--
-- PostgreSQL database dump
--

-- Dumped from database version 18.0 (Ubuntu 18.0-1.pgdg24.04+3)
-- Dumped by pg_dump version 18.0

-- Started on 2026-01-02 11:37:41

SET statement_timeout = 0;
SET lock_timeout = 0;
SET idle_in_transaction_session_timeout = 0;
SET transaction_timeout = 0;
SET client_encoding = 'UTF8';
SET standard_conforming_strings = on;
SELECT pg_catalog.set_config('search_path', '', false);
SET check_function_bodies = false;
SET xmloption = content;
SET client_min_messages = warning;
SET row_security = off;

--
-- TOC entry 6 (class 2615 OID 1846212)
-- Name: census; Type: SCHEMA; Schema: -; Owner: postgres
--

CREATE SCHEMA "census";


ALTER SCHEMA "census" OWNER TO "postgres";

--
-- TOC entry 7 (class 2615 OID 2200)
-- Name: public; Type: SCHEMA; Schema: -; Owner: postgres
--

-- *not* creating schema, since initdb creates it


ALTER SCHEMA "public" OWNER TO "postgres";

--
-- TOC entry 3623 (class 0 OID 0)
-- Dependencies: 7
-- Name: SCHEMA "public"; Type: COMMENT; Schema: -; Owner: postgres
--

COMMENT ON SCHEMA "public" IS 'standard public schema';


--
-- TOC entry 8 (class 2615 OID 1846213)
-- Name: se; Type: SCHEMA; Schema: -; Owner: postgres
--

CREATE SCHEMA "se";


ALTER SCHEMA "se" OWNER TO "postgres";

--
-- TOC entry 9 (class 2615 OID 1846214)
-- Name: staging; Type: SCHEMA; Schema: -; Owner: postgres
--

CREATE SCHEMA "staging";


ALTER SCHEMA "staging" OWNER TO "postgres";

--
-- TOC entry 2 (class 3079 OID 1846215)
-- Name: dict_xsyn; Type: EXTENSION; Schema: -; Owner: -
--

CREATE EXTENSION IF NOT EXISTS "dict_xsyn" WITH SCHEMA "public";


--
-- TOC entry 3624 (class 0 OID 0)
-- Dependencies: 2
-- Name: EXTENSION "dict_xsyn"; Type: COMMENT; Schema: -; Owner: 
--

COMMENT ON EXTENSION "dict_xsyn" IS 'text search dictionary template for extended synonym processing';


--
-- TOC entry 253 (class 1255 OID 1846220)
-- Name: strip_tags("text"); Type: FUNCTION; Schema: se; Owner: postgres
--

CREATE FUNCTION "se"."strip_tags"("text") RETURNS "text"
    LANGUAGE "sql"
    AS '
     SELECT regexp_replace(
        regexp_replace($1, E''(?x)<[^>]*?(\s alt \s* = \s* ([\''"]) ([^>]*?) \2) [^>]*? >'', E''\3''), 
       E''(?x)(< [^>]*? >)'', '''', ''g'')
 ';


ALTER FUNCTION "se"."strip_tags"("text") OWNER TO "postgres";

SET default_tablespace = '';

SET default_table_access_method = "heap";

--
-- TOC entry 223 (class 1259 OID 1846221)
-- Name: facts; Type: TABLE; Schema: census; Owner: postgres
--

CREATE TABLE "census"."facts" (
    "fact_type_id" integer NOT NULL,
    "tract_id" character varying(11) NOT NULL,
    "yr" integer NOT NULL,
    "val" numeric(12,3),
    "perc" numeric(6,2)
);


ALTER TABLE "census"."facts" OWNER TO "postgres";

--
-- TOC entry 224 (class 1259 OID 1846227)
-- Name: hisp_pop; Type: TABLE; Schema: census; Owner: postgres
--

CREATE TABLE "census"."hisp_pop" (
    "tract_id" character varying(11) NOT NULL,
    "hispanic_or_latino" numeric(12,3),
    "white_alone" numeric(12,3),
    "black_or_african_american_alone" numeric(12,3),
    "american_indian_and_alaska_native_alone" numeric(12,3),
    "asian_alone" numeric(12,3),
    "native_hawaiian_and_other_pacific_islander_alone" numeric(12,3),
    "some_other_race_alone" numeric(12,3),
    "two_or_more_races" numeric(12,3)
);


ALTER TABLE "census"."hisp_pop" OWNER TO "postgres";

--
-- TOC entry 225 (class 1259 OID 1846231)
-- Name: lu_fact_types; Type: TABLE; Schema: census; Owner: postgres
--

CREATE TABLE "census"."lu_fact_types" (
    "fact_type_id" integer NOT NULL,
    "category" character varying(100),
    "fact_subcats" character varying(255)[],
    "short_name" character varying(50)
);


ALTER TABLE "census"."lu_fact_types" OWNER TO "postgres";

--
-- TOC entry 226 (class 1259 OID 1846237)
-- Name: lu_fact_types_fact_type_id_seq; Type: SEQUENCE; Schema: census; Owner: postgres
--

CREATE SEQUENCE "census"."lu_fact_types_fact_type_id_seq"
    START WITH 1
    INCREMENT BY 1
    NO MINVALUE
    NO MAXVALUE
    CACHE 1;


ALTER SEQUENCE "census"."lu_fact_types_fact_type_id_seq" OWNER TO "postgres";

--
-- TOC entry 3625 (class 0 OID 0)
-- Dependencies: 226
-- Name: lu_fact_types_fact_type_id_seq; Type: SEQUENCE OWNED BY; Schema: census; Owner: postgres
--

ALTER SEQUENCE "census"."lu_fact_types_fact_type_id_seq" OWNED BY "census"."lu_fact_types"."fact_type_id";


--
-- TOC entry 227 (class 1259 OID 1846238)
-- Name: lu_tracts; Type: TABLE; Schema: census; Owner: postgres
--

CREATE TABLE "census"."lu_tracts" (
    "tract_id" character varying(11) NOT NULL,
    "tract_long_id" character varying(25),
    "tract_name" character varying(150)
);


ALTER TABLE "census"."lu_tracts" OWNER TO "postgres";

--
-- TOC entry 228 (class 1259 OID 1846242)
-- Name: vw_facts; Type: VIEW; Schema: census; Owner: postgres
--

CREATE VIEW "census"."vw_facts" AS
 SELECT "y"."fact_type_id",
    "y"."category",
    "y"."fact_subcats",
    "y"."short_name",
    "x"."tract_id",
    "x"."yr",
    "x"."val",
    "x"."perc"
   FROM ("census"."facts" "x"
     JOIN "census"."lu_fact_types" "y" ON (("x"."fact_type_id" = "y"."fact_type_id")));


ALTER VIEW "census"."vw_facts" OWNER TO "postgres";

--
-- TOC entry 229 (class 1259 OID 1846246)
-- Name: chickens; Type: TABLE; Schema: public; Owner: postgres
--

CREATE TABLE "public"."chickens" (
    "id" integer NOT NULL
);


ALTER TABLE "public"."chickens" OWNER TO "postgres";

--
-- TOC entry 230 (class 1259 OID 1846250)
-- Name: coin_tosses; Type: TABLE; Schema: public; Owner: postgres
--

CREATE TABLE "public"."coin_tosses" (
    "id" integer NOT NULL,
    "player" character varying(50),
    "toss" character(1)
);


ALTER TABLE "public"."coin_tosses" OWNER TO "postgres";

--
-- TOC entry 231 (class 1259 OID 1846254)
-- Name: coin_tosses_id_seq; Type: SEQUENCE; Schema: public; Owner: postgres
--

CREATE SEQUENCE "public"."coin_tosses_id_seq"
    START WITH 1
    INCREMENT BY 1
    NO MINVALUE
    NO MAXVALUE
    CACHE 1;


ALTER SEQUENCE "public"."coin_tosses_id_seq" OWNER TO "postgres";

--
-- TOC entry 3626 (class 0 OID 0)
-- Dependencies: 231
-- Name: coin_tosses_id_seq; Type: SEQUENCE OWNED BY; Schema: public; Owner: postgres
--

ALTER SEQUENCE "public"."coin_tosses_id_seq" OWNED BY "public"."coin_tosses"."id";


--
-- TOC entry 232 (class 1259 OID 1846255)
-- Name: ducks; Type: TABLE; Schema: public; Owner: postgres
--

CREATE TABLE "public"."ducks" (
    "id" integer NOT NULL,
    "chickens" "public"."chickens"[]
);


ALTER TABLE "public"."ducks" OWNER TO "postgres";

--
-- TOC entry 233 (class 1259 OID 1846261)
-- Name: film; Type: TABLE; Schema: public; Owner: postgres
--

CREATE TABLE "public"."film" (
    "film_id" integer NOT NULL,
    "title" character varying(255) NOT NULL,
    "description" "text",
    "fts" "tsvector"
);


ALTER TABLE "public"."film" OWNER TO "postgres";

--
-- TOC entry 234 (class 1259 OID 1846268)
-- Name: game_throws; Type: TABLE; Schema: public; Owner: postgres
--

CREATE TABLE "public"."game_throws" (
    "id" integer NOT NULL,
    "player" character varying(100) NOT NULL,
    "roll" integer
);


ALTER TABLE "public"."game_throws" OWNER TO "postgres";

--
-- TOC entry 235 (class 1259 OID 1846273)
-- Name: game_throws_id_seq; Type: SEQUENCE; Schema: public; Owner: postgres
--

CREATE SEQUENCE "public"."game_throws_id_seq"
    START WITH 1
    INCREMENT BY 1
    NO MINVALUE
    NO MAXVALUE
    CACHE 1;


ALTER SEQUENCE "public"."game_throws_id_seq" OWNER TO "postgres";

--
-- TOC entry 3627 (class 0 OID 0)
-- Dependencies: 235
-- Name: game_throws_id_seq; Type: SEQUENCE OWNED BY; Schema: public; Owner: postgres
--

ALTER SEQUENCE "public"."game_throws_id_seq" OWNED BY "public"."game_throws"."id";


--
-- TOC entry 236 (class 1259 OID 1846274)
-- Name: interval_periods; Type: TABLE; Schema: public; Owner: postgres
--

CREATE TABLE "public"."interval_periods" (
    "i_type" interval
);


ALTER TABLE "public"."interval_periods" OWNER TO "postgres";

--
-- TOC entry 237 (class 1259 OID 1846277)
-- Name: persons; Type: TABLE; Schema: public; Owner: postgres
--

CREATE TABLE "public"."persons" (
    "id" integer NOT NULL,
    "person" json
);


ALTER TABLE "public"."persons" OWNER TO "postgres";

--
-- TOC entry 238 (class 1259 OID 1846283)
-- Name: persons_b; Type: TABLE; Schema: public; Owner: postgres
--

CREATE TABLE "public"."persons_b" (
    "id" integer NOT NULL,
    "person" "jsonb"
);


ALTER TABLE "public"."persons_b" OWNER TO "postgres";

--
-- TOC entry 239 (class 1259 OID 1846289)
-- Name: persons_b_id_seq; Type: SEQUENCE; Schema: public; Owner: postgres
--

CREATE SEQUENCE "public"."persons_b_id_seq"
    START WITH 1
    INCREMENT BY 1
    NO MINVALUE
    NO MAXVALUE
    CACHE 1;


ALTER SEQUENCE "public"."persons_b_id_seq" OWNER TO "postgres";

--
-- TOC entry 3628 (class 0 OID 0)
-- Dependencies: 239
-- Name: persons_b_id_seq; Type: SEQUENCE OWNED BY; Schema: public; Owner: postgres
--

ALTER SEQUENCE "public"."persons_b_id_seq" OWNED BY "public"."persons_b"."id";


--
-- TOC entry 240 (class 1259 OID 1846290)
-- Name: persons_id_seq; Type: SEQUENCE; Schema: public; Owner: postgres
--

CREATE SEQUENCE "public"."persons_id_seq"
    START WITH 1
    INCREMENT BY 1
    NO MINVALUE
    NO MAXVALUE
    CACHE 1;


ALTER SEQUENCE "public"."persons_id_seq" OWNER TO "postgres";

--
-- TOC entry 3629 (class 0 OID 0)
-- Dependencies: 240
-- Name: persons_id_seq; Type: SEQUENCE OWNED BY; Schema: public; Owner: postgres
--

ALTER SEQUENCE "public"."persons_id_seq" OWNED BY "public"."persons"."id";


--
-- TOC entry 241 (class 1259 OID 1846291)
-- Name: posts; Type: TABLE; Schema: public; Owner: postgres
--

CREATE TABLE "public"."posts" (
    "id" bigint NOT NULL,
    "post_type_id" integer,
    "creation_date" timestamp without time zone,
    "score" double precision,
    "view_count" integer,
    "title" "text",
    "body" "text",
    "tags" "text",
    "close_date" timestamp without time zone,
    "fts" "tsvector"
);


ALTER TABLE "public"."posts" OWNER TO "postgres";

--
-- TOC entry 242 (class 1259 OID 1846297)
-- Name: roonga_posts; Type: TABLE; Schema: public; Owner: postgres
--

CREATE TABLE "public"."roonga_posts" (
    "id" bigint,
    "post_type_id" integer,
    "creation_date" timestamp without time zone,
    "score" double precision,
    "view_count" integer,
    "title" "text",
    "body" "text",
    "tags" "text",
    "close_date" timestamp without time zone,
    "fts" "tsvector"
);


ALTER TABLE "public"."roonga_posts" OWNER TO "postgres";

--
-- TOC entry 243 (class 1259 OID 1846302)
-- Name: s; Type: SEQUENCE; Schema: public; Owner: postgres
--

CREATE SEQUENCE "public"."s"
    START WITH 1
    INCREMENT BY 1
    NO MINVALUE
    NO MAXVALUE
    CACHE 1;


ALTER SEQUENCE "public"."s" OWNER TO "postgres";

--
-- TOC entry 244 (class 1259 OID 1846303)
-- Name: stuff; Type: TABLE; Schema: public; Owner: postgres
--

CREATE TABLE "public"."stuff" (
    "id" bigint DEFAULT "nextval"('"public"."s"'::"regclass") NOT NULL,
    "name" "text"
);


ALTER TABLE "public"."stuff" OWNER TO "postgres";

--
-- TOC entry 245 (class 1259 OID 1846310)
-- Name: test_scores; Type: TABLE; Schema: public; Owner: postgres
--

CREATE TABLE "public"."test_scores" (
    "student" character varying(100) NOT NULL,
    "subject" character varying(100) NOT NULL,
    "score" numeric(5,0),
    "test_date" "date" NOT NULL
);


ALTER TABLE "public"."test_scores" OWNER TO "postgres";

--
-- TOC entry 246 (class 1259 OID 1846316)
-- Name: turkeys; Type: TABLE; Schema: public; Owner: postgres
--

CREATE TABLE "public"."turkeys" (
    "id" integer NOT NULL,
    "ducks" "public"."ducks"[]
);


ALTER TABLE "public"."turkeys" OWNER TO "postgres";

--
-- TOC entry 247 (class 1259 OID 1846322)
-- Name: x; Type: TABLE; Schema: public; Owner: postgres
--

CREATE TABLE "public"."x" (
    "i" integer,
    "f" double precision,
    "g" double precision
);


ALTER TABLE "public"."x" OWNER TO "postgres";

--
-- TOC entry 248 (class 1259 OID 1846325)
-- Name: feed; Type: TABLE; Schema: se; Owner: postgres
--

CREATE TABLE "se"."feed" (
    "file_name" "text" NOT NULL,
    "data" "xml"
);


ALTER TABLE "se"."feed" OWNER TO "postgres";

--
-- TOC entry 249 (class 1259 OID 1846331)
-- Name: factfinder_import; Type: TABLE; Schema: staging; Owner: postgres
--

CREATE TABLE "staging"."factfinder_import" (
    "geo_id" character varying(255),
    "geo_id2" character varying(255),
    "geo_display" character varying(255),
    "s01" character varying(255),
    "s01_perc" character varying(255),
    "s02" character varying(255),
    "s02_perc" character varying(255),
    "s03" character varying(255),
    "s03_perc" character varying(255),
    "s04" character varying(255),
    "s04_perc" character varying(255),
    "s05" character varying(255),
    "s05_perc" character varying(255),
    "s06" character varying(255),
    "s06_perc" character varying(255),
    "s07" character varying(255),
    "s07_perc" character varying(255),
    "s08" character varying(255),
    "s08_perc" character varying(255),
    "s09" character varying(255),
    "s09_perc" character varying(255),
    "s10" character varying(255),
    "s10_perc" character varying(255),
    "s11" character varying(255),
    "s11_perc" character varying(255),
    "s12" character varying(255),
    "s12_perc" character varying(255),
    "s13" character varying(255),
    "s13_perc" character varying(255),
    "s14" character varying(255),
    "s14_perc" character varying(255),
    "s15" character varying(255),
    "s15_perc" character varying(255),
    "s16" character varying(255),
    "s16_perc" character varying(255),
    "s17" character varying(255),
    "s17_perc" character varying(255),
    "s18" character varying(255),
    "s18_perc" character varying(255),
    "s19" character varying(255),
    "s19_perc" character varying(255),
    "s20" character varying(255),
    "s20_perc" character varying(255),
    "s21" character varying(255),
    "s21_perc" character varying(255),
    "s22" character varying(255),
    "s22_perc" character varying(255),
    "s23" character varying(255),
    "s23_perc" character varying(255),
    "s24" character varying(255),
    "s24_perc" character varying(255),
    "s25" character varying(255),
    "s25_perc" character varying(255),
    "s26" character varying(255),
    "s26_perc" character varying(255),
    "s27" character varying(255),
    "s27_perc" character varying(255),
    "s28" character varying(255),
    "s28_perc" character varying(255),
    "s29" character varying(255),
    "s29_perc" character varying(255),
    "s30" character varying(255),
    "s30_perc" character varying(255),
    "s31" character varying(255),
    "s31_perc" character varying(255),
    "s32" character varying(255),
    "s32_perc" character varying(255),
    "s33" character varying(255),
    "s33_perc" character varying(255),
    "s34" character varying(255),
    "s34_perc" character varying(255),
    "s35" character varying(255),
    "s35_perc" character varying(255),
    "s36" character varying(255),
    "s36_perc" character varying(255),
    "s37" character varying(255),
    "s37_perc" character varying(255),
    "s38" character varying(255),
    "s38_perc" character varying(255),
    "s39" character varying(255),
    "s39_perc" character varying(255),
    "s40" character varying(255),
    "s40_perc" character varying(255),
    "s41" character varying(255),
    "s41_perc" character varying(255),
    "s42" character varying(255),
    "s42_perc" character varying(255),
    "s43" character varying(255),
    "s43_perc" character varying(255),
    "s44" character varying(255),
    "s44_perc" character varying(255),
    "s45" character varying(255),
    "s45_perc" character varying(255),
    "s46" character varying(255),
    "s46_perc" character varying(255),
    "s47" character varying(255),
    "s47_perc" character varying(255),
    "s48" character varying(255),
    "s48_perc" character varying(255),
    "s49" character varying(255),
    "s49_perc" character varying(255),
    "s50" character varying(255),
    "s50_perc" character varying(255),
    "s51" character varying(255),
    "s51_perc" character varying(255)
);


ALTER TABLE "staging"."factfinder_import" OWNER TO "postgres";

--
-- TOC entry 250 (class 1259 OID 1846336)
-- Name: pop_import; Type: TABLE; Schema: staging; Owner: postgres
--

CREATE TABLE "staging"."pop_import" (
    "geo_id" character varying(255),
    "geo_id2" character varying(255),
    "geo_display" character varying(255),
    "d001" character varying(255),
    "d002" character varying(255),
    "d003" character varying(255),
    "d004" character varying(255),
    "d005" character varying(255),
    "d006" character varying(255),
    "d007" character varying(255),
    "d008" character varying(255),
    "d009" character varying(255),
    "d010" character varying(255),
    "d011" character varying(255),
    "d012" character varying(255),
    "d013" character varying(255),
    "d014" character varying(255),
    "d015" character varying(255),
    "d016" character varying(255),
    "d017" character varying(255)
);


ALTER TABLE "staging"."pop_import" OWNER TO "postgres";

--
-- TOC entry 3403 (class 2604 OID 1846341)
-- Name: lu_fact_types fact_type_id; Type: DEFAULT; Schema: census; Owner: postgres
--

ALTER TABLE ONLY "census"."lu_fact_types" ALTER COLUMN "fact_type_id" SET DEFAULT "nextval"('"census"."lu_fact_types_fact_type_id_seq"'::"regclass");


--
-- TOC entry 3404 (class 2604 OID 1846342)
-- Name: coin_tosses id; Type: DEFAULT; Schema: public; Owner: postgres
--

ALTER TABLE ONLY "public"."coin_tosses" ALTER COLUMN "id" SET DEFAULT "nextval"('"public"."coin_tosses_id_seq"'::"regclass");


--
-- TOC entry 3405 (class 2604 OID 1846343)
-- Name: game_throws id; Type: DEFAULT; Schema: public; Owner: postgres
--

ALTER TABLE ONLY "public"."game_throws" ALTER COLUMN "id" SET DEFAULT "nextval"('"public"."game_throws_id_seq"'::"regclass");


--
-- TOC entry 3406 (class 2604 OID 1846344)
-- Name: persons id; Type: DEFAULT; Schema: public; Owner: postgres
--

ALTER TABLE ONLY "public"."persons" ALTER COLUMN "id" SET DEFAULT "nextval"('"public"."persons_id_seq"'::"regclass");


--
-- TOC entry 3407 (class 2604 OID 1846345)
-- Name: persons_b id; Type: DEFAULT; Schema: public; Owner: postgres
--

ALTER TABLE ONLY "public"."persons_b" ALTER COLUMN "id" SET DEFAULT "nextval"('"public"."persons_b_id_seq"'::"regclass");


--
-- TOC entry 3591 (class 0 OID 1846221)
-- Dependencies: 223
-- Data for Name: facts; Type: TABLE DATA; Schema: census; Owner: postgres
--

INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001010400', 2011, 2359.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001010500', 2011, 2985.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001010100', 2011, 4494.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001010206', 2011, 4305.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001010208', 2011, 3077.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001010304', 2011, 2706.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001010306', 2011, 3254.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001010600', 2011, 2998.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001010700', 2011, 4345.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001010800', 2011, 3788.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001010900', 2011, 4160.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001011002', 2011, 3512.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001011100', 2011, 3084.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001011200', 2011, 3688.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001011300', 2011, 2690.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001011400', 2011, 2660.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001011500', 2011, 2715.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001011600', 2011, 4812.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001011700', 2011, 2709.000, 100.00);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (1, '25001011801', 2011, 1713.000, 100.00);
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INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (68, '25027757300', 2011, 59.000, NULL);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (68, '25027757400', 2011, 29.000, NULL);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (68, '25027757500', 2011, 54.000, NULL);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (68, '25027758101', 2011, 0.000, NULL);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (68, '25027758102', 2011, 20.000, NULL);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (68, '25027759100', 2011, 3.000, NULL);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (68, '25027760100', 2011, 4.000, NULL);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (68, '25027761100', 2011, 24.000, NULL);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (68, '25027761200', 2011, 12.000, NULL);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (68, '25027761300', 2011, 4.000, NULL);
INSERT INTO "census"."facts" ("fact_type_id", "tract_id", "yr", "val", "perc") VALUES (68, '25027761400', 2011, 18.000, NULL);


--
-- TOC entry 3592 (class 0 OID 1846227)
-- Dependencies: 224
-- Data for Name: hisp_pop; Type: TABLE DATA; Schema: census; Owner: postgres
--

INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021417602', 140.000, 77.000, 16.000, 1.000, 3.000, 0.000, 34.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017350900', 127.000, 87.000, 11.000, 1.000, 1.000, 0.000, 16.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021414200', 172.000, 105.000, 15.000, 5.000, 1.000, 0.000, 29.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023502200', 86.000, 58.000, 4.000, 0.000, 0.000, 0.000, 17.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025100300', 352.000, 60.000, 107.000, 1.000, 0.000, 0.000, 138.000, 46.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017351300', 313.000, 159.000, 12.000, 16.000, 1.000, 1.000, 97.000, 27.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009208200', 159.000, 75.000, 4.000, 4.000, 0.000, 0.000, 54.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017374700', 168.000, 118.000, 6.000, 1.000, 0.000, 0.000, 40.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017311900', 590.000, 242.000, 34.000, 3.000, 6.000, 0.000, 270.000, 35.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001011002', 69.000, 31.000, 0.000, 5.000, 3.000, 0.000, 27.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009250800', 4388.000, 1770.000, 373.000, 69.000, 12.000, 3.000, 1820.000, 341.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021402200', 156.000, 106.000, 1.000, 2.000, 0.000, 0.000, 41.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005640800', 422.000, 188.000, 30.000, 4.000, 5.000, 2.000, 153.000, 40.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015821000', 124.000, 85.000, 0.000, 1.000, 0.000, 0.000, 28.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021417902', 180.000, 100.000, 11.000, 1.000, 2.000, 0.000, 62.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017354200', 95.000, 75.000, 2.000, 3.000, 0.000, 0.000, 6.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021408102', 116.000, 70.000, 5.000, 5.000, 3.000, 0.000, 21.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017338400', 86.000, 70.000, 0.000, 0.000, 1.000, 0.000, 12.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017353101', 266.000, 141.000, 28.000, 3.000, 3.000, 0.000, 77.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025100200', 413.000, 67.000, 134.000, 10.000, 0.000, 0.000, 159.000, 43.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017331101', 57.000, 38.000, 1.000, 0.000, 0.000, 0.000, 15.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025070402', 158.000, 44.000, 18.000, 0.000, 0.000, 0.000, 75.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013810601', 467.000, 227.000, 29.000, 4.000, 0.000, 0.000, 174.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015821903', 388.000, 169.000, 17.000, 4.000, 2.000, 0.000, 173.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017353400', 241.000, 97.000, 39.000, 8.000, 1.000, 0.000, 71.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017384002', 329.000, 166.000, 28.000, 3.000, 0.000, 0.000, 110.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009210300', 338.000, 187.000, 20.000, 14.000, 10.000, 0.000, 85.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015822401', 117.000, 83.000, 2.000, 0.000, 1.000, 0.000, 23.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000601', 288.000, 165.000, 12.000, 0.000, 1.000, 0.000, 95.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017382200', 158.000, 113.000, 8.000, 0.000, 0.000, 0.000, 25.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027757400', 454.000, 230.000, 9.000, 0.000, 1.000, 0.000, 185.000, 29.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732201', 220.000, 115.000, 19.000, 2.000, 0.000, 0.000, 63.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013810403', 222.000, 115.000, 6.000, 4.000, 0.000, 0.000, 73.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017352101', 232.000, 178.000, 5.000, 0.000, 1.000, 0.000, 40.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025140105', 955.000, 354.000, 104.000, 10.000, 11.000, 0.000, 399.000, 77.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025082000', 534.000, 120.000, 106.000, 11.000, 0.000, 0.000, 243.000, 54.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027722100', 103.000, 72.000, 12.000, 0.000, 1.000, 0.000, 14.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017339400', 153.000, 91.000, 6.000, 0.000, 0.000, 0.000, 45.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009207100', 1670.000, 472.000, 73.000, 27.000, 8.000, 18.000, 965.000, 107.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009250100', 1737.000, 521.000, 123.000, 25.000, 3.000, 0.000, 919.000, 146.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801901', 2356.000, 845.000, 195.000, 26.000, 4.000, 0.000, 1146.000, 140.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021417702', 65.000, 52.000, 5.000, 1.000, 0.000, 0.000, 4.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009212100', 640.000, 218.000, 51.000, 30.000, 2.000, 3.000, 291.000, 45.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017322300', 376.000, 254.000, 2.000, 12.000, 2.000, 0.000, 79.000, 27.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027710200', 1124.000, 499.000, 55.000, 6.000, 2.000, 0.000, 479.000, 83.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027731101', 1024.000, 448.000, 68.000, 11.000, 1.000, 3.000, 410.000, 83.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812702', 902.000, 489.000, 36.000, 7.000, 0.000, 0.000, 283.000, 87.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021420302', 418.000, 182.000, 85.000, 2.000, 3.000, 0.000, 99.000, 47.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017337101', 78.000, 41.000, 8.000, 2.000, 0.000, 0.000, 24.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027752100', 139.000, 107.000, 10.000, 1.000, 1.000, 0.000, 10.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023525104', 70.000, 49.000, 3.000, 0.000, 0.000, 0.000, 10.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023506101', 83.000, 56.000, 2.000, 1.000, 0.000, 0.000, 14.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025100500', 968.000, 244.000, 228.000, 26.000, 0.000, 1.000, 374.000, 95.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015822700', 37.000, 30.000, 0.000, 0.000, 0.000, 0.000, 6.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017316400', 227.000, 167.000, 12.000, 1.000, 3.000, 0.000, 34.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011040502', 76.000, 58.000, 1.000, 1.000, 0.000, 0.000, 6.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021417501', 105.000, 64.000, 4.000, 1.000, 2.000, 0.000, 27.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005654200', 47.000, 30.000, 0.000, 0.000, 0.000, 0.000, 9.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017366202', 98.000, 74.000, 1.000, 0.000, 1.000, 0.000, 16.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027709501', 92.000, 61.000, 2.000, 0.000, 0.000, 0.000, 16.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017354500', 112.000, 89.000, 6.000, 2.000, 2.000, 0.000, 11.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017339801', 219.000, 104.000, 14.000, 1.000, 1.000, 0.000, 76.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023505101', 72.000, 51.000, 4.000, 1.000, 2.000, 0.000, 2.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021422400', 192.000, 124.000, 16.000, 0.000, 0.000, 0.000, 32.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017311200', 481.000, 124.000, 31.000, 4.000, 21.000, 0.000, 250.000, 51.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813401', 160.000, 93.000, 7.000, 0.000, 0.000, 0.000, 40.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027731800', 2295.000, 1142.000, 161.000, 24.000, 4.000, 1.000, 784.000, 179.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027713100', 655.000, 391.000, 52.000, 2.000, 0.000, 0.000, 169.000, 41.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017353102', 561.000, 400.000, 20.000, 1.000, 8.000, 2.000, 78.000, 52.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017353300', 170.000, 103.000, 15.000, 1.000, 2.000, 0.000, 37.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003922300', 24.000, 14.000, 2.000, 0.000, 1.000, 0.000, 3.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025070500', 1070.000, 460.000, 84.000, 15.000, 1.000, 1.000, 419.000, 90.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021418004', 219.000, 105.000, 27.000, 7.000, 5.000, 0.000, 59.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000602', 641.000, 264.000, 52.000, 11.000, 2.000, 1.000, 259.000, 52.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005642000', 373.000, 213.000, 14.000, 2.000, 0.000, 4.000, 107.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017314101', 215.000, 122.000, 11.000, 1.000, 3.000, 0.000, 59.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013811500', 2060.000, 789.000, 136.000, 25.000, 0.000, 12.000, 943.000, 155.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017354000', 264.000, 194.000, 5.000, 5.000, 5.000, 0.000, 30.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017368300', 1209.000, 604.000, 15.000, 8.000, 5.000, 8.000, 482.000, 87.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000504', 318.000, 195.000, 23.000, 1.000, 4.000, 0.000, 72.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001012900', 72.000, 38.000, 2.000, 2.000, 0.000, 0.000, 19.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025100100', 1603.000, 398.000, 368.000, 8.000, 5.000, 0.000, 697.000, 127.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017354900', 563.000, 187.000, 95.000, 15.000, 5.000, 0.000, 180.000, 81.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005617101', 59.000, 34.000, 1.000, 3.000, 0.000, 0.000, 21.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000202', 596.000, 250.000, 57.000, 5.000, 2.000, 1.000, 220.000, 61.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017341300', 224.000, 123.000, 19.000, 1.000, 0.000, 2.000, 69.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021402400', 316.000, 160.000, 18.000, 3.000, 0.000, 0.000, 101.000, 34.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001010900', 97.000, 80.000, 1.000, 2.000, 0.000, 2.000, 7.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017328100', 44.000, 38.000, 1.000, 0.000, 1.000, 0.000, 0.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023511100', 380.000, 172.000, 38.000, 0.000, 2.000, 0.000, 122.000, 46.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027739100', 280.000, 190.000, 6.000, 0.000, 0.000, 0.000, 63.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023545100', 124.000, 78.000, 9.000, 5.000, 0.000, 0.000, 24.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017313102', 138.000, 82.000, 0.000, 2.000, 2.000, 0.000, 31.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023542101', 115.000, 91.000, 6.000, 0.000, 0.000, 1.000, 7.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017373500', 222.000, 179.000, 2.000, 4.000, 1.000, 0.000, 22.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025981000', 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005600202', 121.000, 71.000, 0.000, 0.000, 0.000, 0.000, 34.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023511301', 609.000, 233.000, 71.000, 5.000, 0.000, 6.000, 234.000, 60.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009252400', 2605.000, 918.000, 193.000, 34.000, 6.000, 3.000, 1244.000, 207.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000502', 446.000, 307.000, 18.000, 2.000, 3.000, 0.000, 91.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013811900', 388.000, 219.000, 17.000, 0.000, 0.000, 1.000, 130.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013800102', 1559.000, 819.000, 75.000, 12.000, 2.000, 0.000, 520.000, 131.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021411200', 137.000, 95.000, 15.000, 3.000, 1.000, 0.000, 13.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027706100', 63.000, 45.000, 5.000, 1.000, 0.000, 0.000, 10.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017363102', 215.000, 144.000, 1.000, 2.000, 1.000, 0.000, 49.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027716100', 858.000, 428.000, 60.000, 6.000, 1.000, 2.000, 313.000, 48.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017322200', 135.000, 83.000, 2.000, 0.000, 2.000, 0.000, 34.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025092101', 689.000, 214.000, 80.000, 15.000, 19.000, 0.000, 299.000, 62.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801401', 1737.000, 547.000, 143.000, 35.000, 5.000, 6.000, 914.000, 87.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027730700', 371.000, 195.000, 41.000, 5.000, 8.000, 1.000, 83.000, 38.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021417502', 145.000, 85.000, 13.000, 1.000, 1.000, 0.000, 37.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005631200', 281.000, 178.000, 4.000, 1.000, 0.000, 0.000, 75.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015822200', 353.000, 239.000, 5.000, 2.000, 3.000, 1.000, 59.000, 44.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027728400', 88.000, 45.000, 11.000, 1.000, 0.000, 0.000, 23.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023530200', 89.000, 53.000, 2.000, 1.000, 0.000, 0.000, 27.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027709701', 938.000, 532.000, 42.000, 10.000, 0.000, 0.000, 275.000, 79.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812500', 713.000, 437.000, 26.000, 9.000, 2.000, 0.000, 192.000, 47.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025080300', 508.000, 115.000, 90.000, 8.000, 0.000, 0.000, 245.000, 50.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023543100', 36.000, 23.000, 1.000, 6.000, 0.000, 0.000, 3.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005652400', 376.000, 164.000, 25.000, 6.000, 0.000, 0.000, 154.000, 27.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017318200', 68.000, 55.000, 0.000, 1.000, 0.000, 0.000, 6.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027707100', 300.000, 192.000, 18.000, 4.000, 0.000, 0.000, 52.000, 34.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013800700', 3501.000, 1312.000, 232.000, 20.000, 0.000, 0.000, 1720.000, 217.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027736300', 107.000, 83.000, 4.000, 0.000, 0.000, 0.000, 14.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005642300', 35.000, 21.000, 0.000, 2.000, 0.000, 0.000, 6.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005644200', 76.000, 60.000, 0.000, 0.000, 0.000, 0.000, 14.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023521201', 69.000, 44.000, 1.000, 0.000, 0.000, 0.000, 18.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017311700', 681.000, 275.000, 40.000, 2.000, 15.000, 0.000, 289.000, 60.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017317101', 126.000, 88.000, 8.000, 2.000, 1.000, 0.000, 13.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017352700', 228.000, 96.000, 31.000, 3.000, 3.000, 0.000, 62.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027716200', 422.000, 238.000, 34.000, 3.000, 1.000, 1.000, 115.000, 30.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813205', 266.000, 203.000, 3.000, 2.000, 0.000, 0.000, 34.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017310602', 567.000, 280.000, 40.000, 3.000, 7.000, 0.000, 200.000, 37.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017312502', 274.000, 173.000, 11.000, 1.000, 1.000, 0.000, 86.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023522101', 49.000, 35.000, 6.000, 0.000, 0.000, 0.000, 4.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001010600', 50.000, 29.000, 5.000, 0.000, 0.000, 0.000, 10.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009269100', 67.000, 55.000, 2.000, 0.000, 1.000, 0.000, 8.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003935300', 110.000, 61.000, 8.000, 2.000, 0.000, 0.000, 25.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027750100', 244.000, 159.000, 10.000, 0.000, 0.000, 0.000, 63.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003900400', 215.000, 102.000, 17.000, 1.000, 0.000, 0.000, 76.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023545400', 94.000, 47.000, 7.000, 2.000, 0.000, 0.000, 23.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025120700', 354.000, 148.000, 36.000, 1.000, 0.000, 1.000, 130.000, 38.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013802200', 1717.000, 624.000, 108.000, 21.000, 1.000, 0.000, 883.000, 80.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017312100', 658.000, 277.000, 33.000, 1.000, 7.000, 0.000, 291.000, 49.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009208400', 190.000, 109.000, 9.000, 0.000, 2.000, 0.000, 52.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017369000', 80.000, 62.000, 4.000, 0.000, 0.000, 0.000, 13.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812404', 223.000, 117.000, 13.000, 0.000, 0.000, 0.000, 71.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017370202', 159.000, 108.000, 2.000, 5.000, 0.000, 0.000, 32.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010701', 136.000, 96.000, 4.000, 0.000, 4.000, 0.000, 22.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021415300', 60.000, 39.000, 0.000, 0.000, 0.000, 0.000, 13.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009251300', 3330.000, 889.000, 178.000, 39.000, 15.000, 3.000, 1933.000, 273.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017341902', 762.000, 346.000, 70.000, 9.000, 8.000, 0.000, 286.000, 43.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005651002', 590.000, 258.000, 30.000, 11.000, 1.000, 0.000, 256.000, 34.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017330200', 110.000, 84.000, 0.000, 1.000, 0.000, 0.000, 19.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027707200', 137.000, 90.000, 8.000, 8.000, 0.000, 0.000, 22.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009261101', 136.000, 95.000, 3.000, 3.000, 2.000, 0.000, 20.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017368500', 856.000, 321.000, 12.000, 8.000, 0.000, 12.000, 453.000, 50.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000301', 190.000, 100.000, 7.000, 6.000, 3.000, 0.000, 60.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025091500', 917.000, 219.000, 153.000, 3.000, 1.000, 1.000, 460.000, 80.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009210800', 739.000, 263.000, 53.000, 13.000, 0.000, 0.000, 356.000, 54.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017340000', 163.000, 86.000, 8.000, 1.000, 0.000, 0.000, 51.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003901100', 72.000, 42.000, 5.000, 0.000, 0.000, 0.000, 22.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017312501', 402.000, 209.000, 10.000, 2.000, 6.000, 0.000, 147.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017338300', 56.000, 50.000, 1.000, 0.000, 0.000, 1.000, 2.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025060301', 28.000, 17.000, 2.000, 0.000, 0.000, 0.000, 5.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017340100', 160.000, 77.000, 10.000, 3.000, 2.000, 0.000, 44.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017352500', 284.000, 123.000, 38.000, 1.000, 2.000, 2.000, 93.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017341901', 127.000, 78.000, 7.000, 0.000, 1.000, 0.000, 30.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025020302', 61.000, 49.000, 0.000, 1.000, 0.000, 0.000, 9.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017339802', 285.000, 140.000, 30.000, 6.000, 0.000, 0.000, 98.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013810100', 161.000, 91.000, 8.000, 1.000, 0.000, 0.000, 35.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017337300', 243.000, 132.000, 22.000, 2.000, 2.000, 0.000, 60.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005614102', 220.000, 148.000, 12.000, 1.000, 0.000, 1.000, 37.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013811102', 1858.000, 930.000, 168.000, 36.000, 4.000, 9.000, 583.000, 128.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023510300', 455.000, 133.000, 82.000, 4.000, 0.000, 1.000, 175.000, 60.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003922100', 29.000, 20.000, 2.000, 0.000, 1.000, 0.000, 1.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027735100', 223.000, 135.000, 9.000, 0.000, 0.000, 1.000, 57.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009250400', 3490.000, 1105.000, 322.000, 67.000, 13.000, 7.000, 1649.000, 327.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015821200', 140.000, 77.000, 12.000, 2.000, 2.000, 0.000, 33.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017322100', 118.000, 66.000, 0.000, 2.000, 1.000, 0.000, 45.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017383700', 361.000, 205.000, 6.000, 0.000, 0.000, 3.000, 112.000, 35.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015821100', 294.000, 174.000, 9.000, 0.000, 2.000, 0.000, 84.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027733101', 255.000, 126.000, 11.000, 7.000, 0.000, 0.000, 79.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021409102', 55.000, 48.000, 0.000, 2.000, 0.000, 0.000, 4.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021400600', 217.000, 153.000, 13.000, 2.000, 5.000, 0.000, 33.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017336100', 126.000, 104.000, 1.000, 1.000, 1.000, 0.000, 13.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025050400', 1204.000, 704.000, 24.000, 4.000, 4.000, 0.000, 406.000, 62.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027710100', 962.000, 484.000, 54.000, 2.000, 1.000, 1.000, 349.000, 71.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017364101', 240.000, 162.000, 8.000, 1.000, 0.000, 0.000, 53.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005644102', 58.000, 38.000, 0.000, 0.000, 0.000, 0.000, 10.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017336402', 133.000, 74.000, 8.000, 3.000, 0.000, 2.000, 37.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025980700', 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009206200', 2832.000, 1016.000, 147.000, 45.000, 5.000, 0.000, 1359.000, 260.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015821901', 129.000, 75.000, 0.000, 2.000, 0.000, 2.000, 44.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021409101', 747.000, 482.000, 54.000, 9.000, 5.000, 0.000, 176.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025160300', 461.000, 208.000, 19.000, 7.000, 1.000, 0.000, 191.000, 35.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011041400', 247.000, 116.000, 8.000, 4.000, 0.000, 3.000, 84.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812103', 1777.000, 695.000, 66.000, 15.000, 2.000, 0.000, 895.000, 104.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812403', 191.000, 116.000, 4.000, 2.000, 0.000, 0.000, 58.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021416400', 79.000, 51.000, 6.000, 0.000, 0.000, 0.000, 12.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010300', 457.000, 177.000, 36.000, 3.000, 7.000, 1.000, 215.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025180101', 279.000, 141.000, 5.000, 1.000, 1.000, 0.000, 103.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021402101', 193.000, 121.000, 8.000, 6.000, 6.000, 0.000, 33.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017361100', 83.000, 69.000, 0.000, 2.000, 0.000, 0.000, 8.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025980300', 68.000, 43.000, 8.000, 0.000, 0.000, 0.000, 14.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005613300', 169.000, 97.000, 8.000, 4.000, 3.000, 0.000, 34.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005640500', 289.000, 131.000, 23.000, 1.000, 3.000, 0.000, 97.000, 34.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013810901', 625.000, 260.000, 33.000, 2.000, 0.000, 0.000, 282.000, 48.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813209', 164.000, 98.000, 13.000, 4.000, 1.000, 0.000, 35.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001011600', 68.000, 36.000, 5.000, 3.000, 0.000, 0.000, 16.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017342600', 1177.000, 454.000, 18.000, 12.000, 3.000, 0.000, 623.000, 67.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023501102', 100.000, 79.000, 4.000, 1.000, 0.000, 0.000, 9.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009250600', 4651.000, 1182.000, 362.000, 24.000, 9.000, 13.000, 2608.000, 453.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801605', 965.000, 460.000, 144.000, 10.000, 0.000, 10.000, 269.000, 72.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025081001', 950.000, 332.000, 115.000, 16.000, 9.000, 0.000, 388.000, 90.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009206500', 1576.000, 432.000, 130.000, 11.000, 6.000, 2.000, 880.000, 115.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009260100', 1741.000, 899.000, 118.000, 18.000, 6.000, 6.000, 579.000, 115.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017342300', 1345.000, 507.000, 34.000, 2.000, 1.000, 0.000, 703.000, 98.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017350700', 475.000, 199.000, 35.000, 1.000, 0.000, 1.000, 200.000, 39.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001014900', 57.000, 42.000, 2.000, 0.000, 2.000, 0.000, 7.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017316101', 92.000, 65.000, 0.000, 0.000, 0.000, 0.000, 17.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009217300', 402.000, 174.000, 21.000, 7.000, 1.000, 0.000, 153.000, 46.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025092200', 422.000, 119.000, 84.000, 1.000, 0.000, 0.000, 172.000, 46.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023500103', 25.000, 21.000, 0.000, 1.000, 0.000, 0.000, 1.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025020303', 373.000, 317.000, 11.000, 0.000, 2.000, 0.000, 35.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001990000', 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021415101', 88.000, 52.000, 11.000, 4.000, 4.000, 1.000, 10.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025080601', 743.000, 223.000, 103.000, 6.000, 1.000, 0.000, 344.000, 66.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027756102', 259.000, 176.000, 14.000, 3.000, 5.000, 0.000, 37.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027736400', 103.000, 69.000, 6.000, 1.000, 0.000, 0.000, 20.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027731001', 216.000, 151.000, 12.000, 1.000, 3.000, 0.000, 42.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009260200', 639.000, 262.000, 38.000, 6.000, 0.000, 0.000, 293.000, 40.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017358300', 149.000, 126.000, 3.000, 0.000, 0.000, 0.000, 12.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015821601', 320.000, 149.000, 14.000, 2.000, 6.000, 0.000, 108.000, 41.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027730902', 225.000, 111.000, 8.000, 5.000, 1.000, 0.000, 84.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023508102', 56.000, 33.000, 1.000, 3.000, 1.000, 0.000, 13.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023511701', 324.000, 144.000, 38.000, 0.000, 2.000, 0.000, 104.000, 36.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025060600', 92.000, 71.000, 4.000, 0.000, 0.000, 0.000, 11.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017312400', 970.000, 437.000, 52.000, 6.000, 8.000, 0.000, 404.000, 63.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027726100', 92.000, 51.000, 3.000, 3.000, 0.000, 0.000, 22.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017359100', 181.000, 116.000, 10.000, 1.000, 0.000, 0.000, 33.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025051101', 2175.000, 1236.000, 92.000, 22.000, 5.000, 0.000, 669.000, 151.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021411301', 63.000, 48.000, 1.000, 0.000, 0.000, 0.000, 12.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025170300', 1220.000, 650.000, 22.000, 3.000, 11.000, 0.000, 455.000, 79.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017316102', 103.000, 72.000, 6.000, 0.000, 0.000, 0.000, 22.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009207200', 1234.000, 322.000, 75.000, 16.000, 4.000, 0.000, 721.000, 96.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021420301', 529.000, 205.000, 111.000, 12.000, 2.000, 1.000, 135.000, 63.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021419500', 55.000, 37.000, 1.000, 0.000, 1.000, 0.000, 11.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812201', 437.000, 235.000, 18.000, 0.000, 2.000, 0.000, 152.000, 30.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023525300', 346.000, 251.000, 20.000, 2.000, 0.000, 0.000, 65.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025140106', 595.000, 179.000, 74.000, 7.000, 1.000, 0.000, 271.000, 63.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011041300', 242.000, 146.000, 9.000, 2.000, 1.000, 0.000, 63.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017363103', 80.000, 56.000, 2.000, 2.000, 0.000, 0.000, 11.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025100700', 101.000, 54.000, 5.000, 3.000, 0.000, 0.000, 30.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005641700', 144.000, 75.000, 14.000, 1.000, 2.000, 4.000, 37.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005650500', 345.000, 95.000, 26.000, 21.000, 1.000, 0.000, 162.000, 40.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009254100', 163.000, 89.000, 3.000, 0.000, 2.000, 1.000, 50.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009268100', 51.000, 41.000, 0.000, 4.000, 0.000, 0.000, 4.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025140202', 1135.000, 368.000, 154.000, 18.000, 1.000, 5.000, 467.000, 122.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017383501', 607.000, 257.000, 38.000, 4.000, 1.000, 0.000, 273.000, 34.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021416300', 186.000, 98.000, 33.000, 1.000, 3.000, 0.000, 35.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027757200', 982.000, 509.000, 35.000, 1.000, 4.000, 0.000, 361.000, 72.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021400700', 169.000, 108.000, 8.000, 0.000, 0.000, 0.000, 36.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017332100', 83.000, 67.000, 0.000, 1.000, 0.000, 0.000, 11.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025100900', 350.000, 72.000, 120.000, 5.000, 3.000, 3.000, 105.000, 42.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027730401', 1164.000, 610.000, 97.000, 14.000, 0.000, 0.000, 355.000, 88.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812901', 155.000, 116.000, 1.000, 14.000, 0.000, 0.000, 16.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025140102', 755.000, 249.000, 91.000, 4.000, 3.000, 3.000, 339.000, 66.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005641101', 232.000, 66.000, 23.000, 10.000, 0.000, 0.000, 123.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005641600', 43.000, 28.000, 0.000, 2.000, 0.000, 0.000, 9.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025120103', 77.000, 51.000, 0.000, 1.000, 0.000, 0.000, 12.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003932300', 18.000, 11.000, 0.000, 3.000, 0.000, 0.000, 4.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001011801', 19.000, 19.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017338500', 91.000, 78.000, 0.000, 1.000, 2.000, 0.000, 3.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017335301', 114.000, 65.000, 3.000, 3.000, 1.000, 0.000, 31.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025061200', 156.000, 99.000, 20.000, 0.000, 2.000, 0.000, 29.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009254402', 378.000, 171.000, 19.000, 7.000, 5.000, 1.000, 146.000, 29.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001013100', 116.000, 82.000, 6.000, 3.000, 0.000, 0.000, 15.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017318400', 55.000, 36.000, 0.000, 2.000, 1.000, 0.000, 4.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017333300', 299.000, 150.000, 5.000, 1.000, 1.000, 0.000, 112.000, 30.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023507103', 65.000, 54.000, 1.000, 0.000, 0.000, 0.000, 6.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023500104', 96.000, 49.000, 5.000, 16.000, 1.000, 0.000, 15.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023526100', 81.000, 55.000, 0.000, 2.000, 0.000, 0.000, 8.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009254302', 81.000, 59.000, 2.000, 2.000, 4.000, 0.000, 9.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005610203', 128.000, 94.000, 3.000, 0.000, 2.000, 0.000, 14.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027730802', 145.000, 83.000, 13.000, 1.000, 0.000, 0.000, 24.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017388200', 561.000, 474.000, 5.000, 4.000, 1.000, 1.000, 59.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001014402', 123.000, 85.000, 2.000, 2.000, 0.000, 0.000, 22.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021422200', 127.000, 71.000, 5.000, 0.000, 0.000, 0.000, 30.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009250900', 2040.000, 603.000, 192.000, 35.000, 1.000, 2.000, 1023.000, 184.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017374400', 179.000, 129.000, 11.000, 1.000, 3.000, 0.000, 16.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003900500', 92.000, 70.000, 7.000, 0.000, 0.000, 0.000, 8.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025130406', 1060.000, 337.000, 167.000, 10.000, 0.000, 0.000, 450.000, 96.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813304', 79.000, 68.000, 1.000, 0.000, 0.000, 0.000, 4.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005650201', 129.000, 85.000, 12.000, 0.000, 0.000, 0.000, 21.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017333502', 243.000, 119.000, 9.000, 0.000, 1.000, 0.000, 91.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025040801', 613.000, 285.000, 66.000, 5.000, 1.000, 0.000, 204.000, 52.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027717100', 44.000, 37.000, 1.000, 2.000, 0.000, 0.000, 4.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027761200', 280.000, 195.000, 27.000, 1.000, 0.000, 0.000, 45.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005651400', 442.000, 159.000, 45.000, 9.000, 1.000, 1.000, 180.000, 47.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023510800', 827.000, 238.000, 142.000, 8.000, 2.000, 1.000, 337.000, 99.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025110201', 734.000, 271.000, 78.000, 22.000, 3.000, 0.000, 297.000, 63.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017366201', 81.000, 65.000, 6.000, 0.000, 1.000, 0.000, 4.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017368600', 1340.000, 550.000, 32.000, 7.000, 3.000, 8.000, 659.000, 81.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025081300', 2261.000, 638.000, 338.000, 38.000, 6.000, 0.000, 998.000, 243.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013810300', 74.000, 36.000, 2.000, 3.000, 0.000, 0.000, 19.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021412300', 82.000, 56.000, 4.000, 0.000, 1.000, 0.000, 14.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027709201', 816.000, 407.000, 25.000, 1.000, 5.000, 0.000, 310.000, 68.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021400500', 237.000, 183.000, 4.000, 8.000, 0.000, 0.000, 32.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017359300', 175.000, 143.000, 9.000, 1.000, 0.000, 4.000, 11.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023501201', 33.000, 29.000, 2.000, 0.000, 0.000, 0.000, 0.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017351204', 346.000, 173.000, 4.000, 6.000, 0.000, 2.000, 125.000, 36.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813000', 123.000, 78.000, 6.000, 2.000, 0.000, 0.000, 15.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023505200', 69.000, 54.000, 1.000, 0.000, 2.000, 0.000, 11.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025090400', 1205.000, 299.000, 255.000, 25.000, 4.000, 1.000, 477.000, 144.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005641900', 117.000, 76.000, 4.000, 4.000, 1.000, 0.000, 25.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017354600', 234.000, 145.000, 32.000, 1.000, 1.000, 0.000, 38.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023530300', 81.000, 57.000, 2.000, 0.000, 2.000, 0.000, 15.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017364102', 137.000, 93.000, 3.000, 0.000, 1.000, 0.000, 27.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005610202', 98.000, 68.000, 4.000, 0.000, 0.000, 4.000, 16.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017356601', 137.000, 91.000, 6.000, 1.000, 0.000, 0.000, 22.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023511200', 554.000, 205.000, 73.000, 1.000, 0.000, 0.000, 220.000, 55.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025060400', 90.000, 60.000, 1.000, 2.000, 1.000, 0.000, 24.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013811200', 334.000, 181.000, 17.000, 5.000, 0.000, 1.000, 93.000, 37.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025050600', 1374.000, 755.000, 17.000, 3.000, 1.000, 1.000, 478.000, 119.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017383101', 1143.000, 607.000, 84.000, 13.000, 7.000, 21.000, 339.000, 72.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025130300', 270.000, 140.000, 31.000, 0.000, 2.000, 0.000, 77.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017342500', 1582.000, 701.000, 51.000, 13.000, 5.000, 0.000, 702.000, 110.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017354400', 67.000, 48.000, 11.000, 1.000, 0.000, 0.000, 4.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005650101', 182.000, 111.000, 7.000, 2.000, 0.000, 0.000, 34.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005653204', 46.000, 33.000, 5.000, 0.000, 0.000, 0.000, 5.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732400', 2427.000, 1159.000, 219.000, 9.000, 3.000, 5.000, 846.000, 186.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015821904', 147.000, 76.000, 2.000, 2.000, 0.000, 0.000, 57.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023523202', 121.000, 90.000, 4.000, 2.000, 0.000, 0.000, 15.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025040401', 144.000, 68.000, 17.000, 1.000, 1.000, 0.000, 42.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005652500', 584.000, 252.000, 28.000, 21.000, 1.000, 2.000, 236.000, 44.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003935200', 93.000, 53.000, 7.000, 0.000, 0.000, 0.000, 25.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005641200', 263.000, 152.000, 27.000, 0.000, 0.000, 0.000, 63.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017311800', 581.000, 223.000, 9.000, 17.000, 6.000, 0.000, 284.000, 42.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021416200', 388.000, 118.000, 108.000, 8.000, 0.000, 0.000, 111.000, 43.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005615100', 104.000, 79.000, 1.000, 0.000, 2.000, 0.000, 9.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027736500', 64.000, 45.000, 1.000, 1.000, 0.000, 0.000, 13.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023530100', 113.000, 55.000, 11.000, 3.000, 0.000, 0.000, 33.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017334100', 126.000, 83.000, 5.000, 5.000, 1.000, 0.000, 24.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025101101', 479.000, 39.000, 143.000, 1.000, 0.000, 1.000, 241.000, 54.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025981202', 37.000, 29.000, 5.000, 0.000, 0.000, 0.000, 3.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027710800', 1196.000, 479.000, 68.000, 4.000, 1.000, 0.000, 566.000, 78.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017374000', 207.000, 128.000, 9.000, 4.000, 2.000, 0.000, 46.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011040501', 146.000, 94.000, 5.000, 1.000, 0.000, 0.000, 36.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017333200', 162.000, 93.000, 6.000, 1.000, 0.000, 0.000, 44.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025160502', 2750.000, 1029.000, 97.000, 20.000, 6.000, 0.000, 1390.000, 208.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023506202', 22.000, 14.000, 0.000, 0.000, 0.000, 0.000, 5.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027755100', 132.000, 77.000, 3.000, 1.000, 0.000, 0.000, 41.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027749100', 144.000, 92.000, 8.000, 9.000, 0.000, 0.000, 29.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005640901', 543.000, 190.000, 34.000, 8.000, 1.000, 0.000, 275.000, 35.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025070600', 114.000, 64.000, 5.000, 0.000, 0.000, 0.000, 34.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027753200', 143.000, 88.000, 1.000, 3.000, 0.000, 1.000, 35.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009206400', 1531.000, 477.000, 140.000, 10.000, 6.000, 1.000, 771.000, 126.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017339500', 236.000, 170.000, 11.000, 0.000, 1.000, 0.000, 35.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027761100', 120.000, 78.000, 1.000, 1.000, 1.000, 0.000, 15.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001015200', 23.000, 19.000, 0.000, 0.000, 1.000, 0.000, 3.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009204500', 340.000, 143.000, 47.000, 9.000, 3.000, 0.000, 99.000, 39.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009252602', 385.000, 200.000, 22.000, 5.000, 0.000, 0.000, 124.000, 34.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021417802', 127.000, 44.000, 23.000, 4.000, 3.000, 0.000, 36.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025110301', 692.000, 272.000, 79.000, 5.000, 1.000, 0.000, 286.000, 49.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017357300', 97.000, 65.000, 3.000, 5.000, 3.000, 0.000, 12.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013802602', 145.000, 68.000, 5.000, 2.000, 0.000, 0.000, 58.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813303', 88.000, 69.000, 3.000, 0.000, 0.000, 0.000, 9.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021411100', 221.000, 139.000, 11.000, 0.000, 1.000, 0.000, 56.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001013300', 28.000, 20.000, 2.000, 0.000, 0.000, 0.000, 6.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017357400', 86.000, 57.000, 1.000, 0.000, 0.000, 0.000, 18.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732801', 320.000, 199.000, 20.000, 0.000, 0.000, 0.000, 76.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003900300', 170.000, 79.000, 9.000, 0.000, 0.000, 2.000, 66.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025092300', 458.000, 113.000, 92.000, 4.000, 0.000, 0.000, 201.000, 48.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001011802', 57.000, 32.000, 2.000, 2.000, 0.000, 0.000, 15.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021402102', 344.000, 178.000, 27.000, 4.000, 0.000, 0.000, 110.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027709600', 505.000, 303.000, 17.000, 3.000, 0.000, 0.000, 155.000, 27.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009261102', 462.000, 218.000, 44.000, 2.000, 1.000, 0.000, 158.000, 39.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017342202', 718.000, 349.000, 35.000, 10.000, 1.000, 0.000, 281.000, 42.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021403300', 99.000, 88.000, 0.000, 0.000, 1.000, 0.000, 7.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023525203', 99.000, 72.000, 4.000, 0.000, 1.000, 0.000, 14.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009203302', 122.000, 71.000, 11.000, 5.000, 0.000, 0.000, 27.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001010208', 41.000, 28.000, 1.000, 1.000, 0.000, 0.000, 5.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005655200', 73.000, 39.000, 4.000, 0.000, 1.000, 0.000, 24.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017317102', 100.000, 70.000, 4.000, 0.000, 0.000, 0.000, 20.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025140400', 1229.000, 362.000, 258.000, 28.000, 2.000, 0.000, 475.000, 104.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027710300', 396.000, 240.000, 39.000, 6.000, 1.000, 0.000, 78.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027740200', 84.000, 54.000, 6.000, 0.000, 0.000, 1.000, 14.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005640400', 70.000, 38.000, 0.000, 0.000, 0.000, 0.000, 23.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801700', 1840.000, 646.000, 224.000, 23.000, 1.000, 13.000, 793.000, 140.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732500', 694.000, 255.000, 38.000, 13.000, 0.000, 0.000, 318.000, 70.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009205300', 419.000, 166.000, 12.000, 0.000, 0.000, 0.000, 185.000, 56.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021400800', 272.000, 195.000, 9.000, 3.000, 2.000, 1.000, 29.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001012200', 43.000, 23.000, 0.000, 1.000, 0.000, 2.000, 10.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021418003', 153.000, 89.000, 12.000, 2.000, 0.000, 0.000, 40.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023561200', 119.000, 56.000, 12.000, 0.000, 2.000, 0.000, 38.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005651100', 958.000, 417.000, 96.000, 19.000, 0.000, 0.000, 334.000, 92.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017316300', 241.000, 163.000, 3.000, 0.000, 1.000, 0.000, 53.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021422100', 105.000, 69.000, 11.000, 1.000, 2.000, 0.000, 17.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025120105', 150.000, 87.000, 8.000, 6.000, 1.000, 0.000, 22.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005646104', 24.000, 20.000, 0.000, 1.000, 0.000, 0.000, 1.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009253201', 172.000, 121.000, 5.000, 0.000, 0.000, 0.000, 35.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017314301', 338.000, 162.000, 15.000, 2.000, 0.000, 1.000, 116.000, 42.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013810412', 93.000, 78.000, 2.000, 0.000, 0.000, 0.000, 9.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025180200', 266.000, 156.000, 4.000, 3.000, 0.000, 0.000, 80.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025082100', 1661.000, 338.000, 262.000, 22.000, 2.000, 0.000, 883.000, 154.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009217400', 254.000, 131.000, 18.000, 2.000, 1.000, 0.000, 82.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013810700', 701.000, 298.000, 37.000, 4.000, 0.000, 1.000, 309.000, 52.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005613400', 289.000, 144.000, 36.000, 8.000, 0.000, 0.000, 64.000, 37.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009260301', 207.000, 94.000, 6.000, 2.000, 1.000, 0.000, 94.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001012700', 113.000, 77.000, 5.000, 3.000, 0.000, 1.000, 23.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025100400', 457.000, 91.000, 141.000, 2.000, 2.000, 0.000, 159.000, 62.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009205500', 1925.000, 659.000, 66.000, 45.000, 4.000, 6.000, 970.000, 175.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009223200', 79.000, 48.000, 2.000, 1.000, 0.000, 0.000, 20.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025130404', 349.000, 172.000, 28.000, 2.000, 1.000, 0.000, 136.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25007200300', 99.000, 41.000, 17.000, 3.000, 4.000, 2.000, 31.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025101002', 693.000, 117.000, 206.000, 16.000, 1.000, 3.000, 265.000, 85.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025120301', 1821.000, 616.000, 161.000, 36.000, 2.000, 0.000, 815.000, 191.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009217600', 147.000, 92.000, 1.000, 6.000, 0.000, 0.000, 29.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017357500', 59.000, 33.000, 0.000, 2.000, 1.000, 0.000, 10.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021403400', 160.000, 120.000, 13.000, 0.000, 2.000, 0.000, 19.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025101001', 594.000, 118.000, 142.000, 12.000, 1.000, 0.000, 260.000, 61.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009267101', 56.000, 44.000, 2.000, 1.000, 0.000, 0.000, 4.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017374800', 157.000, 104.000, 25.000, 2.000, 0.000, 0.000, 21.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027754400', 202.000, 99.000, 19.000, 1.000, 0.000, 0.000, 56.000, 27.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001013800', 95.000, 53.000, 3.000, 1.000, 1.000, 2.000, 24.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009210500', 205.000, 114.000, 16.000, 3.000, 1.000, 0.000, 53.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017311100', 792.000, 283.000, 53.000, 25.000, 3.000, 2.000, 363.000, 63.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813101', 104.000, 62.000, 3.000, 1.000, 0.000, 0.000, 23.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017385201', 194.000, 94.000, 10.000, 2.000, 0.000, 3.000, 69.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021422301', 46.000, 25.000, 1.000, 0.000, 0.000, 0.000, 12.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005600204', 108.000, 52.000, 5.000, 1.000, 0.000, 0.000, 35.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017353700', 354.000, 242.000, 11.000, 3.000, 4.000, 0.000, 61.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021441203', 39.000, 27.000, 0.000, 1.000, 0.000, 0.000, 4.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027721102', 146.000, 115.000, 1.000, 0.000, 0.000, 0.000, 21.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025040200', 351.000, 147.000, 54.000, 3.000, 0.000, 0.000, 131.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025100603', 129.000, 67.000, 23.000, 0.000, 0.000, 1.000, 34.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017338200', 100.000, 64.000, 4.000, 0.000, 2.000, 0.000, 17.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025091400', 802.000, 217.000, 106.000, 5.000, 1.000, 0.000, 414.000, 59.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017373200', 269.000, 148.000, 13.000, 0.000, 2.000, 0.000, 87.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021456101', 97.000, 59.000, 10.000, 1.000, 0.000, 0.000, 19.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005640700', 60.000, 25.000, 3.000, 0.000, 0.000, 3.000, 27.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017365201', 119.000, 87.000, 4.000, 2.000, 0.000, 3.000, 14.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010203', 569.000, 347.000, 20.000, 4.000, 7.000, 1.000, 145.000, 45.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027709702', 225.000, 126.000, 3.000, 0.000, 0.000, 0.000, 71.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023540102', 24.000, 20.000, 0.000, 0.000, 0.000, 0.000, 4.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009206600', 1028.000, 310.000, 61.000, 38.000, 5.000, 0.000, 507.000, 107.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009267102', 72.000, 50.000, 1.000, 0.000, 1.000, 0.000, 12.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021404100', 192.000, 136.000, 1.000, 6.000, 2.000, 0.000, 32.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003924100', 59.000, 50.000, 0.000, 0.000, 0.000, 0.000, 8.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009204400', 171.000, 84.000, 11.000, 3.000, 0.000, 0.000, 46.000, 27.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812001', 1774.000, 689.000, 125.000, 24.000, 5.000, 1.000, 845.000, 85.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017339900', 263.000, 157.000, 13.000, 1.000, 0.000, 0.000, 66.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017385202', 332.000, 215.000, 6.000, 3.000, 1.000, 0.000, 87.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015822402', 250.000, 127.000, 10.000, 0.000, 1.000, 1.000, 80.000, 31.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017326101', 69.000, 47.000, 0.000, 0.000, 0.000, 0.000, 14.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005633200', 105.000, 70.000, 2.000, 0.000, 0.000, 1.000, 17.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017352900', 138.000, 104.000, 10.000, 1.000, 0.000, 0.000, 21.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021408101', 134.000, 115.000, 2.000, 5.000, 0.000, 0.000, 9.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025981201', 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003920101', 198.000, 124.000, 8.000, 2.000, 0.000, 3.000, 38.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005985500', 164.000, 84.000, 20.000, 0.000, 2.000, 0.000, 45.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813301', 203.000, 128.000, 8.000, 1.000, 2.000, 0.000, 51.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025020200', 174.000, 121.000, 6.000, 0.000, 3.000, 0.000, 36.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801604', 349.000, 230.000, 38.000, 0.000, 1.000, 0.000, 55.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813802', 79.000, 69.000, 1.000, 0.000, 0.000, 2.000, 3.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005652800', 171.000, 96.000, 3.000, 1.000, 0.000, 0.000, 60.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023503101', 85.000, 60.000, 4.000, 1.000, 0.000, 0.000, 13.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005653101', 146.000, 105.000, 3.000, 0.000, 2.000, 0.000, 27.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813204', 59.000, 35.000, 0.000, 1.000, 0.000, 0.000, 20.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017317201', 52.000, 43.000, 2.000, 0.000, 0.000, 0.000, 3.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021423100', 96.000, 83.000, 1.000, 3.000, 4.000, 0.000, 2.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027761300', 79.000, 61.000, 2.000, 0.000, 0.000, 0.000, 12.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013811800', 2364.000, 927.000, 107.000, 25.000, 1.000, 1.000, 1201.000, 102.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021422700', 111.000, 87.000, 8.000, 1.000, 0.000, 0.000, 5.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017373800', 219.000, 158.000, 6.000, 0.000, 0.000, 0.000, 28.000, 27.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021456302', 167.000, 99.000, 19.000, 1.000, 1.000, 0.000, 30.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023501202', 82.000, 62.000, 3.000, 2.000, 0.000, 0.000, 12.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005652200', 104.000, 58.000, 6.000, 3.000, 0.000, 0.000, 29.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017334200', 70.000, 44.000, 0.000, 3.000, 0.000, 0.000, 13.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027711000', 801.000, 355.000, 32.000, 10.000, 1.000, 0.000, 363.000, 40.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027744101', 309.000, 216.000, 6.000, 2.000, 0.000, 0.000, 65.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021442102', 110.000, 94.000, 1.000, 0.000, 0.000, 1.000, 11.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023523201', 26.000, 16.000, 0.000, 1.000, 0.000, 0.000, 7.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000704', 575.000, 336.000, 29.000, 10.000, 5.000, 1.000, 156.000, 38.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001014700', 62.000, 52.000, 1.000, 4.000, 0.000, 0.000, 2.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013800600', 2369.000, 839.000, 123.000, 29.000, 4.000, 9.000, 1189.000, 176.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025110401', 1309.000, 538.000, 102.000, 11.000, 1.000, 1.000, 529.000, 127.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005613600', 257.000, 133.000, 18.000, 1.000, 0.000, 0.000, 62.000, 43.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027760100', 44.000, 34.000, 0.000, 2.000, 0.000, 0.000, 4.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005651001', 172.000, 86.000, 4.000, 5.000, 0.000, 0.000, 55.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005651700', 384.000, 161.000, 44.000, 4.000, 0.000, 0.000, 133.000, 42.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017368101', 306.000, 199.000, 9.000, 1.000, 3.000, 0.000, 66.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25019950307', 4.000, 0.000, 0.000, 0.000, 0.000, 0.000, 4.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000302', 229.000, 177.000, 7.000, 0.000, 4.000, 0.000, 26.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021422302', 206.000, 115.000, 23.000, 4.000, 2.000, 0.000, 49.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021442103', 115.000, 92.000, 4.000, 2.000, 0.000, 0.000, 11.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001013200', 88.000, 56.000, 6.000, 1.000, 0.000, 0.000, 10.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021407100', 136.000, 115.000, 3.000, 1.000, 0.000, 1.000, 14.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021457200', 95.000, 73.000, 0.000, 1.000, 0.000, 0.000, 13.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025160501', 3956.000, 1606.000, 111.000, 72.000, 9.000, 0.000, 1884.000, 274.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001013400', 44.000, 27.000, 0.000, 1.000, 0.000, 0.000, 1.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017324101', 61.000, 49.000, 0.000, 1.000, 2.000, 0.000, 1.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025090901', 768.000, 274.000, 101.000, 10.000, 0.000, 0.000, 332.000, 51.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009252201', 423.000, 178.000, 23.000, 4.000, 4.000, 0.000, 176.000, 38.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017318300', 121.000, 101.000, 3.000, 1.000, 0.000, 0.000, 13.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027728100', 98.000, 66.000, 5.000, 0.000, 0.000, 0.000, 13.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025030500', 93.000, 68.000, 3.000, 0.000, 1.000, 0.000, 19.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021417200', 158.000, 72.000, 8.000, 1.000, 5.000, 0.000, 63.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801402', 641.000, 268.000, 61.000, 3.000, 1.000, 1.000, 261.000, 46.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025110501', 461.000, 178.000, 27.000, 9.000, 0.000, 0.000, 187.000, 60.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027728300', 116.000, 80.000, 4.000, 0.000, 0.000, 0.000, 21.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801101', 1223.000, 478.000, 79.000, 10.000, 1.000, 1.000, 585.000, 69.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015822300', 223.000, 139.000, 2.000, 0.000, 0.000, 0.000, 67.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017327102', 89.000, 59.000, 10.000, 1.000, 0.000, 1.000, 9.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021414100', 132.000, 71.000, 25.000, 0.000, 0.000, 0.000, 26.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023524101', 63.000, 49.000, 0.000, 0.000, 0.000, 0.000, 9.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027730801', 137.000, 86.000, 8.000, 0.000, 0.000, 0.000, 35.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013802601', 2149.000, 760.000, 135.000, 10.000, 11.000, 0.000, 1066.000, 167.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021404302', 96.000, 90.000, 0.000, 1.000, 0.000, 0.000, 5.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023510600', 131.000, 63.000, 25.000, 1.000, 0.000, 0.000, 31.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023521101', 80.000, 51.000, 3.000, 0.000, 0.000, 0.000, 18.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009252102', 185.000, 67.000, 6.000, 2.000, 1.000, 0.000, 93.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011040600', 104.000, 77.000, 1.000, 3.000, 1.000, 0.000, 12.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023510700', 430.000, 172.000, 83.000, 2.000, 0.000, 0.000, 130.000, 43.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003931300', 14.000, 9.000, 1.000, 0.000, 1.000, 0.000, 0.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009223300', 96.000, 74.000, 1.000, 6.000, 0.000, 0.000, 11.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009252603', 246.000, 103.000, 9.000, 4.000, 0.000, 0.000, 112.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013800500', 1962.000, 682.000, 101.000, 20.000, 5.000, 0.000, 1010.000, 144.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025091700', 632.000, 154.000, 155.000, 16.000, 0.000, 0.000, 234.000, 73.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027757100', 1190.000, 602.000, 58.000, 5.000, 5.000, 0.000, 441.000, 79.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017382500', 142.000, 88.000, 3.000, 3.000, 4.000, 0.000, 32.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021410400', 156.000, 90.000, 10.000, 1.000, 0.000, 0.000, 34.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025060200', 13.000, 10.000, 0.000, 0.000, 1.000, 0.000, 2.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025060501', 72.000, 36.000, 1.000, 1.000, 0.000, 0.000, 17.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025120500', 1195.000, 420.000, 115.000, 32.000, 0.000, 0.000, 507.000, 121.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021413200', 244.000, 122.000, 14.000, 2.000, 0.000, 1.000, 70.000, 35.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009253204', 113.000, 76.000, 5.000, 4.000, 1.000, 0.000, 21.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027730600', 358.000, 235.000, 23.000, 4.000, 6.000, 1.000, 60.000, 29.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017310200', 1113.000, 518.000, 39.000, 4.000, 4.000, 0.000, 455.000, 93.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017317202', 63.000, 52.000, 0.000, 1.000, 0.000, 0.000, 5.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023510501', 237.000, 96.000, 53.000, 2.000, 0.000, 0.000, 64.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005653301', 79.000, 59.000, 0.000, 1.000, 2.000, 0.000, 15.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017363201', 61.000, 54.000, 0.000, 1.000, 0.000, 0.000, 4.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025050200', 3427.000, 1660.000, 65.000, 14.000, 0.000, 1.000, 1422.000, 265.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017373400', 144.000, 81.000, 7.000, 4.000, 2.000, 0.000, 35.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021404201', 76.000, 65.000, 0.000, 1.000, 0.000, 0.000, 6.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021405100', 83.000, 59.000, 3.000, 0.000, 3.000, 1.000, 12.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009206900', 1040.000, 271.000, 108.000, 17.000, 5.000, 3.000, 534.000, 102.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003921300', 192.000, 102.000, 18.000, 2.000, 1.000, 0.000, 53.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005651600', 431.000, 146.000, 41.000, 5.000, 0.000, 0.000, 194.000, 45.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017337102', 95.000, 60.000, 3.000, 2.000, 0.000, 0.000, 17.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017383400', 942.000, 497.000, 53.000, 16.000, 2.000, 0.000, 304.000, 70.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017387202', 75.000, 53.000, 1.000, 0.000, 5.000, 0.000, 13.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021400100', 279.000, 193.000, 3.000, 3.000, 2.000, 1.000, 57.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017352300', 346.000, 223.000, 24.000, 4.000, 1.000, 0.000, 56.000, 38.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021419700', 62.000, 48.000, 2.000, 0.000, 0.000, 0.000, 11.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017352200', 190.000, 91.000, 31.000, 1.000, 0.000, 0.000, 53.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025110502', 876.000, 426.000, 56.000, 11.000, 2.000, 0.000, 310.000, 71.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027724100', 63.000, 35.000, 1.000, 6.000, 0.000, 0.000, 12.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009205600', 915.000, 236.000, 96.000, 4.000, 4.000, 0.000, 452.000, 123.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011040702', 92.000, 39.000, 5.000, 1.000, 0.000, 0.000, 32.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023521102', 70.000, 44.000, 0.000, 0.000, 0.000, 0.000, 16.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021406101', 93.000, 72.000, 1.000, 0.000, 0.000, 0.000, 13.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015822605', 18.000, 10.000, 1.000, 0.000, 0.000, 0.000, 3.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021416101', 112.000, 79.000, 9.000, 0.000, 2.000, 0.000, 16.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023542200', 69.000, 64.000, 1.000, 0.000, 0.000, 0.000, 2.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003933400', 28.000, 21.000, 0.000, 1.000, 0.000, 0.000, 4.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005632100', 95.000, 57.000, 4.000, 6.000, 0.000, 0.000, 21.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017341101', 160.000, 77.000, 17.000, 2.000, 3.000, 0.000, 43.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005650300', 221.000, 105.000, 24.000, 22.000, 0.000, 0.000, 58.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027707300', 228.000, 108.000, 16.000, 4.000, 0.000, 5.000, 69.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005642400', 83.000, 47.000, 10.000, 1.000, 1.000, 0.000, 18.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017316202', 100.000, 62.000, 0.000, 3.000, 0.000, 0.000, 20.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017356300', 243.000, 144.000, 27.000, 0.000, 1.000, 0.000, 53.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023501101', 26.000, 20.000, 0.000, 0.000, 0.000, 0.000, 1.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023505102', 50.000, 43.000, 1.000, 0.000, 0.000, 0.000, 2.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023508101', 73.000, 55.000, 3.000, 1.000, 0.000, 0.000, 12.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013800202', 359.000, 184.000, 26.000, 1.000, 1.000, 0.000, 123.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013802300', 3454.000, 1188.000, 258.000, 27.000, 1.000, 13.000, 1716.000, 251.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023506204', 48.000, 30.000, 0.000, 1.000, 0.000, 0.000, 14.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023561100', 52.000, 41.000, 0.000, 0.000, 0.000, 0.000, 5.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25007200200', 110.000, 71.000, 11.000, 3.000, 0.000, 4.000, 11.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017314302', 60.000, 44.000, 1.000, 0.000, 2.000, 0.000, 9.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021456102', 172.000, 109.000, 18.000, 2.000, 0.000, 0.000, 31.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009252502', 570.000, 328.000, 23.000, 2.000, 0.000, 0.000, 192.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021404301', 125.000, 116.000, 1.000, 0.000, 0.000, 0.000, 7.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027704200', 51.000, 40.000, 0.000, 0.000, 0.000, 0.000, 8.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021417100', 85.000, 45.000, 7.000, 1.000, 2.000, 0.000, 21.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023510502', 521.000, 210.000, 78.000, 8.000, 0.000, 2.000, 182.000, 41.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009211200', 144.000, 83.000, 9.000, 4.000, 1.000, 0.000, 34.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005631300', 249.000, 118.000, 7.000, 3.000, 2.000, 3.000, 99.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015821300', 195.000, 126.000, 4.000, 0.000, 1.000, 0.000, 45.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013810200', 56.000, 26.000, 5.000, 0.000, 0.000, 0.000, 11.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017980000', 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001010100', 142.000, 88.000, 4.000, 7.000, 0.000, 1.000, 37.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027737300', 156.000, 106.000, 0.000, 5.000, 0.000, 0.000, 31.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023511702', 141.000, 80.000, 24.000, 1.000, 1.000, 0.000, 25.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801603', 679.000, 310.000, 66.000, 16.000, 3.000, 0.000, 235.000, 49.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017330100', 121.000, 99.000, 0.000, 6.000, 0.000, 0.000, 10.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025170400', 1206.000, 574.000, 37.000, 14.000, 2.000, 2.000, 482.000, 95.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009204600', 531.000, 207.000, 39.000, 9.000, 3.000, 4.000, 203.000, 66.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017337201', 80.000, 55.000, 4.000, 0.000, 0.000, 0.000, 19.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027751102', 38.000, 22.000, 1.000, 4.000, 0.000, 0.000, 6.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023510200', 487.000, 192.000, 59.000, 1.000, 1.000, 0.000, 179.000, 55.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017326102', 124.000, 100.000, 1.000, 0.000, 1.000, 0.000, 14.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017353800', 257.000, 210.000, 7.000, 0.000, 3.000, 0.000, 24.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010204', 178.000, 114.000, 3.000, 1.000, 3.000, 1.000, 44.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025981300', 71.000, 57.000, 0.000, 0.000, 2.000, 1.000, 10.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015821700', 111.000, 63.000, 2.000, 2.000, 0.000, 0.000, 39.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017370101', 384.000, 198.000, 25.000, 4.000, 8.000, 0.000, 117.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25007200400', 57.000, 31.000, 3.000, 1.000, 0.000, 0.000, 12.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015820203', 280.000, 169.000, 9.000, 2.000, 1.000, 2.000, 69.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021403500', 133.000, 98.000, 0.000, 0.000, 2.000, 0.000, 23.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023504101', 83.000, 75.000, 0.000, 0.000, 0.000, 0.000, 3.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027709400', 1342.000, 721.000, 62.000, 7.000, 11.000, 4.000, 460.000, 77.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732302', 669.000, 307.000, 44.000, 1.000, 2.000, 0.000, 255.000, 60.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017358100', 103.000, 86.000, 1.000, 1.000, 0.000, 0.000, 3.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027703100', 188.000, 98.000, 16.000, 6.000, 0.000, 0.000, 40.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027725100', 89.000, 62.000, 0.000, 5.000, 0.000, 0.000, 8.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005611102', 131.000, 58.000, 18.000, 2.000, 1.000, 0.000, 40.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027733000', 1386.000, 498.000, 73.000, 19.000, 3.000, 0.000, 646.000, 147.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017356100', 120.000, 88.000, 1.000, 2.000, 1.000, 0.000, 18.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005645103', 56.000, 53.000, 1.000, 1.000, 0.000, 0.000, 1.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005633100', 78.000, 50.000, 2.000, 0.000, 2.000, 0.000, 18.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009206000', 1808.000, 487.000, 123.000, 47.000, 2.000, 0.000, 993.000, 156.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023507104', 50.000, 38.000, 1.000, 0.000, 1.000, 0.000, 8.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001010500', 20.000, 17.000, 1.000, 0.000, 0.000, 0.000, 2.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003925100', 424.000, 249.000, 12.000, 13.000, 0.000, 0.000, 104.000, 46.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009263100', 66.000, 48.000, 2.000, 1.000, 0.000, 0.000, 3.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025140201', 341.000, 104.000, 44.000, 3.000, 0.000, 0.000, 157.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732902', 210.000, 123.000, 16.000, 0.000, 3.000, 1.000, 52.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005600203', 103.000, 71.000, 9.000, 1.000, 1.000, 0.000, 17.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009211300', 113.000, 73.000, 6.000, 0.000, 0.000, 0.000, 24.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015820204', 105.000, 68.000, 0.000, 1.000, 0.000, 0.000, 17.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023521202', 48.000, 26.000, 0.000, 3.000, 3.000, 0.000, 5.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027756101', 115.000, 79.000, 1.000, 0.000, 1.000, 1.000, 20.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812902', 142.000, 92.000, 8.000, 0.000, 2.000, 0.000, 34.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017321500', 1225.000, 598.000, 3.000, 26.000, 1.000, 6.000, 521.000, 70.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001013500', 81.000, 51.000, 2.000, 5.000, 1.000, 0.000, 13.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005614101', 338.000, 187.000, 35.000, 4.000, 2.000, 1.000, 75.000, 34.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023506102', 103.000, 57.000, 0.000, 0.000, 0.000, 0.000, 31.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003933200', 9.000, 4.000, 0.000, 1.000, 0.000, 0.000, 4.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005990000', 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005650700', 1175.000, 271.000, 60.000, 308.000, 2.000, 3.000, 456.000, 75.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027731202', 59.000, 27.000, 6.000, 0.000, 0.000, 0.000, 15.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017365100', 128.000, 102.000, 2.000, 0.000, 1.000, 0.000, 10.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021419400', 81.000, 46.000, 1.000, 0.000, 0.000, 0.000, 22.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000803', 576.000, 398.000, 21.000, 2.000, 15.000, 0.000, 108.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009221400', 166.000, 55.000, 12.000, 3.000, 0.000, 5.000, 75.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023510100', 322.000, 156.000, 27.000, 3.000, 0.000, 0.000, 99.000, 37.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021420202', 403.000, 120.000, 82.000, 17.000, 2.000, 0.000, 131.000, 51.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009266400', 60.000, 37.000, 5.000, 0.000, 0.000, 0.000, 13.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813403', 82.000, 63.000, 1.000, 2.000, 0.000, 0.000, 7.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025020301', 108.000, 82.000, 3.000, 0.000, 1.000, 0.000, 19.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25019990000', 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017327103', 57.000, 42.000, 0.000, 2.000, 0.000, 1.000, 7.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017318100', 89.000, 61.000, 6.000, 2.000, 0.000, 0.000, 16.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025080801', 1345.000, 450.000, 155.000, 25.000, 4.000, 0.000, 602.000, 109.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017373900', 196.000, 146.000, 8.000, 0.000, 4.000, 0.000, 26.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009205400', 177.000, 88.000, 16.000, 3.000, 1.000, 0.000, 59.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023542300', 159.000, 105.000, 12.000, 8.000, 2.000, 1.000, 17.000, 14.000);
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INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812101', 365.000, 209.000, 4.000, 5.000, 3.000, 0.000, 126.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017354100', 147.000, 120.000, 3.000, 1.000, 2.000, 0.000, 13.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000503', 96.000, 59.000, 2.000, 1.000, 1.000, 0.000, 26.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005631800', 290.000, 161.000, 7.000, 1.000, 0.000, 2.000, 98.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005632200', 156.000, 103.000, 6.000, 1.000, 0.000, 0.000, 37.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025091200', 697.000, 180.000, 96.000, 6.000, 2.000, 1.000, 335.000, 77.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027754200', 346.000, 174.000, 31.000, 1.000, 0.000, 0.000, 117.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009268200', 107.000, 72.000, 3.000, 2.000, 1.000, 0.000, 17.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021441202', 43.000, 38.000, 0.000, 2.000, 0.000, 0.000, 0.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023530400', 68.000, 45.000, 4.000, 8.000, 0.000, 0.000, 5.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021401000', 247.000, 145.000, 13.000, 4.000, 1.000, 0.000, 59.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027742300', 251.000, 105.000, 13.000, 1.000, 4.000, 0.000, 105.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017369100', 217.000, 136.000, 1.000, 0.000, 0.000, 0.000, 73.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001015002', 81.000, 54.000, 4.000, 10.000, 0.000, 0.000, 6.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017333100', 177.000, 117.000, 4.000, 1.000, 1.000, 0.000, 44.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027730200', 435.000, 240.000, 30.000, 1.000, 0.000, 0.000, 133.000, 31.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009251700', 3136.000, 931.000, 212.000, 54.000, 8.000, 4.000, 1745.000, 182.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023511302', 362.000, 151.000, 52.000, 8.000, 0.000, 0.000, 110.000, 41.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732001', 2243.000, 967.000, 168.000, 7.000, 11.000, 0.000, 851.000, 239.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005616100', 90.000, 69.000, 3.000, 1.000, 2.000, 0.000, 9.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001013600', 120.000, 81.000, 4.000, 1.000, 1.000, 0.000, 23.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025020101', 139.000, 114.000, 3.000, 0.000, 1.000, 0.000, 12.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025170100', 2007.000, 835.000, 76.000, 29.000, 3.000, 3.000, 927.000, 134.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017311300', 520.000, 196.000, 21.000, 3.000, 4.000, 0.000, 247.000, 49.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027703300', 140.000, 84.000, 0.000, 6.000, 0.000, 0.000, 38.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009260900', 1148.000, 421.000, 74.000, 19.000, 5.000, 0.000, 541.000, 88.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013802100', 1970.000, 678.000, 126.000, 20.000, 1.000, 2.000, 1012.000, 131.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017383300', 352.000, 112.000, 20.000, 5.000, 4.000, 0.000, 179.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021419600', 125.000, 73.000, 12.000, 3.000, 3.000, 0.000, 27.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017341102', 241.000, 125.000, 3.000, 0.000, 11.000, 0.000, 80.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027709502', 359.000, 243.000, 20.000, 0.000, 0.000, 0.000, 86.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017353600', 246.000, 171.000, 12.000, 2.000, 1.000, 0.000, 37.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732901', 1116.000, 548.000, 60.000, 10.000, 2.000, 0.000, 386.000, 110.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009252202', 101.000, 45.000, 0.000, 0.000, 2.000, 0.000, 46.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013800800', 1701.000, 803.000, 102.000, 18.000, 2.000, 5.000, 678.000, 93.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009250700', 4090.000, 1209.000, 283.000, 45.000, 5.000, 4.000, 2146.000, 398.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001012601', 148.000, 104.000, 8.000, 4.000, 1.000, 1.000, 27.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013810902', 852.000, 330.000, 51.000, 2.000, 0.000, 0.000, 406.000, 63.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025120201', 800.000, 283.000, 86.000, 11.000, 3.000, 0.000, 346.000, 71.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025160200', 3347.000, 841.000, 55.000, 31.000, 1.000, 1.000, 2224.000, 194.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005651300', 435.000, 138.000, 62.000, 9.000, 0.000, 0.000, 186.000, 40.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000703', 182.000, 113.000, 8.000, 0.000, 10.000, 0.000, 42.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017322400', 185.000, 126.000, 9.000, 0.000, 0.000, 0.000, 34.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003900200', 542.000, 276.000, 33.000, 2.000, 0.000, 1.000, 169.000, 61.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001011700', 36.000, 26.000, 1.000, 0.000, 0.000, 0.000, 4.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801300', 1772.000, 729.000, 190.000, 25.000, 6.000, 1.000, 663.000, 158.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813207', 243.000, 148.000, 10.000, 6.000, 3.000, 0.000, 60.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027731002', 1081.000, 503.000, 98.000, 18.000, 1.000, 0.000, 360.000, 101.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021456301', 88.000, 23.000, 14.000, 5.000, 0.000, 0.000, 32.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027735200', 191.000, 109.000, 6.000, 0.000, 0.000, 0.000, 60.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025990101', 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027708100', 193.000, 139.000, 3.000, 3.000, 0.000, 0.000, 29.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011040800', 207.000, 107.000, 13.000, 3.000, 0.000, 0.000, 59.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017388100', 113.000, 83.000, 0.000, 1.000, 1.000, 0.000, 18.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005985600', 149.000, 90.000, 6.000, 1.000, 1.000, 1.000, 41.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023520201', 78.000, 25.000, 7.000, 5.000, 0.000, 0.000, 38.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000201', 407.000, 190.000, 32.000, 3.000, 5.000, 0.000, 139.000, 38.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025050300', 1028.000, 416.000, 81.000, 11.000, 1.000, 0.000, 424.000, 95.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027723100', 61.000, 36.000, 2.000, 1.000, 0.000, 0.000, 12.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027731500', 2419.000, 1029.000, 207.000, 12.000, 6.000, 5.000, 1054.000, 106.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812002', 1971.000, 850.000, 86.000, 28.000, 6.000, 0.000, 906.000, 95.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017353900', 578.000, 446.000, 14.000, 1.000, 3.000, 0.000, 72.000, 42.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021419300', 214.000, 109.000, 23.000, 4.000, 7.000, 0.000, 41.000, 30.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027715100', 87.000, 72.000, 1.000, 0.000, 0.000, 0.000, 5.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017315403', 19.000, 12.000, 0.000, 0.000, 0.000, 0.000, 2.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017350800', 77.000, 49.000, 6.000, 1.000, 0.000, 0.000, 15.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021456402', 186.000, 129.000, 10.000, 1.000, 0.000, 0.000, 38.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017321200', 455.000, 271.000, 20.000, 1.000, 0.000, 0.000, 137.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017367100', 90.000, 80.000, 1.000, 0.000, 0.000, 0.000, 6.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021443102', 174.000, 99.000, 15.000, 0.000, 2.000, 1.000, 49.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005653203', 75.000, 38.000, 2.000, 8.000, 0.000, 0.000, 19.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011040100', 47.000, 33.000, 4.000, 3.000, 0.000, 0.000, 5.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017368902', 499.000, 321.000, 17.000, 2.000, 1.000, 0.000, 130.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025130100', 265.000, 160.000, 12.000, 3.000, 0.000, 0.000, 66.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005611201', 45.000, 30.000, 3.000, 0.000, 0.000, 0.000, 4.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017374300', 70.000, 53.000, 6.000, 0.000, 0.000, 0.000, 6.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009210600', 381.000, 115.000, 36.000, 3.000, 1.000, 0.000, 193.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025071101', 513.000, 180.000, 50.000, 7.000, 1.000, 0.000, 234.000, 41.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027738202', 93.000, 54.000, 0.000, 0.000, 0.000, 0.000, 28.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001013002', 36.000, 22.000, 2.000, 0.000, 0.000, 0.000, 5.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003912100', 109.000, 69.000, 1.000, 0.000, 0.000, 0.000, 33.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005655100', 52.000, 35.000, 0.000, 2.000, 0.000, 0.000, 12.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017301102', 72.000, 54.000, 1.000, 0.000, 0.000, 0.000, 11.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025081400', 586.000, 145.000, 132.000, 19.000, 1.000, 0.000, 238.000, 51.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812701', 448.000, 245.000, 30.000, 3.000, 1.000, 1.000, 144.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005646103', 47.000, 38.000, 1.000, 1.000, 0.000, 0.000, 4.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017300100', 57.000, 32.000, 3.000, 3.000, 0.000, 0.000, 9.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025180301', 125.000, 93.000, 4.000, 1.000, 1.000, 1.000, 20.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027703200', 85.000, 54.000, 2.000, 0.000, 1.000, 0.000, 21.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017354800', 76.000, 48.000, 1.000, 2.000, 2.000, 0.000, 15.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015822601', 41.000, 24.000, 5.000, 0.000, 0.000, 0.000, 7.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017312200', 598.000, 269.000, 25.000, 0.000, 13.000, 0.000, 255.000, 36.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025060800', 106.000, 53.000, 4.000, 5.000, 0.000, 0.000, 35.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021420100', 510.000, 220.000, 77.000, 3.000, 1.000, 0.000, 171.000, 38.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027710600', 2001.000, 877.000, 94.000, 17.000, 2.000, 0.000, 879.000, 132.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027738100', 156.000, 98.000, 4.000, 10.000, 0.000, 0.000, 32.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013811700', 1443.000, 594.000, 71.000, 20.000, 3.000, 2.000, 662.000, 91.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025051200', 822.000, 507.000, 29.000, 4.000, 5.000, 5.000, 227.000, 45.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027701100', 300.000, 170.000, 20.000, 4.000, 1.000, 0.000, 75.000, 30.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009220102', 50.000, 36.000, 3.000, 1.000, 0.000, 0.000, 6.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017370400', 222.000, 138.000, 5.000, 1.000, 6.000, 0.000, 50.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021400200', 383.000, 260.000, 11.000, 0.000, 4.000, 0.000, 70.000, 38.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017350103', 111.000, 43.000, 3.000, 0.000, 0.000, 0.000, 50.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021422800', 97.000, 73.000, 5.000, 2.000, 0.000, 0.000, 12.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017332200', 295.000, 189.000, 5.000, 4.000, 5.000, 1.000, 72.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027712101', 151.000, 86.000, 10.000, 1.000, 4.000, 0.000, 40.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021403100', 131.000, 118.000, 0.000, 0.000, 1.000, 0.000, 8.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021417400', 48.000, 29.000, 3.000, 0.000, 0.000, 0.000, 13.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027700100', 155.000, 96.000, 6.000, 2.000, 0.000, 0.000, 36.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009221800', 7.000, 6.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017373700', 76.000, 58.000, 2.000, 2.000, 0.000, 0.000, 8.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021420201', 197.000, 82.000, 35.000, 5.000, 1.000, 0.000, 56.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009217500', 156.000, 101.000, 3.000, 3.000, 3.000, 0.000, 36.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009266200', 111.000, 82.000, 3.000, 3.000, 0.000, 0.000, 15.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021419200', 135.000, 83.000, 10.000, 0.000, 0.000, 0.000, 37.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023506203', 59.000, 52.000, 0.000, 1.000, 0.000, 0.000, 4.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017351000', 309.000, 196.000, 11.000, 7.000, 3.000, 0.000, 70.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021413500', 313.000, 170.000, 9.000, 5.000, 0.000, 0.000, 100.000, 29.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017313101', 127.000, 71.000, 2.000, 3.000, 2.000, 0.000, 34.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017352400', 350.000, 134.000, 37.000, 5.000, 1.000, 0.000, 129.000, 44.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023530500', 58.000, 43.000, 0.000, 1.000, 0.000, 0.000, 10.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017356400', 172.000, 124.000, 3.000, 9.000, 0.000, 1.000, 19.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023509102', 55.000, 42.000, 2.000, 0.000, 0.000, 0.000, 8.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009211402', 139.000, 79.000, 4.000, 2.000, 0.000, 0.000, 42.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001012102', 114.000, 74.000, 7.000, 3.000, 1.000, 0.000, 19.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017387100', 66.000, 54.000, 0.000, 0.000, 0.000, 0.000, 10.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003900600', 304.000, 125.000, 43.000, 2.000, 0.000, 0.000, 92.000, 42.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25007200100', 118.000, 74.000, 3.000, 1.000, 1.000, 0.000, 17.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017321300', 1366.000, 698.000, 33.000, 6.000, 2.000, 3.000, 546.000, 78.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017383102', 2353.000, 1169.000, 198.000, 19.000, 4.000, 3.000, 856.000, 104.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021413300', 178.000, 108.000, 22.000, 3.000, 0.000, 0.000, 27.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013811302', 423.000, 237.000, 28.000, 8.000, 6.000, 0.000, 116.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017331200', 75.000, 50.000, 2.000, 1.000, 0.000, 1.000, 17.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027719100', 158.000, 130.000, 3.000, 1.000, 1.000, 0.000, 18.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005640300', 561.000, 305.000, 56.000, 5.000, 0.000, 0.000, 166.000, 29.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009202102', 187.000, 108.000, 7.000, 0.000, 0.000, 0.000, 43.000, 29.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025070300', 161.000, 108.000, 5.000, 0.000, 1.000, 0.000, 27.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017350200', 882.000, 325.000, 34.000, 10.000, 1.000, 3.000, 433.000, 76.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027721101', 46.000, 37.000, 5.000, 0.000, 0.000, 0.000, 1.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009221600', 157.000, 75.000, 9.000, 2.000, 0.000, 0.000, 47.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025071201', 758.000, 250.000, 74.000, 4.000, 2.000, 0.000, 371.000, 57.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003900700', 69.000, 43.000, 0.000, 0.000, 0.000, 0.000, 19.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812800', 422.000, 241.000, 9.000, 3.000, 0.000, 0.000, 141.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027750300', 129.000, 103.000, 2.000, 1.000, 0.000, 0.000, 16.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027750200', 118.000, 89.000, 4.000, 0.000, 0.000, 0.000, 10.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017315402', 50.000, 35.000, 1.000, 0.000, 0.000, 0.000, 9.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009206100', 1894.000, 687.000, 147.000, 29.000, 0.000, 0.000, 848.000, 183.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023509101', 85.000, 64.000, 2.000, 2.000, 0.000, 0.000, 5.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009221100', 58.000, 39.000, 3.000, 1.000, 0.000, 1.000, 12.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025040300', 384.000, 132.000, 45.000, 7.000, 3.000, 1.000, 141.000, 55.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023530600', 224.000, 184.000, 9.000, 1.000, 0.000, 0.000, 22.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011041000', 167.000, 84.000, 14.000, 1.000, 2.000, 0.000, 46.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017331102', 129.000, 84.000, 9.000, 0.000, 0.000, 0.000, 16.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021456401', 91.000, 42.000, 16.000, 0.000, 0.000, 0.000, 29.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025050500', 996.000, 599.000, 31.000, 0.000, 2.000, 0.000, 298.000, 66.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027746100', 73.000, 43.000, 5.000, 0.000, 0.000, 0.000, 23.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017352600', 265.000, 136.000, 27.000, 2.000, 0.000, 0.000, 76.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025090100', 1029.000, 166.000, 256.000, 11.000, 2.000, 1.000, 485.000, 108.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003921400', 94.000, 45.000, 5.000, 0.000, 0.000, 0.000, 28.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017350400', 181.000, 130.000, 6.000, 0.000, 2.000, 1.000, 22.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003911100', 41.000, 32.000, 4.000, 0.000, 0.000, 0.000, 4.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009210200', 103.000, 67.000, 1.000, 2.000, 2.000, 0.000, 28.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027751101', 77.000, 65.000, 0.000, 5.000, 0.000, 0.000, 6.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001012800', 72.000, 45.000, 3.000, 1.000, 0.000, 0.000, 9.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017320103', 55.000, 38.000, 0.000, 0.000, 0.000, 0.000, 11.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017321600', 510.000, 266.000, 7.000, 2.000, 2.000, 0.000, 191.000, 42.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009221300', 62.000, 50.000, 1.000, 1.000, 1.000, 0.000, 4.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017311500', 244.000, 79.000, 12.000, 2.000, 7.000, 0.000, 111.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021422600', 153.000, 112.000, 4.000, 0.000, 0.000, 0.000, 36.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017341600', 368.000, 212.000, 27.000, 2.000, 0.000, 0.000, 97.000, 30.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017368700', 869.000, 344.000, 17.000, 13.000, 1.000, 5.000, 447.000, 42.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025090700', 379.000, 150.000, 24.000, 4.000, 0.000, 0.000, 177.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021400400', 230.000, 161.000, 0.000, 0.000, 3.000, 0.000, 51.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015821400', 163.000, 104.000, 6.000, 1.000, 3.000, 0.000, 36.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021417801', 136.000, 79.000, 10.000, 1.000, 4.000, 0.000, 26.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023545300', 96.000, 50.000, 9.000, 6.000, 0.000, 0.000, 17.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005650800', 812.000, 217.000, 24.000, 44.000, 0.000, 5.000, 458.000, 64.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000401', 287.000, 155.000, 12.000, 4.000, 0.000, 0.000, 87.000, 29.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009253203', 66.000, 51.000, 0.000, 0.000, 0.000, 0.000, 11.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005613901', 238.000, 112.000, 30.000, 1.000, 0.000, 0.000, 73.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027730500', 560.000, 298.000, 39.000, 9.000, 4.000, 0.000, 146.000, 64.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027711100', 257.000, 172.000, 7.000, 0.000, 0.000, 0.000, 62.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027759100', 51.000, 42.000, 0.000, 0.000, 0.000, 0.000, 6.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023525204', 55.000, 34.000, 7.000, 0.000, 0.000, 0.000, 11.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027755200', 198.000, 117.000, 15.000, 2.000, 2.000, 0.000, 43.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017356602', 111.000, 68.000, 4.000, 0.000, 1.000, 0.000, 23.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021440100', 147.000, 94.000, 8.000, 3.000, 1.000, 0.000, 28.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023511600', 756.000, 286.000, 108.000, 8.000, 6.000, 0.000, 262.000, 86.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009221902', 61.000, 43.000, 7.000, 0.000, 0.000, 0.000, 10.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027744300', 689.000, 380.000, 14.000, 25.000, 3.000, 0.000, 228.000, 39.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025030400', 78.000, 58.000, 1.000, 1.000, 0.000, 0.000, 16.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025070101', 360.000, 229.000, 25.000, 0.000, 2.000, 1.000, 79.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025070800', 337.000, 172.000, 45.000, 2.000, 1.000, 0.000, 84.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009268300', 68.000, 47.000, 0.000, 1.000, 0.000, 0.000, 14.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013800101', 1289.000, 462.000, 74.000, 12.000, 2.000, 0.000, 639.000, 100.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801502', 1285.000, 447.000, 60.000, 6.000, 3.000, 0.000, 662.000, 107.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025070200', 160.000, 80.000, 16.000, 1.000, 3.000, 0.000, 46.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025170501', 814.000, 470.000, 22.000, 9.000, 6.000, 0.000, 251.000, 56.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009260600', 794.000, 410.000, 53.000, 0.000, 0.000, 0.000, 275.000, 56.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025110103', 1746.000, 668.000, 128.000, 20.000, 5.000, 0.000, 785.000, 140.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801601', 606.000, 300.000, 59.000, 4.000, 0.000, 0.000, 186.000, 57.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813701', 66.000, 45.000, 0.000, 0.000, 0.000, 0.000, 8.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021413401', 106.000, 64.000, 9.000, 0.000, 0.000, 0.000, 27.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009205100', 640.000, 193.000, 40.000, 1.000, 4.000, 0.000, 341.000, 61.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025081700', 782.000, 188.000, 165.000, 16.000, 5.000, 0.000, 330.000, 78.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005651500', 735.000, 299.000, 66.000, 13.000, 2.000, 0.000, 309.000, 46.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027754300', 378.000, 233.000, 9.000, 1.000, 0.000, 0.000, 113.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027730100', 399.000, 251.000, 18.000, 4.000, 1.000, 0.000, 88.000, 37.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023542102', 24.000, 20.000, 0.000, 0.000, 0.000, 0.000, 2.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017334400', 92.000, 67.000, 0.000, 0.000, 0.000, 0.000, 15.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015820202', 41.000, 26.000, 1.000, 0.000, 0.000, 0.000, 9.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023520100', 102.000, 69.000, 5.000, 1.000, 1.000, 0.000, 14.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010103', 347.000, 226.000, 18.000, 1.000, 10.000, 0.000, 64.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027731300', 2007.000, 698.000, 108.000, 9.000, 3.000, 1.000, 991.000, 197.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027758102', 210.000, 136.000, 0.000, 2.000, 0.000, 0.000, 52.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015821500', 47.000, 28.000, 0.000, 0.000, 1.000, 0.000, 13.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017315200', 157.000, 106.000, 8.000, 0.000, 0.000, 0.000, 33.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021411302', 230.000, 163.000, 8.000, 1.000, 0.000, 0.000, 46.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025110403', 1002.000, 391.000, 127.000, 4.000, 5.000, 0.000, 366.000, 109.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023510400', 573.000, 228.000, 71.000, 6.000, 0.000, 4.000, 217.000, 47.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023511000', 465.000, 166.000, 64.000, 7.000, 2.000, 0.000, 161.000, 65.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017320102', 88.000, 75.000, 1.000, 1.000, 0.000, 0.000, 9.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001011200', 35.000, 23.000, 4.000, 4.000, 0.000, 0.000, 3.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015820801', 578.000, 244.000, 37.000, 16.000, 8.000, 0.000, 215.000, 58.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017350104', 1665.000, 605.000, 65.000, 23.000, 4.000, 5.000, 879.000, 84.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003934300', 32.000, 18.000, 0.000, 0.000, 0.000, 0.000, 7.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009260302', 79.000, 52.000, 2.000, 1.000, 0.000, 0.000, 13.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025060101', 44.000, 31.000, 2.000, 0.000, 0.000, 1.000, 9.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025092000', 1051.000, 256.000, 209.000, 6.000, 3.000, 6.000, 467.000, 104.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015820802', 80.000, 39.000, 4.000, 1.000, 1.000, 0.000, 22.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017351100', 301.000, 169.000, 18.000, 1.000, 4.000, 0.000, 87.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003932200', 43.000, 27.000, 0.000, 1.000, 0.000, 0.000, 15.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005652000', 329.000, 121.000, 23.000, 12.000, 1.000, 4.000, 144.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021404400', 292.000, 172.000, 13.000, 6.000, 9.000, 6.000, 55.000, 31.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017370102', 420.000, 272.000, 9.000, 0.000, 3.000, 2.000, 113.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001015001', 127.000, 84.000, 2.000, 10.000, 2.000, 0.000, 14.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021402300', 149.000, 96.000, 8.000, 2.000, 2.000, 0.000, 23.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25007990000', 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013811400', 2207.000, 887.000, 74.000, 58.000, 1.000, 0.000, 1087.000, 100.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017350300', 264.000, 152.000, 7.000, 3.000, 1.000, 0.000, 80.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005651200', 572.000, 141.000, 41.000, 65.000, 0.000, 8.000, 299.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009204702', 586.000, 229.000, 33.000, 7.000, 0.000, 0.000, 251.000, 66.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009207000', 878.000, 296.000, 69.000, 18.000, 0.000, 0.000, 437.000, 58.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005613700', 223.000, 104.000, 13.000, 3.000, 1.000, 0.000, 81.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009205200', 1315.000, 413.000, 90.000, 4.000, 9.000, 1.000, 691.000, 107.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023507101', 69.000, 58.000, 1.000, 0.000, 0.000, 0.000, 7.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009208300', 260.000, 176.000, 2.000, 0.000, 0.000, 0.000, 71.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005612200', 177.000, 116.000, 5.000, 4.000, 2.000, 0.000, 32.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017315401', 116.000, 104.000, 4.000, 2.000, 1.000, 0.000, 4.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017368800', 1132.000, 491.000, 20.000, 11.000, 3.000, 6.000, 540.000, 61.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013811000', 319.000, 215.000, 9.000, 2.000, 0.000, 0.000, 78.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027727100', 151.000, 106.000, 18.000, 1.000, 0.000, 0.000, 13.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027741102', 141.000, 100.000, 2.000, 3.000, 2.000, 0.000, 12.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027754100', 222.000, 129.000, 3.000, 1.000, 6.000, 0.000, 55.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027739300', 106.000, 75.000, 0.000, 0.000, 4.000, 0.000, 20.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003926100', 105.000, 51.000, 2.000, 2.000, 0.000, 0.000, 39.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005631500', 213.000, 81.000, 2.000, 1.000, 2.000, 2.000, 111.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001010800', 69.000, 46.000, 3.000, 0.000, 2.000, 0.000, 14.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027720100', 49.000, 41.000, 2.000, 1.000, 0.000, 0.000, 1.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027739400', 193.000, 131.000, 8.000, 3.000, 0.000, 0.000, 43.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003900100', 316.000, 125.000, 19.000, 7.000, 0.000, 0.000, 130.000, 35.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025070700', 194.000, 110.000, 33.000, 0.000, 1.000, 0.000, 31.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027731203', 1966.000, 943.000, 132.000, 23.000, 0.000, 6.000, 715.000, 147.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003921500', 80.000, 38.000, 0.000, 0.000, 2.000, 0.000, 33.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009206800', 1706.000, 466.000, 162.000, 46.000, 4.000, 0.000, 891.000, 137.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009254200', 266.000, 137.000, 6.000, 0.000, 6.000, 1.000, 89.000, 27.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025030100', 68.000, 50.000, 1.000, 0.000, 1.000, 0.000, 15.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017317301', 85.000, 57.000, 2.000, 0.000, 1.000, 0.000, 16.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023990003', 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009252501', 1019.000, 379.000, 70.000, 9.000, 5.000, 4.000, 492.000, 60.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009220101', 61.000, 43.000, 1.000, 0.000, 0.000, 0.000, 7.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017358500', 101.000, 74.000, 2.000, 2.000, 0.000, 0.000, 13.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025101102', 521.000, 119.000, 157.000, 10.000, 2.000, 0.000, 189.000, 44.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023524102', 58.000, 39.000, 3.000, 0.000, 0.000, 0.000, 15.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025140300', 1474.000, 483.000, 219.000, 8.000, 0.000, 4.000, 627.000, 133.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023530700', 62.000, 40.000, 0.000, 0.000, 0.000, 0.000, 13.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005613100', 155.000, 74.000, 7.000, 1.000, 0.000, 0.000, 51.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813702', 87.000, 70.000, 8.000, 0.000, 0.000, 0.000, 2.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027731700', 759.000, 404.000, 52.000, 7.000, 2.000, 0.000, 260.000, 34.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027710400', 271.000, 155.000, 4.000, 5.000, 0.000, 0.000, 85.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005646101', 67.000, 56.000, 4.000, 0.000, 0.000, 0.000, 4.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017316201', 73.000, 45.000, 0.000, 4.000, 0.000, 0.000, 21.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009204300', 1811.000, 669.000, 190.000, 15.000, 1.000, 0.000, 747.000, 189.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009251200', 1169.000, 365.000, 98.000, 49.000, 1.000, 2.000, 585.000, 69.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011040400', 60.000, 33.000, 6.000, 4.000, 0.000, 0.000, 9.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801501', 1585.000, 577.000, 129.000, 27.000, 11.000, 2.000, 723.000, 116.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027707500', 552.000, 381.000, 17.000, 3.000, 0.000, 1.000, 131.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027748100', 41.000, 32.000, 2.000, 0.000, 0.000, 0.000, 4.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005614000', 550.000, 214.000, 53.000, 2.000, 2.000, 0.000, 209.000, 70.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027702200', 84.000, 42.000, 5.000, 8.000, 0.000, 0.000, 17.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025040600', 58.000, 49.000, 2.000, 0.000, 0.000, 0.000, 4.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027705100', 153.000, 96.000, 6.000, 3.000, 0.000, 0.000, 26.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009209200', 79.000, 59.000, 5.000, 0.000, 0.000, 0.000, 10.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005650400', 412.000, 236.000, 30.000, 2.000, 0.000, 1.000, 107.000, 36.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017357200', 98.000, 76.000, 1.000, 0.000, 0.000, 0.000, 7.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017368200', 364.000, 160.000, 20.000, 3.000, 1.000, 0.000, 151.000, 29.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017315100', 111.000, 73.000, 1.000, 0.000, 0.000, 0.000, 28.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021401200', 328.000, 197.000, 37.000, 2.000, 2.000, 0.000, 63.000, 27.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023530901', 155.000, 115.000, 9.000, 2.000, 0.000, 0.000, 24.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009213100', 145.000, 111.000, 5.000, 3.000, 0.000, 0.000, 14.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017383200', 1576.000, 680.000, 48.000, 22.000, 2.000, 0.000, 714.000, 110.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009210100', 152.000, 97.000, 8.000, 0.000, 0.000, 0.000, 40.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027709100', 394.000, 250.000, 20.000, 1.000, 3.000, 0.000, 104.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017339600', 272.000, 128.000, 18.000, 8.000, 3.000, 0.000, 90.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017368901', 387.000, 250.000, 16.000, 14.000, 3.000, 2.000, 76.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25019950200', 602.000, 432.000, 17.000, 0.000, 0.000, 0.000, 136.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011041502', 35.000, 23.000, 0.000, 0.000, 1.000, 0.000, 5.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025140107', 618.000, 271.000, 64.000, 4.000, 2.000, 0.000, 199.000, 78.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017321400', 161.000, 90.000, 4.000, 1.000, 0.000, 0.000, 53.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025080401', 984.000, 288.000, 187.000, 12.000, 1.000, 0.000, 368.000, 128.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017373600', 381.000, 268.000, 15.000, 5.000, 7.000, 1.000, 51.000, 34.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005650900', 1098.000, 383.000, 92.000, 26.000, 2.000, 0.000, 505.000, 90.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017382400', 105.000, 62.000, 6.000, 2.000, 0.000, 0.000, 27.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017333400', 352.000, 191.000, 13.000, 3.000, 1.000, 0.000, 99.000, 45.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017357800', 116.000, 81.000, 6.000, 0.000, 2.000, 0.000, 15.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021401100', 172.000, 120.000, 11.000, 0.000, 1.000, 0.000, 22.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025160101', 5715.000, 2037.000, 171.000, 113.000, 5.000, 0.000, 3057.000, 332.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027709202', 1229.000, 787.000, 43.000, 2.000, 1.000, 3.000, 313.000, 80.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027726200', 243.000, 137.000, 10.000, 3.000, 5.000, 3.000, 70.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027731204', 1120.000, 482.000, 69.000, 17.000, 1.000, 0.000, 473.000, 78.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013800900', 2972.000, 1326.000, 166.000, 41.000, 2.000, 7.000, 1287.000, 143.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017315500', 149.000, 111.000, 0.000, 3.000, 0.000, 0.000, 26.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017351404', 936.000, 429.000, 26.000, 12.000, 1.000, 1.000, 415.000, 52.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027758101', 21.000, 15.000, 0.000, 0.000, 0.000, 0.000, 6.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001012502', 134.000, 82.000, 8.000, 0.000, 1.000, 0.000, 35.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003913100', 138.000, 95.000, 3.000, 3.000, 0.000, 0.000, 29.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017363104', 143.000, 98.000, 3.000, 0.000, 2.000, 0.000, 30.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021421100', 261.000, 110.000, 20.000, 6.000, 2.000, 0.000, 90.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005630200', 118.000, 77.000, 3.000, 1.000, 2.000, 0.000, 18.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001013900', 40.000, 22.000, 3.000, 4.000, 0.000, 0.000, 3.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025080900', 313.000, 147.000, 31.000, 5.000, 0.000, 0.000, 89.000, 41.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009217201', 156.000, 84.000, 1.000, 2.000, 1.000, 0.000, 57.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017332400', 95.000, 64.000, 8.000, 2.000, 0.000, 0.000, 11.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025091800', 713.000, 162.000, 149.000, 3.000, 1.000, 0.000, 366.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009251500', 4292.000, 1347.000, 345.000, 72.000, 5.000, 10.000, 2157.000, 356.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025092400', 1470.000, 354.000, 320.000, 20.000, 4.000, 0.000, 625.000, 147.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025090200', 865.000, 184.000, 108.000, 14.000, 0.000, 4.000, 481.000, 74.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009990100', 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010404', 386.000, 240.000, 10.000, 6.000, 7.000, 1.000, 93.000, 29.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013810404', 145.000, 79.000, 8.000, 0.000, 0.000, 0.000, 51.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003900900', 280.000, 129.000, 15.000, 1.000, 0.000, 0.000, 124.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017361300', 207.000, 128.000, 8.000, 4.000, 1.000, 0.000, 44.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010600', 156.000, 116.000, 7.000, 1.000, 1.000, 0.000, 27.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010702', 102.000, 64.000, 6.000, 0.000, 2.000, 0.000, 23.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005655300', 40.000, 32.000, 1.000, 0.000, 0.000, 0.000, 5.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005610100', 102.000, 63.000, 8.000, 0.000, 0.000, 0.000, 24.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017363202', 61.000, 46.000, 0.000, 0.000, 2.000, 0.000, 10.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009210400', 336.000, 154.000, 36.000, 3.000, 2.000, 0.000, 97.000, 44.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027710700', 579.000, 259.000, 22.000, 0.000, 0.000, 0.000, 256.000, 42.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027728200', 71.000, 43.000, 1.000, 1.000, 4.000, 0.000, 15.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009215101', 64.000, 45.000, 4.000, 2.000, 0.000, 0.000, 6.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027716300', 564.000, 295.000, 35.000, 13.000, 0.000, 0.000, 189.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009266300', 69.000, 45.000, 3.000, 1.000, 0.000, 1.000, 10.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017342400', 1277.000, 559.000, 47.000, 9.000, 3.000, 2.000, 572.000, 85.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027737200', 38.000, 23.000, 4.000, 1.000, 0.000, 0.000, 4.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009254401', 129.000, 89.000, 6.000, 0.000, 3.000, 0.000, 24.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005612100', 70.000, 38.000, 1.000, 0.000, 6.000, 0.000, 13.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021419800', 104.000, 60.000, 6.000, 3.000, 0.000, 0.000, 19.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015820400', 604.000, 281.000, 87.000, 5.000, 8.000, 0.000, 153.000, 70.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015820700', 441.000, 200.000, 24.000, 9.000, 9.000, 0.000, 135.000, 64.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025110601', 122.000, 86.000, 1.000, 3.000, 1.000, 0.000, 19.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009217100', 237.000, 147.000, 13.000, 0.000, 2.000, 0.000, 49.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023530902', 53.000, 38.000, 3.000, 0.000, 0.000, 0.000, 12.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000402', 189.000, 129.000, 3.000, 0.000, 1.000, 0.000, 45.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025180400', 93.000, 52.000, 0.000, 0.000, 0.000, 0.000, 31.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001010400', 61.000, 41.000, 2.000, 1.000, 1.000, 0.000, 10.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025110607', 487.000, 250.000, 33.000, 6.000, 4.000, 0.000, 148.000, 46.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001015100', 84.000, 46.000, 2.000, 7.000, 0.000, 0.000, 22.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009251800', 2669.000, 814.000, 154.000, 45.000, 8.000, 1.000, 1468.000, 179.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027753100', 321.000, 187.000, 8.000, 2.000, 1.000, 0.000, 103.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009221700', 91.000, 39.000, 2.000, 3.000, 1.000, 0.000, 39.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017341800', 824.000, 381.000, 33.000, 1.000, 13.000, 0.000, 334.000, 62.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017332300', 105.000, 73.000, 1.000, 0.000, 0.000, 0.000, 27.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021418002', 288.000, 133.000, 11.000, 10.000, 4.000, 0.000, 96.000, 34.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001012001', 123.000, 66.000, 12.000, 0.000, 0.000, 0.000, 32.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812903', 100.000, 44.000, 7.000, 2.000, 2.000, 0.000, 30.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017310300', 1415.000, 661.000, 61.000, 13.000, 6.000, 0.000, 551.000, 123.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023503102', 43.000, 32.000, 0.000, 1.000, 1.000, 1.000, 7.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025981100', 85.000, 45.000, 18.000, 0.000, 1.000, 0.000, 20.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009251000', 1512.000, 447.000, 118.000, 29.000, 4.000, 0.000, 819.000, 95.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025981502', 2.000, 2.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025091300', 532.000, 151.000, 57.000, 6.000, 2.000, 1.000, 256.000, 59.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801800', 2029.000, 643.000, 179.000, 15.000, 10.000, 0.000, 1083.000, 99.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001012101', 187.000, 77.000, 10.000, 1.000, 1.000, 0.000, 69.000, 29.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017311600', 458.000, 162.000, 16.000, 6.000, 4.000, 0.000, 248.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017341200', 439.000, 198.000, 21.000, 0.000, 19.000, 0.000, 160.000, 41.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005631400', 517.000, 246.000, 4.000, 7.000, 0.000, 0.000, 217.000, 43.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017383902', 149.000, 77.000, 3.000, 0.000, 3.000, 0.000, 56.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025100800', 412.000, 142.000, 97.000, 1.000, 8.000, 0.000, 103.000, 61.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025160602', 1982.000, 793.000, 53.000, 31.000, 3.000, 0.000, 919.000, 183.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005641500', 117.000, 75.000, 9.000, 0.000, 1.000, 0.000, 29.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017352800', 214.000, 78.000, 36.000, 0.000, 0.000, 0.000, 74.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025090300', 1082.000, 270.000, 193.000, 12.000, 9.000, 1.000, 515.000, 82.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017358600', 128.000, 92.000, 6.000, 1.000, 1.000, 0.000, 18.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021400900', 242.000, 138.000, 29.000, 2.000, 4.000, 0.000, 50.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021456200', 75.000, 33.000, 19.000, 1.000, 0.000, 0.000, 3.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017383800', 285.000, 138.000, 10.000, 7.000, 2.000, 1.000, 104.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003931100', 16.000, 14.000, 0.000, 0.000, 0.000, 0.000, 0.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005645101', 43.000, 33.000, 1.000, 1.000, 0.000, 0.000, 2.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017382300', 153.000, 99.000, 7.000, 2.000, 6.000, 1.000, 25.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001012002', 121.000, 65.000, 7.000, 7.000, 2.000, 0.000, 22.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005645102', 74.000, 56.000, 4.000, 2.000, 1.000, 0.000, 4.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812300', 964.000, 430.000, 52.000, 24.000, 4.000, 0.000, 381.000, 73.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010408', 133.000, 84.000, 5.000, 0.000, 1.000, 0.000, 32.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005613800', 420.000, 200.000, 20.000, 9.000, 5.000, 1.000, 138.000, 47.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025070900', 714.000, 243.000, 86.000, 7.000, 0.000, 0.000, 319.000, 59.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009204102', 431.000, 145.000, 56.000, 11.000, 1.000, 0.000, 192.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017350500', 44.000, 32.000, 2.000, 1.000, 0.000, 1.000, 6.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023540101', 31.000, 26.000, 2.000, 0.000, 0.000, 0.000, 0.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027729200', 61.000, 49.000, 0.000, 0.000, 0.000, 0.000, 10.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015822000', 151.000, 76.000, 12.000, 2.000, 2.000, 0.000, 34.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017388300', 1640.000, 648.000, 73.000, 9.000, 9.000, 0.000, 779.000, 122.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021402500', 195.000, 150.000, 14.000, 2.000, 0.000, 0.000, 20.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025081900', 828.000, 195.000, 169.000, 5.000, 0.000, 0.000, 376.000, 83.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025170200', 846.000, 370.000, 10.000, 6.000, 0.000, 0.000, 403.000, 57.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005652300', 401.000, 172.000, 28.000, 12.000, 0.000, 0.000, 156.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009204101', 614.000, 188.000, 53.000, 22.000, 2.000, 0.000, 292.000, 57.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009265102', 78.000, 56.000, 1.000, 2.000, 1.000, 0.000, 12.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017342101', 897.000, 420.000, 32.000, 9.000, 7.000, 0.000, 377.000, 52.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025100601', 478.000, 115.000, 124.000, 1.000, 5.000, 0.000, 172.000, 61.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025981600', 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001014800', 45.000, 29.000, 2.000, 1.000, 2.000, 0.000, 6.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005641400', 253.000, 128.000, 10.000, 0.000, 3.000, 0.000, 78.000, 34.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009206700', 1214.000, 366.000, 91.000, 15.000, 2.000, 2.000, 611.000, 127.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015820500', 390.000, 219.000, 31.000, 4.000, 2.000, 0.000, 91.000, 43.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023541100', 40.000, 31.000, 0.000, 0.000, 0.000, 0.000, 2.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013810800', 688.000, 290.000, 38.000, 5.000, 0.000, 0.000, 299.000, 56.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027731900', 1960.000, 980.000, 134.000, 17.000, 13.000, 0.000, 699.000, 117.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027740101', 119.000, 69.000, 1.000, 4.000, 2.000, 0.000, 39.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005650600', 546.000, 170.000, 37.000, 48.000, 0.000, 0.000, 236.000, 55.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009223100', 57.000, 43.000, 1.000, 1.000, 0.000, 0.000, 10.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017335200', 111.000, 88.000, 2.000, 1.000, 0.000, 0.000, 17.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009264100', 85.000, 60.000, 3.000, 2.000, 0.000, 0.000, 13.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801200', 1582.000, 603.000, 106.000, 27.000, 0.000, 0.000, 754.000, 92.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005610204', 159.000, 103.000, 18.000, 0.000, 0.000, 0.000, 27.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027730402', 417.000, 227.000, 25.000, 0.000, 0.000, 0.000, 146.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025080100', 942.000, 606.000, 89.000, 12.000, 0.000, 0.000, 187.000, 48.000);
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INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017360100', 192.000, 119.000, 18.000, 3.000, 3.000, 0.000, 33.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027729100', 343.000, 292.000, 2.000, 0.000, 0.000, 0.000, 43.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001011500', 86.000, 43.000, 10.000, 9.000, 0.000, 0.000, 12.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009262100', 115.000, 82.000, 4.000, 2.000, 0.000, 0.000, 18.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009203200', 84.000, 55.000, 0.000, 0.000, 3.000, 0.000, 21.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005642200', 260.000, 143.000, 17.000, 1.000, 0.000, 0.000, 70.000, 29.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009205900', 1456.000, 491.000, 101.000, 46.000, 3.000, 5.000, 715.000, 95.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017333600', 280.000, 175.000, 13.000, 4.000, 1.000, 0.000, 60.000, 27.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027757300', 1164.000, 431.000, 60.000, 19.000, 4.000, 0.000, 591.000, 59.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025050901', 2704.000, 1557.000, 29.000, 16.000, 7.000, 0.000, 931.000, 164.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009260800', 1799.000, 653.000, 90.000, 24.000, 0.000, 3.000, 891.000, 138.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001013700', 80.000, 49.000, 0.000, 1.000, 0.000, 0.000, 20.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005640200', 338.000, 145.000, 20.000, 6.000, 0.000, 0.000, 132.000, 35.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005652700', 1093.000, 539.000, 43.000, 18.000, 0.000, 0.000, 421.000, 72.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009215102', 57.000, 42.000, 0.000, 0.000, 3.000, 0.000, 11.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017339700', 281.000, 142.000, 20.000, 4.000, 1.000, 0.000, 79.000, 35.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25019950500', 19.000, 13.000, 1.000, 0.000, 0.000, 0.000, 3.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025981700', 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021415200', 238.000, 115.000, 29.000, 1.000, 1.000, 0.000, 61.000, 31.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009270100', 59.000, 45.000, 1.000, 0.000, 0.000, 0.000, 9.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027738201', 78.000, 58.000, 6.000, 0.000, 0.000, 0.000, 8.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017331300', 142.000, 97.000, 0.000, 2.000, 0.000, 0.000, 30.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017382601', 198.000, 102.000, 8.000, 0.000, 1.000, 0.000, 68.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021418101', 163.000, 97.000, 3.000, 1.000, 3.000, 0.000, 33.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017341400', 708.000, 332.000, 50.000, 3.000, 10.000, 0.000, 230.000, 83.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023545200', 188.000, 105.000, 16.000, 3.000, 3.000, 0.000, 36.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025030200', 69.000, 57.000, 0.000, 1.000, 1.000, 0.000, 4.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017336300', 143.000, 98.000, 2.000, 6.000, 0.000, 1.000, 30.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027747101', 98.000, 83.000, 3.000, 0.000, 0.000, 0.000, 8.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013810602', 63.000, 36.000, 4.000, 3.000, 0.000, 0.000, 18.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813102', 94.000, 67.000, 3.000, 0.000, 0.000, 0.000, 13.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017311400', 855.000, 364.000, 34.000, 15.000, 5.000, 0.000, 361.000, 76.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017366100', 136.000, 109.000, 5.000, 0.000, 0.000, 0.000, 10.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005651800', 279.000, 97.000, 27.000, 17.000, 0.000, 1.000, 103.000, 34.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017354700', 106.000, 71.000, 2.000, 5.000, 1.000, 0.000, 19.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005650102', 691.000, 294.000, 55.000, 15.000, 1.000, 0.000, 297.000, 29.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017335302', 113.000, 79.000, 1.000, 1.000, 0.000, 0.000, 22.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017365202', 103.000, 68.000, 6.000, 1.000, 0.000, 0.000, 23.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025051000', 1572.000, 919.000, 43.000, 19.000, 4.000, 1.000, 508.000, 78.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812104', 1734.000, 727.000, 53.000, 16.000, 0.000, 1.000, 806.000, 131.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021417901', 203.000, 102.000, 19.000, 3.000, 2.000, 0.000, 53.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021457100', 121.000, 45.000, 20.000, 4.000, 0.000, 0.000, 35.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009252300', 1794.000, 681.000, 120.000, 21.000, 2.000, 2.000, 811.000, 157.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003923100', 35.000, 28.000, 1.000, 0.000, 0.000, 0.000, 6.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023530801', 68.000, 43.000, 0.000, 0.000, 0.000, 0.000, 11.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009251400', 3896.000, 1183.000, 222.000, 42.000, 16.000, 0.000, 2148.000, 285.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017335400', 113.000, 80.000, 3.000, 1.000, 0.000, 0.000, 22.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017356500', 199.000, 117.000, 4.000, 1.000, 3.000, 0.000, 50.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010403', 293.000, 144.000, 22.000, 0.000, 4.000, 0.000, 95.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013802500', 817.000, 412.000, 53.000, 2.000, 3.000, 0.000, 260.000, 87.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017353200', 361.000, 239.000, 31.000, 2.000, 1.000, 0.000, 49.000, 39.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021414300', 69.000, 50.000, 5.000, 1.000, 1.000, 0.000, 6.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023520202', 130.000, 77.000, 17.000, 0.000, 1.000, 0.000, 25.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017382602', 120.000, 78.000, 8.000, 6.000, 0.000, 0.000, 19.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011040900', 132.000, 86.000, 7.000, 2.000, 2.000, 0.000, 22.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25019950100', 30.000, 23.000, 0.000, 0.000, 0.000, 0.000, 3.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009203301', 58.000, 37.000, 4.000, 1.000, 0.000, 0.000, 9.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017337202', 138.000, 75.000, 3.000, 2.000, 2.000, 0.000, 42.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001010306', 35.000, 25.000, 0.000, 0.000, 0.000, 3.000, 4.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005631700', 241.000, 114.000, 3.000, 0.000, 0.000, 0.000, 91.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023502101', 101.000, 65.000, 10.000, 0.000, 0.000, 0.000, 17.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027736100', 67.000, 45.000, 11.000, 0.000, 0.000, 0.000, 9.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017370201', 128.000, 91.000, 3.000, 2.000, 0.000, 0.000, 24.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001010700', 58.000, 45.000, 0.000, 1.000, 0.000, 0.000, 12.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812202', 511.000, 208.000, 11.000, 4.000, 0.000, 0.000, 239.000, 49.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013802000', 2039.000, 718.000, 115.000, 28.000, 2.000, 2.000, 1058.000, 116.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021404202', 239.000, 177.000, 10.000, 0.000, 3.000, 0.000, 42.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025120600', 301.000, 137.000, 17.000, 5.000, 2.000, 0.000, 93.000, 47.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813602', 136.000, 96.000, 16.000, 3.000, 1.000, 0.000, 12.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017350600', 188.000, 119.000, 9.000, 3.000, 4.000, 0.000, 37.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732802', 408.000, 170.000, 33.000, 2.000, 0.000, 0.000, 171.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017353500', 175.000, 88.000, 22.000, 2.000, 1.000, 0.000, 32.000, 30.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017374500', 217.000, 147.000, 6.000, 4.000, 0.000, 0.000, 47.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003935100', 36.000, 19.000, 2.000, 2.000, 1.000, 0.000, 6.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025160601', 1722.000, 780.000, 80.000, 17.000, 10.000, 4.000, 713.000, 118.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021442101', 122.000, 83.000, 13.000, 2.000, 0.000, 0.000, 21.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021442201', 74.000, 58.000, 4.000, 0.000, 1.000, 0.000, 6.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017324102', 60.000, 40.000, 8.000, 3.000, 0.000, 0.000, 5.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023511500', 557.000, 168.000, 79.000, 5.000, 0.000, 0.000, 248.000, 57.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017342102', 689.000, 340.000, 32.000, 11.000, 0.000, 3.000, 247.000, 56.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017387201', 105.000, 73.000, 0.000, 1.000, 1.000, 0.000, 21.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017357700', 132.000, 91.000, 0.000, 3.000, 0.000, 0.000, 26.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025981800', 3.000, 2.000, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025081200', 1720.000, 564.000, 210.000, 36.000, 2.000, 0.000, 726.000, 182.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003931400', 4.000, 3.000, 0.000, 1.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025160400', 1922.000, 656.000, 59.000, 24.000, 7.000, 0.000, 1025.000, 151.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017310601', 611.000, 350.000, 15.000, 1.000, 1.000, 2.000, 168.000, 74.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027712102', 89.000, 63.000, 1.000, 0.000, 0.000, 0.000, 12.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013802400', 300.000, 177.000, 32.000, 3.000, 4.000, 0.000, 51.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010802', 119.000, 88.000, 3.000, 0.000, 2.000, 0.000, 19.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017367200', 204.000, 138.000, 20.000, 3.000, 0.000, 0.000, 30.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005600100', 94.000, 75.000, 0.000, 0.000, 0.000, 0.000, 10.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801902', 2167.000, 814.000, 138.000, 39.000, 4.000, 0.000, 1047.000, 125.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001014002', 48.000, 33.000, 4.000, 1.000, 0.000, 0.000, 4.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017382100', 118.000, 91.000, 5.000, 1.000, 0.000, 0.000, 19.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009209100', 123.000, 89.000, 5.000, 0.000, 0.000, 0.000, 12.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009214100', 112.000, 87.000, 4.000, 1.000, 2.000, 0.000, 9.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017357100', 93.000, 77.000, 0.000, 0.000, 2.000, 0.000, 8.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013800201', 2342.000, 945.000, 130.000, 31.000, 2.000, 1.000, 1048.000, 185.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001011300', 39.000, 25.000, 1.000, 0.000, 0.000, 2.000, 4.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017339100', 162.000, 110.000, 2.000, 12.000, 1.000, 0.000, 24.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023523100', 57.000, 29.000, 3.000, 0.000, 0.000, 0.000, 9.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015820102', 253.000, 121.000, 4.000, 4.000, 0.000, 0.000, 92.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005653102', 266.000, 182.000, 12.000, 2.000, 3.000, 0.000, 54.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009202101', 46.000, 24.000, 1.000, 0.000, 0.000, 0.000, 14.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005642100', 274.000, 147.000, 22.000, 4.000, 0.000, 0.000, 70.000, 31.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009261000', 363.000, 170.000, 23.000, 5.000, 1.000, 0.000, 140.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021422502', 206.000, 120.000, 20.000, 1.000, 0.000, 1.000, 49.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023508200', 64.000, 48.000, 0.000, 1.000, 2.000, 0.000, 11.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003914100', 261.000, 156.000, 4.000, 1.000, 0.000, 1.000, 75.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023500101', 52.000, 30.000, 1.000, 1.000, 1.000, 0.000, 9.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025060700', 877.000, 218.000, 108.000, 4.000, 9.000, 0.000, 456.000, 82.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017338100', 72.000, 54.000, 3.000, 4.000, 1.000, 0.000, 6.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017355000', 168.000, 101.000, 13.000, 2.000, 4.000, 0.000, 25.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009205700', 918.000, 383.000, 71.000, 6.000, 2.000, 0.000, 374.000, 82.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017383502', 271.000, 136.000, 15.000, 3.000, 2.000, 0.000, 99.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017335100', 124.000, 80.000, 6.000, 2.000, 0.000, 0.000, 32.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010500', 291.000, 193.000, 14.000, 1.000, 0.000, 0.000, 61.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017321100', 457.000, 271.000, 13.000, 3.000, 1.000, 0.000, 129.000, 40.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023544200', 73.000, 43.000, 1.000, 0.000, 0.000, 0.000, 16.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017312000', 716.000, 351.000, 52.000, 5.000, 8.000, 0.000, 241.000, 59.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021417300', 72.000, 44.000, 8.000, 0.000, 0.000, 0.000, 19.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009221901', 39.000, 29.000, 2.000, 1.000, 0.000, 0.000, 1.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017374100', 234.000, 138.000, 10.000, 1.000, 3.000, 0.000, 65.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021413402', 213.000, 129.000, 28.000, 0.000, 1.000, 2.000, 44.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017360200', 124.000, 91.000, 11.000, 0.000, 1.000, 0.000, 13.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005641800', 81.000, 58.000, 7.000, 2.000, 0.000, 0.000, 12.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017312300', 496.000, 261.000, 18.000, 3.000, 4.000, 1.000, 177.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801602', 973.000, 432.000, 94.000, 13.000, 11.000, 3.000, 371.000, 49.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001011400', 77.000, 42.000, 6.000, 1.000, 0.000, 1.000, 17.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025130200', 207.000, 141.000, 11.000, 7.000, 1.000, 0.000, 21.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027731400', 2428.000, 1045.000, 135.000, 11.000, 7.000, 3.000, 1078.000, 149.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025170502', 266.000, 167.000, 6.000, 6.000, 0.000, 0.000, 61.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005655400', 33.000, 26.000, 0.000, 0.000, 1.000, 0.000, 4.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009253202', 645.000, 268.000, 46.000, 1.000, 2.000, 1.000, 280.000, 47.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001014500', 139.000, 68.000, 4.000, 7.000, 0.000, 0.000, 38.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017339300', 134.000, 57.000, 15.000, 6.000, 0.000, 1.000, 29.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011041501', 59.000, 48.000, 1.000, 1.000, 0.000, 0.000, 2.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005640600', 289.000, 109.000, 35.000, 5.000, 4.000, 0.000, 103.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732301', 325.000, 192.000, 9.000, 0.000, 0.000, 0.000, 87.000, 37.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027742402', 98.000, 48.000, 0.000, 1.000, 3.000, 0.000, 38.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005653304', 29.000, 20.000, 3.000, 2.000, 0.000, 0.000, 2.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027741101', 131.000, 100.000, 7.000, 0.000, 0.000, 1.000, 16.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015820600', 217.000, 132.000, 14.000, 1.000, 2.000, 0.000, 40.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025081100', 608.000, 278.000, 75.000, 5.000, 6.000, 1.000, 185.000, 58.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015820300', 447.000, 244.000, 32.000, 9.000, 1.000, 2.000, 96.000, 63.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812401', 145.000, 96.000, 0.000, 1.000, 2.000, 0.000, 25.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25019950400', 302.000, 253.000, 6.000, 0.000, 0.000, 0.000, 35.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009211100', 86.000, 61.000, 1.000, 0.000, 1.000, 0.000, 21.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017351403', 1174.000, 401.000, 24.000, 19.000, 3.000, 0.000, 647.000, 80.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011040701', 346.000, 163.000, 15.000, 9.000, 0.000, 1.000, 128.000, 30.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015821602', 329.000, 148.000, 24.000, 8.000, 1.000, 4.000, 110.000, 34.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017357600', 73.000, 41.000, 1.000, 0.000, 3.000, 0.000, 26.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027740102', 178.000, 122.000, 8.000, 2.000, 3.000, 0.000, 32.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017333501', 211.000, 132.000, 5.000, 5.000, 0.000, 0.000, 61.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009260500', 239.000, 92.000, 16.000, 3.000, 1.000, 0.000, 95.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023544100', 56.000, 37.000, 3.000, 0.000, 0.000, 0.000, 12.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005651900', 633.000, 247.000, 48.000, 6.000, 0.000, 0.000, 269.000, 63.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009260402', 300.000, 134.000, 7.000, 6.000, 0.000, 0.000, 104.000, 49.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732203', 591.000, 334.000, 28.000, 6.000, 0.000, 1.000, 181.000, 41.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025080500', 1329.000, 413.000, 304.000, 9.000, 2.000, 2.000, 481.000, 118.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021418200', 285.000, 151.000, 40.000, 3.000, 9.000, 0.000, 60.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027761400', 264.000, 219.000, 5.000, 3.000, 0.000, 0.000, 19.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003900800', 72.000, 35.000, 0.000, 0.000, 0.000, 0.000, 26.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005631600', 665.000, 272.000, 15.000, 21.000, 1.000, 3.000, 293.000, 60.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009260700', 714.000, 377.000, 28.000, 5.000, 0.000, 0.000, 241.000, 63.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017356701', 214.000, 141.000, 10.000, 2.000, 1.000, 0.000, 28.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027739200', 209.000, 126.000, 14.000, 3.000, 0.000, 0.000, 48.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027707400', 213.000, 130.000, 9.000, 1.000, 2.000, 0.000, 52.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027733102', 292.000, 133.000, 8.000, 0.000, 1.000, 0.000, 132.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732202', 394.000, 147.000, 27.000, 17.000, 0.000, 0.000, 164.000, 39.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021443101', 235.000, 155.000, 3.000, 3.000, 1.000, 0.000, 56.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027730300', 378.000, 209.000, 30.000, 3.000, 4.000, 0.000, 99.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009208102', 211.000, 112.000, 9.000, 1.000, 0.000, 2.000, 56.000, 31.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027744400', 403.000, 211.000, 12.000, 0.000, 0.000, 1.000, 144.000, 35.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021418102', 206.000, 99.000, 13.000, 1.000, 2.000, 0.000, 78.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009201100', 52.000, 33.000, 1.000, 0.000, 0.000, 0.000, 14.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003922200', 45.000, 26.000, 1.000, 7.000, 0.000, 0.000, 10.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017314200', 240.000, 146.000, 2.000, 0.000, 1.000, 3.000, 57.000, 31.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017316500', 199.000, 143.000, 2.000, 3.000, 4.000, 0.000, 40.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021412100', 105.000, 79.000, 2.000, 2.000, 0.000, 0.000, 10.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015822603', 30.000, 18.000, 1.000, 1.000, 0.000, 0.000, 3.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005613902', 199.000, 108.000, 12.000, 0.000, 1.000, 4.000, 59.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005630102', 152.000, 84.000, 2.000, 12.000, 0.000, 0.000, 44.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005641300', 865.000, 401.000, 35.000, 17.000, 2.000, 7.000, 328.000, 75.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027744102', 290.000, 171.000, 29.000, 1.000, 1.000, 0.000, 68.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801102', 603.000, 209.000, 30.000, 5.000, 1.000, 2.000, 331.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027744200', 624.000, 311.000, 15.000, 8.000, 3.000, 3.000, 249.000, 35.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005652100', 224.000, 101.000, 20.000, 1.000, 0.000, 0.000, 85.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000701', 333.000, 176.000, 19.000, 4.000, 2.000, 0.000, 109.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027710500', 1140.000, 507.000, 60.000, 14.000, 6.000, 5.000, 451.000, 97.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025061000', 895.000, 242.000, 133.000, 13.000, 4.000, 0.000, 386.000, 117.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017370300', 375.000, 219.000, 23.000, 3.000, 1.000, 2.000, 83.000, 44.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017310500', 433.000, 192.000, 28.000, 0.000, 2.000, 1.000, 172.000, 38.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025091900', 824.000, 187.000, 218.000, 18.000, 0.000, 0.000, 320.000, 81.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005631100', 309.000, 197.000, 10.000, 0.000, 0.000, 2.000, 81.000, 19.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013800300', 1721.000, 697.000, 64.000, 19.000, 4.000, 0.000, 834.000, 103.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010104', 409.000, 299.000, 1.000, 2.000, 7.000, 0.000, 72.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017362100', 100.000, 73.000, 6.000, 0.000, 1.000, 0.000, 12.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017353000', 313.000, 177.000, 22.000, 2.000, 1.000, 0.000, 82.000, 29.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025130402', 300.000, 163.000, 30.000, 2.000, 0.000, 1.000, 84.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021410300', 72.000, 54.000, 6.000, 1.000, 0.000, 0.000, 11.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003933300', 27.000, 18.000, 4.000, 0.000, 0.000, 0.000, 5.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009252101', 582.000, 239.000, 52.000, 8.000, 0.000, 0.000, 203.000, 80.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009260401', 210.000, 110.000, 7.000, 0.000, 0.000, 0.000, 86.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017342201', 1107.000, 531.000, 61.000, 12.000, 3.000, 0.000, 419.000, 81.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013811101', 1355.000, 781.000, 89.000, 44.000, 4.000, 3.000, 383.000, 51.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023560100', 57.000, 42.000, 0.000, 1.000, 0.000, 0.000, 12.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009218100', 76.000, 61.000, 1.000, 0.000, 3.000, 0.000, 4.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027745100', 127.000, 79.000, 1.000, 1.000, 0.000, 0.000, 30.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009250200', 3978.000, 1093.000, 226.000, 53.000, 5.000, 0.000, 2393.000, 208.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017310400', 1123.000, 506.000, 63.000, 6.000, 15.000, 4.000, 435.000, 94.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021415102', 210.000, 116.000, 22.000, 1.000, 2.000, 0.000, 49.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021417601', 140.000, 84.000, 9.000, 0.000, 1.000, 0.000, 42.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013812600', 215.000, 128.000, 3.000, 9.000, 1.000, 1.000, 57.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027730901', 210.000, 96.000, 5.000, 4.000, 3.000, 0.000, 69.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005640100', 547.000, 309.000, 41.000, 8.000, 3.000, 0.000, 165.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009206300', 1539.000, 389.000, 121.000, 26.000, 3.000, 1.000, 859.000, 140.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017317103', 76.000, 56.000, 0.000, 0.000, 0.000, 0.000, 10.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021442202', 199.000, 97.000, 11.000, 18.000, 0.000, 0.000, 52.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025061101', 935.000, 361.000, 140.000, 13.000, 0.000, 4.000, 331.000, 86.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013801503', 1295.000, 492.000, 115.000, 10.000, 0.000, 0.000, 567.000, 111.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005630400', 115.000, 76.000, 3.000, 0.000, 2.000, 0.000, 23.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009253205', 81.000, 63.000, 4.000, 1.000, 1.000, 0.000, 9.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027739500', 173.000, 131.000, 7.000, 0.000, 0.000, 0.000, 27.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023530802', 59.000, 44.000, 1.000, 2.000, 0.000, 0.000, 6.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021413100', 173.000, 110.000, 9.000, 0.000, 2.000, 0.000, 35.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027757500', 662.000, 343.000, 33.000, 5.000, 0.000, 0.000, 227.000, 54.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025081500', 516.000, 159.000, 117.000, 3.000, 0.000, 0.000, 182.000, 55.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009253100', 321.000, 210.000, 19.000, 0.000, 0.000, 0.000, 70.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017358700', 112.000, 92.000, 2.000, 0.000, 0.000, 0.000, 10.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025981501', 1.000, 1.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013800400', 3398.000, 1363.000, 164.000, 48.000, 4.000, 3.000, 1565.000, 251.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025030300', 239.000, 153.000, 15.000, 2.000, 0.000, 0.000, 56.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017386100', 57.000, 42.000, 0.000, 0.000, 0.000, 0.000, 9.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017373300', 104.000, 50.000, 6.000, 0.000, 5.000, 0.000, 20.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011041100', 82.000, 48.000, 1.000, 1.000, 0.000, 0.000, 15.000, 17.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003934200', 12.000, 4.000, 1.000, 0.000, 0.000, 0.000, 4.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025170701', 538.000, 316.000, 16.000, 4.000, 1.000, 0.000, 173.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017314102', 296.000, 171.000, 17.000, 5.000, 4.000, 0.000, 63.000, 36.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021421200', 213.000, 104.000, 29.000, 0.000, 2.000, 1.000, 57.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017334300', 90.000, 70.000, 0.000, 0.000, 1.000, 0.000, 13.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017351500', 412.000, 229.000, 14.000, 2.000, 2.000, 0.000, 140.000, 25.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025091100', 466.000, 183.000, 50.000, 0.000, 2.000, 9.000, 190.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005630101', 122.000, 65.000, 2.000, 1.000, 1.000, 0.000, 47.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009266100', 70.000, 45.000, 1.000, 3.000, 0.000, 0.000, 16.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023522102', 46.000, 30.000, 2.000, 1.000, 0.000, 0.000, 10.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009251100', 2152.000, 713.000, 140.000, 32.000, 8.000, 0.000, 1115.000, 144.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025980101', 134.000, 45.000, 13.000, 0.000, 0.000, 0.000, 66.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027736200', 85.000, 42.000, 2.000, 0.000, 0.000, 0.000, 20.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005641000', 213.000, 101.000, 15.000, 0.000, 0.000, 0.000, 87.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021412200', 50.000, 39.000, 3.000, 0.000, 0.000, 0.000, 3.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001011100', 74.000, 43.000, 1.000, 6.000, 0.000, 0.000, 18.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017368102', 256.000, 130.000, 4.000, 6.000, 0.000, 0.000, 108.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732002', 471.000, 297.000, 28.000, 3.000, 0.000, 0.000, 111.000, 32.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025170702', 2893.000, 1394.000, 40.000, 15.000, 3.000, 0.000, 1270.000, 171.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025040100', 41.000, 25.000, 1.000, 1.000, 0.000, 0.000, 12.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017385100', 214.000, 139.000, 6.000, 2.000, 1.000, 0.000, 45.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000802', 1466.000, 673.000, 41.000, 16.000, 7.000, 0.000, 596.000, 133.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813601', 257.000, 163.000, 14.000, 3.000, 0.000, 0.000, 49.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001014300', 50.000, 38.000, 1.000, 1.000, 0.000, 0.000, 6.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009204701', 1090.000, 341.000, 97.000, 23.000, 0.000, 0.000, 497.000, 132.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015822500', 85.000, 45.000, 2.000, 0.000, 1.000, 0.000, 22.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017323100', 122.000, 90.000, 0.000, 2.000, 5.000, 0.000, 15.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017374600', 316.000, 159.000, 12.000, 5.000, 1.000, 0.000, 118.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009217202', 45.000, 34.000, 6.000, 0.000, 1.000, 0.000, 3.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009204200', 891.000, 311.000, 64.000, 8.000, 2.000, 0.000, 416.000, 90.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025090600', 492.000, 140.000, 101.000, 8.000, 0.000, 0.000, 194.000, 49.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011041200', 115.000, 54.000, 3.000, 1.000, 0.000, 0.000, 44.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009265101', 65.000, 39.000, 0.000, 5.000, 0.000, 0.000, 12.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017310700', 776.000, 208.000, 27.000, 12.000, 5.000, 0.000, 465.000, 59.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732700', 1046.000, 545.000, 88.000, 18.000, 8.000, 2.000, 326.000, 59.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009208101', 246.000, 147.000, 3.000, 6.000, 2.000, 0.000, 65.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005617102', 50.000, 36.000, 3.000, 1.000, 0.000, 0.000, 5.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009250500', 3565.000, 1102.000, 298.000, 48.000, 12.000, 4.000, 1705.000, 396.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025170601', 1515.000, 610.000, 26.000, 6.000, 5.000, 0.000, 771.000, 97.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025081800', 736.000, 138.000, 152.000, 3.000, 1.000, 0.000, 350.000, 92.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017301101', 91.000, 64.000, 1.000, 1.000, 1.000, 0.000, 16.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017361200', 365.000, 251.000, 17.000, 0.000, 0.000, 0.000, 86.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017325100', 421.000, 215.000, 14.000, 4.000, 4.000, 5.000, 133.000, 46.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013811301', 511.000, 276.000, 25.000, 2.000, 1.000, 0.000, 180.000, 27.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025180500', 303.000, 127.000, 9.000, 5.000, 1.000, 0.000, 138.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013810414', 723.000, 593.000, 13.000, 7.000, 1.000, 0.000, 93.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009268400', 65.000, 54.000, 0.000, 0.000, 0.000, 0.000, 10.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017317302', 128.000, 79.000, 1.000, 2.000, 7.000, 0.000, 31.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005644101', 57.000, 47.000, 2.000, 0.000, 0.000, 0.000, 4.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813404', 115.000, 90.000, 2.000, 0.000, 1.000, 0.000, 12.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017341700', 473.000, 303.000, 42.000, 5.000, 1.000, 0.000, 74.000, 48.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017356702', 199.000, 140.000, 4.000, 5.000, 2.000, 0.000, 26.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023510900', 406.000, 143.000, 65.000, 5.000, 2.000, 1.000, 133.000, 57.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009210700', 747.000, 300.000, 60.000, 8.000, 0.000, 0.000, 334.000, 45.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027742401', 88.000, 72.000, 2.000, 1.000, 0.000, 0.000, 10.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027743100', 119.000, 83.000, 5.000, 8.000, 0.000, 0.000, 13.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017339200', 119.000, 82.000, 5.000, 1.000, 0.000, 0.000, 17.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017336200', 105.000, 77.000, 2.000, 1.000, 0.000, 1.000, 12.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023510503', 290.000, 137.000, 44.000, 2.000, 0.000, 0.000, 81.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001015300', 186.000, 79.000, 21.000, 0.000, 0.000, 4.000, 52.000, 30.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017383901', 261.000, 189.000, 11.000, 2.000, 1.000, 1.000, 39.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001010206', 41.000, 31.000, 0.000, 0.000, 0.000, 0.000, 8.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009205800', 1298.000, 430.000, 107.000, 27.000, 5.000, 6.000, 634.000, 89.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025120104', 407.000, 194.000, 39.000, 3.000, 0.000, 0.000, 141.000, 30.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017352102', 127.000, 77.000, 5.000, 3.000, 3.000, 0.000, 31.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025170800', 1310.000, 662.000, 26.000, 28.000, 1.000, 2.000, 506.000, 85.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009252601', 621.000, 237.000, 23.000, 3.000, 6.000, 0.000, 304.000, 48.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017374200', 140.000, 109.000, 2.000, 2.000, 1.000, 0.000, 15.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023504102', 45.000, 28.000, 1.000, 0.000, 0.000, 0.000, 10.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023502102', 161.000, 93.000, 7.000, 5.000, 0.000, 0.000, 36.000, 20.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027731102', 291.000, 120.000, 29.000, 1.000, 0.000, 0.000, 113.000, 28.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025000100', 621.000, 240.000, 51.000, 3.000, 1.000, 0.000, 263.000, 63.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009221500', 146.000, 112.000, 4.000, 0.000, 0.000, 4.000, 15.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009254403', 102.000, 69.000, 2.000, 0.000, 3.000, 0.000, 19.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023511400', 530.000, 180.000, 61.000, 2.000, 2.000, 0.000, 252.000, 33.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025050101', 3140.000, 1556.000, 76.000, 20.000, 6.000, 0.000, 1266.000, 216.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017383600', 455.000, 225.000, 30.000, 3.000, 3.000, 0.000, 172.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025120400', 566.000, 300.000, 45.000, 12.000, 3.000, 0.000, 162.000, 44.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001014600', 89.000, 53.000, 6.000, 5.000, 0.000, 0.000, 7.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005652600', 1219.000, 488.000, 105.000, 85.000, 2.000, 10.000, 417.000, 112.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017317203', 56.000, 44.000, 3.000, 0.000, 0.000, 0.000, 7.000, 2.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017351203', 212.000, 138.000, 5.000, 2.000, 1.000, 0.000, 42.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013811600', 3230.000, 1092.000, 167.000, 42.000, 5.000, 2.000, 1712.000, 210.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017384001', 77.000, 39.000, 3.000, 4.000, 0.000, 0.000, 24.000, 7.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813500', 75.000, 60.000, 0.000, 0.000, 0.000, 0.000, 14.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009251600', 4394.000, 1338.000, 305.000, 81.000, 13.000, 2.000, 2413.000, 242.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023525101', 149.000, 79.000, 15.000, 5.000, 0.000, 0.000, 37.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005611101', 82.000, 55.000, 3.000, 0.000, 5.000, 0.000, 14.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005611202', 116.000, 73.000, 7.000, 4.000, 0.000, 6.000, 16.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015820101', 136.000, 70.000, 3.000, 1.000, 3.000, 0.000, 38.000, 21.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017354300', 246.000, 115.000, 29.000, 3.000, 0.000, 3.000, 70.000, 26.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025050700', 2906.000, 1226.000, 42.000, 29.000, 2.000, 7.000, 1417.000, 183.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813206', 126.000, 91.000, 4.000, 0.000, 1.000, 0.000, 21.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021410100', 81.000, 56.000, 1.000, 0.000, 0.000, 0.000, 16.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021422501', 169.000, 70.000, 31.000, 0.000, 0.000, 1.000, 52.000, 15.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005630300', 168.000, 116.000, 0.000, 5.000, 2.000, 0.000, 31.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001014100', 94.000, 50.000, 3.000, 1.000, 1.000, 0.000, 31.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009210900', 211.000, 87.000, 6.000, 0.000, 0.000, 0.000, 94.000, 24.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009222100', 46.000, 37.000, 1.000, 3.000, 1.000, 0.000, 3.000, 1.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017341500', 666.000, 305.000, 24.000, 5.000, 7.000, 0.000, 291.000, 34.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005654100', 72.000, 62.000, 1.000, 0.000, 0.000, 0.000, 6.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017327101', 48.000, 35.000, 0.000, 3.000, 1.000, 0.000, 5.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027732600', 1276.000, 649.000, 53.000, 10.000, 0.000, 0.000, 447.000, 117.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813208', 82.000, 37.000, 13.000, 3.000, 1.000, 0.000, 17.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009254301', 77.000, 48.000, 10.000, 0.000, 0.000, 0.000, 7.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027737100', 112.000, 59.000, 8.000, 0.000, 0.000, 1.000, 28.000, 16.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027749200', 113.000, 86.000, 2.000, 3.000, 0.000, 0.000, 9.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017336401', 156.000, 80.000, 18.000, 0.000, 1.000, 0.000, 43.000, 14.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011040300', 56.000, 29.000, 0.000, 1.000, 1.000, 0.000, 17.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25005650202', 173.000, 94.000, 12.000, 1.000, 0.000, 0.000, 53.000, 13.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25003920102', 122.000, 71.000, 4.000, 0.000, 2.000, 0.000, 23.000, 22.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25011040200', 37.000, 23.000, 0.000, 1.000, 0.000, 0.000, 9.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015822606', 61.000, 43.000, 0.000, 2.000, 0.000, 0.000, 8.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027731600', 1051.000, 515.000, 65.000, 3.000, 6.000, 3.000, 399.000, 60.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25013813801', 86.000, 63.000, 1.000, 0.000, 0.000, 0.000, 16.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017373100', 177.000, 100.000, 6.000, 0.000, 0.000, 0.000, 59.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25015820900', 130.000, 80.000, 2.000, 2.000, 1.000, 0.000, 36.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021419100', 114.000, 49.000, 6.000, 6.000, 3.000, 2.000, 37.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010405', 435.000, 233.000, 45.000, 8.000, 2.000, 0.000, 116.000, 31.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025091001', 212.000, 64.000, 18.000, 1.000, 0.000, 0.000, 111.000, 18.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009202200', 122.000, 92.000, 0.000, 6.000, 1.000, 0.000, 17.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017358400', 120.000, 97.000, 2.000, 0.000, 0.000, 0.000, 13.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25027718100', 64.000, 40.000, 1.000, 0.000, 0.000, 0.000, 19.000, 4.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025010801', 139.000, 104.000, 10.000, 0.000, 0.000, 0.000, 17.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25025091600', 486.000, 104.000, 64.000, 0.000, 8.000, 0.000, 263.000, 47.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021417701', 171.000, 99.000, 8.000, 1.000, 2.000, 0.000, 38.000, 23.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001010304', 40.000, 35.000, 0.000, 0.000, 1.000, 0.000, 1.000, 3.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009211401', 136.000, 92.000, 9.000, 0.000, 0.000, 0.000, 24.000, 11.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017320104', 124.000, 102.000, 0.000, 2.000, 1.000, 0.000, 7.000, 12.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017368400', 765.000, 451.000, 25.000, 0.000, 6.000, 1.000, 235.000, 47.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021416102', 116.000, 66.000, 11.000, 0.000, 2.000, 0.000, 29.000, 8.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25001012602', 410.000, 236.000, 14.000, 10.000, 0.000, 1.000, 120.000, 29.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021400300', 188.000, 128.000, 11.000, 1.000, 2.000, 0.000, 37.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25023540103', 37.000, 29.000, 0.000, 0.000, 0.000, 0.000, 2.000, 6.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25017310100', 1402.000, 672.000, 74.000, 12.000, 9.000, 0.000, 522.000, 113.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021441204', 51.000, 28.000, 1.000, 0.000, 0.000, 0.000, 13.000, 9.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009216100', 95.000, 64.000, 1.000, 2.000, 0.000, 0.000, 23.000, 5.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009203100', 153.000, 106.000, 5.000, 4.000, 1.000, 0.000, 27.000, 10.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25009250300', 1874.000, 455.000, 115.000, 18.000, 1.000, 0.000, 1119.000, 166.000);
INSERT INTO "census"."hisp_pop" ("tract_id", "hispanic_or_latino", "white_alone", "black_or_african_american_alone", "american_indian_and_alaska_native_alone", "asian_alone", "native_hawaiian_and_other_pacific_islander_alone", "some_other_race_alone", "two_or_more_races") VALUES ('25021406102', 87.000, 70.000, 1.000, 1.000, 0.000, 0.000, 12.000, 3.000);


--
-- TOC entry 3593 (class 0 OID 1846231)
-- Dependencies: 225
-- Data for Name: lu_fact_types; Type: TABLE DATA; Schema: census; Owner: postgres
--

INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (1, 'Housing', '{S01,"OCCUPANCY STATUS","Total housing units",HD01,Number}', 's01');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (2, 'Housing', '{S02,"OCCUPANCY STATUS","Total housing units","Occupied housing units",HD01,Number}', 's02');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (3, 'Housing', '{S03,"OCCUPANCY STATUS","Total housing units","Vacant housing units",HD01,Number}', 's03');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (4, 'Housing', '{S04,TENURE,"Occupied housing units",HD01,Number}', 's04');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (5, 'Housing', '{S05,TENURE,"Occupied housing units","Owner occupied",HD01,Number}', 's05');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (6, 'Housing', '{S06,TENURE,"Occupied housing units","Owner occupied","Owned with a mortgage or loan",HD01,Number}', 's06');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (7, 'Housing', '{S07,TENURE,"Occupied housing units","Owner occupied","Owned free and clear",HD01,Number}', 's07');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (8, 'Housing', '{S08,TENURE,"Occupied housing units","Renter occupied",HD01,Number}', 's08');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (9, 'Housing', '{S09,"VACANCY STATUS","Vacant housing units",HD01,Number}', 's09');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (10, 'Housing', '{S10,"VACANCY STATUS","Vacant housing units","For rent",HD01,Number}', 's10');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (11, 'Housing', '{S11,"VACANCY STATUS","Vacant housing units","Rented, not occupied",HD01,Number}', 's11');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (12, 'Housing', '{S12,"VACANCY STATUS","Vacant housing units","For sale only",HD01,Number}', 's12');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (13, 'Housing', '{S13,"VACANCY STATUS","Vacant housing units","Sold, not occupied",HD01,Number}', 's13');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (14, 'Housing', '{S14,"VACANCY STATUS","Vacant housing units","For seasonal, recreational, or occasional use",HD01,Number}', 's14');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (15, 'Housing', '{S15,"VACANCY STATUS","Vacant housing units","For migratory workers",HD01,Number}', 's15');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (16, 'Housing', '{S16,"VACANCY STATUS","Vacant housing units","Other vacant",HD01,Number}', 's16');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (17, 'Housing', '{S17,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units",HD01,Number}', 's17');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (18, 'Housing', '{S18,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units",HD01,Number}', 's18');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (19, 'Housing', '{S19,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Not Hispanic or Latino householder",HD01,Number}', 's19');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (20, 'Housing', '{S20,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Not Hispanic or Latino householder","White alone householder",HD01,Number}', 's20');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (21, 'Housing', '{S21,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Not Hispanic or Latino householder","Black or African American alone householder",HD01,Number}', 's21');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (22, 'Housing', '{S22,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Not Hispanic or Latino householder","American Indian and Alaska Native alone householder",HD01,Number}', 's22');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (23, 'Housing', '{S23,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Not Hispanic or Latino householder","Asian alone householder",HD01,Number}', 's23');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (24, 'Housing', '{S24,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Not Hispanic or Latino householder","Native Hawaiian and Other Pacific Islander alone householder",HD01,Number}', 's24');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (25, 'Housing', '{S25,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Not Hispanic or Latino householder","Some Other Race alone householder",HD01,Number}', 's25');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (26, 'Housing', '{S26,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Not Hispanic or Latino householder","Two or More Races householder",HD01,Number}', 's26');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (27, 'Housing', '{S27,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Hispanic or Latino householder",HD01,Number}', 's27');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (28, 'Housing', '{S28,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Hispanic or Latino householder","White alone householder",HD01,Number}', 's28');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (29, 'Housing', '{S29,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Hispanic or Latino householder","Black or African American alone householder",HD01,Number}', 's29');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (30, 'Housing', '{S30,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Hispanic or Latino householder","American Indian and Alaska Native alone householder",HD01,Number}', 's30');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (31, 'Housing', '{S31,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Hispanic or Latino householder","Asian alone householder",HD01,Number}', 's31');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (32, 'Housing', '{S32,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Hispanic or Latino householder","Native Hawaiian and Other Pacific Islander alone householder",HD01,Number}', 's32');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (33, 'Housing', '{S33,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Hispanic or Latino householder","Some Other Race alone householder",HD01,Number}', 's33');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (34, 'Housing', '{S34,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Owner-occupied housing units","Hispanic or Latino householder","Two or More Races householder",HD01,Number}', 's34');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (35, 'Housing', '{S35,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units",HD01,Number}', 's35');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (36, 'Housing', '{S36,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Not Hispanic or Latino householder",HD01,Number}', 's36');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (37, 'Housing', '{S37,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Not Hispanic or Latino householder","White alone householder",HD01,Number}', 's37');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (38, 'Housing', '{S38,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Not Hispanic or Latino householder","Black or African American alone householder",HD01,Number}', 's38');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (39, 'Housing', '{S39,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Not Hispanic or Latino householder","American Indian and Alaska Native alone householder",HD01,Number}', 's39');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (40, 'Housing', '{S40,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Not Hispanic or Latino householder","Asian alone householder",HD01,Number}', 's40');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (41, 'Housing', '{S41,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Not Hispanic or Latino householder","Native Hawaiian and Other Pacific Islander alone householder",HD01,Number}', 's41');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (42, 'Housing', '{S42,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Not Hispanic or Latino householder","Some Other Race alone householder",HD01,Number}', 's42');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (43, 'Housing', '{S43,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Not Hispanic or Latino householder","Two or More Races householder",HD01,Number}', 's43');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (44, 'Housing', '{S44,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Hispanic or Latino householder",HD01,Number}', 's44');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (45, 'Housing', '{S45,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Hispanic or Latino householder","White alone householder",HD01,Number}', 's45');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (46, 'Housing', '{S46,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Hispanic or Latino householder","Black or African American alone householder",HD01,Number}', 's46');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (47, 'Housing', '{S47,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Hispanic or Latino householder","American Indian and Alaska Native alone householder",HD01,Number}', 's47');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (48, 'Housing', '{S48,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Hispanic or Latino householder","Asian alone householder",HD01,Number}', 's48');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (49, 'Housing', '{S49,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Hispanic or Latino householder","Native Hawaiian and Other Pacific Islander alone householder",HD01,Number}', 's49');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (50, 'Housing', '{S50,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Hispanic or Latino householder","Some Other Race alone householder",HD01,Number}', 's50');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (51, 'Housing', '{S51,"TENURE BY HISPANIC OR LATINO ORIGIN OF HOUSEHOLDER BY RACE OF HOUSEHOLDER","Occupied housing units","Renter-occupied housing units","Hispanic or Latino householder","Two or More Races householder",HD01,Number}', 's51');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (52, 'Population', '{D001,Total:}', 'd001');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (53, 'Population', '{D002,Total:,"Not Hispanic or Latino:"}', 'd002');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (54, 'Population', '{D003,Total:,"Not Hispanic or Latino:","White alone"}', 'd003');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (55, 'Population', '{D004,Total:,"Not Hispanic or Latino:","Black or African American alone"}', 'd004');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (56, 'Population', '{D005,Total:,"Not Hispanic or Latino:","American Indian and Alaska Native alone"}', 'd005');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (57, 'Population', '{D006,Total:,"Not Hispanic or Latino:","Asian alone"}', 'd006');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (58, 'Population', '{D007,Total:,"Not Hispanic or Latino:","Native Hawaiian and Other Pacific Islander alone"}', 'd007');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (59, 'Population', '{D008,Total:,"Not Hispanic or Latino:","Some Other Race alone"}', 'd008');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (60, 'Population', '{D009,Total:,"Not Hispanic or Latino:","Two or More Races"}', 'd009');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (61, 'Population', '{D010,Total:,"Hispanic or Latino:"}', 'd010');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (62, 'Population', '{D011,Total:,"Hispanic or Latino:","White alone"}', 'd011');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (63, 'Population', '{D012,Total:,"Hispanic or Latino:","Black or African American alone"}', 'd012');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (64, 'Population', '{D013,Total:,"Hispanic or Latino:","American Indian and Alaska Native alone"}', 'd013');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (65, 'Population', '{D014,Total:,"Hispanic or Latino:","Asian alone"}', 'd014');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (66, 'Population', '{D015,Total:,"Hispanic or Latino:","Native Hawaiian and Other Pacific Islander alone"}', 'd015');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (67, 'Population', '{D016,Total:,"Hispanic or Latino:","Some Other Race alone"}', 'd016');
INSERT INTO "census"."lu_fact_types" ("fact_type_id", "category", "fact_subcats", "short_name") VALUES (68, 'Population', '{D017,Total:,"Hispanic or Latino:","Two or More Races"}', 'd017');


--
-- TOC entry 3595 (class 0 OID 1846238)
-- Dependencies: 227
-- Data for Name: lu_tracts; Type: TABLE DATA; Schema: census; Owner: postgres
--

INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001010400', '1400000US25001010400', 'Census Tract 104, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001010500', '1400000US25001010500', 'Census Tract 105, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001010100', '1400000US25001010100', 'Census Tract 101, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001010206', '1400000US25001010206', 'Census Tract 102.06, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001010208', '1400000US25001010208', 'Census Tract 102.08, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001010304', '1400000US25001010304', 'Census Tract 103.04, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001010306', '1400000US25001010306', 'Census Tract 103.06, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001010600', '1400000US25001010600', 'Census Tract 106, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001010700', '1400000US25001010700', 'Census Tract 107, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001010800', '1400000US25001010800', 'Census Tract 108, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001010900', '1400000US25001010900', 'Census Tract 109, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001011002', '1400000US25001011002', 'Census Tract 110.02, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001011100', '1400000US25001011100', 'Census Tract 111, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001011200', '1400000US25001011200', 'Census Tract 112, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001011300', '1400000US25001011300', 'Census Tract 113, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001011400', '1400000US25001011400', 'Census Tract 114, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001011500', '1400000US25001011500', 'Census Tract 115, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001011600', '1400000US25001011600', 'Census Tract 116, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001011700', '1400000US25001011700', 'Census Tract 117, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001011801', '1400000US25001011801', 'Census Tract 118.01, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001011802', '1400000US25001011802', 'Census Tract 118.02, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001012001', '1400000US25001012001', 'Census Tract 120.01, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001012002', '1400000US25001012002', 'Census Tract 120.02, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001012101', '1400000US25001012101', 'Census Tract 121.01, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001012102', '1400000US25001012102', 'Census Tract 121.02, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001012200', '1400000US25001012200', 'Census Tract 122, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001012502', '1400000US25001012502', 'Census Tract 125.02, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001012601', '1400000US25001012601', 'Census Tract 126.01, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001012602', '1400000US25001012602', 'Census Tract 126.02, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001012700', '1400000US25001012700', 'Census Tract 127, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001012800', '1400000US25001012800', 'Census Tract 128, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001012900', '1400000US25001012900', 'Census Tract 129, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001013002', '1400000US25001013002', 'Census Tract 130.02, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001013100', '1400000US25001013100', 'Census Tract 131, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001013200', '1400000US25001013200', 'Census Tract 132, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001013300', '1400000US25001013300', 'Census Tract 133, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001013400', '1400000US25001013400', 'Census Tract 134, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001013500', '1400000US25001013500', 'Census Tract 135, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001013600', '1400000US25001013600', 'Census Tract 136, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001013700', '1400000US25001013700', 'Census Tract 137, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001013800', '1400000US25001013800', 'Census Tract 138, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001013900', '1400000US25001013900', 'Census Tract 139, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001014002', '1400000US25001014002', 'Census Tract 140.02, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001014100', '1400000US25001014100', 'Census Tract 141, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001014300', '1400000US25001014300', 'Census Tract 143, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001014402', '1400000US25001014402', 'Census Tract 144.02, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001014500', '1400000US25001014500', 'Census Tract 145, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001014600', '1400000US25001014600', 'Census Tract 146, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001014700', '1400000US25001014700', 'Census Tract 147, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001014800', '1400000US25001014800', 'Census Tract 148, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001014900', '1400000US25001014900', 'Census Tract 149, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001015001', '1400000US25001015001', 'Census Tract 150.01, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001015002', '1400000US25001015002', 'Census Tract 150.02, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001015100', '1400000US25001015100', 'Census Tract 151, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001015200', '1400000US25001015200', 'Census Tract 152, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001015300', '1400000US25001015300', 'Census Tract 153, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25001990000', '1400000US25001990000', 'Census Tract 9900, Barnstable County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003900100', '1400000US25003900100', 'Census Tract 9001, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003900200', '1400000US25003900200', 'Census Tract 9002, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003900300', '1400000US25003900300', 'Census Tract 9003, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003900400', '1400000US25003900400', 'Census Tract 9004, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003900500', '1400000US25003900500', 'Census Tract 9005, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003900600', '1400000US25003900600', 'Census Tract 9006, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003900700', '1400000US25003900700', 'Census Tract 9007, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003900800', '1400000US25003900800', 'Census Tract 9008, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003900900', '1400000US25003900900', 'Census Tract 9009, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003901100', '1400000US25003901100', 'Census Tract 9011, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003911100', '1400000US25003911100', 'Census Tract 9111, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003912100', '1400000US25003912100', 'Census Tract 9121, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003913100', '1400000US25003913100', 'Census Tract 9131, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003914100', '1400000US25003914100', 'Census Tract 9141, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003920101', '1400000US25003920101', 'Census Tract 9201.01, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003920102', '1400000US25003920102', 'Census Tract 9201.02, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003921300', '1400000US25003921300', 'Census Tract 9213, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003921400', '1400000US25003921400', 'Census Tract 9214, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003921500', '1400000US25003921500', 'Census Tract 9215, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003922100', '1400000US25003922100', 'Census Tract 9221, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003922200', '1400000US25003922200', 'Census Tract 9222, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003922300', '1400000US25003922300', 'Census Tract 9223, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003923100', '1400000US25003923100', 'Census Tract 9231, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003924100', '1400000US25003924100', 'Census Tract 9241, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003925100', '1400000US25003925100', 'Census Tract 9251, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003926100', '1400000US25003926100', 'Census Tract 9261, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003931100', '1400000US25003931100', 'Census Tract 9311, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003931300', '1400000US25003931300', 'Census Tract 9313, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003931400', '1400000US25003931400', 'Census Tract 9314, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003932200', '1400000US25003932200', 'Census Tract 9322, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003932300', '1400000US25003932300', 'Census Tract 9323, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003933200', '1400000US25003933200', 'Census Tract 9332, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003933300', '1400000US25003933300', 'Census Tract 9333, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003933400', '1400000US25003933400', 'Census Tract 9334, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003934200', '1400000US25003934200', 'Census Tract 9342, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003934300', '1400000US25003934300', 'Census Tract 9343, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003935100', '1400000US25003935100', 'Census Tract 9351, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003935200', '1400000US25003935200', 'Census Tract 9352, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25003935300', '1400000US25003935300', 'Census Tract 9353, Berkshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005600100', '1400000US25005600100', 'Census Tract 6001, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005600202', '1400000US25005600202', 'Census Tract 6002.02, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005600203', '1400000US25005600203', 'Census Tract 6002.03, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005600204', '1400000US25005600204', 'Census Tract 6002.04, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005610100', '1400000US25005610100', 'Census Tract 6101, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005610202', '1400000US25005610202', 'Census Tract 6102.02, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005610203', '1400000US25005610203', 'Census Tract 6102.03, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005610204', '1400000US25005610204', 'Census Tract 6102.04, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005611101', '1400000US25005611101', 'Census Tract 6111.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005611102', '1400000US25005611102', 'Census Tract 6111.02, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005611201', '1400000US25005611201', 'Census Tract 6112.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005611202', '1400000US25005611202', 'Census Tract 6112.02, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005612100', '1400000US25005612100', 'Census Tract 6121, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005612200', '1400000US25005612200', 'Census Tract 6122, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005613100', '1400000US25005613100', 'Census Tract 6131, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005613300', '1400000US25005613300', 'Census Tract 6133, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005613400', '1400000US25005613400', 'Census Tract 6134, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005613600', '1400000US25005613600', 'Census Tract 6136, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005613700', '1400000US25005613700', 'Census Tract 6137, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005613800', '1400000US25005613800', 'Census Tract 6138, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005613901', '1400000US25005613901', 'Census Tract 6139.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005613902', '1400000US25005613902', 'Census Tract 6139.02, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005614000', '1400000US25005614000', 'Census Tract 6140, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005614101', '1400000US25005614101', 'Census Tract 6141.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005614102', '1400000US25005614102', 'Census Tract 6141.02, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005615100', '1400000US25005615100', 'Census Tract 6151, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005616100', '1400000US25005616100', 'Census Tract 6161, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005617101', '1400000US25005617101', 'Census Tract 6171.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005617102', '1400000US25005617102', 'Census Tract 6171.02, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005630101', '1400000US25005630101', 'Census Tract 6301.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005630102', '1400000US25005630102', 'Census Tract 6301.02, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005630200', '1400000US25005630200', 'Census Tract 6302, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005630300', '1400000US25005630300', 'Census Tract 6303, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005630400', '1400000US25005630400', 'Census Tract 6304, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005631100', '1400000US25005631100', 'Census Tract 6311, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005631200', '1400000US25005631200', 'Census Tract 6312, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005631300', '1400000US25005631300', 'Census Tract 6313, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005631400', '1400000US25005631400', 'Census Tract 6314, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005631500', '1400000US25005631500', 'Census Tract 6315, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005631600', '1400000US25005631600', 'Census Tract 6316, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005631700', '1400000US25005631700', 'Census Tract 6317, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005631800', '1400000US25005631800', 'Census Tract 6318, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005632100', '1400000US25005632100', 'Census Tract 6321, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005632200', '1400000US25005632200', 'Census Tract 6322, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005633100', '1400000US25005633100', 'Census Tract 6331, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005633200', '1400000US25005633200', 'Census Tract 6332, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005640100', '1400000US25005640100', 'Census Tract 6401, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005640200', '1400000US25005640200', 'Census Tract 6402, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005640300', '1400000US25005640300', 'Census Tract 6403, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005640400', '1400000US25005640400', 'Census Tract 6404, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005640500', '1400000US25005640500', 'Census Tract 6405, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005640600', '1400000US25005640600', 'Census Tract 6406, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005640700', '1400000US25005640700', 'Census Tract 6407, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005640800', '1400000US25005640800', 'Census Tract 6408, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005640901', '1400000US25005640901', 'Census Tract 6409.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005641000', '1400000US25005641000', 'Census Tract 6410, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005641101', '1400000US25005641101', 'Census Tract 6411.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005641200', '1400000US25005641200', 'Census Tract 6412, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005641300', '1400000US25005641300', 'Census Tract 6413, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005641400', '1400000US25005641400', 'Census Tract 6414, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005641500', '1400000US25005641500', 'Census Tract 6415, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005641600', '1400000US25005641600', 'Census Tract 6416, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005641700', '1400000US25005641700', 'Census Tract 6417, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005641800', '1400000US25005641800', 'Census Tract 6418, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005641900', '1400000US25005641900', 'Census Tract 6419, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005642000', '1400000US25005642000', 'Census Tract 6420, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005642100', '1400000US25005642100', 'Census Tract 6421, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005642200', '1400000US25005642200', 'Census Tract 6422, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005642300', '1400000US25005642300', 'Census Tract 6423, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005642400', '1400000US25005642400', 'Census Tract 6424, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005642500', '1400000US25005642500', 'Census Tract 6425, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005644101', '1400000US25005644101', 'Census Tract 6441.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005644102', '1400000US25005644102', 'Census Tract 6441.02, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005644200', '1400000US25005644200', 'Census Tract 6442, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005645101', '1400000US25005645101', 'Census Tract 6451.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005645102', '1400000US25005645102', 'Census Tract 6451.02, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005645103', '1400000US25005645103', 'Census Tract 6451.03, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005646101', '1400000US25005646101', 'Census Tract 6461.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005646103', '1400000US25005646103', 'Census Tract 6461.03, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005646104', '1400000US25005646104', 'Census Tract 6461.04, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005650101', '1400000US25005650101', 'Census Tract 6501.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005650102', '1400000US25005650102', 'Census Tract 6501.02, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005650201', '1400000US25005650201', 'Census Tract 6502.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005650202', '1400000US25005650202', 'Census Tract 6502.02, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005650300', '1400000US25005650300', 'Census Tract 6503, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005650400', '1400000US25005650400', 'Census Tract 6504, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005650500', '1400000US25005650500', 'Census Tract 6505, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005650600', '1400000US25005650600', 'Census Tract 6506, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005650700', '1400000US25005650700', 'Census Tract 6507, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005650800', '1400000US25005650800', 'Census Tract 6508, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005650900', '1400000US25005650900', 'Census Tract 6509, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005651001', '1400000US25005651001', 'Census Tract 6510.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005651002', '1400000US25005651002', 'Census Tract 6510.02, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005651100', '1400000US25005651100', 'Census Tract 6511, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005651200', '1400000US25005651200', 'Census Tract 6512, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005651300', '1400000US25005651300', 'Census Tract 6513, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005651400', '1400000US25005651400', 'Census Tract 6514, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005651500', '1400000US25005651500', 'Census Tract 6515, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005651600', '1400000US25005651600', 'Census Tract 6516, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005651700', '1400000US25005651700', 'Census Tract 6517, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005651800', '1400000US25005651800', 'Census Tract 6518, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005651900', '1400000US25005651900', 'Census Tract 6519, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005652000', '1400000US25005652000', 'Census Tract 6520, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005652100', '1400000US25005652100', 'Census Tract 6521, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005652200', '1400000US25005652200', 'Census Tract 6522, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005652300', '1400000US25005652300', 'Census Tract 6523, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005652400', '1400000US25005652400', 'Census Tract 6524, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005652500', '1400000US25005652500', 'Census Tract 6525, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005652600', '1400000US25005652600', 'Census Tract 6526, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005652700', '1400000US25005652700', 'Census Tract 6527, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005652800', '1400000US25005652800', 'Census Tract 6528, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005653101', '1400000US25005653101', 'Census Tract 6531.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005653102', '1400000US25005653102', 'Census Tract 6531.02, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005653203', '1400000US25005653203', 'Census Tract 6532.03, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005653204', '1400000US25005653204', 'Census Tract 6532.04, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005653301', '1400000US25005653301', 'Census Tract 6533.01, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005653304', '1400000US25005653304', 'Census Tract 6533.04, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005654100', '1400000US25005654100', 'Census Tract 6541, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005654200', '1400000US25005654200', 'Census Tract 6542, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005655100', '1400000US25005655100', 'Census Tract 6551, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005655200', '1400000US25005655200', 'Census Tract 6552, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005655300', '1400000US25005655300', 'Census Tract 6553, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005655400', '1400000US25005655400', 'Census Tract 6554, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005985500', '1400000US25005985500', 'Census Tract 9855, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005985600', '1400000US25005985600', 'Census Tract 9856, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25005990000', '1400000US25005990000', 'Census Tract 9900, Bristol County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25007200100', '1400000US25007200100', 'Census Tract 2001, Dukes County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25007200200', '1400000US25007200200', 'Census Tract 2002, Dukes County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25007200300', '1400000US25007200300', 'Census Tract 2003, Dukes County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25007200400', '1400000US25007200400', 'Census Tract 2004, Dukes County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25007990000', '1400000US25007990000', 'Census Tract 9900, Dukes County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009201100', '1400000US25009201100', 'Census Tract 2011, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009202101', '1400000US25009202101', 'Census Tract 2021.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009202102', '1400000US25009202102', 'Census Tract 2021.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009202200', '1400000US25009202200', 'Census Tract 2022, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009205400', '1400000US25009205400', 'Census Tract 2054, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009203100', '1400000US25009203100', 'Census Tract 2031, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009203200', '1400000US25009203200', 'Census Tract 2032, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009203301', '1400000US25009203301', 'Census Tract 2033.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009203302', '1400000US25009203302', 'Census Tract 2033.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009204101', '1400000US25009204101', 'Census Tract 2041.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009204102', '1400000US25009204102', 'Census Tract 2041.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009204200', '1400000US25009204200', 'Census Tract 2042, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009204300', '1400000US25009204300', 'Census Tract 2043, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009204400', '1400000US25009204400', 'Census Tract 2044, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009204500', '1400000US25009204500', 'Census Tract 2045, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009204600', '1400000US25009204600', 'Census Tract 2046, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009204701', '1400000US25009204701', 'Census Tract 2047.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009204702', '1400000US25009204702', 'Census Tract 2047.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009205100', '1400000US25009205100', 'Census Tract 2051, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009205200', '1400000US25009205200', 'Census Tract 2052, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009205300', '1400000US25009205300', 'Census Tract 2053, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009205500', '1400000US25009205500', 'Census Tract 2055, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009205600', '1400000US25009205600', 'Census Tract 2056, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009205700', '1400000US25009205700', 'Census Tract 2057, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009205800', '1400000US25009205800', 'Census Tract 2058, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009205900', '1400000US25009205900', 'Census Tract 2059, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009206000', '1400000US25009206000', 'Census Tract 2060, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009206100', '1400000US25009206100', 'Census Tract 2061, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009206200', '1400000US25009206200', 'Census Tract 2062, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009206300', '1400000US25009206300', 'Census Tract 2063, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009206400', '1400000US25009206400', 'Census Tract 2064, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009206500', '1400000US25009206500', 'Census Tract 2065, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009206600', '1400000US25009206600', 'Census Tract 2066, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009206700', '1400000US25009206700', 'Census Tract 2067, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009206800', '1400000US25009206800', 'Census Tract 2068, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009206900', '1400000US25009206900', 'Census Tract 2069, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009207000', '1400000US25009207000', 'Census Tract 2070, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009207100', '1400000US25009207100', 'Census Tract 2071, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009207200', '1400000US25009207200', 'Census Tract 2072, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009208101', '1400000US25009208101', 'Census Tract 2081.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009208102', '1400000US25009208102', 'Census Tract 2081.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009208200', '1400000US25009208200', 'Census Tract 2082, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009208300', '1400000US25009208300', 'Census Tract 2083, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009208400', '1400000US25009208400', 'Census Tract 2084, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009209100', '1400000US25009209100', 'Census Tract 2091, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009209200', '1400000US25009209200', 'Census Tract 2092, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009210100', '1400000US25009210100', 'Census Tract 2101, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009210200', '1400000US25009210200', 'Census Tract 2102, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009210300', '1400000US25009210300', 'Census Tract 2103, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009210400', '1400000US25009210400', 'Census Tract 2104, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009210500', '1400000US25009210500', 'Census Tract 2105, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009210600', '1400000US25009210600', 'Census Tract 2106, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009210700', '1400000US25009210700', 'Census Tract 2107, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009210800', '1400000US25009210800', 'Census Tract 2108, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009210900', '1400000US25009210900', 'Census Tract 2109, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009211100', '1400000US25009211100', 'Census Tract 2111, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009211200', '1400000US25009211200', 'Census Tract 2112, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009211300', '1400000US25009211300', 'Census Tract 2113, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009211401', '1400000US25009211401', 'Census Tract 2114.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009211402', '1400000US25009211402', 'Census Tract 2114.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009212100', '1400000US25009212100', 'Census Tract 2121, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009213100', '1400000US25009213100', 'Census Tract 2131, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009214100', '1400000US25009214100', 'Census Tract 2141, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009215101', '1400000US25009215101', 'Census Tract 2151.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009215102', '1400000US25009215102', 'Census Tract 2151.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009216100', '1400000US25009216100', 'Census Tract 2161, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009217100', '1400000US25009217100', 'Census Tract 2171, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009217201', '1400000US25009217201', 'Census Tract 2172.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009217202', '1400000US25009217202', 'Census Tract 2172.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009217300', '1400000US25009217300', 'Census Tract 2173, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009217400', '1400000US25009217400', 'Census Tract 2174, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009217500', '1400000US25009217500', 'Census Tract 2175, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009217600', '1400000US25009217600', 'Census Tract 2176, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009218100', '1400000US25009218100', 'Census Tract 2181, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009220101', '1400000US25009220101', 'Census Tract 2201.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009220102', '1400000US25009220102', 'Census Tract 2201.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009221100', '1400000US25009221100', 'Census Tract 2211, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009221300', '1400000US25009221300', 'Census Tract 2213, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009221400', '1400000US25009221400', 'Census Tract 2214, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009221500', '1400000US25009221500', 'Census Tract 2215, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009221600', '1400000US25009221600', 'Census Tract 2216, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009221700', '1400000US25009221700', 'Census Tract 2217, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009221800', '1400000US25009221800', 'Census Tract 2218, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009221901', '1400000US25009221901', 'Census Tract 2219.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009221902', '1400000US25009221902', 'Census Tract 2219.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009222100', '1400000US25009222100', 'Census Tract 2221, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009223100', '1400000US25009223100', 'Census Tract 2231, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009223200', '1400000US25009223200', 'Census Tract 2232, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009223300', '1400000US25009223300', 'Census Tract 2233, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009250100', '1400000US25009250100', 'Census Tract 2501, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009250200', '1400000US25009250200', 'Census Tract 2502, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009250300', '1400000US25009250300', 'Census Tract 2503, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009250400', '1400000US25009250400', 'Census Tract 2504, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009250500', '1400000US25009250500', 'Census Tract 2505, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009250600', '1400000US25009250600', 'Census Tract 2506, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009250700', '1400000US25009250700', 'Census Tract 2507, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009250800', '1400000US25009250800', 'Census Tract 2508, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009250900', '1400000US25009250900', 'Census Tract 2509, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009251000', '1400000US25009251000', 'Census Tract 2510, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009251100', '1400000US25009251100', 'Census Tract 2511, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009251200', '1400000US25009251200', 'Census Tract 2512, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009251300', '1400000US25009251300', 'Census Tract 2513, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009251400', '1400000US25009251400', 'Census Tract 2514, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009251500', '1400000US25009251500', 'Census Tract 2515, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009251600', '1400000US25009251600', 'Census Tract 2516, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009251700', '1400000US25009251700', 'Census Tract 2517, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009251800', '1400000US25009251800', 'Census Tract 2518, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009252101', '1400000US25009252101', 'Census Tract 2521.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009252102', '1400000US25009252102', 'Census Tract 2521.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009252201', '1400000US25009252201', 'Census Tract 2522.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009252202', '1400000US25009252202', 'Census Tract 2522.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009252300', '1400000US25009252300', 'Census Tract 2523, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009252400', '1400000US25009252400', 'Census Tract 2524, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009252501', '1400000US25009252501', 'Census Tract 2525.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009252502', '1400000US25009252502', 'Census Tract 2525.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009252601', '1400000US25009252601', 'Census Tract 2526.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009252602', '1400000US25009252602', 'Census Tract 2526.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009252603', '1400000US25009252603', 'Census Tract 2526.03, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009253100', '1400000US25009253100', 'Census Tract 2531, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009253201', '1400000US25009253201', 'Census Tract 2532.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009253202', '1400000US25009253202', 'Census Tract 2532.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009253203', '1400000US25009253203', 'Census Tract 2532.03, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009253204', '1400000US25009253204', 'Census Tract 2532.04, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009253205', '1400000US25009253205', 'Census Tract 2532.05, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009254100', '1400000US25009254100', 'Census Tract 2541, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009254200', '1400000US25009254200', 'Census Tract 2542, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009254301', '1400000US25009254301', 'Census Tract 2543.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009254302', '1400000US25009254302', 'Census Tract 2543.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009254401', '1400000US25009254401', 'Census Tract 2544.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009254402', '1400000US25009254402', 'Census Tract 2544.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009254403', '1400000US25009254403', 'Census Tract 2544.03, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009260100', '1400000US25009260100', 'Census Tract 2601, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009260200', '1400000US25009260200', 'Census Tract 2602, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009260301', '1400000US25009260301', 'Census Tract 2603.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009260302', '1400000US25009260302', 'Census Tract 2603.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009260401', '1400000US25009260401', 'Census Tract 2604.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009260402', '1400000US25009260402', 'Census Tract 2604.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009260500', '1400000US25009260500', 'Census Tract 2605, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009260600', '1400000US25009260600', 'Census Tract 2606, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009260700', '1400000US25009260700', 'Census Tract 2607, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009260800', '1400000US25009260800', 'Census Tract 2608, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009260900', '1400000US25009260900', 'Census Tract 2609, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009261000', '1400000US25009261000', 'Census Tract 2610, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009261101', '1400000US25009261101', 'Census Tract 2611.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009261102', '1400000US25009261102', 'Census Tract 2611.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009262100', '1400000US25009262100', 'Census Tract 2621, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009263100', '1400000US25009263100', 'Census Tract 2631, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009264100', '1400000US25009264100', 'Census Tract 2641, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009265101', '1400000US25009265101', 'Census Tract 2651.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009265102', '1400000US25009265102', 'Census Tract 2651.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009266100', '1400000US25009266100', 'Census Tract 2661, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009266200', '1400000US25009266200', 'Census Tract 2662, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009266300', '1400000US25009266300', 'Census Tract 2663, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009266400', '1400000US25009266400', 'Census Tract 2664, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009267101', '1400000US25009267101', 'Census Tract 2671.01, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009267102', '1400000US25009267102', 'Census Tract 2671.02, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009268100', '1400000US25009268100', 'Census Tract 2681, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009268200', '1400000US25009268200', 'Census Tract 2682, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009268300', '1400000US25009268300', 'Census Tract 2683, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009268400', '1400000US25009268400', 'Census Tract 2684, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009269100', '1400000US25009269100', 'Census Tract 2691, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009270100', '1400000US25009270100', 'Census Tract 2701, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25009990100', '1400000US25009990100', 'Census Tract 9901, Essex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011040100', '1400000US25011040100', 'Census Tract 401, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011040200', '1400000US25011040200', 'Census Tract 402, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011040300', '1400000US25011040300', 'Census Tract 403, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011040400', '1400000US25011040400', 'Census Tract 404, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011040501', '1400000US25011040501', 'Census Tract 405.01, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011040502', '1400000US25011040502', 'Census Tract 405.02, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011040600', '1400000US25011040600', 'Census Tract 406, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011040701', '1400000US25011040701', 'Census Tract 407.01, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011040702', '1400000US25011040702', 'Census Tract 407.02, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011040800', '1400000US25011040800', 'Census Tract 408, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011040900', '1400000US25011040900', 'Census Tract 409, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011041000', '1400000US25011041000', 'Census Tract 410, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011041100', '1400000US25011041100', 'Census Tract 411, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011041200', '1400000US25011041200', 'Census Tract 412, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011041300', '1400000US25011041300', 'Census Tract 413, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011041400', '1400000US25011041400', 'Census Tract 414, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011041501', '1400000US25011041501', 'Census Tract 415.01, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25011041502', '1400000US25011041502', 'Census Tract 415.02, Franklin County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013800101', '1400000US25013800101', 'Census Tract 8001.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013800102', '1400000US25013800102', 'Census Tract 8001.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013800201', '1400000US25013800201', 'Census Tract 8002.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013800202', '1400000US25013800202', 'Census Tract 8002.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013800300', '1400000US25013800300', 'Census Tract 8003, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013800400', '1400000US25013800400', 'Census Tract 8004, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013800500', '1400000US25013800500', 'Census Tract 8005, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013800600', '1400000US25013800600', 'Census Tract 8006, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013800700', '1400000US25013800700', 'Census Tract 8007, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013800800', '1400000US25013800800', 'Census Tract 8008, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013800900', '1400000US25013800900', 'Census Tract 8009, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801101', '1400000US25013801101', 'Census Tract 8011.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801102', '1400000US25013801102', 'Census Tract 8011.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801200', '1400000US25013801200', 'Census Tract 8012, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801300', '1400000US25013801300', 'Census Tract 8013, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801401', '1400000US25013801401', 'Census Tract 8014.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801402', '1400000US25013801402', 'Census Tract 8014.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801501', '1400000US25013801501', 'Census Tract 8015.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801502', '1400000US25013801502', 'Census Tract 8015.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801503', '1400000US25013801503', 'Census Tract 8015.03, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801601', '1400000US25013801601', 'Census Tract 8016.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801602', '1400000US25013801602', 'Census Tract 8016.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801603', '1400000US25013801603', 'Census Tract 8016.03, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801604', '1400000US25013801604', 'Census Tract 8016.04, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801605', '1400000US25013801605', 'Census Tract 8016.05, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801700', '1400000US25013801700', 'Census Tract 8017, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801800', '1400000US25013801800', 'Census Tract 8018, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801901', '1400000US25013801901', 'Census Tract 8019.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013801902', '1400000US25013801902', 'Census Tract 8019.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013802000', '1400000US25013802000', 'Census Tract 8020, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013802100', '1400000US25013802100', 'Census Tract 8021, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013802200', '1400000US25013802200', 'Census Tract 8022, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013802300', '1400000US25013802300', 'Census Tract 8023, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013802400', '1400000US25013802400', 'Census Tract 8024, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013802500', '1400000US25013802500', 'Census Tract 8025, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013802601', '1400000US25013802601', 'Census Tract 8026.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013802602', '1400000US25013802602', 'Census Tract 8026.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013810100', '1400000US25013810100', 'Census Tract 8101, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013810200', '1400000US25013810200', 'Census Tract 8102, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013810300', '1400000US25013810300', 'Census Tract 8103, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013810403', '1400000US25013810403', 'Census Tract 8104.03, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013810404', '1400000US25013810404', 'Census Tract 8104.04, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013810412', '1400000US25013810412', 'Census Tract 8104.12, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013810414', '1400000US25013810414', 'Census Tract 8104.14, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013810601', '1400000US25013810601', 'Census Tract 8106.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013810602', '1400000US25013810602', 'Census Tract 8106.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013810700', '1400000US25013810700', 'Census Tract 8107, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013810800', '1400000US25013810800', 'Census Tract 8108, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013810901', '1400000US25013810901', 'Census Tract 8109.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013810902', '1400000US25013810902', 'Census Tract 8109.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013811000', '1400000US25013811000', 'Census Tract 8110, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013811101', '1400000US25013811101', 'Census Tract 8111.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013811102', '1400000US25013811102', 'Census Tract 8111.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013811200', '1400000US25013811200', 'Census Tract 8112, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013811301', '1400000US25013811301', 'Census Tract 8113.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013811302', '1400000US25013811302', 'Census Tract 8113.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013811400', '1400000US25013811400', 'Census Tract 8114, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013811500', '1400000US25013811500', 'Census Tract 8115, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013811600', '1400000US25013811600', 'Census Tract 8116, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013811700', '1400000US25013811700', 'Census Tract 8117, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013811800', '1400000US25013811800', 'Census Tract 8118, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013811900', '1400000US25013811900', 'Census Tract 8119, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812001', '1400000US25013812001', 'Census Tract 8120.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812002', '1400000US25013812002', 'Census Tract 8120.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812101', '1400000US25013812101', 'Census Tract 8121.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812103', '1400000US25013812103', 'Census Tract 8121.03, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812104', '1400000US25013812104', 'Census Tract 8121.04, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812201', '1400000US25013812201', 'Census Tract 8122.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812202', '1400000US25013812202', 'Census Tract 8122.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812300', '1400000US25013812300', 'Census Tract 8123, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812401', '1400000US25013812401', 'Census Tract 8124.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812403', '1400000US25013812403', 'Census Tract 8124.03, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812404', '1400000US25013812404', 'Census Tract 8124.04, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812500', '1400000US25013812500', 'Census Tract 8125, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812600', '1400000US25013812600', 'Census Tract 8126, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812701', '1400000US25013812701', 'Census Tract 8127.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812702', '1400000US25013812702', 'Census Tract 8127.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812800', '1400000US25013812800', 'Census Tract 8128, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812901', '1400000US25013812901', 'Census Tract 8129.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812902', '1400000US25013812902', 'Census Tract 8129.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013812903', '1400000US25013812903', 'Census Tract 8129.03, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813000', '1400000US25013813000', 'Census Tract 8130, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813101', '1400000US25013813101', 'Census Tract 8131.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813102', '1400000US25013813102', 'Census Tract 8131.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813204', '1400000US25013813204', 'Census Tract 8132.04, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813205', '1400000US25013813205', 'Census Tract 8132.05, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813206', '1400000US25013813206', 'Census Tract 8132.06, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813207', '1400000US25013813207', 'Census Tract 8132.07, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813208', '1400000US25013813208', 'Census Tract 8132.08, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813209', '1400000US25013813209', 'Census Tract 8132.09, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813301', '1400000US25013813301', 'Census Tract 8133.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813303', '1400000US25013813303', 'Census Tract 8133.03, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813304', '1400000US25013813304', 'Census Tract 8133.04, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813401', '1400000US25013813401', 'Census Tract 8134.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813403', '1400000US25013813403', 'Census Tract 8134.03, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813404', '1400000US25013813404', 'Census Tract 8134.04, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813500', '1400000US25013813500', 'Census Tract 8135, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813601', '1400000US25013813601', 'Census Tract 8136.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813602', '1400000US25013813602', 'Census Tract 8136.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813701', '1400000US25013813701', 'Census Tract 8137.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813702', '1400000US25013813702', 'Census Tract 8137.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813801', '1400000US25013813801', 'Census Tract 8138.01, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25013813802', '1400000US25013813802', 'Census Tract 8138.02, Hampden County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015820101', '1400000US25015820101', 'Census Tract 8201.01, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015820102', '1400000US25015820102', 'Census Tract 8201.02, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015820202', '1400000US25015820202', 'Census Tract 8202.02, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015820203', '1400000US25015820203', 'Census Tract 8202.03, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015820204', '1400000US25015820204', 'Census Tract 8202.04, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015820300', '1400000US25015820300', 'Census Tract 8203, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015820400', '1400000US25015820400', 'Census Tract 8204, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015820500', '1400000US25015820500', 'Census Tract 8205, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015820600', '1400000US25015820600', 'Census Tract 8206, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015820700', '1400000US25015820700', 'Census Tract 8207, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015820801', '1400000US25015820801', 'Census Tract 8208.01, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015820802', '1400000US25015820802', 'Census Tract 8208.02, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015820900', '1400000US25015820900', 'Census Tract 8209, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015821000', '1400000US25015821000', 'Census Tract 8210, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015821100', '1400000US25015821100', 'Census Tract 8211, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015821200', '1400000US25015821200', 'Census Tract 8212, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015821300', '1400000US25015821300', 'Census Tract 8213, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015821400', '1400000US25015821400', 'Census Tract 8214, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015821500', '1400000US25015821500', 'Census Tract 8215, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015821601', '1400000US25015821601', 'Census Tract 8216.01, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015821602', '1400000US25015821602', 'Census Tract 8216.02, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015821700', '1400000US25015821700', 'Census Tract 8217, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015821901', '1400000US25015821901', 'Census Tract 8219.01, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015821903', '1400000US25015821903', 'Census Tract 8219.03, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015821904', '1400000US25015821904', 'Census Tract 8219.04, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015822000', '1400000US25015822000', 'Census Tract 8220, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015822200', '1400000US25015822200', 'Census Tract 8222, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015822300', '1400000US25015822300', 'Census Tract 8223, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015822401', '1400000US25015822401', 'Census Tract 8224.01, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015822402', '1400000US25015822402', 'Census Tract 8224.02, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015822500', '1400000US25015822500', 'Census Tract 8225, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015822601', '1400000US25015822601', 'Census Tract 8226.01, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015822603', '1400000US25015822603', 'Census Tract 8226.03, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015822605', '1400000US25015822605', 'Census Tract 8226.05, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015822606', '1400000US25015822606', 'Census Tract 8226.06, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25015822700', '1400000US25015822700', 'Census Tract 8227, Hampshire County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017300100', '1400000US25017300100', 'Census Tract 3001, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017301101', '1400000US25017301101', 'Census Tract 3011.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017301102', '1400000US25017301102', 'Census Tract 3011.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017310100', '1400000US25017310100', 'Census Tract 3101, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017310200', '1400000US25017310200', 'Census Tract 3102, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017310300', '1400000US25017310300', 'Census Tract 3103, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017310400', '1400000US25017310400', 'Census Tract 3104, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017310500', '1400000US25017310500', 'Census Tract 3105, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017310601', '1400000US25017310601', 'Census Tract 3106.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017310602', '1400000US25017310602', 'Census Tract 3106.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017310700', '1400000US25017310700', 'Census Tract 3107, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017311100', '1400000US25017311100', 'Census Tract 3111, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017311200', '1400000US25017311200', 'Census Tract 3112, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017311300', '1400000US25017311300', 'Census Tract 3113, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017311400', '1400000US25017311400', 'Census Tract 3114, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017311500', '1400000US25017311500', 'Census Tract 3115, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017311600', '1400000US25017311600', 'Census Tract 3116, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017311700', '1400000US25017311700', 'Census Tract 3117, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017311800', '1400000US25017311800', 'Census Tract 3118, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017311900', '1400000US25017311900', 'Census Tract 3119, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017312000', '1400000US25017312000', 'Census Tract 3120, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017312100', '1400000US25017312100', 'Census Tract 3121, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017312200', '1400000US25017312200', 'Census Tract 3122, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017312300', '1400000US25017312300', 'Census Tract 3123, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017312400', '1400000US25017312400', 'Census Tract 3124, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017312501', '1400000US25017312501', 'Census Tract 3125.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017312502', '1400000US25017312502', 'Census Tract 3125.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017313101', '1400000US25017313101', 'Census Tract 3131.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017313102', '1400000US25017313102', 'Census Tract 3131.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017314101', '1400000US25017314101', 'Census Tract 3141.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017314102', '1400000US25017314102', 'Census Tract 3141.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017314200', '1400000US25017314200', 'Census Tract 3142, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017314301', '1400000US25017314301', 'Census Tract 3143.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017314302', '1400000US25017314302', 'Census Tract 3143.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017315100', '1400000US25017315100', 'Census Tract 3151, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017315200', '1400000US25017315200', 'Census Tract 3152, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017315401', '1400000US25017315401', 'Census Tract 3154.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017315402', '1400000US25017315402', 'Census Tract 3154.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017315403', '1400000US25017315403', 'Census Tract 3154.03, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017315500', '1400000US25017315500', 'Census Tract 3155, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017316101', '1400000US25017316101', 'Census Tract 3161.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017316102', '1400000US25017316102', 'Census Tract 3161.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017316201', '1400000US25017316201', 'Census Tract 3162.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017316202', '1400000US25017316202', 'Census Tract 3162.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017316300', '1400000US25017316300', 'Census Tract 3163, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017316400', '1400000US25017316400', 'Census Tract 3164, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017316500', '1400000US25017316500', 'Census Tract 3165, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017317101', '1400000US25017317101', 'Census Tract 3171.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017317102', '1400000US25017317102', 'Census Tract 3171.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017317103', '1400000US25017317103', 'Census Tract 3171.03, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017317201', '1400000US25017317201', 'Census Tract 3172.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017317202', '1400000US25017317202', 'Census Tract 3172.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017317203', '1400000US25017317203', 'Census Tract 3172.03, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017317301', '1400000US25017317301', 'Census Tract 3173.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017317302', '1400000US25017317302', 'Census Tract 3173.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017318100', '1400000US25017318100', 'Census Tract 3181, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017318200', '1400000US25017318200', 'Census Tract 3182, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017318300', '1400000US25017318300', 'Census Tract 3183, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017318400', '1400000US25017318400', 'Census Tract 3184, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017320102', '1400000US25017320102', 'Census Tract 3201.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017320103', '1400000US25017320103', 'Census Tract 3201.03, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017320104', '1400000US25017320104', 'Census Tract 3201.04, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017321100', '1400000US25017321100', 'Census Tract 3211, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017321200', '1400000US25017321200', 'Census Tract 3212, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017321300', '1400000US25017321300', 'Census Tract 3213, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017321400', '1400000US25017321400', 'Census Tract 3214, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017321500', '1400000US25017321500', 'Census Tract 3215, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017321600', '1400000US25017321600', 'Census Tract 3216, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017322100', '1400000US25017322100', 'Census Tract 3221, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017322200', '1400000US25017322200', 'Census Tract 3222, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017322300', '1400000US25017322300', 'Census Tract 3223, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017322400', '1400000US25017322400', 'Census Tract 3224, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017323100', '1400000US25017323100', 'Census Tract 3231, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017324101', '1400000US25017324101', 'Census Tract 3241.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017324102', '1400000US25017324102', 'Census Tract 3241.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017325100', '1400000US25017325100', 'Census Tract 3251, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017326101', '1400000US25017326101', 'Census Tract 3261.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017326102', '1400000US25017326102', 'Census Tract 3261.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017327101', '1400000US25017327101', 'Census Tract 3271.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017327102', '1400000US25017327102', 'Census Tract 3271.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017327103', '1400000US25017327103', 'Census Tract 3271.03, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017328100', '1400000US25017328100', 'Census Tract 3281, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017330100', '1400000US25017330100', 'Census Tract 3301, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017330200', '1400000US25017330200', 'Census Tract 3302, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017331101', '1400000US25017331101', 'Census Tract 3311.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017331102', '1400000US25017331102', 'Census Tract 3311.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017331200', '1400000US25017331200', 'Census Tract 3312, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017331300', '1400000US25017331300', 'Census Tract 3313, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017332100', '1400000US25017332100', 'Census Tract 3321, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017332200', '1400000US25017332200', 'Census Tract 3322, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017332300', '1400000US25017332300', 'Census Tract 3323, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017332400', '1400000US25017332400', 'Census Tract 3324, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017333100', '1400000US25017333100', 'Census Tract 3331, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017333200', '1400000US25017333200', 'Census Tract 3332, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017333300', '1400000US25017333300', 'Census Tract 3333, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017333400', '1400000US25017333400', 'Census Tract 3334, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017333501', '1400000US25017333501', 'Census Tract 3335.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017333502', '1400000US25017333502', 'Census Tract 3335.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017333600', '1400000US25017333600', 'Census Tract 3336, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017334100', '1400000US25017334100', 'Census Tract 3341, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017334200', '1400000US25017334200', 'Census Tract 3342, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017334300', '1400000US25017334300', 'Census Tract 3343, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017334400', '1400000US25017334400', 'Census Tract 3344, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017335100', '1400000US25017335100', 'Census Tract 3351, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017335200', '1400000US25017335200', 'Census Tract 3352, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017335301', '1400000US25017335301', 'Census Tract 3353.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017335302', '1400000US25017335302', 'Census Tract 3353.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017335400', '1400000US25017335400', 'Census Tract 3354, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017336100', '1400000US25017336100', 'Census Tract 3361, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017336200', '1400000US25017336200', 'Census Tract 3362, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017336300', '1400000US25017336300', 'Census Tract 3363, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017336401', '1400000US25017336401', 'Census Tract 3364.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017336402', '1400000US25017336402', 'Census Tract 3364.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017337101', '1400000US25017337101', 'Census Tract 3371.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017337102', '1400000US25017337102', 'Census Tract 3371.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017337201', '1400000US25017337201', 'Census Tract 3372.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017337202', '1400000US25017337202', 'Census Tract 3372.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017337300', '1400000US25017337300', 'Census Tract 3373, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017338100', '1400000US25017338100', 'Census Tract 3381, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017338200', '1400000US25017338200', 'Census Tract 3382, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017338300', '1400000US25017338300', 'Census Tract 3383, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017338400', '1400000US25017338400', 'Census Tract 3384, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017338500', '1400000US25017338500', 'Census Tract 3385, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017339100', '1400000US25017339100', 'Census Tract 3391, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017339200', '1400000US25017339200', 'Census Tract 3392, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017339300', '1400000US25017339300', 'Census Tract 3393, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017339400', '1400000US25017339400', 'Census Tract 3394, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017339500', '1400000US25017339500', 'Census Tract 3395, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017339600', '1400000US25017339600', 'Census Tract 3396, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017339700', '1400000US25017339700', 'Census Tract 3397, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017339801', '1400000US25017339801', 'Census Tract 3398.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017339802', '1400000US25017339802', 'Census Tract 3398.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017339900', '1400000US25017339900', 'Census Tract 3399, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017340000', '1400000US25017340000', 'Census Tract 3400, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017340100', '1400000US25017340100', 'Census Tract 3401, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017341101', '1400000US25017341101', 'Census Tract 3411.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017341102', '1400000US25017341102', 'Census Tract 3411.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017341200', '1400000US25017341200', 'Census Tract 3412, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017341300', '1400000US25017341300', 'Census Tract 3413, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017341400', '1400000US25017341400', 'Census Tract 3414, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017341500', '1400000US25017341500', 'Census Tract 3415, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017341600', '1400000US25017341600', 'Census Tract 3416, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017341700', '1400000US25017341700', 'Census Tract 3417, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017341800', '1400000US25017341800', 'Census Tract 3418, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017341901', '1400000US25017341901', 'Census Tract 3419.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017341902', '1400000US25017341902', 'Census Tract 3419.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017342101', '1400000US25017342101', 'Census Tract 3421.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017342102', '1400000US25017342102', 'Census Tract 3421.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017342201', '1400000US25017342201', 'Census Tract 3422.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017342202', '1400000US25017342202', 'Census Tract 3422.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017342300', '1400000US25017342300', 'Census Tract 3423, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017342400', '1400000US25017342400', 'Census Tract 3424, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017342500', '1400000US25017342500', 'Census Tract 3425, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017342600', '1400000US25017342600', 'Census Tract 3426, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017350103', '1400000US25017350103', 'Census Tract 3501.03, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017350104', '1400000US25017350104', 'Census Tract 3501.04, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017350200', '1400000US25017350200', 'Census Tract 3502, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017350300', '1400000US25017350300', 'Census Tract 3503, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017350400', '1400000US25017350400', 'Census Tract 3504, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017350500', '1400000US25017350500', 'Census Tract 3505, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017350600', '1400000US25017350600', 'Census Tract 3506, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017350700', '1400000US25017350700', 'Census Tract 3507, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017350800', '1400000US25017350800', 'Census Tract 3508, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017350900', '1400000US25017350900', 'Census Tract 3509, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017351000', '1400000US25017351000', 'Census Tract 3510, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017351100', '1400000US25017351100', 'Census Tract 3511, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017351203', '1400000US25017351203', 'Census Tract 3512.03, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017351204', '1400000US25017351204', 'Census Tract 3512.04, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017351300', '1400000US25017351300', 'Census Tract 3513, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017351403', '1400000US25017351403', 'Census Tract 3514.03, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017351404', '1400000US25017351404', 'Census Tract 3514.04, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017351500', '1400000US25017351500', 'Census Tract 3515, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017352101', '1400000US25017352101', 'Census Tract 3521.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017352102', '1400000US25017352102', 'Census Tract 3521.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017352200', '1400000US25017352200', 'Census Tract 3522, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017352300', '1400000US25017352300', 'Census Tract 3523, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017352400', '1400000US25017352400', 'Census Tract 3524, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017352500', '1400000US25017352500', 'Census Tract 3525, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017352600', '1400000US25017352600', 'Census Tract 3526, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017352700', '1400000US25017352700', 'Census Tract 3527, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017352800', '1400000US25017352800', 'Census Tract 3528, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017352900', '1400000US25017352900', 'Census Tract 3529, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017353000', '1400000US25017353000', 'Census Tract 3530, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017353101', '1400000US25017353101', 'Census Tract 3531.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017353102', '1400000US25017353102', 'Census Tract 3531.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017353200', '1400000US25017353200', 'Census Tract 3532, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017353300', '1400000US25017353300', 'Census Tract 3533, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017353400', '1400000US25017353400', 'Census Tract 3534, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017353500', '1400000US25017353500', 'Census Tract 3535, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017353600', '1400000US25017353600', 'Census Tract 3536, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017353700', '1400000US25017353700', 'Census Tract 3537, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017353800', '1400000US25017353800', 'Census Tract 3538, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017353900', '1400000US25017353900', 'Census Tract 3539, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017354000', '1400000US25017354000', 'Census Tract 3540, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017354100', '1400000US25017354100', 'Census Tract 3541, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017354200', '1400000US25017354200', 'Census Tract 3542, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017354300', '1400000US25017354300', 'Census Tract 3543, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017354400', '1400000US25017354400', 'Census Tract 3544, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017354500', '1400000US25017354500', 'Census Tract 3545, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017354600', '1400000US25017354600', 'Census Tract 3546, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017354700', '1400000US25017354700', 'Census Tract 3547, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017354800', '1400000US25017354800', 'Census Tract 3548, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017354900', '1400000US25017354900', 'Census Tract 3549, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017355000', '1400000US25017355000', 'Census Tract 3550, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017356100', '1400000US25017356100', 'Census Tract 3561, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017356300', '1400000US25017356300', 'Census Tract 3563, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017356400', '1400000US25017356400', 'Census Tract 3564, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017356500', '1400000US25017356500', 'Census Tract 3565, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017356601', '1400000US25017356601', 'Census Tract 3566.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017356602', '1400000US25017356602', 'Census Tract 3566.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017356701', '1400000US25017356701', 'Census Tract 3567.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017356702', '1400000US25017356702', 'Census Tract 3567.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017357100', '1400000US25017357100', 'Census Tract 3571, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017357200', '1400000US25017357200', 'Census Tract 3572, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017357300', '1400000US25017357300', 'Census Tract 3573, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017357400', '1400000US25017357400', 'Census Tract 3574, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017357500', '1400000US25017357500', 'Census Tract 3575, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017357600', '1400000US25017357600', 'Census Tract 3576, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017357700', '1400000US25017357700', 'Census Tract 3577, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017357800', '1400000US25017357800', 'Census Tract 3578, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017358100', '1400000US25017358100', 'Census Tract 3581, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017358300', '1400000US25017358300', 'Census Tract 3583, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017358400', '1400000US25017358400', 'Census Tract 3584, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017358500', '1400000US25017358500', 'Census Tract 3585, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017358600', '1400000US25017358600', 'Census Tract 3586, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017358700', '1400000US25017358700', 'Census Tract 3587, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017359100', '1400000US25017359100', 'Census Tract 3591, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017359300', '1400000US25017359300', 'Census Tract 3593, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017360100', '1400000US25017360100', 'Census Tract 3601, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017360200', '1400000US25017360200', 'Census Tract 3602, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017361100', '1400000US25017361100', 'Census Tract 3611, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017361200', '1400000US25017361200', 'Census Tract 3612, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017361300', '1400000US25017361300', 'Census Tract 3613, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017362100', '1400000US25017362100', 'Census Tract 3621, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017363102', '1400000US25017363102', 'Census Tract 3631.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017363103', '1400000US25017363103', 'Census Tract 3631.03, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017363104', '1400000US25017363104', 'Census Tract 3631.04, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017363201', '1400000US25017363201', 'Census Tract 3632.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017363202', '1400000US25017363202', 'Census Tract 3632.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017364101', '1400000US25017364101', 'Census Tract 3641.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017364102', '1400000US25017364102', 'Census Tract 3641.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017365100', '1400000US25017365100', 'Census Tract 3651, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017365201', '1400000US25017365201', 'Census Tract 3652.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017365202', '1400000US25017365202', 'Census Tract 3652.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017366100', '1400000US25017366100', 'Census Tract 3661, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017366201', '1400000US25017366201', 'Census Tract 3662.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017366202', '1400000US25017366202', 'Census Tract 3662.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017367100', '1400000US25017367100', 'Census Tract 3671, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017367200', '1400000US25017367200', 'Census Tract 3672, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017368101', '1400000US25017368101', 'Census Tract 3681.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017368102', '1400000US25017368102', 'Census Tract 3681.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017368200', '1400000US25017368200', 'Census Tract 3682, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017368300', '1400000US25017368300', 'Census Tract 3683, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017368400', '1400000US25017368400', 'Census Tract 3684, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017368500', '1400000US25017368500', 'Census Tract 3685, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017368600', '1400000US25017368600', 'Census Tract 3686, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017368700', '1400000US25017368700', 'Census Tract 3687, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017368800', '1400000US25017368800', 'Census Tract 3688, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017368901', '1400000US25017368901', 'Census Tract 3689.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017368902', '1400000US25017368902', 'Census Tract 3689.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017369000', '1400000US25017369000', 'Census Tract 3690, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017369100', '1400000US25017369100', 'Census Tract 3691, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017370101', '1400000US25017370101', 'Census Tract 3701.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017370102', '1400000US25017370102', 'Census Tract 3701.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017370201', '1400000US25017370201', 'Census Tract 3702.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017370202', '1400000US25017370202', 'Census Tract 3702.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017370300', '1400000US25017370300', 'Census Tract 3703, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017370400', '1400000US25017370400', 'Census Tract 3704, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017373100', '1400000US25017373100', 'Census Tract 3731, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017373200', '1400000US25017373200', 'Census Tract 3732, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017373300', '1400000US25017373300', 'Census Tract 3733, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017373400', '1400000US25017373400', 'Census Tract 3734, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017373500', '1400000US25017373500', 'Census Tract 3735, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017373600', '1400000US25017373600', 'Census Tract 3736, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017373700', '1400000US25017373700', 'Census Tract 3737, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017373800', '1400000US25017373800', 'Census Tract 3738, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017373900', '1400000US25017373900', 'Census Tract 3739, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017374000', '1400000US25017374000', 'Census Tract 3740, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017374100', '1400000US25017374100', 'Census Tract 3741, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017374200', '1400000US25017374200', 'Census Tract 3742, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017374300', '1400000US25017374300', 'Census Tract 3743, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017374400', '1400000US25017374400', 'Census Tract 3744, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017374500', '1400000US25017374500', 'Census Tract 3745, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017374600', '1400000US25017374600', 'Census Tract 3746, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017374700', '1400000US25017374700', 'Census Tract 3747, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017374800', '1400000US25017374800', 'Census Tract 3748, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017382100', '1400000US25017382100', 'Census Tract 3821, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017382200', '1400000US25017382200', 'Census Tract 3822, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017382300', '1400000US25017382300', 'Census Tract 3823, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017382400', '1400000US25017382400', 'Census Tract 3824, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017382500', '1400000US25017382500', 'Census Tract 3825, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017382601', '1400000US25017382601', 'Census Tract 3826.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017382602', '1400000US25017382602', 'Census Tract 3826.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017383101', '1400000US25017383101', 'Census Tract 3831.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017383102', '1400000US25017383102', 'Census Tract 3831.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017383200', '1400000US25017383200', 'Census Tract 3832, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017383300', '1400000US25017383300', 'Census Tract 3833, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017383400', '1400000US25017383400', 'Census Tract 3834, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017383501', '1400000US25017383501', 'Census Tract 3835.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017383502', '1400000US25017383502', 'Census Tract 3835.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017383600', '1400000US25017383600', 'Census Tract 3836, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017383700', '1400000US25017383700', 'Census Tract 3837, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017383800', '1400000US25017383800', 'Census Tract 3838, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017383901', '1400000US25017383901', 'Census Tract 3839.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017383902', '1400000US25017383902', 'Census Tract 3839.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017384001', '1400000US25017384001', 'Census Tract 3840.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017384002', '1400000US25017384002', 'Census Tract 3840.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017385100', '1400000US25017385100', 'Census Tract 3851, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017385201', '1400000US25017385201', 'Census Tract 3852.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017385202', '1400000US25017385202', 'Census Tract 3852.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017386100', '1400000US25017386100', 'Census Tract 3861, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017387100', '1400000US25017387100', 'Census Tract 3871, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017387201', '1400000US25017387201', 'Census Tract 3872.01, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017387202', '1400000US25017387202', 'Census Tract 3872.02, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017388100', '1400000US25017388100', 'Census Tract 3881, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017388200', '1400000US25017388200', 'Census Tract 3882, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017388300', '1400000US25017388300', 'Census Tract 3883, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25017980000', '1400000US25017980000', 'Census Tract 9800, Middlesex County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25019950100', '1400000US25019950100', 'Census Tract 9501, Nantucket County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25019950200', '1400000US25019950200', 'Census Tract 9502, Nantucket County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25019950307', '1400000US25019950307', 'Census Tract 9503.07, Nantucket County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25019950400', '1400000US25019950400', 'Census Tract 9504, Nantucket County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25019950500', '1400000US25019950500', 'Census Tract 9505, Nantucket County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25019990000', '1400000US25019990000', 'Census Tract 9900, Nantucket County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021400100', '1400000US25021400100', 'Census Tract 4001, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021400200', '1400000US25021400200', 'Census Tract 4002, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021400300', '1400000US25021400300', 'Census Tract 4003, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021400400', '1400000US25021400400', 'Census Tract 4004, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021400500', '1400000US25021400500', 'Census Tract 4005, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021400600', '1400000US25021400600', 'Census Tract 4006, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021400700', '1400000US25021400700', 'Census Tract 4007, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021400800', '1400000US25021400800', 'Census Tract 4008, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021400900', '1400000US25021400900', 'Census Tract 4009, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021401000', '1400000US25021401000', 'Census Tract 4010, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021401100', '1400000US25021401100', 'Census Tract 4011, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021401200', '1400000US25021401200', 'Census Tract 4012, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021402101', '1400000US25021402101', 'Census Tract 4021.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021402102', '1400000US25021402102', 'Census Tract 4021.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021402200', '1400000US25021402200', 'Census Tract 4022, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021402300', '1400000US25021402300', 'Census Tract 4023, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021402400', '1400000US25021402400', 'Census Tract 4024, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021402500', '1400000US25021402500', 'Census Tract 4025, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021403100', '1400000US25021403100', 'Census Tract 4031, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021403300', '1400000US25021403300', 'Census Tract 4033, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021403400', '1400000US25021403400', 'Census Tract 4034, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021403500', '1400000US25021403500', 'Census Tract 4035, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021404100', '1400000US25021404100', 'Census Tract 4041, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021404201', '1400000US25021404201', 'Census Tract 4042.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021404202', '1400000US25021404202', 'Census Tract 4042.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021404301', '1400000US25021404301', 'Census Tract 4043.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021404302', '1400000US25021404302', 'Census Tract 4043.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021404400', '1400000US25021404400', 'Census Tract 4044, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021405100', '1400000US25021405100', 'Census Tract 4051, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021406101', '1400000US25021406101', 'Census Tract 4061.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021406102', '1400000US25021406102', 'Census Tract 4061.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021407100', '1400000US25021407100', 'Census Tract 4071, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021408101', '1400000US25021408101', 'Census Tract 4081.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021408102', '1400000US25021408102', 'Census Tract 4081.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021409101', '1400000US25021409101', 'Census Tract 4091.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021409102', '1400000US25021409102', 'Census Tract 4091.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021410100', '1400000US25021410100', 'Census Tract 4101, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021410300', '1400000US25021410300', 'Census Tract 4103, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021410400', '1400000US25021410400', 'Census Tract 4104, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021411100', '1400000US25021411100', 'Census Tract 4111, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021411200', '1400000US25021411200', 'Census Tract 4112, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021411301', '1400000US25021411301', 'Census Tract 4113.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021411302', '1400000US25021411302', 'Census Tract 4113.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021412100', '1400000US25021412100', 'Census Tract 4121, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021412200', '1400000US25021412200', 'Census Tract 4122, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021412300', '1400000US25021412300', 'Census Tract 4123, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021413100', '1400000US25021413100', 'Census Tract 4131, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021413200', '1400000US25021413200', 'Census Tract 4132, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021413300', '1400000US25021413300', 'Census Tract 4133, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021413401', '1400000US25021413401', 'Census Tract 4134.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021413402', '1400000US25021413402', 'Census Tract 4134.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021413500', '1400000US25021413500', 'Census Tract 4135, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021414100', '1400000US25021414100', 'Census Tract 4141, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021414200', '1400000US25021414200', 'Census Tract 4142, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021414300', '1400000US25021414300', 'Census Tract 4143, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021415101', '1400000US25021415101', 'Census Tract 4151.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021415102', '1400000US25021415102', 'Census Tract 4151.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021415200', '1400000US25021415200', 'Census Tract 4152, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021415300', '1400000US25021415300', 'Census Tract 4153, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021416101', '1400000US25021416101', 'Census Tract 4161.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021416102', '1400000US25021416102', 'Census Tract 4161.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021416200', '1400000US25021416200', 'Census Tract 4162, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021416300', '1400000US25021416300', 'Census Tract 4163, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021416400', '1400000US25021416400', 'Census Tract 4164, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021417100', '1400000US25021417100', 'Census Tract 4171, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021417200', '1400000US25021417200', 'Census Tract 4172, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021417300', '1400000US25021417300', 'Census Tract 4173, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021417400', '1400000US25021417400', 'Census Tract 4174, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021417501', '1400000US25021417501', 'Census Tract 4175.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021417502', '1400000US25021417502', 'Census Tract 4175.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021417601', '1400000US25021417601', 'Census Tract 4176.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021417602', '1400000US25021417602', 'Census Tract 4176.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021417701', '1400000US25021417701', 'Census Tract 4177.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021417702', '1400000US25021417702', 'Census Tract 4177.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021417801', '1400000US25021417801', 'Census Tract 4178.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021417802', '1400000US25021417802', 'Census Tract 4178.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021417901', '1400000US25021417901', 'Census Tract 4179.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021417902', '1400000US25021417902', 'Census Tract 4179.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021418002', '1400000US25021418002', 'Census Tract 4180.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021418003', '1400000US25021418003', 'Census Tract 4180.03, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021418004', '1400000US25021418004', 'Census Tract 4180.04, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021418101', '1400000US25021418101', 'Census Tract 4181.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021418102', '1400000US25021418102', 'Census Tract 4181.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021418200', '1400000US25021418200', 'Census Tract 4182, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021419100', '1400000US25021419100', 'Census Tract 4191, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021419200', '1400000US25021419200', 'Census Tract 4192, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021419300', '1400000US25021419300', 'Census Tract 4193, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021419400', '1400000US25021419400', 'Census Tract 4194, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021419500', '1400000US25021419500', 'Census Tract 4195, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021419600', '1400000US25021419600', 'Census Tract 4196, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021419700', '1400000US25021419700', 'Census Tract 4197, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021419800', '1400000US25021419800', 'Census Tract 4198, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021420100', '1400000US25021420100', 'Census Tract 4201, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021420201', '1400000US25021420201', 'Census Tract 4202.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021420202', '1400000US25021420202', 'Census Tract 4202.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021420301', '1400000US25021420301', 'Census Tract 4203.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021420302', '1400000US25021420302', 'Census Tract 4203.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021421100', '1400000US25021421100', 'Census Tract 4211, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021421200', '1400000US25021421200', 'Census Tract 4212, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021422100', '1400000US25021422100', 'Census Tract 4221, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021422200', '1400000US25021422200', 'Census Tract 4222, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021422301', '1400000US25021422301', 'Census Tract 4223.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021422302', '1400000US25021422302', 'Census Tract 4223.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021422400', '1400000US25021422400', 'Census Tract 4224, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021422501', '1400000US25021422501', 'Census Tract 4225.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021422502', '1400000US25021422502', 'Census Tract 4225.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021422600', '1400000US25021422600', 'Census Tract 4226, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021422700', '1400000US25021422700', 'Census Tract 4227, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021422800', '1400000US25021422800', 'Census Tract 4228, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021423100', '1400000US25021423100', 'Census Tract 4231, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021440100', '1400000US25021440100', 'Census Tract 4401, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021441202', '1400000US25021441202', 'Census Tract 4412.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021441203', '1400000US25021441203', 'Census Tract 4412.03, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021441204', '1400000US25021441204', 'Census Tract 4412.04, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021442101', '1400000US25021442101', 'Census Tract 4421.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021442102', '1400000US25021442102', 'Census Tract 4421.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021442103', '1400000US25021442103', 'Census Tract 4421.03, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021442201', '1400000US25021442201', 'Census Tract 4422.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021442202', '1400000US25021442202', 'Census Tract 4422.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021443101', '1400000US25021443101', 'Census Tract 4431.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021443102', '1400000US25021443102', 'Census Tract 4431.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021456101', '1400000US25021456101', 'Census Tract 4561.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021456102', '1400000US25021456102', 'Census Tract 4561.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021456200', '1400000US25021456200', 'Census Tract 4562, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021456301', '1400000US25021456301', 'Census Tract 4563.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021456302', '1400000US25021456302', 'Census Tract 4563.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021456401', '1400000US25021456401', 'Census Tract 4564.01, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021456402', '1400000US25021456402', 'Census Tract 4564.02, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021457100', '1400000US25021457100', 'Census Tract 4571, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25021457200', '1400000US25021457200', 'Census Tract 4572, Norfolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023500101', '1400000US25023500101', 'Census Tract 5001.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023500103', '1400000US25023500103', 'Census Tract 5001.03, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023500104', '1400000US25023500104', 'Census Tract 5001.04, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023501101', '1400000US25023501101', 'Census Tract 5011.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023501102', '1400000US25023501102', 'Census Tract 5011.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023501201', '1400000US25023501201', 'Census Tract 5012.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023501202', '1400000US25023501202', 'Census Tract 5012.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023502101', '1400000US25023502101', 'Census Tract 5021.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023502102', '1400000US25023502102', 'Census Tract 5021.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023502200', '1400000US25023502200', 'Census Tract 5022, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023503101', '1400000US25023503101', 'Census Tract 5031.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023503102', '1400000US25023503102', 'Census Tract 5031.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023504101', '1400000US25023504101', 'Census Tract 5041.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023504102', '1400000US25023504102', 'Census Tract 5041.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023505101', '1400000US25023505101', 'Census Tract 5051.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023505102', '1400000US25023505102', 'Census Tract 5051.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023505200', '1400000US25023505200', 'Census Tract 5052, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023506101', '1400000US25023506101', 'Census Tract 5061.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023506102', '1400000US25023506102', 'Census Tract 5061.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023506202', '1400000US25023506202', 'Census Tract 5062.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023506203', '1400000US25023506203', 'Census Tract 5062.03, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023506204', '1400000US25023506204', 'Census Tract 5062.04, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023507101', '1400000US25023507101', 'Census Tract 5071.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023507103', '1400000US25023507103', 'Census Tract 5071.03, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023507104', '1400000US25023507104', 'Census Tract 5071.04, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023508101', '1400000US25023508101', 'Census Tract 5081.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023508102', '1400000US25023508102', 'Census Tract 5081.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023508200', '1400000US25023508200', 'Census Tract 5082, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023509101', '1400000US25023509101', 'Census Tract 5091.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023509102', '1400000US25023509102', 'Census Tract 5091.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023510100', '1400000US25023510100', 'Census Tract 5101, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023510200', '1400000US25023510200', 'Census Tract 5102, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023510300', '1400000US25023510300', 'Census Tract 5103, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023510400', '1400000US25023510400', 'Census Tract 5104, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023510501', '1400000US25023510501', 'Census Tract 5105.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023510502', '1400000US25023510502', 'Census Tract 5105.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023510503', '1400000US25023510503', 'Census Tract 5105.03, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023510600', '1400000US25023510600', 'Census Tract 5106, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023510700', '1400000US25023510700', 'Census Tract 5107, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023510800', '1400000US25023510800', 'Census Tract 5108, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023510900', '1400000US25023510900', 'Census Tract 5109, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023511000', '1400000US25023511000', 'Census Tract 5110, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023511100', '1400000US25023511100', 'Census Tract 5111, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023511200', '1400000US25023511200', 'Census Tract 5112, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023511301', '1400000US25023511301', 'Census Tract 5113.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023511302', '1400000US25023511302', 'Census Tract 5113.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023511400', '1400000US25023511400', 'Census Tract 5114, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023511500', '1400000US25023511500', 'Census Tract 5115, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023511600', '1400000US25023511600', 'Census Tract 5116, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023511701', '1400000US25023511701', 'Census Tract 5117.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023511702', '1400000US25023511702', 'Census Tract 5117.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023520100', '1400000US25023520100', 'Census Tract 5201, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023520201', '1400000US25023520201', 'Census Tract 5202.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023520202', '1400000US25023520202', 'Census Tract 5202.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023521101', '1400000US25023521101', 'Census Tract 5211.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023521102', '1400000US25023521102', 'Census Tract 5211.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023521201', '1400000US25023521201', 'Census Tract 5212.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023521202', '1400000US25023521202', 'Census Tract 5212.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023522101', '1400000US25023522101', 'Census Tract 5221.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023522102', '1400000US25023522102', 'Census Tract 5221.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023523100', '1400000US25023523100', 'Census Tract 5231, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023523201', '1400000US25023523201', 'Census Tract 5232.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023523202', '1400000US25023523202', 'Census Tract 5232.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023524101', '1400000US25023524101', 'Census Tract 5241.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023524102', '1400000US25023524102', 'Census Tract 5241.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023525101', '1400000US25023525101', 'Census Tract 5251.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023525104', '1400000US25023525104', 'Census Tract 5251.04, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023525203', '1400000US25023525203', 'Census Tract 5252.03, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023525204', '1400000US25023525204', 'Census Tract 5252.04, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023525300', '1400000US25023525300', 'Census Tract 5253, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023526100', '1400000US25023526100', 'Census Tract 5261, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023530100', '1400000US25023530100', 'Census Tract 5301, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023530200', '1400000US25023530200', 'Census Tract 5302, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023530300', '1400000US25023530300', 'Census Tract 5303, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023530400', '1400000US25023530400', 'Census Tract 5304, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023530500', '1400000US25023530500', 'Census Tract 5305, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023530600', '1400000US25023530600', 'Census Tract 5306, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023530700', '1400000US25023530700', 'Census Tract 5307, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023530801', '1400000US25023530801', 'Census Tract 5308.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023530802', '1400000US25023530802', 'Census Tract 5308.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023530901', '1400000US25023530901', 'Census Tract 5309.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023530902', '1400000US25023530902', 'Census Tract 5309.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023540101', '1400000US25023540101', 'Census Tract 5401.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023540102', '1400000US25023540102', 'Census Tract 5401.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023540103', '1400000US25023540103', 'Census Tract 5401.03, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023541100', '1400000US25023541100', 'Census Tract 5411, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023542101', '1400000US25023542101', 'Census Tract 5421.01, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023542102', '1400000US25023542102', 'Census Tract 5421.02, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023542200', '1400000US25023542200', 'Census Tract 5422, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023542300', '1400000US25023542300', 'Census Tract 5423, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023543100', '1400000US25023543100', 'Census Tract 5431, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023544100', '1400000US25023544100', 'Census Tract 5441, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023544200', '1400000US25023544200', 'Census Tract 5442, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023545100', '1400000US25023545100', 'Census Tract 5451, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023545200', '1400000US25023545200', 'Census Tract 5452, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023545300', '1400000US25023545300', 'Census Tract 5453, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023545400', '1400000US25023545400', 'Census Tract 5454, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023560100', '1400000US25023560100', 'Census Tract 5601, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023561100', '1400000US25023561100', 'Census Tract 5611, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023561200', '1400000US25023561200', 'Census Tract 5612, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25023990003', '1400000US25023990003', 'Census Tract 9900.03, Plymouth County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000100', '1400000US25025000100', 'Census Tract 1, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000201', '1400000US25025000201', 'Census Tract 2.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000202', '1400000US25025000202', 'Census Tract 2.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000301', '1400000US25025000301', 'Census Tract 3.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000302', '1400000US25025000302', 'Census Tract 3.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000401', '1400000US25025000401', 'Census Tract 4.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000402', '1400000US25025000402', 'Census Tract 4.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000502', '1400000US25025000502', 'Census Tract 5.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000503', '1400000US25025000503', 'Census Tract 5.03, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000504', '1400000US25025000504', 'Census Tract 5.04, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000601', '1400000US25025000601', 'Census Tract 6.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000602', '1400000US25025000602', 'Census Tract 6.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000701', '1400000US25025000701', 'Census Tract 7.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000703', '1400000US25025000703', 'Census Tract 7.03, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000704', '1400000US25025000704', 'Census Tract 7.04, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000802', '1400000US25025000802', 'Census Tract 8.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025000803', '1400000US25025000803', 'Census Tract 8.03, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010103', '1400000US25025010103', 'Census Tract 101.03, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010104', '1400000US25025010104', 'Census Tract 101.04, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010203', '1400000US25025010203', 'Census Tract 102.03, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010204', '1400000US25025010204', 'Census Tract 102.04, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010300', '1400000US25025010300', 'Census Tract 103, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010403', '1400000US25025010403', 'Census Tract 104.03, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010404', '1400000US25025010404', 'Census Tract 104.04, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010405', '1400000US25025010405', 'Census Tract 104.05, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010408', '1400000US25025010408', 'Census Tract 104.08, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010500', '1400000US25025010500', 'Census Tract 105, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010600', '1400000US25025010600', 'Census Tract 106, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010701', '1400000US25025010701', 'Census Tract 107.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010702', '1400000US25025010702', 'Census Tract 107.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010801', '1400000US25025010801', 'Census Tract 108.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025010802', '1400000US25025010802', 'Census Tract 108.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025020101', '1400000US25025020101', 'Census Tract 201.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025020200', '1400000US25025020200', 'Census Tract 202, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025020301', '1400000US25025020301', 'Census Tract 203.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025020302', '1400000US25025020302', 'Census Tract 203.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025020303', '1400000US25025020303', 'Census Tract 203.03, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025030100', '1400000US25025030100', 'Census Tract 301, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025030200', '1400000US25025030200', 'Census Tract 302, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025030300', '1400000US25025030300', 'Census Tract 303, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025030400', '1400000US25025030400', 'Census Tract 304, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025030500', '1400000US25025030500', 'Census Tract 305, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025040100', '1400000US25025040100', 'Census Tract 401, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025040200', '1400000US25025040200', 'Census Tract 402, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025040300', '1400000US25025040300', 'Census Tract 403, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025040401', '1400000US25025040401', 'Census Tract 404.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025040600', '1400000US25025040600', 'Census Tract 406, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025040801', '1400000US25025040801', 'Census Tract 408.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025050101', '1400000US25025050101', 'Census Tract 501.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025050200', '1400000US25025050200', 'Census Tract 502, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025050300', '1400000US25025050300', 'Census Tract 503, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025050400', '1400000US25025050400', 'Census Tract 504, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025050500', '1400000US25025050500', 'Census Tract 505, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025050600', '1400000US25025050600', 'Census Tract 506, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025050700', '1400000US25025050700', 'Census Tract 507, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025050901', '1400000US25025050901', 'Census Tract 509.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025051000', '1400000US25025051000', 'Census Tract 510, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025051101', '1400000US25025051101', 'Census Tract 511.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025051200', '1400000US25025051200', 'Census Tract 512, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025060101', '1400000US25025060101', 'Census Tract 601.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025060200', '1400000US25025060200', 'Census Tract 602, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025060301', '1400000US25025060301', 'Census Tract 603.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025060400', '1400000US25025060400', 'Census Tract 604, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025060501', '1400000US25025060501', 'Census Tract 605.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025060600', '1400000US25025060600', 'Census Tract 606, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025060700', '1400000US25025060700', 'Census Tract 607, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025060800', '1400000US25025060800', 'Census Tract 608, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025061000', '1400000US25025061000', 'Census Tract 610, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025061101', '1400000US25025061101', 'Census Tract 611.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025061200', '1400000US25025061200', 'Census Tract 612, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025070101', '1400000US25025070101', 'Census Tract 701.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025070200', '1400000US25025070200', 'Census Tract 702, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025070300', '1400000US25025070300', 'Census Tract 703, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025070402', '1400000US25025070402', 'Census Tract 704.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025070500', '1400000US25025070500', 'Census Tract 705, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025070600', '1400000US25025070600', 'Census Tract 706, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025070700', '1400000US25025070700', 'Census Tract 707, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025070800', '1400000US25025070800', 'Census Tract 708, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025070900', '1400000US25025070900', 'Census Tract 709, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025071101', '1400000US25025071101', 'Census Tract 711.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025071201', '1400000US25025071201', 'Census Tract 712.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025080100', '1400000US25025080100', 'Census Tract 801, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025080300', '1400000US25025080300', 'Census Tract 803, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025080401', '1400000US25025080401', 'Census Tract 804.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025080500', '1400000US25025080500', 'Census Tract 805, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025080601', '1400000US25025080601', 'Census Tract 806.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025080801', '1400000US25025080801', 'Census Tract 808.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025080900', '1400000US25025080900', 'Census Tract 809, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025081001', '1400000US25025081001', 'Census Tract 810.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025081100', '1400000US25025081100', 'Census Tract 811, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025081200', '1400000US25025081200', 'Census Tract 812, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025081300', '1400000US25025081300', 'Census Tract 813, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025081400', '1400000US25025081400', 'Census Tract 814, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025081500', '1400000US25025081500', 'Census Tract 815, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025081700', '1400000US25025081700', 'Census Tract 817, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025081800', '1400000US25025081800', 'Census Tract 818, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025081900', '1400000US25025081900', 'Census Tract 819, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025082000', '1400000US25025082000', 'Census Tract 820, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025082100', '1400000US25025082100', 'Census Tract 821, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025090100', '1400000US25025090100', 'Census Tract 901, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025090200', '1400000US25025090200', 'Census Tract 902, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025090300', '1400000US25025090300', 'Census Tract 903, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025090400', '1400000US25025090400', 'Census Tract 904, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025090600', '1400000US25025090600', 'Census Tract 906, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025090700', '1400000US25025090700', 'Census Tract 907, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025090901', '1400000US25025090901', 'Census Tract 909.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025091001', '1400000US25025091001', 'Census Tract 910.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025091100', '1400000US25025091100', 'Census Tract 911, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025091200', '1400000US25025091200', 'Census Tract 912, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025091300', '1400000US25025091300', 'Census Tract 913, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025091400', '1400000US25025091400', 'Census Tract 914, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025091500', '1400000US25025091500', 'Census Tract 915, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025091600', '1400000US25025091600', 'Census Tract 916, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025091700', '1400000US25025091700', 'Census Tract 917, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025091800', '1400000US25025091800', 'Census Tract 918, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025091900', '1400000US25025091900', 'Census Tract 919, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025092000', '1400000US25025092000', 'Census Tract 920, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025092101', '1400000US25025092101', 'Census Tract 921.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025092200', '1400000US25025092200', 'Census Tract 922, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025092300', '1400000US25025092300', 'Census Tract 923, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025092400', '1400000US25025092400', 'Census Tract 924, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025100100', '1400000US25025100100', 'Census Tract 1001, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025100200', '1400000US25025100200', 'Census Tract 1002, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025100300', '1400000US25025100300', 'Census Tract 1003, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025100400', '1400000US25025100400', 'Census Tract 1004, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025100500', '1400000US25025100500', 'Census Tract 1005, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025100601', '1400000US25025100601', 'Census Tract 1006.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025100603', '1400000US25025100603', 'Census Tract 1006.03, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025100700', '1400000US25025100700', 'Census Tract 1007, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025100800', '1400000US25025100800', 'Census Tract 1008, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025100900', '1400000US25025100900', 'Census Tract 1009, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025101001', '1400000US25025101001', 'Census Tract 1010.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025101002', '1400000US25025101002', 'Census Tract 1010.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025101101', '1400000US25025101101', 'Census Tract 1011.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025101102', '1400000US25025101102', 'Census Tract 1011.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025110103', '1400000US25025110103', 'Census Tract 1101.03, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025110201', '1400000US25025110201', 'Census Tract 1102.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025110301', '1400000US25025110301', 'Census Tract 1103.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025110401', '1400000US25025110401', 'Census Tract 1104.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025110403', '1400000US25025110403', 'Census Tract 1104.03, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025110501', '1400000US25025110501', 'Census Tract 1105.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025110502', '1400000US25025110502', 'Census Tract 1105.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025110601', '1400000US25025110601', 'Census Tract 1106.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025110607', '1400000US25025110607', 'Census Tract 1106.07, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025120103', '1400000US25025120103', 'Census Tract 1201.03, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025120104', '1400000US25025120104', 'Census Tract 1201.04, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025120105', '1400000US25025120105', 'Census Tract 1201.05, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025120201', '1400000US25025120201', 'Census Tract 1202.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025120301', '1400000US25025120301', 'Census Tract 1203.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025120400', '1400000US25025120400', 'Census Tract 1204, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025120500', '1400000US25025120500', 'Census Tract 1205, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025120600', '1400000US25025120600', 'Census Tract 1206, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025120700', '1400000US25025120700', 'Census Tract 1207, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025130100', '1400000US25025130100', 'Census Tract 1301, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025130200', '1400000US25025130200', 'Census Tract 1302, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025130300', '1400000US25025130300', 'Census Tract 1303, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025130402', '1400000US25025130402', 'Census Tract 1304.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025130404', '1400000US25025130404', 'Census Tract 1304.04, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025130406', '1400000US25025130406', 'Census Tract 1304.06, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025140102', '1400000US25025140102', 'Census Tract 1401.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025140105', '1400000US25025140105', 'Census Tract 1401.05, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025140106', '1400000US25025140106', 'Census Tract 1401.06, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025140107', '1400000US25025140107', 'Census Tract 1401.07, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025140201', '1400000US25025140201', 'Census Tract 1402.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025140202', '1400000US25025140202', 'Census Tract 1402.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025140300', '1400000US25025140300', 'Census Tract 1403, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025140400', '1400000US25025140400', 'Census Tract 1404, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025160101', '1400000US25025160101', 'Census Tract 1601.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025160200', '1400000US25025160200', 'Census Tract 1602, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025160300', '1400000US25025160300', 'Census Tract 1603, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025160400', '1400000US25025160400', 'Census Tract 1604, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025160501', '1400000US25025160501', 'Census Tract 1605.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025160502', '1400000US25025160502', 'Census Tract 1605.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025160601', '1400000US25025160601', 'Census Tract 1606.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025160602', '1400000US25025160602', 'Census Tract 1606.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025170100', '1400000US25025170100', 'Census Tract 1701, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025170200', '1400000US25025170200', 'Census Tract 1702, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025170300', '1400000US25025170300', 'Census Tract 1703, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025170400', '1400000US25025170400', 'Census Tract 1704, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025170501', '1400000US25025170501', 'Census Tract 1705.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025170502', '1400000US25025170502', 'Census Tract 1705.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025170601', '1400000US25025170601', 'Census Tract 1706.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025170701', '1400000US25025170701', 'Census Tract 1707.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025170702', '1400000US25025170702', 'Census Tract 1707.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025170800', '1400000US25025170800', 'Census Tract 1708, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025180101', '1400000US25025180101', 'Census Tract 1801.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025180200', '1400000US25025180200', 'Census Tract 1802, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025180301', '1400000US25025180301', 'Census Tract 1803.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025180400', '1400000US25025180400', 'Census Tract 1804, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025180500', '1400000US25025180500', 'Census Tract 1805, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025980101', '1400000US25025980101', 'Census Tract 9801.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025980300', '1400000US25025980300', 'Census Tract 9803, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025980700', '1400000US25025980700', 'Census Tract 9807, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025981000', '1400000US25025981000', 'Census Tract 9810, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025981100', '1400000US25025981100', 'Census Tract 9811, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025981201', '1400000US25025981201', 'Census Tract 9812.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025981202', '1400000US25025981202', 'Census Tract 9812.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025981300', '1400000US25025981300', 'Census Tract 9813, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025981501', '1400000US25025981501', 'Census Tract 9815.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025981502', '1400000US25025981502', 'Census Tract 9815.02, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025981600', '1400000US25025981600', 'Census Tract 9816, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025981700', '1400000US25025981700', 'Census Tract 9817, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025981800', '1400000US25025981800', 'Census Tract 9818, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25025990101', '1400000US25025990101', 'Census Tract 9901.01, Suffolk County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027700100', '1400000US25027700100', 'Census Tract 7001, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027701100', '1400000US25027701100', 'Census Tract 7011, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027702200', '1400000US25027702200', 'Census Tract 7022, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027703100', '1400000US25027703100', 'Census Tract 7031, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027703200', '1400000US25027703200', 'Census Tract 7032, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027703300', '1400000US25027703300', 'Census Tract 7033, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027704200', '1400000US25027704200', 'Census Tract 7042, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027705100', '1400000US25027705100', 'Census Tract 7051, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027706100', '1400000US25027706100', 'Census Tract 7061, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027707100', '1400000US25027707100', 'Census Tract 7071, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027707200', '1400000US25027707200', 'Census Tract 7072, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027707300', '1400000US25027707300', 'Census Tract 7073, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027707400', '1400000US25027707400', 'Census Tract 7074, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027707500', '1400000US25027707500', 'Census Tract 7075, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027708100', '1400000US25027708100', 'Census Tract 7081, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027709100', '1400000US25027709100', 'Census Tract 7091, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027709201', '1400000US25027709201', 'Census Tract 7092.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027709202', '1400000US25027709202', 'Census Tract 7092.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027709400', '1400000US25027709400', 'Census Tract 7094, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027709501', '1400000US25027709501', 'Census Tract 7095.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027709502', '1400000US25027709502', 'Census Tract 7095.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027709600', '1400000US25027709600', 'Census Tract 7096, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027709701', '1400000US25027709701', 'Census Tract 7097.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027709702', '1400000US25027709702', 'Census Tract 7097.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027710100', '1400000US25027710100', 'Census Tract 7101, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027710200', '1400000US25027710200', 'Census Tract 7102, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027710300', '1400000US25027710300', 'Census Tract 7103, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027710400', '1400000US25027710400', 'Census Tract 7104, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027710500', '1400000US25027710500', 'Census Tract 7105, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027710600', '1400000US25027710600', 'Census Tract 7106, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027710700', '1400000US25027710700', 'Census Tract 7107, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027710800', '1400000US25027710800', 'Census Tract 7108, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027711000', '1400000US25027711000', 'Census Tract 7110, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027711100', '1400000US25027711100', 'Census Tract 7111, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027712101', '1400000US25027712101', 'Census Tract 7121.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027712102', '1400000US25027712102', 'Census Tract 7121.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027713100', '1400000US25027713100', 'Census Tract 7131, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027715100', '1400000US25027715100', 'Census Tract 7151, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027716100', '1400000US25027716100', 'Census Tract 7161, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027716200', '1400000US25027716200', 'Census Tract 7162, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027716300', '1400000US25027716300', 'Census Tract 7163, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027717100', '1400000US25027717100', 'Census Tract 7171, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027718100', '1400000US25027718100', 'Census Tract 7181, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027719100', '1400000US25027719100', 'Census Tract 7191, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027720100', '1400000US25027720100', 'Census Tract 7201, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027721101', '1400000US25027721101', 'Census Tract 7211.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027721102', '1400000US25027721102', 'Census Tract 7211.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027722100', '1400000US25027722100', 'Census Tract 7221, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027723100', '1400000US25027723100', 'Census Tract 7231, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027724100', '1400000US25027724100', 'Census Tract 7241, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027725100', '1400000US25027725100', 'Census Tract 7251, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027726100', '1400000US25027726100', 'Census Tract 7261, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027726200', '1400000US25027726200', 'Census Tract 7262, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027727100', '1400000US25027727100', 'Census Tract 7271, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027728100', '1400000US25027728100', 'Census Tract 7281, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027728200', '1400000US25027728200', 'Census Tract 7282, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027728300', '1400000US25027728300', 'Census Tract 7283, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027728400', '1400000US25027728400', 'Census Tract 7284, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027729100', '1400000US25027729100', 'Census Tract 7291, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027729200', '1400000US25027729200', 'Census Tract 7292, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027730100', '1400000US25027730100', 'Census Tract 7301, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027730200', '1400000US25027730200', 'Census Tract 7302, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027730300', '1400000US25027730300', 'Census Tract 7303, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027730401', '1400000US25027730401', 'Census Tract 7304.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027730402', '1400000US25027730402', 'Census Tract 7304.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027730500', '1400000US25027730500', 'Census Tract 7305, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027730600', '1400000US25027730600', 'Census Tract 7306, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027730700', '1400000US25027730700', 'Census Tract 7307, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027730801', '1400000US25027730801', 'Census Tract 7308.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027730802', '1400000US25027730802', 'Census Tract 7308.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027730901', '1400000US25027730901', 'Census Tract 7309.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027730902', '1400000US25027730902', 'Census Tract 7309.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027731001', '1400000US25027731001', 'Census Tract 7310.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027731002', '1400000US25027731002', 'Census Tract 7310.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027731101', '1400000US25027731101', 'Census Tract 7311.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027731102', '1400000US25027731102', 'Census Tract 7311.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027731202', '1400000US25027731202', 'Census Tract 7312.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027731203', '1400000US25027731203', 'Census Tract 7312.03, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027731204', '1400000US25027731204', 'Census Tract 7312.04, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027731300', '1400000US25027731300', 'Census Tract 7313, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027731400', '1400000US25027731400', 'Census Tract 7314, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027731500', '1400000US25027731500', 'Census Tract 7315, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027731600', '1400000US25027731600', 'Census Tract 7316, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027731700', '1400000US25027731700', 'Census Tract 7317, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027731800', '1400000US25027731800', 'Census Tract 7318, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027731900', '1400000US25027731900', 'Census Tract 7319, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732001', '1400000US25027732001', 'Census Tract 7320.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732002', '1400000US25027732002', 'Census Tract 7320.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732201', '1400000US25027732201', 'Census Tract 7322.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732202', '1400000US25027732202', 'Census Tract 7322.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732203', '1400000US25027732203', 'Census Tract 7322.03, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732301', '1400000US25027732301', 'Census Tract 7323.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732302', '1400000US25027732302', 'Census Tract 7323.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732400', '1400000US25027732400', 'Census Tract 7324, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732500', '1400000US25027732500', 'Census Tract 7325, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732600', '1400000US25027732600', 'Census Tract 7326, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732700', '1400000US25027732700', 'Census Tract 7327, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732801', '1400000US25027732801', 'Census Tract 7328.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732802', '1400000US25027732802', 'Census Tract 7328.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732901', '1400000US25027732901', 'Census Tract 7329.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027732902', '1400000US25027732902', 'Census Tract 7329.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027733000', '1400000US25027733000', 'Census Tract 7330, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027733101', '1400000US25027733101', 'Census Tract 7331.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027733102', '1400000US25027733102', 'Census Tract 7331.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027735100', '1400000US25027735100', 'Census Tract 7351, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027735200', '1400000US25027735200', 'Census Tract 7352, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027736100', '1400000US25027736100', 'Census Tract 7361, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027736200', '1400000US25027736200', 'Census Tract 7362, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027736300', '1400000US25027736300', 'Census Tract 7363, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027736400', '1400000US25027736400', 'Census Tract 7364, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027736500', '1400000US25027736500', 'Census Tract 7365, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027737100', '1400000US25027737100', 'Census Tract 7371, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027737200', '1400000US25027737200', 'Census Tract 7372, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027737300', '1400000US25027737300', 'Census Tract 7373, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027738100', '1400000US25027738100', 'Census Tract 7381, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027738201', '1400000US25027738201', 'Census Tract 7382.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027738202', '1400000US25027738202', 'Census Tract 7382.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027739100', '1400000US25027739100', 'Census Tract 7391, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027739200', '1400000US25027739200', 'Census Tract 7392, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027739300', '1400000US25027739300', 'Census Tract 7393, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027739400', '1400000US25027739400', 'Census Tract 7394, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027739500', '1400000US25027739500', 'Census Tract 7395, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027740101', '1400000US25027740101', 'Census Tract 7401.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027740102', '1400000US25027740102', 'Census Tract 7401.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027740200', '1400000US25027740200', 'Census Tract 7402, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027741101', '1400000US25027741101', 'Census Tract 7411.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027741102', '1400000US25027741102', 'Census Tract 7411.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027742300', '1400000US25027742300', 'Census Tract 7423, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027742401', '1400000US25027742401', 'Census Tract 7424.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027742402', '1400000US25027742402', 'Census Tract 7424.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027743100', '1400000US25027743100', 'Census Tract 7431, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027744101', '1400000US25027744101', 'Census Tract 7441.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027744102', '1400000US25027744102', 'Census Tract 7441.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027744200', '1400000US25027744200', 'Census Tract 7442, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027744300', '1400000US25027744300', 'Census Tract 7443, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027744400', '1400000US25027744400', 'Census Tract 7444, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027745100', '1400000US25027745100', 'Census Tract 7451, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027746100', '1400000US25027746100', 'Census Tract 7461, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027747101', '1400000US25027747101', 'Census Tract 7471.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027747102', '1400000US25027747102', 'Census Tract 7471.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027748100', '1400000US25027748100', 'Census Tract 7481, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027749100', '1400000US25027749100', 'Census Tract 7491, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027749200', '1400000US25027749200', 'Census Tract 7492, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027750100', '1400000US25027750100', 'Census Tract 7501, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027750200', '1400000US25027750200', 'Census Tract 7502, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027750300', '1400000US25027750300', 'Census Tract 7503, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027751101', '1400000US25027751101', 'Census Tract 7511.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027751102', '1400000US25027751102', 'Census Tract 7511.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027752100', '1400000US25027752100', 'Census Tract 7521, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027753100', '1400000US25027753100', 'Census Tract 7531, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027753200', '1400000US25027753200', 'Census Tract 7532, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027754100', '1400000US25027754100', 'Census Tract 7541, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027754200', '1400000US25027754200', 'Census Tract 7542, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027754300', '1400000US25027754300', 'Census Tract 7543, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027754400', '1400000US25027754400', 'Census Tract 7544, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027755100', '1400000US25027755100', 'Census Tract 7551, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027755200', '1400000US25027755200', 'Census Tract 7552, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027756101', '1400000US25027756101', 'Census Tract 7561.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027756102', '1400000US25027756102', 'Census Tract 7561.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027757100', '1400000US25027757100', 'Census Tract 7571, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027757200', '1400000US25027757200', 'Census Tract 7572, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027757300', '1400000US25027757300', 'Census Tract 7573, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027757400', '1400000US25027757400', 'Census Tract 7574, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027757500', '1400000US25027757500', 'Census Tract 7575, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027758101', '1400000US25027758101', 'Census Tract 7581.01, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027758102', '1400000US25027758102', 'Census Tract 7581.02, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027759100', '1400000US25027759100', 'Census Tract 7591, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027760100', '1400000US25027760100', 'Census Tract 7601, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027761100', '1400000US25027761100', 'Census Tract 7611, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027761200', '1400000US25027761200', 'Census Tract 7612, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027761300', '1400000US25027761300', 'Census Tract 7613, Worcester County, Massachusetts');
INSERT INTO "census"."lu_tracts" ("tract_id", "tract_long_id", "tract_name") VALUES ('25027761400', '1400000US25027761400', 'Census Tract 7614, Worcester County, Massachusetts');


--
-- TOC entry 3596 (class 0 OID 1846246)
-- Dependencies: 229
-- Data for Name: chickens; Type: TABLE DATA; Schema: public; Owner: postgres
--



--
-- TOC entry 3597 (class 0 OID 1846250)
-- Dependencies: 230
-- Data for Name: coin_tosses; Type: TABLE DATA; Schema: public; Owner: postgres
--

INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (1, 'regina', 'H');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (2, 'leo', 'T');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (3, 'sonia', 'T');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (4, 'alex', 'H');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (5, 'regina', 'H');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (6, 'leo', 'H');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (7, 'sonia', 'T');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (8, 'alex', 'H');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (9, 'regina', 'T');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (10, 'leo', 'H');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (11, 'sonia', 'T');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (12, 'alex', 'H');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (13, 'regina', 'T');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (14, 'leo', 'T');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (15, 'sonia', 'T');
INSERT INTO "public"."coin_tosses" ("id", "player", "toss") VALUES (16, 'alex', 'H');


--
-- TOC entry 3599 (class 0 OID 1846255)
-- Dependencies: 232
-- Data for Name: ducks; Type: TABLE DATA; Schema: public; Owner: postgres
--

INSERT INTO "public"."ducks" ("id", "chickens") VALUES (1, '{(1),(1)}');


--
-- TOC entry 3600 (class 0 OID 1846261)
-- Dependencies: 233
-- Data for Name: film; Type: TABLE DATA; Schema: public; Owner: postgres
--

INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (1, 'ACADEMY DINOSAUR', 'A Epic Drama of a Feminist And a Mad Scientist who must Battle a Teacher in The Canadian Rockies', '''academi'':1A ''battl'':15B ''canadian'':20B ''dinosaur'':2A ''drama'':5B ''epic'':4B ''feminist'':8B ''mad'':11B ''must'':14B ''rocki'':21B ''scientist'':12B ''teacher'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (2, 'ACE GOLDFINGER', 'A Astounding Epistle of a Database Administrator And a Explorer who must Find a Car in Ancient China', '''ace'':1A ''administr'':9B ''ancient'':19B ''astound'':4B ''car'':17B ''china'':20B ''databas'':8B ''epistl'':5B ''explor'':12B ''find'':15B ''goldfing'':2A ''must'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (3, 'ADAPTATION HOLES', 'A Astounding Reflection of a Lumberjack And a Car who must Sink a Lumberjack in A Baloon Factory', '''adapt'':1A ''astound'':4B ''baloon'':19B ''car'':11B ''factori'':20B ''hole'':2A ''lumberjack'':8B,16B ''must'':13B ''reflect'':5B ''sink'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (4, 'AFFAIR PREJUDICE', 'A Fanciful Documentary of a Frisbee And a Lumberjack who must Chase a Monkey in A Shark Tank', '''affair'':1A ''chase'':14B ''documentari'':5B ''fanci'':4B ''frisbe'':8B ''lumberjack'':11B ''monkey'':16B ''must'':13B ''prejudic'':2A ''shark'':19B ''tank'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (5, 'AFRICAN EGG', 'A Fast-Paced Documentary of a Pastry Chef And a Dentist who must Pursue a Forensic Psychologist in The Gulf of Mexico', '''african'':1A ''chef'':11B ''dentist'':14B ''documentari'':7B ''egg'':2A ''fast'':5B ''fast-pac'':4B ''forens'':19B ''gulf'':23B ''mexico'':25B ''must'':16B ''pace'':6B ''pastri'':10B ''psychologist'':20B ''pursu'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (6, 'AGENT TRUMAN', 'A Intrepid Panorama of a Robot And a Boy who must Escape a Sumo Wrestler in Ancient China', '''agent'':1A ''ancient'':19B ''boy'':11B ''china'':20B ''escap'':14B ''intrepid'':4B ''must'':13B ''panorama'':5B ''robot'':8B ''sumo'':16B ''truman'':2A ''wrestler'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (7, 'AIRPLANE SIERRA', 'A Touching Saga of a Hunter And a Butler who must Discover a Butler in A Jet Boat', '''airplan'':1A ''boat'':20B ''butler'':11B,16B ''discov'':14B ''hunter'':8B ''jet'':19B ''must'':13B ''saga'':5B ''sierra'':2A ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (8, 'AIRPORT POLLOCK', 'A Epic Tale of a Moose And a Girl who must Confront a Monkey in Ancient India', '''airport'':1A ''ancient'':18B ''confront'':14B ''epic'':4B ''girl'':11B ''india'':19B ''monkey'':16B ''moos'':8B ''must'':13B ''pollock'':2A ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (9, 'ALABAMA DEVIL', 'A Thoughtful Panorama of a Database Administrator And a Mad Scientist who must Outgun a Mad Scientist in A Jet Boat', '''administr'':9B ''alabama'':1A ''boat'':23B ''databas'':8B ''devil'':2A ''jet'':22B ''mad'':12B,18B ''must'':15B ''outgun'':16B ''panorama'':5B ''scientist'':13B,19B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (10, 'ALADDIN CALENDAR', 'A Action-Packed Tale of a Man And a Lumberjack who must Reach a Feminist in Ancient China', '''action'':5B ''action-pack'':4B ''aladdin'':1A ''ancient'':20B ''calendar'':2A ''china'':21B ''feminist'':18B ''lumberjack'':13B ''man'':10B ''must'':15B ''pack'':6B ''reach'':16B ''tale'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (11, 'ALAMO VIDEOTAPE', 'A Boring Epistle of a Butler And a Cat who must Fight a Pastry Chef in A MySQL Convention', '''alamo'':1A ''bore'':4B ''butler'':8B ''cat'':11B ''chef'':17B ''convent'':21B ''epistl'':5B ''fight'':14B ''must'':13B ''mysql'':20B ''pastri'':16B ''videotap'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (12, 'ALASKA PHANTOM', 'A Fanciful Saga of a Hunter And a Pastry Chef who must Vanquish a Boy in Australia', '''alaska'':1A ''australia'':19B ''boy'':17B ''chef'':12B ''fanci'':4B ''hunter'':8B ''must'':14B ''pastri'':11B ''phantom'':2A ''saga'':5B ''vanquish'':15B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (213, 'DATE SPEED', 'A Touching Saga of a Composer And a Moose who must Discover a Dentist in A MySQL Convention', '''compos'':8B ''convent'':20B ''date'':1A ''dentist'':16B ''discov'':14B ''moos'':11B ''must'':13B ''mysql'':19B ''saga'':5B ''speed'':2A ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (13, 'ALI FOREVER', 'A Action-Packed Drama of a Dentist And a Crocodile who must Battle a Feminist in The Canadian Rockies', '''action'':5B ''action-pack'':4B ''ali'':1A ''battl'':16B ''canadian'':21B ''crocodil'':13B ''dentist'':10B ''drama'':7B ''feminist'':18B ''forev'':2A ''must'':15B ''pack'':6B ''rocki'':22B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (14, 'ALICE FANTASIA', 'A Emotional Drama of a A Shark And a Database Administrator who must Vanquish a Pioneer in Soviet Georgia', '''administr'':13B ''alic'':1A ''databas'':12B ''drama'':5B ''emot'':4B ''fantasia'':2A ''georgia'':21B ''must'':15B ''pioneer'':18B ''shark'':9B ''soviet'':20B ''vanquish'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (15, 'ALIEN CENTER', 'A Brilliant Drama of a Cat And a Mad Scientist who must Battle a Feminist in A MySQL Convention', '''alien'':1A ''battl'':15B ''brilliant'':4B ''cat'':8B ''center'':2A ''convent'':21B ''drama'':5B ''feminist'':17B ''mad'':11B ''must'':14B ''mysql'':20B ''scientist'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (16, 'ALLEY EVOLUTION', 'A Fast-Paced Drama of a Robot And a Composer who must Battle a Astronaut in New Orleans', '''alley'':1A ''astronaut'':18B ''battl'':16B ''compos'':13B ''drama'':7B ''evolut'':2A ''fast'':5B ''fast-pac'':4B ''must'':15B ''new'':20B ''orlean'':21B ''pace'':6B ''robot'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (17, 'ALONE TRIP', 'A Fast-Paced Character Study of a Composer And a Dog who must Outgun a Boat in An Abandoned Fun House', '''abandon'':22B ''alon'':1A ''boat'':19B ''charact'':7B ''compos'':11B ''dog'':14B ''fast'':5B ''fast-pac'':4B ''fun'':23B ''hous'':24B ''must'':16B ''outgun'':17B ''pace'':6B ''studi'':8B ''trip'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (18, 'ALTER VICTORY', 'A Thoughtful Drama of a Composer And a Feminist who must Meet a Secret Agent in The Canadian Rockies', '''agent'':17B ''alter'':1A ''canadian'':20B ''compos'':8B ''drama'':5B ''feminist'':11B ''meet'':14B ''must'':13B ''rocki'':21B ''secret'':16B ''thought'':4B ''victori'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (19, 'AMADEUS HOLY', 'A Emotional Display of a Pioneer And a Technical Writer who must Battle a Man in A Baloon', '''amadeus'':1A ''baloon'':20B ''battl'':15B ''display'':5B ''emot'':4B ''holi'':2A ''man'':17B ''must'':14B ''pioneer'':8B ''technic'':11B ''writer'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (20, 'AMELIE HELLFIGHTERS', 'A Boring Drama of a Woman And a Squirrel who must Conquer a Student in A Baloon', '''ameli'':1A ''baloon'':19B ''bore'':4B ''conquer'':14B ''drama'':5B ''hellfight'':2A ''must'':13B ''squirrel'':11B ''student'':16B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (21, 'AMERICAN CIRCUS', 'A Insightful Drama of a Girl And a Astronaut who must Face a Database Administrator in A Shark Tank', '''administr'':17B ''american'':1A ''astronaut'':11B ''circus'':2A ''databas'':16B ''drama'':5B ''face'':14B ''girl'':8B ''insight'':4B ''must'':13B ''shark'':20B ''tank'':21B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (22, 'AMISTAD MIDSUMMER', 'A Emotional Character Study of a Dentist And a Crocodile who must Meet a Sumo Wrestler in California', '''amistad'':1A ''california'':20B ''charact'':5B ''crocodil'':12B ''dentist'':9B ''emot'':4B ''meet'':15B ''midsumm'':2A ''must'':14B ''studi'':6B ''sumo'':17B ''wrestler'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (23, 'ANACONDA CONFESSIONS', 'A Lacklusture Display of a Dentist And a Dentist who must Fight a Girl in Australia', '''anaconda'':1A ''australia'':18B ''confess'':2A ''dentist'':8B,11B ''display'':5B ''fight'':14B ''girl'':16B ''lacklustur'':4B ''must'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (24, 'ANALYZE HOOSIERS', 'A Thoughtful Display of a Explorer And a Pastry Chef who must Overcome a Feminist in The Sahara Desert', '''analyz'':1A ''chef'':12B ''desert'':21B ''display'':5B ''explor'':8B ''feminist'':17B ''hoosier'':2A ''must'':14B ''overcom'':15B ''pastri'':11B ''sahara'':20B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (25, 'ANGELS LIFE', 'A Thoughtful Display of a Woman And a Astronaut who must Battle a Robot in Berlin', '''angel'':1A ''astronaut'':11B ''battl'':14B ''berlin'':18B ''display'':5B ''life'':2A ''must'':13B ''robot'':16B ''thought'':4B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (26, 'ANNIE IDENTITY', 'A Amazing Panorama of a Pastry Chef And a Boat who must Escape a Woman in An Abandoned Amusement Park', '''abandon'':20B ''amaz'':4B ''amus'':21B ''anni'':1A ''boat'':12B ''chef'':9B ''escap'':15B ''ident'':2A ''must'':14B ''panorama'':5B ''park'':22B ''pastri'':8B ''woman'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (27, 'ANONYMOUS HUMAN', 'A Amazing Reflection of a Database Administrator And a Astronaut who must Outrace a Database Administrator in A Shark Tank', '''administr'':9B,18B ''amaz'':4B ''anonym'':1A ''astronaut'':12B ''databas'':8B,17B ''human'':2A ''must'':14B ''outrac'':15B ''reflect'':5B ''shark'':21B ''tank'':22B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (28, 'ANTHEM LUKE', 'A Touching Panorama of a Waitress And a Woman who must Outrace a Dog in An Abandoned Amusement Park', '''abandon'':19B ''amus'':20B ''anthem'':1A ''dog'':16B ''luke'':2A ''must'':13B ''outrac'':14B ''panorama'':5B ''park'':21B ''touch'':4B ''waitress'':8B ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (29, 'ANTITRUST TOMATOES', 'A Fateful Yarn of a Womanizer And a Feminist who must Succumb a Database Administrator in Ancient India', '''administr'':17B ''ancient'':19B ''antitrust'':1A ''databas'':16B ''fate'':4B ''feminist'':11B ''india'':20B ''must'':13B ''succumb'':14B ''tomato'':2A ''woman'':8B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (30, 'ANYTHING SAVANNAH', 'A Epic Story of a Pastry Chef And a Woman who must Chase a Feminist in An Abandoned Fun House', '''abandon'':20B ''anyth'':1A ''chase'':15B ''chef'':9B ''epic'':4B ''feminist'':17B ''fun'':21B ''hous'':22B ''must'':14B ''pastri'':8B ''savannah'':2A ''stori'':5B ''woman'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (31, 'APACHE DIVINE', 'A Awe-Inspiring Reflection of a Pastry Chef And a Teacher who must Overcome a Sumo Wrestler in A U-Boat', '''apach'':1A ''awe'':5B ''awe-inspir'':4B ''boat'':25B ''chef'':11B ''divin'':2A ''inspir'':6B ''must'':16B ''overcom'':17B ''pastri'':10B ''reflect'':7B ''sumo'':19B ''teacher'':14B ''u'':24B ''u-boat'':23B ''wrestler'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (32, 'APOCALYPSE FLAMINGOS', 'A Astounding Story of a Dog And a Squirrel who must Defeat a Woman in An Abandoned Amusement Park', '''abandon'':19B ''amus'':20B ''apocalyps'':1A ''astound'':4B ''defeat'':14B ''dog'':8B ''flamingo'':2A ''must'':13B ''park'':21B ''squirrel'':11B ''stori'':5B ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (33, 'APOLLO TEEN', 'A Action-Packed Reflection of a Crocodile And a Explorer who must Find a Sumo Wrestler in An Abandoned Mine Shaft', '''abandon'':22B ''action'':5B ''action-pack'':4B ''apollo'':1A ''crocodil'':10B ''explor'':13B ''find'':16B ''mine'':23B ''must'':15B ''pack'':6B ''reflect'':7B ''shaft'':24B ''sumo'':18B ''teen'':2A ''wrestler'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (34, 'ARABIA DOGMA', 'A Touching Epistle of a Madman And a Mad Cow who must Defeat a Student in Nigeria', '''arabia'':1A ''cow'':12B ''defeat'':15B ''dogma'':2A ''epistl'':5B ''mad'':11B ''madman'':8B ''must'':14B ''nigeria'':19B ''student'':17B ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (35, 'ARACHNOPHOBIA ROLLERCOASTER', 'A Action-Packed Reflection of a Pastry Chef And a Composer who must Discover a Mad Scientist in The First Manned Space Station', '''action'':5B ''action-pack'':4B ''arachnophobia'':1A ''chef'':11B ''compos'':14B ''discov'':17B ''first'':23B ''mad'':19B ''man'':24B ''must'':16B ''pack'':6B ''pastri'':10B ''reflect'':7B ''rollercoast'':2A ''scientist'':20B ''space'':25B ''station'':26B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (36, 'ARGONAUTS TOWN', 'A Emotional Epistle of a Forensic Psychologist And a Butler who must Challenge a Waitress in An Abandoned Mine Shaft', '''abandon'':20B ''argonaut'':1A ''butler'':12B ''challeng'':15B ''emot'':4B ''epistl'':5B ''forens'':8B ''mine'':21B ''must'':14B ''psychologist'':9B ''shaft'':22B ''town'':2A ''waitress'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (37, 'ARIZONA BANG', 'A Brilliant Panorama of a Mad Scientist And a Mad Cow who must Meet a Pioneer in A Monastery', '''arizona'':1A ''bang'':2A ''brilliant'':4B ''cow'':13B ''mad'':8B,12B ''meet'':16B ''monasteri'':21B ''must'':15B ''panorama'':5B ''pioneer'':18B ''scientist'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (38, 'ARK RIDGEMONT', 'A Beautiful Yarn of a Pioneer And a Monkey who must Pursue a Explorer in The Sahara Desert', '''ark'':1A ''beauti'':4B ''desert'':20B ''explor'':16B ''monkey'':11B ''must'':13B ''pioneer'':8B ''pursu'':14B ''ridgemont'':2A ''sahara'':19B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (39, 'ARMAGEDDON LOST', 'A Fast-Paced Tale of a Boat And a Teacher who must Succumb a Composer in An Abandoned Mine Shaft', '''abandon'':21B ''armageddon'':1A ''boat'':10B ''compos'':18B ''fast'':5B ''fast-pac'':4B ''lost'':2A ''mine'':22B ''must'':15B ''pace'':6B ''shaft'':23B ''succumb'':16B ''tale'':7B ''teacher'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (40, 'ARMY FLINTSTONES', 'A Boring Saga of a Database Administrator And a Womanizer who must Battle a Waitress in Nigeria', '''administr'':9B ''armi'':1A ''battl'':15B ''bore'':4B ''databas'':8B ''flintston'':2A ''must'':14B ''nigeria'':19B ''saga'':5B ''waitress'':17B ''woman'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (41, 'ARSENIC INDEPENDENCE', 'A Fanciful Documentary of a Mad Cow And a Womanizer who must Find a Dentist in Berlin', '''arsenic'':1A ''berlin'':19B ''cow'':9B ''dentist'':17B ''documentari'':5B ''fanci'':4B ''find'':15B ''independ'':2A ''mad'':8B ''must'':14B ''woman'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (42, 'ARTIST COLDBLOODED', 'A Stunning Reflection of a Robot And a Moose who must Challenge a Woman in California', '''artist'':1A ''california'':18B ''challeng'':14B ''coldblood'':2A ''moos'':11B ''must'':13B ''reflect'':5B ''robot'':8B ''stun'':4B ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (43, 'ATLANTIS CAUSE', 'A Thrilling Yarn of a Feminist And a Hunter who must Fight a Technical Writer in A Shark Tank', '''atlanti'':1A ''caus'':2A ''feminist'':8B ''fight'':14B ''hunter'':11B ''must'':13B ''shark'':20B ''tank'':21B ''technic'':16B ''thrill'':4B ''writer'':17B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (44, 'ATTACKS HATE', 'A Fast-Paced Panorama of a Technical Writer And a Mad Scientist who must Find a Feminist in An Abandoned Mine Shaft', '''abandon'':23B ''attack'':1A ''fast'':5B ''fast-pac'':4B ''feminist'':20B ''find'':18B ''hate'':2A ''mad'':14B ''mine'':24B ''must'':17B ''pace'':6B ''panorama'':7B ''scientist'':15B ''shaft'':25B ''technic'':10B ''writer'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (45, 'ATTRACTION NEWTON', 'A Astounding Panorama of a Composer And a Frisbee who must Reach a Husband in Ancient Japan', '''ancient'':18B ''astound'':4B ''attract'':1A ''compos'':8B ''frisbe'':11B ''husband'':16B ''japan'':19B ''must'':13B ''newton'':2A ''panorama'':5B ''reach'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (46, 'AUTUMN CROW', 'A Beautiful Tale of a Dentist And a Mad Cow who must Battle a Moose in The Sahara Desert', '''autumn'':1A ''battl'':15B ''beauti'':4B ''cow'':12B ''crow'':2A ''dentist'':8B ''desert'':21B ''mad'':11B ''moos'':17B ''must'':14B ''sahara'':20B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (47, 'BABY HALL', 'A Boring Character Study of a A Shark And a Girl who must Outrace a Feminist in An Abandoned Mine Shaft', '''abandon'':21B ''babi'':1A ''bore'':4B ''charact'':5B ''feminist'':18B ''girl'':13B ''hall'':2A ''mine'':22B ''must'':15B ''outrac'':16B ''shaft'':23B ''shark'':10B ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (48, 'BACKLASH UNDEFEATED', 'A Stunning Character Study of a Mad Scientist And a Mad Cow who must Kill a Car in A Monastery', '''backlash'':1A ''car'':19B ''charact'':5B ''cow'':14B ''kill'':17B ''mad'':9B,13B ''monasteri'':22B ''must'':16B ''scientist'':10B ''studi'':6B ''stun'':4B ''undef'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (49, 'BADMAN DAWN', 'A Emotional Panorama of a Pioneer And a Composer who must Escape a Mad Scientist in A Jet Boat', '''badman'':1A ''boat'':21B ''compos'':11B ''dawn'':2A ''emot'':4B ''escap'':14B ''jet'':20B ''mad'':16B ''must'':13B ''panorama'':5B ''pioneer'':8B ''scientist'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (50, 'BAKED CLEOPATRA', 'A Stunning Drama of a Forensic Psychologist And a Husband who must Overcome a Waitress in A Monastery', '''bake'':1A ''cleopatra'':2A ''drama'':5B ''forens'':8B ''husband'':12B ''monasteri'':20B ''must'':14B ''overcom'':15B ''psychologist'':9B ''stun'':4B ''waitress'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (51, 'BALLOON HOMEWARD', 'A Insightful Panorama of a Forensic Psychologist And a Mad Cow who must Build a Mad Scientist in The First Manned Space Station', '''balloon'':1A ''build'':16B ''cow'':13B ''first'':22B ''forens'':8B ''homeward'':2A ''insight'':4B ''mad'':12B,18B ''man'':23B ''must'':15B ''panorama'':5B ''psychologist'':9B ''scientist'':19B ''space'':24B ''station'':25B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (52, 'BALLROOM MOCKINGBIRD', 'A Thrilling Documentary of a Composer And a Monkey who must Find a Feminist in California', '''ballroom'':1A ''california'':18B ''compos'':8B ''documentari'':5B ''feminist'':16B ''find'':14B ''mockingbird'':2A ''monkey'':11B ''must'':13B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (53, 'BANG KWAI', 'A Epic Drama of a Madman And a Cat who must Face a A Shark in An Abandoned Amusement Park', '''abandon'':20B ''amus'':21B ''bang'':1A ''cat'':11B ''drama'':5B ''epic'':4B ''face'':14B ''kwai'':2A ''madman'':8B ''must'':13B ''park'':22B ''shark'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (890, 'TIGHTS DAWN', 'A Thrilling Epistle of a Boat And a Secret Agent who must Face a Boy in A Baloon', '''agent'':12B ''baloon'':20B ''boat'':8B ''boy'':17B ''dawn'':2A ''epistl'':5B ''face'':15B ''must'':14B ''secret'':11B ''thrill'':4B ''tight'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (54, 'BANGER PINOCCHIO', 'A Awe-Inspiring Drama of a Car And a Pastry Chef who must Chase a Crocodile in The First Manned Space Station', '''awe'':5B ''awe-inspir'':4B ''banger'':1A ''car'':10B ''chase'':17B ''chef'':14B ''crocodil'':19B ''drama'':7B ''first'':22B ''inspir'':6B ''man'':23B ''must'':16B ''pastri'':13B ''pinocchio'':2A ''space'':24B ''station'':25B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (55, 'BARBARELLA STREETCAR', 'A Awe-Inspiring Story of a Feminist And a Cat who must Conquer a Dog in A Monastery', '''awe'':5B ''awe-inspir'':4B ''barbarella'':1A ''cat'':13B ''conquer'':16B ''dog'':18B ''feminist'':10B ''inspir'':6B ''monasteri'':21B ''must'':15B ''stori'':7B ''streetcar'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (56, 'BAREFOOT MANCHURIAN', 'A Intrepid Story of a Cat And a Student who must Vanquish a Girl in An Abandoned Amusement Park', '''abandon'':19B ''amus'':20B ''barefoot'':1A ''cat'':8B ''girl'':16B ''intrepid'':4B ''manchurian'':2A ''must'':13B ''park'':21B ''stori'':5B ''student'':11B ''vanquish'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (57, 'BASIC EASY', 'A Stunning Epistle of a Man And a Husband who must Reach a Mad Scientist in A Jet Boat', '''basic'':1A ''boat'':21B ''easi'':2A ''epistl'':5B ''husband'':11B ''jet'':20B ''mad'':16B ''man'':8B ''must'':13B ''reach'':14B ''scientist'':17B ''stun'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (58, 'BEACH HEARTBREAKERS', 'A Fateful Display of a Womanizer And a Mad Scientist who must Outgun a A Shark in Soviet Georgia', '''beach'':1A ''display'':5B ''fate'':4B ''georgia'':21B ''heartbreak'':2A ''mad'':11B ''must'':14B ''outgun'':15B ''scientist'':12B ''shark'':18B ''soviet'':20B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (59, 'BEAR GRACELAND', 'A Astounding Saga of a Dog And a Boy who must Kill a Teacher in The First Manned Space Station', '''astound'':4B ''bear'':1A ''boy'':11B ''dog'':8B ''first'':19B ''graceland'':2A ''kill'':14B ''man'':20B ''must'':13B ''saga'':5B ''space'':21B ''station'':22B ''teacher'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (60, 'BEAST HUNCHBACK', 'A Awe-Inspiring Epistle of a Student And a Squirrel who must Defeat a Boy in Ancient China', '''ancient'':20B ''awe'':5B ''awe-inspir'':4B ''beast'':1A ''boy'':18B ''china'':21B ''defeat'':16B ''epistl'':7B ''hunchback'':2A ''inspir'':6B ''must'':15B ''squirrel'':13B ''student'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (61, 'BEAUTY GREASE', 'A Fast-Paced Display of a Composer And a Moose who must Sink a Robot in An Abandoned Mine Shaft', '''abandon'':21B ''beauti'':1A ''compos'':10B ''display'':7B ''fast'':5B ''fast-pac'':4B ''greas'':2A ''mine'':22B ''moos'':13B ''must'':15B ''pace'':6B ''robot'':18B ''shaft'':23B ''sink'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (62, 'BED HIGHBALL', 'A Astounding Panorama of a Lumberjack And a Dog who must Redeem a Woman in An Abandoned Fun House', '''abandon'':19B ''astound'':4B ''bed'':1A ''dog'':11B ''fun'':20B ''highbal'':2A ''hous'':21B ''lumberjack'':8B ''must'':13B ''panorama'':5B ''redeem'':14B ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (63, 'BEDAZZLED MARRIED', 'A Astounding Character Study of a Madman And a Robot who must Meet a Mad Scientist in An Abandoned Fun House', '''abandon'':21B ''astound'':4B ''bedazzl'':1A ''charact'':5B ''fun'':22B ''hous'':23B ''mad'':17B ''madman'':9B ''marri'':2A ''meet'':15B ''must'':14B ''robot'':12B ''scientist'':18B ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (64, 'BEETHOVEN EXORCIST', 'A Epic Display of a Pioneer And a Student who must Challenge a Butler in The Gulf of Mexico', '''beethoven'':1A ''butler'':16B ''challeng'':14B ''display'':5B ''epic'':4B ''exorcist'':2A ''gulf'':19B ''mexico'':21B ''must'':13B ''pioneer'':8B ''student'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (65, 'BEHAVIOR RUNAWAY', 'A Unbelieveable Drama of a Student And a Husband who must Outrace a Sumo Wrestler in Berlin', '''behavior'':1A ''berlin'':19B ''drama'':5B ''husband'':11B ''must'':13B ''outrac'':14B ''runaway'':2A ''student'':8B ''sumo'':16B ''unbeliev'':4B ''wrestler'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (66, 'BENEATH RUSH', 'A Astounding Panorama of a Man And a Monkey who must Discover a Man in The First Manned Space Station', '''astound'':4B ''beneath'':1A ''discov'':14B ''first'':19B ''man'':8B,16B,20B ''monkey'':11B ''must'':13B ''panorama'':5B ''rush'':2A ''space'':21B ''station'':22B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (67, 'BERETS AGENT', 'A Taut Saga of a Crocodile And a Boy who must Overcome a Technical Writer in Ancient China', '''agent'':2A ''ancient'':19B ''beret'':1A ''boy'':11B ''china'':20B ''crocodil'':8B ''must'':13B ''overcom'':14B ''saga'':5B ''taut'':4B ''technic'':16B ''writer'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (68, 'BETRAYED REAR', 'A Emotional Character Study of a Boat And a Pioneer who must Find a Explorer in A Shark Tank', '''betray'':1A ''boat'':9B ''charact'':5B ''emot'':4B ''explor'':17B ''find'':15B ''must'':14B ''pioneer'':12B ''rear'':2A ''shark'':20B ''studi'':6B ''tank'':21B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (69, 'BEVERLY OUTLAW', 'A Fanciful Documentary of a Womanizer And a Boat who must Defeat a Madman in The First Manned Space Station', '''bever'':1A ''boat'':11B ''defeat'':14B ''documentari'':5B ''fanci'':4B ''first'':19B ''madman'':16B ''man'':20B ''must'':13B ''outlaw'':2A ''space'':21B ''station'':22B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (70, 'BIKINI BORROWERS', 'A Astounding Drama of a Astronaut And a Cat who must Discover a Woman in The First Manned Space Station', '''astound'':4B ''astronaut'':8B ''bikini'':1A ''borrow'':2A ''cat'':11B ''discov'':14B ''drama'':5B ''first'':19B ''man'':20B ''must'':13B ''space'':21B ''station'':22B ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (71, 'BILKO ANONYMOUS', 'A Emotional Reflection of a Teacher And a Man who must Meet a Cat in The First Manned Space Station', '''anonym'':2A ''bilko'':1A ''cat'':16B ''emot'':4B ''first'':19B ''man'':11B,20B ''meet'':14B ''must'':13B ''reflect'':5B ''space'':21B ''station'':22B ''teacher'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (72, 'BILL OTHERS', 'A Stunning Saga of a Mad Scientist And a Forensic Psychologist who must Challenge a Squirrel in A MySQL Convention', '''bill'':1A ''challeng'':16B ''convent'':22B ''forens'':12B ''mad'':8B ''must'':15B ''mysql'':21B ''other'':2A ''psychologist'':13B ''saga'':5B ''scientist'':9B ''squirrel'':18B ''stun'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (73, 'BINGO TALENTED', 'A Touching Tale of a Girl And a Crocodile who must Discover a Waitress in Nigeria', '''bingo'':1A ''crocodil'':11B ''discov'':14B ''girl'':8B ''must'':13B ''nigeria'':18B ''tale'':5B ''talent'':2A ''touch'':4B ''waitress'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (74, 'BIRCH ANTITRUST', 'A Fanciful Panorama of a Husband And a Pioneer who must Outgun a Dog in A Baloon', '''antitrust'':2A ''baloon'':19B ''birch'':1A ''dog'':16B ''fanci'':4B ''husband'':8B ''must'':13B ''outgun'':14B ''panorama'':5B ''pioneer'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (75, 'BIRD INDEPENDENCE', 'A Thrilling Documentary of a Car And a Student who must Sink a Hunter in The Canadian Rockies', '''bird'':1A ''canadian'':19B ''car'':8B ''documentari'':5B ''hunter'':16B ''independ'':2A ''must'':13B ''rocki'':20B ''sink'':14B ''student'':11B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (76, 'BIRDCAGE CASPER', 'A Fast-Paced Saga of a Frisbee And a Astronaut who must Overcome a Feminist in Ancient India', '''ancient'':20B ''astronaut'':13B ''birdcag'':1A ''casper'':2A ''fast'':5B ''fast-pac'':4B ''feminist'':18B ''frisbe'':10B ''india'':21B ''must'':15B ''overcom'':16B ''pace'':6B ''saga'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (77, 'BIRDS PERDITION', 'A Boring Story of a Womanizer And a Pioneer who must Face a Dog in California', '''bird'':1A ''bore'':4B ''california'':18B ''dog'':16B ''face'':14B ''must'':13B ''perdit'':2A ''pioneer'':11B ''stori'':5B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (78, 'BLACKOUT PRIVATE', 'A Intrepid Yarn of a Pastry Chef And a Mad Scientist who must Challenge a Secret Agent in Ancient Japan', '''agent'':19B ''ancient'':21B ''blackout'':1A ''challeng'':16B ''chef'':9B ''intrepid'':4B ''japan'':22B ''mad'':12B ''must'':15B ''pastri'':8B ''privat'':2A ''scientist'':13B ''secret'':18B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (79, 'BLADE POLISH', 'A Thoughtful Character Study of a Frisbee And a Pastry Chef who must Fight a Dentist in The First Manned Space Station', '''blade'':1A ''charact'':5B ''chef'':13B ''dentist'':18B ''fight'':16B ''first'':21B ''frisbe'':9B ''man'':22B ''must'':15B ''pastri'':12B ''polish'':2A ''space'':23B ''station'':24B ''studi'':6B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (80, 'BLANKET BEVERLY', 'A Emotional Documentary of a Student And a Girl who must Build a Boat in Nigeria', '''bever'':2A ''blanket'':1A ''boat'':16B ''build'':14B ''documentari'':5B ''emot'':4B ''girl'':11B ''must'':13B ''nigeria'':18B ''student'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (81, 'BLINDNESS GUN', 'A Touching Drama of a Robot And a Dentist who must Meet a Hunter in A Jet Boat', '''blind'':1A ''boat'':20B ''dentist'':11B ''drama'':5B ''gun'':2A ''hunter'':16B ''jet'':19B ''meet'':14B ''must'':13B ''robot'':8B ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (82, 'BLOOD ARGONAUTS', 'A Boring Drama of a Explorer And a Man who must Kill a Lumberjack in A Manhattan Penthouse', '''argonaut'':2A ''blood'':1A ''bore'':4B ''drama'':5B ''explor'':8B ''kill'':14B ''lumberjack'':16B ''man'':11B ''manhattan'':19B ''must'':13B ''penthous'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (83, 'BLUES INSTINCT', 'A Insightful Documentary of a Boat And a Composer who must Meet a Forensic Psychologist in An Abandoned Fun House', '''abandon'':20B ''blue'':1A ''boat'':8B ''compos'':11B ''documentari'':5B ''forens'':16B ''fun'':21B ''hous'':22B ''insight'':4B ''instinct'':2A ''meet'':14B ''must'':13B ''psychologist'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (84, 'BOILED DARES', 'A Awe-Inspiring Story of a Waitress And a Dog who must Discover a Dentist in Ancient Japan', '''ancient'':20B ''awe'':5B ''awe-inspir'':4B ''boil'':1A ''dare'':2A ''dentist'':18B ''discov'':16B ''dog'':13B ''inspir'':6B ''japan'':21B ''must'':15B ''stori'':7B ''waitress'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (85, 'BONNIE HOLOCAUST', 'A Fast-Paced Story of a Crocodile And a Robot who must Find a Moose in Ancient Japan', '''ancient'':20B ''bonni'':1A ''crocodil'':10B ''fast'':5B ''fast-pac'':4B ''find'':16B ''holocaust'':2A ''japan'':21B ''moos'':18B ''must'':15B ''pace'':6B ''robot'':13B ''stori'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (86, 'BOOGIE AMELIE', 'A Lacklusture Character Study of a Husband And a Sumo Wrestler who must Succumb a Technical Writer in The Gulf of Mexico', '''ameli'':2A ''boogi'':1A ''charact'':5B ''gulf'':22B ''husband'':9B ''lacklustur'':4B ''mexico'':24B ''must'':15B ''studi'':6B ''succumb'':16B ''sumo'':12B ''technic'':18B ''wrestler'':13B ''writer'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (87, 'BOONDOCK BALLROOM', 'A Fateful Panorama of a Crocodile And a Boy who must Defeat a Monkey in The Gulf of Mexico', '''ballroom'':2A ''boondock'':1A ''boy'':11B ''crocodil'':8B ''defeat'':14B ''fate'':4B ''gulf'':19B ''mexico'':21B ''monkey'':16B ''must'':13B ''panorama'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (88, 'BORN SPINAL', 'A Touching Epistle of a Frisbee And a Husband who must Pursue a Student in Nigeria', '''born'':1A ''epistl'':5B ''frisbe'':8B ''husband'':11B ''must'':13B ''nigeria'':18B ''pursu'':14B ''spinal'':2A ''student'':16B ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (89, 'BORROWERS BEDAZZLED', 'A Brilliant Epistle of a Teacher And a Sumo Wrestler who must Defeat a Man in An Abandoned Fun House', '''abandon'':20B ''bedazzl'':2A ''borrow'':1A ''brilliant'':4B ''defeat'':15B ''epistl'':5B ''fun'':21B ''hous'':22B ''man'':17B ''must'':14B ''sumo'':11B ''teacher'':8B ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (90, 'BOULEVARD MOB', 'A Fateful Epistle of a Moose And a Monkey who must Confront a Lumberjack in Ancient China', '''ancient'':18B ''boulevard'':1A ''china'':19B ''confront'':14B ''epistl'':5B ''fate'':4B ''lumberjack'':16B ''mob'':2A ''monkey'':11B ''moos'':8B ''must'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (91, 'BOUND CHEAPER', 'A Thrilling Panorama of a Database Administrator And a Astronaut who must Challenge a Lumberjack in A Baloon', '''administr'':9B ''astronaut'':12B ''baloon'':20B ''bound'':1A ''challeng'':15B ''cheaper'':2A ''databas'':8B ''lumberjack'':17B ''must'':14B ''panorama'':5B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (92, 'BOWFINGER GABLES', 'A Fast-Paced Yarn of a Waitress And a Composer who must Outgun a Dentist in California', '''bowfing'':1A ''california'':20B ''compos'':13B ''dentist'':18B ''fast'':5B ''fast-pac'':4B ''gabl'':2A ''must'':15B ''outgun'':16B ''pace'':6B ''waitress'':10B ''yarn'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (93, 'BRANNIGAN SUNRISE', 'A Amazing Epistle of a Moose And a Crocodile who must Outrace a Dog in Berlin', '''amaz'':4B ''berlin'':18B ''brannigan'':1A ''crocodil'':11B ''dog'':16B ''epistl'':5B ''moos'':8B ''must'':13B ''outrac'':14B ''sunris'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (94, 'BRAVEHEART HUMAN', 'A Insightful Story of a Dog And a Pastry Chef who must Battle a Girl in Berlin', '''battl'':15B ''berlin'':19B ''braveheart'':1A ''chef'':12B ''dog'':8B ''girl'':17B ''human'':2A ''insight'':4B ''must'':14B ''pastri'':11B ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (95, 'BREAKFAST GOLDFINGER', 'A Beautiful Reflection of a Student And a Student who must Fight a Moose in Berlin', '''beauti'':4B ''berlin'':18B ''breakfast'':1A ''fight'':14B ''goldfing'':2A ''moos'':16B ''must'':13B ''reflect'':5B ''student'':8B,11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (96, 'BREAKING HOME', 'A Beautiful Display of a Secret Agent And a Monkey who must Battle a Sumo Wrestler in An Abandoned Mine Shaft', '''abandon'':21B ''agent'':9B ''battl'':15B ''beauti'':4B ''break'':1A ''display'':5B ''home'':2A ''mine'':22B ''monkey'':12B ''must'':14B ''secret'':8B ''shaft'':23B ''sumo'':17B ''wrestler'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (97, 'BRIDE INTRIGUE', 'A Epic Tale of a Robot And a Monkey who must Vanquish a Man in New Orleans', '''bride'':1A ''epic'':4B ''intrigu'':2A ''man'':16B ''monkey'':11B ''must'':13B ''new'':18B ''orlean'':19B ''robot'':8B ''tale'':5B ''vanquish'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (98, 'BRIGHT ENCOUNTERS', 'A Fateful Yarn of a Lumberjack And a Feminist who must Conquer a Student in A Jet Boat', '''boat'':20B ''bright'':1A ''conquer'':14B ''encount'':2A ''fate'':4B ''feminist'':11B ''jet'':19B ''lumberjack'':8B ''must'':13B ''student'':16B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (99, 'BRINGING HYSTERICAL', 'A Fateful Saga of a A Shark And a Technical Writer who must Find a Woman in A Jet Boat', '''boat'':22B ''bring'':1A ''fate'':4B ''find'':16B ''hyster'':2A ''jet'':21B ''must'':15B ''saga'':5B ''shark'':9B ''technic'':12B ''woman'':18B ''writer'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (100, 'BROOKLYN DESERT', 'A Beautiful Drama of a Dentist And a Composer who must Battle a Sumo Wrestler in The First Manned Space Station', '''battl'':14B ''beauti'':4B ''brooklyn'':1A ''compos'':11B ''dentist'':8B ''desert'':2A ''drama'':5B ''first'':20B ''man'':21B ''must'':13B ''space'':22B ''station'':23B ''sumo'':16B ''wrestler'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (101, 'BROTHERHOOD BLANKET', 'A Fateful Character Study of a Butler And a Technical Writer who must Sink a Astronaut in Ancient Japan', '''ancient'':20B ''astronaut'':18B ''blanket'':2A ''brotherhood'':1A ''butler'':9B ''charact'':5B ''fate'':4B ''japan'':21B ''must'':15B ''sink'':16B ''studi'':6B ''technic'':12B ''writer'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (102, 'BUBBLE GROSSE', 'A Awe-Inspiring Panorama of a Crocodile And a Moose who must Confront a Girl in A Baloon', '''awe'':5B ''awe-inspir'':4B ''baloon'':21B ''bubbl'':1A ''confront'':16B ''crocodil'':10B ''girl'':18B ''gross'':2A ''inspir'':6B ''moos'':13B ''must'':15B ''panorama'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (103, 'BUCKET BROTHERHOOD', 'A Amazing Display of a Girl And a Womanizer who must Succumb a Lumberjack in A Baloon Factory', '''amaz'':4B ''baloon'':19B ''brotherhood'':2A ''bucket'':1A ''display'':5B ''factori'':20B ''girl'':8B ''lumberjack'':16B ''must'':13B ''succumb'':14B ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (104, 'BUGSY SONG', 'A Awe-Inspiring Character Study of a Secret Agent And a Boat who must Find a Squirrel in The First Manned Space Station', '''agent'':12B ''awe'':5B ''awe-inspir'':4B ''boat'':15B ''bugsi'':1A ''charact'':7B ''find'':18B ''first'':23B ''inspir'':6B ''man'':24B ''must'':17B ''secret'':11B ''song'':2A ''space'':25B ''squirrel'':20B ''station'':26B ''studi'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (105, 'BULL SHAWSHANK', 'A Fanciful Drama of a Moose And a Squirrel who must Conquer a Pioneer in The Canadian Rockies', '''bull'':1A ''canadian'':19B ''conquer'':14B ''drama'':5B ''fanci'':4B ''moos'':8B ''must'':13B ''pioneer'':16B ''rocki'':20B ''shawshank'':2A ''squirrel'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (106, 'BULWORTH COMMANDMENTS', 'A Amazing Display of a Mad Cow And a Pioneer who must Redeem a Sumo Wrestler in The Outback', '''amaz'':4B ''bulworth'':1A ''command'':2A ''cow'':9B ''display'':5B ''mad'':8B ''must'':14B ''outback'':21B ''pioneer'':12B ''redeem'':15B ''sumo'':17B ''wrestler'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (107, 'BUNCH MINDS', 'A Emotional Story of a Feminist And a Feminist who must Escape a Pastry Chef in A MySQL Convention', '''bunch'':1A ''chef'':17B ''convent'':21B ''emot'':4B ''escap'':14B ''feminist'':8B,11B ''mind'':2A ''must'':13B ''mysql'':20B ''pastri'':16B ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (108, 'BUTCH PANTHER', 'A Lacklusture Yarn of a Feminist And a Database Administrator who must Face a Hunter in New Orleans', '''administr'':12B ''butch'':1A ''databas'':11B ''face'':15B ''feminist'':8B ''hunter'':17B ''lacklustur'':4B ''must'':14B ''new'':19B ''orlean'':20B ''panther'':2A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (109, 'BUTTERFLY CHOCOLAT', 'A Fateful Story of a Girl And a Composer who must Conquer a Husband in A Shark Tank', '''butterfli'':1A ''chocolat'':2A ''compos'':11B ''conquer'':14B ''fate'':4B ''girl'':8B ''husband'':16B ''must'':13B ''shark'':19B ''stori'':5B ''tank'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (110, 'CABIN FLASH', 'A Stunning Epistle of a Boat And a Man who must Challenge a A Shark in A Baloon Factory', '''baloon'':20B ''boat'':8B ''cabin'':1A ''challeng'':14B ''epistl'':5B ''factori'':21B ''flash'':2A ''man'':11B ''must'':13B ''shark'':17B ''stun'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (111, 'CADDYSHACK JEDI', 'A Awe-Inspiring Epistle of a Woman And a Madman who must Fight a Robot in Soviet Georgia', '''awe'':5B ''awe-inspir'':4B ''caddyshack'':1A ''epistl'':7B ''fight'':16B ''georgia'':21B ''inspir'':6B ''jedi'':2A ''madman'':13B ''must'':15B ''robot'':18B ''soviet'':20B ''woman'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (112, 'CALENDAR GUNFIGHT', 'A Thrilling Drama of a Frisbee And a Lumberjack who must Sink a Man in Nigeria', '''calendar'':1A ''drama'':5B ''frisbe'':8B ''gunfight'':2A ''lumberjack'':11B ''man'':16B ''must'':13B ''nigeria'':18B ''sink'':14B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (113, 'CALIFORNIA BIRDS', 'A Thrilling Yarn of a Database Administrator And a Robot who must Battle a Database Administrator in Ancient India', '''administr'':9B,18B ''ancient'':20B ''battl'':15B ''bird'':2A ''california'':1A ''databas'':8B,17B ''india'':21B ''must'':14B ''robot'':12B ''thrill'':4B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (114, 'CAMELOT VACATION', 'A Touching Character Study of a Woman And a Waitress who must Battle a Pastry Chef in A MySQL Convention', '''battl'':15B ''camelot'':1A ''charact'':5B ''chef'':18B ''convent'':22B ''must'':14B ''mysql'':21B ''pastri'':17B ''studi'':6B ''touch'':4B ''vacat'':2A ''waitress'':12B ''woman'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (115, 'CAMPUS REMEMBER', 'A Astounding Drama of a Crocodile And a Mad Cow who must Build a Robot in A Jet Boat', '''astound'':4B ''boat'':21B ''build'':15B ''campus'':1A ''cow'':12B ''crocodil'':8B ''drama'':5B ''jet'':20B ''mad'':11B ''must'':14B ''rememb'':2A ''robot'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (116, 'CANDIDATE PERDITION', 'A Brilliant Epistle of a Composer And a Database Administrator who must Vanquish a Mad Scientist in The First Manned Space Station', '''administr'':12B ''brilliant'':4B ''candid'':1A ''compos'':8B ''databas'':11B ''epistl'':5B ''first'':21B ''mad'':17B ''man'':22B ''must'':14B ''perdit'':2A ''scientist'':18B ''space'':23B ''station'':24B ''vanquish'':15B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (117, 'CANDLES GRAPES', 'A Fanciful Character Study of a Monkey And a Explorer who must Build a Astronaut in An Abandoned Fun House', '''abandon'':20B ''astronaut'':17B ''build'':15B ''candl'':1A ''charact'':5B ''explor'':12B ''fanci'':4B ''fun'':21B ''grape'':2A ''hous'':22B ''monkey'':9B ''must'':14B ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (118, 'CANYON STOCK', 'A Thoughtful Reflection of a Waitress And a Feminist who must Escape a Squirrel in A Manhattan Penthouse', '''canyon'':1A ''escap'':14B ''feminist'':11B ''manhattan'':19B ''must'':13B ''penthous'':20B ''reflect'':5B ''squirrel'':16B ''stock'':2A ''thought'':4B ''waitress'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (119, 'CAPER MOTIONS', 'A Fateful Saga of a Moose And a Car who must Pursue a Woman in A MySQL Convention', '''caper'':1A ''car'':11B ''convent'':20B ''fate'':4B ''moos'':8B ''motion'':2A ''must'':13B ''mysql'':19B ''pursu'':14B ''saga'':5B ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (120, 'CARIBBEAN LIBERTY', 'A Fanciful Tale of a Pioneer And a Technical Writer who must Outgun a Pioneer in A Shark Tank', '''caribbean'':1A ''fanci'':4B ''liberti'':2A ''must'':14B ''outgun'':15B ''pioneer'':8B,17B ''shark'':20B ''tale'':5B ''tank'':21B ''technic'':11B ''writer'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (394, 'HAMLET WISDOM', 'A Touching Reflection of a Man And a Man who must Sink a Robot in The Outback', '''hamlet'':1A ''man'':8B,11B ''must'':13B ''outback'':19B ''reflect'':5B ''robot'':16B ''sink'':14B ''touch'':4B ''wisdom'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (121, 'CAROL TEXAS', 'A Astounding Character Study of a Composer And a Student who must Overcome a Composer in A Monastery', '''astound'':4B ''carol'':1A ''charact'':5B ''compos'':9B,17B ''monasteri'':20B ''must'':14B ''overcom'':15B ''student'':12B ''studi'':6B ''texa'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (122, 'CARRIE BUNCH', 'A Amazing Epistle of a Student And a Astronaut who must Discover a Frisbee in The Canadian Rockies', '''amaz'':4B ''astronaut'':11B ''bunch'':2A ''canadian'':19B ''carri'':1A ''discov'':14B ''epistl'':5B ''frisbe'':16B ''must'':13B ''rocki'':20B ''student'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (123, 'CASABLANCA SUPER', 'A Amazing Panorama of a Crocodile And a Forensic Psychologist who must Pursue a Secret Agent in The First Manned Space Station', '''agent'':18B ''amaz'':4B ''casablanca'':1A ''crocodil'':8B ''first'':21B ''forens'':11B ''man'':22B ''must'':14B ''panorama'':5B ''psychologist'':12B ''pursu'':15B ''secret'':17B ''space'':23B ''station'':24B ''super'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (124, 'CASPER DRAGONFLY', 'A Intrepid Documentary of a Boat And a Crocodile who must Chase a Robot in The Sahara Desert', '''boat'':8B ''casper'':1A ''chase'':14B ''crocodil'':11B ''desert'':20B ''documentari'':5B ''dragonfli'':2A ''intrepid'':4B ''must'':13B ''robot'':16B ''sahara'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (125, 'CASSIDY WYOMING', 'A Intrepid Drama of a Frisbee And a Hunter who must Kill a Secret Agent in New Orleans', '''agent'':17B ''cassidi'':1A ''drama'':5B ''frisbe'':8B ''hunter'':11B ''intrepid'':4B ''kill'':14B ''must'':13B ''new'':19B ''orlean'':20B ''secret'':16B ''wyom'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (126, 'CASUALTIES ENCINO', 'A Insightful Yarn of a A Shark And a Pastry Chef who must Face a Boy in A Monastery', '''boy'':18B ''casualti'':1A ''chef'':13B ''encino'':2A ''face'':16B ''insight'':4B ''monasteri'':21B ''must'':15B ''pastri'':12B ''shark'':9B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (127, 'CAT CONEHEADS', 'A Fast-Paced Panorama of a Girl And a A Shark who must Confront a Boy in Ancient India', '''ancient'':21B ''boy'':19B ''cat'':1A ''conehead'':2A ''confront'':17B ''fast'':5B ''fast-pac'':4B ''girl'':10B ''india'':22B ''must'':16B ''pace'':6B ''panorama'':7B ''shark'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (128, 'CATCH AMISTAD', 'A Boring Reflection of a Lumberjack And a Feminist who must Discover a Woman in Nigeria', '''amistad'':2A ''bore'':4B ''catch'':1A ''discov'':14B ''feminist'':11B ''lumberjack'':8B ''must'':13B ''nigeria'':18B ''reflect'':5B ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (129, 'CAUSE DATE', 'A Taut Tale of a Explorer And a Pastry Chef who must Conquer a Hunter in A MySQL Convention', '''caus'':1A ''chef'':12B ''conquer'':15B ''convent'':21B ''date'':2A ''explor'':8B ''hunter'':17B ''must'':14B ''mysql'':20B ''pastri'':11B ''tale'':5B ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (130, 'CELEBRITY HORN', 'A Amazing Documentary of a Secret Agent And a Astronaut who must Vanquish a Hunter in A Shark Tank', '''agent'':9B ''amaz'':4B ''astronaut'':12B ''celebr'':1A ''documentari'':5B ''horn'':2A ''hunter'':17B ''must'':14B ''secret'':8B ''shark'':20B ''tank'':21B ''vanquish'':15B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (131, 'CENTER DINOSAUR', 'A Beautiful Character Study of a Sumo Wrestler And a Dentist who must Find a Dog in California', '''beauti'':4B ''california'':20B ''center'':1A ''charact'':5B ''dentist'':13B ''dinosaur'':2A ''dog'':18B ''find'':16B ''must'':15B ''studi'':6B ''sumo'':9B ''wrestler'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (132, 'CHAINSAW UPTOWN', 'A Beautiful Documentary of a Boy And a Robot who must Discover a Squirrel in Australia', '''australia'':18B ''beauti'':4B ''boy'':8B ''chainsaw'':1A ''discov'':14B ''documentari'':5B ''must'':13B ''robot'':11B ''squirrel'':16B ''uptown'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (133, 'CHAMBER ITALIAN', 'A Fateful Reflection of a Moose And a Husband who must Overcome a Monkey in Nigeria', '''chamber'':1A ''fate'':4B ''husband'':11B ''italian'':2A ''monkey'':16B ''moos'':8B ''must'':13B ''nigeria'':18B ''overcom'':14B ''reflect'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (134, 'CHAMPION FLATLINERS', 'A Amazing Story of a Mad Cow And a Dog who must Kill a Husband in A Monastery', '''amaz'':4B ''champion'':1A ''cow'':9B ''dog'':12B ''flatlin'':2A ''husband'':17B ''kill'':15B ''mad'':8B ''monasteri'':20B ''must'':14B ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (135, 'CHANCE RESURRECTION', 'A Astounding Story of a Forensic Psychologist And a Forensic Psychologist who must Overcome a Moose in Ancient China', '''ancient'':20B ''astound'':4B ''chanc'':1A ''china'':21B ''forens'':8B,12B ''moos'':18B ''must'':15B ''overcom'':16B ''psychologist'':9B,13B ''resurrect'':2A ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (136, 'CHAPLIN LICENSE', 'A Boring Drama of a Dog And a Forensic Psychologist who must Outrace a Explorer in Ancient India', '''ancient'':19B ''bore'':4B ''chaplin'':1A ''dog'':8B ''drama'':5B ''explor'':17B ''forens'':11B ''india'':20B ''licens'':2A ''must'':14B ''outrac'':15B ''psychologist'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (137, 'CHARADE DUFFEL', 'A Action-Packed Display of a Man And a Waitress who must Build a Dog in A MySQL Convention', '''action'':5B ''action-pack'':4B ''build'':16B ''charad'':1A ''convent'':22B ''display'':7B ''dog'':18B ''duffel'':2A ''man'':10B ''must'':15B ''mysql'':21B ''pack'':6B ''waitress'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (138, 'CHARIOTS CONSPIRACY', 'A Unbelieveable Epistle of a Robot And a Husband who must Chase a Robot in The First Manned Space Station', '''chariot'':1A ''chase'':14B ''conspiraci'':2A ''epistl'':5B ''first'':19B ''husband'':11B ''man'':20B ''must'':13B ''robot'':8B,16B ''space'':21B ''station'':22B ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (139, 'CHASING FIGHT', 'A Astounding Saga of a Technical Writer And a Butler who must Battle a Butler in A Shark Tank', '''astound'':4B ''battl'':15B ''butler'':12B,17B ''chase'':1A ''fight'':2A ''must'':14B ''saga'':5B ''shark'':20B ''tank'':21B ''technic'':8B ''writer'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (140, 'CHEAPER CLYDE', 'A Emotional Character Study of a Pioneer And a Girl who must Discover a Dog in Ancient Japan', '''ancient'':19B ''charact'':5B ''cheaper'':1A ''clyde'':2A ''discov'':15B ''dog'':17B ''emot'':4B ''girl'':12B ''japan'':20B ''must'':14B ''pioneer'':9B ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (141, 'CHICAGO NORTH', 'A Fateful Yarn of a Mad Cow And a Waitress who must Battle a Student in California', '''battl'':15B ''california'':19B ''chicago'':1A ''cow'':9B ''fate'':4B ''mad'':8B ''must'':14B ''north'':2A ''student'':17B ''waitress'':12B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (142, 'CHICKEN HELLFIGHTERS', 'A Emotional Drama of a Dog And a Explorer who must Outrace a Technical Writer in Australia', '''australia'':19B ''chicken'':1A ''dog'':8B ''drama'':5B ''emot'':4B ''explor'':11B ''hellfight'':2A ''must'':13B ''outrac'':14B ''technic'':16B ''writer'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (143, 'CHILL LUCK', 'A Lacklusture Epistle of a Boat And a Technical Writer who must Fight a A Shark in The Canadian Rockies', '''boat'':8B ''canadian'':21B ''chill'':1A ''epistl'':5B ''fight'':15B ''lacklustur'':4B ''luck'':2A ''must'':14B ''rocki'':22B ''shark'':18B ''technic'':11B ''writer'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (144, 'CHINATOWN GLADIATOR', 'A Brilliant Panorama of a Technical Writer And a Lumberjack who must Escape a Butler in Ancient India', '''ancient'':19B ''brilliant'':4B ''butler'':17B ''chinatown'':1A ''escap'':15B ''gladiat'':2A ''india'':20B ''lumberjack'':12B ''must'':14B ''panorama'':5B ''technic'':8B ''writer'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (145, 'CHISUM BEHAVIOR', 'A Epic Documentary of a Sumo Wrestler And a Butler who must Kill a Car in Ancient India', '''ancient'':19B ''behavior'':2A ''butler'':12B ''car'':17B ''chisum'':1A ''documentari'':5B ''epic'':4B ''india'':20B ''kill'':15B ''must'':14B ''sumo'':8B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (146, 'CHITTY LOCK', 'A Boring Epistle of a Boat And a Database Administrator who must Kill a Sumo Wrestler in The First Manned Space Station', '''administr'':12B ''boat'':8B ''bore'':4B ''chitti'':1A ''databas'':11B ''epistl'':5B ''first'':21B ''kill'':15B ''lock'':2A ''man'':22B ''must'':14B ''space'':23B ''station'':24B ''sumo'':17B ''wrestler'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (147, 'CHOCOLAT HARRY', 'A Action-Packed Epistle of a Dentist And a Moose who must Meet a Mad Cow in Ancient Japan', '''action'':5B ''action-pack'':4B ''ancient'':21B ''chocolat'':1A ''cow'':19B ''dentist'':10B ''epistl'':7B ''harri'':2A ''japan'':22B ''mad'':18B ''meet'':16B ''moos'':13B ''must'':15B ''pack'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (148, 'CHOCOLATE DUCK', 'A Unbelieveable Story of a Mad Scientist And a Technical Writer who must Discover a Composer in Ancient China', '''ancient'':20B ''china'':21B ''chocol'':1A ''compos'':18B ''discov'':16B ''duck'':2A ''mad'':8B ''must'':15B ''scientist'':9B ''stori'':5B ''technic'':12B ''unbeliev'':4B ''writer'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (149, 'CHRISTMAS MOONSHINE', 'A Action-Packed Epistle of a Feminist And a Astronaut who must Conquer a Boat in A Manhattan Penthouse', '''action'':5B ''action-pack'':4B ''astronaut'':13B ''boat'':18B ''christma'':1A ''conquer'':16B ''epistl'':7B ''feminist'':10B ''manhattan'':21B ''moonshin'':2A ''must'':15B ''pack'':6B ''penthous'':22B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (150, 'CIDER DESIRE', 'A Stunning Character Study of a Composer And a Mad Cow who must Succumb a Cat in Soviet Georgia', '''cat'':18B ''charact'':5B ''cider'':1A ''compos'':9B ''cow'':13B ''desir'':2A ''georgia'':21B ''mad'':12B ''must'':15B ''soviet'':20B ''studi'':6B ''stun'':4B ''succumb'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (151, 'CINCINATTI WHISPERER', 'A Brilliant Saga of a Pastry Chef And a Hunter who must Confront a Butler in Berlin', '''berlin'':19B ''brilliant'':4B ''butler'':17B ''chef'':9B ''cincinatti'':1A ''confront'':15B ''hunter'':12B ''must'':14B ''pastri'':8B ''saga'':5B ''whisper'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (152, 'CIRCUS YOUTH', 'A Thoughtful Drama of a Pastry Chef And a Dentist who must Pursue a Girl in A Baloon', '''baloon'':20B ''chef'':9B ''circus'':1A ''dentist'':12B ''drama'':5B ''girl'':17B ''must'':14B ''pastri'':8B ''pursu'':15B ''thought'':4B ''youth'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (153, 'CITIZEN SHREK', 'A Fanciful Character Study of a Technical Writer And a Husband who must Redeem a Robot in The Outback', '''charact'':5B ''citizen'':1A ''fanci'':4B ''husband'':13B ''must'':15B ''outback'':21B ''redeem'':16B ''robot'':18B ''shrek'':2A ''studi'':6B ''technic'':9B ''writer'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (154, 'CLASH FREDDY', 'A Amazing Yarn of a Composer And a Squirrel who must Escape a Astronaut in Australia', '''amaz'':4B ''astronaut'':16B ''australia'':18B ''clash'':1A ''compos'':8B ''escap'':14B ''freddi'':2A ''must'':13B ''squirrel'':11B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (155, 'CLEOPATRA DEVIL', 'A Fanciful Documentary of a Crocodile And a Technical Writer who must Fight a A Shark in A Baloon', '''baloon'':21B ''cleopatra'':1A ''crocodil'':8B ''devil'':2A ''documentari'':5B ''fanci'':4B ''fight'':15B ''must'':14B ''shark'':18B ''technic'':11B ''writer'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (156, 'CLERKS ANGELS', 'A Thrilling Display of a Sumo Wrestler And a Girl who must Confront a Man in A Baloon', '''angel'':2A ''baloon'':20B ''clerk'':1A ''confront'':15B ''display'':5B ''girl'':12B ''man'':17B ''must'':14B ''sumo'':8B ''thrill'':4B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (157, 'CLOCKWORK PARADISE', 'A Insightful Documentary of a Technical Writer And a Feminist who must Challenge a Cat in A Baloon', '''baloon'':20B ''cat'':17B ''challeng'':15B ''clockwork'':1A ''documentari'':5B ''feminist'':12B ''insight'':4B ''must'':14B ''paradis'':2A ''technic'':8B ''writer'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (158, 'CLONES PINOCCHIO', 'A Amazing Drama of a Car And a Robot who must Pursue a Dentist in New Orleans', '''amaz'':4B ''car'':8B ''clone'':1A ''dentist'':16B ''drama'':5B ''must'':13B ''new'':18B ''orlean'':19B ''pinocchio'':2A ''pursu'':14B ''robot'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (159, 'CLOSER BANG', 'A Unbelieveable Panorama of a Frisbee And a Hunter who must Vanquish a Monkey in Ancient India', '''ancient'':18B ''bang'':2A ''closer'':1A ''frisbe'':8B ''hunter'':11B ''india'':19B ''monkey'':16B ''must'':13B ''panorama'':5B ''unbeliev'':4B ''vanquish'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (160, 'CLUB GRAFFITI', 'A Epic Tale of a Pioneer And a Hunter who must Escape a Girl in A U-Boat', '''boat'':21B ''club'':1A ''epic'':4B ''escap'':14B ''girl'':16B ''graffiti'':2A ''hunter'':11B ''must'':13B ''pioneer'':8B ''tale'':5B ''u'':20B ''u-boat'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (161, 'CLUE GRAIL', 'A Taut Tale of a Butler And a Mad Scientist who must Build a Crocodile in Ancient China', '''ancient'':19B ''build'':15B ''butler'':8B ''china'':20B ''clue'':1A ''crocodil'':17B ''grail'':2A ''mad'':11B ''must'':14B ''scientist'':12B ''tale'':5B ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (162, 'CLUELESS BUCKET', 'A Taut Tale of a Car And a Pioneer who must Conquer a Sumo Wrestler in An Abandoned Fun House', '''abandon'':20B ''bucket'':2A ''car'':8B ''clueless'':1A ''conquer'':14B ''fun'':21B ''hous'':22B ''must'':13B ''pioneer'':11B ''sumo'':16B ''tale'':5B ''taut'':4B ''wrestler'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (163, 'CLYDE THEORY', 'A Beautiful Yarn of a Astronaut And a Frisbee who must Overcome a Explorer in A Jet Boat', '''astronaut'':8B ''beauti'':4B ''boat'':20B ''clyde'':1A ''explor'':16B ''frisbe'':11B ''jet'':19B ''must'':13B ''overcom'':14B ''theori'':2A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (164, 'COAST RAINBOW', 'A Astounding Documentary of a Mad Cow And a Pioneer who must Challenge a Butler in The Sahara Desert', '''astound'':4B ''butler'':17B ''challeng'':15B ''coast'':1A ''cow'':9B ''desert'':21B ''documentari'':5B ''mad'':8B ''must'':14B ''pioneer'':12B ''rainbow'':2A ''sahara'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (165, 'COLDBLOODED DARLING', 'A Brilliant Panorama of a Dentist And a Moose who must Find a Student in The Gulf of Mexico', '''brilliant'':4B ''coldblood'':1A ''darl'':2A ''dentist'':8B ''find'':14B ''gulf'':19B ''mexico'':21B ''moos'':11B ''must'':13B ''panorama'':5B ''student'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (166, 'COLOR PHILADELPHIA', 'A Thoughtful Panorama of a Car And a Crocodile who must Sink a Monkey in The Sahara Desert', '''car'':8B ''color'':1A ''crocodil'':11B ''desert'':20B ''monkey'':16B ''must'':13B ''panorama'':5B ''philadelphia'':2A ''sahara'':19B ''sink'':14B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (167, 'COMA HEAD', 'A Awe-Inspiring Drama of a Boy And a Frisbee who must Escape a Pastry Chef in California', '''awe'':5B ''awe-inspir'':4B ''boy'':10B ''california'':21B ''chef'':19B ''coma'':1A ''drama'':7B ''escap'':16B ''frisbe'':13B ''head'':2A ''inspir'':6B ''must'':15B ''pastri'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (168, 'COMANCHEROS ENEMY', 'A Boring Saga of a Lumberjack And a Monkey who must Find a Monkey in The Gulf of Mexico', '''bore'':4B ''comanchero'':1A ''enemi'':2A ''find'':14B ''gulf'':19B ''lumberjack'':8B ''mexico'':21B ''monkey'':11B,16B ''must'':13B ''saga'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (169, 'COMFORTS RUSH', 'A Unbelieveable Panorama of a Pioneer And a Husband who must Meet a Mad Cow in An Abandoned Mine Shaft', '''abandon'':20B ''comfort'':1A ''cow'':17B ''husband'':11B ''mad'':16B ''meet'':14B ''mine'':21B ''must'':13B ''panorama'':5B ''pioneer'':8B ''rush'':2A ''shaft'':22B ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (170, 'COMMAND DARLING', 'A Awe-Inspiring Tale of a Forensic Psychologist And a Woman who must Challenge a Database Administrator in Ancient Japan', '''administr'':20B ''ancient'':22B ''awe'':5B ''awe-inspir'':4B ''challeng'':17B ''command'':1A ''darl'':2A ''databas'':19B ''forens'':10B ''inspir'':6B ''japan'':23B ''must'':16B ''psychologist'':11B ''tale'':7B ''woman'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (171, 'COMMANDMENTS EXPRESS', 'A Fanciful Saga of a Student And a Mad Scientist who must Battle a Hunter in An Abandoned Mine Shaft', '''abandon'':20B ''battl'':15B ''command'':1A ''express'':2A ''fanci'':4B ''hunter'':17B ''mad'':11B ''mine'':21B ''must'':14B ''saga'':5B ''scientist'':12B ''shaft'':22B ''student'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (172, 'CONEHEADS SMOOCHY', 'A Touching Story of a Womanizer And a Composer who must Pursue a Husband in Nigeria', '''compos'':11B ''conehead'':1A ''husband'':16B ''must'':13B ''nigeria'':18B ''pursu'':14B ''smoochi'':2A ''stori'':5B ''touch'':4B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (173, 'CONFESSIONS MAGUIRE', 'A Insightful Story of a Car And a Boy who must Battle a Technical Writer in A Baloon', '''baloon'':20B ''battl'':14B ''boy'':11B ''car'':8B ''confess'':1A ''insight'':4B ''maguir'':2A ''must'':13B ''stori'':5B ''technic'':16B ''writer'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (174, 'CONFIDENTIAL INTERVIEW', 'A Stunning Reflection of a Cat And a Woman who must Find a Astronaut in Ancient Japan', '''ancient'':18B ''astronaut'':16B ''cat'':8B ''confidenti'':1A ''find'':14B ''interview'':2A ''japan'':19B ''must'':13B ''reflect'':5B ''stun'':4B ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (175, 'CONFUSED CANDLES', 'A Stunning Epistle of a Cat And a Forensic Psychologist who must Confront a Pioneer in A Baloon', '''baloon'':20B ''candl'':2A ''cat'':8B ''confront'':15B ''confus'':1A ''epistl'':5B ''forens'':11B ''must'':14B ''pioneer'':17B ''psychologist'':12B ''stun'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (176, 'CONGENIALITY QUEST', 'A Touching Documentary of a Cat And a Pastry Chef who must Find a Lumberjack in A Baloon', '''baloon'':20B ''cat'':8B ''chef'':12B ''congeni'':1A ''documentari'':5B ''find'':15B ''lumberjack'':17B ''must'':14B ''pastri'':11B ''quest'':2A ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (177, 'CONNECTICUT TRAMP', 'A Unbelieveable Drama of a Crocodile And a Mad Cow who must Reach a Dentist in A Shark Tank', '''connecticut'':1A ''cow'':12B ''crocodil'':8B ''dentist'':17B ''drama'':5B ''mad'':11B ''must'':14B ''reach'':15B ''shark'':20B ''tank'':21B ''tramp'':2A ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (178, 'CONNECTION MICROCOSMOS', 'A Fateful Documentary of a Crocodile And a Husband who must Face a Husband in The First Manned Space Station', '''connect'':1A ''crocodil'':8B ''documentari'':5B ''face'':14B ''fate'':4B ''first'':19B ''husband'':11B,16B ''man'':20B ''microcosmo'':2A ''must'':13B ''space'':21B ''station'':22B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (179, 'CONQUERER NUTS', 'A Taut Drama of a Mad Scientist And a Man who must Escape a Pioneer in An Abandoned Mine Shaft', '''abandon'':20B ''conquer'':1A ''drama'':5B ''escap'':15B ''mad'':8B ''man'':12B ''mine'':21B ''must'':14B ''nut'':2A ''pioneer'':17B ''scientist'':9B ''shaft'':22B ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (180, 'CONSPIRACY SPIRIT', 'A Awe-Inspiring Story of a Student And a Frisbee who must Conquer a Crocodile in An Abandoned Mine Shaft', '''abandon'':21B ''awe'':5B ''awe-inspir'':4B ''conquer'':16B ''conspiraci'':1A ''crocodil'':18B ''frisbe'':13B ''inspir'':6B ''mine'':22B ''must'':15B ''shaft'':23B ''spirit'':2A ''stori'':7B ''student'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (181, 'CONTACT ANONYMOUS', 'A Insightful Display of a A Shark And a Monkey who must Face a Database Administrator in Ancient India', '''administr'':18B ''ancient'':20B ''anonym'':2A ''contact'':1A ''databas'':17B ''display'':5B ''face'':15B ''india'':21B ''insight'':4B ''monkey'':12B ''must'':14B ''shark'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (182, 'CONTROL ANTHEM', 'A Fateful Documentary of a Robot And a Student who must Battle a Cat in A Monastery', '''anthem'':2A ''battl'':14B ''cat'':16B ''control'':1A ''documentari'':5B ''fate'':4B ''monasteri'':19B ''must'':13B ''robot'':8B ''student'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (183, 'CONVERSATION DOWNHILL', 'A Taut Character Study of a Husband And a Waitress who must Sink a Squirrel in A MySQL Convention', '''charact'':5B ''convent'':21B ''convers'':1A ''downhil'':2A ''husband'':9B ''must'':14B ''mysql'':20B ''sink'':15B ''squirrel'':17B ''studi'':6B ''taut'':4B ''waitress'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (184, 'CORE SUIT', 'A Unbelieveable Tale of a Car And a Explorer who must Confront a Boat in A Manhattan Penthouse', '''boat'':16B ''car'':8B ''confront'':14B ''core'':1A ''explor'':11B ''manhattan'':19B ''must'':13B ''penthous'':20B ''suit'':2A ''tale'':5B ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (185, 'COWBOY DOOM', 'A Astounding Drama of a Boy And a Lumberjack who must Fight a Butler in A Baloon', '''astound'':4B ''baloon'':19B ''boy'':8B ''butler'':16B ''cowboy'':1A ''doom'':2A ''drama'':5B ''fight'':14B ''lumberjack'':11B ''must'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (186, 'CRAFT OUTFIELD', 'A Lacklusture Display of a Explorer And a Hunter who must Succumb a Database Administrator in A Baloon Factory', '''administr'':17B ''baloon'':20B ''craft'':1A ''databas'':16B ''display'':5B ''explor'':8B ''factori'':21B ''hunter'':11B ''lacklustur'':4B ''must'':13B ''outfield'':2A ''succumb'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (187, 'CRANES RESERVOIR', 'A Fanciful Documentary of a Teacher And a Dog who must Outgun a Forensic Psychologist in A Baloon Factory', '''baloon'':20B ''crane'':1A ''documentari'':5B ''dog'':11B ''factori'':21B ''fanci'':4B ''forens'':16B ''must'':13B ''outgun'':14B ''psychologist'':17B ''reservoir'':2A ''teacher'':8B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (189, 'CREATURES SHAKESPEARE', 'A Emotional Drama of a Womanizer And a Squirrel who must Vanquish a Crocodile in Ancient India', '''ancient'':18B ''creatur'':1A ''crocodil'':16B ''drama'':5B ''emot'':4B ''india'':19B ''must'':13B ''shakespear'':2A ''squirrel'':11B ''vanquish'':14B ''woman'':8B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (191, 'CROOKED FROGMEN', 'A Unbelieveable Drama of a Hunter And a Database Administrator who must Battle a Crocodile in An Abandoned Amusement Park', '''abandon'':20B ''administr'':12B ''amus'':21B ''battl'':15B ''crocodil'':17B ''crook'':1A ''databas'':11B ''drama'':5B ''frogmen'':2A ''hunter'':8B ''must'':14B ''park'':22B ''unbeliev'':4B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (193, 'CROSSROADS CASUALTIES', 'A Intrepid Documentary of a Sumo Wrestler And a Astronaut who must Battle a Composer in The Outback', '''astronaut'':12B ''battl'':15B ''casualti'':2A ''compos'':17B ''crossroad'':1A ''documentari'':5B ''intrepid'':4B ''must'':14B ''outback'':20B ''sumo'':8B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (194, 'CROW GREASE', 'A Awe-Inspiring Documentary of a Woman And a Husband who must Sink a Database Administrator in The First Manned Space Station', '''administr'':19B ''awe'':5B ''awe-inspir'':4B ''crow'':1A ''databas'':18B ''documentari'':7B ''first'':22B ''greas'':2A ''husband'':13B ''inspir'':6B ''man'':23B ''must'':15B ''sink'':16B ''space'':24B ''station'':25B ''woman'':10B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (196, 'CRUELTY UNFORGIVEN', 'A Brilliant Tale of a Car And a Moose who must Battle a Dentist in Nigeria', '''battl'':14B ''brilliant'':4B ''car'':8B ''cruelti'':1A ''dentist'':16B ''moos'':11B ''must'':13B ''nigeria'':18B ''tale'':5B ''unforgiven'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (197, 'CRUSADE HONEY', 'A Fast-Paced Reflection of a Explorer And a Butler who must Battle a Madman in An Abandoned Amusement Park', '''abandon'':21B ''amus'':22B ''battl'':16B ''butler'':13B ''crusad'':1A ''explor'':10B ''fast'':5B ''fast-pac'':4B ''honey'':2A ''madman'':18B ''must'':15B ''pace'':6B ''park'':23B ''reflect'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (198, 'CRYSTAL BREAKING', 'A Fast-Paced Character Study of a Feminist And a Explorer who must Face a Pastry Chef in Ancient Japan', '''ancient'':22B ''break'':2A ''charact'':7B ''chef'':20B ''crystal'':1A ''explor'':14B ''face'':17B ''fast'':5B ''fast-pac'':4B ''feminist'':11B ''japan'':23B ''must'':16B ''pace'':6B ''pastri'':19B ''studi'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (199, 'CUPBOARD SINNERS', 'A Emotional Reflection of a Frisbee And a Boat who must Reach a Pastry Chef in An Abandoned Amusement Park', '''abandon'':20B ''amus'':21B ''boat'':11B ''chef'':17B ''cupboard'':1A ''emot'':4B ''frisbe'':8B ''must'':13B ''park'':22B ''pastri'':16B ''reach'':14B ''reflect'':5B ''sinner'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (200, 'CURTAIN VIDEOTAPE', 'A Boring Reflection of a Dentist And a Mad Cow who must Chase a Secret Agent in A Shark Tank', '''agent'':18B ''bore'':4B ''chase'':15B ''cow'':12B ''curtain'':1A ''dentist'':8B ''mad'':11B ''must'':14B ''reflect'':5B ''secret'':17B ''shark'':21B ''tank'':22B ''videotap'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (201, 'CYCLONE FAMILY', 'A Lacklusture Drama of a Student And a Monkey who must Sink a Womanizer in A MySQL Convention', '''convent'':20B ''cyclon'':1A ''drama'':5B ''famili'':2A ''lacklustur'':4B ''monkey'':11B ''must'':13B ''mysql'':19B ''sink'':14B ''student'':8B ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (202, 'DADDY PITTSBURGH', 'A Epic Story of a A Shark And a Student who must Confront a Explorer in The Gulf of Mexico', '''confront'':15B ''daddi'':1A ''epic'':4B ''explor'':17B ''gulf'':20B ''mexico'':22B ''must'':14B ''pittsburgh'':2A ''shark'':9B ''stori'':5B ''student'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (203, 'DAISY MENAGERIE', 'A Fast-Paced Saga of a Pastry Chef And a Monkey who must Sink a Composer in Ancient India', '''ancient'':21B ''chef'':11B ''compos'':19B ''daisi'':1A ''fast'':5B ''fast-pac'':4B ''india'':22B ''menageri'':2A ''monkey'':14B ''must'':16B ''pace'':6B ''pastri'':10B ''saga'':7B ''sink'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (204, 'DALMATIONS SWEDEN', 'A Emotional Epistle of a Moose And a Hunter who must Overcome a Robot in A Manhattan Penthouse', '''dalmat'':1A ''emot'':4B ''epistl'':5B ''hunter'':11B ''manhattan'':19B ''moos'':8B ''must'':13B ''overcom'':14B ''penthous'':20B ''robot'':16B ''sweden'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (205, 'DANCES NONE', 'A Insightful Reflection of a A Shark And a Dog who must Kill a Butler in An Abandoned Amusement Park', '''abandon'':20B ''amus'':21B ''butler'':17B ''danc'':1A ''dog'':12B ''insight'':4B ''kill'':15B ''must'':14B ''none'':2A ''park'':22B ''reflect'':5B ''shark'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (206, 'DANCING FEVER', 'A Stunning Story of a Explorer And a Forensic Psychologist who must Face a Crocodile in A Shark Tank', '''crocodil'':17B ''danc'':1A ''explor'':8B ''face'':15B ''fever'':2A ''forens'':11B ''must'':14B ''psychologist'':12B ''shark'':20B ''stori'':5B ''stun'':4B ''tank'':21B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (207, 'DANGEROUS UPTOWN', 'A Unbelieveable Story of a Mad Scientist And a Woman who must Overcome a Dog in California', '''california'':19B ''danger'':1A ''dog'':17B ''mad'':8B ''must'':14B ''overcom'':15B ''scientist'':9B ''stori'':5B ''unbeliev'':4B ''uptown'':2A ''woman'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (208, 'DARES PLUTO', 'A Fateful Story of a Robot And a Dentist who must Defeat a Astronaut in New Orleans', '''astronaut'':16B ''dare'':1A ''defeat'':14B ''dentist'':11B ''fate'':4B ''must'':13B ''new'':18B ''orlean'':19B ''pluto'':2A ''robot'':8B ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (209, 'DARKNESS WAR', 'A Touching Documentary of a Husband And a Hunter who must Escape a Boy in The Sahara Desert', '''boy'':16B ''dark'':1A ''desert'':20B ''documentari'':5B ''escap'':14B ''hunter'':11B ''husband'':8B ''must'':13B ''sahara'':19B ''touch'':4B ''war'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (210, 'DARKO DORADO', 'A Stunning Reflection of a Frisbee And a Husband who must Redeem a Dog in New Orleans', '''darko'':1A ''dog'':16B ''dorado'':2A ''frisbe'':8B ''husband'':11B ''must'':13B ''new'':18B ''orlean'':19B ''redeem'':14B ''reflect'':5B ''stun'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (211, 'DARLING BREAKING', 'A Brilliant Documentary of a Astronaut And a Squirrel who must Succumb a Student in The Gulf of Mexico', '''astronaut'':8B ''break'':2A ''brilliant'':4B ''darl'':1A ''documentari'':5B ''gulf'':19B ''mexico'':21B ''must'':13B ''squirrel'':11B ''student'':16B ''succumb'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (212, 'DARN FORRESTER', 'A Fateful Story of a A Shark And a Explorer who must Succumb a Technical Writer in A Jet Boat', '''boat'':22B ''darn'':1A ''explor'':12B ''fate'':4B ''forrest'':2A ''jet'':21B ''must'':14B ''shark'':9B ''stori'':5B ''succumb'':15B ''technic'':17B ''writer'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (214, 'DAUGHTER MADIGAN', 'A Beautiful Tale of a Hunter And a Mad Scientist who must Confront a Squirrel in The First Manned Space Station', '''beauti'':4B ''confront'':15B ''daughter'':1A ''first'':20B ''hunter'':8B ''mad'':11B ''madigan'':2A ''man'':21B ''must'':14B ''scientist'':12B ''space'':22B ''squirrel'':17B ''station'':23B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (215, 'DAWN POND', 'A Thoughtful Documentary of a Dentist And a Forensic Psychologist who must Defeat a Waitress in Berlin', '''berlin'':19B ''dawn'':1A ''defeat'':15B ''dentist'':8B ''documentari'':5B ''forens'':11B ''must'':14B ''pond'':2A ''psychologist'':12B ''thought'':4B ''waitress'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (216, 'DAY UNFAITHFUL', 'A Stunning Documentary of a Composer And a Mad Scientist who must Find a Technical Writer in A U-Boat', '''boat'':23B ''compos'':8B ''day'':1A ''documentari'':5B ''find'':15B ''mad'':11B ''must'':14B ''scientist'':12B ''stun'':4B ''technic'':17B ''u'':22B ''u-boat'':21B ''unfaith'':2A ''writer'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (217, 'DAZED PUNK', 'A Action-Packed Story of a Pioneer And a Technical Writer who must Discover a Forensic Psychologist in An Abandoned Amusement Park', '''abandon'':23B ''action'':5B ''action-pack'':4B ''amus'':24B ''daze'':1A ''discov'':17B ''forens'':19B ''must'':16B ''pack'':6B ''park'':25B ''pioneer'':10B ''psychologist'':20B ''punk'':2A ''stori'':7B ''technic'':13B ''writer'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (218, 'DECEIVER BETRAYED', 'A Taut Story of a Moose And a Squirrel who must Build a Husband in Ancient India', '''ancient'':18B ''betray'':2A ''build'':14B ''deceiv'':1A ''husband'':16B ''india'':19B ''moos'':8B ''must'':13B ''squirrel'':11B ''stori'':5B ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (219, 'DEEP CRUSADE', 'A Amazing Tale of a Crocodile And a Squirrel who must Discover a Composer in Australia', '''amaz'':4B ''australia'':18B ''compos'':16B ''crocodil'':8B ''crusad'':2A ''deep'':1A ''discov'':14B ''must'':13B ''squirrel'':11B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (672, 'PERFECT GROOVE', 'A Thrilling Yarn of a Dog And a Dog who must Build a Husband in A Baloon', '''baloon'':19B ''build'':14B ''dog'':8B,11B ''groov'':2A ''husband'':16B ''must'':13B ''perfect'':1A ''thrill'':4B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (220, 'DEER VIRGINIAN', 'A Thoughtful Story of a Mad Cow And a Womanizer who must Overcome a Mad Scientist in Soviet Georgia', '''cow'':9B ''deer'':1A ''georgia'':21B ''mad'':8B,17B ''must'':14B ''overcom'':15B ''scientist'':18B ''soviet'':20B ''stori'':5B ''thought'':4B ''virginian'':2A ''woman'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (221, 'DELIVERANCE MULHOLLAND', 'A Astounding Saga of a Monkey And a Moose who must Conquer a Butler in A Shark Tank', '''astound'':4B ''butler'':16B ''conquer'':14B ''deliver'':1A ''monkey'':8B ''moos'':11B ''mulholland'':2A ''must'':13B ''saga'':5B ''shark'':19B ''tank'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (222, 'DESERT POSEIDON', 'A Brilliant Documentary of a Butler And a Frisbee who must Build a Astronaut in New Orleans', '''astronaut'':16B ''brilliant'':4B ''build'':14B ''butler'':8B ''desert'':1A ''documentari'':5B ''frisbe'':11B ''must'':13B ''new'':18B ''orlean'':19B ''poseidon'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (223, 'DESIRE ALIEN', 'A Fast-Paced Tale of a Dog And a Forensic Psychologist who must Meet a Astronaut in The First Manned Space Station', '''alien'':2A ''astronaut'':19B ''desir'':1A ''dog'':10B ''fast'':5B ''fast-pac'':4B ''first'':22B ''forens'':13B ''man'':23B ''meet'':17B ''must'':16B ''pace'':6B ''psychologist'':14B ''space'':24B ''station'':25B ''tale'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (224, 'DESPERATE TRAINSPOTTING', 'A Epic Yarn of a Forensic Psychologist And a Teacher who must Face a Lumberjack in California', '''california'':19B ''desper'':1A ''epic'':4B ''face'':15B ''forens'':8B ''lumberjack'':17B ''must'':14B ''psychologist'':9B ''teacher'':12B ''trainspot'':2A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (225, 'DESTINATION JERK', 'A Beautiful Yarn of a Teacher And a Cat who must Build a Car in A U-Boat', '''beauti'':4B ''boat'':21B ''build'':14B ''car'':16B ''cat'':11B ''destin'':1A ''jerk'':2A ''must'':13B ''teacher'':8B ''u'':20B ''u-boat'':19B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (226, 'DESTINY SATURDAY', 'A Touching Drama of a Crocodile And a Crocodile who must Conquer a Explorer in Soviet Georgia', '''conquer'':14B ''crocodil'':8B,11B ''destini'':1A ''drama'':5B ''explor'':16B ''georgia'':19B ''must'':13B ''saturday'':2A ''soviet'':18B ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (227, 'DETAILS PACKER', 'A Epic Saga of a Waitress And a Composer who must Face a Boat in A U-Boat', '''boat'':16B,21B ''compos'':11B ''detail'':1A ''epic'':4B ''face'':14B ''must'':13B ''packer'':2A ''saga'':5B ''u'':20B ''u-boat'':19B ''waitress'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (228, 'DETECTIVE VISION', 'A Fanciful Documentary of a Pioneer And a Woman who must Redeem a Hunter in Ancient Japan', '''ancient'':18B ''detect'':1A ''documentari'':5B ''fanci'':4B ''hunter'':16B ''japan'':19B ''must'':13B ''pioneer'':8B ''redeem'':14B ''vision'':2A ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (229, 'DEVIL DESIRE', 'A Beautiful Reflection of a Monkey And a Dentist who must Face a Database Administrator in Ancient Japan', '''administr'':17B ''ancient'':19B ''beauti'':4B ''databas'':16B ''dentist'':11B ''desir'':2A ''devil'':1A ''face'':14B ''japan'':20B ''monkey'':8B ''must'':13B ''reflect'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (230, 'DIARY PANIC', 'A Thoughtful Character Study of a Frisbee And a Mad Cow who must Outgun a Man in Ancient India', '''ancient'':20B ''charact'':5B ''cow'':13B ''diari'':1A ''frisbe'':9B ''india'':21B ''mad'':12B ''man'':18B ''must'':15B ''outgun'':16B ''panic'':2A ''studi'':6B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (231, 'DINOSAUR SECRETARY', 'A Action-Packed Drama of a Feminist And a Girl who must Reach a Robot in The Canadian Rockies', '''action'':5B ''action-pack'':4B ''canadian'':21B ''dinosaur'':1A ''drama'':7B ''feminist'':10B ''girl'':13B ''must'':15B ''pack'':6B ''reach'':16B ''robot'':18B ''rocki'':22B ''secretari'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (232, 'DIRTY ACE', 'A Action-Packed Character Study of a Forensic Psychologist And a Girl who must Build a Dentist in The Outback', '''ace'':2A ''action'':5B ''action-pack'':4B ''build'':18B ''charact'':7B ''dentist'':20B ''dirti'':1A ''forens'':11B ''girl'':15B ''must'':17B ''outback'':23B ''pack'':6B ''psychologist'':12B ''studi'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (233, 'DISCIPLE MOTHER', 'A Touching Reflection of a Mad Scientist And a Boat who must Face a Moose in A Shark Tank', '''boat'':12B ''discipl'':1A ''face'':15B ''mad'':8B ''moos'':17B ''mother'':2A ''must'':14B ''reflect'':5B ''scientist'':9B ''shark'':20B ''tank'':21B ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (234, 'DISTURBING SCARFACE', 'A Lacklusture Display of a Crocodile And a Butler who must Overcome a Monkey in A U-Boat', '''boat'':21B ''butler'':11B ''crocodil'':8B ''display'':5B ''disturb'':1A ''lacklustur'':4B ''monkey'':16B ''must'':13B ''overcom'':14B ''scarfac'':2A ''u'':20B ''u-boat'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (235, 'DIVIDE MONSTER', 'A Intrepid Saga of a Man And a Forensic Psychologist who must Reach a Squirrel in A Monastery', '''divid'':1A ''forens'':11B ''intrepid'':4B ''man'':8B ''monasteri'':20B ''monster'':2A ''must'':14B ''psychologist'':12B ''reach'':15B ''saga'':5B ''squirrel'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (236, 'DIVINE RESURRECTION', 'A Boring Character Study of a Man And a Womanizer who must Succumb a Teacher in An Abandoned Amusement Park', '''abandon'':20B ''amus'':21B ''bore'':4B ''charact'':5B ''divin'':1A ''man'':9B ''must'':14B ''park'':22B ''resurrect'':2A ''studi'':6B ''succumb'':15B ''teacher'':17B ''woman'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (237, 'DIVORCE SHINING', 'A Unbelieveable Saga of a Crocodile And a Student who must Discover a Cat in Ancient India', '''ancient'':18B ''cat'':16B ''crocodil'':8B ''discov'':14B ''divorc'':1A ''india'':19B ''must'':13B ''saga'':5B ''shine'':2A ''student'':11B ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (238, 'DOCTOR GRAIL', 'A Insightful Drama of a Womanizer And a Waitress who must Reach a Forensic Psychologist in The Outback', '''doctor'':1A ''drama'':5B ''forens'':16B ''grail'':2A ''insight'':4B ''must'':13B ''outback'':20B ''psychologist'':17B ''reach'':14B ''waitress'':11B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (239, 'DOGMA FAMILY', 'A Brilliant Character Study of a Database Administrator And a Monkey who must Succumb a Astronaut in New Orleans', '''administr'':10B ''astronaut'':18B ''brilliant'':4B ''charact'':5B ''databas'':9B ''dogma'':1A ''famili'':2A ''monkey'':13B ''must'':15B ''new'':20B ''orlean'':21B ''studi'':6B ''succumb'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (240, 'DOLLS RAGE', 'A Thrilling Display of a Pioneer And a Frisbee who must Escape a Teacher in The Outback', '''display'':5B ''doll'':1A ''escap'':14B ''frisbe'':11B ''must'':13B ''outback'':19B ''pioneer'':8B ''rage'':2A ''teacher'':16B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (241, 'DONNIE ALLEY', 'A Awe-Inspiring Tale of a Butler And a Frisbee who must Vanquish a Teacher in Ancient Japan', '''alley'':2A ''ancient'':20B ''awe'':5B ''awe-inspir'':4B ''butler'':10B ''donni'':1A ''frisbe'':13B ''inspir'':6B ''japan'':21B ''must'':15B ''tale'':7B ''teacher'':18B ''vanquish'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (242, 'DOOM DANCING', 'A Astounding Panorama of a Car And a Mad Scientist who must Battle a Lumberjack in A MySQL Convention', '''astound'':4B ''battl'':15B ''car'':8B ''convent'':21B ''danc'':2A ''doom'':1A ''lumberjack'':17B ''mad'':11B ''must'':14B ''mysql'':20B ''panorama'':5B ''scientist'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (243, 'DOORS PRESIDENT', 'A Awe-Inspiring Display of a Squirrel And a Woman who must Overcome a Boy in The Gulf of Mexico', '''awe'':5B ''awe-inspir'':4B ''boy'':18B ''display'':7B ''door'':1A ''gulf'':21B ''inspir'':6B ''mexico'':23B ''must'':15B ''overcom'':16B ''presid'':2A ''squirrel'':10B ''woman'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (244, 'DORADO NOTTING', 'A Action-Packed Tale of a Sumo Wrestler And a A Shark who must Meet a Frisbee in California', '''action'':5B ''action-pack'':4B ''california'':22B ''dorado'':1A ''frisbe'':20B ''meet'':18B ''must'':17B ''not'':2A ''pack'':6B ''shark'':15B ''sumo'':10B ''tale'':7B ''wrestler'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (245, 'DOUBLE WRATH', 'A Thoughtful Yarn of a Womanizer And a Dog who must Challenge a Madman in The Gulf of Mexico', '''challeng'':14B ''dog'':11B ''doubl'':1A ''gulf'':19B ''madman'':16B ''mexico'':21B ''must'':13B ''thought'':4B ''woman'':8B ''wrath'':2A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (246, 'DOUBTFIRE LABYRINTH', 'A Intrepid Panorama of a Butler And a Composer who must Meet a Mad Cow in The Sahara Desert', '''butler'':8B ''compos'':11B ''cow'':17B ''desert'':21B ''doubtfir'':1A ''intrepid'':4B ''labyrinth'':2A ''mad'':16B ''meet'':14B ''must'':13B ''panorama'':5B ''sahara'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (247, 'DOWNHILL ENOUGH', 'A Emotional Tale of a Pastry Chef And a Forensic Psychologist who must Succumb a Monkey in The Sahara Desert', '''chef'':9B ''desert'':22B ''downhil'':1A ''emot'':4B ''enough'':2A ''forens'':12B ''monkey'':18B ''must'':15B ''pastri'':8B ''psychologist'':13B ''sahara'':21B ''succumb'':16B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (248, 'DOZEN LION', 'A Taut Drama of a Cat And a Girl who must Defeat a Frisbee in The Canadian Rockies', '''canadian'':19B ''cat'':8B ''defeat'':14B ''dozen'':1A ''drama'':5B ''frisbe'':16B ''girl'':11B ''lion'':2A ''must'':13B ''rocki'':20B ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (249, 'DRACULA CRYSTAL', 'A Thrilling Reflection of a Feminist And a Cat who must Find a Frisbee in An Abandoned Fun House', '''abandon'':19B ''cat'':11B ''crystal'':2A ''dracula'':1A ''feminist'':8B ''find'':14B ''frisbe'':16B ''fun'':20B ''hous'':21B ''must'':13B ''reflect'':5B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (250, 'DRAGON SQUAD', 'A Taut Reflection of a Boy And a Waitress who must Outgun a Teacher in Ancient China', '''ancient'':18B ''boy'':8B ''china'':19B ''dragon'':1A ''must'':13B ''outgun'':14B ''reflect'':5B ''squad'':2A ''taut'':4B ''teacher'':16B ''waitress'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (251, 'DRAGONFLY STRANGERS', 'A Boring Documentary of a Pioneer And a Man who must Vanquish a Man in Nigeria', '''bore'':4B ''documentari'':5B ''dragonfli'':1A ''man'':11B,16B ''must'':13B ''nigeria'':18B ''pioneer'':8B ''stranger'':2A ''vanquish'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (252, 'DREAM PICKUP', 'A Epic Display of a Car And a Composer who must Overcome a Forensic Psychologist in The Gulf of Mexico', '''car'':8B ''compos'':11B ''display'':5B ''dream'':1A ''epic'':4B ''forens'':16B ''gulf'':20B ''mexico'':22B ''must'':13B ''overcom'':14B ''pickup'':2A ''psychologist'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (253, 'DRIFTER COMMANDMENTS', 'A Epic Reflection of a Womanizer And a Squirrel who must Discover a Husband in A Jet Boat', '''boat'':20B ''command'':2A ''discov'':14B ''drifter'':1A ''epic'':4B ''husband'':16B ''jet'':19B ''must'':13B ''reflect'':5B ''squirrel'':11B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (254, 'DRIVER ANNIE', 'A Lacklusture Character Study of a Butler And a Car who must Redeem a Boat in An Abandoned Fun House', '''abandon'':20B ''anni'':2A ''boat'':17B ''butler'':9B ''car'':12B ''charact'':5B ''driver'':1A ''fun'':21B ''hous'':22B ''lacklustur'':4B ''must'':14B ''redeem'':15B ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (255, 'DRIVING POLISH', 'A Action-Packed Yarn of a Feminist And a Technical Writer who must Sink a Boat in An Abandoned Mine Shaft', '''abandon'':22B ''action'':5B ''action-pack'':4B ''boat'':19B ''drive'':1A ''feminist'':10B ''mine'':23B ''must'':16B ''pack'':6B ''polish'':2A ''shaft'':24B ''sink'':17B ''technic'':13B ''writer'':14B ''yarn'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (256, 'DROP WATERFRONT', 'A Fanciful Documentary of a Husband And a Explorer who must Reach a Madman in Ancient China', '''ancient'':18B ''china'':19B ''documentari'':5B ''drop'':1A ''explor'':11B ''fanci'':4B ''husband'':8B ''madman'':16B ''must'':13B ''reach'':14B ''waterfront'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (257, 'DRUMLINE CYCLONE', 'A Insightful Panorama of a Monkey And a Sumo Wrestler who must Outrace a Mad Scientist in The Canadian Rockies', '''canadian'':21B ''cyclon'':2A ''drumlin'':1A ''insight'':4B ''mad'':17B ''monkey'':8B ''must'':14B ''outrac'':15B ''panorama'':5B ''rocki'':22B ''scientist'':18B ''sumo'':11B ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (258, 'DRUMS DYNAMITE', 'A Epic Display of a Crocodile And a Crocodile who must Confront a Dog in An Abandoned Amusement Park', '''abandon'':19B ''amus'':20B ''confront'':14B ''crocodil'':8B,11B ''display'':5B ''dog'':16B ''drum'':1A ''dynamit'':2A ''epic'':4B ''must'':13B ''park'':21B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (259, 'DUCK RACER', 'A Lacklusture Yarn of a Teacher And a Squirrel who must Overcome a Dog in A Shark Tank', '''dog'':16B ''duck'':1A ''lacklustur'':4B ''must'':13B ''overcom'':14B ''racer'':2A ''shark'':19B ''squirrel'':11B ''tank'':20B ''teacher'':8B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (260, 'DUDE BLINDNESS', 'A Stunning Reflection of a Husband And a Lumberjack who must Face a Frisbee in An Abandoned Fun House', '''abandon'':19B ''blind'':2A ''dude'':1A ''face'':14B ''frisbe'':16B ''fun'':20B ''hous'':21B ''husband'':8B ''lumberjack'':11B ''must'':13B ''reflect'':5B ''stun'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (261, 'DUFFEL APOCALYPSE', 'A Emotional Display of a Boat And a Explorer who must Challenge a Madman in A MySQL Convention', '''apocalyps'':2A ''boat'':8B ''challeng'':14B ''convent'':20B ''display'':5B ''duffel'':1A ''emot'':4B ''explor'':11B ''madman'':16B ''must'':13B ''mysql'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (262, 'DUMBO LUST', 'A Touching Display of a Feminist And a Dentist who must Conquer a Husband in The Gulf of Mexico', '''conquer'':14B ''dentist'':11B ''display'':5B ''dumbo'':1A ''feminist'':8B ''gulf'':19B ''husband'':16B ''lust'':2A ''mexico'':21B ''must'':13B ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (263, 'DURHAM PANKY', 'A Brilliant Panorama of a Girl And a Boy who must Face a Mad Scientist in An Abandoned Mine Shaft', '''abandon'':20B ''boy'':11B ''brilliant'':4B ''durham'':1A ''face'':14B ''girl'':8B ''mad'':16B ''mine'':21B ''must'':13B ''panki'':2A ''panorama'':5B ''scientist'':17B ''shaft'':22B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (264, 'DWARFS ALTER', 'A Emotional Yarn of a Girl And a Dog who must Challenge a Composer in Ancient Japan', '''alter'':2A ''ancient'':18B ''challeng'':14B ''compos'':16B ''dog'':11B ''dwarf'':1A ''emot'':4B ''girl'':8B ''japan'':19B ''must'':13B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (265, 'DYING MAKER', 'A Intrepid Tale of a Boat And a Monkey who must Kill a Cat in California', '''boat'':8B ''california'':18B ''cat'':16B ''die'':1A ''intrepid'':4B ''kill'':14B ''maker'':2A ''monkey'':11B ''must'':13B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (266, 'DYNAMITE TARZAN', 'A Intrepid Documentary of a Forensic Psychologist And a Mad Scientist who must Face a Explorer in A U-Boat', '''boat'':23B ''documentari'':5B ''dynamit'':1A ''explor'':18B ''face'':16B ''forens'':8B ''intrepid'':4B ''mad'':12B ''must'':15B ''psychologist'':9B ''scientist'':13B ''tarzan'':2A ''u'':22B ''u-boat'':21B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (267, 'EAGLES PANKY', 'A Thoughtful Story of a Car And a Boy who must Find a A Shark in The Sahara Desert', '''boy'':11B ''car'':8B ''desert'':21B ''eagl'':1A ''find'':14B ''must'':13B ''panki'':2A ''sahara'':20B ''shark'':17B ''stori'':5B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (268, 'EARLY HOME', 'A Amazing Panorama of a Mad Scientist And a Husband who must Meet a Woman in The Outback', '''amaz'':4B ''earli'':1A ''home'':2A ''husband'':12B ''mad'':8B ''meet'':15B ''must'':14B ''outback'':20B ''panorama'':5B ''scientist'':9B ''woman'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (269, 'EARRING INSTINCT', 'A Stunning Character Study of a Dentist And a Mad Cow who must Find a Teacher in Nigeria', '''charact'':5B ''cow'':13B ''dentist'':9B ''earring'':1A ''find'':16B ''instinct'':2A ''mad'':12B ''must'':15B ''nigeria'':20B ''studi'':6B ''stun'':4B ''teacher'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (270, 'EARTH VISION', 'A Stunning Drama of a Butler And a Madman who must Outrace a Womanizer in Ancient India', '''ancient'':18B ''butler'':8B ''drama'':5B ''earth'':1A ''india'':19B ''madman'':11B ''must'':13B ''outrac'':14B ''stun'':4B ''vision'':2A ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (271, 'EASY GLADIATOR', 'A Fateful Story of a Monkey And a Girl who must Overcome a Pastry Chef in Ancient India', '''ancient'':19B ''chef'':17B ''easi'':1A ''fate'':4B ''girl'':11B ''gladiat'':2A ''india'':20B ''monkey'':8B ''must'':13B ''overcom'':14B ''pastri'':16B ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (272, 'EDGE KISSING', 'A Beautiful Yarn of a Composer And a Mad Cow who must Redeem a Mad Scientist in A Jet Boat', '''beauti'':4B ''boat'':22B ''compos'':8B ''cow'':12B ''edg'':1A ''jet'':21B ''kiss'':2A ''mad'':11B,17B ''must'':14B ''redeem'':15B ''scientist'':18B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (273, 'EFFECT GLADIATOR', 'A Beautiful Display of a Pastry Chef And a Pastry Chef who must Outgun a Forensic Psychologist in A Manhattan Penthouse', '''beauti'':4B ''chef'':9B,13B ''display'':5B ''effect'':1A ''forens'':18B ''gladiat'':2A ''manhattan'':22B ''must'':15B ''outgun'':16B ''pastri'':8B,12B ''penthous'':23B ''psychologist'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (274, 'EGG IGBY', 'A Beautiful Documentary of a Boat And a Sumo Wrestler who must Succumb a Database Administrator in The First Manned Space Station', '''administr'':18B ''beauti'':4B ''boat'':8B ''databas'':17B ''documentari'':5B ''egg'':1A ''first'':21B ''igbi'':2A ''man'':22B ''must'':14B ''space'':23B ''station'':24B ''succumb'':15B ''sumo'':11B ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (275, 'EGYPT TENENBAUMS', 'A Intrepid Story of a Madman And a Secret Agent who must Outrace a Astronaut in An Abandoned Amusement Park', '''abandon'':20B ''agent'':12B ''amus'':21B ''astronaut'':17B ''egypt'':1A ''intrepid'':4B ''madman'':8B ''must'':14B ''outrac'':15B ''park'':22B ''secret'':11B ''stori'':5B ''tenenbaum'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (276, 'ELEMENT FREDDY', 'A Awe-Inspiring Reflection of a Waitress And a Squirrel who must Kill a Mad Cow in A Jet Boat', '''awe'':5B ''awe-inspir'':4B ''boat'':23B ''cow'':19B ''element'':1A ''freddi'':2A ''inspir'':6B ''jet'':22B ''kill'':16B ''mad'':18B ''must'':15B ''reflect'':7B ''squirrel'':13B ''waitress'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (277, 'ELEPHANT TROJAN', 'A Beautiful Panorama of a Lumberjack And a Forensic Psychologist who must Overcome a Frisbee in A Baloon', '''baloon'':20B ''beauti'':4B ''eleph'':1A ''forens'':11B ''frisbe'':17B ''lumberjack'':8B ''must'':14B ''overcom'':15B ''panorama'':5B ''psychologist'':12B ''trojan'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (278, 'ELF MURDER', 'A Action-Packed Story of a Frisbee And a Woman who must Reach a Girl in An Abandoned Mine Shaft', '''abandon'':21B ''action'':5B ''action-pack'':4B ''elf'':1A ''frisbe'':10B ''girl'':18B ''mine'':22B ''murder'':2A ''must'':15B ''pack'':6B ''reach'':16B ''shaft'':23B ''stori'':7B ''woman'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (279, 'ELIZABETH SHANE', 'A Lacklusture Display of a Womanizer And a Dog who must Face a Sumo Wrestler in Ancient Japan', '''ancient'':19B ''display'':5B ''dog'':11B ''elizabeth'':1A ''face'':14B ''japan'':20B ''lacklustur'':4B ''must'':13B ''shane'':2A ''sumo'':16B ''woman'':8B ''wrestler'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (280, 'EMPIRE MALKOVICH', 'A Amazing Story of a Feminist And a Cat who must Face a Car in An Abandoned Fun House', '''abandon'':19B ''amaz'':4B ''car'':16B ''cat'':11B ''empir'':1A ''face'':14B ''feminist'':8B ''fun'':20B ''hous'':21B ''malkovich'':2A ''must'':13B ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (281, 'ENCINO ELF', 'A Astounding Drama of a Feminist And a Teacher who must Confront a Husband in A Baloon', '''astound'':4B ''baloon'':19B ''confront'':14B ''drama'':5B ''elf'':2A ''encino'':1A ''feminist'':8B ''husband'':16B ''must'':13B ''teacher'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (282, 'ENCOUNTERS CURTAIN', 'A Insightful Epistle of a Pastry Chef And a Womanizer who must Build a Boat in New Orleans', '''boat'':17B ''build'':15B ''chef'':9B ''curtain'':2A ''encount'':1A ''epistl'':5B ''insight'':4B ''must'':14B ''new'':19B ''orlean'':20B ''pastri'':8B ''woman'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (283, 'ENDING CROWDS', 'A Unbelieveable Display of a Dentist And a Madman who must Vanquish a Squirrel in Berlin', '''berlin'':18B ''crowd'':2A ''dentist'':8B ''display'':5B ''end'':1A ''madman'':11B ''must'':13B ''squirrel'':16B ''unbeliev'':4B ''vanquish'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (284, 'ENEMY ODDS', 'A Fanciful Panorama of a Mad Scientist And a Woman who must Pursue a Astronaut in Ancient India', '''ancient'':19B ''astronaut'':17B ''enemi'':1A ''fanci'':4B ''india'':20B ''mad'':8B ''must'':14B ''odd'':2A ''panorama'':5B ''pursu'':15B ''scientist'':9B ''woman'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (285, 'ENGLISH BULWORTH', 'A Intrepid Epistle of a Pastry Chef And a Pastry Chef who must Pursue a Crocodile in Ancient China', '''ancient'':20B ''bulworth'':2A ''chef'':9B,13B ''china'':21B ''crocodil'':18B ''english'':1A ''epistl'':5B ''intrepid'':4B ''must'':15B ''pastri'':8B,12B ''pursu'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (286, 'ENOUGH RAGING', 'A Astounding Character Study of a Boat And a Secret Agent who must Find a Mad Cow in The Sahara Desert', '''agent'':13B ''astound'':4B ''boat'':9B ''charact'':5B ''cow'':19B ''desert'':23B ''enough'':1A ''find'':16B ''mad'':18B ''must'':15B ''rage'':2A ''sahara'':22B ''secret'':12B ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (287, 'ENTRAPMENT SATISFACTION', 'A Thoughtful Panorama of a Hunter And a Teacher who must Reach a Mad Cow in A U-Boat', '''boat'':22B ''cow'':17B ''entrap'':1A ''hunter'':8B ''mad'':16B ''must'':13B ''panorama'':5B ''reach'':14B ''satisfact'':2A ''teacher'':11B ''thought'':4B ''u'':21B ''u-boat'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (288, 'ESCAPE METROPOLIS', 'A Taut Yarn of a Astronaut And a Technical Writer who must Outgun a Boat in New Orleans', '''astronaut'':8B ''boat'':17B ''escap'':1A ''metropoli'':2A ''must'':14B ''new'':19B ''orlean'':20B ''outgun'':15B ''taut'':4B ''technic'':11B ''writer'':12B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (289, 'EVE RESURRECTION', 'A Awe-Inspiring Yarn of a Pastry Chef And a Database Administrator who must Challenge a Teacher in A Baloon', '''administr'':15B ''awe'':5B ''awe-inspir'':4B ''baloon'':23B ''challeng'':18B ''chef'':11B ''databas'':14B ''eve'':1A ''inspir'':6B ''must'':17B ''pastri'':10B ''resurrect'':2A ''teacher'':20B ''yarn'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (290, 'EVERYONE CRAFT', 'A Fateful Display of a Waitress And a Dentist who must Reach a Butler in Nigeria', '''butler'':16B ''craft'':2A ''dentist'':11B ''display'':5B ''everyon'':1A ''fate'':4B ''must'':13B ''nigeria'':18B ''reach'':14B ''waitress'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (291, 'EVOLUTION ALTER', 'A Fanciful Character Study of a Feminist And a Madman who must Find a Explorer in A Baloon Factory', '''alter'':2A ''baloon'':20B ''charact'':5B ''evolut'':1A ''explor'':17B ''factori'':21B ''fanci'':4B ''feminist'':9B ''find'':15B ''madman'':12B ''must'':14B ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (292, 'EXCITEMENT EVE', 'A Brilliant Documentary of a Monkey And a Car who must Conquer a Crocodile in A Shark Tank', '''brilliant'':4B ''car'':11B ''conquer'':14B ''crocodil'':16B ''documentari'':5B ''eve'':2A ''excit'':1A ''monkey'':8B ''must'':13B ''shark'':19B ''tank'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (293, 'EXORCIST STING', 'A Touching Drama of a Dog And a Sumo Wrestler who must Conquer a Mad Scientist in Berlin', '''berlin'':20B ''conquer'':15B ''dog'':8B ''drama'':5B ''exorcist'':1A ''mad'':17B ''must'':14B ''scientist'':18B ''sting'':2A ''sumo'':11B ''touch'':4B ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (294, 'EXPECATIONS NATURAL', 'A Amazing Drama of a Butler And a Husband who must Reach a A Shark in A U-Boat', '''amaz'':4B ''boat'':22B ''butler'':8B ''drama'':5B ''expec'':1A ''husband'':11B ''must'':13B ''natur'':2A ''reach'':14B ''shark'':17B ''u'':21B ''u-boat'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (295, 'EXPENDABLE STALLION', 'A Amazing Character Study of a Mad Cow And a Squirrel who must Discover a Hunter in A U-Boat', '''amaz'':4B ''boat'':23B ''charact'':5B ''cow'':10B ''discov'':16B ''expend'':1A ''hunter'':18B ''mad'':9B ''must'':15B ''squirrel'':13B ''stallion'':2A ''studi'':6B ''u'':22B ''u-boat'':21B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (296, 'EXPRESS LONELY', 'A Boring Drama of a Astronaut And a Boat who must Face a Boat in California', '''astronaut'':8B ''boat'':11B,16B ''bore'':4B ''california'':18B ''drama'':5B ''express'':1A ''face'':14B ''lone'':2A ''must'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (297, 'EXTRAORDINARY CONQUERER', 'A Stunning Story of a Dog And a Feminist who must Face a Forensic Psychologist in Berlin', '''berlin'':19B ''conquer'':2A ''dog'':8B ''extraordinari'':1A ''face'':14B ''feminist'':11B ''forens'':16B ''must'':13B ''psychologist'':17B ''stori'':5B ''stun'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (298, 'EYES DRIVING', 'A Thrilling Story of a Cat And a Waitress who must Fight a Explorer in The Outback', '''cat'':8B ''drive'':2A ''explor'':16B ''eye'':1A ''fight'':14B ''must'':13B ''outback'':19B ''stori'':5B ''thrill'':4B ''waitress'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (299, 'FACTORY DRAGON', 'A Action-Packed Saga of a Teacher And a Frisbee who must Escape a Lumberjack in The Sahara Desert', '''action'':5B ''action-pack'':4B ''desert'':22B ''dragon'':2A ''escap'':16B ''factori'':1A ''frisbe'':13B ''lumberjack'':18B ''must'':15B ''pack'':6B ''saga'':7B ''sahara'':21B ''teacher'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (300, 'FALCON VOLUME', 'A Fateful Saga of a Sumo Wrestler And a Hunter who must Redeem a A Shark in New Orleans', '''falcon'':1A ''fate'':4B ''hunter'':12B ''must'':14B ''new'':20B ''orlean'':21B ''redeem'':15B ''saga'':5B ''shark'':18B ''sumo'':8B ''volum'':2A ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (301, 'FAMILY SWEET', 'A Epic Documentary of a Teacher And a Boy who must Escape a Woman in Berlin', '''berlin'':18B ''boy'':11B ''documentari'':5B ''epic'':4B ''escap'':14B ''famili'':1A ''must'':13B ''sweet'':2A ''teacher'':8B ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (302, 'FANTASIA PARK', 'A Thoughtful Documentary of a Mad Scientist And a A Shark who must Outrace a Feminist in Australia', '''australia'':20B ''documentari'':5B ''fantasia'':1A ''feminist'':18B ''mad'':8B ''must'':15B ''outrac'':16B ''park'':2A ''scientist'':9B ''shark'':13B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (303, 'FANTASY TROOPERS', 'A Touching Saga of a Teacher And a Monkey who must Overcome a Secret Agent in A MySQL Convention', '''agent'':17B ''convent'':21B ''fantasi'':1A ''monkey'':11B ''must'':13B ''mysql'':20B ''overcom'':14B ''saga'':5B ''secret'':16B ''teacher'':8B ''touch'':4B ''trooper'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (304, 'FARGO GANDHI', 'A Thrilling Reflection of a Pastry Chef And a Crocodile who must Reach a Teacher in The Outback', '''chef'':9B ''crocodil'':12B ''fargo'':1A ''gandhi'':2A ''must'':14B ''outback'':20B ''pastri'':8B ''reach'':15B ''reflect'':5B ''teacher'':17B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (305, 'FATAL HAUNTED', 'A Beautiful Drama of a Student And a Secret Agent who must Confront a Dentist in Ancient Japan', '''agent'':12B ''ancient'':19B ''beauti'':4B ''confront'':15B ''dentist'':17B ''drama'':5B ''fatal'':1A ''haunt'':2A ''japan'':20B ''must'':14B ''secret'':11B ''student'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (306, 'FEATHERS METAL', 'A Thoughtful Yarn of a Monkey And a Teacher who must Find a Dog in Australia', '''australia'':18B ''dog'':16B ''feather'':1A ''find'':14B ''metal'':2A ''monkey'':8B ''must'':13B ''teacher'':11B ''thought'':4B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (307, 'FELLOWSHIP AUTUMN', 'A Lacklusture Reflection of a Dentist And a Hunter who must Meet a Teacher in A Baloon', '''autumn'':2A ''baloon'':19B ''dentist'':8B ''fellowship'':1A ''hunter'':11B ''lacklustur'':4B ''meet'':14B ''must'':13B ''reflect'':5B ''teacher'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (308, 'FERRIS MOTHER', 'A Touching Display of a Frisbee And a Frisbee who must Kill a Girl in The Gulf of Mexico', '''display'':5B ''ferri'':1A ''frisbe'':8B,11B ''girl'':16B ''gulf'':19B ''kill'':14B ''mexico'':21B ''mother'':2A ''must'':13B ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (309, 'FEUD FROGMEN', 'A Brilliant Reflection of a Database Administrator And a Mad Cow who must Chase a Woman in The Canadian Rockies', '''administr'':9B ''brilliant'':4B ''canadian'':21B ''chase'':16B ''cow'':13B ''databas'':8B ''feud'':1A ''frogmen'':2A ''mad'':12B ''must'':15B ''reflect'':5B ''rocki'':22B ''woman'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (310, 'FEVER EMPIRE', 'A Insightful Panorama of a Cat And a Boat who must Defeat a Boat in The Gulf of Mexico', '''boat'':11B,16B ''cat'':8B ''defeat'':14B ''empir'':2A ''fever'':1A ''gulf'':19B ''insight'':4B ''mexico'':21B ''must'':13B ''panorama'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (311, 'FICTION CHRISTMAS', 'A Emotional Yarn of a A Shark And a Student who must Battle a Robot in An Abandoned Mine Shaft', '''abandon'':20B ''battl'':15B ''christma'':2A ''emot'':4B ''fiction'':1A ''mine'':21B ''must'':14B ''robot'':17B ''shaft'':22B ''shark'':9B ''student'':12B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (312, 'FIDDLER LOST', 'A Boring Tale of a Squirrel And a Dog who must Challenge a Madman in The Gulf of Mexico', '''bore'':4B ''challeng'':14B ''dog'':11B ''fiddler'':1A ''gulf'':19B ''lost'':2A ''madman'':16B ''mexico'':21B ''must'':13B ''squirrel'':8B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (313, 'FIDELITY DEVIL', 'A Awe-Inspiring Drama of a Technical Writer And a Composer who must Reach a Pastry Chef in A U-Boat', '''awe'':5B ''awe-inspir'':4B ''boat'':25B ''chef'':20B ''compos'':14B ''devil'':2A ''drama'':7B ''fidel'':1A ''inspir'':6B ''must'':16B ''pastri'':19B ''reach'':17B ''technic'':10B ''u'':24B ''u-boat'':23B ''writer'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (314, 'FIGHT JAWBREAKER', 'A Intrepid Panorama of a Womanizer And a Girl who must Escape a Girl in A Manhattan Penthouse', '''escap'':14B ''fight'':1A ''girl'':11B,16B ''intrepid'':4B ''jawbreak'':2A ''manhattan'':19B ''must'':13B ''panorama'':5B ''penthous'':20B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (315, 'FINDING ANACONDA', 'A Fateful Tale of a Database Administrator And a Girl who must Battle a Squirrel in New Orleans', '''administr'':9B ''anaconda'':2A ''battl'':15B ''databas'':8B ''fate'':4B ''find'':1A ''girl'':12B ''must'':14B ''new'':19B ''orlean'':20B ''squirrel'':17B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (316, 'FIRE WOLVES', 'A Intrepid Documentary of a Frisbee And a Dog who must Outrace a Lumberjack in Nigeria', '''documentari'':5B ''dog'':11B ''fire'':1A ''frisbe'':8B ''intrepid'':4B ''lumberjack'':16B ''must'':13B ''nigeria'':18B ''outrac'':14B ''wolv'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (317, 'FIREBALL PHILADELPHIA', 'A Amazing Yarn of a Dentist And a A Shark who must Vanquish a Madman in An Abandoned Mine Shaft', '''abandon'':20B ''amaz'':4B ''dentist'':8B ''firebal'':1A ''madman'':17B ''mine'':21B ''must'':14B ''philadelphia'':2A ''shaft'':22B ''shark'':12B ''vanquish'':15B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (343, 'FULL FLATLINERS', 'A Beautiful Documentary of a Astronaut And a Moose who must Pursue a Monkey in A Shark Tank', '''astronaut'':8B ''beauti'':4B ''documentari'':5B ''flatlin'':2A ''full'':1A ''monkey'':16B ''moos'':11B ''must'':13B ''pursu'':14B ''shark'':19B ''tank'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (318, 'FIREHOUSE VIETNAM', 'A Awe-Inspiring Character Study of a Boat And a Boy who must Kill a Pastry Chef in The Sahara Desert', '''awe'':5B ''awe-inspir'':4B ''boat'':11B ''boy'':14B ''charact'':7B ''chef'':20B ''desert'':24B ''firehous'':1A ''inspir'':6B ''kill'':17B ''must'':16B ''pastri'':19B ''sahara'':23B ''studi'':8B ''vietnam'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (319, 'FISH OPUS', 'A Touching Display of a Feminist And a Girl who must Confront a Astronaut in Australia', '''astronaut'':16B ''australia'':18B ''confront'':14B ''display'':5B ''feminist'':8B ''fish'':1A ''girl'':11B ''must'':13B ''opus'':2A ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (320, 'FLAMINGOS CONNECTICUT', 'A Fast-Paced Reflection of a Composer And a Composer who must Meet a Cat in The Sahara Desert', '''cat'':18B ''compos'':10B,13B ''connecticut'':2A ''desert'':22B ''fast'':5B ''fast-pac'':4B ''flamingo'':1A ''meet'':16B ''must'':15B ''pace'':6B ''reflect'':7B ''sahara'':21B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (321, 'FLASH WARS', 'A Astounding Saga of a Moose And a Pastry Chef who must Chase a Student in The Gulf of Mexico', '''astound'':4B ''chase'':15B ''chef'':12B ''flash'':1A ''gulf'':20B ''mexico'':22B ''moos'':8B ''must'':14B ''pastri'':11B ''saga'':5B ''student'':17B ''war'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (322, 'FLATLINERS KILLER', 'A Taut Display of a Secret Agent And a Waitress who must Sink a Robot in An Abandoned Mine Shaft', '''abandon'':20B ''agent'':9B ''display'':5B ''flatlin'':1A ''killer'':2A ''mine'':21B ''must'':14B ''robot'':17B ''secret'':8B ''shaft'':22B ''sink'':15B ''taut'':4B ''waitress'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (323, 'FLIGHT LIES', 'A Stunning Character Study of a Crocodile And a Pioneer who must Pursue a Teacher in New Orleans', '''charact'':5B ''crocodil'':9B ''flight'':1A ''lie'':2A ''must'':14B ''new'':19B ''orlean'':20B ''pioneer'':12B ''pursu'':15B ''studi'':6B ''stun'':4B ''teacher'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (324, 'FLINTSTONES HAPPINESS', 'A Fateful Story of a Husband And a Moose who must Vanquish a Boy in California', '''boy'':16B ''california'':18B ''fate'':4B ''flintston'':1A ''happi'':2A ''husband'':8B ''moos'':11B ''must'':13B ''stori'':5B ''vanquish'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (325, 'FLOATS GARDEN', 'A Action-Packed Epistle of a Robot And a Car who must Chase a Boat in Ancient Japan', '''action'':5B ''action-pack'':4B ''ancient'':20B ''boat'':18B ''car'':13B ''chase'':16B ''epistl'':7B ''float'':1A ''garden'':2A ''japan'':21B ''must'':15B ''pack'':6B ''robot'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (326, 'FLYING HOOK', 'A Thrilling Display of a Mad Cow And a Dog who must Challenge a Frisbee in Nigeria', '''challeng'':15B ''cow'':9B ''display'':5B ''dog'':12B ''fli'':1A ''frisbe'':17B ''hook'':2A ''mad'':8B ''must'':14B ''nigeria'':19B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (327, 'FOOL MOCKINGBIRD', 'A Lacklusture Tale of a Crocodile And a Composer who must Defeat a Madman in A U-Boat', '''boat'':21B ''compos'':11B ''crocodil'':8B ''defeat'':14B ''fool'':1A ''lacklustur'':4B ''madman'':16B ''mockingbird'':2A ''must'':13B ''tale'':5B ''u'':20B ''u-boat'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (328, 'FOREVER CANDIDATE', 'A Unbelieveable Panorama of a Technical Writer And a Man who must Pursue a Frisbee in A U-Boat', '''boat'':22B ''candid'':2A ''forev'':1A ''frisbe'':17B ''man'':12B ''must'':14B ''panorama'':5B ''pursu'':15B ''technic'':8B ''u'':21B ''u-boat'':20B ''unbeliev'':4B ''writer'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (329, 'FORREST SONS', 'A Thrilling Documentary of a Forensic Psychologist And a Butler who must Defeat a Explorer in A Jet Boat', '''boat'':21B ''butler'':12B ''defeat'':15B ''documentari'':5B ''explor'':17B ''forens'':8B ''forrest'':1A ''jet'':20B ''must'':14B ''psychologist'':9B ''son'':2A ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (330, 'FORRESTER COMANCHEROS', 'A Fateful Tale of a Squirrel And a Forensic Psychologist who must Redeem a Man in Nigeria', '''comanchero'':2A ''fate'':4B ''forens'':11B ''forrest'':1A ''man'':17B ''must'':14B ''nigeria'':19B ''psychologist'':12B ''redeem'':15B ''squirrel'':8B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (331, 'FORWARD TEMPLE', 'A Astounding Display of a Forensic Psychologist And a Mad Scientist who must Challenge a Girl in New Orleans', '''astound'':4B ''challeng'':16B ''display'':5B ''forens'':8B ''forward'':1A ''girl'':18B ''mad'':12B ''must'':15B ''new'':20B ''orlean'':21B ''psychologist'':9B ''scientist'':13B ''templ'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (332, 'FRANKENSTEIN STRANGER', 'A Insightful Character Study of a Feminist And a Pioneer who must Pursue a Pastry Chef in Nigeria', '''charact'':5B ''chef'':18B ''feminist'':9B ''frankenstein'':1A ''insight'':4B ''must'':14B ''nigeria'':20B ''pastri'':17B ''pioneer'':12B ''pursu'':15B ''stranger'':2A ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (333, 'FREAKY POCUS', 'A Fast-Paced Documentary of a Pastry Chef And a Crocodile who must Chase a Squirrel in The Gulf of Mexico', '''chase'':17B ''chef'':11B ''crocodil'':14B ''documentari'':7B ''fast'':5B ''fast-pac'':4B ''freaki'':1A ''gulf'':22B ''mexico'':24B ''must'':16B ''pace'':6B ''pastri'':10B ''pocus'':2A ''squirrel'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (334, 'FREDDY STORM', 'A Intrepid Saga of a Man And a Lumberjack who must Vanquish a Husband in The Outback', '''freddi'':1A ''husband'':16B ''intrepid'':4B ''lumberjack'':11B ''man'':8B ''must'':13B ''outback'':19B ''saga'':5B ''storm'':2A ''vanquish'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (335, 'FREEDOM CLEOPATRA', 'A Emotional Reflection of a Dentist And a Mad Cow who must Face a Squirrel in A Baloon', '''baloon'':20B ''cleopatra'':2A ''cow'':12B ''dentist'':8B ''emot'':4B ''face'':15B ''freedom'':1A ''mad'':11B ''must'':14B ''reflect'':5B ''squirrel'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (336, 'FRENCH HOLIDAY', 'A Thrilling Epistle of a Dog And a Feminist who must Kill a Madman in Berlin', '''berlin'':18B ''dog'':8B ''epistl'':5B ''feminist'':11B ''french'':1A ''holiday'':2A ''kill'':14B ''madman'':16B ''must'':13B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (337, 'FRIDA SLIPPER', 'A Fateful Story of a Lumberjack And a Car who must Escape a Boat in An Abandoned Mine Shaft', '''abandon'':19B ''boat'':16B ''car'':11B ''escap'':14B ''fate'':4B ''frida'':1A ''lumberjack'':8B ''mine'':20B ''must'':13B ''shaft'':21B ''slipper'':2A ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (338, 'FRISCO FORREST', 'A Beautiful Documentary of a Woman And a Pioneer who must Pursue a Mad Scientist in A Shark Tank', '''beauti'':4B ''documentari'':5B ''forrest'':2A ''frisco'':1A ''mad'':16B ''must'':13B ''pioneer'':11B ''pursu'':14B ''scientist'':17B ''shark'':20B ''tank'':21B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (339, 'FROGMEN BREAKING', 'A Unbelieveable Yarn of a Mad Scientist And a Cat who must Chase a Lumberjack in Australia', '''australia'':19B ''break'':2A ''cat'':12B ''chase'':15B ''frogmen'':1A ''lumberjack'':17B ''mad'':8B ''must'':14B ''scientist'':9B ''unbeliev'':4B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (340, 'FRONTIER CABIN', 'A Emotional Story of a Madman And a Waitress who must Battle a Teacher in An Abandoned Fun House', '''abandon'':19B ''battl'':14B ''cabin'':2A ''emot'':4B ''frontier'':1A ''fun'':20B ''hous'':21B ''madman'':8B ''must'':13B ''stori'':5B ''teacher'':16B ''waitress'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (341, 'FROST HEAD', 'A Amazing Reflection of a Lumberjack And a Cat who must Discover a Husband in A MySQL Convention', '''amaz'':4B ''cat'':11B ''convent'':20B ''discov'':14B ''frost'':1A ''head'':2A ''husband'':16B ''lumberjack'':8B ''must'':13B ''mysql'':19B ''reflect'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (342, 'FUGITIVE MAGUIRE', 'A Taut Epistle of a Feminist And a Sumo Wrestler who must Battle a Crocodile in Australia', '''australia'':19B ''battl'':15B ''crocodil'':17B ''epistl'':5B ''feminist'':8B ''fugit'':1A ''maguir'':2A ''must'':14B ''sumo'':11B ''taut'':4B ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (344, 'FURY MURDER', 'A Lacklusture Reflection of a Boat And a Forensic Psychologist who must Fight a Waitress in A Monastery', '''boat'':8B ''fight'':15B ''forens'':11B ''furi'':1A ''lacklustur'':4B ''monasteri'':20B ''murder'':2A ''must'':14B ''psychologist'':12B ''reflect'':5B ''waitress'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (345, 'GABLES METROPOLIS', 'A Fateful Display of a Cat And a Pioneer who must Challenge a Pastry Chef in A Baloon Factory', '''baloon'':20B ''cat'':8B ''challeng'':14B ''chef'':17B ''display'':5B ''factori'':21B ''fate'':4B ''gabl'':1A ''metropoli'':2A ''must'':13B ''pastri'':16B ''pioneer'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (346, 'GALAXY SWEETHEARTS', 'A Emotional Reflection of a Womanizer And a Pioneer who must Face a Squirrel in Berlin', '''berlin'':18B ''emot'':4B ''face'':14B ''galaxi'':1A ''must'':13B ''pioneer'':11B ''reflect'':5B ''squirrel'':16B ''sweetheart'':2A ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (347, 'GAMES BOWFINGER', 'A Astounding Documentary of a Butler And a Explorer who must Challenge a Butler in A Monastery', '''astound'':4B ''bowfing'':2A ''butler'':8B,16B ''challeng'':14B ''documentari'':5B ''explor'':11B ''game'':1A ''monasteri'':19B ''must'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (348, 'GANDHI KWAI', 'A Thoughtful Display of a Mad Scientist And a Secret Agent who must Chase a Boat in Berlin', '''agent'':13B ''berlin'':20B ''boat'':18B ''chase'':16B ''display'':5B ''gandhi'':1A ''kwai'':2A ''mad'':8B ''must'':15B ''scientist'':9B ''secret'':12B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (349, 'GANGS PRIDE', 'A Taut Character Study of a Woman And a A Shark who must Confront a Frisbee in Berlin', '''berlin'':20B ''charact'':5B ''confront'':16B ''frisbe'':18B ''gang'':1A ''must'':15B ''pride'':2A ''shark'':13B ''studi'':6B ''taut'':4B ''woman'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (350, 'GARDEN ISLAND', 'A Unbelieveable Character Study of a Womanizer And a Madman who must Reach a Man in The Outback', '''charact'':5B ''garden'':1A ''island'':2A ''madman'':12B ''man'':17B ''must'':14B ''outback'':20B ''reach'':15B ''studi'':6B ''unbeliev'':4B ''woman'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (351, 'GASLIGHT CRUSADE', 'A Amazing Epistle of a Boy And a Astronaut who must Redeem a Man in The Gulf of Mexico', '''amaz'':4B ''astronaut'':11B ''boy'':8B ''crusad'':2A ''epistl'':5B ''gaslight'':1A ''gulf'':19B ''man'':16B ''mexico'':21B ''must'':13B ''redeem'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (352, 'GATHERING CALENDAR', 'A Intrepid Tale of a Pioneer And a Moose who must Conquer a Frisbee in A MySQL Convention', '''calendar'':2A ''conquer'':14B ''convent'':20B ''frisbe'':16B ''gather'':1A ''intrepid'':4B ''moos'':11B ''must'':13B ''mysql'':19B ''pioneer'':8B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (353, 'GENTLEMEN STAGE', 'A Awe-Inspiring Reflection of a Monkey And a Student who must Overcome a Dentist in The First Manned Space Station', '''awe'':5B ''awe-inspir'':4B ''dentist'':18B ''first'':21B ''gentlemen'':1A ''inspir'':6B ''man'':22B ''monkey'':10B ''must'':15B ''overcom'':16B ''reflect'':7B ''space'':23B ''stage'':2A ''station'':24B ''student'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (354, 'GHOST GROUNDHOG', 'A Brilliant Panorama of a Madman And a Composer who must Succumb a Car in Ancient India', '''ancient'':18B ''brilliant'':4B ''car'':16B ''compos'':11B ''ghost'':1A ''groundhog'':2A ''india'':19B ''madman'':8B ''must'':13B ''panorama'':5B ''succumb'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (355, 'GHOSTBUSTERS ELF', 'A Thoughtful Epistle of a Dog And a Feminist who must Chase a Composer in Berlin', '''berlin'':18B ''chase'':14B ''compos'':16B ''dog'':8B ''elf'':2A ''epistl'':5B ''feminist'':11B ''ghostbust'':1A ''must'':13B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (356, 'GIANT TROOPERS', 'A Fateful Display of a Feminist And a Monkey who must Vanquish a Monkey in The Canadian Rockies', '''canadian'':19B ''display'':5B ''fate'':4B ''feminist'':8B ''giant'':1A ''monkey'':11B,16B ''must'':13B ''rocki'':20B ''trooper'':2A ''vanquish'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (357, 'GILBERT PELICAN', 'A Fateful Tale of a Man And a Feminist who must Conquer a Crocodile in A Manhattan Penthouse', '''conquer'':14B ''crocodil'':16B ''fate'':4B ''feminist'':11B ''gilbert'':1A ''man'':8B ''manhattan'':19B ''must'':13B ''pelican'':2A ''penthous'':20B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (358, 'GILMORE BOILED', 'A Unbelieveable Documentary of a Boat And a Husband who must Succumb a Student in A U-Boat', '''boat'':8B,21B ''boil'':2A ''documentari'':5B ''gilmor'':1A ''husband'':11B ''must'':13B ''student'':16B ''succumb'':14B ''u'':20B ''u-boat'':19B ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (359, 'GLADIATOR WESTWARD', 'A Astounding Reflection of a Squirrel And a Sumo Wrestler who must Sink a Dentist in Ancient Japan', '''ancient'':19B ''astound'':4B ''dentist'':17B ''gladiat'':1A ''japan'':20B ''must'':14B ''reflect'':5B ''sink'':15B ''squirrel'':8B ''sumo'':11B ''westward'':2A ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (360, 'GLASS DYING', 'A Astounding Drama of a Frisbee And a Astronaut who must Fight a Dog in Ancient Japan', '''ancient'':18B ''astound'':4B ''astronaut'':11B ''die'':2A ''dog'':16B ''drama'':5B ''fight'':14B ''frisbe'':8B ''glass'':1A ''japan'':19B ''must'':13B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (362, 'GLORY TRACY', 'A Amazing Saga of a Woman And a Womanizer who must Discover a Cat in The First Manned Space Station', '''amaz'':4B ''cat'':16B ''discov'':14B ''first'':19B ''glori'':1A ''man'':20B ''must'':13B ''saga'':5B ''space'':21B ''station'':22B ''traci'':2A ''woman'':8B,11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (363, 'GO PURPLE', 'A Fast-Paced Display of a Car And a Database Administrator who must Battle a Woman in A Baloon', '''administr'':14B ''baloon'':22B ''battl'':17B ''car'':10B ''databas'':13B ''display'':7B ''fast'':5B ''fast-pac'':4B ''go'':1A ''must'':16B ''pace'':6B ''purpl'':2A ''woman'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (364, 'GODFATHER DIARY', 'A Stunning Saga of a Lumberjack And a Squirrel who must Chase a Car in The Outback', '''car'':16B ''chase'':14B ''diari'':2A ''godfath'':1A ''lumberjack'':8B ''must'':13B ''outback'':19B ''saga'':5B ''squirrel'':11B ''stun'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (365, 'GOLD RIVER', 'A Taut Documentary of a Database Administrator And a Waitress who must Reach a Mad Scientist in A Baloon Factory', '''administr'':9B ''baloon'':21B ''databas'':8B ''documentari'':5B ''factori'':22B ''gold'':1A ''mad'':17B ''must'':14B ''reach'':15B ''river'':2A ''scientist'':18B ''taut'':4B ''waitress'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (366, 'GOLDFINGER SENSIBILITY', 'A Insightful Drama of a Mad Scientist And a Hunter who must Defeat a Pastry Chef in New Orleans', '''chef'':18B ''defeat'':15B ''drama'':5B ''goldfing'':1A ''hunter'':12B ''insight'':4B ''mad'':8B ''must'':14B ''new'':20B ''orlean'':21B ''pastri'':17B ''scientist'':9B ''sensibl'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (367, 'GOLDMINE TYCOON', 'A Brilliant Epistle of a Composer And a Frisbee who must Conquer a Husband in The Outback', '''brilliant'':4B ''compos'':8B ''conquer'':14B ''epistl'':5B ''frisbe'':11B ''goldmin'':1A ''husband'':16B ''must'':13B ''outback'':19B ''tycoon'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (368, 'GONE TROUBLE', 'A Insightful Character Study of a Mad Cow And a Forensic Psychologist who must Conquer a A Shark in A Manhattan Penthouse', '''charact'':5B ''conquer'':17B ''cow'':10B ''forens'':13B ''gone'':1A ''insight'':4B ''mad'':9B ''manhattan'':23B ''must'':16B ''penthous'':24B ''psychologist'':14B ''shark'':20B ''studi'':6B ''troubl'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (369, 'GOODFELLAS SALUTE', 'A Unbelieveable Tale of a Dog And a Explorer who must Sink a Mad Cow in A Baloon Factory', '''baloon'':20B ''cow'':17B ''dog'':8B ''explor'':11B ''factori'':21B ''goodfella'':1A ''mad'':16B ''must'':13B ''salut'':2A ''sink'':14B ''tale'':5B ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (370, 'GORGEOUS BINGO', 'A Action-Packed Display of a Sumo Wrestler And a Car who must Overcome a Waitress in A Baloon Factory', '''action'':5B ''action-pack'':4B ''baloon'':22B ''bingo'':2A ''car'':14B ''display'':7B ''factori'':23B ''gorgeous'':1A ''must'':16B ''overcom'':17B ''pack'':6B ''sumo'':10B ''waitress'':19B ''wrestler'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (371, 'GOSFORD DONNIE', 'A Epic Panorama of a Mad Scientist And a Monkey who must Redeem a Secret Agent in Berlin', '''agent'':18B ''berlin'':20B ''donni'':2A ''epic'':4B ''gosford'':1A ''mad'':8B ''monkey'':12B ''must'':14B ''panorama'':5B ''redeem'':15B ''scientist'':9B ''secret'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (372, 'GRACELAND DYNAMITE', 'A Taut Display of a Cat And a Girl who must Overcome a Database Administrator in New Orleans', '''administr'':17B ''cat'':8B ''databas'':16B ''display'':5B ''dynamit'':2A ''girl'':11B ''graceland'':1A ''must'':13B ''new'':19B ''orlean'':20B ''overcom'':14B ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (373, 'GRADUATE LORD', 'A Lacklusture Epistle of a Girl And a A Shark who must Meet a Mad Scientist in Ancient China', '''ancient'':20B ''china'':21B ''epistl'':5B ''girl'':8B ''graduat'':1A ''lacklustur'':4B ''lord'':2A ''mad'':17B ''meet'':15B ''must'':14B ''scientist'':18B ''shark'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (374, 'GRAFFITI LOVE', 'A Unbelieveable Epistle of a Sumo Wrestler And a Hunter who must Build a Composer in Berlin', '''berlin'':19B ''build'':15B ''compos'':17B ''epistl'':5B ''graffiti'':1A ''hunter'':12B ''love'':2A ''must'':14B ''sumo'':8B ''unbeliev'':4B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (375, 'GRAIL FRANKENSTEIN', 'A Unbelieveable Saga of a Teacher And a Monkey who must Fight a Girl in An Abandoned Mine Shaft', '''abandon'':19B ''fight'':14B ''frankenstein'':2A ''girl'':16B ''grail'':1A ''mine'':20B ''monkey'':11B ''must'':13B ''saga'':5B ''shaft'':21B ''teacher'':8B ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (376, 'GRAPES FURY', 'A Boring Yarn of a Mad Cow And a Sumo Wrestler who must Meet a Robot in Australia', '''australia'':20B ''bore'':4B ''cow'':9B ''furi'':2A ''grape'':1A ''mad'':8B ''meet'':16B ''must'':15B ''robot'':18B ''sumo'':12B ''wrestler'':13B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (377, 'GREASE YOUTH', 'A Emotional Panorama of a Secret Agent And a Waitress who must Escape a Composer in Soviet Georgia', '''agent'':9B ''compos'':17B ''emot'':4B ''escap'':15B ''georgia'':20B ''greas'':1A ''must'':14B ''panorama'':5B ''secret'':8B ''soviet'':19B ''waitress'':12B ''youth'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (378, 'GREATEST NORTH', 'A Astounding Character Study of a Secret Agent And a Robot who must Build a A Shark in Berlin', '''agent'':10B ''astound'':4B ''berlin'':21B ''build'':16B ''charact'':5B ''greatest'':1A ''must'':15B ''north'':2A ''robot'':13B ''secret'':9B ''shark'':19B ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (379, 'GREEDY ROOTS', 'A Amazing Reflection of a A Shark And a Butler who must Chase a Hunter in The Canadian Rockies', '''amaz'':4B ''butler'':12B ''canadian'':20B ''chase'':15B ''greedi'':1A ''hunter'':17B ''must'':14B ''reflect'':5B ''rocki'':21B ''root'':2A ''shark'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (380, 'GREEK EVERYONE', 'A Stunning Display of a Butler And a Teacher who must Confront a A Shark in The First Manned Space Station', '''butler'':8B ''confront'':14B ''display'':5B ''everyon'':2A ''first'':20B ''greek'':1A ''man'':21B ''must'':13B ''shark'':17B ''space'':22B ''station'':23B ''stun'':4B ''teacher'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (381, 'GRINCH MASSAGE', 'A Intrepid Display of a Madman And a Feminist who must Pursue a Pioneer in The First Manned Space Station', '''display'':5B ''feminist'':11B ''first'':19B ''grinch'':1A ''intrepid'':4B ''madman'':8B ''man'':20B ''massag'':2A ''must'':13B ''pioneer'':16B ''pursu'':14B ''space'':21B ''station'':22B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (382, 'GRIT CLOCKWORK', 'A Thoughtful Display of a Dentist And a Squirrel who must Confront a Lumberjack in A Shark Tank', '''clockwork'':2A ''confront'':14B ''dentist'':8B ''display'':5B ''grit'':1A ''lumberjack'':16B ''must'':13B ''shark'':19B ''squirrel'':11B ''tank'':20B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (383, 'GROOVE FICTION', 'A Unbelieveable Reflection of a Moose And a A Shark who must Defeat a Lumberjack in An Abandoned Mine Shaft', '''abandon'':20B ''defeat'':15B ''fiction'':2A ''groov'':1A ''lumberjack'':17B ''mine'':21B ''moos'':8B ''must'':14B ''reflect'':5B ''shaft'':22B ''shark'':12B ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (384, 'GROSSE WONDERFUL', 'A Epic Drama of a Cat And a Explorer who must Redeem a Moose in Australia', '''australia'':18B ''cat'':8B ''drama'':5B ''epic'':4B ''explor'':11B ''gross'':1A ''moos'':16B ''must'':13B ''redeem'':14B ''wonder'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (385, 'GROUNDHOG UNCUT', 'A Brilliant Panorama of a Astronaut And a Technical Writer who must Discover a Butler in A Manhattan Penthouse', '''astronaut'':8B ''brilliant'':4B ''butler'':17B ''discov'':15B ''groundhog'':1A ''manhattan'':20B ''must'':14B ''panorama'':5B ''penthous'':21B ''technic'':11B ''uncut'':2A ''writer'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (386, 'GUMP DATE', 'A Intrepid Yarn of a Explorer And a Student who must Kill a Husband in An Abandoned Mine Shaft', '''abandon'':19B ''date'':2A ''explor'':8B ''gump'':1A ''husband'':16B ''intrepid'':4B ''kill'':14B ''mine'':20B ''must'':13B ''shaft'':21B ''student'':11B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (387, 'GUN BONNIE', 'A Boring Display of a Sumo Wrestler And a Husband who must Build a Waitress in The Gulf of Mexico', '''bonni'':2A ''bore'':4B ''build'':15B ''display'':5B ''gulf'':20B ''gun'':1A ''husband'':12B ''mexico'':22B ''must'':14B ''sumo'':8B ''waitress'':17B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (388, 'GUNFIGHT MOON', 'A Epic Reflection of a Pastry Chef And a Explorer who must Reach a Dentist in The Sahara Desert', '''chef'':9B ''dentist'':17B ''desert'':21B ''epic'':4B ''explor'':12B ''gunfight'':1A ''moon'':2A ''must'':14B ''pastri'':8B ''reach'':15B ''reflect'':5B ''sahara'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (389, 'GUNFIGHTER MUSSOLINI', 'A Touching Saga of a Robot And a Boy who must Kill a Man in Ancient Japan', '''ancient'':18B ''boy'':11B ''gunfight'':1A ''japan'':19B ''kill'':14B ''man'':16B ''mussolini'':2A ''must'':13B ''robot'':8B ''saga'':5B ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (390, 'GUYS FALCON', 'A Boring Story of a Woman And a Feminist who must Redeem a Squirrel in A U-Boat', '''boat'':21B ''bore'':4B ''falcon'':2A ''feminist'':11B ''guy'':1A ''must'':13B ''redeem'':14B ''squirrel'':16B ''stori'':5B ''u'':20B ''u-boat'':19B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (391, 'HALF OUTFIELD', 'A Epic Epistle of a Database Administrator And a Crocodile who must Face a Madman in A Jet Boat', '''administr'':9B ''boat'':21B ''crocodil'':12B ''databas'':8B ''epic'':4B ''epistl'':5B ''face'':15B ''half'':1A ''jet'':20B ''madman'':17B ''must'':14B ''outfield'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (392, 'HALL CASSIDY', 'A Beautiful Panorama of a Pastry Chef And a A Shark who must Battle a Pioneer in Soviet Georgia', '''battl'':16B ''beauti'':4B ''cassidi'':2A ''chef'':9B ''georgia'':21B ''hall'':1A ''must'':15B ''panorama'':5B ''pastri'':8B ''pioneer'':18B ''shark'':13B ''soviet'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (393, 'HALLOWEEN NUTS', 'A Amazing Panorama of a Forensic Psychologist And a Technical Writer who must Fight a Dentist in A U-Boat', '''amaz'':4B ''boat'':23B ''dentist'':18B ''fight'':16B ''forens'':8B ''halloween'':1A ''must'':15B ''nut'':2A ''panorama'':5B ''psychologist'':9B ''technic'':12B ''u'':22B ''u-boat'':21B ''writer'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (395, 'HANDICAP BOONDOCK', 'A Beautiful Display of a Pioneer And a Squirrel who must Vanquish a Sumo Wrestler in Soviet Georgia', '''beauti'':4B ''boondock'':2A ''display'':5B ''georgia'':20B ''handicap'':1A ''must'':13B ''pioneer'':8B ''soviet'':19B ''squirrel'':11B ''sumo'':16B ''vanquish'':14B ''wrestler'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (396, 'HANGING DEEP', 'A Action-Packed Yarn of a Boat And a Crocodile who must Build a Monkey in Berlin', '''action'':5B ''action-pack'':4B ''berlin'':20B ''boat'':10B ''build'':16B ''crocodil'':13B ''deep'':2A ''hang'':1A ''monkey'':18B ''must'':15B ''pack'':6B ''yarn'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (397, 'HANKY OCTOBER', 'A Boring Epistle of a Database Administrator And a Explorer who must Pursue a Madman in Soviet Georgia', '''administr'':9B ''bore'':4B ''databas'':8B ''epistl'':5B ''explor'':12B ''georgia'':20B ''hanki'':1A ''madman'':17B ''must'':14B ''octob'':2A ''pursu'':15B ''soviet'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (398, 'HANOVER GALAXY', 'A Stunning Reflection of a Girl And a Secret Agent who must Succumb a Boy in A MySQL Convention', '''agent'':12B ''boy'':17B ''convent'':21B ''galaxi'':2A ''girl'':8B ''hanov'':1A ''must'':14B ''mysql'':20B ''reflect'':5B ''secret'':11B ''stun'':4B ''succumb'':15B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (399, 'HAPPINESS UNITED', 'A Action-Packed Panorama of a Husband And a Feminist who must Meet a Forensic Psychologist in Ancient Japan', '''action'':5B ''action-pack'':4B ''ancient'':21B ''feminist'':13B ''forens'':18B ''happi'':1A ''husband'':10B ''japan'':22B ''meet'':16B ''must'':15B ''pack'':6B ''panorama'':7B ''psychologist'':19B ''unit'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (400, 'HARDLY ROBBERS', 'A Emotional Character Study of a Hunter And a Car who must Kill a Woman in Berlin', '''berlin'':19B ''car'':12B ''charact'':5B ''emot'':4B ''hard'':1A ''hunter'':9B ''kill'':15B ''must'':14B ''robber'':2A ''studi'':6B ''woman'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (401, 'HAROLD FRENCH', 'A Stunning Saga of a Sumo Wrestler And a Student who must Outrace a Moose in The Sahara Desert', '''desert'':21B ''french'':2A ''harold'':1A ''moos'':17B ''must'':14B ''outrac'':15B ''saga'':5B ''sahara'':20B ''student'':12B ''stun'':4B ''sumo'':8B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (402, 'HARPER DYING', 'A Awe-Inspiring Reflection of a Woman And a Cat who must Confront a Feminist in The Sahara Desert', '''awe'':5B ''awe-inspir'':4B ''cat'':13B ''confront'':16B ''desert'':22B ''die'':2A ''feminist'':18B ''harper'':1A ''inspir'':6B ''must'':15B ''reflect'':7B ''sahara'':21B ''woman'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (403, 'HARRY IDAHO', 'A Taut Yarn of a Technical Writer And a Feminist who must Outrace a Dog in California', '''california'':19B ''dog'':17B ''feminist'':12B ''harri'':1A ''idaho'':2A ''must'':14B ''outrac'':15B ''taut'':4B ''technic'':8B ''writer'':9B ''yarn'':5B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (413, 'HEDWIG ALTER', 'A Action-Packed Yarn of a Womanizer And a Lumberjack who must Chase a Sumo Wrestler in A Monastery', '''action'':5B ''action-pack'':4B ''alter'':2A ''chase'':16B ''hedwig'':1A ''lumberjack'':13B ''monasteri'':22B ''must'':15B ''pack'':6B ''sumo'':18B ''woman'':10B ''wrestler'':19B ''yarn'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (414, 'HELLFIGHTERS SIERRA', 'A Taut Reflection of a A Shark And a Dentist who must Battle a Boat in Soviet Georgia', '''battl'':15B ''boat'':17B ''dentist'':12B ''georgia'':20B ''hellfight'':1A ''must'':14B ''reflect'':5B ''shark'':9B ''sierra'':2A ''soviet'':19B ''taut'':4B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (420, 'HOLES BRANNIGAN', 'A Fast-Paced Reflection of a Technical Writer And a Student who must Fight a Boy in The Canadian Rockies', '''boy'':19B ''brannigan'':2A ''canadian'':22B ''fast'':5B ''fast-pac'':4B ''fight'':17B ''hole'':1A ''must'':16B ''pace'':6B ''reflect'':7B ''rocki'':23B ''student'':14B ''technic'':10B ''writer'':11B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (426, 'HOME PITY', 'A Touching Panorama of a Man And a Secret Agent who must Challenge a Teacher in A MySQL Convention', '''agent'':12B ''challeng'':15B ''convent'':21B ''home'':1A ''man'':8B ''must'':14B ''mysql'':20B ''panorama'':5B ''piti'':2A ''secret'':11B ''teacher'':17B ''touch'':4B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (429, 'HONEY TIES', 'A Taut Story of a Waitress And a Crocodile who must Outrace a Lumberjack in A Shark Tank', '''crocodil'':11B ''honey'':1A ''lumberjack'':16B ''must'':13B ''outrac'':14B ''shark'':19B ''stori'':5B ''tank'':20B ''taut'':4B ''tie'':2A ''waitress'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (430, 'HOOK CHARIOTS', 'A Insightful Story of a Boy And a Dog who must Redeem a Boy in Australia', '''australia'':18B ''boy'':8B,16B ''chariot'':2A ''dog'':11B ''hook'':1A ''insight'':4B ''must'':13B ''redeem'':14B ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (431, 'HOOSIERS BIRDCAGE', 'A Astounding Display of a Explorer And a Boat who must Vanquish a Car in The First Manned Space Station', '''astound'':4B ''birdcag'':2A ''boat'':11B ''car'':16B ''display'':5B ''explor'':8B ''first'':19B ''hoosier'':1A ''man'':20B ''must'':13B ''space'':21B ''station'':22B ''vanquish'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (432, 'HOPE TOOTSIE', 'A Amazing Documentary of a Student And a Sumo Wrestler who must Outgun a A Shark in A Shark Tank', '''amaz'':4B ''documentari'':5B ''hope'':1A ''must'':14B ''outgun'':15B ''shark'':18B,21B ''student'':8B ''sumo'':11B ''tank'':22B ''tootsi'':2A ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (433, 'HORN WORKING', 'A Stunning Display of a Mad Scientist And a Technical Writer who must Succumb a Monkey in A Shark Tank', '''display'':5B ''horn'':1A ''mad'':8B ''monkey'':18B ''must'':15B ''scientist'':9B ''shark'':21B ''stun'':4B ''succumb'':16B ''tank'':22B ''technic'':12B ''work'':2A ''writer'':13B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (435, 'HOTEL HAPPINESS', 'A Thrilling Yarn of a Pastry Chef And a A Shark who must Challenge a Mad Scientist in The Outback', '''challeng'':16B ''chef'':9B ''happi'':2A ''hotel'':1A ''mad'':18B ''must'':15B ''outback'':22B ''pastri'':8B ''scientist'':19B ''shark'':13B ''thrill'':4B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (436, 'HOURS RAGE', 'A Fateful Story of a Explorer And a Feminist who must Meet a Technical Writer in Soviet Georgia', '''explor'':8B ''fate'':4B ''feminist'':11B ''georgia'':20B ''hour'':1A ''meet'':14B ''must'':13B ''rage'':2A ''soviet'':19B ''stori'':5B ''technic'':16B ''writer'':17B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (438, 'HUMAN GRAFFITI', 'A Beautiful Reflection of a Womanizer And a Sumo Wrestler who must Chase a Database Administrator in The Gulf of Mexico', '''administr'':18B ''beauti'':4B ''chase'':15B ''databas'':17B ''graffiti'':2A ''gulf'':21B ''human'':1A ''mexico'':23B ''must'':14B ''reflect'':5B ''sumo'':11B ''woman'':8B ''wrestler'':12B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (441, 'HUNTER ALTER', 'A Emotional Drama of a Mad Cow And a Boat who must Redeem a Secret Agent in A Shark Tank', '''agent'':18B ''alter'':2A ''boat'':12B ''cow'':9B ''drama'':5B ''emot'':4B ''hunter'':1A ''mad'':8B ''must'':14B ''redeem'':15B ''secret'':17B ''shark'':21B ''tank'':22B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (477, 'JAWBREAKER BROOKLYN', 'A Stunning Reflection of a Boat And a Pastry Chef who must Succumb a A Shark in A Jet Boat', '''boat'':8B,22B ''brooklyn'':2A ''chef'':12B ''jawbreak'':1A ''jet'':21B ''must'':14B ''pastri'':11B ''reflect'':5B ''shark'':18B ''stun'':4B ''succumb'':15B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (480, 'JEEPERS WEDDING', 'A Astounding Display of a Composer And a Dog who must Kill a Pastry Chef in Soviet Georgia', '''astound'':4B ''chef'':17B ''compos'':8B ''display'':5B ''dog'':11B ''georgia'':20B ''jeeper'':1A ''kill'':14B ''must'':13B ''pastri'':16B ''soviet'':19B ''wed'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (481, 'JEKYLL FROGMEN', 'A Fanciful Epistle of a Student And a Astronaut who must Kill a Waitress in A Shark Tank', '''astronaut'':11B ''epistl'':5B ''fanci'':4B ''frogmen'':2A ''jekyl'':1A ''kill'':14B ''must'':13B ''shark'':19B ''student'':8B ''tank'':20B ''waitress'':16B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (483, 'JERICHO MULAN', 'A Amazing Yarn of a Hunter And a Butler who must Defeat a Boy in A Jet Boat', '''amaz'':4B ''boat'':20B ''boy'':16B ''butler'':11B ''defeat'':14B ''hunter'':8B ''jericho'':1A ''jet'':19B ''mulan'':2A ''must'':13B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (484, 'JERK PAYCHECK', 'A Touching Character Study of a Pastry Chef And a Database Administrator who must Reach a A Shark in Ancient Japan', '''administr'':14B ''ancient'':22B ''charact'':5B ''chef'':10B ''databas'':13B ''japan'':23B ''jerk'':1A ''must'':16B ''pastri'':9B ''paycheck'':2A ''reach'':17B ''shark'':20B ''studi'':6B ''touch'':4B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (488, 'JOON NORTHWEST', 'A Thrilling Panorama of a Technical Writer And a Car who must Discover a Forensic Psychologist in A Shark Tank', '''car'':12B ''discov'':15B ''forens'':17B ''joon'':1A ''must'':14B ''northwest'':2A ''panorama'':5B ''psychologist'':18B ''shark'':21B ''tank'':22B ''technic'':8B ''thrill'':4B ''writer'':9B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (490, 'JUMANJI BLADE', 'A Intrepid Yarn of a Husband And a Womanizer who must Pursue a Mad Scientist in New Orleans', '''blade'':2A ''husband'':8B ''intrepid'':4B ''jumanji'':1A ''mad'':16B ''must'':13B ''new'':19B ''orlean'':20B ''pursu'':14B ''scientist'':17B ''woman'':11B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (491, 'JUMPING WRATH', 'A Touching Epistle of a Monkey And a Feminist who must Discover a Boat in Berlin', '''berlin'':18B ''boat'':16B ''discov'':14B ''epistl'':5B ''feminist'':11B ''jump'':1A ''monkey'':8B ''must'':13B ''touch'':4B ''wrath'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (492, 'JUNGLE CLOSER', 'A Boring Character Study of a Boy And a Woman who must Battle a Astronaut in Australia', '''astronaut'':17B ''australia'':19B ''battl'':15B ''bore'':4B ''boy'':9B ''charact'':5B ''closer'':2A ''jungl'':1A ''must'':14B ''studi'':6B ''woman'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (493, 'KANE EXORCIST', 'A Epic Documentary of a Composer And a Robot who must Overcome a Car in Berlin', '''berlin'':18B ''car'':16B ''compos'':8B ''documentari'':5B ''epic'':4B ''exorcist'':2A ''kane'':1A ''must'':13B ''overcom'':14B ''robot'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (494, 'KARATE MOON', 'A Astounding Yarn of a Womanizer And a Dog who must Reach a Waitress in A MySQL Convention', '''astound'':4B ''convent'':20B ''dog'':11B ''karat'':1A ''moon'':2A ''must'':13B ''mysql'':19B ''reach'':14B ''waitress'':16B ''woman'':8B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (495, 'KENTUCKIAN GIANT', 'A Stunning Yarn of a Woman And a Frisbee who must Escape a Waitress in A U-Boat', '''boat'':21B ''escap'':14B ''frisbe'':11B ''giant'':2A ''kentuckian'':1A ''must'':13B ''stun'':4B ''u'':20B ''u-boat'':19B ''waitress'':16B ''woman'':8B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (496, 'KICK SAVANNAH', 'A Emotional Drama of a Monkey And a Robot who must Defeat a Monkey in New Orleans', '''defeat'':14B ''drama'':5B ''emot'':4B ''kick'':1A ''monkey'':8B,16B ''must'':13B ''new'':18B ''orlean'':19B ''robot'':11B ''savannah'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (497, 'KILL BROTHERHOOD', 'A Touching Display of a Hunter And a Secret Agent who must Redeem a Husband in The Outback', '''agent'':12B ''brotherhood'':2A ''display'':5B ''hunter'':8B ''husband'':17B ''kill'':1A ''must'':14B ''outback'':20B ''redeem'':15B ''secret'':11B ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (498, 'KILLER INNOCENT', 'A Fanciful Character Study of a Student And a Explorer who must Succumb a Composer in An Abandoned Mine Shaft', '''abandon'':20B ''charact'':5B ''compos'':17B ''explor'':12B ''fanci'':4B ''innoc'':2A ''killer'':1A ''mine'':21B ''must'':14B ''shaft'':22B ''student'':9B ''studi'':6B ''succumb'':15B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (499, 'KING EVOLUTION', 'A Action-Packed Tale of a Boy And a Lumberjack who must Chase a Madman in A Baloon', '''action'':5B ''action-pack'':4B ''baloon'':21B ''boy'':10B ''chase'':16B ''evolut'':2A ''king'':1A ''lumberjack'':13B ''madman'':18B ''must'':15B ''pack'':6B ''tale'':7B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (504, 'KWAI HOMEWARD', 'A Amazing Drama of a Car And a Squirrel who must Pursue a Car in Soviet Georgia', '''amaz'':4B ''car'':8B,16B ''drama'':5B ''georgia'':19B ''homeward'':2A ''kwai'':1A ''must'':13B ''pursu'':14B ''soviet'':18B ''squirrel'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (505, 'LABYRINTH LEAGUE', 'A Awe-Inspiring Saga of a Composer And a Frisbee who must Succumb a Pioneer in The Sahara Desert', '''awe'':5B ''awe-inspir'':4B ''compos'':10B ''desert'':22B ''frisbe'':13B ''inspir'':6B ''labyrinth'':1A ''leagu'':2A ''must'':15B ''pioneer'':18B ''saga'':7B ''sahara'':21B ''succumb'':16B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (507, 'LADYBUGS ARMAGEDDON', 'A Fateful Reflection of a Dog And a Mad Scientist who must Meet a Mad Scientist in New Orleans', '''armageddon'':2A ''dog'':8B ''fate'':4B ''ladybug'':1A ''mad'':11B,17B ''meet'':15B ''must'':14B ''new'':20B ''orlean'':21B ''reflect'':5B ''scientist'':12B,18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (508, 'LAMBS CINCINATTI', 'A Insightful Story of a Man And a Feminist who must Fight a Composer in Australia', '''australia'':18B ''cincinatti'':2A ''compos'':16B ''feminist'':11B ''fight'':14B ''insight'':4B ''lamb'':1A ''man'':8B ''must'':13B ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (509, 'LANGUAGE COWBOY', 'A Epic Yarn of a Cat And a Madman who must Vanquish a Dentist in An Abandoned Amusement Park', '''abandon'':19B ''amus'':20B ''cat'':8B ''cowboy'':2A ''dentist'':16B ''epic'':4B ''languag'':1A ''madman'':11B ''must'':13B ''park'':21B ''vanquish'':14B ''yarn'':5B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (511, 'LAWRENCE LOVE', 'A Fanciful Yarn of a Database Administrator And a Mad Cow who must Pursue a Womanizer in Berlin', '''administr'':9B ''berlin'':20B ''cow'':13B ''databas'':8B ''fanci'':4B ''lawrenc'':1A ''love'':2A ''mad'':12B ''must'':15B ''pursu'':16B ''woman'':18B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (512, 'LEAGUE HELLFIGHTERS', 'A Thoughtful Saga of a A Shark And a Monkey who must Outgun a Student in Ancient China', '''ancient'':19B ''china'':20B ''hellfight'':2A ''leagu'':1A ''monkey'':12B ''must'':14B ''outgun'':15B ''saga'':5B ''shark'':9B ''student'':17B ''thought'':4B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (515, 'LEGALLY SECRETARY', 'A Astounding Tale of a A Shark And a Moose who must Meet a Womanizer in The Sahara Desert', '''astound'':4B ''desert'':21B ''legal'':1A ''meet'':15B ''moos'':12B ''must'':14B ''sahara'':20B ''secretari'':2A ''shark'':9B ''tale'':5B ''woman'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (516, 'LEGEND JEDI', 'A Awe-Inspiring Epistle of a Pioneer And a Student who must Outgun a Crocodile in The Outback', '''awe'':5B ''awe-inspir'':4B ''crocodil'':18B ''epistl'':7B ''inspir'':6B ''jedi'':2A ''legend'':1A ''must'':15B ''outback'':21B ''outgun'':16B ''pioneer'':10B ''student'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (517, 'LESSON CLEOPATRA', 'A Emotional Display of a Man And a Explorer who must Build a Boy in A Manhattan Penthouse', '''boy'':16B ''build'':14B ''cleopatra'':2A ''display'':5B ''emot'':4B ''explor'':11B ''lesson'':1A ''man'':8B ''manhattan'':19B ''must'':13B ''penthous'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (518, 'LIAISONS SWEET', 'A Boring Drama of a A Shark And a Explorer who must Redeem a Waitress in The Canadian Rockies', '''bore'':4B ''canadian'':20B ''drama'':5B ''explor'':12B ''liaison'':1A ''must'':14B ''redeem'':15B ''rocki'':21B ''shark'':9B ''sweet'':2A ''waitress'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (519, 'LIBERTY MAGNIFICENT', 'A Boring Drama of a Student And a Cat who must Sink a Technical Writer in A Baloon', '''baloon'':20B ''bore'':4B ''cat'':11B ''drama'':5B ''liberti'':1A ''magnific'':2A ''must'':13B ''sink'':14B ''student'':8B ''technic'':16B ''writer'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (520, 'LICENSE WEEKEND', 'A Insightful Story of a Man And a Husband who must Overcome a Madman in A Monastery', '''husband'':11B ''insight'':4B ''licens'':1A ''madman'':16B ''man'':8B ''monasteri'':19B ''must'':13B ''overcom'':14B ''stori'':5B ''weekend'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (521, 'LIES TREATMENT', 'A Fast-Paced Character Study of a Dentist And a Moose who must Defeat a Composer in The First Manned Space Station', '''charact'':7B ''compos'':19B ''defeat'':17B ''dentist'':11B ''fast'':5B ''fast-pac'':4B ''first'':22B ''lie'':1A ''man'':23B ''moos'':14B ''must'':16B ''pace'':6B ''space'':24B ''station'':25B ''studi'':8B ''treatment'':2A');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (523, 'LIGHTS DEER', 'A Unbelieveable Epistle of a Dog And a Woman who must Confront a Moose in The Gulf of Mexico', '''confront'':14B ''deer'':2A ''dog'':8B ''epistl'':5B ''gulf'':19B ''light'':1A ''mexico'':21B ''moos'':16B ''must'':13B ''unbeliev'':4B ''woman'':11B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (526, 'LOCK REAR', 'A Thoughtful Character Study of a Squirrel And a Technical Writer who must Outrace a Student in Ancient Japan', '''ancient'':20B ''charact'':5B ''japan'':21B ''lock'':1A ''must'':15B ''outrac'':16B ''rear'':2A ''squirrel'':9B ''student'':18B ''studi'':6B ''technic'':12B ''thought'':4B ''writer'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (527, 'LOLA AGENT', 'A Astounding Tale of a Mad Scientist And a Husband who must Redeem a Database Administrator in Ancient Japan', '''administr'':18B ''agent'':2A ''ancient'':20B ''astound'':4B ''databas'':17B ''husband'':12B ''japan'':21B ''lola'':1A ''mad'':8B ''must'':14B ''redeem'':15B ''scientist'':9B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (528, 'LOLITA WORLD', 'A Thrilling Drama of a Girl And a Robot who must Redeem a Waitress in An Abandoned Mine Shaft', '''abandon'':19B ''drama'':5B ''girl'':8B ''lolita'':1A ''mine'':20B ''must'':13B ''redeem'':14B ''robot'':11B ''shaft'':21B ''thrill'':4B ''waitress'':16B ''world'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (529, 'LONELY ELEPHANT', 'A Intrepid Story of a Student And a Dog who must Challenge a Explorer in Soviet Georgia', '''challeng'':14B ''dog'':11B ''eleph'':2A ''explor'':16B ''georgia'':19B ''intrepid'':4B ''lone'':1A ''must'':13B ''soviet'':18B ''stori'':5B ''student'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (530, 'LORD ARIZONA', 'A Action-Packed Display of a Frisbee And a Pastry Chef who must Pursue a Crocodile in A Jet Boat', '''action'':5B ''action-pack'':4B ''arizona'':2A ''boat'':23B ''chef'':14B ''crocodil'':19B ''display'':7B ''frisbe'':10B ''jet'':22B ''lord'':1A ''must'':16B ''pack'':6B ''pastri'':13B ''pursu'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (531, 'LOSE INCH', 'A Stunning Reflection of a Student And a Technical Writer who must Battle a Butler in The First Manned Space Station', '''battl'':15B ''butler'':17B ''first'':20B ''inch'':2A ''lose'':1A ''man'':21B ''must'':14B ''reflect'':5B ''space'':22B ''station'':23B ''student'':8B ''stun'':4B ''technic'':11B ''writer'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (532, 'LOSER HUSTLER', 'A Stunning Drama of a Robot And a Feminist who must Outgun a Butler in Nigeria', '''butler'':16B ''drama'':5B ''feminist'':11B ''hustler'':2A ''loser'':1A ''must'':13B ''nigeria'':18B ''outgun'':14B ''robot'':8B ''stun'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (533, 'LOST BIRD', 'A Emotional Character Study of a Robot And a A Shark who must Defeat a Technical Writer in A Manhattan Penthouse', '''bird'':2A ''charact'':5B ''defeat'':16B ''emot'':4B ''lost'':1A ''manhattan'':22B ''must'':15B ''penthous'':23B ''robot'':9B ''shark'':13B ''studi'':6B ''technic'':18B ''writer'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (534, 'LOUISIANA HARRY', 'A Lacklusture Drama of a Girl And a Technical Writer who must Redeem a Monkey in A Shark Tank', '''drama'':5B ''girl'':8B ''harri'':2A ''lacklustur'':4B ''louisiana'':1A ''monkey'':17B ''must'':14B ''redeem'':15B ''shark'':20B ''tank'':21B ''technic'':11B ''writer'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (535, 'LOVE SUICIDES', 'A Brilliant Panorama of a Hunter And a Explorer who must Pursue a Dentist in An Abandoned Fun House', '''abandon'':19B ''brilliant'':4B ''dentist'':16B ''explor'':11B ''fun'':20B ''hous'':21B ''hunter'':8B ''love'':1A ''must'':13B ''panorama'':5B ''pursu'':14B ''suicid'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (536, 'LOVELY JINGLE', 'A Fanciful Yarn of a Crocodile And a Forensic Psychologist who must Discover a Crocodile in The Outback', '''crocodil'':8B,17B ''discov'':15B ''fanci'':4B ''forens'':11B ''jingl'':2A ''love'':1A ''must'':14B ''outback'':20B ''psychologist'':12B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (537, 'LOVER TRUMAN', 'A Emotional Yarn of a Robot And a Boy who must Outgun a Technical Writer in A U-Boat', '''boat'':22B ''boy'':11B ''emot'':4B ''lover'':1A ''must'':13B ''outgun'':14B ''robot'':8B ''technic'':16B ''truman'':2A ''u'':21B ''u-boat'':20B ''writer'':17B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (538, 'LOVERBOY ATTACKS', 'A Boring Story of a Car And a Butler who must Build a Girl in Soviet Georgia', '''attack'':2A ''bore'':4B ''build'':14B ''butler'':11B ''car'':8B ''georgia'':19B ''girl'':16B ''loverboy'':1A ''must'':13B ''soviet'':18B ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (539, 'LUCK OPUS', 'A Boring Display of a Moose And a Squirrel who must Outrace a Teacher in A Shark Tank', '''bore'':4B ''display'':5B ''luck'':1A ''moos'':8B ''must'':13B ''opus'':2A ''outrac'':14B ''shark'':19B ''squirrel'':11B ''tank'':20B ''teacher'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (540, 'LUCKY FLYING', 'A Lacklusture Character Study of a A Shark And a Man who must Find a Forensic Psychologist in A U-Boat', '''boat'':24B ''charact'':5B ''find'':16B ''fli'':2A ''forens'':18B ''lacklustur'':4B ''lucki'':1A ''man'':13B ''must'':15B ''psychologist'':19B ''shark'':10B ''studi'':6B ''u'':23B ''u-boat'':22B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (541, 'LUKE MUMMY', 'A Taut Character Study of a Boy And a Robot who must Redeem a Mad Scientist in Ancient India', '''ancient'':20B ''boy'':9B ''charact'':5B ''india'':21B ''luke'':1A ''mad'':17B ''mummi'':2A ''must'':14B ''redeem'':15B ''robot'':12B ''scientist'':18B ''studi'':6B ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (542, 'LUST LOCK', 'A Fanciful Panorama of a Hunter And a Dentist who must Meet a Secret Agent in The Sahara Desert', '''agent'':17B ''dentist'':11B ''desert'':21B ''fanci'':4B ''hunter'':8B ''lock'':2A ''lust'':1A ''meet'':14B ''must'':13B ''panorama'':5B ''sahara'':20B ''secret'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (543, 'MADIGAN DORADO', 'A Astounding Character Study of a A Shark And a A Shark who must Discover a Crocodile in The Outback', '''astound'':4B ''charact'':5B ''crocodil'':19B ''discov'':17B ''dorado'':2A ''madigan'':1A ''must'':16B ''outback'':22B ''shark'':10B,14B ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (544, 'MADISON TRAP', 'A Awe-Inspiring Reflection of a Monkey And a Dentist who must Overcome a Pioneer in A U-Boat', '''awe'':5B ''awe-inspir'':4B ''boat'':23B ''dentist'':13B ''inspir'':6B ''madison'':1A ''monkey'':10B ''must'':15B ''overcom'':16B ''pioneer'':18B ''reflect'':7B ''trap'':2A ''u'':22B ''u-boat'':21B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (545, 'MADNESS ATTACKS', 'A Fanciful Tale of a Squirrel And a Boat who must Defeat a Crocodile in The Gulf of Mexico', '''attack'':2A ''boat'':11B ''crocodil'':16B ''defeat'':14B ''fanci'':4B ''gulf'':19B ''mad'':1A ''mexico'':21B ''must'':13B ''squirrel'':8B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (546, 'MADRE GABLES', 'A Intrepid Panorama of a Sumo Wrestler And a Forensic Psychologist who must Discover a Moose in The First Manned Space Station', '''discov'':16B ''first'':21B ''forens'':12B ''gabl'':2A ''intrepid'':4B ''madr'':1A ''man'':22B ''moos'':18B ''must'':15B ''panorama'':5B ''psychologist'':13B ''space'':23B ''station'':24B ''sumo'':8B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (547, 'MAGIC MALLRATS', 'A Touching Documentary of a Pastry Chef And a Pastry Chef who must Build a Mad Scientist in California', '''build'':16B ''california'':21B ''chef'':9B,13B ''documentari'':5B ''mad'':18B ''magic'':1A ''mallrat'':2A ''must'':15B ''pastri'':8B,12B ''scientist'':19B ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (548, 'MAGNIFICENT CHITTY', 'A Insightful Story of a Teacher And a Hunter who must Face a Mad Cow in California', '''california'':19B ''chitti'':2A ''cow'':17B ''face'':14B ''hunter'':11B ''insight'':4B ''mad'':16B ''magnific'':1A ''must'':13B ''stori'':5B ''teacher'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (549, 'MAGNOLIA FORRESTER', 'A Thoughtful Documentary of a Composer And a Explorer who must Conquer a Dentist in New Orleans', '''compos'':8B ''conquer'':14B ''dentist'':16B ''documentari'':5B ''explor'':11B ''forrest'':2A ''magnolia'':1A ''must'':13B ''new'':18B ''orlean'':19B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (550, 'MAGUIRE APACHE', 'A Fast-Paced Reflection of a Waitress And a Hunter who must Defeat a Forensic Psychologist in A Baloon', '''apach'':2A ''baloon'':22B ''defeat'':16B ''fast'':5B ''fast-pac'':4B ''forens'':18B ''hunter'':13B ''maguir'':1A ''must'':15B ''pace'':6B ''psychologist'':19B ''reflect'':7B ''waitress'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (551, 'MAIDEN HOME', 'A Lacklusture Saga of a Moose And a Teacher who must Kill a Forensic Psychologist in A MySQL Convention', '''convent'':21B ''forens'':16B ''home'':2A ''kill'':14B ''lacklustur'':4B ''maiden'':1A ''moos'':8B ''must'':13B ''mysql'':20B ''psychologist'':17B ''saga'':5B ''teacher'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (552, 'MAJESTIC FLOATS', 'A Thrilling Character Study of a Moose And a Student who must Escape a Butler in The First Manned Space Station', '''butler'':17B ''charact'':5B ''escap'':15B ''first'':20B ''float'':2A ''majest'':1A ''man'':21B ''moos'':9B ''must'':14B ''space'':22B ''station'':23B ''student'':12B ''studi'':6B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (553, 'MAKER GABLES', 'A Stunning Display of a Moose And a Database Administrator who must Pursue a Composer in A Jet Boat', '''administr'':12B ''boat'':21B ''compos'':17B ''databas'':11B ''display'':5B ''gabl'':2A ''jet'':20B ''maker'':1A ''moos'':8B ''must'':14B ''pursu'':15B ''stun'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (554, 'MALKOVICH PET', 'A Intrepid Reflection of a Waitress And a A Shark who must Kill a Squirrel in The Outback', '''intrepid'':4B ''kill'':15B ''malkovich'':1A ''must'':14B ''outback'':20B ''pet'':2A ''reflect'':5B ''shark'':12B ''squirrel'':17B ''waitress'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (555, 'MALLRATS UNITED', 'A Thrilling Yarn of a Waitress And a Dentist who must Find a Hunter in A Monastery', '''dentist'':11B ''find'':14B ''hunter'':16B ''mallrat'':1A ''monasteri'':19B ''must'':13B ''thrill'':4B ''unit'':2A ''waitress'':8B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (556, 'MALTESE HOPE', 'A Fast-Paced Documentary of a Crocodile And a Sumo Wrestler who must Conquer a Explorer in California', '''california'':21B ''conquer'':17B ''crocodil'':10B ''documentari'':7B ''explor'':19B ''fast'':5B ''fast-pac'':4B ''hope'':2A ''maltes'':1A ''must'':16B ''pace'':6B ''sumo'':13B ''wrestler'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (557, 'MANCHURIAN CURTAIN', 'A Stunning Tale of a Mad Cow And a Boy who must Battle a Boy in Berlin', '''battl'':15B ''berlin'':19B ''boy'':12B,17B ''cow'':9B ''curtain'':2A ''mad'':8B ''manchurian'':1A ''must'':14B ''stun'':4B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (558, 'MANNEQUIN WORST', 'A Astounding Saga of a Mad Cow And a Pastry Chef who must Discover a Husband in Ancient India', '''ancient'':20B ''astound'':4B ''chef'':13B ''cow'':9B ''discov'':16B ''husband'':18B ''india'':21B ''mad'':8B ''mannequin'':1A ''must'':15B ''pastri'':12B ''saga'':5B ''worst'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (559, 'MARRIED GO', 'A Fanciful Story of a Womanizer And a Dog who must Face a Forensic Psychologist in The Sahara Desert', '''desert'':21B ''dog'':11B ''face'':14B ''fanci'':4B ''forens'':16B ''go'':2A ''marri'':1A ''must'':13B ''psychologist'':17B ''sahara'':20B ''stori'':5B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (560, 'MARS ROMAN', 'A Boring Drama of a Car And a Dog who must Succumb a Madman in Soviet Georgia', '''bore'':4B ''car'':8B ''dog'':11B ''drama'':5B ''georgia'':19B ''madman'':16B ''mar'':1A ''must'':13B ''roman'':2A ''soviet'':18B ''succumb'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (561, 'MASK PEACH', 'A Boring Character Study of a Student And a Robot who must Meet a Woman in California', '''bore'':4B ''california'':19B ''charact'':5B ''mask'':1A ''meet'':15B ''must'':14B ''peach'':2A ''robot'':12B ''student'':9B ''studi'':6B ''woman'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (562, 'MASKED BUBBLE', 'A Fanciful Documentary of a Pioneer And a Boat who must Pursue a Pioneer in An Abandoned Mine Shaft', '''abandon'':19B ''boat'':11B ''bubbl'':2A ''documentari'':5B ''fanci'':4B ''mask'':1A ''mine'':20B ''must'':13B ''pioneer'':8B,16B ''pursu'':14B ''shaft'':21B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (564, 'MASSAGE IMAGE', 'A Fateful Drama of a Frisbee And a Crocodile who must Vanquish a Dog in The First Manned Space Station', '''crocodil'':11B ''dog'':16B ''drama'':5B ''fate'':4B ''first'':19B ''frisbe'':8B ''imag'':2A ''man'':20B ''massag'':1A ''must'':13B ''space'':21B ''station'':22B ''vanquish'':14B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (566, 'MAUDE MOD', 'A Beautiful Documentary of a Forensic Psychologist And a Cat who must Reach a Astronaut in Nigeria', '''astronaut'':17B ''beauti'':4B ''cat'':12B ''documentari'':5B ''forens'':8B ''maud'':1A ''mod'':2A ''must'':14B ''nigeria'':19B ''psychologist'':9B ''reach'':15B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (567, 'MEET CHOCOLATE', 'A Boring Documentary of a Dentist And a Butler who must Confront a Monkey in A MySQL Convention', '''bore'':4B ''butler'':11B ''chocol'':2A ''confront'':14B ''convent'':20B ''dentist'':8B ''documentari'':5B ''meet'':1A ''monkey'':16B ''must'':13B ''mysql'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (568, 'MEMENTO ZOOLANDER', 'A Touching Epistle of a Squirrel And a Explorer who must Redeem a Pastry Chef in The Sahara Desert', '''chef'':17B ''desert'':21B ''epistl'':5B ''explor'':11B ''memento'':1A ''must'':13B ''pastri'':16B ''redeem'':14B ''sahara'':20B ''squirrel'':8B ''touch'':4B ''zooland'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (569, 'MENAGERIE RUSHMORE', 'A Unbelieveable Panorama of a Composer And a Butler who must Overcome a Database Administrator in The First Manned Space Station', '''administr'':17B ''butler'':11B ''compos'':8B ''databas'':16B ''first'':20B ''man'':21B ''menageri'':1A ''must'':13B ''overcom'':14B ''panorama'':5B ''rushmor'':2A ''space'':22B ''station'':23B ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (570, 'MERMAID INSECTS', 'A Lacklusture Drama of a Waitress And a Husband who must Fight a Husband in California', '''california'':18B ''drama'':5B ''fight'':14B ''husband'':11B,16B ''insect'':2A ''lacklustur'':4B ''mermaid'':1A ''must'':13B ''waitress'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (571, 'METAL ARMAGEDDON', 'A Thrilling Display of a Lumberjack And a Crocodile who must Meet a Monkey in A Baloon Factory', '''armageddon'':2A ''baloon'':19B ''crocodil'':11B ''display'':5B ''factori'':20B ''lumberjack'':8B ''meet'':14B ''metal'':1A ''monkey'':16B ''must'':13B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (572, 'METROPOLIS COMA', 'A Emotional Saga of a Database Administrator And a Pastry Chef who must Confront a Teacher in A Baloon Factory', '''administr'':9B ''baloon'':21B ''chef'':13B ''coma'':2A ''confront'':16B ''databas'':8B ''emot'':4B ''factori'':22B ''metropoli'':1A ''must'':15B ''pastri'':12B ''saga'':5B ''teacher'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (573, 'MICROCOSMOS PARADISE', 'A Touching Character Study of a Boat And a Student who must Sink a A Shark in Nigeria', '''boat'':9B ''charact'':5B ''microcosmo'':1A ''must'':14B ''nigeria'':20B ''paradis'':2A ''shark'':18B ''sink'':15B ''student'':12B ''studi'':6B ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (574, 'MIDNIGHT WESTWARD', 'A Taut Reflection of a Husband And a A Shark who must Redeem a Pastry Chef in A Monastery', '''chef'':18B ''husband'':8B ''midnight'':1A ''monasteri'':21B ''must'':14B ''pastri'':17B ''redeem'':15B ''reflect'':5B ''shark'':12B ''taut'':4B ''westward'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (575, 'MIDSUMMER GROUNDHOG', 'A Fateful Panorama of a Moose And a Dog who must Chase a Crocodile in Ancient Japan', '''ancient'':18B ''chase'':14B ''crocodil'':16B ''dog'':11B ''fate'':4B ''groundhog'':2A ''japan'':19B ''midsumm'':1A ''moos'':8B ''must'':13B ''panorama'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (576, 'MIGHTY LUCK', 'A Astounding Epistle of a Mad Scientist And a Pioneer who must Escape a Database Administrator in A MySQL Convention', '''administr'':18B ''astound'':4B ''convent'':22B ''databas'':17B ''epistl'':5B ''escap'':15B ''luck'':2A ''mad'':8B ''mighti'':1A ''must'':14B ''mysql'':21B ''pioneer'':12B ''scientist'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (577, 'MILE MULAN', 'A Lacklusture Epistle of a Cat And a Husband who must Confront a Boy in A MySQL Convention', '''boy'':16B ''cat'':8B ''confront'':14B ''convent'':20B ''epistl'':5B ''husband'':11B ''lacklustur'':4B ''mile'':1A ''mulan'':2A ''must'':13B ''mysql'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (578, 'MILLION ACE', 'A Brilliant Documentary of a Womanizer And a Squirrel who must Find a Technical Writer in The Sahara Desert', '''ace'':2A ''brilliant'':4B ''desert'':21B ''documentari'':5B ''find'':14B ''million'':1A ''must'':13B ''sahara'':20B ''squirrel'':11B ''technic'':16B ''woman'':8B ''writer'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (579, 'MINDS TRUMAN', 'A Taut Yarn of a Mad Scientist And a Crocodile who must Outgun a Database Administrator in A Monastery', '''administr'':18B ''crocodil'':12B ''databas'':17B ''mad'':8B ''mind'':1A ''monasteri'':21B ''must'':14B ''outgun'':15B ''scientist'':9B ''taut'':4B ''truman'':2A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (580, 'MINE TITANS', 'A Amazing Yarn of a Robot And a Womanizer who must Discover a Forensic Psychologist in Berlin', '''amaz'':4B ''berlin'':19B ''discov'':14B ''forens'':16B ''mine'':1A ''must'':13B ''psychologist'':17B ''robot'':8B ''titan'':2A ''woman'':11B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (581, 'MINORITY KISS', 'A Insightful Display of a Lumberjack And a Sumo Wrestler who must Meet a Man in The Outback', '''display'':5B ''insight'':4B ''kiss'':2A ''lumberjack'':8B ''man'':17B ''meet'':15B ''minor'':1A ''must'':14B ''outback'':20B ''sumo'':11B ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (582, 'MIRACLE VIRTUAL', 'A Touching Epistle of a Butler And a Boy who must Find a Mad Scientist in The Sahara Desert', '''boy'':11B ''butler'':8B ''desert'':21B ''epistl'':5B ''find'':14B ''mad'':16B ''miracl'':1A ''must'':13B ''sahara'':20B ''scientist'':17B ''touch'':4B ''virtual'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (583, 'MISSION ZOOLANDER', 'A Intrepid Story of a Sumo Wrestler And a Teacher who must Meet a A Shark in An Abandoned Fun House', '''abandon'':21B ''fun'':22B ''hous'':23B ''intrepid'':4B ''meet'':15B ''mission'':1A ''must'':14B ''shark'':18B ''stori'':5B ''sumo'':8B ''teacher'':12B ''wrestler'':9B ''zooland'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (584, 'MIXED DOORS', 'A Taut Drama of a Womanizer And a Lumberjack who must Succumb a Pioneer in Ancient India', '''ancient'':18B ''door'':2A ''drama'':5B ''india'':19B ''lumberjack'':11B ''mix'':1A ''must'':13B ''pioneer'':16B ''succumb'':14B ''taut'':4B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (585, 'MOB DUFFEL', 'A Unbelieveable Documentary of a Frisbee And a Boat who must Meet a Boy in The Canadian Rockies', '''boat'':11B ''boy'':16B ''canadian'':19B ''documentari'':5B ''duffel'':2A ''frisbe'':8B ''meet'':14B ''mob'':1A ''must'':13B ''rocki'':20B ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (586, 'MOCKINGBIRD HOLLYWOOD', 'A Thoughtful Panorama of a Man And a Car who must Sink a Composer in Berlin', '''berlin'':18B ''car'':11B ''compos'':16B ''hollywood'':2A ''man'':8B ''mockingbird'':1A ''must'':13B ''panorama'':5B ''sink'':14B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (587, 'MOD SECRETARY', 'A Boring Documentary of a Mad Cow And a Cat who must Build a Lumberjack in New Orleans', '''bore'':4B ''build'':15B ''cat'':12B ''cow'':9B ''documentari'':5B ''lumberjack'':17B ''mad'':8B ''mod'':1A ''must'':14B ''new'':19B ''orlean'':20B ''secretari'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (588, 'MODEL FISH', 'A Beautiful Panorama of a Boat And a Crocodile who must Outrace a Dog in Australia', '''australia'':18B ''beauti'':4B ''boat'':8B ''crocodil'':11B ''dog'':16B ''fish'':2A ''model'':1A ''must'':13B ''outrac'':14B ''panorama'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (589, 'MODERN DORADO', 'A Awe-Inspiring Story of a Butler And a Sumo Wrestler who must Redeem a Boy in New Orleans', '''awe'':5B ''awe-inspir'':4B ''boy'':19B ''butler'':10B ''dorado'':2A ''inspir'':6B ''modern'':1A ''must'':16B ''new'':21B ''orlean'':22B ''redeem'':17B ''stori'':7B ''sumo'':13B ''wrestler'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (590, 'MONEY HAROLD', 'A Touching Tale of a Explorer And a Boat who must Defeat a Robot in Australia', '''australia'':18B ''boat'':11B ''defeat'':14B ''explor'':8B ''harold'':2A ''money'':1A ''must'':13B ''robot'':16B ''tale'':5B ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (591, 'MONSOON CAUSE', 'A Astounding Tale of a Crocodile And a Car who must Outrace a Squirrel in A U-Boat', '''astound'':4B ''boat'':21B ''car'':11B ''caus'':2A ''crocodil'':8B ''monsoon'':1A ''must'':13B ''outrac'':14B ''squirrel'':16B ''tale'':5B ''u'':20B ''u-boat'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (592, 'MONSTER SPARTACUS', 'A Fast-Paced Story of a Waitress And a Cat who must Fight a Girl in Australia', '''australia'':20B ''cat'':13B ''fast'':5B ''fast-pac'':4B ''fight'':16B ''girl'':18B ''monster'':1A ''must'':15B ''pace'':6B ''spartacus'':2A ''stori'':7B ''waitress'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (593, 'MONTEREY LABYRINTH', 'A Awe-Inspiring Drama of a Monkey And a Composer who must Escape a Feminist in A U-Boat', '''awe'':5B ''awe-inspir'':4B ''boat'':23B ''compos'':13B ''drama'':7B ''escap'':16B ''feminist'':18B ''inspir'':6B ''labyrinth'':2A ''monkey'':10B ''monterey'':1A ''must'':15B ''u'':22B ''u-boat'':21B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (594, 'MONTEZUMA COMMAND', 'A Thrilling Reflection of a Waitress And a Butler who must Battle a Butler in A Jet Boat', '''battl'':14B ''boat'':20B ''butler'':11B,16B ''command'':2A ''jet'':19B ''montezuma'':1A ''must'':13B ''reflect'':5B ''thrill'':4B ''waitress'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (595, 'MOON BUNCH', 'A Beautiful Tale of a Astronaut And a Mad Cow who must Challenge a Cat in A Baloon Factory', '''astronaut'':8B ''baloon'':20B ''beauti'':4B ''bunch'':2A ''cat'':17B ''challeng'':15B ''cow'':12B ''factori'':21B ''mad'':11B ''moon'':1A ''must'':14B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (596, 'MOONSHINE CABIN', 'A Thoughtful Display of a Astronaut And a Feminist who must Chase a Frisbee in A Jet Boat', '''astronaut'':8B ''boat'':20B ''cabin'':2A ''chase'':14B ''display'':5B ''feminist'':11B ''frisbe'':16B ''jet'':19B ''moonshin'':1A ''must'':13B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (597, 'MOONWALKER FOOL', 'A Epic Drama of a Feminist And a Pioneer who must Sink a Composer in New Orleans', '''compos'':16B ''drama'':5B ''epic'':4B ''feminist'':8B ''fool'':2A ''moonwalk'':1A ''must'':13B ''new'':18B ''orlean'':19B ''pioneer'':11B ''sink'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (598, 'MOSQUITO ARMAGEDDON', 'A Thoughtful Character Study of a Waitress And a Feminist who must Build a Teacher in Ancient Japan', '''ancient'':19B ''armageddon'':2A ''build'':15B ''charact'':5B ''feminist'':12B ''japan'':20B ''mosquito'':1A ''must'':14B ''studi'':6B ''teacher'':17B ''thought'':4B ''waitress'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (599, 'MOTHER OLEANDER', 'A Boring Tale of a Husband And a Boy who must Fight a Squirrel in Ancient China', '''ancient'':18B ''bore'':4B ''boy'':11B ''china'':19B ''fight'':14B ''husband'':8B ''mother'':1A ''must'':13B ''oleand'':2A ''squirrel'':16B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (600, 'MOTIONS DETAILS', 'A Awe-Inspiring Reflection of a Dog And a Student who must Kill a Car in An Abandoned Fun House', '''abandon'':21B ''awe'':5B ''awe-inspir'':4B ''car'':18B ''detail'':2A ''dog'':10B ''fun'':22B ''hous'':23B ''inspir'':6B ''kill'':16B ''motion'':1A ''must'':15B ''reflect'':7B ''student'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (601, 'MOULIN WAKE', 'A Astounding Story of a Forensic Psychologist And a Cat who must Battle a Teacher in An Abandoned Mine Shaft', '''abandon'':20B ''astound'':4B ''battl'':15B ''cat'':12B ''forens'':8B ''mine'':21B ''moulin'':1A ''must'':14B ''psychologist'':9B ''shaft'':22B ''stori'':5B ''teacher'':17B ''wake'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (602, 'MOURNING PURPLE', 'A Lacklusture Display of a Waitress And a Lumberjack who must Chase a Pioneer in New Orleans', '''chase'':14B ''display'':5B ''lacklustur'':4B ''lumberjack'':11B ''mourn'':1A ''must'':13B ''new'':18B ''orlean'':19B ''pioneer'':16B ''purpl'':2A ''waitress'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (603, 'MOVIE SHAKESPEARE', 'A Insightful Display of a Database Administrator And a Student who must Build a Hunter in Berlin', '''administr'':9B ''berlin'':19B ''build'':15B ''databas'':8B ''display'':5B ''hunter'':17B ''insight'':4B ''movi'':1A ''must'':14B ''shakespear'':2A ''student'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (604, 'MULAN MOON', 'A Emotional Saga of a Womanizer And a Pioneer who must Overcome a Dentist in A Baloon', '''baloon'':19B ''dentist'':16B ''emot'':4B ''moon'':2A ''mulan'':1A ''must'':13B ''overcom'':14B ''pioneer'':11B ''saga'':5B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (605, 'MULHOLLAND BEAST', 'A Awe-Inspiring Display of a Husband And a Squirrel who must Battle a Sumo Wrestler in A Jet Boat', '''awe'':5B ''awe-inspir'':4B ''battl'':16B ''beast'':2A ''boat'':23B ''display'':7B ''husband'':10B ''inspir'':6B ''jet'':22B ''mulholland'':1A ''must'':15B ''squirrel'':13B ''sumo'':18B ''wrestler'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (606, 'MUMMY CREATURES', 'A Fateful Character Study of a Crocodile And a Monkey who must Meet a Dentist in Australia', '''australia'':19B ''charact'':5B ''creatur'':2A ''crocodil'':9B ''dentist'':17B ''fate'':4B ''meet'':15B ''monkey'':12B ''mummi'':1A ''must'':14B ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (607, 'MUPPET MILE', 'A Lacklusture Story of a Madman And a Teacher who must Kill a Frisbee in The Gulf of Mexico', '''frisbe'':16B ''gulf'':19B ''kill'':14B ''lacklustur'':4B ''madman'':8B ''mexico'':21B ''mile'':2A ''muppet'':1A ''must'':13B ''stori'':5B ''teacher'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (608, 'MURDER ANTITRUST', 'A Brilliant Yarn of a Car And a Database Administrator who must Escape a Boy in A MySQL Convention', '''administr'':12B ''antitrust'':2A ''boy'':17B ''brilliant'':4B ''car'':8B ''convent'':21B ''databas'':11B ''escap'':15B ''murder'':1A ''must'':14B ''mysql'':20B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (609, 'MUSCLE BRIGHT', 'A Stunning Panorama of a Sumo Wrestler And a Husband who must Redeem a Madman in Ancient India', '''ancient'':19B ''bright'':2A ''husband'':12B ''india'':20B ''madman'':17B ''muscl'':1A ''must'':14B ''panorama'':5B ''redeem'':15B ''stun'':4B ''sumo'':8B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (610, 'MUSIC BOONDOCK', 'A Thrilling Tale of a Butler And a Astronaut who must Battle a Explorer in The First Manned Space Station', '''astronaut'':11B ''battl'':14B ''boondock'':2A ''butler'':8B ''explor'':16B ''first'':19B ''man'':20B ''music'':1A ''must'':13B ''space'':21B ''station'':22B ''tale'':5B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (611, 'MUSKETEERS WAIT', 'A Touching Yarn of a Student And a Moose who must Fight a Mad Cow in Australia', '''australia'':19B ''cow'':17B ''fight'':14B ''mad'':16B ''moos'':11B ''musket'':1A ''must'':13B ''student'':8B ''touch'':4B ''wait'':2A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (612, 'MUSSOLINI SPOILERS', 'A Thrilling Display of a Boat And a Monkey who must Meet a Composer in Ancient China', '''ancient'':18B ''boat'':8B ''china'':19B ''compos'':16B ''display'':5B ''meet'':14B ''monkey'':11B ''mussolini'':1A ''must'':13B ''spoiler'':2A ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (613, 'MYSTIC TRUMAN', 'A Epic Yarn of a Teacher And a Hunter who must Outgun a Explorer in Soviet Georgia', '''epic'':4B ''explor'':16B ''georgia'':19B ''hunter'':11B ''must'':13B ''mystic'':1A ''outgun'':14B ''soviet'':18B ''teacher'':8B ''truman'':2A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (614, 'NAME DETECTIVE', 'A Touching Saga of a Sumo Wrestler And a Cat who must Pursue a Mad Scientist in Nigeria', '''cat'':12B ''detect'':2A ''mad'':17B ''must'':14B ''name'':1A ''nigeria'':20B ''pursu'':15B ''saga'':5B ''scientist'':18B ''sumo'':8B ''touch'':4B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (615, 'NASH CHOCOLAT', 'A Epic Reflection of a Monkey And a Mad Cow who must Kill a Forensic Psychologist in An Abandoned Mine Shaft', '''abandon'':21B ''chocolat'':2A ''cow'':12B ''epic'':4B ''forens'':17B ''kill'':15B ''mad'':11B ''mine'':22B ''monkey'':8B ''must'':14B ''nash'':1A ''psychologist'':18B ''reflect'':5B ''shaft'':23B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (616, 'NATIONAL STORY', 'A Taut Epistle of a Mad Scientist And a Girl who must Escape a Monkey in California', '''california'':19B ''epistl'':5B ''escap'':15B ''girl'':12B ''mad'':8B ''monkey'':17B ''must'':14B ''nation'':1A ''scientist'':9B ''stori'':2A ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (617, 'NATURAL STOCK', 'A Fast-Paced Story of a Sumo Wrestler And a Girl who must Defeat a Car in A Baloon Factory', '''baloon'':22B ''car'':19B ''defeat'':17B ''factori'':23B ''fast'':5B ''fast-pac'':4B ''girl'':14B ''must'':16B ''natur'':1A ''pace'':6B ''stock'':2A ''stori'':7B ''sumo'':10B ''wrestler'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (618, 'NECKLACE OUTBREAK', 'A Astounding Epistle of a Database Administrator And a Mad Scientist who must Pursue a Cat in California', '''administr'':9B ''astound'':4B ''california'':20B ''cat'':18B ''databas'':8B ''epistl'':5B ''mad'':12B ''must'':15B ''necklac'':1A ''outbreak'':2A ''pursu'':16B ''scientist'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (619, 'NEIGHBORS CHARADE', 'A Fanciful Reflection of a Crocodile And a Astronaut who must Outrace a Feminist in An Abandoned Amusement Park', '''abandon'':19B ''amus'':20B ''astronaut'':11B ''charad'':2A ''crocodil'':8B ''fanci'':4B ''feminist'':16B ''must'':13B ''neighbor'':1A ''outrac'':14B ''park'':21B ''reflect'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (620, 'NEMO CAMPUS', 'A Lacklusture Reflection of a Monkey And a Squirrel who must Outrace a Womanizer in A Manhattan Penthouse', '''campus'':2A ''lacklustur'':4B ''manhattan'':19B ''monkey'':8B ''must'':13B ''nemo'':1A ''outrac'':14B ''penthous'':20B ''reflect'':5B ''squirrel'':11B ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (621, 'NETWORK PEAK', 'A Unbelieveable Reflection of a Butler And a Boat who must Outgun a Mad Scientist in California', '''boat'':11B ''butler'':8B ''california'':19B ''mad'':16B ''must'':13B ''network'':1A ''outgun'':14B ''peak'':2A ''reflect'':5B ''scientist'':17B ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (622, 'NEWSIES STORY', 'A Action-Packed Character Study of a Dog And a Lumberjack who must Outrace a Moose in The Gulf of Mexico', '''action'':5B ''action-pack'':4B ''charact'':7B ''dog'':11B ''gulf'':22B ''lumberjack'':14B ''mexico'':24B ''moos'':19B ''must'':16B ''newsi'':1A ''outrac'':17B ''pack'':6B ''stori'':2A ''studi'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (623, 'NEWTON LABYRINTH', 'A Intrepid Character Study of a Moose And a Waitress who must Find a A Shark in Ancient India', '''ancient'':20B ''charact'':5B ''find'':15B ''india'':21B ''intrepid'':4B ''labyrinth'':2A ''moos'':9B ''must'':14B ''newton'':1A ''shark'':18B ''studi'':6B ''waitress'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (624, 'NIGHTMARE CHILL', 'A Brilliant Display of a Robot And a Butler who must Fight a Waitress in An Abandoned Mine Shaft', '''abandon'':19B ''brilliant'':4B ''butler'':11B ''chill'':2A ''display'':5B ''fight'':14B ''mine'':20B ''must'':13B ''nightmar'':1A ''robot'':8B ''shaft'':21B ''waitress'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (625, 'NONE SPIKING', 'A Boring Reflection of a Secret Agent And a Astronaut who must Face a Composer in A Manhattan Penthouse', '''agent'':9B ''astronaut'':12B ''bore'':4B ''compos'':17B ''face'':15B ''manhattan'':20B ''must'':14B ''none'':1A ''penthous'':21B ''reflect'':5B ''secret'':8B ''spike'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (626, 'NOON PAPI', 'A Unbelieveable Character Study of a Mad Scientist And a Astronaut who must Find a Pioneer in A Manhattan Penthouse', '''astronaut'':13B ''charact'':5B ''find'':16B ''mad'':9B ''manhattan'':21B ''must'':15B ''noon'':1A ''papi'':2A ''penthous'':22B ''pioneer'':18B ''scientist'':10B ''studi'':6B ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (627, 'NORTH TEQUILA', 'A Beautiful Character Study of a Mad Cow And a Robot who must Reach a Womanizer in New Orleans', '''beauti'':4B ''charact'':5B ''cow'':10B ''mad'':9B ''must'':15B ''new'':20B ''north'':1A ''orlean'':21B ''reach'':16B ''robot'':13B ''studi'':6B ''tequila'':2A ''woman'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (628, 'NORTHWEST POLISH', 'A Boring Character Study of a Boy And a A Shark who must Outrace a Womanizer in The Outback', '''bore'':4B ''boy'':9B ''charact'':5B ''must'':15B ''northwest'':1A ''outback'':21B ''outrac'':16B ''polish'':2A ''shark'':13B ''studi'':6B ''woman'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (629, 'NOTORIOUS REUNION', 'A Amazing Epistle of a Woman And a Squirrel who must Fight a Hunter in A Baloon', '''amaz'':4B ''baloon'':19B ''epistl'':5B ''fight'':14B ''hunter'':16B ''must'':13B ''notori'':1A ''reunion'':2A ''squirrel'':11B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (630, 'NOTTING SPEAKEASY', 'A Thoughtful Display of a Butler And a Womanizer who must Find a Waitress in The Canadian Rockies', '''butler'':8B ''canadian'':19B ''display'':5B ''find'':14B ''must'':13B ''not'':1A ''rocki'':20B ''speakeasi'':2A ''thought'':4B ''waitress'':16B ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (631, 'NOVOCAINE FLIGHT', 'A Fanciful Display of a Student And a Teacher who must Outgun a Crocodile in Nigeria', '''crocodil'':16B ''display'':5B ''fanci'':4B ''flight'':2A ''must'':13B ''nigeria'':18B ''novocain'':1A ''outgun'':14B ''student'':8B ''teacher'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (632, 'NUTS TIES', 'A Thoughtful Drama of a Explorer And a Womanizer who must Meet a Teacher in California', '''california'':18B ''drama'':5B ''explor'':8B ''meet'':14B ''must'':13B ''nut'':1A ''teacher'':16B ''thought'':4B ''tie'':2A ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (633, 'OCTOBER SUBMARINE', 'A Taut Epistle of a Monkey And a Boy who must Confront a Husband in A Jet Boat', '''boat'':20B ''boy'':11B ''confront'':14B ''epistl'':5B ''husband'':16B ''jet'':19B ''monkey'':8B ''must'':13B ''octob'':1A ''submarin'':2A ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (634, 'ODDS BOOGIE', 'A Thrilling Yarn of a Feminist And a Madman who must Battle a Hunter in Berlin', '''battl'':14B ''berlin'':18B ''boogi'':2A ''feminist'':8B ''hunter'':16B ''madman'':11B ''must'':13B ''odd'':1A ''thrill'':4B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (635, 'OKLAHOMA JUMANJI', 'A Thoughtful Drama of a Dentist And a Womanizer who must Meet a Husband in The Sahara Desert', '''dentist'':8B ''desert'':20B ''drama'':5B ''husband'':16B ''jumanji'':2A ''meet'':14B ''must'':13B ''oklahoma'':1A ''sahara'':19B ''thought'':4B ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (636, 'OLEANDER CLUE', 'A Boring Story of a Teacher And a Monkey who must Succumb a Forensic Psychologist in A Jet Boat', '''boat'':21B ''bore'':4B ''clue'':2A ''forens'':16B ''jet'':20B ''monkey'':11B ''must'':13B ''oleand'':1A ''psychologist'':17B ''stori'':5B ''succumb'':14B ''teacher'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (637, 'OPEN AFRICAN', 'A Lacklusture Drama of a Secret Agent And a Explorer who must Discover a Car in A U-Boat', '''african'':2A ''agent'':9B ''boat'':22B ''car'':17B ''discov'':15B ''drama'':5B ''explor'':12B ''lacklustur'':4B ''must'':14B ''open'':1A ''secret'':8B ''u'':21B ''u-boat'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (638, 'OPERATION OPERATION', 'A Intrepid Character Study of a Man And a Frisbee who must Overcome a Madman in Ancient China', '''ancient'':19B ''charact'':5B ''china'':20B ''frisbe'':12B ''intrepid'':4B ''madman'':17B ''man'':9B ''must'':14B ''oper'':1A,2A ''overcom'':15B ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (639, 'OPPOSITE NECKLACE', 'A Fateful Epistle of a Crocodile And a Moose who must Kill a Explorer in Nigeria', '''crocodil'':8B ''epistl'':5B ''explor'':16B ''fate'':4B ''kill'':14B ''moos'':11B ''must'':13B ''necklac'':2A ''nigeria'':18B ''opposit'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (640, 'OPUS ICE', 'A Fast-Paced Drama of a Hunter And a Boy who must Discover a Feminist in The Sahara Desert', '''boy'':13B ''desert'':22B ''discov'':16B ''drama'':7B ''fast'':5B ''fast-pac'':4B ''feminist'':18B ''hunter'':10B ''ice'':2A ''must'':15B ''opus'':1A ''pace'':6B ''sahara'':21B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (641, 'ORANGE GRAPES', 'A Astounding Documentary of a Butler And a Womanizer who must Face a Dog in A U-Boat', '''astound'':4B ''boat'':21B ''butler'':8B ''documentari'':5B ''dog'':16B ''face'':14B ''grape'':2A ''must'':13B ''orang'':1A ''u'':20B ''u-boat'':19B ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (642, 'ORDER BETRAYED', 'A Amazing Saga of a Dog And a A Shark who must Challenge a Cat in The Sahara Desert', '''amaz'':4B ''betray'':2A ''cat'':17B ''challeng'':15B ''desert'':21B ''dog'':8B ''must'':14B ''order'':1A ''saga'':5B ''sahara'':20B ''shark'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (643, 'ORIENT CLOSER', 'A Astounding Epistle of a Technical Writer And a Teacher who must Fight a Squirrel in The Sahara Desert', '''astound'':4B ''closer'':2A ''desert'':21B ''epistl'':5B ''fight'':15B ''must'':14B ''orient'':1A ''sahara'':20B ''squirrel'':17B ''teacher'':12B ''technic'':8B ''writer'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (644, 'OSCAR GOLD', 'A Insightful Tale of a Database Administrator And a Dog who must Face a Madman in Soviet Georgia', '''administr'':9B ''databas'':8B ''dog'':12B ''face'':15B ''georgia'':20B ''gold'':2A ''insight'':4B ''madman'':17B ''must'':14B ''oscar'':1A ''soviet'':19B ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (645, 'OTHERS SOUP', 'A Lacklusture Documentary of a Mad Cow And a Madman who must Sink a Moose in The Gulf of Mexico', '''cow'':9B ''documentari'':5B ''gulf'':20B ''lacklustur'':4B ''mad'':8B ''madman'':12B ''mexico'':22B ''moos'':17B ''must'':14B ''other'':1A ''sink'':15B ''soup'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (671, 'PERDITION FARGO', 'A Fast-Paced Story of a Car And a Cat who must Outgun a Hunter in Berlin', '''berlin'':20B ''car'':10B ''cat'':13B ''fargo'':2A ''fast'':5B ''fast-pac'':4B ''hunter'':18B ''must'':15B ''outgun'':16B ''pace'':6B ''perdit'':1A ''stori'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (646, 'OUTBREAK DIVINE', 'A Unbelieveable Yarn of a Database Administrator And a Woman who must Succumb a A Shark in A U-Boat', '''administr'':9B ''boat'':23B ''databas'':8B ''divin'':2A ''must'':14B ''outbreak'':1A ''shark'':18B ''succumb'':15B ''u'':22B ''u-boat'':21B ''unbeliev'':4B ''woman'':12B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (647, 'OUTFIELD MASSACRE', 'A Thoughtful Drama of a Husband And a Secret Agent who must Pursue a Database Administrator in Ancient India', '''administr'':18B ''agent'':12B ''ancient'':20B ''databas'':17B ''drama'':5B ''husband'':8B ''india'':21B ''massacr'':2A ''must'':14B ''outfield'':1A ''pursu'':15B ''secret'':11B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (648, 'OUTLAW HANKY', 'A Thoughtful Story of a Astronaut And a Composer who must Conquer a Dog in The Sahara Desert', '''astronaut'':8B ''compos'':11B ''conquer'':14B ''desert'':20B ''dog'':16B ''hanki'':2A ''must'':13B ''outlaw'':1A ''sahara'':19B ''stori'':5B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (649, 'OZ LIAISONS', 'A Epic Yarn of a Mad Scientist And a Cat who must Confront a Womanizer in A Baloon Factory', '''baloon'':20B ''cat'':12B ''confront'':15B ''epic'':4B ''factori'':21B ''liaison'':2A ''mad'':8B ''must'':14B ''oz'':1A ''scientist'':9B ''woman'':17B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (650, 'PACIFIC AMISTAD', 'A Thrilling Yarn of a Dog And a Moose who must Kill a Pastry Chef in A Manhattan Penthouse', '''amistad'':2A ''chef'':17B ''dog'':8B ''kill'':14B ''manhattan'':20B ''moos'':11B ''must'':13B ''pacif'':1A ''pastri'':16B ''penthous'':21B ''thrill'':4B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (651, 'PACKER MADIGAN', 'A Epic Display of a Sumo Wrestler And a Forensic Psychologist who must Build a Woman in An Abandoned Amusement Park', '''abandon'':21B ''amus'':22B ''build'':16B ''display'':5B ''epic'':4B ''forens'':12B ''madigan'':2A ''must'':15B ''packer'':1A ''park'':23B ''psychologist'':13B ''sumo'':8B ''woman'':18B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (652, 'PAJAMA JAWBREAKER', 'A Emotional Drama of a Boy And a Technical Writer who must Redeem a Sumo Wrestler in California', '''boy'':8B ''california'':20B ''drama'':5B ''emot'':4B ''jawbreak'':2A ''must'':14B ''pajama'':1A ''redeem'':15B ''sumo'':17B ''technic'':11B ''wrestler'':18B ''writer'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (653, 'PANIC CLUB', 'A Fanciful Display of a Teacher And a Crocodile who must Succumb a Girl in A Baloon', '''baloon'':19B ''club'':2A ''crocodil'':11B ''display'':5B ''fanci'':4B ''girl'':16B ''must'':13B ''panic'':1A ''succumb'':14B ''teacher'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (654, 'PANKY SUBMARINE', 'A Touching Documentary of a Dentist And a Sumo Wrestler who must Overcome a Boy in The Gulf of Mexico', '''boy'':17B ''dentist'':8B ''documentari'':5B ''gulf'':20B ''mexico'':22B ''must'':14B ''overcom'':15B ''panki'':1A ''submarin'':2A ''sumo'':11B ''touch'':4B ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (655, 'PANTHER REDS', 'A Brilliant Panorama of a Moose And a Man who must Reach a Teacher in The Gulf of Mexico', '''brilliant'':4B ''gulf'':19B ''man'':11B ''mexico'':21B ''moos'':8B ''must'':13B ''panorama'':5B ''panther'':1A ''reach'':14B ''red'':2A ''teacher'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (656, 'PAPI NECKLACE', 'A Fanciful Display of a Car And a Monkey who must Escape a Squirrel in Ancient Japan', '''ancient'':18B ''car'':8B ''display'':5B ''escap'':14B ''fanci'':4B ''japan'':19B ''monkey'':11B ''must'':13B ''necklac'':2A ''papi'':1A ''squirrel'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (657, 'PARADISE SABRINA', 'A Intrepid Yarn of a Car And a Moose who must Outrace a Crocodile in A Manhattan Penthouse', '''car'':8B ''crocodil'':16B ''intrepid'':4B ''manhattan'':19B ''moos'':11B ''must'':13B ''outrac'':14B ''paradis'':1A ''penthous'':20B ''sabrina'':2A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (658, 'PARIS WEEKEND', 'A Intrepid Story of a Squirrel And a Crocodile who must Defeat a Monkey in The Outback', '''crocodil'':11B ''defeat'':14B ''intrepid'':4B ''monkey'':16B ''must'':13B ''outback'':19B ''pari'':1A ''squirrel'':8B ''stori'':5B ''weekend'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (659, 'PARK CITIZEN', 'A Taut Epistle of a Sumo Wrestler And a Girl who must Face a Husband in Ancient Japan', '''ancient'':19B ''citizen'':2A ''epistl'':5B ''face'':15B ''girl'':12B ''husband'':17B ''japan'':20B ''must'':14B ''park'':1A ''sumo'':8B ''taut'':4B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (660, 'PARTY KNOCK', 'A Fateful Display of a Technical Writer And a Butler who must Battle a Sumo Wrestler in An Abandoned Mine Shaft', '''abandon'':21B ''battl'':15B ''butler'':12B ''display'':5B ''fate'':4B ''knock'':2A ''mine'':22B ''must'':14B ''parti'':1A ''shaft'':23B ''sumo'':17B ''technic'':8B ''wrestler'':18B ''writer'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (661, 'PAST SUICIDES', 'A Intrepid Tale of a Madman And a Astronaut who must Challenge a Hunter in A Monastery', '''astronaut'':11B ''challeng'':14B ''hunter'':16B ''intrepid'':4B ''madman'':8B ''monasteri'':19B ''must'':13B ''past'':1A ''suicid'':2A ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (662, 'PATHS CONTROL', 'A Astounding Documentary of a Butler And a Cat who must Find a Frisbee in Ancient China', '''ancient'':18B ''astound'':4B ''butler'':8B ''cat'':11B ''china'':19B ''control'':2A ''documentari'':5B ''find'':14B ''frisbe'':16B ''must'':13B ''path'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (663, 'PATIENT SISTER', 'A Emotional Epistle of a Squirrel And a Robot who must Confront a Lumberjack in Soviet Georgia', '''confront'':14B ''emot'':4B ''epistl'':5B ''georgia'':19B ''lumberjack'':16B ''must'':13B ''patient'':1A ''robot'':11B ''sister'':2A ''soviet'':18B ''squirrel'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (664, 'PATRIOT ROMAN', 'A Taut Saga of a Robot And a Database Administrator who must Challenge a Astronaut in California', '''administr'':12B ''astronaut'':17B ''california'':19B ''challeng'':15B ''databas'':11B ''must'':14B ''patriot'':1A ''robot'':8B ''roman'':2A ''saga'':5B ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (665, 'PATTON INTERVIEW', 'A Thrilling Documentary of a Composer And a Secret Agent who must Succumb a Cat in Berlin', '''agent'':12B ''berlin'':19B ''cat'':17B ''compos'':8B ''documentari'':5B ''interview'':2A ''must'':14B ''patton'':1A ''secret'':11B ''succumb'':15B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (666, 'PAYCHECK WAIT', 'A Awe-Inspiring Reflection of a Boy And a Man who must Discover a Moose in The Sahara Desert', '''awe'':5B ''awe-inspir'':4B ''boy'':10B ''desert'':22B ''discov'':16B ''inspir'':6B ''man'':13B ''moos'':18B ''must'':15B ''paycheck'':1A ''reflect'':7B ''sahara'':21B ''wait'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (667, 'PEACH INNOCENT', 'A Action-Packed Drama of a Monkey And a Dentist who must Chase a Butler in Berlin', '''action'':5B ''action-pack'':4B ''berlin'':20B ''butler'':18B ''chase'':16B ''dentist'':13B ''drama'':7B ''innoc'':2A ''monkey'':10B ''must'':15B ''pack'':6B ''peach'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (668, 'PEAK FOREVER', 'A Insightful Reflection of a Boat And a Secret Agent who must Vanquish a Astronaut in An Abandoned Mine Shaft', '''abandon'':20B ''agent'':12B ''astronaut'':17B ''boat'':8B ''forev'':2A ''insight'':4B ''mine'':21B ''must'':14B ''peak'':1A ''reflect'':5B ''secret'':11B ''shaft'':22B ''vanquish'':15B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (669, 'PEARL DESTINY', 'A Lacklusture Yarn of a Astronaut And a Pastry Chef who must Sink a Dog in A U-Boat', '''astronaut'':8B ''boat'':22B ''chef'':12B ''destini'':2A ''dog'':17B ''lacklustur'':4B ''must'':14B ''pastri'':11B ''pearl'':1A ''sink'':15B ''u'':21B ''u-boat'':20B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (670, 'PELICAN COMFORTS', 'A Epic Documentary of a Boy And a Monkey who must Pursue a Astronaut in Berlin', '''astronaut'':16B ''berlin'':18B ''boy'':8B ''comfort'':2A ''documentari'':5B ''epic'':4B ''monkey'':11B ''must'':13B ''pelican'':1A ''pursu'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (673, 'PERSONAL LADYBUGS', 'A Epic Saga of a Hunter And a Technical Writer who must Conquer a Cat in Ancient Japan', '''ancient'':19B ''cat'':17B ''conquer'':15B ''epic'':4B ''hunter'':8B ''japan'':20B ''ladybug'':2A ''must'':14B ''person'':1A ''saga'':5B ''technic'':11B ''writer'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (674, 'PET HAUNTING', 'A Unbelieveable Reflection of a Explorer And a Boat who must Conquer a Woman in California', '''boat'':11B ''california'':18B ''conquer'':14B ''explor'':8B ''haunt'':2A ''must'':13B ''pet'':1A ''reflect'':5B ''unbeliev'':4B ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (675, 'PHANTOM GLORY', 'A Beautiful Documentary of a Astronaut And a Crocodile who must Discover a Madman in A Monastery', '''astronaut'':8B ''beauti'':4B ''crocodil'':11B ''discov'':14B ''documentari'':5B ''glori'':2A ''madman'':16B ''monasteri'':19B ''must'':13B ''phantom'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (676, 'PHILADELPHIA WIFE', 'A Taut Yarn of a Hunter And a Astronaut who must Conquer a Database Administrator in The Sahara Desert', '''administr'':17B ''astronaut'':11B ''conquer'':14B ''databas'':16B ''desert'':21B ''hunter'':8B ''must'':13B ''philadelphia'':1A ''sahara'':20B ''taut'':4B ''wife'':2A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (677, 'PIANIST OUTFIELD', 'A Intrepid Story of a Boy And a Technical Writer who must Pursue a Lumberjack in A Monastery', '''boy'':8B ''intrepid'':4B ''lumberjack'':17B ''monasteri'':20B ''must'':14B ''outfield'':2A ''pianist'':1A ''pursu'':15B ''stori'':5B ''technic'':11B ''writer'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (678, 'PICKUP DRIVING', 'A Touching Documentary of a Husband And a Boat who must Meet a Pastry Chef in A Baloon Factory', '''baloon'':20B ''boat'':11B ''chef'':17B ''documentari'':5B ''drive'':2A ''factori'':21B ''husband'':8B ''meet'':14B ''must'':13B ''pastri'':16B ''pickup'':1A ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (679, 'PILOT HOOSIERS', 'A Awe-Inspiring Reflection of a Crocodile And a Sumo Wrestler who must Meet a Forensic Psychologist in An Abandoned Mine Shaft', '''abandon'':23B ''awe'':5B ''awe-inspir'':4B ''crocodil'':10B ''forens'':19B ''hoosier'':2A ''inspir'':6B ''meet'':17B ''mine'':24B ''must'':16B ''pilot'':1A ''psychologist'':20B ''reflect'':7B ''shaft'':25B ''sumo'':13B ''wrestler'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (680, 'PINOCCHIO SIMON', 'A Action-Packed Reflection of a Mad Scientist And a A Shark who must Find a Feminist in California', '''action'':5B ''action-pack'':4B ''california'':22B ''feminist'':20B ''find'':18B ''mad'':10B ''must'':17B ''pack'':6B ''pinocchio'':1A ''reflect'':7B ''scientist'':11B ''shark'':15B ''simon'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (681, 'PIRATES ROXANNE', 'A Stunning Drama of a Woman And a Lumberjack who must Overcome a A Shark in The Canadian Rockies', '''canadian'':20B ''drama'':5B ''lumberjack'':11B ''must'':13B ''overcom'':14B ''pirat'':1A ''rocki'':21B ''roxann'':2A ''shark'':17B ''stun'':4B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (682, 'PITTSBURGH HUNCHBACK', 'A Thrilling Epistle of a Boy And a Boat who must Find a Student in Soviet Georgia', '''boat'':11B ''boy'':8B ''epistl'':5B ''find'':14B ''georgia'':19B ''hunchback'':2A ''must'':13B ''pittsburgh'':1A ''soviet'':18B ''student'':16B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (683, 'PITY BOUND', 'A Boring Panorama of a Feminist And a Moose who must Defeat a Database Administrator in Nigeria', '''administr'':17B ''bore'':4B ''bound'':2A ''databas'':16B ''defeat'':14B ''feminist'':8B ''moos'':11B ''must'':13B ''nigeria'':19B ''panorama'':5B ''piti'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (684, 'PIZZA JUMANJI', 'A Epic Saga of a Cat And a Squirrel who must Outgun a Robot in A U-Boat', '''boat'':21B ''cat'':8B ''epic'':4B ''jumanji'':2A ''must'':13B ''outgun'':14B ''pizza'':1A ''robot'':16B ''saga'':5B ''squirrel'':11B ''u'':20B ''u-boat'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (685, 'PLATOON INSTINCT', 'A Thrilling Panorama of a Man And a Woman who must Reach a Woman in Australia', '''australia'':18B ''instinct'':2A ''man'':8B ''must'':13B ''panorama'':5B ''platoon'':1A ''reach'':14B ''thrill'':4B ''woman'':11B,16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (686, 'PLUTO OLEANDER', 'A Action-Packed Reflection of a Car And a Moose who must Outgun a Car in A Shark Tank', '''action'':5B ''action-pack'':4B ''car'':10B,18B ''moos'':13B ''must'':15B ''oleand'':2A ''outgun'':16B ''pack'':6B ''pluto'':1A ''reflect'':7B ''shark'':21B ''tank'':22B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (687, 'POCUS PULP', 'A Intrepid Yarn of a Frisbee And a Dog who must Build a Astronaut in A Baloon Factory', '''astronaut'':16B ''baloon'':19B ''build'':14B ''dog'':11B ''factori'':20B ''frisbe'':8B ''intrepid'':4B ''must'':13B ''pocus'':1A ''pulp'':2A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (688, 'POLISH BROOKLYN', 'A Boring Character Study of a Database Administrator And a Lumberjack who must Reach a Madman in The Outback', '''administr'':10B ''bore'':4B ''brooklyn'':2A ''charact'':5B ''databas'':9B ''lumberjack'':13B ''madman'':18B ''must'':15B ''outback'':21B ''polish'':1A ''reach'':16B ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (689, 'POLLOCK DELIVERANCE', 'A Intrepid Story of a Madman And a Frisbee who must Outgun a Boat in The Sahara Desert', '''boat'':16B ''deliver'':2A ''desert'':20B ''frisbe'':11B ''intrepid'':4B ''madman'':8B ''must'':13B ''outgun'':14B ''pollock'':1A ''sahara'':19B ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (690, 'POND SEATTLE', 'A Stunning Drama of a Teacher And a Boat who must Battle a Feminist in Ancient China', '''ancient'':18B ''battl'':14B ''boat'':11B ''china'':19B ''drama'':5B ''feminist'':16B ''must'':13B ''pond'':1A ''seattl'':2A ''stun'':4B ''teacher'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (691, 'POSEIDON FOREVER', 'A Thoughtful Epistle of a Womanizer And a Monkey who must Vanquish a Dentist in A Monastery', '''dentist'':16B ''epistl'':5B ''forev'':2A ''monasteri'':19B ''monkey'':11B ''must'':13B ''poseidon'':1A ''thought'':4B ''vanquish'':14B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (692, 'POTLUCK MIXED', 'A Beautiful Story of a Dog And a Technical Writer who must Outgun a Student in A Baloon', '''baloon'':20B ''beauti'':4B ''dog'':8B ''mix'':2A ''must'':14B ''outgun'':15B ''potluck'':1A ''stori'':5B ''student'':17B ''technic'':11B ''writer'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (693, 'POTTER CONNECTICUT', 'A Thrilling Epistle of a Frisbee And a Cat who must Fight a Technical Writer in Berlin', '''berlin'':19B ''cat'':11B ''connecticut'':2A ''epistl'':5B ''fight'':14B ''frisbe'':8B ''must'':13B ''potter'':1A ''technic'':16B ''thrill'':4B ''writer'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (694, 'PREJUDICE OLEANDER', 'A Epic Saga of a Boy And a Dentist who must Outrace a Madman in A U-Boat', '''boat'':21B ''boy'':8B ''dentist'':11B ''epic'':4B ''madman'':16B ''must'':13B ''oleand'':2A ''outrac'':14B ''prejudic'':1A ''saga'':5B ''u'':20B ''u-boat'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (695, 'PRESIDENT BANG', 'A Fateful Panorama of a Technical Writer And a Moose who must Battle a Robot in Soviet Georgia', '''bang'':2A ''battl'':15B ''fate'':4B ''georgia'':20B ''moos'':12B ''must'':14B ''panorama'':5B ''presid'':1A ''robot'':17B ''soviet'':19B ''technic'':8B ''writer'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (696, 'PRIDE ALAMO', 'A Thoughtful Drama of a A Shark And a Forensic Psychologist who must Vanquish a Student in Ancient India', '''alamo'':2A ''ancient'':20B ''drama'':5B ''forens'':12B ''india'':21B ''must'':15B ''pride'':1A ''psychologist'':13B ''shark'':9B ''student'':18B ''thought'':4B ''vanquish'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (697, 'PRIMARY GLASS', 'A Fateful Documentary of a Pastry Chef And a Butler who must Build a Dog in The Canadian Rockies', '''build'':15B ''butler'':12B ''canadian'':20B ''chef'':9B ''documentari'':5B ''dog'':17B ''fate'':4B ''glass'':2A ''must'':14B ''pastri'':8B ''primari'':1A ''rocki'':21B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (698, 'PRINCESS GIANT', 'A Thrilling Yarn of a Pastry Chef And a Monkey who must Battle a Monkey in A Shark Tank', '''battl'':15B ''chef'':9B ''giant'':2A ''monkey'':12B,17B ''must'':14B ''pastri'':8B ''princess'':1A ''shark'':20B ''tank'':21B ''thrill'':4B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (699, 'PRIVATE DROP', 'A Stunning Story of a Technical Writer And a Hunter who must Succumb a Secret Agent in A Baloon', '''agent'':18B ''baloon'':21B ''drop'':2A ''hunter'':12B ''must'':14B ''privat'':1A ''secret'':17B ''stori'':5B ''stun'':4B ''succumb'':15B ''technic'':8B ''writer'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (700, 'PRIX UNDEFEATED', 'A Stunning Saga of a Mad Scientist And a Boat who must Overcome a Dentist in Ancient China', '''ancient'':19B ''boat'':12B ''china'':20B ''dentist'':17B ''mad'':8B ''must'':14B ''overcom'':15B ''prix'':1A ''saga'':5B ''scientist'':9B ''stun'':4B ''undef'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (701, 'PSYCHO SHRUNK', 'A Amazing Panorama of a Crocodile And a Explorer who must Fight a Husband in Nigeria', '''amaz'':4B ''crocodil'':8B ''explor'':11B ''fight'':14B ''husband'':16B ''must'':13B ''nigeria'':18B ''panorama'':5B ''psycho'':1A ''shrunk'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (702, 'PULP BEVERLY', 'A Unbelieveable Display of a Dog And a Crocodile who must Outrace a Man in Nigeria', '''bever'':2A ''crocodil'':11B ''display'':5B ''dog'':8B ''man'':16B ''must'':13B ''nigeria'':18B ''outrac'':14B ''pulp'':1A ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (703, 'PUNK DIVORCE', 'A Fast-Paced Tale of a Pastry Chef And a Boat who must Face a Frisbee in The Canadian Rockies', '''boat'':14B ''canadian'':22B ''chef'':11B ''divorc'':2A ''face'':17B ''fast'':5B ''fast-pac'':4B ''frisbe'':19B ''must'':16B ''pace'':6B ''pastri'':10B ''punk'':1A ''rocki'':23B ''tale'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (704, 'PURE RUNNER', 'A Thoughtful Documentary of a Student And a Madman who must Challenge a Squirrel in A Manhattan Penthouse', '''challeng'':14B ''documentari'':5B ''madman'':11B ''manhattan'':19B ''must'':13B ''penthous'':20B ''pure'':1A ''runner'':2A ''squirrel'':16B ''student'':8B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (705, 'PURPLE MOVIE', 'A Boring Display of a Pastry Chef And a Sumo Wrestler who must Discover a Frisbee in An Abandoned Amusement Park', '''abandon'':21B ''amus'':22B ''bore'':4B ''chef'':9B ''discov'':16B ''display'':5B ''frisbe'':18B ''movi'':2A ''must'':15B ''park'':23B ''pastri'':8B ''purpl'':1A ''sumo'':12B ''wrestler'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (706, 'QUEEN LUKE', 'A Astounding Story of a Girl And a Boy who must Challenge a Composer in New Orleans', '''astound'':4B ''boy'':11B ''challeng'':14B ''compos'':16B ''girl'':8B ''luke'':2A ''must'':13B ''new'':18B ''orlean'':19B ''queen'':1A ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (707, 'QUEST MUSSOLINI', 'A Fateful Drama of a Husband And a Sumo Wrestler who must Battle a Pastry Chef in A Baloon Factory', '''baloon'':21B ''battl'':15B ''chef'':18B ''drama'':5B ''factori'':22B ''fate'':4B ''husband'':8B ''mussolini'':2A ''must'':14B ''pastri'':17B ''quest'':1A ''sumo'':11B ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (708, 'QUILLS BULL', 'A Thoughtful Story of a Pioneer And a Woman who must Reach a Moose in Australia', '''australia'':18B ''bull'':2A ''moos'':16B ''must'':13B ''pioneer'':8B ''quill'':1A ''reach'':14B ''stori'':5B ''thought'':4B ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (709, 'RACER EGG', 'A Emotional Display of a Monkey And a Waitress who must Reach a Secret Agent in California', '''agent'':17B ''california'':19B ''display'':5B ''egg'':2A ''emot'':4B ''monkey'':8B ''must'':13B ''racer'':1A ''reach'':14B ''secret'':16B ''waitress'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (710, 'RAGE GAMES', 'A Fast-Paced Saga of a Astronaut And a Secret Agent who must Escape a Hunter in An Abandoned Amusement Park', '''abandon'':22B ''agent'':14B ''amus'':23B ''astronaut'':10B ''escap'':17B ''fast'':5B ''fast-pac'':4B ''game'':2A ''hunter'':19B ''must'':16B ''pace'':6B ''park'':24B ''rage'':1A ''saga'':7B ''secret'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (711, 'RAGING AIRPLANE', 'A Astounding Display of a Secret Agent And a Technical Writer who must Escape a Mad Scientist in A Jet Boat', '''agent'':9B ''airplan'':2A ''astound'':4B ''boat'':23B ''display'':5B ''escap'':16B ''jet'':22B ''mad'':18B ''must'':15B ''rage'':1A ''scientist'':19B ''secret'':8B ''technic'':12B ''writer'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (712, 'RAIDERS ANTITRUST', 'A Amazing Drama of a Teacher And a Feminist who must Meet a Woman in The First Manned Space Station', '''amaz'':4B ''antitrust'':2A ''drama'':5B ''feminist'':11B ''first'':19B ''man'':20B ''meet'':14B ''must'':13B ''raider'':1A ''space'':21B ''station'':22B ''teacher'':8B ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (713, 'RAINBOW SHOCK', 'A Action-Packed Story of a Hunter And a Boy who must Discover a Lumberjack in Ancient India', '''action'':5B ''action-pack'':4B ''ancient'':20B ''boy'':13B ''discov'':16B ''hunter'':10B ''india'':21B ''lumberjack'':18B ''must'':15B ''pack'':6B ''rainbow'':1A ''shock'':2A ''stori'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (714, 'RANDOM GO', 'A Fateful Drama of a Frisbee And a Student who must Confront a Cat in A Shark Tank', '''cat'':16B ''confront'':14B ''drama'':5B ''fate'':4B ''frisbe'':8B ''go'':2A ''must'':13B ''random'':1A ''shark'':19B ''student'':11B ''tank'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (715, 'RANGE MOONWALKER', 'A Insightful Documentary of a Hunter And a Dentist who must Confront a Crocodile in A Baloon', '''baloon'':19B ''confront'':14B ''crocodil'':16B ''dentist'':11B ''documentari'':5B ''hunter'':8B ''insight'':4B ''moonwalk'':2A ''must'':13B ''rang'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (716, 'REAP UNFAITHFUL', 'A Thrilling Epistle of a Composer And a Sumo Wrestler who must Challenge a Mad Cow in A MySQL Convention', '''challeng'':15B ''compos'':8B ''convent'':22B ''cow'':18B ''epistl'':5B ''mad'':17B ''must'':14B ''mysql'':21B ''reap'':1A ''sumo'':11B ''thrill'':4B ''unfaith'':2A ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (717, 'REAR TRADING', 'A Awe-Inspiring Reflection of a Forensic Psychologist And a Secret Agent who must Succumb a Pastry Chef in Soviet Georgia', '''agent'':15B ''awe'':5B ''awe-inspir'':4B ''chef'':21B ''forens'':10B ''georgia'':24B ''inspir'':6B ''must'':17B ''pastri'':20B ''psychologist'':11B ''rear'':1A ''reflect'':7B ''secret'':14B ''soviet'':23B ''succumb'':18B ''trade'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (718, 'REBEL AIRPORT', 'A Intrepid Yarn of a Database Administrator And a Boat who must Outrace a Husband in Ancient India', '''administr'':9B ''airport'':2A ''ancient'':19B ''boat'':12B ''databas'':8B ''husband'':17B ''india'':20B ''intrepid'':4B ''must'':14B ''outrac'':15B ''rebel'':1A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (719, 'RECORDS ZORRO', 'A Amazing Drama of a Mad Scientist And a Composer who must Build a Husband in The Outback', '''amaz'':4B ''build'':15B ''compos'':12B ''drama'':5B ''husband'':17B ''mad'':8B ''must'':14B ''outback'':20B ''record'':1A ''scientist'':9B ''zorro'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (720, 'REDEMPTION COMFORTS', 'A Emotional Documentary of a Dentist And a Woman who must Battle a Mad Scientist in Ancient China', '''ancient'':19B ''battl'':14B ''china'':20B ''comfort'':2A ''dentist'':8B ''documentari'':5B ''emot'':4B ''mad'':16B ''must'':13B ''redempt'':1A ''scientist'':17B ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (721, 'REDS POCUS', 'A Lacklusture Yarn of a Sumo Wrestler And a Squirrel who must Redeem a Monkey in Soviet Georgia', '''georgia'':20B ''lacklustur'':4B ''monkey'':17B ''must'':14B ''pocus'':2A ''red'':1A ''redeem'':15B ''soviet'':19B ''squirrel'':12B ''sumo'':8B ''wrestler'':9B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (722, 'REEF SALUTE', 'A Action-Packed Saga of a Teacher And a Lumberjack who must Battle a Dentist in A Baloon', '''action'':5B ''action-pack'':4B ''baloon'':21B ''battl'':16B ''dentist'':18B ''lumberjack'':13B ''must'':15B ''pack'':6B ''reef'':1A ''saga'':7B ''salut'':2A ''teacher'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (723, 'REIGN GENTLEMEN', 'A Emotional Yarn of a Composer And a Man who must Escape a Butler in The Gulf of Mexico', '''butler'':16B ''compos'':8B ''emot'':4B ''escap'':14B ''gentlemen'':2A ''gulf'':19B ''man'':11B ''mexico'':21B ''must'':13B ''reign'':1A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (724, 'REMEMBER DIARY', 'A Insightful Tale of a Technical Writer And a Waitress who must Conquer a Monkey in Ancient India', '''ancient'':19B ''conquer'':15B ''diari'':2A ''india'':20B ''insight'':4B ''monkey'':17B ''must'':14B ''rememb'':1A ''tale'':5B ''technic'':8B ''waitress'':12B ''writer'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (725, 'REQUIEM TYCOON', 'A Unbelieveable Character Study of a Cat And a Database Administrator who must Pursue a Teacher in A Monastery', '''administr'':13B ''cat'':9B ''charact'':5B ''databas'':12B ''monasteri'':21B ''must'':15B ''pursu'':16B ''requiem'':1A ''studi'':6B ''teacher'':18B ''tycoon'':2A ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (726, 'RESERVOIR ADAPTATION', 'A Intrepid Drama of a Teacher And a Moose who must Kill a Car in California', '''adapt'':2A ''california'':18B ''car'':16B ''drama'':5B ''intrepid'':4B ''kill'':14B ''moos'':11B ''must'':13B ''reservoir'':1A ''teacher'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (727, 'RESURRECTION SILVERADO', 'A Epic Yarn of a Robot And a Explorer who must Challenge a Girl in A MySQL Convention', '''challeng'':14B ''convent'':20B ''epic'':4B ''explor'':11B ''girl'':16B ''must'':13B ''mysql'':19B ''resurrect'':1A ''robot'':8B ''silverado'':2A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (728, 'REUNION WITCHES', 'A Unbelieveable Documentary of a Database Administrator And a Frisbee who must Redeem a Mad Scientist in A Baloon Factory', '''administr'':9B ''baloon'':21B ''databas'':8B ''documentari'':5B ''factori'':22B ''frisbe'':12B ''mad'':17B ''must'':14B ''redeem'':15B ''reunion'':1A ''scientist'':18B ''unbeliev'':4B ''witch'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (729, 'RIDER CADDYSHACK', 'A Taut Reflection of a Monkey And a Womanizer who must Chase a Moose in Nigeria', '''caddyshack'':2A ''chase'':14B ''monkey'':8B ''moos'':16B ''must'':13B ''nigeria'':18B ''reflect'':5B ''rider'':1A ''taut'':4B ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (730, 'RIDGEMONT SUBMARINE', 'A Unbelieveable Drama of a Waitress And a Composer who must Sink a Mad Cow in Ancient Japan', '''ancient'':19B ''compos'':11B ''cow'':17B ''drama'':5B ''japan'':20B ''mad'':16B ''must'':13B ''ridgemont'':1A ''sink'':14B ''submarin'':2A ''unbeliev'':4B ''waitress'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (731, 'RIGHT CRANES', 'A Fateful Character Study of a Boat And a Cat who must Find a Database Administrator in A Jet Boat', '''administr'':18B ''boat'':9B,22B ''cat'':12B ''charact'':5B ''crane'':2A ''databas'':17B ''fate'':4B ''find'':15B ''jet'':21B ''must'':14B ''right'':1A ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (732, 'RINGS HEARTBREAKERS', 'A Amazing Yarn of a Sumo Wrestler And a Boat who must Conquer a Waitress in New Orleans', '''amaz'':4B ''boat'':12B ''conquer'':15B ''heartbreak'':2A ''must'':14B ''new'':19B ''orlean'':20B ''ring'':1A ''sumo'':8B ''waitress'':17B ''wrestler'':9B ''yarn'':5B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (734, 'ROAD ROXANNE', 'A Boring Character Study of a Waitress And a Astronaut who must Fight a Crocodile in Ancient Japan', '''ancient'':19B ''astronaut'':12B ''bore'':4B ''charact'':5B ''crocodil'':17B ''fight'':15B ''japan'':20B ''must'':14B ''road'':1A ''roxann'':2A ''studi'':6B ''waitress'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (735, 'ROBBERS JOON', 'A Thoughtful Story of a Mad Scientist And a Waitress who must Confront a Forensic Psychologist in Soviet Georgia', '''confront'':15B ''forens'':17B ''georgia'':21B ''joon'':2A ''mad'':8B ''must'':14B ''psychologist'':18B ''robber'':1A ''scientist'':9B ''soviet'':20B ''stori'':5B ''thought'':4B ''waitress'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (736, 'ROBBERY BRIGHT', 'A Taut Reflection of a Robot And a Squirrel who must Fight a Boat in Ancient Japan', '''ancient'':18B ''boat'':16B ''bright'':2A ''fight'':14B ''japan'':19B ''must'':13B ''reflect'':5B ''robberi'':1A ''robot'':8B ''squirrel'':11B ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (737, 'ROCK INSTINCT', 'A Astounding Character Study of a Robot And a Moose who must Overcome a Astronaut in Ancient India', '''ancient'':19B ''astound'':4B ''astronaut'':17B ''charact'':5B ''india'':20B ''instinct'':2A ''moos'':12B ''must'':14B ''overcom'':15B ''robot'':9B ''rock'':1A ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (738, 'ROCKETEER MOTHER', 'A Awe-Inspiring Character Study of a Robot And a Sumo Wrestler who must Discover a Womanizer in A Shark Tank', '''awe'':5B ''awe-inspir'':4B ''charact'':7B ''discov'':18B ''inspir'':6B ''mother'':2A ''must'':17B ''robot'':11B ''rocket'':1A ''shark'':23B ''studi'':8B ''sumo'':14B ''tank'':24B ''woman'':20B ''wrestler'':15B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (739, 'ROCKY WAR', 'A Fast-Paced Display of a Squirrel And a Explorer who must Outgun a Mad Scientist in Nigeria', '''display'':7B ''explor'':13B ''fast'':5B ''fast-pac'':4B ''mad'':18B ''must'':15B ''nigeria'':21B ''outgun'':16B ''pace'':6B ''rocki'':1A ''scientist'':19B ''squirrel'':10B ''war'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (740, 'ROLLERCOASTER BRINGING', 'A Beautiful Drama of a Robot And a Lumberjack who must Discover a Technical Writer in A Shark Tank', '''beauti'':4B ''bring'':2A ''discov'':14B ''drama'':5B ''lumberjack'':11B ''must'':13B ''robot'':8B ''rollercoast'':1A ''shark'':20B ''tank'':21B ''technic'':16B ''writer'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (741, 'ROMAN PUNK', 'A Thoughtful Panorama of a Mad Cow And a Student who must Battle a Forensic Psychologist in Berlin', '''battl'':15B ''berlin'':20B ''cow'':9B ''forens'':17B ''mad'':8B ''must'':14B ''panorama'':5B ''psychologist'':18B ''punk'':2A ''roman'':1A ''student'':12B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (742, 'ROOF CHAMPION', 'A Lacklusture Reflection of a Car And a Explorer who must Find a Monkey in A Baloon', '''baloon'':19B ''car'':8B ''champion'':2A ''explor'':11B ''find'':14B ''lacklustur'':4B ''monkey'':16B ''must'':13B ''reflect'':5B ''roof'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (743, 'ROOM ROMAN', 'A Awe-Inspiring Panorama of a Composer And a Secret Agent who must Sink a Composer in A Shark Tank', '''agent'':14B ''awe'':5B ''awe-inspir'':4B ''compos'':10B,19B ''inspir'':6B ''must'':16B ''panorama'':7B ''roman'':2A ''room'':1A ''secret'':13B ''shark'':22B ''sink'':17B ''tank'':23B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (744, 'ROOTS REMEMBER', 'A Brilliant Drama of a Mad Cow And a Hunter who must Escape a Hunter in Berlin', '''berlin'':19B ''brilliant'':4B ''cow'':9B ''drama'':5B ''escap'':15B ''hunter'':12B,17B ''mad'':8B ''must'':14B ''rememb'':2A ''root'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (745, 'ROSES TREASURE', 'A Astounding Panorama of a Monkey And a Secret Agent who must Defeat a Woman in The First Manned Space Station', '''agent'':12B ''astound'':4B ''defeat'':15B ''first'':20B ''man'':21B ''monkey'':8B ''must'':14B ''panorama'':5B ''rose'':1A ''secret'':11B ''space'':22B ''station'':23B ''treasur'':2A ''woman'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (746, 'ROUGE SQUAD', 'A Awe-Inspiring Drama of a Astronaut And a Frisbee who must Conquer a Mad Scientist in Australia', '''astronaut'':10B ''australia'':21B ''awe'':5B ''awe-inspir'':4B ''conquer'':16B ''drama'':7B ''frisbe'':13B ''inspir'':6B ''mad'':18B ''must'':15B ''roug'':1A ''scientist'':19B ''squad'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (747, 'ROXANNE REBEL', 'A Astounding Story of a Pastry Chef And a Database Administrator who must Fight a Man in The Outback', '''administr'':13B ''astound'':4B ''chef'':9B ''databas'':12B ''fight'':16B ''man'':18B ''must'':15B ''outback'':21B ''pastri'':8B ''rebel'':2A ''roxann'':1A ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (748, 'RUGRATS SHAKESPEARE', 'A Touching Saga of a Crocodile And a Crocodile who must Discover a Technical Writer in Nigeria', '''crocodil'':8B,11B ''discov'':14B ''must'':13B ''nigeria'':19B ''rugrat'':1A ''saga'':5B ''shakespear'':2A ''technic'':16B ''touch'':4B ''writer'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (749, 'RULES HUMAN', 'A Beautiful Epistle of a Astronaut And a Student who must Confront a Monkey in An Abandoned Fun House', '''abandon'':19B ''astronaut'':8B ''beauti'':4B ''confront'':14B ''epistl'':5B ''fun'':20B ''hous'':21B ''human'':2A ''monkey'':16B ''must'':13B ''rule'':1A ''student'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (750, 'RUN PACIFIC', 'A Touching Tale of a Cat And a Pastry Chef who must Conquer a Pastry Chef in A MySQL Convention', '''cat'':8B ''chef'':12B,18B ''conquer'':15B ''convent'':22B ''must'':14B ''mysql'':21B ''pacif'':2A ''pastri'':11B,17B ''run'':1A ''tale'':5B ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (751, 'RUNAWAY TENENBAUMS', 'A Thoughtful Documentary of a Boat And a Man who must Meet a Boat in An Abandoned Fun House', '''abandon'':19B ''boat'':8B,16B ''documentari'':5B ''fun'':20B ''hous'':21B ''man'':11B ''meet'':14B ''must'':13B ''runaway'':1A ''tenenbaum'':2A ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (752, 'RUNNER MADIGAN', 'A Thoughtful Documentary of a Crocodile And a Robot who must Outrace a Womanizer in The Outback', '''crocodil'':8B ''documentari'':5B ''madigan'':2A ''must'':13B ''outback'':19B ''outrac'':14B ''robot'':11B ''runner'':1A ''thought'':4B ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (753, 'RUSH GOODFELLAS', 'A Emotional Display of a Man And a Dentist who must Challenge a Squirrel in Australia', '''australia'':18B ''challeng'':14B ''dentist'':11B ''display'':5B ''emot'':4B ''goodfella'':2A ''man'':8B ''must'':13B ''rush'':1A ''squirrel'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (754, 'RUSHMORE MERMAID', 'A Boring Story of a Woman And a Moose who must Reach a Husband in A Shark Tank', '''bore'':4B ''husband'':16B ''mermaid'':2A ''moos'':11B ''must'':13B ''reach'':14B ''rushmor'':1A ''shark'':19B ''stori'':5B ''tank'':20B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (755, 'SABRINA MIDNIGHT', 'A Emotional Story of a Squirrel And a Crocodile who must Succumb a Husband in The Sahara Desert', '''crocodil'':11B ''desert'':20B ''emot'':4B ''husband'':16B ''midnight'':2A ''must'':13B ''sabrina'':1A ''sahara'':19B ''squirrel'':8B ''stori'':5B ''succumb'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (756, 'SADDLE ANTITRUST', 'A Stunning Epistle of a Feminist And a A Shark who must Battle a Woman in An Abandoned Fun House', '''abandon'':20B ''antitrust'':2A ''battl'':15B ''epistl'':5B ''feminist'':8B ''fun'':21B ''hous'':22B ''must'':14B ''saddl'':1A ''shark'':12B ''stun'':4B ''woman'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (757, 'SAGEBRUSH CLUELESS', 'A Insightful Story of a Lumberjack And a Hunter who must Kill a Boy in Ancient Japan', '''ancient'':18B ''boy'':16B ''clueless'':2A ''hunter'':11B ''insight'':4B ''japan'':19B ''kill'':14B ''lumberjack'':8B ''must'':13B ''sagebrush'':1A ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (758, 'SAINTS BRIDE', 'A Fateful Tale of a Technical Writer And a Composer who must Pursue a Explorer in The Gulf of Mexico', '''bride'':2A ''compos'':12B ''explor'':17B ''fate'':4B ''gulf'':20B ''mexico'':22B ''must'':14B ''pursu'':15B ''saint'':1A ''tale'':5B ''technic'':8B ''writer'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (759, 'SALUTE APOLLO', 'A Awe-Inspiring Character Study of a Boy And a Feminist who must Sink a Crocodile in Ancient China', '''ancient'':21B ''apollo'':2A ''awe'':5B ''awe-inspir'':4B ''boy'':11B ''charact'':7B ''china'':22B ''crocodil'':19B ''feminist'':14B ''inspir'':6B ''must'':16B ''salut'':1A ''sink'':17B ''studi'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (760, 'SAMURAI LION', 'A Fast-Paced Story of a Pioneer And a Astronaut who must Reach a Boat in A Baloon', '''astronaut'':13B ''baloon'':21B ''boat'':18B ''fast'':5B ''fast-pac'':4B ''lion'':2A ''must'':15B ''pace'':6B ''pioneer'':10B ''reach'':16B ''samurai'':1A ''stori'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (761, 'SANTA PARIS', 'A Emotional Documentary of a Moose And a Car who must Redeem a Mad Cow in A Baloon Factory', '''baloon'':20B ''car'':11B ''cow'':17B ''documentari'':5B ''emot'':4B ''factori'':21B ''mad'':16B ''moos'':8B ''must'':13B ''pari'':2A ''redeem'':14B ''santa'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (762, 'SASSY PACKER', 'A Fast-Paced Documentary of a Dog And a Teacher who must Find a Moose in A Manhattan Penthouse', '''documentari'':7B ''dog'':10B ''fast'':5B ''fast-pac'':4B ''find'':16B ''manhattan'':21B ''moos'':18B ''must'':15B ''pace'':6B ''packer'':2A ''penthous'':22B ''sassi'':1A ''teacher'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (763, 'SATISFACTION CONFIDENTIAL', 'A Lacklusture Yarn of a Dentist And a Butler who must Meet a Secret Agent in Ancient China', '''agent'':17B ''ancient'':19B ''butler'':11B ''china'':20B ''confidenti'':2A ''dentist'':8B ''lacklustur'':4B ''meet'':14B ''must'':13B ''satisfact'':1A ''secret'':16B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (764, 'SATURDAY LAMBS', 'A Thoughtful Reflection of a Mad Scientist And a Moose who must Kill a Husband in A Baloon', '''baloon'':20B ''husband'':17B ''kill'':15B ''lamb'':2A ''mad'':8B ''moos'':12B ''must'':14B ''reflect'':5B ''saturday'':1A ''scientist'':9B ''thought'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (765, 'SATURN NAME', 'A Fateful Epistle of a Butler And a Boy who must Redeem a Teacher in Berlin', '''berlin'':18B ''boy'':11B ''butler'':8B ''epistl'':5B ''fate'':4B ''must'':13B ''name'':2A ''redeem'':14B ''saturn'':1A ''teacher'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (766, 'SAVANNAH TOWN', 'A Awe-Inspiring Tale of a Astronaut And a Database Administrator who must Chase a Secret Agent in The Gulf of Mexico', '''administr'':14B ''agent'':20B ''astronaut'':10B ''awe'':5B ''awe-inspir'':4B ''chase'':17B ''databas'':13B ''gulf'':23B ''inspir'':6B ''mexico'':25B ''must'':16B ''savannah'':1A ''secret'':19B ''tale'':7B ''town'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (767, 'SCALAWAG DUCK', 'A Fateful Reflection of a Car And a Teacher who must Confront a Waitress in A Monastery', '''car'':8B ''confront'':14B ''duck'':2A ''fate'':4B ''monasteri'':19B ''must'':13B ''reflect'':5B ''scalawag'':1A ''teacher'':11B ''waitress'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (768, 'SCARFACE BANG', 'A Emotional Yarn of a Teacher And a Girl who must Find a Teacher in A Baloon Factory', '''baloon'':19B ''bang'':2A ''emot'':4B ''factori'':20B ''find'':14B ''girl'':11B ''must'':13B ''scarfac'':1A ''teacher'':8B,16B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (769, 'SCHOOL JACKET', 'A Intrepid Yarn of a Monkey And a Boy who must Fight a Composer in A Manhattan Penthouse', '''boy'':11B ''compos'':16B ''fight'':14B ''intrepid'':4B ''jacket'':2A ''manhattan'':19B ''monkey'':8B ''must'':13B ''penthous'':20B ''school'':1A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (770, 'SCISSORHANDS SLUMS', 'A Awe-Inspiring Drama of a Girl And a Technical Writer who must Meet a Feminist in The Canadian Rockies', '''awe'':5B ''awe-inspir'':4B ''canadian'':22B ''drama'':7B ''feminist'':19B ''girl'':10B ''inspir'':6B ''meet'':17B ''must'':16B ''rocki'':23B ''scissorhand'':1A ''slum'':2A ''technic'':13B ''writer'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (788, 'SHIP WONDERLAND', 'A Thrilling Saga of a Monkey And a Frisbee who must Escape a Explorer in The Outback', '''escap'':14B ''explor'':16B ''frisbe'':11B ''monkey'':8B ''must'':13B ''outback'':19B ''saga'':5B ''ship'':1A ''thrill'':4B ''wonderland'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (771, 'SCORPION APOLLO', 'A Awe-Inspiring Documentary of a Technical Writer And a Husband who must Meet a Monkey in An Abandoned Fun House', '''abandon'':22B ''apollo'':2A ''awe'':5B ''awe-inspir'':4B ''documentari'':7B ''fun'':23B ''hous'':24B ''husband'':14B ''inspir'':6B ''meet'':17B ''monkey'':19B ''must'':16B ''scorpion'':1A ''technic'':10B ''writer'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (772, 'SEA VIRGIN', 'A Fast-Paced Documentary of a Technical Writer And a Pastry Chef who must Escape a Moose in A U-Boat', '''boat'':25B ''chef'':15B ''documentari'':7B ''escap'':18B ''fast'':5B ''fast-pac'':4B ''moos'':20B ''must'':17B ''pace'':6B ''pastri'':14B ''sea'':1A ''technic'':10B ''u'':24B ''u-boat'':23B ''virgin'':2A ''writer'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (773, 'SEABISCUIT PUNK', 'A Insightful Saga of a Man And a Forensic Psychologist who must Discover a Mad Cow in A MySQL Convention', '''convent'':22B ''cow'':18B ''discov'':15B ''forens'':11B ''insight'':4B ''mad'':17B ''man'':8B ''must'':14B ''mysql'':21B ''psychologist'':12B ''punk'':2A ''saga'':5B ''seabiscuit'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (774, 'SEARCHERS WAIT', 'A Fast-Paced Tale of a Car And a Mad Scientist who must Kill a Womanizer in Ancient Japan', '''ancient'':21B ''car'':10B ''fast'':5B ''fast-pac'':4B ''japan'':22B ''kill'':17B ''mad'':13B ''must'':16B ''pace'':6B ''scientist'':14B ''searcher'':1A ''tale'':7B ''wait'':2A ''woman'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (775, 'SEATTLE EXPECATIONS', 'A Insightful Reflection of a Crocodile And a Sumo Wrestler who must Meet a Technical Writer in The Sahara Desert', '''crocodil'':8B ''desert'':22B ''expec'':2A ''insight'':4B ''meet'':15B ''must'':14B ''reflect'':5B ''sahara'':21B ''seattl'':1A ''sumo'':11B ''technic'':17B ''wrestler'':12B ''writer'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (776, 'SECRET GROUNDHOG', 'A Astounding Story of a Cat And a Database Administrator who must Build a Technical Writer in New Orleans', '''administr'':12B ''astound'':4B ''build'':15B ''cat'':8B ''databas'':11B ''groundhog'':2A ''must'':14B ''new'':20B ''orlean'':21B ''secret'':1A ''stori'':5B ''technic'':17B ''writer'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (777, 'SECRETARY ROUGE', 'A Action-Packed Panorama of a Mad Cow And a Composer who must Discover a Robot in A Baloon Factory', '''action'':5B ''action-pack'':4B ''baloon'':22B ''compos'':14B ''cow'':11B ''discov'':17B ''factori'':23B ''mad'':10B ''must'':16B ''pack'':6B ''panorama'':7B ''robot'':19B ''roug'':2A ''secretari'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (778, 'SECRETS PARADISE', 'A Fateful Saga of a Cat And a Frisbee who must Kill a Girl in A Manhattan Penthouse', '''cat'':8B ''fate'':4B ''frisbe'':11B ''girl'':16B ''kill'':14B ''manhattan'':19B ''must'':13B ''paradis'':2A ''penthous'':20B ''saga'':5B ''secret'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (779, 'SENSE GREEK', 'A Taut Saga of a Lumberjack And a Pastry Chef who must Escape a Sumo Wrestler in An Abandoned Fun House', '''abandon'':21B ''chef'':12B ''escap'':15B ''fun'':22B ''greek'':2A ''hous'':23B ''lumberjack'':8B ''must'':14B ''pastri'':11B ''saga'':5B ''sens'':1A ''sumo'':17B ''taut'':4B ''wrestler'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (780, 'SENSIBILITY REAR', 'A Emotional Tale of a Robot And a Sumo Wrestler who must Redeem a Pastry Chef in A Baloon Factory', '''baloon'':21B ''chef'':18B ''emot'':4B ''factori'':22B ''must'':14B ''pastri'':17B ''rear'':2A ''redeem'':15B ''robot'':8B ''sensibl'':1A ''sumo'':11B ''tale'':5B ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (781, 'SEVEN SWARM', 'A Unbelieveable Character Study of a Dog And a Mad Cow who must Kill a Monkey in Berlin', '''berlin'':20B ''charact'':5B ''cow'':13B ''dog'':9B ''kill'':16B ''mad'':12B ''monkey'':18B ''must'':15B ''seven'':1A ''studi'':6B ''swarm'':2A ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (782, 'SHAKESPEARE SADDLE', 'A Fast-Paced Panorama of a Lumberjack And a Database Administrator who must Defeat a Madman in A MySQL Convention', '''administr'':14B ''convent'':23B ''databas'':13B ''defeat'':17B ''fast'':5B ''fast-pac'':4B ''lumberjack'':10B ''madman'':19B ''must'':16B ''mysql'':22B ''pace'':6B ''panorama'':7B ''saddl'':2A ''shakespear'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (783, 'SHANE DARKNESS', 'A Action-Packed Saga of a Moose And a Lumberjack who must Find a Woman in Berlin', '''action'':5B ''action-pack'':4B ''berlin'':20B ''dark'':2A ''find'':16B ''lumberjack'':13B ''moos'':10B ''must'':15B ''pack'':6B ''saga'':7B ''shane'':1A ''woman'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (784, 'SHANGHAI TYCOON', 'A Fast-Paced Character Study of a Crocodile And a Lumberjack who must Build a Husband in An Abandoned Fun House', '''abandon'':22B ''build'':17B ''charact'':7B ''crocodil'':11B ''fast'':5B ''fast-pac'':4B ''fun'':23B ''hous'':24B ''husband'':19B ''lumberjack'':14B ''must'':16B ''pace'':6B ''shanghai'':1A ''studi'':8B ''tycoon'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (785, 'SHAWSHANK BUBBLE', 'A Lacklusture Story of a Moose And a Monkey who must Confront a Butler in An Abandoned Amusement Park', '''abandon'':19B ''amus'':20B ''bubbl'':2A ''butler'':16B ''confront'':14B ''lacklustur'':4B ''monkey'':11B ''moos'':8B ''must'':13B ''park'':21B ''shawshank'':1A ''stori'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (786, 'SHEPHERD MIDSUMMER', 'A Thoughtful Drama of a Robot And a Womanizer who must Kill a Lumberjack in A Baloon', '''baloon'':19B ''drama'':5B ''kill'':14B ''lumberjack'':16B ''midsumm'':2A ''must'':13B ''robot'':8B ''shepherd'':1A ''thought'':4B ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (787, 'SHINING ROSES', 'A Awe-Inspiring Character Study of a Astronaut And a Forensic Psychologist who must Challenge a Madman in Ancient India', '''ancient'':22B ''astronaut'':11B ''awe'':5B ''awe-inspir'':4B ''challeng'':18B ''charact'':7B ''forens'':14B ''india'':23B ''inspir'':6B ''madman'':20B ''must'':17B ''psychologist'':15B ''rose'':2A ''shine'':1A ''studi'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (789, 'SHOCK CABIN', 'A Fateful Tale of a Mad Cow And a Crocodile who must Meet a Husband in New Orleans', '''cabin'':2A ''cow'':9B ''crocodil'':12B ''fate'':4B ''husband'':17B ''mad'':8B ''meet'':15B ''must'':14B ''new'':19B ''orlean'':20B ''shock'':1A ''tale'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (790, 'SHOOTIST SUPERFLY', 'A Fast-Paced Story of a Crocodile And a A Shark who must Sink a Pioneer in Berlin', '''berlin'':21B ''crocodil'':10B ''fast'':5B ''fast-pac'':4B ''must'':16B ''pace'':6B ''pioneer'':19B ''shark'':14B ''shootist'':1A ''sink'':17B ''stori'':7B ''superfli'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (791, 'SHOW LORD', 'A Fanciful Saga of a Student And a Girl who must Find a Butler in Ancient Japan', '''ancient'':18B ''butler'':16B ''fanci'':4B ''find'':14B ''girl'':11B ''japan'':19B ''lord'':2A ''must'':13B ''saga'':5B ''show'':1A ''student'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (792, 'SHREK LICENSE', 'A Fateful Yarn of a Secret Agent And a Feminist who must Find a Feminist in A Jet Boat', '''agent'':9B ''boat'':21B ''fate'':4B ''feminist'':12B,17B ''find'':15B ''jet'':20B ''licens'':2A ''must'':14B ''secret'':8B ''shrek'':1A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (793, 'SHRUNK DIVINE', 'A Fateful Character Study of a Waitress And a Technical Writer who must Battle a Hunter in A Baloon', '''baloon'':21B ''battl'':16B ''charact'':5B ''divin'':2A ''fate'':4B ''hunter'':18B ''must'':15B ''shrunk'':1A ''studi'':6B ''technic'':12B ''waitress'':9B ''writer'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (794, 'SIDE ARK', 'A Stunning Panorama of a Crocodile And a Womanizer who must Meet a Feminist in The Canadian Rockies', '''ark'':2A ''canadian'':19B ''crocodil'':8B ''feminist'':16B ''meet'':14B ''must'':13B ''panorama'':5B ''rocki'':20B ''side'':1A ''stun'':4B ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (795, 'SIEGE MADRE', 'A Boring Tale of a Frisbee And a Crocodile who must Vanquish a Moose in An Abandoned Mine Shaft', '''abandon'':19B ''bore'':4B ''crocodil'':11B ''frisbe'':8B ''madr'':2A ''mine'':20B ''moos'':16B ''must'':13B ''shaft'':21B ''sieg'':1A ''tale'':5B ''vanquish'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (796, 'SIERRA DIVIDE', 'A Emotional Character Study of a Frisbee And a Mad Scientist who must Build a Madman in California', '''build'':16B ''california'':20B ''charact'':5B ''divid'':2A ''emot'':4B ''frisbe'':9B ''mad'':12B ''madman'':18B ''must'':15B ''scientist'':13B ''sierra'':1A ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (797, 'SILENCE KANE', 'A Emotional Drama of a Sumo Wrestler And a Dentist who must Confront a Sumo Wrestler in A Baloon', '''baloon'':21B ''confront'':15B ''dentist'':12B ''drama'':5B ''emot'':4B ''kane'':2A ''must'':14B ''silenc'':1A ''sumo'':8B,17B ''wrestler'':9B,18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (798, 'SILVERADO GOLDFINGER', 'A Stunning Epistle of a Sumo Wrestler And a Man who must Challenge a Waitress in Ancient India', '''ancient'':19B ''challeng'':15B ''epistl'':5B ''goldfing'':2A ''india'':20B ''man'':12B ''must'':14B ''silverado'':1A ''stun'':4B ''sumo'':8B ''waitress'':17B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (799, 'SIMON NORTH', 'A Thrilling Documentary of a Technical Writer And a A Shark who must Face a Pioneer in A Shark Tank', '''documentari'':5B ''face'':16B ''must'':15B ''north'':2A ''pioneer'':18B ''shark'':13B,21B ''simon'':1A ''tank'':22B ''technic'':8B ''thrill'':4B ''writer'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (800, 'SINNERS ATLANTIS', 'A Epic Display of a Dog And a Boat who must Succumb a Mad Scientist in An Abandoned Mine Shaft', '''abandon'':20B ''atlanti'':2A ''boat'':11B ''display'':5B ''dog'':8B ''epic'':4B ''mad'':16B ''mine'':21B ''must'':13B ''scientist'':17B ''shaft'':22B ''sinner'':1A ''succumb'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (801, 'SISTER FREDDY', 'A Stunning Saga of a Butler And a Woman who must Pursue a Explorer in Australia', '''australia'':18B ''butler'':8B ''explor'':16B ''freddi'':2A ''must'':13B ''pursu'':14B ''saga'':5B ''sister'':1A ''stun'':4B ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (802, 'SKY MIRACLE', 'A Epic Drama of a Mad Scientist And a Explorer who must Succumb a Waitress in An Abandoned Fun House', '''abandon'':20B ''drama'':5B ''epic'':4B ''explor'':12B ''fun'':21B ''hous'':22B ''mad'':8B ''miracl'':2A ''must'':14B ''scientist'':9B ''sky'':1A ''succumb'':15B ''waitress'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (803, 'SLACKER LIAISONS', 'A Fast-Paced Tale of a A Shark And a Student who must Meet a Crocodile in Ancient China', '''ancient'':21B ''china'':22B ''crocodil'':19B ''fast'':5B ''fast-pac'':4B ''liaison'':2A ''meet'':17B ''must'':16B ''pace'':6B ''shark'':11B ''slacker'':1A ''student'':14B ''tale'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (804, 'SLEEPING SUSPECTS', 'A Stunning Reflection of a Sumo Wrestler And a Explorer who must Sink a Frisbee in A MySQL Convention', '''convent'':21B ''explor'':12B ''frisbe'':17B ''must'':14B ''mysql'':20B ''reflect'':5B ''sink'':15B ''sleep'':1A ''stun'':4B ''sumo'':8B ''suspect'':2A ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (805, 'SLEEPLESS MONSOON', 'A Amazing Saga of a Moose And a Pastry Chef who must Escape a Butler in Australia', '''amaz'':4B ''australia'':19B ''butler'':17B ''chef'':12B ''escap'':15B ''monsoon'':2A ''moos'':8B ''must'':14B ''pastri'':11B ''saga'':5B ''sleepless'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (806, 'SLEEPY JAPANESE', 'A Emotional Epistle of a Moose And a Composer who must Fight a Technical Writer in The Outback', '''compos'':11B ''emot'':4B ''epistl'':5B ''fight'':14B ''japanes'':2A ''moos'':8B ''must'':13B ''outback'':20B ''sleepi'':1A ''technic'':16B ''writer'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (807, 'SLEUTH ORIENT', 'A Fateful Character Study of a Husband And a Dog who must Find a Feminist in Ancient India', '''ancient'':19B ''charact'':5B ''dog'':12B ''fate'':4B ''feminist'':17B ''find'':15B ''husband'':9B ''india'':20B ''must'':14B ''orient'':2A ''sleuth'':1A ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (808, 'SLING LUKE', 'A Intrepid Character Study of a Robot And a Monkey who must Reach a Secret Agent in An Abandoned Amusement Park', '''abandon'':21B ''agent'':18B ''amus'':22B ''charact'':5B ''intrepid'':4B ''luke'':2A ''monkey'':12B ''must'':14B ''park'':23B ''reach'':15B ''robot'':9B ''secret'':17B ''sling'':1A ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (809, 'SLIPPER FIDELITY', 'A Taut Reflection of a Secret Agent And a Man who must Redeem a Explorer in A MySQL Convention', '''agent'':9B ''convent'':21B ''explor'':17B ''fidel'':2A ''man'':12B ''must'':14B ''mysql'':20B ''redeem'':15B ''reflect'':5B ''secret'':8B ''slipper'':1A ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (810, 'SLUMS DUCK', 'A Amazing Character Study of a Teacher And a Database Administrator who must Defeat a Waitress in A Jet Boat', '''administr'':13B ''amaz'':4B ''boat'':22B ''charact'':5B ''databas'':12B ''defeat'':16B ''duck'':2A ''jet'':21B ''must'':15B ''slum'':1A ''studi'':6B ''teacher'':9B ''waitress'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (811, 'SMILE EARRING', 'A Intrepid Drama of a Teacher And a Butler who must Build a Pastry Chef in Berlin', '''berlin'':19B ''build'':14B ''butler'':11B ''chef'':17B ''drama'':5B ''earring'':2A ''intrepid'':4B ''must'':13B ''pastri'':16B ''smile'':1A ''teacher'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (812, 'SMOKING BARBARELLA', 'A Lacklusture Saga of a Mad Cow And a Mad Scientist who must Sink a Cat in A MySQL Convention', '''barbarella'':2A ''cat'':18B ''convent'':22B ''cow'':9B ''lacklustur'':4B ''mad'':8B,12B ''must'':15B ''mysql'':21B ''saga'':5B ''scientist'':13B ''sink'':16B ''smoke'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (813, 'SMOOCHY CONTROL', 'A Thrilling Documentary of a Husband And a Feminist who must Face a Mad Scientist in Ancient China', '''ancient'':19B ''china'':20B ''control'':2A ''documentari'':5B ''face'':14B ''feminist'':11B ''husband'':8B ''mad'':16B ''must'':13B ''scientist'':17B ''smoochi'':1A ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (814, 'SNATCH SLIPPER', 'A Insightful Panorama of a Woman And a Feminist who must Defeat a Forensic Psychologist in Berlin', '''berlin'':19B ''defeat'':14B ''feminist'':11B ''forens'':16B ''insight'':4B ''must'':13B ''panorama'':5B ''psychologist'':17B ''slipper'':2A ''snatch'':1A ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (815, 'SNATCHERS MONTEZUMA', 'A Boring Epistle of a Sumo Wrestler And a Woman who must Escape a Man in The Canadian Rockies', '''bore'':4B ''canadian'':20B ''epistl'':5B ''escap'':15B ''man'':17B ''montezuma'':2A ''must'':14B ''rocki'':21B ''snatcher'':1A ''sumo'':8B ''woman'':12B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (816, 'SNOWMAN ROLLERCOASTER', 'A Fateful Display of a Lumberjack And a Girl who must Succumb a Mad Cow in A Manhattan Penthouse', '''cow'':17B ''display'':5B ''fate'':4B ''girl'':11B ''lumberjack'':8B ''mad'':16B ''manhattan'':20B ''must'':13B ''penthous'':21B ''rollercoast'':2A ''snowman'':1A ''succumb'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (817, 'SOLDIERS EVOLUTION', 'A Lacklusture Panorama of a A Shark And a Pioneer who must Confront a Student in The First Manned Space Station', '''confront'':15B ''evolut'':2A ''first'':20B ''lacklustur'':4B ''man'':21B ''must'':14B ''panorama'':5B ''pioneer'':12B ''shark'':9B ''soldier'':1A ''space'':22B ''station'':23B ''student'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (818, 'SOMETHING DUCK', 'A Boring Character Study of a Car And a Husband who must Outgun a Frisbee in The First Manned Space Station', '''bore'':4B ''car'':9B ''charact'':5B ''duck'':2A ''first'':20B ''frisbe'':17B ''husband'':12B ''man'':21B ''must'':14B ''outgun'':15B ''someth'':1A ''space'':22B ''station'':23B ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (819, 'SONG HEDWIG', 'A Amazing Documentary of a Man And a Husband who must Confront a Squirrel in A MySQL Convention', '''amaz'':4B ''confront'':14B ''convent'':20B ''documentari'':5B ''hedwig'':2A ''husband'':11B ''man'':8B ''must'':13B ''mysql'':19B ''song'':1A ''squirrel'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (820, 'SONS INTERVIEW', 'A Taut Character Study of a Explorer And a Mad Cow who must Battle a Hunter in Ancient China', '''ancient'':20B ''battl'':16B ''charact'':5B ''china'':21B ''cow'':13B ''explor'':9B ''hunter'':18B ''interview'':2A ''mad'':12B ''must'':15B ''son'':1A ''studi'':6B ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (821, 'SORORITY QUEEN', 'A Fast-Paced Display of a Squirrel And a Composer who must Fight a Forensic Psychologist in A Jet Boat', '''boat'':23B ''compos'':13B ''display'':7B ''fast'':5B ''fast-pac'':4B ''fight'':16B ''forens'':18B ''jet'':22B ''must'':15B ''pace'':6B ''psychologist'':19B ''queen'':2A ''soror'':1A ''squirrel'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (822, 'SOUP WISDOM', 'A Fast-Paced Display of a Robot And a Butler who must Defeat a Butler in A MySQL Convention', '''butler'':13B,18B ''convent'':22B ''defeat'':16B ''display'':7B ''fast'':5B ''fast-pac'':4B ''must'':15B ''mysql'':21B ''pace'':6B ''robot'':10B ''soup'':1A ''wisdom'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (823, 'SOUTH WAIT', 'A Amazing Documentary of a Car And a Robot who must Escape a Lumberjack in An Abandoned Amusement Park', '''abandon'':19B ''amaz'':4B ''amus'':20B ''car'':8B ''documentari'':5B ''escap'':14B ''lumberjack'':16B ''must'':13B ''park'':21B ''robot'':11B ''south'':1A ''wait'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (824, 'SPARTACUS CHEAPER', 'A Thrilling Panorama of a Pastry Chef And a Secret Agent who must Overcome a Student in A Manhattan Penthouse', '''agent'':13B ''cheaper'':2A ''chef'':9B ''manhattan'':21B ''must'':15B ''overcom'':16B ''panorama'':5B ''pastri'':8B ''penthous'':22B ''secret'':12B ''spartacus'':1A ''student'':18B ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (825, 'SPEAKEASY DATE', 'A Lacklusture Drama of a Forensic Psychologist And a Car who must Redeem a Man in A Manhattan Penthouse', '''car'':12B ''date'':2A ''drama'':5B ''forens'':8B ''lacklustur'':4B ''man'':17B ''manhattan'':20B ''must'':14B ''penthous'':21B ''psychologist'':9B ''redeem'':15B ''speakeasi'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (826, 'SPEED SUIT', 'A Brilliant Display of a Frisbee And a Mad Scientist who must Succumb a Robot in Ancient China', '''ancient'':19B ''brilliant'':4B ''china'':20B ''display'':5B ''frisbe'':8B ''mad'':11B ''must'':14B ''robot'':17B ''scientist'':12B ''speed'':1A ''succumb'':15B ''suit'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (827, 'SPICE SORORITY', 'A Fateful Display of a Pioneer And a Hunter who must Defeat a Husband in An Abandoned Mine Shaft', '''abandon'':19B ''defeat'':14B ''display'':5B ''fate'':4B ''hunter'':11B ''husband'':16B ''mine'':20B ''must'':13B ''pioneer'':8B ''shaft'':21B ''soror'':2A ''spice'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (828, 'SPIKING ELEMENT', 'A Lacklusture Epistle of a Dentist And a Technical Writer who must Find a Dog in A Monastery', '''dentist'':8B ''dog'':17B ''element'':2A ''epistl'':5B ''find'':15B ''lacklustur'':4B ''monasteri'':20B ''must'':14B ''spike'':1A ''technic'':11B ''writer'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (829, 'SPINAL ROCKY', 'A Lacklusture Epistle of a Sumo Wrestler And a Squirrel who must Defeat a Explorer in California', '''california'':19B ''defeat'':15B ''epistl'':5B ''explor'':17B ''lacklustur'':4B ''must'':14B ''rocki'':2A ''spinal'':1A ''squirrel'':12B ''sumo'':8B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (830, 'SPIRIT FLINTSTONES', 'A Brilliant Yarn of a Cat And a Car who must Confront a Explorer in Ancient Japan', '''ancient'':18B ''brilliant'':4B ''car'':11B ''cat'':8B ''confront'':14B ''explor'':16B ''flintston'':2A ''japan'':19B ''must'':13B ''spirit'':1A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (831, 'SPIRITED CASUALTIES', 'A Taut Story of a Waitress And a Man who must Face a Car in A Baloon Factory', '''baloon'':19B ''car'':16B ''casualti'':2A ''face'':14B ''factori'':20B ''man'':11B ''must'':13B ''spirit'':1A ''stori'':5B ''taut'':4B ''waitress'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (832, 'SPLASH GUMP', 'A Taut Saga of a Crocodile And a Boat who must Conquer a Hunter in A Shark Tank', '''boat'':11B ''conquer'':14B ''crocodil'':8B ''gump'':2A ''hunter'':16B ''must'':13B ''saga'':5B ''shark'':19B ''splash'':1A ''tank'':20B ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (833, 'SPLENDOR PATTON', 'A Taut Story of a Dog And a Explorer who must Find a Astronaut in Berlin', '''astronaut'':16B ''berlin'':18B ''dog'':8B ''explor'':11B ''find'':14B ''must'':13B ''patton'':2A ''splendor'':1A ''stori'':5B ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (834, 'SPOILERS HELLFIGHTERS', 'A Fanciful Story of a Technical Writer And a Squirrel who must Defeat a Dog in The Gulf of Mexico', '''defeat'':15B ''dog'':17B ''fanci'':4B ''gulf'':20B ''hellfight'':2A ''mexico'':22B ''must'':14B ''spoiler'':1A ''squirrel'':12B ''stori'':5B ''technic'':8B ''writer'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (835, 'SPY MILE', 'A Thrilling Documentary of a Feminist And a Feminist who must Confront a Feminist in A Baloon', '''baloon'':19B ''confront'':14B ''documentari'':5B ''feminist'':8B,11B,16B ''mile'':2A ''must'':13B ''spi'':1A ''thrill'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (836, 'SQUAD FISH', 'A Fast-Paced Display of a Pastry Chef And a Dog who must Kill a Teacher in Berlin', '''berlin'':21B ''chef'':11B ''display'':7B ''dog'':14B ''fast'':5B ''fast-pac'':4B ''fish'':2A ''kill'':17B ''must'':16B ''pace'':6B ''pastri'':10B ''squad'':1A ''teacher'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (837, 'STAGE WORLD', 'A Lacklusture Panorama of a Woman And a Frisbee who must Chase a Crocodile in A Jet Boat', '''boat'':20B ''chase'':14B ''crocodil'':16B ''frisbe'':11B ''jet'':19B ''lacklustur'':4B ''must'':13B ''panorama'':5B ''stage'':1A ''woman'':8B ''world'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (838, 'STAGECOACH ARMAGEDDON', 'A Touching Display of a Pioneer And a Butler who must Chase a Car in California', '''armageddon'':2A ''butler'':11B ''california'':18B ''car'':16B ''chase'':14B ''display'':5B ''must'':13B ''pioneer'':8B ''stagecoach'':1A ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (839, 'STALLION SUNDANCE', 'A Fast-Paced Tale of a Car And a Dog who must Outgun a A Shark in Australia', '''australia'':21B ''car'':10B ''dog'':13B ''fast'':5B ''fast-pac'':4B ''must'':15B ''outgun'':16B ''pace'':6B ''shark'':19B ''stallion'':1A ''sundanc'':2A ''tale'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (840, 'STAMPEDE DISTURBING', 'A Unbelieveable Tale of a Woman And a Lumberjack who must Fight a Frisbee in A U-Boat', '''boat'':21B ''disturb'':2A ''fight'':14B ''frisbe'':16B ''lumberjack'':11B ''must'':13B ''stamped'':1A ''tale'':5B ''u'':20B ''u-boat'':19B ''unbeliev'':4B ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (841, 'STAR OPERATION', 'A Insightful Character Study of a Girl And a Car who must Pursue a Mad Cow in A Shark Tank', '''car'':12B ''charact'':5B ''cow'':18B ''girl'':9B ''insight'':4B ''mad'':17B ''must'':14B ''oper'':2A ''pursu'':15B ''shark'':21B ''star'':1A ''studi'':6B ''tank'':22B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (843, 'STEEL SANTA', 'A Fast-Paced Yarn of a Composer And a Frisbee who must Face a Moose in Nigeria', '''compos'':10B ''face'':16B ''fast'':5B ''fast-pac'':4B ''frisbe'':13B ''moos'':18B ''must'':15B ''nigeria'':20B ''pace'':6B ''santa'':2A ''steel'':1A ''yarn'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (844, 'STEERS ARMAGEDDON', 'A Stunning Character Study of a Car And a Girl who must Succumb a Car in A MySQL Convention', '''armageddon'':2A ''car'':9B,17B ''charact'':5B ''convent'':21B ''girl'':12B ''must'':14B ''mysql'':20B ''steer'':1A ''studi'':6B ''stun'':4B ''succumb'':15B');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (848, 'STONE FIRE', 'A Intrepid Drama of a Astronaut And a Crocodile who must Find a Boat in Soviet Georgia', '''astronaut'':8B ''boat'':16B ''crocodil'':11B ''drama'':5B ''find'':14B ''fire'':2A ''georgia'':19B ''intrepid'':4B ''must'':13B ''soviet'':18B ''stone'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (849, 'STORM HAPPINESS', 'A Insightful Drama of a Feminist And a A Shark who must Vanquish a Boat in A Shark Tank', '''boat'':17B ''drama'':5B ''feminist'':8B ''happi'':2A ''insight'':4B ''must'':14B ''shark'':12B,20B ''storm'':1A ''tank'':21B ''vanquish'':15B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (850, 'STORY SIDE', 'A Lacklusture Saga of a Boy And a Cat who must Sink a Dentist in An Abandoned Mine Shaft', '''abandon'':19B ''boy'':8B ''cat'':11B ''dentist'':16B ''lacklustur'':4B ''mine'':20B ''must'':13B ''saga'':5B ''shaft'':21B ''side'':2A ''sink'':14B ''stori'':1A');
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INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (852, 'STRANGELOVE DESIRE', 'A Awe-Inspiring Panorama of a Lumberjack And a Waitress who must Defeat a Crocodile in An Abandoned Amusement Park', '''abandon'':21B ''amus'':22B ''awe'':5B ''awe-inspir'':4B ''crocodil'':18B ''defeat'':16B ''desir'':2A ''inspir'':6B ''lumberjack'':10B ''must'':15B ''panorama'':7B ''park'':23B ''strangelov'':1A ''waitress'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (853, 'STRANGER STRANGERS', 'A Awe-Inspiring Yarn of a Womanizer And a Explorer who must Fight a Woman in The First Manned Space Station', '''awe'':5B ''awe-inspir'':4B ''explor'':13B ''fight'':16B ''first'':21B ''inspir'':6B ''man'':22B ''must'':15B ''space'':23B ''station'':24B ''stranger'':1A,2A ''woman'':10B,18B ''yarn'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (854, 'STRANGERS GRAFFITI', 'A Brilliant Character Study of a Secret Agent And a Man who must Find a Cat in The Gulf of Mexico', '''agent'':10B ''brilliant'':4B ''cat'':18B ''charact'':5B ''find'':16B ''graffiti'':2A ''gulf'':21B ''man'':13B ''mexico'':23B ''must'':15B ''secret'':9B ''stranger'':1A ''studi'':6B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (855, 'STREAK RIDGEMONT', 'A Astounding Character Study of a Hunter And a Waitress who must Sink a Man in New Orleans', '''astound'':4B ''charact'':5B ''hunter'':9B ''man'':17B ''must'':14B ''new'':19B ''orlean'':20B ''ridgemont'':2A ''sink'':15B ''streak'':1A ''studi'':6B ''waitress'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (856, 'STREETCAR INTENTIONS', 'A Insightful Character Study of a Waitress And a Crocodile who must Sink a Waitress in The Gulf of Mexico', '''charact'':5B ''crocodil'':12B ''gulf'':20B ''insight'':4B ''intent'':2A ''mexico'':22B ''must'':14B ''sink'':15B ''streetcar'':1A ''studi'':6B ''waitress'':9B,17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (857, 'STRICTLY SCARFACE', 'A Touching Reflection of a Crocodile And a Dog who must Chase a Hunter in An Abandoned Fun House', '''abandon'':19B ''chase'':14B ''crocodil'':8B ''dog'':11B ''fun'':20B ''hous'':21B ''hunter'':16B ''must'':13B ''reflect'':5B ''scarfac'':2A ''strict'':1A ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (858, 'SUBMARINE BED', 'A Amazing Display of a Car And a Monkey who must Fight a Teacher in Soviet Georgia', '''amaz'':4B ''bed'':2A ''car'':8B ''display'':5B ''fight'':14B ''georgia'':19B ''monkey'':11B ''must'':13B ''soviet'':18B ''submarin'':1A ''teacher'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (859, 'SUGAR WONKA', 'A Touching Story of a Dentist And a Database Administrator who must Conquer a Astronaut in An Abandoned Amusement Park', '''abandon'':20B ''administr'':12B ''amus'':21B ''astronaut'':17B ''conquer'':15B ''databas'':11B ''dentist'':8B ''must'':14B ''park'':22B ''stori'':5B ''sugar'':1A ''touch'':4B ''wonka'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (860, 'SUICIDES SILENCE', 'A Emotional Character Study of a Car And a Girl who must Face a Composer in A U-Boat', '''boat'':22B ''car'':9B ''charact'':5B ''compos'':17B ''emot'':4B ''face'':15B ''girl'':12B ''must'':14B ''silenc'':2A ''studi'':6B ''suicid'':1A ''u'':21B ''u-boat'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (861, 'SUIT WALLS', 'A Touching Panorama of a Lumberjack And a Frisbee who must Build a Dog in Australia', '''australia'':18B ''build'':14B ''dog'':16B ''frisbe'':11B ''lumberjack'':8B ''must'':13B ''panorama'':5B ''suit'':1A ''touch'':4B ''wall'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (862, 'SUMMER SCARFACE', 'A Emotional Panorama of a Lumberjack And a Hunter who must Meet a Girl in A Shark Tank', '''emot'':4B ''girl'':16B ''hunter'':11B ''lumberjack'':8B ''meet'':14B ''must'':13B ''panorama'':5B ''scarfac'':2A ''shark'':19B ''summer'':1A ''tank'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (863, 'SUN CONFESSIONS', 'A Beautiful Display of a Mad Cow And a Dog who must Redeem a Waitress in An Abandoned Amusement Park', '''abandon'':20B ''amus'':21B ''beauti'':4B ''confess'':2A ''cow'':9B ''display'':5B ''dog'':12B ''mad'':8B ''must'':14B ''park'':22B ''redeem'':15B ''sun'':1A ''waitress'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (864, 'SUNDANCE INVASION', 'A Epic Drama of a Lumberjack And a Explorer who must Confront a Hunter in A Baloon Factory', '''baloon'':19B ''confront'':14B ''drama'':5B ''epic'':4B ''explor'':11B ''factori'':20B ''hunter'':16B ''invas'':2A ''lumberjack'':8B ''must'':13B ''sundanc'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (865, 'SUNRISE LEAGUE', 'A Beautiful Epistle of a Madman And a Butler who must Face a Crocodile in A Manhattan Penthouse', '''beauti'':4B ''butler'':11B ''crocodil'':16B ''epistl'':5B ''face'':14B ''leagu'':2A ''madman'':8B ''manhattan'':19B ''must'':13B ''penthous'':20B ''sunris'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (866, 'SUNSET RACER', 'A Awe-Inspiring Reflection of a Astronaut And a A Shark who must Defeat a Forensic Psychologist in California', '''astronaut'':10B ''awe'':5B ''awe-inspir'':4B ''california'':22B ''defeat'':17B ''forens'':19B ''inspir'':6B ''must'':16B ''psychologist'':20B ''racer'':2A ''reflect'':7B ''shark'':14B ''sunset'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (867, 'SUPER WYOMING', 'A Action-Packed Saga of a Pastry Chef And a Explorer who must Discover a A Shark in The Outback', '''action'':5B ''action-pack'':4B ''chef'':11B ''discov'':17B ''explor'':14B ''must'':16B ''outback'':23B ''pack'':6B ''pastri'':10B ''saga'':7B ''shark'':20B ''super'':1A ''wyom'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (868, 'SUPERFLY TRIP', 'A Beautiful Saga of a Lumberjack And a Teacher who must Build a Technical Writer in An Abandoned Fun House', '''abandon'':20B ''beauti'':4B ''build'':14B ''fun'':21B ''hous'':22B ''lumberjack'':8B ''must'':13B ''saga'':5B ''superfli'':1A ''teacher'':11B ''technic'':16B ''trip'':2A ''writer'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (869, 'SUSPECTS QUILLS', 'A Emotional Epistle of a Pioneer And a Crocodile who must Battle a Man in A Manhattan Penthouse', '''battl'':14B ''crocodil'':11B ''emot'':4B ''epistl'':5B ''man'':16B ''manhattan'':19B ''must'':13B ''penthous'':20B ''pioneer'':8B ''quill'':2A ''suspect'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (870, 'SWARM GOLD', 'A Insightful Panorama of a Crocodile And a Boat who must Conquer a Sumo Wrestler in A MySQL Convention', '''boat'':11B ''conquer'':14B ''convent'':21B ''crocodil'':8B ''gold'':2A ''insight'':4B ''must'':13B ''mysql'':20B ''panorama'':5B ''sumo'':16B ''swarm'':1A ''wrestler'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (871, 'SWEDEN SHINING', 'A Taut Documentary of a Car And a Robot who must Conquer a Boy in The Canadian Rockies', '''boy'':16B ''canadian'':19B ''car'':8B ''conquer'':14B ''documentari'':5B ''must'':13B ''robot'':11B ''rocki'':20B ''shine'':2A ''sweden'':1A ''taut'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (872, 'SWEET BROTHERHOOD', 'A Unbelieveable Epistle of a Sumo Wrestler And a Hunter who must Chase a Forensic Psychologist in A Baloon', '''baloon'':21B ''brotherhood'':2A ''chase'':15B ''epistl'':5B ''forens'':17B ''hunter'':12B ''must'':14B ''psychologist'':18B ''sumo'':8B ''sweet'':1A ''unbeliev'':4B ''wrestler'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (873, 'SWEETHEARTS SUSPECTS', 'A Brilliant Character Study of a Frisbee And a Sumo Wrestler who must Confront a Woman in The Gulf of Mexico', '''brilliant'':4B ''charact'':5B ''confront'':16B ''frisbe'':9B ''gulf'':21B ''mexico'':23B ''must'':15B ''studi'':6B ''sumo'':12B ''suspect'':2A ''sweetheart'':1A ''woman'':18B ''wrestler'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (874, 'TADPOLE PARK', 'A Beautiful Tale of a Frisbee And a Moose who must Vanquish a Dog in An Abandoned Amusement Park', '''abandon'':19B ''amus'':20B ''beauti'':4B ''dog'':16B ''frisbe'':8B ''moos'':11B ''must'':13B ''park'':2A,21B ''tadpol'':1A ''tale'':5B ''vanquish'':14B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (875, 'TALENTED HOMICIDE', 'A Lacklusture Panorama of a Dentist And a Forensic Psychologist who must Outrace a Pioneer in A U-Boat', '''boat'':22B ''dentist'':8B ''forens'':11B ''homicid'':2A ''lacklustur'':4B ''must'':14B ''outrac'':15B ''panorama'':5B ''pioneer'':17B ''psychologist'':12B ''talent'':1A ''u'':21B ''u-boat'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (876, 'TARZAN VIDEOTAPE', 'A Fast-Paced Display of a Lumberjack And a Mad Scientist who must Succumb a Sumo Wrestler in The Sahara Desert', '''desert'':24B ''display'':7B ''fast'':5B ''fast-pac'':4B ''lumberjack'':10B ''mad'':13B ''must'':16B ''pace'':6B ''sahara'':23B ''scientist'':14B ''succumb'':17B ''sumo'':19B ''tarzan'':1A ''videotap'':2A ''wrestler'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (877, 'TAXI KICK', 'A Amazing Epistle of a Girl And a Woman who must Outrace a Waitress in Soviet Georgia', '''amaz'':4B ''epistl'':5B ''georgia'':19B ''girl'':8B ''kick'':2A ''must'':13B ''outrac'':14B ''soviet'':18B ''taxi'':1A ''waitress'':16B ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (878, 'TEEN APOLLO', 'A Awe-Inspiring Drama of a Dog And a Man who must Escape a Robot in A Shark Tank', '''apollo'':2A ''awe'':5B ''awe-inspir'':4B ''dog'':10B ''drama'':7B ''escap'':16B ''inspir'':6B ''man'':13B ''must'':15B ''robot'':18B ''shark'':21B ''tank'':22B ''teen'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (879, 'TELEGRAPH VOYAGE', 'A Fateful Yarn of a Husband And a Dog who must Battle a Waitress in A Jet Boat', '''battl'':14B ''boat'':20B ''dog'':11B ''fate'':4B ''husband'':8B ''jet'':19B ''must'':13B ''telegraph'':1A ''voyag'':2A ''waitress'':16B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (880, 'TELEMARK HEARTBREAKERS', 'A Action-Packed Panorama of a Technical Writer And a Man who must Build a Forensic Psychologist in A Manhattan Penthouse', '''action'':5B ''action-pack'':4B ''build'':17B ''forens'':19B ''heartbreak'':2A ''man'':14B ''manhattan'':23B ''must'':16B ''pack'':6B ''panorama'':7B ''penthous'':24B ''psychologist'':20B ''technic'':10B ''telemark'':1A ''writer'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (881, 'TEMPLE ATTRACTION', 'A Action-Packed Saga of a Forensic Psychologist And a Woman who must Battle a Womanizer in Soviet Georgia', '''action'':5B ''action-pack'':4B ''attract'':2A ''battl'':17B ''forens'':10B ''georgia'':22B ''must'':16B ''pack'':6B ''psychologist'':11B ''saga'':7B ''soviet'':21B ''templ'':1A ''woman'':14B,19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (882, 'TENENBAUMS COMMAND', 'A Taut Display of a Pioneer And a Man who must Reach a Girl in The Gulf of Mexico', '''command'':2A ''display'':5B ''girl'':16B ''gulf'':19B ''man'':11B ''mexico'':21B ''must'':13B ''pioneer'':8B ''reach'':14B ''taut'':4B ''tenenbaum'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (883, 'TEQUILA PAST', 'A Action-Packed Panorama of a Mad Scientist And a Robot who must Challenge a Student in Nigeria', '''action'':5B ''action-pack'':4B ''challeng'':17B ''mad'':10B ''must'':16B ''nigeria'':21B ''pack'':6B ''panorama'':7B ''past'':2A ''robot'':14B ''scientist'':11B ''student'':19B ''tequila'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (884, 'TERMINATOR CLUB', 'A Touching Story of a Crocodile And a Girl who must Sink a Man in The Gulf of Mexico', '''club'':2A ''crocodil'':8B ''girl'':11B ''gulf'':19B ''man'':16B ''mexico'':21B ''must'':13B ''sink'':14B ''stori'':5B ''termin'':1A ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (885, 'TEXAS WATCH', 'A Awe-Inspiring Yarn of a Student And a Teacher who must Fight a Teacher in An Abandoned Amusement Park', '''abandon'':21B ''amus'':22B ''awe'':5B ''awe-inspir'':4B ''fight'':16B ''inspir'':6B ''must'':15B ''park'':23B ''student'':10B ''teacher'':13B,18B ''texa'':1A ''watch'':2A ''yarn'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (886, 'THEORY MERMAID', 'A Fateful Yarn of a Composer And a Monkey who must Vanquish a Womanizer in The First Manned Space Station', '''compos'':8B ''fate'':4B ''first'':19B ''man'':20B ''mermaid'':2A ''monkey'':11B ''must'':13B ''space'':21B ''station'':22B ''theori'':1A ''vanquish'':14B ''woman'':16B ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (887, 'THIEF PELICAN', 'A Touching Documentary of a Madman And a Mad Scientist who must Outrace a Feminist in An Abandoned Mine Shaft', '''abandon'':20B ''documentari'':5B ''feminist'':17B ''mad'':11B ''madman'':8B ''mine'':21B ''must'':14B ''outrac'':15B ''pelican'':2A ''scientist'':12B ''shaft'':22B ''thief'':1A ''touch'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (888, 'THIN SAGEBRUSH', 'A Emotional Drama of a Husband And a Lumberjack who must Build a Cat in Ancient India', '''ancient'':18B ''build'':14B ''cat'':16B ''drama'':5B ''emot'':4B ''husband'':8B ''india'':19B ''lumberjack'':11B ''must'':13B ''sagebrush'':2A ''thin'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (889, 'TIES HUNGER', 'A Insightful Saga of a Astronaut And a Explorer who must Pursue a Mad Scientist in A U-Boat', '''astronaut'':8B ''boat'':22B ''explor'':11B ''hunger'':2A ''insight'':4B ''mad'':16B ''must'':13B ''pursu'':14B ''saga'':5B ''scientist'':17B ''tie'':1A ''u'':21B ''u-boat'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (891, 'TIMBERLAND SKY', 'A Boring Display of a Man And a Dog who must Redeem a Girl in A U-Boat', '''boat'':21B ''bore'':4B ''display'':5B ''dog'':11B ''girl'':16B ''man'':8B ''must'':13B ''redeem'':14B ''sky'':2A ''timberland'':1A ''u'':20B ''u-boat'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (892, 'TITANIC BOONDOCK', 'A Brilliant Reflection of a Feminist And a Dog who must Fight a Boy in A Baloon Factory', '''baloon'':19B ''boondock'':2A ''boy'':16B ''brilliant'':4B ''dog'':11B ''factori'':20B ''feminist'':8B ''fight'':14B ''must'':13B ''reflect'':5B ''titan'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (893, 'TITANS JERK', 'A Unbelieveable Panorama of a Feminist And a Sumo Wrestler who must Challenge a Technical Writer in Ancient China', '''ancient'':20B ''challeng'':15B ''china'':21B ''feminist'':8B ''jerk'':2A ''must'':14B ''panorama'':5B ''sumo'':11B ''technic'':17B ''titan'':1A ''unbeliev'':4B ''wrestler'':12B ''writer'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (894, 'TOMATOES HELLFIGHTERS', 'A Thoughtful Epistle of a Madman And a Astronaut who must Overcome a Monkey in A Shark Tank', '''astronaut'':11B ''epistl'':5B ''hellfight'':2A ''madman'':8B ''monkey'':16B ''must'':13B ''overcom'':14B ''shark'':19B ''tank'':20B ''thought'':4B ''tomato'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (895, 'TOMORROW HUSTLER', 'A Thoughtful Story of a Moose And a Husband who must Face a Secret Agent in The Sahara Desert', '''agent'':17B ''desert'':21B ''face'':14B ''husband'':11B ''hustler'':2A ''moos'':8B ''must'':13B ''sahara'':20B ''secret'':16B ''stori'':5B ''thought'':4B ''tomorrow'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (896, 'TOOTSIE PILOT', 'A Awe-Inspiring Documentary of a Womanizer And a Pastry Chef who must Kill a Lumberjack in Berlin', '''awe'':5B ''awe-inspir'':4B ''berlin'':21B ''chef'':14B ''documentari'':7B ''inspir'':6B ''kill'':17B ''lumberjack'':19B ''must'':16B ''pastri'':13B ''pilot'':2A ''tootsi'':1A ''woman'':10B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (897, 'TORQUE BOUND', 'A Emotional Display of a Crocodile And a Husband who must Reach a Man in Ancient Japan', '''ancient'':18B ''bound'':2A ''crocodil'':8B ''display'':5B ''emot'':4B ''husband'':11B ''japan'':19B ''man'':16B ''must'':13B ''reach'':14B ''torqu'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (898, 'TOURIST PELICAN', 'A Boring Story of a Butler And a Astronaut who must Outrace a Pioneer in Australia', '''astronaut'':11B ''australia'':18B ''bore'':4B ''butler'':8B ''must'':13B ''outrac'':14B ''pelican'':2A ''pioneer'':16B ''stori'':5B ''tourist'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (899, 'TOWERS HURRICANE', 'A Fateful Display of a Monkey And a Car who must Sink a Husband in A MySQL Convention', '''car'':11B ''convent'':20B ''display'':5B ''fate'':4B ''hurrican'':2A ''husband'':16B ''monkey'':8B ''must'':13B ''mysql'':19B ''sink'':14B ''tower'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (900, 'TOWN ARK', 'A Awe-Inspiring Documentary of a Moose And a Madman who must Meet a Dog in An Abandoned Mine Shaft', '''abandon'':21B ''ark'':2A ''awe'':5B ''awe-inspir'':4B ''documentari'':7B ''dog'':18B ''inspir'':6B ''madman'':13B ''meet'':16B ''mine'':22B ''moos'':10B ''must'':15B ''shaft'':23B ''town'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (901, 'TRACY CIDER', 'A Touching Reflection of a Database Administrator And a Madman who must Build a Lumberjack in Nigeria', '''administr'':9B ''build'':15B ''cider'':2A ''databas'':8B ''lumberjack'':17B ''madman'':12B ''must'':14B ''nigeria'':19B ''reflect'':5B ''touch'':4B ''traci'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (902, 'TRADING PINOCCHIO', 'A Emotional Character Study of a Student And a Explorer who must Discover a Frisbee in The First Manned Space Station', '''charact'':5B ''discov'':15B ''emot'':4B ''explor'':12B ''first'':20B ''frisbe'':17B ''man'':21B ''must'':14B ''pinocchio'':2A ''space'':22B ''station'':23B ''student'':9B ''studi'':6B ''trade'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (903, 'TRAFFIC HOBBIT', 'A Amazing Epistle of a Squirrel And a Lumberjack who must Succumb a Database Administrator in A U-Boat', '''administr'':17B ''amaz'':4B ''boat'':22B ''databas'':16B ''epistl'':5B ''hobbit'':2A ''lumberjack'':11B ''must'':13B ''squirrel'':8B ''succumb'':14B ''traffic'':1A ''u'':21B ''u-boat'':20B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (904, 'TRAIN BUNCH', 'A Thrilling Character Study of a Robot And a Squirrel who must Face a Dog in Ancient India', '''ancient'':19B ''bunch'':2A ''charact'':5B ''dog'':17B ''face'':15B ''india'':20B ''must'':14B ''robot'':9B ''squirrel'':12B ''studi'':6B ''thrill'':4B ''train'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (905, 'TRAINSPOTTING STRANGERS', 'A Fast-Paced Drama of a Pioneer And a Mad Cow who must Challenge a Madman in Ancient Japan', '''ancient'':21B ''challeng'':17B ''cow'':14B ''drama'':7B ''fast'':5B ''fast-pac'':4B ''japan'':22B ''mad'':13B ''madman'':19B ''must'':16B ''pace'':6B ''pioneer'':10B ''stranger'':2A ''trainspot'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (906, 'TRAMP OTHERS', 'A Brilliant Display of a Composer And a Cat who must Succumb a A Shark in Ancient India', '''ancient'':19B ''brilliant'':4B ''cat'':11B ''compos'':8B ''display'':5B ''india'':20B ''must'':13B ''other'':2A ''shark'':17B ''succumb'':14B ''tramp'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (907, 'TRANSLATION SUMMER', 'A Touching Reflection of a Man And a Monkey who must Pursue a Womanizer in A MySQL Convention', '''convent'':20B ''man'':8B ''monkey'':11B ''must'':13B ''mysql'':19B ''pursu'':14B ''reflect'':5B ''summer'':2A ''touch'':4B ''translat'':1A ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (908, 'TRAP GUYS', 'A Unbelieveable Story of a Boy And a Mad Cow who must Challenge a Database Administrator in The Sahara Desert', '''administr'':18B ''boy'':8B ''challeng'':15B ''cow'':12B ''databas'':17B ''desert'':22B ''guy'':2A ''mad'':11B ''must'':14B ''sahara'':21B ''stori'':5B ''trap'':1A ''unbeliev'':4B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (909, 'TREASURE COMMAND', 'A Emotional Saga of a Car And a Madman who must Discover a Pioneer in California', '''california'':18B ''car'':8B ''command'':2A ''discov'':14B ''emot'':4B ''madman'':11B ''must'':13B ''pioneer'':16B ''saga'':5B ''treasur'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (910, 'TREATMENT JEKYLL', 'A Boring Story of a Teacher And a Student who must Outgun a Cat in An Abandoned Mine Shaft', '''abandon'':19B ''bore'':4B ''cat'':16B ''jekyl'':2A ''mine'':20B ''must'':13B ''outgun'':14B ''shaft'':21B ''stori'':5B ''student'':11B ''teacher'':8B ''treatment'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (911, 'TRIP NEWTON', 'A Fanciful Character Study of a Lumberjack And a Car who must Discover a Cat in An Abandoned Amusement Park', '''abandon'':20B ''amus'':21B ''car'':12B ''cat'':17B ''charact'':5B ''discov'':15B ''fanci'':4B ''lumberjack'':9B ''must'':14B ''newton'':2A ''park'':22B ''studi'':6B ''trip'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (912, 'TROJAN TOMORROW', 'A Astounding Panorama of a Husband And a Sumo Wrestler who must Pursue a Boat in Ancient India', '''ancient'':19B ''astound'':4B ''boat'':17B ''husband'':8B ''india'':20B ''must'':14B ''panorama'':5B ''pursu'':15B ''sumo'':11B ''tomorrow'':2A ''trojan'':1A ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (913, 'TROOPERS METAL', 'A Fanciful Drama of a Monkey And a Feminist who must Sink a Man in Berlin', '''berlin'':18B ''drama'':5B ''fanci'':4B ''feminist'':11B ''man'':16B ''metal'':2A ''monkey'':8B ''must'':13B ''sink'':14B ''trooper'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (914, 'TROUBLE DATE', 'A Lacklusture Panorama of a Forensic Psychologist And a Woman who must Kill a Explorer in Ancient Japan', '''ancient'':19B ''date'':2A ''explor'':17B ''forens'':8B ''japan'':20B ''kill'':15B ''lacklustur'':4B ''must'':14B ''panorama'':5B ''psychologist'':9B ''troubl'':1A ''woman'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (915, 'TRUMAN CRAZY', 'A Thrilling Epistle of a Moose And a Boy who must Meet a Database Administrator in A Monastery', '''administr'':17B ''boy'':11B ''crazi'':2A ''databas'':16B ''epistl'':5B ''meet'':14B ''monasteri'':20B ''moos'':8B ''must'':13B ''thrill'':4B ''truman'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (916, 'TURN STAR', 'A Stunning Tale of a Man And a Monkey who must Chase a Student in New Orleans', '''chase'':14B ''man'':8B ''monkey'':11B ''must'':13B ''new'':18B ''orlean'':19B ''star'':2A ''student'':16B ''stun'':4B ''tale'':5B ''turn'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (917, 'TUXEDO MILE', 'A Boring Drama of a Man And a Forensic Psychologist who must Face a Frisbee in Ancient India', '''ancient'':19B ''bore'':4B ''drama'':5B ''face'':15B ''forens'':11B ''frisbe'':17B ''india'':20B ''man'':8B ''mile'':2A ''must'':14B ''psychologist'':12B ''tuxedo'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (918, 'TWISTED PIRATES', 'A Touching Display of a Frisbee And a Boat who must Kill a Girl in A MySQL Convention', '''boat'':11B ''convent'':20B ''display'':5B ''frisbe'':8B ''girl'':16B ''kill'':14B ''must'':13B ''mysql'':19B ''pirat'':2A ''touch'':4B ''twist'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (919, 'TYCOON GATHERING', 'A Emotional Display of a Husband And a A Shark who must Succumb a Madman in A Manhattan Penthouse', '''display'':5B ''emot'':4B ''gather'':2A ''husband'':8B ''madman'':17B ''manhattan'':20B ''must'':14B ''penthous'':21B ''shark'':12B ''succumb'':15B ''tycoon'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (920, 'UNBREAKABLE KARATE', 'A Amazing Character Study of a Robot And a Student who must Chase a Robot in Australia', '''amaz'':4B ''australia'':19B ''charact'':5B ''chase'':15B ''karat'':2A ''must'':14B ''robot'':9B,17B ''student'':12B ''studi'':6B ''unbreak'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (921, 'UNCUT SUICIDES', 'A Intrepid Yarn of a Explorer And a Pastry Chef who must Pursue a Mad Cow in A U-Boat', '''boat'':23B ''chef'':12B ''cow'':18B ''explor'':8B ''intrepid'':4B ''mad'':17B ''must'':14B ''pastri'':11B ''pursu'':15B ''suicid'':2A ''u'':22B ''u-boat'':21B ''uncut'':1A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (922, 'UNDEFEATED DALMATIONS', 'A Unbelieveable Display of a Crocodile And a Feminist who must Overcome a Moose in An Abandoned Amusement Park', '''abandon'':19B ''amus'':20B ''crocodil'':8B ''dalmat'':2A ''display'':5B ''feminist'':11B ''moos'':16B ''must'':13B ''overcom'':14B ''park'':21B ''unbeliev'':4B ''undef'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (923, 'UNFAITHFUL KILL', 'A Taut Documentary of a Waitress And a Mad Scientist who must Battle a Technical Writer in New Orleans', '''battl'':15B ''documentari'':5B ''kill'':2A ''mad'':11B ''must'':14B ''new'':20B ''orlean'':21B ''scientist'':12B ''taut'':4B ''technic'':17B ''unfaith'':1A ''waitress'':8B ''writer'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (924, 'UNFORGIVEN ZOOLANDER', 'A Taut Epistle of a Monkey And a Sumo Wrestler who must Vanquish a A Shark in A Baloon Factory', '''baloon'':21B ''epistl'':5B ''factori'':22B ''monkey'':8B ''must'':14B ''shark'':18B ''sumo'':11B ''taut'':4B ''unforgiven'':1A ''vanquish'':15B ''wrestler'':12B ''zooland'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (925, 'UNITED PILOT', 'A Fast-Paced Reflection of a Cat And a Mad Cow who must Fight a Car in The Sahara Desert', '''car'':19B ''cat'':10B ''cow'':14B ''desert'':23B ''fast'':5B ''fast-pac'':4B ''fight'':17B ''mad'':13B ''must'':16B ''pace'':6B ''pilot'':2A ''reflect'':7B ''sahara'':22B ''unit'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (926, 'UNTOUCHABLES SUNRISE', 'A Amazing Documentary of a Woman And a Astronaut who must Outrace a Teacher in An Abandoned Fun House', '''abandon'':19B ''amaz'':4B ''astronaut'':11B ''documentari'':5B ''fun'':20B ''hous'':21B ''must'':13B ''outrac'':14B ''sunris'':2A ''teacher'':16B ''untouch'':1A ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (927, 'UPRISING UPTOWN', 'A Fanciful Reflection of a Boy And a Butler who must Pursue a Woman in Berlin', '''berlin'':18B ''boy'':8B ''butler'':11B ''fanci'':4B ''must'':13B ''pursu'':14B ''reflect'':5B ''upris'':1A ''uptown'':2A ''woman'':16B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (928, 'UPTOWN YOUNG', 'A Fateful Documentary of a Dog And a Hunter who must Pursue a Teacher in An Abandoned Amusement Park', '''abandon'':19B ''amus'':20B ''documentari'':5B ''dog'':8B ''fate'':4B ''hunter'':11B ''must'':13B ''park'':21B ''pursu'':14B ''teacher'':16B ''uptown'':1A ''young'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (929, 'USUAL UNTOUCHABLES', 'A Touching Display of a Explorer And a Lumberjack who must Fight a Forensic Psychologist in A Shark Tank', '''display'':5B ''explor'':8B ''fight'':14B ''forens'':16B ''lumberjack'':11B ''must'':13B ''psychologist'':17B ''shark'':20B ''tank'':21B ''touch'':4B ''untouch'':2A ''usual'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (930, 'VACATION BOONDOCK', 'A Fanciful Character Study of a Secret Agent And a Mad Scientist who must Reach a Teacher in Australia', '''agent'':10B ''australia'':21B ''boondock'':2A ''charact'':5B ''fanci'':4B ''mad'':13B ''must'':16B ''reach'':17B ''scientist'':14B ''secret'':9B ''studi'':6B ''teacher'':19B ''vacat'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (931, 'VALENTINE VANISHING', 'A Thrilling Display of a Husband And a Butler who must Reach a Pastry Chef in California', '''butler'':11B ''california'':19B ''chef'':17B ''display'':5B ''husband'':8B ''must'':13B ''pastri'':16B ''reach'':14B ''thrill'':4B ''valentin'':1A ''vanish'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (932, 'VALLEY PACKER', 'A Astounding Documentary of a Astronaut And a Boy who must Outrace a Sumo Wrestler in Berlin', '''astound'':4B ''astronaut'':8B ''berlin'':19B ''boy'':11B ''documentari'':5B ''must'':13B ''outrac'':14B ''packer'':2A ''sumo'':16B ''valley'':1A ''wrestler'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (933, 'VAMPIRE WHALE', 'A Epic Story of a Lumberjack And a Monkey who must Confront a Pioneer in A MySQL Convention', '''confront'':14B ''convent'':20B ''epic'':4B ''lumberjack'':8B ''monkey'':11B ''must'':13B ''mysql'':19B ''pioneer'':16B ''stori'':5B ''vampir'':1A ''whale'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (934, 'VANILLA DAY', 'A Fast-Paced Saga of a Girl And a Forensic Psychologist who must Redeem a Girl in Nigeria', '''day'':2A ''fast'':5B ''fast-pac'':4B ''forens'':13B ''girl'':10B,19B ''must'':16B ''nigeria'':21B ''pace'':6B ''psychologist'':14B ''redeem'':17B ''saga'':7B ''vanilla'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (935, 'VANISHED GARDEN', 'A Intrepid Character Study of a Squirrel And a A Shark who must Kill a Lumberjack in California', '''california'':20B ''charact'':5B ''garden'':2A ''intrepid'':4B ''kill'':16B ''lumberjack'':18B ''must'':15B ''shark'':13B ''squirrel'':9B ''studi'':6B ''vanish'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (936, 'VANISHING ROCKY', 'A Brilliant Reflection of a Man And a Woman who must Conquer a Pioneer in A MySQL Convention', '''brilliant'':4B ''conquer'':14B ''convent'':20B ''man'':8B ''must'':13B ''mysql'':19B ''pioneer'':16B ''reflect'':5B ''rocki'':2A ''vanish'':1A ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (937, 'VARSITY TRIP', 'A Action-Packed Character Study of a Astronaut And a Explorer who must Reach a Monkey in A MySQL Convention', '''action'':5B ''action-pack'':4B ''astronaut'':11B ''charact'':7B ''convent'':23B ''explor'':14B ''monkey'':19B ''must'':16B ''mysql'':22B ''pack'':6B ''reach'':17B ''studi'':8B ''trip'':2A ''varsiti'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (938, 'VELVET TERMINATOR', 'A Lacklusture Tale of a Pastry Chef And a Technical Writer who must Confront a Crocodile in An Abandoned Amusement Park', '''abandon'':21B ''amus'':22B ''chef'':9B ''confront'':16B ''crocodil'':18B ''lacklustur'':4B ''must'':15B ''park'':23B ''pastri'':8B ''tale'':5B ''technic'':12B ''termin'':2A ''velvet'':1A ''writer'':13B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (939, 'VERTIGO NORTHWEST', 'A Unbelieveable Display of a Mad Scientist And a Mad Scientist who must Outgun a Mad Cow in Ancient Japan', '''ancient'':21B ''cow'':19B ''display'':5B ''japan'':22B ''mad'':8B,12B,18B ''must'':15B ''northwest'':2A ''outgun'':16B ''scientist'':9B,13B ''unbeliev'':4B ''vertigo'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (940, 'VICTORY ACADEMY', 'A Insightful Epistle of a Mad Scientist And a Explorer who must Challenge a Cat in The Sahara Desert', '''academi'':2A ''cat'':17B ''challeng'':15B ''desert'':21B ''epistl'':5B ''explor'':12B ''insight'':4B ''mad'':8B ''must'':14B ''sahara'':20B ''scientist'':9B ''victori'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (941, 'VIDEOTAPE ARSENIC', 'A Lacklusture Display of a Girl And a Astronaut who must Succumb a Student in Australia', '''arsenic'':2A ''astronaut'':11B ''australia'':18B ''display'':5B ''girl'':8B ''lacklustur'':4B ''must'':13B ''student'':16B ''succumb'':14B ''videotap'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (942, 'VIETNAM SMOOCHY', 'A Lacklusture Display of a Butler And a Man who must Sink a Explorer in Soviet Georgia', '''butler'':8B ''display'':5B ''explor'':16B ''georgia'':19B ''lacklustur'':4B ''man'':11B ''must'':13B ''sink'':14B ''smoochi'':2A ''soviet'':18B ''vietnam'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (943, 'VILLAIN DESPERATE', 'A Boring Yarn of a Pioneer And a Feminist who must Redeem a Cat in An Abandoned Amusement Park', '''abandon'':19B ''amus'':20B ''bore'':4B ''cat'':16B ''desper'':2A ''feminist'':11B ''must'':13B ''park'':21B ''pioneer'':8B ''redeem'':14B ''villain'':1A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (944, 'VIRGIN DAISY', 'A Awe-Inspiring Documentary of a Robot And a Mad Scientist who must Reach a Database Administrator in A Shark Tank', '''administr'':20B ''awe'':5B ''awe-inspir'':4B ''daisi'':2A ''databas'':19B ''documentari'':7B ''inspir'':6B ''mad'':13B ''must'':16B ''reach'':17B ''robot'':10B ''scientist'':14B ''shark'':23B ''tank'':24B ''virgin'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (945, 'VIRGINIAN PLUTO', 'A Emotional Panorama of a Dentist And a Crocodile who must Meet a Boy in Berlin', '''berlin'':18B ''boy'':16B ''crocodil'':11B ''dentist'':8B ''emot'':4B ''meet'':14B ''must'':13B ''panorama'':5B ''pluto'':2A ''virginian'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (946, 'VIRTUAL SPOILERS', 'A Fateful Tale of a Database Administrator And a Squirrel who must Discover a Student in Soviet Georgia', '''administr'':9B ''databas'':8B ''discov'':15B ''fate'':4B ''georgia'':20B ''must'':14B ''soviet'':19B ''spoiler'':2A ''squirrel'':12B ''student'':17B ''tale'':5B ''virtual'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (947, 'VISION TORQUE', 'A Thoughtful Documentary of a Dog And a Man who must Sink a Man in A Shark Tank', '''documentari'':5B ''dog'':8B ''man'':11B,16B ''must'':13B ''shark'':19B ''sink'':14B ''tank'':20B ''thought'':4B ''torqu'':2A ''vision'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (948, 'VOICE PEACH', 'A Amazing Panorama of a Pioneer And a Student who must Overcome a Mad Scientist in A Manhattan Penthouse', '''amaz'':4B ''mad'':16B ''manhattan'':20B ''must'':13B ''overcom'':14B ''panorama'':5B ''peach'':2A ''penthous'':21B ''pioneer'':8B ''scientist'':17B ''student'':11B ''voic'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (949, 'VOLCANO TEXAS', 'A Awe-Inspiring Yarn of a Hunter And a Feminist who must Challenge a Dentist in The Outback', '''awe'':5B ''awe-inspir'':4B ''challeng'':16B ''dentist'':18B ''feminist'':13B ''hunter'':10B ''inspir'':6B ''must'':15B ''outback'':21B ''texa'':2A ''volcano'':1A ''yarn'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (950, 'VOLUME HOUSE', 'A Boring Tale of a Dog And a Woman who must Meet a Dentist in California', '''bore'':4B ''california'':18B ''dentist'':16B ''dog'':8B ''hous'':2A ''meet'':14B ''must'':13B ''tale'':5B ''volum'':1A ''woman'':11B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (951, 'VOYAGE LEGALLY', 'A Epic Tale of a Squirrel And a Hunter who must Conquer a Boy in An Abandoned Mine Shaft', '''abandon'':19B ''boy'':16B ''conquer'':14B ''epic'':4B ''hunter'':11B ''legal'':2A ''mine'':20B ''must'':13B ''shaft'':21B ''squirrel'':8B ''tale'':5B ''voyag'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (952, 'WAGON JAWS', 'A Intrepid Drama of a Moose And a Boat who must Kill a Explorer in A Manhattan Penthouse', '''boat'':11B ''drama'':5B ''explor'':16B ''intrepid'':4B ''jaw'':2A ''kill'':14B ''manhattan'':19B ''moos'':8B ''must'':13B ''penthous'':20B ''wagon'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (953, 'WAIT CIDER', 'A Intrepid Epistle of a Woman And a Forensic Psychologist who must Succumb a Astronaut in A Manhattan Penthouse', '''astronaut'':17B ''cider'':2A ''epistl'':5B ''forens'':11B ''intrepid'':4B ''manhattan'':20B ''must'':14B ''penthous'':21B ''psychologist'':12B ''succumb'':15B ''wait'':1A ''woman'':8B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (954, 'WAKE JAWS', 'A Beautiful Saga of a Feminist And a Composer who must Challenge a Moose in Berlin', '''beauti'':4B ''berlin'':18B ''challeng'':14B ''compos'':11B ''feminist'':8B ''jaw'':2A ''moos'':16B ''must'':13B ''saga'':5B ''wake'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (955, 'WALLS ARTIST', 'A Insightful Panorama of a Teacher And a Teacher who must Overcome a Mad Cow in An Abandoned Fun House', '''abandon'':20B ''artist'':2A ''cow'':17B ''fun'':21B ''hous'':22B ''insight'':4B ''mad'':16B ''must'':13B ''overcom'':14B ''panorama'':5B ''teacher'':8B,11B ''wall'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (956, 'WANDA CHAMBER', 'A Insightful Drama of a A Shark And a Pioneer who must Find a Womanizer in The Outback', '''chamber'':2A ''drama'':5B ''find'':15B ''insight'':4B ''must'':14B ''outback'':20B ''pioneer'':12B ''shark'':9B ''wanda'':1A ''woman'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (957, 'WAR NOTTING', 'A Boring Drama of a Teacher And a Sumo Wrestler who must Challenge a Secret Agent in The Canadian Rockies', '''agent'':18B ''bore'':4B ''canadian'':21B ''challeng'':15B ''drama'':5B ''must'':14B ''not'':2A ''rocki'':22B ''secret'':17B ''sumo'':11B ''teacher'':8B ''war'':1A ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (958, 'WARDROBE PHANTOM', 'A Action-Packed Display of a Mad Cow And a Astronaut who must Kill a Car in Ancient India', '''action'':5B ''action-pack'':4B ''ancient'':21B ''astronaut'':14B ''car'':19B ''cow'':11B ''display'':7B ''india'':22B ''kill'':17B ''mad'':10B ''must'':16B ''pack'':6B ''phantom'':2A ''wardrob'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (959, 'WARLOCK WEREWOLF', 'A Astounding Yarn of a Pioneer And a Crocodile who must Defeat a A Shark in The Outback', '''astound'':4B ''crocodil'':11B ''defeat'':14B ''must'':13B ''outback'':20B ''pioneer'':8B ''shark'':17B ''warlock'':1A ''werewolf'':2A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (960, 'WARS PLUTO', 'A Taut Reflection of a Teacher And a Database Administrator who must Chase a Madman in The Sahara Desert', '''administr'':12B ''chase'':15B ''databas'':11B ''desert'':21B ''madman'':17B ''must'':14B ''pluto'':2A ''reflect'':5B ''sahara'':20B ''taut'':4B ''teacher'':8B ''war'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (961, 'WASH HEAVENLY', 'A Awe-Inspiring Reflection of a Cat And a Pioneer who must Escape a Hunter in Ancient China', '''ancient'':20B ''awe'':5B ''awe-inspir'':4B ''cat'':10B ''china'':21B ''escap'':16B ''heaven'':2A ''hunter'':18B ''inspir'':6B ''must'':15B ''pioneer'':13B ''reflect'':7B ''wash'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (962, 'WASTELAND DIVINE', 'A Fanciful Story of a Database Administrator And a Womanizer who must Fight a Database Administrator in Ancient China', '''administr'':9B,18B ''ancient'':20B ''china'':21B ''databas'':8B,17B ''divin'':2A ''fanci'':4B ''fight'':15B ''must'':14B ''stori'':5B ''wasteland'':1A ''woman'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (963, 'WATCH TRACY', 'A Fast-Paced Yarn of a Dog And a Frisbee who must Conquer a Hunter in Nigeria', '''conquer'':16B ''dog'':10B ''fast'':5B ''fast-pac'':4B ''frisbe'':13B ''hunter'':18B ''must'':15B ''nigeria'':20B ''pace'':6B ''traci'':2A ''watch'':1A ''yarn'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (964, 'WATERFRONT DELIVERANCE', 'A Unbelieveable Documentary of a Dentist And a Technical Writer who must Build a Womanizer in Nigeria', '''build'':15B ''deliver'':2A ''dentist'':8B ''documentari'':5B ''must'':14B ''nigeria'':19B ''technic'':11B ''unbeliev'':4B ''waterfront'':1A ''woman'':17B ''writer'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (965, 'WATERSHIP FRONTIER', 'A Emotional Yarn of a Boat And a Crocodile who must Meet a Moose in Soviet Georgia', '''boat'':8B ''crocodil'':11B ''emot'':4B ''frontier'':2A ''georgia'':19B ''meet'':14B ''moos'':16B ''must'':13B ''soviet'':18B ''watership'':1A ''yarn'':5B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (966, 'WEDDING APOLLO', 'A Action-Packed Tale of a Student And a Waitress who must Conquer a Lumberjack in An Abandoned Mine Shaft', '''abandon'':21B ''action'':5B ''action-pack'':4B ''apollo'':2A ''conquer'':16B ''lumberjack'':18B ''mine'':22B ''must'':15B ''pack'':6B ''shaft'':23B ''student'':10B ''tale'':7B ''waitress'':13B ''wed'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (967, 'WEEKEND PERSONAL', 'A Fast-Paced Documentary of a Car And a Butler who must Find a Frisbee in A Jet Boat', '''boat'':22B ''butler'':13B ''car'':10B ''documentari'':7B ''fast'':5B ''fast-pac'':4B ''find'':16B ''frisbe'':18B ''jet'':21B ''must'':15B ''pace'':6B ''person'':2A ''weekend'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (968, 'WEREWOLF LOLA', 'A Fanciful Story of a Man And a Sumo Wrestler who must Outrace a Student in A Monastery', '''fanci'':4B ''lola'':2A ''man'':8B ''monasteri'':20B ''must'':14B ''outrac'':15B ''stori'':5B ''student'':17B ''sumo'':11B ''werewolf'':1A ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (969, 'WEST LION', 'A Intrepid Drama of a Butler And a Lumberjack who must Challenge a Database Administrator in A Manhattan Penthouse', '''administr'':17B ''butler'':8B ''challeng'':14B ''databas'':16B ''drama'':5B ''intrepid'':4B ''lion'':2A ''lumberjack'':11B ''manhattan'':20B ''must'':13B ''penthous'':21B ''west'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (970, 'WESTWARD SEABISCUIT', 'A Lacklusture Tale of a Butler And a Husband who must Face a Boy in Ancient China', '''ancient'':18B ''boy'':16B ''butler'':8B ''china'':19B ''face'':14B ''husband'':11B ''lacklustur'':4B ''must'':13B ''seabiscuit'':2A ''tale'':5B ''westward'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (971, 'WHALE BIKINI', 'A Intrepid Story of a Pastry Chef And a Database Administrator who must Kill a Feminist in A MySQL Convention', '''administr'':13B ''bikini'':2A ''chef'':9B ''convent'':22B ''databas'':12B ''feminist'':18B ''intrepid'':4B ''kill'':16B ''must'':15B ''mysql'':21B ''pastri'':8B ''stori'':5B ''whale'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (972, 'WHISPERER GIANT', 'A Intrepid Story of a Dentist And a Hunter who must Confront a Monkey in Ancient Japan', '''ancient'':18B ''confront'':14B ''dentist'':8B ''giant'':2A ''hunter'':11B ''intrepid'':4B ''japan'':19B ''monkey'':16B ''must'':13B ''stori'':5B ''whisper'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (973, 'WIFE TURN', 'A Awe-Inspiring Epistle of a Teacher And a Feminist who must Confront a Pioneer in Ancient Japan', '''ancient'':20B ''awe'':5B ''awe-inspir'':4B ''confront'':16B ''epistl'':7B ''feminist'':13B ''inspir'':6B ''japan'':21B ''must'':15B ''pioneer'':18B ''teacher'':10B ''turn'':2A ''wife'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (974, 'WILD APOLLO', 'A Beautiful Story of a Monkey And a Sumo Wrestler who must Conquer a A Shark in A MySQL Convention', '''apollo'':2A ''beauti'':4B ''conquer'':15B ''convent'':22B ''monkey'':8B ''must'':14B ''mysql'':21B ''shark'':18B ''stori'':5B ''sumo'':11B ''wild'':1A ''wrestler'':12B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (975, 'WILLOW TRACY', 'A Brilliant Panorama of a Boat And a Astronaut who must Challenge a Teacher in A Manhattan Penthouse', '''astronaut'':11B ''boat'':8B ''brilliant'':4B ''challeng'':14B ''manhattan'':19B ''must'':13B ''panorama'':5B ''penthous'':20B ''teacher'':16B ''traci'':2A ''willow'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (976, 'WIND PHANTOM', 'A Touching Saga of a Madman And a Forensic Psychologist who must Build a Sumo Wrestler in An Abandoned Mine Shaft', '''abandon'':21B ''build'':15B ''forens'':11B ''madman'':8B ''mine'':22B ''must'':14B ''phantom'':2A ''psychologist'':12B ''saga'':5B ''shaft'':23B ''sumo'':17B ''touch'':4B ''wind'':1A ''wrestler'':18B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (977, 'WINDOW SIDE', 'A Astounding Character Study of a Womanizer And a Hunter who must Escape a Robot in A Monastery', '''astound'':4B ''charact'':5B ''escap'':15B ''hunter'':12B ''monasteri'':20B ''must'':14B ''robot'':17B ''side'':2A ''studi'':6B ''window'':1A ''woman'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (978, 'WISDOM WORKER', 'A Unbelieveable Saga of a Forensic Psychologist And a Student who must Face a Squirrel in The First Manned Space Station', '''face'':15B ''first'':20B ''forens'':8B ''man'':21B ''must'':14B ''psychologist'':9B ''saga'':5B ''space'':22B ''squirrel'':17B ''station'':23B ''student'':12B ''unbeliev'':4B ''wisdom'':1A ''worker'':2A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (979, 'WITCHES PANIC', 'A Awe-Inspiring Drama of a Secret Agent And a Hunter who must Fight a Moose in Nigeria', '''agent'':11B ''awe'':5B ''awe-inspir'':4B ''drama'':7B ''fight'':17B ''hunter'':14B ''inspir'':6B ''moos'':19B ''must'':16B ''nigeria'':21B ''panic'':2A ''secret'':10B ''witch'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (980, 'WIZARD COLDBLOODED', 'A Lacklusture Display of a Robot And a Girl who must Defeat a Sumo Wrestler in A MySQL Convention', '''coldblood'':2A ''convent'':21B ''defeat'':14B ''display'':5B ''girl'':11B ''lacklustur'':4B ''must'':13B ''mysql'':20B ''robot'':8B ''sumo'':16B ''wizard'':1A ''wrestler'':17B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (981, 'WOLVES DESIRE', 'A Fast-Paced Drama of a Squirrel And a Robot who must Succumb a Technical Writer in A Manhattan Penthouse', '''desir'':2A ''drama'':7B ''fast'':5B ''fast-pac'':4B ''manhattan'':22B ''must'':15B ''pace'':6B ''penthous'':23B ''robot'':13B ''squirrel'':10B ''succumb'':16B ''technic'':18B ''wolv'':1A ''writer'':19B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (982, 'WOMEN DORADO', 'A Insightful Documentary of a Waitress And a Butler who must Vanquish a Composer in Australia', '''australia'':18B ''butler'':11B ''compos'':16B ''documentari'':5B ''dorado'':2A ''insight'':4B ''must'':13B ''vanquish'':14B ''waitress'':8B ''women'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (983, 'WON DARES', 'A Unbelieveable Documentary of a Teacher And a Monkey who must Defeat a Explorer in A U-Boat', '''boat'':21B ''dare'':2A ''defeat'':14B ''documentari'':5B ''explor'':16B ''monkey'':11B ''must'':13B ''teacher'':8B ''u'':20B ''u-boat'':19B ''unbeliev'':4B ''won'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (984, 'WONDERFUL DROP', 'A Boring Panorama of a Woman And a Madman who must Overcome a Butler in A U-Boat', '''boat'':21B ''bore'':4B ''butler'':16B ''drop'':2A ''madman'':11B ''must'':13B ''overcom'':14B ''panorama'':5B ''u'':20B ''u-boat'':19B ''woman'':8B ''wonder'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (985, 'WONDERLAND CHRISTMAS', 'A Awe-Inspiring Character Study of a Waitress And a Car who must Pursue a Mad Scientist in The First Manned Space Station', '''awe'':5B ''awe-inspir'':4B ''car'':14B ''charact'':7B ''christma'':2A ''first'':23B ''inspir'':6B ''mad'':19B ''man'':24B ''must'':16B ''pursu'':17B ''scientist'':20B ''space'':25B ''station'':26B ''studi'':8B ''waitress'':11B ''wonderland'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (986, 'WONKA SEA', 'A Brilliant Saga of a Boat And a Mad Scientist who must Meet a Moose in Ancient India', '''ancient'':19B ''boat'':8B ''brilliant'':4B ''india'':20B ''mad'':11B ''meet'':15B ''moos'':17B ''must'':14B ''saga'':5B ''scientist'':12B ''sea'':2A ''wonka'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (987, 'WORDS HUNTER', 'A Action-Packed Reflection of a Composer And a Mad Scientist who must Face a Pioneer in A MySQL Convention', '''action'':5B ''action-pack'':4B ''compos'':10B ''convent'':23B ''face'':17B ''hunter'':2A ''mad'':13B ''must'':16B ''mysql'':22B ''pack'':6B ''pioneer'':19B ''reflect'':7B ''scientist'':14B ''word'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (988, 'WORKER TARZAN', 'A Action-Packed Yarn of a Secret Agent And a Technical Writer who must Battle a Sumo Wrestler in The First Manned Space Station', '''action'':5B ''action-pack'':4B ''agent'':11B ''battl'':18B ''first'':24B ''man'':25B ''must'':17B ''pack'':6B ''secret'':10B ''space'':26B ''station'':27B ''sumo'':20B ''tarzan'':2A ''technic'':14B ''worker'':1A ''wrestler'':21B ''writer'':15B ''yarn'':7B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (989, 'WORKING MICROCOSMOS', 'A Stunning Epistle of a Dentist And a Dog who must Kill a Madman in Ancient China', '''ancient'':18B ''china'':19B ''dentist'':8B ''dog'':11B ''epistl'':5B ''kill'':14B ''madman'':16B ''microcosmo'':2A ''must'':13B ''stun'':4B ''work'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (990, 'WORLD LEATHERNECKS', 'A Unbelieveable Tale of a Pioneer And a Astronaut who must Overcome a Robot in An Abandoned Amusement Park', '''abandon'':19B ''amus'':20B ''astronaut'':11B ''leatherneck'':2A ''must'':13B ''overcom'':14B ''park'':21B ''pioneer'':8B ''robot'':16B ''tale'':5B ''unbeliev'':4B ''world'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (991, 'WORST BANGER', 'A Thrilling Drama of a Madman And a Dentist who must Conquer a Boy in The Outback', '''banger'':2A ''boy'':16B ''conquer'':14B ''dentist'':11B ''drama'':5B ''madman'':8B ''must'':13B ''outback'':19B ''thrill'':4B ''worst'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (992, 'WRATH MILE', 'A Intrepid Reflection of a Technical Writer And a Hunter who must Defeat a Sumo Wrestler in A Monastery', '''defeat'':15B ''hunter'':12B ''intrepid'':4B ''mile'':2A ''monasteri'':21B ''must'':14B ''reflect'':5B ''sumo'':17B ''technic'':8B ''wrath'':1A ''wrestler'':18B ''writer'':9B');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (993, 'WRONG BEHAVIOR', 'A Emotional Saga of a Crocodile And a Sumo Wrestler who must Discover a Mad Cow in New Orleans', '''behavior'':2A ''cow'':18B ''crocodil'':8B ''discov'':15B ''emot'':4B ''mad'':17B ''must'':14B ''new'':20B ''orlean'':21B ''saga'':5B ''sumo'':11B ''wrestler'':12B ''wrong'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (994, 'WYOMING STORM', 'A Awe-Inspiring Panorama of a Robot And a Boat who must Overcome a Feminist in A U-Boat', '''awe'':5B ''awe-inspir'':4B ''boat'':13B,23B ''feminist'':18B ''inspir'':6B ''must'':15B ''overcom'':16B ''panorama'':7B ''robot'':10B ''storm'':2A ''u'':22B ''u-boat'':21B ''wyom'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (995, 'YENTL IDAHO', 'A Amazing Display of a Robot And a Astronaut who must Fight a Womanizer in Berlin', '''amaz'':4B ''astronaut'':11B ''berlin'':18B ''display'':5B ''fight'':14B ''idaho'':2A ''must'':13B ''robot'':8B ''woman'':16B ''yentl'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (996, 'YOUNG LANGUAGE', 'A Unbelieveable Yarn of a Boat And a Database Administrator who must Meet a Boy in The First Manned Space Station', '''administr'':12B ''boat'':8B ''boy'':17B ''databas'':11B ''first'':20B ''languag'':2A ''man'':21B ''meet'':15B ''must'':14B ''space'':22B ''station'':23B ''unbeliev'':4B ''yarn'':5B ''young'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (997, 'YOUTH KICK', 'A Touching Drama of a Teacher And a Cat who must Challenge a Technical Writer in A U-Boat', '''boat'':22B ''cat'':11B ''challeng'':14B ''drama'':5B ''kick'':2A ''must'':13B ''teacher'':8B ''technic'':16B ''touch'':4B ''u'':21B ''u-boat'':20B ''writer'':17B ''youth'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (998, 'ZHIVAGO CORE', 'A Fateful Yarn of a Composer And a Man who must Face a Boy in The Canadian Rockies', '''boy'':16B ''canadian'':19B ''compos'':8B ''core'':2A ''face'':14B ''fate'':4B ''man'':11B ''must'':13B ''rocki'':20B ''yarn'':5B ''zhivago'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (999, 'ZOOLANDER FICTION', 'A Fateful Reflection of a Waitress And a Boat who must Discover a Sumo Wrestler in Ancient China', '''ancient'':19B ''boat'':11B ''china'':20B ''discov'':14B ''fate'':4B ''fiction'':2A ''must'':13B ''reflect'':5B ''sumo'':16B ''waitress'':8B ''wrestler'':17B ''zooland'':1A');
INSERT INTO "public"."film" ("film_id", "title", "description", "fts") VALUES (1000, 'ZORRO ARK', 'A Intrepid Panorama of a Mad Scientist And a Boy who must Redeem a Boy in A Monastery', '''ark'':2A ''boy'':12B,17B ''intrepid'':4B ''mad'':8B ''monasteri'':20B ''must'':14B ''panorama'':5B ''redeem'':15B ''scientist'':9B ''zorro'':1A');


--
-- TOC entry 3601 (class 0 OID 1846268)
-- Dependencies: 234
-- Data for Name: game_throws; Type: TABLE DATA; Schema: public; Owner: postgres
--

INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (1, 'regina', 3);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (2, 'leo', 2);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (3, 'sonia', 1);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (4, 'alex', 3);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (5, 'regina', 2);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (6, 'leo', 4);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (7, 'sonia', 3);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (8, 'alex', 3);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (9, 'regina', 1);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (10, 'leo', 2);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (11, 'sonia', 3);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (12, 'alex', 3);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (13, 'regina', 3);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (14, 'leo', 4);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (15, 'sonia', 1);
INSERT INTO "public"."game_throws" ("id", "player", "roll") VALUES (16, 'alex', 3);


--
-- TOC entry 3603 (class 0 OID 1846274)
-- Dependencies: 236
-- Data for Name: interval_periods; Type: TABLE DATA; Schema: public; Owner: postgres
--

INSERT INTO "public"."interval_periods" ("i_type") VALUES ('5 mons');
INSERT INTO "public"."interval_periods" ("i_type") VALUES ('132 days');
INSERT INTO "public"."interval_periods" ("i_type") VALUES ('4862:00:00');


--
-- TOC entry 3604 (class 0 OID 1846277)
-- Dependencies: 237
-- Data for Name: persons; Type: TABLE DATA; Schema: public; Owner: postgres
--

INSERT INTO "public"."persons" ("id", "person") VALUES (1, '{
        "name":"Sonia",
        "spouse":
        {
            "name":"Alex",
            "parents":
            {
                "father":"Rafael",
                "mother":"Ofelia"
            },
            "phones":
            [
                {
                    "type":"work",
                    "number":"619-722-6719"
                },
                {
                    "type":"cell",
                    "number":"619-852-5083"
                }
            ]
        },
        "children": 
        [
            {
                "name":"Brandon",
                "gender":"M"
            },
            {
                "name":"Azaleah",
                "girl":true,
                "phones": []
            }
        ]
    }');


--
-- TOC entry 3605 (class 0 OID 1846283)
-- Dependencies: 238
-- Data for Name: persons_b; Type: TABLE DATA; Schema: public; Owner: postgres
--

INSERT INTO "public"."persons_b" ("id", "person") VALUES (1, '{"name": "Sonia", "spouse": {"name": "Alex", "phones": [{"type": "work", "number": "619-722-6719"}, {"type": "cell", "number": "619-852-5083"}], "parents": {"father": "Rafael", "mother": "Ofelia"}}, "children": [{"name": "Brandon", "gender": "M"}, {"name": "Azaleah", "gender": "F", "phones": []}]}');


--
-- TOC entry 3608 (class 0 OID 1846291)
-- Dependencies: 241
-- Data for Name: posts; Type: TABLE DATA; Schema: public; Owner: postgres
--

INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (7, 1, '2014-05-14 00:11:06.457', 2, 266, 'What open-source books (or other materials) provide a relatively thorough overview of data science?', '&lt;p&gt;As a researcher and instructor, I''m looking for open-source books (or similar materials) that provide a relatively thorough overview of data science from an applied perspective. To be clear, I''m especially interested in a thorough overview that provides material suitable for a college-level course, not particular pieces or papers.&lt;/p&gt;
', 'education  open-source', NULL, '''/p'':77C ''appli'':49C ''book'':5A,34C ''clear'':53C ''colleg'':69C ''college-level'':68C ''cours'':71C ''data'':15A,45C ''educ'':17B ''especi'':56C ''instructor'':26C ''interest'':57C ''level'':70C ''look'':29C ''m'':28C,55C ''materi'':8A,37C,64C ''open'':3A,19B,32C ''open-sourc'':2A,18B,31C ''overview'':13A,43C,61C ''p'':21C ''paper'':76C ''particular'':73C ''perspect'':50C ''piec'':74C ''provid'':9A,39C,63C ''relat'':11A,41C ''research'':24C ''scienc'':16A,46C ''similar'':36C ''sourc'':4A,20B,33C ''suitabl'':65C ''thorough'':12A,42C,60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10532, 2, '2016-03-04 19:17:18.713', 3, NULL, NULL, '&lt;p&gt;Assuming you are doing supervized learning to train a model that when deployed will take text as input and output a label (e.g., topic) or class probability, then what you probably want to do is balanced, &lt;a href="https://en.wikipedia.org/wiki/Stratified_sampling" rel="nofollow"&gt;stratified sampling&lt;/a&gt;. Assuming sufficient labelled data, ensure that your final training set has a balanced number of text examples for each class/label. Depending on your situation, you may need to over/under sample or somehow deal with the problem of highly imbalanced classes (see &lt;a href="http://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/" rel="nofollow"&gt;8 tactics to combat imbalanced classes&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;The simplest NLP approach to use a &lt;a href="https://en.wikipedia.org/wiki/Bag-of-words_model" rel="nofollow"&gt;bag of words&lt;/a&gt; technique, simply indicating the presence/absence of a word in the sentence. Thus each sentence becomes represented as vector of length n, where n = the number of unique words in your data set. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data_set &amp;lt;- c("the big dog", "the small cat", "the big and fat cow")
words &amp;lt;- strsplit(data_set, split = " ") #tokenize sentences
words
##[[1]]
##[1] "the" "big" "dog"
##
##[[2]]
##[1] "the"   "small" "cat"  
##
##[[3]]
##[1] "the" "big" "and" "fat" "cow"


vec &amp;lt;- unique(unlist(words)) #vector representation of sentences
##[1] "the"   "big"   "dog"   "small" "cat"   "and"  
##[7] "fat"   "cow" 

m &amp;lt;- matrix(nrow = length(data_set), ncol = length(vec))

for (i in 1:length(words)) { #iterate the index of tokenized sentences
  vec_rep &amp;lt;- vec %in% words[[i]] #create binary word-feature vector
  m[i,] &amp;lt;- vec_rep #update matrix
}

df &amp;lt;- data.frame(m, row.names = NULL)
names(df) &amp;lt;- vec
df
##   the   big   dog small   cat   and   fat   cow
##1 TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
##2 TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
##3 TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Sometimes you can increase performance by adding &lt;a href="https://en.wikipedia.org/wiki/Bigram" rel="nofollow"&gt;bi-gram&lt;/a&gt; and tri-gram features. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;##given: "the big dog"
##unigrams &amp;lt;- {the, big, dog}
##bigrams &amp;lt;- {the big, big dog}
##trigrams &amp;lt;- {the big dog}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Sometimes weighting words by their frequency improves performance or computing the &lt;a href="http://blog.christianperone.com/2011/09/machine-learning-text-feature-extraction-tf-idf-part-i/" rel="nofollow"&gt;tf-idf&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;Another way to increase performance, in my experience, has been custom language feature engineering, especially if the data is from social media sources replete with spelling errors, acronyms, slang, and other word variants. Standard NLP approaches will typically remove &lt;a href="http://datascience.stackexchange.com/questions/5893/how-to-create-a-good-list-of-stopwords?rq=1"&gt;stop words&lt;/a&gt; (e.g., the, a/an, this/that, etc.) from vector representations (because closed class, high frequency words often don''t help discriminate among class/label boundaries). Because vector representations are typically highly dimensional (approximately num of unique words in the corpus/data set), dimensionality reductions techniques can increase performance. For example, one can compute chi-sq, info gain, etc. on a word feature''s distribution across classes -- only keep those features/words above some threshold or below some pre-established p value). &lt;/p&gt;
', NULL, NULL, '''/2011/09/machine-learning-text-feature-extraction-tf-idf-part-i/'':373C ''/a'':47C,102C,122C,326C,379C,428C ''/code'':306C,355C ''/p'':103C,155C,332C,380C,507C ''/pre'':307C,356C ''/questions/5893/how-to-create-a-good-list-of-stopwords?rq=1'':425C ''/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/'':93C ''/wiki/bag-of-words_model'':116C ''/wiki/bigram'':320C ''/wiki/stratified_sampling'':42C ''1'':182C,183C,188C,193C,208C,231C,279C ''2'':187C,288C ''3'':192C,297C ''7'':215C ''8'':96C ''a/an'':431C ''acronym'':409C ''across'':490C ''ad'':315C ''among'':448C ''anoth'':382C ''approach'':108C,417C ''approxim'':458C ''assum'':2C,48C ''bag'':119C ''balanc'':37C,60C ''becom'':137C ''bi'':324C ''bi-gram'':323C ''big'':163C,169C,185C,195C,210C,272C,337C,342C,347C,348C,353C ''bigram'':344C ''binari'':248C ''blog.christianperone.com'':372C ''blog.christianperone.com/2011/09/machine-learning-text-feature-extraction-tf-idf-part-i/'':371C ''boundari'':450C ''c'':161C ''cat'':167C,191C,213C,275C ''chi'':479C ''chi-sq'':478C ''class'':27C,87C,101C,439C,491C ''class/label'':67C,449C ''close'':438C ''code'':157C,334C ''combat'':99C ''comput'':367C,477C ''corpus/data'':465C ''cow'':172C,198C,217C,278C ''creat'':247C ''custom'':392C ''data'':51C,153C,158C,176C,223C,399C ''data.frame'':262C ''datascience.stackexchange.com'':424C ''datascience.stackexchange.com/questions/5893/how-to-create-a-good-list-of-stopwords?rq=1'':423C ''deal'':80C ''depend'':68C ''deploy'':14C ''df'':260C,267C,270C ''dimension'':457C,467C ''discrimin'':447C ''distribut'':489C ''dog'':164C,186C,211C,273C,338C,343C,349C,354C ''e.g'':24C,429C ''en.wikipedia.org'':41C,115C,319C ''en.wikipedia.org/wiki/bag-of-words_model'':114C ''en.wikipedia.org/wiki/bigram'':318C ''en.wikipedia.org/wiki/stratified_sampling'':40C ''engin'':395C ''ensur'':52C ''error'':408C ''especi'':396C ''establish'':504C ''etc'':433C,483C ''exampl'':64C,474C ''experi'':389C ''fals'':283C,284C,285C,286C,287C,290C,291C,294C,295C,296C,300C,301C,302C ''fat'':171C,197C,216C,277C ''featur'':251C,331C,394C,487C ''features/words'':495C ''final'':55C ''frequenc'':363C,441C ''gain'':482C ''given'':335C ''gram'':325C,330C ''help'':446C ''high'':85C,440C,456C ''href'':39C,90C,113C,317C,370C,422C ''idf'':378C ''imbalanc'':86C,100C ''improv'':364C ''increas'':312C,385C,471C ''index'':236C ''indic'':125C ''info'':481C ''input'':19C ''iter'':234C ''keep'':493C ''label'':23C,50C ''languag'':393C ''learn'':7C ''length'':142C,222C,226C,232C ''lt'':160C,174C,200C,219C,242C,255C,261C,268C,340C,345C,351C ''m'':218C,253C,263C ''machinelearningmastery.com'':92C ''machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/'':91C ''matrix'':220C,259C ''may'':73C ''media'':403C ''model'':11C ''n'':143C,145C ''name'':266C ''ncol'':225C ''need'':74C ''nlp'':107C,416C ''nofollow'':44C,95C,118C,322C,375C ''nrow'':221C ''null'':265C ''num'':459C ''number'':61C,147C ''often'':443C ''one'':475C ''output'':21C ''over/under'':76C ''p'':1C,104C,308C,357C,381C,505C ''perform'':313C,365C,386C,472C ''pre'':156C,333C,503C ''pre-establish'':502C ''presence/absence'':127C ''probabl'':28C,32C ''problem'':83C ''reduct'':468C ''rel'':43C,94C,117C,321C,374C ''remov'':420C ''rep'':241C,257C ''replet'':405C ''repres'':138C ''represent'':205C,436C,453C ''row.names'':264C ''sampl'':46C,77C ''see'':88C ''sentenc'':133C,136C,180C,207C,239C ''set'':57C,154C,159C,177C,224C,466C ''simplest'':106C ''simpli'':124C ''situat'':71C ''slang'':410C ''small'':166C,190C,212C,274C ''social'':402C ''somehow'':79C ''sometim'':309C,358C ''sourc'':404C ''spell'':407C ''split'':178C ''sq'':480C ''standard'':415C ''stop'':426C ''stratifi'':45C ''strsplit'':175C ''suffici'':49C ''superv'':6C ''tactic'':97C ''take'':16C ''techniqu'':123C,469C ''text'':17C,63C ''tf'':377C ''tf-idf'':376C ''this/that'':432C ''threshold'':498C ''thus'':134C ''token'':179C,238C ''topic'':25C ''train'':9C,56C ''tri'':329C ''tri-gram'':328C ''trigram'':350C ''true'':280C,281C,282C,289C,292C,293C,298C,299C,303C,304C,305C ''typic'':419C,455C ''unigram'':339C ''uniqu'':149C,201C,461C ''unlist'':202C ''updat'':258C ''use'':110C ''valu'':506C ''variant'':414C ''vec'':199C,227C,240C,243C,256C,269C ''vector'':140C,204C,252C,435C,452C ''want'':33C ''way'':383C ''weight'':359C ''word'':121C,130C,150C,173C,181C,203C,233C,245C,250C,360C,413C,427C,442C,462C,486C ''word-featur'':249C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5, 1, '2014-05-13 23:58:30.457', 7, 286, 'How can I do simple machine learning without hard-coding behavior?', '&lt;p&gt;I''ve always been interested in machine learning, but I can''t figure out one thing about starting out with a simple "Hello World" example - how can I avoid hard-coding behavior?&lt;/p&gt;

&lt;p&gt;For example, if I wanted to "teach" a bot how to avoid randomly placed obstacles, I couldn''t just use relative motion, because the obstacles move around, but I don''t want to hard code, say, distance, because that ruins the whole point of machine learning.&lt;/p&gt;

&lt;p&gt;Obviously, randomly generating code would be impractical, so how could I do this?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':50C,98C,113C ''alway'':19C ''around'':78C ''avoid'':45C,63C ''behavior'':12A,49C ''bot'':60C ''code'':11A,48C,86C,103C ''could'':109C ''couldn'':68C ''distanc'':88C ''exampl'':41C,53C ''figur'':29C ''generat'':102C ''hard'':10A,47C,85C ''hard-cod'':9A,46C ''hello'':39C ''impract'':106C ''interest'':21C ''learn'':7A,15B,24C,97C ''machin'':6A,14B,23C,96C ''machine-learn'':13B ''motion'':73C ''move'':77C ''obstacl'':66C,76C ''obvious'':100C ''one'':31C ''p'':16C,51C,99C ''place'':65C ''point'':94C ''random'':64C,101C ''relat'':72C ''ruin'':91C ''say'':87C ''simpl'':5A,38C ''start'':34C ''teach'':58C ''thing'':32C ''use'':71C ''ve'':18C ''want'':56C,83C ''whole'':93C ''without'':8A ''world'':40C ''would'':104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (53, 2, '2014-05-14 15:42:02.393', 10, NULL, NULL, '&lt;p&gt;For windows, use the task scheduler to set the task to run for example daily at 4:00 AM&lt;/p&gt;

&lt;p&gt;It gives you many other options regarding frequency etc.
&lt;a href="http://en.wikipedia.org/wiki/Windows_Task_Scheduler"&gt;http://en.wikipedia.org/wiki/Windows_Task_Scheduler&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':21C ''/wiki/windows_task_scheduler'':36C ''/wiki/windows_task_scheduler&lt;/a&gt;&lt;/p&gt;'':39C ''00'':19C ''4'':18C ''daili'':16C ''en.wikipedia.org'':35C,38C ''en.wikipedia.org/wiki/windows_task_scheduler'':34C ''en.wikipedia.org/wiki/windows_task_scheduler&lt;/a&gt;&lt;/p&gt;'':37C ''etc'':31C ''exampl'':15C ''frequenc'':30C ''give'':24C ''href'':33C ''mani'':26C ''option'':28C ''p'':1C,22C ''regard'':29C ''run'':13C ''schedul'':7C ''set'':9C ''task'':6C,11C ''use'':4C ''window'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9, 2, '2014-05-14 00:36:31.077', 4, NULL, NULL, '&lt;p&gt;Not sure if this fits the scope of this SE, but here''s a stab at an answer anyway.&lt;/p&gt;

&lt;p&gt;With all AI approaches you have to decide what it is you''re modelling and what kind of uncertainty there is. Once you pick a framework that allows modelling of your situation, you then see which elements are "fixed" and which are flexible. For example, the model may allow you to define your own network structure (or even learn it) with certain constraints. You have to decide whether this flexibility is sufficient for your purposes. Then within a particular network structure, you can learn parameters given a specific training dataset.&lt;/p&gt;

&lt;p&gt;You rarely hard-code behavior in AI/ML solutions. It''s all about modelling the underlying situation and accommodating different situations by tweaking elements of the model.&lt;/p&gt;

&lt;p&gt;In your example, perhaps you might have the robot learn how to detect obstacles (by analyzing elements in the environment), or you might have it keep track of where the obstacles were and which way they were moving.&lt;/p&gt;
', NULL, NULL, '''/p'':21C,113C,142C,182C ''accommod'':133C ''ai'':25C ''ai/ml'':122C ''allow'':50C,71C ''analyz'':159C ''answer'':19C ''anyway'':20C ''approach'':26C ''behavior'':120C ''certain'':84C ''code'':119C ''constraint'':85C ''dataset'':112C ''decid'':30C,89C ''defin'':74C ''detect'':156C ''differ'':134C ''element'':59C,138C,160C ''environ'':163C ''even'':80C ''exampl'':67C,146C ''fit'':6C ''fix'':61C ''flexibl'':65C,92C ''framework'':48C ''given'':108C ''hard'':118C ''hard-cod'':117C ''keep'':169C ''kind'':39C ''learn'':81C,106C,153C ''may'':70C ''might'':149C,166C ''model'':36C,51C,69C,128C,141C ''move'':181C ''network'':77C,102C ''obstacl'':157C,174C ''p'':1C,22C,114C,143C ''paramet'':107C ''particular'':101C ''perhap'':147C ''pick'':46C ''purpos'':97C ''rare'':116C ''re'':35C ''robot'':152C ''scope'':8C ''se'':11C ''see'':57C ''situat'':54C,131C,135C ''solut'':123C ''specif'':110C ''stab'':16C ''structur'':78C,103C ''suffici'':94C ''sure'':3C ''track'':170C ''train'':111C ''tweak'':137C ''uncertainti'':41C ''under'':130C ''way'':178C ''whether'':90C ''within'':99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10, 2, '2014-05-14 00:53:43.273', 9, NULL, NULL, '&lt;p&gt;One book that''s freely available is "The Elements of Statistical Learning" by Hastie, Tibshirani, and Friedman (published by Springer): &lt;a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/"&gt;see Tibshirani''s website&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Another fantastic source, although it isn''t a book, is Andrew Ng''s Machine Learning course on Coursera. This has a much more applied-focus than the above book, and Prof. Ng does a great job of explaining the thinking behind several different machine learning algorithms/situations.&lt;/p&gt;
', NULL, NULL, '''/a'':31C ''/p'':32C,81C ''/~tibs/elemstatlearn/'':26C ''algorithms/situations'':80C ''although'':37C ''andrew'':44C ''anoth'':34C ''appli'':58C ''applied-focus'':57C ''avail'':7C ''behind'':75C ''book'':3C,42C,63C ''cours'':49C ''coursera'':51C ''differ'':77C ''element'':10C ''explain'':72C ''fantast'':35C ''focus'':59C ''freeli'':6C ''friedman'':18C ''great'':69C ''hasti'':15C ''href'':23C ''isn'':39C ''job'':70C ''learn'':13C,48C,79C ''machin'':47C,78C ''much'':55C ''ng'':45C,66C ''one'':2C ''p'':1C,33C ''prof'':65C ''publish'':19C ''see'':27C ''sever'':76C ''sourc'':36C ''springer'':21C ''statist'':12C ''statweb.stanford.edu'':25C ''statweb.stanford.edu/~tibs/elemstatlearn/'':24C ''think'':74C ''tibshirani'':16C,28C ''websit'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (14, 1, '2014-05-14 01:25:59.677', 14, 686, 'Is Data Science the Same as Data Mining?', '&lt;p&gt;I am sure data science as will be discussed in this forum has several synonyms or at least related fields where large data is analyzed.&lt;/p&gt;

&lt;p&gt;My particular question is in regards to Data Mining.  I took a graduate class in Data Mining a few years back.  What are the differences between Data Science and Data Mining and in particular what more would I need to look at to become proficient in Data Mining?&lt;/p&gt;
', 'data-mining  definitions', NULL, '''/p'':39C,89C ''analyz'':38C ''back'':61C ''becom'':84C ''class'':54C ''data'':2A,7A,10B,17C,36C,48C,56C,67C,70C,87C ''data-min'':9B ''definit'':12B ''differ'':65C ''discuss'':22C ''field'':33C ''forum'':25C ''graduat'':53C ''larg'':35C ''least'':31C ''look'':81C ''mine'':8A,11B,49C,57C,71C,88C ''need'':79C ''p'':13C,40C ''particular'':42C,74C ''profici'':85C ''question'':43C ''regard'':46C ''relat'':32C ''scienc'':3A,18C,68C ''sever'':27C ''sure'':16C ''synonym'':28C ''took'':51C ''would'':77C ''year'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6982, 5, '2015-09-02 09:01:54.15', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (178, 4, '2014-05-21 07:05:44.78', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (180, 5, '2014-05-21 07:10:45.827', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (181, 4, '2014-05-21 07:10:45.827', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8888, 5, '2015-11-18 11:23:07.643', 0, NULL, NULL, '&lt;p&gt;Datasets are structured data files in any format, collected together with the documentation that explains their production or use.&lt;/p&gt;
', NULL, NULL, '''/p'':21C ''collect'':10C ''data'':5C ''dataset'':2C ''document'':14C ''explain'':16C ''file'':6C ''format'':9C ''p'':1C ''product'':18C ''structur'':4C ''togeth'':11C ''use'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (16, 1, '2014-05-14 01:57:56.88', 15, 213, 'Use liblinear on big data for semantic analysis', '&lt;p&gt;I use &lt;a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm/"&gt;Libsvm&lt;/a&gt; to train data and predict classification on &lt;strong&gt;semantic analysis&lt;/strong&gt; problem. But it has a &lt;strong&gt;performance&lt;/strong&gt; issue on large-scale data, because semantic analysis concerns &lt;strong&gt;&lt;em&gt;n-dimension&lt;/em&gt;&lt;/strong&gt; problem.&lt;/p&gt;

&lt;p&gt;Last year, &lt;a href="http://www.csie.ntu.edu.tw/~cjlin/liblinear/"&gt;Liblinear&lt;/a&gt; was release, and it can solve performance bottleneck.
But it cost too much &lt;strong&gt;memory&lt;/strong&gt;. Is &lt;strong&gt;MapReduce&lt;/strong&gt; the only way to solve semantic analysis problem on big data? Or are there any other methods that can improve memory bottleneck on &lt;strong&gt;Liblinear&lt;/strong&gt;?&lt;/p&gt;
', 'machine-learning  bigdata  libsvm', NULL, '''/a'':23C,71C ''/em'':58C ''/p'':61C,118C ''/strong'':34C,42C,59C,87C,91C,117C ''/~cjlin/liblinear/'':69C ''/~cjlin/libsvm/'':21C ''analysi'':8A,33C,51C,98C ''big'':4A,101C ''bigdata'':12B ''bottleneck'':79C,113C ''classif'':29C ''concern'':52C ''cost'':82C ''data'':5A,26C,48C,102C ''dimens'':57C ''em'':54C ''href'':18C,66C ''improv'':111C ''issu'':43C ''larg'':46C ''large-scal'':45C ''last'':63C ''learn'':11B ''liblinear'':2A,70C,116C ''libsvm'':13B,22C ''machin'':10B ''machine-learn'':9B ''mapreduc'':90C ''memori'':86C,112C ''method'':108C ''much'':84C ''n'':56C ''n-dimens'':55C ''p'':14C,62C ''perform'':41C,78C ''predict'':28C ''problem'':35C,60C,99C ''releas'':73C ''scale'':47C ''semant'':7A,32C,50C,97C ''solv'':77C,96C ''strong'':31C,40C,53C,85C,89C,115C ''train'':25C ''use'':1A,16C ''way'':94C ''www.csie.ntu.edu.tw'':20C,68C ''www.csie.ntu.edu.tw/~cjlin/liblinear/'':67C ''www.csie.ntu.edu.tw/~cjlin/libsvm/'':19C ''year'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (17, 5, '2014-05-14 02:49:14.58', 0, NULL, NULL, '&lt;p&gt;&lt;a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm/" rel="nofollow"&gt;LIBSVM&lt;/a&gt; is a library for support vector classification (SVM) and regression.
It was created by Chih-Chung Chang and Chih-Jen Lin in 2001.&lt;/p&gt;
', NULL, NULL, '''/a'':10C ''/p'':36C ''/~cjlin/libsvm/'':6C ''2001'':35C ''chang'':28C ''chih'':26C,31C ''chih-chung'':25C ''chih-jen'':30C ''chung'':27C ''classif'':17C ''creat'':23C ''href'':3C ''jen'':32C ''librari'':13C ''libsvm'':9C ''lin'':33C ''nofollow'':8C ''p'':1C ''regress'':20C ''rel'':7C ''support'':15C ''svm'':18C ''vector'':16C ''www.csie.ntu.edu.tw'':5C ''www.csie.ntu.edu.tw/~cjlin/libsvm/'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8342, 2, '2015-10-07 04:38:27.75', 1, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;LinkedIn&lt;/li&gt;
&lt;li&gt;Github&lt;/li&gt;
&lt;li&gt;Personal Website (of course)&lt;/li&gt;
&lt;li&gt;Coderbits (not popular, but you can display your profile link on the resume/LinkedIn)&lt;/li&gt;
&lt;li&gt;Kaggle&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':4C,7C,13C,28C,31C ''/ol'':32C ''coderbit'':15C ''cours'':12C ''display'':21C ''github'':6C ''kaggl'':30C ''li'':2C,5C,8C,14C,29C ''link'':24C ''linkedin'':3C ''ol'':1C ''person'':9C ''popular'':17C ''profil'':23C ''resume/linkedin'':27C ''websit'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (19, 1, '2014-05-14 03:56:20.963', 45, 1710, 'How big is big data?', '&lt;p&gt;Lots of people use the term &lt;em&gt;big data&lt;/em&gt; in a rather &lt;em&gt;commercial&lt;/em&gt; way, as a means of indicating that large datasets are involved in the computation, and therefore potential solutions must have good performance. Of course, &lt;em&gt;big data&lt;/em&gt; always carry associated terms, like scalability and efficiency, but what exactly defines a problem as a &lt;em&gt;big data&lt;/em&gt; problem?&lt;/p&gt;

&lt;p&gt;Does the computation have to be related to some set of specific purposes, like data mining/information retrieval, or could an algorithm for general graph problems be labeled &lt;em&gt;big data&lt;/em&gt; if the dataset was &lt;em&gt;big enough&lt;/em&gt;? Also, how &lt;em&gt;big&lt;/em&gt; is &lt;em&gt;big enough&lt;/em&gt; (if this is possible to define)?&lt;/p&gt;
', 'bigdata  scalability  efficiency  performance', NULL, '''/em'':20C,26C,54C,74C,108C,116C,121C,126C ''/p'':76C,133C ''algorithm'':98C ''also'':117C ''alway'':55C ''associ'':57C ''big'':2A,4A,18C,52C,72C,106C,114C,120C,124C ''bigdata'':6B ''carri'':56C ''commerci'':25C ''comput'':40C,80C ''could'':96C ''cours'':50C ''data'':5A,19C,53C,73C,92C,107C ''dataset'':35C,111C ''defin'':66C,132C ''effici'':8B,62C ''em'':17C,24C,51C,71C,105C,113C,119C,123C ''enough'':115C,125C ''exact'':65C ''general'':100C ''good'':47C ''graph'':101C ''indic'':32C ''involv'':37C ''label'':104C ''larg'':34C ''like'':59C,91C ''lot'':11C ''mean'':30C ''mining/information'':93C ''must'':45C ''p'':10C,77C ''peopl'':13C ''perform'':9B,48C ''possibl'':130C ''potenti'':43C ''problem'':68C,75C,102C ''purpos'':90C ''rather'':23C ''relat'':84C ''retriev'':94C ''scalabl'':7B,60C ''set'':87C ''solut'':44C ''specif'':89C ''term'':16C,58C ''therefor'':42C ''use'':14C ''way'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (20, 1, '2014-05-14 05:37:46.78', 14, 205, 'the data on our relational DBMS is getting big, is it the time to move to NoSQL?', '&lt;p&gt;we created this social network application for eLearning purposes, it''s an experimental thing we are researching on in our lab. it has been used by some case studies for a while and the data on our relational DBMS (SQL Server 2008) is getting big, it''s a few gigabytes now and the tables are highly connected to each other. the performance is still fine, but when should we consider other options? is it the matter of performance?  &lt;/p&gt;
', 'nosql  relational-dbms', NULL, '''/p'':101C ''2008'':64C ''applic'':28C ''big'':9A,67C ''case'':50C ''connect'':79C ''consid'':92C ''creat'':24C ''data'':2A,57C ''dbms'':6A,21B,61C ''elearn'':30C ''experiment'':35C ''fine'':87C ''get'':8A,66C ''gigabyt'':72C ''high'':78C ''lab'':43C ''matter'':98C ''move'':15A ''network'':27C ''nosql'':17A,18B ''option'':94C ''p'':22C ''perform'':84C,100C ''purpos'':31C ''relat'':5A,20B,60C ''relational-dbm'':19B ''research'':39C ''server'':63C ''social'':26C ''sql'':62C ''still'':86C ''studi'':51C ''tabl'':76C ''thing'':36C ''time'':13A ''use'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (27, 2, '2014-05-14 07:53:02.56', 8, NULL, NULL, '&lt;p&gt;To answer this question you have to answer which kind of compromise you can afford. RDBMs implements &lt;a href="http://en.wikipedia.org/wiki/ACID"&gt;ACID&lt;/a&gt;. This is expensive in terms of resources. There are no NoSQL solutions which are ACID. See &lt;a href="http://en.wikipedia.org/wiki/CAP_theorem"&gt;CAP theorem&lt;/a&gt; to dive deep into these ideas. &lt;/p&gt;

&lt;p&gt;So you have to understand each compromise given by each solution and choose the one which is the most appropriate for your problem.&lt;/p&gt;
', NULL, NULL, '''/a'':25C,49C ''/p'':56C,81C ''/wiki/acid'':23C ''/wiki/cap_theorem'':46C ''acid'':24C,40C ''afford'':16C ''answer'':3C,9C ''appropri'':77C ''cap'':47C ''choos'':70C ''compromis'':13C,64C ''deep'':52C ''dive'':51C ''en.wikipedia.org'':22C,45C ''en.wikipedia.org/wiki/acid'':21C ''en.wikipedia.org/wiki/cap_theorem'':44C ''expens'':28C ''given'':65C ''href'':20C,43C ''idea'':55C ''implement'':18C ''kind'':11C ''nosql'':36C ''one'':72C ''p'':1C,57C ''problem'':80C ''question'':5C ''rdbms'':17C ''resourc'':32C ''see'':41C ''solut'':37C,68C ''term'':30C ''theorem'':48C ''understand'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (28, 2, '2014-05-14 07:55:40.133', 4, NULL, NULL, '&lt;p&gt;There is free ebook "&lt;a href="http://jsresearch.net/" rel="nofollow"&gt;Introduction to Data Science&lt;/a&gt;" based on &lt;a href="/questions/tagged/r" class="post-tag" title="show questions tagged &amp;#39;r&amp;#39;" rel="tag"&gt;r&lt;/a&gt; language&lt;/p&gt;
', NULL, NULL, '''/a'':15C,35C ''/p'':37C ''/questions/tagged/r'':20C ''39'':29C,31C ''base'':16C ''class'':21C ''data'':13C ''ebook'':5C ''free'':4C ''href'':7C,19C ''introduct'':11C ''jsresearch.net'':8C ''languag'':36C ''nofollow'':10C ''p'':1C ''post'':23C ''post-tag'':22C ''question'':27C ''r'':30C,34C ''rel'':9C,32C ''scienc'':14C ''show'':26C ''tag'':24C,28C,33C ''titl'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (21, 2, '2014-05-14 05:44:29.34', 22, NULL, NULL, '&lt;p&gt;As you rightly note, these days "big data" is something everyone wants to say they''ve got, which entails a certain looseness in how people define the term.  Generally, though, I''d say you''re certainly dealing with big data if the scale is such that it''s no longer feasible to manage with more traditional technologies such as RDBMS, at least without complementing them with big data technologies such as Hadoop.&lt;/p&gt;

&lt;p&gt;How big your data has to actually be for that to be the case is debatable.  Here''s a (somewhat provocative) &lt;a href="http://www.chrisstucchio.com/blog/2013/hadoop_hatred.html"&gt;blog post&lt;/a&gt; that claims that it''s not really the case for less than 5 TB of data.  (To be clear, it doesn''t claim "Less than 5 TB isn''t big data", but just "Less than 5 TB isn''t big enough that you need Hadoop".)&lt;/p&gt;

&lt;p&gt;But even on smaller datasets, big data technologies like Hadoop can have other advantages, including being well suited to batch operations, playing well with unstructured data (as well as data whose structure isn''t known in advance or could change), horizontal scalability (scaling by adding more nodes instead of beefing up your existing servers), and (as one of the commenters on the above-linked post notes) the ability to integrate your data processing with external data sets (think of a map-reduce where the mapper makes a call to another server).  Other technologies associated with big data, like NoSql databases, emphasize fast performance and consistent availability while dealing with large sets of data, as well also being able to handle semi-unstructured data and to scale horizontally.&lt;/p&gt;

&lt;p&gt;Of course, traditional RDBMS have their own advantages including ACID guarantees (Atomicity, Consistency, Isolation, Durability) and better performance for certain operations, as well as being more standardized, more mature, and (for many users) more familiar.  So even for indisputably "big" data, it may make sense to load at least a portion of your data into a traditional SQL database and use that in conjunction with big data technologies.&lt;/p&gt;

&lt;p&gt;So, a more generous definition would be that you have big data so long as it''s big enough that big data technologies provide some added value for you.  But as you can see, that can depend not just on the size of your data but on how you want to work with it and what sort of requirements you have in terms of flexibility, consistency, and performance.  &lt;em&gt;How&lt;/em&gt; you''re using your data is more relevant to the question than what you''re using it &lt;em&gt;for&lt;/em&gt; (e.g. data mining).  That said, uses like data mining and machine learning are more likely to yield useful results if you have a big enough data set to work with.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (29, 2, '2014-05-14 07:56:34.437', 17, NULL, NULL, '&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/users/36/statsrus"&gt;@statsRus&lt;/a&gt; starts to lay the groundwork for your answer in another question &lt;a href="http://datascience.stackexchange.com/questions/1/what-characterises-the-difference-between-data-science-and-statistics"&gt;What characterises the difference between data science and statistics?&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;ul&gt;
  &lt;li&gt;&lt;strong&gt;Data collection&lt;/strong&gt;: web scraping and online surveys&lt;/li&gt;
  &lt;li&gt;&lt;strong&gt;Data manipulation&lt;/strong&gt;: recoding messy data and extracting meaning from linguistic and social network data&lt;/li&gt;
  &lt;li&gt;&lt;strong&gt;Data scale&lt;/strong&gt;: working with extremely large data sets&lt;/li&gt;
  &lt;li&gt;&lt;strong&gt;Data mining&lt;/strong&gt;: finding patterns in large, complex data sets, with an emphasis on algorithmic techniques&lt;/li&gt;
  &lt;li&gt;&lt;strong&gt;Data communication&lt;/strong&gt;: helping turn "machine-readable" data into "human-readable" information via visualization&lt;/li&gt;
  &lt;/ul&gt;
&lt;/blockquote&gt;

&lt;h2&gt;Definition&lt;/h2&gt;

&lt;p&gt;&lt;a href="/questions/tagged/data-mining" class="post-tag" title="show questions tagged &amp;#39;data-mining&amp;#39;" rel="tag"&gt;data-mining&lt;/a&gt; can be seen as one item (or set of skills and applications) in the toolkit of the data scientist.  I like how he separates the definition of mining from collection in a sort of trade-specific jargon.&lt;/p&gt;

&lt;p&gt;However, I think that &lt;em&gt;data-mining&lt;/em&gt; would be synonymous with &lt;em&gt;data-collection&lt;/em&gt; in a US-English colloquial definition.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;As to where to go to become proficient?&lt;/em&gt;  I think that question is too broad as it is currently stated and would receive answers that are primarily opinion based.  Perhaps if you could refine your question, it might be easier to see what you are asking.&lt;/p&gt;
', NULL, NULL, '''/a'':8C,34C,144C ''/blockquote'':118C ''/em'':193C,202C,221C ''/h2'':121C ''/li'':48C,66C,78C,97C,116C ''/p'':35C,183C,210C,260C ''/questions/1/what-characterises-the-difference-between-data-science-and-statistics'':24C ''/questions/tagged/data-mining'':125C ''/strong'':42C,53C,71C,83C,102C ''/ul'':117C ''/users/36/statsrus'':6C ''39'':134C,138C ''algorithm'':95C ''anoth'':18C ''answer'':16C,237C ''applic'':156C ''ask'':259C ''base'':242C ''becom'':219C ''blockquot'':36C ''broad'':228C ''characteris'':26C ''class'':126C ''collect'':41C,174C,201C ''colloqui'':208C ''communic'':101C ''complex'':88C ''could'':246C ''current'':232C ''data'':30C,40C,51C,56C,65C,69C,76C,81C,89C,100C,108C,136C,142C,162C,191C,200C ''data-collect'':199C ''data-min'':135C,141C,190C ''datascience.stackexchange.com'':5C,23C ''datascience.stackexchange.com/questions/1/what-characterises-the-difference-between-data-science-and-statistics'':22C ''datascience.stackexchange.com/users/36/statsrus'':4C ''definit'':120C,170C,209C ''differ'':28C ''easier'':253C ''em'':189C,198C,212C ''emphasi'':93C ''english'':207C ''extract'':58C ''extrem'':74C ''find'':84C ''go'':217C ''groundwork'':13C ''h2'':119C ''help'':103C ''howev'':185C ''href'':3C,21C,124C ''human'':111C ''human-read'':110C ''inform'':113C ''item'':150C ''jargon'':182C ''larg'':75C,87C ''lay'':11C ''li'':38C,49C,67C,79C,98C ''like'':165C ''linguist'':61C ''machin'':106C ''machine-read'':105C ''manipul'':52C ''mean'':59C ''messi'':55C ''might'':251C ''mine'':82C,137C,143C,172C,192C ''network'':64C ''one'':149C ''onlin'':46C ''opinion'':241C ''p'':1C,122C,184C,211C ''pattern'':85C ''perhap'':243C ''post'':128C ''post-tag'':127C ''primarili'':240C ''profici'':220C ''question'':19C,132C,225C,249C ''readabl'':107C,112C ''receiv'':236C ''recod'':54C ''refin'':247C ''rel'':139C ''scale'':70C ''scienc'':31C ''scientist'':163C ''scrape'':44C ''see'':255C ''seen'':147C ''separ'':168C ''set'':77C,90C,152C ''show'':131C ''skill'':154C ''social'':63C ''sort'':177C ''specif'':181C ''start'':9C ''state'':233C ''statist'':33C ''statsrus'':7C ''strong'':39C,50C,68C,80C,99C ''survey'':47C ''synonym'':196C ''tag'':129C,133C,140C ''techniqu'':96C ''think'':187C,223C ''titl'':130C ''toolkit'':159C ''trade'':180C ''trade-specif'':179C ''turn'':104C ''ul'':37C ''us'':206C ''us-english'':205C ''via'':114C ''visual'':115C ''web'':43C ''work'':72C ''would'':194C,235C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2341, 2, '2014-10-23 21:12:27.35', 1, NULL, NULL, '&lt;p&gt;I would think about starting with a power analysis: i.e. how many data points do you need to measure the effect (or parameter) that you are interested to a specified level of confidence, &lt;em&gt;ceteris paribus&lt;/em&gt;? Then, you estimate a cost. &lt;/p&gt;
', NULL, NULL, '''/em'':38C ''/p'':44C ''analysi'':10C ''ceteri'':36C ''confid'':34C ''cost'':43C ''data'':14C ''effect'':22C ''em'':35C ''estim'':41C ''i.e'':11C ''interest'':28C ''level'':32C ''mani'':13C ''measur'':20C ''need'':18C ''p'':1C ''paramet'':24C ''paribus'':37C ''point'':15C ''power'':9C ''specifi'':31C ''start'':6C ''think'':4C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (30, 2, '2014-05-14 08:03:28.117', 19, NULL, NULL, '&lt;p&gt;Total amount of data in the world: 2.8 zetabytes in 2012, estimated to reach 8 zetabytes by 2015 (&lt;a href="http://siliconangle.com/blog/2012/05/21/when-will-the-world-reach-8-zetabytes-of-stored-data-infographic/"&gt;source&lt;/a&gt;) and with a doubling time of 40 months. Can''t get bigger than that :)&lt;/p&gt;

&lt;p&gt;As an example of a single large organization, Facebook pulls in 500 terabytes per day, into a 100 petabyte warehouse, and runs 70k queries per day on it as of 2012 (&lt;a href="http://gigaom.com/2012/08/22/facebook-is-collecting-your-data-500-terabytes-a-day/"&gt;source&lt;/a&gt;)  Their current warehouse is &gt;300 petabytes.&lt;/p&gt;

&lt;p&gt;Big data is probably something that is a good fraction of the Facebook numbers (1/100 probably yes, 1/10000 probably not: it''s a spectrum not a single number).&lt;/p&gt;

&lt;p&gt;In addition to size, some of the features that make it "big" are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;it is actively analyzed, not just stored  (quote "If you arenât taking advantage of big data, then you donât have big data, you have just a pile of data" Jay Parikh @ Facebook)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;building and running a data warehouse is a major infrastructure project&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;it is growing at a significant rate&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;it is unstructured or has irregular structure&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Gartner definition: "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing" (The 3Vs)  So they also think "bigness" isn''t entirely about the size of the dataset, but also about the velocity and structure and the kind of tools needed.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (22, 1, '2014-05-14 05:58:21.927', 36, 16558, 'K-Means clustering for mixed numeric and categorical data', '&lt;p&gt;My data set contains a number of numeric attributes and one categorical.&lt;/p&gt;

&lt;p&gt;Say, &lt;code&gt;NumericAttr1, NumericAttr2, ..., NumericAttrN, CategoricalAttr&lt;/code&gt;, &lt;/p&gt;

&lt;p&gt;where &lt;code&gt;CategoricalAttr&lt;/code&gt; takes one of three possible values: &lt;code&gt;CategoricalAttrValue1&lt;/code&gt;, &lt;code&gt;CategoricalAttrValue2&lt;/code&gt; or &lt;code&gt;CategoricalAttrValue3&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;I''m using default k-means clustering algorithm implementation for Octave &lt;a href="https://blog.west.uni-koblenz.de/2012-07-14/a-working-k-means-code-for-octave/"&gt;https://blog.west.uni-koblenz.de/2012-07-14/a-working-k-means-code-for-octave/&lt;/a&gt;.
It works with numeric data only.&lt;/p&gt;

&lt;p&gt;So my question: is it correct to split the categorical attribute &lt;code&gt;CategoricalAttr&lt;/code&gt; into three numeric (binary) variables, like &lt;code&gt;IsCategoricalAttrValue1, IsCategoricalAttrValue2, IsCategoricalAttrValue3&lt;/code&gt; ?&lt;/p&gt;
', 'data-mining  clustering  octave  k-means  categorical-data', NULL, '''/2012-07-14/a-working-k-means-code-for-octave/'':84C ''/2012-07-14/a-working-k-means-code-for-octave/&lt;/a&gt;.'':87C ''/code'':43C,49C,58C,61C,65C,109C,120C ''/p'':35C,44C,66C,94C,121C ''algorithm'':76C ''attribut'':31C,106C ''binari'':113C ''blog.west.uni-koblenz.de'':83C,86C ''blog.west.uni-koblenz.de/2012-07-14/a-working-k-means-code-for-octave/'':82C ''blog.west.uni-koblenz.de/2012-07-14/a-working-k-means-code-for-octave/&lt;/a&gt;.'':85C ''categor'':9A,20B,34C,105C ''categorical-data'':19B ''categoricalattr'':42C,48C,108C ''categoricalattrvalue1'':57C ''categoricalattrvalue2'':60C ''categoricalattrvalue3'':64C ''cluster'':4A,14B,75C ''code'':38C,47C,56C,59C,63C,107C,116C ''contain'':26C ''correct'':101C ''data'':10A,12B,21B,24C,92C ''data-min'':11B ''default'':71C ''href'':81C ''implement'':77C ''iscategoricalattrvalue1'':117C ''iscategoricalattrvalue2'':118C ''iscategoricalattrvalue3'':119C ''k'':2A,17B,73C ''k-mean'':1A,16B,72C ''like'':115C ''m'':69C ''mean'':3A,18B,74C ''mine'':13B ''mix'':6A ''number'':28C ''numer'':7A,30C,91C,112C ''numericattr1'':39C ''numericattr2'':40C ''numericattrn'':41C ''octav'':15B,79C ''one'':33C,51C ''p'':22C,36C,45C,67C,95C ''possibl'':54C ''question'':98C ''say'':37C ''set'':25C ''split'':103C ''take'':50C ''three'':53C,111C ''use'':70C ''valu'':55C ''variabl'':114C ''work'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (23, 2, '2014-05-14 06:06:13.603', 6, NULL, NULL, '&lt;p&gt;Data Science specialization from Johns Hopkins University at Coursera would be a great start.
&lt;a href="https://www.coursera.org/specialization/jhudatascience/1"&gt;https://www.coursera.org/specialization/jhudatascience/1&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/specialization/jhudatascience/1'':20C ''/specialization/jhudatascience/1&lt;/a&gt;&lt;/p&gt;'':23C ''coursera'':10C ''data'':2C ''great'':14C ''hopkin'':7C ''href'':17C ''john'':6C ''p'':1C ''scienc'':3C ''special'':4C ''start'':15C ''univers'':8C ''would'':11C ''www.coursera.org'':19C,22C ''www.coursera.org/specialization/jhudatascience/1'':18C ''www.coursera.org/specialization/jhudatascience/1&lt;/a&gt;&lt;/p&gt;'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (24, 2, '2014-05-14 06:26:27.163', 34, NULL, NULL, '&lt;p&gt;The standard k-means algorithm isn''t directly applicable to categorical data, for various reasons.  The sample space for categorical data is discrete, and doesn''t have a natural origin.  A Euclidean distance function on such a space isn''t really meaningful.  As someone put it, "The fact a snake possesses neither wheels nor legs allows us to say nothing about the relative value of wheels and legs." (from &lt;a href="http://www.daylight.com/meetings/mug04/Bradshaw/why_k-modes.html"&gt;here&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;There''s a variation of k-means known as k-modes, introduced in &lt;a href="http://www.cs.ust.hk/~qyang/Teaching/537/Papers/huang98extensions.pdf"&gt;this paper&lt;/a&gt; by Zhexue Huang, which is suitable for categorical data.   Note that the solutions you get are sensitive to initial conditions, as discussed &lt;a href="http://arxiv.org/ftp/cs/papers/0603/0603120.pdf"&gt;here&lt;/a&gt; (PDF), for instance.&lt;/p&gt;

&lt;p&gt;Huang''s paper (linked above) also has a section on "k-protoypes" which applies to data with a mix of categorical and numeric features.  It uses a distance measure which mixes the Hamming distance for categorical features and the Euclidean distance for numeric features.&lt;/p&gt;

&lt;p&gt;A Google search for "k-means mix of categorical data" turns up quite a few more recent papers on various algorithms for k-means-like clustering with a mix of categorical and numeric data.  (I haven''t yet read them, so I can''t comment on their merits.)  &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Actually, what you suggest (converting categorical attributes to binary values, and then doing k-means as if these were numeric values) is another approach that has been tried before (predating k-modes).  (See Ralambondrainy, H. 1995. A conceptual version of the k-means algorithm. Pattern Recognition Letters, 16:1147â1157.)  But I believe the k-modes approach is preferred for the reasons I indicated above.&lt;/p&gt;
', NULL, NULL, '''/a'':78C,103C,132C ''/ftp/cs/papers/0603/0603120.pdf'':130C ''/meetings/mug04/bradshaw/why_k-modes.html'':76C ''/p'':79C,136C,183C,235C,307C ''/~qyang/teaching/537/papers/huang98extensions.pdf'':100C ''1147â'':289C ''1157'':290C ''16'':288C ''1995'':275C ''actual'':238C ''algorithm'':7C,206C,284C ''allow'':58C ''also'':143C ''anoth'':261C ''appli'':152C ''applic'':11C ''approach'':262C,298C ''arxiv.org'':129C ''arxiv.org/ftp/cs/papers/0603/0603120.pdf'':128C ''attribut'':244C ''believ'':293C ''binari'':246C ''categor'':13C,22C,111C,159C,174C,194C,217C,243C ''cluster'':212C ''comment'':231C ''conceptu'':277C ''condit'':123C ''convert'':242C ''data'':14C,23C,112C,154C,195C,220C ''direct'':10C ''discret'':25C ''discuss'':125C ''distanc'':35C,166C,172C,179C ''doesn'':27C ''euclidean'':34C,178C ''fact'':50C ''featur'':162C,175C,182C ''function'':36C ''get'':118C ''googl'':186C ''h'':274C ''ham'':171C ''haven'':222C ''hr'':236C ''href'':73C,97C,127C ''huang'':106C,138C ''indic'':305C ''initi'':122C ''instanc'':135C ''introduc'':94C ''isn'':8C,41C ''k'':5C,87C,92C,149C,190C,209C,252C,270C,282C,296C ''k-mean'':4C,86C,189C,251C,281C ''k-means-lik'':208C ''k-mode'':91C,269C,295C ''k-protoyp'':148C ''known'':89C ''leg'':57C,70C ''letter'':287C ''like'':211C ''link'':141C ''mean'':6C,88C,191C,210C,253C,283C ''meaning'':44C ''measur'':167C ''merit'':234C ''mix'':157C,169C,192C,215C ''mode'':93C,271C,297C ''natur'':31C ''neither'':54C ''note'':113C ''noth'':62C ''numer'':161C,181C,219C,258C ''origin'':32C ''p'':1C,80C,137C,184C,237C ''paper'':102C,140C,203C ''pattern'':285C ''pdf'':133C ''possess'':53C ''predat'':268C ''prefer'':300C ''protoyp'':150C ''put'':47C ''quit'':198C ''ralambondraini'':273C ''read'':225C ''realli'':43C ''reason'':17C,303C ''recent'':202C ''recognit'':286C ''relat'':65C ''sampl'':19C ''say'':61C ''search'':187C ''section'':146C ''see'':272C ''sensit'':120C ''snake'':52C ''solut'':116C ''someon'':46C ''space'':20C,40C ''standard'':3C ''suggest'':241C ''suitabl'':109C ''tri'':266C ''turn'':196C ''us'':59C ''use'':164C ''valu'':66C,247C,259C ''variat'':84C ''various'':16C,205C ''version'':278C ''wheel'':55C,68C ''www.cs.ust.hk'':99C ''www.cs.ust.hk/~qyang/teaching/537/papers/huang98extensions.pdf'':98C ''www.daylight.com'':75C ''www.daylight.com/meetings/mug04/bradshaw/why_k-modes.html'':74C ''yet'':224C ''zhexu'':105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (25, 2, '2014-05-14 07:26:04.39', 5, NULL, NULL, '&lt;p&gt;Big Data is defined by the volume of data, that''s right, but not only. The particularity of big data is that you need to store a &lt;strong&gt;lots&lt;/strong&gt; of &lt;strong&gt;various&lt;/strong&gt; and sometimes &lt;strong&gt;unstructured&lt;/strong&gt; stuffs &lt;strong&gt;all the times&lt;/strong&gt; and from a &lt;strong&gt;tons of sensors&lt;/strong&gt;, usually &lt;strong&gt;for years or decade&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Furthermore you need something scalable, so that it doesn''t take you half a year to find a data back.&lt;/p&gt;

&lt;p&gt;So here''s come Big Data, where traditional method won''t work anymore. SQL is not scalable. And SQL works with very structured and linked data (with all those Primary and foreign key mess, innerjoin, imbricated request...). &lt;/p&gt;

&lt;p&gt;Basically, because storage becomes cheaper and cheaper and data becomes more and more valuable, big manager ask engineer to records everything. Add to this tons of new sensors with all those mobile, social network, embeded stuff ...etc. So as classic methods won''t work, they have to find new technologies (storing everything in files, in json format, with big index, what we call noSQL). &lt;/p&gt;

&lt;p&gt;So Big Data may be very big but can be not so big but complexe unstructured or various data which has to be store quickly and on-the-run in a raw format. We focus and storing at first, and then we look at how to link everything together.&lt;/p&gt;
', NULL, NULL, '''/p'':62C,84C,123C,189C,241C ''/strong'':31C,35C,40C,46C,54C,61C ''add'':146C ''anymor'':98C ''ask'':141C ''back'':83C ''basic'':125C ''becom'':128C,134C ''big'':2C,20C,90C,139C,183C,192C,197C,203C ''call'':187C ''cheaper'':129C,131C ''classic'':164C ''come'':89C ''complex'':205C ''data'':3C,10C,21C,82C,91C,111C,133C,193C,209C ''decad'':60C ''defin'':5C ''doesn'':72C ''embed'':159C ''engin'':142C ''etc'':161C ''everyth'':145C,176C,239C ''file'':178C ''find'':80C,172C ''first'':230C ''focus'':226C ''foreign'':117C ''format'':181C,224C ''furthermor'':64C ''half'':76C ''imbric'':121C ''index'':184C ''innerjoin'':120C ''json'':180C ''key'':118C ''link'':110C,238C ''look'':234C ''lot'':30C ''manag'':140C ''may'':194C ''mess'':119C ''method'':94C,165C ''mobil'':156C ''need'':25C,66C ''network'':158C ''new'':151C,173C ''nosql'':188C ''on-the-run'':217C ''p'':1C,63C,85C,124C,190C ''particular'':18C ''primari'':115C ''quick'':215C ''raw'':223C ''record'':144C ''request'':122C ''right'':13C ''run'':220C ''scalabl'':68C,102C ''sensor'':53C,152C ''social'':157C ''someth'':67C ''sometim'':37C ''sql'':99C,104C ''storag'':127C ''store'':27C,175C,214C,228C ''strong'':29C,33C,38C,42C,50C,56C ''structur'':108C ''stuff'':41C,160C ''take'':74C ''technolog'':174C ''time'':45C ''togeth'':240C ''ton'':51C,149C ''tradit'':93C ''unstructur'':39C,206C ''usual'':55C ''valuabl'':138C ''various'':34C,208C ''volum'':8C ''won'':95C,166C ''work'':97C,105C,168C ''year'':58C,78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (26, 2, '2014-05-14 07:38:31.103', 12, NULL, NULL, '&lt;p&gt;A few gigabytes is not very "&lt;strong&gt;big&lt;/strong&gt;". It''s more like the normal size of an enterprise DB. As long as you go over PK when joining tables it should work out really well, even in the future (as long as you don''t get TB''s of data a day).&lt;/p&gt;

&lt;p&gt;Most professionals working in a big data environment consider &lt;strong&gt;&gt; ~5TB&lt;/strong&gt; as the &lt;em&gt;beginning&lt;/em&gt; of the term big data. But even then it''s not always the best way to just install the next best nosql database. You should always think about the task that you want to archive with the data (aggregate,read,search,mine,..) to find the best tools for you problem.&lt;/p&gt;

&lt;p&gt;i.e. if you do alot of searches in you database it would probably be better to run a solr instance/cluster and denormalize your data from a DBMS like Postgres or your SQL Server from time to time and put it into solr instead of just moving the data from sql to nosql in term of persistence and performance.&lt;/p&gt;
', NULL, NULL, '''/em'':73C ''/p'':55C,124C,184C ''/strong'':10C,68C ''5tb'':67C ''aggreg'':112C ''alot'':130C ''alway'':85C,99C ''archiv'':108C ''begin'':72C ''best'':87C,94C,119C ''better'':140C ''big'':9C,62C,77C ''consid'':65C ''data'':52C,63C,78C,111C,149C,173C ''databas'':96C,135C ''day'':54C ''db'':21C ''dbms'':152C ''denorm'':147C ''em'':71C ''enterpris'':20C ''environ'':64C ''even'':38C,80C ''find'':117C ''futur'':41C ''get'':48C ''gigabyt'':4C ''go'':26C ''i.e'':126C ''instal'':91C ''instance/cluster'':145C ''instead'':168C ''join'':30C ''like'':14C,153C ''long'':23C,43C ''mine'':115C ''move'':171C ''next'':93C ''normal'':16C ''nosql'':95C,177C ''p'':1C,56C,125C ''perform'':183C ''persist'':181C ''pk'':28C ''postgr'':154C ''probabl'':138C ''problem'':123C ''profession'':58C ''put'':164C ''read'':113C ''realli'':36C ''run'':142C ''search'':114C,132C ''server'':158C ''size'':17C ''solr'':144C,167C ''sql'':157C,175C ''strong'':8C,66C ''tabl'':31C ''task'':103C ''tb'':49C ''term'':76C,179C ''think'':100C ''time'':160C,162C ''tool'':120C ''want'':106C ''way'':88C ''well'':37C ''work'':34C,59C ''would'':137C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (192, 1, '2014-05-22 15:15:41.133', 6, 185, 'What are the most popular data science application use cases for consumer web companies', '&lt;p&gt;The most popular use case seem to be recommender systems of different kinds (such as recommending shopping items, users in social networks etc.).&lt;/p&gt;

&lt;p&gt;But what are other typical data science applications, which may be used in a different verticals?&lt;/p&gt;

&lt;p&gt;For example: customer churn prediction with machine learning, evaluating customer lifetime value, sales forecasting.&lt;/p&gt;
', 'usecase  consumerweb', NULL, '''/p'':41C,59C,75C ''applic'':8A,50C ''case'':10A,22C ''churn'':64C ''compani'':14A ''consum'':12A ''consumerweb'':16B ''custom'':63C,70C ''data'':6A,48C ''differ'':29C,57C ''etc'':40C ''evalu'':69C ''exampl'':62C ''forecast'':74C ''item'':35C ''kind'':30C ''learn'':68C ''lifetim'':71C ''machin'':67C ''may'':52C ''network'':39C ''p'':17C,42C,60C ''popular'':5A,20C ''predict'':65C ''recommend'':26C,33C ''sale'':73C ''scienc'':7A,49C ''seem'':23C ''shop'':34C ''social'':38C ''system'':27C ''typic'':47C ''use'':9A,21C,54C ''usecas'':15B ''user'':36C ''valu'':72C ''vertic'':58C ''web'':13A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (31, 1, '2014-05-14 08:38:07.007', 6, 467, 'Clustering customer data stored in ElasticSearch', '&lt;p&gt;I have a bunch of customer profiles stored in a &lt;a href="/questions/tagged/elasticsearch" class="post-tag" title="show questions tagged &amp;#39;elasticsearch&amp;#39;" rel="tag"&gt;elasticsearch&lt;/a&gt; cluster. These profiles are now used for creation of target groups for our email subscriptions. &lt;/p&gt;

&lt;p&gt;Target groups are now formed manually using elasticsearch faceted search capabilities (like  get all male customers of age 23 with one car and 3 children).&lt;/p&gt;

&lt;p&gt;How could I search for interesting groups &lt;strong&gt;automatically&lt;/strong&gt; - using data science, machine learning, clustering or something else?&lt;/p&gt;

&lt;p&gt;&lt;a href="/questions/tagged/r" class="post-tag" title="show questions tagged &amp;#39;r&amp;#39;" rel="tag"&gt;r&lt;/a&gt; programming language seems to be a good tool for this task, but I can''t form a methodology of such group search. One solution is to somehow find the largest clusters of customers and use them as target groups, so the question is:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How can I automatically choose largest clusters of similar customers (similar by parameters that I don''t know at this moment)?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;For example: my program will connect to elasticsearch, offload customer data to CSV and using R language script will find that large portion of customers are male with no children and another large portion of customers have a car and their eye color is brown.&lt;/p&gt;
', 'data-mining  clustering', NULL, '''/a'':39C,122C ''/p'':55C,82C,103C,166C,191C,238C ''/questions/tagged/elasticsearch'':24C ''/questions/tagged/r'':107C ''/strong'':93C,190C ''23'':75C ''3'':80C ''39'':33C,35C,116C,118C ''age'':74C ''anoth'':224C ''automat'':92C,172C ''brown'':237C ''bunch'':15C ''capabl'':67C ''car'':78C,231C ''children'':81C,222C ''choos'':173C ''class'':25C,108C ''cluster'':1A,10B,40C,99C,153C,175C ''color'':235C ''connect'':198C ''could'':85C ''creation'':47C ''csv'':205C ''custom'':2A,17C,72C,155C,178C,202C,217C,228C ''data'':3A,8B,95C,203C ''data-min'':7B ''elasticsearch'':6A,34C,38C,64C,200C ''els'':102C ''email'':53C ''exampl'':194C ''eye'':234C ''facet'':65C ''find'':150C,212C ''form'':61C,138C ''get'':69C ''good'':129C ''group'':50C,58C,90C,143C,161C ''href'':23C,106C ''interest'':89C ''know'':186C ''languag'':124C,209C ''larg'':214C,225C ''largest'':152C,174C ''learn'':98C ''like'':68C ''machin'':97C ''male'':71C,219C ''manual'':62C ''methodolog'':140C ''mine'':9B ''moment'':189C ''offload'':201C ''one'':77C,145C ''p'':11C,56C,83C,104C,167C,192C ''paramet'':181C ''portion'':215C,226C ''post'':27C,110C ''post-tag'':26C,109C ''profil'':18C,42C ''program'':123C,196C ''question'':31C,114C,164C ''r'':117C,121C,208C ''rel'':36C,119C ''scienc'':96C ''script'':210C ''search'':66C,87C,144C ''seem'':125C ''show'':30C,113C ''similar'':177C,179C ''solut'':146C ''somehow'':149C ''someth'':101C ''store'':4A,19C ''strong'':91C,168C ''subscript'':54C ''tag'':28C,32C,37C,111C,115C,120C ''target'':49C,57C,160C ''task'':133C ''titl'':29C,112C ''tool'':130C ''use'':45C,63C,94C,157C,207C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (33, 2, '2014-05-14 09:34:15.477', 4, NULL, NULL, '&lt;p&gt;Depends on 2 things: the nature/structure of your data; and your performance.&lt;/p&gt;

&lt;p&gt;SQL databases excel when your data is well structured (e.g. when it can be modeled as a table or Excel spreadsheet). A set of rows with a fixed # of columns. Also, when you need to do a lot of table joins (which it sounds like you do).&lt;/p&gt;

&lt;p&gt;NoSQL databases excel when the data is UN-structured beyond key-value pairs.&lt;/p&gt;

&lt;p&gt;Performance wise, you gotta ask yourself one question: is your current SQL solution slow? 
If it isn''t, go with "IIABDFI" principle. &lt;/p&gt;
', NULL, NULL, '''/p'':14C,63C,80C,104C ''2'':4C ''also'':46C ''ask'':86C ''beyond'':75C ''column'':45C ''current'':92C ''data'':10C,21C,70C ''databas'':17C,66C ''depend'':2C ''e.g'':25C ''excel'':18C,35C,67C ''fix'':43C ''go'':100C ''gotta'':85C ''iiabdfi'':102C ''isn'':98C ''join'':56C ''key'':77C ''key-valu'':76C ''like'':60C ''lot'':53C ''model'':30C ''nature/structure'':7C ''need'':49C ''nosql'':65C ''one'':88C ''p'':1C,15C,64C,81C ''pair'':79C ''perform'':13C,82C ''principl'':103C ''question'':89C ''row'':40C ''set'':38C ''slow'':95C ''solut'':94C ''sound'':59C ''spreadsheet'':36C ''sql'':16C,93C ''structur'':24C,74C ''tabl'':33C,55C ''thing'':5C ''un'':73C ''un-structur'':72C ''valu'':78C ''well'':23C ''wise'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2650, 2, '2014-12-08 22:32:05.817', 1, NULL, NULL, '&lt;p&gt;Neil is correct. There are partial derivatives evwrywhere in gradient computation for machine learning models training.&lt;/p&gt;

&lt;p&gt;For instance you can look at the gradient descent method used in the backpropagation method for a neural network. The course from AndrewNg on coursera describes it very well.&lt;/p&gt;
', NULL, NULL, '''/p'':18C,49C ''andrewng'':42C ''backpropag'':33C ''comput'':12C ''correct'':4C ''cours'':40C ''coursera'':44C ''deriv'':8C ''descent'':28C ''describ'':45C ''evwrywher'':9C ''gradient'':11C,27C ''instanc'':21C ''learn'':15C ''look'':24C ''machin'':14C ''method'':29C,34C ''model'':16C ''neil'':2C ''network'':38C ''neural'':37C ''p'':1C,19C ''partial'':7C ''train'':17C ''use'':30C ''well'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (35, 1, '2014-05-14 09:51:54.753', 16, 210, 'How to scale up algorithm development?', '&lt;p&gt;In working on exploratory data analysis, and developing algorithms, I find that most of my time is spent in a cycle of visualize, write some code, run on small dataset, repeat.   The data I have tends to be computer vision/sensor fusion type stuff, and algorithms are vision-heavy (for example object detection and tracking, etc), and the off the shelf algorithms don''t work in this context.  I find that this takes a lot of iterations (for example, to dial in the type of algorithm or tune the parameters in the algorithm, or to get a visualization right) and also the run times even on a small dataset are quite long, so all together it takes a while. &lt;/p&gt;

&lt;p&gt;How can the algorithm development itself be sped up and made more scalable?&lt;/p&gt;

&lt;p&gt;Some specific challenges: &lt;/p&gt;

&lt;p&gt;How can the number of iterations be reduced?  (Esp. when what kind of algorithm, let alone the specifics of it, does not seem to be easily foreseeable without trying different versions and examining their behavior)&lt;/p&gt;

&lt;p&gt;How to run on bigger datasets during development?  (Often going from small to large dataset is when a bunch of new behavior and new issues is seen)&lt;/p&gt;

&lt;p&gt;How can algorithm parameters be tuned faster?&lt;/p&gt;

&lt;p&gt;How to apply machine learning type tools to algorithm development itself?  (For example, instead of writing the algorithm by hand, write some simple building blocks and combine them in a way learned from the problem, etc)&lt;/p&gt;
', 'algorithms', NULL, '''/p'':128C,143C,148C,185C,214C,223C,261C ''algorithm'':5A,7B,17C,53C,70C,94C,101C,133C,163C,218C,233C,242C ''alon'':165C ''also'':109C ''analysi'':14C ''appli'':227C ''behavior'':184C,208C ''bigger'':191C ''block'':249C ''build'':248C ''bunch'':205C ''challeng'':147C ''code'':34C ''combin'':251C ''comput'':47C ''context'':76C ''cycl'':29C ''data'':13C,41C ''dataset'':38C,117C,192C,201C ''detect'':61C ''develop'':6A,16C,134C,194C,234C ''dial'':89C ''differ'':179C ''easili'':175C ''esp'':158C ''etc'':64C,260C ''even'':113C ''examin'':182C ''exampl'':59C,87C,237C ''exploratori'':12C ''faster'':222C ''find'':19C,78C ''forese'':176C ''fusion'':49C ''get'':104C ''go'':196C ''hand'':244C ''heavi'':57C ''instead'':238C ''issu'':211C ''iter'':85C,155C ''kind'':161C ''larg'':200C ''learn'':229C,256C ''let'':164C ''long'':120C ''lot'':83C ''machin'':228C ''made'':140C ''new'':207C,210C ''number'':153C ''object'':60C ''often'':195C ''p'':8C,129C,144C,149C,186C,215C,224C ''paramet'':98C,219C ''problem'':259C ''quit'':119C ''reduc'':157C ''repeat'':39C ''right'':107C ''run'':35C,111C,189C ''scalabl'':142C ''scale'':3A ''seem'':172C ''seen'':213C ''shelf'':69C ''simpl'':247C ''small'':37C,116C,198C ''specif'':146C,167C ''sped'':137C ''spent'':26C ''stuff'':51C ''take'':81C,125C ''tend'':44C ''time'':24C,112C ''togeth'':123C ''tool'':231C ''track'':63C ''tri'':178C ''tune'':96C,221C ''type'':50C,92C,230C ''version'':180C ''vision'':56C ''vision-heavi'':55C ''vision/sensor'':48C ''visual'':31C,106C ''way'':255C ''without'':177C ''work'':10C,73C ''write'':32C,240C,245C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (66, 4, '2014-05-14 18:45:23.917', 0, NULL, NULL, 'Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization.', NULL, NULL, '''analysi'':43C ''applic'':33C ''becom'':18C ''big'':1C ''captur'':37C ''challeng'':35C ''collect'':8C ''complex'':15C ''curat'':38C ''data'':2C,10C,31C ''databas'':26C ''difficult'':19C ''hand'':25C ''includ'':36C ''larg'':13C ''manag'':27C ''on-hand'':23C ''process'':21C,32C ''search'':40C ''set'':11C ''share'':41C ''storag'':39C ''term'':5C ''tool'':28C ''tradit'':30C ''transfer'':42C ''use'':22C ''visual'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (540, 1, '2014-06-23 15:38:10.92', 1, 220, 'First steps on a new cleaned dataset', '&lt;p&gt;What is the very first thing you do when you get your hands on a new data set (assuming it is cleaned and well structured)? Please share sample code snippets as I am sure this would be extremely helpful for both beginners and experienced. &lt;/p&gt;
', 'knowledge-base  dataset', NULL, '''/p'':57C ''assum'':31C ''base'':10B ''beginn'':54C ''clean'':6A,34C ''code'':41C ''data'':29C ''dataset'':7A,11B ''experienc'':56C ''extrem'':50C ''first'':1A,17C ''get'':23C ''hand'':25C ''help'':51C ''knowledg'':9B ''knowledge-bas'':8B ''new'':5A,28C ''p'':12C ''pleas'':38C ''sampl'':40C ''set'':30C ''share'':39C ''snippet'':42C ''step'':2A ''structur'':37C ''sure'':46C ''thing'':18C ''well'':36C ''would'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (37, 2, '2014-05-14 10:41:23.823', 55, NULL, NULL, '&lt;p&gt;To me (coming from a relational database background), "Big Data" is not primarily about the data size (which is the bulk of what the other answers are so far).&lt;/p&gt;

&lt;p&gt;"Big Data" and "Bad Data" are closely related. Relational Databases require ''pristine data''. If the data is in the database, it is accurate, clean, and 100% reliable. Relational Databases require "Great Data"  and a huge amount of time, money, and accountability is put on to making sure the data is well prepared before loading it in to the database. If the data is in the database, it is ''gospel'', and it defines the system understanding of reality.&lt;/p&gt;

&lt;p&gt;"Big Data" tackles this problem from the other direction. The data is poorly defined, much of it may be inaccurate, and much of it may in fact be missing. The structure and layout of the data is linear as opposed to relational.&lt;/p&gt;

&lt;p&gt;Big Data has to have enough volume so that the amount of bad data, or missing data becomes statistically insignificant. When the errors in your data are common enough to cancel each other out, and when the missing data is proportionally small enough to be negligible. When your data access requirements and algorithms are functional even with incomplete and inaccurate data, then you have "Big Data".&lt;/p&gt;

&lt;p&gt;"Big Data" is not really about the volume, it is about the characteristics of the data.&lt;/p&gt;
', NULL, NULL, '''/p'':31C,110C,154C,222C,240C ''100'':58C ''access'':205C ''account'':73C ''accur'':55C ''algorithm'':208C ''amount'':68C,166C ''answer'':27C ''background'':9C ''bad'':36C,168C ''becom'':173C ''big'':10C,33C,112C,156C,220C,224C ''bulk'':22C ''cancel'':186C ''characterist'':236C ''clean'':56C ''close'':39C ''come'':4C ''common'':183C ''data'':11C,17C,34C,37C,45C,48C,64C,81C,94C,113C,122C,147C,157C,169C,172C,181C,194C,204C,216C,221C,225C,239C ''databas'':8C,42C,52C,61C,91C,98C ''defin'':104C,125C ''direct'':120C ''enough'':161C,184C,198C ''error'':178C ''even'':211C ''fact'':138C ''far'':30C ''function'':210C ''gospel'':101C ''great'':63C ''huge'':67C ''inaccur'':131C,215C ''incomplet'':213C ''insignific'':175C ''layout'':144C ''linear'':149C ''load'':86C ''make'':78C ''may'':129C,136C ''miss'':140C,171C,193C ''money'':71C ''much'':126C,133C ''neglig'':201C ''oppos'':151C ''p'':1C,32C,111C,155C,223C ''poor'':124C ''prepar'':84C ''primarili'':14C ''pristin'':44C ''problem'':116C ''proport'':196C ''put'':75C ''realiti'':109C ''realli'':228C ''relat'':7C,40C,41C,60C,153C ''reliabl'':59C ''requir'':43C,62C,206C ''size'':18C ''small'':197C ''statist'':174C ''structur'':142C ''sure'':79C ''system'':106C ''tackl'':114C ''time'':70C ''understand'':107C ''volum'':162C,231C ''well'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (38, 1, '2014-05-14 10:44:58.933', 12, 1569, 'What is the difference between Hadoop and noSQL', '&lt;p&gt;I heard about many tools / frameworks for helping people to process their data (big data environment). &lt;/p&gt;

&lt;p&gt;One is called Hadoop and the other is the noSQL concept. What is the difference in point of processing? &lt;/p&gt;

&lt;p&gt;Are they complementary?&lt;/p&gt;
', 'nosql  tools  processing  hadoop', NULL, '''/p'':30C,51C,56C ''big'':27C ''call'':34C ''complementari'':55C ''concept'':42C ''data'':26C,28C ''differ'':4A,46C ''environ'':29C ''framework'':19C ''hadoop'':6A,12B,35C ''heard'':15C ''help'':21C ''mani'':17C ''nosql'':8A,9B,41C ''one'':32C ''p'':13C,31C,52C ''peopl'':22C ''point'':48C ''process'':11B,24C,50C ''tool'':10B,18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (52, 1, '2014-05-14 15:25:21.7', 24, 1044, 'Organized processes to clean data', '&lt;p&gt;From my limited dabbling with data science using R, I realized that cleaning bad data is a very important part of preparing data for analysis. &lt;/p&gt;

&lt;p&gt;Are there any best practices or processes for cleaning data before processing it? If so, are there any automated or semi-automated tools which implement some of these best practices?&lt;/p&gt;
', 'r  data-cleaning', NULL, '''/p'':36C,69C ''analysi'':35C ''autom'':56C,60C ''bad'':24C ''best'':41C,67C ''clean'':4A,9B,23C,46C ''dabbl'':14C ''data'':5A,8B,16C,25C,33C,47C ''data-clean'':7B ''implement'':63C ''import'':29C ''limit'':13C ''organ'':1A ''p'':10C,37C ''part'':30C ''practic'':42C,68C ''prepar'':32C ''process'':2A,44C,49C ''r'':6B,19C ''realiz'':21C ''scienc'':17C ''semi'':59C ''semi-autom'':58C ''tool'':61C ''use'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1045, 1, '2014-08-26 21:37:12.107', 2, 70, 'Using Shark with Apache Spark', '&lt;p&gt;Have anyone used Shark as repository from resulting datasets from Apache Spark?&lt;/p&gt;

&lt;p&gt;I''m starting some tests with Spark and read about this database tecnology. Have anyone been using it?&lt;/p&gt;
', 'hadoop', NULL, '''/p'':20C,40C ''anyon'':9C,36C ''apach'':4A,18C ''databas'':33C ''dataset'':16C ''hadoop'':6B ''m'':23C ''p'':7C,21C ''read'':30C ''repositori'':13C ''result'':15C ''shark'':2A,11C ''spark'':5A,19C,28C ''start'':24C ''tecnolog'':34C ''test'':26C ''use'':1A,10C,38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6455, 2, '2015-07-14 10:02:18.477', 1, NULL, NULL, '&lt;p&gt;You can also modify your cost function so that instances in the smaller class have more weight. This makes training computationally less demanding, when compared to your option 1.&lt;/p&gt;
', NULL, NULL, '''/p'':31C ''1'':30C ''also'':4C ''class'':15C ''compar'':26C ''comput'':22C ''cost'':7C ''demand'':24C ''function'':8C ''instanc'':11C ''less'':23C ''make'':20C ''modifi'':5C ''option'':29C ''p'':1C ''smaller'':14C ''train'':21C ''weight'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8986, 4, '2015-11-23 10:57:43.013', 0, NULL, NULL, 'A Markov process is a stochastic process for which the Markov property holds: If you know the current state, then the next state is independent of all past states.', NULL, NULL, '''current'':18C ''hold'':13C ''independ'':25C ''know'':16C ''markov'':2C,11C ''next'':22C ''past'':28C ''process'':3C,7C ''properti'':12C ''state'':19C,23C,29C ''stochast'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (65, 5, '2014-05-14 18:45:23.917', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (40, 2, '2014-05-14 11:12:03.88', 6, NULL, NULL, '&lt;p&gt;Big Data is actually not so about the "how big it is". &lt;/p&gt;

&lt;p&gt;First, few gigabytes is not big at all, it''s almost nothing. So don''t bother yourself, your system will continu to work efficiently for some time I think.&lt;/p&gt;

&lt;p&gt;Then you have to think of how do you use your data. &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;SQL approach: Every data is precious, well collected and selected, and the focus is put on storing high valuable and well structured data. This can be costly, everything is interlink, and it''s good for well stuctured system and functionnal data.&lt;/li&gt;
&lt;li&gt;Big Data approach: In big data you basically store almost everything, regardless of the value it has, and then do a active analytics process. Things are not linked, they are copied. For example let''s say I have a blog entry. In Big Data there will not be a link to its author, but the author will be embedded inside the blog entry. Way more scalable, but require a different and more complex approach.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If your storing "functionnal" data use by your application, I will suggest you to remain on SQL. If your storing data in order to search on them later or to do reporting, and if this amount of data may increase quickly, I will suggest big data.
In my opinion, big data is useful when you are dealing with real data that have to be collect and analyzed continuously.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (41, 1, '2014-05-14 11:15:40.907', 28, 1897, 'Is the R language suitable for Big Data', '&lt;p&gt;R has many libraries which are aimed at Data Analysis (e.g. JAGS, BUGS, ARULES etc..), and is mentioned in popular textbooks such as: J.Krusche, Doing Bayesian Data Analysis; B.Lantz, "Machine Learning with R".&lt;/p&gt;

&lt;p&gt;I''ve seen a guideline of 5TB for a dataset to be considered as Big Data.&lt;/p&gt;

&lt;p&gt;My question is: Is R suitable for the amount of Data typically seen in Big Data problems? 
Are there strategies to be employed when using R with this size of dataset?&lt;/p&gt;
', 'bigdata  r', NULL, '''/p'':45C,63C,96C ''5tb'':53C ''aim'':18C ''amount'':73C ''analysi'':21C,39C ''arul'':25C ''b.lantz'':40C ''bayesian'':37C ''big'':7A,61C,79C ''bigdata'':9B ''bug'':24C ''consid'':59C ''data'':8A,20C,38C,62C,75C,80C ''dataset'':56C,95C ''e.g'':22C ''employ'':87C ''etc'':26C ''guidelin'':51C ''j.krusche'':35C ''jag'':23C ''languag'':4A ''learn'':42C ''librari'':15C ''machin'':41C ''mani'':14C ''mention'':29C ''p'':11C,46C,64C ''popular'':31C ''problem'':81C ''question'':66C ''r'':3A,10B,12C,44C,69C,90C ''seen'':49C,77C ''size'':93C ''strategi'':84C ''suitabl'':5A,70C ''textbook'':32C ''typic'':76C ''use'':89C ''ve'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (42, 2, '2014-05-14 11:21:31.5', 4, NULL, NULL, '&lt;p&gt;NoSQL is a way to store data that does not require there to be some sort of relation. The simplicity of its design and horizontal scale-ability, one way they store data is the &lt;code&gt;key : value&lt;/code&gt; pair design. This lends itself to processing that is similar to Hadoop. The use of a NoSQL db really depends on the type of problem that one is after.&lt;/p&gt;

&lt;p&gt;Here is a good wikipedia link &lt;a href="https://en.wikipedia.org/wiki/NoSQL" rel="nofollow"&gt;NoSQL&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Hadoop is a system that is meant to store and process huge chunks of data. It is a distributed file system dfs. The reason it does this is that central to its design it makes the assumption that hardware failures are common, thus making multiple copies of the same piece of information and spreading it across multiple machines and racks, so if one goes down, no problem, we have two more copies. Here is a great link for Hadoop from wikipedia as well, you will see that it is, in my opinion more than just storage, but also processing:
 &lt;a href="https://en.wikipedia.org/wiki/Apache_Hadoop" rel="nofollow"&gt;Hadoop&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (43, 2, '2014-05-14 11:23:25.913', 14, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Hadoop is not a database&lt;/strong&gt;, hadoop is an entire ecosystem.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/oOYp7.png" alt="the hadoop ecosystem"&gt;&lt;/p&gt;

&lt;p&gt;Most people will refer to &lt;a href="http://de.wikipedia.org/wiki/MapReduce" rel="nofollow"&gt;mapreduce&lt;/a&gt; jobs while talking about hadoop. A mapreduce job splits big datasets in some little chunks of data and spread them over a cluster of nodes to get proceed. In the end the result from each node will be put together again as one dataset.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Let''s assume you load into hadoop a set of &lt;code&gt;&amp;lt;String, Integer&amp;gt;&lt;/code&gt; with the population of some neighborhoods within a city and you want to get the average population over the whole neighborhoods of each city(figure 1).&lt;/p&gt;

&lt;p&gt;&lt;em&gt;figure 1&lt;/em&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    [new york, 40394]
    [new york, 134]
    [la, 44]
    [la, 647]
    ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now hadoop will first map each value by using the keys (figure 2)&lt;/p&gt;

&lt;p&gt;&lt;em&gt;figure 2&lt;/em&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[new york, [40394,134]]
[la, [44,647]]
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;After the mapping it will reduce the values of each key to a new value (in this example the average over the value set of each key)(figure 3)&lt;/p&gt;

&lt;p&gt;&lt;em&gt;figure 3&lt;/em&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[new york, [20264]]
[la, [346]]
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;now hadoop would be done with everything. You can now load the result into the HDFS (hadoop distributed file system) or into any DBMS or file.&lt;/p&gt;

&lt;p&gt;Thats just one &lt;strong&gt;very basic&lt;/strong&gt; and &lt;strong&gt;simple&lt;/strong&gt; example of what hadoop can do. You can run much more complicated tasks in hadoop.&lt;/p&gt;

&lt;p&gt;As you already mentioned in your question, hadoop and noSQL are complementary. I know a few setups where i.e. billions of datasets from sensors are stored in HBase and get then through hadoop to finally be stored in a DBMS.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (44, 2, '2014-05-14 11:24:39.53', 35, NULL, NULL, '&lt;p&gt;Actually this is coming around. In the book R in a Nutshell there is even a section on using R with Hadoop for big data processing. There are some work arounds that need to be done because R does all it''s work in memory, so you are basically limited to the amount of RAM you have available to you.&lt;/p&gt;

&lt;p&gt;A mature project for R and Hadoop is &lt;a href="https://github.com/RevolutionAnalytics/RHadoop"&gt;RHadoop&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;RHadoop has been divided into several sub-projects, rhdfs, rhbase, rmr2, plyrmr, and quickcheck (&lt;a href="https://github.com/RevolutionAnalytics/RHadoop/wiki"&gt;wiki&lt;/a&gt;).&lt;/p&gt;
', NULL, NULL, '''/a'':78C,102C ''/p'':62C,79C,103C ''/revolutionanalytics/rhadoop'':76C ''/revolutionanalytics/rhadoop/wiki'':100C ''actual'':2C ''amount'':54C ''around'':6C,32C ''avail'':59C ''basic'':50C ''big'':25C ''book'':9C ''come'':5C ''data'':26C ''divid'':84C ''done'':37C ''even'':16C ''github.com'':75C,99C ''github.com/revolutionanalytics/rhadoop'':74C ''github.com/revolutionanalytics/rhadoop/wiki'':98C ''hadoop'':23C,70C ''href'':73C,97C ''limit'':51C ''matur'':65C ''memori'':46C ''need'':34C ''nutshel'':13C ''p'':1C,63C,80C ''plyrmr'':93C ''process'':27C ''project'':66C,89C ''quickcheck'':95C ''r'':10C,21C,39C,68C ''ram'':56C ''rhadoop'':77C,81C ''rhbase'':91C ''rhdfs'':90C ''rmr2'':92C ''section'':18C ''sever'':86C ''sub'':88C ''sub-project'':87C ''use'':20C ''wiki'':101C ''work'':31C,44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (80, 4, '2014-05-15 03:19:40.36', 0, NULL, NULL, 'An activity that seeks patterns in large, complex data sets. It usually emphasizes algorithmic techniques, but may also involve any set of related skills, applications, or methodologies with that goal.', NULL, NULL, '''activ'':2C ''algorithm'':14C ''also'':18C ''applic'':25C ''complex'':8C ''data'':9C ''emphas'':13C ''goal'':30C ''involv'':19C ''larg'':7C ''may'':17C ''methodolog'':27C ''pattern'':5C ''relat'':23C ''seek'':4C ''set'':10C,21C ''skill'':24C ''techniqu'':15C ''usual'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (272, 2, '2014-06-10 11:53:07.737', 12, NULL, NULL, '&lt;p&gt;I would recommend to start with some MOOC on machine learning. For example Andrew Ng''s &lt;a href="https://www.coursera.org/course/ml"&gt;course&lt;/a&gt; at coursera.&lt;/p&gt;

&lt;p&gt;You should also take a look at &lt;a href="http://orange.biolab.si/"&gt;Orange&lt;/a&gt; application. It has a graphical interface and probably it is easier to understand some ML techniques using it. &lt;/p&gt;
', NULL, NULL, '''/a'':24C,40C ''/course/ml'':22C ''/p'':27C,59C ''also'':31C ''andrew'':15C ''applic'':41C ''cours'':23C ''coursera'':26C ''easier'':51C ''exampl'':14C ''graphic'':45C ''href'':19C,37C ''interfac'':46C ''learn'':12C ''look'':34C ''machin'':11C ''ml'':55C ''mooc'':9C ''ng'':16C ''orang'':39C ''orange.biolab.si'':38C ''p'':1C,28C ''probabl'':48C ''recommend'':4C ''start'':6C ''take'':32C ''techniqu'':56C ''understand'':53C ''use'':57C ''would'':3C ''www.coursera.org'':21C ''www.coursera.org/course/ml'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (45, 2, '2014-05-14 11:26:40.58', 6, NULL, NULL, '&lt;p&gt;First off, if your data has as many variations (in function of time, context, and others) as to make it hard to apply a single strategy to cope with it, you may be interested in doing a prior temporal/contextual/... characterization of the dataset. Characterizing data, i.e., extracting information about how the volume or specifics of the content varies according to some criteria, usually provides with a better understanding (more consise and precise) than simply inferring algorithms on a brute-force fashion.&lt;/p&gt;

&lt;p&gt;So, answering each question:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;characterization is definitely a means of reducing the number of &lt;em&gt;iterations&lt;/em&gt; while trying to select proper algorithms for specific data;&lt;/li&gt;
&lt;li&gt;if you have a discrete set of criterias on which your data varies, it becomes much easier to &lt;em&gt;scale up&lt;/em&gt; solutions, as will know what information you''d gain/lose if simpler/specific solutions were applied;&lt;/li&gt;
&lt;li&gt;after a characterization, you should be also easier to select parameters, since you''d know what kind of &lt;em&gt;specific data&lt;/em&gt; you''d be dealing with;&lt;/li&gt;
&lt;li&gt;finally, you may use data mining/machine learning algorithms to support this characterization. This includes using:
&lt;ul&gt;
&lt;li&gt;clustering algorithms, to reduce the dimensionality of data;&lt;/li&gt;
&lt;li&gt;classification algorithms, to help deciding on specific properties the data in function of time/context/... may present;&lt;/li&gt;
&lt;li&gt;association rules, to predict particular knowledge from the dataset, while also improving/fine-graining the data used for later analysis;&lt;/li&gt;
&lt;li&gt;and other possible strategies and analyses.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;And &lt;a href="http://www.cs.cmu.edu/Groups/sage/sagedc.html"&gt;here&lt;/a&gt; is a list of some criterias on which to analyse data, which you may find helpful.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (46, 2, '2014-05-14 12:32:29.503', 10, NULL, NULL, '&lt;p&gt;Note that there is an early version of LIBLINEAR ported to &lt;a href="http://spark.apache.org"&gt;Apache Spark&lt;/a&gt;. See &lt;a href="http://apache-spark-user-list.1001560.n3.nabble.com/Spark-LIBLINEAR-td5546.html"&gt;mailing list comments&lt;/a&gt; for some early details, and the &lt;a href="http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/distributed-liblinear/"&gt;project site&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':18C,28C,42C ''/p'':43C ''/spark-liblinear-td5546.html'':24C ''/~cjlin/libsvmtools/distributed-liblinear/'':39C ''apach'':16C ''apache-spark-user-list.1001560.n3.nabble.com'':23C ''apache-spark-user-list.1001560.n3.nabble.com/spark-liblinear-td5546.html'':22C ''comment'':27C ''detail'':32C ''earli'':7C,31C ''href'':14C,21C,36C ''liblinear'':10C ''list'':26C ''mail'':25C ''note'':2C ''p'':1C ''port'':11C ''project'':40C ''see'':19C ''site'':41C ''spark'':17C ''spark.apache.org'':15C ''version'':8C ''www.csie.ntu.edu.tw'':38C ''www.csie.ntu.edu.tw/~cjlin/libsvmtools/distributed-liblinear/'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (47, 2, '2014-05-14 12:39:41.197', 25, NULL, NULL, '&lt;p&gt;The main problem with using R for large data sets is the RAM constraint. The reason behind keeping all the data in RAM is that it provides much faster access and data manipulations than would storing on HDDs. If you are willing to take a hit on performance, then yes, it is quite practical to work with &lt;a href="http://statistics.org.il/wp-content/uploads/2010/04/Big_Memory%20V0.pdf"&gt;large datasets in R&lt;/a&gt;.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;RODBC Package: Allows connecting to external DB from R to retrieve and handle data. Hence, the data being &lt;em&gt;manipulated&lt;/em&gt; is restricted to your RAM. The overall data set can go much larger.&lt;/li&gt;
&lt;li&gt;The ff package allows using larger than RAM data sets by utilising memory-mapped pages.&lt;/li&gt;
&lt;li&gt;BigLM: It builds generalized linear models on big data. It loads data into memory in chunks.&lt;/li&gt;
&lt;li&gt;bigmemory : An R package which allows powerful and memory-efficient parallel 
analyses and data mining of massive data sets. It permits storing large objects (matrices etc.) in memory (on the RAM) using external pointer objects to refer to them. &lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (48, 5, '2014-05-14 13:08:26.647', 0, NULL, NULL, '&lt;p&gt;&lt;a href="http://www.r-project.org" rel="nofollow"&gt;R&lt;/a&gt; is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&amp;amp;T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.&lt;/p&gt;

&lt;p&gt;R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.&lt;/p&gt;

&lt;p&gt;One of R''s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.&lt;/p&gt;

&lt;p&gt;R was created by &lt;a href="http://www.stat.auckland.ac.nz/~ihaka/" rel="nofollow"&gt;Ross Ihaka&lt;/a&gt; and &lt;a href="http://www.gene.com/scientists/our-scientists/robert-gentleman" rel="nofollow"&gt;Robert Gentleman&lt;/a&gt; and is now developed by the &lt;a href="http://www.r-project.org/contributors.html" rel="nofollow"&gt;R Development Core Team&lt;/a&gt;. The R environment is easily extended through a packaging system on &lt;a href="http://cran.r-project.org" rel="nofollow" title="CRAN The Comprehensive R Archive Network"&gt;CRAN&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;R is available as Free Software under the terms of the Free Software Foundation''s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and Mac OS.&lt;/p&gt;
', NULL, NULL, '''/a'':8C,196C,207C,225C,250C ''/contributors.html'':218C ''/p'':76C,131C,181C,251C,298C ''/scientists/our-scientists/robert-gentleman'':202C ''/~ihaka/'':191C ''activ'':130C ''amp'':41C ''analysi'':95C ''archiv'':247C ''avail'':255C ''bell'':37C ''care'':161C ''chamber'':48C ''choic'':113C,172C ''classic'':89C ''classif'':96C ''cluster'':97C ''code'':68C,274C ''colleagu'':50C ''compil'':277C ''comprehens'':245C ''comput'':16C ''consid'':54C ''control'':180C ''core'':223C ''cran'':243C,249C ''cran.r-project.org'':239C ''creat'':185C ''default'':167C ''design'':145C,171C ''develop'':35C,211C,222C ''differ'':57C,65C ''eas'':140C ''easili'':230C ''environ'':13C,32C,228C ''extend'':231C ''extens'':104C ''form'':275C ''former'':39C ''formula'':157C ''foundat'':266C ''free'':257C,264C ''freebsd'':291C ''full'':179C ''general'':269C ''gentleman'':206C ''gnu'':22C,268C ''graphic'':18C,99C,174C ''great'':160C ''high'':103C ''href'':3C,188C,199C,215C,238C ''ihaka'':195C ''implement'':58C ''import'':64C ''includ'':153C,290C ''john'':47C ''laboratori'':38C ''languag'':11C,30C,107C ''licens'':271C ''linear'':85C ''linux'':293C ''lucent'':44C ''mac'':296C ''mathemat'':154C ''methodolog'':118C ''minor'':170C ''model'':88C ''much'':67C ''need'':159C ''network'':248C ''nofollow'':6C,193C,204C,220C,241C ''nonlinear'':87C ''often'':109C ''one'':133C ''open'':123C ''os'':297C ''p'':1C,77C,132C,182C,252C ''packag'':234C ''particip'':127C ''platform'':286C ''plot'':149C ''produc'':152C ''project'':23C ''provid'':79C,121C ''public'':147C,270C ''publication-qu'':146C ''qualiti'':148C ''r'':7C,51C,75C,78C,120C,135C,183C,221C,227C,246C,253C ''rel'':5C,192C,203C,219C,240C ''research'':115C ''retain'':178C ''robert'':205C ''ross'':194C ''rout'':125C ''run'':72C,279C ''seri'':94C ''similar'':26C,288C ''softwar'':258C,265C ''sourc'':124C,273C ''statist'':15C,84C,90C,117C ''strength'':137C ''symbol'':155C ''system'':235C,289C ''taken'':164C ''team'':224C ''techniqu'':100C ''technolog'':45C ''term'':261C ''test'':91C ''time'':93C ''time-seri'':92C ''titl'':242C ''unalt'':73C ''unix'':285C ''user'':177C ''varieti'':82C,283C ''vehicl'':111C ''well'':144C ''well-design'':143C ''wide'':81C,282C ''window'':294C ''written'':69C ''www.gene.com'':201C ''www.gene.com/scientists/our-scientists/robert-gentleman'':200C ''www.r-project.org'':4C,217C ''www.r-project.org/contributors.html'':216C ''www.stat.auckland.ac.nz'':190C ''www.stat.auckland.ac.nz/~ihaka/'':189C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (49, 4, '2014-05-14 13:08:26.647', 0, NULL, NULL, 'R is a free, open-source programming language and software environment for statistical computing, bioinformatics, and graphics.', NULL, NULL, '''bioinformat'':16C ''comput'':15C ''environ'':12C ''free'':4C ''graphic'':18C ''languag'':9C ''open'':6C ''open-sourc'':5C ''program'':8C ''r'':1C ''softwar'':11C ''sourc'':7C ''statist'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (50, 1, '2014-05-14 14:26:54.313', 5, 159, 'Running an R script programmatically', '&lt;p&gt;I have an R script that generates a report based on the current contents of a database. This database is constantly in flux with records being added/deleted many times each day. How can I ask my computer to run this every night at 4 am so that I have an up to date report waiting for me in the morning? Or perhaps I want it to re-run once a certain number of new records have been added to the database. How might I go about automating this? I should mention I''m on Windows, but I could easily put this script on my Linux machine if that would simplify the process. &lt;/p&gt;
', 'r  databases  efficiency  tools', NULL, '''/p'':124C ''4'':54C ''ad'':89C ''added/deleted'':37C ''ask'':45C ''autom'':98C ''base'':20C ''certain'':82C ''comput'':47C ''constant'':31C ''content'':24C ''could'':109C ''current'':23C ''databas'':7B,27C,29C,92C ''date'':63C ''day'':41C ''easili'':110C ''effici'':8B ''everi'':51C ''flux'':33C ''generat'':17C ''go'':96C ''linux'':116C ''m'':104C ''machin'':117C ''mani'':38C ''mention'':102C ''might'':94C ''morn'':70C ''new'':85C ''night'':52C ''number'':83C ''p'':10C ''perhap'':72C ''process'':123C ''programmat'':5A ''put'':111C ''r'':3A,6B,14C ''re'':78C ''re-run'':77C ''record'':35C,86C ''report'':19C,64C ''run'':1A,49C,79C ''script'':4A,15C,113C ''simplifi'':121C ''time'':39C ''tool'':9B ''wait'':65C ''want'':74C ''window'':106C ''would'':120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (51, 2, '2014-05-14 14:48:32.18', 9, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;How can I ask my computer to run this every night at 4 am so that I have an up to date report waiting for me in the morning?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;You can set up a cronjob on a Linux system. These are run at the set time, if the computer is on. To do so, open a terminal and type:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;crontab -e
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And add:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;00 4 * * * r source(/home/FilePath/.../myRscript.R)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Source: &lt;a href="http://stackoverflow.com/a/14710307/1488917"&gt;Stack Overflow&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':95C ''/a/14710307/1488917'':92C ''/blockquote'':33C ''/code'':70C,84C ''/home/filepath'':82C ''/myrscript.r'':83C ''/p'':32C,65C,75C,96C ''/pre'':71C,85C ''00'':78C ''4'':15C,79C ''add'':74C ''ask'':6C ''blockquot'':1C ''code'':67C,77C ''comput'':8C,54C ''cronjob'':40C ''crontab'':68C ''date'':24C ''e'':69C ''everi'':12C ''href'':89C ''linux'':43C ''morn'':31C ''night'':13C ''open'':60C ''overflow'':94C ''p'':2C,34C,72C,86C ''pre'':66C,76C ''r'':80C ''report'':25C ''run'':10C,47C ''set'':37C,50C ''sourc'':81C,87C ''stack'':93C ''stackoverflow.com'':91C ''stackoverflow.com/a/14710307/1488917'':90C ''system'':44C ''termin'':62C ''time'':51C ''type'':64C ''wait'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5429, 2, '2015-04-01 22:37:39.3', 9, NULL, NULL, '&lt;p&gt;The general approach is to do traditional statistical analysis on your data set to define a multidimensional random process that will generate data with the same statistical characteristics.  The virtue of this approach is that your synthetic data is independent of your ML model, but statistically "close" to your data. (see below for discussion of your alternative)&lt;/p&gt;

&lt;p&gt;In essence, you are estimating the multivariate probability distribution associated with the process.  Once you have estimated the distribution, you can generate synthetic data through the Monte Carlo method or similar repeated sampling methods.  If your data resembles some parametric distribution (e.g. lognormal) then this approach is straightforward and reliable.  The tricky part is to estimate the dependence between variables. See: &lt;a href="https://www.encyclopediaofmath.org/index.php/Multi-dimensional_statistical_analysis" rel="nofollow"&gt;https://www.encyclopediaofmath.org/index.php/Multi-dimensional_statistical_analysis&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;If your data is irregular, then non-parametric methods are easier and probably more robust.  &lt;a href="https://en.wikipedia.org/wiki/Multivariate_kernel_density_estimation" rel="nofollow"&gt;Multivariate kernal density estimation&lt;/a&gt; is a method that is accessible and appealing to people with ML background. For a general introduction and links to specific methods, see: &lt;a href="https://en.wikipedia.org/wiki/Nonparametric_statistics" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Nonparametric_statistics&lt;/a&gt; .&lt;/p&gt;

&lt;p&gt;To validate that this process worked for you, you go through the machine learning process again with the synthesized data, and you should end up with a model that is fairly close to your original.  Likewise, if you put the synthesized data into your ML model, you should get outputs that have similar distribution as your original outputs.&lt;/p&gt;

&lt;p&gt;In contrast, you are proposing this:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;[original data --&gt; build machine learning model --&gt; use ml model to generate synthetic data....!!!]&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This accomplishes something different that the method I just described.  This would solve the &lt;em&gt;&lt;a href="https://en.wikipedia.org/wiki/Inverse_problem#Conceptual_understanding" rel="nofollow"&gt;inverse problem&lt;/a&gt;&lt;/em&gt;: "what inputs could generate any given set of model outputs". Unless your ML model is over-fitted to your original data, this synthesized data &lt;em&gt;will not&lt;/em&gt; look like your original data in every respect, or even most.&lt;/p&gt;

&lt;p&gt;Consider a linear regression model. The same linear regression model can have identical fit to data that have very different characteristics. A famous demonstration of this is through &lt;a href="http://en.wikipedia.org/wiki/Anscombe%27s_quartet" rel="nofollow"&gt;Anscombe''s quartet&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/P9s4w.png" alt="All four sets are identical when examined using simple summary statistics, but vary considerably when graphed"&gt;&lt;/p&gt;

&lt;p&gt;Thought I don''t have references, I believe this problem can also arise in logistic regression, generalized linear models, SVM, and K-means clustering.&lt;/p&gt;

&lt;p&gt;There are some ML model types (e.g. decision tree) where it''s possible to inverse them to generate synthetic data, though it takes some work.  See: &lt;a href="http://csce.uark.edu/~cwt/DOCS/2008-06--IEEE-Internet-Computing--Reverse-Data-Mining--Eno-Thompson.pdf" rel="nofollow"&gt;Generating Synthetic Data to Match Data Mining Patterns&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5474, 2, '2015-04-08 00:00:33.73', 2, NULL, NULL, '&lt;p&gt;this might take a few iterations, but perhaps it can be moved to chat if it gets too long.  are you wondering about a specific application or just the general concept?  as far as i know, the canonical form of something is the unique/simplest arrangement/representation of that something.  as for isomorphism (one to one relationship, a function gets you from one set to the other), you can oversimplify it and think about factors of numbers.&lt;/p&gt;

&lt;p&gt;for a moment consider an analogy - instead of graphs, using sets of numbers.  this is randomly off the top of my head so hopefully there aren''t too many holes in it.  we have&lt;/p&gt;

&lt;p&gt;$A = 6, 10, 14$&lt;/p&gt;

&lt;p&gt;and&lt;/p&gt;

&lt;p&gt;$B = 18, 30, 42$&lt;/p&gt;

&lt;p&gt;let''s call $A$ and $B$ isomorphic because to get between any value in $A$ to the corresponding value in $B$, you multiply or divide by 3.  now let''s introduce another isomorphic set:&lt;/p&gt;

&lt;p&gt;$C = 3, 5, 7$&lt;/p&gt;

&lt;p&gt;it looks like $A$, $B$, and $C$ are all isomorphic because there is a one to one relationship between any of their initial values.  the same can be said for any of their middle values, or any of their ending values.  while it''s great that they''re all isomorphic, we''ll consider something additional about $C$.  i''m going to call $C$ canonical because if you remove the greatest common factor from $A$, or you remove the greatest common factor from $B$, you get $C$.  there is no common factor we can remove to reduce $C$ itself.&lt;/p&gt;

&lt;p&gt;extending this, you can see that a canonical representation of a graph &lt;em&gt;is&lt;/em&gt; an isomorphic version of that graph, but not necessarily the other way around.  all isomorphic versions of said graph will reduce to the same canonical form.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;that being said, the definition of canonical is more complicated for graphs.  first, for graph isomorphism, &lt;a href="http://www.tcs.hut.fi/Software/bliss/index.html" rel="nofollow"&gt;this page about a code named bliss&lt;/a&gt; presents some small graphs as an example.  they define $G_{1}$ as&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/xWHFw.png" alt="g1"&gt;
and $G_{2}$ as
&lt;img src="http://i.stack.imgur.com/gKwj1.png" alt="g2"&gt;&lt;/p&gt;

&lt;p&gt;and offer two transformations for $G_{1}$ that will make $G_{1}$ and $G_{2}$ isomorphic.  first, they consider $\{\langle 1\to 2, 2\to 4, 3\to 1, 4\to 3\rangle\}$, and then $\{\langle 1\to 2, 2\to 4, 3\to 3, 4\to 1\rangle\}$ - both of these transformations swap vertices.  the transformations result in the following isomorphic graphs:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Ovd06.png" alt="transformationResults"&gt;&lt;/p&gt;

&lt;p&gt;each pair of connected vertices has a corresponding pair in $G_{2}$ ($2\to 1,3,4; 1\to 4; 3\to 4$).&lt;/p&gt;

&lt;p&gt;now, to discuss canonical graphs, we must identify a cost function with which we can measure what is the ''most canonical'' form.  &lt;a href="http://www.csie.ntu.edu.tw/~r94054/paper/draw_plane_graph.pdf" rel="nofollow"&gt;this paper&lt;/a&gt; mentions metrics such as area, minimum angle, and total number of bends.  for simplicity, i''ll try to minimize bends and favor angles that are closer to $90^{\circ}$.&lt;/p&gt;

&lt;p&gt;if you use a coordinate transformation, you can see that this simple graph&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Atqpw.png" alt="firstIsomorphic"&gt;&lt;/p&gt;

&lt;p&gt;and this graph&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/kG7mg.png" alt="otherIsomorphic"&gt;&lt;/p&gt;

&lt;p&gt;are isomorphic.  the same edges connect the same pairs of vertices, and if you move vertices from one around, you can make the other.  this graph&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/CtUNC.png" alt="canonical"&gt;&lt;/p&gt;

&lt;p&gt;is also isomorphic with the first two, but since it has the optimal amount of bent edges (zero) and optimal angles (four right angles), it is also the canonical representation for these graphs.&lt;/p&gt;

&lt;p&gt;for further reading, you might consider &lt;a href="http://www.borgelt.net/papers/mgts_05.pdf" rel="nofollow"&gt;this paper&lt;/a&gt;, which discusses several algorithms used to find canonical representations for web mining, or &lt;a href="http://www.ecs.umass.edu/ece/labs/vlsicad/papers/dd-chapter.pdf" rel="nofollow"&gt;this paper&lt;/a&gt;, which discusses canonical representations of binary decision diagrams, word-level decision diagrams, binary moment diagrams, taylor expansion diagrams, and finite field decision diagrams.  &lt;/p&gt;

&lt;p&gt;tl;dr:&lt;/p&gt;

&lt;p&gt;just have a look at the &lt;strong&gt;minion with bananas&lt;/strong&gt; &lt;a href="http://webhome.cs.uvic.ca/~wendym/courses/422/14/notes/422_14_group_theory.pdf" rel="nofollow"&gt;on slide 10 of this presentation&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5512, 2, '2015-04-12 21:54:11.407', 2, NULL, NULL, '&lt;p&gt;Here''s a solution using the caret package for R. A Random Forest is first trained on the data. All observations for which the probability (from the voting) is less than 99% are then passed to model 2, linear discriminant analysis. Only the probabilities from unseen resampling observations are used, since the Random Forest will otherwise fit the training data perfectly. That is what caret is needed for.&lt;/p&gt;

&lt;p&gt;The accuracy is a little higher for the uncertain cases, but this is probably overfitting since I have tried several different models and the data set is small.&lt;/p&gt;

&lt;p&gt;I''d like to know if this really improves the performance in your application for the out of sample test data. Are there any papers that recommend this approach? This approach seems to resemble boosting. I tried it on some of my data but could not improve the out of sample performance I got from model 1 (a Random Forest).&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data(iris)
library(caret)
myTrainControl &amp;lt;- trainControl(method = "cv", number = 10,
                               savePredictions = T,
                               classProbs = TRUE)
set.seed(4213) # To get the same resamples every time
M1 &amp;lt;- train(y = iris$Species,
            x = iris[, !(names(iris) == "Species")],
            method = "rf",
            trControl = myTrainControl)

M1pred &amp;lt;- M1$pred[M1$pred$mtry == 2, ]
confusionMatrix(M1pred$pred, M1pred$obs)
# Accuracy 96%

# Inspect the predicted class probabilities:
probs &amp;lt;- cbind(M1pred$setosa,
                   M1pred$versicolor,
               M1pred$virginica)
colnames(probs) &amp;lt;- c("setosa", "versicolor", "virginica")
maxCol &amp;lt;- max.col(probs)
probs &amp;lt;- cbind(probs, maxCol)
maxProbs &amp;lt;- apply(probs, 1, function(x) x[x["maxCol"]])
summary(maxProbs)
# Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
# 0.5120  0.9805  0.9980  0.9645  1.0000  1.0000

# Let''s define anything below 99% as uncertain:
uncertain &amp;lt;- which(maxProbs &amp;lt; 0.99)
length(uncertain) # 45
# Find rows in iris data that belong to ''uncertain'' cases:
uncertainIndex &amp;lt;- M1pred$rowIndex[uncertain]
    confusionMatrix(M1pred$pred[uncertain],
                reference = M1pred$obs[uncertain])
# M1 Performance in uncertain cases:
#              Reference
# Prediction   setosa versicolor virginica
# setosa          5          0         0
# versicolor      0         17         3
# virginica       0          3        17
#
# Overall Statistics
# Accuracy : 0.8667

# Train new model on uncertain data only:
irisUncertain &amp;lt;- iris[uncertainIndex, ]
set.seed(4213) # To get the same resamples every time
M2 &amp;lt;- train(y = irisUncertain$Species,
            x = irisUncertain[, !(names(irisUncertain) == "Species")],
            method = "lda",
            trControl = myTrainControl)

M2pred &amp;lt;- M2$pred

confusionMatrix(M2pred$pred, reference = M2pred$obs)
#              Reference
# Prediction   setosa versicolor virginica
# setosa          4          0         0
# versicolor      1         16         0
# virginica       0          4        20
# 
# Overall Statistics
# Accuracy : 0.8889 
# (Small discrepancy: Why does caret report an accuracy of 88,5% for M2?)

# For new data predictions can be made as follows:
# (just as an example from the original data again)
# Some ''uncertain'' cases are ''certain'' now using the full M2 model
newdat &amp;lt;- irisUncertain[30:34, -5]
M1maxProbs &amp;lt;- apply(predict(M1, newdat, type = "prob"), 1, max)
ifelse(M1maxProbs &amp;lt; 0.99,
       paste("M2:", predict(M2, newdat)),
       paste("M1:", predict(M1, newdat)))
# 85               94              107              126 
# "M2: versicolor" "M1: versicolor"  "M2: virginica"  "M1: virginica" 
# 71 
# "M2: virginica"  
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (62, 2, '2014-05-14 18:27:56.043', 34, NULL, NULL, '&lt;p&gt;Overfitting is &lt;em&gt;empirically&lt;/em&gt; bad.  Suppose you have a data set which you split in two, test and training.  An overfitted model is one that performs much worse on the test dataset than on training dataset.  It is often observed that models like that also in general perform worse on additional (new) test datasets than models which are not overfitted.  &lt;/p&gt;

&lt;p&gt;One way to understand that intuitively is that a model may use some relevant parts of the data (signal) and some irrelevant parts (noise).  An overfitted model uses more of the noise, which increases its performance in the case of known noise (training data) and decreases its performance in the case of novel noise (test data).  The difference in performance between training and test data indicates how much noise the model picks up; and picking up noise directly translates into worse performance on test data (including future data).&lt;/p&gt;

&lt;p&gt;Summary: overfitting is bad by definition, this has not much to do with either complexity or ability to generalize, but rather has to do with mistaking noise for signal.&lt;/p&gt;

&lt;p&gt;P.S. On the "ability to generalize" part of the question, it is very possible to have a model which has inherently limited ability to generalize due to the structure of the model (for example linear SVM, ...) but is still prone to overfitting.  In a sense overfitting is just one way that generalization may fail.&lt;/p&gt;
', NULL, NULL, '''/em'':6C ''/p'':63C,153C,184C,240C ''abil'':171C,189C,208C ''addit'':53C ''also'':47C ''bad'':7C,158C ''case'':103C,115C ''complex'':169C ''data'':12C,82C,108C,120C,129C,149C,152C ''dataset'':34C,38C,56C ''decreas'':110C ''definit'':160C ''differ'':122C ''direct'':142C ''due'':211C ''either'':168C ''em'':4C ''empir'':5C ''exampl'':219C ''fail'':239C ''futur'':151C ''general'':49C,173C,191C,210C,237C ''includ'':150C ''increas'':98C ''indic'':130C ''inher'':206C ''intuit'':70C ''irrelev'':86C ''known'':105C ''like'':45C ''limit'':207C ''linear'':220C ''may'':75C,238C ''mistak'':180C ''model'':24C,44C,58C,74C,91C,135C,203C,217C ''much'':29C,132C,164C ''new'':54C ''nois'':88C,96C,106C,118C,133C,141C,181C ''novel'':117C ''observ'':42C ''often'':41C ''one'':26C,65C,234C ''overfit'':2C,23C,62C,90C,156C,227C,231C ''p'':1C,64C,154C,185C ''p.s'':186C ''part'':79C,87C,192C ''perform'':28C,50C,100C,112C,124C,146C ''pick'':136C,139C ''possibl'':199C ''prone'':225C ''question'':195C ''rather'':175C ''relev'':78C ''sens'':230C ''set'':13C ''signal'':83C,183C ''split'':16C ''still'':224C ''structur'':214C ''summari'':155C ''suppos'':8C ''svm'':221C ''test'':19C,33C,55C,119C,128C,148C ''train'':21C,37C,107C,126C ''translat'':143C ''two'':18C ''understand'':68C ''use'':76C,92C ''way'':66C,235C ''wors'':30C,51C,145C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (81, 1, '2014-05-15 04:59:54.317', 12, 169, 'Parallel and distributed computing', '&lt;p&gt;What is(are) the difference(s) between parallel and distributed computing? When it comes to scalability and efficiency, it is very common to see solutions dealing with computations in clusters of machines, and sometimes it is referred to as a parallel processing, or as distributed processing.&lt;/p&gt;

&lt;p&gt;In a certain way, the computation seems to be always parallel, since there are things running concurrently. But is the distributed computation simply related to the use of more than one machine, or are there any further specificities that distinguishes these two kinds of processing? Wouldn''t it be redundant to say, for example, that a computation is &lt;em&gt;parallel AND distributed&lt;/em&gt;?&lt;/p&gt;
', 'definitions  parallel  distributed', NULL, '''/em'':119C ''/p'':55C,120C ''alway'':66C ''certain'':59C ''cluster'':38C ''come'':22C ''common'':30C ''comput'':4A,19C,36C,62C,78C,113C ''concurr'':73C ''deal'':34C ''definit'':5B ''differ'':13C ''distinguish'':96C ''distribut'':3A,7B,18C,53C,77C,118C ''effici'':26C ''em'':115C ''exampl'':110C ''kind'':99C ''machin'':40C,88C ''one'':87C ''p'':8C,56C ''parallel'':1A,6B,16C,49C,67C,116C ''process'':50C,54C,101C ''redund'':106C ''refer'':45C ''relat'':80C ''run'':72C ''say'':108C ''scalabl'':24C ''see'':32C ''seem'':63C ''simpli'':79C ''sinc'':68C ''solut'':33C ''sometim'':42C ''specif'':94C ''thing'':71C ''two'':98C ''use'':83C ''way'':60C ''wouldn'':102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (57, 2, '2014-05-14 16:29:39.927', 15, NULL, NULL, '&lt;p&gt;From my point of view, this question is suitable for a two-step answer. The first part, let us call it &lt;em&gt;soft preprocessing&lt;/em&gt;, could be taken as the usage of different data mining algorithms to preprocess data in such a way that makes it suitable for further analyses. Notice that this could be the analysis itself, in case the goal is simple enough to be tackled in a single shot.&lt;/p&gt;

&lt;p&gt;The second part, the &lt;em&gt;hard preprocessing&lt;/em&gt;, actually comes prior to any other process, and is may be taken as the usage of simple tools or scripts to clean up data, selecting specific contents to be processed. To this problem, POSIX provides us with a wonderous set of magic tools, which can be used to compose concise -- and very powerful -- preprocessing scripts.&lt;/p&gt;

&lt;p&gt;For example, for people who deal with data coming from social websites (twitter, facebook, ...), the &lt;em&gt;data retrieval&lt;/em&gt; usually yields files with very specific format -- although not always nicely structure, as they may contain missing fields, and so. For these cases, a simple &lt;code&gt;awk&lt;/code&gt; script could clean up the data, producing a &lt;em&gt;valid&lt;/em&gt; input file for later processing. From the magic set, one may also point out &lt;code&gt;grep&lt;/code&gt;, &lt;code&gt;sed&lt;/code&gt;, &lt;code&gt;cut&lt;/code&gt;, &lt;code&gt;join&lt;/code&gt;, &lt;code&gt;paste&lt;/code&gt;, &lt;code&gt;sort&lt;/code&gt;, and a whole multitude of other tools.&lt;/p&gt;

&lt;p&gt;In case simple the source file has too many nitty-gritties, it may also be necessary to produce a bundle of methods to clean up data. In such cases, it is usually better to use scripting languages (other than shell ones), such as Python, Ruby, and Perl. This allows for building up &lt;em&gt;API&lt;/em&gt;''s to select specific data in a very straightforward and reusable way. Such &lt;em&gt;API&lt;/em&gt;''s are sometimes made public by their writers, such as &lt;a href="http://imdbpy.sourceforge.net/"&gt;IMDbPY&lt;/a&gt;, &lt;a href="https://api.stackexchange.com/docs"&gt;Stack Exchange API&lt;/a&gt;, and many others.&lt;/p&gt;

&lt;p&gt;So, answering the question: are there any best practices? It usually depends on your task. If you will always deal with the same data format, it''s commonly best to write an &lt;em&gt;organized&lt;/em&gt; script to preprocess it; whereas, if you just need a simple and fast clean up on some dataset, count on POSIX tools for concise shell scripts that will do the whole job &lt;strong&gt;much&lt;/strong&gt; faster than a Python script, or so. Since the &lt;em&gt;clean up&lt;/em&gt; depends both on the dataset and on your purposes, it''s hard to have everything already done. Yet, there are lots of API''s that puts you halfway through with the problem.&lt;/p&gt;
', NULL, NULL, '''/a'':327C,336C ''/code'':188C,216C,219C,222C,225C,228C,231C ''/docs'':332C ''/em'':27C,84C,160C,199C,296C,312C,376C,424C ''/p'':75C,140C,239C,340C,457C ''/strong'':411C ''actual'':85C ''algorithm'':38C ''allow'':290C ''alreadi'':440C ''also'':211C,255C ''although'':168C ''alway'':170C,360C ''analys'':52C ''analysi'':59C ''answer'':16C,343C ''api'':295C,311C,335C,447C ''api.stackexchange.com'':331C ''api.stackexchange.com/docs'':330C ''awk'':187C ''best'':349C,370C ''better'':274C ''build'':292C ''bundl'':261C ''call'':22C ''case'':62C,183C,242C,270C ''clean'':106C,191C,265C,390C,422C ''code'':186C,214C,217C,220C,223C,226C,229C ''come'':86C,150C ''common'':369C ''compos'':133C ''concis'':134C,400C ''contain'':176C ''content'':111C ''could'':28C,56C,190C ''count'':395C ''cut'':221C ''data'':36C,41C,108C,149C,158C,194C,267C,301C,365C ''dataset'':394C,429C ''deal'':147C,361C ''depend'':353C,425C ''differ'':35C ''done'':441C ''em'':24C,81C,157C,197C,294C,310C,374C,421C ''enough'':67C ''everyth'':439C ''exampl'':143C ''exchang'':334C ''facebook'':155C ''fast'':389C ''faster'':412C ''field'':178C ''file'':163C,201C,246C ''first'':18C ''format'':167C,366C ''goal'':64C ''grep'':215C ''gritti'':252C ''halfway'':452C ''hard'':82C,436C ''href'':324C,329C ''imdbpi'':326C ''imdbpy.sourceforge.net'':325C ''input'':200C ''job'':408C ''join'':224C ''languag'':278C ''later'':203C ''let'':20C ''lot'':445C ''made'':316C ''magic'':126C,207C ''make'':47C ''mani'':249C,338C ''may'':94C,175C,210C,254C ''method'':263C ''mine'':37C ''miss'':177C ''much'':410C ''multitud'':235C ''necessari'':257C ''need'':385C ''nice'':171C ''nitti'':251C ''nitty-gritti'':250C ''notic'':53C ''one'':209C,282C ''organ'':375C ''other'':339C ''p'':1C,76C,141C,240C,341C ''part'':19C,79C ''past'':227C ''peopl'':145C ''perl'':288C ''point'':4C,212C ''posix'':118C,397C ''power'':137C ''practic'':350C ''preprocess'':26C,40C,83C,138C,379C ''prior'':87C ''problem'':117C,456C ''process'':91C,114C,204C ''produc'':195C,259C ''provid'':119C ''public'':317C ''purpos'':433C ''put'':450C ''python'':285C,415C ''question'':8C,345C ''retriev'':159C ''reusabl'':307C ''rubi'':286C ''script'':104C,139C,189C,277C,377C,402C,416C ''second'':78C ''sed'':218C ''select'':109C,299C ''set'':124C,208C ''shell'':281C,401C ''shot'':74C ''simpl'':66C,101C,185C,243C,387C ''sinc'':419C ''singl'':73C ''social'':152C ''soft'':25C ''sometim'':315C ''sort'':230C ''sourc'':245C ''specif'':110C,166C,300C ''stack'':333C ''step'':15C ''straightforward'':305C ''strong'':409C ''structur'':172C ''suitabl'':10C,49C ''tackl'':70C ''taken'':30C,96C ''task'':356C ''tool'':102C,127C,238C,398C ''twitter'':154C ''two'':14C ''two-step'':13C ''us'':21C,120C ''usag'':33C,99C ''use'':131C,276C ''usual'':161C,273C,352C ''valid'':198C ''view'':6C ''way'':45C,308C ''websit'':153C ''wherea'':381C ''whole'':234C,407C ''wonder'':123C ''write'':372C ''writer'':320C ''yet'':442C ''yield'':162C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (58, 2, '2014-05-14 17:06:33.337', 9, NULL, NULL, '&lt;p&gt;You can check out &lt;a href="http://hunch.net/~vw/"&gt;vowpal wabbit&lt;/a&gt;. It is quite popular for large-scale learning and includes parallel provisions.&lt;/p&gt;

&lt;p&gt;From their website:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;VW is the essence of speed in machine learning, able to learn from terafeature datasets with ease. Via parallel learning, it can exceed the throughput of any single machine network interface when doing linear learning, a first amongst learning algorithms.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/a'':13C ''/blockquote'':76C ''/p'':27C,32C,75C ''/~vw/'':10C ''abl'':44C ''algorithm'':74C ''amongst'':72C ''blockquot'':33C ''check'':4C ''dataset'':49C ''eas'':51C ''essenc'':38C ''exceed'':57C ''first'':71C ''href'':7C ''hunch.net'':9C ''hunch.net/~vw/'':8C ''includ'':24C ''interfac'':65C ''larg'':20C ''large-scal'':19C ''learn'':22C,43C,46C,54C,69C,73C ''linear'':68C ''machin'':42C,63C ''network'':64C ''p'':1C,28C,34C ''parallel'':25C,53C ''popular'':17C ''provis'':26C ''quit'':16C ''scale'':21C ''singl'':62C ''speed'':40C ''terafeatur'':48C ''throughput'':59C ''via'':52C ''vowpal'':11C ''vw'':35C ''wabbit'':12C ''websit'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (59, 1, '2014-05-14 17:48:21.24', 9, 625, 'What are R''s memory constraints?', '&lt;p&gt;In reviewing â&lt;a href="http://rads.stackoverflow.com/amzn/click/1461468485" rel="nofollow"&gt;Applied Predictive Modeling&lt;/a&gt;" a &lt;a href="http://www.information-management.com/blogs/applied-predictive-modeling-10024771-1.html" rel="nofollow"&gt;reviewer states&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;One critique I have of statistical learning (SL) pedagogy is the
  absence of computation performance considerations in the evaluation of
  different modeling techniques. With its emphases on bootstrapping and
  cross-validation to tune/test models, SL is quite compute-intensive.
  Add to that the re-sampling that''s embedded in techniques like bagging
  and boosting, and you have the specter of computation hell for
  supervised learning of large data sets. &lt;strong&gt;In fact, R''s memory
  constraints impose pretty severe limits on the size of models that can
  be fit by top-performing methods like random forests.&lt;/strong&gt; Though SL does a
  good job calibrating model performance against small data sets, it''d
  sure be nice to understand performance versus computational cost for
  larger data.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;What are R''s memory constraints, and do they impose severe limits on the size of models that can be fit by top-performing methods like &lt;a href="http://en.wikipedia.org/wiki/Random_forest" rel="nofollow"&gt;random forests&lt;/a&gt;?&lt;/p&gt;
', 'hadoop  r', NULL, '''/a'':23C,34C,205C ''/amzn/click/1461468485'':17C ''/blockquote'':167C ''/blogs/applied-predictive-modeling-10024771-1.html'':29C ''/p'':35C,166C,206C ''/strong'':138C ''/wiki/random_forest'':200C ''absenc'':49C ''add'':79C ''appli'':20C ''bag'':92C ''blockquot'':36C ''boost'':94C ''bootstrap'':65C ''calibr'':145C ''comput'':51C,77C,101C,161C ''compute-intens'':76C ''consider'':53C ''constraint'':6A,116C,174C ''cost'':162C ''critiqu'':39C ''cross'':68C ''cross-valid'':67C ''d'':153C ''data'':108C,150C,165C ''differ'':58C ''embed'':88C ''emphas'':63C ''en.wikipedia.org'':199C ''en.wikipedia.org/wiki/random_forest'':198C ''evalu'':56C ''fact'':112C ''fit'':129C,189C ''forest'':137C,204C ''good'':143C ''hadoop'':7B ''hell'':102C ''href'':14C,26C,197C ''impos'':117C,178C ''intens'':78C ''job'':144C ''larg'':107C ''larger'':164C ''learn'':44C,105C ''like'':91C,135C,195C ''limit'':120C,180C ''memori'':5A,115C,173C ''method'':134C,194C ''model'':22C,59C,72C,125C,146C,185C ''nice'':156C ''nofollow'':19C,31C,202C ''one'':38C ''p'':9C,37C,168C ''pedagogi'':46C ''perform'':52C,133C,147C,159C,193C ''predict'':21C ''pretti'':118C ''quit'':75C ''r'':3A,8B,113C,171C ''rads.stackoverflow.com'':16C ''rads.stackoverflow.com/amzn/click/1461468485'':15C ''random'':136C,203C ''re'':84C ''re-sampl'':83C ''rel'':18C,30C,201C ''review'':11C,32C ''sampl'':85C ''set'':109C,151C ''sever'':119C,179C ''size'':123C,183C ''sl'':45C,73C,140C ''small'':149C ''specter'':99C ''state'':33C ''statist'':43C ''strong'':110C ''supervis'':104C ''sure'':154C ''techniqu'':60C,90C ''though'':139C ''top'':132C,192C ''top-perform'':131C,191C ''tune/test'':71C ''understand'':158C ''valid'':69C ''versus'':160C ''www.information-management.com'':28C ''www.information-management.com/blogs/applied-predictive-modeling-10024771-1.html'':27C ''â'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (60, 2, '2014-05-14 17:58:48.297', 6, NULL, NULL, '&lt;p&gt;R performs all computation in-memory so you can''t perform operation on a dataset that is larger than available RAM amount. However there are some libraries that allow bigdata processing using R and one of popular libraries for bigdata processing like Hadoop.&lt;/p&gt;
', NULL, NULL, '''/p'':46C ''allow'':31C ''amount'':24C ''avail'':22C ''bigdata'':32C,42C ''comput'':5C ''dataset'':17C ''hadoop'':45C ''howev'':25C ''in-memori'':6C ''larger'':20C ''librari'':29C,40C ''like'':44C ''memori'':8C ''one'':37C ''oper'':14C ''p'':1C ''perform'':3C,13C ''popular'':39C ''process'':33C,43C ''r'':2C,35C ''ram'':23C ''use'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (61, 1, '2014-05-14 18:09:01.94', 25, 2258, 'Why Is Overfitting Bad in Machine Learning?', '&lt;p&gt;Logic often states that by overfitting a model, its capacity to generalize is limited, though this might only mean that overfitting stops a model from improving after a certain complexity. Does overfitting cause models to become worse regardless of the complexity of data, and if so, why is this the case?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Related:&lt;/strong&gt; Followup to the question above, "&lt;a href="http://datascience.stackexchange.com/questions/361/when-is-a-model-underfitted"&gt;When is a Model Underfitted?&lt;/a&gt;"&lt;/p&gt;
', 'machine-learning  predictive-modeling', NULL, '''/a'':87C ''/p'':66C,88C ''/questions/361/when-is-a-model-underfitted'':81C ''/strong'':71C ''bad'':4A ''becom'':50C ''capac'':24C ''case'':65C ''caus'':47C ''certain'':43C ''complex'':44C,55C ''data'':57C ''datascience.stackexchange.com'':80C ''datascience.stackexchange.com/questions/361/when-is-a-model-underfitted'':79C ''followup'':72C ''general'':26C ''hr'':67C ''href'':78C ''improv'':40C ''learn'':7A,10B ''limit'':28C ''logic'':15C ''machin'':6A,9B ''machine-learn'':8B ''mean'':33C ''might'':31C ''model'':13B,22C,38C,48C,85C ''often'':16C ''overfit'':3A,20C,35C,46C ''p'':14C,68C ''predict'':12B ''predictive-model'':11B ''question'':75C ''regardless'':52C ''relat'':70C ''state'':17C ''stop'':36C ''strong'':69C ''though'':29C ''underfit'':86C ''wors'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (64, 2, '2014-05-14 18:37:52.333', 12, NULL, NULL, '&lt;p&gt;Overfitting, in a nutshell, means take into account &lt;strong&gt;too much&lt;/strong&gt; information from your data and/or prior knowledge, and use it in a model. To make it more straightforward, consider the following example: you''re hired by some scientists to provide them with a model to predict the growth of some kind of plants. The scientists have given you information collected from their work 
with such plants throughout a whole year, and they shall continuously give you information on the future development of their plantation.&lt;/p&gt;

&lt;p&gt;So, you run through the data received, and build up a model out of it. Now suppose that, in your model, you considered just as many characteristics as possible to always find out the exact behavior of the plants you saw in the initial dataset. Now, as the production continues, you''ll always take into account those characteristics, and will produce very &lt;em&gt;fine-grained&lt;/em&gt; results. However, if the plantation eventually suffer from some seasonal change, the results you will receive may fit your model in such a way that your predictions will begin to fail (either saying that the growth will slow down, while it shall actually speed up, or the opposite).&lt;/p&gt;

&lt;p&gt;Apart from being unable to detect such small variations, and to usually classify your entries incorrectly, the &lt;em&gt;fine-grain&lt;/em&gt; on the model, i.e., the great amount of variables, may cause the processing to be too costly. Now, imagine that your data is already complex. Overfitting your model to the data not only will make the classification/evaluation very complex, but will most probably make you error the prediction over the slightest variation you may have on the input.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit&lt;/strong&gt;: &lt;a href="https://www.youtube.com/watch?v=DQWI1kvmwRg"&gt;This&lt;/a&gt; might as well be of some use, perhaps adding dynamicity to the above explanation :D&lt;/p&gt;
', NULL, NULL, '''/a'':298C ''/em'':156C,228C ''/p'':88C,205C,287C,314C ''/strong'':13C,291C ''/watch?v=dqwi1kvmwrg'':296C ''account'':9C,145C ''actual'':199C ''ad'':307C ''alreadi'':252C ''alway'':120C,142C ''amount'':235C ''and/or'':18C ''apart'':207C ''begin'':185C ''behavior'':125C ''build'':98C ''caus'':239C ''chang'':167C ''characterist'':116C,147C ''classifi'':219C ''classification/evaluation'':265C ''collect'':63C ''complex'':253C,267C ''consid'':32C,112C ''continu'':77C,139C ''cost'':245C ''d'':313C ''data'':17C,95C,250C,259C ''dataset'':134C ''detect'':212C ''develop'':84C ''dynam'':308C ''edit'':290C ''either'':188C ''em'':152C,224C ''entri'':221C ''error'':274C ''eventu'':162C ''exact'':124C ''exampl'':35C ''explan'':312C ''fail'':187C ''find'':121C ''fine'':154C,226C ''fine-grain'':153C,225C ''fit'':174C ''follow'':34C ''futur'':83C ''give'':78C ''given'':60C ''grain'':155C,227C ''great'':234C ''growth'':51C,192C ''hire'':38C ''howev'':158C ''href'':293C ''i.e'':232C ''imagin'':247C ''incorrect'':222C ''inform'':14C,62C,80C ''initi'':133C ''input'':286C ''kind'':54C ''knowledg'':20C ''ll'':141C ''make'':28C,263C,272C ''mani'':115C ''may'':173C,238C,282C ''mean'':6C ''might'':299C ''model'':26C,47C,101C,110C,176C,231C,256C ''much'':12C ''nutshel'':5C ''opposit'':204C ''overfit'':2C,254C ''p'':1C,89C,206C,288C ''perhap'':306C ''plant'':56C,69C,128C ''plantat'':87C,161C ''possibl'':118C ''predict'':49C,183C,276C ''prior'':19C ''probabl'':271C ''process'':241C ''produc'':150C ''product'':138C ''provid'':43C ''re'':37C ''receiv'':96C,172C ''result'':157C,169C ''run'':92C ''saw'':130C ''say'':189C ''scientist'':41C,58C ''season'':166C ''shall'':76C,198C ''slightest'':279C ''slow'':194C ''small'':214C ''speed'':200C ''straightforward'':31C ''strong'':10C,289C ''suffer'':163C ''suppos'':106C ''take'':7C,143C ''throughout'':70C ''unabl'':210C ''use'':22C,305C ''usual'':218C ''variabl'':237C ''variat'':215C,280C ''way'':180C ''well'':301C ''whole'':72C ''work'':66C ''www.youtube.com'':295C ''www.youtube.com/watch?v=dqwi1kvmwrg'':294C ''year'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (69, 1, '2014-05-14 20:03:15.233', 2, 59, 'Is it possible to automate generating reproducibility documentation?', '&lt;p&gt;First, think it''s worth me stating what I mean by replication &amp;amp; reproducibility:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Replication of analysis A results in an exact copy of all inputs and processes that are supply and result in incidental outputs in analysis B.&lt;/li&gt;
&lt;li&gt;Reproducibility of analysis A results in inputs, processes, and outputs that are semantically incidental to analysis A, without access to the exact inputs and processes.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Putting aside how easy it might be to replicate a given build, especially an ad-hoc one, to me replication always possible if it''s planned for and worth doing. That said, it is unclear to me is how to execute a data science workflow that allows for reproducibility.&lt;/p&gt;

&lt;p&gt;The closet comparison I''m able to think of is &lt;a href="http://en.wikipedia.org/wiki/Documentation_generator" rel="nofollow"&gt;documentation generators&lt;/a&gt; that generates software documentation intended for programmers - though the main difference I see is that in theory, if two sets of analysis ran the "reproducibility documentation generators" the documentation should match.&lt;/p&gt;

&lt;p&gt;Another issue, is that while I get the concept of reproducibility documentation, I am having a hard time imagining what it would look like in usable form without just being a guide to replicating the analysis.&lt;/p&gt;

&lt;p&gt;Lastly, whole intent of this is to understand if it''s possible to "bake-in" reproducibility documentation as you build out a stack, not after the stack is built.&lt;/p&gt;

&lt;p&gt;So, Is it possible to automate generating reproducibility documentation, and if so how, and what would it look like?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;UPDATE:&lt;/strong&gt; Please note that this is the second draft of this question and that &lt;a href="http://datascience.stackexchange.com/users/178/christopher-louden"&gt;Christopher Louden&lt;/a&gt; was kind enough to let me edit the question after I realized it was likely the first draft was unclear. Thanks!&lt;/em&gt;&lt;/p&gt;
', 'processing', NULL, '''/a'':154C,304C ''/em'':326C ''/li'':53C,80C ''/p'':25C,133C,186C,224C,256C,277C,327C ''/strong'':283C ''/ul'':81C ''/users/178/christopher-louden'':301C ''/wiki/documentation_generator'':149C ''abl'':140C ''access'':73C ''ad'':98C ''ad-hoc'':97C ''allow'':130C ''alway'':104C ''amp'':23C ''analysi'':30C,51C,57C,70C,176C,223C ''anoth'':188C ''asid'':84C ''autom'':5A,263C ''b'':52C ''bake'':240C ''bake-in'':239C ''build'':94C,246C ''built'':255C ''christoph'':302C ''closet'':136C ''comparison'':137C ''concept'':196C ''copi'':36C ''data'':126C ''datascience.stackexchange.com'':300C ''datascience.stackexchange.com/users/178/christopher-louden'':299C ''differ'':165C ''document'':8A,152C,158C,180C,183C,199C,243C,266C ''draft'':291C,322C ''easi'':86C ''edit'':311C ''em'':281C ''en.wikipedia.org'':148C ''en.wikipedia.org/wiki/documentation_generator'':147C ''enough'':307C ''especi'':95C ''exact'':35C,76C ''execut'':124C ''first'':11C,321C ''form'':214C ''generat'':6A,153C,156C,181C,264C ''get'':194C ''given'':93C ''guid'':219C ''hard'':204C ''hoc'':99C ''hr'':278C ''href'':146C,298C ''imagin'':206C ''incident'':48C,68C ''input'':39C,61C,77C ''intend'':159C ''intent'':228C ''issu'':189C ''kind'':306C ''last'':226C ''let'':309C ''li'':27C,54C ''like'':211C,276C,319C ''look'':210C,275C ''louden'':303C ''m'':139C ''main'':164C ''match'':185C ''mean'':20C ''might'':88C ''nofollow'':151C ''note'':285C ''one'':100C ''output'':49C,64C ''p'':10C,82C,134C,187C,225C,257C,279C ''plan'':109C ''pleas'':284C ''possibl'':3A,105C,237C,261C ''process'':9B,41C,62C,79C ''programm'':161C ''put'':83C ''question'':294C,313C ''ran'':177C ''realiz'':316C ''rel'':150C ''replic'':22C,28C,91C,103C,221C ''reproduc'':7A,24C,55C,132C,179C,198C,242C,265C ''result'':32C,46C,59C ''said'':115C ''scienc'':127C ''second'':290C ''see'':167C ''semant'':67C ''set'':174C ''softwar'':157C ''stack'':249C,253C ''state'':17C ''strong'':280C ''suppli'':44C ''thank'':325C ''theori'':171C ''think'':12C,142C ''though'':162C ''time'':205C ''two'':173C ''ul'':26C ''unclear'':118C,324C ''understand'':233C ''updat'':282C ''usabl'':213C ''whole'':227C ''without'':72C,215C ''workflow'':128C ''worth'':15C,112C ''would'':209C,273C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (70, 2, '2014-05-14 22:03:50.597', 2, NULL, NULL, '&lt;p&gt;To be reproducible without being just a replication, you would need to redo the experiment with new data, following the same technique as before.  The work flow is not as important as the techniques used.  Sample data in the same way, use the same type of models.  It doesn''t matter if you switch from one language to another, so long as the models and the data manipulations are the same.&lt;/p&gt;

&lt;p&gt;This type of replication will show that the results you got in the first experiment are less likely to be a fluke than they were earlier.&lt;/p&gt;
', NULL, NULL, '''/p'':73C,101C ''anoth'':60C ''data'':19C,38C,68C ''doesn'':50C ''earlier'':100C ''experi'':16C,89C ''first'':88C ''flow'':28C ''fluke'':96C ''follow'':20C ''got'':85C ''import'':32C ''languag'':58C ''less'':91C ''like'':92C ''long'':62C ''manipul'':69C ''matter'':52C ''model'':48C,65C ''need'':12C ''new'':18C ''one'':57C ''p'':1C,74C ''redo'':14C ''replic'':9C,78C ''reproduc'':4C ''result'':83C ''sampl'':37C ''show'':80C ''switch'':55C ''techniqu'':23C,35C ''type'':46C,76C ''use'':36C,43C ''way'':42C ''without'':5C ''work'':27C ''would'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (71, 1, '2014-05-14 22:12:37.203', 10, 280, 'When are p-values deceptive?', '&lt;p&gt;What are the data conditions that we should watch out for, where p-values may not be the best way of deciding statistical significance?  Are there specific problem types that fall into this category?&lt;/p&gt;
', 'bigdata  statistics', NULL, '''/p'':45C ''best'':29C ''bigdata'':7B ''categori'':44C ''condit'':14C ''data'':13C ''decept'':6A ''decid'':32C ''fall'':41C ''may'':25C ''p'':4A,9C,23C ''p-valu'':3A,22C ''problem'':38C ''signific'':34C ''specif'':37C ''statist'':8B,33C ''type'':39C ''valu'':5A,24C ''watch'':18C ''way'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (72, 2, '2014-05-14 22:40:40.363', 3, NULL, NULL, '&lt;p&gt;One algorithm that can be used for this is the &lt;a href="http://en.wikipedia.org/wiki/K-means_clustering" rel="nofollow"&gt;k-means clustering algorithm&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Basically:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Randomly choose k datapoints from your set, m_1, ..., m_k.&lt;/li&gt;
&lt;li&gt;&lt;p&gt;"Until convergence":&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Assign your data points to k clusters, where cluster i is the set of points for which m_i is the closest of your current means&lt;/li&gt;
&lt;li&gt;Replace each m_i by the mean of all points assigned to cluster i.&lt;/li&gt;
&lt;/ol&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;It is good practice to repeat this algorithm several times, then choose the outcome that minimizes distances between the points of each cluster i and the center m_i.&lt;/p&gt;

&lt;p&gt;Of course, you have to know k to start here; you can use cross-validation to choose this parameter, though.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (146, 5, '2014-05-18 15:01:24.08', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (85, 2, '2014-05-15 08:44:47.327', 3, NULL, NULL, '&lt;p&gt;See also &lt;a href="http://datascience.stackexchange.com/questions/71/when-are-p-values-deceptive/84#84"&gt;When are p-values deceptive?&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;When there are a lot of variables that can be tested for pair-wise correlation (for example), the replacement is to use any of the corrections for &lt;a href="http://en.wikipedia.org/wiki/False_discovery_rate" rel="nofollow"&gt;False discovery rate&lt;/a&gt; (to limit probability that any given discovery is false) or &lt;a href="http://en.wikipedia.org/wiki/Familywise_error_rate" rel="nofollow"&gt;Familywise error rate&lt;/a&gt; (to limit probability of one or more false discoveries).  For example, you might use the HolmâBonferroni method.&lt;/p&gt;

&lt;p&gt;In the case of a large sample rather than a lot of variables, something else is needed.  As Christopher said, magnitude of effect a way to treat this.  Combining these two ideas, you might use a confidence interval around your magnitude of effect, and apply a false discovery rate correction to the p-value of the confidence interval.  The effects for which even the lowest bound of the corrected confidence interval is high are likely to be strong effects, regardless of huge data set size.  I am not aware of any published paper that combines confidence intervals with false discovery rate correction in this way, but it seems like a straightforward and intuitively understandable approach.&lt;/p&gt;

&lt;p&gt;To make this even better, use a non-parametric way to estimate confidence intervals.  Assuming a distribution is likely to give very optimistic estimates here, and even fitting a distribution to the data is likely to be inaccurate.  Since the information about the shape of the distribution past the edges of the confidence interval comes from a relatively small subsample of the data, this is where it really pays to be careful.  You can use bootstrapping to get a non-parametric confidence interval.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (73, 2, '2014-05-14 22:43:23.587', 2, NULL, NULL, '&lt;p&gt;You shouldn''t consider the p-value out of context.&lt;/p&gt;

&lt;p&gt;One rather basic point (as illustrated by &lt;a href="http://xkcd.com/882/" rel="nofollow"&gt;xkcd&lt;/a&gt;) is that you need to consider how many tests you''re actually doing.  Obviously, you shouldn''t be shocked to see p &amp;lt; 0.05 for one out of 20 tests, even if the null hypothesis is true every time.  &lt;/p&gt;

&lt;p&gt;A more subtle example of this occurs in high-energy physics, and is known as the &lt;a href="https://en.wikipedia.org/wiki/Look-elsewhere_effect" rel="nofollow"&gt;look-elsewhere effect&lt;/a&gt;.  The larger the parameter space you search for a signal that might represent a new particle, the more likely you are to see an apparent signal that''s really just due to random fluctuations. &lt;/p&gt;
', NULL, NULL, '''/882/'':26C ''/a'':30C,100C ''/p'':13C,70C,135C ''/wiki/look-elsewhere_effect'':93C ''0.05'':54C ''20'':59C ''actual'':42C ''appar'':125C ''basic'':17C ''consid'':5C,36C ''context'':12C ''due'':131C ''effect'':99C ''elsewher'':98C ''en.wikipedia.org'':92C ''en.wikipedia.org/wiki/look-elsewhere_effect'':91C ''energi'':82C ''even'':61C ''everi'':68C ''exampl'':75C ''fluctuat'':134C ''high'':81C ''high-energi'':80C ''href'':23C,90C ''hypothesi'':65C ''illustr'':20C ''known'':86C ''larger'':102C ''like'':119C ''look'':97C ''look-elsewher'':96C ''lt'':53C ''mani'':38C ''might'':112C ''need'':34C ''new'':115C ''nofollow'':28C,95C ''null'':64C ''obvious'':44C ''occur'':78C ''one'':15C,56C ''p'':1C,8C,14C,52C,71C ''p-valu'':7C ''paramet'':104C ''particl'':116C ''physic'':83C ''point'':18C ''random'':133C ''rather'':16C ''re'':41C ''realli'':129C ''rel'':27C,94C ''repres'':113C ''search'':107C ''see'':51C,123C ''shock'':49C ''shouldn'':3C,46C ''signal'':110C,126C ''space'':105C ''subtl'':74C ''test'':39C,60C ''time'':69C ''true'':67C ''valu'':9C ''xkcd'':29C ''xkcd.com'':25C ''xkcd.com/882/'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (74, 2, '2014-05-14 22:58:11.583', 2, NULL, NULL, '&lt;p&gt;One thing you should be aware of is the sample size you are using. Very large samples, such as economists using census data, will lead to deflated p-values. This paper &lt;a href="http://galitshmueli.com/system/files/Print%20Version.pdf" rel="nofollow" title="Too Big to Fail: Large Samples and the p-Value Problem"&gt;"Too Big to Fail: Large Samples and the p-Value Problem"&lt;/a&gt; covers some of the issues. &lt;/p&gt;
', NULL, NULL, '''/a'':66C ''/p'':72C ''/system/files/print%20version.pdf'':38C ''awar'':7C ''big'':43C,55C ''census'':23C ''cover'':67C ''data'':24C ''deflat'':28C ''economist'':21C ''fail'':45C,57C ''galitshmueli.com'':37C ''galitshmueli.com/system/files/print%20version.pdf'':36C ''href'':35C ''issu'':71C ''larg'':17C,46C,58C ''lead'':26C ''nofollow'':40C ''one'':2C ''p'':1C,30C,51C,63C ''p-valu'':29C,50C,62C ''paper'':33C ''problem'':53C,65C ''rel'':39C ''sampl'':11C,18C,47C,59C ''size'':12C ''thing'':3C ''titl'':41C ''use'':15C,22C ''valu'':31C,52C,64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (75, 1, '2014-05-15 00:26:11.387', 4, 101, 'Is there a replacement for small p-values in big data?', '&lt;p&gt;If small p-values are plentiful in big data, what is a comparable replacement for p-values in data with million of samples?&lt;/p&gt;
', 'statistics', NULL, '''/p'':40C ''big'':11A,23C ''compar'':28C ''data'':12A,24C,35C ''million'':37C ''p'':8A,14C,18C,32C ''p-valu'':7A,17C,31C ''plenti'':21C ''replac'':4A,29C ''sampl'':39C ''small'':6A,16C ''statist'':13B ''valu'':9A,19C,33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (76, 1, '2014-05-15 00:39:33.433', 6, 114, 'Which Big Data technology stack is most suitable for processing tweets, extracting/expanding URLs and pushing (only) new links into 3rd party system?', '&lt;p&gt;&lt;em&gt;(Note: Pulled this question from the &lt;a href="http://area51.stackexchange.com/proposals/55053/data-science/57398#57398"&gt;list of questions in Area51&lt;/a&gt;, but believe the question is self explanatory. That said, believe I get the general intent of the question, and as a result likely able to field any questions on the question that might pop-up.)&lt;/em&gt; &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Which Big Data technology stack is most suitable for processing tweets, extracting/expanding URLs and pushing (only) new links into 3rd party system?&lt;/strong&gt;&lt;/p&gt;
', 'bigdata  tools  data-stream-mining', NULL, '''/a'':47C ''/em'':84C ''/p'':85C,111C ''/proposals/55053/data-science/57398#57398'':41C ''/strong'':110C ''3rd'':20A,107C ''abl'':71C ''area51'':46C ''area51.stackexchange.com'':40C ''area51.stackexchange.com/proposals/55053/data-science/57398#57398'':39C ''believ'':49C,57C ''big'':2A,89C ''bigdata'':23B ''data'':3A,26B,90C ''data-stream-min'':25B ''em'':30C ''explanatori'':54C ''extracting/expanding'':12A,99C ''field'':73C ''general'':61C ''get'':59C ''href'':38C ''intent'':62C ''like'':70C ''link'':18A,105C ''list'':42C ''might'':80C ''mine'':28B ''new'':17A,104C ''note'':31C ''p'':29C,86C ''parti'':21A,108C ''pop'':82C ''pop-up'':81C ''process'':10A,97C ''pull'':32C ''push'':15A,102C ''question'':34C,44C,51C,65C,75C,78C ''result'':69C ''said'':56C ''self'':53C ''stack'':5A,92C ''stream'':27B ''strong'':87C ''suitabl'':8A,95C ''system'':22A,109C ''technolog'':4A,91C ''tool'':24B ''tweet'':11A,98C ''url'':13A,100C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (77, 1, '2014-05-15 01:22:35.167', 7, 249, 'Is this Neo4j comparison to RDBMS execution time correct?', '&lt;p&gt;&lt;strong&gt;Background:&lt;/strong&gt; Following is from the book &lt;a href="http://rads.stackoverflow.com/amzn/click/1449356265"&gt;Graph Databases&lt;/a&gt;, which covers a performance test mentioned in the book &lt;a href="http://rads.stackoverflow.com/amzn/click/1617290769"&gt;Neo4j in Action&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Relationships in a graph naturally form paths. Querying, or
  traversing, the graph involves following paths. Because of the
  fundamentally path-oriented nature of the datamodel, the majority of
  path-based graph database operations are highly aligned with the way
  in which the data is laid out, making them extremely efficient. In
  their book Neo4j in Action, Partner and Vukotic perform an experiment
  using a relational store and Neo4j.&lt;/p&gt;
  
  &lt;p&gt;The comparison shows that the graph database is substantially quicker
  for connected data than a relational store.Partner and Vukoticâs
  experiment seeks to find friends-of-friends in a social network, to a
  maximum depth of five. Given any two persons chosen at random, is
  there a path that connects them which is at most five relationships
  long? For a social network containing 1,000,000 people, each with
  approximately 50 friends, the results strongly suggest that graph
  databases are the best choice for connected data, as we see in Table
  2-1.&lt;/p&gt;
  
  &lt;p&gt;Table 2-1. Finding extended friends in a relational database versus efficient finding in Neo4j&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Depth   RDBMS Execution time (s)    Neo4j Execution time (s)     Records returned
2       0.016                       0.01                         ~2500    
3       30.267                      0.168                        ~110,000 
4       1543.505                    1.359                        ~600,000 
5       Unfinished                  2.132                        ~800,000
&lt;/code&gt;&lt;/pre&gt;
  
  &lt;p&gt;At depth two (friends-of-friends) both the relational database and the graph database perform well enough for us to consider using them in an online system. While the Neo4j query runs in two-thirds the time of the relational one, an end-user would barely notice the the difference in milliseconds between the two. By the time we reach depth three (friend-of-friend-of-friend), however, itâs clear that the relational database can no longer deal with the query in a reasonable timeframe: the thirty seconds it takes to complete would be completely unacceptable for an online system. In contrast, Neo4jâs response time remains relatively flat: just a fraction of a second to perform the queryâdefinitely quick enough for an online system.&lt;/p&gt;
  
  &lt;p&gt;At depth four the relational database exhibits crippling latency,
  making it practically useless for an online system. Neo4jâs timings
  have deteriorated a little too, but the latency here is at the
  periphery of being acceptable for a responsive online system. Finally,
  at depth five, the relational database simply takes too long to
  complete the query. Neo4j, in contrast, returns a result in around two
  seconds. At depth five, it transpires almost the entire network is our
  friend: for many real-world use cases, weâd likely trim the results,
  and the timings.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Questions are:&lt;/strong&gt; &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Is this a reasonable test to emulate what one might except to find in a social network? &lt;em&gt;(Meaning do real social networks normally have nodes with approximately 50 friends for example; seems like the "&lt;a href="http://en.wikipedia.org/wiki/The_rich_get_richer_%28statistics%29"&gt;rich get richer&lt;/a&gt;" model would be more natural for social networks, though might be wrong.)&lt;/em&gt;&lt;/li&gt;
&lt;li&gt;Regardless of the naturalness of the emulation, is there any reason to believe the results are off, or unreproducible? &lt;/li&gt;
&lt;/ul&gt;
', 'databases  nosql  neo4j', NULL, '''-1'':216C,221C ''/a'':29C,47C,550C ''/amzn/click/1449356265'':26C ''/amzn/click/1617290769'':43C ''/blockquote'':498C ''/code'':267C ''/em'':563C ''/li'':564C,585C ''/p'':48C,121C,217C,234C,401C,497C,504C ''/pre'':268C ''/strong'':16C,503C ''/ul'':586C ''/wiki/the_rich_get_richer_%28statistics%29'':546C ''0.01'':250C ''0.016'':249C ''0.168'':254C ''000'':188C,189C,256C,261C,266C ''1'':187C ''1.359'':259C ''110'':255C ''1543.505'':258C ''2'':215C,220C,248C ''2.132'':264C ''2500'':251C ''3'':252C ''30.267'':253C ''4'':257C ''5'':262C ''50'':194C,535C ''600'':260C ''800'':265C ''accept'':438C ''action'':46C,108C ''align'':88C ''almost'':474C ''approxim'':193C,534C ''around'':466C ''background'':15C ''bare'':318C ''base'':82C ''believ'':578C ''best'':205C ''blockquot'':49C ''book'':21C,38C,105C ''case'':487C ''choic'':206C ''chosen'':165C ''clear'':344C ''code'':236C ''comparison'':4A,124C ''complet'':366C,369C,456C ''connect'':134C,173C,208C ''consid'':291C ''contain'':186C ''contrast'':376C,461C ''correct'':9A ''cover'':31C ''crippl'':410C ''d'':489C ''data'':95C,135C,209C ''databas'':10B,28C,84C,129C,202C,228C,280C,284C,348C,408C,450C ''datamodel'':76C ''deal'':352C ''definit'':394C ''depth'':158C,237C,271C,333C,404C,446C,470C ''deterior'':424C ''differ'':322C ''effici'':102C,230C ''em'':524C ''emul'':513C,572C ''en.wikipedia.org'':545C ''en.wikipedia.org/wiki/the_rich_get_richer_%28statistics%29'':544C ''end'':315C ''end-us'':314C ''enough'':287C,396C ''entir'':476C ''exampl'':538C ''except'':517C ''execut'':7A,239C,243C ''exhibit'':409C ''experi'':114C,143C ''extend'':223C ''extrem'':101C ''final'':444C ''find'':146C,222C,231C,519C ''five'':160C,179C,447C,471C ''flat'':383C ''follow'':17C,64C ''form'':56C ''four'':405C ''fraction'':386C ''friend'':148C,150C,195C,224C,274C,276C,336C,338C,340C,480C,536C ''friend-of-friend-of-friend'':335C ''friends-of-friend'':147C,273C ''fundament'':69C ''get'':548C ''given'':161C ''graph'':27C,54C,62C,83C,128C,201C,283C ''high'':87C ''howev'':341C ''href'':23C,40C,543C ''involv'':63C ''itâ'':342C ''laid'':97C ''latenc'':411C,430C ''li'':506C,565C ''like'':490C,540C ''littl'':426C ''long'':181C,454C ''longer'':351C ''major'':78C ''make'':99C,412C ''mani'':482C ''maximum'':157C ''mean'':525C ''mention'':35C ''might'':516C,560C ''millisecond'':324C ''model'':551C ''natur'':55C,73C,555C,569C ''neo4j'':3A,12B,44C,106C,120C,233C,242C,300C,459C ''neo4jâ'':377C,420C ''network'':154C,185C,477C,523C,529C,558C ''node'':532C ''normal'':530C ''nosql'':11B ''notic'':319C ''one'':312C,515C ''onlin'':296C,373C,399C,418C,442C ''oper'':85C ''orient'':72C ''p'':13C,50C,122C,218C,269C,402C,499C ''partner'':109C ''path'':57C,65C,71C,81C,171C ''path-bas'':80C ''path-ori'':70C ''peopl'':190C ''perform'':33C,112C,285C,391C ''peripheri'':435C ''person'':164C ''practic'':414C ''pre'':235C ''queri'':58C,301C,355C,458C ''queryâ'':393C ''question'':501C ''quick'':395C ''quicker'':132C ''rads.stackoverflow.com'':25C,42C ''rads.stackoverflow.com/amzn/click/1449356265'':24C ''rads.stackoverflow.com/amzn/click/1617290769'':41C ''random'':167C ''rdbms'':6A,238C ''reach'':332C ''real'':484C,527C ''real-world'':483C ''reason'':358C,510C,576C ''record'':246C ''regardless'':566C ''relat'':117C,138C,227C,279C,311C,347C,382C,407C,449C ''relationship'':51C,180C ''remain'':381C ''respons'':379C,441C ''result'':197C,464C,493C,580C ''return'':247C,462C ''rich'':547C ''richer'':549C ''run'':302C ''second'':362C,389C,468C ''see'':212C ''seek'':144C ''seem'':539C ''show'':125C ''simpli'':451C ''social'':153C,184C,522C,528C,557C ''store'':118C ''store.partner'':139C ''strong'':14C,198C,500C ''substanti'':131C ''suggest'':199C ''system'':297C,374C,400C,419C,443C ''tabl'':214C,219C ''take'':364C,452C ''test'':34C,511C ''third'':306C ''thirti'':361C ''though'':559C ''three'':334C ''time'':8A,240C,244C,308C,330C,380C,422C,496C ''timefram'':359C ''transpir'':473C ''travers'':60C ''trim'':491C ''two'':163C,272C,305C,327C,467C ''two-third'':304C ''ul'':505C ''unaccept'':370C ''unfinish'':263C ''unreproduc'':584C ''us'':289C ''use'':115C,292C,486C ''useless'':415C ''user'':316C ''versus'':229C ''vukot'':111C ''vukoticâ'':141C ''way'':91C ''well'':286C ''weâ'':488C ''world'':485C ''would'':317C,367C,552C ''wrong'':562C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8943, 1, '2015-11-20 14:15:37.753', 1, 36, 'What''s the most robust way to predict the value of some noisy time series data?', '&lt;p&gt;I have some time series data (time, value) such as:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;281,97.0
284,98.0
287,98.0
290,98.0
293,98.0
297,99.0
309,100.0
320,101.0
333,101.0
346,103.0
359,104.0
372,105.0
385,107.0
396,108.0
405,108.0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am trying to predict when the value will be 160.&lt;/p&gt;

&lt;p&gt;The data is somewhat noisy. The values go mostly up, but could go down sharply every now and then, or stay the same for a short time. When these distortions occur, the prediction is far off for a period of time.&lt;/p&gt;

&lt;p&gt;What algorithms or heuristics would work the best to deliver a robust prediction that doesn''t vary to much?&lt;/p&gt;

&lt;p&gt;I am currently using a linear fit over a time window of 60 data points. The results are not stable enough.&lt;/p&gt;
', 'predictive-modeling  time-series', NULL, '''/code'':67C ''/p'':34C,81C,124C,145C,168C ''/pre'':68C ''100.0'':50C ''101.0'':52C,54C ''103.0'':56C ''104.0'':58C ''105.0'':60C ''107.0'':62C ''108.0'':64C,66C ''160'':80C ''281'':37C ''284'':39C ''287'':41C ''290'':43C ''293'':45C ''297'':47C ''309'':49C ''320'':51C ''333'':53C ''346'':55C ''359'':57C ''372'':59C ''385'':61C ''396'':63C ''405'':65C ''60'':159C ''97.0'':38C ''98.0'':40C,42C,44C,46C ''99.0'':48C ''algorithm'':127C ''best'':133C ''code'':36C ''could'':94C ''current'':149C ''data'':16A,29C,84C,160C ''deliv'':135C ''distort'':112C ''doesn'':140C ''enough'':167C ''everi'':98C ''far'':117C ''fit'':153C ''go'':90C,95C ''heurist'':129C ''linear'':152C ''model'':19B ''most'':91C ''much'':144C ''noisi'':13A,87C ''occur'':113C ''p'':23C,69C,82C,125C,146C ''period'':121C ''point'':161C ''pre'':35C ''predict'':8A,18B,74C,115C,138C ''predictive-model'':17B ''result'':163C ''robust'':5A,137C ''seri'':15A,22B,28C ''sharpli'':97C ''short'':108C ''somewhat'':86C ''stabl'':166C ''stay'':103C ''time'':14A,21B,27C,30C,109C,123C,156C ''time-seri'':20B ''tri'':72C ''use'':150C ''valu'':10A,31C,77C,89C ''vari'':142C ''way'':6A ''window'':157C ''work'':131C ''would'':130C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5676, 2, '2015-05-03 04:22:43.507', 3, NULL, NULL, '&lt;p&gt;I have worked on building a fraud detection solution using text mining, so I understand the scenario that has led to this question. I''ll talk about the approach/techniques that is to be followed to build the fraud detection solution.I''ll divide it into 4 sections&lt;/p&gt;

&lt;hr&gt;

&lt;ul&gt;
&lt;li&gt;Build line of business fraud dictionary&lt;/li&gt;
&lt;li&gt;Fraud detection &amp;amp; scoring&lt;/li&gt;
&lt;li&gt;Tools&lt;/li&gt;
&lt;li&gt;Additional notes&lt;/li&gt;
&lt;/ul&gt;

&lt;hr&gt;

&lt;h3&gt;Building Fraud Dictionary:&lt;/h3&gt;

&lt;p&gt;As a first step you''ll have to identify fraud concepts with help from subject matter expert for the line of business in concern. Fraud concept is a person, characteristic, entity, or event that represents a suspicious scenario and similar concepts can be grouped together to form a concept category. Each concept is further represented by words, phrases, entities etc. The presence of these words/phrases in the document implies occurrence of fraud concept in that transaction. The end result of this exercise will be a fraud dictionary that is a repository of concepts and suspicious key words.&lt;/p&gt;

&lt;p&gt;For example considering the data set mentioned in the question, the &lt;code&gt;numeric patterns&lt;/code&gt; become the concept category, and &lt;code&gt;fictitious invoice numbers&lt;/code&gt; and &lt;code&gt;fictitiously-generated transaction amounts&lt;/code&gt; are 2 different concepts. There will be a keywords/phrases that is associated to this concept which should be captured onto the fraud dictionary.&lt;/p&gt;

&lt;p&gt;You can use NLP technique to build the dictionary. &lt;/p&gt;

&lt;p&gt;Steps:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1) Convert the sentence to lowercase

2) Remove stopwords, these are common words found in a language. Words like for, very, and, of, are, etc, are common stop words

3) Extract n-gram i.e., a contiguous sequence of n items from a given sequence of text. simply increasing n, model can be used to store more context

4) Assign a syntactic label (noun, verb etc.)

5) Knowledge extraction from text through semantic/syntactic analysis approach i.e., try to retain words that hold higher weight in a sentence like Noun/Verb
&lt;/code&gt;&lt;/pre&gt;

&lt;h3&gt;Fraud detection &amp;amp; scoring:&lt;/h3&gt;

&lt;p&gt;Phrases/Keywords to be semantically matched with Phrases/Keywords from Fraud Dictionary and Fraud concepts in transactions can be identified. At this point, care needs to be taken to maintain the context (negation, positive sense) in which a keyword/phrase has been used to avoid false positives.&lt;/p&gt;

&lt;p&gt;For each identified occurrence of Fraud concept in the transaction, a weight of 1 can be assigned. This is done for calculating a suspicious score for the transaction. Higher the score high suspecious.&lt;/p&gt;

&lt;p&gt;Note that using a combination of larger data set with high textual content, and extensive fraud dictionary would result in higher number potentially identified.&lt;/p&gt;

&lt;h3&gt;Tools:&lt;/h3&gt;

&lt;p&gt;You can consider using OpenNLP / StanfordNLP for Part of Speech tagging. Most of the programming language have supporting library for OpenNLP/StanfordNLP. You can choose the language based on your comfort. &lt;/p&gt;

&lt;h3&gt;Additional notes:&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;You can refer &lt;a href="http://datascience.stackexchange.com/a/5388/8465"&gt;here&lt;/a&gt; to get an idea of extracting concepts from sentences&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Fruther read my blog on &lt;a href="https://manoharswamynathan.wordpress.com/category/text-mining/" rel="nofollow"&gt;Text mining 101&lt;/a&gt; to learn more about &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;TM process overview&lt;/li&gt;
&lt;li&gt;Calculate term weight (TF-IDF)&lt;/li&gt;
&lt;li&gt;Similarity distance measure (Cosine)&lt;/li&gt;
&lt;li&gt;Overview of key text mining techniques&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Hope this helps.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (78, 2, '2014-05-15 01:46:28.467', 5, NULL, NULL, '&lt;p&gt;There is no replacement in the strict sense of the word.  Instead you should look at other measures.&lt;/p&gt;

&lt;p&gt;The other measures you look at depend on what you type of problem you are solving.  In general, if you have a small p-value, also consider the magnitude of the effect size.  It may be highly statistically significant but in practice meaningless.  It is also helpful to report the confidence interval of the effect size.&lt;/p&gt;

&lt;p&gt;I would consider &lt;a href="http://galitshmueli.com/system/files/Print%20Version.pdf"&gt;this paper&lt;/a&gt; as mentoned in DanC''s answer to &lt;a href="http://datascience.stackexchange.com/questions/71/when-are-p-values-deceptive"&gt;this question&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':91C,106C ''/p'':20C,79C,107C ''/questions/71/when-are-p-values-deceptive'':103C ''/system/files/print%20version.pdf'':88C ''also'':48C,68C ''answer'':97C ''confid'':73C ''consid'':49C,83C ''danc'':95C ''datascience.stackexchange.com'':102C ''datascience.stackexchange.com/questions/71/when-are-p-values-deceptive'':101C ''depend'':28C ''effect'':54C,77C ''galitshmueli.com'':87C ''galitshmueli.com/system/files/print%20version.pdf'':86C ''general'':39C ''help'':69C ''high'':59C ''href'':85C,100C ''instead'':13C ''interv'':74C ''look'':16C,26C ''magnitud'':51C ''may'':57C ''meaningless'':65C ''measur'':19C,24C ''menton'':93C ''p'':1C,21C,46C,80C ''p-valu'':45C ''paper'':90C ''practic'':64C ''problem'':34C ''question'':105C ''replac'':5C ''report'':71C ''sens'':9C ''signific'':61C ''size'':55C,78C ''small'':44C ''solv'':37C ''statist'':60C ''strict'':8C ''type'':32C ''valu'':47C ''word'':12C ''would'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (145, 4, '2014-05-18 14:58:34.853', 0, NULL, NULL, 'Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval etc.', NULL, NULL, '''analysi'':2C,61C,72C ''anoth'':34C ''call'':24C ''cluster'':1C,4C,26C,44C ''common'':56C ''data'':52C,60C ''etc'':75C ''exploratori'':51C ''field'':65C ''group'':9C,23C,43C ''imag'':71C ''includ'':66C ''inform'':73C ''learn'':68C ''machin'':67C ''main'':48C ''mani'':64C ''mine'':53C ''object'':13C,19C ''pattern'':69C ''recognit'':70C ''retriev'':74C ''sens'':32C ''set'':11C ''similar'':29C ''statist'':59C ''task'':7C,49C ''techniqu'':57C ''use'':62C ''way'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (79, 5, '2014-05-15 03:19:40.36', 0, NULL, NULL, '&lt;p&gt;Conceptually speaking, &lt;em&gt;data-mining&lt;/em&gt; can be thought of as one item (or set of skills and applications) in the toolkit of the data scientist.&lt;/p&gt;

&lt;p&gt;More specifically, data-mining is an activity that seeks patterns in large, complex data sets. It usually emphasizes algorithmic techniques, but may also involve any set of related skills, applications, or methodologies with that goal.&lt;/p&gt;

&lt;p&gt;In US-English colloquial speech, data-mining and data-collection are often used interchangeably.&lt;/p&gt;

&lt;p&gt;However, a main difference between these two related activities is &lt;em&gt;intentionality&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;&lt;em&gt;Definition inspired mostly by the contributions of &lt;a href="http://datascience.stackexchange.com/users/36/statsrus"&gt;@statsRus&lt;/a&gt; to Data Science.SE&lt;/em&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':117C ''/em'':8C,100C,121C ''/p'':29C,67C,86C,101C,122C ''/users/36/statsrus'':115C ''activ'':38C,96C ''algorithm'':50C ''also'':54C ''applic'':21C,61C ''collect'':81C ''colloqui'':73C ''complex'':44C ''conceptu'':2C ''contribut'':109C ''data'':6C,27C,34C,45C,76C,80C,119C ''data-collect'':79C ''data-min'':5C,33C,75C ''datascience.stackexchange.com'':114C ''datascience.stackexchange.com/users/36/statsrus'':113C ''definit'':104C ''differ'':91C ''em'':4C,98C,103C ''emphas'':49C ''english'':72C ''goal'':66C ''howev'':88C ''href'':112C ''inspir'':105C ''intent'':99C ''interchang'':85C ''involv'':55C ''item'':15C ''larg'':43C ''main'':90C ''may'':53C ''methodolog'':63C ''mine'':7C,35C,77C ''most'':106C ''often'':83C ''one'':14C ''p'':1C,30C,68C,87C,102C ''pattern'':41C ''relat'':59C,95C ''science.se'':120C ''scientist'':28C ''seek'':40C ''set'':17C,46C,57C ''skill'':19C,60C ''speak'':3C ''specif'':32C ''speech'':74C ''statsrus'':116C ''techniqu'':51C ''thought'':11C ''toolkit'':24C ''two'':94C ''us'':71C ''us-english'':70C ''use'':84C ''usual'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (82, 2, '2014-05-15 05:19:34.757', 13, NULL, NULL, '&lt;p&gt;Simply set, ''parallel'' means running concurrently on distinct resources (CPUs), while ''distributed'' means running across distinct computers, involving issues related to networks.&lt;/p&gt;

&lt;p&gt;Parallel computing using for instance &lt;a href="http://en.wikipedia.org/wiki/OpenMP"&gt;OpenMP&lt;/a&gt; is not distributed, while parallel computing with &lt;a href="http://en.wikipedia.org/wiki/Message_Passing_Interface"&gt;Message Passing&lt;/a&gt; is often distributed.&lt;/p&gt;

&lt;p&gt;Being in a ''distributed but not parallel'' setting would mean under-using resources so it is seldom encountered but it is conceptually possible.&lt;/p&gt;
', NULL, NULL, '''/a'':37C,52C ''/p'':24C,56C,82C ''/wiki/message_passing_interface'':49C ''/wiki/openmp'':35C ''across'':16C ''comput'':18C,27C,43C ''conceptu'':80C ''concurr'':7C ''cpus'':11C ''distinct'':9C,17C ''distribut'':13C,40C,55C,61C ''en.wikipedia.org'':34C,48C ''en.wikipedia.org/wiki/message_passing_interface'':47C ''en.wikipedia.org/wiki/openmp'':33C ''encount'':76C ''href'':32C,46C ''instanc'':30C ''involv'':19C ''issu'':20C ''mean'':5C,14C,67C ''messag'':50C ''network'':23C ''often'':54C ''openmp'':36C ''p'':1C,25C,57C ''parallel'':4C,26C,42C,64C ''pass'':51C ''possibl'':81C ''relat'':21C ''resourc'':10C,71C ''run'':6C,15C ''seldom'':75C ''set'':3C,65C ''simpli'':2C ''under-us'':68C ''use'':28C,70C ''would'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8656, 1, '2015-10-30 02:49:08.59', 3, 43, 'How to predict the duration of burst given several series', '&lt;p&gt;I''ve got sets of time series data collected from Weibo. It contains the number of posts under certain topics over a year. It can be found that from time to time there''re bursts of discussion on certain topic. What''s interesting is some burst die down quickly while others sustain on the agenda for quite a long time and die down gradually. How to predict and model the decays given a time series data. Also, I''ve got data of some exogenous data including time series data of links, pictures, and retweeting and original data in the community. Can I predict the duration of decay from these variables?&lt;/p&gt;
', 'bigdata  predictive-modeling  time-series', NULL, '''/p'':129C ''agenda'':73C ''also'':95C ''bigdata'':11B ''burst'':7A,53C,64C ''certain'':37C,57C ''collect'':27C ''communiti'':118C ''contain'':31C ''data'':26C,94C,99C,103C,107C,115C ''decay'':89C,125C ''die'':65C,80C ''discuss'':55C ''durat'':5A,123C ''exogen'':102C ''found'':45C ''given'':8A,90C ''got'':21C,98C ''gradual'':82C ''includ'':104C ''interest'':61C ''link'':109C ''long'':77C ''model'':14B,87C ''number'':33C ''origin'':114C ''other'':69C ''p'':18C ''pictur'':110C ''post'':35C ''predict'':3A,13B,85C,121C ''predictive-model'':12B ''quick'':67C ''quit'':75C ''re'':52C ''retweet'':112C ''seri'':10A,17B,25C,93C,106C ''set'':22C ''sever'':9A ''sustain'':70C ''time'':16B,24C,48C,50C,78C,92C,105C ''time-seri'':15B ''topic'':38C,58C ''variabl'':128C ''ve'':20C,97C ''weibo'':29C ''year'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5716, 2, '2015-05-07 20:36:54.033', 3, NULL, NULL, '&lt;p&gt;&lt;em&gt;(While I think that your question is usually not regarded as a good question for this site, I think that it should be preserved because almost everyone new to the field is similarly confused at first and it''s hard to find straight, sober, and balanced information about it.)&lt;/em&gt;&lt;/p&gt;

&lt;h2&gt;Is the Semantic Web dead? Are ontologies dead?&lt;/h2&gt;

&lt;p&gt;Many people moved away from the Semantic Web. On the other hand, there are still many using it. There''s always been a lot of confusion about where its value lies. There are use cases that genuinely benefit from the semantic web technologies but they are quite few and far between.&lt;/p&gt;

&lt;h2&gt;Linked Open Data&lt;/h2&gt;

&lt;p&gt;That''s partially why the subfield of Linked Open Data was created. You can see Linked Data as a more pragmatic stripped down version of the Semantic Web (or as a necessary enabler of the grander semantic web vision).&lt;/p&gt;

&lt;p&gt;Linked Data, while more pragmatic, still uses ontologies. It is just not so uptight about using OWL and designing your ontologies first and in a very formal way.&lt;/p&gt;

&lt;p&gt;A knowledge base such as Freebase (now being replaced by &lt;a href="http://www.wikidata.org/wiki/Wikidata:Main_Page" rel="nofollow"&gt;Wikidata&lt;/a&gt;) doesn''t even build on Semantic Web technologies (it''s always been available also in the form of RDF dumps though). On the other hand, even Freebase builds on concepts similar to those in the Semantic Web and it does have a form of ontologies.&lt;/p&gt;

&lt;h2&gt;Software to generate ontologies&lt;/h2&gt;

&lt;p&gt;There is no software that would create high-quality ontologies automatically for you. At least not in the sense of OWL/first-order logic ontologies. On the other hand, many kinds of software, including Cratilo mentioned by you, can help you to build a lower step in the &lt;a href="http://en.wikipedia.org/wiki/Semantic_spectrum" rel="nofollow"&gt;semantic spectrum&lt;/a&gt; such as a glossary or maybe even a folksonomy which can later be transformed into more of a taxonomy or ontology by other algorithms. There might be even software to create fullblown OWL ontologies but I think that''s still rather an active area of research.&lt;/p&gt;

&lt;h2&gt;Ontologies and methodologies&lt;/h2&gt;

&lt;p&gt;As always, it depends on what exactly your needs and goals are. In fields such as biomedicine and life sciences in general, they create complex formal ontologies because they have data with highly varied structure and ontologies help them keep things organized and usable. In contrast, an eshop might be better off sticking with normal relational database modelling and adding only global identifiers (URIs) in the spirit of Linked Data if they want to build a proper knowledge graph later. &lt;/p&gt;

&lt;p&gt;Even if you need ontologies you might want to skip the methodologies first. Especially if you are really new to the field and have little idea what your needs are.&lt;/p&gt;

&lt;h2&gt;Jena, Sesame, ...&lt;/h2&gt;

&lt;p&gt;Jena, Sesame, Virtuoso, etc. are &lt;a href="http://en.wikipedia.org/wiki/Triplestore" rel="nofollow"&gt;triplestores&lt;/a&gt; - they are used to store and query RDF. Most ontologies can be represented in RDF. Even those written in OWL. RDF has the semantic part, RDF/S, which allows you to formulate some basic ontologies. That might well be enough for start. Even RDF/S can get confusing and convoluted when you start to think about blank nodes, named graphs (are they fixed? are they dynamic?), etc.&lt;/p&gt;

&lt;p&gt;While I think that your question is usually not regarded as a good question for this site, I think that it should be preserved because almost everyone new to the field is similarly confused at first and it''s hard to find straight, sober, and balanced information about it.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (91, 2, '2014-05-15 11:56:43.607', 11, NULL, NULL, '&lt;p&gt;Well, I''m not sure if it is MapReduce that solves the problem, but it surely wouldn''t be MapReduce alone to solve all these questions you raised. But here are important things to take into account, and that make it &lt;em&gt;feasible&lt;/em&gt; to have such low latency on queries from all these TBs of data in different machines:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;distributed computing: by being distributed does not mean that the indexes are simply distributed in different machines, they are actually replicated along different clusters, which allows for lots of users performing different queries with low retrieval time (yes, huge companies can afford for that much of machines);&lt;/li&gt;
&lt;li&gt;caching: caches tremendously reduce execution time, be it for the crawling step, for the retrieval of pages, or for the ranking and exihibition of results;&lt;/li&gt;
&lt;li&gt;lots of tweaking: all the above and very efficient algorithms/solutions can only be effective if the implementation is also efficient. There are tons of (hard coded) optimizations, such as locality of reference, compression, caching; all of them usually appliable to different parts of the processing.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Considering that, lets try to address your questions:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;but I imagine it infeasible for the results of every single possible query to be indexed&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Yes, it would be, and actually is infeasible to have results for &lt;em&gt;every single possible query&lt;/em&gt;. There is an infinite number of terms in the world (even if you assume that only terms properly spelled will be entered), and there is an exponential number of queries from these &lt;code&gt;n -&amp;gt; inf&lt;/code&gt; terms (&lt;code&gt;2^n&lt;/code&gt;). So what is done? Caching. But if there are so many queries/results, which ones to cache? Caching policies. The most frequent/popular/relevant-for-the-user queries are the ones cached.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;wouldn''t the hardware latency in Google''s hardware be huge? Even if the data in Google were all stored in TB/s SSDs&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Nowdays, with such highly developed processors, people tend to think that every possible task that must finish within a second (or less), and that deals with so much data, must be processed by extremely powerful processors with multiple cores and lots of memory. However, the one thing &lt;em&gt;ruling&lt;/em&gt; market is money, and the investors are not interested in wasting it. So what is done?&lt;/p&gt;

&lt;p&gt;The preference is actually for having lots of machines, each using simple/accessible (in terms of cost) processors, which lowers the price of building up the multitude of clusters there are. And yes, it does work. The main bottleneck always boils down to disk, if you consider simple measurements of &lt;a href="http://i.stack.imgur.com/Uf6al.gif"&gt;performance&lt;/a&gt;. But once there are so many machines, one can afford to load things up to main memory, instead of working on hard disks.&lt;/p&gt;

&lt;p&gt;Memory cards are &lt;em&gt;expensive&lt;/em&gt; for us, mere human beings, but they are very cheap for enterprises that buy lots of such cards at once. Since it''s not costly, having much memory as needed to load indexes and keep caches at hand is not a problem. And since there are so many machines, there is no need for super fast processors, as you can direct queries to different places, and have clusters of machines responsible for attending &lt;em&gt;specific geographical regions&lt;/em&gt;, which allows for more &lt;em&gt;specialized&lt;/em&gt; data caching, and even better response times.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Does MapReduce help solve this problem?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Although I don''t think that using or not MapReduce is restricted information inside Google, I''m not conversant about this point. However, Google''s implementation of MapReduce (which is surely &lt;em&gt;not&lt;/em&gt; Hadoop) must have lots of optimizations, many involving the aspects discussed above. So, the architecture of MapReduce probably helps guiding how the computations are physically distributed, but there are many other points to be considered to justify such speed in querying time.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Okay, so I understand that popular searches can be cached in memory. But what about unpopular searches?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The graph below presents a curve of how the &lt;em&gt;kinds&lt;/em&gt; of queries occur. You can see that there are three main kinds of searches, each of them holding approximately 1/3 of the volume of queries (area below curve). The plot shows power law, and reinforces the fact that smaller queries are the most popular. The second third of queries are still feasible to process, since they hold few words. But the set of so-called &lt;em&gt;obscure queries&lt;/em&gt;, which usually consist of non-experienced users'' queries, are not a negligible part of the queries.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/CpcNf.jpg" alt="Heavy-tailed distribution"&gt;&lt;/p&gt;

&lt;p&gt;And there lies space for novel solutions. Since it''s not just one or two queries (but one third of them), they must have &lt;em&gt;relevant&lt;/em&gt; results. If you type in something &lt;em&gt;much too obscure&lt;/em&gt; in a Google search, it shan''t take longer to return a list of results, but will most probably show you something it &lt;em&gt;inferred&lt;/em&gt; you''d like to say. Or it may simply state that there was no document with such terms -- or even cut down your search to 32 words (which just happened to me in a random test here).&lt;/p&gt;

&lt;p&gt;There are dozens of appliable heuristics, which may be either to ignore some words, or to try to break the query into smaller ones, and gather the most &lt;em&gt;popular&lt;/em&gt; results. And all these solutions can be tailored and tweaked to respect &lt;em&gt;feasible waiting times&lt;/em&gt; of, say, less then a second? :D&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (96, 2, '2014-05-15 15:10:30.243', 6, NULL, NULL, '&lt;p&gt;For my part I can say that I use click frequency on i.e. eCommerce products. When you combine it with the days of the year it can even bring you great suggestions.&lt;/p&gt;

&lt;p&gt;i.e.: We have historical data from 1 year over 2 products (Snowboots[], Sandalettes[])&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Snowboots[1024,1253,652,123,50,12,8,4,50,148,345,896]
Sandalettes[23,50,73,100,534,701,1053,1503,1125,453,213,25]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where [0] = January&lt;/p&gt;

&lt;p&gt;As you can see, snowboots are much more searched in January than sandalettes, so you should suggest snowboots to someone searching shoes on your site or /we on january.&lt;/p&gt;

&lt;p&gt;You can also see if something is "fresh" at this time, like when people often click a unknown product it could be an insight for a new comming trend or something.&lt;/p&gt;

&lt;p&gt;That are just some examples where you could use click frequency as an insight. I think there are &lt;strong&gt;no rules&lt;/strong&gt; for what you can use or not in recommendations, &lt;strong&gt;as long as it makes sense&lt;/strong&gt;.&lt;/p&gt;
', NULL, NULL, '''/code'':78C ''/p'':34C,49C,84C,115C,148C,189C ''/pre'':79C ''/strong'':171C,188C ''/we'':112C ''0'':82C ''1'':42C ''100'':69C ''1024'':53C ''1053'':72C ''1125'':74C ''12'':58C ''123'':56C ''1253'':54C ''148'':62C ''1503'':73C ''2'':45C ''213'':76C ''23'':66C ''25'':77C ''345'':63C ''4'':60C ''453'':75C ''50'':57C,61C,67C ''534'':70C ''652'':55C ''701'':71C ''73'':68C ''8'':59C ''896'':64C ''also'':119C ''bring'':30C ''click'':11C,132C,159C ''code'':51C ''com'':144C ''combin'':19C ''could'':137C,157C ''data'':40C ''day'':23C ''ecommerc'':15C ''even'':29C ''exampl'':154C ''frequenc'':12C,160C ''fresh'':124C ''great'':32C ''histor'':39C ''i.e'':14C,36C ''insight'':140C,163C ''januari'':83C,96C,114C ''like'':128C ''long'':183C ''make'':186C ''much'':92C ''new'':143C ''often'':131C ''p'':1C,35C,80C,85C,116C,149C ''part'':4C ''peopl'':130C ''pre'':50C ''product'':16C,46C,135C ''recommend'':180C ''rule'':170C ''sandalett'':48C,65C,98C ''say'':7C ''search'':94C,106C ''see'':89C,120C ''sens'':187C ''shoe'':107C ''site'':110C ''snowboot'':47C,52C,90C,103C ''someon'':105C ''someth'':122C,147C ''strong'':168C,181C ''suggest'':33C,102C ''think'':165C ''time'':127C ''trend'':145C ''unknown'':134C ''use'':10C,158C,176C ''year'':26C,43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (83, 2, '2014-05-15 07:47:44.71', 7, NULL, NULL, '&lt;p&gt;I posted a pretty detailed answer on stackoverflow about when it is appropriate to use relational vs document (or NoSQL) database, here:  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;a href="http://stackoverflow.com/questions/13528216/motivations-for-using-relational-database-orm-or-document-database-odm/13599767#13599767"&gt;Motivations for using relational database / ORM or document database / ODM&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Summary:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;for small stuff, go with whatever tools you are familiar with&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;a few gigabytes is definitely small stuff: it doesn''t get big until it is too big to fit in a single &lt;a href="http://www.mysql.com/products/cluster/scalability.html"&gt;MySQL Cluster&lt;/a&gt; with a reasonable number of nodes (16-32), which means maybe 8-16TB data and a few million transactions per second (or a more conventional hard-drive-based database with up to 100''s of TB data and a few thousand transactions per second).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;if you''re stuck with another database (not MySQL Cluster), get more mileage out of it by throwing in FusionIO hardware.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;once you have data larger than a few TB &lt;em&gt;and&lt;/em&gt; faster than thousands of transactions per second, it is a good time to look at moving to logical sharding in the application code first and then to NoSQL.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://cassandra.apache.org/"&gt;Cassandra&lt;/a&gt; :)&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (84, 2, '2014-05-15 08:19:40.577', 7, NULL, NULL, '&lt;p&gt;You are asking about &lt;a href="http://en.wikipedia.org/wiki/Data_dredging"&gt;Data Dredging&lt;/a&gt;, which is what happens when testing a very large number of hypotheses against a data set, or testing hypotheses against a data set that were suggested by the same data.  &lt;/p&gt;

&lt;p&gt;In particular, check out &lt;a href="http://en.wikipedia.org/wiki/Multiple_comparisons"&gt;Multiple hypothesis hazard&lt;/a&gt;, and &lt;a href="http://en.wikipedia.org/wiki/Testing_hypotheses_suggested_by_the_data"&gt;Testing hypotheses suggested by the data&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The solution is to use some kind of correction for &lt;a href="http://en.wikipedia.org/wiki/False_discovery_rate"&gt;False discovery rate&lt;/a&gt; or &lt;a href="http://en.wikipedia.org/wiki/Familywise_error_rate"&gt;Familywise error rate&lt;/a&gt;, such as &lt;a href="http://en.wikipedia.org/wiki/Scheff%C3%A9%27s_method"&gt;ScheffÃ©''s method&lt;/a&gt; or the (very old-school) &lt;a href="http://en.wikipedia.org/wiki/Bonferroni_correction"&gt;Bonferroni correction&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In a somewhat less rigorous way, it may help to filter your discoveries by the confidence interval for the odds ratio (OR) for each statistical result.  If the 99% confidence interval for the odds ratio is 10-12, then the OR is &amp;lt;= 1 with some &lt;em&gt;extremely&lt;/em&gt; small probability, especially if the sample size is also large.  If you find something like this, it is probably a strong effect even if it came out of a test of millions of hypotheses.  &lt;/p&gt;
', NULL, NULL, '''-12'':167C ''/a'':13C,58C,71C,92C,102C,113C,127C ''/em'':178C ''/p'':44C,72C,128C,213C ''/wiki/bonferroni_correction'':124C ''/wiki/data_dredging'':10C ''/wiki/false_discovery_rate'':88C ''/wiki/familywise_error_rate'':98C ''/wiki/multiple_comparisons'':54C ''/wiki/scheff%c3%a9%27s_method'':109C ''/wiki/testing_hypotheses_suggested_by_the_data'':64C ''1'':173C ''10'':166C ''99'':158C ''also'':187C ''ask'':4C ''bonferroni'':125C ''came'':204C ''check'':48C ''confid'':145C,159C ''correct'':82C,126C ''data'':11C,28C,35C,43C,70C ''discoveri'':90C,142C ''dredg'':12C ''effect'':200C ''em'':176C ''en.wikipedia.org'':9C,53C,63C,87C,97C,108C,123C ''en.wikipedia.org/wiki/bonferroni_correction'':122C ''en.wikipedia.org/wiki/data_dredging'':8C ''en.wikipedia.org/wiki/false_discovery_rate'':86C ''en.wikipedia.org/wiki/familywise_error_rate'':96C ''en.wikipedia.org/wiki/multiple_comparisons'':52C ''en.wikipedia.org/wiki/scheff%c3%a9%27s_method'':107C ''en.wikipedia.org/wiki/testing_hypotheses_suggested_by_the_data'':62C ''error'':100C ''especi'':181C ''even'':201C ''extrem'':177C ''fals'':89C ''familywis'':99C ''filter'':140C ''find'':191C ''happen'':17C ''hazard'':57C ''help'':138C ''href'':7C,51C,61C,85C,95C,106C,121C ''hypothes'':25C,32C,66C,212C ''hypothesi'':56C ''interv'':146C,160C ''kind'':80C ''larg'':22C,188C ''less'':133C ''like'':193C ''lt'':172C ''may'':137C ''method'':112C ''million'':210C ''multipl'':55C ''number'':23C ''odd'':149C,163C ''old'':118C ''old-school'':117C ''p'':1C,45C,73C,129C ''particular'':47C ''probabl'':180C,197C ''rate'':91C,101C ''ratio'':150C,164C ''result'':155C ''rigor'':134C ''sampl'':184C ''scheffã'':110C ''school'':119C ''set'':29C,36C ''size'':185C ''small'':179C ''solut'':75C ''someth'':192C ''somewhat'':132C ''statist'':154C ''strong'':199C ''suggest'':39C,67C ''test'':19C,31C,65C,208C ''use'':78C ''way'':135C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (375, 1, '2014-06-14 23:52:10.49', 3, 1292, 'Amazon S3 vs Google Drive', '&lt;p&gt;The majority of people use S3. However, Google Drive seems a promising alternative solution for storing large amounts of data. Are there specific reasons why one is better than the other?&lt;/p&gt;
', 'bigdata', NULL, '''/p'':39C ''altern'':20C ''amazon'':1A ''amount'':25C ''better'':35C ''bigdata'':6B ''data'':27C ''drive'':5A,16C ''googl'':4A,15C ''howev'':14C ''larg'':24C ''major'':9C ''one'':33C ''p'':7C ''peopl'':11C ''promis'':19C ''reason'':31C ''s3'':2A,13C ''seem'':17C ''solut'':21C ''specif'':30C ''store'':23C ''use'':12C ''vs'':3A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (92, 2, '2014-05-15 13:18:38.693', 8, NULL, NULL, '&lt;p&gt;MapReduce has nothing to do with real-time anything. It is a batch-oriented processing framework suitable for some offline tasks, like ETL and index building. Google has moved off of MapReduce for most jobs now, and even the Hadoop ecosystem is doing the same.&lt;/p&gt;

&lt;p&gt;The answer to low latency is generally to keep precomputed indices in memory. Anything that touches disk is hard to make fast and scale. This is how newer-generation Hadoop-based SQL engines like &lt;a href="http://www.cloudera.com/content/cloudera/en/products-and-services/cdh/impala.html"&gt;Impala&lt;/a&gt; get so much speed compared to MapReduce-based infrastructure like &lt;a href="http://hive.apache.org/"&gt;Hive&lt;/a&gt;, for example.&lt;/p&gt;

&lt;p&gt;Search infrastructure can''t cache the results of every single query. But it sure can cache intermediate results, or, more complete results for top queries. With a little caching you can serve results for a significant minority of all queries.&lt;/p&gt;

&lt;p&gt;Search is also split across servers. So one machine can delegate to 100 to each get a part of the result and then combine them.&lt;/p&gt;

&lt;p&gt;You can also get away with some degree of approximation. Google does not literally form a thousand pages of search results; it just has to get the first page about right.&lt;/p&gt;

&lt;p&gt;Keep in mind that Google has &lt;em&gt;millions&lt;/em&gt; of computers around the globe. Your queries are going to a data center geographically near to you and that is only serving your geography. This cuts out most of the latency, which is network and not processing time in the data center.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (86, 1, '2014-05-15 09:04:09.71', 9, 418, 'Clustering unique visitors by useragent, ip, session_id', '&lt;p&gt;Given website access data in the form &lt;code&gt;session_id, ip, user_agent&lt;/code&gt;, and optionally timestamp, following the conditions below, how would you best cluster the sessions into unique visitors?&lt;/p&gt;

&lt;p&gt;&lt;code&gt;session_id&lt;/code&gt;: is an id given to every new visitor. It does not expire, however if the user doesn''t accept cookies/clears cookies/changes browser/changes device, he will not be recognised anymore&lt;/p&gt;

&lt;p&gt;&lt;code&gt;IP&lt;/code&gt; can be shared between different users (Imagine a free wi-fi cafe, or your ISP reassigning IPs), and they will often have at least 2, home and work.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;User_agent&lt;/code&gt; is the browser+OS version, allowing to distinguish between devices. For example a user is likely to use both phone and laptop, but is unlikely to use windows+apple laptops. It is unlikely that the same session id has multiple useragents.&lt;/p&gt;

&lt;p&gt;Data might look as the fiddle here:
&lt;a href="http://sqlfiddle.com/#!2/c4de40/1"&gt;http://sqlfiddle.com/#!2/c4de40/1&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Of course, we are talking about assumptions, but it''s about getting as close to reality as possible. For example, if we encounter the same ip and useragent in a limited time frame with a different session_id, it would be a fair assumption that it''s the same user, with some edge case exceptions.&lt;/p&gt;

&lt;p&gt;Edit: Language in which the problem is solved is irellevant, it''s mostly about logic and not implementation. Pseudocode is fine.&lt;/p&gt;

&lt;p&gt;Edit: due to the slow nature of the fiddle, you can alternatively read/run the mysql:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;select session_id, floor(rand()*256*256*256*256) as ip_num , floor(rand()*1000) as user_agent_id
from 
    (select 1+a.nr+10*b.nr as session_id, ceil(rand()*3) as nr
    from
        (select 1 as nr union all select 2 union all select 3   union all select 4 union all select 5
        union all select 6 union all select 7 union all select 8 union all select 9 union all select 0)a
    join
        (select 1 as nr union all select 2 union all select 3   union all select 4 union all select 5
        union all select 6 union all select 7 union all select 8 union all select 9 union all select 0)b
        order by 1
    )d
inner join
    (select 1 as nr union all select 2 union all select 3   union all select 4 union all select 5
    union all select 6 union all select 7 union all select 8 union all select 9 )e
    on d.nr&amp;gt;=e.nr
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (87, 2, '2014-05-15 09:30:36.46', 6, NULL, NULL, '&lt;p&gt;Looking at this document called &lt;a href="https://www.facebook.com/notes/facebook-data-team/anatomy-of-facebook/10150388519243859"&gt;Anatomy of Facebook&lt;/a&gt; I note that the median is 100. Looking at the cumulative function plot I can bet that the average is higher, near 200. So 50 seems to not be the best number here. However I think that this is not the main issue here. &lt;/p&gt;

&lt;p&gt;The main issue is the lack of information on how the database was used.&lt;/p&gt;

&lt;p&gt;It seems reasonable that a data storage designed specially for graph structures to be more efficient than traditional RDBMs. However, even if the RDBMs are not in the latest trends as a data storage of choice, these systems evolved continuously in a race with the data set dimensions. There are various types of possible designs, various ways of indexing data, improvements related with concurrency and so on. &lt;/p&gt;

&lt;p&gt;To conclude I think that regarding reproducibility, the study lack a proper description of how the database schema was designed. I do not expect that a database to dominate on such king of interrogations, however I would expect that with a well-tuned design the differences to not be such massive.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (89, 1, '2014-05-15 11:22:27.293', 8, 211, 'How does a query into a huge database return with negligible latency?', '&lt;p&gt;For example, when searching something in Google, results return nigh-instantly.&lt;/p&gt;

&lt;p&gt;I understand that Google sorts and indexes pages with algorithms etc., but I imagine it infeasible for the results of every single possible query to be indexed (and results are personalized, which renders this even more infeasible)?&lt;/p&gt;

&lt;p&gt;Moreover, wouldn''t the hardware latency in Google''s hardware be huge? Even if the data in Google were all stored in TB/s SSDs, I imagine the hardware latency to be huge, given the sheer amount of data to process.&lt;/p&gt;

&lt;p&gt;Does MapReduce help solve this problem?&lt;/p&gt;

&lt;p&gt;EDIT: Okay, so I understand that popular searches can be cached in memory. But what about unpopular searches? Even for the most obscure search I have conducted, I don''t think the search has ever been reported to be larger than 5 seconds. How is this possible?&lt;/p&gt;
', 'bigdata  google  search', NULL, '''/p'':29C,68C,110C,118C,167C ''5'':161C ''algorithm'':40C ''amount'':105C ''bigdata'':13B ''cach'':130C ''conduct'':146C ''data'':85C,107C ''databas'':8A ''edit'':120C ''etc'':41C ''even'':65C,82C,138C ''ever'':154C ''everi'':51C ''exampl'':18C ''given'':102C ''googl'':14B,23C,34C,77C,87C ''hardwar'':74C,79C,97C ''help'':114C ''huge'':7A,81C,101C ''imagin'':44C,95C ''index'':37C,57C ''infeas'':46C,67C ''instant'':28C ''larger'':159C ''latenc'':12A,75C,98C ''mapreduc'':113C ''memori'':132C ''moreov'':70C ''neglig'':11A ''nigh'':27C ''nigh-inst'':26C ''obscur'':142C ''okay'':121C ''p'':16C,30C,69C,111C,119C ''page'':38C ''person'':61C ''popular'':126C ''possibl'':53C,166C ''problem'':117C ''process'':109C ''queri'':4A,54C ''render'':63C ''report'':156C ''result'':24C,49C,59C ''return'':9A,25C ''search'':15B,20C,127C,137C,143C,152C ''second'':162C ''sheer'':104C ''singl'':52C ''solv'':115C ''someth'':21C ''sort'':35C ''ssds'':93C ''store'':90C ''tb/s'':92C ''think'':150C ''understand'':32C,124C ''unpopular'':136C ''wouldn'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (90, 2, '2014-05-15 11:46:38.17', 4, NULL, NULL, '&lt;p&gt;The terms "parallel computing" and "distributed computing" certainly have a large overlap, but can be differentiated further. Actually, you already did this in your question, by later asking about "parallel processing" and "distributed processing". &lt;/p&gt;

&lt;p&gt;One could consider "distributed computing" as the more general term that involves "distributed processing" as well as, for example, "distributed storage". The common term, "distributed", usually refers to some sort of &lt;a href="http://en.wikipedia.org/wiki/Message_Passing_Interface" rel="nofollow"&gt;Message Passing&lt;/a&gt; over a network, between machines that are physically separated.&lt;/p&gt;

&lt;p&gt;The term "parallel computing" is also in the process of being further defined, e.g. by explicitly differentiating between the terms "parallel" and "concurrent", where - roughly - the first one refers &lt;a href="http://en.wikipedia.org/wiki/Data_parallelism" rel="nofollow"&gt;data parallelism&lt;/a&gt; and the latter to &lt;a href="http://en.wikipedia.org/wiki/Task_parallelism" rel="nofollow"&gt;task parallelism&lt;/a&gt;, although there are hardly really strict and binding defintions. &lt;/p&gt;

&lt;p&gt;So one could say that &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;"distributed processing" usually (although not necessarily) means that it also is "parallel processing"&lt;/li&gt;
&lt;li&gt;"distributed computing" is more general, and also covers aspects that are not related to parallelism&lt;/li&gt;
&lt;li&gt;and obviously, "parallel computing"/"parallel processing" does not imply that it is "distributed"&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':78C,128C,142C ''/li'':175C,192C,207C ''/p'':36C,88C,152C,159C ''/ul'':208C ''/wiki/data_parallelism'':123C ''/wiki/message_passing_interface'':73C ''/wiki/task_parallelism'':137C ''actual'':19C ''alreadi'':21C ''also'':95C,171C,183C ''although'':143C,165C ''ask'':29C ''aspect'':185C ''bind'':150C ''certain'':9C ''common'':60C ''comput'':5C,8C,42C,93C,178C,197C ''concurr'':112C ''consid'':40C ''could'':39C,156C ''cover'':184C ''data'':126C ''defin'':102C ''defint'':151C ''differenti'':17C,106C ''distribut'':7C,34C,41C,50C,57C,62C,162C,177C,206C ''e.g'':103C ''en.wikipedia.org'':72C,122C,136C ''en.wikipedia.org/wiki/data_parallelism'':121C ''en.wikipedia.org/wiki/message_passing_interface'':71C ''en.wikipedia.org/wiki/task_parallelism'':135C ''exampl'':56C ''explicit'':105C ''first'':116C ''general'':46C,181C ''hard'':146C ''href'':70C,120C,134C ''impli'':202C ''involv'':49C ''larg'':12C ''later'':28C ''latter'':131C ''li'':161C,176C,193C ''machin'':83C ''mean'':168C ''messag'':76C ''necessarili'':167C ''network'':81C ''nofollow'':75C,125C,139C ''obvious'':195C ''one'':38C,117C,155C ''overlap'':13C ''p'':1C,37C,89C,153C ''parallel'':4C,31C,92C,110C,127C,141C,173C,191C,196C,198C ''pass'':77C ''physic'':86C ''process'':32C,35C,51C,98C,163C,174C,199C ''question'':26C ''realli'':147C ''refer'':64C,118C ''rel'':74C,124C,138C ''relat'':189C ''rough'':114C ''say'':157C ''separ'':87C ''sort'':67C ''storag'':58C ''strict'':148C ''task'':140C ''term'':3C,47C,61C,91C,109C ''ul'':160C ''usual'':63C,164C ''well'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (231, 1, '2014-06-05 09:00:27.95', 6, 332, 'How to get an aggregate confusion matrix from n different classifications', '&lt;p&gt;I want to test the accuracy of a methodology. I ran it ~400 times, and I got a different classification for each run. I also have the ground truth, i.e., the real classification to test against.&lt;/p&gt;

&lt;p&gt;For each classification I computed a confusion matrix. Now I want to aggregate these results in order to get the overall confusion matrix. How can I achieve it?&lt;/p&gt;

&lt;p&gt;May I sum all confusion matrices in order to obtain the overall one?&lt;/p&gt;
', 'classification  confusion-matrix  accuracy', NULL, '''/p'':54C,84C,99C ''400'':30C ''accuraci'':16B,23C ''achiev'':82C ''aggreg'':5A,68C ''also'':42C ''classif'':11A,12B,37C,50C,58C ''comput'':60C ''confus'':6A,14B,62C,77C,90C ''confusion-matrix'':13B ''differ'':10A,36C ''get'':3A,74C ''got'':34C ''ground'':45C ''i.e'':47C ''matric'':91C ''matrix'':7A,15B,63C,78C ''may'':86C ''methodolog'':26C ''n'':9A ''obtain'':95C ''one'':98C ''order'':72C,93C ''overal'':76C,97C ''p'':17C,55C,85C ''ran'':28C ''real'':49C ''result'':70C ''run'':40C ''sum'':88C ''test'':21C,52C ''time'':31C ''truth'':46C ''want'':19C,66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (93, 2, '2014-05-15 13:30:04.27', 5, NULL, NULL, '&lt;p&gt;There''s not much you can do with just this data, but what little you can do does not rely on machine learning. &lt;/p&gt;

&lt;p&gt;Yes, sessions from the same IP but different User-Agents are almost certainly distinct users. Sessions with the same IP and User-Agent are usually the same user, except in the case of proxies / wi-fi access points. Those you might identify by looking at the distribution of session count per IP to identify likely ''aggregate'' IPs. Sessions from the same IP / User-Agent that overlap in time are almost surely distinct.&lt;/p&gt;

&lt;p&gt;To further distinguish users you would need more info. For example, the sites or IP addresses that the user is connecting to would be a very strong basis for differentiating sessions. Then you could get into more sophisticated learning to figure out when sessions are the same or different users.&lt;/p&gt;
', NULL, NULL, '''/p'':25C,103C,155C ''access'':66C ''address'':120C ''agent'':37C,51C,94C ''aggreg'':85C ''almost'':39C,100C ''basi'':132C ''case'':60C ''certain'':40C ''connect'':125C ''could'':138C ''count'':79C ''data'':12C ''differ'':34C,153C ''differenti'':134C ''distinct'':41C,102C ''distinguish'':107C ''distribut'':76C ''exampl'':115C ''except'':57C ''fi'':65C ''figur'':145C ''get'':139C ''identifi'':71C,83C ''info'':113C ''ip'':32C,47C,81C,86C,91C,119C ''learn'':24C,143C ''like'':84C ''littl'':15C ''look'':73C ''machin'':23C ''might'':70C ''much'':5C ''need'':111C ''overlap'':96C ''p'':1C,26C,104C ''per'':80C ''point'':67C ''proxi'':62C ''reli'':21C ''session'':28C,43C,78C,87C,135C,148C ''site'':117C ''sophist'':142C ''strong'':131C ''sure'':101C ''time'':98C ''user'':36C,42C,50C,56C,93C,108C,123C,154C ''user-ag'':35C,49C,92C ''usual'':53C ''wi'':64C ''wi-fi'':63C ''would'':110C,127C ''yes'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (94, 1, '2014-05-15 14:41:24.02', 15, 125, 'Does click frequency account for relevance?', '&lt;p&gt;While building a rank, say for a search engine, or a recommendation system, is it valid to rely on click frequency to determine the relevance of an entry?&lt;/p&gt;
', 'recommendation  information-retrieval', NULL, '''/p'':40C ''account'':4A ''build'':13C ''click'':2A,31C ''determin'':34C ''engin'':20C ''entri'':39C ''frequenc'':3A,32C ''inform'':9B ''information-retriev'':8B ''p'':11C ''rank'':15C ''recommend'':7B,23C ''relev'':6A,36C ''reli'':29C ''retriev'':10B ''say'':16C ''search'':19C ''system'':24C ''valid'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10450, 1, '2016-02-29 17:16:57.917', 0, 10, 'sklearn VoteClassifier integrate self-defined classifiers', '&lt;p&gt;I''m trying to integrate several user defined classifiers in sklearn.ensemble.VotingClassifier. More specifically, I followed a Kaggle example to write an XgboostClassifier wrapper class for xgboost with predict(), predict_proba(), fit() etc, passed it to VotingClassifer and called predict_proba() on VC.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;logreg = LogisticRegression(C=0.75,penalty=''l1'')
logreg.fit(X_train, y_train)
xc = XGBoostClassifier(
            eval_metric = ''logloss'',
            num_class = 3,
            nthread = 4,
            eta = 0.1,
            max_depth = 7,
            subsample = 0.9,
            colsample_bytree = 0.35,
            silent = 1,
            early_stopping_rounds=10,
        )
xc.fit(X_train,y_train,num_boost_round=20)
# Create voting classifier
eclf3 = VotingClassifier(estimators=[(''lr'', logreg),(''xc'',xc)],voting=''soft'', weights=[1,2])
eclf3.fit(X_train, y_train)
print(''Ensemble LogLoss {score}''.format(score=log_loss(y_test, eclf3.predict_proba(X_test))))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The Xgboost wrapper class is like below. Credit to &lt;a href="https://www.kaggle.com/tanitter/introducing-kaggle-scripts/grid-search-xgboost-with-scikit-learn/run/23363" rel="nofollow"&gt;this link&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;class XGBoostClassifier():
    def __init__(self, num_boost_round=10, **params):
        self.clf = None
        self.num_boost_round = num_boost_round
        self.params = params
        self.params.update({''objective'': ''multi:softprob''})
    def fit(self, X, y, num_boost_round=None):
        num_boost_round = num_boost_round or self.num_boost_round
        self.label2num = dict((label, i) for i, label in enumerate(sorted(set(y))))
        dtrain = xgb.DMatrix(X, label=[self.label2num[label] for label in y])
        self.clf = xgb.train(params=self.params, dtrain=dtrain, num_boost_round=num_boost_round)
    def predict(self, X):
        num2label = dict((i, label)for label, i in self.label2num.items())
        Y = self.predict_proba(X)
        y = np.argmax(Y, axis=1)
        return np.array([num2label[i] for i in y])
    def predict_proba(self, X):
        dtest = xgb.DMatrix(X)
        return self.clf.predict(dtest)
    def score(self, X, y):
        Y = self.predict_proba(X)
        return 1 / logloss(y, Y)
    def get_params(self, deep=True):
        return self.params
    def set_params(self, **params):
        if ''num_boost_round'' in params:
            self.num_boost_round = params.pop(''num_boost_round'')
        if ''objective'' in params:
            del params[''objective'']
        self.params.update(params)
        return self
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But it seems to me that a user-written class that implements several functions that predict() and predict_proba() cannot be directly used by VoteClassifier. The ensemble returns the following error:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Traceback (most recent call last):
  File "/home/bitnami/PycharmProjects/xgboostTest/ensemble.py", line 231, in &amp;lt;module&amp;gt;
ec.fit(trainSample,trainLabel,testSample,testLabel)
  File "/home/bitnami/PycharmProjects/xgboostTest/ensemble.py", line 155, in fit
print(''Ensemble LogLoss {score}''.format(score=log_loss(y_test, eclf3.predict_proba(X_test))))
  File "/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/voting_classifier.py", line 166, in _predict_proba
avg = np.average(self._collect_probas(X), axis=0, weights=self.weights)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/voting_classifier.py", line 162, in _collect_probas
return np.asarray([clf.predict_proba(X) for clf in self.estimators_])
AttributeError: ''NoneType'' object has no attribute ''predict_proba''
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;After some checking I found the XgboostClassifer was lost after the eclf3.fit(). The eclf3.estimators_ is &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[LogisticRegression(C=0.75, class_weight=None, dual=False, fit_intercept=True,
      intercept_scaling=1, max_iter=100, multi_class=''ovr'', n_jobs=1,
      penalty=''l1'', random_state=None, solver=''liblinear'', tol=0.0001,
      verbose=0, warm_start=False), None]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Please kindly advise whether it is possible to integrate user defined classes in the VotingClassifier. I''m not good with Python. I checked the code of VC but I see no requirement on class inheritance etc. &lt;/p&gt;

&lt;p&gt;Thanks in advance!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (119, 4, '2014-05-17 13:41:20.283', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (95, 2, '2014-05-15 15:06:24.6', 4, NULL, NULL, '&lt;p&gt;Is it valid to &lt;em&gt;use&lt;/em&gt; click frequency, then &lt;strong&gt;yes&lt;/strong&gt;. Is it valid to use &lt;strong&gt;only&lt;/strong&gt; the click frequency, then probably &lt;strong&gt;no&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Search relevance is much more complicated than just one metric. &lt;a href="http://www.amazon.ca/s/ref=nb_sb_noss?url=search-alias=aps&amp;amp;field-keywords=search%20ranking" rel="nofollow"&gt;There are entire books on the subject&lt;/a&gt;. Extending this answer beyond a simple yes/no would likely make the answer far too broad (and opinionated)&lt;/p&gt;
', NULL, NULL, '''/a'':57C ''/em'':8C ''/p'':31C,75C ''/s/ref=nb_sb_noss?url=search-alias=aps&amp;amp;field-keywords=search%20ranking'':47C ''/strong'':14C,22C,30C ''answer'':60C,69C ''beyond'':61C ''book'':53C ''broad'':72C ''click'':9C,24C ''complic'':38C ''em'':6C ''entir'':52C ''extend'':58C ''far'':70C ''frequenc'':10C,25C ''href'':44C ''like'':66C ''make'':67C ''metric'':42C ''much'':36C ''nofollow'':49C ''one'':41C ''opinion'':74C ''p'':1C,32C ''probabl'':27C ''rel'':48C ''relev'':34C ''search'':33C ''simpl'':63C ''strong'':12C,20C,28C ''subject'':56C ''use'':7C,19C ''valid'':4C,17C ''would'':65C ''www.amazon.ca'':46C ''www.amazon.ca/s/ref=nb_sb_noss?url=search-alias=aps&amp;amp;field-keywords=search%20ranking'':45C ''yes'':13C ''yes/no'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (273, 2, '2014-06-10 12:10:28.713', 1, NULL, NULL, '&lt;p&gt;You can apply data science techniques to data on one machine so the answer to the question as the OP phrased it, is no.&lt;/p&gt;
', NULL, NULL, '''/p'':26C ''answer'':15C ''appli'':4C ''data'':5C,9C ''machin'':12C ''one'':11C ''op'':21C ''p'':1C ''phrase'':22C ''question'':18C ''scienc'':6C ''techniqu'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (97, 2, '2014-05-15 17:14:36.817', 13, NULL, NULL, '&lt;p&gt;&lt;a href="http://research.microsoft.com/pubs/169639/cikm-clickpatterns.pdf"&gt;Depends on the user''s intent&lt;/a&gt;, for starters. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.seoresearcher.com/distribution-of-clicks-on-googles-serps-and-eye-tracking-analysis.htm"&gt;Users normally only view the first set of links&lt;/a&gt;, which means that unless the link is viewable, it''s not getting clicks; meaning you''d have to be positive those are the best links, otherwise the clicks are most likely going to reflect placement, not relevance. For example, here''s a &lt;a href="http://www.seoresearcher.com/distribution-of-clicks-on-googles-serps-and-eye-tracking-analysis.htm"&gt;click and attention distribution heat-map&lt;/a&gt; for  Google search results:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/8kO5S.jpg" alt="Google SEPR Click and Attention distribution âheat-mapâ"&gt;&lt;/p&gt;

&lt;p&gt;Further, using click frequency to account for relevance is not a direct measure of the resource''s relevance. Also, using clicks is problematic, since issues like click-inflation, click-fraud, etc. will pop-up and are hard to counter. &lt;/p&gt;

&lt;p&gt;That said, if you''re interested in using user interaction to model relevance, I would suggest you attempt to measure post-click engagement, not how users respond to search results; see "&lt;a href="http://www.youtube.com/watch?v=BsCeNCVb-d8"&gt;YouTube''s head of engineering speaking about clicks vs engagement&lt;/a&gt;" for more information, though note that the &lt;a href="http://www.orbitmedia.com/blog/ideal-blog-post-length/"&gt;size itself of the content is a factor&lt;/a&gt; too.&lt;/p&gt;

&lt;p&gt;Might be worth noting that historically Google was known for &lt;a href="http://en.wikipedia.org/wiki/PageRank"&gt;PageRank algorithm&lt;/a&gt; though it''s possible your intent is only to review click-streams, so I won''t delve &lt;a href="https://www.google.com/search?q=google%20ranking%20factors"&gt;Google ranking factors&lt;/a&gt;; if you are interested in the Google''s approach, you might find a review of &lt;a href="http://static.googleusercontent.com/media/www.google.com/en/us/insidesearch/howsearchworks/assets/searchqualityevaluatorguidelines.pdf"&gt;Google''s Search Quality Rating Guidelines&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/8ko5s.jpg'':98C ''/a'':13C,32C,87C,203C,224C,245C,272C,299C ''/blog/ideal-blog-post-length/'':215C ''/distribution-of-clicks-on-googles-serps-and-eye-tracking-analysis.htm'':22C,79C ''/media/www.google.com/en/us/insidesearch/howsearchworks/assets/searchqualityevaluatorguidelines.pdf'':292C ''/p'':16C,92C,110C,154C,226C,300C ''/pubs/169639/cikm-clickpatterns.pdf'':6C ''/search?q=google%20ranking%20factors'':268C ''/watch?v=bscencvb-d8'':192C ''/wiki/pagerank'':242C ''account'':117C ''algorithm'':244C ''also'':130C ''alt'':99C ''approach'':281C ''attempt'':173C ''attent'':82C,104C ''best'':56C ''click'':45C,60C,80C,102C,114C,132C,139C,142C,178C,200C,257C ''click-fraud'':141C ''click-infl'':138C ''click-stream'':256C ''content'':220C ''counter'':153C ''d'':48C ''delv'':263C ''depend'':7C ''direct'':123C ''distribut'':83C,105C ''en.wikipedia.org'':241C ''en.wikipedia.org/wiki/pagerank'':240C ''engag'':179C,202C ''engin'':197C ''etc'':144C ''exampl'':71C ''factor'':223C,271C ''find'':284C ''first'':28C ''fraud'':143C ''frequenc'':115C ''get'':44C ''go'':64C ''googl'':89C,100C,234C,269C,279C,293C ''guidelin'':298C ''hard'':151C ''head'':195C ''heat'':85C,108C ''heat-map'':84C ''heat-mapâ'':107C ''histor'':233C ''href'':3C,19C,76C,189C,212C,239C,265C,289C ''i.stack.imgur.com'':97C ''i.stack.imgur.com/8ko5s.jpg'':96C ''img'':94C ''inflat'':140C ''inform'':206C ''intent'':12C,251C ''interact'':165C ''interest'':161C,276C ''issu'':136C ''known'':236C ''like'':63C,137C ''link'':31C,38C,57C ''map'':86C ''mapâ'':109C ''mean'':34C,46C ''measur'':124C,175C ''might'':228C,283C ''model'':167C ''normal'':24C ''note'':208C,231C ''otherwis'':58C ''p'':1C,17C,93C,111C,155C,227C ''pagerank'':243C ''placement'':67C ''pop'':147C ''pop-up'':146C ''posit'':52C ''possibl'':249C ''post'':177C ''post-click'':176C ''problemat'':134C ''qualiti'':296C ''rank'':270C ''rate'':297C ''re'':160C ''reflect'':66C ''relev'':69C,119C,129C,168C ''research.microsoft.com'':5C ''research.microsoft.com/pubs/169639/cikm-clickpatterns.pdf'':4C ''resourc'':127C ''respond'':183C ''result'':91C,186C ''review'':255C,286C ''said'':157C ''search'':90C,185C,295C ''see'':187C ''sepr'':101C ''set'':29C ''sinc'':135C ''size'':216C ''speak'':198C ''src'':95C ''starter'':15C ''static.googleusercontent.com'':291C ''static.googleusercontent.com/media/www.google.com/en/us/insidesearch/howsearchworks/assets/searchqualityevaluatorguidelines.pdf'':290C ''stream'':258C ''suggest'':171C ''though'':207C,246C ''unless'':36C ''use'':113C,131C,163C ''user'':10C,23C,164C,182C ''view'':26C ''viewabl'':40C ''vs'':201C ''won'':261C ''worth'':230C ''would'':170C ''www.google.com'':267C ''www.google.com/search?q=google%20ranking%20factors'':266C ''www.orbitmedia.com'':214C ''www.orbitmedia.com/blog/ideal-blog-post-length/'':213C ''www.seoresearcher.com'':21C,78C ''www.seoresearcher.com/distribution-of-clicks-on-googles-serps-and-eye-tracking-analysis.htm'':20C,77C ''www.youtube.com'':191C ''www.youtube.com/watch?v=bscencvb-d8'':190C ''youtub'':193C ''â'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (101, 2, '2014-05-15 21:41:22.703', 7, NULL, NULL, '&lt;p&gt;One possibility here (and this is really an extension of what Sean Owen posted) is to define a "stable user."&lt;/p&gt;

&lt;p&gt;For the given info you have you can imagine making a user_id that is a hash of ip and some user agent info (pseudo code):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;uid = MD5Hash(ip + UA.device + UA.model)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then you flag these ids with "stable" or "unstable" based on usage heuristics you observe for your users.  This can be a threshold of # of visits in a given time window, length of time their cookies persist, some end action on your site (I realize this wasn''t stated in your original log), etc...&lt;/p&gt;

&lt;p&gt;The idea here is to separate the users that don''t drop cookies from those that do.&lt;/p&gt;

&lt;p&gt;From here you can attribute session_ids to stable uids from your logs.  You will then have "left over" session_ids for unstable users that you are relatively unsure about.  You may be over or under counting sessions, attributing behavior to multiple people when there is only one, etc...  But this is at least limited to the users you are now "less certain" about.&lt;/p&gt;

&lt;p&gt;You then perform analytics on your stable group and project that to the unstable group.  Take a user count for example, you know the total # of sessions, but you are unsure of how many users generated those sessions.  You can find the # sessions / unique stable user and use this to project the "estimated" number of unique users in the unstable group since you know the number of sessions attributed to that group.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;projected_num_unstable_users = num_sess_unstable / num_sess_per_stable_uid
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This doesn''t help with per user level investigation on unstable users but you can at least get some mileage out of a cohort of stable users that persist for some time.  You can, by various methods, project behavior and counts into the unstable group.  The above is a simple example of something you might want to know.  The general idea is again to define a set of users you are confident persist, measure what you want to measure, and use certain ground truths (num searches, visits, clicks, etc...) to project into the unknown user space and estimate counts for them.&lt;/p&gt;

&lt;p&gt;This is a longstanding problem in unique user counting, logging, etc... for services that don''t require log in.&lt;/p&gt;
', NULL, NULL, '''/code'':58C,289C ''/p'':22C,50C,115C,134C,200C,274C,393C,414C ''/pre'':59C,290C ''action'':100C ''agent'':46C ''analyt'':205C ''attribut'':140C,174C,270C ''base'':70C ''behavior'':175C,330C ''certain'':198C,373C ''click'':379C ''code'':49C,52C,276C ''cohort'':315C ''confid'':363C ''cooki'':96C,129C ''count'':172C,220C,332C,390C,403C ''defin'':18C,356C ''doesn'':293C ''drop'':128C ''end'':99C ''estim'':254C,389C ''etc'':114C,184C,380C,405C ''exampl'':222C,342C ''extens'':10C ''find'':242C ''flag'':63C ''general'':351C ''generat'':237C ''get'':309C ''given'':26C,89C ''ground'':374C ''group'':209C,216C,262C,273C,336C ''hash'':40C ''help'':295C ''heurist'':73C ''id'':36C,65C,142C,156C ''idea'':118C,352C ''imagin'':32C ''info'':27C,47C ''investig'':300C ''ip'':42C,55C ''know'':224C,265C,349C ''least'':189C,308C ''left'':153C ''length'':92C ''less'':197C ''level'':299C ''limit'':190C ''log'':113C,148C,404C,412C ''longstand'':398C ''make'':33C ''mani'':235C ''may'':167C ''md5hash'':54C ''measur'':365C,370C ''method'':328C ''might'':346C ''mileag'':311C ''multipl'':177C ''num'':278C,281C,284C,376C ''number'':255C,267C ''observ'':75C ''one'':2C,183C ''origin'':112C ''owen'':14C ''p'':1C,23C,60C,116C,135C,201C,291C,394C ''peopl'':178C ''per'':286C,297C ''perform'':204C ''persist'':97C,320C,364C ''possibl'':3C ''post'':15C ''pre'':51C,275C ''problem'':399C ''project'':211C,252C,277C,329C,382C ''pseudo'':48C ''realiz'':105C ''realli'':8C ''relat'':163C ''requir'':411C ''sean'':13C ''search'':377C ''separ'':122C ''servic'':407C ''sess'':282C,285C ''session'':141C,155C,173C,228C,239C,244C,269C ''set'':358C ''simpl'':341C ''sinc'':263C ''site'':103C ''someth'':344C ''space'':387C ''stabl'':20C,67C,144C,208C,246C,287C,317C ''state'':109C ''take'':217C ''threshold'':83C ''time'':90C,94C,323C ''total'':226C ''truth'':375C ''ua.device'':56C ''ua.model'':57C ''uid'':53C,145C,288C ''uniqu'':245C,257C,401C ''unknown'':385C ''unstabl'':69C,158C,215C,261C,279C,283C,302C,335C ''unsur'':164C,232C ''usag'':72C ''use'':249C,372C ''user'':21C,35C,45C,78C,124C,159C,193C,219C,236C,247C,258C,280C,298C,303C,318C,360C,386C,402C ''various'':327C ''visit'':86C,378C ''want'':347C,368C ''wasn'':107C ''window'':91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (102, 1, '2014-05-16 05:09:33.557', 6, 214, 'What is the Best NoSQL backend for a mobile game', '&lt;p&gt;What is the best noSQL backend to use for a mobile game? Users can make a lot of servers requests, it needs also to retrieve users'' historical records (like app purchasing) and analytics of usage behavior.&lt;/p&gt;
', 'nosql  performance', NULL, '''/p'':50C ''also'':36C ''analyt'':46C ''app'':43C ''backend'':6A,19C ''behavior'':49C ''best'':4A,17C ''game'':10A,25C ''histor'':40C ''like'':42C ''lot'':30C ''make'':28C ''mobil'':9A,24C ''need'':35C ''nosql'':5A,11B,18C ''p'':13C ''perform'':12B ''purchas'':44C ''record'':41C ''request'':33C ''retriev'':38C ''server'':32C ''usag'':48C ''use'':21C ''user'':26C,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (103, 1, '2014-05-16 14:26:12.27', 12, 473, 'Clustering based on similarity scores', '&lt;p&gt;Assume that we have a set of elements &lt;em&gt;E&lt;/em&gt; and a similarity (&lt;strong&gt;not distance&lt;/strong&gt;) function &lt;em&gt;sim(ei, ej)&lt;/em&gt; between two elements &lt;em&gt;ei,ej â E&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;How could we (efficiently) cluster the elements of &lt;em&gt;E&lt;/em&gt;, using &lt;em&gt;sim&lt;/em&gt;?&lt;/p&gt;

&lt;p&gt;&lt;em&gt;k&lt;/em&gt;-means, for example, requires a given &lt;em&gt;k&lt;/em&gt;, Canopy Clustering requires two threshold values. What if we don''t want such predefined parameters?&lt;/p&gt;

&lt;p&gt;Note, that &lt;em&gt;sim&lt;/em&gt; is not neccessarily a metric (i.e. the triangle inequality may, or may not hold). Moreover, it doesn''t matter if the clusters are disjoint (partitions of &lt;em&gt;E&lt;/em&gt;).&lt;/p&gt;
', 'clustering  algorithms  similarity', NULL, '''/em'':20C,33C,42C,55C,59C,64C,73C,95C,124C ''/p'':43C,60C,89C,125C ''/strong'':27C ''algorithm'':7B ''assum'':10C ''base'':2A ''canopi'':74C ''cluster'':1A,6B,49C,75C,117C ''could'':46C ''disjoint'':119C ''distanc'':26C ''doesn'':112C ''e'':19C,41C,54C,123C ''effici'':48C ''ei'':31C,38C ''ej'':32C,39C ''element'':17C,36C,51C ''em'':18C,29C,37C,53C,57C,62C,71C,93C,122C ''exampl'':67C ''function'':28C ''given'':70C ''hold'':109C ''i.e'':101C ''inequ'':104C ''k'':63C,72C ''matter'':114C ''may'':105C,107C ''mean'':65C ''metric'':100C ''moreov'':110C ''neccessarili'':98C ''note'':91C ''p'':9C,44C,61C,90C ''paramet'':88C ''partit'':120C ''predefin'':87C ''requir'':68C,76C ''score'':5A ''set'':15C ''sim'':30C,58C,94C ''similar'':4A,8B,23C ''strong'':24C ''threshold'':78C ''triangl'':103C ''two'':35C,77C ''use'':56C ''valu'':79C ''want'':85C ''â'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (104, 5, '2014-05-16 15:35:51.42', 0, NULL, NULL, '&lt;h2&gt;Use the &lt;a href="/questions/tagged/definitions" class="post-tag" title="show questions tagged &amp;#39;definitions&amp;#39;" rel="tag"&gt;definitions&lt;/a&gt; tag when:&lt;/h2&gt;

&lt;p&gt;You think we should create an official definition.&lt;/p&gt;

&lt;p&gt;An existing Tag Wiki needs a more precise definition to avoid confusion and we need to create consensus before an edit.&lt;/p&gt;

&lt;p&gt;(rough draft - needs filling out)&lt;/p&gt;
', NULL, NULL, '''/a'':21C ''/h2'':24C ''/p'':34C,57C,64C ''/questions/tagged/definitions'':6C ''39'':15C,17C ''avoid'':46C ''class'':7C ''confus'':47C ''consensus'':53C ''creat'':30C,52C ''definit'':16C,20C,33C,44C ''draft'':60C ''edit'':56C ''exist'':37C ''fill'':62C ''h2'':1C ''href'':5C ''need'':40C,50C,61C ''offici'':32C ''p'':25C,35C,58C ''post'':9C ''post-tag'':8C ''precis'':43C ''question'':13C ''rel'':18C ''rough'':59C ''show'':12C ''tag'':10C,14C,19C,22C,38C ''think'':27C ''titl'':11C ''use'':2C ''wiki'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (105, 4, '2014-05-16 15:35:51.42', 0, NULL, NULL, 'a discussion (meta) tag used when there exists *disagreement* or *confusion* about the everyday meaning of a term or phrase.', NULL, NULL, '''confus'':11C ''disagr'':9C ''discuss'':2C ''everyday'':14C ''exist'':8C ''mean'':15C ''meta'':3C ''phrase'':20C ''tag'':4C ''term'':18C ''use'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (106, 2, '2014-05-16 16:25:58.25', 2, NULL, NULL, '&lt;p&gt;There are many overlaps between data mining and datascience. I would say that people with the role of datamining are concerned with data collection and the extraction of features from unfiltered, unorganised and mostly raw/wild datasets. Some very important data may be difficult to extract, not do to the implementation issues but because it may have foreign artifacts. &lt;/p&gt;

&lt;p&gt;Eg. if I needed someone to look at financial data from written tax returns in the 70s which were scanned and machine read to find out if people saved more on car insurance; a dataminer would be the person to get. &lt;/p&gt;

&lt;p&gt;If I needed someone to examine the influence Nike''s Twitter profile in the tweets of Brazil and identify key positive features from the profile, I would look for a datascientist.&lt;/p&gt;
', NULL, NULL, '''/p'':60C,103C,136C ''70s'':78C ''artifact'':59C ''brazil'':121C ''car'':93C ''collect'':25C ''concern'':22C ''data'':7C,24C,41C,71C ''datamin'':20C,96C ''datasci'':10C ''datascientist'':135C ''dataset'':37C ''difficult'':44C ''eg'':62C ''examin'':110C ''extract'':28C,46C ''featur'':30C,126C ''financi'':70C ''find'':86C ''foreign'':58C ''get'':102C ''identifi'':123C ''implement'':51C ''import'':40C ''influenc'':112C ''insur'':94C ''issu'':52C ''key'':124C ''look'':68C,132C ''machin'':83C ''mani'':4C ''may'':42C,56C ''mine'':8C ''most'':35C ''need'':65C,107C ''nike'':113C ''overlap'':5C ''p'':1C,61C,104C ''peopl'':15C,89C ''person'':100C ''posit'':125C ''profil'':116C,129C ''raw/wild'':36C ''read'':84C ''return'':75C ''role'':18C ''save'':90C ''say'':13C ''scan'':81C ''someon'':66C,108C ''tax'':74C ''tweet'':119C ''twitter'':115C ''unfilt'':32C ''unorganis'':33C ''would'':12C,97C,131C ''written'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (107, 1, '2014-05-16 20:07:50.983', 9, 69, 'Opensource tools for help in mining stream of leader board scores', '&lt;p&gt;Consider a stream containing &lt;a href="http://en.m.wikipedia.org/wiki/Tuple" rel="nofollow"&gt;tuples&lt;/a&gt; &lt;code&gt;(user, new_score)&lt;/code&gt; representing users'' scores in an online game. The stream could have 100-1,000 new elements per second. The game has 200K to 300K unique players. &lt;/p&gt;

&lt;p&gt;I would like to have some standing queries like: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Which players posted more than x scores in a sliding window of one hour&lt;/li&gt;
&lt;li&gt;Which players gained x% score in a sliding window of one hour&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;My question is which open source tools can I employ to jumpstart this project? I am considering &lt;a href="http://esper.codehaus.org/" rel="nofollow"&gt;Esper&lt;/a&gt; at the moment. &lt;/p&gt;

&lt;p&gt;Note: I have just completed reading "Mining Data Streams" (chapter 4 of &lt;a href="http://infolab.stanford.edu/~ullman/mmds.html" rel="nofollow"&gt;Mining of Massive Datasets&lt;/a&gt;) and I am quite new to mining data streams.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (109, 4, '2014-05-16 20:24:38.98', 0, NULL, NULL, 'An activity that seeks patterns in a continuous stream of data elements, usually involving summarizing the stream in some way.', NULL, NULL, '''activ'':2C ''continu'':8C ''data'':11C ''element'':12C ''involv'':14C ''pattern'':5C ''seek'':4C ''stream'':9C,17C ''summar'':15C ''usual'':13C ''way'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (270, 2, '2014-06-10 11:39:19.603', 3, NULL, NULL, '&lt;p&gt;Machine learning and statistical techniques can improve the forecast, but nobody can predict the real result.&lt;/p&gt;

&lt;p&gt;There was a kaggle competition a few month ago about &lt;a href="https://www.kaggle.com/c/march-machine-learning-mania" rel="nofollow"&gt;predicting the 2014 NCAA Tournament&lt;/a&gt;. You can read the Competition Forum to get a better idea on what people did and what results did they achieve.&lt;/p&gt;
', NULL, NULL, '''/a'':42C ''/c/march-machine-learning-mania'':34C ''/p'':18C,64C ''2014'':39C ''achiev'':63C ''ago'':28C ''better'':52C ''competit'':24C,47C ''forecast'':10C ''forum'':48C ''get'':50C ''href'':31C ''idea'':53C ''improv'':8C ''kaggl'':23C ''learn'':3C ''machin'':2C ''month'':27C ''ncaa'':40C ''nobodi'':12C ''nofollow'':36C ''p'':1C,19C ''peopl'':56C ''predict'':14C,37C ''read'':45C ''real'':16C ''rel'':35C ''result'':17C,60C ''statist'':5C ''techniqu'':6C ''tournament'':41C ''www.kaggle.com'':33C ''www.kaggle.com/c/march-machine-learning-mania'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (147, 4, '2014-05-18 15:01:24.08', 0, NULL, NULL, 'Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of humanâcomputer interaction. Many challenges in NLP involve natural language understanding, that is, enabling computers to derive meaning from human or natural language input, and others involve natural language generation.', NULL, NULL, '''area'':32C ''artifici'':11C ''challeng'':38C ''comput'':9C,20C,35C,48C ''concern'':15C ''deriv'':50C ''enabl'':47C ''field'':7C ''generat'':63C ''human'':22C,53C ''humanâ'':34C ''input'':57C ''intellig'':12C ''interact'':18C,36C ''involv'':41C,60C ''languag'':2C,24C,43C,56C,62C ''linguist'':14C ''mani'':37C ''mean'':51C ''natur'':1C,23C,42C,55C,61C ''nlp'':4C,27C,40C ''other'':59C ''process'':3C ''relat'':29C ''scienc'':10C ''understand'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8664, 1, '2015-10-30 23:10:31.113', 1, 44, 'Is this a correct way improving a statistical model?', '&lt;p&gt;I am building a fair amount of statistical models - text classifiers and sequence taggers. The statistical models are linear in features - Logistic Regression and linear chain first order CRF.&lt;/p&gt;

&lt;p&gt;The models undergo several stages starting from a rough, initial version all the way to a mature production model. Given a specification and some dataset (often with incomplete annotation, noisy labels and features), I build the first model from a small, hand labeled dataset. This dataset is then grown iteratively by adding more labeled examples (e.g. using crowdsourcing).&lt;/p&gt;

&lt;p&gt;Then comes the test phase. The statistical model is actively evaluated within a larger system by testers. I, as a developer, start getting the failure reports on their input. I am supposed to fix the model, update it in the system and deploy it.&lt;/p&gt;

&lt;p&gt;Every individual error report is fairly small to manually go through and fix every misclassification. Obviously it is incorrect just to simply add the failure cases with correct labels to the training set. You never know if the model learned that new pattern.
I do this by comparing the failure cases with a vast unlabeled dataset of examples (real usage data but no labels) and extract similar examples. I label those similar examples, add to the training set and re-train the model. Then I check if the new model fixes the originally reported issues.&lt;/p&gt;

&lt;p&gt;My question is: Is this a correct approach to build and continuously improve the statistical models? May be there are some hidden caveats that will bias my model in some sense?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (111, 2, '2014-05-17 03:07:59.707', 7, NULL, NULL, '&lt;p&gt;Some factors you might consider:&lt;/p&gt;

&lt;p&gt;Developer familiarity: go with whatever you or your developers are familiar with.  Mongo, Couch, Riak, DynamoDB etc all have their strengths but all should do ok here, so rather than going for an unfamiliar solution that might be slightly better go for familiar and save a bunch of development time.&lt;/p&gt;

&lt;p&gt;Ease of cloud deployment:  for example, if you are using Amazon AWS, then DynamoDB is likely an excellent choice.  Sure, you could use Mongo on AWS, but why bother?  Other cloud providers have their own preferred db, for example if you are using Google AppEngine, it makes sense to use BigTable or Cloud Datastore. &lt;/p&gt;

&lt;p&gt;Your use case seems both well suited to NoSQL and not very challenging since your data has a natural partition by user.  I think you''d be technically ok with anything, which is why I''m mainly covering other factors.&lt;/p&gt;
', NULL, NULL, '''/p'':7C,59C,115C,157C ''amazon'':71C ''anyth'':147C ''appengin'':105C ''aw'':72C,86C ''better'':48C ''bigtabl'':111C ''bother'':89C ''bunch'':55C ''case'':119C ''challeng'':129C ''choic'':79C ''cloud'':63C,91C,113C ''consid'':6C ''couch'':22C ''could'':82C ''cover'':154C ''d'':142C ''data'':132C ''datastor'':114C ''db'':97C ''deploy'':64C ''develop'':9C,17C,57C ''dynamodb'':24C,74C ''eas'':61C ''etc'':25C ''exampl'':66C,99C ''excel'':78C ''factor'':3C,156C ''familiar'':10C,19C,51C ''go'':11C,39C,49C ''googl'':104C ''like'':76C ''m'':152C ''main'':153C ''make'':107C ''might'':5C,45C ''mongo'':21C,84C ''natur'':135C ''nosql'':125C ''ok'':34C,145C ''p'':1C,8C,60C,116C ''partit'':136C ''prefer'':96C ''provid'':92C ''rather'':37C ''riak'':23C ''save'':53C ''seem'':120C ''sens'':108C ''sinc'':130C ''slight'':47C ''solut'':43C ''strength'':29C ''suit'':123C ''sure'':80C ''technic'':144C ''think'':140C ''time'':58C ''unfamiliar'':42C ''use'':70C,83C,103C,110C,118C ''user'':138C ''well'':122C ''whatev'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (112, 2, '2014-05-17 04:18:10.02', 7, NULL, NULL, '&lt;p&gt;This isn''t a full solution, but you may want to look into &lt;a href="http://www.orientechnologies.com/"&gt;OrientDB&lt;/a&gt; as part of your stack. Orient is a Graph-Document database server written entirely in Java. &lt;/p&gt;

&lt;p&gt;In graph databases, relationships are considered first class citizens and therefore traversing those relationships can be done pretty quickly. Orient is also a document database which would allow you the kind of schema-free architecture it sounds like you would need. The real reason I suggest Orient, however, is because of its extensiblity. It supports streaming via sockets, and the entire database can be embedded into another application. Finally, it can be scaled efficiently and/or can work entirely through memory. So, with some Java expertise, you can actually run your preset queries against the database in memory.&lt;/p&gt;

&lt;p&gt;We are doing something similar. In creating an app/site for social science research collaboration, we found ourselves with immensely complex data models. We ended up writing several of the queries using the Gremlin Traversal Language (a subset of Groovy, which is, of course, Java at its heart), and then exposing those queries through the binary connection server of the OrientDB. So, the client opens a TCP socket, sends a short binary message, and the query is executing in Java directly against the in-memory database.&lt;/p&gt;

&lt;p&gt;OrientDB also supports writing function queries in Javascript, and you can use Node.js to interact directly with an Orient instance.&lt;/p&gt;

&lt;p&gt;For something of this size, I would want to use Orient in conjunction with Hadoop or something like that. You can also use Orient in conjunction with esper.&lt;/p&gt;

&lt;p&gt;Consider:
An introduction to orient: &lt;a href="http://www.sitepoint.com/a-look-at-orientdb-the-graph-document-nosql/"&gt;http://www.sitepoint.com/a-look-at-orientdb-the-graph-document-nosql/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Complex, real-time queries: &lt;a href="http://www.gft-blog.com/business-trends/leveraging-real-time-scoring-through-bigdata-to-detect-insurance-fraud/"&gt;http://www.gft-blog.com/business-trends/leveraging-real-time-scoring-through-bigdata-to-detect-insurance-fraud/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;A discussion about streaming options with java and orient: &lt;a href="https://github.com/orientechnologies/orientdb/issues/1227"&gt;https://github.com/orientechnologies/orientdb/issues/1227&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (113, 1, '2014-05-17 04:53:03.913', 10, 113, 'When a relational database has better performance than a no relational', '&lt;p&gt;When a relational database like mySQL has better performance than a no relational, like mongo?&lt;/p&gt;

&lt;p&gt;I saw a question on Quora other day, about why Quora still uses mySQL as their backend. And how their performance is still good.&lt;/p&gt;
', 'bigdata  nosql  databases', NULL, '''/p'':31C,57C ''backend'':49C ''better'':6A,23C ''bigdata'':12B ''databas'':4A,14B,19C ''day'':40C ''good'':56C ''like'':20C,29C ''mongo'':30C ''mysql'':21C,46C ''nosql'':13B ''p'':15C,32C ''perform'':7A,24C,53C ''question'':36C ''quora'':38C,43C ''relat'':3A,11A,18C,28C ''saw'':34C ''still'':44C,55C ''use'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (115, 1, '2014-05-17 08:45:08.42', 6, 174, 'Is there any APIs for crawling abstract of paper?', '&lt;p&gt;If I have a very long list of paper names, how could I get abstract of these papers from internet or any database?&lt;/p&gt;

&lt;p&gt;The paper names are like "Assessment of Utility in Web Mining for the Domain of Public Health".&lt;/p&gt;

&lt;p&gt;Does any one know any API that can give me a solution? I tried to crawl google scholar, however, google blocked my crawler.&lt;/p&gt;
', 'data-mining  machine-learning', NULL, '''/p'':40C,59C,84C ''abstract'':7A,31C ''api'':4A,66C ''assess'':47C ''block'':81C ''could'':28C ''crawl'':6A,76C ''crawler'':83C ''data'':11B ''data-min'':10B ''databas'':39C ''domain'':55C ''get'':30C ''give'':69C ''googl'':77C,80C ''health'':58C ''howev'':79C ''internet'':36C ''know'':64C ''learn'':15B ''like'':46C ''list'':23C ''long'':22C ''machin'':14B ''machine-learn'':13B ''mine'':12B,52C ''name'':26C,44C ''one'':63C ''p'':16C,41C,60C ''paper'':9A,25C,34C,43C ''public'':57C ''scholar'':78C ''solut'':72C ''tri'':74C ''util'':49C ''web'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (116, 1, '2014-05-17 09:16:18.823', 23, 1294, 'Machine learning techniques for estimating users'' age based on Facebook sites they like', '&lt;p&gt;I have a database from my Facebook application and I am trying to use machine learning to estimate users'' age based on what Facebook sites they like.&lt;/p&gt;

&lt;p&gt;There are three crucial characteristics of my database:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;the age distribution in my training set (12k of users in sum) is skewed towards younger users (i.e. I have 1157 users aged 27, and 23 users aged 65);&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;many sites have no more than 5 likers (I filtered out the FB sites with less than 5 likers).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;there''s many more features than samples.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So, my questions are: what strategy would you suggest to prepare the data for further analysis? Should I perform some sort of dimensionality reduction? Which ML method would be most appropriate to use in this case?&lt;/p&gt;

&lt;p&gt;I mainly use Python, so Python-specific hints would be greatly appreciated.&lt;/p&gt;
', 'machine-learning  dimensionality-reduction  python', NULL, '''/li'':93C,116C,127C ''/p'':49C,59C,92C,115C,126C,166C,181C ''/ul'':128C ''1157'':83C ''12k'':70C ''23'':88C ''27'':86C ''5'':102C,113C ''65'':91C ''age'':7A,41C,64C,85C,90C ''analysi'':145C ''applic'':29C ''appreci'':180C ''appropri'':160C ''base'':8A,42C ''case'':165C ''characterist'':55C ''crucial'':54C ''data'':142C ''databas'':25C,58C ''dimension'':18B,152C ''dimensionality-reduct'':17B ''distribut'':65C ''estim'':5A,39C ''facebook'':10A,28C,45C ''fb'':108C ''featur'':123C ''filter'':105C ''great'':179C ''hint'':176C ''i.e'':80C ''learn'':2A,16B,37C ''less'':111C ''li'':61C,94C,117C ''like'':13A,48C ''liker'':103C,114C ''machin'':1A,15B,36C ''machine-learn'':14B ''main'':169C ''mani'':96C,121C ''method'':156C ''ml'':155C ''p'':21C,50C,62C,95C,118C,129C,167C ''perform'':148C ''prepar'':140C ''python'':20B,171C,174C ''python-specif'':173C ''question'':132C ''reduct'':19B,153C ''sampl'':125C ''set'':69C ''site'':11A,46C,97C,109C ''skew'':76C ''sort'':150C ''specif'':175C ''strategi'':135C ''suggest'':138C ''sum'':74C ''techniqu'':3A ''three'':53C ''toward'':77C ''train'':68C ''tri'':33C ''ul'':60C ''use'':35C,162C,170C ''user'':6A,40C,72C,79C,84C,89C ''would'':136C,157C,177C ''younger'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (148, 5, '2014-05-18 15:08:08.913', 0, NULL, NULL, '&lt;p&gt;In Computer science and Technologies The Data is the most important part.
Since to work with data in an efficient manner to store the data in mediums and reuse, we use some techniques which named in general indexing.&lt;/p&gt;

&lt;p&gt;This tag interests with these techniques, efficiency, and anything about using stored data with indexing&lt;/p&gt;
', NULL, NULL, '''/p'':40C,57C ''anyth'':50C ''comput'':3C ''data'':8C,18C,26C,54C ''effici'':21C,48C ''general'':38C ''import'':12C ''index'':39C,56C ''interest'':44C ''manner'':22C ''medium'':28C ''name'':36C ''p'':1C,41C ''part'':13C ''reus'':30C ''scienc'':4C ''sinc'':14C ''store'':24C,53C ''tag'':43C ''techniqu'':34C,47C ''technolog'':6C ''use'':32C,52C ''work'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (118, 5, '2014-05-17 13:41:20.283', 0, NULL, NULL, '&lt;p&gt;&lt;strong&gt;NoSQL&lt;/strong&gt; (sometimes expanded to "not only &lt;a href="/questions/tagged/sql" class="post-tag" title="show questions tagged &amp;#39;sql&amp;#39;" rel="tag"&gt;sql&lt;/a&gt;") is a broad class of database management systems that differ from the classic model of the relational database management system (&lt;a href="/questions/tagged/rdbms" class="post-tag" title="show questions tagged &amp;#39;rdbms&amp;#39;" rel="tag"&gt;rdbms&lt;/a&gt;) in some significant ways.&lt;/p&gt;

&lt;h3&gt;NoSQL systems:&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;Specifically designed for high load&lt;/li&gt;
&lt;li&gt;Natively support horizontal scalability&lt;/li&gt;
&lt;li&gt;Fault tolerant&lt;/li&gt;
&lt;li&gt;Store data in denormalised manner&lt;/li&gt;
&lt;li&gt;Do not usually enforce strict database schema&lt;/li&gt;
&lt;li&gt;Do not usually store data in a table&lt;/li&gt;
&lt;li&gt;Sometimes provide eventual consistency instead of ACID transactions&lt;/li&gt;
&lt;/ul&gt;

&lt;h3&gt;In contrast to RDBMS, NoSQL systems:&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;Do not guarantee data consistency&lt;/li&gt;
&lt;li&gt;Usually support a limited query language (subset of SQL or another custom query language)&lt;/li&gt;
&lt;li&gt;May not provide support for transactions/distributed transactions&lt;/li&gt;
&lt;li&gt;Do not usually use some advanced concepts of RDBMS, such as triggers, views, stored procedures&lt;/li&gt;
&lt;/ul&gt;

&lt;h3&gt;NoSQL implementations can be categorised by their manner of implementation:&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Column-oriented_DBMS" rel="nofollow"&gt;Column-oriented&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Document-oriented_database" rel="nofollow"&gt;Document store&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Graph_database" rel="nofollow"&gt;Graph&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://dba.stackexchange.com/questions/607/what-is-a-key-value-store-database"&gt;Key-value store&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/MultiValue" rel="nofollow"&gt;Multivalue databases&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Object_database" rel="nofollow"&gt;Object databases&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Triplestore" rel="nofollow"&gt;Tripplestore&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/NoSQL#Tuple_store" rel="nofollow"&gt;Tuple store&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;h3&gt;Free NoSQL Books&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://books.couchdb.org/relax/" rel="nofollow"&gt;CouchDB: The Definitive Guide&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://openmymind.net/2011/3/28/The-Little-MongoDB-Book" rel="nofollow"&gt;The Little MongoDB Book&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://openmymind.net/2012/1/23/The-Little-Redis-Book/" rel="nofollow"&gt;The Little Redis Book&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/2011/3/28/the-little-mongodb-book'':323C ''/2012/1/23/the-little-redis-book/'':337C ''/a'':27C,65C,213C,225C,236C,248C,260C,272C,283C,295C,316C,330C,344C ''/h3'':74C,137C,200C,302C ''/li'':82C,88C,92C,99C,108C,118C,128C,145C,161C,170C,187C,214C,226C,237C,249C,261C,273C,284C,296C,317C,331C,345C ''/p'':70C ''/questions/607/what-is-a-key-value-store-database'':243C ''/questions/tagged/rdbms'':50C ''/questions/tagged/sql'':12C ''/relax/'':309C ''/strong'':4C ''/ul'':129C,188C,297C,346C ''/wiki/column-oriented_dbms'':207C ''/wiki/document-oriented_database'':220C ''/wiki/graph_database'':232C ''/wiki/multivalue'':255C ''/wiki/nosql#tuple_store'':290C ''/wiki/object_database'':267C ''/wiki/triplestore'':279C ''39'':21C,23C,59C,61C ''acid'':126C ''advanc'':177C ''anoth'':157C ''book'':301C,329C,343C ''books.couchdb.org'':308C ''books.couchdb.org/relax/'':307C ''broad'':30C ''categoris'':194C ''class'':13C,31C,51C ''classic'':40C ''column'':211C ''column-ori'':210C ''concept'':178C ''consist'':123C,144C ''contrast'':132C ''couchdb'':312C ''custom'':158C ''data'':95C,114C,143C ''databas'':33C,45C,106C,259C,271C ''dba.stackexchange.com'':242C ''dba.stackexchange.com/questions/607/what-is-a-key-value-store-database'':241C ''definit'':314C ''denormalis'':97C ''design'':78C ''differ'':37C ''document'':223C ''en.wikipedia.org'':206C,219C,231C,254C,266C,278C,289C ''en.wikipedia.org/wiki/column-oriented_dbms'':205C ''en.wikipedia.org/wiki/document-oriented_database'':218C ''en.wikipedia.org/wiki/graph_database'':230C ''en.wikipedia.org/wiki/multivalue'':253C ''en.wikipedia.org/wiki/nosql#tuple_store'':288C ''en.wikipedia.org/wiki/object_database'':265C ''en.wikipedia.org/wiki/triplestore'':277C ''enforc'':104C ''eventu'':122C ''expand'':6C ''fault'':90C ''free'':299C ''graph'':235C ''guarante'':142C ''guid'':315C ''h3'':71C,130C,189C,298C ''high'':80C ''horizont'':86C ''href'':11C,49C,204C,217C,229C,240C,252C,264C,276C,287C,306C,320C,334C ''implement'':191C,199C ''instead'':124C ''key'':245C ''key-valu'':244C ''languag'':152C,160C ''li'':76C,83C,89C,93C,100C,109C,119C,139C,146C,162C,171C,202C,215C,227C,238C,250C,262C,274C,285C,304C,318C,332C ''limit'':150C ''littl'':327C,341C ''load'':81C ''manag'':34C,46C ''manner'':98C,197C ''may'':163C ''model'':41C ''mongodb'':328C ''multivalu'':258C ''nativ'':84C ''nofollow'':209C,222C,234C,257C,269C,281C,292C,311C,325C,339C ''nosql'':3C,72C,135C,190C,300C ''object'':270C ''openmymind.net'':322C,336C ''openmymind.net/2011/3/28/the-little-mongodb-book'':321C ''openmymind.net/2012/1/23/the-little-redis-book/'':335C ''orient'':212C ''p'':1C ''post'':15C,53C ''post-tag'':14C,52C ''procedur'':186C ''provid'':121C,165C ''queri'':151C,159C ''question'':19C,57C ''rdbms'':60C,64C,134C,180C ''redi'':342C ''rel'':24C,62C,208C,221C,233C,256C,268C,280C,291C,310C,324C,338C ''relat'':44C ''scalabl'':87C ''schema'':107C ''show'':18C,56C ''signific'':68C ''sometim'':5C,120C ''specif'':77C ''sql'':22C,26C,155C ''store'':94C,113C,185C,224C,247C,294C ''strict'':105C ''strong'':2C ''subset'':153C ''support'':85C,148C,166C ''system'':35C,47C,73C,136C ''tabl'':117C ''tag'':16C,20C,25C,54C,58C,63C ''titl'':17C,55C ''toler'':91C ''transact'':127C,169C ''transactions/distributed'':168C ''trigger'':183C ''tripplestor'':282C ''tupl'':293C ''ul'':75C,138C,201C,303C ''use'':175C ''usual'':103C,112C,147C,174C ''valu'':246C ''view'':184C ''way'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (120, 2, '2014-05-17 18:15:11.937', 2, NULL, NULL, '&lt;p&gt;arXiv has an &lt;a href="http://arxiv.org/help/bulk_data" rel="nofollow"&gt;API and bulk download&lt;/a&gt; but if you want something for paid journals it will be hard to come by without paying an indexer like pubmed or elsevier or the like.&lt;/p&gt;
', NULL, NULL, '''/a'':16C ''/help/bulk_data'':9C ''/p'':43C ''api'':12C ''arxiv'':2C ''arxiv.org'':8C ''arxiv.org/help/bulk_data'':7C ''bulk'':14C ''come'':30C ''download'':15C ''elsevi'':39C ''hard'':28C ''href'':6C ''index'':35C ''journal'':24C ''like'':36C,42C ''nofollow'':11C ''p'':1C ''paid'':23C ''pay'':33C ''pubm'':37C ''rel'':10C ''someth'':21C ''want'':20C ''without'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (130, 1, '2014-05-18 06:26:15.673', 25, 3812, 'What is dimensionality reduction? What is the difference between feature selection and extraction?', '&lt;p&gt;From wikipedia, &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;dimensionality reduction or dimension reduction is the process of
  reducing the number of random variables under consideration, and
  can be divided into feature selection and feature extraction.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;What is the difference between feature selection and feature extraction?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;What is an example of dimensionality reduction in a Natural Language Processing task?&lt;/strong&gt;&lt;/p&gt;
', 'feature-selection  feature-extraction  dimensionality-reduction', NULL, '''/blockquote'':57C ''/p'':26C,56C,71C,88C ''/strong'':70C,87C ''blockquot'':27C ''consider'':45C ''differ'':8A,63C ''dimens'':32C ''dimension'':3A,21B,29C,79C ''dimensionality-reduct'':20B ''divid'':49C ''exampl'':77C ''extract'':13A,19B,55C,69C ''featur'':10A,15B,18B,51C,54C,65C,68C ''feature-extract'':17B ''feature-select'':14B ''languag'':84C ''natur'':83C ''number'':40C ''p'':23C,28C,58C,72C ''process'':36C,85C ''random'':42C ''reduc'':38C ''reduct'':4A,22B,30C,33C,80C ''select'':11A,16B,52C,66C ''strong'':59C,73C ''task'':86C ''variabl'':43C ''wikipedia'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (230, 2, '2014-06-02 15:03:35.94', 4, NULL, NULL, '&lt;p&gt;In general, you don''t want to use XML tags to tag documents in this way because tags may overlap.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://uima.apache.org/" rel="nofollow"&gt;UIMA&lt;/a&gt;, &lt;a href="http://gate.ac.uk/" rel="nofollow"&gt;GATE&lt;/a&gt; and similar NLP frameworks denote the tags separate from the text.  Each tag, such as &lt;code&gt;Person&lt;/code&gt;, &lt;code&gt;ACME&lt;/code&gt;, &lt;code&gt;John&lt;/code&gt; etc. is stored as the position that the tag begins and the position that it ends.  So, for the tag &lt;code&gt;ACME&lt;/code&gt;, it would be stored as starting a position 11 and ending at position 17. &lt;/p&gt;
', NULL, NULL, '''/a'':30C,37C ''/code'':55C,58C,61C,84C ''/p'':22C,99C ''11'':93C ''17'':98C ''acm'':57C,83C ''begin'':71C ''code'':53C,56C,59C,82C ''denot'':42C ''document'':14C ''end'':77C,95C ''etc'':62C ''framework'':41C ''gate'':36C ''gate.ac.uk'':33C ''general'':3C ''href'':25C,32C ''john'':60C ''may'':20C ''nlp'':40C ''nofollow'':28C,35C ''overlap'':21C ''p'':1C,23C ''person'':54C ''posit'':67C,74C,92C,97C ''rel'':27C,34C ''separ'':45C ''similar'':39C ''start'':90C ''store'':64C,88C ''tag'':11C,13C,19C,44C,50C,70C,81C ''text'':48C ''uima'':29C ''uima.apache.org'':26C ''use'':9C ''want'':7C ''way'':17C ''would'':86C ''xml'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (154, 2, '2014-05-18 17:53:37.75', 3, NULL, NULL, '&lt;p&gt;Check &lt;a href="http://www.martinfowler.com" rel="nofollow"&gt;Martin Fowler''s Personal website&lt;/a&gt; He write good, and specially answer of your question One of his book : "NoSQL Distilled" &lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/p'':29C ''answer'':19C ''book'':26C ''check'':2C ''distil'':28C ''fowler'':9C ''good'':16C ''href'':4C ''martin'':8C ''nofollow'':7C ''nosql'':27C ''one'':23C ''p'':1C ''person'':11C ''question'':22C ''rel'':6C ''special'':18C ''websit'':12C ''write'':15C ''www.martinfowler.com'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (121, 2, '2014-05-17 18:53:30.123', 14, NULL, NULL, '&lt;p&gt;One thing to start off with would be k-NN.  The idea here is that you have a user/item matrix and for some of the users you have a reported age.  The age for a person in the user item matrix might be well determined by something like the mean or median age of some nearest neighbors in the item space.&lt;/p&gt;

&lt;p&gt;So you have each user expressed as a vector in item space, find the k nearest neighbors and assign the vector in question some summary stat of the nearest neighbor ages.  You can choose k on a distance cutoff or more realistically by iteratively assigning ages to a train hold out and choosing the k that minimizes the error in that assignment.&lt;/p&gt;

&lt;p&gt;If the dimensionality is a problem you can easily perform reduction in this setup by single value decomposition choosing the m vectors that capture the most variance across the group.&lt;/p&gt;

&lt;p&gt;In all cases since each feature is binary it seems that cosine similarity would be your go to distance metric.&lt;/p&gt;

&lt;p&gt;I need to think a bit more about other approaches (regression, rf, etc...) given the narrow focus of your feature space (all variants of the same action, liking) I think the user/item approach might be the best.&lt;/p&gt;

&lt;p&gt;One note of caution, if the ages you have for train are self reported you might need to correct some of them.  People on facebook tend to report ages in the decade they were born.  Plot a histogram of the birth dates (derived from ages) and see if you have spikes at decades like 70s, 80s, 90s.&lt;/p&gt;
', NULL, NULL, '''/p'':64C,128C,160C,182C,221C,280C ''70s'':277C ''80s'':278C ''90s'':279C ''across'':157C ''action'':210C ''age'':33C,35C,55C,96C,111C,229C,251C,267C ''approach'':193C,216C ''assign'':84C,110C,127C ''best'':220C ''binari'':169C ''birth'':263C ''bit'':189C ''born'':257C ''captur'':153C ''case'':164C ''caution'':226C ''choos'':99C,118C,148C ''correct'':241C ''cosin'':173C ''cutoff'':104C ''date'':264C ''decad'':254C,275C ''decomposit'':147C ''deriv'':265C ''determin'':47C ''dimension'':132C ''distanc'':103C,180C ''easili'':138C ''error'':124C ''etc'':196C ''express'':71C ''facebook'':247C ''featur'':167C,203C ''find'':78C ''focus'':200C ''given'':197C ''go'':178C ''group'':159C ''histogram'':260C ''hold'':115C ''idea'':14C ''item'':42C,62C,76C ''iter'':109C ''k'':11C,80C,100C,120C ''k-nn'':10C ''like'':50C,211C,276C ''m'':150C ''matrix'':22C,43C ''mean'':52C ''median'':54C ''metric'':181C ''might'':44C,217C,238C ''minim'':122C ''narrow'':199C ''nearest'':58C,81C,94C ''need'':185C,239C ''neighbor'':59C,82C,95C ''nn'':12C ''note'':224C ''one'':2C,223C ''p'':1C,65C,129C,161C,183C,222C ''peopl'':245C ''perform'':139C ''person'':38C ''plot'':258C ''problem'':135C ''question'':88C ''realist'':107C ''reduct'':140C ''regress'':194C ''report'':32C,236C,250C ''rf'':195C ''see'':269C ''seem'':171C ''self'':235C ''setup'':143C ''similar'':174C ''sinc'':165C ''singl'':145C ''someth'':49C ''space'':63C,77C,204C ''spike'':273C ''start'':5C ''stat'':91C ''summari'':90C ''tend'':248C ''thing'':3C ''think'':187C,213C ''train'':114C,233C ''user'':28C,41C,70C ''user/item'':21C,215C ''valu'':146C ''varianc'':156C ''variant'':206C ''vector'':74C,86C,151C ''well'':46C ''would'':8C,175C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (122, 2, '2014-05-17 20:56:15.577', 6, NULL, NULL, '&lt;p&gt;It depends on your data and what you''re doing with it. For example, if the processing you have to do requires transactions to synchronize across nodes, it will likely be faster to use transactions implemented in an RDBMS rather than implementing it yourself on top of NoSQL databases which don''t support it natively. &lt;/p&gt;
', NULL, NULL, '''/p'':57C ''across'':27C ''data'':6C ''databas'':50C ''depend'':3C ''exampl'':15C ''faster'':33C ''implement'':37C,43C ''like'':31C ''nativ'':56C ''node'':28C ''nosql'':49C ''p'':1C ''process'':18C ''rather'':41C ''rdbms'':40C ''re'':10C ''requir'':23C ''support'':54C ''synchron'':26C ''top'':47C ''transact'':24C,36C ''use'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2574, 2, '2014-12-01 12:23:03.083', 1, NULL, NULL, '&lt;p&gt;I suggest using machine learning libraries with already functional linear regression
&lt;a href="https://spark.apache.org/docs/1.1.0/mllib-guide.html" rel="nofollow"&gt;Spark MLlib&lt;/a&gt; or &lt;a href="https://github.com/myui/hivemall" rel="nofollow"&gt;hivemall&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':22C,32C ''/docs/1.1.0/mllib-guide.html'':17C ''/myui/hivemall'':28C ''/p'':33C ''alreadi'':9C ''function'':10C ''github.com'':27C ''github.com/myui/hivemall'':26C ''hivemal'':31C ''href'':14C,25C ''learn'':6C ''librari'':7C ''linear'':11C ''machin'':5C ''mllib'':21C ''nofollow'':19C,30C ''p'':1C ''regress'':12C ''rel'':18C,29C ''spark'':20C ''spark.apache.org'':16C ''spark.apache.org/docs/1.1.0/mllib-guide.html'':15C ''suggest'':3C ''use'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (123, 5, '2014-05-17 21:10:41.99', 0, NULL, NULL, '&lt;p&gt;The most basic relationship to describe is a &lt;strong&gt;linear relationship&lt;/strong&gt; between variables, &lt;em&gt;x&lt;/em&gt; and &lt;em&gt;y&lt;/em&gt;, such that they can be said to be highly-correlated when every increase in &lt;em&gt;x&lt;/em&gt; results in a proportional increase in &lt;em&gt;y&lt;/em&gt;. They can also be said to be &lt;em&gt;inversely proportional&lt;/em&gt; so that when &lt;em&gt;x&lt;/em&gt; increases, &lt;em&gt;y&lt;/em&gt; decreases. And finally, the two variables can be said to be &lt;a href="http://en.wikipedia.org/wiki/Independence_%28probability_theory%29" rel="nofollow"&gt;independent&lt;/a&gt; in the event that there is no linear relationship between the two (they are uncorrelated, or have a &lt;strong&gt;Pearson correlation coefficient&lt;/strong&gt; of 0. [LaTeX support would be highly desirable at this point.]&lt;/p&gt;

&lt;h2&gt;Different correlation coefficients and their uses:&lt;/h2&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient" rel="nofollow"&gt;Pearson correlation coefficient&lt;/a&gt; is useful.....
[draft]&lt;/p&gt;
', NULL, NULL, '''/a'':90C,145C ''/em'':18C,22C,40C,49C,60C,66C,70C ''/h2'':133C ''/p'':125C,149C ''/strong'':13C,113C ''/wiki/independence_%28probability_theory%29'':86C ''/wiki/pearson_product-moment_correlation_coefficient'':139C ''0'':115C ''also'':52C ''basic'':4C ''coeffici'':112C,129C,144C ''correl'':33C,111C,128C,143C ''decreas'':71C ''describ'':7C ''desir'':121C ''differ'':127C ''draft'':148C ''em'':16C,20C,38C,47C,57C,64C,68C ''en.wikipedia.org'':85C,138C ''en.wikipedia.org/wiki/independence_%28probability_theory%29'':84C ''en.wikipedia.org/wiki/pearson_product-moment_correlation_coefficient'':137C ''event'':93C ''everi'':35C ''final'':73C ''h2'':126C ''high'':32C,120C ''highly-correl'':31C ''href'':83C,136C ''increas'':36C,45C,67C ''independ'':89C ''invers'':58C ''latex'':116C ''linear'':11C,98C ''nofollow'':88C,141C ''p'':1C,134C ''pearson'':110C,142C ''point'':124C ''proport'':44C,59C ''rel'':87C,140C ''relationship'':5C,12C,99C ''result'':41C ''said'':28C,54C,79C ''strong'':10C,109C ''support'':117C ''two'':75C,102C ''uncorrel'':105C ''use'':132C,147C ''variabl'':15C,76C ''would'':118C ''x'':17C,39C,65C ''y'':21C,48C,69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (124, 4, '2014-05-17 21:10:41.99', 0, NULL, NULL, 'A statistics term used to describe a type of dependence between variables (or data sets). Correlations are often used as an indicator of predictability. However, correlation does NOT imply causation. Different methods of calculating correlation exist to capture more complicated relationships between the variables being studied.', NULL, NULL, '''calcul'':34C ''captur'':38C ''causat'':30C ''complic'':40C ''correl'':16C,26C,35C ''data'':14C ''depend'':10C ''describ'':6C ''differ'':31C ''exist'':36C ''howev'':25C ''impli'':29C ''indic'':22C ''method'':32C ''often'':18C ''predict'':24C ''relationship'':41C ''set'':15C ''statist'':2C ''studi'':46C ''term'':3C ''type'':8C ''use'':4C,19C ''variabl'':12C,44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (125, 1, '2014-05-17 21:52:34.563', -1, 90, 'How to learn noSQL databases and how to know when SQL or noSQL is better', '&lt;p&gt;I want learn about NoSQL and when is better to use SQL or NoSQL. I know that this question depends on the case, but I''m asking for a good documentation on NoSQL, and some explanation of when is better to use SQL or NoSQL (use cases, etc). Also, your opinions on NoSQL databases, and any recommendations for learning about this topic are welcome.&lt;/p&gt;
', 'nosql', NULL, '''/p'':82C ''also'':66C ''ask'':44C ''better'':15A,26C,57C ''case'':40C,64C ''databas'':5A,71C ''depend'':37C ''document'':48C ''etc'':65C ''explan'':53C ''good'':47C ''know'':9A,33C ''learn'':3A,20C,76C ''m'':43C ''nosql'':4A,13A,16B,22C,31C,50C,62C,70C ''opinion'':68C ''p'':17C ''question'':36C ''recommend'':74C ''sql'':11A,29C,60C ''topic'':79C ''use'':28C,59C,63C ''want'':19C ''welcom'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (126, 2, '2014-05-17 23:53:42.7', 2, NULL, NULL, '&lt;p&gt;Please have a look at my answer here: &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;a href="http://stackoverflow.com/questions/13528216/motivations-for-using-relational-database-orm-or-document-database-odm/13599767#13599767"&gt;Motivations for using relational database / ORM or document database / ODM&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Short version:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Use NoSQL is data size and number of transactions per second forces it, which typically happens above a few tens of TB and millions of transactions per second (db in memory, running on cluster), &lt;em&gt;or&lt;/em&gt; at hundreds of TB and thousands of transactions per second (traditional db on disk, transactions per second is &lt;em&gt;highly&lt;/em&gt; dependent on the usage pattern). Traditional SQL scales up to that point just fine.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;NoSQL is well suited for some problems (data has a natural sharding, schema is flexible, eventual consistency is ok).  You can use there even if scaling doesn''t force you to.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Developer familiarity with tools and ops ease of deployment are &lt;em&gt;major&lt;/em&gt; factors, don''t overlook those.  A solution may be technically better but you may have a hard time using it, make sure you need it and make sure you budget for the learning curve.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;As to how to learn it: fire up a MongoDB image on AWS, or DynamoDB, and have fun! 
&lt;a href="http://docs.mongodb.org/ecosystem/platforms/amazon-ec2/" rel="nofollow"&gt;http://docs.mongodb.org/ecosystem/platforms/amazon-ec2/&lt;/a&gt;
&lt;a href="http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GettingStartedDynamoDB.html" rel="nofollow"&gt;http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GettingStartedDynamoDB.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':28C ''/amazondynamodb/latest/developerguide/gettingstarteddynamodb.html'':232C ''/amazondynamodb/latest/developerguide/gettingstarteddynamodb.html&lt;/a&gt;&lt;/p&gt;'':237C ''/ecosystem/platforms/amazon-ec2/'':222C ''/ecosystem/platforms/amazon-ec2/&lt;/a&gt;'':227C ''/em'':74C,95C,161C ''/li'':111C,146C,197C ''/p'':10C,30C,34C,110C,145C,196C ''/questions/13528216/motivations-for-using-relational-database-orm-or-document-database-odm/13599767#13599767'':17C ''/strong'':29C ''/ul'':198C ''answer'':8C ''aw'':212C ''better'':172C ''budget'':191C ''cluster'':71C ''consist'':130C ''curv'':195C ''data'':41C,121C ''databas'':22C,26C ''db'':66C,86C ''depend'':96C ''deploy'':157C ''develop'':149C ''disk'':88C ''docs.aws.amazon.com'':231C,236C ''docs.aws.amazon.com/amazondynamodb/latest/developerguide/gettingstarteddynamodb.html'':230C ''docs.aws.amazon.com/amazondynamodb/latest/developerguide/gettingstarteddynamodb.html&lt;/a&gt;&lt;/p&gt;'':235C ''docs.mongodb.org'':221C,226C ''docs.mongodb.org/ecosystem/platforms/amazon-ec2/'':220C ''docs.mongodb.org/ecosystem/platforms/amazon-ec2/&lt;/a&gt;'':225C ''document'':25C ''doesn'':140C ''dynamodb'':214C ''eas'':155C ''em'':72C,93C,159C ''even'':137C ''eventu'':129C ''factor'':162C ''familiar'':150C ''fine'':109C ''fire'':206C ''flexibl'':128C ''forc'':49C,142C ''fun'':217C ''happen'':53C ''hard'':178C ''high'':94C ''href'':14C,219C,229C ''hundr'':76C ''imag'':210C ''learn'':194C,204C ''li'':36C,112C,147C ''look'':5C ''major'':160C ''make'':182C,188C ''may'':169C,175C ''memori'':68C ''million'':61C ''mongodb'':209C ''motiv'':18C ''natur'':124C ''need'':185C ''nofollow'':224C,234C ''nosql'':39C,114C ''number'':44C ''odm'':27C ''ok'':132C ''op'':154C ''orm'':23C ''overlook'':165C ''p'':1C,11C,31C,37C,113C,148C,199C ''pattern'':100C ''per'':47C,64C,83C,90C ''pleas'':2C ''point'':107C ''problem'':120C ''rel'':223C,233C ''relat'':21C ''run'':69C ''scale'':103C,139C ''schema'':126C ''second'':48C,65C,84C,91C ''shard'':125C ''short'':32C ''size'':42C ''solut'':168C ''sql'':102C ''stackoverflow.com'':16C ''stackoverflow.com/questions/13528216/motivations-for-using-relational-database-orm-or-document-database-odm/13599767#13599767'':15C ''strong'':12C ''suit'':117C ''sure'':183C,189C ''tb'':59C,78C ''technic'':171C ''ten'':57C ''thousand'':80C ''time'':179C ''tool'':152C ''tradit'':85C,101C ''transact'':46C,63C,82C,89C ''typic'':52C ''ul'':35C ''usag'':99C ''use'':20C,38C,135C,180C ''version'':33C ''well'':116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (128, 1, '2014-05-18 06:10:52.543', 25, 2376, 'Latent Dirichlet Allocation vs Hierarchical Dirichlet Process', '&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation"&gt;Latent Dirichlet Allocation (LDA)&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/Hierarchical_Dirichlet_process"&gt;Hierarchical Dirichlet Process (HDP)&lt;/a&gt; are both topic modeling processes. The major difference is LDA requires the specification of the number of topics, and HDP doesn''t. Why is that so? And what are the differences, pros, and cons of both topic modelling methods?&lt;/p&gt;
', 'nlp  topic-model  lda', NULL, '''/a'':23C,34C ''/p'':74C ''/wiki/hierarchical_dirichlet_process'':29C ''/wiki/latent_dirichlet_allocation'':18C ''alloc'':3A,21C ''con'':68C ''differ'':42C,65C ''dirichlet'':2A,6A,20C,31C ''doesn'':55C ''en.wikipedia.org'':17C,28C ''en.wikipedia.org/wiki/hierarchical_dirichlet_process'':27C ''en.wikipedia.org/wiki/latent_dirichlet_allocation'':16C ''hdp'':33C,54C ''hierarch'':5A,30C ''href'':15C,26C ''latent'':1A,19C ''lda'':12B,22C,44C ''major'':41C ''method'':73C ''model'':11B,38C,72C ''nlp'':8B ''number'':50C ''p'':13C ''process'':7A,32C,39C ''pros'':66C ''requir'':45C ''specif'':47C ''topic'':10B,37C,52C,71C ''topic-model'':9B ''vs'':4A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (129, 1, '2014-05-18 06:17:37.587', 4, 90, 'What is generative and discriminative model? How are they used in Natural Language Processing?', '&lt;p&gt;&lt;a href="http://stackoverflow.com/questions/879432/what-is-the-difference-between-a-generative-and-discriminative-algorithm"&gt;This question&lt;/a&gt; asks about generative vs. discriminative algorithm, but can someone give an example of the difference between these forms when applied to Natural Language Processing? &lt;strong&gt;How are generative and discriminative models used in NLP?&lt;/strong&gt;&lt;/p&gt;
', 'nlp  language-model', NULL, '''/a'':27C ''/p'':63C ''/questions/879432/what-is-the-difference-between-a-generative-and-discriminative-algorithm'':24C ''/strong'':62C ''algorithm'':33C ''appli'':47C ''ask'':28C ''differ'':42C ''discrimin'':5A,32C,57C ''exampl'':39C ''form'':45C ''generat'':3A,30C,55C ''give'':37C ''href'':21C ''languag'':13A,17B,50C ''language-model'':16B ''model'':6A,18B,58C ''natur'':12A,49C ''nlp'':15B,61C ''p'':19C ''process'':14A,51C ''question'':26C ''someon'':36C ''stackoverflow.com'':23C ''stackoverflow.com/questions/879432/what-is-the-difference-between-a-generative-and-discriminative-algorithm'':22C ''strong'':52C ''use'':10A,59C ''vs'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (131, 2, '2014-05-18 06:54:08.56', 4, NULL, NULL, '&lt;p&gt;Look it up on:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Google Scholar &lt;a href="http://scholar.google.com/scholar?hl=en&amp;amp;q=Assessment%20of%20Utility%20in%20Web%20Mining%20for%20the%20Domain%20of%20Public%20Health&amp;amp;btnG=&amp;amp;as_sdt=1,5&amp;amp;as_sdtp=" rel="nofollow"&gt;link&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Citeseer &lt;a href="http://citeseerx.ist.psu.edu/search?q=Assessment%20of%20Utility%20in%20Web%20Mining%20for%20the%20Domain%20of%20Public%20Health&amp;amp;submit=Search&amp;amp;sort=rlv&amp;amp;t=doc" rel="nofollow"&gt;link&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If you get a single exact title match then you have probably found the right article, and can fill in the rest of the info from there.  Both give you download links and bibtex-style output.  What you would likely want to do though to get perfect metadata is download and parse the pdf (if any) and look for DOI-style identifier.&lt;/p&gt;

&lt;p&gt;Please be nice and rate-limit your requests if you do this.&lt;/p&gt;
', NULL, NULL, '''/a'':19C,31C ''/li'':20C,32C ''/p'':6C,99C,114C ''/scholar?hl=en&amp;amp;q=assessment%20of%20utility%20in%20web%20mining%20for%20the%20domain%20of%20public%20health&amp;amp;btng=&amp;amp;as_sdt=1,5&amp;amp;as_sdtp='':15C ''/search?q=assessment%20of%20utility%20in%20web%20mining%20for%20the%20domain%20of%20public%20health&amp;amp;submit=search&amp;amp;sort=rlv&amp;amp;t=doc'':27C ''/ul'':33C ''articl'':50C ''bibtex'':69C ''bibtex-styl'':68C ''cites'':22C ''citeseerx.ist.psu.edu'':26C ''citeseerx.ist.psu.edu/search?q=assessment%20of%20utility%20in%20web%20mining%20for%20the%20domain%20of%20public%20health&amp;amp;submit=search&amp;amp;sort=rlv&amp;amp;t=doc'':25C ''doi'':96C ''doi-styl'':95C ''download'':65C,85C ''exact'':40C ''fill'':53C ''found'':47C ''get'':37C,81C ''give'':63C ''googl'':9C ''href'':12C,24C ''identifi'':98C ''info'':59C ''li'':8C,21C ''like'':75C ''limit'':107C ''link'':18C,30C,66C ''look'':2C,93C ''match'':42C ''metadata'':83C ''nice'':103C ''nofollow'':17C,29C ''output'':71C ''p'':1C,34C,100C ''pars'':87C ''pdf'':89C ''perfect'':82C ''pleas'':101C ''probabl'':46C ''rate'':106C ''rate-limit'':105C ''rel'':16C,28C ''request'':109C ''rest'':56C ''right'':49C ''scholar'':10C ''scholar.google.com'':14C ''scholar.google.com/scholar?hl=en&amp;amp;q=assessment%20of%20utility%20in%20web%20mining%20for%20the%20domain%20of%20public%20health&amp;amp;btng=&amp;amp;as_sdt=1,5&amp;amp;as_sdtp='':13C ''singl'':39C ''style'':70C,97C ''though'':79C ''titl'':41C ''ul'':7C ''want'':76C ''would'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (132, 2, '2014-05-18 07:53:58.203', 22, NULL, NULL, '&lt;p&gt;Simply put: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;feature selection: you select a subset of the original feature set; while&lt;/li&gt;
&lt;li&gt;feature extraction: you build a new set of features from the original feature set. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Examples of feature extraction: extraction of contours in images, extraction of digrams from a text, extraction of phonemes from recording of spoken text, etc.&lt;/p&gt;

&lt;p&gt;Feature extraction involves a transformation of the features, which often is not reversible because some information is lost in the process of dimensionality reduction.&lt;/p&gt;
', NULL, NULL, '''/li'':19C,35C ''/p'':4C,62C,88C ''/ul'':36C ''build'':24C ''contour'':44C ''digram'':49C ''dimension'':86C ''etc'':61C ''exampl'':38C ''extract'':22C,41C,42C,47C,53C,65C ''featur'':7C,16C,21C,29C,33C,40C,64C,71C ''imag'':46C ''inform'':79C ''involv'':66C ''li'':6C,20C ''lost'':81C ''new'':26C ''often'':73C ''origin'':15C,32C ''p'':1C,37C,63C ''phonem'':55C ''process'':84C ''put'':3C ''record'':57C ''reduct'':87C ''revers'':76C ''select'':8C,10C ''set'':17C,27C,34C ''simpli'':2C ''spoken'':59C ''subset'':12C ''text'':52C,60C ''transform'':68C ''ul'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (133, 2, '2014-05-18 09:09:47.78', 6, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;&lt;p&gt;I think a number of clustering algorithms that normally use a metric, do not actually rely on the metric properties (other than commutativity, but I think you''d have that here).  For example, DBSCAN uses epsilon-neighborhoods around a point; there is nothing in there that specifically says the triangle inequality matters.  So you can probably use DBSCAN, although you may have to do some kind of nonstandard spatial index to do efficient lookups in your case.  Your version of epsilon-neighborhood will likely be sim &gt; 1/epsilon rather than the other way around.  Same story with k-means and related algorithms.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Can you construct a metric from your similarity?  One possibility: dist(ei, ej) = min( sim(ei, ek) + sim(ek, ej) ) for all k ...  Alternately, can you provide an upper  bound such that sim(ei, ej) &amp;lt; sim(ei, ek) + sim(ek, ej) + d, for all k and some positive constant d?  Intuitively, large sim values means closer together: is 1/sim metric-like?  What about 1/(sim + constant)?  What about min( 1/sim(ei, ek) + 1/sim(ek, ej) ) for all k? (that last is guaranteed to be a metric, btw)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;An alternate construction of a metric is to do an embedding.  As a first step, you can try to map your points ei -&gt; xi, such that xi minimize sum( abs( sim(ei, ej) - f( dist(xi, xj) ) ), for some suitable function f and metric dist.  The function f converts distance in the embedding to a similarity-like value; you''d have to experiment a bit, but 1/dist or exp^-dist are good starting points.  You''d also have to experiment on the best dimension for xi.  From there, you can use conventional clustering on xi.  The idea here is that you can almost (in a best fit sense) convert your distances in the embedding to similarity values, so they would cluster correctly.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;On the use of predefined parameters, all algorithms have some tuning.  DBSCAN can find the number of clusters, but you still need to give it some parameters.  In general, tuning requires multiple runs of the algorithm with different values for the tunable parameters, together with some function that evaluates goodness-of-clustering (either calculated separately, provided by the clustering algorithm itself, or just eyeballed :)  If the character of your data doesn''t change, you can tune once and then use those fixed parameters; if it changes then you have to tune for each run.  You can find that out by tuning for each run and then comparing how well the parameters from one run work on another, compared to the parameters specifically tuned for that.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':109C,202C,329C,459C ''/ol'':460C ''/p'':108C,201C,328C,458C ''1'':177C ''1/dist'':272C ''1/epsilon'':92C ''1/sim'':171C,183C,186C ''ab'':234C ''actual'':18C ''algorithm'':10C,107C,339C,367C,392C ''almost'':308C ''also'':282C ''altern'':135C,206C ''although'':63C ''anoth'':449C ''around'':42C,98C ''best'':288C,311C ''bit'':270C ''bound'':141C ''btw'':200C ''calcul'':386C ''case'':81C ''chang'':405C,418C ''charact'':399C ''closer'':168C ''cluster'':9C,298C,326C,349C,384C,391C ''commut'':26C ''compar'':439C,450C ''constant'':161C,179C ''construct'':114C,207C ''convent'':297C ''convert'':253C,314C ''correct'':327C ''d'':31C,154C,162C,265C,281C ''data'':402C ''dbscan'':37C,62C,343C ''differ'':369C ''dimens'':289C ''dist'':122C,239C,249C,275C ''distanc'':254C,316C ''doesn'':403C ''effici'':77C ''ei'':123C,127C,145C,149C,184C,227C,236C ''either'':385C ''ej'':124C,131C,146C,153C,188C,237C ''ek'':128C,130C,150C,152C,185C,187C ''embed'':215C,257C,319C ''epsilon'':40C,86C ''epsilon-neighborhood'':39C,85C ''evalu'':380C ''exampl'':36C ''exp'':274C ''experi'':268C,285C ''eyebal'':396C ''f'':238C,246C,252C ''find'':345C,429C ''first'':218C ''fit'':312C ''fix'':414C ''function'':245C,251C,378C ''general'':360C ''give'':355C ''good'':277C,382C ''goodness-of-clust'':381C ''guarante'':195C ''idea'':302C ''index'':74C ''inequ'':55C ''intuit'':163C ''k'':103C,134C,157C,191C ''k-mean'':102C ''kind'':70C ''larg'':164C ''last'':193C ''li'':2C,110C,203C,330C ''like'':89C,174C,262C ''lookup'':78C ''lt'':147C ''map'':224C ''matter'':56C ''may'':65C ''mean'':104C,167C ''metric'':15C,22C,116C,173C,199C,210C,248C ''metric-lik'':172C ''min'':125C,182C ''minim'':232C ''multipl'':363C ''need'':353C ''neighborhood'':41C,87C ''nonstandard'':72C ''normal'':12C ''noth'':47C ''number'':7C,347C ''ol'':1C ''one'':120C,445C ''p'':3C,111C,204C,331C ''paramet'':337C,358C,374C,415C,443C,453C ''point'':44C,226C,279C ''posit'':160C ''possibl'':121C ''predefin'':336C ''probabl'':60C ''properti'':23C ''provid'':138C,388C ''rather'':93C ''relat'':106C ''reli'':19C ''requir'':362C ''run'':364C,426C,436C,446C ''say'':52C ''sens'':313C ''separ'':387C ''sim'':91C,126C,129C,144C,148C,151C,165C,178C,235C ''similar'':119C,261C,321C ''similarity-lik'':260C ''spatial'':73C ''specif'':51C,454C ''start'':278C ''step'':219C ''still'':352C ''stori'':100C ''suitabl'':244C ''sum'':233C ''think'':5C,29C ''togeth'':169C,375C ''tri'':222C ''triangl'':54C ''tunabl'':373C ''tune'':342C,361C,408C,423C,433C,455C ''upper'':140C ''use'':13C,38C,61C,296C,334C,412C ''valu'':166C,263C,322C,370C ''version'':83C ''way'':97C ''well'':441C ''work'':447C ''would'':325C ''xi'':228C,231C,240C,291C,300C ''xj'':241C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (134, 1, '2014-05-18 12:03:21.65', 10, 405, 'Can map-reduce algorithms written for MongoDB be ported to Hadoop later?', '&lt;p&gt;In our company, we have a MongoDB database containing a lot of unstructured data, on which we need to run map-reduce algorithms to generate reports and other analyses. We have two approaches to select from for implementing the required analyses:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;One approach is to extract the data from MongoDB to a Hadoop cluster and do the analysis completely in Hadoop platform. However, this requires considerable investment on preparing the platform (software and hardware) and educating the team to work with Hadoop and write map-reduce tasks for it.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Another approach is to just put our effort on designing the map-reduce algorithms, and run the algorithms on MongoDB map-reduce functionalities. This way, we can create an initial prototype of final system that can generate the reports. I know that the MongoDB''s map-reduce functionalities are much slower compared to Hadoop, but currently the data is not that big that makes this a bottleneck yet, at least not for the next six months.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The question is, using the second approach and writing the algorithms for MongoDB, can them be later ported to Hadoop with little needed modification and algorithm redesign? MongoDB just supports JavaScript but programming language differences are easy to handle. However, is there any fundamental differences in the map-reduce model of MongoDB and Hadoop that may force us to redesign algorithms substantially for porting to Hadoop?&lt;/p&gt;
', 'scalability  hadoop  map-reduce  mongodb', NULL, '''/li'':117C,200C ''/ol'':201C ''/p'':63C,116C,199C,270C ''algorithm'':5A,44C,134C,138C,213C,228C,264C ''analys'':50C,62C ''analysi'':83C ''anoth'':120C ''approach'':54C,68C,121C,209C ''big'':184C ''bottleneck'':189C ''cluster'':79C ''compani'':23C ''compar'':174C ''complet'':84C ''consider'':91C ''contain'':29C ''creat'':149C ''current'':178C ''data'':34C,73C,180C ''databas'':28C ''design'':129C ''differ'':237C,247C ''easi'':239C ''educ'':101C ''effort'':127C ''extract'':71C ''final'':154C ''forc'':260C ''function'':144C,170C ''fundament'':246C ''generat'':46C,158C ''hadoop'':12A,15B,78C,86C,107C,176C,222C,257C,269C ''handl'':241C ''hardwar'':99C ''howev'':88C,242C ''implement'':59C ''initi'':151C ''invest'':92C ''javascript'':233C ''know'':162C ''languag'':236C ''later'':13A,219C ''least'':192C ''li'':65C,118C ''littl'':224C ''lot'':31C ''make'':186C ''map'':3A,17B,42C,111C,132C,142C,168C,251C ''map-reduc'':2A,16B,41C,110C,131C,141C,167C,250C ''may'':259C ''model'':253C ''modif'':226C ''mongodb'':8A,19B,27C,75C,140C,165C,215C,230C,255C ''month'':198C ''much'':172C ''need'':38C,225C ''next'':196C ''ol'':64C ''one'':67C ''p'':20C,66C,119C,202C ''platform'':87C,96C ''port'':10A,220C,267C ''prepar'':94C ''program'':235C ''prototyp'':152C ''put'':125C ''question'':204C ''redesign'':229C,263C ''reduc'':4A,18B,43C,112C,133C,143C,169C,252C ''report'':47C,160C ''requir'':61C,90C ''run'':40C,136C ''scalabl'':14B ''second'':208C ''select'':56C ''six'':197C ''slower'':173C ''softwar'':97C ''substanti'':265C ''support'':232C ''system'':155C ''task'':113C ''team'':103C ''two'':53C ''unstructur'':33C ''us'':261C ''use'':206C ''way'':146C ''work'':105C ''write'':109C,211C ''written'':6A ''yet'':190C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (135, 2, '2014-05-18 12:30:06.853', 8, NULL, NULL, '&lt;p&gt;Considering another criteria, I think that in some cases using Python may be much superior to R for Big Data. I know the wide-spread use of R in data science educational materials and the good data analysis libraries available for it, but sometimes it just depend on the team.&lt;/p&gt;

&lt;p&gt;In my experience, for people already familiar with programming, using Python provides much more flexibility and productivity boost compared to a language like R, which is not as well-designed and powerful compared to Python in terms of a programming language. As an evidence, in a data mining course in my university, the best final project was written in Python, although the others has access to R''s rich data analysis library. That is, sometimes the overall productivity (considering learning materials, documentation, etc.) for Python may be better than R even in the lack of special-purpose data analysis libraries for Python. Also, there are some good articles explaining the fast pace of Python in data science: &lt;a href="http://readwrite.com/2013/11/25/python-displacing-r-as-the-programming-language-for-data-science"&gt;Python Displacing R&lt;/a&gt; and &lt;a href="http://wesmckinney.com/blog/?p=77"&gt;Rich Scientific Data Structures in Python&lt;/a&gt; that may soon fill the gap of available libraries for R.&lt;/p&gt;

&lt;p&gt;Another important reason for not using R is when working with real world Big Data problems, contrary to academical only problems, there is much need for other tools and techniques, like data parsing, cleaning, visualization, web scrapping, and a lot of others that are much easier using a general purpose programming language. This may be why the default language used in many Hadoop courses (including the Udacity''s &lt;a href="https://www.udacity.com/course/ud617"&gt;online course&lt;/a&gt;) is Python.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Recently DARPA has also invested $3 million to help fund Python''s data processing and visualization capabilities for big data jobs, which is clearly a sign of Python''s future in Big Data. (&lt;a href="http://www.computerworld.com/s/article/9236558/Python_gets_a_big_data_boost_from_DARPA"&gt;details&lt;/a&gt;)&lt;/p&gt;
', NULL, NULL, '''/2013/11/25/python-displacing-r-as-the-programming-language-for-data-science'':178C ''/a'':182C,195C,284C,333C ''/blog/?p=77'':188C ''/course/ud617'':281C ''/p'':53C,207C,287C,292C,334C ''/s/article/9236558/python_gets_a_big_data_boost_from_darpa'':331C ''/strong'':291C ''3'':299C ''academ'':227C ''access'':120C ''alreadi'':60C ''also'':159C,297C ''although'':116C ''analysi'':40C,126C,155C ''anoth'':3C,209C ''articl'':164C ''avail'':42C,203C ''best'':109C ''better'':143C ''big'':20C,222C,312C,325C ''boost'':72C ''capabl'':310C ''case'':10C ''clean'':242C ''clear'':317C ''compar'':73C,88C ''consid'':2C,134C ''contrari'':225C ''cours'':104C,272C,283C ''criteria'':4C ''darpa'':295C ''data'':21C,32C,39C,102C,125C,154C,172C,191C,223C,240C,306C,313C,326C ''default'':266C ''depend'':49C ''design'':85C ''detail'':332C ''displac'':180C ''document'':137C ''easier'':254C ''edit'':290C ''educ'':34C ''etc'':138C ''even'':146C ''evid'':99C ''experi'':57C ''explain'':165C ''familiar'':61C ''fast'':167C ''fill'':199C ''final'':110C ''flexibl'':69C ''fund'':303C ''futur'':323C ''gap'':201C ''general'':257C ''good'':38C,163C ''hadoop'':271C ''help'':302C ''href'':175C,185C,278C,328C ''import'':210C ''includ'':273C ''invest'':298C ''job'':314C ''know'':23C ''lack'':149C ''languag'':76C,96C,260C,267C ''learn'':135C ''librari'':41C,127C,156C,204C ''like'':77C,239C ''lot'':248C ''mani'':270C ''materi'':35C,136C ''may'':13C,141C,197C,262C ''million'':300C ''mine'':103C ''much'':15C,67C,232C,253C ''need'':233C ''onlin'':282C ''other'':118C,250C ''overal'':132C ''p'':1C,54C,208C,288C,293C ''pace'':168C ''pars'':241C ''peopl'':59C ''power'':87C ''problem'':224C,229C ''process'':307C ''product'':71C,133C ''program'':63C,95C,259C ''project'':111C ''provid'':66C ''purpos'':153C,258C ''python'':12C,65C,90C,115C,140C,158C,170C,179C,194C,286C,304C,321C ''r'':18C,30C,78C,122C,145C,181C,206C,215C ''readwrite.com'':177C ''readwrite.com/2013/11/25/python-displacing-r-as-the-programming-language-for-data-science'':176C ''real'':220C ''reason'':211C ''recent'':294C ''rich'':124C,189C ''scienc'':33C,173C ''scientif'':190C ''scrap'':245C ''sign'':319C ''sometim'':46C,130C ''soon'':198C ''special'':152C ''special-purpos'':151C ''spread'':27C ''strong'':289C ''structur'':192C ''superior'':16C ''team'':52C ''techniqu'':238C ''term'':92C ''think'':6C ''tool'':236C ''udac'':275C ''univers'':107C ''use'':11C,28C,64C,214C,255C,268C ''visual'':243C,309C ''web'':244C ''well'':84C ''well-design'':83C ''wesmckinney.com'':187C ''wesmckinney.com/blog/?p=77'':186C ''wide'':26C ''wide-spread'':25C ''work'':218C ''world'':221C ''written'':113C ''www.computerworld.com'':330C ''www.computerworld.com/s/article/9236558/python_gets_a_big_data_boost_from_darpa'':329C ''www.udacity.com'':280C ''www.udacity.com/course/ud617'':279C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (136, 5, '2014-05-18 12:55:39.657', 0, NULL, NULL, '&lt;p&gt;MapReduce is a framework for processing parallelizable problems across huge datasets using a large number of computers (nodes), collectively referred to as a cluster (if all nodes are on the same local network and use similar hardware) or a grid (if the nodes are shared across geographically and administratively distributed systems, and use more heterogenous hardware). Computational processing can occur on data stored either in a filesystem (unstructured) or in a database (structured). MapReduce can take advantage of locality of data, processing it on or near the storage assets in order to reduce the distance over which it must be transmitted.&lt;/p&gt;
', NULL, NULL, '''/p'':103C ''across'':10C,47C ''administr'':50C ''advantag'':78C ''asset'':90C ''cluster'':25C ''collect'':20C ''comput'':18C,58C ''data'':63C,82C ''databas'':73C ''dataset'':12C ''distanc'':96C ''distribut'':51C ''either'':65C ''filesystem'':68C ''framework'':5C ''geograph'':48C ''grid'':41C ''hardwar'':38C,57C ''heterogen'':56C ''huge'':11C ''larg'':15C ''local'':33C,80C ''mapreduc'':2C,75C ''must'':100C ''near'':87C ''network'':34C ''node'':19C,28C,44C ''number'':16C ''occur'':61C ''order'':92C ''p'':1C ''paralleliz'':8C ''problem'':9C ''process'':7C,59C,83C ''reduc'':94C ''refer'':21C ''share'':46C ''similar'':37C ''storag'':89C ''store'':64C ''structur'':74C ''system'':52C ''take'':77C ''transmit'':102C ''unstructur'':69C ''use'':13C,36C,54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (137, 4, '2014-05-18 12:55:39.657', 0, NULL, NULL, 'MapReduce is a programming model for processing large data sets with a parallel, distributed algorithm on a cluster.', NULL, NULL, '''algorithm'':15C ''cluster'':18C ''data'':9C ''distribut'':14C ''larg'':8C ''mapreduc'':1C ''model'':5C ''parallel'':13C ''process'':7C ''program'':4C ''set'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9081, 2, '2015-11-27 14:55:43.017', 0, NULL, NULL, '&lt;p&gt;I recommend calculating 1-gram, 2-grams and ... frequencies, sorting based on frequencies and observing what pops up. Sometimes you see patterns that need extra data cleansing. &lt;/p&gt;
', NULL, NULL, '''/p'':29C ''1'':5C ''2'':7C ''base'':12C ''calcul'':4C ''cleans'':28C ''data'':27C ''extra'':26C ''frequenc'':10C,14C ''gram'':6C,8C ''need'':25C ''observ'':16C ''p'':1C ''pattern'':23C ''pop'':18C ''recommend'':3C ''see'':22C ''sometim'':20C ''sort'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (138, 1, '2014-05-18 14:02:51.35', 8, 159, 'Why is it hard to grant efficiency while using libraries?', '&lt;p&gt;Any small database processing can be easily tackled by Python/Perl/... scripts, that uses libraries and/or even utilities from the language itself. However, when it comes to performance, people tend to reach out for C/C++/low-level languages. The possibility of tailoring the code to the needs seems to be what makes these languages so appealing for BigData -- be it concerning memory management, parallelism, disk access, or even low-level optimizations (via assembly constructs at C/C++ level).&lt;/p&gt;

&lt;p&gt;Of course such set of benefits would not come without a cost: writing the code, and sometimes even &lt;em&gt;reinventing the wheel&lt;/em&gt;, can be quite expensive/tiresome. Although there are lots of libraries available, people are inclined to write the code by themselves whenever they need to &lt;em&gt;grant&lt;/em&gt; performance. What &lt;em&gt;disables&lt;/em&gt; performance assertions from using libraries while processing large databases?&lt;/p&gt;

&lt;p&gt;For example, consider an entreprise that continuously crawls webpages and parses the data collected. For each sliding-window, different data mining algorithms are run upon the data extracted. Why would the developers ditch off using available libraries/frameworks (be it for crawling, text processing, and data mining)? Using stuff already implemented would not only ease the burden of coding the whole process, but also would save a lot of time.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;In a single shot&lt;/strong&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;what makes writing the code by oneself a &lt;em&gt;guarantee&lt;/em&gt; of performance?&lt;/li&gt;
&lt;li&gt;why is it &lt;em&gt;risky&lt;/em&gt; to rely on a frameworks/libraries when you must &lt;strong&gt;assure&lt;/strong&gt; high performance?&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (139, 2, '2014-05-18 14:04:37.87', 3, NULL, NULL, '&lt;p&gt;I''d suggest &lt;a href="http://kafka.apache.org" rel="nofollow"&gt;Apache Kafka&lt;/a&gt; as message store and any stream processing solution of your choice like &lt;a href="https://camel.apache.org" rel="nofollow"&gt;Apache Camel&lt;/a&gt; or &lt;a href="https://github.com/apache/incubator-storm" rel="nofollow"&gt;Twitter Storm&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':12C,32C,43C ''/apache/incubator-storm'':38C ''/p'':44C ''apach'':10C,30C ''camel'':31C ''camel.apache.org'':27C ''choic'':23C ''d'':3C ''github.com'':37C ''github.com/apache/incubator-storm'':36C ''href'':6C,26C,35C ''kafka'':11C ''kafka.apache.org'':7C ''like'':24C ''messag'':14C ''nofollow'':9C,29C,40C ''p'':1C ''process'':19C ''rel'':8C,28C,39C ''solut'':20C ''store'':15C ''storm'':42C ''stream'':18C ''suggest'':4C ''twitter'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (140, 2, '2014-05-18 14:30:10.553', 2, NULL, NULL, '&lt;p&gt;I''ve read very good &lt;a href="http://www.michael-noll.com/blog/2013/01/18/implementing-real-time-trending-topics-in-storm/" rel="nofollow"&gt;article&lt;/a&gt; recently that suggests using &lt;a href="https://github.com/nathanmarz/storm" rel="nofollow"&gt;Twitter storm&lt;/a&gt; for a task that looks pretty similar to yours.&lt;/p&gt;
', NULL, NULL, '''/a'':15C,29C ''/blog/2013/01/18/implementing-real-time-trending-topics-in-storm/'':11C ''/nathanmarz/storm'':24C ''/p'':39C ''articl'':14C ''github.com'':23C ''github.com/nathanmarz/storm'':22C ''good'':6C ''href'':8C,21C ''look'':34C ''nofollow'':13C,26C ''p'':1C ''pretti'':35C ''read'':4C ''recent'':16C ''rel'':12C,25C ''similar'':36C ''storm'':28C ''suggest'':18C ''task'':32C ''twitter'':27C ''use'':19C ''ve'':3C ''www.michael-noll.com'':10C ''www.michael-noll.com/blog/2013/01/18/implementing-real-time-trending-topics-in-storm/'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (142, 4, '2014-05-18 14:36:16.35', 0, NULL, NULL, 'Efficiency, in algorithmic processing, is usually associated to resource usage. The metrics to evaluate the efficiency of a process are commonly account for execution time, memory/disk or storage requirements, network usage and power consumption.', NULL, NULL, '''account'':22C ''algorithm'':3C ''associ'':7C ''common'':21C ''consumpt'':34C ''effici'':1C,16C ''evalu'':14C ''execut'':24C ''memory/disk'':26C ''metric'':12C ''network'':30C ''power'':33C ''process'':4C,19C ''requir'':29C ''resourc'':9C ''storag'':28C ''time'':25C ''usag'':10C,31C ''usual'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (143, 1, '2014-05-18 14:37:20.477', 7, 212, 'What is the most efficient data indexing technique', '&lt;p&gt;As we all know, there are some data indexing techniques, using by well-known indexing apps, like Lucene (for java) or Lucene.NET (for .NET), MurMurHash, B+Tree etc. For a No-Sql / Object Oriented Database (which I try to write/play a little around with C#), which technique you suggest?&lt;/p&gt;

&lt;p&gt;I read about MurMurhash-2 and specially v3 comments say Murmur is very fast. Also Lucene.Net has good comments on it. But what about their memory footprints in general? Is there any efficient solution which uses less footprint (and of course if faster is preferable) than Lucene or Murmur? Or should I write a special index structure to get the best results?&lt;/p&gt;

&lt;p&gt;If I try to write my own, then is there any accepted scale for a good indexing, something like 1% of data-node, or 5% of data-node? Any useful hint will be appreciated.&lt;/p&gt;
', 'nosql  efficiency  indexing  data-indexing-techniques  .net', NULL, '''-2'':75C ''/p'':69C,133C,171C ''1'':154C ''5'':160C ''accept'':146C ''also'':85C ''app'':34C ''appreci'':170C ''around'':62C ''b'':44C ''best'':131C ''c'':64C ''comment'':79C,89C ''cours'':111C ''data'':6A,13B,25C,157C,163C ''data-indexing-techniqu'':12B ''data-nod'':156C,162C ''databas'':54C ''effici'':5A,10B,103C ''etc'':46C ''fast'':84C ''faster'':113C ''footprint'':97C,108C ''general'':99C ''get'':129C ''good'':88C,150C ''hint'':167C ''index'':7A,11B,14B,26C,33C,126C,151C ''java'':38C ''know'':21C ''known'':32C ''less'':107C ''like'':35C,153C ''littl'':61C ''lucen'':36C,117C ''lucene.net'':40C,86C ''memori'':96C ''murmur'':81C,119C ''murmurhash'':43C,74C ''net'':16B,42C ''no-sql'':49C ''node'':158C,164C ''nosql'':9B ''object'':52C ''orient'':53C ''p'':17C,70C,134C ''prefer'':115C ''read'':72C ''result'':132C ''say'':80C ''scale'':147C ''solut'':104C ''someth'':152C ''special'':77C,125C ''sql'':51C ''structur'':127C ''suggest'':68C ''techniqu'':8A,15B,27C,66C ''tree'':45C ''tri'':57C,137C ''use'':28C,106C,166C ''v3'':78C ''well'':31C ''well-known'':30C ''write'':123C,139C ''write/play'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (144, 5, '2014-05-18 14:58:34.853', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (149, 4, '2014-05-18 15:08:08.913', 0, NULL, NULL, 'In Computer science and Technologies The Data is the most important part.&#x0d;
Since to work with data in an efficient manner to store the data in mediums and reuse, we use some techniques which named in general indexing.&#x0d;
&#x0d;
This tag interests with these techniques, efficiency, and anything about using stored data with indexing', NULL, NULL, '''anyth'':47C ''comput'':2C ''data'':7C,17C,25C,51C ''effici'':20C,45C ''general'':37C ''import'':11C ''index'':38C,53C ''interest'':41C ''manner'':21C ''medium'':27C ''name'':35C ''part'':12C ''reus'':29C ''scienc'':3C ''sinc'':13C ''store'':23C,50C ''tag'':40C ''techniqu'':33C,44C ''technolog'':5C ''use'':31C,49C ''work'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (151, 5, '2014-05-18 15:34:16.437', 0, NULL, NULL, '&lt;p&gt;Indexing is the almost most important part of data to get an efficient, properly storing and retrieval data from mediums&lt;/p&gt;

&lt;p&gt;In different Programming Languages, there are different indexing algorithms and structures can be found.&lt;/p&gt;

&lt;p&gt;As an Example, in Java Language the Apache Foundation''s Lucene is very popular.&lt;/p&gt;

&lt;p&gt;In years there are very efficient, fast, scalable Indexing Algorithms projectioned to the computer-world such as MurMurHashing Algorithm (using by some NoSQL Databases), B+Tree Algorithm (using by versions of Windows OS itself), or Lucene (very popular in web technologies) and its variations.&lt;/p&gt;

&lt;p&gt;Also there can be found problem-specific Indexing within Chemicals or Medical Data Representations in Digital World&lt;/p&gt;
', NULL, NULL, '''/p'':22C,38C,54C,101C,121C ''algorithm'':32C,65C,75C,83C ''almost'':5C ''also'':103C ''apach'':47C ''b'':81C ''chemic'':113C ''comput'':70C ''computer-world'':69C ''data'':10C,19C,116C ''databas'':80C ''differ'':25C,30C ''digit'':119C ''effici'':14C,61C ''exampl'':42C ''fast'':62C ''found'':37C,107C ''foundat'':48C ''get'':12C ''import'':7C ''index'':2C,31C,64C,111C ''java'':44C ''languag'':27C,45C ''lucen'':50C,92C ''medic'':115C ''medium'':21C ''murmurhash'':74C ''nosql'':79C ''os'':89C ''p'':1C,23C,39C,55C,102C ''part'':8C ''popular'':53C,94C ''problem'':109C ''problem-specif'':108C ''program'':26C ''project'':66C ''proper'':15C ''represent'':117C ''retriev'':18C ''scalabl'':63C ''specif'':110C ''store'':16C ''structur'':34C ''technolog'':97C ''tree'':82C ''use'':76C,84C ''variat'':100C ''version'':86C ''web'':96C ''window'':88C ''within'':112C ''world'':71C,120C ''year'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (152, 4, '2014-05-18 15:34:16.437', 0, NULL, NULL, 'Indexing is the almost most important part of data to get an efficient, properly storing and retrieval data from mediums', NULL, NULL, '''almost'':4C ''data'':9C,18C ''effici'':13C ''get'':11C ''import'':6C ''index'':1C ''medium'':20C ''part'':7C ''proper'':14C ''retriev'':17C ''store'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (266, 1, '2014-06-10 11:05:47.273', 9, 1941, 'What are some easy to learn machine-learning applications?', '&lt;p&gt;Being new to machine-learning in general, I''d like to start playing around and see what the possibilities are.&lt;/p&gt;

&lt;p&gt;I''m curious as to what applications you might recommend that would offer the fastest time from installation to producing a meaningful result.&lt;/p&gt;

&lt;p&gt;Also, any recommendations for good getting-started materials on the subject of machine-learning in general would be appreciated.&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':36C,61C,84C ''also'':63C ''applic'':10A,44C ''appreci'':83C ''around'':29C ''curious'':40C ''d'':24C ''easi'':4A ''fastest'':52C ''general'':22C,80C ''get'':69C ''getting-start'':68C ''good'':67C ''instal'':55C ''learn'':6A,9A,13B,20C,78C ''like'':25C ''m'':39C ''machin'':8A,12B,19C,77C ''machine-learn'':7A,11B,18C,76C ''materi'':71C ''meaning'':59C ''might'':46C ''new'':16C ''offer'':50C ''p'':14C,37C,62C ''play'':28C ''possibl'':34C ''produc'':57C ''recommend'':47C,65C ''result'':60C ''see'':31C ''start'':27C,70C ''subject'':74C ''time'':53C ''would'':49C,81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (153, 2, '2014-05-18 17:38:01.383', 3, NULL, NULL, '&lt;p&gt;The answers presented so far are very nice, but I was also expecting an emphasis on a particular difference between parallel and distributed processing: the code executed. Considering parallel processes, the code executed is the same, regardless of the level of parallelism (instruction, data, task). You write a &lt;em&gt;single code&lt;/em&gt;, and it will be executed by different threads/processors, e.g., while computing matrices products, or generating permutations.&lt;/p&gt;

&lt;p&gt;On the other hand, distributed computing involves the execution of different algorithms/programs at the same time in different processors (from one or more machines). Such computations are later merged into a intermediate/final results by using the available means of data communication/synchronization (shared memory, network). Further, distributed computing is very appealing for BigData processing, as it allows for exploiting disk parallelism (usually the bottleneck for large databases).&lt;/p&gt;

&lt;p&gt;Finally, for the level of parallelism, it may be taken rather as a constraint on the synchronization. For example, in GPGPU, which is single-instruction multiple-data (SIMD), the parallelism occurs by having different inputs for a single instruction, each pair &lt;em&gt;(data_i, instruction)&lt;/em&gt; being executed by a different thread. Such is the restraint that, in case of divergent branches, it is necessary to discard lots of unnecessary computations, until the threads reconverge. For CPU threads, though, they commonly diverge; yet, one may use synchronization structures to grant concurrent execution of specific sections of the code.&lt;/p&gt;
', NULL, NULL, '''/em'':53C,187C ''/p'':70C,138C,240C ''algorithms/programs'':83C ''allow'':127C ''also'':13C ''answer'':3C ''appeal'':121C ''avail'':108C ''bigdata'':123C ''bottleneck'':134C ''branch'':203C ''case'':200C ''code'':27C,33C,52C,239C ''common'':222C ''communication/synchronization'':112C ''comput'':64C,77C,97C,118C,212C ''concurr'':232C ''consid'':29C ''constraint'':153C ''cpu'':218C ''data'':45C,111C,168C,184C ''databas'':137C ''differ'':20C,60C,82C,89C,175C,192C ''discard'':208C ''disk'':130C ''distribut'':24C,76C,117C ''diverg'':202C,223C ''e.g'':62C ''em'':50C,183C ''emphasi'':16C ''exampl'':158C ''execut'':28C,34C,58C,80C,189C,233C ''expect'':14C ''exploit'':129C ''far'':6C ''final'':140C ''generat'':68C ''gpgpu'':160C ''grant'':231C ''hand'':75C ''input'':176C ''instruct'':44C,165C,180C,186C ''intermediate/final'':103C ''involv'':78C ''larg'':136C ''later'':99C ''level'':41C,143C ''lot'':209C ''machin'':95C ''matric'':65C ''may'':147C,226C ''mean'':109C ''memori'':114C ''merg'':100C ''multipl'':167C ''multiple-data'':166C ''necessari'':206C ''network'':115C ''nice'':9C ''occur'':172C ''one'':92C,225C ''p'':1C,71C,139C ''pair'':182C ''parallel'':22C,30C,43C,131C,145C,171C ''particular'':19C ''permut'':69C ''present'':4C ''process'':25C,31C,124C ''processor'':90C ''product'':66C ''rather'':150C ''reconverg'':216C ''regardless'':38C ''restraint'':197C ''result'':104C ''section'':236C ''share'':113C ''simd'':169C ''singl'':51C,164C,179C ''single-instruct'':163C ''specif'':235C ''structur'':229C ''synchron'':156C,228C ''taken'':149C ''task'':46C ''though'':220C ''thread'':193C,215C,219C ''threads/processors'':61C ''time'':87C ''unnecessari'':211C ''use'':106C,227C ''usual'':132C ''write'':48C ''yet'':224C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (155, 1, '2014-05-18 18:45:38.957', 76, 5667, 'Publicly Available Datasets', '&lt;p&gt;One of the common problems in data science is gathering data from various sources in a somehow cleaned (semi-structured) format and combining metrics from various sources for making a higher level analysis. Looking at the other people''s effort, especially other questions on this site, it appears that many people in this field are doing somewhat repetitive work. For example analyzing tweets, facebook posts, Wikipedia articles etc. is a part of a lot of big data problems.&lt;/p&gt;

&lt;p&gt;Some of these data sets are accessible using public APIs provided by the provider site, but usually some valuable information or metrics are missing from these APIs and everyone has to do the same analyses again and again. For example, although clustering users may depend on different use cases and selection of features, but having a base clustering of Twitter/Facebook users can be useful in many Big Data applications, which is neither provided by the API, nor available publicly in independent data sets.&lt;/p&gt;

&lt;p&gt;Is there any index or publicly available data set hosting site containing valuable data sets that can be reused in solving other big data problems? I mean something like GitHub (or a group of sites/public data sets or at least a comprehensive listing) for the data science. If not, what are the reasons of not having such a platform for data science? Commercial value of data, need to frequently update data sets, ...? Can we not have an open-source model for sharing data sets devised for data scientists?&lt;/p&gt;
', 'open-source  dataset', NULL, '''/p'':88C,173C,264C ''access'':96C ''although'':130C ''analys'':124C ''analysi'':42C ''analyz'':71C ''api'':99C,116C,165C ''appear'':57C ''applic'':158C ''articl'':76C ''avail'':2A,167C,181C ''base'':146C ''big'':85C,156C,197C ''case'':138C ''clean'':26C ''cluster'':131C,147C ''combin'':32C ''commerci'':237C ''common'':12C ''comprehens'':216C ''contain'':186C ''data'':15C,19C,86C,93C,157C,171C,182C,188C,198C,210C,220C,235C,240C,245C,258C,262C ''dataset'':3A,7B ''depend'':134C ''devis'':260C ''differ'':136C ''effort'':49C ''especi'':50C ''etc'':77C ''everyon'':118C ''exampl'':70C,129C ''facebook'':73C ''featur'':142C ''field'':63C ''format'':30C ''frequent'':243C ''gather'':18C ''github'':204C ''group'':207C ''higher'':40C ''host'':184C ''independ'':170C ''index'':178C ''inform'':109C ''least'':214C ''level'':41C ''like'':203C ''list'':217C ''look'':43C ''lot'':83C ''make'':38C ''mani'':59C,155C ''may'':133C ''mean'':201C ''metric'':33C,111C ''miss'':113C ''model'':255C ''need'':241C ''neither'':161C ''one'':9C ''open'':5B,253C ''open-sourc'':4B,252C ''p'':8C,89C,174C ''part'':80C ''peopl'':47C,60C ''platform'':233C ''post'':74C ''problem'':13C,87C,199C ''provid'':100C,103C,162C ''public'':1A,98C,168C,180C ''question'':52C ''reason'':227C ''repetit'':67C ''reus'':193C ''scienc'':16C,221C,236C ''scientist'':263C ''select'':140C ''semi'':28C ''semi-structur'':27C ''set'':94C,172C,183C,189C,211C,246C,259C ''share'':257C ''site'':55C,104C,185C ''sites/public'':209C ''solv'':195C ''somehow'':25C ''someth'':202C ''somewhat'':66C ''sourc'':6B,22C,36C,254C ''structur'':29C ''tweet'':72C ''twitter/facebook'':149C ''updat'':244C ''use'':97C,137C,153C ''user'':132C,150C ''usual'':106C ''valu'':238C ''valuabl'':108C,187C ''various'':21C,35C ''wikipedia'':75C ''work'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (156, 2, '2014-05-18 19:19:44.24', 20, NULL, NULL, '&lt;p&gt;&lt;a href="https://www.freebase.com"&gt;Freebase&lt;/a&gt; is a free community driven database that spans many interesting topics and contains about 2,5 billion facts in machine readable format. It is also have good API to perform data queries.&lt;/p&gt;
', NULL, NULL, '''/a'':6C ''/p'':39C ''2'':21C ''5'':22C ''also'':31C ''api'':34C ''billion'':23C ''communiti'':10C ''contain'':19C ''data'':37C ''databas'':12C ''driven'':11C ''fact'':24C ''format'':28C ''free'':9C ''freebas'':5C ''good'':33C ''href'':3C ''interest'':16C ''machin'':26C ''mani'':15C ''p'':1C ''perform'':36C ''queri'':38C ''readabl'':27C ''span'':14C ''topic'':17C ''www.freebase.com'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1007, 1, '2014-08-20 14:12:03.87', -3, 422, 'Looking for Web scraping tool for unstructured data', '&lt;p&gt;I want to scrape some data from a website. 
I have used import.io but still not much satisfied.. can any of you suggest about it.. whats the best tool to get the unstructured data from web&lt;/p&gt;
', 'tools  crawling', NULL, '''..'':30C,38C ''/p'':50C ''best'':41C ''crawl'':10B ''data'':8A,17C,47C ''get'':44C ''import.io'':24C ''look'':1A ''much'':28C ''p'':11C ''satisfi'':29C ''scrape'':4A,15C ''still'':26C ''suggest'':35C ''tool'':5A,9B,42C ''unstructur'':7A,46C ''use'':23C ''want'':13C ''web'':3A,49C ''websit'':20C ''what'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (291, 2, '2014-06-10 18:55:39.01', 7, NULL, NULL, '&lt;p&gt;If I were you I would take a MOOC or two (e.g., &lt;a href="https://www.coursera.org/course/algs4partI"&gt;Algorithms, Part I&lt;/a&gt;, &lt;a href="https://www.coursera.org/course/algs4partII"&gt;Algorithms, Part II&lt;/a&gt;, &lt;a href="https://www.coursera.org/course/progfun"&gt;Functional Programming Principles in Scala&lt;/a&gt;), a good book on data structures and algorithms, then just code as much as possible. You could implement some statistics or ML algorithms, for example; that would be good practice for you and useful to the community.&lt;/p&gt;

&lt;p&gt;For a PhD program, however, I would also make sure I were familiar with the type of maths they use. If you want to see what it''s like at the deep end, browse the papers at the &lt;a href="http://jmlr.org/papers/"&gt;JMLR&lt;/a&gt;. That will let you calibrate yourself in regards to theory; can you sort of follow the maths?&lt;/p&gt;

&lt;p&gt;Oh, and you don''t need a PhD to work at top companies, unless you want to join research departments like his. But then you''ll spend more time doing development, and you''ll need good coding skills...&lt;/p&gt;
', NULL, NULL, '''/a'':22C,31C,42C,126C ''/course/algs4parti'':18C ''/course/algs4partii'':27C ''/course/progfun'':36C ''/p'':80C,144C,184C ''/papers/'':124C ''algorithm'':19C,28C,50C,65C ''also'':89C ''book'':45C ''brows'':115C ''calibr'':131C ''code'':53C,182C ''communiti'':79C ''compani'':158C ''could'':59C ''data'':47C ''deep'':113C ''depart'':165C ''develop'':176C ''e.g'':13C ''end'':114C ''exampl'':67C ''familiar'':94C ''follow'':141C ''function'':37C ''good'':44C,71C,181C ''howev'':86C ''href'':15C,24C,33C,121C ''ii'':30C ''implement'':60C ''jmlr'':125C ''jmlr.org'':123C ''jmlr.org/papers/'':122C ''join'':163C ''let'':129C ''like'':110C,166C ''ll'':171C,179C ''make'':90C ''math'':99C,143C ''ml'':64C ''mooc'':10C ''much'':55C ''need'':151C,180C ''oh'':146C ''p'':1C,81C,145C ''paper'':117C ''part'':20C,29C ''phd'':84C,153C ''possibl'':57C ''practic'':72C ''principl'':39C ''program'':38C,85C ''regard'':134C ''research'':164C ''scala'':41C ''see'':106C ''skill'':183C ''sort'':139C ''spend'':172C ''statist'':62C ''structur'':48C ''sure'':91C ''take'':8C ''theori'':136C ''time'':174C ''top'':157C ''two'':12C ''type'':97C ''unless'':159C ''use'':76C,101C ''want'':104C,161C ''work'':155C ''would'':7C,69C,88C ''www.coursera.org'':17C,26C,35C ''www.coursera.org/course/algs4parti'':16C ''www.coursera.org/course/algs4partii'':25C ''www.coursera.org/course/progfun'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (157, 2, '2014-05-18 19:22:05.16', 10, NULL, NULL, '&lt;p&gt;R is great for "big data"! However, you need a workflow since R is limited (with some simplification) by the amount of RAM in the operating system. The approach I take is to interact with a relational database (see the &lt;code&gt;RSQLite&lt;/code&gt; package for creating and interacting with a SQLite databse), run SQL-style queries to understand the structure of the data, and then extract particular subsets of the data for computationally-intensive statistical analysis.&lt;/p&gt;

&lt;p&gt;This just one approach, however: there are packages that allow you to interact with other databases (e.g., Monet) or run analyses in R with fewer memory limitations (e.g., see &lt;code&gt;pbdR&lt;/code&gt;).&lt;/p&gt;
', NULL, NULL, '''/code'':44C,113C ''/p'':80C,114C ''allow'':91C ''amount'':22C ''analys'':102C ''analysi'':79C ''approach'':30C,85C ''big'':6C ''code'':42C,111C ''comput'':76C ''computationally-intens'':75C ''creat'':47C ''data'':7C,65C,73C ''databas'':39C,97C ''databs'':53C ''e.g'':98C,109C ''extract'':68C ''fewer'':106C ''great'':4C ''howev'':8C,86C ''intens'':77C ''interact'':35C,49C,94C ''limit'':16C,108C ''memori'':107C ''monet'':99C ''need'':10C ''one'':84C ''oper'':27C ''p'':1C,81C ''packag'':45C,89C ''particular'':69C ''pbdr'':112C ''queri'':58C ''r'':2C,14C,104C ''ram'':24C ''relat'':38C ''rsqlite'':43C ''run'':54C,101C ''see'':40C,110C ''simplif'':19C ''sinc'':13C ''sql'':56C ''sql-style'':55C ''sqlite'':52C ''statist'':78C ''structur'':62C ''style'':57C ''subset'':70C ''system'':28C ''take'':32C ''understand'':60C ''workflow'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (158, 2, '2014-05-18 19:29:53.53', 42, NULL, NULL, '&lt;p&gt;There is, in fact, a very reasonable list of publicly-available datasets, supported by different enterprises/sources. Here are some of them:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://aws.amazon.com/publicdatasets/"&gt;Public Datasets on Amazon WebServices&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://fimi.ua.ac.be/data/"&gt;Frequent Itemset Mining Implementation Repository&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://archive.ics.uci.edu/ml/datasets.html"&gt;UCI Machine Learning Repository&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.kdnuggets.com/datasets/index.html"&gt;KDnuggets&lt;/a&gt; -- big list of lots of public repositories.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Now, two considerations on your question. First one, regarding policies of database sharing. From personal experience, there are some databases that can''t be made publicly available, either for involving privacy restraints (as for some social network informations), or for concerning government information (like health system databases).&lt;/p&gt;

&lt;p&gt;Another point concerns the usage/application of the dataset. Although some bases can be reprocessed to suit the needs of the application, it would be great to have some &lt;em&gt;nice organization&lt;/em&gt; of the datasets by purpose. The &lt;em&gt;taxonomy&lt;/em&gt; should involve social graph analysis, itemset mining, classification, and lots of other reasearch areas there may be.&lt;/p&gt;
', NULL, NULL, '''/a'':37C,50C,62C,71C ''/data/'':44C ''/datasets/index.html'':69C ''/em'':162C,171C ''/li'':38C,51C,63C,79C ''/ml/datasets.html'':57C ''/p'':24C,129C,189C ''/publicdatasets/'':31C ''/ul'':80C ''although'':139C ''amazon'':35C ''analysi'':176C ''anoth'':131C ''applic'':151C ''archive.ics.uci.edu'':56C ''archive.ics.uci.edu/ml/datasets.html'':55C ''area'':185C ''avail'':13C,108C ''aws.amazon.com'':30C ''aws.amazon.com/publicdatasets/'':29C ''base'':141C ''big'':72C ''classif'':179C ''concern'':122C,133C ''consider'':84C ''databas'':93C,101C,128C ''dataset'':14C,33C,138C,165C ''differ'':17C ''either'':109C ''em'':159C,169C ''enterprises/sources'':18C ''experi'':97C ''fact'':5C ''fimi.ua.ac.be'':43C ''fimi.ua.ac.be/data/'':42C ''first'':88C ''frequent'':45C ''govern'':123C ''graph'':175C ''great'':155C ''health'':126C ''href'':28C,41C,54C,66C ''implement'':48C ''inform'':119C,124C ''involv'':111C,173C ''itemset'':46C,177C ''kdnugget'':70C ''learn'':60C ''li'':26C,39C,52C,64C ''like'':125C ''list'':9C,73C ''lot'':75C,181C ''machin'':59C ''made'':106C ''may'':187C ''mine'':47C,178C ''need'':148C ''network'':118C ''nice'':160C ''one'':89C ''organ'':161C ''p'':1C,81C,130C ''person'':96C ''point'':132C ''polici'':91C ''privaci'':112C ''public'':12C,32C,77C,107C ''publicly-avail'':11C ''purpos'':167C ''question'':87C ''reasearch'':184C ''reason'':8C ''regard'':90C ''repositori'':49C,61C,78C ''reprocess'':144C ''restraint'':113C ''share'':94C ''social'':117C,174C ''suit'':146C ''support'':15C ''system'':127C ''taxonomi'':170C ''two'':83C ''uci'':58C ''ul'':25C ''usage/application'':135C ''webservic'':36C ''would'':153C ''www.kdnuggets.com'':68C ''www.kdnuggets.com/datasets/index.html'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (159, 1, '2014-05-18 19:46:44.653', 4, 335, 'Is Data Science just a trend or is a long term concept?', '&lt;p&gt;I see a lot of courses in Data Science emerging in the last 2 years. Even big universities like Stanford and Columbia offers MS specifically in Data Science. But as long as I see, it looks like data science is just a mix of computer science and statistics techniques.
So I always think about this. If it is just a trend and if in 10 years from now, someone will still mention Data Science as an entire field or just a subject/topic inside CS or stats.
What do you think?&lt;/p&gt;
', 'bigdata  machine-learning  databases  statistics  education', NULL, '''/p'':111C ''10'':85C ''2'':34C ''alway'':72C ''big'':37C ''bigdata'':13B ''columbia'':42C ''comput'':65C ''concept'':12A ''cours'':26C ''cs'':104C ''data'':2A,28C,47C,58C,93C ''databas'':17B ''educ'':19B ''emerg'':30C ''entir'':97C ''even'':36C ''field'':98C ''insid'':103C ''last'':33C ''learn'':16B ''like'':39C,57C ''long'':10A,51C ''look'':56C ''lot'':24C ''machin'':15B ''machine-learn'':14B ''mention'':92C ''mix'':63C ''ms'':44C ''offer'':43C ''p'':20C ''scienc'':3A,29C,48C,59C,66C,94C ''see'':22C,54C ''someon'':89C ''specif'':45C ''stanford'':40C ''stat'':106C ''statist'':18B,68C ''still'':91C ''subject/topic'':102C ''techniqu'':69C ''term'':11A ''think'':73C,110C ''trend'':6A,81C ''univers'':38C ''year'':35C,86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (182, 2, '2014-05-21 07:19:32.297', 3, NULL, NULL, '&lt;p&gt;So, a few of your rows will have too many columns by one or more as a result. That''s easy to detect, but harder to infer where the error was -- which two columns are actually one? which delimiter is not a delimiter?&lt;/p&gt;

&lt;p&gt;In some cases, you can use the metadata, because it helps you know when an interpretation of the columns can''t be right. For example, if just the one column can have a text value, and all the others must be numeric, it''s unambiguous where the error is. Any additional columns created by this error occur right after the text column.&lt;/p&gt;

&lt;p&gt;If they''re all text, this doesn''t work of course.&lt;/p&gt;

&lt;p&gt;You might be able to leverage more than the metadata''s column type. For example you may know that some fields are from an enumerated set of values, and use that to determine when a column assignment is wrong.&lt;/p&gt;
', NULL, NULL, '''/p'':45C,109C,122C,163C ''abl'':127C ''actual'':37C ''addit'':97C ''assign'':160C ''case'':49C ''column'':12C,35C,65C,76C,98C,108C,135C,159C ''cours'':121C ''creat'':99C ''delimit'':40C,44C ''detect'':24C ''determin'':156C ''doesn'':117C ''easi'':22C ''enumer'':148C ''error'':31C,94C,102C ''exampl'':71C,138C ''field'':144C ''harder'':26C ''help'':57C ''infer'':28C ''interpret'':62C ''know'':59C,141C ''leverag'':129C ''mani'':11C ''may'':140C ''metadata'':54C,133C ''might'':125C ''must'':86C ''numer'':88C ''occur'':103C ''one'':14C,38C,75C ''other'':85C ''p'':1C,46C,110C,123C ''re'':113C ''result'':19C ''right'':69C,104C ''row'':7C ''set'':149C ''text'':80C,107C,115C ''two'':34C ''type'':136C ''unambigu'':91C ''use'':52C,153C ''valu'':81C,151C ''work'':119C ''wrong'':162C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8812, 2, '2015-11-12 07:18:48.46', 4, NULL, NULL, '&lt;p&gt;Know I''m a bit late, but to get probabilities from &lt;code&gt;xgboost&lt;/code&gt; you should specify &lt;code&gt;multi:softmax&lt;/code&gt; objective like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;xgboost(param, data = x_mat, label = y_mat,nround = 3000, objective=''multi:softprob'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;From the &lt;code&gt;?xgb.train&lt;/code&gt;: &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;multi:softprob same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/blockquote'':88C ''/code'':15C,22C,42C,49C ''/p'':26C,50C,87C ''/pre'':43C ''3000'':38C ''belong'':83C ''bit'':6C ''blockquot'':51C ''class'':86C ''code'':13C,19C,28C,47C ''contain'':76C ''data'':31C,81C ''get'':10C ''know'':2C ''label'':34C ''late'':7C ''like'':24C ''m'':4C ''mat'':33C,36C ''matrix'':73C ''multi'':20C,40C,53C ''nclass'':64C,72C ''ndata'':63C,71C ''nround'':37C ''object'':23C,39C ''output'':59C ''p'':1C,44C,52C ''param'':30C ''point'':82C ''pre'':27C ''predict'':77C ''probabl'':11C,78C ''reshap'':69C ''result'':75C ''softmax'':21C,57C ''softprob'':41C,54C ''specifi'':18C ''vector'':61C ''x'':32C ''xgb.train'':48C ''xgboost'':14C,29C ''y'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2406, 2, '2014-11-05 17:03:40.513', 9, NULL, NULL, '&lt;p&gt;Drew Conway published the &lt;a href="http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram" rel="nofollow"&gt;Data Science Venn Diagram&lt;/a&gt;, with which I heartily agree:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/7W0VM.png" alt="Data Science Venn Diagram"&gt;&lt;/p&gt;

&lt;p&gt;On the one hand, you should really read his post. On the other hand, I can offer my own experience: my subject matter expertise (which I like better as a term than "Substantive Expertise", because you should really also have "Substantive Expertise" in math/stats and hacking) is in the retail business, my math/stats are forecasting and inferential statistics, and my hacking skills lie in R.&lt;/p&gt;

&lt;p&gt;From this vantage point, I can talk to and understand retailers, and someone who doesn''t have at least a passing knowledge of this field will have to face a &lt;em&gt;steep&lt;/em&gt; learning curve in a project with retailers. As a side gig, I do statistics in psychology, and it''s exactly the same there. And even with quite some knowledge of the hacking/math/stats part of the diagram, I would have a hard time getting up to speed in, say, credit scoring or some other new subject field.&lt;/p&gt;

&lt;p&gt;Once you have a certain amount of math/stats and hacking skills, it is &lt;em&gt;much&lt;/em&gt; better to acquire a grounding in one or more subjects than in adding &lt;em&gt;yet&lt;/em&gt; another programming language to your hacking skills, or &lt;em&gt;yet&lt;/em&gt; another machine learning algorithm to your math/stats portfolio. After all, once you have a solid math/stats/hacking grounding, you could if need be learn such new tools from the web or from textbooks in a relative short time period. But the subject matter expertise, on the other hand, you will likely not be able to learn from scratch if you start from zero. And clients will rather work with some data scientist A who understands their specific field than with another data scientist B who first needs to learn the basics - even if B is better in math/stats/hacking.&lt;/p&gt;

&lt;p&gt;Of course, all this will also mean that you will never become an expert in &lt;em&gt;either&lt;/em&gt; of the three fields. But that''s fine, because you are a data scientist, not a programmer or a statistician or a subject matter expert. There will always be people in the three separate circles who you can learn from. Which is part of what I like about data science.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;EDIT: A little while and a few thoughts later, I''d like to update this post with a new version of the diagram. I still think that Hacking Skills, Math &amp;amp; Statistics Knowledge and Substantive Expertise (shortened to "Programming", "Statistics" and "Business" for legibility) are important... but I think that the role of &lt;em&gt;Communication&lt;/em&gt; is important, too. All the insights you derive by leveraging your hacking, stats and business expertise won''t make a bit of a difference unless you can communicate them to people who may &lt;em&gt;not&lt;/em&gt; have that unique blend of knowledge. You may need to explain your statistical insights to a business manager who needs to be convinced to spend money or change processes. Or to a programmer who doesn''t think statistically.&lt;/p&gt;

&lt;p&gt;So here is the new data science Venn diagram, which also includes communication as one indispensable ingredient. I have labeled the areas in ways that should guarantee maximum flaming, while being easy to remember.&lt;/p&gt;

&lt;p&gt;Comment away.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/aiQeT.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/aiQeT.png" alt="new data science Venn diagram"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;R code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;draw.ellipse &amp;lt;- function(center,angle,semimajor,semiminor,radius,h,s,v,...) {
    shape &amp;lt;- rbind(c(cos(angle),-sin(angle)),c(sin(angle),cos(angle))) %*% diag(c(semimajor,semiminor))
    tt &amp;lt;- seq(0,2*pi,length.out=1000)
    foo &amp;lt;- matrix(center,nrow=2,ncol=length(tt),byrow=FALSE) + shape%*%(radius*rbind(cos(tt),sin(tt)))
    polygon(foo[1,],foo[2,],col=hsv(h,s,v,alpha=0.5),border="black",...)
}
name &amp;lt;- function(x,y,label,cex=1.2,...) text(x,y,label,cex=cex,...)

png("Venn.png",width=600,height=600)
    opar &amp;lt;- par(mai=c(0,0,0,0),lwd=3,font=2)
        plot(c(0,100),c(0,90),type="n",bty="n",xaxt="n",yaxt="n",xlab="",ylab="")
        draw.ellipse(center=c(30,30),angle=0.75*pi,semimajor=2,semiminor=1,radius=20,h=60/360,s=.068,v=.976)
        draw.ellipse(center=c(70,30),angle=0.25*pi,semimajor=2,semiminor=1,radius=20,h=83/360,s=.482,v=.894)
        draw.ellipse(center=c(48,40),angle=0.7*pi,semimajor=2,semiminor=1,radius=20,h=174/360,s=.397,v=.8)
        draw.ellipse(center=c(52,40),angle=0.3*pi,semimajor=2,semiminor=1,radius=20,h=200/360,s=.774,v=.745)

        name(50,90,"The Data Scientist Venn Diagram",pos=1,cex=2)
        name(8,62,"Communi-\ncation",cex=1.5,pos=3)
        name(30,78,"Statistics",cex=1.5)
        name(70,78,"Programming",cex=1.5)
        name(92,62,"Business",cex=1.5,pos=3)

        name(10,45,"Hot\nAir")
        name(90,45,"The\nAccountant")
        name(33,65,"The\nData\nNerd")
        name(67,65,"The\nHacker")
        name(27,50,"The\nStats\nProf")
        name(73,50,"The\nIT\nGuy")
        name(50,55,"R\nCore\nTeam")
        name(38,38,"The\nGood\nConsultant")
        name(62,38,"Drew\nConway''s\nData\nScientist")
        name(50,24,"The\nperfect\nData\nScientist!")
        name(31,18,"Comp\nSci\nProf")
        name(69,18,"The\nNumber\nCruncher")
        name(42,11,"Head\nof IT")
        name(58,11,"Ana-\nlyst")
        name(50,5,"The\nSalesperson")
    par(opar)
dev.off()
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (160, 2, '2014-05-18 21:05:28.99', 10, NULL, NULL, '&lt;p&gt;The one thing that you can say for sure is: Nobody can say this for sure. And it might indeed be opinion-based to some extent. The introduction of terms like "Big Data" that some people consider as "hypes" or "buzzwords" don''t make it easier to flesh out an appropriate answer here. But I''ll try. &lt;/p&gt;

&lt;p&gt;In general, interdisciplinary fields often seem to have the problem of not being taken serious by either of the fields they are spanning. However, the more research is invested into a particular field, the greater is the urge to split this field into several sub-topics. And these sub-topics sonner of later have to be re-combined in new ways, in order to prevent an overspecialization, and to increase and broaden the applicability of techniques that are developed by the (over?)specialized experts in the different fields. &lt;/p&gt;

&lt;p&gt;And I consider "Data Science" as such an approach to combine the expertise and findings from different fields. You described it as&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;...a mix of computer science and statistics techniques&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;And indeed, several questions here aim at the differentiation between data science and statistics. But a pure statistician will most likely not be able to set up a Hadoop cluster and show the results of his analysis in an interactive HTML5 dashboard. And someone who can implement a nice HTML5 dashboard might not be so familiar with the mathematical background of a Chi-Squared-Test. &lt;/p&gt;

&lt;p&gt;It is reasonable to assume that giving students enough knowledge to &lt;em&gt;apply&lt;/em&gt; the most important techniques from the different fields that are covered by data science will lead to new applications of these techniques, and be beneficial - also for the "purists" in these fields. The combination of these techniques is not straightforward in many cases, and can justify an own branch of research. &lt;/p&gt;

&lt;p&gt;You also asked whether in 10 years, data science will be considered as "just a topic inside computer science". Again: Nobody can say for sure. But I wonder at which point people stopped asking the question whether "Computer Science" will one day only be considered only as a mix of (or a subject of) Electrical Engineering and Mathematics...&lt;/p&gt;
', NULL, NULL, '''/blockquote'':189C ''/em'':272C ''/p'':60C,153C,177C,188C,257C,324C,384C ''10'':331C ''abl'':214C ''aim'':196C ''also'':298C,327C ''analysi'':227C ''answer'':54C ''appli'':271C ''applic'':138C,291C ''approach'':163C ''appropri'':53C ''ask'':328C,359C ''assum'':263C ''background'':250C ''base'':25C ''benefici'':297C ''big'':34C ''blockquot'':178C ''branch'':321C ''broaden'':136C ''buzzword'':43C ''case'':315C ''chi'':254C ''chi-squared-test'':253C ''cluster'':220C ''combin'':122C,165C,306C ''comput'':183C,343C,363C ''consid'':39C,157C,337C,370C ''cover'':283C ''dashboard'':232C,241C ''data'':35C,158C,201C,285C,333C ''day'':367C ''describ'':174C ''develop'':143C ''differ'':151C,171C,279C ''differenti'':199C ''easier'':48C ''either'':78C ''electr'':380C ''em'':270C ''engin'':381C ''enough'':267C ''expert'':148C ''expertis'':167C ''extent'':28C ''familiar'':246C ''field'':65C,81C,94C,103C,152C,172C,280C,304C ''find'':169C ''flesh'':50C ''general'':63C ''give'':265C ''greater'':96C ''hadoop'':219C ''howev'':85C ''html5'':231C,240C ''hype'':41C ''implement'':237C ''import'':275C ''increas'':134C ''inde'':21C,192C ''insid'':342C ''interact'':230C ''interdisciplinari'':64C ''introduct'':30C ''invest'':90C ''justifi'':318C ''knowledg'':268C ''later'':116C ''lead'':288C ''like'':33C,211C ''ll'':58C ''make'':46C ''mani'':314C ''mathemat'':249C,383C ''might'':20C,242C ''mix'':181C,374C ''new'':124C,290C ''nice'':239C ''nobodi'':12C,346C ''often'':66C ''one'':3C,366C ''opinion'':24C ''opinion-bas'':23C ''order'':127C ''overspeci'':131C ''p'':1C,61C,154C,179C,190C,258C,325C ''particular'':93C ''peopl'':38C,357C ''point'':356C ''prevent'':129C ''problem'':71C ''pure'':207C ''purist'':301C ''question'':194C,361C ''re'':121C ''re-combin'':120C ''reason'':261C ''research'':88C,323C ''result'':224C ''say'':8C,14C,348C ''scienc'':159C,184C,202C,286C,334C,344C,364C ''seem'':67C ''serious'':76C ''set'':216C ''sever'':105C,193C ''show'':222C ''someon'':234C ''sonner'':114C ''span'':84C ''special'':147C ''split'':101C ''squar'':255C ''statist'':186C,204C ''statistician'':208C ''stop'':358C ''straightforward'':312C ''student'':266C ''sub'':107C,112C ''sub-top'':106C,111C ''subject'':378C ''sure'':10C,17C,350C ''taken'':75C ''techniqu'':140C,187C,276C,294C,309C ''term'':32C ''test'':256C ''thing'':4C ''topic'':108C,113C,341C ''tri'':59C ''urg'':99C ''way'':125C ''whether'':329C,362C ''wonder'':353C ''year'':332C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (161, 2, '2014-05-18 21:25:29.04', 6, NULL, NULL, '&lt;p&gt;I don''t think that everyone reaches for C/C++ when performance is an issue.&lt;/p&gt;

&lt;p&gt;The advantage to writing low-level code is using fewer CPU cycles, or sometimes, less memory. But I''d note that higher-level languages can call down to lower-level languages, and do, to get some of this value. Python and JVM languages can do this.&lt;/p&gt;

&lt;p&gt;The data scientist using, for example, scikit-learn on her desktop is already calling heavily optimized native routines to do the number crunching. There is no point in writing new code for speed.&lt;/p&gt;

&lt;p&gt;In the distributed "big data" context, you are more typically bottleneck on data movement: network transfer and I/O. Native code does not help. What helps is not writing the same code to run faster, but writing smarter code.&lt;/p&gt;

&lt;p&gt;Higher-level languages are going to let you implement more sophisticated distributed algorithms in a given amount of developer time than C/C++. At scale, the smarter algorithm with better data movement will beat dumb native code.&lt;/p&gt;

&lt;p&gt;It''s also usually true that developer time, and bugs, cost loads more than new hardware. A year of a senior developer''s time might be $200K fully loaded; over a year that also rents hundreds of servers worth of computation time. It may just not make sense in most cases to bother optimizing over throwing more hardware at it.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;I don''t understand the follow up about "grant" and "disable" and "assert"?&lt;/em&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (162, 2, '2014-05-18 22:16:19.3', 21, NULL, NULL, '&lt;p&gt;There are many openly available data sets, one many people often overlook is &lt;a href="http://www.data.gov/"&gt;data.gov&lt;/a&gt;. As mentioned previously Freebase is great, so are all the examples posted by @Rubens&lt;/p&gt;
', NULL, NULL, '''/a'':19C ''/p'':34C ''avail'':6C ''data'':7C ''data.gov'':18C ''exampl'':30C ''freebas'':23C ''great'':25C ''href'':16C ''mani'':4C,10C ''mention'':21C ''often'':12C ''one'':9C ''open'':5C ''overlook'':13C ''p'':1C ''peopl'':11C ''post'':31C ''previous'':22C ''ruben'':33C ''set'':8C ''www.data.gov'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (163, 2, '2014-05-18 23:21:07.22', 3, NULL, NULL, '&lt;p&gt;As all we know, in Digital world there are many ways to do the same work / get expected results..&lt;/p&gt;

&lt;p&gt;And responsibilities / risks which comes from the code are on developers'' shoulders..&lt;/p&gt;

&lt;p&gt;This is small but i guess a very useful example from .NET world..&lt;/p&gt;

&lt;p&gt;So Many .NET developers use the built-in BinaryReader - BinaryWriter on their data serialization for performance / get control over the process..&lt;/p&gt;

&lt;p&gt;This is CSharp source code of the FrameWork''s built in BinaryWriter class'' one of the overloaded Write Methods :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;// Writes a boolean to this stream. A single byte is written to the stream
// with the value 0 representing false or the value 1 representing true.
// 
public virtual void Write(bool value) 
{
     //_buffer is a byte array which declared in ctor / init codes of the class
    _buffer = ((byte) (value? 1:0));

    //OutStream is the stream instance which BinaryWriter Writes the value(s) into it.
    OutStream.WriteByte(_buffer[0]);
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As you see, this method could written without the extra assigning to _buffer variable:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;public virtual void Write(bool value) 
{
    OutStream.WriteByte((byte) (value ? 1 : 0));
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Without assigning we could gain few milliseconds..This few milliseconds can accept as "almost nothing" but what if there are multi-thousands of writing (i.e. in a server process)?&lt;/p&gt;

&lt;p&gt;Lets suppose that "few" is 2 (milliseconds) and multi-Thousands instances are only 2.000..
This means 4 seconds more process time..4 seconds later returning..&lt;/p&gt;

&lt;p&gt;If we continue to subject from .NET and if you can check the source codes of BCL - .NET Base Class Library- from MSDN you can see a lot of performance losts from the developer decides..&lt;/p&gt;

&lt;p&gt;Any of the point from BCL source It''s normal that you see developer decided to use while() or foreach() loops which could implement a faster for() loop in their code.&lt;/p&gt;

&lt;p&gt;This small gains give us the total performance..&lt;/p&gt;

&lt;p&gt;And if we return to the BinaryWriter.Write() Method..&lt;/p&gt;

&lt;p&gt;Actually extra assigning to a _buffer implementation is not a developer fault..This is exactly decide to "stay in safe" !&lt;/p&gt;

&lt;p&gt;Suppose that we decide to not use _buffer and decided to implement the second method..If we try to send multi-thousands bytes over a wire (i.e. upload / download a BLOB or CLOB data) with the second method, it can fail commonly because of connection lost..Cause we try to send all data without any checks and controlling mechanism.When connection lost, Both the server and Client never know the sent data completed or not.&lt;/p&gt;

&lt;p&gt;If the developer decides "stay in safe" then normally it means performance costs depends to implemented "stay in safe" mechanism(s).&lt;/p&gt;

&lt;p&gt;But if the developer decides "get risky, gain performance" this is not a fault also..Till there are some discussions about "risky" coding.&lt;/p&gt;

&lt;p&gt;And as a small note : Commercial library developers always try to stay in safe because they can''t know where their code will use.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (164, 2, '2014-05-19 00:09:17.9', 6, NULL, NULL, '&lt;p&gt;What @Clayton posted seems about right to me, for those terms, and for "data mining" being one tool of the data scientist. However, I haven''t really used the term "data collection," and it doesn''t strike me as synonymous with "data mining."&lt;/p&gt;

&lt;p&gt;My own answer to your question: &lt;strong&gt;no&lt;/strong&gt;, the terms aren''t the same. Definitions may be loose in this field, but I haven''t seen those terms used interchangeably. In my work, we sometimes use them to differentiate between goals, or methodologies. For us, &lt;a href="/questions/tagged/data-science" class="post-tag" title="show questions tagged &amp;#39;data-science&amp;#39;" rel="tag"&gt;data-science&lt;/a&gt; is more about testing a hypothesis, and typically the data have been collected just for that purpose. &lt;a href="/questions/tagged/data-mining" class="post-tag" title="show questions tagged &amp;#39;data-mining&amp;#39;" rel="tag"&gt;data-mining&lt;/a&gt; is more about sifting through existing data, looking for structure, and perhaps generating hypotheses. Data mining can start with a hypothesis, but it''s often very weak or general, and can be difficult to resolve with confidence. (Dig long enough and you''ll find &lt;em&gt;something&lt;/em&gt;, though it may turn out to be pyrite.)&lt;/p&gt;

&lt;p&gt;However, we also have used "data science" as a wider term, to include "data mining." We also talk about "data modeling," which for us is about finding a model for a system of interest, based on data as well as other knowledge and objectives. Sometimes that means trying to find the math that explains the real system, and sometimes it means finding a predictive model that is good enough for a purpose.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8828, 1, '2015-11-13 07:21:29.24', 1, 33, 'Learning Data Science and Analytic at Middle School age?', '&lt;p&gt;My son who is in middle school, has just been offered by his school to learn Data Science and analytic for the next 4 years. According to son''s teacher, the boys will be the first batch of guinea pigs. I am uncertain if it''s advisable to learn data science at this young age. Will it be too challenging for the young mind? Are there any benefit learning Data Science at this age? Thank you.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (193, 2, '2014-05-22 15:43:57.16', 3, NULL, NULL, '&lt;p&gt;It depends, of course, on the focus of the company: commerce, service, etc.  In adition to the use cases you suggested, some other use cases would be:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Funnel analysis: Analyzing the way in which consumers use a website and complete a sale may include data science techniques, especially if the company operates at a large scale.&lt;/li&gt;
&lt;li&gt;Advertising: Companies that place ads use &lt;em&gt;a lot&lt;/em&gt; of machine learning techniques to analyze and predict which ads would be most effective or most remunerative give the user''s demographics that would view them.&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (165, 2, '2014-05-19 08:46:47.683', 9, NULL, NULL, '&lt;p&gt;I think you messed up some things in your question. Lucene (I know nothing about Lucene,NET, but I suppose is the same) is a library used to analyze, split in tokens, and store documents in order to be able to query and retrieve them later. Lucene has a pretty old but effective model, it uses inverted trees to find and retrieve documents. Without further details, all documents are split in tokens (terms), and for each term is maintained a data structure, which stores all the documents which contains the given term. As a data structure could be used a BTree, a hash table and in the latest major revisions you can even plug in your own data structures.&lt;/p&gt;

&lt;p&gt;A BTree (see &lt;a href="http://en.wikipedia.org/wiki/B-tree"&gt;Wikipedia page&lt;/a&gt; for further details), is a kind of a tree data structure, which is appropriate for working with big chunks of data and is often used for storing tree-like ordered structures on disk. For in-memory other trees performs better.&lt;/p&gt;

&lt;p&gt;Murmur hash (see &lt;a href="http://en.wikipedia.org/wiki/MurmurHash"&gt;Wikipedia page&lt;/a&gt; for further details), is a family of hash functions used in hash table. The implementation of the hash table is not important, it could be a standard chained implementation or more advanced open hash addressing scheme. The idea is that the hash tables allows one to get fast a key, from an unordered set of keys, and can answer to tasks like: is this key part of this set of keys? which is the value associated with this key? &lt;/p&gt;

&lt;p&gt;Now back to your main problem. You have one library (Lucene) and to data structures, both data structures are used in Lucene. Now you see that it is not possible to answer your question in these terms since they are not comparable.&lt;/p&gt;

&lt;p&gt;However, regarding you footprint and performance part of the question. First of all you have to know which kind of operations you need to implement. &lt;/p&gt;

&lt;p&gt;&lt;em&gt;Do you need only get value for key, or do you need to find all elements in a range? In other words do you need order or not?&lt;/em&gt; If you do, than a tree can help. If you do not, than a hash table, which is faster could be used instead. &lt;/p&gt;

&lt;p&gt;&lt;em&gt;Do you have a lot of data which does not fit the memory?&lt;/em&gt; If yes than a disk-based solution would help (like BTree). If your data fit the memory, than use the fastest in-memory solution and use disk only as a storage (with a different structure, much simpler).&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (166, 2, '2014-05-19 11:13:48.067', 4, NULL, NULL, '&lt;p&gt;Let''s say you are predicting the topic of a document given its words.&lt;/p&gt;

&lt;p&gt;A generative model describes how likely each topic is, and how likely words are given the topic. This is how it says documents are actually "generated" by the world -- a topic arises according to some distribution, words arise because of the topic, you have a document. Classifying documents of words W into topic T is a matter of maximizing the joint likelihood: P(T,W) = P(W|T)P(T)&lt;/p&gt;

&lt;p&gt;A discriminative model operates by only describing how likely a topic is given the words. It says nothing about how likely the words or topic are by themselves. The task is to model P(T|W) directly and find the T that maximizes this. These approaches do not care about P(T) or P(W) directly.&lt;/p&gt;
', NULL, NULL, '''/p'':16C,88C,146C ''accord'':50C ''actual'':42C ''approach'':135C ''aris'':49C,55C ''care'':138C ''classifi'':64C ''describ'':21C,96C ''direct'':126C,145C ''discrimin'':91C ''distribut'':53C ''document'':12C,40C,63C,65C ''find'':128C ''generat'':19C,43C ''given'':13C,32C,102C ''joint'':78C ''let'':2C ''like'':23C,29C,98C,110C ''likelihood'':79C ''matter'':74C ''maxim'':76C,132C ''model'':20C,92C,122C ''noth'':107C ''oper'':93C ''p'':1C,17C,80C,83C,86C,89C,123C,140C,143C ''predict'':7C ''say'':4C,39C,106C ''task'':119C ''topic'':9C,25C,34C,48C,59C,70C,100C,114C ''w'':68C,82C,84C,125C,144C ''word'':15C,30C,54C,67C,104C,112C ''world'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (167, 5, '2014-05-19 12:17:45.96', 0, NULL, NULL, '&lt;p&gt;&lt;strong&gt;.NET&lt;/strong&gt; is a very popular Object Oriented Programming Language Family.This Family includes members such as &lt;strong&gt;C#&lt;/strong&gt; (pronounced &lt;strong&gt;CSharp&lt;/strong&gt;), &lt;strong&gt;VB.NET&lt;/strong&gt;, &lt;strong&gt;F#&lt;/strong&gt; (pronounced &lt;strong&gt;Fsharp&lt;/strong&gt;), &lt;strong&gt;J#&lt;/strong&gt; (pronounced &lt;strong&gt;JSharp&lt;/strong&gt;) and much more. The &lt;strong&gt;.NET&lt;/strong&gt; Family offers programming with small effort with well-known high speed of compiled languages such as &lt;strong&gt;C&lt;/strong&gt; and &lt;strong&gt;C++&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This Tag &lt;strong&gt;aims&lt;/strong&gt; to group &lt;strong&gt;Data Science Questions and Answers&lt;/strong&gt; which users want to operate their processes &lt;strong&gt;under .NET&lt;/strong&gt; Programming Language Family&lt;/p&gt;
', NULL, NULL, '''/p'':74C,104C ''/strong'':4C,21C,25C,28C,31C,35C,38C,42C,49C,69C,73C,80C,89C,100C ''aim'':79C ''answer'':88C ''c'':20C,68C,72C ''compil'':63C ''csharp'':24C ''data'':84C ''effort'':55C ''f'':30C ''famili'':14C,50C,103C ''family.this'':13C ''fsharp'':34C ''group'':82C ''high'':60C ''includ'':15C ''j'':37C ''jsharp'':41C ''known'':59C ''languag'':12C,64C,102C ''member'':16C ''much'':44C ''net'':3C,48C,99C ''object'':9C ''offer'':51C ''oper'':94C ''orient'':10C ''p'':1C,75C ''popular'':8C ''process'':96C ''program'':11C,52C,101C ''pronounc'':22C,32C,39C ''question'':86C ''scienc'':85C ''small'':54C ''speed'':61C ''strong'':2C,19C,23C,26C,29C,33C,36C,40C,47C,67C,71C,78C,83C,97C ''tag'':77C ''user'':91C ''vb.net'':27C ''want'':92C ''well'':58C ''well-known'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (168, 4, '2014-05-19 12:17:45.96', 0, NULL, NULL, '.NET is a very popular Object Oriented Programming Language Family which includes members such as C# (pronounced CSharp), VB.NET, F# (pronounced FSharp), J# (pronounced JSharp) and much more. The .NET Family offers programming with small effort with well-known high speed of compiled languages such as C and C++&#x0d;
&#x0d;
This Tag aims to group Data Science Questions and Answers which users want to operate their processes under .NET Programming Language Family', NULL, NULL, '''aim'':53C ''answer'':60C ''c'':16C,48C,50C ''compil'':44C ''csharp'':18C ''data'':56C ''effort'':36C ''f'':20C ''famili'':10C,31C,72C ''fsharp'':22C ''group'':55C ''high'':41C ''includ'':12C ''j'':23C ''jsharp'':25C ''known'':40C ''languag'':9C,45C,71C ''member'':13C ''much'':27C ''net'':1C,30C,69C ''object'':6C ''offer'':32C ''oper'':65C ''orient'':7C ''popular'':5C ''process'':67C ''program'':8C,33C,70C ''pronounc'':17C,21C,24C ''question'':58C ''scienc'':57C ''small'':35C ''speed'':42C ''tag'':52C ''user'':62C ''vb.net'':19C ''want'':63C ''well'':39C ''well-known'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9337, 2, '2015-12-11 10:13:12.463', 2, NULL, NULL, '&lt;p&gt;Yes, the parameter &lt;code&gt;nonnegative&lt;/code&gt; is for constraining values for both matrices: user features and item features to not have values below zeros while optimizing using ALS algorithm. It drive which solver to use: CholeskySolver or NNLS(conjugated gradient). Both are implemented in Spark mllib. &lt;/p&gt;

&lt;p&gt;The flag is of course connected with the input. If You have ratings below zeros and You directly pump all of Your data to model, the prediction of negatives values while You have &lt;code&gt;nonnegative&lt;/code&gt; constrain check will be somehow hard :-)&lt;/p&gt;

&lt;p&gt;In Your case, what''s important is that You have implicit feedback. So, You should set appropriate flag or use &lt;code&gt;trainImplicit&lt;/code&gt; method. The easiest way to find out what works for Your case best with the &lt;code&gt;nonnegative&lt;/code&gt; flag is to threat it as another hyper-parameter which needs cross-validation check. And for me, the measurement of the model while validating is a more crucial aspect. If You are trying to do top-N recommendations, go for ranking measures like Recall@N, MRR@N or AUC.&lt;/p&gt;
', NULL, NULL, '''/code'':7C,85C,116C,134C ''/p'':48C,92C,187C ''al'':29C ''algorithm'':30C ''anoth'':141C ''appropri'':110C ''aspect'':165C ''auc'':186C ''best'':129C ''case'':96C,128C ''check'':87C,150C ''choleskysolv'':37C ''code'':5C,83C,114C,132C ''conjug'':40C ''connect'':55C ''constrain'':10C,86C ''cours'':54C ''cross'':148C ''cross-valid'':147C ''crucial'':164C ''data'':72C ''direct'':67C ''drive'':32C ''easiest'':119C ''featur'':16C,19C ''feedback'':105C ''find'':122C ''flag'':51C,111C,135C ''go'':176C ''gradient'':41C ''hard'':91C ''hyper'':143C ''hyper-paramet'':142C ''implement'':44C ''implicit'':104C ''import'':99C ''input'':58C ''item'':18C ''like'':180C ''matric'':14C ''measur'':155C,179C ''method'':117C ''mllib'':47C ''model'':74C,158C ''mrr'':183C ''n'':174C,182C,184C ''need'':146C ''negat'':78C ''nnls'':39C ''nonneg'':6C,84C,133C ''optim'':27C ''p'':1C,49C,93C ''paramet'':4C,144C ''predict'':76C ''pump'':68C ''rank'':178C ''rate'':62C ''recal'':181C ''recommend'':175C ''set'':109C ''solver'':34C ''somehow'':90C ''spark'':46C ''threat'':138C ''top'':173C ''top-n'':172C ''trainimplicit'':115C ''tri'':169C ''use'':28C,36C,113C ''user'':15C ''valid'':149C,160C ''valu'':11C,23C,79C ''way'':120C ''work'':125C ''yes'':2C ''zero'':25C,64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (169, 1, '2014-05-19 15:55:24.983', 11, 295, 'How to specify important attributes?', '&lt;p&gt;Assume a set of loosely structured data (e.g. Web tables/Linked Open Data), composed of many data sources. There is no common schema followed by the data and each source can use synonym attributes to describe the values (e.g. "nationality" vs "bornIn"). &lt;/p&gt;

&lt;p&gt;My goal is to find some "important" attributes that somehow "define" the entities that they describe. So, when I find the same value for such an attribute, I will know that the two descriptions are most likely about the same entity (e.g. the same person).&lt;/p&gt;

&lt;p&gt;For example, the attribute "lastName" is more discriminative than the attribute "nationality". &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How could I (statistically) find such attributes that are more important than others?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;A naive solution would be to take the average IDF of the values of each attribute and make this the "importance" factor of the attribute. A similar approach would be to count how many distinct values appear for each attribute.&lt;/p&gt;

&lt;p&gt;I have seen the term feature, or attribute selection in machine learning, but I don''t want to discard the remaining attributes, I just want to put higher weights to the most important ones.&lt;/p&gt;
', 'machine-learning  statistics  feature-selection', NULL, '''/p'':55C,102C,116C,133C,175C,211C ''/strong'':132C ''appear'':171C ''approach'':162C ''assum'':14C ''attribut'':5A,46C,64C,83C,107C,114C,125C,150C,159C,174C,184C,198C ''averag'':143C ''bornin'':54C ''common'':34C ''compos'':26C ''could'':120C ''count'':166C ''data'':20C,25C,29C,39C ''defin'':67C ''describ'':48C,72C ''descript'':90C ''discard'':195C ''discrimin'':111C ''distinct'':169C ''e.g'':21C,51C,98C ''entiti'':69C,97C ''exampl'':105C ''factor'':156C ''featur'':11B,182C ''feature-select'':10B ''find'':61C,76C,123C ''follow'':36C ''goal'':58C ''higher'':204C ''idf'':144C ''import'':4A,63C,129C,155C,209C ''know'':86C ''lastnam'':108C ''learn'':8B,188C ''like'':93C ''loos'':18C ''machin'':7B,187C ''machine-learn'':6B ''make'':152C ''mani'':28C,168C ''naiv'':136C ''nation'':52C,115C ''one'':210C ''open'':24C ''other'':131C ''p'':13C,56C,103C,117C,134C,176C ''person'':101C ''put'':203C ''remain'':197C ''schema'':35C ''seen'':179C ''select'':12B,185C ''set'':16C ''similar'':161C ''solut'':137C ''somehow'':66C ''sourc'':30C,42C ''specifi'':3A ''statist'':9B,122C ''strong'':118C ''structur'':19C ''synonym'':45C ''tables/linked'':23C ''take'':141C ''term'':181C ''two'':89C ''use'':44C ''valu'':50C,79C,147C,170C ''vs'':53C ''want'':193C,201C ''web'':22C ''weight'':205C ''would'':138C,163C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (170, 2, '2014-05-19 18:08:32.327', 13, NULL, NULL, '&lt;p&gt;A possible solution is to calculate the &lt;a href="http://en.wikipedia.org/wiki/Decision_tree_learning#Information_gain"&gt;information gain&lt;/a&gt; associated to each attribute:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/sUheW.png" alt="Information Gain"&gt;&lt;/p&gt;

&lt;p&gt;Initially you have the whole dataset, and compute the information gain of each item. The item with the best information gain is the one you should use to partition the dataset (considering the item''s values). Then, perform the same computations for each item (but the ones selected), and always choose the one which best &lt;em&gt;describes/differentiates&lt;/em&gt; the entries from your dataset.&lt;/p&gt;

&lt;p&gt;There are implementations available for such computations. &lt;a href="http://en.wikipedia.org/wiki/Decision_tree_learning"&gt;Decision trees&lt;/a&gt; usually base their feature selection on the features with best information gain. You may use the resulting tree structure to find these &lt;em&gt;important&lt;/em&gt; items.&lt;/p&gt;
', NULL, NULL, '''/a'':16C,112C ''/em'':90C,137C ''/p'':21C,31C,96C,139C ''/suhew.png'':27C ''/wiki/decision_tree_learning'':109C ''/wiki/decision_tree_learning#information_gain'':13C ''alt'':28C ''alway'':82C ''associ'':17C ''attribut'':20C ''avail'':101C ''base'':114C ''best'':51C,87C,122C ''calcul'':7C ''choos'':83C ''comput'':40C,73C,104C ''consid'':64C ''dataset'':38C,63C,95C ''decis'':110C ''describes/differentiates'':89C ''em'':88C,135C ''en.wikipedia.org'':12C,108C ''en.wikipedia.org/wiki/decision_tree_learning'':107C ''en.wikipedia.org/wiki/decision_tree_learning#information_gain'':11C ''entri'':92C ''featur'':116C,120C ''find'':133C ''gain'':15C,30C,43C,53C,124C ''href'':10C,106C ''i.stack.imgur.com'':26C ''i.stack.imgur.com/suhew.png'':25C ''img'':23C ''implement'':100C ''import'':136C ''inform'':14C,29C,42C,52C,123C ''initi'':33C ''item'':46C,48C,66C,76C,138C ''may'':126C ''one'':56C,79C,85C ''p'':1C,22C,32C,97C ''partit'':61C ''perform'':70C ''possibl'':3C ''result'':129C ''select'':80C,117C ''solut'':4C ''src'':24C ''structur'':131C ''tree'':111C,130C ''use'':59C,127C ''usual'':113C ''valu'':68C ''whole'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (171, 2, '2014-05-19 19:44:48.5', 4, NULL, NULL, '&lt;p&gt;Two things you might find useful:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Meta_learning_%28computer_science%29" rel="nofollow"&gt;meta-learning&lt;/a&gt; to speedup the search for the right model and the optimal parameters.
Meta learning consists in applying machine learning tools to the problem of finding the right machine learning tool/parameters for the problem at hand. This for instance &lt;a href="http://link.springer.com/chapter/10.1007/978-3-642-14464-6_11" rel="nofollow"&gt;this paper&lt;/a&gt; for a practical example;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units" rel="nofollow"&gt;gpucomputing&lt;/a&gt; to speedup the algorithm on larger datasets. For instance, &lt;a href="http://docs.opencv.org/modules/gpu/doc/introduction.html" rel="nofollow"&gt;OpenCV can use GPUs&lt;/a&gt;, which are very effective at processing images/videos and can bring 10 to 100 speedups with respect to CPUs. As your computer most probably has a gpucomputing-able GPU, you could gain lots of time using it.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':22C,70C,87C,108C ''/chapter/10.1007/978-3-642-14464-6_11'':65C ''/li'':76C,147C ''/modules/gpu/doc/introduction.html'':101C ''/ol'':148C ''/p'':8C,75C,146C ''/wiki/general-purpose_computing_on_graphics_processing_units'':83C ''/wiki/meta_learning_%28computer_science%29'':16C ''10'':119C ''100'':121C ''abl'':136C ''algorithm'':91C ''appli'':39C ''bring'':118C ''comput'':129C ''consist'':37C ''could'':139C ''cpus'':126C ''dataset'':94C ''docs.opencv.org'':100C ''docs.opencv.org/modules/gpu/doc/introduction.html'':99C ''effect'':112C ''en.wikipedia.org'':15C,82C ''en.wikipedia.org/wiki/general-purpose_computing_on_graphics_processing_units'':81C ''en.wikipedia.org/wiki/meta_learning_%28computer_science%29'':14C ''exampl'':74C ''find'':6C,47C ''gain'':140C ''gpu'':137C ''gpucomput'':86C,135C ''gpucomputing-'':134C ''gpus'':107C ''hand'':57C ''href'':13C,62C,80C,98C ''images/videos'':115C ''instanc'':60C,96C ''larger'':93C ''learn'':21C,36C,41C,51C ''li'':10C,77C ''link.springer.com'':64C ''link.springer.com/chapter/10.1007/978-3-642-14464-6_11'':63C ''lot'':141C ''machin'':40C,50C ''meta'':20C,35C ''meta-learn'':19C ''might'':5C ''model'':30C ''nofollow'':18C,67C,85C,103C ''ol'':9C ''opencv'':104C ''optim'':33C ''p'':1C,11C,78C ''paper'':69C ''paramet'':34C ''practic'':73C ''probabl'':131C ''problem'':45C,55C ''process'':114C ''rel'':17C,66C,84C,102C ''respect'':124C ''right'':29C,49C ''search'':26C ''speedup'':24C,89C,122C ''thing'':3C ''time'':143C ''tool'':42C ''tool/parameters'':52C ''two'':2C ''use'':7C,106C,144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (172, 1, '2014-05-19 23:59:58.07', 16, 2986, 'Is there a straightforward way to run pandas.DataFrame.isin in parallel?', '&lt;p&gt;I have a modeling and scoring program that makes heavy use of the &lt;code&gt;DataFrame.isin&lt;/code&gt; function of pandas, searching through lists of facebook "like" records of individual users for each of a few thousand specific pages. This is the most time-consuming part of the program, more so than the modeling or scoring pieces, simply because it only runs on one core while the rest runs on a few dozen simultaneously.&lt;/p&gt;

&lt;p&gt;Though I know I could manually break up the dataframe into chunks and run the operation in parallel, is there any straightforward way to do that automatically? In other words, is there any kind of package out there that will recognize I''m running an easily-delegated operation and automatically distribute it? Perhaps that''s asking for too much, but I''ve been surprised enough in the past by what''s already available in Python, so I figure it''s worth asking.&lt;/p&gt;

&lt;p&gt;Any other suggestions about how this might be accomplished (even if not by some magic unicorn package!) would also be appreciated. Mainly, just trying to find a way to shave off 15-20 minutes per run without spending an equal amount of time coding the solution.&lt;/p&gt;
', 'performance  python  pandas  parallel', NULL, '''-20'':208C ''/code'':31C ''/p'':89C,174C,222C ''15'':207C ''accomplish'':184C ''alreadi'':163C ''also'':194C ''amount'':216C ''appreci'':196C ''ask'':147C,173C ''automat'':117C,141C ''avail'':164C ''break'':97C ''chunk'':102C ''code'':29C,219C ''consum'':59C ''core'':79C ''could'':95C ''datafram'':100C ''dataframe.isin'':30C ''deleg'':138C ''distribut'':142C ''dozen'':87C ''easili'':137C ''easily-deleg'':136C ''enough'':156C ''equal'':215C ''even'':185C ''facebook'':39C ''figur'':169C ''find'':201C ''function'':32C ''heavi'':25C ''individu'':43C ''kind'':124C ''know'':93C ''like'':40C ''list'':37C ''m'':133C ''magic'':190C ''main'':197C ''make'':24C ''manual'':96C ''might'':182C ''minut'':209C ''model'':19C,68C ''much'':150C ''one'':78C ''oper'':106C,139C ''p'':15C,90C,175C ''packag'':126C,192C ''page'':52C ''panda'':13B,34C ''pandas.dataframe.isin'':8A ''parallel'':10A,14B,108C ''part'':60C ''past'':159C ''per'':210C ''perform'':11B ''perhap'':144C ''piec'':71C ''program'':22C,63C ''python'':12B,166C ''recogn'':131C ''record'':41C ''rest'':82C ''run'':7A,76C,83C,104C,134C,211C ''score'':21C,70C ''search'':35C ''shave'':205C ''simpli'':72C ''simultan'':88C ''solut'':221C ''specif'':51C ''spend'':213C ''straightforward'':4A,112C ''suggest'':178C ''surpris'':155C ''though'':91C ''thousand'':50C ''time'':58C,218C ''time-consum'':57C ''tri'':199C ''unicorn'':191C ''use'':26C ''user'':44C ''ve'':153C ''way'':5A,113C,203C ''without'':212C ''word'':120C ''worth'':172C ''would'':193C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (568, 2, '2014-06-24 20:34:18.473', 3, NULL, NULL, '&lt;p&gt;I suggest the use of Hidden Markov Models, with two possible states: (1) high levels and (0) low levels. &lt;/p&gt;

&lt;p&gt;This technique might be helpful to decode your signal. Probably you would need a specific HMM for each codification.&lt;/p&gt;

&lt;p&gt;If noise is an issue an FIR filter with a Blackman-Harris window function would allow you to isolate the frequency you''re concerned with. &lt;/p&gt;
', NULL, NULL, '''/p'':21C,42C,70C ''0'':18C ''1'':14C ''allow'':60C ''blackman'':55C ''blackman-harri'':54C ''codif'':41C ''concern'':68C ''decod'':29C ''filter'':51C ''fir'':50C ''frequenc'':65C ''function'':58C ''harri'':56C ''help'':27C ''hidden'':7C ''high'':15C ''hmm'':38C ''isol'':63C ''issu'':48C ''level'':16C,20C ''low'':19C ''markov'':8C ''might'':25C ''model'':9C ''need'':35C ''nois'':45C ''p'':1C,22C,43C ''possibl'':12C ''probabl'':32C ''re'':67C ''signal'':31C ''specif'':37C ''state'':13C ''suggest'':3C ''techniqu'':24C ''two'':11C ''use'':5C ''window'':57C ''would'':34C,59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (173, 2, '2014-05-20 03:56:43.147', 3, NULL, NULL, '&lt;p&gt;Guessing it''s likely you''ve seen this &lt;a href="http://www.youtube.com/watch?v=1GhNXHCQGsM" rel="nofollow"&gt;YouTube demo&lt;/a&gt; and the related &lt;a href="http://www.youtube.com/watch?v=lmG_FjG4Dy8" rel="nofollow"&gt;Google Tech Talk&lt;/a&gt;, which is related to these papers: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://eprints.pascal-network.org/archive/00006951/01/cvpr2010.pdf" rel="nofollow"&gt;P-N Learning: Bootstrapping Binary Classifiers by Structural Constraints&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://epubs.surrey.ac.uk/713800/1/Kalal-PAMI-2011%281%29.pdf" rel="nofollow"&gt;Tracking-Learning-Detection&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;And this set of code on GitHub for &lt;a href="https://github.com/zk00006/OpenTLD/" rel="nofollow"&gt;OpenTLD&lt;/a&gt;. If you check the "read me" on GitHub here, you''ll notice that &lt;a href="https://github.com/zk00006/OpenTLD/blob/master/README" rel="nofollow"&gt;author''s email (Zdenek Kalal)&lt;/a&gt; is listed, so it might be worth sending him an email about your questions, or even inviting him to reply to this question too.&lt;/p&gt;
', NULL, NULL, '''/713800/1/kalal-pami-2011%281%29.pdf'':67C ''/a'':19C,33C,60C,74C,94C,120C ''/archive/00006951/01/cvpr2010.pdf'':47C ''/li'':61C,75C ''/p'':40C,145C ''/ul'':76C ''/watch?v=1ghnxhcqgsm'':14C ''/watch?v=lmg_fjg4dy8'':27C ''/zk00006/opentld/'':90C ''/zk00006/opentld/blob/master/readme'':112C ''author'':115C ''binari'':55C ''bootstrap'':54C ''check'':97C ''classifi'':56C ''code'':82C ''constraint'':59C ''demo'':18C ''detect'':73C ''email'':117C,131C ''eprints.pascal-network.org'':46C ''eprints.pascal-network.org/archive/00006951/01/cvpr2010.pdf'':45C ''epubs.surrey.ac.uk'':66C ''epubs.surrey.ac.uk/713800/1/kalal-pami-2011%281%29.pdf'':65C ''even'':136C ''github'':84C,102C ''github.com'':89C,111C ''github.com/zk00006/opentld/'':88C ''github.com/zk00006/opentld/blob/master/readme'':110C ''googl'':30C ''guess'':2C ''href'':11C,24C,44C,64C,87C,109C ''invit'':137C ''kalal'':119C ''learn'':53C,72C ''li'':42C,62C ''like'':5C ''list'':122C ''ll'':105C ''might'':125C ''n'':52C ''nofollow'':16C,29C,49C,69C,92C,114C ''notic'':106C ''opentld'':93C ''p'':1C,51C,77C ''p-n'':50C ''paper'':39C ''question'':134C,143C ''read'':99C ''rel'':15C,28C,48C,68C,91C,113C ''relat'':22C,36C ''repli'':140C ''seen'':8C ''send'':128C ''set'':80C ''structur'':58C ''talk'':32C ''tech'':31C ''track'':71C ''tracking-learning-detect'':70C ''ul'':41C ''ve'':7C ''worth'':127C ''www.youtube.com'':13C,26C ''www.youtube.com/watch?v=1ghnxhcqgsm'':12C ''www.youtube.com/watch?v=lmg_fjg4dy8'':25C ''youtub'':17C ''zdenek'':118C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9952, 2, '2016-01-25 19:37:47.28', 1, NULL, NULL, '&lt;p&gt;The most common approach is to create business rules handmade, based on the univariate and multivariate analysis of the variable.&lt;/p&gt;

&lt;p&gt;Basically, do some frequency count, see if you could isolate some subset of your data just looking at one or two variables.&lt;/p&gt;

&lt;p&gt;Then when you have your labels, create a linear or so model with this new variable as output. For exemple, a &lt;a href="https://en.wikipedia.org/wiki/Linear_discriminant_analysis" rel="nofollow"&gt;linear discriminant analysis&lt;/a&gt;. The analysis will supply you with new insights on your group.&lt;/p&gt;

&lt;p&gt;If you want to rely on an algorithm, two solutions:&lt;/p&gt;

&lt;p&gt;As you don''t seems to have a lot of variables, a non-supervised segmentation could do the job. For exemple, a k-Nearest Neighbor or a decision tree are basic and good aproachs.&lt;/p&gt;

&lt;p&gt;With a few more variables, I like is to do a &lt;a href="https://en.wikipedia.org/wiki/Principal_component_analysis" rel="nofollow"&gt;principal component analysis&lt;/a&gt; then a non-supervised classification to define your group on the result of the PCA.
Note that a PCA + handmade rules based on the analysis of your PCA result may be enough.&lt;/p&gt;

&lt;p&gt;Each time, in the end, a discriminant analysis and a profile of your groups to asses the quality of your results.&lt;/p&gt;
', NULL, NULL, '''/a'':79C,164C ''/p'':22C,46C,91C,103C,141C,198C,221C ''/wiki/linear_discriminant_analysis'':73C ''/wiki/principal_component_analysis'':158C ''algorithm'':100C ''analysi'':18C,78C,81C,163C,190C,207C ''approach'':5C ''aproach'':140C ''ass'':215C ''base'':12C,187C ''basic'':24C,137C ''busi'':9C ''classif'':170C ''common'':4C ''compon'':162C ''could'':32C,121C ''count'':28C ''creat'':8C,54C ''data'':38C ''decis'':134C ''defin'':172C ''discrimin'':77C,206C ''en.wikipedia.org'':72C,157C ''en.wikipedia.org/wiki/linear_discriminant_analysis'':71C ''en.wikipedia.org/wiki/principal_component_analysis'':156C ''end'':204C ''enough'':197C ''exempl'':67C,126C ''frequenc'':27C ''good'':139C ''group'':90C,174C,213C ''handmad'':11C,185C ''href'':70C,155C ''insight'':87C ''isol'':33C ''job'':124C ''k'':129C ''k-nearest'':128C ''label'':53C ''like'':149C ''linear'':56C,76C ''look'':40C ''lot'':113C ''may'':195C ''model'':59C ''multivari'':17C ''nearest'':130C ''neighbor'':131C ''new'':62C,86C ''nofollow'':75C,160C ''non'':118C,168C ''non-supervis'':117C,167C ''note'':181C ''one'':42C ''output'':65C ''p'':1C,23C,47C,92C,104C,142C,199C ''pca'':180C,184C,193C ''princip'':161C ''profil'':210C ''qualiti'':217C ''rel'':74C,159C ''reli'':97C ''result'':177C,194C,220C ''rule'':10C,186C ''see'':29C ''seem'':109C ''segment'':120C ''solut'':102C ''subset'':35C ''supervis'':119C,169C ''suppli'':83C ''time'':201C ''tree'':135C ''two'':44C,101C ''univari'':15C ''variabl'':21C,45C,63C,115C,147C ''want'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (174, 2, '2014-05-20 09:24:30.697', 4, NULL, NULL, '&lt;p&gt;Another suggestion is to test the &lt;a href="http://en.wikipedia.org/wiki/Logistic_regression" rel="nofollow"&gt;logistic regression&lt;/a&gt;. As an added bonus, the  weights (coefficients) of the model will give you an idea of which sites are age-distriminant.  &lt;/p&gt;

&lt;p&gt;Sklearn offers the &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html" rel="nofollow"&gt;sklearn.linear_model.LogisticRegression&lt;/a&gt; package that is designed to handle sparse data as well.&lt;/p&gt;

&lt;p&gt;As mentionned in the comments, in the present case, with more input variables than samples, you need to regularize the model (with &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html" rel="nofollow"&gt;sklearn.linear_model.LogisticRegression&lt;/a&gt; use the &lt;code&gt;penalty=''l1''&lt;/code&gt; argument).&lt;/p&gt;
', NULL, NULL, '''/a'':17C,53C,96C ''/code'':102C ''/p'':40C,64C,104C ''/stable/modules/generated/sklearn.linear_model.logisticregression.html'':49C,92C ''/wiki/logistic_regression'':12C ''ad'':20C ''age'':38C ''age-distrimin'':37C ''anoth'':2C ''argument'':103C ''bonus'':21C ''case'':74C ''code'':99C ''coeffici'':24C ''comment'':70C ''data'':61C ''design'':57C ''distrimin'':39C ''en.wikipedia.org'':11C ''en.wikipedia.org/wiki/logistic_regression'':10C ''give'':29C ''handl'':59C ''href'':9C,46C,89C ''idea'':32C ''input'':77C ''l1'':101C ''logist'':15C ''mention'':67C ''model'':27C,86C ''need'':82C ''nofollow'':14C,51C,94C ''offer'':43C ''p'':1C,41C,65C ''packag'':54C ''penalti'':100C ''present'':73C ''regress'':16C ''regular'':84C ''rel'':13C,50C,93C ''sampl'':80C ''scikit-learn.org'':48C,91C ''scikit-learn.org/stable/modules/generated/sklearn.linear_model.logisticregression.html'':47C,90C ''site'':35C ''sklearn'':42C ''sklearn.linear_model.logisticregression'':52C,95C ''spars'':60C ''suggest'':3C ''test'':6C ''use'':97C ''variabl'':78C ''weight'':23C ''well'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (175, 1, '2014-05-20 22:14:02.927', 2, 35, 'Can metadata be used to adapt parsing for an unescaped in field use of the delimiter?', '&lt;p&gt;I have data coming from a source system that is pipe delimited. Pipe was selected over comma since it was believed no pipes appeared in field, while it was known that commas do occur. After ingesting this data into Hive however it has been discovered that rarely a field does in fact contain a pipe character.&lt;/p&gt;

&lt;p&gt;Due to a constraint we are unable to regenerate from source to escape the delimiter or change delimiters in the usual way. However we have the metadata used to create the Hive table. Could we use knowledge of the fields around the problem field to reprocess the file on our side to escape it or to change the file delimiter prior to reloading the data into Hive?&lt;/p&gt;
', 'metadata  parsing', NULL, '''/p'':76C,145C ''adapt'':6A ''appear'':43C ''around'':118C ''believ'':40C ''chang'':94C,134C ''charact'':75C ''come'':23C ''comma'':36C,51C ''constraint'':81C ''contain'':72C ''could'':111C ''creat'':107C ''data'':22C,57C,142C ''delimit'':16A,31C,92C,95C,137C ''discov'':64C ''due'':78C ''escap'':90C,130C ''fact'':71C ''field'':12A,45C,68C,117C,121C ''file'':125C,136C ''hive'':59C,109C,144C ''howev'':60C,100C ''ingest'':55C ''knowledg'':114C ''known'':49C ''metadata'':2A,17B,104C ''occur'':53C ''p'':19C,77C ''pars'':7A,18B ''pipe'':30C,32C,42C,74C ''prior'':138C ''problem'':120C ''rare'':66C ''regener'':86C ''reload'':140C ''reprocess'':123C ''select'':34C ''side'':128C ''sinc'':37C ''sourc'':26C,88C ''system'':27C ''tabl'':110C ''unabl'':84C ''unescap'':10A ''use'':4A,13A,105C,113C ''usual'':98C ''way'':99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (316, 2, '2014-06-11 16:25:34.747', 8, NULL, NULL, '&lt;p&gt;As Konstantin has pointed, R performs all its computation in the system''s memory i.e. RAM. Hence, RAM capacity is a very important constraint for computation intensive operations in R. Overcoming this constraint, data is being stored these days in HDFS systems, where data isn''t loaded onto memory and program is run instead, program goes to the data and performs the operations, thus overcoming the memory constraints.  RHadoop (&lt;a href="https://github.com/RevolutionAnalytics/RHadoop/wiki"&gt;https://github.com/RevolutionAnalytics/RHadoop/wiki&lt;/a&gt;) is the connector you are looking for.&lt;/p&gt;

&lt;p&gt;Coming to the impact on algorithms which are computation intensive, Random Forests/Decision Trees/Ensemble methods on a considerable amount of data (minimum 50,000 observations in my experience) take up a lot of memory and are considerably slow. To speed up the process, parallelization is the way to go and parallelization is inherently available in Hadoop! That''s where, Hadoop is really efficient.&lt;/p&gt;

&lt;p&gt;So, if you are going for ensemble methods which are compute intensive and are slow, you would want to try out on the HDFS system which gives a considerable performance improvement.&lt;/p&gt;
', NULL, NULL, '''/p'':86C,150C,183C ''/revolutionanalytics/rhadoop/wiki'':75C ''/revolutionanalytics/rhadoop/wiki&lt;/a&gt;)'':78C ''000'':110C ''50'':109C ''algorithm'':93C ''amount'':105C ''avail'':140C ''capac'':20C ''come'':88C ''comput'':10C,27C,96C,162C ''connector'':81C ''consider'':104C,123C,180C ''constraint'':25C,34C,69C ''data'':35C,45C,60C,107C ''day'':40C ''effici'':149C ''ensembl'':158C ''experi'':114C ''forests/decision'':99C ''github.com'':74C,77C ''github.com/revolutionanalytics/rhadoop/wiki'':73C ''github.com/revolutionanalytics/rhadoop/wiki&lt;/a&gt;)'':76C ''give'':178C ''go'':135C,156C ''goe'':57C ''hadoop'':142C,146C ''hdfs'':42C,175C ''henc'':18C ''href'':72C ''i.e'':16C ''impact'':91C ''import'':24C ''improv'':182C ''inher'':139C ''instead'':55C ''intens'':28C,97C,163C ''isn'':46C ''konstantin'':3C ''load'':48C ''look'':84C ''lot'':118C ''memori'':15C,50C,68C,120C ''method'':101C,159C ''minimum'':108C ''observ'':111C ''onto'':49C ''oper'':29C,64C ''overcom'':32C,66C ''p'':1C,87C,151C ''parallel'':130C,137C ''perform'':7C,62C,181C ''point'':5C ''process'':129C ''program'':52C,56C ''r'':6C,31C ''ram'':17C,19C ''random'':98C ''realli'':148C ''rhadoop'':70C ''run'':54C ''slow'':124C,166C ''speed'':126C ''store'':38C ''system'':13C,43C,176C ''take'':115C ''thus'':65C ''trees/ensemble'':100C ''tri'':171C ''want'':169C ''way'':133C ''would'':168C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (176, 1, '2014-05-21 05:29:36.787', 9, 836, 'How to animate growth of a social network?', '&lt;p&gt;I am seeking for a library/tool to visualize how social network changes when new nodes/edges are added to it.&lt;/p&gt;

&lt;p&gt;One of the existing solutions is &lt;a href="http://www.stanford.edu/group/sonia/"&gt;SoNIA: Social Network Image Animator&lt;/a&gt;. It let''s you make movies like &lt;a href="https://www.youtube.com/watch?v=yGSNCED6mDc"&gt;this one&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;SoNIA''s documentation says that it''s broken at the moment, and besides this I would prefer JavaScript-based solution instead. So, my question is: are you familiar with any tools or are you able to point me to some libraries which would make this task as easy as possible?&lt;/p&gt;

&lt;p&gt;Right after posting this question I''ll dig into &lt;a href="http://sigmajs.org/"&gt;sigma.js&lt;/a&gt;, so please consider this library covered.&lt;/p&gt;

&lt;p&gt;In general, my input data would be something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;time_elapsed; node1; node2
1; A; B
2; A; C
3; B; C
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So, here we have three points in time (1, 2, 3), three nodes (A, B, C), and three edges, which represent a triadic closure between the three considered nodes.&lt;/p&gt;

&lt;p&gt;Moreover, every node will have two attributes (age and gender), so I would like to be able to change the shape/colour of the nodes.&lt;/p&gt;

&lt;p&gt;Also, after adding a new node, it would be perfect to have some ForceAtlas2 or similar algorithm to adjust the layout of the graph.&lt;/p&gt;
', 'social-network-analysis  time-series  javascript  visualization', NULL, '''/a'':56C,71C,140C ''/code'':175C ''/group/sonia/'':50C ''/p'':38C,72C,125C,147C,159C,207C,233C,259C ''/pre'':176C ''/watch?v=ygsnced6mdc'':68C ''1'':166C,186C ''2'':169C,187C ''3'':172C,188C ''abl'':109C,225C ''ad'':35C,237C ''adjust'':253C ''age'':216C ''algorithm'':251C ''also'':235C ''analysi'':12B ''anim'':3A,55C ''attribut'':215C ''b'':168C,173C,192C ''base'':93C ''besid'':86C ''broken'':81C ''c'':171C,174C,193C ''chang'':30C,227C ''closur'':201C ''code'':161C ''consid'':143C,205C ''cover'':146C ''data'':153C ''dig'':134C ''document'':76C ''easi'':122C ''edg'':196C ''elaps'':163C ''everi'':210C ''exist'':43C ''familiar'':102C ''forceatlas2'':248C ''gender'':218C ''general'':150C ''graph'':258C ''growth'':4A ''href'':47C,65C,137C ''imag'':54C ''input'':152C ''instead'':95C ''javascript'':16B,92C ''javascript-bas'':91C ''layout'':255C ''let'':58C ''librari'':115C,145C ''library/tool'':24C ''like'':63C,157C,222C ''ll'':133C ''make'':61C,118C ''moment'':84C ''moreov'':209C ''movi'':62C ''network'':8A,11B,29C,53C ''new'':32C,239C ''node'':190C,206C,211C,232C,240C ''node1'':164C ''node2'':165C ''nodes/edges'':33C ''one'':40C,70C ''p'':18C,39C,73C,126C,148C,177C,208C,234C ''perfect'':244C ''pleas'':142C ''point'':111C,183C ''possibl'':124C ''post'':129C ''pre'':160C ''prefer'':90C ''question'':98C,131C ''repres'':198C ''right'':127C ''say'':77C ''seek'':21C ''seri'':15B ''shape/colour'':229C ''sigma.js'':139C ''sigmajs.org'':138C ''similar'':250C ''social'':7A,10B,28C,52C ''social-network-analysi'':9B ''solut'':44C,94C ''someth'':156C ''sonia'':51C,74C ''task'':120C ''three'':182C,189C,195C,204C ''time'':14B,162C,185C ''time-seri'':13B ''tool'':105C ''triadic'':200C ''two'':214C ''visual'':17B,26C ''would'':89C,117C,154C,221C,242C ''www.stanford.edu'':49C ''www.stanford.edu/group/sonia/'':48C ''www.youtube.com'':67C ''www.youtube.com/watch?v=ygsnced6mdc'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (448, 2, '2014-06-18 05:35:35.7', 1, NULL, NULL, '&lt;p&gt;Not sure about the YARN, but I think that Spark makes a real difference compared to Hadoop (advertised as 100 times faster) if data can fit nicely in the memory of the computational nodes. Simply because it avoids hard disk access. If data doesn''t fit memory there''s still some gain because of buffering.&lt;/p&gt;
', NULL, NULL, '''/p'':57C ''100'':21C ''access'':42C ''advertis'':19C ''avoid'':39C ''buffer'':56C ''compar'':16C ''comput'':34C ''data'':25C,44C ''differ'':15C ''disk'':41C ''doesn'':45C ''faster'':23C ''fit'':27C,47C ''gain'':53C ''hadoop'':18C ''hard'':40C ''make'':12C ''memori'':31C,48C ''nice'':28C ''node'':35C ''p'':1C ''real'':14C ''simpli'':36C ''spark'':11C ''still'':51C ''sure'':3C ''think'':9C ''time'':22C ''yarn'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8944, 2, '2015-11-20 14:30:49.877', 1, NULL, NULL, '&lt;p&gt;How much help this community can be depends on where in the process you are.  If you have a dataset and are looking on ways to &lt;a href="https://en.wikipedia.org/wiki/Feature_engineering" rel="nofollow"&gt;determine the most predictive features&lt;/a&gt;, then you could include some information as to what kind of data you''ve collected/have access to.  &lt;/p&gt;

&lt;p&gt;If instead your question is specifically "what data should I collect to predict purchases from banks, and what models should I use," you are unlikely to get a very specific answer.  &lt;/p&gt;
', NULL, NULL, '''/a'':40C ''/p'':58C,91C ''/wiki/feature_engineering'':32C ''access'':56C ''answer'':90C ''bank'':75C ''collect'':70C ''collected/have'':55C ''communiti'':6C ''could'':43C ''data'':52C,67C ''dataset'':21C ''depend'':9C ''determin'':35C ''en.wikipedia.org'':31C ''en.wikipedia.org/wiki/feature_engineering'':30C ''featur'':39C ''get'':86C ''help'':4C ''href'':29C ''includ'':44C ''inform'':46C ''instead'':61C ''kind'':50C ''look'':24C ''model'':78C ''much'':3C ''nofollow'':34C ''p'':1C,59C ''predict'':38C,72C ''process'':14C ''purchas'':73C ''question'':63C ''rel'':33C ''specif'':65C,89C ''unlik'':84C ''use'':81C ''ve'':54C ''way'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (179, 2, '2014-05-21 07:09:20.093', 4, NULL, NULL, '&lt;p&gt;My first guess is to &lt;a href="https://www.google.com/search?q=visualize%20social%20network%20in%20tableau" rel="nofollow"&gt;visualize social network in Tableau&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;And particularly: &lt;a href="http://www.clearlyandsimply.com/clearly_and_simply/2012/12/build-network-graphs-in-tableau.html" rel="nofollow"&gt;building network graphs in Tableau&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;What you need is to add time dimension to the "Pages" section to be able to see network change dynamics.&lt;/p&gt;

&lt;p&gt;This is screen from the link above.
&lt;img src="http://i.stack.imgur.com/Quq8G.png" alt="enter image description here"&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':19C,36C ''/clearly_and_simply/2012/12/build-network-graphs-in-tableau.html'':28C ''/p'':20C,37C,59C,78C ''/quq8g.png'':72C ''/search?q=visualize%20social%20network%20in%20tableau'':11C ''abl'':53C ''add'':44C ''alt'':73C ''build'':31C ''chang'':57C ''descript'':76C ''dimens'':46C ''dynam'':58C ''enter'':74C ''first'':3C ''graph'':33C ''guess'':4C ''href'':8C,25C ''i.stack.imgur.com'':71C ''i.stack.imgur.com/quq8g.png'':70C ''imag'':75C ''img'':68C ''link'':66C ''need'':41C ''network'':16C,32C,56C ''nofollow'':13C,30C ''p'':1C,21C,38C,60C ''page'':49C ''particular'':23C ''rel'':12C,29C ''screen'':63C ''section'':50C ''see'':55C ''social'':15C ''src'':69C ''tableau'':18C,35C ''time'':45C ''visual'':14C ''www.clearlyandsimply.com'':27C ''www.clearlyandsimply.com/clearly_and_simply/2012/12/build-network-graphs-in-tableau.html'':26C ''www.google.com'':10C ''www.google.com/search?q=visualize%20social%20network%20in%20tableau'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (229, 2, '2014-06-01 19:48:41.693', 8, NULL, NULL, '&lt;p&gt;This is kind of like asking about the tradeoffs between frying pan and your drawer of silverware. They are not two things you compare, really. You might use them together as part of a larger project. &lt;/p&gt;

&lt;p&gt;Hadoop itself is not one thing, but a name for a federation of services, like HDFS, Hive, HBase, MapReduce, etc. Storm is something you use with some of these services, like HDFS or HBase. It is a stream-processing framework. There are others within the extended Hadoop ecosystem, like Spark Streaming.&lt;/p&gt;

&lt;p&gt;When would you choose a stream-processing framework? when you need to react to new data in near-real-time. If you need this kind of tool, you deploy this kind of tool, too.&lt;/p&gt;
', NULL, NULL, '''/p'':38C,92C,130C ''ask'':7C ''choos'':97C ''compar'':25C ''data'':110C ''deploy'':124C ''drawer'':16C ''ecosystem'':88C ''etc'':59C ''extend'':86C ''feder'':51C ''framework'':80C,102C ''fri'':12C ''hadoop'':40C,87C ''hbase'':57C,73C ''hdfs'':55C,71C ''hive'':56C ''kind'':4C,120C,126C ''larger'':36C ''like'':6C,54C,70C,89C ''mapreduc'':58C ''might'':28C ''name'':48C ''near'':113C ''near-real-tim'':112C ''need'':105C,118C ''new'':109C ''one'':44C ''other'':83C ''p'':1C,39C,93C ''pan'':13C ''part'':33C ''process'':79C,101C ''project'':37C ''react'':107C ''real'':114C ''realli'':26C ''servic'':53C,69C ''silverwar'':18C ''someth'':62C ''spark'':90C ''storm'':60C ''stream'':78C,91C,100C ''stream-process'':77C,99C ''thing'':23C,45C ''time'':115C ''togeth'':31C ''tool'':122C,128C ''tradeoff'':10C ''two'':22C ''use'':29C,64C ''within'':84C ''would'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (218, 1, '2014-05-29 13:08:09.06', 2, 468, 'built-in cov in pandas DataFrame results ValueError array is too big', '&lt;p&gt;So, I have a dataset with 39.949 variables and 180 rows. dataset is successfully 
saved in DataFrame but when I try to find cov() it result an error.
here is the code&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  import pandas as pd
  cov_data=pd.DataFrame(dataset).cov()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here is the error&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;File "/home/syahdeini/Desktop/FP/pca_2.py", line 44, in find_eagen
cov_data=pd.DataFrame(data_mat).cov()
File "/usr/lib/python2.7/dist-packages/pandas/core/frame.py", line 3716, in cov
baseCov = np.cov(mat.T)
File "/usr/lib/python2.7/dist-packages/numpy/lib/function_base.py", line 1766, in cov
return (dot(X, X.T.conj()) / fact).squeeze()
ValueError: array is too big.
&lt;/code&gt;&lt;/pre&gt;
', 'python  pandas', NULL, '''/code'':61C,110C ''/home/syahdeini/desktop/fp/pca_2.py'':72C ''/p'':49C,68C ''/pre'':62C,111C ''/usr/lib/python2.7/dist-packages/numpy/lib/function_base.py'':94C ''/usr/lib/python2.7/dist-packages/pandas/core/frame.py'':85C ''1766'':96C ''180'':26C ''3716'':87C ''39.949'':23C ''44'':74C ''array'':10A,106C ''basecov'':90C ''big'':13A,109C ''built'':2A ''built-in'':1A ''code'':48C,51C,70C ''cov'':4A,40C,56C,60C,78C,83C,89C,98C ''data'':57C,79C,81C ''datafram'':7A,33C ''dataset'':21C,28C,59C ''dot'':100C ''eagen'':77C ''error'':44C,67C ''fact'':103C ''file'':71C,84C,93C ''find'':39C,76C ''import'':52C ''line'':73C,86C,95C ''mat'':82C ''mat.t'':92C ''np.cov'':91C ''p'':16C,63C ''panda'':6A,15B,53C ''pd'':55C ''pd.dataframe'':58C,80C ''pre'':50C,69C ''python'':14B ''result'':8A,42C ''return'':99C ''row'':27C ''save'':31C ''squeez'':104C ''success'':30C ''tri'':37C ''valueerror'':9A,105C ''variabl'':24C ''x'':101C ''x.t.conj'':102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (488, 1, '2014-06-19 18:02:24.65', 6, 1366, 'Is GLM a statistical or machine learning model?', '&lt;p&gt;I thought that generalized linear model (GLM) would be considered a statistical model, but a friend told me that some papers classify it as a machine learning technique. Which one is true (or more precise)? Any explanation would be appreciated.&lt;/p&gt;
', 'machine-learning  statistics  glm', NULL, '''/p'':55C ''appreci'':54C ''classifi'':36C ''consid'':24C ''explan'':51C ''friend'':30C ''general'':18C ''glm'':2A,13B,21C ''learn'':7A,11B,41C ''linear'':19C ''machin'':6A,10B,40C ''machine-learn'':9B ''model'':8A,20C,27C ''one'':44C ''p'':14C ''paper'':35C ''precis'':49C ''statist'':4A,12B,26C ''techniqu'':42C ''thought'':16C ''told'':31C ''true'':46C ''would'':22C,52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (183, 2, '2014-05-21 10:53:26.87', 9, NULL, NULL, '&lt;h3&gt;Fancy animations are cool&lt;/h3&gt;

&lt;p&gt;I was very impressed when I saw &lt;a href="http://youtu.be/T7Ncmq6scck"&gt;this animation&lt;/a&gt; of the &lt;a href="http://www.discourse.org"&gt;discourse&lt;/a&gt; git repository. They used &lt;a href="https://code.google.com/p/gource/"&gt;Gourse&lt;/a&gt; which is specifically for git. But it may give ideas about how to represent the dynamics of growth.&lt;/p&gt;

&lt;h3&gt;You can create animations with matplotlib&lt;/h3&gt;

&lt;p&gt;&lt;a href="http://stackoverflow.com/a/13571425/1083707"&gt;This stackoverflow answer&lt;/a&gt; seems to point at a python/networkx/matplotlib solution.&lt;/p&gt;

&lt;h3&gt;But D3.js provides interaction&lt;/h3&gt;

&lt;p&gt;If you''re looking for a web-based solution then d3.js is excellent. See &lt;a href="http://bl.ocks.org/mbostock/4062045"&gt;this&lt;/a&gt;, &lt;a href="http://mbostock.github.io/d3/talk/20111116/force-collapsible.html"&gt;this&lt;/a&gt; and &lt;a href="http://bl.ocks.org/mbostock/929623"&gt;this&lt;/a&gt; for example.
See also &lt;a href="http://stackoverflow.com/questions/7416659/interactive-graph-visualisation"&gt;this stackoverflow question&lt;/a&gt;, the accepted answer points to D3.js again.&lt;/p&gt;

&lt;h2&gt;Conclusion&lt;/h2&gt;

&lt;p&gt;I would be drawn towards the python/networkx options for network analysis (possibly to add attributes to your raw data file for example). Then, for visualisation and dissemination D3.js is perfect. You might be surprised how easy it can be to write d3.js once you get into it. I believe &lt;a href="https://www.youtube.com/watch?v=8UtoIR2IEkI"&gt;it even works within an ipython notebook!&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':22C,29C,40C,77C,114C,121C,129C,142C,216C ''/a/13571425/1083707'':73C ''/d3/talk/20111116/force-collapsible.html'':119C ''/h2'':153C ''/h3'':6C,67C,91C ''/mbostock/4062045'':112C ''/mbostock/929623'':127C ''/p'':59C,85C,150C,217C ''/p/gource/'':38C ''/questions/7416659/interactive-graph-visualisation'':138C ''/t7ncmq6scck'':19C ''/watch?v=8utoir2ieki'':208C ''accept'':144C ''add'':168C ''also'':133C ''analysi'':165C ''anim'':3C,21C,64C ''answer'':76C,145C ''attribut'':169C ''base'':101C ''believ'':203C ''bl.ocks.org'':111C,126C ''bl.ocks.org/mbostock/4062045'':110C ''bl.ocks.org/mbostock/929623'':125C ''code.google.com'':37C ''code.google.com/p/gource/'':36C ''conclus'':152C ''cool'':5C ''creat'':63C ''d3.js'':88C,104C,148C,182C,196C ''data'':173C ''discours'':28C ''dissemin'':181C ''drawn'':158C ''dynam'':56C ''easi'':190C ''even'':210C ''exampl'':131C,176C ''excel'':106C ''fanci'':2C ''file'':174C ''get'':199C ''git'':30C,45C ''give'':49C ''gours'':39C ''growth'':58C ''h2'':151C ''h3'':1C,60C,86C ''href'':16C,26C,35C,70C,109C,116C,124C,135C,205C ''idea'':50C ''impress'':11C ''interact'':90C ''ipython'':214C ''look'':96C ''matplotlib'':66C ''may'':48C ''mbostock.github.io'':118C ''mbostock.github.io/d3/talk/20111116/force-collapsible.html'':117C ''might'':186C ''network'':164C ''notebook'':215C ''option'':162C ''p'':7C,68C,92C,154C ''perfect'':184C ''point'':80C,146C ''possibl'':166C ''provid'':89C ''python/networkx'':161C ''python/networkx/matplotlib'':83C ''question'':141C ''raw'':172C ''re'':95C ''repositori'':31C ''repres'':54C ''saw'':14C ''see'':107C,132C ''seem'':78C ''solut'':84C,102C ''specif'':43C ''stackoverflow'':75C,140C ''stackoverflow.com'':72C,137C ''stackoverflow.com/a/13571425/1083707'':71C ''stackoverflow.com/questions/7416659/interactive-graph-visualisation'':136C ''surpris'':188C ''toward'':159C ''use'':33C ''visualis'':179C ''web'':100C ''web-bas'':99C ''within'':212C ''work'':211C ''would'':156C ''write'':195C ''www.discourse.org'':27C ''www.youtube.com'':207C ''www.youtube.com/watch?v=8utoir2ieki'':206C ''youtu.be'':18C ''youtu.be/t7ncmq6scck'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (184, 1, '2014-05-21 11:22:34.657', 6, 360, 'Google prediction API: What training/prediction methods Google Prediction API employs?', '&lt;p&gt;The details of the Google Prediction API are on this &lt;a href="https://developers.google.com/prediction/"&gt;page&lt;/a&gt;, but I am not able to find any details about the prediction algorithms running behind the API. &lt;/p&gt;

&lt;p&gt;So far I have gathered that they let you provide your preprocessing steps in PMML format.&lt;/p&gt;
', 'tools', NULL, '''/a'':29C ''/p'':47C,65C ''/prediction/'':27C ''abl'':34C ''algorithm'':42C ''api'':3A,9A,19C,46C ''behind'':44C ''detail'':14C,38C ''developers.google.com'':26C ''developers.google.com/prediction/'':25C ''employ'':10A ''far'':50C ''find'':36C ''format'':64C ''gather'':53C ''googl'':1A,7A,17C ''href'':24C ''let'':56C ''method'':6A ''p'':12C,48C ''page'':28C ''pmml'':63C ''predict'':2A,8A,18C,41C ''preprocess'':60C ''provid'':58C ''run'':43C ''step'':61C ''tool'':11B ''training/prediction'':5A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (185, 2, '2014-05-21 14:14:38.797', 4, NULL, NULL, '&lt;p&gt;If you take a look over the specifications of PMML which you can find &lt;a href="http://www.dmg.org/v3-0/GeneralStructure.html" rel="nofollow"&gt;here&lt;/a&gt; you can see on the left menu what options you have (like ModelTree, NaiveBayes, Neural Nets and so on).&lt;/p&gt;
', NULL, NULL, '''/a'':24C ''/p'':44C ''/v3-0/generalstructure.html'':20C ''find'':15C ''href'':17C ''left'':30C ''like'':36C ''look'':6C ''menu'':31C ''modeltre'':37C ''naivebay'':38C ''net'':40C ''neural'':39C ''nofollow'':22C ''option'':33C ''p'':1C ''pmml'':11C ''rel'':21C ''see'':27C ''specif'':9C ''take'':4C ''www.dmg.org'':19C ''www.dmg.org/v3-0/generalstructure.html'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (186, 1, '2014-05-21 15:12:18.98', 5, 59, 'Using SVM as a binary classifier, is the label for a data point chosen by consensus?', '&lt;p&gt;I''m learning &lt;a href="http://en.wikipedia.org/wiki/Support_vector_machine"&gt;Support Vector Machines&lt;/a&gt;, and I''m unable to understand how a class label is chosen for a data point in a binary classifier. Is it chosen by consensus with respect to the classification in each dimension of the separating hyperplane?&lt;/p&gt;
', 'svm  classification  binary', NULL, '''/a'':32C ''/p'':70C ''/wiki/support_vector_machine'':28C ''binari'':5A,19B,51C ''chosen'':14A,44C,55C ''class'':41C ''classif'':18B,62C ''classifi'':6A,52C ''consensus'':16A,57C ''data'':12A,47C ''dimens'':65C ''en.wikipedia.org'':27C ''en.wikipedia.org/wiki/support_vector_machine'':26C ''href'':25C ''hyperplan'':69C ''label'':9A,42C ''learn'':23C ''m'':22C,35C ''machin'':31C ''p'':20C ''point'':13A,48C ''respect'':59C ''separ'':68C ''support'':29C ''svm'':2A,17B ''unabl'':36C ''understand'':38C ''use'':1A ''vector'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2405, 2, '2014-11-05 15:16:36.867', -1, NULL, NULL, '&lt;p&gt;Try out AirXcell : &lt;a href="http://airxcell.com" rel="nofollow"&gt;AirXcell calculation software&lt;/a&gt;.
See documentation &lt;a href="http://www.airxcell.com/doc/howTo/useConsole.html" rel="nofollow"&gt;Use AirXCell as an r Console&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':13C,29C ''/doc/howto/useconsole.html'':20C ''/p'':30C ''airxcel'':4C,10C,24C ''airxcell.com'':7C ''calcul'':11C ''consol'':28C ''document'':15C ''href'':6C,17C ''nofollow'':9C,22C ''p'':1C ''r'':27C ''rel'':8C,21C ''see'':14C ''softwar'':12C ''tri'':2C ''use'':23C ''www.airxcell.com'':19C ''www.airxcell.com/doc/howto/useconsole.html'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (187, 2, '2014-05-21 15:39:54.83', 6, NULL, NULL, '&lt;p&gt;The term &lt;em&gt;consensus&lt;/em&gt;, as far as I''m concerned, is used rather for cases when you have more a than one source of metric/measure/choice from which to make a decision. And, in order to choose a possible result, you perform some &lt;em&gt;average evaluation/consensus&lt;/em&gt; over the values available.&lt;/p&gt;

&lt;p&gt;This is not the case for SVM. The algorithm is based on a &lt;a href="http://upload.wikimedia.org/wikipedia/commons/2/2a/Svm_max_sep_hyperplane_with_margin.png"&gt;quadratic optimization&lt;/a&gt;, that maximizes the distance from the closest documents of two different classes, using a hyperplane to make the split.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/CCO7Z.png" alt="Hyperplane separating two different classes"&gt;&lt;/p&gt;

&lt;p&gt;So, the only &lt;em&gt;consensus&lt;/em&gt; here is the resulting hyperplane, computed from the closest documents of each class. In other words, the classes are attributed to each point by calculating the distance from the point to the hyperplane derived. If the distance is positive, it belongs to a certain class, otherwise, it belongs to the other one.&lt;/p&gt;
', NULL, NULL, '''/a'':75C ''/cco7z.png'':101C ''/em'':6C,48C,115C ''/p'':53C,95C,108C,168C ''/wikipedia/commons/2/2a/svm_max_sep_hyperplane_with_margin.png'':72C ''algorithm'':63C ''alt'':102C ''attribut'':135C ''avail'':52C ''averag'':46C ''base'':65C ''belong'':156C,163C ''calcul'':140C ''case'':17C,59C ''certain'':159C ''choos'':38C ''class'':87C,107C,128C,133C,160C ''closest'':82C,124C ''comput'':121C ''concern'':12C ''consensus'':5C,114C ''decis'':33C ''deriv'':149C ''differ'':86C,106C ''distanc'':79C,142C,152C ''document'':83C,125C ''em'':4C,45C,113C ''evaluation/consensus'':47C ''far'':8C ''href'':69C ''hyperplan'':90C,103C,120C,148C ''i.stack.imgur.com'':100C ''i.stack.imgur.com/cco7z.png'':99C ''img'':97C ''m'':11C ''make'':31C,92C ''maxim'':77C ''metric/measure/choice'':27C ''one'':24C,167C ''optim'':74C ''order'':36C ''otherwis'':161C ''p'':1C,54C,96C,109C ''perform'':43C ''point'':138C,145C ''posit'':154C ''possibl'':40C ''quadrat'':73C ''rather'':15C ''result'':41C,119C ''separ'':104C ''sourc'':25C ''split'':94C ''src'':98C ''svm'':61C ''term'':3C ''two'':85C,105C ''upload.wikimedia.org'':71C ''upload.wikimedia.org/wikipedia/commons/2/2a/svm_max_sep_hyperplane_with_margin.png'':70C ''use'':14C,88C ''valu'':51C ''word'':131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (188, 2, '2014-05-21 16:13:25.59', 4, NULL, NULL, '&lt;p&gt;You can use map reduce algorithms in Hadoop without programming them in Java. It is called streaming and works like Linux piping. If you believe that you can port your functions to read and write to terminal, it should work nicely. &lt;a href="http://www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/" rel="nofollow"&gt;Here&lt;/a&gt; is example blog post which shows how to use map reduce functions written in Python in Hadoop.&lt;/p&gt;
', NULL, NULL, '''/a'':51C ''/p'':69C ''/tutorials/writing-an-hadoop-mapreduce-program-in-python/'':47C ''algorithm'':7C ''believ'':26C ''blog'':54C ''call'':17C ''exampl'':53C ''function'':32C,63C ''hadoop'':9C,68C ''href'':44C ''java'':14C ''like'':21C ''linux'':22C ''map'':5C,61C ''nice'':42C ''nofollow'':49C ''p'':1C ''pipe'':23C ''port'':30C ''post'':55C ''program'':11C ''python'':66C ''read'':34C ''reduc'':6C,62C ''rel'':48C ''show'':57C ''stream'':18C ''termin'':38C ''use'':4C,60C ''without'':10C ''work'':20C,41C ''write'':36C ''written'':64C ''www.michael-noll.com'':46C ''www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (196, 1, '2014-05-22 21:47:26.98', 6, 627, 'Algorithm for generating classification rules', '&lt;p&gt;So we have potential for a machine learning application that fits fairly neatly into the traditional problem domain solved by classifiers, i.e., we have a set of attributes describing an item and a "bucket" that they end up in. However, rather than create models of probabilities like in Naive Bayes or similar classifiers, we want our output to be a set of roughly human-readable rules that can be reviewed and modified by an end user.&lt;/p&gt;

&lt;p&gt;Association rule learning looks like the family of algorithms that solves this type of problem, but these algorithms seem to focus on identifying common combinations of features and don''t include the concept of a final bucket that those features might point to. For example, our data set looks something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Item A { 4-door, small, steel } =&amp;gt; { sedan }
Item B { 2-door, big,   steel } =&amp;gt; { truck }
Item C { 2-door, small, steel } =&amp;gt; { coupe }
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I just want the rules that say "if it''s big and a 2-door, it''s a truck," not the rules that say "if it''s a 4-door it''s also small." &lt;/p&gt;

&lt;p&gt;One workaround I can think of is to simply use association rule learning algorithms and ignore the rules that don''t involve an end bucket, but that seems a bit hacky. Have I missed some family of algorithms out there? Or perhaps I''m approaching the problem incorrectly to begin with?&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/code'':169C ''/p'':88C,142C,206C,259C ''/pre'':170C ''2'':155C,163C,185C ''4'':147C,200C ''algorithm'':1A,98C,107C,221C,245C ''also'':204C ''applic'':19C ''approach'':252C ''associ'':90C,218C ''attribut'':38C ''b'':154C ''bay'':60C ''begin'':257C ''big'':157C,182C ''bit'':237C ''bucket'':44C,126C,232C ''c'':162C ''classif'':4A,9B ''classifi'':31C,63C ''code'':144C ''combin'':114C ''common'':113C ''concept'':122C ''coup'':168C ''creat'':53C ''data'':136C ''describ'':39C ''domain'':28C ''door'':148C,156C,164C,186C,201C ''end'':47C,86C,231C ''exampl'':134C ''fair'':22C ''famili'':96C,243C ''featur'':116C,129C ''final'':125C ''fit'':21C ''focus'':110C ''generat'':3A ''gt'':151C,159C,167C ''hacki'':238C ''howev'':50C ''human'':75C ''human-read'':74C ''i.e'':32C ''identifi'':112C ''ignor'':223C ''includ'':120C ''incorrect'':255C ''involv'':229C ''item'':41C,145C,153C,161C ''learn'':8B,18C,92C,220C ''like'':57C,94C,140C ''look'':93C,138C ''m'':251C ''machin'':7B,17C ''machine-learn'':6B ''might'':130C ''miss'':241C ''model'':54C ''modifi'':83C ''naiv'':59C ''neat'':23C ''one'':208C ''output'':67C ''p'':10C,89C,171C,207C ''perhap'':249C ''point'':131C ''potenti'':14C ''pre'':143C ''probabl'':56C ''problem'':27C,104C,254C ''rather'':51C ''readabl'':76C ''review'':81C ''rough'':73C ''rule'':5A,77C,91C,176C,193C,219C,225C ''say'':178C,195C ''sedan'':152C ''seem'':108C,235C ''set'':36C,71C,137C ''similar'':62C ''simpli'':216C ''small'':149C,165C,205C ''solv'':29C,100C ''someth'':139C ''steel'':150C,158C,166C ''think'':212C ''tradit'':26C ''truck'':160C,190C ''type'':102C ''use'':217C ''user'':87C ''want'':65C,174C ''workaround'':209C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (189, 1, '2014-05-21 19:41:19.857', 2, 251, 'Open source solver for large mixed integer programming task?', '&lt;p&gt;I''m currently using &lt;a href="http://en.wikipedia.org/wiki/General_Algebraic_Modeling_System" rel="nofollow"&gt;General Algebraic Modeling System&lt;/a&gt; (GAMS), and more specifically CPLEX within GAMS, to solve a very large mixed integer programming problem. This allows me to parallelize the process over 4 cores (although I have more, CPLEX utilizes a maximum of 4 cores), and it finds an optimal solution in a relatively short amount of time.&lt;/p&gt;

&lt;p&gt;Is there an open source mixed integer programming tool that I could use as an alternative to GAMS and CPLEX? It must be comparable in speed or faster for me to consider it. I have a preference for R based solutions, but I''m open to suggestions of all kinds, and other users may be interested in different solutions.&lt;/p&gt;
', 'r  open-source  parallel  optimization', NULL, '''/a'':32C ''/p'':83C,144C ''/wiki/general_algebraic_modeling_system'':25C ''4'':57C,68C ''algebra'':29C ''allow'':50C ''altern'':100C ''although'':59C ''amount'':80C ''base'':124C ''compar'':108C ''consid'':116C ''core'':58C,69C ''could'':96C ''cplex'':37C,63C,104C ''current'':19C ''differ'':142C ''en.wikipedia.org'':24C ''en.wikipedia.org/wiki/general_algebraic_modeling_system'':23C ''faster'':112C ''find'':72C ''gam'':33C,39C,102C ''general'':28C ''href'':22C ''integ'':7A,46C,91C ''interest'':140C ''kind'':134C ''larg'':5A,44C ''m'':18C,128C ''maximum'':66C ''may'':138C ''mix'':6A,45C,90C ''model'':30C ''must'':106C ''nofollow'':27C ''open'':1A,12B,88C,129C ''open-sourc'':11B ''optim'':15B,74C ''p'':16C,84C ''parallel'':14B,53C ''prefer'':121C ''problem'':48C ''process'':55C ''program'':8A,47C,92C ''r'':10B,123C ''rel'':26C ''relat'':78C ''short'':79C ''solut'':75C,125C,143C ''solv'':41C ''solver'':3A ''sourc'':2A,13B,89C ''specif'':36C ''speed'':110C ''suggest'':131C ''system'':31C ''task'':9A ''time'':82C ''tool'':93C ''use'':20C,97C ''user'':137C ''util'':64C ''within'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (190, 2, '2014-05-22 12:14:49.727', 3, NULL, NULL, '&lt;p&gt;It turned out that this task was quite easy to accomplish using &lt;a href="http://visjs.org/" rel="nofollow"&gt;vis.js&lt;/a&gt;. &lt;a href="http://visjs.org/examples/graph/20_navigation.html" rel="nofollow"&gt;This&lt;/a&gt; was the best example code which I have found.&lt;/p&gt;

&lt;p&gt;The example of what I have built upon this is &lt;a href="http://laboratoriumdanych.pl/jak-powstaje-siec/" rel="nofollow"&gt;here&lt;/a&gt; (scroll to the bottom of this post). This graph represents the growth of a subnetwork of Facebook friends. Green dots are females, blue ones are males. The darker the colour, the older the user. By clicking "Dodaj wÄzÅy" you can add more nodes and edges to the graph.&lt;/p&gt;

&lt;p&gt;Anyway, I am still interested in other ways to accomplish this task, so I won''t accept any answer as for now.&lt;/p&gt;

&lt;p&gt;Thanks for your contributions!&lt;/p&gt;
', NULL, NULL, '''/a'':20C,29C,59C ''/examples/graph/20_navigation.html'':25C ''/jak-powstaje-siec/'':55C ''/p'':39C,110C,134C,140C ''accept'':128C ''accomplish'':12C,121C ''add'':102C ''answer'':130C ''anyway'':112C ''best'':32C ''blue'':82C ''bottom'':63C ''built'':47C ''click'':95C ''code'':34C ''colour'':89C ''contribut'':139C ''darker'':87C ''dodaj'':96C ''dot'':79C ''easi'':10C ''edg'':106C ''exampl'':33C,42C ''facebook'':76C ''femal'':81C ''found'':38C ''friend'':77C ''graph'':68C,109C ''green'':78C ''growth'':71C ''href'':15C,22C,52C ''interest'':116C ''laboratoriumdanych.pl'':54C ''laboratoriumdanych.pl/jak-powstaje-siec/'':53C ''male'':85C ''node'':104C ''nofollow'':18C,27C,57C ''older'':91C ''one'':83C ''p'':1C,40C,111C,135C ''post'':66C ''quit'':9C ''rel'':17C,26C,56C ''repres'':69C ''scroll'':60C ''still'':115C ''subnetwork'':74C ''task'':7C,123C ''thank'':136C ''turn'':3C ''upon'':48C ''use'':13C ''user'':93C ''vis.js'':19C ''visjs.org'':16C,24C ''visjs.org/examples/graph/20_navigation.html'':23C ''way'':119C ''won'':126C ''wä'':97C ''y'':99C ''zå'':98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (191, 1, '2014-05-22 13:36:24.12', 5, 196, 'Multi layer back propagation Neural network for classification', '&lt;p&gt;Can someone explain me, how to classify a data like MNIST with MLBP-Neural network if I make more than one output (e.g 8), I mean if I just use one output I can easily classify the data, but if I use more than one, which output should I choose ?&lt;/p&gt;
', 'neuralnetwork', NULL, '''/p'':62C ''8'':35C ''back'':3A ''choos'':61C ''classif'':8A ''classifi'':17C,47C ''data'':19C,49C ''e.g'':34C ''easili'':46C ''explain'':13C ''layer'':2A ''like'':20C ''make'':29C ''mean'':37C ''mlbp'':24C ''mlbp-neural'':23C ''mnist'':21C ''multi'':1A ''network'':6A,26C ''neural'':5A,25C ''neuralnetwork'':9B ''one'':32C,42C,56C ''output'':33C,43C,58C ''p'':10C ''propag'':4A ''someon'':12C ''use'':41C,53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (194, 2, '2014-05-22 19:20:14.13', 4, NULL, NULL, '&lt;p&gt;Suppose that you need to classify something in K classes, where K &gt; 2. In this case the most often setup I use is one hot encoding. You will have K output columns, and in the training set you will set all values to 0, except the one which has the category index, which could have value 1. Thus, for each training data set instance you will have all outputs with values 0 or 1, all outputs sum to 1 for each instance.&lt;/p&gt;

&lt;p&gt;This looks like a probability, which reminds me of a technique used often to connect some outputs which are modeled as probability. This is called softmax function, more details &lt;a href="http://en.wikipedia.org/wiki/Softmax_activation_function" rel="nofollow"&gt;on Wikipedia&lt;/a&gt;. This will allow you to put some constraints on the output values (it is basically a logistic function generalization) so that the output values will be modeled as probabilities. &lt;/p&gt;

&lt;p&gt;Finally, with or without softmax you can use the output as a discriminant function to select the proper category.&lt;/p&gt;

&lt;p&gt;Another final thought would be to avoid to encode you variables in a connected way. For example you can have the binary representation of the category index. This would induce to the learner an artificial connection between some outputs which are arbitrary. The one hot encoding has the advantage that is neutral to how labels are indexed.&lt;/p&gt;
', NULL, NULL, '''/a'':124C ''/p'':84C,154C,175C,234C ''/wiki/softmax_activation_function'':119C ''0'':45C,73C ''1'':58C,75C,80C ''2'':14C ''advantag'':225C ''allow'':127C ''anoth'':177C ''arbitrari'':218C ''artifici'':211C ''avoid'':183C ''basic'':139C ''binari'':198C ''call'':110C ''case'':17C ''categori'':52C,174C,202C ''class'':11C ''classifi'':7C ''column'':33C ''connect'':100C,190C,212C ''constraint'':132C ''could'':55C ''data'':63C ''detail'':114C ''discrimin'':168C ''en.wikipedia.org'':118C ''en.wikipedia.org/wiki/softmax_activation_function'':117C ''encod'':27C,185C,222C ''exampl'':193C ''except'':46C ''final'':156C,178C ''function'':112C,142C,169C ''general'':143C ''hot'':26C,221C ''href'':116C ''index'':53C,203C,233C ''induc'':206C ''instanc'':65C,83C ''k'':10C,13C,31C ''label'':231C ''learner'':209C ''like'':88C ''logist'':141C ''look'':87C ''model'':105C,151C ''need'':5C ''neutral'':228C ''nofollow'':121C ''often'':20C,98C ''one'':25C,48C,220C ''output'':32C,70C,77C,102C,135C,147C,165C,215C ''p'':1C,85C,155C,176C ''probabl'':90C,107C,153C ''proper'':173C ''put'':130C ''rel'':120C ''remind'':92C ''represent'':199C ''select'':171C ''set'':38C,41C,64C ''setup'':21C ''softmax'':111C,160C ''someth'':8C ''sum'':78C ''suppos'':2C ''techniqu'':96C ''thought'':179C ''thus'':59C ''train'':37C,62C ''use'':23C,97C,163C ''valu'':43C,57C,72C,136C,148C ''variabl'':187C ''way'':191C ''wikipedia'':123C ''without'':159C ''would'':180C,205C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (195, 2, '2014-05-22 20:48:55.297', 4, NULL, NULL, '&lt;p&gt;Satisfaction is a huge one that I run into a lot.  Huge referring to importance/difficulty/complexity.&lt;/p&gt;

&lt;p&gt;The bottom line is that for very large services (search engines, facebook, linkedin, etc...) your users are simply a collection of log lines.  You have little ability to solicit feed back from them (not a hard and fast rule necessarily).  So you have to infer their positive or negative feedback most of the time.&lt;/p&gt;

&lt;p&gt;This means finding ways, even outside of predictive modelling, to truly tell, from a collection of log lines, whether or not someone actually liked something they experienced.  This simple act is even more fundamental (in my biased opinion) than a/b testing since you''re talking about metrics you will eventually track on a test scorecard.&lt;/p&gt;

&lt;p&gt;Once you have a handle on good SAT metrics then you can start making predictive models and experimenting.  But even deciding what piece of log instrumentation can tell you about SAT is non-trivial (and often changes).&lt;/p&gt;
', NULL, NULL, '''/p'':17C,73C,130C,170C ''a/b'':114C ''abil'':45C ''act'':104C ''actual'':97C ''back'':49C ''bias'':111C ''bottom'':20C ''chang'':169C ''collect'':38C,89C ''decid'':152C ''engin'':29C ''etc'':32C ''even'':79C,106C,151C ''eventu'':124C ''experi'':149C ''experienc'':101C ''facebook'':30C ''fast'':56C ''feed'':48C ''feedback'':68C ''find'':77C ''fundament'':108C ''good'':138C ''handl'':136C ''hard'':54C ''huge'':5C,13C ''importance/difficulty/complexity'':16C ''infer'':63C ''instrument'':157C ''larg'':26C ''like'':98C ''line'':21C,41C,92C ''linkedin'':31C ''littl'':44C ''log'':40C,91C,156C ''lot'':12C ''make'':145C ''mean'':76C ''metric'':121C,140C ''model'':83C,147C ''necessarili'':58C ''negat'':67C ''non'':165C ''non-trivi'':164C ''often'':168C ''one'':6C ''opinion'':112C ''outsid'':80C ''p'':1C,18C,74C,131C ''piec'':154C ''posit'':65C ''predict'':82C,146C ''re'':118C ''refer'':14C ''rule'':57C ''run'':9C ''sat'':139C,162C ''satisfact'':2C ''scorecard'':129C ''search'':28C ''servic'':27C ''simpl'':103C ''simpli'':36C ''sinc'':116C ''solicit'':47C ''someon'':96C ''someth'':99C ''start'':144C ''talk'':119C ''tell'':86C,159C ''test'':115C,128C ''time'':72C ''track'':125C ''trivial'':166C ''truli'':85C ''user'':34C ''way'':78C ''whether'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (197, 2, '2014-05-22 21:54:05.66', 7, NULL, NULL, '&lt;p&gt;C45 made by Quinlan is able to produce rule for prediction. Check this &lt;a href="http://en.wikipedia.org/wiki/C4.5_algorithm"&gt;Wikipedia&lt;/a&gt; page. I know that in &lt;a href="http://www.cs.waikato.ac.nz/~ml/weka/"&gt;Weka&lt;/a&gt; its name is J48. I have no idea which are implementations in R or Python. Anyway, from this kind of decision tree  you should be able to infer rules for prediction.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Later edit&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;Also you might be interested in algorithms for directly inferring rules for classification. RIPPER is one, which again in Weka it received a different name JRip. See the original paper for RIPPER: &lt;a href="http://www.google.ro/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;ved=0CCYQFjAA&amp;amp;url=http://www.cs.utsa.edu/~bylander/cs6243/cohen95ripper.pdf&amp;amp;ei=-XJ-U-7pGoqtyAOej4Ag&amp;amp;usg=AFQjCNFqLnuJWi3gGXVCrugmv3NTRhHHLA&amp;amp;bvm=bv.67229260,d.bGQ&amp;amp;cad=rja"&gt;Fast Effective Rule Induction, W.W. Cohen 1995&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/a'':21C,33C,117C ''/em'':70C ''/p'':65C,71C,118C ''/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;ved=0ccyqfjaa&amp;amp;url=http://www.cs.utsa.edu/~bylander/cs6243/cohen95ripper.pdf&amp;amp;ei=-xj-u-7pgoqtyaoej4ag&amp;amp;usg=afqjcnfqlnujwi3ggxvcrugmv3ntrhhhla&amp;amp;bvm=bv.67229260,d.bgq&amp;amp;cad=rja'':109C ''/wiki/c4.5_algorithm'':19C ''/~ml/weka/'':31C ''1995'':116C ''abl'':7C,59C ''algorithm'':79C ''also'':73C ''anyway'':49C ''c45'':2C ''check'':13C ''classif'':85C ''cohen'':115C ''decis'':54C ''differ'':96C ''direct'':81C ''edit'':69C ''effect'':111C ''em'':67C ''en.wikipedia.org'':18C ''en.wikipedia.org/wiki/c4.5_algorithm'':17C ''fast'':110C ''href'':16C,28C,106C ''idea'':41C ''implement'':44C ''induct'':113C ''infer'':61C,82C ''interest'':77C ''j48'':37C ''jrip'':98C ''kind'':52C ''know'':24C ''later'':68C ''made'':3C ''might'':75C ''name'':35C,97C ''one'':88C ''origin'':101C ''p'':1C,66C,72C ''page'':22C ''paper'':102C ''predict'':12C,64C ''produc'':9C ''python'':48C ''quinlan'':5C ''r'':46C ''receiv'':94C ''ripper'':86C,104C ''rule'':10C,62C,83C,112C ''see'':99C ''tree'':55C ''w.w'':114C ''weka'':32C,92C ''wikipedia'':20C ''www.cs.waikato.ac.nz'':30C ''www.cs.waikato.ac.nz/~ml/weka/'':29C ''www.google.ro'':108C ''www.google.ro/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;ved=0ccyqfjaa&amp;amp;url=http://www.cs.utsa.edu/~bylander/cs6243/cohen95ripper.pdf&amp;amp;ei=-xj-u-7pgoqtyaoej4ag&amp;amp;usg=afqjcnfqlnujwi3ggxvcrugmv3ntrhhhla&amp;amp;bvm=bv.67229260,d.bgq&amp;amp;cad=rja'':107C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (198, 2, '2014-05-23 03:05:57.99', 4, NULL, NULL, '&lt;p&gt;Also, there seem to be a very comprehensive list of data science use cases by function and by vertical on Kaggle - &lt;a href="http://www.kaggle.com/wiki/DataScienceUseCases" rel="nofollow"&gt;"Data Science Use Cases"&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':34C ''/p'':35C ''/wiki/datascienceusecases'':27C ''also'':2C ''case'':15C,33C ''comprehens'':9C ''data'':12C,30C ''function'':17C ''href'':24C ''kaggl'':22C ''list'':10C ''nofollow'':29C ''p'':1C ''rel'':28C ''scienc'':13C,31C ''seem'':4C ''use'':14C,32C ''vertic'':20C ''www.kaggle.com'':26C ''www.kaggle.com/wiki/datascienceusecases'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (199, 1, '2014-05-23 06:25:50.48', 8, 1218, 'What does the alpha and beta hyperparameters contribute to in Latent Dirichlet allocation?', '&lt;p&gt;LDA has two hyperparameters, tuning them changes the induced topics. &lt;/p&gt;

&lt;p&gt;What does the alpha and beta hyperparameters contribute to LDA? &lt;/p&gt;

&lt;p&gt;How does the topic change if one or the other hyperparameters increase or decrease? &lt;/p&gt;

&lt;p&gt;Why are they hyperparamters and not just parameters?&lt;/p&gt;
', 'topic-model  lda  parameter', NULL, '''/p'':30C,42C,58C,68C ''alloc'':13A ''alpha'':4A,35C ''beta'':6A,37C ''chang'':26C,48C ''contribut'':8A,39C ''decreas'':57C ''dirichlet'':12A ''hyperparamet'':7A,23C,38C,54C ''hyperparamt'':63C ''increas'':55C ''induc'':28C ''latent'':11A ''lda'':17B,20C,41C ''model'':16B ''one'':50C ''p'':19C,31C,43C,59C ''paramet'':18B,67C ''topic'':15B,29C,47C ''topic-model'':14B ''tune'':24C ''two'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (200, 2, '2014-05-23 08:34:38.9', 3, NULL, NULL, '&lt;p&gt;You also can create a MongoDB-Hadoop &lt;a href="http://docs.mongodb.org/ecosystem/tutorial/getting-started-with-hadoop/" rel="nofollow"&gt;connection&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':18C ''/ecosystem/tutorial/getting-started-with-hadoop/'':14C ''/p'':19C ''also'':3C ''connect'':17C ''creat'':5C ''docs.mongodb.org'':13C ''docs.mongodb.org/ecosystem/tutorial/getting-started-with-hadoop/'':12C ''hadoop'':9C ''href'':11C ''mongodb'':8C ''mongodb-hadoop'':7C ''nofollow'':16C ''p'':1C ''rel'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (206, 2, '2014-05-23 21:01:18.63', 4, NULL, NULL, '&lt;p&gt;All things considered equal (cost, CPU perf, etc.) you could choose the smallest instance that can hold all of my dataset in memory and scale out. That way &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;you make sure not to induce unnecessary latencies due to network communications, and&lt;/li&gt;
&lt;li&gt;you tend to maximize the overall available memory bandwidth for your processes.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Assuming you are running some sort of &lt;a href="http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29" rel="nofollow"&gt;cross-validation scheme&lt;/a&gt; to optimize some &lt;a href="http://en.wikipedia.org/wiki/Meta-optimization" rel="nofollow"&gt;meta parameter&lt;/a&gt; of your model, assign each core a value to test and choose an many instances as needed to cover all the parameter space in as few rounds as you see fit.&lt;/p&gt;

&lt;p&gt;If your data does not fit in the memory of one system, of course you''ll need to distribute across instances. Then it is a matter of balancing memory latency (better with many instances) with network latency (better with fewer instances) but given the nature of EC2 I''d bet you''ll often prefer to work with few fat instances.&lt;/p&gt;
', NULL, NULL, '''/a'':81C,94C ''/li'':46C,60C ''/p'':30C,126C,188C ''/ul'':61C ''/wiki/cross-validation_%28statistics%29'':74C ''/wiki/meta-optimization'':89C ''across'':147C ''assign'':98C ''assum'':63C ''avail'':54C ''balanc'':155C ''bandwidth'':56C ''bet'':177C ''better'':158C,165C ''choos'':12C,106C ''communic'':44C ''consid'':4C ''core'':100C ''cost'':6C ''could'':11C ''cours'':141C ''cover'':113C ''cpu'':7C ''cross'':78C ''cross-valid'':77C ''d'':176C ''data'':130C ''dataset'':22C ''distribut'':146C ''due'':41C ''ec2'':174C ''en.wikipedia.org'':73C,88C ''en.wikipedia.org/wiki/cross-validation_%28statistics%29'':72C ''en.wikipedia.org/wiki/meta-optimization'':87C ''equal'':5C ''etc'':9C ''fat'':186C ''fewer'':167C ''fit'':125C,133C ''given'':170C ''hold'':18C ''href'':71C,86C ''induc'':38C ''instanc'':15C,109C,148C,161C,168C,187C ''latenc'':40C,157C,164C ''li'':32C,47C ''ll'':143C,179C ''make'':34C ''mani'':108C,160C ''matter'':153C ''maxim'':51C ''memori'':24C,55C,136C,156C ''meta'':92C ''model'':97C ''natur'':172C ''need'':111C,144C ''network'':43C,163C ''nofollow'':76C,91C ''often'':180C ''one'':138C ''optim'':83C ''overal'':53C ''p'':1C,62C,127C ''paramet'':93C,116C ''perf'':8C ''prefer'':181C ''process'':59C ''rel'':75C,90C ''round'':121C ''run'':66C ''scale'':26C ''scheme'':80C ''see'':124C ''smallest'':14C ''sort'':68C ''space'':117C ''sure'':35C ''system'':139C ''tend'':49C ''test'':104C ''thing'':3C ''ul'':31C ''unnecessari'':39C ''valid'':79C ''valu'':102C ''way'':29C ''work'':183C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (201, 2, '2014-05-23 09:09:44.49', 29, NULL, NULL, '&lt;p&gt;Update:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Kaggle.com&lt;/strong&gt;, a home of modern data science &amp;amp; machine learning enthusiasts:), opened &lt;strong&gt;&lt;a href="https://www.kaggle.com/datasets" rel="nofollow"&gt;it''s own repository of the data sets&lt;/a&gt;&lt;/strong&gt;.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;In addition to the listed sources.&lt;/p&gt;

&lt;p&gt;Some social network data sets:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://snap.stanford.edu/data/" rel="nofollow"&gt;Stanford University large network dataset collection (SNAP)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://blog.infochimps.com/2008/12/29/massive-scrape-of-twitters-friend-graph/" rel="nofollow"&gt;A huge twitter dataset that includes followers&lt;/a&gt; + &lt;a href="http://www.infochimps.com/collections/twitter-census" rel="nofollow"&gt;large collection of twitter datasets here&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://mtg.upf.edu/node/1671" rel="nofollow"&gt;LastFM data set&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;There are plenty of sources listed at Stats SE:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/questions/7/locating-freely-available-data-samples/"&gt;Locating freely available data samples&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/questions/12670/data-apis-feeds-available-as-packages-in-r"&gt;Data APIs/feeds available as packages in R&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/questions/973/free-data-set-for-very-high-dimensional-classification"&gt;Free data set for very high dimensional classification&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/2008/12/29/massive-scrape-of-twitters-friend-graph/'':77C ''/a'':35C,70C,87C,101C,114C,140C,155C,171C ''/collections/twitter-census'':92C ''/data/'':60C ''/datasets'':24C ''/li'':71C,102C,115C,141C,156C,172C ''/node/1671'':108C ''/p'':3C,37C,46C,53C,127C ''/questions/12670/data-apis-feeds-available-as-packages-in-r'':147C ''/questions/7/locating-freely-available-data-samples/'':134C ''/questions/973/free-data-set-for-very-high-dimensional-classification'':162C ''/strong'':7C,36C ''/ul'':116C,173C ''addit'':41C ''amp'':14C ''apis/feeds'':149C ''avail'':137C,150C ''blog.infochimps.com'':76C ''blog.infochimps.com/2008/12/29/massive-scrape-of-twitters-friend-graph/'':75C ''classif'':170C ''collect'':68C,96C ''data'':12C,33C,51C,112C,138C,148C,164C ''dataset'':67C,83C,99C ''dimension'':169C ''enthusiast'':17C ''follow'':86C ''free'':163C ''freeli'':136C ''high'':168C ''home'':9C ''hr'':38C ''href'':21C,57C,74C,89C,105C,131C,144C,159C ''huge'':81C ''includ'':85C ''kaggle.com'':6C ''larg'':65C,95C ''lastfm'':111C ''learn'':16C ''li'':55C,72C,103C,129C,142C,157C ''list'':44C,123C ''locat'':135C ''machin'':15C ''modern'':11C ''mtg.upf.edu'':107C ''mtg.upf.edu/node/1671'':106C ''network'':50C,66C ''nofollow'':26C,62C,79C,94C,110C ''open'':18C ''p'':1C,4C,39C,47C,117C ''packag'':152C ''plenti'':120C ''r'':154C ''rel'':25C,61C,78C,93C,109C ''repositori'':30C ''sampl'':139C ''scienc'':13C ''se'':126C ''set'':34C,52C,113C,165C ''snap'':69C ''snap.stanford.edu'':59C ''snap.stanford.edu/data/'':58C ''social'':49C ''sourc'':45C,122C ''stanford'':63C ''stat'':125C ''stats.stackexchange.com'':133C,146C,161C ''stats.stackexchange.com/questions/12670/data-apis-feeds-available-as-packages-in-r'':145C ''stats.stackexchange.com/questions/7/locating-freely-available-data-samples/'':132C ''stats.stackexchange.com/questions/973/free-data-set-for-very-high-dimensional-classification'':160C ''strong'':5C,19C ''twitter'':82C,98C ''ul'':54C,128C ''univers'':64C ''updat'':2C ''www.infochimps.com'':91C ''www.infochimps.com/collections/twitter-census'':90C ''www.kaggle.com'':23C ''www.kaggle.com/datasets'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (203, 2, '2014-05-23 14:28:41.563', 1, NULL, NULL, '&lt;p&gt;Never done stuff &lt;strong&gt;on that scale&lt;/strong&gt;, but as no-one else has jumped in yet have you seen these two papers that discuss non-commercial solutions?  Symphony and COIN-OR seem to be the dominant suggestions.&lt;/p&gt;

&lt;p&gt;Linderoth, Jeffrey T., and Andrea Lodi. "MILP software." Wiley encyclopedia of operations research and management science (2010). &lt;a href="http://homepages.cae.wisc.edu/~Linderot/papers/Linderoth-Lodi-10.pdf" rel="nofollow"&gt;PDF version&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Linderoth, Jeffrey T., and Ted K. Ralphs. "Noncommercial software for mixed-integer linear programming." Integer programming: theory and practice 3 (2005): 253-303. &lt;a href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.5872" rel="nofollow"&gt;Compares performance&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''-303'':97C ''/a'':71C,107C ''/p'':43C,72C,108C ''/strong'':9C ''/viewdoc/summary?doi=10.1.1.147.5872'':102C ''/~linderot/papers/linderoth-lodi-10.pdf'':66C ''2005'':95C ''2010'':61C ''253'':96C ''3'':94C ''andrea'':49C ''citeseerx.ist.psu.edu'':101C ''citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.5872'':100C ''coin'':35C ''coin-or'':34C ''commerci'':30C ''compar'':105C ''discuss'':27C ''domin'':41C ''done'':3C ''els'':15C ''encyclopedia'':54C ''homepages.cae.wisc.edu'':65C ''homepages.cae.wisc.edu/~linderot/papers/linderoth-lodi-10.pdf'':64C ''href'':63C,99C ''integ'':86C,89C ''jeffrey'':46C,75C ''jump'':17C ''k'':79C ''linderoth'':45C,74C ''linear'':87C ''lodi'':50C ''manag'':59C ''milp'':51C ''mix'':85C ''mixed-integ'':84C ''never'':2C ''no-on'':12C ''nofollow'':68C,104C ''non'':29C ''non-commerci'':28C ''noncommerci'':81C ''one'':14C ''oper'':56C ''p'':1C,44C,73C ''paper'':25C ''pdf'':69C ''perform'':106C ''practic'':93C ''program'':88C,90C ''ralph'':80C ''rel'':67C,103C ''research'':57C ''scale'':8C ''scienc'':60C ''seem'':37C ''seen'':22C ''softwar'':52C,82C ''solut'':31C ''strong'':5C ''stuff'':4C ''suggest'':42C ''symphoni'':32C ''ted'':78C ''theori'':91C ''two'':24C ''version'':70C ''wiley'':53C ''yet'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (208, 2, '2014-05-24 11:18:26.497', 8, NULL, NULL, '&lt;p&gt;When using IPython, you very nearly don''t have to worry about it (at the expense of some loss of efficiency/greater communication overhead).  The parallel IPython plugin in StarCluster will by default start one engine per physical core on each node (I believe this is configurable but not sure where).  You just run whatever you want across all engines by using the DirectView api (map_sync, apply_sync, ...) or the %px magic commands.   If you are already using IPython in parallel on one machine, using it on a cluster is no different.&lt;/p&gt;

&lt;p&gt;Addressing some of your specific questions:&lt;/p&gt;

&lt;p&gt;"how to reconcile distributing work across cores on an instance vs. instances on a cluster" - You get one engine per core (at least); work is automatically distributed across all cores and across all instances.&lt;/p&gt;

&lt;p&gt;"Is it even practical to parallelize across instances as well as across cores on each instance?" - Yes :)  If the code you are running is embarrassingly parallel (exact same algo on multiple data sets) then you can mostly ignore where a particular engine is running.  If the core requires a lot of communication between engines, then of course you need to structure it so that engines primarily communicate with other engines on the same physical machine; but that kind of problem is not ideally suited for IPython, I think.&lt;/p&gt;

&lt;p&gt;"If so, can anyone give a quick rundown of the pros + cons of running many instances with few cores each vs. a few instances with many cores? Is there a rule of thumb for choosing the right ratio of instances to cores per instance?" - Use the largest c3 instances for compute-bound, and the smallest for memory-bandwidth-bound problems (or small enough that the problem almost stops being memory-bandwidth-bound); for message-passing-bound problems, also use the largest instances but try to partition the problem so that each partition runs on one physical machine and most message passing is within the same partition.  Problems which run significantly slower on N quadruple c3 than on 2N double c3 are rare (an artificial example may be running multiple simple filters on a large number of images, where you go through all images for each filter rather than all filters for the same image).  Using largest instances is a good rule of thumb.&lt;/p&gt;
', NULL, NULL, '''/p'':94C,102C,138C,228C,397C ''2n'':351C ''across'':58C,109C,131C,135C,146C,151C ''address'':96C ''algo'':168C ''almost'':298C ''alreadi'':78C ''also'':311C ''anyon'':233C ''api'':65C ''appli'':68C ''artifici'':357C ''automat'':129C ''bandwidth'':289C,303C ''believ'':44C ''bound'':282C,290C,304C,309C ''c3'':277C,348C,353C ''choos'':264C ''cluster'':90C,118C ''code'':159C ''command'':74C ''communic'':23C,191C,206C ''comput'':281C ''compute-bound'':280C ''con'':241C ''configur'':47C ''core'':39C,110C,124C,133C,152C,186C,248C,256C,271C ''cours'':196C ''data'':171C ''default'':33C ''differ'':93C ''directview'':64C ''distribut'':107C,130C ''doubl'':352C ''efficiency/greater'':22C ''embarrass'':164C ''engin'':36C,60C,122C,181C,193C,204C,209C ''enough'':294C ''even'':142C ''exact'':166C ''exampl'':358C ''expens'':17C ''filter'':364C,379C,383C ''get'':120C ''give'':234C ''go'':373C ''good'':393C ''ideal'':222C ''ignor'':177C ''imag'':370C,376C,387C ''instanc'':113C,115C,137C,147C,155C,245C,253C,269C,273C,278C,315C,390C ''ipython'':4C,27C,80C,225C ''kind'':217C ''larg'':367C ''largest'':276C,314C,389C ''least'':126C ''loss'':20C ''lot'':189C ''machin'':85C,214C,330C ''magic'':73C ''mani'':244C,255C ''map'':66C ''may'':359C ''memori'':288C,302C ''memory-bandwidth-bound'':287C,301C ''messag'':307C,333C ''message-passing-bound'':306C ''most'':176C ''multipl'':170C,362C ''n'':346C ''near'':7C ''need'':198C ''node'':42C ''number'':368C ''one'':35C,84C,121C,328C ''overhead'':24C ''p'':1C,95C,103C,139C,229C ''parallel'':26C,82C,145C,165C ''particular'':180C ''partit'':319C,325C,339C ''pass'':308C,334C ''per'':37C,123C,272C ''physic'':38C,213C,329C ''plugin'':28C ''practic'':143C ''primarili'':205C ''problem'':219C,291C,297C,310C,321C,340C ''pros'':240C ''px'':72C ''quadrupl'':347C ''question'':101C ''quick'':236C ''rare'':355C ''rather'':380C ''ratio'':267C ''reconcil'':106C ''requir'':187C ''right'':266C ''rule'':260C,394C ''run'':54C,162C,183C,243C,326C,342C,361C ''rundown'':237C ''set'':172C ''signific'':343C ''simpl'':363C ''slower'':344C ''small'':293C ''smallest'':285C ''specif'':100C ''starclust'':30C ''start'':34C ''stop'':299C ''structur'':200C ''suit'':223C ''sure'':50C ''sync'':67C,69C ''think'':227C ''thumb'':262C,396C ''tri'':317C ''use'':3C,62C,79C,86C,274C,312C,388C ''vs'':114C,250C ''want'':57C ''well'':149C ''whatev'':55C ''within'':336C ''work'':108C,127C ''worri'':12C ''yes'':156C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (204, 2, '2014-05-23 19:28:01.903', 6, NULL, NULL, '&lt;p&gt;I recently did a similar project in Python (predicting opinions using FB like data), and had good results with the following basic process:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Read in the training set (n = N) by iterating over comma-delimited like records line-by-line and use a counter to identify the most popular pages&lt;/li&gt;
&lt;li&gt;For each of the K most popular pages (I used about 5000, but you can play around with different values), use pandas.DataFrame.isin to test whether each individual in the training set likes each page, then make a N x K dataframe of the results (I''ll call it xdata_train)&lt;/li&gt;
&lt;li&gt;Create a series (I''ll call it ydata_train) containing all of the outcome variables (in my case opinions, in yours age) with the same index as xdata_train&lt;/li&gt;
&lt;li&gt;Set up a random forest classifier through scikit-learn to predict
ydata_train based on xdata_train&lt;/li&gt;
&lt;li&gt;Use scikit-learn''s cross-validation testing to tweak parameters and
refine accuracy (tweaking number of popular pages, number of trees,
min leaf size, etc.)&lt;/li&gt;
&lt;li&gt;Output random forest classifier and list of most popular pages with pickle (or keep in memory if you are doing everything at once)&lt;/li&gt;
&lt;li&gt;Load in the rest of your data, load the list of popular pages (if necessary), and repeat step 2 to produce xdata_new&lt;/li&gt;
&lt;li&gt;Load the random forest classifier (if necessary) and use it to predict values for the xdata_new data&lt;/li&gt;
&lt;li&gt;Output the predicted scores to a new CSV or other output format of your choosing&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;In your case, you''d need to swap out the classifier for a regressor (so see here: &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html"&gt;http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html&lt;/a&gt;) but otherwise the same process should work without much trouble. &lt;/p&gt;

&lt;p&gt;Also, you should be aware of the most amazing feature of random forests in Python: instant parallelization! Those of us who started out doing this in R and then moved over are always amazed, especially when you get to work on a machine with a few dozen cores (see here: &lt;a href="http://blog.yhathq.com/posts/comparing-random-forests-in-python-and-r.html"&gt;http://blog.yhathq.com/posts/comparing-random-forests-in-python-and-r.html&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;Finally, note that this would be a perfect application for network analysis if you have the data on friends as well as the individuals themselves. If you can analyze the ages of a user''s friends, the age of the user will almost certainly be within a year or two of the median among his or her friends, particularly if the users are young enough to have built their friend networks while still in school (since most will be classmates). That prediction would likely trump any you would get from modeling---this is a textbook example of a problem where the right data &gt; the right model every time. &lt;/p&gt;

&lt;p&gt;Good luck!&lt;/p&gt;
', NULL, NULL, '''/li'':57C,109C,140C,160C,189C,214C,239C,259C,276C ''/ol'':277C ''/p'':25C,314C,483C,487C ''/posts/comparing-random-forests-in-python-and-r.html'':370C ''/posts/comparing-random-forests-in-python-and-r.html&lt;/a&gt;).&lt;/p&gt;'':373C ''/stable/modules/generated/sklearn.ensemble.randomforestregressor.html'':300C ''/stable/modules/generated/sklearn.ensemble.randomforestregressor.html&lt;/a&gt;)'':303C ''2'':234C ''5000'':70C ''accuraci'':176C ''age'':132C,405C,412C ''almost'':417C ''also'':316C ''alway'':348C ''amaz'':324C,349C ''among'':428C ''analysi'':386C ''analyz'':403C ''applic'':383C ''around'':75C ''awar'':320C ''base'':156C ''basic'':23C ''blog.yhathq.com'':369C,372C ''blog.yhathq.com/posts/comparing-random-forests-in-python-and-r.html'':368C ''blog.yhathq.com/posts/comparing-random-forests-in-python-and-r.html&lt;/a&gt;).&lt;/p&gt;'':371C ''built'':442C ''call'':105C,116C ''case'':128C,281C ''certain'':418C ''choos'':275C ''classifi'':147C,194C,245C,289C ''classmat'':454C ''comma'':39C ''comma-delimit'':38C ''contain'':120C ''core'':363C ''counter'':50C ''creat'':111C ''cross'':168C ''cross-valid'':167C ''csv'':268C ''d'':283C ''data'':15C,222C,258C,391C,477C ''datafram'':99C ''delimit'':40C ''differ'':77C ''dozen'':362C ''enough'':439C ''especi'':350C ''etc'':188C ''everi'':481C ''everyth'':211C ''exampl'':470C ''fb'':13C ''featur'':325C ''final'':375C ''follow'':22C ''forest'':146C,193C,244C,328C ''format'':272C ''friend'':393C,410C,432C,444C ''get'':353C,463C ''good'':18C,485C ''href'':297C,367C ''identifi'':52C ''index'':136C ''individu'':85C,398C ''instant'':331C ''iter'':36C ''k'':63C,98C ''keep'':204C ''leaf'':186C ''learn'':151C,165C ''li'':27C,58C,110C,141C,161C,190C,215C,240C,260C ''like'':14C,41C,90C,458C ''line'':44C,46C ''line-by-lin'':43C ''list'':196C,225C ''ll'':104C,115C ''load'':216C,223C,241C ''luck'':486C ''machin'':358C ''make'':94C ''median'':427C ''memori'':206C ''min'':185C ''model'':465C,480C ''move'':345C ''much'':312C ''n'':33C,34C,96C ''necessari'':230C,247C ''need'':284C ''network'':385C,445C ''new'':238C,257C,267C ''note'':376C ''number'':178C,182C ''ol'':26C ''opinion'':11C,129C ''otherwis'':305C ''outcom'':124C ''output'':191C,261C,271C ''p'':1C,278C,315C,374C,484C ''page'':56C,66C,92C,181C,200C,228C ''pandas.dataframe.isin'':80C ''parallel'':332C ''paramet'':173C ''particular'':433C ''perfect'':382C ''pickl'':202C ''play'':74C ''popular'':55C,65C,180C,199C,227C ''predict'':10C,153C,252C,263C,456C ''problem'':473C ''process'':24C,308C ''produc'':236C ''project'':7C ''python'':9C,330C ''r'':342C ''random'':145C,192C,243C,327C ''read'':28C ''recent'':3C ''record'':42C ''refin'':175C ''regressor'':292C ''repeat'':232C ''rest'':219C ''result'':19C,102C ''right'':476C,479C ''school'':449C ''scikit'':150C,164C ''scikit-learn'':149C,163C ''scikit-learn.org'':299C,302C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.randomforestregressor.html'':298C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.randomforestregressor.html&lt;/a&gt;)'':301C ''score'':264C ''see'':294C,364C ''seri'':113C ''set'':32C,89C,142C ''similar'':6C ''sinc'':450C ''size'':187C ''start'':337C ''step'':233C ''still'':447C ''swap'':286C ''test'':82C,170C ''textbook'':469C ''time'':482C ''train'':31C,88C,108C,119C,139C,155C,159C ''tree'':184C ''troubl'':313C ''trump'':459C ''tweak'':172C,177C ''two'':424C ''us'':335C ''use'':12C,48C,68C,79C,162C,249C ''user'':408C,415C,436C ''valid'':169C ''valu'':78C,253C ''variabl'':125C ''well'':395C ''whether'':83C ''within'':420C ''without'':311C ''work'':310C,355C ''would'':379C,457C,462C ''x'':97C ''xdata'':107C,138C,158C,237C,256C ''ydata'':118C,154C ''year'':422C ''young'':438C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9001, 2, '2015-11-24 10:45:09.197', 4, NULL, NULL, '&lt;p&gt;In &lt;a href="http://dataskeptic.com/" rel="nofollow"&gt;&lt;strong&gt;Data Skeptic&lt;/strong&gt;&lt;/a&gt; they talk about different aspects of data science:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Data Skeptic is a podcast that alternates between short mini episodes with the host explaining concepts from data science to his non-data scientist wife, and longer interviews featuring practitioners and experts on interesting topics related to data, all through the eye of scientific skepticism.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This is a good intro to Data Science which also explains the basic concepts in a not too technical way.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;A bit more technical is &lt;a href="http://www.partiallyderivative.com/" rel="nofollow"&gt;&lt;strong&gt;Partially Derivative&lt;/strong&gt;&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Partially Derivative is a podcast about the data of everything. Each week we look at a new way that data has changed how we understand and experience the world. From ancient warfare to modern love and everything in between.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;hr&gt;

&lt;p&gt;A related Quora question: &lt;a href="https://www.quora.com/What-are-the-best-data-science-podcasts" rel="nofollow"&gt;What are the best data science podcasts?&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':12C,110C,175C ''/blockquote'':71C,154C ''/p'':21C,70C,93C,111C,153C,176C ''/strong'':11C,109C ''/what-are-the-best-data-science-podcasts'':165C ''also'':82C ''altern'':30C ''ancient'':144C ''aspect'':17C ''basic'':85C ''best'':171C ''bit'':97C ''blockquot'':22C,112C ''chang'':135C ''concept'':39C,86C ''data'':9C,19C,24C,41C,47C,62C,79C,121C,133C,172C ''dataskeptic.com'':5C ''deriv'':108C,115C ''differ'':16C ''episod'':34C ''everyth'':123C,150C ''experi'':140C ''expert'':56C ''explain'':38C,83C ''eye'':66C ''featur'':53C ''good'':76C ''host'':37C ''hr'':94C,155C ''href'':4C,102C,162C ''interest'':58C ''interview'':52C ''intro'':77C ''longer'':51C ''look'':127C ''love'':148C ''mini'':33C ''modern'':147C ''new'':130C ''nofollow'':7C,105C,167C ''non'':46C ''non-data'':45C ''p'':1C,23C,72C,95C,113C,156C ''partial'':107C,114C ''podcast'':28C,118C,174C ''practition'':54C ''question'':160C ''quora'':159C ''rel'':6C,104C,166C ''relat'':60C,158C ''scienc'':20C,42C,80C,173C ''scientif'':68C ''scientist'':48C ''short'':32C ''skeptic'':10C,25C,69C ''strong'':8C,106C ''talk'':14C ''technic'':91C,99C ''topic'':59C ''understand'':138C ''warfar'':145C ''way'':92C,131C ''week'':125C ''wife'':49C ''world'':142C ''www.partiallyderivative.com'':103C ''www.quora.com'':164C ''www.quora.com/what-are-the-best-data-science-podcasts'':163C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (205, 1, '2014-05-23 19:45:54.283', 8, 303, 'Instances vs. cores when using EC2', '&lt;p&gt;Working on what could often be called "medium data" projects, I''ve been able to parallelize my code (mostly for modeling and prediction in Python) on a single system across anywhere from 4 to 32 cores. Now I''m looking at scaling up to clusters on EC2 (probably with StarCluster/IPython, but open to other suggestions as well), and have been puzzled by how to reconcile distributing work across cores on an instance vs. instances on a cluster.&lt;/p&gt;

&lt;p&gt;Is it even practical to parallelize across instances as well as across cores on each instance? If so, can anyone give a quick rundown of the pros + cons of running many instances with few cores each vs. a few instances with many cores? Is there a rule of thumb for choosing the right ratio of instances to cores per instance? &lt;/p&gt;

&lt;p&gt;Bandwidth and RAM are non-trivial concerns in my projects, but it''s easy to spot when those are the bottlenecks and readjust. It''s much harder, I''d imagine, to benchmark the right mix of cores to instances without repeated testing, and my projects vary too much for any single test to apply to all circumstances. Thanks in advance, and if I''ve just failed to google this one properly, feel free to point me to the right answer somewhere else!&lt;/p&gt;
', 'parallel  clusters  aws', NULL, '''/p'':88C,150C,235C ''32'':45C ''4'':43C ''abl'':24C ''across'':40C,78C,96C,101C ''advanc'':212C ''answer'':232C ''anyon'':109C ''anywher'':41C ''appli'':206C ''aw'':9B ''bandwidth'':152C ''benchmark'':184C ''bottleneck'':173C ''call'':17C ''choos'':140C ''circumst'':209C ''cluster'':8B,55C,87C ''code'':28C ''con'':117C ''concern'':159C ''core'':3A,46C,79C,102C,124C,132C,147C,189C ''could'':14C ''d'':181C ''data'':19C ''distribut'':76C ''easi'':166C ''ec2'':6A,57C ''els'':234C ''even'':92C ''fail'':218C ''feel'':224C ''free'':225C ''give'':110C ''googl'':220C ''harder'':179C ''imagin'':182C ''instanc'':1A,82C,84C,97C,105C,121C,129C,145C,149C,191C ''look'':50C ''m'':49C ''mani'':120C,131C ''medium'':18C ''mix'':187C ''model'':31C ''most'':29C ''much'':178C,200C ''non'':157C ''non-trivi'':156C ''often'':15C ''one'':222C ''open'':62C ''p'':10C,89C,151C ''parallel'':7B,26C,95C ''per'':148C ''point'':227C ''practic'':93C ''predict'':33C ''probabl'':58C ''project'':20C,162C,197C ''proper'':223C ''pros'':116C ''puzzl'':71C ''python'':35C ''quick'':112C ''ram'':154C ''ratio'':143C ''readjust'':175C ''reconcil'':75C ''repeat'':193C ''right'':142C,186C,231C ''rule'':136C ''run'':119C ''rundown'':113C ''scale'':52C ''singl'':38C,203C ''somewher'':233C ''spot'':168C ''starcluster/ipython'':60C ''suggest'':65C ''system'':39C ''test'':194C,204C ''thank'':210C ''thumb'':138C ''trivial'':158C ''use'':5A ''vari'':198C ''ve'':22C,216C ''vs'':2A,83C,126C ''well'':67C,99C ''without'':192C ''work'':11C,77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (268, 2, '2014-06-10 11:36:19.287', 4, NULL, NULL, '&lt;p&gt;I think &lt;a href="http://www.cs.waikato.ac.nz/ml/weka/" rel="nofollow"&gt;Weka&lt;/a&gt; is a good starting point. You can do a bunch of stuff like supervised learning or clustering and easily compare a large set of algorithms na methodologies.&lt;/p&gt;

&lt;p&gt;Weka''s manual is actually a book on machine learning and data mining that can be used as introductory material. &lt;/p&gt;
', NULL, NULL, '''/a'':12C ''/ml/weka/'':8C ''/p'':40C,62C ''actual'':46C ''algorithm'':37C ''book'':48C ''bunch'':22C ''cluster'':29C ''compar'':32C ''data'':53C ''easili'':31C ''good'':15C ''href'':5C ''introductori'':60C ''larg'':34C ''learn'':27C,51C ''like'':25C ''machin'':50C ''manual'':44C ''materi'':61C ''methodolog'':39C ''mine'':54C ''na'':38C ''nofollow'':10C ''p'':1C,41C ''point'':17C ''rel'':9C ''set'':35C ''start'':16C ''stuff'':24C ''supervis'':26C ''think'':3C ''use'':58C ''weka'':11C,42C ''www.cs.waikato.ac.nz'':7C ''www.cs.waikato.ac.nz/ml/weka/'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (212, 2, '2014-05-27 10:58:00.62', 4, NULL, NULL, '&lt;p&gt;Your system isn''t just trained on items that are recommended right? if so you have a big feedback loop here. You want to learn from all clicks/views, I hope.&lt;/p&gt;

&lt;p&gt;You suggest that not-looking at an item is a negative signal. I strongly suggest you do not treat it that way. Not interacting with something is almost always best treated as no information. If you have an explicit signal that indicates a dislike, like a down vote (or, maybe watched 10 seconds of a video and stopped), maybe that''s valid.&lt;/p&gt;

&lt;p&gt;I would not construe this input as rating-like data. (Although in your case, you may get away with it.) Instead think of them as weights, which is exactly the treatment in the Hu Koren Volinsky paper on ALS that @Trey mentions in a comment. This lets you record relative strength of positive/negative interactions.&lt;/p&gt;

&lt;p&gt;Finally I would note that this paper, while is very likely to be what you''re looking for, does not provide for negative weights. It is simple to extend in this way. If you get that far I can point you to the easy extension, which exists already in two implementations that I know of, in &lt;a href="https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala" rel="nofollow"&gt;Spark&lt;/a&gt; and &lt;a href="https://github.com/cloudera/oryx/blob/master/als-common/src/main/java/com/cloudera/oryx/als/common/factorizer/als/AlternatingLeastSquares.java" rel="nofollow"&gt;Oryx&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':220C,230C ''/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/recommendation/als.scala'':216C ''/cloudera/oryx/blob/master/als-common/src/main/java/com/cloudera/oryx/als/common/factorizer/als/alternatingleastsquares.java'':226C ''/p'':32C,97C,154C,231C ''10'':86C ''al'':138C ''almost'':62C ''alreadi'':203C ''although'':110C ''alway'':63C ''away'':117C ''best'':64C ''big'':19C ''case'':113C ''clicks/views'':29C ''comment'':144C ''constru'':102C ''data'':109C ''dislik'':78C ''easi'':199C ''exact'':128C ''exist'':202C ''explicit'':73C ''extend'':184C ''extens'':200C ''far'':192C ''feedback'':20C ''final'':156C ''get'':116C,190C ''github.com'':215C,225C ''github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/recommendation/als.scala'':214C ''github.com/cloudera/oryx/blob/master/als-common/src/main/java/com/cloudera/oryx/als/common/factorizer/als/alternatingleastsquares.java'':224C ''hope'':31C ''href'':213C,223C ''hu'':133C ''implement'':206C ''indic'':76C ''inform'':68C ''input'':104C ''instead'':120C ''interact'':58C,153C ''isn'':4C ''item'':9C,42C ''know'':209C ''koren'':134C ''learn'':26C ''let'':146C ''like'':79C,108C,166C ''look'':39C,172C ''loop'':21C ''may'':115C ''mayb'':84C,93C ''mention'':141C ''negat'':45C,178C ''nofollow'':218C,228C ''not-look'':37C ''note'':159C ''oryx'':229C ''p'':1C,33C,98C,155C ''paper'':136C,162C ''point'':195C ''positive/negative'':152C ''provid'':176C ''rate'':107C ''rating-lik'':106C ''re'':171C ''recommend'':12C ''record'':148C ''rel'':217C,227C ''relat'':149C ''right'':13C ''second'':87C ''signal'':46C,74C ''simpl'':182C ''someth'':60C ''spark'':219C ''stop'':92C ''strength'':150C ''strong'':48C ''suggest'':35C,49C ''system'':3C ''think'':121C ''train'':7C ''treat'':53C,65C ''treatment'':130C ''trey'':140C ''two'':205C ''valid'':96C ''video'':90C ''volinski'':135C ''vote'':82C ''want'':24C ''watch'':85C ''way'':56C,187C ''weight'':125C,179C ''would'':100C,158C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (207, 2, '2014-05-24 10:36:58.987', 8, NULL, NULL, '&lt;p&gt;A general rule of thumb is to not distribute until you have to. It''s usually more efficient to have N servers of a certain capacity than 2N servers of half that capacity. More of the data access will be local, and therefore fast in memory versus slow across the network.&lt;/p&gt;

&lt;p&gt;At a certain point, scaling up one machine becomes uneconomical because the cost of additional resource scales more than linearly. However this point is still amazingly high.&lt;/p&gt;

&lt;p&gt;On Amazon in particular though, the economics of each instance type can vary a lot if you are using spot market instances. The default pricing more or less means that the same amount of resource costs about the same regardless of the instance type, that can vary a lot; large instances can be cheaper than small ones, or N small instances can be much cheaper than one large machine with equivalent resources.&lt;/p&gt;

&lt;p&gt;One massive consideration here is that the computation paradigm can change quite a lot when you move from one machine to multiple machines. The tradeoffs that the communication overhead induce may force you to, for example, adopt a data-parallel paradigm to scale. That means a different choice of tools and algorithm. For example, SGD looks quite different in-memory and in Python than on MapReduce. So you would have to consider this before parallelizing.&lt;/p&gt;

&lt;p&gt;You may choose to distribute work across a cluster, even if a single node and non-distributed paradigms work for you, for reliability. If a single node fails, you lose all of the computation; a distributed computation can potentially recover and complete just the part of the computation that was lost.&lt;/p&gt;
', NULL, NULL, '''/p'':53C,82C,156C,235C,289C ''2n'':29C ''access'':39C ''across'':50C,243C ''addit'':69C ''adopt'':194C ''algorithm'':210C ''amaz'':80C ''amazon'':85C ''amount'':116C ''becom'':63C ''capac'':27C,34C ''certain'':26C,57C ''chang'':168C ''cheaper'':137C,148C ''choic'':206C ''choos'':239C ''cluster'':245C ''communic'':185C ''complet'':279C ''comput'':165C,271C,274C,285C ''consid'':231C ''consider'':160C ''cost'':67C,119C ''data'':38C,197C ''data-parallel'':196C ''default'':107C ''differ'':205C,216C ''distribut'':10C,241C,254C,273C ''econom'':90C ''effici'':19C ''equival'':154C ''even'':246C ''exampl'':193C,212C ''fail'':265C ''fast'':45C ''forc'':189C ''general'':3C ''half'':32C ''high'':81C ''howev'':75C ''in-memori'':217C ''induc'':187C ''instanc'':93C,105C,126C,134C,144C ''larg'':133C,151C ''less'':111C ''linear'':74C ''local'':42C ''look'':214C ''lose'':267C ''lost'':288C ''lot'':98C,132C,171C ''machin'':62C,152C,177C,180C ''mapreduc'':225C ''market'':104C ''massiv'':159C ''may'':188C,238C ''mean'':112C,203C ''memori'':47C,219C ''move'':174C ''much'':147C ''multipl'':179C ''n'':22C,142C ''network'':52C ''node'':250C,264C ''non'':253C ''non-distribut'':252C ''one'':61C,140C,150C,158C,176C ''overhead'':186C ''p'':1C,54C,83C,157C,236C ''paradigm'':166C,199C,255C ''parallel'':198C,234C ''part'':282C ''particular'':87C ''point'':58C,77C ''potenti'':276C ''price'':108C ''python'':222C ''quit'':169C,215C ''recov'':277C ''regardless'':123C ''reliabl'':260C ''resourc'':70C,118C,155C ''rule'':4C ''scale'':59C,71C,201C ''server'':23C,30C ''sgd'':213C ''singl'':249C,263C ''slow'':49C ''small'':139C,143C ''spot'':103C ''still'':79C ''therefor'':44C ''though'':88C ''thumb'':6C ''tool'':208C ''tradeoff'':182C ''type'':94C,127C ''uneconom'':64C ''use'':102C ''usual'':17C ''vari'':96C,130C ''versus'':48C ''work'':242C,256C ''would'':228C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (324, 1, '2014-06-12 03:11:00.033', -3, 210, 'How can I extract news about a particular company from various websites using RODBC package in R? And perform sentiment analysis on the data?', '&lt;p&gt;I want to extract news about a company from online news by using RODBC package in R. And I want to use the extracted data for sentiment analysis. I want to accomplish this in such a way that the positive news is assigned a value of +1, the negative news is assigned a value of -1 and the neutral news is assigned a value of 0.&lt;/p&gt;
', 'r  text-mining', NULL, '''+1'':76C ''-1'':85C ''/p'':96C ''0'':95C ''accomplish'':61C ''analysi'':21A,57C ''assign'':72C,81C,91C ''compani'':9A,37C ''data'':24A,54C ''extract'':4A,33C,53C ''mine'':28B ''negat'':78C ''neutral'':88C ''news'':5A,34C,40C,70C,79C,89C ''onlin'':39C ''p'':29C ''packag'':15A,44C ''particular'':8A ''perform'':19A ''posit'':69C ''r'':17A,25B,46C ''rodbc'':14A,43C ''sentiment'':20A,56C ''text'':27B ''text-min'':26B ''use'':13A,42C,51C ''valu'':74C,83C,93C ''various'':11A ''want'':31C,49C,59C ''way'':66C ''websit'':12A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (209, 1, '2014-05-25 13:57:52.657', 5, 155, 'How should one deal with implicit data in recommendation', '&lt;p&gt;A recommendation system keeps a log of what recommendations have been made to a particular user and whether that user accepts the recommendation. It''s like&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;user_id item_id result
1       4       1
1       7       -1
5       19      1
5       80      1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where 1 means the user accepted the recommendation while -1 means the user did not respond to the recommendation. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Question:&lt;/strong&gt; If I am going to make recommendations to a bunch of users based on the kind of log described above, and I want to maximize MAP@3 scores, how should I deal with the implicit data (1 or -1)?&lt;/p&gt;

&lt;p&gt;My idea is to treat 1 and -1 as ratings, and predict the rating using factorization machines-type algorithms. But this does not seem right, given the asymmetry of the implicit data (-1 does not mean the user does not like the recommendation).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit 1&lt;/strong&gt;
Let us think about it in the context of a matrix factorization approach. If we treat -1 and 1 as ratings, there will be some problem. For example, user 1 likes movie A which scores high in one factor (e.g. having glorious background music) in the latent factor space. The system recommends movie B which also scores high in "glorious background music", but for some reason user 1 is too busy to look into the recommendation, and we have a -1 rating movie B. If we just treat 1 or -1 equally, then the system might be discouraged to recommend movie with glorious BGM to user 1 while user 1 still loves movie with glorious BGM. I think this situation is to be avoided.&lt;/p&gt;
', 'recommendation', NULL, '''-1'':51C,70C,123C,133C,159C,192C,256C,266C ''/code'':58C ''/p'':38C,80C,124C,170C,300C ''/pre'':59C ''/strong'':84C,175C ''1'':46C,48C,49C,54C,57C,62C,121C,131C,174C,194C,205C,243C,264C,282C,285C ''19'':53C ''3'':111C ''4'':47C ''5'':52C,55C ''7'':50C ''80'':56C ''accept'':32C,66C ''algorithm'':145C ''also'':231C ''approach'':188C ''asymmetri'':154C ''avoid'':299C ''b'':229C,259C ''background'':218C,236C ''base'':97C ''bgm'':279C,291C ''bunch'':94C ''busi'':246C ''code'':40C ''context'':183C ''data'':7A,120C,158C ''deal'':4A,116C ''describ'':103C ''discourag'':273C ''e.g'':215C ''edit'':173C ''equal'':267C ''exampl'':203C ''factor'':141C,187C,214C,223C ''given'':152C ''glorious'':217C,235C,278C,290C ''go'':88C ''high'':211C,233C ''id'':42C,44C ''idea'':127C ''implicit'':6A,119C,157C ''item'':43C ''keep'':15C ''kind'':100C ''latent'':222C ''let'':176C ''like'':37C,167C,206C ''log'':17C,102C ''look'':248C ''love'':287C ''machin'':143C ''machines-typ'':142C ''made'':23C ''make'':90C ''map'':110C ''matrix'':186C ''maxim'':109C ''mean'':63C,71C,162C ''might'':271C ''movi'':207C,228C,258C,276C,288C ''music'':219C,237C ''one'':3A,213C ''p'':11C,60C,81C,125C,171C ''particular'':26C ''pre'':39C ''predict'':137C ''problem'':201C ''question'':83C ''rate'':135C,139C,196C,257C ''reason'':241C ''recommend'':9A,10B,13C,20C,34C,68C,79C,91C,169C,227C,251C,275C ''respond'':76C ''result'':45C ''right'':151C ''score'':112C,210C,232C ''seem'':150C ''situat'':295C ''space'':224C ''still'':286C ''strong'':82C,172C ''system'':14C,226C,270C ''think'':178C,293C ''treat'':130C,191C,263C ''type'':144C ''us'':177C ''use'':140C ''user'':27C,31C,41C,65C,73C,96C,164C,204C,242C,281C,284C ''want'':107C ''whether'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (210, 2, '2014-05-26 04:07:32.39', 1, NULL, NULL, '&lt;p&gt;Assuming symmetric Dirichlet distributions (for simplicity), a low alpha value places more weight on having each document composed of only a few dominant topics (whereas a high value will return many more relatively dominant topics). Similarly, a low beta value places more weight on having each topic composed of only a few dominant words.&lt;/p&gt;
', NULL, NULL, '''/p'':56C ''alpha'':10C ''assum'':2C ''beta'':40C ''compos'':19C,49C ''dirichlet'':4C ''distribut'':5C ''document'':18C ''domin'':24C,35C,54C ''high'':28C ''low'':9C,39C ''mani'':32C ''p'':1C ''place'':12C,42C ''relat'':34C ''return'':31C ''similar'':37C ''simplic'':7C ''symmetr'':3C ''topic'':25C,36C,48C ''valu'':11C,29C,41C ''weight'':14C,44C ''wherea'':26C ''word'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (211, 1, '2014-05-27 10:41:33.22', 5, 1155, 'Human activity recognition using smartphone data set problem', '&lt;p&gt;I''m new to this community and hopefully my question will well fit in here.
As part of my undergraduate data analytics course I have choose to do the project on human activity recognition using smartphone data sets. As far as I''m concern this topic relates to Machine Learning and Support Vector Machines. I''m not well familiar with this technologies yet so I will need some help. &lt;/p&gt;

&lt;p&gt;I have decided to follow this project idea &lt;a href="http://www.inf.ed.ac.uk/teaching/courses/dme/2014/datasets.html"&gt;http://www.inf.ed.ac.uk/teaching/courses/dme/2014/datasets.html&lt;/a&gt; (first project on the top)
The project goal is determine what activity a person is engaging in (e.g., WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) from data recorded by a smartphone (Samsung Galaxy S II) on the subject''s waist. Using its embedded accelerometer and gyroscope, the data includes 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz.&lt;/p&gt;

&lt;p&gt;All the data set is given in one folder with some description and feature labels. The data is divided for ''test'' and ''train'' files in which data is represented in this format:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  2.5717778e-001 -2.3285230e-002 -1.4653762e-002 -9.3840400e-001 -9.2009078e-001 -6.6768331e-001 -9.5250112e-001 -9.2524867e-001 -6.7430222e-001 -8.9408755e-001 -5.5457721e-001 -4.6622295e-001  7.1720847e-001  6.3550240e-001  7.8949666e-001 -8.7776423e-001 -9.9776606e-001 -9.9841381e-001 -9.3434525e-001 -9.7566897e-001 -9.4982365e-001 -8.3047780e-001 -1.6808416e-001 -3.7899553e-001  2.4621698e-001  5.2120364e-001 -4.8779311e-001  4.8228047e-001 -4.5462113e-002  2.1195505e-001 -1.3489443e-001  1.3085848e-001 -1.4176313e-002 -1.0597085e-001  7.3544013e-002 -1.7151642e-001  4.0062978e-002  7.6988933e-002 -4.9054573e-001 -7.0900265e-001
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And that''s only a very small sample of what the file contain. &lt;/p&gt;

&lt;p&gt;I don''t really know what this data represents and how can be interpreted. Also for analyzing, classification and clustering of the data, what tools will I need to use? 
Is there any way I can put this data into excel with labels included and for example use R or python to extract sample data and work on this?&lt;/p&gt;

&lt;p&gt;Any hints/tips would be much appreciated.&lt;/p&gt;
', 'bigdata  machine-learning  databases  clustering  data-mining', NULL, '''-1.0597085e-001'':241C ''-1.3489443e-001'':238C ''-1.4176313e-002'':240C ''-1.4653762e-002'':210C ''-1.6808416e-001'':230C ''-1.7151642e-001'':243C ''-2.3285230e-002'':209C ''-3.7899553e-001'':231C ''-4.5462113e-002'':236C ''-4.6622295e-001'':219C ''-4.8779311e-001'':234C ''-4.9054573e-001'':246C ''-5.5457721e-001'':218C ''-6.6768331e-001'':213C ''-6.7430222e-001'':216C ''-7.0900265e-001'':247C ''-8.3047780e-001'':229C ''-8.7776423e-001'':223C ''-8.9408755e-001'':217C ''-9.2009078e-001'':212C ''-9.2524867e-001'':215C ''-9.3434525e-001'':226C ''-9.3840400e-001'':211C ''-9.4982365e-001'':228C ''-9.5250112e-001'':214C ''-9.7566897e-001'':227C ''-9.9776606e-001'':224C ''-9.9841381e-001'':225C ''/code'':248C ''/p'':88C,171C,205C,264C,325C,333C ''/pre'':249C ''/teaching/courses/dme/2014/datasets.html'':102C ''/teaching/courses/dme/2014/datasets.html&lt;/a&gt;'':105C ''1.3085848e-001'':239C ''2.1195505e-001'':237C ''2.4621698e-001'':232C ''2.5717778e-001'':208C ''3'':156C,161C ''4.0062978e-002'':244C ''4.8228047e-001'':235C ''5.2120364e-001'':233C ''50hz'':170C ''6.3550240e-001'':221C ''7.1720847e-001'':220C ''7.3544013e-002'':242C ''7.6988933e-002'':245C ''7.8949666e-001'':222C ''acceler'':159C ''acceleromet'':150C ''activ'':2A,51C,117C ''also'':280C ''analyt'':40C ''analyz'':282C ''angular'':163C ''appreci'':332C ''axial'':157C,162C ''bigdata'':9B ''choos'':44C ''classif'':283C ''cluster'':14B,285C ''code'':207C ''communiti'':24C ''concern'':62C ''constant'':167C ''contain'':263C ''cours'':41C ''data'':6A,16B,39C,55C,133C,154C,175C,189C,199C,273C,288C,304C,320C ''data-min'':15B ''databas'':13B ''decid'':92C ''descript'':184C ''determin'':115C ''divid'':191C ''downstair'':128C ''e.g'':123C ''embed'':149C ''engag'':121C ''exampl'':312C ''excel'':306C ''extract'':318C ''familiar'':77C ''far'':58C ''featur'':186C ''file'':196C,262C ''first'':106C ''fit'':31C ''folder'':181C ''follow'':94C ''format'':204C ''galaxi'':139C ''given'':178C ''goal'':113C ''gyroscop'':152C ''help'':87C ''hints/tips'':328C ''hope'':26C ''href'':99C ''human'':1A,50C ''idea'':97C ''ii'':141C ''includ'':155C,309C ''interpret'':279C ''know'':270C ''label'':187C,308C ''lay'':131C ''learn'':12B,68C ''linear'':158C ''m'':20C,61C,74C ''machin'':11B,67C,72C ''machine-learn'':10B ''mine'':17B ''much'':331C ''need'':85C,293C ''new'':21C ''one'':180C ''p'':18C,89C,172C,250C,265C,326C ''part'':35C ''person'':119C ''pre'':206C ''problem'':8A ''project'':48C,96C,107C,112C ''put'':302C ''python'':316C ''question'':28C ''r'':314C ''rate'':168C ''realli'':269C ''recognit'':3A,52C ''record'':134C ''relat'':65C ''repres'':201C,274C ''sampl'':258C,319C ''samsung'':138C ''set'':7A,56C,176C ''sit'':129C ''small'':257C ''smartphon'':5A,54C,137C ''stand'':130C ''subject'':144C ''support'':70C ''technolog'':80C ''test'':193C ''tool'':290C ''top'':110C ''topic'':64C ''train'':195C ''undergradu'':38C ''upstair'':126C ''use'':4A,53C,147C,295C,313C ''vector'':71C ''veloc'':164C ''waist'':146C ''walk'':124C,125C,127C ''way'':299C ''well'':30C,76C ''work'':322C ''would'':329C ''www.inf.ed.ac.uk'':101C,104C ''www.inf.ed.ac.uk/teaching/courses/dme/2014/datasets.html'':100C ''www.inf.ed.ac.uk/teaching/courses/dme/2014/datasets.html&lt;/a&gt;'':103C ''yet'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (275, 2, '2014-06-10 13:17:48.433', 14, NULL, NULL, '&lt;p&gt;For time series data in particular, &lt;a href="http://www.quandl.com/"&gt;Quandl&lt;/a&gt; is an excellent resource -- an easily browsable directory of (mostly) clean time series.&lt;/p&gt;

&lt;p&gt;One of their coolest features is &lt;a href="http://blog.quandl.com/blog/quandl-open-data/"&gt;open-data stock prices&lt;/a&gt; -- i.e. financial data that can be edited wiki-style, and isn''t encumbered by licensing.&lt;/p&gt;
', NULL, NULL, '''/a'':12C,44C ''/blog/quandl-open-data/'':38C ''/p'':26C,61C ''blog.quandl.com'':37C ''blog.quandl.com/blog/quandl-open-data/'':36C ''browsabl'':19C ''clean'':23C ''coolest'':31C ''data'':5C,41C,47C ''directori'':20C ''easili'':18C ''edit'':51C ''encumb'':58C ''excel'':15C ''featur'':32C ''financi'':46C ''href'':9C,35C ''i.e'':45C ''isn'':56C ''licens'':60C ''most'':22C ''one'':28C ''open'':40C ''open-data'':39C ''p'':1C,27C ''particular'':7C ''price'':43C ''quandl'':11C ''resourc'':16C ''seri'':4C,25C ''stock'':42C ''style'':54C ''time'':3C,24C ''wiki'':53C ''wiki-styl'':52C ''www.quandl.com'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (213, 2, '2014-05-27 12:07:45.92', 6, NULL, NULL, '&lt;p&gt;The data set definitions are on the page here:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones#"&gt;Attribute Information at the bottom&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;or you can see inside the ZIP folder the file named activity_labels, that has your column headings inside of it, make sure you read the README carefully, it has some good info in it. You can easily bring in a &lt;code&gt;.csv&lt;/code&gt; file in R using the &lt;code&gt;read.csv&lt;/code&gt; command.&lt;/p&gt;

&lt;p&gt;For example if you name you file &lt;code&gt;samsungdata&lt;/code&gt; you can open R and run this command:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data &amp;lt;- read.csv("directory/where/file/is/located/samsungdata.csv", header = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Or if you are already inside of the working directory in R you can just run the following&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data &amp;lt;- read.csv("samsungdata.csv", header = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Where the name &lt;code&gt;data&lt;/code&gt; can be changed to whatever you want to call your data set.&lt;/p&gt;
', NULL, NULL, '''/a'':23C ''/code'':69C,77C,90C,108C,138C,146C ''/ml/datasets/human+activity+recognition+using+smartphones#'':17C ''/p'':11C,24C,79C,99C,129C,159C ''/pre'':109C,139C ''activ'':37C ''alreadi'':115C ''archive.ics.uci.edu'':16C ''archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones#'':15C ''attribut'':18C ''bottom'':22C ''bring'':64C ''call'':155C ''care'':53C ''chang'':149C ''code'':67C,75C,88C,101C,131C,144C ''column'':42C ''command'':78C,98C ''csv'':68C ''data'':3C,102C,132C,145C,157C ''definit'':5C ''directori'':120C ''directory/where/file/is/located/samsungdata.csv'':105C ''easili'':63C ''exampl'':82C ''file'':35C,70C,87C ''folder'':33C ''follow'':128C ''good'':57C ''head'':43C ''header'':106C,136C ''href'':14C ''info'':58C ''inform'':19C ''insid'':30C,44C,116C ''label'':38C ''lt'':103C,133C ''make'':47C ''name'':36C,85C,143C ''open'':93C ''p'':1C,12C,25C,80C,110C,140C ''page'':9C ''pre'':100C,130C ''r'':72C,94C,122C ''read'':50C ''read.csv'':76C,104C,134C ''readm'':52C ''run'':96C,126C ''samsungdata'':89C ''samsungdata.csv'':135C ''see'':29C ''set'':4C,158C ''sure'':48C ''true'':107C,137C ''use'':73C ''want'':153C ''whatev'':151C ''work'':119C ''zip'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (325, 2, '2014-06-12 05:17:46.453', 2, NULL, NULL, '&lt;p&gt;This isn''t a question with a simple answer, so all I can really do is point you in the right direction.&lt;/p&gt;

&lt;p&gt;The RODBC package isn''t meant to extract data online, it''s meant to pull data from a database.  If you will be leveraging that package, it will be after you pull data down from the web.&lt;/p&gt;

&lt;p&gt;Jeffrey Bean put together a &lt;a href="http://jeffreybreen.wordpress.com/2011/07/04/twitter-text-mining-r-slides/" rel="nofollow"&gt;slideshow tutorial&lt;/a&gt; for doing sentiment analysis with Twitter data a few years back.  He used the Twitter stream as well as some data pulled in from web scraping.  It''s a good starting point.&lt;/p&gt;

&lt;p&gt;There''s also this site that discusses a few different approaches to this problem in detail, including Bean''s, the sentiment package, and ViralHeat (which is a commercial sentiment analysis service who''s data you can pull into R).  Sentiment has since been removed (&lt;a href="http://cran.us.r-project.org/src/contrib/Archive/sentiment/" rel="nofollow"&gt;archived versions here&lt;/a&gt;), but the &lt;a href="http://cran.us.r-project.org/web/packages/qdap/index.html" rel="nofollow"&gt;qdap package&lt;/a&gt; is available and is designed for use in transcript analysis.&lt;/p&gt;
', NULL, NULL, '''/2011/07/04/twitter-text-mining-r-slides/'':74C ''/a'':79C,168C,180C ''/p'':24C,63C,112C,191C ''/src/contrib/archive/sentiment/'':162C ''/web/packages/qdap/index.html'':175C ''also'':116C ''analysi'':83C,143C,190C ''answer'':10C ''approach'':124C ''archiv'':165C ''avail'':182C ''back'':90C ''bean'':66C,131C ''commerci'':141C ''cran.us.r-project.org'':161C,174C ''cran.us.r-project.org/src/contrib/archive/sentiment/'':160C ''cran.us.r-project.org/web/packages/qdap/index.html'':173C ''data'':34C,41C,58C,86C,100C,147C ''databas'':44C ''design'':185C ''detail'':129C ''differ'':123C ''direct'':23C ''discuss'':120C ''extract'':33C ''good'':109C ''href'':71C,159C,172C ''includ'':130C ''isn'':3C,29C ''jeffrey'':65C ''jeffreybreen.wordpress.com'':73C ''jeffreybreen.wordpress.com/2011/07/04/twitter-text-mining-r-slides/'':72C ''leverag'':49C ''meant'':31C,38C ''nofollow'':76C,164C,177C ''onlin'':35C ''p'':1C,25C,64C,113C ''packag'':28C,51C,135C,179C ''point'':18C,111C ''problem'':127C ''pull'':40C,57C,101C,150C ''put'':67C ''qdap'':178C ''question'':6C ''r'':152C ''realli'':15C ''rel'':75C,163C,176C ''remov'':157C ''right'':22C ''rodbc'':27C ''scrape'':105C ''sentiment'':82C,134C,142C,153C ''servic'':144C ''simpl'':9C ''sinc'':155C ''site'':118C ''slideshow'':77C ''start'':110C ''stream'':95C ''togeth'':68C ''transcript'':189C ''tutori'':78C ''twitter'':85C,94C ''use'':92C,187C ''version'':166C ''viralheat'':137C ''web'':62C,104C ''well'':97C ''year'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (214, 2, '2014-05-27 16:05:02.883', 17, NULL, NULL, '&lt;p&gt;Public Data Sets&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://www.opensciencedatacloud.org/publicdata/"&gt;https://www.opensciencedatacloud.org/publicdata/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Google Public Data Sets&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;
&lt;a href="http://www.google.com/publicdata/directory"&gt;http://www.google.com/publicdata/directory&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Amazon Web Services&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://aws.amazon.com/publicdatasets/"&gt;https://aws.amazon.com/publicdatasets/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Finding Data on the Internet&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.inside-r.org/howto/finding-data-internet"&gt;http://www.inside-r.org/howto/finding-data-internet&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/howto/finding-data-internet'':66C ''/howto/finding-data-internet&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':69C ''/p'':5C,23C,39C,58C ''/publicdata/'':13C ''/publicdata/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':16C ''/publicdata/directory'':30C ''/publicdata/directory&lt;/a&gt;&lt;/li&gt;'':33C ''/publicdatasets/'':47C ''/publicdatasets/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':50C ''/ul'':17C,34C,51C,70C ''amazon'':36C ''aws.amazon.com'':46C,49C ''aws.amazon.com/publicdatasets/'':45C ''aws.amazon.com/publicdatasets/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':48C ''data'':3C,21C,54C ''find'':53C ''googl'':19C ''href'':10C,27C,44C,63C ''internet'':57C ''li'':7C,25C,41C,60C ''p'':1C,8C,18C,35C,42C,52C,61C ''public'':2C,20C ''servic'':38C ''set'':4C,22C ''ul'':6C,24C,40C,59C ''web'':37C ''www.google.com'':29C,32C ''www.google.com/publicdata/directory'':28C ''www.google.com/publicdata/directory&lt;/a&gt;&lt;/li&gt;'':31C ''www.inside-r.org'':65C,68C ''www.inside-r.org/howto/finding-data-internet'':64C ''www.inside-r.org/howto/finding-data-internet&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':67C ''www.opensciencedatacloud.org'':12C,15C ''www.opensciencedatacloud.org/publicdata/'':11C ''www.opensciencedatacloud.org/publicdata/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (215, 1, '2014-05-27 21:07:48.973', 13, 476, 'Where in the workflow should we deal with missing data?', '&lt;p&gt;I''m building a workflow for creating machine learning models (in my case, using Python''s &lt;code&gt;pandas&lt;/code&gt; and &lt;code&gt;sklearn&lt;/code&gt; packages) from data pulled from a very large database (here, Vertica by way of SQL and &lt;code&gt;pyodbc&lt;/code&gt;), and a critical step in that process involves imputing missing values of the predictors. This is straightforward within a single analytics or stats platform---be it Python, R, Stata, etc.---but I''m curious where best to locate this step in a multi-platform workflow.&lt;/p&gt;

&lt;p&gt;It''s simple enough to do this in Python, either with the &lt;a href="http://scikit-learn.org/stable/modules/preprocessing.html#imputation-of-missing-values"&gt;&lt;code&gt;sklearn.preprocessing.Imputer&lt;/code&gt;&lt;/a&gt; class, using the &lt;a href="http://pandas.pydata.org/pandas-docs/version/0.13.1/generated/pandas.DataFrame.fillna.html"&gt;&lt;code&gt;pandas.DataFrame.fillna&lt;/code&gt;&lt;/a&gt; method, or by hand (depending upon the complexity of the imputation method used). But since I''m going to be using this for dozens or hundreds of columns across hundreds of millions of records, I wonder if there''s a more efficient way to do this directly through SQL ahead of time. Aside from the potential efficiencies of doing this in a distributed platform like Vertica, this would have the added benefit of allowing us to create an automated pipeline for building "complete" versions of tables, so we don''t need to fill in a new set of missing values from scratch every time we want to run a model.&lt;/p&gt;

&lt;p&gt;I haven''t been able to find much guidance about this, but I imagine that we could:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;create a table of substitute values (e.g., mean/median/mode, either overall or by group) for each incomplete column&lt;/li&gt;
&lt;li&gt;join the substitute value table with the original table to assign a substitute value for each row and incomplete column&lt;/li&gt;
&lt;li&gt;use a series of case statements to take the original value if available and the substitute value otherwise&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Is this a reasonable thing to do in Vertica/SQL, or is there a good reason not to bother and just handle it in Python instead? And if the latter, is there a strong case for doing this in pandas rather than sklearn or vice-versa? Thanks!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (216, 2, '2014-05-28 07:08:05.393', 11, NULL, NULL, '&lt;p&gt;My strong opinion regarding automated tasks like imputation (but, here I can include also scaling, centering, feature selection, etc) is to avoid in any way do such things without carefully inspecting your data. &lt;/p&gt;

&lt;p&gt;Of course, after deciding what kind of imputation to apply it can be automated (under the assumption that the new data has the same shape/problems).&lt;/p&gt;

&lt;p&gt;So, before anything, take a wise decision. I often wasted time trying to automate this things, destroying my data. I will give you some examples:
- a marketplace encoded as N/A, which I missed and considered to be North/America
- numbers like -999.0, because the data producer could not find a better replacement for missing data
- number like 0 for blood pressure or body temperature, instead of missing data (it is hard to imagine a living human with 0 blood pressure)
- multiple placeholders for missing data, due to the fact that the data was collected from various sources&lt;/p&gt;

&lt;p&gt;After that you need to understand what kind of imputation would resemble better the information from your data for a given task. This is often much harder to do it right than it seems.&lt;/p&gt;

&lt;p&gt;After all those things, my advice is to delay your imputation task to an upper layer where you have tools to reproduce on new data and to inspect if the assumptions for the new data are not violated (if it is possible).&lt;/p&gt;
', NULL, NULL, '''-999.0'':104C ''/p'':35C,62C,160C,196C,240C ''0'':120C,140C ''advic'':203C ''also'':15C ''anyth'':66C ''appli'':46C ''assumpt'':53C,228C ''autom'':6C,50C,77C ''avoid'':23C ''better'':113C,174C ''blood'':122C,141C ''bodi'':125C ''care'':31C ''center'':17C ''collect'':156C ''consid'':98C ''could'':109C ''cours'':38C ''data'':34C,57C,82C,107C,117C,130C,147C,154C,179C,222C,232C ''decid'':40C ''decis'':70C ''delay'':206C ''destroy'':80C ''due'':148C ''encod'':91C ''etc'':20C ''exampl'':88C ''fact'':151C ''featur'':18C ''find'':111C ''give'':85C ''given'':182C ''hard'':133C ''harder'':188C ''human'':138C ''imagin'':135C ''imput'':9C,44C,171C,208C ''includ'':14C ''inform'':176C ''inspect'':32C,225C ''instead'':127C ''kind'':42C,169C ''layer'':213C ''like'':8C,103C,119C ''live'':137C ''marketplac'':90C ''miss'':96C,116C,129C,146C ''much'':187C ''multipl'':143C ''n/a'':93C ''need'':165C ''new'':56C,221C,231C ''north/america'':101C ''number'':102C,118C ''often'':72C,186C ''opinion'':4C ''p'':1C,36C,63C,161C,197C ''placehold'':144C ''possibl'':239C ''pressur'':123C,142C ''produc'':108C ''regard'':5C ''replac'':114C ''reproduc'':219C ''resembl'':173C ''right'':192C ''scale'':16C ''seem'':195C ''select'':19C ''shape/problems'':61C ''sourc'':159C ''strong'':3C ''take'':67C ''task'':7C,183C,209C ''temperatur'':126C ''thing'':29C,79C,201C ''time'':74C ''tool'':217C ''tri'':75C ''understand'':167C ''upper'':212C ''various'':158C ''violat'':235C ''wast'':73C ''way'':26C ''wise'':69C ''without'':30C ''would'':172C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (217, 2, '2014-05-28 09:43:54.197', 4, NULL, NULL, '&lt;p&gt;It looks like this (or very similar data set) is used for Coursera courses. Cleaning this dataset is task for &lt;a href="https://www.coursera.org/course/getdata" rel="nofollow"&gt;Getting and Cleaning Data&lt;/a&gt;, but it is also used for case study for &lt;a href="https://class.coursera.org/exdata-002" rel="nofollow"&gt;Exploratory Data analysis&lt;/a&gt;. Video from this case study is available in videos for week 4 of EDA course-ware. It might help you with starting with this data.&lt;/p&gt;
', NULL, NULL, '''/a'':33C,53C ''/course/getdata'':26C ''/exdata-002'':47C ''/p'':80C ''4'':65C ''also'':37C ''analysi'':52C ''avail'':60C ''case'':40C,57C ''class.coursera.org'':46C ''class.coursera.org/exdata-002'':45C ''clean'':16C,31C ''cours'':15C,69C ''course-war'':68C ''coursera'':14C ''data'':9C,32C,51C,79C ''dataset'':18C ''eda'':67C ''exploratori'':50C ''get'':29C ''help'':73C ''href'':23C,44C ''like'':4C ''look'':3C ''might'':72C ''nofollow'':28C,49C ''p'':1C ''rel'':27C,48C ''set'':10C ''similar'':8C ''start'':76C ''studi'':41C,58C ''task'':20C ''use'':12C,38C ''video'':54C,62C ''ware'':70C ''week'':64C ''www.coursera.org'':25C ''www.coursera.org/course/getdata'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (276, 2, '2014-06-10 13:38:31.207', 8, NULL, NULL, '&lt;p&gt;Not all government data is listed on data.gov - &lt;a href="http://sunlightfoundation.com/blog/2014/02/21/open-data-inventories-ready-for-human-consumption/"&gt;Sunlight Foundation&lt;/a&gt; put together a &lt;a href="https://drive.google.com/folderview?id=0B4QuErjcV2a0WXVDOURwbzh6S2s&amp;amp;usp=sharing"&gt;set of spreadsheets&lt;/a&gt; back in February describing sets of available data.&lt;/p&gt;
', NULL, NULL, '''/a'':17C,29C ''/blog/2014/02/21/open-data-inventories-ready-for-human-consumption/'':14C ''/folderview?id=0b4querjcv2a0wxvdourwbzh6s2s&amp;amp;usp=sharing'':25C ''/p'':38C ''avail'':36C ''back'':30C ''data'':5C,37C ''data.gov'':9C ''describ'':33C ''drive.google.com'':24C ''drive.google.com/folderview?id=0b4querjcv2a0wxvdourwbzh6s2s&amp;amp;usp=sharing'':23C ''februari'':32C ''foundat'':16C ''govern'':4C ''href'':11C,22C ''list'':7C ''p'':1C ''put'':18C ''set'':26C,34C ''spreadsheet'':28C ''sunlight'':15C ''sunlightfoundation.com'':13C ''sunlightfoundation.com/blog/2014/02/21/open-data-inventories-ready-for-human-consumption/'':12C ''togeth'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (327, 2, '2014-06-12 07:05:05.653', 9, NULL, NULL, '&lt;p&gt;There is nothing like "python is better" or "R is much better than x". &lt;/p&gt;

&lt;p&gt;The only fact I know is that in the industry allots of people stick to python because that is what they learned at the university. The python community is really active and have a few great frameworks for ML and data mining etc. &lt;/p&gt;

&lt;p&gt;But to be honest, if you get a good c programmer he can do the same as people do in python or r, if you got a good java programmer he can also do (near to) everything in java. &lt;/p&gt;

&lt;p&gt;So just stick with the language you are comfortable with.&lt;/p&gt;
', NULL, NULL, '''/p'':16C,61C,102C,114C ''activ'':48C ''allot'':28C ''also'':95C ''better'':8C,13C ''c'':72C ''comfort'':112C ''communiti'':45C ''data'':58C ''etc'':60C ''everyth'':99C ''fact'':20C ''framework'':54C ''get'':69C ''good'':71C,90C ''got'':88C ''great'':53C ''honest'':66C ''industri'':27C ''java'':91C,101C ''know'':22C ''languag'':109C ''learn'':39C ''like'':5C ''mine'':59C ''ml'':56C ''much'':12C ''near'':97C ''noth'':4C ''p'':1C,17C,62C,103C ''peopl'':30C,80C ''programm'':73C,92C ''python'':6C,33C,44C,83C ''r'':10C,85C ''realli'':47C ''stick'':31C,106C ''univers'':42C ''x'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (220, 2, '2014-05-29 14:13:00.317', 4, NULL, NULL, '&lt;p&gt;Christopher is right about the size of the array. To be simplistic about it, if this translates to 1.6B floats, at 16 bytes per float (32-bit version; 64-bit is bigger), then you''re trying to create an array of about 26 GB. Even if you have the RAM for that, I''d imagine that it''s probably going to overload something else along the way. &lt;/p&gt;

&lt;p&gt;(Maybe not, but generally speaking, any operations that are that computationally intensive should first raise the question of whether you are doing the right calculation in the first place. And if you do need to do something of that magnitude, you should then try to break it down into more manageable chunks that can be run in parallel or distributed across machines.)&lt;/p&gt;

&lt;p&gt;But given that you are describing a very, very wide dataset (~40k columns x 180 rows), I wonder whether you really want to take the covariance matrix of the transposed dataset (so 180x180 = 32,400 covariances)? That would be a far more tractable problem, and it''s easier to see how it might be useful.&lt;/p&gt;

&lt;p&gt;In any case, you''re probably far better off calculating each pairwise covariance (or at least, the vector of cov(x_i,x_k) for all x_k != x_i) at the point where you''ll actually use it, rather than calculating a giant matrix initially then referring back to it later. Memory issues aside, it''ll make your life much easier if you start running things in parallel, and will help ensure you don''t waste resources on unnecessary calculations.&lt;/p&gt;
', NULL, NULL, '''/p'':70C,134C,191C,274C ''1.6'':20C ''16'':24C ''180'':150C ''180x180'':168C ''26'':45C ''32'':28C,169C ''400'':170C ''40k'':147C ''64'':31C ''across'':132C ''actual'':229C ''along'':67C ''array'':10C,42C ''asid'':247C ''b'':21C ''back'':241C ''better'':200C ''bigger'':34C ''bit'':29C,32C ''break'':117C ''byte'':25C ''calcul'':96C,202C,234C,273C ''case'':195C ''christoph'':2C ''chunk'':123C ''column'':148C ''comput'':82C ''cov'':212C ''covari'':161C,171C,205C ''creat'':40C ''d'':56C ''dataset'':146C,166C ''describ'':141C ''distribut'':131C ''easier'':183C,254C ''els'':66C ''ensur'':265C ''even'':47C ''far'':176C,199C ''first'':85C,99C ''float'':22C,27C ''gb'':46C ''general'':75C ''giant'':236C ''given'':137C ''go'':62C ''help'':264C ''imagin'':57C ''initi'':238C ''intens'':83C ''issu'':246C ''k'':216C,220C ''later'':244C ''least'':208C ''life'':252C ''ll'':228C,249C ''machin'':133C ''magnitud'':111C ''make'':250C ''manag'':122C ''matrix'':162C,237C ''mayb'':72C ''memori'':245C ''might'':188C ''much'':253C ''need'':105C ''oper'':78C ''overload'':64C ''p'':1C,71C,135C,192C ''pairwis'':204C ''parallel'':129C,261C ''per'':26C ''place'':100C ''point'':225C ''probabl'':61C,198C ''problem'':179C ''question'':88C ''rais'':86C ''ram'':52C ''rather'':232C ''re'':37C,197C ''realli'':156C ''refer'':240C ''resourc'':270C ''right'':4C,95C ''row'':151C ''run'':127C,258C ''see'':185C ''simplist'':13C ''size'':7C ''someth'':65C,108C ''speak'':76C ''start'':257C ''take'':159C ''thing'':259C ''tractabl'':178C ''translat'':18C ''transpos'':165C ''tri'':38C,115C ''unnecessari'':272C ''use'':190C,230C ''vector'':210C ''version'':30C ''want'':157C ''wast'':269C ''way'':69C ''whether'':90C,154C ''wide'':145C ''wonder'':153C ''would'':173C ''x'':149C,213C,215C,219C,221C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (221, 2, '2014-05-29 14:30:21.357', 4, NULL, NULL, '&lt;p&gt;It''s actually even simpler than that, from what you describe---you''re just looking for a basic classification tree algorithm (so no need for slightly more complex variants like C4.5 which are optimized for prediction accuracy). The canonical text is:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0412048418" rel="nofollow"&gt;http://www.amazon.com/Classification-Regression-Wadsworth-Statistics-Probability/dp/0412048418&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This is readily implemented in R: &lt;/p&gt;

&lt;p&gt;&lt;a href="http://cran.r-project.org/web/packages/tree/tree.pdf" rel="nofollow"&gt;http://cran.r-project.org/web/packages/tree/tree.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;and Python:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/modules/tree.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/tree.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/amzn/click/0412048418'':49C ''/classification-regression-wadsworth-statistics-probability/dp/0412048418&lt;/a&gt;&lt;/p&gt;'':54C ''/p'':43C,62C,77C ''/stable/modules/tree.html'':83C ''/stable/modules/tree.html&lt;/a&gt;&lt;/p&gt;'':88C ''/web/packages/tree/tree.pdf'':68C ''/web/packages/tree/tree.pdf&lt;/a&gt;&lt;/p&gt;'':73C ''accuraci'':38C ''actual'':4C ''algorithm'':22C ''basic'':19C ''c4.5'':32C ''canon'':40C ''classif'':20C ''complex'':29C ''cran.r-project.org'':67C,72C ''cran.r-project.org/web/packages/tree/tree.pdf'':66C ''cran.r-project.org/web/packages/tree/tree.pdf&lt;/a&gt;&lt;/p&gt;'':71C ''describ'':12C ''even'':5C ''href'':46C,65C,80C ''implement'':59C ''like'':31C ''look'':16C ''need'':25C ''nofollow'':51C,70C,85C ''optim'':35C ''p'':1C,44C,55C,63C,74C,78C ''predict'':37C ''python'':76C ''r'':61C ''rads.stackoverflow.com'':48C ''rads.stackoverflow.com/amzn/click/0412048418'':47C ''re'':14C ''readili'':58C ''rel'':50C,69C,84C ''scikit-learn.org'':82C,87C ''scikit-learn.org/stable/modules/tree.html'':81C ''scikit-learn.org/stable/modules/tree.html&lt;/a&gt;&lt;/p&gt;'':86C ''simpler'':6C ''slight'':27C ''text'':41C ''tree'':21C ''variant'':30C ''www.amazon.com'':53C ''www.amazon.com/classification-regression-wadsworth-statistics-probability/dp/0412048418&lt;/a&gt;&lt;/p&gt;'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (241, 2, '2014-06-09 21:51:53.793', 2, NULL, NULL, '&lt;p&gt;It seems to me that the premise of a PhD is to expand knowledge in some little slice of the world. Since a "data scientist" is by nature is somewhat of a jack-of-all-trades it does seem a little odd to me. A masters program seems much more appropriate.&lt;/p&gt;

&lt;p&gt;What do you hope to gain from a PhD? If the rigor scares (or bores) you, then what about a more applied area? Signal processing, robotics, applied physics, operations research, etc.&lt;/p&gt;
', NULL, NULL, '''/p'':54C,87C ''appli'':77C,82C ''appropri'':53C ''area'':78C ''bore'':70C ''data'':25C ''etc'':86C ''expand'':14C ''gain'':61C ''hope'':59C ''jack'':35C ''jack-of-all-trad'':34C ''knowledg'':15C ''littl'':18C,43C ''master'':48C ''much'':51C ''natur'':29C ''odd'':44C ''oper'':84C ''p'':1C,55C ''phd'':11C,64C ''physic'':83C ''premis'':8C ''process'':80C ''program'':49C ''research'':85C ''rigor'':67C ''robot'':81C ''scare'':68C ''scientist'':26C ''seem'':3C,41C,50C ''signal'':79C ''sinc'':23C ''slice'':19C ''somewhat'':31C ''trade'':38C ''world'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (222, 2, '2014-05-29 19:02:13.21', 11, NULL, NULL, '&lt;p&gt;&lt;a href="http://enigma.io"&gt;Enigma&lt;/a&gt; is a repository of public available datasets. Its free plan offers public data search, with 10k API calls per month. Not all public databases are listed, but the list is enough for common cases.&lt;/p&gt;

&lt;p&gt;I used it for academic research and it saved me a lot of time.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Another interesting source of data is the &lt;a href="http://theunitedstates.io/"&gt;@unitedstates project&lt;/a&gt;, containing data and tools to collect them, about the United States (members of Congress, geographic shapesâ¦).&lt;/p&gt;
', NULL, NULL, '''/a'':6C,72C ''/p'':41C,57C,89C ''10k'':22C ''academ'':47C ''anoth'':60C ''api'':23C ''avail'':12C ''call'':24C ''case'':40C ''collect'':78C ''common'':39C ''congress'':86C ''contain'':73C ''data'':19C,64C,74C ''databas'':30C ''dataset'':13C ''enigma'':5C ''enigma.io'':4C ''enough'':37C ''free'':15C ''geograph'':87C ''hr'':58C ''href'':3C,68C ''interest'':61C ''list'':32C,35C ''lot'':54C ''member'':84C ''month'':26C ''offer'':17C ''p'':1C,42C,59C ''per'':25C ''plan'':16C ''project'':71C ''public'':11C,18C,29C ''repositori'':9C ''research'':48C ''save'':51C ''search'':20C ''shapesâ'':88C ''sourc'':62C ''state'':83C ''theunitedstates.io'':69C ''time'':56C ''tool'':76C ''unit'':82C ''unitedst'':70C ''use'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (223, 1, '2014-05-29 20:11:16.327', 9, 302, 'How to annotate text documents with meta-data?', '&lt;p&gt;Having a lot of text documents (in natural language, unstructured), what are the possible ways of annotating them with some semantic meta-data? For example, consider a short document:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;I saw the company''s manager last day.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;To be able to extract information from it, it must be annotated with additional data to be less ambiguous. The process of finding such meta-data is not in question, so assume it is done manually. The question is how to store these data in a way that further analysis on it can be done more conveniently/efficiently?&lt;/p&gt;

&lt;p&gt;A possible approach is to use XML tags (see below), but it seems too verbose, and maybe there are better approaches/guidelines for storing such meta-data on text documents.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;Person name="John"&amp;gt;I&amp;lt;/Person&amp;gt; saw the &amp;lt;Organization name="ACME"&amp;gt;company&amp;lt;/Organization&amp;gt;''s
manager &amp;lt;Time value="2014-5-29"&amp;gt;last day&amp;lt;/Time&amp;gt;.
&lt;/code&gt;&lt;/pre&gt;
', 'nlp  metadata  data-cleaning  text-mining', NULL, '''-29'':183C ''-5'':182C ''/code'':60C,190C ''/organization'':174C ''/p'':49C,121C,153C ''/person'':163C ''/pre'':61C,191C ''/time'':188C ''2014'':181C ''abl'':65C ''acm'':170C ''addit'':76C ''ambigu'':81C ''analysi'':113C ''annot'':3A,35C,74C ''approach'':125C ''approaches/guidelines'':143C ''assum'':95C ''better'':142C ''clean'':14B ''code'':51C,155C ''compani'':55C,172C ''consid'':45C ''conveniently/efficiently'':120C ''data'':9A,13B,42C,77C,89C,107C,149C ''data-clean'':12B ''day'':59C,186C ''document'':5A,24C,48C,152C ''done'':98C,118C ''exampl'':44C ''extract'':67C ''find'':85C ''gt'':160C,164C,171C,175C,184C,189C ''inform'':68C ''john'':159C ''languag'':27C ''last'':58C,185C ''less'':80C ''lot'':21C ''lt'':156C,162C,167C,173C,178C,187C ''manag'':57C,177C ''manual'':99C ''mayb'':139C ''meta'':8A,41C,88C,148C ''meta-data'':7A,40C,87C,147C ''metadata'':11B ''mine'':17B ''must'':72C ''name'':158C,169C ''natur'':26C ''nlp'':10B ''organ'':168C ''p'':18C,62C,122C ''person'':157C ''possibl'':32C,124C ''pre'':50C,154C ''process'':83C ''question'':93C,101C ''saw'':53C,165C ''see'':131C ''seem'':135C ''semant'':39C ''short'':47C ''store'':105C,145C ''tag'':130C ''text'':4A,16B,23C,151C ''text-min'':15B ''time'':179C ''unstructur'':28C ''use'':128C ''valu'':180C ''verbos'':137C ''way'':33C,110C ''xml'':129C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (250, 2, '2014-06-10 04:47:13.04', 2, NULL, NULL, '&lt;p&gt;Google does not publish the models they use, but they specifically do not support models from the PMML specification.&lt;/p&gt;

&lt;p&gt;If you look closely at the documentation on &lt;a href="https://developers.google.com/prediction/docs/pmml-schema" rel="nofollow"&gt;this page&lt;/a&gt;, you will notice that the model selection within the schema is greyed out indicating that it is an unsupported feature of the schema.&lt;/p&gt;

&lt;p&gt;The &lt;a href="https://developers.google.com/prediction/docs/developer-guide#whatisprediction" rel="nofollow"&gt;documentation does spell out&lt;/a&gt; that by default it will use a regression model for training data that has numeric answers, and an unspecified categorization model for training data that results in text based answers.&lt;/p&gt;

&lt;p&gt;The Google Prediction API also supports hosted models (although only a few demo models are currently available), and models specified with a PMML transform.  The documentation does contain an &lt;a href="https://developers.google.com/prediction/docs/pmml-schema" rel="nofollow"&gt;example of a model defined by a PMML transform&lt;/a&gt;.  (There is also a note on that page stating that PMML ...Model elements are not supported).  &lt;/p&gt;

&lt;p&gt;The PMML standard that google partially supports is &lt;a href="http://www.dmg.org//pmml-v4-0-1.html" rel="nofollow"&gt;version 4.0.1&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''//pmml-v4-0-1.html'':187C ''/a'':40C,78C,156C,192C ''/p'':21C,64C,109C,173C,193C ''/prediction/docs/developer-guide#whatisprediction'':71C ''/prediction/docs/pmml-schema'':35C,144C ''4.0.1'':191C ''also'':115C,159C ''although'':119C ''answer'':94C,108C ''api'':114C ''avail'':127C ''base'':107C ''categor'':98C ''close'':26C ''contain'':138C ''current'':126C ''data'':90C,102C ''default'':81C ''defin'':151C ''demo'':123C ''developers.google.com'':34C,70C,143C ''developers.google.com/prediction/docs/developer-guide#whatisprediction'':69C ''developers.google.com/prediction/docs/pmml-schema'':33C,142C ''document'':29C,74C,136C ''element'':169C ''exampl'':147C ''featur'':60C ''googl'':2C,112C,179C ''grey'':52C ''host'':117C ''href'':32C,68C,141C,184C ''indic'':54C ''look'':25C ''model'':7C,16C,46C,87C,99C,118C,124C,129C,150C,168C ''nofollow'':37C,73C,146C,189C ''note'':161C ''notic'':43C ''numer'':93C ''p'':1C,22C,65C,110C,174C ''page'':39C,164C ''partial'':180C ''pmml'':19C,133C,154C,167C,176C ''predict'':113C ''publish'':5C ''regress'':86C ''rel'':36C,72C,145C,188C ''result'':104C ''schema'':50C,63C ''select'':47C ''specif'':12C,20C ''specifi'':130C ''spell'':76C ''standard'':177C ''state'':165C ''support'':15C,116C,172C,181C ''text'':106C ''train'':89C,101C ''transform'':134C,155C ''unspecifi'':97C ''unsupport'':59C ''use'':9C,84C ''version'':190C ''within'':48C ''www.dmg.org'':186C ''www.dmg.org//pmml-v4-0-1.html'':185C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (224, 1, '2014-05-31 14:28:42.317', 2, 200, 'How to get phrase tables from word alignments?', '&lt;p&gt;The output of my word alignment file looks as such:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;I wish to say with regard to the initiative of the Portuguese Presidency that we support the spirit and the political intention behind it . In bezug auf die Initiative der portugiesischen PrÃ¤sidentschaft mÃ¶chte ich zum Ausdruck bringen , daÃ wir den Geist und die politische Absicht , die dahinter stehen , unterstÃ¼tzen .   0-0 5-1 5-2 2-3 8-4 7-5 11-6 12-7 1-8 0-9 9-10 3-11 10-12 13-13 13-14 14-15 16-16 17-17 18-18 16-19 20-20 21-21 19-22 19-23 22-24 22-25 23-26 15-27 24-28
It may not be an ideal initiative in terms of its structure but we accept Mr President-in-Office , that it is rooted in idealism and for that reason we are inclined to support it .    Von der Struktur her ist es vielleicht keine ideale Initiative , aber , Herr amtierender RatsprÃ¤sident , wir akzeptieren , daÃ sie auf Idealismus fuÃt , und sind deshalb geneigt , sie mitzutragen .   0-0 11-2 8-3 0-4 3-5 1-6 2-7 5-8 6-9 12-11 17-12 15-13 16-14 16-15 17-16 13-17 14-18 17-19 18-20 19-21 21-22 23-23 21-24 26-25 24-26 29-27 27-28 30-29 31-30 33-31 32-32 34-33
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;How can I produce the phrase tables that are used by MOSES from this output?&lt;/p&gt;
', 'untagged', NULL, '''-0'':77C,200C ''-1'':79C ''-10'':97C ''-11'':99C,218C ''-12'':101C,220C ''-13'':103C,222C ''-14'':105C,224C ''-15'':107C,226C ''-16'':109C,228C ''-17'':111C,230C ''-18'':113C,232C ''-19'':115C,234C ''-2'':81C,202C ''-20'':117C,236C ''-21'':119C,238C ''-22'':121C,240C ''-23'':123C,242C ''-24'':125C,244C ''-25'':127C,246C ''-26'':129C,248C ''-27'':131C,250C ''-28'':133C,252C ''-29'':254C ''-3'':83C,204C ''-30'':256C ''-31'':258C ''-32'':260C ''-33'':262C ''-4'':85C,206C ''-5'':87C,208C ''-6'':89C,210C ''-7'':91C,212C ''-8'':93C,214C ''-9'':95C,216C ''/code'':263C ''/p'':21C,281C ''/pre'':264C ''0'':76C,94C,199C,205C ''1'':92C,209C ''10'':100C ''11'':88C,201C ''12'':90C,217C ''13'':102C,104C,229C ''14'':106C,231C ''15'':130C,221C ''16'':108C,114C,223C,225C ''17'':110C,219C,227C,233C ''18'':112C,235C ''19'':120C,122C,237C ''2'':82C,211C ''20'':116C ''21'':118C,239C,243C ''22'':124C,126C ''23'':128C,241C ''24'':132C,247C ''26'':245C ''27'':251C ''29'':249C ''3'':98C,207C ''30'':253C ''31'':255C ''32'':259C ''33'':257C ''34'':261C ''5'':78C,80C,213C ''6'':215C ''7'':86C ''8'':84C,203C ''9'':96C ''aber'':180C ''absicht'':70C ''accept'':148C ''akzeptieren'':186C ''align'':8A,16C ''amtierend'':182C ''auf'':50C,189C ''ausdruck'':61C ''behind'':46C ''bezug'':49C ''bringen'':62C ''chte'':58C ''code'':23C ''dahint'':72C ''daã'':63C,187C ''den'':65C ''der'':53C,171C ''deshalb'':195C ''die'':51C,68C,71C ''es'':175C ''file'':17C ''fuã'':191C ''geist'':66C ''geneigt'':196C ''get'':3A ''herr'':181C ''ich'':59C ''ideal'':139C,159C,178C ''idealismus'':190C ''inclin'':166C ''initi'':32C,52C,140C,179C ''intent'':45C ''ist'':174C ''kein'':177C ''look'':18C ''may'':135C ''mitzutragen'':198C ''mose'':277C ''mr'':149C ''mã'':57C ''offic'':153C ''output'':12C,280C ''p'':10C,265C ''phrase'':4A,271C ''polit'':44C ''politisch'':69C ''portugiesischen'':54C ''portugues'':35C ''pre'':22C ''presid'':36C,151C ''president-in-offic'':150C ''produc'':269C ''prã'':55C ''ratsprã'':183C ''reason'':163C ''regard'':29C ''root'':157C ''say'':27C ''sident'':184C ''sidentschaft'':56C ''sie'':188C,197C ''sind'':194C ''spirit'':41C ''stehen'':73C ''structur'':145C ''struktur'':172C ''support'':39C,168C ''tabl'':5A,272C ''term'':142C ''tzen'':75C ''und'':67C,193C ''untag'':9B ''unterstã'':74C ''use'':275C ''vielleicht'':176C ''von'':170C ''wir'':64C,185C ''wish'':25C ''word'':7A,15C ''zum'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10488, 1, '2016-03-02 18:20:21.793', 0, 2, 'Hadoop Pseudo Distribution Set-up Exception in thread âmainâ java.lang.RuntimeException: java.net.ConnectException', '&lt;p&gt;I am a beginner in Hadoop and I am trying to execute some program (jar file) in pseudo-distribution mode on windows using cygwin. I have done the preliminary steps such as changed all the xml files under conf folder and have ssh configured as well for pseudo mode. I am able to start all the nodes using the command bin/start-all.sh and I am able to bring up &lt;a href="http://localhost:50030/jobtracker.jsp" rel="nofollow"&gt;http://localhost:50030/jobtracker.jsp&lt;/a&gt; locally. But when i tried to execute the jar files which I am testing for pseudo-mode I am getting the below error.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;16/03/02 01:50:14 INFO ipc.Client: Retrying connect to server:    
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 0 time(s).
16/03/02 01:50:16 INFO ipc.Client: Retrying connect to server:   
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 1 time(s).
16/03/02 01:50:18 INFO ipc.Client: Retrying connect to server:  
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 2 time(s).
16/03/02 01:50:20 INFO ipc.Client: Retrying connect to server: 
localhost/127.0.0                                                                                                                   
.1:9000. Already tried 3 time(s).
16/03/02 01:50:22 INFO ipc.Client: Retrying connect to server:  
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 4 time(s).
16/03/02 01:50:24 INFO ipc.Client: Retrying connect to server:  
localhost/127.0.0                                                                                                                 
.1:9000. Already tried 5 time(s).
16/03/02 01:50:26 INFO ipc.Client: Retrying connect to server: 
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 6 time(s).
16/03/02 01:50:28 INFO ipc.Client: Retrying connect to server:  
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 7 time(s).
16/03/02 01:50:30 INFO ipc.Client: Retrying connect to server:  
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 8 time(s).
16/03/02 01:50:32 INFO ipc.Client: Retrying connect to server:  
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 9 time(s).
Exception in thread "main" java.lang.RuntimeException:  
java.net.ConnectException                                                                                                                 
: Call to localhost/127.0.0.1:9000 failed on connection exception:  
java.net.Conn                                                                                                                   
ectException: Connection refused: no further information
at org.apache.hadoop.mapred.JobConf.getWorkingDirectory(JobConf.java:371                                                                                                                  
)
at org.apache.hadoop.mapred.FileInputFormat.setInputPaths(FileInputForma                                                                                                                
t.java:309)
at SpeciesGraphBuilder.main(SpeciesGraphBuilder.java:56)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.                                                                                                                
java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAcces                                                                                                                
sorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.util.RunJar.main(RunJar.java:156)
Caused by: java.net.ConnectException: Call to localhost/127.0.0.1:9000   
failed on                                                                                                                  
connection exception: java.net.ConnectException: Connection refused: no   
further                                                                                                               
information
at org.apache.hadoop.ipc.Client.wrapException(Client.java:767)
at org.apache.hadoop.ipc.Client.call(Client.java:743)
at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:220)
    at com.sun.proxy.$Proxy0.getProtocolVersion(Unknown Source)
at org.apache.hadoop.ipc.RPC.getProxy(RPC.java:359)
at org.apache.hadoop.hdfs.DFSClient.createRPCNamenode(DFSClient.java:106                                                                                                                 
)
at org.apache.hadoop.hdfs.DFSClient.&amp;lt;init&amp;gt;(DFSClient.java:207)
at org.apache.hadoop.hdfs.DFSClient.&amp;lt;init&amp;gt;(DFSClient.java:170)
at org.apache.hadoop.hdfs.DistributedFileSystem.initialize(DistributedFi                                                                                                                 
leSystem.java:82)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:1378                                                                                                                 
)
at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:66)
    at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:1390)
at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:196)
at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:95)
at org.apache.hadoop.mapred.JobConf.getWorkingDirectory(JobConf.java:367                                                                                                                 
)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have done all the initial testing by refreshing the name nodes, starting the name node, data node, job trackers etc and all went fine. But I am unable to tackle this strange error. I am using hadoop 0.20.2 version and trying to run it on windows. I am providing the changes that I made in xml files:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;core-site.xml&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;configuration&amp;gt;
 &amp;lt;property&amp;gt;
  &amp;lt;name&amp;gt;hadoop.tmp.dir&amp;lt;/name&amp;gt;
  &amp;lt;value&amp;gt;/tmp/hadoop-Sriram&amp;lt;/value&amp;gt;
 &amp;lt;/property&amp;gt;

 &amp;lt;property&amp;gt;
   &amp;lt;name&amp;gt;fs.default.name&amp;lt;/name&amp;gt;
   &amp;lt;value&amp;gt;hdfs://localhost:9000&amp;lt;/value&amp;gt;
 &amp;lt;/property&amp;gt;    
&amp;lt;/configuration&amp;gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;HDFS-Site.xml&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;configuration&amp;gt;
 &amp;lt;property&amp;gt;
  &amp;lt;name&amp;gt;dfs.replication&amp;lt;/name&amp;gt;
  &amp;lt;value&amp;gt;1&amp;lt;/value&amp;gt;
 &amp;lt;/property&amp;gt;

 &amp;lt;property&amp;gt;
  &amp;lt;name&amp;gt;dfs.name.dir&amp;lt;/name&amp;gt;
  &amp;lt;value&amp;gt;/home/Sriram/hdfs/name&amp;lt;/value&amp;gt;
 &amp;lt;/property&amp;gt;

 &amp;lt;property&amp;gt;
  &amp;lt;name&amp;gt;dfs.data.dir&amp;lt;/name&amp;gt;
  &amp;lt;value&amp;gt;/home/Sriram/hdfs/data&amp;lt;/value&amp;gt;
 &amp;lt;/property&amp;gt;
&amp;lt;/configuration&amp;gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Mapred-site.xml&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;configuration&amp;gt;
 &amp;lt;property&amp;gt;
  &amp;lt;name&amp;gt;mapred.job.tracker&amp;lt;/name&amp;gt;
  &amp;lt;value&amp;gt;localhost:9001&amp;lt;/value&amp;gt;
 &amp;lt;/property&amp;gt;

 &amp;lt;property&amp;gt;
  &amp;lt;name&amp;gt;mapred.child.java.opts&amp;lt;/name&amp;gt;
  &amp;lt;value&amp;gt;-Xmx2g&amp;lt;/value&amp;gt;
 &amp;lt;/property&amp;gt;
&amp;lt;/configuration&amp;gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have referred previous posts for this issue, but could not find one for windows. I need to track the jobs locally once the execution starts (task tracker/job tracker). Any help would be greatly appreciated as I am blocked at this state.&lt;/p&gt;
', 'bigdata  dataset  hadoop  java', NULL, '''/a'':96C ''/code'':429C,545C,620C,676C ''/configuration'':543C,618C,674C ''/home/sriram/hdfs/data'':610C ''/home/sriram/hdfs/name'':590C ''/name'':509C,529C,565C,585C,605C,640C,661C ''/p'':121C,490C,495C,551C,626C,721C ''/pre'':430C,546C,621C,677C ''/property'':519C,540C,575C,595C,615C,651C,671C ''/strong'':494C,550C,625C ''/tmp/hadoop-sriram'':514C ''/value'':516C,537C,572C,592C,612C,648C,668C ''0'':139C ''0.20.2'':470C ''01'':125C,143C,161C,179C,197C,215C,233C,251C,269C,287C ''1'':135C,153C,157C,171C,189C,207C,225C,243C,261C,279C,297C,570C ''14'':127C ''16'':145C ''16/03/02'':124C,142C,160C,178C,196C,214C,232C,250C,268C,286C ''18'':163C ''2'':175C ''20'':181C ''200'':414C ''22'':199C ''24'':217C ''26'':235C ''28'':253C ''3'':193C ''30'':271C ''32'':289C ''4'':211C ''5'':229C ''50'':126C,144C,162C,180C,198C,216C,234C,252C,270C,288C ''50030/jobtracker.jsp'':91C,95C ''6'':247C ''62'':344C ''7'':265C ''8'':283C ''9'':301C ''9000'':136C,154C,172C,190C,208C,226C,244C,262C,280C,298C,313C,361C,535C ''9001'':646C ''abl'':71C,84C ''alreadi'':137C,155C,173C,191C,209C,227C,245C,263C,281C,299C ''appreci'':713C ''beginn'':22C ''bigdata'':14B ''bin/start-all.sh'':80C ''block'':717C ''bring'':86C ''cache.get'':418C ''call'':310C,358C ''caus'':355C ''chang'':52C,483C ''client.java:743'':377C ''client.java:767'':374C ''code'':123C,497C,553C,628C ''com.sun.proxy'':383C ''command'':79C ''conf'':58C ''configur'':63C,499C,555C,630C ''connect'':131C,149C,167C,185C,203C,221C,239C,257C,275C,293C,316C,320C,364C,367C ''core-site.xml'':493C ''could'':688C ''cygwin'':43C ''data'':448C ''dataset'':15B ''delegatingmethodacc'':347C ''dfs.data.dir'':603C ''dfs.name.dir'':583C ''dfs.replication'':563C ''dfsclient.java:106'':392C ''dfsclient.java:170'':404C ''dfsclient.java:207'':398C ''distribut'':3A,38C ''distributedfi'':407C ''done'':46C,434C ''ectexcept'':319C ''error'':120C,465C ''etc'':452C ''except'':7A,304C,317C,365C ''execut'':30C,103C,703C ''fail'':314C,362C ''file'':34C,56C,106C,489C ''fileinputforma'':330C ''filesystem.java:1378'':411C ''filesystem.java:1390'':419C ''filesystem.java:196'':422C ''filesystem.java:66'':415C ''filesystem.java:95'':425C ''find'':690C ''fine'':456C ''folder'':59C ''fs.default.name'':527C ''get'':117C ''great'':712C ''gt'':397C,403C,500C,503C,506C,510C,513C,517C,520C,523C,526C,530C,533C,538C,541C,544C,556C,559C,562C,566C,569C,573C,576C,579C,582C,586C,589C,593C,596C,599C,602C,606C,609C,613C,616C,619C,631C,634C,637C,641C,644C,649C,652C,655C,658C,662C,665C,669C,672C,675C ''hadoop'':1A,16B,24C,469C ''hadoop.tmp.dir'':507C ''hdfs-site.xml'':549C ''help'':709C ''href'':89C ''info'':128C,146C,164C,182C,200C,218C,236C,254C,272C,290C ''inform'':324C,371C ''init'':396C,402C ''initi'':437C ''invoke0'':337C ''invoker.invoke'':380C ''ipc.client'':129C,147C,165C,183C,201C,219C,237C,255C,273C,291C ''issu'':686C ''jar'':33C,105C ''java'':17B,343C ''java.lang.reflect.method.invoke'':350C ''java.lang.runtimeexception'':12A,308C ''java.net.conn'':318C ''java.net.connectexception'':13A,309C,357C,366C ''job'':450C,699C ''jobconf.java:367'':428C ''jobconf.java:371'':327C ''lesystem.java:82'':408C ''local'':97C,700C ''localhost'':90C,94C,534C,645C ''localhost/127.0.0'':134C,152C,170C,188C,206C,224C,242C,260C,278C,296C ''localhost/127.0.0.1'':312C,360C ''lt'':395C,401C,498C,501C,504C,508C,511C,515C,518C,521C,524C,528C,531C,536C,539C,542C,554C,557C,560C,564C,567C,571C,574C,577C,580C,584C,587C,591C,594C,597C,600C,604C,607C,611C,614C,617C,629C,632C,635C,639C,642C,647C,650C,653C,656C,660C,663C,667C,670C,673C ''made'':486C ''main'':307C ''mainâ'':11A ''mapred-site.xml'':624C ''mapred.child.java.opts'':659C ''mapred.job.tracker'':638C ''method'':339C ''method.java:498'':351C ''mode'':39C,68C,114C ''name'':442C,446C,505C,525C,561C,581C,601C,636C,657C ''nativ'':338C ''nativemethodaccessorimpl'':342C ''need'':695C ''node'':76C,443C,447C,449C ''nofollow'':93C ''one'':691C ''org.apache.hadoop.fs.filesystem'':417C ''org.apache.hadoop.fs.filesystem.access'':413C ''org.apache.hadoop.fs.filesystem.createfilesystem'':410C ''org.apache.hadoop.fs.filesystem.get'':421C,424C ''org.apache.hadoop.hdfs.dfsclient'':394C,400C ''org.apache.hadoop.hdfs.dfsclient.createrpcnamenode'':391C ''org.apache.hadoop.hdfs.distributedfilesystem.initialize'':406C ''org.apache.hadoop.ipc.client.call'':376C ''org.apache.hadoop.ipc.client.wrapexception'':373C ''org.apache.hadoop.ipc.rpc'':379C ''org.apache.hadoop.ipc.rpc.getproxy'':388C ''org.apache.hadoop.mapred.fileinputformat.setinputpaths'':329C ''org.apache.hadoop.mapred.jobconf.getworkingdirectory'':326C,427C ''org.apache.hadoop.util.runjar.main'':353C ''p'':18C,431C,491C,547C,622C,678C ''post'':683C ''pre'':122C,496C,552C,627C ''preliminari'':48C ''previous'':682C ''program'':32C ''properti'':502C,522C,558C,578C,598C,633C,654C ''provid'':481C ''proxy0.getprotocolversion'':384C ''pseudo'':2A,37C,67C,113C ''pseudo-distribut'':36C ''pseudo-mod'':112C ''refer'':681C ''refresh'':440C ''refus'':321C,368C ''rel'':92C ''retri'':130C,148C,166C,184C,202C,220C,238C,256C,274C,292C ''rpc.java:220'':381C ''rpc.java:359'':389C ''run'':475C ''runjar.java:156'':354C ''server'':133C,151C,169C,187C,205C,223C,241C,259C,277C,295C ''set'':5A ''set-up'':4A ''sorimpl.java:43'':348C ''sourc'':386C ''speciesgraphbuilder.java:56'':334C ''speciesgraphbuilder.main'':333C ''ssh'':62C ''start'':73C,444C,704C ''state'':720C ''step'':49C ''strang'':464C ''strong'':492C,548C,623C ''sun.reflect.delegatingmethodaccessorimpl.invoke'':346C ''sun.reflect.nativemethodaccessorimpl'':336C ''sun.reflect.nativemethodaccessorimpl.invoke'':341C ''t.java:309'':331C ''tackl'':462C ''task'':705C ''test'':110C,438C ''thread'':9A,306C ''time'':140C,158C,176C,194C,212C,230C,248C,266C,284C,302C ''track'':697C ''tracker'':451C,707C ''tracker/job'':706C ''tri'':28C,101C,138C,156C,174C,192C,210C,228C,246C,264C,282C,300C,473C ''unabl'':460C ''unknown'':385C ''use'':42C,77C,468C ''valu'':512C,532C,568C,588C,608C,643C,664C ''version'':471C ''well'':65C ''went'':455C ''window'':41C,478C,693C ''would'':710C ''xml'':55C,488C ''xmx2g'':666C ''â'':10A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (226, 2, '2014-05-31 19:59:15.563', 11, NULL, NULL, '&lt;p&gt;I''ve found this link in Data Science Central with a list of free datasets: &lt;a href="http://www.datasciencecentral.com/profiles/blogs/big-data-sets-available-for-free"&gt;Big data sets available for free&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':28C ''/p'':29C ''/profiles/blogs/big-data-sets-available-for-free'':21C ''avail'':25C ''big'':22C ''central'':10C ''data'':8C,23C ''dataset'':16C ''found'':4C ''free'':15C,27C ''href'':18C ''link'':6C ''list'':13C ''p'':1C ''scienc'':9C ''set'':24C ''ve'':3C ''www.datasciencecentral.com'':20C ''www.datasciencecentral.com/profiles/blogs/big-data-sets-available-for-free'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (227, 1, '2014-06-01 10:25:51.163', 7, 3756, 'Tradeoffs between Storm and Hadoop (MapReduce)', '&lt;p&gt;Can someone kindly tell me about the trade-offs involved when choosing between Storm and MapReduce in Hadoop Cluster for data processing? Of course, aside from the obvious one, that Hadoop (processing via MapReduce in a Hadoop Cluster) is a batch processing system, and Storm is a real-time processing system.&lt;/p&gt;

&lt;p&gt;I have worked a bit with Hadoop Eco System, but I haven''t worked with Storm. After looking through a lot of presentations and articles, I still haven''t been able to find a satisfactory and comprehensive answer.&lt;/p&gt;

&lt;p&gt;Note: The term tradeoff here is not meant to compare to similar things. It is meant to represent the consequences of getting results real-time that are absent from a batch processing system. &lt;/p&gt;
', 'bigdata  efficiency  hadoop  distributed', NULL, '''/p'':65C,105C,141C ''abl'':97C ''absent'':135C ''answer'':104C ''articl'':91C ''asid'':37C ''batch'':53C,138C ''bigdata'':7B ''bit'':71C ''choos'':24C ''cluster'':31C,50C ''compar'':116C ''comprehens'':103C ''consequ'':126C ''cours'':36C ''data'':33C ''distribut'':10B ''eco'':74C ''effici'':8B ''find'':99C ''get'':128C ''hadoop'':5A,9B,30C,43C,49C,73C ''haven'':78C,94C ''involv'':22C ''kind'':14C ''look'':84C ''lot'':87C ''mapreduc'':6A,28C,46C ''meant'':114C,122C ''note'':107C ''obvious'':40C ''off'':21C ''one'':41C ''p'':11C,66C,106C ''present'':89C ''process'':34C,44C,54C,63C,139C ''real'':61C,131C ''real-tim'':60C,130C ''repres'':124C ''result'':129C ''satisfactori'':101C ''similar'':118C ''someon'':13C ''still'':93C ''storm'':3A,26C,57C,82C ''system'':55C,64C,75C,140C ''tell'':15C ''term'':109C ''thing'':119C ''time'':62C,132C ''trade'':20C ''trade-off'':19C ''tradeoff'':1A,110C ''via'':45C ''work'':69C,80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (228, 1, '2014-06-01 12:51:25.04', 6, 50, 'Cascaded Error in Apache Storm', '&lt;p&gt;Going through the presentation and material of Summingbird by Twitter, one of the reasons that is mentioned for using Storm and Hadoop clusters together in Summingbird is that processing through Storm results in cascading of error. In order to avoid this cascading of error and accumulation of it, Hadoop cluster is used to batch process the data and discard the Storm results after the same data is processed by Hadoop. &lt;/p&gt;

&lt;p&gt;What is the reasons for generation of this accumulation of error? and why is it not present in Hadoop? Since I have not worked with Storm, I do not know the reasons for it. Is it because Storm uses some approximate algorithm to process the data in order to process them in real time? or is the cause something else?&lt;/p&gt;
', 'bigdata  hadoop', NULL, '''/p'':79C,141C ''accumul'':54C,89C ''algorithm'':122C ''apach'':4A ''approxim'':121C ''avoid'':48C ''batch'':62C ''bigdata'':6B ''cascad'':1A,42C,50C ''caus'':138C ''cluster'':31C,58C ''data'':65C,74C,126C ''discard'':67C ''els'':140C ''error'':2A,44C,52C,91C ''generat'':86C ''go'':9C ''hadoop'':7B,30C,57C,78C,99C ''know'':110C ''materi'':14C ''mention'':25C ''one'':19C ''order'':46C,128C ''p'':8C,80C ''present'':12C,97C ''process'':37C,63C,76C,124C,130C ''real'':133C ''reason'':22C,84C,112C ''result'':40C,70C ''sinc'':100C ''someth'':139C ''storm'':5A,28C,39C,69C,106C,118C ''summingbird'':16C,34C ''time'':134C ''togeth'':32C ''twitter'':18C ''use'':27C,60C,119C ''work'':104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (243, 2, '2014-06-10 01:36:27.52', 2, NULL, NULL, '&lt;p&gt;A variety of methods are available to the user.   The support documentation gives walkthroughs and tips for when one or another model is most appropriate. &lt;/p&gt;

&lt;p&gt;&lt;a href="https://developers.google.com/prediction/docs/pmml-schema" rel="nofollow"&gt;This page&lt;/a&gt; shows the following learning methods:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;"AssociationModel"&lt;/li&gt;
&lt;li&gt;"ClusteringModel" &lt;/li&gt;
&lt;li&gt;"GeneralRegressionModel"&lt;/li&gt;
&lt;li&gt;"MiningModel" &lt;/li&gt;
&lt;li&gt;"NaiveBayesModel" &lt;/li&gt;
&lt;li&gt;"NeuralNetwork" &lt;/li&gt;
&lt;li&gt;"RegressionModel"&lt;/li&gt;
&lt;li&gt;"RuleSetModel" &lt;/li&gt;
&lt;li&gt;"SequenceModel" &lt;/li&gt;
&lt;li&gt;"SupportVectorMachineModel"&lt;/li&gt;
&lt;li&gt;"TextModel" &lt;/li&gt;
&lt;li&gt;"TimeSeriesModel" &lt;/li&gt;
&lt;li&gt;"TreeModel"&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;EDIT: I don''t see any specific information about the algorithms, though.  For example, does the tree model use information gain or gini index for splits? &lt;/p&gt;
', NULL, NULL, '''/a'':38C ''/li'':48C,51C,54C,57C,60C,63C,66C,69C,72C,75C,78C,81C,84C ''/p'':27C,44C,113C ''/prediction/docs/pmml-schema'':33C ''/ul'':85C ''algorithm'':97C ''anoth'':22C ''appropri'':26C ''associationmodel'':47C ''avail'':7C ''clusteringmodel'':50C ''developers.google.com'':32C ''developers.google.com/prediction/docs/pmml-schema'':31C ''document'':13C ''edit'':87C ''exampl'':100C ''follow'':41C ''gain'':107C ''generalregressionmodel'':53C ''gini'':109C ''give'':14C ''href'':30C ''index'':110C ''inform'':94C,106C ''learn'':42C ''li'':46C,49C,52C,55C,58C,61C,64C,67C,70C,73C,76C,79C,82C ''method'':5C,43C ''miningmodel'':56C ''model'':23C,104C ''naivebayesmodel'':59C ''neuralnetwork'':62C ''nofollow'':35C ''one'':20C ''p'':1C,28C,86C ''page'':37C ''regressionmodel'':65C ''rel'':34C ''rulesetmodel'':68C ''see'':91C ''sequencemodel'':71C ''show'':39C ''specif'':93C ''split'':112C ''support'':12C ''supportvectormachinemodel'':74C ''textmodel'':77C ''though'':98C ''timeseriesmodel'':80C ''tip'':17C ''tree'':103C ''treemodel'':83C ''ul'':45C ''use'':105C ''user'':10C ''varieti'':3C ''walkthrough'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (262, 1, '2014-06-10 09:26:06.593', 13, 402, 'Hierarchical Data Format. What are the advantages compared to alternative formats?', '&lt;p&gt;What are the main benefits from storing data in HDF? And what are the main data science tasks where HDF is really suitable and useful?&lt;/p&gt;
', 'data-formats  hierarchical-data-format', NULL, '''/p'':45C ''advantag'':7A ''altern'':10A ''benefit'':24C ''compar'':8A ''data'':2A,13B,17B,27C,35C ''data-format'':12B ''format'':3A,11A,14B,18B ''hdf'':29C,39C ''hierarch'':1A,16B ''hierarchical-data-format'':15B ''main'':23C,34C ''p'':19C ''realli'':41C ''scienc'':36C ''store'':26C ''suitabl'':42C ''task'':37C ''use'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8945, 2, '2015-11-20 19:30:24.2', 6, NULL, NULL, '&lt;p&gt;PyData - talks about Python Data tools&lt;br&gt;
Link: &lt;a href="http://pydata.org/events/" rel="nofollow"&gt;http://pydata.org/events/&lt;/a&gt;  &lt;/p&gt;

&lt;p&gt;There is one PyData conference on the east coast and one on the west coast each year.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;NIPS - Neural Information Processing Systems (NIPS)&lt;br&gt;
Link: &lt;a href="https://nips.cc/" rel="nofollow"&gt;https://nips.cc/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This is one of the hardest / most prestigious academic Machine Learning conferences to get an abstract / poster accepted.    &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;The 5th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (IEEE IPDPS 2016)&lt;br&gt;
Link: &lt;a href="http://parlearning.ecs.fullerton.edu/" rel="nofollow"&gt;http://parlearning.ecs.fullerton.edu/&lt;/a&gt;  &lt;/p&gt;

&lt;p&gt;This one is also an academic conference with paper submission.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Note:
I am not sure if you want academic or nor academic conferences (have conference proceedings / papers associated with the conference).
Some conferences are not about new data science methodologies  but the tools and libraries (e.g. PyData) that implement existing methodologies.
Also, data science is very broad and includes Stat, Machine Learning and data warehousing / mining etc.&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':110C ''/&lt;/a&gt;&lt;/p&gt;'':57C ''/events/'':14C ''/events/&lt;/a&gt;'':19C ''/p'':20C,39C,77C,111C,123C,183C ''2016'':100C ''5th'':81C ''abstract'':74C ''academ'':67C,118C,134C,137C ''accept'':76C ''also'':116C,167C ''analyt'':97C ''associ'':143C ''big'':95C ''br'':8C,48C,101C ''broad'':172C ''coast'':30C,36C ''comput'':88C ''confer'':26C,70C,119C,138C,140C,146C,148C ''data'':6C,96C,153C,168C,179C ''distribut'':87C ''e.g'':161C ''east'':29C ''etc'':182C ''exist'':165C ''get'':72C ''hardest'':64C ''hr'':40C,78C,124C ''href'':11C,51C,104C ''ieee'':98C ''implement'':164C ''includ'':174C ''inform'':44C ''intern'':82C ''ipdp'':99C ''larg'':90C ''learn'':69C,93C,177C ''librari'':160C ''link'':9C,49C,102C ''machin'':68C,92C,176C ''methodolog'':155C,166C ''mine'':181C ''neural'':43C ''new'':152C ''nip'':42C,47C ''nips.cc'':52C,56C ''nips.cc/&lt;/a&gt;&lt;/p&gt;'':55C ''nofollow'':16C,54C,107C ''note'':126C ''one'':24C,32C,61C,114C ''p'':1C,21C,41C,58C,79C,112C,125C ''paper'':121C,142C ''parallel'':85C ''parlearning.ecs.fullerton.edu'':105C,109C ''parlearning.ecs.fullerton.edu/&lt;/a&gt;'':108C ''poster'':75C ''prestigi'':66C ''proceed'':141C ''process'':45C ''pydata'':2C,25C,162C ''pydata.org'':13C,18C ''pydata.org/events/'':12C ''pydata.org/events/&lt;/a&gt;'':17C ''python'':5C ''rel'':15C,53C,106C ''scale'':91C ''scienc'':154C,169C ''stat'':175C ''submiss'':122C ''sure'':130C ''system'':46C ''talk'':3C ''tool'':7C,158C ''want'':133C ''wareh'':180C ''west'':35C ''workshop'':83C ''year'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (232, 2, '2014-06-06 14:55:18.867', 3, NULL, NULL, '&lt;p&gt;There are a few ways to achieve your "master confusion matrix".&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Sum all the confusion matrices together:  Like you suggested, summing this results in a confusion matrix.  The problem with this is you can not interpret totals.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Average the entries.  This method is the same as number one, but you divide each entry by the number of trials (~400 in your case).  This would be my preferred method because then you can actually translate each category to a (mean) +- (an error measurement) and actually see which categories are the most volatile or stable.  Careful with interpreting this ''error measurement'' though.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Report a problem specific measurement of the confusion numbers.  For example, if your numbers have outliers, medians would preferred over means.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;There are other statistics that are possible to report as well.  You can redo the method to keep track of individual classifications.  Then we can say other important stats like ''% of classifications that stay the same and are accurate'', etc...&lt;/p&gt;
', NULL, NULL, '''/li'':44C,111C,136C ''/ol'':137C ''/p'':13C,43C,110C,135C,179C ''400'':68C ''accur'':177C ''achiev'':8C ''actual'':82C,93C ''averag'':47C ''care'':103C ''case'':71C ''categori'':85C,96C ''classif'':160C,170C ''confus'':11C,20C,31C,121C ''divid'':60C ''entri'':49C,62C ''error'':90C,107C ''etc'':178C ''exampl'':124C ''import'':166C ''individu'':159C ''interpret'':41C,105C ''keep'':156C ''li'':15C,45C,112C ''like'':23C,168C ''master'':10C ''matric'':21C ''matrix'':12C,32C ''mean'':88C,134C ''measur'':91C,108C,118C ''median'':130C ''method'':51C,77C,154C ''number'':56C,65C,122C,127C ''ol'':14C ''one'':57C ''outlier'':129C ''p'':1C,16C,46C,113C,138C ''possibl'':145C ''prefer'':76C,132C ''problem'':34C,116C ''redo'':152C ''report'':114C,147C ''result'':28C ''say'':164C ''see'':94C ''specif'':117C ''stabl'':102C ''stat'':167C ''statist'':142C ''stay'':172C ''suggest'':25C ''sum'':17C,26C ''though'':109C ''togeth'':22C ''total'':42C ''track'':157C ''translat'':83C ''trial'':67C ''volatil'':100C ''way'':6C ''well'':149C ''would'':73C,131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (233, 2, '2014-06-08 07:03:43.71', 2, NULL, NULL, '&lt;p&gt;To complete Damien''s answer, an example of dimensionality reduction in NLP is a &lt;a href="http://en.wikipedia.org/wiki/Topic_model" rel="nofollow"&gt;topic model&lt;/a&gt;, where you represent the document by a vector indicating the weights of its constituent topics.&lt;/p&gt;
', NULL, NULL, '''/a'':25C ''/p'':41C ''/wiki/topic_model'':20C ''answer'':6C ''complet'':3C ''constitu'':39C ''damien'':4C ''dimension'':10C ''document'':30C ''en.wikipedia.org'':19C ''en.wikipedia.org/wiki/topic_model'':18C ''exampl'':8C ''href'':17C ''indic'':34C ''model'':24C ''nlp'':13C ''nofollow'':22C ''p'':1C ''reduct'':11C ''rel'':21C ''repres'':28C ''topic'':23C,40C ''vector'':33C ''weight'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (234, 1, '2014-06-09 04:43:03.497', 4, 285, 'Data science Ph.D. program, what do you think?', '&lt;p&gt;As Yann LeCun &lt;a href="http://www.reddit.com/r/MachineLearning/comments/25lnbt/ama_yann_lecun/chisdw1" rel="nofollow"&gt;mentioned&lt;/a&gt;, a number of PhD programs in data science will be popping up in the next few years.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://datascience.nyu.edu/academics/programs/" rel="nofollow"&gt;NYU&lt;/a&gt; already have one, where Prof.LeCun is at right now.&lt;/p&gt;

&lt;p&gt;A statistics or cs PhD in machine learning is probably more rigorous than a data science one.  Is data science PhD for the less mathy people like myself? &lt;/p&gt;

&lt;p&gt;Are these cash cow programs?&lt;/p&gt;

&lt;p&gt;There is a huge industry demand for big data, but what is the academic value of these programs, as you probably can''t be a professor or publish any paper.&lt;/p&gt;
', 'knowledge-base', NULL, '''/a'':24C,52C ''/academics/programs/'':48C ''/p'':42C,62C,92C,99C,131C ''/r/machinelearning/comments/25lnbt/ama_yann_lecun/chisdw1'':20C ''academ'':114C ''alreadi'':53C ''base'':11B ''big'':108C ''cash'':96C ''cow'':97C ''cs'':67C ''data'':1A,31C,78C,82C,109C ''datascience.nyu.edu'':47C ''datascience.nyu.edu/academics/programs/'':46C ''demand'':106C ''href'':17C,45C ''huge'':104C ''industri'':105C ''knowledg'':10B ''knowledge-bas'':9B ''learn'':71C ''lecun'':15C ''less'':87C ''like'':90C ''machin'':70C ''mathi'':88C ''mention'':23C ''next'':39C ''nofollow'':22C,50C ''number'':26C ''nyu'':51C ''one'':55C,80C ''p'':12C,43C,63C,93C,100C ''paper'':130C ''peopl'':89C ''ph.d'':3A ''phd'':28C,68C,84C ''pop'':35C ''probabl'':73C,121C ''prof.lecun'':57C ''professor'':126C ''program'':4A,29C,98C,118C ''publish'':128C ''rel'':21C,49C ''right'':60C ''rigor'':75C ''scienc'':2A,32C,79C,83C ''statist'':65C ''think'':8A ''valu'':115C ''www.reddit.com'':19C ''www.reddit.com/r/machinelearning/comments/25lnbt/ama_yann_lecun/chisdw1'':18C ''yann'':14C ''year'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (235, 1, '2014-06-09 08:34:29.337', 0, 573, 'Are there any python based data visualization toolkit?', '&lt;p&gt;Data visualization is an important sub-field in data science and python programmers would need to have available toolkits for them.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is there a Python API to Tableau?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Are there any Python-based data visualization toolkits?&lt;/strong&gt;&lt;/p&gt;
', 'python  visualization', NULL, '''/p'':34C,45C,58C ''/strong'':44C,57C ''api'':41C ''avail'':30C ''base'':5A,53C ''data'':6A,12C,21C,54C ''field'':19C ''import'':16C ''need'':27C ''p'':11C,35C,46C ''programm'':25C ''python'':4A,9B,24C,40C,52C ''python-bas'':51C ''scienc'':22C ''strong'':36C,47C ''sub'':18C ''sub-field'':17C ''tableau'':43C ''toolkit'':8A,31C,56C ''visual'':7A,10B,13C,55C ''would'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (236, 2, '2014-06-09 18:02:00.613', 1, NULL, NULL, '&lt;p&gt;No-one knows since no-one''s completed one of these PhD programs yet! However, I would look at the syllabus and the teachers to base my decision. It all depends on what you want to do; industry or academia?&lt;/p&gt;
', NULL, NULL, '''/p'':44C ''academia'':43C ''base'':29C ''complet'':11C ''decis'':31C ''depend'':34C ''howev'':18C ''industri'':41C ''know'':5C ''look'':21C ''no-on'':2C,7C ''one'':4C,9C,12C ''p'':1C ''phd'':15C ''program'':16C ''sinc'':6C ''syllabus'':24C ''teacher'':27C ''want'':38C ''would'':20C ''yet'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (237, 2, '2014-06-09 19:52:41.847', 7, NULL, NULL, '&lt;p&gt;There is a Tablaeu API and you can use Python to use it, but maybe not in the sense that you think. There is a Data Extract API that you could use to import your data into Python and do your visualizations there, so I do not know if this is going to answer your question entirely.&lt;/p&gt;

&lt;p&gt;As in the first comment you can use Matplotlib from &lt;a href="http://www.matplotlib.org"&gt;Matplotlib website&lt;/a&gt;, or you could install Canopy from Enthought which has it available, there is also Pandas, which you could also use for data analysis and some visualizations. There is also a package called &lt;code&gt;ggplot&lt;/code&gt; which is used in &lt;code&gt;R&lt;/code&gt; alot, but is also made for Python, which you can find here &lt;a href="https://pypi.python.org/pypi/ggplot"&gt;ggplot for python&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The Tableau data extract API and some information about it can be found &lt;a href="http://www.tableausoftware.com/new-features/data-engine-api-0"&gt;at this link&lt;/a&gt;. There are a few web sources that I found concerning it using duckduckgo &lt;a href="https://duckduckgo.com/?q=tableau%20PYTHON%20API&amp;amp;kp=1&amp;amp;kd=-1"&gt;at this link&lt;/a&gt;.
Here are some samples:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.interworks.com/blogs/bbickell/2012/12/06/introducing-python-tableau-data-extract-api-csv-extract-example"&gt;Link 1&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://ryrobes.com/python/building-tableau-data-extract-files-with-python-in-tableau-8-sample-usage/"&gt;Link 2&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://nbviewer.ipython.org/github/Btibert3/tableau-r/blob/master/Python-R-Tableau-Predictive-Modeling.ipynb"&gt;Link 3&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;As far as an API like matplotlib, I cannot say for certain that one exists. Hopefully this gives some sort of reference to help answer your question.&lt;/p&gt;

&lt;p&gt;Also to help avoid closure flags and downvotes you should try and show some of what you have tried to do or find, this makes for a better question and helps to illicit responses.&lt;/p&gt;
', NULL, NULL, '''/?q=tableau%20python%20api&amp;amp;kp=1&amp;amp;kd=-1'':181C ''/a'':76C,139C,163C,185C,199C,209C,219C ''/blogs/bbickell/2012/12/06/introducing-python-tableau-data-extract-api-csv-extract-example'':196C ''/code'':111C,118C ''/github/btibert3/tableau-r/blob/master/python-r-tableau-predictive-modeling.ipynb'':216C ''/new-features/data-engine-api-0'':159C ''/p'':59C,140C,190C,200C,210C,220C,249C,285C ''/pypi/ggplot'':135C ''/python/building-tableau-data-extract-files-with-python-in-tableau-8-sample-usage/'':206C ''1'':198C ''2'':208C ''3'':218C ''alot'':119C ''also'':90C,95C,105C,122C,251C ''analysi'':99C ''answer'':55C,246C ''api'':6C,29C,146C,226C ''avail'':87C ''avoid'':254C ''better'':278C ''call'':108C ''cannot'':230C ''canopi'':81C ''certain'':233C ''closur'':255C ''code'':109C,116C ''comment'':65C ''concern'':173C ''could'':32C,79C,94C ''data'':27C,37C,98C,144C ''downvot'':258C ''duckduckgo'':176C ''duckduckgo.com'':180C ''duckduckgo.com/?q=tableau%20python%20api&amp;amp;kp=1&amp;amp;kd=-1'':179C ''enthought'':83C ''entir'':58C ''exist'':236C ''extract'':28C,145C ''far'':223C ''find'':129C,273C ''first'':64C ''flag'':256C ''found'':154C,172C ''ggplot'':110C,136C ''give'':239C ''go'':53C ''help'':245C,253C,281C ''hope'':237C ''href'':72C,132C,156C,178C,193C,203C,213C ''illicit'':283C ''import'':35C ''inform'':149C ''instal'':80C ''know'':49C ''like'':227C ''link'':162C,184C,197C,207C,217C ''made'':123C ''make'':275C ''matplotlib'':69C,74C,228C ''mayb'':16C ''nbviewer.ipython.org'':215C ''nbviewer.ipython.org/github/btibert3/tableau-r/blob/master/python-r-tableau-predictive-modeling.ipynb'':214C ''one'':235C ''p'':1C,60C,141C,191C,201C,211C,221C,250C ''packag'':107C ''panda'':91C ''pypi.python.org'':134C ''pypi.python.org/pypi/ggplot'':133C ''python'':11C,39C,125C,138C ''question'':57C,248C,279C ''r'':117C ''refer'':243C ''respons'':284C ''ryrobes.com'':205C ''ryrobes.com/python/building-tableau-data-extract-files-with-python-in-tableau-8-sample-usage/'':204C ''sampl'':189C ''say'':231C ''sens'':20C ''show'':263C ''sort'':241C ''sourc'':169C ''tablaeu'':5C ''tableau'':143C ''think'':23C ''tri'':261C,269C ''use'':10C,13C,33C,68C,96C,114C,175C ''visual'':43C,102C ''web'':168C ''websit'':75C ''www.interworks.com'':195C ''www.interworks.com/blogs/bbickell/2012/12/06/introducing-python-tableau-data-extract-api-csv-extract-example'':194C ''www.matplotlib.org'':73C ''www.tableausoftware.com'':158C ''www.tableausoftware.com/new-features/data-engine-api-0'':157C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (238, 2, '2014-06-09 21:36:44.297', 1, NULL, NULL, '&lt;p&gt;I think this question assumes a false premise. As a student at NYU, I only know of a Masters in Data Science. You linked to a page that confirms this.&lt;/p&gt;

&lt;p&gt;It''s hard to gauge the benefit of a program that doesn''t exist yet.&lt;/p&gt;
', NULL, NULL, '''/p'':32C,49C ''assum'':6C ''benefit'':40C ''confirm'':30C ''data'':22C ''doesn'':45C ''exist'':47C ''fals'':8C ''gaug'':38C ''hard'':36C ''know'':17C ''link'':25C ''master'':20C ''nyu'':14C ''p'':1C,33C ''page'':28C ''premis'':9C ''program'':43C ''question'':5C ''scienc'':23C ''student'':12C ''think'':3C ''yet'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (242, 2, '2014-06-09 21:57:30.24', 10, NULL, NULL, '&lt;p&gt;&lt;strong&gt;MapReduce&lt;/strong&gt;: A fault tolerant distributed computational framework. MapReduce allows you to operate over huge amounts of data- with a lot of work put in to prevent failure due to hardware. MapReduce is a poor choice for computing results on the fly because it is slow. (A typical MapReduce job takes on the order of minutes or hours, not microseconds)&lt;/p&gt;

&lt;p&gt;A MapReduce job takes a file (or some data store) as an input and writes a file of results. If you want these results available to an application, it is your responsibility to put this data in a place that is accessible. This is likely slow, and there will be a lag between the values you can display, and the values that represent your system in its current state.&lt;/p&gt;

&lt;p&gt;An important distinction to make when considering using MapReduce in building realtime systems is that of training your model, and applying your model. If you think your model parameters do not change quickly, you can fit them with MapReduce, and then have a mechanism for accessing these pre-fit parameters when you want to apply your model.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Storm&lt;/strong&gt;: A real-time, streaming computational system. Storm is online framework, meaning, in this sense, a service that interacts with a running application. In contrast to MapReduce, it receives small pieces of data (not a whole file) as they are processed in your application. You define a DAG of operations to perform on the data. A common and simple use case for Storm is tracking counters, and using that information to populate a real-time dashboard.&lt;/p&gt;

&lt;p&gt;Storm doesn''t have anything (necessarily) to do with persisting your data. Here, streaming is another way to say keeping the information you care about and throwing the rest away. In reality, you probably have a persistence layer in your application that has already recorded the data, and so this a good and justified separation of concerns.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;If you want to know more...&lt;/strong&gt;
If you would like to learn more about real-time systems that that fit parameters with MR and apply the models a different way &lt;a href="http://www.slideshare.net/cloudera/hbasecon-2013-24063525"&gt;here are slides for a talk I gave on building real-time recommendation engines on HBase.&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;An excellent paper that marries real-time counting and persistence in an interesting way is &lt;a href="http://dl.acm.org/citation.cfm?id=1242610"&gt;Google News Personalization: Scalable Online Collaborative Filtering&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;Another interesting marriage of MR and Storm is &lt;a href="https://github.com/twitter/summingbird/wiki"&gt;SummingBird&lt;/a&gt;. Summingbird allows you to define data analysis operations that can be applied via Storm or MR.&lt;/p&gt;
', NULL, NULL, '''/a'':392C,423C,440C ''/citation.cfm?id=1242610'':415C ''/cloudera/hbasecon-2013-24063525'':374C ''/p'':63C,134C,194C,276C,335C,393C,424C,457C ''/strong'':4C,198C,344C ''/twitter/summingbird/wiki'':438C ''access'':106C,181C ''allow'':12C,442C ''alreadi'':321C ''amount'':18C ''analysi'':447C ''anoth'':293C,426C ''anyth'':282C ''appli'':156C,191C,364C,452C ''applic'':92C,221C,242C,318C ''avail'':89C ''away'':307C ''build'':146C,384C ''care'':301C ''case'':259C ''chang'':167C ''choic'':38C ''collabor'':421C ''common'':255C ''comput'':9C,40C,204C ''concern'':334C ''consid'':142C ''contrast'':223C ''count'':403C ''counter'':264C ''current'':132C ''dag'':246C ''dashboard'':275C ''data'':20C,73C,100C,231C,253C,289C,324C,446C ''defin'':244C,445C ''differ'':368C ''display'':122C ''distinct'':138C ''distribut'':8C ''dl.acm.org'':414C ''dl.acm.org/citation.cfm?id=1242610'':413C ''doesn'':279C ''due'':31C ''engin'':389C ''excel'':396C ''failur'':30C ''fault'':6C ''file'':70C,81C,235C ''filter'':422C ''fit'':171C,185C,359C ''fli'':44C ''framework'':10C,209C ''gave'':382C ''github.com'':437C ''github.com/twitter/summingbird/wiki'':436C ''good'':329C ''googl'':416C ''hardwar'':33C ''hbase'':391C ''hour'':60C ''href'':371C,412C,435C ''huge'':17C ''import'':137C ''inform'':268C,299C ''input'':77C ''interact'':217C ''interest'':408C,427C ''job'':52C,67C ''justifi'':331C ''keep'':297C ''know'':342C ''lag'':116C ''layer'':315C ''learn'':350C ''like'':109C,348C ''lot'':23C ''make'':140C ''mapreduc'':3C,11C,34C,51C,66C,144C,174C,225C ''marri'':399C ''marriag'':428C ''mean'':210C ''mechan'':179C ''microsecond'':62C ''minut'':58C ''model'':154C,158C,163C,193C,366C ''mr'':362C,430C,456C ''necessarili'':283C ''news'':417C ''onlin'':208C,420C ''oper'':15C,248C,448C ''order'':56C ''p'':1C,64C,135C,195C,277C,336C,394C,425C ''paper'':397C ''paramet'':164C,186C,360C ''perform'':250C ''persist'':287C,314C,405C ''person'':418C ''piec'':229C ''place'':103C ''poor'':37C ''popul'':270C ''pre'':184C ''pre-fit'':183C ''prevent'':29C ''probabl'':311C ''process'':239C ''put'':26C,98C ''quick'':168C ''real'':201C,273C,354C,386C,401C ''real-tim'':200C,272C,353C,385C,400C ''realiti'':309C ''realtim'':147C ''receiv'':227C ''recommend'':388C ''record'':322C ''repres'':127C ''respons'':96C ''rest'':306C ''result'':41C,83C,88C ''run'':220C ''say'':296C ''scalabl'':419C ''sens'':213C ''separ'':332C ''servic'':215C ''simpl'':257C ''slide'':377C ''slow'':48C,110C ''small'':228C ''state'':133C ''store'':74C ''storm'':197C,206C,261C,278C,432C,454C ''stream'':203C,291C ''strong'':2C,196C,337C ''summingbird'':439C,441C ''system'':129C,148C,205C,356C ''take'':53C,68C ''talk'':380C ''think'':161C ''throw'':304C ''time'':202C,274C,355C,387C,402C ''toler'':7C ''track'':263C ''train'':152C ''typic'':50C ''use'':143C,258C,266C ''valu'':119C,125C ''via'':453C ''want'':86C,189C,340C ''way'':294C,369C,409C ''whole'':234C ''work'':25C ''would'':347C ''write'':79C ''www.slideshare.net'':373C ''www.slideshare.net/cloudera/hbasecon-2013-24063525'':372C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (274, 2, '2014-06-10 12:57:04.307', 7, NULL, NULL, '&lt;p&gt;One benefit is wide support - C, Java, Perl, Python, and R all have HDF5 bindings.&lt;/p&gt;

&lt;p&gt;Another benefit is speed.  I haven''t ever seen it benchmarked, but HDF is supposed to be faster than SQL databases.&lt;/p&gt;

&lt;p&gt;I understand that it is very good when used with both large sets of scientific data and time series data - network monitoring, usage tracking, etc.&lt;/p&gt;

&lt;p&gt;I don''t believe there is a size limitation for HDF files (although OS limits would still apply.&lt;/p&gt;
', NULL, NULL, '''/p'':17C,40C,67C,87C ''although'':81C ''anoth'':19C ''appli'':86C ''believ'':72C ''benchmark'':29C ''benefit'':3C,20C ''bind'':16C ''c'':7C ''data'':57C,61C ''databas'':39C ''etc'':66C ''ever'':26C ''faster'':36C ''file'':80C ''good'':48C ''haven'':24C ''hdf'':31C,79C ''hdf5'':15C ''java'':8C ''larg'':53C ''limit'':77C,83C ''monitor'':63C ''network'':62C ''one'':2C ''os'':82C ''p'':1C,18C,41C,68C ''perl'':9C ''python'':10C ''r'':12C ''scientif'':56C ''seen'':27C ''seri'':60C ''set'':54C ''size'':76C ''speed'':22C ''sql'':38C ''still'':85C ''support'':6C ''suppos'':33C ''time'':59C ''track'':65C ''understand'':43C ''usag'':64C ''use'':50C ''wide'':5C ''would'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1046, 2, '2014-08-26 21:51:07.247', 1, NULL, NULL, '&lt;p&gt;Try &lt;a href="http://deeplearning4j.org/word2vec.html" rel="nofollow"&gt;http://deeplearning4j.org/word2vec.html&lt;/a&gt;. This has an implementation of Word2Vec used instead of Bag of Words for NER and other NLP tasks.&lt;/p&gt;
', NULL, NULL, '''/p'':31C ''/word2vec.html'':7C ''/word2vec.html&lt;/a&gt;.'':12C ''bag'':22C ''deeplearning4j.org'':6C,11C ''deeplearning4j.org/word2vec.html'':5C ''deeplearning4j.org/word2vec.html&lt;/a&gt;.'':10C ''href'':4C ''implement'':16C ''instead'':20C ''ner'':26C ''nlp'':29C ''nofollow'':9C ''p'':1C ''rel'':8C ''task'':30C ''tri'':2C ''use'':19C ''word'':24C ''word2vec'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (244, 2, '2014-06-10 01:40:23.263', 3, NULL, NULL, '&lt;p&gt;Coming from a programmers perspective, frameworks rarely target performance as the highest priority.  If your library is going to be widely leveraged the things people are likely to value most are ease of use, flexibility, and reliability.&lt;/p&gt;

&lt;p&gt;Performance is generally valued in secondary competitive libraries.  "X library is better because it''s faster."  Even then very frequently those libraries will trade off the most optimal solution for one that can be widely leveraged.  &lt;/p&gt;

&lt;p&gt;By using any framework you are inherently taking a risk that a faster solution exists.  I might go so far as to say that a faster solution almost always exists.&lt;/p&gt;

&lt;p&gt;Writing something yourself is not a guarantee of performance, but if you know what you are doing and have a fairly limited set of requirements it can help.&lt;/p&gt;

&lt;p&gt;An example might be JSON parsing.  There are a hundred libraries out there for a variety of languages that will turn JSON into a referable object and vice versa.  I know of one implementation that does it all in CPU registers.  It''s measurably faster than all other parsers, but it is also very limited and that limitation will vary based on what CPU you are working with.&lt;/p&gt;

&lt;p&gt;Is the task of building a high-performant environment specific JSON parser a good idea?  I would leverage a respected library 99 times out of 100.  In that one separate instance a few extra CPU cycles multiplied by a million iterations would make the development time worth it.&lt;/p&gt;
', NULL, NULL, '''/p'':39C,77C,109C,139C,209C,260C ''100'':237C ''99'':233C ''almost'':106C ''also'':193C ''alway'':107C ''base'':201C ''better'':52C ''build'':215C ''come'':2C ''competit'':47C ''cpu'':180C,204C,246C ''cycl'':247C ''develop'':256C ''eas'':33C ''environ'':220C ''even'':57C ''exampl'':142C ''exist'':93C,108C ''extra'':245C ''fair'':131C ''far'':98C ''faster'':56C,91C,104C,185C ''flexibl'':36C ''framework'':7C,82C ''frequent'':60C ''general'':43C ''go'':19C,96C ''good'':225C ''guarante'':117C ''help'':138C ''high'':218C ''high-perform'':217C ''highest'':13C ''hundr'':150C ''idea'':226C ''implement'':174C ''inher'':85C ''instanc'':242C ''iter'':252C ''json'':145C,162C,222C ''know'':123C,171C ''languag'':158C ''leverag'':23C,76C,229C ''librari'':17C,48C,50C,62C,151C,232C ''like'':28C ''limit'':132C,195C,198C ''make'':254C ''measur'':184C ''might'':95C,143C ''million'':251C ''multipli'':248C ''object'':166C ''one'':71C,173C,240C ''optim'':68C ''p'':1C,40C,78C,110C,140C,210C ''pars'':146C ''parser'':189C,223C ''peopl'':26C ''perform'':10C,41C,119C,219C ''perspect'':6C ''prioriti'':14C ''programm'':5C ''rare'':8C ''refer'':165C ''regist'':181C ''reliabl'':38C ''requir'':135C ''respect'':231C ''risk'':88C ''say'':101C ''secondari'':46C ''separ'':241C ''set'':133C ''solut'':69C,92C,105C ''someth'':112C ''specif'':221C ''take'':86C ''target'':9C ''task'':213C ''thing'':25C ''time'':234C,257C ''trade'':64C ''turn'':161C ''use'':35C,80C ''valu'':30C,44C ''vari'':200C ''varieti'':156C ''versa'':169C ''vice'':168C ''wide'':22C,75C ''work'':207C ''worth'':258C ''would'':228C,253C ''write'':111C ''x'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (245, 2, '2014-06-10 01:54:40.647', 2, NULL, NULL, '&lt;p&gt;Computer Science is itself a multi-disciplinary field which has varying requirements among universities.  For example, Stockholm University does not require any math above algebra for its CS programs (some courses may have higher requirements, but not often).  &lt;/p&gt;

&lt;p&gt;I am not sure what you mean by a machine learning program being more rigorous.  They are just two different programs.  Data Science would likely take a broader view and focus on application and management (business courses are maybe on offer?).  The research could be rigorous in its own right, but it definitely won''t be tailored to someone who wants to optimize new algorithms or solve the low-level problems of machine learning.  &lt;/p&gt;

&lt;p&gt;I don''t see the Ph.D program listed yet in the link you provided.  Will you please follow up here if you get more specific information?&lt;/p&gt;
', NULL, NULL, '''/p'':41C,118C,146C ''algebra'':27C ''algorithm'':107C ''among'':15C ''applic'':75C ''broader'':70C ''busi'':78C ''comput'':2C ''could'':86C ''cours'':33C,79C ''cs'':30C ''data'':64C ''definit'':95C ''differ'':62C ''disciplinari'':9C ''exampl'':18C ''field'':10C ''focus'':73C ''follow'':137C ''get'':142C ''higher'':36C ''inform'':145C ''learn'':53C,117C ''level'':113C ''like'':67C ''link'':131C ''list'':127C ''low'':112C ''low-level'':111C ''machin'':52C,116C ''manag'':77C ''math'':25C ''may'':34C ''mayb'':81C ''mean'':49C ''multi'':8C ''multi-disciplinari'':7C ''new'':106C ''offer'':83C ''often'':40C ''optim'':105C ''p'':1C,42C,119C ''ph.d'':125C ''pleas'':136C ''problem'':114C ''program'':31C,54C,63C,126C ''provid'':133C ''requir'':14C,23C,37C ''research'':85C ''right'':92C ''rigor'':57C,88C ''scienc'':3C,65C ''see'':123C ''solv'':109C ''someon'':101C ''specif'':144C ''stockholm'':19C ''sure'':46C ''tailor'':99C ''take'':68C ''two'':61C ''univers'':16C,20C ''vari'':13C ''view'':71C ''want'':103C ''won'':96C ''would'':66C ''yet'':128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2509, 2, '2014-11-20 16:28:48.343', 2, NULL, NULL, '&lt;p&gt;How about considering each string as a process trace and applying alpha-algorithm?
That would give you a graph and nodes with a big number out-edges will most likely point to values.&lt;/p&gt;

&lt;p&gt;You can mark these nodes and for every new string parse/traverse the graph until you reach those areas.&lt;/p&gt;
', NULL, NULL, '''/p'':37C,57C ''algorithm'':15C ''alpha'':14C ''alpha-algorithm'':13C ''appli'':12C ''area'':56C ''big'':26C ''consid'':4C ''edg'':30C ''everi'':46C ''give'':18C ''graph'':21C,51C ''like'':33C ''mark'':41C ''new'':47C ''node'':23C,43C ''number'':27C ''out-edg'':28C ''p'':1C,38C ''parse/traverse'':49C ''point'':34C ''process'':9C ''reach'':54C ''string'':6C,48C ''trace'':10C ''valu'':36C ''would'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (246, 2, '2014-06-10 02:42:02.05', 2, NULL, NULL, '&lt;p&gt;There will definitely be a translation task at the end if you prototype using just mongo.&lt;/p&gt;

&lt;p&gt;When you run a MapReduce task on mongodb, it has the data source and structure built in.  When you eventually convert to hadoop, your data structures might not look the same.  You could leverage the mongodb-hadoop connector to access mongo data directly from within hadoop, but that won''t be quite as straightforward as you might think.  The time to figure out how exactly to do the conversion most optimally will be easier to justify once you have a prototype in place, IMO.&lt;/p&gt;

&lt;p&gt;While you will need to translate mapreduce functions, the basic pseudocode should apply well to both systems.  You won''t find anything that can be done in MongoDB that can''t be done using Java or that is significantly more complex to do with Java.&lt;/p&gt;
', NULL, NULL, '''/p'':18C,105C,152C ''access'':60C ''anyth'':128C ''appli'':119C ''basic'':116C ''built'':35C ''complex'':147C ''connector'':58C ''convers'':89C ''convert'':40C ''could'':52C ''data'':31C,44C,62C ''definit'':4C ''direct'':63C ''done'':132C,139C ''easier'':94C ''end'':11C ''eventu'':39C ''exact'':85C ''figur'':82C ''find'':127C ''function'':114C ''hadoop'':42C,57C,66C ''imo'':104C ''java'':141C,151C ''justifi'':96C ''leverag'':53C ''look'':48C ''mapreduc'':24C,113C ''might'':46C,77C ''mongo'':17C,61C ''mongodb'':27C,56C,134C ''mongodb-hadoop'':55C ''need'':110C ''optim'':91C ''p'':1C,19C,106C ''place'':103C ''prototyp'':14C,101C ''pseudocod'':117C ''quit'':72C ''run'':22C ''signific'':145C ''sourc'':32C ''straightforward'':74C ''structur'':34C,45C ''system'':123C ''task'':8C,25C ''think'':78C ''time'':80C ''translat'':7C,112C ''use'':15C,140C ''well'':120C ''within'':65C ''won'':69C,125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (247, 2, '2014-06-10 02:50:51.153', 7, NULL, NULL, '&lt;p&gt;&lt;a href="http://bokeh.pydata.org/"&gt;Bokeh&lt;/a&gt; is an excellent data visualization library for python.
&lt;a href="http://www.cityinabottle.org/nodebox/"&gt;NodeBox&lt;/a&gt; is another that comes to mind.&lt;/p&gt;
', NULL, NULL, '''/a'':6C,21C ''/nodebox/'':19C ''/p'':28C ''anoth'':23C ''bokeh'':5C ''bokeh.pydata.org'':4C ''come'':25C ''data'':10C ''excel'':9C ''href'':3C,16C ''librari'':12C ''mind'':27C ''nodebox'':20C ''p'':1C ''python'':14C ''visual'':11C ''www.cityinabottle.org'':18C ''www.cityinabottle.org/nodebox/'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (248, 2, '2014-06-10 03:21:56.473', 2, NULL, NULL, '&lt;p&gt;A cash cow program?  No.  PhD programs are never cash cows.&lt;/p&gt;

&lt;p&gt;I don''t know why you couldn''t be a professor with a PhD in data science.  Rarely does a professor of a given course have to have a specific degree in order to teach it.&lt;/p&gt;

&lt;p&gt;As far as publishing goes, there are any number of related journals that would accept papers from somebody on topics that would be covered by the topic of Data Science.&lt;/p&gt;

&lt;p&gt;When I went to college, MIS, Computer Engineering, and Computer Science were new subjects.  Most of the people in my graduating class for Computer Science couldn''t program anything significant at graduation.  Within a few years, CS programs around the country matured significantly.&lt;/p&gt;

&lt;p&gt;When you are part of a new program, sometimes it''s possible to help define what it is that''s required for graduation.  Being a part of that puts you in rare company for that field.&lt;/p&gt;

&lt;p&gt;As far as mathematical rigor is concerned, I would expect Data Science to leverage a heavy dose of mathematically based material.  I wouldn''t expect anything particularly new - statistics, calculus, etc. should have been covered in undergrad.  Masters and PhD programs should be more about applying that knowledge and not so much about learning it.&lt;/p&gt;
', NULL, NULL, '''/p'':13C,51C,83C,128C,166C,223C ''accept'':67C ''anyth'':113C,193C ''appli'':213C ''around'':123C ''base'':187C ''calculus'':197C ''cash'':3C,11C ''class'':106C ''colleg'':89C ''compani'':162C ''comput'':91C,94C,108C ''concern'':174C ''couldn'':21C,110C ''countri'':125C ''cours'':39C ''cover'':76C,202C ''cow'':4C,12C ''cs'':121C ''data'':30C,81C,178C ''defin'':144C ''degre'':45C ''dose'':184C ''engin'':92C ''etc'':198C ''expect'':177C,192C ''far'':54C,169C ''field'':165C ''given'':38C ''goe'':57C ''graduat'':105C,116C,152C ''heavi'':183C ''help'':143C ''journal'':64C ''know'':18C ''knowledg'':215C ''learn'':221C ''leverag'':181C ''master'':205C ''materi'':188C ''mathemat'':171C,186C ''matur'':126C ''mis'':90C ''much'':219C ''never'':10C ''new'':97C,136C,195C ''number'':61C ''order'':47C ''p'':1C,14C,52C,84C,129C,167C ''paper'':68C ''part'':133C,155C ''particular'':194C ''peopl'':102C ''phd'':7C,28C,207C ''possibl'':141C ''professor'':25C,35C ''program'':5C,8C,112C,122C,137C,208C ''publish'':56C ''put'':158C ''rare'':32C,161C ''relat'':63C ''requir'':150C ''rigor'':172C ''scienc'':31C,82C,95C,109C,179C ''signific'':114C,127C ''somebodi'':70C ''sometim'':138C ''specif'':44C ''statist'':196C ''subject'':98C ''teach'':49C ''topic'':72C,79C ''undergrad'':204C ''went'':87C ''within'':117C ''would'':66C,74C,176C ''wouldn'':190C ''year'':120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (249, 2, '2014-06-10 03:42:51.637', 3, NULL, NULL, '&lt;p&gt;Twitter uses Storm for real-time processing of data.  Problems can happen with real-time data.  Systems might go down.  Data might be inadvertently processed twice.  Network connections can be lost.  A lot can happen in a real-time system.  &lt;/p&gt;

&lt;p&gt;They use hadoop to reliably process historical data.  I don''t know specifics, but for instance, getting solid information from aggregated logs is probably more reliable than attaching to the stream.&lt;/p&gt;

&lt;p&gt;If they simply relied on Storm for everything - Storm would have problems due to the nature of providing real-time information at scale.  If they relied on hadoop for everything, there''s a good deal of latency involved.  Combining the two with Summingbird is the next logical step.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (271, 2, '2014-06-10 11:49:23.777', 3, NULL, NULL, '&lt;p&gt;It has been shown before that machine learning techniques can be applied for predicting sport results. Simple google search should give you a bunch of results.&lt;/p&gt;

&lt;p&gt;However, it has also been showed (for NFL btw) that very complex predictive models, simple predictive models, questioning people, or crowd knowledge by utilising betting info, they all perform more or less the same. Source: "&lt;a href="http://everythingisobvious.com/" rel="nofollow"&gt;Everything is obvious once you know the answer - How common sense Fails&lt;/a&gt;", Chapter 7, by Duncan Watts. &lt;/p&gt;
', NULL, NULL, '''/a'':82C ''/p'':28C,88C ''7'':84C ''also'':33C ''answer'':77C ''appli'':13C ''bet'':54C ''btw'':38C ''bunch'':25C ''chapter'':83C ''common'':79C ''complex'':41C ''crowd'':50C ''duncan'':86C ''everyth'':70C ''everythingisobvious.com'':67C ''fail'':81C ''give'':22C ''googl'':19C ''howev'':30C ''href'':66C ''info'':55C ''know'':75C ''knowledg'':51C ''learn'':9C ''less'':61C ''machin'':8C ''model'':43C,46C ''nfl'':37C ''nofollow'':69C ''obvious'':72C ''p'':1C,29C ''peopl'':48C ''perform'':58C ''predict'':15C,42C,45C ''question'':47C ''rel'':68C ''result'':17C,27C ''search'':20C ''sens'':80C ''show'':35C ''shown'':5C ''simpl'':18C,44C ''sourc'':64C ''sport'':16C ''techniqu'':10C ''utilis'':53C ''watt'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (300, 2, '2014-06-10 23:20:16.67', 2, NULL, NULL, '&lt;p&gt;Topological Data Analysis is a method explicitly designed for the setting you describe. Rather than a global distance metric, it relies only on a local metric of proximity or neighborhood. See: &lt;a href="http://www.ams.org/bull/2009-46-02/S0273-0979-09-01249-X/S0273-0979-09-01249-X.pdf" rel="nofollow"&gt;Topology and data&lt;/a&gt; and &lt;a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3566620/" rel="nofollow"&gt;Extracting insights from the shape of complex data using topology&lt;/a&gt;. You can find additional resources at the website for Ayasdi.&lt;/p&gt;
', NULL, NULL, '''/a'':43C,62C ''/bull/2009-46-02/s0273-0979-09-01249-x/s0273-0979-09-01249-x.pdf'':37C ''/p'':73C ''/pmc/articles/pmc3566620/'':49C ''addit'':66C ''analysi'':4C ''ayasdi'':72C ''complex'':58C ''data'':3C,42C,59C ''describ'':14C ''design'':9C ''distanc'':19C ''explicit'':8C ''extract'':52C ''find'':65C ''global'':18C ''href'':34C,46C ''insight'':53C ''local'':26C ''method'':7C ''metric'':20C,27C ''neighborhood'':31C ''nofollow'':39C,51C ''p'':1C ''proxim'':29C ''rather'':15C ''rel'':38C,50C ''reli'':22C ''resourc'':67C ''see'':32C ''set'':12C ''shape'':56C ''topolog'':2C,40C,61C ''use'':60C ''websit'':70C ''www.ams.org'':36C ''www.ams.org/bull/2009-46-02/s0273-0979-09-01249-x/s0273-0979-09-01249-x.pdf'':35C ''www.ncbi.nlm.nih.gov'':48C ''www.ncbi.nlm.nih.gov/pmc/articles/pmc3566620/'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (251, 2, '2014-06-10 05:57:13.897', 3, NULL, NULL, '&lt;p&gt;Having done the rewriting game over and over myself (and still doing it), my immediate reaction was &lt;em&gt;adaptability&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;While frameworks and libraries have a huge arsenal of (possibly intertwinable) routines for standard tasks, their framework property often (always?) disallows shortcuts.  In fact, most frameworks have some sort of core infrastructure around which a core layer of basic functionality is implemented.  More specific functionality makes use of the basic layer and is placed in a second layer around the core.&lt;/p&gt;

&lt;p&gt;Now by shortcuts I mean going straight from a second layer routine to another second layer routine without using the core.  Typical example (from my domain) would be timestamps: You have a timestamped data source of some kind.  Thus far the job is simply to read the data off the wire and pass it to the core so your other code can feast on it.&lt;/p&gt;

&lt;p&gt;Now your industry changes the default timestamp format for a very good reason (in my case they went from unix time to GPS time).  Unless your framework is industry-specific it is very unlikely that they''re willing to change the core representation of time, so you end up using a framework that &lt;em&gt;almost&lt;/em&gt; does what you want.  Every time you access your data you have to convert it to industry-time-format first, and every time you want it modified you have to convert it back to whatever the core deems appropriate.  There is no way that you can hand over data straight from the source to a sink without double conversion.&lt;/p&gt;

&lt;p&gt;This is where your hand-crafted frameworks will shine, it''s just a minor change and you''re back modelling the real world whereas all other (non-industry-specific) frameworks will now have a performance disadvantage.&lt;/p&gt;

&lt;p&gt;Over time, the discrepancy between the real world and the model will add up.  With an off-the-shelf framework you''d soon be facing questions like: How can I represent &lt;code&gt;this&lt;/code&gt; in &lt;code&gt;that&lt;/code&gt; or how do make routine &lt;code&gt;X&lt;/code&gt; accept/produce &lt;code&gt;Y&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;So far this wasn''t about C/C++.  But if, for some reason, you can''t change the framework, i.e. you do have to put up with double conversion of data to go from one end to another, then you''d typically employ something that minimises the additional overhead.  In my case, a TAI-&gt;UTC or UTC-&gt;TAI converter is best left to raw C (or an FPGA).  There is no elegance possible, no profound smart data structure that makes the problem trivial.  It''s just a boring switch statement, and why not use a language whose compilers are good at optimising exactly that?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (252, 1, '2014-06-10 06:19:46.51', 4, 326, 'HBase connector - Thrift or REST', '&lt;p&gt;There seem to be at least 2 ways to connect to HBase from external application, with language other then Java (i.e. Python):&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;HBase Thrift API&lt;/li&gt;
&lt;li&gt;HBase Stargate (REST API) &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Does anyone know which one should be used in which circumstances?
I.e. what are their main differences, and pros/cons?&lt;/p&gt;
', 'bigdata  hadoop', NULL, '''/li'':37C,43C ''/ol'':44C ''/p'':31C,65C ''2'':15C ''anyon'':47C ''api'':36C,42C ''applic'':23C ''bigdata'':6B ''circumst'':56C ''connect'':18C ''connector'':2A ''differ'':62C ''extern'':22C ''hadoop'':7B ''hbase'':1A,20C,34C,39C ''i.e'':29C,57C ''java'':28C ''know'':48C ''languag'':25C ''least'':14C ''li'':33C,38C ''main'':61C ''ol'':32C ''one'':50C ''p'':8C,45C ''pros/cons'':64C ''python'':30C ''rest'':5A,41C ''seem'':10C ''stargat'':40C ''thrift'':3A,35C ''use'':53C ''way'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (253, 1, '2014-06-10 06:20:20.817', 24, 4977, 'Do I need to learn Hadoop to be a Data Scientist?', '&lt;p&gt;An aspiring data scientist here. I don''t know anything about Hadoop, but as I have been reading about Data Science and Big Data, I see a lot of talk about Hadoop. Is it absolutely necessary to learn Hadoop to be a Data Scientist? &lt;/p&gt;
', 'bigdata  hadoop', NULL, '''/p'':59C ''absolut'':49C ''anyth'':24C ''aspir'':16C ''big'':37C ''bigdata'':12B ''data'':10A,17C,34C,38C,57C ''hadoop'':6A,13B,26C,46C,53C ''know'':23C ''learn'':5A,52C ''lot'':42C ''necessari'':50C ''need'':3A ''p'':14C ''read'':32C ''scienc'':35C ''scientist'':11A,18C,58C ''see'':40C ''talk'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (254, 2, '2014-06-10 07:10:45.057', 6, NULL, NULL, '&lt;p&gt;Actually there are more than one question to answer here:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;How to work on schemaless/loose/missing data&lt;/li&gt;
&lt;li&gt;How to label a person (from what I understand unsupervised) and create an identifier&lt;/li&gt;
&lt;li&gt;How to train your system so that it can tell you which attributes you should use in order to identify the person &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;As Rubens mentioned, you can use &lt;strong&gt;decision tree&lt;/strong&gt; methods, specifically &lt;a href="http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm"&gt;Random Forests&lt;/a&gt; for calculating the most important attributes based on information gain if you have already found a way to identify how to label a person.&lt;/p&gt;

&lt;p&gt;However, if you do not have any label information maybe you can use some expert view for preliminary attribute selection. After that you make &lt;strong&gt;unsupervised classification&lt;/strong&gt; in order to retrieve your labels. Lastly, you can select the most important fields using &lt;strong&gt;Random Forest&lt;/strong&gt; or other methods like &lt;strong&gt;Bayesian Belief Networks&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;In order to achieve all that, you also need complete data set. If your data set is loose you have to manually or heuristically find a way to couple attributes indicating same thing with different names. What is more, you can use &lt;em&gt;imputation&lt;/em&gt; techniques such as &lt;a href="http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm"&gt;Expectation Maximization&lt;/a&gt; method and complete your data set. Or you can also work with Bayesian Networks and can leave missing fields as they are.&lt;/p&gt;
', NULL, NULL, '''/a'':83C,223C ''/em'':212C ''/li'':21C,37C,61C ''/ol'':62C ''/p'':12C,108C,166C,246C ''/strong'':73C,137C,156C,165C ''/wiki/expectation%e2%80%93maximization_algorithm'':220C ''/~breiman/randomforests/cc_home.htm'':80C ''achiev'':171C ''actual'':2C ''alreadi'':97C ''also'':175C,233C ''answer'':10C ''attribut'':51C,89C,128C,197C ''base'':90C ''bayesian'':162C,236C ''belief'':163C ''calcul'':85C ''classif'':136C ''complet'':177C,226C ''coupl'':196C ''creat'':34C ''data'':20C,178C,182C,228C ''decis'':71C ''differ'':202C ''em'':210C ''en.wikipedia.org'':219C ''en.wikipedia.org/wiki/expectation%e2%80%93maximization_algorithm'':218C ''expect'':221C ''expert'':124C ''field'':151C,242C ''find'':192C ''forest'':82C,155C ''found'':98C ''gain'':93C ''heurist'':191C ''howev'':110C ''href'':77C,217C ''identifi'':36C,58C,102C ''import'':88C,150C ''imput'':211C ''indic'':198C ''inform'':92C,118C ''label'':25C,105C,117C,143C ''last'':144C ''leav'':240C ''li'':14C,22C,38C ''like'':160C ''loos'':185C ''make'':133C ''manual'':189C ''maxim'':222C ''mayb'':119C ''mention'':66C ''method'':74C,159C,224C ''miss'':241C ''name'':203C ''need'':176C ''network'':164C,237C ''ol'':13C ''one'':7C ''order'':56C,139C,169C ''p'':1C,63C,109C,167C ''person'':27C,60C,107C ''preliminari'':127C ''question'':8C ''random'':81C,154C ''retriev'':141C ''ruben'':65C ''schemaless/loose/missing'':19C ''select'':129C,147C ''set'':179C,183C,229C ''specif'':75C ''strong'':70C,134C,153C,161C ''system'':43C ''techniqu'':213C ''tell'':48C ''thing'':200C ''train'':41C ''tree'':72C ''understand'':31C ''unsupervis'':32C,135C ''use'':54C,69C,122C,152C,209C ''view'':125C ''way'':100C,194C ''work'':17C,234C ''www.stat.berkeley.edu'':79C ''www.stat.berkeley.edu/~breiman/randomforests/cc_home.htm'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (255, 2, '2014-06-10 07:28:49.127', 2, NULL, NULL, '&lt;p&gt;You should learn Hadoop if you want to be work as data scientist, but maybe before starting with Hadoop you should read something about ETL or Big Data... this book could be a good starting point: &lt;a href="http://rads.stackoverflow.com/amzn/click/1617290343" rel="nofollow"&gt;http://www.amazon.com/Big-Data-Principles-practices-scalable/dp/1617290343&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Hope it helps and good luck!&lt;/p&gt;
', NULL, NULL, '''/amzn/click/1617290343'':42C ''/big-data-principles-practices-scalable/dp/1617290343&lt;/a&gt;&lt;/p&gt;'':47C ''/p'':55C ''big'':28C ''book'':31C ''could'':32C ''data'':13C,29C ''etl'':26C ''good'':35C,53C ''hadoop'':5C,20C ''help'':51C ''hope'':49C ''href'':39C ''learn'':4C ''luck'':54C ''mayb'':16C ''nofollow'':44C ''p'':1C,48C ''point'':37C ''rads.stackoverflow.com'':41C ''rads.stackoverflow.com/amzn/click/1617290343'':40C ''read'':23C ''rel'':43C ''scientist'':14C ''someth'':24C ''start'':18C,36C ''want'':8C ''work'':11C ''www.amazon.com'':46C ''www.amazon.com/big-data-principles-practices-scalable/dp/1617290343&lt;/a&gt;&lt;/p&gt;'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (269, 2, '2014-06-10 11:37:28.293', 4, NULL, NULL, '&lt;p&gt;Definitely they can.
I can target you to a &lt;strong&gt;&lt;a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.7448&amp;amp;rep=rep1&amp;amp;type=pdf" rel="nofollow"&gt;nice paper&lt;/a&gt;&lt;/strong&gt;. Once I used it for soccer league results prediction algorithm implementation, primarily aiming at having some value against bookmakers.&lt;/p&gt;

&lt;p&gt;From paper''s abstract:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;a Bayesian dynamic generalized model to estimate the time dependent skills of all teams in a league, and to predict next weekend''s soccer matches.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Keywords:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Dynamic Models, Generalized Linear Models, Graphical Models, Markov
  Chain Monte Carlo Methods, Prediction of Soccer Matches&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Citation:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Rue, Havard, and Oyvind Salvesen. "Prediction and retrospective analysis of soccer matches in a league." Journal of the Royal Statistical Society: Series D (The Statistician) 49.3 (2000): 399-418.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''-418'':134C ''/a'':21C ''/blockquote'':77C,100C,136C ''/p'':42C,48C,76C,80C,99C,103C,135C ''/strong'':22C ''/viewdoc/download?doi=10.1.1.56.7448&amp;amp;rep=rep1&amp;amp;type=pdf'':16C ''2000'':132C ''399'':133C ''49.3'':131C ''abstract'':47C ''aim'':35C ''algorithm'':32C ''analysi'':114C ''bayesian'':52C ''blockquot'':49C,81C,104C ''bookmak'':41C ''carlo'':93C ''chain'':91C ''citat'':102C ''citeseerx.ist.psu.edu'':15C ''citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.7448&amp;amp;rep=rep1&amp;amp;type=pdf'':14C ''d'':128C ''definit'':2C ''depend'':60C ''dynam'':53C,83C ''estim'':57C ''general'':54C,85C ''graphic'':88C ''havard'':107C ''href'':13C ''implement'':33C ''journal'':121C ''keyword'':79C ''leagu'':29C,67C,120C ''linear'':86C ''markov'':90C ''match'':75C,98C,117C ''method'':94C ''model'':55C,84C,87C,89C ''mont'':92C ''next'':71C ''nice'':19C ''nofollow'':18C ''oyvind'':109C ''p'':1C,43C,50C,78C,82C,101C,105C ''paper'':20C,45C ''predict'':31C,70C,95C,111C ''primarili'':34C ''rel'':17C ''result'':30C ''retrospect'':113C ''royal'':124C ''rue'':106C ''salvesen'':110C ''seri'':127C ''skill'':61C ''soccer'':28C,74C,97C,116C ''societi'':126C ''statist'':125C ''statistician'':130C ''strong'':11C ''target'':7C ''team'':64C ''time'':59C ''use'':25C ''valu'':39C ''weekend'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (256, 2, '2014-06-10 07:30:53.51', 29, NULL, NULL, '&lt;p&gt;Different people use different tools for different things.  Terms like Data Science are generic for a reason.  A data scientist could spend an entire career without having to learn a particular tool like hadoop.  Hadoop is widely used, but it is not the only platform that is capable of managing and manipulating data, even large scale data.&lt;/p&gt;

&lt;p&gt;I would say that a data scientist should be familiar with concepts like MapReduce, distributed systems, distributed file systems, and the like, but I wouldn''t judge someone for not knowing about such things.  &lt;/p&gt;

&lt;p&gt;It''s a big field.  There is a sea of knowledge and most people are capable of learning and being an expert in a single drop.  The key to being a scientist is having the desire to learn and the motivation to know that which you don''t already know.&lt;/p&gt;

&lt;p&gt;As an example:  I could hand the right person a hundred structured CSV files containing information about classroom performance in one particular class over a decade.  A data scientist would be able to spend a year gleaning insights from the data without ever needing to spread computation across multiple machines.  You could apply machine learning algorithms, analyze it using visualizations, combine it with external data about the region, ethnic makeup, changes to environment over time, political information, weather patterns, etc.  All of that would be "data science" in my opinion.  It might take something like hadoop to test and apply anything you learned to data comprising an entire country of students rather than just a classroom, but that final step doesn''t necessarily make someone a data scientist.  And not taking that final step doesn''t necessarily disqualify someone from being a data scientist.&lt;/p&gt;
', NULL, NULL, '''/p'':59C,95C,147C,293C ''abl'':180C ''across'':196C ''algorithm'':204C ''alreadi'':145C ''analyz'':205C ''anyth'':249C ''appli'':201C,248C ''big'':100C ''capabl'':49C,112C ''career'':26C ''chang'':219C ''class'':171C ''classroom'':166C,264C ''combin'':209C ''compris'':254C ''comput'':195C ''concept'':72C ''contain'':163C ''could'':22C,153C,200C ''countri'':257C ''csv'':161C ''data'':12C,20C,54C,58C,66C,176C,189C,213C,234C,253C,275C,291C ''decad'':174C ''desir'':132C ''differ'':2C,5C,8C ''disqualifi'':286C ''distribut'':75C,77C ''doesn'':269C,283C ''drop'':122C ''entir'':25C,256C ''environ'':221C ''etc'':228C ''ethnic'':217C ''even'':55C ''ever'':191C ''exampl'':151C ''expert'':118C ''extern'':212C ''familiar'':70C ''field'':101C ''file'':78C,162C ''final'':267C,281C ''generic'':15C ''glean'':185C ''hadoop'':35C,36C,244C ''hand'':154C ''hundr'':159C ''inform'':164C,225C ''insight'':186C ''judg'':87C ''key'':124C ''know'':91C,139C,146C ''knowledg'':107C ''larg'':56C ''learn'':30C,114C,134C,203C,251C ''like'':11C,34C,73C,82C,243C ''machin'':198C,202C ''make'':272C ''makeup'':218C ''manag'':51C ''manipul'':53C ''mapreduc'':74C ''might'':240C ''motiv'':137C ''multipl'':197C ''necessarili'':271C,285C ''need'':192C ''one'':169C ''opinion'':238C ''p'':1C,60C,96C,148C ''particular'':32C,170C ''pattern'':227C ''peopl'':3C,110C ''perform'':167C ''person'':157C ''platform'':46C ''polit'':224C ''rather'':260C ''reason'':18C ''region'':216C ''right'':156C ''say'':63C ''scale'':57C ''scienc'':13C,235C ''scientist'':21C,67C,128C,177C,276C,292C ''sea'':105C ''singl'':121C ''someon'':88C,273C,287C ''someth'':242C ''spend'':23C,182C ''spread'':194C ''step'':268C,282C ''structur'':160C ''student'':259C ''system'':76C,79C ''take'':241C,279C ''term'':10C ''test'':246C ''thing'':9C,94C ''time'':223C ''tool'':6C,33C ''use'':4C,39C,207C ''visual'':208C ''weather'':226C ''wide'':38C ''without'':27C,190C ''would'':62C,178C,232C ''wouldn'':85C ''year'':184C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (257, 2, '2014-06-10 07:42:57.47', 4, NULL, NULL, '&lt;p&gt;Yes, you should learn a platform that is capable of dissecting your problem as a data parallel problem. Hadoop is one. For your simple needs (design patterns like counting, aggregation, filtering etc.) you need Hadoop and for more complex Machine Learning stuff like doing some Bayesian, SVM you need Mahout which in turn needs Hadoop (Now Apache Spark) to solve your problem using a data-parallel approach.&lt;/p&gt;

&lt;p&gt;So Hadoop is a good platform to learn and really important for your batch processing needs. Not only Hadoop but you also need to know Spark (Mahout runs it''s algorithms utilizing Spark) &amp;amp; Twitter Storm (for your real time analytics needs). This list will continue and evolve so if you are good with the building blocks (Distributed Computing, Data-Parallel Problems and so on) and know how one such platform (say Hadoop) operates you will fairly quickly be up to speed on others.&lt;/p&gt;
', NULL, NULL, '''/p'':70C,157C ''aggreg'':31C ''algorithm'':102C ''also'':93C ''amp'':105C ''analyt'':112C ''apach'':58C ''approach'':69C ''batch'':85C ''bayesian'':47C ''block'':128C ''build'':127C ''capabl'':10C ''complex'':40C ''comput'':130C ''continu'':117C ''count'':30C ''data'':17C,67C,132C ''data-parallel'':66C,131C ''design'':27C ''dissect'':12C ''distribut'':129C ''etc'':33C ''evolv'':119C ''fair'':149C ''filter'':32C ''good'':76C,124C ''hadoop'':20C,36C,56C,73C,90C,145C ''import'':82C ''know'':96C,139C ''learn'':5C,42C,79C ''like'':29C,44C ''list'':115C ''machin'':41C ''mahout'':51C,98C ''need'':26C,35C,50C,55C,87C,94C,113C ''one'':22C,141C ''oper'':146C ''other'':156C ''p'':1C,71C ''parallel'':18C,68C,133C ''pattern'':28C ''platform'':7C,77C,143C ''problem'':14C,19C,63C,134C ''process'':86C ''quick'':150C ''real'':110C ''realli'':81C ''run'':99C ''say'':144C ''simpl'':25C ''solv'':61C ''spark'':59C,97C,104C ''speed'':154C ''storm'':107C ''stuff'':43C ''svm'':48C ''time'':111C ''turn'':54C ''twitter'':106C ''use'':64C ''util'':103C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (258, 2, '2014-06-10 07:56:45.587', 2, NULL, NULL, '&lt;p&gt;Thrift is generally faster because the data being exchanged is smaller.  Stargate offers a web service which is an integration method that is widely supported, which is a concern when you are working with commercial products with limited integration possibilities.&lt;/p&gt;

&lt;p&gt;In a closed environment where everything is controlled, I would prefer Thrift.  If I''m exposing data to external teams or systems I would prefer Stargate.&lt;/p&gt;
', NULL, NULL, '''/p'':42C,70C ''close'':46C ''commerci'':36C ''concern'':30C ''control'':51C ''data'':8C,60C ''environ'':47C ''everyth'':49C ''exchang'':10C ''expos'':59C ''extern'':62C ''faster'':5C ''general'':4C ''integr'':21C,40C ''limit'':39C ''m'':58C ''method'':22C ''offer'':14C ''p'':1C,43C ''possibl'':41C ''prefer'':54C,68C ''product'':37C ''servic'':17C ''smaller'':12C ''stargat'':13C,69C ''support'':26C ''system'':65C ''team'':63C ''thrift'':2C,55C ''web'':16C ''wide'':25C ''work'':34C ''would'':53C,67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (259, 2, '2014-06-10 08:04:20.4', 11, NULL, NULL, '&lt;p&gt;I would like to point to &lt;a href="http://national.census.okfn.org/"&gt;The Open Data Census&lt;/a&gt;. It is an initiative of the Open Knowledge Foundation based on contributions from open data advocates and experts around the world. &lt;/p&gt;

&lt;p&gt;The value of Open data Census is open, community driven, and systematic effort to collect and update the database of open datasets globally on country and, in some cases, &lt;a href="http://us-city.census.okfn.org/"&gt;like U.S., on city level&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;Also, it presents an opportunity to compare different countries and cities on in selected areas of interest.&lt;/p&gt;
', NULL, NULL, '''/a'':15C,76C ''/p'':37C,77C,96C ''advoc'':31C ''also'':79C ''area'':93C ''around'':34C ''base'':25C ''case'':67C ''census'':14C,44C ''citi'':74C,89C ''collect'':53C ''communiti'':47C ''compar'':85C ''contribut'':27C ''countri'':63C,87C ''data'':13C,30C,43C ''databas'':57C ''dataset'':60C ''differ'':86C ''driven'':48C ''effort'':51C ''expert'':33C ''foundat'':24C ''global'':61C ''href'':9C,69C ''initi'':19C ''interest'':95C ''knowledg'':23C ''level'':75C ''like'':4C,71C ''national.census.okfn.org'':10C ''open'':12C,22C,29C,42C,46C,59C ''opportun'':83C ''p'':1C,38C,78C ''point'':6C ''present'':81C ''select'':92C ''systemat'':50C ''u.s'':72C ''updat'':55C ''us-city.census.okfn.org'':70C ''valu'':40C ''world'':36C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (260, 2, '2014-06-10 08:38:27.093', 1, NULL, NULL, '&lt;p&gt;The algorithm that is used in this case is called &lt;a href="https://class.coursera.org/ml-003/lecture/38" rel="nofollow"&gt;one-vs-all classifier&lt;/a&gt; or multiclass classifier. &lt;/p&gt;

&lt;p&gt;In your case you have to take one class, e. g. number 1 , mark it as positive and combine the rest seven classes in one negative class. The neural network will output the probability of this case being class number 1 vs the rest of the classes. &lt;/p&gt;

&lt;p&gt;Afterwords, you have to assign as positive another class, e.g. number 2, assign all other classes as one big negative class and get the predicted probability from the network again.&lt;/p&gt;

&lt;p&gt;After repeating this procedure for all eight classes, assign each case to the the class that had the maximum probability from all the classes outputted from the neural network. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (261, 2, '2014-06-10 09:21:14.013', 2, NULL, NULL, '&lt;p&gt;You could take a look at CN2 rule learner in Orange 2 &lt;a href="http://orange.biolab.si/orange2/" rel="nofollow"&gt;http://orange.biolab.si/orange2/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/orange2/'':18C ''/orange2/&lt;/a&gt;&lt;/p&gt;'':23C ''2'':13C ''cn2'':8C ''could'':3C ''href'':15C ''learner'':10C ''look'':6C ''nofollow'':20C ''orang'':12C ''orange.biolab.si'':17C,22C ''orange.biolab.si/orange2/'':16C ''orange.biolab.si/orange2/&lt;/a&gt;&lt;/p&gt;'':21C ''p'':1C ''rel'':19C ''rule'':9C ''take'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1200, 2, '2014-10-03 11:39:11.24', 2, NULL, NULL, '&lt;p&gt;I think I''d try to model the &lt;em&gt;rate&lt;/em&gt; at which the ambulances are released instead of the time it takes. That would let me use Poisson regression, which is the canonical type of GLM for rates (in R''s GLM, set family = "poisson".) However, in order to use PR, the data needs to have its variance equal to its mean, or at least close to it. &lt;/p&gt;
', NULL, NULL, '''/em'':12C ''/p'':71C ''ambul'':16C ''canon'':35C ''close'':68C ''d'':5C ''data'':55C ''em'':10C ''equal'':61C ''famili'':46C ''glm'':38C,44C ''howev'':48C ''instead'':19C ''least'':67C ''let'':27C ''mean'':64C ''model'':8C ''need'':56C ''order'':50C ''p'':1C ''poisson'':30C,47C ''pr'':53C ''r'':42C ''rate'':11C,40C ''regress'':31C ''releas'':18C ''set'':45C ''take'':24C ''think'':3C ''time'':22C ''tri'':6C ''type'':36C ''use'':29C,52C ''varianc'':60C ''would'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (263, 2, '2014-06-10 09:41:34.697', 6, NULL, NULL, '&lt;p&gt;Unfortunately, parallelization is not yet implemented in pandas. You can join &lt;a href="http://github.com/pydata/pandas/issues/5751"&gt;this github issue&lt;/a&gt; if you want to participate in the development of this feature.&lt;/p&gt;

&lt;p&gt;I don''t know any "magic unicorn package" for this purposes, so the best thing will be write your own solution. But if you still don''t want to spend time on that and want to learn something new â you can try the two methods built into MongoDB (map reduce and agg framework).  See &lt;a href="http://docs.mongodb.org/manual/core/aggregation/"&gt;mongodb_agg_framework&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':21C,98C ''/manual/core/aggregation/'':94C ''/p'':33C,99C ''/pydata/pandas/issues/5751'':17C ''agg'':87C,96C ''best'':48C ''built'':81C ''develop'':29C ''docs.mongodb.org'':93C ''docs.mongodb.org/manual/core/aggregation/'':92C ''featur'':32C ''framework'':88C,97C ''github'':19C ''github.com'':16C ''github.com/pydata/pandas/issues/5751'':15C ''href'':14C,91C ''implement'':7C ''issu'':20C ''join'':12C ''know'':38C ''learn'':71C ''magic'':40C ''map'':84C ''method'':80C ''mongodb'':83C,95C ''new'':73C ''p'':1C,34C ''packag'':42C ''panda'':9C ''parallel'':3C ''particip'':26C ''purpos'':45C ''reduc'':85C ''see'':89C ''solut'':55C ''someth'':72C ''spend'':64C ''still'':59C ''thing'':49C ''time'':65C ''tri'':77C ''two'':79C ''unfortun'':2C ''unicorn'':41C ''want'':24C,62C,69C ''write'':52C ''yet'':6C ''â'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (264, 2, '2014-06-10 10:48:58.457', 0, NULL, NULL, '&lt;p&gt;You can also give the Expectation Maximization clustering algorithm a try.  It can work on categorical data and will give you a statistical likelihood of which categorical value (or values) a cluster is most likely to take on.&lt;/p&gt;
', NULL, NULL, '''/p'':40C ''algorithm'':10C ''also'':4C ''categor'':17C,28C ''cluster'':9C,33C ''data'':18C ''expect'':7C ''give'':5C,21C ''like'':36C ''likelihood'':25C ''maxim'':8C ''p'':1C ''statist'':24C ''take'':38C ''tri'':12C ''valu'':29C,31C ''work'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (265, 1, '2014-06-10 10:58:58.447', 19, 11227, 'Can machine learning algorithms predict sports scores or plays?', '&lt;p&gt;I have a variety of NFL datasets that I think might make a good side-project, but I haven''t done anything with them just yet.&lt;/p&gt;

&lt;p&gt;Coming to this site made me think of machine learning algorithms and I wondering how good they might be at either predicting the outcome of football games or even the next play.&lt;/p&gt;

&lt;p&gt;It seems to me that there would be some trends that could be identified - on 3rd down and 1, a team with a strong running back &lt;em&gt;theoretically should&lt;/em&gt; have a tendency to run the ball in that situation.&lt;/p&gt;

&lt;p&gt;Scoring might be more difficult to predict, but the winning team might be.&lt;/p&gt;

&lt;p&gt;My question is whether these are good questions to throw at a machine learning algorithm.  It could be that a thousand people have tried it before, but the nature of sports makes it an unreliable topic.&lt;/p&gt;
', 'machine-learning  sports', NULL, '''/em'':107C ''/p'':42C,76C,118C,133C,171C ''1'':96C ''3rd'':93C ''algorithm'':4A,54C,149C ''anyth'':37C ''back'':103C ''ball'':114C ''come'':44C ''could'':89C,151C ''dataset'':21C ''difficult'':124C ''done'':36C ''either'':64C ''em'':104C ''even'':72C ''footbal'':69C ''game'':70C ''good'':28C,59C,141C ''haven'':34C ''identifi'':91C ''learn'':3A,12B,53C,148C ''machin'':2A,11B,52C,147C ''machine-learn'':10B ''made'':48C ''make'':26C,166C ''might'':25C,61C,121C,131C ''natur'':163C ''next'':74C ''nfl'':20C ''outcom'':67C ''p'':14C,43C,77C,119C,134C ''peopl'':156C ''play'':9A,75C ''predict'':5A,65C,126C ''project'':31C ''question'':136C,142C ''run'':102C,112C ''score'':7A,120C ''seem'':79C ''side'':30C ''side-project'':29C ''site'':47C ''situat'':117C ''sport'':6A,13B,165C ''strong'':101C ''team'':98C,130C ''tendenc'':110C ''theoret'':105C ''think'':24C,50C ''thousand'':155C ''throw'':144C ''topic'':170C ''trend'':87C ''tri'':158C ''unreli'':169C ''varieti'':18C ''whether'':138C ''win'':129C ''wonder'':57C ''would'':84C ''yet'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1076, 2, '2014-09-05 06:17:28.533', 3, NULL, NULL, '&lt;p&gt;You could look at scikit-learn or Orange module in Python.
Scikit-learn has a SGD classifier and regressor that could do a partial fit data in case of online learning.
In R, take a look at caret package &lt;/p&gt;
', NULL, NULL, '''/p'':43C ''caret'':41C ''case'':31C ''classifi'':20C ''could'':3C,24C ''data'':29C ''fit'':28C ''learn'':8C,16C,34C ''look'':4C,39C ''modul'':11C ''onlin'':33C ''orang'':10C ''p'':1C ''packag'':42C ''partial'':27C ''python'':13C ''r'':36C ''regressor'':22C ''scikit'':7C,15C ''scikit-learn'':6C,14C ''sgd'':19C ''take'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (393, 2, '2014-06-15 22:33:17.67', 4, NULL, NULL, '&lt;p&gt;Linear SVM should be a good starting point. Take a look at &lt;a href="http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html" rel="nofollow"&gt;this&lt;/a&gt; guide to choose the right estimator.&lt;/p&gt;
', NULL, NULL, '''/a'':22C ''/p'':29C ''/stable/tutorial/machine_learning_map/index.html'':18C ''choos'':25C ''estim'':28C ''good'':7C ''guid'':23C ''href'':15C ''linear'':2C ''look'':12C ''nofollow'':20C ''p'':1C ''point'':9C ''rel'':19C ''right'':27C ''scikit-learn.org'':17C ''scikit-learn.org/stable/tutorial/machine_learning_map/index.html'':16C ''start'':8C ''svm'':3C ''take'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (277, 2, '2014-06-10 14:25:41.903', 10, NULL, NULL, '&lt;p&gt;To be honest, I think that doing some projects will teach you much more than doing a full course. One reason is that doing a project is more motivating and open-ended than doing assignments.&lt;/p&gt;

&lt;p&gt;A course, if you have the time AND motivation (real motivation), is better than doing a project. The other commentators have made good platform recommendations on tech. &lt;/p&gt;

&lt;p&gt;I think, from a fun project standpoint, you should ask a question and get a computer to learn to answer it. &lt;/p&gt;

&lt;p&gt;Some good classic questions that have good examples are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Neural Networks for recognizing hand written digits&lt;/li&gt;
&lt;li&gt;Spam email classification using logistic regression&lt;/li&gt;
&lt;li&gt;Classification of objects using Gaussian Mixture models&lt;/li&gt;
&lt;li&gt;Some use of linear regression, perhaps forecasting of grocery prices given neighborhoods&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;These projects have the math done, code done, and can be found with Google readily.&lt;/p&gt;

&lt;p&gt;Other cool subjects can be done by you!&lt;/p&gt;

&lt;p&gt;Lastly, I research robotics, so for me the most FUN applications are behavioral.
Examples can include (if you can play with an arduino)&lt;/p&gt;

&lt;p&gt;Create a application, that uses logistic regression perhaps, that learns when to turn the fan off and on given the inner temperature, and the status of the light in the room.&lt;/p&gt;

&lt;p&gt;Create an application that teaches a robot to move an actuator, perhaps a wheel, based on sensor input (perhaps a button press), using Gaussian Mixture Models (learning from demonstration).&lt;/p&gt;

&lt;p&gt;Anyway, those are pretty advanced. The point I''m making is that if you pick a project that you (really really) like, and spend a few week on it, you will learn a massive amount, and understand so much more than you will get doing a few assignments.&lt;/p&gt;
', NULL, NULL, '''/li'':111C,119C,128C,142C ''/p'':38C,67C,90C,101C,160C,170C,195C,228C,259C,309C ''/ul'':143C ''actuat'':240C ''advanc'':265C ''amount'':295C ''answer'':88C ''anyway'':261C ''applic'':182C,199C,232C ''arduino'':194C ''ask'':78C ''assign'':37C,308C ''base'':244C ''behavior'':184C ''better'':52C ''button'':250C ''classic'':94C ''classif'':115C,121C ''code'':151C ''comment'':59C ''comput'':84C ''cool'':163C ''cours'':20C,41C ''creat'':197C,230C ''demonstr'':258C ''digit'':110C ''done'':150C,152C,167C ''email'':114C ''end'':34C ''exampl'':99C,185C ''fan'':211C ''forecast'':136C ''found'':156C ''full'':19C ''fun'':73C,181C ''gaussian'':125C,253C ''get'':82C,304C ''given'':140C,215C ''good'':62C,93C,98C ''googl'':158C ''groceri'':138C ''hand'':108C ''honest'':4C ''includ'':187C ''inner'':217C ''input'':247C ''last'':172C ''learn'':86C,206C,256C,292C ''li'':103C,112C,120C,129C ''light'':224C ''like'':282C ''linear'':133C ''logist'':117C,202C ''m'':269C ''made'':61C ''make'':270C ''massiv'':294C ''math'':149C ''mixtur'':126C,254C ''model'':127C,255C ''motiv'':30C,48C,50C ''move'':238C ''much'':14C,299C ''neighborhood'':141C ''network'':105C ''neural'':104C ''object'':123C ''one'':21C ''open'':33C ''open-end'':32C ''p'':1C,39C,68C,91C,144C,161C,171C,196C,229C,260C ''perhap'':135C,204C,241C,248C ''pick'':275C ''platform'':63C ''play'':191C ''point'':267C ''press'':251C ''pretti'':264C ''price'':139C ''project'':10C,27C,56C,74C,146C,277C ''question'':80C,95C ''readili'':159C ''real'':49C ''realli'':280C,281C ''reason'':22C ''recogn'':107C ''recommend'':64C ''regress'':118C,134C,203C ''research'':174C ''robot'':175C,236C ''room'':227C ''sensor'':246C ''spam'':113C ''spend'':284C ''standpoint'':75C ''status'':221C ''subject'':164C ''teach'':12C,234C ''tech'':66C ''temperatur'':218C ''think'':6C,70C ''time'':46C ''turn'':209C ''ul'':102C ''understand'':297C ''use'':116C,124C,131C,201C,252C ''week'':287C ''wheel'':243C ''written'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (278, 2, '2014-06-10 14:30:33.667', 1, NULL, NULL, '&lt;p&gt;Assuming you''re familiar with programming I would recommend looking at &lt;a href="http://scikit-learn.org/stable/" rel="nofollow"&gt;scikit-learn&lt;/a&gt;. It has especially nice help pages that can serve as mini-tutorials/a quick tour through machine learning. Pick an area you find interesting and work through the examples. &lt;/p&gt;
', NULL, NULL, '''/a'':23C,37C ''/p'':54C ''/stable/'':17C ''area'':45C ''assum'':2C ''especi'':26C ''exampl'':53C ''familiar'':5C ''find'':47C ''help'':28C ''href'':14C ''interest'':48C ''learn'':22C,42C ''look'':11C ''machin'':41C ''mini'':35C ''mini-tutori'':34C ''nice'':27C ''nofollow'':19C ''p'':1C ''page'':29C ''pick'':43C ''program'':7C ''quick'':38C ''re'':4C ''recommend'':10C ''rel'':18C ''scikit'':21C ''scikit-learn'':20C ''scikit-learn.org'':16C ''scikit-learn.org/stable/'':15C ''serv'':32C ''tour'':39C ''tutori'':36C ''work'':50C ''would'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (279, 2, '2014-06-10 14:57:47.81', 11, NULL, NULL, '&lt;p&gt;There is also another resource provided by The Guardian, the British Daily on their website. The datasets published by the Guardian Datablog are all hosted. Datasets related to Football Premier League Clubs'' accounts, Inflation and GDP details of UK, Grammy awards data etc.
The datasets are available at &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.theguardian.com/news/datablog/interactive/2013/jan/14/all-our-datasets-index"&gt;http://www.theguardian.com/news/datablog/interactive/2013/jan/14/all-our-datasets-index&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Some more resources. Some of the datasets are in R format or R commads exist for directly importing data to R.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.inside-r.org/howto/finding-data-internet"&gt;http://www.inside-r.org/howto/finding-data-internet&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/howto/finding-data-internet'':91C ''/howto/finding-data-internet&lt;/a&gt;&lt;/li&gt;'':94C ''/news/datablog/interactive/2013/jan/14/all-our-datasets-index'':57C ''/news/datablog/interactive/2013/jan/14/all-our-datasets-index&lt;/a&gt;&lt;/li&gt;'':60C ''/p'':50C,84C ''/ul'':61C,95C ''account'':34C ''also'':4C ''anoth'':5C ''avail'':48C ''award'':42C ''british'':12C ''club'':33C ''commad'':76C ''daili'':13C ''data'':43C,81C ''datablog'':23C ''dataset'':18C,27C,46C,69C ''detail'':38C ''direct'':79C ''etc'':44C ''exist'':77C ''footbal'':30C ''format'':73C ''gdp'':37C ''grammi'':41C ''guardian'':10C,22C ''host'':26C ''href'':54C,88C ''import'':80C ''inflat'':35C ''leagu'':32C ''li'':52C,86C ''p'':1C,62C ''premier'':31C ''provid'':7C ''publish'':19C ''r'':72C,75C,83C ''relat'':28C ''resourc'':6C,65C ''uk'':40C ''ul'':51C,85C ''websit'':16C ''www.inside-r.org'':90C,93C ''www.inside-r.org/howto/finding-data-internet'':89C ''www.inside-r.org/howto/finding-data-internet&lt;/a&gt;&lt;/li&gt;'':92C ''www.theguardian.com'':56C,59C ''www.theguardian.com/news/datablog/interactive/2013/jan/14/all-our-datasets-index'':55C ''www.theguardian.com/news/datablog/interactive/2013/jan/14/all-our-datasets-index&lt;/a&gt;&lt;/li&gt;'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (280, 1, '2014-06-10 15:08:54.073', 6, 106, 'Feature selection for tracking user activity within an application', '&lt;p&gt;I am developing a system that is intended to capture the "context" of user activity within an application; it is a framework that web applications can use to tag user activity based on requests made to the system.  It is hoped that this data can then power ML features such as context aware information retrieval.&lt;/p&gt;

&lt;p&gt;I''m having trouble deciding on what features to select in addition to these user tags - the URL being requested, approximate time spent with any given resource, estimating the current "activity" within the system.&lt;/p&gt;

&lt;p&gt;I am interested to know if there are good examples of this kind of technology or any prior research on the subject - a cursory search of the ACM DL revealed some related papers but nothing really spot-on.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (282, 2, '2014-06-10 16:25:24.223', 6, NULL, NULL, '&lt;p&gt;Yes. Why not?!
With so much of data being recorded in each sport in each game, smart use of data could lead us in obtaining important insights regarding player performance.&lt;/p&gt;

&lt;p&gt;Some examples:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Baseball&lt;/strong&gt;: In the movie Moneyball (which is an adaptation of the MoneyBall book), Brad Pitt plays a character who analyses player statistics to come up with a team that performs tremendously well! It was a depiction of the real life story of Oakland Athletics baseball team. For more info, &lt;a href="http://www.theatlantic.com/entertainment/archive/2013/09/forget-2002-this-years-oakland-as-are-the-real-em-moneyball-em-team/279927/"&gt;http://www.theatlantic.com/entertainment/archive/2013/09/forget-2002-this-years-oakland-as-are-the-real-em-moneyball-em-team/279927/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Cricket&lt;/strong&gt;: SAP Labs has come up with an auction analytics tool that has given insights about impact players to buy in the 2014 Indian Premier League auction for the Kolkata Knight Riders team, which eventually went on to win the 2014 IPL &lt;strong&gt;Championship&lt;/strong&gt;. For more info, &lt;a href="http://scn.sap.com/community/hana-in-memory/blog/2014/06/10/sap-hana-academy-cricket-demo--how-sap-hana-powered-the-kolkata-knight-riders-to-ipl-championship"&gt;http://scn.sap.com/community/hana-in-memory/blog/2014/06/10/sap-hana-academy-cricket-demo--how-sap-hana-powered-the-kolkata-knight-riders-to-ipl-championship&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So, yes, statistical analysis of the player records can give us insights about &lt;strong&gt;which players are more likely to perform but not which players will perform&lt;/strong&gt;. So, machine learning, a close cousin of statistical analysis will be proving to be a game changer.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (284, 2, '2014-06-10 17:06:54.95', 4, NULL, NULL, '&lt;p&gt;Well, this may not answer the question thoroughly, but since you''re dealing with information retrieval, it may be of some use. &lt;a href="http://moz.com/search-ranking-factors" rel="nofollow"&gt;This page&lt;/a&gt; mantains a set of features and associated &lt;em&gt;correlations&lt;/em&gt; with page-ranking methods of search engines. As a disclaimer from the webpage itself:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Note that these factors are not "proof" of what search engines use to rank websites, but simply show the characteristics of web pages that tend to rank higher.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The list pointed may give you some insights on which features would be nice to select. For example, considering the second most correlated feature, # of google +1''s, it may be possible to add some probability of a user making use of such service if he/she accesses many pages with high # of google +1 (infer "user context"). Thus, you could try to "guess" some other relations that may shed light on interesting features for your tracking app.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (285, 2, '2014-06-10 17:15:52.953', 5, NULL, NULL, '&lt;p&gt;There are a lot of good questions about Football (and sports, in general) that would be awesome to throw to an algorithm and see what comes out. The tricky part is to know &lt;em&gt;what&lt;/em&gt; to throw to the algorithm.&lt;/p&gt;

&lt;p&gt;A team with a good RB could just pass on 3rd-and-short just because the opponents would probably expect run, for instance. So, in order to actually produce some worthy results, I''d break the problem in smaller pieces and analyse them statistically while throwing them to the machines!&lt;/p&gt;

&lt;p&gt;There are a few (good) websites that try to do the same, you should check''em out and use whatever they found to help you out:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.footballoutsiders.com/"&gt;Football Outsiders&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.advancedfootballanalytics.com/"&gt;Advanced Football Analytics&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;And if you truly want to explore Sports Data Analysis, you should definitely check the &lt;a href="http://www.sloansportsconference.com/"&gt;Sloan Sports Conference&lt;/a&gt; videos. There''s a lot of them spread on Youtube.&lt;/p&gt;
', NULL, NULL, '''/a'':132C,141C,166C ''/em'':37C ''/li'':133C,142C ''/p'':43C,96C,124C,177C ''/ul'':143C ''3rd'':56C ''3rd-and-short'':55C ''actual'':73C ''advanc'':138C ''algorithm'':23C,42C ''analys'':87C ''analysi'':154C ''analyt'':140C ''awesom'':18C ''break'':80C ''check'':112C,158C ''come'':27C ''confer'':165C ''could'':51C ''d'':79C ''data'':153C ''definit'':157C ''em'':35C,113C ''expect'':65C ''explor'':151C ''footbal'':10C,130C,139C ''found'':119C ''general'':14C ''good'':7C,49C,102C ''help'':121C ''href'':128C,136C,161C ''instanc'':68C ''know'':34C ''li'':126C,134C ''lot'':5C,171C ''machin'':95C ''oppon'':62C ''order'':71C ''outsid'':131C ''p'':1C,44C,97C,144C ''part'':31C ''pass'':53C ''piec'':85C ''probabl'':64C ''problem'':82C ''produc'':74C ''question'':8C ''rb'':50C ''result'':77C ''run'':66C ''see'':25C ''short'':58C ''sloan'':163C ''smaller'':84C ''sport'':12C,152C,164C ''spread'':174C ''statist'':89C ''team'':46C ''throw'':20C,39C,91C ''tri'':105C ''tricki'':30C ''truli'':148C ''ul'':125C ''use'':116C ''video'':167C ''want'':149C ''websit'':103C ''whatev'':117C ''worthi'':76C ''would'':16C,63C ''www.advancedfootballanalytics.com'':137C ''www.footballoutsiders.com'':129C ''www.sloansportsconference.com'':162C ''youtub'':176C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (287, 2, '2014-06-10 17:32:19.12', 3, NULL, NULL, '&lt;p&gt;I do not know a standard answer to this, but I thought about it some times ago and I have some ideas to share. &lt;/p&gt;

&lt;p&gt;When you have one confusion matrix, you have more or less a picture of how you classification model confuse (mis-classify) classes. When you repeat classification tests you will end up having multiple confusion matrices. The question is how to get a meaningful aggregate confusion matrix. The answer depends on what is the meaning of meaningful (pun intended). I think there is not a single version of meaningful.&lt;/p&gt;

&lt;p&gt;One way is to follow the rough idea of multiple testing. In general, you test something multiple times in order to get more accurate results. As a general principle one can reason that averaging on the results of the multiple tests reduces the variance of the estimates, so as a consequence, it increases the precision of the estimates. You can proceed in this way, of course, by summing position by position and then dividing by the number of tests. You can go further and instead of estimating only a value for each cell of the confusion matrix, you can also compute some confidence intervals, t-values and so on. This is OK from my point of view. But it tell only one side of the story.&lt;/p&gt;

&lt;p&gt;The other side of the story which might be investigated is how stable are the results for the same instances. To exemplify that I will take an extreme example. Suppose you have a classification model for 3 classes. Suppose that these classes are in the same proportion. If your model is able to predict one class perfectly and the other 2 classes with random like performance, you will end up having 0.33 + 0.166 + 0.166 = 0.66 misclassification ratio. This might seem good, but even if you take a look on a single confusion matrix you will not know that your performance on the last 2 classes varies wildly. Multiple tests can help. But averaging the confusion matrices would reveal this? My belief is not. The averaging will give the same result more or less, and doing multiple tests will only decrease the variance of the estimation. However it says nothing about the wild instability of prediction.&lt;/p&gt;

&lt;p&gt;So another way to do compose the confusion matrices would better involve a prediction density for each instance. One can build this density by counting for each instance, the number of times it was predicted a given class. After normalization, you will have for each instance a prediction density rather a single prediction label. You can see that a single prediction label is similar with a degenerated density where you have probability of 1 for the predicted class and 0 for the other classes for each separate instance. Now having this densities one can build a confusion matrix by adding the probabilities from each instance and predicted class to the corresponding cell of the aggregated confusion matrix. &lt;/p&gt;

&lt;p&gt;One can argue that this would give similar results like the previous method. However I think that this might be the case sometimes, often when the model has low variance, the second method is less affected by how the samples from the tests are drawn, and thus more stable and closer to the reality. &lt;/p&gt;

&lt;p&gt;Also the second method might be altered in order to obtain a third method, where one can assign as prediction the label with highest density from the prediction of a given instance. &lt;/p&gt;

&lt;p&gt;I do not implemented those things but I plan to study further because I believe might worth spending some time. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (288, 2, '2014-06-10 17:36:11.58', 4, NULL, NULL, '&lt;p&gt;The goal determines the features, so I would initially take as many as possible, then use cross validation to select the optimal subset.&lt;/p&gt;

&lt;p&gt;My educated guess is that a Markov model would work. If you discretize the action space (e.g., select this menu item, press that button, etc.), you can predict the next action based on the past ones. It''s a &lt;em&gt;sequence&lt;/em&gt; or &lt;a href="http://en.wikipedia.org/wiki/Structured_prediction" rel="nofollow"&gt;structured prediction&lt;/a&gt; problem.&lt;/p&gt;

&lt;p&gt;For commercial offerings, search &lt;strong&gt;app analytics&lt;/strong&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (289, 1, '2014-06-10 17:56:34.847', 7, 286, 'Qualifications for PhD Programs', '&lt;p&gt;Yann LeCun mentioned in his &lt;a href="http://www.reddit.com/r/MachineLearning/comments/25lnbt/ama_yann_lecun/" rel="nofollow"&gt;AMA&lt;/a&gt; that he considers having a PhD very important in order to get a job at a top company.&lt;/p&gt;

&lt;p&gt;I have a masters in statistics and my undergrad was in economics and applied math, but I am now looking into ML PhD programs. Most programs say there are no absolutely necessary CS courses; however I tend to think most accepted students have at least a very strong CS background. I am currently working as a data scientist/statistician but my company will pay for courses. Should I take some intro software engineering courses at my local University to make myself a stronger candidate? What other advice you have for someone applying to PhD programs from outside the CS field?  &lt;/p&gt;

&lt;p&gt;edit: I have taken a few MOOCs (Machine Learning, Recommender Systems, NLP) and code R/python on a daily basis. I have a lot of coding experience with statistical languages and implement ML algorithms daily. I am more concerned with things that I can put on applications.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (301, 2, '2014-06-11 00:32:54.887', 9, NULL, NULL, '&lt;p&gt;One reason that data cleaning is rarely fully automated is that there is so much judgment required to define what "clean" means given your particular problem, methods, and goals.&lt;/p&gt;

&lt;p&gt;It may be as simple as imputing values for any missing data, or it might be as complex as diagnosing data entry errors or data transformation errors from previous automated processes (e.g. coding, censoring, transforming).  In these last two cases, the data &lt;em&gt;looks good&lt;/em&gt; by outward appearance but it''s really erroneous.  Such diagnosis often requires manual analysis and inspection, and also out-of-band information such as information about the data sources and methods they used.&lt;/p&gt;

&lt;p&gt;Also, some data analysis methods work better when erroneous or missing data is left blank (or N/A) rather than imputed or given a default value.  This is true when there is explicit representations of uncertainty and ignorance, such as Dempster-Shafer Belief functions.&lt;/p&gt;

&lt;p&gt;Finally, it''s useful to have specific diagnostics and metrics for the cleaning process.  Are missing or erroneous values randomly distributed or are they concentrated in any way that might affect the outcome of the analysis.  It''s useful to test the effects of alternative cleaning strategies or algorithms to see if they affect the final results.&lt;/p&gt;

&lt;p&gt;Given these concerns, I''m very suspicious of any method or process that treats data cleaning in a superficial, cavalier or full-automated fashion.  There are many devils hiding in those details and it pays to give them serious attention.&lt;/p&gt;
', NULL, NULL, '''/em'':78C ''/p'':31C,113C,159C,218C,261C ''affect'':191C,214C ''algorithm'':209C ''also'':96C,115C ''altern'':205C ''analysi'':92C,118C,196C ''appear'':81C ''attent'':260C ''autom'':10C,62C,243C ''band'':100C ''belief'':157C ''better'':121C ''blank'':129C ''case'':72C ''cavali'':239C ''censor'':66C ''clean'':6C,22C,173C,206C,235C ''code'':65C ''complex'':50C ''concentr'':185C ''concern'':222C ''data'':5C,44C,53C,57C,74C,107C,117C,126C,234C ''default'':138C ''defin'':20C ''dempster'':155C ''dempster-shaf'':154C ''detail'':252C ''devil'':248C ''diagnos'':52C ''diagnosi'':88C ''diagnost'':168C ''distribut'':181C ''e.g'':64C ''effect'':203C ''em'':75C ''entri'':54C ''erron'':86C,123C,178C ''error'':55C,59C ''explicit'':146C ''fashion'':244C ''final'':161C,216C ''full'':242C ''full-autom'':241C ''fulli'':9C ''function'':158C ''give'':257C ''given'':24C,136C,220C ''goal'':30C ''good'':77C ''hide'':249C ''ignor'':151C ''imput'':39C,134C ''inform'':101C,104C ''inspect'':94C ''judgment'':17C ''last'':70C ''left'':128C ''look'':76C ''m'':224C ''mani'':247C ''manual'':91C ''may'':34C ''mean'':23C ''method'':28C,110C,119C,229C ''metric'':170C ''might'':47C,190C ''miss'':43C,125C,176C ''much'':16C ''n/a'':131C ''often'':89C ''one'':2C ''out-of-band'':97C ''outcom'':193C ''outward'':80C ''p'':1C,32C,114C,160C,219C ''particular'':26C ''pay'':255C ''previous'':61C ''problem'':27C ''process'':63C,174C,231C ''random'':180C ''rare'':8C ''rather'':132C ''realli'':85C ''reason'':3C ''represent'':147C ''requir'':18C,90C ''result'':217C ''see'':211C ''serious'':259C ''shafer'':156C ''simpl'':37C ''sourc'':108C ''specif'':167C ''strategi'':207C ''superfici'':238C ''suspici'':226C ''test'':201C ''transform'':58C,67C ''treat'':233C ''true'':142C ''two'':71C ''uncertainti'':149C ''use'':112C,164C,199C ''valu'':40C,139C,179C ''way'':188C ''work'':120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (290, 2, '2014-06-10 18:22:32.61', 1, NULL, NULL, '&lt;p&gt;I found the pluralsight course &lt;a href="http://pluralsight.com/training/courses/TableOfContents?courseName=introduction-to-machine-learning-encog&amp;amp;highlight=abhishek-kumar_introduction-to-machine-learning-encog-m2-applications!abhishek-kumar_introduction-to-machine-learning-encog-m3-tasks!abhishek-kumar_introduction-to-machine-learning-encog-m1-intro%2a1#introduction-to-machine-learning-encog-m2-applications" rel="nofollow"&gt;Introduction to machine learning encog&lt;/a&gt; a great resource so start with. It uses the &lt;a href="http://www.heatonresearch.com/encog" rel="nofollow"&gt;Encog library&lt;/a&gt; to quickly explore different ml techniques. &lt;/p&gt;
', NULL, NULL, '''/a'':19C,38C ''/encog'':33C ''/p'':45C ''/training/courses/tableofcontents?coursename=introduction-to-machine-learning-encog&amp;amp;highlight=abhishek-kumar_introduction-to-machine-learning-encog-m2-applications!abhishek-kumar_introduction-to-machine-learning-encog-m3-tasks!abhishek-kumar_introduction-to-machine-learning-encog-m1-intro%2a1#introduction-to-machine-learning-encog-m2-applications'':11C ''cours'':6C ''differ'':42C ''encog'':18C,36C ''explor'':41C ''found'':3C ''great'':21C ''href'':8C,30C ''introduct'':14C ''learn'':17C ''librari'':37C ''machin'':16C ''ml'':43C ''nofollow'':13C,35C ''p'':1C ''pluralsight'':5C ''pluralsight.com'':10C ''pluralsight.com/training/courses/tableofcontents?coursename=introduction-to-machine-learning-encog&amp;amp;highlight=abhishek-kumar_introduction-to-machine-learning-encog-m2-applications!abhishek-kumar_introduction-to-machine-learning-encog-m3-tasks!abhishek-kumar_introduction-to-machine-learning-encog-m1-intro%2a1#introduction-to-machine-learning-encog-m2-applications'':9C ''quick'':40C ''rel'':12C,34C ''resourc'':22C ''start'':24C ''techniqu'':44C ''use'':27C ''www.heatonresearch.com'':32C ''www.heatonresearch.com/encog'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (292, 2, '2014-06-10 19:43:11.86', 6, NULL, NULL, '&lt;p&gt;Your time would probably be better spent on Kaggle than in a PhD program. When you read the stories by winners (&lt;a href="http://blog.kaggle.com/"&gt;Kaggle blog&lt;/a&gt;) you''ll see that it takes a large amount of practice and the winners are not just experts of one single method.&lt;/p&gt;

&lt;p&gt;On the other hand, being active and having a plan in a PhD program can get you connections that you otherwise would probably not get.&lt;/p&gt;

&lt;p&gt;I guess the real question is for you - what are the reasons for wanting a job at a top company?&lt;/p&gt;
', NULL, NULL, '''/a'':28C ''/p'':51C,78C,100C ''activ'':58C ''amount'':37C ''better'':7C ''blog'':27C ''blog.kaggle.com'':25C ''compani'':99C ''connect'':70C ''expert'':46C ''get'':68C,77C ''guess'':81C ''hand'':56C ''href'':24C ''job'':95C ''kaggl'':10C,26C ''larg'':36C ''ll'':30C ''method'':50C ''one'':48C ''otherwis'':73C ''p'':1C,52C,79C ''phd'':14C,65C ''plan'':62C ''practic'':39C ''probabl'':5C,75C ''program'':15C,66C ''question'':84C ''read'':18C ''real'':83C ''reason'':91C ''see'':31C ''singl'':49C ''spent'':8C ''stori'':20C ''take'':34C ''time'':3C ''top'':98C ''want'':93C ''winner'':22C,42C ''would'':4C,74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9082, 1, '2015-11-27 16:11:32.24', 1, 27, 'result of correlation function in r', '&lt;p&gt;I would like to ask if the matrix that is returned from the R cor() function applied on a dataset, is considered a confusion matrix. Thank you&lt;/p&gt;
', 'r  correlation  confusion-matrix', NULL, '''/p'':40C ''appli'':29C ''ask'':17C ''confus'':10B,36C ''confusion-matrix'':9B ''consid'':34C ''cor'':27C ''correl'':3A,8B ''dataset'':32C ''function'':4A,28C ''like'':15C ''matrix'':11B,20C,37C ''p'':12C ''r'':6A,7B,26C ''result'':1A ''return'':23C ''thank'':38C ''would'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (293, 2, '2014-06-10 20:28:54.613', 10, NULL, NULL, '&lt;p&gt;Perhaps a good way to paraphrase the question is, what are the advantages compared to alternative formats?  &lt;/p&gt;

&lt;p&gt;The main alternatives are, I think: a database, text files, or another packed/binary format.&lt;/p&gt;

&lt;p&gt;The database options to consider are probably a columnar store or NoSQL, or for small self-contained datasets SQLite.  The main advantage of the database is the ability to work with data much larger than memory, to have random or indexed access, and to add/append/modify data quickly.  The main *dis*advantage is that it is much slower than HDF, for problems in which the entire dataset needs to be read in and processed.  Another disadvantage is that, with the exception of embedded-style databases like SQLite, a database is a system (requiring admnistration, setup, maintenance, etc) rather than a simple self-contained data store.  &lt;/p&gt;

&lt;p&gt;The text file format options are XML/JSON/CSV.  They are cross-platform/language/toolkit, and are a good archival format due to the ability to be self-describing (or obvious :).  If uncompressed, they are huge (10x-100x HDF), but if compressed, they can be fairly space-efficient (compressed XML is about the same as HDF).  The main disadvantage here is again speed: parsing text is much, much slower than HDF.&lt;/p&gt;

&lt;p&gt;The other binary formats (npy/npz numpy files, blz blaze files, protocol buffers, Avro, ...) have very similar properties to HDF, except they are less widely supported (may be limited to just one platform: numpy) and may have specific other limitations.  They typically do not offer a compelling advantage.&lt;/p&gt;

&lt;p&gt;HDF is a good complement to databases, it may make sense to run a query to produce a roughly memory-sized dataset and then cache it in HDF if the same data would be used more than once.  If you have a dataset which is fixed, and usually processed as a whole, storing it as a collection of appropriately sized HDF files is not a bad option.  If you have a dataset which is updated often, staging some of it as HDF files periodically might still be helpful.&lt;/p&gt;

&lt;p&gt;To summarize, HDF is a good format for data which is read (or written) typically as a whole; it is the lingua franca or common/preferred interchange format for many applications due to wide support and compatibility, decent as an archival format, and very fast.&lt;/p&gt;

&lt;p&gt;P.S. To give this some practical context, my most recent experience comparing HDF to alternatives, a certain small (much less than memory-sized) dataset took 2 seconds to read as HDF (and most of this is probably overhead from Pandas); ~1 minute to read from JSON; and 1 &lt;em&gt;hour&lt;/em&gt; to write to database.  Certainly the database write could be sped up, but you''d better have a good DBA!  This is how it works out of the box.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (302, 2, '2014-06-11 00:44:20.517', 2, NULL, NULL, '&lt;p&gt;I''ve seen a few similar systems over the years.  I remember a company called ClickTrax which if I''m not mistaken got bought by Google and some of their features are now part of Google Analytics.&lt;/p&gt;

&lt;p&gt;Their purpose was marketing, but the same concept can be applied to user experience analytics.  The beauty of their system was that what was tracked was defined by the webmaster - in your case the application developer.&lt;/p&gt;

&lt;p&gt;I can imagine as an application developer I would want to be able to see statistical data on two things - task accomplishment, and general feature usage.&lt;/p&gt;

&lt;p&gt;As an example of task accomplishment, I might have 3 ways to print a page - Ctrl+P, File-&gt;Print, and a toolbar button.  I would want to be able to compare usage to see if the screenspace utilized by the toolbar button was actually worth it.&lt;/p&gt;

&lt;p&gt;As an example of general feature usage, I would want to define a set of features within my application and focus my development efforts on expanding the features used most by my end users.  Some features that take maybe 5 clicks and are popular, I might want to provide a hotkey for, or slim down the number of clicks to activate that feature.  There is also event timing.  Depending on the application, I might want to know the average amount of time spent on a particular feature.  &lt;/p&gt;

&lt;p&gt;Another thing I would want to look at are click streams.  How are people getting from point A to point B in my application?  What are the most popular point B''s?  What are the most popular starting points?  &lt;/p&gt;
', NULL, NULL, '''/p'':39C,77C,105C,153C,242C,283C ''3'':116C ''5'':194C ''abl'':91C,135C ''accomplish'':100C,112C ''activ'':215C ''actual'':150C ''also'':220C ''amount'':234C ''analyt'':38C,55C ''anoth'':244C ''appli'':51C ''applic'':75C,84C,173C,226C,267C ''averag'':233C ''b'':264C,274C ''beauti'':57C ''bought'':25C ''button'':129C,148C ''call'':16C ''case'':73C ''click'':195C,213C,253C ''clicktrax'':17C ''compani'':15C ''compar'':137C ''concept'':48C ''ctrl'':122C ''data'':95C ''defin'':67C,166C ''depend'':223C ''develop'':76C,85C,177C ''effort'':178C ''end'':187C ''event'':221C ''exampl'':109C,157C ''expand'':180C ''experi'':54C ''featur'':32C,103C,160C,170C,182C,190C,217C,241C ''file'':124C ''focus'':175C ''general'':102C,159C ''get'':258C ''googl'':27C,37C ''got'':24C ''hotkey'':205C ''imagin'':81C ''know'':231C ''look'':250C ''m'':21C ''market'':44C ''mayb'':193C ''might'':114C,200C,228C ''mistaken'':23C ''number'':211C ''p'':1C,40C,78C,106C,123C,154C,243C ''page'':121C ''part'':35C ''particular'':240C ''peopl'':257C ''point'':260C,263C,273C,282C ''popular'':198C,272C,280C ''print'':119C,125C ''provid'':203C ''purpos'':42C ''rememb'':13C ''screenspac'':143C ''see'':93C,140C ''seen'':4C ''set'':168C ''similar'':7C ''slim'':208C ''spent'':237C ''start'':281C ''statist'':94C ''stream'':254C ''system'':8C,60C ''take'':192C ''task'':99C,111C ''thing'':98C,245C ''time'':222C,236C ''toolbar'':128C,147C ''track'':65C ''two'':97C ''usag'':104C,138C,161C ''use'':183C ''user'':53C,188C ''util'':144C ''ve'':3C ''want'':88C,132C,164C,201C,229C,248C ''way'':117C ''webmast'':70C ''within'':171C ''worth'':151C ''would'':87C,131C,163C,247C ''year'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (294, 2, '2014-06-10 20:40:25.623', 8, NULL, NULL, '&lt;p&gt;As a former Hadoop engineer, it is not needed but it helps. Hadoop is just one system - the most common system, based on Java, and a ecosystem of products, which apply a particular technique "Map/Reduce" to obtain results in a timely manner. Hadoop is not used at Google, though I assure you they use big data analytics. Google uses their own systems, developed in C++. In fact, Hadoop was created as a result of Google publishing their Map/Reduce and BigTable (HBase in Hadoop) white papers.&lt;/p&gt;

&lt;p&gt;Data scientists will interface with hadoop engineers, though at smaller places you may be required to wear both hats. If you are strictly a data scientist, then whatever you use for your analytics, R, Excel, Tableau, etc, will operate only on a small subset, then will need to be converted to run against the full data set involving hadoop. &lt;/p&gt;
', NULL, NULL, '''/p'':87C,148C ''analyt'':58C,121C ''appli'':32C ''assur'':52C ''base'':23C ''big'':56C ''bigtabl'':81C ''c'':66C ''common'':21C ''convert'':138C ''creat'':71C ''data'':57C,89C,113C,144C ''develop'':64C ''ecosystem'':28C ''engin'':6C,95C ''etc'':125C ''excel'':123C ''fact'':68C ''former'':4C ''full'':143C ''googl'':49C,59C,76C ''hadoop'':5C,14C,44C,69C,84C,94C,147C ''hat'':107C ''hbase'':82C ''help'':13C ''interfac'':92C ''involv'':146C ''java'':25C ''manner'':43C ''map/reduce'':36C,79C ''may'':101C ''need'':10C,135C ''obtain'':38C ''one'':17C ''oper'':127C ''p'':1C,88C ''paper'':86C ''particular'':34C ''place'':99C ''product'':30C ''publish'':77C ''r'':122C ''requir'':103C ''result'':39C,74C ''run'':140C ''scientist'':90C,114C ''set'':145C ''small'':131C ''smaller'':98C ''strict'':111C ''subset'':132C ''system'':18C,22C,63C ''tableau'':124C ''techniqu'':35C ''though'':50C,96C ''time'':42C ''use'':47C,55C,60C,118C ''wear'':105C ''whatev'':116C ''white'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (326, 1, '2014-06-12 06:04:48.243', 28, 18307, 'Python vs R for machine learning', '&lt;p&gt;I''m just starting to develop a &lt;a href="https://en.wikipedia.org/wiki/Machine_learning"&gt;machine learning&lt;/a&gt; application for academic purposes. I''m currently using &lt;strong&gt;R&lt;/strong&gt; and training myself in it. However, in a lot of places, I saw people using &lt;strong&gt;Python&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;What are people using in academia and industry, and what is the recommendation?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (295, 2, '2014-06-10 20:43:28.533', 3, NULL, NULL, '&lt;p&gt;I am glad you also found Yann LeCun''s AMA page, it''s very useful.&lt;/p&gt;

&lt;p&gt;Here are my opinions&lt;br&gt;
Q: Should I take some intro software engineering courses at my local University to make myself a stronger candidate?&lt;br&gt;
A: No, you need to take more math courses.  It''s not the applied stuff that''s hard, it''s the theory stuff.  I don''t know what your school offers.  Take theoretical math courses, along with some computer science courses.  &lt;/p&gt;

&lt;p&gt;Q:What other advice you have for someone applying to PhD programs from outside the CS field?&lt;br&gt;
A:  How closely related are you looking for.  Without a specific question, it''s hard to give a specific answer.  &lt;/p&gt;
', NULL, NULL, '''/p'':17C,85C,125C ''advic'':90C ''along'':79C ''also'':6C ''ama'':11C ''answer'':124C ''appli'':57C,95C ''br'':23C,43C,104C ''candid'':42C ''close'':107C ''comput'':82C ''cours'':32C,52C,78C,84C ''cs'':102C ''engin'':31C ''field'':103C ''found'':7C ''give'':121C ''glad'':4C ''hard'':61C,119C ''intro'':29C ''know'':70C ''lecun'':9C ''local'':35C ''look'':111C ''make'':38C ''math'':51C,77C ''need'':47C ''offer'':74C ''opinion'':22C ''outsid'':100C ''p'':1C,18C,86C ''page'':12C ''phd'':97C ''program'':98C ''q'':24C,87C ''question'':116C ''relat'':108C ''school'':73C ''scienc'':83C ''softwar'':30C ''someon'':94C ''specif'':115C,123C ''stronger'':41C ''stuff'':58C,66C ''take'':27C,49C,75C ''theoret'':76C ''theori'':65C ''univers'':36C ''use'':16C ''without'':113C ''yann'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (744, 1, '2014-07-15 21:30:11.6', 11, 3483, 'Cosine similarity versus dot product as distance metrics', '&lt;p&gt;It looks like the cosine similarity of two features is just their dot product scaled by the product of their magnitudes. When does cosine similarity make a better distance metric than the dot product? I.e. do the dot product and cosine similarity have different strengths or weaknesses in different situations?&lt;/p&gt;
', 'classification', NULL, '''/p'':61C ''better'':38C ''classif'':9B ''cosin'':1A,15C,34C,51C ''differ'':54C,59C ''distanc'':7A,39C ''dot'':4A,23C,43C,48C ''featur'':19C ''i.e'':45C ''like'':13C ''look'':12C ''magnitud'':31C ''make'':36C ''metric'':8A,40C ''p'':10C ''product'':5A,24C,28C,44C,49C ''scale'':25C ''similar'':2A,16C,35C,52C ''situat'':60C ''strength'':55C ''two'':18C ''versus'':3A ''weak'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (296, 2, '2014-06-10 21:50:51.347', 15, NULL, NULL, '&lt;p&gt;HDP is an extension of LDA, designed to address the case where the number of mixture components (the number of "topics" in document-modeling terms) is not known a priori.  So that''s the reason why there''s a difference.&lt;/p&gt;

&lt;p&gt;Using LDA for document modeling, one treats each "topic" as a distribution of words in some known vocabulary.  For each document a mixture of topics is drawn from a Dirichlet distribution, and then each word in the document is an independent draw from that mixture (that is, selecting a topic and then using it to generate a word).&lt;/p&gt;

&lt;p&gt;For HDP (applied to document modeling), one also uses a Dirichlet process to capture the uncertainty in the number of topics.  So a common base distribution is selected which represents the countably-infinite set of possible topics for the corpus, and then the finite distribution of topics for each document is sampled from this base distribution.&lt;/p&gt;

&lt;p&gt;As far as pros and cons, HDP has the advantage that the maximum number of topics can be unbounded and learned from the data rather than specified in advance.  I suppose though it is more complicated to implement, and unnecessary in the case where a bounded number of topics is acceptable.&lt;/p&gt;
', NULL, NULL, '''/p'':43C,103C,162C,215C ''accept'':214C ''address'':10C ''advanc'':192C ''advantag'':173C ''also'':112C ''appli'':107C ''base'':129C,160C ''bound'':209C ''captur'':118C ''case'':12C,206C ''common'':128C ''complic'':199C ''compon'':18C ''con'':169C ''corpus'':145C ''countabl'':137C ''countably-infinit'':136C ''data'':187C ''design'':8C ''differ'':42C ''dirichlet'':74C,115C ''distribut'':56C,75C,130C,150C,161C ''document'':25C,48C,65C,82C,109C,155C ''document-model'':24C ''draw'':86C ''drawn'':71C ''extens'':5C ''far'':165C ''finit'':149C ''generat'':100C ''hdp'':2C,106C,170C ''implement'':201C ''independ'':85C ''infinit'':138C ''known'':30C,61C ''lda'':7C,46C ''learn'':184C ''maximum'':176C ''mixtur'':17C,67C,89C ''model'':26C,49C,110C ''number'':15C,20C,123C,177C,210C ''one'':50C,111C ''p'':1C,44C,104C,163C ''possibl'':141C ''priori'':32C ''process'':116C ''pros'':167C ''rather'':188C ''reason'':37C ''repres'':134C ''sampl'':157C ''select'':92C,132C ''set'':139C ''specifi'':190C ''suppos'':194C ''term'':27C ''though'':195C ''topic'':22C,53C,69C,94C,125C,142C,152C,179C,212C ''treat'':51C ''unbound'':182C ''uncertainti'':120C ''unnecessari'':203C ''use'':45C,97C,113C ''vocabulari'':62C ''word'':58C,79C,102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (297, 2, '2014-06-10 22:26:53.623', 3, NULL, NULL, '&lt;p&gt;As in @damienfrancois answer feature selection is about selecting a subset of features. So in NLP it would be selecting a set of specific words (the typical in NLP is that each word represents a feature with value equal to the frequency of the word or some other weight based on TF/IDF or similar).&lt;/p&gt;

&lt;p&gt;Dimensionality reduction is the introduction of new feature space where the original features are represented. The new space is of lower dimension that the original space. In case of text an example would be the &lt;a href="http://en.wikipedia.org/wiki/Feature_hashing" rel="nofollow"&gt;hashing trick&lt;/a&gt; where a piece of text is reduced to a vector of few bits (say 16 or 32) or bytes. The amazing thing is that the geometry of the space is preserved (given enough bits), so relative distances between documents remain the same as in the original space, so you can deploy standard machine learning techniques without having to deal with unbound (and huge number of) dimensions found in text.&lt;/p&gt;
', NULL, NULL, '''/a'':102C ''/p'':56C,172C ''/wiki/feature_hashing'':97C ''16'':117C ''32'':119C ''amaz'':123C ''answer'':5C ''base'':51C ''bit'':115C,136C ''byte'':121C ''case'':85C ''damienfrancoi'':4C ''deal'':161C ''deploy'':153C ''dimens'':79C,168C ''dimension'':58C ''distanc'':139C ''document'':141C ''en.wikipedia.org'':96C ''en.wikipedia.org/wiki/feature_hashing'':95C ''enough'':135C ''equal'':40C ''exampl'':89C ''featur'':6C,14C,37C,65C,70C ''found'':169C ''frequenc'':43C ''geometri'':128C ''given'':134C ''hash'':100C ''href'':94C ''huge'':165C ''introduct'':62C ''learn'':156C ''lower'':78C ''machin'':155C ''new'':64C,74C ''nlp'':17C,30C ''nofollow'':99C ''number'':166C ''origin'':69C,82C,148C ''p'':1C,57C ''piec'':105C ''preserv'':133C ''reduc'':109C ''reduct'':59C ''rel'':98C ''relat'':138C ''remain'':142C ''repres'':35C,72C ''say'':116C ''select'':7C,10C,21C ''set'':23C ''similar'':55C ''space'':66C,75C,83C,131C,149C ''specif'':25C ''standard'':154C ''subset'':12C ''techniqu'':157C ''text'':87C,107C,171C ''tf/idf'':53C ''thing'':124C ''trick'':101C ''typic'':28C ''unbound'':163C ''valu'':39C ''vector'':112C ''weight'':50C ''without'':158C ''word'':26C,34C,46C ''would'':19C,90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (298, 2, '2014-06-10 22:29:52.873', 6, NULL, NULL, '&lt;p&gt;You already have a Masters in Statistics, which is great! In general, I''d suggest to people to take as much statistics as they can, especially Bayesian Data Analysis. &lt;/p&gt;

&lt;p&gt;Depending on what you want to do with your PhD, you would benefit from foundational courses in the discipline(s) in your application area.  You already have Economics but if you want to do Data Science on social behavior, then courses in Sociology would be valuable.  If you want to work in fraud prevention, then a courses in banking and financial transactions would be good.  If you want to work in information security, then taking a few security courses would be good.&lt;/p&gt;

&lt;p&gt;There are people who argue that it''s not valuable for Data Scientists to spend time on courses in sociology or other disciplines.  But consider the recent case of the Google Flu Trends project.  In &lt;a href="http://www.uvm.edu/~cdanfort/csc-reading-group/lazer-flu-science-2014.pdf"&gt;this article&lt;/a&gt; their methods were strongly criticized for making avoidable mistakes.  The critics call it "Big Data hubris".&lt;/p&gt;

&lt;p&gt;There''s another reason for building strength in social science disciplines: personal competitive advantage.  With the rush of academic degree programs, certificate programs, and MOOCs, there is a mad rush of students into the Data Science field.  Most will come out with capabilities for core Machine Learning methods and tools.  PhD graduates will have more depth and more theoretical knowledge, but they are all competing for the same sorts of jobs, delivering the same sorts of value.  With this flood of graduates, I expect that they won''t be able to command premium salaries.&lt;/p&gt;

&lt;p&gt;But if you can differentiate yourself with a combination of formal education and practical experience in a particular domain and application area, then you should be able to set yourself apart from the crowd.&lt;/p&gt;

&lt;p&gt;(Context: I''m in a PhD program in Computational Social Science, which has a heavy focus on modeling, evolutionary computation, and social science disciplines, and less emphasis on ML and other empirical data analysis topics).&lt;/p&gt;
', NULL, NULL, '''/a'':159C ''/p'':31C,115C,176C,272C,308C,345C ''/~cdanfort/csc-reading-group/lazer-flu-science-2014.pdf'':156C ''abl'':267C,300C ''academ'':196C ''advantag'':191C ''alreadi'':3C,58C ''analysi'':30C,343C ''anoth'':180C ''apart'':304C ''applic'':55C,294C ''area'':56C,295C ''argu'':121C ''articl'':158C ''avoid'':167C ''bank'':91C ''bayesian'':28C ''behavior'':71C ''benefit'':45C ''big'':173C ''build'':183C ''call'':171C ''capabl'':220C ''case'':144C ''certif'':199C ''combin'':282C ''come'':217C ''command'':269C ''compet'':242C ''competit'':190C ''comput'':318C,329C ''consid'':141C ''context'':310C ''core'':222C ''cours'':48C,73C,89C,111C,134C ''critic'':164C,170C ''crowd'':307C ''d'':15C ''data'':29C,67C,128C,174C,212C,342C ''degre'':197C ''deliv'':249C ''depend'':33C ''depth'':233C ''differenti'':278C ''disciplin'':51C,139C,188C,333C ''domain'':292C ''econom'':60C ''educ'':285C ''emphasi'':336C ''empir'':341C ''especi'':27C ''evolutionari'':328C ''expect'':261C ''experi'':288C ''field'':214C ''financi'':93C ''flood'':257C ''flu'':148C ''focus'':325C ''formal'':284C ''foundat'':47C ''fraud'':85C ''general'':13C ''good'':97C,114C ''googl'':147C ''graduat'':229C,259C ''great'':11C ''heavi'':324C ''href'':153C ''hubri'':175C ''inform'':104C ''job'':248C ''knowledg'':237C ''learn'':224C ''less'':335C ''m'':312C ''machin'':223C ''mad'':206C ''make'':166C ''master'':6C ''method'':161C,225C ''mistak'':168C ''ml'':338C ''model'':327C ''mooc'':202C ''much'':22C ''p'':1C,32C,116C,177C,273C,309C ''particular'':291C ''peopl'':18C,119C ''person'':189C ''phd'':42C,228C,315C ''practic'':287C ''premium'':270C ''prevent'':86C ''program'':198C,200C,316C ''project'':150C ''reason'':181C ''recent'':143C ''rush'':194C,207C ''salari'':271C ''scienc'':68C,187C,213C,320C,332C ''scientist'':129C ''secur'':105C,110C ''set'':302C ''social'':70C,186C,319C,331C ''sociolog'':75C,136C ''sort'':246C,252C ''spend'':131C ''statist'':8C,23C ''strength'':184C ''strong'':163C ''student'':209C ''suggest'':16C ''take'':20C,107C ''theoret'':236C ''time'':132C ''tool'':227C ''topic'':344C ''transact'':94C ''trend'':149C ''valu'':254C ''valuabl'':78C,126C ''want'':37C,64C,81C,100C ''won'':264C ''work'':83C,102C ''would'':44C,76C,95C,112C ''www.uvm.edu'':155C ''www.uvm.edu/~cdanfort/csc-reading-group/lazer-flu-science-2014.pdf'':154C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1202, 2, '2014-10-03 16:07:30.327', 3, NULL, NULL, '&lt;p&gt;Try out some generative models like HMM. 
Just check the following link:
&lt;a href="http://stats.stackexchange.com/questions/91290/how-do-i-train-hmms-for-classification"&gt;http://stats.stackexchange.com/questions/91290/how-do-i-train-hmms-for-classification&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/questions/91290/how-do-i-train-hmms-for-classification'':18C ''/questions/91290/how-do-i-train-hmms-for-classification&lt;/a&gt;&lt;/p&gt;'':21C ''check'':10C ''follow'':12C ''generat'':5C ''hmm'':8C ''href'':15C ''like'':7C ''link'':13C ''model'':6C ''p'':1C ''stats.stackexchange.com'':17C,20C ''stats.stackexchange.com/questions/91290/how-do-i-train-hmms-for-classification'':16C ''stats.stackexchange.com/questions/91290/how-do-i-train-hmms-for-classification&lt;/a&gt;&lt;/p&gt;'':19C ''tri'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (303, 2, '2014-06-11 01:09:06.1', 1, NULL, NULL, '&lt;p&gt;This isn''t my area of specialty and I''m not familiar with Moses, but I found this after some searching.  &lt;/p&gt;

&lt;p&gt;I think you are looking for GIZA++.  You''ll see GIZA++ listed in the "Training" section (left menu) on the Moses home page, as the second step. GIZA++ is briefly described in tutorial fashion &lt;a href="http://stackoverflow.com/questions/5752043/is-there-a-tutorial-about-giza"&gt;here&lt;/a&gt;. Here are a few tutorial PowerPoint slides: &lt;a href="http://www.tc.umn.edu/~bthomson/wordalignment/GIZA.ppt" rel="nofollow"&gt;http://www.tc.umn.edu/~bthomson/wordalignment/GIZA.ppt&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':65C ''/p'':23C ''/questions/5752043/is-there-a-tutorial-about-giza'':63C ''/~bthomson/wordalignment/giza.ppt'':77C ''/~bthomson/wordalignment/giza.ppt&lt;/a&gt;&lt;/p&gt;'':82C ''area'':6C ''briefli'':54C ''describ'':55C ''familiar'':13C ''fashion'':58C ''found'':18C ''giza'':31C,35C,52C ''home'':46C ''href'':60C,74C ''isn'':3C ''left'':41C ''list'':36C ''ll'':33C ''look'':29C ''m'':11C ''menu'':42C ''mose'':15C,45C ''nofollow'':79C ''p'':1C,24C ''page'':47C ''powerpoint'':71C ''rel'':78C ''search'':22C ''second'':50C ''section'':40C ''see'':34C ''slide'':72C ''specialti'':8C ''stackoverflow.com'':62C ''stackoverflow.com/questions/5752043/is-there-a-tutorial-about-giza'':61C ''step'':51C ''think'':26C ''train'':39C ''tutori'':57C,70C ''www.tc.umn.edu'':76C,81C ''www.tc.umn.edu/~bthomson/wordalignment/giza.ppt'':75C ''www.tc.umn.edu/~bthomson/wordalignment/giza.ppt&lt;/a&gt;&lt;/p&gt;'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (305, 1, '2014-06-11 04:24:04.183', 9, 2008, 'Does Amazon RedShift replace Hadoop for ~1XTB data?', '&lt;p&gt;There is plenty of hype surrounding Hadoop and its eco-system.  However, in practice, where many data sets are in the terabyte range, is it not more reasonable to use &lt;a href="http://aws.amazon.com/redshift/"&gt;Amazon RedShift&lt;/a&gt; for querying large data sets, rather than spending time and effort building a Hadoop cluster? &lt;/p&gt;

&lt;p&gt;Also, how does Amazon Redshift compare with Hadoop with respect to setup complexity, cost, and performance?&lt;/p&gt;
', 'hadoop  map-reduce  aws', NULL, '''/a'':53C ''/p'':69C,87C ''/redshift/'':50C ''1xtb'':7A ''also'':71C ''amazon'':2A,51C,74C ''aw'':13B ''aws.amazon.com'':49C ''aws.amazon.com/redshift/'':48C ''build'':65C ''cluster'':68C ''compar'':76C ''complex'':83C ''cost'':84C ''data'':8A,32C,57C ''eco'':25C ''eco-system'':24C ''effort'':64C ''hadoop'':5A,9B,21C,67C,78C ''howev'':27C ''href'':47C ''hype'':19C ''larg'':56C ''mani'':31C ''map'':11B ''map-reduc'':10B ''p'':14C,70C ''perform'':86C ''plenti'':17C ''practic'':29C ''queri'':55C ''rang'':38C ''rather'':59C ''reason'':43C ''redshift'':3A,52C,75C ''reduc'':12B ''replac'':4A ''respect'':80C ''set'':33C,58C ''setup'':82C ''spend'':61C ''surround'':20C ''system'':26C ''terabyt'':37C ''time'':62C ''use'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (306, 2, '2014-06-11 05:17:12.253', 2, NULL, NULL, '&lt;p&gt;Personally, I don''t think it''s all that difficult to set up a hadoop cluster, but I know that it is sometimes painful when you are getting started.&lt;/p&gt;

&lt;p&gt;HDFS size limitations well exceed a TB (or did you mean exabyte?).  If I''m not mistaken it scales to yottabytes or some other measurement that I don''t even know the word for.  Whatever it is, it''s really big.&lt;/p&gt;

&lt;p&gt;Tools like Redshift have their place, but I always worry about vendor specific solutions.  My main concern is always "what do I do when I am dissatisfied with their service?" - I can go to google and shift my analysis work into their paradigm or I can go to hadoop and shift that same work into that system.  Either way, I''m going to have to learn something new and do a lot of work translating things.&lt;/p&gt;

&lt;p&gt;That being said, it''s nice to be able to upload a dataset and get to work quickly - especially if what I''m doing has a short lifecycle.  Amazon has done a good job of answering the data security problem.&lt;/p&gt;

&lt;p&gt;If you want to avoid hadoop, there will always be an alternative.  But it''s not all that difficult to work with once you get going with it.&lt;/p&gt;
', NULL, NULL, '''/p'':31C,74C,152C,194C,224C ''abl'':162C ''altern'':207C ''alway'':84C,94C,204C ''amazon'':182C ''analysi'':114C ''answer'':189C ''avoid'':200C ''big'':73C ''cluster'':17C ''concern'':92C ''data'':191C ''dataset'':166C ''difficult'':11C,214C ''dissatisfi'':102C ''done'':184C ''either'':133C ''especi'':172C ''even'':62C ''exabyt'':44C ''exceed'':37C ''get'':29C,168C,220C ''go'':108C,122C,137C,221C ''good'':186C ''googl'':110C ''hadoop'':16C,124C,201C ''hdfs'':33C ''job'':187C ''know'':20C,63C ''learn'':141C ''lifecycl'':181C ''like'':77C ''limit'':35C ''lot'':147C ''m'':47C,136C,176C ''main'':91C ''mean'':43C ''measur'':57C ''mistaken'':49C ''new'':143C ''nice'':159C ''p'':1C,32C,75C,153C,195C ''pain'':25C ''paradigm'':118C ''person'':2C ''place'':81C ''problem'':193C ''quick'':171C ''realli'':72C ''redshift'':78C ''said'':156C ''scale'':51C ''secur'':192C ''servic'':105C ''set'':13C ''shift'':112C,126C ''short'':180C ''size'':34C ''solut'':89C ''someth'':142C ''sometim'':24C ''specif'':88C ''start'':30C ''system'':132C ''tb'':39C ''thing'':151C ''think'':6C ''tool'':76C ''translat'':150C ''upload'':164C ''vendor'':87C ''want'':198C ''way'':134C ''well'':36C ''whatev'':67C ''word'':65C ''work'':115C,129C,149C,170C,216C ''worri'':85C ''yottabyt'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (307, 1, '2014-06-11 06:07:45.767', 11, 1356, 'Big data case study or use case example', '&lt;p&gt;I have read lot of blogs\article on how different type of industries are using Big Data Analytic. But most of these article fails to mention&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;What kinda data these companies used. What was the size of the data&lt;/li&gt;
&lt;li&gt;What kinda of tools technologies they used to process the data&lt;/li&gt;
&lt;li&gt;What was the problem they were facing and how the insight they got the data helped them to resolve the issue.&lt;/li&gt;
&lt;li&gt;How they selected the tool\technology to suit their need.&lt;/li&gt;
&lt;li&gt;What kinda pattern they identified from the data &amp;amp; what kind of patterns they were looking from the data.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I wonder if someone can provide me answer to all these questions or a link which at-least answer some of the the questions. I am looking for real world example. &lt;/p&gt;

&lt;p&gt;It would be great if someone share how finance industry is making use of Big Data Analytic.&lt;/p&gt;
', 'bigdata  hadoop  data-mining  social-network-analysis  usecase', NULL, '''/li'':62C,75C,98C,110C,131C ''/ol'':132C ''/p'':46C,166C,185C ''amp'':120C ''analysi'':17B ''analyt'':37C,184C ''answer'':141C,153C ''articl'':26C,42C ''at-least'':150C ''big'':1A,35C,182C ''bigdata'':9B ''blog'':25C ''case'':3A,7A ''compani'':53C ''data'':2A,12B,36C,51C,61C,74C,91C,119C,130C,183C ''data-min'':11B ''differ'':29C ''exampl'':8A,165C ''face'':83C ''fail'':43C ''financ'':176C ''got'':89C ''great'':171C ''hadoop'':10B ''help'':92C ''identifi'':116C ''industri'':32C,177C ''insight'':87C ''issu'':97C ''kind'':122C ''kinda'':50C,65C,113C ''least'':152C ''li'':48C,63C,76C,99C,111C ''link'':148C ''look'':127C,161C ''lot'':23C ''make'':179C ''mention'':45C ''mine'':13B ''need'':109C ''network'':16B ''ol'':47C ''p'':19C,133C,167C ''pattern'':114C,124C ''problem'':80C ''process'':72C ''provid'':139C ''question'':145C,158C ''read'':22C ''real'':163C ''resolv'':95C ''select'':102C ''share'':174C ''size'':58C ''social'':15B ''social-network-analysi'':14B ''someon'':137C,173C ''studi'':4A ''suit'':107C ''technolog'':68C,105C ''tool'':67C,104C ''type'':30C ''use'':6A,34C,54C,70C,180C ''usecas'':18B ''wonder'':135C ''world'':164C ''would'':169C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (309, 2, '2014-06-11 06:51:19.143', 11, NULL, NULL, '&lt;p&gt;&lt;strong&gt;tl;dr:&lt;/strong&gt; They markedly differ in many aspects and I can''t think Redshift will replace Hadoop.  &lt;/p&gt;

&lt;p&gt;-Function&lt;br&gt;
You can''t run anything other than SQL on Redshift. Perhaps most importantly, you can''t run any type of custom functions on Redshift. In Hadoop you can, using many languages (Java, Python, Ruby.. you name it). For example, NLP in Hadoop is easy, while it''s more or less impossible in Redshift. I.e. there are lots of things you can do in Hadoop but not on Redshift. This is probably the most important difference.&lt;/p&gt;

&lt;p&gt;-Performance Profile&lt;br&gt;
Query execution on Redshift is in most cases significantly more efficient than on Hadoop. However, this efficiency comes from the indexing that is done when the data is loaded into Redshift (I''m using the term &lt;code&gt;indexing&lt;/code&gt; very loose here). Therefore, it''s great if you load your data once and execute multiple queries, but if you want to execute only one query for example, you might actually lose out in performance overall.&lt;/p&gt;

&lt;p&gt;-Cost Profile&lt;br&gt;
Which solution wins out in cost depends on the situation (like performance), but you probably need quite a lot of queries in order to make it cheaper than Hadoop (more specifically Amazon''s Elastic Map Reduce). For example, if you are doing OLAP, it''s very likely that Redshift comes out cheaper. If you do daily batch ETLs, Hadoop is more likely to come out cheaper.  &lt;/p&gt;

&lt;p&gt;Having said that, we''ve replaced part of our ETL that was done in Hive to Redshift, and it was a pretty great experience; mostly for the ease of development. Redshift''s Query Engine is based on PostgreSQL and is very mature, compared to Hive''s. Its ACID characteristics make it easier to reason about it, and the quicker response time allows more testing to be done. It''s a great tool to have, but it won''t replace Hadoop.  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT&lt;/strong&gt;:  As for setup complexity, I''d even say it''s easier with Hadoop if you use AWS''s EMR. Their tools are so mature that it''s ridiculously easy to have your Hadoop job running. Tools and mechanisms surrounding Redshift''s operation aren''t that mature yet. For example, Redshift can''t handle trickle loading and thus you have to come up with something that turns that into a batched load, which can add some complexity to your ETL. &lt;/p&gt;
', NULL, NULL, '''..'':59C ''/code'':144C ''/p'':21C,101C,181C,252C,334C,418C ''/strong'':5C,338C ''acid'':301C ''actual'':175C ''add'':412C ''allow'':315C ''amazon'':217C ''anyth'':29C ''aren'':381C ''aspect'':11C ''aw'':355C ''base'':289C ''batch'':242C,408C ''br'':24C,105C,185C ''case'':113C ''characterist'':302C ''cheaper'':212C,237C,251C ''code'':142C ''come'':123C,235C,249C,399C ''compar'':296C ''complex'':342C,414C ''cost'':183C,191C ''custom'':45C ''d'':344C ''daili'':241C ''data'':132C,156C ''depend'':192C ''develop'':283C ''differ'':8C,100C ''done'':129C,266C,320C ''dr'':4C ''eas'':281C ''easi'':69C,367C ''easier'':305C,349C ''edit'':337C ''effici'':116C,122C ''elast'':219C ''emr'':357C ''engin'':287C ''etl'':243C,263C,417C ''even'':345C ''exampl'':64C,172C,223C,387C ''execut'':107C,159C,167C ''experi'':277C ''function'':23C,46C ''great'':151C,276C,324C ''hadoop'':20C,50C,67C,89C,119C,214C,244C,333C,351C,371C ''handl'':391C ''hive'':268C,298C ''howev'':120C ''i.e'':79C ''import'':37C,99C ''imposs'':76C ''index'':126C,143C ''java'':56C ''job'':372C ''languag'':55C ''less'':75C ''like'':196C,232C,247C ''load'':134C,154C,393C,409C ''loos'':146C ''lose'':176C ''lot'':82C,204C ''m'':138C ''make'':210C,303C ''mani'':10C,54C ''map'':220C ''mark'':7C ''matur'':295C,362C,384C ''mechan'':376C ''might'':174C ''most'':278C ''multipl'':160C ''name'':61C ''need'':201C ''nlp'':65C ''olap'':228C ''one'':169C ''oper'':380C ''order'':208C ''overal'':180C ''p'':1C,22C,102C,182C,253C,335C ''part'':260C ''perform'':103C,179C,197C ''perhap'':35C ''postgresql'':291C ''pretti'':275C ''probabl'':96C,200C ''profil'':104C,184C ''python'':57C ''queri'':106C,161C,170C,206C,286C ''quicker'':312C ''quit'':202C ''reason'':307C ''redshift'':17C,34C,48C,78C,93C,109C,136C,234C,270C,284C,378C,388C ''reduc'':221C ''replac'':19C,259C,332C ''respons'':313C ''ridicul'':366C ''rubi'':58C ''run'':28C,41C,373C ''said'':255C ''say'':346C ''setup'':341C ''signific'':114C ''situat'':195C ''solut'':187C ''someth'':402C ''specif'':216C ''sql'':32C ''strong'':2C,336C ''surround'':377C ''term'':141C ''test'':317C ''therefor'':148C ''thing'':84C ''think'':16C ''thus'':395C ''time'':314C ''tl'':3C ''tool'':325C,359C,374C ''trickl'':392C ''turn'':404C ''type'':43C ''use'':53C,139C,354C ''ve'':258C ''want'':165C ''win'':188C ''won'':330C ''yet'':385C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (310, 1, '2014-06-11 10:11:59.397', 8, 467, 'One-Class discriminatory classification with imbalanced, heterogenous Negative background?', '&lt;p&gt;I''m working on improving an existing supervised classifier, for classifying {protein} sequences as belonging to a specific class (Neuropeptide hormone precursors), or not.&lt;/p&gt;

&lt;p&gt;There are about 1,150 known "positives", against a background of about 13 million protein sequences ("Unknown/poorly annotated background"), or about 100,000 reviewed, relevant proteins, annotated with a variety of properties (but very few annotated in an explicitly "negative" way). &lt;/p&gt;

&lt;p&gt;My previous implementation looked at this as a binary classification problem: 
Positive set = Proteins marked as Neuropeptides.
Negative set: Random sampling of 1,300 samples (total) from among the remaining proteins of a roughly similar length-wise distribution. &lt;/p&gt;

&lt;p&gt;That worked, but I want to greatly improve the machines discriminatory abilities (Currently, it''s at about 83-86% in terms of accuracy, AUC, F1, measured by CV, on multiple randomly sampled negative sets).&lt;/p&gt;

&lt;p&gt;My thoughts were to:
1) Make this a multiclass problem, choosing 2-3 different classes of protein that will definetly be negatives, by their properties/functional class, along with (maybe) another randomly sampled set.
 (Priority here would be negative sets that are similar in their characteristics/features to the positive set, while still having defining characteristics) . 
2) One class learning - Would be nice, but as I understand it, it''s meant just for anomaly detection, and has poorer performance than discriminatory approaches.&lt;/p&gt;

&lt;p&gt;*) I''ve heard of P-U learning, which sounds neat, but I''m a programming N00b, and I don''t know of any existing implementations for it. (In Python/sci-kit learn).&lt;/p&gt;

&lt;p&gt;So, does approach 1 make sense in a theoretical POV? Is there a best way to make multiple negative sets? (I could also simply use a massive [50K] pick of the "negative" proteins, but they''re all very very different from each other, so I don''t know how well the classifier would handle them as one big , unbalanced mix).
Thanks!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (311, 2, '2014-06-11 11:24:19.963', 4, NULL, NULL, '&lt;p&gt;The way I would attack the problem, in general, is to leverage statistical analysis like Principal Component Analysis or Ordinary Least Squares to help determine what attributes within these protein sequences are best suited to classify proteins as Neuropeptide hormone precursors.  &lt;/p&gt;

&lt;p&gt;In order to do that, you''ll have to convert the protein sequences into numeric data, but I believe some work has already been done in that regard using formulas leveraged in Amino Acid PCA.&lt;/p&gt;

&lt;p&gt;See these two links:
&lt;a href="http://www.ncbi.nlm.nih.gov/pubmed/24496727" rel="nofollow"&gt;http://www.ncbi.nlm.nih.gov/pubmed/24496727&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.ncbi.nlm.nih.gov/pubmed/16615809" rel="nofollow"&gt;http://www.ncbi.nlm.nih.gov/pubmed/16615809&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Once that work has been done, I would attempt to classify using the entire dataset and a reinforcement learning algorithm, like &lt;a href="http://findingscience.com/ankusa/hbase/hadoop/ruby/2010/12/02/naive-bayes-classification-in-ruby-using-hadoop-and-hbase.html" rel="nofollow"&gt;Naive Bayes&lt;/a&gt; while slimming down the data into that which PCA has identified as important.  &lt;/p&gt;

&lt;p&gt;The reason I would try to use Bayes is because it has proven to be one of the best methods for determining spam vs. regular email, which has a similarly skewed dataset.&lt;/p&gt;

&lt;p&gt;Having said all of that...&lt;/p&gt;

&lt;p&gt;Slimming down the number or type of negative classifications might skew your results a few points one way or the other, but I don''t think you''ll see the long term effectiveness change substantially until you do the leg work of determining how to best remove the fuzziness from your training data.  That will either require a field expert or statistical analysis.&lt;/p&gt;

&lt;p&gt;I could be completely off base.  I am interested in seeing some other answers, but that is my 2 cents.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (312, 2, '2014-06-11 11:32:13.433', 3, NULL, NULL, '&lt;p&gt;Therriault, really happy to hear you are using Vertica! Full disclosure, I am the chief data scientist there :) . The workflow you describe is exactly what I encounter quite frequently and I am a true believer in preprocessing those very large datasets in the database prior to any pyODBC and pandas work. I''d suggest creating a view or table via a file based query just to ensure reproducible work. Good Luck&lt;/p&gt;
', NULL, NULL, '''/p'':73C ''base'':64C ''believ'':36C ''chief'':16C ''creat'':56C ''d'':54C ''data'':17C ''databas'':45C ''dataset'':42C ''describ'':23C ''disclosur'':12C ''encount'':28C ''ensur'':68C ''exact'':25C ''file'':63C ''frequent'':30C ''full'':11C ''good'':71C ''happi'':4C ''hear'':6C ''larg'':41C ''luck'':72C ''p'':1C ''panda'':51C ''preprocess'':38C ''prior'':46C ''pyodbc'':49C ''queri'':65C ''quit'':29C ''realli'':3C ''reproduc'':69C ''scientist'':18C ''suggest'':55C ''tabl'':60C ''therriault'':2C ''true'':35C ''use'':9C ''vertica'':10C ''via'':61C ''view'':58C ''work'':52C,70C ''workflow'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (313, 1, '2014-06-11 13:28:35.98', 19, 1116, 'Books about the "Science" in Data Science?', '&lt;p&gt;What are the books about the science and mathematics behind data science? It feels like so many "data science" books are programming tutorials and don''t touch things like data generating processes and statistical inference. I can already code, what I am weak on is the math/stats/theory behind what I am doing.&lt;/p&gt;

&lt;p&gt;If I am ready to burn $1000 on books (so around 10 books... sigh), what could I buy?&lt;/p&gt;

&lt;p&gt;Examples: Agresti''s &lt;a href="http://rads.stackoverflow.com/amzn/click/0470463635"&gt;Categorical Data Analysis&lt;/a&gt;, &lt;a href="http://rads.stackoverflow.com/amzn/click/1441902996"&gt;Linear Mixed Models for Longitudinal Data&lt;/a&gt;, etc... etc...&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (314, 2, '2014-06-11 13:49:08.97', 10, NULL, NULL, '&lt;p&gt;If I could only recomend one to you, it would be: &lt;a href="http://rads.stackoverflow.com/amzn/click/0387848576"&gt;The Elements of Statistical Learning and Prediction&lt;/a&gt; by Hastie, Tibshirani and Friedman.  It provides the math/statistics behind a lot of commonly used techniques in data science.&lt;/p&gt;

&lt;p&gt;For Bayesian Techniques, &lt;a href="http://rads.stackoverflow.com/amzn/click/1439840954"&gt;Bayesian Data Analysis&lt;/a&gt; by Gelman, Carlin, Stern, Dunson, Vehtari and Rubin is excellent.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0534243126"&gt;Statistical Inference&lt;/a&gt; by Casella and Berger is a good graduate-level textbook on the theoretical foundation of statistics.  This book does require a pretty high level of comfort with math (probability theory is based on measure theory, which is not trivial to understand).&lt;/p&gt;

&lt;p&gt;With respect to data generating processes, I don''t have a recommendation for a book.  What I can say is that a good understanding of the assumptions of the techniques used and ensuring that the data was collected or generated in a manner that does not violate those assumptions goes a long way towards a good analysis.&lt;/p&gt;
', NULL, NULL, '''/a'':25C,58C,78C ''/amzn/click/0387848576'':17C ''/amzn/click/0534243126'':75C ''/amzn/click/1439840954'':54C ''/p'':45C,69C,121C,180C ''analysi'':57C,179C ''assumpt'':149C,171C ''base'':111C ''bayesian'':48C,55C ''behind'':35C ''berger'':82C ''book'':97C,137C ''carlin'':61C ''casella'':80C ''collect'':160C ''comfort'':105C ''common'':39C ''could'':4C ''data'':43C,56C,126C,158C ''dunson'':63C ''element'':19C ''ensur'':155C ''excel'':68C ''foundat'':93C ''friedman'':30C ''gelman'':60C ''generat'':127C,162C ''goe'':172C ''good'':85C,145C,178C ''graduat'':87C ''graduate-level'':86C ''hasti'':27C ''high'':102C ''href'':14C,51C,72C ''infer'':77C ''learn'':22C ''level'':88C,103C ''long'':174C ''lot'':37C ''manner'':165C ''math'':107C ''math/statistics'':34C ''measur'':113C ''one'':7C ''p'':1C,46C,70C,122C ''predict'':24C ''pretti'':101C ''probabl'':108C ''process'':128C ''provid'':32C ''rads.stackoverflow.com'':16C,53C,74C ''rads.stackoverflow.com/amzn/click/0387848576'':15C ''rads.stackoverflow.com/amzn/click/0534243126'':73C ''rads.stackoverflow.com/amzn/click/1439840954'':52C ''recomend'':6C ''recommend'':134C ''requir'':99C ''respect'':124C ''rubin'':66C ''say'':141C ''scienc'':44C ''statist'':21C,76C,95C ''stern'':62C ''techniqu'':41C,49C,152C ''textbook'':89C ''theoret'':92C ''theori'':109C,114C ''tibshirani'':28C ''toward'':176C ''trivial'':118C ''understand'':120C,146C ''use'':40C,153C ''vehtari'':64C ''violat'':169C ''way'':175C ''would'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (328, 2, '2014-06-12 08:30:49.757', 5, NULL, NULL, '&lt;p&gt;There is no "better" language. I have tried both of them and I am comfortable with Python so I work with Python only. Though I am still learning stuff, but I haven''t encounter any roadblock with Python till now. The good thing about Python is community is too good and you can get a lot of help on the Internet easily. Other than that, I would say go with the language you like not the one people recommend. &lt;/p&gt;
', NULL, NULL, '''/p'':81C ''better'':5C ''comfort'':16C ''communiti'':48C ''easili'':63C ''encount'':35C ''get'':55C ''go'':70C ''good'':43C,51C ''haven'':33C ''help'':59C ''internet'':62C ''languag'':6C,73C ''learn'':29C ''like'':75C ''lot'':57C ''one'':78C ''p'':1C ''peopl'':79C ''python'':18C,23C,39C,46C ''recommend'':80C ''roadblock'':37C ''say'':69C ''still'':28C ''stuff'':30C ''thing'':44C ''though'':25C ''till'':40C ''tri'':9C ''work'':21C ''would'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (317, 2, '2014-06-11 16:56:59.66', 1, NULL, NULL, '&lt;p&gt;If you already know R Studio, then the caret package is a good place to start. Here are some tutorials: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="https://class.coursera.org/predmachlearn-002" rel="nofollow"&gt;https://class.coursera.org/predmachlearn-002&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://caret.r-forge.r-project.org/index.html" rel="nofollow"&gt;http://caret.r-forge.r-project.org/index.html&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;With R and caret you can easily load and splice data sets, feature reduction, principal component analysis, and train and predict using various algorithms.&lt;/p&gt;
', NULL, NULL, '''/index.html'':40C ''/index.html&lt;/a&gt;&lt;/li&gt;'':45C ''/ol'':46C ''/p'':22C,72C ''/predmachlearn-002'':29C ''/predmachlearn-002&lt;/a&gt;&lt;/li&gt;'':34C ''algorithm'':71C ''alreadi'':4C ''analysi'':64C ''caret'':10C,51C ''caret.r-forge.r-project.org'':39C,44C ''caret.r-forge.r-project.org/index.html'':38C ''caret.r-forge.r-project.org/index.html&lt;/a&gt;&lt;/li&gt;'':43C ''class.coursera.org'':28C,33C ''class.coursera.org/predmachlearn-002'':27C ''class.coursera.org/predmachlearn-002&lt;/a&gt;&lt;/li&gt;'':32C ''compon'':63C ''data'':58C ''easili'':54C ''featur'':60C ''good'':14C ''href'':26C,37C ''know'':5C ''li'':24C,35C ''load'':55C ''nofollow'':31C,42C ''ol'':23C ''p'':1C,47C ''packag'':11C ''place'':15C ''predict'':68C ''princip'':62C ''r'':6C,49C ''reduct'':61C ''rel'':30C,41C ''set'':59C ''splice'':57C ''start'':17C ''studio'':7C ''train'':66C ''tutori'':21C ''use'':69C ''various'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (318, 2, '2014-06-11 17:24:53.61', 1, NULL, NULL, '&lt;p&gt;If you can reproduce the 6x3 grid of graphs from the banner of the &lt;a href="http://scikit-learn.org/" rel="nofollow"&gt;http://scikit-learn.org/&lt;/a&gt; page then you will have learnt some ML and some Python. You didn''t mention a language. Python is easy enough to learn very quickly, and scikit-learn has a wide range of algorithms implemented.&lt;/p&gt;

&lt;p&gt;Then try on your own data!&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':23C ''/p'':60C,68C ''6x3'':7C ''algorithm'':58C ''banner'':13C ''data'':67C ''didn'':36C ''easi'':43C ''enough'':44C ''graph'':10C ''grid'':8C ''href'':17C ''implement'':59C ''languag'':40C ''learn'':46C,52C ''learnt'':29C ''mention'':38C ''ml'':31C ''nofollow'':20C ''p'':1C,61C ''page'':24C ''python'':34C,41C ''quick'':48C ''rang'':56C ''rel'':19C ''reproduc'':5C ''scikit'':51C ''scikit-learn'':50C ''scikit-learn.org'':18C,22C ''scikit-learn.org/&lt;/a&gt;'':21C ''tri'':63C ''wide'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (319, 1, '2014-06-11 18:22:36.267', 6, 739, 'Debugging Neural Networks', '&lt;p&gt;I''ve built an artificial neural network in python using the scipy.optimize.minimize (Conjugate gradient) optimization function.&lt;/p&gt;

&lt;p&gt;I''ve implemented gradient checking, double checked everything etc and I''m pretty certain it''s working correctly.&lt;/p&gt;

&lt;p&gt;I''ve run it a few times and it reaches ''Optimization terminated successfully'' however when I increase the number of hidden layers, the cost of the hypothesis increases (everything else is kept the same) after it has successfully terminated.&lt;/p&gt;

&lt;p&gt;Intuitively it feels as if the cost should decrease when the number of hidden layers is increased, as it is able to generate a more complex hypothesis which can fit the data better, however this appears not to be the case.&lt;/p&gt;

&lt;p&gt;I''d be interested to understand what''s going on here, or if I''ve implemented neural net incorrectly?&lt;/p&gt;
', 'machine-learning  python  neuralnetwork', NULL, '''/p'':26C,46C,87C,130C,151C ''abl'':109C ''appear'':124C ''artifici'':14C ''better'':121C ''built'':12C ''case'':129C ''certain'':41C ''check'':32C,34C ''complex'':114C ''conjug'':22C ''correct'':45C ''cost'':71C,95C ''d'':133C ''data'':120C ''debug'':1A ''decreas'':97C ''doubl'':33C ''els'':77C ''etc'':36C ''everyth'':35C,76C ''feel'':91C ''fit'':118C ''function'':25C ''generat'':111C ''go'':140C ''gradient'':23C,31C ''hidden'':68C,102C ''howev'':61C,122C ''hypothesi'':74C,115C ''implement'':30C,147C ''incorrect'':150C ''increas'':64C,75C,105C ''interest'':135C ''intuit'':89C ''kept'':79C ''layer'':69C,103C ''learn'':6B ''m'':39C ''machin'':5B ''machine-learn'':4B ''net'':149C ''network'':3A,16C ''neural'':2A,15C,148C ''neuralnetwork'':8B ''number'':66C,100C ''optim'':24C,58C ''p'':9C,27C,47C,88C,131C ''pretti'':40C ''python'':7B,18C ''reach'':57C ''run'':50C ''scipy.optimize.minimize'':21C ''success'':60C,85C ''termin'':59C,86C ''time'':54C ''understand'':137C ''use'':19C ''ve'':11C,29C,49C,146C ''work'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (320, 2, '2014-06-11 20:34:51.873', 7, NULL, NULL, '&lt;p&gt;There are so many ways to go wrong with a neural net that it''s going to be difficult to debug. Also, to address your intuition, each additional hidden layer makes learning much harder. With that said, here are some possibilities:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;You have added weight decay. Adding more layers adds more weights which increases your regularization cost.&lt;/li&gt;
&lt;li&gt;The problem is simple enough that a model with a single hidden layer is sufficient. Adding more hidden layers makes it harder for the network to learn (harder optimization problem).&lt;/li&gt;
&lt;li&gt;The optimization method is not doing a great job (I prefer climin to scipy.optimize).&lt;/li&gt;
&lt;li&gt;You are using the sigmoid/tanh activation function. The sigmoid function causes the vanishing gradient problem which makes learning hard with more layers. Try using the ReLu function.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Training neural nets takes a lot of practice, luck, and patience. Good luck.&lt;/p&gt;
', NULL, NULL, '''/li'':62C,94C,110C,139C ''/ol'':140C ''/p'':43C,155C ''activ'':117C ''ad'':48C,51C,79C ''add'':54C ''addit'':29C ''address'':25C ''also'':23C ''caus'':122C ''climin'':107C ''cost'':61C ''debug'':22C ''decay'':50C ''difficult'':20C ''enough'':68C ''function'':118C,121C,138C ''go'':8C,17C ''good'':153C ''gradient'':125C ''great'':103C ''hard'':130C ''harder'':35C,85C,91C ''hidden'':30C,75C,81C ''increas'':58C ''intuit'':27C ''job'':104C ''layer'':31C,53C,76C,82C,133C ''learn'':33C,90C,129C ''li'':45C,63C,95C,111C ''lot'':147C ''luck'':150C,154C ''make'':32C,83C,128C ''mani'':5C ''method'':98C ''model'':71C ''much'':34C ''net'':13C,144C ''network'':88C ''neural'':12C,143C ''ol'':44C ''optim'':92C,97C ''p'':1C,141C ''patienc'':152C ''possibl'':42C ''practic'':149C ''prefer'':106C ''problem'':65C,93C,126C ''regular'':60C ''relu'':137C ''said'':38C ''scipy.optimize'':109C ''sigmoid'':120C ''sigmoid/tanh'':116C ''simpl'':67C ''singl'':74C ''suffici'':78C ''take'':145C ''train'':142C ''tri'':134C ''use'':114C,135C ''vanish'':124C ''way'':6C ''weight'':49C,56C ''wrong'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (323, 1, '2014-06-11 23:52:37.823', 4, 796, 'How can we calculate AUC for a simple decision tree?', '&lt;p&gt;The setup is simple: binary classification using a simple decision tree, each node of the tree has a single threshold applied on a single feature. In general, building a ROC curve requires moving a decision threshold over different values and computing the effect of that change on the true positive rate and the false positives rate of predictions. What''s that decision threshold in the case of a simple fixed decision tree? &lt;/p&gt;
', 'machine-learning', NULL, '''/p'':87C ''appli'':35C ''auc'':5A ''binari'':19C ''build'':42C ''calcul'':4A ''case'':80C ''chang'':60C ''classif'':20C ''comput'':55C ''curv'':45C ''decis'':9A,24C,49C,76C,85C ''differ'':52C ''effect'':57C ''fals'':68C ''featur'':39C ''fix'':84C ''general'':41C ''learn'':13B ''machin'':12B ''machine-learn'':11B ''move'':47C ''node'':27C ''p'':14C ''posit'':64C,69C ''predict'':72C ''rate'':65C,70C ''requir'':46C ''roc'':44C ''setup'':16C ''simpl'':8A,18C,23C,83C ''singl'':33C,38C ''threshold'':34C,50C,77C ''tree'':10A,25C,30C,86C ''true'':63C ''use'':21C ''valu'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (489, 2, '2014-06-19 18:05:51.07', 14, NULL, NULL, '&lt;p&gt;A GLM is absolutely a statistical model, but statistical models and machine learning techniques are not mutually exclusive. In general, statistics is more concerned with inferring parameters, whereas in machine learning, prediction is the ultimate goal.&lt;/p&gt;
', NULL, NULL, '''/p'':38C ''absolut'':5C ''concern'':25C ''exclus'':19C ''general'':21C ''glm'':3C ''goal'':37C ''infer'':27C ''learn'':14C,32C ''machin'':13C,31C ''model'':8C,11C ''mutual'':18C ''p'':1C ''paramet'':28C ''predict'':33C ''statist'':7C,10C,22C ''techniqu'':15C ''ultim'':36C ''wherea'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (332, 2, '2014-06-12 10:27:14.48', 1, NULL, NULL, '&lt;p&gt;In order to build the ROC curve and AUC (Area under curve) you have to have a binary classifier which provides you at classification time, the distribution (or at least a score), not the classification label. To give you an example, suppose you have a binary classification model, with classes c1 and c2. For a given instance, your classifier would have to return a score for c1 and another for c2. If this score is a probability-like (preferrable), than something like p(c1), p(c2) would work. In plain English is translated like "I (the model) classify this instance as c1 with probability p(c1), and as c2 with probability  p(c2)=1-p(c1)". &lt;/p&gt;

&lt;p&gt;This applies for all type of classifiers, not only for decision trees. Having these scores you can than compute ROC or AUC by varying a threshold on p(c1) values, from the smallest to the greatest value.&lt;/p&gt;

&lt;p&gt;Now, if you have an implementation of a decision tree and you want to change that implementation to return scores instead of labels you have to compute those values. The most used way for decision trees is to use the proportion of classes from the leaf nodes. So, for example you have built a decision tree and when you predict the class for an instance you arrive at a leaf node which have (stored from the learning phase) 10 instances of class c1 and 15 instances of class c2, you can use the ratios as the scores. So, in our example, you would return p(c1) = 10 / (10+15) = 0.4 probability of class c1 and p(c2) = 15/(10+15)=0.6 probability of being class c2.&lt;/p&gt;

&lt;p&gt;For further reading on the ROC curves, the best and inspiring source of information I found to be the Tom Fawcett paper called &lt;a href="https://www.google.ie/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;ved=0CCsQFjAA&amp;amp;url=https://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf&amp;amp;ei=DYCZU96QGPOV7AaAx4CgBA&amp;amp;usg=AFQjCNECLoecin6ieT-0ymLQ--FoMjkMZw&amp;amp;sig2=3bGE2y1N3I2Re_YWn0EDFA" rel="nofollow"&gt;An Introduction to ROC Analysis&lt;/a&gt;, it''s solid gold on this topic.&lt;/p&gt;
', NULL, NULL, '''+15'':268C,279C ''/a'':323C ''/p'':119C,158C,286C,331C ''/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;ved=0ccsqfjaa&amp;amp;url=https://ccrma.stanford.edu/workshops/mir2009/references/rocintro.pdf&amp;amp;ei=dyczu96qgpov7aaax4cgba&amp;amp;usg=afqjcnecloecin6iet-0ymlq--fomjkmzw&amp;amp;sig2=3bge2y1n3i2re_ywn0edfa'':315C ''0.4'':269C ''0.6'':280C ''1'':116C ''10'':238C,266C,267C,278C ''15'':244C,277C ''analysi'':322C ''anoth'':70C ''appli'':122C ''area'':11C ''arriv'':226C ''auc'':10C,142C ''best'':296C ''binari'':19C,47C ''build'':5C ''built'':212C ''c1'':52C,68C,86C,104C,108C,118C,149C,242C,265C,273C ''c2'':54C,72C,88C,111C,115C,248C,276C,285C ''call'':310C ''chang'':174C ''class'':51C,202C,221C,241C,247C,272C,284C ''classif'':25C,36C,48C ''classifi'':20C,60C,100C,127C ''comput'':139C,186C ''curv'':8C,13C,294C ''decis'':131C,168C,194C,214C ''distribut'':28C ''english'':93C ''exampl'':42C,209C,260C ''fawcett'':308C ''found'':303C ''give'':39C ''given'':57C ''gold'':327C ''greatest'':156C ''href'':312C ''implement'':165C,176C ''inform'':301C ''inspir'':298C ''instanc'':58C,102C,224C,239C,245C ''instead'':180C ''introduct'':319C ''label'':37C,182C ''leaf'':205C,229C ''learn'':236C ''least'':31C ''like'':80C,84C,96C ''model'':49C,99C ''node'':206C,230C ''nofollow'':317C ''order'':3C ''p'':1C,85C,87C,107C,114C,117C,120C,148C,159C,264C,275C,287C ''paper'':309C ''phase'':237C ''plain'':92C ''predict'':219C ''preferr'':81C ''probability-lik'':78C ''probabl'':79C,106C,113C,270C,281C ''proport'':200C ''provid'':22C ''ratio'':253C ''read'':290C ''rel'':316C ''return'':64C,178C,263C ''roc'':7C,140C,293C,321C ''score'':33C,66C,75C,135C,179C,256C ''smallest'':153C ''solid'':326C ''someth'':83C ''sourc'':299C ''store'':233C ''suppos'':43C ''threshold'':146C ''time'':26C ''tom'':307C ''topic'':330C ''translat'':95C ''tree'':132C,169C,195C,215C ''type'':125C ''use'':191C,198C,251C ''valu'':150C,157C,188C ''vari'':144C ''want'':172C ''way'':192C ''work'':90C ''would'':61C,89C,262C ''www.google.ie'':314C ''www.google.ie/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;ved=0ccsqfjaa&amp;amp;url=https://ccrma.stanford.edu/workshops/mir2009/references/rocintro.pdf&amp;amp;ei=dyczu96qgpov7aaax4cgba&amp;amp;usg=afqjcnecloecin6iet-0ymlq--fomjkmzw&amp;amp;sig2=3bge2y1n3i2re_ywn0edfa'':313C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (334, 1, '2014-06-12 10:52:03.41', 23, 4782, 'What do you think of Data Science certifications?', '&lt;p&gt;I''ve now seen two data science certification programs - the &lt;a href="https://www.coursera.org/specialization/jhudatascience/1?utm_medium=listingPage"&gt;John Hopkins one available at Coursera&lt;/a&gt; and the &lt;a href="http://cloudera.com/content/cloudera/en/training/certification/ccp-ds.html"&gt;Cloudera one&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I''m sure there are others out there.&lt;/p&gt;

&lt;p&gt;The John Hopkins set of classes is focused on R as a toolset, but covers a range of topics:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;R Programming&lt;/li&gt;
&lt;li&gt;cleaning and obtaining data&lt;/li&gt;
&lt;li&gt;Data Analysis&lt;/li&gt;
&lt;li&gt;Reproducible Research&lt;/li&gt;
&lt;li&gt;Statistical Inference&lt;/li&gt;
&lt;li&gt;Regression Models&lt;/li&gt;
&lt;li&gt;Machine Learning&lt;/li&gt;
&lt;li&gt;Developing Data Products&lt;/li&gt;
&lt;li&gt;And what looks to be a Project based completion task similar to Cloudera''s Data Science Challenge&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The Cloudera program looks thin on the surface, but looks to answer the two important questions - "Do you know the tools", "Can you apply the tools in the real world".  Their program consists of:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Introduction to Data Science&lt;/li&gt;
&lt;li&gt;Data Science Essentials Exam&lt;/li&gt;
&lt;li&gt;Data Science Challenge (a real world data science project scenario)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I am not looking for a recommendation on a program or a quality comparison.&lt;/p&gt;

&lt;p&gt;I am curious about other certifications out there, the topics they cover, and how seriously DS certifications are viewed at this point by the community.&lt;/p&gt;

&lt;p&gt;EDIT: These are all great answers.  I''m choosing the correct answer by votes.&lt;/p&gt;
', 'education', NULL, '''/a'':32C,42C ''/content/cloudera/en/training/certification/ccp-ds.html'':39C ''/li'':79C,85C,89C,93C,97C,101C,105C,110C,129C,173C,179C,191C ''/p'':43C,53C,74C,166C,208C,235C,251C ''/specialization/jhudatascience/1?utm_medium=listingpage'':25C ''/ul'':130C,192C ''analysi'':88C ''answer'':143C,242C,248C ''appli'':155C ''avail'':29C ''base'':119C ''certif'':8A,18C,215C,226C ''challeng'':128C,183C ''choos'':245C ''class'':60C ''clean'':81C ''cloudera'':40C,124C,133C ''cloudera.com'':38C ''cloudera.com/content/cloudera/en/training/certification/ccp-ds.html'':37C ''communiti'':234C ''comparison'':207C ''complet'':120C ''consist'':164C ''correct'':247C ''coursera'':31C ''cover'':69C,221C ''curious'':212C ''data'':6A,16C,84C,87C,108C,126C,171C,175C,181C,187C ''develop'':107C ''ds'':225C ''edit'':237C ''educ'':9B ''essenti'':177C ''exam'':178C ''focus'':62C ''great'':241C ''hopkin'':27C,57C ''href'':22C,36C ''import'':146C ''infer'':96C ''introduct'':169C ''john'':26C,56C ''know'':150C ''learn'':104C ''li'':76C,80C,86C,90C,94C,98C,102C,106C,111C,168C,174C,180C ''look'':114C,135C,141C,197C ''m'':46C,244C ''machin'':103C ''model'':100C ''obtain'':83C ''one'':28C,41C ''other'':50C ''p'':10C,44C,54C,131C,193C,209C,236C ''point'':231C ''product'':109C ''program'':19C,78C,134C,163C,203C ''project'':118C,189C ''qualiti'':206C ''question'':147C ''r'':64C,77C ''rang'':71C ''real'':160C,185C ''recommend'':200C ''regress'':99C ''reproduc'':91C ''research'':92C ''scenario'':190C ''scienc'':7A,17C,127C,172C,176C,182C,188C ''seen'':14C ''serious'':224C ''set'':58C ''similar'':122C ''statist'':95C ''sure'':47C ''surfac'':139C ''task'':121C ''thin'':136C ''think'':4A ''tool'':152C,157C ''toolset'':67C ''topic'':73C,219C ''two'':15C,145C ''ul'':75C,167C ''ve'':12C ''view'':228C ''vote'':250C ''world'':161C,186C ''www.coursera.org'':24C ''www.coursera.org/specialization/jhudatascience/1?utm_medium=listingpage'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (335, 2, '2014-06-12 11:11:35.6', 8, NULL, NULL, '&lt;p&gt;The certification programs you mentioned are really entry level courses. Personally, I think these certificates show only person''s persistence and they can be only useful to those who is applying for internships, not the real data science jobs.&lt;/p&gt;
', NULL, NULL, '''/p'':41C ''appli'':32C ''certif'':3C,16C ''cours'':11C ''data'':38C ''entri'':9C ''internship'':34C ''job'':40C ''level'':10C ''mention'':6C ''p'':1C ''persist'':21C ''person'':12C,19C ''program'':4C ''real'':37C ''realli'':8C ''scienc'':39C ''show'':17C ''think'':14C ''use'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (336, 2, '2014-06-12 11:30:20.943', 7, NULL, NULL, '&lt;p&gt;There isn''t a silver bullet language that can be used to solve each and every data related problem. The language choice depends on the context of the problem, size of data and if you are working at a workplace you have to stick to what they use.&lt;/p&gt;

&lt;p&gt;Personally I use R more often than Python due to its visualization libraries and interactive style. But if I need more performance or structured code I definitely use Python since it has some of the best libraries as SciKit-Learn, numpy, scipy etc. I use both R and Python in my projects interchangeably. &lt;/p&gt;

&lt;p&gt;So if you are starting on data science work I suggest you to learn both and it''s not difficult since Python also provides a similar interface to R with &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;If you have to deal with much larger datasets, you can''t escape eco-systems built with Java(Hadoop, Pig, Hbase etc).&lt;/p&gt;
', NULL, NULL, '''/a'':142C ''/p'':50C,106C,143C,168C ''also'':130C ''best'':87C ''built'':161C ''bullet'':7C ''choic'':23C ''code'':76C ''context'':27C ''data'':18C,33C,114C ''dataset'':153C ''deal'':149C ''definit'':78C ''depend'':24C ''difficult'':127C ''due'':60C ''eco'':159C ''eco-system'':158C ''escap'':157C ''etc'':95C,167C ''everi'':17C ''hadoop'':164C ''hbase'':166C ''href'':139C ''interact'':66C ''interchang'':105C ''interfac'':134C ''isn'':3C ''java'':163C ''languag'':8C,22C ''larger'':152C ''learn'':92C,121C ''librari'':64C,88C ''much'':151C ''need'':71C ''numpi'':93C ''often'':57C ''p'':1C,51C,107C,144C ''panda'':141C ''pandas.pydata.org'':140C ''perform'':73C ''person'':52C ''pig'':165C ''problem'':20C,30C ''project'':104C ''provid'':131C ''python'':59C,80C,101C,129C ''r'':55C,99C,136C ''relat'':19C ''scienc'':115C ''scikit'':91C ''scikit-learn'':90C ''scipi'':94C ''silver'':6C ''similar'':133C ''sinc'':81C,128C ''size'':31C ''solv'':14C ''start'':112C ''stick'':45C ''structur'':75C ''style'':67C ''suggest'':118C ''system'':160C ''use'':12C,49C,54C,79C,97C ''visual'':63C ''work'':38C,116C ''workplac'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (337, 2, '2014-06-12 11:54:59.14', 9, NULL, NULL, '&lt;p&gt;I would add to what others have said till now. There is no single answer that one language is better than other.&lt;/p&gt;

&lt;p&gt;Having said that, R has a better community for data exploration and learning. It has extensive visualization capabilities. Python, on the other hand, has become better at data handling since introduction of pandas. Learning and development time is very less in Python, as compared to R (R being a low level language).&lt;/p&gt;

&lt;p&gt;I think it ultimately boils down to the eco-system you are in and personal preferences. For more details, you can look at this comparison &lt;a href="http://www.analyticsvidhya.com/blog/2014/03/sas-vs-vs-python-tool-learn/"&gt;here&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':112C ''/blog/2014/03/sas-vs-vs-python-tool-learn/'':110C ''/p'':24C,78C,113C ''add'':4C ''answer'':16C ''becom'':50C ''better'':21C,32C,51C ''boil'':84C ''capabl'':43C ''communiti'':33C ''compar'':69C ''comparison'':105C ''data'':35C,53C ''detail'':99C ''develop'':61C ''eco'':89C ''eco-system'':88C ''explor'':36C ''extens'':41C ''hand'':48C ''handl'':54C ''href'':107C ''introduct'':56C ''languag'':19C,77C ''learn'':38C,59C ''less'':65C ''level'':76C ''look'':102C ''low'':75C ''one'':18C ''other'':7C ''p'':1C,25C,79C ''panda'':58C ''person'':95C ''prefer'':96C ''python'':44C,67C ''r'':29C,71C,72C ''said'':9C,27C ''sinc'':55C ''singl'':15C ''system'':90C ''think'':81C ''till'':10C ''time'':62C ''ultim'':83C ''visual'':42C ''would'':3C ''www.analyticsvidhya.com'':109C ''www.analyticsvidhya.com/blog/2014/03/sas-vs-vs-python-tool-learn/'':108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (338, 2, '2014-06-12 12:13:26.94', 9, NULL, NULL, '&lt;p&gt;I did the first 2 courses and I''m planning to do all the others too.  If you don''t know R, it''s a really good program. There are assignments and quizzes every week. Many people find some courses very difficult. You are going to have hard time if you don''t have any programming experience (even if they say it''s not required). &lt;/p&gt;

&lt;p&gt;Just remember.. it''s not because you can drive a car that you are a F1 pilot ;) &lt;/p&gt;
', NULL, NULL, '''..'':71C ''/p'':67C,87C ''2'':6C ''assign'':32C ''car'':80C ''cours'':7C,41C ''difficult'':43C ''drive'':78C ''even'':59C ''everi'':35C ''experi'':58C ''f1'':85C ''find'':39C ''first'':5C ''go'':46C ''good'':28C ''hard'':49C ''know'':22C ''m'':10C ''mani'':37C ''other'':16C ''p'':1C,68C ''peopl'':38C ''pilot'':86C ''plan'':11C ''program'':29C,57C ''quizz'':34C ''r'':23C ''realli'':27C ''rememb'':70C ''requir'':66C ''say'':62C ''time'':50C ''week'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (339, 2, '2014-06-12 12:59:00.663', 34, NULL, NULL, '&lt;p&gt;Some real important differences to consider when you are choosing &lt;strong&gt;R&lt;/strong&gt; or &lt;strong&gt;Python&lt;/strong&gt; over one another:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Machine Learning&lt;/strong&gt; has 2 phases. Model Building and Prediction phase. Typically, model building is performed as a batch process and &lt;strong&gt;predictions are done realtime&lt;/strong&gt;. The model building process is a compute intensive process while the prediction happens in a jiffy. Therefore, performance of an algorithm in Python or R doesn''t really affect the turn-around time of the user. Python 1, R 1.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Production:&lt;/strong&gt; The real difference between Python and R comes in being production ready. Python, as such is a full fledged programming language and many organisations use it in their production systems. R is a statistical programming software favoured by many academia and due to the rise in data science and availability of libraries and being open source, the industry has started using R. Many of these organisations have their production systems either in Java, C++, C#, Python etc. So, ideally they would like to have the &lt;strong&gt;prediction system&lt;/strong&gt; in the same language to reduce the latency and maintenance issues.
Python 2, R 1.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Libraries:&lt;/strong&gt; Both the languages have enormous and reliable libraries. R has over 5000 libraries catering to many domains while Python has some incredible packages like &lt;strong&gt;Pandas, NumPy, SciPy, Scikit Learn, Matplotlib&lt;/strong&gt;. Python 3, R 2.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Development:&lt;/strong&gt; Both the language are interpreted languages. Many say that python has a good learning curve, it''s almost like reading english (to put it on a lighter note) but R has a reputation of having a steeper learning curve. Also, both of them have good IDEs (Spyder etc for Python and RStudio for R). Python 4, R 2.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Speed:&lt;/strong&gt; R software initially had problems with large computations (say, like nxn matrix multiplications). But, this issue is addressed with the introduction of R by Revolution Analytics. They have re-written computation intensive operations in C which is blazingly fast. Python being a high level language is relatively slow. Python 4, R 3.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Visualizations:&lt;/strong&gt; In data science, we frequently tend to plot data to showcase patterns to users. Therefore, visualisations become an important criteria in choosing a software and R completely kills Python in this regard. Thanks to Hadley Wickham for an incredible ggplot2 package. R wins hands down. Python 4, R 4.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Dealing with Big Data:&lt;/strong&gt; One of the constraints of R is it stores the data in system memory (RAM). So, RAM capacity becomes a constraint when you are handling Big Data. Python does well, but I would say, as both R and Python have HDFS connectors, leveraging Hadoop infrastructure would give substantial performance improvement. So, Python 5, R 5.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So, both the languages are equally good. Therefore, depending upon your domain and the place you work, you have to smartly choose the right language. The technology world usually prefers using a single language. Business users (marketing analytics, retail analytics) usually go with statistical programming languages like R, since they frequently do quick prototyping and build visualisations (which is faster done in R than Python).&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (355, 2, '2014-06-13 10:08:14.087', 11, NULL, NULL, '&lt;p&gt;The downvotes are because of the topic, but I''ll attempt to answer your question as best I can since it''s here.&lt;/p&gt;

&lt;p&gt;Data science is a term that is thrown around as loosely as Big Data.  Everyone has a rough idea of what they mean by the term, but when you look at the actual work tasks, a data scientist''s responsibilities will vary greatly from company to company.&lt;/p&gt;

&lt;p&gt;Statistical analysis could encompass the entirety of the workload in one job, and not even be a consideration for another.&lt;/p&gt;

&lt;p&gt;I wouldn''t chase after a job title per se.  If you are interested in the field, network (like you are doing now) and find a good fit.  If you are perusing job ads, just look for the ones that stress statistical and informatics backgrounds.  Hadoop and SQL are both easy to become familiar with given the time and motivation, but I would stick with the areas you are strongest in and go from there.&lt;/p&gt;
', NULL, NULL, '''/p'':25C,74C,96C,173C ''actual'':59C ''ad'':131C ''analysi'':77C ''anoth'':95C ''answer'':14C ''area'':164C ''around'':35C ''attempt'':12C ''background'':142C ''becom'':150C ''best'':18C ''big'':39C ''chase'':101C ''compani'':71C,73C ''consider'':93C ''could'':78C ''data'':27C,40C,63C ''downvot'':3C ''easi'':148C ''encompass'':79C ''entireti'':81C ''even'':90C ''everyon'':41C ''familiar'':151C ''field'':114C ''find'':122C ''fit'':125C ''given'':153C ''go'':170C ''good'':124C ''great'':69C ''hadoop'':143C ''idea'':45C ''informat'':141C ''interest'':111C ''job'':87C,104C,130C ''like'':116C ''ll'':11C ''look'':56C,133C ''loos'':37C ''mean'':49C ''motiv'':157C ''network'':115C ''one'':86C,136C ''p'':1C,26C,75C,97C ''per'':106C ''perus'':129C ''question'':16C ''respons'':66C ''rough'':44C ''scienc'':28C ''scientist'':64C ''se'':107C ''sinc'':21C ''sql'':145C ''statist'':76C,139C ''stick'':161C ''stress'':138C ''strongest'':167C ''task'':61C ''term'':31C,52C ''thrown'':34C ''time'':155C ''titl'':105C ''topic'':8C ''vari'':68C ''work'':60C ''workload'':84C ''would'':160C ''wouldn'':99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (446, 2, '2014-06-17 23:38:20.133', 9, NULL, NULL, '&lt;p&gt;One of the references I mentioned in the OP led me to a potential solution that seems quite powerful, described in "Privacy-preserving record linkage using Bloom filters" (&lt;a href="http://www.biomedcentral.com/1472-6947/9/41"&gt;doi:10.1186/1472-6947-9-41&lt;/a&gt;):&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;A new protocol for privacy-preserving record linkage with encrypted identifiers allowing for errors in identifiers has been developed. The protocol is based on Bloom filters on q-grams of identifiers.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The article goes into detail about the method, which I will summarize here to the best of my ability.&lt;/p&gt;

&lt;p&gt;A Bloom filter is a fixed-length series of bits storing the results of a fixed set of independent hash functions, each computed on the same input value. The output of each hash function should be an index value from among the possible indexes in the filter; i.e., if you have a 0-indexed series of 10 bits, hash functions should return (or be mapped to) values from 0 to 9. &lt;/p&gt;

&lt;p&gt;The filter starts with each bit set to 0. After hashing the input value with each function from the set of hash functions, each bit corresponding to an index value returned by any hash function is set to 1. If the same index is returned by more than one hash function, the bit at that index is only set once. You could consider the Bloom filter to be a superposition of the set of hashes onto the fixed range of bits.&lt;/p&gt;

&lt;p&gt;The protocol described in the above-linked article divides strings into n-grams, which are in this case sets of characters. As an example, &lt;code&gt;"hello"&lt;/code&gt; might yield the following set of 2-grams:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;["_h", "he", "el", "ll", "lo", "o_"]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Padding the front and back with spaces seems to be generally optional when constructing n-grams; the examples given in the paper that proposes this method use such padding.&lt;/p&gt;

&lt;p&gt;Each n-gram can be hashed to produce a Bloom filter, and this set of Bloom filters can be superimposed on itself (bitwise OR operation) to produce the Bloom filter for the string.&lt;/p&gt;

&lt;p&gt;If the filter contains many more bits than there are hash functions or n-grams, arbitrary strings are relatively unlikely to produce exactly the same filter. However, the more n-grams two strings have in common, the more bits their filters will ultimately share. You can then compare any two filters &lt;code&gt;A, B&lt;/code&gt; by means of their Dice coefficient:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;D&lt;sub&gt;A, B&lt;/sub&gt; = 2h / (a + b)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Where &lt;code&gt;h&lt;/code&gt; is the number of bits that are set to 1 in both filters, &lt;code&gt;a&lt;/code&gt; is the number of bits set to 1 in &lt;em&gt;only&lt;/em&gt; filter A, and &lt;code&gt;b&lt;/code&gt; is the number of bits set to 1 in &lt;em&gt;only&lt;/em&gt; filter B. If the strings are exactly the same, the Dice coefficient will be 1; the more they differ, the closer the coefficient will be to &lt;code&gt;0&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Because the hash functions are mapping an indeterminate number of unique inputs to a small number of possible bit indexes, different inputs may produce the same filter, so the coefficient indicates only a &lt;em&gt;probability&lt;/em&gt; that the strings are the same or similar. The number of different hash functions and the number of bits in the filter are important parameters for determining the likelihood of false positives - pairs of inputs that are much less similar than the Dice coefficient produced by this method predicts.&lt;/p&gt;

&lt;p&gt;I found &lt;a href="http://billmill.org/bloomfilter-tutorial/"&gt;this tutorial&lt;/a&gt; to be very helpful for understanding the Bloom filter.&lt;/p&gt;

&lt;p&gt;There is some flexibility in the implementation of this method; see also &lt;a href="https://www.uni-due.de/~hq0215/documents/2010/Bachteler_2010_An_Empirical_Comparison_Of_Approaches_To_Approximate_String_Matching_In_Private_Record_Linkage.pdf"&gt;this 2010 paper&lt;/a&gt; (also linked at the end of the question) for some indications of how performant it is in relation to other methods, and with various parameters.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (340, 2, '2014-06-12 13:38:10.877', 4, NULL, NULL, '&lt;p&gt;There''s plenty. If you''ve ever used ggplot2 in R and want to do that in python:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://pypi.python.org/pypi/ggplot/0.5.9" rel="nofollow"&gt;https://pypi.python.org/pypi/ggplot/0.5.9&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you want to use a similar visualisation grammar (Vega) and go via D3 then:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/wrobstory/vincent" rel="nofollow"&gt;https://github.com/wrobstory/vincent&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Or if you want the full-on 3d shizzle:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://docs.enthought.com/mayavi/mayavi/" rel="nofollow"&gt;http://docs.enthought.com/mayavi/mayavi/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/mayavi/mayavi/'':77C ''/mayavi/mayavi/&lt;/a&gt;&lt;/p&gt;'':82C ''/p'':20C,48C,71C ''/pypi/ggplot/0.5.9'':26C ''/pypi/ggplot/0.5.9&lt;/a&gt;&lt;/p&gt;'':31C ''/wrobstory/vincent'':54C ''/wrobstory/vincent&lt;/a&gt;&lt;/p&gt;'':59C ''3d'':69C ''d3'':46C ''docs.enthought.com'':76C,81C ''docs.enthought.com/mayavi/mayavi/'':75C ''docs.enthought.com/mayavi/mayavi/&lt;/a&gt;&lt;/p&gt;'':80C ''ever'':8C ''full'':67C ''full-on'':66C ''ggplot2'':10C ''github.com'':53C,58C ''github.com/wrobstory/vincent'':52C ''github.com/wrobstory/vincent&lt;/a&gt;&lt;/p&gt;'':57C ''go'':44C ''grammar'':41C ''href'':23C,51C,74C ''nofollow'':28C,56C,79C ''p'':1C,21C,32C,49C,60C,72C ''plenti'':4C ''pypi.python.org'':25C,30C ''pypi.python.org/pypi/ggplot/0.5.9'':24C ''pypi.python.org/pypi/ggplot/0.5.9&lt;/a&gt;&lt;/p&gt;'':29C ''python'':19C ''r'':12C ''rel'':27C,55C,78C ''shizzl'':70C ''similar'':39C ''use'':9C,37C ''ve'':7C ''vega'':42C ''via'':45C ''visualis'':40C ''want'':14C,35C,64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (341, 2, '2014-06-12 13:42:05.383', 6, NULL, NULL, '&lt;p&gt;You have to first make it clear what do you mean by "learn Hadoop". If you mean using Hadoop, such as learning to program in MapReduce, then most probably it is a good idea. But fundamental knowledge (database, machine learning, statistics) may play a bigger role as time goes on.&lt;/p&gt;
', NULL, NULL, '''/p'':52C ''bigger'':46C ''clear'':8C ''databas'':39C ''first'':5C ''fundament'':37C ''goe'':50C ''good'':34C ''hadoop'':15C,20C ''idea'':35C ''knowledg'':38C ''learn'':14C,23C,41C ''machin'':40C ''make'':6C ''mapreduc'':27C ''may'':43C ''mean'':12C,18C ''p'':1C ''play'':44C ''probabl'':30C ''program'':25C ''role'':47C ''statist'':42C ''time'':49C ''use'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (343, 2, '2014-06-12 15:22:16.247', 2, NULL, NULL, '&lt;p&gt;Increasing the number of hidden layers for a standard neural network actually won''t improve results in a majority of cases. Changing the &lt;em&gt;size&lt;/em&gt; of the hidden layer will.&lt;/p&gt;

&lt;p&gt;This fact (that the number of hidden layers does very little) has actually was noted historically and is the motivation behind the field of deep learning. Deep learning is effectively clever ways of training multilayer neural networks by, for example, isolating subsets of features when training different layers.&lt;/p&gt;

&lt;p&gt;Good introductory video on this topic on &lt;a href="https://www.youtube.com/watch?v=vXMpKYRhpmI&amp;amp;index=52&amp;amp;list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH" rel="nofollow"&gt;YouTube&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':100C ''/em'':27C ''/p'':33C,83C,101C ''/watch?v=vxmpkyrhpmi&amp;amp;index=52&amp;amp;list=pl6xpj9i5qxyecohn7tqghaj6naprnmubh'':96C ''actual'':13C,47C ''behind'':55C ''case'':22C ''chang'':23C ''clever'':65C ''deep'':59C,61C ''differ'':81C ''effect'':64C ''em'':25C ''exampl'':74C ''fact'':36C ''featur'':78C ''field'':57C ''good'':85C ''hidden'':6C,30C,41C ''histor'':50C ''href'':93C ''improv'':16C ''increas'':2C ''introductori'':86C ''isol'':75C ''layer'':7C,31C,42C,82C ''learn'':60C,62C ''littl'':45C ''major'':20C ''motiv'':54C ''multilay'':69C ''network'':12C,71C ''neural'':11C,70C ''nofollow'':98C ''note'':49C ''number'':4C,39C ''p'':1C,34C,84C ''rel'':97C ''result'':17C ''size'':26C ''standard'':10C ''subset'':76C ''topic'':90C ''train'':68C,80C ''video'':87C ''way'':66C ''won'':14C ''www.youtube.com'':95C ''www.youtube.com/watch?v=vxmpkyrhpmi&amp;amp;index=52&amp;amp;list=pl6xpj9i5qxyecohn7tqghaj6naprnmubh'':94C ''youtub'':99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (345, 2, '2014-06-12 16:52:46.557', 17, NULL, NULL, '&lt;p&gt;Introductory:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1107422221"&gt;Machine Learning: The Art and Science of Algorithms that Make Sense of Data (Flach)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1600490069"&gt;Learning From Data (Abu-Mostafa et al.)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1461471370"&gt;Introduction to Statistical Learning (James et al.)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Digging deeper:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0387848576"&gt;Elements of Statistical Learning (Hastie et al.)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0387310738"&gt;Pattern Recognition and Machine Learning (Bishop)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Some special interest examples:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0521833787"&gt;Convex Optimization (Boyd)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.cs.ucl.ac.uk/staff/d.barber/brml/"&gt;Bayesian Reasoning and Machine Learning (Barber)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0262013193"&gt;Probabilistic Graphical Models (Koller)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0198538642"&gt;Neural Networks for Pattern Recognition (Bishop)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Broader reference works on machine learning (not really what you asked for, but for completeness):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0262018020"&gt;Machine Learning: A Probabilistic Perspective (Murphy)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0136042597"&gt;Artificial Intelligence: A Modern Approach (Russell &amp;amp; Norvig)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Bonus paper:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://projecteuclid.org/euclid.ss/1009213726"&gt;Statistical Modeling: The Two Cultures (Breiman)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':25C,41C,56C,77C,91C,110C,124C,136C,150C,183C,199C,219C ''/amzn/click/0136042597'':190C ''/amzn/click/0198538642'':143C ''/amzn/click/0262013193'':131C ''/amzn/click/0262018020'':176C ''/amzn/click/0387310738'':84C ''/amzn/click/0387848576'':69C ''/amzn/click/0521833787'':106C ''/amzn/click/1107422221'':10C ''/amzn/click/1461471370'':48C ''/amzn/click/1600490069'':32C ''/euclid.ss/1009213726'':212C ''/li'':26C,42C,57C,78C,92C,111C,125C,137C,151C,184C,200C,220C ''/p'':3C,62C,99C,169C,205C ''/staff/d.barber/brml/'':117C ''/ul'':58C,93C,152C,201C,221C ''abu'':37C ''abu-mostafa'':36C ''al'':40C,55C,76C ''algorithm'':18C ''amp'':197C ''approach'':195C ''art'':14C ''artifici'':191C ''ask'':164C ''barber'':123C ''bayesian'':118C ''bishop'':90C,149C ''bonus'':203C ''boyd'':109C ''breiman'':218C ''broader'':154C ''complet'':168C ''convex'':107C ''cultur'':217C ''data'':23C,35C ''deeper'':61C ''dig'':60C ''element'':70C ''et'':39C,54C,75C ''exampl'':98C ''flach'':24C ''graphic'':133C ''hasti'':74C ''href'':7C,29C,45C,66C,81C,103C,114C,128C,140C,173C,187C,209C ''intellig'':192C ''interest'':97C ''introduct'':49C ''introductori'':2C ''jame'':53C ''koller'':135C ''learn'':12C,33C,52C,73C,89C,122C,159C,178C ''li'':5C,27C,43C,64C,79C,101C,112C,126C,138C,171C,185C,207C ''machin'':11C,88C,121C,158C,177C ''make'':20C ''model'':134C,214C ''modern'':194C ''mostafa'':38C ''murphi'':182C ''network'':145C ''neural'':144C ''norvig'':198C ''optim'':108C ''p'':1C,59C,94C,153C,202C ''paper'':204C ''pattern'':85C,147C ''perspect'':181C ''probabilist'':132C,180C ''projecteuclid.org'':211C ''projecteuclid.org/euclid.ss/1009213726'':210C ''rads.stackoverflow.com'':9C,31C,47C,68C,83C,105C,130C,142C,175C,189C ''rads.stackoverflow.com/amzn/click/0136042597'':188C ''rads.stackoverflow.com/amzn/click/0198538642'':141C ''rads.stackoverflow.com/amzn/click/0262013193'':129C ''rads.stackoverflow.com/amzn/click/0262018020'':174C ''rads.stackoverflow.com/amzn/click/0387310738'':82C ''rads.stackoverflow.com/amzn/click/0387848576'':67C ''rads.stackoverflow.com/amzn/click/0521833787'':104C ''rads.stackoverflow.com/amzn/click/1107422221'':8C ''rads.stackoverflow.com/amzn/click/1461471370'':46C ''rads.stackoverflow.com/amzn/click/1600490069'':30C ''realli'':161C ''reason'':119C ''recognit'':86C,148C ''refer'':155C ''russel'':196C ''scienc'':16C ''sens'':21C ''special'':96C ''statist'':51C,72C,213C ''two'':216C ''ul'':4C,63C,100C,170C,206C ''work'':156C ''www.cs.ucl.ac.uk'':116C ''www.cs.ucl.ac.uk/staff/d.barber/brml/'':115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (346, 2, '2014-06-12 17:03:15.733', 0, NULL, NULL, '&lt;p&gt;In addition to the courses and tutorials posted, I would suggest something a bit more ''hands on'': &lt;a href="http://www.kaggle.com/competitions" rel="nofollow"&gt;Kaggle&lt;/a&gt; has some introductory competitions that might pique your interest (most people start with the Titanic competition). And there''s a large variety of subjects to explore and compete in when you want to get more experience.&lt;/p&gt;
', NULL, NULL, '''/a'':27C ''/competitions'':23C ''/p'':64C ''addit'':3C ''bit'':15C ''compet'':55C ''competit'':31C,43C ''cours'':6C ''experi'':63C ''explor'':53C ''get'':61C ''hand'':17C ''href'':20C ''interest'':36C ''introductori'':30C ''kaggl'':26C ''larg'':48C ''might'':33C ''nofollow'':25C ''p'':1C ''peopl'':38C ''piqu'':34C ''post'':9C ''rel'':24C ''someth'':13C ''start'':39C ''subject'':51C ''suggest'':12C ''titan'':42C ''tutori'':8C ''varieti'':49C ''want'':59C ''would'':11C ''www.kaggle.com'':22C ''www.kaggle.com/competitions'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8666, 2, '2015-10-31 01:57:54.07', 0, NULL, NULL, '&lt;p&gt;As everyone here knows, data science as a field is broad, and similarly the experience required for a particular job is broad. &lt;/p&gt;

&lt;p&gt;Depending on where you want to end up, either degree could suit you better. If you would like more of a traditional statistician''s job which has been rebranded to data scientist in order to gain more attention, then perhaps the MSc Stats would be more applicable. However, if your field requires more complex computer programming or database management, you may want to look into the masters in data science or masters in computer science, depending on your background and interest. Going with a "hot" degree like data science means that you will likely learn a lot of common analyses in superficial detail in order to be employable quickly, but with a more traditionally academic MSc Stats or MSc Com sci, you will learn the theoretical basis with the potential loss of applicability. My personal bias is towards to academic side (MSc/PhD Stats or applied math), but I think this is a decision you should think hard about. Regardless of your decision, in general data science / analysis / programming / whatever this field is requires quite a lot of self directed learning, so whatever program you choose &lt;strong&gt;must&lt;/strong&gt; provide you with the skills and tools to be able to teach yourself whatever you need to know.  &lt;/p&gt;

&lt;p&gt;I would personally take a hard look at what specific kinds of jobs appeal to you, and which programs may prepare you better. Talk to the admissions officers and look up the career paths of past students. Where did they end up? Is that where you want to be? Keep in mind the kind of life you want to live and the kind of work you want to do. I personally find anything financially or profit driven absolutely soul-sucking, but enjoying doing almost the same work in the medical field. Different skill sets, different programming languages, and different vocabulary are required, and the more you know before you enter the field the better you can prepare yourself for the the journey to come.&lt;/p&gt;

&lt;p&gt;Regardless, have fun, and don''t worry too much about making mistakes. No one knows how things will work out in the long run. You can prepare yourself with as much knowledge as possible, but you''ll never know which was the truly right choice, just which one was your choice. &lt;/p&gt;
', NULL, NULL, '''/p'':24C,232C,359C,412C ''/strong'':213C ''abl'':223C ''absolut'':311C ''academ'':140C,165C ''admiss'':260C ''almost'':318C ''analys'':125C ''analysi'':192C ''anyth'':306C ''appeal'':247C ''appli'':170C ''applic'':72C,158C ''attent'':63C ''background'':104C ''basi'':152C ''better'':39C,256C,348C ''bias'':161C ''broad'':12C,23C ''career'':266C ''choic'':405C,411C ''choos'':210C ''com'':145C ''come'':358C ''common'':124C ''complex'':79C ''comput'':80C,99C ''could'':36C ''data'':6C,56C,94C,113C,190C ''databas'':83C ''decis'':178C,187C ''degre'':35C,111C ''depend'':26C,101C ''detail'':128C ''differ'':326C,329C,333C ''direct'':204C ''driven'':310C ''either'':34C ''employ'':133C ''end'':32C,274C ''enjoy'':316C ''enter'':344C ''everyon'':3C ''experi'':16C ''field'':10C,76C,196C,325C,346C ''financi'':307C ''find'':305C ''fun'':363C ''gain'':61C ''general'':189C ''go'':107C ''hard'':182C,239C ''hot'':110C ''howev'':73C ''interest'':106C ''job'':21C,50C,246C ''journey'':356C ''keep'':283C ''kind'':244C,287C,296C ''know'':5C,231C,341C,375C,399C ''knowledg'':392C ''languag'':331C ''learn'':120C,149C,205C ''life'':289C ''like'':43C,112C,119C ''live'':293C ''ll'':397C ''long'':383C ''look'':89C,240C,263C ''loss'':156C ''lot'':122C,201C ''make'':371C ''manag'':84C ''master'':92C,97C ''math'':171C ''may'':86C,253C ''mean'':115C ''medic'':324C ''mind'':285C ''mistak'':372C ''msc'':67C,141C,144C ''msc/phd'':167C ''much'':369C,391C ''must'':212C ''need'':229C ''never'':398C ''offic'':261C ''one'':374C,408C ''order'':59C,130C ''p'':1C,25C,233C,360C ''particular'':20C ''past'':269C ''path'':267C ''perhap'':65C ''person'':160C,236C,304C ''possibl'':394C ''potenti'':155C ''prepar'':254C,351C,387C ''profit'':309C ''program'':81C,193C,208C,252C,330C ''provid'':214C ''quick'':134C ''quit'':199C ''rebrand'':54C ''regardless'':184C,361C ''requir'':17C,77C,198C,336C ''right'':404C ''run'':384C ''sci'':146C ''scienc'':7C,95C,100C,114C,191C ''scientist'':57C ''self'':203C ''set'':328C ''side'':166C ''similar'':14C ''skill'':218C,327C ''soul'':313C ''soul-suck'':312C ''specif'':243C ''stat'':68C,142C,168C ''statistician'':48C ''strong'':211C ''student'':270C ''suck'':314C ''suit'':37C ''superfici'':127C ''take'':237C ''talk'':257C ''teach'':225C ''theoret'':151C ''thing'':377C ''think'':174C,181C ''tool'':220C ''toward'':163C ''tradit'':47C,139C ''truli'':403C ''vocabulari'':334C ''want'':30C,87C,280C,291C,300C ''whatev'':194C,207C,227C ''work'':298C,321C,379C ''worri'':367C ''would'':42C,69C,235C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (354, 1, '2014-06-13 07:28:37.763', 4, 160, 'Data Science as a Social Scientist?', '&lt;p&gt;as I am very interested in programming and statistics, Data Science seems like a great career path to me - I like both fields and would like to combine them. Unfortunately, I have studied political science with a non-statistical sounding Master. I focused on statistics in this Master, visiting optional courses and writing a statistical thesis on a rather large dataset.&lt;/p&gt;

&lt;p&gt;Since almost all job adds are requiring  a degree in informatics, physics or some other techy-field, I am wondering if there is a chance to become a data scientist or if I should drop that idea.&lt;/p&gt;

&lt;p&gt;I am lacking knowledge in machine learning, sql and hadoop, while having a rather strong informatics and statistics background. &lt;/p&gt;

&lt;p&gt;So can somebody tell me how feasible my goal of becoming a data scientist is? &lt;/p&gt;
', 'statistics', NULL, '''/p'':71C,111C,132C,149C ''add'':77C ''almost'':74C ''background'':131C ''becom'':100C,144C ''career'':24C ''chanc'':98C ''combin'':36C ''cours'':60C ''data'':1A,18C,102C,146C ''dataset'':70C ''degre'':81C ''drop'':108C ''feasibl'':140C ''field'':31C,90C ''focus'':52C ''goal'':142C ''great'':23C ''hadoop'':122C ''idea'':110C ''informat'':83C,128C ''interest'':13C ''job'':76C ''knowledg'':116C ''lack'':115C ''larg'':69C ''learn'':119C ''like'':21C,29C,34C ''machin'':118C ''master'':50C,57C ''non'':47C ''non-statist'':46C ''option'':59C ''p'':8C,72C,112C,133C ''path'':25C ''physic'':84C ''polit'':42C ''program'':15C ''rather'':68C,126C ''requir'':79C ''scienc'':2A,19C,43C ''scientist'':6A,103C,147C ''seem'':20C ''sinc'':73C ''social'':5A ''somebodi'':136C ''sound'':49C ''sql'':120C ''statist'':7B,17C,48C,54C,64C,130C ''strong'':127C ''studi'':41C ''techi'':89C ''techy-field'':88C ''tell'':137C ''thesi'':65C ''unfortun'':38C ''visit'':58C ''wonder'':93C ''would'':33C ''write'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (347, 2, '2014-06-12 17:58:21.467', 0, NULL, NULL, '&lt;p&gt;As mentioned in above answers grasp the basics of ML by following MOOCs by Prof.Andrew Ng and &lt;a href="http://work.caltech.edu/telecourse.html" rel="nofollow"&gt;''Learning From Data''&lt;/a&gt; by Prof. Yaser Abu-Mostafa.  &lt;/p&gt;

&lt;p&gt;R is the &lt;a href="http://www.kaggle.com/wiki/Software" rel="nofollow"&gt;clear winner&lt;/a&gt; as the most used tool in Kaggle competitions. (Don''t forget to check the resources on Kaggle wiki and forums)&lt;/p&gt;

&lt;p&gt;Learn basic R and Python. Coursera ''Data Science'' track has an &lt;a href="https://www.coursera.org/course/rprog" rel="nofollow"&gt;introductory R course&lt;/a&gt;. Almost all the algorithms can be found in Python and R libraries. Feel free to use the algorithms you learned in few kaggle competitions. As a starting point compare the performance of several algorithms on Titanic dataset and Digit recognizer dataset on &lt;a href="http://www.kaggle.com/" rel="nofollow"&gt;kaggle&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;And do continue practising on various datasets!&lt;/p&gt;
', NULL, NULL, '''/a'':29C,50C,94C,143C ''/course/rprog'':88C ''/p'':36C,71C,144C,153C ''/telecourse.html'':23C ''/wiki/software'':45C ''abu'':34C ''abu-mostafa'':33C ''algorithm'':98C,112C,128C ''almost'':95C ''answer'':6C ''basic'':9C,74C ''check'':63C ''clear'':48C ''compar'':123C ''competit'':58C,118C ''continu'':148C ''cours'':93C ''coursera'':78C ''data'':28C,79C ''dataset'':131C,135C,152C ''digit'':133C ''feel'':107C ''follow'':13C ''forget'':61C ''forum'':70C ''found'':101C ''free'':108C ''grasp'':7C ''href'':20C,42C,85C,138C ''introductori'':91C ''kaggl'':57C,67C,117C,142C ''learn'':26C,73C,114C ''librari'':106C ''mention'':3C ''ml'':11C ''mooc'':14C ''mostafa'':35C ''ng'':17C ''nofollow'':25C,47C,90C,141C ''p'':1C,37C,72C,145C ''perform'':125C ''point'':122C ''practis'':149C ''prof'':31C ''prof.andrew'':16C ''python'':77C,103C ''r'':38C,75C,92C,105C ''recogn'':134C ''rel'':24C,46C,89C,140C ''resourc'':65C ''scienc'':80C ''sever'':127C ''start'':121C ''titan'':130C ''tool'':55C ''track'':81C ''use'':54C,110C ''various'':151C ''wiki'':68C ''winner'':49C ''work.caltech.edu'':22C ''work.caltech.edu/telecourse.html'':21C ''www.coursera.org'':87C ''www.coursera.org/course/rprog'':86C ''www.kaggle.com'':44C,139C ''www.kaggle.com/wiki/software'':43C ''yaser'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (348, 2, '2014-06-12 18:33:21.54', 5, NULL, NULL, '&lt;p&gt;Not sure about the cloud era one, but one of my friends joined the John Hopkins one and in his words it''s "brilliant to get you started". It has also been recommended by a lot of people. I am planning to join it in few weeks. As far as seriousness is concerned, I don''t think these certifications are gonna help you land a job, but they sure will help you learn.  &lt;/p&gt;
', NULL, NULL, '''/p'':75C ''also'':32C ''brilliant'':25C ''certif'':60C ''cloud'':6C ''concern'':54C ''era'':7C ''far'':50C ''friend'':13C ''get'':27C ''gonna'':62C ''help'':63C,72C ''hopkin'':17C ''job'':67C ''john'':16C ''join'':14C,44C ''land'':65C ''learn'':74C ''lot'':37C ''one'':8C,10C,18C ''p'':1C ''peopl'':39C ''plan'':42C ''recommend'':34C ''serious'':52C ''start'':29C ''sure'':3C,70C ''think'':58C ''week'':48C ''word'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (349, 2, '2014-06-12 20:51:59.93', 24, NULL, NULL, '&lt;p&gt;As a former analytics manager and a current lead data scientist, I am very leery of the need for data science certificates.  The term data scientist is pretty vague and the field of data science is in it''s infancy.  A certificates implies some sort of uniform standard which is just lacking in data science, it is still very much the wild west. &lt;/p&gt;

&lt;p&gt;While a certificate is probably not going to hurt you, I think your time would be better spent developing the experience to know when to use a certain approach, and depth of understanding to be able to explain that approach to a non-technical audience.&lt;/p&gt;
', NULL, NULL, '''/p'':65C,113C ''abl'':102C ''analyt'':5C ''approach'':95C,106C ''audienc'':112C ''better'':83C ''certain'':94C ''certif'':23C,43C,69C ''current'':9C ''data'':11C,21C,26C,35C,55C ''depth'':97C ''develop'':85C ''experi'':87C ''explain'':104C ''field'':33C ''former'':4C ''go'':73C ''hurt'':75C ''impli'':44C ''infanc'':41C ''know'':89C ''lack'':53C ''lead'':10C ''leeri'':16C ''manag'':6C ''much'':61C ''need'':19C ''non'':110C ''non-techn'':109C ''p'':1C,66C ''pretti'':29C ''probabl'':71C ''scienc'':22C,36C,56C ''scientist'':12C,27C ''sort'':46C ''spent'':84C ''standard'':49C ''still'':59C ''technic'':111C ''term'':25C ''think'':78C ''time'':80C ''understand'':99C ''uniform'':48C ''use'':92C ''vagu'':30C ''west'':64C ''wild'':63C ''would'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (350, 1, '2014-06-12 22:11:46.607', -3, 554, 'Example tasks of a data scientist and the necessary knowledge', '&lt;p&gt;Could you give some examples of typical tasks that a data scientist does in his daily job, and the must-know minimum for each of the levels (like junior, senior, etc. if there are any)? If possible, something like a &lt;a href="http://www.starling-software.com/employment/programmer-competency-matrix.html" rel="nofollow"&gt;Programmer competency matrix&lt;/a&gt;.&lt;/p&gt;
', 'knowledge-base', NULL, '''/a'':66C ''/employment/programmer-competency-matrix.html'':60C ''/p'':67C ''base'':13B ''compet'':64C ''could'':15C ''daili'':30C ''data'':5A,25C ''etc'':46C ''exampl'':1A,19C ''give'':17C ''href'':57C ''job'':31C ''junior'':44C ''know'':36C ''knowledg'':10A,12B ''knowledge-bas'':11B ''level'':42C ''like'':43C,54C ''matrix'':65C ''minimum'':37C ''must'':35C ''must-know'':34C ''necessari'':9A ''nofollow'':62C ''p'':14C ''possibl'':52C ''programm'':63C ''rel'':61C ''scientist'':6A,26C ''senior'':45C ''someth'':53C ''task'':2A,22C ''typic'':21C ''www.starling-software.com'':59C ''www.starling-software.com/employment/programmer-competency-matrix.html'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9962, 1, '2016-01-26 02:50:56.333', 0, 15, 'How to generate combination shapes dataset using sklearn dataset generator?', '&lt;p&gt;I need to generate a dataset with many shapes ( like  an outer ring with two blobs overlapped in the center ). Is it possible using sklearn ? If not which package in python can I use instead ?&lt;/p&gt;
', 'machine-learning  python  clustering  sklearn', NULL, '''/p'':53C ''blob'':33C ''center'':37C ''cluster'':15B ''combin'':4A ''dataset'':6A,9A,23C ''generat'':3A,10A,21C ''instead'':52C ''learn'':13B ''like'':27C ''machin'':12B ''machine-learn'':11B ''mani'':25C ''need'':19C ''outer'':29C ''overlap'':34C ''p'':17C ''packag'':46C ''possibl'':40C ''python'':14B,48C ''ring'':30C ''shape'':5A,26C ''sklearn'':8A,16B,42C ''two'':32C ''use'':7A,41C,51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (351, 2, '2014-06-13 03:50:24.61', 7, NULL, NULL, '&lt;p&gt;There are multiple certifications going on, but they have different focus area and style of teaching.&lt;/p&gt;

&lt;p&gt;I prefer The Analytics Edge on eDX lot more over John Hopkins specialization, as it is more intensive and hands on. The expectation in John Hopkins specialization is to put in 3 - 4 hours a week vs. 11 - 12 hours a week on Analytics Edge.&lt;/p&gt;

&lt;p&gt;From an industry perspective, I take these certifications as a sign of interest and not level of knowledge a person possesses. There are too many dropouts in these MOOCs. I value other experience (like participating in Kaggle competitions) lot more than undergoing XYZ certification on MOOC.&lt;/p&gt;
', NULL, NULL, '''/p'':18C,65C,113C ''11'':57C ''12'':58C ''3'':51C ''4'':52C ''analyt'':23C,63C ''area'':13C ''certif'':5C,74C,110C ''competit'':104C ''differ'':11C ''dropout'':92C ''edg'':24C,64C ''edx'':26C ''expect'':42C ''experi'':99C ''focus'':12C ''go'':6C ''hand'':39C ''hopkin'':31C,45C ''hour'':53C,59C ''industri'':69C ''intens'':37C ''interest'':79C ''john'':30C,44C ''kaggl'':103C ''knowledg'':84C ''level'':82C ''like'':100C ''lot'':27C,105C ''mani'':91C ''mooc'':95C,112C ''multipl'':4C ''p'':1C,19C,66C ''particip'':101C ''person'':86C ''perspect'':70C ''possess'':87C ''prefer'':21C ''put'':49C ''sign'':77C ''special'':32C,46C ''style'':15C ''take'':72C ''teach'':17C ''undergo'':108C ''valu'':97C ''vs'':56C ''week'':55C,61C ''xyz'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (352, 1, '2014-06-13 05:40:39.36', 6, 110, 'How do I figure out if subsets can be plotted in a normal Euler diagram?', '&lt;p&gt;In some cases, &lt;a href="http://www.ncbi.nlm.nih.gov/pubmed/20975147"&gt;it may be impossible&lt;/a&gt; to draw Euler diagrams with overlapping circles to represent all the overlapping subsets in the correct proportions. This type of data then requires using polygons or other figures to represent each set. When dealing with data that describes overlapping subsets, how can I figure out whether a simple Euler diagram is possible?&lt;/p&gt;
', 'visualization', NULL, '''/a'':30C ''/p'':83C ''/pubmed/20975147'':25C ''case'':20C ''circl'':37C ''correct'':46C ''data'':51C,66C ''deal'':64C ''describ'':68C ''diagram'':15A,34C,80C ''draw'':32C ''euler'':14A,33C,79C ''figur'':4A,58C,74C ''href'':22C ''imposs'':29C ''may'':27C ''normal'':13A ''overlap'':36C,42C,69C ''p'':17C ''plot'':10A ''polygon'':55C ''possibl'':82C ''proport'':47C ''repres'':39C,60C ''requir'':53C ''set'':62C ''simpl'':78C ''subset'':7A,43C,70C ''type'':49C ''use'':54C ''visual'':16B ''whether'':76C ''www.ncbi.nlm.nih.gov'':24C ''www.ncbi.nlm.nih.gov/pubmed/20975147'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (356, 1, '2014-06-13 10:57:10.623', 5, 99, 'How to best accomplish high speed comparison of like data?', '&lt;p&gt;I attack this problem frequently with inefficiency because it''s always pretty low on the priority list and my clients are resistant to change until things break.  I would like some input on how to speed things up.  &lt;/p&gt;

&lt;p&gt;I have multiple datasets of information in a SQL database.  The database is vendor-designed, so I have little control over the structure.  It''s a sql representation of a class-based structure.  It looks a little bit like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Main-class table
 -sub-class table 1
 -sub-class table 2
  -sub-sub-class table
 ...
 -sub-class table n
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Each table contains fields for each attribute of the class.  A join exists which contains all of the fields for each of the sub-classes which contains all of the fields in the class table and all of the fields in each parent class'' table, joined by a unique identifier.&lt;/p&gt;

&lt;p&gt;There are hundreds of classes. which means thousands of views and tens of thousands of columns.&lt;/p&gt;

&lt;p&gt;Beyond that, there are multiple datasets, indicated by a field value in the Main-class table.  There is the production dataset, visible to all end users, and there are several other datasets comprised of the most current version of the same data from various integration sources.  &lt;/p&gt;

&lt;p&gt;Daily, we run jobs that compare the production dataset to the live datasets and based on a set of rules we merge the data, purge the live datasets, then start all over again.  The rules are in place because we might trust one source of data more than another for a particular value of a particular class.&lt;/p&gt;

&lt;p&gt;The jobs are essentially a series of SQL statements that go row-by-row through each dataset, and field by field within each row.  The common changes are limited to a handful of fields in each row, but since anything can change we compare each value.&lt;/p&gt;

&lt;p&gt;There are 10s of millions of rows of data and in some environments the merge jobs can take longer than 24 hours.  We resolve that problem generally, by throwing more hardware at it, but this isn''t a hadoop environment currently so there''s a pretty finite limit to what can be done in that regard.&lt;/p&gt;

&lt;p&gt;How would you go about scaling a solution to this problem such that there were no limitations?  And how would you go about accomplishing the most efficient data-merge?  (currently it is field by field comparisons... painfully slow).&lt;/p&gt;
', 'efficiency  scalability  sql', NULL, '''/code'':124C ''/p'':53C,97C,178C,196C,245C,304C,353C,411C,452C ''/pre'':125C ''1'':108C ''10s'':357C ''2'':113C ''24'':375C ''accomplish'':4A,436C ''alway'':25C ''anoth'':295C ''anyth'':346C ''attack'':16C ''attribut'':133C ''base'':88C,261C ''best'':3A ''beyond'':198C ''bit'':94C ''break'':41C ''chang'':38C,333C,348C ''class'':87C,102C,106C,111C,117C,121C,136C,152C,161C,171C,184C,213C,303C ''class-bas'':86C ''client'':34C ''code'':99C ''column'':195C ''common'':332C ''compar'':252C,350C ''comparison'':7A,449C ''compris'':231C ''contain'':129C,141C,154C ''control'':75C ''current'':235C,395C,443C ''daili'':247C ''data'':10A,240C,270C,292C,363C,441C ''data-merg'':440C ''databas'':64C,66C ''dataset'':58C,203C,219C,230C,255C,259C,274C,323C ''design'':70C ''done'':407C ''effici'':11B,439C ''end'':223C ''environ'':367C,394C ''essenti'':309C ''exist'':139C ''field'':130C,145C,158C,167C,207C,325C,327C,340C,446C,448C ''finit'':401C ''frequent'':19C ''general'':381C ''go'':316C,416C,434C ''hadoop'':393C ''hand'':338C ''hardwar'':385C ''high'':5A ''hour'':376C ''hundr'':182C ''identifi'':177C ''indic'':204C ''ineffici'':21C ''inform'':60C ''input'':46C ''integr'':243C ''isn'':390C ''job'':250C,307C,370C ''join'':138C,173C ''like'':9A,44C,95C ''limit'':335C,402C,429C ''list'':31C ''littl'':74C,93C ''live'':258C,273C ''longer'':373C ''look'':91C ''low'':27C ''main'':101C,212C ''main-class'':100C,211C ''mean'':186C ''merg'':268C,369C,442C ''might'':287C ''million'':359C ''multipl'':57C,202C ''n'':123C ''one'':289C ''p'':14C,54C,126C,179C,197C,246C,305C,354C,412C ''pain'':450C ''parent'':170C ''particular'':298C,302C ''place'':284C ''pre'':98C ''pretti'':26C,400C ''prioriti'':30C ''problem'':18C,380C,423C ''product'':218C,254C ''purg'':271C ''regard'':410C ''represent'':83C ''resist'':36C ''resolv'':378C ''row'':318C,320C,330C,343C,361C ''row-by-row'':317C ''rule'':266C,281C ''run'':249C ''scalabl'':12B ''scale'':418C ''seri'':311C ''set'':264C ''sever'':228C ''sinc'':345C ''slow'':451C ''solut'':420C ''sourc'':244C,290C ''speed'':6A,50C ''sql'':13B,63C,82C,313C ''start'':276C ''statement'':314C ''structur'':78C,89C ''sub'':105C,110C,115C,116C,120C,151C ''sub-class'':104C,109C,119C,150C ''sub-sub-class'':114C ''tabl'':103C,107C,112C,118C,122C,128C,162C,172C,214C ''take'':372C ''ten'':191C ''thing'':40C,51C ''thousand'':187C,193C ''throw'':383C ''trust'':288C ''uniqu'':176C ''user'':224C ''valu'':208C,299C,352C ''various'':242C ''vendor'':69C ''vendor-design'':68C ''version'':236C ''view'':189C ''visibl'':220C ''within'':328C ''would'':43C,414C,432C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (357, 2, '2014-06-13 12:18:53.83', 7, NULL, NULL, '&lt;p&gt;I lead data science teams for a major Internet company and I have screened hundreds of profiles and interviewed dozens for our teams around the world. Many candidates have passed the aforementioned courses and programs or bring similar credentials. Personally, I have also taken the courses, some are good, others are disappointing but none of them makes you a "data scientist".&lt;/p&gt;

&lt;p&gt;In general, I agree with the others here. A certificate from Coursera or Cloudera just signalizes an interest but it does not move the needle.  There is a lot more to consider and you can have a bigger impact by providing a comprehensive repository of your work (github profile for example) and by networking with other data scientists. Anyone hiring for a data science profile will always prefer to see your previous work and coding style/abilities.&lt;/p&gt;
', NULL, NULL, '''/p'':63C,141C ''aforement'':33C ''agre'':68C ''also'':44C ''alway'':131C ''anyon'':123C ''around'':25C ''bigger'':102C ''bring'':38C ''candid'':29C ''certif'':74C ''cloudera'':78C ''code'':139C ''compani'':11C ''comprehens'':107C ''consid'':96C ''cours'':34C,47C ''coursera'':76C ''credenti'':40C ''data'':4C,61C,121C,127C ''disappoint'':53C ''dozen'':21C ''exampl'':115C ''general'':66C ''github'':112C ''good'':50C ''hire'':124C ''hundr'':16C ''impact'':103C ''interest'':82C ''internet'':10C ''interview'':20C ''lead'':3C ''lot'':93C ''major'':9C ''make'':58C ''mani'':28C ''move'':87C ''needl'':89C ''network'':118C ''none'':55C ''other'':51C,71C ''p'':1C,64C ''pass'':31C ''person'':41C ''prefer'':132C ''previous'':136C ''profil'':18C,113C,129C ''program'':36C ''provid'':105C ''repositori'':108C ''scienc'':5C,128C ''scientist'':62C,122C ''screen'':15C ''see'':134C ''signal'':80C ''similar'':39C ''style/abilities'':140C ''taken'':45C ''team'':6C,24C ''work'':111C,137C ''world'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (358, 2, '2014-06-13 12:30:45.29', 3, NULL, NULL, '&lt;p&gt;Sounds interesting. Could the solution be to dump the data out, build a fast custom processing thingie to run it through and then import it back to the database? I''ve seen some blazing fast Java-based text processing tools for topic modeling that handle millions of lines of text per second.&lt;/p&gt;

&lt;p&gt;If it''s an option then you can build a shell script to first dump the data in as good as format as possible. Then some pre-processing to separate the datasets, then real processing of comparison and changes. Lastly something that writes it back to a good format for input into database.&lt;/p&gt;

&lt;p&gt;Definately not a one-afternoon project, but you could probably get it to work in a couple of weeks.&lt;/p&gt;
', NULL, NULL, '''/p'':55C,111C,133C ''afternoon'':118C ''back'':27C,102C ''base'':39C ''blaze'':35C ''build'':13C,65C ''chang'':96C ''comparison'':94C ''could'':4C,122C ''coupl'':130C ''custom'':16C ''data'':11C,73C ''databas'':30C,110C ''dataset'':89C ''defin'':113C ''dump'':9C,71C ''fast'':15C,36C ''first'':70C ''format'':78C,106C ''get'':124C ''good'':76C,105C ''handl'':47C ''import'':25C ''input'':108C ''interest'':3C ''java'':38C ''java-bas'':37C ''last'':97C ''line'':50C ''million'':48C ''model'':45C ''one'':117C ''one-afternoon'':116C ''option'':61C ''p'':1C,56C,112C ''per'':53C ''possibl'':80C ''pre'':84C ''pre-process'':83C ''probabl'':123C ''process'':17C,41C,85C,92C ''project'':119C ''real'':91C ''run'':20C ''script'':68C ''second'':54C ''seen'':33C ''separ'':87C ''shell'':67C ''solut'':6C ''someth'':98C ''sound'':2C ''text'':40C,52C ''thingi'':18C ''tool'':42C ''topic'':44C ''ve'':32C ''week'':132C ''work'':127C ''write'':100C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (359, 2, '2014-06-13 13:49:35.777', 2, NULL, NULL, '&lt;p&gt;&lt;a href="http://nirvacana.com/thoughts/becoming-a-data-scientist/" rel="nofollow"&gt;Becoming a Data Scientist â Curriculum via Metromap&lt;/a&gt; is a popular reference for this kind of question.&lt;/p&gt;
', NULL, NULL, '''/a'':17C ''/p'':27C ''/thoughts/becoming-a-data-scientist/'':6C ''becom'':9C ''curriculum'':14C ''data'':11C ''href'':3C ''kind'':24C ''metromap'':16C ''nirvacana.com'':5C ''nirvacana.com/thoughts/becoming-a-data-scientist/'':4C ''nofollow'':8C ''p'':1C ''popular'':20C ''question'':26C ''refer'':21C ''rel'':7C ''scientist'':12C ''via'':15C ''â'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (360, 2, '2014-06-13 14:46:30.393', 3, NULL, NULL, '&lt;p&gt;That''s because something called &lt;a href="http://en.wikipedia.org/wiki/Bias%E2%80%93variance_dilemma" rel="nofollow"&gt;bias-variance dilema&lt;/a&gt;. The overfitted model means that we will have more complex decision boundary if we give more variance on model. The thing is, not only too simple models but also complex models are likely to have dis-classified result on unseen data. Consequently, over-fitted model is not good as under-fitted model. That''s why overfitting is bad and we need to fit the model somewhere in the middle.&lt;/p&gt;
', NULL, NULL, '''/a'':18C ''/p'':91C ''/wiki/bias%e2%80%93variance_dilemma'':11C ''also'':47C ''bad'':79C ''bias'':15C ''bias-vari'':14C ''boundari'':30C ''call'':6C ''classifi'':56C ''complex'':28C,48C ''consequ'':61C ''data'':60C ''decis'':29C ''dilema'':17C ''dis'':55C ''dis-classifi'':54C ''en.wikipedia.org'':10C ''en.wikipedia.org/wiki/bias%e2%80%93variance_dilemma'':9C ''fit'':64C,72C,84C ''give'':33C ''good'':68C ''href'':8C ''like'':51C ''mean'':22C ''middl'':90C ''model'':21C,37C,45C,49C,65C,73C,86C ''need'':82C ''nofollow'':13C ''over-fit'':62C ''overfit'':20C,77C ''p'':1C ''rel'':12C ''result'':57C ''simpl'':44C ''someth'':5C ''somewher'':87C ''thing'':39C ''under-fit'':70C ''unseen'':59C ''varianc'':16C,35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1060, 2, '2014-08-29 10:42:14.957', 2, NULL, NULL, '&lt;p&gt;Checkout Breeze and apache commons math for the maths, and ScalaLab for some nice examples of how to plot things in Scala.&lt;/p&gt;

&lt;p&gt;I''ve managed to get an environment setup where this would just be a couple of lines. I dont actually use ScalaLab, rather borrow some of its code, I use Intellij worksheets instead.&lt;/p&gt;
', NULL, NULL, '''/p'':24C,59C ''actual'':45C ''apach'':5C ''borrow'':49C ''breez'':3C ''checkout'':2C ''code'':53C ''common'':6C ''coupl'':40C ''dont'':44C ''environ'':32C ''exampl'':16C ''get'':30C ''instead'':58C ''intellij'':56C ''line'':42C ''manag'':28C ''math'':7C,10C ''nice'':15C ''p'':1C,25C ''plot'':20C ''rather'':48C ''scala'':23C ''scalalab'':12C,47C ''setup'':33C ''thing'':21C ''use'':46C,55C ''ve'':27C ''worksheet'':57C ''would'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (361, 1, '2014-06-13 16:44:29.323', 21, 5424, 'When is a Model Underfitted?', '&lt;p&gt;Logic often states that by underfitting a model, it''s capacity to generalize is increased. That said, clearly at some point underfitting a model cause models to become worse regardless of the complexity of data.&lt;/p&gt;

&lt;p&gt;How do you know when your model has struck the right balance and is not underfitting the data it seeks to model?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Note:&lt;/strong&gt; This is a followup to my question, "&lt;a href="http://datascience.stackexchange.com/questions/61/why-is-overfitting-bad/"&gt;Why Is Overfitting Bad?&lt;/a&gt;"&lt;/p&gt;
', 'efficiency  algorithms  parameter', NULL, '''/a'':91C ''/p'':45C,69C,92C ''/questions/61/why-is-overfitting-bad/'':86C ''/strong'':74C ''algorithm'':7B ''bad'':90C ''balanc'':58C ''becom'':37C ''capac'':20C ''caus'':34C ''clear'':27C ''complex'':42C ''data'':44C,64C ''datascience.stackexchange.com'':85C ''datascience.stackexchange.com/questions/61/why-is-overfitting-bad/'':84C ''effici'':6B ''followup'':78C ''general'':22C ''hr'':70C ''href'':83C ''increas'':24C ''know'':50C ''logic'':10C ''model'':4A,17C,33C,35C,53C,68C ''note'':73C ''often'':11C ''overfit'':89C ''p'':9C,46C,71C ''paramet'':8B ''point'':30C ''question'':81C ''regardless'':39C ''right'':57C ''said'':26C ''seek'':66C ''state'':12C ''strong'':72C ''struck'':55C ''underfit'':5A,15C,31C,62C ''wors'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (362, 2, '2014-06-13 17:14:26.26', 4, NULL, NULL, '&lt;p&gt;Can''t you create a hash for each classes, and then merge rows by rows, field by field only the classes where the hash changed ? It should be faster if most of the classes don''t change..&lt;/p&gt;

&lt;p&gt;Or a hash of each rows or maybe columns.. depending on how the data normally change.. &lt;/p&gt;
', NULL, NULL, '''..'':50C,58C ''/p'':39C,59C ''chang'':26C,38C,57C ''class'':10C,22C,35C ''column'':49C ''creat'':5C ''data'':55C ''depend'':51C ''faster'':30C ''field'':17C,19C ''hash'':7C,25C,43C ''mayb'':48C ''merg'':13C ''normal'':56C ''p'':1C,40C ''row'':14C,16C,46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (363, 2, '2014-06-13 17:14:57.517', 6, NULL, NULL, '&lt;p&gt;Models are but abstractions of what is seen in real life. They are designed in order to abstract-away nitty-gritties of the real system in observation, while keeping sufficient information to support desired analysis.&lt;/p&gt;

&lt;p&gt;If a model is overfit, it takes into account too many details about what is being observed, and small changes on such object may cause the model to lose precision. On the other hand, if a model is underfit, it evaluates so few attributes that noteworthy changes on the object may be ignored.&lt;/p&gt;

&lt;p&gt;Note also that underfit may be seen as an &lt;em&gt;overfit&lt;/em&gt;, depending on the dataset. If your input can be 99%-correctly-classified with a single attribute, you &lt;em&gt;overfit&lt;/em&gt; the model to the data by simplifying the abstraction to a single characteristic. And, in this case, you''d be generalizing too much the 1% of the base into the 99%-class -- or also specifying the model so much that it can only &lt;em&gt;see&lt;/em&gt; one class.&lt;/p&gt;

&lt;p&gt;A reasonable way to say that a model is neither over nor underfit is by performing cross-validations. You split your dataset into &lt;em&gt;k&lt;/em&gt; parts, and say, pick one of them to perform your analysis, while using the other &lt;em&gt;k - 1&lt;/em&gt; parts to train your model. Considering that the input itself is not biased, you should be able to have as much variance of data to train and evaluate as you''d have while using the model in real life processing.&lt;/p&gt;
', NULL, NULL, '''/em'':107C,128C,174C,205C,224C ''/p'':39C,94C,177C,265C ''1'':153C,223C ''99'':117C,159C ''abl'':241C ''abstract'':5C,20C,137C ''abstract-away'':19C ''account'':49C ''also'':97C,162C ''analysi'':38C,216C ''attribut'':84C,124C ''away'':21C ''base'':156C ''bias'':237C ''case'':145C ''caus'':65C ''chang'':60C,87C ''characterist'':141C ''class'':160C,176C ''classifi'':120C ''consid'':230C ''correct'':119C ''correctly-classifi'':118C ''cross'':196C ''cross-valid'':195C ''d'':147C,255C ''data'':133C,248C ''dataset'':111C,201C ''depend'':108C ''design'':15C ''desir'':37C ''detail'':52C ''em'':105C,126C,172C,203C,221C ''evalu'':81C,252C ''general'':149C ''gritti'':24C ''hand'':74C ''ignor'':93C ''inform'':34C ''input'':114C,233C ''k'':204C,222C ''keep'':32C ''life'':12C,263C ''lose'':69C ''mani'':51C ''may'':64C,91C,100C ''model'':2C,43C,67C,77C,130C,165C,186C,229C,260C ''much'':151C,167C,245C ''neither'':188C ''nitti'':23C ''nitty-gritti'':22C ''note'':96C ''noteworthi'':86C ''object'':63C,90C ''observ'':30C,57C ''one'':175C,210C ''order'':17C ''overfit'':45C,106C,127C ''p'':1C,40C,95C,178C ''part'':206C,225C ''perform'':194C,214C ''pick'':209C ''precis'':70C ''process'':264C ''real'':11C,27C,262C ''reason'':180C ''say'':183C,208C ''see'':173C ''seen'':9C,102C ''simplifi'':135C ''singl'':123C,140C ''small'':59C ''specifi'':163C ''split'':199C ''suffici'':33C ''support'':36C ''system'':28C ''take'':47C ''train'':227C,250C ''underfit'':79C,99C,191C ''use'':218C,258C ''valid'':197C ''varianc'':246C ''way'':181C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (865, 1, '2014-07-30 10:24:54.18', 1, 440, 'How to build parse tree with BNF', '&lt;p&gt;I need to build parse tree for some source code (on Python or any program language that describe by CFG).&lt;/p&gt;

&lt;p&gt;So, I have source code on some programming language and BNF this language.&lt;/p&gt;

&lt;p&gt;Can anybody give some advice how can I build parse tree in this case?
Preferably, with tools for Python.&lt;/p&gt;
', 'python  parsing', NULL, '''/p'':31C,46C,67C ''advic'':52C ''anybodi'':49C ''bnf'':7A,43C ''build'':3A,14C,56C ''case'':61C ''cfg'':30C ''code'':20C,37C ''describ'':28C ''give'':50C ''languag'':26C,41C,45C ''need'':12C ''p'':10C,32C,47C ''pars'':4A,9B,15C,57C ''prefer'':62C ''program'':25C,40C ''python'':8B,22C,66C ''sourc'':19C,36C ''tool'':64C ''tree'':5A,16C,58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (364, 2, '2014-06-13 17:36:21.937', 0, NULL, NULL, '&lt;p&gt;The &lt;a href="http://sijinjoseph.com/programmer-competency-matrix/" rel="nofollow"&gt;Programmer Competency Matrix&lt;/a&gt; is just a set of skills, which are more likely to occur when being a real programmer than other skills, they are not a checklist to being a programmer, or for that matter, required to be a programmer; most common way to know someone is a programmer is that they''re paid to be a programmer, which honestly has nothing to do with programming skills.&lt;/p&gt;

&lt;p&gt;To be a data scientist, do data science.&lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/p'':79C,89C ''/programmer-competency-matrix/'':7C ''checklist'':38C ''common'':53C ''compet'':11C ''data'':84C,87C ''honest'':71C ''href'':4C ''know'':56C ''like'':23C ''matrix'':12C ''matter'':46C ''nofollow'':9C ''noth'':73C ''occur'':25C ''p'':1C,80C ''paid'':65C ''program'':77C ''programm'':10C,30C,42C,51C,60C,69C ''re'':64C ''real'':29C ''rel'':8C ''requir'':47C ''scienc'':88C ''scientist'':85C ''set'':17C ''sijinjoseph.com'':6C ''sijinjoseph.com/programmer-competency-matrix/'':5C ''skill'':19C,33C,78C ''someon'':57C ''way'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (403, 1, '2014-06-16 13:30:01.32', 1, 165, 'Why is there such a mismatch between the Model''s predicted probability and theoretical probability in logistic regression?', '&lt;p&gt;&lt;img src="http://i.stack.imgur.com/4Ih6o.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;I am trying to do Logistic Regression using SAS Enterprise Miner. 
My Independent variables are &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;CPR/Inc (Categorical 1 to 7)
OD/Inc (Categorical 1 to 4)
Insurance (Binary 0 or 1)
Income Loss (Binary 0 or 1)
Living Arrangement (Categorical 1 to 7)
Employment Status (categorical 1 to 8)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;My Dependent Variable is Default (Binary 0 or 1)&lt;/p&gt;

&lt;p&gt;The following is the output from running Regression Model.&lt;/p&gt;

&lt;p&gt;Analysis of Maximum Likelihood Estimates&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;                                  Standard          Wald
Parameter       DF    Estimate       Error    Chi-Square    Pr &amp;gt; ChiSq    Exp(Est)

Intercept        1     -0.4148      0.0645         41.30        &amp;lt;.0001       0.660
CPR___Inc  1     1     -0.8022      0.1051         58.26        &amp;lt;.0001       0.448
CPR___Inc  2     1     -0.4380      0.0966         20.57        &amp;lt;.0001       0.645
CPR___Inc  3     1      0.3100      0.0871         12.68        0.0004       1.363
CPR___Inc  4     1    -0.00304      0.0898          0.00        0.9730       0.997
CPR___Inc  5     1      0.1331      0.0885          2.26        0.1324       1.142
CPR___Inc  6     1      0.1694      0.0881          3.70        0.0546       1.185
Emp_Status 1     1     -0.2289      0.1006          5.18        0.0229       0.795
Emp_Status 2     1      0.4061      0.0940         18.66        &amp;lt;.0001       1.501
Emp_Status 3     1     -0.2119      0.1004          4.46        0.0347       0.809
Emp_Status 4     1      0.1100      0.0963          1.30        0.2534       1.116
Emp_Status 5     1     -0.2280      0.1007          5.12        0.0236       0.796
Emp_Status 6     1      0.3761      0.0943         15.91        &amp;lt;.0001       1.457
Emp_Status 7     1     -0.3337      0.1026         10.59        0.0011       0.716
Inc_Loss   0     1     -0.1996      0.0449         19.76        &amp;lt;.0001       0.819
Insurance  0     1      0.1256      0.0559          5.05        0.0246       1.134
Liv_Arran  1     1     -0.1128      0.0916          1.52        0.2178       0.893
Liv_Arran  2     1      0.2576      0.0880          8.57        0.0034       1.294
Liv_Arran  3     1      0.0235      0.0904          0.07        0.7950       1.024
Liv_Arran  4     1      0.0953      0.0887          1.16        0.2825       1.100
Liv_Arran  5     1     -0.0493      0.0907          0.29        0.5871       0.952
Liv_Arran  6     1     -0.3732      0.0966         14.93        0.0001       0.689
OD___Inc   1     1     -0.2136      0.0557         14.72        0.0001       0.808
OD___Inc   2     1     -0.0279      0.0792          0.12        0.7248       0.973
OD___Inc   3     1     -0.0249      0.0793          0.10        0.7534       0.975
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now I used this Model to Score a new set of data. An example row of my new data is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;CPR - 7
OD - 4
Living Arrangement - 4
Employment Status - 4
Insurance - 0
Income Loss - 1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For this sample row, the model predicted output (Probability of default = 1) as 0.7335 
To check this manually, I added the estimates&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Intercept + Emp Status 4 + Liv Arran 4 + Insurance 0
-0.4148   + 0.1100  +   0.0953   +   0.1256    =   -0.0839
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Odds ratio = Exponential(-0.0839) = 0.9195&lt;/p&gt;

&lt;p&gt;Hence probability = 0.9195 / (1 + 0.9195)  =   0.4790&lt;/p&gt;

&lt;p&gt;I am unable to understand why there is such a mismatch between the Model''s predicted probability and theoretical probability. &lt;/p&gt;

&lt;p&gt;Any help would be much appreciated .
Thanks&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (365, 2, '2014-06-13 18:59:11.493', 3, NULL, NULL, '&lt;p&gt;@OP: Choosing answers by votes is the WORST idea.  &lt;/p&gt;

&lt;p&gt;Your question becomes a popularity contest.  You should seek the right answer, I doubt you know what you are asking, know what you are looking for.  &lt;/p&gt;

&lt;p&gt;To answer your question:&lt;br&gt;
Q: how seriously DS certifications are viewed at this point by the community.  &lt;/p&gt;

&lt;p&gt;A: What is your goal from taking these courses?  For work, for school, for self-improvement, etc?  Coursera classes are very applied, you will not learn much theory, they are intentionally reserved for classroom setting.  &lt;/p&gt;

&lt;p&gt;Nonetheless, Coursera classes are very useful.  I''d say it is equivalent to one year of stat grad class, out of a two year Master program.  &lt;/p&gt;

&lt;p&gt;I am not sure of its industry recognition yet, because the problem of how did you actually take the course?  How much time did you spend?  It''s a lot easier to get A''s in these courses than a classroom paper-pencil exam.  So, there is be a huge quality variation from person to person.&lt;/p&gt;
', NULL, NULL, '''/p'':11C,39C,59C,97C,125C,184C ''actual'':143C ''answer'':4C,24C,42C ''appli'':83C ''ask'':32C ''becom'':15C ''br'':45C ''certif'':50C ''choos'':3C ''class'':80C,101C,117C ''classroom'':95C,167C ''communiti'':58C ''contest'':18C ''cours'':69C,146C,164C ''coursera'':79C,100C ''d'':106C ''doubt'':26C ''ds'':49C ''easier'':157C ''equival'':110C ''etc'':78C ''exam'':171C ''get'':159C ''goal'':65C ''grad'':116C ''huge'':177C ''idea'':10C ''improv'':77C ''industri'':133C ''intent'':92C ''know'':28C,33C ''learn'':87C ''look'':37C ''lot'':156C ''master'':123C ''much'':88C,148C ''nonetheless'':99C ''one'':112C ''op'':2C ''p'':1C,12C,40C,60C,98C,126C ''paper'':169C ''paper-pencil'':168C ''pencil'':170C ''person'':181C,183C ''point'':55C ''popular'':17C ''problem'':138C ''program'':124C ''q'':46C ''qualiti'':178C ''question'':14C,44C ''recognit'':134C ''reserv'':93C ''right'':23C ''say'':107C ''school'':73C ''seek'':21C ''self'':76C ''self-improv'':75C ''serious'':48C ''set'':96C ''spend'':152C ''stat'':115C ''sure'':130C ''take'':67C,144C ''theori'':89C ''time'':149C ''two'':121C ''use'':104C ''variat'':179C ''view'':52C ''vote'':6C ''work'':71C ''worst'':9C ''year'':113C,122C ''yet'':135C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (366, 2, '2014-06-13 20:13:01.913', 7, NULL, NULL, '&lt;p&gt;To answer your question it is important to understand the frame of reference you are looking for, if you are looking for what philosophically you are trying to achieve in model fitting, check out Rubens answer he does a good job of explaining that context.&lt;/p&gt;

&lt;p&gt;However, in practice your question is almost entirely defined by business objectives.  &lt;/p&gt;

&lt;p&gt;To give a concrete example, lets say that you are a loan officer, you issued loans that are \$3,000 and when people pay you back you make \$50.  Naturally you are trying to build a model that predicts how if a person defaults on their loan.  Lets keep this simple and say that the outcomes are either full payment, or default.&lt;/p&gt;

&lt;p&gt;From a business perspective you can sum up a models performance with a contingency matrix:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/RgWr9.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;When the model predicts someone is going to default, do they?  To determining the downsides of over and under fitting I find it helpful to think of it as an optimization problem, because in each cross section of predicted verses actual model performance there is either a cost or profit to be made:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/78kH2.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;In this example predicting a default that is a default means avoiding any risk, and predicted a non-default which doesn''t default will make \$50 per loan issued.  Where things get dicey is when you are wrong, if you default when you predicted non-default you lose the entire loan principal and if you predict default when a customer actually would not have you suffer \$50 of missed opportunity.  The numbers here are not important, just the approach.&lt;/p&gt;

&lt;p&gt;With this framework we can now begin to understand the difficulties associated with over and under fitting.&lt;/p&gt;

&lt;p&gt;Over fitting in this case would mean that your model works far better on you development/test data then it does in production.  Or to put it another way, your model in production will far underperform what you saw in development, this false confidence will probably cause you to take on far more risky loans then you otherwise would and leaves you very vulnerable to losing money.&lt;/p&gt;

&lt;p&gt;On the other hand, under fitting in this context will leave you with a model that just does a poor job of matching reality.  While the results of this can be wildly unpredictable, (the opposite word you want to describe your predictive models), commonly what happens is standards are tightened up to compensate for this, leading to less overall customers leading to lost good customers.  &lt;/p&gt;

&lt;p&gt;Under fitting suffers a kind of opposite difficulty that over fitting does, which is under fitting gives you lower confidence.  Insidiously, the lack of predictability still leads you to take on unexpected risk, all of which is bad news.&lt;/p&gt;

&lt;p&gt;In my experience the best way to avoid both of these situations is validating your model on data that is completely outside the scope of your training data, so you can have some confidence that you have a representative sample of what you will see ''in the wild''.  &lt;/p&gt;

&lt;p&gt;Additionally, it is always a good practice to revalidate your models periodically, to determine how quickly your model is degrading, and if it is still accomplishing your objectives.&lt;/p&gt;

&lt;p&gt;Just to some things up, your model is under fitted when it does a poor job of predicting both the development and production data.&lt;/p&gt;
', NULL, NULL, '''/78kh2.png'':215C ''/p'':47C,61C,125C,142C,154C,209C,221C,304C,323C,391C,458C,499C,549C,579C,605C ''/rgwr9.png'':148C ''000'':82C ''3'':81C ''50'':91C,249C,291C ''accomplish'':576C ''achiev'':30C ''actual'':196C,285C ''addit'':551C ''almost'':55C ''alt'':149C,216C ''alway'':554C ''anoth'':351C ''answer'':3C,37C ''approach'':303C ''associ'':317C ''avoid'':234C,508C ''back'':88C ''bad'':497C ''begin'':312C ''best'':505C ''better'':337C ''build'':97C ''busi'':59C,129C ''case'':329C ''caus'':370C ''check'':34C ''common'':436C ''compens'':445C ''complet'':521C ''concret'':66C ''confid'':367C,479C,534C ''context'':46C,401C ''conting'':140C ''cost'':203C ''cross'':191C ''custom'':284C,452C,457C ''data'':341C,518C,528C,604C ''default'':106C,124C,164C,228C,232C,242C,246C,264C,270C,281C ''defin'':57C ''degrad'':570C ''describ'':432C ''descript'':152C,219C ''determin'':168C,564C ''develop'':364C,601C ''development/test'':340C ''dicey'':256C ''difficulti'':316C,467C ''doesn'':244C ''downsid'':170C ''either'':120C,201C ''enter'':150C,217C ''entir'':56C,274C ''exampl'':67C,225C ''experi'':503C ''explain'':44C ''fals'':366C ''far'':336C,358C,375C ''find'':177C ''fit'':33C,175C,322C,326C,398C,461C,470C,475C,590C ''frame'':12C ''framework'':308C ''full'':121C ''get'':255C ''give'':64C,476C ''go'':162C ''good'':41C,456C,556C ''hand'':396C ''happen'':438C ''help'':179C ''howev'':49C ''i.stack.imgur.com'':147C,214C ''i.stack.imgur.com/78kh2.png'':213C ''i.stack.imgur.com/rgwr9.png'':146C ''imag'':151C,218C ''img'':144C,211C ''import'':8C,300C ''insidi'':480C ''issu'':77C,252C ''job'':42C,413C,596C ''keep'':111C ''kind'':464C ''lack'':482C ''lead'':448C,453C,486C ''leav'':384C,403C ''less'':450C ''let'':68C,110C ''loan'':74C,78C,109C,251C,275C,378C ''look'':17C,22C ''lose'':272C,389C ''lost'':455C ''lower'':478C ''made'':208C ''make'':90C,248C ''match'':415C ''matrix'':141C ''mean'':233C,331C ''miss'':293C ''model'':32C,99C,136C,158C,197C,334C,354C,407C,435C,516C,561C,568C,587C ''money'':390C ''natur'':92C ''news'':498C ''non'':241C,269C ''non-default'':240C,268C ''number'':296C ''object'':60C,578C ''offic'':75C ''opportun'':294C ''opposit'':427C,466C ''optim'':186C ''otherwis'':381C ''outcom'':118C ''outsid'':522C ''overal'':451C ''p'':1C,48C,62C,126C,143C,155C,210C,222C,305C,324C,392C,459C,500C,550C,580C ''pay'':86C ''payment'':122C ''peopl'':85C ''per'':250C ''perform'':137C,198C ''period'':562C ''person'':105C ''perspect'':130C ''philosoph'':25C ''poor'':412C,595C ''practic'':51C,557C ''predict'':101C,159C,194C,226C,238C,267C,280C,434C,484C,598C ''princip'':276C ''probabl'':369C ''problem'':187C ''product'':346C,356C,603C ''profit'':205C ''put'':349C ''question'':5C,53C ''quick'':566C ''realiti'':416C ''refer'':14C ''repres'':539C ''result'':419C ''revalid'':559C ''risk'':236C,492C ''riski'':377C ''ruben'':36C ''sampl'':540C ''saw'':362C ''say'':69C,115C ''scope'':524C ''section'':192C ''see'':545C ''simpl'':113C ''situat'':512C ''someon'':160C ''src'':145C,212C ''standard'':440C ''still'':485C,575C ''suffer'':290C,462C ''sum'':133C ''take'':373C,489C ''thing'':254C,584C ''think'':181C ''tighten'':442C ''train'':527C ''tri'':28C,95C ''underperform'':359C ''understand'':10C,314C ''unexpect'':491C ''unpredict'':425C ''valid'':514C ''vers'':195C ''vulner'':387C ''want'':430C ''way'':352C,506C ''wild'':424C,548C ''word'':428C ''work'':335C ''would'':286C,330C,382C ''wrong'':261C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (368, 2, '2014-06-14 07:34:37.643', 2, NULL, NULL, '&lt;p&gt;If you know R and it''s ggplot library, you could try ggplot for python:&lt;/p&gt;

&lt;p&gt;I like it, because I do work in R and python, and both are virtually identical.&lt;/p&gt;

&lt;p&gt;But if you are not familiar you have to deal with a very "unpythonic" syntax. But I think it''s an easy library overall.&lt;/p&gt;
', NULL, NULL, '''/p'':17C,35C,61C ''could'':12C ''deal'':46C ''easi'':58C ''familiar'':42C ''ggplot'':9C,14C ''ident'':34C ''know'':4C ''librari'':10C,59C ''like'':20C ''overal'':60C ''p'':1C,18C,36C ''python'':16C,29C ''r'':5C,27C ''syntax'':51C ''think'':54C ''tri'':13C ''unpython'':50C ''virtual'':33C ''work'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (369, 1, '2014-06-14 18:53:32.243', 5, 479, 'Difference between using RMSE and nDCG to evaluate Recommender Systems', '&lt;p&gt;What kind of error measures do RMSE and nDCG give while evaluating a recommender system, and how do I know when to use one over the other? If you could give an example of when to use each, that would be great as well!&lt;/p&gt;
', 'machine-learning  recommendation  evaluation', NULL, '''/p'':61C ''could'':46C ''differ'':1A ''error'':20C ''evalu'':8A,15B,28C ''exampl'':49C ''give'':26C,47C ''great'':58C ''kind'':18C ''know'':36C ''learn'':13B ''machin'':12B ''machine-learn'':11B ''measur'':21C ''ndcg'':6A,25C ''one'':40C ''p'':16C ''recommend'':9A,14B,30C ''rmse'':4A,23C ''system'':10A,31C ''use'':3A,39C,53C ''well'':60C ''would'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (370, 1, '2014-06-14 19:54:53.193', 8, 467, 'Data Science oriented dataset/research question for Statistics MSc thesis', '&lt;p&gt;I''d like to explore ''data science''. The term seems a little vague to me, but I expect it to require:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;machine learning (rather than traditional statistics);&lt;/li&gt;
&lt;li&gt;a large enough dataset that you have to run analyses on clusters.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;What are some good datasets and problems, accessible to a statistician with some programming background, that I can use to explore the field of data science?&lt;/p&gt;

&lt;p&gt;To keep this as narrow as possible, I''d ideally like links to open, well used datasets and example problems.&lt;/p&gt;
', 'statistics  education  knowledge-base  definitions', NULL, '''/li'':47C,61C ''/ol'':62C ''/p'':38C,90C,112C ''access'':71C ''analys'':58C ''background'':78C ''base'':14B ''cluster'':60C ''d'':18C,100C ''data'':1A,22C,88C ''dataset'':52C,68C,108C ''dataset/research'':4A ''definit'':15B ''educ'':11B ''enough'':51C ''exampl'':110C ''expect'':34C ''explor'':21C,84C ''field'':86C ''good'':67C ''ideal'':101C ''keep'':93C ''knowledg'':13B ''knowledge-bas'':12B ''larg'':50C ''learn'':42C ''li'':40C,48C ''like'':19C,102C ''link'':103C ''littl'':28C ''machin'':41C ''msc'':8A ''narrow'':96C ''ol'':39C ''open'':105C ''orient'':3A ''p'':16C,63C,91C ''possibl'':98C ''problem'':70C,111C ''program'':77C ''question'':5A ''rather'':43C ''requir'':37C ''run'':57C ''scienc'':2A,23C,89C ''seem'':26C ''statist'':7A,10B,46C ''statistician'':74C ''term'':25C ''thesi'':9A ''tradit'':45C ''use'':82C,107C ''vagu'':29C ''well'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (371, 1, '2014-06-14 20:32:06.143', 5, 120, 'How to process natural language queries?', '&lt;p&gt;I''m curious about natural language querying.  Stanford has what looks to be a strong set of &lt;a href="http://nlp.stanford.edu/software/index.shtml"&gt;software for processing natural language&lt;/a&gt;.  I''ve also seen the &lt;a href="http://opennlp.apache.org/documentation/1.5.3/manual/opennlp.html"&gt;Apache OpenNLP library&lt;/a&gt;, and the &lt;a href="http://gate.ac.uk/science.html"&gt;General Architecture for Text Engineering&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;There are an incredible amount of uses for natural language processing and that makes the documentation of these projects difficult to quickly absorb.&lt;/p&gt;

&lt;p&gt;Can you simplify things for me a bit and at a high level outline the tasks necessary for performing a basic translation of simple questions into SQL?&lt;/p&gt;

&lt;p&gt;The first rectangle on my flow chart is a bit of a mystery.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/wJPx9.png" alt="enter image description here"&gt;  &lt;/p&gt;

&lt;p&gt;For example, I might want to know:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;How many books were sold last month?
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And I''d want that translated into&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Select count(*) 
  from sales 
  where 
   item_type=''book'' and 
   sales_date &amp;gt;= ''5/1/2014'' and 
   sales_date &amp;lt;= ''5/31/2014''
&lt;/code&gt;&lt;/pre&gt;
', 'nlp', NULL, '''/a'':36C,50C,63C ''/code'':164C,195C ''/documentation/1.5.3/manual/opennlp.html'':46C ''/p'':64C,89C,118C,133C,145C,154C,174C ''/pre'':165C,196C ''/science.html'':57C ''/software/index.shtml'':30C ''/wjpx9.png'':139C ''5/1/2014'':189C ''5/31/2014'':194C ''absorb'':88C ''also'':39C ''alt'':140C ''amount'':70C ''apach'':47C ''architectur'':59C ''basic'':111C ''bit'':98C,129C ''book'':159C,184C ''chart'':126C ''code'':156C,176C ''count'':178C ''curious'':11C ''d'':169C ''date'':187C,192C ''descript'':143C ''difficult'':85C ''document'':81C ''engin'':62C ''enter'':141C ''exampl'':148C ''first'':121C ''flow'':125C ''gate.ac.uk'':56C ''gate.ac.uk/science.html'':55C ''general'':58C ''gt'':188C ''high'':102C ''href'':27C,43C,54C ''i.stack.imgur.com'':138C ''i.stack.imgur.com/wjpx9.png'':137C ''imag'':142C ''img'':135C ''incred'':69C ''item'':182C ''know'':153C ''languag'':5A,14C,35C,75C ''last'':162C ''level'':103C ''librari'':49C ''look'':19C ''lt'':193C ''m'':10C ''make'':79C ''mani'':158C ''might'':150C ''month'':163C ''mysteri'':132C ''natur'':4A,13C,34C,74C ''necessari'':107C ''nlp'':7B ''nlp.stanford.edu'':29C ''nlp.stanford.edu/software/index.shtml'':28C ''opennlp'':48C ''opennlp.apache.org'':45C ''opennlp.apache.org/documentation/1.5.3/manual/opennlp.html'':44C ''outlin'':104C ''p'':8C,65C,90C,119C,134C,146C,166C ''perform'':109C ''pre'':155C,175C ''process'':3A,33C,76C ''project'':84C ''queri'':6A,15C ''question'':115C ''quick'':87C ''rectangl'':122C ''sale'':180C,186C,191C ''seen'':40C ''select'':177C ''set'':24C ''simpl'':114C ''simplifi'':93C ''softwar'':31C ''sold'':161C ''sql'':117C ''src'':136C ''stanford'':16C ''strong'':23C ''task'':106C ''text'':61C ''thing'':94C ''translat'':112C,172C ''type'':183C ''use'':72C ''ve'':38C ''want'':151C,170C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (372, 2, '2014-06-14 20:52:00.873', 8, NULL, NULL, '&lt;p&gt;Just head to kaggle.com; it''ll keep you busy for a long time. For open data there''s the &lt;a href="http://archive.ics.uci.edu/ml/"&gt;UC Irvine Machine Learning Repository&lt;/a&gt;. In fact, there''s a whole &lt;a href="http://opendata.stackexchange.com/"&gt;Stackexchange site&lt;/a&gt; devoted to this; look there.&lt;/p&gt;
', NULL, NULL, '''/a'':31C,43C ''/ml/'':25C ''/p'':49C ''archive.ics.uci.edu'':24C ''archive.ics.uci.edu/ml/'':23C ''busi'':10C ''data'':17C ''devot'':44C ''fact'':33C ''head'':3C ''href'':22C,39C ''irvin'':27C ''kaggle.com'':5C ''keep'':8C ''learn'':29C ''ll'':7C ''long'':13C ''look'':47C ''machin'':28C ''open'':16C ''opendata.stackexchange.com'':40C ''p'':1C ''repositori'':30C ''site'':42C ''stackexchang'':41C ''time'':14C ''uc'':26C ''whole'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8829, 1, '2015-11-13 09:29:35.127', 1, 25, 'Examining a DocumentTermMatrix in RTextTools', '&lt;p&gt;I created a DocumentTermMatrix  for text mining using RTextTools. The rows for this DocumentTermMatrix  correspond to dataframe rows and matix columns correspond to words. My question is : How can I get the words (labels vector) for examining the DocumentTermMatrix  ? In other words, How can I get the vector of these 904 words?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;require(RTextTools,quietly=TRUE)
data(USCongress)

doc_matrix &amp;lt;- create_matrix(USCongress$text, language="english",   removeNumbers=TRUE, stemWords=TRUE, removeSparseTerms=.998)

dim(USCongress)
[1] 4449    6

dim(doc_matrix)
[1] 4449  904
&lt;/code&gt;&lt;/pre&gt;
', 'r  text-mining', NULL, '''/code'':98C ''/p'':63C ''/pre'':99C ''1'':89C,95C ''4449'':90C,96C ''6'':91C ''904'':61C,97C ''998'':86C ''code'':65C ''column'':31C ''correspond'':25C,32C ''creat'':12C,75C ''data'':70C ''datafram'':27C ''dim'':87C,92C ''doc'':72C,93C ''documenttermmatrix'':3A,14C,24C,49C ''english'':80C ''examin'':1A,47C ''get'':41C,56C ''label'':44C ''languag'':79C ''lt'':74C ''matix'':30C ''matrix'':73C,76C,94C ''mine'':9B,17C ''p'':10C ''pre'':64C ''question'':36C ''quiet'':68C ''r'':6B ''removenumb'':81C ''removesparseterm'':85C ''requir'':66C ''row'':21C,28C ''rtexttool'':5A,19C,67C ''stemword'':83C ''text'':8B,16C,78C ''text-min'':7B ''true'':69C,82C,84C ''uscongress'':71C,77C,88C ''use'':18C ''vector'':45C,58C ''word'':34C,43C,52C,62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (373, 2, '2014-06-14 21:13:20.11', 5, NULL, NULL, '&lt;p&gt;The &lt;a href="http://sunlightfoundation.com/"&gt;Sunlight Foundation&lt;/a&gt; is an organization that is focused on opening up and encouraging non-partisan analysis of government data.&lt;/p&gt;

&lt;p&gt;There is a ton of analysis out there in the wild that can be used for comparison, and a wide variety of topics.&lt;/p&gt;

&lt;p&gt;They provide &lt;a href="http://sunlightfoundation.com/tools/"&gt;tools&lt;/a&gt; and &lt;a href="http://sunlightfoundation.com/api/"&gt;apis&lt;/a&gt; for accessing data, and have helped push to make data available in places like &lt;a href="http://www.data.gov/"&gt;data.gov&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;One interesting project is &lt;a href="http://influenceexplorer.com/"&gt;Influence Explorer&lt;/a&gt;.  You can get &lt;a href="http://data.influenceexplorer.com/"&gt;source data here&lt;/a&gt; as well as access to real time data.&lt;/p&gt;

&lt;p&gt;You might also want to take a look at one of our more popular questions:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/questions/155/publicly-available-datasets"&gt;Publicly available datasets&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':8C,62C,70C,89C,101C,111C,147C ''/api/'':68C ''/p'':27C,52C,90C,120C,137C,148C ''/questions/155/publicly-available-datasets'':143C ''/tools/'':60C ''access'':72C,115C ''also'':124C ''analysi'':23C,34C ''api'':69C ''avail'':81C,145C ''comparison'':45C ''data'':26C,73C,80C,109C,119C ''data.gov'':88C ''data.influenceexplorer.com'':107C ''datascience.stackexchange.com'':142C ''datascience.stackexchange.com/questions/155/publicly-available-datasets'':141C ''dataset'':146C ''encourag'':19C ''explor'':100C ''focus'':14C ''foundat'':7C ''get'':104C ''govern'':25C ''help'':76C ''href'':4C,57C,65C,86C,97C,106C,140C ''influenc'':99C ''influenceexplorer.com'':98C ''interest'':93C ''like'':84C ''look'':129C ''make'':79C ''might'':123C ''non'':21C ''non-partisan'':20C ''one'':92C,131C ''open'':16C ''organ'':11C ''p'':1C,28C,53C,91C,121C,138C ''partisan'':22C ''place'':83C ''popular'':135C ''project'':94C ''provid'':55C ''public'':144C ''push'':77C ''question'':136C ''real'':117C ''sourc'':108C ''sunlight'':6C ''sunlightfoundation.com'':5C,59C,67C ''sunlightfoundation.com/api/'':66C ''sunlightfoundation.com/tools/'':58C ''take'':127C ''time'':118C ''ton'':32C ''tool'':61C ''topic'':51C ''use'':43C ''varieti'':49C ''want'':125C ''well'':113C ''wide'':48C ''wild'':39C ''www.data.gov'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (569, 2, '2014-06-24 20:35:44.713', 1, NULL, NULL, '&lt;p&gt;Is it a bird? Is it a cat? We have black-and-white cat-sized! magpies here. so that would fail.&lt;/p&gt;

&lt;p&gt;First thing would be to exclude all areas that are green, cats are seldom green.&lt;/p&gt;

&lt;p&gt;Then compare the rest to a reference image to remove static things like stones and stairs.&lt;/p&gt;

&lt;p&gt;Detecting objects of a minimum size should be possible, but for a classification the resolution is too low. Could be also your neighbor testing his new remote controlled drone.&lt;/p&gt;

&lt;p&gt;With two cameras you could do a 3d mapping of the objects and eliminate flying objects.&lt;/p&gt;
', NULL, NULL, '''/p'':25C,42C,60C,91C,109C ''3d'':100C ''also'':82C ''area'':34C ''bird'':5C ''black'':13C ''black-and-whit'':12C ''camera'':95C ''cat'':9C,17C,38C ''cat-siz'':16C ''classif'':74C ''compar'':45C ''control'':89C ''could'':80C,97C ''detect'':62C ''drone'':90C ''elimin'':106C ''exclud'':32C ''fail'':24C ''first'':27C ''fli'':107C ''green'':37C,41C ''imag'':51C ''like'':56C ''low'':79C ''magpi'':19C ''map'':101C ''minimum'':66C ''neighbor'':84C ''new'':87C ''object'':63C,104C,108C ''p'':1C,26C,43C,61C,92C ''possibl'':70C ''refer'':50C ''remot'':88C ''remov'':53C ''resolut'':76C ''rest'':47C ''seldom'':40C ''size'':18C,67C ''stair'':59C ''static'':54C ''stone'':57C ''test'':85C ''thing'':28C,55C ''two'':94C ''white'':15C ''would'':23C,29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (374, 2, '2014-06-14 21:39:15.833', 3, NULL, NULL, '&lt;p&gt;Natural language querying poses very many intricacies which can be very difficult to generalize.  From a high level, I would start with trying to think of things in terms of nouns and verbs.&lt;/p&gt;

&lt;p&gt;So for the sentence: How many books were sold last month?&lt;/p&gt;

&lt;p&gt;You would start by breaking the sentence down with a parser which will return a tree format similar to this:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ogoiY.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;You can see that there is a subject books, a compound verbal phrase indicating the past action of sell, and then a noun phrase where you have the time focus of a month.&lt;/p&gt;

&lt;p&gt;We can further break down the subject for modifiers: "how many" for books, and "last" for month.&lt;/p&gt;

&lt;p&gt;Once you have broken the sentence down you need to map those elements to sql language e.g.: how many =&gt; count, books =&gt; book, sold =&gt; sales, month =&gt; sales_date (interval), and so on.  &lt;/p&gt;

&lt;p&gt;Finally, once you have the elements of the language you just need to come up with a set of rules for how different entities interact with each other, which leaves you with:&lt;/p&gt;

&lt;p&gt;Select count(*) 
  from sales 
  where 
   item_type=''book'' and 
   sales_date &gt;= ''5/1/2014'' and 
   sales_date &amp;lt;= ''5/31/2014''&lt;/p&gt;

&lt;p&gt;This is at a high level how I would begin, while almost every step I have mentioned is non-trivial and really the rabbit hole can be endless, this should give you many of the dots to connect.  &lt;/p&gt;
', NULL, NULL, '''/ogoiy.png'':76C ''/p'':35C,48C,70C,82C,117C,136C,169C,203C,222C,263C ''5/1/2014'':216C ''5/31/2014'':221C ''action'':100C ''almost'':235C ''alt'':77C ''begin'':233C ''book'':43C,92C,131C,158C,159C,212C ''break'':54C,122C ''broken'':141C ''come'':184C ''compound'':94C ''connect'':262C ''count'':157C,206C ''date'':164C,215C,219C ''descript'':80C ''differ'':193C ''difficult'':13C ''dot'':260C ''e.g'':154C ''element'':150C,176C ''endless'':252C ''enter'':78C ''entiti'':194C ''everi'':236C ''final'':171C ''focus'':113C ''format'':66C ''general'':15C ''give'':255C ''high'':18C,228C ''hole'':249C ''i.stack.imgur.com'':75C ''i.stack.imgur.com/ogoiy.png'':74C ''imag'':79C ''img'':72C ''indic'':97C ''interact'':195C ''interv'':165C ''intricaci'':8C ''item'':210C ''languag'':3C,153C,179C ''last'':46C,133C ''leav'':200C ''level'':19C,229C ''lt'':220C ''mani'':7C,42C,129C,156C,257C ''map'':148C ''mention'':240C ''modifi'':127C ''month'':47C,116C,135C,162C ''natur'':2C ''need'':146C,182C ''non'':243C ''non-trivi'':242C ''noun'':32C,106C ''p'':1C,36C,49C,71C,83C,118C,137C,170C,204C,223C ''parser'':60C ''past'':99C ''phrase'':96C,107C ''pose'':5C ''queri'':4C ''rabbit'':248C ''realli'':246C ''return'':63C ''rule'':190C ''sale'':161C,163C,208C,214C,218C ''see'':86C ''select'':205C ''sell'':102C ''sentenc'':40C,56C,143C ''set'':188C ''similar'':67C ''sold'':45C,160C ''sql'':152C ''src'':73C ''start'':22C,52C ''step'':237C ''subject'':91C,125C ''term'':30C ''thing'':28C ''think'':26C ''time'':112C ''tree'':65C ''tri'':24C ''trivial'':244C ''type'':211C ''verb'':34C ''verbal'':95C ''would'':21C,51C,232C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (376, 1, '2014-06-15 00:00:51.657', 6, 77, 'How to compare experiments run over different infrastructures', '&lt;p&gt;I''m developing a distributed algorithm, and to improve efficiency, it relies both on the number of disks (one per machine), and on an efficient load balance strategy. With more disks, we''re able to reduce the time spent with I/O; and with an efficient load balance policy, we can distribute tasks without much data replication overhead.&lt;/p&gt;

&lt;p&gt;There are many studies on the literature that deal with the same problem, and each of them runs different experiments to evaluate their proposal. Some experiments are specific of the strategy presented, and some others, like weak scaling (scalability) and strong scaling (speedup), are common to all of the works.&lt;/p&gt;

&lt;p&gt;The problem is the experiments are usually executed over entirely different infrastructures (disks, processors, # machines, network), and depending on what is being evaluated, it may raise &lt;em&gt;false/unfair&lt;/em&gt; comparisons. For example, I may get 100% of speedup in my application running on 10 machines with Infiniband connection, whereas I could get the same or even worse results if my connection was Ethernet.&lt;/p&gt;

&lt;p&gt;So, how can one honestly compare different experiments to point out efficiency gains? &lt;/p&gt;
', 'bigdata  efficiency  performance  scalability  distributed', NULL, '''/em'':154C ''/p'':72C,124C,189C,204C ''10'':169C ''100'':161C ''abl'':48C ''algorithm'':20C ''applic'':166C ''balanc'':41C,61C ''bigdata'':9B ''common'':118C ''compar'':3A,196C ''comparison'':155C ''connect'':173C,186C ''could'':176C ''data'':69C ''deal'':82C ''depend'':143C ''develop'':17C ''differ'':7A,92C,136C,197C ''disk'':32C,45C,138C ''distribut'':13B,19C,65C ''effici'':10B,24C,39C,59C,202C ''em'':152C ''entir'':135C ''ethernet'':188C ''evalu'':95C,148C ''even'':181C ''exampl'':157C ''execut'':133C ''experi'':4A,93C,99C,130C,198C ''false/unfair'':153C ''gain'':203C ''get'':160C,177C ''honest'':195C ''i/o'':55C ''improv'':23C ''infiniband'':172C ''infrastructur'':8A,137C ''like'':109C ''literatur'':80C ''load'':40C,60C ''m'':16C ''machin'':35C,140C,170C ''mani'':76C ''may'':150C,159C ''much'':68C ''network'':141C ''number'':30C ''one'':33C,194C ''other'':108C ''overhead'':71C ''p'':14C,73C,125C,190C ''per'':34C ''perform'':11B ''point'':200C ''polici'':62C ''present'':105C ''problem'':86C,127C ''processor'':139C ''propos'':97C ''rais'':151C ''re'':47C ''reduc'':50C ''reli'':26C ''replic'':70C ''result'':183C ''run'':5A,91C,167C ''scalabl'':12B,112C ''scale'':111C,115C ''specif'':101C ''speedup'':116C,163C ''spent'':53C ''strategi'':42C,104C ''strong'':114C ''studi'':77C ''task'':66C ''time'':52C ''usual'':132C ''weak'':110C ''wherea'':174C ''without'':67C ''work'':123C ''wors'':182C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (377, 2, '2014-06-15 00:11:54.95', 4, NULL, NULL, '&lt;p&gt;From our perspective on here, the big benefit of S3 is the ease of accessing the data from within EC2.&lt;/p&gt;

&lt;p&gt;Google Drive is directly accessible from the Google Cloud platform.&lt;/p&gt;

&lt;p&gt;There are a host of other differences that might matter depending on your usage requirements, but that''s the one that would matter most around here.&lt;/p&gt;

&lt;p&gt;The only other difference I can think of that would matter to the DS community is that when you are sharing something, you have no control of the address of a given file on google drive.&lt;/p&gt;
', NULL, NULL, '''/p'':22C,34C,62C,100C ''access'':16C,28C ''address'':92C ''around'':60C ''benefit'':9C ''big'':8C ''cloud'':32C ''communiti'':78C ''control'':89C ''data'':18C ''depend'':46C ''differ'':42C,67C ''direct'':27C ''drive'':25C,99C ''ds'':77C ''eas'':14C ''ec2'':21C ''file'':96C ''given'':95C ''googl'':24C,31C,98C ''host'':39C ''matter'':45C,58C,74C ''might'':44C ''one'':55C ''p'':1C,23C,35C,63C ''perspect'':4C ''platform'':33C ''requir'':50C ''s3'':11C ''share'':84C ''someth'':85C ''think'':70C ''usag'':49C ''within'':20C ''would'':57C,73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8830, 2, '2015-11-13 10:00:41.03', 2, NULL, NULL, '&lt;p&gt;a documenttermmatrix is a simple_triplet_matrix. You can turn this into a simple matrix with the as.matrix command and then use all matrix functions.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# turn into simple matrix
mat &amp;lt;- as.matrix(doc_matrix)

# vector of the words 
word_vector &amp;lt;- colnames(mat)

# Dataframe containing words and their frequency
df_words &amp;lt;- data.frame(words = colnames(mat), frequency = colSums(mat), row.names = NULL)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':66C ''/p'':27C ''/pre'':67C ''as.matrix'':19C,36C ''code'':29C ''colnam'':46C,59C ''colsum'':62C ''command'':20C ''contain'':49C ''data.frame'':57C ''datafram'':48C ''df'':54C ''doc'':37C ''documenttermmatrix'':3C ''frequenc'':53C,61C ''function'':26C ''lt'':35C,45C,56C ''mat'':34C,47C,60C,63C ''matrix'':8C,16C,25C,33C,38C ''null'':65C ''p'':1C ''pre'':28C ''row.names'':64C ''simpl'':6C,15C,32C ''triplet'':7C ''turn'':11C,30C ''use'':23C ''vector'':39C,44C ''word'':42C,43C,50C,55C,58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (379, 2, '2014-06-15 01:25:48.563', 5, NULL, NULL, '&lt;p&gt;Financial Services is a big user of Big Data, and innovator too.  One example is mortgage bond trading.  To answer your questions for it:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;What kinda data these companies used. What was the size of the data?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;ul&gt;
&lt;li&gt;Long histories of each mortgage issued for the past many years, and payments by month against them.  (Billions of rows)&lt;/li&gt;
&lt;li&gt;Long histories of credit histories.  (Billions of rows)&lt;/li&gt;
&lt;li&gt;Home price indices.  (Not as big)&lt;/li&gt;
&lt;/ul&gt;

&lt;blockquote&gt;
  &lt;p&gt;What kinda of tools technologies they used to process the data?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;It varies.  Some use in-house solutions built on databases like Netezza or Teradata.  Others access the data via systems provided by the data providers.  (Corelogic, Experian, etc)  Some banks use columnal database technologies like KDB, or 1010data.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;What was the problem they were facing and how the insight they got the
  data helped them to resolve the issue.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The key issue is determining when mortgage bonds (mortgage backed-securities) will prepay or default.  This is especially important for bonds that lack the government guarantee.  By digging into payment histories, credit files, and understanding the current value of the house, it''s possible to predict the likelihood of a default.  Adding an interest rate model and prepayment model also helps predict the likelihood of a prepayment.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;How they selected the tool\technology to suit their need.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;If the project is driven by internal IT, usually it''s based off of a large database vendor like Oracle, Teradata or Netezza.  If it''s driven by the quants, then they are more likely to go straight to the data vendor, or a 3rd party "All in" system.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;What kinda pattern they identified from the data &amp;amp; what kind of
  patterns they were looking from the data.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Linking the data gives great insights into who is likely to default on their loans, and prepay them.  When you aggregated the loans into bonds, it can be the difference between a bond issued at $100,000,000 being worth that amount, or as little as $20,000,000.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':43C,100C,166C,250C,324C ''/li'':66C,76C,84C ''/p'':26C,42C,99C,141C,165C,236C,249C,301C,323C,375C ''/ul'':85C ''000'':362C,363C,373C,374C ''100'':361C ''1010data'':140C ''20'':372C ''3rd'':296C ''access'':118C ''ad'':220C ''aggreg'':346C ''also'':228C ''amount'':367C ''amp'':312C ''answer'':21C ''back'':178C ''backed-secur'':177C ''bank'':132C ''base'':263C ''big'':6C,9C,83C ''billion'':63C,73C ''blockquot'':27C,86C,142C,237C,302C ''bond'':18C,175C,189C,350C,358C ''built'':110C ''column'':134C ''compani'':33C ''corelog'':128C ''credit'':71C,200C ''current'':205C ''data'':10C,31C,41C,98C,120C,126C,158C,292C,311C,322C,328C ''databas'':112C,135C,268C ''default'':183C,219C,337C ''determin'':172C ''differ'':355C ''dig'':196C ''driven'':256C,278C ''especi'':186C ''etc'':130C ''exampl'':15C ''experian'':129C ''face'':150C ''file'':201C ''financi'':2C ''give'':329C ''go'':288C ''got'':156C ''govern'':193C ''great'':330C ''guarante'':194C ''help'':159C,229C ''histori'':47C,69C,72C,199C ''home'':78C ''hous'':108C,209C ''identifi'':308C ''import'':187C ''in-hous'':106C ''indic'':80C ''innov'':12C ''insight'':154C,331C ''interest'':222C ''intern'':258C ''issu'':51C,164C,170C,359C ''kdb'':138C ''key'':169C ''kind'':314C ''kinda'':30C,89C,305C ''lack'':191C ''larg'':267C ''li'':45C,67C,77C ''like'':113C,137C,270C,286C,335C ''likelihood'':216C,232C ''link'':326C ''littl'':370C ''loan'':340C,348C ''long'':46C,68C ''look'':319C ''mani'':55C ''model'':224C,227C ''month'':60C ''mortgag'':17C,50C,174C,176C ''need'':248C ''netezza'':114C,274C ''one'':14C ''oracl'':271C ''other'':117C ''p'':1C,28C,87C,101C,143C,167C,238C,251C,303C,325C ''parti'':297C ''past'':54C ''pattern'':306C,316C ''payment'':58C,198C ''possibl'':212C ''predict'':214C,230C ''prepay'':181C,226C,235C,342C ''price'':79C ''problem'':147C ''process'':96C ''project'':254C ''provid'':123C,127C ''quant'':281C ''question'':23C ''rate'':223C ''resolv'':162C ''row'':65C,75C ''secur'':179C ''select'':241C ''servic'':3C ''size'':38C ''solut'':109C ''straight'':289C ''suit'':246C ''system'':122C,300C ''technolog'':92C,136C,244C ''teradata'':116C,272C ''tool'':91C,243C ''trade'':19C ''ul'':44C ''understand'':203C ''use'':34C,94C,105C,133C ''user'':7C ''usual'':260C ''valu'':206C ''vari'':103C ''vendor'':269C,293C ''via'':121C ''worth'':365C ''year'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (380, 2, '2014-06-15 01:29:15.24', 4, NULL, NULL, '&lt;p&gt;I suspect this will get closed since it is very narrow, but my 2 cents...&lt;/p&gt;

&lt;p&gt;Data Science requires 3 skills:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Math/Stats&lt;/li&gt;
&lt;li&gt;Programming&lt;/li&gt;
&lt;li&gt;Domain Knowledge&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It can be very hard to show all three.  #1 and #2 can be signaled via degrees, but a hiring manager who may not have them doesn''t want to trust a liberal arts degree.  If you''re looking to get into Data Science, position yourself as a domain expert first.  Publish election predictions.  If you''re correct, cite them.  That will get you noticed.&lt;/p&gt;

&lt;p&gt;If you''re Domain knowledge is A+ level, you don''t need A+ level programming skills, but learn programming enough so that you don''t need someone else to fetch data for you.&lt;/p&gt;
', NULL, NULL, '''/li'':28C,31C,35C ''/p'':17C,24C,103C,138C ''/ul'':36C ''1'':47C ''2'':15C,49C ''3'':22C ''art'':71C ''cent'':16C ''cite'':96C ''close'':7C ''correct'':95C ''data'':19C,80C,135C ''degre'':54C,72C ''doesn'':64C ''domain'':33C,86C,108C ''elect'':90C ''els'':132C ''enough'':124C ''expert'':87C ''fetch'':134C ''first'':88C ''get'':6C,78C,100C ''hard'':42C ''hire'':57C ''knowledg'':34C,109C ''learn'':122C ''level'':112C,118C ''li'':26C,29C,32C ''liber'':70C ''look'':76C ''manag'':58C ''math/stats'':27C ''may'':60C ''narrow'':12C ''need'':116C,130C ''notic'':102C ''p'':1C,18C,37C,104C ''posit'':82C ''predict'':91C ''program'':30C,119C,123C ''publish'':89C ''re'':75C,94C,107C ''requir'':21C ''scienc'':20C,81C ''show'':44C ''signal'':52C ''sinc'':8C ''skill'':23C,120C ''someon'':131C ''suspect'':3C ''three'':46C ''trust'':68C ''ul'':25C ''via'':53C ''want'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (400, 2, '2014-06-16 11:45:05.8', 5, NULL, NULL, '&lt;p&gt;From my experience, the points to keep in mind when considering a data analysis platform are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Can it handle the size of the data that I need? If your data sets fit in memory, there''s usually no big trouble, although AFAIK Python is somewhat more memory-efficient than R. If you need to handle larger-than-memory data sets, the platform need to handle it conveniently. In this case, SQL would cover for basic statistics, Python + Apache Spark is another option.&lt;/li&gt;
&lt;li&gt;Does the platform covers all of my analysis needs? The greatest annoyance I''ve encountered in data mining projects is having to juggle between several tools, because tool A handles web connections well, tool B does the statistics and tool C renders nice pictures. You want your weapon-of-choice to cover as many aspects of your projects as possible. When considering this issue, Python is very comprehensive, but R has a lot of build-in statistical tests ready-to-use, if that''s what you need. &lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':89C,181C ''/ol'':182C ''/p'':18C ''afaik'':46C ''although'':45C ''analysi'':15C,98C ''annoy'':102C ''anoth'':87C ''apach'':84C ''aspect'':146C ''b'':125C ''basic'':81C ''big'':43C ''build'':167C ''build-in'':166C ''c'':131C ''case'':76C ''choic'':141C ''comprehens'':159C ''connect'':122C ''consid'':12C,153C ''conveni'':73C ''cover'':79C,94C,143C ''data'':14C,28C,34C,65C,107C ''effici'':53C ''encount'':105C ''experi'':4C ''fit'':36C ''greatest'':101C ''handl'':23C,60C,71C,120C ''issu'':155C ''juggl'':113C ''keep'':8C ''larger'':62C ''larger-than-memori'':61C ''li'':20C,90C ''lot'':164C ''mani'':145C ''memori'':38C,52C,64C ''memory-effici'':51C ''mind'':10C ''mine'':108C ''need'':31C,58C,69C,99C,180C ''nice'':133C ''ol'':19C ''option'':88C ''p'':1C ''pictur'':134C ''platform'':16C,68C,93C ''point'':6C ''possibl'':151C ''project'':109C,149C ''python'':47C,83C,156C ''r'':55C,161C ''readi'':172C ''ready-to-us'':171C ''render'':132C ''set'':35C,66C ''sever'':115C ''size'':25C ''somewhat'':49C ''spark'':85C ''sql'':77C ''statist'':82C,128C,169C ''test'':170C ''tool'':116C,118C,124C,130C ''troubl'':44C ''use'':174C ''usual'':41C ''ve'':104C ''want'':136C ''weapon'':139C ''weapon-of-choic'':138C ''web'':121C ''well'':123C ''would'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (381, 2, '2014-06-15 01:36:51.693', 3, NULL, NULL, '&lt;p&gt;CAPM (Capital Asset Pricing Model) in Finance is a classic example of an underfit model.  It was built on the beautiful theory that "Investors only pay for risk they can''t diversify away" so expected excess returns are equal to correlation to market returns.&lt;/p&gt;

&lt;p&gt;As a formula [0] Ra = Rf + B (Rm - Rf)
where Ra is the expected return of the asset, Rf is the risk free rate, Rm is the market rate of return, and Beta is the correlation to the Equity premium (Rm - Rf)&lt;/p&gt;

&lt;p&gt;This is beautiful, elegant, and wrong.  Investors seem to require a higher rate of small stocks and value (defined by book to market, or dividend yield) stocks.  &lt;/p&gt;

&lt;p&gt;Fama and French [1] presented an update to the model, which adds additional Betas for Size and Value.  &lt;/p&gt;

&lt;p&gt;So how do you know in a general sense?  When the predictions you are making are wrong, and another variable with a logical explanation increases the prediction quality.  It''s easy to understand why someone might think small stocks are risky, independent of non-diversifiable risk.  It''s a good story, backed by the data.&lt;/p&gt;

&lt;p&gt;[0]  &lt;a href="http://www.investopedia.com/terms/c/capm.asp" rel="nofollow"&gt;http://www.investopedia.com/terms/c/capm.asp&lt;/a&gt;
[1]  &lt;a href="http://en.wikipedia.org/wiki/Fama%E2%80%93French_three-factor_model" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Fama%E2%80%93French_three-factor_model&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':46C,90C,119C,139C,197C ''/terms/c/capm.asp'':204C ''/terms/c/capm.asp&lt;/a&gt;'':209C ''/wiki/fama%e2%80%93french_three-factor_model'':215C ''/wiki/fama%e2%80%93french_three-factor_model&lt;/a&gt;&lt;/p&gt;'':220C ''0'':51C,199C ''1'':124C,210C ''add'':132C ''addit'':133C ''anoth'':159C ''asset'':4C,65C ''away'':34C ''b'':54C ''back'':193C ''beauti'':22C,94C ''beta'':80C,134C ''book'':112C ''built'':19C ''capit'':3C ''capm'':2C ''classic'':11C ''correl'':42C,83C ''data'':196C ''defin'':110C ''diversifi'':33C,186C ''dividend'':116C ''easi'':171C ''eleg'':95C ''en.wikipedia.org'':214C,219C ''en.wikipedia.org/wiki/fama%e2%80%93french_three-factor_model'':213C ''en.wikipedia.org/wiki/fama%e2%80%93french_three-factor_model&lt;/a&gt;&lt;/p&gt;'':218C ''equal'':40C ''equiti'':86C ''exampl'':12C ''excess'':37C ''expect'':36C,61C ''explan'':164C ''fama'':121C ''financ'':8C ''formula'':50C ''free'':70C ''french'':123C ''general'':148C ''good'':191C ''higher'':103C ''href'':201C,212C ''increas'':165C ''independ'':182C ''investor'':25C,98C ''know'':145C ''logic'':163C ''make'':155C ''market'':44C,75C,114C ''might'':176C ''model'':6C,16C,130C ''nofollow'':206C,217C ''non'':185C ''non-diversifi'':184C ''p'':1C,47C,91C,120C,140C,198C ''pay'':27C ''predict'':152C,167C ''premium'':87C ''present'':125C ''price'':5C ''qualiti'':168C ''ra'':52C,58C ''rate'':71C,76C,104C ''rel'':205C,216C ''requir'':101C ''return'':38C,45C,62C,78C ''rf'':53C,56C,66C,89C ''risk'':29C,69C,187C ''riski'':181C ''rm'':55C,72C,88C ''seem'':99C ''sens'':149C ''size'':136C ''small'':106C,178C ''someon'':175C ''stock'':107C,118C,179C ''stori'':192C ''theori'':23C ''think'':177C ''underfit'':15C ''understand'':173C ''updat'':127C ''valu'':109C,138C ''variabl'':160C ''wrong'':97C,157C ''www.investopedia.com'':203C,208C ''www.investopedia.com/terms/c/capm.asp'':202C ''www.investopedia.com/terms/c/capm.asp&lt;/a&gt;'':207C ''yield'':117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (382, 1, '2014-06-15 12:26:50.06', 8, 140, 'How to debug data analysis?', '&lt;p&gt;I''ve came across the following problem, that I recon is rather typical.&lt;/p&gt;

&lt;p&gt;I have some large data, say, a few million rows. I run some non-trivial analysis on it, e.g. an SQL query consisting of several sub-queries. I get some result, stating, for example, that property X is increasing over time.&lt;/p&gt;

&lt;p&gt;Now, there are two possible things that could lead to that:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;X is indeed increasing over time&lt;/li&gt;
&lt;li&gt;I have a bug in my analysis&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;How can I test that the first happened, rather than the second? A step-wise debugger, even if one exists, won''t help, since intermediate results can still consist of millions of lines.&lt;/p&gt;

&lt;p&gt;The only thing I could think of was to somehow generate a small, synthetic data set with the property that I want to test and run the analysis on it as a unit test. Are there tools to do this? Particularly, but not limited to, SQL.&lt;/p&gt;
', 'data-mining  sql  experiments', NULL, '''/li'':92C,101C ''/ol'':102C ''/p'':25C,70C,83C,138C,186C ''across'':15C ''analysi'':5A,43C,100C,167C ''bug'':97C ''came'':14C ''consist'':50C,133C ''could'':79C,144C ''data'':4A,7B,31C,154C ''data-min'':6B ''debug'':3A ''debugg'':120C ''e.g'':46C ''even'':121C ''exampl'':62C ''exist'':124C ''experi'':10B ''first'':110C ''follow'':17C ''generat'':150C ''get'':57C ''happen'':111C ''help'':127C ''increas'':67C,89C ''inde'':88C ''intermedi'':129C ''larg'':30C ''lead'':80C ''li'':85C,93C ''limit'':183C ''line'':137C ''million'':35C,135C ''mine'':8B ''non'':41C ''non-trivi'':40C ''ol'':84C ''one'':123C ''p'':11C,26C,71C,103C,139C ''particular'':180C ''possibl'':76C ''problem'':18C ''properti'':64C,158C ''queri'':49C,55C ''rather'':23C,112C ''recon'':21C ''result'':59C,130C ''row'':36C ''run'':38C,165C ''say'':32C ''second'':115C ''set'':155C ''sever'':52C ''sinc'':128C ''small'':152C ''somehow'':149C ''sql'':9B,48C,185C ''state'':60C ''step'':118C ''step-wis'':117C ''still'':132C ''sub'':54C ''sub-queri'':53C ''synthet'':153C ''test'':107C,163C,173C ''thing'':77C,142C ''think'':145C ''time'':69C,91C ''tool'':176C ''trivial'':42C ''two'':75C ''typic'':24C ''unit'':172C ''ve'':13C ''want'':161C ''wise'':119C ''won'':125C ''x'':65C,86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (384, 1, '2014-06-15 14:01:38.233', 8, 1944, 'Choose binary classification algorithm', '&lt;p&gt;I have a binary classification problem:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Approximately 1000 samples in training set&lt;/li&gt;
&lt;li&gt;10 attributes, including binary, numeric and categorical&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Which algorithm is the best choice for this type of problem?&lt;/p&gt;

&lt;p&gt;By default I''m going to start with SVM (preliminary having nominal attributes values converted to binary features), as it is considered the best for relatively clean and  not noisy data. &lt;/p&gt;
', 'classification  binary  svm  random-forest  logistic-regression', NULL, '''/li'':30C,39C ''/p'':21C,53C,86C ''/ul'':40C ''10'':32C ''1000'':25C ''algorithm'':4A,43C ''approxim'':24C ''attribut'':33C,67C ''best'':46C,78C ''binari'':2A,6B,18C,35C,71C ''categor'':38C ''choic'':47C ''choos'':1A ''classif'':3A,5B,19C ''clean'':81C ''consid'':76C ''convert'':69C ''data'':85C ''default'':56C ''featur'':72C ''forest'':10B ''go'':59C ''includ'':34C ''li'':23C,31C ''logist'':12B ''logistic-regress'':11B ''m'':58C ''noisi'':84C ''nomin'':66C ''numer'':36C ''p'':14C,41C,54C ''preliminari'':64C ''problem'':20C,52C ''random'':9B ''random-forest'':8B ''regress'':13B ''relat'':80C ''sampl'':26C ''set'':29C ''start'':61C ''svm'':7B,63C ''train'':28C ''type'':50C ''ul'':22C ''valu'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (387, 1, '2014-06-15 15:11:29.97', 6, 153, 'Filtering spam from retrieved data', '&lt;p&gt;I once heard that filtering spam by using blacklists is not a good approach, since some user searching for entries in your dataset may be looking for particular information from the sources blocked. Also it''d become a burden to continuously validate the &lt;em&gt;current state&lt;/em&gt; of each spammer blocked, checking if the site/domain still disseminate spam data.&lt;/p&gt;

&lt;p&gt;Considering that any approach must be efficient and scalable, so as to support filtering on very large datasets, what are the strategies available to get rid of spam in a non-biased manner?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit&lt;/strong&gt;: if possible, any example of strategy, even if just the intuition behind it, would be very welcome along with the answer.&lt;/p&gt;
', 'bigdata  efficiency', NULL, '''/em'':55C ''/p'':68C,104C,130C ''/strong'':108C ''along'':126C ''also'':42C ''answer'':129C ''approach'':22C,73C ''avail'':92C ''becom'':45C ''behind'':120C ''bias'':102C ''bigdata'':6B ''blacklist'':17C ''block'':41C,59C ''burden'':47C ''check'':60C ''consid'':70C ''continu'':49C ''current'':53C ''d'':44C ''data'':5A,67C ''dataset'':31C,87C ''dissemin'':65C ''edit'':107C ''effici'':7B,76C ''em'':52C ''entri'':28C ''even'':115C ''exampl'':112C ''filter'':1A,13C,83C ''get'':94C ''good'':21C ''heard'':11C ''inform'':37C ''intuit'':119C ''larg'':86C ''look'':34C ''manner'':103C ''may'':32C ''must'':74C ''non'':101C ''non-bias'':100C ''p'':8C,69C,105C ''particular'':36C ''possibl'':110C ''retriev'':4A ''rid'':95C ''scalabl'':78C ''search'':26C ''sinc'':23C ''site/domain'':63C ''sourc'':40C ''spam'':2A,14C,66C,97C ''spammer'':58C ''state'':54C ''still'':64C ''strategi'':91C,114C ''strong'':106C ''support'':82C ''use'':16C ''user'':25C ''valid'':50C ''welcom'':125C ''would'':122C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (388, 2, '2014-06-15 15:20:31.3', 5, NULL, NULL, '&lt;p&gt;Spam filtering, especially in email, has been revolutionized by neural networks, here are a couple papers that provide good reading on the subject:&lt;/p&gt;

&lt;p&gt;On Neural Networks And The Future Of Spam 
A. C. Cosoi, M. S. Vlad, V. Sgarciu 
&lt;a href="http://ceai.srait.ro/index.php/ceai/article/viewFile/18/8" rel="nofollow"&gt;http://ceai.srait.ro/index.php/ceai/article/viewFile/18/8&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Intelligent Word-Based Spam Filter Detection Using 
Multi-Neural Networks 
Ann Nosseir, Khaled Nagati and Islam Taj-Eddin
&lt;a href="http://www.ijcsi.org/papers/IJCSI-10-2-1-17-21.pdf" rel="nofollow"&gt;http://www.ijcsi.org/papers/IJCSI-10-2-1-17-21.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Spam Detection using Adaptive Neural Networks: Adaptive Resonance Theory 
David Ndumiyana, Richard Gotora, and Tarisai Mupamombe
&lt;a href="http://onlineresearchjournals.org/JPESR/pdf/2013/apr/Ndumiyana%20et%20al.pdf" rel="nofollow"&gt;http://onlineresearchjournals.org/JPESR/pdf/2013/apr/Ndumiyana%20et%20al.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;EDIT:
The basic intuition behind using a neural network to help with spam filtering is by providing a weight to terms based on how often they are associated with spam.&lt;/p&gt;

&lt;p&gt;Neural networks can be trained most quickly in a supervised -- you explicitly provide the classification of the sentence in the training set -- environment.  Without going into the nitty gritty the basic idea can be illustrated with these sentences:&lt;/p&gt;

&lt;p&gt;Text = "How is the loss of the Viagra patent going to affect Pfizer", Spam = false
Text = "Cheap Viagra Buy Now", Spam = true
Text = "Online pharmacy Viagra Cialis Lipitor", Spam = true&lt;/p&gt;

&lt;p&gt;For a two stage neural network, the first stage will calculate the likelihood of spam based off of if the word exists in the sentence.  So from our example:&lt;/p&gt;

&lt;p&gt;viagra =&gt; 66%
buy =&gt; 100%
Pfizer =&gt; 0%
etc..&lt;/p&gt;

&lt;p&gt;Then for the second stage the results in the first stage are used as variables in the second stage:&lt;/p&gt;

&lt;p&gt;viagra &amp;amp; buy =&gt; 100%
Pfizer &amp;amp; viagra=&gt; 0%&lt;/p&gt;

&lt;p&gt;This basic idea is run for many of the permutations of the all the words in your training data.  The end results once trained is basically just an equation that based of the context of the words in the sentence can assign a probability of being spam.  Set spamminess threshold, and filter out any data higher then said threshold.&lt;/p&gt;
', NULL, NULL, '''/index.php/ceai/article/viewfile/18/8'':47C ''/index.php/ceai/article/viewfile/18/8&lt;/a&gt;&lt;/p&gt;'':52C ''/jpesr/pdf/2013/apr/ndumiyana%20et%20al.pdf'':106C ''/jpesr/pdf/2013/apr/ndumiyana%20et%20al.pdf&lt;/a&gt;&lt;/p&gt;'':111C ''/p'':25C,143C,183C,215C,246C,255C,276C,286C,347C ''/papers/ijcsi-10-2-1-17-21.pdf'':79C ''/papers/ijcsi-10-2-1-17-21.pdf&lt;/a&gt;&lt;/p&gt;'':84C ''0'':253C,285C ''100'':251C,281C ''66'':249C ''adapt'':89C,92C ''affect'':196C ''amp'':279C,283C ''ann'':66C ''assign'':329C ''associ'':140C ''base'':57C,134C,232C,318C ''basic'':115C,175C,289C,313C ''behind'':117C ''buy'':203C,250C,280C ''c'':36C ''calcul'':227C ''ceai.srait.ro'':46C,51C ''ceai.srait.ro/index.php/ceai/article/viewfile/18/8'':45C ''ceai.srait.ro/index.php/ceai/article/viewfile/18/8&lt;/a&gt;&lt;/p&gt;'':50C ''cheap'':201C ''ciali'':211C ''classif'':159C ''context'':321C ''cosoi'':37C ''coupl'':16C ''data'':306C,342C ''david'':95C ''detect'':60C,87C ''eddin'':74C ''edit'':113C ''email'':6C ''end'':308C ''environ'':167C ''equat'':316C ''especi'':4C ''etc'':254C ''exampl'':245C ''exist'':238C ''explicit'':156C ''fals'':199C ''filter'':3C,59C,126C,339C ''first'':224C,266C ''futur'':32C ''go'':169C,194C ''good'':20C ''gotora'':98C ''gritti'':173C ''help'':123C ''higher'':343C ''href'':44C,76C,103C ''idea'':176C,290C ''illustr'':179C ''intellig'':54C ''intuit'':116C ''islam'':71C ''khale'':68C ''likelihood'':229C ''lipitor'':212C ''loss'':189C ''m'':38C ''mani'':294C ''multi'':63C ''multi-neur'':62C ''mupamomb'':101C ''nagati'':69C ''ndumiyana'':96C ''network'':12C,29C,65C,91C,121C,146C,222C ''neural'':11C,28C,64C,90C,120C,145C,221C ''nitti'':172C ''nofollow'':49C,81C,108C ''nosseir'':67C ''often'':137C ''onlin'':208C ''onlineresearchjournals.org'':105C,110C ''onlineresearchjournals.org/jpesr/pdf/2013/apr/ndumiyana%20et%20al.pdf'':104C ''onlineresearchjournals.org/jpesr/pdf/2013/apr/ndumiyana%20et%20al.pdf&lt;/a&gt;&lt;/p&gt;'':109C ''p'':1C,26C,53C,85C,112C,144C,184C,216C,247C,256C,277C,287C ''paper'':17C ''patent'':193C ''permut'':297C ''pfizer'':197C,252C,282C ''pharmaci'':209C ''probabl'':331C ''provid'':19C,129C,157C ''quick'':151C ''read'':21C ''rel'':48C,80C,107C ''reson'':93C ''result'':263C,309C ''revolution'':9C ''richard'':97C ''run'':292C ''said'':345C ''second'':260C,274C ''sentenc'':162C,182C,241C,327C ''set'':166C,335C ''sgarciu'':42C ''spam'':2C,34C,58C,86C,125C,142C,198C,205C,213C,231C,334C ''spammi'':336C ''stage'':220C,225C,261C,267C,275C ''subject'':24C ''supervis'':154C ''taj'':73C ''taj-eddin'':72C ''tarisai'':100C ''term'':133C ''text'':185C,200C,207C ''theori'':94C ''threshold'':337C,346C ''train'':149C,165C,305C,311C ''true'':206C,214C ''two'':219C ''use'':61C,88C,118C,269C ''v'':41C ''variabl'':271C ''viagra'':192C,202C,210C,248C,278C,284C ''vlad'':40C ''weight'':131C ''without'':168C ''word'':56C,237C,302C,324C ''word-bas'':55C ''www.ijcsi.org'':78C,83C ''www.ijcsi.org/papers/ijcsi-10-2-1-17-21.pdf'':77C ''www.ijcsi.org/papers/ijcsi-10-2-1-17-21.pdf&lt;/a&gt;&lt;/p&gt;'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (389, 2, '2014-06-15 15:49:12.907', 4, NULL, NULL, '&lt;p&gt;Here is a suggestion:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Code your analysis in such a way that it can be run on sub-samples.&lt;/li&gt;
&lt;li&gt;Code a complementary routine which can sample, either randomly, or by time, or by region, or ...  This may be domain-specific. This is where your knowledge enters.&lt;/li&gt;
&lt;li&gt;Combine the two and see if the results are stable across subsamples.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':25C,55C,69C ''/p'':6C ''/ul'':70C ''across'':67C ''analysi'':11C ''code'':9C,27C ''combin'':57C ''complementari'':29C ''domain'':47C ''domain-specif'':46C ''either'':34C ''enter'':54C ''knowledg'':53C ''li'':8C,26C,56C ''may'':44C ''p'':1C ''random'':35C ''region'':41C ''result'':64C ''routin'':30C ''run'':20C ''sampl'':24C,33C ''see'':61C ''specif'':48C ''stabl'':66C ''sub'':23C ''sub-sampl'':22C ''subsampl'':68C ''suggest'':5C ''time'':38C ''two'':59C ''ul'':7C ''way'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (391, 2, '2014-06-15 16:59:50.19', 1, NULL, NULL, '&lt;p&gt;This is what I normally do - take up the most important variables (basis your business understanding and hypothesis - you can always revise it later), group by on these attributes to reduce the number of rows, which can then be imported into a Pivot. You should include the sum and count of the relevant metrics on each row.&lt;/p&gt;

&lt;p&gt;Make sure that you don''t put any filters in the previous step. Once you have entire data at a summarized level, you can play around in Pivot tables and see what things are changing / increasing or decreasing.&lt;/p&gt;

&lt;p&gt;If the data is too big to be summarized even on important parameters, you need to partition it in 3 - 4 subsets and then do this again.&lt;/p&gt;

&lt;p&gt;Hope it helps.&lt;/p&gt;
', NULL, NULL, '''/p'':59C,99C,128C,133C ''3'':120C ''4'':121C ''alway'':22C ''around'':86C ''attribut'':30C ''basi'':14C ''big'':106C ''busi'':16C ''chang'':95C ''count'':51C ''data'':78C,103C ''decreas'':98C ''entir'':77C ''even'':110C ''filter'':69C ''group'':26C ''help'':132C ''hope'':130C ''hypothesi'':19C ''import'':12C,41C,112C ''includ'':47C ''increas'':96C ''later'':25C ''level'':82C ''make'':61C ''metric'':55C ''need'':115C ''normal'':6C ''number'':34C ''p'':1C,60C,100C,129C ''paramet'':113C ''partit'':117C ''pivot'':44C,88C ''play'':85C ''previous'':72C ''put'':67C ''reduc'':32C ''relev'':54C ''revis'':23C ''row'':36C,58C ''see'':91C ''step'':73C ''subset'':122C ''sum'':49C ''summar'':81C,109C ''sure'':62C ''tabl'':89C ''take'':8C ''thing'':93C ''understand'':17C ''variabl'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (570, 2, '2014-06-25 00:13:50.803', 2, NULL, NULL, '&lt;p&gt;Personally, I think Netflix got it right.  Break it down into a confidence rating from 1-5 and show your recommendations based on the number of yellow stars.  &lt;/p&gt;

&lt;p&gt;It doesn''t have to be stars, but those icon based graphs are very easy to interpret and get the point across clearly.&lt;/p&gt;
', NULL, NULL, '''-5'':18C ''/p'':30C,55C ''1'':17C ''across'':53C ''base'':23C,42C ''break'':9C ''clear'':54C ''confid'':14C ''doesn'':33C ''easi'':46C ''get'':50C ''got'':6C ''graph'':43C ''icon'':41C ''interpret'':48C ''netflix'':5C ''number'':26C ''p'':1C,31C ''person'':2C ''point'':52C ''rate'':15C ''recommend'':22C ''right'':8C ''show'':20C ''star'':29C,38C ''think'':4C ''yellow'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (394, 2, '2014-06-16 04:21:36.34', 5, NULL, NULL, '&lt;p&gt;Personally, we use S3 on top of GCE and really love it. Depending on how much data you''re dealing with, Google Drive just doesn''t quite match the 5 TB max that S3 gives you. Also, if you''re using python, &lt;code&gt;boto&lt;/code&gt; does a pretty fantastic job of making most aws services pretty accessible regardless of what stack you''re dealing with. Even if you''re not using python, they''ve got a pretty straightforward API that generally is more accessible than Google Drive.&lt;/p&gt;

&lt;p&gt;Instead of google drive, though google did recently release a cloud storage service, apart from drive, that lets you more closely integrate your storage with any gce instance you''ve got, &lt;a href="https://cloud.google.com/products/cloud-storage/"&gt;https://cloud.google.com/products/cloud-storage/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;They''ve got an API which seems to be pretty comparable to S3''s, but I can''t profess to having really played around with it much. Pricing-wise the two are identical, but I think that the large community and experience with aws in general still puts S3 squarely above both google''s cloud storage and google drive.&lt;/p&gt;
', NULL, NULL, '''/code'':46C ''/p'':89C,191C ''/products/cloud-storage/'':126C ''/products/cloud-storage/&lt;/a&gt;&lt;/p&gt;'':129C ''5'':31C ''access'':58C,85C ''also'':38C ''apart'':104C ''api'':80C,135C ''around'':154C ''aw'':55C,175C ''boto'':45C ''close'':111C ''cloud'':101C,186C ''cloud.google.com'':125C,128C ''cloud.google.com/products/cloud-storage/'':124C ''cloud.google.com/products/cloud-storage/&lt;/a&gt;&lt;/p&gt;'':127C ''code'':44C ''communiti'':171C ''compar'':141C ''data'':18C ''deal'':21C,65C ''depend'':14C ''doesn'':26C ''drive'':24C,88C,94C,106C,190C ''even'':67C ''experi'':173C ''fantast'':50C ''gce'':9C,117C ''general'':82C,177C ''give'':36C ''googl'':23C,87C,93C,96C,184C,189C ''got'':76C,121C,133C ''href'':123C ''ident'':164C ''instanc'':118C ''instead'':91C ''integr'':112C ''job'':51C ''larg'':170C ''let'':108C ''love'':12C ''make'':53C ''match'':29C ''max'':33C ''much'':17C,157C ''p'':1C,90C,130C ''person'':2C ''play'':153C ''pretti'':49C,57C,78C,140C ''price'':159C ''pricing-wis'':158C ''profess'':149C ''put'':179C ''python'':43C,73C ''quit'':28C ''re'':20C,41C,64C,70C ''realli'':11C,152C ''recent'':98C ''regardless'':59C ''releas'':99C ''s3'':5C,35C,143C,180C ''seem'':137C ''servic'':56C,103C ''squar'':181C ''stack'':62C ''still'':178C ''storag'':102C,114C,187C ''straightforward'':79C ''tb'':32C ''think'':167C ''though'':95C ''top'':7C ''two'':162C ''use'':4C,42C,72C ''ve'':75C,120C,132C ''wise'':160C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (502, 2, '2014-06-20 10:01:03.363', 12, NULL, NULL, '&lt;p&gt;Regarding prediction, statistics and machine learning sciences started to solve mostly the same problem from different perspectives. &lt;/p&gt;

&lt;p&gt;Basically statistics assumes that the data were produced by a given stochastic model. So, from a statistical perspective, a model is assumed and given various assumptions the errors are treated and the model parameters and other questions are inferred. &lt;/p&gt;

&lt;p&gt;Machine learning comes from a computer science perspective. The models are algorithmic and usually very few assumptions are required regarding the data. We work with hypothesis space and learning bias. The best exposition of machine learning I found is contained in Tom Mitchell''s book called &lt;a href="http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html"&gt;Machine Learning&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;For a more exhaustive and complete idea regarding the two cultures you can read the Leo Broiman paper called &lt;a href="http://www.google.ie/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;cad=rja&amp;amp;uact=8&amp;amp;ved=0CDIQFjAA&amp;amp;url=http://strimmerlab.org/courses/ss09/current-topics/download/breiman2001.pdf&amp;amp;ei=RQGkU_DgH8Le7AaRmID4DA&amp;amp;usg=AFQjCNGNnrlqadmBT2fZMT_NfoUQ1rEuow&amp;amp;sig2=g5qovUTsuuJUGGE64g0nrQ"&gt;Statistical Modeling: The Two Cultures&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;However what must be added is that even if the two sciences started with different perspectives, both of them now now share a fair amount of common knowledge and techniques. Why, because the problems were the same, but the tools were different. So now machine learning is mostly treated from a statistical perspective (check the Hastie,Tibshirani, Friedman book &lt;a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/"&gt;The Elements of Statistical Learning&lt;/a&gt; from a machine learning point of view with a statistical treatement, and perhaps Kevin P. Murphy ''s book &lt;a href="http://mitpress.mit.edu/books/machine-learning-2"&gt;Machine Learning: A probabilistic perspective&lt;/a&gt;, to name just a few of the best books available today). &lt;/p&gt;

&lt;p&gt;Even the history of the development of this field show the benefits of this merge of perspectives. I will describe two events. &lt;/p&gt;

&lt;p&gt;The first is the creation of CART trees, which was created by Breiman with a solid statistical background. At approximately the same time, Quinlan developed ID3,C45,See5, and so on, decision tree suite with a more computer science background. Now both this families of trees and the ensemble methods like bagging and forests become quite similar. &lt;/p&gt;

&lt;p&gt;The second story is about boosting. Initially they were developed by Freund and Shapire when they discovered AdaBoost. The choices for designing AdaBoost were done mostly from a computational perspective. Even the authors did not understood well why it works. Only 5 years later Breiman (again!) described the adaboost model from a statistical perspective and gave an explanation for why that works. Since then, various eminent scientists, with both type of backgrounds, developed further those ideas leading to a Pleiads of boosting algorithms, like logistic boosting, gradient boosting, gentle boosting ans so on. It is hard now to think at boosting without a solid statistical background.&lt;/p&gt;

&lt;p&gt;GLM is a statistical development. However new Bayesian treatments puts this algorithm also in machine learning playground. So I believe both claims could be right, since the interpretation and treatment of how it works could be different. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (395, 2, '2014-06-16 04:37:58.817', 10, NULL, NULL, '&lt;p&gt;It''s hard to say without knowing a little more about your dataset, and how separable your dataset is based on your feature vector, but I would probably suggest using extreme random forest over standard random forests because of your relatively small sample set.&lt;/p&gt;

&lt;p&gt;Extreme random forests are pretty similar to standard random forests with the one exception that instead of optimizing splits on trees, extreme random forest makes splits at random. Initially this would seem like a negative, but it generally means that you have significantly better generalization and speed, though the AUC on your training set is likely to be a little worse.&lt;/p&gt;

&lt;p&gt;Logistic regression is also a pretty solid bet for these kinds of tasks, though with your relatively low dimensionality and small sample size I would be worried about overfitting. You might want to check out using K-Nearest Neighbors since it often performs very will with low dimensionalities, but it doesn''t usually handle categorical variables very well.&lt;/p&gt;

&lt;p&gt;If I had to pick one without knowing more about the problem I would certainly place my bets on extreme random forest, as it''s very likely to give you good generalization on this kind of dataset, and it also handles a mix of numerical and categorical data better than most other methods.&lt;/p&gt;
', NULL, NULL, '''/p'':46C,109C,170C,225C ''also'':114C,211C ''auc'':97C ''base'':21C ''bet'':118C,189C ''better'':91C,220C ''categor'':166C,218C ''certain'':186C ''check'':144C ''data'':219C ''dataset'':14C,19C,208C ''dimension'':129C,159C ''doesn'':162C ''except'':61C ''extrem'':32C,48C,69C,191C ''featur'':24C ''forest'':34C,38C,50C,57C,71C,193C ''general'':85C,92C,203C ''give'':200C ''good'':202C ''handl'':165C,212C ''hard'':4C ''initi'':76C ''instead'':63C ''k'':148C ''k-nearest'':147C ''kind'':121C,206C ''know'':8C,179C ''like'':80C,103C,198C ''littl'':10C,107C ''logist'':111C ''low'':128C,158C ''make'':72C ''mean'':86C ''method'':224C ''might'':141C ''mix'':214C ''nearest'':149C ''negat'':82C ''neighbor'':150C ''numer'':216C ''often'':153C ''one'':60C,177C ''optim'':65C ''overfit'':139C ''p'':1C,47C,110C,171C ''perform'':154C ''pick'':176C ''place'':187C ''pretti'':52C,116C ''probabl'':29C ''problem'':183C ''random'':33C,37C,49C,56C,70C,75C,192C ''regress'':112C ''relat'':42C,127C ''sampl'':44C,132C ''say'':6C ''seem'':79C ''separ'':17C ''set'':45C,101C ''signific'':90C ''similar'':53C ''sinc'':151C ''size'':133C ''small'':43C,131C ''solid'':117C ''speed'':94C ''split'':66C,73C ''standard'':36C,55C ''suggest'':30C ''task'':123C ''though'':95C,124C ''train'':100C ''tree'':68C ''use'':31C,146C ''usual'':164C ''variabl'':167C ''vector'':25C ''want'':142C ''well'':169C ''without'':7C,178C ''worri'':137C ''wors'':108C ''would'':28C,78C,135C,185C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (442, 2, '2014-06-17 21:17:31.21', 8, NULL, NULL, '&lt;p&gt;Christopher''s answers seem very reasonable. In particular tree based methods do well with this sort of data because they branch on discriminative features. It''s a little hard to say without knowing your specific application, but in general if you think that some of your features might be significantly more discriminative than others, you could try some dimensionality reduction techniques to clean this up a bit.&lt;/p&gt;

&lt;p&gt;Also if you use a dimensionality reduction technique you end up getting a slightly more robust format for your feature vector (they generally end up being straight numerical vectors instead of mixed data types), which might let you leverage different methods. You could also look into hand engineering features. With properly hand engineered features &lt;code&gt;Random Forest&lt;/code&gt; will get you very close to state of the art on most tasks.&lt;/p&gt;
', NULL, NULL, '''/code'':128C ''/p'':69C,142C ''also'':71C,114C ''answer'':4C ''applic'':37C ''art'':138C ''base'':11C ''bit'':68C ''branch'':22C ''christoph'':2C ''clean'':64C ''close'':133C ''code'':125C ''could'':57C,113C ''data'':19C,103C ''differ'':110C ''dimension'':60C,76C ''discrimin'':24C,53C ''end'':80C,94C ''engin'':118C,123C ''featur'':25C,48C,90C,119C,124C ''forest'':127C ''format'':87C ''general'':40C,93C ''get'':82C,130C ''hand'':117C,122C ''hard'':30C ''instead'':100C ''know'':34C ''let'':107C ''leverag'':109C ''littl'':29C ''look'':115C ''method'':12C,111C ''might'':49C,106C ''mix'':102C ''numer'':98C ''other'':55C ''p'':1C,70C ''particular'':9C ''proper'':121C ''random'':126C ''reason'':7C ''reduct'':61C,77C ''robust'':86C ''say'':32C ''seem'':5C ''signific'':51C ''slight'':84C ''sort'':17C ''specif'':36C ''state'':135C ''straight'':97C ''task'':141C ''techniqu'':62C,78C ''think'':43C ''tree'':10C ''tri'':58C ''type'':104C ''use'':74C ''vector'':91C,99C ''well'':14C ''without'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (402, 2, '2014-06-16 13:25:48.453', 3, NULL, NULL, '&lt;p&gt;The &lt;a href="http://brat.nlplab.org/" rel="nofollow"&gt;brat annotation tool&lt;/a&gt; might be useful for you as per my comment.  I have tried many of them and this is the best I have found.  It has a nice user interface and can support a number of different types of annotations.  The annotations are stored in a separate .annot file which contain each annotation as well as its location within the original document.  A word of warning though, if you ultimately want to feed the annotations into a classifier like the Stanford NER tool then you will have to do some manipulation to get the data into a format that it will accept.&lt;/p&gt;
', NULL, NULL, '''/a'':11C ''/p'':113C ''accept'':112C ''annot'':9C,50C,52C,58C,63C,85C ''best'':31C ''brat'':8C ''brat.nlplab.org'':5C ''classifi'':88C ''comment'':20C ''contain'':61C ''data'':105C ''differ'':47C ''document'':72C ''feed'':83C ''file'':59C ''format'':108C ''found'':34C ''get'':103C ''href'':4C ''interfac'':40C ''like'':89C ''locat'':68C ''mani'':24C ''manipul'':101C ''might'':12C ''ner'':92C ''nice'':38C ''nofollow'':7C ''number'':45C ''origin'':71C ''p'':1C ''per'':18C ''rel'':6C ''separ'':57C ''stanford'':91C ''store'':54C ''support'':43C ''though'':77C ''tool'':10C,93C ''tri'':23C ''type'':48C ''ultim'':80C ''use'':14C ''user'':39C ''want'':81C ''warn'':76C ''well'':65C ''within'':69C ''word'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (396, 2, '2014-06-16 04:51:47.847', 6, NULL, NULL, '&lt;p&gt;Alex made a number of good points, though I might have to push back a bit on his implication that DBSCAN is the best clustering algorithm to use here. Depending on your implementation, and whether or not you''re using accelerated indices (many implementations do not), your time and space complexity will both be &lt;code&gt;O(n2)&lt;/code&gt;, which is far from ideal.&lt;/p&gt;

&lt;p&gt;Personally, my go-to clustering algorithms are OpenOrd for winner-takes-all clustering and FLAME for fuzzy clustering. Both methods are indifferent to whether the metrics used are similarity or distance (FLAME in particular is nearly identical in both constructions). The implementation of OpenOrd in Gephi is &lt;code&gt;O(nlogn)&lt;/code&gt; and is known to be more scalable than any of the other clustering algorithms present in the Gephi package.&lt;/p&gt;

&lt;p&gt;FLAME on the other hand is great if you''re looking for a fuzzy clustering method. While the complexity of FLAME is a little harder to determine since it''s an iterative process, it has been shown to be sub-quadratic, and similar in run-speed to knn.&lt;/p&gt;
', NULL, NULL, '''/code'':59C,119C ''/p'':65C,139C,191C ''acceler'':42C ''alex'':2C ''algorithm'':27C,73C,133C ''back'':15C ''best'':25C ''bit'':17C ''cluster'':26C,72C,81C,86C,132C,155C ''code'':56C,116C ''complex'':52C,159C ''construct'':108C ''dbscan'':22C ''depend'':31C ''determin'':167C ''distanc'':99C ''far'':62C ''flame'':83C,100C,141C,161C ''fuzzi'':85C,154C ''gephi'':114C,137C ''go'':70C ''go-to'':69C ''good'':7C ''great'':147C ''hand'':145C ''harder'':165C ''ideal'':64C ''ident'':105C ''implement'':34C,45C,110C ''implic'':20C ''indic'':43C ''indiffer'':90C ''iter'':172C ''knn'':190C ''known'':122C ''littl'':164C ''look'':151C ''made'':3C ''mani'':44C ''method'':88C,156C ''metric'':94C ''might'':11C ''n2'':58C ''near'':104C ''nlogn'':118C ''number'':5C ''o'':57C,117C ''openord'':75C,112C ''p'':1C,66C,140C ''packag'':138C ''particular'':102C ''person'':67C ''point'':8C ''present'':134C ''process'':173C ''push'':14C ''quadrat'':182C ''re'':40C,150C ''run'':187C ''run-spe'':186C ''scalabl'':126C ''shown'':177C ''similar'':97C,184C ''sinc'':168C ''space'':51C ''speed'':188C ''sub'':181C ''sub-quadrat'':180C ''take'':79C ''though'':9C ''time'':49C ''use'':29C,41C,95C ''whether'':36C,92C ''winner'':78C ''winner-takes-al'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8950, 2, '2015-11-21 00:34:08.703', 2, NULL, NULL, '&lt;p&gt;My favorites ones are Wrangle, Spark Summit and ampcamp.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;ol&gt;
  &lt;li&gt;&lt;p&gt;Wrangle is a new, single-day, single-track industry event about the principles, practice, and application of Data Science, across multiple data-rich industries. It includes talks from data scientists from companies like Salesforce, Pinterest, Facebook, and Uber about the hardest problems they''ve faced, and the solutions they found for them.
  If you''re a practicing Data Scientist, Wrangle is for you!&lt;/p&gt;&lt;/li&gt;
  &lt;li&gt;&lt;p&gt;spark-summit. DATA SCIENCE AND ENGINEERING AT SCALE &lt;/p&gt;&lt;/li&gt;
  &lt;li&gt;&lt;p&gt;AMP Camps are Big Data training events organized by the UC Berkeley AMPLab about big data analytics, machine learning, and popular open-source software projects produced by the AMPLab. All AMP Camp curricula, and whenever possible videos of instructional talks presented at AMP Camps, are published here and accessible for free.&lt;/p&gt;&lt;/li&gt;
  &lt;/ol&gt;
&lt;/blockquote&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.wrangleconf.com/" rel="nofollow"&gt;http://www.wrangleconf.com/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://spark-summit.org/" rel="nofollow"&gt;https://spark-summit.org/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://ampcamp.berkeley.edu/" rel="nofollow"&gt;http://ampcamp.berkeley.edu/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':165C,175C,185C ''/blockquote'':154C ''/li'':83C,96C,152C ''/ol'':153C,186C ''/p'':11C,82C,95C,151C ''access'':148C ''across'':37C ''amp'':99C,130C,142C ''ampcamp'':10C ''ampcamp.berkeley.edu'':180C,184C ''ampcamp.berkeley.edu/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':183C ''amplab'':111C,128C ''analyt'':115C ''applic'':33C ''berkeley'':110C ''big'':102C,113C ''blockquot'':12C ''camp'':100C,131C,143C ''compani'':50C ''curricula'':132C ''data'':35C,40C,47C,76C,89C,103C,114C ''data-rich'':39C ''day'':22C ''engin'':92C ''event'':27C,105C ''face'':63C ''facebook'':54C ''favorit'':3C ''found'':68C ''free'':150C ''hardest'':59C ''href'':159C,169C,179C ''includ'':44C ''industri'':26C,42C ''instruct'':138C ''learn'':117C ''li'':14C,84C,97C,156C,166C,176C ''like'':51C ''machin'':116C ''multipl'':38C ''new'':19C ''nofollow'':162C,172C,182C ''ol'':13C,155C ''one'':4C ''open'':121C ''open-sourc'':120C ''organ'':106C ''p'':1C,15C,85C,98C,157C,167C,177C ''pinterest'':53C ''popular'':119C ''possibl'':135C ''practic'':31C,75C ''present'':140C ''principl'':30C ''problem'':60C ''produc'':125C ''project'':124C ''publish'':145C ''re'':73C ''rel'':161C,171C,181C ''rich'':41C ''salesforc'':52C ''scale'':94C ''scienc'':36C,90C ''scientist'':48C,77C ''singl'':21C,24C ''single-day'':20C ''single-track'':23C ''softwar'':123C ''solut'':66C ''sourc'':122C ''spark'':7C,87C ''spark-summit'':86C ''spark-summit.org'':170C,174C ''spark-summit.org/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':173C ''summit'':8C,88C ''talk'':45C,139C ''track'':25C ''train'':104C ''uber'':56C ''uc'':109C ''ve'':62C ''video'':136C ''whenev'':134C ''wrangl'':6C,16C,78C ''www.wrangleconf.com'':160C,164C ''www.wrangleconf.com/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':163C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (397, 2, '2014-06-16 06:34:53.683', 1, NULL, NULL, '&lt;p&gt;First you need to verify that your implementation of the algorithm is accurate. For that use a small sample of data and check whether the result is correct. At this stage the sample doesn''t need to be representative of the population. &lt;/p&gt;

&lt;p&gt;Once the implementation is verified, you need to verify that there is a significant relationship among the variables that you try to predict. To do that define null hypothesis and try to reject the null hypothesis with a significant confidence level. (&lt;a href="http://stattrek.com/regression/slope-test.aspx" rel="nofollow"&gt;hypothesis testing for linear regression&lt;/a&gt;) &lt;/p&gt;

&lt;p&gt;There might be unit test frameworks for your SQL distribution. But using a programming language like R will be more easier to implement.&lt;/p&gt;
', NULL, NULL, '''/a'':99C ''/p'':44C,100C,125C ''/regression/slope-test.aspx'':91C ''accur'':14C ''algorithm'':12C ''among'':61C ''check'':24C ''confid'':85C ''correct'':29C ''data'':22C ''defin'':72C ''distribut'':111C ''doesn'':35C ''easier'':122C ''first'':2C ''framework'':107C ''href'':88C ''hypothesi'':74C,81C,94C ''implement'':9C,48C,124C ''languag'':116C ''level'':86C ''like'':117C ''linear'':97C ''might'':103C ''need'':4C,37C,52C ''nofollow'':93C ''null'':73C,80C ''p'':1C,45C,101C ''popul'':43C ''predict'':68C ''program'':115C ''r'':118C ''regress'':98C ''reject'':78C ''rel'':92C ''relationship'':60C ''repres'':40C ''result'':27C ''sampl'':20C,34C ''signific'':59C,84C ''small'':19C ''sql'':110C ''stage'':32C ''stattrek.com'':90C ''stattrek.com/regression/slope-test.aspx'':89C ''test'':95C,106C ''tri'':66C,76C ''unit'':105C ''use'':17C,113C ''variabl'':63C ''verifi'':6C,50C,54C ''whether'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (398, 1, '2014-06-16 07:32:29.137', 6, 646, 'What to consider before learning a new language for data analysis', '&lt;p&gt;I''m currently in the very early stages of preparing a new research-project (still at the funding-application stage), and expect that data-analysis and especially visualisation tools will play a role in this project.&lt;/p&gt;

&lt;p&gt;In view of this I face the following dilemma: Should I learn Python to be able to use its extensive scientific libraries (Pandas, Numpy, Scipy, ...), or should I just dive into similar packages of a language I''m already acquainted with (Racket, or to a lesser extent Scala)?&lt;/p&gt;

&lt;p&gt;(Ideally I would learn Python in parallel with using statistical libraries in Racket, but I''m not sure I''ll have time for both)&lt;/p&gt;

&lt;p&gt;I''m not looking for an answer to this dilemma, but rather for feedback on my different considerations:&lt;/p&gt;

&lt;p&gt;My current position is as follows:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;In favour of Python:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Extensively used libraries&lt;/li&gt;
&lt;li&gt;Widely used (may be decisive in case of collaboration with others)&lt;/li&gt;
&lt;li&gt;A lot of online material to start learning it&lt;/li&gt;
&lt;li&gt;Conferences that are specifically dedicated to Scientific Computing with Python&lt;/li&gt;
&lt;li&gt;Learning Python won''t be a waste of time anyway&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;In favour of a language I already know:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;It''s a way to deepen my knowledge of one language rather than getting superficial knowledge of one more language (under the motto: you should at least know one language really well)&lt;/li&gt;
&lt;li&gt;It is feasible. Both Racket and Scala have good mathematics and statistics libraries&lt;/li&gt;
&lt;li&gt;I can start right away with learning what I need to know rather than first having to learn the basics&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Two concrete questions:&lt;/strong&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;What am I forgetting?&lt;/li&gt;
&lt;li&gt;How big of a nuisance could the Python 2 vs 3 issue be?&lt;/li&gt;
&lt;/ol&gt;
', 'python  visualization', NULL, '''/li'':172C,185C,196C,208C,220C,268C,283C,305C,320C,335C ''/ol'':336C ''/p'':54C,104C,130C,150C,158C,166C,233C,313C ''/strong'':165C,232C,312C ''/ul'':221C,306C ''2'':330C ''3'':332C ''abl'':71C ''acquaint'':95C ''alreadi'':94C,230C ''analysi'':11A,42C ''answer'':138C ''anyway'':219C ''applic'':35C ''away'':289C ''basic'':304C ''big'':323C ''case'':180C ''collabor'':182C ''comput'':205C ''concret'':310C ''confer'':198C ''consid'':3A ''consider'':149C ''could'':327C ''current'':17C,153C ''data'':10A,41C ''data-analysi'':40C ''decis'':178C ''dedic'':202C ''deepen'':241C ''differ'':148C ''dilemma'':64C,141C ''dive'':85C ''earli'':21C ''especi'':44C ''expect'':38C ''extens'':75C,169C ''extent'':102C ''face'':61C ''favour'':162C,225C ''feasibl'':272C ''feedback'':145C ''first'':299C ''follow'':63C,157C ''forget'':319C ''fund'':34C ''funding-appl'':33C ''get'':249C ''good'':278C ''ideal'':106C ''issu'':333C ''know'':231C,263C,296C ''knowledg'':243C,251C ''languag'':8A,91C,228C,246C,255C,265C ''learn'':5A,67C,109C,194C,210C,291C,302C ''least'':262C ''lesser'':101C ''li'':168C,173C,186C,197C,209C,235C,269C,284C,315C,321C ''librari'':77C,116C,171C,282C ''ll'':125C ''look'':135C ''lot'':188C ''m'':16C,93C,121C,133C ''materi'':191C ''mathemat'':279C ''may'':176C ''motto'':258C ''need'':294C ''new'':7A,26C ''nuisanc'':326C ''numpi'':79C ''ol'':314C ''one'':245C,253C,264C ''onlin'':190C ''other'':184C ''p'':14C,55C,105C,131C,151C,159C,222C,307C ''packag'':88C ''panda'':78C ''parallel'':112C ''play'':48C ''posit'':154C ''prepar'':24C ''project'':29C,53C ''python'':12B,68C,110C,164C,207C,211C,329C ''question'':311C ''racket'':97C,118C,274C ''rather'':143C,247C,297C ''realli'':266C ''research'':28C ''research-project'':27C ''right'':288C ''role'':50C ''scala'':103C,276C ''scientif'':76C,204C ''scipi'':80C ''similar'':87C ''specif'':201C ''stage'':22C,36C ''start'':193C,287C ''statist'':115C,281C ''still'':30C ''strong'':160C,223C,308C ''superfici'':250C ''sure'':123C ''time'':127C,218C ''tool'':46C ''two'':309C ''ul'':167C,234C ''use'':73C,114C,170C,175C ''view'':57C ''visual'':13B ''visualis'':45C ''vs'':331C ''wast'':216C ''way'':239C ''well'':267C ''wide'':174C ''won'':212C ''would'':108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (399, 2, '2014-06-16 09:52:24.913', 0, NULL, NULL, '&lt;p&gt;According to me, all the factors, you have mentioned are superficial in nature. You have not considered the core of tool selection. In this case, there are 2 aspects, you mentioned:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Data analysis - What kind of analysis are you working on? There might be some analysis which are easier in some languages and more difficult in other.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Visualization - R provides similar community and learning material (as Python) and has the best visualizations compared to other languages here.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;At this stage, you can be flexible with what language to learn, since you are starting from scratch.&lt;/p&gt;

&lt;p&gt;Hope this helps.&lt;/p&gt;
', NULL, NULL, '''/li'':64C,88C ''/ol'':89C ''/p'':33C,63C,87C,109C,114C ''2'':29C ''accord'':2C ''analysi'':38C,42C,51C ''aspect'':30C ''best'':80C ''case'':26C ''communiti'':71C ''compar'':82C ''consid'':18C ''core'':20C ''data'':37C ''difficult'':60C ''easier'':54C ''factor'':7C ''flexibl'':97C ''help'':113C ''hope'':111C ''kind'':40C ''languag'':57C,85C,100C ''learn'':73C,102C ''li'':35C,65C ''materi'':74C ''mention'':10C,32C ''might'':48C ''natur'':14C ''ol'':34C ''p'':1C,36C,66C,90C,110C ''provid'':69C ''python'':76C ''r'':68C ''scratch'':108C ''select'':23C ''similar'':70C ''sinc'':103C ''stage'':93C ''start'':106C ''superfici'':12C ''tool'':22C ''visual'':67C,81C ''work'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (401, 2, '2014-06-16 13:21:58.777', 1, NULL, NULL, '&lt;p&gt;I like a multiple step strategy:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Write clean easy to understand code, as opposed to short-tricky code. I know statisticians like tricky code, but spotting problems in tricky code is dangerous. 
( I am mentioning this because a supervisor of mine was fond of undocumented 500 lines python scrips - have fun debugging that mess and I have seen that pattern a lot, especially from people who are not from an IT background)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Break down your code in smaller functions, which can be tested and evaluated in smaller stes.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Look for connected elements, e.g. the number of cases with condition X is Y - so this query MUST return Y. Most often this is more complex, but doable.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;When you are running your script the first time, test it with a small subsample and carefully check if everything is in order. While I like unit tests in IT, bugs in statistics scripts are often so pronounced that they are easily visible doing a carefully check. Or they are methodical errors, which are probably never caught by unit tests.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;That should suffice to ensure a clean "one - off " job. But for a time series as you seem to have, I would add that you should check for values out of range, impossible combinations etc. For me, most scripts that have reached step 4 are probably bug free - and they will stay that way unless something changes. And most often, the data are changing - and that is something which should be checked for every run. Writing code for that can be time consuming and annoying, but it beats subtle errors due to data entry errors.&lt;/p&gt;
', NULL, NULL, '''/li'':80C,100C,132C,196C ''/ol'':197C ''/p'':8C,79C,99C,131C,195C,294C ''4'':242C ''500'':52C ''add'':221C ''annoy'':283C ''background'':78C ''beat'':286C ''break'':83C ''bug'':165C,245C ''care'':151C,180C ''case'':111C ''caught'':191C ''chang'':255C,262C ''check'':152C,181C,225C,270C ''clean'':13C,205C ''code'':17C,24C,30C,36C,86C,275C ''combin'':232C ''complex'':128C ''condit'':113C ''connect'':105C ''consum'':281C ''danger'':38C ''data'':260C,291C ''debug'':58C ''doabl'':130C ''due'':289C ''e.g'':107C ''easi'':14C ''easili'':176C ''element'':106C ''ensur'':203C ''entri'':292C ''error'':186C,288C,293C ''especi'':69C ''etc'':233C ''evalu'':95C ''everi'':272C ''everyth'':154C ''first'':142C ''fond'':49C ''free'':246C ''fun'':57C ''function'':89C ''imposs'':231C ''job'':208C ''know'':26C ''li'':10C,81C,101C,133C ''like'':3C,28C,160C ''line'':53C ''look'':103C ''lot'':68C ''mention'':41C ''mess'':60C ''method'':185C ''mine'':47C ''multipl'':5C ''must'':120C ''never'':190C ''number'':109C ''often'':124C,170C,258C ''ol'':9C ''one'':206C ''oppos'':19C ''order'':157C ''p'':1C,11C,82C,102C,134C,198C ''pattern'':66C ''peopl'':71C ''probabl'':189C,244C ''problem'':33C ''pronounc'':172C ''python'':54C ''queri'':119C ''rang'':230C ''reach'':240C ''return'':121C ''run'':138C,273C ''scrip'':55C ''script'':140C,168C,237C ''seem'':216C ''seen'':64C ''seri'':213C ''short'':22C ''short-tricki'':21C ''small'':148C ''smaller'':88C,97C ''someth'':254C,266C ''spot'':32C ''statist'':167C ''statistician'':27C ''stay'':250C ''step'':6C,241C ''stes'':98C ''strategi'':7C ''subsampl'':149C ''subtl'':287C ''suffic'':201C ''supervisor'':45C ''test'':93C,144C,162C,194C ''time'':143C,212C,280C ''tricki'':23C,29C,35C ''understand'':16C ''undocu'':51C ''unit'':161C,193C ''unless'':253C ''valu'':227C ''visibl'':177C ''way'':252C ''would'':220C ''write'':12C,274C ''x'':114C ''y'':116C,122C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (404, 2, '2014-06-16 14:35:20.98', 10, NULL, NULL, '&lt;p&gt;Personally I would advocate using something that is both not-specific to the NLP field, and something that is sufficiently general that it can still be used as a tool even when you''ve started moving beyond this level of metadata. I would especially pick a format that can be used regardless of development environment and one that can keep some basic structure if that becomes relevant (like tokenization)&lt;/p&gt;

&lt;p&gt;It might seem strange, but I would honestly suggest &lt;code&gt;JSON&lt;/code&gt;. It''s extremely well supported, supports a lot of structure, and is flexible enough that you shouldn''t have to move from it for not being powerful enough. For your example, something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;{''text'': ''I saw the company''s manager last day.", {''Person'': [{''name'': ''John''}, {''indices'': [0:1]}, etc...]}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The one big advantage you''ve got over any NLP-specific formats here is that &lt;code&gt;JSON&lt;/code&gt; can be parsed in any environment, and since you''ll probably have to edit your format anyway, JSON lends itself to very simple edits that give you a short distance to other formats.&lt;/p&gt;

&lt;p&gt;You can also implicitly store tokenization information if you want:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;{"text": ["I", "saw", "the", "company''s", "manager", "last", "day."]}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;EDIT: To clarify the mapping of metadata is pretty open, but here''s an example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;{''body'': ''&amp;lt;some_text&amp;gt;'',
 ''metadata'': 
  {''&amp;lt;entity&amp;gt;'':
    {''&amp;lt;attribute&amp;gt;'': ''&amp;lt;value&amp;gt;'',
     ''location'': [&amp;lt;start_index&amp;gt;, &amp;lt;end_index&amp;gt;]
    }
  }
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Hope that helps, let me know if you''ve got any more questions.&lt;/p&gt;
', NULL, NULL, '''/code'':85C,139C,160C,218C,263C ''/p'':72C,120C,194C,206C,236C,279C ''/pre'':140C,219C,264C ''0'':136C ''1'':137C ''advantag'':145C ''advoc'':5C ''also'':198C ''anyway'':177C ''attribut'':249C ''basic'':64C ''becom'':68C ''beyond'':39C ''big'':144C ''bodi'':239C ''clarifi'':223C ''code'':83C,122C,158C,208C,238C ''compani'':127C,213C ''day'':131C,217C ''develop'':56C ''distanc'':190C ''edit'':174C,184C,221C ''end'':260C ''enough'':99C,113C ''entiti'':246C ''environ'':57C,166C ''especi'':46C ''etc'':138C ''even'':33C ''exampl'':116C,235C ''extrem'':88C ''field'':17C ''flexibl'':98C ''format'':49C,154C,176C,193C ''general'':23C ''give'':186C ''got'':148C,275C ''gt'':243C,247C,250C,253C,258C,262C ''help'':268C ''honest'':81C ''hope'':266C ''implicit'':199C ''index'':257C,261C ''indic'':135C ''inform'':202C ''john'':134C ''json'':84C,159C,178C ''keep'':62C ''know'':271C ''last'':130C,216C ''lend'':179C ''let'':269C ''level'':41C ''like'':70C,118C ''ll'':170C ''locat'':254C ''lot'':93C ''lt'':240C,245C,248C,251C,255C,259C ''manag'':129C,215C ''map'':225C ''metadata'':43C,227C,244C ''might'':75C ''move'':38C,106C ''name'':133C ''nlp'':16C,152C ''nlp-specif'':151C ''not-specif'':11C ''one'':59C,143C ''open'':230C ''p'':1C,73C,141C,195C,220C,265C ''pars'':163C ''person'':2C,132C ''pick'':47C ''power'':112C ''pre'':121C,207C,237C ''pretti'':229C ''probabl'':171C ''question'':278C ''regardless'':54C ''relev'':69C ''saw'':125C,211C ''seem'':76C ''short'':189C ''shouldn'':102C ''simpl'':183C ''sinc'':168C ''someth'':7C,19C,117C ''specif'':13C,153C ''start'':37C,256C ''still'':27C ''store'':200C ''strang'':77C ''structur'':65C,95C ''suffici'':22C ''suggest'':82C ''support'':90C,91C ''text'':123C,209C,242C ''token'':71C,201C ''tool'':32C ''use'':6C,29C,53C ''valu'':252C ''ve'':36C,147C,274C ''want'':205C ''well'':89C ''would'':4C,45C,80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10101, 2, '2016-02-06 03:14:11.303', 2, NULL, NULL, '&lt;p&gt;Unless you are trying to do this as a learning exercise, just use Spark which has ML libraries made for distributed computing.  See &lt;a href="http://spark.apache.org/docs/latest/mllib-clustering.html" rel="nofollow"&gt;http://spark.apache.org/docs/latest/mllib-clustering.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/docs/latest/mllib-clustering.html'':29C ''/docs/latest/mllib-clustering.html&lt;/a&gt;&lt;/p&gt;'':34C ''comput'':23C ''distribut'':22C ''exercis'':12C ''href'':26C ''learn'':11C ''librari'':19C ''made'':20C ''ml'':18C ''nofollow'':31C ''p'':1C ''rel'':30C ''see'':24C ''spark'':15C ''spark.apache.org'':28C,33C ''spark.apache.org/docs/latest/mllib-clustering.html'':27C ''spark.apache.org/docs/latest/mllib-clustering.html&lt;/a&gt;&lt;/p&gt;'':32C ''tri'':5C ''unless'':2C ''use'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (405, 2, '2014-06-16 15:00:04.577', 13, NULL, NULL, '&lt;p&gt;Personally going to make a strong argument in favor of Python here. There are a large number of reasons for this, but I''m going to build on some of the points that other people have mentioned here:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;strong&gt;Picking a single language:&lt;/strong&gt; It''s definitely possible to mix and match languages, picking &lt;code&gt;d3&lt;/code&gt; for your visualization needs, &lt;code&gt;FORTRAN&lt;/code&gt; for your fast matrix multiplies, and &lt;code&gt;python&lt;/code&gt; for all of your networking and scripting. You can do this down the line, but keeping your stack as simple as possible is a good move, especially early on.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Picking something bigger than you:&lt;/strong&gt; You never want to be pushing up against the barriers of the language you want to use. This is a huge issue when it comes to languages like &lt;code&gt;Julia&lt;/code&gt; and &lt;code&gt;FORTRAN&lt;/code&gt;, which simply don''t offer the full functionality of languages like &lt;code&gt;python&lt;/code&gt; or &lt;code&gt;R&lt;/code&gt;.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Pick Community&lt;/strong&gt;: The one most difficult thing to find in any language is community. &lt;code&gt;Python&lt;/code&gt; is the clear winner here. If you get stuck, you ask something on SO, and someone will answer in a matter of minutes, which is simply not the case for most other languages. If you''re learning something in a vacuum you will simply learn much slower.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;In terms of the minus points, I might actually push back on them.&lt;/p&gt;

&lt;p&gt;Deepening your knowledge of one language is a decent idea, but knowing &lt;em&gt;only&lt;/em&gt; one language, without having practice generalizing that knowledge to other languages is a good way to shoot yourself in the foot. I have changed my entire favored development stack three time over as many years, moving from &lt;code&gt;MATLAB&lt;/code&gt; to &lt;code&gt;Java&lt;/code&gt; to &lt;code&gt;haskell&lt;/code&gt; to &lt;code&gt;python&lt;/code&gt;. Learning to transfer your knowledge to another language is far more valuable than just knowing one.&lt;/p&gt;

&lt;p&gt;As far as feasibility, this is something you''re going to see again and again in any programming career. Turing completeness means you could technically do everything with &lt;code&gt;HTML4&lt;/code&gt; and &lt;code&gt;CSS3&lt;/code&gt;, but you want to pick the right tool for the job. If you see the ideal tool and decide to leave it by the roadside you''re going to find yourself slowed down wishing you had some of the tools you left behind.&lt;/p&gt;

&lt;p&gt;A great example of that last point is trying to deploy &lt;code&gt;R&lt;/code&gt; code. ''R''''s networking capabilities are hugely lacking compared to &lt;code&gt;python&lt;/code&gt;, and if you want to deploy a service, or use slightly off-the-beaten path packages, the fact that &lt;code&gt;pip&lt;/code&gt; has an order of magnitude more packages than &lt;code&gt;CRAN&lt;/code&gt; is a huge help.&lt;/p&gt;
', NULL, NULL, '''/code'':61C,68C,77C,146C,150C,164C,168C,189C,309C,313C,317C,321C,370C,374C,433C,446C,469C,480C ''/em'':269C ''/li'':107C,169C,237C ''/ol'':238C ''/p'':40C,253C,338C,418C,485C ''/strong'':48C,115C,174C ''actual'':248C ''anoth'':328C ''answer'':207C ''argument'':8C ''ask'':200C ''back'':250C ''barrier'':125C ''beaten'':461C ''behind'':417C ''bigger'':112C ''build'':28C ''capabl'':438C ''career'':358C ''case'':218C ''chang'':293C ''clear'':192C ''code'':59C,66C,75C,144C,148C,162C,166C,187C,307C,311C,315C,319C,368C,372C,431C,434C,444C,467C,478C ''come'':140C ''communiti'':173C,186C ''compar'':442C ''complet'':360C ''could'':363C ''cran'':479C ''css3'':373C ''d3'':60C ''decent'':263C ''decid'':393C ''deepen'':255C ''definit'':51C ''deploy'':430C,452C ''develop'':297C ''difficult'':178C ''earli'':105C ''em'':267C ''entir'':295C ''especi'':104C ''everyth'':366C ''exampl'':422C ''fact'':465C ''far'':331C,341C ''fast'':71C ''favor'':10C,296C ''feasibl'':343C ''find'':181C,404C ''foot'':290C ''fortran'':67C,149C ''full'':157C ''function'':158C ''general'':275C ''get'':197C ''go'':3C,26C,349C,402C ''good'':102C,283C ''great'':421C ''haskel'':316C ''help'':484C ''html4'':369C ''huge'':136C,440C,483C ''idea'':264C ''ideal'':390C ''issu'':137C ''java'':312C ''job'':385C ''julia'':145C ''keep'':93C ''know'':266C,336C ''knowledg'':257C,277C,326C ''lack'':441C ''languag'':47C,57C,128C,142C,160C,184C,222C,260C,271C,280C,329C ''larg'':17C ''last'':425C ''learn'':226C,234C,322C ''leav'':395C ''left'':416C ''li'':42C,108C,170C ''like'':143C,161C ''line'':91C ''m'':25C ''magnitud'':474C ''make'':5C ''mani'':303C ''match'':56C ''matlab'':308C ''matrix'':72C ''matter'':210C ''mean'':361C ''mention'':38C ''might'':247C ''minus'':244C ''minut'':212C ''mix'':54C ''move'':103C,305C ''much'':235C ''multipli'':73C ''need'':65C ''network'':82C,437C ''never'':117C ''number'':18C ''off-the-beaten'':458C ''offer'':155C ''ol'':41C ''one'':176C,259C,270C,337C ''order'':472C ''p'':1C,239C,254C,339C,419C ''packag'':463C,476C ''path'':462C ''peopl'':36C ''person'':2C ''pick'':44C,58C,110C,172C,379C ''pip'':468C ''point'':33C,245C,426C ''possibl'':52C,99C ''practic'':274C ''program'':357C ''push'':121C,249C ''python'':12C,76C,163C,188C,320C,445C ''r'':167C,432C,435C ''re'':225C,348C,401C ''reason'':20C ''right'':381C ''roadsid'':399C ''script'':84C ''see'':351C,388C ''servic'':454C ''shoot'':286C ''simpl'':97C ''simpli'':152C,215C,233C ''singl'':46C ''slight'':457C ''slow'':406C ''slower'':236C ''someon'':205C ''someth'':111C,201C,227C,346C ''stack'':95C,298C ''strong'':7C,43C,109C,171C ''stuck'':198C ''technic'':364C ''term'':241C ''thing'':179C ''three'':299C ''time'':300C ''tool'':382C,391C,414C ''transfer'':324C ''tri'':428C ''ture'':359C ''use'':132C,456C ''vacuum'':230C ''valuabl'':333C ''visual'':64C ''want'':118C,130C,377C,450C ''way'':284C ''winner'':193C ''wish'':408C ''without'':272C ''year'':304C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (406, 1, '2014-06-16 15:49:55.673', 11, 1817, 'How can I predict traffic based on previous time series data?', '&lt;p&gt;If I have a retail store and have a way to measure how many people enter my store every minute, and timestamp that data, how can I predict future foot traffic?  &lt;/p&gt;

&lt;p&gt;I have looked into machine learning algorithms, but I''m not sure which one to use. In my test data, a year over year trend is more accurate compared to other things I''ve tried, like KNN (with what I think are sensible parameters and distance function).&lt;/p&gt;

&lt;p&gt;It almost seems like this could be similar to financial modeling, where you deal with time series data. Any ideas?&lt;/p&gt;
', 'machine-learning  time-series', NULL, '''/p'':50C,99C,121C ''accur'':79C ''algorithm'':58C ''almost'':102C ''base'':6A ''compar'':80C ''could'':106C ''data'':11A,42C,71C,118C ''deal'':114C ''distanc'':97C ''enter'':34C ''everi'':37C ''financi'':110C ''foot'':48C ''function'':98C ''futur'':47C ''idea'':120C ''knn'':88C ''learn'':14B,57C ''like'':87C,104C ''look'':54C ''m'':61C ''machin'':13B,56C ''machine-learn'':12B ''mani'':32C ''measur'':30C ''minut'':38C ''model'':111C ''one'':65C ''p'':18C,51C,100C ''paramet'':95C ''peopl'':33C ''predict'':4A,46C ''previous'':8A ''retail'':23C ''seem'':103C ''sensibl'':94C ''seri'':10A,17B,117C ''similar'':108C ''store'':24C,36C ''sure'':63C ''test'':70C ''thing'':83C ''think'':92C ''time'':9A,16B,116C ''time-seri'':15B ''timestamp'':40C ''traffic'':5A,49C ''trend'':76C ''tri'':86C ''use'':67C ''ve'':85C ''way'':28C ''year'':73C,75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (429, 2, '2014-06-17 09:56:49.18', 7, NULL, NULL, '&lt;p&gt;It''s not a social network per se, but Stackexchange publish their entire database dump periodically:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://archive.org/details/stackexchange"&gt;Stackexchange data dump hosted on the archive.org&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://meta.stackoverflow.com/questions/2677/database-schema-documentation-for-the-public-data-dump-and-sede"&gt;Post describing the database dump schema&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You can extract some social information by analyzing which users ask and answer to each other. One nice thing is that since posts are tagged, you can analyze sub-communities easily.&lt;/p&gt;
', NULL, NULL, '''/a'':33C,47C ''/details/stackexchange'':25C ''/li'':34C,48C ''/p'':18C,83C ''/questions/2677/database-schema-documentation-for-the-public-data-dump-and-sede'':40C ''/ul'':49C ''analyz'':58C,78C ''answer'':63C ''archive.org'':24C,32C ''archive.org/details/stackexchange'':23C ''ask'':61C ''communiti'':81C ''data'':27C ''databas'':15C,44C ''describ'':42C ''dump'':16C,28C,45C ''easili'':82C ''entir'':14C ''extract'':53C ''host'':29C ''href'':22C,37C ''inform'':56C ''li'':20C,35C ''meta.stackoverflow.com'':39C ''meta.stackoverflow.com/questions/2677/database-schema-documentation-for-the-public-data-dump-and-sede'':38C ''network'':7C ''nice'':68C ''one'':67C ''p'':1C,50C ''per'':8C ''period'':17C ''post'':41C,73C ''publish'':12C ''schema'':46C ''se'':9C ''sinc'':72C ''social'':6C,55C ''stackexchang'':11C,26C ''sub'':80C ''sub-commun'':79C ''tag'':75C ''thing'':69C ''ul'':19C ''user'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (407, 2, '2014-06-16 16:10:34.423', 6, NULL, NULL, '&lt;p&gt;Other answers recommended a good set of books about the mathematics behind data science. But as you mentioned, its not just mathematics and activities like data collection and inference from data has their own rules and theories, even if not being as rigorous as mathematical backgrounds (yet).&lt;/p&gt;

&lt;p&gt;For theses parts, I suggest the book &lt;a href="http://rads.stackoverflow.com/amzn/click/0596157118"&gt;Beautiful Data: The Stories Behind Elegant Data Solutions&lt;/a&gt; which contains twenty case-study like chapters written by people really engaged with real world data analysis problems. It does not contain any mathematics, but explores areas like collecting data, finding practical ways of using data in analyses, scaling and selecting the best solutions very well.&lt;/p&gt;

&lt;p&gt;Another really interesting book is &lt;a href="http://rads.stackoverflow.com/amzn/click/1449362931"&gt;Thinking with Data: How to Turn Information into Insights&lt;/a&gt;, which is not technical (=programming tutorial) either, but covers important topics on how to really use the data science power in decision making and real world problems.&lt;/p&gt;
', NULL, NULL, '''/a'':71C,140C ''/amzn/click/0596157118'':62C ''/amzn/click/1449362931'':130C ''/p'':49C,119C,168C ''activ'':25C ''analys'':110C ''analysi'':89C ''anoth'':121C ''answer'':3C ''area'':99C ''background'':47C ''beauti'':63C ''behind'':13C,67C ''best'':115C ''book'':9C,57C,124C ''case'':76C ''case-studi'':75C ''chapter'':79C ''collect'':28C,101C ''contain'':73C,94C ''cover'':149C ''data'':14C,27C,32C,64C,69C,88C,102C,108C,133C,158C ''decis'':162C ''either'':147C ''eleg'':68C ''engag'':84C ''even'':39C ''explor'':98C ''find'':103C ''good'':6C ''href'':59C,127C ''import'':150C ''infer'':30C ''inform'':137C ''insight'':139C ''interest'':123C ''like'':26C,78C,100C ''make'':163C ''mathemat'':12C,23C,46C,96C ''mention'':19C ''p'':1C,50C,120C ''part'':53C ''peopl'':82C ''power'':160C ''practic'':104C ''problem'':90C,167C ''program'':145C ''rads.stackoverflow.com'':61C,129C ''rads.stackoverflow.com/amzn/click/0596157118'':60C ''rads.stackoverflow.com/amzn/click/1449362931'':128C ''real'':86C,165C ''realli'':83C,122C,155C ''recommend'':4C ''rigor'':44C ''rule'':36C ''scale'':111C ''scienc'':15C,159C ''select'':113C ''set'':7C ''solut'':70C,116C ''stori'':66C ''studi'':77C ''suggest'':55C ''technic'':144C ''theori'':38C ''these'':52C ''think'':131C ''topic'':151C ''turn'':136C ''tutori'':146C ''twenti'':74C ''use'':107C,156C ''way'':105C ''well'':118C ''world'':87C,166C ''written'':80C ''yet'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (408, 2, '2014-06-16 16:34:50.317', 11, NULL, NULL, '&lt;p&gt;The problem with models like KNN is that they do not take into account seasonality (time-dependent variations in trend).  To take those into account, you should use Time Series analysis.&lt;/p&gt;

&lt;p&gt;For count data, such as yours, you can use generalized linear auto-regressive moving average models (GLARMA).  Fortunately, there is an R package that implements them (&lt;a href="http://cran.r-project.org/web/packages/glarma/index.html"&gt;glarma&lt;/a&gt;).  &lt;/p&gt;

&lt;p&gt;The &lt;a href="http://cran.r-project.org/web/packages/glarma/vignettes/glarma.pdf"&gt;vignette&lt;/a&gt; is a good resource for the theory behind the tool.&lt;/p&gt;
', NULL, NULL, '''/a'':69C,79C ''/p'':34C,70C,90C ''/web/packages/glarma/index.html'':67C ''/web/packages/glarma/vignettes/glarma.pdf'':77C ''account'':15C,27C ''analysi'':33C ''auto'':48C ''auto-regress'':47C ''averag'':51C ''behind'':87C ''count'':37C ''cran.r-project.org'':66C,76C ''cran.r-project.org/web/packages/glarma/index.html'':65C ''cran.r-project.org/web/packages/glarma/vignettes/glarma.pdf'':75C ''data'':38C ''depend'':19C ''fortun'':54C ''general'':45C ''glarma'':53C,68C ''good'':82C ''href'':64C,74C ''implement'':61C ''knn'':7C ''like'':6C ''linear'':46C ''model'':5C,52C ''move'':50C ''p'':1C,35C,71C ''packag'':59C ''problem'':3C ''r'':58C ''regress'':49C ''resourc'':83C ''season'':16C ''seri'':32C ''take'':13C,24C ''theori'':86C ''time'':18C,31C ''time-depend'':17C ''tool'':89C ''trend'':22C ''use'':30C,44C ''variat'':20C ''vignett'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (409, 2, '2014-06-16 17:26:17.697', 7, NULL, NULL, '&lt;p&gt;I think Christopher''s answers above are entirely sensible.  As an alternate approach (or perhaps just in addition to the advise he''s given), I might start by just visualizing the data a bit to try get a rough sense of what''s going on.&lt;/p&gt;

&lt;p&gt;If you haven''t already done this, you might try adding a date''s month and day of week as features -- if you end up sticking with KNN, this will help the model pick up seasonality. &lt;/p&gt;

&lt;p&gt;As a different way of taking this on, you might consider starting with a really, really basic model (like OLS).. these often go a long way in generating reasonable predictions.  &lt;/p&gt;

&lt;p&gt;Finally, the more we know about your data, the easier it will be for us to help generate suggestions -- What time frame are you observing?  What are the features you''re currently using?  etc.&lt;/p&gt;

&lt;p&gt;Hope this helps --&lt;/p&gt;
', NULL, NULL, '''/p'':47C,85C,117C,153C,158C ''ad'':59C ''addit'':19C ''advis'':22C ''alreadi'':53C ''altern'':13C ''answer'':6C ''approach'':14C ''basic'':103C ''bit'':35C ''christoph'':4C ''consid'':97C ''current'':150C ''data'':33C,126C ''date'':61C ''day'':65C ''differ'':89C ''done'':54C ''easier'':128C ''end'':72C ''entir'':9C ''etc'':152C ''featur'':69C,147C ''final'':119C ''frame'':140C ''generat'':114C,136C ''get'':38C ''given'':25C ''go'':45C,109C ''haven'':51C ''help'':79C,135C,157C ''hope'':155C ''knn'':76C ''know'':123C ''like'':105C ''long'':111C ''might'':27C,57C,96C ''model'':81C,104C ''month'':63C ''observ'':143C ''often'':108C ''ol'':106C ''p'':1C,48C,86C,118C,154C ''perhap'':16C ''pick'':82C ''predict'':116C ''re'':149C ''realli'':101C,102C ''reason'':115C ''rough'':40C ''season'':84C ''sens'':41C ''sensibl'':10C ''start'':28C,98C ''stick'':74C ''suggest'':137C ''take'':92C ''think'':3C ''time'':139C ''tri'':37C,58C ''us'':133C ''use'':151C ''visual'':31C ''way'':90C,112C ''week'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (473, 1, '2014-06-19 08:56:46.847', 4, 557, 'Is logistic regression actually a regression algorithm?', '&lt;p&gt;The usual definition of regression (as far as I am aware) is &lt;em&gt;predicting a continuous output variable from a given set of input variables&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;Logistic regression is a binary classification algorithm, so it produces a categorical output.&lt;/p&gt;

&lt;p&gt;Is it really a regression algorithm? If so, why?&lt;/p&gt;
', 'algorithms  logistic-regression', NULL, '''/em'':38C ''/p'':39C,54C,65C ''actual'':4A ''algorithm'':7A,8B,47C,61C ''awar'':23C ''binari'':45C ''categor'':52C ''classif'':46C ''continu'':28C ''definit'':15C ''em'':25C ''far'':19C ''given'':33C ''input'':36C ''logist'':2A,10B,41C ''logistic-regress'':9B ''output'':29C,53C ''p'':12C,40C,55C ''predict'':26C ''produc'':50C ''realli'':58C ''regress'':3A,6A,11B,17C,42C,60C ''set'':34C ''usual'':14C ''variabl'':30C,37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (410, 1, '2014-06-16 18:08:38.623', 27, 4171, 'Choosing a learning rate', '&lt;p&gt;I''m currently working on implementing Stochastic Gradient Descent (SGD) for neural nets using backpropagation, and while I understand its purpose I have some questions about how to choose values for the learning rate.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Is the learning rate related to the shape of the error gradient, as it dictates the rate of descent?&lt;/li&gt;
&lt;li&gt;If so, how do you use this information to inform your decision about a value?&lt;/li&gt;
&lt;li&gt;If it''s not what sort of values should I choose, and how should I choose them?&lt;/li&gt;
&lt;li&gt;It seems like you would want small values to avoid overshooting, but how do you choose one such that you don''t get stuck in local minima or take to long to descend?&lt;/li&gt;
&lt;li&gt;Does it make sense to have a constant learning rate, or should I use some metric to alter its value as I get nearer a minimum in the gradient?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In short: How do I choose the learning rate for SGD?&lt;/p&gt;
', 'machine-learning  neuralnetwork  algorithms  optimization  hyperparameter', NULL, '''/li'':69C,86C,105C,140C,171C ''/p'':47C,185C ''/ul'':172C ''algorithm'':9B ''alter'':159C ''avoid'':116C ''backpropag'':27C ''choos'':1A,41C,98C,103C,122C,179C ''constant'':149C ''current'':15C ''decis'':82C ''descend'':139C ''descent'':21C,68C ''dictat'':64C ''error'':60C ''get'':129C,164C ''gradient'':20C,61C,170C ''hyperparamet'':11B ''implement'':18C ''inform'':78C,80C ''learn'':3A,7B,45C,52C,150C,181C ''li'':49C,70C,87C,106C,141C ''like'':109C ''local'':132C ''long'':137C ''m'':14C ''machin'':6B ''machine-learn'':5B ''make'':144C ''metric'':157C ''minima'':133C ''minimum'':167C ''nearer'':165C ''net'':25C ''neural'':24C ''neuralnetwork'':8B ''one'':123C ''optim'':10B ''overshoot'':117C ''p'':12C,173C ''purpos'':33C ''question'':37C ''rate'':4A,46C,53C,66C,151C,182C ''relat'':54C ''seem'':108C ''sens'':145C ''sgd'':22C,184C ''shape'':57C ''short'':175C ''small'':113C ''sort'':93C ''stochast'':19C ''stuck'':130C ''take'':135C ''ul'':48C ''understand'':31C ''use'':26C,76C,155C ''valu'':42C,85C,95C,114C,161C ''want'':112C ''work'':16C ''would'':111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (411, 1, '2014-06-16 19:14:38.553', 6, 985, 'Best languages for scientific computing', '&lt;p&gt;It seems as though most languages have some number of scientific computing libraries available. &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Python has &lt;code&gt;Scipy&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;Rust&lt;/code&gt; has &lt;code&gt;SciRust&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;C++&lt;/code&gt; has several including &lt;code&gt;ViennaCL&lt;/code&gt; and &lt;code&gt;Armadillo&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;Java&lt;/code&gt; has &lt;code&gt;Java Numerics&lt;/code&gt; and &lt;code&gt;Colt&lt;/code&gt; as well as several other&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Not to mention languages like &lt;code&gt;R&lt;/code&gt; and &lt;code&gt;Julia&lt;/code&gt; designed explicitly for scientific computing.&lt;/p&gt;

&lt;p&gt;With so many options how do you choose the best language for a task? Additionally which languages will be the most performant? &lt;code&gt;Python&lt;/code&gt; and &lt;code&gt;R&lt;/code&gt; seem to have the most traction in the space, but logically a compiled language seems like it would be a better choice. And will anything ever outperform &lt;code&gt;Fortran&lt;/code&gt;? Additionally compiled languages tend to have GPU acceleration, while interpreted languages like &lt;code&gt;R&lt;/code&gt; and &lt;code&gt;Python&lt;/code&gt; don''t. What should I take into account when choosing a language, and which languages provide the best balance of utility and performance? Also are there any languages with significant scientific computing resources that I''ve missed?&lt;/p&gt;
', 'efficiency  statistics  tools  knowledge-base', NULL, '''/code'':34C,39C,43C,48C,54C,58C,63C,68C,72C,88C,92C,124C,128C,158C,173C,177C ''/li'':35C,44C,59C,78C ''/p'':27C,98C,215C ''/ul'':79C ''acceler'':166C ''account'':185C ''addit'':114C,159C ''also'':201C ''anyth'':153C ''armadillo'':57C ''avail'':26C ''balanc'':196C ''base'':11B ''best'':1A,109C,195C ''better'':149C ''c'':47C ''choic'':150C ''choos'':107C,187C ''code'':32C,37C,41C,46C,52C,56C,61C,65C,70C,86C,90C,122C,126C,156C,171C,175C ''colt'':71C ''compil'':141C,160C ''comput'':5A,24C,97C,209C ''design'':93C ''effici'':6B ''ever'':154C ''explicit'':94C ''fortran'':157C ''gpu'':165C ''includ'':51C ''interpret'':168C ''java'':62C,66C ''julia'':91C ''knowledg'':10B ''knowledge-bas'':9B ''languag'':2A,18C,84C,110C,116C,142C,161C,169C,189C,192C,205C ''li'':29C,36C,45C,60C ''librari'':25C ''like'':85C,144C,170C ''logic'':139C ''mani'':102C ''mention'':83C ''miss'':214C ''number'':21C ''numer'':67C ''option'':103C ''outperform'':155C ''p'':12C,80C,99C ''perform'':121C,200C ''provid'':193C ''python'':30C,123C,176C ''r'':87C,127C,172C ''resourc'':210C ''rust'':38C ''scientif'':4A,23C,96C,208C ''scipi'':33C ''scirust'':42C ''seem'':14C,129C,143C ''sever'':50C,76C ''signific'':207C ''space'':137C ''statist'':7B ''take'':183C ''task'':113C ''tend'':162C ''though'':16C ''tool'':8B ''traction'':134C ''ul'':28C ''util'':198C ''ve'':213C ''viennacl'':53C ''well'':74C ''would'':146C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (412, 1, '2014-06-16 19:48:31.797', 26, 525, 'How can I transform names in a confidential data set to make it anonymous, but preserve some of the characteristics of the names?', '&lt;h1&gt;Motivation&lt;/h1&gt;

&lt;p&gt;I work with datasets that contain personally identifiable information (PII) and sometimes need to share part of a dataset with third parties, in a way that doesn''t expose PII and subject my employer to liability. Our usual approach here is to withhold data entirely, or in some cases to reduce its resolution; e.g., replacing an exact street address with the corresponding county or census tract. &lt;/p&gt;

&lt;p&gt;This means that certain types of analysis and processing must be done in-house, even when a third party has resources and expertise more suited to the task. Since the source data is not disclosed, the way we go about this analysis and processing lacks transparency. As a result, any third party''s ability to perform QA/QC, adjust parameters or make refinements may be very limited.&lt;/p&gt;

&lt;h1&gt;Anonymizing Confidential Data&lt;/h1&gt;

&lt;p&gt;One task involves identifying individuals by their names, in user-submitted data, while taking into account errors and inconsistencies. A private individual might be recorded in one place as "Dave" and in another as "David," commercial entities can have many different abbreviations, and there are always some typos. I''ve developed scripts based on a number of criteria that determine when two records with non-identical names represent the same individual, and assign them a common ID. &lt;/p&gt;

&lt;p&gt;At this point we can make the dataset anonymous by withholding the names and replacing them with this personal ID number. But this means the recipient has almost no information about e.g. the strength of the match. We would prefer to be able to pass along as much information as possible without divulging identity.&lt;/p&gt;

&lt;h1&gt;What Doesn''t Work&lt;/h1&gt;

&lt;p&gt;For instance, it would be great to be able to encrypt strings while preserving edit distance. This way, third parties could do some of their own QA/QC, or choose to do further processing on their own, without ever accessing (or being able to potentially reverse-engineer) PII. Perhaps we match strings in-house with edit distance &amp;lt;= 2, and the recipient wants to look at the implications of tightening that tolerance to edit distance &amp;lt;= 1.&lt;/p&gt;

&lt;p&gt;But the only method I am familiar with that does this is &lt;a href="http://www.techrepublic.com/blog/it-security/cryptographys-running-gag-rot13/" rel="nofollow"&gt;ROT13&lt;/a&gt; (more generally, any &lt;a href="https://en.wikipedia.org/wiki/Caesar_cipher" rel="nofollow"&gt;shift cipher&lt;/a&gt;), which hardly even counts as encryption; it''s like writing the names upside down and saying, "Promise you won''t flip the paper over?"&lt;/p&gt;

&lt;p&gt;Another &lt;strong&gt;bad&lt;/strong&gt; solution would be to abbreviate everything. "Ellen Roberts" becomes "ER" and so forth. This is a poor solution because in some cases the initials, in association with public data, will reveal a person''s identity, and in other cases it''s too ambiguous; "Benjamin Othello Ames" and "Bank of America" will have the same initials, but their names are otherwise dissimilar. So it doesn''t do either of the things we want.&lt;/p&gt;

&lt;p&gt;An inelegant alternative is to introduce additional fields to track certain attributes of the name, e.g.:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;+-----+----+-------------------+-----------+--------+
| Row | ID | Name              | WordChars | Origin |
+-----+----+-------------------+-----------+--------+
| 1   | 17 | "AMELIA BEDELIA"  | (6, 7)    | Eng    |
+-----+----+-------------------+-----------+--------+
| 2   | 18 | "CHRISTOPH BAUER" | (9, 5)    | Ger    |
+-----+----+-------------------+-----------+--------+
| 3   | 18 | "C J BAUER"       | (1, 1, 5) | Ger    |
+-----+----+-------------------+-----------+--------+
| 4   | 19 | "FRANZ HELLER"    | (5, 6)    | Ger    |
+-----+----+-------------------+-----------+--------+
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I call this "inelegant" because it requires anticipating which qualities might be interesting and it''s relatively coarse. If the names are removed, there''s not much you can reasonably conclude about the strength of the match between rows 2 &amp;amp; 3, or about the distance between rows 2 &amp;amp; 4 (i.e., how close they are to matching).&lt;/p&gt;

&lt;h1&gt;Conclusion&lt;/h1&gt;

&lt;p&gt;The goal is to transform strings in such a way that as many useful qualities of the original string are preserved as possible while obscuring the original string. Decryption should be impossible, or so impractical as to be effectively impossible, no matter the size of the data set. In particular, a method that preserves the edit distance between arbitrary strings would be very useful.&lt;/p&gt;

&lt;p&gt;I''ve found a couple papers that might be relevant, but they''re a bit over my head:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.merl.com/publications/docs/TR2010-109.pdf" rel="nofollow"&gt;Privacy Preserving String Comparisons Based on Levenshtein Distance&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.uni-due.de/~hq0215/documents/2010/Bachteler_2010_An_Empirical_Comparison_Of_Approaches_To_Approximate_String_Matching_In_Private_Record_Linkage.pdf" rel="nofollow"&gt;An Empirical Comparison of Approaches to Approximate String 
Matching in Private Record Linkage&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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''pii'':41C,61C,364C ''place'':202C ''point'':257C ''poor'':477C ''possibl'':305C,677C ''potenti'':360C ''pre'':552C ''prefer'':294C ''preserv'':16A,330C,675C,708C,750C ''privaci'':749C ''privat'':195C,777C ''process'':108C,144C,349C ''promis'':447C ''public'':488C ''qa/qc'':157C,343C ''qualiti'':601C,669C ''re'':733C ''reason'':621C ''recipi'':280C,379C ''record'':199C,237C,778C ''reduc'':82C ''refin'':162C ''rel'':414C,426C,747C,765C ''relat'':608C ''relev'':730C ''remov'':614C ''replac'':86C,269C ''repres'':243C ''requir'':598C ''resolut'':84C ''resourc'':121C ''result'':149C ''reveal'':491C ''revers'':362C ''reverse-engin'':361C ''robert'':468C ''rot13'':416C ''row'':554C,630C,639C ''say'':446C ''script'':226C ''set'':10A,702C ''share'':46C ''shift'':428C ''sinc'':129C ''size'':698C ''solut'':461C,478C ''sometim'':43C ''sourc'':131C ''street'':89C ''strength'':288C,625C ''string'':328C,368C,660C,673C,682C,714C,751C,774C ''strong'':458C ''subject'':63C ''submit'':185C ''suit'':125C ''take'':188C ''task'':128C,175C ''thing'':530C ''third'':52C,118C,151C,335C ''tighten'':387C ''toler'':389C ''track'':544C ''tract'':97C ''transform'':4A,659C ''transpar'':146C ''two'':236C ''type'':104C ''typo'':222C ''ul'':740C ''upsid'':443C ''use'':668C,718C ''user'':184C ''user-submit'':183C ''usual'':69C ''ve'':224C,722C ''want'':380C,532C ''way'':56C,137C,334C,664C ''withhold'':74C,265C ''without'':306C,353C ''won'':449C ''wordchar'':557C ''work'':33C,314C ''would'':293C,320C,462C,715C ''write'':440C ''www.merl.com'':745C ''www.merl.com/publications/docs/tr2010-109.pdf'':744C ''www.techrepublic.com'':412C ''www.techrepublic.com/blog/it-security/cryptographys-running-gag-rot13/'':411C ''www.uni-due.de'':763C ''www.uni-due.de/~hq0215/documents/2010/bachteler_2010_an_empirical_comparison_of_approaches_to_approximate_string_matching_in_private_record_linkage.pdf'':762C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5276, 2, '2015-03-04 06:05:05.32', 5, NULL, NULL, '&lt;p&gt;Terms that covered more or less the same topics that Data Science covers today:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Pattern Recognition&lt;/li&gt;
&lt;li&gt;Machine Learning &lt;/li&gt;
&lt;li&gt;Data Mining &lt;/li&gt;
&lt;li&gt;Quantitative methods&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':21C,25C,29C,33C ''/p'':16C ''/ul'':34C ''cover'':4C,14C ''data'':12C,27C ''learn'':24C ''less'':7C ''li'':18C,22C,26C,30C ''machin'':23C ''method'':32C ''mine'':28C ''p'':1C ''pattern'':19C ''quantit'':31C ''recognit'':20C ''scienc'':13C ''term'':2C ''today'':15C ''topic'':10C ''ul'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (413, 2, '2014-06-16 19:49:24.377', 8, NULL, NULL, '&lt;p&gt;Dimensionality reduction is typically choosing a basis or mathematical representation within which you can describe most but not all of the variance within your data, thereby retaining the relevant information, while reducing the amount of information necessary to represent it. There are a variety of techniques for doing this including but not limited to &lt;code&gt;PCA&lt;/code&gt;, &lt;code&gt;ICA&lt;/code&gt;, and &lt;code&gt;Matrix Feature Factorization&lt;/code&gt;. These will take existing data and reduce it to the most discriminative components.These all allow you to represent most of the information in your dataset with fewer, more discriminative features.&lt;/p&gt;

&lt;p&gt;Feature Selection is hand selecting features which are highly discriminative. This has a lot more to do with feature engineering than analysis, and requires significantly more work on the part of the data scientist. It requires an understanding of what aspects of your dataset are important in whatever predictions you''re making, and which aren''t. Feature extraction usually involves generating new features which are composites of existing features. Both of these techniques fall into the category of feature engineering. Generally feature engineering is important if you want to obtain the best results, as it involves creating information that may not exist in your dataset, and increasing your signal to noise ratio.&lt;/p&gt;
', NULL, NULL, '''/code'':58C,61C,67C ''/p'':98C,212C ''allow'':82C ''amount'':35C ''analysi'':121C ''aren'':154C ''aspect'':140C ''basi'':8C ''best'':191C ''categori'':176C ''choos'':6C ''code'':56C,59C,63C ''components.these'':80C ''composit'':165C ''creat'':196C ''data'':26C,72C,132C ''dataset'':92C,143C,204C ''describ'':16C ''dimension'':2C ''discrimin'':79C,96C,109C ''engin'':119C,179C,182C ''exist'':71C,167C,201C ''extract'':157C ''factor'':66C ''fall'':173C ''featur'':65C,97C,100C,105C,118C,156C,162C,168C,178C,181C ''fewer'':94C ''general'':180C ''generat'':160C ''hand'':103C ''high'':108C ''ica'':60C ''import'':145C,184C ''includ'':51C ''increas'':206C ''inform'':31C,37C,89C,197C ''involv'':159C,195C ''limit'':54C ''lot'':113C ''make'':151C ''mathemat'':10C ''matrix'':64C ''may'':199C ''necessari'':38C ''new'':161C ''nois'':210C ''obtain'':189C ''p'':1C,99C ''part'':129C ''pca'':57C ''predict'':148C ''ratio'':211C ''re'':150C ''reduc'':33C,74C ''reduct'':3C ''relev'':30C ''repres'':40C,85C ''represent'':11C ''requir'':123C,135C ''result'':192C ''retain'':28C ''scientist'':133C ''select'':101C,104C ''signal'':208C ''signific'':124C ''take'':70C ''techniqu'':47C,172C ''therebi'':27C ''typic'':5C ''understand'':137C ''usual'':158C ''varianc'':23C ''varieti'':45C ''want'':187C ''whatev'':147C ''within'':12C,24C ''work'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (414, 2, '2014-06-16 19:53:09.957', 27, NULL, NULL, '&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Is the learning rate related to the shape of the error gradient, as
it dictates the rate of descent?&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;In plain SGD, no. A global learning rate is used which is indifferent to the error gradient. However the intuition you are getting at has inspired various modifications of the SGD update rule.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;If so, how do you use this information to inform your decision about a value?&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Adagrad is the most widely known of these and scales a global learning rate &lt;em&gt;Î·&lt;/em&gt; on each dimension based on l2 norm of the history of the error gradient &lt;em&gt;gt&lt;/em&gt; on each dimension:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/aP96K.png" alt="enter image description here"&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://arxiv.org/pdf/1212.5701v1.pdf"&gt;Adadelta&lt;/a&gt; is another such training algorithm which uses both the error gradient history like adagrad and the weight update history and has the advantage of &lt;em&gt;not having to set a learning rate at all&lt;/em&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;If it''s not what sort of values should I choose, and how should I choose them?&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Setting learning rates for plain SGD in neural nets is usually a
process of starting with a sane value such as 0.01 and then doing cross validation
to find an optimal value. Typical values range over a few orders of
magnitude from 0.0001 up to 1.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;It seems like you would want small values to avoid overshooting, but
how do you choose one such that you don''t get stuck in local minima
or take to long to descend? Does it make sense to have a constant learning rate, or should I use some metric to alter its value as I get nearer a minimum in the gradient?&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Usually the value that''s best is near the highest stable learning
rate and learning rate decay/annealing (either linear or
exponentially) is used over the course of training. The reason behind this is that early on there is a clear learning signal so aggressive updates encourage exploration while later on the smaller learning rates allow for more delicate exploitation of local error surface.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':146C ''/ap96k.png'':130C ''/em'':103C,120C,181C ''/li'':61C,63C,137C,183C,185C,256C,258C,391C,393C ''/p'':25C,83C,124C,136C,182C,207C,325C ''/pdf/1212.5701v1.pdf'':144C ''/strong'':24C,82C,206C,324C ''/ul'':62C,184C,257C,392C,394C ''0.0001'':252C ''0.01'':231C ''1'':255C ''adadelta'':145C ''adagrad'':87C,160C ''advantag'':169C ''aggress'':371C ''algorithm'':151C ''allow'':382C ''alt'':131C ''alter'':312C ''anoth'':148C ''arxiv.org'':143C ''arxiv.org/pdf/1212.5701v1.pdf'':142C ''avoid'':271C ''base'':107C ''behind'':358C ''best'':333C ''choos'':199C,204C,277C ''clear'':367C ''constant'':302C ''cours'':353C ''cross'':235C ''decay/annealing'':344C ''decis'':78C ''delic'':385C ''descend'':294C ''descent'':23C ''descript'':134C ''dictat'':19C ''dimens'':106C,123C ''earli'':362C ''either'':345C ''em'':101C,118C,171C ''encourag'':373C ''enter'':132C ''error'':15C,43C,116C,156C,389C ''exploit'':386C ''explor'':374C ''exponenti'':348C ''find'':238C ''get'':50C,284C,317C ''global'':33C,98C ''gradient'':16C,44C,117C,157C,323C ''gt'':119C ''highest'':337C ''histori'':113C,158C,165C ''howev'':45C ''href'':141C ''i.stack.imgur.com'':129C ''i.stack.imgur.com/ap96k.png'':128C ''imag'':133C ''img'':126C ''indiffer'':40C ''inform'':74C,76C ''inspir'':53C ''intuit'':47C ''known'':92C ''l2'':109C ''later'':376C ''learn'':7C,34C,99C,177C,211C,303C,339C,342C,368C,380C ''li'':2C,27C,64C,85C,138C,186C,209C,259C,327C ''like'':159C,264C ''linear'':346C ''local'':287C,388C ''long'':292C ''magnitud'':250C ''make'':297C ''metric'':310C ''minima'':288C ''minimum'':320C ''modif'':55C ''near'':335C ''nearer'':318C ''net'':218C ''neural'':217C ''norm'':110C ''one'':278C ''optim'':240C ''order'':248C ''overshoot'':272C ''p'':3C,65C,86C,125C,139C,187C,260C ''plain'':29C,214C ''process'':222C ''rang'':244C ''rate'':8C,21C,35C,100C,178C,212C,304C,340C,343C,381C ''reason'':357C ''relat'':9C ''rule'':60C ''sane'':227C ''scale'':96C ''seem'':263C ''sens'':298C ''set'':175C,210C ''sgd'':30C,58C,215C ''shape'':12C ''signal'':369C ''small'':268C ''smaller'':379C ''sort'':194C ''src'':127C ''stabl'':338C ''start'':224C ''strong'':4C,66C,188C,261C ''stuck'':285C ''surfac'':390C ''take'':290C ''train'':150C,355C ''typic'':242C ''ul'':1C,26C,84C,208C,326C ''updat'':59C,164C,372C ''use'':37C,72C,153C,308C,350C ''usual'':220C,328C ''valid'':236C ''valu'':81C,196C,228C,241C,243C,269C,314C,330C ''various'':54C ''want'':267C ''weight'':163C ''wide'':91C ''would'':266C ''î'':102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (421, 1, '2014-06-17 04:31:34.067', 7, 539, 'Online machine learning tutorial', '&lt;p&gt;Does anyone know some good tutorials on online machine learning technics?
I.e. how it can be used in real-time environments, what are key differences compared to normal machine learning methods etc.&lt;/p&gt;

&lt;p&gt;UPD: Thank you everyone for answers, by "online" I mean methods which can be trained in a real-time mode, based on a new inputs one by one.&lt;/p&gt;
', 'machine-learning  education  beginner', NULL, '''/p'':44C,75C ''answer'':51C ''anyon'':12C ''base'':67C ''beginn'':9B ''compar'':37C ''differ'':36C ''educ'':8B ''environ'':32C ''etc'':43C ''everyon'':49C ''good'':15C ''i.e'':22C ''input'':71C ''key'':35C ''know'':13C ''learn'':3A,7B,20C,41C ''machin'':2A,6B,19C,40C ''machine-learn'':5B ''mean'':55C ''method'':42C,56C ''mode'':66C ''new'':70C ''normal'':39C ''one'':72C,74C ''onlin'':1A,18C,53C ''p'':10C,45C ''real'':30C,64C ''real-tim'':29C,63C ''technic'':21C ''thank'':47C ''time'':31C,65C ''train'':60C ''tutori'':4A,16C ''upd'':46C ''use'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (415, 2, '2014-06-16 20:10:12.167', 11, NULL, NULL, '&lt;p&gt;This is a pretty massive question, so this is not intended to be a full answer, but hopefully this can help to inform general practice around determining the best tool for the job when it comes to data science. Generally, I have a relatively short list of qualifications I look for when it comes to any tool in this space. In no particular order they are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Performance&lt;/strong&gt;: Basically boils down to how quickly the language does matrix multiplication, as that is more or less the most important task in data science.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Scalability&lt;/strong&gt;: At least for me personally, this comes down to ease of building a distributed system. This is somewhere where languages like &lt;code&gt;Julia&lt;/code&gt; really shine.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Community&lt;/strong&gt;: With any language, you''re really looking for an active community that can help you when you get stuck using whichever tool you''re using. This is where &lt;code&gt;python&lt;/code&gt; pulls very far ahead of most other languages. &lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Flexibility&lt;/strong&gt;: Nothing is worse than being limited by the language that you use. It doesn''t happen very often, but trying to represent graph structures in &lt;code&gt;haskell&lt;/code&gt; is a notorious pain, and &lt;code&gt;Julia&lt;/code&gt; is filled with a lot of code architectures pains as a result of being such a young language.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Ease of Use&lt;/strong&gt;: If you want to use something in a larger environment, you want to make sure that setup is a straightforward and it can be automated. Nothing is worse than having to set up a finnicky build on half a dozen machines.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;There are a ton of articles out there about performance and scalability, but in general you''re going to be looking at a performance differential of maybe 5-10x between languages, which may or may not matter depending on your specific application. As far as GPU acceleration goes, &lt;code&gt;cudamat&lt;/code&gt; is a really seamless way of getting it working with &lt;code&gt;python&lt;/code&gt;, and the &lt;code&gt;cuda&lt;/code&gt; library in general has made GPU acceleration far more accessible than it used to be.&lt;/p&gt;

&lt;p&gt;The two primary metrics I use for both community and flexibility are to look at the language''s package manager, and the language questions on a site like SO. If there are a large number of high-quality questions and answers, it''s a good sign that the community is active. Number of packages and the general activity on those packages can also be a good proxy for this metric.&lt;/p&gt;

&lt;p&gt;As far as ease of use goes, I am a firm believer that the only way to actually know is to actually set it up yourself. There''s a lot of superstition around a lot of Data Science tools, specifically things like databases and distributed computing architecture, but there''s no way to really know if something is easy or hard to setup up and deploy without just building it yourself.&lt;/p&gt;
', NULL, NULL, '''-10'':311C ''/code'':126C,164C,205C,213C,334C,347C,352C ''/li'':98C,129C,173C,232C,280C ''/p'':68C,368C,441C,514C ''/strong'':73C,102C,133C,177C,238C ''/ul'':281C ''5'':310C ''acceler'':330C,359C ''access'':362C ''activ'':143C,421C,428C ''actual'':460C,464C ''ahead'':168C ''also'':433C ''answer'':17C,411C ''applic'':325C ''architectur'':221C,489C ''around'':27C,475C ''articl'':288C ''autom'':263C ''basic'':74C ''believ'':454C ''best'':30C ''boil'':75C ''build'':114C,274C,511C ''code'':124C,162C,203C,211C,220C,332C,345C,350C ''come'':37C,55C,109C ''communiti'':132C,144C,378C,419C ''comput'':488C ''cuda'':351C ''cudamat'':333C ''data'':39C,96C,479C ''databas'':485C ''depend'':321C ''deploy'':508C ''determin'':28C ''differenti'':307C ''distribut'':116C,487C ''doesn'':191C ''dozen'':278C ''eas'':112C,235C,446C ''easi'':501C ''environ'':248C ''far'':167C,327C,360C,444C ''fill'':215C ''finnicki'':273C ''firm'':453C ''flexibl'':176C,380C ''full'':16C ''general'':25C,41C,297C,355C,427C ''get'':151C,341C ''go'':300C ''goe'':331C,449C ''good'':415C,436C ''gpu'':329C,358C ''graph'':200C ''half'':276C ''happen'':193C ''hard'':503C ''haskel'':204C ''help'':22C,147C ''high'':407C ''high-qual'':406C ''hope'':19C ''import'':93C ''inform'':24C ''intend'':12C ''job'':34C ''julia'':125C,212C ''know'':461C,497C ''languag'':81C,122C,136C,172C,186C,231C,314C,386C,392C ''larg'':403C ''larger'':247C ''least'':104C ''less'':90C ''li'':70C,99C,130C,174C,233C ''librari'':353C ''like'':123C,397C,484C ''limit'':183C ''list'':47C ''look'':51C,140C,303C,383C ''lot'':218C,472C,477C ''machin'':279C ''made'':357C ''make'':252C ''manag'':389C ''massiv'':6C ''matrix'':83C ''matter'':320C ''may'':316C,318C ''mayb'':309C ''metric'':373C,440C ''multipl'':84C ''noth'':178C,264C ''notori'':208C ''number'':404C,422C ''often'':195C ''order'':65C ''p'':1C,282C,369C,442C ''packag'':388C,424C,431C ''pain'':209C,222C ''particular'':64C ''perform'':72C,292C,306C ''person'':107C ''practic'':26C ''pretti'':5C ''primari'':372C ''proxi'':437C ''pull'':165C ''python'':163C,346C ''qualif'':49C ''qualiti'':408C ''question'':7C,393C,409C ''quick'':79C ''re'':138C,157C,299C ''realli'':127C,139C,337C,496C ''relat'':45C ''repres'':199C ''result'':225C ''scalabl'':101C,294C ''scienc'':40C,97C,480C ''seamless'':338C ''set'':270C,465C ''setup'':255C,505C ''shine'':128C ''short'':46C ''sign'':416C ''site'':396C ''someth'':244C,499C ''somewher'':120C ''space'':61C ''specif'':324C,482C ''straightforward'':258C ''strong'':71C,100C,131C,175C,234C ''structur'':201C ''stuck'':152C ''superstit'':474C ''sure'':253C ''system'':117C ''task'':94C ''thing'':483C ''ton'':286C ''tool'':31C,58C,155C,481C ''tri'':197C ''two'':371C ''ul'':69C ''use'':153C,158C,189C,237C,243C,365C,375C,448C ''want'':241C,250C ''way'':339C,458C,494C ''whichev'':154C ''without'':509C ''work'':343C ''wors'':180C,266C ''x'':312C ''young'':230C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (416, 2, '2014-06-16 20:12:39.13', 6, NULL, NULL, '&lt;p&gt;If feasible I would link related records (e.g., Dave, David, etc.) and replace them with a sequence number (1,2,3, etc.) or a &lt;a href="http://en.wikipedia.org/wiki/Salt_%28cryptography%29" rel="nofollow"&gt;salted&lt;/a&gt; &lt;a href="http://www.cse.yorku.ca/~oz/hash.html" rel="nofollow"&gt;hash of the string&lt;/a&gt; that is used to represent all related records (e.g., David instead of Dave).&lt;/p&gt;

&lt;p&gt;I assume that third parties need not have any idea what the real name is, otherwise you might as well give it to them.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;edit&lt;/strong&gt;: You need to define and justify what kind of operations the third party needs to be able to do. For example, what is wrong with using initials followed by a number (e.g., BOA-1, BOA-2, etc.) to disambiguate Bank of America from Benjamin Othello Ames? If that''s too revealing, you could bin some of the letters or names; e.g., [A-E] -&gt; 1, [F-J] -&gt; 2, etc. so BOA would become 1OA, or ["Bank", "Barry", "Bruce", etc.] -&gt; 1 so Bank of America is again 1OA. &lt;/p&gt;

&lt;p&gt;For more information see &lt;a href="http://en.wikipedia.org/wiki/K-anonymity" rel="nofollow"&gt;k-anonymity&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''-1'':124C ''-2'':126C ''/a'':34C,46C,195C ''/p'':60C,86C,179C,196C ''/strong'':90C ''/wiki/k-anonymity'':189C ''/wiki/salt_%28cryptography%29'':30C ''/~oz/hash.html'':39C ''1'':20C,155C,171C ''1oa'':165C,178C ''2'':21C,159C ''3'':22C ''a-e'':152C ''abl'':107C ''ame'':136C ''america'':132C,175C ''anonym'':194C ''assum'':63C ''bank'':130C,167C,173C ''barri'':168C ''becom'':164C ''benjamin'':134C ''bin'':144C ''boa'':123C,125C,162C ''bruce'':169C ''could'':143C ''dave'':10C,59C ''david'':11C,56C ''defin'':94C ''disambigu'':129C ''e'':154C ''e.g'':9C,55C,122C,151C ''edit'':89C ''en.wikipedia.org'':29C,188C ''en.wikipedia.org/wiki/k-anonymity'':187C ''en.wikipedia.org/wiki/salt_%28cryptography%29'':28C ''etc'':12C,23C,127C,160C,170C ''exampl'':111C ''f'':157C ''f-j'':156C ''feasibl'':3C ''follow'':118C ''give'':82C ''hash'':42C ''href'':27C,36C,186C ''idea'':71C ''inform'':183C ''initi'':117C ''instead'':57C ''j'':158C ''justifi'':96C ''k'':193C ''k-anonym'':192C ''kind'':98C ''letter'':148C ''link'':6C ''might'':79C ''name'':75C,150C ''need'':67C,92C,104C ''nofollow'':32C,41C,191C ''number'':19C,121C ''oper'':100C ''othello'':135C ''otherwis'':77C ''p'':1C,61C,87C,180C ''parti'':66C,103C ''real'':74C ''record'':8C,54C ''rel'':31C,40C,190C ''relat'':7C,53C ''replac'':14C ''repres'':51C ''reveal'':141C ''salt'':33C ''see'':184C ''sequenc'':18C ''string'':45C ''strong'':88C ''third'':65C,102C ''use'':49C,116C ''well'':81C ''would'':5C,163C ''wrong'':114C ''www.cse.yorku.ca'':38C ''www.cse.yorku.ca/~oz/hash.html'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (417, 2, '2014-06-16 20:18:50.123', 1, NULL, NULL, '&lt;p&gt;Blacklists aren''t  have value for a number of reasons:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;They''re easy to set up and scale - it''s just a key/value store, and you can probably just re-use some of your caching logic for the most basic implementation.&lt;/li&gt;
&lt;li&gt;Depending on the size and type of the spam attack, there will probably be some very specific terms or URLs being used. It''s much faster to throw that term into a blacklist than wait for your model to adapt.&lt;/li&gt;
&lt;li&gt;You can remove items just as quickly as you added them.&lt;/li&gt;
&lt;li&gt;Everybody understands how they work and any admin can use them.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The key to fighting spam is &lt;em&gt;monitoring&lt;/em&gt;. Make sure you have some sort of interface showing which items are on your blacklist, how often they''ve been hit in the last 10 minutes / hour / day / month, and the ability to easily add and remove items.&lt;/p&gt;

&lt;p&gt;You''ll want to combine a number of different spam detection models and tactics. Neural nets seem to be a good suggestion, and I''d recommend looking at &lt;em&gt;user behavior patterns&lt;/em&gt; in addition to just content. Normal humans don''t do things like send batches of 1,000 emails every 30 seconds for 12 consecutive hours.&lt;/p&gt;
', NULL, NULL, '''/em'':126C,199C ''/li'':47C,89C,102C,115C ''/ol'':116C ''/p'':12C,165C,225C ''000'':216C ''1'':215C ''10'':151C ''12'':222C ''30'':219C ''abil'':158C ''ad'':100C ''adapt'':88C ''add'':161C ''addit'':201C ''admin'':111C ''aren'':3C ''attack'':58C ''basic'':45C ''batch'':213C ''behavior'':197C ''blacklist'':2C,81C,141C ''cach'':40C ''combin'':171C ''consecut'':223C ''content'':204C ''d'':191C ''day'':154C ''depend'':49C ''detect'':177C ''differ'':175C ''easi'':17C ''easili'':160C ''em'':124C,195C ''email'':217C ''everi'':218C ''everybodi'':104C ''faster'':74C ''fight'':121C ''good'':187C ''hit'':147C ''hour'':153C,224C ''human'':206C ''implement'':46C ''interfac'':134C ''item'':94C,137C,164C ''key'':119C ''key/value'':27C ''last'':150C ''li'':14C,48C,90C,103C ''like'':211C ''ll'':168C ''logic'':41C ''look'':193C ''make'':127C ''minut'':152C ''model'':86C,178C ''monitor'':125C ''month'':155C ''much'':73C ''net'':182C ''neural'':181C ''normal'':205C ''number'':9C,173C ''often'':143C ''ol'':13C ''p'':1C,117C,166C ''pattern'':198C ''probabl'':32C,61C ''quick'':97C ''re'':16C,35C ''re-us'':34C ''reason'':11C ''recommend'':192C ''remov'':93C,163C ''scale'':22C ''second'':220C ''seem'':183C ''send'':212C ''set'':19C ''show'':135C ''size'':52C ''sort'':132C ''spam'':57C,122C,176C ''specif'':65C ''store'':28C ''suggest'':188C ''sure'':128C ''tactic'':180C ''term'':66C,78C ''thing'':210C ''throw'':76C ''type'':54C ''understand'':105C ''url'':68C ''use'':36C,70C,113C ''user'':196C ''valu'':6C ''ve'':145C ''wait'':83C ''want'':169C ''work'':108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (418, 1, '2014-06-17 00:16:24.287', 5, 277, 'Best way to classify datasets with mixed types of attributes', '&lt;p&gt;I would like to know what is the best way to classify a data set composed of mixed types of attributes, for example, textual and numerical. I know I can convert textual to boolean, but the vocabulary is diverse and data become too sparse. I also tried to classify the types of attributes separately and combine the results through meta-learning techniques, but it did not work well.&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/p'':85C ''also'':61C ''attribut'':10A,36C,68C ''becom'':57C ''best'':1A,24C ''boolean'':49C ''classif'':14B ''classifi'':4A,27C,64C ''combin'':71C ''compos'':31C ''convert'':46C ''data'':29C,56C ''dataset'':5A ''divers'':54C ''exampl'':38C ''know'':20C,43C ''learn'':13B,77C ''like'':18C ''machin'':12B ''machine-learn'':11B ''meta'':76C ''meta-learn'':75C ''mix'':7A,33C ''numer'':41C ''p'':15C ''result'':73C ''separ'':69C ''set'':30C ''spars'':59C ''techniqu'':78C ''textual'':39C,47C ''tri'':62C ''type'':8A,34C,66C ''vocabulari'':52C ''way'':2A,25C ''well'':84C ''work'':83C ''would'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (437, 1, '2014-06-17 18:13:46.23', 5, 113, 'What are good sources to learn about Bootstrap?', '&lt;p&gt;I think that Bootstrap can be useful in my work, where we have a lot a variables that we don''t know the distribution of it. So, simulations could help.
What are good sources to learn about Bootstrap/other useful simulation methods?&lt;/p&gt;
', 'data-mining  statistics  education', NULL, '''/p'':56C ''bootstrap'':8A,18C ''bootstrap/other'':52C ''could'':43C ''data'':10B ''data-min'':9B ''distribut'':38C ''educ'':13B ''good'':3A,47C ''help'':44C ''know'':36C ''learn'':6A,50C ''lot'':29C ''method'':55C ''mine'':11B ''p'':14C ''simul'':42C,54C ''sourc'':4A,48C ''statist'':12B ''think'':16C ''use'':21C,53C ''variabl'':31C ''work'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5334, 2, '2015-03-17 07:30:40.047', 1, NULL, NULL, '&lt;p&gt;Only reason for such a long subset time is that you''re running out of memory and the OS starts to swap. AFAIK base data.frame will create a copy of the data in memory before writing it to file.&lt;/p&gt;

&lt;p&gt;What you can do is use either dplyr or data.table packages as they manage some of these steps without copying the data.&lt;/p&gt;
', NULL, NULL, '''/p'':41C,65C ''afaik'':24C ''base'':25C ''copi'':30C,62C ''creat'':28C ''data'':33C,64C ''data.frame'':26C ''data.table'':52C ''dplyr'':50C ''either'':49C ''file'':40C ''long'':7C ''manag'':56C ''memori'':17C,35C ''os'':20C ''p'':1C,42C ''packag'':53C ''re'':13C ''reason'':3C ''run'':14C ''start'':21C ''step'':60C ''subset'':8C ''swap'':23C ''time'':9C ''use'':48C ''without'':61C ''write'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (419, 2, '2014-06-17 00:19:09.773', 4, NULL, NULL, '&lt;p&gt;First you need to decide what you want to do, then look for the right tool for that task.&lt;/p&gt;

&lt;p&gt;A very general approach is to use R for first versions and to see if your approach is correct. It lacks a little in speed, but has very powerful commands and addon libraries, that you can try almost anything with it:
&lt;a href="http://www.r-project.org/" rel="nofollow"&gt;http://www.r-project.org/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The second idea is if you want to understand the algorithms behind the libraries, you might wanna take a look at the Numerical Recipies. They are available for different languages and free to use for learning. If you want to use them in commercial products, you need to ourchase a licence:
&lt;a href="http://en.wikipedia.org/wiki/Numerical_Recipes" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Numerical_Recipes&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Most of the time performance will not be the issue but finding the right algorithms and parameters for them, so it is important to have a fast scripting language instead of a monster program that first needs to compile 10 mins before calculating two numbers and putting out the result.&lt;/p&gt;

&lt;p&gt;And a big plus in using R is that it has built-in functions or libraries for almost any kind of diagram you might wanna need to visualize your data.&lt;/p&gt;

&lt;p&gt;If you then have a working version, it is almost easy to port it to any other language you think is more performant.&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':71C ''/p'':21C,185C,218C,243C ''/wiki/numerical_recipes'':128C ''/wiki/numerical_recipes&lt;/a&gt;&lt;/p&gt;'':133C ''10'':174C ''addon'':54C ''algorithm'':83C,149C ''almost'':60C,205C,229C ''anyth'':61C ''approach'':26C,39C ''avail'':99C ''behind'':84C ''big'':189C ''built'':199C ''built-in'':198C ''calcul'':177C ''command'':52C ''commerci'':116C ''compil'':173C ''correct'':41C ''data'':217C ''decid'':6C ''diagram'':209C ''differ'':101C ''easi'':230C ''en.wikipedia.org'':127C,132C ''en.wikipedia.org/wiki/numerical_recipes'':126C ''en.wikipedia.org/wiki/numerical_recipes&lt;/a&gt;&lt;/p&gt;'':131C ''fast'':161C ''find'':146C ''first'':2C,32C,170C ''free'':104C ''function'':201C ''general'':25C ''href'':65C,125C ''idea'':75C ''import'':157C ''instead'':164C ''issu'':144C ''kind'':207C ''lack'':43C ''languag'':102C,163C,237C ''learn'':108C ''librari'':55C,86C,203C ''licenc'':123C ''littl'':45C ''look'':13C,92C ''might'':88C,211C ''min'':175C ''monster'':167C ''need'':4C,119C,171C,213C ''nofollow'':68C,130C ''number'':179C ''numer'':95C ''ourchas'':121C ''p'':1C,22C,72C,134C,186C,219C ''paramet'':151C ''perform'':139C,242C ''plus'':190C ''port'':232C ''power'':51C ''product'':117C ''program'':168C ''put'':181C ''r'':30C,193C ''recipi'':96C ''rel'':67C,129C ''result'':184C ''right'':16C,148C ''script'':162C ''second'':74C ''see'':36C ''speed'':47C ''take'':90C ''task'':20C ''think'':239C ''time'':138C ''tool'':17C ''tri'':59C ''two'':178C ''understand'':81C ''use'':29C,106C,113C,192C ''version'':33C,226C ''visual'':215C ''wanna'':89C,212C ''want'':9C,79C,111C ''work'':225C ''www.r-project.org'':66C,70C ''www.r-project.org/&lt;/a&gt;&lt;/p&gt;'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (420, 2, '2014-06-17 00:39:15.99', 4, NULL, NULL, '&lt;p&gt;It is hard to answer this question without knowing more about the data.  That said, I would offer the following advice:&lt;/p&gt;

&lt;p&gt;Most machine learning techniques can handle mixed-type data.  Tree based methods (such as AdaBoost and Random Forests) do well with this type of data.  The more important issue is actually the dimensionality, about which you are correct to be concerned.&lt;/p&gt;

&lt;p&gt;I would suggest that you do something to reduce that dimensionality.  For example, look for the words or phrases that separate the data the best and discard the other words (note: tree based methods do this automatically).&lt;/p&gt;
', NULL, NULL, '''/p'':23C,67C,106C ''actual'':56C ''adaboost'':40C ''advic'':22C ''answer'':6C ''automat'':105C ''base'':36C,101C ''best'':93C ''concern'':66C ''correct'':63C ''data'':14C,34C,50C,91C ''dimension'':58C,79C ''discard'':95C ''exampl'':81C ''follow'':21C ''forest'':43C ''handl'':30C ''hard'':4C ''import'':53C ''issu'':54C ''know'':10C ''learn'':27C ''look'':82C ''machin'':26C ''method'':37C,102C ''mix'':32C ''mixed-typ'':31C ''note'':99C ''offer'':19C ''p'':1C,24C,68C ''phrase'':87C ''question'':8C ''random'':42C ''reduc'':77C ''said'':16C ''separ'':89C ''someth'':75C ''suggest'':71C ''techniqu'':28C ''tree'':35C,100C ''type'':33C,48C ''well'':45C ''without'':9C ''word'':85C,98C ''would'':18C,70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (422, 1, '2014-06-17 05:29:11.83', 13, 4285, 'Publicly available social network datasets/APIs', '&lt;p&gt;As an extension to our great list of &lt;a href="http://datascience.stackexchange.com/questions/155/publicly-available-datasets"&gt;publicly available datasets&lt;/a&gt;, I''d like to know if there is any list of publicly available social network datasets/crawling APIs. It would be very nice if alongside with a link to the dataset/API, characteristics of the data available were added. Such information should be, and is not limited to:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;the name of the social network;&lt;/li&gt;
&lt;li&gt;what kind of user information it provides (posts, profile, friendship network, ...);&lt;/li&gt;
&lt;li&gt;whether it allows for crawling its contents via an API (and rate: 10/min, 1k/month, ...);&lt;/li&gt;
&lt;li&gt;whether it simply provides a snapshot of the whole dataset.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Any suggestions and further characteristics to be added are very welcome.&lt;/p&gt;
', 'open-source  dataset  crawling', NULL, '''/a'':28C ''/li'':84C,97C,113C,125C ''/p'':75C,139C ''/questions/155/publicly-available-datasets'':24C ''/ul'':126C ''10/min'':111C ''1k/month'':112C ''ad'':65C,135C ''allow'':101C ''alongsid'':52C ''api'':45C,108C ''avail'':2A,26C,41C,63C ''characterist'':59C,132C ''content'':105C ''crawl'':10B,103C ''d'':30C ''data'':62C ''datascience.stackexchange.com'':23C ''datascience.stackexchange.com/questions/155/publicly-available-datasets'':22C ''dataset'':9B,27C,124C ''dataset/api'':58C ''datasets/apis'':5A ''datasets/crawling'':44C ''extens'':14C ''friendship'':95C ''great'':17C ''href'':21C ''inform'':67C,90C ''kind'':87C ''know'':33C ''li'':77C,85C,98C,114C ''like'':31C ''limit'':73C ''link'':55C ''list'':18C,38C ''name'':79C ''network'':4A,43C,83C,96C ''nice'':50C ''open'':7B ''open-sourc'':6B ''p'':11C,127C ''post'':93C ''profil'':94C ''provid'':92C,118C ''public'':1A,25C,40C ''rate'':110C ''simpli'':117C ''snapshot'':120C ''social'':3A,42C,82C ''sourc'':8B ''suggest'':129C ''ul'':76C ''user'':89C ''via'':106C ''welcom'':138C ''whether'':99C,115C ''whole'':123C ''would'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (469, 2, '2014-06-19 05:52:57.527', 5, NULL, NULL, '&lt;p&gt;I suggest this tutorial on ordered logit: &lt;a href="http://www.ats.ucla.edu/stat/r/dae/ologit.htm"&gt;http://www.ats.ucla.edu/stat/r/dae/ologit.htm&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;It showcases the use of &lt;code&gt;polr&lt;/code&gt; in the &lt;code&gt;MASS&lt;/code&gt; package, and also explains the assumptions and how to interpret the results.&lt;/p&gt;
', NULL, NULL, '''/code'':25C,30C ''/p'':43C ''/stat/r/dae/ologit.htm'':13C ''/stat/r/dae/ologit.htm&lt;/a&gt;&lt;/p&gt;'':16C ''also'':33C ''assumpt'':36C ''code'':23C,28C ''explain'':34C ''href'':10C ''interpret'':40C ''logit'':8C ''mass'':29C ''order'':7C ''p'':1C,17C ''packag'':31C ''polr'':24C ''result'':42C ''showcas'':19C ''suggest'':3C ''tutori'':5C ''use'':21C ''www.ats.ucla.edu'':12C,15C ''www.ats.ucla.edu/stat/r/dae/ologit.htm'':11C ''www.ats.ucla.edu/stat/r/dae/ologit.htm&lt;/a&gt;&lt;/p&gt;'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9124, 2, '2015-11-30 05:28:55.88', 2, NULL, NULL, '&lt;p&gt;Depends on how you define "hubs". In Network Science, a hub is simply a node with high degree i.e. those node who contribute to the power-law nature of the degree distribution the most. But you can also find other definitions for instance according to the information flow in the network where hubs are defined as those node that are critical in the process information flow (also called central nodes). &lt;/p&gt;

&lt;h2&gt;My Suggestions&lt;/h2&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Degree Distribution:&lt;/strong&gt; The simplest approach would be to choose high degree nodes as hubs. To ensure your results a bit more I recommend to calculate the summation of all correlations correspond to each node and have a look at these numbers as well. In this case you are looking for nodes which have high degrees and contain a larger value of correlation.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Centrality Measure:&lt;/strong&gt; or &lt;em&gt;Betweenness&lt;/em&gt; introduced by Linton Freeman which again somehow measures the influence of a node in the process of information flow over the network. Calculating the centrality for a vertex $v$ in a graph has basically 3 steps:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;For each pair of vertices $(s,t)$, compute the shortest paths between them.&lt;/li&gt;
&lt;li&gt;For each pair of vertices $(s,t)$, determine the fraction of shortest paths that pass through the vertex in question (here, vertex $v$).&lt;/li&gt;
&lt;li&gt;Sum this fraction over all pairs of vertices $(s,t)$.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;For more information read &lt;a href="https://en.wikipedia.org/wiki/Centrality" rel="nofollow"&gt;this&lt;/a&gt; carefully and in case you need any help (specially for implementation) just drop me a line in the comments :)&lt;/p&gt;

&lt;p&gt;Good Luck!&lt;/p&gt;
', NULL, NULL, '''/a'':263C ''/em'':156C ''/h2'':77C ''/li'':146C,209C,234C,246C,248C ''/ol'':249C ''/p'':73C,145C,193C,283C,287C ''/strong'':84C,152C ''/ul'':247C ''/wiki/centrality'':259C ''3'':191C ''accord'':46C ''also'':40C,69C ''approach'':87C ''basic'':190C ''between'':155C ''bit'':102C ''calcul'':107C,179C ''call'':70C ''care'':264C ''case'':128C,267C ''central'':71C,150C,181C ''choos'':91C ''comment'':282C ''comput'':203C ''contain'':139C ''contribut'':24C ''correl'':112C,144C ''correspond'':113C ''critic'':63C ''defin'':6C,57C ''definit'':43C ''degre'':19C,33C,82C,93C,137C ''depend'':2C ''determin'':218C ''distribut'':34C,83C ''drop'':276C ''em'':154C ''en.wikipedia.org'':258C ''en.wikipedia.org/wiki/centrality'':257C ''ensur'':98C ''find'':41C ''flow'':50C,68C,175C ''fraction'':220C,238C ''freeman'':160C ''good'':285C ''graph'':188C ''h2'':74C ''help'':271C ''high'':18C,92C,136C ''href'':256C ''hub'':7C,12C,55C,96C ''i.e'':20C ''implement'':274C ''influenc'':166C ''inform'':49C,67C,174C,253C ''instanc'':45C ''introduc'':157C ''larger'':141C ''law'':29C ''li'':79C,147C,195C,210C,235C ''line'':279C ''linton'':159C ''look'':120C,131C ''luck'':286C ''measur'':151C,164C ''natur'':30C ''need'':269C ''network'':9C,53C,178C ''node'':16C,22C,60C,72C,94C,116C,133C,169C ''nofollow'':261C ''number'':123C ''ol'':78C ''p'':1C,80C,148C,250C,284C ''pair'':198C,213C,241C ''pass'':225C ''path'':206C,223C ''power'':28C ''power-law'':27C ''process'':66C,172C ''question'':230C ''read'':254C ''recommend'':105C ''rel'':260C ''result'':100C ''scienc'':10C ''shortest'':205C,222C ''simplest'':86C ''simpli'':14C ''somehow'':163C ''special'':272C ''step'':192C ''strong'':81C,149C ''suggest'':76C ''sum'':236C ''summat'':109C ''ul'':194C ''v'':185C,233C ''valu'':142C ''vertex'':184C,228C,232C ''vertic'':200C,215C,243C ''well'':125C ''would'':88C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (423, 1, '2014-06-17 05:55:04.71', 5, 180, 'How to measure execution time on distributed system', '&lt;p&gt;I''m planning to run experiments with large datasets on distributed system in order to evaluate efficiency gains in comparison with previous proposals.&lt;/p&gt;

&lt;p&gt;I have limited number of machines nearly ten machines having 200 GB of free space on hard disk on each. On the contrary, I wished to perform experiments on more than available nodes in order to measure scalability, &lt;strong&gt;more precisely&lt;/strong&gt;. Since I don''t have any, I thought about using a commodity cluster. However, I''m not sure about the policies of usage, and I need to reliably measure execution times. &lt;/p&gt;

&lt;p&gt;Are there commodity services which will grant me that only my application would be running at a given time?&lt;/p&gt;
', 'bigdata  scalability  distributed', NULL, '''/p'':36C,111C,132C ''/strong'':79C ''200'':48C ''applic'':124C ''avail'':69C ''bigdata'':9B ''cluster'':92C ''commod'':91C,115C ''comparison'':32C ''contrari'':60C ''dataset'':21C ''disk'':55C ''distribut'':7A,11B,23C ''effici'':29C ''evalu'':28C ''execut'':4A,109C ''experi'':18C,65C ''free'':51C ''gain'':30C ''gb'':49C ''given'':130C ''grant'':119C ''hard'':54C ''howev'':93C ''larg'':20C ''limit'':40C ''m'':14C,95C ''machin'':43C,46C ''measur'':3A,74C,108C ''near'':44C ''need'':105C ''node'':70C ''number'':41C ''order'':26C,72C ''p'':12C,37C,112C ''perform'':64C ''plan'':15C ''polici'':100C ''precis'':78C ''previous'':34C ''propos'':35C ''reliabl'':107C ''run'':17C,127C ''scalabl'':10B,75C ''servic'':116C ''sinc'':80C ''space'':52C ''strong'':76C ''sure'':97C ''system'':8A,24C ''ten'':45C ''thought'':87C ''time'':5A,110C,131C ''usag'':102C ''use'':89C ''wish'':62C ''would'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (424, 1, '2014-06-17 06:05:39.653', 13, 271, 'How to grow a list of related words based on initial keywords?', '&lt;p&gt;I recently saw a cool feature that &lt;a href="https://support.google.com/docs/answer/3543688?hl=en"&gt;was once available&lt;/a&gt; in Google Sheets: you start by writing a few related keywords in consecutive cells, say: "blue", "green", "yellow", and it automatically generates similar keywords (in this case, other colors). See more examples in &lt;a href="http://youtu.be/dlslNhfrQmw"&gt;this YouTube video&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I would like to reproduce this in my own program. I''m thinking of using Freebase, and it would work like this intuitively: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Retrieve the list of given words in Freebase;&lt;/li&gt;
&lt;li&gt;Find their "common denominator(s)" and construct a distance metric based on this;&lt;/li&gt;
&lt;li&gt;Rank other concepts based on their "distance" to the original keywords;&lt;/li&gt;
&lt;li&gt;Display the next closest concepts.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;As I''m not familiar with this area, my questions are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Is there a better way to do this?&lt;/li&gt;
&lt;li&gt;What tools are available for each step?&lt;/li&gt;
&lt;/ul&gt;
', 'nlp  text-mining  freebase', NULL, '''/a'':34C,76C ''/dlslnhfrqmw'':72C ''/docs/answer/3543688?hl=en'':30C ''/li'':113C,128C,141C,148C,173C,182C ''/ol'':149C ''/p'':77C,102C,162C ''/ul'':183C ''area'':158C ''automat'':55C ''avail'':33C,178C ''base'':9A,125C,133C ''better'':168C ''blue'':50C ''case'':61C ''cell'':48C ''closest'':146C ''color'':63C ''common'':117C ''concept'':132C,147C ''consecut'':47C ''construct'':121C ''cool'':23C ''denomin'':118C ''display'':143C ''distanc'':123C,136C ''exampl'':66C ''familiar'':155C ''featur'':24C ''find'':115C ''freebas'':17B,94C,112C ''generat'':56C ''given'':109C ''googl'':36C ''green'':51C ''grow'':3A ''href'':27C,69C ''initi'':11A ''intuit'':101C ''keyword'':12A,45C,58C,140C ''li'':104C,114C,129C,142C,164C,174C ''like'':81C,99C ''list'':5A,107C ''m'':90C,153C ''metric'':124C ''mine'':16B ''next'':145C ''nlp'':13B ''ol'':103C ''origin'':139C ''p'':18C,78C,150C ''program'':88C ''question'':160C ''rank'':130C ''recent'':20C ''relat'':7A,44C ''reproduc'':83C ''retriev'':105C ''saw'':21C ''say'':49C ''see'':64C ''sheet'':37C ''similar'':57C ''start'':39C ''step'':181C ''support.google.com'':29C ''support.google.com/docs/answer/3543688?hl=en'':28C ''text'':15B ''text-min'':14B ''think'':91C ''tool'':176C ''ul'':163C ''use'':93C ''video'':75C ''way'':169C ''word'':8A,110C ''work'':98C ''would'':80C,97C ''write'':41C ''yellow'':52C ''youtu.be'':71C ''youtu.be/dlslnhfrqmw'':70C ''youtub'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (425, 2, '2014-06-17 06:32:22.883', 5, NULL, NULL, '&lt;p&gt;An example from germany: Xing a site similar to linkedin but limited to german speaking countries.&lt;/p&gt;

&lt;p&gt;Link to it''s developer central: &lt;a href="https://dev.xing.com/overview"&gt;https://dev.xing.com/overview&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Provides access to: User profiles, Conversations between users (limited to the user itself), Job advertisings, Contacts and Contacts of Contacts, news from the network and some geolocation api.&lt;/p&gt;

&lt;p&gt;Yes it has an api, but I did not find information about the rate. But it seems to me, that some information is limited to the consent of the user.&lt;/p&gt;
', NULL, NULL, '''/overview'':30C ''/overview&lt;/a&gt;&lt;/p&gt;'':33C ''/p'':18C,63C,95C ''access'':36C ''advertis'':49C ''api'':62C,69C ''central'':25C ''consent'':91C ''contact'':50C,52C,54C ''convers'':40C ''countri'':17C ''dev.xing.com'':29C,32C ''dev.xing.com/overview'':28C ''dev.xing.com/overview&lt;/a&gt;&lt;/p&gt;'':31C ''develop'':24C ''exampl'':3C ''find'':74C ''geoloc'':61C ''german'':15C ''germani'':5C ''href'':27C ''inform'':75C,86C ''job'':48C ''limit'':13C,43C,88C ''link'':20C ''linkedin'':11C ''network'':58C ''news'':55C ''p'':1C,19C,34C,64C ''profil'':39C ''provid'':35C ''rate'':78C ''seem'':81C ''similar'':9C ''site'':8C ''speak'':16C ''user'':38C,42C,46C,94C ''xing'':6C ''yes'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (426, 2, '2014-06-17 06:53:44.007', 0, NULL, NULL, '&lt;p&gt;There are plenty on youtube and here''s a famous one by Andrew Ng from coursea: &lt;a href="https://www.coursera.org/course/ml" rel="nofollow"&gt;https://www.coursera.org/course/ml&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/course/ml'':22C ''/course/ml&lt;/a&gt;&lt;/p&gt;'':27C ''andrew'':14C ''coursea'':17C ''famous'':11C ''href'':19C ''ng'':15C ''nofollow'':24C ''one'':12C ''p'':1C ''plenti'':4C ''rel'':23C ''www.coursera.org'':21C,26C ''www.coursera.org/course/ml'':20C ''www.coursera.org/course/ml&lt;/a&gt;&lt;/p&gt;'':25C ''youtub'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (427, 1, '2014-06-17 07:46:39.783', 3, 430, 'does anyone use Julia programming language?', '&lt;p&gt;Are anyone using &lt;code&gt;Julia&lt;/code&gt; (&lt;a href="http://julialang.org/" rel="nofollow"&gt;http://julialang.org/&lt;/a&gt;) for professional jobs?&lt;/p&gt;

&lt;p&gt;Or use it instead of R , Matlab or Mathematica? &lt;/p&gt;

&lt;p&gt;Is it a good language?&lt;/p&gt;

&lt;p&gt;If you have to predict next 5-10 years: Do you think it grow up enough to became such a standard in datascience like R or similar?&lt;/p&gt;
', 'tools', NULL, '''-10'':53C ''/&lt;/a&gt;)'':22C ''/code'':14C ''/p'':26C,37C,44C,73C ''5'':52C ''anyon'':2A,10C ''becam'':63C ''code'':12C ''datasci'':68C ''enough'':61C ''good'':42C ''grow'':59C ''href'':16C ''instead'':31C ''job'':25C ''julia'':4A,13C ''julialang.org'':17C,21C ''julialang.org/&lt;/a&gt;)'':20C ''languag'':6A,43C ''like'':69C ''mathematica'':36C ''matlab'':34C ''next'':51C ''nofollow'':19C ''p'':8C,27C,38C,45C ''predict'':50C ''profession'':24C ''program'':5A ''r'':33C,70C ''rel'':18C ''similar'':72C ''standard'':66C ''think'':57C ''tool'':7B ''use'':3A,11C,29C ''year'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (428, 2, '2014-06-17 09:33:37.23', 1, NULL, NULL, '&lt;p&gt;This critique is no longer justified:&lt;/p&gt;

&lt;p&gt;While it is true that most of the standard and most respected R libraries were restricted to in-memory computations, there is a growing number of specialized libraries to deal with data that doesn''t fit into memory.&lt;br&gt;
For instance, for random forests on large datasets, you have the library &lt;code&gt;bigrf&lt;/code&gt;. More info here: &lt;a href="http://cran.r-project.org/web/packages/bigrf/" rel="nofollow"&gt;http://cran.r-project.org/web/packages/bigrf/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Another area of growth is R''s connectedness to big data environments like hadoop, which opens another world of possibilities.&lt;/p&gt;
', NULL, NULL, '''/code'':64C ''/p'':8C,99C ''/web/packages/bigrf/'':72C ''/web/packages/bigrf/&lt;/a&gt;&lt;/p&gt;'':77C ''anoth'':79C,95C ''area'':80C ''big'':88C ''bigrf'':63C ''br'':49C ''code'':62C ''comput'':30C ''connected'':86C ''cran.r-project.org'':71C,76C ''cran.r-project.org/web/packages/bigrf/'':70C ''cran.r-project.org/web/packages/bigrf/&lt;/a&gt;&lt;/p&gt;'':75C ''critiqu'':3C ''data'':42C,89C ''dataset'':57C ''deal'':40C ''doesn'':44C ''environ'':90C ''fit'':46C ''forest'':54C ''grow'':34C ''growth'':82C ''hadoop'':92C ''href'':69C ''in-memori'':27C ''info'':66C ''instanc'':51C ''justifi'':7C ''larg'':56C ''librari'':23C,38C,61C ''like'':91C ''longer'':6C ''memori'':29C,48C ''nofollow'':74C ''number'':35C ''open'':94C ''p'':1C,9C,78C ''possibl'':98C ''r'':22C,84C ''random'':53C ''rel'':73C ''respect'':21C ''restrict'':25C ''special'':37C ''standard'':18C ''true'':13C ''world'':96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5521, 2, '2015-04-13 23:54:08.857', 0, NULL, NULL, '&lt;p&gt;Since you have your clusters set up already you should be able to calculate cluster centroids and then determine the distance between a each cluster centroid and the new data point. You could follow the same process with random samples of each cluster. &lt;/p&gt;
', NULL, NULL, '''/p'':45C ''abl'':13C ''alreadi'':9C ''calcul'':15C ''centroid'':17C,27C ''cluster'':6C,16C,26C,44C ''could'':34C ''data'':31C ''determin'':20C ''distanc'':22C ''follow'':35C ''new'':30C ''p'':1C ''point'':32C ''process'':38C ''random'':40C ''sampl'':41C ''set'':7C ''sinc'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (430, 1, '2014-06-17 10:37:22.987', 12, 742, 'Looking for example infrastructure stacks/workflows/pipelines', '&lt;p&gt;I''m trying to understand how all the "big data" components play together in a real world use case, e.g. hadoop, monogodb/nosql, storm, kafka, ... I know that this is quite a wide range of tools used for different types, but I''d like to get to know more about their interaction in applications, e.g. thinking machine learning for an app, webapp, online shop.&lt;/p&gt;

&lt;p&gt;I have vistors/session, transaction data etc and store that; but if I want to make recommendations on the fly, I can''t run slow map/reduce jobs for that on some big database of logs I have. Where can I learn more about the infrastructure aspects? I think I can use most of the tools on their own, but plugging them into each other seems to be an art of its own.&lt;/p&gt;

&lt;p&gt;Are there any public examples/use cases etc available? I understand that the individual pipelines strongly depend on the use case and the user, but just examples will probably be very useful to me.&lt;/p&gt;
', 'machine-learning  bigdata  efficiency  scalability  distributed', NULL, '''/p'':77C,150C,185C ''app'':73C ''applic'':66C ''art'':146C ''aspect'':123C ''avail'':159C ''big'':22C,109C ''bigdata'':9B ''case'':32C,157C,171C ''compon'':24C ''d'':55C ''data'':23C,83C ''databas'':110C ''depend'':167C ''differ'':51C ''distribut'':12B ''e.g'':33C,67C ''effici'':10B ''etc'':84C,158C ''exampl'':3A,177C ''examples/use'':156C ''fli'':97C ''get'':58C ''hadoop'':34C ''individu'':164C ''infrastructur'':4A,122C ''interact'':64C ''job'':104C ''kafka'':37C ''know'':39C,60C ''learn'':8B,70C,118C ''like'':56C ''log'':112C ''look'':1A ''m'':15C ''machin'':7B,69C ''machine-learn'':6B ''make'':93C ''map/reduce'':103C ''monogodb/nosql'':35C ''onlin'':75C ''p'':13C,78C,151C ''pipelin'':165C ''play'':25C ''plug'':137C ''probabl'':179C ''public'':155C ''quit'':43C ''rang'':46C ''real'':29C ''recommend'':94C ''run'':101C ''scalabl'':11B ''seem'':142C ''shop'':76C ''slow'':102C ''stacks/workflows/pipelines'':5A ''store'':86C ''storm'':36C ''strong'':166C ''think'':68C,125C ''togeth'':26C ''tool'':48C,132C ''transact'':82C ''tri'':16C ''type'':52C ''understand'':18C,161C ''use'':31C,49C,128C,170C,182C ''user'':174C ''vistors/session'':81C ''want'':91C ''webapp'':74C ''wide'':45C ''world'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (431, 2, '2014-06-17 12:37:17.15', 1, NULL, NULL, '&lt;p&gt;A small collection of such links can be found at &lt;a href="http://lgylym.github.io/big-graph/dataset.html" rel="nofollow"&gt;here&lt;/a&gt;. Many of them are social graphs.&lt;/p&gt;
', NULL, NULL, '''/a'':20C ''/big-graph/dataset.html'':16C ''/p'':27C ''collect'':4C ''found'':10C ''graph'':26C ''href'':13C ''lgylym.github.io'':15C ''lgylym.github.io/big-graph/dataset.html'':14C ''link'':7C ''mani'':21C ''nofollow'':18C ''p'':1C ''rel'':17C ''small'':3C ''social'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (432, 2, '2014-06-17 12:41:58.9', 5, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;There is a very nice library of online machine learning algorithms from a group at NTU, called &lt;a href="http://libol.stevenhoi.org/" rel="nofollow"&gt;&lt;code&gt;LIBOL&lt;/code&gt;&lt;/a&gt;. This would be a very good place to start experimenting with the algorithms.&lt;br&gt;
The &lt;a href="http://libol.stevenhoi.org/LIBOL_manual.pdf" rel="nofollow"&gt;accompanying user guide&lt;/a&gt;, and &lt;a href="http://jmlr.org/papers/v15/hoi14a.html" rel="nofollow"&gt;associated JMLR publication&lt;/a&gt; are very nice introductions to the basic algorithms in this field.  &lt;/li&gt;
&lt;li&gt;Avrim Blum has an older and more technical &lt;a href="http://www.cs.cmu.edu/~avrim/Papers/survey.pdf" rel="nofollow"&gt;survey paper&lt;/a&gt; on online learning algorithms. &lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':28C,54C,66C,97C ''/code'':27C ''/li'':78C,102C ''/libol_manual.pdf'':48C ''/ol'':103C ''/papers/v15/hoi14a.html'':60C ''/~avrim/papers/survey.pdf'':92C ''accompani'':51C ''algorithm'':13C,41C,74C,101C ''associ'':63C ''avrim'':80C ''basic'':73C ''blum'':81C ''br'':42C ''call'':19C ''code'':25C ''experi'':38C ''field'':77C ''good'':34C ''group'':16C ''guid'':53C ''href'':21C,45C,57C,89C ''introduct'':70C ''jmlr'':64C ''jmlr.org'':59C ''jmlr.org/papers/v15/hoi14a.html'':58C ''learn'':12C,100C ''li'':2C,79C ''libol'':26C ''libol.stevenhoi.org'':22C,47C ''libol.stevenhoi.org/libol_manual.pdf'':46C ''librari'':8C ''machin'':11C ''nice'':7C,69C ''nofollow'':24C,50C,62C,94C ''ntu'':18C ''ol'':1C ''older'':84C ''onlin'':10C,99C ''paper'':96C ''place'':35C ''public'':65C ''rel'':23C,49C,61C,93C ''start'':37C ''survey'':95C ''technic'':87C ''user'':52C ''would'':30C ''www.cs.cmu.edu'':91C ''www.cs.cmu.edu/~avrim/papers/survey.pdf'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (440, 2, '2014-06-17 18:59:14.947', 11, NULL, NULL, '&lt;p&gt;The &lt;a href="https://code.google.com/p/word2vec/"&gt;word2vec algorithm&lt;/a&gt; may be a good way to retrieve more elements for a list of similar words. It is an unsupervised "deep learning" algorithm that has previously been demonstrated with Wikipedia-based training data (helper scripts are provided on the Google code page).&lt;/p&gt;

&lt;p&gt;There are currently &lt;a href="https://code.google.com/p/word2vec/"&gt;C&lt;/a&gt; and &lt;a href="http://radimrehurek.com/gensim/models/word2vec.html"&gt;Python&lt;/a&gt; implementations. This &lt;a href="http://radimrehurek.com/2014/02/word2vec-tutorial"&gt;tutorial&lt;/a&gt; by &lt;a href="http://radimrehurek.com/"&gt;Radim ÅehÅ¯Åek&lt;/a&gt;, the author of the &lt;a href="http://radimrehurek.com/gensim/"&gt;Gensim topic modelling library&lt;/a&gt;, is an excellent place to start.&lt;/p&gt;

&lt;p&gt;The &lt;a href="http://radimrehurek.com/2014/02/word2vec-tutorial#single"&gt;"single topic"&lt;/a&gt; demonstration on the tutorial is a good example of retreiving similar words to a single term (try searching on ''red'' or ''yellow''). It should be possible to extend this technique to find the words that have the greatest overall similarity to a set of input words.&lt;/p&gt;
', NULL, NULL, '''/2014/02/word2vec-tutorial'':79C ''/2014/02/word2vec-tutorial#single'':119C ''/a'':10C,64C,72C,81C,91C,105C,122C ''/gensim/'':100C ''/gensim/models/word2vec.html'':70C ''/p'':53C,112C,169C ''/p/word2vec/'':7C,62C ''algorithm'':9C,32C ''author'':93C ''base'':41C ''c'':63C ''code'':51C ''code.google.com'':6C,61C ''code.google.com/p/word2vec/'':5C,60C ''current'':57C ''data'':43C ''deep'':30C ''demonstr'':37C,123C ''ehå'':88C ''ek'':90C ''element'':19C ''exampl'':130C ''excel'':108C ''extend'':150C ''find'':154C ''gensim'':101C ''good'':14C,129C ''googl'':50C ''greatest'':160C ''helper'':44C ''href'':4C,59C,67C,76C,84C,97C,116C ''implement'':73C ''input'':167C ''learn'':31C ''librari'':104C ''list'':22C ''may'':11C ''model'':103C ''overal'':161C ''p'':1C,54C,113C ''page'':52C ''place'':109C ''possibl'':148C ''previous'':35C ''provid'':47C ''python'':71C ''radim'':86C ''radimrehurek.com'':69C,78C,85C,99C,118C ''radimrehurek.com/2014/02/word2vec-tutorial'':77C ''radimrehurek.com/2014/02/word2vec-tutorial#single'':117C ''radimrehurek.com/gensim/'':98C ''radimrehurek.com/gensim/models/word2vec.html'':68C ''red'':142C ''retreiv'':132C ''retriev'':17C ''script'':45C ''search'':140C ''set'':165C ''similar'':24C,133C,162C ''singl'':120C,137C ''start'':111C ''techniqu'':152C ''term'':138C ''topic'':102C,121C ''train'':42C ''tri'':139C ''tutori'':80C,126C ''unsupervis'':29C ''way'':15C ''wikipedia'':40C ''wikipedia-bas'':39C ''word'':25C,134C,156C,168C ''word2vec'':8C ''yellow'':144C ''å'':87C,89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (433, 2, '2014-06-17 13:31:30.503', 7, NULL, NULL, '&lt;p&gt;One of the most detailed and clear explanations of setting up a complex analytics pipeline is from the folks over at &lt;a href="http://www.twitch.tv/"&gt;Twitch&lt;/a&gt;.&lt;br&gt;
They give detailed motivations of each of the architecture choices for collection, transportation, coordination, processing, storage, and querying their data.&lt;br&gt;
Compelling reading! Find it &lt;a href="http://blog.twitch.tv/2014/04/twitch-data-analysis-part-1-the-twitch-statistics-pipeline/"&gt;here&lt;/a&gt; and &lt;a href="http://blog.twitch.tv/2014/04/twitch-data-analysis-part-2-architectural-decisions/"&gt;here&lt;/a&gt;. &lt;/p&gt;
', NULL, NULL, '''/2014/04/twitch-data-analysis-part-1-the-twitch-statistics-pipeline/'':58C ''/2014/04/twitch-data-analysis-part-2-architectural-decisions/'':66C ''/a'':27C,60C,68C ''/p'':69C ''analyt'':15C ''architectur'':37C ''blog.twitch.tv'':57C,65C ''blog.twitch.tv/2014/04/twitch-data-analysis-part-1-the-twitch-statistics-pipeline/'':56C ''blog.twitch.tv/2014/04/twitch-data-analysis-part-2-architectural-decisions/'':64C ''br'':28C,49C ''choic'':38C ''clear'':8C ''collect'':40C ''compel'':50C ''complex'':14C ''coordin'':42C ''data'':48C ''detail'':6C,31C ''explan'':9C ''find'':52C ''folk'':20C ''give'':30C ''href'':24C,55C,63C ''motiv'':32C ''one'':2C ''p'':1C ''pipelin'':16C ''process'':43C ''queri'':46C ''read'':51C ''set'':11C ''storag'':44C ''transport'':41C ''twitch'':26C ''www.twitch.tv'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (434, 2, '2014-06-17 13:44:43.807', 6, NULL, NULL, '&lt;p&gt;There is really no question here as you ask for pure conjectures but consider at least that &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;this week has &lt;a href="http://juliacon.org/"&gt;Julia Con&lt;/a&gt;, the first Julia conference&lt;/li&gt;
&lt;li&gt;you could search GitHub and/or the registered Julia modules&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':30C ''/li'':35C,46C ''/p'':19C ''/ul'':47C ''and/or'':41C ''ask'':10C ''con'':29C ''confer'':34C ''conjectur'':13C ''consid'':15C ''could'':38C ''first'':32C ''github'':40C ''href'':26C ''julia'':28C,33C,44C ''juliacon.org'':27C ''least'':17C ''li'':21C,36C ''modul'':45C ''p'':1C ''pure'':12C ''question'':6C ''realli'':4C ''regist'':43C ''search'':39C ''ul'':20C ''week'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (544, 2, '2014-06-23 17:56:32.917', 3, NULL, NULL, '&lt;p&gt;You could also try:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.quora.com" rel="nofollow"&gt;Quora&lt;/a&gt; &lt;/li&gt;
&lt;li&gt;&lt;a href="http://paper.li/MachineCoding/1370791453" rel="nofollow"&gt;Machine Learning Daily&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':15C,28C ''/li'':16C,29C ''/machinecoding/1370791453'':22C ''/p'':6C ''/ul'':30C ''also'':4C ''could'':3C ''daili'':27C ''href'':10C,19C ''learn'':26C ''li'':8C,17C ''machin'':25C ''nofollow'':13C,24C ''p'':1C ''paper.li'':21C ''paper.li/machinecoding/1370791453'':20C ''quora'':14C ''rel'':12C,23C ''tri'':5C ''ul'':7C ''www.quora.com'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6894, 1, '2015-08-25 12:37:54.423', 0, 39, 'For logistic regression, Predict.glm() outputs $p$ or $ln(p/1-p)$?', '&lt;p&gt;I''m performing a logistic regression in R.&lt;br&gt;
I wanted to know if the function &lt;em&gt;predict.glm&lt;/em&gt; outputs $p$ (probability of event occurring) or log odds i.e. $log(p/1-p)$?&lt;/p&gt;
', 'r  logistic-regression', NULL, '''/em'':33C ''/p'':46C ''br'':23C ''em'':31C ''event'':38C ''function'':30C ''i.e'':43C ''know'':27C ''ln'':8A ''log'':41C,44C ''logist'':2A,12B,19C ''logistic-regress'':11B ''m'':16C ''occur'':39C ''odd'':42C ''output'':5A,34C ''p'':6A,14C,35C ''p/1-p'':9A,45C ''perform'':17C ''predict.glm'':4A,32C ''probabl'':36C ''r'':10B,22C ''regress'':3A,13B,20C ''want'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (435, 1, '2014-06-17 13:46:06.367', 9, 165, 'Relational Data Mining without ILP', '&lt;p&gt;I have a huge dataset from a relational database which I need to create a classification model for. Normally for this situation I would use &lt;a href="http://en.wikipedia.org/wiki/Inductive_logic_programming"&gt;Inductive Logic Programming&lt;/a&gt; (ILP), but due to special circumstances I can''t do that.&lt;/p&gt;

&lt;p&gt;The other way to tackle this would be just to try to aggregate the values when I have a foreign relation. However, I have thousands of important and distinct rows for some nominal attributes (e.g.: A patient with a relation to several distinct drug prescriptions). So, I just can''t do that without creating a new attribute for each distinct row of that nominal attribute, and furthermore most of the new columns would have NULL values if I do that.&lt;/p&gt;

&lt;p&gt;Is there any non-ILP algorithm that allows me to data mine relational databases without resorting to techniques like pivoting, which would create thousands of new columns?&lt;/p&gt;
', 'data-mining  classification  relational-dbms', NULL, '''/a'':47C ''/p'':59C,141C,171C ''/wiki/inductive_logic_programming'':43C ''aggreg'':73C ''algorithm'':149C ''allow'':151C ''attribut'':94C,117C,125C ''circumst'':53C ''classif'':9B,29C ''column'':132C,170C ''creat'':27C,114C,166C ''data'':2A,7B,154C ''data-min'':6B ''databas'':22C,157C ''dataset'':18C ''dbms'':12B ''distinct'':89C,103C,120C ''drug'':104C ''due'':50C ''e.g'':95C ''en.wikipedia.org'':42C ''en.wikipedia.org/wiki/inductive_logic_programming'':41C ''foreign'':80C ''furthermor'':127C ''howev'':82C ''href'':40C ''huge'':17C ''ilp'':5A,48C,148C ''import'':87C ''induct'':44C ''like'':162C ''logic'':45C ''mine'':3A,8B,155C ''model'':30C ''need'':25C ''new'':116C,131C,169C ''nomin'':93C,124C ''non'':147C ''non-ilp'':146C ''normal'':32C ''null'':135C ''p'':13C,60C,142C ''patient'':97C ''pivot'':163C ''prescript'':105C ''program'':46C ''relat'':1A,11B,21C,81C,100C,156C ''relational-dbm'':10B ''resort'':159C ''row'':90C,121C ''sever'':102C ''situat'':35C ''special'':52C ''tackl'':65C ''techniqu'':161C ''thousand'':85C,167C ''tri'':71C ''use'':38C ''valu'':75C,136C ''way'':63C ''without'':4A,113C,158C ''would'':37C,67C,133C,165C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (436, 2, '2014-06-17 14:55:47.103', 9, NULL, NULL, '&lt;p&gt;I personally have used &lt;code&gt;Julia&lt;/code&gt; for a good number of professional projects, and while, as Dirk mentioned, this is purely conjecture, I can give some insights on where Julia really stands out. The question of whether or not these reasons will prove enough to have &lt;code&gt;Julia&lt;/code&gt; succeed as a language is anyone''s guess.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Distributed Systems&lt;/strong&gt;: Julia is the easiest language I''ve ever dealt with in terms of building distributed systems. This is becoming more and more relevant in computing, and will potentially become a deciding factor, but the question of whether or not &lt;code&gt;Julia&lt;/code&gt;''a relative ease decides this is up for debate&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;JIT Performance&lt;/strong&gt;: Julia''s JIT compiler is extremely fast, and while there is a lot of debate as to how accurate these benchmark numbers are, the &lt;a href="http://julialang.org/"&gt;Julia Website&lt;/a&gt; shows a series of relevant benchmarks&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Community&lt;/strong&gt;: This is an area where &lt;code&gt;Julia&lt;/code&gt; just isn''t quite there. The community that is there is generally supportive, but not quite as knowledgable as the &lt;code&gt;R&lt;/code&gt; or &lt;code&gt;python&lt;/code&gt; communities, which is a definite minus.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Extensibility&lt;/strong&gt;: This is another place where &lt;code&gt;Julia&lt;/code&gt; is currently lacking, there is a large disconnect between the implies code patterns that &lt;code&gt;Julia&lt;/code&gt; steers you toward and what it can actually support. The type system is currently overly bulky and difficult to use effectively.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Again, can''t say what this means for the future, but these are just a couple of relevant points when it comes to evaluating &lt;code&gt;Julia&lt;/code&gt; in my opinion.&lt;/p&gt;
', NULL, NULL, '''/a'':153C ''/code'':8C,51C,108C,172C,195C,199C,218C,235C,286C ''/li'':118C,160C,206C,257C ''/p'':60C,290C ''/strong'':66C,123C,164C,210C ''/ul'':258C ''accur'':142C ''actual'':243C ''anoth'':213C ''anyon'':57C ''area'':168C ''becom'':85C,95C ''benchmark'':144C,159C ''build'':80C ''bulki'':251C ''code'':6C,49C,106C,170C,193C,197C,216C,230C,233C,284C ''come'':281C ''communiti'':163C,179C,200C ''compil'':127C ''comput'':91C ''conjectur'':24C ''coupl'':275C ''current'':220C,249C ''dealt'':75C ''debat'':117C,138C ''decid'':97C,112C ''definit'':204C ''difficult'':253C ''dirk'':19C ''disconnect'':226C ''distribut'':64C,81C ''eas'':111C ''easiest'':70C ''effect'':256C ''enough'':46C ''evalu'':283C ''ever'':74C ''extens'':209C ''extrem'':129C ''factor'':98C ''fast'':130C ''futur'':269C ''general'':184C ''give'':27C ''good'':11C ''guess'':59C ''href'':149C ''impli'':229C ''insight'':29C ''isn'':174C ''jit'':121C,126C ''julia'':7C,32C,50C,67C,107C,124C,151C,171C,217C,234C,285C ''julialang.org'':150C ''knowledg'':190C ''lack'':221C ''languag'':55C,71C ''larg'':225C ''li'':62C,119C,161C,207C ''lot'':136C ''mean'':266C ''mention'':20C ''minus'':205C ''number'':12C,145C ''opinion'':289C ''over'':250C ''p'':1C,259C ''pattern'':231C ''perform'':122C ''person'':3C ''place'':214C ''point'':278C ''potenti'':94C ''profession'':14C ''project'':15C ''prove'':45C ''pure'':23C ''python'':198C ''question'':37C,101C ''quit'':176C,188C ''r'':194C ''realli'':33C ''reason'':43C ''relat'':110C ''relev'':89C,158C,277C ''say'':263C ''seri'':156C ''show'':154C ''stand'':34C ''steer'':236C ''strong'':63C,120C,162C,208C ''succeed'':52C ''support'':185C,244C ''system'':65C,82C,247C ''term'':78C ''toward'':238C ''type'':246C ''ul'':61C ''use'':5C,255C ''ve'':73C ''websit'':152C ''whether'':39C,103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (439, 2, '2014-06-17 18:42:55.423', 6, NULL, NULL, '&lt;p&gt;Halfway through reading your question, I realized Levenshtein Distance could be a nice solution to your problem.  Its good to see that you have a link to a paper on the topic, let me see if I can shed some light into what a Levenshtein solution would look like.&lt;/p&gt;

&lt;p&gt;Levenshtein distance is used across many industries for entity resolution, what makes it useful is that it is a measure of the difference between two sequences.  In the case of string comparison it is just sequences characters.&lt;/p&gt;

&lt;p&gt;This could help solve your problem by allowing you to provide one number that gives a measure of how similar the text of another field is.  &lt;/p&gt;

&lt;p&gt;Here is an example of a basic way of using Levenshtein with the data you gave:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/vpaAH.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;This provides an ok solution, the distance of 8 provides some indication of a relationship, and it is very PII compliant.  However, it is still not super useful, let see what happens if we do some text magic to take only the first initial of the first name and the full last name dropping anything in the middle:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/JlPu9.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;As you can see the Levenshtein distance of 0 is pretty indicative of a relationship.  Commonly data providers will combine a bunch of Levenshtein permutations of the first and last names with 1, 2, or all of the characters just to give some dimensionality as to how entities are related while still maintaining anonymity within the data.&lt;/p&gt;
', NULL, NULL, '''/jlpu9.png'':214C ''/p'':51C,90C,118C,136C,148C,208C,220C,279C ''/vpaah.png'':142C ''0'':230C ''1'':254C ''2'':255C ''8'':158C ''across'':57C ''allow'':99C ''alt'':143C,215C ''anonym'':275C ''anoth'':115C ''anyth'':204C ''basic'':126C ''bunch'':243C ''case'':81C ''charact'':89C,260C ''combin'':241C ''common'':237C ''comparison'':84C ''compliant'':170C ''could'':11C,93C ''data'':133C,238C,278C ''descript'':146C,218C ''differ'':75C ''dimension'':265C ''distanc'':10C,54C,156C,228C ''drop'':203C ''enter'':144C,216C ''entiti'':61C,269C ''exampl'':123C ''field'':116C ''first'':192C,196C,249C ''full'':200C ''gave'':135C ''give'':106C,263C ''good'':20C ''halfway'':2C ''happen'':181C ''help'':94C ''howev'':171C ''i.stack.imgur.com'':141C,213C ''i.stack.imgur.com/jlpu9.png'':212C ''i.stack.imgur.com/vpaah.png'':140C ''imag'':145C,217C ''img'':138C,210C ''indic'':161C,233C ''industri'':59C ''initi'':193C ''last'':201C,251C ''let'':34C,178C ''levenshtein'':9C,46C,53C,130C,227C,245C ''light'':42C ''like'':50C ''link'':27C ''look'':49C ''magic'':187C ''maintain'':274C ''make'':64C ''mani'':58C ''measur'':72C,108C ''middl'':207C ''name'':197C,202C,252C ''nice'':14C ''number'':104C ''ok'':153C ''one'':103C ''p'':1C,52C,91C,119C,137C,149C,209C,221C ''paper'':30C ''permut'':246C ''pii'':169C ''pretti'':232C ''problem'':18C,97C ''provid'':102C,151C,159C,239C ''question'':6C ''read'':4C ''realiz'':8C ''relat'':271C ''relationship'':164C,236C ''resolut'':62C ''see'':22C,36C,179C,225C ''sequenc'':78C,88C ''shed'':40C ''similar'':111C ''solut'':15C,47C,154C ''solv'':95C ''src'':139C,211C ''still'':174C,273C ''string'':83C ''super'':176C ''take'':189C ''text'':113C,186C ''topic'':33C ''two'':77C ''use'':56C,66C,129C,177C ''way'':127C ''within'':276C ''would'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5623, 2, '2015-04-27 07:33:24.683', 0, NULL, NULL, '&lt;p&gt;&lt;a href="http://www.kdnuggets.com/datasets/index.html" rel="nofollow"&gt;http://www.kdnuggets.com/datasets/index.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This should get you maximum datasets for your classification exercise.&lt;/p&gt;
', NULL, NULL, '''/datasets/index.html'':6C ''/datasets/index.html&lt;/a&gt;&lt;/p&gt;'':11C ''/p'':23C ''classif'':21C ''dataset'':18C ''exercis'':22C ''get'':15C ''href'':3C ''maximum'':17C ''nofollow'':8C ''p'':1C,12C ''rel'':7C ''www.kdnuggets.com'':5C,10C ''www.kdnuggets.com/datasets/index.html'':4C ''www.kdnuggets.com/datasets/index.html&lt;/a&gt;&lt;/p&gt;'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (441, 1, '2014-06-17 20:48:35.267', 23, 8265, 'What are the use cases for Apache Spark vs Hadoop', '&lt;p&gt;With Hadoop 2.0 and YARN Hadoop is supposedly no longer tied only map-reduce solutions. With that advancement, what are the use cases for Apache Spark vs Hadoop considering both sit atop of HDFS? I''ve read through the introduction documentation for Spark, but I''m curious if anyone has encountered a problem that was more efficient and easier to solve with Spark compared to Hadoop.&lt;/p&gt;
', 'hadoop  distributed  knowledge-base', NULL, '''/p'':84C ''2.0'':19C ''advanc'':35C ''anyon'':66C ''apach'':7A,42C ''atop'':49C ''base'':15B ''case'':5A,40C ''compar'':81C ''consid'':46C ''curious'':64C ''distribut'':12B ''document'':58C ''easier'':76C ''effici'':74C ''encount'':68C ''hadoop'':10A,11B,18C,22C,45C,83C ''hdfs'':51C ''introduct'':57C ''knowledg'':14B ''knowledge-bas'':13B ''longer'':26C ''m'':63C ''map'':30C ''map-reduc'':29C ''p'':16C ''problem'':70C ''read'':54C ''reduc'':31C ''sit'':48C ''solut'':32C ''solv'':78C ''spark'':8A,43C,60C,80C ''suppos'':24C ''tie'':27C ''use'':4A,39C ''ve'':53C ''vs'':9A,44C ''yarn'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (474, 1, '2014-06-19 09:42:28.16', 4, 93, 'Network structure: k-cliques vs. p-cliques', '&lt;p&gt;In network structure, what is the difference between &lt;strong&gt;k-cliques&lt;/strong&gt; and &lt;strong&gt;p-cliques&lt;/strong&gt;, can anyone give a brief explaination with examples? Thanks in advanced!&lt;/p&gt;

&lt;p&gt;============================
&lt;br&gt;EDIT:
I found an online &lt;a href="http://open.umich.edu/sites/default/files/SI508-F08-Week7-Lab6.ppt" rel="nofollow"&gt;ppt&lt;/a&gt; while I am googling, please take a look on &lt;strong&gt;p.37&lt;/strong&gt; and &lt;strong&gt;p.39&lt;/strong&gt;, can you comment on them?&lt;/p&gt;
', 'definitions  social-network-analysis  graphs', NULL, '''/a'':63C ''/p'':47C,85C ''/sites/default/files/si508-f08-week7-lab6.ppt'':59C ''/strong'':29C,35C,75C,79C ''advanc'':46C ''analysi'':14B ''anyon'':37C ''br'':49C ''brief'':40C ''cliqu'':5A,9A,28C,34C ''comment'':82C ''definit'':10B ''differ'':23C ''edit'':50C ''exampl'':43C ''explain'':41C ''found'':52C ''give'':38C ''googl'':67C ''graph'':15B ''href'':56C ''k'':4A,27C ''k-cliqu'':3A,26C ''look'':71C ''network'':1A,13B,18C ''nofollow'':61C ''onlin'':54C ''open.umich.edu'':58C ''open.umich.edu/sites/default/files/si508-f08-week7-lab6.ppt'':57C ''p'':8A,16C,33C,48C ''p-cliqu'':7A,32C ''p.37'':74C ''p.39'':78C ''pleas'':68C ''ppt'':62C ''rel'':60C ''social'':12B ''social-network-analysi'':11B ''strong'':25C,31C,73C,77C ''structur'':2A,19C ''take'':69C ''thank'':44C ''vs'':6A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (443, 2, '2014-06-17 22:28:36.07', 5, NULL, NULL, '&lt;p&gt;One option (depending on your dataset size) is to just provide edit distances (or other measures of similarity you''re using) as an additional dataset.&lt;/p&gt;

&lt;p&gt;E.g.:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Generate a set of unique names in the dataset&lt;/li&gt;
&lt;li&gt;For each name, calculate edit distance to each other name&lt;/li&gt;
&lt;li&gt;Generate an ID or irreversable hash for each name&lt;/li&gt;
&lt;li&gt;Replace names in the original dataset with this ID&lt;/li&gt;
&lt;li&gt;Provide matrix of edit distances between ID numbers as new dataset&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Though there''s still a lot that could be done to deanonymise the data from these even. &lt;/p&gt;

&lt;p&gt;E.g. if "Tim" is known to be the most popular name for a boy, frequency counting of IDs that closely match the known percentage of Tims across the population might give that away.  From there you could then look for names with an edit distance of 1, and conclude that those IDs might refer to "Tom" or "Jim" (when combined with other info).&lt;/p&gt;
', NULL, NULL, '''/li'':42C,54C,65C,76C,89C ''/ol'':90C ''/p'':27C,30C,109C,174C ''1'':157C ''across'':137C ''addit'':25C ''away'':143C ''boy'':124C ''calcul'':47C ''close'':130C ''combin'':170C ''conclud'':159C ''could'':99C,147C ''count'':126C ''data'':105C ''dataset'':7C,26C,41C,72C,88C ''deanonymis'':103C ''depend'':4C ''distanc'':14C,49C,82C,155C ''done'':101C ''e.g'':29C,111C ''edit'':13C,48C,81C,154C ''even'':108C ''frequenc'':125C ''generat'':33C,56C ''give'':141C ''hash'':61C ''id'':58C,75C,84C,128C,162C ''info'':173C ''irrevers'':60C ''jim'':168C ''known'':115C,133C ''li'':32C,43C,55C,66C,77C ''look'':149C ''lot'':97C ''match'':131C ''matrix'':79C ''measur'':17C ''might'':140C,163C ''name'':38C,46C,53C,64C,68C,121C,151C ''new'':87C ''number'':85C ''ol'':31C ''one'':2C ''option'':3C ''origin'':71C ''p'':1C,28C,91C,110C ''percentag'':134C ''popul'':139C ''popular'':120C ''provid'':12C,78C ''re'':21C ''refer'':164C ''replac'':67C ''set'':35C ''similar'':19C ''size'':8C ''still'':95C ''though'':92C ''tim'':113C,136C ''tom'':166C ''uniqu'':37C ''use'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (444, 2, '2014-06-17 22:29:36.72', 3, NULL, NULL, '&lt;p&gt;A classic book is by B. Efron who created the technique:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Bradley Efron; Robert Tibshirani (1994). An Introduction to the Bootstrap. Chapman &amp;amp; Hall/CRC. ISBN 978-0-412-04231-7.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''-0'':31C ''-04231'':33C ''-412'':32C ''-7'':34C ''/li'':35C ''/p'':13C ''/ul'':36C ''1994'':20C ''978'':30C ''amp'':27C ''b'':7C ''book'':4C ''bootstrap'':25C ''bradley'':16C ''chapman'':26C ''classic'':3C ''creat'':10C ''efron'':8C,17C ''hall/crc'':28C ''introduct'':22C ''isbn'':29C ''li'':15C ''p'':1C ''robert'':18C ''techniqu'':12C ''tibshirani'':19C ''ul'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (445, 2, '2014-06-17 23:30:45.897', 5, NULL, NULL, '&lt;p&gt;Is your Masters in Computer Science?  Statistics?&lt;/p&gt;

&lt;p&gt;Is ''data science'' going to be at the center of your thesis?  Or a side topic?&lt;/p&gt;

&lt;p&gt;I''ll assume your in Statistics and that you want to focus your thesis on a ''data science'' problem.  If so, then I''m going to go against the grain and suggest that you &lt;em&gt;should not&lt;/em&gt; start with a data set or an ML method.  Instead, you should seek an interesting research problem that''s poorly understood or where ML methods have not yet been proven successful, or where there are many competing ML methods but none seem better than others.&lt;/p&gt;

&lt;p&gt;Consider this data source: &lt;a href="http://snap.stanford.edu/data/"&gt;Stanford Large Network Dataset Collection&lt;/a&gt;.  While you &lt;em&gt;could&lt;/em&gt; pick one of these data sets, make up a problem statement, and then run some list of ML methods, that approach really doesn''t tell you very much about what &lt;em&gt;data science&lt;/em&gt; is all about, and in my opinion doesn''t lead to a very good Masters thesis.&lt;/p&gt;

&lt;p&gt;Instead, you might do this: look for all the research papers that use ML on some specific category -- e.g. Collaboration networks (a.k.a. co-authorship). As you read each paper, try to find out what they &lt;em&gt;were&lt;/em&gt; able to accomplish with each ML method and what they weren''t able to address.  Especially look for their suggestions for "future research".&lt;/p&gt;

&lt;p&gt;Maybe they all use the same method, but never tried competing ML methods.  Or maybe they don''t adequately validate their results, or maybe there data sets are small, or maybe their research questions and hypothesis were simplistic or limited.&lt;/p&gt;

&lt;p&gt;Most important: try to find out where this line of research is going.  Why are they even bothering to do this?  What is significant about it?  Where and why are they encountering difficulties?&lt;/p&gt;
', NULL, NULL, '''/a'':128C ''/data/'':122C ''/em'':66C,133C,167C,224C ''/p'':9C,27C,112C,184C,248C,290C,325C ''a.k.a'':207C ''abl'':225C,237C ''accomplish'':227C ''address'':239C ''adequ'':268C ''approach'':154C ''assum'':31C ''authorship'':210C ''better'':109C ''bother'':309C ''categori'':203C ''center'':19C ''co'':209C ''co-authorship'':208C ''collabor'':205C ''collect'':127C ''compet'':103C,260C ''comput'':6C ''consid'':114C ''could'':132C ''data'':12C,45C,70C,116C,138C,165C,275C ''dataset'':126C ''difficulti'':324C ''doesn'':156C,175C ''e.g'':204C ''em'':63C,131C,164C,222C ''encount'':323C ''especi'':240C ''even'':308C ''find'':218C,296C ''focus'':40C ''futur'':246C ''go'':14C,53C,55C,304C ''good'':181C ''grain'':58C ''href'':119C ''hypothesi'':285C ''import'':293C ''instead'':76C,186C ''interest'':81C ''larg'':124C ''lead'':177C ''limit'':289C ''line'':300C ''list'':149C ''ll'':30C ''look'':191C,241C ''m'':52C ''make'':140C ''mani'':102C ''master'':4C,182C ''mayb'':250C,264C,273C,280C ''method'':75C,91C,105C,152C,231C,256C,262C ''might'':188C ''ml'':74C,90C,104C,151C,199C,230C,261C ''much'':161C ''network'':125C,206C ''never'':258C ''none'':107C ''one'':135C ''opinion'':174C ''other'':111C ''p'':1C,10C,28C,113C,185C,249C,291C ''paper'':196C,215C ''pick'':134C ''poor'':86C ''problem'':47C,83C,143C ''proven'':96C ''question'':283C ''read'':213C ''realli'':155C ''research'':82C,195C,247C,282C,302C ''result'':271C ''run'':147C ''scienc'':7C,13C,46C,166C ''seek'':79C ''seem'':108C ''set'':71C,139C,276C ''side'':25C ''signific'':315C ''simplist'':287C ''small'':278C ''snap.stanford.edu'':121C ''snap.stanford.edu/data/'':120C ''sourc'':117C ''specif'':202C ''stanford'':123C ''start'':67C ''statement'':144C ''statist'':8C,34C ''success'':97C ''suggest'':60C,244C ''tell'':158C ''thesi'':22C,42C,183C ''topic'':26C ''tri'':216C,259C,294C ''understood'':87C ''use'':198C,253C ''valid'':269C ''want'':38C ''weren'':235C ''yet'':94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (580, 2, '2014-06-25 14:30:10.597', 1, NULL, NULL, '&lt;p&gt;I have read some about it and I had the following blog in mind:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://fellgernon.tumblr.com/post/46117939292/predicting-who-will-win-a-nfl-match-at-half-time#.UtehM7TWtQg" rel="nofollow"&gt;http://fellgernon.tumblr.com/post/46117939292/predicting-who-will-win-a-nfl-match-at-half-time#.UtehM7TWtQg&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This blog deals with the prediction of a NFL match after the half time is already over. The prediction is 80% accurate with simple GLM model.&lt;/p&gt;

&lt;p&gt;I do not know if that is suitable for soccer.&lt;/p&gt;
', NULL, NULL, '''/p'':16C,55C,67C ''/post/46117939292/predicting-who-will-win-a-nfl-match-at-half-time#.utehm7twtqg'':22C ''/post/46117939292/predicting-who-will-win-a-nfl-match-at-half-time#.utehm7twtqg&lt;/a&gt;&lt;/p&gt;'':27C ''80'':49C ''accur'':50C ''alreadi'':44C ''blog'':13C,30C ''deal'':31C ''fellgernon.tumblr.com'':21C,26C ''fellgernon.tumblr.com/post/46117939292/predicting-who-will-win-a-nfl-match-at-half-time#.utehm7twtqg'':20C ''fellgernon.tumblr.com/post/46117939292/predicting-who-will-win-a-nfl-match-at-half-time#.utehm7twtqg&lt;/a&gt;&lt;/p&gt;'':25C ''follow'':12C ''glm'':53C ''half'':41C ''href'':19C ''know'':60C ''match'':38C ''mind'':15C ''model'':54C ''nfl'':37C ''nofollow'':24C ''p'':1C,17C,28C,56C ''predict'':34C,47C ''read'':4C ''rel'':23C ''simpl'':52C ''soccer'':66C ''suitabl'':64C ''time'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (447, 1, '2014-06-18 03:19:07.557', 8, 129, 'Learning signal encoding', '&lt;p&gt;I have a large number of samples which represent Manchester encoded bit streams as audio signals. The frequency at which they are encoded is the primary frequency component when it is high, and there is a consistent amount of white noise in the background.&lt;/p&gt;

&lt;p&gt;I have manually decoded these streams, but I was wondering if I could use some sort of machine learning technique to learn the encoding schemes. This would save a great deal of time manually recognizing these schemes. The difficulty is that different signals are encoded differently.&lt;/p&gt;

&lt;p&gt;Is it possible to build a model which can learn to decode more than one encoding scheme? How robust would such a model be, and what sort of techniques would I want to employ? &lt;a href="http://en.wikipedia.org/wiki/Independent_component_analysis" rel="nofollow"&gt;Independent Component Analysis&lt;/a&gt; (ICA) seems like could be useful for isolating the frequency I care about, but how would I learn the encoding scheme?&lt;/p&gt;
', 'machine-learning  data-mining  scalability  algorithms  feature-selection', NULL, '''/a'':154C ''/p'':60C,108C,176C ''/wiki/independent_component_analysis'':148C ''algorithm'':11B ''amount'':53C ''analysi'':153C ''audio'':30C ''background'':59C ''bit'':27C ''build'':114C ''care'':166C ''compon'':43C,152C ''consist'':52C ''could'':74C,158C ''data'':8B ''data-min'':7B ''deal'':92C ''decod'':65C,121C ''differ'':103C,107C ''difficulti'':100C ''employ'':143C ''en.wikipedia.org'':147C ''en.wikipedia.org/wiki/independent_component_analysis'':146C ''encod'':3A,26C,38C,85C,106C,125C,174C ''featur'':13B ''feature-select'':12B ''frequenc'':33C,42C,164C ''great'':91C ''high'':47C ''href'':145C ''ica'':155C ''independ'':151C ''isol'':162C ''larg'':19C ''learn'':1A,6B,80C,83C,119C,172C ''like'':157C ''machin'':5B,79C ''machine-learn'':4B ''manchest'':25C ''manual'':64C,95C ''mine'':9B ''model'':116C,132C ''nofollow'':150C ''nois'':56C ''number'':20C ''one'':124C ''p'':15C,61C,109C ''possibl'':112C ''primari'':41C ''recogn'':96C ''rel'':149C ''repres'':24C ''robust'':128C ''sampl'':22C ''save'':89C ''scalabl'':10B ''scheme'':86C,98C,126C,175C ''seem'':156C ''select'':14B ''signal'':2A,31C,104C ''sort'':77C,136C ''stream'':28C,67C ''techniqu'':81C,138C ''time'':94C ''use'':75C,160C ''want'':141C ''white'':55C ''wonder'':71C ''would'':88C,129C,139C,170C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10277, 1, '2016-02-17 09:49:45.097', 1, 17, 'Predict quantitative response variable from quantitative and qualitative variables', '&lt;p&gt;I have to predict a quantitative response variable Y from p quantitative and q qualitative variables. There''s around 100,000 rows in the data frame. What predictive methods can I use for that purpose? &lt;/p&gt;
', 'data-mining', NULL, '''/p'':49C ''000'':34C ''100'':33C ''around'':32C ''data'':11B,38C ''data-min'':10B ''frame'':39C ''method'':42C ''mine'':12B ''p'':13C,24C ''predict'':1A,17C,41C ''purpos'':48C ''q'':27C ''qualit'':8A,28C ''quantit'':2A,6A,19C,25C ''respons'':3A,20C ''row'':35C ''use'':45C ''variabl'':4A,9A,21C,29C ''y'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9134, 1, '2015-11-30 16:08:12.507', 3, 131, 'Gradient boosting algorithm example', '&lt;p&gt;I''m trying to fully understand the gradient boosting (GB) method. I''ve read some wiki pages and papers about it, but it would really help me to see a full simple example carried out step-by-step. Can anyone provide one for me, or give me a link to such an example? Straightforward source code without tricky optimizations will also meet my needs.&lt;/p&gt;
', 'machine-learning  classification  algorithms  xgboost', NULL, '''/p'':77C ''algorithm'':3A,9B ''also'':73C ''anyon'':52C ''boost'':2A,20C ''carri'':45C ''classif'':8B ''code'':68C ''exampl'':4A,44C,65C ''full'':42C ''fulli'':16C ''gb'':21C ''give'':58C ''gradient'':1A,19C ''help'':37C ''learn'':7B ''link'':61C ''m'':13C ''machin'':6B ''machine-learn'':5B ''meet'':74C ''method'':22C ''need'':76C ''one'':54C ''optim'':71C ''p'':11C ''page'':28C ''paper'':30C ''provid'':53C ''read'':25C ''realli'':36C ''see'':40C ''simpl'':43C ''sourc'':67C ''step'':48C,50C ''step-by-step'':47C ''straightforward'':66C ''tri'':14C ''tricki'':70C ''understand'':17C ''ve'':24C ''wiki'':27C ''without'':69C ''would'':35C ''xgboost'':10B');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (449, 2, '2014-06-18 07:27:22.677', 32, NULL, NULL, '&lt;p&gt;Hadoop means HDFS, YARN, MapReduce, and a lot of other things. Do you mean Spark vs &lt;em&gt;MapReduce&lt;/em&gt;? Because Spark runs on/with Hadoop, which is rather the point.&lt;/p&gt;

&lt;p&gt;The primary reason to use Spark is for speed, and this comes from the fact that its execution can keep data in memory between stages rather than always persist back to HDFS after a Map or Reduce. This advantage is very pronounced for iterative computations, which have tens of stages each of which is touching the same data. This is where things might be "100x" faster. For simple, one-pass ETL-like jobs for which MapReduce was designed, it''s not in general faster.&lt;/p&gt;

&lt;p&gt;Another reason to use Spark is its nicer high-level language compared to MapReduce. It provides a functional programming-like view that mimics Scala, which is far nicer than writing MapReduce code. (Although you have to either use Scala, or adopt the slightly-less-developed Java or Python APIs for Spark). &lt;a href="http://crunch.apache.org"&gt;Crunch&lt;/a&gt; and &lt;a href="http://cascading.org"&gt;Cascading&lt;/a&gt; already provide a similar abstraction on top of MapReduce, but this is still an area where Spark is nice.&lt;/p&gt;

&lt;p&gt;Finally Spark has as-yet-young but promising subprojects for ML, graph analysis, and streaming, which expose a similar, coherent API. With MapReduce, you would have to turn to several different other projects for this (Mahout, Giraph, Storm). It''s nice to have it in one package, albeit not yet ''baked''.&lt;/p&gt;

&lt;p&gt;Why would you not use Spark? &lt;a href="https://www.quora.com/Apache-Spark/Assuming-you-have-a-system-with-both-Hadoop-and-Spark-installed-say-under-Yarn-is-there-any-reason-to-use-Hadoop-map-reduce-instead-of-the-equivalent-Spark-commands"&gt;paraphrasing&lt;/a&gt; myself:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Spark is primarily Scala, with ported Java APIs; MapReduce might be friendlier and more native for Java-based developers&lt;/li&gt;
&lt;li&gt;There is more MapReduce expertise out there now than Spark&lt;/li&gt;
&lt;li&gt;For the data-parallel, one-pass, ETL-like jobs MapReduce was designed for, MapReduce is lighter-weight compared to the Spark equivalent&lt;/li&gt;
&lt;li&gt;Spark is fairly mature, and so is YARN now, but Spark-on-YARN is still pretty new. The two may not be optimally integrated yet. For example until recently I don''t think Spark could ask YARN for allocations based on number of cores? That is: MapReduce might be easier to understand, manage and tune&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':179C,185C,273C ''/apache-spark/assuming-you-have-a-system-with-both-hadoop-and-spark-installed-say-under-yarn-is-there-any-reason-to-use-hadoop-map-reduce-instead-of-the-equivalent-spark-commands'':271C ''/em'':20C ''/li'':298C,310C,338C,396C ''/p'':31C,119C,205C,259C,275C ''/ul'':397C ''100x'':97C ''abstract'':190C ''adopt'':163C ''advantag'':71C ''albeit'':255C ''alloc'':379C ''alreadi'':186C ''although'':155C ''alway'':60C ''analysi'':220C ''anoth'':121C ''api'':172C,228C,285C ''area'':200C ''as-yet-young'':210C ''ask'':376C ''back'':62C ''bake'':258C ''base'':296C,380C ''cascad'':184C ''cascading.org'':183C ''code'':154C ''coher'':227C ''come'':44C ''compar'':133C,333C ''comput'':77C ''core'':384C ''could'':375C ''crunch'':178C ''crunch.apache.org'':177C ''data'':53C,90C,315C ''data-parallel'':314C ''design'':112C,326C ''develop'':168C,297C ''differ'':238C ''easier'':390C ''either'':159C ''em'':18C ''equival'':337C ''etl'':105C,321C ''etl-lik'':104C,320C ''exampl'':367C ''execut'':50C ''expertis'':304C ''expos'':224C ''fact'':47C ''fair'':342C ''far'':149C ''faster'':98C,118C ''final'':207C ''friendlier'':289C ''function'':139C ''general'':117C ''giraph'':244C ''graph'':219C ''hadoop'':2C,25C ''hdfs'':4C,64C ''high'':130C ''high-level'':129C ''href'':176C,182C,268C ''integr'':364C ''iter'':76C ''java'':169C,284C,295C ''java-bas'':294C ''job'':107C,323C ''keep'':52C ''languag'':132C ''less'':167C ''level'':131C ''li'':277C,299C,311C,339C ''lighter'':331C ''lighter-weight'':330C ''like'':106C,142C,322C ''lot'':9C ''mahout'':243C ''manag'':393C ''map'':67C ''mapreduc'':6C,19C,110C,135C,153C,194C,230C,286C,303C,324C,328C,387C ''matur'':343C ''may'':360C ''mean'':3C,15C ''memori'':55C ''might'':95C,287C,388C ''mimic'':145C ''ml'':218C ''nativ'':292C ''new'':357C ''nice'':204C,248C ''nicer'':128C,150C ''number'':382C ''on/with'':24C ''one'':102C,253C,318C ''one-pass'':101C,317C ''optim'':363C ''p'':1C,32C,120C,206C,260C ''packag'':254C ''parallel'':316C ''paraphras'':272C ''pass'':103C,319C ''persist'':61C ''point'':30C ''port'':283C ''pretti'':356C ''primari'':34C ''primarili'':280C ''program'':141C ''programming-lik'':140C ''project'':240C ''promis'':215C ''pronounc'':74C ''provid'':137C,187C ''python'':171C ''rather'':28C,58C ''reason'':35C,122C ''recent'':369C ''reduc'':69C ''run'':23C ''scala'':146C,161C,281C ''sever'':237C ''similar'':189C,226C ''simpl'':100C ''slight'':166C ''slightly-less-develop'':165C ''spark'':16C,22C,38C,125C,174C,202C,208C,266C,278C,309C,336C,340C,351C,374C ''spark-on-yarn'':350C ''speed'':41C ''stage'':57C,82C ''still'':198C,355C ''storm'':245C ''stream'':222C ''subproject'':216C ''ten'':80C ''thing'':12C,94C ''think'':373C ''top'':192C ''touch'':87C ''tune'':395C ''turn'':235C ''two'':359C ''ul'':276C ''understand'':392C ''use'':37C,124C,160C,265C ''view'':143C ''vs'':17C ''weight'':332C ''would'':232C,262C ''write'':152C ''www.quora.com'':270C ''www.quora.com/apache-spark/assuming-you-have-a-system-with-both-hadoop-and-spark-installed-say-under-yarn-is-there-any-reason-to-use-hadoop-map-reduce-instead-of-the-equivalent-spark-commands'':269C ''yarn'':5C,347C,353C,377C ''yet'':212C,257C,365C ''young'':213C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (475, 2, '2014-06-19 09:50:53.657', 1, NULL, NULL, '&lt;p&gt;As you discuss the definition of regression is predicting a continuous variable. &lt;a href="http://en.wikipedia.org/wiki/Logistic_regression" rel="nofollow"&gt;Logistic regression&lt;/a&gt; is a binary classifier.  Logistic regression is the application of a logit function on the output of a usual regression approach. Logit function turns (-inf,+inf) to [0,1]. I think it is just for historical reasons that keeps that name. &lt;/p&gt;

&lt;p&gt;Saying something like "I did some regression to classify images. In particular I used logistic regression." is wrong.&lt;/p&gt;
', NULL, NULL, '''/a'':23C ''/p'':65C,85C ''/wiki/logistic_regression'':18C ''0'':51C ''1'':52C ''applic'':32C ''approach'':44C ''binari'':26C ''classifi'':27C,75C ''continu'':12C ''definit'':6C ''discuss'':4C ''en.wikipedia.org'':17C ''en.wikipedia.org/wiki/logistic_regression'':16C ''function'':36C,46C ''histor'':59C ''href'':15C ''imag'':76C ''inf'':48C,49C ''keep'':62C ''like'':69C ''logist'':21C,28C,81C ''logit'':35C,45C ''name'':64C ''nofollow'':20C ''output'':39C ''p'':1C,66C ''particular'':78C ''predict'':10C ''reason'':60C ''regress'':8C,22C,29C,43C,73C,82C ''rel'':19C ''say'':67C ''someth'':68C ''think'':54C ''turn'':47C ''use'':80C ''usual'':42C ''variabl'':13C ''wrong'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2354, 2, '2014-10-27 04:15:00.747', 5, NULL, NULL, '&lt;p&gt;I think your best bet would be &lt;strong&gt;&lt;a href="http://pythonhosted.org/rosetta/"&gt;rosetta&lt;/a&gt;&lt;/strong&gt;. I''m finding it extremely useful and easy. Check its &lt;a href="http://pythonhosted.org/rosetta/#module-rosetta.parallel.pandas_easy"&gt;pandas methods&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;You can get it by &lt;strong&gt;pip&lt;/strong&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':16C,35C ''/p'':36C,46C ''/rosetta/'':14C ''/rosetta/#module-rosetta.parallel.pandas_easy'':32C ''/strong'':17C,45C ''best'':5C ''bet'':6C ''check'':26C ''easi'':25C ''extrem'':22C ''find'':20C ''get'':40C ''href'':11C,29C ''m'':19C ''method'':34C ''p'':1C,37C ''panda'':33C ''pip'':44C ''pythonhosted.org'':13C,31C ''pythonhosted.org/rosetta/'':12C ''pythonhosted.org/rosetta/#module-rosetta.parallel.pandas_easy'':30C ''rosetta'':15C ''strong'':9C,43C ''think'':3C ''use'':23C ''would'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (450, 2, '2014-06-18 14:15:37.957', 5, NULL, NULL, '&lt;p&gt;I''m not quite sure, but maybe  locality-sensitive hashing is a good solution. It does hashing of input data (in your case - names), so original strings would be preserved. On the other side, the main idea of LSH is to maximize hashes likelihood for similar items. There are a lot of different LSH-implementations. I tried &lt;a href="http://spdp.di.unimi.it/papers/pdcs04.pdf"&gt;Nilsimsa-hash&lt;/a&gt; for comparing tweet texts, and it worked quite well. But I''m not sure, how well it will work in case of short strings (names) - this issue require testing. I tried your examples, and here is the result (name A, name B, "distance" - maximum is 120):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1. AMELIA BEDELIA  - CHRISTOPH BAUER - 107
2. AMELIA BEDELIA  - C J BAUER       - 82
3. AMELIA BEDELIA  - FRANZ HELLER    - 91
4. CHRISTOPH BAUER - C J BAUER       - 81
5. CHRISTOPH BAUER - FRANZ HELLER    - 98
6. C J BAUER       - FRANZ HELLER    - 83
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As you see, CHRISTOPH BAUER and C J BAUER turned up to be the closest pair. But difference is not significant.
And just for example - hash representation of these names:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;AMELIA BEDELIA  6b208299602b5000c3005a048122a43a828020889042240005011c1880864502
CHRISTOPH BAUER 22226448000ab10102e2860b52062487ff0000928e0822ee106028016cc01237
C J BAUER       2282204100961060048050004400240006032400148000802000a80130402002
FRANZ HELLER    58002002400880080b49172044020008030002442631e004009195020ad01158
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':69C ''/code'':158C,207C ''/p'':116C,191C ''/papers/pdcs04.pdf'':65C ''/pre'':159C,208C ''1'':119C ''107'':124C ''120'':115C ''2'':125C ''22226448000ab10102e2860b52062487ff0000928e0822ee106028016cc01237'':199C ''2282204100961060048050004400240006032400148000802000a80130402002'':203C ''3'':132C ''4'':138C ''5'':145C ''58002002400880080b49172044020008030002442631e004009195020ad01158'':206C ''6'':151C ''6b208299602b5000c3005a048122a43a828020889042240005011c1880864502'':196C ''81'':144C ''82'':131C ''83'':157C ''91'':137C ''98'':150C ''amelia'':120C,126C,133C,194C ''b'':111C ''bauer'':123C,130C,140C,143C,147C,154C,165C,169C,198C,202C ''bedelia'':121C,127C,134C,195C ''c'':128C,141C,152C,167C,200C ''case'':25C,90C ''christoph'':122C,139C,146C,164C,197C ''closest'':175C ''code'':118C,193C ''compar'':71C ''data'':22C ''differ'':55C,178C ''distanc'':112C ''exampl'':102C,185C ''franz'':135C,148C,155C,204C ''good'':15C ''hash'':12C,19C,45C,68C,186C ''heller'':136C,149C,156C,205C ''href'':62C ''idea'':39C ''implement'':58C ''input'':21C ''issu'':96C ''item'':49C ''j'':129C,142C,153C,168C,201C ''likelihood'':46C ''local'':10C ''locality-sensit'':9C ''lot'':53C ''lsh'':41C,57C ''lsh-implement'':56C ''m'':3C,81C ''main'':38C ''maxim'':44C ''maximum'':113C ''mayb'':8C ''name'':26C,94C,108C,110C,190C ''nilsimsa'':67C ''nilsimsa-hash'':66C ''origin'':28C ''p'':1C,160C ''pair'':176C ''pre'':117C,192C ''preserv'':32C ''quit'':5C,77C ''represent'':187C ''requir'':97C ''result'':107C ''see'':163C ''sensit'':11C ''short'':92C ''side'':36C ''signific'':181C ''similar'':48C ''solut'':16C ''spdp.di.unimi.it'':64C ''spdp.di.unimi.it/papers/pdcs04.pdf'':63C ''string'':29C,93C ''sure'':6C,83C ''test'':98C ''text'':73C ''tri'':60C,100C ''turn'':170C ''tweet'':72C ''well'':78C,85C ''work'':76C,88C ''would'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (451, 1, '2014-06-18 14:36:07.98', 5, 371, 'How to speedup message passing between computing nodes', '&lt;p&gt;I''m developing a distributed application, and as it''s been designed, there''ll be a great load of communication during the processing. Since the communication is already as much &lt;em&gt;spread&lt;/em&gt; along the entire process as possible, I''m wondering if there any standard solutions to improve the performance of the message passing layer of my application.&lt;/p&gt;

&lt;p&gt;What changes/improvements could I apply to my code to reduce the time spent sending messages? For what it''s worth, I''m communicating up to 10GB between 9 computing nodes, and the framework I''m using is implemented with OpenMPI.&lt;/p&gt;
', 'efficiency  distributed  performance', NULL, '''/em'':45C ''/p'':72C,114C ''10gb'':99C ''9'':101C ''along'':46C ''alreadi'':40C ''appli'':78C ''applic'':18C,71C ''changes/improvements'':75C ''code'':81C ''communic'':32C,38C,96C ''comput'':7A,102C ''could'':76C ''design'':24C ''develop'':15C ''distribut'':10B,17C ''effici'':9B ''em'':43C ''entir'':48C ''framework'':106C ''great'':29C ''implement'':111C ''improv'':61C ''layer'':68C ''ll'':26C ''load'':30C ''m'':14C,53C,95C,108C ''messag'':4A,66C,88C ''much'':42C ''node'':8A,103C ''openmpi'':113C ''p'':12C,73C ''pass'':5A,67C ''perform'':11B,63C ''possibl'':51C ''process'':35C,49C ''reduc'':83C ''send'':87C ''sinc'':36C ''solut'':59C ''speedup'':3A ''spent'':86C ''spread'':44C ''standard'':58C ''time'':85C ''use'':109C ''wonder'':54C ''worth'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (470, 2, '2014-06-19 06:09:43.963', 8, NULL, NULL, '&lt;p&gt;Just an idea - your data is highly seasonal: daily and weekly cycles are quite perceptible. So first of all, try to decompose your variables (gas and electricity consumption, temperature, and solar radiation). &lt;a href="http://www.r-bloggers.com/time-series-decomposition/"&gt;Here is&lt;/a&gt; a nice tutorial on time series decomposition for R. &lt;/p&gt;

&lt;p&gt;After obtaining trend and seasonal components, the most interesting part begins. It''s just an assumption, but I think, gas and electricity consumption variables would be quite predictable by means of time series analysis (e.g., &lt;a href="http://statsmodels.sourceforge.net/devel/examples/notebooks/generated/tsa_arma_0.html"&gt;ARIMA model&lt;/a&gt;). From my point of view, the most exiting part here is to try to predict residuals after decomposition, using available data (temperature anomalies, solar radiation, wind speed). I suppose, these residuals would be outliers, you are looking for. Hope, you will find this useful.&lt;/p&gt;
', NULL, NULL, '''/a'':41C,95C ''/devel/examples/notebooks/generated/tsa_arma_0.html'':92C ''/p'':51C,140C ''/time-series-decomposition/'':38C ''analysi'':86C ''anomali'':118C ''arima'':93C ''assumpt'':68C ''avail'':115C ''begin'':63C ''compon'':58C ''consumpt'':29C,75C ''cycl'':13C ''daili'':10C ''data'':6C,116C ''decompos'':23C ''decomposit'':48C,113C ''e.g'':87C ''electr'':28C,74C ''exit'':103C ''find'':137C ''first'':18C ''gas'':26C,72C ''high'':8C ''hope'':134C ''href'':35C,89C ''idea'':4C ''interest'':61C ''look'':132C ''mean'':82C ''model'':94C ''nice'':43C ''obtain'':54C ''outlier'':129C ''p'':1C,52C ''part'':62C,104C ''percept'':16C ''point'':98C ''predict'':80C,110C ''quit'':15C,79C ''r'':50C ''radiat'':33C,120C ''residu'':111C,126C ''season'':9C,57C ''seri'':47C,85C ''solar'':32C,119C ''speed'':122C ''statsmodels.sourceforge.net'':91C ''statsmodels.sourceforge.net/devel/examples/notebooks/generated/tsa_arma_0.html'':90C ''suppos'':124C ''temperatur'':30C,117C ''think'':71C ''time'':46C,84C ''trend'':55C ''tri'':21C,108C ''tutori'':44C ''use'':114C,139C ''variabl'':25C,76C ''view'':100C ''week'':12C ''wind'':121C ''would'':77C,127C ''www.r-bloggers.com'':37C ''www.r-bloggers.com/time-series-decomposition/'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (494, 1, '2014-06-19 19:33:40.62', 6, 129, 'Techniques for trend extraction from unbalanced panel data', '&lt;p&gt;My data set is formatted like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;User-id | Threat_score
aaa       45
bbb       32
ccc       20
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The list contains the top 100 users with the highest threat scores. I generate such a list monthly and store each month''s list in its own file.&lt;/p&gt;

&lt;p&gt;There are three things I would like to get from this data:&lt;/p&gt;

&lt;ul&gt;1. Users who are consistently showing up in this list&lt;/ul&gt;

&lt;ul&gt;2. Users who are consistently showing up in this list with high threat scores&lt;/ul&gt;

&lt;ul&gt;3. Users whose threat scores are increasing very quickly&lt;/ul&gt;

&lt;p&gt;I am thinking a visual summary would be nice; each month (somehow) decide which users I want to plot on a graph of historic threat scores.&lt;/p&gt;

&lt;p&gt;Are there any known visualization techniques that deal with similar requirements?&lt;/p&gt;

&lt;p&gt;How should I be transforming my current data to achieve what I am looking for?&lt;/p&gt;
', 'visualization  dataset  graphs', NULL, '''/code'':34C ''/p'':20C,65C,79C,146C,159C,176C ''/pre'':35C ''/ul'':91C,107C,118C ''1'':81C ''100'':42C ''2'':93C ''20'':33C ''3'':109C ''32'':31C ''45'':29C ''aaa'':28C ''achiev'':170C ''bbb'':30C ''ccc'':32C ''code'':22C ''consist'':85C,97C ''contain'':39C ''current'':167C ''data'':8A,14C,78C,168C ''dataset'':10B ''deal'':155C ''decid'':132C ''extract'':4A ''file'':64C ''format'':17C ''generat'':50C ''get'':75C ''graph'':11B,141C ''high'':104C ''highest'':46C ''histor'':143C ''id'':25C ''increas'':115C ''known'':151C ''like'':18C,73C ''list'':38C,53C,60C,90C,102C ''look'':174C ''month'':54C,58C,130C ''nice'':128C ''p'':12C,36C,66C,119C,147C,160C ''panel'':7A ''plot'':138C ''pre'':21C ''quick'':117C ''requir'':158C ''score'':27C,48C,106C,113C,145C ''set'':15C ''show'':86C,98C ''similar'':157C ''somehow'':131C ''store'':56C ''summari'':125C ''techniqu'':1A,153C ''thing'':70C ''think'':122C ''threat'':26C,47C,105C,112C,144C ''three'':69C ''top'':41C ''transform'':165C ''trend'':3A ''ul'':80C,92C,108C ''unbalanc'':6A ''user'':24C,43C,82C,94C,110C,134C ''user-id'':23C ''visual'':9B,124C,152C ''want'':136C ''whose'':111C ''would'':72C,126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (452, 2, '2014-06-18 15:10:22.637', 2, NULL, NULL, '&lt;p&gt;As @Christopher Lauden mentioned above, time-series analysis is most appropriate for this sort of thing. If, however, you wished to do a more traditional "machine learning approach", something that I have done in the past is to block up your data into overlapping windows of time as features, then use it to predict the next days (or weeks) traffic.&lt;/p&gt;

&lt;p&gt;Your feature matrix would be something like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;t1 | t2 | ... | tN
t2 | t3 | ... | tN+1
t3 | t4 | ... | tN+2
...
tW | tW+1 | ... |tN+W
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where &lt;code&gt;tI&lt;/code&gt; is the traffic on day &lt;code&gt;I&lt;/code&gt;. The feature you''ll be predicting is the traffic on the day after the last column. In essence, use a window of traffic to predict the next day''s traffic. &lt;/p&gt;

&lt;p&gt;Any sort of ML model would work for this. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;In response to the question, "can you elaborate on how you use this feature matrix":&lt;/p&gt;

&lt;p&gt;The feature matrix has values indicating past traffic over a period of time (for instance, hourly traffic over 1 week), and we use this to predict traffic for some specified time period in the future. We take our historic data and build a feature matrix of historic traffic and label this with the traffic at some period in the future (e.g. 2 days after the window in the feature). Using some sort of regression machine learning model, we can take historic traffic data, and try and build a model that can predict how traffic moved in our historic data set. The presumption is that future traffic will resemble past traffic.&lt;/p&gt;
', NULL, NULL, '''+1'':81C,88C ''+2'':85C ''/code'':91C,97C,105C ''/p'':63C,72C,136C,147C,152C,169C,281C ''/pre'':92C ''/strong'':151C ''1'':189C ''2'':232C ''analysi'':10C ''approach'':30C ''appropri'':13C ''block'':41C ''build'':212C,257C ''christoph'':3C ''code'':74C,95C,103C ''column'':121C ''data'':44C,210C,253C,269C ''day'':59C,102C,117C,133C,233C ''done'':35C ''e.g'':231C ''edit'':150C ''elabor'':161C ''essenc'':123C ''featur'':51C,66C,107C,167C,172C,214C,239C ''futur'':205C,230C,275C ''histor'':209C,217C,251C,268C ''hour'':186C ''howev'':20C ''indic'':176C ''instanc'':185C ''label'':220C ''last'':120C ''lauden'':4C ''learn'':29C,246C ''like'':71C ''ll'':109C ''machin'':28C,245C ''matrix'':67C,168C,173C,215C ''mention'':5C ''ml'':141C ''model'':142C,247C,259C ''move'':265C ''next'':58C,132C ''overlap'':46C ''p'':1C,64C,93C,137C,148C,153C,170C ''past'':38C,177C,279C ''period'':181C,202C,227C ''pre'':73C ''predict'':56C,111C,130C,196C,262C ''presumpt'':272C ''question'':158C ''regress'':244C ''resembl'':278C ''respons'':155C ''seri'':9C ''set'':270C ''someth'':31C,70C ''sort'':16C,139C,242C ''specifi'':200C ''strong'':149C ''t1'':75C ''t2'':76C,78C ''t3'':79C,82C ''t4'':83C ''take'':207C,250C ''thing'':18C ''ti'':96C ''time'':8C,49C,183C,201C ''time-seri'':7C ''tn'':77C,80C,84C,89C ''tradit'':27C ''traffic'':62C,100C,114C,128C,135C,178C,187C,197C,218C,224C,252C,264C,276C,280C ''tri'':255C ''tw'':86C,87C ''use'':53C,124C,165C,193C,240C ''valu'':175C ''w'':90C ''week'':61C,190C ''window'':47C,126C,236C ''wish'':22C ''work'':144C ''would'':68C,143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (453, 1, '2014-06-18 15:27:23.313', 6, 79, 'What is the difference between global and universal compression methods?', '&lt;p&gt;I understand that compression methods may be split into two main sets: global and local. The first set works regardless of the data being processed, i.e., they do not rely on any characteristic of the data, and thus need not to perform any preprocessing over any part of the dataset (before the compression itself). On the other hand, local methods analyze the data, extracting informations that usually improve the compression rate.&lt;/p&gt;

&lt;p&gt;While reading about some of these methods, I noticed that &lt;a href="http://en.wikipedia.org/wiki/Universal_code_%28data_compression%29#Universal_and_non-universal_codes"&gt;the unary method is not universal&lt;/a&gt;, which surprised me since I thought "globality" and "universality" referred to the same thing. The unary method does not rely on characteristics of the data to yield its encoding (i.e., it is a global method), and therefore it should be global/universal, shouldn''t it?&lt;/p&gt;

&lt;p&gt;What is the difference between universal and global methods? Aren''t these classifications synonyms?&lt;/p&gt;
', 'algorithms', NULL, '''/a'':107C ''/p'':84C,152C,168C ''/wiki/universal_code_%28data_compression%29#universal_and_non-universal_codes'':100C ''algorithm'':11B ''analyz'':73C ''aren'':163C ''characterist'':45C,129C ''classif'':166C ''compress'':9A,16C,65C,82C ''data'':35C,48C,75C,132C ''dataset'':62C ''differ'':4A,157C ''en.wikipedia.org'':99C ''en.wikipedia.org/wiki/universal_code_%28data_compression%29#universal_and_non-universal_codes'':98C ''encod'':136C ''extract'':76C ''first'':29C ''global'':6A,25C,114C,141C,161C ''global/universal'':148C ''hand'':70C ''href'':97C ''i.e'':38C,137C ''improv'':80C ''inform'':77C ''local'':27C,71C ''main'':23C ''may'':18C ''method'':10A,17C,72C,92C,103C,124C,142C,162C ''need'':51C ''notic'':94C ''p'':12C,85C,153C ''part'':59C ''perform'':54C ''preprocess'':56C ''process'':37C ''rate'':83C ''read'':87C ''refer'':117C ''regardless'':32C ''reli'':42C,127C ''set'':24C,30C ''shouldn'':149C ''sinc'':111C ''split'':20C ''surpris'':109C ''synonym'':167C ''therefor'':144C ''thing'':121C ''thought'':113C ''thus'':50C ''two'':22C ''unari'':102C,123C ''understand'':14C ''univers'':8A,106C,116C,159C ''usual'':79C ''work'':31C ''yield'':134C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (454, 1, '2014-06-18 15:48:19.497', 8, 419, 'What are the implications for training a Tree Ensemble with highly biased datasets?', '&lt;p&gt;I have a highly biased binary dataset - I have 1000x more examples of the negative class than the positive class. I would like to train a Tree Ensemble (like Extra Random Trees or a Random Forest) on this data but it''s difficult to create training datasets that contain enough examples of the positive class.&lt;/p&gt;

&lt;p&gt;What would be the implications of doing a stratified sampling approach to normalize the number of positive and negative examples? In other words, is it a bad idea to, for instance, artificially inflate (by resampling) the number of positive class examples in the training set?&lt;/p&gt;
', 'machine-learning  feature-selection  unbalanced-classes', NULL, '''/p'':79C,126C ''1000x'':33C ''approach'':91C ''artifici'':112C ''bad'':107C ''bias'':12A,28C ''binari'':29C ''class'':22B,39C,43C,78C,120C ''contain'':72C ''creat'':68C ''data'':62C ''dataset'':13A,30C,70C ''difficult'':66C ''enough'':73C ''ensembl'':9A,51C ''exampl'':35C,74C,100C,121C ''extra'':53C ''featur'':18B ''feature-select'':17B ''forest'':59C ''high'':11A,27C ''idea'':108C ''implic'':4A,85C ''inflat'':113C ''instanc'':111C ''learn'':16B ''like'':46C,52C ''machin'':15B ''machine-learn'':14B ''negat'':38C,99C ''normal'':93C ''number'':95C,117C ''p'':23C,80C ''posit'':42C,77C,97C,119C ''random'':54C,58C ''resampl'':115C ''sampl'':90C ''select'':19B ''set'':125C ''stratifi'':89C ''train'':6A,48C,69C,124C ''tree'':8A,50C,55C ''unbalanc'':21B ''unbalanced-class'':20B ''word'':103C ''would'':45C,82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (471, 2, '2014-06-19 07:22:38.987', 5, NULL, NULL, '&lt;p&gt;One of the most widely used test collection for text categorization research (link below). I''ve used many times. Enjoy your exploration :)&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.daviddlewis.com/resources/testcollections/reuters21578/"&gt;http://www.daviddlewis.com/resources/testcollections/reuters21578/&lt;/a&gt;
or
&lt;a href="http://archive.ics.uci.edu/ml/datasets/Reuters-21578+Text+Categorization+Collection"&gt;http://archive.ics.uci.edu/ml/datasets/Reuters-21578+Text+Categorization+Collection&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/ml/datasets/reuters-21578+text+categorization+collection'':39C ''/ml/datasets/reuters-21578+text+categorization+collection&lt;/a&gt;&lt;/p&gt;'':42C ''/p'':24C ''/resources/testcollections/reuters21578/'':30C ''/resources/testcollections/reuters21578/&lt;/a&gt;'':33C ''archive.ics.uci.edu'':38C,41C ''archive.ics.uci.edu/ml/datasets/reuters-21578+text+categorization+collection'':37C ''archive.ics.uci.edu/ml/datasets/reuters-21578+text+categorization+collection&lt;/a&gt;&lt;/p&gt;'':40C ''categor'':12C ''collect'':9C ''enjoy'':21C ''explor'':23C ''href'':27C,36C ''link'':14C ''mani'':19C ''one'':2C ''p'':1C,25C ''research'':13C ''test'':8C ''text'':11C ''time'':20C ''use'':7C,18C ''ve'':17C ''wide'':6C ''www.daviddlewis.com'':29C,32C ''www.daviddlewis.com/resources/testcollections/reuters21578/'':28C ''www.daviddlewis.com/resources/testcollections/reuters21578/&lt;/a&gt;'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (455, 1, '2014-06-18 16:21:12.203', 5, 331, 'Suggest text classifier training datasets', '&lt;p&gt;Which freely available datasets can I use to train a text classifier?&lt;/p&gt;

&lt;p&gt;We are trying to enhance our users engagement by recommending the most related content for him, so we thought If we classified our content based on a predefined bag of words we can recommend to him engaging content by getting his feedback on random number of posts already classified before.&lt;/p&gt;

&lt;p&gt;We can use this info to recommend for him pulses labeled with those classes. But we found If we used a predefined bag of words not related to our content the feature vector will be full of zeros, also categories may be not relevant to our content. so for those reasons we tried another solution that will be clustering our content not classifying it.&lt;/p&gt;

&lt;p&gt;Thanks :)&lt;/p&gt;
', 'machine-learning  classification  dataset  clustering  text-mining', NULL, '''/p'':28C,80C,146C,149C ''alreadi'':77C ''also'':120C ''anoth'':135C ''avail'':18C ''bag'':58C,104C ''base'':54C ''categori'':121C ''class'':95C ''classif'':9B ''classifi'':3A,27C,51C,78C,144C ''cluster'':11B,140C ''content'':43C,53C,67C,111C,128C,142C ''dataset'':5A,10B,19C ''engag'':37C,66C ''enhanc'':34C ''featur'':113C ''feedback'':71C ''found'':98C ''freeli'':17C ''full'':117C ''get'':69C ''info'':86C ''label'':92C ''learn'':8B ''machin'':7B ''machine-learn'':6B ''may'':122C ''mine'':14B ''number'':74C ''p'':15C,29C,81C,147C ''post'':76C ''predefin'':57C,103C ''puls'':91C ''random'':73C ''reason'':132C ''recommend'':39C,63C,88C ''relat'':42C,108C ''relev'':125C ''solut'':136C ''suggest'':1A ''text'':2A,13B,26C ''text-min'':12B ''thank'':148C ''thought'':48C ''train'':4A,24C ''tri'':32C,134C ''use'':22C,84C,101C ''user'':36C ''vector'':114C ''word'':60C,106C ''zero'':119C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (511, 1, '2014-06-20 17:57:46.363', 6, 1367, 'Cross-validation: K-fold vs Repeated random sub-sampling', '&lt;p&gt;I wonder which type of model cross-validation to choose for classification problem: K-fold or random sub-sampling (bootstrap sampling)?&lt;/p&gt;

&lt;p&gt;My best guess is to use 2/3 of the data set (which is ~1000 items) for training and 1/3 for validation.&lt;/p&gt;

&lt;p&gt;In this case K-fold gives only three iterations(folds), which is not enough to see stable average error.&lt;/p&gt;

&lt;p&gt;On the other hand I don''t like random sub-sampling feature: that some items won''t be ever selected for training/validation, and some will be used more than once.&lt;/p&gt;

&lt;p&gt;Classification algorithms used: random forest &amp;amp; logistic regression.&lt;/p&gt;
', 'cross-validation  sampling', NULL, '''/p'':42C,65C,87C,120C,130C ''1/3'':62C ''1000'':57C ''2/3'':50C ''algorithm'':123C ''amp'':127C ''averag'':85C ''best'':45C ''bootstrap'':40C ''case'':69C ''choos'':28C ''classif'':30C,122C ''cross'':2A,14B,25C ''cross-valid'':1A,13B,24C ''data'':53C ''enough'':81C ''error'':86C ''ever'':108C ''featur'':101C ''fold'':6A,34C,72C,77C ''forest'':126C ''give'':73C ''guess'':46C ''hand'':92C ''item'':58C,104C ''iter'':76C ''k'':5A,33C,71C ''k-fold'':4A,32C,70C ''like'':96C ''logist'':128C ''model'':23C ''p'':17C,43C,66C,88C,121C ''problem'':31C ''random'':9A,36C,97C,125C ''regress'':129C ''repeat'':8A ''sampl'':12A,16B,39C,41C,100C ''see'':83C ''select'':109C ''set'':54C ''stabl'':84C ''sub'':11A,38C,99C ''sub-sampl'':10A,37C,98C ''three'':75C ''train'':60C ''training/validation'':111C ''type'':21C ''use'':49C,116C,124C ''valid'':3A,15B,26C,64C ''vs'':7A ''won'':105C ''wonder'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (462, 2, '2014-06-18 22:27:06.503', 10, NULL, NULL, '&lt;p&gt;I would recommend training on more balanced subsets of your data. Training random forest on sets of randomly selected positive example with a similar number of negative samples. In particular if the discriminative features exhibit a lot of variance this will be fairly effective and avoid over-fitting. However in stratification it is important to find balance as over-fitting can become a problem regardless. I would suggest seeing how the model does with the whole data set then progressively increasing the ratio of positive to negative samples approaching an even ratio, and selecting for the one that maximizes your performance metric on some representative hold out data. &lt;/p&gt;

&lt;p&gt;This paper seems fairly relevant &lt;a href="http://statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf"&gt;http://statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf&lt;/a&gt; it talks about a &lt;code&gt;weighted Random Forest&lt;/code&gt; which more heavily penalizes misclassification of the minority class.  &lt;/p&gt;
', NULL, NULL, '''/code'':135C ''/p'':112C,145C ''/sites/default/files/tech-reports/666.pdf'':123C ''/sites/default/files/tech-reports/666.pdf&lt;/a&gt;'':126C ''approach'':92C ''avoid'':47C ''balanc'':8C,59C ''becom'':65C ''class'':144C ''code'':131C ''data'':12C,80C,111C ''discrimin'':34C ''effect'':45C ''even'':94C ''exampl'':22C ''exhibit'':36C ''fair'':44C,117C ''featur'':35C ''find'':58C ''fit'':50C,63C ''forest'':15C,134C ''heavili'':138C ''hold'':109C ''howev'':51C ''href'':120C ''import'':56C ''increas'':84C ''lot'':38C ''maxim'':102C ''metric'':105C ''minor'':143C ''misclassif'':140C ''model'':75C ''negat'':28C,90C ''number'':26C ''one'':100C ''over-fit'':48C,61C ''p'':1C,113C ''paper'':115C ''particular'':31C ''penal'':139C ''perform'':104C ''posit'':21C,88C ''problem'':67C ''progress'':83C ''random'':14C,19C,133C ''ratio'':86C,95C ''recommend'':4C ''regardless'':68C ''relev'':118C ''repres'':108C ''sampl'':29C,91C ''see'':72C ''seem'':116C ''select'':20C,97C ''set'':17C,81C ''similar'':25C ''statistics.berkeley.edu'':122C,125C ''statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf'':121C ''statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf&lt;/a&gt;'':124C ''stratif'':53C ''subset'':9C ''suggest'':71C ''talk'':128C ''train'':5C,13C ''varianc'':40C ''weight'':132C ''whole'':79C ''would'':3C,70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (581, 2, '2014-06-25 14:36:29.163', 5, NULL, NULL, '&lt;p&gt;In my opinion, there are solutions to deal with categorical data in clustering. R comes with a specific distance for categorical data. This distance is called Gower (&lt;a href="http://www.inside-r.org/packages/cran/StatMatch/docs/gower.dist"&gt;http://www.inside-r.org/packages/cran/StatMatch/docs/gower.dist&lt;/a&gt;) and it works pretty well.&lt;/p&gt;
', NULL, NULL, '''/p'':42C ''/packages/cran/statmatch/docs/gower.dist'':33C ''/packages/cran/statmatch/docs/gower.dist&lt;/a&gt;)'':36C ''call'':27C ''categor'':11C,22C ''cluster'':14C ''come'':16C ''data'':12C,23C ''deal'':9C ''distanc'':20C,25C ''gower'':28C ''href'':30C ''opinion'':4C ''p'':1C ''pretti'':40C ''r'':15C ''solut'':7C ''specif'':19C ''well'':41C ''work'':39C ''www.inside-r.org'':32C,35C ''www.inside-r.org/packages/cran/statmatch/docs/gower.dist'':31C ''www.inside-r.org/packages/cran/statmatch/docs/gower.dist&lt;/a&gt;)'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (586, 1, '2014-06-25 17:37:23.38', 3, 253, 'What are the main types of NLP annotators?', '&lt;p&gt;I''m new to the world of text mining and have been reading up on annotators at places like the &lt;a href="http://uima.apache.org/" rel="nofollow"&gt;UIMA website&lt;/a&gt;. I''m encountering many new terms like named entity recognition, tokenizer, lemmatizer, gazetteer, etc. Coming from a layman background, this is all very confusing so can anyone tell me or link to resources that can explain what the main categories of annotators are and what they do?&lt;/p&gt;
', 'nlp  text-mining', NULL, '''/a'':41C ''/p'':89C ''annot'':8A,29C,83C ''anyon'':68C ''background'':60C ''categori'':81C ''come'':56C ''confus'':65C ''encount'':44C ''entiti'':50C ''etc'':55C ''explain'':77C ''gazett'':54C ''href'':35C ''layman'':59C ''lemmat'':53C ''like'':32C,48C ''link'':72C ''m'':15C,43C ''main'':4A,80C ''mani'':45C ''mine'':12B,22C ''name'':49C ''new'':16C,46C ''nlp'':7A,9B ''nofollow'':38C ''p'':13C ''place'':31C ''read'':26C ''recognit'':51C ''rel'':37C ''resourc'':74C ''tell'':69C ''term'':47C ''text'':11B,21C ''text-min'':10B ''token'':52C ''type'':5A ''uima'':39C ''uima.apache.org'':36C ''websit'':40C ''world'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (456, 2, '2014-06-18 17:22:46.207', 4, NULL, NULL, '&lt;p&gt;If you expect (or find) that nodes are requesting the same data more than once, perhaps you could benefit from a caching strategy? Especially where some data is used much more often than others, so you can target only the most frequently-used information.&lt;/p&gt;

&lt;p&gt;If the data is mutable, you also need a way to confirm that it hasn''t changed since the last request that''s less expensive than repeating the request.&lt;/p&gt;

&lt;p&gt;This is further complicated if each node has its own separate cache. Depending on the nature of your system and task(s), you could consider adding a node dedicated to serving information between the processing nodes, and building a single cache on that node.&lt;/p&gt;

&lt;p&gt;For an example of when that &lt;em&gt;might&lt;/em&gt; be a good idea, let''s suppose I retrieve some data from a remote data store over a low-bandwidth connection, and I have some task(s) requiring that data, which are distributed exclusively among local nodes. I definitely wouldn''t want each node requesting information separately over that low-bandwidth connection, which another node might have previously requested. Since my local I/O is much less expensive than my I/O over the low-bandwidth connection, I might add a node between the processing nodes and the remote source that acts as an intermediate server. This node would take requests from the processing nodes, communicate with the remote data store, and cache frequently-requested data to minimize the use of that low-bandwidth connection.&lt;/p&gt;

&lt;p&gt;The core concepts here that &lt;em&gt;may&lt;/em&gt; be applicable to your specific case are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Eliminate or reduce redundant I/O;&lt;/li&gt;
&lt;li&gt;Take advantage of trade-offs between memory use and computation time;&lt;/li&gt;
&lt;li&gt;Not all I/O is created equal.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/em'':134C,272C ''/li'':288C,302C,310C ''/p'':47C,78C,124C,263C,280C ''/ul'':311C ''act'':227C ''ad'':105C ''add'':215C ''advantag'':291C ''also'':55C ''among'':170C ''anoth'':190C ''applic'':274C ''bandwidth'':155C,187C,211C,261C ''benefit'':20C ''build'':117C ''cach'':23C,91C,120C,248C ''case'':278C ''chang'':65C ''communic'':241C ''complic'':83C ''comput'':300C ''concept'':267C ''confirm'':60C ''connect'':156C,188C,212C,262C ''consid'':104C ''core'':266C ''could'':19C,103C ''creat'':308C ''data'':13C,28C,51C,145C,149C,165C,245C,252C ''dedic'':108C ''definit'':174C ''depend'':92C ''distribut'':168C ''elimin'':283C ''em'':132C,270C ''equal'':309C ''especi'':25C ''exampl'':128C ''exclus'':169C ''expect'':4C ''expens'':73C,203C ''find'':6C ''frequent'':44C,250C ''frequently-request'':249C ''frequently-us'':43C ''good'':137C ''hasn'':63C ''i/o'':199C,206C,287C,306C ''idea'':138C ''inform'':46C,111C,181C ''intermedi'':230C ''last'':68C ''less'':72C,202C ''let'':139C ''li'':282C,289C,303C ''local'':171C,198C ''low'':154C,186C,210C,260C ''low-bandwidth'':153C,185C,209C,259C ''may'':271C ''memori'':297C ''might'':133C,192C,214C ''minim'':254C ''much'':31C,201C ''mutabl'':53C ''natur'':95C ''need'':56C ''node'':8C,86C,107C,115C,123C,172C,179C,191C,217C,221C,233C,240C ''off'':295C ''often'':33C ''other'':35C ''p'':1C,48C,79C,125C,264C ''perhap'':17C ''previous'':194C ''process'':114C,220C,239C ''reduc'':285C ''redund'':286C ''remot'':148C,224C,244C ''repeat'':75C ''request'':10C,69C,77C,180C,195C,236C,251C ''requir'':163C ''retriev'':143C ''separ'':90C,182C ''serv'':110C ''server'':231C ''sinc'':66C,196C ''singl'':119C ''sourc'':225C ''specif'':277C ''store'':150C,246C ''strategi'':24C ''suppos'':141C ''system'':98C ''take'':235C,290C ''target'':39C ''task'':100C,161C ''time'':301C ''trade'':294C ''trade-off'':293C ''ul'':281C ''use'':30C,45C,256C,298C ''want'':177C ''way'':58C ''would'':234C ''wouldn'':175C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (457, 2, '2014-06-18 18:22:11.68', 6, NULL, NULL, '&lt;p&gt;Firstly, I would generally agree with everything that AirThomas suggested. Caching things is generally good if you can, but I find it slightly brittle since that''s very dependent on exactly what your application is. Data compression is another very solid suggestion, but my impression on both of these is that the speedups you''re looking at are going to be relatively marginal. Maybe as high as 2-5x, but I would be very surprised if they were any faster than that.&lt;/p&gt;

&lt;p&gt;Under the assumption that pure I/O (writing to/reading from memory) is &lt;em&gt;not&lt;/em&gt; your limiting factor (if it is, you''re probably not going to get a lot faster), I would make a strong plug for &lt;a href="http://zeromq.org/"&gt;zeromq&lt;/a&gt;. In the words of the creators:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;We took a normal TCP socket, injected it with a mix of radioactive
  isotopes stolen from a secret Soviet atomic research project,
  bombarded it with 1950-era cosmic rays, and put it into the hands of a
  drug-addled comic book author with a badly-disguised fetish for
  bulging muscles clad in spandex. Yes, ÃMQ sockets are the world-saving
  superheroes of the networking world.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;While that may be a little dramatic, &lt;code&gt;zeromq&lt;/code&gt; sockets in my opinion are one of the most amazing pieces of software that the world of computer networks has put together in several years. I''m not sure what you''re using for your message-passing layer right now, but if you''re using something traditional like &lt;code&gt;rabbitmq&lt;/code&gt;, you''re liable to see speedups of multiple orders of magnitude (personally noticed about 500x, but depends a lot of architecture)&lt;/p&gt;

&lt;p&gt;Check out some basic benchmarks &lt;a href="http://blog.x-aeon.com/2013/04/10/a-quick-message-queue-benchmark-activemq-rabbitmq-hornetq-qpid-apollo/"&gt;here.&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''-5'':70C ''/2013/04/10/a-quick-message-queue-benchmark-activemq-rabbitmq-hornetq-qpid-apollo/'':304C ''/a'':128C,306C ''/blockquote'':208C ''/code'':219C,271C ''/em'':100C ''/p'':85C,135C,207C,293C,307C ''1950'':163C ''2'':69C ''500x'':286C ''addl'':177C ''agre'':6C ''airthoma'':10C ''amaz'':229C ''anoth'':40C ''applic'':35C ''architectur'':292C ''assumpt'':89C ''atom'':157C ''author'':180C ''bad'':184C ''badly-disguis'':183C ''basic'':298C ''benchmark'':299C ''blockquot'':136C ''blog.x-aeon.com'':303C ''blog.x-aeon.com/2013/04/10/a-quick-message-queue-benchmark-activemq-rabbitmq-hornetq-qpid-apollo/'':302C ''bombard'':160C ''book'':179C ''brittl'':25C ''bulg'':188C ''cach'':12C ''check'':295C ''clad'':190C ''code'':217C,269C ''comic'':178C ''compress'':38C ''comput'':237C ''cosmic'':165C ''creator'':134C ''data'':37C ''depend'':30C,288C ''disguis'':185C ''dramat'':216C ''drug'':176C ''drug-addl'':175C ''em'':98C ''era'':164C ''everyth'':8C ''exact'':32C ''factor'':103C ''faster'':82C,116C ''fetish'':186C ''find'':22C ''first'':2C ''general'':5C,15C ''get'':113C ''go'':60C,111C ''good'':16C ''hand'':172C ''high'':67C ''href'':125C,301C ''i/o'':92C ''impress'':46C ''inject'':144C ''isotop'':151C ''layer'':258C ''liabl'':274C ''like'':268C ''limit'':102C ''littl'':215C ''look'':57C ''lot'':115C,290C ''m'':246C ''magnitud'':282C ''make'':119C ''margin'':64C ''may'':212C ''mayb'':65C ''memori'':96C ''messag'':256C ''message-pass'':255C ''mix'':148C ''mq'':195C ''multipl'':279C ''muscl'':189C ''network'':205C,238C ''normal'':141C ''notic'':284C ''one'':225C ''opinion'':223C ''order'':280C ''p'':1C,86C,137C,209C,294C ''pass'':257C ''person'':283C ''piec'':230C ''plug'':122C ''probabl'':109C ''project'':159C ''pure'':91C ''put'':168C,240C ''rabbitmq'':270C ''radioact'':150C ''ray'':166C ''re'':56C,108C,251C,264C,273C ''relat'':63C ''research'':158C ''right'':259C ''save'':201C ''secret'':155C ''see'':276C ''sever'':243C ''sinc'':26C ''slight'':24C ''socket'':143C,196C,220C ''softwar'':232C ''solid'':42C ''someth'':266C ''soviet'':156C ''spandex'':192C ''speedup'':54C,277C ''stolen'':152C ''strong'':121C ''suggest'':11C,43C ''superhero'':202C ''sure'':248C ''surpris'':77C ''tcp'':142C ''thing'':13C ''to/reading'':94C ''togeth'':241C ''took'':139C ''tradit'':267C ''use'':252C,265C ''word'':131C ''world'':200C,206C,235C ''world-sav'':199C ''would'':4C,74C,118C ''write'':93C ''x'':71C ''year'':244C ''yes'':193C ''zeromq'':127C,218C ''zeromq.org'':126C ''ã'':194C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (458, 1, '2014-06-18 19:48:54.883', 8, 413, 'K-means vs. online K-means', '&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/K-means_clustering"&gt;K-means&lt;/a&gt; is a well known algorithm for clustering, but there is also an online variation of such algorithm (online K-means). What are the pros and cons of these approaches, and when should each be preferred?&lt;/p&gt;
', 'algorithms  clustering', NULL, '''/a'':20C ''/p'':57C ''/wiki/k-means_clustering'':16C ''algorithm'':9B,25C,37C ''also'':31C ''approach'':50C ''cluster'':10B,27C ''con'':47C ''en.wikipedia.org'':15C ''en.wikipedia.org/wiki/k-means_clustering'':14C ''href'':13C ''k'':2A,7A,18C,40C ''k-mean'':1A,6A,17C,39C ''known'':24C ''mean'':3A,8A,19C,41C ''onlin'':5A,33C,38C ''p'':11C ''prefer'':56C ''pros'':45C ''variat'':34C ''vs'':4A ''well'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (459, 2, '2014-06-18 20:07:05.017', 8, NULL, NULL, '&lt;p&gt;Online k-means (more commonly known as &lt;a href="http://stackoverflow.com/questions/3698532/online-k-means-clustering"&gt;sequential k-means&lt;/a&gt;) and traditional k-means are very similar.  The difference is that online k-means allows you to update the model as new data is received.&lt;/p&gt;

&lt;p&gt;Online k-means should be used when you expect the data to be received one by one (or maybe in chunks).  This allows you to update your model as you get more information about it.  The drawback of this method is that it is dependent on the order in which the data is received (&lt;a href="http://www.cs.princeton.edu/courses/archive/fall08/cos436/Duda/C/sk_means.htm"&gt;ref&lt;/a&gt;).  &lt;/p&gt;
', NULL, NULL, '''/a'':19C,110C ''/courses/archive/fall08/cos436/duda/c/sk_means.htm'':108C ''/p'':47C,111C ''/questions/3698532/online-k-means-clustering'':14C ''allow'':36C,72C ''chunk'':70C ''common'':7C ''data'':44C,60C,101C ''depend'':94C ''differ'':29C ''drawback'':86C ''expect'':58C ''get'':80C ''href'':11C,105C ''inform'':82C ''k'':4C,17C,23C,34C,51C ''k-mean'':3C,16C,22C,33C,50C ''known'':8C ''mayb'':68C ''mean'':5C,18C,24C,35C,52C ''method'':89C ''model'':41C,77C ''new'':43C ''one'':64C,66C ''onlin'':2C,32C,49C ''order'':97C ''p'':1C,48C ''receiv'':46C,63C,103C ''ref'':109C ''sequenti'':15C ''similar'':27C ''stackoverflow.com'':13C ''stackoverflow.com/questions/3698532/online-k-means-clustering'':12C ''tradit'':21C ''updat'':39C,75C ''use'':55C ''www.cs.princeton.edu'':107C ''www.cs.princeton.edu/courses/archive/fall08/cos436/duda/c/sk_means.htm'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (460, 2, '2014-06-18 21:56:29.147', 4, NULL, NULL, '&lt;p&gt;A fast, easy an often effective way to approach this imbalance would be to randomly subsample the bigger class (which in your case is the negative class), run the classification N number of times with members from the two classes (one full and the other subsampled) and report the average metric values, the average being computed over N (say 1000) iterations.&lt;/p&gt;

&lt;p&gt;A more methodical approach would be to execute the Mapping Convergence (MC) algorithm, which involves identifying a subset of strong negative samples with the help of a one-class classifier, such as OSVM or SVDD, and then iteratively execute binary classification on the set of strong negative and positive samples. More details of the MC algorithm can be found in this &lt;a href="http://link.springer.com/article/10.1007/s10994-005-1122-7#page-1" rel="nofollow"&gt;paper&lt;/a&gt;. &lt;/p&gt;
', NULL, NULL, '''/a'':135C ''/article/10.1007/s10994-005-1122-7#page-1'':131C ''/p'':63C,136C ''1000'':61C ''algorithm'':77C,121C ''approach'':10C,68C ''averag'':51C,55C ''bigger'':19C ''binari'':105C ''case'':24C ''class'':20C,28C,41C,94C ''classif'':31C,106C ''classifi'':95C ''comput'':57C ''converg'':75C ''detail'':117C ''easi'':4C ''effect'':7C ''execut'':72C,104C ''fast'':3C ''found'':124C ''full'':43C ''help'':89C ''href'':128C ''identifi'':80C ''imbal'':12C ''involv'':79C ''iter'':62C,103C ''link.springer.com'':130C ''link.springer.com/article/10.1007/s10994-005-1122-7#page-1'':129C ''map'':74C ''mc'':76C,120C ''member'':37C ''method'':67C ''metric'':52C ''n'':32C,59C ''negat'':27C,85C,112C ''nofollow'':133C ''number'':33C ''often'':6C ''one'':42C,93C ''one-class'':92C ''osvm'':98C ''p'':1C,64C ''paper'':134C ''posit'':114C ''random'':16C ''rel'':132C ''report'':49C ''run'':29C ''sampl'':86C,115C ''say'':60C ''set'':109C ''strong'':84C,111C ''subsampl'':17C,47C ''subset'':82C ''svdd'':100C ''time'':35C ''two'':40C ''valu'':53C ''way'':8C ''would'':13C,69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (571, 1, '2014-06-25 02:33:22.673', 5, 111, 'Standardize numbers for ranking ratios', '&lt;p&gt;I''m trying to rank some percentages. I have numerators and denominators for each ratio. To give a concrete example, consider ratio as &lt;code&gt;total graduates / total students&lt;/code&gt; in a school.&lt;/p&gt;

&lt;p&gt;But the issue is that &lt;code&gt;total students&lt;/code&gt; vary over a long range (1000-20000). Smaller schools seem to have higher percentage of students graduating, but I want to standardize it, and not let the size of the school affect the ranking. Is there a way to do it?&lt;/p&gt;
', 'statistics', NULL, '''-20000'':57C ''/code'':36C,50C ''/p'':40C,92C ''1000'':56C ''affect'':82C ''code'':31C,47C ''concret'':26C ''consid'':28C ''denomin'':19C ''exampl'':27C ''give'':24C ''graduat'':33C,67C ''higher'':63C ''issu'':44C ''let'':76C ''long'':54C ''m'':9C ''number'':2A ''numer'':17C ''p'':7C,41C ''percentag'':14C,64C ''rang'':55C ''rank'':4A,12C,84C ''ratio'':5A,22C,29C ''school'':39C,59C,81C ''seem'':60C ''size'':78C ''smaller'':58C ''standard'':1A,72C ''statist'':6B ''student'':35C,49C,66C ''total'':32C,34C,48C ''tri'':10C ''vari'':51C ''want'':70C ''way'':88C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (629, 2, '2014-06-30 01:51:11.027', 2, NULL, NULL, '&lt;h1&gt;Do you need a VM?&lt;/h1&gt;

&lt;p&gt;You need to keep in mind that a virtual machine is a software emulation of your own or another machine hardware configuration that can run an operating systems. In most basic terms, it acts as a layer interfacing between the virtual OS, and your own OS which then communicates with the lower level hardware to provide support to the virtual OS. What this means for you is:&lt;/p&gt;

&lt;h1&gt;Cons&lt;/h1&gt;

&lt;h3&gt;Hardware Support&lt;/h3&gt;

&lt;p&gt;A drawback of virtual machine technology is that it supports only the hardware that both the virtual machine hypervisor and the guest operating system support. Even if the guest operating system supports the physical hardware, it sees only the virtual hardware presented by the virtual machine. 
The second aspect of virtual machine hardware support is the hardware presented to the guest operating system. No matter the hardware in the host, the hardware presented to the guest environment is usually the same (with the exception of the CPU, which shows through). For example, VMware GSX Server presents an AMD PCnet32 Fast Ethernet card or an optimized VMware-proprietary network card, depending on which you choose. The network card in the host machine does not matter. VMware GSX Server performs the translation between the guest environment''s network card and the host environment''s network card. This is great for standardization, but it also means that host hardware that VMware does not understand will not be present in the guest environment.&lt;/p&gt;

&lt;h3&gt;Performance Penalty&lt;/h3&gt;

&lt;p&gt;Virtual machine technology imposes a performance penalty from running an additional layer above the physical hardware but beneath the guest operating system. The performance penalty varies based on the virtualization software used and the guest software being run. This is significant.&lt;/p&gt;

&lt;h1&gt;Pros&lt;/h1&gt;

&lt;h3&gt;Isolation&lt;/h3&gt;

&lt;blockquote&gt;
  &lt;p&gt;One of the key reasons to employ virtualization is to isolate applications from each other. Running everything on one machine would be great if it all worked, but many times it results in undesirable interactions or even outright conflicts. The cause often is software problems or business requirements, such as the need for isolated security. Virtual machines allow you to isolate each application (or group of applications) in its own sandbox environment. The virtual machines can run on the same physical machine (simplifying IT hardware management), yet appear as independent machines to the software you are running. For all intents and purposesâexcept performance, the virtual machines are independent machines. If one virtual machine goes down due to application or operating system error, the others continue running, providing services your business needs to function smoothly.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;h3&gt;Standardization&lt;/h3&gt;

&lt;blockquote&gt;
  &lt;p&gt;Another key benefit virtual machines provide is standardization. The hardware that is presented to the guest operating system is uniform for the most part, usually with the CPU being the only component that is "pass-through" in the sense that the guest sees what is on the host. A standardized hardware platform reduces support costs and increases the share of IT resources that you can devote to accomplishing goals that give your business a competitive advantage. The host machines can be different (as indeed they often are when hardware is acquired at different times), but the virtual machines will appear to be the same across all of them.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;h3&gt;Ease of Testing&lt;/h3&gt;

&lt;blockquote&gt;
  &lt;p&gt;Virtual machines let you test scenarios easily. Most virtual machine software today provides snapshot and rollback capabilities. This means you can stop a virtual machine, create a snapshot, perform more operations in the virtual machine, and then roll back again and again until you have finished your testing. This is very handy for software development, but it is also useful for system administration. Admins can snapshot a system and install some software or make some configuration changes that they suspect may destabilize the system. If the software installs or changes work, then the admin can commit the updates. If the updates damage or destroy the system, the admin can roll them back.
  Virtual machines also facilitate scenario testing by enabling virtual networks. In VMware Workstation, for example, you can set up multiple virtual machines on a virtual network with configurable parameters, such as packet loss from congestion and latency. You can thus test timing-sensitive or load-sensitive applications to see how they perform under the stress of a simulated heavy workload.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;h3&gt;Mobility&lt;/h3&gt;

&lt;blockquote&gt;
  &lt;p&gt;Virtual machines are easy to move between physical machines. Most of the virtual machine software on the market today stores a whole disk in the guest environment as a single file in the host environment. Snapshot and rollback capabilities are implemented by storing the change in state in a separate file in the host information. Having a single file represent an entire guest environment disk promotes the mobility of virtual machines. Transferring the virtual machine to another physical machine is as easy as moving the virtual disk file and some configuration files to the other physical machine. Deploying another copy of a virtual machine is the same as transferring a virtual machine, except that instead of moving the files, you copy them.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;h1&gt;Which VM should I use if I am starting out?&lt;/h1&gt;

&lt;p&gt;The Data Science Box or the Data Science Toolbox are your best bets if you just getting into data science. They have the basic software that you will need, with the primary difference being the virtual environment in which each of these can run. The DSB can run on AWS while the DST can run on Virtual Box (which is the most common tool used for VMs).&lt;/p&gt;

&lt;h1&gt;Sources&lt;/h1&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.devx.com/vmspecialreport/Article/30383" rel="nofollow"&gt;http://www.devx.com/vmspecialreport/Article/30383&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://jeroenjanssens.com/2013/12/07/lean-mean-data-science-machine.html" rel="nofollow"&gt;http://jeroenjanssens.com/2013/12/07/lean-mean-data-science-machine.html&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (522, 2, '2014-06-22 13:18:38.76', 2, NULL, NULL, '&lt;p&gt;I have been able to optimize very strange functions with simulated annealing, and it does not require a gradient. Instead, it uses random numbers in a way very similar to Markov Chain Monte Carlo, which helps it avoid getting stuck in local optima. A decent explanation that gives the intuition behind it can be found in this lecture: &lt;a href="http://iacs-courses.seas.harvard.edu/courses/am207/blog/lecture-13.html" rel="nofollow"&gt;Simulated Annealing&lt;/a&gt;. scipy 0.14 includes this algorithm in its optimization module: &lt;a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.anneal.html" rel="nofollow"&gt;scipy.optimize.anneal&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':69C,87C ''/courses/am207/blog/lecture-13.html'':64C ''/doc/scipy/reference/generated/scipy.optimize.anneal.html'':83C ''/p'':88C ''0.14'':71C ''abl'':5C ''algorithm'':74C ''anneal'':13C,68C ''avoid'':39C ''behind'':52C ''carlo'':35C ''chain'':33C ''decent'':46C ''docs.scipy.org'':82C ''docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.anneal.html'':81C ''explan'':47C ''found'':56C ''function'':10C ''get'':40C ''give'':49C ''gradient'':20C ''help'':37C ''href'':61C,80C ''iacs-courses.seas.harvard.edu'':63C ''iacs-courses.seas.harvard.edu/courses/am207/blog/lecture-13.html'':62C ''includ'':72C ''instead'':21C ''intuit'':51C ''lectur'':59C ''local'':43C ''markov'':32C ''modul'':78C ''mont'':34C ''nofollow'':66C,85C ''number'':25C ''optim'':7C,77C ''optima'':44C ''p'':1C ''random'':24C ''rel'':65C,84C ''requir'':18C ''scipi'':70C ''scipy.optimize.anneal'':86C ''similar'':30C ''simul'':12C,67C ''strang'':9C ''stuck'':41C ''use'':23C ''way'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5298, 2, '2015-03-09 21:54:19.297', 4, NULL, NULL, '&lt;p&gt;They are not related in any meaningful sense. Sure, you can use them both to extract features, or do any number of things, but the same can be said about a many techniques. I would have asked "what kind of neural network?" to see if the interviewer had something specific in mind.&lt;/p&gt;
', NULL, NULL, '''/p'':54C ''ask'':38C ''extract'':17C ''featur'':18C ''interview'':48C ''kind'':40C ''mani'':33C ''meaning'':8C ''mind'':53C ''network'':43C ''neural'':42C ''number'':22C ''p'':1C ''relat'':5C ''said'':30C ''see'':45C ''sens'':9C ''someth'':50C ''specif'':51C ''sure'':10C ''techniqu'':34C ''thing'':24C ''use'':13C ''would'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (463, 2, '2014-06-18 22:45:25.677', 8, NULL, NULL, '&lt;p&gt;My first suggestion would be to somehow map the non-quantifiable attributes to quantities with the help of suitable mapping functions. Otherwise, simply leave them out.&lt;/p&gt;

&lt;p&gt;Secondly, I don''t think that you need to assume that the list of attributes is not finite. A standard and intuitive approach is to represent each attribute as an individual dimension in a vector space. Each product is then simply a point in this space. In that case, if you want to dynamically add more attributes you simply have to remap the product vectors into the new feature space (with additional dimensions).    &lt;/p&gt;

&lt;p&gt;With this representation, a seller is a point in the feature space with product attributes and a buyer is a point in the same feature space with the preference attributes. The task is then to find out the most similar buyer point for a given seller point.&lt;/p&gt;

&lt;p&gt;If your dataset (i.e. the number of buyers/sellers) is not very large, you can solve this with a nearest neighbour approach implemented with the help of k-d trees.&lt;/p&gt;

&lt;p&gt;For very large sized data, you can take an IR approach. Index the set of sellers (i.e. the product attributes) by treating each attribute as a separate term with the term-weight being set to the attribute value. A query in this case is a buyer which is also encoded in the term space as a query vector with appropriate term weights. The retrieval step would return you a list of top K most similar matches.&lt;/p&gt;
', NULL, NULL, '''/p'':29C,104C,153C,185C,264C ''add'':85C ''addit'':102C ''also'':236C ''approach'':53C,175C,197C ''appropri'':247C ''assum'':40C ''attribut'':14C,45C,58C,87C,120C,135C,206C,210C,224C ''buyer'':123C,146C,233C ''buyers/sellers'':162C ''case'':79C,230C ''d'':183C ''data'':191C ''dataset'':157C ''dimens'':62C,103C ''dynam'':84C ''encod'':237C ''featur'':99C,116C,130C ''find'':141C ''finit'':48C ''first'':3C ''function'':23C ''given'':150C ''help'':19C,179C ''i.e'':158C,203C ''implement'':176C ''index'':198C ''individu'':61C ''intuit'':52C ''ir'':196C ''k'':182C,260C ''k-d'':181C ''larg'':166C,189C ''leav'':26C ''list'':43C,257C ''map'':9C,22C ''match'':263C ''nearest'':173C ''need'':38C ''neighbour'':174C ''new'':98C ''non'':12C ''non-quantifi'':11C ''number'':160C ''otherwis'':24C ''p'':1C,30C,105C,154C,186C ''point'':73C,113C,126C,147C,152C ''prefer'':134C ''product'':68C,94C,119C,205C ''quantifi'':13C ''quantiti'':16C ''queri'':227C,244C ''remap'':92C ''repres'':56C ''represent'':108C ''retriev'':251C ''return'':254C ''second'':31C ''seller'':110C,151C,202C ''separ'':213C ''set'':200C,221C ''similar'':145C,262C ''simpli'':25C,71C,89C ''size'':190C ''solv'':169C ''somehow'':8C ''space'':66C,76C,100C,117C,131C,241C ''standard'':50C ''step'':252C ''suggest'':4C ''suitabl'':21C ''take'':194C ''task'':137C ''term'':214C,218C,240C,248C ''term-weight'':217C ''think'':35C ''top'':259C ''treat'':208C ''tree'':184C ''valu'':225C ''vector'':65C,95C,245C ''want'':82C ''weight'':219C,249C ''would'':5C,253C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (464, 2, '2014-06-18 22:48:53.35', 10, NULL, NULL, '&lt;p&gt;Some standard datasets for text classification are the 20-News group, Reuters (with 8 and 52 classes) and WebKb. You can find all of them &lt;a href="http://web.ist.utl.pt/~acardoso/datasets/"&gt;here&lt;/a&gt;. &lt;/p&gt;
', NULL, NULL, '''/a'':33C ''/p'':34C ''/~acardoso/datasets/'':31C ''20'':10C ''52'':17C ''8'':15C ''class'':18C ''classif'':7C ''dataset'':4C ''find'':23C ''group'':12C ''href'':28C ''news'':11C ''p'':1C ''reuter'':13C ''standard'':3C ''text'':6C ''web.ist.utl.pt'':30C ''web.ist.utl.pt/~acardoso/datasets/'':29C ''webkb'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (465, 2, '2014-06-18 22:58:35.26', 5, NULL, NULL, '&lt;p&gt;nDCG is used to evaluate a golden ranked list (typically human judged) against your output ranked list. The more is the correlation between the two ranked lists, i.e. the more similar are the ranks of the relevant items in the two lists, the closer is the value of nDCG to 1.&lt;/p&gt;

&lt;p&gt;RMSE (Root Mean Squared Error) is typically used to evaluate regression problems where the output (a predicted scalar value) is compared with the true scalar value output for a given data point.&lt;/p&gt;

&lt;p&gt;So, if you are simply recommending a score (such as recommending a movie rating), then use RMSE. Whereas, if you are recommending a list of items (such as a list of related movies), then use nDCG.  &lt;/p&gt;
', NULL, NULL, '''/p'':53C,87C,125C ''1'':52C ''closer'':45C ''compar'':75C ''correl'':23C ''data'':85C ''error'':59C ''evalu'':6C,64C ''given'':84C ''golden'':8C ''human'':12C ''i.e'':29C ''item'':39C,114C ''judg'':13C ''list'':10C,18C,28C,43C,112C,118C ''mean'':57C ''movi'':101C,121C ''ndcg'':2C,50C,124C ''output'':16C,69C,81C ''p'':1C,54C,88C ''point'':86C ''predict'':71C ''problem'':66C ''rank'':9C,17C,27C,35C ''rate'':102C ''recommend'':94C,99C,110C ''regress'':65C ''relat'':120C ''relev'':38C ''rmse'':55C,105C ''root'':56C ''scalar'':72C,79C ''score'':96C ''similar'':32C ''simpli'':93C ''squar'':58C ''true'':78C ''two'':26C,42C ''typic'':11C,61C ''use'':4C,62C,104C,123C ''valu'':48C,73C,80C ''wherea'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (466, 1, '2014-06-18 23:02:54.2', 9, 279, 'Gas consumption outliers detection - Neural network project. Bad results', '&lt;p&gt;I tried to detect outliers in the energy gas consumption of some dutch buildings, building a neural network model. I have very bad results, but I don''t find the reason. &lt;/p&gt;

&lt;p&gt;I am not an expert so I would like to ask you what I can improve and what I''m doing wrong. This is the complete description: &lt;a href="https://github.com/denadai2/Gas-consumption-outliers"&gt;https://github.com/denadai2/Gas-consumption-outliers&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The neural network is a FeedFoward Network with Back Propagation. As described &lt;a href="http://nbviewer.ipython.org/github/denadai2/Gas-consumption-outliers/blob/master/3-%20Regression_NN.ipynb"&gt;here&lt;/a&gt; I splitted the dataset in a "small" dataset of 41''000 rows, 9 features and I tried to add more features. &lt;/p&gt;

&lt;p&gt;I trained the networks but the results have 14.14 RMSE, so it can''t predict so well the gas consumptions, consecutevely I can''t run a good outlier detection mechanism. I see that in some papers that even if they predict daily or hourly consumption in the electric power, they have errors like MSE = 0.01.&lt;/p&gt;

&lt;p&gt;What can I improve? What am I doing wrong? Can you have a look of my description?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (468, 1, '2014-06-19 03:43:23.853', 8, 261, 'Learning ordinal regression in R?', '&lt;p&gt;I''m working on a project and need resources to get me up to speed.&lt;/p&gt;

&lt;p&gt;The dataset is around 35000 observations on 30 or so variables.  About half the variables are categorical with some having many different possible values, i.e. if you split the categorical variables into dummy variables you would have a lot more than 30 variables.  But still probably on the order of a couple of hundred max.  (n&gt;p).  &lt;/p&gt;

&lt;p&gt;The response we want to predict is ordinal with 5 levels (1,2,3,4,5).  Predictors are a mix of continuous and categorical, about half of each.  These are my thoughts/plans so far:
1.  Treat the response as continuous and run vanilla linear regression.
2.  Run nominal and ordinal logistic and probit regression
3.  Use MARS and/or another flavor of non-linear regression&lt;/p&gt;

&lt;p&gt;I''m familiar with linear regression.  MARS is well enough described by Hastie and Tibshirani.  But I''m at a loss when it comes to ordinal logit/probit, especially with so many variables and a big data set.  &lt;/p&gt;

&lt;p&gt;The r package &lt;a href="http://cran.r-project.org/web/packages/glmnetcr/index.html"&gt;glmnetcr&lt;/a&gt; seems to be my best bet so far, but the documentation hardly suffices to get me where I need to be.&lt;/p&gt;

&lt;p&gt;Where can I go to learn more?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (477, 1, '2014-06-18 22:15:43.82', 2, 64, 'Preference Matching Algorithm', '&lt;p&gt;There''s this side project I''m working on where I need to structure a solution to the following problem.&lt;/p&gt;

&lt;p&gt;I have two groups of people (clients). Group "A" intends to buy and group "B" intends to sell a determined product "X". &lt;/p&gt;

&lt;p&gt;The product has a series of attributes x_i and my objective is to facilitate the transaction between "A" e "B" by matching their preferences. The main idea is to point out to each member of "A" a corresponding in "B" whoâs product better suits his needs, and vice versa. &lt;/p&gt;

&lt;p&gt;Some complicating aspects of the problem:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;The list of attributes is not finite. The buyer might be interested in a very particular characteristic or some kind of design which is rare among the population and I canât predict. Canât previously list all the attributes;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Attributes might be continuous, binary or non-quantifiable (ex: price, functionality, design).&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Any suggestion on how to approach this problem and solve it in an automated way?&lt;br&gt;
The idea is to really think out of the box here so feel free to "go wild" on your suggestions. &lt;/p&gt;

&lt;p&gt;I would also appreciate some references to other similar problems if possible. &lt;/p&gt;
', 'algorithms', NULL, '''/li'':156C,173C ''/ol'':174C ''/p'':26C,50C,103C,111C,155C,172C,212C,226C ''algorithm'':3A,4B ''also'':216C ''among'':140C ''appreci'':217C ''approach'':181C ''aspect'':107C ''attribut'':58C,118C,154C,159C ''autom'':189C ''b'':42C,72C,92C ''better'':96C ''binari'':163C ''box'':201C ''br'':191C ''buy'':39C ''buyer'':123C ''canâ'':145C,148C ''characterist'':131C ''client'':34C ''complic'':106C ''continu'':162C ''correspond'':90C ''design'':136C,171C ''determin'':47C ''e'':71C ''ex'':168C ''facilit'':66C ''feel'':204C ''finit'':121C ''follow'':24C ''free'':205C ''function'':170C ''go'':207C ''group'':31C,35C,41C ''idea'':79C,193C ''intend'':37C,43C ''interest'':126C ''kind'':134C ''li'':113C,157C ''list'':116C,151C ''m'':12C ''main'':78C ''match'':2A,74C ''member'':86C ''might'':124C,160C ''need'':17C,99C ''non'':166C ''non-quantifi'':165C ''object'':63C ''ol'':112C ''p'':5C,27C,51C,104C,114C,158C,175C,213C ''particular'':130C ''peopl'':33C ''point'':82C ''popul'':142C ''possibl'':225C ''predict'':147C ''prefer'':1A,76C ''previous'':150C ''price'':169C ''problem'':25C,110C,183C,223C ''product'':48C,53C,95C ''project'':10C ''quantifi'':167C ''rare'':139C ''realli'':196C ''refer'':219C ''sell'':45C ''seri'':56C ''side'':9C ''similar'':222C ''solut'':21C ''solv'':185C ''structur'':19C ''suggest'':177C,211C ''suit'':97C ''think'':197C ''transact'':68C ''two'':30C ''versa'':102C ''vice'':101C ''way'':190C ''whoâ'':93C ''wild'':208C ''work'':13C ''would'':215C ''x'':49C,59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (478, 2, '2014-06-19 03:20:00.847', 1, NULL, NULL, '&lt;p&gt;This can be a cross between machine learning and simple matching exercise.&lt;/p&gt;

&lt;p&gt;I think X_i tend to be rather defined and finite, while A_i can be vague and not finite. From a pure algorithm perspective I would search for instances where X_i = A_i and store the results into a container of sort. The more hits for certain X''es where X_i_n = A_i_k the more points X scores. X''es are then presented to A in the order of points from best match to lowest match.&lt;/p&gt;

&lt;p&gt;Onto the machine learning mechanism, as the algorithm serves a lot of As (by that mean thousands and thousands, even millions) patterns will start to develop and certain combination of A_i''s will be more prevalent, or in other words, worth more to other A_i''s for a certain category of A. Using these patterns, the weighting of points will be re-balanced for higher chance of hitting the correct offers.&lt;/p&gt;

&lt;p&gt;Kind of like how a search engine works.&lt;/p&gt;
', NULL, NULL, '''/p'':14C,97C,173C,183C ''algorithm'':39C,106C ''balanc'':164C ''best'':92C ''categori'':150C ''certain'':64C,126C,149C ''chanc'':167C ''combin'':127C ''contain'':57C ''correct'':171C ''cross'':6C ''defin'':24C ''develop'':124C ''engin'':181C ''es'':66C,80C ''even'':118C ''exercis'':13C ''finit'':26C,35C ''higher'':166C ''hit'':62C,169C ''instanc'':45C ''k'':73C ''kind'':175C ''learn'':9C,102C ''like'':177C ''lot'':109C ''lowest'':95C ''machin'':8C,101C ''match'':12C,93C,96C ''mean'':114C ''mechan'':103C ''million'':119C ''n'':70C ''offer'':172C ''onto'':99C ''order'':88C ''p'':1C,15C,98C,174C ''pattern'':120C,155C ''perspect'':40C ''point'':76C,90C,159C ''present'':83C ''preval'':135C ''pure'':38C ''rather'':23C ''re'':163C ''re-balanc'':162C ''result'':54C ''score'':78C ''search'':43C,180C ''serv'':107C ''simpl'':11C ''sort'':59C ''start'':122C ''store'':52C ''tend'':20C ''think'':17C ''thousand'':115C,117C ''use'':153C ''vagu'':32C ''weight'':157C ''word'':139C ''work'':182C ''worth'':140C ''would'':42C ''x'':18C,47C,65C,68C,77C,79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (480, 2, '2014-06-19 10:35:37.19', 4, NULL, NULL, '&lt;p&gt;One fairly powerful R package for regression with an ordinal categorical response is VGAM, on the CRAN. The vignette contains some examples of ordinal regression, but admittedly I have never tried it on such a large dataset, so I cannot estimate how long it may take. You may find some additional material about VGAM on the author''s &lt;a href="https://www.stat.auckland.ac.nz/~yee/VGAM/" rel="nofollow"&gt;page&lt;/a&gt;. Alternatively you could take a look at Laura Thompson''s &lt;a href="https://www.google.com/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=3&amp;amp;cad=rja&amp;amp;uact=8&amp;amp;ved=0CDUQFjAC&amp;amp;url=http://home.comcast.net/~lthompson221/Splusdiscrete2.pdf&amp;amp;ei=8bmiU4HSFcjA7Abk4YHgDg&amp;amp;usg=AFQjCNHuBp2_nRpaPOFgdkcQWJGuSO9V6A&amp;amp;sig2=hAy4d3mu9WCJZulqxCzraw" rel="nofollow"&gt;companion&lt;/a&gt; to Agresti''s book "Categorical Data Analysis". Chapter 7 of Thompson''s book describes cumulative logit models, which are frequently used with ordinal responses.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/a'':68C,87C ''/p'':112C,117C ''/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=3&amp;amp;cad=rja&amp;amp;uact=8&amp;amp;ved=0cduqfjac&amp;amp;url=http://home.comcast.net/~lthompson221/splusdiscrete2.pdf&amp;amp;ei=8bmiu4hsfcja7abk4yhgdg&amp;amp;usg=afqjcnhubp2_nrpapofgdkcqwjguso9v6a&amp;amp;sig2=hay4d3mu9wcjzulqxczraw'':83C ''/~yee/vgam/'':64C ''7'':96C ''addit'':52C ''admit'':28C ''agresti'':89C ''altern'':69C ''analysi'':94C ''author'':58C ''book'':91C,100C ''cannot'':41C ''categor'':12C,92C ''chapter'':95C ''companion'':86C ''contain'':21C ''could'':71C ''cran'':18C ''cumul'':102C ''data'':93C ''dataset'':38C ''describ'':101C ''estim'':42C ''exampl'':23C ''fair'':3C ''find'':50C ''frequent'':107C ''help'':116C ''hope'':114C ''href'':61C,80C ''larg'':37C ''laura'':76C ''logit'':103C ''long'':44C ''look'':74C ''materi'':53C ''may'':46C,49C ''model'':104C ''never'':31C ''nofollow'':66C,85C ''one'':2C ''ordin'':11C,25C,110C ''p'':1C,113C ''packag'':6C ''page'':67C ''power'':4C ''r'':5C ''regress'':8C,26C ''rel'':65C,84C ''respons'':13C,111C ''take'':47C,72C ''thompson'':77C,98C ''tri'':32C ''use'':108C ''vgam'':15C,55C ''vignett'':20C ''www.google.com'':82C ''www.google.com/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=3&amp;amp;cad=rja&amp;amp;uact=8&amp;amp;ved=0cduqfjac&amp;amp;url=http://home.comcast.net/~lthompson221/splusdiscrete2.pdf&amp;amp;ei=8bmiu4hsfcja7abk4yhgdg&amp;amp;usg=afqjcnhubp2_nrpapofgdkcqwjguso9v6a&amp;amp;sig2=hay4d3mu9wcjzulqxczraw'':81C ''www.stat.auckland.ac.nz'':63C ''www.stat.auckland.ac.nz/~yee/vgam/'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (481, 2, '2014-06-19 12:22:42.017', 1, NULL, NULL, '&lt;p&gt;In graph theory a clique indicates a fully connected set of nodes: &lt;a href="http://books.google.com/books?id=E3-OSVSPbU0C&amp;amp;pg=PA40&amp;amp;lpg=PA40&amp;amp;dq=%22graph%20theory%22,%20%22p-clique%22&amp;amp;source=bl&amp;amp;ots=smbhcK-9AC&amp;amp;sig=X0v_EbqSqB4WBbudgPbqo_j1pvk&amp;amp;hl=en&amp;amp;sa=X&amp;amp;ei=VNWiU8KaA9WxsQSQhYGIAg&amp;amp;ved=0CDEQ6AEwBA#v=onepage&amp;amp;q=%22graph%20theory%22,%20%22p-clique%22&amp;amp;f=false" rel="nofollow"&gt;as noted here&lt;/a&gt;, a p-clique simply indicates a clique comoprised of p nodes. A k-clique is an undirected graph and a number k, and the output is a clique of size k if one exists.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Clique_problem" rel="nofollow"&gt;Clique Problem&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (482, 2, '2014-06-19 13:23:53.387', 7, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Short Answer&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Yes, logistic regression is a regression algorithm and it does predict a continuous outcome: the probability of an event.  That we use it as a binary classifier is due to the interpretation of the outcome.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Detail&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Logistic regression is a type of generalize linear regression model.&lt;/p&gt;

&lt;p&gt;In an ordinary linear regression model, a continuous outcome, &lt;code&gt;y&lt;/code&gt;, is modeled as the sum of the product of predictors and their effect:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;y = b_0 + b_1 * x_1 + b_2 * x_2 + ... b_n * x_n + e
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where &lt;code&gt;e&lt;/code&gt; is the error.&lt;/p&gt;

&lt;p&gt;Generalized linear models do not model &lt;code&gt;y&lt;/code&gt; directly.  Instead, they use transformations to expand the domain of &lt;code&gt;y&lt;/code&gt; to all real numbers.  This transformation is called the link function.  For logistic regression the link function is the logit function (usually, see note below).&lt;/p&gt;

&lt;p&gt;The logit function is defined as &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ln(y/(1 + y))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Thus the form of logistic regression is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ln(y/(1 + y)) = b_0 + b_1 * x_1 + b_2 * x_2 + ... b_n * x_n + e
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where &lt;code&gt;y&lt;/code&gt; is the probability of an event.&lt;/p&gt;

&lt;p&gt;The fact that we use it as a binary classifier is due to the interpretation of the outcome.&lt;/p&gt;

&lt;p&gt;Note: probit is another link function used for logistic regression but logit is the most widely used.&lt;/p&gt;
', NULL, NULL, '''/code'':73C,106C,112C,126C,139C,180C,212C,218C ''/p'':6C,43C,48C,60C,87C,116C,165C,173C,190C,225C,245C,264C ''/pre'':107C,181C,213C ''/strong'':5C,47C ''0'':92C,198C ''1'':94C,96C,178C,195C,200C,202C ''2'':98C,100C,204C,206C ''algorithm'':14C ''anoth'':250C ''answer'':4C ''b'':91C,93C,97C,101C,197C,199C,203C,207C ''binari'':33C,235C ''call'':147C ''classifi'':34C,236C ''code'':71C,89C,110C,124C,137C,175C,192C,216C ''continu'':20C,69C ''defin'':171C ''detail'':46C ''direct'':127C ''domain'':135C ''due'':36C,238C ''e'':105C,111C,211C ''effect'':86C ''error'':115C ''event'':26C,224C ''expand'':133C ''fact'':228C ''form'':185C ''function'':150C,156C,160C,169C,252C ''general'':56C,118C ''instead'':128C ''interpret'':39C,241C ''linear'':57C,65C,119C ''link'':149C,155C,251C ''ln'':176C,193C ''logist'':9C,50C,152C,187C,255C ''logit'':159C,168C,258C ''model'':59C,67C,75C,120C,123C ''n'':102C,104C,208C,210C ''note'':163C,247C ''number'':143C ''ordinari'':64C ''outcom'':21C,42C,70C,244C ''p'':1C,7C,44C,49C,61C,108C,117C,166C,182C,214C,226C,246C ''pre'':88C,174C,191C ''predict'':18C ''predictor'':83C ''probabl'':23C,221C ''probit'':248C ''product'':81C ''real'':142C ''regress'':10C,13C,51C,58C,66C,153C,188C,256C ''see'':162C ''short'':3C ''strong'':2C,45C ''sum'':78C ''thus'':183C ''transform'':131C,145C ''type'':54C ''use'':29C,130C,231C,253C,263C ''usual'':161C ''wide'':262C ''x'':95C,99C,103C,201C,205C,209C ''y'':72C,90C,125C,138C,177C,179C,194C,196C,217C ''yes'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (483, 2, '2014-06-19 14:15:07.433', 2, NULL, NULL, '&lt;p&gt;You could try Neural Network. You can find 2 great explanations on how to apply NN on time series &lt;a href="http://stats.stackexchange.com/questions/10162/how-to-apply-neural-network-to-time-series-forecasting"&gt;here&lt;/a&gt; and &lt;a href="http://stackoverflow.com/questions/18670558/prediction-using-recurrent-neural-network-on-time-series-dataset"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Note that it is best practice to :&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Deseasonalize/detrend the input data (so that the NN will not learn the seasonality).&lt;/li&gt;
&lt;li&gt;Rescale/Normalize the input data.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Because what you are looking for is a regression problem, the activation functions should be &lt;code&gt;linear&lt;/code&gt; and not &lt;code&gt;sigmoid&lt;/code&gt; or &lt;code&gt;tanh&lt;/code&gt; and you aim to minimize the &lt;code&gt;sum-of-squares error&lt;/code&gt; (as opposition to the maximization of the &lt;code&gt;negative log-likelihood&lt;/code&gt; in a classification problem).&lt;/p&gt;
', NULL, NULL, '''/a'':27C,35C ''/code'':87C,92C,96C,109C,122C ''/li'':61C,67C ''/p'':36C,45C,127C ''/questions/10162/how-to-apply-neural-network-to-time-series-forecasting'':25C ''/questions/18670558/prediction-using-recurrent-neural-network-on-time-series-dataset'':33C ''/ul'':68C ''2'':10C ''activ'':81C ''aim'':99C ''appli'':16C ''best'':42C ''classif'':125C ''code'':85C,90C,94C,103C,117C ''could'':3C ''data'':51C,66C ''deseasonalize/detrend'':48C ''error'':108C ''explan'':12C ''find'':9C ''function'':82C ''great'':11C ''href'':22C,30C ''input'':50C,65C ''learn'':58C ''li'':47C,62C ''likelihood'':121C ''linear'':86C ''log'':120C ''log-likelihood'':119C ''look'':74C ''maxim'':114C ''minim'':101C ''negat'':118C ''network'':6C ''neural'':5C ''nn'':17C,55C ''note'':38C ''opposit'':111C ''p'':1C,37C,69C ''practic'':43C ''problem'':79C,126C ''regress'':78C ''rescale/normalize'':63C ''season'':60C ''seri'':20C ''sigmoid'':91C ''squar'':107C ''stackoverflow.com'':32C ''stackoverflow.com/questions/18670558/prediction-using-recurrent-neural-network-on-time-series-dataset'':31C ''stats.stackexchange.com'':24C ''stats.stackexchange.com/questions/10162/how-to-apply-neural-network-to-time-series-forecasting'':23C ''sum'':105C ''sum-of-squar'':104C ''tanh'':95C ''time'':19C ''tri'':4C ''ul'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (727, 2, '2014-07-11 21:35:22.677', 3, NULL, NULL, '&lt;p&gt;Your problem is that the resets aren''t part of your linear model. You either have to cut your data into different fragments at the resets, so that no reset occurs within each fragment, and you can fit a linear model to each fragment. Or you can build a more complicated model that allows for resets. In this case, either the time of occurrence of the resets has to be put into the model manually, or the time of resets has to be a free parameter in the model that is determined by fitting the model to the data.&lt;/p&gt;
', NULL, NULL, '''/p'':101C ''allow'':55C ''aren'':8C ''build'':49C ''case'':60C ''complic'':52C ''cut'':19C ''data'':21C,100C ''determin'':93C ''differ'':23C ''either'':16C,61C ''fit'':39C,95C ''fragment'':24C,35C,45C ''free'':86C ''linear'':13C,41C ''manual'':76C ''model'':14C,42C,53C,75C,90C,97C ''occur'':32C ''occurr'':65C ''p'':1C ''paramet'':87C ''part'':10C ''problem'':3C ''put'':72C ''reset'':7C,27C,31C,57C,68C,81C ''time'':63C,79C ''within'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (484, 2, '2014-06-19 14:52:59.877', 13, NULL, NULL, '&lt;p&gt;Logistic regression is regression, first and foremost. It becomes a classifier by adding a decision rule. I will give an example that goes backwards. That is, instead of taking data and fitting a model, I''m going to start with the model in order to show how this is truly a regression problem.&lt;/p&gt;

&lt;p&gt;In logistic regression, we are modeling the log odds, or logit, that an event occurs, which is a continuous quantity. If the probability that event A occurs is P(A), the odds are:&lt;/p&gt;

&lt;p&gt;P(A) / (1 - P(A)) &lt;/p&gt;

&lt;p&gt;The log odds, then, are:&lt;/p&gt;

&lt;p&gt;log { (P(A) / (1 - P(A))) }&lt;/p&gt;

&lt;p&gt;As in linear regression, we model this with a linear combination of coefficients and predictors:&lt;/p&gt;

&lt;p&gt;logit = b0 + b1 * x1 + b2 * x2 ...&lt;/p&gt;

&lt;p&gt;Imagine we are given a model of whether a person has gray hair. Our model uses age as the only predictor. Here, our event A = a person has gray hair:&lt;/p&gt;

&lt;p&gt;log odds of gray hair = -10 + 0.25 * age&lt;/p&gt;

&lt;p&gt;...Regression! Here is some Python code and a plot:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

x = np.linspace(0, 100, 100)

def log_odds(x):
    return -10 + .25 * x

plt.plot(x, log_odds(x))
plt.xlabel("age")
plt.ylabel("log odds of gray hair")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/xR0OT.png" alt="plot of the log odds for our toy example"&gt;&lt;/p&gt;

&lt;p&gt;Now, let''s make it a classifier. First, we need to transform the log odds to get out our probability P(A). We can use the sigmoid function:&lt;/p&gt;

&lt;p&gt;P(A) = 1 / (1 + exp(-log odds))&lt;/p&gt;

&lt;p&gt;Here''s the code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;plt.plot(x, 1 / (1 + np.exp(-log_odds(x))))
plt.xlabel("age")
plt.ylabel("probability of gray hair")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/hSpCa.png" alt="plot of the probability of gray hair for our toy example"&gt;&lt;/p&gt;

&lt;p&gt;The last thing we need to make this a classifier is to add a decision rule. One very common rule is to classify a success whenever P(A) &gt; 0.5. We will adopt that rule, which implies that our classifier will predict gray hair whenever a person is older than 40 and will predict non-gray hair whenever a person is under 40.&lt;/p&gt;

&lt;p&gt;Logistic regression works great as a classifier in more realistic examples too, but before it can be a classifier, it must be a regression technique!&lt;/p&gt;
', NULL, NULL, '''-10'':176C,217C ''/code'':233C,314C ''/hspca.png'':321C ''/p'':55C,90C,97C,104C,112C,129C,137C,169C,179C,190C,251C,281C,290C,296C,334C,399C,426C ''/pre'':234C,315C ''/xr0ot.png'':240C ''0'':209C ''0.25'':177C ''0.5'':364C ''1'':94C,109C,285C,286C,301C,302C ''100'':210C,211C ''25'':218C ''40'':385C,398C ''ad'':14C ''add'':348C ''adopt'':367C ''age'':155C,178C,226C,308C ''alt'':241C,322C ''b0'':132C ''b1'':133C ''b2'':135C ''backward'':25C ''becom'':10C ''classifi'':12C,259C,345C,358C,374C,407C,419C ''code'':186C,192C,295C,298C ''coeffici'':126C ''combin'':124C ''common'':354C ''continu'':75C ''data'':31C ''decis'':16C,350C ''def'':212C ''event'':70C,81C,162C ''exampl'':22C,250C,333C,411C ''exp'':287C ''first'':6C,260C ''fit'':33C ''foremost'':8C ''function'':280C ''get'':269C ''give'':20C ''given'':142C ''go'':38C ''goe'':24C ''gray'':150C,167C,174C,231C,312C,328C,377C,391C ''great'':404C ''hair'':151C,168C,175C,232C,313C,329C,378C,392C ''i.stack.imgur.com'':239C,320C ''i.stack.imgur.com/hspca.png'':319C ''i.stack.imgur.com/xr0ot.png'':238C ''imagin'':139C ''img'':236C,317C ''impli'':371C ''import'':195C,199C,203C ''inlin'':194C ''instead'':28C ''last'':337C ''let'':254C ''linear'':116C,123C ''log'':64C,100C,106C,171C,213C,222C,228C,245C,266C,288C,304C ''logist'':2C,58C,401C ''logit'':67C,131C ''m'':37C ''make'':256C,342C ''matplotlib'':193C ''matplotlib.pyplot'':196C ''model'':35C,43C,62C,119C,144C,153C ''must'':421C ''need'':262C,340C ''non'':390C ''non-gray'':389C ''np'':202C ''np.exp'':303C ''np.linspace'':208C ''numpi'':200C ''occur'':71C,83C ''odd'':65C,88C,101C,172C,214C,223C,229C,246C,267C,289C,305C ''older'':383C ''one'':352C ''order'':45C ''p'':1C,56C,85C,91C,92C,95C,98C,105C,107C,110C,113C,130C,138C,170C,180C,235C,252C,273C,282C,283C,291C,316C,335C,362C,400C ''person'':148C,165C,381C,395C ''plot'':189C,242C,323C ''plt'':198C ''plt.plot'':220C,299C ''plt.xlabel'':225C,307C ''plt.ylabel'':227C,309C ''pre'':191C,297C ''predict'':376C,388C ''predictor'':128C,159C ''probabl'':79C,272C,310C,326C ''problem'':54C ''python'':185C ''quantiti'':76C ''realist'':410C ''regress'':3C,5C,53C,59C,117C,181C,402C,424C ''return'':216C ''rule'':17C,351C,355C,369C ''seaborn'':204C ''show'':47C ''sigmoid'':279C ''sns'':206C ''src'':237C,318C ''start'':40C ''success'':360C ''take'':30C ''techniqu'':425C ''thing'':338C ''toy'':249C,332C ''transform'':264C ''truli'':51C ''use'':154C,277C ''whenev'':361C,379C,393C ''whether'':146C ''work'':403C ''x'':207C,215C,219C,221C,224C,300C,306C ''x1'':134C ''x2'':136C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (485, 2, '2014-06-19 16:07:29.907', 3, NULL, NULL, '&lt;p&gt;My answer would be no. I consider Data mining to be one of the miscellaneous fields in Data science. Data Mining is mostly considered on yielding questions rather than answering them. It is often termed as "detecting something new", when compared to Data science, where the data scientist try to solve complex problems to be able to reach their end results. However both terms have many commonalities between them.
For example..if u have an agricultural land where u aim to find the affected plants..Here spatial data mining plays a key role in doing this job.There are good chances that you may end  up with not only finding out the affected plants in the land but also the extent to which they are affected.......this is something that is not possible with data science.&lt;/p&gt;
', NULL, NULL, '''/p'':137C ''abl'':57C ''affect'':85C,113C,126C ''agricultur'':77C ''aim'':81C ''also'':119C ''answer'':3C,31C ''chanc'':101C ''common'':68C ''compar'':42C ''complex'':53C ''consid'':8C,25C ''data'':9C,19C,21C,44C,48C,89C,135C ''detect'':38C ''end'':61C,105C ''exampl'':72C ''extent'':121C ''field'':17C ''find'':83C,110C ''good'':100C ''howev'':63C ''job.there'':98C ''key'':93C ''land'':78C,117C ''mani'':67C ''may'':104C ''mine'':10C,22C,90C ''miscellan'':16C ''most'':24C ''new'':40C ''often'':35C ''one'':13C ''p'':1C ''plant'':86C,114C ''play'':91C ''possibl'':133C ''problem'':54C ''question'':28C ''rather'':29C ''reach'':59C ''result'':62C ''role'':94C ''scienc'':20C,45C,136C ''scientist'':49C ''solv'':52C ''someth'':39C,129C ''spatial'':88C ''term'':36C,65C ''tri'':50C ''u'':74C,80C ''would'':4C ''yield'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (487, 2, '2014-06-19 18:01:36.12', 5, NULL, NULL, '&lt;p&gt;A good list of publicly available social network datasets can be found on the Stanford Network Analysis Project website: &lt;/p&gt;

&lt;p&gt;&lt;a href="https://snap.stanford.edu/data/"&gt;SNAP datasets&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The site contains internet social network data (Facebook, Twitter, Google Plus), Citation networks for academic journals, co-purchasing networks from Amazon and several others kinds of networks. They have directed, undirected, and bipartite graphs and all datasets are snapshots that can be downloaded in compressed form.&lt;/p&gt;
', NULL, NULL, '''/a'':30C ''/data/'':27C ''/p'':21C,31C,80C ''academ'':47C ''amazon'':54C ''analysi'':18C ''avail'':7C ''bipartit'':66C ''citat'':44C ''co'':50C ''co-purchas'':49C ''compress'':78C ''contain'':35C ''data'':39C ''dataset'':10C,29C,70C ''direct'':63C ''download'':76C ''facebook'':40C ''form'':79C ''found'':13C ''good'':3C ''googl'':42C ''graph'':67C ''href'':24C ''internet'':36C ''journal'':48C ''kind'':58C ''list'':4C ''network'':9C,17C,38C,45C,52C,60C ''other'':57C ''p'':1C,22C,32C ''plus'':43C ''project'':19C ''public'':6C ''purchas'':51C ''sever'':56C ''site'':34C ''snap'':28C ''snap.stanford.edu'':26C ''snap.stanford.edu/data/'':25C ''snapshot'':72C ''social'':8C,37C ''stanford'':16C ''twitter'':41C ''undirect'':64C ''websit'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (491, 2, '2014-06-19 18:24:32.023', 6, NULL, NULL, '&lt;p&gt;As suggested, &lt;strong&gt;going wild&lt;/strong&gt;. First of all, correct me if Iâm wrong:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Just a few features exist for each unique product;&lt;/li&gt;
&lt;li&gt;There is no ultimate features list, and clients are able to add new features to their products.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If so, constructing full product-feature table could be computational expensive. And final data table would be extremely sparse.&lt;/p&gt;

&lt;p&gt;The first step is narrowing customers (products) list for matching. Letâs build a bipartite graph, where sellers would be type-1 nodes, and buyers would be type-2 nodes. Create an edge between any seller and buyer every time they reference a similar product feature, as in the following sketch:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/q1JTN.png" alt="graph"&gt;&lt;/p&gt;

&lt;p&gt;Using the above graph, for every unique sellerâs product you can select only buyers who are interested in features that match the product (itâs possible to filter &lt;strong&gt;at least one&lt;/strong&gt; common feature, match the full set of features, or set a threshold level). But certainly, thatâs not enough. The next step is to compare feature values, as described by the seller and buyer. There are a lot of variants (e.g., k-Nearest-Neighbors). But why not try to solve this question using the existing graph? Letâs add weights to the edges:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;for continuous features (e.g., price): &lt;/p&gt;

&lt;p&gt;&lt;img src="https://chart.googleapis.com/chart?cht=tx&amp;amp;chl=weight%20%3D%20%5Cfrac%7B%7Cprice_%7Bsell%7D%20-%20price_%7Bbuy%7D%7C%7D%7B%5Cmax%28price%29%20-%20%5Cmin%28price%29%7D" alt="price_weight"&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;for binary and non-quantifiable features - just logical biconditional:&lt;/p&gt;

&lt;p&gt;&lt;img src="https://chart.googleapis.com/chart?cht=tx&amp;amp;chl=weight%20%3D%20feature_%7Bsell%7D%20%5Cleftrightarrow%20feature_%7Bbuy%7D" alt="feature_weight"&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The main idea here is to &lt;strong&gt;scale&lt;/strong&gt; every feature to the interval &lt;code&gt;[0, 1]&lt;/code&gt;. Additionally, we can use feature coefficients to determine most important features. E.g., assuming price is twice as important as availability of some rare function: &lt;/p&gt;

&lt;p&gt;&lt;img src="https://chart.googleapis.com/chart?cht=tx&amp;amp;chl=adj.price.weight%20%3D%202%20%5Ctimes%20price.weight" alt="adj_w_1"&gt;&lt;/p&gt;

&lt;p&gt;&lt;img src="https://chart.googleapis.com/chart?cht=tx&amp;amp;chl=adj.feature.weight%20%3D%201%20%5Ctimes%20feature.weight" alt="adj_w_2"&gt;&lt;/p&gt;

&lt;p&gt;One of the final steps is simplifying the graph structure and reducing many edges to one edge with weight equal to the sum of the previously calculated weights of each feature. With such a reduced structure every pair of customers/products could have only one edge (no parallel edges). So, to find the best deal for exact seller you just need to select connected buyers with max weighted edges.&lt;/p&gt;

&lt;p&gt;Future challenge: introduce a cheap method for weighting edges on first step :)&lt;/p&gt;
', NULL, NULL, '''-1'':94C ''-2'':101C ''/chart?cht=tx&amp;amp;chl=adj.feature.weight%20%3d%201%20%5ctimes%20feature.weight'':339C ''/chart?cht=tx&amp;amp;chl=adj.price.weight%20%3d%202%20%5ctimes%20price.weight'':328C ''/chart?cht=tx&amp;amp;chl=weight%20%3d%20%5cfrac%7b%7cprice_%7bsell%7d%20-%20price_%7bbuy%7d%7c%7d%7b%5cmax%28price%29%20-%20%5cmin%28price%29%7d'':248C ''/chart?cht=tx&amp;amp;chl=weight%20%3d%20feature_%7bsell%7d%20%5cleftrightarrow%20feature_%7bbuy%7d'':272C ''/code'':297C ''/li'':29C,48C,253C,277C ''/p'':17C,71C,124C,133C,233C,242C,252C,266C,276C,322C,333C,344C,414C,428C ''/q1jtn.png'':130C ''/strong'':7C,168C,288C ''/ul'':49C,278C ''0'':295C ''1'':296C,332C ''2'':343C ''abl'':40C ''add'':42C,228C ''addit'':298C ''adj'':330C,341C ''alt'':131C,249C,273C,329C,340C ''assum'':310C ''avail'':317C ''best'':398C ''bicondit'':265C ''binari'':257C ''bipartit'':87C ''build'':85C ''buyer'':97C,110C,149C,202C,409C ''calcul'':372C ''certain'':183C ''challeng'':417C ''chart.googleapis.com'':247C,271C,327C,338C ''chart.googleapis.com/chart?cht=tx&amp;amp;chl=adj.feature.weight%20%3d%201%20%5ctimes%20feature.weight'':337C ''chart.googleapis.com/chart?cht=tx&amp;amp;chl=adj.price.weight%20%3d%202%20%5ctimes%20price.weight'':326C ''chart.googleapis.com/chart?cht=tx&amp;amp;chl=weight%20%3d%20%5cfrac%7b%7cprice_%7bsell%7d%20-%20price_%7bbuy%7d%7c%7d%7b%5cmax%28price%29%20-%20%5cmin%28price%29%7d'':246C ''chart.googleapis.com/chart?cht=tx&amp;amp;chl=weight%20%3d%20feature_%7bsell%7d%20%5cleftrightarrow%20feature_%7bbuy%7d'':270C ''cheap'':420C ''client'':38C ''code'':294C ''coeffici'':303C ''common'':169C ''compar'':193C ''comput'':61C ''connect'':408C ''construct'':53C ''continu'':238C ''correct'':11C ''could'':59C,386C ''creat'':103C ''custom'':78C ''customers/products'':385C ''data'':65C ''deal'':399C ''describ'':197C ''determin'':305C ''e.g'':209C,240C,309C ''edg'':105C,232C,359C,362C,390C,393C,413C,424C ''enough'':187C ''equal'':365C ''everi'':111C,140C,289C,382C ''exact'':401C ''exist'':24C,224C ''expens'':62C ''extrem'':69C ''featur'':23C,35C,44C,57C,118C,154C,170C,176C,194C,239C,262C,274C,290C,302C,308C,376C ''filter'':163C ''final'':64C,349C ''find'':396C ''first'':8C,74C,426C ''follow'':122C ''full'':54C,173C ''function'':321C ''futur'':416C ''go'':5C ''graph'':88C,132C,138C,225C,354C ''i.stack.imgur.com'':129C ''i.stack.imgur.com/q1jtn.png'':128C ''idea'':282C ''img'':126C,244C,268C,324C,335C ''import'':307C,315C ''interest'':152C ''interv'':293C ''introduc'':418C ''itâ'':159C ''iâ'':14C ''k'':211C ''k-nearest-neighbor'':210C ''least'':166C ''letâ'':83C,226C ''level'':181C ''li'':19C,30C,235C,254C ''list'':36C,80C ''logic'':264C ''lot'':206C ''m'':15C ''main'':281C ''mani'':358C ''match'':82C,156C,171C ''max'':411C ''method'':421C ''narrow'':77C ''nearest'':212C ''need'':405C ''neighbor'':213C ''new'':43C ''next'':189C ''node'':95C,102C ''non'':260C ''non-quantifi'':259C ''one'':167C,346C,361C,389C ''p'':1C,50C,72C,125C,134C,236C,243C,255C,267C,279C,323C,334C,345C,415C ''pair'':383C ''parallel'':392C ''possibl'':161C ''previous'':371C ''price'':241C,250C,311C ''product'':28C,47C,56C,79C,117C,144C,158C ''product-featur'':55C ''quantifi'':261C ''question'':221C ''rare'':320C ''reduc'':357C,380C ''refer'':114C ''scale'':287C ''select'':147C,407C ''seller'':90C,108C,200C,402C ''sellerâ'':142C ''set'':174C,178C ''similar'':116C ''simplifi'':352C ''sketch'':123C ''solv'':219C ''spars'':70C ''src'':127C,245C,269C,325C,336C ''step'':75C,190C,350C,427C ''strong'':4C,164C,286C ''structur'':355C,381C ''suggest'':3C ''sum'':368C ''tabl'':58C,66C ''thatâ'':184C ''threshold'':180C ''time'':112C ''tri'':217C ''twice'':313C ''type'':93C,100C ''ul'':18C,234C ''ultim'':34C ''uniqu'':27C,141C ''use'':135C,222C,301C ''valu'':195C ''variant'':208C ''w'':331C,342C ''weight'':229C,251C,275C,364C,373C,412C,423C ''wild'':6C ''would'':67C,91C,98C ''wrong'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (492, 1, '2014-06-19 19:29:57.797', 11, 1755, 'Word2Vec for Named Entity Recognition', '&lt;p&gt;I''m looking to use google''s word2vec implementation to build a named entity recognition system.  I''ve heard that recursive neural nets with back propagation through structure are well suited for named entity recognition tasks, but I''ve been unable to find a decent implementation or a decent tutorial for that type of model. Because I''m working with an atypical corpus, standard NER tools in NLTK and similar have performed very poorly, and it looks like I''ll have to train my own system.   &lt;/p&gt;

&lt;p&gt;In short, what resources are available for this kind of problem?  Is there a standard recursive neural net implementation available?&lt;/p&gt;
', 'machine-learning  python  nlp  neuralnetwork  recommendation', NULL, '''/p'':100C,122C ''atyp'':75C ''avail'':107C,121C ''back'':38C ''build'':24C ''corpus'':76C ''decent'':58C,62C ''entiti'':4A,27C,47C ''find'':56C ''googl'':19C ''heard'':32C ''implement'':22C,59C,120C ''kind'':110C ''learn'':8B ''like'':91C ''ll'':93C ''look'':16C,90C ''m'':15C,71C ''machin'':7B ''machine-learn'':6B ''model'':68C ''name'':3A,26C,46C ''ner'':78C ''net'':36C,119C ''neural'':35C,118C ''neuralnetwork'':11B ''nlp'':10B ''nltk'':81C ''p'':13C,101C ''perform'':85C ''poor'':87C ''problem'':112C ''propag'':39C ''python'':9B ''recognit'':5A,28C,48C ''recommend'':12B ''recurs'':34C,117C ''resourc'':105C ''short'':103C ''similar'':83C ''standard'':77C,116C ''structur'':41C ''suit'':44C ''system'':29C,99C ''task'':49C ''tool'':79C ''train'':96C ''tutori'':63C ''type'':66C ''unabl'':54C ''use'':18C ''ve'':31C,52C ''well'':43C ''word2vec'':1A,21C ''work'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (493, 2, '2014-06-19 19:33:12.067', 1, NULL, NULL, '&lt;p&gt;Well, firstly, I would not even use things like Machine learning without having in depth knowledge. Simplistic things I would do if I had this time series is:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Write sql queries to understand which of the times you have the busiest, average and low foot traffic.&lt;/li&gt;
&lt;li&gt;Then try to visualize the whole time series, and you could use basic pattern matching algorithms to pick up patterns.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;This two things will help you understand what your data set is telling you. Then, with that in hand, you will probably be in a better state to use machine learning algorithms.&lt;/p&gt;

&lt;p&gt;Also, I''m currently working in building something on time series, and using time series analysis will help you much more than machine learning. For example, there are pattern recognition algorithms that you can use that uses every day data to show patterns, and ones which use up to as much as 3 to 6 months of data to catch a pattern.&lt;/p&gt;
', NULL, NULL, '''/li'':51C,73C ''/ol'':74C ''/p'':30C,108C,172C ''3'':162C ''6'':164C ''algorithm'':68C,107C,140C ''also'':110C ''analysi'':125C ''averag'':46C ''basic'':65C ''better'':101C ''build'':116C ''busiest'':45C ''catch'':169C ''could'':63C ''current'':113C ''data'':85C,149C,167C ''day'':148C ''depth'':16C ''even'':7C ''everi'':147C ''exampl'':135C ''first'':3C ''foot'':49C ''hand'':94C ''help'':80C,127C ''knowledg'':17C ''learn'':12C,106C,133C ''li'':32C,52C ''like'':10C ''low'':48C ''m'':112C ''machin'':11C,105C,132C ''match'':67C ''month'':165C ''much'':129C,160C ''ol'':31C ''one'':154C ''p'':1C,75C,109C ''pattern'':66C,72C,138C,152C,171C ''pick'':70C ''probabl'':97C ''queri'':35C ''recognit'':139C ''seri'':28C,60C,120C,124C ''set'':86C ''show'':151C ''simplist'':18C ''someth'':117C ''sql'':34C ''state'':102C ''tell'':88C ''thing'':9C,19C,78C ''time'':27C,41C,59C,119C,123C ''traffic'':50C ''tri'':54C ''two'':77C ''understand'':37C,82C ''use'':8C,64C,104C,122C,144C,146C,156C ''visual'':56C ''well'':2C ''whole'':58C ''without'':13C ''work'':114C ''would'':5C,21C ''write'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (749, 1, '2014-07-16 09:24:51.78', 7, 372, 'Meaning of latent features?', '&lt;p&gt;I am learning about matrix factorization for recommender systems and I am seeing the term &lt;code&gt;latent features&lt;/code&gt; occurring too frequently but I am unable to understand what it means. I know what a feature is but I don''t understand the idea of latent features. Could please explain it? Or at least point me to a paper/place where I can read about it?&lt;/p&gt;
', 'machine-learning  data-mining  recommendation', NULL, '''/code'':31C ''/p'':78C ''code'':28C ''could'':60C ''data'':9B ''data-min'':8B ''explain'':62C ''factor'':18C ''featur'':4A,30C,48C,59C ''frequent'':34C ''idea'':56C ''know'':45C ''latent'':3A,29C,58C ''learn'':7B,15C ''least'':66C ''machin'':6B ''machine-learn'':5B ''matrix'':17C ''mean'':1A,43C ''mine'':10B ''occur'':32C ''p'':12C ''paper/place'':71C ''pleas'':61C ''point'':67C ''read'':75C ''recommend'':11B,20C ''see'':25C ''system'':21C ''term'':27C ''unabl'':38C ''understand'':40C,54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (495, 2, '2014-06-19 22:02:44.05', 6, NULL, NULL, '&lt;p&gt;I would add a third column called &lt;code&gt;month&lt;/code&gt; and then concatenate each list. So if you have a top 100 list for 5 months you will create one big table with 500 entries:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;User-id | Threat_score | month
aaa       45             1
bbb       32             1
ccc       20             1
...       ...            ...
bbb       64             2
ccc       29             2
...       ...            ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then, to answer your first question, you could simply count the occurrences of each user-id. For example, if user &lt;code&gt;bbb&lt;/code&gt; is in your concatenated table five times, then you know that person made your list all five months.&lt;/p&gt;

&lt;p&gt;To answer you second question, you could do a &lt;code&gt;group by&lt;/code&gt; operation to compute some aggregate function of the users. A &lt;code&gt;group by&lt;/code&gt; operation with an average function is a little crude and sensitive to outliers, but it would probably get you close to what you are looking for.&lt;/p&gt;

&lt;p&gt;One possibility for the third question is to compute the difference in threat score between month &lt;code&gt;n-1&lt;/code&gt; and month &lt;code&gt;n&lt;/code&gt;. That is, for each month (not including the first month) you subtract the user''s previous threat score from the current threat score. You can make this a new column so your table would now look like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;User-id | Threat_score | month | difference
aaa       45             1       null
bbb       32             1       null
ccc       20             1       null
...       ...            ...     ...
bbb       64             2       32
ccc       29             2       9
...       ...            ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;With this table, you could again do a &lt;code&gt;group by&lt;/code&gt; operation to find people who consistently have a higher threat score than the previous month or you could simply find people with a large difference between the current month and the previous month.&lt;/p&gt;

&lt;p&gt;As you suggest, visualizing this data is a really good idea. If you care about these threat scores over time (which I think you do), I strongly recommend a simple line chart, with month on the x-axis and threat score on the y-axis. It''s not fancy, but it''s extremely easy to interpret and should give you useful information about the trends.&lt;/p&gt;

&lt;p&gt;Most of this stuff (not the visualization) can be done in SQL and all of it can be done in R or Python (and many other languages). Good luck!&lt;/p&gt;
', NULL, NULL, '''-1'':181C ''/code'':11C,61C,87C,120C,134C,182C,187C,255C,269C ''/p'':37C,106C,161C,225C,303C,372C,403C ''/pre'':62C,256C ''1'':48C,51C,54C,237C,241C,245C ''100'':23C ''2'':57C,60C,249C,253C ''20'':53C,244C ''29'':59C,252C ''32'':50C,240C,250C ''45'':47C,236C ''5'':26C ''500'':35C ''64'':56C,248C ''9'':254C ''aaa'':46C,235C ''add'':4C ''aggreg'':125C ''answer'':66C,109C ''averag'':138C ''axi'':343C,351C ''bbb'':49C,55C,86C,239C,247C ''big'':32C ''call'':8C ''care'':318C ''ccc'':52C,58C,243C,251C ''chart'':336C ''close'':154C ''code'':9C,39C,85C,117C,131C,179C,185C,227C,266C ''column'':7C,217C ''comput'':123C,171C ''concaten'':14C,91C ''consist'':275C ''could'':71C,114C,262C,287C ''count'':73C ''creat'':30C ''crude'':143C ''current'':208C,297C ''data'':310C ''differ'':173C,234C,294C ''done'':383C,392C ''easi'':360C ''entri'':36C ''exampl'':82C ''extrem'':359C ''fanci'':355C ''find'':272C,289C ''first'':68C,196C ''five'':93C,104C ''function'':126C,139C ''get'':152C ''give'':365C ''good'':314C,401C ''group'':118C,132C,267C ''higher'':278C ''id'':42C,80C,230C ''idea'':315C ''includ'':194C ''inform'':368C ''interpret'':362C ''know'':97C ''languag'':400C ''larg'':293C ''like'':224C ''line'':335C ''list'':16C,24C,102C ''littl'':142C ''look'':159C,223C ''luck'':402C ''made'':100C ''make'':213C ''mani'':398C ''month'':10C,27C,45C,105C,178C,184C,192C,197C,233C,284C,298C,302C,338C ''n'':180C,186C ''new'':216C ''null'':238C,242C,246C ''occurr'':75C ''one'':31C,163C ''oper'':121C,135C,270C ''outlier'':147C ''p'':1C,63C,107C,162C,257C,304C,373C ''peopl'':273C,290C ''person'':99C ''possibl'':164C ''pre'':38C,226C ''previous'':203C,283C,301C ''probabl'':151C ''python'':396C ''question'':69C,112C,168C ''r'':394C ''realli'':313C ''recommend'':332C ''score'':44C,176C,205C,210C,232C,280C,322C,346C ''second'':111C ''sensit'':145C ''simpl'':334C ''simpli'':72C,288C ''sql'':385C ''strong'':331C ''stuff'':377C ''subtract'':199C ''suggest'':307C ''tabl'':33C,92C,220C,260C ''think'':327C ''third'':6C,167C ''threat'':43C,175C,204C,209C,231C,279C,321C,345C ''time'':94C,324C ''top'':22C ''trend'':371C ''use'':367C ''user'':41C,79C,84C,129C,201C,229C ''user-id'':40C,78C,228C ''visual'':308C,380C ''would'':3C,150C,221C ''x'':342C ''x-axi'':341C ''y'':350C ''y-axi'':349C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (496, 2, '2014-06-20 03:18:07.653', 3, NULL, NULL, '&lt;p&gt;You have three questions to answer and 100 records per month to analyze.&lt;/p&gt;

&lt;p&gt;Based on this size, I''d recommend doing analysis in a simple SQL database or a spreadsheet to start off with.  The first two questions are fairly easy to figure out.  The third is a little more difficult.&lt;/p&gt;

&lt;p&gt;I''d definitely add a column for month and group all of that data together into a spreadsheet or database table given the questions you want to answer.&lt;/p&gt;

&lt;p&gt;question 1. Users who are consistently showing up in this list&lt;/p&gt;

&lt;p&gt;In excel, this answer should help you out:  &lt;a href="http://superuser.com/questions/442653/ms-excel-how-count-occurence-of-item-in-a-list"&gt;http://superuser.com/questions/442653/ms-excel-how-count-occurence-of-item-in-a-list&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;For a SQL database:  &lt;a href="http://stackoverflow.com/questions/2516546/select-count-duplicates"&gt;http://stackoverflow.com/questions/2516546/select-count-duplicates&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;question 2. Users who are consistently showing up in this list with high risk score &lt;/p&gt;

&lt;p&gt;This is just adding a little complexity to the above.  For SQL, you would further qualify your query based on a minimum risk score value.&lt;/p&gt;

&lt;p&gt;In excel, a straight pivot isn''t going to work, you''ll have to copy the unique values in one column to another, then drag a CountIf function adjacent to each unique value, qualifying the countif function with a minimum risk score.&lt;/p&gt;

&lt;p&gt;question 3. Users who have/reaching the high risk level very fast.&lt;/p&gt;

&lt;p&gt;A fast rise in risk level could be defined as the difference between two months being larger than a given value.&lt;/p&gt;

&lt;p&gt;For each user record you want to know the previous month''s threat value, or assume zero as the previous threat value.&lt;/p&gt;

&lt;p&gt;If that difference is greater than your risk threshold, you want to include it in your report.  If not, they can be filtered from the list.  &lt;/p&gt;

&lt;p&gt;If I had to do this month after month, I would spend the two hours it might take to automate a report after the first couple of months.  I''d throw all the data in a SQL database and write a quick script in perl or java to iterate through the 100 records, do the calculation, and output the users who crossed the threshold.&lt;/p&gt;

&lt;p&gt;If I needed it to look pretty, I''d use a reporting tool.  I''m not particularly partial to any of them.&lt;/p&gt;

&lt;p&gt;If I needed to trend threshold values over time, I''d output the results for all people into a second table add records to that table each month.&lt;/p&gt;

&lt;p&gt;If I just needed to do it once or twice, figuring out how to do it in excel by adding a new column using VLookUp and some basic math and a filter would probably be the fastest and easiest way to get it done.  I tend to avoid using excel for things I''ll need to use with consistency because there are limits that you run into when your data gets sizeable.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (498, 2, '2014-06-20 04:09:04.23', 4, NULL, NULL, '&lt;p&gt;Have you considered a frequency-based approach exploiting simple word co-occurence in corpora? At least, that''s what I''ve seen most folks use for this. I think it might be covered briefly in Manning and SchÃ¼tze''s book, and I seem to remember something like this as a homework assignment back in grad school...&lt;/p&gt;

&lt;p&gt;More background here: &lt;a href="http://nlp.stanford.edu/IR-book/html/htmledition/automatic-thesaurus-generation-1.html" rel="nofollow"&gt;http://nlp.stanford.edu/IR-book/html/htmledition/automatic-thesaurus-generation-1.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;For this step:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Rank other concepts based on their "distance" to the original keywords;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;There are several semantic similarity metrics you could look into. Here''s a link to some slides I put together for a class project using a few of these similarity metrics in WordNet: &lt;a href="http://www.eecis.udel.edu/~trnka/CISC889-11S/lectures/greenbacker-WordNet-Similarity.pdf" rel="nofollow"&gt;http://www.eecis.udel.edu/~trnka/CISC889-11S/lectures/greenbacker-WordNet-Similarity.pdf&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (499, 2, '2014-06-20 04:27:02.467', 8, NULL, NULL, '&lt;p&gt;Anecdotally, I''ve never been impressed with the output from hierarchical LDA. It just doesn''t seem to find an optimal level of granularity for choosing the number of topics. I''ve gotten much better results by running a few iterations of regular LDA, manually inspecting the topics it produced, deciding whether to increase or decrease the number of topics, and continue iterating until I get the granularity I''m looking for.&lt;/p&gt;

&lt;p&gt;Remember: hierarchical LDA can''t read your mind... it doesn''t know what you actually intend to use the topic modeling for. Just like with k-means clustering, you should choose the k that makes the most sense for your use case.&lt;/p&gt;
', NULL, NULL, '''/p'':74C,119C ''actual'':90C ''anecdot'':2C ''better'':36C ''case'':118C ''choos'':27C,107C ''cluster'':104C ''continu'':63C ''decid'':52C ''decreas'':57C ''doesn'':16C,85C ''find'':20C ''get'':67C ''gotten'':34C ''granular'':25C,69C ''hierarch'':12C,77C ''impress'':7C ''increas'':55C ''inspect'':47C ''intend'':91C ''iter'':42C,64C ''k'':102C,109C ''k-mean'':101C ''know'':87C ''lda'':13C,45C,78C ''level'':23C ''like'':99C ''look'':72C ''m'':71C ''make'':111C ''manual'':46C ''mean'':103C ''mind'':83C ''model'':96C ''much'':35C ''never'':5C ''number'':29C,59C ''optim'':22C ''output'':10C ''p'':1C,75C ''produc'':51C ''read'':81C ''regular'':44C ''rememb'':76C ''result'':37C ''run'':39C ''seem'':18C ''sens'':114C ''topic'':31C,49C,61C,95C ''use'':93C,117C ''ve'':4C,33C ''whether'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (512, 2, '2014-06-20 18:56:52.11', 2, NULL, NULL, '&lt;p&gt;Here''s an approach I didn''t see mentioned: separate the process into two steps: the first step focused on encoding names so that alternative versions of the same name are encoded the same (or nearly the same), and the second step focused on making them anonymous.&lt;/p&gt;

&lt;p&gt;For the first step, you could use one of the &lt;a href="https://en.wikipedia.org/wiki/Phonetic_encoding" rel="nofollow"&gt;Phonetic Algorithms (Soundex and variants)&lt;/a&gt;, applied to first name, last name, and initials in various orders.  (See &lt;a href="http://www.stevemorse.org/phonetics/bmpm2.htm" rel="nofollow"&gt;this article&lt;/a&gt;, also).  It''s in this step where you resolve similarities vs. differences in names to balance false positives from false negatives.&lt;/p&gt;

&lt;p&gt;For the second step, you can pick any hashing or cryptographic method you like, without concern for how that method affects name matching.  This gives you freedom to use a method that has the best characteristics for both performance, robustness, and anonymity.&lt;/p&gt;
', NULL, NULL, '''/a'':73C,95C ''/p'':49C,117C,161C ''/phonetics/bmpm2.htm'':90C ''/wiki/phonetic_encoding'':65C ''affect'':139C ''algorithm'':69C ''also'':96C ''altern'':26C ''anonym'':48C,160C ''appli'':74C ''approach'':5C ''articl'':94C ''balanc'':111C ''best'':153C ''characterist'':154C ''concern'':134C ''could'':56C ''cryptograph'':129C ''didn'':7C ''differ'':107C ''en.wikipedia.org'':64C ''en.wikipedia.org/wiki/phonetic_encoding'':63C ''encod'':22C,33C ''fals'':112C,115C ''first'':18C,53C,76C ''focus'':20C,44C ''freedom'':145C ''give'':143C ''hash'':127C ''href'':62C,87C ''initi'':81C ''last'':78C ''like'':132C ''make'':46C ''match'':141C ''mention'':10C ''method'':130C,138C,149C ''name'':23C,31C,77C,79C,109C,140C ''near'':37C ''negat'':116C ''nofollow'':67C,92C ''one'':58C ''order'':84C ''p'':1C,50C,118C ''perform'':157C ''phonet'':68C ''pick'':125C ''posit'':113C ''process'':13C ''rel'':66C,91C ''resolv'':104C ''robust'':158C ''second'':42C,121C ''see'':9C,85C ''separ'':11C ''similar'':105C ''soundex'':70C ''step'':16C,19C,43C,54C,101C,122C ''two'':15C ''use'':57C,147C ''variant'':72C ''various'':83C ''version'':27C ''vs'':106C ''without'':133C ''www.stevemorse.org'':89C ''www.stevemorse.org/phonetics/bmpm2.htm'':88C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (500, 2, '2014-06-20 06:49:25.237', 4, NULL, NULL, '&lt;p&gt;In addition to Ben''s answer, the subtle distinction between statistical models and machine learning models is that, in statistical models, you explicitly decide the output equation structure prior to building the model. The model is built to compute the parameters/coefficients.&lt;/p&gt;

&lt;p&gt;Take linear model or GLM for example,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;y = a1x1 + a2x2 + a3x3
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Your independent variables are x1, x2, x3 and the coefficients to be determined are a1,a2,a3. You define your equation structure this way prior to building the model and compute a1,a2,a3. If you believe that y is somehow correlated to x2 in a non-linear way, you could try something like this.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;y = a1x1 + a2(x2)^2 + a3x3.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Thus, you put a restriction in terms of the output structure. Inherently statistical models are linear models unless you explicitly apply transformations like sigmoid or kernel to make them nonlinear (GLM and SVM).&lt;/p&gt;

&lt;p&gt;In case of machine learning models, you rarely specify output structure and algorithms like decision trees are inherently non-linear and work efficiently.&lt;/p&gt;

&lt;p&gt;Contrary to what Ben pointed out, machine learning models aren''t just about prediction, they do classification, regression etc which can be used to make predictions which are also done by various statistical models.&lt;/p&gt;
', NULL, NULL, '''/code'':59C,127C ''/p'':43C,52C,118C,163C,189C,225C ''/pre'':60C,128C ''2'':125C ''a1'':76C,93C ''a1x1'':56C,122C ''a2'':77C,94C,123C ''a2x2'':57C ''a3'':78C,95C ''a3x3'':58C,126C ''addit'':3C ''algorithm'':177C ''also'':219C ''answer'':7C ''appli'':150C ''aren'':200C ''believ'':98C ''ben'':5C,194C ''build'':32C,88C ''built'':38C ''case'':166C ''classif'':207C ''code'':54C,120C ''coeffici'':71C ''comput'':40C,92C ''contrari'':191C ''correl'':103C ''could'':113C ''decid'':25C ''decis'':179C ''defin'':80C ''determin'':74C ''distinct'':10C ''done'':220C ''effici'':188C ''equat'':28C,82C ''etc'':209C ''exampl'':51C ''explicit'':24C,149C ''glm'':49C,160C ''independ'':63C ''inher'':141C,182C ''kernel'':155C ''learn'':16C,169C,198C ''like'':116C,152C,178C ''linear'':46C,110C,145C,185C ''machin'':15C,168C,197C ''make'':157C,215C ''model'':13C,17C,22C,34C,36C,47C,90C,143C,146C,170C,199C,224C ''non'':109C,184C ''non-linear'':108C,183C ''nonlinear'':159C ''output'':27C,139C,174C ''p'':1C,44C,61C,129C,164C,190C ''parameters/coefficients'':42C ''point'':195C ''pre'':53C,119C ''predict'':204C,216C ''prior'':30C,86C ''put'':132C ''rare'':172C ''regress'':208C ''restrict'':134C ''sigmoid'':153C ''somehow'':102C ''someth'':115C ''specifi'':173C ''statist'':12C,21C,142C,223C ''structur'':29C,83C,140C,175C ''subtl'':9C ''svm'':162C ''take'':45C ''term'':136C ''thus'':130C ''transform'':151C ''tree'':180C ''tri'':114C ''unless'':147C ''use'':213C ''variabl'':64C ''various'':222C ''way'':85C,111C ''work'':187C ''x1'':66C ''x2'':67C,105C,124C ''x3'':68C ''y'':55C,100C,121C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (501, 2, '2014-06-20 07:11:40.053', 5, NULL, NULL, '&lt;p&gt;The best language depends on what you want to do. First remark: don''t limit yourself to one language. Learning a new language is always a good thing, but at some point you will need to choose. Facilities offered by the language itself are an obvious thing to keep into account &lt;em&gt;but&lt;/em&gt; in my opinion the following are more important:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;available libraries&lt;/strong&gt;: do you have to implement everything from scratch or can you reuse existing stuff? Note that this these libraries need not be in whatever language you are considering, as long as you can interface easily. Working in a language without library access won''t help you get things done.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;number of experts&lt;/strong&gt;: if you want external developers or start working in a team, you have to consider how many people actually know the language. As an extreme example: if you decide to work in Brainfuck because you happen to like it, know that you will likely work alone. Many surveys exists that can help assess the popularity of languages, including the number of questions per language on SO.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;toolchain&lt;/strong&gt;: do you have access to &lt;em&gt;good&lt;/em&gt; debuggers, profilers, documentation tools and (if you''re into that) IDEs?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I am aware that most of my points favor established languages. This is from a ''get-things-done'' perspective.&lt;/p&gt;

&lt;p&gt;That said, I personally believe it is far better to become proficient in a low level language and a high level language:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;low level: C++, C, Fortran, ... using which you can implement certain profiling hot spots &lt;em&gt;only if you need to&lt;/em&gt; because developing in these languages is typically slower (though this is subject to debate). These languages remain king of the hill in terms of critical performance and are likely to stay on top for a long time.&lt;/li&gt;
&lt;li&gt;high level: Python, R, Clojure, ... to ''glue'' stuff together and do non-performance critical stuff (preprocessing, data handling, ...). I find this to be important simply because it is much easier to do rapid development and prototyping in these languages.&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (503, 2, '2014-06-20 14:17:27.97', 2, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Edit: Warning, i leave my message but my answer seems wrong, please check out the comment below!&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I''m not an expert but I guess the main problem is to answer this question:&lt;/p&gt;

&lt;p&gt;Has an/any event affected the number of hits on a certain day?&lt;/p&gt;

&lt;p&gt;But I don''t know how to treat multiple events, so I would try to answer this question:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Does event X affected the number of hits on a certain day?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Which can be answered using hypothesis testing with p-values (what scientist do to evaluate for instance if a medicine affects a disease or not).&lt;/p&gt;

&lt;p&gt;By using p-values, you could determinate if the number of hits in a certain day were mere random and acceptable under normal circumstances or that they must correspond to a change in your model.&lt;/p&gt;

&lt;p&gt;You can read more about p-values in &lt;a href="http://www.openintro.org/stat/" rel="nofollow"&gt;Open Intro to Statistics Book&lt;/a&gt;, I''ve actually learn about them from there.&lt;/p&gt;

&lt;p&gt;Then, the other parts of the problem are how to identify your events and calculate the necessary parameters to answer your question (average/median, variance, etc.) and also how to keep that up-to-date and working.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6292, 1, '2015-07-01 06:13:41.82', 2, 247, 'Is there any working alternatives to Netvizz?', '&lt;p&gt;I''d like to download my friends data as a graph/network from Facebook. In Feb 2015, Facebook banned the awesome application Netvizz. Do you know any alternative application for my purpose?&lt;/p&gt;
', 'graphs  social-network-analysis', NULL, '''/p'':45C ''2015'':29C ''altern'':5A,40C ''analysi'':12B ''applic'':34C,41C ''awesom'':33C ''ban'':31C ''d'':15C ''data'':21C ''download'':18C ''facebook'':26C,30C ''feb'':28C ''friend'':20C ''graph'':8B ''graph/network'':24C ''know'':38C ''like'':16C ''netvizz'':7A,35C ''network'':11B ''p'':13C ''purpos'':44C ''social'':10B ''social-network-analysi'':9B ''work'':4A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (504, 1, '2014-06-20 14:58:09.32', 6, 92, 'Dealing with diverse text data', '&lt;p&gt;I''m currently working with a dataset with a wide range of document lengths -- anywhere from a single word to a full page of text.  In addition, the grammatical structure and use of punctuation varies wildly from document to document.  The goal is to classify those documents into one of about 10-15 categories.  I''m currently using ridge regression and logistic regression for the task, and CV for the alpha values of ridge.  The feature vectors are tf-idf ngrams.&lt;/p&gt;

&lt;p&gt;Recently I''ve noticed that longer documents are much less likely to be categorized. Why might this be the case, and how can one "normalize" for this kind of variation?  As a more general question, how does one typically deal with diverse data sets?  Should documents be grouped based off of metrics like document length, use of punctuation, grammatical rigor, etc. and then fed through different classifiers?  &lt;/p&gt;
', 'classification  nlp', NULL, '''-15'':61C ''/p'':91C,160C ''10'':60C ''addit'':35C ''alpha'':79C ''anywher'':23C ''base'':141C ''case'':112C ''categor'':106C ''categori'':62C ''classif'':6B ''classifi'':53C,159C ''current'':11C,65C ''cv'':76C ''data'':5A,135C ''dataset'':15C ''deal'':1A,132C ''differ'':158C ''divers'':3A,134C ''document'':21C,46C,48C,55C,99C,138C,146C ''etc'':153C ''featur'':84C ''fed'':156C ''full'':30C ''general'':126C ''goal'':50C ''grammat'':37C,151C ''group'':140C ''idf'':89C ''kind'':120C ''length'':22C,147C ''less'':102C ''like'':103C,145C ''logist'':70C ''longer'':98C ''m'':10C,64C ''metric'':144C ''might'':108C ''much'':101C ''ngram'':90C ''nlp'':7B ''normal'':117C ''notic'':96C ''one'':57C,116C,130C ''p'':8C,92C ''page'':31C ''punctuat'':42C,150C ''question'':127C ''rang'':19C ''recent'':93C ''regress'':68C,71C ''ridg'':67C,82C ''rigor'':152C ''set'':136C ''singl'':26C ''structur'':38C ''task'':74C ''text'':4A,33C ''tf'':88C ''tf-idf'':87C ''typic'':131C ''use'':40C,66C,148C ''valu'':80C ''vari'':43C ''variat'':122C ''ve'':95C ''vector'':85C ''wide'':18C ''wild'':44C ''word'':27C ''work'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (508, 2, '2014-06-20 16:56:41.16', 4, NULL, NULL, '&lt;p&gt;I''m not sure how you are applying a regression framework for document classification. The way I''d approach the problem is to apply a standard discriminative classification approach such as SVM.&lt;/p&gt;

&lt;p&gt;In a discriminative classification approach the notion of similarity or inverse distance between data points (documents in this case) is pivotal. Fortunately for documents, there is a standard way of defining pairwise similarity. This is the standard &lt;a href="http://en.wikipedia.org/wiki/Cosine_similarity" rel="nofollow"&gt;cosine similarity&lt;/a&gt; measure which makes use of document length normalization to take different document lengths into account.&lt;/p&gt;

&lt;p&gt;Thus, practically speaking, in cosine similarity you would work with relative term weights normalized by document lengths and hence document length diversity should not be a major issue in the similarity computation.&lt;/p&gt;

&lt;p&gt;One also has to be careful when applying idf in term weights. If the number of documents is not significantly large the idf measure may be statistically imprecise thus adding noise to the term weights. It''s also a standard practice to ignore stopwords and punctuations.&lt;/p&gt;
', NULL, NULL, '''/a'':82C ''/p'':34C,98C,132C,180C ''/wiki/cosine_similarity'':77C ''account'':97C ''ad'':163C ''also'':135C,171C ''appli'':9C,25C,141C ''approach'':20C,30C,40C ''care'':139C ''case'':54C ''classif'':15C,29C,39C ''comput'':131C ''cosin'':80C,104C ''d'':19C ''data'':49C ''defin'':66C ''differ'':93C ''discrimin'':28C,38C ''distanc'':47C ''divers'':121C ''document'':14C,51C,59C,88C,94C,115C,119C,150C ''en.wikipedia.org'':76C ''en.wikipedia.org/wiki/cosine_similarity'':75C ''fortun'':57C ''framework'':12C ''henc'':118C ''href'':74C ''idf'':142C,156C ''ignor'':176C ''imprecis'':161C ''invers'':46C ''issu'':127C ''larg'':154C ''length'':89C,95C,116C,120C ''m'':3C ''major'':126C ''make'':85C ''may'':158C ''measur'':83C,157C ''nofollow'':79C ''nois'':164C ''normal'':90C,113C ''notion'':42C ''number'':148C ''one'':134C ''p'':1C,35C,99C,133C ''pairwis'':67C ''pivot'':56C ''point'':50C ''practic'':101C,174C ''problem'':22C ''punctuat'':179C ''regress'':11C ''rel'':78C ''relat'':110C ''signific'':153C ''similar'':44C,68C,81C,105C,130C ''speak'':102C ''standard'':27C,63C,72C,173C ''statist'':160C ''stopword'':177C ''sure'':5C ''svm'':33C ''take'':92C ''term'':111C,144C,167C ''thus'':100C,162C ''use'':86C ''way'':17C,64C ''weight'':112C,145C,168C ''work'':108C ''would'':107C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (509, 2, '2014-06-20 17:30:39.097', -2, NULL, NULL, '&lt;p&gt;For example...if u have an agricultural land then selecting one particular area of that land would be feature selection.If u aim to find the affected plants in that area den u need to observe each plant based on a particular feature that is common in each plant so as to find the abnormalities...for this u would be considering feature extraction.In this example the original agricultural land corresponds to Dimensionality reduction.&lt;/p&gt;
', NULL, NULL, '''/p'':74C ''abnorm'':55C ''affect'':27C ''agricultur'':8C,68C ''aim'':23C ''area'':14C,31C ''base'':39C ''common'':46C ''consid'':61C ''correspond'':70C ''den'':32C ''dimension'':72C ''exampl'':3C,65C ''extraction.in'':63C ''featur'':20C,43C,62C ''find'':25C,53C ''land'':9C,17C,69C ''need'':34C ''observ'':36C ''one'':12C ''origin'':67C ''p'':1C ''particular'':13C,42C ''plant'':28C,38C,49C ''reduct'':73C ''select'':11C ''selection.if'':21C ''u'':5C,22C,33C,58C ''would'':18C,59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (510, 2, '2014-06-20 17:34:51.633', 9, NULL, NULL, '&lt;p&gt;A couple of words about social networks APIs. About a year ago I wrote a review of popular social networksâ APIs for researchers. Unfortunately, it is in Russian. Here is a summary:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Twitter&lt;/strong&gt; (&lt;a href="https://dev.twitter.com/docs/api/1.1"&gt;https://dev.twitter.com/docs/api/1.1&lt;/a&gt;)&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;almost all data about tweets/texts and users is available;&lt;/li&gt;
&lt;li&gt;lack of sociodemographic data;&lt;/li&gt;
&lt;li&gt;great streaming API: useful for real time text processing;&lt;/li&gt;
&lt;li&gt;a lot of wrappers for programing languages;&lt;/li&gt;
&lt;li&gt;getting network structure (connections) is possible, but time-expensive (1 request per 1 minute).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Facebook&lt;/strong&gt; (&lt;a href="https://developers.facebook.com/docs/reference/api/"&gt;https://developers.facebook.com/docs/reference/api/&lt;/a&gt;)&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;rate limits: about 1 request per second;&lt;/li&gt;
&lt;li&gt;well documented, sandbox present;&lt;/li&gt;
&lt;li&gt;FQL (SQL-like) and Â«regular RestÂ» Graph API;&lt;/li&gt;
&lt;li&gt;friendship data and sociodemographic features present;&lt;/li&gt;
&lt;li&gt;a lot of data is beyond &lt;em&gt;event horizon&lt;/em&gt;: only friends'' and friends'' of friends data is more or less complete, almost nothing could be investigated about random user;&lt;/li&gt;
&lt;li&gt;some strange API bugs, and looks like nobody cares about it (e.g., some features available through FQL, but not through Graph API synonym).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Instagram&lt;/strong&gt; (&lt;a href="http://instagram.com/developer/"&gt;http://instagram.com/developer/&lt;/a&gt;)&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;rate limits: 5000 requests per hour;&lt;/li&gt;
&lt;li&gt;real-time API (like Streaming API for Twitter, but with photos) - connection to it is a little bit tricky: callbacks are used;&lt;/li&gt;
&lt;li&gt;lack of sociodemographic data;&lt;/li&gt;
&lt;li&gt;photos, filters data available;&lt;/li&gt;
&lt;li&gt;unexpected imperfections (e.g., itâs possible to collect only 150 comments to post/photo).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Foursquare&lt;/strong&gt; (&lt;a href="https://developer.foursquare.com/overview/"&gt;https://developer.foursquare.com/overview/&lt;/a&gt;)&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;rate limits: 5000 requests per hour;&lt;/li&gt;
&lt;li&gt;kingdom of geosocial data :)&lt;/li&gt;
&lt;li&gt;quite closed from researches because of privacy issues. To collect checkins data one need to build composite parser working with 4sq, bit.ly, and twitter APIs at once;&lt;/li&gt;
&lt;li&gt;again: lack of sociodemographic data.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Google+&lt;/strong&gt; (&lt;a href="https://developers.google.com/+/api/latest/"&gt;https://developers.google.com/+/api/latest/&lt;/a&gt;)&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;about 5 requests per second (try to verify);&lt;/li&gt;
&lt;li&gt;main methods: activities and people;&lt;/li&gt;
&lt;li&gt;like on Facebook, a lot of personal data for random user is hidden;&lt;/li&gt;
&lt;li&gt;lack of user connections data.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;And out-of-competition: I reviewed social networks for Russian readers, and #1 network here is &lt;a href="http://en.wikipedia.org/wiki/VK_%28social_network%29"&gt;&lt;strong&gt;vk.com&lt;/strong&gt;&lt;/a&gt;. Itâs translated to many languages, but popular only in Russia and other CIS countries. API docs link: &lt;a href="http://vk.com/dev/"&gt;http://vk.com/dev/&lt;/a&gt;. And from my point of view, itâs the best choice for homebrew social media research. At least, in Russia. Thatâs why:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;rate limits: 3 requests per second;&lt;/li&gt;
&lt;li&gt;public text and media data available;&lt;/li&gt;
&lt;li&gt;sociodemographic data available: for random user availability level is about 60-70%;&lt;/li&gt;
&lt;li&gt;connections between users are also available: almost all friendships data for random user is available;&lt;/li&gt;
&lt;li&gt;some special methods: e.g., there is a method to get online/offline status for exact user in realtime, and one could build schedule for his audience.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''-70'':506C ''/+/api/latest/'':355C ''/+/api/latest/&lt;/a&gt;)&lt;/p&gt;'':358C ''/a'':426C ''/dev/'':449C ''/dev/&lt;/a&gt;.'':452C ''/developer/'':217C ''/developer/&lt;/a&gt;)&lt;/p&gt;'':220C ''/docs/api/1.1'':43C ''/docs/api/1.1&lt;/a&gt;)&lt;/p&gt;'':46C ''/docs/reference/api/'':111C ''/docs/reference/api/&lt;/a&gt;)&lt;/p&gt;'':114C ''/em'':161C ''/li'':58C,64C,75C,84C,101C,124C,130C,142C,150C,182C,207C,229C,254C,260C,266C,281C,303C,309C,338C,345C,369C,376C,391C,398C,485C,493C,507C,524C,551C ''/overview/'':291C ''/overview/&lt;/a&gt;)&lt;/p&gt;'':294C ''/p'':34C,476C ''/strong'':38C,106C,212C,286C,350C,425C ''/ul'':102C,208C,282C,346C,399C,552C ''/wiki/vk_%28social_network%29'':422C ''1'':96C,99C,120C,414C ''150'':277C ''3'':481C ''4sq'':331C ''5'':362C ''5000'':225C,299C ''60'':505C ''activ'':373C ''ago'':13C ''almost'':49C,174C,515C ''also'':513C ''api'':9C,22C,68C,141C,186C,205C,234C,237C,335C,442C ''audienc'':550C ''avail'':57C,198C,265C,492C,497C,501C,514C,523C ''best'':462C ''beyond'':157C ''bit'':249C ''bit.ly'':332C ''bug'':187C ''build'':326C,546C ''callback'':251C ''care'':192C ''checkin'':321C ''choic'':463C ''cis'':440C ''close'':312C ''collect'':275C,320C ''comment'':278C ''competit'':405C ''complet'':173C ''composit'':327C ''connect'':89C,243C,396C,509C ''could'':176C,545C ''countri'':441C ''coupl'':3C ''data'':51C,63C,145C,155C,168C,259C,264C,308C,322C,344C,385C,397C,491C,496C,518C ''dev.twitter.com'':42C,45C ''dev.twitter.com/docs/api/1.1'':41C ''dev.twitter.com/docs/api/1.1&lt;/a&gt;)&lt;/p&gt;'':44C ''developer.foursquare.com'':290C,293C ''developer.foursquare.com/overview/'':289C ''developer.foursquare.com/overview/&lt;/a&gt;)&lt;/p&gt;'':292C ''developers.facebook.com'':110C,113C ''developers.facebook.com/docs/reference/api/'':109C ''developers.facebook.com/docs/reference/api/&lt;/a&gt;)&lt;/p&gt;'':112C ''developers.google.com'':354C,357C ''developers.google.com/+/api/latest/'':353C ''developers.google.com/+/api/latest/&lt;/a&gt;)&lt;/p&gt;'':356C ''doc'':443C ''document'':127C ''e.g'':195C,270C,529C ''em'':158C ''en.wikipedia.org'':421C ''en.wikipedia.org/wiki/vk_%28social_network%29'':420C ''event'':159C ''exact'':539C ''expens'':95C ''facebook'':105C,380C ''featur'':148C,197C ''filter'':263C ''foursquar'':285C ''fql'':132C,200C ''friend'':163C,165C,167C ''friendship'':144C,517C ''geosoci'':307C ''get'':86C,535C ''googl'':349C ''graph'':140C,204C ''great'':66C ''hidden'':390C ''homebrew'':465C ''horizon'':160C ''hour'':228C,302C ''href'':40C,108C,214C,288C,352C,419C,446C ''imperfect'':269C ''instagram'':211C ''instagram.com'':216C,219C ''instagram.com/developer/'':215C ''instagram.com/developer/&lt;/a&gt;)&lt;/p&gt;'':218C ''investig'':178C ''issu'':318C ''itâ'':271C,427C,459C ''kingdom'':305C ''lack'':60C,256C,341C,393C ''languag'':83C,432C ''least'':470C ''less'':172C ''level'':502C ''li'':48C,59C,65C,76C,85C,116C,125C,131C,143C,151C,183C,222C,230C,255C,261C,267C,296C,304C,310C,339C,360C,370C,377C,392C,478C,486C,494C,508C,525C ''like'':135C,190C,235C,378C ''limit'':118C,224C,298C,480C ''link'':444C ''littl'':248C ''look'':189C ''lot'':78C,153C,382C ''main'':371C ''mani'':431C ''media'':467C,490C ''method'':372C,528C,533C ''minut'':100C ''need'':324C ''network'':8C,87C,409C,415C ''networksâ'':21C ''nobodi'':191C ''noth'':175C ''one'':323C,544C ''online/offline'':536C ''out-of-competit'':402C ''p'':1C,35C,103C,209C,283C,347C,400C ''parser'':328C ''peopl'':375C ''per'':98C,122C,227C,301C,364C,483C ''person'':384C ''photo'':242C,262C ''point'':456C ''popular'':19C,434C ''possibl'':91C,273C ''post/photo'':280C ''present'':129C,149C ''privaci'':317C ''process'':74C ''program'':82C ''public'':487C ''quit'':311C ''random'':180C,387C,499C,520C ''rate'':117C,223C,297C,479C ''reader'':412C ''real'':71C,232C ''real-tim'':231C ''realtim'':542C ''regular'':138C ''request'':97C,121C,226C,300C,363C,482C ''research'':24C,314C,468C ''restâ'':139C ''review'':17C,407C ''russia'':437C,472C ''russian'':29C,411C ''sandbox'':128C ''schedul'':547C ''second'':123C,365C,484C ''social'':7C,20C,408C,466C ''sociodemograph'':62C,147C,258C,343C,495C ''special'':527C ''sql'':134C ''sql-like'':133C ''status'':537C ''strang'':185C ''stream'':67C,236C ''strong'':36C,104C,210C,284C,348C,423C ''structur'':88C ''summari'':33C ''synonym'':206C ''text'':73C,488C ''thatâ'':473C ''time'':72C,94C,233C ''time-expens'':93C ''translat'':429C ''tri'':366C ''tricki'':250C ''tweets/texts'':53C ''twitter'':37C,239C,334C ''ul'':47C,115C,221C,295C,359C,477C ''unexpect'':268C ''unfortun'':25C ''use'':69C,253C ''user'':55C,181C,388C,395C,500C,511C,521C,540C ''verifi'':368C ''view'':458C ''vk.com'':424C,448C,451C ''vk.com/dev/'':447C ''vk.com/dev/&lt;/a&gt;.'':450C ''well'':126C ''word'':5C ''work'':329C ''wrapper'':80C ''wrote'':15C ''year'':12C ''â'':137C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5926, 2, '2015-05-27 13:27:44.253', 0, NULL, NULL, '&lt;p&gt;If you want to have some data science programming experience in R try &lt;a href="https://www.datacamp.com/" rel="nofollow"&gt;https://www.datacamp.com/&lt;/a&gt; Is'' an interactive online coding environment for learning R programming. &lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':22C ''/p'':33C ''code'':27C ''data'':8C ''environ'':28C ''experi'':11C ''href'':16C ''interact'':25C ''learn'':30C ''nofollow'':19C ''onlin'':26C ''p'':1C ''program'':10C,32C ''r'':13C,31C ''rel'':18C ''scienc'':9C ''tri'':14C ''want'':4C ''www.datacamp.com'':17C,21C ''www.datacamp.com/&lt;/a&gt;'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (526, 2, '2014-06-22 18:50:07.193', 1, NULL, NULL, '&lt;p&gt;Chapter 1 of Practical Data Science with R (&lt;a href="http://www.manning.com/zumel/" rel="nofollow"&gt;http://www.manning.com/zumel/&lt;/a&gt;) has a great breakdown of the data science process, including team roles and how they relate to specific tasks. The book follows the models laid out in the chapter by referencing which stages/personnel this or that particular task would be performed by.  &lt;/p&gt;
', NULL, NULL, '''/p'':62C ''/zumel/'':14C ''/zumel/&lt;/a&gt;)'':19C ''1'':3C ''book'':40C ''breakdown'':23C ''chapter'':2C,48C ''data'':6C,26C ''follow'':41C ''great'':22C ''href'':11C ''includ'':29C ''laid'':44C ''model'':43C ''nofollow'':16C ''p'':1C ''particular'':56C ''perform'':60C ''practic'':5C ''process'':28C ''r'':9C ''referenc'':50C ''rel'':15C ''relat'':35C ''role'':31C ''scienc'':7C,27C ''specif'':37C ''stages/personnel'':52C ''task'':38C,57C ''team'':30C ''would'':58C ''www.manning.com'':13C,18C ''www.manning.com/zumel/'':12C ''www.manning.com/zumel/&lt;/a&gt;)'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (513, 2, '2014-06-20 19:15:52.093', 4, NULL, NULL, '&lt;p&gt;If you have an adequate number of samples and want to use all the data, then k-fold cross-validation is the way to go. Having ~1,500 seems like a lot but whether it is adequate for k-fold cross-validation also depends on the dimensionality of the data (number of attributes and number of attribute values). For example, if each observation has 100 attributes, then 1,500 observations is low.&lt;/p&gt;

&lt;p&gt;Another potential downside to k-fold cross-validation is the possibility of a single, extreme outlier skewing the results. For example, if you have one extreme outlier that can heavily bias your classifier, then in a 10-fold cross-validation, 9 of the 10 partitions will be affected (though for random forests, I don''t think you would have that problem).&lt;/p&gt;

&lt;p&gt;Random subsampling (e.g., bootstrap sampling) is preferable when you are either undersampled or when you have the situation above, where you don''t want each observation to appear in k-1 folds.&lt;/p&gt;
', NULL, NULL, '''-1'':176C ''/p'':78C,144C,178C ''1'':30C,73C ''10'':118C,126C ''100'':70C ''500'':31C,74C ''9'':123C ''adequ'':6C,40C ''affect'':130C ''also'':48C ''anoth'':80C ''appear'':173C ''attribut'':58C,62C,71C ''bias'':112C ''bootstrap'':149C ''classifi'':114C ''cross'':22C,46C,88C,121C ''cross-valid'':21C,45C,87C,120C ''data'':16C,55C ''depend'':49C ''dimension'':52C ''downsid'':82C ''e.g'':148C ''either'':156C ''exampl'':65C,102C ''extrem'':96C,107C ''fold'':20C,44C,86C,119C,177C ''forest'':134C ''go'':28C ''heavili'':111C ''k'':19C,43C,85C,175C ''k-fold'':18C,42C,84C ''like'':33C ''lot'':35C ''low'':77C ''number'':7C,56C,60C ''observ'':68C,75C,171C ''one'':106C ''outlier'':97C,108C ''p'':1C,79C,145C ''partit'':127C ''possibl'':92C ''potenti'':81C ''prefer'':152C ''problem'':143C ''random'':133C,146C ''result'':100C ''sampl'':9C,150C ''seem'':32C ''singl'':95C ''situat'':163C ''skew'':98C ''subsampl'':147C ''think'':138C ''though'':131C ''undersampl'':157C ''use'':13C ''valid'':23C,47C,89C,122C ''valu'':63C ''want'':11C,169C ''way'':26C ''whether'':37C ''would'':140C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (514, 2, '2014-06-20 20:03:51.247', 6, NULL, NULL, '&lt;p&gt;I like Amir Ali Akbari''s suggestions, and I''ll add a few of my own, focusing on topics and skills that are not adequately covered in most machine learning and data analysis books that focus on math and/or programming.&lt;/p&gt;

&lt;p&gt;Data Cleaning:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1412988012"&gt;Osborne 2012, &lt;em&gt;Best Practices in Data Cleaning&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1449321887"&gt;McCallom 2012, &lt;em&gt;Bad Data Handbook: Cleaning Up The Data So You Can Get Back To Work&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Bayesian Data Analysis (alternative to Fisher-style Null Hypothesis Significance Testing):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0123814855"&gt;Kruschke 2011, &lt;em&gt;Doing Bayesian Data Analysis&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Inference in the face of uncertainty, incompleteness, contradictions, ambiguity, imprecision, ignorance, etc.:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0810118211"&gt;Schum &amp;amp; Starace, 2001, &lt;em&gt;The Evidential Foundations of Probabilistic Reasoning&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1844074749"&gt;Bammer &amp;amp; Smithson 2008, &lt;em&gt;Uncertainty and Risk: Multidisciplinary Perspectives&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0387969454"&gt;Smithson 1989, &lt;em&gt;Ignorance and Uncertainty&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/Tradecraft%20Primer-apr09.pdf"&gt;CIA 2008, &lt;em&gt;A Tradecraft Primer: Structured Analytic Techniques for Improving Intelligence Analysis&lt;/em&gt;&lt;/a&gt; (FREE! as PDF)&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0521671930"&gt;Morgan &amp;amp; Winship 2007, &lt;em&gt;Counterfactuals and Causal Inference: Methods and Principles for Social Research&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Experiments:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0691159270"&gt;Glennerster &amp;amp; Takavarasha 2013, &lt;em&gt;Running Randomized Evaluations: A Practical Guide&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1107698006"&gt;Dunning 2012, &lt;em&gt;Natural Experiments in the Social Sciences&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Simulation:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0691125473"&gt;Epstein 2006, &lt;em&gt;Generative Social Science: Studies in Agent-Based Computational Modeling&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0486477703"&gt;Nelson 2010, &lt;em&gt;Stochastic Modeling: Analysis and Simulation&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Expert elicitation, probabilistic estimation:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1118539273"&gt;Hubbard 2014, &lt;em&gt;How to Measure Anything: Finding the Value of Intangibles in Business&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':63C,89C,121C,157C,176C,191C,213C,240C,265C,283C,310C,327C,358C ''/amzn/click/0123814855'':112C ''/amzn/click/0387969454'':183C ''/amzn/click/0486477703'':317C ''/amzn/click/0521671930'':223C ''/amzn/click/0691125473'':295C ''/amzn/click/0691159270'':252C ''/amzn/click/0810118211'':144C ''/amzn/click/1107698006'':272C ''/amzn/click/1118539273'':342C ''/amzn/click/1412988012'':53C ''/amzn/click/1449321887'':70C ''/amzn/click/1844074749'':164C ''/em'':62C,88C,120C,156C,175C,190C,212C,239C,264C,282C,309C,326C,357C ''/li'':64C,90C,122C,158C,177C,192C,217C,241C,266C,284C,311C,328C,359C ''/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/tradecraft%20primer-apr09.pdf'':198C ''/p'':42C,46C,105C,137C,245C,288C,335C ''/ul'':91C,123C,242C,285C,329C,360C ''1989'':185C ''2001'':148C ''2006'':297C ''2007'':227C ''2008'':168C,200C ''2010'':319C ''2011'':114C ''2012'':55C,72C,274C ''2013'':256C ''2014'':344C ''add'':12C ''adequ'':26C ''agent'':305C ''agent-bas'':304C ''akbari'':6C ''ali'':5C ''altern'':96C ''ambigu'':133C ''amir'':4C ''amp'':146C,166C,225C,254C ''analysi'':34C,95C,119C,211C,323C ''analyt'':206C ''and/or'':40C ''anyth'':349C ''back'':85C ''bad'':74C ''bammer'':165C ''base'':306C ''bayesian'':93C,117C ''best'':57C ''book'':35C ''busi'':356C ''causal'':231C ''cia'':199C ''clean'':45C,61C,77C ''comput'':307C ''contradict'':132C ''counterfactu'':229C ''cover'':27C ''data'':33C,44C,60C,75C,80C,94C,118C ''dun'':273C ''elicit'':332C ''em'':56C,73C,115C,149C,169C,186C,201C,228C,257C,275C,298C,320C,345C ''epstein'':296C ''estim'':334C ''etc'':136C ''evalu'':260C ''evidenti'':151C ''experi'':244C,277C ''expert'':331C ''face'':128C ''find'':350C ''fisher'':99C ''fisher-styl'':98C ''focus'':18C,37C ''foundat'':152C ''free'':214C ''generat'':299C ''get'':84C ''glennerst'':253C ''guid'':263C ''handbook'':76C ''href'':50C,67C,109C,141C,161C,180C,195C,220C,249C,269C,292C,314C,339C ''hubbard'':343C ''hypothesi'':102C ''ignor'':135C,187C ''imprecis'':134C ''improv'':209C ''incomplet'':131C ''infer'':125C,232C ''intang'':354C ''intellig'':210C ''kruschk'':113C ''learn'':31C ''li'':48C,65C,107C,139C,159C,178C,193C,218C,247C,267C,290C,312C,337C ''like'':3C ''ll'':11C ''machin'':30C ''math'':39C ''mccallom'':71C ''measur'':348C ''method'':233C ''model'':308C,322C ''morgan'':224C ''multidisciplinari'':173C ''natur'':276C ''nelson'':318C ''null'':101C ''osborn'':54C ''p'':1C,43C,92C,124C,243C,286C,330C ''pdf'':216C ''perspect'':174C ''practic'':58C,262C ''primer'':204C ''principl'':235C ''probabilist'':154C,333C ''program'':41C ''rads.stackoverflow.com'':52C,69C,111C,143C,163C,182C,222C,251C,271C,294C,316C,341C ''rads.stackoverflow.com/amzn/click/0123814855'':110C ''rads.stackoverflow.com/amzn/click/0387969454'':181C ''rads.stackoverflow.com/amzn/click/0486477703'':315C ''rads.stackoverflow.com/amzn/click/0521671930'':221C ''rads.stackoverflow.com/amzn/click/0691125473'':293C ''rads.stackoverflow.com/amzn/click/0691159270'':250C ''rads.stackoverflow.com/amzn/click/0810118211'':142C ''rads.stackoverflow.com/amzn/click/1107698006'':270C ''rads.stackoverflow.com/amzn/click/1118539273'':340C ''rads.stackoverflow.com/amzn/click/1412988012'':51C ''rads.stackoverflow.com/amzn/click/1449321887'':68C ''rads.stackoverflow.com/amzn/click/1844074749'':162C ''random'':259C ''reason'':155C ''research'':238C ''risk'':172C ''run'':258C ''schum'':145C ''scienc'':281C,301C ''signific'':103C ''simul'':287C,325C ''skill'':22C ''smithson'':167C,184C ''social'':237C,280C,300C ''starac'':147C ''stochast'':321C ''structur'':205C ''studi'':302C ''style'':100C ''suggest'':8C ''takavarasha'':255C ''techniqu'':207C ''test'':104C ''topic'':20C ''tradecraft'':203C ''ul'':47C,106C,138C,246C,289C,336C ''uncertainti'':130C,170C,189C ''valu'':352C ''winship'':226C ''work'':87C ''www.cia.gov'':197C ''www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/tradecraft%20primer-apr09.pdf'':196C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (516, 1, '2014-06-21 04:59:06.62', 5, 116, 'Efficient solution of fmincg without providing gradient?', '&lt;p&gt;I''m working on multiclass logistic regression model with a large number of features (numFeatures &gt; 100). Using a Maximum Likelihood Estimation based on the cost function and gradient, the fmincg algorithm solves the problem quickly. However, I''m also experimenting with a different cost function and do not have a gradient.&lt;/p&gt;

&lt;p&gt;Is there a good way to speed up the calculation process? E.g., is there a different algorithm or fmincg setting that I can use?&lt;/p&gt;
', 'bigdata  data-mining', NULL, '''/p'':64C,90C ''100'':28C ''algorithm'':43C,82C ''also'':51C ''base'':34C ''bigdata'':8B ''calcul'':75C ''cost'':37C,56C ''data'':10B ''data-min'':9B ''differ'':55C,81C ''e.g'':77C ''effici'':1A ''estim'':33C ''experi'':52C ''featur'':26C ''fmincg'':4A,42C,84C ''function'':38C,57C ''good'':69C ''gradient'':7A,40C,63C ''howev'':48C ''larg'':23C ''likelihood'':32C ''logist'':18C ''m'':14C,50C ''maximum'':31C ''mine'':11B ''model'':20C ''multiclass'':17C ''number'':24C ''numfeatur'':27C ''p'':12C,65C ''problem'':46C ''process'':76C ''provid'':6A ''quick'':47C ''regress'':19C ''set'':85C ''solut'':2A ''solv'':44C ''speed'':72C ''use'':29C,89C ''way'':70C ''without'':5A ''work'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (517, 2, '2014-06-21 07:02:10.687', 3, NULL, NULL, '&lt;p&gt;If you do not have a gradient available, but the problem is convex, you can use the &lt;a href="http://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method" rel="nofollow"&gt;Nelder-Mead simplex method&lt;/a&gt;. It is available in most optimization packages, for example in &lt;a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize" rel="nofollow"&gt;scipy.optimize&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':31C,50C ''/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize'':46C ''/p'':51C ''/wiki/nelder%e2%80%93mead_method'':23C ''avail'':9C,34C ''convex'':14C ''docs.scipy.org'':45C ''docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize'':44C ''en.wikipedia.org'':22C ''en.wikipedia.org/wiki/nelder%e2%80%93mead_method'':21C ''exampl'':40C ''gradient'':8C ''href'':20C,43C ''mead'':28C ''method'':30C ''nelder'':27C ''nelder-mead'':26C ''nofollow'':25C,48C ''optim'':37C ''p'':1C ''packag'':38C ''problem'':12C ''rel'':24C,47C ''scipy.optimize'':49C ''simplex'':29C ''use'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (518, 1, '2014-06-21 10:55:41.7', -2, 84, 'Online k-means explanation', '&lt;p&gt;Please, could someone recommend a paper or blog post that describes the online k-means algorithm.&lt;/p&gt;

&lt;p&gt;Thanks :)&lt;/p&gt;
', 'machine-learning  clustering', NULL, '''/p'':28C,31C ''algorithm'':27C ''blog'':18C ''cluster'':9B ''could'':12C ''describ'':21C ''explan'':5A ''k'':3A,25C ''k-mean'':2A,24C ''learn'':8B ''machin'':7B ''machine-learn'':6B ''mean'':4A,26C ''onlin'':1A,23C ''p'':10C,29C ''paper'':16C ''pleas'':11C ''post'':19C ''recommend'':14C ''someon'':13C ''thank'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (519, 2, '2014-06-21 12:59:15.373', 2, NULL, NULL, '&lt;p&gt;The main problem here is that even before attempting to apply anomaly detection algorithms, you are not getting good enough predictions of gas consumption using neural networks. &lt;/p&gt;

&lt;p&gt;If the main goal here is to reach the stage when anomaly detection algorithms could be used and you state that you have access to examples of successful application of linear regression for this problem, this approach could be more productive. One of the principles of successful machine learning application is that several different algorithms can be tried out before final selection based on results.&lt;/p&gt;

&lt;p&gt;It you choose to tune your neural network performance, &lt;a href="http://stackoverflow.com/questions/4617365/what-is-a-learning-curve-in-machine-learning"&gt;learning curve&lt;/a&gt; plotting the effect of change in different hyperparameters on the error rate can be used. Hyperparameters that can be modified are: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;number of features&lt;/li&gt;
&lt;li&gt;order of the polynomial&lt;/li&gt;
&lt;li&gt;regularization parameter &lt;/li&gt;
&lt;li&gt;number of layers in the network&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Best settings can be selected by the performance on cross validation set.&lt;/p&gt;
', NULL, NULL, '''/a'':114C ''/li'':142C,148C,152C,160C ''/p'':29C,96C,136C,175C ''/questions/4617365/what-is-a-learning-curve-in-machine-learning'':111C ''/ul'':161C ''access'':54C ''algorithm'':15C,44C,85C ''anomali'':13C,42C ''appli'':12C ''applic'':59C,80C ''approach'':67C ''attempt'':10C ''base'':93C ''best'':163C ''chang'':119C ''choos'':100C ''consumpt'':25C ''could'':45C,68C ''cross'':172C ''curv'':113C ''detect'':14C,43C ''differ'':84C,121C ''effect'':117C ''enough'':21C ''error'':125C ''even'':8C ''exampl'':56C ''featur'':141C ''final'':91C ''gas'':24C ''get'':19C ''goal'':34C ''good'':20C ''href'':108C ''hyperparamet'':122C,130C ''layer'':156C ''learn'':79C,112C ''li'':138C,143C,149C,153C ''linear'':61C ''machin'':78C ''main'':3C,33C ''modifi'':134C ''network'':28C,105C,159C ''neural'':27C,104C ''number'':139C,154C ''one'':72C ''order'':144C ''p'':1C,30C,97C,162C ''paramet'':151C ''perform'':106C,170C ''plot'':115C ''polynomi'':147C ''predict'':22C ''principl'':75C ''problem'':4C,65C ''product'':71C ''rate'':126C ''reach'':38C ''regress'':62C ''regular'':150C ''result'':95C ''select'':92C,167C ''set'':164C,174C ''sever'':83C ''stackoverflow.com'':110C ''stackoverflow.com/questions/4617365/what-is-a-learning-curve-in-machine-learning'':109C ''stage'':40C ''state'':50C ''success'':58C,77C ''tri'':88C ''tune'':102C ''ul'':137C ''use'':26C,47C,129C ''valid'':173C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (520, 2, '2014-06-21 19:13:47.757', 3, NULL, NULL, '&lt;p&gt;In your training notebook you present results for training with 20 epochs. Have you tried varying that parameter, to see if it affects your performance? This is an important parameter for back-propagation. &lt;/p&gt;

&lt;p&gt;For estimating your model parameters, as user tomaskazemekas pointed out, plotting Learning Curves is a very good approach. In addition to that, you could also create a plot using a model parameter (e.g. training epochs or hidden layer size) vs. Training and Validation error. This will allow you to understand the bias/variance tradeoff, and help you pick a good value for your parameters. &lt;a href="http://www.astroml.org/sklearn_tutorial/practical.html#cross-validation-and-testing" rel="nofollow"&gt;Some info can be found here&lt;/a&gt;. Naturally, it is a good idea to keep a small percentage of your data for a (third) Test set.&lt;/p&gt;

&lt;p&gt;As a side note, it seems that increasing the number of neurons in your model show no significant improvement for your RMSE. This suggests that you could also try with a simpler model, i.e. with less neurons and see how your model behaves.&lt;/p&gt;

&lt;p&gt;In fact, I would suggest (if you haven''t done so already) trying a simple model with few or no parameters first e.g. Linear Regression, and compare your results with the literature, just as a sanity check. &lt;/p&gt;
', NULL, NULL, '''/a'':114C ''/p'':36C,134C,179C,218C ''/sklearn_tutorial/practical.html#cross-validation-and-testing'':105C ''20'':12C ''addit'':57C ''affect'':24C ''allow'':84C ''alreadi'':192C ''also'':62C,163C ''approach'':55C ''back'':34C ''back-propag'':33C ''behav'':178C ''bias/variance'':89C ''check'':217C ''compar'':207C ''could'':61C,162C ''creat'':63C ''curv'':50C ''data'':128C ''done'':190C ''e.g'':70C,203C ''epoch'':13C,72C ''error'':81C ''estim'':39C ''fact'':182C ''first'':202C ''found'':112C ''good'':54C,96C,119C ''haven'':188C ''help'':92C ''hidden'':74C ''href'':102C ''i.e'':169C ''idea'':120C ''import'':30C ''improv'':154C ''increas'':143C ''info'':109C ''keep'':122C ''layer'':75C ''learn'':49C ''less'':171C ''linear'':204C ''literatur'':212C ''model'':41C,68C,150C,168C,177C,196C ''natur'':115C ''neuron'':147C,172C ''nofollow'':107C ''note'':139C ''notebook'':5C ''number'':145C ''p'':1C,37C,135C,180C ''paramet'':19C,31C,42C,69C,100C,201C ''percentag'':125C ''perform'':26C ''pick'':94C ''plot'':48C,65C ''point'':46C ''present'':7C ''propag'':35C ''regress'':205C ''rel'':106C ''result'':8C,209C ''rmse'':157C ''saniti'':216C ''see'':21C,174C ''seem'':141C ''set'':133C ''show'':151C ''side'':138C ''signific'':153C ''simpl'':195C ''simpler'':167C ''size'':76C ''small'':124C ''suggest'':159C,185C ''test'':132C ''third'':131C ''tomaskazemeka'':45C ''tradeoff'':90C ''train'':4C,10C,71C,78C ''tri'':16C,164C,193C ''understand'':87C ''use'':66C ''user'':44C ''valid'':80C ''valu'':97C ''vari'':17C ''vs'':77C ''would'':184C ''www.astroml.org'':104C ''www.astroml.org/sklearn_tutorial/practical.html#cross-validation-and-testing'':103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (582, 2, '2014-06-25 14:43:53.68', 3, NULL, NULL, '&lt;p&gt;Some research from &lt;a href="http://www.dongnguyen.nl/publications.html" rel="nofollow"&gt;D. Nguyen et al.&lt;/a&gt; try to predict twitter user''s age based on their tweets. Maybe you find them useful. They use logistic and linear regression.&lt;/p&gt;
', NULL, NULL, '''/a'':16C ''/p'':39C ''/publications.html'':9C ''age'':23C ''al'':15C ''base'':24C ''d'':12C ''et'':14C ''find'':30C ''href'':6C ''linear'':37C ''logist'':35C ''mayb'':28C ''nguyen'':13C ''nofollow'':11C ''p'':1C ''predict'':19C ''regress'':38C ''rel'':10C ''research'':3C ''tri'':17C ''tweet'':27C ''twitter'':20C ''use'':32C,34C ''user'':21C ''www.dongnguyen.nl'':8C ''www.dongnguyen.nl/publications.html'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (521, 2, '2014-06-22 12:02:52.773', 2, NULL, NULL, '&lt;p&gt;Conjugate Gradient -- the cg in Function MINimization (nonlinear) Conjugate Gradiant -- requires you to have a gradient function (or approximation) since that is a critical part of the algorithm itself: it needs to find the steepest descent direction quickly.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;fminsearch&lt;/code&gt; implements Nelder-Mead, a nonlinear gradient-free method. Its convergence properties are not anywhere near as good.&lt;/p&gt;

&lt;p&gt;What is your cost function? Are there approximations that are differentiable (pref. twice so you can use the very powerful quasi-Newton methods)?&lt;/p&gt;
', NULL, NULL, '''/code'':44C ''/p'':40C,64C,90C ''algorithm'':29C ''anywher'':60C ''approxim'':20C,73C ''cg'':5C ''code'':42C ''conjug'':2C,10C ''converg'':56C ''cost'':69C ''critic'':25C ''descent'':37C ''differenti'':76C ''direct'':38C ''find'':34C ''fminsearch'':43C ''free'':53C ''function'':7C,18C,70C ''good'':63C ''gradiant'':11C ''gradient'':3C,17C,52C ''gradient-fre'':51C ''implement'':45C ''mead'':48C ''method'':54C,89C ''minim'':8C ''near'':61C ''need'':32C ''nelder'':47C ''nelder-mead'':46C ''newton'':88C ''nonlinear'':9C,50C ''p'':1C,41C,65C ''part'':26C ''power'':85C ''pref'':77C ''properti'':57C ''quasi'':87C ''quasi-newton'':86C ''quick'':39C ''requir'':12C ''sinc'':21C ''steepest'':36C ''twice'':78C ''use'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5333, 2, '2015-03-17 07:21:54.11', 1, NULL, NULL, '&lt;p&gt;If you filter something out by choosing one branch over another branch in the tree, the observations you did not choose are forever lost.&lt;/p&gt;

&lt;p&gt;But to directly answer your question - no, it does not always make it more general. If you construct a tree where all the decision are exactly the same, then pruning does not make it more general.&lt;/p&gt;
', NULL, NULL, '''/p'':26C,64C ''alway'':38C ''anoth'':12C ''answer'':31C ''branch'':10C,13C ''choos'':8C,22C ''construct'':45C ''decis'':51C ''direct'':30C ''exact'':53C ''filter'':4C ''forev'':24C ''general'':42C,63C ''lost'':25C ''make'':39C,60C ''observ'':18C ''one'':9C ''p'':1C,27C ''prune'':57C ''question'':33C ''someth'':5C ''tree'':16C,47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (710, 1, '2014-07-09 11:05:40.813', 1, 214, 'Commercial Text Summarization Tools', '&lt;p&gt;I''m looking for commercial text summarization tools (APIs, Libraries,...) which are able to perform any of the following tasks:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Extractive Multi-Document Summarization (Generic or query-based)&lt;/li&gt;
&lt;li&gt;Extractive Single-Document Summarization (Generic or query-based)&lt;/li&gt;
&lt;li&gt;Generative Single-Document Summarization (Generic or query-based)&lt;/li&gt;
&lt;li&gt;Generative Multi-Document Summarization (Generic or query-based)&lt;/li&gt;
&lt;/ol&gt;
', 'text-mining', NULL, '''/li'':42C,54C,66C,78C ''/ol'':79C ''/p'':29C ''abl'':21C ''api'':17C ''base'':41C,53C,65C,77C ''commerci'':1A,13C ''document'':35C,47C,59C,71C ''extract'':32C,44C ''follow'':27C ''generat'':56C,68C ''generic'':37C,49C,61C,73C ''li'':31C,43C,55C,67C ''librari'':18C ''look'':11C ''m'':10C ''mine'':7B ''multi'':34C,70C ''multi-docu'':33C,69C ''ol'':30C ''p'':8C ''perform'':23C ''queri'':40C,52C,64C,76C ''query-bas'':39C,51C,63C,75C ''singl'':46C,58C ''single-docu'':45C,57C ''summar'':3A,15C,36C,48C,60C,72C ''task'':28C ''text'':2A,6B,14C ''text-min'':5B ''tool'':4A,16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (523, 2, '2014-06-22 16:03:39.047', 2, NULL, NULL, '&lt;p&gt;In your notebooks, I did not see your neural network model, can you point which library is using, how many layers you have and what type of neural network are you using?&lt;/p&gt;

&lt;p&gt;In your notebooks, it seems you are using the noisy and outlier dataset to train the neural network, I think you should train the neural network on the dataset that you do not have any outliers so that you could see the observation distance from the prediction of the neural network to label the observation either outlier or not. &lt;/p&gt;

&lt;p&gt;I wrote &lt;a href="http://bugra.github.io/work/notes/2014-03-31/outlier-detection-in-time-series-signals-fft-median-filtering/" rel="nofollow"&gt;couple&lt;/a&gt; of &lt;a href="http://bugra.github.io/work/notes/2014-05-11/robust-regression-and-outlier-detection-via-gaussian-processes/" rel="nofollow"&gt;things&lt;/a&gt; on outlier detection in time-series signals, your data is highly seasonal as sobach mentioned and you could use FFT(first link above) to get the overall trend in the signal. After you get the frequency component in the gas consumption, you could look at the high frequency components to get the outliers. &lt;/p&gt;

&lt;p&gt;Also if you want to insist on using neural network for seasonal data, you may want to check recurrent neural networks out as they could incorporate the past observations better than a vanilla neural network, and supposedly may provide a better result for the data that you have. &lt;/p&gt;
', NULL, NULL, '''/a'':107C,117C ''/p'':34C,95C,172C,222C ''/work/notes/2014-03-31/outlier-detection-in-time-series-signals-fft-median-filtering/'':103C ''/work/notes/2014-05-11/robust-regression-and-outlier-detection-via-gaussian-processes/'':113C ''also'':174C ''better'':203C,214C ''bugra.github.io'':102C,112C ''bugra.github.io/work/notes/2014-03-31/outlier-detection-in-time-series-signals-fft-median-filtering/'':101C ''bugra.github.io/work/notes/2014-05-11/robust-regression-and-outlier-detection-via-gaussian-processes/'':111C ''check'':191C ''compon'':155C,167C ''consumpt'':159C ''could'':75C,136C,161C,198C ''coupl'':106C ''data'':127C,186C,218C ''dataset'':48C,64C ''detect'':120C ''distanc'':79C ''either'':91C ''fft'':138C ''first'':139C ''frequenc'':154C,166C ''gas'':158C ''get'':143C,152C,169C ''high'':129C,165C ''href'':100C,110C ''incorpor'':199C ''insist'':179C ''label'':88C ''layer'':22C ''librari'':17C ''link'':140C ''look'':162C ''mani'':21C ''may'':188C,211C ''mention'':133C ''model'':12C ''network'':11C,30C,53C,61C,86C,183C,194C,208C ''neural'':10C,29C,52C,60C,85C,182C,193C,207C ''nofollow'':105C,115C ''noisi'':45C ''notebook'':4C,38C ''observ'':78C,90C,202C ''outlier'':47C,71C,92C,119C,171C ''overal'':145C ''p'':1C,35C,96C,173C ''past'':201C ''point'':15C ''predict'':82C ''provid'':212C ''recurr'':192C ''rel'':104C,114C ''result'':215C ''season'':130C,185C ''see'':8C,76C ''seem'':40C ''seri'':124C ''signal'':125C,149C ''sobach'':132C ''suppos'':210C ''thing'':116C ''think'':55C ''time'':123C ''time-seri'':122C ''train'':50C,58C ''trend'':146C ''type'':27C ''use'':19C,33C,43C,137C,181C ''vanilla'':206C ''want'':177C,189C ''wrote'':98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (525, 2, '2014-06-22 18:35:22.207', 10, NULL, NULL, '&lt;p&gt;In order to understand the variety of ways machine learning can be integrated into production applications, I think it is useful to look at open source projects and papers/blog posts from companies describing their infrastructure.&lt;/p&gt;

&lt;p&gt;The common theme that these systems have is the separation of model training from model application. In production systems, model application needs to be fast, on the order of 100s of ms, but there is more freedom in how frequently fitted model parameters (or equivalent) need to be updated.&lt;/p&gt;

&lt;p&gt;People use a wide range of solutions for model training and deployment:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Build a model, then export and deploy it with PMML&lt;/p&gt;

&lt;blockquote&gt;
  &lt;ul&gt;
  &lt;li&gt;&lt;p&gt;&lt;a href="http://nerds.airbnb.com/architecting-machine-learning-system-risk/"&gt;AirBnB describes their model training&lt;/a&gt; in R/Python and deployment of PMML models via OpenScoring.&lt;/p&gt;&lt;/li&gt;
  &lt;li&gt;&lt;p&gt;&lt;a href="http://www.cascading.org/projects/pattern/"&gt;Pattern&lt;/a&gt; is project related to &lt;a href="http://www.cascading.org/"&gt;Cascading&lt;/a&gt; that can consume PMML and deploy predictive models.&lt;/p&gt;&lt;/li&gt;
  &lt;/ul&gt;
&lt;/blockquote&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Build a model in MapReduce and access values in a custom system&lt;/p&gt;

&lt;blockquote&gt;
  &lt;ul&gt;
  &lt;li&gt;&lt;p&gt;&lt;a href="http://codeascraft.com/2014/06/18/conjecture-scalable-machine-learning-in-hadoop-with-scalding/"&gt;Conjecture is an open source project from Etsy&lt;/a&gt; that allows for model training with &lt;a href="https://github.com/twitter/scalding"&gt;Scalding&lt;/a&gt;, an easier to use scala wrapper around MapReduce, and  deployment via Php.&lt;/p&gt;&lt;/li&gt;
  &lt;li&gt;&lt;p&gt;&lt;a href="http://www.kiji.org/"&gt;Kiji is an open source project from WibiData&lt;/a&gt; that allows for real-time model scoring (application) as well as functioanlity for persisting user data and training models on that data via &lt;a href="https://github.com/twitter/scalding"&gt;Scalding.&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
  &lt;/ul&gt;
&lt;/blockquote&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Use an online system that allows for continuously updating model parameters.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;ul&gt;
  &lt;li&gt;&lt;a href="http://dl.acm.org/citation.cfm?id=1242610"&gt;Google released a great paper about an online collaborative filtering&lt;/a&gt; they implemented to deal with recommendations in Google News.&lt;/li&gt;
  &lt;/ul&gt;
&lt;/blockquote&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/2014/06/18/conjecture-scalable-machine-learning-in-hadoop-with-scalding/'':197C ''/a'':131C,151C,160C,206C,219C,247C,278C,316C ''/architecting-machine-learning-system-risk/'':125C ''/blockquote'':172C,282C,328C ''/citation.cfm?id=1242610'':305C ''/li'':142C,170C,173C,233C,280C,283C,326C,329C ''/p'':37C,88C,102C,116C,141C,169C,188C,232C,279C,297C ''/projects/pattern/'':149C ''/twitter/scalding'':217C,276C ''/ul'':171C,281C,327C,330C ''100s'':68C ''access'':182C ''airbnb'':126C ''allow'':208C,249C,291C ''applic'':17C,54C,59C,256C ''around'':226C ''blockquot'':117C,189C,298C ''build'':106C,176C ''cascad'':159C ''codeascraft.com'':196C ''codeascraft.com/2014/06/18/conjecture-scalable-machine-learning-in-hadoop-with-scalding/'':195C ''collabor'':314C ''common'':40C ''compani'':33C ''conjectur'':198C ''consum'':163C ''continu'':293C ''custom'':186C ''data'':264C,270C ''deal'':320C ''deploy'':101C,112C,135C,166C,229C ''describ'':34C,127C ''dl.acm.org'':304C ''dl.acm.org/citation.cfm?id=1242610'':303C ''easier'':221C ''equival'':83C ''etsi'':205C ''export'':110C ''fast'':63C ''filter'':315C ''fit'':79C ''freedom'':75C ''frequent'':78C ''functioanl'':260C ''github.com'':216C,275C ''github.com/twitter/scalding'':215C,274C ''googl'':306C,324C ''great'':309C ''href'':122C,146C,157C,194C,214C,237C,273C,302C ''implement'':318C ''infrastructur'':36C ''integr'':14C ''kiji'':239C ''learn'':11C ''li'':104C,119C,143C,174C,191C,234C,284C,300C ''look'':24C ''machin'':10C ''mapreduc'':180C,227C ''model'':50C,53C,58C,80C,98C,108C,129C,138C,168C,178C,210C,254C,267C,295C ''ms'':70C ''need'':60C,84C ''nerds.airbnb.com'':124C ''nerds.airbnb.com/architecting-machine-learning-system-risk/'':123C ''news'':325C ''onlin'':288C,313C ''open'':26C,201C,242C ''openscor'':140C ''order'':3C,66C ''p'':1C,38C,89C,105C,120C,144C,175C,192C,235C,285C ''paper'':310C ''papers/blog'':30C ''paramet'':81C,296C ''pattern'':150C ''peopl'':90C ''persist'':262C ''php'':231C ''pmml'':115C,137C,164C ''post'':31C ''predict'':167C ''product'':16C,56C ''project'':28C,153C,203C,244C ''r/python'':133C ''rang'':94C ''real'':252C ''real-tim'':251C ''recommend'':322C ''relat'':154C ''releas'':307C ''scala'':224C ''scald'':218C,277C ''score'':255C ''separ'':48C ''solut'':96C ''sourc'':27C,202C,243C ''system'':44C,57C,187C,289C ''theme'':41C ''think'':19C ''time'':253C ''train'':51C,99C,130C,211C,266C ''ul'':103C,118C,190C,299C ''understand'':5C ''updat'':87C,294C ''use'':22C,91C,223C,286C ''user'':263C ''valu'':183C ''varieti'':7C ''via'':139C,230C,271C ''way'':9C ''well'':258C ''wibidata'':246C ''wide'':93C ''wrapper'':225C ''www.cascading.org'':148C,158C ''www.cascading.org/projects/pattern/'':147C ''www.kiji.org'':238C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (527, 1, '2014-06-22 21:45:35.8', 6, 380, 'Score matrix string similarity', '&lt;p&gt;I have a load of documents, which have a load of key value pairs in them. The key might not be unique so there might be multiple keys of the same type with different values.&lt;/p&gt;

&lt;p&gt;I want to compare the similarity of the keys between 2 documents. More specifically the string similarity of these values. I am thinking of using something like the &lt;a href="http://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm"&gt;Smith-Waterman Algorithm&lt;/a&gt; to compare the similarity.&lt;/p&gt;

&lt;p&gt;So I''ve drawn a picture of how I''m thinking about representing the data - &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/VC4em.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;The values in the cells are the result of the smith-waterman algorithm (or some other string similarity metric).&lt;/p&gt;

&lt;p&gt;Image that this matrix represents a key type of "things" I then need to add the "things" similarity score into a vector of 0 or 1. Thats ok.&lt;/p&gt;

&lt;p&gt;What I can''t figure out is how I determine if the matrix is similar or not similar - ideally I want to convert the matrix to an number between 0 and 1 and then I''ll just set a threshold to score it as either 0 or 1.&lt;/p&gt;

&lt;p&gt;Any ideas how I can create a score of the matrix? Does anyone know any algorithms that do this type of thing (obviously things like how smith waterman works is kind of applicable).&lt;/p&gt;
', 'algorithms  similarity', NULL, '''/a'':82C ''/p'':43C,87C,104C,116C,138C,168C,218C,253C ''/vc4em.png'':110C ''/wiki/smith%e2%80%93waterman_algorithm'':77C ''0'':163C,199C,215C ''1'':165C,201C,217C ''2'':55C ''add'':154C ''algorithm'':5B,81C,131C,235C ''alt'':111C ''anyon'':232C ''applic'':252C ''cell'':122C ''compar'':48C,84C ''convert'':192C ''creat'':225C ''data'':103C ''descript'':114C ''determin'':179C ''differ'':41C ''document'':13C,56C ''drawn'':92C ''either'':214C ''en.wikipedia.org'':76C ''en.wikipedia.org/wiki/smith%e2%80%93waterman_algorithm'':75C ''enter'':112C ''figur'':174C ''href'':74C ''i.stack.imgur.com'':109C ''i.stack.imgur.com/vc4em.png'':108C ''idea'':221C ''ideal'':188C ''imag'':113C,140C ''img'':106C ''key'':19C,25C,35C,53C,146C ''kind'':250C ''know'':233C ''like'':71C,244C ''ll'':205C ''load'':11C,17C ''m'':98C ''matrix'':2A,143C,182C,194C,230C ''metric'':137C ''might'':26C,32C ''multipl'':34C ''need'':152C ''number'':197C ''obvious'':242C ''ok'':167C ''p'':7C,44C,88C,105C,117C,139C,169C,219C ''pair'':21C ''pictur'':94C ''repres'':101C,144C ''result'':125C ''score'':1A,158C,211C,227C ''set'':207C ''similar'':4A,6B,50C,61C,86C,136C,157C,184C,187C ''smith'':79C,129C,246C ''smith-waterman'':78C,128C ''someth'':70C ''specif'':58C ''src'':107C ''string'':3A,60C,135C ''that'':166C ''thing'':149C,156C,241C,243C ''think'':67C,99C ''threshold'':209C ''type'':39C,147C,239C ''uniqu'':29C ''use'':69C ''valu'':20C,42C,64C,119C ''ve'':91C ''vector'':161C ''want'':46C,190C ''waterman'':80C,130C,247C ''work'':248C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (529, 2, '2014-06-23 03:44:57.643', 3, NULL, NULL, '&lt;p&gt;&lt;a href="http://nerds.airbnb.com/architecting-machine-learning-system-risk/" rel="nofollow"&gt;Airbnb&lt;/a&gt; and &lt;a href="http://codeascraft.com/2014/06/18/conjecture-scalable-machine-learning-in-hadoop-with-scalding/" rel="nofollow"&gt;Etsy&lt;/a&gt; both recently posted detailed information about their workflows.&lt;/p&gt;
', NULL, NULL, '''/2014/06/18/conjecture-scalable-machine-learning-in-hadoop-with-scalding/'':16C ''/a'':10C,20C ''/architecting-machine-learning-system-risk/'':6C ''/p'':29C ''airbnb'':9C ''codeascraft.com'':15C ''codeascraft.com/2014/06/18/conjecture-scalable-machine-learning-in-hadoop-with-scalding/'':14C ''detail'':24C ''etsi'':19C ''href'':3C,13C ''inform'':25C ''nerds.airbnb.com'':5C ''nerds.airbnb.com/architecting-machine-learning-system-risk/'':4C ''nofollow'':8C,18C ''p'':1C ''post'':23C ''recent'':22C ''rel'':7C,17C ''workflow'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (530, 1, '2014-06-23 04:39:26.623', 4, 269, 'How to select algorithms for ensemble methods?', '&lt;p&gt;There is a general recommendation that algorithms in ensemble learning combinations should be different in nature. Is there a classification table, a scale or some rules that allow to evaluate how far away are the algorithms from each other? What are the best combinations? &lt;/p&gt;
', 'machine-learning', NULL, '''/p'':56C ''algorithm'':4A,18C,47C ''allow'':39C ''away'':44C ''best'':54C ''classif'':31C ''combin'':22C,55C ''differ'':25C ''ensembl'':6A,20C ''evalu'':41C ''far'':43C ''general'':15C ''learn'':10B,21C ''machin'':9B ''machine-learn'':8B ''method'':7A ''natur'':27C ''p'':11C ''recommend'':16C ''rule'':37C ''scale'':34C ''select'':3A ''tabl'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (538, 1, '2014-06-23 13:31:41.443', 2, 241, 'What are your favorite sources for news about Machine Learning and Data Science?', '&lt;p&gt;Data Science and Machine Learning include a lot of different topics and itÂ´s hard to stay up-to-date about all the news about papers, researches or new tutorials and tools.&lt;/p&gt;

&lt;p&gt;What sources do you use to get all the information?&lt;/p&gt;

&lt;p&gt;I use mostly Reddit as my first source and the subreddits Machine Learning and R
&lt;a href="http://www.reddit.com/r/MachineLearning" rel="nofollow"&gt;http://www.reddit.com/r/MachineLearning&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.reddit.com/r/rstats" rel="nofollow"&gt;http://www.reddit.com/r/rstats&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;But also datatau.com and of course the great KDNuggets page &lt;/p&gt;
', 'machine-learning', NULL, '''/p'':51C,63C,112C ''/r/machinelearning'':84C ''/r/machinelearning&lt;/a&gt;&lt;/p&gt;'':89C ''/r/rstats'':95C ''/r/rstats&lt;/a&gt;&lt;/p&gt;'':100C ''also'':103C ''cours'':107C ''data'':12A,18C ''datatau.com'':104C ''date'':38C ''differ'':27C ''favorit'':4A ''first'':71C ''get'':59C ''great'':109C ''hard'':32C ''href'':81C,92C ''includ'':23C ''inform'':62C ''itâ'':30C ''kdnugget'':110C ''learn'':10A,16B,22C,77C ''lot'':25C ''machin'':9A,15B,21C,76C ''machine-learn'':14B ''most'':67C ''new'':47C ''news'':7A,42C ''nofollow'':86C,97C ''p'':17C,52C,64C,90C,101C ''page'':111C ''paper'':44C ''r'':79C ''reddit'':68C ''rel'':85C,96C ''research'':45C ''scienc'':13A,19C ''sourc'':5A,54C,72C ''stay'':34C ''subreddit'':75C ''tool'':50C ''topic'':28C ''tutori'':48C ''up-to-d'':35C ''use'':57C,66C ''www.reddit.com'':83C,88C,94C,99C ''www.reddit.com/r/machinelearning'':82C ''www.reddit.com/r/machinelearning&lt;/a&gt;&lt;/p&gt;'':87C ''www.reddit.com/r/rstats'':93C ''www.reddit.com/r/rstats&lt;/a&gt;&lt;/p&gt;'':98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (539, 2, '2014-06-23 13:36:18.273', 3, NULL, NULL, '&lt;p&gt;I like following certain tags on stats.stackexchange.com (a.k.a. CrossValidated), as well as the &lt;code&gt;r&lt;/code&gt; tag on StackOverflow.&lt;/p&gt;

&lt;p&gt;Arxiv.org is also pretty cool, it has several subsections devoted to stats and ML. &lt;/p&gt;
', NULL, NULL, '''/code'':17C ''/p'':21C,37C ''a.k.a'':9C ''also'':25C ''arxiv.org'':23C ''certain'':5C ''code'':15C ''cool'':27C ''crossvalid'':10C ''devot'':32C ''follow'':4C ''like'':3C ''ml'':36C ''p'':1C,22C ''pretti'':26C ''r'':16C ''sever'':30C ''stackoverflow'':20C ''stat'':34C ''stats.stackexchange.com'':8C ''subsect'':31C ''tag'':6C,18C ''well'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (877, 1, '2014-07-31 18:52:56.307', 7, 151, 'What is the use of user data collection besides serving ads?', '&lt;p&gt;Well this looks like the most suited place for this question.   &lt;/p&gt;

&lt;p&gt;Every website collect data of the user, some just for usability and personalization, but the majority like social networks track every move on the web, some free apps on your phone scan text messages, call history and so on.&lt;/p&gt;

&lt;p&gt;All this data siphoning is just for selling your profile for advertisers?&lt;/p&gt;
', 'data-mining', NULL, '''/p'':27C,68C,82C ''ad'':11A ''advertis'':81C ''app'':56C ''besid'':9A ''call'':63C ''collect'':8A,31C ''data'':7A,13B,32C,72C ''data-min'':12B ''everi'':29C,49C ''free'':55C ''histori'':64C ''like'':19C,45C ''look'':18C ''major'':44C ''messag'':62C ''mine'':14B ''move'':50C ''network'':47C ''p'':15C,28C,69C ''person'':41C ''phone'':59C ''place'':23C ''profil'':79C ''question'':26C ''scan'':60C ''sell'':77C ''serv'':10A ''siphon'':73C ''social'':46C ''suit'':22C ''text'':61C ''track'':48C ''usabl'':39C ''use'':4A ''user'':6A,35C ''web'':53C ''websit'':30C ''well'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (531, 1, '2014-06-23 07:03:15.643', 7, 452, 'Binary classification model for sparse / biased data', '&lt;p&gt;I have a dataset with following specifications:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Training dataset with 193176 samples with 2821 positives&lt;/li&gt;
&lt;li&gt;Test Dataset with 82887 samples with 673 positives&lt;/li&gt;
&lt;li&gt;There are 10 features.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I want to perform a binary classification ( say, 0/1 ). The issue I am facing is that the data is very biased or rather sparse. After normalization and scaling the data along with some feature engineering and using a couple of different algorithms, these are the best results I could achieve:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;mean square error : 0.00804710026904
Confusion matrix : [[82214   667]
                   [    0     6]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;i.e only 6 correct positive hits. This is using logistic regression. Here are the various things I tried with this:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Different algorithms like RandomForest, DecisionTree, SVM&lt;/li&gt;
&lt;li&gt;Changing parameters value to call the function&lt;/li&gt;
&lt;li&gt;Some intuition based feature engineering to include compounded features&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Now, my questions are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;What can I do to improve the number of positive hits ? &lt;/li&gt;
&lt;li&gt;How can one determine if there is an overfit in such a case ? ( I have tried plotting etc. )&lt;/li&gt;
&lt;li&gt;At what point could one conclude if maybe this is the best possible fit I could have? ( which seems sad considering only 6 hits out of 673 )&lt;/li&gt;
&lt;li&gt;Is there a way I could make the positive sample instances weigh more so the pattern recognition improves leading to more hits ?&lt;/li&gt;
&lt;li&gt;Which graphical plots could help detect outliers or some intuition about which pattern would fit the best?&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I am using the scikit-learn library with Python and all implementations are library functions.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;edit:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Here are the results with a few other algorithms:&lt;/p&gt;

&lt;p&gt;Random Forest Classifier(n_estimators=100)&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[[82211   667]
[    3     6]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Decision Trees:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[[78611   635]
[ 3603    38]]
&lt;/code&gt;&lt;/pre&gt;
', 'machine-learning  python  classification  logistic-regression  topic-model', NULL, '''/code'':120C,335C,347C ''/li'':38C,48C,54C,152C,161C,172C,193C,213C,242C,266C,285C ''/ol'':286C ''/p'':27C,107C,143C,179C,304C,309C,320C,328C,340C ''/pre'':121C,336C,348C ''/strong'':308C ''/ul'':55C,173C ''0'':118C ''0.00804710026904'':113C ''0/1'':65C ''10'':52C ''100'':327C ''193176'':33C ''2821'':36C ''3'':333C ''3603'':345C ''38'':346C ''6'':119C,125C,237C,334C ''635'':344C ''667'':117C,332C ''673'':46C,241C ''78611'':343C ''82211'':331C ''82214'':116C ''82887'':43C ''achiev'':106C ''algorithm'':98C,147C,319C ''along'':87C ''base'':165C ''best'':102C,226C,284C ''bias'':6A,77C ''binari'':1A,62C ''call'':158C ''case'':207C ''chang'':154C ''classif'':2A,12B,63C ''classifi'':324C ''code'':109C,330C,342C ''compound'':170C ''conclud'':220C ''confus'':114C ''consid'':235C ''correct'':126C ''could'':105C,218C,230C,249C,271C ''coupl'':95C ''data'':7A,74C,86C ''dataset'':23C,31C,41C ''decis'':338C ''decisiontre'':150C ''detect'':273C ''determin'':198C ''differ'':97C,146C ''edit'':307C ''engin'':91C,167C ''error'':112C ''estim'':326C ''etc'':212C ''face'':70C ''featur'':53C,90C,166C,171C ''fit'':228C,282C ''follow'':25C ''forest'':323C ''function'':160C,303C ''graphic'':269C ''help'':272C ''hit'':128C,192C,238C,265C ''i.e'':123C ''implement'':300C ''improv'':187C,261C ''includ'':169C ''instanc'':254C ''intuit'':164C,277C ''issu'':67C ''lead'':262C ''learn'':10B,294C ''li'':29C,39C,49C,145C,153C,162C,181C,194C,214C,243C,267C ''librari'':295C,302C ''like'':148C ''logist'':14B,132C ''logistic-regress'':13B ''machin'':9B ''machine-learn'':8B ''make'':250C ''matrix'':115C ''mayb'':222C ''mean'':110C ''model'':3A,18B ''n'':325C ''normal'':82C ''number'':189C ''ol'':180C ''one'':197C,219C ''outlier'':274C ''overfit'':203C ''p'':19C,56C,122C,174C,287C,305C,310C,321C,337C ''paramet'':155C ''pattern'':259C,280C ''perform'':60C ''plot'':211C,270C ''point'':217C ''posit'':37C,47C,127C,191C,252C ''possibl'':227C ''pre'':108C,329C,341C ''python'':11B,297C ''question'':177C ''random'':322C ''randomforest'':149C ''rather'':79C ''recognit'':260C ''regress'':15B,133C ''result'':103C,314C ''sad'':234C ''sampl'':34C,44C,253C ''say'':64C ''scale'':84C ''scikit'':293C ''scikit-learn'':292C ''seem'':233C ''spars'':5A,80C ''specif'':26C ''squar'':111C ''strong'':306C ''svm'':151C ''test'':40C ''thing'':138C ''topic'':17B ''topic-model'':16B ''train'':30C ''tree'':339C ''tri'':140C,210C ''ul'':28C,144C ''use'':93C,131C,290C ''valu'':156C ''various'':137C ''want'':58C ''way'':247C ''weigh'':255C ''would'':281C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (583, 2, '2014-06-25 15:42:19.287', 3, NULL, NULL, '&lt;p&gt;The original MacQueen k-means publication (the first to use the name "kmeans") is an online algorithm.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;MacQueen, J. B. (1967). "Some Methods for classification and Analysis of Multivariate Observations". Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability 1. University of California Press. pp. 281â297&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;After assigning each point, the mean is incrementally updated.&lt;/p&gt;

&lt;p&gt;As far as I can tell, it was also meant to be a single pass over the data only, although it can be trivially repeated multiple times to reassign points until convergence.&lt;/p&gt;

&lt;p&gt;MacQueen usually takes fewer iterations than Lloyds to converge if your data is shuffled. On ordered data, it can have problems. On the downside, it requires more computation for each object, so each iteration takes slightly longer.&lt;/p&gt;

&lt;p&gt;When you implement a parallel version of k-means, make sure to study the update formulas in MacQueens publication. They''re useful.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':55C ''/p'':20C,54C,66C,100C,139C,164C ''1'':46C ''1967'':26C ''281â'':52C ''297'':53C ''5th'':38C ''algorithm'':19C ''also'':76C ''although'':87C ''analysi'':32C ''assign'':58C ''b'':25C ''berkeley'':39C ''blockquot'':21C ''california'':49C ''classif'':30C ''comput'':129C ''converg'':99C,110C ''data'':85C,113C,118C ''downsid'':125C ''far'':69C ''fewer'':105C ''first'':10C ''formula'':157C ''implement'':143C ''increment'':64C ''iter'':106C,135C ''j'':24C ''k'':6C,149C ''k-mean'':5C,148C ''kmean'':15C ''lloyd'':108C ''longer'':138C ''macqueen'':4C,23C,102C,159C ''make'':151C ''mathemat'':42C ''mean'':7C,62C,150C ''meant'':77C ''method'':28C ''multipl'':93C ''multivari'':34C ''name'':14C ''object'':132C ''observ'':35C ''onlin'':18C ''order'':117C ''origin'':3C ''p'':1C,22C,56C,67C,101C,140C ''parallel'':145C ''pass'':82C ''point'':60C,97C ''pp'':51C ''press'':50C ''probabl'':45C ''problem'':122C ''proceed'':36C ''public'':8C,160C ''re'':162C ''reassign'':96C ''repeat'':92C ''requir'':127C ''shuffl'':115C ''singl'':81C ''slight'':137C ''statist'':43C ''studi'':154C ''sure'':152C ''symposium'':40C ''take'':104C,136C ''tell'':73C ''time'':94C ''trivial'':91C ''univers'':47C ''updat'':65C,156C ''use'':12C,163C ''usual'':103C ''version'':146C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (532, 2, '2014-06-23 08:03:50.327', 7, NULL, NULL, '&lt;p&gt;In general in an ensemble you try to combine the opinions of multiple classifiers. The idea is like asking a bunch of experts on the same thing. You get multiple opinions and you later have to combine their answers (e.g. by a voting scheme). For this trick to work you want the classifiers to be different from each other, that is you don''t want to ask the same "expert" twice for the same thing.&lt;/p&gt;

&lt;p&gt;In practice, the classifiers do not have to be different in the sense of a different algorithm. What you can do is train the same algorithm with different subset of the data or a different subset of features (or both). If you use different training sets you end up with different models and different "independent" classifiers. &lt;/p&gt;

&lt;p&gt;There is no golden rule on what works best in general. You have to try to see if there is an improvement for your specific problem. &lt;/p&gt;
', NULL, NULL, '''/p'':77C,135C,163C ''algorithm'':95C,104C ''answer'':40C ''ask'':20C,68C ''best'':145C ''bunch'':22C ''classifi'':15C,54C,82C,134C ''combin'':10C,38C ''data'':110C ''differ'':57C,88C,94C,106C,113C,122C,129C,132C ''e.g'':41C ''end'':126C ''ensembl'':6C ''expert'':24C,71C ''featur'':116C ''general'':3C,147C ''get'':30C ''golden'':140C ''idea'':17C ''improv'':158C ''independ'':133C ''later'':35C ''like'':19C ''model'':130C ''multipl'':14C,31C ''opinion'':12C,32C ''p'':1C,78C,136C ''practic'':80C ''problem'':162C ''rule'':141C ''scheme'':45C ''see'':153C ''sens'':91C ''set'':124C ''specif'':161C ''subset'':107C,114C ''thing'':28C,76C ''train'':101C,123C ''tri'':8C,151C ''trick'':48C ''twice'':72C ''use'':121C ''vote'':44C ''want'':52C,66C ''work'':50C,144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (533, 2, '2014-06-23 08:13:22.837', 6, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Since you are doing binary classification, have you tried adjusting the classification threshold? Since your algorithm seems rather insensitive, I would try lowering it and check if there is an improvement.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;You can always use &lt;a href="http://stackoverflow.com/questions/4617365/what-is-a-learning-curve-in-machine-learning"&gt;Learning Curves&lt;/a&gt;, or a plot of one model parameter vs. Training and Validation error to determine whether your model is overfitting. It seems it is under fitting in your case, but that''s just intuition.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Well, ultimately it depends on your dataset, and the different models you have tried. At this point, and without further testing, there can not be a definite answer.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Without claiming to be an expert on the topic, there are a number of different techniques you may follow (hint: &lt;a href="http://florianhartl.com/thoughts-on-machine-learning-dealing-with-skewed-classes.html"&gt;first link on google&lt;/a&gt;), but in my opinion you should first make sure you choose your cost function carefully, so that it represents what you are actually looking for. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Not sure what you mean by pattern intuition, can you elaborate?&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;By the way, what were your results with the different algorithms you tried? Were they any different?&lt;/p&gt;
', NULL, NULL, '''/a'':50C,149C ''/li'':36C,85C,117C,176C,191C ''/ol'':192C ''/p'':35C,84C,116C,175C,190C,211C ''/questions/4617365/what-is-a-learning-curve-in-machine-learning'':47C ''/thoughts-on-machine-learning-dealing-with-skewed-classes.html'':144C ''actual'':172C ''adjust'':13C ''algorithm'':19C,204C ''alway'':41C ''answer'':115C ''binari'':8C ''care'':164C ''case'':78C ''check'':29C ''choos'':160C ''claim'':121C ''classif'':9C,15C ''cost'':162C ''curv'':49C ''dataset'':94C ''definit'':114C ''depend'':91C ''determin'':64C ''differ'':97C,134C,203C,210C ''elabor'':189C ''error'':62C ''expert'':125C ''first'':145C,156C ''fit'':75C ''florianhartl.com'':143C ''florianhartl.com/thoughts-on-machine-learning-dealing-with-skewed-classes.html'':142C ''follow'':138C ''function'':163C ''googl'':148C ''hint'':139C ''href'':44C,141C ''improv'':34C ''insensit'':22C ''intuit'':83C,186C ''learn'':48C ''li'':2C,37C,86C,118C,177C ''link'':146C ''look'':173C ''lower'':26C ''make'':157C ''may'':137C ''mean'':183C ''model'':56C,67C,98C ''number'':132C ''ol'':1C ''one'':55C ''opinion'':153C ''overfit'':69C ''p'':3C,38C,87C,119C,178C,193C ''paramet'':57C ''pattern'':185C ''plot'':53C ''point'':104C ''rather'':21C ''repres'':168C ''result'':200C ''seem'':20C,71C ''sinc'':4C,17C ''stackoverflow.com'':46C ''stackoverflow.com/questions/4617365/what-is-a-learning-curve-in-machine-learning'':45C ''sure'':158C,180C ''techniqu'':135C ''test'':108C ''threshold'':16C ''topic'':128C ''train'':59C ''tri'':12C,25C,101C,206C ''ultim'':89C ''use'':42C ''valid'':61C ''vs'':58C ''way'':196C ''well'':88C ''whether'':65C ''without'':106C,120C ''would'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (534, 2, '2014-06-23 09:23:27.617', 2, NULL, NULL, '&lt;p&gt;If your goal is to transform your matrix into a number (your similarity measure), you may want to use a &lt;a href="http://en.wikipedia.org/wiki/Matrix_norm" rel="nofollow"&gt;matrix norm&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;For instance, using the &lt;a href="http://en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm" rel="nofollow"&gt;Frobenius norm&lt;/a&gt; on your example would return 1.488086.&lt;/p&gt;
', NULL, NULL, '''/a'':31C,47C ''/p'':32C,54C ''/wiki/matrix_norm'':26C ''/wiki/matrix_norm#frobenius_norm'':42C ''1.488086'':53C ''en.wikipedia.org'':25C,41C ''en.wikipedia.org/wiki/matrix_norm'':24C ''en.wikipedia.org/wiki/matrix_norm#frobenius_norm'':40C ''exampl'':50C ''frobenius'':45C ''goal'':4C ''href'':23C,39C ''instanc'':35C ''matrix'':9C,29C ''may'':17C ''measur'':15C ''nofollow'':28C,44C ''norm'':30C,46C ''number'':12C ''p'':1C,33C ''rel'':27C,43C ''return'':52C ''similar'':14C ''transform'':7C ''use'':20C,36C ''want'':18C ''would'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (535, 2, '2014-06-23 09:42:46.45', 2, NULL, NULL, '&lt;p&gt;As I understood, Document 1 and Document 2 may have different number of keys. And you wand to get final similarity evaluation between 0 and 1. If so, I would propose following algorithm:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Sum of max. vals is equal to 0.&lt;/li&gt;
&lt;li&gt;Select maximum value from doc-doc matrix and add it to Sum of max. vals.&lt;/li&gt;
&lt;li&gt;Remove row and column with maximum value from the matrix.&lt;/li&gt;
&lt;li&gt;Repeat steps 2-3 until rows or columns are ended.&lt;/li&gt;
&lt;li&gt;Denominate Sum of max. vals by average number of key words in two texts.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Final estimation would be equal to 1, if both documents have identical length, and every word from Doc 1 has equivalent in Doc 2.&lt;/p&gt;

&lt;p&gt;You haven''t mentioned software, you are using, but here is &lt;strong&gt;R&lt;/strong&gt; example of function, computing such similarity (it takes object of class matrix as input):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;eval.sim &amp;lt;- function(sim.matrix){
  similarity &amp;lt;- 0
  denominator &amp;lt;- sum(dim(sim.matrix)) / 2
  for(i in 1:(min(c(nrow(sim.matrix), ncol(sim.matrix))) - 1)){
    extract &amp;lt;- which(sim.matrix == max(sim.matrix), arr.ind=T)[1, ]
    similarity &amp;lt;- similarity + sim.matrix[extract[1], extract[2]]
    sim.matrix &amp;lt;- sim.matrix[-extract[1], -extract[2]]
  }
  similarity &amp;lt;- similarity + max(sm.copy)
  similarity &amp;lt;- similarity / denominator
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In python - &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np

def score_matrix(sim_matrix):
    similarity = 0
    denominator = sum(sim_matrix.shape) / 2
    for i in range(min(sim_matrix.shape)):
        x, y = np.where(sim_matrix == np.max(sim_matrix))[0][0], np.where(sim_matrix == np.max(sim_matrix))[1][0]
        similarity += sim_matrix[x, y]
        sim_matrix = np.delete(sim_matrix,(x),axis=0)
        sim_matrix = np.delete(sim_matrix,(y),axis=1)
    return similarity / denominator
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (550, 2, '2014-06-24 03:50:41.41', 2, NULL, NULL, '&lt;p&gt;Feature extraction is always a challenge and the less addressed topic in literature, since it''s widely application dependant.&lt;/p&gt;

&lt;p&gt;Some ideas you can try:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Raw data, measured day-by-day. That''s kind of obvious with some implications and extra preprocessing (normalisation) in order to make timelines of different length comparable.&lt;/li&gt;
&lt;li&gt;Higher moments: skewness, kurtosis, etc&lt;/li&gt;
&lt;li&gt;Derivative(s): speed of evolution&lt;/li&gt;
&lt;li&gt;Time span is not that large but maybe it is worth trying some time series analysis features like for example autocorrelation. &lt;/li&gt;
&lt;li&gt;Some customised features like breaking timeline in weeks and measure the quantities you already measure in each week separately. Then a non-linear classifier would be able to combine e.g first-week features with last-week features in order to get insight of evolution in time.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':59C,66C,73C,96C,147C ''/p'':21C,28C ''/ul'':148C ''abl'':125C ''address'':11C ''alreadi'':111C ''alway'':5C ''analysi'':90C ''applic'':19C ''autocorrel'':95C ''break'':102C ''challeng'':7C ''classifi'':122C ''combin'':127C ''compar'':58C ''customis'':99C ''data'':32C ''day'':35C,37C ''day-by-day'':34C ''depend'':20C ''deriv'':68C ''differ'':56C ''e.g'':128C ''etc'':65C ''evolut'':72C,144C ''exampl'':94C ''extra'':47C ''extract'':3C ''featur'':2C,91C,100C,132C,137C ''first'':130C ''first-week'':129C ''get'':141C ''higher'':61C ''idea'':24C ''implic'':45C ''insight'':142C ''kind'':40C ''kurtosi'':64C ''larg'':80C ''last'':135C ''last-week'':134C ''length'':57C ''less'':10C ''li'':30C,60C,67C,74C,97C ''like'':92C,101C ''linear'':121C ''literatur'':14C ''make'':53C ''mayb'':82C ''measur'':33C,107C,112C ''moment'':62C ''non'':120C ''non-linear'':119C ''normalis'':49C ''obvious'':42C ''order'':51C,139C ''p'':1C,22C ''preprocess'':48C ''quantiti'':109C ''raw'':31C ''separ'':116C ''seri'':89C ''sinc'':15C ''skew'':63C ''span'':76C ''speed'':70C ''time'':75C,88C,146C ''timelin'':54C,103C ''topic'':12C ''tri'':27C,86C ''ul'':29C ''week'':105C,115C,131C,136C ''wide'':18C ''worth'':85C ''would'':123C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (549, 2, '2014-06-24 00:54:13.11', 2, NULL, NULL, '&lt;p&gt;What you''re trying to do here is reduce the dimensionality of your features. You can search for dimensionality reduction to get several options, but one very popular technique is principal components analysis (PCA). Principal components are not interpretable like the options you''ve mentioned, but they do a good job of summarizing all of the information.&lt;/p&gt;
', NULL, NULL, '''/p'':59C ''analysi'':34C ''compon'':33C,37C ''dimension'':12C,20C ''featur'':15C ''get'':23C ''good'':51C ''inform'':58C ''interpret'':40C ''job'':52C ''like'':41C ''mention'':46C ''one'':27C ''option'':25C,43C ''p'':1C ''pca'':35C ''popular'':29C ''princip'':32C,36C ''re'':4C ''reduc'':10C ''reduct'':21C ''search'':18C ''sever'':24C ''summar'':54C ''techniqu'':30C ''tri'':5C ''ve'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (536, 1, '2014-06-23 11:03:09.767', 5, 203, 'Good practices for manual modifications of data', '&lt;p&gt;More often than not, data I am working with is not 100% clean. Even if it is reasonably clean, still there are portions that need to be fixed. &lt;/p&gt;

&lt;p&gt;When a fraction of data needs it, I write a script and incorporate it in data processing.&lt;/p&gt;

&lt;p&gt;But what to do if only a few entries needs to be fixed (e.g. misspelled city or zip code)? Let''s focus on "small data",  such as that in CSV files or a relational database.&lt;/p&gt;

&lt;p&gt;The practical problems I encountered:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Writing a general script trying solve all similar errors may give unintended consequences (e.g. matching cities that are different but happen to have similar names).&lt;/li&gt;
&lt;li&gt;Copying and modifying data may make a mess, as:
&lt;ul&gt;
&lt;li&gt;Generating it again will destroy all fixes.&lt;/li&gt;
&lt;li&gt;When there are more errors of different kinds, too many copies of the same file result, and it is hard to keep track of them all.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;Writing a script to modify particular entries seems the best, but there is overhead in comparison to opening CSV and fixing it (but still, &lt;em&gt;seems&lt;/em&gt; to be the best); and either we need to create more copies of data (as in the previous point) or run the script every time we load data.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;What are the best practices in such a case as this?&lt;/p&gt;

&lt;p&gt;EDIT: The question is on the workflow, not whether to use it or not.&lt;/p&gt;

&lt;p&gt;(In my particular case I don''t want the end-user to see misspelled cities and, even worse, see two points of data, for the same city but with different spelling; the data is small, ~500 different cities, so manual corrections do make sense.)&lt;/p&gt;
', 'data-cleaning', NULL, '''/em'':209C ''/li'':131C,151C,179C,181C,238C ''/p'':40C,59C,96C,103C,252C,268C,315C ''/ul'':180C,239C ''100'':23C ''500'':306C ''best'':192C,213C,244C ''case'':249C,273C ''citi'':76C,121C,285C,297C,308C ''clean'':10B,24C,30C ''code'':79C ''comparison'':198C ''consequ'':118C ''copi'':133C,163C,221C ''correct'':311C ''creat'':219C ''csv'':90C,201C ''data'':7A,9B,16C,46C,57C,85C,136C,223C,237C,293C,303C ''data-clean'':8B ''databas'':95C ''destroy'':148C ''differ'':124C,159C,300C,307C ''e.g'':74C,119C ''edit'':254C ''either'':215C ''em'':207C ''encount'':102C ''end'':280C ''end-us'':279C ''entri'':69C,189C ''error'':114C,157C ''even'':25C,287C ''everi'':233C ''file'':91C,167C ''fix'':39C,73C,150C,203C ''focus'':82C ''fraction'':44C ''general'':108C ''generat'':144C ''give'':116C ''good'':1A ''happen'':126C ''hard'':172C ''incorpor'':54C ''keep'':174C ''kind'':160C ''let'':80C ''li'':105C,132C,143C,152C,182C ''load'':236C ''make'':138C,313C ''mani'':162C ''manual'':4A,310C ''match'':120C ''may'':115C,137C ''mess'':140C ''misspel'':75C,284C ''modif'':5A ''modifi'':135C,187C ''name'':130C ''need'':36C,47C,70C,217C ''often'':13C ''open'':200C ''overhead'':196C ''p'':11C,41C,60C,97C,240C,253C,269C ''particular'':188C,272C ''point'':228C,291C ''portion'':34C ''practic'':2A,99C,245C ''previous'':227C ''problem'':100C ''process'':58C ''question'':256C ''reason'':29C ''relat'':94C ''result'':168C ''run'':230C ''script'':52C,109C,185C,232C ''see'':283C,289C ''seem'':190C,208C ''sens'':314C ''similar'':113C,129C ''small'':84C,305C ''solv'':111C ''spell'':301C ''still'':31C,206C ''time'':234C ''track'':175C ''tri'':110C ''two'':290C ''ul'':104C,142C ''unintend'':117C ''use'':264C ''user'':281C ''want'':277C ''whether'':262C ''work'':19C ''workflow'':260C ''wors'':288C ''write'':50C,106C,183C ''zip'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (543, 2, '2014-06-23 17:50:51.32', 1, NULL, NULL, '&lt;p&gt;Well, you mention in your question of the data being ''clean and well structured''. In practice, close to 70% of the time is spent in doing these two steps. Of course the first thing you do is to separate the training and test data. Considering the plethora of libraries and tools available irrespective of which technology/language you prefer to use, the next step would be to understand the data via graph plots and drawing useful intuitions specific to your target goal. This would then be followed by various other problem specific methods. As pointed out, the question is very broad and citing code snippets is simply not feasible.&lt;/p&gt;
', NULL, NULL, '''/p'':110C ''70'':20C ''avail'':53C ''broad'':101C ''cite'':103C ''clean'':12C ''close'':18C ''code'':104C ''consid'':46C ''cours'':32C ''data'':10C,45C,70C ''draw'':75C ''feasibl'':109C ''first'':34C ''follow'':87C ''goal'':82C ''graph'':72C ''intuit'':77C ''irrespect'':54C ''librari'':50C ''mention'':4C ''method'':93C ''next'':63C ''p'':1C ''plethora'':48C ''plot'':73C ''point'':95C ''practic'':17C ''prefer'':59C ''problem'':91C ''question'':7C,98C ''separ'':40C ''simpli'':107C ''snippet'':105C ''specif'':78C,92C ''spent'':25C ''step'':30C,64C ''structur'':15C ''target'':81C ''technology/language'':57C ''test'':44C ''thing'':35C ''time'':23C ''tool'':52C ''train'':42C ''two'':29C ''understand'':68C ''use'':61C,76C ''various'':89C ''via'':71C ''well'':2C,14C ''would'':65C,84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (545, 2, '2014-06-23 18:38:34.26', 2, NULL, NULL, '&lt;p&gt;I think this is a reasonable question. Here is what I do:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Peak at the first few rows&lt;/li&gt;
&lt;li&gt;Visualize the distribution of the features I care about (histograms)&lt;/li&gt;
&lt;li&gt;Visualize the relationship between pairs of features (scatterplots)&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I downloaded the abalone dataset from the UCI Machine Learning repository &lt;a href="https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data" rel="nofollow"&gt;here&lt;/a&gt;. Let''s say I care about how height and diameter can be used to predict whole weight. For completeness, I''ve included the step of reading the data from file.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

data = pd.read_csv("abalone.data", header=False)
data.columns = ["sex", "length", "diameter", "height", 
                "whole_weight", "shucked_weight",
                "viscera_weight", "shell_weight", "rings"]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now we can take a peak at the first few rows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data.head()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ZEFH6.png" alt="Head of dataset"&gt;&lt;/p&gt;

&lt;p&gt;Now, I know that the variables I care about are floating point values and they can be treated as continuous. I want to take a look to see how these three variables are distributed:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;fig = plt.figure(figsize=(20,5))
plt.subplot(1, 3, 1)
plt.hist(data[''diameter''], normed=True)
plt.title("Diameter")
plt.subplot(1, 3, 2)
plt.hist(data[''height''], normed=True)
plt.title("Height")
plt.subplot(1, 3, 3)
plt.hist(data[''whole_weight''], normed=True)
plt.title("Whole Weight")
plt.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Rmq4n.png" alt="histograms"&gt;&lt;/p&gt;

&lt;p&gt;Great! Now, I know that diameter and whole weight are skewed left and right (respectively). I also know that there are some outliers in terms of height (which is why matplotlib gives me extra room to the right of the distribution). Finally, I''d like to see if I can find any visual patterns between my predictors and outcome variable. I use a scatter plot for this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;plt.figure(figsize=(15,5))
plt.subplot(1, 2, 1)
plt.plot(data[''diameter''], data[''whole_weight''], ''o'')
plt.title("Diameter vs. Whole Weight")
plt.ylabel("Whole Weight")
plt.xlabel("Diameter")
plt.subplot(1, 2, 2)
plt.plot(data[''height''], data[''whole_weight''], ''o'')
plt.title("Height vs. Whole Weight")
plt.ylabel("Whole Weight")
plt.xlabel("Height")
plt.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/FMmN1.png" alt="scatterplots"&gt;&lt;/p&gt;

&lt;p&gt;Here, I see there is a non-linear relationship between diameter and whole weight and I''m going to have to deal with my height outliers. Now, I''m ready to do some analysis!&lt;/p&gt;
', NULL, NULL, '''/a'':67C ''/code'':133C,151C,243C,372C ''/fmmn1.png'':379C ''/li'':23C,35C,45C ''/ml/machine-learning-databases/abalone/abalone.data'':63C ''/ol'':46C ''/p'':14C,98C,147C,163C,199C,253C,322C,382C,419C ''/pre'':134C,152C,244C,373C ''/rmq4n.png'':250C ''/zefh6.png'':158C ''1'':208C,210C,219C,230C,330C,332C,351C ''15'':327C ''2'':221C,331C,352C,353C ''20'':205C ''3'':209C,220C,231C,232C ''5'':206C,328C ''abalon'':51C ''abalone.data'':116C ''also'':271C ''alt'':159C,251C,380C ''analysi'':418C ''archive.ics.uci.edu'':62C ''archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data'':61C ''care'':32C,72C,172C ''code'':100C,149C,201C,324C ''complet'':86C ''continu'':184C ''csv'':115C ''d'':298C ''data'':95C,113C,212C,223C,234C,334C,336C,355C,357C ''data.columns'':119C ''data.head'':150C ''dataset'':52C,162C ''deal'':406C ''diamet'':77C,122C,213C,217C,260C,335C,341C,349C,395C ''distribut'':27C,198C,295C ''download'':49C ''extra'':288C ''fals'':118C ''featur'':30C,43C ''fig'':202C ''figsiz'':204C,326C ''file'':97C ''final'':296C ''find'':305C ''first'':20C,144C ''float'':175C ''give'':286C ''go'':402C ''great'':255C ''head'':160C ''header'':117C ''height'':75C,123C,224C,228C,281C,356C,362C,370C,409C ''histogram'':34C,252C ''href'':60C ''i.stack.imgur.com'':157C,249C,378C ''i.stack.imgur.com/fmmn1.png'':377C ''i.stack.imgur.com/rmq4n.png'':248C ''i.stack.imgur.com/zefh6.png'':156C ''img'':154C,246C,375C ''import'':101C,105C,109C ''includ'':89C ''know'':167C,258C,272C ''learn'':57C ''left'':266C ''length'':121C ''let'':68C ''li'':16C,24C,36C ''like'':299C ''linear'':392C ''look'':190C ''m'':401C,413C ''machin'':56C ''matplotlib'':285C ''matplotlib.pyplot'':106C ''nofollow'':65C ''non'':391C ''non-linear'':390C ''norm'':214C,225C,237C ''o'':339C,360C ''ol'':15C ''outcom'':313C ''outlier'':277C,410C ''p'':1C,47C,135C,153C,164C,245C,254C,374C,383C ''pair'':41C ''panda'':102C ''pattern'':308C ''pd'':104C ''pd.read'':114C ''peak'':17C,141C ''plot'':319C ''plt'':108C ''plt.figure'':203C,325C ''plt.hist'':211C,222C,233C ''plt.plot'':333C,354C ''plt.show'':242C,371C ''plt.subplot'':207C,218C,229C,329C,350C ''plt.title'':216C,227C,239C,340C,361C ''plt.xlabel'':348C,369C ''plt.ylabel'':345C,366C ''point'':176C ''pre'':99C,148C,200C,323C ''predict'':82C ''predictor'':311C ''question'':8C ''read'':93C ''readi'':414C ''reason'':7C ''rel'':64C ''relationship'':39C,393C ''repositori'':58C ''respect'':269C ''right'':268C,292C ''ring'':132C ''room'':289C ''row'':22C,146C ''say'':70C ''scatter'':318C ''scatterplot'':44C,381C ''seaborn'':110C ''see'':192C,301C,386C ''sex'':120C ''shell'':130C ''shuck'':126C ''skew'':265C ''sns'':112C ''src'':155C,247C,376C ''step'':91C ''take'':139C,188C ''term'':279C ''think'':3C ''three'':195C ''treat'':182C ''true'':215C,226C,238C ''uci'':55C ''use'':80C,316C ''valu'':177C ''variabl'':170C,196C,314C ''ve'':88C ''viscera'':128C ''visual'':25C,37C,307C ''vs'':342C,363C ''want'':186C ''weight'':84C,125C,127C,129C,131C,236C,241C,263C,338C,344C,347C,359C,365C,368C,398C ''whole'':83C,124C,235C,240C,262C,337C,343C,346C,358C,364C,367C,397C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (546, 2, '2014-06-23 18:56:11.577', 11, NULL, NULL, '&lt;p&gt;Even if you are effectively modifying certain records by hand, as in the city name example you give, I would recommend doing it in code. The reason to strongly prefer code over hand-tweaking records is that the code makes a result reproducible. You want to make sure that you can always go from raw data to final result without any human intervention.&lt;/p&gt;

&lt;p&gt;Here''s a quick example. Let''s say I have a list of city names in a pandas dataframe and I am certain they should all be "omaha" (you need to be absolutely certain, because changing values by hand is fraught with danger). But instead I have the following strings:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;pd.unique(data.city)
array([''omaha'', ''omahd'', ''imaha'', ''omaka''], dtype=object)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You could make the change like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data.city.values[data.city.values == ''omahd''] = ''omaha''
data.city.values[data.city.values == ''imaha''] = ''omaha''
data.city.values[data.city.values == ''omaka''] = ''omaha''
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;That code is ugly, but if you run it on the same raw dataset, you will &lt;em&gt;always&lt;/em&gt; get the same result.&lt;/p&gt;
', NULL, NULL, '''/code'':130C,155C ''/em'':176C ''/p'':66C,118C,140C,181C ''/pre'':131C,156C ''absolut'':100C ''alway'':54C,175C ''array'':123C ''certain'':8C,90C,101C ''chang'':103C,137C ''citi'':15C,81C ''code'':26C,32C,41C,120C,142C,159C ''could'':134C ''danger'':110C ''data'':58C ''data.city'':122C ''data.city.values'':143C,144C,147C,148C,151C,152C ''datafram'':86C ''dataset'':171C ''dtype'':128C ''effect'':6C ''em'':174C ''even'':2C ''exampl'':17C,72C ''final'':60C ''follow'':116C ''fraught'':108C ''get'':177C ''give'':19C ''go'':55C ''hand'':11C,35C,106C ''hand-tweak'':34C ''human'':64C ''imaha'':126C,149C ''instead'':112C ''intervent'':65C ''let'':73C ''like'':138C ''list'':79C ''make'':42C,49C,135C ''modifi'':7C ''name'':16C,82C ''need'':97C ''object'':129C ''omaha'':95C,124C,146C,150C,154C ''omahd'':125C,145C ''omaka'':127C,153C ''p'':1C,67C,132C,157C ''panda'':85C ''pd.unique'':121C ''pre'':119C,141C ''prefer'':31C ''quick'':71C ''raw'':57C,170C ''reason'':28C ''recommend'':22C ''record'':9C,37C ''reproduc'':45C ''result'':44C,61C,180C ''run'':165C ''say'':75C ''string'':117C ''strong'':30C ''sure'':50C ''tweak'':36C ''ugli'':161C ''valu'':104C ''want'':47C ''without'':62C ''would'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (548, 1, '2014-06-23 23:20:36.18', 4, 794, 'Feature Extraction Technique - Summarizing a Sequence of Data', '&lt;p&gt;I often am building a model (classification or regression) where I have some predictor variables that are sequences and I have been trying to find technique recommendations for summarizing them in the best way possible for inclusion as predictors in the model.&lt;/p&gt;

&lt;p&gt;As a concrete example, say a model is being built to predict if a customer will leave the company in the next 90 days (anytime between t and t+90; thus a binary outcome). One of the predictors available is the level of the customers financial balance for periods t_0 to t-1. Maybe this represents monthly observations for the prior 12 months (i.e. 12 measurements). &lt;/p&gt;

&lt;p&gt;I am looking for ways to construct features from this series. I use descriptives of each customers series such as the mean, high, low, std dev., fit a OLS regression to get the trend. Are their other methods of calculating features? Other measures of change or volatility? &lt;/p&gt;

&lt;p&gt;ADD:&lt;/p&gt;

&lt;p&gt;As mentioned in a response below, I also considered (but forgot to add here) using Dynamic Time Warping (DTW) and then hierarchical clustering on the resulting distance matrix - creating some number of clusters and then using the cluster membership as a feature. Scoring test data would likely have to follow a process where the DTW was done on new cases and the cluster centroids - matching the new data series to their closest centroids... &lt;/p&gt;
', 'machine-learning  feature-selection  time-series', NULL, '''+90'':92C ''-1'':116C ''/p'':61C,130C,179C,182C,257C ''0'':113C ''12'':125C,128C ''90'':85C ''add'':181C,196C ''also'':191C ''anytim'':87C ''avail'':101C ''balanc'':109C ''best'':51C ''binari'':95C ''build'':22C ''built'':72C ''calcul'':171C ''case'':243C ''centroid'':247C,256C ''chang'':176C ''classif'':25C ''closest'':255C ''cluster'':206C,216C,221C,246C ''compani'':81C ''concret'':65C ''consid'':192C ''construct'':138C ''creat'':212C ''custom'':77C,107C,148C ''data'':8A,228C,251C ''day'':86C ''descript'':145C ''dev'':157C ''distanc'':210C ''done'':240C ''dtw'':202C,238C ''dynam'':199C ''exampl'':66C ''extract'':2A ''featur'':1A,13B,139C,172C,225C ''feature-select'':12B ''financi'':108C ''find'':43C ''fit'':158C ''follow'':233C ''forgot'':194C ''get'':163C ''hierarch'':205C ''high'':154C ''i.e'':127C ''inclus'':55C ''learn'':11B ''leav'':79C ''level'':104C ''like'':230C ''look'':134C ''low'':155C ''machin'':10B ''machine-learn'':9B ''match'':248C ''matrix'':211C ''mayb'':117C ''mean'':153C ''measur'':129C,174C ''membership'':222C ''mention'':185C ''method'':169C ''model'':24C,60C,69C ''month'':120C,126C ''new'':242C,250C ''next'':84C ''number'':214C ''observ'':121C ''often'':20C ''ol'':160C ''one'':97C ''outcom'':96C ''p'':18C,62C,131C,180C,183C ''period'':111C ''possibl'':53C ''predict'':74C ''predictor'':32C,57C,100C ''prior'':124C ''process'':235C ''recommend'':45C ''regress'':27C,161C ''repres'':119C ''respons'':188C ''result'':209C ''say'':67C ''score'':226C ''select'':14B ''sequenc'':6A,36C ''seri'':17B,142C,149C,252C ''std'':156C ''summar'':4A,47C ''techniqu'':3A,44C ''test'':227C ''thus'':93C ''time'':16B,200C ''time-seri'':15B ''trend'':165C ''tri'':41C ''use'':144C,198C,219C ''variabl'':33C ''volatil'':178C ''warp'':201C ''way'':52C,136C ''would'':229C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8482, 2, '2015-10-17 21:24:01.29', 2, NULL, NULL, '&lt;p&gt;Boosting, together with bagging, falls into the realm of so cold ensemble models: you randomly draw a sample from the data, fit a model, adjust your predictions, sample once again. Unless your samples are fixed, every time you run the algo you''ll get slightly different results.&lt;/p&gt;
', NULL, NULL, '''/p'':49C ''adjust'':26C ''algo'':42C ''bag'':5C ''boost'':2C ''cold'':12C ''data'':22C ''differ'':47C ''draw'':17C ''ensembl'':13C ''everi'':37C ''fall'':6C ''fit'':23C ''fix'':36C ''get'':45C ''ll'':44C ''model'':14C,25C ''p'':1C ''predict'':28C ''random'':16C ''realm'':9C ''result'':48C ''run'':40C ''sampl'':19C,29C,34C ''slight'':46C ''time'':38C ''togeth'':3C ''unless'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (745, 1, '2014-07-16 00:06:02.16', 3, 865, 'ARMA/ARIMA on energy forecasts timeseries: strange prediction', '&lt;p&gt;I''m trying to use ARMA/ARIMA with the &lt;a href="http://statsmodels.sourceforge.net/devel/tsa.html#descriptive-statistics-and-tests" rel="nofollow"&gt;statsmodel Python package&lt;/a&gt;, in order to predict the gas consumption. I tried with &lt;a href="https://github.com/denadai2/Gas-consumption-outliers/blob/master/exportWeb.csv" rel="nofollow"&gt;a dataset&lt;/a&gt; of this format:&lt;/p&gt;

&lt;p&gt;&lt;img src="https://i.imgur.com/ZUvBlUP.png" alt="with this format"&gt;&lt;/p&gt;

&lt;p&gt;Using only the gas column.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from pandas.tseries.offsets import *

arma_mod20 = sm.tsa.ARMA(januaryFeb[[''gas [m3]'']], (5,3)).fit()
predict_sunspots = arma_mod20.predict(''2012-01-13'', ''2012-01-14'', dynamic=True)
ax = januaryFeb.ix[''2012-01-13 00:00:00'':''2012-01-15 22:00:00''][''gas [m3]''].plot(figsize=(12,8))
ax = predict_sunspots.plot(ax=ax, style=''r--'', label=''Dynamic Prediction'');
ax.legend();
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="https://i.imgur.com/oCPonu7.png" alt="result"&gt;&lt;/p&gt;

&lt;p&gt;Why is the prediction so bad?&lt;/p&gt;
', 'machine-learning  python', NULL, '''-01'':92C,95C,102C,108C ''-13'':93C,103C ''-14'':96C ''-15'':109C ''/a'':31C,51C ''/code'':129C ''/denadai2/gas-consumption-outliers/blob/master/exportweb.csv'':46C ''/devel/tsa.html#descriptive-statistics-and-tests'':25C ''/ocponu7.png'':136C ''/p'':55C,66C,73C,139C,147C ''/pre'':130C ''/zuvblup.png'':61C ''00'':104C,105C,106C,111C,112C ''12'':117C ''2012'':91C,94C,101C,107C ''22'':110C ''3'':86C ''5'':85C ''8'':118C ''alt'':62C,137C ''arma'':79C ''arma/arima'':1A,18C ''arma_mod20.predict'':90C ''ax'':99C,119C,121C,122C ''ax.legend'':128C ''bad'':146C ''code'':75C ''column'':72C ''consumpt'':38C ''dataset'':50C ''dynam'':97C,126C ''energi'':3A ''figsiz'':116C ''fit'':87C ''forecast'':4A ''format'':54C,65C ''gas'':37C,71C,83C,113C ''github.com'':45C ''github.com/denadai2/gas-consumption-outliers/blob/master/exportweb.csv'':44C ''href'':22C,43C ''i.imgur.com'':60C,135C ''i.imgur.com/ocponu7.png'':134C ''i.imgur.com/zuvblup.png'':59C ''img'':57C,132C ''import'':78C ''januaryfeb'':82C ''januaryfeb.ix'':100C ''label'':125C ''learn'':10B ''m'':14C ''m3'':84C,114C ''machin'':9B ''machine-learn'':8B ''mod20'':80C ''nofollow'':27C,48C ''order'':33C ''p'':12C,56C,67C,131C,140C ''packag'':30C ''pandas.tseries.offsets'':77C ''plot'':115C ''pre'':74C ''predict'':7A,35C,88C,127C,144C ''predict_sunspots.plot'':120C ''python'':11B,29C ''r'':124C ''rel'':26C,47C ''result'':138C ''sm.tsa.arma'':81C ''src'':58C,133C ''statsmodel'':28C ''statsmodels.sourceforge.net'':24C ''statsmodels.sourceforge.net/devel/tsa.html#descriptive-statistics-and-tests'':23C ''strang'':6A ''style'':123C ''sunspot'':89C ''timeseri'':5A ''tri'':15C,40C ''true'':98C ''use'':17C,68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (552, 2, '2014-06-24 07:03:35.567', 2, NULL, NULL, '&lt;p&gt;At first glance, you need to extract features from your time series (x - 12) - x. One possible approach is to compute summary metrics: average, dispersion, etc. But doing so, you will loose all time-series related information. But data, extracted from curve shape may be quite useful. I recommend you to look through &lt;a href="http://cs.stanford.edu/people/jure/pubs/memeshapes-wsdm11.pdf" rel="nofollow"&gt;this&lt;/a&gt; article, where authors propose algorithm for time series clustering. Hope, it will be useful. Additionally to such clustering you can add summary statistics to your feature list. &lt;/p&gt;
', NULL, NULL, '''/a'':64C ''/p'':92C ''/people/jure/pubs/memeshapes-wsdm11.pdf'':60C ''12'':15C ''add'':85C ''addit'':79C ''algorithm'':69C ''approach'':19C ''articl'':65C ''author'':67C ''averag'':25C ''cluster'':73C,82C ''comput'':22C ''cs.stanford.edu'':59C ''cs.stanford.edu/people/jure/pubs/memeshapes-wsdm11.pdf'':58C ''curv'':44C ''data'':41C ''dispers'':26C ''etc'':27C ''extract'':8C,42C ''featur'':9C,90C ''first'':3C ''glanc'':4C ''hope'':74C ''href'':57C ''inform'':39C ''list'':91C ''look'':54C ''loos'':33C ''may'':46C ''metric'':24C ''need'':6C ''nofollow'':62C ''one'':17C ''p'':1C ''possibl'':18C ''propos'':68C ''quit'':48C ''recommend'':51C ''rel'':61C ''relat'':38C ''seri'':13C,37C,72C ''shape'':45C ''statist'':87C ''summari'':23C,86C ''time'':12C,36C,71C ''time-seri'':35C ''use'':49C,78C ''x'':14C,16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (554, 1, '2014-06-24 07:29:57.787', 10, 276, 'Datasets understanding best practices', '&lt;p&gt;I am a CS master student in data mining. My supervisor once told me that before I run any classifier or do anything with a dataset I must fully understand the data and make sure that the data is clean and correct.&lt;/p&gt;

&lt;p&gt;My questions:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;What are the best practices to understand a dataset (high dimensional with numerical and nominal attributes)?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Practices to make sure the dataset is clean?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Practices to make sure the dataset doesn''t have wrong values or so?&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', 'statistics  dataset', NULL, '''/li'':75C,87C,104C ''/p'':50C,54C,74C,86C,103C ''/ul'':105C ''anyth'':30C ''attribut'':73C ''best'':3A,61C ''classifi'':27C ''clean'':47C,85C ''correct'':49C ''cs'':11C ''data'':15C,39C,45C ''dataset'':1A,6B,33C,66C,83C,95C ''dimension'':68C ''doesn'':96C ''fulli'':36C ''high'':67C ''li'':56C,76C,88C ''make'':41C,80C,92C ''master'':12C ''mine'':16C ''must'':35C ''nomin'':72C ''numer'':70C ''p'':7C,51C,57C,77C,89C ''practic'':4A,62C,78C,90C ''question'':53C ''run'':25C ''statist'':5B ''student'':13C ''supervisor'':18C ''sure'':42C,81C,93C ''told'':20C ''ul'':55C ''understand'':2A,37C,64C ''valu'':100C ''wrong'':99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (555, 2, '2014-06-24 07:35:16.93', 12, NULL, NULL, '&lt;p&gt;&lt;strong&gt;1.  Do not modify the original data&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Having the original data source intact is important.  You may find that updates you make to the data are not valid.  You may also find a more efficient way to make updates and you will want to regression test those updates.&lt;/p&gt;

&lt;p&gt;Always work with a copy of the data, and add columns/properties/metadata that includes any processed corrections.&lt;/p&gt;

&lt;p&gt;Example, if your data is a .csv file that includes a city name that contains several misspellings:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1. Copy the .csv to a new file
2. Add a column for containing "adjusted_city_name"
3. Copy data from the city_name column to the 
   adjusted_city_name column and make corrections in that column.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;2. Document proposed changes&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Any changes you want to make to data should be documented so that they can be replicated moving forward.&lt;/p&gt;

&lt;p&gt;Version control and timestamp the document every time you change it.  That will help in troubleshooting at a later date.&lt;/p&gt;

&lt;p&gt;Be explicit.  Do not simply state "correct capitalization problems", state "ensure that the first letter of each city name begins with a capital letter and the remaining letters are lower-case."&lt;/p&gt;

&lt;p&gt;Update the document with references to any automation routines that have been built to manage data cleansing.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;3. Decide on a standard data cleansing technology&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Whether you use perl, python, java, a particular utility, a manual process or something else is not the issue.  The issue is that in the future you want to hand the data cleansing process to somebody else.  If they have to know 12 different data cleansing technologies, delegating the cleansing procedure will be very difficult.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;4. Standardize the workflow&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;There should be a standard way to handle new data.  Ideally, it will be as simple as dropping a file in a specific location and a predictable automated process cleanses it and hands off a cleansed set of data to the next processing step.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;5. Make as few changes as is absolutely necessary&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;It''s always better to have a fault tolerant analysis than one that makes assumptions about the data.  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;6. Avoid manual updates&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;It''s always tempting, but people are error-prone and again it makes delegation difficult.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Notes on manual processing&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;To more completely address the original question as to whether there''s a "good" way to do manual processing, I would say no, there is not.  My answer is based on experience and is not one that I make arbitrarily.&lt;/p&gt;

&lt;p&gt;I have had more than one project lose days of time due to a client insisting that a manual data cleansing process was just fine and could be handled internally.  You do not want your projects to be dependent on a single individual accomplishing a judgement based task of varying scale.&lt;/p&gt;

&lt;p&gt;It''s much better to have that individual build and document a rule set based on what they would do than to have them manually cleanse data.  (And then automating that rule set)&lt;/p&gt;

&lt;p&gt;If automation fails you in the end or is simply not possible, the ability to delegate that rule set to others without domain specific knowledge is vital.&lt;/p&gt;

&lt;p&gt;In the end, routines to do something like correct city names can be applied to other data sets.&lt;/p&gt;
', NULL, NULL, '''/code'':131C ''/p'':11C,54C,72C,91C,140C,161C,183C,217C,236C,248C,305C,313C,359C,372C,392C,400C,418C,426C,468C,521C,556C,585C,605C ''/pre'':132C ''/strong'':10C,139C,247C,312C,371C,399C,425C ''1'':3C,94C ''12'':292C ''2'':102C,135C ''3'':111C,239C ''4'':308C ''5'':362C ''6'':395C ''abil'':571C ''absolut'':369C ''accomplish'':513C ''add'':65C,103C ''address'':431C ''adjust'':108C,121C ''also'':36C ''alway'':56C,376C,404C ''analysi'':383C ''answer'':455C ''appli'':600C ''arbitrarili'':467C ''assumpt'':388C ''autom'':226C,342C,552C,559C ''avoid'':396C ''base'':457C,516C,537C ''begin'':204C ''better'':377C,526C ''build'':531C ''built'':231C ''capit'':192C,207C ''case'':216C ''chang'':138C,143C,172C,366C ''citi'':85C,109C,116C,122C,202C,596C ''cleans'':235C,245C,282C,295C,299C,344C,350C,490C,548C ''client'':484C ''code'':93C ''column'':105C,118C,124C,130C ''columns/properties/metadata'':66C ''complet'':430C ''contain'':88C,107C ''control'':164C ''copi'':60C,95C,112C ''correct'':71C,127C,191C,595C ''could'':496C ''csv'':80C,97C ''data'':9C,16C,30C,63C,77C,113C,149C,234C,244C,281C,294C,324C,353C,391C,489C,549C,603C ''date'':182C ''day'':478C ''decid'':240C ''deleg'':297C,416C,573C ''depend'':508C ''differ'':293C ''difficult'':304C,417C ''document'':136C,152C,168C,221C,533C ''domain'':580C ''drop'':332C ''due'':481C ''effici'':40C ''els'':264C,286C ''end'':564C,589C ''ensur'':195C ''error'':410C ''error-pron'':409C ''everi'':169C ''exampl'':74C ''experi'':459C ''explicit'':186C ''fail'':560C ''fault'':381C ''file'':81C,101C,334C ''find'':23C,37C ''fine'':494C ''first'':198C ''forward'':160C ''futur'':275C ''good'':441C ''hand'':279C,347C ''handl'':322C,498C ''help'':176C ''ideal'':325C ''import'':20C ''includ'':68C,83C ''individu'':512C,530C ''insist'':485C ''intact'':18C ''intern'':499C ''issu'':268C,270C ''java'':255C ''judgement'':515C ''know'':291C ''knowledg'':582C ''later'':181C ''letter'':199C,208C,212C ''like'':594C ''locat'':338C ''lose'':477C ''lower'':215C ''lower-cas'':214C ''make'':27C,43C,126C,147C,363C,387C,415C,466C ''manag'':233C ''manual'':260C,397C,423C,445C,488C,547C ''may'':22C,35C ''misspel'':90C ''modifi'':6C ''move'':159C ''much'':525C ''name'':86C,110C,117C,123C,203C,597C ''necessari'':370C ''new'':100C,323C ''next'':356C ''note'':421C ''one'':385C,463C,475C ''origin'':8C,15C,433C ''other'':578C ''p'':1C,12C,55C,73C,133C,141C,162C,184C,218C,237C,249C,306C,314C,360C,373C,393C,401C,419C,427C,469C,522C,557C,586C ''particular'':257C ''peopl'':407C ''perl'':253C ''possibl'':569C ''pre'':92C ''predict'':341C ''problem'':193C ''procedur'':300C ''process'':70C,261C,283C,343C,357C,424C,446C,491C ''project'':476C,505C ''prone'':411C ''propos'':137C ''python'':254C ''question'':434C ''refer'':223C ''regress'':50C ''remain'':211C ''replic'':158C ''routin'':227C,590C ''rule'':535C,554C,575C ''say'':449C ''scale'':520C ''set'':351C,536C,555C,576C,604C ''sever'':89C ''simpl'':330C ''simpli'':189C,567C ''singl'':511C ''somebodi'':285C ''someth'':263C,593C ''sourc'':17C ''specif'':337C,581C ''standard'':243C,309C,319C ''state'':190C,194C ''step'':358C ''strong'':2C,134C,238C,307C,361C,394C,420C ''task'':517C ''technolog'':246C,296C ''tempt'':405C ''test'':51C ''time'':170C,480C ''timestamp'':166C ''toler'':382C ''troubleshoot'':178C ''updat'':25C,44C,53C,219C,398C ''use'':252C ''util'':258C ''valid'':33C ''vari'':519C ''version'':163C ''vital'':584C ''want'':48C,145C,277C,503C ''way'':41C,320C,442C ''whether'':250C,437C ''without'':579C ''work'':57C ''workflow'':311C ''would'':448C,541C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (556, 2, '2014-06-24 08:09:49.3', 8, NULL, NULL, '&lt;p&gt;There are basic things you can do with any set of data:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Validate values (String length tolerance, data type, formatting masks, required field presence, etc.)&lt;/li&gt;
&lt;li&gt;Range correctness (Does this seemingly correct data fall within expected ranges of values)&lt;/li&gt;
&lt;li&gt;Preliminary processing (If I attempt to analyze this data, can I perform the basics without running into errors)&lt;/li&gt;
&lt;li&gt;Preliminary reporting (run a report against a data set and ensure that it passes a sanity test)&lt;/li&gt;
&lt;li&gt;Defining null vs. empty vs. zero vs. False for any given column of data&lt;/li&gt;
&lt;li&gt;Identifying data that is out of place (numeric values dramatically different than other values in a data set, string values that look like they might be misspelled, etc.)&lt;/li&gt;
&lt;li&gt;Eliminating or correcting obviously errant data&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Understanding data to identify errors is a whole different ball game, and it is very important.&lt;/p&gt;

&lt;p&gt;For instance, you can have a rule that says a serial number must be present in a given data set and that serial number must be alphanumeric with a maximum string length of 255 and a minimum string length of 5.&lt;/p&gt;

&lt;p&gt;Looking at the data, you may find one particular serial number value reads &lt;code&gt;"PLEASE ENTER SERIAL"&lt;/code&gt;  It''s perfectly valid, but wrong.&lt;/p&gt;

&lt;p&gt;That''s kind of an obvious one, but say you''re processing stock data and you had a price range for 1000 stocks that was under a dollar.  A lot of people would not know that a stock price so low is invalid on certain exchanges and perfectly valid on others.  You need knowledge about your data to understand if what you are seeing is problematic or not.  &lt;/p&gt;

&lt;p&gt;In the real world, you don''t always have the luxury of understanding your data intimately.&lt;/p&gt;

&lt;p&gt;The way I avoid problems is by leveraging the people around me.  For small data sets, I can ask someone to review the data in it''s entirety.  For large ones, pulling a set of random samples and asking someone to do a sanity check on the data is more appropriate.&lt;/p&gt;

&lt;p&gt;Further, questioning the source of the data and how well that data source can be trusted is imperative.  I often have multiple conflicting sources of data and we create rules to determine the "source of truth".  Sometimes one data set has great data in a given aspect, but other data sets are stronger in other areas.&lt;/p&gt;

&lt;p&gt;Manually entered data is usually what I''m most skeptical about, but in some cases it is stronger than anything that can be acquired through automation.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (559, 1, '2014-06-24 12:28:10.99', 15, 1790, 'Detecting cats visually by means of anomaly detection', '&lt;p&gt;I have a hobby project which I am contemplating committing to as a way of increasing my so far limited experience of machine learning. I have taken and completed the Coursera MOOC on the topic. My question is with regards to the feasibility of the project.&lt;/p&gt;

&lt;p&gt;The task is the following:&lt;/p&gt;

&lt;p&gt;Neighboring cats are from time to time visiting my garden, which I dislike since they tend to defecate on my lawn. I would like to have a warning system that alerts me when there''s a cat present so that I may go chase it off using my super soaker. For simplicity''s sake, say that I only care about a cat with black and white coloring.&lt;/p&gt;

&lt;p&gt;I have setup a raspberry pi with camera module that can capture video and/or pictures of a part of the garden. &lt;/p&gt;

&lt;p&gt;Sample image:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/cNqus.jpg" alt="Sample garden image"&gt;&lt;/p&gt;

&lt;p&gt;My first idea was to train a classifier to identify cat or cat-like objects, but after realizing that I will be unable to obtain a large enough number of positive samples, I have abandoned that in favor of anomaly detection.&lt;/p&gt;

&lt;p&gt;I estimate that if I captured a photo every second of the day, I would end up with maybe five photos containing cats (out of about 60,000 with sunlight) per day. &lt;/p&gt;

&lt;p&gt;Is this feasible using anomaly detection? If so, what features would you suggest? My ideas so far would be to simply count the number of pixels with that has certain colors; do some kind of blob detection/image segmenting (which I do not know how do to, and would thus like to avoid) and perform the same color analysis on them.&lt;/p&gt;
', 'machine-learning', NULL, '''/cnqus.jpg'':168C ''/p'':59C,66C,135C,158C,162C,173C,217C,251C,313C ''000'':246C ''60'':245C ''abandon'':210C ''alert'':98C ''alt'':169C ''analysi'':310C ''and/or'':150C ''anomali'':7A,215C,257C ''avoid'':304C ''black'':131C ''blob'':288C ''camera'':144C ''captur'':148C,224C ''care'':126C ''cat'':2A,69C,104C,129C,185C,188C,241C ''cat-lik'':187C ''certain'':282C ''chase'':111C ''classifi'':182C ''color'':134C,283C,309C ''commit'':22C ''complet'':41C ''contain'':240C ''contempl'':21C ''count'':274C ''coursera'':43C ''day'':231C,250C ''defec'':85C ''detect'':1A,8A,216C,258C ''detection/image'':289C ''dislik'':80C ''end'':234C ''enough'':203C ''estim'':220C ''everi'':227C ''experi'':33C ''far'':31C,269C ''favor'':213C ''feasibl'':55C,255C ''featur'':262C ''first'':176C ''five'':238C ''follow'':65C ''garden'':77C,157C,171C ''go'':110C ''hobbi'':16C ''i.stack.imgur.com'':167C ''i.stack.imgur.com/cnqus.jpg'':166C ''idea'':177C,267C ''identifi'':184C ''imag'':161C,172C ''img'':164C ''increas'':28C ''kind'':286C ''know'':295C ''larg'':202C ''lawn'':88C ''learn'':11B,36C ''like'':91C,189C,302C ''limit'':32C ''machin'':10B,35C ''machine-learn'':9B ''may'':109C ''mayb'':237C ''mean'':5A ''modul'':145C ''mooc'':44C ''neighbor'':68C ''number'':204C,276C ''object'':190C ''obtain'':200C ''p'':12C,60C,67C,136C,159C,163C,174C,218C,252C ''part'':154C ''per'':249C ''perform'':306C ''photo'':226C,239C ''pi'':142C ''pictur'':151C ''pixel'':278C ''posit'':206C ''present'':105C ''project'':17C,58C ''question'':49C ''raspberri'':141C ''realiz'':193C ''regard'':52C ''sake'':121C ''sampl'':160C,170C,207C ''say'':122C ''second'':228C ''segment'':290C ''setup'':139C ''simpli'':273C ''simplic'':119C ''sinc'':81C ''soaker'':117C ''src'':165C ''suggest'':265C ''sunlight'':248C ''super'':116C ''system'':96C ''taken'':39C ''task'':62C ''tend'':83C ''thus'':301C ''time'':72C,74C ''topic'':47C ''train'':180C ''unabl'':198C ''use'':114C,256C ''video'':149C ''visit'':75C ''visual'':3A ''warn'':95C ''way'':26C ''white'':133C ''would'':90C,233C,263C,270C,300C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5300, 2, '2015-03-10 09:29:06.893', 2, NULL, NULL, '&lt;p&gt;Also: "Business Intelligence developer"&lt;/p&gt;
', NULL, NULL, '''/p'':6C ''also'':2C ''busi'':3C ''develop'':5C ''intellig'':4C ''p'':1C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (560, 2, '2014-06-24 12:55:26.457', 4, NULL, NULL, '&lt;p&gt;This is an interesting and also quite ambitious project :)&lt;/p&gt;

&lt;p&gt;I am not sure anomaly detection (at least in the sense described in the course you followed) would be a very fitting algorithm in this case. &lt;/p&gt;

&lt;p&gt;I would consider a more viable approach to be what has been discussed at the end of that course where a Photo OCR workflow was demonstrated.&lt;/p&gt;

&lt;p&gt;The approach would consist of segmenting your image in smaller "blocks", and going through them one-by-one using a supervised learning algorithm and try to classify each block according to whether it contains a cat or not. If one block contains a cat, the alarm goes off. As a bonus, you get the position of the cat as well, so that you may think of incorporating some "automatic" response as a future step to your project. &lt;/p&gt;

&lt;p&gt;The benefit here is that you will not have to train your algorithm using a dataset specific to your garden (which, as you mention is difficult to create), but you can use images of cats taken off the net (e.g. perhaps you can search for "cat on grass" or something), and perhaps patches of photos from your (or other) gardens. Therefore you don;t have to spend your time collecting photos from your camera, and you avoid the risk of having a very small (comparable) sample of positives (i.e. cats).&lt;/p&gt;

&lt;p&gt;Now, of course how easy it is to build an accurate cat detector is another topic..&lt;/p&gt;
', NULL, NULL, '''/p'':11C,39C,67C,147C,239C,257C ''accord'':99C ''accur'':251C ''alarm'':115C ''algorithm'':35C,92C,161C ''also'':7C ''ambiti'':9C ''anomali'':17C ''anoth'':255C ''approach'':47C,70C ''automat'':138C ''avoid'':225C ''benefit'':150C ''block'':79C,98C,110C ''bonus'':120C ''build'':249C ''camera'':222C ''case'':38C ''cat'':105C,113C,127C,183C,194C,238C,252C ''classifi'':96C ''collect'':218C ''compar'':233C ''consid'':43C ''consist'':72C ''contain'':103C,111C ''cours'':27C,59C,243C ''creat'':176C ''dataset'':164C ''demonstr'':66C ''describ'':24C ''detect'':18C ''detector'':253C ''difficult'':174C ''discuss'':53C ''e.g'':188C ''easi'':245C ''end'':56C ''fit'':34C ''follow'':29C ''futur'':142C ''garden'':168C,208C ''get'':122C ''go'':81C ''goe'':116C ''grass'':196C ''i.e'':237C ''imag'':76C,181C ''incorpor'':136C ''interest'':5C ''learn'':91C ''least'':20C ''may'':133C ''mention'':172C ''net'':187C ''ocr'':63C ''one'':85C,87C,109C ''one-by-on'':84C ''p'':1C,12C,40C,68C,148C,240C ''patch'':201C ''perhap'':189C,200C ''photo'':62C,203C,219C ''posit'':124C,236C ''project'':10C,146C ''quit'':8C ''respons'':139C ''risk'':227C ''sampl'':234C ''search'':192C ''segment'':74C ''sens'':23C ''small'':232C ''smaller'':78C ''someth'':198C ''specif'':165C ''spend'':215C ''step'':143C ''supervis'':90C ''sure'':16C ''taken'':184C ''therefor'':209C ''think'':134C ''time'':217C ''topic'':256C ''train'':159C ''tri'':94C ''use'':88C,162C,180C ''viabl'':46C ''well'':129C ''whether'':101C ''workflow'':64C ''would'':30C,42C,71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (561, 2, '2014-06-24 13:34:12.107', 7, NULL, NULL, '&lt;p&gt;You could simplify your problem significantly by using a motion/change detection approach. For example, you could compare each image/frame with one from an early time (e.g., a minute earlier), then only consider pixels that have changed since the earlier time. You could then extract the rectangular region of change and use that as the basis for your classification or anomaly detection.&lt;/p&gt;

&lt;p&gt;Taking this type of approach can significantly simplify your classifier and reduce your false target rate because you can ignore anything that is not roughly the size of a cat (e.g., a person or bird). You would then use the extracted change regions that were not filtered out to form the training set for your classifier (or anomaly detector).&lt;/p&gt;

&lt;p&gt;Just be sure to get your false target rate sufficiently low before mounting a laser turret to your feline intrusion detection system.&lt;/p&gt;
', NULL, NULL, '''/p'':63C,124C,148C ''anomali'':61C,122C ''anyth'':85C ''approach'':13C,69C ''basi'':56C ''bird'':99C ''cat'':94C ''chang'':37C,50C,106C ''classif'':59C ''classifi'':74C,120C ''compar'':18C ''consid'':33C ''could'':3C,17C,43C ''detect'':12C,62C,146C ''detector'':123C ''e.g'':27C,95C ''earli'':25C ''earlier'':30C,40C ''exampl'':15C ''extract'':45C,105C ''fals'':78C,132C ''felin'':144C ''filter'':111C ''form'':114C ''get'':130C ''ignor'':84C ''image/frame'':20C ''intrus'':145C ''laser'':140C ''low'':136C ''minut'':29C ''motion/change'':11C ''mount'':138C ''one'':22C ''p'':1C,64C,125C ''person'':97C ''pixel'':34C ''problem'':6C ''rate'':80C,134C ''rectangular'':47C ''reduc'':76C ''region'':48C,107C ''rough'':89C ''set'':117C ''signific'':7C,71C ''simplifi'':4C,72C ''sinc'':38C ''size'':91C ''suffici'':135C ''sure'':128C ''system'':147C ''take'':65C ''target'':79C,133C ''time'':26C,41C ''train'':116C ''turret'':141C ''type'':67C ''use'':9C,52C,103C ''would'':101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (562, 2, '2014-06-24 15:44:52.54', 3, NULL, NULL, '&lt;p&gt;As a rule of thumb I always propose three different options:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Use a bagging learning technique, similar to that one followed by Random Forest. This technique allows the training of ''small'' classifiers which see a small portion of the whole data. Afterwards, a simple voting scheme (as in Random Forest) will lead you to a very interesting and robust classification.&lt;/li&gt;
&lt;li&gt;Use any technique related to fusioning information or probabilistic fusion. This is a very suitable solution in order to combine different likelihoods from different classifiers.&lt;/li&gt;
&lt;li&gt;My last suggestion is the use of fuzzy logic, a very adequate tool in order to combine information properly from a probabilistic (belonging) perspective.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The selection of specific methods or strategies will depend enormously on the data.&lt;/p&gt;
', NULL, NULL, '''/li'':65C,92C,118C ''/p'':13C,134C ''/ul'':119C ''adequ'':105C ''afterward'':46C ''allow'':31C ''alway'':8C ''bag'':18C ''belong'':116C ''classif'':64C ''classifi'':36C,91C ''combin'':86C,110C ''data'':45C,133C ''depend'':129C ''differ'':11C,87C,90C ''enorm'':130C ''follow'':25C ''forest'':28C,54C ''fusion'':72C,76C ''fuzzi'':101C ''inform'':73C,111C ''interest'':61C ''last'':95C ''lead'':56C ''learn'':19C ''li'':15C,66C,93C ''likelihood'':88C ''logic'':102C ''method'':125C ''one'':24C ''option'':12C ''order'':84C,108C ''p'':1C,120C ''perspect'':117C ''portion'':41C ''probabilist'':75C,115C ''proper'':112C ''propos'':9C ''random'':27C,53C ''relat'':70C ''robust'':63C ''rule'':4C ''scheme'':50C ''see'':38C ''select'':122C ''similar'':21C ''simpl'':48C ''small'':35C,40C ''solut'':82C ''specif'':124C ''strategi'':127C ''suggest'':96C ''suitabl'':81C ''techniqu'':20C,30C,69C ''three'':10C ''thumb'':6C ''tool'':106C ''train'':33C ''ul'':14C ''use'':16C,67C,99C ''vote'':49C ''whole'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (563, 2, '2014-06-24 15:55:37.11', 3, NULL, NULL, '&lt;p&gt;The strategy of motion/change detection is certainly adequate, but I would add an extra operation. I would detect those regions that are more likely to be changed, for instance, the ladder seems a place where humans can be (also cats) and grass where dogs, cats or humans can be.&lt;/p&gt;

&lt;p&gt;I would capture a map with size of the object and trajectory and with this I would create a cluster with the aim of detecting an object (with specific size within the image in terms of pixels) that moves with a certain speed and trajectory.&lt;/p&gt;

&lt;p&gt;You can achieve this by using R or I would suggest OpenCV in order to detect movement and follow different objects.&lt;/p&gt;
', NULL, NULL, '''/p'':51C,98C,121C ''achiev'':102C ''add'':13C ''adequ'':9C ''aim'':75C ''also'':40C ''captur'':55C ''cat'':41C,46C ''certain'':8C,94C ''chang'':28C ''cluster'':72C ''creat'':70C ''detect'':6C,19C,77C,115C ''differ'':119C ''dog'':45C ''extra'':15C ''follow'':118C ''grass'':43C ''human'':37C,48C ''imag'':85C ''instanc'':30C ''ladder'':32C ''like'':25C ''map'':57C ''motion/change'':5C ''move'':91C ''movement'':116C ''object'':62C,79C,120C ''opencv'':111C ''oper'':16C ''order'':113C ''p'':1C,52C,99C ''pixel'':89C ''place'':35C ''r'':106C ''region'':21C ''seem'':33C ''size'':59C,82C ''specif'':81C ''speed'':95C ''strategi'':3C ''suggest'':110C ''term'':87C ''trajectori'':64C,97C ''use'':105C ''within'':83C ''would'':12C,18C,54C,69C,109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (564, 1, '2014-06-24 18:59:30.56', 5, 113, 'What visualization technique to best describe a recommendation dataset?', '&lt;p&gt;I''ve written a simple recommender which generates recommendations for users based on what they have clicked. The recommender generates a data file with the following format:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;userid,userid,simmilarity (between 0 and 1 - closer to 0 the more similar the users)
a,b,.2
a,c,.3
a,d,.4
a,e,.1
e,b,.3
e,c,.5
e,d,.8
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''ve looked at &lt;a href="https://github.com/mbostock/d3/wiki/Gallery" rel="nofollow"&gt;some graphs&lt;/a&gt;, but I''m not sure which one to use, or if are there other ones that will better display the user similarities from the dataset above. Any suggestions?&lt;/p&gt;

&lt;p&gt;I''m aiming this visualization at business users who are not at all technical. I would just like to show them an easy to understand visual that details how similar some users are and so convince the business that for these users the recommendation system is useful.&lt;/p&gt;

&lt;p&gt;@Steve Kallestad do you mean something like this : &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/4zyQR.png" alt="enter image description here"&gt;&lt;/p&gt;
', 'visualization  javascript', NULL, '''/4zyqr.png'':189C ''/a'':95C ''/code'':79C ''/mbostock/d3/wiki/gallery'':90C ''/p'':40C,124C,173C,183C,195C ''/pre'':80C ''0'':47C,52C ''1'':49C,69C ''2'':60C ''3'':63C,72C ''4'':66C ''5'':75C ''8'':78C ''aim'':128C ''alt'':190C ''b'':59C,71C ''base'':24C ''best'':5A ''better'':113C ''busi'':132C,163C ''c'':62C,74C ''click'':29C ''closer'':50C ''code'':42C ''convinc'':161C ''d'':65C,77C ''data'':34C ''dataset'':9A,120C ''describ'':6A ''descript'':193C ''detail'':153C ''display'':114C ''e'':68C,70C,73C,76C ''easi'':148C ''enter'':191C ''file'':35C ''follow'':38C ''format'':39C ''generat'':20C,32C ''github.com'':89C ''github.com/mbostock/d3/wiki/gallery'':88C ''graph'':94C ''href'':87C ''i.stack.imgur.com'':188C ''i.stack.imgur.com/4zyqr.png'':187C ''imag'':192C ''img'':185C ''javascript'':11B ''kallestad'':176C ''like'':143C,181C ''look'':84C ''m'':98C,127C ''mean'':179C ''nofollow'':92C ''one'':102C,110C ''p'':12C,81C,125C,174C,184C ''pre'':41C ''recommend'':8A,18C,21C,31C,169C ''rel'':91C ''show'':145C ''similar'':55C,117C,155C ''simmilar'':45C ''simpl'':17C ''someth'':180C ''src'':186C ''steve'':175C ''suggest'':123C ''sure'':100C ''system'':170C ''technic'':139C ''techniqu'':3A ''understand'':150C ''use'':104C,172C ''user'':23C,57C,116C,133C,157C,167C ''userid'':43C,44C ''ve'':14C,83C ''visual'':2A,10B,130C,151C ''would'':141C ''written'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (565, 1, '2014-06-24 19:43:24.643', 6, 709, 'Why does Gradient Boosting regression predict negative values when there are no negative y-values in my training set?', '&lt;p&gt;As I increase the number of trees in &lt;a href="http://scikit-learn.org/stable/" rel="nofollow"&gt;scikit learn&lt;/a&gt;''s &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html" rel="nofollow"&gt;&lt;code&gt;GradientBoostingRegressor&lt;/code&gt;&lt;/a&gt;, I get more negative predictions, even though there are no negative values in my training or testing set. I have about 10 features, most of which are binary.&lt;/p&gt;

&lt;p&gt;Some of the parameters that I was tuning were:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;the number of trees/iterations;&lt;/li&gt;
&lt;li&gt;learning depth;&lt;/li&gt;
&lt;li&gt;and learning rate.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The percentage of negative values seemed to max at ~2%. The learning depth of 1(stumps) seemed to have the largest % of negative values. This percentage also seemed to increase with more trees and a smaller learning rate. The dataset is from one of the kaggle playground competitions.&lt;/p&gt;

&lt;p&gt;My code is something like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from sklearn.ensemble import GradientBoostingRegressor

X_train, X_test, y_train, y_test = train_test_split(X, y)

reg = GradientBoostingRegressor(n_estimators=8000, max_depth=1, loss = ''ls'', learning_rate = .01)

reg.fit(X_train, y_train)

ypred = reg.predict(X_test)
&lt;/code&gt;&lt;/pre&gt;
', 'machine-learning  python  algorithms  scikit  kaggle', NULL, '''/a'':46C,58C ''/code'':57C,214C ''/li'':105C,109C,114C ''/p'':87C,98C,165C,172C ''/pre'':215C ''/stable/'':41C ''/stable/modules/generated/sklearn.ensemble.gradientboostingregressor.html'':52C ''/ul'':115C ''01'':204C ''1'':131C,199C ''10'':80C ''2'':126C ''8000'':196C ''algorithm'':25B ''also'':143C ''binari'':86C ''boost'':4A ''code'':55C,168C,174C ''competit'':164C ''dataset'':156C ''depth'':108C,129C,198C ''estim'':195C ''even'':64C ''featur'':81C ''get'':60C ''gradient'':3A ''gradientboostingregressor'':56C,178C,193C ''href'':38C,49C ''import'':177C ''increas'':31C,146C ''kaggl'':27B,162C ''largest'':137C ''learn'':23B,45C,107C,112C,128C,153C,202C ''li'':100C,106C,110C ''like'':171C ''loss'':200C ''ls'':201C ''machin'':22B ''machine-learn'':21B ''max'':124C,197C ''n'':194C ''negat'':7A,13A,62C,69C,120C,139C ''nofollow'':43C,54C ''number'':33C,102C ''one'':159C ''p'':28C,88C,116C,166C ''paramet'':92C ''percentag'':118C,142C ''playground'':163C ''pre'':173C ''predict'':6A,63C ''python'':24B ''rate'':113C,154C,203C ''reg'':192C ''reg.fit'':205C ''reg.predict'':211C ''regress'':5A ''rel'':42C,53C ''scikit'':26B,44C ''scikit-learn.org'':40C,51C ''scikit-learn.org/stable/'':39C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.gradientboostingregressor.html'':50C ''seem'':122C,133C,144C ''set'':20A,76C ''sklearn.ensemble'':176C ''smaller'':152C ''someth'':170C ''split'':189C ''stump'':132C ''test'':75C,182C,186C,188C,213C ''though'':65C ''train'':19A,73C,180C,184C,187C,207C,209C ''tree'':35C,149C ''trees/iterations'':104C ''tune'':96C ''ul'':99C ''valu'':8A,16A,70C,121C,140C ''x'':179C,181C,190C,206C,212C ''y'':15A,183C,185C,191C,208C ''y-valu'':14A ''ypred'':210C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (566, 1, '2014-06-24 17:06:10.31', 5, 405, 'Named Entity Recognition: NLTK using Regular Expression', '&lt;p&gt;Many times &lt;a href="http://en.wikipedia.org/wiki/Named-entity_recognition" rel="nofollow"&gt;Named Entity Recognition&lt;/a&gt; (NER) doesn''t tag consecutive NNPs as one NE. I think editing the NER to use RegexpTagger also can improve the NER.&lt;/p&gt;

&lt;p&gt;For example, consider the following input:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;"Barack Obama is a great person."&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;And the output:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Tree(''S'', [Tree(''PERSON'', [(''Barack'', ''NNP'')]), Tree(''ORGANIZATION'', [(''Obama'', ''NNP'')]),
    (''is'', ''VBZ''), (''a'', ''DT''), (''great'', ''JJ''), (''person'', ''NN''), (''.'', ''.'')])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where as for the input: &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;''Former Vice President Dick Cheney told conservative radio host Laura Ingraham that he "was honored" to be compared to Darth Vader while in office.''&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;the output is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Tree(''S'', [(''Former'', ''JJ''), (''Vice'', ''NNP''), (''President'', ''NNP''),
    Tree(''NE'', [(''Dick'', ''NNP''), (''Cheney'', ''NNP'')]), (''told'', ''VBD''), (''conservative'', ''JJ''),
    (''radio'', ''NN''), (''host'', ''NN''), Tree(''NE'', [(''Laura'', ''NNP''), (''Ingraham'', ''NNP'')]),
    (''that'', ''IN''), (''he'', ''PRP''), (''``'', ''``''), (''was'', ''VBD''), (''honored'', ''VBN''),
    ("''''", "''''"), (''to'', ''TO''), (''be'', ''VB''), (''compared'', ''VBN''), (''to'', ''TO''),
    Tree(''NE'', [(''Darth'', ''NNP''), (''Vader'', ''NNP'')]), (''while'', ''IN''), (''in'', ''IN''),
    (''office'', ''NN''), (''.'', ''.'')])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here &lt;code&gt;Vice/NNP, President/NNP, (Dick/NNP, Cheney/NNP)&lt;/code&gt; is correctly extracted. So, I think if &lt;code&gt;nltk.ne_chunk&lt;/code&gt; is used first, and then if two consecutive trees are NNP, there are higher chances that both refer to one entity. &lt;/p&gt;

&lt;p&gt;I have been playing with NLTK toolkit, and I came across this problem a lot, but couldn''t find a satisfying answer. Any suggestion will be really appreciated. I''m looking for flaws in my approach.&lt;/p&gt;
', 'nlp', NULL, '''/a'':22C ''/blockquote'':63C,125C ''/code'':89C,189C,198C,209C ''/p'':45C,53C,62C,68C,97C,124C,130C,231C,269C ''/pre'':90C,190C ''/wiki/named-entity_recognition'':16C ''across'':243C ''also'':40C ''answer'':254C ''appreci'':260C ''approach'':268C ''barack'':56C,75C ''blockquot'':54C,98C ''came'':242C ''chanc'':224C ''cheney'':104C,145C ''cheney/nnp'':197C ''chunk'':208C ''code'':70C,132C,193C,206C ''compar'':117C,173C ''consecut'':27C,217C ''conserv'':106C,149C ''consid'':49C ''correct'':200C ''couldn'':249C ''darth'':119C,179C ''dick'':103C,143C ''dick/nnp'':196C ''doesn'':24C ''dt'':84C ''edit'':34C ''en.wikipedia.org'':15C ''en.wikipedia.org/wiki/named-entity_recognition'':14C ''entiti'':2A,20C,230C ''exampl'':48C ''express'':7A ''extract'':201C ''find'':251C ''first'':212C ''flaw'':265C ''follow'':51C ''former'':100C,135C ''great'':60C,85C ''higher'':223C ''honor'':114C,167C ''host'':108C,153C ''href'':13C ''improv'':42C ''ingraham'':110C,159C ''input'':52C,96C ''jj'':86C,136C,150C ''laura'':109C,157C ''look'':263C ''lot'':247C ''m'':262C ''mani'':10C ''name'':1A,19C ''ne'':31C,142C,156C,178C ''ner'':23C,36C,44C ''nlp'':8B ''nltk'':4A,238C ''nltk.ne'':207C ''nn'':88C,152C,154C,188C ''nnp'':76C,80C,138C,140C,144C,146C,158C,160C,180C,182C,220C ''nnps'':28C ''nofollow'':18C ''obama'':57C,79C ''offic'':123C,187C ''one'':30C,229C ''organ'':78C ''output'':67C,128C ''p'':9C,46C,55C,64C,91C,99C,126C,191C,232C ''person'':61C,74C,87C ''play'':236C ''pre'':69C,131C ''presid'':102C,139C ''president/nnp'':195C ''problem'':245C ''prp'':164C ''radio'':107C,151C ''realli'':259C ''recognit'':3A,21C ''refer'':227C ''regexptagg'':39C ''regular'':6A ''rel'':17C ''satisfi'':253C ''suggest'':256C ''tag'':26C ''think'':33C,204C ''time'':11C ''told'':105C,147C ''toolkit'':239C ''tree'':71C,73C,77C,133C,141C,155C,177C,218C ''two'':216C ''use'':5A,38C,211C ''vader'':120C,181C ''vb'':172C ''vbd'':148C,166C ''vbn'':168C,174C ''vbz'':82C ''vice'':101C,137C ''vice/nnp'':194C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (567, 2, '2014-06-24 20:28:33.693', 5, NULL, NULL, '&lt;p&gt;I think you''re looking for a &lt;a href="http://cgi.mtc.sri.com/Cluster-Lab/"&gt;similarity matrix&lt;/a&gt; (see bottom of the page). If you don''t have data on similarity between certain pairs, you can always leave them as grey or white. Also, this will only work for data sets small enough to actually make out what''s going on. I''d say 25 rows / columns maximum.&lt;/p&gt;

&lt;p&gt;In a similarity matrix, x, and y coordinates correspond to the two things you''re comparing, while a &lt;a href="http://en.wikipedia.org/wiki/False_color"&gt;colormap&lt;/a&gt; magnitude represents similarity &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT&lt;/strong&gt;:
One thing you could do to replace the colormap is the insert, say, circles of different sizes according to the similarity metric. Or you could insert the numbers themselves, again, varying the size of the number as the magnitude of that number varies. Size usually works best is business visualizations.&lt;/p&gt;
', NULL, NULL, '''/a'':16C,93C ''/cluster-lab/'':13C ''/p'':67C,97C,152C ''/strong'':101C ''/wiki/false_color'':91C ''25'':63C ''accord'':119C ''actual'':53C ''also'':42C ''alway'':35C ''best'':148C ''bottom'':18C ''busi'':150C ''certain'':31C ''cgi.mtc.sri.com'':12C ''cgi.mtc.sri.com/cluster-lab/'':11C ''circl'':115C ''colormap'':92C,110C ''column'':65C ''compar'':84C ''coordin'':76C ''correspond'':77C ''could'':105C,126C ''d'':61C ''data'':27C,48C ''differ'':117C ''edit'':100C ''en.wikipedia.org'':90C ''en.wikipedia.org/wiki/false_color'':89C ''enough'':51C ''go'':58C ''grey'':39C ''href'':10C,88C ''insert'':113C,127C ''leav'':36C ''look'':6C ''magnitud'':94C,140C ''make'':54C ''matrix'':15C,72C ''maximum'':66C ''metric'':123C ''number'':129C,137C,143C ''one'':102C ''p'':1C,68C,98C ''page'':21C ''pair'':32C ''re'':5C,83C ''replac'':108C ''repres'':95C ''row'':64C ''say'':62C,114C ''see'':17C ''set'':49C ''similar'':14C,29C,71C,96C,122C ''size'':118C,134C,145C ''small'':50C ''strong'':99C ''thing'':81C,103C ''think'':3C ''two'':80C ''usual'':146C ''vari'':132C,144C ''visual'':151C ''white'':41C ''work'':46C,147C ''x'':73C ''y'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (572, 2, '2014-06-25 03:11:02.457', 5, NULL, NULL, '&lt;p&gt;This is relatively simple to do mathematically.  First, fit a regression line to the scatter plot of "total graduates" (y) vs. "total students" (x).  You will probably see a downward sloping line if your assertion is correct (smaller schools graduate a higher %).&lt;/p&gt;

&lt;p&gt;You can identify the slope and y-intercept for this line to convert it into an equation y = mx + b, and then do a little algebra to convert the equation  into normalized form: "y / x = m + b / x"&lt;/p&gt;

&lt;p&gt;Then, with all the ratios in your data , you should &lt;em&gt;subtract&lt;/em&gt; this RHS:  &lt;/p&gt;

&lt;p&gt;normalized ratio = (total grads / total students) - (m + b / total students)&lt;/p&gt;

&lt;p&gt;If the result is postive, then the ratio is above normal for that size (i.e. above the regression line) and if it is negative it is below the regression line.  If you want all positive numbers, you can add a positive constant to move all results above zero.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;This is how to do it mathematically, but I suggest that you consider whether it is wise, from a data analysis point of view, to normalize by school size.  This depends on the purpose of your analysis and specifically how this ratio is being analyzed in relation to other data.&lt;/p&gt;
', NULL, NULL, '''/em'':99C ''/p'':44C,85C,102C,114C,164C,217C ''add'':154C ''algebra'':72C ''analysi'':187C,203C ''analyz'':211C ''assert'':36C ''b'':66C,83C,111C ''consid'':179C ''constant'':157C ''convert'':59C,74C ''correct'':38C ''data'':94C,186C,216C ''depend'':197C ''downward'':31C ''em'':97C ''equat'':63C,76C ''first'':9C ''fit'':10C ''form'':79C ''grad'':107C ''graduat'':20C,41C ''higher'':43C ''hr'':165C ''i.e'':130C ''identifi'':48C ''intercept'':54C ''line'':13C,33C,57C,134C,145C ''littl'':71C ''m'':82C,110C ''mathemat'':8C,173C ''move'':159C ''mx'':65C ''negat'':139C ''normal'':78C,104C,126C,192C ''number'':151C ''p'':1C,45C,86C,103C,115C,166C ''plot'':17C ''point'':188C ''posit'':150C,156C ''postiv'':120C ''probabl'':28C ''purpos'':200C ''ratio'':91C,105C,123C,208C ''regress'':12C,133C,144C ''relat'':4C,213C ''result'':118C,161C ''rhs'':101C ''scatter'':16C ''school'':40C,194C ''see'':29C ''simpl'':5C ''size'':129C,195C ''slope'':32C,50C ''smaller'':39C ''specif'':205C ''student'':24C,109C,113C ''subtract'':98C ''suggest'':176C ''total'':19C,23C,106C,108C,112C ''view'':190C ''vs'':22C ''want'':148C ''whether'':180C ''wise'':183C ''x'':25C,81C,84C ''y'':21C,53C,64C,80C ''y-intercept'':52C ''zero'':163C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (573, 2, '2014-06-25 03:30:19.787', 2, NULL, NULL, '&lt;p&gt;OpenCV''s &lt;a href="http://docs.opencv.org/trunk/doc/tutorials/video/background_subtraction/background_subtraction.html" rel="nofollow" title="background subtraction"&gt;background subtraction&lt;/a&gt; will find objects moving about your harden. After that you could use a classifier or shape analysis to differentiate between cats, people, trees and etc.&lt;/p&gt;
', NULL, NULL, '''/a'':16C ''/p'':42C ''/trunk/doc/tutorials/video/background_subtraction/background_subtraction.html'':8C ''analysi'':33C ''background'':12C,14C ''cat'':37C ''classifi'':30C ''could'':27C ''differenti'':35C ''docs.opencv.org'':7C ''docs.opencv.org/trunk/doc/tutorials/video/background_subtraction/background_subtraction.html'':6C ''etc'':41C ''find'':18C ''harden'':23C ''href'':5C ''move'':20C ''nofollow'':10C ''object'':19C ''opencv'':2C ''p'':1C ''peopl'':38C ''rel'':9C ''shape'':32C ''subtract'':13C,15C ''titl'':11C ''tree'':39C ''use'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (574, 2, '2014-06-25 07:22:33.17', 1, NULL, NULL, '&lt;p&gt;This is a very interesting problem.&lt;/p&gt;

&lt;p&gt;I faced a similar one by analyzing the pictures users upload to the social network. I did the following approach:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Rather than associating data to ages (15 y.o., 27 y.o., ...) what I did is to establish different groups of ages: Less than 18, from 18 to 30 and greater than 30 (this is due to the specific problem we were facing, but you can choose whatever intervals you want). This division helps a lot to solve the problem.&lt;/li&gt;
&lt;li&gt;Afterwards, I created a hierarchical clustering (divisive or aggregative). Then I choose those branches where I had users with known ages (or group ages) and then for that branch I extended the same age to that group.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;This approach is &lt;strong&gt;semi-supervised learning&lt;/strong&gt; and I recommended it in case you only have some data labeled.&lt;/p&gt;

&lt;p&gt;Please, notice that on a social network, people usually lie about the age (just for fun, or sometimes because they want to camuflate themselves on the social net).&lt;/p&gt;
', NULL, NULL, '''/li'':91C,130C ''/p'':8C,30C,154C,184C ''/strong'':141C ''/ul'':131C ''15'':39C ''18'':55C,57C ''27'':41C ''30'':59C,63C ''afterward'':93C ''age'':38C,52C,113C,116C,126C,168C ''aggreg'':101C ''analyz'':16C ''approach'':29C,134C ''associ'':35C ''branch'':106C,121C ''camufl'':178C ''case'':147C ''choos'':77C,104C ''cluster'':98C ''creat'':95C ''data'':36C,152C ''differ'':49C ''divis'':83C,99C ''due'':66C ''establish'':48C ''extend'':123C ''face'':11C,73C ''follow'':28C ''fun'':171C ''greater'':61C ''group'':50C,115C,129C ''help'':84C ''hierarch'':97C ''interest'':6C ''interv'':79C ''known'':112C ''label'':153C ''learn'':140C ''less'':53C ''li'':32C,92C ''lie'':165C ''lot'':86C ''net'':183C ''network'':24C,162C ''notic'':157C ''one'':14C ''p'':1C,9C,132C,155C ''peopl'':163C ''pictur'':18C ''pleas'':156C ''problem'':7C,70C,90C ''rather'':33C ''recommend'':144C ''semi'':138C ''semi-supervis'':137C ''similar'':13C ''social'':23C,161C,182C ''solv'':88C ''sometim'':173C ''specif'':69C ''strong'':136C ''supervis'':139C ''ul'':31C ''upload'':20C ''user'':19C,110C ''usual'':164C ''want'':81C,176C ''whatev'':78C ''y.o'':40C,42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (575, 2, '2014-06-25 09:19:25.56', 3, NULL, NULL, '&lt;p&gt;This is a very good question and a common situation.&lt;/p&gt;

&lt;p&gt;In my opinion there are three different factors that must be controlled:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Data: There exist already different benchmarks in order to evaluate algorithms and architectures. These data must be publicly available so that everybody can contrast their approaches.&lt;/li&gt;
&lt;li&gt;Architecture: My suggestion is to test everything on the cloud, so that everyone can contrast their results and also there is no doubt the same machines and software is used.&lt;/li&gt;
&lt;li&gt;Algorithms: If you have developed a distributed algorithm, it would be great to compare your algorithm on a specific data. In this case, algorithms must not be public.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So, answering your question, if you want to compare different experiments and state to what extent your distributed algorithm outperforms others, you should try to replicate as accurate as possible the same environment (data and architecture) where the experiments were carried out.&lt;/p&gt;

&lt;p&gt;If this is not possible, my suggestion is that you test your algorithm with public data and cloud architecture so that &lt;strong&gt;you become a referent&lt;/strong&gt; as you are facilitating the comparison of future algorithms.&lt;/p&gt;
', NULL, NULL, '''/li'':55C,87C,117C ''/p'':12C,26C,162C,200C ''/strong'':190C ''/ul'':118C ''accur'':147C ''algorithm'':39C,89C,96C,104C,112C,138C,176C,199C ''alreadi'':32C ''also'':75C ''answer'':121C ''approach'':54C ''architectur'':41C,57C,155C,182C ''avail'':47C ''becom'':187C ''benchmark'':34C ''carri'':160C ''case'':111C ''cloud'':66C,181C ''common'':10C ''compar'':102C,128C ''comparison'':196C ''contrast'':52C,71C ''control'':25C ''data'':29C,43C,108C,153C,179C ''develop'':93C ''differ'':20C,33C,129C ''distribut'':95C,137C ''doubt'':79C ''environ'':152C ''evalu'':38C ''everybodi'':50C ''everyon'':69C ''everyth'':63C ''exist'':31C ''experi'':130C,158C ''extent'':135C ''facilit'':194C ''factor'':21C ''futur'':198C ''good'':6C ''great'':100C ''li'':28C,56C,88C ''machin'':82C ''must'':23C,44C,113C ''opinion'':16C ''order'':36C ''other'':140C ''outperform'':139C ''p'':1C,13C,119C,163C ''possibl'':149C,168C ''public'':46C,116C,178C ''question'':7C,123C ''refer'':189C ''replic'':145C ''result'':73C ''situat'':11C ''softwar'':84C ''specif'':107C ''state'':132C ''strong'':185C ''suggest'':59C,170C ''test'':62C,174C ''three'':19C ''tri'':143C ''ul'':27C ''use'':86C ''want'':126C ''would'':98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (576, 2, '2014-06-25 10:15:31.477', 0, NULL, NULL, '&lt;p&gt;You should try &lt;em&gt;arules&lt;/em&gt; package in R. It allows you to create not only the association rules but also to specify the length of each rule, the importance of each rule and also you can filter them, which is what you are looking for (try the rhs() command of this package).&lt;/p&gt;
', NULL, NULL, '''/em'':7C ''/p'':55C ''allow'':12C ''also'':22C,36C ''arul'':6C ''associ'':19C ''command'':51C ''creat'':15C ''em'':5C ''filter'':39C ''import'':31C ''length'':26C ''look'':46C ''p'':1C ''packag'':8C,54C ''r'':10C ''rhs'':50C ''rule'':20C,29C,34C ''specifi'':24C ''tri'':4C,48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (577, 2, '2014-06-25 10:59:56.737', 2, NULL, NULL, '&lt;p&gt;Though it''s easy to say, it''s better to treat the environment that changes as variables, describe/estimate your algorithm''s performance base on these variables. And hopefully others will do the same. Of interest, &lt;a href="http://infolab.stanford.edu/~ullman/pub/experiments.pdf" rel="nofollow"&gt;Experiments as Research Validation -- Have We Gone too Far?&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':53C ''/p'':54C ''/~ullman/pub/experiments.pdf'':41C ''algorithm'':21C ''base'':24C ''better'':10C ''chang'':16C ''describe/estimate'':19C ''easi'':5C ''environ'':14C ''experi'':44C ''far'':52C ''gone'':50C ''hope'':29C ''href'':38C ''infolab.stanford.edu'':40C ''infolab.stanford.edu/~ullman/pub/experiments.pdf'':39C ''interest'':36C ''nofollow'':43C ''other'':30C ''p'':1C ''perform'':23C ''rel'':42C ''research'':46C ''say'':7C ''though'':2C ''treat'':12C ''valid'':47C ''variabl'':18C,27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (584, 2, '2014-06-25 15:46:21.267', 6, NULL, NULL, '&lt;p&gt;The original MacQueen k-means publication (the first to use the name "kmeans") is an online algorithm.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;MacQueen, J. B. (1967). "Some Methods for classification and Analysis of Multivariate Observations". Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability 1. University of California Press. pp. 281â297&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;After assigning each point, the mean is incrementally updated using a simple weighted-average formula (old mean is weighted with n, the new observation is weighted with 1, if the mean had n observations before).&lt;/p&gt;

&lt;p&gt;As far as I can tell, it was also meant to be a single pass over the data only, although it can be trivially repeated multiple times to reassign points until convergence.&lt;/p&gt;

&lt;p&gt;MacQueen usually takes fewer iterations than Lloyds to converge if your data is shuffled (because it updates the mean faster!). On ordered data, it can have problems. On the downside, it requires more computation for each object, so each iteration takes slightly longer (additional math operations, obviously).&lt;/p&gt;
', NULL, NULL, '''/blockquote'':55C ''/p'':20C,54C,93C,127C,176C ''1'':46C,85C ''1967'':26C ''281â'':52C ''297'':53C ''5th'':38C ''addit'':172C ''algorithm'':19C ''also'':103C ''although'':114C ''analysi'':32C ''assign'':58C ''averag'':71C ''b'':25C ''berkeley'':39C ''blockquot'':21C ''california'':49C ''classif'':30C ''comput'':162C ''converg'':126C,137C ''data'':112C,140C,151C ''downsid'':158C ''far'':96C ''faster'':148C ''fewer'':132C ''first'':10C ''formula'':72C ''increment'':64C ''iter'':133C,168C ''j'':24C ''k'':6C ''k-mean'':5C ''kmean'':15C ''lloyd'':135C ''longer'':171C ''macqueen'':4C,23C,129C ''math'':173C ''mathemat'':42C ''mean'':7C,62C,74C,88C,147C ''meant'':104C ''method'':28C ''multipl'':120C ''multivari'':34C ''n'':78C,90C ''name'':14C ''new'':80C ''object'':165C ''observ'':35C,81C,91C ''obvious'':175C ''old'':73C ''onlin'':18C ''oper'':174C ''order'':150C ''origin'':3C ''p'':1C,22C,56C,94C,128C ''pass'':109C ''point'':60C,124C ''pp'':51C ''press'':50C ''probabl'':45C ''problem'':155C ''proceed'':36C ''public'':8C ''reassign'':123C ''repeat'':119C ''requir'':160C ''shuffl'':142C ''simpl'':68C ''singl'':108C ''slight'':170C ''statist'':43C ''symposium'':40C ''take'':131C,169C ''tell'':100C ''time'':121C ''trivial'':118C ''univers'':47C ''updat'':65C,145C ''use'':12C,66C ''usual'':130C ''weight'':70C,76C,83C ''weighted-averag'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (585, 2, '2014-06-25 15:53:30.723', 1, NULL, NULL, '&lt;p&gt;DBSCAN (see also: Generalized DBSCAN) does not require a distance.
All it needs is a &lt;em&gt;binary decision&lt;/em&gt;. Commonly, one would use "distance &amp;lt; epsilon" but nothing says you cannot use "similarity &gt; epsilon" instead. Triangle inequality etc. are not required.&lt;/p&gt;

&lt;p&gt;Affinity propagation, as the name says, uses similarities.&lt;/p&gt;

&lt;p&gt;Hierarchical clustering, except for maybe Ward linkage, does not make any assumption. In many implementations you can just use negative distances when you have similarities, and it will work just fine. Because all that is needed is min, max, and &amp;lt;.&lt;/p&gt;

&lt;p&gt;Kernel k-means could work IF your similarity is a good kernel function. Think of it as computing k-means in a different vector space, where Euclidean distance corresponds to your similarity function. But then you need to know k.&lt;/p&gt;

&lt;p&gt;PAM (K-medoids) should work. Assign each object to the most similary medoid, then choose the object with the highest average similarity as new medoid... no triangle inequality needed.&lt;/p&gt;

&lt;p&gt;... and probably many many more. There are literally hundreds of clustering algorithms. &lt;strong&gt;Most should work&lt;/strong&gt; IMHO. Very few seem to actually require metric properties. K-means has probably the strongest requirements: it minimizes &lt;em&gt;variance&lt;/em&gt; (not distance, or similarity), and you must be able to compute means.&lt;/p&gt;
', NULL, NULL, '''/em'':20C,212C ''/p'':43C,53C,96C,140C,172C,225C ''/strong'':190C ''abl'':221C ''actual'':196C ''affin'':45C ''algorithm'':185C ''also'':4C ''assign'':148C ''assumpt'':66C ''averag'':163C ''binari'':18C ''cannot'':32C ''choos'':157C ''cluster'':56C,184C ''common'':21C ''comput'':116C,223C ''correspond'':128C ''could'':102C ''dbscan'':2C,6C ''decis'':19C ''differ'':122C ''distanc'':11C,25C,75C,127C,214C ''em'':17C,210C ''epsilon'':27C,35C ''etc'':39C ''euclidean'':126C ''except'':57C ''fine'':85C ''function'':111C,132C ''general'':5C ''good'':109C ''hierarch'':55C ''highest'':162C ''hundr'':182C ''imho'':191C ''implement'':69C ''inequ'':38C,170C ''instead'':36C ''k'':100C,118C,139C,144C,201C ''k-mean'':99C,117C,200C ''k-medoid'':143C ''kernel'':98C,110C ''know'':138C ''linkag'':61C ''liter'':181C ''lt'':26C,95C ''make'':64C ''mani'':68C,176C,177C ''max'':93C ''mayb'':59C ''mean'':101C,119C,202C,224C ''medoid'':145C,155C,167C ''metric'':198C ''min'':92C ''minim'':209C ''must'':219C ''name'':49C ''need'':14C,90C,136C,171C ''negat'':74C ''new'':166C ''noth'':29C ''object'':150C,159C ''one'':22C ''p'':1C,44C,54C,97C,141C,173C ''pam'':142C ''probabl'':175C,204C ''propag'':46C ''properti'':199C ''requir'':9C,42C,197C,207C ''say'':30C,50C ''see'':3C ''seem'':194C ''similar'':34C,52C,79C,106C,131C,164C,216C ''similari'':154C ''space'':124C ''strong'':186C ''strongest'':206C ''think'':112C ''triangl'':37C,169C ''use'':24C,33C,51C,73C ''varianc'':211C ''vector'':123C ''ward'':60C ''work'':83C,103C,147C,189C ''would'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (587, 1, '2014-06-25 18:06:14.293', 8, 4238, 'Where can I download historical market capitalization and daily turnover data for stocks?', '&lt;p&gt;There are plenty of sources which provide the historical stock data but they only provide the OHLC fields along with volume and adjusted close. Also a couple of sources I found provide market cap data sets but they''re restricted to US stocks. Yahoo Finance provides this data online but there''s no option to download it ( or none I am aware of ). &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Where can I download this data for stocks belonging to various top stock exchanges across countries by using their ticker name ?&lt;/li&gt;
&lt;li&gt;Is there some way to download it via Yahoo Finance or Google Finance ?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I need data for the last decade or so and hence need some script or API which would do this.&lt;/p&gt;
', 'dataset', NULL, '''/li'':103C,118C ''/p'':79C,141C ''/ul'':119C ''across'':96C ''adjust'':38C ''along'':34C ''also'':40C ''api'':136C ''awar'':77C ''belong'':90C ''cap'':49C ''capit'':7A ''close'':39C ''countri'':97C ''coupl'':42C ''daili'':9A ''data'':11A,26C,50C,63C,87C,123C ''dataset'':14B ''decad'':127C ''download'':4A,71C,85C,110C ''exchang'':95C ''field'':33C ''financ'':60C,114C,117C ''found'':46C ''googl'':116C ''henc'':131C ''histor'':5A,24C ''last'':126C ''li'':81C,104C ''market'':6A,48C ''name'':102C ''need'':122C,132C ''none'':74C ''ohlc'':32C ''onlin'':64C ''option'':69C ''p'':15C,120C ''plenti'':18C ''provid'':22C,30C,47C,61C ''re'':54C ''restrict'':55C ''script'':134C ''set'':51C ''sourc'':20C,44C ''stock'':13A,25C,58C,89C,94C ''ticker'':101C ''top'':93C ''turnov'':10A ''ul'':80C ''us'':57C ''use'':99C ''various'':92C ''via'':112C ''volum'':36C ''way'':108C ''would'':138C ''yahoo'':59C,113C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (588, 2, '2014-06-25 18:12:04.01', 4, NULL, NULL, '&lt;p&gt;Quant SE is better place for questions related to getting financial data:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://quant.stackexchange.com/questions/141/what-data-sources-are-available-online"&gt;What data sources are available online&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://quant.stackexchange.com/search?q=market+capitalization+data"&gt;http://quant.stackexchange.com/search?q=market+capitalization+data&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':28C ''/li'':29C ''/p'':14C ''/questions/141/what-data-sources-are-available-online'':21C ''/search?q=market+capitalization+data'':35C ''/search?q=market+capitalization+data&lt;/a&gt;&lt;/li&gt;'':38C ''/ul'':39C ''avail'':26C ''better'':5C ''data'':13C,23C ''financi'':12C ''get'':11C ''href'':18C,32C ''li'':16C,30C ''onlin'':27C ''p'':1C ''place'':6C ''quant'':2C ''quant.stackexchange.com'':20C,34C,37C ''quant.stackexchange.com/questions/141/what-data-sources-are-available-online'':19C ''quant.stackexchange.com/search?q=market+capitalization+data'':33C ''quant.stackexchange.com/search?q=market+capitalization+data&lt;/a&gt;&lt;/li&gt;'':36C ''question'':8C ''relat'':9C ''se'':3C ''sourc'':24C ''ul'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (591, 2, '2014-06-26 06:51:12.243', 2, NULL, NULL, '&lt;p&gt;If you are totally unfamiliar with ordinal regression, I would try to read the Tabachnick / Fidell (&lt;a href="http://www.pearsonhighered.com/educator/product/Using-Multivariate-Statistics-6E/0205849571.page" rel="nofollow"&gt;http://www.pearsonhighered.com/educator/product/Using-Multivariate-Statistics-6E/0205849571.page&lt;/a&gt;)  chapter on the topic first - while not written for R, the book is very good at conveying the general logic and the "do''s" and "do nots". &lt;/p&gt;

&lt;p&gt;As a question: What are your response catgeories exactly? If they are some sort of scale, like "good - bad" it would be ok to use a linear regression (market research does it all the time...), but if the items are more disjunct, an ordinal regression might be better.
I dimly remember that some books about structural equatiotion modelling mentioned that linear regression was superior for good scales than probit - bit I cannot recall the book at the moment, sorry!&lt;/p&gt;

&lt;p&gt;The most serious problem might be the number of dummy variables - a couple of hundred dummy variables will make the analysis slow, hard to interpret and probably unstable - are there enough cases for each dummy / dummy-combination?&lt;/p&gt;
', NULL, NULL, '''/educator/product/using-multivariate-statistics-6e/0205849571.page'':22C ''/educator/product/using-multivariate-statistics-6e/0205849571.page&lt;/a&gt;)'':27C ''/p'':55C,136C,176C ''analysi'':158C ''bad'':75C ''better'':104C ''bit'':126C ''book'':39C,110C,131C ''cannot'':128C ''case'':169C ''catgeori'':64C ''chapter'':28C ''combin'':175C ''convey'':44C ''coupl'':150C ''dim'':106C ''disjunct'':98C ''dummi'':147C,153C,172C,174C ''dummy-combin'':173C ''enough'':168C ''equatiot'':113C ''exact'':65C ''fidel'':17C ''first'':32C ''general'':46C ''good'':42C,74C,122C ''hard'':160C ''href'':19C ''hundr'':152C ''interpret'':162C ''item'':95C ''like'':73C ''linear'':83C,117C ''logic'':47C ''make'':156C ''market'':85C ''mention'':115C ''might'':102C,142C ''model'':114C ''moment'':134C ''nofollow'':24C ''not'':54C ''number'':145C ''ok'':79C ''ordin'':8C,100C ''p'':1C,56C,137C ''probabl'':164C ''probit'':125C ''problem'':141C ''question'':59C ''r'':37C ''read'':14C ''recal'':129C ''regress'':9C,84C,101C,118C ''rel'':23C ''rememb'':107C ''research'':86C ''respons'':63C ''scale'':72C,123C ''serious'':140C ''slow'':159C ''sorri'':135C ''sort'':70C ''structur'':112C ''superior'':120C ''tabachnick'':16C ''time'':91C ''topic'':31C ''total'':5C ''tri'':12C ''unfamiliar'':6C ''unstabl'':165C ''use'':81C ''variabl'':148C,154C ''would'':11C,77C ''written'':35C ''www.pearsonhighered.com'':21C,26C ''www.pearsonhighered.com/educator/product/using-multivariate-statistics-6e/0205849571.page'':20C ''www.pearsonhighered.com/educator/product/using-multivariate-statistics-6e/0205849571.page&lt;/a&gt;)'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (592, 2, '2014-06-26 07:41:52.403', 4, NULL, NULL, '&lt;p&gt;I usually take a two-step approach &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;compute univariate (variable by variable) summary statistics such as mean, range, variance, number of missing, cardinality, etc. for each variable and look for oddities (e.g. range not plausible given the meaning of the variable). Plot histograms for those odd variables.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;split the data into manageable subsets (choose a meaningful variable and split the data according to it e.g. all positive examples, and all negative) and explore them visually (e.g. with &lt;a href="http://www.ggobi.org/" rel="nofollow"&gt;ggobi&lt;/a&gt; ). Especially use tools like brushing and scatter plots to understand how variables are linked together.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;And when you start building models, make sure to plot the residuals, looking for extreme errors that might be due to an outlier, or look at the confusion matrix and make sure it is balanced. Use k-fold cross validation to optimize your models and look at the variance of the training error for each fold, if one fold performs much worse than the others, it may contain outliers.&lt;/p&gt;
', NULL, NULL, '''/a'':94C ''/li'':55C,111C ''/ol'':112C ''/p'':10C,54C,110C,184C ''accord'':72C ''approach'':9C ''balanc'':148C ''brush'':99C ''build'':118C ''cardin'':29C ''choos'':64C ''comput'':14C ''confus'':141C ''contain'':182C ''cross'':153C ''data'':60C,71C ''due'':133C ''e.g'':38C,75C,86C ''error'':129C,167C ''especi'':95C ''etc'':30C ''exampl'':78C ''explor'':83C ''extrem'':128C ''fold'':152C,170C,173C ''ggobi'':93C ''given'':42C ''histogram'':49C ''href'':89C ''k'':151C ''k-fold'':150C ''li'':12C,56C ''like'':98C ''link'':108C ''look'':35C,126C,138C,160C ''make'':120C,144C ''manag'':62C ''matrix'':142C ''may'':181C ''mean'':23C,44C ''meaning'':66C ''might'':131C ''miss'':28C ''model'':119C,158C ''much'':175C ''negat'':81C ''nofollow'':92C ''number'':26C ''odd'':52C ''odditi'':37C ''ol'':11C ''one'':172C ''optim'':156C ''other'':179C ''outlier'':136C,183C ''p'':1C,13C,57C,113C ''perform'':174C ''plausibl'':41C ''plot'':48C,102C,123C ''posit'':77C ''rang'':24C,39C ''rel'':91C ''residu'':125C ''scatter'':101C ''split'':58C,69C ''start'':117C ''statist'':20C ''step'':8C ''subset'':63C ''summari'':19C ''sure'':121C,145C ''take'':4C ''togeth'':109C ''tool'':97C ''train'':166C ''two'':7C ''two-step'':6C ''understand'':104C ''univari'':15C ''use'':96C,149C ''usual'':3C ''valid'':154C ''variabl'':16C,18C,33C,47C,53C,67C,106C ''varianc'':25C,163C ''visual'':85C ''wors'':176C ''www.ggobi.org'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (868, 2, '2014-07-30 15:32:44.557', 4, NULL, NULL, '&lt;p&gt;There is a paper you should look into:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://cs.smith.edu/dftwiki/images/6/68/MapReduceDistributedComputingMachineLearning.pdf" rel="nofollow"&gt;MapReduce: Distributed Computing for Machine Learning&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;They distinguish 3 classes of machine-learning problems that are reasonable to address with MapReduce:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Single pass algorithms&lt;/li&gt;
&lt;li&gt;Iterative algorithms&lt;/li&gt;
&lt;li&gt;Query based algorithms&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;They also give examples for each class.&lt;/p&gt;
', NULL, NULL, '''/a'':25C ''/dftwiki/images/6/68/mapreducedistributedcomputingmachinelearning.pdf'':16C ''/li'':50C,54C,59C ''/ol'':60C ''/p'':10C,26C,44C,69C ''3'':30C ''address'':41C ''algorithm'':49C,53C,58C ''also'':63C ''base'':57C ''class'':31C,68C ''comput'':21C ''cs.smith.edu'':15C ''cs.smith.edu/dftwiki/images/6/68/mapreducedistributedcomputingmachinelearning.pdf'':14C ''distinguish'':29C ''distribut'':20C ''exampl'':65C ''give'':64C ''href'':13C ''iter'':52C ''learn'':24C,35C ''li'':46C,51C,55C ''look'':8C ''machin'':23C,34C ''machine-learn'':33C ''mapreduc'':19C,43C ''nofollow'':18C ''ol'':45C ''p'':1C,11C,27C,61C ''paper'':5C ''pass'':48C ''problem'':36C ''queri'':56C ''reason'':39C ''rel'':17C ''singl'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (593, 2, '2014-06-26 08:01:40.763', 3, NULL, NULL, '&lt;p&gt;As many have answered, it is always best to avoid anything done manually as it is less reproducible/documentable. Your point about the overhead of writing a script vs. opening and fixing the file is valid, though.&lt;/p&gt;

&lt;p&gt;Best practice is often to&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;keep an untouched version of the data&lt;/li&gt;
&lt;li&gt;build a working copy of the data with errors fixed&lt;/li&gt;
&lt;li&gt;have a way to re-create a working copy from the original data&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The last point can be done with a script. Then make sure to be as specific as needed to modify only the data you want to modify, and to write the script in such a way that adding a fix by modifying the script is as easy as modifying the data directly.&lt;/p&gt;

&lt;p&gt;If your data lie in files, you can also use diffs/patches to store the original data along with the patches needed to produce the working data. To generate them, duplicate your working copy, perform the change, extract the diff/patch, save it, and delete the previous working copy.&lt;/p&gt;
', NULL, NULL, '''/li'':55C,67C,83C ''/p'':38C,45C,138C,187C ''/ul'':84C ''ad'':123C ''along'':156C ''also'':148C ''alway'':8C ''answer'':5C ''anyth'':12C ''avoid'':11C ''best'':9C,40C ''build'':57C ''chang'':175C ''copi'':60C,78C,172C,186C ''creat'':75C ''data'':54C,63C,82C,108C,136C,142C,155C,165C ''delet'':182C ''diff/patch'':178C ''diffs/patches'':150C ''direct'':137C ''done'':13C,91C ''duplic'':169C ''easi'':132C ''error'':65C ''extract'':176C ''file'':34C,145C ''fix'':32C,66C,125C ''generat'':167C ''keep'':48C ''last'':87C ''less'':18C ''li'':47C,56C,68C ''lie'':143C ''make'':96C ''mani'':3C ''manual'':14C ''modifi'':105C,112C,127C,134C ''need'':103C,160C ''often'':43C ''open'':30C ''origin'':81C,154C ''overhead'':24C ''p'':1C,39C,85C,139C ''patch'':159C ''perform'':173C ''point'':21C,88C ''practic'':41C ''previous'':184C ''produc'':162C ''re'':74C ''re-creat'':73C ''reproducible/documentable'':19C ''save'':179C ''script'':28C,94C,117C,129C ''specif'':101C ''store'':152C ''sure'':97C ''though'':37C ''ul'':46C ''untouch'':50C ''use'':149C ''valid'':36C ''version'':51C ''vs'':29C ''want'':110C ''way'':71C,121C ''work'':59C,77C,164C,171C,185C ''write'':26C,115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8818, 1, '2015-11-12 15:41:30.003', 4, 54, 'how to increase the coverage of wordnet using wikipedia', '&lt;p&gt;I want to increase the coverage of wordnet by increasing the synset count.&lt;/p&gt;

&lt;p&gt;Is there a way in which I can proceed and increase sysnets. I am trying to work on Hindi wordnet with Princeton wordnet  as the  standard&lt;/p&gt;
', 'knowledge-base', NULL, '''/p'':27C,55C ''base'':12B ''count'':26C ''coverag'':5A,19C ''hindi'':47C ''increas'':3A,17C,23C,39C ''knowledg'':11B ''knowledge-bas'':10B ''p'':13C,28C ''princeton'':50C ''proceed'':37C ''standard'':54C ''synset'':25C ''sysnet'':40C ''tri'':43C ''use'':8A ''want'':15C ''way'':32C ''wikipedia'':9A ''wordnet'':7A,21C,48C,51C ''work'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (594, 1, '2014-06-26 12:11:51.253', 5, 156, 'How does Google categorize results from its image search?', '&lt;p&gt;While doing a Google image search, the page displays some figured out categories for the images of the topic being searched for. I''m interested in learning how this works, and how it chooses and creates categories.&lt;/p&gt;

&lt;p&gt;Unfortunately, I couldn''t find much about it at all. Is anyone able to shed some light on algorithms they may be using to do this, and what basis these categories are created from?&lt;/p&gt;

&lt;p&gt;For example, if I search for "animals" I get the categories:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;"cute", "baby", "wild", "farm", "zoo", "clipart".&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;If I go into "wild", I then have subcategories:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;"forest", "baby", "africa", "clipart", "rainforest", "domestic".&lt;/p&gt;
&lt;/blockquote&gt;
', 'machine-learning  classification  google  search', NULL, '''/blockquote'':113C,134C ''/p'':54C,90C,103C,112C,124C,133C ''abl'':68C ''africa'':129C ''algorithm'':74C ''anim'':98C ''anyon'':67C ''babi'':107C,128C ''basi'':84C ''blockquot'':104C,125C ''categor'':4A ''categori'':29C,53C,86C,102C ''choos'':50C ''classif'':13B ''clipart'':111C,130C ''couldn'':58C ''creat'':52C,88C ''cute'':106C ''display'':25C ''domest'':132C ''exampl'':93C ''farm'':109C ''figur'':27C ''find'':60C ''forest'':127C ''get'':100C ''go'':117C ''googl'':3A,14B,20C ''imag'':8A,21C,32C ''interest'':41C ''learn'':12B,43C ''light'':72C ''m'':40C ''machin'':11B ''machine-learn'':10B ''may'':76C ''much'':61C ''p'':16C,55C,91C,105C,114C,126C ''page'':24C ''rainforest'':131C ''result'':5A ''search'':9A,15B,22C,37C,96C ''shed'':70C ''subcategori'':123C ''topic'':35C ''unfortun'':56C ''use'':78C ''wild'':108C,119C ''work'':46C ''zoo'':110C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (595, 1, '2014-06-26 12:25:55.663', 1, 178, 'How to use neural networks with large and variable number of inputs?', '&lt;p&gt;I''m new to machine learning, but I have an interesting problem. I have a large sample of people and visited sites. Some people have indicated gender, age, and other parameters. Now I want to restore these parameters to each user.&lt;/p&gt;

&lt;p&gt;Which way do I look for? Which algorithm is suitable to solve this problem? I''m familiar with Neural Networks (supervised learning), but it seems they don''t fit.&lt;/p&gt;
', 'machine-learning  data-mining  algorithms  neuralnetwork', NULL, '''/p'':63C,94C ''age'':49C ''algorithm'':19B,72C ''data'':17B ''data-min'':16B ''familiar'':81C ''fit'':93C ''gender'':48C ''indic'':47C ''input'':12A ''interest'':32C ''larg'':7A,37C ''learn'':15B,27C,86C ''look'':69C ''m'':23C,80C ''machin'':14B,26C ''machine-learn'':13B ''mine'':18B ''network'':5A,84C ''neural'':4A,83C ''neuralnetwork'':20B ''new'':24C ''number'':10A ''p'':21C,64C ''paramet'':52C,59C ''peopl'':40C,45C ''problem'':33C,78C ''restor'':57C ''sampl'':38C ''seem'':89C ''site'':43C ''solv'':76C ''suitabl'':74C ''supervis'':85C ''use'':3A ''user'':62C ''variabl'':9A ''visit'':42C ''want'':55C ''way'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (964, 2, '2014-08-12 07:59:13.06', 4, NULL, NULL, '&lt;p&gt;Topic models are usually &lt;strong&gt;unsupervised&lt;/strong&gt;. There are "supervised topic models", too; but even then they try to model &lt;strong&gt;topics within a classes&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;E.g. you may have a class "football", but there may be topics inside this class that relate to particular matches or teams.&lt;/p&gt;

&lt;p&gt;The challenge with topics is that they change over time; consider the matches example above. Such topics may emerge, and disappear again.&lt;/p&gt;
', NULL, NULL, '''/p'':28C,52C,76C ''/strong'':8C,27C ''challeng'':55C ''chang'':61C ''class'':26C,35C,44C ''consid'':64C ''disappear'':74C ''e.g'':30C ''emerg'':72C ''even'':16C ''exampl'':67C ''footbal'':36C ''insid'':42C ''match'':49C,66C ''may'':32C,39C,71C ''model'':3C,13C,21C ''p'':1C,29C,53C ''particular'':48C ''relat'':46C ''strong'':6C,22C ''supervis'':11C ''team'':51C ''time'':63C ''topic'':2C,12C,23C,41C,57C,70C ''tri'':19C ''unsupervis'':7C ''usual'':5C ''within'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (596, 1, '2014-06-26 13:32:18.05', 8, 170, 'Network analysis classic datasets', '&lt;p&gt;There are several classic datasets for machine learning classification/regression tasks. The most popular are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html"&gt;Iris Flower Data Set&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.kaggle.com/c/titanic-gettingStarted"&gt;Titanic Data Set&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stat.ethz.ch/R-manual/R-devel/library/datasets/html/mtcars.html"&gt;Motor Trend Cars&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;etc.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;But does anyone know similar datasets for networks analysis / graph theory? More concrete - I''m looking for &lt;strong&gt;Gold standard&lt;/strong&gt; datasets for comparing/evaluating/learning:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;centrality measures;&lt;/li&gt;
&lt;li&gt;network clustering algorithms.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I don''t need a huge list of publicly available networks/graphs, but a couple of actually must-know datasets.&lt;/p&gt;

&lt;p&gt;EDIT:&lt;/p&gt;

&lt;p&gt;It''s quite difficult to provide exact features for "gold standard dataset", but here are some thoughts. I think, real classic dataset should satisfy these criteria:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Multiple references in articles and textbooks;&lt;/li&gt;
&lt;li&gt;Inclusion in well-known network analysis software packages;&lt;/li&gt;
&lt;li&gt;Sufficient time of existence;&lt;/li&gt;
&lt;li&gt;Usage in a number of courses on graph analysis.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Concerning my field of interest, I also need labeled classes for vertices and/or precomputed (or predefined) "authority scores" (i.e. centrality estimates). After asking this question I continued searching, and here are some suitable examples:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://networkdata.ics.uci.edu/data.php?id=105"&gt;Zachary''s Karate Club&lt;/a&gt;: introduced in 1977, cited more than 1.5k times (according to Google Scholar), vertexes have attribute Faction (which can be used for clustering).&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.orgnet.com/Erdos.html"&gt;Erdos Collaboration Network&lt;/a&gt;: unfortunately, I haven''t find this network in form of data-file, but it''s rather famous, and if someone will enrich network with  mathematicians'' specialisations data, it also could be used for testing clustering algorithms.&lt;/li&gt;
&lt;/ul&gt;
', 'dataset  graphs', NULL, '''/a'':34C,45C,56C,237C,271C ''/c/titanic-gettingstarted'':41C ''/data.php?id=105'':232C ''/erdos.html'':267C ''/li'':35C,46C,57C,60C,92C,97C,160C,171C,177C,188C,261C,309C ''/ol'':98C ''/p'':22C,87C,120C,123C,151C,225C ''/r-manual/r-devel/library/datasets/html/mtcars.html'':52C ''/stable/auto_examples/datasets/plot_iris_dataset.html'':29C ''/strong'':83C ''/ul'':61C,189C,310C ''1.5'':244C ''1977'':240C ''accord'':247C ''actual'':115C ''algorithm'':96C,308C ''also'':197C,301C ''analysi'':2A,71C,168C,187C ''and/or'':203C ''anyon'':65C ''articl'':157C ''ask'':213C ''attribut'':253C ''author'':207C ''avail'':109C ''car'':55C ''central'':90C,210C ''cite'':241C ''class'':200C ''classic'':3A,11C,145C ''classification/regression'':16C ''club'':236C ''cluster'':95C,260C,307C ''collabor'':269C ''comparing/evaluating/learning'':86C ''concern'':191C ''concret'':75C ''continu'':217C ''could'':302C ''coupl'':113C ''cours'':184C ''criteria'':150C ''data'':32C,43C,283C,299C ''data-fil'':282C ''dataset'':4A,5B,12C,68C,84C,119C,136C,146C ''difficult'':128C ''edit'':122C ''enrich'':294C ''erdo'':268C ''estim'':211C ''etc'':59C ''exact'':131C ''exampl'':224C ''exist'':176C ''faction'':254C ''famous'':289C ''featur'':132C ''field'':193C ''file'':284C ''find'':276C ''flower'':31C ''form'':280C ''gold'':81C,134C ''googl'':249C ''graph'':6B,72C,186C ''haven'':274C ''href'':26C,38C,49C,229C,264C ''huge'':105C ''i.e'':209C ''inclus'':162C ''interest'':195C ''introduc'':238C ''iri'':30C ''k'':245C ''karat'':235C ''know'':66C,118C ''known'':166C ''label'':199C ''learn'':15C ''li'':24C,36C,47C,58C,89C,93C,153C,161C,172C,178C,227C,262C ''list'':106C ''look'':78C ''m'':77C ''machin'':14C ''mathematician'':297C ''measur'':91C ''motor'':53C ''multipl'':154C ''must'':117C ''must-know'':116C ''need'':103C,198C ''network'':1A,70C,94C,167C,270C,278C,295C ''networkdata.ics.uci.edu'':231C ''networkdata.ics.uci.edu/data.php?id=105'':230C ''networks/graphs'':110C ''number'':182C ''ol'':88C ''p'':7C,62C,99C,121C,124C,190C ''packag'':170C ''popular'':20C ''precomput'':204C ''predefin'':206C ''provid'':130C ''public'':108C ''question'':215C ''quit'':127C ''rather'':288C ''real'':144C ''refer'':155C ''satisfi'':148C ''scholar'':250C ''scikit-learn.org'':28C ''scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html'':27C ''score'':208C ''search'':218C ''set'':33C,44C ''sever'':10C ''similar'':67C ''softwar'':169C ''someon'':292C ''specialis'':298C ''standard'':82C,135C ''stat.ethz.ch'':51C ''stat.ethz.ch/r-manual/r-devel/library/datasets/html/mtcars.html'':50C ''strong'':80C ''suffici'':173C ''suitabl'':223C ''task'':17C ''test'':306C ''textbook'':159C ''theori'':73C ''think'':143C ''thought'':141C ''time'':174C,246C ''titan'':42C ''trend'':54C ''ul'':23C,152C,226C ''unfortun'':272C ''usag'':179C ''use'':258C,304C ''vertex'':251C ''vertic'':202C ''well'':165C ''well-known'':164C ''www.kaggle.com'':40C ''www.kaggle.com/c/titanic-gettingstarted'':39C ''www.orgnet.com'':266C ''www.orgnet.com/erdos.html'':265C ''zachari'':233C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (597, 2, '2014-06-26 13:45:16.573', 2, NULL, NULL, '&lt;p&gt;I am not working at Google, but I think it is some sort of recommendation system based on the words which millions of users searched before. So those people who search for "animals" often search for "wild animals" for example. Like in many online stores they recommend you to buy something in addition to the product you are looking for based on the previous purchases of other users.&lt;/p&gt;

&lt;p&gt;There are many approaches how to build such recommendation system using machine learning, no one knows for sure what google uses.&lt;/p&gt;
', NULL, NULL, '''/p'':70C,93C ''addit'':54C ''anim'':34C,39C ''approach'':75C ''base'':18C,62C ''build'':78C ''buy'':51C ''exampl'':41C ''googl'':7C,91C ''know'':87C ''learn'':84C ''like'':42C ''look'':60C ''machin'':83C ''mani'':44C,74C ''million'':23C ''often'':35C ''one'':86C ''onlin'':45C ''p'':1C,71C ''peopl'':30C ''previous'':65C ''product'':57C ''purchas'':66C ''recommend'':16C,48C,80C ''search'':26C,32C,36C ''someth'':52C ''sort'':14C ''store'':46C ''sure'':89C ''system'':17C,81C ''think'':10C ''use'':82C,92C ''user'':25C,69C ''wild'':38C ''word'':21C ''work'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (615, 2, '2014-06-27 13:22:22.577', 2, NULL, NULL, '&lt;p&gt;One standard reference written from social science perspective is &lt;a href="http://rads.stackoverflow.com/amzn/click/0803973748" rel="nofollow"&gt;J Scott Long''s Limited Dependent Variables&lt;/a&gt; book. It goes much deeper than say Tabachnik suggested in &lt;a href="http://datascience.stackexchange.com/a/591/1237"&gt;another answer&lt;/a&gt;: Tabachnik is a cookbook at best, with little to no explanations of the "why", and it seems like you would benefit from figuring this out in more detail that can be found in Long''s book. Ordinal regression should be covered in most introductory econometrics courses (Wooldridge''s &lt;a href="http://rads.stackoverflow.com/amzn/click/0262232588" rel="nofollow"&gt;Cross-Section and Panel Data&lt;/a&gt; is a great graduate-level book), as well as quantitative social science courses (sociology, psychology), although I would imagine that the latter will loop back to Long''s book.&lt;/p&gt;

&lt;p&gt;Given that your number of variables is wa-a-ay lower than the sample size, the R package you should be looking is probably &lt;a href="http://cran.r-project.org/web/packages/ordinal/" rel="nofollow"&gt;&lt;code&gt;ordinal&lt;/code&gt;&lt;/a&gt; rather than &lt;code&gt;glmnetcr&lt;/code&gt;. &lt;a href="http://datascience.stackexchange.com/a/469/1237"&gt;Another answer&lt;/a&gt; mentioned that you can find this functionality in a more mainstream &lt;code&gt;MASS&lt;/code&gt; package.&lt;/p&gt;
', NULL, NULL, '''/a'':25C,43C,105C,173C,186C ''/a/469/1237'':183C ''/a/591/1237'':40C ''/amzn/click/0262232588'':96C ''/amzn/click/0803973748'':15C ''/code'':172C,178C,200C ''/p'':136C,202C ''/web/packages/ordinal/'':167C ''although'':122C ''anoth'':41C,184C ''answer'':42C,185C ''ay'':148C ''back'':131C ''benefit'':64C ''best'':49C ''book'':26C,79C,112C,135C ''code'':170C,176C,198C ''cookbook'':47C ''cours'':89C,119C ''cover'':84C ''cran.r-project.org'':166C ''cran.r-project.org/web/packages/ordinal/'':165C ''cross'':100C ''cross-sect'':99C ''data'':104C ''datascience.stackexchange.com'':39C,182C ''datascience.stackexchange.com/a/469/1237'':181C ''datascience.stackexchange.com/a/591/1237'':38C ''deeper'':30C ''depend'':23C ''detail'':71C ''econometr'':88C ''explan'':54C ''figur'':66C ''find'':191C ''found'':75C ''function'':193C ''given'':138C ''glmnetcr'':177C ''goe'':28C ''graduat'':110C ''graduate-level'':109C ''great'':108C ''href'':12C,37C,93C,164C,180C ''imagin'':125C ''introductori'':87C ''j'':18C ''latter'':128C ''level'':111C ''like'':61C ''limit'':22C ''littl'':51C ''long'':20C,77C,133C ''look'':160C ''loop'':130C ''lower'':149C ''mainstream'':197C ''mass'':199C ''mention'':187C ''much'':29C ''nofollow'':17C,98C,169C ''number'':141C ''one'':2C ''ordin'':80C,171C ''p'':1C,137C ''packag'':156C,201C ''panel'':103C ''perspect'':9C ''probabl'':162C ''psycholog'':121C ''quantit'':116C ''r'':155C ''rads.stackoverflow.com'':14C,95C ''rads.stackoverflow.com/amzn/click/0262232588'':94C ''rads.stackoverflow.com/amzn/click/0803973748'':13C ''rather'':174C ''refer'':4C ''regress'':81C ''rel'':16C,97C,168C ''sampl'':152C ''say'':32C ''scienc'':8C,118C ''scott'':19C ''section'':101C ''seem'':60C ''size'':153C ''social'':7C,117C ''sociolog'':120C ''standard'':3C ''suggest'':34C ''tabachnik'':33C,44C ''variabl'':24C,143C ''wa'':146C ''wa-a-ay'':145C ''well'':114C ''wooldridg'':90C ''would'':63C,124C ''written'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (598, 2, '2014-06-26 14:17:44.077', 2, NULL, NULL, '&lt;p&gt;There exist many possibilities for populating empty gaps on data.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Most repeated value&lt;/strong&gt;: Fill the gaps with the most common value.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Create a distribution&lt;/strong&gt;: Make the histogram and drop values according to that distribution.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Create a new label&lt;/strong&gt;: Since you do not have information, do not assume any value and create another label/category to indicate that value is empty.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Create a classifier&lt;/strong&gt;: Make a relation among the variable with empty gaps and the rest of the data and create a simple classifier. With this, populate the rest of the data.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;There exist many others, but these are the most common strategies. My suggestion is not to populate and to keep unknown what is unknown.&lt;/p&gt;
', NULL, NULL, '''/li'':28C,45C,74C,109C ''/p'':12C,136C ''/strong'':19C,34C,52C,80C ''/ul'':110C ''accord'':41C ''among'':84C ''anoth'':66C ''assum'':61C ''classifi'':79C,100C ''common'':26C,121C ''creat'':31C,48C,65C,77C,97C ''data'':11C,95C,108C ''distribut'':33C,44C ''drop'':39C ''empti'':8C,73C,88C ''exist'':3C,113C ''fill'':20C ''gap'':9C,22C,89C ''histogram'':37C ''indic'':69C ''inform'':58C ''keep'':131C ''label'':51C ''label/category'':67C ''li'':14C,29C,46C,75C ''make'':35C,81C ''mani'':4C,114C ''new'':50C ''other'':115C ''p'':1C,111C ''popul'':7C,103C,128C ''possibl'':5C ''relat'':83C ''repeat'':17C ''rest'':92C,105C ''simpl'':99C ''sinc'':53C ''strategi'':122C ''strong'':15C,30C,47C,76C ''suggest'':124C ''ul'':13C ''unknown'':132C,135C ''valu'':18C,27C,40C,63C,71C ''variabl'':86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (599, 2, '2014-06-26 14:21:10.513', 1, NULL, NULL, '&lt;p&gt;The only thing I know about is benchmark data for Graph Databases, such as Neo4j.&lt;/p&gt;

&lt;p&gt;You may find links similar to this one:
&lt;a href="http://istc-bigdata.org/index.php/benchmarking-graph-databases/" rel="nofollow"&gt;http://istc-bigdata.org/index.php/benchmarking-graph-databases/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;where you can find data to test network analysis and graph theory.&lt;/p&gt;

&lt;p&gt;Furthermore, you could play with the API of Twitter/Facebook to collect your own data. This is also a suggestion in case you do not find the data you are looking for.&lt;/p&gt;
', NULL, NULL, '''/index.php/benchmarking-graph-databases/'':31C ''/index.php/benchmarking-graph-databases/&lt;/a&gt;&lt;/p&gt;'':36C ''/p'':17C,50C,83C ''also'':68C ''analysi'':46C ''api'':58C ''benchmark'':9C ''case'':72C ''collect'':62C ''could'':54C ''data'':10C,42C,65C,78C ''databas'':13C ''find'':21C,41C,76C ''furthermor'':52C ''graph'':12C,48C ''href'':28C ''istc-bigdata.org'':30C,35C ''istc-bigdata.org/index.php/benchmarking-graph-databases/'':29C ''istc-bigdata.org/index.php/benchmarking-graph-databases/&lt;/a&gt;&lt;/p&gt;'':34C ''know'':6C ''link'':22C ''look'':81C ''may'':20C ''neo4j'':16C ''network'':45C ''nofollow'':33C ''one'':26C ''p'':1C,18C,37C,51C ''play'':55C ''rel'':32C ''similar'':23C ''suggest'':70C ''test'':44C ''theori'':49C ''thing'':4C ''twitter/facebook'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (600, 1, '2014-06-26 14:39:06.41', 7, 276, 'How do you create an optimized walk list given longitude and latitude coordinates?', '&lt;p&gt;I am working on a political campaign where dozens of volunteers will be conducting door-knocking promotions over the next few weeks. Given a list with names, addresses and long/lat coordinates, what algorithms can be used to create an optimized walk list.&lt;/p&gt;
', 'algorithms', NULL, '''/p'':59C ''address'':44C ''algorithm'':14B,49C ''campaign'':22C ''conduct'':29C ''coordin'':13A,47C ''creat'':4A,54C ''door'':31C ''door-knock'':30C ''dozen'':24C ''given'':9A,39C ''knock'':32C ''latitud'':12A ''list'':8A,41C,58C ''long/lat'':46C ''longitud'':10A ''name'':43C ''next'':36C ''optim'':6A,56C ''p'':15C ''polit'':21C ''promot'':33C ''use'':52C ''volunt'':26C ''walk'':7A,57C ''week'':38C ''work'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (606, 2, '2014-06-26 20:49:38.47', 3, NULL, NULL, '&lt;p&gt;Folks here stated great steps, but I think there are great information at the following link &lt;a href="http://simplystatistics.org/2014/06/13/what-i-do-when-i-get-a-new-data-set-as-told-through-tweets/" rel="nofollow"&gt;what I do when I get a new data set as told through tweets&lt;/a&gt;,
It sums up the steps the folks tweeted answering the great @hmason question "Data people: What is the very first thing you do when you get your hands on a new data set?"&lt;/p&gt;

&lt;p&gt;Hope it will be useful. &lt;/p&gt;
', NULL, NULL, '''/2014/06/13/what-i-do-when-i-get-a-new-data-set-as-told-through-tweets/'':22C ''/a'':39C ''/p'':73C,80C ''answer'':48C ''data'':33C,53C,71C ''first'':59C ''folk'':2C,46C ''follow'':16C ''get'':30C,65C ''great'':5C,12C,50C ''hand'':67C ''hmason'':51C ''hope'':75C ''href'':19C ''inform'':13C ''link'':17C ''new'':32C,70C ''nofollow'':24C ''p'':1C,74C ''peopl'':54C ''question'':52C ''rel'':23C ''set'':34C,72C ''simplystatistics.org'':21C ''simplystatistics.org/2014/06/13/what-i-do-when-i-get-a-new-data-set-as-told-through-tweets/'':20C ''state'':4C ''step'':6C,44C ''sum'':41C ''thing'':60C ''think'':9C ''told'':36C ''tweet'':38C,47C ''use'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (621, 2, '2014-06-28 01:07:27.263', 3, NULL, NULL, '&lt;p&gt;There is a bunch of datasets made free by UC Irvine &lt;a href="https://archive.ics.uci.edu/ml/datasets.html" rel="nofollow"&gt;to play with here&lt;/a&gt;. Among those datasets, &lt;a href="https://archive.ics.uci.edu/ml/datasets.html?format=&amp;amp;task=&amp;amp;att=&amp;amp;area=&amp;amp;numAtt=&amp;amp;numIns=&amp;amp;type=text&amp;amp;sort=nameUp&amp;amp;view=table" rel="nofollow"&gt;there are a few dozen textual datasets&lt;/a&gt; that might help you guys with your task.&lt;/p&gt;

&lt;p&gt;Those are kind of generic datasets, so depending on your purpose they should not be used as the only data to train your models, or else your model -- while it might work -- will not produce quality results.&lt;/p&gt;
', NULL, NULL, '''/a'':24C,42C ''/ml/datasets.html'':17C ''/ml/datasets.html?format=&amp;amp;task=&amp;amp;att=&amp;amp;area=&amp;amp;numatt=&amp;amp;numins=&amp;amp;type=text&amp;amp;sort=nameup&amp;amp;view=table'':32C ''/p'':51C,90C ''among'':25C ''archive.ics.uci.edu'':16C,31C ''archive.ics.uci.edu/ml/datasets.html'':15C ''archive.ics.uci.edu/ml/datasets.html?format=&amp;amp;task=&amp;amp;att=&amp;amp;area=&amp;amp;numatt=&amp;amp;numins=&amp;amp;type=text&amp;amp;sort=nameup&amp;amp;view=table'':30C ''bunch'':5C ''data'':72C ''dataset'':7C,27C,41C,58C ''depend'':60C ''dozen'':39C ''els'':78C ''free'':9C ''generic'':57C ''guy'':47C ''help'':45C ''href'':14C,29C ''irvin'':12C ''kind'':55C ''made'':8C ''might'':44C,83C ''model'':76C,80C ''nofollow'':19C,34C ''p'':1C,52C ''play'':21C ''produc'':87C ''purpos'':63C ''qualiti'':88C ''rel'':18C,33C ''result'':89C ''task'':50C ''textual'':40C ''train'':74C ''uc'':11C ''use'':68C ''work'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (622, 2, '2014-06-28 14:34:17.487', 1, NULL, NULL, '&lt;p&gt;I''m not sure if your question classifies as a clustering problem. In clustering you are trying to discover clusters of similar examples using unlabelled data. Here, it seems you wish to enumerate existing "clusters" of nearby nodes. &lt;/p&gt;

&lt;p&gt;To be honest, I have no idea of such a function in R. But, as far as the algorithm is concerned, I believe what you are looking for is &lt;a href="http://en.wikipedia.org/wiki/Connected-component_labeling" rel="nofollow"&gt;Connected-Component Labeling&lt;/a&gt;. Kind of a bucket fill, for matrices.&lt;/p&gt;

&lt;p&gt;The wikipedia article is linked above. One of the algorithms presented there, termed as single-pass algorithm, is as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;One-Pass(Image)
        [M, N]=size(Image);
        Connected = zeros(M,N);
        Mark = Value;
        Difference = Increment;
        Offsets = [-1; M; 1; -M];
        Index = [];
        No_of_Objects = 0; 

   for i: 1:M :
       for j: 1:N:
            if(Image(i,j)==1)            
                 No_of_Objects = No_of_Objects +1;            
                 Index = [((j-1)*M + i)];           
                 Connected(Index)=Mark;            
                 while ~isempty(Index)                
                      Image(Index)=0;                
                      Neighbors = bsxfun(@plus, Index, Offsets'');
                      Neighbors = unique(Neighbors(:));                
                      Index = Neighbors(find(Image(Neighbors)));                                
                      Connected(Index)=Mark;
                 end            
                 Mark = Mark + Difference;
            end
      end
  end
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I guess it''d be easy to roll your own using the above.&lt;/p&gt;
', NULL, NULL, '''+1'':161C ''-1'':133C,164C ''/a'':82C ''/code'':199C ''/p'':40C,90C,113C,215C ''/pre'':200C ''/wiki/connected-component_labeling'':75C ''0'':141C,175C ''1'':135C,144C,148C,154C ''algorithm'':60C,101C,109C ''articl'':94C ''believ'':64C ''bsxfun'':177C ''bucket'':86C ''classifi'':9C ''cluster'':12C,15C,21C,36C ''code'':115C ''compon'':80C ''concern'':62C ''connect'':79C,124C,167C,189C ''connected-compon'':78C ''d'':205C ''data'':27C ''differ'':130C,195C ''discov'':20C ''easi'':207C ''en.wikipedia.org'':74C ''en.wikipedia.org/wiki/connected-component_labeling'':73C ''end'':192C,196C,197C,198C ''enumer'':34C ''exampl'':24C ''exist'':35C ''far'':57C ''fill'':87C ''find'':186C ''follow'':112C ''function'':52C ''guess'':203C ''honest'':44C ''href'':72C ''idea'':48C ''imag'':119C,123C,151C,173C,187C ''increment'':131C ''index'':137C,162C,168C,172C,174C,179C,184C,190C ''isempti'':171C ''j'':147C,153C,163C ''kind'':83C ''label'':81C ''link'':96C ''look'':68C ''m'':3C,120C,126C,134C,136C,145C,165C ''mark'':128C,169C,191C,193C,194C ''matric'':89C ''n'':121C,127C,149C ''nearbi'':38C ''neighbor'':176C,181C,183C,185C,188C ''node'':39C ''nofollow'':77C ''object'':140C,157C,160C ''offset'':132C,180C ''one'':98C,117C ''one-pass'':116C ''p'':1C,41C,91C,201C ''pass'':108C,118C ''plus'':178C ''pre'':114C ''present'':102C ''problem'':13C ''question'':8C ''r'':54C ''rel'':76C ''roll'':209C ''seem'':30C ''similar'':23C ''singl'':107C ''single-pass'':106C ''size'':122C ''sure'':5C ''term'':104C ''tri'':18C ''uniqu'':182C ''unlabel'':26C ''use'':25C,212C ''valu'':129C ''wikipedia'':93C ''wish'':32C ''zero'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8969, 4, '2015-11-22 05:40:44.037', 0, NULL, NULL, 'A measure of the degree of linear association among a pair of variables.', NULL, NULL, '''among'':9C ''associ'':8C ''degre'':5C ''linear'':7C ''measur'':2C ''pair'':11C ''variabl'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (602, 2, '2014-06-26 16:19:46.48', 3, NULL, NULL, '&lt;p&gt;I thought of expanding a bit on the answer by Stanpol. While recommendation system is one approach of suggesting related queries, one more standard information retrieval based approach is the query expansion technique.&lt;/p&gt;

&lt;p&gt;Generally speaking, query expansion involves selecting additional terms from the top ranked documents retrieved in response to an initial query. Terms are typically selected by a combination of a term scoring function such as tf-idf and a co-occurrence based measure.&lt;/p&gt;

&lt;p&gt;For example, in response to a query term "animal", a term selection function may choose the term "zoo", because&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;"zoo" may be a dominating term (high tf-idf) in the top (say 10) documents retrieved in response to the query "animal"&lt;/li&gt;
&lt;li&gt;"zoo" may co-occur frequently (in close proximity) with the original query term "animal" in these documents&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':128C,148C ''/p'':35C,81C,102C ''/ul'':149C ''10'':119C ''addit'':43C ''anim'':91C,127C,144C ''answer'':10C ''approach'':18C,29C ''base'':28C,79C ''bit'':7C ''choos'':97C ''close'':137C ''co'':77C,133C ''co-occur'':132C ''co-occurr'':76C ''combin'':63C ''document'':49C,120C,147C ''domin'':109C ''exampl'':84C ''expand'':5C ''expans'':33C,40C ''frequent'':135C ''function'':68C,95C ''general'':37C ''high'':111C ''idf'':73C,114C ''inform'':26C ''initi'':55C ''involv'':41C ''li'':104C,129C ''may'':96C,106C,131C ''measur'':80C ''occur'':134C ''occurr'':78C ''one'':17C,23C ''origin'':141C ''p'':1C,36C,82C ''proxim'':138C ''queri'':22C,32C,39C,56C,89C,126C,142C ''rank'':48C ''recommend'':14C ''relat'':21C ''respons'':52C,86C,123C ''retriev'':27C,50C,121C ''say'':118C ''score'':67C ''select'':42C,60C,94C ''speak'':38C ''standard'':25C ''stanpol'':12C ''suggest'':20C ''system'':15C ''techniqu'':34C ''term'':44C,57C,66C,90C,93C,99C,110C,143C ''tf'':72C,113C ''tf-idf'':71C,112C ''thought'':3C ''top'':47C,117C ''typic'':59C ''ul'':103C ''zoo'':100C,105C,130C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (641, 1, '2014-06-30 21:02:05.053', 9, 1225, 'Dataset for Named Entity Recognition on Informal Text', '&lt;p&gt;I''m currently searching for labeled datasets to use to train a model to extract named entities from informal text (think something similar to tweets). Because capitalization and grammar are often lacking in the documents in my dataset, I''m looking for out of domain data that''s a bit more "informal" than the news articles and journal entries that many of today''s state of the art named entity recognition systems are trained on.  Any recommendations?  So far I''ve only been able to locate 50k tokens from twitter published here: &lt;a href="https://github.com/aritter/twitter_nlp/blob/master/data/annotated/ner.txt"&gt;https://github.com/aritter/twitter_nlp/blob/master/data/annotated/ner.txt&lt;/a&gt;&lt;/p&gt;
', 'dataset  nlp', NULL, '''/aritter/twitter_nlp/blob/master/data/annotated/ner.txt'':108C ''/aritter/twitter_nlp/blob/master/data/annotated/ner.txt&lt;/a&gt;&lt;/p&gt;'':111C ''50k'':98C ''abl'':95C ''art'':79C ''articl'':67C ''bit'':61C ''capit'':38C ''current'':14C ''data'':57C ''dataset'':1A,9B,18C,49C ''document'':46C ''domain'':56C ''entiti'':4A,28C,81C ''entri'':70C ''extract'':26C ''far'':90C ''github.com'':107C,110C ''github.com/aritter/twitter_nlp/blob/master/data/annotated/ner.txt'':106C ''github.com/aritter/twitter_nlp/blob/master/data/annotated/ner.txt&lt;/a&gt;&lt;/p&gt;'':109C ''grammar'':40C ''href'':105C ''inform'':7A,30C,63C ''journal'':69C ''label'':17C ''lack'':43C ''locat'':97C ''look'':52C ''m'':13C,51C ''mani'':72C ''model'':24C ''name'':3A,27C,80C ''news'':66C ''nlp'':10B ''often'':42C ''p'':11C ''publish'':102C ''recognit'':5A,82C ''recommend'':88C ''search'':15C ''similar'':34C ''someth'':33C ''state'':76C ''system'':83C ''text'':8A,31C ''think'':32C ''today'':74C ''token'':99C ''train'':22C,85C ''tweet'':36C ''twitter'':101C ''use'':20C ''ve'':92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (603, 2, '2014-06-26 18:01:06.443', 4, NULL, NULL, '&lt;p&gt;People see something closely related to the &lt;a href="http://en.wikipedia.org/wiki/Travelling_salesman_problem" rel="nofollow"&gt;Travelling Salesman Problem&lt;/a&gt; and think that it can''t be solved. &lt;/p&gt;

&lt;p&gt;A good deal of work has been done on this topic and not all of it indicates that a solution is not available.  Depending on the parameters and the desired solution, you may be able to find something that will work.&lt;/p&gt;

&lt;p&gt;You may want to give a look at the &lt;a href="http://openopt.org/TSP" rel="nofollow"&gt;OpenOpt&lt;/a&gt; python library. &lt;/p&gt;

&lt;p&gt;Another resource to look at would  be the &lt;a href="http://tspsg.info/" rel="nofollow"&gt;TSP Solver and Generator&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;If you are using R, there is a &lt;a href="http://tsp.r-forge.r-project.org/" rel="nofollow"&gt;TSP package available&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Actually implementing a solution to your problem is a little too much to cover here, but this should provide a good starting point.  Within these packages and at the documentation within the links that I provided for you, you will find that there are a fairly wide variety of algorithmic strategies available.  You have a small geographic region and a small set of "salespeople", so the computational power needed to calculate a strategy within a reasonable time frame should be available on your desktop.  &lt;/p&gt;

&lt;p&gt;In practical terms, you don''t need to find the absolutely most optimal strategy.  You just need a very good one.  Pick a TSP package that looks the least overwhelming and give it a go.&lt;/p&gt;
', NULL, NULL, '''/a'':19C,90C,112C,131C ''/p'':28C,71C,93C,113C,132C,218C,255C ''/tsp'':86C ''/wiki/travelling_salesman_problem'':13C ''abl'':64C ''absolut'':230C ''actual'':134C ''algorithm'':183C ''anoth'':95C ''avail'':52C,130C,185C,214C ''calcul'':204C ''close'':5C ''comput'':200C ''cover'':147C ''deal'':32C ''depend'':53C ''desir'':59C ''desktop'':217C ''document'':163C ''done'':37C ''en.wikipedia.org'':12C ''en.wikipedia.org/wiki/travelling_salesman_problem'':11C ''fair'':179C ''find'':66C,174C,228C ''frame'':211C ''generat'':111C ''geograph'':190C ''give'':77C,251C ''go'':254C ''good'':31C,154C,239C ''href'':10C,83C,104C,124C ''implement'':135C ''indic'':46C ''least'':248C ''librari'':92C ''link'':166C ''littl'':143C ''look'':79C,98C,246C ''may'':62C,74C ''much'':145C ''need'':202C,226C,236C ''nofollow'':15C,88C,107C,127C ''one'':240C ''openopt'':89C ''openopt.org'':85C ''openopt.org/tsp'':84C ''optim'':232C ''overwhelm'':249C ''p'':1C,29C,72C,94C,114C,133C,219C ''packag'':129C,159C,244C ''paramet'':56C ''peopl'':2C ''pick'':241C ''point'':156C ''power'':201C ''practic'':221C ''problem'':18C,140C ''provid'':152C,169C ''python'':91C ''r'':119C ''reason'':209C ''region'':191C ''rel'':14C,87C,106C,126C ''relat'':6C ''resourc'':96C ''salesman'':17C ''salespeopl'':197C ''see'':3C ''set'':195C ''small'':189C,194C ''solut'':49C,60C,137C ''solv'':27C ''solver'':109C ''someth'':4C,67C ''start'':155C ''strategi'':184C,206C,233C ''term'':222C ''think'':21C ''time'':210C ''topic'':40C ''travel'':16C ''tsp'':108C,128C,243C ''tsp.r-forge.r-project.org'':125C ''tspsg.info'':105C ''use'':118C ''varieti'':181C ''want'':75C ''wide'':180C ''within'':157C,164C,207C ''work'':34C,70C ''would'':100C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (604, 2, '2014-06-26 19:16:31.47', 2, NULL, NULL, '&lt;p&gt;Google isn''t going to give away their proprietary work, but we can speculate.&lt;/p&gt;

&lt;p&gt;Here''s what I can gather from my limited usage:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;The recommendations do not seem to be user, geography, or history specific.&lt;/li&gt;
&lt;li&gt;There is never an empty recommendation (one that returns no results)&lt;/li&gt;
&lt;li&gt;There is not always a recommendation (some searches just return images)&lt;/li&gt;
&lt;li&gt;The recommendations are not always the same (consecutive searches sometimes return different recommendations)&lt;/li&gt;
&lt;li&gt;Result ordering shifts regularly (search for a specific image and it won''t always be in the same place)&lt;/li&gt;
&lt;li&gt;Very popular searches seem to be pre-calculated and more static than unpopular searches.&lt;/li&gt;
&lt;li&gt;Recommendations are not always one additional word, and recommendations do not always include the base query.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;It seems to me that they do this based on the history of the general end user population, they rotate recommendations when there are many popular ones, and they do some additional processing to determine that the result set is of a reasonable size.&lt;/p&gt;

&lt;p&gt;I would postulate that it works as follows:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Use consecutive search strings from users (short-to-long-tail searches) as training data for a machine-learning algorithm.&lt;/li&gt;
&lt;li&gt;Run searches that occur &gt; N amount of times a week against that recommendation algorithm.&lt;/li&gt;
&lt;li&gt;Validate and clean results.&lt;/li&gt;
&lt;li&gt;Push them out to the general population in rotation/A-B testing.&lt;/li&gt;
&lt;li&gt;Track click-throughs.&lt;/li&gt;
&lt;li&gt;Refine results over time.&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':43C,56C,69C,84C,105C,122C,140C,221C,237C,243C,255C,261C,267C ''/ol'':141C,268C ''/p'':16C,28C,187C,197C ''addit'':129C,174C ''algorithm'':220C,236C ''alway'':61C,75C,99C,127C,135C ''amount'':228C ''away'':8C ''base'':138C,151C ''calcul'':115C ''clean'':241C ''click'':259C ''click-through'':258C ''consecut'':78C,201C ''data'':214C ''determin'':177C ''differ'':82C ''empti'':49C ''end'':158C ''follow'':196C ''gather'':23C ''general'':157C,250C ''geographi'':39C ''give'':7C ''go'':5C ''googl'':2C ''histori'':41C,154C ''imag'':68C,94C ''includ'':136C ''isn'':3C ''learn'':219C ''li'':30C,44C,57C,70C,85C,106C,123C,199C,222C,238C,244C,256C,262C ''limit'':26C ''long'':209C ''machin'':218C ''machine-learn'':217C ''mani'':167C ''n'':227C ''never'':47C ''occur'':226C ''ol'':29C,198C ''one'':51C,128C,169C ''order'':87C ''p'':1C,17C,142C,188C ''place'':104C ''popul'':160C,251C ''popular'':108C,168C ''postul'':191C ''pre'':114C ''pre-calcul'':113C ''process'':175C ''proprietari'':10C ''push'':245C ''queri'':139C ''reason'':185C ''recommend'':32C,50C,63C,72C,83C,124C,132C,163C,235C ''refin'':263C ''regular'':89C ''result'':55C,86C,180C,242C,264C ''return'':53C,67C,81C ''rotat'':162C ''rotation/a-b'':253C ''run'':223C ''search'':65C,79C,90C,109C,121C,202C,211C,224C ''seem'':35C,110C,144C ''set'':181C ''shift'':88C ''short'':207C ''short-to-long-tail'':206C ''size'':186C ''sometim'':80C ''specif'':42C,93C ''specul'':15C ''static'':118C ''string'':203C ''tail'':210C ''test'':254C ''through'':260C ''time'':230C,266C ''track'':257C ''train'':213C ''unpopular'':120C ''usag'':27C ''use'':200C ''user'':38C,159C,205C ''valid'':239C ''week'':232C ''won'':97C ''word'':130C ''work'':11C,194C ''would'':190C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (654, 2, '2014-07-02 14:14:22.593', 7, NULL, NULL, '&lt;p&gt;What you are asking about is &lt;a href="http://en.wikipedia.org/wiki/Instance-based_learning"&gt;Instance-Based Learning&lt;/a&gt;. k-Nearest Neighbors (kNN) appears to be the most popular of these methods and is applicable to a wide variety of problem domains. Another general type of instance-based learning is &lt;a href="http://en.wikipedia.org/wiki/Analogical_modeling"&gt;Analogical Modeling&lt;/a&gt;, which uses instances as exemplars for comparison with new data.&lt;/p&gt;

&lt;p&gt;You referred to kNN as an application that uses training but that is not correct (the Wikipedia entry you linked is somewhat misleading in that regard). Yes, there are "training examples" (labeled instances) but the classifier doesn''t learn/train from these data. Rather, they are only used whenever you actually want to classify a new instance, which is why it is considered a "lazy" learner.&lt;/p&gt;

&lt;p&gt;Note that the Nearest Template Prediction method you mention effectively is a form of kNN with &lt;code&gt;k=1&lt;/code&gt; and cosine distance as the distance measure.&lt;/p&gt;
', NULL, NULL, '''/a'':17C,58C ''/code'':157C ''/p'':69C,136C,165C ''/wiki/analogical_modeling'':55C ''/wiki/instance-based_learning'':12C ''1'':156C ''actual'':120C ''analog'':56C ''anoth'':42C ''appear'':23C ''applic'':34C,77C ''ask'':5C ''base'':15C,48C ''classifi'':106C,123C ''code'':154C ''comparison'':65C ''consid'':132C ''correct'':85C ''cosin'':159C ''data'':68C,112C ''distanc'':160C,163C ''doesn'':107C ''domain'':41C ''effect'':147C ''en.wikipedia.org'':11C,54C ''en.wikipedia.org/wiki/analogical_modeling'':53C ''en.wikipedia.org/wiki/instance-based_learning'':10C ''entri'':88C ''exampl'':101C ''exemplar'':63C ''form'':150C ''general'':43C ''href'':9C,52C ''instanc'':14C,47C,61C,103C,126C ''instance-bas'':13C,46C ''k'':19C,155C ''k-nearest'':18C ''knn'':22C,74C,152C ''label'':102C ''lazi'':134C ''learn'':16C,49C ''learn/train'':109C ''learner'':135C ''link'':90C ''measur'':164C ''mention'':146C ''method'':31C,144C ''mislead'':93C ''model'':57C ''nearest'':20C,141C ''neighbor'':21C ''new'':67C,125C ''note'':138C ''p'':1C,70C,137C ''popular'':28C ''predict'':143C ''problem'':40C ''rather'':113C ''refer'':72C ''regard'':96C ''somewhat'':92C ''templat'':142C ''train'':80C,100C ''type'':44C ''use'':60C,79C,117C ''varieti'':38C ''want'':121C ''whenev'':118C ''wide'':37C ''wikipedia'':87C ''yes'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (605, 1, '2014-06-26 19:23:46.043', 6, 283, 'How to normalize results of Singular Value Decomposition (SVD) between 0 and 1?', '&lt;p&gt;I''m building a recommender system and using SVD as one of the preprocessing techniques.
However, I want to normalize all my preprocessed data between 0 and 1 because all of my similarity measures (cosine, pearson, euclidean) depend on that assumption.&lt;/p&gt;

&lt;p&gt;After I take the SVD (A = USV^T), is there a standard way to normalize the matrix ''A'' between 0 and 1? Thanks!&lt;/p&gt;

&lt;p&gt;Edit: I want all of my similarity measurements to give results between 0 and 1 and my normalized euclidean distance in particular fails if the input matrix does not have values between 0 and 1.&lt;/p&gt;
', 'machine-learning  statistics  recommendation  feature-selection', NULL, '''/p'':64C,89C,126C ''0'':11A,48C,85C,103C,123C ''1'':13A,50C,87C,105C,125C ''assumpt'':63C ''build'':25C ''cosin'':57C ''data'':46C ''decomposit'':8A ''depend'':60C ''distanc'':110C ''edit'':91C ''euclidean'':59C,109C ''fail'':113C ''featur'':20B ''feature-select'':19B ''give'':100C ''howev'':38C ''input'':116C ''learn'':16B ''m'':24C ''machin'':15B ''machine-learn'':14B ''matrix'':82C,117C ''measur'':56C,98C ''normal'':3A,42C,80C,108C ''one'':33C ''p'':22C,65C,90C ''particular'':112C ''pearson'':58C ''preprocess'':36C,45C ''recommend'':18B,27C ''result'':4A,101C ''select'':21B ''similar'':55C,97C ''singular'':6A ''standard'':77C ''statist'':17B ''svd'':9A,31C,70C ''system'':28C ''take'':68C ''techniqu'':37C ''thank'':88C ''use'':30C ''usv'':72C ''valu'':7A,121C ''want'':40C,93C ''way'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (645, 2, '2014-07-01 17:01:12.263', 3, NULL, NULL, '&lt;p&gt;You need to look at the confidence interval of the statistic.  This helps measure how much uncertainty in the statistic, which is largely a function of sample size.&lt;/p&gt;
', NULL, NULL, '''/p'':30C ''confid'':8C ''function'':26C ''help'':14C ''interv'':9C ''larg'':24C ''look'':5C ''measur'':15C ''much'':17C ''need'':3C ''p'':1C ''sampl'':28C ''size'':29C ''statist'':12C,21C ''uncertainti'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (607, 1, '2014-06-26 22:17:52.57', 5, 277, 'Do you need a virtual machine as an instrument for your data science practice?', '&lt;p&gt;I am brand new to the field of data science, want to break into it, and there are so many tools out there.  These VMs have alot of software on them, but I haven''t been able to find any side-by-side comparison.  &lt;/p&gt;

&lt;p&gt;Here''s a start from my research, but if someone could tell me that one is objectively more rich-featured, with a larger community of support, and useful to get started then that would help greatly:&lt;/p&gt;

&lt;p&gt;datasciencetoolKIT.org -&gt; vm is on vagrant cloud (4 GB) and seems to be more "hip" with R, iPython notebook, and other useful command-line tools (html-&gt;txt, json-&gt;xml, etc). There is a book being released in August with detail.&lt;/p&gt;

&lt;p&gt;datasciencetoolBOX.org -&gt; vm is a vagrant box (24 GB) downloadable from their website. There seems to be more features here, and more literature.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (651, 2, '2014-07-02 08:50:55.98', 2, NULL, NULL, '&lt;p&gt;The number of data in the class is sometimes referred to as the &lt;code&gt;support&lt;/code&gt; of the classifier. It tells how much you can trust your result, like a p-value would allow you to trust or distrust some test.&lt;/p&gt;

&lt;p&gt;One approach you can use is to compute several classifier performance measures, not only precision and recall, but also true positive rate, false positive rate, specificity, sensitivity, positive likelihood, negative likelihood, etc. and see whether they are consistent with one another. If one of the measure maxes out (100%) and the other do not, it is often, in my experience, indicative of something went wrong (e.g. poor support, trivial classifier, biased classifier, etc.). See &lt;a href="http://www.damienfrancois.be/blog/files/modelperfcheatsheet.pdf" rel="nofollow"&gt;this&lt;/a&gt; for a list of classifier performance measures.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (627, 2, '2014-06-29 22:44:02.56', 8, NULL, NULL, '&lt;p&gt;Roughly speaking, over-fitting typically occurs when the ratio&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/AVVpB.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;is too high.&lt;/p&gt;

&lt;p&gt;Think of over-fitting as a situation where your model learn the training data by heart instead of learning the big pictures which prevent it from being able to generalized to the test data: this happens when the model is too complex with respect to the size of the training data, that is to say when the size of the training data is to small in comparison with the model complexity.&lt;/p&gt;

&lt;p&gt;Examples: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;if your data is in two dimensions, you have 10000 points in the training set and the model is a line, you are likely to &lt;em&gt;under&lt;/em&gt;-fit.&lt;/li&gt;
&lt;li&gt;if your data is in two dimensions, you have 10 points in the training set and the model is 100-degree polynomial, you are likely to &lt;em&gt;over&lt;/em&gt;-fit.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/HH23h.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;From a theoretical standpoint, the amount of data you need to properly train your model is a crucial yet far-to-be-answered question in machine learning. One such approach to answer this question is the &lt;a href="http://math.stackexchange.com/a/656167/24265"&gt;VC dimension&lt;/a&gt;. Another is the &lt;a href="https://www.quora.com/Machine-Learning/What-is-an-intuitive-explanation-for-bias-variance-tradeoff/answer/Franck-Dernoncourt?share=1"&gt;bias-variance tradeoff&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;From an empirical standpoint, people typically plot the training error and the test error on the same plot and make sure that they don''t reduce the training error at the expense of the test error:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/I7LiT.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;I would advise to watch &lt;a href="https://www.coursera.org/course/ml"&gt;Coursera'' Machine Learning course&lt;/a&gt;, section "10: Advice for applying Machine Learning".&lt;/p&gt;

&lt;p&gt;(PS: please go &lt;a href="http://meta.datascience.stackexchange.com/q/6/843"&gt;here&lt;/a&gt; to ask for TeX support on this SE.) &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (608, 1, '2014-06-26 22:58:32.38', 5, 79, 'Any differences in regularisation in MLP between batch and individual updates?', '&lt;p&gt;I have just learned about regularisation as an approach to control over-fitting, and I would like to incorporate the idea into a simple implementation of backpropagation and &lt;a href="http://en.wikipedia.org/wiki/Multilayer_perceptron" rel="nofollow"&gt;Multilayer perceptron&lt;/a&gt; (MLP) that I put together.&lt;/p&gt;

&lt;p&gt;Currently to avoid over-fitting, I cross-validate and keep the network with best score so far on the validation set. This works OK, but adding regularisation would benefit me in that correct choice of the regularisation algorithm and parameter would make my network converge on a non-overfit model more systematically.&lt;/p&gt;

&lt;p&gt;The formula I have for the update term (from Coursera ML course) is stated as a batch update e.g. for each weight, after summing all the applicable deltas for the whole training set from error propagation, an adjustment of &lt;code&gt;lambda * current_weight&lt;/code&gt; is added as well before the combined delta is subtracted at the end of the batch, where &lt;code&gt;lambda&lt;/code&gt; is the regularisation parameter.&lt;/p&gt;

&lt;p&gt;My implementation of backpropagation uses per-item weight updates. I am concerned that I cannot just copy the batch approach, although it looks OK intuitively to me. &lt;em&gt;Does a smaller regularisation term per item work just as well?&lt;/em&gt; &lt;/p&gt;

&lt;p&gt;For instance &lt;code&gt;lambda * current_weight / N&lt;/code&gt; where N is size of training set - at first glance this looks reasonable. I could not find anything on the subject though, and I wonder if that is because regularisation does not work as well with a per-item update, or even goes under a different name or altered formula.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (609, 2, '2014-06-26 23:46:22.487', 6, NULL, NULL, '&lt;p&gt;Below is a very good note (page 12) on learning rate in Neural Nets (Back Propagation) by Andrew Ng. You will find details relating to learning rate. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://web.stanford.edu/class/cs294a/sparseAutoencoder_2011new.pdf" rel="nofollow"&gt;http://web.stanford.edu/class/cs294a/sparseAutoencoder_2011new.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;For your 4th point, you''re right that normally one has to choose a "balanced" learning rate, that should neither overshoot nor converge too slowly. One can plot the learning rate w.r.t. the descent of the cost function to diagnose/fine tune. In practice, Andrew normally uses the L-BFGS algorithm (mentioned in page 12) to get a "good enough" learning rate.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (611, 2, '2014-06-27 07:06:14.297', 0, NULL, NULL, '&lt;p&gt;I had almost the same problem: ''restoring'' age, gender, location for social network users. But I used users'' ego-networks, not visited sites statistics. And I faced with two almost independent tasks:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;''Restoring'' or ''predicting'' data. You can use a bunch of different technics to complete this task, but my vote is for simplest ones (KISS, yes). E.g., in my case, for age prediction, mean of ego-network users'' ages gave satisfactory results (for about 70% of users error was less than +/-3 years, in my case it was enough). It''s just an idea, but you can try to use for age prediction weighted average, defining weight as similarity measure between visited sites sets of current user and others.&lt;/li&gt;
&lt;li&gt;Evaluating prediction quality. Algorithm from task-1 will produce prediction almost in all cases. And second task is to determine, if prediction is reliable. E.g., in case of ego network and age prediction: can we trust in prediction, if a user has only one ''friend'' in his ego network? This task is more about machine-learning: it''s a binary classification problem. You need to compose features set, form training and test samples from your data with both right and wrong predictions. Creating appropriate classifier will help you to filter out unpredictable users. But you need to determine, what are your features set. I used a number of network metrics, and summary statistics on feature of interest distribution among ego-network.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;This approach wouldn''t populate all the gaps, but only predictable ones.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (612, 2, '2014-06-27 07:19:11.42', 2, NULL, NULL, '&lt;p&gt;Regularization is relevant in per-item learning as well. I would suggest to start with a basic validation approach for finding out lambda, whether you are doing batch or per-item learning. This is the easiest and safest approach. Try manually with a number of different values. e.g. 0.001. 0.003, 0.01, 0.03, 0.1 etc. and see how your validation set behaves. Later on you may automate this process by introducing a linear or local search method. &lt;/p&gt;

&lt;p&gt;As a side note, I believe the value of lambda should be considered in relation to the updates of the parameter vector, rather than the training set size. For batch training you have one parameter update &lt;em&gt;per dataset pass&lt;/em&gt;, while for online one update &lt;em&gt;per sample&lt;/em&gt; (regardless of the training set size).&lt;/p&gt;

&lt;p&gt;I recently stumbled upon this &lt;a href="http://stats.stackexchange.com/questions/64224/regularization-and-feature-scaling-in-online-learning"&gt;Crossvalidated Question&lt;/a&gt;, which seems quite similar to yours. There is a link to a paper about &lt;a href="http://leon.bottou.org/publications/pdf/jmlr-2009.pdf" rel="nofollow"&gt;a new SGD algorithm&lt;/a&gt;, with some relevant content. It might be useful to take a look (especially pages 1742-1743).&lt;/p&gt;
', NULL, NULL, '''-1743'':194C ''/a'':152C,178C ''/em'':122C,131C ''/p'':80C,138C,195C ''/publications/pdf/jmlr-2009.pdf'':171C ''/questions/64224/regularization-and-feature-scaling-in-online-learning'':149C ''0.001'':52C ''0.003'':53C ''0.01'':54C ''0.03'':55C ''0.1'':56C ''1742'':193C ''algorithm'':177C ''approach'':21C,42C ''autom'':69C ''basic'':19C ''batch'':30C,111C ''behav'':64C ''believ'':87C ''consid'':94C ''content'':182C ''crossvalid'':150C ''dataset'':120C ''differ'':49C ''e.g'':51C ''easiest'':39C ''em'':118C,128C ''especi'':191C ''etc'':57C ''find'':23C ''href'':146C,168C ''introduc'':73C ''item'':8C,34C ''lambda'':25C,91C ''later'':65C ''learn'':9C,35C ''leon.bottou.org'':170C ''leon.bottou.org/publications/pdf/jmlr-2009.pdf'':169C ''linear'':75C ''link'':162C ''local'':77C ''look'':190C ''manual'':44C ''may'':68C ''method'':79C ''might'':184C ''new'':175C ''nofollow'':173C ''note'':85C ''number'':47C ''one'':115C,126C ''onlin'':125C ''p'':1C,81C,139C ''page'':192C ''paper'':165C ''paramet'':102C,116C ''pass'':121C ''per'':7C,33C,119C,129C ''per-item'':6C,32C ''process'':71C ''question'':151C ''quit'':155C ''rather'':104C ''recent'':141C ''regardless'':132C ''regular'':2C ''rel'':172C ''relat'':96C ''relev'':4C,181C ''safest'':41C ''sampl'':130C ''search'':78C ''see'':59C ''seem'':154C ''set'':63C,108C,136C ''sgd'':176C ''side'':84C ''similar'':156C ''size'':109C,137C ''start'':16C ''stats.stackexchange.com'':148C ''stats.stackexchange.com/questions/64224/regularization-and-feature-scaling-in-online-learning'':147C ''stumbl'':142C ''suggest'':14C ''take'':188C ''train'':107C,112C,135C ''tri'':43C ''updat'':99C,117C,127C ''upon'':143C ''use'':186C ''valid'':20C,62C ''valu'':50C,89C ''vector'':103C ''well'':11C ''whether'':26C ''would'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (613, 1, '2014-06-27 12:03:53.357', 2, 515, 'Difference Between Hadoop Mapreduce(Java) and RHadoop mapreduce', '&lt;p&gt;I understand Hadoop MapReduce and its features but I am confused about R MapReduce.&lt;/p&gt;

&lt;p&gt;One difference I have read is that R utilizes maximum RAM. So do perform parallel processing integrated R with Hadoop.&lt;/p&gt;

&lt;h2&gt;My doubt is:&lt;/h2&gt;

&lt;ol&gt;
&lt;li&gt;R can do all stats, math and data science related stuff, but why R MapReduce?&lt;/li&gt;
&lt;li&gt;Is there any new task I can achieve by using R MapReduce instead of Hadoop MapReduce? If yes, please specify.&lt;/li&gt;
&lt;li&gt;We can achieve the task by using R with Hadoop (directly) but what is the importance of MapReduce in R and how it is different from normal MapReduce?&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  r  hadoop  map-reduce', NULL, '''/h2'':59C ''/li'':77C,99C,129C ''/ol'':130C ''/p'':32C,54C ''achiev'':86C,103C ''confus'':28C ''data'':69C ''differ'':1A,35C,125C ''direct'':111C ''doubt'':57C ''featur'':24C ''h2'':55C ''hadoop'':3A,13B,20C,53C,93C,110C ''import'':116C ''instead'':91C ''integr'':50C ''java'':5A ''learn'':11B ''li'':61C,78C,100C ''machin'':10B ''machine-learn'':9B ''map'':15B ''map-reduc'':14B ''mapreduc'':4A,8A,21C,31C,76C,90C,94C,118C,128C ''math'':67C ''maximum'':43C ''new'':82C ''normal'':127C ''ol'':60C ''one'':34C ''p'':17C,33C ''parallel'':48C ''perform'':47C ''pleas'':97C ''process'':49C ''r'':12B,30C,41C,51C,62C,75C,89C,108C,120C ''ram'':44C ''read'':38C ''reduc'':16B ''relat'':71C ''rhadoop'':7A ''scienc'':70C ''specifi'':98C ''stat'':66C ''stuff'':72C ''task'':83C,105C ''understand'':19C ''use'':88C,107C ''util'':42C ''yes'':96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (614, 2, '2014-06-27 12:28:25.467', 2, NULL, NULL, '&lt;p&gt;To complement what &lt;strong&gt;insys&lt;/strong&gt; said :&lt;/p&gt;

&lt;p&gt;Regularization is used when computing the backpropagation for all the weights in your MLP.
Therefore, instead of computing the gradient in regard to all the input of the training set (&lt;code&gt;batch&lt;/code&gt;) you only use some/one item(s) (&lt;code&gt;stochastic or semi-stochastic&lt;/code&gt;).
You will end up limiting a result of the update in regard to one item instead of all which is also correct.&lt;/p&gt;

&lt;p&gt;Also, if i remember correctly, Andrew NG used &lt;code&gt;L2-regularization&lt;/code&gt;.
The &lt;code&gt;/N&lt;/code&gt; in &lt;code&gt;lambda * current_weight / N&lt;/code&gt; is not mandatory, it just helps rescaling the input. However if you choose not to use it, you will have (in most of the case) to select another value for &lt;code&gt;lambda&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;You can also use the &lt;a href="http://en.wikipedia.org/wiki/Hyperparameter_optimization" rel="nofollow"&gt;Grid-search algorithm&lt;/a&gt; to choose the best value for &lt;code&gt;lambda&lt;/code&gt; (the &lt;em&gt;hyperparameter&lt;/em&gt; =&gt; the one you have to choose).&lt;/p&gt;
', NULL, NULL, '''/a'':156C ''/code'':43C,56C,93C,97C,104C,137C,165C ''/em'':169C ''/n'':96C ''/p'':9C,79C,138C,176C ''/strong'':7C ''/wiki/hyperparameter_optimization'':149C ''algorithm'':155C ''also'':77C,81C,142C ''andrew'':86C ''anoth'':132C ''backpropag'':17C ''batch'':42C ''best'':160C ''case'':129C ''choos'':117C,158C,175C ''code'':41C,50C,89C,95C,99C,135C,163C ''complement'':3C ''comput'':15C,28C ''correct'':78C,85C ''current'':101C ''em'':167C ''en.wikipedia.org'':148C ''en.wikipedia.org/wiki/hyperparameter_optimization'':147C ''end'':59C ''gradient'':30C ''grid'':153C ''grid-search'':152C ''help'':110C ''howev'':114C ''href'':146C ''hyperparamet'':168C ''input'':36C,113C ''insi'':6C ''instead'':26C,72C ''item'':48C,71C ''l2'':91C ''l2-regularization'':90C ''lambda'':100C,136C,164C ''limit'':61C ''mandatori'':107C ''mlp'':24C ''n'':103C ''ng'':87C ''nofollow'':151C ''one'':70C,171C ''p'':1C,10C,80C,139C ''regard'':32C,68C ''regular'':11C,92C ''rel'':150C ''rememb'':84C ''rescal'':111C ''result'':63C ''said'':8C ''search'':154C ''select'':131C ''semi'':54C ''semi-stochast'':53C ''set'':40C ''some/one'':47C ''stochast'':51C,55C ''strong'':5C ''therefor'':25C ''train'':39C ''updat'':66C ''use'':13C,46C,88C,120C,143C ''valu'':133C,161C ''weight'':21C,102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (807, 2, '2014-07-22 04:10:18.353', 16, NULL, NULL, '&lt;p&gt;Really great question, and one that I find that most people don''t really understand on an intuitive level. &lt;code&gt;AUC&lt;/code&gt; is in fact often predicted over accuracy for binary classification for a number of different reasons. First though, let''s talk about exactly what &lt;code&gt;AUC&lt;/code&gt; is. Honestly, for being one of the most widely used efficacy metrics, it''s surprisingly obtuse to figure out exactly how &lt;code&gt;AUC&lt;/code&gt; works.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;AUC&lt;/code&gt; stands for &lt;code&gt;Area Under the Curve&lt;/code&gt;, which curve you ask? Well that would be the &lt;code&gt;ROC&lt;/code&gt; curve. &lt;code&gt;ROC&lt;/code&gt; stands for &lt;a href="http://en.wikipedia.org/wiki/Receiver_operating_characteristic"&gt;Receiver Operating Characteristic&lt;/a&gt;, which is actually slightly non-intuitive. The implicit goal of &lt;code&gt;AUC&lt;/code&gt; is to deal with situations where you have a very skewed sample distribution, and don''t want to overfit to a single class.&lt;/p&gt;

&lt;p&gt;A great example is in spam detection. Generally spam data sets are STRONGLY biased towards ham, or not-spam. If your data set is 90% ham, you can get a pretty damn good accuracy by just saying that every single email is ham, which is obviously something that indicates a non-ideal classifier. Let''s start with a couple of metrics that are a little more useful for us, specifically the true positive rate (&lt;code&gt;TPR&lt;/code&gt;) and the false positive rate (&lt;code&gt;FPR&lt;/code&gt;):&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/hNxTl.png" alt="ROC axes"&gt;&lt;/p&gt;

&lt;p&gt;Now in this graph, &lt;code&gt;TPR&lt;/code&gt; is specifically the ratio of true positive to all positives, and &lt;code&gt;FPR&lt;/code&gt; is the ratio of false positives to all negatives. (Keep in mind, this is only for binary classification.) On a graph like this, it should be pretty straightforward to figure out that a prediction of all 0''s or all 1''s will result in the points of &lt;code&gt;(0,0)&lt;/code&gt; and &lt;code&gt;(1,1)&lt;/code&gt; respectively. If you draw a line through these lines you get something like this:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/B1WT1.png" alt="Kind of like a triangle"&gt;&lt;/p&gt;

&lt;p&gt;Which looks basically like a diagonal line (it is), and by some easy geometry, you can see that the &lt;code&gt;AUC&lt;/code&gt; of such a model would be &lt;code&gt;0.5&lt;/code&gt; (height and base are both 1). Similarly, if you predict a random assortment of 0''s and 1''s, let''s say 90% 1''s, you could get the point &lt;code&gt;(0.9, 0.9)&lt;/code&gt;, which again falls along that diagonal line.&lt;/p&gt;

&lt;p&gt;Now comes the interesting part. What if we weren''t only predicting 0''s and 1''s? What if instead we wanted to say that, theoretically we were going to set a cutoff, above which every result was a 1, and below which every result were a 0. This would mean that at the extremes you get the original situation where you have all 0''s and all 1''s (at a cutoff of 0 and 1 respectively), but also a series of intermediate states that fall within the &lt;code&gt;1x1&lt;/code&gt; graph that contains your &lt;code&gt;ROC&lt;/code&gt;. In practice you get something like this:
&lt;img src="http://i.stack.imgur.com/13McM.png" alt="Courtesy of Wikipedia"&gt;&lt;/p&gt;

&lt;p&gt;So basically, what you''re actually getting when you do an &lt;code&gt;AUC&lt;/code&gt; over accuracy is something that will strongly discourage people going for models that are representative, but not discriminative, as this will only actually select for models that achieve false positive and true positive rates that are significantly above random chance, which is not guaranteed for accuracy.&lt;/p&gt;
', NULL, NULL, '''/13mcm.png'':545C ''/a'':115C ''/b1wt1.png'':352C ''/code'':23C,50C,74C,80C,88C,100C,104C,129C,233C,241C,260C,274C,326C,331C,382C,391C,425C,526C,533C,565C ''/hnxtl.png'':248C ''/p'':76C,153C,242C,252C,346C,359C,433C,550C,612C ''/wiki/receiver_operating_characteristic'':111C ''0'':311C,324C,325C,406C,447C,482C,499C,509C ''0.5'':390C ''0.9'':423C,424C ''1'':315C,329C,330C,397C,409C,415C,450C,474C,503C,511C ''1x1'':525C ''90'':180C,414C ''accuraci'':30C,189C,567C,611C ''achiev'':593C ''actual'':118C,557C,588C ''along'':429C ''also'':514C ''alt'':249C,353C,546C ''area'':84C ''ask'':92C ''assort'':404C ''auc'':22C,49C,73C,79C,128C,381C,564C ''axe'':251C ''base'':394C ''basic'':363C,553C ''bias'':168C ''binari'':32C,291C ''chanc'':605C ''characterist'':114C ''class'':152C ''classif'':33C,292C ''classifi'':209C ''code'':21C,48C,72C,78C,83C,98C,102C,127C,231C,239C,258C,272C,323C,328C,380C,389C,422C,524C,531C,563C ''come'':436C ''contain'':529C ''could'':418C ''coupl'':215C ''courtesi'':547C ''curv'':87C,90C,101C ''cutoff'':467C,507C ''damn'':187C ''data'':164C,177C ''deal'':132C ''detect'':161C ''diagon'':366C,431C ''differ'':38C ''discourag'':573C ''discrimin'':583C ''distribut'':142C ''draw'':335C ''easi'':373C ''efficaci'':61C ''email'':196C ''en.wikipedia.org'':110C ''en.wikipedia.org/wiki/receiver_operating_characteristic'':109C ''everi'':194C,470C,478C ''exact'':46C,70C ''exampl'':157C ''extrem'':489C ''fact'':26C ''fall'':428C,521C ''fals'':236C,279C,594C ''figur'':68C,304C ''find'':9C ''first'':40C ''fpr'':240C,273C ''general'':162C ''geometri'':374C ''get'':184C,342C,419C,491C,537C,558C ''go'':463C,575C ''goal'':125C ''good'':188C ''graph'':257C,295C,527C ''great'':3C,156C ''guarante'':609C ''ham'':170C,181C,198C ''height'':392C ''honest'':52C ''href'':108C ''i.stack.imgur.com'':247C,351C,544C ''i.stack.imgur.com/13mcm.png'':543C ''i.stack.imgur.com/b1wt1.png'':350C ''i.stack.imgur.com/hnxtl.png'':246C ''ideal'':208C ''img'':244C,348C,541C ''implicit'':124C ''indic'':204C ''instead'':454C ''interest'':438C ''intermedi'':518C ''intuit'':19C,122C ''keep'':284C ''kind'':354C ''let'':42C,210C,411C ''level'':20C ''like'':296C,344C,356C,364C,539C ''line'':337C,340C,367C,432C ''littl'':221C ''look'':362C ''mean'':485C ''metric'':62C,217C ''mind'':286C ''model'':386C,577C,591C ''negat'':283C ''non'':121C,207C ''non-id'':206C ''non-intuit'':120C ''not-spam'':172C ''number'':36C ''obtus'':66C ''obvious'':201C ''often'':27C ''one'':6C,55C ''oper'':113C ''origin'':493C ''overfit'':148C ''p'':1C,77C,154C,243C,253C,347C,360C,434C,551C ''part'':439C ''peopl'':12C,574C ''point'':321C,421C ''posit'':229C,237C,267C,270C,280C,595C,598C ''practic'':535C ''predict'':28C,308C,401C,446C ''pretti'':186C,301C ''question'':4C ''random'':403C,604C ''rate'':230C,238C,599C ''ratio'':264C,277C ''re'':556C ''realli'':2C,15C ''reason'':39C ''receiv'':112C ''repres'':580C ''respect'':332C,512C ''result'':318C,471C,479C ''roc'':99C,103C,250C,532C ''sampl'':141C ''say'':192C,413C,458C ''see'':377C ''select'':589C ''seri'':516C ''set'':165C,178C,465C ''signific'':602C ''similar'':398C ''singl'':151C,195C ''situat'':134C,494C ''skew'':140C ''slight'':119C ''someth'':202C,343C,538C,569C ''spam'':160C,163C,174C ''specif'':226C,262C ''src'':245C,349C,542C ''stand'':81C,105C ''start'':212C ''state'':519C ''straightforward'':302C ''strong'':167C,572C ''surpris'':65C ''talk'':44C ''theoret'':460C ''though'':41C ''toward'':169C ''tpr'':232C,259C ''triangl'':358C ''true'':228C,266C,597C ''understand'':16C ''us'':225C ''use'':60C,223C ''want'':146C,456C ''well'':93C ''weren'':443C ''wide'':59C ''wikipedia'':549C ''within'':522C ''work'':75C ''would'':95C,387C,484C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (616, 1, '2014-06-27 15:58:19.34', 5, 187, 'Identifying âclustersâ or âgroupsâ in a matrix', '&lt;p&gt;I have a matrix that is populated with discrete elements, and I need to cluster them (using R) into intact groups. So, for example, take this matrix:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[A B B C A]  
[A A B A A]  
[A B B C C]  
[A A A A A]  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;There would be two separate clusters for A, two separate clusters for C, and one cluster for B.&lt;/p&gt;

&lt;p&gt;The output I''m looking for would ideally assign a unique ID to each cluster, something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[1 2 2 3 4]  
[1 1 2 4 4]  
[1 2 2 5 5]  
[1 1 1 1 1]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Right now I wrote a code that does this recursively by just iteratively checking nearest neighbor, but it quickly overflows when the matrix gets large (i.e., 100x100).&lt;/p&gt;

&lt;p&gt;Is there a built in function in R that can do this? I looked into raster and image processing, but no luck. I''m convinced it must be out there.&lt;/p&gt;
', 'r  clustering', NULL, '''/code'':63C,127C ''/p'':40C,84C,104C,157C,189C ''/pre'':64C,128C ''1'':107C,112C,113C,117C,122C,123C,124C,125C,126C ''100x100'':156C ''2'':108C,109C,114C,118C,119C ''3'':110C ''4'':111C,115C,116C ''5'':120C,121C ''assign'':94C ''b'':44C,45C,50C,54C,55C,83C ''built'':162C ''c'':46C,56C,57C,78C ''check'':143C ''cluster'':11B,27C,71C,76C,81C,100C ''clustersâ'':3A ''code'':42C,106C,135C ''convinc'':183C ''discret'':21C ''element'':22C ''exampl'':36C ''function'':164C ''get'':153C ''group'':33C ''groupsâ'':6A ''i.e'':155C ''id'':97C ''ideal'':93C ''identifi'':1A ''imag'':176C ''intact'':32C ''iter'':142C ''larg'':154C ''like'':102C ''look'':90C,172C ''luck'':180C ''m'':89C,182C ''matrix'':9A,16C,39C,152C ''must'':185C ''nearest'':144C ''need'':25C ''neighbor'':145C ''one'':80C ''output'':87C ''overflow'':149C ''p'':12C,65C,85C,129C,158C ''popul'':19C ''pre'':41C,105C ''process'':177C ''quick'':148C ''r'':10B,30C,166C ''raster'':174C ''recurs'':139C ''right'':130C ''separ'':70C,75C ''someth'':101C ''take'':37C ''two'':69C,74C ''uniqu'':96C ''use'':29C ''would'':67C,92C ''wrote'':133C ''â'':2A,5A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (617, 2, '2014-06-27 16:40:17.393', 2, NULL, NULL, '&lt;p&gt;What do you think is distance measure in your case?&lt;/p&gt;

&lt;p&gt;I assume there are three dimensions here:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;RowN&lt;/code&gt; (row number)&lt;/li&gt;
&lt;li&gt;&lt;code&gt;ColN&lt;/code&gt; (column number)&lt;/li&gt;
&lt;li&gt;&lt;code&gt;Value&lt;/code&gt; (value: A, B or C)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;That means data you get from &lt;code&gt;4x5&lt;/code&gt; matrix looks like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Sample1 -&amp;gt; (1, 1, A)
Sample2 -&amp;gt; (1, 2, B)
...
Sample5 -&amp;gt; (1, 5, A)
Sample6 -&amp;gt; (2, 1, A)
...
Sample15 -&amp;gt; (3, 5, C)
...
Sample20 -&amp;gt; (4, 5, A)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is &lt;code&gt;value&lt;/code&gt; scaled? In other words, is &lt;code&gt;A &amp;lt; B &amp;lt; C&lt;/code&gt;? &lt;/p&gt;

&lt;p&gt;If yes, then&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;you can replace &lt;code&gt;{A, B, C}&lt;/code&gt; with &lt;code&gt;{0, 1, 2}&lt;/code&gt; (or may be &lt;code&gt;{10, 11, 12}&lt;/code&gt;, if you want this difference be less important than RowN and ColN attributes)&lt;/li&gt;
&lt;li&gt;normalize your data&lt;/li&gt;
&lt;li&gt;use, for example, K-Means clustering algorithm (&lt;a href="http://stat.ethz.ch/R-manual/R-patched/library/stats/html/kmeans.html" rel="nofollow"&gt;http://stat.ethz.ch/R-manual/R-patched/library/stats/html/kmeans.html&lt;/a&gt;) from &lt;code&gt;stats&lt;/code&gt; R package&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In that case the distance between two will be:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Sqrt( (RowN1-RowN2)^2 + (ColN1-ColN2)^2 + (Value1-Value2)^2 )
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If &lt;code&gt;value&lt;/code&gt; is not scaled (regular categorical variable), use some &lt;a href="http://datascience.stackexchange.com/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data"&gt;modifications of K-Means that work with categorical data&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;So in case of 100x100 matrix you have 10000 observations and three variables, which is pretty trivial sample size.&lt;/p&gt;
', NULL, NULL, '''/a'':245C ''/code'':26C,33C,40C,57C,94C,100C,112C,128C,134C,142C,184C,215C,221C ''/li'':29C,36C,46C,156C,161C,187C ''/p'':12C,21C,61C,113C,118C,199C,246C,267C ''/pre'':95C,216C ''/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data'':234C ''/r-manual/r-patched/library/stats/html/kmeans.html'':175C ''/r-manual/r-patched/library/stats/html/kmeans.html&lt;/a&gt;)'':180C ''/ul'':47C,188C ''0'':131C ''1'':66C,67C,71C,76C,82C,132C ''10'':139C ''10000'':256C ''100x100'':252C ''11'':140C ''12'':141C ''2'':72C,81C,133C,206C,210C,214C ''3'':86C ''4'':91C ''4x5'':56C ''5'':77C,87C,92C ''algorithm'':170C ''assum'':15C ''attribut'':155C ''b'':43C,73C,109C,126C ''c'':45C,88C,111C,127C ''case'':11C,192C,250C ''categor'':226C,243C ''cluster'':169C ''code'':24C,31C,38C,55C,63C,98C,106C,124C,130C,138C,182C,201C,219C ''coln'':32C,154C ''coln1'':208C ''coln1-coln2'':207C ''coln2'':209C ''column'':34C ''data'':51C,160C,244C ''datascience.stackexchange.com'':233C ''datascience.stackexchange.com/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data'':232C ''differ'':147C ''dimens'':19C ''distanc'':7C,194C ''exampl'':165C ''get'':53C ''gt'':65C,70C,75C,80C,85C,90C ''href'':172C,231C ''import'':150C ''k'':167C,238C ''k-mean'':166C,237C ''less'':149C ''li'':23C,30C,37C,120C,157C,162C ''like'':60C ''look'':59C ''lt'':108C,110C ''matrix'':58C,253C ''may'':136C ''mean'':50C,168C,239C ''measur'':8C ''modif'':235C ''nofollow'':177C ''normal'':158C ''number'':28C,35C ''observ'':257C ''p'':1C,13C,48C,96C,114C,189C,217C,247C ''packag'':186C ''pre'':62C,200C ''pretti'':263C ''r'':185C ''regular'':225C ''rel'':176C ''replac'':123C ''row'':27C ''rown'':25C,152C ''rown1'':204C ''rown1-rown2'':203C ''rown2'':205C ''sampl'':265C ''sample1'':64C ''sample15'':84C ''sample2'':69C ''sample20'':89C ''sample5'':74C ''sample6'':79C ''scale'':101C,224C ''size'':266C ''sqrt'':202C ''stat'':183C ''stat.ethz.ch'':174C,179C ''stat.ethz.ch/r-manual/r-patched/library/stats/html/kmeans.html'':173C ''stat.ethz.ch/r-manual/r-patched/library/stats/html/kmeans.html&lt;/a&gt;)'':178C ''think'':5C ''three'':18C,259C ''trivial'':264C ''two'':196C ''ul'':22C,119C ''use'':163C,228C ''valu'':39C,41C,99C,220C ''value1'':212C ''value1-value2'':211C ''value2'':213C ''variabl'':227C,260C ''want'':145C ''word'':104C ''work'':241C ''yes'':116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (618, 2, '2014-06-27 19:18:11.433', 1, NULL, NULL, '&lt;p&gt;Although adesantos has already given a good answer, I would like to add a little background information.&lt;/p&gt;

&lt;p&gt;The name for the problem you are looking at is "imputation". As adesantos already said, one of the possibilities is to fit a distribution. For example, you could fit a multivariate Gaussian to the data. You will get the mean only from the samples you know and you calculate the covariances only from the samples you know. You can then use standard MVG results to impute the missing data linearly.&lt;/p&gt;

&lt;p&gt;This is probably the simplest probabilistic method of imputation and it is already quite involved. If you are a neural networks, a recently proposed method that can do so are deep latent gaussian models by Rezende et al. However, understand the method will require a lot of neural net knowledge, quite some variational Bayes knowledge about Markov chains.&lt;/p&gt;

&lt;p&gt;Another method, which I have hear to work well is to train a generative stochastic network (Bengio et al). This is done by training a denoising auto encoder on the data you have (neglecting missing values in the reconstruction loss). Say you have a reconstruction function f and a input x. Then you will reconstruct it via x'' = f(x). You then reset the values of x'' with those you know from x. (I.e. you only keep the values that were missing before reconstruction.) If you do so many times, you are guaranteed to sample from the distribution given the values you know.&lt;/p&gt;

&lt;p&gt;But in either case, these methods require quite some knowledge about statistics and neural nets.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (619, 2, '2014-06-27 19:37:44.847', 1, NULL, NULL, '&lt;p&gt;&lt;a href="https://github.com/RevolutionAnalytics/RHadoop" rel="nofollow"&gt;rhadoop&lt;/a&gt; (the part you are interested in is now called &lt;a href="https://github.com/RevolutionAnalytics/rmr2" rel="nofollow"&gt;rmr2&lt;/a&gt;) is simply a client API for MapReduce written in R. You invoke MapReduce using R package APIs, and send an R function to the workers, where it is executed by an R interpreter locally. But it is otherwise exactly the same MapReduce.&lt;/p&gt;

&lt;p&gt;You can call anything you like in R this way, but no R functions are themselves parallelized to use MapReduce in this way. The point is simply that you can invoke M/R from R. I don''t think it somehow lets you do anything more magical than that.&lt;/p&gt;
', NULL, NULL, '''/a'':10C,28C ''/p'':71C,121C ''/revolutionanalytics/rhadoop'':6C ''/revolutionanalytics/rmr2'':24C ''anyth'':76C,116C ''api'':33C,45C ''call'':19C,75C ''client'':32C ''exact'':67C ''execut'':57C ''function'':50C,86C ''github.com'':5C,23C ''github.com/revolutionanalytics/rhadoop'':4C ''github.com/revolutionanalytics/rmr2'':22C ''href'':3C,21C ''interest'':15C ''interpret'':61C ''invok'':40C,103C ''let'':113C ''like'':78C ''local'':62C ''m/r'':104C ''magic'':118C ''mapreduc'':35C,41C,70C,92C ''nofollow'':8C,26C ''otherwis'':66C ''p'':1C,72C ''packag'':44C ''parallel'':89C ''part'':12C ''point'':97C ''r'':38C,43C,49C,60C,80C,85C,106C ''rel'':7C,25C ''rhadoop'':9C ''rmr2'':27C ''send'':47C ''simpli'':30C,99C ''somehow'':112C ''think'':110C ''use'':42C,91C ''way'':82C,95C ''worker'':53C ''written'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (620, 2, '2014-06-27 21:45:23.237', 6, NULL, NULL, '&lt;p&gt;Yes, it''s problematic.  If you oversample the minority, you risk overfitting.  If you undersample the majority, you risk missing aspects of the majority class.  Stratified sampling, btw, is the equivalent to assigning non-uniform misclassification costs.  &lt;/p&gt;

&lt;p&gt;Alternatives:&lt;/p&gt;

&lt;p&gt;(1) Independently sampling several subsets from the majority class and making multiple classifiers by combining each subset with all the minority class data, as suggested in the answer from @Debasis and described in this &lt;a href="http://cse.seu.edu.cn/people/xyliu/publication/tsmcb09.pdf"&gt;EasyEnsemble paper&lt;/a&gt;, &lt;/p&gt;

&lt;p&gt;(2) &lt;a href="http://arxiv.org/pdf/1106.1813.pdf"&gt;SMOTE (Synthetic Minority Oversampling Technique)&lt;/a&gt; or &lt;a href="http://www3.nd.edu/~nchawla/papers/ECML03.pdf"&gt;SMOTEBoost, (combining SMOTE with boosting)&lt;/a&gt; to create synthetic instances of the minority class by making nearest neighbors in the feature space.  SMOTE is implemented in R in &lt;a href="http://cran.r-project.org/web/packages/DMwR/index.html"&gt;the DMwR package&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1055, 2, '2014-08-28 11:24:38.743', 1, NULL, NULL, '&lt;p&gt;Scala is the only real language that has Big Data at it''s core. You have MLLib that sits on Spark, and as Scala is Functional it makes parallel computing really natural.  R, Python and Matlab are not suitable for industry productization, well some would say Python''s horrible dynamic typing can be handled a little using special build tools, but really its not type safe and there is no way to solve that problem.&lt;/p&gt;
', NULL, NULL, '''/p'':77C ''big'':10C ''build'':60C ''comput'':31C ''core'':15C ''data'':11C ''dynam'':51C ''function'':27C ''handl'':55C ''horribl'':50C ''industri'':42C ''languag'':7C ''littl'':57C ''make'':29C ''matlab'':37C ''mllib'':18C ''natur'':33C ''p'':1C ''parallel'':30C ''problem'':76C ''product'':43C ''python'':35C,48C ''r'':34C ''real'':6C ''realli'':32C,63C ''safe'':67C ''say'':47C ''scala'':2C,25C ''sit'':20C ''solv'':74C ''spark'':22C ''special'':59C ''suitabl'':40C ''tool'':61C ''type'':52C,66C ''use'':58C ''way'':72C ''well'':44C ''would'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (623, 1, '2014-06-28 14:57:18.177', 9, 153, 'Measuring performance of different classifiers with different sample sizes', '&lt;p&gt;I''m currently using several different classifiers on various entities extracted from text, and using precision/recall as a summary of how well each separate classifier performs across a given dataset.&lt;/p&gt;

&lt;p&gt;I''m wondering if there''s a meaningful way of comparing the performance of these classifiers in a similar way, but which also takes into account the total numbers of each entity in the test data that''s being classified?&lt;/p&gt;

&lt;p&gt;Currently, I''m using precision/recall as a measure of performance, so might have something like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;                    Precision Recall
Person classifier   65%       40%
Company classifier  98%       90%
Cheese classifier   10%       50%
Egg classifier      100%      100%
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However, the dataset I''m running these on might contain 100k people, 5k companies, 500 cheeses, and 1 egg.&lt;/p&gt;

&lt;p&gt;So is there a summary statistic I can add to the above table which also takes into account the total number of each item? Or is there some way of measuring the fact that e.g. 100% prec/rec on the Egg classifier might not be meaningful with only 1 data item?&lt;/p&gt;

&lt;p&gt;Let''s say we had hundreds of such classifiers, I guess I''m looking for a good way to answer questions like "Which classifiers are underperforming? Which classifiers lack sufficient test data to tell whether they''re underperforming?". &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (624, 2, '2014-06-29 07:06:00.43', 2, NULL, NULL, '&lt;p&gt;In most cases a practicing data scientist creates his own working environment on personal computed installing preferred software packages. Normally it is sufficient and efficient use of computing resources, because to run a virtual machine (VM) on your main machine you have to allocate a significant portion of RAM for it. The software will run noticeably slower on both the main and the virtual machine unless a lot of RAM. &lt;/p&gt;

&lt;p&gt;Due to this impact on speed it is not common to use VMs as main working environment but they are a good solution in several cases when there is a need of additional working environment. &lt;/p&gt;

&lt;p&gt;The VMs be considered when:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;There is a need to easily replicate a number of identical computing
environments when teaching a course or doing a presentation on a
conference.&lt;/li&gt;
&lt;li&gt;There is a need to save and recreate an exact environment for an experiment or a calculation.&lt;/li&gt;
&lt;li&gt;There is a need to run a different OS or to test a solution on a tool that runs on a different OS. &lt;/li&gt;
&lt;li&gt;One wants to try out a bundle of software tools before installing
them on the main machine. E.g. there is an opportunity to instal an instance of Hadoop (CDH) on a VM during an &lt;a href="https://www.udacity.com/course/ud617" rel="nofollow"&gt;Intro to Hadoop&lt;/a&gt; course on Udacity. &lt;/li&gt;
&lt;li&gt;VMs are sometimes used for fast deployment in the cloud like AWS EC, Rackspace etc.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The VMs mentioned in the original question are made as easily installable data science software bundles. There are more than these two. This &lt;a href="http://jeroenjanssens.com/2013/12/07/lean-mean-data-science-machine.html" rel="nofollow"&gt;blog post&lt;/a&gt; by Jeroen Janssens gives a comparison of at least four:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Data Science Toolbox &lt;/li&gt;
&lt;li&gt;Mining the Social Web &lt;/li&gt;
&lt;li&gt;Data Science Toolkit &lt;/li&gt;
&lt;li&gt;Data Science Box  &lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (625, 2, '2014-06-29 12:48:08.84', 3, NULL, NULL, '&lt;p&gt;Maybe you can check here - &lt;a href="http://snap.stanford.edu/data/" rel="nofollow"&gt;http://snap.stanford.edu/data/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;For each data set you will also see references of the works where they have been used&lt;/p&gt;
', NULL, NULL, '''/data/'':11C ''/data/&lt;/a&gt;&lt;/p&gt;'':16C ''/p'':35C ''also'':24C ''check'':5C ''data'':20C ''href'':8C ''mayb'':2C ''nofollow'':13C ''p'':1C,17C ''refer'':26C ''rel'':12C ''see'':25C ''set'':21C ''snap.stanford.edu'':10C,15C ''snap.stanford.edu/data/'':9C ''snap.stanford.edu/data/&lt;/a&gt;&lt;/p&gt;'':14C ''use'':34C ''work'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (626, 2, '2014-06-29 21:11:47.727', 2, NULL, NULL, '&lt;p&gt;In my opinion, it is difficult to compare the performance when there is such a big difference of size. On this link, (please check it out here in Wikipedia &lt;a href="http://en.wikipedia.org/wiki/Effect_size" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Effect_size&lt;/a&gt;), you may see different strategies.&lt;/p&gt;

&lt;p&gt;The one I suggest is one related to the variance. For instance, consider the performance of the classifier (100%) and the person classifier (65%). The minimum error you commit with the former classifier is 100%. However, the minimum error you can commit with the latter classifier is 10e-5.&lt;/p&gt;

&lt;p&gt;So one way to compare classifier is to have on mind this &lt;strong&gt;Rule of Three&lt;/strong&gt; (&lt;a href="http://en.wikipedia.org/wiki/Rule_of_three_%28statistics%29" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Rule_of_three_(statistics)&lt;/a&gt; where you can compare the performance and its variability.&lt;/p&gt;

&lt;p&gt;Other possibility is &lt;strong&gt;F-measure&lt;/strong&gt; which is a combination of Precision and Recall and it is somehow independent to the effect size.&lt;/p&gt;
', NULL, NULL, '''/p'':46C,96C,134C,161C ''/strong'':114C,143C ''/wiki/effect_size'':35C ''/wiki/effect_size&lt;/a&gt;),'':40C ''/wiki/rule_of_three_%28statistics%29'':119C ''/wiki/rule_of_three_(statistics)&lt;/a&gt;'':124C ''100'':66C,82C ''10e-5'':95C ''65'':71C ''big'':17C ''check'':25C ''classifi'':65C,70C,80C,93C,103C ''combin'':147C ''commit'':76C,89C ''compar'':9C,102C,128C ''consid'':60C ''differ'':18C,44C ''difficult'':7C ''effect'':159C ''en.wikipedia.org'':34C,39C,118C,123C ''en.wikipedia.org/wiki/effect_size'':33C ''en.wikipedia.org/wiki/effect_size&lt;/a&gt;),'':38C ''en.wikipedia.org/wiki/rule_of_three_%28statistics%29'':117C ''en.wikipedia.org/wiki/rule_of_three_(statistics)&lt;/a&gt;'':122C ''error'':74C,86C ''f'':141C ''f-measur'':140C ''former'':79C ''howev'':83C ''href'':32C,116C ''independ'':156C ''instanc'':59C ''latter'':92C ''link'':23C ''may'':42C ''measur'':142C ''mind'':108C ''minimum'':73C,85C ''nofollow'':37C,121C ''one'':49C,53C,99C ''opinion'':4C ''p'':1C,47C,97C,135C ''perform'':11C,62C,130C ''person'':69C ''pleas'':24C ''possibl'':137C ''precis'':149C ''recal'':151C ''rel'':36C,120C ''relat'':54C ''rule'':111C ''see'':43C ''size'':20C,160C ''somehow'':155C ''strategi'':45C ''strong'':110C,139C ''suggest'':51C ''three'':113C ''variabl'':133C ''varianc'':57C ''way'':100C ''wikipedia'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (965, 1, '2014-08-12 08:54:34.903', 1, 42, 'Tool for finding correlations between data after some event', '&lt;p&gt;I''m looking for a product that allows us to take in a collection of datastreams, and then after some event, will find any data that changes or correlates with that event. (For example, having a headache, and identifying that I drank too much beer last night and didn''t drink enough water)&lt;/p&gt;
', 'correlation', NULL, '''/p'':65C ''allow'':19C ''beer'':56C ''chang'':38C ''collect'':25C ''correl'':4A,10B,40C ''data'':6A,36C ''datastream'':27C ''didn'':60C ''drank'':53C ''drink'':62C ''enough'':63C ''event'':9A,32C,43C ''exampl'':45C ''find'':3A,34C ''headach'':48C ''identifi'':50C ''last'':57C ''look'':14C ''m'':13C ''much'':55C ''night'':58C ''p'':11C ''product'':17C ''take'':22C ''tool'':1A ''us'':20C ''water'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (628, 2, '2014-06-29 23:14:39.513', 16, NULL, NULL, '&lt;p&gt;A model underfits when it is too simple with regards to the data it is trying to model.&lt;/p&gt;

&lt;p&gt;One way to detect such situation is to use the &lt;a href="http://en.wikipedia.org/wiki/Bias%E2%80%93variance_dilemma"&gt;biasâvariance approach&lt;/a&gt;, which can represented like this:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/t0zit.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Your model is underfitted when you have a high bias.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;To know whether you have a too high bias or a too high variance, you view the phenomenon in terms of training and test errors:&lt;/p&gt;

&lt;p&gt;High bias: This learning curve shows high error on both the training and test sets, so the algorithm is suffering from high bias:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/KFjM4.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;High variance: This learning curve shows a large gap between training and test set errors, so the algorithm is suffering from high variance.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Ypj9y.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;If an algorithm is suffering from high variance:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;more data will probably help&lt;/li&gt;
&lt;li&gt;otherwise reduce the model complexity&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If an algorithm is suffering from high bias:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;increase the model complexity&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I would advise to watch &lt;a href="https://www.coursera.org/course/ml"&gt;Coursera'' Machine Learning course&lt;/a&gt;, section "10: Advice for applying Machine Learning", from which I took the above graphs.&lt;/p&gt;
', NULL, NULL, '''/a'':40C,232C ''/course/ml'':227C ''/kfjm4.png'':129C ''/li'':190C,197C,215C ''/p'':20C,46C,58C,70C,98C,123C,135C,160C,172C,182C,208C,247C ''/t0zit.png'':52C ''/ul'':198C,216C ''/wiki/bias%e2%80%93variance_dilemma'':36C ''/ypj9y.png'':166C ''10'':234C ''advic'':235C ''advis'':220C ''algorithm'':117C,154C,176C,202C ''alt'':53C,130C,167C ''appli'':237C ''approach'':39C ''bias'':69C,81C,101C,122C,207C ''biasâ'':37C ''complex'':196C,214C ''cours'':231C ''coursera'':228C ''curv'':104C,141C ''data'':14C,186C ''descript'':56C,133C,170C ''detect'':25C ''en.wikipedia.org'':35C ''en.wikipedia.org/wiki/bias%e2%80%93variance_dilemma'':34C ''enter'':54C,131C,168C ''error'':97C,107C,151C ''gap'':145C ''graph'':246C ''help'':189C ''high'':68C,80C,85C,100C,106C,121C,137C,158C,180C,206C ''hr'':71C ''href'':33C,224C ''i.stack.imgur.com'':51C,128C,165C ''i.stack.imgur.com/kfjm4.png'':127C ''i.stack.imgur.com/t0zit.png'':50C ''i.stack.imgur.com/ypj9y.png'':164C ''imag'':55C,132C,169C ''img'':48C,125C,162C ''increas'':211C ''know'':74C ''larg'':144C ''learn'':103C,140C,230C,239C ''li'':184C,191C,210C ''like'':44C ''machin'':229C,238C ''model'':3C,19C,61C,195C,213C ''one'':22C ''otherwis'':192C ''p'':1C,21C,47C,59C,72C,99C,124C,136C,161C,173C,199C,217C ''phenomenon'':90C ''probabl'':188C ''reduc'':193C ''regard'':11C ''repres'':43C ''section'':233C ''set'':114C,150C ''show'':105C,142C ''simpl'':9C ''situat'':27C ''src'':49C,126C,163C ''suffer'':119C,156C,178C,204C ''term'':92C ''test'':96C,113C,149C ''took'':243C ''train'':94C,111C,147C ''tri'':17C ''ul'':183C,209C ''underfit'':4C,63C ''use'':30C ''varianc'':38C,86C,138C,159C,181C ''view'':88C ''watch'':222C ''way'':23C ''whether'':75C ''would'':219C ''www.coursera.org'':226C ''www.coursera.org/course/ml'':225C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (754, 2, '2014-07-16 14:22:38.677', 2, NULL, NULL, '&lt;p&gt;Most of the recent Frequent Pattern approaches that I''ve seen in the literature are based on optimizing FPGrowth. I have to admit, I haven''t seen many developments within the literature in FPM in many years.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Frequent_Pattern_Mining/The_FP-Growth_Algorithm" rel="nofollow"&gt;This wikibook&lt;/a&gt; highlights many of the variants on FPGrowth that are out there.&lt;/p&gt;
', NULL, NULL, '''/a'':50C ''/p'':39C,62C ''/wiki/data_mining_algorithms_in_r/frequent_pattern_mining/the_fp-growth_algorithm'':45C ''admit'':24C ''approach'':8C ''base'':17C ''develop'':30C ''en.wikibooks.org'':44C ''en.wikibooks.org/wiki/data_mining_algorithms_in_r/frequent_pattern_mining/the_fp-growth_algorithm'':43C ''fpgrowth'':20C,57C ''fpm'':35C ''frequent'':6C ''haven'':26C ''highlight'':51C ''href'':42C ''literatur'':15C,33C ''mani'':29C,37C,52C ''nofollow'':47C ''optim'':19C ''p'':1C,40C ''pattern'':7C ''recent'':5C ''rel'':46C ''seen'':12C,28C ''variant'':55C ''ve'':11C ''wikibook'':49C ''within'':31C ''year'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (630, 2, '2014-06-30 04:00:35.077', 1, NULL, NULL, '&lt;p&gt;Here''s a crazy idea: talk to the volunteers who know the neighborhoods and who have done door-to-door work before.  Get their advice and ideas.  They will probably have insights that no algorithm will produce, and those modifications will be valuable to any computer-generated route list.  One example: Avoiding crossing heavily traveled streets with slow lights or no lights.  Another example: pairs of volunteers working on opposite sides of the same street will feel safer than a volunteer working that street alone.&lt;/p&gt;
', NULL, NULL, '''/p'':89C ''advic'':27C ''algorithm'':37C ''alon'':88C ''anoth'':66C ''avoid'':55C ''comput'':49C ''computer-gener'':48C ''crazi'':5C ''cross'':56C ''done'':18C ''door'':20C,22C ''door-to-door'':19C ''exampl'':54C,67C ''feel'':80C ''generat'':50C ''get'':25C ''heavili'':57C ''idea'':6C,29C ''insight'':34C ''know'':12C ''light'':62C,65C ''list'':52C ''modif'':42C ''neighborhood'':14C ''one'':53C ''opposit'':73C ''p'':1C ''pair'':68C ''probabl'':32C ''produc'':39C ''rout'':51C ''safer'':81C ''side'':74C ''slow'':61C ''street'':59C,78C,87C ''talk'':7C ''travel'':58C ''valuabl'':45C ''volunt'':10C,70C,84C ''work'':23C,71C,85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (634, 1, '2014-06-30 09:43:01.94', 8, 86, 'Handling a regularly increasing feature set', '&lt;p&gt;I''m working on a fraud detection system. In this field, new frauds appear regularly, so that new features have to be added to the model on ongoing basis. &lt;/p&gt;

&lt;p&gt;I wonder what is the best way to handle it (from the development process perspective)? Just adding a new feature into the feature vector and re-training the classifier seems to be a naive approach, because too much time will be spent for re-learning of the old features.&lt;/p&gt;

&lt;p&gt;I''m thinking along the way of training a classifier for each feature (or a couple of related features), and then combining the results of those classifiers with an overall classifier. Are there any drawbacks of this approach? How can I choose an algorithm for the overall classifier?&lt;/p&gt;
', 'machine-learning  bigdata', NULL, '''/p'':41C,94C,144C ''ad'':34C,59C ''algorithm'':139C ''along'':99C ''appear'':25C ''approach'':78C,133C ''basi'':40C ''best'':48C ''bigdata'':10B ''choos'':137C ''classifi'':72C,105C,122C,126C,143C ''combin'':117C ''coupl'':111C ''detect'':18C ''develop'':55C ''drawback'':130C ''featur'':5A,30C,62C,65C,93C,108C,114C ''field'':22C ''fraud'':17C,24C ''handl'':1A,51C ''increas'':4A ''learn'':9B,89C ''m'':13C,97C ''machin'':8B ''machine-learn'':7B ''model'':37C ''much'':81C ''naiv'':77C ''new'':23C,29C,61C ''old'':92C ''ongo'':39C ''overal'':125C,142C ''p'':11C,42C,95C ''perspect'':57C ''process'':56C ''re'':69C,88C ''re-learn'':87C ''re-train'':68C ''regular'':3A,26C ''relat'':113C ''result'':119C ''seem'':73C ''set'':6A ''spent'':85C ''system'':19C ''think'':98C ''time'':82C ''train'':70C,103C ''vector'':66C ''way'':49C,101C ''wonder'':44C ''work'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (635, 2, '2014-06-30 10:47:06.453', 4, NULL, NULL, '&lt;p&gt;In an ideal world, you retain all of your historical data, and do indeed run a new model with the new feature extracted retroactively from historical data. I''d argue that the computing resource spent on this is quite useful actually. Is it really a problem?&lt;/p&gt;

&lt;p&gt;Yes, it''s a widely accepted technique to build an ensemble of classifiers and combine their results. You can build a new model in parallel just on new features and average in its prediction. This should add value, but, you will never capture interaction between the new and old features this way, since they will never appear together in a classifier. &lt;/p&gt;
', NULL, NULL, '''/p'':48C,111C ''accept'':55C ''actual'':42C ''add'':86C ''appear'':106C ''argu'':31C ''averag'':80C ''build'':58C,69C ''captur'':92C ''classifi'':62C,110C ''combin'':64C ''comput'':34C ''d'':30C ''data'':12C,28C ''ensembl'':60C ''extract'':24C ''featur'':23C,78C,99C ''histor'':11C,27C ''ideal'':4C ''inde'':15C ''interact'':93C ''model'':19C,72C ''never'':91C,105C ''new'':18C,22C,71C,77C,96C ''old'':98C ''p'':1C,49C ''parallel'':74C ''predict'':83C ''problem'':47C ''quit'':40C ''realli'':45C ''resourc'':35C ''result'':66C ''retain'':7C ''retroact'':25C ''run'':16C ''sinc'':102C ''spent'':36C ''techniqu'':56C ''togeth'':107C ''use'':41C ''valu'':87C ''way'':101C ''wide'':54C ''world'':5C ''yes'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (636, 1, '2014-06-30 12:05:38.597', 2, 390, 'Data preparation and machine learning algorithm for click prediction', '&lt;p&gt;I am new to machine learning. I have a task at hand of predicting click probability given user information like city, state, OS version, OS family, device, browser family, browser version, etc. I have been advised to try logit since logit seems to be what MS and Google are using. I have some questions regarding logistic regression:&lt;/p&gt;

&lt;p&gt;Click and non click is a very very unbalanced class and the simple GLM predictions do not look good. How can I make the data work better with the GLM?&lt;/p&gt;

&lt;p&gt;All the variables I have are categorical and things like device and city can be numerous. Also the frequency of occurrence of some devices or some cities can be very very low. How can I deal with this distribution of categorical variables?&lt;/p&gt;

&lt;p&gt;One of the variables that we get is device ID. This is a very unique feature that can be translated to a user''s identity. How can I make use of it in logit, or should it be used in a completely different model based on user identity?&lt;/p&gt;
', 'machine-learning  bigdata  data-mining  dataset  data-cleaning', NULL, '''/p'':79C,111C,155C,205C ''advis'':57C ''algorithm'':6A ''also'':129C ''base'':201C ''better'':107C ''bigdata'':13B ''browser'':49C,51C ''categor'':119C,153C ''citi'':42C,125C,139C ''class'':90C ''clean'':20B ''click'':8A,36C,81C,84C ''complet'':198C ''data'':1A,15B,19B,105C ''data-clean'':18B ''data-min'':14B ''dataset'':17B ''deal'':148C ''devic'':48C,123C,136C,165C ''differ'':199C ''distribut'':151C ''etc'':53C ''famili'':47C,50C ''featur'':172C ''frequenc'':131C ''get'':163C ''given'':38C ''glm'':94C,110C ''good'':99C ''googl'':69C ''hand'':33C ''id'':166C ''ident'':181C,204C ''inform'':40C ''learn'':5A,12B,27C ''like'':41C,122C ''logist'':77C ''logit'':60C,62C,190C ''look'':98C ''low'':144C ''machin'':4A,11B,26C ''machine-learn'':10B ''make'':103C,185C ''mine'':16B ''model'':200C ''ms'':67C ''new'':24C ''non'':83C ''numer'':128C ''occurr'':133C ''one'':157C ''os'':44C,46C ''p'':21C,80C,112C,156C ''predict'':9A,35C,95C ''prepar'':2A ''probabl'':37C ''question'':75C ''regard'':76C ''regress'':78C ''seem'':63C ''simpl'':93C ''sinc'':61C ''state'':43C ''task'':31C ''thing'':121C ''translat'':176C ''tri'':59C ''unbalanc'':89C ''uniqu'':171C ''use'':71C,186C,195C ''user'':39C,179C,203C ''variabl'':115C,154C,160C ''version'':45C,52C ''work'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (637, 2, '2014-06-30 13:56:10.753', 2, NULL, NULL, '&lt;p&gt;Here''s an idea that just popped out of the blue â what if you make use of &lt;a href="http://en.wikipedia.org/wiki/Random_subspace_method" rel="nofollow"&gt;Random Subspace Sampling&lt;/a&gt; (as in fact Sean Owen already suggested) to train a bunch of new classifiers every time a new feature appears (using a random feature subset, including the new set of features). You could train those models on a subset of samples as well to save some training time.&lt;/p&gt;

&lt;p&gt;This way you can have new classifiers possibly taking on both new and old features, and at the same time keeping your old classifiers. You might even, perhaps using a cross validation technique to measure each classifier''s performance, be able to kill-off the worst performing ones after a while, to avoid a bloated model.&lt;/p&gt;
', NULL, NULL, '''/a'':30C ''/p'':79C,138C ''/wiki/random_subspace_method'':24C ''abl'':121C ''alreadi'':36C ''appear'':50C ''avoid'':134C ''bloat'':136C ''blue'':12C ''bunch'':41C ''classifi'':44C,87C,104C,117C ''could'':63C ''cross'':111C ''en.wikipedia.org'':23C ''en.wikipedia.org/wiki/random_subspace_method'':22C ''even'':107C ''everi'':45C ''fact'':33C ''featur'':49C,54C,61C,95C ''href'':21C ''idea'':5C ''includ'':56C ''keep'':101C ''kill'':124C ''kill-off'':123C ''make'':17C ''measur'':115C ''might'':106C ''model'':66C,137C ''new'':43C,48C,58C,86C,92C ''nofollow'':26C ''old'':94C,103C ''one'':129C ''owen'':35C ''p'':1C,80C ''perform'':119C,128C ''perhap'':108C ''pop'':8C ''possibl'':88C ''random'':27C,53C ''rel'':25C ''sampl'':29C,71C ''save'':75C ''sean'':34C ''set'':59C ''subset'':55C,69C ''subspac'':28C ''suggest'':37C ''take'':89C ''techniqu'':113C ''time'':46C,78C,100C ''train'':39C,64C,77C ''use'':18C,51C,109C ''valid'':112C ''way'':82C ''well'':73C ''worst'':127C ''â'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5034, 2, '2015-02-04 00:16:15.323', 0, NULL, NULL, '&lt;p&gt;The hotel_id should not be a feature.&lt;/p&gt;

&lt;p&gt;Let''s see if I understand you correctly.&lt;/p&gt;

&lt;p&gt;At testing time you give your model a whole set of feature values for a particular hotel you are interested in. This hotel has an id, which is known to you.&lt;/p&gt;

&lt;p&gt;Your model should be able to take both an id and a set of feature values as input, so it can print both to an output file.&lt;/p&gt;

&lt;p&gt;Does that answer your question?&lt;/p&gt;
', NULL, NULL, '''/p'':10C,20C,53C,82C,89C ''abl'':59C ''answer'':86C ''correct'':19C ''featur'':9C,33C,69C ''file'':81C ''give'':26C ''hotel'':3C,38C,44C ''id'':4C,47C,64C ''input'':72C ''interest'':41C ''known'':50C ''let'':12C ''model'':28C,56C ''output'':80C ''p'':1C,11C,21C,54C,83C ''particular'':37C ''print'':76C ''question'':88C ''see'':14C ''set'':31C,67C ''take'':61C ''test'':23C ''time'':24C ''understand'':17C ''valu'':34C,70C ''whole'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (640, 1, '2014-06-30 20:51:58.64', 4, 63, 'Large Scale Personalization - Per User vs Global Models', '&lt;p&gt;I''m currently working on a project that would benefit from personalized predictions.  Given an input document, a set of output documents, and a history of user behavior, I''d like to predict which of the output documents are clicked.  &lt;/p&gt;

&lt;p&gt;In short, I''m wondering what the typical approach to this kind of personalization problem is.  Are models trained per user, or does a single global model take in summary statistics of past user behavior to help inform that decision?  Per user models won''t be accurate until the user has been active for a while, while most global models have to take in a fixed length feature vector (meaning we more or less have to compress a stream of past events into a smaller number of summary statistics).  &lt;/p&gt;
', 'classification', NULL, '''/p'':51C,142C ''accur'':99C ''activ'':105C ''approach'':61C ''behavior'':38C,87C ''benefit'':20C ''classif'':9B ''click'':50C ''compress'':129C ''current'':13C ''d'':40C ''decis'':92C ''document'':27C,32C,48C ''event'':134C ''featur'':120C ''fix'':118C ''given'':24C ''global'':7A,78C,111C ''help'':89C ''histori'':35C ''inform'':90C ''input'':26C ''kind'':64C ''larg'':1A ''length'':119C ''less'':126C ''like'':41C ''m'':12C,56C ''mean'':122C ''model'':8A,70C,79C,95C,112C ''number'':138C ''output'':31C,47C ''p'':10C,52C ''past'':85C,133C ''per'':4A,72C,93C ''person'':3A,22C,66C ''predict'':23C,43C ''problem'':67C ''project'':17C ''scale'':2A ''set'':29C ''short'':54C ''singl'':77C ''smaller'':137C ''statist'':83C,141C ''stream'':131C ''summari'':82C,140C ''take'':80C,115C ''train'':71C ''typic'':60C ''user'':5A,37C,73C,86C,94C,102C ''vector'':121C ''vs'':6A ''won'':96C ''wonder'':57C ''work'':14C ''would'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (642, 2, '2014-06-30 23:10:53.397', 3, NULL, NULL, '&lt;p&gt;The answer to this question is going to vary pretty wildly depending on the size and nature of your data. At a high level, you could think of it as a special case of multilevel models; you have the option of estimating a model with complete pooling (i.e., a universal model that doesn''t distinguish between users), models with no pooling (a separate model for each user), and partially pooled models (a mixture of the two). You should really read Andrew Gelman on this topic if you''re interested.&lt;/p&gt;

&lt;p&gt;You can also think of this as a learning-to-rank problem that either tries to produce point-wise estimates using a single function or instead tries to optimize on some list-wise loss function (e.g., NDCG). &lt;/p&gt;

&lt;p&gt;As with most machine learning problems, it all depends on what kind of data you have, the quality of it, the sparseness of it, and what kinds of features you are able to extract from it. If you have reason to believe that each and every user is going to be pretty unique in their behavior, you might want to build a per-user model, but that''s going to be unwieldy fast -- and what do you do when you are faced with a new user?&lt;/p&gt;
', NULL, NULL, '''/p'':91C,133C,222C ''abl'':166C ''also'':95C ''andrew'':82C ''answer'':3C ''behavior'':190C ''believ'':176C ''build'':195C ''case'':34C ''complet'':47C ''could'':27C ''data'':21C,148C ''depend'':13C,143C ''distinguish'':56C ''doesn'':54C ''e.g'':131C ''either'':107C ''estim'':43C,114C ''everi'':180C ''extract'':168C ''face'':217C ''fast'':208C ''featur'':163C ''function'':118C,130C ''gelman'':83C ''go'':8C,183C,204C ''high'':24C ''i.e'':49C ''instead'':120C ''interest'':90C ''kind'':146C,161C ''learn'':102C,139C ''learning-to-rank'':101C ''level'':25C ''list'':127C ''list-wis'':126C ''loss'':129C ''machin'':138C ''might'':192C ''mixtur'':74C ''model'':37C,45C,52C,59C,65C,72C,200C ''multilevel'':36C ''natur'':18C ''ndcg'':132C ''new'':220C ''optim'':123C ''option'':41C ''p'':1C,92C,134C ''partial'':70C ''per'':198C ''per-us'':197C ''point'':112C ''point-wis'':111C ''pool'':48C,62C,71C ''pretti'':11C,186C ''problem'':105C,140C ''produc'':110C ''qualiti'':152C ''question'':6C ''rank'':104C ''re'':89C ''read'':81C ''realli'':80C ''reason'':174C ''separ'':64C ''singl'':117C ''size'':16C ''spars'':156C ''special'':33C ''think'':28C,96C ''topic'':86C ''tri'':108C,121C ''two'':77C ''uniqu'':187C ''univers'':51C ''unwieldi'':207C ''use'':115C ''user'':58C,68C,181C,199C,221C ''vari'':10C ''want'':193C ''wild'':12C ''wise'':113C,128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (643, 2, '2014-07-01 08:04:30.127', 2, NULL, NULL, '&lt;p&gt;I guess you say that you want to use 3-fold cross-validation because you know something about your data (that using k=10 would cause overfitting? I''m curious to your reasoning). I am not sure that you know this, if not then you can simply use a larger k.&lt;/p&gt;

&lt;p&gt;If you still think that you cannot use standard k-fold cross-validation, then you could modify the algorithm a bit: say that you split the data into 30 folds and each time use 20 for training and 10 for evaluation (and then shift up one fold and use the first and the last 9 as evaluation and the rest as training). This means that you''re able to use all your data.&lt;/p&gt;

&lt;p&gt;When I use k-fold cross-validation I usually run the process multiple times with a different randomisation to make sure that I have sufficient data, if you don''t you will see different performances depending on the randomisation. In such cases I would suggest sampling. The trick then is to do it often enough.&lt;/p&gt;
', NULL, NULL, '''/p'':54C,131C,190C ''10'':26C,96C ''20'':92C ''3'':11C ''30'':86C ''9'':112C ''abl'':125C ''algorithm'':76C ''bit'':78C ''cannot'':62C ''case'':176C ''caus'':28C ''could'':73C ''cross'':14C,69C,140C ''cross-valid'':13C,68C,139C ''curious'':32C ''data'':22C,84C,130C,160C ''depend'':170C ''differ'':151C,168C ''enough'':189C ''evalu'':98C,114C ''first'':108C ''fold'':12C,67C,87C,104C,138C ''guess'':3C ''k'':25C,53C,66C,137C ''k-fold'':65C,136C ''know'':18C,42C ''larger'':52C ''last'':111C ''m'':31C ''make'':154C ''mean'':121C ''modifi'':74C ''multipl'':147C ''often'':188C ''one'':103C ''overfit'':29C ''p'':1C,55C,132C ''perform'':169C ''process'':146C ''randomis'':152C,173C ''re'':124C ''reason'':35C ''rest'':117C ''run'':144C ''sampl'':180C ''say'':5C,79C ''see'':167C ''shift'':101C ''simpli'':49C ''someth'':19C ''split'':82C ''standard'':64C ''still'':58C ''suffici'':159C ''suggest'':179C ''sure'':39C,155C ''think'':59C ''time'':90C,148C ''train'':94C,119C ''trick'':182C ''use'':10C,24C,50C,63C,91C,106C,127C,135C ''usual'':143C ''valid'':15C,70C,141C ''want'':8C ''would'':27C,178C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (644, 1, '2014-07-01 13:44:23.29', 4, 310, 'Create most "average" cosine similarity observation', '&lt;p&gt;For a recommendation system I''m using cosine similarity to compute similarities between items. However, for items with small amounts of data I''d like to bin them under a general "average" category (in the general not mathematical sense). To accomplish this I''m currently trying to create a synthetic observation to represent that middle of the road point.&lt;/p&gt;

&lt;p&gt;So for example if these were my observations (rows are observations, cols are features):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[[0, 0, 0, 1, 1, 1, 0, 1, 0],
 [1, 0, 1, 0, 0, 0, 1, 0, 0],
 [1, 1, 1, 1, 0, 1, 0, 1, 1],
 [0, 0, 1, 0, 0, 1, 0, 1, 0]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;A strategy where I''d simply take the actual average of all features across observations would generate a synthetic datapoint such as follows, which I''d then append to the matrix before doing the similarity calculation.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[ 0.5 ,  0.25,  0.75,  0.5 ,  0.25,  0.75,  0.25,  0.75,  0.25]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;While this might work well with certain similarity metrics (e.g. L1 distance) I''m sure there are much better ways for cosine similarity. Though, at the moment, I''m having trouble reasoning my way through angles between lines in high dimensional space.&lt;/p&gt;

&lt;p&gt;Any ideas?&lt;/p&gt;
', 'recommendation  similarity', NULL, '''/code'':124C,175C ''/p'':69C,85C,163C,220C,224C ''/pre'':125C,176C ''0'':88C,89C,90C,94C,96C,98C,100C,101C,102C,104C,105C,110C,112C,115C,116C,118C,119C,121C,123C ''0.25'':167C,170C,172C,174C ''0.5'':166C,169C ''0.75'':168C,171C,173C ''1'':91C,92C,93C,95C,97C,99C,103C,106C,107C,108C,109C,111C,113C,114C,117C,120C,122C ''accomplish'':50C ''across'':140C ''actual'':135C ''amount'':29C ''angl'':213C ''append'':154C ''averag'':3A,41C,136C ''better'':196C ''bin'':36C ''calcul'':162C ''categori'':42C ''certain'':184C ''code'':87C,165C ''col'':82C ''comput'':20C ''cosin'':4A,17C,199C ''creat'':1A,57C ''current'':54C ''d'':33C,131C,152C ''data'':31C ''datapoint'':146C ''dimension'':218C ''distanc'':189C ''e.g'':187C ''exampl'':73C ''featur'':84C,139C ''follow'':149C ''general'':40C,45C ''generat'':143C ''high'':217C ''howev'':24C ''idea'':223C ''item'':23C,26C ''l1'':188C ''like'':34C ''line'':215C ''m'':15C,53C,191C,206C ''mathemat'':47C ''matrix'':157C ''metric'':186C ''middl'':64C ''might'':180C ''moment'':204C ''much'':195C ''observ'':6A,60C,78C,81C,141C ''p'':9C,70C,126C,177C,221C ''point'':68C ''pre'':86C,164C ''reason'':209C ''recommend'':7B,12C ''repres'':62C ''road'':67C ''row'':79C ''sens'':48C ''similar'':5A,8B,18C,21C,161C,185C,200C ''simpli'':132C ''small'':28C ''space'':219C ''strategi'':128C ''sure'':192C ''synthet'':59C,145C ''system'':13C ''take'':133C ''though'':201C ''tri'':55C ''troubl'':208C ''use'':16C ''way'':197C,211C ''well'':182C ''work'':181C ''would'':142C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (646, 1, '2014-07-01 17:03:31.907', 2, 19, 'Spatial index for variable kernel nonparametric density', '&lt;p&gt;I''m trying to build a nonparametric density function for a fairly large dataset that can be evaluated efficently, and can be updated efficiently when new points are added. There will only ever be a maximum of 4 independent variables, but we can start off with 2. Lets use a gaussian kernel. Let the result be a probability density function, i.e. its volume will be 1.&lt;/p&gt;

&lt;p&gt;In each evaluation, we can omit all points for which the evaluation point is outside a certain ellipsoid corresponding to the minimum gaussian value we care about. We can change this threshold for accuracy or performance, and the maximum number of points inside the threshold will depend on the chosen covariance matrix of the kernel. Then, we can evaluate the distribution approximately using the subset of points.&lt;/p&gt;

&lt;p&gt;If we use a fixed kernel, then we can use the eigenvalues and eigenvectors we get from the covariance matrix to transform each point so that the threshold ellipsoid is a fixed circle. We can then shove all the transformed points into a spatial index, and efficiently find all points within the required radius of the evaluation point.&lt;/p&gt;

&lt;p&gt;However, we would the kernel to be variable for two reasons. (1) to fit the data better, and (2) because adding or modifying points would require the fixed kernel to be updated, which would mean that the entire data set would need to be reindexed. With a variable kernel, we could make new/updated points only affect the closest points.&lt;/p&gt;

&lt;p&gt;Specifically, is there a spatial index that can efficiently find ellipses surrounding a given point from a set of around 10 million ellipses of different shapes and sizes?&lt;/p&gt;

&lt;p&gt;More generally though, does my approach look sound? I am open to answers like "give up and precalculate a grid of results". Answers much appreciated!&lt;/p&gt;
', 'data-indexing-techniques', NULL, '''/p'':79C,148C,208C,269C,299C,326C ''1'':78C,221C ''10'':291C ''2'':59C,228C ''4'':50C ''accuraci'':114C ''ad'':41C,230C ''affect'':265C ''answer'':313C,323C ''appreci'':325C ''approach'':306C ''approxim'':142C ''around'':290C ''better'':226C ''build'':17C ''care'':106C ''certain'':97C ''chang'':110C ''chosen'':130C ''circl'':182C ''closest'':267C ''correspond'':99C ''could'':260C ''covari'':131C,168C ''data'':9B,225C,248C ''data-indexing-techniqu'':8B ''dataset'':26C ''densiti'':7A,20C,71C ''depend'':127C ''differ'':295C ''distribut'':141C ''effic'':31C ''effici'':36C,196C,279C ''eigenvalu'':161C ''eigenvector'':163C ''ellips'':281C,293C ''ellipsoid'':98C,178C ''entir'':247C ''evalu'':30C,83C,92C,139C,206C ''ever'':45C ''fair'':24C ''find'':197C,280C ''fit'':223C ''fix'':154C,181C,237C ''function'':21C,72C ''gaussian'':63C,103C ''general'':302C ''get'':165C ''give'':315C ''given'':284C ''grid'':320C ''howev'':210C ''i.e'':73C ''independ'':51C ''index'':2A,10B,194C,276C ''insid'':123C ''kernel'':5A,64C,135C,155C,214C,238C,258C ''larg'':25C ''let'':60C,65C ''like'':314C ''look'':307C ''m'':14C ''make'':261C ''matrix'':132C,169C ''maximum'':48C,119C ''mean'':244C ''million'':292C ''minimum'':102C ''modifi'':232C ''much'':324C ''need'':251C ''new'':38C ''new/updated'':262C ''nonparametr'':6A,19C ''number'':120C ''omit'':86C ''open'':311C ''outsid'':95C ''p'':12C,80C,149C,209C,270C,300C ''perform'':116C ''point'':39C,88C,93C,122C,147C,173C,190C,199C,207C,233C,263C,268C,285C ''precalcul'':318C ''probabl'':70C ''radius'':203C ''reason'':220C ''reindex'':254C ''requir'':202C,235C ''result'':67C,322C ''set'':249C,288C ''shape'':296C ''shove'':186C ''size'':298C ''sound'':308C ''spatial'':1A,193C,275C ''specif'':271C ''start'':56C ''subset'':145C ''surround'':282C ''techniqu'':11B ''though'':303C ''threshold'':112C,125C,177C ''transform'':171C,189C ''tri'':15C ''two'':219C ''updat'':35C,241C ''use'':61C,143C,152C,159C ''valu'':104C ''variabl'':4A,52C,217C,257C ''volum'':75C ''within'':200C ''would'':212C,234C,243C,250C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (648, 2, '2014-07-01 18:48:13.757', 3, NULL, NULL, '&lt;p&gt;Singular Value Decomposition is a linear algebraic technique as a result of which the notion of normalization is hard to define. In principle, you can do this normalization by dividing each element A(i,j) of the matrix by the sum (or max) of the elements in that particular (ith) row, i.e. A(i,j) = A(i, j) / \sum_{k=1}^{n} A(i,k)&lt;/p&gt;

&lt;p&gt;However, a more elegant way to achieve this would be to apply a probabilistic dimensionality reduction technique such as &lt;a href="http://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis" rel="nofollow"&gt;PLSA&lt;/a&gt; or &lt;a href="http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation" rel="nofollow"&gt;LDA&lt;/a&gt;. These dimensionality reduction techniques ensure that you always end up with probability values strictly between 0 and 1.  &lt;/p&gt;
', NULL, NULL, '''/a'':96C,106C ''/p'':67C,125C ''/wiki/latent_dirichlet_allocation'':102C ''/wiki/probabilistic_latent_semantic_analysis'':92C ''0'':122C ''1'':62C,124C ''achiev'':75C ''algebra'':8C ''alway'':114C ''appli'':80C ''decomposit'':4C ''defin'':22C ''dimension'':83C,108C ''divid'':31C ''eleg'':72C ''element'':33C,47C ''en.wikipedia.org'':91C,101C ''en.wikipedia.org/wiki/latent_dirichlet_allocation'':100C ''en.wikipedia.org/wiki/probabilistic_latent_semantic_analysis'':90C ''end'':115C ''ensur'':111C ''hard'':20C ''howev'':69C ''href'':89C,99C ''i.e'':53C ''ith'':51C ''j'':36C,56C,59C ''k'':61C,66C ''lda'':105C ''linear'':7C ''matrix'':39C ''max'':44C ''n'':63C ''nofollow'':94C,104C ''normal'':18C,29C ''notion'':16C ''p'':1C,68C ''particular'':50C ''plsa'':95C ''principl'':24C ''probabilist'':82C ''probabl'':118C ''reduct'':84C,109C ''rel'':93C,103C ''result'':12C ''row'':52C ''singular'':2C ''strict'':120C ''sum'':42C,60C ''techniqu'':9C,85C,110C ''valu'':3C,119C ''way'':73C ''would'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (649, 2, '2014-07-01 23:11:35.627', 6, NULL, NULL, '&lt;p&gt;You are doing the correct thing. Technically, this averaging leads to computing the &lt;a href="http://en.wikipedia.org/wiki/Centroid"&gt;centroid&lt;/a&gt; in the Euclidean space of a set of N points. The centroid works pretty well with cosine similarities (cosine of the angles between normalized vectors), e.g. &lt;a href="http://en.wikipedia.org/wiki/Rocchio_algorithm"&gt;the Rocchio algorithm&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':21C,56C ''/p'':57C ''/wiki/centroid'':19C ''/wiki/rocchio_algorithm'':52C ''algorithm'':55C ''angl'':43C ''averag'':10C ''centroid'':20C,33C ''comput'':13C ''correct'':6C ''cosin'':38C,40C ''e.g'':47C ''en.wikipedia.org'':18C,51C ''en.wikipedia.org/wiki/centroid'':17C ''en.wikipedia.org/wiki/rocchio_algorithm'':50C ''euclidean'':24C ''href'':16C,49C ''lead'':11C ''n'':30C ''normal'':45C ''p'':1C ''point'':31C ''pretti'':35C ''rocchio'':54C ''set'':28C ''similar'':39C ''space'':25C ''technic'':8C ''thing'':7C ''vector'':46C ''well'':36C ''work'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (650, 2, '2014-07-02 08:40:09.773', 1, NULL, NULL, '&lt;p&gt;As @Debasis writes, what you are doing is correct. Do not worry about the relative values as the cosine similarity focusses on the angle, not the length of the vector. &lt;/p&gt;

&lt;p&gt;Note that the averages that you compute actually are the proportion of observations that have that feature in your subset. If the subset is sufficiently large, you can interpret them as probabilities.&lt;/p&gt;
', NULL, NULL, '''/p'':32C,66C ''actual'':41C ''angl'':25C ''averag'':37C ''comput'':40C ''correct'':10C ''cosin'':20C ''debasi'':3C ''featur'':50C ''focuss'':22C ''interpret'':62C ''larg'':59C ''length'':28C ''note'':34C ''observ'':46C ''p'':1C,33C ''probabl'':65C ''proport'':44C ''relat'':16C ''similar'':21C ''subset'':53C,56C ''suffici'':58C ''valu'':17C ''vector'':31C ''worri'':13C ''write'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (652, 2, '2014-07-02 08:58:25.413', 3, NULL, NULL, '&lt;p&gt;Simply, one common approach is to increase the complexity of the model, making it simple, and most probably underfitting at first, and increasing the complexity of the model  until early signs of overfitting are witnessed using a resampling technique such as cross validation, bootstrap, etc.&lt;/p&gt;

&lt;p&gt;You increase the complexity either by adding parameters (number of hidden neurons for artificial neural networks, number of trees in a random forest) or by relaxing the regularization (often named lambda, or C for support vector machines) term in your model.&lt;/p&gt;
', NULL, NULL, '''/p'':47C,90C ''ad'':55C ''approach'':5C ''artifici'':62C ''bootstrap'':45C ''c'':81C ''common'':4C ''complex'':10C,26C,52C ''cross'':43C ''earli'':31C ''either'':53C ''etc'':46C ''first'':22C ''forest'':71C ''hidden'':59C ''increas'':8C,24C,50C ''lambda'':79C ''machin'':85C ''make'':14C ''model'':13C,29C,89C ''name'':78C ''network'':64C ''neural'':63C ''neuron'':60C ''number'':57C,65C ''often'':77C ''one'':3C ''overfit'':34C ''p'':1C,48C ''paramet'':56C ''probabl'':19C ''random'':70C ''regular'':76C ''relax'':74C ''resampl'':39C ''sign'':32C ''simpl'':16C ''simpli'':2C ''support'':83C ''techniqu'':40C ''term'':86C ''tree'':67C ''underfit'':20C ''use'':37C ''valid'':44C ''vector'':84C ''wit'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6486, 1, '2015-07-17 08:58:09.017', 2, 372, 'Data.frame vs Data.table in R?', '&lt;p&gt;Data.table package is claimed to be faster than Data.frame. what are the implementation changes that made this possible? How can one leverage the power of this package for data analysis?&lt;/p&gt;
', 'data-mining  r  data-cleaning', NULL, '''/p'':44C ''analysi'':43C ''chang'':27C ''claim'':17C ''clean'':12B ''data'':7B,11B,42C ''data-clean'':10B ''data-min'':6B ''data.frame'':1A,22C ''data.table'':3A,14C ''faster'':20C ''implement'':26C ''leverag'':35C ''made'':29C ''mine'':8B ''one'':34C ''p'':13C ''packag'':15C,40C ''possibl'':31C ''power'':37C ''r'':5A,9B ''vs'':2A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (653, 1, '2014-07-02 13:40:27', 4, 132, 'Which non-training classification methods are available?', '&lt;p&gt;I am trying to find which classification methods, that do not use a training phase, are available. &lt;/p&gt;

&lt;p&gt;The scenario is gene expression based classification, in which you have a matrix of gene expression of m genes (features) and n samples (observations).
A signature for each class is also provided (that is a list of the features to consider to define to which class belongs a sample).&lt;/p&gt;

&lt;p&gt;An application (non-training) is the &lt;a href="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0015543" rel="nofollow"&gt;Nearest Template Prediction&lt;/a&gt; method. In this case it is computed the cosine distance between each sample and each signature (on the common set of features). Then each sample is assigned to the nearest class (the sample-class comparison resulting in a smaller distance). No already classified samples are needed in this case.&lt;/p&gt;

&lt;p&gt;A different application (training) is the &lt;a href="http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm" rel="nofollow"&gt;kNN&lt;/a&gt; method, in which we have a set of already labeled samples. Then, each new sample is labeled depending on how are labeled the k nearest samples.&lt;/p&gt;

&lt;p&gt;Are there any other non-training methods?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'classification', NULL, '''/a'':98C,165C ''/article/info%3adoi%2f10.1371%2fjournal.pone.0015543'':92C ''/p'':28C,79C,149C,192C,202C,205C ''/wiki/k-nearest_neighbors_algorithm'':161C ''alreadi'':141C,174C ''also'':60C ''applic'':82C,153C ''assign'':125C ''avail'':8A,27C ''base'':35C ''belong'':76C ''case'':102C,148C ''class'':58C,75C,129C,133C ''classif'':5A,9B,17C,36C ''classifi'':142C ''common'':117C ''comparison'':134C ''comput'':105C ''consid'':70C ''cosin'':107C ''defin'':72C ''depend'':183C ''differ'':152C ''distanc'':108C,139C ''en.wikipedia.org'':160C ''en.wikipedia.org/wiki/k-nearest_neighbors_algorithm'':159C ''express'':34C,45C ''featur'':49C,68C,120C ''find'':15C ''gene'':33C,44C,48C ''href'':89C,158C ''k'':189C ''knn'':164C ''label'':175C,182C,187C ''list'':65C ''m'':47C ''matrix'':42C ''method'':6A,18C,99C,166C,201C ''n'':51C ''nearest'':95C,128C,190C ''need'':145C ''new'':179C ''nofollow'':94C,163C ''non'':3A,84C,199C ''non-train'':2A,83C,198C ''observ'':53C ''p'':10C,29C,80C,150C,193C,203C ''phase'':25C ''predict'':97C ''provid'':61C ''rel'':93C,162C ''result'':135C ''sampl'':52C,78C,111C,123C,132C,143C,176C,180C,191C ''sample-class'':131C ''scenario'':31C ''set'':118C,172C ''signatur'':55C,114C ''smaller'':138C ''templat'':96C ''thank'':204C ''train'':4A,24C,85C,154C,200C ''tri'':13C ''use'':22C ''www.plosone.org'':91C ''www.plosone.org/article/info%3adoi%2f10.1371%2fjournal.pone.0015543'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (732, 2, '2014-07-13 09:58:16.387', 21, NULL, NULL, '&lt;p&gt;The problem with deep networks is that they have lots of hyperparameters to tune and very small solution space. Thus, finding good ones is more like an art rather than engineering task. I would start with working example from tutorial and play around with its parameters to see how results change - this gives a good intuition (though not formal explanation) about dependencies between parameters and results (both - final and intermediate). &lt;/p&gt;

&lt;p&gt;Also I found following papers very useful: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://yosinski.com/media/papers/Yosinski2012VisuallyDebuggingRestrictedBoltzmannMachine.pdf"&gt;Visually Debugging Restricted Boltzmann Machine Training
with a 3D Example&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf"&gt;A Practical Guide to Training Restricted Boltzmann
Machines&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;They both describe RBMs, but contain some insights on deep networks in general. For example, one of key points is that networks need to be debugged layer-wise - if previous layer doesn''t provide good representation of features, further layers have almost no chance to fix it. &lt;/p&gt;
', NULL, NULL, '''/a'':99C,115C ''/li'':100C,116C ''/media/papers/yosinski2012visuallydebuggingrestrictedboltzmannmachine.pdf'':88C ''/p'':72C,81C,167C ''/ul'':117C ''/~hinton/absps/guidetr.pdf'':106C ''3d'':97C ''almost'':161C ''also'':74C ''around'':44C ''art'':29C ''boltzmann'':92C,113C ''chanc'':163C ''chang'':52C ''contain'':124C ''debug'':90C,144C ''deep'':5C,128C ''depend'':63C ''describ'':121C ''doesn'':151C ''engin'':32C ''exampl'':39C,98C,133C ''explan'':61C ''featur'':157C ''final'':69C ''find'':22C ''fix'':165C ''follow'':77C ''formal'':60C ''found'':76C ''general'':131C ''give'':54C ''good'':23C,56C,154C ''guid'':109C ''href'':85C,103C ''hyperparamet'':13C ''insight'':126C ''intermedi'':71C ''intuit'':57C ''key'':136C ''layer'':146C,150C,159C ''layer-wis'':145C ''li'':83C,101C ''like'':27C ''lot'':11C ''machin'':93C,114C ''need'':141C ''network'':6C,129C,140C ''one'':24C,134C ''p'':1C,73C,118C ''paper'':78C ''paramet'':47C,65C ''play'':43C ''point'':137C ''practic'':108C ''previous'':149C ''problem'':3C ''provid'':153C ''rather'':30C ''rbms'':122C ''represent'':155C ''restrict'':91C,112C ''result'':51C,67C ''see'':49C ''small'':18C ''solut'':19C ''space'':20C ''start'':36C ''task'':33C ''though'':58C ''thus'':21C ''train'':94C,111C ''tune'':15C ''tutori'':41C ''ul'':82C ''use'':80C ''visual'':89C ''wise'':147C ''work'':38C ''would'':35C ''www.cs.toronto.edu'':105C ''www.cs.toronto.edu/~hinton/absps/guidetr.pdf'':104C ''yosinski.com'':87C ''yosinski.com/media/papers/yosinski2012visuallydebuggingrestrictedboltzmannmachine.pdf'':86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (655, 1, '2014-07-02 14:40:55.413', 8, 315, 'Foreign exchange market forecasting with neural networks', '&lt;p&gt;I would like to use ANN to automate trading currencies, preferably USD/EUR or USD/GBP. I know this is hard and may not be straightforward. I have already read some papers and done some experiments but without much luck. I would like to get advice from EXPERTS to make this work.&lt;/p&gt;

&lt;p&gt;Here is what I did so far:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;I got tick by tick data for the month of july 2013. It has bid/ask/bid volume/ask volume.&lt;/li&gt;
&lt;li&gt;Extracted all ticks for the time frame 12PM to 14PM for all days.&lt;/li&gt;
&lt;li&gt;From this data, created a data set where each entry consists of n bid values in sequence. &lt;/li&gt;
&lt;li&gt;Used that data to train an ANN with n-1 inputs and the output is the forecasted nth bid value.&lt;/li&gt;
&lt;li&gt;The ANN had n-1 inputs neurons, (n-1)*2 + 1 hidden and 1 output neuron. Input layer had linear TF, hidden had log TF and output had linear TF.&lt;/li&gt;
&lt;li&gt;Trained the network with back propagation with n-125 first and then 10.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;For both n, the MSE did not drop below 0.5 and stayed at that value during full training. Assuming that this could be due to the time series being totally random, I used the R package to find partial autocorrelation on the data set (pacf). This gave non zero values for 2 and 3 lags only. &lt;/p&gt;

&lt;p&gt;Question 1: What does this mean exactly? &lt;/p&gt;

&lt;p&gt;Then I used hurst exponent to evaluate the randomness. In R, hurst(values) showed values above 0.9.&lt;/p&gt;

&lt;p&gt;Question 2: It is supposed to be nearly random. Should it have a value closer to 0.5?&lt;/p&gt;

&lt;p&gt;I repeated the training of the ANN with n=3. The ANN was trained and was able to obtain a pretty low value for MSE. However, the calculated output from this ANN does not differ much from the (n-1)th bid value. It looks like ANN just takes the last bid as the next bid! I tried different network structures (all multilayer perceptions), different training parameters, etc, but results are same.&lt;/p&gt;

&lt;p&gt;Question 3: How can I improve the accuracy? Are there any other training methods than backpropagation?&lt;/p&gt;
', 'neuralnetwork', NULL, '''-1'':134C,151C,155C,339C ''-125'':187C ''/li'':89C,104C,123C,145C,177C,192C ''/ol'':193C ''/p'':60C,69C,251C,260C,279C,298C,372C,390C ''0.5'':204C,297C ''0.9'':278C ''1'':157C,160C,254C ''10'':191C ''12pm'':98C ''14pm'':100C ''2'':156C,246C,282C ''2013'':83C ''3'':248C,309C,375C ''abl'':316C ''accuraci'':381C ''advic'':53C ''alreadi'':36C ''ann'':15C,131C,148C,306C,311C,331C,346C ''assum'':213C ''autocorrel'':234C ''autom'':17C ''back'':183C ''backpropag'':389C ''bid'':119C,143C,341C,351C,355C ''bid/ask/bid'':86C ''calcul'':327C ''closer'':295C ''consist'':116C ''could'':216C ''creat'':109C ''currenc'':19C ''data'':77C,108C,111C,127C,237C ''day'':103C ''differ'':334C,358C,364C ''done'':41C ''drop'':202C ''due'':218C ''entri'':115C ''etc'':367C ''evalu'':268C ''exact'':259C ''exchang'':2A ''experi'':43C ''expert'':55C ''expon'':266C ''extract'':91C ''far'':68C ''find'':232C ''first'':188C ''forecast'':4A,141C ''foreign'':1A ''frame'':97C ''full'':211C ''gave'':241C ''get'':52C ''got'':73C ''hard'':28C ''hidden'':158C,168C ''howev'':325C ''hurst'':265C,273C ''improv'':379C ''input'':135C,152C,163C ''juli'':82C ''know'':25C ''lag'':249C ''last'':350C ''layer'':164C ''li'':71C,90C,105C,124C,146C,178C ''like'':12C,50C,345C ''linear'':166C,175C ''log'':170C ''look'':344C ''low'':321C ''luck'':47C ''make'':57C ''market'':3A ''may'':30C ''mean'':258C ''method'':387C ''month'':80C ''mse'':199C,324C ''much'':46C,335C ''multilay'':362C ''n'':118C,133C,150C,154C,186C,197C,308C,338C ''near'':288C ''network'':7A,181C,359C ''neural'':6A ''neuralnetwork'':8B ''neuron'':153C,162C ''next'':354C ''non'':242C ''nth'':142C ''obtain'':318C ''ol'':70C ''output'':138C,161C,173C,328C ''p'':9C,61C,194C,252C,261C,280C,299C,373C ''pacf'':239C ''packag'':230C ''paper'':39C ''paramet'':366C ''partial'':233C ''percept'':363C ''prefer'':20C ''pretti'':320C ''propag'':184C ''question'':253C,281C,374C ''r'':229C,272C ''random'':225C,270C,289C ''read'':37C ''repeat'':301C ''result'':369C ''sequenc'':122C ''seri'':222C ''set'':112C,238C ''show'':275C ''stay'':206C ''straightforward'':33C ''structur'':360C ''suppos'':285C ''take'':348C ''tf'':167C,171C,176C ''th'':340C ''tick'':74C,76C,93C ''time'':96C,221C ''total'':224C ''trade'':18C ''train'':129C,179C,212C,303C,313C,365C,386C ''tri'':357C ''usd/eur'':21C ''usd/gbp'':23C ''use'':14C,125C,227C,264C ''valu'':120C,144C,209C,244C,274C,276C,294C,322C,342C ''volum'':88C ''volume/ask'':87C ''without'':45C ''work'':59C ''would'':11C,49C ''zero'':243C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (656, 2, '2014-07-02 15:52:44.47', 3, NULL, NULL, '&lt;p&gt;Some suggestions&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Remove items appearing too infrequently in the data. That will reduce the dimensionality by several orders of magnitude. If a feature occurs less than say 10 times, it''s likely that it''s not adding any predictive value, and it may lead to overfitting due to low frequency&lt;/li&gt;
&lt;li&gt;Try a Linear SVM instead. They handle large dimensional data very well in terms of not overfitting. They also often have the option to assign relative weights to different classes, which may help address your unbalanced problem above. The sklearn svm (which simply wraps some other packages such as libsvm) has this option.&lt;/li&gt;
&lt;li&gt;Don''t use the ID column. Producing a model per user will most probably lead to overfitting. Instead, feed in attributes that describe the user that allows the model to generalize over similar users. You could try fitting a separate model per user, but you need a lot of data per user to do this well.&lt;/li&gt;
&lt;li&gt;It sounds like you really need to try some feature selection here, to reduce the dimensionality of the problem. But try 1 and 2 first, as they may give you good results sooner (although the end solution may still work better with some good feature selection). Sklearn again has a number of options for feature selection.&lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (657, 1, '2014-07-02 16:41:47.467', 3, 48, 'How are selected the features for a decision tree in CART?', '&lt;p&gt;Suppose I want to use CART as classification tree (I want a categorical response). I have the training set, and I split it using observation labels.&lt;/p&gt;

&lt;p&gt;Now, to build the decision tree (classification tree) how are selected the features to decide which label apply to testing observations?&lt;/p&gt;

&lt;p&gt;Supposing we are working on gene expression matrix, in which each element is a real number, is that done using features that are more distant between classes?&lt;/p&gt;
', 'classification', NULL, '''/p'':40C,63C,92C ''appli'':59C ''build'':44C ''cart'':11A,19C ''categor'':26C ''class'':91C ''classif'':12B,21C,48C ''decid'':56C ''decis'':8A,46C ''distant'':89C ''done'':83C ''element'':76C ''express'':71C ''featur'':5A,54C,85C ''gene'':70C ''label'':39C,58C ''matrix'':72C ''number'':80C ''observ'':38C,62C ''p'':13C,41C,64C ''real'':79C ''respons'':27C ''select'':3A,52C ''set'':32C ''split'':35C ''suppos'':14C,65C ''test'':61C ''train'':31C ''tree'':9A,22C,47C,49C ''use'':18C,37C,84C ''want'':16C,24C ''work'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (658, 1, '2014-07-02 20:54:53.767', 8, 108, 'Prediction with non-atomic features', '&lt;p&gt;I would like to use non-atomic data, as a feature for a prediction. 
Suppose I have a Table with these features:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;- Column 1: Categorical - House
- Column 2: Numerical - 23.22
- Column 3: A Vector - [ 12, 22, 32 ]
- Column 4: A Tree - [ [ 2323, 2323 ],[2323, 2323] , [ Boolean, Categorical ] ]
- Column 5: A List [ 122, Boolean ]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I would like to predict/classify, for instance, Column 2.&lt;/p&gt;

&lt;p&gt;I am making something to automatically respond to questions, any type of question, like "Where was Foo Born?" ...&lt;/p&gt;

&lt;p&gt;I first make a query to a search engine, then I get some text data as a result, then I do all the parsing stuff (tagging, stemming, parsing, splitting ... )&lt;/p&gt;

&lt;p&gt;My first approach was to make a table, each row with a line of text and a lot of features, like "First Word", "Tag of First Word", "Chunks", etc...&lt;/p&gt;

&lt;p&gt;But with this approach I am missing the relationships between the sentences. &lt;/p&gt;

&lt;p&gt;I would like to know if there is an algorithm that looks inside the tree structures (or vectors) and makes the relations and extract whatever is relevant for predicting/classifying. I''d prefer to know about a library that does that than an algorithm that I have to implement.&lt;/p&gt;
', 'machine-learning', NULL, '''/code'':68C ''/p'':34C,80C,100C,131C,162C,176C,226C ''/pre'':69C ''1'':38C ''12'':49C ''122'':66C ''2'':42C,79C ''22'':50C ''23.22'':44C ''2323'':56C,57C,58C,59C ''3'':46C ''32'':51C ''4'':53C ''5'':63C ''algorithm'':187C,220C ''approach'':135C,167C ''atom'':5A,18C ''automat'':87C ''boolean'':60C,67C ''born'':99C ''categor'':39C,61C ''chunk'':160C ''code'':36C ''column'':37C,41C,45C,52C,62C,78C ''d'':208C ''data'':19C,116C ''engin'':110C ''etc'':161C ''extract'':201C ''featur'':6A,22C,33C,152C ''first'':103C,134C,154C,158C ''foo'':98C ''get'':113C ''hous'':40C ''implement'':225C ''insid'':190C ''instanc'':77C ''know'':182C,211C ''learn'':9B ''librari'':214C ''like'':13C,73C,95C,153C,180C ''line'':145C ''list'':65C ''look'':189C ''lot'':150C ''machin'':8B ''machine-learn'':7B ''make'':84C,104C,138C,197C ''miss'':170C ''non'':4A,17C ''non-atom'':3A,16C ''numer'':43C ''p'':10C,70C,81C,101C,132C,163C,177C ''pars'':125C,129C ''pre'':35C ''predict'':1A,25C ''predict/classify'':75C ''predicting/classifying'':206C ''prefer'':209C ''queri'':106C ''question'':90C,94C ''relat'':199C ''relationship'':172C ''relev'':204C ''respond'':88C ''result'':119C ''row'':142C ''search'':109C ''sentenc'':175C ''someth'':85C ''split'':130C ''stem'':128C ''structur'':193C ''stuff'':126C ''suppos'':26C ''tabl'':30C,140C ''tag'':127C,156C ''text'':115C,147C ''tree'':55C,192C ''type'':92C ''use'':15C ''vector'':48C,195C ''whatev'':202C ''word'':155C,159C ''would'':12C,72C,179C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (659, 2, '2014-07-03 08:00:10.823', 6, NULL, NULL, '&lt;p&gt;As Steve Kallestad has said, this is a TSP problem, and there are wonderful free solvers to find approximate solutions.&lt;/p&gt;

&lt;p&gt;It may be too much work for what you are looking for, but you may try use one of those solvers in combination with the Google Maps API, to find real walking distances beetwen your coordinates:
&lt;a href="https://developers.google.com/maps/documentation/directions/#DirectionsRequests"&gt;https://developers.google.com/maps/documentation/directions/#DirectionsRequests&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;(I have never used this API, so I don''t know how easy or effective it would be)&lt;/p&gt;
', NULL, NULL, '''/maps/documentation/directions/#directionsrequests'':64C ''/maps/documentation/directions/#directionsrequests&lt;/a&gt;&lt;/p&gt;'':67C ''/p'':22C,87C ''api'':51C,74C ''approxim'':20C ''beetwen'':57C ''combin'':46C ''coordin'':59C ''developers.google.com'':63C,66C ''developers.google.com/maps/documentation/directions/#directionsrequests'':62C ''developers.google.com/maps/documentation/directions/#directionsrequests&lt;/a&gt;&lt;/p&gt;'':65C ''distanc'':56C ''easi'':81C ''effect'':83C ''find'':19C,53C ''free'':16C ''googl'':49C ''href'':61C ''kallestad'':4C ''know'':79C ''look'':34C ''map'':50C ''may'':25C,38C ''much'':28C ''never'':71C ''one'':41C ''p'':1C,23C,68C ''problem'':11C ''real'':54C ''said'':6C ''solut'':21C ''solver'':17C,44C ''steve'':3C ''tri'':39C ''tsp'':10C ''use'':40C,72C ''walk'':55C ''wonder'':15C ''work'':29C ''would'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (792, 2, '2014-07-21 11:59:49.203', 2, NULL, NULL, '&lt;p&gt;To handle such amount of data, programming language is not the main concern but the programming framework is. Frameworks such as MapReduce or Spark have bindings to many languages including Python. These frameworks certainly have many ready-to-use packages for data analysis tasks. But in the end it all comes to your requirement, i.e., what is your task? People have different definitions of data analysis tasks, some of them can be easily solved with relational databases. In that case, SQL is much better than all other alternatives.&lt;/p&gt;
', NULL, NULL, '''/p'':91C ''altern'':90C ''amount'':5C ''analysi'':45C,68C ''better'':86C ''bind'':27C ''case'':82C ''certain'':35C ''come'':53C ''concern'':14C ''data'':7C,44C,67C ''databas'':79C ''definit'':65C ''differ'':64C ''easili'':75C ''end'':50C ''framework'':18C,20C,34C ''handl'':3C ''i.e'':57C ''includ'':31C ''languag'':9C,30C ''main'':13C ''mani'':29C,37C ''mapreduc'':23C ''much'':85C ''p'':1C ''packag'':42C ''peopl'':62C ''program'':8C,17C ''python'':32C ''readi'':39C ''ready-to-us'':38C ''relat'':78C ''requir'':56C ''solv'':76C ''spark'':25C ''sql'':83C ''task'':46C,61C,69C ''use'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (660, 1, '2014-07-03 10:49:50.993', 6, 764, 'Linear Regression in R Mapreduce(RHadoop)', '&lt;p&gt;I m new to RHadoop and also to RMR...
I had an requirement to write a Mapreduce Job in R Mapreduce. I have Tried writing but While executing this it gives an Error.
Tring to read the file from hdfs&lt;/p&gt;

&lt;h2&gt;Error:&lt;/h2&gt;

&lt;pre&gt;&lt;code&gt;Error in mr(map = map, reduce = reduce, combine = combine, vectorized.reduce,  : 
   hadoop streaming failed with error code 1
&lt;/code&gt;&lt;/pre&gt;

&lt;h2&gt;Code :&lt;/h2&gt;

&lt;pre&gt;&lt;code&gt;Sys.setenv(HADOOP_HOME="/opt/cloudera/parcels/CDH-4.7.0-1.cdh4.7.0.p0.40/lib/hadoop")
Sys.setenv(HADOOP_CMD="/opt/cloudera/parcels/CDH-4.7.0-1.cdh4.7.0.p0.40/bin/hadoop")

Sys.setenv(HADOOP_STREAMING="/opt/cloudera/parcels/CDH-4.7.0-1.cdh4.7.0.p0.40/lib/hadoop-0.20-mapreduce/contrib/streaming/hadoop-streaming-2.0.0-mr1-cdh4.7.0.jar")
library(rmr2)
library(rhdfs)
hdfs.init()
day_file = hdfs.file("/hdfs/bikes_LR/day.csv","r")
day_read = hdfs.read(day_file)
c = rawToChar(day_read)

XtX =
  values(from.dfs(
    mapreduce(
      input = "/hdfs/bikes_LR/day.csv",
      map=
        function(.,Xi){
         yi =c[Xi[,1],]
         Xi = Xi[,-1]
         keyval(1,list(t(Xi)%*%Xi))
       },
  reduce = function(k,v )
  {
    vals =as.numeric(v)
    keyval(k,sum(vals))
  } ,
  combine = TRUE)))[[1]]

XtY =
 values(from.dfs(
    mapreduce(
     input = "/hdfs/bikes_LR/day.csv",
     map=
       function(.,Xi){
         yi =c[Xi[,1],]
         Xi = Xi[,-1]
        keyval(1,list(t(Xi)%*%yi))
       },
     reduce = TRUE ,
     combine = TRUE)))[[1]]
solve(XtX,XtY)



Input:
------------

instant,dteday,season,yr,mnth,holiday,weekday,workingday,weathersit,temp,atemp,hum,windspeed,casual,registered,cnt
1,2011-01-01,1,0,1,0,6,0,2,0.344167,0.363625,0.805833,0.160446,331,654,985
2,2011-01-02,1,0,1,0,0,0,2,0.363478,0.353739,0.696087,0.248539,131,670,801
3,2011-01-03,1,0,1,0,1,1,1,0.196364,0.189405,0.437273,0.248309,120,1229,1349
4,2011-01-04,1,0,1,0,2,1,1,0.2,0.212122,0.590435,0.160296,108,1454,1562
5,2011-01-05,1,0,1,0,3,1,1,0.226957,0.22927,0.436957,0.1869,82,1518,1600
6,2011-01-06,1,0,1,0,4,1,1,0.204348,0.233209,0.518261,0.0895652,88,1518,1606
7,2011-01-07,1,0,1,0,5,1,2,0.196522,0.208839,0.498696,0.168726,148,1362,1510
8,2011-01-08,1,0,1,0,6,0,2,0.165,0.162254,0.535833,0.266804,68,891,959
9,2011-01-09,1,0,1,0,0,0,1,0.138333,0.116175,0.434167,0.36195,54,768,822
10,2011-01-10,1,0,1,0,1,1,1,0.150833,0.150888,0.482917,0.223267,41,1280,1321



 Please Suggest me any mistakes.
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (661, 1, '2014-07-03 14:18:14.387', 2, 906, 'Cannot make user directory on a new CDH5 installation (Hadoop)', '&lt;p&gt;I download and installed CDH 5 package succesfully on a single linux node in pseudo-distributed Mode on my CentOS 6.5&lt;/p&gt;

&lt;p&gt;Starting Hadoop and verifying it is Working Properly as in &lt;a href="http://www.cloudera.com/content/cloudera-content/cloudera-docs/CDH5/latest/CDH5-Quick-Start/cdh5qs_mrv1_pseudo.html" rel="nofollow"&gt;this link&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I succesfully finished the following steps&lt;/p&gt;

&lt;p&gt;Step 1: Format the NameNode.&lt;/p&gt;

&lt;p&gt;Step 2: Start HDFS&lt;/p&gt;

&lt;p&gt;Step 3: Create the /tmp Directory&lt;/p&gt;

&lt;p&gt;Step 4: Create the MapReduce system directories:&lt;/p&gt;

&lt;p&gt;Step 5: Verify the HDFS File Structure&lt;/p&gt;

&lt;p&gt;Step 6: Start MapReduce&lt;/p&gt;

&lt;p&gt;while following command in step 7 I get the following error.&lt;/p&gt;

&lt;p&gt;Step 7: Create User Directories&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;$ sudo -u hdfs hadoop fs -mkdir -p /user/hadoopuser&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;mkdir: ''/user/hadoopuser'': No such file or directory&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;(where hadoopuser is my linux login username)&lt;/p&gt;

&lt;p&gt;If I create the directory manually as /user/hadoopuser in the filesystem, it is not accepting.&lt;/p&gt;

&lt;p&gt;How to success the step 7:?&lt;/p&gt;

&lt;p&gt;Please provide the sloution to procced the remaining installation.&lt;/p&gt;
', 'bigdata  hadoop', NULL, '''/a'':57C ''/content/cloudera-content/cloudera-docs/cdh5/latest/cdh5-quick-start/cdh5qs_mrv1_pseudo.html'':52C ''/em'':143C,156C ''/p'':36C,58C,66C,73C,79C,87C,96C,105C,111C,124C,131C,145C,158C,167C,184C,192C,203C ''/strong'':144C,157C ''/tmp'':85C ''/user/hadoopuser'':142C,150C,176C ''1'':69C ''2'':76C ''3'':82C ''4'':90C ''5'':19C,99C ''6'':108C ''6.5'':35C ''7'':118C,127C,191C ''accept'':183C ''bigdata'':11B ''cannot'':1A ''cdh'':18C ''cdh5'':8A ''cento'':34C ''command'':115C ''creat'':83C,91C,128C,171C ''directori'':4A,86C,95C,130C,155C,173C ''distribut'':30C ''download'':15C ''em'':134C,148C ''error'':123C ''file'':103C,153C ''filesystem'':179C ''finish'':62C ''follow'':64C,114C,122C ''format'':70C ''fs'':139C ''get'':120C ''hadoop'':10A,12B,39C,138C ''hadoopus'':161C ''hdfs'':78C,102C,137C ''href'':49C ''instal'':9A,17C,202C ''link'':56C ''linux'':25C,164C ''login'':165C ''make'':2A ''manual'':174C ''mapreduc'':93C,110C ''mkdir'':140C,149C ''mode'':31C ''namenod'':72C ''new'':7A ''node'':26C ''nofollow'':54C ''p'':13C,37C,59C,67C,74C,80C,88C,97C,106C,112C,125C,132C,141C,146C,159C,168C,185C,193C ''packag'':20C ''pleas'':194C ''procc'':199C ''proper'':45C ''provid'':195C ''pseudo'':29C ''pseudo-distribut'':28C ''rel'':53C ''remain'':201C ''singl'':24C ''sloution'':197C ''start'':38C,77C,109C ''step'':65C,68C,75C,81C,89C,98C,107C,117C,126C,190C ''strong'':133C,147C ''structur'':104C ''succes'':21C,61C ''success'':188C ''sudo'':135C ''system'':94C ''u'':136C ''user'':3A,129C ''usernam'':166C ''verifi'':41C,100C ''work'':44C ''www.cloudera.com'':51C ''www.cloudera.com/content/cloudera-content/cloudera-docs/cdh5/latest/cdh5-quick-start/cdh5qs_mrv1_pseudo.html'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (662, 1, '2014-07-03 16:11:22.637', 9, 3317, 'What algorithms should I use to perform job classification based on resume data?', '&lt;p&gt;Note that I am doing everything in R. &lt;/p&gt;

&lt;p&gt;The problem goes as follow: &lt;/p&gt;

&lt;p&gt;Basically, I have a list of resumes (CVs). Some candidates will have work experience before and some don''t. The goal here is to: based on the text on their CVs, I want to classify them into different job sectors. I am particular in those cases, in which the candidates do not have any experience / is a student, and I want to make a prediction to classify which job sectors this candidate will most likely belongs to after graduation . &lt;/p&gt;

&lt;p&gt;Question 1: I know machine learning algorithms. However, I have never done NLP before. I came across Latent Dirichlet allocation on the internet. However, I am not sure if this is the best approach to tackle my problem. &lt;/p&gt;

&lt;p&gt;My original idea:  &lt;em&gt;make this a supervised learning problem&lt;/em&gt;. 
Suppose we already have large amount of labelled data, meaning that we have correctly labelled the job sectors for a list of candidates. We train the model up using ML algorithms (i.e. nearest neighbor... )and feed in those &lt;em&gt;unlabelled data&lt;/em&gt;, which are candidates that have no work experience / are students, and try to predict which job sector they will belong to. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Update&lt;/strong&gt;
Question 2: Would it be a good idea to create an text file by extracting everything in a resume and print these data out in the text file, so that each resume is associated with a text file,which contains unstructured strings, and then we applied text mining techniques to the text files and make the data become structured or even to create a  frequency matrix of terms used out of the text files ? For example, the text file may look something like this:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;I deployed ML algorithm in this project and... Skills: Java, Python, c++ ...&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;This is what I meant by ''unstructured'', i.e. collapsing everything into a single line string.&lt;/p&gt;

&lt;p&gt;Is this approach wrong ? Please correct me if you think my approach is wrong. &lt;/p&gt;

&lt;p&gt;Question 3: The tricky part is: how to &lt;strong&gt;identify and extract the keywords&lt;/strong&gt; ? Using the &lt;code&gt;tm&lt;/code&gt; package in R ? what algorithm is the &lt;code&gt;tm&lt;/code&gt;   package based on ?  Should I use NLP algorithms ? If yes, what algorithms should I look at ? Please point me to some good resources to look at as well. &lt;/p&gt;

&lt;p&gt;Any ideas would be great.&lt;/p&gt;
', 'machine-learning  classification  nlp  text-mining', NULL, '''/code'':339C,394C,404C ''/em'':171C,213C ''/p'':31C,38C,119C,159C,235C,324C,340C,357C,373C,433C,440C ''/strong'':239C,389C ''1'':122C ''2'':241C ''3'':376C ''across'':137C ''algorithm'':2A,127C,202C,330C,399C,412C,416C ''alloc'':140C ''alreadi'':174C ''amount'':177C ''appli'':285C ''approach'':154C,361C,370C ''associ'':273C ''base'':10A,64C,406C ''basic'':40C ''becom'':297C ''belong'':115C,233C ''best'':153C ''c'':338C ''came'':136C ''candid'':49C,89C,111C,194C,216C ''case'':85C ''classif'':9A,17B ''classifi'':74C,106C ''code'':326C,392C,402C ''collaps'':350C ''contain'':279C ''correct'':185C,364C ''creat'':249C,302C ''cvs'':47C,70C ''data'':13A,180C,212C,262C,296C ''deploy'':328C ''differ'':77C ''dirichlet'':139C ''done'':132C ''em'':164C,210C ''even'':300C ''everyth'':28C,255C,351C ''exampl'':315C ''experi'':53C,94C,221C ''extract'':254C,386C ''feed'':207C ''file'':252C,267C,277C,292C,313C,318C ''follow'':37C ''frequenc'':304C ''goal'':60C ''goe'':35C ''good'':246C,426C ''graduat'':118C ''great'':439C ''howev'':128C,144C ''i.e'':203C,349C ''idea'':163C,247C,436C ''identifi'':384C ''internet'':143C ''java'':336C ''job'':8A,78C,108C,188C,229C ''keyword'':388C ''know'':124C ''label'':179C,186C ''larg'':176C ''latent'':138C ''learn'':16B,126C,169C ''like'':114C,322C ''line'':355C ''list'':44C,192C ''look'':320C,419C,429C ''machin'':15B,125C ''machine-learn'':14B ''make'':102C,165C,294C ''matrix'':305C ''may'':319C ''mean'':181C ''meant'':346C ''mine'':21B,287C ''ml'':201C,329C ''model'':198C ''nearest'':204C ''neighbor'':205C ''never'':131C ''nlp'':18B,133C,411C ''note'':23C ''origin'':162C ''p'':22C,32C,39C,120C,160C,236C,325C,341C,358C,374C,434C ''packag'':395C,405C ''part'':379C ''particular'':82C ''perform'':7A ''pleas'':363C,421C ''point'':422C ''predict'':104C,227C ''print'':260C ''problem'':34C,158C,170C ''project'':333C ''python'':337C ''question'':121C,240C,375C ''r'':30C,397C ''resourc'':427C ''resum'':12A,46C,258C,271C ''sector'':79C,109C,189C,230C ''singl'':354C ''skill'':335C ''someth'':321C ''string'':281C,356C ''strong'':237C,383C ''structur'':298C ''student'':97C,223C ''supervis'':168C ''suppos'':172C ''sure'':148C ''tackl'':156C ''techniqu'':288C ''term'':307C ''text'':20B,67C,251C,266C,276C,286C,291C,312C,317C ''text-min'':19B ''think'':368C ''tm'':393C,403C ''train'':196C ''tri'':225C ''tricki'':378C ''unlabel'':211C ''unstructur'':280C,348C ''updat'':238C ''use'':5A,200C,308C,390C,410C ''want'':72C,100C ''well'':432C ''work'':52C,220C ''would'':242C,437C ''wrong'':362C,372C ''yes'':414C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (664, 1, '2014-07-03 17:11:01.77', 9, 92, 'Fisher Scoring v/s Coordinate Descent for MLE in R', '&lt;p&gt;R base function &lt;code&gt;glm()&lt;/code&gt; uses Fishers Scoring for MLE, while the &lt;code&gt;glmnet&lt;/code&gt; appears to use the coordinate descent method to solve the same equation. Coordinate descent is more time-efficient than Fisher Scoring, as Fisher Scoring calculates the second order derivative matrix, in addition to some other matrix operations. which makes expensive to perform, while coordinate descent can do the same task in O(np) time.&lt;/p&gt;

&lt;p&gt;Why would R base function use Fisher Scoring? Does this method have an advantage over other optimization methods? How does coordinate descent and Fisher Scoring compare? I am relatively new to do this field so any help or resource will be helpful.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (665, 2, '2014-07-03 17:52:00.963', 3, NULL, NULL, '&lt;p&gt;At each split point, CART will choose the feature which "best" splits the observations. What qualifies as best varies, but generally the split is done so that the subsequent nodes are more homogenous/pure with respect to the target. There are different ways of measuring homogeneity, for example Gini, Entropy, Chi-square. If you are using software, it may allow you to choose the measure of homogenity that the tree algorithm will use.&lt;/p&gt;

&lt;p&gt;Distance is not a factor with trees - what matters is whether the value is greater than or less than the split point, not the distance from the split point.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (863, 2, '2014-07-29 13:43:39.06', 13, NULL, NULL, '&lt;p&gt;Let''s first split it into parts. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Data Science&lt;/strong&gt; is about making knowledge from raw data. It uses machine learning, statistics and other fields to simplify (or even automate) decision making. Data science techniques may work with any data size, but more data means better predictions and thus more precise decisions. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Hadoop&lt;/strong&gt; is a common name for a set of tools intended to work with large amounts of data. Two most important components in Hadoop are HDFS and MapReduce.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;HDFS&lt;/strong&gt;, or Hadoop Distributed File System, is a special distributed storage capable of holding really large data amounts. Large files on HDFS are split into blocks, and for each block HDFS API exposes its &lt;em&gt;location&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;MapReduce&lt;/strong&gt; is framework for running computations on nodes with data. MapReduce heavily uses &lt;em&gt;data locality&lt;/em&gt; exposed by HDFS: when possible, data is not transferred between nodes, but instead code is copied to the nodes with data. &lt;/p&gt;

&lt;p&gt;So basically any problem (including data science tasks) that doesn''t break data locality principle may be efficiently implemented using MapReduce (and a number of other problems may be solved not that efficiently, but still simply enough).&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Let''s take some examples. Very often analyst only needs some simple statistics over his tabular data. In this case &lt;a href="https://hive.apache.org/"&gt;&lt;strong&gt;Hive&lt;/strong&gt;&lt;/a&gt;, which is basically SQL engine over MapReduce, works pretty well (there are also Impala, Shark and others, but they don''t use Hadoop''s MapReduce, so more on them later). &lt;/p&gt;

&lt;p&gt;In other cases analyst (or developer) may want to work with previously unstructured data. Pure MapReduce is pretty good for &lt;strong&gt;transforming&lt;/strong&gt; and &lt;strong&gt;standardizing&lt;/strong&gt; data.&lt;/p&gt;

&lt;p&gt;Some people are used to exploratory statistics and visualization using tools like R. It''s possible to apply this approach to big data amounts using &lt;a href="https://github.com/RevolutionAnalytics/RHadoop/wiki"&gt;&lt;strong&gt;RHadoop&lt;/strong&gt;&lt;/a&gt; package. &lt;/p&gt;

&lt;p&gt;And when it comes to MapReduce-based machine learning &lt;a href="https://mahout.apache.org/"&gt;&lt;strong&gt;Apache Mahout&lt;/strong&gt;&lt;/a&gt; is the first to mention. &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;There''s, however, one type of algorithms that work pretty slowly on Hadoop even in presence of data locality, namely, iterative algorithms. Iterative algorithms tend to have multiple Map and Reduce stages. Hadoop''s MR framework &lt;strong&gt;reads&lt;/strong&gt; and &lt;strong&gt;writes&lt;/strong&gt; data to disk on each stage (and sometimes in between), which makes iterative (as well as any multi-stage) tasks terribly slow. &lt;/p&gt;

&lt;p&gt;Fortunately, there are alternative frameworks that can both - use data locality and keep data in memory between stages. Probably, the most notable of them is &lt;a href="https://spark.apache.org/"&gt;&lt;strong&gt;Apache Spark&lt;/strong&gt;&lt;/a&gt;. Spark is complete replacement for Hadoop''s MapReduce that uses its own runtime and exposes pretty rich API for manipulating your distributed dataset. Spark has several sub-projects, closely related to data science: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://shark.cs.berkeley.edu/"&gt;&lt;strong&gt;Shark&lt;/strong&gt;&lt;/a&gt; and &lt;a href="https://spark.apache.org/sql/"&gt;&lt;strong&gt;Spark SQL&lt;/strong&gt;&lt;/a&gt;  provide alternative SQL-like interfaces to data stored on HDFS&lt;/li&gt;
&lt;li&gt;&lt;a href="https://spark.apache.org/streaming/"&gt;&lt;strong&gt;Spark Streaming&lt;/strong&gt;&lt;/a&gt; makes it easy to work with continuous data streams (e.g. Twitter feed)&lt;/li&gt;
&lt;li&gt;&lt;a href="https://spark.apache.org/mllib/"&gt;&lt;strong&gt;MLlib&lt;/strong&gt;&lt;/a&gt; implements a number of machine learning algorithms with a pretty simple and flexible API&lt;/li&gt;
&lt;li&gt;&lt;a href="https://spark.apache.org/graphx/"&gt;&lt;strong&gt;GraphX&lt;/strong&gt;&lt;/a&gt; enables large-scale graph processing&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So there''s pretty large set of data science problems that you can solve with Hadoop and related projects. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (666, 2, '2014-07-03 18:36:36.05', 2, NULL, NULL, '&lt;p&gt;As far as gathering data goes, you can check out &lt;strong&gt;Quandl&lt;/strong&gt; (there''s a tutorial on using it with &lt;strong&gt;R&lt;/strong&gt; on &lt;a href="https://www.datacamp.com/courses/how-to-work-with-quandl-in-r" rel="nofollow"&gt;DataCamp&lt;/a&gt; if you''re interested).&lt;/p&gt;

&lt;p&gt;In addition, Aswath Damodaran''s &lt;a href="http://people.stern.nyu.edu/adamodar/New_Home_Page/data.html" rel="nofollow"&gt;site&lt;/a&gt; contains a lot of helpful datasets. Though they aren''t updated that frequently, they may still be useful, especially as a benchmark for comparing your own output (from the scripts you will inevitably need to write to calculate the necessary metrics).  &lt;/p&gt;

&lt;p&gt;And, again, &lt;strong&gt;Quant SE&lt;/strong&gt; is probably a better place to be looking...&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (667, 2, '2014-07-03 20:47:20.147', 5, NULL, NULL, '&lt;p&gt;This is a tricky problem. There are many ways to handle it. I guess, resumes can be treated as semi-structured documents. Sometimes, it''s beneficial to have some minimal structure in the documents. I believe, in resumes you would see some tabular data. You might want to treat these as attribute value pairs. For example, you would get a list of terms for the attribute "Skill set".&lt;/p&gt;

&lt;p&gt;The key idea is to manually configure a list of key phrases such as "skill", "education", "publication" etc. The next step is to extract terms which pertain to these key phrases either by exploiting the structure in some way (such as tables) or by utilizing the proximity of terms around these key phrases, e.g. the fact that the word "Java" is in close proximity to the term "skill" might indicate that the person is skilled in Java.&lt;/p&gt;

&lt;p&gt;After you extract these information, the next step could be to build up a feature vector for each of these key phrases. You can then represent a document as a vector with different fields (one each for a key phrase). For example, consider the following two resumes represented with two fields, namely &lt;em&gt;project&lt;/em&gt; and &lt;em&gt;education&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Doc1: {project: (java, 3) (c, 4)}, {education: (computer, 2), (physics, 1)}&lt;/p&gt;

&lt;p&gt;Doc2: {project: (java, 3) (python, 2)}, {education: (maths, 3), (computer, 2)}&lt;/p&gt;

&lt;p&gt;In the above example, I show a term with the frequency. Of course, while extracting the terms you need to stem and remove stop-words. It is clear from the examples that the person whose resume is Doc1 is more skilled in C than that of D2. Implementation wise, it''s very easy to represent documents as field vectors in Lucene.&lt;/p&gt;

&lt;p&gt;Now, the next step is to retrieve a ranked list of resumes given a job specification. In fact, that''s fairly straight forward if you represent queries (job specs) as field vectors as well. You just need to retrieve a ranked list of candidates (resumes) using Lucene from a collection of indexed resumes.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (668, 2, '2014-07-03 20:51:59.51', 1, NULL, NULL, '&lt;p&gt;I''ve done some research in this area. I''ve found first order Markov chains work well for predicting within game scoring dynamics across a variety of sports.&lt;/p&gt;

&lt;p&gt;You can read in more detail here:
&lt;a href="http://www.epjdatascience.com/content/3/1/4" rel="nofollow"&gt;http://www.epjdatascience.com/content/3/1/4&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (669, 2, '2014-07-03 23:00:42.35', 6, NULL, NULL, '&lt;p&gt;The results you''re seeing aren''t a byproduct of your training product, but rather that &lt;code&gt;neural nets&lt;/code&gt; are not a great choice for this task. &lt;code&gt;Neural nets&lt;/code&gt; are effectively a means to create a high order non-linear function by composing a number of simpler functions. This is often a really good thing, because it allows neural nets to fit very complex patterns.&lt;/p&gt;

&lt;p&gt;However, in a stock exchange any complex pattern, when traded upon will quickly decay. Detecting a complicated pattern will generally not generate useful results, because it is typically complex patterns in the short term. Additionally, depending on the metric you choose, there are a number of ways of performing well that actually won''t pay off in investing (such as just predicting the last value in your example).&lt;/p&gt;

&lt;p&gt;In addition the stock market is startlingly chaotic which can result in a &lt;code&gt;neural net&lt;/code&gt; overfitting. This means that the patterns it learns will generalize poorly. Something along the lines of just seeing a stock decrease over a day and uniformly deciding that the stock will always decrease just because it was seen on a relatively short term. Instead techniques like &lt;code&gt;ridge&lt;/code&gt; and &lt;code&gt;robust regression&lt;/code&gt;, which will identify more general, less complex patterns, do better.&lt;/p&gt;

&lt;p&gt;The winner of a similar Kaggle competition used &lt;code&gt;robust regression&lt;/code&gt; for this very reason. You are likely to see better results if you switch to a shallow learning model that will find functions of a lower polynomial order, over the deep complex functions of a neural net.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (670, 1, '2014-07-04 02:31:42.08', 3, 56, 'Dealing with events that have not yet happened when building a model', '&lt;p&gt;I was building a model that predicts user churn for a website, where I have data on all users, both past and present.&lt;/p&gt;

&lt;p&gt;I can build a model that only uses those users that have left, but then I''m leaving 2/3 of the total user population unused.&lt;/p&gt;

&lt;p&gt;Is there a good way to incorporate data from these users into a model from a conceptual standpoint?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (671, 1, '2014-07-04 05:12:44.707', 6, 110, 'Linearly increasing data with manual reset', '&lt;p&gt;I have a linearly increasing time series dataset of a sensor, with value ranges between 50 and 150. I''ve implemented a &lt;a href="http://en.wikipedia.org/wiki/Simple_linear_regression" rel="nofollow"&gt;Simple Linear Regression&lt;/a&gt; algorithm to fit a regression line on such data, and I''m predicting the date when the series would reach 120.&lt;/p&gt;

&lt;p&gt;All works fine when the series move upwards. But, there are cases in which the sensor reaches around 110 or 115, and it is reset; in such cases the values would start over again at, say, 50 or 60.&lt;/p&gt;

&lt;p&gt;This is where I start facing issues with the regression line, as it starts moving downwards, and it starts predicting old date. I think I should be considering only the subset of data from where it was previously reset. However, I''m trying to understand if there are any algorithms available that consider this case.&lt;/p&gt;

&lt;p&gt;I''m new to data science, would appreciate any pointers to move further.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit: nfmcclure''s suggestions applied&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Before applying the suggestions&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ZsyyQ.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Below is the snapshot of what I''ve got after splitting the dataset where the reset occurs, and the slope of two set.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/OEQCw.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;finding the mean of the two slopes and drawing the line from the mean.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/i2qv5.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Is this OK?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (874, 2, '2014-07-30 21:53:13.79', 4, NULL, NULL, '&lt;p&gt;I have some thoughts about your question. I hope it may help you to solve your problem.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;I''m planning to run some experiments with very large data sets, and I''d like to distribute the computation.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;In &lt;a href="http://www.datasciencecentral.com/profiles/blogs/meeting-justice-of-data-science" rel="nofollow"&gt;one of my posts&lt;/a&gt;, I have done research on topic of &lt;em&gt;evaluation methods of Data Science&lt;/em&gt;. With &lt;strong&gt;Learning Curve&lt;/strong&gt;, you can evaluate your experiments &lt;em&gt;learning ability&lt;/em&gt;. To talk a bit more, you will &lt;strong&gt;fix&lt;/strong&gt; commodity configuration, and then will run same experiment on the same number of machines with different size of data set you have (from starting from small chunk in size, incrementally increase the size until you reach the whole data set). &lt;/p&gt;

&lt;p&gt;To point on, you should avoid having &lt;em&gt;power distribution&lt;/em&gt; for result of performance test being run with different size of data sets. To avoid, you should carefully choose &lt;em&gt;step size&lt;/em&gt; (step size = amount of increments).&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;I have about ten machines available, each with 200 GB of free space on hard disk. However, I would like to perform experiments on a greater number of nodes, to measure scalability more precisely.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;For this type question, I have intuitively searched and read materials; afterwards, published as a &lt;a href="http://www.datasciencecentral.com/profiles/blogs/scale-your-vision" rel="nofollow"&gt;blog post&lt;/a&gt;. At the end of the post, I have briefly talked about how to test your hypothesis on real complex system. If you let me, I want to briefly talk about;&lt;/p&gt;

&lt;p&gt;First of all, &lt;strong&gt;base requirement&lt;/strong&gt; should be formed in order to run data set as a whole. The minimum requirement will build your &lt;strong&gt;baseline evaluation score&lt;/strong&gt; which is calculated with one/combination of evaluation metrics you have chosen, or with one/combination of methods being used to calculate &lt;code&gt;Running Time = Computation complexity + Communication cost + Synchronization cost&lt;/code&gt;. &lt;/p&gt;

&lt;p&gt;After those steps, with an &lt;em&gt;evaluation strategy&lt;/em&gt;, add new elements, e.g. new node, to the system you have doing scalability test; meanwhile, for each addition, measure performance w.r.t new system configuration. &lt;/p&gt;

&lt;p&gt;Just to note, &lt;em&gt;evaluation strategy&lt;/em&gt; must be planned along with considerations of default behavior of parallel and distributed systems. For example, what I mean by behavior is that adding just more cores will, after some point, automatically drop performance of the system not due to your algorithm characteristics. It is because more cores need more RAMs, more hard driver, or etc. In other words, there is a &lt;strong&gt;N-Way&lt;/strong&gt; relationship between hardware components. As a second example, adding more nodes to the distributed system will punished you with more communication and synchronization costs.&lt;/p&gt;

&lt;p&gt;As a last step, you will sketch two different graphs with your evaluation results via data analysis program or language (As a recommendation, use GNU Plot or R programming language). Print out and put those results at your desktop, and start to examine them, carefully. According to your investigation, modify/erase + rebuild &lt;em&gt;evaluation strategy&lt;/em&gt; and re-do the performance test.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Are there commodity services which would grant me that only my application would be running at a given time? Has anyone used such services yet?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I have no much experiment on commodity services, but I can easily say whether it grants or not depends on your configuration of services. If you configured say Hadoop to your node as an only service, Hadoop will grant your code will be only running at any time.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (680, 2, '2014-07-06 00:01:41.327', 4, NULL, NULL, '&lt;p&gt;There''s a number of semantic distance measures, each with its pros and cons. Here are just a few of them: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Cosine_similarity" rel="nofollow"&gt;&lt;strong&gt;cosine distance&lt;/strong&gt;&lt;/a&gt;, inner product between document feature vectors;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Latent_semantic_analysis" rel="nofollow"&gt;&lt;strong&gt;LSA&lt;/strong&gt;&lt;/a&gt;, another vector-based model, but utilizing SVD for de-noising original term-document matrix;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/WordNet" rel="nofollow"&gt;&lt;strong&gt;WordNet&lt;/strong&gt;&lt;/a&gt;-based, human verified, though hardly extensible.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Start with a simplest approach and then move further based on issues for your specific case. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (672, 2, '2014-07-04 06:46:19.18', 5, NULL, NULL, '&lt;p&gt;This setting is common in reliability, health care, and mortality. The statistical analysis method is called &lt;a href="http://en.wikipedia.org/wiki/Survival_analysis"&gt;Survival Analysis&lt;/a&gt;. All users are coded according to their start date (or week or month).  You use the empirical data to estimate the survival function, which is the probability that the time of defection is later than some specified time &lt;strong&gt;&lt;em&gt;t&lt;/em&gt;&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Your baseline model will estimate survival function for all users.  Then you can do more sophisticated modeling to estimate what factors or behaviors might predict defection (churn), given your baseline survival function.  Basically, any model that is predictive will yield a survival probability that is significantly lower than the baseline.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;There''s another approach which involves attempting to identify precursor events patterns or user behavior pattern that foreshadow defection. Any given event/behavior pattern might occur for users that defect, or for users that stay. For this analysis, you may need to censor your data to only include users that have been members for some minimum period of time. The minimum time period can be estimated using your estimate of survival function, or even simple histogram analysis of the distribution of membership period for users who have defected. &lt;/p&gt;
', NULL, NULL, '''/a'':25C ''/em'':67C ''/p'':69C,121C,211C ''/strong'':68C ''/wiki/survival_analysis'':22C ''accord'':30C ''analysi'':14C,24C,160C,199C ''anoth'':126C ''approach'':127C ''attempt'':130C ''baselin'':72C,100C,120C ''basic'':103C ''behavior'':93C,138C ''call'':17C ''care'':9C ''censor'':165C ''churn'':97C ''code'':29C ''common'':5C ''data'':43C,167C ''date'':34C ''defect'':57C,96C,142C,152C,210C ''distribut'':202C ''em'':65C ''empir'':42C ''en.wikipedia.org'':21C ''en.wikipedia.org/wiki/survival_analysis'':20C ''estim'':45C,75C,89C,188C,191C ''even'':196C ''event'':134C ''event/behavior'':145C ''factor'':91C ''foreshadow'':141C ''function'':48C,77C,102C,194C ''given'':98C,144C ''health'':8C ''histogram'':198C ''hr'':122C ''href'':19C ''identifi'':132C ''includ'':170C ''involv'':129C ''later'':59C ''lower'':117C ''may'':162C ''member'':175C ''membership'':204C ''method'':15C ''might'':94C,147C ''minimum'':178C,183C ''model'':73C,87C,105C ''month'':38C ''mortal'':11C ''need'':163C ''occur'':148C ''p'':1C,70C,123C ''pattern'':135C,139C,146C ''period'':179C,185C,205C ''precursor'':133C ''predict'':95C,108C ''probabl'':52C,113C ''reliabl'':7C ''set'':3C ''signific'':116C ''simpl'':197C ''sophist'':86C ''specifi'':62C ''start'':33C ''statist'':13C ''stay'':157C ''strong'':64C ''surviv'':23C,47C,76C,101C,112C,193C ''time'':55C,63C,181C,184C ''use'':40C,189C ''user'':27C,80C,137C,150C,155C,171C,207C ''week'':36C ''yield'':110C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (675, 2, '2014-07-04 22:46:32.13', 5, NULL, NULL, '&lt;p&gt;Just extract &lt;strong&gt;keywords&lt;/strong&gt; and train a &lt;strong&gt;classifier&lt;/strong&gt; on them. That''s all, really. &lt;/p&gt;

&lt;p&gt;Most of the text in CVs is not actually related to skills. E.g. consider sentence "I''m experienced and highly efficient in Java". Here only 1 out of 7 words is a skill name, the rest is just a noise that''s going to put your classification accuracy down. &lt;/p&gt;

&lt;p&gt;Most of CVs are not really structured. Or structured too freely. Or use unusual names for sections. Or file formats that don''t preserve structure when translated to text. I have experience extracting dates, times, names, addresses and even people intents from unstructured text, but not a skill (or university or anything) list, not even closely. &lt;/p&gt;

&lt;p&gt;So just tokenize (and possibly &lt;a href="http://stackoverflow.com/questions/16069406/text-mining-with-the-tm-package-word-stemming"&gt;stem&lt;/a&gt;) your CVs, select only words from predefined list (you can use LinkedIn or something similar to grab this list), create a feature vector and try out a couple of classifiers (say, SVM and Naive Bayes). &lt;/p&gt;

&lt;p&gt;(Note: I used a similar approach to classify LinkedIn profiles into more than 50 classes with accuracy &gt; 90%, so I''m pretty sure even naive implementation will work well.)&lt;/p&gt;
', NULL, NULL, '''/a'':141C ''/p'':19C,70C,128C,177C,208C ''/questions/16069406/text-mining-with-the-tm-package-word-stemming'':139C ''/strong'':6C,12C ''1'':46C ''50'':192C ''7'':49C ''90'':196C ''accuraci'':68C,195C ''actual'':29C ''address'':108C ''anyth'':123C ''approach'':184C ''bay'':176C ''class'':193C ''classif'':67C ''classifi'':11C,171C,186C ''close'':127C ''consid'':34C ''coupl'':169C ''creat'':161C ''cvs'':26C,74C,143C ''date'':105C ''e.g'':33C ''effici'':41C ''even'':110C,126C,202C ''experi'':103C ''experienc'':38C ''extract'':3C,104C ''featur'':163C ''file'':90C ''format'':91C ''freeli'':82C ''go'':63C ''grab'':158C ''high'':40C ''href'':136C ''implement'':204C ''intent'':112C ''java'':43C ''keyword'':5C ''linkedin'':153C,187C ''list'':124C,149C,160C ''m'':37C,199C ''naiv'':175C,203C ''name'':54C,86C,107C ''nois'':60C ''note'':179C ''p'':1C,20C,71C,129C,178C ''peopl'':111C ''possibl'':134C ''predefin'':148C ''preserv'':95C ''pretti'':200C ''profil'':188C ''put'':65C ''realli'':18C,77C ''relat'':30C ''rest'':56C ''say'':172C ''section'':88C ''select'':144C ''sentenc'':35C ''similar'':156C,183C ''skill'':32C,53C,119C ''someth'':155C ''stackoverflow.com'':138C ''stackoverflow.com/questions/16069406/text-mining-with-the-tm-package-word-stemming'':137C ''stem'':140C ''strong'':4C,10C ''structur'':78C,80C,96C ''sure'':201C ''svm'':173C ''text'':24C,100C,115C ''time'':106C ''token'':132C ''train'':8C ''translat'':98C ''tri'':166C ''univers'':121C ''unstructur'':114C ''unusu'':85C ''use'':84C,152C,181C ''vector'':164C ''well'':207C ''word'':50C,146C ''work'':206C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (676, 2, '2014-07-05 14:48:10.57', 4, NULL, NULL, '&lt;p&gt;In general regression models (any) can behave in an arbitrary way beyond the domain spanned by training samples. In particular, they are free to assume linearity of the modeled function, so if you for instance train a regression model with points:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;X     Y
10    0
20    1
30    2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;it is reasonable to build a model &lt;code&gt;f(x) = x/10-1&lt;/code&gt;, which for &lt;code&gt;x&amp;lt;10&lt;/code&gt; returns negative values.&lt;/p&gt;

&lt;p&gt;The same applies "in between" your data points, it is always possible that due to the assumed famility of functions (which can be modeled by particular method) you will get values "out of your training samples". &lt;/p&gt;

&lt;p&gt;You can think about this in another way - "what is so special about negative values?", why do you find existance of negative values weird (if not provided in training set) while you don''t get alarmed by existance of lets say... value 2131.23? Unless developed in such a way, no model will treat negative values "different" than positive ones. This is just a natural element of the real values which can be attained as any other value.&lt;/p&gt;
', NULL, NULL, '''/code'':54C,68C,75C ''/p'':43C,79C,117C,196C ''/pre'':55C ''0'':49C ''1'':51C ''10'':48C,74C ''2'':53C ''20'':50C ''2131.23'':161C ''30'':52C ''alarm'':154C ''alway'':91C ''anoth'':125C ''appli'':83C ''arbitrari'':11C ''assum'':26C,97C ''attain'':191C ''behav'':8C ''beyond'':13C ''build'':61C ''code'':45C,64C,71C ''data'':87C ''develop'':163C ''differ'':174C ''domain'':15C ''due'':94C ''element'':183C ''exist'':138C,156C ''f'':65C ''famil'':98C ''find'':137C ''free'':24C ''function'':31C,100C ''general'':3C ''get'':110C,153C ''instanc'':36C ''let'':158C ''linear'':27C ''lt'':73C ''method'':107C ''model'':5C,30C,40C,63C,104C,169C ''natur'':182C ''negat'':77C,132C,140C,172C ''one'':177C ''p'':1C,56C,80C,118C ''particular'':21C,106C ''point'':42C,88C ''posit'':176C ''possibl'':92C ''pre'':44C ''provid'':145C ''real'':186C ''reason'':59C ''regress'':4C,39C ''return'':76C ''sampl'':19C,116C ''say'':159C ''set'':148C ''span'':16C ''special'':130C ''think'':121C ''train'':18C,37C,115C,147C ''treat'':171C ''unless'':162C ''valu'':78C,111C,133C,141C,160C,173C,187C,195C ''way'':12C,126C,167C ''weird'':142C ''x'':46C,66C,72C ''x/10-1'':67C ''y'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8961, 1, '2015-11-21 07:17:14.963', 1, 23, 'Can we predict correlation between independent variables based on dependent variables', '&lt;p&gt;I have a correlation matrix for dependent var Vs independent variable as below:&lt;/p&gt;

&lt;p&gt;Year   1       Sales
Sales -0.5453  1.0000
PriceIndex    -0.6089 
Income        -0.5033 
Interest      -0.3842 &lt;/p&gt;

&lt;p&gt;As we can see that all independent var have negative correlation with the dependent variable. However as one of the requirement that i have for my project is that based on these values can i predict correlation between my independent variables.&lt;/p&gt;

&lt;p&gt;Kindly suggest if we can predict the same.
Thanks, Shivi&lt;/p&gt;
', 'statistics  correlation  linear-regression', NULL, '''-0.3842'':44C ''-0.5033'':42C ''-0.5453'':37C ''-0.6089'':40C ''/p'':31C,45C,88C,100C ''1'':34C ''1.0000'':38C ''base'':8A,76C ''correl'':4A,13B,21C,57C,83C ''depend'':10A,24C,60C ''howev'':62C ''incom'':41C ''independ'':6A,27C,53C,86C ''interest'':43C ''kind'':90C ''linear'':15B ''linear-regress'':14B ''matrix'':22C ''negat'':56C ''one'':64C ''p'':17C,32C,46C,89C ''predict'':3A,82C,95C ''priceindex'':39C ''project'':73C ''regress'':16B ''requir'':67C ''sale'':35C,36C ''see'':50C ''shivi'':99C ''statist'':12B ''suggest'':91C ''thank'':98C ''valu'':79C ''var'':25C,54C ''variabl'':7A,11A,28C,61C,87C ''vs'':26C ''year'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (758, 1, '2014-07-16 20:09:08.64', 18, 2450, 'Tools and protocol for reproducible data science using Python', '&lt;p&gt;I am working on a data science project using Python.
The project has several stages.
Each stage comprises of taking a data set, using Python scripts, auxiliary data, configuration and parameters, and creating another data set.
I store the code in git, so that part is covered.
I would like to hear about:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Tools for data version control.&lt;/li&gt;
&lt;li&gt;Tools enabling to reproduce stages and experiments.&lt;/li&gt;
&lt;li&gt;Protocol and suggested directory structure for such a project.&lt;/li&gt;
&lt;li&gt;Automated build/run tools.&lt;/li&gt;
&lt;/ol&gt;
', 'python  tools  version-control  reproducibility', NULL, '''/li'':78C,87C,98C,103C ''/ol'':104C ''/p'':70C ''anoth'':50C ''autom'':100C ''auxiliari'':43C ''build/run'':101C ''code'':56C ''compris'':34C ''configur'':45C ''control'':14B,77C ''cover'':63C ''creat'':49C ''data'':6A,22C,38C,44C,51C,75C ''directori'':92C ''enabl'':81C ''experi'':86C ''git'':58C ''hear'':68C ''li'':72C,79C,88C,99C ''like'':66C ''ol'':71C ''p'':16C ''paramet'':47C ''part'':61C ''project'':24C,28C,97C ''protocol'':3A,89C ''python'':9A,10B,26C,41C ''reproduc'':5A,15B,83C ''scienc'':7A,23C ''script'':42C ''set'':39C,52C ''sever'':30C ''stage'':31C,33C,84C ''store'':54C ''structur'':93C ''suggest'':91C ''take'':36C ''tool'':1A,11B,73C,80C,102C ''use'':8A,25C,40C ''version'':13B,76C ''version-control'':12B ''work'':19C ''would'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (677, 1, '2014-07-05 15:01:43.94', 3, 1076, 'Struggling to integrate sklearn and pandas in simple Kaggle task', '&lt;p&gt;I''m trying to use the sklearn_pandas module to extend the work I do in pandas and dip a toe into machine learning but I''m struggling with an error I don''t really understand how to fix.&lt;/p&gt;

&lt;p&gt;I was working through the following dataset on &lt;a href="https://www.kaggle.com/c/data-science-london-scikit-learn/data" rel="nofollow"&gt;Kaggle&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;It''s essentially an unheadered table (1000 rows, 40 features) with floating point values.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas as pdfrom sklearn import neighbors
from sklearn_pandas import DataFrameMapper, cross_val_score
path_train ="../kaggle/scikitlearn/train.csv"
path_labels ="../kaggle/scikitlearn/trainLabels.csv"
path_test = "../kaggle/scikitlearn/test.csv"

train = pd.read_csv(path_train, header=None)
labels = pd.read_csv(path_labels, header=None)
test = pd.read_csv(path_test, header=None)
mapper_train = DataFrameMapper([(list(train.columns),neighbors.KNeighborsClassifier(n_neighbors=3))])
mapper_train
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Output:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;DataFrameMapper(features=[([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], KNeighborsClassifier(algorithm=''auto'', leaf_size=30, metric=''minkowski'',
       n_neighbors=3, p=2, weights=''uniform''))])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So far so good. But then I try the fit&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;mapper_train.fit_transform(train, labels)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Output:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
&amp;lt;ipython-input-6-e3897d6db1b5&amp;gt; in &amp;lt;module&amp;gt;()
----&amp;gt; 1 mapper_train.fit_transform(train, labels)

//anaconda/lib/python2.7/site-packages/sklearn/base.pyc in fit_transform(self, X, y,     **fit_params)
    409         else:
    410             # fit method of arity 2 (supervised transformation)
--&amp;gt; 411             return self.fit(X, y, **fit_params).transform(X)
    412 
    413 

//anaconda/lib/python2.7/site-packages/sklearn_pandas/__init__.pyc in fit(self, X, y)
    116         for columns, transformer in self.features:
    117             if transformer is not None:
--&amp;gt; 118                 transformer.fit(self._get_col_subset(X, columns))
    119         return self
    120 

TypeError: fit() takes exactly 3 arguments (2 given)`
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What am I doing wrong? While the data in this case is all the same, I''m planning to work up a workflow for mixtures categorical, nominal and floating point features and sklearn_pandas seemed to be a logical fit.&lt;/p&gt;
', 'python  pandas  sklearn', NULL, '''/a'':72C ''/anaconda/lib/python2.7/site-packages/sklearn/base.pyc'':262C ''/anaconda/lib/python2.7/site-packages/sklearn_pandas/__init__.pyc'':293C ''/c/data-science-london-scikit-learn/data'':68C ''/code'':148C,212C,232C,329C ''/kaggle/scikitlearn/test.csv'':115C ''/kaggle/scikitlearn/train.csv'':109C ''/kaggle/scikitlearn/trainlabels.csv'':112C ''/p'':54C,73C,89C,152C,225C,236C,372C ''/pre'':149C,213C,233C,330C ''0'':157C ''1'':158C,257C ''10'':167C ''1000'':81C ''11'':168C ''116'':299C ''117'':305C ''118'':312C ''119'':317C ''12'':169C ''120'':320C ''13'':170C ''14'':171C ''15'':172C ''16'':173C ''17'':174C ''18'':175C ''19'':176C ''2'':159C,209C,278C,327C ''20'':177C ''21'':178C ''22'':179C ''23'':180C ''24'':181C ''25'':182C ''26'':183C ''27'':184C ''28'':185C ''29'':186C ''3'':145C,160C,207C,325C ''30'':187C,202C ''31'':188C ''32'':189C ''33'':190C ''34'':191C ''35'':192C ''36'':193C ''37'':194C ''38'':195C ''39'':196C ''4'':161C ''40'':83C ''409'':271C ''410'':273C ''411'':282C ''412'':291C ''413'':292C ''5'':162C ''6'':163C,249C ''7'':164C ''8'':165C ''9'':166C ''algorithm'':198C ''argument'':326C ''ariti'':277C ''auto'':199C ''call'':243C ''case'':342C ''categor'':357C ''code'':91C,154C,227C,238C ''column'':301C,316C ''cross'':104C ''csv'':118C,125C,132C ''data'':339C ''dataframemapp'':103C,139C,155C ''dataset'':62C ''dip'':33C ''e3897d6db1b5'':250C ''els'':272C ''error'':45C ''essenti'':77C ''exact'':324C ''extend'':25C ''far'':216C ''featur'':84C,156C,362C ''fit'':224C,264C,269C,274C,287C,295C,322C,371C ''fix'':53C ''float'':86C,360C ''follow'':61C ''given'':328C ''good'':218C ''gt'':251C,255C,256C,281C,311C ''header'':121C,128C,135C ''href'':65C ''import'':92C,97C,102C ''input'':248C ''integr'':3A ''ipython'':247C ''ipython-input'':246C ''kaggl'':9A,71C ''kneighborsclassifi'':197C ''label'':111C,123C,127C,231C,261C ''last'':244C ''leaf'':200C ''learn'':38C ''list'':140C ''logic'':370C ''lt'':245C,253C ''m'':16C,41C,348C ''machin'':37C ''mapper'':137C,146C ''mapper_train.fit'':228C,258C ''method'':275C ''metric'':203C ''minkowski'':204C ''mixtur'':356C ''modul'':23C,254C ''n'':143C,205C ''neighbor'':98C,144C,206C ''neighbors.kneighborsclassifier'':142C ''nofollow'':70C ''nomin'':358C ''none'':122C,129C,136C,310C ''output'':151C,235C ''p'':14C,55C,74C,150C,208C,214C,234C,331C ''panda'':6A,12B,22C,31C,93C,101C,365C ''param'':270C,288C ''path'':107C,110C,113C,119C,126C,133C ''pd.read'':117C,124C,131C ''pdfrom'':95C ''plan'':349C ''point'':87C,361C ''pre'':90C,153C,226C,237C ''python'':11B ''realli'':49C ''recent'':242C ''rel'':69C ''return'':283C,318C ''row'':82C ''score'':106C ''seem'':366C ''self'':266C,296C,319C ''self._get_col_subset'':314C ''self.features'':304C ''self.fit'':284C ''simpl'':8A ''size'':201C ''sklearn'':4A,13B,21C,96C,100C,364C ''struggl'':1A,42C ''supervis'':279C ''tabl'':80C ''take'':323C ''task'':10A ''test'':114C,130C,134C ''toe'':35C ''traceback'':240C ''train'':108C,116C,120C,138C,147C,230C,260C ''train.columns'':141C ''transform'':229C,259C,265C,280C,289C,302C,307C ''transformer.fit'':313C ''tri'':17C,222C ''typeerror'':239C,321C ''understand'':50C ''unhead'':79C ''uniform'':211C ''use'':19C ''val'':105C ''valu'':88C ''weight'':210C ''work'':27C,58C,351C ''workflow'':354C ''wrong'':336C ''www.kaggle.com'':67C ''www.kaggle.com/c/data-science-london-scikit-learn/data'':66C ''x'':267C,285C,290C,297C,315C ''y'':268C,286C,298C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (678, 1, '2014-07-05 16:10:21.58', 12, 3293, 'What are some standard ways of computing the distance between documents?', '&lt;p&gt;When I say "document", I have in mind web pages like Wikipedia articles and news stories.  I prefer answers giving either vanilla lexical distance metrics or state-of-the-art semantic distance metrics, with stronger preference for the latter.&lt;/p&gt;
', 'machine-learning  data-mining  nlp  text-mining  similarity', NULL, '''/p'':64C ''answer'':42C ''art'':54C ''articl'':36C ''comput'':7A ''data'':16B ''data-min'':15B ''distanc'':9A,47C,56C ''document'':11A,27C ''either'':44C ''give'':43C ''latter'':63C ''learn'':14B ''lexic'':46C ''like'':34C ''machin'':13B ''machine-learn'':12B ''metric'':48C,57C ''mind'':31C ''mine'':17B,21B ''news'':38C ''nlp'':18B ''p'':23C ''page'':33C ''prefer'':41C,60C ''say'':26C ''semant'':55C ''similar'':22B ''standard'':4A ''state'':51C ''state-of-the-art'':50C ''stori'':39C ''stronger'':59C ''text'':20B ''text-min'':19B ''vanilla'':45C ''way'':5A ''web'':32C ''wikipedia'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (679, 1, '2014-07-05 16:20:17.963', 7, 110, 'What are some standard ways of computing the distance between individual search queries?', '&lt;p&gt;I made a similar question asking about distance between "documents" (Wikipedia articles, news stories, etc.).  I made this a separate question because search queries are considerably smaller than documents and are considerably noisier.  I hence don''t know (and doubt) if the same distance metrics would be used here.  &lt;/p&gt;

&lt;p&gt;Either vanilla lexical distance metrics or state-of-the-art semantic distance metrics are preferred, with stronger preference for the latter.&lt;/p&gt;
', 'machine-learning  nlp  search', NULL, '''/p'':69C,93C ''art'':81C ''articl'':31C ''ask'':25C ''comput'':7A ''consider'':45C,51C ''distanc'':9A,27C,63C,74C,83C ''document'':29C,48C ''doubt'':59C ''either'':71C ''etc'':34C ''henc'':54C ''individu'':11A ''know'':57C ''latter'':92C ''learn'':16B ''lexic'':73C ''machin'':15B ''machine-learn'':14B ''made'':21C,36C ''metric'':64C,75C,84C ''news'':32C ''nlp'':17B ''noisier'':52C ''p'':19C,70C ''prefer'':86C,89C ''queri'':13A,43C ''question'':24C,40C ''search'':12A,18B,42C ''semant'':82C ''separ'':39C ''similar'':23C ''smaller'':46C ''standard'':4A ''state'':78C ''state-of-the-art'':77C ''stori'':33C ''stronger'':88C ''use'':67C ''vanilla'':72C ''way'':5A ''wikipedia'':30C ''would'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (682, 2, '2014-07-06 14:01:57.467', 1, NULL, NULL, '&lt;p&gt;Michael Maouboussin, in his book, "The Success Equation," looks at differentiating luck from skill in various endeavors, including sports.  He actually ranks sports by the amount of luck that contributes to performance in the different sports (p. 23) and about 2/3 of performance in football is attributable to skill.  By contrast, I used MM''s technique to analyze performance in Formula 1 racing, and found that 60% is attributable to skill (less than I was expecting.)&lt;/p&gt;

&lt;p&gt;That said, it seems this kind of analysis would imply that a sufficiently detailed and crafted feature set would allow ML algorithms to predict performance of NFL teams, perhaps even to the play level, with the caveat that significant variance will still exist because of the influence of luck in the game.&lt;/p&gt;
', NULL, NULL, '''/p'':78C,132C ''1'':63C ''2/3'':42C ''23'':39C ''60'':68C ''actual'':22C ''algorithm'':101C ''allow'':99C ''amount'':27C ''analysi'':87C ''analyz'':59C ''attribut'':48C,70C ''book'':6C ''caveat'':116C ''contrast'':52C ''contribut'':31C ''craft'':95C ''detail'':93C ''differ'':36C ''differenti'':12C ''endeavor'':18C ''equat'':9C ''even'':109C ''exist'':122C ''expect'':77C ''featur'':96C ''footbal'':46C ''formula'':62C ''found'':66C ''game'':131C ''impli'':89C ''includ'':19C ''influenc'':126C ''kind'':85C ''less'':73C ''level'':113C ''look'':10C ''luck'':13C,29C,128C ''maouboussin'':3C ''michael'':2C ''ml'':100C ''mm'':55C ''nfl'':106C ''p'':1C,38C,79C ''perform'':33C,44C,60C,104C ''perhap'':108C ''play'':112C ''predict'':103C ''race'':64C ''rank'':23C ''said'':81C ''seem'':83C ''set'':97C ''signific'':118C ''skill'':15C,50C,72C ''sport'':20C,24C,37C ''still'':121C ''success'':8C ''suffici'':92C ''team'':107C ''techniqu'':57C ''use'':54C ''varianc'':119C ''various'':17C ''would'':88C,98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (683, 1, '2014-07-07 09:34:36.95', 9, 136, 'Build a binary classifier with only positive and unlabeled data', '&lt;p&gt;I have 2 datasets, one with positive instances of what I would like to detect, and one with unlabeled instances. What methods can I use ?&lt;/p&gt;

&lt;p&gt;As an example, suppose we want to understand detect spam email based on a few structured email characteristics. We have one dataset of 10000 spam emails, and one dataset of 100000 emails for which we don''t know whether they are spam or not.&lt;/p&gt;

&lt;p&gt;How can we tackle this problem (without labeling manually any of the unlabeled data) ?&lt;/p&gt;

&lt;p&gt;What can we do if we have additional information about the proportion of spam in the unlabeled data (i.e. what if we estimate that between 20-40% of the 100000 unlabeled emails are spam) ?&lt;/p&gt;
', 'classification  semi-supervised-learning', NULL, '''-40'':132C ''/p'':42C,88C,104C,140C ''10000'':67C ''100000'':74C,135C ''2'':19C ''20'':131C ''addit'':113C ''base'':55C ''binari'':3A ''build'':1A ''characterist'':61C ''classif'':11B ''classifi'':4A ''data'':10A,103C,123C ''dataset'':20C,65C,72C ''detect'':31C,52C ''email'':54C,60C,69C,75C,137C ''estim'':128C ''exampl'':46C ''i.e'':124C ''inform'':114C ''instanc'':24C,36C ''know'':81C ''label'':97C ''learn'':15B ''like'':29C ''manual'':98C ''method'':38C ''one'':21C,33C,64C,71C ''p'':16C,43C,89C,105C ''posit'':7A,23C ''problem'':95C ''proport'':117C ''semi'':13B ''semi-supervised-learn'':12B ''spam'':53C,68C,85C,119C,139C ''structur'':59C ''supervis'':14B ''suppos'':47C ''tackl'':93C ''understand'':51C ''unlabel'':9A,35C,102C,122C,136C ''use'':41C ''want'':49C ''whether'':82C ''without'':96C ''would'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (684, 2, '2014-07-07 10:25:55.323', 6, NULL, NULL, '&lt;p&gt;I have never used &lt;code&gt;sklearn_pandas&lt;/code&gt;, but from reading their source code, it looks like this is a bug on their side. If you look for &lt;a href="https://github.com/paulgb/sklearn-pandas/blob/master/sklearn_pandas/__init__.py"&gt;the function that is throwing the exception&lt;/a&gt;, you can notice that they are discarding the &lt;code&gt;y&lt;/code&gt; argument (it does not even survive until the docstring), and the inner &lt;code&gt;fit&lt;/code&gt; function expects one argument more, which is probably &lt;code&gt;y&lt;/code&gt;:&lt;/p&gt;



&lt;pre&gt;&lt;code&gt;def fit(self, X, y=None):
    ''''''
    Fit a transformation from the pipeline

    X       the data to fit
    ''''''
    for columns, transformer in self.features:
        if transformer is not None:
            transformer.fit(self._get_col_subset(X, columns))
    return self
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I would recommend that you open an issue in &lt;a href="https://github.com/paulgb/sklearn-pandas/issues"&gt;their bug tracker&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;You can test this if you run your code from IPython. To summarize, if you use the &lt;code&gt;%pdb on&lt;/code&gt; magic right before you run the problematic call, the exception is captured by the Python debugger, so you can play around a bit and see that calling the &lt;code&gt;fit&lt;/code&gt; function with the label values &lt;code&gt;y[0]&lt;/code&gt; does work  -- see the last line with the &lt;code&gt;pdb&amp;gt;&lt;/code&gt; prompt. (The CSV files are downloaded from Kaggle, except for the largest one which is just a part of the real file).&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;In [1]: import pandas as pd

In [2]: from sklearn import neighbors

In [3]: from sklearn_pandas import DataFrameMapper, cross_val_score

In [4]: path_train ="train.csv"

In [5]: path_labels ="trainLabels.csv"

In [6]: path_test = "test.csv"

In [7]: train = pd.read_csv(path_train, header=None)

In [8]: labels = pd.read_csv(path_labels, header=None)

In [9]: test = pd.read_csv(path_test, header=None)

In [10]: mapper_train = DataFrameMapper([(list(train.columns),neighbors.KNeighborsClassifier(n_neighbors=3))])

In [13]: %pdb on

In [14]: mapper_train.fit_transform(train, labels)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
&amp;lt;ipython-input-14-e3897d6db1b5&amp;gt; in &amp;lt;module&amp;gt;()
----&amp;gt; 1 mapper_train.fit_transform(train, labels)

/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/base.pyc in fit_transform(self, X, y, **fit_params)
    409         else:
    410             # fit method of arity 2 (supervised transformation)
--&amp;gt; 411             return self.fit(X, y, **fit_params).transform(X)
    412
    413

/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn_pandas/__init__.pyc in fit(self, X, y)
    116         for columns, transformer in self.features:
    117             if transformer is not None:
--&amp;gt; 118                 transformer.fit(self._get_col_subset(X, columns))
    119         return self
    120

TypeError: fit() takes exactly 3 arguments (2 given)
&amp;gt; /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn_pandas/__init__.py(118)fit()
    117             if transformer is not None:
--&amp;gt; 118                 transformer.fit(self._get_col_subset(X, columns))
    119         return self

ipdb&amp;gt; l
    113
    114         X       the data to fit
    115         ''''''
    116         for columns, transformer in self.features:
    117             if transformer is not None:
--&amp;gt; 118                 transformer.fit(self._get_col_subset(X, columns))
    119         return self
    120
    121
    122     def transform(self, X):
    123         ''''''
ipdb&amp;gt; transformer.fit(self._get_col_subset(X, columns), y[0])
KNeighborsClassifier(algorithm=''auto'', leaf_size=30, metric=''minkowski'',
           n_neighbors=3, p=2, weights=''uniform'')
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (685, 1, '2014-07-07 11:43:48.43', 4, 642, 'Stochastic gradient descent in logistic regression', '&lt;p&gt;I am very new to machine learning and in my first project have stumbled across a lot of issues which I really want to get through.&lt;/p&gt;

&lt;p&gt;I''m using logistic regression with R''s &lt;code&gt;glmnet&lt;/code&gt; package and alpha = 0 for ridge regression.&lt;/p&gt;

&lt;p&gt;I''m using ridge regression actually since lasso deleted all my variables and gave very low area under curve (0.52) but with ridge there isn''t much of a difference (0.61).&lt;/p&gt;

&lt;p&gt;My dependent variable/output is probability of click, based on if there is a click or not in historical data.&lt;/p&gt;

&lt;p&gt;The independent variables are state, city, device, user age, user gender, IP carrier, keyword, mobile manufacturer, ad template, browser version, browser family, OS version and OS family.&lt;/p&gt;

&lt;p&gt;Of these, for prediction I''m using state, device, user age, user gender, IP carrier, browser version, browser family, OS version and OS family; I am not using keyword or template since we want to reject a user request before deep diving in our system and selecting a keyword or template. I am not using city because they are too many or mobile manufacturer because they are too few.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is that okay or should I be using the rejected variables?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;To start, I create a sparse matrix from my variables which are mapped against the column of clicks that have yes or no values.&lt;/p&gt;

&lt;p&gt;After training the model, I save the coefficients and intercept. These are used for new incoming requests using the formula for logistic regression:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;img src="http://i.stack.imgur.com/AZBRq.png" alt="1 / (1+e^-1*sum(a+k(ith)*x(ith)))"&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Where &lt;code&gt;a&lt;/code&gt; is intercept, &lt;code&gt;k&lt;/code&gt; is the &lt;code&gt;i&lt;/code&gt;th coefficient and &lt;code&gt;x&lt;/code&gt; is the &lt;code&gt;i&lt;/code&gt;th variable value.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is my approach correct so far?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Simple GLM in R (that is where there is no regularized regression, right?) gave me 0.56 AUC. With regularization I get 0.61 but there is no distinct threshold where we could say that above 0.xx its mostly ones and below it most zeros are covered; actually, the max probability that a click didn''t happen is almost always greater than the max probability that a click happened.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;So basically what should I do?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I have read how stochastic gradient descent is an effective technique in logit so how do I implement stochastic gradient descent in R? If it''s not straightforward, is there a way to implement this system in Python? Is SGD implemented after generating a regularized logistic regression model or is it a different process altogether?&lt;/p&gt;

&lt;p&gt;Also there is an algorithm called follow the regularized leader (FTRL) that is used in click-through rate prediction. Is there a sample code and use of FTRL that I could go through?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (686, 2, '2014-07-07 13:23:28.22', 3, NULL, NULL, '&lt;p&gt;State of the art appears to be "paragraph vectors" introduced in a recent paper: &lt;a href="http://cs.stanford.edu/~quocle/paragraph_vector.pdf" rel="nofollow"&gt;http://cs.stanford.edu/~quocle/paragraph_vector.pdf&lt;/a&gt;. Cosine/Euclidean distance between paragraph vectors would likely work better than any other approach. This probably isn''t feasible yet due to lack of open source implementations.&lt;/p&gt;

&lt;p&gt;Next best thing is cosine distance between LSA vectors or cosine distance between raw BOW vectors. Sometimes it works better to choose different weighting schemes, like TF-IDF. &lt;/p&gt;
', NULL, NULL, '''/p'':52C,83C ''/~quocle/paragraph_vector.pdf'':20C ''/~quocle/paragraph_vector.pdf&lt;/a&gt;.'':25C ''appear'':6C ''approach'':38C ''art'':5C ''best'':55C ''better'':34C,73C ''bow'':68C ''choos'':75C ''cosin'':58C,64C ''cosine/euclidean'':26C ''cs.stanford.edu'':19C,24C ''cs.stanford.edu/~quocle/paragraph_vector.pdf'':18C ''cs.stanford.edu/~quocle/paragraph_vector.pdf&lt;/a&gt;.'':23C ''differ'':76C ''distanc'':27C,59C,65C ''due'':45C ''feasibl'':43C ''href'':17C ''idf'':82C ''implement'':51C ''introduc'':11C ''isn'':41C ''lack'':47C ''like'':32C,79C ''lsa'':61C ''next'':54C ''nofollow'':22C ''open'':49C ''p'':1C,53C ''paper'':15C ''paragraph'':9C,29C ''probabl'':40C ''raw'':67C ''recent'':14C ''rel'':21C ''scheme'':78C ''sometim'':70C ''sourc'':50C ''state'':2C ''tf'':81C ''tf-idf'':80C ''thing'':56C ''vector'':10C,30C,62C,69C ''weight'':77C ''work'':33C,72C ''would'':31C ''yet'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10394, 1, '2016-02-25 18:11:34.943', -1, 75, 'which technology should I invest into SAS In-memory analytics or R?', '&lt;p&gt;As I''m starting my career in analytics, I have to choose between SAS in-memory analytics and Openware and largely adopted R, which one should I choose now so that I will have good market value in short-term as well as long-term?&lt;/p&gt;
', 'r  sas', NULL, '''/p'':65C ''adopt'':39C ''analyt'':11A,24C,34C ''career'':22C ''choos'':28C,45C ''good'':52C ''in-memori'':8A,31C ''invest'':5A ''larg'':38C ''long'':63C ''long-term'':62C ''m'':19C ''market'':53C ''memori'':10A,33C ''one'':42C ''openwar'':36C ''p'':16C ''r'':13A,14B,40C ''sas'':7A,15B,30C ''short'':57C ''short-term'':56C ''start'':20C ''technolog'':2A ''term'':58C,64C ''valu'':54C ''well'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (693, 2, '2014-07-07 18:36:23.43', 3, NULL, NULL, '&lt;p&gt;I work for an online jobs site and we build solutions to recommend jobs based on resumes. Our approach take''s a person''s job title (or desired job title if a student and known), along with skills we extract from their resume, and their location (which is very important to most people) and find matches with jobs based on that. &lt;/p&gt;

&lt;p&gt;in terms of document classification, I would take a similar approach. I would recommend computing a tf idf matrix for each resume as a standard bag of words model, extracting just the person''s job title and skills (for which you will need to define a list of skills to look for), and feed that into a ML algorithm. I would recommend trying knn, and an SVM, the latter works very well with high dimensional text data. Linear SVM''s tend to do better than non-linear (e.g. using RBf kernels). If you have that outputting reasonable results, I would then play with extracting features using a natural language parser \ chunker, and also some custom built phrases matched by regex''s.&lt;/p&gt;
', NULL, NULL, '''/p'':63C,187C ''algorithm'':123C ''along'':37C ''also'':178C ''approach'':20C,75C ''bag'':90C ''base'':16C,60C ''better'':148C ''build'':11C ''built'':181C ''chunker'':176C ''classif'':69C ''comput'':79C ''custom'':180C ''data'':141C ''defin'':109C ''desir'':29C ''dimension'':139C ''document'':68C ''e.g'':153C ''extract'':41C,94C,169C ''featur'':170C ''feed'':118C ''find'':56C ''high'':138C ''idf'':82C ''import'':51C ''job'':7C,15C,26C,30C,59C,99C ''kernel'':156C ''knn'':128C ''known'':36C ''languag'':174C ''latter'':133C ''linear'':142C,152C ''list'':111C ''locat'':47C ''look'':115C ''match'':57C,183C ''matrix'':83C ''ml'':122C ''model'':93C ''natur'':173C ''need'':107C ''non'':151C ''non-linear'':150C ''onlin'':6C ''output'':161C ''p'':1C,64C ''parser'':175C ''peopl'':54C ''person'':24C,97C ''phrase'':182C ''play'':167C ''rbf'':155C ''reason'':162C ''recommend'':14C,78C,126C ''regex'':185C ''result'':163C ''resum'':18C,44C,86C ''similar'':74C ''site'':8C ''skill'':39C,102C,113C ''solut'':12C ''standard'':89C ''student'':34C ''svm'':131C,143C ''take'':21C,72C ''tend'':145C ''term'':66C ''text'':140C ''tf'':81C ''titl'':27C,31C,100C ''tri'':127C ''use'':154C,171C ''well'':136C ''word'':92C ''work'':3C,134C ''would'':71C,77C,125C,165C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (694, 1, '2014-07-07 19:17:04.973', 60, 22834, 'Best python library for neural networks', '&lt;p&gt;I''m using Neural Networks to solve different Machine learning problems. I''m using Python and &lt;a href="http://pybrain.org/"&gt;pybrain&lt;/a&gt; but this library is almost discontinued. Are there other good alternatives in Python?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'machine-learning  python  neuralnetwork', NULL, '''/a'':33C ''/p'':47C,50C ''almost'':38C ''altern'':44C ''best'':1A ''differ'':20C ''discontinu'':39C ''good'':43C ''href'':30C ''learn'':9B,22C ''librari'':3A,36C ''m'':14C,25C ''machin'':8B,21C ''machine-learn'':7B ''network'':6A,17C ''neural'':5A,16C ''neuralnetwork'':11B ''p'':12C,48C ''problem'':23C ''pybrain'':32C ''pybrain.org'':31C ''python'':2A,10B,27C,46C ''solv'':19C ''thank'':49C ''use'':15C,26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (687, 2, '2014-07-07 14:15:51.117', 7, NULL, NULL, '&lt;p&gt;My suggestion would be to attempt to build some kind of clustering on your unlabeled data that somewhat approximates a labelled dataset. The rationale is more or less as follows:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;You have some feature vector for representing your documents&lt;/li&gt;
&lt;li&gt;Based on that feature vector, you can come up with a number of different clusterings, with either fuzzy, rough, or class-based clustering methods&lt;/li&gt;
&lt;li&gt;Knowing what a positive example looks like, you can quickly evaluate the overall similarity of a cluster to your positive cluster&lt;/li&gt;
&lt;li&gt;Knowing that there should really only be two clusters, you can adjust the hyperparameters on your clustering method so that the above two metrics are closer and closer to satisfaction&lt;/li&gt;
&lt;li&gt;With the two clusters, you have what is likely a close approximation of a labelled dataset, which you can then use as a silver-standard corpus of sorts to actually train your model&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Hope that makes sense, if you''re specifically looking for clustering algorithms, a few that I personally enjoy that might be good in this scenario are &lt;a href="http://en.wikipedia.org/wiki/FLAME_clustering"&gt;FLAME&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding"&gt;tsne&lt;/a&gt;. Alternately, looking at the spectacular &lt;a href="http://radimrehurek.com/gensim/"&gt;gensim&lt;/a&gt; library in python will get you a long way toward the clustering you''re looking for.&lt;/p&gt;

&lt;p&gt;Hope that helps and makes sense, leave a comment if you''ve got any questions.&lt;/p&gt;
', NULL, NULL, '''/a'':197C,205C,217C ''/gensim/'':215C ''/li'':44C,71C,94C,126C,162C ''/p'':32C,234C,251C ''/ul'':163C ''/wiki/flame_clustering'':195C ''/wiki/t-distributed_stochastic_neighbor_embedding'':203C ''actual'':158C ''adjust'':107C ''algorithm'':176C ''altern'':206C ''approxim'':20C,139C ''attempt'':7C ''base'':46C,68C ''build'':9C ''class'':67C ''class-bas'':66C ''close'':138C ''closer'':121C,123C ''cluster'':13C,60C,69C,89C,93C,104C,112C,131C,175C,229C ''come'':53C ''comment'':244C ''corpus'':154C ''data'':17C ''dataset'':23C,143C ''differ'':59C ''document'':43C ''either'':62C ''en.wikipedia.org'':194C,202C ''en.wikipedia.org/wiki/flame_clustering'':193C ''en.wikipedia.org/wiki/t-distributed_stochastic_neighbor_embedding'':201C ''enjoy'':182C ''evalu'':83C ''exampl'':77C ''featur'':38C,49C ''flame'':196C ''follow'':31C ''fuzzi'':63C ''gensim'':216C ''get'':222C ''good'':186C ''got'':248C ''help'':238C ''hope'':165C,236C ''href'':192C,200C,212C ''hyperparamet'':109C ''kind'':11C ''know'':73C,96C ''label'':22C,142C ''leav'':242C ''less'':29C ''li'':34C,45C,72C,95C,127C ''librari'':218C ''like'':79C,136C ''long'':225C ''look'':78C,173C,207C,232C ''make'':167C,240C ''method'':70C,113C ''metric'':119C ''might'':184C ''model'':161C ''number'':57C ''overal'':85C ''p'':1C,164C,235C ''person'':181C ''posit'':76C,92C ''python'':220C ''question'':250C ''quick'':82C ''radimrehurek.com'':214C ''radimrehurek.com/gensim/'':213C ''rational'':25C ''re'':171C,231C ''realli'':100C ''repres'':41C ''rough'':64C ''satisfact'':125C ''scenario'':189C ''sens'':168C,241C ''silver'':152C ''silver-standard'':151C ''similar'':86C ''somewhat'':19C ''sort'':156C ''specif'':172C ''spectacular'':210C ''standard'':153C ''suggest'':3C ''toward'':227C ''train'':159C ''tsne'':204C ''two'':103C,118C,130C ''ul'':33C ''unlabel'':16C ''use'':148C ''ve'':247C ''vector'':39C,50C ''way'':226C ''would'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (688, 2, '2014-07-07 14:41:59.523', 2, NULL, NULL, '&lt;p&gt;As @SpacedMan has noted &lt;a href="http://datascience.stackexchange.com/questions/600/how-do-you-create-an-optimized-walk-list-given-longitude-and-latitude-coordinate#comment1670_600"&gt;in a comment&lt;/a&gt;, the street layout will have a massive influence on the optimization of the walk list.  You have included only "latitude and longitude" in your question''s title; but solving that problem does not lead to a "walk list", but to a "as-the-crow-flies list".&lt;/p&gt;

&lt;p&gt;Looking at your street layout as a graph, with edge weights describing distances, and trying to find the shortest traversal between all required addresses, will lead you to think of your problem as a "&lt;a href="http://en.wikipedia.org/wiki/Shortest_path_problem" rel="nofollow"&gt;Shortest path problem&lt;/a&gt;". &lt;a href="http://en.wikipedia.org/wiki/Dijkstra%27s_algorithm" rel="nofollow"&gt;Dijkstra''s algorithm&lt;/a&gt; is the best known solution (there are others); in its naive implementation it converges in &lt;em&gt;O(n&lt;sup&gt;2&lt;/sup&gt;)&lt;/em&gt;, which may be acceptable if your lists of addresses are moderate in size.  Otherwise, look for optimized versions in the above links.&lt;/p&gt;

&lt;p&gt;As for libraries and resources to start tackling the problem, since you do not specify languages or platforms, let me point to the &lt;a href="http://wiki.openstreetmap.org/wiki/Routing#Developers" rel="nofollow"&gt;compilation of routing solvers in the Open Street Maps wiki&lt;/a&gt; and in general &lt;a href="http://wiki.openstreetmap.org/wiki/Frameworks" rel="nofollow"&gt;their frameworks and libraries page&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':14C,108C,119C,206C,222C ''/em'':141C ''/p'':62C,164C,223C ''/questions/600/how-do-you-create-an-optimized-walk-list-given-longitude-and-latitude-coordinate#comment1670_600'':10C ''/sup'':140C ''/wiki/dijkstra%27s_algorithm'':113C ''/wiki/frameworks'':214C ''/wiki/routing#developers'':193C ''/wiki/shortest_path_problem'':102C ''2'':139C ''accept'':145C ''address'':87C,150C ''algorithm'':118C ''as-the-crow-fli'':56C ''best'':122C ''comment'':13C ''compil'':196C ''converg'':133C ''crow'':59C ''datascience.stackexchange.com'':9C ''datascience.stackexchange.com/questions/600/how-do-you-create-an-optimized-walk-list-given-longitude-and-latitude-coordinate#comment1670_600'':8C ''describ'':75C ''dijkstra'':116C ''distanc'':76C ''edg'':73C ''em'':135C ''en.wikipedia.org'':101C,112C ''en.wikipedia.org/wiki/dijkstra%27s_algorithm'':111C ''en.wikipedia.org/wiki/shortest_path_problem'':100C ''find'':80C ''fli'':60C ''framework'':218C ''general'':209C ''graph'':71C ''href'':7C,99C,110C,190C,211C ''implement'':131C ''includ'':32C ''influenc'':22C ''known'':123C ''languag'':181C ''latitud'':34C ''layout'':17C,68C ''lead'':48C,89C ''let'':184C ''librari'':168C,220C ''link'':163C ''list'':29C,52C,61C,148C ''longitud'':36C ''look'':64C,156C ''map'':204C ''massiv'':21C ''may'':143C ''moder'':152C ''n'':137C ''naiv'':130C ''nofollow'':104C,115C,195C,216C ''note'':5C ''o'':136C ''open'':202C ''optim'':25C,158C ''other'':127C ''otherwis'':155C ''p'':1C,63C,165C ''page'':221C ''path'':106C ''platform'':183C ''point'':186C ''problem'':45C,95C,107C,175C ''question'':39C ''rel'':103C,114C,194C,215C ''requir'':86C ''resourc'':170C ''rout'':198C ''shortest'':82C,105C ''sinc'':176C ''size'':154C ''solut'':124C ''solv'':43C ''solver'':199C ''spacedman'':3C ''specifi'':180C ''start'':172C ''street'':16C,67C,203C ''sup'':138C ''tackl'':173C ''think'':92C ''titl'':41C ''travers'':83C ''tri'':78C ''version'':159C ''walk'':28C,51C ''weight'':74C ''wiki'':205C ''wiki.openstreetmap.org'':192C,213C ''wiki.openstreetmap.org/wiki/frameworks'':212C ''wiki.openstreetmap.org/wiki/routing#developers'':191C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (689, 2, '2014-07-07 15:36:40.96', 26, NULL, NULL, '&lt;p&gt;There''s a number of different ways of going about this depending on exactly how much semantic information you want to retain and how easy your documents are to tokenize (html documents would probably be pretty difficult to tokenize, but you could conceivably do something with tags and context.)&lt;/p&gt;

&lt;p&gt;Some of them have been mentioned by ffriend, and the paragraph vectors by user1133029 is a really solid one, but I just figured I would go into some more depth about plusses and minuses of different approaches.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Cosine_similarity"&gt;Cosine Distance&lt;/a&gt; - Tried a true, cosine distance is probably the most common distance metric used generically across multiple domains. With that said, there''s very little information in cosine distance that can actually be mapped back to anything semantic, which seems to be non-ideal for this situation.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Levenshtein_distance"&gt;Levenshtein Distance&lt;/a&gt; - Also known as &lt;code&gt;edit distance&lt;/code&gt;, this is usually just used on the individual token level (words, bigrams, etc...). In general I wouldn''t recommend this metric as it not only discards any semantic information, but also tends to treat very different word alterations very similarly, but it is an extremely common metric for this kind of thing&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Latent_semantic_analysis"&gt;LSA&lt;/a&gt; - Is a part of a large arsenal of techniques when it comes to evaluating document similarity called &lt;code&gt;topic modeling&lt;/code&gt;. LSA has gone out of fashion pretty recently, and in my experience, it''s not quite the strongest topic modeling approach, but it is relatively straightforward to implement and has a few open source implementations&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation"&gt;LDA&lt;/a&gt; - Is also a technique used for &lt;code&gt;topic modeling&lt;/code&gt;, but it''s different from &lt;code&gt;LSA&lt;/code&gt; in that it actually learns internal representations that tend to be more smooth and intuitive. In general, the results you get from &lt;code&gt;LDA&lt;/code&gt; are better for modeling document similarity than &lt;code&gt;LSA&lt;/code&gt;, but not quite as good for learning how to discriminate strongly between topics.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Pachinko_allocation"&gt;Pachinko Allocation&lt;/a&gt; - Is a really neat extension on top of LDA. In general, this is just a significantly improved version of &lt;code&gt;LDA&lt;/code&gt;, with the only downside being that it takes a bit longer to train and open-source implementations are a little harder to come by&lt;/li&gt;
&lt;li&gt;&lt;a href="https://code.google.com/p/word2vec/"&gt;word2vec&lt;/a&gt; - Google has been working on a series of techniques for intelligently reducing words and documents to more reasonable vectors than the sparse vectors yielded by techniques such as &lt;code&gt;Count Vectorizers&lt;/code&gt; and &lt;code&gt;TF-IDF&lt;/code&gt;. Word2vec is great because it has a number of open source implementations. Once you have the vector, any other similarity metric (like cosine distance) can be used on top of it with significantly more efficacy.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://cs.stanford.edu/~quocle/paragraph_vector.pdf"&gt;doc2vec&lt;/a&gt; - Also known as &lt;code&gt;paragraph vectors&lt;/code&gt;, this is the latest and greatest in a series of papers by Google, looking into dense vector representations of documents. The &lt;code&gt;gensim&lt;/code&gt; library in python has an implementation of &lt;code&gt;word2vec&lt;/code&gt; that is straightforward enough that it can pretty reasonably be leveraged to build &lt;code&gt;doc2vec&lt;/code&gt;, but make sure to keep the license in mind if you want to go down this route&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Hope that helps, let me know if you''ve got any questions.&lt;/p&gt;
', NULL, NULL, '''/a'':100C,157C,225C,290C,366C,422C,504C ''/code'':164C,246C,300C,308C,333C,343C,388C,454C,460C,511C,535C,545C,561C ''/li'':148C,217C,282C,357C,414C,496C,579C ''/p'':51C,90C,594C ''/p/word2vec/'':420C ''/ul'':580C ''/wiki/cosine_similarity'':97C ''/wiki/latent_dirichlet_allocation'':288C ''/wiki/latent_semantic_analysis'':223C ''/wiki/levenshtein_distance'':154C ''/wiki/pachinko_allocation'':363C ''/~quocle/paragraph_vector.pdf'':502C ''across'':115C ''actual'':131C,312C ''alloc'':365C ''also'':158C,195C,292C,505C ''alter'':202C ''anyth'':136C ''approach'':89C,267C ''arsenal'':232C ''back'':134C ''better'':335C ''bigram'':176C ''bit'':398C ''build'':558C ''call'':242C ''code'':161C,243C,297C,306C,331C,341C,386C,451C,456C,508C,533C,543C,559C ''code.google.com'':419C ''code.google.com/p/word2vec/'':418C ''come'':237C,412C ''common'':110C,210C ''conceiv'':44C ''context'':50C ''cosin'':98C,104C,127C,483C ''could'':43C ''count'':452C ''cs.stanford.edu'':501C ''cs.stanford.edu/~quocle/paragraph_vector.pdf'':500C ''dens'':527C ''depend'':13C ''depth'':82C ''differ'':7C,88C,200C,304C ''difficult'':38C ''discard'':190C ''discrimin'':353C ''distanc'':99C,105C,111C,128C,156C,163C,484C ''doc2vec'':503C,560C ''document'':28C,33C,240C,338C,437C,531C ''domain'':117C ''downsid'':392C ''easi'':26C ''edit'':162C ''efficaci'':495C ''en.wikipedia.org'':96C,153C,222C,287C,362C ''en.wikipedia.org/wiki/cosine_similarity'':95C ''en.wikipedia.org/wiki/latent_dirichlet_allocation'':286C ''en.wikipedia.org/wiki/latent_semantic_analysis'':221C ''en.wikipedia.org/wiki/levenshtein_distance'':152C ''en.wikipedia.org/wiki/pachinko_allocation'':361C ''enough'':549C ''etc'':177C ''evalu'':239C ''exact'':15C ''experi'':258C ''extens'':371C ''extrem'':209C ''fashion'':252C ''ffriend'':60C ''figur'':75C ''general'':179C,325C,377C ''generic'':114C ''gensim'':534C ''get'':329C ''go'':10C,78C,575C ''gone'':249C ''good'':348C ''googl'':423C,524C ''got'':591C ''great'':463C ''greatest'':517C ''harder'':410C ''help'':584C ''hope'':582C ''href'':94C,151C,220C,285C,360C,417C,499C ''html'':32C ''ideal'':144C ''idf'':459C ''implement'':274C,281C,406C,472C,541C ''improv'':383C ''individu'':172C ''inform'':19C,125C,193C ''intellig'':433C ''intern'':314C ''intuit'':323C ''keep'':566C ''kind'':214C ''know'':587C ''known'':159C,506C ''larg'':231C ''latest'':515C ''lda'':289C,332C,375C,387C ''learn'':313C,350C ''let'':585C ''level'':174C ''levenshtein'':155C ''leverag'':556C ''li'':92C,149C,218C,283C,358C,415C,497C ''librari'':536C ''licens'':568C ''like'':482C ''littl'':124C,409C ''longer'':399C ''look'':525C ''lsa'':224C,247C,307C,342C ''make'':563C ''map'':133C ''mention'':58C ''metric'':112C,185C,211C,481C ''mind'':570C ''minus'':86C ''model'':245C,266C,299C,337C ''much'':17C ''multipl'':116C ''neat'':370C ''non'':143C ''non-id'':142C ''number'':5C,468C ''one'':71C ''open'':279C,404C,470C ''open-sourc'':403C ''p'':1C,52C,581C ''pachinko'':364C ''paper'':522C ''paragraph'':63C,509C ''part'':228C ''pluss'':84C ''pretti'':37C,253C,553C ''probabl'':35C,107C ''python'':538C ''question'':593C ''quit'':262C,346C ''realli'':69C,369C ''reason'':440C,554C ''recent'':254C ''recommend'':183C ''reduc'':434C ''relat'':271C ''represent'':315C,529C ''result'':327C ''retain'':23C ''rout'':578C ''said'':120C ''seem'':139C ''semant'':18C,137C,192C ''seri'':429C,520C ''signific'':382C,493C ''similar'':204C,241C,339C,480C ''situat'':147C ''smooth'':321C ''solid'':70C ''someth'':46C ''sourc'':280C,405C,471C ''spars'':444C ''straightforward'':272C,548C ''strong'':354C ''strongest'':264C ''sure'':564C ''tag'':48C ''take'':396C ''techniqu'':234C,294C,431C,448C ''tend'':196C,317C ''tf'':458C ''tf-idf'':457C ''thing'':216C ''token'':31C,40C,173C ''top'':373C,489C ''topic'':244C,265C,298C,356C ''train'':401C ''treat'':198C ''tri'':101C ''true'':103C ''ul'':91C ''use'':113C,169C,295C,487C ''user1133029'':66C ''usual'':167C ''ve'':590C ''vector'':64C,441C,445C,453C,477C,510C,528C ''version'':384C ''want'':21C,573C ''way'':8C ''word'':175C,201C,435C ''word2vec'':421C,461C,544C ''work'':426C ''would'':34C,77C ''wouldn'':181C ''yield'':446C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (690, 2, '2014-07-07 18:06:24.583', 2, NULL, NULL, '&lt;p&gt;Train 2 generative models, one for each dataset (spam only, spam plus ham), that will give you the probability that a datapoint is drawn from the same probability distribution of the training data. Assign emails as spam or ham based on which model gives you the highest probability of the document arising from the training data used to train it. Example generative models are RBM''s, autoencoders (in that case, which model has the lowest reconstruction error). There are likely some bayesian generative models also that will assign a probability to a data point based on some training data.&lt;/p&gt;

&lt;p&gt;The best option though would be to take time to curate a second dataset containing only ham. That will give you higher classification accuracy. Assuming a lower proportion of spam to ham emails, that should not be too hard. You can even use Mechanical Turk if you lack the time or resources (or interns \ graduates students or other cheap labor).&lt;/p&gt;
', NULL, NULL, '''/p'':101C,163C ''2'':3C ''accuraci'':126C ''also'':86C ''aris'':53C ''assign'':35C,89C ''assum'':127C ''autoencod'':68C ''base'':41C,96C ''bayesian'':83C ''best'':104C ''case'':71C ''cheap'':161C ''classif'':125C ''contain'':117C ''curat'':113C ''data'':34C,57C,94C,100C ''datapoint'':23C ''dataset'':9C,116C ''distribut'':30C ''document'':52C ''drawn'':25C ''email'':36C,135C ''error'':78C ''even'':144C ''exampl'':62C ''generat'':4C,63C,84C ''give'':17C,45C,122C ''graduat'':157C ''ham'':14C,40C,119C,134C ''hard'':141C ''higher'':124C ''highest'':48C ''intern'':156C ''labor'':162C ''lack'':150C ''like'':81C ''lower'':129C ''lowest'':76C ''mechan'':146C ''model'':5C,44C,64C,73C,85C ''one'':6C ''option'':105C ''p'':1C,102C ''plus'':13C ''point'':95C ''probabl'':20C,29C,49C,91C ''proport'':130C ''rbm'':66C ''reconstruct'':77C ''resourc'':154C ''second'':115C ''spam'':10C,12C,38C,132C ''student'':158C ''take'':110C ''though'':106C ''time'':111C,152C ''train'':2C,33C,56C,60C,99C ''turk'':147C ''use'':58C,145C ''would'':107C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (691, 2, '2014-07-07 18:20:36.09', 3, NULL, NULL, '&lt;p&gt;Empirically I have found LSA vastly superior to LDA every time and on every dataset I have tried it on. I have talked to other people who have said the same thing. It''s also been used to win a number of the SemEval competitions for measuring semantic similarity between documents, often in combinations with a wordnet based measure, so I wouldn''t say it''s going out of fashion, or is definitely inferior to LDA, which is better for topic modelling and not semantic similarity in my experience, contrary to what some responders have stated. &lt;/p&gt;

&lt;p&gt;If you use gensim (a python library), it has LSA, LDA and word2vec, so you can easily compare the 3. doc2vec is a cool idea, but does not scale very well and you will likely have to implement it yourself as I am unaware of any open source implementations. It does not scale well as for each document, a new and separate model has to be built using SGD, a slow machine learning algorithm. But it will probably give you the most accurate results. LSA and LDA also don''t scale well (word2vec does however), LDA scales worse in general. Gensim''s implementations are very fast however, as it uses iterative SVD.&lt;/p&gt;

&lt;p&gt;One other note, if you use word2vec, you will still have to determine a way to compose vectors from documents, as it gives you a different vector per word. The simplest way to do this is to normalize each vector and take the mean over all word vectors in the document, or take a weighted mean by idf weighting of each word. So it''s not as simple as ''use word2vec'', you will need to do something further to compute document similarity. &lt;/p&gt;

&lt;p&gt;I would personally go with LSA, as I have seen it work well empirically, and gensim''s library scales very well. However, there''s no free lunch, so preferably try each method and see which works better for your data.&lt;/p&gt;
', NULL, NULL, '''/p'':98C,212C,296C,338C ''3'':119C ''accur'':182C ''algorithm'':173C ''also'':36C,187C ''base'':59C ''better'':80C,334C ''built'':166C ''combin'':55C ''compar'':117C ''competit'':46C ''compos'':230C ''comput'':293C ''contrari'':91C ''cool'':123C ''data'':337C ''dataset'':16C ''definit'':74C ''determin'':226C ''differ'':239C ''doc2vec'':120C ''document'':52C,157C,233C,264C,294C ''easili'':116C ''empir'':2C,311C ''everi'':11C,15C ''experi'':90C ''fashion'':71C ''fast'':205C ''found'':5C ''free'':323C ''general'':199C ''gensim'':103C,200C,313C ''give'':178C,236C ''go'':68C,301C ''howev'':194C,206C,319C ''idea'':124C ''idf'':271C ''implement'':137C,148C,202C ''inferior'':75C ''iter'':210C ''lda'':10C,77C,110C,186C,195C ''learn'':172C ''librari'':106C,315C ''like'':134C ''lsa'':6C,109C,184C,303C ''lunch'':324C ''machin'':171C ''mean'':257C,269C ''measur'':48C,60C ''method'':329C ''model'':83C,162C ''need'':287C ''new'':159C ''normal'':251C ''note'':216C ''number'':42C ''often'':53C ''one'':214C ''open'':146C ''p'':1C,99C,213C,297C ''peopl'':27C ''per'':241C ''person'':300C ''prefer'':326C ''probabl'':177C ''python'':105C ''respond'':95C ''result'':183C ''said'':30C ''say'':65C ''scale'':128C,152C,190C,196C,316C ''see'':331C ''seen'':307C ''semant'':49C,86C ''semev'':45C ''separ'':161C ''sgd'':168C ''similar'':50C,87C,295C ''simpl'':281C ''simplest'':244C ''slow'':170C ''someth'':290C ''sourc'':147C ''state'':97C ''still'':223C ''superior'':8C ''svd'':211C ''take'':255C,266C ''talk'':24C ''thing'':33C ''time'':12C ''topic'':82C ''tri'':19C,327C ''unawar'':143C ''use'':38C,102C,167C,209C,219C,283C ''vast'':7C ''vector'':231C,240C,253C,261C ''way'':228C,245C ''weight'':268C,272C ''well'':130C,153C,191C,310C,318C ''win'':40C ''word'':242C,260C,275C ''word2vec'':112C,192C,220C,284C ''wordnet'':58C ''work'':309C,333C ''wors'':197C ''would'':299C ''wouldn'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (692, 2, '2014-07-07 18:28:50.42', 4, NULL, NULL, '&lt;p&gt;The cosine similarity metric does a good (if not perfect) job of controlling for the document length, so comparing the similarity of 2 documents or 2 queries using the cosine metric and tf idf weights for the words should work well in either case. I would also recommend doing LSA first on tf idf weights, and then computing the cosine distance\similarities.&lt;/p&gt;

&lt;p&gt;If you are trying to build a search engine, I would recommend using a free open source search engine like solr or elastic search, or just the raw lucene libraries, as they do most of the work for you, and have good built in methods for handling the query to document similarity problem.&lt;/p&gt;
', NULL, NULL, '''/p'':64C,119C ''2'':24C,27C ''also'':48C ''build'':71C ''built'':108C ''case'':45C ''compar'':20C ''comput'':59C ''control'':14C ''cosin'':3C,31C,61C ''distanc'':62C ''document'':17C,25C,116C ''either'':44C ''elast'':88C ''engin'':74C,84C ''first'':52C ''free'':80C ''good'':8C,107C ''handl'':112C ''idf'':35C,55C ''job'':12C ''length'':18C ''librari'':95C ''like'':85C ''lsa'':51C ''lucen'':94C ''method'':110C ''metric'':5C,32C ''open'':81C ''p'':1C,65C ''perfect'':11C ''problem'':118C ''queri'':28C,114C ''raw'':93C ''recommend'':49C,77C ''search'':73C,83C,89C ''similar'':4C,22C,63C,117C ''solr'':86C ''sourc'':82C ''tf'':34C,54C ''tri'':69C ''use'':29C,78C ''weight'':36C,56C ''well'':42C ''word'':39C ''work'':41C,102C ''would'':47C,76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9805, 1, '2016-01-15 17:53:32.933', 0, 29, 'What techniques do Google use to interpret text typed into the search bar?', '&lt;p&gt;I have a project that I''m working on where I''d love to be able to take in questions from a user and give our results without the rigidity of having explicit questions.&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':52C ''abl'':33C ''bar'':13A ''d'':29C ''explicit'':50C ''give'':42C ''googl'':4A ''interpret'':7A ''learn'':16B ''love'':30C ''m'':24C ''machin'':15B ''machine-learn'':14B ''p'':17C ''project'':21C ''question'':37C,51C ''result'':44C ''rigid'':47C ''search'':12A ''take'':35C ''techniqu'':2A ''text'':8A ''type'':9A ''use'':5A ''user'':40C ''without'':45C ''work'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (701, 1, '2014-07-08 07:37:57.123', 3, 179, 'Clustering pair-wise distance dataset', '&lt;p&gt;I have generated a dataset of pairwise distances as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;id_1 id_2 dist_12
id_2 id_3 dist_23
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to cluster this data so as to identify the pattern. I have been looking at Spectral clustering and DBSCAN, but I haven''t been able to come to a conclusion and have been ambiguous on how to make use of the existing implementations of these algorithms. I have been looking at Python and Java implementations so far.&lt;/p&gt;

&lt;p&gt;Could anyone point me to a tutorial or demo on how to make use of these clustering algorithms to handle the situation in hand?&lt;/p&gt;
', 'data-mining  clustering  dbscan', NULL, '''/code'':38C ''/p'':23C,100C,126C ''/pre'':39C ''1'':27C ''12'':31C ''2'':29C,33C ''23'':37C ''3'':35C ''abl'':67C ''algorithm'':88C,119C ''ambigu'':76C ''anyon'':103C ''cluster'':1A,10B,44C,59C,118C ''code'':25C ''come'':69C ''conclus'':72C ''could'':102C ''data'':8B,46C ''data-min'':7B ''dataset'':6A,17C ''dbscan'':11B,61C ''demo'':110C ''dist'':30C,36C ''distanc'':5A,20C ''exist'':84C ''far'':99C ''follow'':22C ''generat'':15C ''hand'':125C ''handl'':121C ''haven'':64C ''id'':26C,28C,32C,34C ''identifi'':50C ''implement'':85C,97C ''java'':96C ''look'':56C,92C ''make'':80C,114C ''mine'':9B ''p'':12C,40C,101C ''pair'':3A ''pair-wis'':2A ''pairwis'':19C ''pattern'':52C ''point'':104C ''pre'':24C ''python'':94C ''situat'':123C ''spectral'':58C ''tutori'':108C ''use'':81C,115C ''want'':42C ''wise'':4A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (719, 2, '2014-07-10 22:42:13.72', 4, NULL, NULL, '&lt;p&gt;nsl-
I''m a beginner at machine learning, so forgive the lay-like description here, but it sounds like you might be able to use topic modelling, like latent dirichlet analysis (LDA). It''s an algorithm widely used to classify documents, according to what topics they are about, based on the words found and the relative frequencies of those words in the overall corpus.  I bring it up mainly because, in LDA it''s not necessary to define the topics in advance.&lt;/p&gt;

&lt;p&gt;Since the help pages on LDA are mostly written for text analysis, the analogy I would use, in order to apply it to your question, is:
- Treat each gene expression, or feature, as a ''word'' (sometimes called a token in typical LDA text-classification applications)
- Treat each sample as a document (ie it contains an assortment of words, or gene expressions)
- Treat the signatures as pre-existing topics&lt;/p&gt;

&lt;p&gt;If I''m not mistaken, LDA should give weighted probabilities for each topic, as to how strongly it is present in each document.&lt;/p&gt;
', NULL, NULL, '''/p'':85C,157C,182C ''abl'':25C ''accord'':44C ''advanc'':84C ''algorithm'':38C ''analog'':100C ''analysi'':33C,98C ''appli'':107C ''applic'':132C ''assort'':143C ''base'':51C ''beginn'':6C ''bring'':68C ''call'':123C ''classif'':131C ''classifi'':42C ''contain'':141C ''corpus'':66C ''defin'':80C ''descript'':16C ''dirichlet'':32C ''document'':43C,138C,181C ''exist'':155C ''express'':116C,148C ''featur'':118C ''forgiv'':11C ''found'':55C ''frequenc'':59C ''gene'':115C,147C ''give'':166C ''help'':89C ''ie'':139C ''latent'':31C ''lay'':14C ''lay-lik'':13C ''lda'':34C,74C,92C,128C,164C ''learn'':9C ''like'':15C,21C,30C ''m'':4C,161C ''machin'':8C ''main'':71C ''might'':23C ''mistaken'':163C ''model'':29C ''most'':94C ''necessari'':78C ''nsl'':2C ''order'':105C ''overal'':65C ''p'':1C,86C,158C ''page'':90C ''pre'':154C ''pre-exist'':153C ''present'':178C ''probabl'':168C ''question'':111C ''relat'':58C ''sampl'':135C ''signatur'':151C ''sinc'':87C ''sometim'':122C ''sound'':20C ''strong'':175C ''text'':97C,130C ''text-classif'':129C ''token'':125C ''topic'':28C,47C,82C,156C,171C ''treat'':113C,133C,149C ''typic'':127C ''use'':27C,40C,103C ''weight'':167C ''wide'':39C ''word'':54C,62C,121C,145C ''would'':102C ''written'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (718, 2, '2014-07-10 15:43:53.177', 8, NULL, NULL, '&lt;p&gt;A very strong correlation between the new feature and an existing feature is a fairly good sign that the new feature provides little new information.  A low correlation between the new feature and existing features is likely preferable.&lt;/p&gt;

&lt;p&gt;A strong linear correlation between the new feature and the predicted variable is an good sign that a new feature will be valuable, but the absence of a high correlation is not necessary a sign of a poor feature, because neural networks are not restricted to linear combinations of variables.   &lt;/p&gt;

&lt;p&gt;If the new feature was manually constructed from a combination of existing features, consider leaving it out.  The beauty of neural networks is that little feature engineering and preprocessing is required -- features are instead learned by intermediate layers.  Whenever possible, prefer learning features to engineering them.&lt;/p&gt;
', NULL, NULL, '''/p'':40C,92C,140C ''absenc'':67C ''beauti'':112C ''combin'':89C,103C ''consid'':107C ''construct'':100C ''correl'':5C,29C,45C,71C ''engin'':120C,138C ''exist'':12C,35C,105C ''fair'':16C ''featur'':9C,13C,22C,33C,36C,49C,61C,80C,97C,106C,119C,125C,136C ''good'':17C,56C ''high'':70C ''inform'':26C ''instead'':127C ''intermedi'':130C ''layer'':131C ''learn'':128C,135C ''leav'':108C ''like'':38C ''linear'':44C,88C ''littl'':24C,118C ''low'':28C ''manual'':99C ''necessari'':74C ''network'':83C,115C ''neural'':82C,114C ''new'':8C,21C,25C,32C,48C,60C,96C ''p'':1C,41C,93C ''poor'':79C ''possibl'':133C ''predict'':52C ''prefer'':39C,134C ''preprocess'':122C ''provid'':23C ''requir'':124C ''restrict'':86C ''sign'':18C,57C,76C ''strong'':4C,43C ''valuabl'':64C ''variabl'':53C,91C ''whenev'':132C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (695, 2, '2014-07-07 19:55:51.057', 53, NULL, NULL, '&lt;p&gt;UPDATE: the landscape has changed quite a bit since I answered this question in July ''14, and some new players have entered the space.  In particular I would recommend checking out:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Lasagne: &lt;a href="https://github.com/Lasagne/Lasagne"&gt;https://github.com/Lasagne/Lasagne&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Keras: &lt;a href="https://github.com/fchollet/keras"&gt;https://github.com/fchollet/keras&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Deepy: &lt;a href="https://github.com/uaca/deepy"&gt;https://github.com/uaca/deepy&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Nolearn: &lt;a href="https://github.com/dnouri/nolearn"&gt;https://github.com/dnouri/nolearn&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Blocks: &lt;a href="https://github.com/mila-udem/blocks"&gt;https://github.com/mila-udem/blocks&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;TensorFlow: &lt;a href="https://github.com/tensorflow/tensorflow"&gt;https://github.com/tensorflow/tensorflow&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;They each have their strengths and weaknesses, so give them all a go and see which best suits your use case.  Although I would have recommended using pylearn2 a year ago, the community is no longer active so I would recommend looking elsewhere.  My original response to the answer is included below, but is largely irrelevant at this point.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;a href="http://deeplearning.net/software/pylearn2/"&gt;Pylearn2&lt;/a&gt; is generally considered the library of choice for neural networks and deep learning in python.  It''s designed for easy scientific experimentation rather than ease of use, so the learning curve is rather steep, but if you take your time and follow the tutorials I think you''ll be happy with the functionality it provides.  Everything from standard Multilayer Perceptrons to Restricted Boltzmann Machines to Convolutional Nets to Autoencoders is provided.  There''s great GPU support and everything is built on top of Theano, so performance is typically quite good.  The source for Pylearn2 is available &lt;a href="https://github.com/lisa-lab/pylearn2"&gt;on github&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Be aware that Pylearn2 has the opposite problem of pybrain at the moment -- rather than being abandoned, Pylearn2 is under active development and is subject to frequent changes.&lt;/p&gt;
', NULL, NULL, '''/a'':165C,269C ''/dnouri/nolearn'':71C ''/dnouri/nolearn&lt;/a&gt;&lt;/li&gt;'':74C ''/fchollet/keras'':51C ''/fchollet/keras&lt;/a&gt;&lt;/li&gt;'':54C ''/lasagne/lasagne'':41C ''/lasagne/lasagne&lt;/a&gt;&lt;/li&gt;'':44C ''/lisa-lab/pylearn2'':266C ''/mila-udem/blocks'':81C ''/mila-udem/blocks&lt;/a&gt;&lt;/li&gt;'':84C ''/p'':33C,156C,270C,300C ''/software/pylearn2/'':163C ''/tensorflow/tensorflow'':91C ''/tensorflow/tensorflow&lt;/a&gt;&lt;/li&gt;'':94C ''/uaca/deepy'':61C ''/uaca/deepy&lt;/a&gt;&lt;/li&gt;'':64C ''/ul'':95C ''14'':17C ''abandon'':288C ''activ'':133C,292C ''ago'':127C ''although'':118C ''answer'':12C,145C ''autoencod'':234C ''avail'':261C ''awar'':273C ''best'':113C ''bit'':9C ''block'':76C ''boltzmann'':228C ''built'':245C ''case'':117C ''chang'':6C,299C ''check'':31C ''choic'':172C ''communiti'':129C ''consid'':168C ''convolut'':231C ''curv'':196C ''deep'':177C ''deepi'':56C ''deeplearning.net'':162C ''deeplearning.net/software/pylearn2/'':161C ''design'':183C ''develop'':293C ''eas'':190C ''easi'':185C ''elsewher'':139C ''enter'':23C ''everyth'':221C,243C ''experiment'':187C ''follow'':207C ''frequent'':298C ''function'':218C ''general'':167C ''github'':268C ''github.com'':40C,43C,50C,53C,60C,63C,70C,73C,80C,83C,90C,93C,265C ''github.com/dnouri/nolearn'':69C ''github.com/dnouri/nolearn&lt;/a&gt;&lt;/li&gt;'':72C ''github.com/fchollet/keras'':49C ''github.com/fchollet/keras&lt;/a&gt;&lt;/li&gt;'':52C ''github.com/lasagne/lasagne'':39C ''github.com/lasagne/lasagne&lt;/a&gt;&lt;/li&gt;'':42C ''github.com/lisa-lab/pylearn2'':264C ''github.com/mila-udem/blocks'':79C ''github.com/mila-udem/blocks&lt;/a&gt;&lt;/li&gt;'':82C ''github.com/tensorflow/tensorflow'':89C ''github.com/tensorflow/tensorflow&lt;/a&gt;&lt;/li&gt;'':92C ''github.com/uaca/deepy'':59C ''github.com/uaca/deepy&lt;/a&gt;&lt;/li&gt;'':62C ''give'':105C ''go'':109C ''good'':255C ''gpu'':240C ''great'':239C ''happi'':215C ''hr'':157C ''href'':38C,48C,58C,68C,78C,88C,160C,263C ''includ'':147C ''irrelev'':152C ''juli'':16C ''kera'':46C ''landscap'':4C ''larg'':151C ''lasagn'':36C ''learn'':178C,195C ''li'':35C,45C,55C,65C,75C,85C ''librari'':170C ''ll'':213C ''longer'':132C ''look'':138C ''machin'':229C ''moment'':284C ''multilay'':224C ''net'':232C ''network'':175C ''neural'':174C ''new'':20C ''nolearn'':66C ''opposit'':278C ''origin'':141C ''p'':1C,96C,158C,271C ''particular'':27C ''perceptron'':225C ''perform'':251C ''player'':21C ''point'':155C ''problem'':279C ''provid'':220C,236C ''pybrain'':281C ''pylearn2'':124C,164C,259C,275C,289C ''python'':180C ''question'':14C ''quit'':7C,254C ''rather'':188C,198C,285C ''recommend'':30C,122C,137C ''respons'':142C ''restrict'':227C ''scientif'':186C ''see'':111C ''sinc'':10C ''sourc'':257C ''space'':25C ''standard'':223C ''steep'':199C ''strength'':101C ''subject'':296C ''suit'':114C ''support'':241C ''take'':203C ''tensorflow'':86C ''theano'':249C ''think'':211C ''time'':205C ''top'':247C ''tutori'':209C ''typic'':253C ''ul'':34C ''updat'':2C ''use'':116C,123C,192C ''weak'':103C ''would'':29C,120C,136C ''year'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (697, 1, '2014-07-07 21:26:36.83', -1, 178, 'Running huge datasets with R', '&lt;p&gt;I''m trying to run some analysis with some big datasets (eg 400k rows vs. 400 columns) with R (e.g. using neural networks and recommendation systems).
But, it''s taking too long to process the data (with huge matrices, e.g. 400k rows vs. 400k columns).
What are some free/cheap ways to improve R performance?&lt;/p&gt;

&lt;p&gt;I''m accepting packages or web services suggestions (other options are welcome).&lt;/p&gt;
', 'bigdata  r  optimization  processing', NULL, '''/p'':65C,79C ''400'':26C ''400k'':23C,51C,54C ''accept'':69C ''analysi'':17C ''big'':20C ''bigdata'':6B ''column'':27C,55C ''data'':46C ''dataset'':3A,21C ''e.g'':30C,50C ''eg'':22C ''free/cheap'':59C ''huge'':2A,48C ''improv'':62C ''long'':42C ''m'':12C,68C ''matric'':49C ''network'':33C ''neural'':32C ''optim'':8B ''option'':76C ''p'':10C,66C ''packag'':70C ''perform'':64C ''process'':9B,44C ''r'':5A,7B,29C,63C ''recommend'':35C ''row'':24C,52C ''run'':1A,15C ''servic'':73C ''suggest'':74C ''system'':36C ''take'':40C ''tri'':13C ''use'':31C ''vs'':25C,53C ''way'':60C ''web'':72C ''welcom'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (755, 2, '2014-07-16 17:34:24.88', 4, NULL, NULL, '&lt;p&gt;&lt;em&gt;I expected more from random trees&lt;/em&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;With random forests, typically for N features, sqrt(N) features are used for each decision tree construction. Since in your case &lt;em&gt;N&lt;/em&gt;=20, you could try setting &lt;em&gt;max_depth&lt;/em&gt; (the number of sub-features to construct each decision tree) to 5.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Instead of decision trees, linear models have been proposed and evaluated as base estimators in random forests, in particular multinomial logistic regression and naive Bayes. This might improve your accuracy.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;em&gt;On MNIST kNN gives better accuracy, any ideas how to get it higher?&lt;/em&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Try with a higher value of &lt;em&gt;K&lt;/em&gt; (say 5 or 7). A higher value of K would give you more supportive evidence about the class label of a point.&lt;/li&gt;
&lt;li&gt;You could run PCA or Fisher''s Linear Discriminant Analysis before running k-nearest neighbour. By this you could potentially get rid of correlated features while computing distances between the points, and hence your k neighbours would be more robust.&lt;/li&gt;
&lt;li&gt;Try different K values for different points based on the variance in the distances between the K neighbours.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/em'':9C,37C,46C,112C,124C ''/li'':61C,95C,147C,190C,210C ''/p'':10C,60C,94C,113C ''/ul'':96C,211C ''20'':38C ''5'':59C,126C ''7'':128C ''accuraci'':93C,104C ''analysi'':158C ''base'':76C,199C ''bay'':88C ''better'':103C ''case'':34C ''class'':142C ''comput'':176C ''construct'':30C,54C ''correl'':173C ''could'':40C,150C,168C ''decis'':28C,56C,66C ''depth'':45C ''differ'':193C,197C ''discrimin'':157C ''distanc'':177C,205C ''em'':2C,35C,43C,98C,122C ''estim'':77C ''evalu'':74C ''evid'':139C ''expect'':4C ''featur'':20C,23C,52C,174C ''fisher'':154C ''forest'':16C,80C ''get'':109C,170C ''give'':102C,135C ''henc'':182C ''higher'':111C,119C,130C ''idea'':106C ''improv'':91C ''instead'':64C ''k'':123C,133C,162C,184C,194C,208C ''k-nearest'':161C ''knn'':101C ''label'':143C ''li'':12C,62C,115C,148C,191C ''linear'':68C,156C ''logist'':84C ''max'':44C ''might'':90C ''mnist'':100C ''model'':69C ''multinomi'':83C ''n'':19C,22C,36C ''naiv'':87C ''nearest'':163C ''neighbour'':164C,185C,209C ''number'':48C ''p'':1C,13C,63C,97C ''particular'':82C ''pca'':152C ''point'':146C,180C,198C ''potenti'':169C ''propos'':72C ''random'':7C,15C,79C ''regress'':85C ''rid'':171C ''robust'':189C ''run'':151C,160C ''say'':125C ''set'':42C ''sinc'':31C ''sqrt'':21C ''sub'':51C ''sub-featur'':50C ''support'':138C ''tree'':8C,29C,57C,67C ''tri'':41C,116C,192C ''typic'':17C ''ul'':11C,114C ''use'':25C ''valu'':120C,131C,195C ''varianc'':202C ''would'':134C,186C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (698, 2, '2014-07-08 02:08:04.56', 1, NULL, NULL, '&lt;p&gt;They can''t predict, but they can tell you the most likely result. There''s an study about this kind of approach from &lt;strong&gt;Etienne&lt;/strong&gt; - &lt;a href="http://blog.wolfram.com/2014/06/20/predicting-who-will-win-the-world-cup-with-wolfram-language/" rel="nofollow"&gt;Predicting Who Will Win the World Cup with Wolfram Language&lt;/a&gt;. This is a very detailed study, so you can check all the methodology used to get the predictions. &lt;/p&gt;

&lt;p&gt;Interesting enough, 11 from 15 matches were correct!&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;As one might expect, Brazil is the favorite, with a probability to win of 42.5%. This striking result is due to the fact that Brazil has both the highest Elo ranking and plays at home.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;(Let''s go Brazil!)&lt;/p&gt;
', NULL, NULL, '''/2014/06/20/predicting-who-will-win-the-world-cup-with-wolfram-language/'':32C ''/a'':45C ''/blockquote'':113C ''/p'':64C,74C,112C,119C ''/strong'':27C ''11'':68C ''15'':70C ''42.5'':91C ''approach'':23C ''blockquot'':75C ''blog.wolfram.com'':31C ''blog.wolfram.com/2014/06/20/predicting-who-will-win-the-world-cup-with-wolfram-language/'':30C ''brazil'':81C,101C,118C ''check'':55C ''correct'':73C ''cup'':41C ''detail'':50C ''due'':96C ''elo'':106C ''enough'':67C ''etienn'':26C ''expect'':80C ''fact'':99C ''favorit'':84C ''get'':61C ''go'':117C ''highest'':105C ''home'':111C ''href'':29C ''interest'':66C ''kind'':21C ''languag'':44C ''let'':115C ''like'':13C ''match'':71C ''methodolog'':58C ''might'':79C ''nofollow'':34C ''one'':78C ''p'':1C,65C,76C,114C ''play'':109C ''predict'':5C,35C,63C ''probabl'':87C ''rank'':107C ''rel'':33C ''result'':14C,94C ''strike'':93C ''strong'':25C ''studi'':18C,51C ''tell'':9C ''use'':59C ''win'':38C,89C ''wolfram'':43C ''world'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (699, 2, '2014-07-08 06:40:36.923', 4, NULL, NULL, '&lt;p&gt;From my experience only some classes of queries can be classified on lexical features (due to ambiguity of natural language). Instead you can try to use boolean search results (sites or segments of sites, not documents, without ranking) as features for classification (instead on words). This approach works well in classes where there is a big lexical ambiguity in a query but exists a lot of good sites relevant to the query (e.g. movies, music, commercial queries and so on).&lt;/p&gt;

&lt;p&gt;Also, for offline classification you can do LSI on query-site matrix. See "Introduction to Information Retrieval" book for details.&lt;/p&gt;
', NULL, NULL, '''/p'':82C,105C ''also'':84C ''ambigu'':18C,59C ''approach'':48C ''big'':57C ''book'':102C ''boolean'':28C ''class'':7C,52C ''classif'':43C,87C ''classifi'':12C ''commerci'':77C ''detail'':104C ''document'':37C ''due'':16C ''e.g'':74C ''exist'':64C ''experi'':4C ''featur'':15C,41C ''good'':68C ''inform'':100C ''instead'':22C,44C ''introduct'':98C ''languag'':21C ''lexic'':14C,58C ''lot'':66C ''lsi'':91C ''matrix'':96C ''movi'':75C ''music'':76C ''natur'':20C ''offlin'':86C ''p'':1C,83C ''queri'':9C,62C,73C,78C,94C ''query-sit'':93C ''rank'':39C ''relev'':70C ''result'':30C ''retriev'':101C ''search'':29C ''see'':97C ''segment'':33C ''site'':31C,35C,69C,95C ''tri'':25C ''use'':27C ''well'':50C ''without'':38C ''word'':46C ''work'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (700, 1, '2014-07-08 07:29:34.167', 5, 125, 'Efficient dynamic clustering', '&lt;p&gt;I have a set of datapoints from the unit interval (i.e. 1-dimensional dataset with numerical values). I receive some additional datapoints online, and moreover the value of some datapoints might change dynamically. I''m looking for an ideal clustering algorithm which can handle these issues efficiently.&lt;/p&gt;

&lt;p&gt;I know &lt;a href="https://www.cs.princeton.edu/courses/archive/fall08/cos436/Duda/C/sk_means.htm"&gt;sequential k-means clustering&lt;/a&gt; copes with the addition of new instances, and I suppose with minor modification it can work with dynamic instance values (i.e. first taking the modified instance from the respective cluster, then updating the mean of the cluster and finally giving the modified instance as an input to the algorithm just as the addition of an unseen instance).&lt;/p&gt;

&lt;p&gt;My concern with using the k-means algorithm is the requirement of supplying the number of clusters as an input. I know that they beat other clustering algorithms (GAs, MSTs, Hierarchical Methods etc.) in time&amp;amp;space complexity. Honestly I''m not sure, but maybe I can get away with using one of the aforementioned algorithms. Even that my datasets are relatively large, the existence of a single dimension makes me wonder.&lt;/p&gt;

&lt;p&gt;More specifically a typical test case of mine would contain about 10K-200K 1-dimensional datapoints. I would like to complete the clustering preferably under a second. The dynamic changes in the value points are assumed to be smooth, i.e. relatively small. Thus being able to use existing solutions (i.e. being able to continue clustering on the existing one when a value is changed or new one is added) is highly preferred. &lt;/p&gt;

&lt;p&gt;So all in all:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Can you think of an algorithm which will provide a sweet spot between computational efficiency and the accuracy of clusters wrt. the problem defined above?&lt;/p&gt;
  
  &lt;p&gt;Are there some nice heuristics for the k-means algorithm to automatically compute the value of K beforehand?&lt;/p&gt;
&lt;/blockquote&gt;
', 'machine-learning  algorithms  clustering  k-means  hierarchical-data-format', NULL, '''/a'':78C ''/blockquote'':342C ''/courses/archive/fall08/cos436/duda/c/sk_means.htm'':72C ''/p'':64C,136C,211C,286C,292C,320C,341C ''1'':28C,227C ''10k'':225C ''10k-200k'':224C ''200k'':226C ''abl'':258C,265C ''accuraci'':312C ''ad'':282C ''addit'':37C,82C,131C ''aforement'':193C ''algorithm'':7B,57C,127C,146C,166C,194C,300C,332C ''amp'':174C ''assum'':249C ''automat'':334C ''away'':187C ''beat'':163C ''beforehand'':340C ''blockquot'':293C ''case'':218C ''chang'':48C,243C,277C ''cluster'':3A,8B,56C,77C,108C,115C,155C,165C,236C,268C,314C ''complet'':234C ''complex'':176C ''comput'':308C,335C ''concern'':139C ''contain'':222C ''continu'':267C ''cope'':79C ''data'':14B ''datapoint'':22C,38C,46C,229C ''dataset'':30C,198C ''defin'':318C ''dimens'':207C ''dimension'':29C,228C ''dynam'':2A,49C,96C,242C ''effici'':1A,63C,309C ''etc'':171C ''even'':195C ''exist'':203C,261C,271C ''final'':117C ''first'':100C ''format'':15B ''gas'':167C ''get'':186C ''give'':118C ''handl'':60C ''heurist'':326C ''hierarch'':13B,169C ''hierarchical-data-format'':12B ''high'':284C ''honest'':177C ''href'':69C ''i.e'':27C,99C,253C,263C ''ideal'':55C ''input'':124C,158C ''instanc'':85C,97C,104C,121C,135C ''interv'':26C ''issu'':62C ''k'':10B,75C,144C,330C,339C ''k-mean'':9B,74C,143C,329C ''know'':67C,160C ''larg'':201C ''learn'':6B ''like'':232C ''look'':52C ''m'':51C,179C ''machin'':5B ''machine-learn'':4B ''make'':208C ''mayb'':183C ''mean'':11B,76C,112C,145C,331C ''method'':170C ''might'':47C ''mine'':220C ''minor'':90C ''modif'':91C ''modifi'':103C,120C ''moreov'':41C ''msts'':168C ''new'':84C,279C ''nice'':325C ''number'':153C ''numer'':32C ''one'':190C,272C,280C ''onlin'':39C ''p'':16C,65C,137C,212C,287C,294C,321C ''point'':247C ''prefer'':237C,285C ''problem'':317C ''provid'':303C ''receiv'':35C ''relat'':200C,254C ''requir'':149C ''respect'':107C ''second'':240C ''sequenti'':73C ''set'':20C ''singl'':206C ''small'':255C ''smooth'':252C ''solut'':262C ''space'':175C ''specif'':214C ''spot'':306C ''suppli'':151C ''suppos'':88C ''sure'':181C ''sweet'':305C ''take'':101C ''test'':217C ''think'':297C ''thus'':256C ''time'':173C ''typic'':216C ''unit'':25C ''unseen'':134C ''updat'':110C ''use'':141C,189C,260C ''valu'':33C,43C,98C,246C,275C,337C ''wonder'':210C ''work'':94C ''would'':221C,231C ''wrt'':315C ''www.cs.princeton.edu'':71C ''www.cs.princeton.edu/courses/archive/fall08/cos436/duda/c/sk_means.htm'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (709, 2, '2014-07-09 07:08:34.74', 2, NULL, NULL, '&lt;p&gt;Since you mention you are building a recommendation system, I believe you have a sparse matrix which you are working on. Check &lt;a href="http://stat.ethz.ch/R-manual/R-devel/library/Matrix/html/sparseMatrix.html" rel="nofollow"&gt;sparseMatrix&lt;/a&gt; from Matrix package. This should be able to help you with storing your large size matrix in memory and train your model. &lt;/p&gt;
', NULL, NULL, '''/a'':32C ''/p'':55C ''/r-manual/r-devel/library/matrix/html/sparsematrix.html'':28C ''abl'':39C ''believ'':12C ''build'':7C ''check'':23C ''help'':41C ''href'':25C ''larg'':46C ''matrix'':17C,34C,48C ''memori'':50C ''mention'':4C ''model'':54C ''nofollow'':30C ''p'':1C ''packag'':35C ''recommend'':9C ''rel'':29C ''sinc'':2C ''size'':47C ''spars'':16C ''sparsematrix'':31C ''stat.ethz.ch'':27C ''stat.ethz.ch/r-manual/r-devel/library/matrix/html/sparsematrix.html'':26C ''store'':44C ''system'':10C ''train'':52C ''work'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (702, 2, '2014-07-08 09:18:17.99', 1, NULL, NULL, '&lt;p&gt;In the scikit-learn implementation of Spectral clustering and DBSCAN you do not need to precompute the distances, you should input the sample coordinates for all &lt;code&gt;id_1&lt;/code&gt; ... &lt;code&gt;id_n&lt;/code&gt;.  Here is a simplification of the &lt;a href="http://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html" rel="nofollow"&gt;documented example comparison of clustering algorithms&lt;/a&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
from sklearn import cluster
from sklearn.preprocessing import StandardScaler

## Prepare the data
X = np.random.rand(1500, 2)
# When reading from a file of the form: `id_n coord_x coord_y`
# you will need this call instead:
# X = np.loadtxt(''coords.csv'', usecols=(1, 2))
X = StandardScaler().fit_transform(X)

## Instantiate the algorithms
spectral = cluster.SpectralClustering(n_clusters=2,
                                      eigen_solver=''arpack'',
                                      affinity="nearest_neighbors")
dbscan = cluster.DBSCAN(eps=.2)

## Use the algorithms
spectral_labels = spectral.fit_predict(X)
dbscan_labels = dbscan.fit_predict(X)
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (708, 2, '2014-07-09 00:44:28.207', 4, NULL, NULL, '&lt;p&gt;There are many solutions to ease the burden of copying the file from a local machine to the computing nodes in the clusters. A simple approach is to use an interface that allows multi-access to the machines in the cluster, like &lt;a href="http://sourceforge.net/projects/clusterssh/" rel="nofollow"&gt;clusterssh&lt;/a&gt; (cssh). It allows you to type commands to multiple machines at once via a set of terminal screens (each one a ssh connection to a different machine in the cluster).&lt;/p&gt;

&lt;p&gt;Since your cluster seem to have &lt;code&gt;qsub&lt;/code&gt; set up, your problem may be rather related to &lt;em&gt;replicating&lt;/em&gt; the data along the machines (other than simply running a command in each node). So, to address this point, you may either write an &lt;code&gt;scp&lt;/code&gt; script, to copy things to and from each node in the cluster (which is surely better addressed with SVN), or you may set up a NFS. This would allow for a simple and transparent access to the data, and also reduce the need for replicating unnecessary data.&lt;/p&gt;

&lt;p&gt;For example, you could access a node, copy the data to such place, and simply use the data &lt;em&gt;remotely&lt;/em&gt;, via network communication. I''m not acquainted with how to set up a NFS, but you already have access to it (in case your home folder is the same across the machines you access). Then, the scripts and data could be sent to a single place, and later accessed from others. This is akin to the SVN approach, except it''s more transparent/straightforward.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (897, 2, '2014-08-03 20:21:08.15', 3, NULL, NULL, '&lt;p&gt;Having worked with Facebook data a bit (harvested from Facebook users) we stored it just as a pair of values: USER_ID, FRIEND_USER_ID.&lt;/p&gt;

&lt;p&gt;But I guess your questions is a bit deeper? You can store it in different ways, depending on your research question. One interesting option is triads for example - &lt;a href="http://mypersonality.org/wiki/doku.php?id=list_of_variables_available#triads" rel="nofollow"&gt;http://mypersonality.org/wiki/doku.php?id=list_of_variables_available#triads&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':27C ''/wiki/doku.php?id=list_of_variables_available#triads'':61C ''/wiki/doku.php?id=list_of_variables_available#triads&lt;/a&gt;&lt;/p&gt;'':66C ''bit'':8C,36C ''data'':6C ''deeper'':37C ''depend'':45C ''differ'':43C ''exampl'':56C ''facebook'':5C,11C ''friend'':24C ''guess'':31C ''harvest'':9C ''href'':58C ''id'':23C,26C ''interest'':51C ''mypersonality.org'':60C,65C ''mypersonality.org/wiki/doku.php?id=list_of_variables_available#triads'':59C ''mypersonality.org/wiki/doku.php?id=list_of_variables_available#triads&lt;/a&gt;&lt;/p&gt;'':64C ''nofollow'':63C ''one'':50C ''option'':52C ''p'':1C,28C ''pair'':19C ''question'':33C,49C ''rel'':62C ''research'':48C ''store'':14C,40C ''triad'':54C ''user'':12C,22C,25C ''valu'':21C ''way'':44C ''work'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (898, 2, '2014-08-04 09:42:03.59', 10, NULL, NULL, '&lt;h2&gt;Two Categorical Variables&lt;/h2&gt;

&lt;p&gt;Checking if two categorical variables are independent can be done with Chi-Squared test of independence. &lt;/p&gt;

&lt;p&gt;This is a typical &lt;a href="https://en.wikipedia.org/wiki/Chi-square_test"&gt;Chi-Square test&lt;/a&gt;: if we assume that two variables are independent, then the values of the contingency table for these variables should be distributed uniformly. And then we check how far away from uniform the actual values are.&lt;/p&gt;

&lt;p&gt;There also exists a &lt;a href="http://en.wikipedia.org/wiki/Cram%C3%A9r%27s_V"&gt;Crammer''s V&lt;/a&gt; that is a measure of correlation that follows from this test&lt;/p&gt;

&lt;h3&gt;Example&lt;/h3&gt;

&lt;p&gt;Suppose we have two variables&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;gender: male and female&lt;/li&gt;
&lt;li&gt;city: Blois and Tours&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;We observed the following data:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/zcCfV.png" alt="observed values"&gt; &lt;/p&gt;

&lt;p&gt;Are gender and city independent? Let''s perform a Chi-Squred test. Null hypothesis: they are independent, Alternative hypothesis is that they are correlated in some way. &lt;/p&gt;

&lt;p&gt;Under the Null hypothesis, we assume uniform distribution. So our expected values are the following&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/H8bKJ.png" alt="expected value"&gt;&lt;/p&gt;

&lt;p&gt;So we run the chi-squared test and the resulting p-value here can be seen as a measure of correlation between these two variables.&lt;/p&gt;

&lt;p&gt;To compute Crammer''s V we first find the normalizing factor chi-squared-max which is typically the size of the sample, divide the chi-square by it and take a square root &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/v7HY6.png" alt="crammers v"&gt;&lt;/p&gt;

&lt;h3&gt;R&lt;/h3&gt;

&lt;pre&gt;&lt;code&gt;tbl = matrix(data=c(55, 45, 20, 30), nrow=2, ncol=2, byrow=T)
dimnames(tbl) = list(City=c(''B'', ''T''), Gender=c(''M'', ''F''))

chi2 = chisq.test(tbl, correct=F)
c(chi2$statistic, chi2$p.value)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here the p value is 0.08 - quite small, but still not enough to reject the hypothesis of independence. So we can say that the "correlation" here is 0.08&lt;/p&gt;

&lt;p&gt;We also compute V: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;sqrt(chi2$statistic / sum(tbl))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And get 0.14 (the smaller v, the lower the correlation) &lt;/p&gt;

&lt;p&gt;Consider another dataset &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    Gender
City  M  F
   B 51 49
   T 24 26
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For this, it would give the following&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;tbl = matrix(data=c(51, 49, 24, 26), nrow=2, ncol=2, byrow=T)
dimnames(tbl) = list(City=c(''B'', ''T''), Gender=c(''M'', ''F''))

chi2 = chisq.test(tbl, correct=F)
c(chi2$statistic, chi2$p.value)

sqrt(chi2$statistic / sum(tbl))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The p-value is 0.72 which is far closer to 1, and v is 0.03 - very close to 0&lt;/p&gt;

&lt;h2&gt;Categorical vs Numerical Variables&lt;/h2&gt;

&lt;p&gt;For this type we typically perform &lt;a href="http://en.wikipedia.org/wiki/F_test#One-way_ANOVA_example"&gt;One-way ANOVA test&lt;/a&gt;: we calculate in-group variance and intra-group variance and then compare them.&lt;/p&gt;

&lt;h3&gt;Example&lt;/h3&gt;

&lt;p&gt;We want to study the relationship between absorbed fat from donuts vs the type of fat used to produce donuts (example is taken from &lt;a href="http://courses.statistics.com/software/R/R1way.htm"&gt;here&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/LMOS3.png" alt="donuts"&gt;&lt;/p&gt;

&lt;p&gt;Is there any dependence between the variables?
For that we conduct ANOVA test and see that the p-value is just 0.007 - there''s no correlation between these variables. &lt;/p&gt;

&lt;h3&gt;R&lt;/h3&gt;

&lt;pre&gt;&lt;code&gt;t1 = c(164, 172, 168, 177, 156, 195)
t2 = c(178, 191, 197, 182, 185, 177)
t3 = c(175, 193, 178, 171, 163, 176)
t4 = c(155, 166, 149, 164, 170, 168)

val = c(t1, t2, t3, t4)
fac = gl(n=4, k=6, labels=c(''type1'', ''type2'', ''type3'', ''type4''))

aov1 = aov(val ~ fac)
summary(aov1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Output is &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;            Df Sum Sq Mean Sq F value  Pr(&amp;gt;F)   
fac          3   1636   545.5   5.406 0.00688 **
Residuals   20   2018   100.9                   
---
Signif. codes:  0 â***â 0.001 â**â 0.01 â*â 0.05 â.â 0.1 â â 1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So we can take the p-value as the measure of correlation here as well.&lt;/p&gt;

&lt;h2&gt;References&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Chi-square_test"&gt;https://en.wikipedia.org/wiki/Chi-square_test&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://0agr.ru/wiki/index.php/Chi-square_Test_of_Independence"&gt;http://0agr.ru/wiki/index.php/Chi-square_Test_of_Independence&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://courses.statistics.com/software/R/R1way.htm"&gt;http://courses.statistics.com/software/R/R1way.htm&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://0agr.ru/wiki/index.php/One-Way_ANOVA_F-Test"&gt;http://0agr.ru/wiki/index.php/One-Way_ANOVA_F-Test&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://0agr.ru/wiki/index.php/Crammer%27s_Coefficient"&gt;http://0agr.ru/wiki/index.php/Crammer%27s_Coefficient&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (703, 2, '2014-07-08 10:36:44.22', 14, NULL, NULL, '&lt;p&gt;Pylearn relies on Theano and as mentioned in the other answer to use the library is quite complicated, until you get the hold of it.&lt;/p&gt;

&lt;p&gt;In the meantime I would suggest using &lt;a href="https://github.com/lmjohns3/theano-nets/" rel="nofollow"&gt;Theanets&lt;/a&gt;. It also built on top of Theano, but is much more easier to work with. It might be true, that it doesn''t have all the features of Pylearn, but for the basic work it''s sufficient.&lt;/p&gt;

&lt;p&gt;Also it''s open source, so you can add custom networks on the fly, if you dare. :)&lt;/p&gt;

&lt;p&gt;EDIT: Dec 2015. Recently I have started using &lt;a href="http://keras.io/" rel="nofollow"&gt;Keras&lt;/a&gt;. It is a bit lower level than Theanets, but much more powerful. For basic tests the Theanets is appropriate. But if you want to do some research in field of ANN Keras is much more flexible. Plus the Keras can use &lt;strong&gt;Tensorflow&lt;/strong&gt; as a backend.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (770, 2, '2014-07-17 15:35:17.487', 2, NULL, NULL, '&lt;p&gt;I am not an export in using SVMs, but usually (if you are using a machine learning library like Python''s &lt;code&gt;scikit-learn&lt;/code&gt; or R''s &lt;code&gt;libsvm&lt;/code&gt;, there is the &lt;code&gt;class_weight&lt;/code&gt; parameter, or &lt;code&gt;class.weights&lt;/code&gt;, respectively. &lt;/p&gt;

&lt;p&gt;Or if you''d use a Bayes classifier, you would take this "skew" into account via the "prior (class) probabilities" P(&amp;omega;&lt;sub&gt;j&lt;/sub&gt;)&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (704, 1, '2014-07-08 13:45:07.583', 8, 155, 'Working with HPC clusters', '&lt;p&gt;In my university, we have an HPC computing cluster. I use the cluster to train classifiers and so on. So, usually, to send a job to the cluster, (e.g. python scikit-learn script), I need to write a Bash script that contains (among others) a command like &lt;code&gt;qsub script.py&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;However, I find this process very very frustrating. Usually what happens is that I write the python script on my laptop and then I login to the server and update the SVN repository, so I get the same python script there. Then I write that Bash script or edit it, so I can run the bash script.&lt;/p&gt;

&lt;p&gt;As you see this is really frustrating since, for every little update for the python script, I need to do many steps to have it executed at the computing cluster. Of course the task gets even more complicated when I have to put the data on the server and use the datasets'' path on the server.&lt;/p&gt;

&lt;p&gt;I''m sure many people here are using computing clusters for their data science tasks. I just want to know how you guys manage sending the jobs to the clusters?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (705, 2, '2014-07-08 14:32:55.827', 4, NULL, NULL, '&lt;p&gt;Stochastic gradient descent is a method of setting the parameters of the regressor; since the objective for logistic regression is convex (has only one maximum), this won''t be an issue and SGD is generally only needed to improve convergence speed with masses of training data.&lt;/p&gt;

&lt;p&gt;What your numbers suggest to me is that your features are not adequate to separate the classes. Consider adding extra features if you can think any any that are useful. You might also consider interactions and quadratic features in your original feature space.&lt;/p&gt;
', NULL, NULL, '''/p'':48C,93C ''ad'':68C ''adequ'':62C ''also'':82C ''class'':66C ''consid'':67C,83C ''converg'':41C ''convex'':22C ''data'':47C ''descent'':4C ''extra'':69C ''featur'':59C,70C,87C,91C ''general'':36C ''gradient'':3C ''improv'':40C ''interact'':84C ''issu'':32C ''logist'':19C ''mass'':44C ''maximum'':26C ''method'':7C ''might'':81C ''need'':38C ''number'':52C ''object'':17C ''one'':25C ''origin'':90C ''p'':1C,49C ''paramet'':11C ''quadrat'':86C ''regress'':20C ''regressor'':14C ''separ'':64C ''set'':9C ''sgd'':34C ''sinc'':15C ''space'':92C ''speed'':42C ''stochast'':2C ''suggest'':53C ''think'':74C ''train'':46C ''use'':79C ''won'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (707, 2, '2014-07-09 00:37:51.167', 5, NULL, NULL, '&lt;p&gt;Try a recurrent neural network, a model well suited for time series data. They''re notoriously difficult to train, but seem to perform well when trained properly: &lt;a href="http://cs229.stanford.edu/proj2012/BernalFokPidaparthi-FinancialMarketTimeSeriesPredictionwithRecurrentNeural.pdf"&gt;http://cs229.stanford.edu/proj2012/BernalFokPidaparthi-FinancialMarketTimeSeriesPredictionwithRecurrentNeural.pdf&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (711, 1, '2014-07-09 12:22:22.4', 31, 1672, 'Are Support Vector Machines still considered "state of the art" in their niche?', '&lt;p&gt;This question is in response to a comment I saw on another question.&lt;/p&gt;

&lt;p&gt;The comment was regarding the Machine Learning course syllabus on Coursera, and along the lines of "SVMs are not used so much nowadays".&lt;/p&gt;

&lt;p&gt;I have only just finished the relevant lectures myself, and my understanding of SVMs is that they are a robust and efficient learning algorithm for classification, and that when using a kernel, they have a "niche" covering number of features perhaps 10 to 1000 and number of training samples perhaps 100 to 10,000. The limit on training samples is because the core algorithm revolves around optimising results generated from a square matrix with dimensions based on number of training samples, not number of original features.&lt;/p&gt;

&lt;p&gt;So does the comment I saw refer some real change since the course was made, and if so, what is that change: A new algorithm that covers SVM''s "sweet spot" just as well, better CPUs meaning SVM''s computational advantages are not worth as much? Or is it perhaps opinion or personal experience of the commenter?&lt;/p&gt;

&lt;p&gt;I tried a search for e.g. "are support vector machines out of fashion" and found nothing to imply they were being dropped in favour of anything else.&lt;/p&gt;

&lt;p&gt;And Wikipedia has this: &lt;a href="http://en.wikipedia.org/wiki/Support_vector_machine#Issues"&gt;http://en.wikipedia.org/wiki/Support_vector_machine#Issues&lt;/a&gt; . . . the main sticking point appears to be difficulty of interpreting the model. Which makes SVM fine for a black-box predicting engine, but not so good for generating insights. I don''t see that as a major issue, just another minor thing to take into account when picking the right tool for the job (along with nature of the training data and learning task etc).&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (712, 2, '2014-07-09 13:07:13.303', 25, NULL, NULL, '&lt;p&gt;SVM is a powerful classifier. It has some nice advantages (which I guess were responsible for its popularity)... These are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Efficiency: Only the support vectors play a role in determining the classification boundary. All other points from the training set needn''t be stored in memory.&lt;/li&gt;
&lt;li&gt;The so-called power of kernels: With appropriate kernels you can transform feature space into a higher dimension so that it becomes linearly separable. The notion of kernels work with arbitrary objects on which you can define some notion of similarity with the help of inner products... and hence SVMs can classify arbitrary objects such as trees, graphs etc.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;There are some significant disadvantages as well.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Parameter sensitivity: The performance is highly sensitive to the choice of the regularization parameter C, which allows some variance in the model.&lt;/li&gt;
&lt;li&gt;Extra parameter for the Gaussian kernel: The radius of the Gaussian kernel can have a significant impact on classifier accuracy. Typically a grid search has to be conducted to find optimal parameters. LibSVM has a support for grid search.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;SVMs generally belong to the class of "Sparse Kernel Machines". The sparse vectors in the case of SVM are the support vectors which are chosen from the maximum margin criterion. Other sparse vector machines such as the &lt;strong&gt;Relevance Vector Machine&lt;/strong&gt; (RVM) perform better than SVM. The following figure shows a comparative performance of the two. In the figure, the x-axis shows one dimensional data from two classes y={0,1}. The mixture model is defined as P(x|y=0)=Unif(0,1) and P(x|y=1)=Unif(.5,1.5) (Unif denotes uniform distribution). 1000 points were sampled from this mixture and an SVM and an RVM were used to estimate the posterior. The problem of SVM is that the predicted values are far off from the true log odds.  &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/zNYbt.png" alt="RVM vs. SVM"&gt; &lt;/p&gt;

&lt;p&gt;A very effective classifier, which is very popular nowadays, is the &lt;strong&gt;Random Forest&lt;/strong&gt;. The main advantages are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Only one parameter to tune (i.e. the number of trees in the forest)&lt;/li&gt;
&lt;li&gt;Not utterly parameter sensitive&lt;/li&gt;
&lt;li&gt;Can easily be extended to multiple classes&lt;/li&gt;
&lt;li&gt;Is based on probabilistic principles (maximizing mutual information gain with the help of decision trees)&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':51C,113C,148C,189C,375C,381C,390C,407C ''/p'':22C,123C,327C,338C,359C ''/strong'':233C,354C ''/ul'':114C,190C,408C ''/znybt.png'':333C ''0'':264C,275C,277C ''1'':265C,278C,283C ''1.5'':286C ''1000'':291C ''5'':285C ''accuraci'':169C ''advantag'':11C,357C ''allow'':142C ''alt'':334C ''appropri'':61C ''arbitrari'':84C,106C ''axi'':255C ''base'':393C ''becom'':75C ''belong'':194C ''better'':236C ''boundari'':37C ''c'':140C ''call'':56C ''case'':207C ''choic'':135C ''chosen'':216C ''class'':197C,262C,389C ''classif'':36C ''classifi'':6C,105C,168C,343C ''compar'':244C ''conduct'':177C ''criterion'':221C ''data'':259C ''decis'':405C ''defin'':90C,270C ''denot'':288C ''determin'':34C ''dimens'':71C ''dimension'':258C ''disadvantag'':120C ''distribut'':290C ''easili'':384C ''effect'':342C ''effici'':25C ''estim'':307C ''etc'':112C ''extend'':386C ''extra'':150C ''far'':320C ''featur'':66C ''figur'':241C,251C ''find'':179C ''follow'':240C ''forest'':353C,374C ''gain'':400C ''gaussian'':154C,160C ''general'':193C ''graph'':111C ''grid'':172C,187C ''guess'':14C ''help'':97C,403C ''henc'':102C ''high'':131C ''higher'':70C ''i.e'':367C ''i.stack.imgur.com'':332C ''i.stack.imgur.com/znybt.png'':331C ''img'':329C ''impact'':166C ''inform'':399C ''inner'':99C ''kernel'':59C,62C,81C,155C,161C,200C ''li'':24C,52C,125C,149C,361C,376C,382C,391C ''libsvm'':182C ''linear'':76C ''log'':325C ''machin'':201C,225C,232C ''main'':356C ''margin'':220C ''maxim'':397C ''maximum'':219C ''memori'':50C ''mixtur'':267C,297C ''model'':147C,268C ''multipl'':388C ''mutual'':398C ''needn'':45C ''nice'':10C ''notion'':79C,92C ''nowaday'':348C ''number'':369C ''object'':85C,107C ''odd'':326C ''one'':257C,363C ''optim'':180C ''p'':1C,115C,191C,272C,280C,328C,339C ''paramet'':126C,139C,151C,181C,364C,379C ''perform'':129C,235C,245C ''play'':30C ''point'':40C,292C ''popular'':19C,347C ''posterior'':309C ''power'':5C,57C ''predict'':317C ''principl'':396C ''probabilist'':395C ''problem'':311C ''product'':100C ''radius'':157C ''random'':352C ''regular'':138C ''relev'':230C ''respons'':16C ''role'':32C ''rvm'':234C,303C,335C ''sampl'':294C ''search'':173C,188C ''sensit'':127C,132C,380C ''separ'':77C ''set'':44C ''show'':242C,256C ''signific'':119C,165C ''similar'':94C ''so-cal'':54C ''space'':67C ''spars'':199C,203C,223C ''src'':330C ''store'':48C ''strong'':229C,351C ''support'':28C,185C,212C ''svm'':2C,209C,238C,300C,313C,337C ''svms'':103C,192C ''train'':43C ''transform'':65C ''tree'':110C,371C,406C ''true'':324C ''tune'':366C ''two'':248C,261C ''typic'':170C ''ul'':23C,124C,360C ''unif'':276C,284C,287C ''uniform'':289C ''use'':305C ''utter'':378C ''valu'':318C ''varianc'':144C ''vector'':29C,204C,213C,224C,231C ''vs'':336C ''well'':122C ''work'':82C ''x'':254C,273C,281C ''x-axi'':253C ''y'':263C,274C,282C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (713, 1, '2014-07-09 17:51:40.583', 3, 840, 'Cloudera QuickStart VM Error', '&lt;p&gt;I have installed cloudera CDH5 Quick start VM on VM player. When I login through HUE in the first page I am the following error&lt;/p&gt;

&lt;p&gt;âPotential misconfiguration detected. Fix and restart Hue.â&lt;img src="http://i.stack.imgur.com/vnq5P.png" alt="Potential misconfiguration detected. Fix and restart Hue"&gt;&lt;/p&gt;

&lt;p&gt;How to solve this issue.&lt;/p&gt;

&lt;p&gt;Thanks,
Green&lt;/p&gt;
', 'hadoop', NULL, '''/p'':32C,56C,63C,67C ''/vnq5p.png'':47C ''alt'':48C ''cdh5'':11C ''cloudera'':1A,10C ''detect'':37C,51C ''error'':4A,31C ''first'':25C ''fix'':38C,52C ''follow'':30C ''green'':66C ''hadoop'':5B ''hue'':22C,41C,55C ''i.stack.imgur.com'':46C ''i.stack.imgur.com/vnq5p.png'':45C ''img'':43C ''instal'':9C ''issu'':62C ''login'':20C ''misconfigur'':36C,50C ''p'':6C,33C,57C,64C ''page'':26C ''player'':17C ''potenti'':35C,49C ''quick'':12C ''quickstart'':2A ''restart'':40C,54C ''solv'':60C ''src'':44C ''start'':13C ''thank'':65C ''vm'':3A,14C,16C ''â'':34C,42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (714, 2, '2014-07-10 08:38:43.353', 5, NULL, NULL, '&lt;p&gt;Although your question is not very specific so I''ll try to give you some generic solutions. There are couple of things you can do here:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Check sparseMatrix from Matrix package as mentioned by @Sidhha&lt;/li&gt;
&lt;li&gt;Try running your model in parallel using packages like snowfall, &lt;a href="https://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf"&gt;Parallel&lt;/a&gt;. Check this &lt;a href="http://cran.r-project.org/web/views/HighPerformanceComputing.html"&gt;list of packages on Cran&lt;/a&gt; which can help you runnning your model in multicore parallel mode.&lt;/li&gt;
&lt;li&gt;You can also try &lt;a href="http://datatable.r-forge.r-project.org/"&gt;data.table&lt;/a&gt; package. It is quite phenomenal in speed.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Good reads:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="http://theodi.org/blog/fig-data-11-tips-how-handle-big-data-r-and-1-bad-pun"&gt;11 Tips on How to Handle Big Data in R (and 1 Bad Pun)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://adv-r.had.co.nz/Performance.html"&gt;Why R is slow &amp;amp; how to improve its Performance?&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':58C,71C,93C,128C,146C ''/blog/fig-data-11-tips-how-handle-big-data-r-and-1-bad-pun'':113C ''/li'':40C,83C,101C,129C,147C ''/ol'':148C ''/p'':28C,106C ''/performance.html'':135C ''/r-manual/r-devel/library/parallel/doc/parallel.pdf'':56C ''/ul'':102C ''/web/views/highperformancecomputing.html'':65C ''1'':125C ''11'':114C ''adv-r.had.co.nz'':134C ''adv-r.had.co.nz/performance.html'':133C ''also'':87C ''although'':2C ''amp'':140C ''bad'':126C ''big'':120C ''check'':31C,59C ''coupl'':21C ''cran'':70C ''cran.r-project.org'':64C ''cran.r-project.org/web/views/highperformancecomputing.html'':63C ''data'':121C ''data.table'':92C ''datatable.r-forge.r-project.org'':91C ''generic'':17C ''give'':14C ''good'':104C ''handl'':119C ''help'':74C ''href'':53C,62C,90C,110C,132C ''improv'':143C ''li'':30C,41C,84C,108C,130C ''like'':50C ''list'':66C ''ll'':11C ''matrix'':34C ''mention'':37C ''mode'':82C ''model'':45C,78C ''multicor'':80C ''ol'':107C ''p'':1C,103C ''packag'':35C,49C,68C,94C ''parallel'':47C,57C,81C ''perform'':145C ''phenomen'':98C ''pun'':127C ''question'':4C ''quit'':97C ''r'':123C,137C ''read'':105C ''run'':43C ''runn'':76C ''sidhha'':39C ''slow'':139C ''snowfal'':51C ''solut'':18C ''sparsematrix'':32C ''specif'':8C ''speed'':100C ''stat.ethz.ch'':55C ''stat.ethz.ch/r-manual/r-devel/library/parallel/doc/parallel.pdf'':54C ''theodi.org'':112C ''theodi.org/blog/fig-data-11-tips-how-handle-big-data-r-and-1-bad-pun'':111C ''thing'':23C ''tip'':115C ''tri'':12C,42C,88C ''ul'':29C ''use'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (715, 2, '2014-07-10 09:16:39.937', 2, NULL, NULL, '&lt;p&gt;Go into the other link from home - to the cloudera manager.&lt;/p&gt;

&lt;p&gt;From there, you''ll see Hue can be restarted, but there is probably an alert that needs to be resolved in there first.&lt;/p&gt;

&lt;p&gt;If I remember right there''s some initial configuration that needs to be done on the quickstart VM that''s spelled out as soon as you log into the manager application.&lt;/p&gt;
', NULL, NULL, '''/p'':13C,38C,71C ''alert'':29C ''applic'':70C ''cloudera'':11C ''configur'':48C ''done'':53C ''first'':37C ''go'':2C ''home'':8C ''hue'':20C ''initi'':47C ''link'':6C ''ll'':18C ''log'':66C ''manag'':12C,69C ''need'':31C,50C ''p'':1C,14C,39C ''probabl'':27C ''quickstart'':56C ''rememb'':42C ''resolv'':34C ''restart'':23C ''right'':43C ''see'':19C ''soon'':63C ''spell'':60C ''vm'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (716, 1, '2014-07-10 10:07:13.523', 6, 465, 'How to choose the features for a neural network?', '&lt;p&gt;I know that there is no a clear answer for this question, but let''s suppose that I have a huge neural network, with a lot of data and I want to add a new feature in input. The "best" way would be to test the network with the new feature and see the results, but is there a method to test if the feature IS UNLIKELY helpful? Like correlation measures (&lt;a href="http://www3.nd.edu/~mclark19/learn/CorrelationComparison.pdf"&gt;http://www3.nd.edu/~mclark19/learn/CorrelationComparison.pdf&lt;/a&gt;) etc?&lt;/p&gt;
', 'machine-learning  neuralnetwork  feature-selection  feature-extraction', NULL, '''/p'':101C ''/~mclark19/learn/correlationcomparison.pdf'':96C ''/~mclark19/learn/correlationcomparison.pdf&lt;/a&gt;)'':99C ''add'':53C ''answer'':29C ''best'':60C ''choos'':3A ''clear'':28C ''correl'':90C ''data'':48C ''etc'':100C ''extract'':19B ''featur'':5A,15B,18B,56C,71C,85C ''feature-extract'':17B ''feature-select'':14B ''help'':88C ''href'':93C ''huge'':41C ''input'':58C ''know'':22C ''learn'':12B ''let'':34C ''like'':89C ''lot'':46C ''machin'':11B ''machine-learn'':10B ''measur'':91C ''method'':80C ''network'':9A,43C,67C ''neural'':8A,42C ''neuralnetwork'':13B ''new'':55C,70C ''p'':20C ''question'':32C ''result'':75C ''see'':73C ''select'':16B ''suppos'':36C ''test'':65C,82C ''unlik'':87C ''want'':51C ''way'':61C ''would'':62C ''www3.nd.edu'':95C,98C ''www3.nd.edu/~mclark19/learn/correlationcomparison.pdf'':94C ''www3.nd.edu/~mclark19/learn/correlationcomparison.pdf&lt;/a&gt;)'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (717, 1, '2014-07-10 11:55:49.637', 5, 47, 'How to define a custom resampling methodology', '&lt;p&gt;I''m using an experimental design to test the robustness of different classification methods, and now I''m searching for the correct definition of such design.&lt;/p&gt;

&lt;p&gt;I''m creating different subsets of the full dataset by cutting away some samples. Each subset is created independently with respect to the others. Then, I run each classification method on every subset. Finally, I estimate the accuracy of each method as how many classifications on subsets are in agreement with the classification on the full dataset. For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Classification-full     1    2    3    2    1    1    2

Classification-subset1  1    2         2    3    1   
Classification-subset2       2    3         1    1    2
...

Accuracy                1    1    1    1  0.5    1    1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is there a correct name to this methodology? I thought it can fall under &lt;a href="http://en.wikipedia.org/wiki/Bootstrapping_(statistics)" rel="nofollow"&gt;bootstrapping&lt;/a&gt; but I''m not sure about this.&lt;/p&gt;
', 'classification  definitions  accuracy  sampling', NULL, '''/a'':162C ''/code'':137C ''/p'':39C,100C,170C ''/pre'':138C ''/wiki/bootstrapping_(statistics)'':158C ''0.5'':134C ''1'':106C,110C,111C,116C,120C,126C,127C,130C,131C,132C,133C,135C,136C ''2'':107C,109C,112C,117C,118C,124C,128C ''3'':108C,119C,125C ''accuraci'':10B,78C,129C ''agreement'':90C ''away'':52C ''bootstrap'':161C ''classif'':8B,25C,69C,85C,93C,104C,114C,122C ''classification-ful'':103C ''classification-subset1'':113C ''classification-subset2'':121C ''code'':102C ''correct'':34C,143C ''creat'':43C,58C ''custom'':5A ''cut'':51C ''dataset'':49C,97C ''defin'':3A ''definit'':9B,35C ''design'':18C,38C ''differ'':24C,44C ''en.wikipedia.org'':157C ''en.wikipedia.org/wiki/bootstrapping_(statistics)'':156C ''estim'':76C ''everi'':72C ''exampl'':99C ''experiment'':17C ''fall'':152C ''final'':74C ''full'':48C,96C,105C ''href'':155C ''independ'':59C ''m'':14C,30C,42C,165C ''mani'':84C ''method'':26C,70C,81C ''methodolog'':7A,147C ''name'':144C ''nofollow'':160C ''other'':64C ''p'':12C,40C,139C ''pre'':101C ''rel'':159C ''resampl'':6A ''respect'':61C ''robust'':22C ''run'':67C ''sampl'':11B,54C ''search'':31C ''subset'':45C,56C,73C,87C ''subset1'':115C ''subset2'':123C ''sure'':167C ''test'':20C ''thought'':149C ''use'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (720, 2, '2014-07-10 23:38:58.153', 2, NULL, NULL, '&lt;p&gt;There are a couple of open source options I know of - &lt;/p&gt;

&lt;p&gt;LibOTS - &lt;a href="http://libots.sourceforge.net/" rel="nofollow"&gt;http://libots.sourceforge.net/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;DocSum - &lt;a href="http://docsum.sourceforge.net/docsum/web/about.php" rel="nofollow"&gt;http://docsum.sourceforge.net/docsum/web/about.php&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;A couple of commercial solutions - &lt;/p&gt;

&lt;p&gt;Intellix Summarizer Pro - &lt;a href="http://summarizer.intellexer.com/order_summarizer_pro.php" rel="nofollow"&gt;http://summarizer.intellexer.com/order_summarizer_pro.php&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Copernic Summarizer - &lt;a href="http://www.copernic.com/en/products/summarizer/" rel="nofollow"&gt;http://www.copernic.com/en/products/summarizer/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;And this one is a web service - &lt;/p&gt;

&lt;p&gt;TextTeaser - &lt;a href="http://www.textteaser.com/" rel="nofollow"&gt;http://www.textteaser.com/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I''m sure there are plenty of others out there.  I have used Copernic a good deal and it''s pretty good, but I was hoping it could be automated easily, which it can''t - at least it couldn''t when I used it.  &lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':23C,88C ''/docsum/web/about.php'':30C ''/docsum/web/about.php&lt;/a&gt;&lt;/p&gt;'':35C ''/en/products/summarizer/'':64C ''/en/products/summarizer/&lt;/a&gt;&lt;/p&gt;'':69C ''/order_summarizer_pro.php'':51C ''/order_summarizer_pro.php&lt;/a&gt;&lt;/p&gt;'':56C ''/p'':13C,42C,78C,134C ''autom'':119C ''commerci'':40C ''copern'':58C,103C ''could'':117C ''couldn'':128C ''coupl'':5C,38C ''deal'':106C ''docsum'':25C ''docsum.sourceforge.net'':29C,34C ''docsum.sourceforge.net/docsum/web/about.php'':28C ''docsum.sourceforge.net/docsum/web/about.php&lt;/a&gt;&lt;/p&gt;'':33C ''easili'':120C ''good'':105C,111C ''hope'':115C ''href'':17C,27C,48C,61C,82C ''intellix'':44C ''know'':11C ''least'':126C ''libot'':15C ''libots.sourceforge.net'':18C,22C ''libots.sourceforge.net/&lt;/a&gt;&lt;/p&gt;'':21C ''m'':91C ''nofollow'':20C,32C,53C,66C,85C ''one'':73C ''open'':7C ''option'':9C ''other'':97C ''p'':1C,14C,24C,36C,43C,57C,70C,79C,89C ''plenti'':95C ''pretti'':110C ''pro'':46C ''rel'':19C,31C,52C,65C,84C ''servic'':77C ''solut'':41C ''sourc'':8C ''summar'':45C,59C ''summarizer.intellexer.com'':50C,55C ''summarizer.intellexer.com/order_summarizer_pro.php'':49C ''summarizer.intellexer.com/order_summarizer_pro.php&lt;/a&gt;&lt;/p&gt;'':54C ''sure'':92C ''textteas'':80C ''use'':102C,132C ''web'':76C ''www.copernic.com'':63C,68C ''www.copernic.com/en/products/summarizer/'':62C ''www.copernic.com/en/products/summarizer/&lt;/a&gt;&lt;/p&gt;'':67C ''www.textteaser.com'':83C,87C ''www.textteaser.com/&lt;/a&gt;&lt;/p&gt;'':86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (721, 2, '2014-07-11 01:28:24.957', 7, NULL, NULL, '&lt;p&gt;When it comes to dealing with many disparate kinds of data, especially when the relationships between them are unclear, I would strongly recommend a technique based on &lt;a href="http://en.wikipedia.org/wiki/Decision_tree_learning"&gt;decision trees&lt;/a&gt;, the most popular ones today to the best of my knowledge are &lt;a href="http://en.wikipedia.org/wiki/Random_forest"&gt;random forest&lt;/a&gt;, and &lt;a href="http://www.montefiore.ulg.ac.be/~ernst/uploads/news/id63/extremely-randomized-trees.pdf"&gt;extremely randomized trees&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Both have implementations in &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.fit_transform"&gt;sklearn&lt;/a&gt;, and they are pretty straightforward to use. At a very high level, the reason that a &lt;code&gt;decision tree&lt;/code&gt;-based approach is advantageous for multiple disparate kinds of data is because decision trees are largely independent from the specific data they are dealing with, just so long as they are capable of understanding your representation.&lt;/p&gt;

&lt;p&gt;You''ll still have to fit your data into a feature vector of some kind, but based on your example that seems like a pretty straightforward task, and if you''re willing to go a little deeper on your implementation you could certainly come up with a custom tree-splitting rule without actually having to change anything in the underlying algorithm. The &lt;a href="http://www.cs.nyu.edu/~roweis/csc2515-2006/readings/quinlan.pdf"&gt;original paper&lt;/a&gt; is a pretty decent place to start if you want to give that a shot.&lt;/p&gt;

&lt;p&gt;If you want pseudo-structural data from your text data though, I might suggest looking into &lt;code&gt;doc2vec&lt;/code&gt;, recently developed by Google. I don''t think there are any good open-source implementations now, but it''s a pretty straightforward improvement on the &lt;code&gt;word2vec&lt;/code&gt; algorithm, which has implementations in at least &lt;code&gt;C&lt;/code&gt; and &lt;code&gt;python&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Hope that helps! Let me know if you''ve got any more questions.&lt;/p&gt;
', NULL, NULL, '''/a'':36C,56C,66C,79C,208C ''/code'':99C,245C,274C,284C,288C ''/p'':67C,136C,224C,289C,304C ''/stable/modules/generated/sklearn.ensemble.randomforestclassifier.html#sklearn.ensemble.randomforestclassifier.fit_transform'':77C ''/wiki/decision_tree_learning'':33C ''/wiki/random_forest'':53C ''/~ernst/uploads/news/id63/extremely-randomized-trees.pdf'':62C ''/~roweis/csc2515-2006/readings/quinlan.pdf'':205C ''actual'':191C ''advantag'':103C ''algorithm'':199C,275C ''anyth'':195C ''approach'':101C ''base'':27C,100C,154C ''best'':44C ''c'':283C ''capabl'':131C ''certain'':180C ''chang'':194C ''code'':96C,243C,272C,282C,286C ''come'':4C,181C ''could'':179C ''custom'':185C ''data'':12C,109C,120C,145C,232C,236C ''deal'':6C,123C ''decent'':212C ''decis'':34C,97C,112C ''deeper'':174C ''develop'':247C ''dispar'':9C,106C ''doc2vec'':244C ''en.wikipedia.org'':32C,52C ''en.wikipedia.org/wiki/decision_tree_learning'':31C ''en.wikipedia.org/wiki/random_forest'':51C ''especi'':13C ''exampl'':157C ''extrem'':63C ''featur'':148C ''fit'':143C ''forest'':55C ''give'':220C ''go'':171C ''good'':257C ''googl'':249C ''got'':300C ''help'':293C ''high'':90C ''hope'':291C ''href'':30C,50C,59C,74C,202C ''implement'':71C,177C,261C,278C ''improv'':269C ''independ'':116C ''kind'':10C,107C,152C ''know'':296C ''knowledg'':47C ''larg'':115C ''least'':281C ''let'':294C ''level'':91C ''like'':160C ''littl'':173C ''ll'':139C ''long'':127C ''look'':241C ''mani'':8C ''might'':239C ''multipl'':105C ''one'':40C ''open'':259C ''open-sourc'':258C ''origin'':206C ''p'':1C,68C,137C,225C,290C ''paper'':207C ''place'':213C ''popular'':39C ''pretti'':83C,162C,211C,267C ''pseudo'':230C ''pseudo-structur'':229C ''python'':287C ''question'':303C ''random'':54C,64C ''re'':168C ''reason'':93C ''recent'':246C ''recommend'':24C ''relationship'':16C ''represent'':135C ''rule'':189C ''scikit-learn.org'':76C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.randomforestclassifier.html#sklearn.ensemble.randomforestclassifier.fit_transform'':75C ''seem'':159C ''shot'':223C ''sklearn'':78C ''sourc'':260C ''specif'':119C ''split'':188C ''start'':215C ''still'':140C ''straightforward'':84C,163C,268C ''strong'':23C ''structur'':231C ''suggest'':240C ''task'':164C ''techniqu'':26C ''text'':235C ''think'':253C ''though'':237C ''today'':41C ''tree'':35C,65C,98C,113C,187C ''tree-split'':186C ''unclear'':20C ''under'':198C ''understand'':133C ''use'':86C ''ve'':299C ''vector'':149C ''want'':218C,228C ''will'':169C ''without'':190C ''word2vec'':273C ''would'':22C ''www.cs.nyu.edu'':204C ''www.cs.nyu.edu/~roweis/csc2515-2006/readings/quinlan.pdf'':203C ''www.montefiore.ulg.ac.be'':61C ''www.montefiore.ulg.ac.be/~ernst/uploads/news/id63/extremely-randomized-trees.pdf'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (946, 1, '2014-08-08 13:44:52.803', 2, 348, 'Analysis of Split (A/B) tests using Poisson and/or Binomial Distribution', '&lt;p&gt;Cross posting this from Cross Validated:&lt;/p&gt;

&lt;p&gt;I''ve seen this question asked before, but I have yet to come across a definitive source answering the specific questions:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;What''s the most appropriate statistical test to apply to a small A/B test?&lt;/li&gt;
&lt;li&gt;What''s the R code and interpretation to analyze a small A/B test?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I''m running a small test to figure out which ads perform better. I have the following results:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Position 1:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;code&gt;variation,impressions,clicks
row-1,753,26
row-3,767 7
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Position 2:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;code&gt;variation,impressions,clicks
row-1,753,16
row-3,767 13
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Position 3:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;code&gt;variation,impressions,clicks
row-1,753,2
row-3,767 7
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;I think it''s safe to say these numbers are small and likely to be not normally distributed. Also, it''s click data so there''s a binary outcome of clicked or not and the trials are independent.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Appropriate test&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;In analyzing each position for significance, I think comparison with a binomial or Poisson distribution makes the most sense. &lt;/p&gt;

&lt;p&gt;According to &lt;a href="http://www.openintro.org/stat/textbook.php?stat_book=os" rel="nofollow"&gt;the OpenIntro Stats&lt;/a&gt; (and other sources) book, a variable follows a Poisson distribution "... if the event being considered is rare, the population is large, and the events occur independently of each other."&lt;/p&gt;

&lt;p&gt;The same source classifies a binomial variable approximately the same way adding that the probability of success is the same and the number of trials is fixed.&lt;/p&gt;

&lt;p&gt;I appreciate this is not an either/or decision and analysis can be done using both distributions.&lt;/p&gt;

&lt;p&gt;Given A/B (split) testing is a science that has been practiced for several years, I imagine that there is a canonical test. However, looking around the internet, I mostly come across analysis that uses the standard normal distribution. That just seems wrong :)&lt;/p&gt;

&lt;p&gt;Is there a canonical test to use for A/B tests with small #''s of clicks?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Interpretation and R code&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I''ve used the following R code to test significance for each position:&lt;/p&gt;

&lt;p&gt;Position 1:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;binom.test(7, 767, p=(26/753))

Exact binomial test

data:  7 and 767
number of successes = 7, number of trials = 767, p-value = 1.077e-05
alternative hypothesis: true probability of success is not equal to 0.03452855
95 percent confidence interval:
 0.003676962 0.018713125
sample estimates:
probability of success 
           0.009126467
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I interpret this result to mean: The probability of success in the test group is indeed different than the control group with a 95% confidence interval that the success probability is between .368% and 1.87%&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ppois(((26-1)/753), lambda=(7/767), lower.tail = F)
[1] 0.009084947
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I interpret this result to mean: Given a Poisson distribution with a click rate of 7 per 767 trials, there is a 0.9% chance of having a click rate of 26 or more per 753 trials in the same distribution. Contextualized in the ad example,
there is a .1% chance that the control ad actually performs the same as the test ad.&lt;/p&gt;

&lt;p&gt;Is the above interpretation correct? Does the test and interpretation change with the different positions (i.e. are the results of the Poisson test more appropriate for Position 3 given the small numbers)?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (947, 2, '2014-08-08 13:53:36.847', 5, NULL, NULL, '&lt;p&gt;First of all, some basics of classification (and in general any supervised ML tasks), just to make sure we have same set of concepts in mind. &lt;/p&gt;

&lt;p&gt;Any supervised ML algorithm consists of at least 2 components: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Dataset to train and test on.&lt;/li&gt;
&lt;li&gt;Algorithm(s) to handle these data.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Training dataset consists of a set of pairs &lt;code&gt;(x, y)&lt;/code&gt;, where &lt;code&gt;x&lt;/code&gt; is a &lt;strong&gt;vector of features&lt;/strong&gt; and &lt;code&gt;y&lt;/code&gt; is &lt;strong&gt;predicted variable&lt;/strong&gt;. Predicted variable is just what you want to know, i.e. in your case it is exception type. Features are more tricky. You cannot just throw raw text into an algorithm, you need to extract meaningful parts of it and organize them as feature vectors first. You''ve already mentioned a couple of useful features - exception class name (e.g. &lt;code&gt;com.acme.PrintException&lt;/code&gt;) and contained words ("Timeout"). All you need is to translate your row exceptions (and human-categorized exception types) into suitable dataset, e.g.: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ex_class                  contains_timeout  ...   | ex_type
-----------------------------------------------------------
[com.acme.PrintException, 1                , ...] | Availability
[java.lang.Exception    , 0                , ...] | Network
 ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This representation is already much better for ML algorithms. But which one to take? &lt;/p&gt;

&lt;p&gt;Taking into account nature of the task and your current approach natural choice is to use &lt;strong&gt;decision trees&lt;/strong&gt;. This class of algorithms will compute optimal decision criteria for all your exception types and print out resulting tree. This is especially useful, because you will have possibility to manually inspect how decision is made and see how much it corresponds to your manually-crafted rules. &lt;/p&gt;

&lt;p&gt;There''s, however, possibility that some exceptions with exactly the same features will belong to different exception types. In this case probabilistic approach may work well. Despite its name, &lt;strong&gt;Naive Bayes&lt;/strong&gt; classifier works pretty well in most cases. There''s one issue with NB and our dataset representation, though: dataset contains &lt;em&gt;categorical&lt;/em&gt; variables, and Naive Bayes can work with &lt;em&gt;numerical&lt;/em&gt; attributes only*. Standard way to overcome this problem is to use &lt;a href="http://en.wikipedia.org/wiki/Dummy_variable_%28statistics%29"&gt;dummy variables&lt;/a&gt;. In short, dummy variables are binary variables that simply indicate whether specific category presents or not. For example, single variable &lt;code&gt;ex_class&lt;/code&gt; with values &lt;code&gt;{com.acme.PrintException, java.lang.Exception, ...}&lt;/code&gt;, etc. may be split into several variables &lt;code&gt;ex_class_printexception&lt;/code&gt;, &lt;code&gt;ex_class_exception&lt;/code&gt;, etc. with values &lt;code&gt;{0, 1}&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ex_class_printexception  ex_class_exception  contains_timeout | ex_type
-----------------------------------------------------------------------
[1,                    , 0                 , 1              ] | Availability
[0,                    , 1                 , 0              ] | Network
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;One last algorithm to try is &lt;strong&gt;Support Vector Machines (SVM)&lt;/strong&gt;. It neither provides helpful visualisation, nor is probabilistic, but often gives superior results. &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;em&gt;*&lt;/em&gt; - in fact, neither Bayes theorem, nor Naive Bayes itself state anything about variable type, but most software packages that come to mind rely on numerical features. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (722, 2, '2014-07-11 09:49:32.933', 19, NULL, NULL, '&lt;p&gt;R contains some &lt;em&gt;standard&lt;/em&gt; functions for data manipulation, which can be used for data cleaning, in its &lt;strong&gt;base&lt;/strong&gt; package (&lt;code&gt;gsub&lt;/code&gt;, &lt;code&gt;transform&lt;/code&gt;, etc.), as well as in various third-party packages, such as &lt;strong&gt;stringr&lt;/strong&gt;, &lt;strong&gt;reshape&lt;/strong&gt;/&lt;strong&gt;reshape2&lt;/strong&gt;, and &lt;strong&gt;plyr&lt;/strong&gt;/&lt;strong&gt;dplyr&lt;/strong&gt;. Examples and best practices of usage for these packages and their functions are described in the following paper: &lt;a href="http://vita.had.co.nz/papers/tidy-data.pdf"&gt;http://vita.had.co.nz/papers/tidy-data.pdf&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Additionally, R offers some packages specifically &lt;em&gt;focused&lt;/em&gt; on data cleaning and transformation:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;editrules&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/editrules/index.html"&gt;http://cran.r-project.org/web/packages/editrules/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;deducorrect&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/deducorrect/index.html"&gt;http://cran.r-project.org/web/packages/deducorrect/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;StatMatch&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/StatMatch/index.html"&gt;http://cran.r-project.org/web/packages/StatMatch/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;MatchIt&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/MatchIt/index.html"&gt;http://cran.r-project.org/web/packages/MatchIt/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;DataCombine&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/DataCombine"&gt;http://cran.r-project.org/web/packages/DataCombine&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;data.table&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/data.table"&gt;http://cran.r-project.org/web/packages/data.table&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;A comprehensive and coherent approach to &lt;strong&gt;data cleaning&lt;/strong&gt; in R, including examples and use of &lt;strong&gt;editrules&lt;/strong&gt; and &lt;strong&gt;deducorrect&lt;/strong&gt; packages, as well as a description of &lt;em&gt;workflow&lt;/em&gt; (&lt;em&gt;framework&lt;/em&gt;) of data cleaning in R, is presented in the following paper, which I highly recommend: &lt;a href="http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf"&gt;http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/code'':27C,30C ''/doc/contrib/de_jonge+van_der_loo-introduction_to_data_cleaning_with_r.pdf'':232C ''/doc/contrib/de_jonge+van_der_loo-introduction_to_data_cleaning_with_r.pdf&lt;/a&gt;.&lt;/p&gt;'':235C ''/em'':7C,94C,209C,212C ''/p'':100C ''/papers/tidy-data.pdf'':81C ''/papers/tidy-data.pdf&lt;/a&gt;.&lt;/p&gt;'':84C ''/strong'':23C,45C,48C,51C,55C,58C,105C,117C,129C,141C,153C,165C,185C,195C,199C ''/ul'':174C ''/web/packages/data.table'':170C ''/web/packages/data.table&lt;/a&gt;)&lt;/li&gt;'':173C ''/web/packages/datacombine'':158C ''/web/packages/datacombine&lt;/a&gt;)&lt;/li&gt;'':161C ''/web/packages/deducorrect/index.html'':122C ''/web/packages/deducorrect/index.html&lt;/a&gt;)&lt;/li&gt;'':125C ''/web/packages/editrules/index.html'':110C ''/web/packages/editrules/index.html&lt;/a&gt;)&lt;/li&gt;'':113C ''/web/packages/matchit/index.html'':146C ''/web/packages/matchit/index.html&lt;/a&gt;)&lt;/li&gt;'':149C ''/web/packages/statmatch/index.html'':134C ''/web/packages/statmatch/index.html&lt;/a&gt;)&lt;/li&gt;'':137C ''addit'':86C ''approach'':180C ''base'':22C ''best'':61C ''clean'':18C,97C,184C,215C ''code'':25C,28C ''coher'':179C ''comprehens'':177C ''contain'':3C ''cran.r-project.org'':109C,112C,121C,124C,133C,136C,145C,148C,157C,160C,169C,172C,231C,234C ''cran.r-project.org/doc/contrib/de_jonge+van_der_loo-introduction_to_data_cleaning_with_r.pdf'':230C ''cran.r-project.org/doc/contrib/de_jonge+van_der_loo-introduction_to_data_cleaning_with_r.pdf&lt;/a&gt;.&lt;/p&gt;'':233C ''cran.r-project.org/web/packages/data.table'':168C ''cran.r-project.org/web/packages/data.table&lt;/a&gt;)&lt;/li&gt;'':171C ''cran.r-project.org/web/packages/datacombine'':156C ''cran.r-project.org/web/packages/datacombine&lt;/a&gt;)&lt;/li&gt;'':159C ''cran.r-project.org/web/packages/deducorrect/index.html'':120C ''cran.r-project.org/web/packages/deducorrect/index.html&lt;/a&gt;)&lt;/li&gt;'':123C ''cran.r-project.org/web/packages/editrules/index.html'':108C ''cran.r-project.org/web/packages/editrules/index.html&lt;/a&gt;)&lt;/li&gt;'':111C ''cran.r-project.org/web/packages/matchit/index.html'':144C ''cran.r-project.org/web/packages/matchit/index.html&lt;/a&gt;)&lt;/li&gt;'':147C ''cran.r-project.org/web/packages/statmatch/index.html'':132C ''cran.r-project.org/web/packages/statmatch/index.html&lt;/a&gt;)&lt;/li&gt;'':135C ''data'':10C,17C,96C,183C,214C ''data.table'':164C ''datacombin'':152C ''deducorrect'':116C,198C ''describ'':72C ''descript'':205C ''dplyr'':57C ''editrul'':104C,194C ''em'':5C,92C,207C,210C ''etc'':31C ''exampl'':59C,189C ''focus'':93C ''follow'':75C,222C ''framework'':211C ''function'':8C,70C ''gsub'':26C ''high'':226C ''href'':78C,107C,119C,131C,143C,155C,167C,229C ''includ'':188C ''li'':102C,114C,126C,138C,150C,162C ''manipul'':11C ''matchit'':140C ''offer'':88C ''p'':1C,85C,175C ''packag'':24C,40C,67C,90C,200C ''paper'':76C,223C ''parti'':39C ''plyr'':54C ''practic'':62C ''present'':219C ''r'':2C,87C,187C,217C ''recommend'':227C ''reshap'':47C ''reshape2'':50C ''specif'':91C ''standard'':6C ''statmatch'':128C ''stringr'':44C ''strong'':21C,43C,46C,49C,53C,56C,103C,115C,127C,139C,151C,163C,182C,193C,197C ''third'':38C ''third-parti'':37C ''transform'':29C,99C ''ul'':101C ''usag'':64C ''use'':15C,191C ''various'':36C ''vita.had.co.nz'':80C,83C ''vita.had.co.nz/papers/tidy-data.pdf'':79C ''vita.had.co.nz/papers/tidy-data.pdf&lt;/a&gt;.&lt;/p&gt;'':82C ''well'':33C,202C ''workflow'':208C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (723, 2, '2014-07-11 11:32:13.55', 2, NULL, NULL, '&lt;p&gt;Random subsampling seems appropriate, bootstrapping is a bit more generic, but also correct.&lt;/p&gt;

&lt;p&gt;Here are some references and synonyms: &lt;a href="http://www.frank-dieterle.com/phd/2_4_3.html" rel="nofollow"&gt;http://www.frank-dieterle.com/phd/2_4_3.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':15C ''/phd/2_4_3.html'':27C ''/phd/2_4_3.html&lt;/a&gt;&lt;/p&gt;'':32C ''also'':13C ''appropri'':5C ''bit'':9C ''bootstrap'':6C ''correct'':14C ''generic'':11C ''href'':24C ''nofollow'':29C ''p'':1C,16C ''random'':2C ''refer'':20C ''rel'':28C ''seem'':4C ''subsampl'':3C ''synonym'':22C ''www.frank-dieterle.com'':26C,31C ''www.frank-dieterle.com/phd/2_4_3.html'':25C ''www.frank-dieterle.com/phd/2_4_3.html&lt;/a&gt;&lt;/p&gt;'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (724, 2, '2014-07-11 14:13:30.403', 4, NULL, NULL, '&lt;p&gt;Your approach of using a source version repository is a good one and it actually allows you also working on the cluster and then copying everything back.&lt;/p&gt;

&lt;p&gt;If you find yourself making minor edits to your Python script on your laptop, then updating your SVN directory on the cluster, why not work directly on the cluster frontend, make all needed minor edits, and then, at the end of the day, commit everything there and update on your laptop? &lt;/p&gt;

&lt;p&gt;All you need is to get familiar with the environment there (OS, editor, etc.) or install your own environment (I usually install in my home directory the latest version of &lt;a href="http://www.vim.org/" rel="nofollow"&gt;Vim&lt;/a&gt;, &lt;a href="http://tmux.sourceforge.net/" rel="nofollow"&gt;Tmux&lt;/a&gt;, etc. with the proper dotfiles so I feel at home there.)&lt;/p&gt;

&lt;p&gt;Also, you can version your data, and even your intermediate results if size permits. My repositories often comprise code, data (original and cleaned versions), documentation, and paper sources for publishing (latex)&lt;/p&gt;

&lt;p&gt;Finally, you can script your job submission to avoid modifying scripts manually. &lt;code&gt;qsub&lt;/code&gt; accepts a script from stdin and also accepts all &lt;code&gt;#$&lt;/code&gt; comments as command-line arguments. &lt;/p&gt;
', NULL, NULL, '''/a'':120C,127C ''/code'':188C,199C ''/p'':29C,82C,139C,172C,206C ''accept'':189C,196C ''actual'':16C ''allow'':17C ''also'':19C,141C,195C ''approach'':3C ''argument'':205C ''avoid'':182C ''back'':28C ''clean'':163C ''cluster'':23C,52C,59C ''code'':159C,186C,198C ''command'':203C ''command-lin'':202C ''comment'':200C ''commit'':74C ''compris'':158C ''copi'':26C ''data'':146C,160C ''day'':73C ''direct'':56C ''directori'':49C,109C ''document'':165C ''dotfil'':132C ''edit'':37C,65C ''editor'':96C ''end'':70C ''environ'':93C,102C ''etc'':97C,128C ''even'':148C ''everyth'':27C,75C ''familiar'':90C ''feel'':135C ''final'':174C ''find'':33C ''frontend'':60C ''get'':89C ''good'':12C ''home'':108C,137C ''href'':115C,122C ''instal'':99C,105C ''intermedi'':150C ''job'':179C ''laptop'':44C,81C ''latest'':111C ''latex'':171C ''line'':204C ''make'':35C,61C ''manual'':185C ''minor'':36C,64C ''modifi'':183C ''need'':63C,86C ''nofollow'':118C,125C ''often'':157C ''one'':13C ''origin'':161C ''os'':95C ''p'':1C,30C,83C,140C,173C ''paper'':167C ''permit'':154C ''proper'':131C ''publish'':170C ''python'':40C ''qsub'':187C ''rel'':117C,124C ''repositori'':9C,156C ''result'':151C ''script'':41C,177C,184C,191C ''size'':153C ''sourc'':7C,168C ''stdin'':193C ''submiss'':180C ''svn'':48C ''tmux'':126C ''tmux.sourceforge.net'':123C ''updat'':46C,78C ''use'':5C ''usual'':104C ''version'':8C,112C,144C,164C ''vim'':119C ''work'':20C,55C ''www.vim.org'':116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (725, 2, '2014-07-11 14:27:01.603', 0, NULL, NULL, '&lt;p&gt;What you describe falls in the category of &lt;a href="http://en.wikipedia.org/wiki/Concept_drift" rel="nofollow"&gt;concept drift&lt;/a&gt; in machine learning.
You might find interesting and actionable ideas in this &lt;a href="http://arxiv.org/pdf/1010.4784.pdf" rel="nofollow"&gt;summary paper&lt;/a&gt; and you''ll find a taxonomy of the possible approaches in &lt;a href="http://www.cs.waikato.ac.nz/~abifet/PAKDD2011/PAKDD11Tutorial_Handling_Concept_Drift.pdf" rel="nofollow"&gt;these slides&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':19C,41C,62C ''/p'':63C ''/pdf/1010.4784.pdf'':36C ''/wiki/concept_drift'':14C ''/~abifet/pakdd2011/pakdd11tutorial_handling_concept_drift.pdf'':57C ''action'':28C ''approach'':51C ''arxiv.org'':35C ''arxiv.org/pdf/1010.4784.pdf'':34C ''categori'':8C ''concept'':17C ''describ'':4C ''drift'':18C ''en.wikipedia.org'':13C ''en.wikipedia.org/wiki/concept_drift'':12C ''fall'':5C ''find'':25C,45C ''href'':11C,33C,54C ''idea'':29C ''interest'':26C ''learn'':22C ''ll'':44C ''machin'':21C ''might'':24C ''nofollow'':16C,38C,59C ''p'':1C ''paper'':40C ''possibl'':50C ''rel'':15C,37C,58C ''slide'':61C ''summari'':39C ''taxonomi'':47C ''www.cs.waikato.ac.nz'':56C ''www.cs.waikato.ac.nz/~abifet/pakdd2011/pakdd11tutorial_handling_concept_drift.pdf'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (726, 1, '2014-07-11 21:09:58.873', 5, 120, 'Trying to understand free-energy equations in a Karl Friston neuroscience article', '&lt;p&gt;I am trying to understand a neuroscience article:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Friston, Karl J., et al. "Action and behavior: a free-energy formulation." &lt;em&gt;Biological cybernetics&lt;/em&gt; 102.3 (2010): 227-260. (&lt;a href="http://link.springer.com/article/10.1007%2Fs00422-010-0364-z" rel="nofollow"&gt;DOI 10.1007/s00422-010-0364-z&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In this article, Friston gives three equations that are, as I understand him, equivalent or inter-convertertable and refer to both physical and Shannon entropy. They appear on page 231 of the article as equation (5):&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The resulting expression for free-energy can be expressed in three ways (with the use of the Bayes rules and simple rearrangements):&lt;/p&gt;
  
  &lt;p&gt;â¢ Energy minus entropy&lt;/p&gt;
  
  &lt;p&gt;â¢ Divergence plus surprise&lt;/p&gt;
  
  &lt;p&gt;â¢ Complexity minus accuracy&lt;/p&gt;
  
  &lt;p&gt;Mathematically, these correspond to:&lt;/p&gt;
  
  &lt;p&gt;&lt;img src="http://i.stack.imgur.com/0eQgg.png" alt="\begin {aligned} F &amp;amp;= - \langle \ln p(\tilde s, \Psi \vert m) \rangle _q + \langle \ln q(\Psi \vert \mu) \rangle _q \ &amp;amp;= D(q(\Psi \vert \mu) \parallel p(\Psi \vert \tilde s, m)) - \ln p(\tilde s \vert m) &amp;amp; \qquad \qquad &amp;amp; (5) \ &amp;amp;= D(q(\Psi \vert \mu) \parallel p(\Psi \vert m)) - \langle \ln p(\tilde s \vert \Psi, m) \rangle _q \end{aligned}"&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The things I am struggling with at this point are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;the meaning of the || in the 2nd and 3rd versions of the equations;&lt;/li&gt;
&lt;li&gt;and the negative logs.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Any help in understanding how these equations are actually what Fristen claims them to be would be greatly appreciated. For example, in the 1st equation, in what sense is the first term energy, etc?&lt;/p&gt;
', 'neuralnetwork', NULL, '''-260'':47C ''/0eqgg.png'':155C ''/a'':58C ''/article/10.1007%2fs00422-010-0364-z'':52C ''/blockquote'':227C ''/em'':43C ''/li'':59C,255C,261C ''/ol'':262C ''/p'':24C,99C,125C,131C,137C,143C,149C,226C,239C,298C ''/s00422-010-0364-z'':57C ''/ul'':60C ''10.1007'':56C ''102.3'':44C ''1st'':287C ''2010'':45C ''227'':46C ''231'':92C ''2nd'':248C ''3rd'':250C ''5'':98C,202C ''accuraci'':142C ''action'':32C ''actual'':272C ''al'':31C ''align'':158C,225C ''alt'':156C ''amp'':160C,179C,198C,201C,203C ''appear'':89C ''appreci'':282C ''articl'':13A,23C,64C,95C ''bay'':120C ''begin'':157C ''behavior'':34C ''biolog'':41C ''blockquot'':100C ''claim'':275C ''complex'':140C ''convertert'':79C ''correspond'':147C ''cybernet'':42C ''d'':180C,204C ''diverg'':134C ''doi'':55C ''em'':40C ''end'':224C ''energi'':6A,38C,108C,128C,296C ''entropi'':87C,130C ''equat'':7A,68C,97C,254C,270C,288C ''equival'':75C ''et'':30C ''etc'':297C ''exampl'':284C ''express'':104C,111C ''f'':159C ''first'':294C ''formul'':39C ''free'':5A,37C,107C ''free-energi'':4A,36C,106C ''fristen'':274C ''friston'':11A,27C,65C ''give'':66C ''great'':281C ''help'':265C ''href'':49C ''i.stack.imgur.com'':154C ''i.stack.imgur.com/0eqgg.png'':153C ''img'':151C ''inter'':78C ''inter-convertert'':77C ''j'':29C ''karl'':10A,28C ''langl'':161C,171C,214C ''li'':26C,241C,256C ''link.springer.com'':51C ''link.springer.com/article/10.1007%2fs00422-010-0364-z'':50C ''ln'':162C,172C,192C,215C ''log'':260C ''m'':168C,191C,197C,213C,221C ''mathemat'':145C ''mean'':243C ''minus'':129C,141C ''mu'':176C,184C,208C ''negat'':259C ''neuralnetwork'':14B ''neurosci'':12A,22C ''nofollow'':54C ''ol'':240C ''p'':15C,61C,101C,126C,132C,138C,144C,150C,163C,186C,193C,210C,216C,228C,263C ''page'':91C ''parallel'':185C,209C ''physic'':84C ''plus'':135C ''point'':237C ''psi'':166C,174C,182C,187C,206C,211C,220C ''q'':170C,173C,178C,181C,205C,223C ''qquad'':199C,200C ''rangl'':169C,177C,222C ''rearrang'':124C ''refer'':81C ''rel'':53C ''result'':103C ''rule'':121C ''sens'':291C ''shannon'':86C ''simpl'':123C ''src'':152C ''struggl'':233C ''surpris'':136C ''term'':295C ''thing'':230C ''three'':67C,113C ''tild'':164C,189C,194C,217C ''tri'':1A,18C ''ul'':25C ''understand'':3A,20C,73C,267C ''use'':117C ''version'':251C ''vert'':167C,175C,183C,188C,196C,207C,212C,219C ''way'':114C ''would'':279C ''â'':127C,133C,139C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5037, 2, '2015-02-04 02:54:52.773', 0, NULL, NULL, '&lt;p&gt;I''ve found an interesting project With tons of data available. It''s a real data benchmark executed over an industrial valve. This is the website.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://diag.mchtr.pw.edu.pl/damadics/" rel="nofollow"&gt;Industrial Actuator Real Data Benchmark Study.&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':43C ''/damadics/'':34C ''/p'':28C,44C ''actuat'':38C ''avail'':12C ''benchmark'':18C,41C ''data'':11C,17C,40C ''diag.mchtr.pw.edu.pl'':33C ''diag.mchtr.pw.edu.pl/damadics/'':32C ''execut'':19C ''found'':4C ''href'':31C ''industri'':22C,37C ''interest'':6C ''nofollow'':36C ''p'':1C,29C ''project'':7C ''real'':16C,39C ''rel'':35C ''studi'':42C ''ton'':9C ''valv'':23C ''ve'':3C ''websit'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (728, 2, '2014-07-11 22:03:31.95', 4, NULL, NULL, '&lt;p&gt;In addition to excellent previous answers, I''d like to recommend two papers on &lt;strong&gt;data cleaning&lt;/strong&gt;. They are not specific to &lt;em&gt;manual&lt;/em&gt; data cleaning, but, considering the benefits and advice (which I completely agree with) of expressing even &lt;strong&gt;manual&lt;/strong&gt; data transformations &lt;strong&gt;in code&lt;/strong&gt;, these resources can be as valuable. Also, despite the fact that following papers are somewhat R-focused, I believe that general &lt;em&gt;ideas&lt;/em&gt; and &lt;em&gt;workflows&lt;/em&gt; for data cleaning can be easily extracted and are equally applicable to non-R environments, as well.&lt;/p&gt;

&lt;p&gt;The first paper presents the concept of &lt;em&gt;tidy data&lt;/em&gt;, as well as examples and best practices of use of standard and specific R packages in data cleaning: &lt;a href="http://vita.had.co.nz/papers/tidy-data.pdf" rel="nofollow"&gt;http://vita.had.co.nz/papers/tidy-data.pdf&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;A comprehensive and coherent approach to data cleaning in R, including examples, as well as a description of &lt;em&gt;workflow&lt;/em&gt; (&lt;em&gt;framework&lt;/em&gt;) of data cleaning in R, is presented in the following paper, which I highly recommend: &lt;a href="http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf" rel="nofollow"&gt;http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/doc/contrib/de_jonge+van_der_loo-introduction_to_data_cleaning_with_r.pdf'':185C ''/doc/contrib/de_jonge+van_der_loo-introduction_to_data_cleaning_with_r.pdf&lt;/a&gt;.&lt;/p&gt;'':190C ''/em'':27C,77C,81C,112C,162C,165C ''/p'':100C ''/papers/tidy-data.pdf'':135C ''/papers/tidy-data.pdf&lt;/a&gt;.&lt;/p&gt;'':140C ''/strong'':19C,46C,52C ''addit'':3C ''advic'':35C ''agre'':39C ''also'':59C ''answer'':7C ''applic'':92C ''approach'':146C ''believ'':72C ''benefit'':33C ''best'':118C ''clean'':18C,29C,84C,130C,149C,168C ''code'':51C ''coher'':145C ''complet'':38C ''comprehens'':143C ''concept'':107C ''consid'':31C ''cran.r-project.org'':184C,189C ''cran.r-project.org/doc/contrib/de_jonge+van_der_loo-introduction_to_data_cleaning_with_r.pdf'':183C ''cran.r-project.org/doc/contrib/de_jonge+van_der_loo-introduction_to_data_cleaning_with_r.pdf&lt;/a&gt;.&lt;/p&gt;'':188C ''d'':9C ''data'':17C,28C,47C,83C,111C,129C,148C,167C ''descript'':158C ''despit'':60C ''easili'':87C ''em'':25C,75C,79C,109C,160C,163C ''environ'':97C ''equal'':91C ''even'':43C ''exampl'':116C,153C ''excel'':5C ''express'':42C ''extract'':88C ''fact'':62C ''first'':103C ''focus'':70C ''follow'':64C,175C ''framework'':164C ''general'':74C ''high'':179C ''href'':132C,182C ''idea'':76C ''includ'':152C ''like'':10C ''manual'':26C,45C ''nofollow'':137C,187C ''non'':95C ''non-r'':94C ''p'':1C,101C,141C ''packag'':127C ''paper'':14C,65C,104C,176C ''practic'':119C ''present'':105C,172C ''previous'':6C ''r'':69C,96C,126C,151C,170C ''r-focus'':68C ''recommend'':12C,180C ''rel'':136C,186C ''resourc'':54C ''somewhat'':67C ''specif'':23C,125C ''standard'':123C ''strong'':16C,44C,49C ''tidi'':110C ''transform'':48C ''two'':13C ''use'':121C ''valuabl'':58C ''vita.had.co.nz'':134C,139C ''vita.had.co.nz/papers/tidy-data.pdf'':133C ''vita.had.co.nz/papers/tidy-data.pdf&lt;/a&gt;.&lt;/p&gt;'':138C ''well'':99C,114C,155C ''workflow'':80C,161C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (730, 1, '2014-07-12 17:25:52.907', 8, 264, 'Is FPGrowth still considered "state of the art" in frequent pattern mining?', '&lt;p&gt;As far as I know the development of algorithms to solve the Frequent Pattern Mining (FPM) problem, the road of improvements have some main checkpoints. Firstly, the &lt;a href="http://en.wikipedia.org/wiki/Apriori_algorithm"&gt;Apriori&lt;/a&gt; algorithm was proposed in 1993, by &lt;a href="http://dl.acm.org/citation.cfm?id=170072"&gt;Agrawal et al.&lt;/a&gt;, along with the formalization of the problem. The algorithm was able to &lt;em&gt;strip-off&lt;/em&gt; some sets from the &lt;code&gt;2^n - 1&lt;/code&gt; sets (powerset) by using a lattice to maintain the data. A drawback of the approach was the need to re-read the database to compute the frequency of each set expanded.&lt;/p&gt;

&lt;p&gt;Later, on year 1997, &lt;a href="http://www.computer.org/csdl/trans/tk/2000/03/k0372-abs.html"&gt;Zaki et al.&lt;/a&gt; proposed the algorithm &lt;a href="http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Frequent_Pattern_Mining/The_Eclat_Algorithm"&gt;Eclat&lt;/a&gt;, which &lt;em&gt;inserted&lt;/em&gt; the resulting frequency of each set inside the lattice. This was done by adding, at each node of the lattice, the set of transaction-ids that had the items from root to the referred node. The main contribution is that one does not have to re-read the entire dataset to know the frequency of each set, but the memory required to keep such data structure built may exceed the size of the dataset itself.&lt;/p&gt;

&lt;p&gt;In 2000, &lt;a href="http://dl.acm.org/citation.cfm?doid=335191.335372"&gt;Han et al.&lt;/a&gt; proposed an algorithm named &lt;a href="http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Frequent_Pattern_Mining/The_FP-Growth_Algorithm"&gt;FPGrowth&lt;/a&gt;, along with a prefix-tree data structure named FPTree. The algorithm was able to provide significant data compression, while also granting that only frequent itemsets would be yielded (without candidate itemset generation). This was done mainly by sorting the items of each transaction in decreasing order, so that the most frequent items are the ones with the least repetitions in the tree data structure. Since the frequency only descends while traversing the tree in-depth, the algorithm is able to &lt;em&gt;strip-off&lt;/em&gt; non-frequent itemsets.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;&lt;strike&gt;As far as I know, this may be considered a state-of-the-art algorithm, but I''d like to know about other proposed solutions. What other algorithms for FPM are considered "state-of-the-art"? What is the &lt;em&gt;intuition&lt;/em&gt;/&lt;em&gt;main-contribution&lt;/em&gt; of such algorithms?&lt;/strike&gt;&lt;/p&gt;

&lt;p&gt;Is the FPGrowth algorithm still considered "state of the art" in frequent pattern mining? If not, what algorithm(s) may extract frequent itemsets from large datasets more efficiently?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (731, 1, '2014-07-13 09:04:39.703', 26, 966, 'How to fight underfitting in a deep neural net', '&lt;p&gt;When I started with artificial neural networks (NN) I thought I''d have to fight overfitting as the main problem. But in practice I can''t even get my NN to pass the 20% error rate barrier. I can''t even beat my score on random forest!&lt;/p&gt;

&lt;p&gt;I''m seeking some very general or not so general advice on what should one do to make a NN start capturing trends in data.&lt;/p&gt;

&lt;p&gt;For implementing NN I use Theano Stacked Auto Encoder with &lt;a href="https://github.com/lisa-lab/DeepLearningTutorials/blob/master/code/SdA.py"&gt;the code from tutorial&lt;/a&gt; that works great (less than 5% error rate) for classifying the MNIST dataset. It is a multilayer perceptron, with softmax layer on top with each hidden later being pre-trained as autoencoder (fully described at &lt;a href="http://deeplearning.net/tutorial/deeplearning.pdf"&gt;tutorial&lt;/a&gt;, chapter 8). There are ~50 input features and ~10 output classes. The NN has sigmoid neurons and all data are normalized to [0,1]. I tried lots of different configurations: number of hidden layers and neurons in them (100-&gt;100-&gt;100, 60-&gt;60-&gt;60, 60-&gt;30-&gt;15, etc.), different learning and pre-train rates, etc.&lt;/p&gt;

&lt;p&gt;And the best thing I can get is a 20% error rate on the validation set and a 40% error rate on the test set.&lt;/p&gt;

&lt;p&gt;On the other hand, when I try to use Random Forest (from scikit-learn) I easily get a 12% error rate on the validation set and 25%(!) on the test set.&lt;/p&gt;

&lt;p&gt;How can it be that my deep NN with pre-training behaves so badly? What should I try? &lt;/p&gt;
', 'neuralnetwork', NULL, '''/a'':107C,150C ''/lisa-lab/deeplearningtutorials/blob/master/code/sda.py'':102C ''/p'':59C,86C,207C,234C,268C,289C ''/tutorial/deeplearning.pdf'':148C ''0'':173C ''1'':174C ''10'':159C ''100'':189C,190C,191C ''12'':255C ''15'':197C ''20'':45C,218C ''25'':263C ''30'':196C ''40'':227C ''5'':113C ''50'':155C ''60'':192C,193C,194C,195C ''8'':152C ''advic'':71C ''artifici'':16C ''auto'':95C ''autoencod'':140C ''bad'':284C ''barrier'':48C ''beat'':53C ''behav'':282C ''best'':211C ''captur'':82C ''chapter'':151C ''class'':161C ''classifi'':117C ''code'':104C ''configur'':180C ''d'':23C ''data'':85C,169C ''dataset'':120C ''deep'':7A,276C ''deeplearning.net'':147C ''deeplearning.net/tutorial/deeplearning.pdf'':146C ''describ'':142C ''differ'':179C,199C ''easili'':252C ''encod'':96C ''error'':46C,114C,219C,228C,256C ''etc'':198C,206C ''even'':38C,52C ''featur'':157C ''fight'':3A,26C ''forest'':58C,246C ''fulli'':141C ''general'':66C,70C ''get'':39C,215C,253C ''github.com'':101C ''github.com/lisa-lab/deeplearningtutorials/blob/master/code/sda.py'':100C ''great'':110C ''hand'':239C ''hidden'':133C,183C ''href'':99C,145C ''implement'':89C ''input'':156C ''later'':134C ''layer'':128C,184C ''learn'':200C,250C ''less'':111C ''lot'':177C ''m'':62C ''main'':30C ''make'':78C ''mnist'':119C ''multilay'':124C ''net'':9A ''network'':18C ''neural'':8A,17C ''neuralnetwork'':10B ''neuron'':166C,186C ''nn'':19C,41C,80C,90C,163C,277C ''normal'':171C ''number'':181C ''one'':75C ''output'':160C ''overfit'':27C ''p'':11C,60C,87C,208C,235C,269C ''pass'':43C ''perceptron'':125C ''practic'':34C ''pre'':137C,203C,280C ''pre-train'':136C,202C,279C ''problem'':31C ''random'':57C,245C ''rate'':47C,115C,205C,220C,229C,257C ''scikit'':249C ''scikit-learn'':248C ''score'':55C ''seek'':63C ''set'':224C,233C,261C,267C ''sigmoid'':165C ''softmax'':127C ''stack'':94C ''start'':14C,81C ''test'':232C,266C ''theano'':93C ''thing'':212C ''thought'':21C ''top'':130C ''train'':138C,204C,281C ''trend'':83C ''tri'':176C,242C,288C ''tutori'':106C,149C ''underfit'':4A ''use'':92C,244C ''valid'':223C,260C ''work'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (733, 2, '2014-07-13 12:24:36.43', 5, NULL, NULL, '&lt;p&gt;Ask your grid administrator to add your local machine as a "submit host", and install SGE (which we assume you are using, you don''t actually say) so then you can &lt;code&gt;qsub&lt;/code&gt; from your machine.&lt;/p&gt;

&lt;p&gt;OR....&lt;/p&gt;

&lt;p&gt;Use emacs, then you can edit on your HPC via emacs''s "tramp" ssh-connection facilities, and keep a shell open in another emacs window. You don''t say what editor/operating system you like to use. You can even configure emacs to save a file in two places, so you could save to your local machine for running tests and to the HPC file system simultaneously for big jobs.&lt;/p&gt;
', NULL, NULL, '''/code'':35C ''/p'':39C,42C,114C ''actual'':27C ''add'':7C ''administr'':5C ''anoth'':67C ''ask'':2C ''assum'':20C ''big'':112C ''code'':33C ''configur'':84C ''connect'':59C ''could'':95C ''edit'':49C ''editor/operating'':75C ''emac'':45C,54C,68C,85C ''even'':83C ''facil'':60C ''file'':89C,108C ''grid'':4C ''host'':14C ''hpc'':52C,107C ''instal'':16C ''job'':113C ''keep'':62C ''like'':78C ''local'':9C,99C ''machin'':10C,38C,100C ''open'':65C ''p'':1C,40C,43C ''place'':92C ''qsub'':34C ''run'':102C ''save'':87C,96C ''say'':28C,73C ''sge'':17C ''shell'':64C ''simultan'':110C ''ssh'':58C ''ssh-connect'':57C ''submit'':13C ''system'':76C,109C ''test'':103C ''tramp'':56C ''two'':91C ''use'':23C,44C,80C ''via'':53C ''window'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (793, 1, '2014-07-21 13:41:13.427', 10, 1960, 'Uses of NoSQL database in data science', '&lt;p&gt;How can &lt;a href="http://en.wikipedia.org/wiki/NoSQL"&gt;NoSQL&lt;/a&gt; databases like &lt;a href="http://en.wikipedia.org/wiki/MongoDB"&gt;MongoDB&lt;/a&gt; be used for data analysis? What are the features in them that can make data analysis faster and powerful?&lt;/p&gt;
', 'bigdata  nosql  mongodb', NULL, '''/a'':20C,29C ''/p'':49C ''/wiki/mongodb'':27C ''/wiki/nosql'':18C ''analysi'':34C,45C ''bigdata'':8B ''data'':6A,33C,44C ''databas'':4A,21C ''en.wikipedia.org'':17C,26C ''en.wikipedia.org/wiki/mongodb'':25C ''en.wikipedia.org/wiki/nosql'':16C ''faster'':46C ''featur'':38C ''href'':15C,24C ''like'':22C ''make'':43C ''mongodb'':10B,28C ''nosql'':3A,9B,19C ''p'':11C ''power'':48C ''scienc'':7A ''use'':1A,31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5355, 2, '2015-03-19 16:35:56.013', 2, NULL, NULL, '&lt;p&gt;What about &lt;a href="http://ess.r-project.org" rel="nofollow"&gt;ESS&lt;/a&gt;, the R (and other stats languages) package for the Emacs editor?
It''s not formally an IDE, though it has many, if not more of the features of RStudio, just in a different UI (code completion, inline help, object-aware autocomplete, debugging etc.).&lt;/p&gt;
', NULL, NULL, '''/a'':10C ''/p'':55C ''autocomplet'':52C ''awar'':51C ''code'':45C ''complet'':46C ''debug'':53C ''differ'':43C ''editor'':21C ''emac'':20C ''ess'':9C ''ess.r-project.org'':6C ''etc'':54C ''featur'':37C ''formal'':25C ''help'':48C ''href'':5C ''ide'':27C ''inlin'':47C ''languag'':16C ''mani'':31C ''nofollow'':8C ''object'':50C ''object-awar'':49C ''p'':1C ''packag'':17C ''r'':12C ''rel'':7C ''rstudio'':39C ''stat'':15C ''though'':28C ''ui'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (742, 2, '2014-07-15 06:19:31.82', 17, NULL, NULL, '&lt;p&gt;1) I think that there''s no need to question whether your background is adequate for a career in data science. CS degree IMHO is &lt;strong&gt;more than enough&lt;/strong&gt; for data scientist from software engineering point of view. Having said that, theoretical knowledge is not very helpful without matching &lt;strong&gt;practical experience&lt;/strong&gt;, so I would definitely try to &lt;strong&gt;enrich&lt;/strong&gt; my experience through participating in &lt;em&gt;additional school projects, internships or open source projects&lt;/em&gt; (maybe ones, focused on data science / machine learning / artificial intelligence).&lt;/p&gt;

&lt;p&gt;2) I believe your concern about &lt;strong&gt;focusing&lt;/strong&gt; on data science &lt;strong&gt;too early&lt;/strong&gt; is unfounded, as long as you will be practicing software engineering either as a part of your data science job, or additionally in your spare time.&lt;/p&gt;

&lt;p&gt;3) I find the following &lt;strong&gt;definition of a data scientist&lt;/strong&gt; rather accurate and hope it will be helpful in your future career success:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;A &lt;em&gt;data scientist&lt;/em&gt; is someone who is better at statistics than any
  software engineer and better at software engineering than any
  statistician.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;P.S. Today''s &lt;strong&gt;enormous&lt;/strong&gt; number of various resources on data science topics is mind-blowing, but this &lt;strong&gt;open source curriculum for learning data science&lt;/strong&gt; might fill some gaps between your BSc/MSc respective curricula and reality of the data science career (or, at least, provide some direction for further research and maybe answer some of your concerns): &lt;a href="http://datasciencemasters.org"&gt;http://datasciencemasters.org&lt;/a&gt;, or on GitHub: &lt;a href="https://github.com/datasciencemasters/go"&gt;https://github.com/datasciencemasters/go&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':256C ''/blockquote'':189C ''/datasciencemasters/go'':264C ''/datasciencemasters/go&lt;/a&gt;.&lt;/p&gt;'':267C ''/em'':79C,168C ''/p'':90C,134C,161C,188C ''/strong'':31C,55C,64C,100C,107C,147C,196C,219C ''1'':2C ''2'':92C ''3'':136C ''accur'':149C ''addit'':71C,129C ''adequ'':16C ''answer'':247C ''artifici'':88C ''background'':14C ''believ'':94C ''better'':173C,181C ''blockquot'':162C ''blow'':208C ''bsc/msc'':226C ''career'':19C,159C,235C ''concern'':96C,251C ''cs'':23C ''curricula'':228C ''curriculum'':214C ''data'':21C,33C,84C,102C,125C,145C,166C,202C,217C,233C ''datasciencemasters.org'':254C,255C ''definit'':59C,142C ''degre'':24C ''direct'':241C ''earli'':106C ''either'':119C ''em'':70C,165C ''engin'':37C,118C,179C,184C ''enorm'':195C ''enough'':30C ''enrich'':63C ''experi'':54C,66C ''fill'':221C ''find'':138C ''focus'':82C,99C ''follow'':140C ''futur'':158C ''gap'':223C ''github'':259C ''github.com'':263C,266C ''github.com/datasciencemasters/go'':262C ''github.com/datasciencemasters/go&lt;/a&gt;.&lt;/p&gt;'':265C ''help'':49C,155C ''hope'':151C ''href'':253C,261C ''imho'':25C ''intellig'':89C ''internship'':74C ''job'':127C ''knowledg'':45C ''learn'':87C,216C ''least'':238C ''long'':111C ''machin'':86C ''match'':51C ''mayb'':80C,246C ''might'':220C ''mind'':207C ''mind-blow'':206C ''need'':9C ''number'':197C ''one'':81C ''open'':76C,212C ''p'':1C,91C,135C,163C,190C ''p.s'':191C ''part'':122C ''particip'':68C ''point'':38C ''practic'':53C,116C ''project'':73C,78C ''provid'':239C ''question'':11C ''rather'':148C ''realiti'':230C ''research'':244C ''resourc'':200C ''respect'':227C ''said'':42C ''school'':72C ''scienc'':22C,85C,103C,126C,203C,218C,234C ''scientist'':34C,146C,167C ''softwar'':36C,117C,178C,183C ''someon'':170C ''sourc'':77C,213C ''spare'':132C ''statist'':175C ''statistician'':187C ''strong'':27C,52C,62C,98C,104C,141C,194C,211C ''success'':160C ''theoret'':44C ''think'':4C ''time'':133C ''today'':192C ''topic'':204C ''tri'':60C ''unfound'':109C ''various'':199C ''view'':40C ''whether'':12C ''without'':50C ''would'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (734, 2, '2014-07-14 12:08:13.373', 3, NULL, NULL, '&lt;p&gt;I''m not qualified to understand almost all of that paper, but, I might be able to give some intuitions from information theory that help you parse the paper.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;||&lt;/code&gt; denotes the &lt;a href="http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence" rel="nofollow"&gt;Kullback-Leibler divergence&lt;/a&gt;. It measures an information gain between two distributions. I suppose you could say it indicates the information in the real distribution of data that a model fails to capture.&lt;/p&gt;

&lt;p&gt;When you see "negative log" think &lt;a href="http://en.wikipedia.org/wiki/Information_entropy" rel="nofollow"&gt;"entropy"&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In the first equation, think of it as "-ln(...) - -ln(...)". This may help think of it as the difference of entropies. Likewise in the second, read it as "D(...) + -ln(...)". This may help think of it as "plus entropy".&lt;/p&gt;

&lt;p&gt;If you look at the divergence definition, you''ll see it is defined as the log of the ratio of the PDFs. This may help connect it to logs and negative logs. Look at the definition that writes it as cross-entropy minus entropy. Then this is all a question of differences of entropies of things which may be clearer.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (735, 2, '2014-07-14 13:06:05.523', 3, NULL, NULL, '&lt;p&gt;From your question''s wording I assume that you have a local machine and a remote machine where you update two files &amp;mdash; a Python script and a Bash script.  Both files are under SVN control, and both machines have access to the same SVN server.&lt;/p&gt;

&lt;p&gt;I am sorry I do not have any advice specific to your grid system, but let me list some general points I have found important for any deployment.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Keep production changes limited to configuration changes&lt;/strong&gt;. You write that you have to "use the datasets'' path on the server"; this sounds to me like you have the paths hardcoded into your Python script.  This is not a good idea, precisely because you will need to change those paths in every other machine where you move the script to.  If you commit those changes back to SVN, then on your local machine you will have the remote paths, and on and on ...  (What if there are not only paths, but also passwords?  You should not have production passwords in an SVN server.)&lt;/p&gt;

&lt;p&gt;So, keep paths and other setup informations in a &lt;code&gt;.ini&lt;/code&gt; file and use &lt;a href="https://docs.python.org/2/library/configparser.html" rel="nofollow"&gt;ConfigParser&lt;/a&gt; to read it, or use a &lt;code&gt;.json&lt;/code&gt; file and use the &lt;a href="https://docs.python.org/2/library/json.html" rel="nofollow"&gt;json&lt;/a&gt; module. Keep one copy of the file locally and one remotely, both under the same path, both without SVN control, and just keep the path to that configuration file in the Python script (or get it from the command line if you can''t keep both configurations under the same path).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Keep configuration as small as possible&lt;/strong&gt;. Any configuration is a "moving part" of your application, and any system is more robust the less it has moving parts.  A good indicator of something that belongs into configuration is exactly that you have to edit it every time you move the code; things that have not needed editing can remain as constants in the code.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Automate your deployment&lt;/strong&gt;.  You can do it via a Bash script on your local machine; note that you can &lt;a href="http://malcontentcomics.com/systemsboy/2006/07/send-remote-commands-via-ssh.html" rel="nofollow"&gt;run any command on a remote machine&lt;/a&gt; through &lt;code&gt;ssh&lt;/code&gt;.  For instance:&lt;/p&gt;



&lt;pre&gt;&lt;code&gt;svn export yourprojectpath /tmp/exportedproject
tar czf /tmp/yourproject.tgz /tmp/exportedproject
scp /tmp/myproject.tgz youruser@remotemachine:~/dev

## Remote commands are in the right hand side, between ''''
ssh youruser@remotemachine ''tar xzf ~/dev/yourproject.tgz''
ssh youruser@remotemachine ''qsub ~/dev/yourproject/script.py''
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For this to work, you need of course to have a &lt;a href="http://www.linuxproblem.org/art_9.html" rel="nofollow"&gt;passwordless login&lt;/a&gt;, based on public/private keys, set up between your local and the remote machine.&lt;/p&gt;

&lt;p&gt;If you need more than this, you can think of using Python''s &lt;a href="http://www.fabfile.org" rel="nofollow"&gt;Fabric&lt;/a&gt; or the higher-level &lt;a href="https://github.com/sebastien/cuisine" rel="nofollow"&gt;cuisine&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6702, 2, '2015-08-05 11:24:34.843', 1, NULL, NULL, '&lt;p&gt;You can answer this using tapply, which will give you the mean for all your subsets, e.g.&lt;/p&gt;

&lt;p&gt;dummy data&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;df = data.frame(mon=rep(month.abb, times=10)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;calculate mean for all months&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;tapply(df$temp, df$mon, mean, na.rm=T)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':33C,52C ''/p'':19C,23C,41C ''/pre'':34C,53C ''10'':32C ''answer'':4C ''calcul'':36C ''code'':25C,43C ''data'':22C ''data.frame'':27C ''df'':26C,45C,47C ''dummi'':21C ''e.g'':18C ''give'':10C ''mean'':13C,37C,49C ''mon'':28C,48C ''month'':40C ''month.abb'':30C ''na.rm'':50C ''p'':1C,20C,35C ''pre'':24C,42C ''rep'':29C ''subset'':17C ''tappli'':7C,44C ''temp'':46C ''time'':31C ''use'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (736, 1, '2014-07-14 13:53:28.437', 7, 572, 'Skewed multi-class data', '&lt;p&gt;I have a dataset which contains ~100,000 samples of 50 classes. I have been using SVM with an RBF kernel to train and predict new data. The problem though is the dataset is skewed towards different classes. &lt;/p&gt;

&lt;p&gt;For example, Class 1 - 30 (~3% each), Class 31 - 45 (~0.6% each), Class 46 - 50 (~0.2% each)&lt;/p&gt;

&lt;p&gt;I see that the model tends to very rarely predict the classes which occur less frequent in the training set, even though the test set has the same class distribution as the training set. &lt;/p&gt;

&lt;p&gt;I am aware that there are technique such as ''undersampling'' where the majority class is scaled down to the minor class. However, is this applicable here where there are so many different classes? Are there other methods to help handle this case?&lt;/p&gt;
', 'classification  svm', NULL, '''/p'':47C,66C,102C,146C ''0.2'':64C ''0.6'':59C ''000'':16C ''1'':52C ''100'':15C ''3'':54C ''30'':53C ''31'':57C ''45'':58C ''46'':62C ''50'':19C,63C ''applic'':128C ''awar'':106C ''case'':145C ''class'':4A,20C,46C,51C,56C,61C,79C,96C,117C,124C,136C ''classif'':6B ''contain'':14C ''data'':5A,35C ''dataset'':12C,41C ''differ'':45C,135C ''distribut'':97C ''even'':88C ''exampl'':50C ''frequent'':83C ''handl'':143C ''help'':142C ''howev'':125C ''kernel'':29C ''less'':82C ''major'':116C ''mani'':134C ''method'':140C ''minor'':123C ''model'':72C ''multi'':3A ''multi-class'':2A ''new'':34C ''occur'':81C ''p'':8C,48C,67C,103C ''predict'':33C,77C ''problem'':37C ''rare'':76C ''rbf'':28C ''sampl'':17C ''scale'':119C ''see'':69C ''set'':87C,92C,101C ''skew'':1A,43C ''svm'':7B,25C ''techniqu'':110C ''tend'':73C ''test'':91C ''though'':38C,89C ''toward'':44C ''train'':31C,86C,100C ''undersampl'':113C ''use'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (737, 2, '2014-07-14 15:58:42.517', 1, NULL, NULL, '&lt;p&gt;I have faced this problem many times while using SVM with Rbf kernel. Using Linear kernel instead of Rbf kernel solved my problem, but I dealt with lesser number of classes. The results were less skewed and more accurate with the linear kernel. Hope this solves your problem.&lt;/p&gt;

&lt;p&gt;Edit: While I wrote original answer I was naive enough to not consider weighting the classes as one of them correctly answered. Also, while using rbf kernel its important to make sure that the penalty parameter or the ''C'' value as per sklearn''s svm module is too generic. I find that the default value of C=1 is too generic most of the time and I typically end up with a value of C=10000. Hope this helps others who get skewed results with svm(rbf) despite of having good distribution of classes in data.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9958, 1, '2016-01-25 23:45:28.583', 2, 57, 'What would be the best way to structure and mine this set of data?', '&lt;p&gt;&lt;a href="http://pastebin.com/K0eq8cyZ" rel="nofollow"&gt;http://pastebin.com/K0eq8cyZ&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I went through each season of "It''s Always Sunny in Philadelphia" and determined the character groupings (D=Dennis, F=Frank, C=Charlie, M=Mac, B=Sweet Dee) for each episode. I also starred "winners" for some episodes. How best could I organize this data, in what type of database, and what data science tools would extract the most information out of it?&lt;/p&gt;

&lt;p&gt;I was thinking of making an SQL table like so:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;             (1)     (2)      (3)     (4)     (5)
Episode# | Dennis | Frank | Charlie | Mac | Sweet Dee 
008      |    5   |  3,4  |  2,4    | 2,3 |    1
010      |    5   |  3,4,6|  2,4,6  |2,3,6|    1  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;...where all the values are arrays of ints. 6 represents that the character won the episode and each number represents one of the 5 characters. Thoughts?&lt;/p&gt;
', 'machine-learning  clustering  dataset  visualization  data-cleaning', NULL, '''/code'':147C ''/k0eq8cyz'':29C ''/k0eq8cyz&lt;/a&gt;&lt;/p&gt;'':34C ''/p'':99C,111C,176C ''/pre'':148C ''008'':126C ''010'':135C ''1'':114C,134C,146C ''2'':115C,130C,132C,140C,143C ''3'':116C,128C,133C,137C,144C ''4'':117C,129C,131C,138C,141C ''5'':118C,127C,136C,173C ''6'':139C,142C,145C,158C ''also'':68C ''alway'':44C ''array'':155C ''b'':61C ''best'':5A,75C ''c'':57C ''charact'':51C,162C,174C ''charli'':58C,122C ''clean'':23B ''cluster'':18B ''code'':113C ''could'':76C ''d'':53C ''data'':14A,22B,80C,88C ''data-clean'':21B ''databas'':85C ''dataset'':19B ''dee'':63C,125C ''denni'':54C,120C ''determin'':49C ''episod'':66C,73C,119C,165C ''extract'':92C ''f'':55C ''frank'':56C,121C ''group'':52C ''href'':26C ''inform'':95C ''int'':157C ''learn'':17B ''like'':109C ''m'':59C ''mac'':60C,123C ''machin'':16B ''machine-learn'':15B ''make'':105C ''mine'':10A ''nofollow'':31C ''number'':168C ''one'':170C ''organ'':78C ''p'':24C,35C,100C,149C ''pastebin.com'':28C,33C ''pastebin.com/k0eq8cyz'':27C ''pastebin.com/k0eq8cyz&lt;/a&gt;&lt;/p&gt;'':32C ''philadelphia'':47C ''pre'':112C ''rel'':30C ''repres'':159C,169C ''scienc'':89C ''season'':40C ''set'':12A ''sql'':107C ''star'':69C ''structur'':8A ''sunni'':45C ''sweet'':62C,124C ''tabl'':108C ''think'':103C ''thought'':175C ''tool'':90C ''type'':83C ''valu'':153C ''visual'':20B ''way'':6A ''went'':37C ''winner'':70C ''won'':163C ''would'':2A,91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (739, 1, '2014-07-14 19:02:01.67', 11, 1998, 'Starting my career as Data Scientist, is Software Engineering experience required?', '&lt;p&gt;I am an MSc student at the University of Edinburgh, specialized in machine learning and natural language processing. I had some practical courses focused on data mining, and others dealing with machine learning, bayesian statistics and graphical models. My background is a BSc in Computer Science.&lt;/p&gt;

&lt;p&gt;I did some software engineering and I learnt the basic concepts, such as design patterns, but I have never been involved in a large software development project. However, I had a data mining project in my MSc. My question is, if I want to go for a career as Data Scientist, should I apply for a graduate data scientist position first, or should I get a position as graduate software engineer first, maybe something related to data science, such as big data infrastructure or machine learning software development?&lt;/p&gt;

&lt;p&gt;My concern is that I might need good software engineering skills for data science, and I am not sure if these can be obtained by working as a graduate data scientist directly.&lt;/p&gt;

&lt;p&gt;Moreover, at the moment I like Data Mining, but what if I want to change my career to software engineering in the future? It might be difficult if I specialised so much in data science.&lt;/p&gt;

&lt;p&gt;I have not been employed yet, so my knowledge is still limited. Any clarification or advice are welcome, as I am about to finish my MSc and I want to start applying for graduate positions in early October.&lt;/p&gt;
', 'education  definitions  career', NULL, '''/p'':62C,152C,186C,223C,263C ''advic'':240C ''appli'':117C,256C ''background'':55C ''basic'':73C ''bayesian'':49C ''big'':144C ''bsc'':58C ''career'':3A,14B,111C,204C ''chang'':202C ''clarif'':238C ''comput'':60C ''concept'':74C ''concern'':155C ''cours'':38C ''data'':5A,41C,95C,113C,121C,140C,145C,166C,183C,194C,221C ''deal'':45C ''definit'':13B ''design'':77C ''develop'':89C,151C ''difficult'':214C ''direct'':185C ''earli'':261C ''edinburgh'':25C ''educ'':12B ''employ'':229C ''engin'':9A,68C,134C,163C,207C ''experi'':10A ''finish'':248C ''first'':124C,135C ''focus'':39C ''futur'':210C ''get'':128C ''go'':108C ''good'':161C ''graduat'':120C,132C,182C,258C ''graphic'':52C ''howev'':91C ''infrastructur'':146C ''involv'':84C ''knowledg'':233C ''languag'':32C ''larg'':87C ''learn'':29C,48C,149C ''learnt'':71C ''like'':193C ''limit'':236C ''machin'':28C,47C,148C ''mayb'':136C ''might'':159C,212C ''mine'':42C,96C,195C ''model'':53C ''moment'':191C ''moreov'':188C ''msc'':19C,100C,250C ''much'':219C ''natur'':31C ''need'':160C ''never'':82C ''obtain'':177C ''octob'':262C ''other'':44C ''p'':15C,63C,153C,187C,224C ''pattern'':78C ''posit'':123C,130C,259C ''practic'':37C ''process'':33C ''project'':90C,97C ''question'':102C ''relat'':138C ''requir'':11A ''scienc'':61C,141C,167C,222C ''scientist'':6A,114C,122C,184C ''skill'':164C ''softwar'':8A,67C,88C,133C,150C,162C,206C ''someth'':137C ''special'':26C ''specialis'':217C ''start'':1A,255C ''statist'':50C ''still'':235C ''student'':20C ''sure'':172C ''univers'':23C ''want'':106C,200C,253C ''welcom'':242C ''work'':179C ''yet'':230C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (740, 2, '2014-07-14 19:06:44.09', 5, NULL, NULL, '&lt;p&gt;I would suggest you to use libsvm, which already has adjustable class weights implemented in it. Rather than replicating the training samples, one modifies the C parameter for different classes in the SVM optimization. For example if your data has 2 classes, and the first class is only 10% of the data, you would choose class weights to be 10 and 1 for class 1 and 2 respectively. Therefore, margin violations of the first class would cost 10 times more than the margin violations for second class, and per-class accuracies would be more balanced.&lt;/p&gt;
', NULL, NULL, '''/p'':98C ''1'':63C,66C ''10'':50C,61C,79C ''2'':42C,68C ''accuraci'':93C ''adjust'':12C ''alreadi'':10C ''balanc'':97C ''c'':27C ''choos'':56C ''class'':13C,31C,43C,47C,57C,65C,76C,88C,92C ''cost'':78C ''data'':40C,53C ''differ'':30C ''exampl'':37C ''first'':46C,75C ''implement'':15C ''libsvm'':8C ''margin'':71C,84C ''modifi'':25C ''one'':24C ''optim'':35C ''p'':1C ''paramet'':28C ''per'':91C ''per-class'':90C ''rather'':18C ''replic'':20C ''respect'':69C ''sampl'':23C ''second'':87C ''suggest'':4C ''svm'':34C ''therefor'':70C ''time'':80C ''train'':22C ''use'':7C ''violat'':72C,85C ''weight'':14C,58C ''would'':3C,55C,77C,94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (741, 2, '2014-07-15 04:39:11.67', 4, NULL, NULL, '&lt;p&gt;Absolutely. Keep your software skills sharp. You can do this in an academic program if you simply implement by yourself all the algorithms you learn about. &lt;/p&gt;

&lt;p&gt;Good selection of courses, btw. Consider getting an internship too.&lt;/p&gt;
', NULL, NULL, '''/p'':28C,40C ''absolut'':2C ''academ'':14C ''algorithm'':24C ''btw'':34C ''consid'':35C ''cours'':33C ''get'':36C ''good'':30C ''implement'':19C ''internship'':38C ''keep'':3C ''learn'':26C ''p'':1C,29C ''program'':15C ''select'':31C ''sharp'':7C ''simpli'':18C ''skill'':6C ''softwar'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9489, 2, '2015-12-22 17:26:15.973', 3, NULL, NULL, '&lt;p&gt;You could try building multiple xgboost models, with some of them being limited to more recent data, then weighting those results together.  Another idea would be to make a customized evaluation metric that penalizes recent points more heavily which would give them more importance.&lt;/p&gt;
', NULL, NULL, '''/p'':46C ''anoth'':24C ''build'':5C ''could'':3C ''custom'':31C ''data'':18C ''evalu'':32C ''give'':42C ''heavili'':39C ''idea'':25C ''import'':45C ''limit'':14C ''make'':29C ''metric'':33C ''model'':8C ''multipl'':6C ''p'':1C ''penal'':35C ''point'':37C ''recent'':17C,36C ''result'':22C ''togeth'':23C ''tri'':4C ''weight'':20C ''would'':26C,41C ''xgboost'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6703, 2, '2015-08-05 11:28:25.06', 0, NULL, NULL, '&lt;p&gt;To just get the mean for month 6:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;mean(df$temp[df$mon=="Jun"], na.rm=T)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You were nearly there but didn''t assign your subset to a value, if you had:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;x = data[data$X..Month.. == 6,]
mean(x$X..Temp.., na.rm=TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;that should work.&lt;/p&gt;
', NULL, NULL, '''..'':48C ''/code'':21C,56C ''/p'':10C,40C,62C ''/pre'':22C,57C ''6'':9C,49C ''assign'':31C ''code'':12C,42C ''data'':44C,45C ''df'':14C,16C ''didn'':29C ''get'':4C ''jun'':18C ''mean'':6C,13C,50C ''mon'':17C ''month'':8C,47C ''na.rm'':19C,54C ''near'':26C ''p'':1C,23C,58C ''pre'':11C,41C ''subset'':33C ''temp'':15C,53C ''true'':55C ''valu'':36C ''work'':61C ''x'':43C,46C,51C,52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (743, 2, '2014-07-15 09:30:02.183', 6, NULL, NULL, '&lt;p&gt;From the job ads I have seen, the answer depends: There are jobs which are more technical in nature (designing big data projects, doing some analysis) or the exact opposite (doing analysis, storage etc. is someone elses job).&lt;/p&gt;

&lt;p&gt;So I would say that SOME software design skills are extremely useful , but you don''t need the abillity to build a huge program in C# / Java or whatever.
Why I like some SW skills is simply that your code probably looks way better than code from someone who never programmed for the sake of programming. Most of the time, the latter code is very hard do understand / debug for outsiders. Also, sometimes your analysis needs to be integrated in a bigger program,an understand of the needs of the programms certainly helps.&lt;/p&gt;
', NULL, NULL, '''/p'':40C,135C ''abil'':60C ''ad'':5C ''also'':113C ''analysi'':27C,33C,116C ''answer'':10C ''better'':85C ''big'':22C ''bigger'':123C ''build'':62C ''c'':67C ''certain'':133C ''code'':81C,87C,104C ''data'':23C ''debug'':110C ''depend'':11C ''design'':21C,49C ''els'':38C ''etc'':35C ''exact'':30C ''extrem'':52C ''hard'':107C ''help'':134C ''huge'':64C ''integr'':120C ''java'':68C ''job'':4C,14C,39C ''latter'':103C ''like'':73C ''look'':83C ''natur'':20C ''need'':58C,117C,129C ''never'':91C ''opposit'':31C ''outsid'':112C ''p'':1C,41C ''probabl'':82C ''program'':65C,92C,97C,124C ''programm'':132C ''project'':24C ''sake'':95C ''say'':45C ''seen'':8C ''simpli'':78C ''skill'':50C,76C ''softwar'':48C ''someon'':37C,89C ''sometim'':114C ''storag'':34C ''sw'':75C ''technic'':18C ''time'':101C ''understand'':109C,126C ''use'':53C ''way'':84C ''whatev'':70C ''would'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8667, 1, '2015-10-31 05:21:03.62', 5, 158, 'Which algorithms or methods can be used to detect an outlier from this data set?', '&lt;p&gt;Suppose I have a data set : &lt;code&gt;Amount of money (100, 50, 150, 200, 35, 60 ,50, 20, 500)&lt;/code&gt;. I have &lt;a href="https://www.google.co.zw/webhp?sourceid=chrome-instant&amp;amp;ion=1&amp;amp;espv=2&amp;amp;ie=UTF-8#q=Outlier+detection+Algorithms"&gt;Googled&lt;/a&gt; the web looking for techniques that can be used to find a possible outlier in this data set but I ended up confused.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;My question is&lt;/strong&gt;: Which algorithms, techniques or methods can be used to detect possible outlier in this data set?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;PS&lt;/strong&gt;:Consider that the data does not follow a normal distribution. Thanks.&lt;/p&gt;
', 'algorithms  outlier', NULL, '''/a'':47C ''/code'':38C ''/p'':71C,94C,110C ''/strong'':77C,98C ''/webhp?sourceid=chrome-instant&amp;amp;ion=1&amp;amp;espv=2&amp;amp;ie=utf-8#q=outlier+detection+algorithms'':45C ''100'':29C ''150'':31C ''20'':36C ''200'':32C ''35'':33C ''50'':30C,35C ''500'':37C ''60'':34C ''algorithm'':2A,16B,79C ''amount'':26C ''code'':25C ''confus'':70C ''consid'':99C ''data'':14A,23C,64C,92C,102C ''detect'':9A,87C ''distribut'':108C ''end'':68C ''find'':58C ''follow'':105C ''googl'':46C ''href'':42C ''look'':50C ''method'':4A,82C ''money'':28C ''normal'':107C ''outlier'':11A,17B,61C,89C ''p'':18C,72C,95C ''possibl'':60C,88C ''ps'':97C ''question'':75C ''set'':15A,24C,65C,93C ''strong'':73C,96C ''suppos'':19C ''techniqu'':52C,80C ''thank'':109C ''use'':7A,56C,85C ''web'':49C ''www.google.co.zw'':44C ''www.google.co.zw/webhp?sourceid=chrome-instant&amp;amp;ion=1&amp;amp;espv=2&amp;amp;ie=utf-8#q=outlier+detection+algorithms'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (746, 2, '2014-07-16 03:32:16.27', 5, NULL, NULL, '&lt;p&gt;I''m not an expert on time series, but I have a &lt;strong&gt;general advice&lt;/strong&gt;: may I suggest you to try other packages (and various parameters) to see, if there are any differences in results.&lt;/p&gt;

&lt;p&gt;Also, unless you have to use &lt;code&gt;Python&lt;/code&gt;, I''d recommend to take a look at the &lt;code&gt;R&lt;/code&gt;''s extensive &lt;em&gt;ecosystem&lt;/em&gt; for &lt;strong&gt;time series analysis&lt;/strong&gt;: see &lt;a href="http://www.statmethods.net/advstats/timeseries.html"&gt;http://www.statmethods.net/advstats/timeseries.html&lt;/a&gt; and &lt;a href="http://cran.r-project.org/web/views/TimeSeries.html"&gt;http://cran.r-project.org/web/views/TimeSeries.html&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In particular, you may want to check the standard &lt;code&gt;stats&lt;/code&gt; package (including functions &lt;code&gt;arima()&lt;/code&gt; and &lt;code&gt;arima0&lt;/code&gt;), as well as some other packages: &lt;code&gt;FitARMA&lt;/code&gt; (&lt;a href="http://cran.r-project.org/web/packages/FitARMA"&gt;http://cran.r-project.org/web/packages/FitARMA&lt;/a&gt;), &lt;code&gt;forecast&lt;/code&gt; (&lt;a href="http://cran.r-project.org/web/packages/forecast"&gt;http://cran.r-project.org/web/packages/forecast&lt;/a&gt;) and education-focused &lt;code&gt;fArma&lt;/code&gt; (cran.r-project.org/web/packages/fArma), to mention just a few. I hope this is helpful.&lt;/p&gt;
', NULL, NULL, '''/advstats/timeseries.html'':77C ''/advstats/timeseries.html&lt;/a&gt;'':80C ''/code'':48C,60C,102C,108C,112C,121C,132C,147C ''/em'':65C ''/p'':38C,161C ''/strong'':17C,71C ''/web/packages/farma),'':150C ''/web/packages/fitarma'':126C ''/web/packages/fitarma&lt;/a&gt;),'':129C ''/web/packages/forecast'':137C ''/web/packages/forecast&lt;/a&gt;)'':140C ''/web/views/timeseries.html'':86C ''/web/views/timeseries.html&lt;/a&gt;.&lt;/p&gt;'':89C ''advic'':16C ''also'':40C ''analysi'':70C ''arima'':107C ''arima0'':111C ''check'':97C ''code'':46C,58C,100C,106C,110C,119C,130C,145C ''cran.r-project.org'':85C,88C,125C,128C,136C,139C,149C ''cran.r-project.org/web/packages/farma),'':148C ''cran.r-project.org/web/packages/fitarma'':124C ''cran.r-project.org/web/packages/fitarma&lt;/a&gt;),'':127C ''cran.r-project.org/web/packages/forecast'':135C ''cran.r-project.org/web/packages/forecast&lt;/a&gt;)'':138C ''cran.r-project.org/web/views/timeseries.html'':84C ''cran.r-project.org/web/views/timeseries.html&lt;/a&gt;.&lt;/p&gt;'':87C ''d'':50C ''differ'':35C ''ecosystem'':64C ''educ'':143C ''education-focus'':142C ''em'':63C ''expert'':6C ''extens'':62C ''farma'':146C ''fitarma'':120C ''focus'':144C ''forecast'':131C ''function'':105C ''general'':15C ''help'':160C ''hope'':157C ''href'':74C,83C,123C,134C ''includ'':104C ''look'':55C ''m'':3C ''may'':18C,94C ''mention'':152C ''p'':1C,39C,90C ''packag'':25C,103C,118C ''paramet'':28C ''particular'':92C ''python'':47C ''r'':59C ''recommend'':51C ''result'':37C ''see'':30C,72C ''seri'':9C,69C ''standard'':99C ''stat'':101C ''strong'':14C,67C ''suggest'':20C ''take'':53C ''time'':8C,68C ''tri'':23C ''unless'':41C ''use'':45C ''various'':27C ''want'':95C ''well'':114C ''www.statmethods.net'':76C,79C ''www.statmethods.net/advstats/timeseries.html'':75C ''www.statmethods.net/advstats/timeseries.html&lt;/a&gt;'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (747, 2, '2014-07-16 06:45:36.74', 4, NULL, NULL, '&lt;p&gt;About automatic cleaning: You really cannot clean data automatically, because the number of errors and the definition of an error is often dependent on the data. E.g.: Your column "Income" might contain negative values, which are an error - you have to do something about the cases. On the other hand a column "monthly savings" could reasonably contain negative values. &lt;/p&gt;

&lt;p&gt;Such errors are highly domain dependent - so to find them, you must have domain knowledge, something at which humans excel, automated processes not so much.&lt;/p&gt;

&lt;p&gt;Where you can and should automate is repeated projects. E.g. a report which has to produced monthly. If you spot errors, you should place some automated process which can spot  these kinds of errors in subsequent months, freeing your time. &lt;/p&gt;
', NULL, NULL, '''/p'':61C,88C,130C ''autom'':83C,95C,115C ''automat'':3C,10C ''cannot'':7C ''case'':47C ''clean'':4C,8C ''column'':30C,53C ''contain'':33C,58C ''could'':56C ''data'':9C,27C ''definit'':18C ''depend'':24C,68C ''domain'':67C,76C ''e.g'':28C,99C ''error'':15C,21C,39C,64C,110C,123C ''excel'':82C ''find'':71C ''free'':127C ''hand'':51C ''high'':66C ''human'':81C ''incom'':31C ''kind'':121C ''knowledg'':77C ''might'':32C ''month'':54C,106C,126C ''much'':87C ''must'':74C ''negat'':34C,59C ''number'':13C ''often'':23C ''p'':1C,62C,89C ''place'':113C ''process'':84C,116C ''produc'':105C ''project'':98C ''realli'':6C ''reason'':57C ''repeat'':97C ''report'':101C ''save'':55C ''someth'':44C,78C ''spot'':109C,119C ''subsequ'':125C ''time'':129C ''valu'':35C,60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (748, 1, '2014-07-16 07:47:48.603', 6, 65, 'Why use mean revenue in a split test?', '&lt;p&gt;I asked a data science question regarding how to decide on the best variation of a split test on the Statistics section of StackExchange. I hope I will have better luck here. The question is basically, "Why is mean revenue per user the best metric to make your decision on in a split test?"&lt;/p&gt;

&lt;p&gt;The original question is here: &lt;a href="http://stats.stackexchange.com/questions/107599/better-estimator-of-expected-sum-than-mean"&gt;http://stats.stackexchange.com/questions/107599/better-estimator-of-expected-sum-than-mean&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Since it was not well received/understood I simplified the problem to a discrete set of purchases and phrased it as a classical probability problem. That question is here: &lt;a href="http://stats.stackexchange.com/questions/107848/drawing-numbered-balls-from-an-urn"&gt;http://stats.stackexchange.com/questions/107848/drawing-numbered-balls-from-an-urn&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The mean may be the best metric for such a decision but I am not convinced. We often have a lot of prior information so a Bayesian method would likely improve our estimates. I realize that this is a difficult question but Data Scientists are doing such split tests everyday. &lt;/p&gt;
', 'research  cross-validation', NULL, '''/p'':68C,171C ''/questions/107599/better-estimator-of-expected-sum-than-mean'':79C ''/questions/107599/better-estimator-of-expected-sum-than-mean&lt;/a&gt;&lt;/p&gt;'':82C ''/questions/107848/drawing-numbered-balls-from-an-urn'':116C ''/questions/107848/drawing-numbered-balls-from-an-urn&lt;/a&gt;&lt;/p&gt;'':119C ''ask'':15C ''basic'':49C ''bayesian'':147C ''best'':26C,57C,126C ''better'':43C ''classic'':105C ''convinc'':136C ''cross'':11B ''cross-valid'':10B ''data'':17C,163C ''decid'':23C ''decis'':62C,131C ''difficult'':160C ''discret'':96C ''estim'':153C ''everyday'':170C ''hope'':39C ''href'':76C,113C ''improv'':151C ''inform'':144C ''like'':150C ''lot'':141C ''luck'':44C ''make'':60C ''may'':123C ''mean'':3A,52C,122C ''method'':148C ''metric'':58C,127C ''often'':138C ''origin'':71C ''p'':13C,69C,83C,120C ''per'':54C ''phrase'':101C ''prior'':143C ''probabl'':106C ''problem'':93C,107C ''purchas'':99C ''question'':19C,47C,72C,109C,161C ''realiz'':155C ''received/understood'':89C ''regard'':20C ''research'':9B ''revenu'':4A,53C ''scienc'':18C ''scientist'':164C ''section'':35C ''set'':97C ''simplifi'':91C ''sinc'':84C ''split'':7A,30C,66C,168C ''stackexchang'':37C ''statist'':34C ''stats.stackexchange.com'':78C,81C,115C,118C ''stats.stackexchange.com/questions/107599/better-estimator-of-expected-sum-than-mean'':77C ''stats.stackexchange.com/questions/107599/better-estimator-of-expected-sum-than-mean&lt;/a&gt;&lt;/p&gt;'':80C ''stats.stackexchange.com/questions/107848/drawing-numbered-balls-from-an-urn'':114C ''stats.stackexchange.com/questions/107848/drawing-numbered-balls-from-an-urn&lt;/a&gt;&lt;/p&gt;'':117C ''test'':8A,31C,67C,169C ''use'':2A ''user'':55C ''valid'':12B ''variat'':27C ''well'':88C ''would'':149C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5624, 1, '2015-04-27 09:09:55.17', 1, 44, 'Which classifier is efficient in dealing with test query which belongs to no trained class?', '&lt;p&gt;Suppose classifier trained with 5 class, and input query content does not belong to any of the trained class data.&lt;/p&gt;

&lt;p&gt;Naive bayes provides and random class as a result here. Which classifier deals best in such scenario?&lt;/p&gt;
', 'machine-learning  classification  svm', NULL, '''/p'':42C,61C ''5'':26C ''bay'':45C ''belong'':11A,34C ''best'':57C ''class'':15A,27C,40C,49C ''classif'':19B ''classifi'':2A,23C,55C ''content'':31C ''data'':41C ''deal'':6A,56C ''effici'':4A ''input'':29C ''learn'':18B ''machin'':17B ''machine-learn'':16B ''naiv'':44C ''p'':21C,43C ''provid'':46C ''queri'':9A,30C ''random'':48C ''result'':52C ''scenario'':60C ''suppos'':22C ''svm'':20B ''test'':8A ''train'':14A,24C,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (756, 2, '2014-07-16 18:15:42.343', 9, NULL, NULL, '&lt;p&gt;At the expense of over-simplication, latent features are ''hidden'' features to distinguish them from observed features. Latent features are computed from observed features using matrix factorization. An example would be text document analysis. ''words'' extracted from the documents are features. If you factorize the data of words you can find ''topics'', where ''topic'' is a group of words with semantic relevance. Low-rank matrix factorization maps several rows (observed features) to a smaller set of rows (latent features).
To elaborate, the document could have observed features (words) like [sail-boat, schooner, yatch, steamer, cruiser] which would ''factorize'' to latent feature (topic) like ''ship'' and ''boat''. &lt;/p&gt;

&lt;p&gt;[sail-boat, schooner, yatch, steamer, cruiser, ...] -&gt; [ship, boat]&lt;/p&gt;

&lt;p&gt;The underlying idea is that latent features are semantically relevant ''aggregates'' of observered features. When you have large-scale, high-dimensional, and noisy observered features, it makes sense to build your classifier on latent features.&lt;/p&gt;

&lt;p&gt;This is a of course a simplified description to elucidate the concept. You can read the details on Latent Dirichlet Allocation (LDA) or probabilistic Latent Semantic Analysis (pLSA) models for an accurate description.&lt;/p&gt;
', NULL, NULL, '''/p'':111C,122C,161C,196C ''accur'':194C ''aggreg'':134C ''alloc'':183C ''analysi'':36C,189C ''boat'':95C,110C,115C,121C ''build'':155C ''classifi'':157C ''comput'':23C ''concept'':174C ''could'':87C ''cours'':167C ''cruiser'':99C,119C ''data'':48C ''descript'':170C,195C ''detail'':179C ''dimension'':146C ''dirichlet'':182C ''distinguish'':15C ''document'':35C,41C,86C ''elabor'':84C ''elucid'':172C ''exampl'':31C ''expens'':4C ''extract'':38C ''factor'':29C,46C,69C,102C ''featur'':10C,13C,19C,21C,26C,43C,74C,82C,90C,105C,130C,137C,150C,160C ''find'':53C ''group'':59C ''hidden'':12C ''high'':145C ''high-dimension'':144C ''idea'':126C ''larg'':142C ''large-scal'':141C ''latent'':9C,20C,81C,104C,129C,159C,181C,187C ''lda'':184C ''like'':92C,107C ''low'':66C ''low-rank'':65C ''make'':152C ''map'':70C ''matrix'':28C,68C ''model'':191C ''noisi'':148C ''observ'':18C,25C,73C,89C,136C,149C ''over-simpl'':6C ''p'':1C,112C,123C,162C ''plsa'':190C ''probabilist'':186C ''rank'':67C ''read'':177C ''relev'':64C,133C ''row'':72C,80C ''sail'':94C,114C ''sail-boat'':93C,113C ''scale'':143C ''schooner'':96C,116C ''semant'':63C,132C,188C ''sens'':153C ''set'':78C ''sever'':71C ''ship'':108C,120C ''simplic'':8C ''simplifi'':169C ''smaller'':77C ''steamer'':98C,118C ''text'':34C ''topic'':54C,56C,106C ''under'':125C ''use'':27C ''word'':37C,50C,61C,91C ''would'':32C,101C ''yatch'':97C,117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (750, 1, '2014-07-16 09:49:15.933', 11, 1413, 'How to increase accuracy of classifiers?', '&lt;p&gt;I am using OpenCV letter_recog.cpp example to experiment on random trees and other classifiers. This example has implementations of six classifiers - random trees, boosting, MLP, kNN, naive Bayes and SVM. UCI letter recognition dataset with 20000 instances and 16 features is used, which I split in half for training and testing. I have experience with SVM so I quickly set its recognition error to 3.3%. After some experimentation what I got was:&lt;/p&gt;

&lt;p&gt;UCI letter recognition:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;RTrees - 5.3% &lt;/li&gt;
&lt;li&gt;Boost -  13% &lt;/li&gt;
&lt;li&gt;MLP -    7.9% &lt;/li&gt;
&lt;li&gt;kNN(k=3) -   6.5%  &lt;/li&gt;
&lt;li&gt;Bayes -  11.5%  &lt;/li&gt;
&lt;li&gt;SVM -    3.3%&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Parameters used:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;RTrees - max_num_of_trees_in_the_forrest=200, max_depth=20,
min_sample_count=1&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Boost -  boost_type=REAL, weak_count=200, weight_trim_rate=0.95,
max_depth=7&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;MLP -    method=BACKPROP, param=0.001, max_iter=300 (default values - too
slow to experiment) &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;kNN(k=3) -   k=3&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Bayes -  none&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;SVM -    RBF kernel, C=10, gamma=0.01&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;After that I used same parameters and tested on Digits and MNIST datasets by extracting gradient features first (vector size 200 elements):&lt;/p&gt;

&lt;p&gt;Digits:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;RTrees - 5.1%&lt;/li&gt;
&lt;li&gt;Boost -  23.4%&lt;/li&gt;
&lt;li&gt;MLP -    4.3%&lt;/li&gt;
&lt;li&gt;kNN(k=3) -   7.3%&lt;/li&gt;
&lt;li&gt;Bayes -  17.7%&lt;/li&gt;
&lt;li&gt;SVM -    4.2%&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;MNIST:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;RTrees - 1.4%&lt;/li&gt;
&lt;li&gt;Boost -  out of memory&lt;/li&gt;
&lt;li&gt;MLP -    1.0%&lt;/li&gt;
&lt;li&gt;kNN(k=3) -   1.2%&lt;/li&gt;
&lt;li&gt;Bayes -  34.33%&lt;/li&gt;
&lt;li&gt;SVM -    0.6%&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I am new to all classifiers except SVM and kNN, for these two I can say the results seem fine. What about others? I expected more from random trees, on MNIST kNN gives better accuracy, any ideas how to get it higher? Boost and Bayes give very low accuracy. In the end I''d like to use these classifiers to make a multiple classifier system. Any advice?&lt;/p&gt;
', 'machine-learning  classification  svm  accuracy  random-forest', NULL, '''/li'':99C,103C,107C,113C,117C,121C,147C,165C,183C,192C,198C,209C,242C,246C,250C,256C,260C,264C,273C,279C,283C,289C,293C,297C ''/p'':89C,94C,126C,146C,164C,182C,191C,197C,208C,234C,237C,268C,367C ''/ul'':122C,210C,265C,298C ''0.001'':172C ''0.01'':207C ''0.6'':296C ''0.95'':160C ''1'':145C ''1.0'':282C ''1.2'':288C ''1.4'':272C ''10'':205C ''11.5'':116C ''13'':102C ''16'':55C ''17.7'':259C ''20'':141C ''200'':138C,156C,232C ''20000'':52C ''23.4'':245C ''3'':111C,188C,190C,254C,287C ''3.3'':81C,120C ''300'':175C ''34.33'':292C ''4.2'':263C ''4.3'':249C ''5.1'':241C ''5.3'':98C ''6.5'':112C ''7'':163C ''7.3'':255C ''7.9'':106C ''accuraci'':4A,12B,334C,348C ''advic'':366C ''backprop'':170C ''bay'':44C,115C,195C,258C,291C,344C ''better'':333C ''boost'':40C,101C,150C,151C,244C,275C,342C ''c'':204C ''classif'':10B ''classifi'':6A,30C,37C,305C,358C,363C ''count'':144C,155C ''d'':353C ''dataset'':50C,224C ''default'':176C ''depth'':140C,162C ''digit'':221C,236C ''element'':233C ''end'':351C ''error'':79C ''exampl'':22C,32C ''except'':306C ''expect'':324C ''experi'':24C,70C,181C ''experiment'':84C ''extract'':226C ''featur'':56C,228C ''fine'':319C ''first'':229C ''forest'':15B ''forrest'':137C ''gamma'':206C ''get'':339C ''give'':332C,345C ''got'':87C ''gradient'':227C ''half'':63C ''higher'':341C ''idea'':336C ''implement'':34C ''increas'':3A ''instanc'':53C ''iter'':174C ''k'':110C,187C,189C,253C,286C ''kernel'':203C ''knn'':42C,109C,186C,252C,285C,309C,331C ''learn'':9B ''letter'':48C,92C ''letter_recog.cpp'':21C ''li'':96C,100C,104C,108C,114C,118C,128C,148C,166C,184C,193C,199C,239C,243C,247C,251C,257C,261C,270C,274C,280C,284C,290C,294C ''like'':354C ''low'':347C ''machin'':8B ''machine-learn'':7B ''make'':360C ''max'':131C,139C,161C,173C ''memori'':278C ''method'':169C ''min'':142C ''mlp'':41C,105C,168C,248C,281C ''mnist'':223C,267C,330C ''multipl'':362C ''naiv'':43C ''new'':302C ''none'':196C ''num'':132C ''opencv'':20C ''other'':322C ''p'':16C,90C,123C,129C,149C,167C,185C,194C,200C,211C,235C,266C,299C ''param'':171C ''paramet'':124C,217C ''quick'':75C ''random'':14B,26C,38C,327C ''random-forest'':13B ''rate'':159C ''rbf'':202C ''real'':153C ''recognit'':49C,78C,93C ''result'':317C ''rtree'':97C,130C,240C,271C ''sampl'':143C ''say'':315C ''seem'':318C ''set'':76C ''six'':36C ''size'':231C ''slow'':179C ''split'':61C ''svm'':11B,46C,72C,119C,201C,262C,295C,307C ''system'':364C ''test'':67C,219C ''train'':65C ''tree'':27C,39C,134C,328C ''trim'':158C ''two'':312C ''type'':152C ''uci'':47C,91C ''ul'':95C,127C,238C,269C ''use'':19C,58C,125C,215C,356C ''valu'':177C ''vector'':230C ''weak'':154C ''weight'':157C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (751, 2, '2014-07-16 10:42:55.793', 3, NULL, NULL, '&lt;p&gt;You are right, cosine similarity has a lot of common with dot product of vectors. Indeed, it is a dot product, scaled by magnitude. And because of scaling it is normalized between 0 and 1. CS is preferable because it takes into account variability of data and features'' relative frequencies. On the other hand, plain dot product is a little bit "cheaper" (in terms of complexity and implementation).&lt;/p&gt;
', NULL, NULL, '''/p'':70C ''0'':34C ''1'':36C ''account'':44C ''bit'':62C ''cheaper'':63C ''common'':11C ''complex'':67C ''cosin'':5C ''cs'':37C ''data'':47C ''dot'':13C,21C,57C ''featur'':49C ''frequenc'':51C ''hand'':55C ''implement'':69C ''inde'':17C ''littl'':61C ''lot'':9C ''magnitud'':25C ''normal'':32C ''p'':1C ''plain'':56C ''prefer'':39C ''product'':14C,22C,58C ''relat'':50C ''right'':4C ''scale'':23C,29C ''similar'':6C ''take'':42C ''term'':65C ''variabl'':45C ''vector'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (761, 1, '2014-07-17 09:50:41.437', 11, 3905, 'Clustering geo location coordinates (lat,long pairs)', '&lt;p&gt;What is the right approach and clustering algorithm for geo location clustering?&lt;/p&gt;

&lt;p&gt;I''m using the following code to cluster geolocation coordinates :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
import matplotlib.pyplot as plt
from scipy.cluster.vq import kmeans2, whiten

coordinates= np.array([
           [lat, long],
           [lat, long],
            ...
           [lat, long]
           ])
x, y = kmeans2(whiten(coordinates), 3, iter = 20)  
plt.scatter(coordinates[:,0], coordinates[:,1], c=y);
plt.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is it right to use Kmeans for location clustering, as it uses Euclidean distance and not Haversine formula as a distance function?&lt;/p&gt;
', 'machine-learning  python  clustering  k-means  clusters', NULL, '''/code'':82C ''/p'':30C,42C,107C ''/pre'':83C ''0'':76C ''1'':78C ''20'':73C ''3'':71C ''algorithm'':25C ''approach'':22C ''c'':79C ''cluster'':1A,12B,16B,24C,29C,39C,93C ''code'':37C,44C ''coordin'':4A,41C,58C,70C,75C,77C ''distanc'':98C,105C ''euclidean'':97C ''follow'':36C ''formula'':102C ''function'':106C ''geo'':2A,27C ''geoloc'':40C ''haversin'':101C ''import'':45C,49C,55C ''iter'':72C ''k'':14B ''k-mean'':13B ''kmean'':90C ''kmeans2'':56C,68C ''lat'':5A,60C,62C,64C ''learn'':10B ''locat'':3A,28C,92C ''long'':6A,61C,63C,65C ''m'':33C ''machin'':9B ''machine-learn'':8B ''matplotlib.pyplot'':50C ''mean'':15B ''np'':48C ''np.array'':59C ''numpi'':46C ''p'':17C,31C,84C ''pair'':7A ''plt'':52C ''plt.scatter'':74C ''plt.show'':81C ''pre'':43C ''python'':11B ''right'':21C,87C ''scipy.cluster.vq'':54C ''use'':34C,89C,96C ''whiten'':57C,69C ''x'':66C ''y'':67C,80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (762, 1, '2014-07-17 10:04:29.797', 4, 1174, 't-SNE Python implementation: Kullback-Leibler divergence', '&lt;p&gt;t-SNE, as in [1], works by progressively reducing the Kullback-Leibler (KL) divergence, until a certain condition is met.
The creators of t-SNE suggests to use KL divergence as a performance criterion for the visualizations:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;you can compare the Kullback-Leibler divergences that t-SNE reports. It is perfectly fine to run t-SNE ten times, and select the solution with the lowest KL divergence [2]&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I tried two implementations of t-SNE:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;python&lt;/strong&gt;: sklearn.manifold.TSNE().&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;R&lt;/strong&gt;: tsne, from library(tsne).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Both these implementations, when verbosity is set, print the error (Kullback-Leibler divergence) for each iteration. However, they don''t allow the user to get this information, which looks a bit strange to me.&lt;/p&gt;

&lt;p&gt;For example, the code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
from sklearn.manifold import TSNE
X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
model = TSNE(n_components=2, verbose=2, n_iter=200)
t = model.fit_transform(X)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;produces:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[t-SNE] Computing pairwise distances...
[t-SNE] Computed conditional probabilities for sample 4 / 4
[t-SNE] Mean sigma: 1125899906842624.000000
[t-SNE] Iteration 10: error = 6.7213750, gradient norm = 0.0012028
[t-SNE] Iteration 20: error = 6.7192064, gradient norm = 0.0012062
[t-SNE] Iteration 30: error = 6.7178683, gradient norm = 0.0012114
...
[t-SNE] Error after 200 iterations: 0.270186
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now, as far as I understand, &lt;strong&gt;0.270186&lt;/strong&gt; should be the KL divergence. However i cannot get this information, neither from &lt;strong&gt;model&lt;/strong&gt; nor from &lt;strong&gt;t&lt;/strong&gt; (which is a simple numpy.ndarray).&lt;/p&gt;

&lt;p&gt;To solve this problem I could: i) Calculate KL divergence by my self, ii) Do something nasty in python for capturing and parsing TSNE() function''s output [3]. However: i) would be quite stupid to re-calculate KL divergence, when TSNE() has already computed it, ii) would be a bit unusual in terms of code.&lt;/p&gt;

&lt;p&gt;Do you have any other suggestion? Is there a standard way to get this information using this library?&lt;/p&gt;

&lt;p&gt;I mentioned I tried &lt;em&gt;R&lt;/em&gt;''s tsne library, but I''d prefer the answers to focus on the &lt;em&gt;python&lt;/em&gt; sklearn implementation.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;References&lt;/p&gt;

&lt;p&gt;[1] &lt;a href="http://nbviewer.ipython.org/urls/gist.githubusercontent.com/AlexanderFabisch/1a0c648de22eff4a2a3e/raw/59d5bc5ed8f8bfd9ff1f7faa749d1b095aa97d5a/t-SNE.ipynb" rel="nofollow"&gt;http://nbviewer.ipython.org/urls/gist.githubusercontent.com/AlexanderFabisch/1a0c648de22eff4a2a3e/raw/59d5bc5ed8f8bfd9ff1f7faa749d1b095aa97d5a/t-SNE.ipynb&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;[2] &lt;a href="http://homepage.tudelft.nl/19j49/t-SNE.html" rel="nofollow"&gt;http://homepage.tudelft.nl/19j49/t-SNE.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;[3] &lt;a href="http://stackoverflow.com/questions/16571150/how-to-capture-stdout-output-from-a-python-function-call"&gt;http://stackoverflow.com/questions/16571150/how-to-capture-stdout-output-from-a-python-function-call&lt;/a&gt;&lt;/p&gt;
', 'machine-learning  python', NULL, '''/19j49/t-sne.html'':435C ''/19j49/t-sne.html&lt;/a&gt;&lt;/p&gt;'':440C ''/blockquote'':93C ''/code'':202C,269C ''/em'':393C,409C ''/li'':110C,119C ''/p'':55C,92C,103C,157C,163C,206C,307C,365C,385C,412C,416C ''/pre'':203C,270C ''/questions/16571150/how-to-capture-stdout-output-from-a-python-function-call'':447C ''/questions/16571150/how-to-capture-stdout-output-from-a-python-function-call&lt;/a&gt;&lt;/p&gt;'':450C ''/strong'':108C,114C,280C,296C,301C ''/ul'':120C ''/urls/gist.githubusercontent.com/alexanderfabisch/1a0c648de22eff4a2a3e/raw/59d5bc5ed8f8bfd9ff1f7faa749d1b095aa97d5a/t-sne.ipynb'':423C ''/urls/gist.githubusercontent.com/alexanderfabisch/1a0c648de22eff4a2a3e/raw/59d5bc5ed8f8bfd9ff1f7faa749d1b095aa97d5a/t-sne.ipynb&lt;/a&gt;&lt;/p&gt;'':428C ''0'':176C,177C,178C,179C,183C ''0.0012028'':240C ''0.0012062'':250C ''0.0012114'':260C ''0.270186'':268C,279C ''1'':20C,180C,181C,182C,184C,185C,186C,187C,418C ''10'':235C ''1125899906842624.000000'':230C ''2'':91C,192C,194C,430C ''20'':245C ''200'':197C,266C ''3'':336C,442C ''30'':255C ''4'':223C,224C ''6.7178683'':257C ''6.7192064'':247C ''6.7213750'':237C ''allow'':143C ''alreadi'':352C ''answer'':402C ''bit'':153C,359C ''blockquot'':56C ''calcul'':316C,346C ''cannot'':288C ''captur'':329C ''certain'':33C ''code'':162C,165C,208C,364C ''compar'':60C ''compon'':191C ''comput'':212C,218C,353C ''condit'':34C,219C ''could'':314C ''creator'':38C ''criterion'':51C ''d'':399C ''distanc'':214C ''diverg'':9A,30C,47C,65C,90C,135C,285C,318C,348C ''em'':391C,407C ''error'':131C,236C,246C,256C,264C ''exampl'':160C ''far'':274C ''fine'':74C ''focus'':404C ''function'':333C ''get'':147C,289C,379C ''gradient'':238C,248C,258C ''homepage.tudelft.nl'':434C,439C ''homepage.tudelft.nl/19j49/t-sne.html'':433C ''homepage.tudelft.nl/19j49/t-sne.html&lt;/a&gt;&lt;/p&gt;'':438C ''howev'':139C,286C,337C ''hr'':413C ''href'':420C,432C,444C ''ii'':322C,355C ''implement'':5A,98C,124C,411C ''import'':166C,172C ''inform'':149C,291C,381C ''iter'':138C,196C,234C,244C,254C,267C ''kl'':29C,46C,89C,284C,317C,347C ''kullback'':7A,27C,63C,133C ''kullback-leibl'':6A,26C,62C,132C ''learn'':12B ''leibler'':8A,28C,64C,134C ''li'':105C,111C ''librari'':117C,384C,396C ''look'':151C ''lowest'':88C ''machin'':11B ''machine-learn'':10B ''mean'':228C ''mention'':388C ''met'':36C ''model'':188C,295C ''model.fit'':199C ''n'':190C,195C ''nasti'':325C ''nbviewer.ipython.org'':422C,427C ''nbviewer.ipython.org/urls/gist.githubusercontent.com/alexanderfabisch/1a0c648de22eff4a2a3e/raw/59d5bc5ed8f8bfd9ff1f7faa749d1b095aa97d5a/t-sne.ipynb'':421C ''nbviewer.ipython.org/urls/gist.githubusercontent.com/alexanderfabisch/1a0c648de22eff4a2a3e/raw/59d5bc5ed8f8bfd9ff1f7faa749d1b095aa97d5a/t-sne.ipynb&lt;/a&gt;&lt;/p&gt;'':426C ''neither'':292C ''nofollow'':425C,437C ''norm'':239C,249C,259C ''np'':169C ''np.array'':175C ''numpi'':167C ''numpy.ndarray'':306C ''output'':335C ''p'':14C,57C,94C,121C,158C,204C,271C,308C,366C,386C,414C,417C,429C,441C ''pairwis'':213C ''pars'':331C ''perfect'':73C ''perform'':50C ''pre'':164C,207C ''prefer'':400C ''print'':129C ''probabl'':220C ''problem'':312C ''produc'':205C ''progress'':23C ''python'':4A,13B,107C,327C,408C ''quit'':341C ''r'':113C,392C ''re'':345C ''re-calcul'':344C ''reduc'':24C ''refer'':415C ''rel'':424C,436C ''report'':70C ''run'':76C ''sampl'':222C ''select'':83C ''self'':321C ''set'':128C ''sigma'':229C ''simpl'':305C ''sklearn'':410C ''sklearn.manifold'':171C ''sklearn.manifold.tsne'':109C ''sne'':3A,17C,42C,69C,79C,102C,211C,217C,227C,233C,243C,253C,263C ''solut'':85C ''solv'':310C ''someth'':324C ''stackoverflow.com'':446C,449C ''stackoverflow.com/questions/16571150/how-to-capture-stdout-output-from-a-python-function-call'':445C ''stackoverflow.com/questions/16571150/how-to-capture-stdout-output-from-a-python-function-call&lt;/a&gt;&lt;/p&gt;'':448C ''standard'':376C ''strang'':154C ''strong'':106C,112C,278C,294C,299C ''stupid'':342C ''suggest'':43C,372C ''t-sne'':1A,15C,40C,67C,77C,100C,209C,215C,225C,231C,241C,251C,261C ''ten'':80C ''term'':362C ''time'':81C ''transform'':200C ''tri'':96C,390C ''tsne'':115C,118C,173C,189C,332C,350C,395C ''two'':97C ''ul'':104C ''understand'':277C ''unusu'':360C ''use'':45C,382C ''user'':145C ''verbos'':126C,193C ''visual'':54C ''way'':377C ''work'':21C ''would'':339C,356C ''x'':174C,201C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (752, 2, '2014-07-16 11:07:56.467', 2, NULL, NULL, '&lt;p&gt;It seems to me that &lt;em&gt;latent features&lt;/em&gt; is a term used to describe criteria for &lt;strong&gt;classifying entities&lt;/strong&gt; by their structure, in other words, by &lt;em&gt;features&lt;/em&gt; (traits) they contain, instead of &lt;em&gt;classes&lt;/em&gt; they belong to. Meaning of the word "latent" here is most likely similar to its meaning in social sciences, where very popular term latent variable (&lt;a href="http://en.wikipedia.org/wiki/Latent_variable" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Latent_variable&lt;/a&gt;) means unobservable variable (concept).&lt;/p&gt;

&lt;p&gt;Section "Introduction" in the following paper provides a good explanation of latent features'' &lt;strong&gt;meaning&lt;/strong&gt; and use in &lt;strong&gt;modeling&lt;/strong&gt; of social sciences phenomena: &lt;a href="http://papers.nips.cc/paper/3846-nonparametric-latent-feature-models-for-link-prediction.pdf" rel="nofollow"&gt;http://papers.nips.cc/paper/3846-nonparametric-latent-feature-models-for-link-prediction.pdf&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/em'':10C,32C,40C ''/p'':80C ''/paper/3846-nonparametric-latent-feature-models-for-link-prediction.pdf'':112C ''/paper/3846-nonparametric-latent-feature-models-for-link-prediction.pdf&lt;/a&gt;.&lt;/p&gt;'':117C ''/strong'':22C,97C,103C ''/wiki/latent_variable'':70C ''/wiki/latent_variable&lt;/a&gt;)'':75C ''belong'':42C ''class'':39C ''classifi'':20C ''concept'':79C ''contain'':35C ''criteria'':17C ''describ'':16C ''em'':7C,30C,38C ''en.wikipedia.org'':69C,74C ''en.wikipedia.org/wiki/latent_variable'':68C ''en.wikipedia.org/wiki/latent_variable&lt;/a&gt;)'':73C ''entiti'':21C ''explan'':91C ''featur'':9C,31C,94C ''follow'':86C ''good'':90C ''href'':67C,109C ''instead'':36C ''introduct'':83C ''latent'':8C,48C,64C,93C ''like'':52C ''mean'':44C,56C,76C,96C ''model'':102C ''nofollow'':72C,114C ''p'':1C,81C ''paper'':87C ''papers.nips.cc'':111C,116C ''papers.nips.cc/paper/3846-nonparametric-latent-feature-models-for-link-prediction.pdf'':110C ''papers.nips.cc/paper/3846-nonparametric-latent-feature-models-for-link-prediction.pdf&lt;/a&gt;.&lt;/p&gt;'':115C ''phenomena'':107C ''popular'':62C ''provid'':88C ''rel'':71C,113C ''scienc'':59C,106C ''section'':82C ''seem'':3C ''similar'':53C ''social'':58C,105C ''strong'':19C,95C,101C ''structur'':25C ''term'':13C,63C ''trait'':33C ''unobserv'':77C ''use'':14C,99C ''variabl'':65C,78C ''word'':28C,47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (753, 2, '2014-07-16 13:05:41.123', 0, NULL, NULL, '&lt;p&gt;I could try to explain you with words, but these slides explain it very well with pictures. Hope it helps.
&lt;a href="http://www.inf.ed.ac.uk/teaching/courses/mt/lectures/phrase-model.pdf" rel="nofollow"&gt;http://www.inf.ed.ac.uk/teaching/courses/mt/lectures/phrase-model.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Note this slides correspond to the chapter 5 of "Statistical Machine Translation" by Philipp Koehn, highly recommended if you are working on machine translation, and it is easy to read.&lt;/p&gt;
', NULL, NULL, '''/p'':63C ''/teaching/courses/mt/lectures/phrase-model.pdf'':26C ''/teaching/courses/mt/lectures/phrase-model.pdf&lt;/a&gt;&lt;/p&gt;'':31C ''5'':40C ''chapter'':39C ''correspond'':36C ''could'':3C ''easi'':60C ''explain'':6C,13C ''help'':21C ''high'':48C ''hope'':19C ''href'':23C ''koehn'':47C ''machin'':43C,55C ''nofollow'':28C ''note'':33C ''p'':1C,32C ''philipp'':46C ''pictur'':18C ''read'':62C ''recommend'':49C ''rel'':27C ''slide'':12C,35C ''statist'':42C ''translat'':44C,56C ''tri'':4C ''well'':16C ''word'':9C ''work'':53C ''www.inf.ed.ac.uk'':25C,30C ''www.inf.ed.ac.uk/teaching/courses/mt/lectures/phrase-model.pdf'':24C ''www.inf.ed.ac.uk/teaching/courses/mt/lectures/phrase-model.pdf&lt;/a&gt;&lt;/p&gt;'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1228, 2, '2014-10-08 22:12:31.823', -1, NULL, NULL, '&lt;p&gt;Use one of Pandas'' built in functions:
&lt;a href="http://pandas.pydata.org/pandas-docs/stable/computation.html#correlation" rel="nofollow"&gt;http://pandas.pydata.org/pandas-docs/stable/computation.html#correlation&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/pandas-docs/stable/computation.html#correlation'':13C ''/pandas-docs/stable/computation.html#correlation&lt;/a&gt;&lt;/p&gt;'':18C ''built'':6C ''function'':8C ''href'':10C ''nofollow'':15C ''one'':3C ''p'':1C ''panda'':5C ''pandas.pydata.org'':12C,17C ''pandas.pydata.org/pandas-docs/stable/computation.html#correlation'':11C ''pandas.pydata.org/pandas-docs/stable/computation.html#correlation&lt;/a&gt;&lt;/p&gt;'':16C ''rel'':14C ''use'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8671, 1, '2015-10-31 13:35:09.233', 0, 25, '$\chi^{2}$ kernel SVM performance issue', '&lt;p&gt;I am using $\chi^{2}$ kernel for non-linear SVM (using libSVM) for classifying MNIST digits. I am getting very bad performance (worse than random guessing). The $\chi^{2}$ kernel code (in MATLAB) is as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;function D = chi2_kernel(X,Y)
%Computes the chi2 kernel for two Matrices X and Y

 D = zeros(size(X,1),size(Y,1));

for i = 1:size(Y,1)

    d = bsxfun(@minus,X, Y(i,:));
    s = bsxfun(@plus,X,Y(i,:));
    D(:,i) = sum(d.^2 ./ (s/2),2);
end

D = exp(-0.0001.*(1-D));

end
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Using this I computed the kernel matrix as follows :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;% Gram matrix for chi2 kernel
 dist = chi2_kernel(X_train2,X_train2);
dist1 = chi2_kernel(X_test,X_train2);

% Gram matrix for training set to training set


KTrT = [(1:numTrain/50)'', dist];

% Gram matrix for training to test set 

KTT = [(1:numTest)'', dist1];


model = svmtrain(Y_train2, KTrT,''-t 4'');

predClass = svmpredict(Y_test, KTT,model);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Does anyone have suggestions for how to better optimize this code, or an explanation for why I''m observing such poor performance?&lt;/p&gt;
', 'svm  matlab  libsvm', NULL, '''-0.0001'':103C ''/code'':107C,177C ''/p'':48C,119C,202C ''/pre'':108C,178C ''1'':71C,74C,77C,80C,104C,150C,161C ''2'':2A,15C,40C,97C,99C ''4'':170C ''anyon'':181C ''bad'':32C ''better'':187C ''bsxfun'':82C,88C ''chi'':1A,14C,39C ''chi2'':53C,59C,125C,128C,135C ''classifi'':25C ''code'':42C,50C,121C,190C ''comput'':57C,113C ''d'':52C,67C,81C,93C,96C,101C,105C ''digit'':27C ''dist'':127C,152C ''dist1'':134C,163C ''end'':100C,106C ''exp'':102C ''explan'':193C ''follow'':47C,118C ''function'':51C ''get'':30C ''gram'':122C,141C,153C ''guess'':37C ''issu'':6A ''kernel'':3A,16C,41C,54C,60C,115C,126C,129C,136C ''ktrt'':149C,168C ''ktt'':160C,175C ''libsvm'':9B,23C ''linear'':20C ''m'':197C ''matlab'':8B,44C ''matric'':63C ''matrix'':116C,123C,142C,154C ''minus'':83C ''mnist'':26C ''model'':164C,176C ''non'':19C ''non-linear'':18C ''numtest'':162C ''numtrain/50'':151C ''observ'':198C ''optim'':188C ''p'':10C,109C,179C ''perform'':5A,33C,201C ''plus'':89C ''poor'':200C ''pre'':49C,120C ''predclass'':171C ''random'':36C ''s/2'':98C ''set'':145C,148C,159C ''size'':69C,72C,78C ''suggest'':183C ''sum'':95C ''svm'':4A,7B,21C ''svmpredict'':172C ''svmtrain'':165C ''test'':138C,158C,174C ''train'':144C,147C,156C ''train2'':131C,133C,140C,167C ''two'':62C ''use'':13C,22C,110C ''wors'':34C ''x'':55C,64C,70C,84C,90C,130C,132C,137C,139C ''y'':56C,66C,73C,79C,85C,91C,166C,173C ''zero'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (757, 2, '2014-07-16 18:26:54.233', 3, NULL, NULL, '&lt;p&gt;Gas usage has a daily cycle but there are also secondary weekly and annual cycles that the ARIMA may not be able to capture.&lt;/p&gt;

&lt;p&gt;There is a very noticeable difference between the weekday and Saturday data. Try creating a subset of the data for each day of the week or splitting the data into weekday and weekend and applying the model.&lt;/p&gt;

&lt;p&gt;If you can obtain temperature data for the same period check if there is a correlation between the temperature and gas usage.&lt;/p&gt;

&lt;p&gt;As @Aleksandr Blekh said R does have good packages for ARIMA models&lt;/p&gt;
', NULL, NULL, '''/p'':26C,65C,89C,103C ''abl'':23C ''aleksandr'':92C ''also'':11C ''annual'':15C ''appli'':62C ''arima'':19C,101C ''blekh'':93C ''captur'':25C ''check'':77C ''correl'':82C ''creat'':41C ''cycl'':7C,16C ''daili'':6C ''data'':39C,46C,56C,72C ''day'':49C ''differ'':33C ''gas'':2C,87C ''good'':98C ''may'':20C ''model'':64C,102C ''notic'':32C ''obtain'':70C ''p'':1C,27C,66C,90C ''packag'':99C ''period'':76C ''r'':95C ''said'':94C ''saturday'':38C ''secondari'':12C ''split'':54C ''subset'':43C ''temperatur'':71C,85C ''tri'':40C ''usag'':3C,88C ''week'':13C,52C ''weekday'':36C,58C ''weekend'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8285, 2, '2015-10-03 00:44:15', 0, NULL, NULL, '&lt;p&gt;You don''t give many clues about what properties of the images you might be considering, but it seems that what you might want to measure is the difference in the distributions of the training and tests sets. A useful place to start would be with the KullbackâLeibler divergence, which is a measure of the difference of two distributions.&lt;/p&gt;
', NULL, NULL, '''/p'':62C ''clue'':7C ''consid'':17C ''differ'':30C,58C ''distribut'':33C,61C ''diverg'':51C ''give'':5C ''imag'':13C ''kullbackâ'':49C ''leibler'':50C ''mani'':6C ''measur'':27C,55C ''might'':15C,24C ''p'':1C ''place'':42C ''properti'':10C ''seem'':20C ''set'':39C ''start'':44C ''test'':38C ''train'':36C ''two'':60C ''use'':41C ''want'':25C ''would'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (760, 1, '2014-07-17 09:26:11.833', 2, 131, 'Given time series data, how to model the frequency of someone changes his job?', '&lt;p&gt;I am given a time series data vector (ordered by months and years),which contains only &lt;code&gt;0&lt;/code&gt;s and &lt;code&gt;1&lt;/code&gt;s. &lt;code&gt;1&lt;/code&gt; s represent a person changes his job at a particular a month. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Questions:&lt;/strong&gt; What model can i use to determine model how frequently this person change his job ? In addition, this model should be able to predict the probability of this person changing his in the next 6 months.  &lt;/p&gt;

&lt;p&gt;A poisson process ? (I have studied poisson process before however I have no idea when and how to apply it). Any assumptions that data need to meet before applying the poisson process ? &lt;/p&gt;

&lt;p&gt;Would love to gather more information on how to model something like this. Thanks&lt;/p&gt;
', 'data-mining  time-series', NULL, '''/code'':40C,45C,49C ''/p'':62C,103C,137C,153C ''/strong'':66C ''0'':39C ''1'':44C,48C ''6'':101C ''abl'':88C ''addit'':83C ''appli'':123C,133C ''assumpt'':126C ''chang'':12A,54C,79C,96C ''code'':38C,43C,47C ''contain'':36C ''data'':4A,16B,28C,128C ''data-min'':15B ''determin'':73C ''frequenc'':9A ''frequent'':76C ''gather'':142C ''given'':1A,24C ''howev'':114C ''idea'':118C ''inform'':144C ''job'':14A,56C,81C ''like'':150C ''love'':140C ''meet'':131C ''mine'':17B ''model'':7A,68C,74C,85C,148C ''month'':32C,61C,102C ''need'':129C ''next'':100C ''order'':30C ''p'':21C,63C,104C,138C ''particular'':59C ''person'':53C,78C,95C ''poisson'':106C,111C,135C ''predict'':90C ''probabl'':92C ''process'':107C,112C,136C ''question'':65C ''repres'':51C ''seri'':3A,20B,27C ''someon'':11A ''someth'':149C ''strong'':64C ''studi'':110C ''thank'':152C ''time'':2A,19B,26C ''time-seri'':18B ''use'':71C ''vector'':29C ''would'':139C ''year'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (786, 1, '2014-07-19 20:46:52.74', 6, 269, 'NASDAQ Trade Data', '&lt;p&gt;I am trying to find stock data to practice with, is there a good resource for this? I found this: &lt;a href="ftp://emi.nasdaq.com/ITCH/"&gt;ftp://emi.nasdaq.com/ITCH/&lt;/a&gt; but it only has the current year.&lt;/p&gt;

&lt;p&gt;I already have a way of parsing the protocol, but would like to have some more data to compare with. It doesn''t have to be in the same format, as long as it has price, trades, and date statistics.&lt;/p&gt;
', 'data-mining  dataset', NULL, '''/itch/'':33C ''/itch/&lt;/a&gt;'':36C ''/p'':44C,86C ''alreadi'':47C ''compar'':64C ''current'':42C ''data'':3A,5B,15C,62C ''data-min'':4B ''dataset'':7B ''date'':84C ''doesn'':67C ''emi.nasdaq.com'':32C,35C ''emi.nasdaq.com/itch/'':31C ''emi.nasdaq.com/itch/&lt;/a&gt;'':34C ''find'':13C ''format'':75C ''found'':27C ''good'':22C ''href'':30C ''like'':57C ''long'':77C ''mine'':6B ''nasdaq'':1A ''p'':8C,45C ''pars'':52C ''practic'':17C ''price'':81C ''protocol'':54C ''resourc'':23C ''statist'':85C ''stock'':14C ''trade'':2A,82C ''tri'':11C ''way'':50C ''would'':56C ''year'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (763, 2, '2014-07-17 10:26:45.547', 2, NULL, NULL, '&lt;p&gt;A simple and perhaps somewhat naive approach would be to assume that a person changes jobs at a constant rate and that previous job changes have no influence on future ones. Under these assumptions you could model the job changes as a Poisson process and estimate the rate parameter using MLE (&lt;a href="http://en.wikipedia.org/wiki/Poisson_process" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Poisson_process&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/Poisson_distribution" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Poisson_distribution&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;Of course one should explore how well these assumptions hold in the data. To do this, you could study whether or not job changes are independent of one another by computing the correlation between events at various lags (&lt;a href="http://en.wikipedia.org/wiki/Correlation" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Correlation&lt;/a&gt;). You could also plot the distribution of time between job change events. If the process is Poisson-like then you should observe little to no correlation between events at any number of lags and the distribution of time between job change events should be exponentially distributed (&lt;a href="http://en.wikipedia.org/wiki/Exponential_distribution" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Exponential_distribution&lt;/a&gt;).&lt;/p&gt;
', NULL, NULL, '''/wiki/correlation'':117C ''/wiki/correlation&lt;/a&gt;).'':122C ''/wiki/exponential_distribution'':174C ''/wiki/exponential_distribution&lt;/a&gt;).&lt;/p&gt;'':179C ''/wiki/poisson_distribution'':68C ''/wiki/poisson_distribution&lt;/a&gt;).&lt;/p&gt;'':73C ''/wiki/poisson_process'':57C ''/wiki/poisson_process&lt;/a&gt;'':62C ''also'':125C ''anoth'':103C ''approach'':8C ''assum'':12C ''assumpt'':35C,83C ''chang'':16C,26C,41C,98C,133C,164C ''comput'':105C ''constant'':20C ''correl'':107C,149C ''could'':37C,92C,124C ''cours'':76C ''data'':87C ''distribut'':128C,159C,169C ''en.wikipedia.org'':56C,61C,67C,72C,116C,121C,173C,178C ''en.wikipedia.org/wiki/correlation'':115C ''en.wikipedia.org/wiki/correlation&lt;/a&gt;).'':120C ''en.wikipedia.org/wiki/exponential_distribution'':172C ''en.wikipedia.org/wiki/exponential_distribution&lt;/a&gt;).&lt;/p&gt;'':177C ''en.wikipedia.org/wiki/poisson_distribution'':66C ''en.wikipedia.org/wiki/poisson_distribution&lt;/a&gt;).&lt;/p&gt;'':71C ''en.wikipedia.org/wiki/poisson_process'':55C ''en.wikipedia.org/wiki/poisson_process&lt;/a&gt;'':60C ''estim'':47C ''event'':109C,134C,151C,165C ''explor'':79C ''exponenti'':168C ''futur'':31C ''hold'':84C ''href'':54C,65C,114C,171C ''independ'':100C ''influenc'':29C ''job'':17C,25C,40C,97C,132C,163C ''lag'':112C,156C ''like'':141C ''littl'':146C ''mle'':52C ''model'':38C ''naiv'':7C ''nofollow'':59C,70C,119C,176C ''number'':154C ''observ'':145C ''one'':32C,77C,102C ''p'':1C,74C ''paramet'':50C ''perhap'':5C ''person'':15C ''plot'':126C ''poisson'':44C,140C ''poisson-lik'':139C ''previous'':24C ''process'':45C,137C ''rate'':21C,49C ''rel'':58C,69C,118C,175C ''simpl'':3C ''somewhat'':6C ''studi'':93C ''time'':130C,161C ''use'':51C ''various'':111C ''well'':81C ''whether'':94C ''would'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (773, 2, '2014-07-17 20:02:11.227', 11, NULL, NULL, '&lt;p&gt;Think geometrically. Cosine similarity only cares about angle difference, while dot product cares about angle and magnitude. If you normalize your data to have the same magnitude, the two are indistinguishable. Sometimes it is desirable to ignore the magnitude, hence cosine similarity is nice, but if magnitude plays a role, dot product would be better as a similarity measure. Note that neither of them is a "distance metric".&lt;/p&gt;
', NULL, NULL, '''/p'':70C ''angl'':9C,16C ''better'':56C ''care'':7C,14C ''cosin'':4C,42C ''data'':23C ''desir'':36C ''differ'':10C ''distanc'':68C ''dot'':12C,52C ''geometr'':3C ''henc'':41C ''ignor'':38C ''indistinguish'':32C ''magnitud'':18C,28C,40C,48C ''measur'':60C ''metric'':69C ''neither'':63C ''nice'':45C ''normal'':21C ''note'':61C ''p'':1C ''play'':49C ''product'':13C,53C ''role'':51C ''similar'':5C,43C,59C ''sometim'':33C ''think'':2C ''two'':30C ''would'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (764, 2, '2014-07-17 12:34:11.397', 3, NULL, NULL, '&lt;p&gt;K-means should be right in this case. Since k-means tries to group based solely on euclidean distance between objects you will get back clusters of locations that are close to each other. &lt;/p&gt;

&lt;p&gt;To find the optimal number of clusters you can try making an ''elbow'' plot of the within group sum of square distance. This may be helpful (&lt;a href="http://nbviewer.ipython.org/github/nborwankar/LearnDataScience/blob/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb" rel="nofollow"&gt;http://nbviewer.ipython.org/github/nborwankar/LearnDataScience/blob/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb&lt;/a&gt;)&lt;/p&gt;
', NULL, NULL, '''/github/nborwankar/learndatascience/blob/master/notebooks/d3.%20k-means%20clustering%20analysis.ipynb'':70C ''/github/nborwankar/learndatascience/blob/master/notebooks/d3.%20k-means%20clustering%20analysis.ipynb&lt;/a&gt;)&lt;/p&gt;'':75C ''/p'':38C ''back'':28C ''base'':18C ''case'':10C ''close'':34C ''cluster'':29C,46C ''distanc'':22C,61C ''elbow'':52C ''euclidean'':21C ''find'':41C ''get'':27C ''group'':17C,57C ''help'':65C ''href'':67C ''k'':3C,13C ''k-mean'':2C,12C ''locat'':31C ''make'':50C ''may'':63C ''mean'':4C,14C ''nbviewer.ipython.org'':69C,74C ''nbviewer.ipython.org/github/nborwankar/learndatascience/blob/master/notebooks/d3.%20k-means%20clustering%20analysis.ipynb'':68C ''nbviewer.ipython.org/github/nborwankar/learndatascience/blob/master/notebooks/d3.%20k-means%20clustering%20analysis.ipynb&lt;/a&gt;)&lt;/p&gt;'':73C ''nofollow'':72C ''number'':44C ''object'':24C ''optim'':43C ''p'':1C,39C ''plot'':53C ''rel'':71C ''right'':7C ''sinc'':11C ''sole'':19C ''squar'':60C ''sum'':58C ''tri'':15C,49C ''within'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (765, 2, '2014-07-17 13:19:46.817', 0, NULL, NULL, '&lt;p&gt;I''ve seen the same thing: My target variables are non-negative, but sklearn''s GBR sometimes makes negative predictions.  To grossly oversimplify, GBR is just using averages of subsets of the target variable to make predictions, so if the target variables are non-negative, then the predictions should be non-negative. &lt;/p&gt;

&lt;p&gt;Also, I can provide an example, if anyone''s interested.&lt;/p&gt;

&lt;p&gt;I''d make this a comment, but I don''t have enough reputation.&lt;/p&gt;
', NULL, NULL, '''/p'':57C,69C,84C ''also'':59C ''anyon'':66C ''averag'':30C ''comment'':76C ''d'':72C ''enough'':82C ''exampl'':64C ''gbr'':18C,26C ''grossli'':24C ''interest'':68C ''make'':20C,38C,73C ''negat'':14C,21C,48C,56C ''non'':13C,47C,55C ''non-neg'':12C,46C,54C ''oversimplifi'':25C ''p'':1C,58C,70C ''predict'':22C,39C,51C ''provid'':62C ''reput'':83C ''seen'':4C ''sklearn'':16C ''sometim'':19C ''subset'':32C ''target'':9C,35C,43C ''thing'':7C ''use'':29C ''variabl'':10C,36C,44C ''ve'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (766, 2, '2014-07-17 14:07:12.643', 3, NULL, NULL, '&lt;p&gt;The TSNE source in scikit-learn is in pure Python. Fit &lt;code&gt;fit_transform()&lt;/code&gt; method is actually calling a private &lt;code&gt;_fit()&lt;/code&gt; function which then calls a private &lt;code&gt;_tsne()&lt;/code&gt; function. That &lt;code&gt;_tsne()&lt;/code&gt; function has a local variable &lt;code&gt;error&lt;/code&gt; which is printed out at the end of the fit. Seems like you could pretty easily change one or two lines of source code to have that value returned to &lt;code&gt;fit_transform()&lt;/code&gt;.&lt;/p&gt;
', NULL, NULL, '''/code'':17C,26C,35C,40C,48C,82C ''/p'':83C ''actual'':20C ''call'':21C,30C ''chang'':65C ''code'':14C,24C,33C,38C,46C,72C,79C ''could'':62C ''easili'':64C ''end'':55C ''error'':47C ''fit'':13C,15C,25C,58C,80C ''function'':27C,36C,41C ''learn'':8C ''like'':60C ''line'':69C ''local'':44C ''method'':18C ''one'':66C ''p'':1C ''pretti'':63C ''print'':51C ''privat'':23C,32C ''pure'':11C ''python'':12C ''return'':77C ''scikit'':7C ''scikit-learn'':6C ''seem'':59C ''sourc'':4C,71C ''transform'':16C,81C ''tsne'':3C,34C,39C ''two'':68C ''valu'':76C ''variabl'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (767, 2, '2014-07-17 14:28:38.207', 12, NULL, NULL, '&lt;p&gt;The best reproducibility tool is to make a log of your actions, something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;experiment/input ; expected ; observation/output ; current hypothesis and if supported or rejected
exp1 ; expected1 ; obs1 ; some fancy hypothesis, supported
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This can be written down on a paper, but, if your experiments fit in a computational framework, you can use computational tools to partly or completely automate that logging process (particularly by helping you track the input datasets which can be huge, and the output figures).&lt;/p&gt;

&lt;p&gt;A great reproducibility tool for Python with a low learning curve is of course &lt;a href="http://ipython.org/" rel="nofollow"&gt;&lt;strong&gt;IPython/Jupyter Notebook&lt;/strong&gt;&lt;/a&gt; (don''t forget the &lt;a href="https://damontallen.github.io/IPython-quick-ref-sheets/" rel="nofollow"&gt;%logon and %logstart&lt;/a&gt; magics).&lt;/p&gt;

&lt;p&gt;Another great tool that is very recent (2015) is &lt;a href="https://github.com/recipy/recipy" rel="nofollow"&gt;recipy&lt;/a&gt;, which is very like sumatra (see below), but made specifically for Python. I don''t know if it works with Jupyter Notebooks, but I know the author frequently uses them so I guess that if it''s not currently supported, it will be in the future.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Git_(software)" rel="nofollow"&gt;Git&lt;/a&gt; is also awesome, and it''s not tied to Python. It will help you not only to keep a history of all your experiments, code, datasets, figures, etc. but also provide you with tools to maintain (&lt;a href="http://www.philandstuff.com/2014/02/09/git-pickaxe.html" rel="nofollow"&gt;git pickaxe&lt;/a&gt;), collaborate (&lt;a href="http://jfire.io/blog/2012/03/07/code-archaeology-with-git/" rel="nofollow"&gt;blame&lt;/a&gt;) and debug (&lt;a href="http://blogs.perl.org/users/ovid/2014/07/making-git-bisect-more-useful.html" rel="nofollow"&gt;git&lt;/a&gt;-&lt;a href="https://lwn.net/Articles/317154/" rel="nofollow"&gt;bisect&lt;/a&gt;) using a scientific method of debugging (called &lt;a href="https://en.wikipedia.org/wiki/Delta_Debugging" rel="nofollow"&gt;delta debugging&lt;/a&gt;). &lt;a href="https://matthew-brett.github.io/pydagogue/curious_git.html" rel="nofollow"&gt;Here''s a story&lt;/a&gt; of a fictional researcher trying to make his own experiments logging system, until it ends up being a facsimile of Git.&lt;/p&gt;

&lt;p&gt;Another general tool working with any language (with a Python API on &lt;a href="https://pypi.python.org/pypi/Sumatra" rel="nofollow"&gt;pypi&lt;/a&gt;) is &lt;a href="http://neuralensemble.org/sumatra/" rel="nofollow"&gt;&lt;strong&gt;Sumatra&lt;/strong&gt;&lt;/a&gt;, which is specifically designed to help you do &lt;strong&gt;replicable&lt;/strong&gt; research (&lt;em&gt;replicable&lt;/em&gt; aims to produce the same results given the exact same code and softwares, whereas &lt;em&gt;reproducibility&lt;/em&gt; aims to produce the same results given any medium, which is a lot harder and time consuming and not automatable).&lt;/p&gt;

&lt;p&gt;Here is how Sumatra works: for each experiment that you conduct through Sumatra, this software will act like a "save game state" often found in videogames. More precisely, it will will save:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;all the parameters you provided;&lt;/li&gt;
&lt;li&gt;the exact sourcecode state of your whole experimental application and config files;&lt;/li&gt;
&lt;li&gt;the output/plots/results and also any file produced by your experimental application.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It will then construct a database with the timestamp and other metadatas for each of your experiments, that you can later crawl using the webGUI. Since Sumatra saved the full state of your application for a specific experiment at one specific point in time, you can restore the code that produced a specific result at any moment you want, thus you have replicable research at a low cost (except for storage if you work on huge datasets, but you can configure exceptions if you don''t want to save everything everytime).&lt;/p&gt;

&lt;p&gt;In the end, you can use either Git or Sumatra, they will provide you with about the same replicability power, but Sumatra is specifically tailored for scientific research so it provides a few fancy tools like a web GUI to crawl your results, while Git is more tailored towards code maintenance (but it has debugging tools like git-bisect so if your experiments involve codes, it may actually be better). Or of course you can use both!&lt;/p&gt;

&lt;p&gt;/EDIT: &lt;a href="http://datascience.stackexchange.com/a/775"&gt;dsign&lt;/a&gt; touched a very important point here: the replicability of your setup is as important as the replicability of your application. In other words, you should at least provide a &lt;strong&gt;full list of the libraries and compilers&lt;/strong&gt; you used along with their exact &lt;strong&gt;versions&lt;/strong&gt; and the details of your &lt;strong&gt;platform&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Personally, in scientific computing with Python, I have found that packaging an application along with the libraries is just too painful, thus I now just use an all-in-one scientific python package such as &lt;a href="https://store.continuum.io/cshop/anaconda/" rel="nofollow"&gt;Anaconda&lt;/a&gt; (with the great package manager &lt;a href="http://www.continuum.io/blog/conda" rel="nofollow"&gt;conda&lt;/a&gt;), and just advise users to use the same package. Another solution could be to provide a script to automatically generate a &lt;a href="http://docs.python-guide.org/en/latest/dev/virtualenvs/" rel="nofollow"&gt;virtualenv&lt;/a&gt;, or to package everything using the commercial &lt;a href="http://datascience.stackexchange.com/a/775"&gt;Docker application as cited by dsign&lt;/a&gt; or the opensource &lt;a href="http://www.vagrantup.com/" rel="nofollow"&gt;Vagrant&lt;/a&gt; (with for example &lt;a href="http://deeplearning.net/software/pylearn2/#other-methods" rel="nofollow"&gt;pylearn2-in-a-box&lt;/a&gt; which use Vagrant to produce an easily redistributable virtual environment package).&lt;/p&gt;

&lt;p&gt;/EDIT2: Here''s a &lt;a href="https://www.udacity.com/course/viewer#!/c-cs259/l-48648797/m-48680672" rel="nofollow"&gt;great video&lt;/a&gt; summarizing (for debugging but this can also be applied to research) what is fundamental to do reproducible research: logging your experiments and each other steps of the scientific method, a sort of &lt;em&gt;"explicit experimenting"&lt;/em&gt;.&lt;/p&gt;
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''produc'':383C,400C,482C,540C,866C ''provid'':235C,459C,595C,613C,698C,801C ''pylearn2'':857C ''pylearn2-in-a-box'':856C ''pypi'':352C ''pypi.python.org'':348C ''pypi.python.org/pypi/sumatra'':347C ''python'':93C,159C,214C,342C,733C,760C ''recent'':136C ''recipi'':146C ''redistribut'':869C ''reject'':29C ''rel'':105C,121C,144C,201C,246C,257C,268C,277C,293C,303C,350C,360C,769C,783C,813C,842C,854C,884C ''replic'':375C,379C,552C,601C,678C,687C ''reproduc'':4C,90C,396C,905C ''research'':313C,377C,553C,610C,899C,906C ''restor'':536C ''result'':386C,403C,543C,625C ''save'':439C,451C,517C,578C ''scientif'':283C,609C,730C,759C,916C ''script'':803C ''see'':153C ''setup'':681C ''sinc'':515C ''softwar'':393C,434C ''solut'':797C ''someth'':14C ''sort'':919C ''sourcecod'':464C ''specif'':157C,368C,526C,530C,542C,606C ''state'':441C,465C,520C ''step'':913C ''storag'':560C ''store.continuum.io'':767C ''store.continuum.io/cshop/anaconda/'':766C ''stori'':308C ''strong'':107C,362C,374C,700C,715C,723C 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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (768, 2, '2014-07-17 15:19:32.237', 13, NULL, NULL, '&lt;p&gt;Since I started doing research in academia I was constantly looking for a satisfactory workflow.
I think that I finally found something I am happy with:&lt;/p&gt;

&lt;p&gt;1) Put everything under version control, e.g., Git:&lt;/p&gt;

&lt;p&gt;For hobby research projects I use GitHub, for research at work I use the private GitLab server that is provided by our university. I also keep my datasets there.&lt;/p&gt;

&lt;p&gt;2) I do most of my analyses along with the documentation on IPython notebooks. It is very organized (for me) to have the code, the plots, and the discussion/conclusion all in one document
If I am running larger scripts, I would usually put them into separate script .py files, but I would still execute them from the IPython notebook via the %run magic to add information about the purpose, outcome, and other parameters.&lt;/p&gt;

&lt;p&gt;I have written a small cell-magic extension for IPython and IPython notebooks, called "watermark" that I use to conveniently create time stamps and keep track of the different package versions I used and also Git hashs&lt;/p&gt;

&lt;p&gt;For example&lt;/p&gt;

&lt;p&gt;&lt;br&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;%watermark

29/06/2014 01:19:10

CPython 3.4.1
IPython 2.1.0

compiler   : GCC 4.2.1 (Apple Inc. build 5577)
system     : Darwin
release    : 13.2.0
machine    : x86_64
processor  : i386
CPU cores  : 2
interpreter: 64bit
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;br&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;%watermark -d -t

29/06/2014 01:19:11 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;br&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;%watermark -v -m -p numpy,scipy

CPython 3.4.1
IPython 2.1.0

numpy 1.8.1
scipy 0.14.0

compiler   : GCC 4.2.1 (Apple Inc. build 5577)
system     : Darwin
release    : 13.2.0
machine    : x86_64
processor  : i386
CPU cores  : 2
interpreter: 64bit
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For more info, see the &lt;a href="http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb"&gt;documentation here&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (769, 2, '2014-07-17 15:28:25.94', 7, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Dimensionality Reduction&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Another important procedure is to compare the error rates on training and test dataset to see if you are overfitting (due to the "curse of dimensionality"). E.g., if your error rate on the test dataset is much larger than the error on the training data set, this would be one indicator.&lt;br&gt;
In this case, you could try dimensionality reduction techniques, such as PCA or LDA.&lt;/p&gt;

&lt;p&gt;If you are interested, I have written about PCA, LDA and some other techniques here: &lt;a href="http://sebastianraschka.com/index.html#machine_learning"&gt;http://sebastianraschka.com/index.html#machine_learning&lt;/a&gt; and in my GitHub repo here: &lt;a href="https://github.com/rasbt/pattern_classification"&gt;https://github.com/rasbt/pattern_classification&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Cross validation&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Also you may want to take a look at cross-validation techniques in order to evaluate the performance of your classifiers in a more objective manner&lt;/p&gt;
', NULL, NULL, '''/index.html#machine_learning'':95C ''/index.html#machine_learning&lt;/a&gt;'':98C ''/p'':6C,74C,118C,147C ''/rasbt/pattern_classification'':109C ''/rasbt/pattern_classification&lt;/a&gt;&lt;/p&gt;'':112C ''/strong'':5C,117C ''also'':120C ''anoth'':8C ''br'':59C ''case'':62C ''classifi'':141C ''compar'':13C ''could'':64C ''cross'':115C,130C ''cross-valid'':129C ''curs'':31C ''data'':52C ''dataset'':21C,42C ''dimension'':3C,33C,66C ''due'':28C ''e.g'':34C ''error'':15C,37C,48C ''evalu'':136C ''github'':102C ''github.com'':108C,111C ''github.com/rasbt/pattern_classification'':107C ''github.com/rasbt/pattern_classification&lt;/a&gt;&lt;/p&gt;'':110C ''href'':92C,106C ''import'':9C ''indic'':58C ''interest'':79C ''larger'':45C ''lda'':73C,85C ''look'':127C ''manner'':146C ''may'':122C ''much'':44C ''object'':145C ''one'':57C ''order'':134C ''overfit'':27C ''p'':1C,7C,75C,113C,119C ''pca'':71C,84C ''perform'':138C ''procedur'':10C ''rate'':16C,38C ''reduct'':4C,67C ''repo'':103C ''sebastianraschka.com'':94C,97C ''sebastianraschka.com/index.html#machine_learning'':93C ''sebastianraschka.com/index.html#machine_learning&lt;/a&gt;'':96C ''see'':23C ''set'':53C ''strong'':2C,114C ''take'':125C ''techniqu'':68C,89C,132C ''test'':20C,41C ''train'':18C,51C ''tri'':65C ''valid'':116C,131C ''want'':123C ''would'':55C ''written'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (771, 2, '2014-07-17 18:25:44.683', 4, NULL, NULL, '&lt;p&gt;From what I heard, Pylearn2 might be currently the library of choice for most people. This reminds me of a recent blog post a few month ago that lists all the different machine learning libraries with a short explanation&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.cbinsights.com/blog/python-tools-machine-learning" rel="nofollow"&gt;https://www.cbinsights.com/blog/python-tools-machine-learning&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The section you might be interested in here would be "Deep Learning". About Pylearn2, he writes&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;PyLearn2&lt;/p&gt;
  
  &lt;p&gt;There is another library built on top of Theano, called PyLearn2 which
  brings modularity and configurability to Theano where you could create
  your neural network through different configuration files so that it
  would be easier to experiment different parameters. Arguably, it
  provides more modularity by separating the parameters and properties
  of neural network to the configuration file.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/blockquote'':134C ''/blog/python-tools-machine-learning'':47C ''/blog/python-tools-machine-learning&lt;/a&gt;&lt;/p&gt;'':52C ''/p'':41C,70C,74C,133C ''ago'':28C ''anoth'':78C ''arguabl'':115C ''blockquot'':71C ''blog'':23C ''bring'':88C ''built'':80C ''call'':85C ''choic'':13C ''configur'':91C,103C,131C ''could'':96C ''creat'':97C ''current'':9C ''deep'':64C ''differ'':33C,102C,113C ''easier'':110C ''experi'':112C ''explan'':40C ''file'':104C,132C ''heard'':5C ''href'':44C ''interest'':59C ''learn'':35C,65C ''librari'':11C,36C,79C ''list'':30C ''machin'':34C ''might'':7C,57C ''modular'':89C,119C ''month'':27C ''network'':100C,128C ''neural'':99C,127C ''nofollow'':49C ''p'':1C,42C,53C,72C,75C ''paramet'':114C,123C ''peopl'':16C ''post'':24C ''properti'':125C ''provid'':117C ''pylearn2'':6C,67C,73C,86C ''recent'':22C ''rel'':48C ''remind'':18C ''section'':55C ''separ'':121C ''short'':39C ''theano'':84C,93C ''top'':82C ''would'':62C,108C ''write'':69C ''www.cbinsights.com'':46C,51C ''www.cbinsights.com/blog/python-tools-machine-learning'':45C ''www.cbinsights.com/blog/python-tools-machine-learning&lt;/a&gt;&lt;/p&gt;'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (774, 2, '2014-07-18 04:50:58.287', 2, NULL, NULL, '&lt;p&gt;The following &lt;strong&gt;general&lt;/strong&gt; answer is my uneducated guess, so take it with grain of salt. Hopefully, it makes sense. I think that the best way to describe or analyze &lt;em&gt;experiments&lt;/em&gt; (as any other &lt;em&gt;systems&lt;/em&gt;, in general) is to build their &lt;strong&gt;statistical (multivariate) models&lt;/strong&gt; and evaluate them. Depending on whether &lt;em&gt;environments&lt;/em&gt; for your set of experiments are represented by the &lt;strong&gt;same model or different&lt;/strong&gt;, I see the following &lt;strong&gt;approaches&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;1) &lt;strong&gt;Single model approach.&lt;/strong&gt; &lt;em&gt;Define&lt;/em&gt; experiments'' statistical model for all environments (dependent and independent variables, data types, assumptions, constraints). &lt;em&gt;Analyze&lt;/em&gt; it (most likely, using &lt;em&gt;regression analysis&lt;/em&gt;). &lt;em&gt;Compare&lt;/em&gt; results &lt;strong&gt;across variables&lt;/strong&gt;, which determine (influence) different environments.&lt;/p&gt;

&lt;p&gt;2) &lt;strong&gt;Multiple models approach.&lt;/strong&gt; The same steps as previous case, but &lt;em&gt;compare&lt;/em&gt; results &lt;strong&gt;across models&lt;/strong&gt;, corresponding to different environments.&lt;/p&gt;
', NULL, NULL, '''/em'':35C,41C,61C,94C,111C,119C,122C,150C ''/p'':84C,133C,160C ''/strong'':6C,52C,76C,83C,91C,127C,140C,155C ''1'':86C ''2'':135C ''across'':125C,153C ''analysi'':118C ''analyz'':32C,110C ''answer'':7C ''approach'':82C,90C,139C ''assumpt'':107C ''best'':27C ''build'':46C ''case'':146C ''compar'':121C,149C ''constraint'':108C ''correspond'':156C ''data'':105C ''defin'':93C ''depend'':56C,101C ''describ'':30C ''determin'':129C ''differ'':75C,131C,158C ''em'':33C,39C,59C,92C,109C,116C,120C,148C ''environ'':60C,100C,132C,159C ''evalu'':54C ''experi'':34C,66C,95C ''follow'':3C,80C ''general'':5C,43C ''grain'':16C ''guess'':11C ''hope'':19C ''independ'':103C ''influenc'':130C ''like'':114C ''make'':21C ''model'':51C,73C,89C,97C,138C,154C ''multipl'':137C ''multivari'':50C ''p'':1C,85C,134C ''previous'':145C ''regress'':117C ''repres'':68C ''result'':123C,151C ''salt'':18C ''see'':78C ''sens'':22C ''set'':64C ''singl'':88C ''statist'':49C,96C ''step'':143C ''strong'':4C,48C,71C,81C,87C,124C,136C,152C ''system'':40C ''take'':13C ''think'':24C ''type'':106C ''uneduc'':10C ''use'':115C ''variabl'':104C,126C ''way'':28C ''whether'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (775, 2, '2014-07-18 07:43:56.823', 9, NULL, NULL, '&lt;p&gt;Be sure to check out &lt;a href="https://www.docker.com/"&gt;docker&lt;/a&gt;! And in general, all the other good things that software engineering has created along decades for ensuring isolation and reproductibility. &lt;/p&gt;

&lt;p&gt;I would like to stress that it is not enough to have &lt;em&gt;just&lt;/em&gt; reproducible workflows, but also &lt;em&gt;easy&lt;/em&gt; to reproduce workflows. Let me show what I mean. Suppose that your project uses Python, a database X and Scipy. Most surely you will be using a specific library to connect to your database from Python, and Scipy will be in turn using some sparse algebraic routines. This is by all means a very simple setup, but not entirely simple to setup, pun intended. If somebody wants to execute your scripts, she will have to install all the dependencies. Or worse, she might have incompatible versions of it already installed. Fixing those things takes time. It will also take time to you if you at some moment need to move your computations to a cluster, to a different cluster, or to some cloud servers. &lt;/p&gt;

&lt;p&gt;Here is where I find docker useful. Docker is a way to formalize and compile recipes for binary environments. You can write the following in a dockerfile (I''m using here plain English instead of the Dockerfile syntax):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Start with a basic binary environment, like Ubuntu''s&lt;/li&gt;
&lt;li&gt;Install libsparse-dev&lt;/li&gt;
&lt;li&gt;(Pip) Install numpy and scipy&lt;/li&gt;
&lt;li&gt;Install X&lt;/li&gt;
&lt;li&gt;Install libX-dev&lt;/li&gt;
&lt;li&gt;(Pip) Install python-X&lt;/li&gt;
&lt;li&gt;Install IPython-Notebook&lt;/li&gt;
&lt;li&gt;Copy my python scripts/notebooks to my binary environment, these datafiles, and these configurations to do other miscellaneous things. To ensure reproductibility, copy them from a named url instead of a local file. &lt;/li&gt;
&lt;li&gt;Maybe run IPython-Notebook.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Some of the lines will be installing things in Python using pip, since pip can do a very clean work in selecting specific package versions. Check it out too!&lt;/p&gt;

&lt;p&gt;And that''s it. If after you create your Dockerfile it can be built, then it can be built anywhere, by anybody (provided they also have access to your project-specific files, e.g. because you put them in a public url referenced from the Dockerfile). What is best, you can upload the resulting environment (called an "image") to a public or private server (called a "register") for other people to use. So, when you publish your workflow, you have both a fully reproducible recipe in the form of a Dockerfile, and an easy way for you or other people to reproduce what you do:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;docker run dockerregistery.thewheezylab.org/nowyouwillbelieveme
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Or if they want to poke around in your scripts and so forth:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;docker run -i -t dockerregistery.thewheezylab.org/nowyouwillbelieveme /bin/bash
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':11C ''/bin/bash'':482C ''/code'':456C,483C ''/em'':48C,55C ''/li'':232C,238C,245C,249C,255C,262C,268C,302C,309C ''/nowyouwillbelieveme'':455C,481C ''/p'':32C,180C,220C,341C,448C,472C ''/pre'':457C,484C ''/ul'':310C ''access'':369C ''algebra'':101C ''along'':25C ''alreadi'':144C ''also'':52C,153C,367C ''anybodi'':364C ''anywher'':362C ''around'':465C ''basic'':226C ''best'':391C ''binari'':199C,227C,276C ''built'':356C,361C ''call'':398C,407C ''check'':5C,337C ''clean'':330C ''cloud'':178C ''cluster'':170C,174C ''code'':450C,474C ''compil'':196C ''comput'':167C ''configur'':282C ''connect'':86C ''copi'':270C,291C ''creat'':24C,350C ''databas'':72C,89C ''datafil'':279C ''decad'':26C ''depend'':134C ''dev'':237C,254C ''differ'':173C ''docker'':10C,187C,189C,451C,475C ''dockerfil'':208C,218C,352C,388C,433C ''dockerregistery.thewheezylab.org'':454C,480C ''dockerregistery.thewheezylab.org/nowyouwillbelieveme'':453C,479C ''e.g'':376C ''easi'':54C,436C ''em'':46C,53C ''engin'':22C ''english'':214C ''enough'':43C ''ensur'':28C,289C ''entir'':114C ''environ'':200C,228C,277C,397C ''execut'':124C ''file'':301C,375C ''find'':186C ''fix'':146C ''follow'':205C ''form'':430C ''formal'':194C ''forth'':471C ''fulli'':425C ''general'':14C ''good'':18C ''href'':8C ''imag'':400C ''incompat'':140C ''instal'':131C,145C,234C,241C,247C,251C,258C,264C,318C ''instead'':215C,297C ''intend'':119C ''ipython'':266C,307C ''ipython-notebook'':265C,306C ''isol'':29C ''let'':59C ''li'':222C,233C,239C,246C,250C,256C,263C,269C,303C ''librari'':84C ''libspars'':236C ''libsparse-dev'':235C ''libx'':253C ''libx-dev'':252C ''like'':36C,229C ''line'':315C ''local'':300C ''m'':210C ''mayb'':304C ''mean'':64C,107C ''might'':138C ''miscellan'':286C ''moment'':162C ''move'':165C ''name'':295C ''need'':163C ''notebook'':267C,308C ''numpi'':242C ''p'':1C,33C,181C,311C,342C,458C ''packag'':335C ''peopl'':412C,442C ''pip'':240C,257C,323C,325C ''plain'':213C ''poke'':464C ''pre'':449C,473C ''privat'':405C ''project'':68C,373C ''project-specif'':372C ''provid'':365C ''public'':383C,403C ''publish'':418C ''pun'':118C ''put'':379C ''python'':70C,91C,260C,272C,321C ''python-x'':259C ''recip'':197C,427C ''referenc'':385C ''regist'':409C ''reproduc'':49C,57C,426C,444C ''reproduct'':31C,290C ''result'':396C ''routin'':102C ''run'':305C,452C,476C ''scipi'':75C,93C,244C ''script'':126C,468C ''scripts/notebooks'':273C ''select'':333C ''server'':179C,406C ''setup'':111C,117C ''show'':61C ''simpl'':110C,115C ''sinc'':324C ''softwar'':21C ''somebodi'':121C ''spars'':100C ''specif'':83C,334C,374C ''start'':223C ''stress'':38C ''suppos'':65C ''sure'':3C,77C ''syntax'':219C ''take'':149C,154C ''thing'':19C,148C,287C,319C ''time'':150C,155C ''turn'':97C ''ubuntu'':230C ''ul'':221C ''upload'':394C ''url'':296C,384C ''use'':69C,81C,98C,188C,211C,322C,414C ''version'':141C,336C ''want'':122C,462C ''way'':192C,437C ''work'':331C ''workflow'':50C,58C,420C ''wors'':136C ''would'':35C ''write'':203C ''www.docker.com'':9C ''x'':73C,248C,261C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (776, 2, '2014-07-18 15:24:38.007', 0, NULL, NULL, '&lt;p&gt;I am far from an expert, but my understanding of the subject tells me that R (superb in statistics) and e.g. Python (superb in several of those things where R is lacking) complements each other quite well (as pointed out by previous posts). &lt;/p&gt;
', NULL, NULL, '''/p'':45C ''complement'':34C ''e.g'':22C ''expert'':7C ''far'':4C ''lack'':33C ''p'':1C ''point'':40C ''post'':44C ''previous'':43C ''python'':23C ''quit'':37C ''r'':17C,31C ''sever'':26C ''statist'':20C ''subject'':13C ''superb'':18C,24C ''tell'':14C ''thing'':29C ''understand'':10C ''well'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (777, 1, '2014-07-18 22:29:05.017', 1, 81, 'Getting GitHub repository information by different criteria', '&lt;p&gt;New to the Data Science forum, and first poster here!&lt;/p&gt;

&lt;p&gt;This may be kind of a specific question (hopefully not too much so), but one I''d imagine others might be interested in.&lt;/p&gt;

&lt;p&gt;I''m looking for a way to basically query GitHub with something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Give me a collection of all of the public repositories that have more than 10 stars, at
least two forks, and more than three committers.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The result could take any viable form: a JSON data dump, a URL to the web page, etc. It more than likely will consist of information from 10,000 repos or something large.&lt;/p&gt;

&lt;p&gt;Is this sort of thing possible using the API or some other pre-built way, or am I going to have to build out my own custom solution where I try to scrape every page? If so, how feasible is this and how might I approach it?&lt;/p&gt;
', 'bigdata  data-mining  python  dataset', NULL, '''/code'':94C ''/p'':25C,50C,66C,130C,180C ''/pre'':95C ''000'':125C ''10'':83C,124C ''api'':140C ''approach'':178C ''basic'':59C ''bigdata'':8B ''build'':155C ''built'':146C ''code'':68C ''collect'':72C ''committ'':93C ''consist'':120C ''could'':99C ''criteria'':7A ''custom'':159C ''d'':43C ''data'':10B,18C,106C ''data-min'':9B ''dataset'':13B ''differ'':6A ''dump'':107C ''etc'':114C ''everi'':166C ''feasibl'':171C ''first'':22C ''fork'':88C ''form'':103C ''forum'':20C ''get'':1A ''github'':2A,61C ''give'':69C ''go'':151C ''hope'':35C ''imagin'':44C ''inform'':4A,122C ''interest'':48C ''json'':105C ''kind'':30C ''larg'':129C ''least'':86C ''like'':64C,118C ''look'':54C ''m'':53C ''may'':28C ''might'':46C,176C ''mine'':11B ''much'':38C ''new'':15C ''one'':41C ''other'':45C ''p'':14C,26C,51C,96C,131C ''page'':113C,167C ''possibl'':137C ''poster'':23C ''pre'':67C,145C ''pre-built'':144C ''public'':77C ''python'':12B ''queri'':60C ''question'':34C ''repo'':126C ''repositori'':3A,78C ''result'':98C ''scienc'':19C ''scrape'':165C ''solut'':160C ''someth'':63C,128C ''sort'':134C ''specif'':33C ''star'':84C ''take'':100C ''thing'':136C ''three'':92C ''tri'':163C ''two'':87C ''url'':109C ''use'':138C ''viabl'':102C ''way'':57C,147C ''web'':112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (778, 1, '2014-07-18 22:34:48.08', 11, 1148, 'Is Python suitable for big data', '&lt;p&gt;I read in this post &lt;a href="https://datascience.stackexchange.com/questions/41/is-the-r-language-suitable-for-big-data"&gt;Is the R language suitable for Big Data&lt;/a&gt; that big data constitutes &lt;code&gt;5TB&lt;/code&gt;, and while it does a good job of providing information about the feasibility of working with this type of data in &lt;code&gt;R&lt;/code&gt; it provides very little information about &lt;code&gt;Python&lt;/code&gt;. I was wondering if &lt;code&gt;Python&lt;/code&gt; can work with this much data as well. &lt;/p&gt;
', 'bigdata  python', NULL, '''/a'':28C ''/code'':35C,59C,68C,75C ''/p'':84C ''/questions/41/is-the-r-language-suitable-for-big-data'':19C ''5tb'':34C ''big'':5A,26C,30C ''bigdata'':7B ''code'':33C,57C,66C,73C ''constitut'':32C ''data'':6A,27C,31C,55C,81C ''datascience.stackexchange.com'':18C ''datascience.stackexchange.com/questions/41/is-the-r-language-suitable-for-big-data'':17C ''feasibl'':48C ''good'':41C ''href'':16C ''inform'':45C,64C ''job'':42C ''languag'':23C ''littl'':63C ''much'':80C ''p'':9C ''post'':14C ''provid'':44C,61C ''python'':2A,8B,67C,74C ''r'':22C,58C ''read'':11C ''suitabl'':3A,24C ''type'':53C ''well'':83C ''wonder'':71C ''work'':50C,77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (779, 2, '2014-07-18 22:59:39.1', 2, NULL, NULL, '&lt;p&gt;I believe the language itself has little to do with performance capabilities, when it comes to large data. What matters is:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;How large is the data actually&lt;/li&gt;
&lt;li&gt;What processing are you going to perform on it&lt;/li&gt;
&lt;li&gt;What hardware are you going to use&lt;/li&gt;
&lt;li&gt;Which are the specific libraries that you plan to use&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Anyway, Python is well adopted in data science communities.&lt;/p&gt;
', NULL, NULL, '''/li'':32C,43C,52C,64C ''/p'':23C,76C ''/ul'':65C ''actual'':31C ''adopt'':71C ''anyway'':67C ''believ'':3C ''capabl'':13C ''come'':16C ''communiti'':75C ''data'':19C,30C,73C ''go'':38C,49C ''hardwar'':46C ''languag'':5C ''larg'':18C,27C ''li'':25C,33C,44C,53C ''librari'':58C ''littl'':8C ''matter'':21C ''p'':1C,66C ''perform'':12C,40C ''plan'':61C ''process'':35C ''python'':68C ''scienc'':74C ''specif'':57C ''ul'':24C ''use'':51C,63C ''well'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5626, 2, '2015-04-27 11:58:50.443', 0, NULL, NULL, '&lt;p&gt;Another good normalization is &lt;a href="http://en.wikipedia.org/wiki/Standard_score" rel="nofollow"&gt;zScore&lt;/a&gt; normalization. Was already implemented in python in the module &lt;a href="http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.mstats.zscore.html" rel="nofollow"&gt;scipy.stats&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':14C,32C ''/doc/scipy-0.14.0/reference/generated/scipy.stats.mstats.zscore.html'':28C ''/p'':33C ''/wiki/standard_score'':10C ''alreadi'':17C ''anoth'':2C ''docs.scipy.org'':27C ''docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.mstats.zscore.html'':26C ''en.wikipedia.org'':9C ''en.wikipedia.org/wiki/standard_score'':8C ''good'':3C ''href'':7C,25C ''implement'':18C ''modul'':23C ''nofollow'':12C,30C ''normal'':4C,15C ''p'':1C ''python'':20C ''rel'':11C,29C ''scipy.stats'':31C ''zscore'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (780, 2, '2014-07-19 02:19:46.53', 10, NULL, NULL, '&lt;p&gt;Some good answers here. I would like to join the discussion by adding the following two &lt;strong&gt;notes&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;1) The question''s emphasis on the &lt;strong&gt;volume of data&lt;/strong&gt; while referring to &lt;em&gt;Big Data&lt;/em&gt; is certainly understandable and valid, especially considering the &lt;strong&gt;problem&lt;/strong&gt; of data volume growth &lt;strong&gt;outpacing&lt;/strong&gt; technological capacities'' exponential growth per &lt;em&gt;Moore''s Law&lt;/em&gt; (&lt;a href="http://en.wikipedia.org/wiki/Moore%27s_law" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Moore%27s_law&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;2) Having said that, it is important to remember about other aspects of big data concept, based on &lt;em&gt;Gartner&lt;/em&gt;''s definition (emphasis mine - AB): "&lt;em&gt;Big data&lt;/em&gt; is high &lt;strong&gt;volume&lt;/strong&gt;, high &lt;strong&gt;velocity&lt;/strong&gt;, and/or high &lt;strong&gt;variety&lt;/strong&gt; information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization." (usually referred to as the "&lt;strong&gt;3Vs model&lt;/strong&gt;"). I mention this, because it forces data scientists and other analysts to look for and use R packages that focus on &lt;strong&gt;other than volume&lt;/strong&gt; aspects of big data (enabled by the &lt;strong&gt;richness&lt;/strong&gt; of enormous &lt;em&gt;R ecosystem&lt;/em&gt;).&lt;/p&gt;

&lt;p&gt;3) While existing answers mention some R packages, related to big data, for a more &lt;strong&gt;comprehensive coverage&lt;/strong&gt;, I''d recommend to refer to &lt;em&gt;CRAN Task View&lt;/em&gt; &lt;strong&gt;"High-Performance and Parallel Computing with R"&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/views/HighPerformanceComputing.html" rel="nofollow"&gt;http://cran.r-project.org/web/views/HighPerformanceComputing.html&lt;/a&gt;), in particular, sections &lt;strong&gt;"Parallel computing: Hadoop"&lt;/strong&gt; and &lt;strong&gt;"Large memory and out-of-memory data"&lt;/strong&gt;.&lt;/p&gt;
', NULL, NULL, '''/em'':41C,69C,101C,110C,193C,225C ''/p'':21C,194C,265C ''/strong'':20C,34C,52C,59C,115C,119C,124C,151C,177C,187C,214C,235C,253C,264C ''/web/views/highperformancecomputing.html'':240C ''/web/views/highperformancecomputing.html&lt;/a&gt;),'':245C ''/wiki/moore%27s_law'':74C ''/wiki/moore%27s_law&lt;/a&gt;).&lt;/p&gt;'':79C ''1'':23C ''2'':81C ''3'':196C ''3vs'':149C ''ab'':106C ''ad'':14C ''analyst'':162C ''and/or'':120C ''answer'':4C,199C ''aspect'':92C,178C ''asset'':126C ''base'':97C ''big'':39C,94C,108C,180C,206C ''capac'':61C ''certain'':43C ''comprehens'':212C ''comput'':232C,251C ''concept'':96C ''consid'':48C ''coverag'':213C ''cran'':222C ''cran.r-project.org'':239C,244C ''cran.r-project.org/web/views/highperformancecomputing.html'':238C ''cran.r-project.org/web/views/highperformancecomputing.html&lt;/a&gt;),'':243C ''d'':216C ''data'':33C,40C,54C,95C,109C,158C,181C,207C,263C ''decis'':136C ''definit'':103C ''discoveri'':139C ''discuss'':12C ''ecosystem'':192C ''em'':38C,65C,99C,107C,190C,221C ''emphasi'':27C,104C ''en.wikipedia.org'':73C,78C ''en.wikipedia.org/wiki/moore%27s_law'':72C ''en.wikipedia.org/wiki/moore%27s_law&lt;/a&gt;).&lt;/p&gt;'':77C ''enabl'':134C,182C ''enhanc'':135C ''enorm'':189C ''especi'':47C ''exist'':198C ''exponenti'':62C ''focus'':171C ''follow'':16C ''forc'':157C ''form'':130C ''gartner'':100C ''good'':3C ''growth'':56C,63C ''hadoop'':252C ''high'':112C,116C,121C,228C ''high-perform'':227C ''href'':71C,237C ''import'':87C ''inform'':125C ''insight'':138C ''join'':10C ''larg'':256C ''law'':68C ''like'':8C ''look'':164C ''make'':137C ''memori'':257C,262C ''mention'':153C,200C ''mine'':105C ''model'':150C ''moor'':66C ''new'':129C ''nofollow'':76C,242C ''note'':19C ''optim'':142C ''out-of-memori'':259C ''outpac'':58C ''p'':1C,22C,80C,195C ''packag'':169C,203C ''parallel'':231C,250C ''particular'':247C ''per'':64C ''perform'':229C ''problem'':51C ''process'':132C,141C ''question'':25C ''r'':168C,191C,202C,234C ''recommend'':217C ''refer'':36C,144C,219C ''rel'':75C,241C ''relat'':204C ''rememb'':89C ''requir'':128C ''rich'':186C ''said'':83C ''scientist'':159C ''section'':248C ''strong'':18C,30C,50C,57C,113C,117C,122C,148C,173C,185C,211C,226C,249C,255C ''task'':223C ''technolog'':60C ''two'':17C ''understand'':44C ''use'':167C ''usual'':143C ''valid'':46C ''varieti'':123C ''veloc'':118C ''view'':224C ''volum'':31C,55C,114C,176C ''would'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8923, 2, '2015-11-19 13:17:24.097', 5, NULL, NULL, '&lt;p&gt;&lt;a href="https://github.com/pluskid/Mocha.jl"&gt;Mocha.jl&lt;/a&gt; - Mocha is a Deep Learning framework for Julia, inspired by the C++ framework Caffe.&lt;/p&gt;

&lt;p&gt;Project with good &lt;a href="http://mochajl.readthedocs.org/en/latest/"&gt;documentation&lt;/a&gt; and examples.
Can be run on CPU and GPU backend.&lt;/p&gt;
', NULL, NULL, '''/a'':8C,34C ''/en/latest/'':32C ''/p'':23C,45C ''/pluskid/mocha.jl'':6C ''backend'':44C ''c'':20C ''caff'':22C ''cpu'':41C ''deep'':12C ''document'':33C ''exampl'':36C ''framework'':14C,21C ''github.com'':5C ''github.com/pluskid/mocha.jl'':4C ''good'':27C ''gpu'':43C ''href'':3C,29C ''inspir'':17C ''julia'':16C ''learn'':13C ''mocha'':9C ''mocha.jl'':7C ''mochajl.readthedocs.org'':31C ''mochajl.readthedocs.org/en/latest/'':30C ''p'':1C,24C ''project'':25C ''run'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (781, 2, '2014-07-19 03:29:02.647', 12, NULL, NULL, '&lt;p&gt;To clarify, I feel like the original question references by OP probably isn''t be best for a SO-type format, but I will certainly represent &lt;code&gt;python&lt;/code&gt; in this particular case.&lt;/p&gt;

&lt;p&gt;Let me just start by saying that regardless of your data size, &lt;code&gt;python&lt;/code&gt; shouldn''t be your limiting factor. In fact, there are just a couple main issues that you''re going to run into dealing with large datasets:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Reading data into memory&lt;/strong&gt; - This is by far the most common issue faced in the world of big data. Basically, you can''t read in more data than you have memory (RAM) for. The best way to fix this is by making atomic operations on your data instead of trying to read everything in at once.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Storing data&lt;/strong&gt; - This is actually just another form of the earlier issue, by the time to get up to about &lt;code&gt;1TB&lt;/code&gt;, you start having to look elsewhere for storage. AWS S3 is the most common resource, and &lt;code&gt;python&lt;/code&gt; has the fantastic &lt;code&gt;boto&lt;/code&gt; library to facilitate leading with large pieces of data.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Network latency&lt;/strong&gt; - Moving data around between different services is going to be your bottleneck. There''s not a huge amount you can do to fix this, other than trying to pick co-located resources and plugging into the wall.&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (782, 2, '2014-07-19 03:42:34.433', 0, NULL, NULL, '&lt;p&gt;My limited understanding, based on brief browsing &lt;em&gt;GitHub API&lt;/em&gt; documentation, is that currently there is NO &lt;strong&gt;single API request&lt;/strong&gt; that supports &lt;strong&gt;all&lt;/strong&gt; your listed criteria &lt;strong&gt;at once&lt;/strong&gt;. However, I think that you could use the following sequence in order to achieve the goal from your example (at least, I would use this approach):&lt;/p&gt;

&lt;p&gt;1) &lt;strong&gt;Request&lt;/strong&gt; information on all public repositories (API returns &lt;em&gt;summary representations&lt;/em&gt; only): &lt;a href="https://developer.github.com/v3/repos/#list-all-public-repositories" rel="nofollow"&gt;https://developer.github.com/v3/repos/#list-all-public-repositories&lt;/a&gt;;&lt;/p&gt;

&lt;p&gt;2) &lt;strong&gt;Loop&lt;/strong&gt; through the list of all public repositories retrieved in step 1, requesting individual resources, and save it as new (detailed) list (this returns &lt;em&gt;detailed representations&lt;/em&gt;, in other words, all attributes): &lt;a href="https://developer.github.com/v3/repos/#get" rel="nofollow"&gt;https://developer.github.com/v3/repos/#get&lt;/a&gt;;&lt;/p&gt;

&lt;p&gt;3) &lt;strong&gt;Loop&lt;/strong&gt; through the detailed list of all repositories, filtering corresponding fields by your criteria. For your example request, you''d be interested in the following attributes of the &lt;strong&gt;parent&lt;/strong&gt; object: &lt;em&gt;stargazers_count&lt;/em&gt;, &lt;em&gt;forks_count&lt;/em&gt;. In order to filter the repositories by number of committers, you could use a separate API: &lt;a href="https://developer.github.com/v3/repos/#list-contributors" rel="nofollow"&gt;https://developer.github.com/v3/repos/#list-contributors&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Updates or comments from people more familiar with GitHub API are welcome!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (783, 1, '2014-07-19 05:27:22.773', 9, 314, 'Data visualization for pattern analysis (language-independent, but R preferred)', '&lt;p&gt;I want to plot the bytes from a disk image in order to understand a pattern in them. This is mainly an academic task, since I''m almost sure this pattern was created by a disk testing program, but I''d like to reverse-engineer it anyway.&lt;/p&gt;

&lt;p&gt;I already know that the pattern is aligned, with a periodicity of 256 characters.&lt;/p&gt;

&lt;p&gt;I can envision two ways of visualizing this information: either a 16x16 plane viewed through time (3 dimensions), where each pixel''s color is the ASCII code for the character, or a 256 pixel line for each period (2 dimensions).&lt;/p&gt;

&lt;p&gt;This is a snapshot of the pattern (you can see more than one), seen through &lt;code&gt;xxd&lt;/code&gt; (32x16):&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/zOFSK.gif" alt="Pattern to analyze"&gt;&lt;/p&gt;

&lt;p&gt;Either way, I am trying to find a way of visualizing this information. This probably isn''t hard for anyone into signal analysis, but I can''t seem to find a way using open-source software.&lt;/p&gt;

&lt;p&gt;I''d like to avoid Matlab or Mathematica and I''d prefer an answer in R, since I have been learning it recently, but nonetheless, any language is welcome.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Update, 2014-07-25: given Emre''s answer below, this is what the pattern looks like, given the first 30MB of the pattern, aligned at 512 instead of 256 (this alignment looks better):&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/4tDIA.png" alt="Graphical pattern"&gt;&lt;/p&gt;

&lt;p&gt;Any further ideas are welcome!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (796, 2, '2014-07-21 16:58:22.66', 7, NULL, NULL, '&lt;p&gt;There are couple off things you need to understand when dealing with Big data - &lt;/p&gt;

&lt;h3&gt;What is Big data?&lt;/h3&gt;

&lt;p&gt;You might be aware of famous V''s of Big data - Volume, Velocity, Variety... So, Python may not be suitable for all. And it goes with all data science tools available. You need to know which tool is good for what purpose.&lt;/p&gt;

&lt;p&gt;If dealing with large Volume of data:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Pig/Hive/Shark - Data cleaning and ETL work&lt;/li&gt;
&lt;li&gt;Hadoop/Spark - Distributed parallel computing&lt;/li&gt;
&lt;li&gt;Mahout/ML-Lib - Machine Learning&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Now, you can use R/Python in intermediate stages but you''ll realize that they become bottleneck in your entire process.&lt;/p&gt;

&lt;p&gt;If dealing with Velocity of data:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Kafka/Storm - High throughput system&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;People are trying to R/Python here but again it depends on kind of parallelism you want and your model complexity.&lt;/p&gt;

&lt;h3&gt;What sort of analysis you wish to do?&lt;/h3&gt;

&lt;p&gt;If your model demands the entire data to be first brought into memory then your model should not be complex because if the intermediate data is large then the code will break. And if you think of writing it into disk then you''ll face additional delay because disk read/write is slow as compared to RAM.&lt;/p&gt;

&lt;h2&gt;Conclusion&lt;/h2&gt;

&lt;p&gt;You can definitely use Python in Big data space (Definitely, since people are trying with R, why not Python) but know your data and business requirement first. There may be better tools available for same and always remember:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Your tools shouldnât determine how you answer questions. Your questions should determine what tools you use.&lt;/p&gt;
&lt;/blockquote&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (784, 1, '2014-07-19 18:31:36.573', 5, 269, 'Why should I care about seasonal data when I forecast?', '&lt;p&gt;I have a timeseries with hourly gas consumption. I want to use &lt;a href="http://en.wikipedia.org/wiki/Autoregressive%E2%80%93moving-average_model"&gt;ARMA&lt;/a&gt;/&lt;a href="http://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average"&gt;ARIMA&lt;/a&gt; to forecast the consumption on the next hour, basing on the previous. Why should I analyze/find the seasonality (with &lt;a href="https://www.otexts.org/fpp/6/5"&gt;Seasonal and Trend decomposition using Loess&lt;/a&gt; (STL)?)?&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/hYyH8.png" alt="enter image description here"&gt;&lt;/p&gt;
', 'machine-learning  time-series', NULL, '''/a'':36C,43C,74C ''/fpp/6/5'':67C ''/hyyh8.png'':82C ''/p'':76C,88C ''/wiki/autoregressive%e2%80%93moving-average_model'':34C ''/wiki/autoregressive_integrated_moving_average'':41C ''alt'':83C ''analyze/find'':59C ''arima'':42C ''arma'':35C ''base'':52C ''care'':4A ''consumpt'':25C,47C ''data'':7A ''decomposit'':71C ''descript'':86C ''en.wikipedia.org'':33C,40C ''en.wikipedia.org/wiki/autoregressive%e2%80%93moving-average_model'':32C ''en.wikipedia.org/wiki/autoregressive_integrated_moving_average'':39C ''enter'':84C ''forecast'':10A,45C ''gas'':24C ''hour'':23C,51C ''href'':31C,38C,64C ''i.stack.imgur.com'':81C ''i.stack.imgur.com/hyyh8.png'':80C ''imag'':85C ''img'':78C ''learn'':13B ''loess'':73C ''machin'':12B ''machine-learn'':11B ''next'':50C ''p'':17C,77C ''previous'':55C ''season'':6A,61C,68C ''seri'':16B ''src'':79C ''stl'':75C ''time'':15B ''time-seri'':14B ''timeseri'':21C ''trend'':70C ''use'':29C,72C ''want'':27C ''www.otexts.org'':66C ''www.otexts.org/fpp/6/5'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (800, 2, '2014-07-21 21:29:26.27', 3, NULL, NULL, '&lt;p&gt;One benefit of the schema-free NoSQL approach is that you don''t commit prematurely and you can apply the right schema at query time using an appropriate tool like &lt;a href="http://incubator.apache.org/drill/" rel="nofollow"&gt;Apache Drill&lt;/a&gt;. See &lt;a href="http://wiki.apache.org/incubator/DrillProposal?action=AttachFile&amp;amp;do=get&amp;amp;target=Drill+slides.pdf" rel="nofollow"&gt;this presentation&lt;/a&gt; for details. MySQL wouldn''t be my first choice in a big data setting.&lt;/p&gt;
', NULL, NULL, '''/a'':42C,53C ''/drill/'':37C ''/incubator/drillproposal?action=attachfile&amp;amp;do=get&amp;amp;target=drill+slides.pdf'':48C ''/p'':68C ''apach'':40C ''appli'':21C ''approach'':10C ''appropri'':30C ''benefit'':3C ''big'':65C ''choic'':62C ''commit'':16C ''data'':66C ''detail'':55C ''drill'':41C ''first'':61C ''free'':8C ''href'':34C,45C ''incubator.apache.org'':36C ''incubator.apache.org/drill/'':35C ''like'':32C ''mysql'':56C ''nofollow'':39C,50C ''nosql'':9C ''one'':2C ''p'':1C ''prematur'':17C ''present'':52C ''queri'':26C ''rel'':38C,49C ''right'':23C ''schema'':7C,24C ''schema-fre'':6C ''see'':43C ''set'':67C ''time'':27C ''tool'':31C ''use'':28C ''wiki.apache.org'':47C ''wiki.apache.org/incubator/drillproposal?action=attachfile&amp;amp;do=get&amp;amp;target=drill+slides.pdf'':46C ''wouldn'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (785, 2, '2014-07-19 18:47:38.787', 1, NULL, NULL, '&lt;p&gt;I know almost nothing about signal analysis, but 2-dimensional visualization could be easily done using R. Particularly you will need &lt;code&gt;reshape2&lt;/code&gt; and &lt;code&gt;ggplot2&lt;/code&gt; packages. Assuming your data is &lt;strong&gt;wide&lt;/strong&gt; (e.g. [n X 256] size), first you need to transform it to &lt;a href="http://www.cookbook-r.com/Manipulating_data/Converting_data_between_wide_and_long_format/" rel="nofollow"&gt;&lt;strong&gt;long&lt;/strong&gt;&lt;/a&gt; format using &lt;code&gt;melt()&lt;/code&gt; function from &lt;code&gt;reshape2&lt;/code&gt; package. Then use &lt;a href="http://docs.ggplot2.org/current/geom_tile.html" rel="nofollow"&gt;&lt;code&gt;geom_tile&lt;/code&gt;&lt;/a&gt; geometry from &lt;code&gt;ggplot2&lt;/code&gt;. Here is a nice &lt;a href="http://www.r-bloggers.com/simplest-possible-heatmap-with-ggplot2/" rel="nofollow"&gt;recipe&lt;/a&gt; with &lt;a href="https://gist.github.com/dsparks/3710171" rel="nofollow"&gt;gist&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':60C,85C,103C,113C ''/code'':25C,29C,65C,70C,84C,90C ''/current/geom_tile.html'':78C ''/dsparks/3710171'':109C ''/manipulating_data/converting_data_between_wide_and_long_format/'':54C ''/p'':114C ''/simplest-possible-heatmap-with-ggplot2/'':99C ''/strong'':37C,59C ''2'':10C ''256'':41C ''almost'':4C ''analysi'':8C ''assum'':31C ''code'':23C,27C,63C,68C,81C,88C ''could'':13C ''data'':33C ''dimension'':11C ''docs.ggplot2.org'':77C ''docs.ggplot2.org/current/geom_tile.html'':76C ''done'':16C ''e.g'':38C ''easili'':15C ''first'':43C ''format'':61C ''function'':66C ''geom'':82C ''geometri'':86C ''ggplot2'':28C,89C ''gist'':112C ''gist.github.com'':108C ''gist.github.com/dsparks/3710171'':107C ''href'':51C,75C,96C,106C ''know'':3C ''long'':58C ''melt'':64C ''n'':39C ''need'':22C,45C ''nice'':94C ''nofollow'':56C,80C,101C,111C ''noth'':5C ''p'':1C ''packag'':30C,71C ''particular'':19C ''r'':18C ''recip'':102C ''rel'':55C,79C,100C,110C ''reshape2'':24C,69C ''signal'':7C ''size'':42C ''strong'':35C,57C ''tile'':83C ''transform'':47C ''use'':17C,62C,73C ''visual'':12C ''wide'':36C ''www.cookbook-r.com'':53C ''www.cookbook-r.com/manipulating_data/converting_data_between_wide_and_long_format/'':52C ''www.r-bloggers.com'':98C ''www.r-bloggers.com/simplest-possible-heatmap-with-ggplot2/'':97C ''x'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (787, 1, '2014-07-19 20:51:58.527', 2, 1276, 'Hadoop Resource Manager Won''t Start', '&lt;p&gt;I am a relatively new user to Hadoop (using version 2.4.1). I installed hadoop on my first node without a hitch, but I can''t seem to get the Resource Manager to start on my second node. &lt;/p&gt;

&lt;p&gt;I cleared up some "shared library" problems by adding this to yarn-env.sh and hadoop-env.sh:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;export HADOOP_HOME="/usr/local/hadoop"&lt;/p&gt;
  
  &lt;p&gt;export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib"&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I also added this to hadoop-env.sh:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;export HADOOP_COMMON_LIB_NATIVE_DIR=${HADOOP_PREFIX}/lib/native&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;based on the advice of this post at horton works &lt;a href="http://hortonworks.com/community/forums/topic/hdfs-tmp-dir-issue/" rel="nofollow"&gt;http://hortonworks.com/community/forums/topic/hdfs-tmp-dir-issue/&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;That cleared up all of my error messages; when I run /sbin/start-yarn.sh I get this:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;starting yarn daemons&lt;/p&gt;
  
  &lt;p&gt;starting resourcemanager, 
  logging to /usr/local/hadoop/logs/yarn-hduser-resourcemanager-HdNode.out&lt;/p&gt;
  
  &lt;p&gt;localhost: starting nodemanager, 
  logging to /usr/local/hadoop/logs/yarn-hduser-nodemanager-HdNode.out&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The only problem is, JPS says that the Resource Manager isn''t running. &lt;/p&gt;

&lt;p&gt;What''s going on here?&lt;/p&gt;
', 'hadoop', NULL, '''/blockquote'':78C,99C,160C ''/community/forums/topic/hdfs-tmp-dir-issue/'':115C ''/community/forums/topic/hdfs-tmp-dir-issue/&lt;/a&gt;'':120C ''/lib/native'':97C ''/p'':46C,62C,69C,77C,86C,98C,121C,138C,144C,151C,159C,175C,182C ''/sbin/start-yarn.sh'':134C ''/usr/local/hadoop'':68C ''/usr/local/hadoop/logs/yarn-hduser-nodemanager-hdnode.out'':158C ''/usr/local/hadoop/logs/yarn-hduser-resourcemanager-hdnode.out'':150C ''2.4.1'':19C ''ad'':56C,82C ''advic'':104C ''also'':81C ''base'':101C ''blockquot'':63C,87C,139C ''clear'':49C,124C ''common'':91C ''daemon'':143C ''dir'':94C ''djava.library.path'':74C ''error'':129C ''export'':65C,71C,89C ''first'':25C ''get'':36C,136C ''go'':179C ''hadoop'':1A,7B,16C,22C,66C,72C,75C,90C,95C ''hadoop-env.sh'':61C,85C ''hitch'':29C ''home'':67C ''home/lib'':76C ''horton'':109C ''hortonworks.com'':114C,119C ''hortonworks.com/community/forums/topic/hdfs-tmp-dir-issue/'':113C ''hortonworks.com/community/forums/topic/hdfs-tmp-dir-issue/&lt;/a&gt;'':118C ''href'':112C ''instal'':21C ''isn'':172C ''jps'':166C ''lib'':92C ''librari'':53C ''localhost'':153C ''log'':148C,156C ''manag'':3A,39C,171C ''messag'':130C ''nativ'':93C ''new'':13C ''node'':26C,45C ''nodemanag'':155C ''nofollow'':117C ''opt'':73C ''p'':8C,47C,64C,70C,79C,88C,100C,122C,140C,145C,152C,161C,176C ''post'':107C ''prefix'':96C ''problem'':54C,164C ''rel'':116C ''relat'':12C ''resourc'':2A,38C,170C ''resourcemanag'':147C ''run'':133C,174C ''say'':167C ''second'':44C ''seem'':34C ''share'':52C ''start'':6A,41C,141C,146C,154C ''use'':17C ''user'':14C ''version'':18C ''without'':27C ''won'':4A ''work'':110C ''yarn'':142C ''yarn-env.sh'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (788, 2, '2014-07-20 21:37:27.857', 1, NULL, NULL, '&lt;p&gt;Check your version of JPS and make sure it''s the same as the version of java that you are running.  Sometimes you start out with an out of the box jdk install, upgrade, set alternatives for the java bin, but still have the original jps binary being referenced.&lt;/p&gt;

&lt;p&gt;Run &lt;code&gt;ps -ef |grep java&lt;/code&gt; and look for the resource manager threads.  Maybe it''s actually running.  If it is, try &lt;code&gt;update-alternatives --config jps&lt;/code&gt; to see what binary jps is pointing at and compare it with the java binary that you are using.&lt;/p&gt;

&lt;p&gt;If the resource manager is not running, it''s time to do some basic linux troubleshooting.  Check log files and barring that check actual command output.&lt;/p&gt;

&lt;p&gt;On the system I''m looking at now, the log files for resource manager are placed in the &lt;code&gt;hadoop-install/logs&lt;/code&gt; directory in &lt;code&gt;yarn-username-resourcemanager-hostname.log&lt;/code&gt; and &lt;code&gt;yarn-user-resourcemanager-hostname.out&lt;/code&gt;.  Your configuration may place them in /var/logs or what have you.  Also, have a look at the syslog.&lt;/p&gt;

&lt;p&gt;If the logs don''t yield any good information, which can happen, my process is to generally try to figure out the command line from the startup script (usually by prefixing the command line with &lt;code&gt;echo&lt;/code&gt;), and then trying to run the command directly to see the output as it comes out.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;I have actually run into this problem before, but I can''t remember the specific issue.  I''m sure the same result can manifest itself from a variety of problems.  Considering that you are as far as you are in the process of getting set up, I believe it''s likely to be a minor configuration issue.&lt;/p&gt;
', NULL, NULL, '''/code'':59C,82C,155C,160C,164C,222C ''/logs'':154C ''/p'':51C,102C,130C,183C,239C,299C ''/var/logs'':171C ''actual'':70C,127C,244C ''also'':176C ''altern'':37C,79C ''bar'':124C ''basic'':117C ''believ'':289C ''bin'':41C ''binari'':48C,86C,97C ''box'':32C ''check'':2C,120C,126C ''code'':54C,76C,150C,158C,162C,220C ''come'':237C ''command'':128C,207C,217C,229C ''compar'':92C ''config'':80C ''configur'':166C,297C ''consid'':272C ''direct'':230C ''directori'':156C ''echo'':221C ''ef'':56C ''far'':277C ''figur'':204C ''file'':122C,142C ''general'':201C ''get'':285C ''good'':192C ''grep'':57C ''hadoop'':152C ''hadoop-instal'':151C ''happen'':196C ''hr'':240C ''inform'':193C ''instal'':34C,153C ''issu'':257C,298C ''java'':18C,40C,58C,96C ''jdk'':33C ''jps'':6C,47C,81C,87C ''like'':292C ''line'':208C,218C ''linux'':118C ''log'':121C,141C,187C ''look'':61C,137C,179C ''m'':136C,259C ''make'':8C ''manag'':65C,107C,145C ''manifest'':265C ''may'':167C ''mayb'':67C ''minor'':296C ''origin'':46C ''output'':129C,234C ''p'':1C,52C,103C,131C,184C,241C ''place'':147C,168C ''point'':89C ''prefix'':215C ''problem'':248C,271C ''process'':198C,283C ''ps'':55C ''referenc'':50C ''rememb'':254C ''resourc'':64C,106C,144C ''result'':263C ''run'':22C,53C,71C,110C,227C,245C ''script'':212C ''see'':84C,232C ''set'':36C,286C ''sometim'':23C ''specif'':256C ''start'':25C ''startup'':211C ''still'':43C ''sure'':9C,260C ''syslog'':182C ''system'':134C ''thread'':66C ''time'':113C ''tri'':75C,202C,225C ''troubleshoot'':119C ''updat'':78C ''update-altern'':77C ''upgrad'':35C ''use'':101C ''usual'':213C ''varieti'':269C ''version'':4C,16C ''yarn-user-resourcemanager-hostname.out'':163C ''yarn-username-resourcemanager-hostname.log'':159C ''yield'':190C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2272, 2, '2014-10-14 07:02:47.5', 21, NULL, NULL, '&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.r-fiddle.org"&gt;R-fiddle&lt;/a&gt; provides an easy-to-use interactive R console that you can run from your browser. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/cZJu6.png" alt="screenshot-rfiddle"&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://roncloud.com"&gt;R On Cloud&lt;/a&gt; provides also a browser-embedded R-console&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ELpbR.png" alt="screenshot-roncloud"&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.getdatajoy.com"&gt;DataJoy&lt;/a&gt; provides easy to use, online data processing with Python and R (sign-up is required; the free basic plan has the following restrictions: low CPU priority, 256MB memory, 1 minute script timeout). &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/c75NS.png" alt="screenshot-datajoy"&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://jupyter.org/"&gt;Jupyter.org&lt;/a&gt; evolved from the &lt;a href="http://ipython.org/"&gt;IPython Project&lt;/a&gt; (the language-agnostic parts of IPython); supports Python 3, Julia, R, Haskell, Ruby, etc.  &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/mhHkx.png" alt="jupyter screenshot"&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':9C,47C,76C,129C,138C ''/c75ns.png'':117C ''/czju6.png'':33C ''/elpbr.png'':64C ''/li'':26C,57C,110C,154C ''/mhhkx.png'':161C ''/p'':38C,69C,122C,165C ''/ul'':27C,58C,111C,155C ''1'':106C ''256mb'':104C ''3'':148C ''agnost'':142C ''also'':49C ''alt'':34C,65C,118C,162C ''basic'':95C ''browser'':25C,52C ''browser-embed'':51C ''cloud'':46C ''consol'':18C,56C ''cpu'':102C ''data'':82C ''datajoy'':75C,121C ''easi'':13C,78C ''easy-to-us'':12C ''embed'':53C ''etc'':153C ''evolv'':130C ''fiddl'':8C ''follow'':99C ''free'':94C ''haskel'':151C ''href'':4C,42C,73C,126C,134C ''i.stack.imgur.com'':32C,63C,116C,160C ''i.stack.imgur.com/c75ns.png'':115C ''i.stack.imgur.com/czju6.png'':31C ''i.stack.imgur.com/elpbr.png'':62C ''i.stack.imgur.com/mhhkx.png'':159C ''img'':29C,60C,113C,157C ''interact'':16C ''ipython'':136C,145C ''ipython.org'':135C ''julia'':149C ''jupyt'':163C ''jupyter.org'':127C,128C ''languag'':141C ''language-agnost'':140C ''li'':2C,40C,71C,124C ''low'':101C ''memori'':105C ''minut'':107C ''onlin'':81C ''p'':28C,59C,112C,156C ''part'':143C ''plan'':96C ''prioriti'':103C ''process'':83C ''project'':137C ''provid'':10C,48C,77C ''python'':85C,147C ''r'':7C,17C,44C,55C,87C,150C ''r-consol'':54C ''r-fiddl'':6C ''requir'':92C ''restrict'':100C ''rfiddl'':37C ''roncloud'':68C ''roncloud.com'':43C ''rubi'':152C ''run'':22C ''screenshot'':36C,67C,120C,164C ''screenshot-datajoy'':119C ''screenshot-rfiddl'':35C ''screenshot-roncloud'':66C ''script'':108C ''sign'':89C ''sign-up'':88C ''src'':30C,61C,114C,158C ''support'':146C ''timeout'':109C ''ul'':1C,39C,70C,123C ''use'':15C,80C ''www.getdatajoy.com'':74C ''www.r-fiddle.org'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (789, 2, '2014-07-20 23:54:09.07', 3, NULL, NULL, '&lt;p&gt;You can pull stock data very easyly in python and R (probably other languages as well) with the following packages:&lt;/p&gt;

&lt;p&gt;In python with: &lt;a href="https://pypi.python.org/pypi/ystockquote" rel="nofollow"&gt;https://pypi.python.org/pypi/ystockquote&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This is also a really nice tutorial in iPython which shows you how to pull the stock data and play with it: &lt;a href="http://nbviewer.ipython.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20Pandas%20Basics.ipynb" rel="nofollow"&gt;http://nbviewer.ipython.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20Pandas%20Basics.ipynb&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In R with: &lt;a href="http://www.quantmod.com/" rel="nofollow"&gt;http://www.quantmod.com/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;HTH. &lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':81C ''/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20pandas%20basics.ipynb'':64C ''/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20pandas%20basics.ipynb&lt;/a&gt;&lt;/p&gt;'':69C ''/p'':22C,84C ''/pypi/ystockquote'':31C ''/pypi/ystockquote&lt;/a&gt;&lt;/p&gt;'':36C ''also'':40C ''data'':6C,55C ''easyli'':8C ''follow'':20C ''href'':28C,61C,75C ''hth'':83C ''ipython'':46C ''languag'':15C ''nbviewer.ipython.org'':63C,68C ''nbviewer.ipython.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20pandas%20basics.ipynb'':62C ''nbviewer.ipython.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20pandas%20basics.ipynb&lt;/a&gt;&lt;/p&gt;'':67C ''nice'':43C ''nofollow'':33C,66C,78C ''p'':1C,23C,37C,70C,82C ''packag'':21C ''play'':57C ''probabl'':13C ''pull'':4C,52C ''pypi.python.org'':30C,35C ''pypi.python.org/pypi/ystockquote'':29C ''pypi.python.org/pypi/ystockquote&lt;/a&gt;&lt;/p&gt;'':34C ''python'':10C,25C ''r'':12C,72C ''realli'':42C ''rel'':32C,65C,77C ''show'':48C ''stock'':5C,54C ''tutori'':44C ''well'':17C ''www.quantmod.com'':76C,80C ''www.quantmod.com/&lt;/a&gt;&lt;/p&gt;'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (790, 2, '2014-07-21 00:35:13.44', 4, NULL, NULL, '&lt;p&gt;Absolutely. When you''re working with data at that scale it''s common to use a big data framework, in which case python or whatever language you''re using is merely an interface. See for example &lt;a href="http://spark.apache.org/docs/0.9.1/python-programming-guide.html" rel="nofollow"&gt;Spark''s Python Programming Guide&lt;/a&gt;. What kind of data do you have and what do you want to do with it?&lt;/p&gt;
', NULL, NULL, '''/a'':50C ''/docs/0.9.1/python-programming-guide.html'':42C ''/p'':67C ''absolut'':2C ''big'':18C ''case'':23C ''common'':14C ''data'':8C,19C,54C ''exampl'':37C ''framework'':20C ''guid'':49C ''href'':39C ''interfac'':34C ''kind'':52C ''languag'':27C ''mere'':32C ''nofollow'':44C ''p'':1C ''program'':48C ''python'':24C,47C ''re'':5C,29C ''rel'':43C ''scale'':11C ''see'':35C ''spark'':45C ''spark.apache.org'':41C ''spark.apache.org/docs/0.9.1/python-programming-guide.html'':40C ''use'':16C,30C ''want'':62C ''whatev'':26C ''work'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (791, 1, '2014-07-21 09:00:04.917', 5, 237, 'Job title similarity', '&lt;p&gt;I''m trying to define a metric between job titles in IT field. For this I need some metric between words of job titles that are not appearing together in the same job title, e.g. metric between the words &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;senior, primary, lead, head, vp, director, stuff, principal, chief, &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;or the words &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;analyst, expert, modeler, researcher, scientist, developer, engineer, architect.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;How can I get all such possible words with their distance ?&lt;/p&gt;
', 'machine-learning  dataset', NULL, '''/blockquote'':61C,78C ''/p'':48C,60C,66C,77C,91C ''analyst'':69C ''appear'':36C ''architect'':76C ''blockquot'':49C,67C ''chief'':59C ''dataset'':7B ''defin'':13C ''develop'':74C ''director'':56C ''distanc'':90C ''e.g'':43C ''engin'':75C ''expert'':70C ''field'':21C ''get'':83C ''head'':54C ''job'':1A,17C,31C,41C ''lead'':53C ''learn'':6B ''m'':10C ''machin'':5B ''machine-learn'':4B ''metric'':15C,27C,44C ''model'':71C ''need'':25C ''p'':8C,50C,62C,68C,79C ''possibl'':86C ''primari'':52C ''princip'':58C ''research'':72C ''scientist'':73C ''senior'':51C ''similar'':3A ''stuff'':57C ''titl'':2A,18C,32C,42C ''togeth'':37C ''tri'':11C ''vp'':55C ''word'':29C,47C,65C,87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5316, 1, '2015-03-13 16:41:29.28', 6, 710, 'General approach to extract key text from sentence (nlp)', '&lt;p&gt;Given a sentence like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Complimentary gym access for two for the length of stay ($12 value per person per day)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What general approach can I take to identify the word gym or gym access?&lt;/p&gt;
', 'machine-learning  nlp  text-mining  data-cleaning', NULL, '''/code'':44C ''/p'':25C,61C ''/pre'':45C ''12'':38C ''access'':30C,60C ''approach'':2A,49C ''clean'':19B ''code'':27C ''complimentari'':28C ''data'':18B ''data-clean'':17B ''day'':43C ''extract'':4A ''general'':1A,48C ''given'':21C ''gym'':29C,57C,59C ''identifi'':54C ''key'':5A ''learn'':12B ''length'':35C ''like'':24C ''machin'':11B ''machine-learn'':10B ''mine'':16B ''nlp'':9A,13B ''p'':20C,46C ''per'':40C,42C ''person'':41C ''pre'':26C ''sentenc'':8A,23C ''stay'':37C ''take'':52C ''text'':6A,15B ''text-min'':14B ''two'':32C ''valu'':39C ''word'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (794, 2, '2014-07-21 15:35:59.54', 6, NULL, NULL, '&lt;p&gt;Pylearn2 seems to be the library of choice, however I find their YAML configuration files off-putting.&lt;/p&gt;

&lt;p&gt;Python itself was designed to be an easy language for prototyping, why would you &lt;strong&gt;not&lt;/strong&gt; use it to define the network properties themselves? We have great editors with autocompletion that would make your life much easier and Python is not like C++ where you have to wait for long builds to finish before you can run your code.&lt;/p&gt;

&lt;p&gt;YAML files on the other hand you have to edit using a standard text editor with no assistance whatsoever and this makes the learning curve even steeper. &lt;/p&gt;

&lt;p&gt;I may be missing the big picture but I still don''t understand what were they thinking, I don''t think prototyping in code would be much slower. For that reason I''m considering Theanets or using Theano directly.&lt;/p&gt;
', NULL, NULL, '''/p'':20C,82C,111C,152C ''/strong'':38C ''assist'':101C ''autocomplet'':52C ''big'':118C ''build'':73C ''c'':65C ''choic'':9C ''code'':81C,136C ''configur'':15C ''consid'':146C ''curv'':108C ''defin'':42C ''design'':25C ''direct'':151C ''easi'':29C ''easier'':59C ''edit'':93C ''editor'':50C,98C ''even'':109C ''file'':16C,85C ''find'':12C ''finish'':75C ''great'':49C ''hand'':89C ''howev'':10C ''languag'':30C ''learn'':107C ''librari'':7C ''life'':57C ''like'':64C ''long'':72C ''m'':145C ''make'':55C,105C ''may'':114C ''miss'':116C ''much'':58C,139C ''network'':44C ''off-put'':17C ''p'':1C,21C,83C,112C ''pictur'':119C ''properti'':45C ''prototyp'':32C,134C ''put'':19C ''pylearn2'':2C ''python'':22C,61C ''reason'':143C ''run'':79C ''seem'':3C ''slower'':140C ''standard'':96C ''steeper'':110C ''still'':122C ''strong'':36C ''text'':97C ''theanet'':147C ''theano'':150C ''think'':129C,133C ''understand'':125C ''use'':39C,94C,149C ''wait'':70C ''whatsoev'':102C ''would'':34C,54C,137C ''yaml'':14C,84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (795, 2, '2014-07-21 16:15:07.01', 1, NULL, NULL, '&lt;p&gt;Not sure if this is exactly what you''re looking for, but r-base has a function called "adist" which creates a distance matrix of approximate string distances (according to the Levenshtein distance). Type ''?adist'' for more.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;words = c("senior", "primary", "lead", "head", "vp", "director", "stuff", "principal", "chief")
adist(words)

      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
 [1,]    0    6    5    5    6    5    5    7    5
 [2,]    6    0    6    6    7    7    7    6    6
 [3,]    5    6    0    1    4    7    5    8    5
 [4,]    5    6    1    0    4    7    5    8    4
 [5,]    6    7    4    4    0    8    5    8    5
 [6,]    5    7    7    7    8    0    8    8    7
 [7,]    5    7    5    5    5    8    0    9    4
 [8,]    7    6    8    8    8    8    9    0    8
 [9,]    5    6    5    4    5    7    4    8    0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Also, if R isn''t an option, the Levenshtein distance algorithm is implemented in many languages here:
&lt;a href="http://en.wikibooks.org/wiki/Algorithm_Implementation/Strings/Levenshtein_distance" rel="nofollow"&gt;http://en.wikibooks.org/wiki/Algorithm_Implementation/Strings/Levenshtein_distance&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/code'':155C ''/p'':40C ''/pre'':156C ''/wiki/algorithm_implementation/strings/levenshtein_distance'':179C ''/wiki/algorithm_implementation/strings/levenshtein_distance&lt;/a&gt;&lt;/p&gt;'':184C ''0'':66C,77C,88C,99C,110C,121C,132C,143C,154C ''1'':56C,65C,89C,98C ''2'':57C,75C ''3'':58C,85C ''4'':59C,90C,95C,100C,104C,108C,109C,134C,149C,152C ''5'':60C,68C,69C,71C,72C,74C,86C,92C,94C,96C,102C,105C,112C,114C,116C,126C,128C,129C,130C,146C,148C,150C ''6'':61C,67C,70C,76C,78C,79C,83C,84C,87C,97C,106C,115C,137C,147C ''7'':62C,73C,80C,81C,82C,91C,101C,107C,117C,118C,119C,124C,125C,127C,136C,151C ''8'':63C,93C,103C,111C,113C,120C,122C,123C,131C,135C,138C,139C,140C,141C,144C,153C ''9'':64C,133C,142C,145C ''accord'':31C ''adist'':21C,37C,54C ''algorithm'':168C ''also'':158C ''approxim'':28C ''base'':16C ''c'':44C ''call'':20C ''chief'':53C ''code'':42C ''creat'':23C ''director'':50C ''distanc'':25C,30C,35C,167C ''en.wikibooks.org'':178C,183C ''en.wikibooks.org/wiki/algorithm_implementation/strings/levenshtein_distance'':177C ''en.wikibooks.org/wiki/algorithm_implementation/strings/levenshtein_distance&lt;/a&gt;&lt;/p&gt;'':182C ''exact'':7C ''function'':19C ''head'':48C ''href'':176C ''implement'':170C ''isn'':161C ''languag'':173C ''lead'':47C ''levenshtein'':34C,166C ''look'':11C ''mani'':172C ''matrix'':26C ''nofollow'':181C ''option'':164C ''p'':1C,157C ''pre'':41C ''primari'':46C ''princip'':52C ''r'':15C,160C ''r-base'':14C ''re'':10C ''rel'':180C ''senior'':45C ''string'':29C ''stuff'':51C ''sure'':3C ''type'':36C ''vp'':49C ''word'':43C,55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (797, 2, '2014-07-21 19:06:43.223', 17, NULL, NULL, '&lt;p&gt;To be perfectly honest, most NoSQL databases are not very well suited to applications in big data. For the vast majority of all big data applications, the performance of &lt;a href="https://en.wikipedia.org/wiki/MongoDB" rel="nofollow"&gt;MongoDB&lt;/a&gt; compared to a relational database like &lt;a href="http://en.wikipedia.org/wiki/MySQL" rel="nofollow"&gt;MySQL&lt;/a&gt; is &lt;a href="http://www.moredevs.com/mysql-vs-mongodb-performance-benchmark/" rel="nofollow"&gt;significantly&lt;/a&gt; is poor enough to warrant staying away from something like MongoDB entirely.&lt;/p&gt;

&lt;p&gt;With that said, there are a couple of really useful properties of NoSQL databases that certainly work in your favor when you''re working with large data sets, though the chance of those benefits outweighing the generally poor performance of NoSQL compared to &lt;a href="http://en.wikipedia.org/wiki/SQL" rel="nofollow"&gt;SQL&lt;/a&gt; for read-intensive operations (most similar to typical big data use cases) is low.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;No Schema&lt;/strong&gt; - If you''re working with a lot of unstructured data, it might be hard to actually decide on and rigidly apply a schema. NoSQL databases in general are very supporting of this, and will allow you to insert schema-less documents on the fly, which is certainly not something an SQL database will support.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;&lt;a href="http://en.wikipedia.org/wiki/JSON" rel="nofollow"&gt;JSON&lt;/a&gt;&lt;/strong&gt; - If you happen to be working with JSON-style documents instead of with &lt;a href="http://en.wikipedia.org/wiki/Comma-separated_values" rel="nofollow"&gt;CSV&lt;/a&gt; files, then you''ll see a lot of advantage in using something like MongoDB for a database-layer. Generally the workflow savings don''t outweigh the increased query-times though.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Ease of Use&lt;/strong&gt; - I''m not saying that SQL databases are always hard to use, or that &lt;a href="http://en.wikipedia.org/wiki/Apache_Cassandra" rel="nofollow"&gt;Cassandra&lt;/a&gt; is the easiest thing in the world to set up, but in general NoSQL databases are easier to set up and use than SQL databases. MongoDB is a particularly strong example of this, known for being one of the easiest database layers to use (outside of &lt;a href="http://en.wikipedia.org/wiki/SQLite" rel="nofollow"&gt;SQLite&lt;/a&gt;). SQL also deals with a lot of normalization and there''s a large legacy of SQL best practices that just generally bogs down the development process.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Personally I might suggest you also check out &lt;a href="http://en.wikipedia.org/wiki/Graph_database" rel="nofollow"&gt;graph databases&lt;/a&gt; such as &lt;a href="http://en.wikipedia.org/wiki/Neo4j" rel="nofollow"&gt;Neo4j&lt;/a&gt; that show really good performance for certain types of queries if you''re looking into picking out a backend for your data science applications.&lt;/p&gt;
', NULL, NULL, '''/a'':39C,54C,64C,130C,219C,243C,305C,360C,407C,418C ''/li'':208C,276C,387C ''/mysql-vs-mongodb-performance-benchmark/'':60C ''/p'':77C,146C,443C ''/strong'':152C,220C,282C ''/ul'':388C ''/wiki/apache_cassandra'':301C ''/wiki/comma-separated_values'':239C ''/wiki/graph_database'':402C ''/wiki/json'':215C ''/wiki/mongodb'':35C ''/wiki/mysql'':50C ''/wiki/neo4j'':414C ''/wiki/sql'':126C ''/wiki/sqlite'':356C ''actual'':168C ''advantag'':252C ''allow'':187C ''also'':362C,395C ''alway'':291C ''appli'':173C ''applic'':15C,27C,442C ''away'':71C ''backend'':437C ''benefit'':112C ''best'':377C ''big'':17C,25C,140C ''bog'':382C ''case'':143C ''cassandra'':304C ''certain'':94C,200C,425C ''chanc'':109C ''check'':396C ''compar'':40C,120C ''coupl'':85C ''csv'':242C ''data'':18C,26C,105C,141C,162C,440C ''databas'':8C,44C,92C,177C,205C,261C,289C,320C,330C,346C,406C ''database-lay'':260C ''deal'':363C ''decid'':169C ''develop'':385C ''document'':194C,231C ''eas'':279C ''easier'':322C ''easiest'':308C,345C ''en.wikipedia.org'':34C,49C,125C,214C,238C,300C,355C,401C,413C ''en.wikipedia.org/wiki/apache_cassandra'':299C ''en.wikipedia.org/wiki/comma-separated_values'':237C ''en.wikipedia.org/wiki/graph_database'':400C ''en.wikipedia.org/wiki/json'':213C ''en.wikipedia.org/wiki/mongodb'':33C ''en.wikipedia.org/wiki/mysql'':48C ''en.wikipedia.org/wiki/neo4j'':412C ''en.wikipedia.org/wiki/sql'':124C ''en.wikipedia.org/wiki/sqlite'':354C ''enough'':67C ''entir'':76C ''exampl'':336C ''favor'':98C ''file'':244C ''fli'':197C ''general'':115C,179C,263C,318C,381C ''good'':422C ''graph'':405C ''happen'':223C ''hard'':166C,292C ''honest'':5C ''href'':32C,47C,57C,123C,212C,236C,298C,353C,399C,411C ''increas'':271C ''insert'':190C ''instead'':232C ''intens'':134C ''json'':218C,229C ''json-styl'':228C ''known'':339C ''larg'':104C,373C ''layer'':262C,347C ''legaci'':374C ''less'':193C ''li'':148C,209C,277C ''like'':45C,74C,256C ''ll'':247C ''look'':432C ''lot'':159C,250C,366C ''low'':145C ''m'':284C ''major'':22C ''might'':164C,392C ''mongodb'':38C,75C,257C,331C ''mysql'':53C ''neo4j'':417C ''nofollow'':37C,52C,62C,128C,217C,241C,303C,358C,404C,416C ''normal'':368C ''nosql'':7C,91C,119C,176C,319C ''one'':342C ''oper'':135C ''outsid'':350C ''outweigh'':113C,269C ''p'':1C,78C,389C ''particular'':334C ''perfect'':4C ''perform'':29C,117C,423C ''person'':390C ''pick'':434C ''poor'':66C,116C ''practic'':378C ''process'':386C ''properti'':89C ''queri'':273C,428C ''query-tim'':272C ''re'':101C,155C,431C ''read'':133C ''read-intens'':132C ''realli'':87C,421C ''rel'':36C,51C,61C,127C,216C,240C,302C,357C,403C,415C ''relat'':43C ''rigid'':172C ''said'':81C ''save'':266C ''say'':286C ''schema'':151C,175C,192C ''schema-less'':191C ''scienc'':441C ''see'':248C ''set'':106C,314C,324C ''show'':420C ''signific'':63C ''similar'':137C ''someth'':73C,202C,255C ''sql'':129C,204C,288C,329C,361C,376C ''sqlite'':359C ''stay'':70C ''strong'':149C,210C,278C,335C ''style'':230C ''suggest'':393C ''suit'':13C ''support'':182C,207C ''thing'':309C ''though'':107C,275C ''time'':274C ''type'':426C ''typic'':139C ''ul'':147C ''unstructur'':161C ''use'':88C,142C,254C,281C,294C,327C,349C ''vast'':21C ''warrant'':69C ''well'':12C ''work'':95C,102C,156C,226C ''workflow'':265C ''world'':312C ''www.moredevs.com'':59C ''www.moredevs.com/mysql-vs-mongodb-performance-benchmark/'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (798, 2, '2014-07-21 20:42:00.143', 2, NULL, NULL, '&lt;p&gt;If I understand your question, you can look at the co-occurrence matrix formed using the terms following the title; e.g., senior FOO, primary BAR, etc. Then you can compute the similarity between any pair of terms, such as "senior" and "primary", using a suitable metric; e.g., the cosine similarity.&lt;/p&gt;
', NULL, NULL, '''/p'':53C ''bar'':27C ''co'':13C ''co-occurr'':12C ''comput'':32C ''cosin'':51C ''e.g'':23C,49C ''etc'':28C ''follow'':20C ''foo'':25C ''form'':16C ''look'':9C ''matrix'':15C ''metric'':48C ''occurr'':14C ''p'':1C ''pair'':37C ''primari'':26C,44C ''question'':6C ''senior'':24C,42C ''similar'':34C,52C ''suitabl'':47C ''term'':19C,39C ''titl'':22C ''understand'':4C ''use'':17C,45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (799, 2, '2014-07-21 21:17:07.303', 5, NULL, NULL, '&lt;p&gt;I would use a visual analysis. Since you know there is a repetition every 256 bytes, create an image 256 pixels wide by however many deep, and encode the data using brightness. In (i)python it would look like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import os, numpy, matplotlib.pyplot as plt

%matplotlib inline

def read_in_chunks(infile, chunk_size=256):
    while True:
        chunk = infile.read(chunk_size)
        if chunk:
            yield chunk
        else:
            # The chunk was empty, which means we''re at the end
            # of the file
            return

fname = ''enter something here''
srcfile = open(fname, ''rb'')
height = 1 + os.path.getsize(fname)/256
data = numpy.zeros((height, 256), dtype=numpy.uint8)    

for i, line in enumerate(read_in_chunks(srcfile)):
    vals = list(map(int, line))
    data[i,:len(vals)] = vals

plt.imshow(data, aspect=1e-2);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This is what a PDF looks like:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/bicgF.png" alt="A PDF file visualized"&gt;&lt;/p&gt;

&lt;p&gt;A 256 byte periodic pattern would have manifested itself as vertical lines. Except for the header and tail it looks pretty noisy.&lt;/p&gt;
', NULL, NULL, '''/256'':99C ''/bicgf.png'':145C ''/code'':129C ''/p'':42C,139C,151C,175C ''/pre'':130C ''1'':96C ''1e-2'':128C ''256'':16C,21C,60C,103C,154C ''alt'':146C ''analysi'':7C ''aspect'':127C ''bright'':33C ''byte'':17C,155C ''chunk'':56C,58C,63C,65C,68C,70C,73C,113C ''code'':44C ''creat'':18C ''data'':31C,100C,120C,126C ''deep'':27C ''def'':53C ''dtype'':104C ''els'':71C ''empti'':75C ''encod'':29C ''end'':82C ''enter'':88C ''enumer'':110C ''everi'':15C ''except'':165C ''file'':85C,149C ''fname'':87C,93C,98C ''header'':168C ''height'':95C,102C ''howev'':25C ''i.stack.imgur.com'':144C ''i.stack.imgur.com/bicgf.png'':143C ''imag'':20C ''img'':141C ''import'':45C ''infil'':57C ''infile.read'':64C ''inlin'':52C ''int'':118C ''know'':10C ''len'':122C ''like'':40C,138C ''line'':108C,119C,164C ''list'':116C ''look'':39C,137C,172C ''mani'':26C ''manifest'':160C ''map'':117C ''matplotlib'':51C ''matplotlib.pyplot'':48C ''mean'':77C ''noisi'':174C ''numpi'':47C ''numpy.uint8'':105C ''numpy.zeros'':101C ''open'':92C ''os'':46C ''os.path.getsize'':97C ''p'':1C,131C,140C,152C ''pattern'':157C ''pdf'':136C,148C ''period'':156C ''pixel'':22C ''plt'':50C ''plt.imshow'':125C ''pre'':43C ''pretti'':173C ''python'':36C ''rb'':94C ''re'':79C ''read'':54C,111C ''repetit'':14C ''return'':86C ''sinc'':8C ''size'':59C,66C ''someth'':89C ''src'':142C ''srcfile'':91C,114C ''tail'':170C ''true'':62C ''use'':4C,32C ''val'':115C,123C,124C ''vertic'':163C ''visual'':6C,150C ''wide'':23C ''would'':3C,38C,158C ''yield'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5927, 1, '2015-05-27 15:20:16.18', 1, 45, 'Wordnet Lemmatization', '&lt;p&gt;I tried finding about exception lists in wordnet lemmatizers. "Morphy() uses inflectional ending rules and exception lists to handle different possibilities" which I read from &lt;a href="http://www.nltk.org/howto/wordnet.html" rel="nofollow"&gt;http://www.nltk.org/howto/wordnet.html&lt;/a&gt; . Can you explain what is an exception list. Thank you.&lt;/p&gt;
', 'machine-learning  nlp  text-mining', NULL, '''/howto/wordnet.html'':40C ''/howto/wordnet.html&lt;/a&gt;'':45C ''/p'':56C ''differ'':30C ''end'':23C ''except'':15C,26C,52C ''explain'':48C ''find'':13C ''handl'':29C ''href'':37C ''inflect'':22C ''learn'':5B ''lemmat'':2A,19C ''list'':16C,27C,53C ''machin'':4B ''machine-learn'':3B ''mine'':9B ''morphi'':20C ''nlp'':6B ''nofollow'':42C ''p'':10C ''possibl'':31C ''read'':34C ''rel'':41C ''rule'':24C ''text'':8B ''text-min'':7B ''thank'':54C ''tri'':12C ''use'':21C ''wordnet'':1A,18C ''www.nltk.org'':39C,44C ''www.nltk.org/howto/wordnet.html'':38C ''www.nltk.org/howto/wordnet.html&lt;/a&gt;'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (801, 2, '2014-07-21 21:32:12.963', 4, NULL, NULL, '&lt;p&gt;That''s an interesting problem, thanks for bring out here on stack.&lt;/p&gt;

&lt;p&gt;I think this problem is similar to when we apply &lt;a href="http://en.wikipedia.org/wiki/Latent_semantic_analysis" rel="nofollow"&gt;LSA(Latent Semantic Analysis)&lt;/a&gt; in sentiment analysis to find list of positive and negative words with polarity with respect to some predefined positive and negative words.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Good reads:&lt;/em&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://cs.stanford.edu/people/ang/papers/acl11-WordVectorsSentimentAnalysis.pdf" rel="nofollow"&gt;Learning Word Vectors for Sentiment Analysis&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://arxiv.org/pdf/cs/0212012.pdf" rel="nofollow"&gt;Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So, according to me LSA is your best approach to begin with in this situation as it learns the underlying relation between the words from the corpus and probably that''s what you are looking for.&lt;/p&gt;
', NULL, NULL, '''/a'':37C,82C,104C ''/em'':65C ''/li'':83C,105C ''/p'':14C,60C,66C,144C ''/pdf/cs/0212012.pdf'':89C ''/people/ang/papers/acl11-wordvectorssentimentanalysis.pdf'':73C ''/ul'':106C ''/wiki/latent_semantic_analysis'':30C ''accord'':109C ''analysi'':36C,40C,81C ''appli'':25C ''approach'':116C ''arxiv.org'':88C ''arxiv.org/pdf/cs/0212012.pdf'':87C ''begin'':118C ''best'':115C ''billion'':101C ''bring'':9C ''corpus'':103C,134C ''cs.stanford.edu'':72C ''cs.stanford.edu/people/ang/papers/acl11-wordvectorssentimentanalysis.pdf'':71C ''em'':62C ''en.wikipedia.org'':29C ''en.wikipedia.org/wiki/latent_semantic_analysis'':28C ''find'':42C ''good'':63C ''href'':27C,70C,86C ''hundr'':100C ''hundred-billion-word'':99C ''interest'':5C ''latent'':34C ''learn'':76C,93C,125C ''li'':68C,84C ''list'':43C ''look'':142C ''lsa'':33C,112C ''negat'':47C,58C ''nofollow'':32C,75C,91C ''orient'':96C ''p'':1C,15C,61C,107C ''polar'':50C ''posit'':45C,56C ''predefin'':55C ''probabl'':136C ''problem'':6C,19C ''read'':64C ''rel'':31C,74C,90C ''relat'':128C ''respect'':52C ''semant'':35C,95C ''sentiment'':39C,80C ''similar'':21C ''situat'':122C ''stack'':13C ''thank'':7C ''think'':17C ''ul'':67C ''under'':127C ''unsupervis'':92C ''vector'':78C ''word'':48C,59C,77C,102C,131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (867, 1, '2014-07-30 13:55:36.187', 2, 418, 'Relative merits of different open source natural language generators', '&lt;p&gt;Does anyone know what (from your experience) is the best open source natural language generators (NLG) out there? What are the relative merits of each?&lt;/p&gt;

&lt;p&gt;I''m looking to do sophisticated text summarization and would like to use theme extraction/semantic modeling in conjunction with NLG tools to create accurate, context-aware, and natural-sounding text summaries.&lt;/p&gt;
', 'nlp  text-mining', NULL, '''/p'':40C,75C ''accur'':65C ''anyon'':16C ''awar'':68C ''best'':24C ''conjunct'':59C ''context'':67C ''context-awar'':66C ''creat'':64C ''differ'':4A ''experi'':21C ''extraction/semantic'':56C ''generat'':9A,29C ''know'':17C ''languag'':8A,28C ''like'':52C ''look'':44C ''m'':43C ''merit'':2A,37C ''mine'':13B ''model'':57C ''natur'':7A,27C,71C ''natural-sound'':70C ''nlg'':30C,61C ''nlp'':10B ''open'':5A,25C ''p'':14C,41C ''relat'':1A,36C ''sophist'':47C ''sound'':72C ''sourc'':6A,26C ''summar'':49C ''summari'':74C ''text'':12B,48C,73C ''text-min'':11B ''theme'':55C ''tool'':62C ''use'':54C ''would'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (814, 2, '2014-07-22 16:40:17.733', 13, NULL, NULL, '&lt;p&gt;I also suggest &lt;code&gt;Gephi&lt;/code&gt; software (&lt;a href="https://gephi.github.io"&gt;https://gephi.github.io&lt;/a&gt;), which seems to be quite powerful. Some additional information on using &lt;code&gt;Gephi&lt;/code&gt; with &lt;strong&gt;large networks&lt;/strong&gt; can be found &lt;a href="https://forum.gephi.org/viewtopic.php?t=1554"&gt;here&lt;/a&gt; and, more generally, &lt;a href="https://forum.gephi.org/viewforum.php?f=25"&gt;here&lt;/a&gt;. &lt;code&gt;Cytoscape&lt;/code&gt; (&lt;a href="http://www.cytoscape.org"&gt;http://www.cytoscape.org&lt;/a&gt;) is an alternative to &lt;code&gt;Gephi&lt;/code&gt;, being an another popular platform for complex network analysis and visualization.&lt;/p&gt;

&lt;p&gt;If you''d like to work with networks &lt;strong&gt;programmatically&lt;/strong&gt; (including visualization) in R, Python or C/C++, you can check &lt;code&gt;igraph&lt;/code&gt; collection of libraries. Speaking of R, you may find interesting the following blog posts: on &lt;strong&gt;using R with Cytoscape&lt;/strong&gt; (&lt;a href="http://www.vesnam.com/Rblog/viznets1"&gt;http://www.vesnam.com/Rblog/viznets1&lt;/a&gt;) and on &lt;strong&gt;using R with Gephi&lt;/strong&gt; (&lt;a href="http://www.vesnam.com/Rblog/viznets2"&gt;http://www.vesnam.com/Rblog/viznets2&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;For &lt;strong&gt;extensive lists&lt;/strong&gt; of &lt;em&gt;network analysis and visualization software&lt;/em&gt;, including some comparison and reviews, you might want to check the following pages: 1) &lt;a href="http://wiki.cytoscape.org/Network_analysis_links"&gt;http://wiki.cytoscape.org/Network_analysis_links&lt;/a&gt;; 2) &lt;a href="http://www.kdnuggets.com/software/social-network-analysis.html"&gt;http://www.kdnuggets.com/software/social-network-analysis.html&lt;/a&gt;; 3) &lt;a href="http://www.activatenetworks.net/social-network-analysis-sna-software-review"&gt;http://www.activatenetworks.net/social-network-analysis-sna-software-review&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':13C,42C,52C,60C ''/code'':7C,27C,55C,67C,104C ''/em'':163C ''/network_analysis_links'':182C ''/network_analysis_links&lt;/a&gt;;'':185C ''/p'':79C ''/rblog/viznets1'':130C ''/rblog/viznets1&lt;/a&gt;)'':133C ''/rblog/viznets2'':146C ''/rblog/viznets2&lt;/a&gt;).&lt;/p&gt;'':149C ''/social-network-analysis-sna-software-review'':200C ''/social-network-analysis-sna-software-review&lt;/a&gt;.&lt;/p&gt;'':203C ''/software/social-network-analysis.html'':191C ''/software/social-network-analysis.html&lt;/a&gt;;'':194C ''/strong'':32C,91C,125C,141C,155C ''/viewforum.php?f=25'':50C ''/viewtopic.php?t=1554'':40C ''1'':177C ''2'':186C ''3'':195C ''addit'':21C ''also'':3C ''altern'':63C ''analysi'':76C,159C ''anoth'':70C ''blog'':117C ''c/c'':98C ''check'':101C,173C ''code'':5C,25C,53C,65C,102C ''collect'':105C ''comparison'':166C ''complex'':74C ''cytoscap'':54C,124C ''d'':83C ''em'':157C ''extens'':153C ''find'':113C ''follow'':116C,175C ''forum.gephi.org'':39C,49C ''forum.gephi.org/viewforum.php?f=25'':48C ''forum.gephi.org/viewtopic.php?t=1554'':38C ''found'':35C ''general'':45C ''gephi'':6C,26C,66C,140C ''gephi.github.io'':11C,12C ''href'':10C,37C,47C,57C,127C,143C,179C,188C,197C ''igraph'':103C ''includ'':92C,164C ''inform'':22C ''interest'':114C ''larg'':30C ''librari'':107C ''like'':84C ''list'':154C ''may'':112C ''might'':170C ''network'':31C,75C,88C,158C ''p'':1C,80C,150C ''page'':176C ''platform'':72C ''popular'':71C ''post'':118C ''power'':19C ''programmat'':90C ''python'':96C ''quit'':18C ''r'':95C,110C,122C,138C ''review'':168C ''seem'':15C ''softwar'':8C,162C ''speak'':108C ''strong'':29C,89C,120C,136C,152C ''suggest'':4C ''use'':24C,121C,137C ''visual'':78C,93C,161C ''want'':171C ''wiki.cytoscape.org'':181C,184C ''wiki.cytoscape.org/network_analysis_links'':180C ''wiki.cytoscape.org/network_analysis_links&lt;/a&gt;;'':183C ''work'':86C ''www.activatenetworks.net'':199C,202C ''www.activatenetworks.net/social-network-analysis-sna-software-review'':198C ''www.activatenetworks.net/social-network-analysis-sna-software-review&lt;/a&gt;.&lt;/p&gt;'':201C ''www.cytoscape.org'':58C,59C ''www.kdnuggets.com'':190C,193C ''www.kdnuggets.com/software/social-network-analysis.html'':189C ''www.kdnuggets.com/software/social-network-analysis.html&lt;/a&gt;;'':192C ''www.vesnam.com'':129C,132C,145C,148C ''www.vesnam.com/rblog/viznets1'':128C ''www.vesnam.com/rblog/viznets1&lt;/a&gt;)'':131C ''www.vesnam.com/rblog/viznets2'':144C ''www.vesnam.com/rblog/viznets2&lt;/a&gt;).&lt;/p&gt;'':147C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (802, 1, '2014-07-21 23:53:11.12', 10, 528, 'Efficient database model for storing data indexed by n-grams', '&lt;p&gt;I''m working on an application which requires creating a very large database of n-grams that exist in a large text corpus.&lt;/p&gt;

&lt;p&gt;I need three efficient operation types: Lookup and insertion indexed by the n-gram itself, and querying for all n-grams that contain a sub-n-gram.&lt;/p&gt;

&lt;p&gt;This sounds to me like the database should be a gigantic document tree, and document databases, e.g. Mongo, should be able to do the job well, but I''ve never used those at scale.&lt;/p&gt;

&lt;p&gt;Knowing the Stack Exchange question format, I''d like to clarify that I''m not asking for suggestions on specific technologies, but rather a type of database that I should be looking for to implement something like this at scale.&lt;/p&gt;
', 'nlp  databases', NULL, '''/p'':39C,71C,107C,149C ''abl'':93C ''applic'':20C ''ask'':124C ''clarifi'':119C ''contain'':65C ''corpus'':38C ''creat'':23C ''d'':116C ''data'':6A ''databas'':2A,13B,27C,79C,88C,135C ''document'':84C,87C ''e.g'':89C ''effici'':1A,44C ''exchang'':112C ''exist'':33C ''format'':114C ''gigant'':83C ''gram'':11A,31C,55C,63C,70C ''implement'':143C ''index'':7A,50C ''insert'':49C ''job'':97C ''know'':109C ''larg'':26C,36C ''like'':77C,117C,145C ''look'':140C ''lookup'':47C ''m'':16C,122C ''model'':3A ''mongo'':90C ''n'':10A,30C,54C,62C,69C ''n-gram'':9A,29C,53C,61C ''need'':42C ''never'':102C ''nlp'':12B ''oper'':45C ''p'':14C,40C,72C,108C ''queri'':58C ''question'':113C ''rather'':131C ''requir'':22C ''scale'':106C,148C ''someth'':144C ''sound'':74C ''specif'':128C ''stack'':111C ''store'':5A ''sub'':68C ''sub-n-gram'':67C ''suggest'':126C ''technolog'':129C ''text'':37C ''three'':43C ''tree'':85C ''type'':46C,133C ''use'':103C ''ve'':101C ''well'':98C ''work'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (803, 2, '2014-07-22 00:06:10.5', 1, NULL, NULL, '&lt;p&gt;I haven''t done this before but it sounds like a job for a graph database given the functionality you want. &lt;a href="http://www.rene-pickhardt.de/download-google-n-gram-data-set-and-neo4j-source-code-for-storing-it/" rel="nofollow"&gt;Here''s a demo in neo4j&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':36C ''/download-google-n-gram-data-set-and-neo4j-source-code-for-storing-it/'':27C ''/p'':37C ''databas'':17C ''demo'':33C ''done'':5C ''function'':20C ''given'':18C ''graph'':16C ''haven'':3C ''href'':24C ''job'':13C ''like'':11C ''neo4j'':35C ''nofollow'':29C ''p'':1C ''rel'':28C ''sound'':10C ''want'':22C ''www.rene-pickhardt.de'':26C ''www.rene-pickhardt.de/download-google-n-gram-data-set-and-neo4j-source-code-for-storing-it/'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (804, 1, '2014-07-22 01:12:03.86', 3, 38, 'What''s the difference between data products and intelligent systems?', '&lt;p&gt;Basically, both are software systems that are based on data and algorithms.&lt;/p&gt;
', 'tools  education  definitions', NULL, '''/p'':27C ''algorithm'':26C ''base'':22C ''basic'':15C ''data'':6A,24C ''definit'':13B ''differ'':4A ''educ'':12B ''intellig'':9A ''p'':14C ''product'':7A ''softwar'':18C ''system'':10A,19C ''tool'':11B');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (805, 2, '2014-07-22 02:27:43.71', 1, NULL, NULL, '&lt;p&gt;This is a very vague question. However, I will try to make sense of it. Considering rules of &lt;em&gt;logic&lt;/em&gt; as well as your statement that both &lt;em&gt;entities&lt;/em&gt; are "software systems that are based on data and algorithms", it appears that &lt;strong&gt;data products&lt;/strong&gt; are &lt;em&gt;intelligent systems&lt;/em&gt; and &lt;strong&gt;intelligent systems&lt;/strong&gt; are, to some degree, &lt;em&gt;data products&lt;/em&gt;. Therefore, it can be argued that the difference between the terms "data products" and "intelligent systems" is purely in the &lt;strong&gt;focus&lt;/strong&gt; (&lt;em&gt;source&lt;/em&gt; of information or &lt;em&gt;purpose&lt;/em&gt; of system dimensions) of each type of systems (&lt;em&gt;data&lt;/em&gt; &lt;strong&gt;vs.&lt;/strong&gt; &lt;em&gt;intelligence/algorithms&lt;/em&gt;).&lt;/p&gt;
', NULL, NULL, '''/em'':22C,32C,54C,67C,93C,99C,110C,116C ''/p'':117C ''/strong'':49C,59C,90C,113C ''algorithm'':42C ''appear'':44C ''argu'':72C ''base'':38C ''consid'':17C ''data'':40C,47C,65C,79C,109C ''degre'':63C ''differ'':75C ''dimens'':102C ''em'':20C,30C,51C,64C,91C,97C,108C,114C ''entiti'':31C ''focus'':89C ''howev'':8C ''inform'':95C ''intellig'':52C,57C,82C ''intelligence/algorithms'':115C ''logic'':21C ''make'':13C ''p'':1C ''product'':48C,66C,80C ''pure'':85C ''purpos'':98C ''question'':7C ''rule'':18C ''sens'':14C ''softwar'':34C ''sourc'':92C ''statement'':27C ''strong'':46C,56C,88C,111C ''system'':35C,53C,58C,83C,101C,107C ''term'':78C ''therefor'':68C ''tri'':11C ''type'':105C ''vagu'':6C ''vs'':112C ''well'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (806, 1, '2014-07-22 03:43:20.327', 16, 1830, 'Advantages of AUC vs standard accuracy', '&lt;p&gt;I was starting to look into area under curve(AUC) and am a little confused about its usefulness. When first explained to me, AUC seemed to be a great measure of performance but in my research I''ve found that some claim its advantage is mostly marginal in that it is best for catching ''lucky'' models with high standard accuracy measurements and low AUC.&lt;/p&gt;

&lt;p&gt;So should I avoid relying on AUC for validating models or would a combination be best? Thanks for all your help.&lt;/p&gt;
', 'machine-learning  accuracy', NULL, '''/p'':76C,99C ''accuraci'':6A,10B,71C ''advantag'':1A,55C ''area'':18C ''auc'':3A,21C,35C,75C,84C ''avoid'':81C ''best'':63C,93C ''catch'':65C ''claim'':53C ''combin'':91C ''confus'':26C ''curv'':20C ''explain'':32C ''first'':31C ''found'':50C ''great'':40C ''help'':98C ''high'':69C ''learn'':9B ''littl'':25C ''look'':16C ''low'':74C ''lucki'':66C ''machin'':8B ''machine-learn'':7B ''margin'':58C ''measur'':41C,72C ''model'':67C,87C ''most'':57C ''p'':11C,77C ''perform'':43C ''reli'':82C ''research'':47C ''seem'':36C ''standard'':5A,70C ''start'':14C ''thank'':94C ''use'':29C ''valid'':86C ''ve'':49C ''vs'':4A ''would'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (813, 2, '2014-07-22 15:44:04.16', 7, NULL, NULL, '&lt;p&gt;&lt;a href="https://gephi.github.io/"&gt;https://gephi.github.io/&lt;/a&gt; says it can handle a million edges. If your graph has 1000000 vertices and only 50000 edges then most of your vertices won''t have any edges anyway.&lt;/p&gt;

&lt;p&gt;In fact the Gephi spec is the dual of your example: "Networks up to 50,000 nodes and 1,000,000 edges"&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':7C ''/p'':36C,60C ''000'':53C,57C,58C ''1'':56C ''1000000'':19C ''50'':52C ''50000'':23C ''anyway'':35C ''dual'':45C ''edg'':14C,24C,34C,59C ''exampl'':48C ''fact'':39C ''gephi'':41C ''gephi.github.io'':4C,6C ''gephi.github.io/&lt;/a&gt;'':5C ''graph'':17C ''handl'':11C ''href'':3C ''million'':13C ''network'':49C ''node'':54C ''p'':1C,37C ''say'':8C ''spec'':42C ''vertic'':20C,29C ''won'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (808, 1, '2014-07-22 08:39:33.81', 8, 589, 'Statistics + Computer Science = Data Science?', '&lt;p&gt;i want to become a &lt;strong&gt;data scientist&lt;/strong&gt;. I studied applied &lt;strong&gt;statistics&lt;/strong&gt; (actuarial science), so i have a great statistical background (regression, stochastic process, time series, just for mention a few). But now,  I am going to do a master degree in &lt;strong&gt;Computer Science&lt;/strong&gt; focus in Intelligent Systems.&lt;/p&gt;

&lt;p&gt;Here is my study plan:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Machine learning&lt;/li&gt;
&lt;li&gt;Advanced machine learning&lt;/li&gt;
&lt;li&gt;Data mining&lt;/li&gt;
&lt;li&gt;Fuzzy logic&lt;/li&gt;
&lt;li&gt;Recommendation Systems&lt;/li&gt;
&lt;li&gt;Distributed Data Systems&lt;/li&gt;
&lt;li&gt;Cloud Computing&lt;/li&gt;
&lt;li&gt;Knowledge discovery&lt;/li&gt;
&lt;li&gt;Business Intelligence&lt;/li&gt;
&lt;li&gt;Information retrieval&lt;/li&gt;
&lt;li&gt;Text mining&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;At the end, with all my statistical  and  computer science knowledge, can i call myself a data scientist? , or am i wrong?&lt;/p&gt;

&lt;p&gt;Thanks for the answers.&lt;/p&gt;
', 'machine-learning  statistics  career', NULL, '''/li'':77C,82C,86C,90C,94C,99C,103C,107C,111C,115C,119C ''/p'':65C,72C,144C,150C ''/strong'':20C,26C,60C ''/ul'':120C ''actuari'':27C ''advanc'':79C ''answer'':149C ''appli'':23C ''background'':35C ''becom'':15C ''busi'':109C ''call'':135C ''career'':10B ''cloud'':101C ''comput'':2A,58C,102C,130C ''data'':4A,18C,84C,97C,138C ''degre'':55C ''discoveri'':106C ''distribut'':96C ''end'':124C ''focus'':61C ''fuzzi'':88C ''go'':50C ''great'':33C ''inform'':113C ''intellig'':63C,110C ''knowledg'':105C,132C ''learn'':8B,76C,81C ''li'':74C,78C,83C,87C,91C,95C,100C,104C,108C,112C,116C ''logic'':89C ''machin'':7B,75C,80C ''machine-learn'':6B ''master'':54C ''mention'':43C ''mine'':85C,118C ''p'':11C,66C,121C,145C ''plan'':71C ''process'':38C ''recommend'':92C ''regress'':36C ''retriev'':114C ''scienc'':3A,5A,28C,59C,131C ''scientist'':19C,139C ''seri'':40C ''statist'':1A,9B,25C,34C,128C ''stochast'':37C ''strong'':17C,24C,57C ''studi'':22C,70C ''system'':64C,93C,98C ''text'':117C ''thank'':146C ''time'':39C ''ul'':73C ''want'':13C ''wrong'':143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (809, 2, '2014-07-22 09:03:49.27', 1, NULL, NULL, '&lt;p&gt;I think that you''re on the right track toward becoming an &lt;strong&gt;expert&lt;/strong&gt; &lt;em&gt;data scientist&lt;/em&gt;. Recently I have answered related question here on Data Science StackExchange: &lt;a href="http://datascience.stackexchange.com/a/742/2452"&gt;http://datascience.stackexchange.com/a/742/2452&lt;/a&gt; (pay attention to the &lt;em&gt;definition&lt;/em&gt; I mention there, as it essentially answers your question by itself, as well as to aspects of &lt;strong&gt;practicing&lt;/strong&gt; &lt;em&gt;software engineering&lt;/em&gt; and &lt;strong&gt;applying&lt;/strong&gt; knowledge to solving &lt;em&gt;real-world&lt;/em&gt; problems). I hope that you will find all that useful. Good luck in your career!&lt;/p&gt;
', NULL, NULL, '''/a/742/2452'':36C ''/a/742/2452&lt;/a&gt;'':39C ''/em'':20C,46C,70C,82C ''/p'':98C ''/strong'':16C,66C,74C ''answer'':24C,53C ''appli'':73C ''aspect'':62C ''attent'':41C ''becom'':12C ''career'':97C ''data'':18C,29C ''datascience.stackexchange.com'':35C,38C ''datascience.stackexchange.com/a/742/2452'':34C ''datascience.stackexchange.com/a/742/2452&lt;/a&gt;'':37C ''definit'':45C ''em'':17C,44C,67C,78C ''engin'':69C ''essenti'':52C ''expert'':15C ''find'':89C ''good'':93C ''hope'':85C ''href'':33C ''knowledg'':75C ''luck'':94C ''mention'':48C ''p'':1C ''pay'':40C ''practic'':65C ''problem'':83C ''question'':26C,55C ''re'':6C ''real'':80C ''real-world'':79C ''recent'':21C ''relat'':25C ''right'':9C ''scienc'':30C ''scientist'':19C ''softwar'':68C ''solv'':77C ''stackexchang'':31C ''strong'':14C,64C,72C ''think'':3C ''toward'':11C ''track'':10C ''use'':92C ''well'':59C ''world'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (810, 1, '2014-07-22 12:29:10.05', 20, 826, 'Should I go for a ''balanced'' dataset or a ''representative'' dataset?', '&lt;p&gt;My ''machine learning'' task is of separating benign Internet traffic from malicious traffic. In the real world scenario, most (say 90% or more) of Internet traffic is benign. Thus I felt that I should go with the similar kind of data for training my models as well. But then I did come across a research paper or two (in my area of work) which have used a balanced data to train models, implying equal number of instances of benign and malicious traffic.&lt;/p&gt;

&lt;p&gt;In general, if I am building ML models, should I go for a dataset which is representative of the real world problem, or is a balanced dataset better suited for building the models (since certain classifiers do not behave well with class imbalance, or due to other reasons not known to me)?&lt;/p&gt;

&lt;p&gt;Can someone shed more light on the &lt;em&gt;pros&lt;/em&gt; and &lt;em&gt;cons&lt;/em&gt; of both the choices, and how to decide which one to go for?&lt;/p&gt;
', 'machine-learning  dataset  unbalanced-classes', NULL, '''/em'':167C,171C ''/p'':102C,156C,185C ''90'':40C ''across'':72C ''area'':80C ''balanc'':6A,87C,129C ''behav'':142C ''benign'':27C,47C,98C ''better'':131C ''build'':109C,134C ''certain'':138C ''choic'':175C ''class'':18B,145C ''classifi'':139C ''come'':71C ''con'':170C ''data'':60C,88C ''dataset'':7A,11A,15B,117C,130C ''decid'':179C ''due'':148C ''em'':165C,169C ''equal'':93C ''felt'':50C ''general'':105C ''go'':3A,54C,114C,183C ''imbal'':146C ''impli'':92C ''instanc'':96C ''internet'':28C,44C ''kind'':58C ''known'':153C ''learn'':14B,22C ''light'':162C ''machin'':13B,21C ''machine-learn'':12B ''malici'':31C,100C ''ml'':110C ''model'':64C,91C,111C,136C ''number'':94C ''one'':181C ''p'':19C,103C,157C ''paper'':75C ''problem'':125C ''pros'':166C ''real'':35C,123C ''reason'':151C ''repres'':10A,120C ''research'':74C ''say'':39C ''scenario'':37C ''separ'':26C ''shed'':160C ''similar'':57C ''sinc'':137C ''someon'':159C ''suit'':132C ''task'':23C ''thus'':48C ''traffic'':29C,32C,45C,101C ''train'':62C,90C ''two'':77C ''unbalanc'':17B ''unbalanced-class'':16B ''use'':85C ''well'':66C,143C ''work'':82C ''world'':36C,124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8322, 1, '2015-10-06 10:51:52.883', 2, 155, 'filling missing data with other than mean values', '&lt;p&gt;What all options are available for filling missing data ?&lt;/p&gt;

&lt;p&gt;One obvious choice is the mean, but if the percentage of missing data is large, it will decrease the accuracy.&lt;/p&gt;

&lt;p&gt;So how do we deal with missing values if they are in great number ?&lt;/p&gt;
', 'data-mining  missing-data', NULL, '''/p'':25C,47C,63C ''accuraci'':46C ''avail'':20C ''choic'':29C ''data'':3A,10B,14B,24C,39C ''data-min'':9B ''deal'':53C ''decreas'':44C ''fill'':1A,22C ''great'':61C ''larg'':41C ''mean'':7A,32C ''mine'':11B ''miss'':2A,13B,23C,38C,55C ''missing-data'':12B ''number'':62C ''obvious'':28C ''one'':27C ''option'':18C ''p'':15C,26C,48C ''percentag'':36C ''valu'':8A,56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (811, 2, '2014-07-22 14:22:49.137', 17, NULL, NULL, '&lt;p&gt;I would say the answer depends on your use case. Based on my experience:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;If you''re trying to build a representative model -- one that describes the data rather than necessarily predicts -- then I would suggest using a representative sample of your data.&lt;/li&gt;
&lt;li&gt;If you want to build a predictive model, particularly one that performs well by measure of AUC or rank-order and plan to use a basic ML framework (i.e. Decision Tree, SVM, Naive Bayes, etc), then I would suggest you feed the framework a balanced dataset. Much of the literature on class imbalance finds that random undersampling (down sampling the majority class to the size of the minority class) can drive performance gains.&lt;/li&gt;
&lt;li&gt;If you''re building a predictive model, but are using a more advanced framework (i.e. something that determines sampling parameters via wrapper or a modification of a bagging framework that samples to class equivalence), then I would suggest again feeding the representative sample and letting the algorithm take care of balancing the data for training.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':48C,124C,181C ''/p'':16C ''/ul'':182C ''advanc'':138C ''algorithm'':172C ''answer'':6C ''auc'':66C ''bag'':153C ''balanc'':95C,176C ''base'':12C ''basic'':76C ''bay'':84C ''build'':24C,54C,129C ''care'':174C ''case'':11C ''class'':102C,112C,119C,158C ''data'':32C,47C,178C ''dataset'':96C ''decis'':80C ''depend'':7C ''describ'':30C ''determin'':143C ''drive'':121C ''equival'':159C ''etc'':85C ''experi'':15C ''feed'':91C,165C ''find'':104C ''framework'':78C,93C,139C,154C ''gain'':123C ''i.e'':79C,140C ''imbal'':103C ''let'':170C ''li'':18C,49C,125C ''literatur'':100C ''major'':111C ''measur'':64C ''minor'':118C ''ml'':77C ''model'':27C,57C,132C ''modif'':150C ''much'':97C ''naiv'':83C ''necessarili'':35C ''one'':28C,59C ''order'':70C ''p'':1C ''paramet'':145C ''particular'':58C ''perform'':61C,122C ''plan'':72C ''predict'':36C,56C,131C ''random'':106C ''rank'':69C ''rank-ord'':68C ''rather'':33C ''re'':21C,128C ''repres'':26C,43C,167C ''sampl'':44C,109C,144C,156C,168C ''say'':4C ''size'':115C ''someth'':141C ''suggest'':40C,89C,163C ''svm'':82C ''take'':173C ''train'':180C ''tree'':81C ''tri'':22C ''ul'':17C ''undersampl'':107C ''use'':10C,41C,74C,135C ''via'':146C ''want'':52C ''well'':62C ''would'':3C,39C,88C,162C ''wrapper'':147C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (812, 1, '2014-07-22 15:17:04.81', 12, 2507, 'Visualizing a graph with a million vertices', '&lt;p&gt;What is the best tool to use to visualize (draw the vertices and edges) a graph with 1000000 vertices? There are about 50000 edges in the graph. And I can compute the location of individual vertices and edges.&lt;/p&gt;

&lt;p&gt;I am thinking about writing a program to generate a svg. Any other suggestions?  &lt;/p&gt;
', 'visualization  graphs', NULL, '''/p'':49C,65C ''1000000'':28C ''50000'':33C ''best'':14C ''comput'':41C ''draw'':20C ''edg'':24C,34C,48C ''generat'':59C ''graph'':3A,9B,26C,37C ''individu'':45C ''locat'':43C ''million'':6A ''p'':10C,50C ''program'':57C ''suggest'':64C ''svg'':61C ''think'':53C ''tool'':15C ''use'':17C ''vertic'':7A,22C,29C,46C ''visual'':1A,8B,19C ''write'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2329, 2, '2014-10-21 10:22:21.39', 2, NULL, NULL, '&lt;p&gt;You could use the &lt;code&gt;wireframe&lt;/code&gt; function from the &lt;code&gt;lattice&lt;/code&gt; package:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library("lattice")
wireframe(volcano[1:30, 1:30], shade=TRUE, zlab="")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/BQHGX.png" alt="wireframe plot example"&gt;&lt;/p&gt;
', NULL, NULL, '''/bqhgx.png'':37C ''/code'':8C,14C,30C ''/p'':16C,42C ''/pre'':31C ''1'':23C,25C ''30'':24C,26C ''alt'':38C ''code'':6C,12C,18C ''could'':3C ''exampl'':41C ''function'':9C ''i.stack.imgur.com'':36C ''i.stack.imgur.com/bqhgx.png'':35C ''img'':33C ''lattic'':13C,20C ''librari'':19C ''p'':1C,32C ''packag'':15C ''plot'':40C ''pre'':17C ''shade'':27C ''src'':34C ''true'':28C ''use'':4C ''volcano'':22C ''wirefram'':7C,21C,39C ''zlab'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (815, 1, '2014-07-22 19:20:47.58', 4, 95, 'is this a good case for NOSQL?', '&lt;p&gt;I''m currently facing a project that I could solve with a relational database in a relatively painful way. Having heard so much about NOSQL, I''m wondering if there is not a more appropriate way of tackling it:&lt;/p&gt;

&lt;p&gt;Suppose we are tracking a group of animals in a forest (n ~ 500) and would like to keep a record of a set of observations (this is a fictional scenario).&lt;/p&gt;

&lt;p&gt;We would like to store the following information in a database:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;a unique identifier for each animal&lt;/li&gt;
&lt;li&gt;a description of the animal with structured fields: Species, Genus, Family, ...&lt;/li&gt;
&lt;li&gt;a free text field with additional information&lt;/li&gt;
&lt;li&gt;each time-point at which it was detected close to a reference point&lt;/li&gt;
&lt;li&gt;a picture of the animal&lt;/li&gt;
&lt;li&gt;an indication whether two given animals are siblings&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;And:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;there might be additional features appearing later as more data comes in&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;We would like to be able to execute the following types of queries:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;return all the animals spotted between in a given time interval&lt;/li&gt;
&lt;li&gt;return all the animals of a given Species or Family&lt;/li&gt;
&lt;li&gt;perform a text search on the free text field&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Which particular database system would you recommend ? Is there any tutorial / examples that I could use as a starting point ?&lt;/p&gt;
', 'nosql  databases', NULL, '''/li'':104C,117C,126C,142C,149C,159C,178C,208C,220C,231C ''/p'':50C,82C,95C,163C,194C,254C ''/ul'':160C,179C,232C ''500'':64C ''abl'':186C ''addit'':124C,169C ''anim'':59C,103C,110C,148C,156C,200C,213C ''appear'':171C ''appropri'':45C ''case'':5A ''close'':137C ''come'':176C ''could'':19C,248C ''current'':13C ''data'':175C ''databas'':9B,24C,94C,236C ''descript'':107C ''detect'':136C ''exampl'':245C ''execut'':188C ''face'':14C ''famili'':116C,219C ''featur'':170C ''fiction'':80C ''field'':113C,122C,230C ''follow'':90C,190C ''forest'':62C ''free'':120C,228C ''genus'':115C ''given'':155C,205C,216C ''good'':4A ''group'':57C ''heard'':31C ''identifi'':100C ''indic'':152C ''inform'':91C,125C ''interv'':207C ''keep'':69C ''later'':172C ''li'':97C,105C,118C,127C,143C,150C,165C,196C,209C,221C ''like'':67C,86C,183C ''m'':12C,37C ''might'':167C ''much'':33C ''n'':63C ''nosql'':7A,8B,35C ''observ'':76C ''p'':10C,51C,83C,161C,180C,233C ''pain'':28C ''particular'':235C ''perform'':222C ''pictur'':145C ''point'':131C,141C,253C ''project'':16C ''queri'':193C ''recommend'':240C ''record'':71C ''refer'':140C ''relat'':23C,27C ''return'':197C,210C ''scenario'':81C ''search'':225C ''set'':74C ''sibl'':158C ''solv'':20C ''speci'':114C,217C ''spot'':201C ''start'':252C ''store'':88C ''structur'':112C ''suppos'':52C ''system'':237C ''tackl'':48C ''text'':121C,224C,229C ''time'':130C,206C ''time-point'':129C ''track'':55C ''tutori'':244C ''two'':154C ''type'':191C ''ul'':96C,164C,195C ''uniqu'':99C ''use'':249C ''way'':29C,46C ''whether'':153C ''wonder'':38C ''would'':66C,85C,182C,238C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (816, 2, '2014-07-22 21:12:08.333', 8, NULL, NULL, '&lt;p&gt;Well it depends on what kind of "Data Science" you wish to get in to.  For basic analytics and reporting statistics will certainly help, but for Machine Learning and Artificial Intelligence then you''ll want a few more skills&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Probability theory&lt;/strong&gt; - you must have a solid background in pure probability so that you can decompose any problem, whether seen before or not, into probabilistic principles.  Statistics helps a lot for already solved problems, but new and unsolved problems require a deep understanding of probability so that you can design appropriate techniques.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Information Theory&lt;/strong&gt; - this (relative to statistics) is quite a new field (though still decades old), the most important work was by Shannon, but even more important and often neglected note in literature is work by Hobson that proved that Kullback-Leibler Divergence is the only mathematical definition that truly captures the notion of a &lt;em&gt;"measure of information"&lt;/em&gt;. Now fundamental to artificial intellgence is being able to quantify information.  Suggest reading "Concepts in Statistical Mechanics" - Arthur Hobson (very expensive book, only available in academic libraries).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Complexity Theory&lt;/strong&gt; - A big problem many Data Scientists face that do not have a solid complexity theory background is that their algorithms do not scale, or just take an extremely long time to run on large data.  Take PCA for example, many peoples favourite answer to the interview question "how do you reduce the number of features in our dataset", but even if you tell the candidate "the data set is really really really large" they still propose various forms of PCA that are O(n^3).  If you want to stand out, you want to be able to solve each problem on it''s own, NOT throw some text book solution at it designed a long time ago before Big Data was such a hip thing.  For that you need to understand how long things take to run, not only theoretically, but practically - so how to use a cluster of computers to distribute an algorithm, or which data structures take up less memory.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Communication Skills&lt;/strong&gt; - A huge part of Data Science is understanding business.  Whether it''s inventing a product driven by data science, or giving business insight driven by data science, being able to communicate well with both the Project and Product Managers, the tech teams, and your fellow data scientists is very important.  You can have an amazing idea, say an awesome AI solution, but if you cannot effectively (a) communicate WHY that will make the business money, (b) convince your collegues it will work and (c) explain to tech people how you need their help to build it, then it wont get done.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/em'':164C ''/li'':100C,193C,363C,472C ''/p'':41C,99C,192C,362C,471C ''/strong'':48C,106C,199C,369C ''/ul'':473C ''3'':284C ''abl'':172C,295C,398C ''academ'':190C ''ago'':316C ''ai'':429C ''algorithm'':219C,353C ''alreadi'':78C ''amaz'':424C ''analyt'':19C ''answer'':242C ''appropri'':97C ''arthur'':182C ''artifici'':31C,168C ''avail'':188C ''awesom'':428C ''b'':445C ''background'':54C,215C ''basic'':18C ''big'':201C,318C ''book'':186C,308C ''build'':464C ''busi'':378C,391C,443C ''c'':453C ''candid'':264C ''cannot'':434C ''captur'':155C ''certain'':24C ''cluster'':347C ''collegu'':448C ''communic'':367C,400C,437C ''complex'':197C,213C ''comput'':349C ''concept'':178C ''convinc'':446C ''data'':9C,204C,234C,266C,319C,356C,374C,387C,395C,415C ''dataset'':257C ''decad'':118C ''decompos'':62C ''deep'':88C ''definit'':152C ''depend'':4C ''design'':96C,312C ''distribut'':351C ''diverg'':147C ''done'':470C ''driven'':385C,393C ''effect'':435C ''em'':160C ''even'':128C,259C ''exampl'':238C ''expens'':185C ''explain'':454C ''extrem'':227C ''face'':206C ''favourit'':241C ''featur'':254C ''fellow'':414C ''field'':115C ''form'':277C ''fundament'':166C ''get'':14C,469C ''give'':390C ''help'':25C,74C,462C ''hip'':323C ''hobson'':140C,183C ''huge'':371C ''idea'':425C ''import'':122C,130C,419C ''inform'':104C,163C,175C ''insight'':392C ''intellg'':169C ''intellig'':32C ''interview'':245C ''invent'':382C ''kind'':7C ''kullback'':145C ''kullback-leibl'':144C ''larg'':233C,272C ''learn'':29C ''leibler'':146C ''less'':360C ''li'':43C,101C,194C,364C ''librari'':191C ''literatur'':136C ''ll'':35C ''long'':228C,314C,332C ''lot'':76C ''machin'':28C ''make'':441C ''manag'':408C ''mani'':203C,239C ''mathemat'':151C ''measur'':161C ''mechan'':181C ''memori'':361C ''money'':444C ''must'':50C ''n'':283C ''need'':328C,460C ''neglect'':133C ''new'':82C,114C ''note'':134C ''notion'':157C ''number'':252C ''o'':282C ''often'':132C ''old'':119C ''p'':1C,44C,102C,195C,365C ''part'':372C ''pca'':236C,279C ''peopl'':240C,457C ''practic'':341C ''principl'':72C ''probabilist'':71C ''probabl'':46C,57C,91C ''problem'':64C,80C,85C,202C,299C ''product'':384C,407C ''project'':405C ''propos'':275C ''prove'':142C ''pure'':56C ''quantifi'':174C ''question'':246C ''quit'':112C ''read'':177C ''realli'':269C,270C,271C ''reduc'':250C ''relat'':108C ''report'':21C ''requir'':86C ''run'':231C,336C ''say'':426C ''scale'':222C ''scienc'':10C,375C,388C,396C ''scientist'':205C,416C ''seen'':66C ''set'':267C ''shannon'':126C ''skill'':40C,368C ''solid'':53C,212C ''solut'':309C,430C ''solv'':79C,297C ''stand'':289C ''statist'':22C,73C,110C,180C ''still'':117C,274C ''strong'':45C,103C,196C,366C ''structur'':357C ''suggest'':176C ''take'':225C,235C,334C,358C ''team'':411C ''tech'':410C,456C ''techniqu'':98C ''tell'':262C ''text'':307C ''theoret'':339C ''theori'':47C,105C,198C,214C ''thing'':324C,333C ''though'':116C ''throw'':305C ''time'':229C,315C ''truli'':154C ''ul'':42C ''understand'':89C,330C,377C ''unsolv'':84C ''use'':345C ''various'':276C ''want'':36C,287C,292C ''well'':2C,401C ''whether'':65C,379C ''wish'':12C ''wont'':468C ''work'':123C,138C,451C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (831, 1, '2014-07-24 07:38:07.163', 3, 63, 'Data Science Association?', '&lt;p&gt;Have you heard of the "Data Science Association"? 
&lt;br&gt;URL: &lt;a href="http://www.datascienceassn.org/" rel="nofollow"&gt;http://www.datascienceassn.org/&lt;/a&gt;
&lt;br&gt;Do you expect it to become a professional body like the Actuaries Institute?
&lt;br&gt;If yes, then why?
&lt;br&gt;If no, then why not and do you see anyone else becoming the professional body?&lt;/p&gt;

&lt;p&gt;Lastly, is this question "on-topic" ?&lt;/p&gt;
', 'knowledge-base', NULL, '''/&lt;/a&gt;'':25C ''/p'':61C,70C ''actuari'':38C ''anyon'':55C ''associ'':3A,15C ''base'':6B ''becom'':32C,57C ''bodi'':35C,60C ''br'':16C,26C,40C,45C ''data'':1A,13C ''els'':56C ''expect'':29C ''heard'':10C ''href'':19C ''institut'':39C ''knowledg'':5B ''knowledge-bas'':4B ''last'':63C ''like'':36C ''nofollow'':22C ''on-top'':67C ''p'':7C,62C ''profession'':34C,59C ''question'':66C ''rel'':21C ''scienc'':2A,14C ''see'':54C ''topic'':69C ''url'':17C ''www.datascienceassn.org'':20C,24C ''www.datascienceassn.org/&lt;/a&gt;'':23C ''yes'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (920, 1, '2014-08-06 09:02:01.033', 1, 99, 'SUMMARIST: Automated Text Summarization', '&lt;p&gt;There is a text summarization project called SUMMARIST. Apparently it is able to perform abstractive text summarization. I want to give it a try but unfortunately the demo links on the website do not work. Does anybody have any information regarding this? How can I test this tool?&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.isi.edu/natural-language/projects/SUMMARIST.html" rel="nofollow"&gt;http://www.isi.edu/natural-language/projects/SUMMARIST.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Regards,
PasMod&lt;/p&gt;
', 'text-mining', NULL, '''/natural-language/projects/summarist.html'':63C ''/natural-language/projects/summarist.html&lt;/a&gt;&lt;/p&gt;'':68C ''/p'':57C,72C ''abl'':20C ''abstract'':23C ''anybodi'':45C ''appar'':17C ''autom'':2A ''call'':15C ''demo'':36C ''give'':29C ''href'':60C ''inform'':48C ''link'':37C ''mine'':7B ''nofollow'':65C ''p'':8C,58C,69C ''pasmod'':71C ''perform'':22C ''project'':14C ''regard'':49C,70C ''rel'':64C ''summar'':4A,13C,25C ''summarist'':1A,16C ''test'':54C ''text'':3A,6B,12C,24C ''text-min'':5B ''tool'':56C ''tri'':32C ''unfortun'':34C ''want'':27C ''websit'':40C ''work'':43C ''www.isi.edu'':62C,67C ''www.isi.edu/natural-language/projects/summarist.html'':61C ''www.isi.edu/natural-language/projects/summarist.html&lt;/a&gt;&lt;/p&gt;'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (864, 2, '2014-07-30 07:49:14.467', 2, NULL, NULL, '&lt;p&gt;map/reduce is most appropriate for parallelizable offline computations. To be more precise, it works best when the result can be found from the result of some function of a partition of the input. Averaging is a trivial example; you can do this with map/reduce by summing each partition, returning the sum and the number of elements in the partition, then computing the overall mean using these intermediate results. It is less appropriate when the intermediate steps depend on the state of the other partitions.&lt;/p&gt;
', NULL, NULL, '''/p'':86C ''appropri'':5C,73C ''averag'':35C ''best'':16C ''comput'':9C,62C ''depend'':78C ''element'':57C ''exampl'':39C ''found'':22C ''function'':28C ''input'':34C ''intermedi'':68C,76C ''less'':72C ''map/reduce'':2C,45C ''mean'':65C ''number'':55C ''offlin'':8C ''overal'':64C ''p'':1C ''paralleliz'':7C ''partit'':31C,49C,60C,85C ''precis'':13C ''result'':19C,25C,69C ''return'':50C ''state'':81C ''step'':77C ''sum'':47C,52C ''trivial'':38C ''use'':66C ''work'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9938, 1, '2016-01-25 10:33:46.983', 3, 54, 'How to define user churn', '&lt;p&gt;I''d like to define our app user churn,  Generally, people will define their app user churn by some simply way, like: if the user do not logon continually 7 days or 1 month, we will define them churn user. So is there a better way?&lt;/p&gt;
', 'bigdata  churn', NULL, '''/p'':55C ''1'':41C ''7'':38C ''app'':15C,23C ''better'':53C ''bigdata'':6B ''churn'':5A,7B,17C,25C,47C ''continu'':37C ''d'':10C ''day'':39C ''defin'':3A,13C,21C,45C ''general'':18C ''like'':11C,30C ''logon'':36C ''month'':42C ''p'':8C ''peopl'':19C ''simpli'':28C ''user'':4A,16C,24C,33C,48C ''way'':29C,54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (817, 2, '2014-07-22 21:24:33.82', 6, NULL, NULL, '&lt;p&gt;Data scientist (to me) a big umbrella term. I would see a data scientist as a person who can proficiently use techniques from the fields of data mining, machine learning, pattern classification, and statistics. &lt;/p&gt;

&lt;p&gt;However, those terms are intertwined to: machine learning is tied together with pattern classification, and also data mining overlaps when it comes finding patterns in data. And all techniques have their underlying statistical principles. I always picture this as a Venn diagram with a huge intersection. &lt;/p&gt;

&lt;p&gt;Computer sciences is related to all those fields too. I would say that you need "data science" techniques to do computer-scientific research, but computer science knowledge is not necessarily implied in "data science". However, programming skills - I see programming and computer science as different professions, where programming is more the tool in order solve problems - are also important to work with the data and to conduct data analysis.&lt;/p&gt;

&lt;p&gt;You have a really nice study plan, and it all makes sense. But I am not sure if you "want" to call yourself just "data scientist", I have the impression that "data scientist" is such a ambiguous term that can mean everything or nothing. What I want to convey is that you will end up being something more - more "specialized" - than "just" a data scientist.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (818, 2, '2014-07-23 05:29:06.903', 7, NULL, NULL, '&lt;p&gt;I think, that &lt;code&gt;Gephi&lt;/code&gt; could face with lack-of-memory issues, you will need at least 8Gb of RAM. Though number of edges is not extremely huge. &lt;/p&gt;

&lt;p&gt;Possibly, more appropriate tool in this case would be &lt;a href="http://www.graphviz.org/"&gt;&lt;code&gt;GraphViz&lt;/code&gt;&lt;/a&gt;. It''s a command line tool for network visualizations, and presumably would be more tolerant to graph size. Moreover, as I remember, in &lt;code&gt;GraphViz&lt;/code&gt; it is possible to use precomputed coordinates to facilitate computations.&lt;/p&gt;

&lt;p&gt;I''ve tried to find a real-world examples of using &lt;code&gt;GraphViz&lt;/code&gt; with huge graphs, but didn''t succeed. Though I found similar discussion on &lt;a href="http://scicomp.stackexchange.com/questions/3315/visualizing-very-large-link-graphs"&gt;&lt;strong&gt;Computational Science&lt;/strong&gt;&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':49C,125C ''/code'':7C,48C,75C,102C ''/p'':32C,86C,126C ''/questions/3315/visualizing-very-large-link-graphs'':120C ''/strong'':124C ''8gb'':21C ''appropri'':36C ''case'':40C ''code'':5C,46C,73C,100C ''command'':53C ''comput'':85C,122C ''coordin'':82C ''could'':8C ''didn'':107C ''discuss'':114C ''edg'':27C ''exampl'':97C ''extrem'':30C ''face'':9C ''facilit'':84C ''find'':92C ''found'':112C ''gephi'':6C ''graph'':66C,105C ''graphviz'':47C,74C,101C ''href'':44C,117C ''huge'':31C,104C ''issu'':15C ''lack'':12C ''lack-of-memori'':11C ''least'':20C ''line'':54C ''memori'':14C ''moreov'':68C ''need'':18C ''network'':57C ''number'':25C ''p'':1C,33C,87C ''possibl'':34C,78C ''precomput'':81C ''presum'':60C ''ram'':23C ''real'':95C ''real-world'':94C ''rememb'':71C ''scicomp.stackexchange.com'':119C ''scicomp.stackexchange.com/questions/3315/visualizing-very-large-link-graphs'':118C ''scienc'':123C ''similar'':113C ''size'':67C ''strong'':121C ''succeed'':109C ''think'':3C ''though'':24C,110C ''toler'':64C ''tool'':37C,55C ''tri'':90C ''use'':80C,99C ''ve'':89C ''visual'':58C ''world'':96C ''would'':41C,61C ''www.graphviz.org'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (819, 2, '2014-07-23 07:01:01.14', 7, NULL, NULL, '&lt;p&gt;Three tables: &lt;strong&gt;animal&lt;/strong&gt;, &lt;strong&gt;observation&lt;/strong&gt;, and &lt;strong&gt;sibling&lt;/strong&gt;. The observation has an &lt;strong&gt;animal_id&lt;/strong&gt; column which links to the animal table, and the sibling table has &lt;strong&gt;animal_1_id&lt;/strong&gt; and &lt;strong&gt;animal_2_id&lt;/strong&gt; columns that indicates two animals are siblings for each row.&lt;/p&gt;

&lt;p&gt;Even with 5000 animals and 100000 observations I don''t think query time will be a problem for something like PostgreSQL for most reasonable queries (obviously you can construct unreasonable queries but you can do that in any system). &lt;/p&gt;

&lt;p&gt;So I don''t see how this is "relatively painful". Relative to what? The only complexity is the sibling table. In NOSQL you might store the full list of siblings in the record for each animal, but then when you add a sibling relationship you have to add it to both sibling''s animal records. With the relational table approach I''ve outlined, it only exists once, but at the expense of having to test against both columns to find an animal''s siblings. &lt;/p&gt;

&lt;p&gt;I''d use PostgreSQL, and that gives you the option of using PostGIS if you have location data - this is a geospatial extension to PostgreSQL that lets you do spatial queries (point in polygon, points near a point etc) which might be something for you. &lt;/p&gt;

&lt;p&gt;I really don''t think the properties of NOSQL databases are a problem here for you - you aren''t changing your schema every ten minutes, you probably &lt;strong&gt;do&lt;/strong&gt; care that your database is ACID-compliant, and you don''t need something web-scale.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.mongodb-is-web-scale.com/"&gt;http://www.mongodb-is-web-scale.com/&lt;/a&gt; [warning: strong language]&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (820, 1, '2014-07-23 08:06:21.417', 6, 76, 'How can we effectively measure the impact of our data decisions', '&lt;p&gt;Apologies if this is very broad question, what I would like to know is how effective is A/B testing (or other methods) of effectively measuring the effects of a design decision.&lt;/p&gt;

&lt;p&gt;For instance we can analyse user interactions or click results, purchase/ browse decisions and then modify/tailor the results presented to the user.&lt;/p&gt;

&lt;p&gt;We could then test the effectiveness of this design change by subjecting 10% of users to the alternative model randomly but then how objective is this?&lt;/p&gt;

&lt;p&gt;How do we avoid influencing the user by the model change, for instance we could decided that search queries for ''David Beckham'' are probably about football so search results become biased towards this but we could equally say that his lifestyle is just as relevant but this never makes it into the top 10 results that are returned.&lt;/p&gt;

&lt;p&gt;I am curious how this is dealt with and how to measure this effectively.&lt;/p&gt;

&lt;p&gt;My thoughts are that you could be in danger of pushing a model that you think is correct and the user obliges and this becomes a self-fulfilling prophecy.&lt;/p&gt;

&lt;p&gt;I''ve read an article on this: &lt;a href="http://techcrunch.com/2014/06/29/ethics-in-a-data-driven-world/"&gt;http://techcrunch.com/2014/06/29/ethics-in-a-data-driven-world/&lt;/a&gt; and also the book: &lt;a href="http://shop.oreilly.com/product/0636920028529.do"&gt;http://shop.oreilly.com/product/0636920028529.do&lt;/a&gt; which discussed this so it piqued my interest.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (821, 2, '2014-07-23 12:57:37.073', 7, NULL, NULL, '&lt;p&gt;"Because its there".&lt;/p&gt;

&lt;p&gt;The data has a seasonal pattern. So you model it. The data has a trend. So you model it. Maybe the data is correlated with the number of sunspots. So you model that. Eventually you hope to get nothing left to model than uncorrelated random noise.&lt;/p&gt;

&lt;p&gt;But I think you''ve screwed up your STL computation here. Your residuals are clearly not serially uncorrelated. I rather suspect you''ve not told the function that your "seasonality" is a 24-hour cycle rather than an annual one. But hey you haven''t given us any code or data so we don''t really have a clue what you''ve done, do we? What do you think "seasonality" even means here? Do you have any idea?&lt;/p&gt;

&lt;p&gt;Your data seems the have three peaks every 24 hours. Really? Is this ''gas''=''gasoline''=''petrol'' or gas in some heating/electric generating system? Either way if you know a priori there''s an 8 hour cycle, or an 8 hour cycle on top of a 24 hour cycle on top of what looks like a very high frequency one or two hour cycle you &lt;strong&gt;put that in your model&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Actually you don''t even say what your x-axis is so maybe its days and then I''d fit a daily cycle, a weekly cycle, and then an annual cycle. But given how it all changes at time=85 or so I''d not expect a model to do well on both sides of that.&lt;/p&gt;

&lt;p&gt;With statistics (which is what this is, sorry to disappoint you but you''re not a data scientist yet) you don''t just robotically go "And.. Now.. I.. Fit.. An... S TL model....". You look at your data, try and get some understanding, then propose a model, fit it, test it, and use the parameters it make inferences about the data. Fitting cyclic seasonal patterns is part of that.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (822, 2, '2014-07-23 13:49:01.423', 3, NULL, NULL, '&lt;p&gt;In A/B testing, bias is handled very well by ensuring visitors are randomly assigned to either version A or version B of the site.  This creates independent samples drawn from the same population.  Because the groups are independent and, on average, only differ in the version of the site seen, the test measures the effect of the design decision.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Slight aside&lt;/em&gt;: Now you might argue that the A group or B group may differ in some demographic.  That commonly happens by random chance.  To a certain degree this can be taken care of by covariate adjusted randomization.  It can also be taken care of by adding covariates to the model that tests the effect of the design decision.  It should be noted that there is still some discussion about the proper way to do this within the statistics community.  Essentially A/B testing is an application of a &lt;a href="http://en.wikipedia.org/wiki/Randomized_controlled_trial" rel="nofollow"&gt;Randomized Control Trial&lt;/a&gt; to website design.  Some people disagree with adding covariates to the test.  Others, such as Frank Harrel (see &lt;a href="http://rads.stackoverflow.com/amzn/click/0387952322" rel="nofollow"&gt;Regression Modeling Strategies&lt;/a&gt;) argue for the use of covariates in such models.&lt;/p&gt;

&lt;p&gt;I would offer the following suggestions:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Design the study in advance so as to take care of as much sources of bias and variation as possible.   &lt;/li&gt;
&lt;li&gt;Let the data speak for itself.  As you get more data (like about searches for David Beckham), let it dominate your assumptions about how the data should be (as how the posterior dominates the prior in Bayesian analysis when the sample size becomes large).    &lt;/li&gt;
&lt;li&gt;Make sure your data matches the assumptions of the model.&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (823, 1, '2014-07-23 14:04:31.057', 14, 172, 'How should ethics be applied in data science', '&lt;p&gt;There was a recent furore with &lt;a href="http://online.wsj.com/articles/furor-erupts-over-facebook-experiment-on-users-1404085840"&gt;facebook experimenting on their users to see if they could alter user''s emotions&lt;/a&gt; and now &lt;a href="http://www.bbc.co.uk/news/technology-28542642"&gt;okcupid&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Whilst I am not a professional data scientist I read about &lt;a href="http://columbiadatascience.com/2013/11/25/data-science-ethics/"&gt;data science ethics&lt;/a&gt; from &lt;a href="http://shop.oreilly.com/product/0636920028529.do"&gt;Cathy O''Neill''s book ''Doing Data Science''&lt;/a&gt; and would like to know if this is something that professionals are taught at academic level (I would expect so) or something that is ignored or is lightly applied in the professional world. Particularly for those who ended up doing data science &lt;em&gt;accidentally&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Whilst the linked article touched on data integrity, the book also discussed the moral ethics behind understanding the impact of the data models that are created and the impact of those models which can have adverse effects when used inappropriately (sometimes unwittingly) or when the models are inaccurate, again producing adverse results.&lt;/p&gt;

&lt;p&gt;The article discusses a code of practice and mentions the &lt;a href="http://www.datascienceassn.org/code-of-conduct.html"&gt;Data Science Association''s Code of conduct&lt;/a&gt;, is this something that is in use? Rule 7 is of particular interest (quoted from their website):&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;(a) A person who consults with a data scientist about the possibility
  of forming a client-data scientist relationship with respect to a
  matter is a prospective client.&lt;/p&gt;
  
  &lt;p&gt;(b) Even when no client-data scientist relationship ensues, a data
  scientist who has learned information from a prospective client shall
  not use or reveal that information.&lt;/p&gt;
  
  &lt;p&gt;(c) A data scientist subject to paragraph (b) shall not provide
  professional data science services for a client with interests
  materially adverse to those of a prospective client in the same or a
  substantially related industry if the data scientist received
  information from the prospective client that could be significantly
  harmful to that person in the matter&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Is this something that is practiced professionally? Many users blindly accept that we get some free service (mail, social network, image hosting, blog platform etc..) and agree to an EULA in order to have ads pushed at us. &lt;/p&gt;

&lt;p&gt;Finally how is this regulated, I often read about users being up in arms when the terms of a service change but it seems that it requires some liberty organisation, class action or a &lt;a href="http://www.cnet.com/news/senator-asks-ftc-to-investigate-facebooks-mood-study/"&gt;senator&lt;/a&gt; to react to such things before something happens.&lt;/p&gt;

&lt;p&gt;By the way I am not making any judgements here or saying that all data scientists behave like this, I''m interested in what is taught academically and practiced professionally.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5411, 2, '2015-03-27 15:45:50.857', 1, NULL, NULL, '&lt;p&gt;I have had success using &lt;a href="https://bitbucket.org/wellaware/mogwai" rel="nofollow"&gt;Mogwai Python Library&lt;/a&gt;  which is more actively maintained than Bulbs at this point.&lt;/p&gt;

&lt;p&gt;Though I agree that this belongs on stack overflow.  &lt;/p&gt;
', NULL, NULL, '''/a'':17C ''/p'':28C,39C ''/wellaware/mogwai'':11C ''activ'':21C ''agre'':32C ''belong'':35C ''bitbucket.org'':10C ''bitbucket.org/wellaware/mogwai'':9C ''bulb'':24C ''href'':8C ''librari'':16C ''maintain'':22C ''mogwai'':14C ''nofollow'':13C ''overflow'':38C ''p'':1C,29C ''point'':27C ''python'':15C ''rel'':12C ''stack'':37C ''success'':5C ''though'':30C ''use'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (824, 2, '2014-07-23 15:46:48.977', 1, NULL, NULL, '&lt;p&gt;(too long for a comment)&lt;/p&gt;

&lt;p&gt;Basically, @Emre''s answer is correct: simple correlation matrix and cosine distance should work well*. There''s one subtlety, though - job titles are too short to carry important context. Let me explain this. &lt;/p&gt;

&lt;p&gt;Imagine LinkedIn profiles (which is pretty good source for data). Normally, they contain 4-10 sentences describing person''s skills and qualifications. It''s pretty likely that you find phrases like "lead data scientist" and "professional knowledge of Matlab and R" in a same profile, but it''s very unlikely to also see "junior Java developer" in it. So we may say that "lead" and "professional" (as well as "data scientist" and "Matlab" and "R") often occur in same contexts, but they are rarely found together with "junior" and "Java". &lt;/p&gt;

&lt;p&gt;Co-occurrence matrix shows exactly this. The more 2 words occur in same context, the more similar their vectors in the matrix will look like. And cosine distance is just a good way to measure this similarity. &lt;/p&gt;

&lt;p&gt;But what about job titles? Normally they are much shorter and don''t actually create enough context to catch similarities. Luckily, you don''t need source data to be titles themselves - you need to find similarities between skills in general, not specifically in titles. So you can simply build co-occurrence matrix from (long) profiles and then use it to measure similarity of titles. &lt;/p&gt;

&lt;p&gt;&lt;em&gt;*&lt;/em&gt; - in fact, it''s already worked for me on a similar project. &lt;/p&gt;
', NULL, NULL, '''-10'':58C ''/em'':244C ''/p'':7C,42C,134C,174C,241C,257C ''2'':145C ''4'':57C ''actual'':189C ''alreadi'':249C ''also'':95C ''answer'':12C ''basic'':9C ''build'':224C ''carri'':35C ''catch'':194C ''co'':137C,226C ''co-occurr'':136C,225C ''comment'':6C ''contain'':56C ''context'':37C,123C,150C,192C ''correct'':14C ''correl'':16C ''cosin'':19C,163C ''creat'':190C ''data'':53C,76C,113C,202C ''describ'':60C ''develop'':99C ''distanc'':20C,164C ''em'':243C ''emr'':10C ''enough'':191C ''exact'':141C ''explain'':40C ''fact'':246C ''find'':72C,210C ''found'':128C ''general'':215C ''good'':50C,168C ''imagin'':44C ''import'':36C ''java'':98C,133C ''job'':29C,179C ''junior'':97C,131C ''knowledg'':80C ''lead'':75C,107C ''let'':38C ''like'':69C,74C,161C ''linkedin'':45C ''long'':3C,230C ''look'':160C ''luckili'':196C ''matlab'':82C,116C ''matrix'':17C,139C,158C,228C ''may'':104C ''measur'':171C,237C ''much'':184C ''need'':200C,208C ''normal'':54C,181C ''occur'':120C,147C ''occurr'':138C,227C ''often'':119C ''one'':26C ''p'':1C,8C,43C,135C,175C,242C ''person'':61C ''phrase'':73C ''pretti'':49C,68C ''profession'':79C,109C ''profil'':46C,88C,231C ''project'':256C ''qualif'':65C ''r'':84C,118C ''rare'':127C ''say'':105C ''scientist'':77C,114C ''see'':96C ''sentenc'':59C ''short'':33C ''shorter'':185C ''show'':140C ''similar'':153C,173C,195C,211C,238C,255C ''simpl'':15C ''simpli'':223C ''skill'':63C,213C ''sourc'':51C,201C ''specif'':217C ''subtleti'':27C ''though'':28C ''titl'':30C,180C,205C,219C,240C ''togeth'':129C ''unlik'':93C ''use'':234C ''vector'':155C ''way'':169C ''well'':23C,111C ''word'':146C ''work'':22C,250C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (825, 1, '2014-07-24 00:01:40.76', 5, 69, 'Are there any annotators or Named Entity Recognition for license plate numbers?', '&lt;p&gt;Most vehicle license/number plate extractors I''ve found involve reading a plate from an image (OCR) but I''m interested in something that could tag instances of license plates in a body of text. Are there any such annotators out there?&lt;/p&gt;
', 'text-mining', NULL, '''/p'':58C ''annot'':4A,55C ''bodi'':48C ''could'':40C ''entiti'':7A ''extractor'':21C ''found'':24C ''imag'':31C ''instanc'':42C ''interest'':36C ''involv'':25C ''licens'':10A,44C ''license/number'':19C ''m'':35C ''mine'':15B ''name'':6A ''number'':12A ''ocr'':32C ''p'':16C ''plate'':11A,20C,28C,45C ''read'':26C ''recognit'':8A ''someth'':38C ''tag'':41C ''text'':14B,50C ''text-min'':13B ''ve'':23C ''vehicl'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (826, 2, '2014-07-24 00:34:41.13', -1, NULL, NULL, '&lt;p&gt;This can be done using &lt;code&gt;regular expressions&lt;/code&gt;. &lt;/p&gt;

&lt;p&gt;2 letters followed by a number (\d denotes digits) would be&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[A-Z]{2} \d*
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;2 or 3 letters followed by a number is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[A-Z]{2,3} \d*
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':10C,32C,53C ''/p'':11C,24C,44C ''/pre'':33C,54C ''2'':13C,30C,35C,50C ''3'':37C,51C ''a-z'':27C,47C ''code'':7C,26C,46C ''d'':19C,31C,52C ''denot'':20C ''digit'':21C ''done'':5C ''express'':9C ''follow'':15C,39C ''letter'':14C,38C ''number'':18C,42C ''p'':1C,12C,34C ''pre'':25C,45C ''regular'':8C ''use'':6C ''would'':22C ''z'':29C,49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (827, 2, '2014-07-24 02:23:23.703', 4, NULL, NULL, '&lt;p&gt;There are a lot of pretty decent tools out there for text annotation in general, and given the broad nature of the task you''re approaching (license plates are about as general as words), the annotation tools you are looking at should probably come from the more classical tools for annotation.&lt;/p&gt;

&lt;p&gt;There was actually a pretty good discussion about annotation tools on &lt;a href="http://datascience.stackexchange.com/questions/223/how-to-annotate-text-documents-with-meta-data/404#404"&gt;this&lt;/a&gt; question, which should actually apply to this problem. The most relied-upon thing in annotation right now is probably &lt;code&gt;brat&lt;/code&gt;. You can learn more about &lt;code&gt;brat&lt;/code&gt; &lt;a href="http://brat.nlplab.org/" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Hope that helps! Let me know if you''ve got any more questions.&lt;/p&gt;
', NULL, NULL, '''/a'':72C,110C ''/code'':95C,103C ''/p'':53C,111C,126C ''/questions/223/how-to-annotate-text-documents-with-meta-data/404#404'':70C ''actual'':57C,76C ''annot'':14C,37C,52C,63C,88C ''appli'':77C ''approach'':27C ''brat'':94C,102C ''brat.nlplab.org'':106C ''broad'':20C ''classic'':49C ''code'':93C,101C ''come'':45C ''datascience.stackexchange.com'':69C ''datascience.stackexchange.com/questions/223/how-to-annotate-text-documents-with-meta-data/404#404'':68C ''decent'':8C ''discuss'':61C ''general'':16C,33C ''given'':18C ''good'':60C ''got'':122C ''help'':115C ''hope'':113C ''href'':67C,105C ''know'':118C ''learn'':98C ''let'':116C ''licens'':28C ''look'':41C ''lot'':5C ''natur'':21C ''nofollow'':108C ''p'':1C,54C,112C ''plate'':29C ''pretti'':7C,59C ''probabl'':44C,92C ''problem'':80C ''question'':73C,125C ''re'':26C ''rel'':107C ''reli'':84C ''relied-upon'':83C ''right'':89C ''task'':24C ''text'':13C ''thing'':86C ''tool'':9C,38C,50C,64C ''upon'':85C ''ve'':121C ''word'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (828, 2, '2014-07-24 03:31:32.087', 3, NULL, NULL, '&lt;p&gt;Reporting back: I ended up coding graphml and using yEd for visualization (just because I am familiar with this combination. I bet gephi or graphviz would work fine and might even be better). Since I computed the location of all nodes, memory was not such big of an issue. Coding graphml is a little easier comparing to coding svg, since I don''t have to explicitly specify the placement of edges.&lt;/p&gt;
', NULL, NULL, '''/p'':73C ''back'':3C ''bet'':23C ''better'':34C ''big'':47C ''code'':7C,51C,59C ''combin'':21C ''compar'':57C ''comput'':37C ''easier'':56C ''edg'':72C ''end'':5C ''even'':32C ''explicit'':67C ''familiar'':18C ''fine'':29C ''gephi'':24C ''graphml'':8C,52C ''graphviz'':26C ''issu'':50C ''littl'':55C ''locat'':39C ''memori'':43C ''might'':31C ''node'':42C ''p'':1C ''placement'':70C ''report'':2C ''sinc'':35C,61C ''specifi'':68C ''svg'':60C ''use'':10C ''visual'':13C ''work'':28C ''would'':27C ''yed'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (829, 1, '2014-07-24 06:26:07.29', 2, 53, 'Pig script code error?', '&lt;p&gt;While running the below pig script I am getting error in line4: 
If it is GROUP then I am getting error.
If I change from ''GROUP'' TO ''group'' in line4, then the script is running.
What is the difference between group and GROUP.&lt;/p&gt;

&lt;p&gt;LINES = LOAD ''/user/cloudera/datapeople.csv'' USING PigStorage('','') AS ( firstname:chararray, lastname:chararray, address:chararray, city:chararray, state:chararray, zip:chararray );&lt;/p&gt;

&lt;p&gt;WORDS = FOREACH LINES GENERATE FLATTEN(TOKENIZE(zip)) AS ZIPS;&lt;/p&gt;

&lt;p&gt;WORDSGROUPED = GROUP WORDS BY ZIPS;&lt;/p&gt;

&lt;p&gt;WORDBYCOUNT = FOREACH WORDSGROUPED GENERATE GROUP AS ZIPS, COUNT(WORDS);&lt;/p&gt;

&lt;p&gt;WORDSSORT = ORDER WORDBYCOUNT BY $1 DESC;&lt;/p&gt;

&lt;p&gt;DUMP WORDSSORT;&lt;/p&gt;
', 'bigdata  hadoop', NULL, '''/p'':51C,71C,82C,89C,100C,108C,112C ''/user/cloudera/datapeople.csv'':55C ''1'':106C ''address'':63C ''bigdata'':5B ''chang'':31C ''chararray'':60C,62C,64C,66C,68C,70C ''citi'':65C ''code'':3A ''count'':98C ''desc'':107C ''differ'':46C ''dump'':110C ''error'':4A,17C,28C ''firstnam'':59C ''flatten'':77C ''foreach'':74C,92C ''generat'':76C,94C ''get'':16C,27C ''group'':23C,33C,35C,48C,50C,85C,95C ''hadoop'':6B ''lastnam'':61C ''line'':53C,75C ''line4'':19C,37C ''load'':54C ''order'':103C ''p'':7C,52C,72C,83C,90C,101C,109C ''pig'':1A,12C ''pigstorag'':57C ''run'':9C,42C ''script'':2A,13C,40C ''state'':67C ''token'':78C ''use'':56C ''word'':73C,86C,99C ''wordbycount'':91C,104C ''wordsgroup'':84C,93C ''wordssort'':102C,111C ''zip'':69C,79C,81C,88C,97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (875, 2, '2014-07-30 23:40:54.7', 2, NULL, NULL, '&lt;p&gt;I suggest you use &lt;a href="http://www.antlr.org/" rel="nofollow"&gt;ANTLR&lt;/a&gt;, which is a very powerful parser generator. It has a good GUI for entering your BNF. It has a &lt;a href="https://theantlrguy.atlassian.net/wiki/display/ANTLR4/Python+Target" rel="nofollow"&gt;Python target&lt;/a&gt; capability.&lt;/p&gt;
', NULL, NULL, '''/a'':12C,41C ''/p'':43C ''/wiki/display/antlr4/python+target'':36C ''antlr'':11C ''bnf'':28C ''capabl'':42C ''enter'':26C ''generat'':19C ''good'':23C ''gui'':24C ''href'':7C,33C ''nofollow'':10C,38C ''p'':1C ''parser'':18C ''power'':17C ''python'':39C ''rel'':9C,37C ''suggest'':3C ''target'':40C ''theantlrguy.atlassian.net'':35C ''theantlrguy.atlassian.net/wiki/display/antlr4/python+target'':34C ''use'':5C ''www.antlr.org'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (830, 2, '2014-07-24 07:00:13.187', 2, NULL, NULL, '&lt;p&gt;''group'' in strictly lower case in the FOREACH is the thing you are looping/grouping over.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://squarecog.wordpress.com/2010/05/11/group-operator-in-apache-pig/" rel="nofollow"&gt;http://squarecog.wordpress.com/2010/05/11/group-operator-in-apache-pig/&lt;/a&gt; says:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;When you group a relation, the result is a new relation with two
  columns: âgroupâ and the name of the original relation.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Column names are case sensitive, so you have to use lower-case ''group'' in your FOREACH.&lt;/p&gt;

&lt;p&gt;''GROUP'' in upper case is the grouping operator. You can''t mix them. So don''t do that.&lt;/p&gt;
', NULL, NULL, '''/2010/05/11/group-operator-in-apache-pig/'':23C ''/2010/05/11/group-operator-in-apache-pig/&lt;/a&gt;'':28C ''/blockquote'':57C ''/p'':17C,30C,56C,76C,96C ''blockquot'':31C ''case'':6C,62C,71C,81C ''column'':46C,59C ''foreach'':9C,75C ''group'':2C,35C,72C,78C,84C ''groupâ'':48C ''href'':20C ''looping/grouping'':15C ''lower'':5C,70C ''lower-cas'':69C ''mix'':89C ''name'':51C,60C ''new'':42C ''nofollow'':25C ''oper'':85C ''origin'':54C ''p'':1C,18C,32C,58C,77C ''rel'':24C ''relat'':37C,43C,55C ''result'':39C ''say'':29C ''sensit'':63C ''squarecog.wordpress.com'':22C,27C ''squarecog.wordpress.com/2010/05/11/group-operator-in-apache-pig/'':21C ''squarecog.wordpress.com/2010/05/11/group-operator-in-apache-pig/&lt;/a&gt;'':26C ''strict'':4C ''thing'':12C ''two'':45C ''upper'':80C ''use'':68C ''â'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8334, 1, '2015-10-06 21:01:20.73', 1, 18, 'finding maximum depth of random forest given the number of features', '&lt;p&gt;How do we find maximum depth of Random Forest if we know the number of features ?&lt;/p&gt;

&lt;p&gt;This is needed for regularizing random forest classifier.&lt;/p&gt;
', 'machine-learning  random-forest', NULL, '''/p'':35C,45C ''classifi'':44C ''depth'':3A,24C ''featur'':11A,34C ''find'':1A,22C ''forest'':6A,17B,27C,43C ''given'':7A ''know'':30C ''learn'':14B ''machin'':13B ''machine-learn'':12B ''maximum'':2A,23C ''need'':39C ''number'':9A,32C ''p'':18C,36C ''random'':5A,16B,26C,42C ''random-forest'':15B ''regular'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (844, 1, '2014-07-25 21:03:44.663', 5, 754, 'Using Apache Spark to do ML. Keep getting serializing errors', '&lt;p&gt;so I''m using Spark to do sentiment analysis, and I keep getting errors with the serializers it uses (I think) to pass python objects around.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;PySpark worker failed with exception:
Traceback (most recent call last):
  File "/Users/abdul/Desktop/RSI/spark-1.0.1-bin-    hadoop1/python/pyspark/worker.py", line 77, in main
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/Users/abdul/Desktop/RSI/spark-1.0.1-bin-    hadoop1/python/pyspark/serializers.py", line 191, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/Users/abdul/Desktop/RSI/spark-1.0.1-bin-    hadoop1/python/pyspark/serializers.py", line 123, in dump_stream
    for obj in iterator:
  File "/Users/abdul/Desktop/RSI/spark-1.0.1-bin-    hadoop1/python/pyspark/serializers.py", line 180, in _batched
    for item in iterator:
TypeError: __init__() takes exactly 3 arguments (2 given)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and the code for serializers is available &lt;a href="https://spark.apache.org/docs/latest/api/python/pyspark.serializers-pysrc.html#PickleSerializer.dumps"&gt;here&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;and my code is &lt;a href="https://github.com/seashark97/Scalable-Sentiment-Analysis/blob/master/spark_test.py"&gt;here&lt;/a&gt;&lt;/p&gt;
', 'bigdata  hadoop  distributed  scalability  map-reduce', NULL, '''/a'':132C,145C ''/code'':116C ''/docs/latest/api/python/pyspark.serializers-pysrc.html#pickleserializer.dumps'':130C ''/p'':45C,133C,146C ''/pre'':117C ''/seashark97/scalable-sentiment-analysis/blob/master/spark_test.py'':143C ''/users/abdul/desktop/rsi/spark-1.0.1-bin-'':59C,73C,86C,98C ''123'':89C ''180'':101C ''191'':76C ''2'':114C ''3'':112C ''77'':62C ''analysi'':27C ''apach'':2A ''argument'':113C ''around'':44C ''avail'':125C ''batch'':103C ''bigdata'':11B ''call'':56C ''code'':47C,121C,137C ''distribut'':13B ''dump'':78C,91C ''error'':10A,32C ''exact'':111C ''except'':52C ''fail'':50C ''file'':58C,72C,85C,97C ''func'':67C ''get'':8A,31C ''github.com'':142C ''github.com/seashark97/scalable-sentiment-analysis/blob/master/spark_test.py'':141C ''given'':115C ''hadoop'':12B ''hadoop1/python/pyspark/serializers.py'':74C,87C,99C ''hadoop1/python/pyspark/worker.py'':60C ''href'':127C,140C ''index'':69C ''init'':109C ''item'':105C ''iter'':70C,83C,96C,107C ''keep'':7A,30C ''last'':57C ''line'':61C,75C,88C,100C ''m'':21C ''main'':64C ''map'':16B ''map-reduc'':15B ''ml'':6A ''obj'':94C ''object'':43C ''outfil'':71C ''p'':18C,118C,134C ''pass'':41C ''pre'':46C ''pyspark'':48C ''python'':42C ''recent'':55C ''reduc'':17B ''scalabl'':14B ''self._batched'':82C ''self.serializer.dump'':80C ''sentiment'':26C ''serial'':9A,35C,123C ''serializer.dump'':65C ''spark'':3A,23C ''spark.apache.org'':129C ''spark.apache.org/docs/latest/api/python/pyspark.serializers-pysrc.html#pickleserializer.dumps'':128C ''split'':68C ''stream'':66C,79C,81C,84C,92C ''take'':110C ''think'':39C ''traceback'':53C ''typeerror'':108C ''use'':1A,22C,37C ''worker'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (832, 1, '2014-07-24 14:04:09.533', 5, 123, 'How do you plot overlapping durations?', '&lt;p&gt;My data contains a set of start times and duration for an action. I would like to plot this so that for a given time slice I can see how many actions are active. I''m currently thinking of this as a histogram with time on the x axis and number of active actions on the y axis.&lt;/p&gt;

&lt;p&gt;My question is, how should I adjust the data so that this is able to be plotted?&lt;/p&gt;

&lt;p&gt;The times for an action can be between 2 seconds and a minute. And, at any given time I would estimate there could be about 100 actions taking place. Ideally a single plot would be able to show hours of data. The accuracy of the data is in milliseconds.&lt;/p&gt;

&lt;p&gt;In the past the way that I have done this is to count for each second how many actions started , ended, or were active. This gave me a count of active actions for each second. The issue I found with this technique was that it made it difficult to adjust the time slice that I was looking at. Looking at a time slice of a minute was difficult to compute and looking at time slices of less than a second was impossible.&lt;/p&gt;

&lt;p&gt;I''m open to any advice on how to think about this issue.&lt;/p&gt;

&lt;p&gt;Thanks in advance!&lt;/p&gt;
', 'visualization', NULL, '''/p'':67C,86C,137C,221C,236C,241C ''100'':113C ''2'':96C ''abl'':82C,123C ''accuraci'':130C ''action'':21C,40C,62C,92C,114C,157C,170C ''activ'':42C,61C,162C,169C ''adjust'':75C,188C ''advanc'':240C ''advic'':228C ''axi'':57C,66C ''comput'':208C ''contain'':11C ''could'':110C ''count'':151C,167C ''current'':45C ''data'':10C,77C,128C,133C ''difficult'':186C,206C ''done'':147C ''durat'':6A,18C ''end'':159C ''estim'':108C ''found'':177C ''gave'':164C ''given'':32C,104C ''histogram'':51C ''hour'':126C ''ideal'':117C ''imposs'':220C ''issu'':175C,235C ''less'':215C ''like'':24C ''look'':195C,197C,210C ''m'':44C,224C ''made'':184C ''mani'':39C,156C ''millisecond'':136C ''minut'':100C,204C ''number'':59C ''open'':225C ''overlap'':5A ''p'':8C,68C,87C,138C,222C,237C ''past'':141C ''place'':116C ''plot'':4A,26C,85C,120C ''question'':70C ''second'':97C,154C,173C,218C ''see'':37C ''set'':13C ''show'':125C ''singl'':119C ''slice'':34C,191C,201C,213C ''start'':15C,158C ''take'':115C ''techniqu'':180C ''thank'':238C ''think'':46C,232C ''time'':16C,33C,53C,89C,105C,190C,200C,212C ''visual'':7B ''way'':143C ''would'':23C,107C,121C ''x'':56C ''y'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (833, 2, '2014-07-24 21:14:45.793', 1, NULL, NULL, '&lt;p&gt;This can be done in &lt;code&gt;R&lt;/code&gt; using &lt;code&gt;ggplot&lt;/code&gt;. Based on &lt;a href="http://stackoverflow.com/questions/18102224/drawing-gantt-charts-with-r-to-sub-second-accuracy"&gt;this&lt;/a&gt; question, it could be done with this code where &lt;code&gt;limits&lt;/code&gt; is the date range of the plot.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;tasks &amp;lt;- c("Task1", "Task2")
dfr &amp;lt;- data.frame(
name        = factor(tasks, levels = tasks),
start.date  = c("2014-08-07 09:03:25.815", "2014-08-07 09:03:25.956"),
end.date    = c("2014-08-07 09:03:28.300", "2014-08-07 09:03:30.409")
)

mdfr &amp;lt;- melt(dfr, measure.vars = c("start.date", "end.date"))


mdfr$time&amp;lt;-as.POSIXct(mdfr$value)

ggplot(mdfr, aes(time,name)) + 
geom_line(size = 6) +
xlab("") + ylab("") +
theme_bw()+
scale_x_datetime(breaks=date_breaks("2 sec"),
limits = as.POSIXct(c(''2014-08-07 09:03:24'',''2014-08-07 09:03:29'')))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Bwwod.png" alt="enter image description here"&gt;&lt;/p&gt;
', NULL, NULL, '''-07'':62C,68C,76C,82C,126C,132C ''-08'':61C,67C,75C,81C,125C,131C ''/a'':22C ''/bwwod.png'':143C ''/code'':9C,13C,34C,136C ''/p'':42C,149C ''/pre'':137C ''/questions/18102224/drawing-gantt-charts-with-r-to-sub-second-accuracy'':20C ''03'':64C,70C,78C,84C,128C,134C ''09'':63C,69C,77C,83C,127C,133C ''2'':119C ''2014'':60C,66C,74C,80C,124C,130C ''24'':129C ''25.815'':65C ''25.956'':71C ''28.300'':79C ''29'':135C ''30.409'':85C ''6'':108C ''ae'':102C ''alt'':144C ''as.posixct'':97C,122C ''base'':14C ''break'':116C,118C ''bw'':112C ''c'':47C,59C,73C,91C,123C ''code'':7C,11C,30C,32C,44C ''could'':25C ''data.frame'':52C ''date'':37C,117C ''datetim'':115C ''descript'':147C ''dfr'':50C,89C ''done'':5C,27C ''end.date'':72C,93C ''enter'':145C ''factor'':54C ''geom'':105C ''ggplot'':12C,100C ''href'':17C ''i.stack.imgur.com'':142C ''i.stack.imgur.com/bwwod.png'':141C ''imag'':146C ''img'':139C ''level'':56C ''limit'':33C,121C ''line'':106C ''lt'':46C,51C,87C,96C ''mdfr'':86C,94C,98C,101C ''measure.vars'':90C ''melt'':88C ''name'':53C,104C ''p'':1C,138C ''plot'':41C ''pre'':43C ''question'':23C ''r'':8C ''rang'':38C ''scale'':113C ''sec'':120C ''size'':107C ''src'':140C ''stackoverflow.com'':19C ''stackoverflow.com/questions/18102224/drawing-gantt-charts-with-r-to-sub-second-accuracy'':18C ''start.date'':58C,92C ''task'':45C,55C,57C ''task1'':48C ''task2'':49C ''theme'':111C ''time'':95C,103C ''use'':10C ''valu'':99C ''x'':114C ''xlab'':109C ''ylab'':110C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (834, 1, '2014-07-25 00:58:12.253', 11, 3780, 'Recommending movies with additional features using collaborative filtering', '&lt;p&gt;I am trying to build a recommendation engine using collaborative filtering. I have the usual [user, movie, rating] information. I would like to incorporate an additional feature like ''language'' or ''duration of movie''. I am not sure what techniques I could use for such a problem. Please suggest references or packages in python/R. &lt;/p&gt;
', 'python  r  recommendation', NULL, '''/p'':66C ''addit'':4A,38C ''build'':17C ''collabor'':7A,22C ''could'':53C ''durat'':43C ''engin'':20C ''featur'':5A,39C ''filter'':8A,23C ''incorpor'':36C ''inform'':31C ''languag'':41C ''like'':34C,40C ''movi'':2A,29C,45C ''p'':12C ''packag'':63C ''pleas'':59C ''problem'':58C ''python'':9B ''python/r'':65C ''r'':10B ''rate'':30C ''recommend'':1A,11B,19C ''refer'':61C ''suggest'':60C ''sure'':49C ''techniqu'':51C ''tri'':15C ''use'':6A,21C,54C ''user'':28C ''usual'':27C ''would'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (886, 2, '2014-08-01 23:21:34.36', 4, NULL, NULL, '&lt;p&gt;I am going to agree with @user1683454''s comment. After importing, your DATE column is of either &lt;code&gt;character&lt;/code&gt;, or &lt;code&gt;factor&lt;/code&gt; class (depending on your settings for &lt;code&gt;stringsAsFactors&lt;/code&gt;). Therefore, I think that you can solve this issue in at least several ways, as follows:&lt;/p&gt;

&lt;p&gt;1) &lt;strong&gt;Convert data&lt;/strong&gt; to correct type &lt;strong&gt;during import&lt;/strong&gt;. To do this, just use the following options of &lt;code&gt;read.csv()&lt;/code&gt;: &lt;code&gt;stringsAsFactors&lt;/code&gt; (or &lt;code&gt;as.is&lt;/code&gt;) and &lt;code&gt;colClasses&lt;/code&gt;. By default, you can specify conversion to &lt;code&gt;Date&lt;/code&gt; or &lt;code&gt;POSIXct&lt;/code&gt; classes. If you need a non-standard format, you have two options. First, if you have a single Date column, you can use &lt;code&gt;as.Date.character()&lt;/code&gt; to pass the desired format to &lt;code&gt;colClasses&lt;/code&gt;. Second, if you have multiple Date columns, you can write a function for that and pass it to &lt;code&gt;colClasses&lt;/code&gt; via &lt;code&gt;setAs()&lt;/code&gt;. Both options are discussed here: &lt;a href="http://stackoverflow.com/questions/13022299/specify-date-format-for-colclasses-argument-in-read-table-read-csv"&gt;http://stackoverflow.com/questions/13022299/specify-date-format-for-colclasses-argument-in-read-table-read-csv&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;2) &lt;strong&gt;Convert data&lt;/strong&gt; to correct format &lt;strong&gt;after import&lt;/strong&gt;. Thus, after calling &lt;code&gt;read.csv()&lt;/code&gt;, you would have to execute the following code: &lt;code&gt;dateColumn &amp;lt;- as.Date(dateColumn, "%m/%d/%Y")&lt;/code&gt; or &lt;code&gt;dateColumn &amp;lt;- strptime(dateColumn, "%m/%d/%Y")&lt;/code&gt; (adjust the format to whatever Date format you need).&lt;/p&gt;
', NULL, NULL, '''/code'':21C,25C,34C,76C,79C,83C,87C,97C,101C,128C,137C,158C,162C,194C,211C,221C ''/p'':51C,231C ''/questions/13022299/specify-date-format-for-colclasses-argument-in-read-table-read-csv'':172C ''/questions/13022299/specify-date-format-for-colclasses-argument-in-read-table-read-csv&lt;/a&gt;.&lt;/p&gt;'':175C ''/strong'':57C,64C,181C,188C ''1'':53C ''2'':177C ''adjust'':222C ''agre'':6C ''as.date'':206C ''as.date.character'':127C ''as.is'':82C ''call'':191C ''charact'':20C ''class'':26C,102C ''code'':19C,23C,32C,74C,77C,81C,85C,95C,99C,126C,135C,156C,160C,192C,202C,203C,213C ''colclass'':86C,136C,157C ''column'':15C,122C,144C ''comment'':10C ''convers'':93C ''convert'':55C,179C ''correct'':59C,183C ''d'':209C,219C ''data'':56C,180C ''date'':14C,96C,121C,143C,227C ''datecolumn'':204C,207C,214C,217C ''default'':89C ''depend'':27C ''desir'':132C ''discuss'':166C ''either'':18C ''execut'':199C ''factor'':24C ''first'':115C ''follow'':50C,71C,201C ''format'':110C,133C,184C,224C,228C ''function'':149C ''go'':4C ''href'':169C ''import'':12C,63C,187C ''issu'':43C ''least'':46C ''lt'':205C,215C ''m'':208C,218C ''multipl'':142C ''need'':105C,230C ''non'':108C ''non-standard'':107C ''option'':72C,114C,164C ''p'':1C,52C,176C ''pass'':130C,153C ''posixct'':100C ''read.csv'':75C,193C ''second'':138C ''set'':30C ''seta'':161C ''sever'':47C ''singl'':120C ''solv'':41C ''specifi'':92C ''stackoverflow.com'':171C,174C ''stackoverflow.com/questions/13022299/specify-date-format-for-colclasses-argument-in-read-table-read-csv'':170C ''stackoverflow.com/questions/13022299/specify-date-format-for-colclasses-argument-in-read-table-read-csv&lt;/a&gt;.&lt;/p&gt;'':173C ''standard'':109C ''stringsasfactor'':33C,78C ''strong'':54C,61C,178C,185C ''strptime'':216C ''therefor'':35C ''think'':37C ''thus'':189C ''two'':113C ''type'':60C ''use'':69C,125C ''user1683454'':8C ''via'':159C ''way'':48C ''whatev'':226C ''would'':196C ''write'':147C ''y'':210C,220C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (835, 2, '2014-07-25 03:05:38.107', 1, NULL, NULL, '&lt;p&gt;Consider, try, and perhaps even use multiple databases. It''s not just a "performance" issue at play here. It''s really going to come down to your requirements. How much data are you talking about? what kind of data? how fast do you need it? Are you more read heavy or write heavy?&lt;/p&gt;

&lt;p&gt;Here''s one thing you can''t do in a SQL database: Calculate sentiment. &lt;a href="http://www.slideshare.net/shift8/mongodb-machine-learning" rel="nofollow"&gt;http://www.slideshare.net/shift8/mongodb-machine-learning&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Of course the speed in that case may not be fast enough for your needs, but it is something that''s possible. With some caching of specific aggregate values, it was quite acceptable even. Why would you do this? Convenience.&lt;/p&gt;

&lt;p&gt;Convenience really is something that you''re going to be persuaded by. That''s exactly why (in my opinion) NoSQL databases were created. Performance too of course, but I''m trying to discount benchmarks and focus more on other concerns.&lt;/p&gt;

&lt;p&gt;MongoDB (and some other NoSQL) databases have some very powerful features such as built-in map/reduce. This could result in a savings both in cost and time over using something like Hadoop. Or it could provide a prototype or MVP to launch a larger business.&lt;/p&gt;

&lt;p&gt;What about graph databases? They''re "NoSQL" too. Look at databases like OrientDB. If you want to argue performance ...I don''t think you''re gonna show me a SQL database that''s faster there =) ...and graph databases have some really amazing application based on what you need to do.&lt;/p&gt;

&lt;p&gt;Rule of technology (and the internet) don''t get too comfortable with one thing. You''re gonna be limited and set yourself up for failure.&lt;/p&gt;
', NULL, NULL, '''/p'':55C,122C,164C,212C,264C,291C ''/shift8/mongodb-machine-learning'':75C ''/shift8/mongodb-machine-learning&lt;/a&gt;&lt;/p&gt;'':80C ''accept'':114C ''aggreg'':109C ''amaz'':255C ''applic'':256C ''argu'':231C ''base'':257C ''benchmark'':157C ''built'':180C ''built-in'':179C ''busi'':211C ''cach'':106C ''calcul'':69C ''case'':88C ''come'':25C ''comfort'':276C ''concern'':163C ''consid'':2C ''conveni'':121C,124C ''cost'':191C ''could'':184C,201C ''cours'':83C,150C ''creat'':146C ''data'':32C,40C ''databas'':9C,68C,144C,171C,217C,224C,244C,251C ''discount'':156C ''enough'':93C ''even'':6C,115C ''exact'':138C ''failur'':290C ''fast'':42C,92C ''faster'':247C ''featur'':176C ''focus'':159C ''get'':274C ''go'':23C,131C ''gonna'':239C,282C ''graph'':216C,250C ''hadoop'':198C ''heavi'':51C,54C ''href'':72C ''internet'':271C ''issu'':16C ''kind'':38C ''larger'':210C ''launch'':208C ''like'':197C,225C ''limit'':284C ''look'':222C ''m'':153C ''map/reduce'':182C ''may'':89C ''mongodb'':166C ''much'':31C ''multipl'':8C ''mvp'':206C ''need'':45C,96C,261C ''nofollow'':77C ''nosql'':143C,170C,220C ''one'':59C,278C ''opinion'':142C ''orientdb'':226C ''p'':1C,56C,81C,123C,165C,213C,265C ''perform'':15C,147C,232C ''perhap'':5C ''persuad'':134C ''play'':18C ''possibl'':103C ''power'':175C ''prototyp'':204C ''provid'':202C ''quit'':113C ''re'':130C,219C,238C,281C ''read'':50C ''realli'':22C,125C,254C ''rel'':76C ''requir'':29C ''result'':185C ''rule'':266C ''save'':188C ''sentiment'':70C ''set'':286C ''show'':240C ''someth'':100C,127C,196C ''specif'':108C ''speed'':85C ''sql'':67C,243C ''talk'':35C ''technolog'':268C ''thing'':60C,279C ''think'':236C ''time'':193C ''tri'':3C,154C ''use'':7C,195C ''valu'':110C ''want'':229C ''would'':117C ''write'':53C ''www.slideshare.net'':74C,79C ''www.slideshare.net/shift8/mongodb-machine-learning'':73C ''www.slideshare.net/shift8/mongodb-machine-learning&lt;/a&gt;&lt;/p&gt;'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (837, 2, '2014-07-25 12:59:00.85', 1, NULL, NULL, '&lt;p&gt;I would look at the &lt;code&gt;raster&lt;/code&gt; package for this, which can read in raw binary data and present it as NxM grids. It can even extract subsets of large binary grids without having to read in the whole file (the R raster object itself is just a proxy to the data, not the data itself).&lt;/p&gt;
', NULL, NULL, '''/code'':9C ''/p'':59C ''binari'':18C,33C ''code'':7C ''data'':19C,54C,57C ''even'':28C ''extract'':29C ''file'':42C ''grid'':25C,34C ''larg'':32C ''look'':4C ''nxm'':24C ''object'':46C ''p'':1C ''packag'':10C ''present'':21C ''proxi'':51C ''r'':44C ''raster'':8C,45C ''raw'':17C ''read'':15C,38C ''subset'':30C ''whole'':41C ''without'':35C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (838, 1, '2014-07-24 06:34:50.083', 2, 46, 'Pig latin code error', '&lt;p&gt;While running the below pig script I am getting error in line4: If it is &lt;code&gt;GROUP&lt;/code&gt; then I am getting error. If I change from &lt;code&gt;GROUP&lt;/code&gt; TO &lt;code&gt;group&lt;/code&gt; in line4, then the script is running. What is the difference between group and GROUP.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;LINES = LOAD ''/user/cloudera/datapeople.csv'' USING PigStorage('','') AS ( firstname:chararray, lastname:chararray, address:chararray, city:chararray, state:chararray, zip:chararray );

WORDS = FOREACH LINES GENERATE FLATTEN(TOKENIZE(zip)) AS ZIPS;

WORDSGROUPED = GROUP WORDS BY ZIPS;

WORDBYCOUNT = FOREACH WORDSGROUPED GENERATE GROUP AS ZIPS, COUNT(WORDS);

WORDSSORT = ORDER WORDBYCOUNT BY $1 DESC;

DUMP WORDSSORT;
&lt;/code&gt;&lt;/pre&gt;
', 'hadoop  bigdata', NULL, '''/code'':25C,37C,41C,109C ''/p'':57C ''/pre'':110C ''/user/cloudera/datapeople.csv'':62C ''1'':105C ''address'':70C ''bigdata'':6B ''chang'':33C ''chararray'':67C,69C,71C,73C,75C,77C ''citi'':72C ''code'':3A,23C,35C,39C,59C ''count'':99C ''desc'':106C ''differ'':52C ''dump'':107C ''error'':4A,17C,30C ''firstnam'':66C ''flatten'':82C ''foreach'':79C,93C ''generat'':81C,95C ''get'':16C,29C ''group'':24C,36C,40C,54C,56C,88C,96C ''hadoop'':5B ''lastnam'':68C ''latin'':2A ''line'':60C,80C ''line4'':19C,43C ''load'':61C ''order'':102C ''p'':7C ''pig'':1A,12C ''pigstorag'':64C ''pre'':58C ''run'':9C,48C ''script'':13C,46C ''state'':74C ''token'':83C ''use'':63C ''word'':78C,89C,100C ''wordbycount'':92C,103C ''wordsgroup'':87C,94C ''wordssort'':101C,108C ''zip'':76C,84C,86C,91C,98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2332, 2, '2014-10-22 08:30:00.99', 3, NULL, NULL, '&lt;p&gt;Since it''s a master''s thesis, how about writing something regarding decision trees, and their "upgrades": boosting and Random Forests? And then integrate that with Map/Reduce, together with showing how to scale a Random Forest on Hadoop using M/R?&lt;/p&gt;
', NULL, NULL, '''/p'':42C ''boost'':19C ''decis'':14C ''forest'':22C,37C ''hadoop'':39C ''integr'':25C ''m/r'':41C ''map/reduce'':28C ''master'':6C ''p'':1C ''random'':21C,36C ''regard'':13C ''scale'':34C ''show'':31C ''sinc'':2C ''someth'':12C ''thesi'':8C ''togeth'':29C ''tree'':15C ''upgrad'':18C ''use'':40C ''write'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (839, 2, '2014-07-25 09:05:09.493', 0, NULL, NULL, '&lt;p&gt;When we do grouping of the data, Pig creates a new key named "group" and puts all the tuple matching that key into a bag and associates the bag with the key. So after the group operation schema of the grouped data will be something like &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;raw = load ''$input'' using PigStorage(''\u0001'') as (id1:int, name:chararray);
groupdata1 = group raw by (id1,name);  
describe groupdata1;
{group: (id1: int,name: chararray),raw: {(id1: int,name: chararray)}}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The ''GROUP'' in line 4 you are trying to access is one of the attribute of the schema from the last statement. These attribute name are case sensitive. It will produce the error saying it doesn''t exist in the schema. So you need to use ''group'' only to access it.&lt;/p&gt;
', NULL, NULL, '''/code'':80C ''/p'':48C,134C ''/pre'':81C ''4'':87C ''access'':92C,132C ''associ'':28C ''attribut'':97C,106C ''bag'':26C,30C ''case'':109C ''chararray'':61C,74C,79C ''code'':50C ''creat'':10C ''data'':8C,43C ''describ'':68C ''doesn'':118C ''error'':115C ''exist'':120C ''group'':5C,15C,37C,42C,63C,70C,84C,129C ''groupdata1'':62C,69C ''id1'':58C,66C,71C,76C ''input'':53C ''int'':59C,72C,77C ''key'':13C,23C,33C ''last'':103C ''like'':47C ''line'':86C ''load'':52C ''match'':21C ''name'':14C,60C,67C,73C,78C,107C ''need'':126C ''new'':12C ''one'':94C ''oper'':38C ''p'':1C,82C ''pig'':9C ''pigstorag'':55C ''pre'':49C ''produc'':113C ''put'':17C ''raw'':51C,64C,75C ''say'':116C ''schema'':39C,100C,123C ''sensit'':110C ''someth'':46C ''statement'':104C ''tri'':90C ''tupl'':20C ''u0001'':56C ''use'':54C,128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (847, 2, '2014-07-26 08:37:52.823', 2, NULL, NULL, '&lt;p&gt;&lt;a href="https://www.coursera.org/course/datasci" rel="nofollow"&gt;Introduction to Data Science&lt;/a&gt; course that is being run on Coursera now includes real-world project assignment where companies post their problems and students are encouraged to solve them. This is done via &lt;a href="https://www.coursolve.org/" rel="nofollow"&gt;coursolve.com&lt;/a&gt; (already mentioned here).&lt;/p&gt;

&lt;p&gt;More information &lt;a href="https://class.coursera.org/datasci-002/wiki/OptionalRealWorldProject" rel="nofollow"&gt;here&lt;/a&gt; (you have to be enrolled in the course to see that link)&lt;/p&gt;
', NULL, NULL, '''/a'':13C,50C,66C ''/course/datasci'':6C ''/datasci-002/wiki/optionalrealworldproject'':62C ''/p'':54C,79C ''alreadi'':51C ''assign'':27C ''class.coursera.org'':61C ''class.coursera.org/datasci-002/wiki/optionalrealworldproject'':60C ''compani'':29C ''cours'':14C,74C ''coursera'':20C ''coursolve.com'':49C ''data'':11C ''done'':42C ''encourag'':36C ''enrol'':71C ''href'':3C,45C,59C ''includ'':22C ''inform'':57C ''introduct'':9C ''link'':78C ''mention'':52C ''nofollow'':8C,48C,64C ''p'':1C,55C ''post'':30C ''problem'':32C ''project'':26C ''real'':24C ''real-world'':23C ''rel'':7C,47C,63C ''run'':18C ''scienc'':12C ''see'':76C ''solv'':38C ''student'':34C ''via'':43C ''world'':25C ''www.coursera.org'':5C ''www.coursera.org/course/datasci'':4C ''www.coursolve.org'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6759, 2, '2015-08-11 12:22:01.997', 1, NULL, NULL, '&lt;p&gt;You are looking for Recurrent Neural Networks for character-level language models. &lt;/p&gt;

&lt;p&gt;Have a look at this. &lt;a href="https://github.com/karpathy/char-rnn" rel="nofollow"&gt;https://github.com/karpathy/char-rnn&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/karpathy/char-rnn'':26C ''/karpathy/char-rnn&lt;/a&gt;&lt;/p&gt;'':31C ''/p'':15C ''charact'':11C ''character-level'':10C ''github.com'':25C,30C ''github.com/karpathy/char-rnn'':24C ''github.com/karpathy/char-rnn&lt;/a&gt;&lt;/p&gt;'':29C ''href'':23C ''languag'':13C ''level'':12C ''look'':4C,19C ''model'':14C ''network'':8C ''neural'':7C ''nofollow'':28C ''p'':1C,16C ''recurr'':6C ''rel'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (840, 1, '2014-07-25 17:18:21.393', 1, 108, 'How to fix similarity matrix in Pandas returning all NaNs?', '&lt;p&gt;I''ve been trying to create a similarity matrix in Pandas from with a matrix multiplication operation on a document-term count matrix with 2264 rows and 20475 columns.&lt;/p&gt;

&lt;p&gt;The calculation completes in IPython but inspection shows the results all come back as NaN.&lt;/p&gt;

&lt;p&gt;I''ve also tried doing the same job in numpy, tried converting the original matrix to_sparse and even re-casting the values as integers, but still no joy.&lt;/p&gt;

&lt;p&gt;Can anyone suggest the best approach to tackle the problem?&lt;/p&gt;

&lt;p&gt;EDIT: Here''s my code thus far:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;path = "../../reuters.db"
%pylab inline
import pandas as pd
import numpy as np
import pandas.io.sql as psql
import sqlite3 as lite
con = lite.connect(path)
with con:
    sql = "SELECT * FROM Frequency"
    df = psql.frame_query(sql, con)
    print df.shape
df = df.rename(columns={"term":"term_id", "count":"count_id"})
pivoted = df.pivot(''docid'', ''term_id'', ''count_id'')
pivoted.to_sparse()
similarity_matrix = pivoted.dot(pivoted.T)
&lt;/code&gt;&lt;/pre&gt;
', 'pandas  similarity', NULL, '''/../reuters.db'':118C ''/code'':175C ''/p'':44C,61C,93C,105C,114C ''/pre'':176C ''20475'':42C ''2264'':39C ''also'':65C ''anyon'':96C ''approach'':100C ''back'':58C ''best'':99C ''calcul'':47C ''cast'':84C ''code'':111C,116C ''column'':43C,155C ''come'':57C ''complet'':48C ''con'':137C,141C,150C ''convert'':74C ''count'':36C,159C,160C,167C ''creat'':19C ''df'':146C,153C ''df.pivot'':163C ''df.rename'':154C ''df.shape'':152C ''docid'':164C ''document'':34C ''document-term'':33C ''edit'':107C ''even'':81C ''far'':113C ''fix'':3A ''frequenc'':145C ''id'':158C,161C,166C,168C ''import'':121C,125C,129C,133C ''inlin'':120C ''inspect'':52C ''integ'':88C ''ipython'':50C ''job'':70C ''joy'':92C ''lite'':136C ''lite.connect'':138C ''matrix'':5A,22C,28C,37C,77C,172C ''multipl'':29C ''nan'':10A,60C ''np'':128C ''numpi'':72C,126C ''oper'':30C ''origin'':76C ''p'':13C,45C,62C,94C,106C ''panda'':7A,11B,24C,122C ''pandas.io.sql'':130C ''path'':117C,139C ''pd'':124C ''pivot'':162C ''pivoted.dot'':173C ''pivoted.t'':174C ''pivoted.to'':169C ''pre'':115C ''print'':151C ''problem'':104C ''psql'':132C ''psql.frame'':147C ''pylab'':119C ''queri'':148C ''re'':83C ''re-cast'':82C ''result'':55C ''return'':8A ''row'':40C ''select'':143C ''show'':53C ''similar'':4A,12B,21C,171C ''spars'':79C,170C ''sql'':142C,149C ''sqlite3'':134C ''still'':90C ''suggest'':97C ''tackl'':102C ''term'':35C,156C,157C,165C ''thus'':112C ''tri'':17C,66C,73C ''valu'':86C ''ve'':15C,64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (841, 2, '2014-07-25 18:12:49.847', 4, NULL, NULL, '&lt;p&gt;Instead of collaborative filtering I would use the matrix factorization approach, wherein users and movies alike a represented by vectors of latent features whose dot products yield the ratings. Normally one merely selects the rank (number of features) without regard to what the features represent, and the algorithm does the rest. Like PCA, the result is not immediately interpretable but it yields good results. What you want to do is extend the movie matrix to include the additional features you mentioned and make sure that they stay fixed as the algorithm estimates the two matrices using regularizastion. The corresponding entries in the user matrix will be initialized randomly, then estimated by the matrix factorization algorithm. It''s a versatile and performant approach but it takes some understanding of machine learning, or linear algebra at least.&lt;/p&gt;

&lt;p&gt;I saw a nice ipython notebook a while back but I can''t find it right now, so I''ll refer you to &lt;a href="http://nbviewer.ipython.org/github/diktat/CPSC540machinelearning/blob/master/1.4%20Collaborative%20Filtering%20for%20Movie%20Recommendation.ipynb" rel="nofollow"&gt;another one&lt;/a&gt; which, while not as nice, still clarifies some of the maths.&lt;/p&gt;
', NULL, NULL, '''/a'':171C ''/github/diktat/cpsc540machinelearning/blob/master/1.4%20collaborative%20filtering%20for%20movie%20recommendation.ipynb'':166C ''/p'':137C,183C ''addit'':79C ''algebra'':134C ''algorithm'':49C,92C,116C ''alik'':17C ''anoth'':169C ''approach'':12C,123C ''back'':147C ''clarifi'':178C ''collabor'':4C ''correspond'':100C ''dot'':26C ''entri'':101C ''estim'':93C,111C ''extend'':72C ''factor'':11C,115C ''featur'':24C,39C,45C,80C ''filter'':5C ''find'':152C ''fix'':89C ''good'':64C ''href'':163C ''immedi'':59C ''includ'':77C ''initi'':108C ''instead'':2C ''interpret'':60C ''ipython'':143C ''latent'':23C ''learn'':131C ''least'':136C ''like'':53C ''linear'':133C ''ll'':158C ''machin'':130C ''make'':84C ''math'':182C ''matric'':96C ''matrix'':10C,75C,105C,114C ''mention'':82C ''mere'':33C ''movi'':16C,74C ''nbviewer.ipython.org'':165C ''nbviewer.ipython.org/github/diktat/cpsc540machinelearning/blob/master/1.4%20collaborative%20filtering%20for%20movie%20recommendation.ipynb'':164C ''nice'':142C,176C ''nofollow'':168C ''normal'':31C ''notebook'':144C ''number'':37C ''one'':32C,170C ''p'':1C,138C ''pca'':54C ''perform'':122C ''product'':27C ''random'':109C ''rank'':36C ''rate'':30C ''refer'':159C ''regard'':41C ''regularizast'':98C ''rel'':167C ''repres'':19C,46C ''rest'':52C ''result'':56C,65C ''right'':154C ''saw'':140C ''select'':34C ''stay'':88C ''still'':177C ''sure'':85C ''take'':126C ''two'':95C ''understand'':128C ''use'':8C,97C ''user'':14C,104C ''vector'':21C ''versatil'':120C ''want'':68C ''wherein'':13C ''whose'':25C ''without'':40C ''would'':7C ''yield'':28C,63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (842, 1, '2014-07-25 18:36:31.34', 12, 17577, 'Data Science Project Ideas', '&lt;p&gt;I don''t know if this is a right place to ask this question, but a community dedicated to Data Science should be the most appropriate place in my opinion.&lt;/p&gt;

&lt;p&gt;I have just started with Data Science and Machine learning. I am looking for long term project ideas which I can work on for like 8 months.&lt;/p&gt;

&lt;p&gt;A mix of Data Science and Machine learning would be great.&lt;/p&gt;

&lt;p&gt;A project big enough to help me understand the core concepts and also implement them at the same time would be very beneficial.&lt;/p&gt;
', 'machine-learning  bigdata  dataset', NULL, '''/p'':41C,70C,83C,108C ''8'':68C ''also'':97C ''appropri'':36C ''ask'':22C ''benefici'':107C ''big'':87C ''bigdata'':8B ''communiti'':27C ''concept'':95C ''core'':94C ''data'':1A,30C,48C,75C ''dataset'':9B ''dedic'':28C ''enough'':88C ''great'':82C ''help'':90C ''idea'':4A,60C ''implement'':98C ''know'':14C ''learn'':7B,52C,79C ''like'':67C ''long'':57C ''look'':55C ''machin'':6B,51C,78C ''machine-learn'':5B ''mix'':73C ''month'':69C ''opinion'':40C ''p'':10C,42C,71C,84C ''place'':20C,37C ''project'':3A,59C,86C ''question'':24C ''right'':19C ''scienc'':2A,31C,49C,76C ''start'':46C ''term'':58C ''time'':103C ''understand'':92C ''work'':64C ''would'':80C,104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (843, 2, '2014-07-25 20:50:14.54', 18, NULL, NULL, '&lt;p&gt;I would try to analyze and solve one or more of the problems published on &lt;strong&gt;Kaggle Competitions&lt;/strong&gt; (&lt;a href="https://www.kaggle.com/competitions"&gt;https://www.kaggle.com/competitions&lt;/a&gt;). Note that the competitions are grouped by their expected &lt;em&gt;complexity&lt;/em&gt;, from &lt;code&gt;101&lt;/code&gt; (bottom of the list) to &lt;code&gt;Research&lt;/code&gt; and &lt;code&gt;Featured&lt;/code&gt; (top of the list). A color-coded vertical band is a &lt;em&gt;visual guideline&lt;/em&gt; for grouping. You can &lt;strong&gt;assess time&lt;/strong&gt; you could spend on a project by &lt;strong&gt;adjusting&lt;/strong&gt; the expected &lt;em&gt;length&lt;/em&gt; of corresponding competition, based on your &lt;em&gt;skills&lt;/em&gt; and &lt;em&gt;experience&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;A number of &lt;strong&gt;data science project ideas&lt;/strong&gt; can be found by browsing the following &lt;code&gt;Coursolve&lt;/code&gt; webpage: &lt;a href="https://www.coursolve.org/browse-needs?query=Data%20Science"&gt;https://www.coursolve.org/browse-needs?query=Data%20Science&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;If you have skills and desire to work on a &lt;strong&gt;real data science project&lt;/strong&gt;, focused on &lt;strong&gt;social impacts&lt;/strong&gt;, visit &lt;code&gt;DataKind&lt;/code&gt; projects page: &lt;a href="http://www.datakind.org/projects"&gt;http://www.datakind.org/projects&lt;/a&gt;. More projects with social impacts focus can be found at &lt;code&gt;Data Science for Social Good&lt;/code&gt; fellowship webpage: &lt;a href="http://dssg.io/projects"&gt;http://dssg.io/projects&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Science Project Ideas&lt;/strong&gt; page at &lt;code&gt;My NASA Data&lt;/code&gt; site looks like another place to visit for inspiration: &lt;a href="http://mynasadata.larc.nasa.gov/804-2"&gt;http://mynasadata.larc.nasa.gov/804-2&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;If you would like to use &lt;strong&gt;open data&lt;/strong&gt;, this long list of applications on &lt;code&gt;Data.gov&lt;/code&gt; can provide you with some interesting &lt;em&gt;data science&lt;/em&gt; project ideas: &lt;a href="http://www.data.gov/applications"&gt;http://www.data.gov/applications&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/804-2'':229C ''/804-2&lt;/a&gt;.&lt;/p&gt;'':232C ''/applications'':269C ''/applications&lt;/a&gt;.&lt;/p&gt;'':272C ''/browse-needs?query=data%20science'':135C ''/browse-needs?query=data%20science&lt;/a&gt;.&lt;/p&gt;'':138C ''/code'':44C,52C,56C,129C,165C,192C,215C,252C ''/competitions'':25C ''/competitions&lt;/a&gt;).'':28C ''/em'':40C,72C,95C,104C,108C,262C ''/p'':109C ''/projects'':172C,199C ''/projects&lt;/a&gt;.'':175C ''/projects&lt;/a&gt;.&lt;/p&gt;'':202C ''/strong'':20C,80C,90C,119C,155C,161C,208C,243C ''101'':43C ''adjust'':89C ''analyz'':6C ''anoth'':219C ''applic'':248C ''assess'':78C ''band'':66C ''base'':99C ''bottom'':45C ''brows'':124C ''code'':42C,50C,54C,64C,127C,163C,186C,211C,250C ''color'':63C ''color-cod'':62C ''competit'':19C,32C,98C ''complex'':39C ''correspond'':97C ''could'':82C ''coursolv'':128C ''data'':115C,152C,187C,214C,242C,260C ''data.gov'':251C ''datakind'':164C ''desir'':145C ''dssg.io'':198C,201C ''dssg.io/projects'':197C ''dssg.io/projects&lt;/a&gt;.&lt;/p&gt;'':200C ''em'':38C,69C,93C,102C,106C,259C ''expect'':37C,92C ''experi'':107C ''featur'':55C ''fellowship'':193C ''focus'':156C,181C ''follow'':126C ''found'':122C,184C ''good'':191C ''group'':34C,74C ''guidelin'':71C ''href'':22C,132C,169C,196C,226C,266C ''idea'':118C,207C,264C ''impact'':160C,180C ''inspir'':224C ''interest'':258C ''kaggl'':18C ''length'':94C ''like'':218C,237C ''list'':48C,60C,246C ''long'':245C ''look'':217C ''mynasadata.larc.nasa.gov'':228C,231C ''mynasadata.larc.nasa.gov/804-2'':227C ''mynasadata.larc.nasa.gov/804-2&lt;/a&gt;.&lt;/p&gt;'':230C ''nasa'':213C ''note'':29C ''number'':112C ''one'':9C ''open'':241C ''p'':1C,110C,139C,203C,233C ''page'':167C,209C ''place'':220C ''problem'':14C ''project'':86C,117C,154C,166C,177C,206C,263C ''provid'':254C ''publish'':15C ''real'':151C ''research'':51C ''scienc'':116C,153C,188C,205C,261C ''site'':216C ''skill'':103C,143C ''social'':159C,179C,190C ''solv'':8C ''spend'':83C ''strong'':17C,77C,88C,114C,150C,158C,204C,240C ''time'':79C ''top'':57C ''tri'':4C ''use'':239C ''vertic'':65C ''visit'':162C,222C ''visual'':70C ''webpag'':130C,194C ''work'':147C ''would'':3C,236C ''www.coursolve.org'':134C,137C ''www.coursolve.org/browse-needs?query=data%20science'':133C ''www.coursolve.org/browse-needs?query=data%20science&lt;/a&gt;.&lt;/p&gt;'':136C ''www.data.gov'':268C,271C ''www.data.gov/applications'':267C ''www.data.gov/applications&lt;/a&gt;.&lt;/p&gt;'':270C ''www.datakind.org'':171C,174C ''www.datakind.org/projects'':170C ''www.datakind.org/projects&lt;/a&gt;.'':173C ''www.kaggle.com'':24C,27C ''www.kaggle.com/competitions'':23C ''www.kaggle.com/competitions&lt;/a&gt;).'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (845, 2, '2014-07-26 00:11:03.637', 6, NULL, NULL, '&lt;p&gt;Most often serialization error in (Py)Spark means that some part of your distributed code (e.g. functions passed to &lt;code&gt;map&lt;/code&gt;) has &lt;strong&gt;dependencies&lt;/strong&gt; on &lt;strong&gt;non-serializable data&lt;/strong&gt;. Consider following example: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;rdd = sc.parallelize(range(5))
rdd = rdd.map(lambda x: x + 1)
rdd.collect()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here you have distributed collection and lambda function to send to all workers. Lambda is completely self-containing, so it''s easy to copy its binary representation to other nodes without any worries. &lt;/p&gt;

&lt;p&gt;Now let''s make things a bit more interesting: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;f = open("/etc/hosts")
rdd = sc.parallelize(range(100))
rdd = rdd.map(lambda x: f.read())
rdd.collect()
f.close()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Boom! Strange error in serialization module! What just happened is that we had attempted to pass &lt;code&gt;f&lt;/code&gt;, which is a file object, to workers. Obviously, file object is a handle to &lt;em&gt;local&lt;/em&gt; data and thus cannot be sent to other machines. &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;So what''s happening in your specific code? Without actual data and knowing record format, I cannot debug it completely, but I guess that problem goes from this line: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def vectorizer(text, vocab=vocab_dict):
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In Python, keyword arguments are initialized when function is called for the first time. When you call &lt;code&gt;sc.parallelize(...).map(vectorizer)&lt;/code&gt; just after its definition, &lt;code&gt;vocab_dict&lt;/code&gt; is available &lt;em&gt;locally&lt;/em&gt;, but &lt;em&gt;remote&lt;/em&gt; workers know absolutely nothing about it. Thus function is called with fewer parameters than it expects which results in &lt;code&gt;__init__() takes exactly 3 arguments (2 given)&lt;/code&gt; error. &lt;/p&gt;

&lt;p&gt;Also note, that you follow very bad pattern of  &lt;code&gt;sc.parallelize(...)...collect()&lt;/code&gt; calls. First you spread your collection to entire cluster, do some computations, and then pull the result. But sending data back and forth is pretty pointless here. Instead, you can just do these computations locally, and run Spark''s parallel processes only when you work with really big datasets (like you main &lt;code&gt;amazon_dataset&lt;/code&gt;, I guess).&lt;/p&gt;
', NULL, NULL, '''/code'':23C,52C,117C,138C,206C,230C,238C,275C,291C,347C ''/em'':155C,243C,247C ''/etc/hosts'':105C ''/p'':38C,89C,100C,165C,197C,277C,350C ''/pre'':53C,118C,207C ''/strong'':27C,34C ''1'':50C ''100'':109C ''2'':273C ''3'':271C ''5'':44C ''absolut'':250C ''actual'':177C ''also'':279C ''amazon'':345C ''argument'':212C,272C ''attempt'':133C ''avail'':240C ''back'':312C ''bad'':285C ''big'':339C ''binari'':81C ''bit'':97C ''boom'':120C ''call'':218C,225C,257C,292C ''cannot'':159C,184C ''cluster'':300C ''code'':16C,21C,40C,102C,136C,175C,199C,226C,235C,267C,288C,344C ''collect'':59C,290C,297C ''complet'':70C,187C ''comput'':303C,325C ''consid'':35C ''contain'':73C ''copi'':79C ''data'':33C,156C,178C,311C ''dataset'':340C,346C ''debug'':185C ''def'':200C ''definit'':234C ''depend'':26C ''dict'':205C,237C ''distribut'':15C,58C ''e.g'':17C ''easi'':77C ''em'':153C,241C,245C ''entir'':299C ''error'':5C,122C,276C ''exact'':270C ''exampl'':37C ''expect'':263C ''f'':103C,137C ''f.close'':116C ''f.read'':114C ''fewer'':259C ''file'':142C,147C ''first'':221C,293C ''follow'':36C,283C ''format'':182C ''forth'':314C ''function'':18C,62C,216C,255C ''given'':274C ''goe'':193C ''guess'':190C,349C ''handl'':151C ''happen'':128C,171C ''hr'':166C ''init'':268C ''initi'':214C ''instead'':319C ''interest'':99C ''keyword'':211C ''know'':180C,249C ''lambda'':47C,61C,68C,112C ''let'':92C ''like'':341C ''line'':196C ''local'':154C,242C,326C ''machin'':164C ''main'':343C ''make'':94C ''map'':22C,228C ''mean'':9C ''modul'':125C ''node'':85C ''non'':31C ''non-serializ'':30C ''note'':280C ''noth'':251C ''object'':143C,148C ''obvious'':146C ''often'':3C ''open'':104C ''p'':1C,54C,90C,119C,167C,208C,278C ''parallel'':331C ''paramet'':260C ''part'':12C ''pass'':19C,135C ''pattern'':286C ''pointless'':317C ''pre'':39C,101C,198C ''pretti'':316C ''problem'':192C ''process'':332C ''pull'':306C ''py'':7C ''python'':210C ''rang'':43C,108C ''rdd'':41C,45C,106C,110C ''rdd.collect'':51C,115C ''rdd.map'':46C,111C ''realli'':338C ''record'':181C ''remot'':246C ''represent'':82C ''result'':265C,308C ''run'':328C ''sc.parallelize'':42C,107C,227C,289C ''self'':72C ''self-contain'':71C ''send'':64C,310C ''sent'':161C ''serial'':4C,124C ''serializ'':32C ''spark'':8C,329C ''specif'':174C ''spread'':295C ''strang'':121C ''strong'':25C,29C ''take'':269C ''text'':202C ''thing'':95C ''thus'':158C,254C ''time'':222C ''vector'':201C,229C ''vocab'':203C,204C,236C ''without'':86C,176C ''work'':336C ''worker'':67C,145C,248C ''worri'':88C ''x'':48C,49C,113C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (846, 2, '2014-07-26 01:12:08.167', 4, NULL, NULL, '&lt;p&gt;Take something from your everyday life. Create predictor of traffic jams in your region, craft personalised music recommender, analyse car market, etc. Choose &lt;strong&gt;real problem&lt;/strong&gt; that you &lt;strong&gt;want to solve&lt;/strong&gt; - this will not only keep you motivated, but also make you go through the whole development circle from data collection to hypothesis testing. &lt;/p&gt;
', NULL, NULL, '''/p'':59C ''/strong'':28C,35C ''also'':44C ''analys'':20C ''car'':21C ''choos'':24C ''circl'':52C ''collect'':55C ''craft'':16C ''creat'':8C ''data'':54C ''develop'':51C ''etc'':23C ''everyday'':6C ''go'':47C ''hypothesi'':57C ''jam'':12C ''keep'':40C ''life'':7C ''make'':45C ''market'':22C ''motiv'':42C ''music'':18C ''p'':1C ''personalis'':17C ''predictor'':9C ''problem'':27C ''real'':26C ''recommend'':19C ''region'':15C ''solv'':34C ''someth'':3C ''strong'':25C,31C ''take'':2C ''test'':58C ''traffic'':11C ''want'':32C ''whole'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (848, 2, '2014-07-26 16:04:25.363', 11, NULL, NULL, '&lt;p&gt;K-means is not the most appropriate algorithm here.&lt;/p&gt;

&lt;p&gt;The reason is that k-means is designed to &lt;strong&gt;minimize variance&lt;/strong&gt;. This is, of course, appearling from a statistical and signal procssing point of view, but your data is not "linear".&lt;/p&gt;

&lt;p&gt;Since your data is in latitude, longitude format, you should use an algorithm that can handle &lt;em&gt;arbitrary&lt;/em&gt; distance functions, in particular geodetic distance functions. Hierarchical clustering, PAM, CLARA, and DBSCAN are popular examples of this.&lt;/p&gt;

&lt;p&gt;The problems of k-means are easy to see when you consider points close to the +-180 degrees wrap-around. Even if you hacked k-means to use Haversine distance, in the update step when it recomputes the &lt;em&gt;mean&lt;/em&gt; the result will be badly screwed. &lt;strong&gt;Worst case is, k-means will never converge!&lt;/strong&gt;&lt;/p&gt;
', NULL, NULL, '''-180'':106C ''/em'':68C,132C ''/p'':12C,48C,87C,150C ''/strong'':27C,149C ''algorithm'':10C,62C ''appearl'':32C ''appropri'':9C ''arbitrari'':67C ''around'':110C ''bad'':137C ''case'':141C ''clara'':79C ''close'':103C ''cluster'':77C ''consid'':101C ''converg'':148C ''cours'':31C ''data'':44C,52C ''dbscan'':81C ''degre'':107C ''design'':22C ''distanc'':69C,74C,121C ''easi'':96C ''em'':66C,130C ''even'':111C ''exampl'':84C ''format'':57C ''function'':70C,75C ''geodet'':73C ''hack'':114C ''handl'':65C ''haversin'':120C ''hierarch'':76C ''k'':3C,19C,93C,116C,144C ''k-mean'':2C,18C,92C,115C,143C ''latitud'':55C ''linear'':47C ''longitud'':56C ''mean'':4C,20C,94C,117C,131C,145C ''minim'':25C ''never'':147C ''p'':1C,13C,49C,88C ''pam'':78C ''particular'':72C ''point'':39C,102C ''popular'':83C ''problem'':90C ''procss'':38C ''reason'':15C ''recomput'':128C ''result'':134C ''screw'':138C ''see'':98C ''signal'':37C ''sinc'':50C ''statist'':35C ''step'':125C ''strong'':24C,139C ''updat'':124C ''use'':60C,119C ''varianc'':26C ''view'':41C ''worst'':140C ''wrap'':109C ''wrap-around'':108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (849, 2, '2014-07-26 21:03:13.47', 2, NULL, NULL, '&lt;p&gt;Since you want to show so much data, I think that your best choice is going interactive. Check out this &lt;a href="http://demo.zunzun.se/intervals/index.html" rel="nofollow"&gt;demo&lt;/a&gt;, it is close to what you want but not quite. &lt;/p&gt;

&lt;p&gt;It is very difficult to show a lot of data in a single diagram, together with the finest details and the bird-eyes view. But you can let the user interact and look for the details. To show counts, one option is to use color-coding. Take a look at this image (code &lt;a href="https://gist.github.com/dsign/4c598cfbfc81f6d491d5" rel="nofollow"&gt;here&lt;/a&gt;): &lt;img src="http://i.stack.imgur.com/xmgHi.png" alt="image"&gt;.&lt;/p&gt;

&lt;p&gt;Here rgb channels have been used to encode (the logarithm of) the number of active events (red), events starting (green) and events ending (blue) for windows of different size. The X axis is time, and the Y axis represents window size, that is, duration. Thus, a point with coordinates (10, 4) represents the interval of time that goes from 10 to 14. &lt;/p&gt;

&lt;p&gt;To make a lot of data more detailed, it could be a good idea to make the diagram zoomable (like in the demo before), and to give the user the possibility of visualizing just one channel/magnitude.&lt;/p&gt;
', NULL, NULL, '''/a'':30C,106C ''/dsign/4c598cfbfc81f6d491d5'':102C ''/intervals/index.html'':26C ''/p'':41C,114C,178C,216C ''/xmghi.png'':111C ''10'':165C,175C ''14'':177C ''4'':166C ''activ'':130C ''alt'':112C ''axi'':147C,153C ''best'':14C ''bird'':65C ''bird-ey'':64C ''blue'':139C ''channel'':118C ''channel/magnitude'':215C ''check'':19C ''choic'':15C ''close'':33C ''code'':90C,97C ''color'':89C ''color-cod'':88C ''coordin'':164C ''could'':189C ''count'':82C ''data'':9C,52C,185C ''demo'':29C,202C ''demo.zunzun.se'':25C ''demo.zunzun.se/intervals/index.html'':24C ''detail'':61C,79C,187C ''diagram'':56C,197C ''differ'':143C ''difficult'':46C ''durat'':159C ''encod'':123C ''end'':138C ''event'':131C,133C,137C ''eye'':66C ''finest'':60C ''gist.github.com'':101C ''gist.github.com/dsign/4c598cfbfc81f6d491d5'':100C ''give'':206C ''go'':17C ''goe'':173C ''good'':192C ''green'':135C ''href'':23C,99C ''i.stack.imgur.com'':110C ''i.stack.imgur.com/xmghi.png'':109C ''idea'':193C ''imag'':96C,113C ''img'':107C ''interact'':18C,74C ''interv'':169C ''let'':71C ''like'':199C ''logarithm'':125C ''look'':76C,93C ''lot'':50C,183C ''make'':181C,195C ''much'':8C ''nofollow'':28C,104C ''number'':128C ''one'':83C,214C ''option'':84C ''p'':1C,42C,115C,179C ''point'':162C ''possibl'':210C ''quit'':40C ''red'':132C ''rel'':27C,103C ''repres'':154C,167C ''rgb'':117C ''show'':6C,48C,81C ''sinc'':2C ''singl'':55C ''size'':144C,156C ''src'':108C ''start'':134C ''take'':91C ''think'':11C ''thus'':160C ''time'':149C,171C ''togeth'':57C ''use'':87C,121C ''user'':73C,208C ''view'':67C ''visual'':212C ''want'':4C,37C ''window'':141C,155C ''x'':146C ''y'':152C ''zoomabl'':198C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (850, 2, '2014-07-27 03:58:22.907', 6, NULL, NULL, '&lt;p&gt;If you''ve got prior information then you should certainly not use simple mean in a split test. I assume you''re trying to just predict which group will produce the greatest amount of revenue overall, by trying to emulate the underlying distribution.&lt;/p&gt;

&lt;p&gt;Firstly, it''s worth noting that any metrics you choose will actually reduce to mean in a pretty trivial way. Eventually mean will necessarily work out, though using a standard bayesian method to estimate the mean is probably your best bet.&lt;/p&gt;

&lt;p&gt;If you''ve got a prior then using a standard bayesian approach to update the prior on your mean revenue is probably the best way to do it. Basically, just take the individual results you get and update a multinomial distribution representing your prior in each case.&lt;/p&gt;

&lt;p&gt;If you want some more full background on multinomial distributions as bayesian priors are pretty well, &lt;a href="http://research.microsoft.com/en-us/um/people/minka/papers/minka-multinomial.pdf"&gt;this&lt;/a&gt; Microsoft paper does a pretty good job of outlining it. In general, I wouldn''t care so much about the fact that your distribution is technically discrete, as a multinomial distribution will effectively interpolate across your solution space, giving you a continuous distribution that is a very good approximation of your discrete space.&lt;/p&gt;
', NULL, NULL, '''/a'':161C ''/en-us/um/people/minka/papers/minka-multinomial.pdf'':159C ''/p'':45C,88C,137C,215C ''across'':196C ''actual'':58C ''amount'':34C ''approach'':101C ''approxim'':210C ''assum'':21C ''background'':145C ''basic'':118C ''bayesian'':77C,100C,150C ''best'':86C,113C ''bet'':87C ''care'':177C ''case'':136C ''certain'':11C ''choos'':56C ''continu'':203C ''discret'':188C,213C ''distribut'':44C,130C,148C,185C,192C,204C ''effect'':194C ''emul'':41C ''estim'':80C ''eventu'':67C ''fact'':182C ''first'':47C ''full'':144C ''general'':173C ''get'':125C ''give'':200C ''good'':167C,209C ''got'':5C,93C ''greatest'':33C ''group'':29C ''href'':156C ''individu'':122C ''inform'':7C ''interpol'':195C ''job'':168C ''mean'':15C,61C,68C,82C,108C ''method'':78C ''metric'':54C ''microsoft'':162C ''much'':179C ''multinomi'':129C,147C,191C ''necessarili'':70C ''note'':51C ''outlin'':170C ''overal'':37C ''p'':1C,46C,89C,138C ''paper'':163C ''predict'':27C ''pretti'':64C,153C,166C ''prior'':6C,95C,105C,133C,151C ''probabl'':84C,111C ''produc'':31C ''re'':23C ''reduc'':59C ''repres'':131C ''research.microsoft.com'':158C ''research.microsoft.com/en-us/um/people/minka/papers/minka-multinomial.pdf'':157C ''result'':123C ''revenu'':36C,109C ''simpl'':14C ''solut'':198C ''space'':199C,214C ''split'':18C ''standard'':76C,99C ''take'':120C ''technic'':187C ''test'':19C ''though'':73C ''tri'':24C,39C ''trivial'':65C ''under'':43C ''updat'':103C,127C ''use'':13C,74C,97C ''ve'':4C,92C ''want'':141C ''way'':66C,114C ''well'':154C ''work'':71C ''worth'':50C ''wouldn'':175C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8947, 2, '2015-11-20 21:11:17.623', 1, NULL, NULL, '&lt;p&gt;Annual Wolfram Technology Conference.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://www.wolfram.com/events/technology-conference/2015/" rel="nofollow"&gt;Wolfram Technology Conference 2015&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Technical training, analysis topics on a wide range of industries, software development, curated data, on so on. &lt;/p&gt;
', NULL, NULL, '''/a'':20C ''/events/technology-conference/2015/'':13C ''/li'':21C ''/p'':6C,41C ''/ul'':22C ''2015'':19C ''analysi'':26C ''annual'':2C ''confer'':5C,18C ''curat'':36C ''data'':37C ''develop'':35C ''href'':10C ''industri'':33C ''li'':8C ''nofollow'':15C ''p'':1C,23C ''rang'':31C ''rel'':14C ''softwar'':34C ''technic'':24C ''technolog'':4C,17C ''topic'':27C ''train'':25C ''ul'':7C ''wide'':30C ''wolfram'':3C,16C ''www.wolfram.com'':12C ''www.wolfram.com/events/technology-conference/2015/'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (851, 1, '2014-07-27 21:54:05.003', 6, 138, 'Computing Image Similarity based on Color Distribution', '&lt;h2&gt;Image Similarity based on Color Palette Distribution&lt;/h2&gt;

&lt;p&gt;I am trying to compute similarity between two images based on their color palette distribution, let''s say I have two sets of key value pairs as follows,&lt;/p&gt;

&lt;p&gt;Img1: &lt;code&gt;{''Brown'': 14, ''White'': 13, ''Black'': 40, ''Gray'': 31}&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Img2: &lt;code&gt;{''Pink'': 82, ''Brown'': 8, ''White'': 7}&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Where the numbers denote the % of that color present in the image. What would be the best way to compute similarity on a scale of 0-100 between the two images?&lt;/p&gt;
', 'data-mining  clustering  similarity', NULL, '''-100'':103C ''/code'':63C,74C ''/h2'':21C ''/p'':51C,64C,75C,108C ''0'':102C ''13'':58C ''14'':56C ''31'':62C ''40'':60C ''7'':73C ''8'':71C ''82'':69C ''base'':4A,16C,32C ''best'':93C ''black'':59C ''brown'':55C,70C ''cluster'':11B ''code'':54C,67C ''color'':6A,18C,35C,84C ''comput'':1A,27C,96C ''data'':9B ''data-min'':8B ''denot'':80C ''distribut'':7A,20C,37C ''follow'':50C ''gray'':61C ''h2'':13C ''imag'':2A,14C,31C,88C,107C ''img1'':53C ''img2'':66C ''key'':46C ''let'':38C ''mine'':10B ''number'':79C ''p'':22C,52C,65C,76C ''pair'':48C ''palett'':19C,36C ''pink'':68C ''present'':85C ''say'':40C ''scale'':100C ''set'':44C ''similar'':3A,12B,15C,28C,97C ''tri'':25C ''two'':30C,43C,106C ''valu'':47C ''way'':94C ''white'':57C,72C ''would'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (872, 1, '2014-07-30 18:45:13.79', 5, 1120, 'R error using package tm (text-mining)', '&lt;p&gt;I am attempting to use the tm package to convert a vector of text strings to a corpus element.&lt;/p&gt;

&lt;p&gt;My code looks something like this&lt;/p&gt;

&lt;p&gt;Corpus(d1$Yes)&lt;/p&gt;

&lt;p&gt;where d1$Yes is a factor with 124 levels, each containing a text string.&lt;/p&gt;

&lt;p&gt;For example, d1$Yes[246] = "So we can get the boat out!"&lt;/p&gt;

&lt;p&gt;I''m receiving the following error:
"Error: inherits(x, "Source") is not TRUE"&lt;/p&gt;

&lt;p&gt;I''m not sure how to remedy this.&lt;/p&gt;
', 'r  text-mining', NULL, '''/p'':33C,41C,46C,62C,76C,91C,101C ''124'':55C ''246'':68C ''attempt'':16C ''boat'':74C ''code'':36C ''contain'':58C ''convert'':23C ''corpus'':31C,43C ''d1'':44C,49C,66C ''element'':32C ''error'':2A,83C,84C ''exampl'':65C ''factor'':53C ''follow'':82C ''get'':72C ''inherit'':85C ''level'':56C ''like'':39C ''look'':37C ''m'':79C,94C ''mine'':8A,12B ''p'':13C,34C,42C,47C,63C,77C,92C ''packag'':4A,21C ''r'':1A,9B ''receiv'':80C ''remedi'':99C ''someth'':38C ''sourc'':87C ''string'':28C,61C ''sure'':96C ''text'':7A,11B,27C,60C ''text-min'':6A,10B ''tm'':5A,20C ''true'':90C ''use'':3A,18C ''vector'':25C ''x'':86C ''yes'':45C,50C,67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (853, 1, '2014-07-28 07:19:49.877', 5, 484, 'Unsupervised Feature Learning for NER', '&lt;p&gt;I have implemented NER system with the use of CRF algorithm with my handcrafted features that gave quite good results. The thing is that I used lots of different features including POS tags and lemmas.&lt;/p&gt;

&lt;p&gt;Now I want to make the same NER for different language. The problem here is that I can''t use POS tags and lemmas. I started reading articles about deep learning and unsupervised feature learning.&lt;/p&gt;

&lt;p&gt;My question is, if it''s possible to use methods for unsupervised feature learning with CRF algorithm. Did anyone try this and got any good result? Is there any article or tutorial about this matter.&lt;/p&gt;

&lt;p&gt;I still don''t completely understand this way of feature creation so I don''t want to spend to much time for something that won''t work. So any information would be really helpful. To create whole NER system based on deep learning is a bit to much for now.&lt;/p&gt;

&lt;p&gt;Thank you in advance.&lt;/p&gt;
', 'nlp  text-mining  feature-extraction', NULL, '''/p'':49C,86C,123C,175C,181C ''advanc'':180C ''algorithm'':24C,104C ''anyon'':106C ''articl'':78C,117C ''base'':164C ''bit'':170C ''complet'':129C ''creat'':160C ''creation'':135C ''crf'':23C,103C ''deep'':80C,166C ''differ'':42C,60C ''extract'':12B ''featur'':2A,11B,28C,43C,84C,100C,134C ''feature-extract'':10B ''gave'':30C ''good'':32C,112C ''got'':110C ''handcraft'':27C ''help'':158C ''implement'':16C ''includ'':44C ''inform'':154C ''languag'':61C ''learn'':3A,81C,85C,101C,167C ''lemma'':48C,74C ''lot'':40C ''make'':55C ''matter'':122C ''method'':97C ''mine'':9B ''much'':144C,172C ''ner'':5A,17C,58C,162C ''nlp'':6B ''p'':13C,50C,87C,124C,176C ''pos'':45C,71C ''possibl'':94C ''problem'':63C ''question'':89C ''quit'':31C ''read'':77C ''realli'':157C ''result'':33C,113C ''someth'':147C ''spend'':142C ''start'':76C ''still'':126C ''system'':18C,163C ''tag'':46C,72C ''text'':8B ''text-min'':7B ''thank'':177C ''thing'':35C ''time'':145C ''tri'':107C ''tutori'':119C ''understand'':130C ''unsupervis'':1A,83C,99C ''use'':21C,39C,70C,96C ''want'':53C,140C ''way'':132C ''whole'':161C ''won'':149C ''work'':151C ''would'':155C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (854, 2, '2014-07-28 08:36:53.633', 5, NULL, NULL, '&lt;p&gt;I like @Kallestad answer very much, but I would like to add a meta-step: Make sure that you understand how the data where collected, and what types of constraints there are. 
I think it is very common to think that there where no non-obvious steps when the data where collected, but this is not the case: Most of the time, some process or indivudal did somethink with the data, and these steps can and will influence the shape of the data.&lt;/p&gt;

&lt;p&gt;Two examples:
I had a study recently where the data where collected by various con
tractors worldwide. I was not at the briefing, so that was opaque to me. Unfortunately, the measurements where off for some parts of france: People all liked ice cram, but we expected a random distribution. There was no obvious reason for this uniformity, so I began to hunt the errors. When I queried the contractors, one had misunderstood the briefing and selected only ice-cream lovers from his database.&lt;/p&gt;

&lt;p&gt;The second error was more challenging: When doing some geographic analysis, I found that a lot of people had extremely large movement patterns, which suggested that a lot of them traveled from Munich to Hamburg in minutes. When I spoke with ppeople upstream, they found a subtle bug in their data aggregation software, which was unnoticed before.&lt;/p&gt;

&lt;p&gt;Conclusions:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Do not assume that your data was collected by perfect processes /humans.&lt;/li&gt;
&lt;li&gt;Do try to understand the limits of your data providers.&lt;/li&gt;
&lt;li&gt;Look at individual patterns / values and try to determine if they are logical (easy for movement / geographic data)&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/humans'':250C ''/li'':251C,263C,283C ''/p'':87C,174C,233C,236C ''/ul'':284C ''add'':13C ''aggreg'':227C ''analysi'':186C ''answer'':5C ''assum'':241C ''began'':149C ''brief'':111C,163C ''bug'':223C ''case'':61C ''challeng'':181C ''collect'':27C,55C,100C,246C ''common'':40C ''con'':103C ''conclus'':235C ''constraint'':32C ''contractor'':158C ''cram'':132C ''cream'':169C ''data'':25C,53C,74C,86C,98C,226C,244C,261C,282C ''databas'':173C ''determin'':273C ''distribut'':138C ''easi'':278C ''error'':153C,178C ''exampl'':90C ''expect'':135C ''extrem'':195C ''found'':188C,220C ''franc'':127C ''geograph'':185C,281C ''hamburg'':210C ''hunt'':151C ''ice'':131C,168C ''ice-cream'':167C ''individu'':267C ''indivud'':69C ''influenc'':81C ''kallestad'':4C ''larg'':196C ''li'':238C,252C,264C ''like'':3C,11C,130C ''limit'':258C ''logic'':277C ''look'':265C ''lot'':191C,203C ''lover'':170C ''make'':18C ''measur'':120C ''meta'':16C ''meta-step'':15C ''minut'':212C ''misunderstood'':161C ''movement'':197C,280C ''much'':7C ''munich'':208C ''non'':48C ''non-obvi'':47C ''obvious'':49C,142C ''one'':159C ''opaqu'':115C ''p'':1C,88C,175C,234C ''part'':125C ''pattern'':198C,268C ''peopl'':128C,193C ''perfect'':248C ''ppeopl'':217C ''process'':67C,249C ''provid'':262C ''queri'':156C ''random'':137C ''reason'':143C ''recent'':95C ''second'':177C ''select'':165C ''shape'':83C ''softwar'':228C ''somethink'':71C ''spoke'':215C ''step'':17C,50C,77C ''studi'':94C ''subtl'':222C ''suggest'':200C ''sure'':19C ''think'':36C,42C ''time'':65C ''tractor'':104C ''travel'':206C ''tri'':254C,271C ''two'':89C ''type'':30C ''ul'':237C ''understand'':22C,256C ''unfortun'':118C ''uniform'':146C ''unnot'':231C ''upstream'':218C ''valu'':269C ''various'':102C ''worldwid'':105C ''would'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (855, 2, '2014-07-28 12:07:25.573', 4, NULL, NULL, '&lt;p&gt;Below you can find a copy of my answer to a related (however, focused on data cleaning aspect) question here on &lt;em&gt;Data Science StackExchange&lt;/em&gt; (&lt;a href="http://datascience.stackexchange.com/a/722/2452"&gt;http://datascience.stackexchange.com/a/722/2452&lt;/a&gt;), provided in its entirety for readers'' convenience. I believe that it partially answers your question as well and hope it is helpful. While the answer is focused on &lt;code&gt;R&lt;/code&gt; ecosystem, similar packages and/or libraries can be found for other &lt;em&gt;data analysis environments&lt;/em&gt;. Moreover, while the two &lt;strong&gt;cited papers&lt;/strong&gt; on data preparation also contain examples in R, these papers present &lt;strong&gt;general&lt;/strong&gt; &lt;strong&gt;workflow (framework)&lt;/strong&gt; and &lt;strong&gt;best practices&lt;/strong&gt; that are applicable to &lt;strong&gt;any&lt;/strong&gt; data analysis environment.&lt;/p&gt;

&lt;p&gt;R contains some &lt;em&gt;standard&lt;/em&gt; functions for data manipulation, which can be used for data cleaning, in its &lt;strong&gt;base&lt;/strong&gt; package (&lt;code&gt;gsub&lt;/code&gt;, &lt;code&gt;transform&lt;/code&gt;, etc.), as well as in various third-party packages, such as &lt;strong&gt;stringr&lt;/strong&gt;, &lt;strong&gt;reshape&lt;/strong&gt;, &lt;strong&gt;reshape2&lt;/strong&gt;, and &lt;strong&gt;plyr&lt;/strong&gt;. Examples and best practices of usage for these packages and their functions are described in the following paper: &lt;a href="http://vita.had.co.nz/papers/tidy-data.pdf" rel="nofollow"&gt;http://vita.had.co.nz/papers/tidy-data.pdf&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Additionally, R offers some packages specifically &lt;em&gt;focused&lt;/em&gt; on data cleaning and transformation:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;editrules&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/editrules/index.html" rel="nofollow"&gt;http://cran.r-project.org/web/packages/editrules/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;deducorrect&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/deducorrect/index.html" rel="nofollow"&gt;http://cran.r-project.org/web/packages/deducorrect/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;StatMatch&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/StatMatch/index.html" rel="nofollow"&gt;http://cran.r-project.org/web/packages/StatMatch/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;MatchIt&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/MatchIt/index.html" rel="nofollow"&gt;http://cran.r-project.org/web/packages/MatchIt/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;DataCombine&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/DataCombine" rel="nofollow"&gt;http://cran.r-project.org/web/packages/DataCombine&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;A comprehensive and coherent approach to &lt;strong&gt;data cleaning&lt;/strong&gt; in R, including examples and use of &lt;strong&gt;editrules&lt;/strong&gt; and &lt;strong&gt;deducorrect&lt;/strong&gt; packages, as well as a description of &lt;em&gt;workflow&lt;/em&gt; (&lt;em&gt;framework&lt;/em&gt;) of data cleaning in R, is presented in the following paper, which I highly recommend: &lt;a href="http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf" rel="nofollow"&gt;http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3703, 2, '2014-12-14 03:42:25.213', 1, NULL, NULL, '&lt;p&gt;Instead of &lt;em&gt;exporting&lt;/em&gt; your models, consider creating an R-based &lt;strong&gt;interoperable environment&lt;/strong&gt; for your &lt;em&gt;modeling&lt;/em&gt; needs. Such environment would consists of &lt;em&gt;R environment&lt;/em&gt; proper as well as &lt;em&gt;integration layers&lt;/em&gt; for your third-party libraries. In particular, for the &lt;em&gt;OpenCV&lt;/em&gt; project, consider either using &lt;code&gt;r-opencv&lt;/code&gt; open source project (&lt;a href="https://code.google.com/p/r-opencv" rel="nofollow"&gt;https://code.google.com/p/r-opencv&lt;/a&gt;), or integration via &lt;em&gt;OpenCV&lt;/em&gt; C++ APIs and &lt;em&gt;R&lt;/em&gt; &lt;code&gt;Rcpp&lt;/code&gt; package (&lt;a href="http://dirk.eddelbuettel.com/code/rcpp.html" rel="nofollow"&gt;http://dirk.eddelbuettel.com/code/rcpp.html&lt;/a&gt;). Finally, if you want to add &lt;em&gt;PMML&lt;/em&gt; support to the mix and create a &lt;strong&gt;deployable-to-cloud solution&lt;/strong&gt;, take a look at the following excellent blog post with relevant examples: &lt;a href="http://things-about-r.tumblr.com/post/37861967022/predictive-modeling-using-r-and-the" rel="nofollow"&gt;http://things-about-r.tumblr.com/post/37861967022/predictive-modeling-using-r-and-the&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (858, 2, '2014-07-28 14:48:37.743', 3, NULL, NULL, '&lt;p&gt;Yes, it is entirely possible to combine unsupervised learning with the CRF model.  In particular, I would recommend that you explore the possibility of using &lt;a href="https://code.google.com/p/word2vec/" rel="nofollow"&gt;word2vec&lt;/a&gt; features as inputs to your CRF.&lt;/p&gt;

&lt;p&gt;Word2vec trains a  to distinguish between words that are appropriate for a given context and words that are randomly selected.  Select weights of the model can then be interpreted as a dense vector representation of a given word.  &lt;/p&gt;

&lt;p&gt;These dense vectors have the appealing property that words that are semantically or syntactically similar have similar vector representations.  Basic vector arithmetic even reveals some interesting learned relationships between words.&lt;br&gt;
For example, vector("Paris") - vector("France") + vector("Italy") yields a vector that is quite similar to vector("Rome").  &lt;/p&gt;

&lt;p&gt;At a high level, you can think of word2vec representations as being similar to LDA or LSA representations, in the sense that you can convert a sparse input vector into a dense output vector that contains word similarity information. &lt;/p&gt;

&lt;p&gt;For that matter, LDA and LSA are also valid options for unsupervised feature learning -- both attempt to represent words as combinations of "topics" and output dense word representations.  &lt;/p&gt;

&lt;p&gt;For English text Google distributes word2vec models pretrained on a huge 100 billion word Google News dataset, but for other languages you''ll have to train your own model.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (859, 1, '2014-07-28 16:17:49.823', 7, 505, 'Data science and MapReduce programming model of Hadoop', '&lt;p&gt;What are the different classes of data science problems that can be solved using mapreduce programming model?&lt;/p&gt;
', 'hadoop  map-reduce', NULL, '''/p'':31C ''class'':18C ''data'':1A,20C ''differ'':17C ''hadoop'':8A,9B ''map'':11B ''map-reduc'':10B ''mapreduc'':4A,28C ''model'':6A,30C ''p'':13C ''problem'':22C ''program'':5A,29C ''reduc'':12B ''scienc'':2A,21C ''solv'':26C ''use'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (860, 2, '2014-07-28 16:23:39.6', 1, NULL, NULL, '&lt;p&gt;I''ll add one thing- if possible, do a reasonableness check by comparing you data against some other source. It seems that whenever I fail to do this, I get burnt:(&lt;/p&gt;
', NULL, NULL, '''/p'':33C ''add'':4C ''burnt'':32C ''check'':12C ''compar'':14C ''data'':16C ''fail'':26C ''get'':31C ''ll'':3C ''one'':5C ''p'':1C ''possibl'':8C ''reason'':11C ''seem'':22C ''sourc'':20C ''thing'':6C ''whenev'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (873, 2, '2014-07-30 19:15:09.383', 5, NULL, NULL, '&lt;p&gt;You have to tell Corpus what kind of source you are using.  Try:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Corpus(VectorSource(d1$Yes))
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':22C ''/p'':15C ''/pre'':23C ''code'':17C ''corpus'':6C,18C ''d1'':20C ''kind'':8C ''p'':1C ''pre'':16C ''sourc'':10C ''tell'':5C ''tri'':14C ''use'':13C ''vectorsourc'':19C ''yes'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (861, 2, '2014-07-28 16:39:45.703', 9, NULL, NULL, '&lt;p&gt;Data Science has many different sub-areas as described in &lt;a href="http://www.datasciencecentral.com/profiles/blogs/ingredients-of-data-science-1"&gt;my post&lt;/a&gt;). Nearly for each area, scientists and developer has significant contributions. To learn more about what can be done, please look at following websites:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Data Mining Algorithms &amp;amp; Machine Learning -&gt; &lt;a href="https://mahout.apache.org/"&gt;Apache Mahout&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Statistics -&gt; &lt;a href="https://github.com/RevolutionAnalytics/RHadoop/wiki"&gt;RHadoop&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Data Warehousing &amp;amp; Database Querying -&gt; &lt;a href="http://www.teradata.com.tr/Teradata-Aster-SQL-MapReduce/?LangType=1055&amp;amp;LangSelect=true"&gt;SQL-MapReduce&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Social Network Analysis -&gt; &lt;a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&amp;amp;arnumber=5636636"&gt;Article&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Bio-informatics -&gt; &lt;a href="http://abhishek-tiwari.com/post/mapreduce-and-hadoop-algorithms-in-bioinformatics-papers?ModPagespeed=noscript"&gt;Article - 1 &lt;/a&gt;, &lt;a href="http://www.biomedcentral.com/1471-2105/11/S12/S1"&gt;Article - 2&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Also, there are some work on MapReduce + Excel + Cloud combination but I have not found the link.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;What are the different classes of Data Science problems ...&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Each "classes" is not purely homogeneous problem domain, i.e. some problem cannot be solved via map and reduce approach due to its communication cost, or algorithm behavior. What I mean by behavior is that some problem wants to have control on all data sets instead of chunks. Thus, I refuse to list type of problem "classes".&lt;/p&gt;

&lt;p&gt;Do not forget that knowing what MapReduce can do is not enough for Data Science. You should also aware of &lt;a href="http://www.analyticbridge.com/profiles/blogs/what-mapreduce-can-t-do"&gt;What MapReduce can''t do&lt;/a&gt;, too.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (862, 2, '2014-07-28 17:33:43.28', 4, NULL, NULL, '&lt;p&gt;I think that there is no universal technique for "cleaning" data before doing actual research. On the other hand, I''m aiming for doing as much &lt;a href="https://en.wikipedia.org/wiki/Reproducibility#Reproducible_research" rel="nofollow"&gt;reproducible research&lt;/a&gt; as possible. By doing reproducible research, if you used cleaning techniques with bugs or with poor parameters/assumptions it could be spot by others.&lt;/p&gt;

&lt;p&gt;There is nice R package &lt;a href="http://yihui.name/knitr/" rel="nofollow"&gt;knitr&lt;/a&gt; which helps a lot in reproducible research.&lt;/p&gt;

&lt;p&gt;Of course, not all research could be fully reproduced (for example live Twitter data) , but at least you can document cleaning, formating and preprocessing steps easily.&lt;/p&gt;

&lt;p&gt;You can check my &lt;a href="http://rpubs.com/QuatnumDamage/storm" rel="nofollow"&gt;assessment&lt;/a&gt; prepared for &lt;a href="https://www.coursera.org/course/repdata" rel="nofollow"&gt;Reproducible Research course at Coursera&lt;/a&gt;. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (869, 1, '2014-07-30 16:27:45.177', 8, 1260, 'Neural Network parse string data?', '&lt;p&gt;So, I''m just starting to learn how a neural network can operate to recognize patterns and categorize inputs, and I''ve seen how an artificial neural network can parse image data and categorize the images (&lt;a href="http://cs.stanford.edu/people/karpathy/convnetjs/demo/mnist.html"&gt;demo with convnetjs&lt;/a&gt;), and the key there is to downsample the image and each pixel stimulates one input neuron into the network.&lt;/p&gt;

&lt;p&gt;However, I''m trying to wrap my head around if this is possible to be done with string inputs? The use-case I''ve got is a "recommendation engine" for movies a user has watched. Movies have lots of string data (title, plot, tags), and I could imagine "downsampling" the text down to a few key words that describe that movie, but even if I parse out the top five words that describe this movie, I think I''d need input neurons for every english word in order to compare a set of movies? I could limit the input neurons just to the words used in the set, but then could it grow/learn by adding new movies (user watches a new movie, with new words)? Most of the libraries I''ve seen don''t allow adding new neurons after the system has been trained?&lt;/p&gt;

&lt;p&gt;Is there a standard way to map string/word/character data to inputs into a neural network? Or is a neural network really not the right tool for the job of parsing string data like this (what''s a better tool for pattern-matching in string data)?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (870, 2, '2014-07-30 17:42:09.797', 2, NULL, NULL, '&lt;p&gt;This is not a problem about neural networks per se, but about representing textual data in machine learning. You can represent the movies, cast, and theme as categorical variables. The plot is more complicated; you''d probably want a &lt;a href="http://en.wikipedia.org/wiki/Topic_model" rel="nofollow"&gt;topic model&lt;/a&gt; for that, but I''d leave that out until you get the hang of things. It does precisely that textual "downsampling" you mentioned.&lt;/p&gt;

&lt;p&gt;Take a look at &lt;a href="http://visualstudiomagazine.com/articles/2013/07/01/neural-network-data-normalization-and-encoding.aspx" rel="nofollow"&gt;this&lt;/a&gt; tutorial to learn how to encode categorical variables for neural networks. And good luck!&lt;/p&gt;
', NULL, NULL, '''/a'':50C,88C ''/articles/2013/07/01/neural-network-data-normalization-and-encoding.aspx'':84C ''/p'':74C,103C ''/wiki/topic_model'':45C ''cast'':25C ''categor'':29C,95C ''complic'':35C ''d'':37C,55C ''data'':16C ''downsampl'':71C ''en.wikipedia.org'':44C ''en.wikipedia.org/wiki/topic_model'':43C ''encod'':94C ''get'':61C ''good'':101C ''hang'':63C ''href'':42C,81C ''learn'':19C,91C ''leav'':56C ''look'':78C ''luck'':102C ''machin'':18C ''mention'':73C ''model'':49C ''movi'':24C ''network'':9C,99C ''neural'':8C,98C ''nofollow'':47C,86C ''p'':1C,75C ''per'':10C ''plot'':32C ''precis'':68C ''probabl'':38C ''problem'':6C ''rel'':46C,85C ''repres'':14C,22C ''se'':11C ''take'':76C ''textual'':15C,70C ''theme'':27C ''thing'':65C ''topic'':48C ''tutori'':89C ''variabl'':30C,96C ''visualstudiomagazine.com'':83C ''visualstudiomagazine.com/articles/2013/07/01/neural-network-data-normalization-and-encoding.aspx'':82C ''want'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (871, 2, '2014-07-30 17:53:40.33', 8, NULL, NULL, '&lt;p&gt;Using a neural network for prediction on natural language data can be a tricky task, but there are tried and true methods for making it possible.  &lt;/p&gt;

&lt;p&gt;In the Natural Language Processing (NLP) field, text is often represented using the bag of words model.  In other words, you have a vector of length n, where n is the number of words in your vocabulary, and each word corresponds to an element in the vector.  In order to convert text to numeric data, you simply count the number of occurrences of each word and place that value at the index of the vector that corresponds to the word. &lt;a href="http://en.wikipedia.org/wiki/Bag-of-words_model."&gt;Wikipedia does an excellent job of describing this conversion process.&lt;/a&gt;  Because the length of the vector is fixed, its difficult to deal with new words that don''t map to an index, but there are ways to help mitigate this problem (lookup &lt;a href="http://en.wikipedia.org/wiki/Feature_hashing"&gt;feature hashing&lt;/a&gt;).  &lt;/p&gt;

&lt;p&gt;This method of representation has many disadvantages -- it does not preserve the relationship between adjacent words, and results in very sparse vectors.  Looking at &lt;a href="http://en.wikipedia.org/wiki/N-gram"&gt;n-grams&lt;/a&gt; helps to fix the problem of preserving word relationships, but for now let''s focus on the second problem: sparsity.  &lt;/p&gt;

&lt;p&gt;It''s difficult to deal directly with these sparse vectors (many linear algebra libraries do a poor job of handling sparse inputs), so often the next step is dimensionality reduction. For that we can refer to the field of &lt;a href="http://en.wikipedia.org/wiki/Topic_model"&gt;topic modeling&lt;/a&gt;:  Techniques like &lt;a href="http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation"&gt;Latent Dirichlet Allocation&lt;/a&gt; (LDA) and &lt;a href="http://en.wikipedia.org/wiki/Latent_semantic_analysis"&gt;Latent Semantic Analysis&lt;/a&gt; (LSA) allow the compression of these sparse vectors into dense vectors by representing a document as a combination of topics.  You can fix the number of topics used, and in doing so fix the size of the output vector producted by LDA or LSA. This dimensionality reduction process drastically reduces the size of the input vector while attempting to lose a minimal amount of information.  &lt;/p&gt;

&lt;p&gt;Finally, after all of these conversions, you can feed the outputs of the topic modeling process into the inputs of your neural network.     &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (876, 2, '2014-07-31 16:39:37.127', 3, NULL, NULL, '&lt;p&gt;"Identify the most influential precursor conditions (comorbidities) for a medical condition like chronic kidney disease"&lt;/p&gt;

&lt;p&gt;I''m not sure that it''s possible to ID &lt;em&gt;the&lt;/em&gt; most influential conditions; I think it will depend on what model you''re using. Just yesterday I fit a random forest and a boosted regression tree to the same data, and the order and relative importance each model gave for the variables were quite different.&lt;/p&gt;
', NULL, NULL, '''/em'':31C ''/p'':17C,77C ''boost'':55C ''chronic'':14C ''comorbid'':8C ''condit'':7C,12C,34C ''data'':61C ''depend'':39C ''differ'':76C ''diseas'':16C ''em'':29C ''fit'':49C ''forest'':52C ''gave'':70C ''id'':28C ''identifi'':2C ''import'':67C ''influenti'':5C,33C ''kidney'':15C ''like'':13C ''m'':20C ''medic'':11C ''model'':42C,69C ''order'':64C ''p'':1C,18C ''possibl'':26C ''precursor'':6C ''quit'':75C ''random'':51C ''re'':44C ''regress'':56C ''relat'':66C ''sure'':22C ''think'':36C ''tree'':57C ''use'':45C ''variabl'':73C ''yesterday'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (878, 2, '2014-07-31 23:03:33.767', 7, NULL, NULL, '&lt;p&gt;A couple of days ago developers from one product company asked me how they can understand why new users were leaving their website. My first question to them was what these users'' profiles looked like and how they were different from those who stayed. &lt;/p&gt;

&lt;p&gt;Advertising is only top of an iceberg. User profiles (either filled by users themselves or computed from users'' behaviour) hold information about: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;user categories&lt;/strong&gt;, i.e. what kind of people tend to use your website/product&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;paying client portraits&lt;/strong&gt;, i.e. who is more likely to use your paid services&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;UX component performance&lt;/strong&gt;, e.g. how long it takes people to find the button they need&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;action performance comparison&lt;/strong&gt;, e.g. what was more efficient - lower price for a weekend or propose gifts with each buy, etc. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So it''s more about improving product and making better user experience rather than selling this data to advertisers. &lt;/p&gt;
', NULL, NULL, '''/li'':87C,104C,123C,147C ''/p'':46C,70C,169C ''/strong'':76C,93C,110C,129C ''/ul'':148C ''action'':126C ''advertis'':48C,168C ''ago'':6C ''ask'':12C ''behaviour'':66C ''better'':159C ''button'':120C ''buy'':145C ''categori'':75C ''client'':91C ''compani'':11C ''comparison'':128C ''compon'':108C ''comput'':63C ''coupl'':3C ''data'':166C ''day'':5C ''develop'':7C ''differ'':41C ''e.g'':111C,130C ''effici'':134C ''either'':57C ''etc'':146C ''experi'':161C ''fill'':58C ''find'':118C ''first'':26C ''gift'':142C ''hold'':67C ''i.e'':77C,94C ''iceberg'':54C ''improv'':155C ''inform'':68C ''kind'':79C ''leav'':22C ''li'':72C,88C,105C,124C ''like'':36C,98C ''long'':113C ''look'':35C ''lower'':135C ''make'':158C ''need'':122C ''new'':19C ''one'':9C ''p'':1C,47C,149C ''paid'':102C ''pay'':90C ''peopl'':81C,116C ''perform'':109C,127C ''portrait'':92C ''price'':136C ''product'':10C,156C ''profil'':34C,56C ''propos'':141C ''question'':27C ''rather'':162C ''sell'':164C ''servic'':103C ''stay'':45C ''strong'':73C,89C,106C,125C ''take'':115C ''tend'':82C ''top'':51C ''ul'':71C ''understand'':17C ''use'':84C,100C ''user'':20C,33C,55C,60C,65C,74C,160C ''ux'':107C ''websit'':24C ''website/product'':86C ''weekend'':139C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8948, 2, '2015-11-20 21:41:21.573', 1, NULL, NULL, '&lt;p&gt;Mean normalization is a form of feature scaling, so these are not really two different approaches. Feature scaling is just a more general term. What kind of feature scaling, e.g. mean normalization,  you need to use ultimately depends on the data.&lt;/p&gt;
', NULL, NULL, '''/p'':43C ''approach'':17C ''data'':42C ''depend'':39C ''differ'':16C ''e.g'':31C ''featur'':8C,18C,29C ''form'':6C ''general'':24C ''kind'':27C ''mean'':2C,32C ''need'':35C ''normal'':3C,33C ''p'':1C ''realli'':14C ''scale'':9C,19C,30C ''term'':25C ''two'':15C ''ultim'':38C ''use'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (879, 2, '2014-08-01 06:03:47.7', 6, NULL, NULL, '&lt;p&gt;Most companies won''t sell the data, not on any small scale anyways.  Most will use it internally.&lt;/p&gt;

&lt;p&gt;User tracking data is important for understanding a lot of things.  There''s basic A/B testing where you provide different experiences to see which is more effective.  There is understanding how your UI is utilized.  Categorizing your end users in different ways for a variety of reasons.  Figuring out where your end user base is, and within that group where the end users that matter are.  Correlating user experiences with social network updates.  Figuring out what will draw people to your product and what drives them away.  The list of potential for data mining and analysis projects could go on for days.  &lt;/p&gt;

&lt;p&gt;Data storage is cheap.  If you track everything out of the gate, you can figure out what you want to do with that data later.  &lt;/p&gt;

&lt;p&gt;Scanning text messages is sketchy territory when there isn''t a good reason for it.  Even when there is a good reason it''s sketchy territory.  I''d love to say that nobody does it, but there have been instances where big companies have done it and there are a lot of cases where no-name apps at least require access to that kind of data for installation.  I generally frown on that kind of thing myself as a consumer, but the data analyst in me would love to see if I could build anything useful from a set of information like that.&lt;/p&gt;
', NULL, NULL, '''/p'':20C,124C,151C,257C ''a/b'':36C ''access'':214C ''analysi'':117C ''analyst'':237C ''anyth'':248C ''anyway'':14C ''app'':210C ''away'':108C ''base'':75C ''basic'':35C ''big'':194C ''build'':247C ''case'':205C ''categor'':57C ''cheap'':129C ''compani'':3C,195C ''consum'':233C ''correl'':88C ''could'':119C,246C ''d'':180C ''data'':8C,24C,114C,126C,149C,219C,236C ''day'':123C ''differ'':41C,62C ''done'':197C ''draw'':99C ''drive'':106C ''effect'':48C ''end'':59C,73C,83C ''even'':168C ''everyth'':133C ''experi'':42C,90C ''figur'':69C,95C,140C ''frown'':224C ''gate'':137C ''general'':223C ''go'':120C ''good'':164C,173C ''group'':80C ''import'':26C ''inform'':254C ''instal'':221C ''instanc'':192C ''intern'':19C ''isn'':161C ''kind'':217C,227C ''later'':150C ''least'':212C ''like'':255C ''list'':110C ''lot'':30C,203C ''love'':181C,241C ''matter'':86C ''messag'':155C ''mine'':115C ''name'':209C ''network'':93C ''no-nam'':207C ''nobodi'':185C ''p'':1C,21C,125C,152C ''peopl'':100C ''potenti'':112C ''product'':103C ''project'':118C ''provid'':40C ''reason'':68C,165C,174C ''requir'':213C ''say'':183C ''scale'':13C ''scan'':153C ''see'':44C,243C ''sell'':6C ''set'':252C ''sketchi'':157C,177C ''small'':12C ''social'':92C ''storag'':127C ''territori'':158C,178C ''test'':37C ''text'':154C ''thing'':32C,229C ''track'':23C,132C ''ui'':54C ''understand'':28C,51C ''updat'':94C ''use'':17C,249C ''user'':22C,60C,74C,84C,89C ''util'':56C ''varieti'':66C ''want'':144C ''way'':63C ''within'':78C ''won'':4C ''would'':240C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (880, 2, '2014-08-01 12:27:41.58', 3, NULL, NULL, '&lt;p&gt;There always is the solution to try both approaches and keep the one that maximizes the expected performances. &lt;/p&gt;

&lt;p&gt;In your case, I would assume you prefer minimizing false negatives at the cost of some false positive, so you want to bias your classifier against the strong negative prior, and address the imbalance by reducing the number of negative examples in your training set.&lt;/p&gt;

&lt;p&gt;Then compute the precision/recall, or sensitivity/specificity, or whatever criterion suits you on the full, imbalanced, dataset to make sure you haven''t ignored a significant pattern present in the real data while building the model on the reduced data.&lt;/p&gt;
', NULL, NULL, '''/p'':20C,67C,108C ''address'':53C ''alway'':3C ''approach'':10C ''assum'':27C ''bias'':44C ''build'':101C ''case'':24C ''classifi'':46C ''comput'':70C ''cost'':35C ''criterion'':77C ''data'':99C,107C ''dataset'':84C ''exampl'':62C ''expect'':18C ''fals'':31C,38C ''full'':82C ''haven'':89C ''ignor'':91C ''imbal'':55C ''imbalanc'':83C ''keep'':12C ''make'':86C ''maxim'':16C ''minim'':30C ''model'':103C ''negat'':32C,50C,61C ''number'':59C ''one'':14C ''p'':1C,21C,68C ''pattern'':94C ''perform'':19C ''posit'':39C ''precision/recall'':72C ''prefer'':29C ''present'':95C ''prior'':51C ''real'':98C ''reduc'':57C,106C ''sensitivity/specificity'':74C ''set'':66C ''signific'':93C ''solut'':6C ''strong'':49C ''suit'':78C ''sure'':87C ''train'':65C ''tri'':8C ''want'':42C ''whatev'':76C ''would'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (881, 2, '2014-08-01 14:08:13.043', 6, NULL, NULL, '&lt;p&gt;I''ve never worked with medical data, but from general reasoning I''d say that relations between variables in healthcare are pretty complicated. Different models, such as random forests, regression, etc. could capture only part of relations and ignore others. In such circumstances it makes sense to use general &lt;strong&gt;statistical exploration&lt;/strong&gt; and &lt;strong&gt;modelling&lt;/strong&gt;. &lt;/p&gt;

&lt;p&gt;For example, the very first thing I would do is finding out &lt;strong&gt;correlations&lt;/strong&gt; between possible precursor conditions and diagnoses. E.g. in what percent of cases chronic kidney disease was preceded by long flu? If it is high, it &lt;a href="http://en.wikipedia.org/wiki/Correlation_does_not_imply_causation"&gt;doesn''t always mean causality&lt;/a&gt;, but gives pretty good food for thought and helps to better understand relations between different conditions. &lt;/p&gt;

&lt;p&gt;Another important step is data visualisation. Does CKD happens in males more often than in females? What about their place of residence? What is distribution of CKD cases by age? It''s hard to grasp large dataset as a set of numbers, plotting them out makes it much easier. &lt;/p&gt;

&lt;p&gt;When you have an idea of what''s going on, perform &lt;a href="http://en.wikipedia.org/wiki/Statistical_hypothesis_testing"&gt;&lt;strong&gt;hypothesis testing&lt;/strong&gt;&lt;/a&gt; to check your assumption. If you reject null hypothesis (basic assumption) in favour of alternative one, congratulations, you''ve made "something real". &lt;/p&gt;

&lt;p&gt;Finally, when you have a good understanding of your data, try to create complete &lt;strong&gt;model&lt;/strong&gt;. It may be something general like &lt;a href="https://www.coursera.org/course/pgm"&gt;PGM&lt;/a&gt; (e.g. manually-crafted Bayesian network), or something more specific like linear regression or &lt;a href="http://en.wikipedia.org/wiki/Support_vector_machine"&gt;SVM&lt;/a&gt;, or anything. But in any way you will already know how this model corresponds to your data and how you can measure its efficiency. &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;As a good starting resource for learning statistical approach I would recommend &lt;a href="https://www.udacity.com/course/st101"&gt;Intro to Statistics&lt;/a&gt; course by Sebastian Thrun. While it''s pretty basic and doesn''t include advanced topics, it describes most important concepts and gives systematic understanding of probability theory and statistics. &lt;/p&gt;
', NULL, NULL, '''/a'':111C,201C,255C,276C,324C ''/course/pgm'':253C ''/course/st101'':320C ''/p'':59C,128C,179C,224C,301C,354C ''/strong'':54C,58C,75C,200C,242C ''/wiki/correlation_does_not_imply_causation'':105C ''/wiki/statistical_hypothesis_testing'':196C ''/wiki/support_vector_machine'':274C ''advanc'':338C ''age'':159C ''alreadi'':285C ''altern'':216C ''alway'':108C ''anoth'':130C ''anyth'':278C ''approach'':312C ''assumpt'':205C,212C ''basic'':211C,333C ''bayesian'':260C ''better'':122C ''captur'':34C ''case'':87C,157C ''causal'':110C ''check'':203C ''chronic'':88C ''circumst'':44C ''ckd'':137C,156C ''complet'':239C ''complic'':24C ''concept'':344C ''condit'':79C,127C ''congratul'':218C ''correl'':74C ''correspond'':290C ''could'':33C ''cours'':325C ''craft'':259C ''creat'':238C ''d'':14C ''data'':8C,134C,235C,293C ''dataset'':166C ''describ'':341C ''diagnos'':81C ''differ'':25C,126C ''diseas'':90C ''distribut'':154C ''doesn'':106C,335C ''e.g'':82C,256C ''easier'':178C ''effici'':300C ''en.wikipedia.org'':104C,195C,273C ''en.wikipedia.org/wiki/correlation_does_not_imply_causation'':103C ''en.wikipedia.org/wiki/statistical_hypothesis_testing'':194C ''en.wikipedia.org/wiki/support_vector_machine'':272C ''etc'':32C ''exampl'':62C ''explor'':53C ''favour'':214C ''femal'':145C ''final'':226C ''find'':71C ''first'':65C ''flu'':95C ''food'':116C ''forest'':30C ''general'':11C,50C,247C ''give'':113C,346C ''go'':189C ''good'':115C,231C,306C ''grasp'':164C ''happen'':138C ''hard'':162C ''healthcar'':21C ''help'':120C ''high'':99C ''hr'':302C ''href'':102C,193C,250C,271C,317C ''hypothesi'':198C,210C ''idea'':185C ''ignor'':40C ''import'':131C,343C ''includ'':337C ''intro'':321C ''kidney'':89C ''know'':286C ''larg'':165C ''learn'':310C ''like'':248C,266C ''linear'':267C ''long'':94C ''made'':221C ''make'':46C,175C ''male'':140C ''manual'':258C ''manually-craft'':257C ''may'':244C ''mean'':109C ''measur'':298C ''medic'':7C ''model'':26C,57C,241C,289C ''much'':177C ''network'':261C ''never'':4C ''null'':209C ''number'':171C ''often'':142C ''one'':217C ''other'':41C ''p'':1C,60C,129C,180C,225C,303C ''part'':36C ''percent'':85C ''perform'':191C ''pgm'':254C ''place'':149C ''plot'':172C ''possibl'':77C ''preced'':92C ''precursor'':78C ''pretti'':23C,114C,332C ''probabl'':350C ''random'':29C ''real'':223C ''reason'':12C ''recommend'':315C ''regress'':31C,268C ''reject'':208C ''relat'':17C,38C,124C ''resid'':151C ''resourc'':308C ''say'':15C ''sebastian'':327C ''sens'':47C ''set'':169C ''someth'':222C,246C,263C ''specif'':265C ''start'':307C ''statist'':52C,311C,323C,353C ''step'':132C ''strong'':51C,56C,73C,197C,240C ''svm'':275C ''systemat'':347C ''test'':199C ''theori'':351C ''thing'':66C ''thought'':118C ''thrun'':328C ''topic'':339C ''tri'':236C ''understand'':123C,232C,348C ''use'':49C ''variabl'':19C ''ve'':3C,220C ''visualis'':135C ''way'':282C ''work'':5C ''would'':68C,314C ''www.coursera.org'':252C ''www.coursera.org/course/pgm'':251C ''www.udacity.com'':319C ''www.udacity.com/course/st101'':318C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (882, 2, '2014-08-01 14:10:38.267', 3, NULL, NULL, '&lt;p&gt;Here''s a practical example of using web data for something other than advertising. Distil Networks (disclaimer, I work there) uses network traffic to determine whether page accesses are from humans or bots - scrapers, click fraud, form spam, etc.&lt;/p&gt;

&lt;p&gt;Another example is some of the work that Webtrends is doing. They allow site users to build a model for each visitor to predict whether they''ll leave, buy, add to cart, etc. Then based on the probability of each action you can change the users experience (e.g. if they''re about to leave, give them a coupon). Here''s the slides from a talk by them: &lt;a href="http://www.oscon.com/oscon2014/public/schedule/detail/34809" rel="nofollow"&gt;http://www.oscon.com/oscon2014/public/schedule/detail/34809&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/oscon2014/public/schedule/detail/34809'':114C ''/oscon2014/public/schedule/detail/34809&lt;/a&gt;&lt;/p&gt;'':119C ''/p'':41C ''access'':29C ''action'':83C ''add'':72C ''advertis'':15C ''allow'':55C ''anoth'':43C ''base'':77C ''bot'':34C ''build'':59C ''buy'':71C ''cart'':74C ''chang'':86C ''click'':36C ''coupon'':100C ''data'':10C ''determin'':26C ''disclaim'':18C ''distil'':16C ''e.g'':90C ''etc'':40C,75C ''exampl'':6C,44C ''experi'':89C ''form'':38C ''fraud'':37C ''give'':97C ''href'':111C ''human'':32C ''leav'':70C,96C ''ll'':69C ''model'':61C ''network'':17C,23C ''nofollow'':116C ''p'':1C,42C ''page'':28C ''practic'':5C ''predict'':66C ''probabl'':80C ''re'':93C ''rel'':115C ''scraper'':35C ''site'':56C ''slide'':104C ''someth'':12C ''spam'':39C ''talk'':107C ''traffic'':24C ''use'':8C,22C ''user'':57C,88C ''visitor'':64C ''web'':9C ''webtrend'':51C ''whether'':27C,67C ''work'':20C,49C ''www.oscon.com'':113C,118C ''www.oscon.com/oscon2014/public/schedule/detail/34809'':112C ''www.oscon.com/oscon2014/public/schedule/detail/34809&lt;/a&gt;&lt;/p&gt;'':117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (883, 1, '2014-08-01 18:13:06.063', 3, 452, 'R aggregate() with dates', '&lt;p&gt;I am working on a data set that has multiple traffic speed measurements per day. My data is from the city of chicago, and it is taken every minute for about six months. I wanted to consolidate this data into days only, so this is what I did:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;traffic &amp;lt;- read.csv("path.csv",header=TRUE)
traffic2 &amp;lt;- aggregate(SPEED~DATE, data=traffic, FUN=MEAN)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;this was perfect because it took all of my data and averaged it by date. For example, my original data looked something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;DATE        SPEED  
12/31/2012   22
12/31/2012   25
12/31/2012   23
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and the final looked like this: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;DATE        SPEED 
10/1/2012    22
10/2/2012    23
10/3/2012    22
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The only problem, is my data is supposed to start at 9/1/2012. I plotted this data, and it turns out the data goes from 10/1/2012-12/31/2012 and then 9/1/2012-9/30/2012.&lt;/p&gt;

&lt;p&gt;What in the world is going on here?&lt;/p&gt;
', 'r  dataset  beginner', NULL, '''/code'':75C,113C,133C ''/p'':57C,102C,122C,164C,174C ''/pre'':76C,114C,134C ''10/1/2012'':127C ''10/1/2012-12/31/2012'':160C ''10/2/2012'':129C ''10/3/2012'':131C ''12/31/2012'':107C,109C,111C ''22'':108C,128C,132C ''23'':112C,130C ''25'':110C ''9/1/2012'':147C ''9/1/2012-9/30/2012'':163C ''aggreg'':2A,68C ''averag'':89C ''beginn'':7B ''chicago'':31C ''citi'':29C ''code'':59C,104C,124C ''consolid'':45C ''data'':14C,25C,47C,71C,87C,97C,141C,151C,157C ''dataset'':6B ''date'':4A,92C,105C,125C ''day'':23C,49C ''everi'':36C ''exampl'':94C ''final'':118C ''fun'':73C ''go'':171C ''goe'':158C ''header'':64C ''like'':100C,120C ''look'':98C,119C ''lt'':61C,67C ''mean'':74C ''measur'':21C ''minut'':37C ''month'':41C ''multipl'':18C ''origin'':96C ''p'':8C,77C,115C,135C,165C ''path.csv'':63C ''per'':22C ''perfect'':80C ''plot'':149C ''pre'':58C,103C,123C ''problem'':138C ''r'':1A,5B ''read.csv'':62C ''set'':15C ''six'':40C ''someth'':99C ''speed'':20C,69C,106C,126C ''start'':145C ''suppos'':143C ''taken'':35C ''took'':83C ''traffic'':19C,60C,72C ''traffic2'':66C ''true'':65C ''turn'':154C ''want'':43C ''work'':11C ''world'':169C ''~date'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (884, 2, '2014-08-01 18:16:57.543', 1, NULL, NULL, '&lt;p&gt;So I was never able to find the error by looking through my logs. I ended up reinstalling it with CDH5 (which was MUCH easier than installing "poor" Hadoop)
Now everything runs fine! &lt;/p&gt;

&lt;p&gt;I''m still having trouble getting things to save to the hdfs, but thats a question for another day... &lt;/p&gt;
', NULL, NULL, '''/p'':35C,56C ''abl'':6C ''anoth'':54C ''cdh5'':22C ''day'':55C ''easier'':26C ''end'':17C ''error'':10C ''everyth'':32C ''find'':8C ''fine'':34C ''get'':42C ''hadoop'':30C ''hdfs'':48C ''instal'':28C ''log'':15C ''look'':12C ''m'':38C ''much'':25C ''never'':5C ''p'':1C,36C ''poor'':29C ''question'':52C ''reinstal'':19C ''run'':33C ''save'':45C ''still'':39C ''that'':50C ''thing'':43C ''troubl'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (896, 1, '2014-08-03 16:38:38.853', 1, 393, 'How to implement Brown Clustering Algorithm in O(|V|k^2)', '&lt;p&gt;I am trying to implement the Brown Clustering Algorithm &lt;a href="http://delivery.acm.org/10.1145/180000/176316/p467-brown.pdf?ip=197.78.151.216&amp;amp;id=176316&amp;amp;acc=OPEN&amp;amp;key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&amp;amp;CFID=522814165&amp;amp;CFTOKEN=99738065&amp;amp;__acm__=1407081874_5a9649adaf4a9d43b8faa5c1a2da47f8" rel="nofollow"&gt;(link to paper)&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The algorithm is supposed to in O(|V|k^2) where |V| is the size of the vocabulary and k is the number of clusters. I am unable to implement it this efficiently. In fact, the best I can manage is O(|V|k^3) which is too slow. My current implementation for the main part of the algorithm is as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for w = number of clusters + 1 to |V|
{
   word = next most frequent word in the corpus

   assign word to a new cluster 

   initialize MaxQuality to 0

   initialize ArgMax vector to (0,0)

   for i = 0 to number of clusters - 1 
   {
      for j = i to number of clusters
      {
         Quality = Mutual Information if we merge cluster i and cluster j

         if Quality &amp;gt; MaxQuality
         {
            MaxQuality = Quality 

            ArgMax = (i,j) 
         }
      }
   }
} 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I compute quality as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1. Before entering the second loop compute the pre-merge quality i.e. quality before doing any merges.
2. Every time a cluster-pair merge step is considered:
    i. assign quality := pre-merge quality
   ii. quality = quality - any terms in the mutual information equation that contain cluster i or cluster j (pre-merge)
  iii. quality = quality + any terms in the mutual information equation that contain (cluster i U cluster j)  (post-merge)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In my implementation, the first loop has approx |V| iterations, the second and third loop approx k iterations each. To compute quality at each step requires approx a further k iterations. In total it runs in (|V|k^3) time.&lt;/p&gt;

&lt;p&gt;How do you get it to run in (|V|k^2)?&lt;/p&gt;
', 'nlp  efficiency  clustering', NULL, '''/10.1145/180000/176316/p467-brown.pdf?ip=197.78.151.216&amp;amp;id=176316&amp;amp;acc=open&amp;amp;key=4d4702b0c3e38b35%2e4d4702b0c3e38b35%2e4d4702b0c3e38b35%2e6d218144511f3437&amp;amp;cfid=522814165&amp;amp;cftoken=99738065&amp;amp;__acm__=1407081874_5a9649adaf4a9d43b8faa5c1a2da47f8'':29C ''/a'':35C ''/code'':170C,257C ''/p'':36C,100C,178C,300C,313C ''/pre'':171C,258C ''0'':128C,133C,134C,137C ''1'':108C,142C,181C ''2'':11A,47C,199C,312C ''3'':82C,298C ''algorithm'':6A,24C,39C,96C ''approx'':267C,275C,286C ''argmax'':130C,167C ''assign'':119C,211C ''best'':74C ''brown'':4A,22C ''cluster'':5A,14B,23C,62C,107C,124C,141C,149C,156C,159C,204C,229C,232C,249C,252C ''cluster-pair'':203C ''code'':102C,180C ''comput'':174C,187C,280C ''consid'':209C ''contain'':228C,248C ''corpus'':118C ''current'':88C ''delivery.acm.org'':28C ''delivery.acm.org/10.1145/180000/176316/p467-brown.pdf?ip=197.78.151.216&amp;amp;id=176316&amp;amp;acc=open&amp;amp;key=4d4702b0c3e38b35%2e4d4702b0c3e38b35%2e4d4702b0c3e38b35%2e6d218144511f3437&amp;amp;cfid=522814165&amp;amp;cftoken=99738065&amp;amp;__acm__=1407081874_5a9649adaf4a9d43b8faa5c1a2da47f8'':27C ''effici'':13B,70C ''enter'':183C ''equat'':226C,246C ''everi'':200C ''fact'':72C ''first'':264C ''follow'':99C,177C ''frequent'':114C ''get'':305C ''gt'':163C ''href'':26C ''i.e'':193C ''ii'':217C ''iii'':237C ''implement'':3A,20C,67C,89C,262C ''inform'':152C,225C,245C ''initi'':125C,129C ''iter'':269C,277C,290C ''j'':144C,160C,169C,233C,253C ''k'':10A,46C,57C,81C,276C,289C,297C,311C ''link'':32C ''loop'':186C,265C,274C ''main'':92C ''manag'':77C ''maxqual'':126C,164C,165C ''merg'':155C,191C,198C,206C,215C,236C,256C ''mutual'':151C,224C,244C ''new'':123C ''next'':112C ''nlp'':12B ''nofollow'':31C ''number'':60C,105C,139C,147C ''o'':8A,44C,79C ''p'':15C,37C,172C,259C,301C ''pair'':205C ''paper'':34C ''part'':93C ''post'':255C ''post-merg'':254C ''pre'':101C,179C,190C,214C,235C ''pre-merg'':189C,213C,234C ''qualiti'':150C,162C,166C,175C,192C,194C,212C,216C,218C,219C,238C,239C,281C ''rel'':30C ''requir'':285C ''run'':294C,308C ''second'':185C,271C ''size'':52C ''slow'':86C ''step'':207C,284C ''suppos'':41C ''term'':221C,241C ''third'':273C ''time'':201C,299C ''total'':292C ''tri'':18C ''u'':251C ''unabl'':65C ''v'':9A,45C,49C,80C,110C,268C,296C,310C ''vector'':131C ''vocabulari'':55C ''w'':104C ''word'':111C,115C,120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (887, 1, '2014-08-02 00:07:09.267', 1, 38, 'Modelling on one Population and Evaluating on another Population', '&lt;p&gt;I am currently on a project that will build a model (train and test) on Client-side Web data, but evaluate this model on Sever-side Web data.  Unfortunately building the model on Server-side data is not an option, nor is it an option to evaluate this model on Client-side data.&lt;/p&gt;

&lt;p&gt;This model will be based on metrics collected on specific visitors.  This is a real time system that will be calculating a likelihood based on metrics collected while visitors browse the website.&lt;/p&gt;

&lt;p&gt;I am looking for approaches to ensure the highest possible accuracy on the model evaluation.&lt;/p&gt;

&lt;p&gt;So far I have the following ideas,&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Clean the Server-side data by removing webpages that are never seen Client-side.&lt;/li&gt;
&lt;li&gt;Collect additional data Server-side data to make the Server-side data more closely resemble Client-side data.&lt;/li&gt;
&lt;li&gt;Collect data on the Client and send this data to the Server.  This is possible and may be the best solution, but is currently undesirable. &lt;/li&gt;
&lt;li&gt;Build one or more models that estimate Client-side Visitor metrics from Server-side Visitor metrics and use these estimates in the Likelihood model.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Any other thoughts on evaluating over one Population while training (and testing) on another Population?&lt;/p&gt;
', 'data-cleaning  evaluation', NULL, '''/li'':151C,174C,201C,229C ''/ol'':230C ''/p'':72C,106C,123C,132C,247C ''accuraci'':118C ''addit'':154C ''anoth'':8A,245C ''approach'':112C ''base'':78C,97C ''best'':195C ''brows'':103C ''build'':23C,46C,203C ''calcul'':94C ''clean'':12B,135C ''client'':31C,69C,149C,171C,180C,211C ''client-sid'':30C,68C,148C,170C,210C ''close'':168C ''collect'':81C,100C,153C,176C ''current'':17C,199C ''data'':11B,34C,44C,53C,71C,140C,155C,159C,166C,173C,177C,184C ''data-clean'':10B ''ensur'':114C ''estim'':209C,224C ''evalu'':6A,13B,36C,64C,122C,236C ''far'':126C ''follow'':130C ''highest'':116C ''idea'':131C ''li'':134C,152C,175C,202C ''likelihood'':96C,227C ''look'':110C ''make'':161C ''may'':192C ''metric'':80C,99C,214C,220C ''model'':1A,25C,38C,48C,66C,75C,121C,207C,228C ''never'':146C ''ol'':133C ''one'':3A,204C,238C ''option'':57C,62C ''p'':14C,73C,107C,124C,231C ''popul'':4A,9A,239C,246C ''possibl'':117C,190C ''project'':20C ''real'':88C ''remov'':142C ''resembl'':169C ''seen'':147C ''send'':182C ''server'':51C,138C,157C,164C,187C,217C ''server-sid'':50C,137C,156C,163C,216C ''sever'':41C ''sever-sid'':40C ''side'':32C,42C,52C,70C,139C,150C,158C,165C,172C,212C,218C ''solut'':196C ''specif'':83C ''system'':90C ''test'':28C,243C ''thought'':234C ''time'':89C ''train'':26C,241C ''undesir'':200C ''unfortun'':45C ''use'':222C ''visitor'':84C,102C,213C,219C ''web'':33C,43C ''webpag'':143C ''websit'':105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (922, 1, '2014-08-06 09:08:08.113', 2, 40, 'Can I classify set of documents using classifying method using limited number of concepts ?', '&lt;p&gt;I have  set of documents and I want classify them to true and false &lt;/p&gt;

&lt;p&gt;My question is I have to take the whole words in the documents then I classify them depend on the similarity words in these documents or I can take only some words that I interested in then I compare it with the documents. Which one is more efficient in classify document and can work with SVM.       &lt;/p&gt;
', 'machine-learning  classification  text-mining', NULL, '''/p'':37C,95C ''classif'':18B ''classifi'':3A,8A,31C,54C,88C ''compar'':77C ''concept'':14A ''depend'':56C ''document'':6A,27C,51C,63C,81C,89C ''effici'':86C ''fals'':36C ''interest'':73C ''learn'':17B ''limit'':11A ''machin'':16B ''machine-learn'':15B ''method'':9A ''mine'':21B ''number'':12A ''one'':83C ''p'':22C,38C ''question'':40C ''set'':4A,25C ''similar'':59C ''svm'':94C ''take'':45C,67C ''text'':20B ''text-min'':19B ''true'':34C ''use'':7A,10A ''want'':30C ''whole'':47C ''word'':48C,60C,70C ''work'':92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (888, 2, '2014-08-02 04:21:49.927', 0, NULL, NULL, '&lt;p&gt;I''m not an expert on this, so take my advice with a grain of salt. It''s not clear for me what is the relationship between server-side and client-side data. Are they both &lt;strong&gt;representative&lt;/strong&gt; of the same population? If Yes, I think it''s OK to use different data sets for testing/training and evaluating your models. If No, I think it might be a good idea to use some &lt;strong&gt;resampling&lt;/strong&gt; technique, such as &lt;em&gt;bootstrapping&lt;/em&gt;, &lt;em&gt;jackknifing&lt;/em&gt; or &lt;em&gt;cross-validation&lt;/em&gt;.&lt;/p&gt;
', NULL, NULL, '''/em'':86C,89C,95C ''/p'':96C ''/strong'':42C,80C ''advic'':12C ''bootstrap'':85C ''clear'':21C ''client'':34C ''client-sid'':33C ''cross'':93C ''cross-valid'':92C ''data'':36C,57C ''differ'':56C ''em'':84C,87C,91C ''evalu'':62C ''expert'':6C ''good'':73C ''grain'':15C ''idea'':74C ''jackknif'':88C ''m'':3C ''might'':70C ''model'':64C ''ok'':53C ''p'':1C ''popul'':46C ''relationship'':27C ''repres'':41C ''resampl'':79C ''salt'':17C ''server'':30C ''server-sid'':29C ''set'':58C ''side'':31C,35C ''strong'':40C,78C ''take'':10C ''techniqu'':81C ''testing/training'':60C ''think'':50C,68C ''use'':55C,76C ''valid'':94C ''yes'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (889, 2, '2014-08-02 04:54:14.757', 2, NULL, NULL, '&lt;p&gt;If the users who you are getting client-side data from are from the same population of users who you would get server-side data from. If that is true, then you aren''t really training on one population and applying to another. The main difference is that the client side data happened in the past (by necessity unless you are constantly refitting your model) and the server side data will come in the future.&lt;/p&gt;

&lt;p&gt;Let''s reformulate the question in terms of models rather than web clients and servers.&lt;/p&gt;

&lt;p&gt;You are fitting a model on one dataset and applying it to another. That is the classic use of predictive modeling/machine learning. Models use features from the data to make estimates of some parameter or parameters. Once you have a fitted (and tested) model, all that you need is the same set of features to feed into the model to get your estimates.&lt;/p&gt;

&lt;p&gt;Just make sure to model on a set of features (aka variables) that are available on the client-side and server-side. If that isn''t possible, ask that question separately.&lt;/p&gt;
', NULL, NULL, '''/p'':79C,96C,161C,196C ''aka'':173C ''anoth'':46C,110C ''appli'':44C,107C ''aren'':36C ''ask'':192C ''avail'':177C ''classic'':114C ''client'':10C,53C,93C,181C ''client-sid'':9C,180C ''come'':75C ''constant'':65C ''data'':12C,28C,55C,73C,125C ''dataset'':105C ''differ'':49C ''estim'':128C,160C ''featur'':122C,151C,172C ''feed'':153C ''fit'':100C,138C ''futur'':78C ''get'':8C,24C,158C ''happen'':56C ''isn'':189C ''learn'':119C ''let'':81C ''main'':48C ''make'':127C,164C ''model'':68C,89C,102C,120C,141C,156C,167C ''modeling/machine'':118C ''necess'':61C ''need'':145C ''one'':41C,104C ''p'':1C,80C,97C,162C ''paramet'':131C,133C ''past'':59C ''popul'':18C,42C ''possibl'':191C ''predict'':117C ''question'':85C,194C ''rather'':90C ''realli'':38C ''refit'':66C ''reformul'':83C ''separ'':195C ''server'':26C,71C,95C,185C ''server-sid'':25C,184C ''set'':149C,170C ''side'':11C,27C,54C,72C,182C,186C ''sure'':165C ''term'':87C ''test'':140C ''train'':39C ''true'':33C ''unless'':62C ''use'':115C,121C ''user'':4C,20C ''variabl'':174C ''web'':92C ''would'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (890, 2, '2014-08-02 14:55:37.347', 1, NULL, NULL, '&lt;p&gt;If your work is parallelizable enough for a distributed network of cpus to make a difference, why not try to run it on the gpu instead? That will require rather less investment than a large network of cpus with individual software licenses and still provide parallel processing which you can do runtime tracking on yourself.&lt;/p&gt;
', NULL, NULL, '''/p'':57C ''cpus'':13C,39C ''differ'':17C ''distribut'':10C ''enough'':7C ''gpu'':26C ''individu'':41C ''instead'':27C ''invest'':33C ''larg'':36C ''less'':32C ''licens'':43C ''make'':15C ''network'':11C,37C ''p'':1C ''parallel'':47C ''paralleliz'':6C ''process'':48C ''provid'':46C ''rather'':31C ''requir'':30C ''run'':22C ''runtim'':53C ''softwar'':42C ''still'':45C ''track'':54C ''tri'':20C ''work'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (891, 2, '2014-08-02 15:02:42.437', 1, NULL, NULL, '&lt;p&gt;As others have pointed out, these are not distance "metrics", because they do not satisfy the metric criteria. Say instead "distance measure".&lt;/p&gt;

&lt;p&gt;Anyway, what are you measuring and why? That information will help us give a more useful answer for your situation.&lt;/p&gt;
', NULL, NULL, '''/p'':24C,46C ''answer'':42C ''anyway'':26C ''criteria'':19C ''distanc'':10C,22C ''give'':38C ''help'':36C ''inform'':34C ''instead'':21C ''measur'':23C,30C ''metric'':11C,18C ''other'':3C ''p'':1C,25C ''point'':5C ''satisfi'':16C ''say'':20C ''situat'':45C ''us'':37C ''use'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (893, 1, '2014-08-03 13:07:24.143', 9, 13035, 'How to get correlation between two categorical variable and a categorical variable and continuous variable?', '&lt;p&gt;I am building a regression model and I need to calculate the below to check for correlations&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Correlation between 2 Multi level categorical variables&lt;/li&gt;
&lt;li&gt;Correlation between a Multi level categorical variable and
continuous variable &lt;/li&gt;
&lt;li&gt;VIF(variance inflation factor) for a Multi
level categorical variables&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I believe its wrong to use Pearson correlation coefficient for the above scenarios because Pearson only works for 2 continuous variables. &lt;/p&gt;

&lt;p&gt;Please answer the below questions&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Which correlation coefficient works best for the above cases ? &lt;/li&gt;
&lt;li&gt;VIF calculation only works for continuous data so what is the
alternative? &lt;/li&gt;
&lt;li&gt;What are the assumptions I need to check before I use the correlation coefficient you suggest? &lt;/li&gt;
&lt;li&gt;How to implement them in SAS &amp;amp; R?&lt;/li&gt;
&lt;/ol&gt;
', 'r  statistics  correlation', NULL, '''/li'':47C,59C,71C,114C,128C,146C,156C ''/ol'':72C,157C ''/p'':37C,95C,102C ''2'':42C,92C ''altern'':127C ''amp'':154C ''answer'':98C ''assumpt'':133C ''believ'':75C ''best'':109C ''build'':22C ''calcul'':30C,117C ''case'':113C ''categor'':7A,11A,45C,54C,69C ''check'':34C,137C ''coeffici'':82C,107C,143C ''continu'':14A,57C,93C,121C ''correl'':4A,18B,36C,40C,49C,81C,106C,142C ''data'':122C ''factor'':64C ''get'':3A ''implement'':150C ''inflat'':63C ''level'':44C,53C,68C ''li'':39C,48C,60C,104C,115C,129C,147C ''model'':25C ''multi'':43C,52C,67C ''need'':28C,135C ''ol'':38C,103C ''p'':19C,73C,96C ''pearson'':80C,88C ''pleas'':97C ''question'':101C ''r'':16B,155C ''regress'':24C ''sas'':153C ''scenario'':86C ''statist'':17B ''suggest'':145C ''two'':6A ''use'':79C,140C ''variabl'':8A,12A,15A,46C,55C,58C,70C,94C ''varianc'':62C ''vif'':61C,116C ''work'':90C,108C,119C ''wrong'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (895, 1, '2014-08-03 16:09:35.75', 4, 134, 'Facebook''s Huge Database', '&lt;p&gt;I assume that each person on Facebook is represented as a node (of a Graph) in Facebook, and relationship/friendship between each person(node) is represented as an edge between the involved nodes.&lt;/p&gt;

&lt;p&gt;Given that there are millions of people on Facebook, how is the Graph stored?&lt;/p&gt;
', 'graphs', NULL, '''/p'':39C,55C ''assum'':8C ''databas'':4A ''edg'':34C ''facebook'':1A,13C,23C,49C ''given'':41C ''graph'':5B,21C,53C ''huge'':3A ''involv'':37C ''million'':45C ''node'':18C,29C,38C ''p'':6C,40C ''peopl'':47C ''person'':11C,28C ''relationship/friendship'':25C ''repres'':15C,31C ''store'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (923, 2, '2014-08-06 09:08:12.117', 3, NULL, NULL, '&lt;p&gt;I think in your case a 10-fold CV will be O.K. &lt;/p&gt;

&lt;p&gt;I think it is more important to randomize the cross validation process than selecting the ideal value for k.&lt;/p&gt;

&lt;p&gt;So repeat the CV process several times randomly and compute the variance of your classification result to determine if the results are realiable or not.&lt;/p&gt;
', NULL, NULL, '''/p'':14C,35C,62C ''10'':8C ''case'':6C ''classif'':51C ''comput'':46C ''cross'':25C ''cv'':10C,40C ''determin'':54C ''fold'':9C ''ideal'':31C ''import'':21C ''k'':34C ''o.k'':13C ''p'':1C,15C,36C ''process'':27C,41C ''random'':23C,44C ''realiabl'':59C ''repeat'':38C ''result'':52C,57C ''select'':29C ''sever'':42C ''think'':3C,17C ''time'':43C ''valid'':26C ''valu'':32C ''varianc'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (899, 2, '2014-08-04 09:48:09.66', 1, NULL, NULL, '&lt;p&gt;When I worked with social network data, we stoted the "friendship" relation in a database in the table &lt;code&gt;Friends(friend_a, friend_b, ...)&lt;/code&gt; with a B-Tree index on &lt;code&gt;(friend_a, friend_b)&lt;/code&gt; plus also some partitioning.&lt;/p&gt;

&lt;p&gt;In our case it was a little bit different since the graph was directed, so it wasn''t really "friendship", but rather "following/follower" relationship. But for friendship I would just store two edges: both &lt;code&gt;(friend_a, friend_b)&lt;/code&gt; and &lt;code&gt;(friend_b, friend_a)&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;We used MySQL to store the data, if it matters, but I guess it shouldn''t.&lt;/p&gt;
', NULL, NULL, '''/code'':26C,39C,85C,92C ''/p'':44C,93C,111C ''also'':41C ''b'':25C,30C,38C,84C,89C ''b-tree'':29C ''bit'':53C ''case'':48C ''code'':20C,34C,80C,87C ''data'':8C,101C ''databas'':16C ''differ'':54C ''direct'':59C ''edg'':78C ''following/follower'':68C ''friend'':21C,22C,24C,35C,37C,81C,83C,88C,90C ''friendship'':12C,65C,72C ''graph'':57C ''guess'':107C ''index'':32C ''littl'':52C ''matter'':104C ''mysql'':97C ''network'':7C ''p'':1C,45C,94C ''partit'':43C ''plus'':40C ''rather'':67C ''realli'':64C ''relat'':13C ''relationship'':69C ''shouldn'':109C ''sinc'':55C ''social'':6C ''store'':76C,99C ''stote'':10C ''tabl'':19C ''tree'':31C ''two'':77C ''use'':96C ''wasn'':62C ''work'':4C ''would'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (900, 2, '2014-08-04 15:48:29.757', 2, NULL, NULL, '&lt;p&gt;I think hierarchical clustering would be more time efficient in your case (with a single dimension).
Depending on your task, you may implement something like this:&lt;/p&gt;

&lt;p&gt;Having N datapoints d&lt;sub&gt;i&lt;/sub&gt; with their 1-dimension value x&lt;sub&gt;i&lt;/sub&gt;:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Sort datapoints based on their x&lt;sub&gt;i&lt;/sub&gt; value. &lt;/li&gt;
&lt;li&gt;Calculate distances between adjacent datapoints (N-1 distances). Each distance must be assigned a pair of original datapoints (d&lt;sub&gt;i&lt;/sub&gt;, d&lt;sub&gt;j&lt;/sub&gt;).&lt;/li&gt;
&lt;li&gt;Sort distances in descending order to generate list of datapoint pairs (d&lt;sub&gt;i&lt;/sub&gt;, d&lt;sub&gt;j&lt;/sub&gt;), starting from the closest one.&lt;/li&gt;
&lt;li&gt;Iteratively unite datapoints (d&lt;sub&gt;i&lt;/sub&gt;, d&lt;sub&gt;j&lt;/sub&gt;) into clusters, starting from beginning of the list (the closest pair). (Depending on current state of d&lt;sub&gt;i&lt;/sub&gt; and d&lt;sub&gt;j&lt;/sub&gt;, uniting them means: (a) creating new cluster for two unclustered datapoints, (b) adding a datapoint to existing cluster and (c) uniting two clusters.)&lt;/li&gt;
&lt;li&gt;Stop uniting, if the distance is over some threshold.&lt;/li&gt;
&lt;li&gt;Create singleton clusters for datapoints which did not get into clusters.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;This algorithm implements &lt;a href="http://en.wikipedia.org/wiki/Single-linkage_clustering" rel="nofollow"&gt;single linkage&lt;/a&gt; clustering. It can be tuned easily to implement average linkage. &lt;a href="http://en.wikipedia.org/wiki/Complete_linkage_clustering" rel="nofollow"&gt;Complete linkage&lt;/a&gt; will be less efficient, but maybe easier ones will give good results depending on your data and task.&lt;/p&gt;

&lt;p&gt;I believe for 200K datapoints it must take under second, if you use proper data structures for above operations.&lt;/p&gt;
', NULL, NULL, '''-1'':67C ''/a'':213C,233C ''/li'':59C,87C,113C,174C,185C,198C ''/ol'':199C ''/p'':28C,46C,252C,273C ''/sub'':36C,45C,57C,82C,86C,103C,107C,121C,125C,145C,150C ''/wiki/complete_linkage_clustering'':228C ''/wiki/single-linkage_clustering'':208C ''1'':39C ''200k'':257C ''ad'':163C ''adjac'':64C ''algorithm'':202C ''assign'':73C ''averag'':222C ''b'':162C ''base'':51C ''begin'':130C ''believ'':255C ''c'':170C ''calcul'':61C ''case'':13C ''closest'':111C,135C ''cluster'':5C,127C,157C,168C,173C,189C,197C,214C ''complet'':231C ''creat'':155C,187C ''current'':139C ''d'':33C,79C,83C,100C,104C,118C,122C,142C,147C ''data'':249C,268C ''datapoint'':32C,50C,65C,78C,98C,117C,161C,165C,191C,258C ''depend'':18C,137C,246C ''descend'':92C ''dimens'':17C,40C ''distanc'':62C,68C,70C,90C,180C ''easier'':240C ''easili'':219C ''effici'':10C,237C ''en.wikipedia.org'':207C,227C ''en.wikipedia.org/wiki/complete_linkage_clustering'':226C ''en.wikipedia.org/wiki/single-linkage_clustering'':206C ''exist'':167C ''generat'':95C ''get'':195C ''give'':243C ''good'':244C ''hierarch'':4C ''href'':205C,225C ''implement'':24C,203C,221C ''iter'':115C ''j'':85C,106C,124C,149C ''less'':236C ''li'':48C,60C,88C,114C,175C,186C ''like'':26C ''linkag'':212C,223C,232C ''list'':96C,133C ''may'':23C ''mayb'':239C ''mean'':153C ''must'':71C,260C ''n'':31C,66C ''new'':156C ''nofollow'':210C,230C ''ol'':47C ''one'':112C,241C ''oper'':272C ''order'':93C ''origin'':77C ''p'':1C,29C,200C,253C ''pair'':75C,99C,136C ''proper'':267C ''rel'':209C,229C ''result'':245C ''second'':263C ''singl'':16C,211C ''singleton'':188C ''someth'':25C ''sort'':49C,89C ''start'':108C,128C ''state'':140C ''stop'':176C ''structur'':269C ''sub'':34C,43C,55C,80C,84C,101C,105C,119C,123C,143C,148C ''take'':261C ''task'':21C,251C ''think'':3C ''threshold'':184C ''time'':9C ''tune'':218C ''two'':159C,172C ''unclust'':160C ''unit'':116C,151C,171C,177C ''use'':266C ''valu'':41C,58C ''would'':6C ''x'':42C,54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (901, 2, '2014-08-04 15:59:12.573', 6, NULL, NULL, '&lt;p&gt;Strange as it sounds, graphs and graph databases are typically implemented as &lt;a href="http://docs.oracle.com/javase/7/docs/api/java/util/LinkedList.html"&gt;linked lists&lt;/a&gt;. As alluded to &lt;a href="http://docs.neo4j.org/chunked/stable/cypher-cookbook-newsfeed.html"&gt;here&lt;/a&gt;, even the most popular/performant graph database out there (neo4j), is secretly using something akin to a doubly-linked list.&lt;/p&gt;

&lt;p&gt;Representing a graph this way has a number of significant benefits, but also a few drawbacks. Firstly, representing a graph this way means that you can do edge-based insertions in near-constant time. Secondly, this means that traversing the graph can happen extremely quickly, if we''re only looking to either step up or down a linked list.&lt;/p&gt;

&lt;p&gt;The biggest drawback of this though comes from something sometimes called The Justin Bieber Effect, where nodes with a large number of connections tend to be extremely slow to evaluate. Imagine having to traverse a million semi-redundant links every time someone linked to Justin Bieber.&lt;/p&gt;

&lt;p&gt;I know that the awesome folks over at Neo4j are working on the second problem, but I''m not sure how they''re going about it, or how much success they''ve had.&lt;/p&gt;
', NULL, NULL, '''/a'':21C,31C ''/chunked/stable/cypher-cookbook-newsfeed.html'':29C ''/javase/7/docs/api/java/util/linkedlist.html'':18C ''/p'':52C,115C,164C,199C ''akin'':45C ''allud'':23C ''also'':66C ''awesom'':170C ''base'':83C ''benefit'':64C ''bieber'':130C,163C ''biggest'':118C ''call'':127C ''come'':123C ''connect'':139C ''constant'':88C ''databas'':9C,37C ''docs.neo4j.org'':28C ''docs.neo4j.org/chunked/stable/cypher-cookbook-newsfeed.html'':27C ''docs.oracle.com'':17C ''docs.oracle.com/javase/7/docs/api/java/util/linkedlist.html'':16C ''doubli'':49C ''doubly-link'':48C ''drawback'':69C,119C ''edg'':82C ''edge-bas'':81C ''effect'':131C ''either'':107C ''evalu'':146C ''even'':32C ''everi'':157C ''extrem'':99C,143C ''first'':70C ''folk'':171C ''go'':189C ''graph'':6C,8C,36C,56C,73C,96C ''happen'':98C ''href'':15C,26C ''imagin'':147C ''implement'':12C ''insert'':84C ''justin'':129C,162C ''know'':167C ''larg'':136C ''link'':19C,50C,113C,156C,160C ''list'':20C,51C,114C ''look'':105C ''m'':183C ''mean'':76C,92C ''million'':152C ''much'':194C ''near'':87C ''near-const'':86C ''neo4j'':40C,174C ''node'':133C ''number'':61C,137C ''p'':1C,53C,116C,165C ''popular/performant'':35C ''problem'':180C ''quick'':100C ''re'':103C,188C ''redund'':155C ''repres'':54C,71C ''second'':90C,179C ''secret'':42C ''semi'':154C ''semi-redund'':153C ''signific'':63C ''slow'':144C ''someon'':159C ''someth'':44C,125C ''sometim'':126C ''sound'':5C ''step'':108C ''strang'':2C ''success'':195C ''sure'':185C ''tend'':140C ''though'':122C ''time'':89C,158C ''travers'':94C,150C ''typic'':11C ''use'':43C ''ve'':197C ''way'':58C,75C ''work'':176C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (902, 1, '2014-08-04 19:10:57.187', 4, 132, 'When is there enough data for generalization?', '&lt;p&gt;Are there any general rules that one can use to infer what can be learned/generalized from a particular data set?  Suppose the dataset was taken from a sample of people.  Can these rules be stated as functions of the sample or total population?&lt;/p&gt;

&lt;p&gt;I understand the above may be vague, so a case scenario: Users participate in a search task, where the data are their queries, clicked results, and the HTML content (text only) of those results.  Each of these are tagged with their user and timestamp.  A user may generate a few pages - for a simple fact-finding task - or hundreds of pages - for a longer-term search task, like for class report.&lt;/p&gt;

&lt;p&gt;Edit:  In addition to generalizing about a population, given a sample, I''m interested in generalizing about an individual''s overall search behavior, given a time slice.  Theory and paper references are a plus!&lt;/p&gt;
', 'machine-learning  data-mining  statistics  search', NULL, '''/p'':60C,135C,171C ''addit'':139C ''behavior'':159C ''case'':71C ''class'':133C ''click'':85C ''content'':90C ''data'':5A,12B,35C,81C ''data-min'':11B ''dataset'':39C ''edit'':137C ''enough'':4A ''fact'':117C ''fact-find'':116C ''find'':118C ''function'':53C ''general'':7A,20C,141C,152C ''generat'':109C ''given'':145C,160C ''html'':89C ''hundr'':121C ''individu'':155C ''infer'':27C ''interest'':150C ''learn'':10B ''learned/generalized'':31C ''like'':131C ''longer'':127C ''longer-term'':126C ''m'':149C ''machin'':9B ''machine-learn'':8B ''may'':66C,108C ''mine'':13B ''one'':23C ''overal'':157C ''p'':16C,61C,136C ''page'':112C,123C ''paper'':166C ''particip'':74C ''particular'':34C ''peopl'':46C ''plus'':170C ''popul'':59C,144C ''queri'':84C ''refer'':167C ''report'':134C ''result'':86C,95C ''rule'':21C,49C ''sampl'':44C,56C,147C ''scenario'':72C ''search'':15B,77C,129C,158C ''set'':36C ''simpl'':115C ''slice'':163C ''state'':51C ''statist'':14B ''suppos'':37C ''tag'':100C ''taken'':41C ''task'':78C,119C,130C ''term'':128C ''text'':91C ''theori'':164C ''time'':162C ''timestamp'':105C ''total'':58C ''understand'':63C ''use'':25C ''user'':73C,103C,107C ''vagu'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (903, 1, '2014-08-04 22:27:10.837', 8, 297, 'Analyzing A/B test results which are not normally distributed, using independent t-test', '&lt;p&gt;I have a set of results from an A/B test (one control group, one feature group) which do not fit a Normal Distribution. 
In fact the distribution resembles more closely the Landau Distribution.&lt;/p&gt;

&lt;p&gt;I believe the independent t-test requires that the samples be at least approximately normally distributed, which discourages me using the t-test as a valid method of significance testing.&lt;/p&gt;

&lt;p&gt;But my question is: 
&lt;strong&gt;At what point can one say that the t-test is not a good method of significance testing?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Or put another way, how can one qualify how reliable the p-values of a t-test are, given only the data set?&lt;/p&gt;
', 'dataset  statistics  ab-test', NULL, '''/p'':54C,88C,115C,142C ''/strong'':114C ''a/b'':2A,29C ''ab'':18B ''ab-test'':17B ''analyz'':1A ''anoth'':119C ''approxim'':70C ''believ'':57C ''close'':50C ''control'':32C ''data'':140C ''dataset'':15B ''discourag'':74C ''distribut'':9A,43C,47C,53C,72C ''fact'':45C ''featur'':35C ''fit'':40C ''given'':137C ''good'':109C ''group'':33C,36C ''independ'':11A,59C ''landau'':52C ''least'':69C ''method'':84C,110C ''normal'':8A,42C,71C ''one'':31C,34C,99C,123C ''p'':20C,55C,89C,116C,129C ''p-valu'':128C ''point'':97C ''put'':118C ''qualifi'':124C ''question'':92C ''reliabl'':126C ''requir'':63C ''resembl'':48C ''result'':4A,26C ''sampl'':66C ''say'':100C ''set'':24C,141C ''signific'':86C,112C ''statist'':16B ''strong'':94C ''t-test'':12A,60C,78C,103C,133C ''test'':3A,14A,19B,30C,62C,80C,87C,105C,113C,135C ''use'':10A,76C ''valid'':83C ''valu'':130C ''way'':120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (909, 2, '2014-08-05 08:12:15.647', 5, NULL, NULL, '&lt;p&gt;The distribution of your data doesn''t need to be normal, it''s the &lt;a href="https://en.wikipedia.org/wiki/Sampling_distribution"&gt;Sampling Distribution&lt;/a&gt; that has to be nearly normal. If your sample size is big enough, then the sampling distribution of means from Landau Distribution should to be nearly normal, due to the &lt;a href="https://en.wikipedia.org/wiki/Central_limit_theorem"&gt;Central Limit Theorem&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;So it means you should be able to safely use t-test with your data.&lt;/p&gt;

&lt;h3&gt;Example&lt;/h3&gt;

&lt;p&gt;Let''s consider this example: suppose we have a population with &lt;a href="https://en.wikipedia.org/wiki/Log-normal_distribution"&gt;Lognormal distribution&lt;/a&gt; with mu=0 and sd=0.5 (it looks a bit similar to Landau)&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Hw5mM.png" alt="lognormal density"&gt;&lt;/p&gt;

&lt;p&gt;So we sample 30 observations 5000 times from this distribution each time calculating the mean of the sample &lt;/p&gt;

&lt;p&gt;And this is what we get &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/wjueS.png" alt="sampling distribution"&gt;&lt;/p&gt;

&lt;p&gt;Looks quite normal, doesn''t it? If we increase the sample size, it''s even more apparent&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/M0FQS.png" alt="sampling distribution"&gt;&lt;/p&gt;

&lt;h3&gt;R code&lt;/h3&gt;

&lt;pre&gt;&lt;code&gt;x = seq(0, 4, 0.05)
y = dlnorm(x, mean=0, sd=0.5)
plot(x, y, type=''l'', bty=''n'')


n = 30
m = 1000

set.seed(0)
samp = rep(NA, m)

for (i in 1:m) {
  samp[i] = mean(rlnorm(n, mean=0, sd=0.5))
}

hist(samp, col=''orange'', probability=T, breaks=25, main=''sample size = 30'')
x = seq(0.5, 1.5, 0.01)
lines(x, dnorm(x, mean=mean(samp), sd=sd(samp)))


n = 300
samp = rep(NA, m)

for (i in 1:m) {
  samp[i] = mean(rlnorm(n, mean=0, sd=0.5))
}

hist(samp, col=''orange'', probability=T, breaks=25, main=''sample size = 300'')
x = seq(1, 1.25, 0.005)
lines(x, dnorm(x, mean=mean(samp), sd=sd(samp)))
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':23C,62C,104C ''/code'':319C ''/h3'':84C,199C ''/hw5mm.png'':124C ''/m0fqs.png'':191C ''/p'':63C,81C,118C,128C,148C,156C,166C,185C,195C ''/pre'':320C ''/wiki/central_limit_theorem'':58C ''/wiki/log-normal_distribution'':101C ''/wiki/sampling_distribution'':20C ''/wjues.png'':162C ''0'':107C,204C,211C,226C,242C,289C ''0.005'':308C ''0.01'':261C ''0.05'':206C ''0.5'':110C,213C,244C,259C,291C ''1'':234C,281C,306C ''1.25'':307C ''1.5'':260C ''1000'':224C ''25'':252C,299C ''30'':133C,222C,256C ''300'':273C,303C ''4'':205C ''5000'':135C ''abl'':71C ''alt'':125C,163C,192C ''appar'':184C ''big'':35C ''bit'':114C ''break'':251C,298C ''bti'':219C ''calcul'':142C ''central'':59C ''code'':198C,201C ''col'':247C,294C ''consid'':88C ''data'':6C,80C ''densiti'':127C ''distribut'':3C,22C,40C,45C,103C,139C,165C,194C ''dlnorm'':208C ''dnorm'':264C,311C ''doesn'':7C,171C ''due'':51C ''en.wikipedia.org'':19C,57C,100C ''en.wikipedia.org/wiki/central_limit_theorem'':56C ''en.wikipedia.org/wiki/log-normal_distribution'':99C ''en.wikipedia.org/wiki/sampling_distribution'':18C ''enough'':36C ''even'':182C ''exampl'':83C,90C ''get'':155C ''h3'':82C,196C ''hist'':245C,292C ''href'':17C,55C,98C ''i.stack.imgur.com'':123C,161C,190C ''i.stack.imgur.com/hw5mm.png'':122C ''i.stack.imgur.com/m0fqs.png'':189C ''i.stack.imgur.com/wjues.png'':160C ''img'':120C,158C,187C ''increas'':176C ''l'':218C ''landau'':44C,117C ''let'':86C ''limit'':60C ''line'':262C,309C ''lognorm'':102C,126C ''look'':112C,168C ''m'':223C,230C,235C,277C,282C ''main'':253C,300C ''mean'':42C,67C,144C,210C,238C,241C,266C,267C,285C,288C,313C,314C ''mu'':106C ''n'':220C,221C,240C,272C,287C ''na'':229C,276C ''near'':28C,49C ''need'':9C ''normal'':12C,29C,50C,170C ''observ'':134C ''orang'':248C,295C ''p'':1C,64C,85C,119C,129C,149C,157C,167C,186C ''plot'':214C ''popul'':95C ''pre'':200C ''probabl'':249C,296C ''quit'':169C ''r'':197C ''rep'':228C,275C ''rlnorm'':239C,286C ''safe'':73C ''samp'':227C,236C,246C,268C,271C,274C,283C,293C,315C,318C ''sampl'':21C,32C,39C,132C,147C,164C,178C,193C,254C,301C ''sd'':109C,212C,243C,269C,270C,290C,316C,317C ''seq'':203C,258C,305C ''set.seed'':225C ''similar'':115C ''size'':33C,179C,255C,302C ''src'':121C,159C,188C ''suppos'':91C ''t-test'':75C ''test'':77C ''theorem'':61C ''time'':136C,141C ''type'':217C ''use'':74C ''x'':202C,209C,215C,257C,263C,265C,304C,310C,312C ''y'':207C,216C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (904, 1, '2014-08-04 19:21:45.067', 12, 4572, 'What do you use to generate a dashboard in R?', '&lt;p&gt;I need to generate periodic (daily, monthly) web analytics dashboard reports. They will be static and don''t require interaction, so imagine a PDF file as the target output. The reports will mix tables and charts (mainly sparkline and bullet graphs created with ggplot2). Think Stephen Few/Perceptual Edge style dashboards, such as: &lt;img src="http://i.stack.imgur.com/Edh2e.png" alt="sample dashboard"&gt;&lt;/p&gt;

&lt;p&gt;but applied to web analytics. &lt;/p&gt;

&lt;p&gt;Any suggestions on what packages to use creating these dashboard reports? &lt;/p&gt;

&lt;p&gt;My first intuition is to use R markdown and knitr, but perhaps you''ve found a better solution. I can''t seem to find rich examples of dashboards generated from R. &lt;/p&gt;
', 'r  visualization', NULL, '''/edh2e.png'':70C ''/p'':74C,81C,94C,127C ''alt'':71C ''analyt'':22C,80C ''appli'':77C ''better'':112C ''bullet'':53C ''chart'':49C ''creat'':55C,90C ''daili'':19C ''dashboard'':8A,23C,63C,73C,92C,123C ''edg'':61C ''exampl'':121C ''few/perceptual'':60C ''file'':38C ''find'':119C ''first'':97C ''found'':110C ''generat'':6A,17C,124C ''ggplot2'':57C ''graph'':54C ''i.stack.imgur.com'':69C ''i.stack.imgur.com/edh2e.png'':68C ''imagin'':35C ''img'':66C ''interact'':33C ''intuit'':98C ''knitr'':105C ''main'':50C ''markdown'':103C ''mix'':46C ''month'':20C ''need'':15C ''output'':42C ''p'':13C,75C,82C,95C ''packag'':87C ''pdf'':37C ''perhap'':107C ''period'':18C ''r'':10A,11B,102C,126C ''report'':24C,44C,93C ''requir'':32C ''rich'':120C ''sampl'':72C ''seem'':117C ''solut'':113C ''sparklin'':51C ''src'':67C ''static'':28C ''stephen'':59C ''style'':62C ''suggest'':84C ''tabl'':47C ''target'':41C ''think'':58C ''use'':4A,89C,101C ''ve'':109C ''visual'':12B ''web'':21C,79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1168, 2, '2014-09-26 08:51:46.727', 1, NULL, NULL, '&lt;p&gt;Please take a look at this paper: &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Survey on Various Natural Language Processing Toolkits&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;In this work several NLP tools are introduced. Examples are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Carabao Language Toolkit&lt;/li&gt;
&lt;li&gt;GATE&lt;/li&gt;
&lt;li&gt;Stanford NLP toolkit&lt;/li&gt;
&lt;li&gt;NLTK&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Just google the title of the paper and you will find the .pdf file.&lt;/p&gt;
', NULL, NULL, '''/li'':38C,41C,46C,49C ''/p'':9C,20C,32C,66C ''/strong'':19C ''/ul'':50C ''carabao'':35C ''exampl'':30C ''file'':65C ''find'':62C ''gate'':40C ''googl'':53C ''introduc'':29C ''languag'':16C,36C ''li'':34C,39C,42C,47C ''look'':5C ''natur'':15C ''nlp'':26C,44C ''nltk'':48C ''p'':1C,10C,21C,51C ''paper'':8C,58C ''pdf'':64C ''pleas'':2C ''process'':17C ''sever'':25C ''stanford'':43C ''strong'':11C ''survey'':12C ''take'':3C ''titl'':55C ''tool'':27C ''toolkit'':18C,37C,45C ''ul'':33C ''various'':14C ''work'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (921, 1, '2014-08-06 09:03:20.857', 3, 102, 'Avoid iterations while calculating average model accuracy', '&lt;p&gt;I am fitting a model in R.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;use &lt;code&gt;createFolds&lt;/code&gt; method to create several &lt;code&gt;k&lt;/code&gt; folds from the data set&lt;/li&gt;
&lt;li&gt;loop through the folds, repeating the following on each iteration:
&lt;ul&gt;
&lt;li&gt;&lt;code&gt;train&lt;/code&gt; the model on k-1 folds&lt;/li&gt;
&lt;li&gt;&lt;code&gt;predict&lt;/code&gt; the outcomes for the i-th fold&lt;/li&gt;
&lt;li&gt;calculate prediction accuracy&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;average the accuracy&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Does R have a function that makes folds itself, repeats model tuning/predictions and gives the average accuracy back?&lt;/p&gt;
', 'r  accuracy  cross-validation  sampling  beginner', NULL, '''-1'':63C ''/code'':29C,36C,58C,69C ''/li'':42C,65C,78C,83C,85C,90C ''/p'':23C,111C ''/ul'':84C,91C ''accuraci'':7A,9B,82C,89C,109C ''averag'':5A,87C,108C ''avoid'':1A ''back'':110C ''beginn'':14B ''calcul'':4A,80C ''code'':27C,34C,56C,67C ''creat'':32C ''createfold'':28C ''cross'':11B ''cross-valid'':10B ''data'':40C ''fit'':18C ''fold'':37C,47C,64C,77C,100C ''follow'':50C ''function'':97C ''give'':106C ''i-th'':74C ''iter'':2A,53C ''k'':35C,62C ''li'':25C,43C,55C,66C,79C,86C ''loop'':44C ''make'':99C ''method'':30C ''model'':6A,20C,60C,103C ''outcom'':71C ''p'':15C,92C ''predict'':68C,81C ''r'':8B,22C,94C ''repeat'':48C,102C ''sampl'':13B ''set'':41C ''sever'':33C ''th'':76C ''train'':57C ''tuning/predictions'':104C ''ul'':24C,54C ''use'':26C ''valid'':12B');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (905, 2, '2014-08-04 19:28:38.173', 8, NULL, NULL, '&lt;p&gt;&lt;a href="http://shiny.rstudio.com"&gt;Shiny&lt;/a&gt; is a framework for generating HTML-based apps that execute R code dynamically. Shiny apps can stand alone or be built into Markdown documents with &lt;code&gt;knitr&lt;/code&gt;, and Shiny development is fully integrated into RStudio. There''s even a free service called &lt;a href="https://www.shinyapps.io"&gt;shinyapps.io&lt;/a&gt; for hosting Shiny apps, the &lt;code&gt;shiny&lt;/code&gt; package has functions for deploying Shiny apps directly from R, and RStudio has a GUI interface for calling those functions. There''s plenty more info in the Tutorial section of the site.&lt;/p&gt;

&lt;p&gt;Since it essentially "compiles" the whole thing to JavaScript and HTML, you can use CSS to freely change the formatting and layout, although Shiny has decent wrapper functionality for this. But it just so happens that their default color scheme is similar to the one in the screenshot you posted.&lt;/p&gt;

&lt;p&gt;edit: I just realized you don''t need them to be dynamic. Shiny still makes very nice-looking webpages out of the box, with lots of options for rearranging elements. There''s also functionality for downloading plots, so you can generate your dashboard every month by just updating your data files in the app, and then saving the resulting image to PDF.&lt;/p&gt;
', NULL, NULL, '''/a'':6C,55C ''/code'':35C,63C ''/p'':96C,148C,213C ''alon'':25C ''also'':183C ''although'':120C ''app'':15C,22C,59C,70C,204C ''base'':14C ''box'':173C ''built'':28C ''call'':50C,81C ''chang'':115C ''code'':19C,33C,61C ''color'':136C ''compil'':101C ''css'':112C ''dashboard'':193C ''data'':200C ''decent'':123C ''default'':135C ''deploy'':68C ''develop'':38C ''direct'':71C ''document'':31C ''download'':186C ''dynam'':20C,161C ''edit'':150C ''element'':180C ''essenti'':100C ''even'':46C ''everi'':194C ''execut'':17C ''file'':201C ''format'':117C ''framework'':9C ''free'':48C ''freeli'':114C ''fulli'':40C ''function'':66C,83C,125C,184C ''generat'':11C,191C ''gui'':78C ''happen'':132C ''host'':57C ''href'':3C,52C ''html'':13C,108C ''html-base'':12C ''imag'':210C ''info'':88C ''integr'':41C ''interfac'':79C ''javascript'':106C ''knitr'':34C ''layout'':119C ''look'':168C ''lot'':175C ''make'':164C ''markdown'':30C ''month'':195C ''need'':157C ''nice'':167C ''nice-look'':166C ''one'':142C ''option'':177C ''p'':1C,97C,149C ''packag'':64C ''pdf'':212C ''plenti'':86C ''plot'':187C ''post'':147C ''r'':18C,73C ''realiz'':153C ''rearrang'':179C ''result'':209C ''rstudio'':43C,75C ''save'':207C ''scheme'':137C ''screenshot'':145C ''section'':92C ''servic'':49C ''shini'':5C,21C,37C,58C,62C,69C,121C,162C ''shiny.rstudio.com'':4C ''shinyapps.io'':54C ''similar'':139C ''sinc'':98C ''site'':95C ''stand'':24C ''still'':163C ''thing'':104C ''tutori'':91C ''updat'':198C ''use'':111C ''webpag'':169C ''whole'':103C ''wrapper'':124C ''www.shinyapps.io'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (906, 2, '2014-08-04 23:37:27.877', 2, NULL, NULL, '&lt;p&gt;&lt;a href="http://mrvar.fdv.uni-lj.si/pajek/PajekXXL.htm" rel="nofollow"&gt;PajekXXL&lt;/a&gt; is designed to handle enormous networks. But Pajek is also kind of a bizarre program with an unintuitive interface.&lt;/p&gt;
', NULL, NULL, '''/a'':10C ''/p'':30C ''/pajek/pajekxxl.htm'':6C ''also'':20C ''bizarr'':24C ''design'':12C ''enorm'':15C ''handl'':14C ''href'':3C ''interfac'':29C ''kind'':21C ''mrvar.fdv.uni-lj.si'':5C ''mrvar.fdv.uni-lj.si/pajek/pajekxxl.htm'':4C ''network'':16C ''nofollow'':8C ''p'':1C ''pajek'':18C ''pajekxxl'':9C ''program'':25C ''rel'':7C ''unintuit'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (907, 2, '2014-08-05 07:17:34.75', 13, NULL, NULL, '&lt;p&gt;I think that &lt;code&gt;Shiny&lt;/code&gt; is an overkill in this situation and doesn''t match your requirement of &lt;em&gt;dashboard reports&lt;/em&gt; to be &lt;strong&gt;static&lt;/strong&gt;. I guess, that your use of the term "dashboard" is a bit confusing, as some people might consider that it has more emphasis of &lt;strong&gt;interactivity&lt;/strong&gt; (&lt;em&gt;real-time dashboards&lt;/em&gt;), rather than &lt;strong&gt;information layout&lt;/strong&gt;, as is my understanding (confirmed by the "static" requirement).&lt;/p&gt;

&lt;p&gt;My recommendation to you is to use &lt;strong&gt;R Markdown&lt;/strong&gt; and &lt;strong&gt;knitr&lt;/strong&gt;, especially since these packages have much lower learning curve than &lt;strong&gt;Shiny&lt;/strong&gt;. Moreover, I have recently run across an R package, which, in my view, ideally suits your requirement of embedding small charts/plots in a report, as presented on your picture above. This package generates static or dynamic &lt;em&gt;graphical tables&lt;/em&gt; and is called &lt;strong&gt;sparkTable&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/sparkTable"&gt;http://cran.r-project.org/web/packages/sparkTable&lt;/a&gt;). Its vignette is available here (there is no link to it on the package''s home page): &lt;a href="http://publik.tuwien.ac.at/files/PubDat_228663.pdf"&gt;http://publik.tuwien.ac.at/files/PubDat_228663.pdf&lt;/a&gt;. Should you ever need some &lt;em&gt;interactivity&lt;/em&gt;, &lt;code&gt;sparkTable&lt;/code&gt; provides some via its simple interface to &lt;code&gt;Shiny&lt;/code&gt;.&lt;/p&gt;
', NULL, NULL, '''/code'':7C,197C,207C ''/em'':24C,62C,147C,194C ''/files/pubdat_228663.pdf'':183C ''/files/pubdat_228663.pdf&lt;/a&gt;.'':186C ''/p'':78C,208C ''/strong'':29C,56C,68C,90C,94C,107C,153C ''/web/packages/sparktable'':158C ''/web/packages/sparktable&lt;/a&gt;).'':161C ''across'':113C ''avail'':165C ''bit'':41C ''call'':150C ''charts/plots'':128C ''code'':5C,195C,205C ''confirm'':73C ''confus'':42C ''consid'':47C ''cran.r-project.org'':157C,160C ''cran.r-project.org/web/packages/sparktable'':156C ''cran.r-project.org/web/packages/sparktable&lt;/a&gt;).'':159C ''curv'':103C ''dashboard'':22C,38C,61C ''doesn'':15C ''dynam'':143C ''em'':21C,57C,144C,192C ''embed'':126C ''emphasi'':52C ''especi'':95C ''ever'':189C ''generat'':140C ''graphic'':145C ''guess'':31C ''home'':177C ''href'':155C,180C ''ideal'':121C ''inform'':66C ''interact'':55C,193C ''interfac'':203C ''knitr'':93C ''layout'':67C ''learn'':102C ''link'':170C ''lower'':101C ''markdown'':89C ''match'':17C ''might'':46C ''moreov'':108C ''much'':100C ''need'':190C ''overkil'':10C ''p'':1C,79C ''packag'':98C,116C,139C,175C ''page'':178C ''peopl'':45C ''pictur'':136C ''present'':133C ''provid'':198C ''publik.tuwien.ac.at'':182C,185C ''publik.tuwien.ac.at/files/pubdat_228663.pdf'':181C ''publik.tuwien.ac.at/files/pubdat_228663.pdf&lt;/a&gt;.'':184C ''r'':88C,115C ''rather'':63C ''real'':59C ''real-tim'':58C ''recent'':111C ''recommend'':81C ''report'':23C,131C ''requir'':19C,77C,124C ''run'':112C ''shini'':6C,106C,206C ''simpl'':202C ''sinc'':96C ''situat'':13C ''small'':127C ''sparktabl'':152C,196C ''static'':28C,76C,141C ''strong'':27C,54C,65C,87C,92C,105C,151C ''suit'':122C ''tabl'':146C ''term'':37C ''think'':3C ''time'':60C ''understand'':72C ''use'':34C,86C ''via'':200C ''view'':120C ''vignett'':163C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (929, 2, '2014-08-06 17:07:17.52', 1, NULL, NULL, '&lt;p&gt;Can you just cast your nparray from train_test_split back into a pandas dataFrame so you can carry out your same strategy. This is very common to what I do when dealing with pandas and scikit. For example,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; a = train_test_split
 new_df = pd.DataFrame(a)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':52C ''/p'':41C ''/pre'':53C ''back'':12C ''carri'':20C ''cast'':5C ''code'':43C ''common'':28C ''datafram'':16C ''deal'':34C ''df'':49C ''exampl'':40C ''new'':48C ''nparray'':7C ''p'':1C ''panda'':15C,36C ''pd.dataframe'':50C ''pre'':42C ''scikit'':38C ''split'':11C,47C ''strategi'':24C ''test'':10C,46C ''train'':9C,45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (908, 2, '2014-08-05 08:09:17.24', 6, NULL, NULL, '&lt;p&gt;It is my understanding that &lt;em&gt;random sampling&lt;/em&gt; is a &lt;strong&gt;mandatory condition&lt;/strong&gt; for making any &lt;em&gt;generalization&lt;/em&gt; statements. IMHO, other parameters, such as sample size, just affect probability level (confidence) of generalization. Furthermore, clarifying the @ffriend''s comment, I believe that you have to &lt;strong&gt;calculate&lt;/strong&gt; needed &lt;em&gt;sample size&lt;/em&gt;, based on desired values of &lt;em&gt;confidence interval&lt;/em&gt;, &lt;em&gt;effect size&lt;/em&gt;, &lt;em&gt;statistical power&lt;/em&gt; and &lt;em&gt;number of predictors&lt;/em&gt; (this is based on Cohen''s work - see References section at the following link). For multiple regression, you can use the following calculator: &lt;a href="http://www.danielsoper.com/statcalc3/calc.aspx?id=1"&gt;http://www.danielsoper.com/statcalc3/calc.aspx?id=1&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;More information on &lt;strong&gt;how to select, calculate and interpret effect sizes&lt;/strong&gt; can be found in the following nice and comprehensive paper, which is freely available: &lt;a href="http://jpepsy.oxfordjournals.org/content/34/9/917.full"&gt;http://jpepsy.oxfordjournals.org/content/34/9/917.full&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;If you''re using &lt;code&gt;R&lt;/code&gt; (and even, if you don''t), you may find the following Web page on &lt;strong&gt;confidence intervals and R&lt;/strong&gt; interesting and useful: &lt;a href="http://osc.centerforopenscience.org/static/CIs_in_r.html"&gt;http://osc.centerforopenscience.org/static/CIs_in_r.html&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Finally, the following &lt;strong&gt;comprehensive guide&lt;/strong&gt; to survey &lt;strong&gt;sampling&lt;/strong&gt; can be helpful, even if you''re not using survey research designs. In my opinion, it contains a wealth of useful information on &lt;em&gt;sampling methods&lt;/em&gt;, &lt;em&gt;sampling size determination&lt;/em&gt; (including calculator) and much more: &lt;a href="http://home.ubalt.edu/ntsbarsh/stat-data/Surveys.htm"&gt;http://home.ubalt.edu/ntsbarsh/stat-data/Surveys.htm&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/code'':155C ''/content/34/9/917.full'':144C ''/content/34/9/917.full&lt;/a&gt;.&lt;/p&gt;'':147C ''/em'':10C,22C,57C,66C,70C,74C,80C,226C,231C ''/ntsbarsh/stat-data/surveys.htm'':241C ''/ntsbarsh/stat-data/surveys.htm&lt;/a&gt;.&lt;/p&gt;'':244C ''/statcalc3/calc.aspx?id=1'':108C ''/statcalc3/calc.aspx?id=1&lt;/a&gt;.&lt;/p&gt;'':111C ''/static/cis_in_r.html'':183C ''/static/cis_in_r.html&lt;/a&gt;.&lt;/p&gt;'':186C ''/strong'':16C,52C,125C,175C,194C,199C ''affect'':32C ''avail'':139C ''base'':58C,83C ''believ'':45C ''calcul'':51C,103C,120C,233C ''clarifi'':39C ''code'':153C ''cohen'':85C ''comment'':43C ''comprehens'':134C,192C ''condit'':15C ''confid'':35C,64C,171C ''contain'':216C ''design'':211C ''desir'':60C ''determin'':230C ''effect'':68C,123C ''em'':7C,20C,54C,63C,67C,71C,76C,223C,227C ''even'':157C,203C ''ffriend'':41C ''final'':188C ''find'':164C ''follow'':93C,102C,131C,166C,190C ''found'':128C ''freeli'':138C ''furthermor'':38C ''general'':21C,37C ''guid'':193C ''help'':202C ''home.ubalt.edu'':240C,243C ''home.ubalt.edu/ntsbarsh/stat-data/surveys.htm'':239C ''home.ubalt.edu/ntsbarsh/stat-data/surveys.htm&lt;/a&gt;.&lt;/p&gt;'':242C ''href'':105C,141C,180C,238C ''imho'':24C ''includ'':232C ''inform'':114C,221C ''interest'':176C ''interpret'':122C ''interv'':65C,172C ''jpepsy.oxfordjournals.org'':143C,146C ''jpepsy.oxfordjournals.org/content/34/9/917.full'':142C ''jpepsy.oxfordjournals.org/content/34/9/917.full&lt;/a&gt;.&lt;/p&gt;'':145C ''level'':34C ''link'':94C ''make'':18C ''mandatori'':14C ''may'':163C ''method'':225C ''much'':235C ''multipl'':96C ''need'':53C ''nice'':132C ''number'':77C ''opinion'':214C ''osc.centerforopenscience.org'':182C,185C ''osc.centerforopenscience.org/static/cis_in_r.html'':181C ''osc.centerforopenscience.org/static/cis_in_r.html&lt;/a&gt;.&lt;/p&gt;'':184C ''p'':1C,112C,148C,187C ''page'':168C ''paper'':135C ''paramet'':26C ''power'':73C ''predictor'':79C ''probabl'':33C ''r'':154C,174C ''random'':8C ''re'':151C,206C ''refer'':89C ''regress'':97C ''research'':210C ''sampl'':9C,29C,55C,198C,224C,228C ''section'':90C ''see'':88C ''select'':119C ''size'':30C,56C,69C,124C,229C ''statement'':23C ''statist'':72C ''strong'':13C,50C,116C,170C,191C,197C ''survey'':196C,209C ''understand'':5C ''use'':100C,152C,178C,208C,220C ''valu'':61C ''wealth'':218C ''web'':167C ''work'':87C ''www.danielsoper.com'':107C,110C ''www.danielsoper.com/statcalc3/calc.aspx?id=1'':106C ''www.danielsoper.com/statcalc3/calc.aspx?id=1&lt;/a&gt;.&lt;/p&gt;'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (911, 2, '2014-08-05 10:15:33.713', 1, NULL, NULL, '&lt;p&gt;Basically an independent t-test or a 2 sample t-test is used to check if the averages of the two samples are significantly different. Or, to put in another words, if there is a significant difference between the means of the two samples. &lt;/p&gt;

&lt;p&gt;Now, the means of those 2 samples are two statistics, which according with CLT, have a normal distribution, if provided enough samples. Note that CLT works no matter of the distribution from which the mean statistic is built. &lt;/p&gt;

&lt;p&gt;Normally one can use a z-test, but if the variances are estimated from the sample (because it is unknown), some additional uncertainty is introduced, which is incorporated in t distribution. That''s why 2-sample t-test applies here.  &lt;/p&gt;
', NULL, NULL, '''/p'':48C,88C,132C ''2'':10C,55C,125C ''accord'':61C ''addit'':112C ''anoth'':33C ''appli'':130C ''averag'':21C ''basic'':2C ''built'':87C ''check'':18C ''clt'':63C,74C ''differ'':28C,40C ''distribut'':67C,80C,121C ''enough'':70C ''estim'':103C ''incorpor'':118C ''independ'':4C ''introduc'':115C ''matter'':77C ''mean'':43C,52C,84C ''normal'':66C,90C ''note'':72C ''one'':91C ''p'':1C,49C,89C ''provid'':69C ''put'':31C ''sampl'':11C,25C,47C,56C,71C,106C,126C ''signific'':27C,39C ''statist'':59C,85C ''t-test'':5C,12C,127C ''test'':7C,14C,97C,129C ''two'':24C,46C,58C ''uncertainti'':113C ''unknown'':110C ''use'':16C,93C ''varianc'':101C ''word'':34C ''work'':75C ''z'':96C ''z-test'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (912, 2, '2014-08-05 12:29:05.3', 2, NULL, NULL, '&lt;p&gt;There is an excellent comparison of the common inner-product-based similarity metrics &lt;a href="http://brenocon.com/blog/2012/03/cosine-similarity-pearson-correlation-and-ols-coefficients/" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In particular, Cosine Similarity is normalized to lie within [0,1], unlike the dot product which can be any real number, but, as everyone else is saying, that will require ignoring the magnitude of the vectors. Personally, I think that''s a good thing. I think of magnitude as an internal (within-vector) structure, and angle between vectors as external (between vector) structure. They are different things and (in my opinion) are often best analyzed separately. I can''t imagine a situation where I would rather compute inner products than compute cosine similarities and just compare the magnitudes afterward.&lt;/p&gt;
', NULL, NULL, '''/a'':24C ''/blog/2012/03/cosine-similarity-pearson-correlation-and-ols-coefficients/'':20C ''/p'':25C,127C ''0'':36C ''1'':37C ''afterward'':126C ''analyz'':102C ''angl'':83C ''base'':13C ''best'':101C ''brenocon.com'':19C ''brenocon.com/blog/2012/03/cosine-similarity-pearson-correlation-and-ols-coefficients/'':18C ''common'':9C ''compar'':123C ''comparison'':6C ''comput'':114C,118C ''cosin'':29C,119C ''differ'':93C ''dot'':40C ''els'':51C ''everyon'':50C ''excel'':5C ''extern'':87C ''good'':69C ''href'':17C ''ignor'':57C ''imagin'':107C ''inner'':11C,115C ''inner-product-bas'':10C ''intern'':77C ''lie'':34C ''magnitud'':59C,74C,125C ''metric'':15C ''nofollow'':22C ''normal'':32C ''number'':47C ''often'':100C ''opinion'':98C ''p'':1C,26C ''particular'':28C ''person'':63C ''product'':12C,41C,116C ''rather'':113C ''real'':46C ''rel'':21C ''requir'':56C ''say'':53C ''separ'':103C ''similar'':14C,30C,120C ''situat'':109C ''structur'':81C,90C ''thing'':70C,94C ''think'':65C,72C ''unlik'':38C ''vector'':62C,80C,85C,89C ''within'':35C,79C ''within-vector'':78C ''would'':112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (972, 1, '2014-08-13 20:30:18.143', 1, 21, 'Subgraph isomorphism and Anti-monotone property', '&lt;p&gt;While finding frequent subgraphs in single large graph, subgraph isomorphism (test) is not considered because its not anti-monotone. How and why subgraph isomorphism is not anti-monotone ?&lt;/p&gt;
', 'bigdata  data-mining  dataset  graphs  similarity', NULL, '''/p'':46C ''anti'':5A,34C,44C ''anti-monoton'':4A,33C,43C ''bigdata'':8B ''consid'':29C ''data'':10B ''data-min'':9B ''dataset'':12B ''find'':17C ''frequent'':18C ''graph'':13B,23C ''isomorph'':2A,25C,40C ''larg'':22C ''mine'':11B ''monoton'':6A,35C,45C ''p'':15C ''properti'':7A ''similar'':14B ''singl'':21C ''subgraph'':1A,19C,24C,39C ''test'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9512, 1, '2015-12-25 09:04:20.853', 2, 24, 'Are there other large margin classifiers than SVMs?', '&lt;p&gt;When reading about SVMs (e.g. on the German Wikipedia) there is a sentence like "an svm is a large-margin classifier).&lt;/p&gt;

&lt;p&gt;Are there other large margin classifiers than SVMs?&lt;/p&gt;
', 'machine-learning  classification  svm', NULL, '''/p'':37C,47C ''classif'':12B ''classifi'':6A,36C,44C ''e.g'':19C ''german'':22C ''larg'':4A,34C,42C ''large-margin'':33C ''learn'':11B ''like'':28C ''machin'':10B ''machine-learn'':9B ''margin'':5A,35C,43C ''p'':14C,38C ''read'':16C ''sentenc'':27C ''svm'':13B,30C ''svms'':8A,18C,46C ''wikipedia'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (913, 2, '2014-08-05 12:58:16', 4, NULL, NULL, '&lt;p&gt;There are two rules for generalizability:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;The sample must be &lt;strong&gt;representative&lt;/strong&gt;. In expectation, at least, the distribution of features in your sample must match the distribution of features in the population. When you are fitting a model with a response variable, &lt;em&gt;this includes features that you do not observe, but that affect any response variables in your model&lt;/em&gt;. Since it is, in many cases, impossible to know what you do not observe, &lt;strong&gt;random sampling&lt;/strong&gt; is used.&lt;/p&gt;

&lt;p&gt;The idea with randomization is that a random sample, up to sampling error, &lt;em&gt;must&lt;/em&gt; accurately reflect the distribution of all features in the population, observed and otherwise. This is why &lt;strong&gt;randomization is the "gold standard,"&lt;/strong&gt; but if sample control is available by some other technique, or it is defensible to argue that there are no omitted features, then it isn''t always necessary.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Your sample must be &lt;strong&gt;large enough&lt;/strong&gt; that the effect of &lt;strong&gt;sampling error&lt;/strong&gt; on the feature distribution is relatively small. This is, again, to ensure representativeness. But deciding who to sample is different from deciding how many people to sample.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Since it sounds like you''re fitting a model, there''s the additional consideration that certain important combinations of features could be relatively rare in the population. This is not an issue for generalizability, but it bears heavily on your considerations for sample size. For instance, I''m working on a project now with (non-big) data that was originally collected to understand the experiences of minorities in college. As such, it was critically important to ensure that &lt;strong&gt;statistical power&lt;/strong&gt; was high &lt;em&gt;specifically in the minority subpopulation&lt;/em&gt;. For this reason, blacks and Latinos were deliberately &lt;strong&gt;oversampled&lt;/strong&gt;. However, the proportion by which they were oversampled was also recorded. These are used to compute survey weights. These can be used to  re-weight the sample so as to reflect the estimated population proportions, in the event that a representative sample is required.&lt;/p&gt;

&lt;p&gt;An additional consideration arises if your model is hierarchical. A canonical use for a hierarchical model is one of children''s behavior in schools. Children are "grouped" by school and share school-level traits. Therefore a representative sample of schools is required, and within each school a representative sample of children is required. This leads to &lt;strong&gt;stratified sampling&lt;/strong&gt;. This and some other sampling designs are reviewed in surprising depth on &lt;a href="http://en.wikipedia.org/wiki/Sampling_(statistics)#Sampling_methods" rel="nofollow"&gt;Wikipedia&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (915, 1, '2014-08-05 18:36:12.753', 7, 165, 'How do various statistical techniques (regression, PCA, etc) scale with sample size and dimension?', '&lt;p&gt;Is there a known general table of statistical techniques that explain how they scale with sample size and dimension? For example, a friend of mine told me the other day that the computation time of simply quick-sorting one dimensional data of size n goes as n*log(n).&lt;/p&gt;

&lt;p&gt;So, for example, if we regress y against X where X is a d-dimensional variable, does it go as O(n^2*d)? How does it scale if I want to find the solution via exact Gauss-Markov solution vs numerical least squares with Newton method? Or simply getting the solution vs using significance tests?&lt;/p&gt;

&lt;p&gt;I guess I more want a good source of answers (like a paper that summarizes the scaling of various statistical techniques) than a good answer here. Like, say, a list that includes the scaling of multiple regression, logistic regression, PCA, cox proportional hazard regression, K-means clustering, etc.&lt;/p&gt;
', 'bigdata  statistics  efficiency  scalability', NULL, '''/p'':70C,130C,181C ''2'':95C ''answer'':141C,156C ''bigdata'':15B ''cluster'':179C ''comput'':52C ''cox'':172C ''d'':86C,96C ''d-dimension'':85C ''data'':61C ''day'':49C ''dimens'':14A,38C ''dimension'':60C,87C ''effici'':17B ''etc'':8A,180C ''exact'':109C ''exampl'':40C,74C ''explain'':30C ''find'':105C ''friend'':42C ''gauss'':111C ''gauss-markov'':110C ''general'':24C ''get'':123C ''go'':91C ''goe'':65C ''good'':138C,155C ''guess'':133C ''hazard'':174C ''includ'':163C ''k'':177C ''k-mean'':176C ''known'':23C ''least'':116C ''like'':142C,158C ''list'':161C ''log'':68C ''logist'':169C ''markov'':112C ''mean'':178C ''method'':120C ''mine'':44C ''multipl'':167C ''n'':64C,67C,69C,94C ''newton'':119C ''numer'':115C ''o'':93C ''one'':59C ''p'':19C,71C,131C ''paper'':144C ''pca'':7A,171C ''proport'':173C ''quick'':57C ''quick-sort'':56C ''regress'':6A,77C,168C,170C,175C ''sampl'':11A,35C ''say'':159C ''scalabl'':18B ''scale'':9A,33C,100C,148C,165C ''signific'':128C ''simpli'':55C,122C ''size'':12A,36C,63C ''solut'':107C,113C,125C ''sort'':58C ''sourc'':139C ''squar'':117C ''statist'':4A,16B,27C,151C ''summar'':146C ''tabl'':25C ''techniqu'':5A,28C,152C ''test'':129C ''time'':53C ''told'':45C ''use'':127C ''variabl'':88C ''various'':3A,150C ''via'':108C ''vs'':114C,126C ''want'':103C,136C ''x'':80C,82C ''y'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (916, 2, '2014-08-05 20:24:09.2', 4, NULL, NULL, '&lt;p&gt;Most of the efficient (and non trivial) statistic algorithms are iterative in nature so that the worst case analysis &lt;code&gt;O()&lt;/code&gt; is irrelevant as the worst case is ''it fails to converge''.&lt;/p&gt;

&lt;p&gt;Nevertheless, when you have a lot of data, even the linear algorithms (&lt;code&gt;O(n)&lt;/code&gt;) can be slow and you then need to focus on the constant ''hidden'' behind the notation. For instance, computing the variance of a single variate is naively done scanning the data twice (once for computing an estimate of the mean, and then once to estimate the variance). But it also can be done in &lt;a href="http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Online_algorithm" rel="nofollow"&gt;one pass&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;For iterative algorithms, what is more important is convergence rate and number of parameters as a function of the data dimensionality, an element that greatly influences convergence. Many models/algorithm grow a number of parameters that is exponential with the number of variables (e.g. splines) while some other grow linearly (e.g. support vector machines, random forests, ...) &lt;/p&gt;
', NULL, NULL, '''/a'':116C ''/code'':23C,52C ''/p'':35C,117C,174C ''/wiki/algorithms_for_calculating_variance#online_algorithm'':111C ''algorithm'':10C,48C,121C ''also'':102C ''analysi'':20C ''behind'':66C ''case'':19C,29C ''code'':21C,49C ''comput'':71C,87C ''constant'':64C ''converg'':34C,127C,145C ''data'':44C,83C,138C ''dimension'':139C ''done'':80C,105C ''e.g'':161C,168C ''effici'':5C ''element'':141C ''en.wikipedia.org'':110C ''en.wikipedia.org/wiki/algorithms_for_calculating_variance#online_algorithm'':109C ''estim'':89C,97C ''even'':45C ''exponenti'':155C ''fail'':32C ''focus'':61C ''forest'':173C ''function'':135C ''great'':143C ''grow'':148C,166C ''hidden'':65C ''href'':108C ''import'':125C ''influenc'':144C ''instanc'':70C ''irrelev'':25C ''iter'':12C,120C ''linear'':47C,167C ''lot'':42C ''machin'':171C ''mani'':146C ''mean'':92C ''models/algorithm'':147C ''n'':51C ''naiv'':79C ''natur'':14C ''need'':59C ''nevertheless'':37C ''nofollow'':113C ''non'':7C ''notat'':68C ''number'':130C,150C,158C ''o'':22C,50C ''one'':114C ''p'':1C,36C,118C ''paramet'':132C,152C ''pass'':115C ''random'':172C ''rate'':128C ''rel'':112C ''scan'':81C ''singl'':76C ''slow'':55C ''spline'':162C ''statist'':9C ''support'':169C ''trivial'':8C ''twice'':84C ''variabl'':160C ''varianc'':73C,99C ''variat'':77C ''vector'':170C ''worst'':18C,28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (917, 1, '2014-08-05 20:45:01.383', 6, 390, 'Solving a system of equations with sparse data', '&lt;p&gt;I am attempting to solve a set of equations which has 40 independent variables (x1, ..., x40) and one dependent variable (y). The total number of equations (number of rows) is ~300, and I want to solve for the set of 40 coefficients that minimizes the total sum-of-square error between y and the predicted value. &lt;/p&gt;

&lt;p&gt;My problem is that the matrix is very sparse and I do not know the best way to solve the system of equations with sparse data. An example of the dataset is shown below:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;   y    x1  x2 x3 x4 x5 x6 ... x40
87169   14  0  1  0  0  2  ... 0 
46449   0   0  4  0  1  4  ... 12
846449  0   0  0  0  0  3  ... 0
....
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am currently using a Genetic Algorithm to solve this and the results are coming out 
with roughly a factor of two difference between observed and expected. &lt;/p&gt;

&lt;p&gt;Can anyone suggest different methods or techniques which are capable of solving a set of equations with sparse data.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (918, 2, '2014-08-05 22:34:04.55', 8, NULL, NULL, '&lt;p&gt;If I understand you correctly, this is the case of &lt;strong&gt;multiple linear regression with sparse data&lt;/strong&gt; (&lt;em&gt;sparse regression&lt;/em&gt;). Assuming that, I hope you will find the following &lt;strong&gt;resources&lt;/strong&gt; useful.&lt;/p&gt;

&lt;p&gt;1) NCSU &lt;strong&gt;lecture slides on sparse regression&lt;/strong&gt; with overview of algorithms, notes, formulas, graphics and references to literature: &lt;a href="http://www.stat.ncsu.edu/people/zhou/courses/st810/notes/lect23sparse.pdf"&gt;http://www.stat.ncsu.edu/people/zhou/courses/st810/notes/lect23sparse.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;2) &lt;code&gt;R&lt;/code&gt; ecosystem offers many &lt;strong&gt;packages&lt;/strong&gt;, useful for sparse regression analysis, including:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Matrix&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/Matrix"&gt;http://cran.r-project.org/web/packages/Matrix&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;SparseM&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/SparseM"&gt;http://cran.r-project.org/web/packages/SparseM&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;MatrixModels&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/MatrixModels"&gt;http://cran.r-project.org/web/packages/MatrixModels&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;glmnet&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/glmnet"&gt;http://cran.r-project.org/web/packages/glmnet&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;flare&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/flare"&gt;http://cran.r-project.org/web/packages/flare&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;3) A blog post with an &lt;strong&gt;example of sparse regression solution&lt;/strong&gt;, based on &lt;code&gt;SparseM&lt;/code&gt;: &lt;a href="http://aleph-nought.blogspot.com/2012/03/multiple-linear-regression-with-sparse.html"&gt;http://aleph-nought.blogspot.com/2012/03/multiple-linear-regression-with-sparse.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;4) A blog post on using &lt;strong&gt;sparse matrices in R&lt;/strong&gt;, which includes a &lt;strong&gt;primer&lt;/strong&gt; on using &lt;code&gt;glmnet&lt;/code&gt;: &lt;a href="http://www.johnmyleswhite.com/notebook/2011/10/31/using-sparse-matrices-in-r"&gt;http://www.johnmyleswhite.com/notebook/2011/10/31/using-sparse-matrices-in-r&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;5) &lt;strong&gt;More examples and some discussion&lt;/strong&gt; on the topic can be found on &lt;strong&gt;StackOverflow&lt;/strong&gt;: &lt;a href="http://stackoverflow.com/questions/3169371/large-scale-regression-in-r-with-a-sparse-feature-matrix"&gt;http://stackoverflow.com/questions/3169371/large-scale-regression-in-r-with-a-sparse-feature-matrix&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE&lt;/strong&gt; (based on your comment):&lt;/p&gt;

&lt;p&gt;If you''re trying to solve an LP problem with constraints, you may find this &lt;strong&gt;theoretical paper&lt;/strong&gt; useful: &lt;a href="http://web.stanford.edu/group/SOL/papers/gmsw84.pdf"&gt;http://web.stanford.edu/group/SOL/papers/gmsw84.pdf&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Also, check R package &lt;strong&gt;limSolve&lt;/strong&gt;: &lt;a href="http://cran.r-project.org/web/packages/limSolve"&gt;http://cran.r-project.org/web/packages/limSolve&lt;/a&gt;. And, in general, check packages in &lt;em&gt;CRAN Task View&lt;/em&gt; &lt;strong&gt;"Optimization and Mathematical Programming"&lt;/strong&gt;: &lt;a href="http://cran.r-project.org/web/views/Optimization.html"&gt;http://cran.r-project.org/web/views/Optimization.html&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Finally, check the book &lt;strong&gt;"Using R for Numerical Analysis in Science and Engineering"&lt;/strong&gt; (by Victor A. Bloomfield). It has a section on solving systems of equations, represented by &lt;strong&gt;sparse matrices&lt;/strong&gt; (section 5.7, pages 99-104), which includes examples, based on some of the above-mentioned packages: &lt;a href="http://books.google.com/books?id=9ph_AwAAQBAJ&amp;amp;pg=PA99&amp;amp;lpg=PA99&amp;amp;dq=r+limsolve+sparse+matrix&amp;amp;source=bl&amp;amp;ots=PHDE8nXljQ&amp;amp;sig=sPi4n5Wk0M02ywkubq7R7KD_b04&amp;amp;hl=en&amp;amp;sa=X&amp;amp;ei=FZjiU-ioIcjmsATGkYDAAg&amp;amp;ved=0CDUQ6AEwAw#v=onepage&amp;amp;q=r%20limsolve%20sparse%20matrix&amp;amp;f=false"&gt;http://books.google.com/books?id=9ph_AwAAQBAJ&amp;amp;pg=PA99&amp;amp;lpg=PA99&amp;amp;dq=r+limsolve+sparse+matrix&amp;amp;source=bl&amp;amp;ots=PHDE8nXljQ&amp;amp;sig=sPi4n5Wk0M02ywkubq7R7KD_b04&amp;amp;hl=en&amp;amp;sa=X&amp;amp;ei=FZjiU-ioIcjmsATGkYDAAg&amp;amp;ved=0CDUQ6AEwAw#v=onepage&amp;amp;q=r%20limsolve%20sparse%20matrix&amp;amp;f=false&lt;/a&gt;. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (919, 1, '2014-08-06 08:41:44.967', 5, 188, 'Which cross-validation type best suits to binary classification problem', '&lt;p&gt;Data set looks like:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;25000 observations&lt;/li&gt;
&lt;li&gt;up to 15 predictors of different types: numeric, multi-class categorical, binary&lt;/li&gt;
&lt;li&gt;target variable is binary&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Which cross validation method is typical for this type of problems?&lt;/p&gt;

&lt;p&gt;By default I''m using K-Fold. How many folds is enough in this case? (One of the models I use is random forest, which is time consuming...)&lt;/p&gt;
', 'classification  cross-validation', NULL, '''/li'':26C,41C,47C ''/p'':21C,61C,92C ''/ul'':48C ''15'':30C ''25000'':24C ''best'':6A ''binari'':9A,40C,46C ''case'':78C ''categor'':39C ''class'':38C ''classif'':10A,12B ''consum'':91C ''cross'':3A,14B,51C ''cross-valid'':2A,13B ''data'':17C ''default'':64C ''differ'':33C ''enough'':75C ''fold'':70C,73C ''forest'':87C ''k'':69C ''k-fold'':68C ''li'':23C,27C,42C ''like'':20C ''look'':19C ''m'':66C ''mani'':72C ''method'':53C ''model'':82C ''multi'':37C ''multi-class'':36C ''numer'':35C ''observ'':25C ''one'':79C ''p'':16C,49C,62C ''predictor'':31C ''problem'':11A,60C ''random'':86C ''set'':18C ''suit'':7A ''target'':43C ''time'':90C ''type'':5A,34C,58C ''typic'':55C ''ul'':22C ''use'':67C,84C ''valid'':4A,15B,52C ''variabl'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8974, 5, '2015-11-22 05:42:25.853', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8665, 1, '2015-10-31 00:28:41.197', 1, 65, 'Master of Statistics or Master of Data Science', '&lt;p&gt;Many Universities now offer Masters Degrees in data science; I''m curious as to people''s opinions on whether a traditional master of statistics is a better to fit for working in the field of in data science.&lt;/p&gt;
', 'statistics  education  academia', NULL, '''/p'':51C ''academia'':11B ''better'':39C ''curious'':24C ''data'':7A,20C,49C ''degre'':18C ''educ'':10B ''field'':46C ''fit'':41C ''m'':23C ''mani'':13C ''master'':1A,5A,17C,34C ''offer'':16C ''opinion'':29C ''p'':12C ''peopl'':27C ''scienc'':8A,21C,50C ''statist'':3A,9B,36C ''tradit'':33C ''univers'':14C ''whether'':31C ''work'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8031, 2, '2015-09-07 17:53:45.567', 1, NULL, NULL, '&lt;p&gt;You have the option of joining a PhD program in business school and information school as well. There are quantitative professors and data scientists in business schools and information schools as well (About US, I am sure there are a lot of schools). This way you are qualified or even over-qualified in terms of quantitative and technical skills and you can spend your time on reinforcing other skills.&lt;/p&gt;
', NULL, NULL, '''/p'':72C ''busi'':12C,27C ''data'':24C ''even'':51C ''inform'':15C,30C ''join'':7C ''lot'':42C ''option'':5C ''over-qualifi'':52C ''p'':1C ''phd'':9C ''professor'':22C ''program'':10C ''qualifi'':49C,54C ''quantit'':21C,58C ''reinforc'':69C ''school'':13C,16C,28C,31C,44C ''scientist'':25C ''skill'':61C,71C ''spend'':65C ''sure'':38C ''technic'':60C ''term'':56C ''time'':67C ''us'':35C ''way'':46C ''well'':18C,33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (924, 2, '2014-08-06 12:16:22.85', 4, NULL, NULL, '&lt;p&gt;Yes, you can do all this using the Caret (&lt;a href="http://caret.r-forge.r-project.org/training.html" rel="nofollow"&gt;http://caret.r-forge.r-project.org/training.html&lt;/a&gt;) package in R. For example,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;fitControl &amp;lt;- trainControl(## 10-fold CV
                           method = "repeatedcv",
                           number = 10,
                           ## repeated ten times
                           repeats = 10)

gbmFit1 &amp;lt;- train(Class ~ ., data = training,
                 method = "gbm",
                 trControl = fitControl,
                ## This last option is actually one
                ## for gbm() that passes through
                verbose = FALSE)
gbmFit1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;which will give the output&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Stochastic Gradient Boosting 

157 samples
 60 predictors
  2 classes: ''M'', ''R'' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 10 times) 

Summary of sample sizes: 142, 142, 140, 142, 142, 141, ... 

Resampling results across tuning parameters:

  interaction.depth  n.trees  Accuracy  Kappa  Accuracy SD  Kappa SD
  1                  50       0.8       0.5    0.1          0.2     
  1                  100      0.8       0.6    0.1          0.2     
  1                  200      0.8       0.6    0.09         0.2     
  2                  50       0.8       0.6    0.1          0.2     
  2                  100      0.8       0.6    0.09         0.2     
  2                  200      0.8       0.6    0.1          0.2     
  3                  50       0.8       0.6    0.09         0.2     
  3                  100      0.8       0.6    0.09         0.2     
  3                  200      0.8       0.6    0.08         0.2     

Tuning parameter ''shrinkage'' was held constant at a value of 0.1
Accuracy was used to select the optimal model using  the largest value.
The final values used for the model were n.trees = 150, interaction.depth = 3     
and shrinkage = 0.1.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Caret offers many other options as well so should be able to suit your needs. &lt;/p&gt;
', NULL, NULL, '''/code'':68C,218C ''/p'':26C,76C,236C ''/pre'':69C,219C ''/training.html'':15C ''/training.html&lt;/a&gt;)'':20C ''0.08'':178C ''0.09'':142C,154C,166C,172C ''0.1'':130C,136C,148C,160C,190C,217C ''0.2'':131C,137C,143C,149C,155C,161C,167C,173C,179C ''0.5'':129C ''0.6'':135C,141C,147C,153C,159C,165C,171C,177C ''0.8'':128C,134C,140C,146C,152C,158C,164C,170C,176C ''1'':126C,132C,138C ''10'':32C,38C,43C,98C,101C ''100'':133C,151C,169C ''140'':109C ''141'':112C ''142'':107C,108C,110C,111C ''150'':212C ''157'':82C ''2'':86C,144C,150C,156C ''200'':139C,157C,175C ''3'':162C,168C,174C,214C ''50'':127C,145C,163C ''60'':84C ''abl'':231C ''accuraci'':120C,122C,191C ''across'':115C ''actual'':58C ''boost'':81C ''caret'':10C,221C ''caret.r-forge.r-project.org'':14C,19C ''caret.r-forge.r-project.org/training.html'':13C ''caret.r-forge.r-project.org/training.html&lt;/a&gt;)'':18C ''class'':47C,87C ''code'':28C,78C ''constant'':185C ''cross'':96C ''cross-valid'':95C ''cv'':34C ''data'':48C ''exampl'':25C ''fals'':66C ''final'':204C ''fitcontrol'':29C,53C ''fold'':33C,99C ''gbm'':51C,61C ''gbmfit1'':44C,67C ''give'':73C ''gradient'':80C ''held'':184C ''href'':12C ''interaction.depth'':118C,213C ''kappa'':121C,124C ''largest'':201C ''last'':55C ''lt'':30C,45C ''m'':88C ''mani'':223C ''method'':35C,50C ''model'':198C,209C ''n.trees'':119C,211C ''need'':235C ''nofollow'':17C ''number'':37C ''offer'':222C ''one'':59C ''optim'':197C ''option'':56C,225C ''output'':75C ''p'':1C,70C,220C ''packag'':21C ''paramet'':117C,181C ''pass'':63C ''pre'':27C,77C,92C ''pre-process'':91C ''predictor'':85C ''process'':93C ''r'':23C,89C ''rel'':16C ''repeat'':39C,42C,100C ''repeatedcv'':36C ''resampl'':94C,113C ''result'':114C ''sampl'':83C,105C ''sd'':123C,125C ''select'':195C ''shrinkag'':182C,216C ''size'':106C ''stochast'':79C ''suit'':233C ''summari'':103C ''ten'':40C ''time'':41C,102C ''train'':46C,49C ''traincontrol'':31C ''trcontrol'':52C ''tune'':116C,180C ''use'':8C,193C,199C,206C ''valid'':97C ''valu'':188C,202C,205C ''verbos'':65C ''well'':227C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (925, 2, '2014-08-06 12:27:31.277', 0, NULL, NULL, '&lt;p&gt;Both methods work. However, if you retain all words in documents you would essentially be working with high dimensional vectors (each term representing one dimension). Consequently, a classifier, e.g. SVM, would take more time to converge.&lt;/p&gt;

&lt;p&gt;It is thus a standard practice to reduce the term-space dimensionality by pre-processing steps such as stop-word removal, stemming, Principal Component Analysis (PCA) etc.&lt;/p&gt;

&lt;p&gt;One approach could be to analyze the document corpora by a topic modelling technique such as LDA and then retaining only those words which are representative of the topics, i.e. those which have high membership values in a single topic class.&lt;/p&gt;

&lt;p&gt;Another approach (inspired by information retrieval) could be to retain the top K tf-idf terms from each document.&lt;/p&gt;
', NULL, NULL, '''/p'':38C,70C,113C,135C ''analysi'':67C ''analyz'':77C ''anoth'':115C ''approach'':73C,116C ''class'':112C ''classifi'':29C ''compon'':66C ''consequ'':27C ''converg'':37C ''corpora'':80C ''could'':74C,121C ''dimens'':26C ''dimension'':20C,52C ''document'':12C,79C,134C ''e.g'':30C ''essenti'':15C ''etc'':69C ''high'':19C,105C ''howev'':5C ''i.e'':101C ''idf'':130C ''inform'':119C ''inspir'':117C ''k'':127C ''lda'':88C ''membership'':106C ''method'':3C ''model'':84C ''one'':25C,72C ''p'':1C,39C,71C,114C ''pca'':68C ''practic'':45C ''pre'':55C ''pre-process'':54C ''princip'':65C ''process'':56C ''reduc'':47C ''remov'':63C ''repres'':24C,97C ''retain'':8C,91C,124C ''retriev'':120C ''singl'':110C ''space'':51C ''standard'':44C ''stem'':64C ''step'':57C ''stop'':61C ''stop-word'':60C ''svm'':31C ''take'':33C ''techniqu'':85C ''term'':23C,50C,131C ''term-spac'':49C ''tf'':129C ''tf-idf'':128C ''thus'':42C ''time'':35C ''top'':126C ''topic'':83C,100C,111C ''valu'':107C ''vector'':21C ''word'':10C,62C,94C ''work'':4C,17C ''would'':14C,32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (926, 2, '2014-08-06 13:42:54.083', 6, NULL, NULL, '&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/a/918/2853"&gt;Aleksandr''s answer&lt;/a&gt; is completely correct.&lt;/p&gt;

&lt;p&gt;However, the way the question is posed implies that this is a straightforward ordinary least squares regression question: minimizing the sum of squared residuals between a dependent variable and a linear combination of predictors.&lt;/p&gt;

&lt;p&gt;Now, while there may be many zeros in your design matrix, your system as such is not overly large: 300 observations on 40 predictors is no more than medium-sized. You can run such a regression using R without any special efforts for sparse data. Just use the &lt;code&gt;lm()&lt;/code&gt; command (for "linear model"). Use &lt;code&gt;?lm&lt;/code&gt; to see the help page. And note that &lt;code&gt;lm&lt;/code&gt; will by default silently add a constant column of ones to your design matrix (the intercept) - include a &lt;code&gt;-1&lt;/code&gt; on the right hand side of your formula to suppress this. Overall, assuming all your data (and nothing else) is in a &lt;code&gt;data.frame&lt;/code&gt; called &lt;code&gt;foo&lt;/code&gt;, you can do this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;model &amp;lt;- lm(y~.-1,data=foo)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And then you can look at parameter estimates etc. like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;summary(model)
residuals(model)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;em&gt;If&lt;/em&gt; your system is &lt;em&gt;much&lt;/em&gt; larger, say on the order of 10,000 observations and hundreds of predictors, looking at specialized sparse solvers as per &lt;a href="http://datascience.stackexchange.com/a/918/2853"&gt;Aleksandr''s answer&lt;/a&gt; may start to make sense.&lt;/p&gt;

&lt;p&gt;Finally, in your comment to &lt;a href="http://datascience.stackexchange.com/a/918/2853"&gt;Aleksandr''s answer&lt;/a&gt;, you mention constraints on your equation. If that is actually your key issue, there are ways to calculate constrained least squares in R. I personally like &lt;code&gt;pcls()&lt;/code&gt; in the &lt;code&gt;mgcv&lt;/code&gt; package. Perhaps you want to edit your question to include the type of constraints (box constraints, nonnegativity constraints, integrality constraints, linear constraints, ...) you face?&lt;/p&gt;
', NULL, NULL, '''-1'':142C,184C ''/a'':10C,248C,269C ''/a/918/2853'':6C,244C,265C ''/code'':103C,111C,122C,143C,168C,172C,187C,208C,298C,303C ''/em'':213C,219C ''/p'':14C,50C,177C,201C,254C,328C ''/pre'':188C,209C ''000'':227C ''10'':226C ''300'':71C ''40'':74C ''actual'':279C ''add'':127C ''aleksandr'':7C,245C,266C ''answer'':9C,247C,268C ''assum'':156C ''box'':318C ''calcul'':287C ''call'':169C ''code'':101C,109C,120C,141C,166C,170C,179C,203C,296C,301C ''column'':130C ''combin'':47C ''command'':104C ''comment'':259C ''complet'':12C ''constant'':129C ''constrain'':288C ''constraint'':272C,317C,319C,321C,323C,325C ''correct'':13C ''data'':97C,159C,185C ''data.frame'':167C ''datascience.stackexchange.com'':5C,243C,264C ''datascience.stackexchange.com/a/918/2853'':4C,242C,263C ''default'':125C ''depend'':42C ''design'':61C,135C ''edit'':309C ''effort'':94C ''els'':162C ''em'':211C,217C ''equat'':275C ''estim'':197C ''etc'':198C ''face'':327C ''final'':256C ''foo'':171C,186C ''formula'':151C ''hand'':147C ''help'':115C ''howev'':16C ''href'':3C,241C,262C ''hundr'':230C ''impli'':23C ''includ'':139C,313C ''integr'':322C ''intercept'':138C ''issu'':282C ''key'':281C ''larg'':70C ''larger'':220C ''least'':30C,289C ''like'':199C,295C ''linear'':46C,106C,324C ''lm'':102C,110C,121C,182C ''look'':194C,233C ''lt'':181C ''make'':252C ''mani'':57C ''matrix'':62C,136C ''may'':55C,249C ''medium'':81C ''medium-s'':80C ''mention'':271C ''mgcv'':302C ''minim'':34C ''model'':107C,180C,205C,207C ''much'':218C ''nonneg'':320C ''note'':118C ''noth'':161C ''observ'':72C,228C ''one'':132C ''order'':224C ''ordinari'':29C ''over'':69C ''overal'':155C ''p'':1C,15C,51C,189C,210C,255C ''packag'':304C ''page'':116C ''paramet'':196C ''pcls'':297C ''per'':239C ''perhap'':305C ''person'':294C ''pose'':22C ''pre'':178C,202C ''predictor'':49C,75C,232C ''question'':20C,33C,311C ''r'':90C,292C ''regress'':32C,88C ''residu'':39C,206C ''right'':146C ''run'':85C ''say'':221C ''see'':113C ''sens'':253C ''side'':148C ''silent'':126C ''size'':82C ''solver'':237C ''spars'':96C,236C ''special'':93C,235C ''squar'':31C,38C,290C ''start'':250C ''straightforward'':28C ''sum'':36C ''summari'':204C ''suppress'':153C ''system'':64C,215C ''type'':315C ''use'':89C,99C,108C ''variabl'':43C ''want'':307C ''way'':18C,285C ''without'':91C ''y'':183C ''zero'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (927, 1, '2014-08-06 15:07:07.457', 2, 357, 'how to impute missing values on numpy array created by train_test_split from pandas.DataFrame?', '&lt;p&gt;I''m working on the dataset with lots of NA values with sklearn and pandas.DataFrame. I implemented different imputation strategies for different columns of the dataFrame based column names. For example NAs predictor ''var1'' I impute with 0''s and for ''var2'' with mean.&lt;/p&gt;

&lt;p&gt;When I try to cross validate my model using train_test_split it returns me a nparray which does not have column names. How can I impute missing values in this nparray?&lt;/p&gt;

&lt;p&gt;P.S. I do not impute missing values in the original data set before splitting on purpose so I keep test and validation sets separately.&lt;/p&gt;
', 'pandas  cross-validation  sklearn', NULL, '''/p'':66C,100C,126C ''0'':59C ''array'':8A ''base'':48C ''column'':44C,49C,89C ''creat'':9A ''cross'':18B,72C ''cross-valid'':17B ''data'':112C ''datafram'':47C ''dataset'':27C ''differ'':39C,43C ''exampl'':52C ''implement'':38C ''imput'':3A,40C,57C,94C,106C ''keep'':120C ''lot'':29C ''m'':23C ''mean'':65C ''miss'':4A,95C,107C ''model'':75C ''na'':31C ''name'':50C,90C ''nas'':53C ''nparray'':84C,99C ''numpi'':7A ''origin'':111C ''p'':21C,67C,101C ''p.s'':102C ''panda'':16B ''pandas.dataframe'':15A,36C ''predictor'':54C ''purpos'':117C ''return'':81C ''separ'':125C ''set'':113C,124C ''sklearn'':20B,34C ''split'':13A,79C,115C ''strategi'':41C ''test'':12A,78C,121C ''train'':11A,77C ''tri'':70C ''use'':76C ''valid'':19B,73C,123C ''valu'':5A,32C,96C,108C ''var1'':55C ''var2'':63C ''work'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (928, 2, '2014-08-06 15:10:59.6', 0, NULL, NULL, '&lt;p&gt;K-Fold should do just fine for binary classification problem. Depending on the time it is taking to train your model and predict the outcome I would use 10-20 folds.&lt;/p&gt;

&lt;p&gt;However sometimes a single fold takes several minutes, in this case I use 3-5 folds but not less than 3. Hope it helps.&lt;/p&gt;
', NULL, NULL, '''-20'':32C ''-5'':50C ''/p'':34C,60C ''10'':31C ''3'':49C,56C ''binari'':10C ''case'':46C ''classif'':11C ''depend'':13C ''fine'':8C ''fold'':4C,33C,40C,51C ''help'':59C ''hope'':57C ''howev'':36C ''k'':3C ''k-fold'':2C ''less'':54C ''minut'':43C ''model'':23C ''outcom'':27C ''p'':1C,35C ''predict'':25C ''problem'':12C ''sever'':42C ''singl'':39C ''sometim'':37C ''take'':19C,41C ''time'':16C ''train'':21C ''use'':30C,48C ''would'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (930, 2, '2014-08-06 18:17:29.7', 0, NULL, NULL, '&lt;p&gt;From the link you mentioned in the comment, the train and test sets should be in the form of a  dataframe if you followed the first explanation.&lt;/p&gt;

&lt;p&gt;In that case, you could do something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;df[variable] = df[variable].fillna(df[variable].median())
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You have options on what to fill the N/A values with, check out the link.
&lt;a href="http://pandas.pydata.org/pandas-docs/stable/missing_data.html" rel="nofollow"&gt;http://pandas.pydata.org/pandas-docs/stable/missing_data.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you followed the second explanation, using sklearn''s cross-validation, you could implement  mike1886''s suggestion of transforming the arrays into dataframes and then use the fillna option.&lt;/p&gt;
', NULL, NULL, '''/code'':51C ''/p'':29C,40C,110C ''/pandas-docs/stable/missing_data.html'':73C ''/pandas-docs/stable/missing_data.html&lt;/a&gt;&lt;/p&gt;'':78C ''/pre'':52C ''array'':101C ''case'':33C ''check'':65C ''code'':42C ''comment'':9C ''could'':35C,93C ''cross'':90C ''cross-valid'':89C ''datafram'':22C,103C ''df'':43C,45C,48C ''explan'':28C,85C ''fill'':60C ''fillna'':47C,108C ''first'':27C ''follow'':25C,82C ''form'':19C ''href'':70C ''implement'':94C ''like'':38C ''link'':4C,68C ''median'':50C ''mention'':6C ''mike1886'':95C ''n/a'':62C ''nofollow'':75C ''option'':56C,109C ''p'':1C,30C,53C,79C ''pandas.pydata.org'':72C,77C ''pandas.pydata.org/pandas-docs/stable/missing_data.html'':71C ''pandas.pydata.org/pandas-docs/stable/missing_data.html&lt;/a&gt;&lt;/p&gt;'':76C ''pre'':41C ''rel'':74C ''second'':84C ''set'':14C ''sklearn'':87C ''someth'':37C ''suggest'':97C ''test'':13C ''train'':11C ''transform'':99C ''use'':86C,106C ''valid'':91C ''valu'':63C ''variabl'':44C,46C,49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5618, 1, '2015-04-26 11:09:49.133', 2, 68, 'Correlation between time to event data and continuous data', '&lt;p&gt;I want to measure the correlation between the survival time which is a time to event data and the patient''s activity count which is measured on continuous scale. What type of correlation coefficient is available to measure the strength of these two variables?&lt;/p&gt;
', 'statistics', NULL, '''/p'':56C ''activ'':33C ''avail'':47C ''coeffici'':45C ''continu'':8A,39C ''correl'':1A,17C,44C ''count'':34C ''data'':6A,9A,28C ''event'':5A,27C ''measur'':15C,37C,49C ''p'':11C ''patient'':31C ''scale'':40C ''statist'':10B ''strength'':51C ''surviv'':20C ''time'':3A,21C,25C ''two'':54C ''type'':42C ''variabl'':55C ''want'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (973, 1, '2014-08-13 20:53:15.443', 1, 45, 'feature weights in structured support vector machine', '&lt;p&gt;I like to find the weight vector for input-space features in a &lt;a href="http://en.wikipedia.org/wiki/Structured_support_vector_machine" rel="nofollow"&gt;structured SVM&lt;/a&gt;. The idea is to identify the most important set of input-space features (based on the magnitude of their corresponding weights). I know that in a binary SVM the weight vector can be written as a &lt;a href="http://pyml.sourceforge.net/doc/howto.pdf" rel="nofollow"&gt;linear combination of examples&lt;/a&gt;, and the magnitude of those weights represents how much they were effective for the prediction problem at hand. But how do you compute the same for an SSVM?&lt;/p&gt;
', 'svm', NULL, '''/a'':33C,82C ''/doc/howto.pdf'':75C ''/p'':111C ''/wiki/structured_support_vector_machine'':28C ''base'':48C ''binari'':61C ''combin'':79C ''comput'':105C ''correspond'':54C ''effect'':94C ''en.wikipedia.org'':27C ''en.wikipedia.org/wiki/structured_support_vector_machine'':26C ''exampl'':81C ''featur'':1A,21C,47C ''find'':13C ''hand'':100C ''href'':25C,72C ''idea'':35C ''identifi'':38C ''import'':41C ''input'':19C,45C ''input-spac'':18C,44C ''know'':57C ''like'':11C ''linear'':78C ''machin'':7A ''magnitud'':51C,85C ''much'':91C ''nofollow'':30C,77C ''p'':9C ''predict'':97C ''problem'':98C ''pyml.sourceforge.net'':74C ''pyml.sourceforge.net/doc/howto.pdf'':73C ''rel'':29C,76C ''repres'':89C ''set'':42C ''space'':20C,46C ''ssvm'':110C ''structur'':4A,31C ''support'':5A ''svm'':8B,32C,62C ''vector'':6A,16C,65C ''weight'':2A,15C,55C,64C,88C ''written'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (931, 2, '2014-08-06 23:37:15.293', 2, NULL, NULL, '&lt;p&gt;To answer a simpler, but related question, namely ''How well can my model generalize on the data that I have?'' the method of learning curves might be applicable. &lt;a href="https://www.youtube.com/watch?v=g4XluwGYPaA" rel="nofollow"&gt;This&lt;/a&gt; is a lecture given by Andrew Ng about them.&lt;/p&gt;

&lt;p&gt;The basic idea is to plot test set error and training set error vs. the complexity of the model you are using (this can be somewhat complicated). If the model is powerful enough to fully ''understand'' your data, at some point the complexity of the model will be high enough that performance on the training set will be close to perfect. However, the variance of a complex model will likely cause the test set performance to increase at some point.&lt;/p&gt;

&lt;p&gt;This analysis tells you two main things, I think. The first is an upper limit on performance. It''s pretty unlikely that you''ll do better on data that you haven''t seen than on your training data. The other thing it tells you is whether or not getting more data might help. If you can demonstrate that you fully understand your training data by driving training error to zero it might be possible, through the inclusion of more data, to drive your test error further down by getting a more complete sample and then training a powerful model on that. &lt;/p&gt;
', NULL, NULL, '''/a'':38C ''/p'':48C,130C,233C ''/watch?v=g4xluwgypaa'':34C ''analysi'':133C ''andrew'':44C ''answer'':3C ''applic'':29C ''basic'':51C ''better'':157C ''caus'':120C ''close'':108C ''complet'':223C ''complex'':65C,92C,116C ''complic'':76C ''curv'':26C ''data'':18C,87C,159C,169C,182C,195C,211C ''demonstr'':188C ''drive'':197C,213C ''enough'':82C,99C ''error'':58C,62C,199C,216C ''first'':142C ''fulli'':84C,191C ''general'':15C ''get'':180C,220C ''given'':42C ''haven'':162C ''help'':184C ''high'':98C ''howev'':111C ''href'':31C ''idea'':52C ''inclus'':208C ''increas'':126C ''learn'':25C ''lectur'':41C ''like'':119C ''limit'':146C ''ll'':155C ''main'':137C ''method'':23C ''might'':27C,183C,203C ''model'':14C,68C,79C,95C,117C,230C ''name'':9C ''ng'':45C ''nofollow'':36C ''p'':1C,49C,131C ''perfect'':110C ''perform'':101C,124C,148C ''plot'':55C ''point'':90C,129C ''possibl'':205C ''power'':81C,229C ''pretti'':151C ''question'':8C ''rel'':35C ''relat'':7C ''sampl'':224C ''seen'':164C ''set'':57C,61C,105C,123C ''simpler'':5C ''somewhat'':75C ''tell'':134C,174C ''test'':56C,122C,215C ''thing'':138C,172C ''think'':140C ''train'':60C,104C,168C,194C,198C,227C ''two'':136C ''understand'':85C,192C ''unlik'':152C ''upper'':145C ''use'':71C ''varianc'':113C ''vs'':63C ''well'':11C ''whether'':177C ''www.youtube.com'':33C ''www.youtube.com/watch?v=g4xluwgypaa'':32C ''zero'':201C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (932, 2, '2014-08-07 03:15:09.933', 1, NULL, NULL, '&lt;p&gt;I have managed to resolve this. There is an excellent and thorough explanation of the optimization steps in the following thesis: &lt;a href="http://cs.stanford.edu/~pliang/papers/meng-thesis.pdf" rel="nofollow"&gt;Semi-Supervised Learning for Natural Language by Percy Liang&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;My mistake was trying to update the quality for all potential clusters pairs. Instead, you should initialize a table with the quality changes of doing each merge. Use this table to find the best merge, and the update the relevant terms that make up the table entries.&lt;/p&gt;
', NULL, NULL, '''/a'':40C ''/p'':41C,90C ''/~pliang/papers/meng-thesis.pdf'':27C ''best'':76C ''chang'':65C ''cluster'':54C ''cs.stanford.edu'':26C ''cs.stanford.edu/~pliang/papers/meng-thesis.pdf'':25C ''entri'':89C ''excel'':11C ''explan'':14C ''find'':74C ''follow'':21C ''href'':24C ''initi'':59C ''instead'':56C ''languag'':36C ''learn'':33C ''liang'':39C ''make'':85C ''manag'':4C ''merg'':69C,77C ''mistak'':44C ''natur'':35C ''nofollow'':29C ''optim'':17C ''p'':1C,42C ''pair'':55C ''perci'':38C ''potenti'':53C ''qualiti'':50C,64C ''rel'':28C ''relev'':82C ''resolv'':6C ''semi'':31C ''semi-supervis'':30C ''step'':18C ''supervis'':32C ''tabl'':61C,72C,88C ''term'':83C ''thesi'':22C ''thorough'':13C ''tri'':46C ''updat'':48C,80C ''use'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (933, 2, '2014-08-07 09:33:50.817', 1, NULL, NULL, '&lt;p&gt;It dates back to 1998, so most likely has been abandoned, or "acquired" by microsoft as the creator currently works there and has done since publishing that research.&lt;/p&gt;

&lt;p&gt;see &lt;a href="http://research.microsoft.com/en-us/people/cyl/ists97.pdf" rel="nofollow"&gt;http://research.microsoft.com/en-us/people/cyl/ists97.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;and &lt;a href="http://research.microsoft.com/en-us/people/cyl" rel="nofollow"&gt;http://research.microsoft.com/en-us/people/cyl&lt;/a&gt; for the author. Maybe you could try to contact him.&lt;/p&gt;
', NULL, NULL, '''/en-us/people/cyl'':49C ''/en-us/people/cyl&lt;/a&gt;'':54C ''/en-us/people/cyl/ists97.pdf'':37C ''/en-us/people/cyl/ists97.pdf&lt;/a&gt;&lt;/p&gt;'':42C ''/p'':30C,65C ''1998'':6C ''abandon'':12C ''acquir'':14C ''author'':57C ''back'':4C ''contact'':63C ''could'':60C ''creator'':19C ''current'':20C ''date'':3C ''done'':25C ''href'':34C,46C ''like'':9C ''mayb'':58C ''microsoft'':16C ''nofollow'':39C,51C ''p'':1C,31C,43C ''publish'':27C ''rel'':38C,50C ''research'':29C ''research.microsoft.com'':36C,41C,48C,53C ''research.microsoft.com/en-us/people/cyl'':47C ''research.microsoft.com/en-us/people/cyl&lt;/a&gt;'':52C ''research.microsoft.com/en-us/people/cyl/ists97.pdf'':35C ''research.microsoft.com/en-us/people/cyl/ists97.pdf&lt;/a&gt;&lt;/p&gt;'':40C ''see'':32C ''sinc'':26C ''tri'':61C ''work'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (934, 1, '2014-08-07 10:45:38.557', 3, 134, 'Handling huge dataset imbalance (2 class values) and appropriate ML algorithm', '&lt;p&gt;I have train and test sets of chronological data consisting of 305000 instances and 70000,appropriately. There are 15 features in each instance and only 2 possible class values ( NEW,OLD). The problem is that there are only 725 OLD instances in the train set and 95 in the test. &lt;/p&gt;

&lt;p&gt;The only algorithm which succeeds for me to handle imbalance is NaiveBayes in Weka (0.02 precision for OLD class), others (trees) classify each instance as NEW.
What is the best approach to handle the imbalance and the appropriate algorithm in such a case?&lt;/p&gt;

&lt;p&gt;Thank you in advance.&lt;/p&gt;
', 'machine-learning  dataset', NULL, '''/p'':67C,112C,118C ''0.02'':83C ''15'':35C ''2'':5A,42C ''305000'':28C ''70000'':31C ''725'':55C ''95'':63C ''advanc'':117C ''algorithm'':11A,71C,107C ''approach'':99C ''appropri'':9A,32C,106C ''best'':98C ''case'':111C ''chronolog'':24C ''class'':6A,44C,87C ''classifi'':90C ''consist'':26C ''data'':25C ''dataset'':3A,15B ''featur'':36C ''handl'':1A,77C,101C ''huge'':2A ''imbal'':4A,78C,103C ''instanc'':29C,39C,57C,92C ''learn'':14B ''machin'':13B ''machine-learn'':12B ''ml'':10A ''naivebay'':80C ''new'':46C,94C ''old'':47C,56C,86C ''other'':88C ''p'':16C,68C,113C ''possibl'':43C ''precis'':84C ''problem'':49C ''set'':22C,61C ''succeed'':73C ''test'':21C,66C ''thank'':114C ''train'':19C,60C ''tree'':89C ''valu'':7A,45C ''weka'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (935, 2, '2014-08-07 12:46:25.123', 5, NULL, NULL, '&lt;p&gt;I''m not allowed to comment, but I have more a suggestion: you could try to implement some "Over-sampling Techniques" like SMOTE:
&lt;a href="http://scholar.google.com/scholar?q=oversampling+minority+classes"&gt;http://scholar.google.com/scholar?q=oversampling+minority+classes&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/scholar?q=oversampling+minority+classes'':30C ''/scholar?q=oversampling+minority+classes&lt;/a&gt;&lt;/p&gt;'':33C ''allow'':5C ''comment'':7C ''could'':15C ''href'':27C ''implement'':18C ''like'':24C ''m'':3C ''over-sampl'':20C ''p'':1C ''sampl'':22C ''scholar.google.com'':29C,32C ''scholar.google.com/scholar?q=oversampling+minority+classes'':28C ''scholar.google.com/scholar?q=oversampling+minority+classes&lt;/a&gt;&lt;/p&gt;'':31C ''smote'':25C ''suggest'':13C ''techniqu'':23C ''tri'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (936, 1, '2014-08-07 15:12:48.617', -1, 97, 'R error using Knitr', '&lt;p&gt;I am attempting to compile code using Knitr in R.&lt;/p&gt;

&lt;p&gt;My code below is returning the following error, and causes errors in the rest of the document.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;miss&amp;lt;-sample$sensor_glucose[!is.na(sample$sensor_glucose)]
# Error: "## Warning: is.na() applied to non-(list or vector) of type ''NULL''"

str(miss)
# int [1:103] 213 113 46 268 186 196 187 153 43 175 ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Does anyone know how to remedy this problem?&lt;/p&gt;

&lt;p&gt;Thanks in advance!&lt;/p&gt;
', 'r  error-handling', NULL, '''/code'':78C ''/p'':20C,39C,89C,94C ''/pre'':79C ''1'':66C ''103'':67C ''113'':69C ''153'':75C ''175'':77C ''186'':72C ''187'':74C ''196'':73C ''213'':68C ''268'':71C ''43'':76C ''46'':70C ''advanc'':93C ''anyon'':82C ''appli'':54C ''attempt'':12C ''caus'':31C ''code'':15C,23C,41C ''compil'':14C ''document'':38C ''error'':2A,7B,29C,32C,51C ''error-handl'':6B ''follow'':28C ''glucos'':46C,50C ''handl'':8B ''int'':65C ''is.na'':47C,53C ''knitr'':4A,17C ''know'':83C ''list'':57C ''lt'':43C ''miss'':42C,64C ''non'':56C ''null'':62C ''p'':9C,21C,80C,90C ''pre'':40C ''problem'':88C ''r'':1A,5B,19C ''remedi'':86C ''rest'':35C ''return'':26C ''sampl'':44C,48C ''sensor'':45C,49C ''str'':63C ''thank'':91C ''type'':61C ''use'':3A,16C ''vector'':59C ''warn'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (937, 1, '2014-08-07 15:33:43.793', 6, 2397, 'Does scikit-learn have forward selection/stepwise regression algorithm?', '&lt;p&gt;I''m working on the problem with too many features and training my models takes way too long. I implemented forward selection algorithm to choose features.&lt;/p&gt;

&lt;p&gt;However, I was wondering does scikit-learn have forward selection/stepwise regression algorithm?&lt;/p&gt;
', 'feature-selection  sklearn  scikit', NULL, '''/p'':42C,57C ''algorithm'':9A,38C,56C ''choos'':40C ''featur'':11B,25C,41C ''feature-select'':10B ''forward'':6A,36C,53C ''howev'':44C ''implement'':35C ''learn'':4A,51C ''long'':33C ''m'':17C ''mani'':24C ''model'':29C ''p'':15C,43C ''problem'':21C ''regress'':8A,55C ''scikit'':3A,14B,50C ''scikit-learn'':2A,49C ''select'':12B,37C ''selection/stepwise'':7A,54C ''sklearn'':13B ''take'':30C ''train'':27C ''way'':31C ''wonder'':47C ''work'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (938, 2, '2014-08-07 17:49:05.64', 1, NULL, NULL, '&lt;p&gt;You can apply a clustering algorithm to the instances in the majority class and train a classifier with the centroids/medoids offered by the cluster algorithm. This is subsampling the majority class, the converse of oversampling the minority class. &lt;/p&gt;
', NULL, NULL, '''/p'':40C ''algorithm'':7C,26C ''appli'':4C ''centroids/medoids'':21C ''class'':14C,32C,39C ''classifi'':18C ''cluster'':6C,25C ''convers'':34C ''instanc'':10C ''major'':13C,31C ''minor'':38C ''offer'':22C ''oversampl'':36C ''p'':1C ''subsampl'':29C ''train'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (939, 2, '2014-08-07 17:53:06.97', 3, NULL, NULL, '&lt;p&gt;You will have best results if you care to build the folds so that each variable (and most importantly the target variable) is approximately identically distributed in each fold. This is called, when applied to the target variable, stratified k-fold. One approach is to cluster the inputs and make sure each fold contains the same number of instances from each cluster proportional to their size.&lt;/p&gt;
', NULL, NULL, '''/p'':69C ''appli'':35C ''approach'':45C ''approxim'':25C ''best'':5C ''build'':11C ''call'':33C ''care'':9C ''cluster'':48C,64C ''contain'':56C ''distribut'':27C ''fold'':13C,30C,43C,55C ''ident'':26C ''import'':20C ''input'':50C ''instanc'':61C ''k'':42C ''k-fold'':41C ''make'':52C ''number'':59C ''one'':44C ''p'':1C ''proport'':65C ''result'':6C ''size'':68C ''stratifi'':40C ''sure'':53C ''target'':22C,38C ''variabl'':17C,23C,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (966, 2, '2014-08-12 09:52:01.153', 15, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Text Classification&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I give you a bunch of documents, each of which has a label attached. I ask you to learn why you think the contents of the documents have been given these labels based on their words. Then I give you new documents and ask what you think the label for each one should be. The labels have meaning to me, not to you necessarily.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Topic Modeling&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I give you a bunch of documents, without labels. I ask you to explain why the documents have the words they do by identifying some topics that each is "about". You tell me the topics, by telling me how much of each is in each document, and I decide what the topics "mean" if anything.&lt;/p&gt;

&lt;p&gt;You''d have to clarify what you me by "identify one topic" or "classify the text".&lt;/p&gt;
', NULL, NULL, '''/p'':6C,72C,78C,135C,153C ''/strong'':5C,77C ''anyth'':134C ''ask'':23C,51C,90C ''attach'':21C ''base'':40C ''bunch'':12C,84C ''clarifi'':141C ''classif'':4C ''classifi'':150C ''content'':31C ''d'':138C ''decid'':128C ''document'':14C,34C,49C,86C,96C,125C ''explain'':93C ''give'':9C,46C,81C ''given'':37C ''identifi'':103C,146C ''label'':20C,39C,56C,63C,88C ''learn'':26C ''mean'':65C,132C ''model'':76C ''much'':119C ''necessarili'':71C ''new'':48C ''one'':59C,147C ''p'':1C,7C,73C,79C,136C ''strong'':2C,74C ''tell'':111C,116C ''text'':3C,152C ''think'':29C,54C ''topic'':75C,105C,114C,131C,148C ''without'':87C ''word'':43C,99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (940, 1, '2014-08-07 22:30:52.913', 2, 208, 'Database for a trie, or other appropriate structure for recommendation engine', '&lt;p&gt;We are storing the information about our users showing interest in our items. Based on this information, we would like to create a simple recommendation engine that will take the items I1, I2, I3 etc of the current user, search for all other users that had shown interest in those items, and then output the items I4, I5, I6 etc of the other users, sorted by their decreasing popularity. So, basically, the standard "other buyer were also interested in..." functionality.&lt;/p&gt;

&lt;p&gt;I''m asking myself what kind of a database is suitable for a realtime recommendation engine like this. My current idea is to build a trie of item IDs, then sort the item IDs of the current user (as the order of items is irrelevant) and to go down the trie; the children of the last trie node will build the needed output.&lt;/p&gt;

&lt;p&gt;The problem is that we have 2 million items so that according to our estimation the trie will have at least 1E12 nodes, so that we probably need a distributed sharded database to store it. Before we reinvent the wheel, are there any ready-to-use databases or generally, non-cloud solutions for recommendation engines out there?&lt;/p&gt;
', 'bigdata  recommendation  databases', NULL, '''/p'':96C,161C,222C ''1e12'':184C ''2'':169C ''accord'':174C ''also'':92C ''appropri'':7A ''ask'':100C ''base'':29C ''basic'':86C ''bigdata'':12B ''build'':121C,157C ''buyer'':90C ''children'':150C ''cloud'':215C ''creat'':37C ''current'':53C,117C,134C ''databas'':1A,14B,106C,194C,210C ''decreas'':83C ''distribut'':192C ''engin'':11A,41C,113C,219C ''estim'':177C ''etc'':50C,75C ''function'':95C ''general'':212C ''go'':145C ''i1'':47C ''i2'':48C ''i3'':49C ''i4'':72C ''i5'':73C ''i6'':74C ''id'':126C,131C ''idea'':118C ''inform'':20C,32C ''interest'':25C,63C,93C ''irrelev'':142C ''item'':28C,46C,66C,71C,125C,130C,140C,171C ''kind'':103C ''last'':153C ''least'':183C ''like'':35C,114C ''m'':99C ''million'':170C ''need'':159C,190C ''node'':155C,185C ''non'':214C ''non-cloud'':213C ''order'':138C ''output'':69C,160C ''p'':15C,97C,162C ''popular'':84C ''probabl'':189C ''problem'':164C ''readi'':207C ''ready-to-us'':206C ''realtim'':111C ''recommend'':10A,13B,40C,112C,218C ''reinvent'':200C ''search'':55C ''shard'':193C ''show'':24C ''shown'':62C ''simpl'':39C ''solut'':216C ''sort'':80C,128C ''standard'':88C ''store'':18C,196C ''structur'':8A ''suitabl'':108C ''take'':44C ''trie'':4A,123C,148C,154C,179C ''use'':209C ''user'':23C,54C,59C,79C,135C ''wheel'':202C ''would'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3777, 2, '2014-12-29 09:21:26.78', 0, NULL, NULL, '&lt;p&gt;Essentially machine learning uses non parametric methods...The assumption is that you have enough data and (computation)  time
 You Identify best model by cross validation (rather than eg  assessing significance of coefficients) ,  and estimate prediction error by using test set.  Confidence intervals can also be generated by bootstrapping. &lt;/p&gt;
', NULL, NULL, '''/p'':50C ''also'':45C ''assess'':30C ''assumpt'':10C ''best'':22C ''bootstrap'':49C ''coeffici'':33C ''comput'':18C ''confid'':42C ''cross'':25C ''data'':16C ''eg'':29C ''enough'':15C ''error'':37C ''essenti'':2C ''estim'':35C ''generat'':47C ''identifi'':21C ''interv'':43C ''learn'':4C ''machin'':3C ''method'':8C ''model'':23C ''non'':6C ''p'':1C ''parametr'':7C ''predict'':36C ''rather'':27C ''set'':41C ''signific'':31C ''test'':40C ''time'':19C ''use'':5C,39C ''valid'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1005, 2, '2014-08-20 03:06:01.753', 3, NULL, NULL, '&lt;p&gt;Another idea is to combine OpenStreetMap project map data, for example, using corresponding nice R package (&lt;a href="http://www.r-bloggers.com/the-openstreetmap-package-opens-up" rel="nofollow"&gt;http://www.r-bloggers.com/the-openstreetmap-package-opens-up&lt;/a&gt;), with census data (population census data, such as the US data: &lt;a href="http://www.census.gov/data/data-tools.html" rel="nofollow"&gt;http://www.census.gov/data/data-tools.html&lt;/a&gt;, as well as census data in other categories: &lt;a href="http://national.census.okfn.org" rel="nofollow"&gt;http://national.census.okfn.org&lt;/a&gt;) to analyze temporal patterns of geosocial trends. HTH.&lt;/p&gt;
', NULL, NULL, '''/a'':63C ''/data/data-tools.html'':43C ''/data/data-tools.html&lt;/a&gt;,'':48C ''/p'':72C ''/the-openstreetmap-package-opens-up'':22C ''/the-openstreetmap-package-opens-up&lt;/a&gt;),'':27C ''analyz'':65C ''anoth'':2C ''categori'':56C ''census'':29C,32C,52C ''combin'':6C ''correspond'':14C ''data'':10C,30C,33C,38C,53C ''exampl'':12C ''geosoci'':69C ''href'':19C,40C,58C ''hth'':71C ''idea'':3C ''map'':9C ''national.census.okfn.org'':59C,62C ''nice'':15C ''nofollow'':24C,45C,61C ''openstreetmap'':7C ''p'':1C ''packag'':17C ''pattern'':67C ''popul'':31C ''project'':8C ''r'':16C ''rel'':23C,44C,60C ''tempor'':66C ''trend'':70C ''us'':37C ''use'':13C ''well'':50C ''www.census.gov'':42C,47C ''www.census.gov/data/data-tools.html'':41C ''www.census.gov/data/data-tools.html&lt;/a&gt;,'':46C ''www.r-bloggers.com'':21C,26C ''www.r-bloggers.com/the-openstreetmap-package-opens-up'':20C ''www.r-bloggers.com/the-openstreetmap-package-opens-up&lt;/a&gt;),'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (941, 2, '2014-08-08 00:15:05.527', 2, NULL, NULL, '&lt;p&gt;I agree with @ssdecontrol that a minimal &lt;em&gt;reproducible example&lt;/em&gt; would be the most helpful. However, looking at your code (pay attention to the sequence &lt;code&gt;Error: ... Warning: ...&lt;/code&gt;), I believe that the issue you are experiencing is due to an inappropriate setting of R''s global &lt;code&gt;warn&lt;/code&gt; &lt;strong&gt;option&lt;/strong&gt;. It appears that your current setting is likely &lt;code&gt;2&lt;/code&gt;, which refers to &lt;strong&gt;converting warnings to errors&lt;/strong&gt;, whereas, you, most likely want the setting &lt;code&gt;1&lt;/code&gt;, which is to treat warnings as such, without converting them to errors. If that is the case, you just need to set the option appropriately:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;options(warn=1)  # print warnings as they occur
options(warn=2)  # treat warnings as errors
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Note for moderators/administrators&lt;/strong&gt;: This question seems not to be a &lt;em&gt;data science&lt;/em&gt; question, but purely an &lt;em&gt;R&lt;/em&gt; question. Therefore, I think it should be moved to &lt;em&gt;StackOverflow&lt;/em&gt;, where it belongs.&lt;/p&gt;
', NULL, NULL, '''/code'':31C,52C,66C,85C,129C ''/em'':12C,147C,154C,166C ''/p'':111C,170C ''/pre'':130C ''/strong'':55C,75C,136C ''1'':84C,116C ''2'':65C,124C ''agre'':3C ''appear'':57C ''appropri'':110C ''attent'':24C ''believ'':33C ''belong'':169C ''case'':102C ''code'':22C,28C,50C,64C,83C,113C ''convert'':71C,94C ''current'':60C ''data'':145C ''due'':41C ''em'':9C,144C,152C,164C ''error'':29C,74C,97C,128C ''exampl'':11C ''experienc'':39C ''global'':49C ''help'':17C ''howev'':18C ''inappropri'':44C ''issu'':36C ''like'':63C,79C ''look'':19C ''minim'':8C ''moderators/administrators'':135C ''move'':162C ''need'':105C ''note'':133C ''occur'':121C ''option'':54C,109C,114C,122C ''p'':1C,131C ''pay'':23C ''pre'':112C ''print'':117C ''pure'':150C ''question'':138C,148C,155C ''r'':47C,153C ''refer'':68C ''reproduc'':10C ''scienc'':146C ''seem'':139C ''sequenc'':27C ''set'':45C,61C,82C,107C ''ssdecontrol'':5C ''stackoverflow'':165C ''strong'':53C,70C,132C ''therefor'':156C ''think'':158C ''treat'':89C,125C ''want'':80C ''warn'':30C,51C,72C,90C,115C,118C,123C,126C ''wherea'':76C ''without'':93C ''would'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8675, 2, '2015-10-31 18:51:06.72', 3, NULL, NULL, '&lt;p&gt;A simple approach would be using the same thing as box plots does: away than 1.5 (median-q1) or 1.5 (q3-median) = outlier.&lt;/p&gt;

&lt;p&gt;I find it useful in lots of cases even it not perfect and maybe too simple.&lt;/p&gt;

&lt;p&gt;It has the advantage to not suppose normality.&lt;/p&gt;
', NULL, NULL, '''/p'':27C,45C,55C ''1.5'':17C,22C ''advantag'':50C ''approach'':4C ''away'':15C ''box'':12C ''case'':36C ''even'':37C ''find'':30C ''lot'':34C ''mayb'':42C ''median'':19C,25C ''median-q1'':18C ''normal'':54C ''outlier'':26C ''p'':1C,28C,46C ''perfect'':40C ''plot'':13C ''q1'':20C ''q3'':24C ''q3-median'':23C ''simpl'':3C,44C ''suppos'':53C ''thing'':10C ''use'':7C,32C ''would'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (942, 2, '2014-08-08 00:25:04.21', 4, NULL, NULL, '&lt;p&gt;You mentioned regression and PCA in the title, and there is a definite answer for each of those.&lt;/p&gt;

&lt;p&gt;The asymptotic complexity of linear regression reduces to O(P^2 * N) if N &gt; P, where P is the number of features and N is the number of observations. More detail in &lt;a href="http://math.stackexchange.com/a/84503/117452"&gt;Computational complexity of least square regression operation&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Vanilla PCA is O(P^2 * N + P ^ 3), as in &lt;a href="http://scicomp.stackexchange.com/q/3220"&gt;Fastest PCA algorithm for high-dimensional data&lt;/a&gt;. However fast algorithms exist for very large matrices, explained in that answer and &lt;a href="http://stats.stackexchange.com/q/2806/36229"&gt;Best PCA Algorithm For Huge Number of Features?&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;However I don''t think anyone''s compiled a single lit review or reference or  book on the subject. Might not be a bad project for my free time...&lt;/p&gt;
', NULL, NULL, '''/a'':66C,93C,120C ''/a/84503/117452'':58C ''/p'':20C,67C,121C,152C ''/q/2806/36229'':111C ''/q/3220'':84C ''2'':32C,74C ''3'':77C ''algorithm'':87C,96C,114C ''answer'':15C,105C ''anyon'':128C ''asymptot'':23C ''bad'':146C ''best'':112C ''book'':138C ''compil'':130C ''complex'':24C,60C ''comput'':59C ''data'':92C ''definit'':14C ''detail'':52C ''dimension'':91C ''exist'':97C ''explain'':102C ''fast'':95C ''fastest'':85C ''featur'':43C,119C ''free'':150C ''high'':90C ''high-dimension'':89C ''howev'':94C,123C ''href'':55C,81C,108C ''huge'':116C ''larg'':100C ''least'':62C ''linear'':26C ''lit'':133C ''math.stackexchange.com'':57C ''math.stackexchange.com/a/84503/117452'':56C ''matric'':101C ''mention'':3C ''might'':142C ''n'':33C,35C,45C,75C ''number'':41C,48C,117C ''o'':30C,72C ''observ'':50C ''oper'':65C ''p'':1C,21C,31C,36C,38C,68C,73C,76C,122C ''pca'':6C,70C,86C,113C ''project'':147C ''reduc'':28C ''refer'':136C ''regress'':4C,27C,64C ''review'':134C ''scicomp.stackexchange.com'':83C ''scicomp.stackexchange.com/q/3220'':82C ''singl'':132C ''squar'':63C ''stats.stackexchange.com'':110C ''stats.stackexchange.com/q/2806/36229'':109C ''subject'':141C ''think'':127C ''time'':151C ''titl'':9C ''vanilla'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (944, 2, '2014-08-08 07:12:52.68', 1, NULL, NULL, '&lt;p&gt;In addition to undersampling the majority class (i.e. taking only a few NEW), you may consider oversampling the minority class (in essence, duplicating your OLDs, but there are other smarter ways to do that)&lt;/p&gt;

&lt;p&gt;Note that oversampling may lead to overfitting, so pay special attention to testing your classifiers &lt;/p&gt;

&lt;p&gt;Check also this answer on CV: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/a/108325/49130"&gt;http://stats.stackexchange.com/a/108325/49130&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a/108325/49130'':68C ''/a/108325/49130&lt;/a&gt;&lt;/li&gt;'':71C ''/p'':36C,53C,61C ''/ul'':72C ''addit'':3C ''also'':56C ''answer'':58C ''attent'':48C ''check'':55C ''class'':8C,21C ''classifi'':52C ''consid'':17C ''cv'':60C ''duplic'':24C ''essenc'':23C ''href'':65C ''i.e'':9C ''lead'':42C ''li'':63C ''major'':7C ''may'':16C,41C ''minor'':20C ''new'':14C ''note'':38C ''old'':26C ''overfit'':44C ''oversampl'':18C,40C ''p'':1C,37C,54C ''pay'':46C ''smarter'':31C ''special'':47C ''stats.stackexchange.com'':67C,70C ''stats.stackexchange.com/a/108325/49130'':66C ''stats.stackexchange.com/a/108325/49130&lt;/a&gt;&lt;/li&gt;'':69C ''take'':10C ''test'':50C ''ul'':62C ''undersampl'':5C ''way'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (950, 2, '2014-08-08 19:23:33.637', 0, NULL, NULL, '&lt;p&gt;The approximation Binomial(k,n,p) ~= Poisson(k,s) (where s = n*p) can be shown under the assumptions: &lt;br/&gt;
1) n &gt;&gt; k (to say that n!/(n-k)! ~= n^k), &lt;br/&gt;
2) p &amp;lt;&amp;lt;1 (to say that (1-p)^(n-k) ~= (1-p)^n). &lt;br/&gt;
It''s up to you whether those are sufficiently satisfied. If the exact calculation can be done quickly, in my opinion, it''s nice to stay with that.&lt;/p&gt;

&lt;p&gt;Also since, if the probability of row 3 sample is different from the row 1 sample, it would almost certainly be on the lower side. It would probably best for you to use &lt;br/&gt;
binom.test(7, 767, p=(26/753), alternative=''less'')
the final option indicating that the alternative to your null hypothesis is that the probability is less than 26/753, not equal to. Of course, that''s simply just the sum of Binomial probabilities from 0 to 7 (you can check yourself), the interpretation being that &lt;em&gt;this&lt;/em&gt; is the probability of having gotten at most 7 rolls from random chance, if the probability truly was 26/753.&lt;/p&gt;

&lt;p&gt;Keep in mind the interpretation of that last sentence. These kinds of tests are generally used when we &lt;em&gt;know&lt;/em&gt; what the inherent probability is that we''re comparing to (e.g. to see if the set of coin flips has a probability significantly different from 1/2 which is what we expect from a fair coin). In this case, we don''t &lt;em&gt;know&lt;/em&gt; what the probability is that we''re comparing to, we''re just making the very crude guess that the 26/753 outcome of row 1 reflects the true probability. It''s better than a regular Normal t-test in this case, but don''t put &lt;em&gt;too&lt;/em&gt; much stock in it unless you have a much higher sample size for row 1.&lt;/p&gt;
', NULL, NULL, '''/em'':170C,212C,256C,304C ''/p'':80C,190C,320C ''0'':157C ''1'':22C,39C,43C,48C,96C,280C,319C ''1/2'':238C ''2'':35C ''26/753'':120C,141C,189C,276C ''3'':89C ''7'':117C,159C,179C ''767'':118C ''almost'':100C ''also'':82C ''altern'':121C,129C ''approxim'':3C ''assumpt'':20C ''best'':110C ''better'':287C ''binom.test'':116C ''binomi'':4C,154C ''br'':21C,34C,51C,115C ''calcul'':65C ''case'':250C,297C ''certain'':101C ''chanc'':183C ''check'':162C ''coin'':230C,247C ''compar'':221C,264C ''cours'':146C ''crude'':272C ''differ'':92C,236C ''done'':68C ''e.g'':223C ''em'':168C,210C,254C,302C ''equal'':143C ''exact'':64C ''expect'':243C ''fair'':246C ''final'':124C ''flip'':231C ''general'':206C ''gotten'':176C ''guess'':273C ''higher'':314C ''hypothesi'':133C ''indic'':126C ''inher'':215C ''interpret'':165C,196C ''k'':5C,9C,24C,31C,33C,47C ''keep'':192C ''kind'':202C ''know'':211C,255C ''last'':199C ''less'':122C,139C ''lower'':105C ''lt'':37C,38C ''make'':269C ''mind'':194C ''much'':305C,313C ''n'':6C,13C,23C,28C,30C,32C,46C,50C ''n-k'':29C,45C ''nice'':75C ''normal'':291C ''null'':132C ''opinion'':72C ''option'':125C ''outcom'':277C ''p'':1C,7C,14C,36C,44C,49C,81C,119C,191C ''poisson'':8C ''probabl'':86C,109C,137C,155C,173C,186C,216C,234C,259C,284C ''put'':301C ''quick'':69C ''random'':182C ''re'':220C,263C,267C ''reflect'':281C ''regular'':290C ''roll'':180C ''row'':88C,95C,279C,318C ''sampl'':90C,97C,315C ''satisfi'':61C ''say'':26C,41C ''see'':225C ''sentenc'':200C ''set'':228C ''shown'':17C ''side'':106C ''signific'':235C ''simpli'':149C ''sinc'':83C ''size'':316C ''stay'':77C ''stock'':306C ''suffici'':60C ''sum'':152C ''t-test'':292C ''test'':204C,294C ''true'':283C ''truli'':187C ''unless'':309C ''use'':114C,207C ''whether'':57C ''would'':99C,108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (988, 2, '2014-08-18 08:49:11.933', 2, NULL, NULL, '&lt;p&gt;Here is a couple of tools that may be worth a look:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Bart, an open source tool that have been used for several languages, including German. &lt;a href="http://www.bart-coref.org/" rel="nofollow"&gt;Available from the website&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Sucre is a tool developed at the University of Stuttgart. I don''t know if it''s available easily. &lt;a href="http://hnk.ffzg.hr/bibl/acl2011/CoNLL-ST/pdf/CoNLL-ST3.pdf" rel="nofollow"&gt;You can see this paper about it&lt;/a&gt;.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':40C,76C ''/bibl/acl2011/conll-st/pdf/conll-st3.pdf'':66C ''/li'':41C,77C ''/p'':14C ''/ul'':78C ''avail'':36C,60C ''bart'':17C ''coupl'':5C ''develop'':47C ''easili'':61C ''german'':30C ''hnk.ffzg.hr'':65C ''hnk.ffzg.hr/bibl/acl2011/conll-st/pdf/conll-st3.pdf'':64C ''href'':32C,63C ''includ'':29C ''know'':56C ''languag'':28C ''li'':16C,42C ''look'':13C ''may'':9C ''nofollow'':35C,68C ''open'':19C ''p'':1C ''paper'':73C ''rel'':34C,67C ''see'':71C ''sever'':27C ''sourc'':20C ''stuttgart'':52C ''sucr'':43C ''tool'':7C,21C,46C ''ul'':15C ''univers'':50C ''use'':25C ''websit'':39C ''worth'':11C ''www.bart-coref.org'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (945, 1, '2014-08-08 10:01:25.4', 4, 126, 'Classifying Java exceptions', '&lt;p&gt;We have a classification algorithm to categorize Java exceptions in Production.
This algorithm is based on hierarchical human defined rules so when a bunch of text forming an exception comes up, it determines what kind of exception is (development, availability, configuration, etc.) and the responsible component (the most inner component responsible of the exception). In Java an exception can have several causing exceptions, and the whole must be analyzed.&lt;/p&gt;

&lt;p&gt;For example, given the following example exception:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;com.myapp.CustomException: Error printing ...
... (stack)
Caused by: com.foo.webservice.RemoteException: Unable to communicate ...
... (stack)
Caused by: com.acme.PrintException: PrintServer002: Timeout ....
... (stack)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;First of all, our algorithm splits the whole stack in three isolated exceptions. Afterwards it starts analyzing these exceptions starting from the most inner one. In this case, it determines that this exception (the second caused by) is of type &lt;code&gt;Availability&lt;/code&gt; and that the responsible component is a "print server". This is because there is a rule that matches containing the word &lt;code&gt;Timeout&lt;/code&gt; associated to the &lt;code&gt;Availability&lt;/code&gt; type. There is also a rule that matches &lt;code&gt;com.acme.PrintException&lt;/code&gt; and determines that the responsible component is a print server. As all the information needed is determined using only the most inner exception, the upper exceptions are ignored, but this is not always the case.&lt;/p&gt;

&lt;p&gt;As you can see this kind of approximation is very complex (and chaotic) as a human have to create new rules as new exceptions appear. Besides, the new rules have to be compatible with the current ones because a new rule for classifying a new exception must not change the classification of any of the already classified exceptions.&lt;/p&gt;

&lt;p&gt;We are thinking about using Machine Learning to automate this process. Obviously, I am not asking for a solution here as I know the complexity but I''d really appreciate some advice to achieve our goal.&lt;/p&gt;
', 'machine-learning  classification  algorithms', NULL, '''/code'':108C,153C,177C,183C,194C ''/p'':79C,88C,230C,290C,328C ''/pre'':109C ''achiev'':325C ''advic'':323C ''afterward'':124C ''algorithm'':8B,14C,22C,115C ''alreadi'':287C ''also'':187C ''alway'':227C ''analyz'':78C,127C ''appear'':256C ''appreci'':321C ''approxim'':239C ''ask'':307C ''associ'':178C ''autom'':300C ''avail'':49C,152C,182C ''base'':24C ''besid'':257C ''bunch'':33C ''case'':138C,229C ''categor'':16C ''caus'':71C,95C,102C,146C ''chang'':280C ''chaotic'':244C ''classif'':7B,13C,282C ''classifi'':1A,274C,288C ''code'':90C,151C,175C,181C,192C ''com.acme.printexception'':104C,193C ''com.foo.webservice.remoteexception'':97C ''com.myapp.customexception'':91C ''come'':39C ''communic'':100C ''compat'':264C ''complex'':242C,316C ''compon'':55C,59C,158C,200C ''configur'':50C ''contain'':172C ''creat'':250C ''current'':267C ''d'':319C ''defin'':28C ''determin'':42C,140C,196C,211C ''develop'':48C ''error'':92C ''etc'':51C ''exampl'':82C,86C ''except'':3A,18C,38C,46C,63C,67C,72C,87C,123C,129C,143C,217C,220C,255C,277C,289C ''first'':111C ''follow'':85C ''form'':36C ''given'':83C ''goal'':327C ''hierarch'':26C ''human'':27C,247C ''ignor'':222C ''inform'':208C ''inner'':58C,134C,216C ''isol'':122C ''java'':2A,17C,65C ''kind'':44C,237C ''know'':314C ''learn'':6B,298C ''machin'':5B,297C ''machine-learn'':4B ''match'':171C,191C ''must'':76C,278C ''need'':209C ''new'':251C,254C,259C,271C,276C ''obvious'':303C ''one'':135C,268C ''p'':9C,80C,110C,231C,291C ''pre'':89C ''print'':93C,161C,203C ''printserver002'':105C ''process'':302C ''product'':20C ''realli'':320C ''respons'':54C,60C,157C,199C ''rule'':29C,169C,189C,252C,260C,272C ''second'':145C ''see'':235C ''server'':162C,204C ''sever'':70C ''solut'':310C ''split'':116C ''stack'':94C,101C,107C,119C ''start'':126C,130C ''text'':35C ''think'':294C ''three'':121C ''timeout'':106C,176C ''type'':150C,184C ''unabl'':98C ''upper'':219C ''use'':212C,296C ''whole'':75C,118C ''word'':174C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (948, 1, '2014-08-08 16:49:13.85', 1, 226, 'Any clear winner for Data Science in Scala?', '&lt;p&gt;I have found a number of libraries and tools for data science in Scala, I would like to know about which one has more adoption and which one is gaining adoption at a faster pace and to what extent this is the case. Basically, which one should I bet for (if any at this point).&lt;/p&gt;

&lt;p&gt;Some of the tools I''ve found are (in no particular order):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Scalding&lt;/li&gt;
&lt;li&gt;Breeze&lt;/li&gt;
&lt;li&gt;Spark&lt;/li&gt;
&lt;li&gt;Saddle&lt;/li&gt;
&lt;li&gt;H2O&lt;/li&gt;
&lt;li&gt;Spire&lt;/li&gt;
&lt;li&gt;Mahout&lt;/li&gt;
&lt;li&gt;Hadoop&lt;/li&gt;
&lt;li&gt;MongoDB&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If I need to be more specific to make the question answerable: I''m not particularly interested in clusters and Big Data at this moment, but I''m interested in sizable data (up to 100 GB) for information integration and predictive analytics.&lt;/p&gt;
', 'tools', NULL, '''/li'':84C,87C,90C,93C,96C,99C,102C,105C,108C ''/p'':66C,80C,153C ''/ul'':109C ''100'':145C ''adopt'':35C,41C ''analyt'':152C ''answer'':122C ''basic'':54C ''bet'':59C ''big'':131C ''breez'':86C ''case'':53C ''clear'':2A ''cluster'':129C ''data'':5A,21C,132C,142C ''extent'':49C ''faster'':44C ''found'':13C,74C ''gain'':40C ''gb'':146C ''h2o'':95C ''hadoop'':104C ''inform'':148C ''integr'':149C ''interest'':127C,139C ''know'':29C ''li'':82C,85C,88C,91C,94C,97C,100C,103C,106C ''librari'':17C ''like'':27C ''m'':124C,138C ''mahout'':101C ''make'':119C ''moment'':135C ''mongodb'':107C ''need'':113C ''number'':15C ''one'':32C,38C,56C ''order'':79C ''p'':10C,67C,110C ''pace'':45C ''particular'':78C,126C ''point'':65C ''predict'':151C ''question'':121C ''saddl'':92C ''scala'':8A,24C ''scald'':83C ''scienc'':6A,22C ''sizabl'':141C ''spark'':89C ''specif'':117C ''spire'':98C ''tool'':9B,19C,71C ''ul'':81C ''ve'':73C ''winner'':3A ''would'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (962, 1, '2014-08-12 03:50:52.303', 10, 2204, 'what is difference between text classification and topic models?', '&lt;p&gt;I know the difference between clustering and classification in machine learning, but I don''t understand the difference between text classification and topic modeling for documents. Can I use topic modeling over documents to identify a topic? Can I use classification methods to classify the text inside these documents?  &lt;/p&gt;
', 'classification  text-mining  topic-model', NULL, '''/p'':67C ''classif'':6A,10B,25C,38C,58C ''classifi'':61C ''cluster'':23C ''differ'':3A,21C,35C ''document'':43C,50C,66C ''identifi'':52C ''insid'':64C ''know'':19C ''learn'':28C ''machin'':27C ''method'':59C ''mine'':13B ''model'':9A,16B,41C,48C ''p'':17C ''text'':5A,12B,37C,63C ''text-min'':11B ''topic'':8A,15B,40C,47C,54C ''topic-model'':14B ''understand'':33C ''use'':46C,57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (949, 1, '2014-08-08 17:32:48.377', 2, 303, 'Pig Rank function not generating rank in output', '&lt;p&gt;I am facing this bizarre issue while using &lt;code&gt;Apache Pig&lt;/code&gt; &lt;strong&gt;rank&lt;/strong&gt; utility. I am executing the following code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;email_id_ranked = rank email_id;
store email_id_ranked into ''/tmp/'';
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So, basically I am trying to get the following result&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1,email1
2,email2
3,email3
... 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Issue is sometime pig dumps the above result but sometimes it dumps only the emails without the rank. Also when I dump the data on screen using &lt;code&gt;dump&lt;/code&gt; function pig returns both the columns. I don''t know where the issue is. Kindly advice.&lt;/p&gt;

&lt;p&gt;Please let me know if you need any more information. Thanks in advance.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Pig version: Apache Pig version 0.11.0-cdh4.6.0&lt;/strong&gt;&lt;/p&gt;
', 'bigdata  hadoop  apache-pig', NULL, '''/code'':26C,52C,74C,106C ''/p'':37C,65C,123C,138C,149C ''/pre'':53C,75C ''/strong'':29C,148C ''/tmp'':51C ''0.11.0'':146C ''1'':68C ''2'':70C ''3'':72C ''advanc'':137C ''advic'':122C ''also'':95C ''apach'':12B,24C,143C ''apache-pig'':11B ''basic'':56C ''bigdata'':9B ''bizarr'':19C ''cdh4.6.0'':147C ''code'':23C,36C,39C,67C,104C ''column'':112C ''data'':100C ''dump'':81C,88C,98C,105C ''email'':40C,44C,47C,91C ''email1'':69C ''email2'':71C ''email3'':73C ''execut'':33C ''face'':17C ''follow'':35C,63C ''function'':3A,107C ''generat'':5A ''get'':61C ''hadoop'':10B ''id'':41C,45C,48C ''inform'':134C ''issu'':20C,77C,119C ''kind'':121C ''know'':116C,128C ''let'':126C ''need'':131C ''output'':8A ''p'':14C,54C,76C,124C,139C ''pig'':1A,13B,25C,80C,108C,141C,144C ''pleas'':125C ''pre'':38C,66C ''rank'':2A,6A,28C,42C,43C,49C,94C ''result'':64C,84C ''return'':109C ''screen'':102C ''sometim'':79C,86C ''store'':46C ''strong'':27C,140C ''thank'':135C ''tri'':59C ''use'':22C,103C ''util'':30C ''version'':142C,145C ''without'':92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (974, 1, '2014-08-14 00:50:51.103', 9, 910, 'Nearest neighbors search for very high dimensional data', '&lt;p&gt;I have a big sparse matrix of users and items they like (in the order of 1M users and 100K items, with a very low level of sparsity). I''m exploring ways in which I could perform kNN search on it. Given the size of my dataset and some initial tests I performed, my assumption is that the method I will use will need to be either parallel or distributed. So I''m considering two classes of possible solutions: one that is either available (or implementable in a reasonably easy way) on a single multicore machine, the other on a Spark cluster, i.e. as a MapReduce program. Here are three broad ideas that I considered:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Assuming a cosine similarity metric, perform the full multiplication of the normalized matrix by its transpose (implemented as a sum of outer products)&lt;/li&gt;
&lt;li&gt;Using locality-sensitive hashing (LSH)&lt;/li&gt;
&lt;li&gt;Reducing first the dimensionality of the problem with a PCA&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I''d appreciate any thoughts or advices about possible other ways in which I could tackle this problem.&lt;/p&gt;
', 'machine-learning  distributed  map-reduce  dimensionality-reduction', NULL, '''/li'':161C,169C,181C ''/p'':135C,202C ''/ul'':182C ''100k'':39C ''1m'':36C ''advic'':190C ''appreci'':186C ''assum'':138C ''assumpt'':74C ''avail'':103C ''big'':23C ''broad'':130C ''class'':95C ''cluster'':121C ''consid'':93C,134C ''cosin'':140C ''could'':55C,198C ''d'':185C ''data'':8A ''dataset'':66C ''dimension'':7A,17B,174C ''dimensionality-reduct'':16B ''distribut'':12B,89C ''easi'':109C ''either'':86C,102C ''explor'':50C ''first'':172C ''full'':145C ''given'':61C ''hash'':167C ''high'':6A ''i.e'':122C ''idea'':131C ''implement'':105C,154C ''initi'':69C ''item'':29C,40C ''knn'':57C ''learn'':11B ''level'':45C ''li'':137C,162C,170C ''like'':31C ''local'':165C ''locality-sensit'':164C ''low'':44C ''lsh'':168C ''m'':49C,92C ''machin'':10B,115C ''machine-learn'':9B ''map'':14B ''map-reduc'':13B ''mapreduc'':125C ''matrix'':25C,150C ''method'':78C ''metric'':142C ''multicor'':114C ''multipl'':146C ''nearest'':1A ''need'':83C ''neighbor'':2A ''normal'':149C ''one'':99C ''order'':34C ''outer'':159C ''p'':19C,183C ''parallel'':87C ''pca'':180C ''perform'':56C,72C,143C ''possibl'':97C,192C ''problem'':177C,201C ''product'':160C ''program'':126C ''reason'':108C ''reduc'':15B,171C ''reduct'':18B ''search'':3A,58C ''sensit'':166C ''similar'':141C ''singl'':113C ''size'':63C ''solut'':98C ''spark'':120C ''spars'':24C ''sparsiti'':47C ''sum'':157C ''tackl'':199C ''test'':70C ''thought'':188C ''three'':129C ''transpos'':153C ''two'':94C ''ul'':136C ''use'':81C,163C ''user'':27C,37C ''way'':51C,110C,194C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (954, 1, '2014-08-11 07:59:22.78', 3, 178, 'OpenNLP Coreference Resolution (German)', '&lt;p&gt;I need to do coreference resolution for German texts and I plan to use OpenNLP to perform this task.&lt;/p&gt;

&lt;p&gt;As far as I know OpenNLP coreference resolution does not support the German language.&lt;/p&gt;

&lt;p&gt;Which components/data do I need to adapt the code such that it is possible to perform coreference resolution for German texts?&lt;/p&gt;
', 'nlp  text-mining', NULL, '''/p'':29C,45C,68C ''adapt'':53C ''code'':55C ''components/data'':48C ''corefer'':2A,14C,37C,63C ''far'':32C ''german'':4A,17C,43C,66C ''know'':35C ''languag'':44C ''mine'':8B ''need'':11C,51C ''nlp'':5B ''opennlp'':1A,24C,36C ''p'':9C,30C,46C ''perform'':26C,62C ''plan'':21C ''possibl'':60C ''resolut'':3A,15C,38C,64C ''support'':41C ''task'':28C ''text'':7B,18C,67C ''text-min'':6B ''use'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (955, 1, '2014-08-11 12:25:47.7', 4, 99, 'Coreference Resolution for German Texts', '&lt;p&gt;does anybody know a libarary for performing coreference resolution on German texts?&lt;/p&gt;

&lt;p&gt;As far as I know OpenNLP and Standord NLP are not able to perform coreference resolution for German Texts.&lt;/p&gt;

&lt;p&gt;The only tool that I know is &lt;a href="http://www.cl.uzh.ch/static/news.php?om=view&amp;amp;nid=163" rel="nofollow"&gt;CorZu&lt;/a&gt; which is a python library.&lt;/p&gt;
', 'nlp', NULL, '''/a'':58C ''/p'':20C,41C,64C ''/static/news.php?om=view&amp;amp;nid=163'':54C ''abl'':33C ''anybodi'':9C ''corefer'':1A,15C,36C ''corzu'':57C ''far'':23C ''german'':4A,18C,39C ''href'':51C ''know'':10C,26C,48C ''libarari'':12C ''librari'':63C ''nlp'':6B,30C ''nofollow'':56C ''opennlp'':27C ''p'':7C,21C,42C ''perform'':14C,35C ''python'':62C ''rel'':55C ''resolut'':2A,16C,37C ''standord'':29C ''text'':5A,19C,40C ''tool'':45C ''www.cl.uzh.ch'':53C ''www.cl.uzh.ch/static/news.php?om=view&amp;amp;nid=163'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (956, 2, '2014-08-11 12:48:52.32', 1, NULL, NULL, '&lt;p&gt;I had to create a web based dashboard. My main charting tool was d3js. But I needed to use ggplot2 to generate few charts. Through d3js''s ggplot2 extension, I could create the same. If your charts can be generated through existing extension, then web has better alternatives. Later you can export them to PDF for distribution. &lt;/p&gt;
', NULL, NULL, '''/p'':59C ''altern'':49C ''base'':8C ''better'':48C ''chart'':12C,25C,38C ''could'':32C ''creat'':5C,33C ''d3js'':15C,27C ''dashboard'':9C ''distribut'':58C ''exist'':43C ''export'':53C ''extens'':30C,44C ''generat'':23C,41C ''ggplot2'':21C,29C ''later'':50C ''main'':11C ''need'':18C ''p'':1C ''pdf'':56C ''tool'':13C ''use'':20C ''web'':7C,46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (963, 1, '2014-08-12 07:57:03.283', 8, 815, 'Sentiment data for Emoji', '&lt;p&gt;For experimenting we''d like to use the &lt;a href="https://en.wikipedia.org/wiki/Emoji"&gt;Emoji&lt;/a&gt; embedded in many Tweets as a ground truth/training data for simple quantitative senitment analysis. Tweets are usually too unstructured for NLP to work well.&lt;/p&gt;

&lt;p&gt;Anyway, there are 722 Emoji in Unicode 6.0, and probably another 250 will be added in Unicode 7.0.&lt;/p&gt;

&lt;p&gt;Is there a database (like e.g. SentiWordNet) that contains sentiment annotations for them?&lt;/p&gt;

&lt;p&gt;(Note that SentiWordNet does allow for &lt;em&gt;ambiguous&lt;/em&gt; meanings, too. Consider e.g. &lt;a href="http://sentiwordnet.isti.cnr.it/search.php?q=funny"&gt;funny&lt;/a&gt;, which is not just positive: "this tastes funny" is probably not positive... same will hold for &lt;code&gt;;-)&lt;/code&gt; for example. But I don''t think this is harder for Emoji than it is for regular words...)&lt;/p&gt;

&lt;p&gt;Also, if you have experience with using them for sentiment analysis, I''d be interested to hear.&lt;/p&gt;
', 'machine-learning  classification  parsing', NULL, '''/a'':25C,106C ''/code'':124C ''/em'':95C ''/p'':50C,70C,85C,143C,162C ''/search.php?q=funny'':104C ''/wiki/emoji'':23C ''250'':63C ''6.0'':59C ''7.0'':69C ''722'':55C ''ad'':66C ''allow'':91C ''also'':145C ''ambigu'':94C ''analysi'':39C,155C ''annot'':82C ''anoth'':62C ''anyway'':52C ''classif'':8B ''code'':123C ''consid'':98C ''contain'':80C ''d'':14C,157C ''data'':2A,34C ''databas'':75C ''e.g'':77C,99C ''em'':93C ''embed'':26C ''emoji'':4A,24C,56C,136C ''en.wikipedia.org'':22C ''en.wikipedia.org/wiki/emoji'':21C ''exampl'':126C ''experi'':12C,149C ''funni'':105C,114C ''ground'':32C ''harder'':134C ''hear'':161C ''hold'':121C ''href'':20C,101C ''interest'':159C ''learn'':7B ''like'':15C,76C ''machin'':6B ''machine-learn'':5B ''mani'':28C ''mean'':96C ''nlp'':46C ''note'':87C ''p'':10C,51C,71C,86C,144C ''pars'':9B ''posit'':111C,118C ''probabl'':61C,116C ''quantit'':37C ''regular'':141C ''senit'':38C ''sentiment'':1A,81C,154C ''sentiwordnet'':78C,89C ''sentiwordnet.isti.cnr.it'':103C ''sentiwordnet.isti.cnr.it/search.php?q=funny'':102C ''simpl'':36C ''tast'':113C ''think'':131C ''truth/training'':33C ''tweet'':29C,40C ''unicod'':58C,68C ''unstructur'':44C ''use'':17C,151C ''usual'':42C ''well'':49C ''word'':142C ''work'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1025, 1, '2014-08-23 13:56:43.813', 9, 133, 'implementing temporal difference in chess', '&lt;p&gt;I have been developing a chess program which makes use of alpha-beta pruning algorithm and an evaluation function that evaluates positions using the following features namely material, kingsafety, mobility, pawn-structure and trapped pieces etc..... My evaluation function is derived from the &lt;/p&gt;

&lt;p&gt;$$f(p) = w_1 \cdot \text{material} + w_2 \cdot \text{kingsafety} + w_3 \cdot \text{mobility} + w_4 \cdot \text{pawn-structure} + w_5 \cdot \text{trapped pieces}$$&lt;/p&gt;

&lt;p&gt;where $w$ is the weight assigned to each feature. At this point i want to tune the weights of my evaluation function using temporal difference, where the agent plays against itself and in the process gather training data from its environment (which is a form of reinforcement learning). I have read some books and articles in order to have an insight on how to implement this in Java but they seem to be theoretical rather than practical. I need a detailed explanation and pseudo codes on how to automatically tune the weights of my evaluation function based on previous games.&lt;/p&gt;
', 'machine-learning  algorithms  reinforcement-learning', NULL, '''/p'':59C,91C,193C ''1'':64C ''2'':69C ''3'':74C ''4'':79C ''5'':86C ''agent'':120C ''algorithm'':9B,29C ''alpha'':26C ''alpha-beta'':25C ''articl'':147C ''assign'':98C ''automat'':181C ''base'':189C ''beta'':27C ''book'':145C ''cdot'':65C,70C,75C,80C,87C ''chess'':5A,19C ''code'':177C ''data'':130C ''deriv'':56C ''detail'':173C ''develop'':17C ''differ'':3A,117C ''environ'':133C ''etc'':51C ''evalu'':32C,35C,53C,113C,187C ''explan'':174C ''f'':61C ''featur'':40C,101C ''follow'':39C ''form'':137C ''function'':33C,54C,114C,188C ''game'':192C ''gather'':128C ''implement'':1A,157C ''insight'':153C ''java'':160C ''kingsafeti'':43C,72C ''learn'':8B,12B,140C ''machin'':7B ''machine-learn'':6B ''make'':22C ''materi'':42C,67C ''mobil'':44C,77C ''name'':41C ''need'':171C ''order'':149C ''p'':13C,60C,62C,92C ''pawn'':46C,83C ''pawn-structur'':45C,82C ''piec'':50C,90C ''play'':121C ''point'':104C ''posit'':36C ''practic'':169C ''previous'':191C ''process'':127C ''program'':20C ''prune'':28C ''pseudo'':176C ''rather'':167C ''read'':143C ''reinforc'':11B,139C ''reinforcement-learn'':10B ''seem'':163C ''structur'':47C,84C ''tempor'':2A,116C ''text'':66C,71C,76C,81C,88C ''theoret'':166C ''train'':129C ''trap'':49C,89C ''tune'':108C,182C ''use'':23C,37C,115C ''w'':63C,68C,73C,78C,85C,94C ''want'':106C ''weight'':97C,110C,184C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (957, 1, '2014-08-11 15:19:02.047', 2, 143, 'Multiple seasonality with ARIMA?', '&lt;p&gt;I know that ARIMA can''t detect multiple seasonality, but it is possible to &lt;a href="http://robjhyndman.com/hyndsight/dailydata/" rel="nofollow"&gt;use fourier functions to add a second seasonality&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I need to forecast gas consumption composed by a daily, weekly (week days-weekend), yearly seasonality. Does it make sense to apply three times the &lt;a href="http://stat.ethz.ch/R-manual/R-devel/library/stats/html/stl.html" rel="nofollow"&gt;STL decomposition by LOESS&lt;/a&gt;? The reason is that I applied the fourier method and I have bad results but I don''t know if it is only because I applied it wrong.&lt;/p&gt;

&lt;p&gt;I''m interested in the theoretical explanation, but here you find also the code:&lt;/p&gt;

&lt;p&gt;ARIMA + 2 STL:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;b &amp;lt;- ts(drop(coredata(dat.ts)), deltat=1/12/30/24, start=1)
fit &amp;lt;- stl(b, s.window="periodic")
b &amp;lt;- seasadj(fit)
dat.ts &amp;lt;- xts(b, index(dat.ts))

# The weekdays are extracted
dat.weekdays &amp;lt;- dat.ts[.indexwday(dat.ts) %in% 1:5]
dat.weekdaysTS &amp;lt;- ts(drop(coredata(dat.weekdays)), frequency=24, start=1)
fit &amp;lt;- stl(dat.weekdaysTS, s.window="periodic")
dat.weekdaysTS &amp;lt;- seasadj(fit)

arima &amp;lt;- Arima(dat.weekdaysTS, order=c(3,0,5))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;With fourier:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dat.weekdays &amp;lt;- dat.ts[.indexwday(dat.ts) %in% 1:5]
dat.weekdaysTS &amp;lt;- ts(drop(coredata(dat.weekdays)), frequency=24, start=1)
z &amp;lt;- fourier(ts(dat.weekdaysTS, frequency=365.25), K=5)
arima &amp;lt;- Arima(dat.weekdaysTS, order=c(3,0,5),xreg=z)
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (958, 2, '2014-08-11 16:05:02.197', 1, NULL, NULL, '&lt;p&gt;Have a look at &lt;a href="https://mahout.apache.org/" rel="nofollow"&gt;Apache Mahout&lt;/a&gt;. Last version features also user-item-based recommenders.&lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/p'':23C ''also'':17C ''apach'':11C ''base'':21C ''featur'':16C ''href'':7C ''item'':20C ''last'':14C ''look'':4C ''mahout'':12C ''mahout.apache.org'':8C ''nofollow'':10C ''p'':1C ''recommend'':22C ''rel'':9C ''user'':19C ''user-item-bas'':18C ''version'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (959, 2, '2014-08-11 18:33:37.023', 5, NULL, NULL, '&lt;p&gt;While I am not a data scientist, I am an epidemiologist working in a clinical setting. Your research question did not specify a time period (ie odds of developing CKD in 1 year, 10 years, lifetime?).&lt;/p&gt;

&lt;p&gt;Generally, I would go through a number of steps before even thinking about modeling (univariate analysis, bivariate analysis, colinearity checks, etc). However, the most commonly used method for trying to predict a binary event (using continuous OR binary variables) is logistic regression. If you wanted to look at CKD as a lab value (urine albumin, eGFR) you would use linear regression (continuous outcome).&lt;/p&gt;

&lt;p&gt;While the methods used should be informed by your data and questions, clinicians are used to seeing odds ratios and risk ratios as these the most commonly reported measures of association in medical journals such as NEJM and JAMA. &lt;/p&gt;

&lt;p&gt;If you are working on this problem from a human health perspective (as opposed to Business Intelligence) this Steyerberg''s &lt;a href="http://www.springer.com/medicine/internal/book/978-0-387-77243-1"&gt;Clinical Prediction Models&lt;/a&gt; is an excellent resource. &lt;/p&gt;
', NULL, NULL, '''/a'':174C ''/medicine/internal/book/978-0-387-77243-1'':170C ''/p'':38C,103C,144C,179C ''1'':33C ''10'':35C ''albumin'':94C ''analysi'':55C,57C ''associ'':135C ''binari'':72C,77C ''bivari'':56C ''busi'':161C ''check'':59C ''ckd'':31C,88C ''clinic'':16C,171C ''clinician'':117C ''colinear'':58C ''common'':64C,131C ''continu'':75C,101C ''data'':7C,114C ''develop'':30C ''egfr'':95C ''epidemiologist'':12C ''etc'':60C ''even'':50C ''event'':73C ''excel'':177C ''general'':40C ''go'':43C ''health'':156C ''howev'':61C ''href'':167C ''human'':155C ''ie'':27C ''inform'':111C ''intellig'':162C ''jama'':143C ''journal'':138C ''lab'':91C ''lifetim'':37C ''linear'':99C ''logist'':80C ''look'':86C ''measur'':133C ''medic'':137C ''method'':66C,107C ''model'':53C,173C ''nejm'':141C ''number'':46C ''odd'':28C,122C ''oppos'':159C ''outcom'':102C ''p'':1C,39C,104C,145C ''period'':26C ''perspect'':157C ''predict'':70C,172C ''problem'':152C ''question'':20C,116C ''ratio'':123C,126C ''regress'':81C,100C ''report'':132C ''research'':19C ''resourc'':178C ''risk'':125C ''scientist'':8C ''see'':121C ''set'':17C ''specifi'':23C ''step'':48C ''steyerberg'':164C ''think'':51C ''time'':25C ''tri'':68C ''univari'':54C ''urin'':93C ''use'':65C,74C,98C,108C,119C ''valu'':92C ''variabl'':78C ''want'':84C ''work'':13C,149C ''would'':42C,97C ''www.springer.com'':169C ''www.springer.com/medicine/internal/book/978-0-387-77243-1'':168C ''year'':34C,36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (960, 2, '2014-08-11 19:21:50.137', 1, NULL, NULL, '&lt;p&gt;I have to agree that k-fold should do "just" fine. However, there is a nice article about the "Bootstrap .632+" method (basically a smoothened cross validation) that is supposed to be superior (however, they did the comparisons on not-binary data as far as I can tell)&lt;/p&gt;

&lt;p&gt;Maybe you want to check out this article here: &lt;a href="http://www.jstor.org/stable/2965703" rel="nofollow"&gt;http://www.jstor.org/stable/2965703&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':52C ''/stable/2965703'':67C ''/stable/2965703&lt;/a&gt;&lt;/p&gt;'':72C ''632'':23C ''agre'':5C ''articl'':19C,61C ''basic'':25C ''binari'':44C ''bootstrap'':22C ''check'':58C ''comparison'':40C ''cross'':28C ''data'':45C ''far'':47C ''fine'':13C ''fold'':9C ''howev'':14C,36C ''href'':64C ''k'':8C ''k-fold'':7C ''mayb'':54C ''method'':24C ''nice'':18C ''nofollow'':69C ''not-binari'':42C ''p'':1C,53C ''rel'':68C ''smoothen'':27C ''superior'':35C ''suppos'':32C ''tell'':51C ''valid'':29C ''want'':56C ''www.jstor.org'':66C,71C ''www.jstor.org/stable/2965703'':65C ''www.jstor.org/stable/2965703&lt;/a&gt;&lt;/p&gt;'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9536, 1, '2015-12-27 16:52:01.12', 4, 131, 'How is it possible to process an image with a few neurons?', '&lt;p&gt;An 1024*1024 pixel image has around one million pixels. If I would like to connect each pixel to an R,G,B input neuron, then more than 3 million neurons are needed.&lt;/p&gt;

&lt;p&gt;It would be really hard, to train a neural network, which has millions of inputs. How is it possible, to reduce the number of neurons?&lt;/p&gt;
', 'machine-learning  neuralnetwork  deep-learning  image-classification', NULL, '''/p'':57C,84C ''1024'':25C,26C ''3'':52C ''around'':30C ''b'':46C ''classif'':22B ''connect'':39C ''deep'':18B ''deep-learn'':17B ''g'':45C ''hard'':63C ''imag'':8A,21B,28C ''image-classif'':20B ''input'':47C,73C ''learn'':15B,19B ''like'':37C ''machin'':14B ''machine-learn'':13B ''million'':32C,53C,71C ''need'':56C ''network'':68C ''neural'':67C ''neuralnetwork'':16B ''neuron'':12A,48C,54C,83C ''number'':81C ''one'':31C ''p'':23C,58C ''pixel'':27C,33C,41C ''possibl'':4A,77C ''process'':6A ''r'':44C ''realli'':62C ''reduc'':79C ''train'':65C ''would'':36C,60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (994, 2, '2014-08-18 17:05:19.957', 4, NULL, NULL, '&lt;p&gt;its worth also looking at the training errors. &lt;/p&gt;

&lt;p&gt;basically I disagree with your  analysis. if logistic regression etc are all giving the same results it would suggest that the ''best model'' is a very simple one (that all models can fit equally well - eg basically linear).&lt;/p&gt;

&lt;p&gt;So then the question might be why is the best model a simple model?:
It might suggest that your variables are not very predictive. Its of course hard to analyse without knowing the data. &lt;/p&gt;
', NULL, NULL, '''/p'':10C,50C,86C ''also'':4C ''analys'':81C ''analysi'':17C ''basic'':12C,48C ''best'':33C,61C ''cours'':78C ''data'':85C ''disagre'':14C ''eg'':47C ''equal'':45C ''error'':9C ''etc'':21C ''fit'':44C ''give'':24C ''hard'':79C ''know'':83C ''linear'':49C ''logist'':19C ''look'':5C ''might'':56C,67C ''model'':34C,42C,62C,65C ''one'':39C ''p'':1C,11C,51C ''predict'':75C ''question'':55C ''regress'':20C ''result'':27C ''simpl'':38C,64C ''suggest'':30C,68C ''train'':8C ''variabl'':71C ''well'':46C ''without'':82C ''worth'':3C ''would'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (989, 1, '2014-08-18 10:46:57.36', 7, 3195, 'SVM using scikit learn runs endlessly and never completes execution', '&lt;p&gt;I am trying to run SVR using scikit learn ( python ) on a training dataset having 595605 rows and 5 columns(features) and test dataset having 397070 rows. The data has been pre-processed and regularized.&lt;/p&gt;

&lt;p&gt;I am able to successfully run the test examples but on executing using my dataset and letting it run for over an hour, I could still not see any output or termination of program. I have tried executing using a different IDE and even from terminal but that doesn''t seem to be the issue.
I have also tried changing the ''C'' parameter value from 1 to 1e3.&lt;/p&gt;

&lt;p&gt;I am facing similar issues with all svm implementations using scikit.&lt;/p&gt;

&lt;p&gt;Am I not waiting enough for it to complete ?
How much time should this execution take ?&lt;/p&gt;

&lt;p&gt;From my experience it shouldn''t require over a few minutes.&lt;/p&gt;

&lt;p&gt;Here is my system configuration:
Ubuntu 14.04, 8GB RAM, lots of free memory, 4th gen i7 processor&lt;/p&gt;
', 'python  svm  scikit', NULL, '''/p'':51C,121C,134C,152C,165C,184C ''1'':118C ''14.04'':173C ''1e3'':120C ''397070'':40C ''4th'':180C ''5'':33C ''595605'':30C ''8gb'':174C ''abl'':55C ''also'':110C ''c'':114C ''chang'':112C ''column'':34C ''complet'':9A,144C ''configur'':171C ''could'':77C ''data'':43C ''dataset'':28C,38C,67C ''differ'':93C ''doesn'':101C ''endless'':6A ''enough'':140C ''even'':96C ''exampl'':61C ''execut'':10A,64C,90C,150C ''experi'':156C ''face'':125C ''featur'':35C ''free'':178C ''gen'':181C ''hour'':75C ''i7'':182C ''ide'':94C ''implement'':131C ''issu'':107C,127C ''learn'':4A,23C ''let'':69C ''lot'':176C ''memori'':179C ''minut'':164C ''much'':146C ''never'':8A ''output'':82C ''p'':14C,52C,122C,135C,153C,166C ''paramet'':115C ''pre'':47C ''pre-process'':46C ''process'':48C ''processor'':183C ''program'':86C ''python'':11B,24C ''ram'':175C ''regular'':50C ''requir'':160C ''row'':31C,41C ''run'':5A,19C,58C,71C ''scikit'':3A,13B,22C,133C ''see'':80C ''seem'':103C ''shouldn'':158C ''similar'':126C ''still'':78C ''success'':57C ''svm'':1A,12B,130C ''svr'':20C ''system'':170C ''take'':151C ''termin'':84C,98C ''test'':37C,60C ''time'':147C ''train'':27C ''tri'':17C,89C,111C ''ubuntu'':172C ''use'':2A,21C,65C,91C,132C ''valu'':116C ''wait'':139C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (961, 1, '2014-08-11 21:13:36.23', 5, 160, 'Why RBM tends to learn very similar weights?', '&lt;p&gt;&lt;img src="http://i.stack.imgur.com/lBxL5.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;These are 4 different weight matrices that I got after training &lt;strong&gt;RBM&lt;/strong&gt; with ~4k visible units and only 96 hidden units/weight vectors. As you can see, weights are extremely similar - even black pixels on the face are reproduced. The other 92 vectors are very similar too, though &lt;em&gt;none&lt;/em&gt; of weights are &lt;em&gt;exactly&lt;/em&gt; the same. &lt;/p&gt;

&lt;p&gt;I can overcome this by increasing number of weight vectors to 512 or more. But I encountered this problem several times earlier with different RBM types (binary, Gaussian, even convolutional), different number of hidden units (including pretty large), different hyper-parameters, etc. &lt;/p&gt;

&lt;p&gt;My question is: what is the &lt;strong&gt;most likely reason&lt;/strong&gt; for weights to get &lt;strong&gt;very similar values&lt;/strong&gt;? Do they all just get to some local minimum? Or is it a sign of overfitting? &lt;/p&gt;

&lt;p&gt;I currently use a kind of Gaussian-Bernoulli RBM, code may be found &lt;a href="https://github.com/faithlessfriend/Milk.jl/blob/master/src/rbm.jl" rel="nofollow"&gt;here&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPD.&lt;/strong&gt; My dataset is based on &lt;a href="http://www.pitt.edu/~emotion/ck-spread.htm" rel="nofollow"&gt;CK+&lt;/a&gt;, which contains &gt; 10k images of 327 individuals. However I do pretty heavy preprocessing. First, I clip only pixels inside of outer contour of a face. Second, I transform each face (using piecewise affine wrapping) to the same grid (e.g. eyebrows, nose, lips etc. are in the same (x,y) position on all images). After preprocessing images look like this: &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/FQMSp.png" alt="enter image description here"&gt; &lt;img src="http://i.stack.imgur.com/Hjcaw.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;When training RBM, I take only non-zero pixels, so outer black region is ignored. &lt;/p&gt;
', 'rbm', NULL, '''/a'':190C,209C ''/em'':74C,80C ''/faithlessfriend/milk.jl/blob/master/src/rbm.jl'':186C ''/fqmsp.png'':275C ''/hjcaw.png'':285C ''/lbxl5.png'':15C ''/p'':21C,83C,128C,166C,191C,269C,291C,309C ''/strong'':36C,140C,149C,195C ''/~emotion/ck-spread.htm'':205C ''10k'':212C ''327'':215C ''4'':25C ''4k'':38C ''512'':96C ''92'':65C ''96'':43C ''affin'':242C ''alt'':16C,276C,286C ''base'':199C ''bernoulli'':176C ''binari'':111C ''black'':56C,305C ''ck'':208C ''clip'':225C ''code'':178C ''contain'':211C ''contour'':231C ''convolut'':114C ''current'':169C ''dataset'':197C ''descript'':19C,279C,289C ''differ'':26C,108C,115C,123C ''e.g'':248C ''earlier'':106C ''em'':72C,78C ''encount'':101C ''enter'':17C,277C,287C ''etc'':127C,252C ''even'':55C,113C ''exact'':79C ''extrem'':53C ''eyebrow'':249C ''face'':60C,234C,239C ''first'':223C ''found'':181C ''gaussian'':112C,175C ''gaussian-bernoulli'':174C ''get'':144C,154C ''github.com'':185C ''github.com/faithlessfriend/milk.jl/blob/master/src/rbm.jl'':184C ''got'':31C ''grid'':247C ''heavi'':221C ''hidden'':44C,118C ''howev'':217C ''href'':183C,202C ''hyper'':125C ''hyper-paramet'':124C ''i.stack.imgur.com'':14C,274C,284C ''i.stack.imgur.com/fqmsp.png'':273C ''i.stack.imgur.com/hjcaw.png'':283C ''i.stack.imgur.com/lbxl5.png'':13C ''ignor'':308C ''imag'':18C,213C,262C,265C,278C,288C ''img'':11C,271C,281C ''includ'':120C ''increas'':90C ''individu'':216C ''insid'':228C ''kind'':172C ''larg'':122C ''learn'':5A ''like'':138C,267C ''lip'':251C ''local'':157C ''look'':266C ''matric'':28C ''may'':179C ''minimum'':158C ''nofollow'':188C,207C ''non'':300C ''non-zero'':299C ''none'':73C ''nose'':250C ''number'':91C,116C ''outer'':230C,304C ''overcom'':87C ''overfit'':165C ''p'':10C,22C,84C,129C,167C,192C,270C,292C ''paramet'':126C ''piecewis'':241C ''pixel'':57C,227C,302C ''posit'':259C ''preprocess'':222C,264C ''pretti'':121C,220C ''problem'':103C ''question'':131C ''rbm'':2A,9B,35C,109C,177C,295C ''reason'':139C ''region'':306C ''rel'':187C,206C ''reproduc'':62C ''second'':235C ''see'':50C ''sever'':104C ''sign'':163C ''similar'':7A,54C,69C,147C ''src'':12C,272C,282C ''strong'':34C,136C,145C,193C ''take'':297C ''tend'':3A ''though'':71C ''time'':105C ''train'':33C,294C ''transform'':237C ''type'':110C ''unit'':40C,119C ''units/weight'':45C ''upd'':194C ''use'':170C,240C ''valu'':148C ''vector'':46C,66C,94C ''visibl'':39C ''weight'':8A,27C,51C,76C,93C,142C ''wrap'':243C ''www.pitt.edu'':204C ''www.pitt.edu/~emotion/ck-spread.htm'':203C ''x'':257C ''y'':258C ''zero'':301C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5432, 2, '2015-04-02 14:08:05.547', 1, NULL, NULL, '&lt;p&gt;I am using this one, so far so good.&lt;br&gt;
Online terminals: &lt;a href="http://www.tutorialspoint.com/codingground.htm" rel="nofollow"&gt;http://www.tutorialspoint.com/codingground.htm&lt;/a&gt; &lt;br&gt;&lt;/p&gt;

&lt;p&gt;Also, R-Fiddle is an option.&lt;br&gt;
&lt;a href="http://www.r-fiddle.org/#/" rel="nofollow"&gt;http://www.r-fiddle.org/#/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/#/'':39C ''/#/&lt;/a&gt;&lt;/p&gt;'':44C ''/codingground.htm'':18C ''/codingground.htm&lt;/a&gt;'':23C ''/p'':25C ''also'':27C ''br'':11C,24C,34C ''far'':8C ''fiddl'':30C ''good'':10C ''href'':15C,36C ''nofollow'':20C,41C ''one'':6C ''onlin'':12C ''option'':33C ''p'':1C,26C ''r'':29C ''r-fiddl'':28C ''rel'':19C,40C ''termin'':13C ''use'':4C ''www.r-fiddle.org'':38C,43C ''www.r-fiddle.org/#/'':37C ''www.r-fiddle.org/#/&lt;/a&gt;&lt;/p&gt;'':42C ''www.tutorialspoint.com'':17C,22C ''www.tutorialspoint.com/codingground.htm'':16C ''www.tutorialspoint.com/codingground.htm&lt;/a&gt;'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (967, 2, '2014-08-12 12:40:44.907', 4, NULL, NULL, '&lt;p&gt;Total of 972 emoji is not really that big not to be able to label them manually, but I doubt that they will work as a good ground truth. Sources like Twitter are full of irony, sarcasm and other tricky settings where emotional symbols (such as emoji or emoticon) mean something different from normal interpretation. For example, someone may write "xxx cheated their clients, and now they are cheated themselves! ha ha ha! :D". This is definitely negative comment, but author is glad to see xxx company in trouble and thus adds positive emoticon. These cases are not that frequent, but definitely not suitable for ground truth. &lt;/p&gt;

&lt;p&gt;Much more common approach is to use emoticon as a &lt;strong&gt;seed for collecting actual data set&lt;/strong&gt;. For example, in &lt;a href="http://www.saifmohammad.com/WebDocs/NRC-Sentiment-JAIR-2014.pdf" rel="nofollow"&gt;this paper&lt;/a&gt; authors use emoticon and emotional hash tags to grab lexicon of words useful for further classification. &lt;/p&gt;
', NULL, NULL, '''/a'':141C ''/p'':109C,158C ''/strong'':128C ''/webdocs/nrc-sentiment-jair-2014.pdf'':136C ''972'':4C ''abl'':14C ''actual'':125C ''add'':93C ''approach'':114C ''author'':82C,142C ''big'':10C ''case'':97C ''cheat'':63C,70C ''classif'':157C ''client'':65C ''collect'':124C ''comment'':80C ''common'':113C ''compani'':88C ''d'':75C ''data'':126C ''definit'':78C,103C ''differ'':53C ''doubt'':21C ''emoji'':5C,48C ''emot'':44C,146C ''emoticon'':50C,95C,118C,144C ''exampl'':58C,130C ''frequent'':101C ''full'':35C ''glad'':84C ''good'':28C ''grab'':150C ''ground'':29C,107C ''ha'':72C,73C,74C ''hash'':147C ''href'':133C ''interpret'':56C ''ironi'':37C ''label'':16C ''lexicon'':151C ''like'':32C ''manual'':18C ''may'':60C ''mean'':51C ''much'':111C ''negat'':79C ''nofollow'':138C ''normal'':55C ''p'':1C,110C ''paper'':140C ''posit'':94C ''realli'':8C ''rel'':137C ''sarcasm'':38C ''see'':86C ''seed'':122C ''set'':42C,127C ''someon'':59C ''someth'':52C ''sourc'':31C ''strong'':121C ''suitabl'':105C ''symbol'':45C ''tag'':148C ''thus'':92C ''total'':2C ''tricki'':41C ''troubl'':90C ''truth'':30C,108C ''twitter'':33C ''use'':117C,143C,154C ''word'':153C ''work'':25C ''write'':61C ''www.saifmohammad.com'':135C ''www.saifmohammad.com/webdocs/nrc-sentiment-jair-2014.pdf'':134C ''xxx'':62C,87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (968, 1, '2014-08-12 17:11:45.447', 4, 214, 'Understanding output stepAIC', '&lt;p&gt;I am using the &lt;code&gt;stepAIC&lt;/code&gt; function in R to do a bi-directional (forward and backward) stepwise regression. I do not understand what each return value from the function means. &lt;/p&gt;

&lt;p&gt;The output is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;          Df     Sum of Sq    RSS       AIC
&amp;lt;none&amp;gt;                        350.71   -5406.0
- aaa      1     0.283        350.99   -5405.9
- bbb      1     0.339        351.05   -5405.4
- ccc      1     0.982        351.69   -5400.5
- ddd      1     0.989        351.70   -5400.5
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Question Are the values listed under &lt;code&gt;Df&lt;/code&gt;, &lt;code&gt;Sum of Sq&lt;/code&gt;, &lt;code&gt;RSS&lt;/code&gt;, and &lt;code&gt;AIC&lt;/code&gt; the values for a model where only one variable would be considered as the independent variable (i.e. y ~
aaa, y ~ bbb, etc.)? &lt;/p&gt;
', 'data-mining  r  feature-selection', NULL, '''-5400.5'':78C,83C ''-5405.4'':73C ''-5405.9'':68C ''-5406.0'':63C ''/code'':18C,84C,95C,100C,103C,107C ''/p'':45C,50C,130C ''/pre'':85C ''0.283'':66C ''0.339'':71C ''0.982'':76C ''0.989'':81C ''1'':65C,70C,75C,80C ''350.71'':62C ''350.99'':67C ''351.05'':72C ''351.69'':77C ''351.70'':82C ''aaa'':64C,126C ''aic'':58C,106C ''backward'':30C ''bbb'':69C,128C ''bi'':26C ''bi-direct'':25C ''ccc'':74C ''code'':16C,52C,93C,96C,101C,105C ''consid'':119C ''data'':5B ''data-min'':4B ''ddd'':79C ''df'':53C,94C ''direct'':27C ''etc'':129C ''featur'':9B ''feature-select'':8B ''forward'':28C ''function'':19C,43C ''gt'':61C ''i.e'':124C ''independ'':122C ''list'':91C ''lt'':59C ''mean'':44C ''mine'':6B ''model'':112C ''none'':60C ''one'':115C ''output'':2A,48C ''p'':11C,46C,86C ''pre'':51C ''question'':87C ''r'':7B,21C ''regress'':32C ''return'':39C ''rss'':57C,102C ''select'':10B ''sq'':56C,99C ''stepaic'':3A,17C ''stepwis'':31C ''sum'':54C,97C ''understand'':1A,36C ''use'':14C ''valu'':40C,90C,109C ''variabl'':116C,123C ''would'':117C ''y'':125C,127C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (969, 2, '2014-08-13 14:18:58.917', 1, NULL, NULL, '&lt;p&gt;After consulting with someone I found out that the  corresponds to a model that would include all the variables, in other words none of the variables were removed. So consider the line in the output for the variable aaa. The listed RSS and AIC are the values for a model that would include all variables but aaa and we see an increase in the RSS and AIC. The other listed results can be considered in the same fashion. The best model is then the one where none are removed since this has the smallest AIC. &lt;/p&gt;
', NULL, NULL, '''/p'':97C ''aaa'':40C,58C ''aic'':45C,68C,96C ''best'':81C ''consid'':31C,75C ''consult'':3C ''correspond'':11C ''fashion'':79C ''found'':7C ''includ'':17C,54C ''increas'':63C ''line'':33C ''list'':42C,71C ''model'':14C,51C,82C ''none'':24C,88C ''one'':86C ''output'':36C ''p'':1C ''remov'':29C,90C ''result'':72C ''rss'':43C,66C ''see'':61C ''sinc'':91C ''smallest'':95C ''someon'':5C ''valu'':48C ''variabl'':20C,27C,39C,56C ''word'':23C ''would'':16C,53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (975, 2, '2014-08-14 04:28:36.933', 10, NULL, NULL, '&lt;p&gt;I hope that the following &lt;strong&gt;resources&lt;/strong&gt; might get you additional &lt;strong&gt;ideas&lt;/strong&gt; toward solving the problem:&lt;/p&gt;

&lt;p&gt;1) Research paper &lt;strong&gt;"Efficient K-Nearest Neighbor Join Algorithms for High Dimensional Sparse Data"&lt;/strong&gt;: &lt;a href="http://arxiv.org/abs/1011.2807"&gt;http://arxiv.org/abs/1011.2807&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;2) Class project paper &lt;strong&gt;"Recommendation System Based on Collaborative Filtering"&lt;/strong&gt; (Stanford University): &lt;a href="http://cs229.stanford.edu/proj2008/Wen-RecommendationSystemBasedOnCollaborativeFiltering.pdf"&gt;http://cs229.stanford.edu/proj2008/Wen-RecommendationSystemBasedOnCollaborativeFiltering.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;3) Project for the &lt;strong&gt;Netflix Prize Competition (&lt;em&gt;k-NN&lt;/em&gt;-based)&lt;/strong&gt;: &lt;a href="http://cs.carleton.edu/cs_comps/0910/netflixprize/final_results/knn/index.html"&gt;http://cs.carleton.edu/cs_comps/0910/netflixprize/final_results/knn/index.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;4) Research paper &lt;strong&gt;"Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data"&lt;/strong&gt; on the &lt;em&gt;curse of dimensionality&lt;/em&gt; phenomenon and its relation to &lt;em&gt;machine learning&lt;/em&gt;, in general, and &lt;em&gt;k-NN algorithm&lt;/em&gt;, in particular: &lt;a href="http://jmlr.org/papers/volume11/radovanovic10a/radovanovic10a.pdf"&gt;http://jmlr.org/papers/volume11/radovanovic10a/radovanovic10a.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;5) &lt;strong&gt;Software for sparse k-NN classification&lt;/strong&gt; (free, but appears not to be open source - might clarify with authors): &lt;a href="http://www.autonlab.org/autonweb/10408.html"&gt;http://www.autonlab.org/autonweb/10408.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;6) Several &lt;strong&gt;discussion threads&lt;/strong&gt; on &lt;em&gt;StackOverflow&lt;/em&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://stackoverflow.com/questions/20333092/knn-with-big-sparse-matrices-in-python"&gt;http://stackoverflow.com/questions/20333092/knn-with-big-sparse-matrices-in-python&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stackoverflow.com/questions/18164348/efficient-nearest-neighbour-search-for-sparse-matrices"&gt;http://stackoverflow.com/questions/18164348/efficient-nearest-neighbour-search-for-sparse-matrices&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stackoverflow.com/questions/21085990/scipy-sparse-distance-matrix-scikit-or-scipy"&gt;http://stackoverflow.com/questions/21085990/scipy-sparse-distance-matrix-scikit-or-scipy&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stackoverflow.com/questions/10472681/handling-incomplete-data-data-sparsity-in-knn"&gt;http://stackoverflow.com/questions/10472681/handling-incomplete-data-data-sparsity-in-knn&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stackoverflow.com/questions/5560218/computing-sparse-pairwise-distance-matrix-in-r"&gt;http://stackoverflow.com/questions/5560218/computing-sparse-pairwise-distance-matrix-in-r&lt;/a&gt; (unlike all previous discussions, which refer to &lt;code&gt;Python&lt;/code&gt;, this one refers to &lt;code&gt;R&lt;/code&gt; ecosystem)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;7) Pay attention to &lt;em&gt;GraphLab&lt;/em&gt;, an open source &lt;strong&gt;parallel framework for machine learning&lt;/strong&gt; (&lt;a href="http://select.cs.cmu.edu/code/graphlab"&gt;http://select.cs.cmu.edu/code/graphlab&lt;/a&gt;), which supports &lt;em&gt;parallel clustering&lt;/em&gt; via &lt;code&gt;MapReduce&lt;/code&gt; model: &lt;a href="http://select.cs.cmu.edu/code/graphlab/clustering.html"&gt;http://select.cs.cmu.edu/code/graphlab/clustering.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You might also check my answer here on Data Science StackExchange on &lt;em&gt;sparse regression&lt;/em&gt; for links to relevant &lt;code&gt;R&lt;/code&gt; packages and &lt;code&gt;CRAN Task View&lt;/code&gt; pages: &lt;a href="http://datascience.stackexchange.com/a/918/2452"&gt;http://datascience.stackexchange.com/a/918/2452&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1026, 2, '2014-08-23 14:15:58.66', 2, NULL, NULL, '&lt;p&gt;I think there are two separate issues to consider: Training time, and prediction accuracy. &lt;/p&gt;

&lt;p&gt;Take a simple example : consider you have two classes, that have a multivariate normal distribution. Basically you need to estimate the respective class means and class covariances.  Now the first thing you care about is your estimate of the difference in the class means: but your performance is limited by the accuracy of the worst estimated mean: its no good estimating one mean to the 100th decimal place - if the other mean is only estimated to 1 decimal place.  So its a waste of computing resources to use all the data - you can instead undersample the  more common class AND reweight the classes appropriately. ( those computing resources can then be used exploring different input variables etc)&lt;/p&gt;

&lt;p&gt;Now the second issue is predictive accuracy: different algorithms use different error metrics, which may or may not agree with your own objectives. eg logistic regression will penalise overall probability error,  so if  most of your data is from one class, then it will tend to try to improve accurate probability estimates ( eg 90 vs 95% probability) of that one class rather than trying to identify the rare class. in that case you would definitely want to try to reweight to emphasize the rare class ( and subsequently adjust the estimate [by adjusting the bias term] to get the probability estimates realigned)&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (976, 2, '2014-08-14 17:28:40.453', 4, NULL, NULL, '&lt;p&gt;I gave a very limited partial answer for the confirmatory factor analysis package that I developed for Stata in this &lt;a href="http://www.stata-journal.com/article.html?article=st0169" rel="nofollow"&gt;Stata Journal article&lt;/a&gt; based on timing the actual simulations. Confirmatory factor analysis was implemented as a maximum likelihood estimation technique, and I could see very easily how the computation time grew with each dimension (sample size &lt;code&gt;n&lt;/code&gt;, number of variables &lt;code&gt;p&lt;/code&gt;, number of factors &lt;code&gt;k&lt;/code&gt;). As it is heavily dependent on how Stata thinks about the data (optimized to compute across columns/observations rather than rows), I found performance to be &lt;code&gt;O(n^{0.68} (k+p)^{2.4})&lt;/code&gt; where 2.4 is the fastest matrix inversion asymptotics (and there''s hell of a lot of that in confirmatory factor analysis iterative maximization). I did not give a reference for the latter, but I think I got this from &lt;a href="http://en.wikipedia.org/wiki/Computational_complexity_of_mathematical_operations#Matrix_algebra" rel="nofollow"&gt;Wikipedia&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Note that there is also a matrix inversion step in OLS. However, for reasons of numerical accuracy, no one would really brute-force inverse the &lt;code&gt;X''X&lt;/code&gt; matrix, and would rather use sweep operators and identify the dangerously collinear variables to deal with precision issues. If you add up $10^8$ numbers that originally were in &lt;a href="http://en.wikipedia.org/wiki/Double-precision_floating-point_format" rel="nofollow"&gt;double precision&lt;/a&gt;, you will likely end up with a number that only has a single precision. Numerical computing issues may become a forgotten corner of big data calculations as you start optimizing for speed.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (987, 1, '2014-08-17 17:29:44.123', 10, 755, 'Text categorization: combining different kind of features', '&lt;p&gt;The problem I am tackling is categorizing short texts into multiple classes. My current approach is to use tf-idf weighted term frequencies and learn a simple linear classifier (logistic regression). This works reasonably well (around 90% macro F-1 on test set, nearly 100% on training set). A big problem are unseen words/n-grams. &lt;/p&gt;

&lt;p&gt;I am trying to improve the classifier by adding other features, e.g. a fixed sized vector computed using distributional similarities (as computed by word2vec) or other categorical features of the examples. My idea was to just add the features to the sparse input features from the bag of words. However, this results in worse performance on the test and training set. The additional features by themselves give about 80% F-1 on the test set, so they aren''t garbage. Scaling the features didn''t help as well. My current thinking is that these kind of features don''t mix well with the (sparse) bag of words features.&lt;/p&gt;

&lt;p&gt;So the question is: assuming the additional features provide additional information, what is the best way to incorporate them? Could training separate classifiers and combining them in some kind of ensemble work (this would probably have the drawback that no interaction between the features of the different classifiers could be captured)? Are there other more complex models I should consider?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10395, 2, '2016-02-25 18:49:53.537', 1, NULL, NULL, '&lt;p&gt;This all depends. SAS is great and is backed by the SAS Institute, meaning if you''re working for an organization that has invested in SAS, you can contact the support team for anything funky happening with the software.&lt;/p&gt;

&lt;p&gt;R is free and open source, and there are also organizations being created that are building on to and supporting R much like SAS Institute has done. The difference being that SAS has been around much longer and is much more structured than R.&lt;/p&gt;

&lt;p&gt;They both have their ups and downs, and it depends on what type of analytics you plan on doing in your career. So, to answer your questions, I would decide what you envision yourself doing. To my understanding R is good for Big Data applications and Machine Learning, SAS is great for statistical analysis (ARIMA, etc).&lt;/p&gt;

&lt;p&gt;SAS and R comparisons:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://support.sas.com/resources/papers/proceedings13/348-2013.pdf" rel="nofollow"&gt;http://support.sas.com/resources/papers/proceedings13/348-2013.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.learnanalytics.in/blog/?p=9" rel="nofollow"&gt;http://www.learnanalytics.in/blog/?p=9&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (977, 1, '2014-08-14 19:09:29.523', 10, 161, 'Solutions for Continuous Online Cluster Identification?', '&lt;p&gt;Let me show you an example of a hypothetical online clustering application:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ZVOQN.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;At time n points 1,2,3,4 are allocated to the blue cluster A and points b,5,6,7 are allocated to the red cluster B.&lt;/p&gt;

&lt;p&gt;At time n+1 a new point a is introduced which is assigned to the blue cluster A but also causes the point b to be assigned to the blue cluster A as well.&lt;/p&gt;

&lt;p&gt;In the end points 1,2,3,4,a,b belong to A and points 5,6,7 to B. To me this seems reasonable.&lt;/p&gt;

&lt;p&gt;What seems simple at first glance is actually a bit tricky - to maintain identifiers across time steps. Let me try to make this point clear with a more borderline example:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/sQj4h.jpg" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;The green point will cause two blue and two red points to be merged into one cluster which I arbitrarily decided to color blue - mind this is already my human heuristical thinking at work!&lt;/p&gt;

&lt;p&gt;A computer to make this decision will have to use rules. For example when points are merged into a cluster then the identity of the cluster is determined by the majority. In this case we would face a draw - both blue and red might be valid choices for the new (here blue colored) cluster. &lt;/p&gt;

&lt;p&gt;Imagine a fifth red point close to the green one. Then the majority would be red (3 red vs 2 blue) so red would be a good choice for the new cluster - but this would contradict the even clearer choice of red for the rightmost cluster as those have been red and probably should stay that way.&lt;/p&gt;

&lt;p&gt;I find it fishy to think about this. At the end of the day I guess there are no perfect rules for this - rather heuristics optimizing some stability criterea.&lt;/p&gt;

&lt;p&gt;This finally leads to my questions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Does this "problem" have a name that it can be referred to?&lt;/li&gt;
&lt;li&gt;Are there "standard" solutions to this and ...&lt;/li&gt;
&lt;li&gt;... is there maybe even an R package for that?&lt;/li&gt;
&lt;/ol&gt;

&lt;hr&gt;

&lt;p&gt;&lt;a href="http://www.joyofdata.de/blog/reasonable-inheritance-of-cluster-identities-in-repetitive-clustering/"&gt;Reasonable Inheritance of Cluster Identities in Repetitive Clustering&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (978, 2, '2014-08-14 21:38:57.63', 7, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;But I don''t know what is difference between text classification and topic models in documents&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Text classification is a form of supervised learning -- the set of possible classes are known/defined in advance and don''t change.&lt;/p&gt;

&lt;p&gt;Topic modeling is a form of unsupervised learning (akin to clustering) -- the set of possible topics are unknown apriori. They''re defined as part of generating the topic models. With a non-deterministic algorithm like LDA, you''ll get different topics each time you run the algorithm.&lt;/p&gt;

&lt;p&gt;Text classification often involves mutually-exclusive classes -- think of these as buckets. But it doesn''t have to -- given the right kind of labeled input data, you can set of a series of non-mutually-exclusive binary classifiers.&lt;/p&gt;

&lt;p&gt;Topic modeling is generally not mutually-exclusive -- the same document can have its probability distribution spread across many topics. In addition, there are also hierarchical topic modeling methods, etc.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Also can I use topic model for the documents to identify one topic later on can I use the classification to classify the text inside this documents ? &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;If you''re asking whether you can take all of the documents assigned to one topic by a topic modeling algorithm and then apply a classifier to that collection, then yes, you certainly can do that. I''m not sure it makes much sense, though -- at a minimum, you''d need to pick a threshold for the topic probability distribution above which you''ll include documents in your collection (typically 0.05-0.1). Can you elaborate on your use case?&lt;/p&gt;

&lt;p&gt;By the way, there''s a great tutorial on topic modeling using the MALLET library for Java available here: &lt;a href="http://programminghistorian.org/lessons/topic-modeling-and-mallet"&gt;Getting Started with Topic Modeling and MALLET&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (979, 1, '2014-08-15 13:10:20.937', 9, 2417, 'Algorithms for text clustering', '&lt;p&gt;I have a problem of clustering huge amount of sentences into groups by their meanings. This is similar to a problem when you have lots of sentences and want to group them by their meanings.&lt;/p&gt;

&lt;p&gt;What algorithms are suggested to do this? I don''t know number of clusters in advance (and as more data is coming clusters can change as well), what features are normally used to represent each sentence?&lt;/p&gt;

&lt;p&gt;I''m trying now the simplest features with just list of words and distance between sentences as next   &lt;img src="http://i.stack.imgur.com/dHB9X.jpg" alt="enter image description here"&gt; A and B are corresponding sets of words in sentence A and B. Does it make sense at all? &lt;/p&gt;

&lt;p&gt;I''m trying to apply &lt;a href="http://scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html#example-cluster-plot-mean-shift-py"&gt;Mean-Shift&lt;/a&gt; algorithm from scikit library to this distance as it does not require number of clusters in advance.&lt;/p&gt;

&lt;p&gt;If anyone will advise better methods/approaches for the problem - it will be very much appreciated as I''m still new to the topic.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2511, 2, '2014-11-20 21:11:12.187', 1, NULL, NULL, '&lt;p&gt;I would add a comment but I do not have enough reputation points. I might suggest Using "R revolution open". It is a Build of R that includes a lot of native support for multi-core processing. I have not used it much as my computer is very old, but it is defiantly worth looking at. Plus it is free. &lt;/p&gt;
', NULL, NULL, '''/p'':63C ''add'':4C ''build'':25C ''comment'':6C ''comput'':48C ''core'':38C ''defiant'':55C ''enough'':12C ''free'':62C ''includ'':29C ''look'':57C ''lot'':31C ''might'':16C ''much'':45C ''multi'':37C ''multi-cor'':36C ''nativ'':33C ''old'':51C ''open'':21C ''p'':1C ''plus'':59C ''point'':14C ''process'':39C ''r'':19C,27C ''reput'':13C ''revolut'':20C ''suggest'':17C ''support'':34C ''use'':18C,43C ''worth'':56C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (980, 2, '2014-08-15 14:00:14.927', 7, NULL, NULL, '&lt;p&gt;Check the &lt;strong&gt;Stanford NLP Group&lt;/strong&gt;''s open source software (&lt;a href="http://www-nlp.stanford.edu/software"&gt;http://www-nlp.stanford.edu/software&lt;/a&gt;), in particular, &lt;strong&gt;Stanford Classifier&lt;/strong&gt; (&lt;a href="http://www-nlp.stanford.edu/software/classifier.shtml"&gt;http://www-nlp.stanford.edu/software/classifier.shtml&lt;/a&gt;). The software is written in &lt;code&gt;Java&lt;/code&gt;, which will likely delight you, but also has bindings for some other languages. Note, the &lt;em&gt;licensing&lt;/em&gt; - if you plan to use their code in commercial products, you have to acquire commercial license.&lt;/p&gt;

&lt;p&gt;Another interesting set of open source libraries, IMHO suitable for this task and much more, is &lt;strong&gt;parallel framework for machine learning GraphLab&lt;/strong&gt; (&lt;a href="http://select.cs.cmu.edu/code/graphlab"&gt;http://select.cs.cmu.edu/code/graphlab&lt;/a&gt;), which includes &lt;strong&gt;clustering library&lt;/strong&gt;, implementing various clustering algorithms (&lt;a href="http://select.cs.cmu.edu/code/graphlab/clustering.html"&gt;http://select.cs.cmu.edu/code/graphlab/clustering.html&lt;/a&gt;). It is especially suitable for &lt;strong&gt;very large volume of data&lt;/strong&gt; (like you have), as it implements &lt;code&gt;MapReduce&lt;/code&gt; model and, thus, supports &lt;em&gt;multicore&lt;/em&gt; and &lt;em&gt;multiprocessor&lt;/em&gt; &lt;strong&gt;parallel processing&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;You most likely are aware of the following, but I will mention it just in case. &lt;strong&gt;Natural Language Toolkit (NLTK)&lt;/strong&gt; for &lt;code&gt;Python&lt;/code&gt; (&lt;a href="http://www.nltk.org"&gt;http://www.nltk.org&lt;/a&gt;) contains modules for clustering/classifying/categorizing text. Check the relevant chapter in the &lt;code&gt;NLTK Book&lt;/code&gt;: &lt;a href="http://www.nltk.org/book/ch06.html"&gt;http://www.nltk.org/book/ch06.html&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Speaking of &lt;strong&gt;algorithms&lt;/strong&gt;, it seems that you''ve tried most of the ones from &lt;code&gt;scikit-learn&lt;/code&gt;, such as illustrated in this topic extraction example: &lt;a href="http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf.html"&gt;http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf.html&lt;/a&gt;. However, you may find useful other libraries, which implement a wide variety of &lt;em&gt;clustering algorithms&lt;/em&gt;, including &lt;em&gt;Non-Negative Matrix Factorization (NMF)&lt;/em&gt;. One of such libraries is &lt;strong&gt;Python Matrix Factorization (PyMF)&lt;/strong&gt; with home page at &lt;a href="https://code.google.com/p/pymf"&gt;https://code.google.com/p/pymf&lt;/a&gt; and source code at &lt;a href="https://github.com/nils-werner/pymf"&gt;https://github.com/nils-werner/pymf&lt;/a&gt;. Another, even more interesting, library, also Python-based, is &lt;strong&gt;NIMFA&lt;/strong&gt;, which implements various &lt;em&gt;NMF algorithms&lt;/em&gt;: &lt;a href="http://nimfa.biolab.si"&gt;http://nimfa.biolab.si&lt;/a&gt;. Here''s a research paper, describing &lt;code&gt;NIMFA&lt;/code&gt;: &lt;a href="http://jmlr.org/papers/volume13/zitnik12a/zitnik12a.pdf"&gt;http://jmlr.org/papers/volume13/zitnik12a/zitnik12a.pdf&lt;/a&gt;. Here''s an example from its documentation, which presents the solution for very similar text processing problem of &lt;em&gt;topic clustering&lt;/em&gt;: &lt;a href="http://nimfa.biolab.si/nimfa.examples.documents.html"&gt;http://nimfa.biolab.si/nimfa.examples.documents.html&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':197C,349C ''/book/ch06.html'':217C ''/book/ch06.html&lt;/a&gt;.&lt;/p&gt;'':220C ''/code'':42C,149C,192C,212C,247C,358C ''/code/graphlab'':107C ''/code/graphlab&lt;/a&gt;),'':110C ''/code/graphlab/clustering.html'':125C ''/code/graphlab/clustering.html&lt;/a&gt;).'':128C ''/em'':60C,156C,160C,280C,289C,344C,388C ''/nils-werner/pymf'':321C ''/nils-werner/pymf&lt;/a&gt;.'':324C ''/nimfa.examples.documents.html'':393C ''/nimfa.examples.documents.html&lt;/a&gt;.&lt;/p&gt;'':396C ''/p'':77C,165C,225C ''/p/pymf'':309C ''/p/pymf&lt;/a&gt;'':312C ''/papers/volume13/zitnik12a/zitnik12a.pdf'':363C ''/papers/volume13/zitnik12a/zitnik12a.pdf&lt;/a&gt;.'':366C ''/software'':17C ''/software&lt;/a&gt;),'':20C ''/software/classifier.shtml'':31C ''/software/classifier.shtml&lt;/a&gt;).'':34C ''/stable/auto_examples/applications/topics_extraction_with_nmf.html'':260C ''/stable/auto_examples/applications/topics_extraction_with_nmf.html&lt;/a&gt;.'':263C ''/strong'':8C,26C,102C,116C,140C,164C,188C,224C,231C,300C,337C ''acquir'':74C ''algorithm'':120C,230C,279C,343C ''also'':49C,330C ''anoth'':79C,325C ''awar'':171C ''base'':333C ''bind'':51C ''book'':211C ''case'':182C ''chapter'':206C ''check'':2C,203C ''classifi'':25C ''cluster'':114C,119C,278C,387C ''clustering/classifying/categorizing'':201C ''code'':40C,67C,147C,190C,209C,243C,315C,356C ''code.google.com'':308C,311C ''code.google.com/p/pymf'':307C ''code.google.com/p/pymf&lt;/a&gt;'':310C ''commerci'':69C,75C ''contain'':198C ''data'':139C ''delight'':46C ''describ'':355C ''document'':373C ''em'':58C,154C,158C,277C,282C,341C,385C ''especi'':131C ''even'':326C ''exampl'':255C,370C ''extract'':254C ''factor'':287C,298C ''find'':267C ''follow'':174C ''framework'':97C ''github.com'':320C,323C ''github.com/nils-werner/pymf'':319C ''github.com/nils-werner/pymf&lt;/a&gt;.'':322C ''graphlab'':101C ''group'':7C ''home'':302C ''howev'':264C ''href'':14C,28C,104C,122C,194C,214C,257C,306C,318C,346C,360C,390C ''illustr'':250C ''imho'':86C ''implement'':117C,146C,272C,339C ''includ'':112C,281C ''interest'':80C,328C ''java'':41C ''jmlr.org'':362C,365C ''jmlr.org/papers/volume13/zitnik12a/zitnik12a.pdf'':361C ''jmlr.org/papers/volume13/zitnik12a/zitnik12a.pdf&lt;/a&gt;.'':364C ''languag'':55C,185C ''larg'':136C ''learn'':100C,246C ''librari'':85C,115C,270C,293C,329C ''licens'':59C,76C ''like'':45C,141C,169C ''machin'':99C ''mapreduc'':148C ''matrix'':286C,297C ''may'':266C ''mention'':178C ''model'':150C ''modul'':199C ''much'':92C ''multicor'':155C ''multiprocessor'':159C ''natur'':184C ''negat'':285C ''nimfa'':336C,357C ''nimfa.biolab.si'':347C,348C,392C,395C ''nimfa.biolab.si/nimfa.examples.documents.html'':391C ''nimfa.biolab.si/nimfa.examples.documents.html&lt;/a&gt;.&lt;/p&gt;'':394C ''nlp'':6C ''nltk'':187C,210C ''nmf'':288C,342C ''non'':284C ''non-neg'':283C ''note'':56C ''one'':241C,290C ''open'':10C,83C ''p'':1C,78C,166C,221C,226C ''page'':303C ''paper'':354C ''parallel'':96C,162C ''particular'':22C ''plan'':63C ''present'':375C ''problem'':383C ''process'':163C,382C ''product'':70C ''pymf'':299C ''python'':191C,296C,332C ''python-bas'':331C ''relev'':205C ''research'':353C ''scikit'':245C ''scikit-learn'':244C ''scikit-learn.org'':259C,262C ''scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf.html'':258C ''scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf.html&lt;/a&gt;.'':261C ''seem'':233C ''select.cs.cmu.edu'':106C,109C,124C,127C ''select.cs.cmu.edu/code/graphlab'':105C ''select.cs.cmu.edu/code/graphlab&lt;/a&gt;),'':108C ''select.cs.cmu.edu/code/graphlab/clustering.html'':123C ''select.cs.cmu.edu/code/graphlab/clustering.html&lt;/a&gt;).'':126C ''set'':81C ''similar'':380C ''softwar'':12C,36C ''solut'':377C ''sourc'':11C,84C,314C ''speak'':227C ''stanford'':5C,24C ''strong'':4C,23C,95C,113C,134C,161C,183C,222C,229C,295C,335C ''suitabl'':87C,132C ''support'':153C ''task'':90C ''text'':202C,381C ''thus'':152C ''toolkit'':186C ''topic'':253C,386C ''tri'':237C ''updat'':223C ''use'':65C,268C ''varieti'':275C ''various'':118C,340C ''ve'':236C ''volum'':137C ''wide'':274C ''written'':38C ''www-nlp.stanford.edu'':16C,19C,30C,33C ''www-nlp.stanford.edu/software'':15C ''www-nlp.stanford.edu/software&lt;/a&gt;),'':18C ''www-nlp.stanford.edu/software/classifier.shtml'':29C ''www-nlp.stanford.edu/software/classifier.shtml&lt;/a&gt;).'':32C ''www.nltk.org'':195C,196C,216C,219C ''www.nltk.org/book/ch06.html'':215C ''www.nltk.org/book/ch06.html&lt;/a&gt;.&lt;/p&gt;'':218C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (981, 2, '2014-08-15 20:22:11.75', 2, NULL, NULL, '&lt;p&gt;Basically for this task you can efficiently use any SQL database with good support of B+tree based indexes (MySQL will suite you needs just perfect). &lt;/p&gt;

&lt;p&gt;Create 3 tables:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Documents table, columns: id/document&lt;/li&gt;
&lt;li&gt;N-grams table: n_gram_id/n_gram&lt;/li&gt;
&lt;li&gt;Mapping between n-grams and documents: document_id/n_gram_id&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Create indexes on N-gram table/n_gram string and Mapping table/n_gram_id, also primary keys will be indexed by default well.&lt;/p&gt;

&lt;p&gt;Your operations will be efficient:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Insertion of document: just extract all n-grams and insert into document table and N-grams table&lt;/li&gt;
&lt;li&gt;Lookup for in_gram will be quick with support of index&lt;/li&gt;
&lt;li&gt;Querying for all n-grams that contain a sub-n-gram: in 2 steps - just query based on index all n-grams which contain sub-n-gram from 2nd table. Then - retrieve all corresponding documents for each of these n-grams.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;You don''t even need to use joins to achieve all these operations so indexes will help a lot. Also if data will not suite in one machine - you can implement sharding scheme, like storing n_grams started from a-n on one server and o-z on another or other suitable scheme. &lt;/p&gt;

&lt;p&gt;Also you can use MongoDB, but I''m not sure how exactly you need to implement indexing scheme. For MongoDB you will get sharding scheme for free as it is already built-in.&lt;/p&gt;
', NULL, NULL, '''/li'':40C,49C,60C,112C,125C,173C ''/ol'':61C,174C ''/p'':28C,33C,83C,90C,231C,267C ''2'':141C ''2nd'':159C ''3'':31C ''a-n'':215C ''achiev'':185C ''alreadi'':263C ''also'':74C,195C,233C ''anoth'':226C ''b'':17C ''base'':19C,145C ''basic'':2C ''built'':265C ''built-in'':264C ''column'':38C ''contain'':134C,153C ''correspond'':164C ''creat'':30C,63C ''data'':197C ''databas'':12C ''default'':81C ''document'':36C,57C,58C,95C,105C,165C ''effici'':8C,89C ''even'':179C ''exact'':244C ''extract'':97C ''free'':259C ''get'':255C ''good'':14C ''gram'':44C,47C,55C,68C,101C,110C,117C,132C,139C,151C,157C,172C,212C ''help'':192C ''id/document'':39C ''id/n_gram'':48C ''id/n_gram_id'':59C ''implement'':206C,248C ''index'':20C,64C,79C,124C,147C,190C,249C ''insert'':93C,103C ''join'':183C ''key'':76C ''li'':35C,41C,50C,92C,113C,126C ''like'':209C ''lookup'':114C ''lot'':194C ''m'':240C ''machin'':203C ''map'':51C,72C ''mongodb'':237C,252C ''mysql'':21C ''n'':43C,46C,54C,67C,100C,109C,131C,138C,150C,156C,171C,211C,217C ''n-gram'':42C,53C,66C,99C,108C,130C,149C,170C ''need'':25C,180C,246C ''o'':223C ''o-z'':222C ''ol'':34C,91C ''one'':202C,219C ''oper'':86C,188C ''p'':1C,29C,62C,84C,175C,232C ''perfect'':27C ''primari'':75C ''queri'':127C,144C ''quick'':120C ''retriev'':162C ''scheme'':208C,230C,250C,257C ''server'':220C ''shard'':207C,256C ''sql'':11C ''start'':213C ''step'':142C ''store'':210C ''string'':70C ''sub'':137C,155C ''sub-n-gram'':136C,154C ''suit'':23C,200C ''suitabl'':229C ''support'':15C,122C ''sure'':242C ''tabl'':32C,37C,45C,106C,111C,160C ''table/n_gram'':69C ''table/n_gram_id'':73C ''task'':5C ''tree'':18C ''use'':9C,182C,236C ''well'':82C ''z'':224C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (982, 2, '2014-08-16 10:25:19.687', 8, NULL, NULL, '&lt;p&gt;See &lt;a href="https://lucene.apache.org/core/4_4_0/analyzers-common/org/apache/lucene/analysis/ngram/NGramTokenizer.html"&gt;Lucene NGramTokenizer&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Are you sure you can''t just use lucene or similar indexing techniques?&lt;/p&gt;

&lt;p&gt;Inverted indexes will store the n-gram only once, then just the document ids that contain the ngram; they don''t store this as highly redundant raw text.&lt;/p&gt;

&lt;p&gt;As for finding ngrams that contain your query sub-n-gram, I would build an index on the observed ngrams, e.g. using a second lucene index, or &lt;a href="https://en.wikipedia.org/wiki/Substring_index"&gt;any other substring index&lt;/a&gt; such as a trie or suffix tree. If your data is dynamic, probably lucene is a reasonable choice, using phrase queries to find your n-grams.&lt;/p&gt;
', NULL, NULL, '''/a'':10C,96C ''/core/4_4_0/analyzers-common/org/apache/lucene/analysis/ngram/ngramtokenizer.html'':7C ''/p'':11C,26C,57C,124C ''/wiki/substring_index'':91C ''build'':73C ''choic'':114C ''contain'':44C,64C ''data'':106C ''document'':41C ''dynam'':108C ''e.g'':80C ''en.wikipedia.org'':90C ''en.wikipedia.org/wiki/substring_index'':89C ''find'':61C,119C ''gram'':35C,70C,123C ''high'':53C ''href'':4C,88C ''id'':42C ''index'':24C,29C,75C,85C,95C ''invert'':28C ''lucen'':8C,21C,84C,110C ''lucene.apache.org'':6C ''lucene.apache.org/core/4_4_0/analyzers-common/org/apache/lucene/analysis/ngram/ngramtokenizer.html'':5C ''n'':34C,69C,122C ''n-gram'':33C,121C ''ngram'':46C,62C,79C ''ngramtoken'':9C ''observ'':78C ''p'':1C,12C,27C,58C ''phrase'':116C ''probabl'':109C ''queri'':66C,117C ''raw'':55C ''reason'':113C ''redund'':54C ''second'':83C ''see'':2C ''similar'':23C ''store'':31C,50C ''sub'':68C ''sub-n-gram'':67C ''substr'':94C ''suffix'':102C ''sure'':15C ''techniqu'':25C ''text'':56C ''tree'':103C ''trie'':100C ''use'':20C,81C,115C ''would'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (984, 1, '2014-08-16 10:42:58.337', 1, 194, 'Can anyone provide the 24 hour challenge dataset from Climate Corporation?', '&lt;p&gt;I have no knowledge about the climate or soil. And I just want to find out more about these kind of dataset. I heard that Climate Corporation asked its candidates to perform statistical analysis on various climate dataset. &lt;/p&gt;

&lt;p&gt;This is why I am asking this question. Please do not get me wrong. I am not trying to get the dataset to prepare myself for an interview, as I know they give out different dataset to people from different background. &lt;/p&gt;

&lt;p&gt;I know that Climate Corporation only hires PHD, which I am not. I only want to play around with their dataset such that I can learn and implement &lt;strong&gt;time series analysis&lt;/strong&gt;. That''s it. &lt;/p&gt;

&lt;p&gt;So, if anyone does not mind sharing their dataset. Please post the link them below. Thank you very much. &lt;/p&gt;
', 'dataset', NULL, '''/p'':52C,95C,133C,154C ''/strong'':129C ''24'':5A ''analysi'':47C,128C ''anyon'':2A,137C ''around'':114C ''ask'':41C,59C ''background'':94C ''candid'':43C ''challeng'':7A ''climat'':10A,20C,39C,50C,100C ''corpor'':11A,40C,101C ''dataset'':8A,12B,35C,51C,75C,89C,117C,143C ''differ'':88C,93C ''find'':28C ''get'':65C,73C ''give'':86C ''heard'':37C ''hire'':103C ''hour'':6A ''implement'':124C ''interview'':81C ''kind'':33C ''know'':84C,98C ''knowledg'':17C ''learn'':122C ''link'':147C ''mind'':140C ''much'':153C ''p'':13C,53C,96C,134C ''peopl'':91C ''perform'':45C ''phd'':104C ''play'':113C ''pleas'':62C,144C ''post'':145C ''prepar'':77C ''provid'':3A ''question'':61C ''seri'':127C ''share'':141C ''soil'':22C ''statist'':46C ''strong'':125C ''thank'':150C ''time'':126C ''tri'':71C ''various'':49C ''want'':26C,111C ''wrong'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1008, 2, '2014-08-20 15:34:00.83', 1, NULL, NULL, '&lt;p&gt;Try BeautifulSoup - &lt;a href="http://www.crummy.com/software/BeautifulSoup/" rel="nofollow"&gt;http://www.crummy.com/software/BeautifulSoup/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;From the website "Beautiful Soup is a Python library designed for quick turnaround projects like screen-scraping."
I have no personally used it, but it often comes up in regards to a nice library for scraping. Here''s a blog post on using it to scrape Craigslist &lt;a href="http://www.gregreda.com/2014/07/27/scraping-craigslist-for-tickets/" rel="nofollow"&gt;http://www.gregreda.com/2014/07/27/scraping-craigslist-for-tickets/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/2014/07/27/scraping-craigslist-for-tickets/'':67C ''/2014/07/27/scraping-craigslist-for-tickets/&lt;/a&gt;&lt;/p&gt;'':72C ''/software/beautifulsoup/'':8C ''/software/beautifulsoup/&lt;/a&gt;&lt;/p&gt;'':13C ''beauti'':18C ''beautifulsoup'':3C ''blog'':55C ''come'':42C ''craigslist'':62C ''design'':24C ''href'':5C,64C ''librari'':23C,49C ''like'':29C ''nice'':48C ''nofollow'':10C,69C ''often'':41C ''p'':1C,14C ''person'':36C ''post'':56C ''project'':28C ''python'':22C ''quick'':26C ''regard'':45C ''rel'':9C,68C ''scrape'':32C,51C,61C ''screen'':31C ''screen-scrap'':30C ''soup'':19C ''tri'':2C ''turnaround'':27C ''use'':37C,58C ''websit'':17C ''www.crummy.com'':7C,12C ''www.crummy.com/software/beautifulsoup/'':6C ''www.crummy.com/software/beautifulsoup/&lt;/a&gt;&lt;/p&gt;'':11C ''www.gregreda.com'':66C,71C ''www.gregreda.com/2014/07/27/scraping-craigslist-for-tickets/'':65C ''www.gregreda.com/2014/07/27/scraping-craigslist-for-tickets/&lt;/a&gt;&lt;/p&gt;'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (985, 1, '2014-08-16 11:05:43.353', 2, 621, 'Can I use unsupervised learning followed by supervised learning?', '&lt;p&gt;I have a question about classifying documents using supervised learning and unsupervised learning.&lt;/p&gt;

&lt;p&gt;For example: - I have a bunch of documents talking about football.&lt;br&gt;
As we know football has different meaning in UK, USA and Australia. Therefore, it is difficult to classify these documents to three different categorizations which are soccer, American football and Australian football.&lt;br&gt;
My approach tries to use cosine similarity terms which is based on unsupervised. After we use the cluster learning, we are able to create a number of clusters based on cosine similarity which each cluster will contain similar documents terms. After we create the clusters, we can use a semantic feature to identify these clusters depend on supervised model like SVM to make accurate categorizations.  &lt;/p&gt;

&lt;p&gt;My goal is to create more accurate categorizations because if I want to test a new document I want know if this document can be related to these categorizations or not.  &lt;/p&gt;
', 'machine-learning  classification  clustering  dimensionality-reduction', NULL, '''/p'':32C,144C,177C ''abl'':100C ''accur'':142C,152C ''american'':73C ''approach'':80C ''australia'':57C ''australian'':76C ''base'':89C,107C ''br'':45C,78C ''bunch'':39C ''categor'':69C,143C,153C,174C ''classif'':13B ''classifi'':24C,63C ''cluster'':14B,96C,106C,113C,123C,133C ''contain'':115C ''cosin'':84C,109C ''creat'':102C,121C,150C ''depend'':134C ''differ'':51C,68C ''difficult'':61C ''dimension'':16B ''dimensionality-reduct'':15B ''document'':25C,41C,65C,117C,162C,168C ''exampl'':35C ''featur'':129C ''follow'':6A ''footbal'':44C,49C,74C,77C ''goal'':147C ''identifi'':131C ''know'':48C,165C ''learn'':5A,9A,12B,28C,31C,97C ''like'':138C ''machin'':11B ''machine-learn'':10B ''make'':141C ''mean'':52C ''model'':137C ''new'':161C ''number'':104C ''p'':18C,33C,145C ''question'':22C ''reduct'':17B ''relat'':171C ''semant'':128C ''similar'':85C,110C,116C ''soccer'':72C ''supervis'':8A,27C,136C ''svm'':139C ''talk'':42C ''term'':86C,118C ''test'':159C ''therefor'':58C ''three'':67C ''tri'':81C ''uk'':54C ''unsupervis'':4A,30C,91C ''usa'':55C ''use'':3A,26C,83C,94C,126C ''want'':157C,164C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10120, 1, '2016-02-07 15:34:18.73', 2, 18, 'PCA on acceleration time series data', '&lt;p&gt;Has anyone attempted Principal component analysis on time series and acceleration data(ie. data from accelerometer and sensors) and tried compressing it as well as regenerating back the data with minimum error?&lt;/p&gt;

&lt;p&gt;I wanted to know if this is possible using PCA. Kindly suggest alternate methods available to compress and regenrate my data.&lt;/p&gt;
', 'data', NULL, '''/p'':41C,64C ''acceler'':3A,19C ''acceleromet'':24C ''altern'':55C ''analysi'':14C ''anyon'':10C ''attempt'':11C ''avail'':57C ''back'':35C ''compon'':13C ''compress'':29C,59C ''data'':6A,7B,20C,22C,37C,63C ''error'':40C ''ie'':21C ''kind'':53C ''know'':46C ''method'':56C ''minimum'':39C ''p'':8C,42C ''pca'':1A,52C ''possibl'':50C ''princip'':12C ''regener'':34C ''regenr'':61C ''sensor'':26C ''seri'':5A,17C ''suggest'':54C ''time'':4A,16C ''tri'':28C ''use'':51C ''want'':44C ''well'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (986, 2, '2014-08-17 14:17:41.307', 1, NULL, NULL, '&lt;p&gt;OK, so here''s your data. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dd &amp;lt;- data.frame(position=rep(1:3, each=2), 
                 variation=rep(c(1,3), 3), 
                 impressions=rep(c(753, 767), 3), 
                 clicks=c(26,7,16,13,2,7))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;which is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  position variation impressions clicks
1        1         1         753     26
2        1         3         767      7
3        2         1         753     16
4        2         3         767     13
5        3         1         753      2
6        3         3         767      7
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The two model assumptions you''re thinking about are Binomial&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;mod.bin &amp;lt;- glm(cbind(clicks, impressions-clicks) ~ variation + position,
               family=binomial, data=dd)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where the dependent variable is constructed to have the count of the event of interest in the first column, and the Poisson&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;md.pois &amp;lt;- glm(clicks ~ variation + position + offset(log(impressions)), 
               family=poisson, data=dd)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where the &lt;code&gt;log(impressions)&lt;/code&gt; offset is necessary whenever the number of trials differs across observations.  This means coefficients are interpretable in terms of change in rate not change in count, which is what you want.  &lt;/p&gt;

&lt;p&gt;The first model generalises the &lt;code&gt;binom.test&lt;/code&gt; to a setting with covariates, which is what you have.  That gets you a more direct answer to your question, and better (if not perfect) measurement of the relevant uncertainty.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Notes&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Both models assume no interaction between variation and position (''independent effects'').  This may or may not be reasonable.  You''d want more replications to investigate that properly.  Swap the &lt;code&gt;+&lt;/code&gt; for a &lt;code&gt;*&lt;/code&gt; to do so.&lt;/p&gt;

&lt;p&gt;In this data &lt;code&gt;summary&lt;/code&gt; confirms that the two models give rather similar results, so concerns about Poisson vs Binomial don''t seem to matter much.&lt;/p&gt;

&lt;p&gt;In the wild, count data is usually overdispersed, that is: more variable than you''d expect from a Poisson with a constant rate or a Binomial with constant click probability, often due to unmodeled determinants of click rate / probability.  If that''s the case then prediction intervals from these models will be too narrow.&lt;/p&gt;
', NULL, NULL, '''/code'':40C,82C,112C,153C,161C,202C,270C,274C,285C ''/p'':8C,45C,95C,137C,193C,233C,238C,278C,307C,363C ''/pre'':41C,83C,113C,154C ''/strong'':237C ''1'':16C,23C,52C,53C,54C,58C,64C,74C ''13'':37C,71C ''16'':36C,66C ''2'':19C,38C,57C,63C,68C,76C ''26'':34C,56C ''3'':17C,24C,25C,31C,59C,62C,69C,73C,78C,79C ''4'':67C ''5'':72C ''6'':77C ''7'':35C,39C,61C,81C ''753'':29C,55C,65C,75C ''767'':30C,60C,70C,80C ''across'':171C ''answer'':219C ''assum'':242C ''assumpt'':88C ''better'':224C ''binom.test'':201C ''binomi'':94C,109C,300C,334C ''c'':22C,28C,33C ''case'':352C ''cbind'':101C ''chang'':181C,185C ''click'':32C,51C,102C,105C,143C,337C,345C ''code'':10C,47C,97C,139C,158C,200C,269C,273C,283C ''coeffici'':175C ''column'':133C ''concern'':296C ''confirm'':286C ''constant'':330C,336C ''construct'':120C ''count'':124C,187C,312C ''covari'':207C ''d'':259C,323C ''data'':7C,110C,151C,282C,313C ''data.frame'':13C ''dd'':11C,111C,152C ''depend'':117C ''determin'':343C ''differ'':170C ''direct'':218C ''due'':340C ''effect'':250C ''event'':127C ''expect'':324C ''famili'':108C,149C ''first'':132C,196C ''generalis'':198C ''get'':214C ''give'':291C ''glm'':100C,142C ''impress'':26C,50C,104C,148C,160C ''impressions-click'':103C ''independ'':249C ''interact'':244C ''interest'':129C ''interpret'':177C ''interv'':355C ''investig'':264C ''log'':147C,159C ''lt'':12C,99C,141C ''matter'':305C ''may'':252C,254C ''md.pois'':140C ''mean'':174C ''measur'':228C ''mod.bin'':98C ''model'':87C,197C,241C,290C,358C ''much'':306C ''narrow'':362C ''necessari'':164C ''note'':236C ''number'':167C ''observ'':172C ''offset'':146C,162C ''often'':339C ''ok'':2C ''overdispers'':316C ''p'':1C,42C,84C,114C,155C,194C,234C,239C,279C,308C ''perfect'':227C ''poisson'':136C,150C,298C,327C ''posit'':14C,48C,107C,145C,248C ''pre'':9C,46C,96C,138C ''predict'':354C ''probabl'':338C,347C ''proper'':266C ''question'':222C ''rate'':183C,331C,346C ''rather'':292C ''re'':90C ''reason'':257C ''relev'':231C ''rep'':15C,21C,27C ''replic'':262C ''result'':294C ''seem'':303C ''set'':205C ''similar'':293C ''strong'':235C ''summari'':284C ''swap'':267C ''term'':179C ''think'':91C ''trial'':169C ''two'':86C,289C ''uncertainti'':232C ''unmodel'':342C ''usual'':315C ''variabl'':118C,320C ''variat'':20C,49C,106C,144C,246C ''vs'':299C ''want'':192C,260C ''whenev'':165C ''wild'':311C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (990, 2, '2014-08-18 11:33:32.617', 3, NULL, NULL, '&lt;p&gt;This makes sense. IIUC, the speed of execution of support vector operations is bound by number of samples, not dimensionality. In other words, it is capped by CPU time and not RAM. I''m not sure exactly how much time this should take, but I''m running some benchmarks to find out.&lt;/p&gt;
', NULL, NULL, '''/p'':54C ''benchmark'':50C ''bound'':15C ''cap'':27C ''cpu'':29C ''dimension'':21C ''exact'':38C ''execut'':9C ''find'':52C ''iiuc'':5C ''m'':35C,47C ''make'':3C ''much'':40C ''number'':17C ''oper'':13C ''p'':1C ''ram'':33C ''run'':48C ''sampl'':19C ''sens'':4C ''speed'':7C ''support'':11C ''sure'':37C ''take'':44C ''time'':30C,41C ''vector'':12C ''word'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (991, 2, '2014-08-18 13:35:28', 7, NULL, NULL, '&lt;p&gt;Linear models simply add their features multiplied by corresponding weights. If, for example, you have 1000 sparse features only 3 or 4 of which are active in each instance (and the others are zeros) and 20 dense features that are all non-zeros, then it''s pretty likely that dense features will make most of the impact while sparse features will add only a little value. You can check this by looking at feature weights for a few instances and how they influence resulting sum.&lt;/p&gt;

&lt;p&gt;One way to fix it is to go away from additive model. Here''s a couple of candidate models. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;SVM&lt;/strong&gt; is based on separating hyperplanes. Though hyperplane is linear model itself, SVM doesn''t sum up its parameters, but instead tries to split feature space in an optimal way. Given the number of features, I''d say that linear SVM should work fine while more complicated kernels may tend to overfit the data. &lt;/p&gt;

&lt;p&gt;Despite its name, &lt;strong&gt;Naive Bayes&lt;/strong&gt; is pretty powerful statistical model that showed good results for text classification. It''s also flexible enough to capture imbalance in frequency of sparse and dense features, so you should definitely give it a try. &lt;/p&gt;

&lt;p&gt;Finally, &lt;strong&gt;random forests&lt;/strong&gt; may work as a good ensemble method in this case. Randomization will ensure that different kinds of features (sparse/dense) will be used as primary decision nodes in different trees. RF/decision trees are also good for inspecting features themselves, so it''s worth to note their structure anyway.&lt;/p&gt;

&lt;p&gt;Note that all of these methods have their drawbacks that may turn them into a garbage in your case. Combing sparse and dense features isn''t really well-studied task, so let us know what of these approaches works best for your case. &lt;/p&gt;
', NULL, NULL, '''/p'':88C,109C,167C,211C,265C,311C ''/strong'':113C,175C,217C ''1000'':17C ''20'':37C ''3'':21C ''4'':23C ''activ'':27C ''add'':5C,64C ''addit'':100C ''also'':190C,250C ''anyway'':264C ''approach'':305C ''away'':98C ''base'':115C ''bay'':174C ''best'':307C ''candid'':107C ''captur'':194C ''case'':227C,285C,310C ''check'':71C ''classif'':187C ''comb'':286C ''complic'':159C ''correspond'':10C ''coupl'':105C ''d'':149C ''data'':166C ''decis'':242C ''definit'':206C ''dens'':38C,52C,201C,289C ''despit'':169C ''differ'':232C,245C ''doesn'':126C ''drawback'':275C ''enough'':192C ''ensembl'':223C ''ensur'':230C ''exampl'':14C ''featur'':7C,19C,39C,53C,62C,76C,137C,147C,202C,235C,254C,290C ''final'':213C ''fine'':156C ''fix'':93C ''flexibl'':191C ''forest'':216C ''frequenc'':197C ''garbag'':282C ''give'':207C ''given'':143C ''go'':97C ''good'':183C,222C,251C ''hyperplan'':118C,120C ''imbal'':195C ''impact'':59C ''influenc'':85C ''inspect'':253C ''instanc'':30C,81C ''instead'':133C ''isn'':291C ''kernel'':160C ''kind'':233C ''know'':301C ''let'':299C ''like'':50C ''linear'':2C,122C,152C ''littl'':67C ''look'':74C ''make'':55C ''may'':161C,218C,277C ''method'':224C,272C ''model'':3C,101C,108C,123C,180C ''multipli'':8C ''naiv'':173C ''name'':171C ''node'':243C ''non'':44C ''non-zero'':43C ''note'':261C,267C ''number'':145C ''one'':90C ''optim'':141C ''other'':33C ''overfit'':164C ''p'':1C,89C,110C,168C,212C,266C ''paramet'':131C ''power'':178C ''pretti'':49C,177C ''primari'':241C ''random'':215C,228C ''realli'':293C ''result'':86C,184C ''rf/decision'':247C ''say'':150C ''separ'':117C ''show'':182C ''simpli'':4C ''space'':138C ''spars'':18C,61C,199C,287C ''sparse/dense'':236C ''split'':136C ''statist'':179C ''strong'':111C,172C,214C ''structur'':263C ''studi'':296C ''sum'':87C,128C ''svm'':112C,125C,153C ''task'':297C ''tend'':162C ''text'':186C ''though'':119C ''tree'':246C,248C ''tri'':134C,210C ''turn'':278C ''us'':300C ''use'':239C ''valu'':68C ''way'':91C,142C ''weight'':11C,77C ''well'':295C ''well-studi'':294C ''work'':155C,219C,306C ''worth'':259C ''zero'':35C,45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (992, 1, '2014-08-18 14:56:13.8', 8, 218, 'Why might several types of models give almost identical results?', '&lt;p&gt;I''ve been analyzing a data set of ~400k records and 9 variables The dependent variable is binary. I''ve fitted a logistic regression, a regression tree, a random forest, and a gradient boosted tree. All of them give virtual identical goodness of fit numbers when I validate them on another data set.&lt;/p&gt;

&lt;p&gt;Why is this so? I''m guessing that it''s because my observations to variable ratio is so high. If this is correct, at what observation to variable ratio will different models start to give different results? &lt;/p&gt;
', 'data-mining  classification  binary', NULL, '''/p'':70C,109C ''400k'':25C ''9'':28C ''almost'':8A ''analyz'':20C ''anoth'':67C ''binari'':15B,34C ''boost'':50C ''classif'':14B ''correct'':94C ''data'':12B,22C,68C ''data-min'':11B ''depend'':31C ''differ'':102C,107C ''fit'':37C,60C ''forest'':46C ''give'':7A,55C,106C ''good'':58C ''gradient'':49C ''guess'':78C ''high'':90C ''ident'':9A,57C ''logist'':39C ''m'':77C ''might'':2A ''mine'':13B ''model'':6A,103C ''number'':61C ''observ'':84C,97C ''p'':16C,71C ''random'':45C ''ratio'':87C,100C ''record'':26C ''regress'':40C,42C ''result'':10A,108C ''set'':23C,69C ''sever'':3A ''start'':104C ''tree'':43C,51C ''type'':4A ''valid'':64C ''variabl'':29C,32C,86C,99C ''ve'':18C,36C ''virtual'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1059, 1, '2014-08-29 06:00:53.42', 6, 433, 'Forecasting Foreign Exchange with Neural Network - Lag in Prediction', '&lt;p&gt;I have a question regarding the use of neural network. I am currently working with R (&lt;a href="http://cran.r-project.org/web/packages/neuralnet/index.html" rel="nofollow"&gt;neuralnet package&lt;/a&gt;) and I am facing the following issue.
My testing and validation set are always late with respect to the historical data. Is there a way of correcting the result?
Maybe something is wrong in my analysis&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;I use the daily log return 
&lt;img src="http://i.stack.imgur.com/A3r16.gif" alt="r(t) = ln(s(t)/s(t-1))"&gt;&lt;/li&gt;
&lt;li&gt;I normalise my data with the sigmoid function (sigma and mu computed on my whole set)&lt;/li&gt;
&lt;li&gt;I train my neural networks with 10 dates and the output is the normalised value that follows these 10 dates.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I tried to add the trend but there is no improvement, I observed 1-2 days late. My process seems ok, what do you think about it?&lt;/p&gt;
', 'r  neuralnetwork  time-series  forecast', NULL, '''-1'':101C ''-2'':159C ''/a'':42C ''/a3r16.gif'':92C ''/li'':102C,120C,142C ''/ol'':143C ''/p'':79C,172C ''/s'':99C ''/web/packages/neuralnet/index.html'':37C ''1'':158C ''10'':128C,140C ''add'':148C ''alt'':93C ''alway'':56C ''analysi'':78C ''comput'':115C ''correct'':69C ''cran.r-project.org'':36C ''cran.r-project.org/web/packages/neuralnet/index.html'':35C ''current'':29C ''daili'':85C ''data'':63C,107C ''date'':129C,141C ''day'':160C ''exchang'':3A ''face'':46C ''follow'':48C,138C ''forecast'':1A,15B ''foreign'':2A ''function'':111C ''histor'':62C ''href'':34C ''i.stack.imgur.com'':91C ''i.stack.imgur.com/a3r16.gif'':90C ''img'':88C ''improv'':155C ''issu'':49C ''lag'':7A ''late'':57C,161C ''li'':81C,103C,121C ''ln'':96C ''log'':86C ''mayb'':72C ''mu'':114C ''network'':6A,26C,126C ''neural'':5A,25C,125C ''neuralnet'':40C ''neuralnetwork'':11B ''nofollow'':39C ''normalis'':105C,135C ''observ'':157C ''ok'':165C ''ol'':80C ''output'':132C ''p'':16C,144C ''packag'':41C ''predict'':9A ''process'':163C ''question'':20C ''r'':10B,32C,94C ''regard'':21C ''rel'':38C ''respect'':59C ''result'':71C ''return'':87C ''seem'':164C ''seri'':14B ''set'':54C,119C ''sigma'':112C ''sigmoid'':110C ''someth'':73C ''src'':89C ''test'':51C ''think'':169C ''time'':13B ''time-seri'':12B ''train'':123C ''trend'':150C ''tri'':146C ''use'':23C,83C ''valid'':53C ''valu'':136C ''way'':67C ''whole'':118C ''work'':30C ''wrong'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (993, 2, '2014-08-18 16:28:02.147', 0, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;I''m guessing that it''s because my observations to variable ratio is so high.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I think this explanation makes perfect sense.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;If this is correct, at what observation to variable ratio will different models start to give different results? &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This will probably depend very much on your specific data (for instance, even whether your nine variables are continuous, factors, ordinary or binary), as well as any tuning decisions you made while fitting your model.&lt;/p&gt;

&lt;p&gt;But you can play around with the observation-to-variable ratio - not by increasing the number of variables, but by decreasing the number of observations. Randomly draw 100 observations, fit models and see whether different models yield different results. (I guess they will.) Do this multiple times with different samples drawn from your total number of observations. Then look at subsamples of 1,000 observations... 10,000 observations... and so forth.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':19C,50C ''/p'':18C,28C,49C,87C,161C ''000'':153C,156C ''1'':152C ''10'':155C ''100'':117C ''around'':93C ''binari'':74C ''blockquot'':1C,29C ''continu'':70C ''correct'':34C ''data'':61C ''decis'':80C ''decreas'':110C ''depend'':55C ''differ'':42C,47C,124C,127C,138C ''draw'':116C ''drawn'':140C ''even'':64C ''explan'':24C ''factor'':71C ''fit'':84C,119C ''forth'':160C ''give'':46C ''guess'':5C,130C ''high'':17C ''increas'':103C ''instanc'':63C ''look'':148C ''m'':4C ''made'':82C ''make'':25C ''model'':43C,86C,120C,125C ''much'':57C ''multipl'':135C ''nine'':67C ''number'':105C,112C,144C ''observ'':11C,37C,97C,114C,118C,146C,154C,157C ''observation-to-vari'':96C ''ordinari'':72C ''p'':2C,20C,30C,51C,88C ''perfect'':26C ''play'':92C ''probabl'':54C ''random'':115C ''ratio'':14C,40C,100C ''result'':48C,128C ''sampl'':139C ''see'':122C ''sens'':27C ''specif'':60C ''start'':44C ''subsampl'':150C ''think'':22C ''time'':136C ''total'':143C ''tune'':79C ''variabl'':13C,39C,68C,99C,107C ''well'':76C ''whether'':65C,123C ''yield'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2365, 2, '2014-10-28 07:14:06.873', 1, NULL, NULL, '&lt;p&gt;Ok all sorted - Bit embarrassing but forgot to normalise the data!&lt;/p&gt;
', NULL, NULL, '''/p'':13C ''bit'':5C ''data'':12C ''embarrass'':6C ''forgot'':8C ''normalis'':10C ''ok'':2C ''p'':1C ''sort'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (995, 2, '2014-08-18 17:25:27.007', 1, NULL, NULL, '&lt;p&gt;As @seanv507 suggested, the similar performance may simply be due to the data being best separated by a linear model. But in general, the statement that it is because the "observations to variable ratio is so high" is incorrect. Even as your ratio of sample size to number of variables goes to infinity, you should not expect different models to perform nearly identically, unless they all provide the same predictive bias.&lt;/p&gt;
', NULL, NULL, '''/p'':73C ''best'':16C ''bias'':72C ''data'':14C ''differ'':59C ''due'':11C ''even'':41C ''expect'':58C ''general'':24C ''goe'':52C ''high'':38C ''ident'':64C ''incorrect'':40C ''infin'':54C ''linear'':20C ''may'':8C ''model'':21C,60C ''near'':63C ''number'':49C ''observ'':32C ''p'':1C ''perform'':7C,62C ''predict'':71C ''provid'':68C ''ratio'':35C,44C ''sampl'':46C ''seanv507'':3C ''separ'':17C ''similar'':6C ''simpli'':9C ''size'':47C ''statement'':26C ''suggest'':4C ''unless'':65C ''variabl'':34C,51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (996, 2, '2014-08-19 00:56:40.89', 8, NULL, NULL, '&lt;p&gt;Kernelized SVMs require the computation of a distance function between each point in the dataset, which is the dominating cost of $\mathcal{O}(n_\text{features} \times n_\text{observations}^2)$. The storage of the distances is a burden on memory, so they''re recomputed on the fly. Thankfully, only the points nearest the decision boundary are needed most of the time. Frequently computed distances are stored in a cache. If the cache is getting thrashed then the running time blows up to $\mathcal{O}(n_\text{features} \times n_\text{observations}^3)$.&lt;/p&gt;

&lt;p&gt;You can increase this cache by invoking SVR as&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;model = SVR(cache_size=7000)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In general, this is not going to work. But all is not lost. You can subsample the data and use the rest as a validation set, or you can pick a different model. Above the 200,000 observation range, it''s wise to choose linear learners.&lt;/p&gt;

&lt;p&gt;Kernel SVM can be approximated, by approximating the kernel matrix and feeding it to a linear SVM. This allows you to trade off between accuracy and performance in linear time.&lt;/p&gt;

&lt;p&gt;A popular means of achieving this is to use 100 or so cluster centers found by kmeans/kmeans++ as the basis of your kernel function. The new derived features are then fed into a linear model. This works very well in practice. Tools like &lt;a href="https://code.google.com/p/sofia-ml/" rel="nofollow"&gt;sophia-ml&lt;/a&gt; and &lt;a href="https://github.com/JohnLangford/vowpal_wabbit/wiki" rel="nofollow"&gt;vowpal wabbit&lt;/a&gt; are how Google, Yahoo and Microsoft do this. Input/output becomes the dominating cost for simple linear learners.&lt;/p&gt;

&lt;p&gt;In the abundance of data, nonparametric models perform roughly the same for most problems. The exceptions being structured inputs, like text, images, time series, audio.&lt;/p&gt;

&lt;h2&gt;Further reading&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://fastml.com/the-secret-of-the-big-guys/" rel="nofollow"&gt;How to implement this.&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://fastml.com/go-non-linear-with-vowpal-wabbit/" rel="nofollow"&gt;How to train an ngram neural network with dropout that scales linearly&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://peekaboo-vision.blogspot.co.uk/2012/12/kernel-approximations-for-efficient.html" rel="nofollow"&gt;Kernel Approximations&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.144.9009&amp;amp;rep=rep1&amp;amp;type=pdf" rel="nofollow"&gt;A formal paper on using kmeans to approximate kernel machines&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/2012/12/kernel-approximations-for-efficient.html'':353C ''/a'':250C,261C,324C,346C,358C,378C ''/code'':114C ''/go-non-linear-with-vowpal-wabbit/'':331C ''/h2'':310C ''/johnlangford/vowpal_wabbit/wiki'':256C ''/li'':325C,347C,359C,379C ''/p'':95C,106C,163C,195C,279C,306C ''/p/sofia-ml/'':244C ''/pre'':115C ''/the-secret-of-the-big-guys/'':317C ''/ul'':380C ''/viewdoc/download?doi=10.1.1.144.9009&amp;amp;rep=rep1&amp;amp;type=pdf'':365C ''000'':153C ''100'':206C ''2'':32C ''200'':152C ''3'':94C ''7000'':113C ''abund'':283C ''accuraci'':189C ''achiev'':201C ''allow'':183C ''approxim'':169C,171C,357C,375C ''audio'':305C ''basi'':216C ''becom'':271C ''blow'':82C ''boundari'':57C ''burden'':40C ''cach'':71C,74C,101C,111C ''center'':210C ''choos'':160C ''citeseerx.ist.psu.edu'':364C ''citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.144.9009&amp;amp;rep=rep1&amp;amp;type=pdf'':363C ''cluster'':209C ''code'':108C ''code.google.com'':243C ''code.google.com/p/sofia-ml/'':242C ''comput'':6C,65C ''cost'':21C,274C ''data'':134C,285C ''dataset'':16C ''decis'':56C ''deriv'':223C ''differ'':148C ''distanc'':9C,37C,66C ''domin'':20C,273C ''dropout'':342C ''except'':296C ''fastml.com'':316C,330C ''fastml.com/go-non-linear-with-vowpal-wabbit/'':329C ''fastml.com/the-secret-of-the-big-guys/'':315C ''featur'':27C,89C,224C ''fed'':227C ''feed'':176C ''fli'':49C ''formal'':369C ''found'':211C ''frequent'':64C ''function'':10C,220C ''general'':118C ''get'':76C ''github.com'':255C ''github.com/johnlangford/vowpal_wabbit/wiki'':254C ''go'':122C ''googl'':264C ''h2'':307C ''href'':241C,253C,314C,328C,350C,362C ''imag'':302C ''implement'':322C ''increas'':99C ''input'':299C ''input/output'':270C ''invok'':103C ''kernel'':2C,165C,173C,219C,356C,376C ''kmean'':373C ''kmeans/kmeans'':213C ''learner'':162C,278C ''li'':312C,326C,348C,360C ''like'':239C,300C ''linear'':161C,180C,193C,230C,277C,345C ''lost'':129C ''machin'':377C ''mathcal'':23C,85C ''matrix'':174C ''mean'':199C ''memori'':42C ''microsoft'':267C ''ml'':249C ''model'':109C,149C,231C,287C ''n'':25C,29C,87C,91C ''nearest'':54C ''need'':59C ''network'':340C ''neural'':339C ''new'':222C ''ngram'':338C ''nofollow'':246C,258C,319C,333C,355C,367C ''nonparametr'':286C ''o'':24C,86C ''observ'':31C,93C,154C ''p'':1C,96C,116C,164C,196C,280C ''paper'':370C ''peekaboo-vision.blogspot.co.uk'':352C ''peekaboo-vision.blogspot.co.uk/2012/12/kernel-approximations-for-efficient.html'':351C ''perform'':191C,288C ''pick'':146C ''point'':13C,53C ''popular'':198C ''practic'':237C ''pre'':107C ''problem'':294C ''rang'':155C ''re'':45C ''read'':309C ''recomput'':46C ''rel'':245C,257C,318C,332C,354C,366C ''requir'':4C ''rest'':138C ''rough'':289C ''run'':80C ''scale'':344C ''seri'':304C ''set'':142C ''simpl'':276C ''size'':112C ''sophia'':248C ''sophia-ml'':247C ''storag'':34C ''store'':68C ''structur'':298C ''subsampl'':132C ''svm'':166C,181C ''svms'':3C ''svr'':104C,110C ''text'':26C,30C,88C,92C,301C ''thank'':50C ''thrash'':77C ''time'':28C,63C,81C,90C,194C,303C ''tool'':238C ''trade'':186C ''train'':336C ''ul'':311C ''use'':136C,205C,372C ''valid'':141C ''vowpal'':259C ''wabbit'':260C ''well'':235C ''wise'':158C ''work'':124C,233C ''yahoo'':265C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (997, 1, '2014-08-19 03:41:24.207', 4, 875, 'Where can I find free spatio-temporal dataset for download?', '&lt;p&gt;Where can I find free spatio-temporal dataset for download so that I can play with it in R ? &lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'dataset  open-source  freebase', NULL, '''/p'':38C,41C ''dataset'':9A,12B,26C ''download'':11A,28C ''find'':4A,21C ''free'':5A,22C ''freebas'':16B ''open'':14B ''open-sourc'':13B ''p'':17C,39C ''play'':33C ''r'':37C ''sourc'':15B ''spatio'':7A,24C ''spatio-tempor'':6A,23C ''tempor'':8A,25C ''thank'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (998, 2, '2014-08-19 13:33:32.453', 4, NULL, NULL, '&lt;p&gt;No, sklearn doesn''t seem to have a forward selection algorithm. However, it does provide recursive feature elimination, which is a greedy feature elimination algorithm similar to sequential backward selection. See the documentation here:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':36C ''/stable/modules/generated/sklearn.feature_selection.rfe.html'':42C ''/stable/modules/generated/sklearn.feature_selection.rfe.html&lt;/a&gt;&lt;/p&gt;'':47C ''algorithm'':12C,26C ''backward'':30C ''document'':34C ''doesn'':4C ''elimin'':19C,25C ''featur'':18C,24C ''forward'':10C ''greedi'':23C ''howev'':13C ''href'':39C ''nofollow'':44C ''p'':1C,37C ''provid'':16C ''recurs'':17C ''rel'':43C ''scikit-learn.org'':41C,46C ''scikit-learn.org/stable/modules/generated/sklearn.feature_selection.rfe.html'':40C ''scikit-learn.org/stable/modules/generated/sklearn.feature_selection.rfe.html&lt;/a&gt;&lt;/p&gt;'':45C ''see'':32C ''seem'':6C ''select'':11C,31C ''sequenti'':29C ''similar'':27C ''sklearn'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (999, 2, '2014-08-19 14:13:07.147', 7, NULL, NULL, '&lt;p&gt;This results means that whatever method you use, you are able to get reasonably close to the optimal decision rule (aka &lt;a href="http://en.wikipedia.org/wiki/Admissible_decision_rule#Bayes_rules_and_generalized_Bayes_rules"&gt;Bayes rule&lt;/a&gt;). The underlying reasons have been explained in Hastie, Tibshirani and Friedman''s &lt;a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/"&gt;"Elements of Statistical Learning"&lt;/a&gt;. They demonstrated how the different methods perform by comparing Figs. 2.1, 2.2, 2.3, 5.11 (in my first edition -- in section on multidimensional splines), 12.2, 12.3 (support vector machines), and probably some others. If you have not read that book, you need to drop everything &lt;strong&gt;RIGHT NOW&lt;/strong&gt; and read it up. (I mean, it isn''t worth losing your job, but it is worth missing a homework or two if you are a student.)&lt;/p&gt;

&lt;p&gt;I don''t think that observations to variable ratio is the explanation. In light of my rationale offered above, it is the relatively simple form of the boundary separating your classes in the multidimensional space that all of the methods you tried have been able to identify.&lt;/p&gt;
', NULL, NULL, '''/a'':30C,52C ''/p'':128C,177C ''/strong'':100C ''/wiki/admissible_decision_rule#bayes_rules_and_generalized_bayes_rules'':27C ''/~tibs/elemstatlearn/'':47C ''12.2'':76C ''12.3'':77C ''2.1'':63C ''2.2'':64C ''2.3'':65C ''5.11'':66C ''abl'':12C,174C ''aka'':22C ''bay'':28C ''book'':91C ''boundari'':157C ''class'':160C ''close'':16C ''compar'':61C ''decis'':20C ''demonstr'':54C ''differ'':57C ''drop'':95C ''edit'':70C ''element'':48C ''en.wikipedia.org'':26C ''en.wikipedia.org/wiki/admissible_decision_rule#bayes_rules_and_generalized_bayes_rules'':25C ''everyth'':96C ''explain'':36C ''explan'':141C ''fig'':62C ''first'':69C ''form'':154C ''friedman'':41C ''get'':14C ''hasti'':38C ''homework'':120C ''href'':24C,44C ''identifi'':176C ''isn'':108C ''job'':113C ''learn'':51C ''light'':143C ''lose'':111C ''machin'':80C ''mean'':4C,106C ''method'':7C,58C,169C ''miss'':118C ''multidimension'':74C,163C ''need'':93C ''observ'':135C ''offer'':147C ''optim'':19C ''other'':84C ''p'':1C,129C ''perform'':59C ''probabl'':82C ''ratio'':138C ''rational'':146C ''read'':89C,102C ''reason'':15C,33C ''relat'':152C ''result'':3C ''right'':98C ''rule'':21C,29C ''section'':72C ''separ'':158C ''simpl'':153C ''space'':164C ''spline'':75C ''statist'':50C ''statweb.stanford.edu'':46C ''statweb.stanford.edu/~tibs/elemstatlearn/'':45C ''strong'':97C ''student'':127C ''support'':78C ''think'':133C ''tibshirani'':39C ''tri'':171C ''two'':122C ''under'':32C ''use'':9C ''variabl'':137C ''vector'':79C ''whatev'':6C ''worth'':110C,117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9705, 1, '2016-01-08 17:39:10.643', 1, 13, 'Calculate the overall accuracy of mined rules using apiriori algorithm', '&lt;p&gt;I have doing association rule mining to discover some interesting patterns on supermarket data - using the apriori algorithm.&lt;/p&gt;

&lt;p&gt;I got 194 rules with minimum support 0.01 and minimum confidence 0.01 and also I have got some measures like support, confidence, lift and conviction for each rule. &lt;/p&gt;

&lt;p&gt;Now I need to measure the overall accuracy of my model, which contains 194 rules. &lt;/p&gt;

&lt;p&gt;How do I calculate the overall accuracy of my model? &lt;/p&gt;
', 'data-mining  predictive-modeling  accuracy', NULL, '''/p'':37C,67C,84C,96C ''0.01'':46C,50C ''194'':41C,82C ''accuraci'':4A,17B,76C,92C ''algorithm'':10A,36C ''also'':52C ''apiriori'':9A ''apriori'':35C ''associ'':22C ''calcul'':1A,89C ''confid'':49C,60C ''contain'':81C ''convict'':63C ''data'':12B,32C ''data-min'':11B ''discov'':26C ''got'':40C,55C ''interest'':28C ''lift'':61C ''like'':58C ''measur'':57C,73C ''mine'':6A,13B,24C ''minimum'':44C,48C ''model'':16B,79C,95C ''need'':71C ''overal'':3A,75C,91C ''p'':18C,38C,68C,85C ''pattern'':29C ''predict'':15B ''predictive-model'':14B ''rule'':7A,23C,42C,66C,83C ''supermarket'':31C ''support'':45C,59C ''use'':8A,33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1000, 2, '2014-08-19 15:21:57.833', 2, NULL, NULL, '&lt;p&gt;You can get some documented, publicly available EEG data from the HeadIT database at UCSD.
&lt;a href="http://headit-beta.ucsd.edu/studies" rel="nofollow"&gt;http://headit-beta.ucsd.edu/studies&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The data itself appears to be in Biosemi Data Format (.bdf) files, described here: &lt;a href="http://www.biosemi.com/faq/file_format.htm" rel="nofollow"&gt;http://www.biosemi.com/faq/file_format.htm&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Biosemi provides links to several open-source methods to access and import .bdf files on their website, including several functions for importing into Matlab, as well as into Python (BioSig) and C/C++ libraries:
&lt;a href="http://www.biosemi.com/download.htm" rel="nofollow"&gt;http://www.biosemi.com/download.htm&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Just as a forewarning, EEG data can be a bit of a bear to work with, due to it''s inherently low signal/noise ratio.&lt;/p&gt;
', NULL, NULL, '''/download.htm'':91C ''/download.htm&lt;/a&gt;&lt;/p&gt;'':96C ''/faq/file_format.htm'':46C ''/faq/file_format.htm&lt;/a&gt;&lt;/p&gt;'':51C ''/p'':122C ''/studies'':21C ''/studies&lt;/a&gt;&lt;/p&gt;'':26C ''access'':63C ''appear'':31C ''avail'':8C ''bdf'':38C,66C ''bear'':110C ''biosemi'':35C,53C ''biosig'':83C ''bit'':107C ''c/c'':85C ''data'':10C,29C,36C,103C ''databas'':14C ''describ'':40C ''document'':6C ''due'':114C ''eeg'':9C,102C ''file'':39C,67C ''forewarn'':101C ''format'':37C ''function'':73C ''get'':4C ''headit'':13C ''headit-beta.ucsd.edu'':20C,25C ''headit-beta.ucsd.edu/studies'':19C ''headit-beta.ucsd.edu/studies&lt;/a&gt;&lt;/p&gt;'':24C ''href'':18C,43C,88C ''import'':65C,75C ''includ'':71C ''inher'':118C ''librari'':86C ''link'':55C ''low'':119C ''matlab'':77C ''method'':61C ''nofollow'':23C,48C,93C ''open'':59C ''open-sourc'':58C ''p'':1C,27C,52C,97C ''provid'':54C ''public'':7C ''python'':82C ''ratio'':121C ''rel'':22C,47C,92C ''sever'':57C,72C ''signal/noise'':120C ''sourc'':60C ''ucsd'':16C ''websit'':70C ''well'':79C ''work'':112C ''www.biosemi.com'':45C,50C,90C,95C ''www.biosemi.com/download.htm'':89C ''www.biosemi.com/download.htm&lt;/a&gt;&lt;/p&gt;'':94C ''www.biosemi.com/faq/file_format.htm'':44C ''www.biosemi.com/faq/file_format.htm&lt;/a&gt;&lt;/p&gt;'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1001, 2, '2014-08-19 15:47:29.413', 4, NULL, NULL, '&lt;p&gt;First thing that came to mind would be one''s personal workout data from running or biking apps.&lt;/p&gt;

&lt;p&gt;Otherwise there is a dataset around NYC''s taxi trip data. Quick Googling brought me this: &lt;a href="http://www.andresmh.com/nyctaxitrips/" rel="nofollow"&gt;http://www.andresmh.com/nyctaxitrips/&lt;/a&gt;. Variables include time and location for both pickups and dropoffs.&lt;/p&gt;

&lt;p&gt;Another dataset comes from Chicago''s bikesharing service. It can be found here: &lt;a href="https://www.divvybikes.com/datachallenge" rel="nofollow"&gt;https://www.divvybikes.com/datachallenge&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/datachallenge'':77C ''/datachallenge&lt;/a&gt;.&lt;/p&gt;'':82C ''/nyctaxitrips/'':42C ''/nyctaxitrips/&lt;/a&gt;.'':47C ''/p'':20C,58C ''anoth'':60C ''app'':19C ''around'':27C ''bike'':18C ''bikeshar'':66C ''brought'':35C ''came'':5C ''chicago'':64C ''come'':62C ''data'':14C,32C ''dataset'':26C,61C ''dropoff'':57C ''first'':2C ''found'':71C ''googl'':34C ''href'':39C,74C ''includ'':49C ''locat'':52C ''mind'':7C ''nofollow'':44C,79C ''nyc'':28C ''one'':10C ''otherwis'':22C ''p'':1C,21C,59C ''person'':12C ''pickup'':55C ''quick'':33C ''rel'':43C,78C ''run'':16C ''servic'':67C ''taxi'':30C ''thing'':3C ''time'':50C ''trip'':31C ''variabl'':48C ''workout'':13C ''would'':8C ''www.andresmh.com'':41C,46C ''www.andresmh.com/nyctaxitrips/'':40C ''www.andresmh.com/nyctaxitrips/&lt;/a&gt;.'':45C ''www.divvybikes.com'':76C,81C ''www.divvybikes.com/datachallenge'':75C ''www.divvybikes.com/datachallenge&lt;/a&gt;.&lt;/p&gt;'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1037, 2, '2014-08-25 03:01:09.837', 2, NULL, NULL, '&lt;p&gt;If I understand you question correctly, there are two reasons why genetic algorithm might not a good idea for optimizing website features:&lt;/p&gt;

&lt;p&gt;1) Feedback data is coming in too slow, say once a day, genetic algorithm might take a while to converge.&lt;/p&gt;

&lt;p&gt;2) In the process of testing genetic algorithm will probably come up with combinations that are ''strange'' and that might not be the risk the company wants to take.&lt;/p&gt;
', NULL, NULL, '''/p'':24C,46C,77C ''1'':26C ''2'':48C ''algorithm'':14C,39C,55C ''combin'':61C ''come'':30C,58C ''compani'':73C ''converg'':45C ''correct'':7C ''data'':28C ''day'':37C ''featur'':23C ''feedback'':27C ''genet'':13C,38C,54C ''good'':18C ''idea'':19C ''might'':15C,40C,67C ''optim'':21C ''p'':1C,25C,47C ''probabl'':57C ''process'':51C ''question'':6C ''reason'':11C ''risk'':71C ''say'':34C ''slow'':33C ''strang'':64C ''take'':41C,76C ''test'':53C ''two'':10C ''understand'':4C ''want'':74C ''websit'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1002, 1, '2014-08-19 17:50:52.583', 3, 363, 'Making sense of large data sets', '&lt;p&gt;Caveat: I am a complete beginner when it comes to machine learning, but eager to learn.&lt;/p&gt;

&lt;p&gt;I have a large dataset and I''m trying to find pattern in it. There may / may not be correlation across the data, either with known variables, or variables that are contained in the data but which I haven''t yet realised are actually variables / relevant.&lt;/p&gt;

&lt;p&gt;I''m guessing this would be a familiar problem in the world of data analysis, so I have a few questions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;The ''silver bullet'' would be to throw this all this data into a stats / data analysis program and for it to crunch the data looking for known / unknown patterns trying to find relations. Is SPSS suitable, or are there other applications which may be better suited.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Should I learn a language like R, and figure out how to manually process the data. Wouldn''t this comprimise finding relations as I would have to manually specify what and how to analyse the data?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;How would a professional data miner approach this problem and what steps would s/he take?&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  data-mining  tools  beginner', NULL, '''/li'':154C,194C,213C ''/ol'':214C ''/p'':32C,80C,103C,153C,193C,212C ''across'':54C ''actual'':77C ''analys'':190C ''analysi'':96C,122C ''applic'':147C ''approach'':203C ''beginn'':14B,21C ''better'':151C ''bullet'':109C ''caveat'':16C ''come'':24C ''complet'':20C ''comprimis'':176C ''contain'':65C ''correl'':53C ''crunch'':128C ''data'':5A,11B,56C,68C,95C,117C,121C,130C,172C,192C,201C ''data-min'':10B ''dataset'':38C ''eager'':29C ''either'':57C ''familiar'':89C ''figur'':165C ''find'':44C,138C,177C ''guess'':84C ''haven'':72C ''known'':59C,133C ''languag'':161C ''larg'':4A,37C ''learn'':9B,27C,31C,159C ''li'':105C,155C,195C ''like'':162C ''look'':131C ''m'':41C,83C ''machin'':8B,26C ''machine-learn'':7B ''make'':1A ''manual'':169C,184C ''may'':49C,50C,149C ''mine'':12B ''miner'':202C ''ol'':104C ''p'':15C,33C,81C,106C,156C,196C ''pattern'':45C,135C ''problem'':90C,205C ''process'':170C ''profession'':200C ''program'':123C ''question'':102C ''r'':163C ''realis'':75C ''relat'':139C,178C ''relev'':79C ''s/he'':210C ''sens'':2A ''set'':6A ''silver'':108C ''specifi'':185C ''spss'':141C ''stat'':120C ''step'':208C ''suit'':152C ''suitabl'':142C ''take'':211C ''throw'':113C ''tool'':13B ''tri'':42C,136C ''unknown'':134C ''variabl'':60C,62C,78C ''world'':93C ''would'':86C,110C,181C,198C,209C ''wouldn'':173C ''yet'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1003, 1, '2014-08-19 18:59:03.013', 3, 179, 'Query similarity: how much data is used in practice?', '&lt;p&gt;I recently read &lt;a href="http://research.microsoft.com/en-us/um/people/sdumais/ecir07-metzlerdumaismeek-final.pdf" rel="nofollow"&gt;Similarity Measures for Short Segments of Text&lt;/a&gt; (Metzler et al.).  It describes basic methods for measuring query similarity, and in the paper, the data consists of queries and their top results. Results are lists of page urls, page titles, and short page snippets.  In the paper, the authors collect 200 results per query.&lt;/p&gt;

&lt;p&gt;When using the public Google APIs to retrieve results, I was only able to collect 4-10 results per query.  There''s a substantial difference between 10 and 200.  Hence, how much data is commonly used in practice to measure query similarity (e.g., how many results per query)?&lt;/p&gt;

&lt;p&gt;References are a plus!&lt;/p&gt;
', 'machine-learning  dataset  text-mining  search', NULL, '''-10'':101C ''/a'':36C ''/en-us/um/people/sdumais/ecir07-metzlerdumaismeek-final.pdf'':26C ''/p'':83C,133C,139C ''10'':111C ''200'':79C,113C ''4'':100C ''abl'':97C ''al'':39C ''api'':90C ''author'':77C ''basic'':42C ''collect'':78C,99C ''common'':119C ''consist'':54C ''data'':5A,53C,117C ''dataset'':13B ''describ'':41C ''differ'':109C ''e.g'':127C ''et'':38C ''googl'':89C ''henc'':114C ''href'':23C ''learn'':12B ''list'':63C ''machin'':11B ''machine-learn'':10B ''mani'':129C ''measur'':30C,45C,124C ''method'':43C ''metzler'':37C ''mine'':16B ''much'':4A,116C ''nofollow'':28C ''p'':18C,84C,134C ''page'':65C,67C,71C ''paper'':51C,75C ''per'':81C,103C,131C ''plus'':138C ''practic'':9A,122C ''public'':88C ''queri'':1A,46C,56C,82C,104C,125C,132C ''read'':21C ''recent'':20C ''refer'':135C ''rel'':27C ''research.microsoft.com'':25C ''research.microsoft.com/en-us/um/people/sdumais/ecir07-metzlerdumaismeek-final.pdf'':24C ''result'':60C,61C,80C,93C,102C,130C ''retriev'':92C ''search'':17B ''segment'':33C ''short'':32C,70C ''similar'':2A,29C,47C,126C ''snippet'':72C ''substanti'':108C ''text'':15B,35C ''text-min'':14B ''titl'':68C ''top'':59C ''url'':66C ''use'':7A,86C,120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1019, 2, '2014-08-22 09:03:13.333', 4, NULL, NULL, '&lt;p&gt;I think it always depends on the scenario. Using a representative data set is not always the solution. Assume that your training set with 1000 negative examples and 20 positive examples. Without any modeification of the classifier, your algorithm will tend to classify all new examples as negative. In some scenarios this is O.K. But in many cases the costs of missing postive examples is high so you have to find a solution for it.&lt;/p&gt;

&lt;p&gt;In such cases you can use a cost sensitive machine learning algorithm. For example in the case of medical diagnosis data analysis.&lt;/p&gt;

&lt;p&gt;In summary: Classification erros do not have the same cost! &lt;/p&gt;
', NULL, NULL, '''/p'':77C,101C,113C ''1000'':26C ''20'':30C ''algorithm'':40C,90C ''alway'':5C,17C ''analysi'':100C ''assum'':20C ''case'':59C,81C,95C ''classif'':105C ''classifi'':38C,44C ''cost'':61C,86C,112C ''data'':13C,99C ''depend'':6C ''diagnosi'':98C ''erro'':106C ''exampl'':28C,32C,47C,65C,92C ''find'':72C ''high'':67C ''learn'':89C ''machin'':88C ''mani'':58C ''medic'':97C ''miss'':63C ''modeif'':35C ''negat'':27C,49C ''new'':46C ''o.k'':55C ''p'':1C,78C,102C ''posit'':31C ''postiv'':64C ''repres'':12C ''scenario'':9C,52C ''sensit'':87C ''set'':14C,24C ''solut'':19C,74C ''summari'':104C ''tend'':42C ''think'':3C ''train'':23C ''use'':10C,84C ''without'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2380, 2, '2014-10-31 04:44:23.1', 2, NULL, NULL, '&lt;p&gt;The importance of a concept in mathematics depends on the circumstances of its application. Sometimes, its importance relies on the fact that it allows you to carry on with what you are doing. &lt;/p&gt;

&lt;p&gt;For example, you usually need column independence (independent variables between predictors) because multiple regression will behave badly with highly correlated variables. Even worst, when some of your columns (or rows) are dependent, your matrix is not invertible. Why? Because matrix inversion A^-1 involves the determinant 1/|A|, which is 0 when columns or rows are linearly dependent.&lt;/p&gt;

&lt;p&gt;Eigenvalues is a common occurrence in calculations related to maximization/minimization in machine learning. Let''s say you are interested in principal component analysis. A very important idea there is dimensional reduction (you have a dataset with many variables and want to reduce the number of variables without losing too much explanatory power.) One solution is to project your data onto a lower dimensional space (e.g. taking your data with 50 variables and reducing them to 5 variables.) Turns out a good projection to use is one that includes as much variation as possible and maximization of this variation results in the eigenvalue equation S u  = Î» u. &lt;/p&gt;

&lt;p&gt;In other cases, you explicitly include the eigenvalue equation of some quantity of interest because in doing so, you''re changing the coordinate system in which you represent the variables. Take the case of a (multivariate) Gaussian distribution in which the argument of the exponent is given by Î = (x-Î¼)^T Î£ (x-Î¼). If you consider the eigenvalue equation of Î£, the exponent can be written as Î = y_1^2 / Î»_1 + y_2^2 / Î»_2 (in two dimensions) This is the equation of an ellipse only if Î»_1 and Î»_2 are positive. Therefore, you obtain the following graphical interpretation (Bishop, PRML, p.81):&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/pNrLb.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Positive semi-definite matrices are used as a matter of convenience. They are well-behaved and well-understood. For instance, their eigenvalues are non-negative, and if you remember the previous paragraph, the argument Î required positive eigenvalues. By now, you can see why some concepts are very popular: You need them for your calculations or they need each other.&lt;/p&gt;

&lt;p&gt;I can recommend a couple of books:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0534998453" rel="nofollow"&gt;Linear Algebra: A Modern Introduction&lt;/a&gt; by David Poole &lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1584888326" rel="nofollow"&gt;Understanding Complex Datasets: Data Mining with Matrix Decompositions &lt;/a&gt; by David Skillicorn. &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The second recommendation is more specialized and requires a decent understanding of the basics, but it is of great help to understand matrix decompositions.&lt;/p&gt;
', NULL, NULL, '''-1'':79C ''/a'':419C,440C ''/amzn/click/0534998453'':411C ''/amzn/click/1584888326'':429C ''/li'':423C,444C ''/ol'':445C ''/p'':35C,95C,204C,317C,329C,395C,404C,471C ''/pnrlb.png'':323C ''0'':87C ''1'':83C,279C,282C,301C ''2'':280C,284C,285C,287C,304C ''5'':172C ''50'':166C ''algebra'':415C ''allow'':25C ''alt'':324C ''analysi'':119C ''applic'':15C ''argument'':247C,368C ''bad'':53C ''basic'':460C ''behav'':52C,347C ''bishop'':314C ''book'':403C ''calcul'':103C,389C ''carri'':28C ''case'':208C,238C ''chang'':226C ''circumst'':12C ''column'':42C,64C,89C ''common'':100C ''complex'':433C ''compon'':118C ''concept'':6C,380C ''consid'':265C ''conveni'':342C ''coordin'':228C ''correl'':56C ''coupl'':401C ''data'':155C,164C,435C ''dataset'':131C,434C ''david'':421C,442C ''decent'':456C ''decomposit'':439C,470C ''definit'':334C ''depend'':9C,68C,94C ''descript'':327C ''determin'':82C ''dimens'':290C ''dimension'':126C,159C ''distribut'':243C ''e.g'':161C ''eigenvalu'':97C,198C,213C,267C,355C,372C ''ellips'':297C ''enter'':325C ''equat'':199C,214C,268C,294C ''even'':58C ''exampl'':38C ''explanatori'':147C ''explicit'':210C ''expon'':250C,272C ''fact'':22C ''follow'':311C ''gaussian'':242C ''given'':252C ''good'':177C ''graphic'':312C ''great'':465C ''help'':466C ''high'':55C ''href'':408C,426C ''i.stack.imgur.com'':322C ''i.stack.imgur.com/pnrlb.png'':321C ''idea'':123C ''imag'':326C ''img'':319C ''import'':3C,18C,122C ''includ'':184C,211C ''independ'':43C,44C ''instanc'':353C ''interest'':115C,219C ''interpret'':313C ''introduct'':418C ''invers'':77C ''invert'':73C ''involv'':80C ''learn'':109C ''let'':110C ''li'':406C,424C ''linear'':93C,414C ''lose'':144C ''lower'':158C ''machin'':108C ''mani'':133C ''mathemat'':8C ''matric'':335C ''matrix'':70C,76C,438C,469C ''matter'':340C ''maxim'':191C ''maximization/minimization'':106C ''mine'':436C ''modern'':417C ''much'':146C,186C ''multipl'':49C ''multivari'':241C ''need'':41C,385C,392C ''negat'':359C ''nofollow'':413C,431C ''non'':358C ''non-neg'':357C ''number'':140C ''obtain'':309C ''occurr'':101C ''ol'':405C ''one'':149C,182C ''onto'':156C ''p'':1C,36C,96C,205C,318C,330C,396C,446C ''p.81'':316C ''paragraph'':366C ''pool'':422C ''popular'':383C ''posit'':306C,331C,371C ''possibl'':189C ''power'':148C ''predictor'':47C ''previous'':365C ''princip'':117C ''prml'':315C ''project'':153C,178C ''quantiti'':217C ''rads.stackoverflow.com'':410C,428C ''rads.stackoverflow.com/amzn/click/0534998453'':409C ''rads.stackoverflow.com/amzn/click/1584888326'':427C ''re'':225C ''recommend'':399C,449C ''reduc'':138C,169C ''reduct'':127C ''regress'':50C ''rel'':412C,430C ''relat'':104C ''reli'':19C ''rememb'':363C ''repres'':233C ''requir'':370C,454C ''result'':195C ''row'':66C,91C ''say'':112C ''second'':448C ''see'':377C ''semi'':333C ''semi-definit'':332C ''skillicorn'':443C ''solut'':150C ''sometim'':16C ''space'':160C ''special'':452C ''src'':320C ''system'':229C ''take'':162C,236C ''therefor'':307C ''turn'':174C ''two'':289C ''u'':201C,203C ''understand'':432C,457C,468C ''understood'':351C ''use'':180C,337C ''usual'':40C ''variabl'':45C,57C,134C,142C,167C,173C,235C ''variat'':187C,194C ''want'':136C ''well'':346C,350C ''well-behav'':345C ''well-understood'':349C ''without'':143C ''worst'':59C ''written'':275C ''x'':256C,261C ''x-î'':255C,260C ''y'':278C,283C ''î'':202C,254C,257C,259C,262C,270C,277C,281C,286C,300C,303C,369C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1009, 2, '2014-08-20 15:53:29.403', 6, NULL, NULL, '&lt;p&gt;If you have R and the &lt;code&gt;spacetime&lt;/code&gt; package then you are only &lt;code&gt;data(package="spacetime")&lt;/code&gt; away from a list of space-time data sets bundled with the package:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Data sets in package âspacetimeâ:

DE_NUTS1 (air)          Air quality data, rural background PM10 in
                        Germany, daily averages 1998-2009
fires                   Northern Los Angeles County Fires
rural (air)             Air quality data, rural background PM10 in
                        Germany, daily averages 1998-2009
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;then for example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; data(fires)
&amp;gt; str(fires)
''data.frame'':   313 obs. of  3 variables:
 $ Time: int  5863 5870 6017 6018 6034 6060 6176 6364 6366 6372 ...
 $ X   : num  63.9 64.3 64.1 64 64.4 ...
 $ Y   : num  19.4 20.1 19.7 19.8 20.3 ...
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''-2009'':58C,78C ''/code'':10C,20C,79C,126C ''/p'':35C,85C ''/pre'':80C,127C ''19.4'':121C ''19.7'':123C ''19.8'':124C ''1998'':57C,77C ''20.1'':122C ''20.3'':125C ''3'':98C ''313'':95C ''5863'':102C ''5870'':103C ''6017'':104C ''6018'':105C ''6034'':106C ''6060'':107C ''6176'':108C ''63.9'':114C ''6364'':109C ''6366'':110C ''6372'':111C ''64'':117C ''64.1'':116C ''64.3'':115C ''64.4'':118C ''air'':46C,47C,66C,67C ''angel'':62C ''averag'':56C,76C ''away'':21C ''background'':51C,71C ''bundl'':31C ''code'':8C,16C,37C,87C ''counti'':63C ''daili'':55C,75C ''data'':17C,29C,38C,49C,69C,89C ''data.frame'':94C ''de'':44C ''exampl'':84C ''fire'':59C,64C,90C,93C ''germani'':54C,74C ''gt'':88C,91C ''int'':101C ''list'':24C ''los'':61C ''northern'':60C ''num'':113C,120C ''nuts1'':45C ''ob'':96C ''p'':1C,81C ''packag'':11C,18C,34C,41C ''pm10'':52C,72C ''pre'':36C,86C ''qualiti'':48C,68C ''r'':5C ''rural'':50C,65C,70C ''set'':30C,39C ''space'':27C ''space-tim'':26C ''spacetim'':9C,19C ''spacetimeâ'':43C ''str'':92C ''time'':28C,100C ''variabl'':99C ''x'':112C ''y'':119C ''â'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1010, 2, '2014-08-20 19:08:39.807', 1, NULL, NULL, '&lt;p&gt;You don''t mention what language you''re programming in (please consider adding it as a tag), so general help would be to seek out a HTML parser and use that to pull the data. Some web sites can have simply awful HTML code and can be very difficult to scrape, and just when you think you have it...&lt;/p&gt;

&lt;p&gt;A HTML parser will parse all the html and allow you to access it in a structured sort of way, whether that''s from an array, an object etc.&lt;/p&gt;
', NULL, NULL, '''/p'':61C,92C ''access'':75C ''ad'':14C ''allow'':72C ''array'':88C ''aw'':43C ''code'':45C ''consid'':13C ''data'':36C ''difficult'':50C ''etc'':91C ''general'':20C ''help'':21C ''html'':28C,44C,64C,70C ''languag'':7C ''mention'':5C ''object'':90C ''p'':1C,62C ''pars'':67C ''parser'':29C,65C ''pleas'':12C ''program'':10C ''pull'':34C ''re'':9C ''scrape'':52C ''seek'':25C ''simpli'':42C ''site'':39C ''sort'':80C ''structur'':79C ''tag'':18C ''think'':57C ''use'':31C ''way'':82C ''web'':38C ''whether'':83C ''would'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1013, 1, '2014-08-20 21:12:49.927', 2, 66, 'Using Heuristic Methods for AB Testing', '&lt;p&gt;I''ve just started reading about AB testing, as it pertains to optimizing website design.  I find it interesting that most of the methods assume that changes to the layout and appearance are independent of each other.  I understand that the most common method of optimization is the &lt;a href="http://en.wikipedia.org/wiki/Multi-armed_bandit" rel="nofollow"&gt;''multi-armed bandit''&lt;/a&gt; procedure.  While I grasp the concept of it, it seems to ignore the fact that changes (changes to the website in this case) are not independent to each other.&lt;/p&gt;

&lt;p&gt;For example, if company is testing the placement and color of the logo on the website, they find the optimal color first then the optimal placement.  Not that I''m some expert on human psychology, but shouldn''t these be related? Can the multi-armed bandit method be efficiently used in this case or more complicated cases?&lt;/p&gt;

&lt;p&gt;My first instinct is to say no.  On that note, why haven''t people used heuristic algorithms to optimize over complicated AB testing sample spaces?  For an example, I thought someone might have used a genetic algorithm to optimize a website layout, but I can find no examples of something like this out there. This leads me to believe that I''m missing something important in my understanding of AB testing as it applies to website optimization.&lt;/p&gt;

&lt;p&gt;Why isn''t heuristic optimization used on more complicated websites?&lt;/p&gt;
', 'optimization  consumerweb', NULL, '''/a'':69C ''/p'':99C,159C,238C,250C ''/wiki/multi-armed_bandit'':62C ''ab'':5A,16C,182C,230C ''algorithm'':177C,197C ''appear'':41C ''appli'':234C ''arm'':67C,146C ''assum'':34C ''bandit'':68C,147C ''believ'':219C ''case'':92C,154C,158C ''chang'':36C,85C,86C ''color'':110C,121C ''common'':52C ''compani'':104C ''complic'':157C,181C,248C ''concept'':75C ''consumerweb'':8B ''design'':24C ''effici'':150C ''en.wikipedia.org'':61C ''en.wikipedia.org/wiki/multi-armed_bandit'':60C ''exampl'':102C,188C,208C ''expert'':132C ''fact'':83C ''find'':26C,118C,206C ''first'':122C,162C ''genet'':196C ''grasp'':73C ''haven'':172C ''heurist'':2A,176C,243C ''href'':59C ''human'':134C ''ignor'':81C ''import'':225C ''independ'':43C,95C ''instinct'':163C ''interest'':28C ''isn'':241C ''layout'':39C,202C ''lead'':216C ''like'':211C ''logo'':113C ''m'':130C,222C ''method'':3A,33C,53C,148C ''might'':192C ''miss'':223C ''multi'':66C,145C ''multi-arm'':65C,144C ''nofollow'':64C ''note'':170C ''optim'':7B,22C,55C,120C,125C,179C,199C,237C,244C ''p'':9C,100C,160C,239C ''peopl'':174C ''pertain'':20C ''placement'':108C,126C ''procedur'':70C ''psycholog'':135C ''read'':14C ''rel'':63C ''relat'':141C ''sampl'':184C ''say'':166C ''seem'':79C ''shouldn'':137C ''someon'':191C ''someth'':210C,224C ''space'':185C ''start'':13C ''test'':6A,17C,106C,183C,231C ''thought'':190C ''understand'':48C,228C ''use'':1A,151C,175C,194C,245C ''ve'':11C ''websit'':23C,89C,116C,201C,236C,249C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1015, 1, '2014-08-21 06:31:50.197', 1, 88, 'Making Factual drake work on Windows 7 64-bit', '&lt;p&gt;I have installed &lt;a href="https://github.com/Factual/drake" rel="nofollow"&gt;Drake&lt;/a&gt; on Windows 7 64-bit.&lt;/p&gt;

&lt;p&gt;I am using JDK 1.7.0_51.&lt;/p&gt;

&lt;p&gt;I tried both using the pre-compiled jar file and
compiling from the Clojure source using &lt;a href="https://github.com/technomancy/leiningen" rel="nofollow"&gt;leiningen&lt;/a&gt;.
The resulting Drake version is 0.1.6, the current development version.&lt;/p&gt;

&lt;p&gt;When running Drake, I get the current version number.&lt;/p&gt;

&lt;p&gt;Next, I tried to go through &lt;a href="https://github.com/Factual/drake/wiki/Tutorial" rel="nofollow"&gt;the tutorial&lt;/a&gt;. The command:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;java -jar drake.jar  -w .\workflow.d
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;results in the following Exception:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;java.lang.Exception: no input data found in locations: D:\tools\drake\in.c
sv
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Even though the file exists and has text inside it. 
The same scenario works in a similar installation on Ubuntu 12.04.
Am I doing something wrong, or is this a Windows-specific bug?&lt;/p&gt;
', 'tools', NULL, '''/a'':23C,64C,103C ''/code'':114C,137C ''/factual/drake'':19C ''/factual/drake/wiki/tutorial'':98C ''/p'':29C,37C,75C,86C,106C,122C,174C ''/pre'':115C,138C ''/technomancy/leiningen'':60C ''0.1.6'':70C ''1.7.0'':35C ''12.04'':160C ''51'':36C ''64'':8A,27C ''7'':7A,26C ''bit'':9A,28C ''bug'':173C ''clojur'':53C ''code'':108C,124C ''command'':105C ''compil'':46C,50C ''current'':72C,83C ''d'':132C ''data'':128C ''develop'':73C ''drake'':3A,22C,67C,79C,134C ''drake.jar'':111C ''even'':140C ''except'':121C ''exist'':144C ''factual'':2A ''file'':48C,143C ''follow'':120C ''found'':129C ''get'':81C ''github.com'':18C,59C,97C ''github.com/factual/drake'':17C ''github.com/factual/drake/wiki/tutorial'':96C ''github.com/technomancy/leiningen'':58C ''go'':92C ''href'':16C,57C,95C ''in.c'':135C ''input'':127C ''insid'':148C ''instal'':14C,157C ''jar'':47C,110C ''java'':109C ''java.lang.exception'':125C ''jdk'':34C ''leiningen'':63C ''locat'':131C ''make'':1A ''next'':88C ''nofollow'':21C,62C,100C ''number'':85C ''p'':11C,30C,38C,76C,87C,116C,139C ''pre'':45C,107C,123C ''pre-compil'':44C ''rel'':20C,61C,99C ''result'':66C,117C ''run'':78C ''scenario'':152C ''similar'':156C ''someth'':164C ''sourc'':54C ''specif'':172C ''sv'':136C ''text'':147C ''though'':141C ''tool'':10B,133C ''tri'':40C,90C ''tutori'':102C ''ubuntu'':159C ''use'':33C,42C,55C ''version'':68C,74C,84C ''w'':112C ''window'':6A,25C,171C ''windows-specif'':170C ''work'':4A,153C ''workflow.d'':113C ''wrong'':165C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1029, 1, '2014-08-23 19:34:09.417', -1, 549, 'Which one will be the dominating programming language for next 5 years for analytics , machine learning . R or python or SAS', '&lt;p&gt;Which one will be the dominating programming language for next 5 years for analytics , machine learning . R verses python verses SAS. Advantage and disadvantage.&lt;/p&gt;
', 'machine-learning  r  python', NULL, '''/p'':52C ''5'':11A,38C ''advantag'':49C ''analyt'':14A,41C ''disadvantag'':51C ''domin'':6A,33C ''languag'':8A,35C ''learn'':16A,24B,43C ''machin'':15A,23B,42C ''machine-learn'':22B ''next'':10A,37C ''one'':2A,29C ''p'':27C ''program'':7A,34C ''python'':19A,26B,46C ''r'':17A,25B,44C ''sas'':21A,48C ''vers'':45C,47C ''year'':12A,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1017, 1, '2014-08-21 10:13:54.13', 3, 53, 'How can I model open environment in reinforcement learning?', '&lt;p&gt;I''m studying reinforcement learning in order to implement a kind of time series pattern analyzer such as market.&lt;/p&gt;

&lt;p&gt;The most examples I have seen are based on the maze environment.&lt;/p&gt;

&lt;p&gt;But in real market environment, the signal changes endlessly as time passes and I can not guess how can I model environment and states.&lt;/p&gt;

&lt;p&gt;Another question is about buy-sell modeling.&lt;/p&gt;

&lt;p&gt;Let''s assume that the agent randomly buy at time $t$ and sell at time $t + \alpha$.&lt;/p&gt;

&lt;p&gt;It''s simple to calculate reward.
The problem is how can I model $Q$ matrix and how can I model signals between buy and sell actions.&lt;/p&gt;

&lt;p&gt;Can you share some source code or guidance for similar situation?&lt;/p&gt;
', 'machine-learning  reinforcement-learning', NULL, '''/p'':36C,50C,76C,86C,105C,133C,146C ''action'':132C ''agent'':93C ''alpha'':104C ''analyz'':32C ''anoth'':78C ''assum'':90C ''base'':45C ''buy'':83C,95C,129C ''buy-sel'':82C ''calcul'':111C ''chang'':59C ''code'':140C ''endless'':60C ''environ'':6A,49C,56C,73C ''exampl'':40C ''guess'':68C ''guidanc'':142C ''implement'':25C ''kind'':27C ''learn'':9A,12B,15B,21C ''let'':88C ''m'':18C ''machin'':11B ''machine-learn'':10B ''market'':35C,55C ''matrix'':121C ''maze'':48C ''model'':4A,72C,85C,119C,126C ''open'':5A ''order'':23C ''p'':16C,37C,51C,77C,87C,106C,134C ''pass'':63C ''pattern'':31C ''problem'':114C ''q'':120C ''question'':79C ''random'':94C ''real'':54C ''reinforc'':8A,14B,20C ''reinforcement-learn'':13B ''reward'':112C ''seen'':43C ''sell'':84C,100C,131C ''seri'':30C ''share'':137C ''signal'':58C,127C ''similar'':144C ''simpl'':109C ''situat'':145C ''sourc'':139C ''state'':75C ''studi'':19C ''time'':29C,62C,97C,102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1020, 1, '2014-08-22 14:01:23.64', 4, 62, 'Available data about ''likers'' as a page on Facebook', '&lt;p&gt;I am hoping to model the characteristics of the users of a specific page on Facebook, which has roughly 2 million likes. I have been looking at the Facebook SDK/API, but I can''t really see if what I would like to do is possible. It seems that the users share quite different amounts of data so I probably discard a lot of users and only use the ones with a quite open public profile. I would like to have the following data:&lt;/p&gt;

&lt;p&gt;1) See the individuals that have ''liked'' the page.&lt;/p&gt;

&lt;p&gt;2) See the list of friends for each person that have ''liked'' the page.&lt;/p&gt;

&lt;p&gt;3) See gender for each person (optional)&lt;/p&gt;

&lt;p&gt;4) See other pages that each person has liked (optional)&lt;/p&gt;

&lt;p&gt;Could anyone tell me if it is possible to get this data? As mentioned earlier it is okay if I discard data for users that don''t like to share this data.&lt;/p&gt;
', 'social-network-analysis', NULL, '''/p'':98C,109C,125C,134C,146C,180C ''1'':100C ''2'':34C,111C ''3'':127C ''4'':136C ''amount'':68C ''analysi'':13B ''anyon'':149C ''avail'':1A ''characterist'':21C ''could'':148C ''data'':2A,70C,97C,159C,169C,179C ''differ'':67C ''discard'':74C,168C ''earlier'':162C ''facebook'':9A,30C,43C ''follow'':96C ''friend'':116C ''gender'':129C ''get'':157C ''hope'':17C ''individu'':103C ''like'':36C,55C,92C,106C,122C,144C,175C ''liker'':4A ''list'':114C ''look'':40C ''lot'':76C ''mention'':161C ''million'':35C ''model'':19C ''network'':12B ''okay'':165C ''one'':83C ''open'':87C ''option'':133C,145C ''p'':14C,99C,110C,126C,135C,147C ''page'':7A,28C,108C,124C,139C ''person'':119C,132C,142C ''possibl'':59C,155C ''probabl'':73C ''profil'':89C ''public'':88C ''quit'':66C,86C ''realli'':49C ''rough'':33C ''sdk/api'':44C ''see'':50C,101C,112C,128C,137C ''seem'':61C ''share'':65C,177C ''social'':11B ''social-network-analysi'':10B ''specif'':27C ''tell'':150C ''use'':81C ''user'':24C,64C,78C,171C ''would'':54C,91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1021, 1, '2014-08-22 15:59:07.097', 8, 148, 'Extract canonical string from a list of noisy strings', '&lt;p&gt;I have thousands of lists of strings, and each list has about 10 strings. Most strings in a given list are very similar, though some strings are (rarely) completely unrelated to the others and some strings contain irrelevant words. They can be considered to be noisy variations of a canonical string. I am looking for an algorithm or a library that will convert each list into this canonical string.&lt;/p&gt;

&lt;p&gt;Here is one such list.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Star Wars: Episode IV A New Hope | StarWars.com&lt;/li&gt;
&lt;li&gt;Star Wars Episode IV - A New Hope (1977)&lt;/li&gt;
&lt;li&gt;Star Wars: Episode IV - A New Hope - Rotten Tomatoes&lt;/li&gt;
&lt;li&gt;Watch Star Wars: Episode IV - A New Hope Online Free&lt;/li&gt;
&lt;li&gt;Star Wars (1977) - Greatest Films&lt;/li&gt;
&lt;li&gt;[REC] 4 poster promises death by outboard motor - SciFiNow&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;For this list, any string matching the regular expression &lt;code&gt;^Star Wars:? Episode IV (- )?A New Hope$&lt;/code&gt; would be acceptable.&lt;/p&gt;

&lt;p&gt;I have looked at Andrew Ng''s course on Machine Learning on Coursera, but I was not able to find a similar problem.&lt;/p&gt;
', 'nlp  similarity  information-retrieval', NULL, '''/code'':174C ''/li'':103C,113C,124C,136C,143C,154C ''/p'':85C,92C,178C,203C ''/ul'':155C ''10'':28C ''1977'':112C,140C ''4'':146C ''abl'':197C ''accept'':177C ''algorithm'':72C ''andrew'':184C ''canon'':2A,65C,83C ''code'':166C ''complet'':44C ''consid'':58C ''contain'':52C ''convert'':78C ''cours'':187C ''coursera'':192C ''death'':149C ''episod'':97C,107C,117C,129C,169C ''express'':165C ''extract'':1A ''film'':142C ''find'':199C ''free'':135C ''given'':34C ''greatest'':141C ''hope'':101C,111C,121C,133C,173C ''inform'':13B ''information-retriev'':12B ''irrelev'':53C ''iv'':98C,108C,118C,130C,170C ''learn'':190C ''li'':94C,104C,114C,125C,137C,144C ''librari'':75C ''list'':6A,20C,25C,35C,80C,91C,159C ''look'':69C,182C ''machin'':189C ''match'':162C ''motor'':152C ''new'':100C,110C,120C,132C,172C ''ng'':185C ''nlp'':10B ''noisi'':8A,61C ''one'':89C ''onlin'':134C ''other'':48C ''outboard'':151C ''p'':15C,86C,156C,179C ''poster'':147C ''problem'':202C ''promis'':148C ''rare'':43C ''rec'':145C ''regular'':164C ''retriev'':14B ''rotten'':122C ''scifinow'':153C ''similar'':11B,38C,201C ''star'':95C,105C,115C,127C,138C,167C ''starwars.com'':102C ''string'':3A,9A,22C,29C,31C,41C,51C,66C,84C,161C ''though'':39C ''thousand'':18C ''tomato'':123C ''ul'':93C ''unrel'':45C ''variat'':62C ''war'':96C,106C,116C,128C,139C,168C ''watch'':126C ''word'':54C ''would'':175C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1022, 2, '2014-08-22 18:27:04.377', 2, NULL, NULL, '&lt;p&gt;Want to wish you good luck. Some time ago faced with the same problem, but didn''t find any satisfying solution. 
First of all, there is no way to get list of users, who "liked" a particular page. Even, if you are an administrator of this page (I was). One only can get list of last 3 or 5 hundred users. &lt;/p&gt;

&lt;p&gt;Friendships data for most of the users is also inaccessible. Looks like gender is the only thing from your list, that you can get. &lt;/p&gt;

&lt;p&gt;Data about pages, that exact user "likes", should be available (as it''s written in docs), but in reality, through API you can collect something only for friends and FoF. Even though this data is available through web interface. So the only way is to try dirty trick with parsing and scraping (but remember, that I didn''t advise it ;) ). &lt;/p&gt;
', NULL, NULL, '''/p'':63C,89C,151C ''3'':58C ''5'':60C ''administr'':45C ''advis'':149C ''ago'':10C ''also'':73C ''api'':111C ''avail'':100C,126C ''collect'':114C ''data'':66C,91C,124C ''didn'':17C,147C ''dirti'':137C ''doc'':106C ''even'':40C,121C ''exact'':95C ''face'':11C ''find'':19C ''first'':23C ''fof'':120C ''friend'':118C ''friendship'':65C ''gender'':77C ''get'':31C,54C,88C ''good'':6C ''hundr'':61C ''inaccess'':74C ''interfac'':129C ''last'':57C ''like'':36C,76C,97C ''list'':32C,55C,84C ''look'':75C ''luck'':7C ''one'':51C ''p'':1C,64C,90C ''page'':39C,48C,93C ''pars'':140C ''particular'':38C ''problem'':15C ''realiti'':109C ''rememb'':144C ''satisfi'':21C ''scrape'':142C ''solut'':22C ''someth'':115C ''thing'':81C ''though'':122C ''time'':9C ''tri'':136C ''trick'':138C ''user'':34C,62C,71C,96C ''want'':2C ''way'':29C,133C ''web'':128C ''wish'':4C ''written'':104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1023, 2, '2014-08-23 09:19:08.577', 2, NULL, NULL, '&lt;p&gt;As a naive solution I would suggest to first select the strings which contain the most frequent tokens inside the list. In this way you can get rid of irrelevant string.&lt;/p&gt;

&lt;p&gt;In the second phrase I would do a majority voting. Assuming the 3 sentences:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Star Wars: Episode IV A New Hope | StarWars.com&lt;/li&gt;
&lt;li&gt;Star Wars Episode IV - A New Hope (1977)&lt;/li&gt;
&lt;li&gt;Star Wars: Episode IV - A New Hope - Rotten Tomatoes&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I would go through the tokens one by one. We start by "Star". It wins as all the string start with it. "Wars" will also win. The next one is ":". It will also win.&lt;/p&gt;

&lt;p&gt;All the tokens will ein in majority voting till "Hope". The next token after "Hope" will be either "|", or "(" or "-". None of the will win in majority voting so I will stop here!&lt;/p&gt;

&lt;p&gt;Another solution would be probably to use &lt;a href="http://en.wikipedia.org/wiki/Longest_common_subsequence_problem" rel="nofollow"&gt;Longest common subsequence&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;As I said I have not though about it much. So there might be much more better solutions to your problem :-)&lt;/p&gt;
', NULL, NULL, '''/a'':172C ''/li'':60C,70C,81C ''/p'':33C,49C,118C,153C,173C,196C ''/ul'':82C ''/wiki/longest_common_subsequence_problem'':166C ''1977'':69C ''3'':47C ''also'':108C,116C ''anoth'':155C ''assum'':45C ''better'':191C ''common'':170C ''contain'':15C ''ein'':124C ''either'':137C ''en.wikipedia.org'':165C ''en.wikipedia.org/wiki/longest_common_subsequence_problem'':164C ''episod'':54C,64C,74C ''first'':10C ''frequent'':18C ''get'':28C ''go'':86C ''hope'':58C,68C,78C,129C,134C ''href'':163C ''insid'':20C ''irrelev'':31C ''iv'':55C,65C,75C ''li'':51C,61C,71C ''list'':22C ''longest'':169C ''major'':43C,126C,146C ''might'':187C ''much'':184C,189C ''naiv'':4C ''new'':57C,67C,77C ''next'':111C,131C ''nofollow'':168C ''none'':140C ''one'':90C,92C,112C ''p'':1C,34C,83C,119C,154C,174C ''phrase'':38C ''probabl'':159C ''problem'':195C ''rel'':167C ''rid'':29C ''rotten'':79C ''said'':177C ''second'':37C ''select'':11C ''sentenc'':48C ''solut'':5C,156C,192C ''star'':52C,62C,72C,96C ''start'':94C,103C ''starwars.com'':59C ''stop'':151C ''string'':13C,32C,102C ''subsequ'':171C ''suggest'':8C ''though'':181C ''till'':128C ''token'':19C,89C,122C,132C ''tomato'':80C ''ul'':50C ''use'':161C ''vote'':44C,127C,147C ''war'':53C,63C,73C,106C ''way'':25C ''win'':98C,109C,117C,144C ''would'':7C,40C,85C,157C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1024, 1, '2014-08-23 13:47:01.907', 3, 83, 'Extrapolating GLM coefficients for year a product was sold into future years?', '&lt;p&gt;I''ve fit a GLM (Poisson) to a data set where one of the variables is categorical for the year a customer bought a product from my company, ranging from 1999 to 2012. There''s a linear trend of the coefficients for the values of the variable as the year of sale increases.&lt;/p&gt;

&lt;p&gt;Is there any problem with trying to improve predictions for 2013 and maybe 2014 by extrapolating to get the coefficients for those years?&lt;/p&gt;
', 'statistics  glm  regression', NULL, '''/p'':70C,95C ''1999'':47C ''2012'':49C ''2013'':82C ''2014'':85C ''bought'':39C ''categor'':33C ''coeffici'':3A,57C,91C ''compani'':44C ''custom'':38C ''data'':25C ''extrapol'':1A,87C ''fit'':19C ''futur'':11A ''get'':89C ''glm'':2A,14B,21C ''improv'':79C ''increas'':69C ''linear'':53C ''mayb'':84C ''one'':28C ''p'':16C,71C ''poisson'':22C ''predict'':80C ''problem'':75C ''product'':7A,41C ''rang'':45C ''regress'':15B ''sale'':68C ''set'':26C ''sold'':9A ''statist'':13B ''trend'':54C ''tri'':77C ''valu'':60C ''variabl'':31C,63C ''ve'':18C ''year'':5A,12A,36C,66C,94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1027, 2, '2014-08-23 15:25:44.903', 2, NULL, NULL, '&lt;p&gt;A first remark, you should watch ''Wargames'' to know what you''re getting yourself into.&lt;/p&gt;

&lt;p&gt;What you want is f(p) such that f(p) is as close as possible to strength of position.&lt;/p&gt;

&lt;p&gt;A very simple solution using genetic algo would be to setup 10000 players with different weights and see which wins. Then keep the top 1000 winners'' weight, copy them 10 times, alter them slightly to explore weight space, and run the simulation again. That''s standard GA, given a functional form, what are the best coefficients for it.&lt;/p&gt;

&lt;p&gt;Another solution is to extract the positions, so you have a table ''(material, kingsafety, mobility, pawn-structure, trappedpieces) -&gt; goodness of position'' where goodness of position is some objective factor (outcome win/lose computed using simulations above or known matches, depth of available tree, number of moves under the tree where one of the 5 factors gets better. You can then try different functional forms for your f(p), regression, svm.&lt;/p&gt;
', NULL, NULL, '''/p'':17C,38C,98C,170C ''10'':69C ''1000'':64C ''10000'':51C ''5'':153C ''algo'':46C ''alter'':71C ''anoth'':100C ''avail'':141C ''best'':94C ''better'':156C ''close'':31C ''coeffici'':95C ''comput'':132C ''copi'':67C ''depth'':139C ''differ'':54C,161C ''explor'':75C ''extract'':104C ''f'':23C,27C,166C ''factor'':129C,154C ''first'':3C ''form'':90C,163C ''function'':89C,162C ''ga'':86C ''genet'':45C ''get'':14C,155C ''given'':87C ''good'':119C,123C ''keep'':61C ''kingsafeti'':113C ''know'':10C ''known'':137C ''match'':138C ''materi'':112C ''mobil'':114C ''move'':145C ''number'':143C ''object'':128C ''one'':150C ''outcom'':130C ''p'':1C,18C,24C,28C,39C,99C,167C ''pawn'':116C ''pawn-structur'':115C ''player'':52C ''posit'':37C,106C,121C,125C ''possibl'':33C ''re'':13C ''regress'':168C ''remark'':4C ''run'':79C ''see'':57C ''setup'':50C ''simpl'':42C ''simul'':81C,134C ''slight'':73C ''solut'':43C,101C ''space'':77C ''standard'':85C ''strength'':35C ''structur'':117C ''svm'':169C ''tabl'':111C ''time'':70C ''top'':63C ''trappedpiec'':118C ''tree'':142C,148C ''tri'':160C ''use'':44C,133C ''want'':21C ''wargam'':8C ''watch'':7C ''weight'':55C,66C,76C ''win'':59C ''win/lose'':131C ''winner'':65C ''would'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1028, 1, '2014-08-23 16:54:06.38', 13, 310, 'Do Random Forest overfit?', '&lt;p&gt;I have been reading around about Random Forests but I cannot really find a definitive answer about the problem of overfitting. According to the original paper of Breiman, they should not overfit when increasing the number of trees in the forest, but it seems that there is not consensus about this. This is creating me quite some confusion about the issue.&lt;/p&gt;

&lt;p&gt;Maybe someone more expert than me can give me a more concrete answer or point me in the right direction to better understand the problem.&lt;/p&gt;
', 'machine-learning  random-forest', NULL, '''/p'':73C,100C ''accord'':33C ''answer'':27C,87C ''around'':16C ''better'':96C ''breiman'':39C ''cannot'':22C ''concret'':86C ''confus'':69C ''consensus'':60C ''creat'':65C ''definit'':26C ''direct'':94C ''expert'':78C ''find'':24C ''forest'':3A,10B,19C,52C ''give'':82C ''increas'':45C ''issu'':72C ''learn'':7B ''machin'':6B ''machine-learn'':5B ''mayb'':75C ''number'':47C ''origin'':36C ''overfit'':4A,32C,43C ''p'':11C,74C ''paper'':37C ''point'':89C ''problem'':30C,99C ''quit'':67C ''random'':2A,9B,18C ''random-forest'':8B ''read'':15C ''realli'':23C ''right'':93C ''seem'':55C ''someon'':76C ''tree'':49C ''understand'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1030, 2, '2014-08-23 20:05:38.7', 4, NULL, NULL, '&lt;p&gt;I believe that this is a case for applying &lt;em&gt;time series analysis&lt;/em&gt;, in particular &lt;em&gt;time series forecasting&lt;/em&gt; (&lt;a href="http://en.wikipedia.org/wiki/Time_series" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Time_series&lt;/a&gt;). Consider the following resources on &lt;strong&gt;time series regression&lt;/strong&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471363553.html" rel="nofollow"&gt;http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471363553.html&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.stats.uwo.ca/faculty/aim/tsar/tsar.pdf" rel="nofollow"&gt;http://www.stats.uwo.ca/faculty/aim/tsar/tsar.pdf&lt;/a&gt; (especially section
4.6)&lt;/li&gt;
&lt;li&gt;&lt;a href="http://arxiv.org/abs/0802.0219" rel="nofollow"&gt;http://arxiv.org/abs/0802.0219&lt;/a&gt; (Bayesian approach)&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/abs/0802.0219'':76C ''/abs/0802.0219&lt;/a&gt;'':81C ''/em'':15C,22C ''/faculty/aim/tsar/tsar.pdf'':61C ''/faculty/aim/tsar/tsar.pdf&lt;/a&gt;'':66C ''/li'':70C,84C ''/p'':43C ''/strong'':42C ''/ul'':85C ''/wiki/time_series'':27C ''/wiki/time_series&lt;/a&gt;).'':32C ''/wileycda/wileytitle/productcd-0471363553.html'':50C ''/wileycda/wileytitle/productcd-0471363553.html&lt;/a&gt;&lt;/li&gt;'':55C ''4.6'':69C ''analysi'':14C ''appli'':10C ''approach'':83C ''arxiv.org'':75C,80C ''arxiv.org/abs/0802.0219'':74C ''arxiv.org/abs/0802.0219&lt;/a&gt;'':79C ''bayesian'':82C ''believ'':3C ''case'':8C ''consid'':33C ''em'':11C,18C ''en.wikipedia.org'':26C,31C ''en.wikipedia.org/wiki/time_series'':25C ''en.wikipedia.org/wiki/time_series&lt;/a&gt;).'':30C ''especi'':67C ''follow'':35C ''forecast'':21C ''href'':24C,47C,58C,73C ''li'':45C,56C,71C ''nofollow'':29C,52C,63C,78C ''p'':1C ''particular'':17C ''regress'':41C ''rel'':28C,51C,62C,77C ''resourc'':36C ''section'':68C ''seri'':13C,20C,40C ''strong'':38C ''time'':12C,19C,39C ''ul'':44C ''www.stats.uwo.ca'':60C,65C ''www.stats.uwo.ca/faculty/aim/tsar/tsar.pdf'':59C ''www.stats.uwo.ca/faculty/aim/tsar/tsar.pdf&lt;/a&gt;'':64C ''www.wiley.com'':49C,54C ''www.wiley.com/wileycda/wileytitle/productcd-0471363553.html'':48C ''www.wiley.com/wileycda/wileytitle/productcd-0471363553.html&lt;/a&gt;&lt;/li&gt;'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1031, 2, '2014-08-24 00:46:03.813', 2, NULL, NULL, '&lt;p&gt;There is a great &lt;a href="http://blog.revolutionanalytics.com/2014/01/in-data-scientist-survey-r-is-the-most-used-tool-other-than-databases.html" rel="nofollow"&gt;survey&lt;/a&gt; published by O''Reilly collected at Strata.&lt;/p&gt;

&lt;p&gt;You can see that SAS is not widely popular, and there is no reason why that should change at this point. One can rule that out.&lt;/p&gt;

&lt;p&gt;R is barely ahead of Python, 43% vs 41%. You can find many blogs expressing the rise of Python in data science. I would go with Python in the near future.&lt;/p&gt;

&lt;p&gt;But 5 years is a very long time. I think Golang will steal a lot of developers from Python in general. This might spill over to data science usage as well. Code can be written to execute in parallel very easily, which makes it a perfect vehicle for Big Data processing. &lt;a href="http://julialang.org/" rel="nofollow"&gt;Julia''s&lt;/a&gt; benchmarks for technical computing are even more impressive, and you can have iPython like stuff with iJulia. Hence Python is likely to lose some steam to both. But there are ways to call Julia functions from R and Python, so you can experiment using best sides of each.&lt;/p&gt;
', NULL, NULL, '''/2014/01/in-data-scientist-survey-r-is-the-most-used-tool-other-than-databases.html'':10C ''/a'':14C,143C ''/p'':22C,50C,83C,192C ''41'':60C ''43'':58C ''5'':86C ''ahead'':55C ''bare'':54C ''benchmark'':144C ''best'':188C ''big'':133C ''blog'':65C ''blog.revolutionanalytics.com'':9C ''blog.revolutionanalytics.com/2014/01/in-data-scientist-survey-r-is-the-most-used-tool-other-than-databases.html'':8C ''call'':176C ''chang'':41C ''code'':116C ''collect'':19C ''comput'':147C ''data'':72C,111C,134C ''develop'':101C ''easili'':125C ''even'':149C ''execut'':121C ''experi'':186C ''express'':66C ''find'':63C ''function'':178C ''futur'':82C ''general'':105C ''go'':76C ''golang'':95C ''great'':5C ''henc'':161C ''href'':7C,137C ''ijulia'':160C ''impress'':151C ''ipython'':156C ''julia'':141C,177C ''julialang.org'':138C ''like'':157C,164C ''long'':91C ''lose'':166C ''lot'':99C ''make'':127C ''mani'':64C ''might'':107C ''near'':81C ''nofollow'':12C,140C ''o'':17C ''one'':45C ''p'':1C,23C,51C,84C ''parallel'':123C ''perfect'':130C ''point'':44C ''popular'':32C ''process'':135C ''publish'':15C ''python'':57C,70C,78C,103C,162C,182C ''r'':52C,180C ''reason'':37C ''reilli'':18C ''rel'':11C,139C ''rise'':68C ''rule'':47C ''sas'':28C ''scienc'':73C,112C ''see'':26C ''side'':189C ''spill'':108C ''steal'':97C ''steam'':168C ''strata'':21C ''stuff'':158C ''survey'':13C ''technic'':146C ''think'':94C ''time'':92C ''usag'':113C ''use'':187C ''vehicl'':131C ''vs'':59C ''way'':174C ''well'':115C ''wide'':31C ''would'':75C ''written'':119C ''year'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1032, 2, '2014-08-24 08:22:23.497', 6, NULL, NULL, '&lt;p&gt;You may want to check &lt;a href="http://stats.stackexchange.com/"&gt;cross-validated&lt;/a&gt; - a stachexchange website for many things, including machine learning. &lt;/p&gt;

&lt;p&gt;In particular, this question (with exactly same title) has already been answered multiple times. Check these links: &lt;a href="http://stats.stackexchange.com/search?q=random+forest+overfit"&gt;http://stats.stackexchange.com/search?q=random+forest+overfit&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;But I may give you the short answer to it: yes, it does overfit, and sometimes you need to control the complexity of the trees in your forest, or even prune when they grow too much - but this depends on the library you use for building the forest. E.g. in &lt;code&gt;randomForest&lt;/code&gt; in R you can only control the complexity&lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/code'':103C ''/p'':23C,112C ''/search?q=random+forest+overfit'':46C ''/search?q=random+forest+overfit&lt;/a&gt;&lt;/p&gt;'':49C ''alreadi'':34C ''answer'':36C,58C ''build'':96C ''check'':6C,39C ''code'':101C ''complex'':72C,111C ''control'':70C,109C ''cross'':11C ''cross-valid'':10C ''depend'':89C ''e.g'':99C ''even'':80C ''exact'':30C ''forest'':78C,98C ''give'':54C ''grow'':84C ''href'':8C,43C ''includ'':20C ''learn'':22C ''librari'':92C ''link'':41C ''machin'':21C ''mani'':18C ''may'':3C,53C ''much'':86C ''multipl'':37C ''need'':68C ''overfit'':64C ''p'':1C,24C,50C ''particular'':26C ''prune'':81C ''question'':28C ''r'':105C ''randomforest'':102C ''short'':57C ''sometim'':66C ''stachexchang'':15C ''stats.stackexchange.com'':9C,45C,48C ''stats.stackexchange.com/search?q=random+forest+overfit'':44C ''stats.stackexchange.com/search?q=random+forest+overfit&lt;/a&gt;&lt;/p&gt;'':47C ''thing'':19C ''time'':38C ''titl'':32C ''tree'':75C ''use'':94C ''valid'':12C ''want'':4C ''websit'':16C ''yes'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1033, 2, '2014-08-24 11:03:08.773', 3, NULL, NULL, '&lt;p&gt;Due to the very Big increase in &lt;strong&gt;Big Data&lt;/strong&gt; (pun intended) and the desire for robust stable scalable applications I actually believe it to be &lt;strong&gt;Scala&lt;/strong&gt;.  &lt;strong&gt;Spark&lt;/strong&gt; will inevitably become the main Big Data Machine Learning tool, and it''s main API is in Scala.  Furthermore you simply cannot build a product with scripting languages like Python and R, one can only experiment with these languages.  What Scala brings is a way to BOTH experiment and produce a product.  More reasons&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Think functionally - write faster code and more readable code&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Scala means the end of the two team development cycle. So better product ownership, more agile cross functional teams, and half as many employees required to make a product as we will no longer need both a "research" team and an engineering team, Data Scientists will be able to do both.  This is because Scala is;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;A production quality language - static typing, but with the flexibility of dynamic typing due to implicits&lt;/li&gt;
&lt;li&gt;Interoperable with rest of Java world (so Apache Commons Math, Databases, Cassandra, HBase, HDFS, Akka, Storm, many many databases, more spark components (e.g. graphx, SparkStreaming)&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Step into Spark code easily and understand it, also helps with debugging&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Scala is awesome:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Amazing IDE support due to static typing&lt;/li&gt;
&lt;li&gt;Property based tests with ScalaCheck - insane unit testing&lt;/li&gt;
&lt;li&gt;Very concise language&lt;/li&gt;
&lt;li&gt;Suits mathematicians perfectly (especially Pure Mathematicians)&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;A little more efficient as compiled not interpreted&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Python Spark API sits on Scala API and therefore will always be behind Scala API&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Much easier to do Mathematics in Scala as it''s a Scalable Language where one can easily define DSLs and due to being so functional&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Akka - another way other than storm to do High Velocity&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Pimp my library pattern makes adding methods to Spark RDDs really easy&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':101C,179C,206C,208C,224C,240C,250C,255C,263C,265C,277C,296C,325C,339C,355C ''/ol'':356C ''/p'':89C,160C,223C,230C,276C,295C,324C,338C,354C ''/strong'':12C,31C,34C ''/ul'':207C,264C ''abl'':151C ''actual'':24C ''ad'':347C ''agil'':119C ''akka'':195C,328C ''also'':219C ''alway'':290C ''amaz'':233C ''anoth'':329C ''apach'':188C ''api'':49C,282C,286C,294C ''applic'':22C ''awesom'':229C ''base'':243C ''becom'':37C ''behind'':292C ''believ'':25C ''better'':115C ''big'':6C,10C,40C ''bring'':76C ''build'':57C ''cannot'':56C ''cassandra'':192C ''code'':96C,100C,214C ''common'':189C ''compil'':273C ''compon'':202C ''concis'':253C ''cross'':120C ''cycl'':113C ''data'':11C,41C,147C ''databas'':191C,199C ''debug'':222C ''defin'':316C ''desir'':17C ''develop'':112C ''dsls'':317C ''due'':2C,176C,236C,319C ''dynam'':174C ''e.g'':203C ''easi'':353C ''easier'':300C ''easili'':215C,315C ''effici'':271C ''employe'':127C ''end'':107C ''engin'':145C ''especi'':260C ''experi'':70C,82C ''faster'':95C ''flexibl'':172C ''function'':93C,121C,323C ''furthermor'':53C ''graphx'':204C ''half'':124C ''hbase'':193C ''hdfs'':194C ''help'':220C ''high'':336C ''ide'':234C ''implicit'':178C ''increas'':7C ''inevit'':36C ''insan'':247C ''intend'':14C ''interoper'':181C ''interpret'':275C ''java'':185C ''languag'':62C,73C,166C,254C,311C ''learn'':43C ''li'':91C,102C,162C,180C,209C,225C,232C,241C,251C,256C,266C,278C,297C,326C,340C ''librari'':344C ''like'':63C ''littl'':269C ''longer'':137C ''machin'':42C ''main'':39C,48C ''make'':130C,346C ''mani'':126C,197C,198C ''math'':190C ''mathemat'':303C ''mathematician'':258C,262C ''mean'':105C ''method'':348C ''much'':299C ''need'':138C ''ol'':90C ''one'':67C,313C ''ownership'':117C ''p'':1C,103C,210C,226C,267C,279C,298C,327C,341C ''pattern'':345C ''perfect'':259C ''pimp'':342C ''produc'':84C ''product'':59C,86C,116C,132C,164C ''properti'':242C ''pun'':13C ''pure'':261C ''python'':64C,280C ''qualiti'':165C ''r'':66C ''rdds'':351C ''readabl'':99C ''realli'':352C ''reason'':88C ''requir'':128C ''research'':141C ''rest'':183C ''robust'':19C ''scala'':30C,52C,75C,104C,158C,227C,285C,293C,305C ''scalabl'':21C,310C ''scalacheck'':246C ''scientist'':148C ''script'':61C ''simpli'':55C ''sit'':283C ''spark'':33C,201C,213C,281C,350C ''sparkstream'':205C ''stabl'':20C ''static'':167C,238C ''step'':211C ''storm'':196C,333C ''strong'':9C,29C,32C ''suit'':257C ''support'':235C ''team'':111C,122C,142C,146C ''test'':244C,249C ''therefor'':288C ''think'':92C ''tool'':44C ''two'':110C ''type'':168C,175C,239C ''ul'':161C,231C ''understand'':217C ''unit'':248C ''veloc'':337C ''way'':79C,330C ''world'':186C ''write'':94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1039, 2, '2014-08-25 11:29:35.047', 1, NULL, NULL, '&lt;p&gt;I think dependencies can be used to improve the accurary of your sentiment classifier. Consider the following examples:&lt;/p&gt;

&lt;p&gt;E1: Bill is not a scientist&lt;/p&gt;

&lt;p&gt;and assume that the token "scientist" has a positive sentiment in a specific domain.&lt;/p&gt;

&lt;p&gt;Knowing the dependency neg(scientist, not) we can see that the example above has a negative sentiment. Without knowing this dependency we would probably classify the sentence as positive.&lt;/p&gt;

&lt;p&gt;Another types of dependencies can be used probably in the same way to improve the accuracy of the classifiers.&lt;/p&gt;
', NULL, NULL, '''/p'':20C,28C,44C,75C,96C ''accuraci'':92C ''accurari'':11C ''anoth'':77C ''assum'':31C ''bill'':23C ''classifi'':15C,70C,95C ''consid'':16C ''depend'':4C,48C,66C,80C ''domain'':43C ''e1'':22C ''exampl'':19C,57C ''follow'':18C ''improv'':9C,90C ''know'':46C,64C ''neg'':49C ''negat'':61C ''p'':1C,21C,29C,45C,76C ''posit'':38C,74C ''probabl'':69C,84C ''scientist'':27C,35C,50C ''see'':54C ''sentenc'':72C ''sentiment'':14C,39C,62C ''specif'':42C ''think'':3C ''token'':34C ''type'':78C ''use'':7C,83C ''way'':88C ''without'':63C ''would'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1034, 1, '2014-08-24 17:09:40.51', 4, 348, 'What features are generally used from Parse trees in classification process in NLP?', '&lt;p&gt;I am exploring different types of parse tree structures. The two widely known parse tree structures are 
a) Constituency based parse tree and 
b) Dependency based parse tree structures. &lt;/p&gt;

&lt;p&gt;I am able to use generate both types of parse tree structures using Stanford NLP package. However, I am not sure how to use these tree structures for my classification task. &lt;/p&gt;

&lt;p&gt;For e.g If I want to do sentiment analysis and want to categorize text into positive and negative classes, what features can I derive from parse tree structures for my classification task?&lt;/p&gt;
', 'machine-learning  nlp  feature-selection  feature-extraction', NULL, '''/p'':54C,87C,121C ''abl'':58C ''analysi'':97C ''b'':48C ''base'':44C,50C ''categor'':101C ''class'':107C ''classif'':10A,85C,119C ''constitu'':43C ''depend'':49C ''deriv'':112C ''differ'':28C ''e.g'':90C ''explor'':27C ''extract'':23B ''featur'':2A,19B,22B,109C ''feature-extract'':21B ''feature-select'':18B ''general'':4A ''generat'':61C ''howev'':72C ''known'':37C ''learn'':16B ''machin'':15B ''machine-learn'':14B ''negat'':106C ''nlp'':13A,17B,70C ''p'':24C,55C,88C ''packag'':71C ''pars'':7A,31C,38C,45C,51C,65C,114C ''posit'':104C ''process'':11A ''select'':20B ''sentiment'':96C ''stanford'':69C ''structur'':33C,40C,53C,67C,82C,116C ''sure'':76C ''task'':86C,120C ''text'':102C ''tree'':8A,32C,39C,46C,52C,66C,81C,115C ''two'':35C ''type'':29C,63C ''use'':5A,60C,68C,79C ''want'':93C,99C ''wide'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1074, 1, '2014-09-02 19:29:07.49', 4, 343, 'Polynomial Kernel Parameters in SVMs', '&lt;p&gt;In SVMs the polynomial kernel is defined as:&lt;/p&gt;

&lt;p&gt;(scale * crossprod(x, y) + offset)^degree&lt;/p&gt;

&lt;p&gt;How do the scale and offset parameters affect the model and what range should they be in? (intuitively please)&lt;/p&gt;

&lt;p&gt;Are the scale and offset for numeric stability only (that''s what it looks like to me), or do they influence prediction accuracy as well?&lt;/p&gt;

&lt;p&gt;Can good values for scale and offset be calculated/estimated when the data is known or is a grid search required? The caret package always sets the offset to 1, but it does a grid search for scale. (Why) is an offset of 1 a good value?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;

&lt;p&gt;PS.: Wikipedia didn''t really help my understanding:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;For degree-d polynomials, the polynomial kernel is defined as&lt;/p&gt;
  
  &lt;p&gt;&lt;img src="http://i.stack.imgur.com/3stCR.png" alt=""&gt;&lt;/p&gt;
  
  &lt;p&gt;where x and y are vectors in the input space, i.e. vectors of features
  computed from training or test samples, &lt;img src="http://i.stack.imgur.com/34dwi.png" alt=""&gt; &lt;strong&gt;is a constant trading&lt;/strong&gt;
  &lt;strong&gt;off the influence of higher-order versus lower-order terms&lt;/strong&gt; in the
  polynomial. When &lt;img src="http://i.stack.imgur.com/79cDG.png" alt=""&gt;, the kernel is called homogeneous.(&lt;strong&gt;A further&lt;/strong&gt;
  &lt;strong&gt;generalized polykernel divides &lt;img src="http://i.stack.imgur.com/Ps0qt.png" alt=""&gt; by a user-specified scalar&lt;/strong&gt;
  &lt;strong&gt;parameter &lt;img src="http://i.stack.imgur.com/sjfS1.png" alt=""&gt;.&lt;/strong&gt;)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Neither did ?polydot''s explanation in R''s help system:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;strong&gt;scale&lt;/strong&gt;: The scaling parameter of the polynomial and tangent kernel is a
  convenient way of normalizing patterns (&amp;lt;-!?) without the need to modify the
  data itself&lt;/p&gt;
  
  &lt;p&gt;&lt;strong&gt;offset&lt;/strong&gt;: The offset used in a polynomial or hyperbolic tangent kernel (&amp;lt;- lol thanks)&lt;/p&gt;
&lt;/blockquote&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1035, 2, '2014-08-24 22:02:01.05', 2, NULL, NULL, '&lt;p&gt;First compute the edit distance between all pairs of strings.  See &lt;a href="http://en.wikipedia.org/wiki/Edit_distance" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Edit_distance&lt;/a&gt; and &lt;a href="http://web.stanford.edu/class/cs124/lec/med.pdf" rel="nofollow"&gt;http://web.stanford.edu/class/cs124/lec/med.pdf&lt;/a&gt;.  Then exclude any outliers strings based on some distance threshold.&lt;/p&gt;

&lt;p&gt;With remaining strings, you can use the distance matrix to identify the most central string.  Depending on the method you use, you might get ambiguous results for some data. No method is perfect for all possibilities.  For your purposes, all you need is some heuristic rules to resolve ambiguities -- i.e. pick two or more candidates.&lt;/p&gt;

&lt;p&gt;Maybe you don''t want to pick "most central" from your list of strings, but instead want to generate a regular expression that captures the pattern common to all the non-outlier strings.  One way to do this is to synthesize a string that is equidistant from all the non-outlier strings.  You can work out the required edit distance from the matrix, and then you''d randomly generate regular using those distances as constraints.  Then you''d test candidate regular expressions and accept the first one that fits the constraints and also accepts all the strings in your non-outlier list.  (Start building regular expressions from longest common substring lists, because those are non-wildcard characters.)&lt;/p&gt;
', NULL, NULL, '''/class/cs124/lec/med.pdf'':28C ''/class/cs124/lec/med.pdf&lt;/a&gt;.'':33C ''/p'':44C,101C,224C ''/wiki/edit_distance'':17C ''/wiki/edit_distance&lt;/a&gt;'':22C ''accept'':188C,198C ''also'':197C ''ambigu'':70C,94C ''base'':39C ''build'':209C ''candid'':100C,184C ''captur'':126C ''central'':59C,111C ''charact'':223C ''common'':129C,214C ''comput'':3C ''constraint'':179C,195C ''d'':171C,182C ''data'':74C ''depend'':61C ''distanc'':6C,42C,53C,164C,177C ''edit'':5C,163C ''en.wikipedia.org'':16C,21C ''en.wikipedia.org/wiki/edit_distance'':15C ''en.wikipedia.org/wiki/edit_distance&lt;/a&gt;'':20C ''equidist'':149C ''exclud'':35C ''express'':124C,186C,211C ''first'':2C,190C ''fit'':193C ''generat'':121C,173C ''get'':69C ''heurist'':90C ''href'':14C,25C ''i.e'':95C ''identifi'':56C ''instead'':118C ''list'':114C,207C,216C ''longest'':213C ''matrix'':54C,167C ''mayb'':103C ''method'':64C,76C ''might'':68C ''need'':87C ''nofollow'':19C,30C ''non'':134C,154C,205C,221C ''non-outli'':133C,153C,204C ''non-wildcard'':220C ''one'':137C,191C ''outlier'':37C,135C,155C,206C ''p'':1C,45C,102C ''pair'':9C ''pattern'':128C ''perfect'':78C ''pick'':96C,109C ''possibl'':81C ''purpos'':84C ''random'':172C ''regular'':123C,174C,185C,210C ''rel'':18C,29C ''remain'':47C ''requir'':162C ''resolv'':93C ''result'':71C ''rule'':91C ''see'':12C ''start'':208C ''string'':11C,38C,48C,60C,116C,136C,146C,156C,201C ''substr'':215C ''synthes'':144C ''test'':183C ''threshold'':43C ''two'':97C ''use'':51C,66C,175C ''want'':107C,119C ''way'':138C ''web.stanford.edu'':27C,32C ''web.stanford.edu/class/cs124/lec/med.pdf'':26C ''web.stanford.edu/class/cs124/lec/med.pdf&lt;/a&gt;.'':31C ''wildcard'':222C ''work'':159C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1047, 2, '2014-08-26 22:48:16.617', 1, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;&lt;p&gt;SPSS is a great tool, but you can accomplish a great deal with resources that you already have on your computer, like Excel, or that are free, like the R-project. Although these tools are powerful, and can help you identify patterns, you''ll need to have a firm grasp of your data before running analyses (I''d recommend running descriptive statistics on your data, and exploring the data with graphs to make sure everything is looking normal). In other words, the tool that you use won''t offer a "silver bullet", because the output will only be as valuable as the input (you know the saying... "garbage in, garbage out"). Much of what I''m saying has already been stated in the reply by Aleksandr - spot on. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;R can be challenging for those of us who aren''t savvy with coding, but the free resources associated with R and its packages are abundant. If you practice learning the program, you''ll quickly gain traction. Again, you''ll need to be familiar with your data and the analyses you want to run anyway, and that fact remains regardless of the statistical tools you utilize. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;I''d begin by getting super familiar with my data (follow the steps outlined in the reply from Aleksandr, for starters). You might consider picking up John Foreman''s book called Data Smart. It''s a hands-on book, as John provides datasets and you follow along with his examples (using Excel) to learn various ways of navigating and exploring data. For beginners, it''s a great resource. &lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':134C,204C,277C ''/ol'':278C ''/p'':133C,203C,276C ''abund'':162C ''accomplish'':12C ''aleksandr'':130C,225C ''along'':254C ''alreadi'':20C,123C ''although'':36C ''analys'':60C,186C ''anyway'':191C ''aren'':146C ''associ'':155C ''begin'':209C ''beginn'':270C ''book'':236C,246C ''bullet'':96C ''call'':237C ''challeng'':140C ''code'':150C ''comput'':24C ''consid'':230C ''d'':62C,208C ''data'':57C,69C,73C,183C,216C,238C,268C ''dataset'':250C ''deal'':15C ''descript'':65C ''everyth'':79C ''exampl'':257C ''excel'':26C,259C ''explor'':71C,267C ''fact'':194C ''familiar'':180C,213C ''firm'':53C ''follow'':217C,253C ''foreman'':234C ''free'':30C,153C ''gain'':172C ''garbag'':112C,114C ''get'':211C ''graph'':75C ''grasp'':54C ''great'':7C,14C,274C ''hand'':244C ''hands-on'':243C ''help'':43C ''identifi'':45C ''input'':107C ''john'':233C,248C ''know'':109C ''learn'':166C,261C ''li'':2C,135C,205C ''like'':25C,31C ''ll'':48C,170C,176C ''look'':81C ''m'':120C ''make'':77C ''might'':229C ''much'':116C ''navig'':265C ''need'':49C,177C ''normal'':82C ''offer'':93C ''ol'':1C ''outlin'':220C ''output'':99C ''p'':3C,136C,206C ''packag'':160C ''pattern'':46C ''pick'':231C ''power'':40C ''practic'':165C ''program'':168C ''project'':35C ''provid'':249C ''quick'':171C ''r'':34C,137C,157C ''r-project'':33C ''recommend'':63C ''regardless'':196C ''remain'':195C ''repli'':128C,223C ''resourc'':17C,154C,275C ''run'':59C,64C,190C ''savvi'':148C ''say'':111C,121C ''silver'':95C ''smart'':239C ''spot'':131C ''spss'':4C ''starter'':227C ''state'':125C ''statist'':66C,199C ''step'':219C ''super'':212C ''sure'':78C ''tool'':8C,38C,87C,200C ''traction'':173C ''us'':144C ''use'':90C,258C ''util'':202C ''valuabl'':104C ''various'':262C ''want'':188C ''way'':263C ''won'':91C ''word'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1036, 1, '2014-08-25 00:28:09.003', 5, 223, 'How to numerically estimate MLE estimators in python when gradients are very small far from the optimal solution?', '&lt;p&gt;I am exploring how to model a data set using normal distributions with both mean and variance defined as linear functions of independent variables.&lt;/p&gt;

&lt;p&gt;Something like N ~ (f(x), g(x)).&lt;/p&gt;

&lt;p&gt;I generate a random sample like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def draw(x):
    return norm(5 * x + 2, 3 *x + 4).rvs(1)[0]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So I want to retrieve 5, 2 and 4 as the parameters for my distribution.&lt;/p&gt;

&lt;p&gt;I generate my sample:&lt;/p&gt;

&lt;p&gt;smp = np.zeros((100,2))&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for i in range(0, len(smp)):
    smp[i][0] = i
    smp[i][1] = draw(i)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The likelihood function is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def lh(p):
    p_loc_b0 = p[0]
    p_loc_b1 = p[1]
    p_scl_b0 = p[2]
    p_scl_b1 = p[3]

    l = 1
    for i in range(0, len(smp)):
        x = smp[i][0]
        y = smp[i][1]
        l = l * norm(p_loc_b0 + p_loc_b1 * x, p_scl_b0 + p_scl_b1 * x).pdf(y)

    return -l
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So the parameters for the linear functions used in the model are given in the p 4-variable vector.&lt;/p&gt;

&lt;p&gt;Using scipy.optimize, I can solve for the MLE parameters using an extremely low xtol, and already giving the solution as the starting point:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;fmin(lh, x0=[2,5,3,4], xtol=1e-35)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Which does not work to well:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Warning: Maximum number of function evaluations has been exceeded.
array([ 3.27491346,  4.69237042,  5.70317719,  3.30395462])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Raising the xtol to higher values does no good.&lt;/p&gt;

&lt;p&gt;So i try using a starting solution far from the real solution:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;&amp;gt;&amp;gt; fmin(lh, x0=[1,1,1,1], xtol=1e-8)
Optimization terminated successfully.
         Current function value: -0.000000
         Iterations: 24
         Function evaluations: 143
array([ 1.,  1.,  1.,  1.])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Which makes me think:&lt;/p&gt;

&lt;p&gt;PDF are largely clustered around the mean, and have very low gradients only a few standard deviations away from the mean, which must be not too good for numerical methods.&lt;/p&gt;

&lt;p&gt;So how does one go about doing these kind of numerical estimation in functions where gradient is very near to zero away from the solution?&lt;/p&gt;
', 'python  statistics', NULL, '''-0.000000'':333C ''/code'':81C,130C,201C,260C,286C,344C ''/p'':46C,55C,64C,99C,105C,111C,137C,223C,248C,269C,298C,312C,351C,383C,410C ''/pre'':82C,131C,202C,261C,287C,345C ''0'':80C,118C,123C,147C,169C,175C ''1'':79C,127C,152C,164C,179C,321C,322C,323C,324C,340C,341C,342C,343C ''100'':109C ''143'':338C ''1e-35'':259C ''1e-8'':326C ''2'':74C,90C,110C,157C,254C ''24'':335C ''3'':75C,162C,256C ''3.27491346'':282C ''3.30395462'':285C ''4'':77C,92C,220C,257C ''4.69237042'':283C ''5'':72C,89C,255C ''5.70317719'':284C ''alreadi'':240C ''around'':357C ''array'':281C,339C ''away'':370C,406C ''b0'':145C,155C,185C,192C ''b1'':150C,160C,188C,195C ''cluster'':356C ''code'':66C,113C,139C,250C,271C,314C ''current'':330C ''data'':29C ''def'':67C,140C ''defin'':39C ''deviat'':369C ''distribut'':33C,98C ''draw'':68C,128C ''estim'':4A,6A,396C ''evalu'':277C,337C ''exceed'':280C ''explor'':24C ''extrem'':236C ''f'':51C ''far'':14A,307C ''fmin'':251C,318C ''function'':42C,135C,210C,276C,331C,336C,398C ''g'':53C ''generat'':58C,102C ''give'':241C ''given'':216C ''go'':389C ''good'':297C,379C ''gradient'':10A,364C,400C ''gt'':315C,316C,317C ''higher'':293C ''independ'':44C ''iter'':334C ''kind'':393C ''l'':163C,180C,181C,200C ''larg'':355C ''len'':119C,170C ''lh'':141C,252C,319C ''like'':49C,62C ''likelihood'':134C ''linear'':41C,209C ''loc'':144C,149C,184C,187C ''low'':237C,363C ''make'':348C ''maximum'':273C ''mean'':36C,359C,373C ''method'':382C ''mle'':5A,232C ''model'':27C,214C ''must'':375C ''n'':50C ''near'':403C ''norm'':71C,182C ''normal'':32C ''np.zeros'':108C ''number'':274C ''numer'':3A,381C,395C ''one'':388C ''optim'':17A,327C ''p'':21C,47C,56C,83C,100C,106C,132C,142C,143C,146C,148C,151C,153C,156C,158C,161C,183C,186C,190C,193C,203C,219C,224C,262C,288C,299C,346C,352C,384C ''paramet'':95C,206C,233C ''pdf'':197C,353C ''point'':247C ''pre'':65C,112C,138C,249C,270C,313C ''python'':8A,19B ''rais'':289C ''random'':60C ''rang'':117C,168C ''real'':310C ''retriev'':88C ''return'':70C,199C ''rvs'':78C ''sampl'':61C,104C ''scipy.optimize'':226C ''scl'':154C,159C,191C,194C ''set'':30C ''small'':13A ''smp'':107C,120C,121C,125C,171C,173C,177C ''solut'':18A,243C,306C,311C,409C ''solv'':229C ''someth'':48C ''standard'':368C ''start'':246C,305C ''statist'':20B ''success'':329C ''termin'':328C ''think'':350C ''tri'':302C ''use'':31C,211C,225C,234C,303C ''valu'':294C,332C ''variabl'':45C,221C ''varianc'':38C ''vector'':222C ''want'':86C ''warn'':272C ''well'':268C ''work'':266C ''x'':52C,54C,69C,73C,76C,172C,189C,196C ''x0'':253C,320C ''xtol'':238C,258C,291C,325C ''y'':176C,198C ''zero'':405C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6157, 2, '2015-06-19 15:28:51.003', 2, NULL, NULL, '&lt;p&gt;Yes it is a challenging task to extract named entities in tweets. Give a go at NLTK NER and also Alan Ritter''s Twitter specific NER and evaluate on their performance and compare to Stanford NER and which one fits in your use. Maybe you want to use more than one to get more named entities if you don''t mind so much of false NEs..&lt;/p&gt;
', NULL, NULL, '''/p'':68C ''alan'':22C ''also'':21C ''challeng'':6C ''compar'':34C ''entiti'':11C,57C ''evalu'':29C ''extract'':9C ''fals'':66C ''fit'':41C ''get'':54C ''give'':14C ''go'':16C ''mayb'':45C ''mind'':62C ''much'':64C ''name'':10C,56C ''ner'':19C,27C,37C ''nes'':67C ''nltk'':18C ''one'':40C,52C ''p'':1C ''perform'':32C ''ritter'':23C ''specif'':26C ''stanford'':36C ''task'':7C ''tweet'':13C ''twitter'':25C ''use'':44C,49C ''want'':47C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1038, 1, '2014-08-25 09:53:13.823', 1, 249, 'Hive External table does not showing in Namenode (Cloudera-QuickstartVm)', '&lt;p&gt;I have created external table in Hive in the hdfs path ''hdfs://localhost.localdomain:8020/user/hive/training''. If I apply describe command I can find the table path as shown below. But when I browse through the namenode web page, the table name does not showing up in the path.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;hive&amp;gt; describe extended testtable4;
OK
firstname   string  
lastname    string  
address string  
city    string  
state   string  
country string  

    ***Detailed Table Information  Table(tableName:testtable4, dbName:default, owner:cloudera, createTime:1408765301, lastAccessTime:0, retention:0, sd:StorageDescriptor(cols:[FieldSchema(name:firstname, type:string, comment:null), FieldSchema(name:lastname, type:string, comment:null), FieldSchema(name:address, type:string, comment:null), FieldSchema(name:city, type:string, comment:null), FieldSchema(name:state, type:string, comment:null), FieldSchema(name:country, type:string, comment:null)], location:hdfs://localhost.localdomain:8020/user/hive/training, inputFormat:org.apache.hadoop.mapred.TextInputFormat, outputFormat:org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat, compressed:false, numBuckets:-1, serdeInfo:SerDeInfo(name:null, serializationLib:org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, parameters:{serialization.format=,, field.delim=,, line.delim=    
    }), bucketCols:[], sortCols:[], parameters:{}, skewedInfo:SkewedInfo(skewedColNames:[], skewedColValues:[], skewedColValueLocationMaps:{}), storedAsSubDirectories:false), partitionKeys:[], parameters:{EXTERNAL=TRUE, transient_lastDdlTime=1408765301}, viewOriginalText:null, viewExpandedText:null, tableType:EXTERNAL_TABLE)       
    Time taken: 0.7 seconds***
&lt;/code&gt;&lt;/pre&gt;
', 'bigdata', NULL, '''-1'':154C ''/code'':193C ''/p'':61C ''/pre'':194C ''/user/hive/training''''.'':27C ''/user/hive/training,'':146C ''0'':95C,97C ''0.7'':191C ''1408765301'':93C,181C ''address'':74C,117C ''appli'':30C ''bigdata'':12B ''brows'':45C ''bucketcol'':165C ''citi'':76C,124C ''cloudera'':10A,91C ''cloudera-quickstartvm'':9A ''code'':63C ''col'':100C ''command'':32C ''comment'':106C,113C,120C,127C,134C,141C ''compress'':151C ''countri'':80C,138C ''creat'':16C ''createtim'':92C ''dbname'':88C ''default'':89C ''describ'':31C,66C ''detail'':82C ''extend'':67C ''extern'':2A,17C,177C,187C ''fals'':152C,174C ''field.delim'':163C ''fieldschema'':101C,108C,115C,122C,129C,136C ''find'':35C ''firstnam'':70C,103C ''gt'':65C ''hdfs'':23C ''hive'':1A,20C,64C ''inform'':84C ''inputformat'':147C ''lastaccesstim'':94C ''lastddltim'':180C ''lastnam'':72C,110C ''line.delim'':164C ''localhost.localdomain:8020'':26C,145C ''localhost.localdomain:8020/user/hive/training''''.'':25C ''localhost.localdomain:8020/user/hive/training,'':144C ''locat'':143C ''name'':53C,102C,109C,116C,123C,130C,137C,157C ''namenod'':8A,48C ''null'':107C,114C,121C,128C,135C,142C,158C,183C,185C ''numbucket'':153C ''ok'':69C ''org.apache.hadoop.hive.ql.io.hiveignorekeytextoutputformat'':150C ''org.apache.hadoop.hive.serde2.lazy.lazysimpleserde'':160C ''org.apache.hadoop.mapred.textinputformat'':148C ''outputformat'':149C ''owner'':90C ''p'':13C ''page'':50C ''paramet'':161C,167C,176C ''partitionkey'':175C ''path'':24C,38C,60C ''pre'':62C ''quickstartvm'':11A ''retent'':96C ''sd'':98C ''second'':192C ''serdeinfo'':155C,156C ''serialization.format'':162C ''serializationlib'':159C ''show'':6A,56C ''shown'':40C ''skewedcolnam'':170C ''skewedcolvalu'':171C ''skewedcolvaluelocationmap'':172C ''skewedinfo'':168C,169C ''sortcol'':166C ''state'':78C,131C ''storagedescriptor'':99C ''storedassubdirectori'':173C ''string'':71C,73C,75C,77C,79C,81C,105C,112C,119C,126C,133C,140C ''tabl'':3A,18C,37C,52C,83C,85C,188C ''tablenam'':86C ''tabletyp'':186C ''taken'':190C ''testtable4'':68C,87C ''time'':189C ''transient'':179C ''true'':178C ''type'':104C,111C,118C,125C,132C,139C ''viewexpandedtext'':184C ''vieworiginaltext'':182C ''web'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1041, 2, '2014-08-26 07:14:54.767', 6, NULL, NULL, '&lt;p&gt;If you suspect your response is linear with year, then put year in as a numeric term in your model rather than a categorical.&lt;/p&gt;

&lt;p&gt;Extrapolation is then perfectly valid based on the usual assumptions of the GLM family. Make sure you correctly get the errors on your extrapolated estimates.&lt;/p&gt;

&lt;p&gt;Just extrapolating the parameters from a categorical variable is wrong for a number of reasons. The first one I can think of is that there may be more observations in some years than others, so any linear extrapolation needs to weight those year''s estimates more. Just eyeballing a line - or even fitting a line to the coefficients - won''t do this. &lt;/p&gt;
', NULL, NULL, '''/p'':26C,53C,117C ''assumpt'':37C ''base'':33C ''categor'':25C,61C ''coeffici'':112C ''correct'':45C ''error'':48C ''estim'':52C,99C ''even'':106C ''extrapol'':28C,51C,56C,92C ''eyebal'':102C ''famili'':41C ''first'':71C ''fit'':107C ''get'':46C ''glm'':40C ''line'':104C,109C ''linear'':8C,91C ''make'':42C ''may'':80C ''model'':21C ''need'':93C ''number'':67C ''numer'':17C ''observ'':83C ''one'':72C ''other'':88C ''p'':1C,27C,54C ''paramet'':58C ''perfect'':31C ''put'':12C ''rather'':22C ''reason'':69C ''respons'':6C ''sure'':43C ''suspect'':4C ''term'':18C ''think'':75C ''usual'':36C ''valid'':32C ''variabl'':62C ''weight'':95C ''won'':113C ''wrong'':64C ''year'':10C,13C,86C,97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1042, 1, '2014-08-26 07:33:40.08', 4, 83, 'Studying social media platforms - key data analysis methods?', '&lt;p&gt;I am seeking a basic list of key data analysis methods used for studying social media platforms online. Are there such key methods, or does this process generally vary according to topic? And is there a standard order in which these methods are applied?(The particular context I''m interested in is how the news is impacting on social media)&lt;/p&gt;
', 'social-network-analysis', NULL, '''/p'':74C ''accord'':43C ''analysi'':7A,12B,23C ''appli'':57C ''basic'':18C ''context'':60C ''data'':6A,22C ''general'':41C ''impact'':70C ''interest'':63C ''key'':5A,21C,35C ''list'':19C ''m'':62C ''media'':3A,29C,73C ''method'':8A,24C,36C,55C ''network'':11B ''news'':68C ''onlin'':31C ''order'':51C ''p'':13C ''particular'':59C ''platform'':4A,30C ''process'':40C ''seek'':16C ''social'':2A,10B,28C,72C ''social-network-analysi'':9B ''standard'':50C ''studi'':1A,27C ''topic'':45C ''use'':25C ''vari'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1044, 1, '2014-08-26 14:53:40.647', -3, 199, 'which programming language has a large library that can do machine learning algorithm, R, matlab or python', '&lt;p&gt;As what I described in the title, we are especially interested in those for dealing with big data----ts efficiency and stability, and used in industry not in experiment or university. Thanks!&lt;/p&gt;
', 'machine-learning  bigdata  data-mining  python  r', NULL, '''/p'':60C ''algorithm'':13A ''big'':44C ''bigdata'':21B ''data'':23B,45C ''data-min'':22B ''deal'':42C ''describ'':31C ''effici'':47C ''especi'':37C ''experi'':56C ''industri'':53C ''interest'':38C ''languag'':3A ''larg'':6A ''learn'':12A,20B ''librari'':7A ''machin'':11A,19B ''machine-learn'':18B ''matlab'':15A ''mine'':24B ''p'':27C ''program'':2A ''python'':17A,25B ''r'':14A,26B ''stabil'':49C ''thank'':59C ''titl'':34C ''ts'':46C ''univers'':58C ''use'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1048, 2, '2014-08-27 03:41:20.317', 2, NULL, NULL, '&lt;p&gt;I''ve making some researches last months and I could find more libraries, contente and active community with Python. Actually I''m using it to ETL processes, some minning jobs and to make map/reduce.&lt;/p&gt;
', NULL, NULL, '''/p'':36C ''activ'':17C ''actual'':21C ''communiti'':18C ''content'':15C ''could'':11C ''etl'':27C ''find'':12C ''job'':31C ''last'':7C ''librari'':14C ''m'':23C ''make'':4C,34C ''map/reduce'':35C ''min'':30C ''month'':8C ''p'':1C ''process'':28C ''python'':20C ''research'':6C ''use'':24C ''ve'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1050, 1, '2014-08-27 13:01:14.643', 7, 274, 'Libraries for (label propagation algorithms/frequent subgraph mining) for graphs in R', '&lt;p&gt;&lt;strong&gt;General description of the problem&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I have a graph where some vertices are labeled with a type with 3 or 4 possible values. For the other vertices, the type is unknown.
My goal is to use the graph to predict the type for vertices that are unlabeled.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Possible framework&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I suspect this fits into the general framework of label propagation problems, based on my reading of the literature (e.g., see &lt;a href="http://lvk.cs.msu.su/~bruzz/articles/classification/zhu02learning.pdf" rel="nofollow"&gt;this paper&lt;/a&gt; and &lt;a href="http://www.csc.ncsu.edu/faculty/samatova/practical-graph-mining-with-R/slides/pdf/Frequent_Subgraph_Mining.pdf" rel="nofollow"&gt;this paper&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;Another method that is mentioned often is &lt;code&gt;Frequent Subgraph Mining&lt;/code&gt;, which includes algorithms like &lt;code&gt;SUBDUE&lt;/code&gt;,&lt;code&gt;SLEUTH&lt;/code&gt;, and &lt;code&gt;gSpan&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Found in R&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The only label propagation implementation I managed to find in &lt;code&gt;R&lt;/code&gt; is &lt;code&gt;label.propagation.community()&lt;/code&gt; from the &lt;code&gt;igraph&lt;/code&gt; library. 
However, as the name suggests, it is mostly used to find communities, not for classifying unlabeled vertices.&lt;/p&gt;

&lt;p&gt;There also seems to be several references to a &lt;code&gt;subgraphMining&lt;/code&gt; library (here for example), but it looks like it is missing from CRAN.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Question&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Do you know of a library or framework for the task described?&lt;/p&gt;
', 'classification  r  graphs', NULL, '''/a'':105C,116C ''/code'':130C,137C,140C,144C,166C,170C,175C,207C ''/faculty/samatova/practical-graph-mining-with-r/slides/pdf/frequent_subgraph_mining.pdf'':111C ''/p'':23C,67C,73C,117C,145C,152C,194C,221C,226C,240C ''/strong'':22C,72C,151C,225C ''/~bruzz/articles/classification/zhu02learning.pdf'':100C ''3'':38C ''4'':40C ''algorithm'':133C ''algorithms/frequent'':5A ''also'':197C ''anoth'':119C ''base'':87C ''classif'':12B ''classifi'':191C ''code'':126C,135C,138C,142C,164C,168C,173C,205C ''communiti'':188C ''cran'':220C ''describ'':239C ''descript'':18C ''e.g'':94C ''exampl'':211C ''find'':162C,187C ''fit'':78C ''found'':148C ''framework'':71C,82C,235C ''frequent'':127C ''general'':17C,81C ''goal'':52C ''graph'':9A,14B,28C,57C ''gspan'':143C ''howev'':177C ''href'':97C,108C ''igraph'':174C ''implement'':158C ''includ'':132C ''know'':230C ''label'':3A,33C,84C,156C ''label.propagation.community'':169C ''librari'':1A,176C,208C,233C ''like'':134C,215C ''literatur'':93C ''look'':214C ''lvk.cs.msu.su'':99C ''lvk.cs.msu.su/~bruzz/articles/classification/zhu02learning.pdf'':98C ''manag'':160C ''mention'':123C ''method'':120C ''mine'':7A,129C ''miss'':218C ''most'':184C ''name'':180C ''nofollow'':102C,113C ''often'':124C ''p'':15C,24C,68C,74C,118C,146C,153C,195C,222C,227C ''paper'':104C,115C ''possibl'':41C,70C ''predict'':59C ''problem'':21C,86C ''propag'':4A,85C,157C ''question'':224C ''r'':11A,13B,150C,165C ''read'':90C ''refer'':202C ''rel'':101C,112C ''see'':95C ''seem'':198C ''sever'':201C ''sleuth'':139C ''strong'':16C,69C,147C,223C ''subdu'':136C ''subgraph'':6A,128C ''subgraphmin'':206C ''suggest'':181C ''suspect'':76C ''task'':238C ''type'':36C,48C,61C ''unknown'':50C ''unlabel'':66C,192C ''use'':55C,185C ''valu'':42C ''vertic'':31C,46C,63C,193C ''www.csc.ncsu.edu'':110C ''www.csc.ncsu.edu/faculty/samatova/practical-graph-mining-with-r/slides/pdf/frequent_subgraph_mining.pdf'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1171, 2, '2014-09-26 16:15:05.58', 0, NULL, NULL, '&lt;p&gt;There''s a NLP toolbox for MATLAB called &lt;a href="https://github.com/faridani/MatlabNLP" rel="nofollow"&gt;&lt;strong&gt;MatlabNLP&lt;/strong&gt;&lt;/a&gt;. It includes modules for tokenization, preprocessing (stop word removal, text cleaning, stemming), and learning algorithms (linear regression, decision trees, support vector machines and a NaÃ¯ve Bayes). A module for POS tagging is coming soon.&lt;/p&gt;
', NULL, NULL, '''/a'':20C ''/faridani/matlabnlp'':14C ''/p'':56C ''/strong'':19C ''algorithm'':35C ''bay'':47C ''call'':9C ''clean'':31C ''come'':54C ''decis'':38C ''github.com'':13C ''github.com/faridani/matlabnlp'':12C ''href'':11C ''includ'':22C ''learn'':34C ''linear'':36C ''machin'':42C ''matlab'':8C ''matlabnlp'':18C ''modul'':23C,49C ''naã'':45C ''nlp'':5C ''nofollow'':16C ''p'':1C ''pos'':51C ''preprocess'':26C ''regress'':37C ''rel'':15C ''remov'':29C ''soon'':55C ''stem'':32C ''stop'':27C ''strong'':17C ''support'':40C ''tag'':52C ''text'':30C ''token'':25C ''toolbox'':6C ''tree'':39C ''ve'':46C ''vector'':41C ''word'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1051, 2, '2014-08-27 18:33:23.11', 1, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;When using the public Google APIs to retrieve results, I was only able to collect 4-10 results per query.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Here''s how to get more than 10 results per query: &lt;a href="https://support.google.com/customsearch/answer/1361951?hl=en" rel="nofollow"&gt;https://support.google.com/customsearch/answer/1361951?hl=en&lt;/a&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Google Custom Search and Google Site Search return up to 10 results per query. If you want to display more than 10 results to the user, you can issue multiple requests (using the start=0, start=11 ... parameters) and display the results on a single page. In this case, Google will consider each request as a separate query, and if you are using Google Site Search, each query will count towards your limit.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;There are other search engine APIs as well (e.g., &lt;a href="http://datamarket.azure.com/dataset/bing/search" rel="nofollow"&gt;Bing&lt;/a&gt;)&lt;/p&gt;
', NULL, NULL, '''-10'':19C ''/a'':142C ''/blockquote'':24C,123C ''/customsearch/answer/1361951?hl=en'':41C ''/customsearch/answer/1361951?hl=en&lt;/a&gt;&lt;/p&gt;'':46C ''/dataset/bing/search'':138C ''/p'':23C,122C,143C ''0'':83C ''10'':33C,59C,70C ''11'':85C ''4'':18C ''abl'':15C ''api'':8C,130C ''bing'':141C ''blockquot'':1C,47C ''case'':97C ''collect'':17C ''consid'':100C ''count'':118C ''custom'':50C ''datamarket.azure.com'':137C ''datamarket.azure.com/dataset/bing/search'':136C ''display'':67C,88C ''e.g'':133C ''engin'':129C ''get'':30C ''googl'':7C,49C,53C,98C,112C ''href'':38C,135C ''issu'':77C ''limit'':121C ''multipl'':78C ''nofollow'':43C,140C ''p'':2C,25C,48C,124C ''page'':94C ''paramet'':86C ''per'':21C,35C,61C ''public'':6C ''queri'':22C,36C,62C,106C,116C ''rel'':42C,139C ''request'':79C,102C ''result'':11C,20C,34C,60C,71C,90C ''retriev'':10C ''return'':56C ''search'':51C,55C,114C,128C ''separ'':105C ''singl'':93C ''site'':54C,113C ''start'':82C,84C ''support.google.com'':40C,45C ''support.google.com/customsearch/answer/1361951?hl=en'':39C ''support.google.com/customsearch/answer/1361951?hl=en&lt;/a&gt;&lt;/p&gt;'':44C ''toward'':119C ''use'':4C,80C,111C ''user'':74C ''want'':65C ''well'':132C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1053, 1, '2014-08-28 01:36:40.54', 6, 569, 'Summarize and visualize a CSV in Java/Scala?', '&lt;p&gt;I would like to &lt;a href="http://stat.ethz.ch/R-manual/R-devel/library/base/html/summary.html"&gt;summarize&lt;/a&gt; (as in R) the contents of a CSV (possibly after &lt;a href="http://www.endmemo.com/program/R/readcsv.php"&gt;loading&lt;/a&gt; it, or storing it somewhere, that''s not a problem). The summary should contain the quartiles, mean, median, min and max of the data in a CSV file for each numeric (integer or real numbers) dimension. The standard deviation would be cool as well.&lt;/p&gt;

&lt;p&gt;I would also like to generate some plots to visualize the data, for example 3 plots for the 3 pairs of variables that are more correlated (&lt;a href="http://www.r-tutor.com/elementary-statistics/numerical-measures/correlation-coefficient"&gt;correlation coefficient&lt;/a&gt;) and 3 plots for the 3 pairs of variables that are least correlated.&lt;/p&gt;

&lt;p&gt;R requires only a few lines to implement this. Are there any libraries (or tools) that would allow a similarly simple (and efficient if possible) implementation in Java or Scala?&lt;/p&gt;

&lt;p&gt;PD: This is a specific use case for a &lt;a href="http://datascience.stackexchange.com/questions/948/any-clear-winner-for-data-science-in-scala"&gt;previous (too broad) question&lt;/a&gt;.&lt;/p&gt;
', 'tools  visualization  scala  csv', NULL, '''/a'':23C,40C,120C,186C ''/elementary-statistics/numerical-measures/correlation-coefficient'':117C ''/p'':85C,134C,166C,187C ''/program/r/readcsv.php'':38C ''/questions/948/any-clear-winner-for-data-science-in-scala'':181C ''/r-manual/r-devel/library/base/html/summary.html'':21C ''3'':101C,105C,122C,126C ''allow'':153C ''also'':89C ''broad'':184C ''case'':174C ''coeffici'':119C ''contain'':54C ''content'':28C ''cool'':82C ''correl'':112C,118C,133C ''csv'':5A,11B,31C,67C ''data'':64C,98C ''datascience.stackexchange.com'':180C ''datascience.stackexchange.com/questions/948/any-clear-winner-for-data-science-in-scala'':179C ''deviat'':79C ''dimens'':76C ''effici'':158C ''exampl'':100C ''file'':68C ''generat'':92C ''href'':18C,35C,114C,178C ''implement'':143C,161C ''integ'':72C ''java'':163C ''java/scala'':7A ''least'':132C ''librari'':148C ''like'':15C,90C ''line'':141C ''load'':39C ''max'':61C ''mean'':57C ''median'':58C ''min'':59C ''number'':75C ''numer'':71C ''p'':12C,86C,135C,167C ''pair'':106C,127C ''pd'':168C ''plot'':94C,102C,123C ''possibl'':32C,160C ''previous'':182C ''problem'':50C ''quartil'':56C ''question'':185C ''r'':26C,136C ''real'':74C ''requir'':137C ''scala'':10B,165C ''similar'':155C ''simpl'':156C ''somewher'':45C ''specif'':172C ''standard'':78C ''stat.ethz.ch'':20C ''stat.ethz.ch/r-manual/r-devel/library/base/html/summary.html'':19C ''store'':43C ''summar'':1A,22C ''summari'':52C ''tool'':8B,150C ''use'':173C ''variabl'':108C,129C ''visual'':3A,9B,96C ''well'':84C ''would'':14C,80C,88C,152C ''www.endmemo.com'':37C ''www.endmemo.com/program/r/readcsv.php'':36C ''www.r-tutor.com'':116C ''www.r-tutor.com/elementary-statistics/numerical-measures/correlation-coefficient'':115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1054, 2, '2014-08-28 06:49:05.21', 5, NULL, NULL, '&lt;p&gt;There are several reasons why you are getting erroneous results.
First, you should consider using log likelihood instead of likelihood. There are numerical issues with multiplying many small numbers(imagine if you had millions of samples you had to multiply millions of small numbers for the lhd). Also taking gradients for optimization methods that require gradients is often easier when you are dealing with the log likelihood. In general, it is good to have an objective which is a sum rather than a product of variables when dealing with optimization problems.&lt;/p&gt;

&lt;p&gt;Second, fmin is using Nelder-Mead simplex algorithm which has no convergence guarantees according to &lt;a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin.html#scipy.optimize.fmin"&gt;scipy documentation&lt;/a&gt;. This means the convergence is totally random and you should not expect to find parameters close to the originals. To get around this, I would suggest you to use a gradient based method like stochastic gradient descent or BFGS. Since you know the generative model (rvs are Gaussian distributed) you can write the likelihood and log likelihood as:
&lt;img src="http://i.stack.imgur.com/bfsvr.png" alt="equations"&gt;&lt;/p&gt;

&lt;p&gt;Where a,b,c and d are your model parameters 5,2,3 and 4 respectively.
Then take the &lt;a href="http://en.wikipedia.org/wiki/Gradient"&gt;gradient&lt;/a&gt; with respect to [a,b,c,d] and feed that into prime input of fmin_bfgs. Note that due to varying variance what could be solved by just linear regression is now a nastier problem.&lt;/p&gt;

&lt;p&gt;Finally, you may also want to check Generalized least squares on &lt;a href="http://en.wikipedia.org/wiki/Linear_regression#Least-squares_estimation_and_related_techniques"&gt;http://en.wikipedia.org/wiki/Linear_regression#Least-squares_estimation_and_related_techniques&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/Heteroscedasticity"&gt;http://en.wikipedia.org/wiki/Heteroscedasticity&lt;/a&gt;, which talk about your problem and offer several available solutions.&lt;/p&gt;

&lt;p&gt;Good luck!&lt;/p&gt;
', NULL, NULL, '''/a'':118C,211C ''/bfsvr.png'':181C ''/doc/scipy/reference/generated/scipy.optimize.fmin.html#scipy.optimize.fmin'':115C ''/p'':93C,184C,247C,287C,291C ''/wiki/gradient'':209C ''/wiki/heteroscedasticity'':273C ''/wiki/heteroscedasticity&lt;/a&gt;,'':276C ''/wiki/linear_regression#least-squares_estimation_and_related_techniques'':264C ''/wiki/linear_regression#least-squares_estimation_and_related_techniques&lt;/a&gt;'':267C ''2'':197C ''3'':198C ''4'':200C ''5'':196C ''accord'':109C ''algorithm'':103C ''also'':49C,252C ''alt'':182C ''around'':140C ''avail'':285C ''b'':188C,216C ''base'':150C ''bfgs'':157C,227C ''c'':189C,217C ''check'':255C ''close'':134C ''consid'':15C ''converg'':107C,122C ''could'':235C ''d'':191C,218C ''deal'':64C,89C ''descent'':155C ''distribut'':167C ''docs.scipy.org'':114C ''docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin.html#scipy.optimize.fmin'':113C ''document'':117C ''due'':230C ''easier'':60C ''en.wikipedia.org'':208C,263C,266C,272C,275C ''en.wikipedia.org/wiki/gradient'':207C ''en.wikipedia.org/wiki/heteroscedasticity'':271C ''en.wikipedia.org/wiki/heteroscedasticity&lt;/a&gt;,'':274C ''en.wikipedia.org/wiki/linear_regression#least-squares_estimation_and_related_techniques'':262C ''en.wikipedia.org/wiki/linear_regression#least-squares_estimation_and_related_techniques&lt;/a&gt;'':265C ''equat'':183C ''erron'':10C ''expect'':130C ''feed'':220C ''final'':249C ''find'':132C ''first'':12C ''fmin'':96C,226C ''gaussian'':166C ''general'':70C,256C ''generat'':162C ''get'':9C,139C ''good'':73C,289C ''gradient'':51C,57C,149C,154C,210C ''guarante'':108C ''href'':112C,206C,261C,270C ''i.stack.imgur.com'':180C ''i.stack.imgur.com/bfsvr.png'':179C ''imagin'':31C ''img'':177C ''input'':224C ''instead'':19C ''issu'':25C ''know'':160C ''least'':257C ''lhd'':48C ''like'':152C ''likelihood'':18C,21C,68C,172C,175C ''linear'':240C ''log'':17C,67C,174C ''luck'':290C ''mani'':28C ''may'':251C ''mead'':101C ''mean'':120C ''method'':54C,151C ''million'':35C,42C ''model'':163C,194C ''multipli'':27C,41C ''nastier'':245C ''nelder'':100C ''nelder-mead'':99C ''note'':228C ''number'':30C,45C ''numer'':24C ''object'':77C ''offer'':283C ''often'':59C ''optim'':53C,91C ''origin'':137C ''p'':1C,94C,185C,248C,288C ''paramet'':133C,195C ''prime'':223C ''problem'':92C,246C,281C ''product'':85C ''random'':125C ''rather'':82C ''reason'':5C ''regress'':241C ''requir'':56C ''respect'':201C,213C ''result'':11C ''rvs'':164C ''sampl'':37C ''scipi'':116C ''second'':95C ''sever'':4C,284C ''simplex'':102C ''sinc'':158C ''small'':29C,44C ''solut'':286C ''solv'':237C ''squar'':258C ''src'':178C ''stochast'':153C ''suggest'':144C ''sum'':81C ''take'':50C,203C ''talk'':278C ''total'':124C ''use'':16C,98C,147C ''vari'':232C ''variabl'':87C ''varianc'':233C ''want'':253C ''would'':143C ''write'':170C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1056, 2, '2014-08-28 16:56:45.577', 1, NULL, NULL, '&lt;p&gt;It sounds as if you want to use unsupervized learning to create a training set. Am I right? You use your cluster analysis to determine which docs come from UK, US or Oz -- or which docs are talking about Soccer, Football or Australian football respectively? Then feed those tagged docs into a supervized learning algorithm of some sort?&lt;/p&gt;

&lt;p&gt;How well this works will depend entirely on how well you can distinguish UK, US and OZ. I would have thought it would be fairly straightforward to find documents where national origin was known, so that you could build a supervized algorithm for detecting language variant. You wouldn''t even need a corpus that talked about football, since dialectical differences show up in other ways that are subject matter independent. (For example, I am clearly from North America, since I just wrote "in ways that are subject matter independent" rather than "Since dialectical differences do not depend on subject matter").&lt;/p&gt;

&lt;p&gt;However, the answer to your question, "can I use unsupervized learning and then supervized learning" is No, if you are looking for supervized learning. If the results of an unsupervized learning algorithm are fed to a supervized learning algorithm, the net result is unsupervized --- there are still no grown-ups in the room. And the classification errors of the resulting process will contain error terms from both stages. You won''t get the same performance as you would if you did a SVM with properly tagged training data. This doesn''t mean you shouldn''t use the method you propose ... it might still work well ... but it won''t be a supervized learning algorithm.&lt;/p&gt;
', NULL, NULL, '''/p'':60C,162C,279C ''algorithm'':56C,103C,195C,202C,278C ''america'':139C ''analysi'':24C ''answer'':166C ''australian'':44C ''build'':100C ''classif'':220C ''clear'':136C ''cluster'':23C ''come'':29C ''contain'':227C ''corpus'':114C ''could'':99C ''creat'':13C ''data'':252C ''depend'':67C,158C ''detect'':105C ''determin'':26C ''dialect'':120C,154C ''differ'':121C,155C ''distinguish'':74C ''doc'':28C,37C,51C ''document'':90C ''doesn'':254C ''entir'':68C ''error'':221C,228C ''even'':111C ''exampl'':133C ''fair'':86C ''fed'':197C ''feed'':48C ''find'':89C ''footbal'':42C,45C,118C ''get'':236C ''grown'':213C ''grown-up'':212C ''howev'':164C ''independ'':131C,150C ''known'':95C ''languag'':106C ''learn'':11C,55C,174C,178C,187C,194C,201C,277C ''look'':184C ''matter'':130C,149C,161C ''mean'':256C ''method'':262C ''might'':266C ''nation'':92C ''need'':112C ''net'':204C ''north'':138C ''origin'':93C ''oz'':34C,78C ''p'':1C,61C,163C ''perform'':239C ''process'':225C ''proper'':249C ''propos'':264C ''question'':169C ''rather'':151C ''respect'':46C ''result'':190C,205C,224C ''right'':19C ''room'':217C ''set'':16C ''shouldn'':258C ''show'':122C ''sinc'':119C,140C,153C ''soccer'':41C ''sort'':59C ''sound'':3C ''stage'':232C ''still'':210C,267C ''straightforward'':87C ''subject'':129C,148C,160C ''superv'':54C,102C,177C,186C,200C,276C ''svm'':247C ''tag'':50C,250C ''talk'':39C,116C ''term'':229C ''thought'':82C ''train'':15C,251C ''uk'':31C,75C ''unsuperv'':10C,173C,193C,207C ''up'':214C ''us'':32C,76C ''use'':9C,21C,172C,260C ''variant'':107C ''want'':7C ''way'':126C,145C ''well'':63C,71C,269C ''won'':234C,272C ''work'':65C,268C ''would'':80C,84C,242C ''wouldn'':109C ''wrote'':143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1057, 2, '2014-08-28 18:53:57.297', 1, NULL, NULL, '&lt;p&gt;You might want to try this book &lt;a href="http://shop.oreilly.com/product/0636920030195.do" rel="nofollow"&gt;Mining the Social Web&lt;/a&gt; for an overview of different techniques. Obviously, the methods you need will depend on the use case. A lot of people do interesting things with graphs, displaying relationships between users, with respect to certain topics. Or you might simply to a timeline showing how a news topic builds in interest and wanes.&lt;/p&gt;
', NULL, NULL, '''/a'':20C ''/p'':73C ''/product/0636920030195.do'':13C ''book'':8C ''build'':68C ''case'':37C ''certain'':54C ''depend'':33C ''differ'':25C ''display'':47C ''graph'':46C ''href'':10C ''interest'':43C,70C ''lot'':39C ''method'':29C ''might'':3C,58C ''mine'':16C ''need'':31C ''news'':66C ''nofollow'':15C ''obvious'':27C ''overview'':23C ''p'':1C ''peopl'':41C ''rel'':14C ''relationship'':48C ''respect'':52C ''shop.oreilly.com'':12C ''shop.oreilly.com/product/0636920030195.do'':11C ''show'':63C ''simpli'':59C ''social'':18C ''techniqu'':26C ''thing'':44C ''timelin'':62C ''topic'':55C,67C ''tri'':6C ''use'':36C ''user'':50C ''wane'':72C ''want'':4C ''web'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1058, 1, '2014-08-29 01:36:36.32', -2, 78, 'How much data space is used by all scientific articles?', '&lt;p&gt;I was wondering if there is any research or study made to calculate the volume of space is used by all scientific articles. It could be in pdf, txt, compressed, or any other format. Is there even a way to measure it?&lt;/p&gt;

&lt;p&gt;Can some one point me towards realizing this study?&lt;/p&gt;

&lt;p&gt;Regards and thanks.&lt;/p&gt;
', 'bigdata  research', NULL, '''/p'':56C,67C,72C ''articl'':10A,36C ''bigdata'':11B ''calcul'':26C ''compress'':43C ''could'':38C ''data'':3A ''even'':50C ''format'':47C ''made'':24C ''measur'':54C ''much'':2A ''one'':60C ''p'':13C,57C,68C ''pdf'':41C ''point'':61C ''realiz'':64C ''regard'':69C ''research'':12B,21C ''scientif'':9A,35C ''space'':4A,30C ''studi'':23C,66C ''thank'':71C ''toward'':63C ''txt'':42C ''use'':6A,32C ''volum'':28C ''way'':52C ''wonder'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1061, 1, '2014-08-29 11:40:28.657', 1, 21, 'Apps to manage/host data sets', '&lt;p&gt;I''m looking for the best solution to manage and host datasets for journalistic pursuits. I am assessing &lt;a href="https://www.documentcloud.org" rel="nofollow"&gt;https://www.documentcloud.org&lt;/a&gt; and &lt;a href="http://datahub.io/" rel="nofollow"&gt;http://datahub.io/&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Can anyone explain the differences between them, or recommend a superior solution?&lt;/p&gt;
', 'dataset  optimization', NULL, '''/&lt;/a&gt;.&lt;/p&gt;'':42C ''/a'':33C ''/p'':56C ''anyon'':45C ''app'':1A ''assess'':26C ''best'':14C ''data'':4A ''datahub.io'':37C,41C ''datahub.io/&lt;/a&gt;.&lt;/p&gt;'':40C ''dataset'':6B,20C ''differ'':48C ''explain'':46C ''host'':19C ''href'':28C,36C ''journalist'':22C ''look'':11C ''m'':10C ''manag'':17C ''manage/host'':3A ''nofollow'':31C,39C ''optim'':7B ''p'':8C,43C ''pursuit'':23C ''recommend'':52C ''rel'':30C,38C ''set'':5A ''solut'':15C,55C ''superior'':54C ''www.documentcloud.org'':29C,32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1099, 2, '2014-09-10 08:15:17.343', 10, NULL, NULL, '&lt;p&gt;No, despite their names they are not equivalent or even that similar. Gini impurity is a measure of misclassification, which applies in a multiclass classifier context. Gini coefficient applies to binary classification, and requires a classifier that can in some way rank examples according to likelihood of being in the positive class. Both could be applied in some cases, but they are different measures for different things. Impurity is what is commonly used in decision trees.&lt;/p&gt;
', NULL, NULL, '''/p'':78C ''accord'':45C ''appli'':22C,30C,57C ''binari'':32C ''case'':60C ''class'':53C ''classif'':33C ''classifi'':26C,37C ''coeffici'':29C ''common'':73C ''context'':27C ''could'':55C ''decis'':76C ''despit'':3C ''differ'':64C,67C ''equival'':9C ''even'':11C ''exampl'':44C ''gini'':14C,28C ''impur'':15C,69C ''likelihood'':47C ''measur'':18C,65C ''misclassif'':20C ''multiclass'':25C ''name'':5C ''p'':1C ''posit'':52C ''rank'':43C ''requir'':35C ''similar'':13C ''thing'':68C ''tree'':77C ''use'':74C ''way'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1062, 2, '2014-08-29 16:19:06.903', 3, NULL, NULL, '&lt;p&gt;You can definitely try to first cluster your data, and then try to see if the cluster information helps your classification task.&lt;/p&gt;

&lt;p&gt;For example if your data looked like this (in 1D):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;AA A AA A A      BBB B B B BB BB BB      AA AA A A AAA
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;then it may be reasonable to run a clustering algorithm on each class, to obtain &lt;em&gt;two different kinds of A&lt;/em&gt;, and learn two separate classifiers for A1 and A2, and just drop the cluster distinction for the final output.&lt;/p&gt;

&lt;p&gt;Other common unsupervised techniques used include PCA.&lt;/p&gt;

&lt;p&gt;As for your football example, the problem is that the unsupervised algorithm does not know what it should be looking for. Instead of learning to separate american football and soccer, it may just as well decide to cluster on international vs. national games. Or Europe vs. U.S.; which may look like it learned about american football and soccer at first, but it put american soccer into the same cluster as american football, and american football teams in Europe into the Europe cluster... because it does not have &lt;em&gt;guidance&lt;/em&gt; on what structure you are interested in; and the continents &lt;em&gt;are&lt;/em&gt; a valid structure, too!&lt;/p&gt;

&lt;p&gt;So usually, I would not blindly assume that unsupervised techniques yield a distrinction that matches your desired result. They can yield &lt;em&gt;any&lt;/em&gt; kind of structure, and you will want to carefully inspect what they found before using it. If you use it blindly, make sure you spend enough time on evaluation (e.g. if the clustering improves your classifier performance, then it probably worked as intended ...)&lt;/p&gt;
', NULL, NULL, '''/code'':56C ''/em'':80C,200C,213C,243C ''/p'':24C,36C,100C,109C,218C,287C ''/pre'':57C ''1d'':35C ''a1'':87C ''a2'':89C ''aa'':39C,41C,51C,52C ''aaa'':55C ''algorithm'':68C,122C ''american'':137C,165C,174C,181C,184C ''assum'':226C ''b'':45C,46C,47C ''bb'':48C,49C,50C ''bbb'':44C ''blind'':225C,264C ''care'':252C ''class'':71C ''classif'':22C ''classifi'':85C,279C ''cluster'':8C,18C,67C,94C,148C,179C,192C,276C ''code'':38C ''common'':103C ''contin'':210C ''data'':10C,30C ''decid'':146C ''definit'':4C ''desir'':236C ''differ'':76C ''distinct'':95C ''distrinct'':232C ''drop'':92C ''e.g'':273C ''em'':74C,198C,211C,241C ''enough'':269C ''europ'':155C,188C,191C ''evalu'':272C ''exampl'':27C,115C ''final'':98C ''first'':7C,170C ''footbal'':114C,138C,166C,182C,185C ''found'':256C ''game'':153C ''guidanc'':199C ''help'':20C ''improv'':277C ''includ'':107C ''inform'':19C ''inspect'':253C ''instead'':132C ''intend'':286C ''interest'':206C ''intern'':150C ''kind'':77C,244C ''know'':125C ''learn'':82C,134C,163C ''like'':32C,161C ''look'':31C,130C,160C ''make'':265C ''match'':234C ''may'':61C,142C,159C ''nation'':152C ''obtain'':73C ''output'':99C ''p'':1C,25C,58C,101C,110C,219C ''pca'':108C ''perform'':280C ''pre'':37C ''probabl'':283C ''problem'':117C ''put'':173C ''reason'':63C ''result'':237C ''run'':65C ''see'':15C ''separ'':84C,136C ''soccer'':140C,168C,175C ''spend'':268C ''structur'':203C,216C,246C ''sure'':266C ''task'':23C ''team'':186C ''techniqu'':105C,229C ''time'':270C ''tri'':5C,13C ''two'':75C,83C ''u.s'':157C ''unsupervis'':104C,121C,228C ''use'':106C,258C,262C ''usual'':221C ''valid'':215C ''vs'':151C,156C ''want'':250C ''well'':145C ''work'':284C ''would'':223C ''yield'':230C,240C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1063, 2, '2014-08-30 17:05:23.197', 1, NULL, NULL, '&lt;p&gt;If your data is numeric, try loading it into ELKI (Java). With the &lt;code&gt;NullAlgorithm&lt;/code&gt; it will give you scatterplots, histograms and parallel coordinate plots. It''s fast in reading the data; only the current Apache Batik-based visualization is slooow because it''s using SVG. :-( I''m mostly using it "headless".&lt;/p&gt;

&lt;p&gt;It also has classes for various statistics (including higher order moments on data streams), but I havn''t seen them in the default UI yet.&lt;/p&gt;
', NULL, NULL, '''/code'':17C ''/p'':56C,83C ''also'':59C ''apach'':38C ''base'':41C ''batik'':40C ''batik-bas'':39C ''class'':61C ''code'':15C ''coordin'':26C ''current'':37C ''data'':4C,34C,70C ''default'':80C ''elki'':11C ''fast'':30C ''give'':20C ''havn'':74C ''headless'':55C ''higher'':66C ''histogram'':23C ''includ'':65C ''java'':12C ''load'':8C ''m'':51C ''moment'':68C ''most'':52C ''nullalgorithm'':16C ''numer'':6C ''order'':67C ''p'':1C,57C ''parallel'':25C ''plot'':27C ''read'':32C ''scatterplot'':22C ''seen'':76C ''slooow'':44C ''statist'':64C ''stream'':71C ''svg'':49C ''tri'':7C ''ui'':81C ''use'':48C,53C ''various'':63C ''visual'':42C ''yet'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1075, 1, '2014-09-04 18:13:57.343', 4, 146, 'Hadoop for grid computing', '&lt;p&gt;Background:
I run a product that compares sets of data (data matching and data reconciliation).
To get the result we need to compare each row in a data set with every N rows on the opposing data set
Now however we get sets of up to 300 000 rows of data in each set to compare and are getting 90 Billion computations to handle.&lt;/p&gt;

&lt;p&gt;So my question is this:
Even though we dont have the data volumes to use Hadoop, we have the computational need for something distributed. Is Hadoop a good choice for us?&lt;/p&gt;
', 'hadoop', NULL, '''/p'':71C,104C ''000'':54C ''300'':53C ''90'':66C ''background'':7C ''billion'':67C ''choic'':101C ''compar'':13C,29C,62C ''comput'':4A,68C,92C ''data'':16C,17C,20C,34C,43C,57C,84C ''distribut'':96C ''dont'':81C ''even'':78C ''everi'':37C ''get'':23C,48C,65C ''good'':100C ''grid'':3A ''hadoop'':1A,5B,88C,98C ''handl'':70C ''howev'':46C ''match'':18C ''n'':38C ''need'':27C,93C ''oppos'':42C ''p'':6C,72C ''product'':11C ''question'':75C ''reconcili'':21C ''result'':25C ''row'':31C,39C,55C ''run'':9C ''set'':14C,35C,44C,49C,60C ''someth'':95C ''though'':79C ''us'':103C ''use'':87C ''volum'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1064, 2, '2014-08-30 18:14:05.577', 2, NULL, NULL, '&lt;p&gt;MapReduce is not used in searching. It was used a long time ago to build the index; but it is a batch processing framework, and most of the web does not change all the time, so the newer architectures are all &lt;em&gt;incremental&lt;/em&gt; instead of batch oriented.&lt;/p&gt;

&lt;p&gt;Search in Google will largely work the same it works in Lucene and Elastic Search, except for a lot of fine tuned extra weighting and optimizations. But at the very heart, they will use some form of an &lt;strong&gt;inverted index&lt;/strong&gt;. In other words, they do &lt;em&gt;not&lt;/em&gt; search several terabytes when you enter a search query (even when it is not cached). They likely don''t look at the actual documents at all. But they use a lookup table that lists which documents match your query term (with stemming, misspellings, synonyms etc. all preprocessed). They probably retrieve the &lt;em&gt;list&lt;/em&gt; of the top 10000 documents for each word (10k integers - just a few kb!) and compute the best matches from that. Only if there aren''t good matches in these lists, they expand to the next such blocks etc.&lt;/p&gt;

&lt;p&gt;Queries for common words can be easily cached; and via preprocessing you can build a list of the top 10k results and then rerank them according to the user profile. There is nothing to be gained by computing an "exact" answer, too. Looking at the top 10k results is likely enough; there is no correct answer; and if a better result somewhere at position 10001 is missed, nobody will know or notice (or care). It likely was already ranked down in preprocessing and would not have made it into the top 10 that is presented to the user at the end (or the top 3, the user actually looks at)&lt;/p&gt;

&lt;p&gt;Rare terms on the other hand aren''t much of a challenge either - one of the lists only contains a few matching documents, and you can immediately discard all others.&lt;/p&gt;

&lt;p&gt;I recommend reading this article:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;strong&gt;The Anatomy of a Large-Scale Hypertextual Web Search Engine&lt;/strong&gt;&lt;br /&gt;
  Sergey Brin and Lawrence Page&lt;br /&gt;
  Computer Science Department, Stanford University, Stanford, CA 94305&lt;br /&gt;
  &lt;a href="http://infolab.stanford.edu/~backrub/google.html" rel="nofollow"&gt;http://infolab.stanford.edu/~backrub/google.html&lt;/a&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;And yes, that''s the Google founders who wrote this. It''s not the latest state, but it will already work at a pretty large scale.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':388C ''/em'':45C,101C,155C ''/p'':50C,195C,307C,339C,346C,416C ''/strong'':93C,361C ''/~backrub/google.html'':382C ''/~backrub/google.html&lt;/a&gt;&lt;/p&gt;'':387C ''10'':288C ''10000'':159C ''10001'':261C ''10k'':164C,216C,243C ''3'':301C ''94305'':376C ''accord'':222C ''actual'':124C,304C ''ago'':14C ''alreadi'':274C,409C ''anatomi'':351C ''answer'':237C,252C ''architectur'':40C ''aren'':180C,315C ''articl'':345C ''batch'':23C,48C ''best'':173C ''better'':256C ''block'':193C ''blockquot'':347C ''br'':362C,368C,377C ''brin'':364C ''build'':16C,210C ''ca'':375C ''cach'':116C,204C ''care'':270C ''challeng'':320C ''chang'':33C ''common'':199C ''comput'':171C,234C,369C ''contain'':327C ''correct'':251C ''depart'':371C ''discard'':336C ''document'':125C,137C,160C,331C ''easili'':203C ''either'':321C ''elast'':65C ''em'':43C,99C,153C ''end'':297C ''engin'':360C ''enough'':247C ''enter'':107C ''etc'':146C,194C ''even'':111C ''exact'':236C ''except'':67C ''expand'':188C ''extra'':74C ''fine'':72C ''form'':87C ''founder'':396C ''framework'':25C ''gain'':232C ''good'':182C ''googl'':54C,395C ''hand'':314C ''heart'':82C ''href'':379C ''hypertextu'':357C ''immedi'':335C ''increment'':44C ''index'':18C,92C ''infolab.stanford.edu'':381C,386C ''infolab.stanford.edu/~backrub/google.html'':380C ''infolab.stanford.edu/~backrub/google.html&lt;/a&gt;&lt;/p&gt;'':385C ''instead'':46C ''integ'':165C ''invert'':91C ''kb'':169C ''know'':266C ''larg'':56C,355C,414C ''large-scal'':354C ''latest'':404C ''lawrenc'':366C ''like'':118C,246C,272C ''list'':135C,154C,186C,212C,325C ''long'':12C ''look'':121C,239C,305C ''lookup'':132C ''lot'':70C ''lucen'':63C ''made'':283C ''mapreduc'':2C ''match'':138C,174C,183C,330C ''miss'':263C ''misspel'':144C ''much'':317C ''newer'':39C ''next'':191C ''nobodi'':264C ''nofollow'':384C ''noth'':229C ''notic'':268C ''one'':322C ''optim'':77C ''orient'':49C ''other'':338C ''p'':1C,51C,196C,308C,340C,348C,389C ''page'':367C ''posit'':260C ''preprocess'':148C,207C,278C ''present'':291C ''pretti'':413C ''probabl'':150C ''process'':24C ''profil'':226C ''queri'':110C,140C,197C ''rank'':275C ''rare'':309C ''read'':343C ''recommend'':342C ''rel'':383C ''rerank'':220C ''result'':217C,244C,257C ''retriev'':151C ''scale'':356C,415C ''scienc'':370C ''search'':7C,52C,66C,102C,109C,359C ''sergey'':363C ''sever'':103C ''somewher'':258C ''stanford'':372C,374C ''state'':405C ''stem'':143C ''strong'':90C,349C ''synonym'':145C ''tabl'':133C ''terabyt'':104C ''term'':141C,310C ''time'':13C,36C ''top'':158C,215C,242C,287C,300C ''tune'':73C ''univers'':373C ''use'':5C,10C,85C,130C ''user'':225C,294C,303C ''via'':206C ''web'':30C,358C ''weight'':75C ''word'':96C,163C,200C ''work'':57C,61C,410C ''would'':280C ''wrote'':398C ''yes'':391C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1073, 1, '2014-09-02 19:17:43.21', 8, 630, 'Libraries for Online Machine Learning', '&lt;p&gt;I am looking for packages (either in python, R, or a standalone package) to perform online learning to predict stock data.&lt;/p&gt;

&lt;p&gt;I have found and read about Vowpal Wabbit (&lt;a href="https://github.com/JohnLangford/vowpal_wabbit/wiki"&gt;https://github.com/JohnLangford/vowpal_wabbit/wiki&lt;/a&gt;),
which seems to be quite promising but I am wondering if there are any other packages out there.&lt;/p&gt;

&lt;p&gt;Thanks in advance.&lt;/p&gt;
', 'machine-learning  online-learning', NULL, '''/johnlangford/vowpal_wabbit/wiki'':48C ''/johnlangford/vowpal_wabbit/wiki&lt;/a&gt;),'':51C ''/p'':34C,70C,75C ''advanc'':74C ''data'':33C ''either'':18C ''found'':38C ''github.com'':47C,50C ''github.com/johnlangford/vowpal_wabbit/wiki'':46C ''github.com/johnlangford/vowpal_wabbit/wiki&lt;/a&gt;),'':49C ''href'':45C ''learn'':5A,8B,11B,29C ''librari'':1A ''look'':15C ''machin'':4A,7B ''machine-learn'':6B ''onlin'':3A,10B,28C ''online-learn'':9B ''p'':12C,35C,71C ''packag'':17C,25C,67C ''perform'':27C ''predict'':31C ''promis'':57C ''python'':20C ''quit'':56C ''r'':21C ''read'':40C ''seem'':53C ''standalon'':24C ''stock'':32C ''thank'':72C ''vowpal'':42C ''wabbit'':43C ''wonder'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1066, 2, '2014-09-01 00:01:58.917', 1, NULL, NULL, '&lt;p&gt;Perhaps you are looking to quantify the amount of filespace used by a specific subset of data that we will label as "academic publications." &lt;/p&gt;

&lt;p&gt;Well, to estimate, you could find stats on how many publications are housed at all the leading libraries (JSTOR, EBSCO, AcademicHost, etc) and then get the mean average size of each. Multiply that by the number of articles and whamo, you''ve got yourself an estimate.&lt;/p&gt;

&lt;p&gt;Here''s the problem, though: PDF files store the text from string &lt;code&gt;s&lt;/code&gt; differently (in size) than, say, a text document stores that same string. Likewise, a compressed JPEG will store an amount of information &lt;code&gt;i&lt;/code&gt; differently than a non-compressed JPEG. So you see we could have two of the same articles containing the same information &lt;code&gt;i&lt;/code&gt; but taking up different amounts of memory &lt;code&gt;m&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Are you looking to get a wordcount on the amount of scientific literature?&lt;/p&gt;

&lt;p&gt;Are you looking to get an approximation of file system space used to store all academically published content in the world? &lt;/p&gt;
', NULL, NULL, '''/code'':90C,115C,140C,150C ''/p'':26C,74C,151C,166C,189C ''academ'':24C,183C ''academichost'':48C ''amount'':9C,110C,145C,162C ''approxim'':174C ''articl'':65C,133C ''averag'':55C ''code'':88C,113C,138C,148C ''compress'':105C,121C ''contain'':134C ''content'':185C ''could'':32C,127C ''data'':18C ''differ'':91C,116C,144C ''document'':98C ''ebsco'':47C ''estim'':30C,73C ''etc'':49C ''file'':82C,176C ''filespac'':11C ''find'':33C ''get'':52C,157C,172C ''got'':70C ''hous'':40C ''inform'':112C,137C ''jpeg'':106C,122C ''jstor'':46C ''label'':22C ''lead'':44C ''librari'':45C ''likewis'':103C ''literatur'':165C ''look'':5C,155C,170C ''m'':149C ''mani'':37C ''mean'':54C ''memori'':147C ''multipli'':59C ''non'':120C ''non-compress'':119C ''number'':63C ''p'':1C,27C,75C,152C,167C ''pdf'':81C ''perhap'':2C ''problem'':79C ''public'':25C,38C ''publish'':184C ''quantifi'':7C ''say'':95C ''scientif'':164C ''see'':125C ''size'':56C,93C ''space'':178C ''specif'':15C ''stat'':34C ''store'':83C,99C,108C,181C ''string'':87C,102C ''subset'':16C ''system'':177C ''take'':142C ''text'':85C,97C ''though'':80C ''two'':129C ''use'':12C,179C ''ve'':69C ''well'':28C ''whamo'':67C ''wordcount'':159C ''world'':188C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1067, 2, '2014-09-01 00:17:56.07', 0, NULL, NULL, '&lt;p&gt;It is likely to be very hard to draw any conclusion if you are training with only 10 input samples.  With more data, your diagnosis that the model is predicting lagged values would have more plausibility.  As it stands, it seems pretty likely that your model is just saying that the last observed value is pretty close to correct.  This isn''t the same as a real lag model, but it is a very reasonable thing to guess if you haven''t seen enough data.&lt;/p&gt;
', NULL, NULL, '''/p'':87C ''10'':19C ''close'':58C ''conclus'':12C ''correct'':60C ''data'':24C,86C ''diagnosi'':26C ''draw'':10C ''enough'':85C ''guess'':79C ''hard'':8C ''haven'':82C ''input'':20C ''isn'':62C ''lag'':32C,69C ''last'':53C ''like'':4C,44C ''model'':29C,47C,70C ''observ'':54C ''p'':1C ''plausibl'':37C ''predict'':31C ''pretti'':43C,57C ''real'':68C ''reason'':76C ''sampl'':21C ''say'':50C ''seem'':42C ''seen'':84C ''stand'':40C ''thing'':77C ''train'':16C ''valu'':33C,55C ''would'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1068, 2, '2014-09-01 08:10:56.967', 0, NULL, NULL, '&lt;p&gt;I''d have a closer look at one of Apache Spark''s modules: &lt;a href="https://spark.apache.org/mllib/" rel="nofollow" title="MLlib"&gt;MLlib&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':25C ''/mllib/'':19C ''/p'':26C ''apach'':11C ''closer'':6C ''d'':3C ''href'':16C ''look'':7C ''mllib'':23C,24C ''modul'':14C ''nofollow'':21C ''one'':9C ''p'':1C ''rel'':20C ''spark'':12C ''spark.apache.org'':18C ''spark.apache.org/mllib/'':17C ''titl'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1120, 2, '2014-09-15 18:14:16.18', 2, NULL, NULL, '&lt;p&gt;This may provide some answer: &lt;a href="http://cran.r-project.org/web/packages/caret/vignettes/caret.pdf" rel="nofollow"&gt;http://cran.r-project.org/web/packages/caret/vignettes/caret.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You may also check out Max Kuhn''s "Applied Predictive Modeling" book. He talks about the caret package at length in this book, including the kappa statistics and how to use it. This may be of some help to you.&lt;/p&gt;
', NULL, NULL, '''/p'':58C ''/web/packages/caret/vignettes/caret.pdf'':11C ''/web/packages/caret/vignettes/caret.pdf&lt;/a&gt;&lt;/p&gt;'':16C ''also'':20C ''answer'':6C ''appli'':26C ''book'':29C,40C ''caret'':34C ''check'':21C ''cran.r-project.org'':10C,15C ''cran.r-project.org/web/packages/caret/vignettes/caret.pdf'':9C ''cran.r-project.org/web/packages/caret/vignettes/caret.pdf&lt;/a&gt;&lt;/p&gt;'':14C ''help'':55C ''href'':8C ''includ'':41C ''kappa'':43C ''kuhn'':24C ''length'':37C ''max'':23C ''may'':3C,19C,51C ''model'':28C ''nofollow'':13C ''p'':1C,17C ''packag'':35C ''predict'':27C ''provid'':4C ''rel'':12C ''statist'':44C ''talk'':31C ''use'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1069, 1, '2014-09-02 03:24:12.793', 5, 72, 'Kappa From Combined Confusion Matrices', '&lt;p&gt;I am trying to evaluate and compare several different machine learning models built with different parameters (i.e. downsampling, outlier removal) and different classifiers (i.e. Bayes Net, SVM, Decision Tree).&lt;/p&gt;

&lt;p&gt;I am performing a type of cross validation where I randomly select 67% of the data for use in the training set and 33% of the data for use in the testing set. I perform this for several iterations, say, 20.&lt;/p&gt;

&lt;p&gt;Now, from each iteration I am able to generate a confusion matrix and compute a kappa. My question is, what are some ways to aggregate these across the iterations? I am also interested in aggregating accuracy and expected accuracy, among other things.&lt;/p&gt;

&lt;p&gt;For the kappa, accuracy, and expected accuracy, I have just been taking the average up to this point. One of the problems is that when I recompute kappa with the aggregated average and expected average, it is not the same with the aggregated kappa.&lt;/p&gt;

&lt;p&gt;For the confusion matrix, I have been first normalizing the confusion matrix from each iteration and then averaging them, in an attempt to avoid an issue of confusion matrices with different numbers of total cases (which is possible with my cross validation scheme).&lt;/p&gt;

&lt;p&gt;When I recompute the kappa from this aggregated confusion matrix, it is also different from the previous two.&lt;/p&gt;

&lt;p&gt;Which one is most correct? Is there another way of computing an average kappa that is more correct?&lt;/p&gt;

&lt;p&gt;Thanks, and if more concrete examples are needed in order to illustrate my question please let me know.&lt;/p&gt;
', 'machine-learning  confusion-matrix', NULL, '''/p'':42C,85C,129C,175C,220C,240C,260C,280C ''20'':84C ''33'':67C ''67'':56C ''abl'':93C ''accuraci'':122C,125C,134C,137C ''across'':113C ''aggreg'':111C,121C,161C,173C,229C ''also'':118C,234C ''among'':126C ''anoth'':249C ''attempt'':198C ''averag'':144C,162C,165C,194C,254C ''avoid'':200C ''bay'':37C ''built'':25C ''case'':211C ''classifi'':35C ''combin'':3A ''compar'':19C ''comput'':100C,252C ''concret'':266C ''confus'':4A,10B,97C,179C,187C,204C,230C ''confusion-matrix'':9B ''correct'':246C,259C ''cross'':50C,217C ''data'':59C,70C ''decis'':40C ''differ'':21C,27C,34C,207C,235C ''downsampl'':30C ''evalu'':17C ''exampl'':267C ''expect'':124C,136C,164C ''first'':184C ''generat'':95C ''i.e'':29C,36C ''illustr'':273C ''interest'':119C ''issu'':202C ''iter'':82C,90C,115C,191C ''kappa'':1A,102C,133C,158C,174C,226C,255C ''know'':279C ''learn'':8B,23C ''let'':277C ''machin'':7B,22C ''machine-learn'':6B ''matric'':5A,205C ''matrix'':11B,98C,180C,188C,231C ''model'':24C ''need'':269C ''net'':38C ''normal'':185C ''number'':208C ''one'':149C,243C ''order'':271C ''outlier'':31C ''p'':12C,43C,86C,130C,176C,221C,241C,261C ''paramet'':28C ''perform'':46C,78C ''pleas'':276C ''point'':148C ''possibl'':214C ''previous'':238C ''problem'':152C ''question'':104C,275C ''random'':54C ''recomput'':157C,224C ''remov'':32C ''say'':83C ''scheme'':219C ''select'':55C ''set'':65C,76C ''sever'':20C,81C ''svm'':39C ''take'':142C ''test'':75C ''thank'':262C ''thing'':128C ''total'':210C ''train'':64C ''tree'':41C ''tri'':15C ''two'':239C ''type'':48C ''use'':61C,72C ''valid'':51C,218C ''way'':109C,250C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1070, 1, '2014-09-02 08:47:38.737', 2, 457, 'SAP HANA vs Exasol', '&lt;p&gt;I am interested in knowing the differences in &lt;strong&gt;functionality&lt;/strong&gt; between SAP HANA and Exasol. Since this is a bit of an open ended question let me be clear. I am not interested in people debating which is "better" or faster. I am only interested in what each was designed to do so please keep your opinions out of it. I suspect it is a bit like comparing HANA to Oracle Exalytics where there is some overlap but the functionality goals are different. &lt;/p&gt;
', 'bigdata', NULL, '''/p'':91C ''/strong'':17C ''better'':46C ''bigdata'':5B ''bit'':27C,73C ''clear'':36C ''compar'':75C ''debat'':43C ''design'':57C ''differ'':13C,90C ''end'':31C ''exalyt'':79C ''exasol'':4A,22C ''faster'':48C ''function'':16C,87C ''goal'':88C ''hana'':2A,20C,76C ''interest'':9C,40C,52C ''keep'':62C ''know'':11C ''let'':33C ''like'':74C ''open'':30C ''opinion'':64C ''oracl'':78C ''overlap'':84C ''p'':6C ''peopl'':42C ''pleas'':61C ''question'':32C ''sap'':1A,19C ''sinc'':23C ''strong'':15C ''suspect'':69C ''vs'':3A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1071, 1, '2014-09-02 09:48:08.15', 1, 34, 'How is the concept of data different for different disciplines?', '&lt;p&gt;How is the concept of data different for different disciplines? Obviously, for physicists and sociologists, "data" is something different.&lt;/p&gt;
', 'definitions', NULL, '''/p'':32C ''concept'':4A,16C ''data'':6A,18C,28C ''definit'':11B ''differ'':7A,9A,19C,21C,31C ''disciplin'':10A,22C ''obvious'':23C ''p'':12C ''physicist'':25C ''sociologist'':27C ''someth'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1072, 2, '2014-09-02 14:01:07.407', 1, NULL, NULL, '&lt;p&gt;There''s not an enormous difference between what you can do with the two databases, it''s more a question of the focus and the way the functionality is implemented and that''s where it becomes difficult to explain without using words like "better" and "faster" (and for sure words like "cheaper") &lt;/p&gt;

&lt;p&gt;EXASOL was designed for speed and ease of use with Analytical processing and is designed to run on clusters of commodity hardware. SAP is a more complex, aims to do more than "just" Analytical processing and runs only on a range of "approved" hardware.&lt;/p&gt;

&lt;p&gt;What type of differences did you have in mind ?&lt;/p&gt;
', NULL, NULL, '''/p'':54C,100C,111C ''aim'':83C ''analyt'':66C,89C ''approv'':98C ''becom'':37C ''better'':45C ''cheaper'':53C ''cluster'':74C ''commod'':76C ''complex'':82C ''databas'':16C ''design'':58C,70C ''differ'':7C,105C ''difficult'':38C ''eas'':62C ''enorm'':6C ''exasol'':56C ''explain'':40C ''faster'':47C ''focus'':24C ''function'':29C ''hardwar'':77C,99C ''implement'':31C ''like'':44C,52C ''mind'':110C ''p'':1C,55C,101C ''process'':67C,90C ''question'':21C ''rang'':96C ''run'':72C,92C ''sap'':78C ''speed'':60C ''sure'':50C ''two'':15C ''type'':103C ''use'':42C,64C ''way'':27C ''without'':41C ''word'':43C,51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1077, 2, '2014-09-05 11:00:22.053', 4, NULL, NULL, '&lt;p&gt;Your job seems like a map-reduce job and hence might be good for Hadoop. Hadoop has a zoo of an ecosystem though. &lt;/p&gt;

&lt;p&gt;Hadoop is a distributed file system. It distributes data on a cluster and because this data is split up it can be analysed in parallel. Out of the box, Hadoop allows you to write map reduce jobs on the platform and this is why it might help with your problem.&lt;/p&gt;

&lt;p&gt;The following technologies work on Hadoop: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;If the data can be represented in a table format you might want to check out technologies like &lt;a href="https://hive.apache.org/" rel="nofollow"&gt;hive&lt;/a&gt; and &lt;a href="http://www.cloudera.com/content/cloudera/en/products-and-services/cdh/impala.html" rel="nofollow"&gt;impala&lt;/a&gt;. Impala uses all the distributed memory across a cluster and is very performant while it allows you to still work with a table structure. &lt;/li&gt;
&lt;li&gt;A more new, but promising alternative might also be &lt;a href="https://spark.apache.org/" rel="nofollow"&gt;spark&lt;/a&gt; which allows for more iterative procedures to be run on the cluster. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Don''t underestimate the amount of time setting up and the amount of time needed to understand Hadoop.&lt;/p&gt;
', NULL, NULL, '''/a'':113C,123C,165C ''/content/cloudera/en/products-and-services/cdh/impala.html'':119C ''/li'':148C,178C ''/p'':26C,78C,86C,199C ''/ul'':179C ''across'':130C ''allow'':58C,139C,167C ''also'':157C ''altern'':155C ''amount'':185C,192C ''analys'':50C ''box'':56C ''check'':103C ''cluster'':39C,132C,177C ''data'':36C,43C,91C ''distribut'':31C,35C,128C ''ecosystem'':24C ''file'':32C ''follow'':81C ''format'':98C ''good'':15C ''hadoop'':17C,18C,28C,57C,85C,198C ''help'':74C ''henc'':12C ''hive'':112C ''hive.apache.org'':109C ''href'':108C,116C,160C ''impala'':122C,124C ''iter'':170C ''job'':3C,10C,64C ''li'':88C,149C ''like'':5C,106C ''map'':8C,62C ''map-reduc'':7C ''memori'':129C ''might'':13C,73C,100C,156C ''need'':195C ''new'':152C ''nofollow'':111C,121C,163C ''p'':1C,27C,79C,180C ''parallel'':52C ''perform'':136C ''platform'':67C ''problem'':77C ''procedur'':171C ''promis'':154C ''reduc'':9C,63C ''rel'':110C,120C,162C ''repres'':94C ''run'':174C ''seem'':4C ''set'':188C ''spark'':164C ''spark.apache.org'':161C ''split'':45C ''still'':142C ''structur'':147C ''system'':33C ''tabl'':97C,146C ''technolog'':82C,105C ''though'':25C ''time'':187C,194C ''ul'':87C ''underestim'':183C ''understand'':197C ''use'':125C ''want'':101C ''work'':83C,143C ''write'':61C ''www.cloudera.com'':118C ''www.cloudera.com/content/cloudera/en/products-and-services/cdh/impala.html'':117C ''zoo'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1078, 1, '2014-09-05 12:08:11.347', 7, 2154, 'Unstructured text classification', '&lt;p&gt;I''m going to classify unstructured text documents, namely web sites of unknown structure. The number of classes to which I am classifying is limited (at this point, I believe there is no more than three). Does anyone have a suggested for how I might get started?&lt;/p&gt;

&lt;p&gt;Is the "bag of words" approach feasible here? Later, I could add another classification stage based on document structure (perhaps decision trees).&lt;/p&gt;

&lt;p&gt;I am somewhat familiar with Mahout and Hadoop, so I prefer Java-based solutions. If needed, I can switch to Scala and/or Spark engine (the ML library).&lt;/p&gt;
', 'machine-learning  classification  text-mining  beginner', NULL, '''/p'':60C,84C,114C ''add'':73C ''and/or'':108C ''anoth'':74C ''anyon'':50C ''approach'':67C ''bag'':64C ''base'':77C,99C ''beginn'':11B ''believ'':42C ''class'':30C ''classif'':3A,7B,75C ''classifi'':17C,35C ''could'':72C ''decis'':82C ''document'':20C,79C ''engin'':110C ''familiar'':89C ''feasibl'':68C ''get'':58C ''go'':15C ''hadoop'':93C ''java'':98C ''java-bas'':97C ''later'':70C ''learn'':6B ''librari'':113C ''limit'':37C ''m'':14C ''machin'':5B ''machine-learn'':4B ''mahout'':91C ''might'':57C ''mine'':10B ''ml'':112C ''name'':21C ''need'':102C ''number'':28C ''p'':12C,61C,85C ''perhap'':81C ''point'':40C ''prefer'':96C ''scala'':107C ''site'':23C ''solut'':100C ''somewhat'':88C ''spark'':109C ''stage'':76C ''start'':59C ''structur'':26C,80C ''suggest'':53C ''switch'':105C ''text'':2A,9B,19C ''text-min'':8B ''three'':48C ''tree'':83C ''unknown'':25C ''unstructur'':1A,18C ''web'':22C ''word'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1138, 2, '2014-09-18 13:14:37.94', 5, NULL, NULL, '&lt;p&gt;Decision trees (and hence Random Forests) are insensitive to monotone transformations of input features. &lt;/p&gt;

&lt;p&gt;Since multiplying by the same factor is a monotone transformation, I''d assume that for Random Forests there indeed is no difference. &lt;/p&gt;

&lt;p&gt;However, you eventually may consider using other classifiers that do not have this property, so it may still make sense to use the entire TF * IDF. &lt;/p&gt;
', NULL, NULL, '''/p'':16C,40C,68C ''assum'':30C ''classifi'':49C ''consid'':46C ''d'':29C ''decis'':2C ''differ'':39C ''entir'':65C ''eventu'':44C ''factor'':23C ''featur'':15C ''forest'':7C,34C ''henc'':5C ''howev'':42C ''idf'':67C ''inde'':36C ''input'':14C ''insensit'':9C ''make'':60C ''may'':45C,58C ''monoton'':11C,26C ''multipli'':19C ''p'':1C,17C,41C ''properti'':55C ''random'':6C,33C ''sens'':61C ''sinc'':18C ''still'':59C ''tf'':66C ''transform'':12C,27C ''tree'':3C ''use'':47C,63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2478, 2, '2014-11-15 03:18:13.223', 1, NULL, NULL, '&lt;p&gt;Data volume is not the only criterion for using Hadoop. Big Data is often characterized by the 3 V''s: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;volume, &lt;/li&gt;
&lt;li&gt;velocity, and &lt;/li&gt;
&lt;li&gt;variety. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;More V''s than these 3 have been invented since. I suppose the V''s were a catchy way to characterize what is Big Data.&lt;/p&gt;

&lt;p&gt;But as hinted, computational intensity is a perfect reason for using Hadoop (if your algorithm is computationally expensive). And, as hinted, the problem you describe is perfect for Hadoop, especially since it is embarrassingly parallel in nature. &lt;/p&gt;

&lt;p&gt;Is Hadoop a good choice for you? I would argue, yes. Why? Because &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Hadoop is open source (compared with proprietary systems which may be expensive and black boxes), &lt;/li&gt;
&lt;li&gt;your problem lends itself well to the MapReduce paradigm (embarassingly parallel, shared-nothing), &lt;/li&gt;
&lt;li&gt;Hadoop is easily scalable with commodity hardware (as opposed to specialized hardware, and you should get linear speed-up in your performance with just throwing hardware at the problem, and you can just spin a cluster as needed on cloud service providers),&lt;/li&gt;
&lt;li&gt;Hadoop allows multiple client languages (Java is only one of many supported languages),&lt;/li&gt;
&lt;li&gt;there might be a library available already to do your cross-product operation, and&lt;/li&gt;
&lt;li&gt;you''re shipping compute code, not data, around the network (which you should benefit from, and as opposed to other distributed platforms where you are shipping data to compute nodes which is the bottleneck).&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Please note, Hadoop is not a distributed file system (as mentioned, and corrected already). Hadoop is distributed storage and processing platform. The distributed storage component of Hadoop is called the Hadoop Distributed File System (HDFS), and the distributed processing component is called MapReduce. &lt;/p&gt;

&lt;p&gt;Hadoop has now evolved slightly. They keep the HDFS part for distributed storage. But they have a new component called YARN (Yet Another Resource Negotiator), which serves to appropriate resources (CPU, RAM) for any compute task (including MapReduce). &lt;/p&gt;

&lt;p&gt;On the "overhead" part, there is noticeable overhead with starting/stopping a Java Virtual Machine (JVM) per tasks (map tasks, reduce tasks). You can specify for your MapReduce Jobs to reuse JVMs to mitigate this issue. &lt;/p&gt;

&lt;p&gt;If "overhead" is really an issue, look into Apache Spark, which is part of the Hadoop ecosystem, and they are orders of magnitude faster than MapReduce, especially for iterative algorithms. &lt;/p&gt;

&lt;p&gt;I have used Hadoop to compute pairwise comparisons (e.g. correlation matrix, similarity matrix) that are O(N^2) (n choose 2) in worst case running time complexity. Imagine computing the correlations between 16,000 variables (16,000 choose 2); Hadoop can easily process and store the results if you have the commodity resources to support the cluster. I did this using the preeminent cloud service provider (I won''t name it, but you can surely guess who it is), and it cost me &amp;lt; $100 and under 18 hours.  &lt;/p&gt;
', NULL, NULL, '''/li'':26C,30C,33C,133C,149C,194C,209C,226C,262C ''/ol'':263C ''/p'':22C,61C,100C,115C,308C,348C,385C,417C,508C ''/ul'':34C ''000'':452C,455C ''100'':503C ''16'':451C,454C ''18'':506C ''2'':436C,439C,457C ''3'':19C,41C ''algorithm'':77C,416C ''allow'':197C ''alreadi'':217C,278C ''anoth'':332C ''apach'':395C ''appropri'':338C ''argu'':111C ''around'':235C ''avail'':216C ''benefit'':241C ''big'':12C,59C ''black'':131C ''bottleneck'':261C ''box'':132C ''call'':293C,306C,329C ''case'':442C ''catchi'':53C ''character'':16C,56C ''choic'':106C ''choos'':438C,456C ''client'':199C ''cloud'':191C,482C ''cluster'':187C,475C ''code'':232C ''commod'':156C,470C ''compar'':122C ''comparison'':426C ''complex'':445C ''compon'':289C,304C,328C ''comput'':66C,79C,231C,256C,344C,424C,447C ''correct'':277C ''correl'':428C,449C ''cost'':500C ''cpu'':340C ''criterion'':8C ''cross'':222C ''cross-product'':221C ''data'':2C,13C,60C,234C,254C ''describ'':87C ''distribut'':248C,271C,281C,287C,296C,302C,321C ''e.g'':427C ''easili'':153C,460C ''ecosystem'':403C ''embarass'':144C ''embarrass'':96C ''especi'':92C,413C ''evolv'':313C ''expens'':80C,129C ''faster'':410C ''file'':272C,297C ''get'':166C ''good'':105C ''guess'':494C ''hadoop'':11C,74C,91C,103C,118C,151C,196C,267C,279C,291C,295C,310C,402C,422C,458C ''hardwar'':157C,162C,177C ''hdfs'':299C,318C ''hint'':65C,83C ''hour'':507C ''imagin'':446C ''includ'':346C ''intens'':67C ''invent'':44C ''issu'':384C,392C ''iter'':415C ''java'':201C,361C ''job'':377C ''jvm'':364C ''jvms'':380C ''keep'':316C ''languag'':200C,208C ''lend'':137C ''li'':24C,27C,31C,117C,134C,150C,195C,210C,227C ''librari'':215C ''linear'':167C ''look'':393C ''lt'':502C ''machin'':363C ''magnitud'':409C ''mani'':206C ''map'':367C ''mapreduc'':142C,307C,347C,376C,412C ''matrix'':429C,431C ''may'':127C ''mention'':275C ''might'':212C ''mitig'':382C ''multipl'':198C ''n'':435C,437C ''name'':488C ''natur'':99C ''need'':189C ''negoti'':334C ''network'':237C ''new'':327C ''node'':257C ''note'':266C ''noth'':148C ''notic'':356C ''o'':434C ''often'':15C ''ol'':116C ''one'':204C ''open'':120C ''oper'':224C ''oppos'':159C,245C ''order'':407C ''overhead'':352C,357C,388C ''p'':1C,35C,62C,101C,264C,309C,349C,386C,418C ''pairwis'':425C ''paradigm'':143C ''parallel'':97C,145C ''part'':319C,353C,399C ''per'':365C ''perfect'':70C,89C ''perform'':173C ''platform'':249C,285C ''pleas'':265C ''preemin'':481C ''problem'':85C,136C,180C ''process'':284C,303C,461C ''product'':223C ''proprietari'':124C ''provid'':193C,484C ''ram'':341C ''re'':229C ''realli'':390C ''reason'':71C ''reduc'':369C ''resourc'':333C,339C,471C ''result'':465C ''reus'':379C ''run'':443C ''scalabl'':154C ''serv'':336C ''servic'':192C,483C ''share'':147C ''shared-noth'':146C ''ship'':230C,253C ''similar'':430C ''sinc'':45C,93C ''slight'':314C ''sourc'':121C ''spark'':396C ''special'':161C ''specifi'':373C ''speed'':169C ''speed-up'':168C ''spin'':185C ''starting/stopping'':359C ''storag'':282C,288C,322C ''store'':463C ''support'':207C,473C ''suppos'':47C ''sure'':493C ''system'':125C,273C,298C ''task'':345C,366C,368C,370C ''throw'':176C ''time'':444C ''ul'':23C ''use'':10C,73C,421C,479C ''v'':20C,37C,49C ''variabl'':453C ''varieti'':32C ''veloc'':28C ''virtual'':362C ''volum'':3C,25C ''way'':54C ''well'':139C ''won'':486C ''worst'':441C ''would'':110C ''yarn'':330C ''yes'':112C ''yet'':331C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1079, 1, '2014-09-05 14:47:52.127', 2, 420, 'Data sets for evaluating text retrieval quality', '&lt;p&gt;I''m searching for data sets for evaluating text retrieval quality.&lt;/p&gt;

&lt;p&gt;TF-IDF is a popular similarity measure, but is it the best choice? And which &lt;em&gt;variant&lt;/em&gt; is the best choice? &lt;a href="https://lucene.apache.org/core/3_6_1/api/all/org/apache/lucene/search/Similarity.html" rel="nofollow"&gt;Lucenes Scoring&lt;/a&gt; for example uses IDF^2, and IDF defined as 1+log(numdocs/(docFreq+1)). TF in lucene is defined as sqrt(frequency)...&lt;/p&gt;

&lt;p&gt;Many more variants exist, including &lt;a href="https://en.wikipedia.org/wiki/Okapi_BM25" rel="nofollow"&gt;Okapi BM25&lt;/a&gt;, which is used by the &lt;a href="http://xapian.org/docs/bm25.html" rel="nofollow"&gt;Xapian search engine&lt;/a&gt;...&lt;/p&gt;

&lt;p&gt;I''d like to study the different variants, and I''m looking for &lt;strong&gt;evaluation data sets&lt;/strong&gt;. Thanks!&lt;/p&gt;
', 'dataset  text-mining  similarity  information-retrieval', NULL, '''+1'':76C ''/a'':62C,101C,117C ''/core/3_6_1/api/all/org/apache/lucene/search/similarity.html'':57C ''/docs/bm25.html'':111C ''/em'':48C ''/p'':28C,85C,118C,139C ''/strong'':137C ''/wiki/okapi_bm25'':96C ''1'':72C ''2'':67C ''best'':42C,51C ''bm25'':100C ''choic'':43C,52C ''d'':121C ''data'':1A,21C,135C ''dataset'':8B ''defin'':70C,81C ''differ'':126C ''docfreq'':75C ''em'':46C ''en.wikipedia.org'':95C ''en.wikipedia.org/wiki/okapi_bm25'':94C ''engin'':116C ''evalu'':4A,24C,134C ''exampl'':64C ''exist'':90C ''frequenc'':84C ''href'':54C,93C,108C ''idf'':32C,66C,69C ''includ'':91C ''inform'':14B ''information-retriev'':13B ''like'':122C ''log'':73C ''look'':131C ''lucen'':60C,79C ''lucene.apache.org'':56C ''lucene.apache.org/core/3_6_1/api/all/org/apache/lucene/search/similarity.html'':55C ''m'':18C,130C ''mani'':87C ''measur'':37C ''mine'':11B ''nofollow'':59C,98C,113C ''numdoc'':74C ''okapi'':99C ''p'':16C,29C,86C,119C ''popular'':35C ''qualiti'':7A,27C ''rel'':58C,97C,112C ''retriev'':6A,15B,26C ''score'':61C ''search'':19C,115C ''set'':2A,22C,136C ''similar'':12B,36C ''sqrt'':83C ''strong'':133C ''studi'':124C ''text'':5A,10B,25C ''text-min'':9B ''tf'':31C,77C ''tf-idf'':30C ''thank'':138C ''use'':65C,104C ''variant'':47C,89C,127C ''xapian'':114C ''xapian.org'':110C ''xapian.org/docs/bm25.html'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1081, 2, '2014-09-05 16:36:03.227', 1, NULL, NULL, '&lt;p&gt;Data is, at it''s most basic reduction, a raw element of something. Data is a raw "thing" that exists in any form from which we can analyze it and construct intelligence. When I was an Intelligence Analyst, we used to define data as "anything and everything that could be used to construct a hypothesis." &lt;/p&gt;

&lt;p&gt;Thus, data for any discipline is interchangeable; as a sociologist, I have a vector of discrete variables indicating ethnicity, as an economist I have a vector with housing prices, and as an anthropologist I have a vector of tablet names used in some long-gone civilization. &lt;/p&gt;

&lt;p&gt;Data is data.&lt;/p&gt;
', NULL, NULL, '''/p'':57C,106C,111C ''analyst'':39C ''analyz'':29C ''anthropologist'':91C ''anyth'':46C ''basic'':8C ''civil'':105C ''construct'':32C,54C ''could'':50C ''data'':2C,15C,44C,60C,108C,110C ''defin'':43C ''disciplin'':63C ''discret'':74C ''economist'':80C ''element'':12C ''ethnic'':77C ''everyth'':48C ''exist'':21C ''form'':24C ''gone'':104C ''hous'':86C ''hypothesi'':56C ''indic'':76C ''intellig'':33C,38C ''interchang'':65C ''long'':103C ''long-gon'':102C ''name'':98C ''p'':1C,58C,107C ''price'':87C ''raw'':11C,18C ''reduct'':9C ''sociologist'':68C ''someth'':14C ''tablet'':97C ''thing'':19C ''thus'':59C ''use'':41C,52C,99C ''variabl'':75C ''vector'':72C,84C,95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1137, 1, '2014-09-18 06:22:13.94', 5, 276, 'Choosing a window size for DTW', '&lt;p&gt;I have time series data from mobile sensors for different motions such as walking, pushups, dumbellifts, rowing and so on. All these motions have different length of time series. For classifying them using &lt;a href="http://en.wikipedia.org/wiki/Dynamic_time_warping"&gt;Dynamic Time Warping (DTW)&lt;/a&gt;, how do I choose an appropriate window size that will give good results?&lt;/p&gt;
', 'classification  time-series  parameter', NULL, '''/a'':55C ''/p'':69C ''/wiki/dynamic_time_warping'':50C ''appropri'':61C ''choos'':1A,59C ''classif'':7B ''classifi'':43C ''data'':17C ''differ'':22C,37C ''dtw'':6A,54C ''dumbellift'':28C ''dynam'':51C ''en.wikipedia.org'':49C ''en.wikipedia.org/wiki/dynamic_time_warping'':48C ''give'':66C ''good'':67C ''href'':47C ''length'':38C ''mobil'':19C ''motion'':23C,35C ''p'':12C ''paramet'':11B ''pushup'':27C ''result'':68C ''row'':29C ''sensor'':20C ''seri'':10B,16C,41C ''size'':4A,63C ''time'':9B,15C,40C,52C ''time-seri'':8B ''use'':45C ''walk'':26C ''warp'':53C ''window'':3A,62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1082, 2, '2014-09-05 19:13:39.993', 2, NULL, NULL, '&lt;p&gt;Here are a couple of really great open source software packages for text classification that should help get you started:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://mallet.cs.umass.edu/" rel="nofollow"&gt;MALLET&lt;/a&gt; is a CPL-licensed Java-based machine learning toolkit built by UMass for working with text data. It includes implementations of several &lt;a href="http://mallet.cs.umass.edu/classification.php" rel="nofollow"&gt;classification&lt;/a&gt; algorithms (e.g., naÃ¯ve Bayes, maximum entropy, decision trees).&lt;/li&gt;
&lt;li&gt;The &lt;a href="http://nlp.stanford.edu/software/classifier.shtml" rel="nofollow"&gt;Stanford Classifier&lt;/a&gt; from the Stanford NLP Group is a GPL-licensed Java implementation of a maximum entropy classifier designed to work with text data.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':31C,64C,86C ''/classification.php'':60C ''/li'':74C,110C ''/p'':22C ''/software/classifier.shtml'':81C ''/ul'':111C ''algorithm'':65C ''base'':39C ''bay'':69C ''built'':43C ''classif'':15C,63C ''classifi'':85C,103C ''coupl'':5C ''cpl'':35C ''cpl-licens'':34C ''data'':50C,109C ''decis'':72C ''design'':104C ''e.g'':66C ''entropi'':71C,102C ''get'':19C ''gpl'':95C ''gpl-licens'':94C ''great'':8C ''group'':91C ''help'':18C ''href'':26C,57C,78C ''implement'':53C,98C ''includ'':52C ''java'':38C,97C ''java-bas'':37C ''learn'':41C ''li'':24C,75C ''licens'':36C,96C ''machin'':40C ''mallet'':30C ''mallet.cs.umass.edu'':27C,59C ''mallet.cs.umass.edu/classification.php'':58C ''maximum'':70C,101C ''naã'':67C ''nlp'':90C ''nlp.stanford.edu'':80C ''nlp.stanford.edu/software/classifier.shtml'':79C ''nofollow'':29C,62C,83C ''open'':9C ''p'':1C ''packag'':12C ''realli'':7C ''rel'':28C,61C,82C ''sever'':55C ''softwar'':11C ''sourc'':10C ''stanford'':84C,89C ''start'':21C ''text'':14C,49C,108C ''toolkit'':42C ''tree'':73C ''ul'':23C ''umass'':45C ''ve'':68C ''work'':47C,106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1086, 1, '2014-09-07 22:51:27.49', 2, 99, 'non query-based document ranking', '&lt;p&gt;We have ~500 biomedical documents each of some 1-2 MB. We want to use a non query-based method to rank the documents in order of their unique content score. I''m calling it "unique content" because our researchers want to know from which document to start reading. All the documents are of the same topic, in the biomedical world we know that there is always a lot of content overlap. So all we want to do is to arrange the documents in the order of their unique content.&lt;/p&gt;

&lt;p&gt;Most Information Retrieval literature suggest query-based ranking which does not fit our need.&lt;/p&gt;
', 'machine-learning  data-mining  text-mining  information-retrieval', NULL, '''-2'':29C ''/p'':111C,128C ''1'':28C ''500'':22C ''alway'':87C ''arrang'':101C ''base'':4A,39C,120C ''biomed'':23C,80C ''call'':54C ''content'':50C,57C,91C,110C ''data'':11B ''data-min'':10B ''document'':5A,24C,44C,66C,72C,103C ''fit'':125C ''inform'':17B,114C ''information-retriev'':16B ''know'':63C,83C ''learn'':9B ''literatur'':116C ''lot'':89C ''m'':53C ''machin'':8B ''machine-learn'':7B ''mb'':30C ''method'':40C ''mine'':12B,15B ''need'':127C ''non'':1A,36C ''order'':46C,106C ''overlap'':92C ''p'':19C,112C ''queri'':3A,38C,119C ''query-bas'':2A,37C,118C ''rank'':6A,42C,121C ''read'':69C ''research'':60C ''retriev'':18B,115C ''score'':51C ''start'':68C ''suggest'':117C ''text'':14B ''text-min'':13B ''topic'':77C ''uniqu'':49C,56C,109C ''use'':34C ''want'':32C,61C,96C ''world'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9674, 4, '2016-01-07 11:23:52.943', 0, NULL, NULL, 'Area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.', NULL, NULL, '''action'':13C ''agent'':9C ''area'':1C ''concern'':5C ''cumul'':24C ''environ'':16C ''learn'':4C ''machin'':3C ''maxim'':20C ''notion'':22C ''ought'':10C ''reward'':25C ''softwar'':8C ''take'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1087, 2, '2014-09-08 00:47:21.59', 0, NULL, NULL, '&lt;p&gt;Here''s a simple initial approach to try:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Calculate the &lt;a href="http://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;TF-IDF&lt;/a&gt; score of each word in each document.&lt;/li&gt;
&lt;li&gt;Sort the documents by the average TF-IDF score of their words.&lt;/li&gt;
&lt;li&gt;The higher the average TF-IDF score, the more unique a document is with respect to the rest of the collection.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;You might also try a clustering-based approach where you look for outliers, or perhaps something with the &lt;a href="http://en.wikipedia.org/wiki/Jaccard_index" rel="nofollow"&gt;Jaccard index&lt;/a&gt; using a bag-of-words model.&lt;/p&gt;
', NULL, NULL, '''/a'':25C,103C ''/li'':33C,48C,72C ''/ol'':73C ''/p'':10C,111C ''/wiki/jaccard_index'':98C ''/wiki/tf%e2%80%93idf'':19C ''also'':77C ''approach'':7C,83C ''averag'':40C,53C ''bag'':107C ''bag-of-word'':106C ''base'':82C ''calcul'':13C ''cluster'':81C ''clustering-bas'':80C ''collect'':71C ''document'':32C,37C,62C ''en.wikipedia.org'':18C,97C ''en.wikipedia.org/wiki/jaccard_index'':96C ''en.wikipedia.org/wiki/tf%e2%80%93idf'':17C ''higher'':51C ''href'':16C,95C ''idf'':24C,43C,56C ''index'':102C ''initi'':6C ''jaccard'':101C ''li'':12C,34C,49C ''look'':86C ''might'':76C ''model'':110C ''nofollow'':21C,100C ''ol'':11C ''outlier'':88C ''p'':1C,74C ''perhap'':90C ''rel'':20C,99C ''respect'':65C ''rest'':68C ''score'':26C,44C,57C ''simpl'':5C ''someth'':91C ''sort'':35C ''tf'':23C,42C,55C ''tf-idf'':22C,41C,54C ''tri'':9C,78C ''uniqu'':60C ''use'':104C ''word'':29C,47C,109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1113, 1, '2014-09-13 17:13:23.373', 2, 107, 'General approahces for grouping a continuous variable based on text data?', '&lt;p&gt;I have a general methodological question. I have two columns of data, with one a column a numeric variable for age and another column a short character variable for text responses to a question. &lt;/p&gt;

&lt;p&gt;My goal is to group the age variable (that is, create cut points for the age variable), based on the text responses. I''m unfamiliar with any general approaches for doing this sort of analysis. What general approaches would you recommend? Ideally I''d like to categorize the age variable based on linguistic similarity of the text responses.&lt;/p&gt;
', 'bigdata  clustering  text-mining', NULL, '''/p'':52C,112C ''age'':38C,60C,69C,102C ''analysi'':88C ''anoth'':40C ''approach'':82C,91C ''approahc'':2A ''base'':8A,71C,104C ''bigdata'':12B ''categor'':100C ''charact'':44C ''cluster'':13B ''column'':27C,33C,41C ''continu'':6A ''creat'':64C ''cut'':65C ''d'':97C ''data'':11A,29C ''general'':1A,21C,81C,90C ''goal'':55C ''group'':4A,58C ''ideal'':95C ''like'':98C ''linguist'':106C ''m'':77C ''methodolog'':22C ''mine'':16B ''numer'':35C ''one'':31C ''p'':17C,53C ''point'':66C ''question'':23C,51C ''recommend'':94C ''respons'':48C,75C,111C ''short'':43C ''similar'':107C ''sort'':86C ''text'':10A,15B,47C,74C,110C ''text-min'':14B ''two'':26C ''unfamiliar'':78C ''variabl'':7A,36C,45C,61C,70C,103C ''would'':92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1088, 2, '2014-09-08 02:45:42.653', 3, NULL, NULL, '&lt;p&gt;Topic Modeling would be a very appropriate method for your problem.  Topic Models are a form of unsupervised learning/discovery, where a specified (or discovered) number of topics are defined by a list of words that have a high probability of appearing together. In a separate step, you can label each topic using subject matter experts, but for your purposes this isn''t necessary since you are only interested in getting to three clusters.&lt;/p&gt;

&lt;p&gt;You treat each document as a bag of words, and pre-process to remove stop words, etc.  With the simplest methods, you pre-specify the number of topics.  In your case, you could either specify "3", which is your fixed limit on categories, or pick a larger number of topics (between 10 and 100), and then in a separate step, form three clusters for documents with common emphasis on topics. K-means or other clustering methods could be used.  (I''d recommend the latter approach)&lt;/p&gt;

&lt;p&gt;You don''t need to code topic modeling software from scratch. Here''s a web page with many resources, including software libraries/packages: &lt;a href="http://www.cs.princeton.edu/~blei/topicmodeling.html" rel="nofollow"&gt;http://www.cs.princeton.edu/~blei/topicmodeling.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;None are in Java, but there are ways to run C++ and Python under Java.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1089, 2, '2014-09-08 03:07:07.413', 1, NULL, NULL, '&lt;p&gt;You could use Topic Modeling as described in this paper:
&lt;a href="http://faculty.chicagobooth.edu/workshops/orgs-markets/pdf/KaplanSwordWin2014.pdf" rel="nofollow"&gt;http://faculty.chicagobooth.edu/workshops/orgs-markets/pdf/KaplanSwordWin2014.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;They performed Topic Modeling on abstracts of patents (limited to 150 words).  They identified papers as "novel" if they were the first to introduce a topic, and measured degree of novelty by how many papers in the following year used the same topic. (Read the paper for details).&lt;/p&gt;

&lt;p&gt;I suggest that you follow their lead and only process paper abstracts.  Processing the body of each paper might reveal some novelty that the abstract does not, but you also run the risk of having much more noise in your topic model (i.e. extraneous topics, extraneous words).&lt;/p&gt;

&lt;p&gt;While you say that all 500 papers are on the same "topic", it''s probably safer to say that they are all on the same "theme" or in the same "sub-category" of Bio-medicine. Topic modeling permits decomposition of the "theme" into "topics".&lt;/p&gt;

&lt;p&gt;The good news is that there are plenty of good packages/libraries for Topic Modeling.  You still have to do preprocessing, but you don''t have to code the algorithms yourself.  See this page for many resources:
&lt;a href="http://www.cs.princeton.edu/~blei/topicmodeling.html" rel="nofollow"&gt;http://www.cs.princeton.edu/~blei/topicmodeling.html&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1090, 1, '2014-09-08 14:57:11.223', 1, 23, 'Freebase Related Models', '&lt;p&gt;It may be unlikely that anyone knows this but I have a specific question about Freebase.  Here is the Freebase page from the &lt;a href="http://www.freebase.com/m/014_d3" rel="nofollow"&gt;Ford Taurus automotive model&lt;/a&gt; .  It has a property called "Related Models".  Does anyone know how this list of related models was compiled.  What is the similarity measure that they use?  I don''t think it is only about other wikipedia pages that link to or from this page.  Alternatively, it may be that this is user generated.  Does anyone know for sure?&lt;/p&gt;
', 'dataset', NULL, '''/a'':40C ''/m/014_d3'':33C ''/p'':99C ''altern'':85C ''anyon'':11C,49C,95C ''automot'':38C ''call'':45C ''compil'':58C ''dataset'':4B ''ford'':36C ''freebas'':1A,21C,25C ''generat'':93C ''href'':30C ''know'':12C,50C,96C ''link'':79C ''list'':53C ''may'':7C,87C ''measur'':63C ''model'':3A,39C,47C,56C ''nofollow'':35C ''p'':5C ''page'':26C,77C,84C ''properti'':44C ''question'':19C ''rel'':34C ''relat'':2A,46C,55C ''similar'':62C ''specif'':18C ''sure'':98C ''taurus'':37C ''think'':70C ''unlik'':9C ''use'':66C ''user'':92C ''wikipedia'':76C ''www.freebase.com'':32C ''www.freebase.com/m/014_d3'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5820, 2, '2015-05-18 09:20:51.007', 3, NULL, NULL, '&lt;p&gt;You can consider that the clusters are defined by a multinomial distribution with 4 classes. Thus you would identify the cluster as a vector of 4 probabilities, one for each class, and the sum of probabilities being 1. &lt;/p&gt;

&lt;p&gt;In this form there are some tests and distance functions which can prove to be useful for you. &lt;/p&gt;

&lt;p&gt;You can ask questions like "is the cluster 1 is different than the cluster 2?". In order to do that you can employ hypothesis testing, and specifically chi square independence test (wikipedia page &lt;a href="http://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test#Test_of_independence" rel="nofollow"&gt;here&lt;/a&gt;). You select from your table only the first 2 columns, compute the marginal totals, compute the estimated counts as $E_{ij} = rowsum_i * colsum_j / grandtotal$. Denoting with $O_{ij}$ the cell counts you can compute a test statistic with 
formula: &lt;/p&gt;

&lt;p&gt;$$chi = \sum_{i=1}^4\sum_{j=1}^2\frac{(E_{ij}-O_{ij})^2}{E_{ij}}$$&lt;/p&gt;

&lt;p&gt;This is known to has a chi square distribution with $(rows-1)(cols-1)=3$ degrees of freedom. Thus using the cumulative distribution of chi square you can compute the p-value as $1-cdf_{\chi_{3}^2}(chi)$. Now if p-value is smaller than any critical value you choose before test (like 0.05, 0.01 or so), you have a statistical proof that the clusters have a different distribution (this follows from the fact that your procedure tests for the independence of cluster and object distributions, which with short derivation is the same as saying the clusters have the same distribution). &lt;/p&gt;

&lt;p&gt;To answer to the same type of question you can also employ G-test (wikipedia page &lt;a href="http://en.wikipedia.org/wiki/G-test" rel="nofollow"&gt;here&lt;/a&gt;) or Fisher exact test (wikipedia page &lt;a href="http://en.wikipedia.org/wiki/Fisher%27s_exact_test" rel="nofollow"&gt;here&lt;/a&gt;).  &lt;/p&gt;

&lt;p&gt;Note also that all those tests can''t prove that two clusters are the same. The logic behind the tests can be formulated as "we consider the distributions the same, as a natural state, accepted without other proofs, and if we see some exceptionally obvious differences, we can bring that as a proof that they are different indeed". &lt;/p&gt;

&lt;p&gt;A different approach would be to build a distance between those two distributions. The advantage of a distance function would be that it could be used for comparisons and eventually some further algorithms based on distances. The drawback of a distance function is that it gives you no hint to answer the first type of questions like "are the differences between 2 distributions small enough in order to be explained by the natural noise in data?". Thus I have two candidates in mind. &lt;/p&gt;

&lt;p&gt;First distance is the chi square distance. This type of distance gives a measure to the distance between two normalized histograms (or, put it simply, between two vectors of probabilities of the same size). Thus, if we denote with $cluster_i = (n_{i1}/n_i, n_{i2}/n_i, .., n_{ik}/n_i] = (p_{i1},p_{i2},..p_{ik})$, the distance between cluster i and j would be: &lt;/p&gt;

&lt;p&gt;$$d(i,j) = \frac{1}{2}\sum_{k=1}^{4}\frac{(p_{ik}-p_{jk})^2}{p_{ik}-p_{jk}}$$&lt;/p&gt;

&lt;p&gt;This distance function has the advantage that it is symmetric regarding $i$ and $j$. To be honest I rarely used it since I did not fully understand it''s meaning. In general I use another function, described below.&lt;/p&gt;

&lt;p&gt;The second distance function is mutual information (see wikipedia page &lt;a href="http://en.wikipedia.org/wiki/Mutual_information" rel="nofollow"&gt;here&lt;/a&gt;). The idea is similar with Kulback-Leibler divergence, however the KL distance is an oriented measure (measures how a distribution can be expressed through another one). The MI distance is a measure of mutual independence between two distributions. In your case the two distributions are two clusters, and the MI measures how dependent they are. I give formula just to illustrate the idea behind it. 
$$MI(x,y) = \sum_{x,y}p(x,y)log(\frac{p(x,y)}{p(x)p(y)})$$
You can see from the formula that when the two variables are independent, the quantity inside the logarithm is $1$, and as a consequence, the MI is $0$. As the difference grows, the function also grows. I will not derive what p means, see what I explained already for chi square test, since is very similar. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1091, 1, '2014-09-08 19:33:00.253', 2, 671, 'Creating Bag of words', '&lt;p&gt;What is the best technology to be used to create my custom bag of words with N-grams to apply to. I want to know a functionality that can be achieved over GUI. I cannot use spot fire as it is not available in the organization. Though i can get SAP Hana or R-hadoop. But R-hadoop is bit challenging, any suggessions.&lt;/p&gt;
', 'bigdata  text-mining', NULL, '''/p'':76C ''achiev'':41C ''appli'':30C ''avail'':53C ''bag'':2A,22C ''best'':13C ''bigdata'':5B ''bit'':72C ''cannot'':45C ''challeng'':73C ''creat'':1A,19C ''custom'':21C ''fire'':48C ''function'':37C ''get'':60C ''gram'':28C ''gui'':43C ''hadoop'':66C,70C ''hana'':62C ''know'':35C ''mine'':8B ''n'':27C ''n-gram'':26C ''organ'':56C ''p'':9C ''r'':65C,69C ''r-hadoop'':64C,68C ''sap'':61C ''spot'':47C ''suggess'':75C ''technolog'':14C ''text'':7B ''text-min'':6B ''though'':57C ''use'':17C,46C ''want'':33C ''word'':4A,24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1092, 1, '2014-09-08 21:25:26.183', 9, 872, 'Machine learning libraries for Ruby', '&lt;p&gt;Are there any machine learning libraries for Ruby that are relatively complete (including a wide variety of algorithms for supervised and unsupervised learning), robustly tested, and well-documented? I love Python''s &lt;a href="http://scikit-learn.org/"&gt;scikit-learn&lt;/a&gt; for its incredible documentation, but a client would prefer to write the code in Ruby since that''s what they''re familiar with.&lt;/p&gt;

&lt;p&gt;Ideally I am looking for a library or set of libraries which, like scikit and numpy, can implement a wide variety of data structures like sparse matrices, as well as learners.&lt;/p&gt;

&lt;p&gt;Some examples of things we''ll need to do are binary classification using SVMs, and  implementing bag of words models which we hope to concatenate with arbitrary numeric data, as described in &lt;a href="http://stackoverflow.com/q/20106940/1435804"&gt;this&lt;/a&gt; Stackoverflow post.&lt;/p&gt;

&lt;p&gt;Thanks in advance!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1094, 1, '2014-09-09 06:33:00.73', 2, 84, 'Creating obligatory combinations of variables for drawing by random forest', '&lt;p&gt;&lt;strong&gt;Problem&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;For my machine learning task, I create a set of predictors.
Predictors come in "bundles" - multi-dimensional measurements (3 or 4 - dimensional in my case).&lt;/p&gt;

&lt;p&gt;The hole "bundle" makes sense only if it has been measured, and taken all together.&lt;/p&gt;

&lt;p&gt;The problem is, different ''bundles'' of predictors can be measured only for small part of the sample, and those parts don''t necessary intersect for different ''bundles''. &lt;/p&gt;

&lt;p&gt;As parts are small, imputing leads to considerable decrease in accuracy(catastrophical to be more accurate)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Possible solutions&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I could create dummy variables that would mark whether the measurement has taken place for each variable. The problem is, when random forests draws random variables, it does so individually.&lt;/p&gt;

&lt;p&gt;So there are two basic ways to solve this problem:
1) Combine each "bundle" into one predictor. That is possible, but it seems information will be lost. 
2) Make random forest draw variables not individually, but by obligatory "bundles".&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Problem for random forest&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;As random forest draws variables randomly, it takes features that are useless (or much less useful) without other from their "bundle". I have a feeling that leads to a loss of accuracy.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Example&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;For example I have variables &lt;code&gt;a&lt;/code&gt;,&lt;code&gt;a_measure&lt;/code&gt;, &lt;code&gt;b&lt;/code&gt;,&lt;code&gt;b_measure&lt;/code&gt;.
The problem is, variables &lt;code&gt;a_measure&lt;/code&gt; make sense only if variable &lt;code&gt;a&lt;/code&gt; is present, same for &lt;code&gt;b&lt;/code&gt;. So I either have to combine &lt;code&gt;a&lt;/code&gt;and &lt;code&gt;a_measure&lt;/code&gt; into one variable, or make random forest draw both, in case at least one of them is drawn.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Question&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;What are the best practice solutions for problems when different sets of predictors are measured for small parts of overall population, and these sets of predictors come in obligatory "bundles"?&lt;/p&gt;

&lt;p&gt;Thank you!&lt;/p&gt;
', 'machine-learning  r  random-forest', NULL, '''/code'':249C,253C,256C,260C,268C,276C,283C,292C,297C ''/p'':22C,50C,67C,96C,114C,120C,152C,193C,201C,235C,240C,316C,321C,353C,357C ''/strong'':21C,119C,200C,239C,320C ''1'':164C ''2'':181C ''3'':43C ''4'':45C ''accur'':113C ''accuraci'':108C,234C ''b'':255C,258C,282C ''basic'':158C ''best'':326C ''bundl'':38C,54C,73C,95C,167C,192C,223C,352C ''case'':49C,308C ''catastroph'':109C ''code'':247C,250C,254C,257C,265C,274C,281C,290C,294C ''combin'':3A,165C,289C ''come'':36C,349C ''consider'':105C ''could'':123C ''creat'':1A,30C,124C ''decreas'':106C ''differ'':72C,94C,332C ''dimension'':41C,46C ''draw'':7A,145C,185C,206C,305C ''drawn'':315C ''dummi'':125C ''either'':286C ''exampl'':238C,243C ''featur'':211C ''feel'':227C ''forest'':10A,17B,144C,184C,199C,205C,304C ''hole'':53C ''imput'':102C ''individu'':151C,188C ''inform'':177C ''intersect'':92C ''lead'':103C,229C ''learn'':13B,27C ''least'':310C ''less'':217C ''loss'':232C ''lost'':180C ''machin'':12B,26C ''machine-learn'':11B ''make'':55C,182C,269C,302C ''mark'':129C ''measur'':42C,62C,78C,132C,252C,259C,267C,296C,337C ''much'':216C ''multi'':40C ''multi-dimension'':39C ''necessari'':91C ''obligatori'':2A,191C,351C ''one'':169C,299C,311C ''overal'':342C ''p'':18C,23C,51C,68C,97C,115C,121C,153C,194C,202C,236C,241C,317C,322C,354C ''part'':82C,88C,99C,340C ''place'':135C ''popul'':343C ''possibl'':117C,173C ''practic'':327C ''predictor'':34C,35C,75C,170C,335C,348C ''present'':278C ''problem'':20C,70C,140C,163C,196C,262C,330C ''question'':319C ''r'':14B ''random'':9A,16B,143C,146C,183C,198C,204C,208C,303C ''random-forest'':15B ''sampl'':85C ''seem'':176C ''sens'':56C,270C ''set'':32C,333C,346C ''small'':81C,101C,339C ''solut'':118C,328C ''solv'':161C ''strong'':19C,116C,195C,237C,318C ''take'':210C ''taken'':64C,134C ''task'':28C ''thank'':355C ''togeth'':66C ''two'':157C ''use'':218C ''useless'':214C ''variabl'':5A,126C,138C,147C,186C,207C,246C,264C,273C,300C ''way'':159C ''whether'':130C ''without'':219C ''would'':128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1095, 1, '2014-09-09 12:44:16.967', 9, 7695, 'Gini coefficient vs Gini impurity - decision trees', '&lt;p&gt;The problem refers to decision trees building. According to Wikipedia ''&lt;a href="http://en.wikipedia.org/wiki/Gini_coefficient"&gt;Gini coefficient&lt;/a&gt;'' should not be confused with ''&lt;a href="http://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity"&gt;Gini impurity&lt;/a&gt;''. However both measures can be used when building a decision tree - these can support our choices when splitting the set of items.&lt;/p&gt;

&lt;p&gt;1) ''Gini impurity'' - it is a standard decision-tree splitting metric (see in the link above);&lt;/p&gt;

&lt;p&gt;2) ''Gini coefficient'' - each splitting can be assessed based on the AUC criterion. For each splitting scenario we can build a ROC curve and compute AUC metric. According to Wikipedia AUC=(GiniCoeff+1)/2;&lt;/p&gt;

&lt;p&gt;Question is: are both these measures equivalent? On the one hand, I am informed that Gini coefficient should not be confused with Gini impurity. On the other hand, both these measures can be used in doing the same thing - assessing the quality of a decision tree split.&lt;/p&gt;
', 'data-mining', NULL, '''+1'':118C ''/2'':119C ''/a'':29C,42C ''/p'':65C,84C,120C,169C ''/wiki/decision_tree_learning#gini_impurity'':39C ''/wiki/gini_coefficient'':26C ''1'':67C ''2'':86C ''accord'':19C,113C ''assess'':93C,161C ''auc'':97C,111C,116C ''base'':94C ''build'':18C,50C,105C ''choic'':58C ''coeffici'':2A,28C,88C,138C ''comput'':110C ''confus'':33C,142C ''criterion'':98C ''curv'':108C ''data'':9B ''data-min'':8B ''decis'':6A,16C,52C,75C,166C ''decision-tre'':74C ''en.wikipedia.org'':25C,38C ''en.wikipedia.org/wiki/decision_tree_learning#gini_impurity'':37C ''en.wikipedia.org/wiki/gini_coefficient'':24C ''equival'':128C ''gini'':1A,4A,27C,40C,68C,87C,137C,144C ''ginicoeff'':117C ''hand'':132C,149C ''howev'':43C ''href'':23C,36C ''impur'':5A,41C,69C,145C ''inform'':135C ''item'':64C ''link'':82C ''measur'':45C,127C,152C ''metric'':78C,112C ''mine'':10B ''one'':131C ''p'':11C,66C,85C,121C ''problem'':13C ''qualiti'':163C ''question'':122C ''refer'':14C ''roc'':107C ''scenario'':102C ''see'':79C ''set'':62C ''split'':60C,77C,90C,101C,168C ''standard'':73C ''support'':56C ''thing'':160C ''tree'':7A,17C,53C,76C,167C ''use'':48C,155C ''vs'':3A ''wikipedia'':21C,115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1096, 2, '2014-09-09 15:05:46.093', 3, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;create my custom bag of words with N-grams to apply to&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;My initial recommendation would be to use the &lt;a href="http://www.nltk.org/" rel="nofollow"&gt;NLTK library for Python&lt;/a&gt;. NLTK offers methods for &lt;a href="http://www.nltk.org/book/ch01.html#bigrams_index_term" rel="nofollow"&gt;easily extracting bigrams from text&lt;/a&gt; or &lt;a href="https://nltk.googlecode.com/svn/trunk/doc/api/nltk.util-module.html#ngrams" rel="nofollow"&gt;ngrams of arbitrary length&lt;/a&gt;, as well as methods for analyzing the &lt;a href="http://www.nltk.org/book/ch01.html#frequency_distribution_index_term" rel="nofollow"&gt;frequency distribution of those items&lt;/a&gt;. However, all of this requires a bit of programming.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;a functionality that can be achieved over GUI&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;That''s tricky. Have you looked into &lt;a href="https://gate.ac.uk/" rel="nofollow"&gt;GATE&lt;/a&gt;? I''m not exactly sure if/how GATE does what you want, but it does offer a GUI.&lt;/p&gt;
', NULL, NULL, '''/a'':36C,53C,66C,86C,123C ''/blockquote'':17C,108C ''/book/ch01.html#bigrams_index_term'':45C ''/book/ch01.html#frequency_distribution_index_term'':78C ''/p'':16C,96C,107C,141C ''/svn/trunk/doc/api/nltk.util-module.html#ngrams'':59C ''achiev'':104C ''analyz'':72C ''appli'':14C ''arbitrari'':64C ''bag'':6C ''bigram'':50C ''bit'':93C ''blockquot'':1C,97C ''creat'':3C ''custom'':5C ''distribut'':82C ''easili'':48C ''exact'':127C ''extract'':49C ''frequenc'':81C ''function'':100C ''gate'':122C,130C ''gate.ac.uk'':119C ''gram'':12C ''gui'':106C,140C ''howev'':87C ''href'':28C,42C,56C,75C,118C ''if/how'':129C ''initi'':20C ''item'':85C ''length'':65C ''librari'':33C ''look'':115C ''m'':125C ''method'':39C,70C ''n'':11C ''n-gram'':10C ''ngram'':62C ''nltk'':32C,37C ''nltk.googlecode.com'':58C ''nltk.googlecode.com/svn/trunk/doc/api/nltk.util-module.html#ngrams'':57C ''nofollow'':31C,47C,61C,80C,121C ''offer'':38C,138C ''p'':2C,18C,98C,109C ''program'':95C ''python'':35C ''recommend'':21C ''rel'':30C,46C,60C,79C,120C ''requir'':91C ''sure'':128C ''text'':52C ''tricki'':112C ''use'':25C ''want'':134C ''well'':68C ''word'':8C ''would'':22C ''www.nltk.org'':29C,44C,77C ''www.nltk.org/book/ch01.html#bigrams_index_term'':43C ''www.nltk.org/book/ch01.html#frequency_distribution_index_term'':76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9676, 4, '2016-01-07 11:24:42.38', 0, NULL, NULL, 'A model-free reinforcement learning technique.', NULL, NULL, '''free'':4C ''learn'':6C ''model'':3C ''model-fre'':2C ''reinforc'':5C ''techniqu'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8773, 2, '2015-11-10 01:00:51.743', 9, NULL, NULL, '&lt;p&gt;&lt;a href="http://www.tensorflow.org/" rel="nofollow"&gt;TensorFlow&lt;/a&gt; (by Google, &lt;a href="https://googleblog.blogspot.com/2015/11/tensorflow-smarter-machine-learning-for.html" rel="nofollow"&gt;released&lt;/a&gt; on 2015-11-09) looks promising.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;open source (Apache 2.0 License) (&lt;a href="https://github.com/tensorflow/tensorflow" rel="nofollow"&gt;GitHub&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;Python (backend in C++)&lt;/li&gt;
&lt;li&gt;CPU/GPU&lt;/li&gt;
&lt;li&gt;Auto-Differentiation&lt;/li&gt;
&lt;li&gt;Portable (even works on mobile devices)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/Suhdk.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Suhdk.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;FYI: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://github.com/zer0n/deepframeworks/blob/master/README.md" rel="nofollow"&gt;Evaluation of Deep Learning Toolkits&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://github.com/soumith/convnet-benchmarks" rel="nofollow"&gt;Easy benchmarking of all public open-source implementations of convnets.&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''-09'':23C ''-11'':22C ''/2015/11/tensorflow-smarter-machine-learning-for.html'':15C ''/a'':8C,19C,42C,85C,104C,125C ''/li'':43C,49C,52C,57C,65C,105C,126C ''/p'':26C,86C,89C ''/soumith/convnet-benchmarks'':111C ''/suhdk.png'':72C,79C ''/tensorflow/tensorflow'':38C ''/ul'':66C,127C ''/zer0n/deepframeworks/blob/master/readme.md'':96C ''2.0'':32C ''2015'':21C ''alt'':80C ''apach'':31C ''auto'':55C ''auto-differenti'':54C ''backend'':46C ''benchmark'':115C ''c'':48C ''convnet'':124C ''cpu/gpu'':51C ''deep'':101C ''descript'':83C ''devic'':64C ''differenti'':56C ''easi'':114C ''enter'':81C ''evalu'':99C ''even'':60C ''fyi'':88C ''github'':41C ''github.com'':37C,95C,110C ''github.com/soumith/convnet-benchmarks'':109C ''github.com/tensorflow/tensorflow'':36C ''github.com/zer0n/deepframeworks/blob/master/readme.md'':94C ''googl'':10C ''googleblog.blogspot.com'':14C ''googleblog.blogspot.com/2015/11/tensorflow-smarter-machine-learning-for.html'':13C ''href'':3C,12C,35C,69C,93C,108C ''i.stack.imgur.com'':71C,78C ''i.stack.imgur.com/suhdk.png'':70C,77C ''imag'':82C ''img'':75C ''implement'':122C ''learn'':102C ''li'':28C,44C,50C,53C,58C,91C,106C ''licens'':33C ''look'':24C ''mobil'':63C ''nofollow'':6C,17C,40C,74C,98C,113C ''open'':29C,120C ''open-sourc'':119C ''p'':1C,67C,87C ''portabl'':59C ''promis'':25C ''public'':118C ''python'':45C ''rel'':5C,16C,39C,73C,97C,112C ''releas'':18C ''sourc'':30C,121C ''src'':76C ''tensorflow'':7C ''toolkit'':103C ''ul'':27C,90C ''work'':61C ''www.tensorflow.org'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1114, 2, '2014-09-13 18:17:33.253', 7, NULL, NULL, '&lt;p&gt;Gradient boosting is also a good choice here.  You can use the gradient boosting classifier in sci-kit learn for example.  Gradient boosting is a principled method of dealing with class imbalance by constructing successive training sets based on incorrectly classified examples.&lt;/p&gt;
', NULL, NULL, '''/p'':45C ''also'':5C ''base'':40C ''boost'':3C,15C,25C ''choic'':8C ''class'':33C ''classifi'':16C,43C ''construct'':36C ''deal'':31C ''exampl'':23C,44C ''good'':7C ''gradient'':2C,14C,24C ''imbal'':34C ''incorrect'':42C ''kit'':20C ''learn'':21C ''method'':29C ''p'':1C ''principl'':28C ''sci'':19C ''sci-kit'':18C ''set'':39C ''success'':37C ''train'':38C ''use'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1097, 2, '2014-09-09 16:58:10.537', 6, NULL, NULL, '&lt;p&gt;I''ll go ahead and post an answer for now; if someone has something better I''ll accept theirs. &lt;/p&gt;

&lt;p&gt;At this point the most powerful option appears to be accessing WEKA using jRuby. We spent yesterday scouring the ''net, and this combination was even used by a &lt;a href="http://www.confreaks.com/videos/867-railsconf2012-practical-machine-learning-and-rails"&gt;talk at RailsConf 2012&lt;/a&gt;, so I would guess if there were a comparable pure ruby package, they would have used it.&lt;/p&gt;

&lt;p&gt;Note that if you know exactly what you need, there are plenty of individual libraries that either &lt;a href="https://github.com/febeling/rb-libsvm"&gt;wrap standalone packages like libsvm&lt;/a&gt; or &lt;a href="https://github.com/alexandru/stuff-classifier"&gt;re-implement some individual algorithms like Naive Bayes in pure Ruby&lt;/a&gt; and will spare you from using jRuby.&lt;/p&gt;

&lt;p&gt;But for a general-purpose library, WEKA and jRuby seem to be the best bet at this time.&lt;/p&gt;
', NULL, NULL, '''/a'':60C,107C,126C ''/alexandru/stuff-classifier'':113C ''/febeling/rb-libsvm'':101C ''/p'':21C,78C,134C,155C ''/videos/867-railsconf2012-practical-machine-learning-and-rails'':55C ''2012'':59C ''accept'':19C ''access'':33C ''ahead'':5C ''algorithm'':119C ''answer'':9C ''appear'':30C ''bay'':122C ''best'':150C ''bet'':151C ''better'':16C ''combin'':45C ''compar'':69C ''either'':96C ''even'':47C ''exact'':85C ''general'':140C ''general-purpos'':139C ''github.com'':100C,112C ''github.com/alexandru/stuff-classifier'':111C ''github.com/febeling/rb-libsvm'':99C ''go'':4C ''guess'':64C ''href'':52C,98C,110C ''implement'':116C ''individu'':93C,118C ''jrubi'':36C,133C,145C ''know'':84C ''librari'':94C,142C ''libsvm'':106C ''like'':105C,120C ''ll'':3C,18C ''naiv'':121C ''need'':88C ''net'':42C ''note'':80C ''option'':29C ''p'':1C,22C,79C,135C ''packag'':72C,104C ''plenti'':91C ''point'':25C ''post'':7C ''power'':28C ''pure'':70C,124C ''purpos'':141C ''railsconf'':58C ''re'':115C ''re-impl'':114C ''rubi'':71C,125C ''scour'':40C ''seem'':146C ''someon'':13C ''someth'':15C ''spare'':129C ''spent'':38C ''standalon'':103C ''talk'':56C ''time'':154C ''use'':35C,48C,76C,132C ''weka'':34C,143C ''would'':63C,74C ''wrap'':102C ''www.confreaks.com'':54C ''www.confreaks.com/videos/867-railsconf2012-practical-machine-learning-and-rails'':53C ''yesterday'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1098, 2, '2014-09-10 07:33:06.47', 1, NULL, NULL, '&lt;p&gt;You can use SKlearn, It is a python library. It is simplest method which i like with minimal code. You can follow this link &lt;a href="http://scikit-learn.org/stable/modules/feature_extraction.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/feature_extraction.html&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/p'':36C ''/stable/modules/feature_extraction.html'':30C ''/stable/modules/feature_extraction.html&lt;/a&gt;'':35C ''code'':20C ''follow'':23C ''href'':27C ''librari'':10C ''like'':17C ''link'':25C ''method'':14C ''minim'':19C ''nofollow'':32C ''p'':1C ''python'':9C ''rel'':31C ''scikit-learn.org'':29C,34C ''scikit-learn.org/stable/modules/feature_extraction.html'':28C ''scikit-learn.org/stable/modules/feature_extraction.html&lt;/a&gt;'':33C ''simplest'':13C ''sklearn'':5C ''use'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1139, 2, '2014-09-18 13:51:58.473', 1, NULL, NULL, '&lt;p&gt;Some of the GREC shared task challenges included a named entity recognition &amp;amp; coreference resolution component (i.e., disambiguation), but I don''t think they''ve run GREC since 2010...?&lt;/p&gt;

&lt;p&gt;&lt;a href="https://sites.google.com/site/genchalrepository/reg-in-context/grec-ner" rel="nofollow"&gt;https://sites.google.com/site/genchalrepository/reg-in-context/grec-ner&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':31C ''/site/genchalrepository/reg-in-context/grec-ner'':37C ''/site/genchalrepository/reg-in-context/grec-ner&lt;/a&gt;&lt;/p&gt;'':42C ''2010'':30C ''amp'':14C ''challeng'':8C ''compon'':17C ''corefer'':15C ''disambigu'':19C ''entiti'':12C ''grec'':5C,28C ''href'':34C ''i.e'':18C ''includ'':9C ''name'':11C ''nofollow'':39C ''p'':1C,32C ''recognit'':13C ''rel'':38C ''resolut'':16C ''run'':27C ''share'':6C ''sinc'':29C ''sites.google.com'':36C,41C ''sites.google.com/site/genchalrepository/reg-in-context/grec-ner'':35C ''sites.google.com/site/genchalrepository/reg-in-context/grec-ner&lt;/a&gt;&lt;/p&gt;'':40C ''task'':7C ''think'':24C ''ve'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1100, 1, '2014-09-10 14:50:13.72', 10, 250, 'Neural net for server monitoring', '&lt;p&gt;I''m looking at pybrain for taking server monitor alarms and determining the root cause of a problem. I''m happy with training it using supervised learning and curating the training data sets. The data is structured something like this:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Server Type &lt;strong&gt;A&lt;/strong&gt; #1
&lt;ul&gt;
&lt;li&gt;Alarm type 1&lt;/li&gt;
&lt;li&gt;Alarm type 2&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;Server Type &lt;strong&gt;A&lt;/strong&gt; #2
&lt;ul&gt;
&lt;li&gt;Alarm type 1&lt;/li&gt;
&lt;li&gt;Alarm type 2&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;Server Type &lt;strong&gt;B&lt;/strong&gt; #1
&lt;ul&gt;
&lt;li&gt;Alarm type &lt;strong&gt;99&lt;/strong&gt;&lt;/li&gt;
&lt;li&gt;Alarm type 2&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So there are &lt;em&gt;n&lt;/em&gt; servers, with &lt;em&gt;x&lt;/em&gt; alarms that can be &lt;code&gt;UP&lt;/code&gt; or &lt;code&gt;DOWN&lt;/code&gt;. Both &lt;code&gt;n&lt;/code&gt; and &lt;code&gt;x&lt;/code&gt; are variable. &lt;/p&gt;

&lt;p&gt;If Server A1 has &lt;em&gt;alarm 1 &amp;amp; 2&lt;/em&gt; as &lt;code&gt;DOWN&lt;/code&gt;, then we can say that &lt;em&gt;service a&lt;/em&gt; is down on that server and is the cause of the problem.&lt;/p&gt;

&lt;p&gt;If &lt;em&gt;alarm 1&lt;/em&gt; is down on all servers, then we can say that &lt;em&gt;service a&lt;/em&gt; is the cause.&lt;/p&gt;

&lt;p&gt;There can potentially be multiple options for the cause, so straight classification doesn''t seem appropriate.&lt;/p&gt;

&lt;p&gt;I would also like to tie later sources of data to the net. Such as just scripts that ping some external service.&lt;/p&gt;

&lt;p&gt;All the appropriate alarms may not be triggered at once, due to serial service checks, so it can start with one server down and then another server down 5 minutes later.&lt;/p&gt;

&lt;p&gt;I''m trying to do some basic stuff at first:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from pybrain.tools.shortcuts import buildNetwork
from pybrain.datasets import SupervisedDataSet
from pybrain.supervised.trainers import BackpropTrainer


INPUTS = 2
OUTPUTS = 1

# Build network

# 2 inputs, 3 hidden, 1 output neurons
net = buildNetwork(INPUTS, 3, OUTPUTS)


# Build dataset

# Dataset with 2 inputs and 1 output
ds = SupervisedDataSet(INPUTS, OUTPUTS)


# Add one sample, iterable of inputs and iterable of outputs
ds.addSample((0, 0), (0,))



# Train the network with the dataset
trainer = BackpropTrainer(net, ds)

# Train 1000 epochs
for x in xrange(10):
    trainer.train()

# Train infinite epochs until the error rate is low
trainer.trainUntilConvergence()


# Run an input over the network
result = net.activate([2, 1])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But I[m having a hard time mapping variable numbers of alarms to static numbers of inputs. For example, if we add an alarm to a server, or add a server, the whole net needs to be rebuilt. If that is something that needs to be done, I can do it, but want to know if there''s a better way.&lt;/p&gt;

&lt;p&gt;Another option I''m trying to think of, is have a different net for each type of server, but I don''t see how I can draw an environment-wide conclusion, since it will just make evaluations on a single host, instead of all hosts at once. &lt;/p&gt;

&lt;p&gt;Which type of algorithm should I use and how do I map the dataset to draw environment-wide conclusions as a whole with variable inputs?&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/code'':134C,138C,142C,146C,164C,396C ''/em'':122C,127C,160C,173C,192C,206C ''/li'':65C,70C,72C,85C,90C,92C,107C,112C,114C ''/p'':51C,149C,186C,210C,228C,252C,285C,297C,460C,510C,538C ''/pre'':397C ''/strong'':58C,78C,98C,106C ''/ul'':71C,91C,113C,115C ''0'':354C,355C,356C ''1'':59C,64C,84C,99C,157C,191C,315C,322C,337C,395C ''10'':374C ''1000'':368C ''2'':69C,79C,89C,111C,159C,313C,318C,334C,394C ''3'':320C,328C ''5'':282C ''99'':105C ''a1'':153C ''add'':343C,420C,427C ''alarm'':20C,62C,67C,82C,87C,102C,109C,128C,156C,190C,257C,410C,422C ''algorithm'':515C ''also'':232C ''amp'':158C ''anoth'':279C,462C ''appropri'':227C,256C ''b'':97C ''backproptrain'':311C,364C ''basic'':293C ''better'':458C ''build'':316C,330C ''buildnetwork'':303C,326C ''caus'':25C,182C,209C,220C ''check'':268C ''classif'':223C ''code'':132C,136C,140C,144C,162C,299C ''conclus'':493C,531C ''curat'':39C ''data'':42C,45C,239C ''dataset'':331C,332C,362C,525C ''determin'':22C ''differ'':473C ''doesn'':224C ''done'':445C ''draw'':488C,527C ''ds'':339C,366C ''ds.addsample'':353C ''due'':264C ''em'':120C,125C,155C,170C,189C,203C ''environ'':491C,529C ''environment-wid'':490C,528C ''epoch'':369C,378C ''error'':381C ''evalu'':499C ''exampl'':417C ''extern'':250C ''first'':296C ''happi'':31C ''hard'':404C ''hidden'':321C ''host'':503C,507C ''import'':302C,306C,310C ''infinit'':377C ''input'':312C,319C,327C,335C,341C,348C,388C,415C,537C ''instead'':504C ''iter'':346C,350C ''know'':453C ''later'':236C,284C ''learn'':8B,37C ''li'':53C,61C,66C,73C,81C,86C,93C,101C,108C ''like'':49C,233C ''look'':13C ''low'':384C ''m'':12C,30C,288C,401C,465C ''machin'':7B ''machine-learn'':6B ''make'':498C ''map'':406C,523C ''may'':258C ''minut'':283C ''monitor'':5A,19C ''multipl'':216C ''n'':121C,141C ''need'':433C,442C ''net'':2A,242C,325C,365C,432C,474C ''net.activate'':393C ''network'':317C,359C,391C ''neural'':1A ''neuralnetwork'':9B ''neuron'':324C ''number'':408C,413C ''one'':274C,344C ''option'':217C,463C ''output'':314C,323C,329C,338C,342C,352C ''p'':10C,116C,150C,187C,211C,229C,253C,286C,398C,461C,511C ''ping'':248C ''potenti'':214C ''pre'':298C ''problem'':28C,185C ''pybrain'':15C ''pybrain.datasets'':305C ''pybrain.supervised.trainers'':309C ''pybrain.tools.shortcuts'':301C ''rate'':382C ''rebuilt'':436C ''result'':392C ''root'':24C ''run'':386C ''sampl'':345C ''say'':168C,201C ''script'':246C ''see'':484C ''seem'':226C ''serial'':266C ''server'':4A,18C,54C,74C,94C,123C,152C,178C,197C,275C,280C,425C,429C,479C ''servic'':171C,204C,251C,267C ''set'':43C ''sinc'':494C ''singl'':502C ''someth'':48C,440C ''sourc'':237C ''start'':272C ''static'':412C ''straight'':222C ''strong'':56C,76C,96C,104C ''structur'':47C ''stuff'':294C ''supervis'':36C ''superviseddataset'':307C,340C ''take'':17C ''think'':468C ''tie'':235C ''time'':405C ''train'':33C,41C,357C,367C,376C ''trainer'':363C ''trainer.train'':375C ''trainer.trainuntilconvergence'':385C ''tri'':289C,466C ''trigger'':261C ''type'':55C,63C,68C,75C,83C,88C,95C,103C,110C,477C,513C ''ul'':52C,60C,80C,100C ''use'':35C,518C ''variabl'':148C,407C,536C ''want'':451C ''way'':459C ''whole'':431C,534C ''wide'':492C,530C ''would'':231C ''x'':126C,145C,371C ''xrang'':373C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10134, 5, '2016-02-08 05:45:45.807', 0, NULL, NULL, '&lt;p&gt;This is a special tag that is designed to be used only by the system for questions that have had all of their other tags removed.&lt;/p&gt;

&lt;p&gt;All questions in the system must have at least one tag. Normally, it is not possible to submit new or edited questions that do not have tags; the system will reject the submission until a valid tag is applied.&lt;/p&gt;

&lt;p&gt;There are a few unusual situations that can cause this tag to be applied. When a tag has existed for six months and only been used on one question, and no tag wiki has been created for the tag, the system deletes the tag. When the community decides that a given tag can never be appropriate &amp;#8212; for example, because it is misspelled or is off-topic &amp;#8212; moderators may remove the tag from the site &lt;em&gt;en masse&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;When one of these deletions removes the last tag from a question, the system adds the &lt;a href="/questions/tagged/untagged" class="post-tag" title="show questions tagged &amp;#39;untagged&amp;#39;" rel="tag"&gt;untagged&lt;/a&gt; tag to that question so that the "all questions must have at least one tag" rule is not broken.&lt;/p&gt;

&lt;p&gt;You can help clean up some of these questions! If they''re good questions, edit them to remove the &lt;a href="/questions/tagged/untagged" class="post-tag" title="show questions tagged &amp;#39;untagged&amp;#39;" rel="tag"&gt;untagged&lt;/a&gt; tag and add more descriptive tags. If they''re not so good, use the usual tools: downvoting, flagging and voting to close/delete.&lt;/p&gt;
', NULL, NULL, '''/a'':187C,245C ''/em'':151C ''/p'':28C,69C,152C,207C,268C ''/questions/tagged/untagged'':172C,230C ''39'':181C,183C,239C,241C ''8212'':127C,139C ''add'':168C,248C ''appli'':68C,84C ''appropri'':126C ''broken'':206C ''caus'':79C ''class'':173C,231C ''clean'':212C ''close/delete'':267C ''communiti'':117C ''creat'':106C ''decid'':118C ''delet'':112C,158C ''descript'':250C ''design'':9C ''downvot'':262C ''edit'':50C,223C ''em'':148C ''en'':149C ''exampl'':129C ''exist'':89C ''flag'':263C ''given'':121C ''good'':221C,257C ''help'':211C ''href'':171C,229C ''last'':161C ''least'':38C,200C ''mass'':150C ''may'':141C ''misspel'':133C ''moder'':140C ''month'':92C ''must'':35C,197C ''never'':124C ''new'':48C ''normal'':41C ''off-top'':136C ''one'':39C,98C,155C,201C ''p'':1C,29C,70C,153C,208C ''possibl'':45C ''post'':175C,233C ''post-tag'':174C,232C ''question'':18C,31C,51C,99C,165C,179C,191C,196C,217C,222C,237C ''re'':220C,254C ''reject'':60C ''rel'':184C,242C ''remov'':27C,142C,159C,226C ''rule'':203C ''show'':178C,236C ''site'':147C ''situat'':76C ''six'':91C ''special'':5C ''submiss'':62C ''submit'':47C ''system'':16C,34C,58C,111C,167C ''tag'':6C,26C,40C,56C,66C,81C,87C,102C,109C,114C,122C,144C,162C,176C,180C,185C,188C,202C,234C,238C,243C,246C,251C ''titl'':177C,235C ''tool'':261C ''topic'':138C ''untag'':182C,186C,240C,244C ''unusu'':75C ''use'':12C,96C,258C ''usual'':260C ''valid'':65C ''vote'':265C ''wiki'':103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1101, 2, '2014-09-10 15:52:04.743', -1, NULL, NULL, '&lt;p&gt;I think they both represent the same concept. &lt;/p&gt;

&lt;p&gt;In classification trees, the Gini Index is used to compute the impurity of a data partition. So Assume the data partition D consisiting of 4 classes each with equal probability. Then the Gini Index (Gini Impurity) will be:
Gini(D) = 1 - (0.25^2 + 0.25^2 + 0.25^2 + 0.25^2)&lt;/p&gt;

&lt;p&gt;In CART we perform binary splits. So The gini index will be computed as the weighted sum of the resulting partitions and we select the split with the smallest gini index.&lt;/p&gt;

&lt;p&gt;So the use of Gini Impurity (Gini Index) is not limited to binary situations.&lt;/p&gt;

&lt;p&gt;Another term for Gini Impurity is Gini Coefficient which is used normally as a measure of income distribution.&lt;/p&gt;
', NULL, NULL, '''/p'':10C,61C,94C,110C,130C ''0.25'':53C,55C,57C,59C ''1'':52C ''2'':54C,56C,58C,60C ''4'':36C ''anoth'':112C ''assum'':29C ''binari'':67C,108C ''cart'':64C ''class'':37C ''classif'':13C ''coeffici'':119C ''comput'':21C,75C ''concept'':9C ''consisit'':34C ''d'':33C,51C ''data'':26C,31C ''distribut'':129C ''equal'':40C ''gini'':16C,44C,46C,50C,71C,92C,100C,102C,115C,118C ''impur'':23C,47C,101C,116C ''incom'':128C ''index'':17C,45C,72C,93C,103C ''limit'':106C ''measur'':126C ''normal'':123C ''p'':1C,11C,62C,95C,111C ''partit'':27C,32C,83C ''perform'':66C ''probabl'':41C ''repres'':6C ''result'':82C ''select'':86C ''situat'':109C ''smallest'':91C ''split'':68C,88C ''sum'':79C ''term'':113C ''think'':3C ''tree'':14C ''use'':19C,98C,122C ''weight'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1118, 2, '2014-09-15 13:03:44.083', 3, NULL, NULL, '&lt;p&gt;There are several approaches. You can start from the second one.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt; Equal-width (distance) partitioning:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;It divides the range into N intervals of equal size: uniform grid&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;if A and B are the lowest and highest values of the attribute, the width of intervals will be: &lt;code&gt;W = (B-A)/N&lt;/code&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;The most straightforward
- Outliers may dominate presentation
- Skewed data is not handled well.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt; Equal-depth (frequency) partitioning:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;It divides the range into 
N intervals, each containing approximately 
same number of samples&lt;/li&gt;
&lt;li&gt;Good data scaling&lt;/li&gt;
&lt;li&gt;Managing categorical attributes can be tricky.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Other Methods&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;Rank&lt;/code&gt;: The rank of a number is its size relative to other values of a numerical variable. First, we sort the list of values, then we assign the position of a value as its rank. Same values receive the same rank but the presence of duplicate values affects the ranks of subsequent values (e.g., 1,2,3,3,5). Rank is a solid binning method with one major drawback, values can have different ranks in different lists.&lt;/li&gt;
&lt;li&gt;&lt;code&gt;Quantiles (median, quartiles, percentiles, ...)&lt;/code&gt;: Quantiles are also very useful binning methods but like Rank, one value can have different quantile if the list of values changes.&lt;/li&gt;
&lt;li&gt;&lt;code&gt;Math functions&lt;/code&gt;: For example, logarithmic binning is an effective method for the numerical variables with highly skewed distribution (e.g., income).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Entropy-based Binning&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.saedsayad.com/supervised_binning.htm" rel="nofollow"&gt;Entropy based method&lt;/a&gt; uses a split approach. The entropy (or the information content) is calculated based on the class label. Intuitively, it finds the best split so that the bins are as pure as possible that is the majority of the values in a bin correspond to have the same class label. Formally, it is characterized by finding the split with the maximal information gain. &lt;/p&gt;
', NULL, NULL, '''/a'':290C ''/code'':67C,138C,222C,250C ''/li'':39C,69C,86C,113C,118C,126C,215C,245C,269C ''/n'':66C ''/p'':13C,22C,38C,68C,85C,96C,133C,278C,353C ''/strong'':21C,95C,132C,277C ''/supervised_binning.htm'':284C ''/ul'':87C,127C,270C ''1'':192C ''2'':193C ''3'':194C,195C ''5'':196C ''affect'':185C ''also'':225C ''approach'':5C,294C ''approxim'':108C ''assign'':164C ''attribut'':54C,122C ''b'':45C,64C ''b-a'':63C ''base'':275C,288C,303C ''best'':312C ''bin'':201C,228C,254C,276C,317C,332C ''calcul'':302C ''categor'':121C ''chang'':244C ''character'':343C ''class'':306C,338C ''code'':61C,136C,217C,247C ''contain'':107C ''content'':300C ''correspond'':333C ''data'':80C,116C ''depth'':92C ''differ'':210C,213C,237C ''distanc'':19C ''distribut'':266C ''divid'':27C,100C ''domin'':77C ''drawback'':206C ''duplic'':183C ''e.g'':191C,267C ''effect'':257C ''entropi'':274C,287C,296C ''entropy-bas'':273C ''equal'':17C,34C,91C ''equal-depth'':90C ''equal-width'':16C ''exampl'':252C ''find'':310C,345C ''first'':155C ''formal'':340C ''frequenc'':93C ''function'':249C ''gain'':352C ''good'':115C ''grid'':37C ''handl'':83C ''high'':264C ''highest'':50C ''href'':281C ''incom'':268C ''inform'':299C,351C ''interv'':32C,58C,105C ''intuit'':308C ''label'':307C,339C ''li'':24C,40C,70C,98C,114C,119C,135C,216C,246C ''like'':231C ''list'':159C,214C,241C ''logarithm'':253C ''lowest'':48C ''major'':205C,326C ''manag'':120C ''math'':248C ''maxim'':350C ''may'':76C ''median'':219C ''method'':131C,202C,229C,258C,289C ''n'':31C,104C ''nofollow'':286C ''number'':110C,143C ''numer'':153C,261C ''one'':12C,204C,233C ''outlier'':75C ''p'':1C,14C,25C,41C,71C,88C,128C,271C,279C ''partit'':20C,94C ''percentil'':221C ''posit'':166C ''possibl'':322C ''presenc'':181C ''present'':78C ''pure'':320C ''quantil'':218C,223C,238C ''quartil'':220C ''rang'':29C,102C ''rank'':137C,140C,172C,178C,187C,197C,211C,232C ''receiv'':175C ''rel'':285C ''relat'':147C ''sampl'':112C ''scale'':117C ''second'':11C ''sever'':4C ''size'':35C,146C ''skew'':79C,265C ''solid'':200C ''sort'':157C ''split'':293C,313C,347C ''start'':8C ''straightforward'':74C ''strong'':15C,89C,129C,272C ''subsequ'':189C ''tricki'':125C ''ul'':23C,97C,134C ''uniform'':36C ''use'':227C,291C ''valu'':51C,150C,161C,169C,174C,184C,190C,207C,234C,243C,329C ''variabl'':154C,262C ''w'':62C ''well'':84C ''width'':18C,56C ''www.saedsayad.com'':283C ''www.saedsayad.com/supervised_binning.htm'':282C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1102, 1, '2014-09-11 17:09:52.313', 4, 282, 'Improve CoreNLP POS tagger and NER tagger?', '&lt;p&gt;The CoreNLP parts of speech tagger and name entity recognition tagger are pretty good out of the box, but I''d like to improve the accuracy further so that the overall program runs better. To explain more about accuracy -- there are situations in which the POS/NER is wrongly tagged. For instance:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;"Oversaw car manufacturing" gets tagged as NNP-NN-NN&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Rather than VB* or something similar, since it''s a verb-like phrase (I''m not a linguist, so take this with a grain of salt).&lt;/p&gt;

&lt;p&gt;So what''s the best way to accomplish accuracy improvement?&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Are there better models out there for POS/NER that can be incorporated into CoreNLP?&lt;/li&gt;
&lt;li&gt;Should I switch to other NLP tools?&lt;/li&gt;
&lt;li&gt;Or create training models with exception rules?&lt;/li&gt;
&lt;/ul&gt;
', 'nlp  language-model', NULL, '''/li'':77C,136C,145C,154C ''/p'':64C,107C,119C ''/ul'':78C,155C ''accomplish'':116C ''accuraci'':38C,51C,117C ''best'':113C ''better'':46C,124C ''box'':30C ''car'':68C ''corenlp'':2A,14C,135C ''creat'':148C ''d'':33C ''entiti'':21C ''except'':152C ''explain'':48C ''get'':70C ''good'':26C ''grain'':104C ''improv'':1A,36C,118C ''incorpor'':133C ''instanc'':63C ''languag'':10B ''language-model'':9B ''li'':66C,121C,137C,146C ''like'':34C,92C ''linguist'':98C ''m'':95C ''manufactur'':69C ''model'':11B,125C,150C ''name'':20C ''ner'':6A ''nlp'':8B,143C ''nn'':75C,76C ''nnp'':74C ''nnp-nn-nn'':73C ''overal'':43C ''oversaw'':67C ''p'':12C,79C,108C ''part'':15C ''phrase'':93C ''pos'':3A ''pos/ner'':58C,129C ''pretti'':25C ''program'':44C ''rather'':80C ''recognit'':22C ''rule'':153C ''run'':45C ''salt'':106C ''similar'':85C ''sinc'':86C ''situat'':54C ''someth'':84C ''speech'':17C ''switch'':140C ''tag'':61C,71C ''tagger'':4A,7A,18C,23C ''take'':100C ''tool'':144C ''train'':149C ''ul'':65C,120C ''vb'':82C ''verb'':91C ''verb-lik'':90C ''way'':114C ''wrong'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1104, 1, '2014-09-12 04:22:38.823', 2, 25, 'Modeling Pipeline Budget', '&lt;p&gt;I have been tasked with creating a pipeline chart with the live data and the budgeted numbers.  &lt;/p&gt;

&lt;p&gt;I know what probability of each phase of reaching the next.  The problem is I have no Idea what to do about the pipeline budgeting with regards to time.  For instance what period of time should I have closed sales in the chart.  &lt;/p&gt;

&lt;p&gt;I have honestly been working on trying to figure it out.  Each successive revision gets me farther from the answer.&lt;/p&gt;
', 'recommendation  time-series', NULL, '''/p'':26C,71C,93C ''answer'':92C ''budget'':3A,24C,52C ''chart'':17C,70C ''close'':66C ''creat'':14C ''data'':21C ''farther'':89C ''figur'':81C ''get'':87C ''honest'':75C ''idea'':45C ''instanc'':58C ''know'':29C ''live'':20C ''model'':1A ''next'':38C ''number'':25C ''p'':8C,27C,72C ''period'':60C ''phase'':34C ''pipelin'':2A,16C,51C ''probabl'':31C ''problem'':40C ''reach'':36C ''recommend'':4B ''regard'':54C ''revis'':86C ''sale'':67C ''seri'':7B ''success'':85C ''task'':12C ''time'':6B,56C,62C ''time-seri'':5B ''tri'':79C ''work'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1105, 1, '2014-09-12 11:06:48.203', 2, 71, 'Logistic Regression implementation does not converge', '&lt;p&gt;I am currently trying to implement logistic regression with iteratively reweightes LS, according to "Pattern Recognition and Machine Learning" by C. Bishop. In a first approach I tried to implement it in C#, where I used Gauss'' algorithm to solve eq. 4.99. For a single feature it gave very promising (nearly exact) results, but whenever I tried to run it with more than one feature my system matrix became singular, and the weights did not converge. I first thought that it was my implementation, but when I implemented it in SciLab the results sustained. The SciLab (more concise due to matrix operators) code I used is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;phi = [1; 0; 1; 1];
t = [1; 0; 0; 0];
w= [1];

w'' * phi(1,:)''

for in=1:100
    y = [];
    R = zeros(size(phi,1));
    R_inv = zeros(size(phi,1));

    for i=1:size(phi,1)
        y(i) = 1/(1+ exp(-(w'' * phi(i,:)'')));
        R(i,i) = y(i)*(1 - y(i));
        R_inv(i,i) = 1/R(i,i);
    end

    z = phi * w - R_inv*(y - t)
    w = inv(phi''*R*phi)*phi''*R*z
end
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;With the values for phi (input/features) and t (output/classes), it yields a weight of  -0.6931472, which is pretty much 1/3, which seems fine to me, for there is 1/3 probability of beeing assigned to class 1, if feature 1 is present (please forgive me, if my terms do not comply with ML-language completely, for I am an software developer). If I now added an intercept feature, which would accord to&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;phi = [1, 1; 1, 0; 1, 1; 1, 1];
w = [1; 1];
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;my R-matrix becomes singular and the last weights value is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;w  =
  - 5.8151677  
  1.290D+30  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;which - to my reading - would mean, that the probability of belonging to class 1 would be close to 1 if feature 1 is present about 3% for the rest. There has got to be any error I made, but I do not get which one. For both implementations yield the same results I suspect that there is some point I''ve been missing or gotten wrong, but I do not understand which one.&lt;/p&gt;
', 'logistic-regression', NULL, '''+30'':309C ''-0.6931472'':214C ''/code'':197C,287C,310C ''/p'':117C,272C,302C,386C ''/pre'':198C,288C,311C ''0'':122C,127C,128C,129C,279C ''1'':121C,123C,124C,126C,131C,134C,137C,144C,150C,153C,156C,159C,160C,170C,235C,238C,276C,277C,278C,280C,281C,282C,283C,285C,286C,326C,331C,334C ''1.290'':307C ''1/3'':219C,228C ''1/r'':177C ''100'':138C ''3'':338C ''4.99'':52C ''5.8151677'':306C ''accord'':23C,270C ''ad'':264C ''algorithm'':48C ''approach'':36C ''assign'':232C ''becam'':79C ''becom'':294C ''bee'':231C ''belong'':323C ''bishop'':32C ''c'':31C,43C ''class'':234C,325C ''close'':329C ''code'':113C,119C,274C,304C ''complet'':254C ''compli'':249C ''concis'':108C ''converg'':6A,86C ''current'':13C ''d'':308C ''develop'':260C ''due'':109C ''end'':180C,196C ''eq'':51C ''error'':348C ''exact'':62C ''exp'':161C ''featur'':56C,75C,237C,267C,333C ''fine'':222C ''first'':35C,88C ''forgiv'':242C ''gauss'':47C ''gave'':58C ''get'':355C ''got'':344C ''gotten'':377C ''implement'':3A,16C,40C,94C,98C,360C ''input/features'':205C ''intercept'':266C ''inv'':146C,174C,185C,189C ''iter'':20C ''languag'':253C ''last'':298C ''learn'':29C ''logist'':1A,8B,17C ''logistic-regress'':7B ''ls'':22C ''machin'':28C ''made'':350C ''matrix'':78C,111C,293C ''mean'':318C ''miss'':375C ''ml'':252C ''ml-languag'':251C ''much'':218C ''near'':61C ''one'':74C,357C,385C ''oper'':112C ''output/classes'':208C ''p'':10C,199C,289C,312C ''pattern'':25C ''phi'':120C,133C,143C,149C,155C,163C,182C,190C,192C,193C,204C,275C ''pleas'':241C ''point'':371C ''pre'':118C,273C,303C ''present'':240C,336C ''pretti'':217C ''probabl'':229C,321C ''promis'':60C ''r'':140C,145C,165C,173C,184C,191C,194C,292C ''r-matrix'':291C ''read'':316C ''recognit'':26C ''regress'':2A,9B,18C ''rest'':341C ''result'':63C,103C,364C ''reweight'':21C ''run'':69C ''scilab'':101C,106C ''seem'':221C ''singl'':55C ''singular'':80C,295C ''size'':142C,148C,154C ''softwar'':259C ''solv'':50C ''suspect'':366C ''sustain'':104C ''system'':77C ''term'':246C ''thought'':89C ''tri'':14C,38C,67C ''understand'':383C ''use'':46C,115C ''valu'':202C,300C ''ve'':373C ''w'':130C,132C,162C,183C,188C,284C,305C ''weight'':83C,212C,299C ''whenev'':65C ''would'':269C,317C,327C ''wrong'':378C ''y'':139C,157C,168C,171C,186C ''yield'':210C,361C ''z'':181C,195C ''zero'':141C,147C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6196, 2, '2015-06-24 01:22:17.71', 3, NULL, NULL, '&lt;p&gt;It is a bad idea to counterpose "unstructure data" to, say, tabular data (as in "non-tabular data"), as you will have to elliminate other alternatives as well (e.g., "non-tabular and non-graph and ... data"). "Plain text" (-- my choice) or "raw text" or "raw data" sound fine.&lt;/p&gt;
', NULL, NULL, '''/p'':53C ''altern'':28C ''bad'':5C ''choic'':44C ''counterpos'':8C ''data'':10C,14C,20C,40C,50C ''e.g'':31C ''ellimin'':26C ''fine'':52C ''graph'':38C ''idea'':6C ''non'':18C,33C,37C ''non-graph'':36C ''non-tabular'':17C,32C ''p'':1C ''plain'':41C ''raw'':46C,49C ''say'':12C ''sound'':51C ''tabular'':13C,19C,34C ''text'':42C,47C ''unstructur'':9C ''well'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10170, 2, '2016-02-09 23:28:57.067', 0, NULL, NULL, '&lt;p&gt;The strided deconvolution is expanding the size of the layer (you can think of it as undo-ing the effect of the pooling without doing unpooling).&lt;/p&gt;

&lt;p&gt;Check out slide 18 &lt;a href="http://www.slideshare.net/nervanasys/anil-thomas-object-recognition?ref=http://neon.nervanasys.com/docs/latest/introductory_resources.html" rel="nofollow"&gt;here&lt;/a&gt;. That might help.&lt;/p&gt;
', NULL, NULL, '''/a'':43C ''/nervanasys/anil-thomas-object-recognition?ref=http://neon.nervanasys.com/docs/latest/introductory_resources.html'':39C ''/p'':29C,47C ''18'':34C ''check'':31C ''deconvolut'':4C ''effect'':22C ''expand'':6C ''help'':46C ''href'':36C ''ing'':20C ''layer'':11C ''might'':45C ''nofollow'':41C ''p'':1C,30C ''pool'':25C ''rel'':40C ''size'':8C ''slide'':33C ''stride'':3C ''think'':14C ''undo'':19C ''undo-'':18C ''unpool'':28C ''without'':26C ''www.slideshare.net'':38C ''www.slideshare.net/nervanasys/anil-thomas-object-recognition?ref=http://neon.nervanasys.com/docs/latest/introductory_resources.html'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1106, 1, '2014-09-12 11:48:21.617', 7, 144, 'How to build a textual search engine?', '&lt;p&gt;I am having an HTML string and want to find out if a word I supply is relevant in that string.&lt;/p&gt;

&lt;p&gt;Relevancy could be measured based on frequency in the text.&lt;/p&gt;

&lt;p&gt;An example to illustrate my problem:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;this is an awesome bike store
bikes can be purchased online.
the bikes we own rock.
check out our bike store now
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now I want to test a few other words:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;bike repairs
dog poo
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;code&gt;bike repairs&lt;/code&gt; should be marked as relevant whereas &lt;code&gt;dog poo&lt;/code&gt; should not be marked as relevant.&lt;/p&gt;

&lt;p&gt;Questions:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;How could this be done?&lt;/li&gt;
&lt;li&gt;How to I filter out ambiguous words like &lt;code&gt;in&lt;/code&gt; or &lt;code&gt;or&lt;/code&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Thanks for your ideas!&lt;/p&gt;

&lt;p&gt;I guess it''s something Google does to figure out what keywords are relevant to a website. I am basically trying to reproduce their on-page rankings.&lt;/p&gt;
', 'machine-learning  data-mining', NULL, '''/code'':81C,100C,106C,116C,146C,150C ''/li'':134C,151C ''/p'':36C,48C,56C,93C,123C,126C,158C,188C ''/pre'':82C,101C ''/ul'':152C ''ambigu'':141C ''awesom'':62C ''base'':42C ''basic'':179C ''bike'':63C,65C,71C,78C,96C,104C ''build'':3A ''check'':75C ''code'':58C,95C,103C,113C,144C,148C ''could'':39C,130C ''data'':12B ''data-min'':11B ''dog'':98C,114C ''done'':133C ''engin'':7A ''exampl'':51C ''figur'':168C ''filter'':139C ''find'':24C ''frequenc'':44C ''googl'':165C ''guess'':161C ''html'':19C ''idea'':157C ''illustr'':53C ''keyword'':171C ''learn'':10B ''li'':128C,135C ''like'':143C ''machin'':9B ''machine-learn'':8B ''mark'':109C,120C ''measur'':41C ''mine'':13B ''on-pag'':184C ''onlin'':69C ''p'':14C,37C,49C,83C,102C,124C,153C,159C ''page'':186C ''poo'':99C,115C ''pre'':57C,94C ''problem'':55C ''purchas'':68C ''question'':125C ''rank'':187C ''relev'':32C,38C,111C,122C,173C ''repair'':97C,105C ''reproduc'':182C ''rock'':74C ''search'':6A ''someth'':164C ''store'':64C,79C ''string'':20C,35C ''suppli'':30C ''test'':88C ''text'':47C ''textual'':5A ''thank'':154C ''tri'':180C ''ul'':127C ''want'':22C,86C ''websit'':176C ''wherea'':112C ''word'':28C,92C,142C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1119, 1, '2014-09-15 13:14:40.797', 4, 358, 'Predictive modeling based on RFM scoring indicators', '&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/RFM_(customer_value)" rel="nofollow"&gt;RFM&lt;/a&gt; - is a ranking model when all customers are ranked according to their purchasing &lt;strong&gt;F&lt;/strong&gt; requency, &lt;strong&gt;R&lt;/strong&gt; recency and &lt;strong&gt;M&lt;/strong&gt; monetary value. This indicator is highly used by marketing departments of various organizations to segment customers into groups according to customer value.&lt;/p&gt;

&lt;p&gt;The question is following: are there any substantial models based on RFM scoring (or related to) which have solid predictive power?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Update&lt;/strong&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;predicting which customer will most likely spend more&lt;/li&gt;
&lt;li&gt;who is going to upgrade/renew subscribtion/refund etc&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Update2&lt;/strong&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;I understand, this is simple problem with three independent variable and one classifier. My guess and experience say these pure three factors do not predict future customer value. But they can be used together with another data or can be an additional input into some model.&lt;/li&gt;
&lt;li&gt;Please share which methodologies worked for you personally and are likely to have high predictive ability. What kind of data you used together with RFM indicators and it worked well?&lt;/li&gt;
&lt;/ul&gt;
', 'marketing  predictive-modeling', NULL, '''/a'':21C ''/li'':108C,117C,172C,204C ''/p'':69C,92C,97C,123C ''/strong'':37C,41C,46C,96C,122C ''/ul'':118C,205C ''/wiki/rfm_(customer_value)'':17C ''abil'':189C ''accord'':31C,65C ''addit'':167C ''anoth'':161C ''base'':3A,80C ''classifi'':138C ''custom'':28C,62C,67C,102C,152C ''data'':162C,193C ''depart'':56C ''en.wikipedia.org'':16C ''en.wikipedia.org/wiki/rfm_(customer_value)'':15C ''etc'':116C ''experi'':142C ''f'':36C ''factor'':147C ''follow'':74C ''futur'':151C ''go'':112C ''group'':64C ''guess'':140C ''high'':52C,187C ''href'':14C ''independ'':134C ''indic'':7A,50C,199C ''input'':168C ''kind'':191C ''li'':99C,109C,125C,173C ''like'':105C,184C ''m'':45C ''market'':8B,55C ''methodolog'':177C ''model'':2A,11B,25C,79C,171C ''monetari'':47C ''nofollow'':19C ''one'':137C ''organ'':59C ''p'':12C,70C,93C,119C ''person'':181C ''pleas'':174C ''power'':91C ''predict'':1A,10B,90C,100C,150C,188C ''predictive-model'':9B ''problem'':131C ''purchas'':34C ''pure'':145C ''question'':72C ''r'':40C ''rank'':24C,30C ''recenc'':42C ''rel'':18C ''relat'':85C ''requenc'':38C ''rfm'':5A,20C,82C,198C ''say'':143C ''score'':6A,83C ''segment'':61C ''share'':175C ''simpl'':130C ''solid'':89C ''spend'':106C ''strong'':35C,39C,44C,94C,120C ''subscribtion/refund'':115C ''substanti'':78C ''three'':133C,146C ''togeth'':159C,196C ''ul'':98C,124C ''understand'':127C ''updat'':95C ''update2'':121C ''upgrade/renew'':114C ''use'':53C,158C,195C ''valu'':48C,68C,153C ''variabl'':135C ''various'':58C ''well'':203C ''work'':178C,202C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1107, 1, '2014-09-12 15:20:51.767', 11, 1422, 'Quick guide into training highly imbalanced data sets', '&lt;p&gt;I have a classification problem with approximately 1000 positive and 10000 negative samples in training set. So this data set is quite unbalanced. Plain random forest is just trying to mark all test samples as a majority class.&lt;/p&gt;

&lt;p&gt;Some good answers about sub-sampling and weighted random forest are given here: &lt;a href="http://datascience.stackexchange.com/questions/454/what-are-the-implications-for-training-a-tree-ensemble-with-highly-biased-datase"&gt;What are the implications for training a Tree Ensemble with highly biased datasets?&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Which classification methods besides RF can handle the problem in the best way?&lt;/p&gt;
', 'machine-learning  classification  dataset  unbalanced-classes', NULL, '''/a'':90C ''/p'':56C,91C,106C ''/questions/454/what-are-the-implications-for-training-a-tree-ensemble-with-highly-biased-datase'':76C ''1000'':25C ''10000'':28C ''answer'':60C ''approxim'':24C ''besid'':96C ''best'':104C ''bias'':88C ''class'':16B,55C ''classif'':12B,21C,94C ''data'':7A,36C ''datascience.stackexchange.com'':75C ''datascience.stackexchange.com/questions/454/what-are-the-implications-for-training-a-tree-ensemble-with-highly-biased-datase'':74C ''dataset'':13B,89C ''ensembl'':85C ''forest'':43C,68C ''given'':70C ''good'':59C ''guid'':2A ''handl'':99C ''high'':5A,87C ''href'':73C ''imbalanc'':6A ''implic'':80C ''learn'':11B ''machin'':10B ''machine-learn'':9B ''major'':54C ''mark'':48C ''method'':95C ''negat'':29C ''p'':17C,57C,92C ''plain'':41C ''posit'':26C ''problem'':22C,101C ''quick'':1A ''quit'':39C ''random'':42C,67C ''rf'':97C ''sampl'':30C,51C,64C ''set'':8A,33C,37C ''sub'':63C ''sub-sampl'':62C ''test'':50C ''train'':4A,32C,82C ''tree'':84C ''tri'':46C ''unbalanc'':15B,40C ''unbalanced-class'':14B ''way'':105C ''weight'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1108, 1, '2014-09-12 16:26:15.827', 3, 386, 'Kappa near to 60% in unbalanced (1:10) data set', '&lt;p&gt;As mentioned &lt;a href="http://datascience.stackexchange.com/questions/1107/quick-guide-into-training-highly-imbalanced-data-sets"&gt;before&lt;/a&gt;, I have a classification problem and unbalanced data set. The majority class contains 88% of all samples.
I have trained a Generalized Boosted Regression model using &lt;code&gt;gbm()&lt;/code&gt; from the &lt;code&gt;gbm&lt;/code&gt; package in &lt;code&gt;R&lt;/code&gt; and get the following output:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  interaction.depth  n.trees  Accuracy  Kappa  Accuracy SD  Kappa SD
  1                  50       0.906     0.523  0.00978      0.0512  
  1                  100      0.91      0.561  0.0108       0.0517  
  1                  150      0.91      0.572  0.0104       0.0492  
  2                  50       0.908     0.569  0.0106       0.0484  
  2                  100      0.91      0.582  0.00965      0.0443  
  2                  150      0.91      0.584  0.00976      0.0437  
  3                  50       0.909     0.578  0.00996      0.0469  
  3                  100      0.91      0.583  0.00975      0.0447  
  3                  150      0.911     0.586  0.00962      0.0443  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Looking at the 90% accuracy I assume that model has labeled all the samples as majority class. That''s clear.
And what is not transparent: how Kappa is calculated.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;What does this Kappa values (near to 60%) really mean? Is it enough to say that the model is not classifying them just by chance? &lt;/li&gt;
&lt;li&gt;What do &lt;code&gt;Accuracy SD&lt;/code&gt; and &lt;code&gt;Kappa SD&lt;/code&gt; mean?&lt;/li&gt;
&lt;/ul&gt;
', 'r  unbalanced-classes  gbm', NULL, '''/a'':25C ''/code'':54C,59C,64C,135C,202C,207C ''/li'':195C,209C ''/p'':70C,167C ''/pre'':136C ''/questions/1107/quick-guide-into-training-highly-imbalanced-data-sets'':23C ''/ul'':210C ''0.00962'':133C ''0.00965'':109C ''0.00975'':127C ''0.00976'':115C ''0.00978'':85C ''0.00996'':121C ''0.0104'':97C ''0.0106'':103C ''0.0108'':91C ''0.0437'':116C ''0.0443'':110C,134C ''0.0447'':128C ''0.0469'':122C ''0.0484'':104C ''0.0492'':98C ''0.0512'':86C ''0.0517'':92C ''0.523'':84C ''0.561'':90C ''0.569'':102C ''0.572'':96C ''0.578'':120C ''0.582'':108C ''0.583'':126C ''0.584'':114C ''0.586'':132C ''0.906'':83C ''0.908'':101C ''0.909'':119C ''0.91'':89C,95C,107C,113C,125C ''0.911'':131C ''1'':7A,81C,87C,93C ''10'':8A ''100'':88C,106C,124C ''150'':94C,112C,130C ''2'':99C,105C,111C ''3'':117C,123C,129C ''50'':82C,100C,118C ''60'':4A,177C ''88'':39C ''90'':141C ''accuraci'':75C,77C,142C,200C ''assum'':144C ''boost'':48C ''calcul'':166C ''chanc'':194C ''class'':14B,37C,154C ''classif'':29C ''classifi'':190C ''clear'':157C ''code'':52C,57C,62C,72C,199C,204C ''contain'':38C ''data'':9A,33C ''datascience.stackexchange.com'':22C ''datascience.stackexchange.com/questions/1107/quick-guide-into-training-highly-imbalanced-data-sets'':21C ''enough'':182C ''follow'':68C ''gbm'':15B,53C,58C ''general'':47C ''get'':66C ''href'':20C ''interaction.depth'':73C ''kappa'':1A,76C,79C,164C,173C,205C ''label'':148C ''li'':169C,196C ''look'':138C ''major'':36C,153C ''mean'':179C,208C ''mention'':18C ''model'':50C,146C,187C ''n.trees'':74C ''near'':2A,175C ''output'':69C ''p'':16C,137C ''packag'':60C ''pre'':71C ''problem'':30C ''r'':11B,63C ''realli'':178C ''regress'':49C ''sampl'':42C,151C ''say'':184C ''sd'':78C,80C,201C,206C ''set'':10A,34C ''train'':45C ''transpar'':162C ''ul'':168C ''unbalanc'':6A,13B,32C ''unbalanced-class'':12B ''use'':51C ''valu'':174C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1109, 2, '2014-09-12 20:30:51.74', 9, NULL, NULL, '&lt;p&gt;Undersampling the majority class is usually the way to go in such situations.&lt;/p&gt;

&lt;p&gt;If you think that you have too few instances of the positive class, you may perform oversampling, for example, sample 5n instances with replacement from the dataset of size n.&lt;/p&gt;

&lt;p&gt;Caveats:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Some methods may be sensitive to changes in the class distribution, e.g. for Naive Bayes - it affects the prior probabilities. &lt;/li&gt;
&lt;li&gt;Oversampling may lead to overfitting  &lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':73C,80C ''/p'':15C,47C,50C ''/ul'':81C ''5n'':37C ''affect'':69C ''bay'':67C ''caveat'':49C ''chang'':59C ''class'':5C,29C,62C ''dataset'':43C ''distribut'':63C ''e.g'':64C ''exampl'':35C ''go'':11C ''instanc'':25C,38C ''lead'':77C ''li'':52C,74C ''major'':4C ''may'':31C,55C,76C ''method'':54C ''n'':46C ''naiv'':66C ''overfit'':79C ''oversampl'':33C,75C ''p'':1C,16C,48C ''perform'':32C ''posit'':28C ''prior'':71C ''probabl'':72C ''replac'':40C ''sampl'':36C ''sensit'':57C ''situat'':14C ''size'':45C ''think'':19C ''ul'':51C ''undersampl'':2C ''usual'':7C ''way'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5945, 1, '2015-05-29 05:13:40.113', 1, 28, 'Compute utility of a notification', '&lt;p&gt;I have no idea whether this is the right StackExchange flavor to post this question in, but here goes:&lt;/p&gt;

&lt;p&gt;I have an agent (an IRC bot that listens to an event stream) that I would like to add "intelligent notifications" to. Let me give a specific example.&lt;/p&gt;

&lt;p&gt;The bot monitors the results from our continuous integration system, so it basically sees a stream of test results associated with changes (and the changes are associated with users.) If I ask it to notify me of interesting events, then for each event it sees, it decides whether to tell me about it (or rather, about the current results so far.)&lt;/p&gt;

&lt;p&gt;I would like it to decide based on how interesting something is for me. Something is interesting if (1) it is a result of a change that I pushed to the continuous integration system, (2) it is a test failure, and (3) the information conveyed by that failure is a significant indicator of whether my change was bad.&lt;/p&gt;

&lt;p&gt;Tests have a baseline probability of failing for no good reason ("intermittent failures"). If one test fails, it does not necessarily mean my change was bad. It might just be a flaky test. (Any given push tends to result in a couple bogus failures, so this isn''t some obscure edge case.) But the baseline probability of an intermittent failure varies according to the test suite running (we have several dozen different test suites that fire off for every change pushed.)&lt;/p&gt;

&lt;p&gt;So I don''t want to be bothered with reports of failing tests that are probably just noise. And if my push &lt;em&gt;is&lt;/em&gt; bad, I don''t want to be flooded with notifications for every failed test. (If I break something, it''ll probably show up in multiple test suites. So if the agent sees two failures and 20 successes, maybe it won''t bother me, but if it then sees another failure or two it should.)&lt;/p&gt;

&lt;p&gt;I am imagining I could look at the change between the prior and posterior probabilities and use that to estimate the entropy of that new test result, and perhaps also compute my personal entropy (if that makes any sense -- as in, the bot should assume I don''t know what any of the results are until it notifies me, at which point it should assume I am aware of the current full set of results and not bother me again until enough additional results have come in to substantially change the probability estimate of my change being bad.)&lt;/p&gt;

&lt;p&gt;Or something like that. I''m really looking for the right mathematical framework to compute things like this. I know next to nothing about machine learning or... well, mathematics in general.&lt;/p&gt;

&lt;p&gt;(The above is simplified; I would additionally like to do crazy things like take into account "labeling", where a third party looks at the test failures and decides whether they are real failures or not. But the time between the failure coming in and when that person labels it for me can also be modeled by a distribution, and I''d like to hold off notifications for interesting results if there''s a good chance that this other person may tell me that the result is not interesting after all. But only if that''s going to happen "soon". Also, we have an automated system for guessing whether something &lt;em&gt;might&lt;/em&gt; be an intermittent vs real failure, and it''d be nice to take its opinion into account as well.)&lt;/p&gt;
', 'beginner', NULL, '''/em'':289C,590C ''/p'':27C,57C,120C,180C,263C,344C,444C,477C,610C ''1'':140C ''2'':156C ''20'':325C ''3'':163C ''accord'':243C ''account'':494C,607C ''add'':47C ''addit'':428C,485C ''agent'':32C,320C ''also'':375C,531C,578C ''anoth'':338C ''ask'':90C ''associ'':78C,85C ''assum'':390C,410C ''autom'':582C ''awar'':413C ''bad'':179C,207C,290C,443C ''base'':128C ''baselin'':185C,236C ''basic'':71C ''beginn'':6B ''bogus'':224C ''bot'':35C,60C,388C ''bother'':272C,331C,423C ''break'':306C ''case'':233C ''chanc'':553C ''chang'':80C,83C,147C,177C,205C,261C,354C,435C,441C ''come'':431C,520C ''comput'':1A,376C,460C ''continu'':66C,153C ''convey'':166C ''could'':350C ''coupl'':223C ''crazi'':489C ''current'':116C,416C ''d'':539C,599C ''decid'':105C,127C,506C ''differ'':253C ''distribut'':536C ''dozen'':252C ''edg'':232C ''em'':287C,588C ''enough'':427C ''entropi'':367C,379C ''estim'':365C,438C ''event'':40C,97C,101C ''everi'':260C,301C ''exampl'':56C ''fail'':188C,198C,276C,302C ''failur'':161C,169C,194C,225C,241C,323C,339C,504C,511C,519C,596C ''far'':119C ''fire'':257C ''flaki'':213C ''flavor'':18C ''flood'':297C ''framework'':458C ''full'':417C ''general'':476C ''give'':53C ''given'':216C ''go'':574C ''goe'':26C ''good'':191C,552C ''guess'':585C ''happen'':576C ''hold'':542C ''idea'':11C ''imagin'':348C ''indic'':173C ''inform'':165C ''integr'':67C,154C ''intellig'':48C ''interest'':96C,131C,138C,546C,566C ''intermitt'':193C,240C,593C ''irc'':34C ''isn'':228C ''know'':394C,465C ''label'':495C,526C ''learn'':471C ''let'':51C ''like'':45C,124C,448C,462C,486C,491C,540C ''listen'':37C ''ll'':309C ''look'':351C,453C,500C ''m'':451C ''machin'':470C ''make'':382C ''mathemat'':457C,474C ''may'':558C ''mayb'':327C ''mean'':203C ''might'':209C,589C ''model'':533C ''monitor'':61C ''multipl'':314C ''necessarili'':202C ''new'':370C ''next'':466C ''nice'':601C ''nois'':282C ''noth'':468C ''notif'':5A,49C,299C,544C ''notifi'':93C,403C ''obscur'':231C ''one'':196C ''opinion'':605C ''p'':7C,28C,58C,121C,181C,264C,345C,445C,478C ''parti'':499C ''perhap'':374C ''person'':378C,525C,557C ''point'':407C ''post'':20C ''posterior'':359C ''prior'':357C ''probabl'':186C,237C,280C,310C,360C,437C ''push'':150C,217C,262C,286C ''question'':22C ''rather'':113C ''real'':510C,595C ''realli'':452C ''reason'':192C ''report'':274C ''result'':63C,77C,117C,144C,220C,372C,399C,420C,429C,547C,563C ''right'':16C,456C ''run'':248C ''see'':72C,103C,321C,337C ''sens'':384C ''set'':418C ''sever'':251C ''show'':311C ''signific'':172C ''simplifi'':482C ''someth'':132C,136C,307C,447C,587C ''soon'':577C ''specif'':55C ''stackexchang'':17C ''stream'':41C,74C ''substanti'':434C ''success'':326C ''suit'':247C,255C,316C ''system'':68C,155C,583C ''take'':492C,603C ''tell'':108C,559C ''tend'':218C ''test'':76C,160C,182C,197C,214C,246C,254C,277C,303C,315C,371C,503C ''thing'':461C,490C ''third'':498C ''time'':516C ''two'':322C,341C ''use'':362C ''user'':87C ''util'':2A ''vari'':242C ''vs'':594C ''want'':269C,294C ''well'':473C,609C ''whether'':12C,106C,175C,507C,586C ''won'':329C ''would'':44C,123C,484C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1110, 1, '2014-09-13 06:33:17.36', 5, 130, 'Binning long-tailed / pareto data before clustering', '&lt;p&gt;I want to cluster a set of long-tailed /pareto-alike data into several bins (Actually the bin number is not determined yet). 
Is there any algorithms or models I can use?&lt;/p&gt;
', 'clustering  k-means', NULL, '''/p'':46C ''/pareto-alike'':24C ''actual'':29C ''algorithm'':40C ''bin'':1A,28C,31C ''cluster'':8A,9B,17C ''data'':6A,25C ''determin'':35C ''k'':11B ''k-mean'':10B ''long'':3A,22C ''long-tail'':2A,21C ''mean'':12B ''model'':42C ''number'':32C ''p'':13C ''pareto'':5A ''set'':19C ''sever'':27C ''tail'':4A,23C ''use'':45C ''want'':15C ''yet'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1112, 2, '2014-09-13 15:36:19.867', 9, NULL, NULL, '&lt;ul&gt;
&lt;li&gt;Max Kuhn covers this well in Ch16 of &lt;em&gt;Applied Predictive Modeling&lt;/em&gt;.     &lt;/li&gt;
&lt;li&gt;As mentioned in the linked thread, imbalanced data is essentially a cost sensitive training problem. Thus any cost sensitive approach is applicable to imbalanced data.&lt;/li&gt;
&lt;li&gt;There are a large number of such approaches. Not all implemented in R: C50, weighted SVMs are options. Jous-boost. Rusboost I think is only available as Matlab code. &lt;/li&gt;
&lt;li&gt;I don''t use Weka, but believe it has a large number of cost sensitive classifiers. &lt;/li&gt;
&lt;li&gt;&lt;em&gt;Handling imbalanced datasets: A review&lt;/em&gt;: Sotiris Kotsiantis, Dimitris Kanellopoulos, Panayiotis Pintelas''&lt;/li&gt;
&lt;li&gt;&lt;em&gt;On the Class Imbalance Problem&lt;/em&gt;: Xinjian Guo, Yilong Yin, Cailing Dong, Gongping Yang, Guangtong Zhou&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/em'':15C,101C,116C ''/li'':16C,43C,75C,93C,108C,127C ''/ul'':128C ''appli'':12C ''applic'':39C ''approach'':37C,52C ''avail'':71C ''believ'':83C ''boost'':65C ''c50'':58C ''cail'':121C ''ch16'':9C ''class'':113C ''classifi'':92C ''code'':74C ''cost'':29C,35C,90C ''cover'':5C ''data'':25C,42C ''dataset'':98C ''dimitri'':104C ''dong'':122C ''em'':11C,95C,110C ''essenti'':27C ''gongp'':123C ''guangtong'':125C ''guo'':118C ''handl'':96C ''imbal'':114C ''imbalanc'':24C,41C,97C ''implement'':55C ''jous'':64C ''jous-boost'':63C ''kanellopoulo'':105C ''kotsianti'':103C ''kuhn'':4C ''larg'':48C,87C ''li'':2C,17C,44C,76C,94C,109C ''link'':22C ''matlab'':73C ''max'':3C ''mention'':19C ''model'':14C ''number'':49C,88C ''option'':62C ''panayioti'':106C ''pintela'':107C ''predict'':13C ''problem'':32C,115C ''r'':57C ''review'':100C ''rusboost'':66C ''sensit'':30C,36C,91C ''sotiri'':102C ''svms'':60C ''think'':68C ''thread'':23C ''thus'':33C ''train'':31C ''ul'':1C ''use'':80C ''weight'':59C ''weka'':81C ''well'':7C ''xinjian'':117C ''yang'':124C ''yilong'':119C ''yin'':120C ''zhou'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1121, 2, '2014-09-15 20:29:16.62', 3, NULL, NULL, '&lt;p&gt;The Kappa is Cohen''s Kappa score for inter-rater agreement. It''s a commonly-used metric for evaluating the performance of machine learning algorithms and human annotaters, particularly when dealing with text/linguistics. &lt;/p&gt;

&lt;p&gt;What it does is compare the level of agreement between the output of the (human or algorithmic) annotater and the ground truth labels, to the level of agreement that would occur through random chance. There''s a very good overview of how to calculate Kappa and use it to evaluate a classifier in this stats.stackexchange.com answer &lt;a href="http://stats.stackexchange.com/questions/82162/kappa-statistic-in-plain-english"&gt;here&lt;/a&gt;, and a more in-depth explanation of Kappa and how to interpret it in &lt;a href="http://www1.cs.columbia.edu/~julia/courses/CS6998/Interrater_agreement.Kappa_statistic.pdf" rel="nofollow"&gt;this paper&lt;/a&gt;, entitled "Understanding Interobserver Agreement: The Kappa Statistic" by Viera &amp;amp; Garrett (2005).&lt;/p&gt;

&lt;p&gt;The benefit of using Kappa, particularly in an unbalanced data set like yours, is that with a 90-10% imbalance between the classes, you can achieve 90% accuracy by simply labeling all of the data points with the label of the more commonly occurring class. The Kappa statistic is describing how well the classifier performs above that baseline level of performance.&lt;/p&gt;

&lt;p&gt;Kappa ranges from -1 to 1, with 0 indicating no agreement between the raters, 1 indicating a perfect agreement, and negative numbers indicating systematic disagreement. While interpretation is somewhat arbitrary (and very task-dependent), Landis &amp;amp; Koch (1977) defined the following interpretation system which can work as a general rule of thumb:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Kappa Agreement
&amp;lt; 0 Less than chance agreement
0.01â0.20 Slight agreement
0.21â 0.40 Fair agreement
0.41â0.60 Moderate agreement
0.61â0.80 Substantial agreement
0.81â0.99 Almost perfect agreement
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Which would indicate that your algorithm is performing moderately well. Accuracy SD and Kappa SD are the respective Standard Deviations of the Accuracy and Kappa scores. I hope this is helpful!&lt;/p&gt;
', NULL, NULL, '''-1'':207C ''-10'':159C ''/a'':101C,126C ''/code'':294C ''/p'':37C,139C,202C,257C,328C ''/pre'':295C ''/questions/82162/kappa-statistic-in-plain-english'':99C ''/~julia/courses/cs6998/interrater_agreement.kappa_statistic.pdf'':121C ''0'':211C,263C ''0.01'':268C ''0.20'':270C ''0.21'':273C ''0.40'':275C ''0.41'':278C ''0.60'':280C ''0.61'':283C ''0.80'':285C ''0.81'':288C ''0.99'':290C ''1'':209C,218C ''1977'':242C ''2005'':138C ''90'':158C,167C ''accuraci'':168C,307C,319C ''achiev'':166C ''agreement'':13C,47C,66C,130C,214C,222C,261C,267C,272C,277C,282C,287C,293C ''algorithm'':28C,55C,302C ''almost'':291C ''amp'':136C,240C ''annotat'':31C,56C ''answer'':94C ''arbitrari'':233C ''baselin'':198C ''benefit'':142C ''calcul'':82C ''chanc'':72C,266C ''class'':163C,185C ''classifi'':90C,194C ''code'':259C ''cohen'':5C ''common'':18C,183C ''commonly-us'':17C ''compar'':43C ''data'':150C,175C ''deal'':34C ''defin'':243C ''depend'':238C ''depth'':107C ''describ'':190C ''deviat'':316C ''disagr'':228C ''entitl'':127C ''evalu'':22C,88C ''explan'':108C ''fair'':276C ''follow'':245C ''garrett'':137C ''general'':253C ''good'':77C ''ground'':59C ''help'':327C ''hope'':324C ''href'':96C,118C ''human'':30C,53C ''imbal'':160C ''in-depth'':105C ''indic'':212C,219C,226C,299C ''inter'':11C ''inter-rat'':10C ''interobserv'':129C ''interpret'':114C,230C,246C ''kappa'':3C,7C,83C,110C,132C,145C,187C,204C,260C,310C,321C ''koch'':241C ''label'':61C,171C,179C ''landi'':239C ''learn'':27C ''less'':264C ''level'':45C,64C,199C ''like'':152C ''lt'':262C ''machin'':26C ''metric'':20C ''moder'':281C,305C ''negat'':224C ''nofollow'':123C ''number'':225C ''occur'':69C,184C ''output'':50C ''overview'':78C ''p'':1C,38C,140C,203C,296C ''paper'':125C ''particular'':32C,146C ''perfect'':221C,292C ''perform'':24C,195C,201C,304C ''point'':176C ''pre'':258C ''random'':71C ''rang'':205C ''rater'':12C,217C ''rel'':122C ''respect'':314C ''rule'':254C ''score'':8C,322C ''sd'':308C,311C ''set'':151C ''simpli'':170C ''slight'':271C ''somewhat'':232C ''standard'':315C ''statist'':133C,188C ''stats.stackexchange.com'':93C,98C ''stats.stackexchange.com/questions/82162/kappa-statistic-in-plain-english'':97C ''substanti'':286C ''system'':247C ''systemat'':227C ''task'':237C ''task-depend'':236C ''text/linguistics'':36C ''thumb'':256C ''truth'':60C ''unbalanc'':149C ''understand'':128C ''use'':19C,85C,144C ''viera'':135C ''well'':192C,306C ''work'':250C ''would'':68C,298C ''www1.cs.columbia.edu'':120C ''www1.cs.columbia.edu/~julia/courses/cs6998/interrater_agreement.kappa_statistic.pdf'':119C ''â'':269C,274C,279C,284C,289C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1122, 2, '2014-09-15 22:43:37.583', 2, NULL, NULL, '&lt;p&gt;A Google search leads to many relevant resources that answer your question:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;USING RFM DATA TO OPTIMIZE DIRECT MARKETING CAMPAIGNS: A LINEAR PROGRAMMING APPROACH &lt;a href="http://www.thefreelibrary.com/Using+RFM+data+to+optimize+direct+marketing+campaigns%3A+a+linear...-a0272246211" rel="nofollow"&gt;http://www.thefreelibrary.com/Using+RFM+data+to+optimize+direct+marketing+campaigns%3A+a+linear...-a0272246211&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Data Mining using RFM Analysis &lt;a href="http://cdn.intechweb.org/pdfs/13162.pdf" rel="nofollow"&gt;http://cdn.intechweb.org/pdfs/13162.pdf&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;em&gt;Libby on Recency, Frequency, Monetary Value&lt;/em&gt; (book) &lt;a href="http://rads.stackoverflow.com/amzn/click/1882222067" rel="nofollow"&gt;http://www.amazon.com/Recency-Frequency-Monetary-Century-Library/dp/1882222067&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;From a data science point of view, there is nothing very special or unique about this problem. You have three independent variables and one dependent variable. Regression, clustering, and classification methods can be applied.&lt;/p&gt;
', NULL, NULL, '''/amzn/click/1882222067'':69C ''/em'':63C ''/p'':14C,111C ''/pdfs/13162.pdf'':49C ''/pdfs/13162.pdf&lt;/a&gt;&lt;/li&gt;'':54C ''/recency-frequency-monetary-century-library/dp/1882222067&lt;/a&gt;&lt;/li&gt;'':74C ''/ul'':75C ''/using+rfm+data+to+optimize+direct+marketing+campaigns%3a+a+linear...-a0272246211'':33C ''/using+rfm+data+to+optimize+direct+marketing+campaigns%3a+a+linear...-a0272246211&lt;/a&gt;&lt;/li&gt;'':38C ''analysi'':44C ''answer'':11C ''appli'':110C ''approach'':28C ''book'':64C ''campaign'':24C ''cdn.intechweb.org'':48C,53C ''cdn.intechweb.org/pdfs/13162.pdf'':47C ''cdn.intechweb.org/pdfs/13162.pdf&lt;/a&gt;&lt;/li&gt;'':52C ''classif'':106C ''cluster'':104C ''data'':19C,40C,79C ''depend'':101C ''direct'':22C ''em'':56C ''frequenc'':60C ''googl'':3C ''href'':30C,46C,66C ''independ'':97C ''lead'':5C ''li'':16C,39C,55C ''libbi'':57C ''linear'':26C ''mani'':7C ''market'':23C ''method'':107C ''mine'':41C ''monetari'':61C ''nofollow'':35C,51C,71C ''noth'':86C ''one'':100C ''optim'':21C ''p'':1C,76C ''point'':81C ''problem'':93C ''program'':27C ''question'':13C ''rads.stackoverflow.com'':68C ''rads.stackoverflow.com/amzn/click/1882222067'':67C ''recenc'':59C ''regress'':103C ''rel'':34C,50C,70C ''relev'':8C ''resourc'':9C ''rfm'':18C,43C ''scienc'':80C ''search'':4C ''special'':88C ''three'':96C ''ul'':15C ''uniqu'':90C ''use'':17C,42C ''valu'':62C ''variabl'':98C,102C ''view'':83C ''www.amazon.com'':73C ''www.amazon.com/recency-frequency-monetary-century-library/dp/1882222067&lt;/a&gt;&lt;/li&gt;'':72C ''www.thefreelibrary.com'':32C,37C ''www.thefreelibrary.com/using+rfm+data+to+optimize+direct+marketing+campaigns%3a+a+linear...-a0272246211'':31C ''www.thefreelibrary.com/using+rfm+data+to+optimize+direct+marketing+campaigns%3a+a+linear...-a0272246211&lt;/a&gt;&lt;/li&gt;'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1131, 2, '2014-09-17 00:44:19.967', 0, NULL, NULL, '&lt;p&gt;The most direct and obvious transformation is from time domain to &lt;a href="http://en.wikipedia.org/wiki/Frequency_domain" rel="nofollow"&gt;frequency domain&lt;/a&gt;. Possible methods include &lt;a href="http://en.wikipedia.org/wiki/Fourier_transform" rel="nofollow"&gt;Fourier transform&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/Wavelet_transform" rel="nofollow"&gt;wavelet transform&lt;/a&gt;.  After the transform the signal is represented by a function of frequency-domain elements which can be operated on using ordinary algebra.&lt;/p&gt;

&lt;p&gt;It''s also possible to model a time series as a trajectory of a dynamical system in a state space (see: &lt;a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9892.1980.tb00300.x/abstract" rel="nofollow"&gt;http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9892.1980.tb00300.x/abstract&lt;/a&gt;, and &lt;a href="http://www3.stat.sinica.edu.tw/statistica/oldpdf/A2n16.pdf" rel="nofollow"&gt;http://www3.stat.sinica.edu.tw/statistica/oldpdf/A2n16.pdf&lt;/a&gt;). Dynamical systems can be modeled symbolically at a course-grain level (see: &lt;a href="http://en.wikipedia.org/wiki/Symbolic_dynamics" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Symbolic_dynamics&lt;/a&gt; and &lt;a href="http://www.math.washington.edu/SymbolicDynamics/" rel="nofollow"&gt;http://www.math.washington.edu/SymbolicDynamics/&lt;/a&gt;)  Symbolic dynamics draws on linear algebra.&lt;/p&gt;
', NULL, NULL, '''/a'':22C,35C,46C ''/doi/10.1111/j.1467-9892.1980.tb00300.x/abstract'':97C ''/doi/10.1111/j.1467-9892.1980.tb00300.x/abstract&lt;/a&gt;,'':102C ''/p'':70C,154C ''/statistica/oldpdf/a2n16.pdf'':108C ''/statistica/oldpdf/a2n16.pdf&lt;/a&gt;).'':113C ''/symbolicdynamics/'':142C ''/symbolicdynamics/&lt;/a&gt;)'':147C ''/wiki/fourier_transform'':30C ''/wiki/frequency_domain'':17C ''/wiki/symbolic_dynamics'':131C ''/wiki/symbolic_dynamics&lt;/a&gt;'':136C ''/wiki/wavelet_transform'':41C ''algebra'':69C,153C ''also'':74C ''cours'':123C ''course-grain'':122C ''direct'':4C ''domain'':11C,21C,60C ''draw'':150C ''dynam'':86C,114C,149C ''element'':61C ''en.wikipedia.org'':16C,29C,40C,130C,135C ''en.wikipedia.org/wiki/fourier_transform'':28C ''en.wikipedia.org/wiki/frequency_domain'':15C ''en.wikipedia.org/wiki/symbolic_dynamics'':129C ''en.wikipedia.org/wiki/symbolic_dynamics&lt;/a&gt;'':134C ''en.wikipedia.org/wiki/wavelet_transform'':39C ''fourier'':33C ''frequenc'':20C,59C ''frequency-domain'':58C ''function'':56C ''grain'':124C ''href'':14C,27C,38C,94C,105C,128C,139C ''includ'':25C ''level'':125C ''linear'':152C ''method'':24C ''model'':77C,118C ''nofollow'':19C,32C,43C,99C,110C,133C,144C ''obvious'':6C ''onlinelibrary.wiley.com'':96C,101C ''onlinelibrary.wiley.com/doi/10.1111/j.1467-9892.1980.tb00300.x/abstract'':95C ''onlinelibrary.wiley.com/doi/10.1111/j.1467-9892.1980.tb00300.x/abstract&lt;/a&gt;,'':100C ''oper'':65C ''ordinari'':68C ''p'':1C,71C ''possibl'':23C,75C ''rel'':18C,31C,42C,98C,109C,132C,143C ''repres'':53C ''see'':92C,126C ''seri'':80C ''signal'':51C ''space'':91C ''state'':90C ''symbol'':119C,148C ''system'':87C,115C ''time'':10C,79C ''trajectori'':83C ''transform'':7C,34C,45C,49C ''use'':67C ''wavelet'':44C ''www.math.washington.edu'':141C,146C ''www.math.washington.edu/symbolicdynamics/'':140C ''www.math.washington.edu/symbolicdynamics/&lt;/a&gt;)'':145C ''www3.stat.sinica.edu.tw'':107C,112C ''www3.stat.sinica.edu.tw/statistica/oldpdf/a2n16.pdf'':106C ''www3.stat.sinica.edu.tw/statistica/oldpdf/a2n16.pdf&lt;/a&gt;).'':111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1123, 1, '2014-09-16 08:01:35.997', 2, 122, 'Combine multiple classifiers to build a multi-modal classifier', '&lt;p&gt;Suppose I am interested in classifying a set of instances composed by different content types, e.g.:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;a piece of &lt;strong&gt;text&lt;/strong&gt;&lt;/li&gt;
&lt;li&gt;an &lt;strong&gt;image&lt;/strong&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;as &lt;code&gt;relevant&lt;/code&gt; or &lt;code&gt;non-relevant&lt;/code&gt; for a specific class &lt;code&gt;C&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;In my classification process I perform the following steps:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Given a sample, I subdivide it in text and image&lt;/li&gt;
&lt;li&gt;A first SVM binary classifier (&lt;code&gt;SVM-text&lt;/code&gt;), trained only on text, classifies the text as &lt;code&gt;relevant&lt;/code&gt;/&lt;code&gt;non-relevant&lt;/code&gt; for the class &lt;code&gt;C&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;A second SVM binary classifier (&lt;code&gt;SVM-image&lt;/code&gt;), trained only on images, classifies the image as &lt;code&gt;relevant&lt;/code&gt;/&lt;code&gt;non-relevant&lt;/code&gt; for the class &lt;code&gt;C&lt;/code&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Both &lt;code&gt;SVM-text&lt;/code&gt; and &lt;code&gt;SVM-image&lt;/code&gt; produce an estimate of the probability of the analyzed content (text or image) of being relevant for the class &lt;code&gt;C&lt;/code&gt;. Given this, I am able to state whether the text is relevant for &lt;code&gt;C&lt;/code&gt; and the image is relevant for &lt;code&gt;C&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;However, these estimates are valid for segments of the original sample (either the text or the image), while it is not clear how to obtain a general opinion on the whole original sample (text+image). How can I combine conveniently the opinions of the two classifiers, so as to obtain a classification for the whole original sample?&lt;/p&gt;
', 'classification  svm', NULL, '''/code'':51C,57C,64C,100C,111C,116C,122C,134C,145C,150C,156C,165C,171C,193C,209C,218C ''/li'':39C,45C,89C,123C,157C ''/ol'':158C ''/p'':30C,65C,76C,219C,278C ''/strong'':38C,44C ''/ul'':46C ''abl'':198C ''analyz'':180C ''binari'':94C,128C ''build'':5A ''c'':63C,121C,155C,192C,208C,217C ''class'':61C,119C,153C,190C ''classif'':11B,69C,272C ''classifi'':3A,10A,19C,95C,105C,129C,139C,266C ''clear'':242C ''code'':49C,53C,62C,96C,109C,112C,120C,130C,143C,146C,154C,161C,167C,191C,207C,216C ''combin'':1A,259C ''compos'':24C ''content'':27C,181C ''conveni'':260C ''differ'':26C ''e.g'':29C ''either'':232C ''estim'':174C,223C ''first'':92C ''follow'':74C ''general'':247C ''given'':79C,194C ''howev'':221C ''imag'':43C,88C,133C,138C,141C,170C,184C,212C,237C,255C ''instanc'':23C ''interest'':17C ''li'':32C,40C,78C,90C,124C ''modal'':9A ''multi'':8A ''multi-mod'':7A ''multipl'':2A ''non'':55C,114C,148C ''non-relev'':54C,113C,147C ''obtain'':245C,270C ''ol'':77C ''opinion'':248C,262C ''origin'':230C,252C,276C ''p'':13C,47C,66C,159C,220C ''perform'':72C ''piec'':34C ''probabl'':177C ''process'':70C ''produc'':172C ''relev'':50C,56C,110C,115C,144C,149C,187C,205C,214C ''sampl'':81C,231C,253C,277C ''second'':126C ''segment'':227C ''set'':21C ''specif'':60C ''state'':200C ''step'':75C ''strong'':36C,42C ''subdivid'':83C ''suppos'':14C ''svm'':12B,93C,98C,127C,132C,163C,169C ''svm-imag'':131C,168C ''svm-text'':97C,162C ''text'':37C,86C,99C,104C,107C,164C,182C,203C,234C,254C ''train'':101C,135C ''two'':265C ''type'':28C ''ul'':31C ''valid'':225C ''whether'':201C ''whole'':251C,275C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5986, 2, '2015-06-02 16:31:29.623', 3, NULL, NULL, '&lt;p&gt;Try before doing LDA look at the data - like doing TF, IDF and TFIDF analysis to identify such words which happen in all subject. If You have some taxonomy of Your product definition - consider using it. In my case it was really helpful.&lt;/p&gt;

&lt;p&gt;I experimented with LDA topic modeling for recommendation systems purposes. I''ve few runs for offers in our marketplace service. It is in some aspect similar to Your problem with products and a list of keywords. But in our case, we do not have a product definition and an offer description is created by a seller. So You can imagine what tokens soup You will get :-)&lt;/p&gt;

&lt;p&gt;The key point for me wast to analyze TF, IDF and TFIDF inside categories where I''ve tested LDA. E.g. for Toys category (&lt;a href="http://allegro.pl/zabawki-11818?ref=simplified-category-tree" rel="nofollow"&gt;http://allegro.pl/zabawki-11818?ref=simplified-category-tree&lt;/a&gt;) ~ .5M items, LDA based only on its names results in:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;TOPIC:
lego    0.02282500803373657
puzzle  0.016679246682968853
gra     0.01092812347216676
klocki  0.010650330607355207
trefl   0.006797740719581059
zabawka 0.006365688767908592
lalka   0.0063546887775893105
maskotka        0.006111593588558912
dzieci  0.005888846995546501
24h     0.005736876716620104

TOPIC:
lego    0.02296167186908205
puzzle  0.016613064812376045
gra     0.010928234683331952
klocki  0.010607432835126579
trefl   0.006689061156804526
lalka   0.006317220992078405
zabawka 0.0062838718987015575
maskotka        0.006121026383268814
dzieci  0.0058569443925760144
24h     0.00578544965708585

TOPIC:
lego    0.02285507528449409
puzzle  0.016539723998111246
gra     0.010892673154789407
klocki  0.010624244094063881
trefl   0.006683424205358961
lalka   0.006350949499009139
zabawka 0.006324510823409019
maskotka        0.006148070280704085
dzieci  0.0058401580163702565
24h     0.005782372531889951
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And the rest looks almost the same. Before running LDA I just did basic text preprocessing - our dictionary based item-name stop words removal and text tokenization (but without steaming). Even giving more terms for topic descriptions results doesn''t look promising. Then, after exporting word frequencies with its offer categories by looking at data and plots I''ve decided to remove terms with TF-IDF above some threshold. Yes, above - I''ve used the calculation provided by Spark 1.3.1 implementation (HasingTF + IDF from mllib, no ml). After doing this I received:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;TOPIC:
interaktywny    0.0026985965530064884
pony    0.0026721637823727343
dmuchany        0.0015777211309733249
baterie 0.0012447186564456534
pojazdy 0.0011171017074143481
thomas  9.476877418459519E-4
dinozaury       8.823212274401862E-4
monsters        7.426822230409613E-4
heroes  7.365256561824247E-4
ninjago 7.344344320326593E-4

TOPIC:
pony    0.0026867880330479327
interaktywny    0.0026803244251279693
dmuchany        0.0015797940843537916
baterie 0.001263424692131187
pojazdy 0.0010685733429671523
thomas  9.87888159025782E-4
heroes  8.26066846602493E-4
ptaszek 7.952454816383102E-4
dinozaury       7.849838129629457E-4
batman  7.408185413186286E-4
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Where results started to differ from each other. Taking like last time only 10-terms for every subject description. Still, it''s not perfect but better. &lt;/p&gt;

&lt;p&gt;So, for me doing the experiments which few categories, the way to overcome problem was to remove it based on TF-IDF value. But, for every category the threshold was calculated separately. Mainly: mean(tf-idf) + 3*sd(tf-idf). &lt;/p&gt;

&lt;p&gt;I know that Idf multiplication factor in Tf-Idf should resolve this problem by itself and the term should be punished for occurring in every document. But, using Spark implementation (idf = log((m + 1) / (d(t) + 1))) and our data it was simpler during a quick and dirty experiment to filter it that way. &lt;/p&gt;

&lt;p&gt;When I find few sec. I will get back on this and place results with code and online to share this.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2379, 2, '2014-10-31 01:38:57.483', 2, NULL, NULL, '&lt;p&gt;A few things where the knowledge of Linear Algebra might be helpful in the context of Machine Learning: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Dimensionality Reduction: There are lots of problems where PCA (a special case of an SVD) followed by a simple Machine Learning method applied on the reduced dataset produces much better results than a non parametric model on the full (non-reduced dataset). For an example see &lt;a href="http://faculty.ucmerced.edu/hbhat/BhatZaelit2012.pdf" rel="nofollow"&gt;Bhat and Zaelit, 2012&lt;/a&gt; where PCA followed by linear regression performs better than more involved non-parametric models. It also suggests reasons why dimensionality reduction performs better in these cases.  &lt;/li&gt;
&lt;li&gt;Visualizing data: Higher dimensional data is complicated to visualize and one often needs to be able to reduce the dimension of the dataset to view it. This comes very handy when one has to "view" the results of clustering on a higher dimensional dataset.  &lt;/li&gt;
&lt;li&gt;Numerical Accuracy: Eigenvalues are generally handy in order to understand condition numbers of matrices and hence be able to figure out if results of Linear regression or other methods that require to solve Ax=b would be numerically accurate. Positive Definiteness of a matrix might also be able to guarantee bounds on numerical accuracies. &lt;/li&gt;
&lt;li&gt;Recommendations: Some methods like collaborative filtering use matrix factorization (SVD) tuned smartly to solve recommendation problems.&lt;/li&gt;
&lt;li&gt;Regularization: Regularization is commonly used to reduce over-fitting in machine learning problems. Most of these regularization techniques like Lasso, Tikhonov etc. have both optimization and Linear Algebra at their heart. &lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':81C ''/hbhat/bhatzaelit2012.pdf'':74C ''/li'':109C,155C,211C,229C,263C ''/ol'':264C ''/p'':20C ''2012'':80C ''abl'':126C,174C,204C ''accur'':195C ''accuraci'':158C,210C ''algebra'':10C,259C ''also'':98C,202C ''appli'':45C ''ax'':190C ''b'':191C ''better'':52C,89C,105C ''bhat'':77C ''bound'':207C ''case'':34C,108C ''cluster'':149C ''collabor'':217C ''come'':138C ''common'':234C ''complic'':117C ''condit'':167C ''context'':16C ''data'':112C,115C ''dataset'':49C,65C,133C,154C ''definit'':197C ''dimens'':130C ''dimension'':23C,102C,114C,153C ''eigenvalu'':159C ''etc'':253C ''exampl'':68C ''factor'':221C ''faculty.ucmerced.edu'':73C ''faculty.ucmerced.edu/hbhat/bhatzaelit2012.pdf'':72C ''figur'':176C ''filter'':218C ''fit'':240C ''follow'':38C,84C ''full'':61C ''general'':161C ''guarante'':206C ''handi'':140C,162C ''heart'':262C ''help'':13C ''henc'':172C ''higher'':113C,152C ''href'':71C ''involv'':92C ''knowledg'':7C ''lasso'':251C ''learn'':19C,43C,243C ''li'':22C,110C,156C,212C,230C ''like'':216C,250C ''linear'':9C,86C,181C,258C ''lot'':27C ''machin'':18C,42C,242C ''matric'':170C ''matrix'':200C,220C ''method'':44C,185C,215C ''might'':11C,201C ''model'':58C,96C ''much'':51C ''need'':123C ''nofollow'':76C ''non'':56C,63C,94C ''non-parametr'':93C ''non-reduc'':62C ''number'':168C ''numer'':157C,194C,209C ''often'':122C ''ol'':21C ''one'':121C,142C ''optim'':256C ''order'':164C ''over-fit'':238C ''p'':1C ''parametr'':57C,95C ''pca'':31C,83C ''perform'':88C,104C ''posit'':196C ''problem'':29C,228C,244C ''produc'':50C ''reason'':100C ''recommend'':213C,227C ''reduc'':48C,64C,128C,237C ''reduct'':24C,103C ''regress'':87C,182C ''regular'':231C,232C,248C ''rel'':75C ''requir'':187C ''result'':53C,147C,179C ''see'':69C ''simpl'':41C ''smart'':224C ''solv'':189C,226C ''special'':33C ''suggest'':99C ''svd'':37C,222C ''techniqu'':249C ''thing'':4C ''tikhonov'':252C ''tune'':223C ''understand'':166C ''use'':219C,235C ''view'':135C,145C ''visual'':111C,119C ''would'':192C ''zaelit'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1124, 1, '2014-09-16 08:14:06.307', 5, 229, 'Difference between tf-idf and tf with Random Forests', '&lt;p&gt;I am working on a text classification problem using Random Forest as classifiers, and a bag-of-words approach. 
I am using the basic implementation of Random Forests (the one present in scikit), that creates a binary condition on a single variable at each split. Given this, is there a difference between using simple tf (term frequency) features. where each word has an associated weight that represents the number of occurrences in the document, or tf-idf (term frequency * inverse document frequency), where the term frequency is also multiplied by a value that represents the ratio between the total number of documents and the number of documents containing the word)?&lt;/p&gt;

&lt;p&gt;In my opinion, there should not be any difference between these two approaches, because the only difference is a scaling factor on each feature, but since the split is done at the level of single features this should not make a difference.&lt;/p&gt;

&lt;p&gt;Am I right in my reasoning? &lt;/p&gt;
', 'classification  text-mining  random-forest', NULL, '''/p'':131C,175C,183C ''also'':108C ''approach'':38C,145C ''associ'':83C ''bag'':35C ''bag-of-word'':34C ''basic'':43C ''binari'':56C ''classif'':11B,25C ''classifi'':31C ''condit'':57C ''contain'':128C ''creat'':54C ''differ'':1A,70C,141C,149C,174C ''document'':93C,101C,122C,127C ''done'':162C ''factor'':153C ''featur'':77C,156C,168C ''forest'':10A,17B,29C,47C ''frequenc'':76C,99C,102C,106C ''given'':65C ''idf'':5A,97C ''implement'':44C ''invers'':100C ''level'':165C ''make'':172C ''mine'':14B ''multipli'':109C ''number'':88C,120C,125C ''occurr'':90C ''one'':49C ''opinion'':135C ''p'':18C,132C,176C ''present'':50C ''problem'':26C ''random'':9A,16B,28C,46C ''random-forest'':15B ''ratio'':116C ''reason'':182C ''repres'':86C,114C ''right'':179C ''scale'':152C ''scikit'':52C ''simpl'':73C ''sinc'':158C ''singl'':60C,167C ''split'':64C,160C ''term'':75C,98C,105C ''text'':13B,24C ''text-min'':12B ''tf'':4A,7A,74C,96C ''tf-idf'':3A,95C ''total'':119C ''two'':144C ''use'':27C,41C,72C ''valu'':112C ''variabl'':61C ''weight'':84C ''word'':37C,80C,130C ''work'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8804, 1, '2015-11-11 18:31:14.463', 0, 58, 'Canopy clustering implementation in Python', '&lt;p&gt;As far as I understand, canopy clustering preprocessing procedure is not implemented in scikit-learn.&lt;/p&gt;

&lt;p&gt;Is there a python library for this?&lt;/p&gt;
', 'python  clustering', NULL, '''/p'':25C,34C ''canopi'':1A,14C ''cluster'':2A,7B,15C ''far'':10C ''implement'':3A,20C ''learn'':24C ''librari'':31C ''p'':8C,26C ''preprocess'':16C ''procedur'':17C ''python'':5A,6B,30C ''scikit'':23C ''scikit-learn'':22C ''understand'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1136, 2, '2014-09-18 01:39:30.063', 2, NULL, NULL, '&lt;p&gt;You &lt;em&gt;do&lt;/em&gt; want to model the traffic, at least over a work day, otherwise it wouldn''t matter what time you traveled! Absent any data, I''d assume there isn''t much variance over the working week, but that''s one thing the data will quickly confirm or refute. If it is varying, you can use a different model for each day. &lt;/p&gt;

&lt;p&gt;You have two variables; the departure times from home and work, respectively. Let''s call them t_h and t_w. Let''s call the commute time T_c(t), where t is the time of day. You can estimate this function from the data, so I''ll assume it is given.&lt;/p&gt;

&lt;p&gt;You want to maximize c t_h - (1-c) t_w subject to the constraints t_h + T_c(t_h) &amp;lt; 9.5 and t_w &gt; t_h + T_c(t_h) + 8&lt;/p&gt;

&lt;p&gt;where c is a constant you can set to adjust the relative importance of leaving home early relative to leaving work early. You should be able to solve this numerical optimization problem with Mathematica, MATLAB, or something similar. I would not recommend Java; it''s not meant for this. The only tricky part is estimating T_c. You know that it''s a non-negative function, so you could use the standard trick of estimating it''s logarithm (say, with kernels) and exponentiating. For implementation with Mathematica see &lt;a href="http://reference.wolfram.com/language/ref/InterpolatingFunction.html" rel="nofollow"&gt;Smoothing Data, Filling Missing Data, and Nonparametric Fitting&lt;/a&gt; and &lt;a href="http://reference.wolfram.com/language/tutorial/ConstrainedOptimizationOverview.html" rel="nofollow"&gt;Constrained Optimization&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':261C,272C ''/em'':5C ''/language/ref/interpolatingfunction.html'':250C ''/language/tutorial/constrainedoptimizationoverview.html'':267C ''/p'':66C,120C,155C,273C ''1'':129C ''8'':154C ''9.5'':144C ''abl'':182C ''absent'':26C ''adjust'':166C ''assum'':31C,116C ''c'':96C,126C,130C,140C,151C,158C,213C ''call'':82C,91C ''commut'':93C ''confirm'':50C ''constant'':161C ''constrain'':270C ''constraint'':136C ''could'':226C ''d'':30C ''data'':28C,47C,112C,254C,257C ''day'':16C,65C,104C ''departur'':73C ''differ'':61C ''earli'':173C,178C ''em'':3C ''estim'':107C,211C,232C ''exponenti'':240C ''fill'':255C ''fit'':260C ''function'':109C,223C ''given'':119C ''h'':85C,128C,138C,142C,149C,153C ''home'':76C,172C ''href'':247C,264C ''implement'':242C ''import'':169C ''isn'':33C ''java'':199C ''kernel'':238C ''know'':215C ''least'':12C ''leav'':171C,176C ''let'':80C,89C ''ll'':115C ''logarithm'':235C ''lt'':143C ''mathematica'':190C,244C ''matlab'':191C ''matter'':21C ''maxim'':125C ''meant'':203C ''miss'':256C ''model'':8C,62C ''much'':35C ''negat'':222C ''nofollow'':252C,269C ''non'':221C ''non-neg'':220C ''nonparametr'':259C ''numer'':186C ''one'':44C ''optim'':187C,271C ''otherwis'':17C ''p'':1C,67C,121C,156C ''part'':209C ''problem'':188C ''quick'':49C ''recommend'':198C ''reference.wolfram.com'':249C,266C ''reference.wolfram.com/language/ref/interpolatingfunction.html'':248C ''reference.wolfram.com/language/tutorial/constrainedoptimizationoverview.html'':265C ''refut'':52C ''rel'':251C,268C ''relat'':168C,174C ''respect'':79C ''say'':236C ''see'':245C ''set'':164C ''similar'':194C ''smooth'':253C ''solv'':184C ''someth'':193C ''standard'':229C ''subject'':133C ''thing'':45C ''time'':23C,74C,94C,102C ''traffic'':10C ''travel'':25C ''trick'':230C ''tricki'':208C ''two'':70C ''use'':59C,227C ''vari'':56C ''variabl'':71C ''varianc'':36C ''w'':88C,132C,147C ''want'':6C,123C ''week'':40C ''work'':15C,39C,78C,177C ''would'':196C ''wouldn'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1125, 1, '2014-09-16 11:41:48.14', 3, 116, 'Named entity disambiguation contests', '&lt;p&gt;I am interested in the field of &lt;strong&gt;named entity disambiguation&lt;/strong&gt; and want to learn more about it. I have heard that there are contests organised by various associations on these kind of research topics. These contests are very helpful as they give a practical experience in these fields. &lt;/p&gt;

&lt;p&gt;I found one such contest organised by Microsoft research &lt;a href="http://web-ngram.research.microsoft.com/erd2014/" rel="nofollow"&gt;here&lt;/a&gt; though the dates have already passed. Can anyone point me to any other such contests ? Also, is there a site which catalogues these contests so that one can just go there and know about all upcoming contests ?&lt;/p&gt;

&lt;p&gt;Thanks in advance.  &lt;/p&gt;
', 'nlp  recommendation', NULL, '''/a'':77C ''/erd2014/'':73C ''/p'':58C,115C,120C ''/strong'':19C ''advanc'':119C ''alreadi'':82C ''also'':93C ''anyon'':85C ''associ'':37C ''catalogu'':99C ''contest'':4A,33C,45C,64C,92C,101C,114C ''date'':80C ''disambigu'':3A,18C ''entiti'':2A,17C ''experi'':54C ''field'':13C,57C ''found'':61C ''give'':51C ''go'':107C ''heard'':29C ''help'':48C ''href'':70C ''interest'':10C ''kind'':40C ''know'':110C ''learn'':23C ''microsoft'':67C ''name'':1A,16C ''nlp'':5B ''nofollow'':75C ''one'':62C,104C ''organis'':34C,65C ''p'':7C,59C,116C ''pass'':83C ''point'':86C ''practic'':53C ''recommend'':6B ''rel'':74C ''research'':42C,68C ''site'':97C ''strong'':15C ''thank'':117C ''though'':78C ''topic'':43C ''upcom'':113C ''various'':36C ''want'':21C ''web-ngram.research.microsoft.com'':72C ''web-ngram.research.microsoft.com/erd2014/'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1126, 2, '2014-09-16 12:32:10.13', 1, NULL, NULL, '&lt;p&gt;Basically, you can do one of two things: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;strong&gt;Combine features&lt;/strong&gt; from both classifiers. I.e., instead of &lt;code&gt;SVM-text&lt;/code&gt; and &lt;code&gt;SVM-image&lt;/code&gt; you may train single &lt;code&gt;SVM&lt;/code&gt; that uses both - textual and visual features. &lt;/li&gt;
&lt;li&gt;Use &lt;a href="http://en.wikipedia.org/wiki/Ensemble_learning" rel="nofollow"&gt;&lt;strong&gt;ensemble learning&lt;/strong&gt;&lt;/a&gt;. If you already have probabilities from separate classifiers, you can simply use them as weights and compute weighted average. For more sophisticated cases there are Bayesian combiners (each classifier has its prior), boosting algorithms (e.g. see &lt;a href="http://en.wikipedia.org/wiki/AdaBoost" rel="nofollow"&gt;AdaBoost&lt;/a&gt;) and others. &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Note, that ensembles where initially created for combining different learners, not different sets if features. In this later case ensembles have advantage mostly in cases when different kinds of features just can''t be combined in a single vector efficiently. But in general, combing features is simpler and more straightforward. &lt;/p&gt;
', NULL, NULL, '''/a'':62C,107C ''/code'':27C,33C,40C ''/li'':48C,110C ''/ol'':111C ''/p'':10C,163C ''/strong'':16C,61C ''/wiki/adaboost'':103C ''/wiki/ensemble_learning'':55C ''adaboost'':106C ''advantag'':134C ''algorithm'':96C ''alreadi'':65C ''averag'':81C ''basic'':2C ''bayesian'':88C ''boost'':95C ''case'':85C,131C,137C ''classifi'':19C,70C,91C ''code'':23C,29C,38C ''comb'':156C ''combin'':14C,89C,120C,147C ''comput'':79C ''creat'':118C ''differ'':121C,124C,139C ''e.g'':97C ''effici'':152C ''en.wikipedia.org'':54C,102C ''en.wikipedia.org/wiki/adaboost'':101C ''en.wikipedia.org/wiki/ensemble_learning'':53C ''ensembl'':59C,115C,132C ''featur'':15C,47C,127C,142C,157C ''general'':155C ''href'':52C,100C ''i.e'':20C ''imag'':32C ''initi'':117C ''instead'':21C ''kind'':140C ''later'':130C ''learn'':60C ''learner'':122C ''li'':12C,49C ''may'':35C ''most'':135C ''nofollow'':57C,105C ''note'':113C ''ol'':11C ''one'':6C ''other'':109C ''p'':1C,112C ''prior'':94C ''probabl'':67C ''rel'':56C,104C ''see'':98C ''separ'':69C ''set'':125C ''simpler'':159C ''simpli'':73C ''singl'':37C,150C ''sophist'':84C ''straightforward'':162C ''strong'':13C,58C ''svm'':25C,31C,39C ''svm-imag'':30C ''svm-text'':24C ''text'':26C ''textual'':44C ''thing'':9C ''train'':36C ''two'':8C ''use'':42C,50C,74C ''vector'':151C ''visual'':46C ''weight'':77C,80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1128, 1, '2014-09-16 15:47:17.43', 5, 282, 'Looking for algebras designed to transform time series', '&lt;p&gt;I am looking for information on (formal) algebraic systems that can be used to transform time-series - in either a practical or academic context.&lt;/p&gt;

&lt;p&gt;I hope that there exists (at least one) small, expressive, set of operators - ranging over (finite) time-series.  I want to compare and contrast different systems  with respect to algebraic completeness, and brevity of representation, of common time-series transformations in various domains.&lt;/p&gt;

&lt;p&gt;I realise this question is broad - but hope it is not too vague for datascience.stackexchange.  I welcome any pointers to relevant literature for specific scenarios, or the general subject.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit... (Attempt to better explain what I meant by an algebraic system...)&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I was thinking about "abstract algebras" as discussed in Wikipedia:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Algebra#Abstract_algebra"&gt;http://en.wikipedia.org/wiki/Algebra#Abstract_algebra&lt;/a&gt;
&lt;a href="http://en.wikipedia.org/wiki/Abstract_algebra#Basic_concepts"&gt;http://en.wikipedia.org/wiki/Abstract_algebra#Basic_concepts&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Boolean Algebras are (very simple) algebras that range over Boolean values.  A simple example of such an algebra would consist the values True and False and the operations AND, OR and NOT. One might argue this algebra is ''complete'' as, from these two constants (free-variables) and three basic operations, arbitrary boolean functions can be constructed/described.&lt;/p&gt;

&lt;p&gt;I am interested to discover algebras where the values are (time-domain) time-series.  I''d like it to be possible to construct "arbitrary" functions, that map time-series to time-series, from a few operations which, individually, map time-series to time-series.  I am open to liberal interpretations of "arbitrary". I would be especially interested in examples of these algebras where the operations consist ''higher-order functions'' - where such operations have been developed for a specific domain.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6021, 1, '2015-06-05 15:03:41.86', 1, 42, 'How do I deal with non-IID data in gradient boosted random forest (for stock market)?', '&lt;p&gt;I am working on a stock market decision system. I have currently centered on gradient boosting as the likely best machine learning solution for the problem. &lt;/p&gt;

&lt;p&gt;However, I have 2 fundamental issues with my data owing to it being from the stock market having to do with it not being IID. &lt;/p&gt;

&lt;p&gt;First, because of the duration of average in some indicators use, some data-points are highly correlated. For example, the 2-year trailing return of a stock is not very different if measured a month ago. My understanding is that this requires a sampling (for ensembles) where I choose datapoints that are "far away" in time to make trees more independent. From what I can tell so far, Matlab does not have functionality to pick a random subspace with this criteria. When I was previously thinking of using simple bagging, I figured I would just build the trees myself from custom subspaces and aggregate them into an ensemble, but this wonât work if I want to do gradient boosting. Now, on this point I am not totally sure that it is so critical to have samples âfar away.â My intuition is that it is better if they are, but even if they are not perhaps by right-sizing the percent of data sampled and having enough trees it gives the same result. I would love any insight on that issue and how I might be able to use LSboost in matlab on custom samples.  (One idea I have is to just to create a small number, like 5-10 custom samples, use LS-Boost on each, and then average them.) &lt;/p&gt;

&lt;p&gt;The second fundamental problem is that data from a given stock is correlated/related to itself. I realized after thinking about it that this is of critical importance. Consider, it would likely be better, if there is enough data, to make a prediction for stock A from training data only or mostly from stock A than to use the entire market. Thus, I had been thinking of a âsystemâ where I train on stock-specific data, stock-group data (where I use a special algorithm to group stocks), and the entire market, and then use a calculation (I can elaborate if interested) that determines which of these models is more likely to give the better result. If the input looks very different from the stock-specific training data, for example, then it will use the group or entire market. I am pretty convicted that some form of taking into account which stock the system is looking at is important to optimizing performance. &lt;/p&gt;

&lt;p&gt;Now, on the second issue the question is what is the best way to organize this. Thinking naively, it would be great to simply feed categories to the predictor that indicate what stock it is looking at. However, my belief here from what I know about these algorithms is that this will have poor results on new data, because this predictor will assume that it has seen the full universe of potential outcomes for each stock, when many times this isnât the case. (Say there is a stock with only a one year history with a big rally â the system will think the rally will continue regardless of how different the new data looks). So I feel like I have to do something like in the previous paragraph. I donât know if there is some way for the system to âautomaticallyâ recognize when new data is sufficiently similar to stock-specific data to focus on a stock-specific prediction vs. when it is different and it should go to the default system with multiple stocks.&lt;/p&gt;

&lt;p&gt;If you have any insights on these issues and/or how to address them in Matlab or otherwise, I would very much appreciate. Thanks in advance. &lt;/p&gt;

&lt;p&gt;Best,
Mike&lt;/p&gt;
', 'random-forest  gradient-descent', NULL, '''-10'':293C ''/p'':51C,78C,306C,473C,656C,683C,687C ''2'':56C,101C ''5'':292C ''abl'':270C ''account'':460C ''address'':669C ''advanc'':682C ''aggreg'':184C ''ago'':116C ''algorithm'':394C,522C ''and/or'':666C ''appreci'':679C ''assum'':537C ''automaticallyâ'':619C ''averag'':86C,304C ''away'':134C,220C ''bag'':170C ''belief'':514C ''best'':44C,486C,685C ''better'':228C,340C,424C ''big'':572C ''boost'':12A,40C,200C,299C ''build'':176C ''calcul'':406C ''case'':558C ''categori'':500C ''center'':37C ''choos'':129C ''consid'':335C ''continu'':582C ''convict'':453C ''correl'':97C ''correlated/related'':320C ''creat'':287C ''criteria'':161C ''critic'':214C,333C ''current'':36C ''custom'':181C,277C,294C ''data'':9A,61C,93C,246C,314C,345C,355C,384C,388C,438C,532C,589C,623C,631C ''data-point'':92C ''datapoint'':130C ''deal'':4A ''decis'':32C ''default'':651C ''descent'':23B ''determin'':413C ''differ'':111C,431C,586C,644C ''donâ'':606C ''durat'':84C ''elabor'':409C ''enough'':250C,344C ''ensembl'':126C,188C ''entir'':366C,400C,448C ''even'':233C ''exampl'':99C,440C ''far'':133C,148C,219C ''feed'':499C ''feel'':593C ''figur'':172C ''first'':80C ''focus'':633C ''forest'':14A,20B ''form'':456C ''full'':543C ''function'':153C ''fundament'':57C,310C ''give'':253C,422C ''given'':317C ''go'':648C ''gradient'':11A,22B,39C,199C ''gradient-desc'':21B ''great'':496C ''group'':387C,396C,446C ''high'':96C ''histori'':569C ''howev'':53C,512C ''idea'':280C ''iid'':8A,77C ''import'':334C,469C ''independ'':141C ''indic'':89C,505C ''input'':428C ''insight'':261C,662C ''interest'':411C ''intuit'':223C ''isnâ'':555C ''issu'':58C,264C,479C,665C ''know'':519C,608C ''learn'':46C ''like'':43C,291C,338C,420C,594C,600C ''look'':429C,466C,510C,590C ''love'':259C ''ls'':298C ''ls-boost'':297C ''lsboost'':273C ''machin'':45C ''make'':138C,347C ''mani'':552C ''market'':17A,31C,69C,367C,401C,449C ''matlab'':149C,275C,672C ''measur'':113C ''might'':268C ''mike'':686C ''model'':417C ''month'':115C ''most'':358C ''much'':678C ''multipl'':654C ''naiv'':492C ''new'':531C,588C,622C ''non'':7A ''non-iid'':6A ''number'':290C ''one'':279C,567C ''optim'':471C ''organ'':489C ''otherwis'':674C ''outcom'':547C ''owe'':62C ''p'':24C,52C,79C,307C,474C,657C,684C ''paragraph'':604C ''percent'':244C ''perform'':472C ''perhap'':238C ''pick'':155C ''point'':94C,204C ''poor'':528C ''potenti'':546C ''predict'':349C,639C ''predictor'':503C,535C ''pretti'':452C ''previous'':165C,603C ''problem'':50C,311C ''question'':481C ''ralli'':573C,580C ''random'':13A,19B,157C ''random-forest'':18B ''realiz'':324C ''recogn'':620C ''regardless'':583C ''requir'':122C ''result'':256C,425C,529C ''return'':104C ''right'':241C ''right-siz'':240C ''sampl'':124C,217C,247C,278C,295C ''say'':559C ''second'':309C,478C ''seen'':541C ''similar'':626C ''simpl'':169C ''simpli'':498C ''size'':242C ''small'':289C ''solut'':47C ''someth'':599C ''special'':393C ''specif'':383C,436C,630C,638C ''stock'':16A,30C,68C,107C,318C,351C,360C,382C,386C,397C,435C,462C,507C,550C,563C,629C,637C,655C ''stock-group'':385C ''stock-specif'':381C,434C,628C,636C ''subspac'':158C,182C ''suffici'':625C ''sure'':209C ''system'':33C,464C,576C,616C,652C ''systemâ'':376C ''take'':458C ''tell'':146C ''thank'':680C ''think'':166C,326C,372C,491C,578C ''thus'':368C ''time'':136C,553C ''total'':208C ''trail'':103C ''train'':354C,379C,437C ''tree'':139C,178C,251C ''understand'':118C ''univers'':544C ''use'':90C,168C,272C,296C,364C,391C,404C,444C ''vs'':640C ''want'':196C ''way'':487C,613C ''wonâ'':191C ''work'':27C,193C ''would'':174C,258C,337C,494C,676C ''year'':102C,568C ''â'':218C,221C,375C,574C,618C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6117, 2, '2015-06-14 19:07:42.017', 5, NULL, NULL, '&lt;p&gt;Please see my comment above and this is my answer according to what I understood from your question:&lt;/p&gt;

&lt;p&gt;As you correctly stated you do not need &lt;em&gt;Clustering&lt;/em&gt; but &lt;em&gt;Segmentation&lt;/em&gt;. Indeed you are looking for &lt;em&gt;Change Points&lt;/em&gt; in your time series. The answer really depends on the complexity of your data. If the data is as simple as above example you can use the difference of vectors which overshoots at changing points and set a threshold detecting those points like bellow:
&lt;img src="http://i.stack.imgur.com/6BKAl.png" alt="enter image description here"&gt;
As you see for instance a threshold of 20 (i.e. $dx&amp;lt;-20$ and $dx&amp;gt;20$) will detect the points. Of course for real data you need to investigate more to find the thresholds.&lt;/p&gt;

&lt;h2&gt;Pre-processing&lt;/h2&gt;

&lt;p&gt;Please note that there is a trade-off between accurate location of the change point and the accurate number of segments i.e. if you use the original data you''ll find the exact change points but the whole method is to sensitive to noise but if you smooth your signals first you may not find the exact changes but the noise effect will be much less as shown in figures bellow:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ceDxi.png" alt="enter image description here"&gt;
&lt;img src="http://i.stack.imgur.com/PLIpr.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;h2&gt;Conclusion&lt;/h2&gt;

&lt;p&gt;My suggestion is to smooth your signals first and go for a simple clustering mthod (e.g. using &lt;a href="http://www.autonlab.org/tutorials/gmm14.pdf"&gt;GMM&lt;/a&gt;s) to find an accurate estimation of the number of segments in signals. Given this information you can start finding changing points constrained by the number of segments you found from previous part.&lt;/p&gt;

&lt;p&gt;I hope it all helped :)&lt;/p&gt;

&lt;p&gt;Good Luck!&lt;/p&gt;

&lt;h2&gt;UPDATE&lt;/h2&gt;

&lt;p&gt;Luckily your data is pretty straightforward and clean. I strongly recommend dimensionality reduction algorithms (e.g. simple &lt;a href="http://sebastianraschka.com/Articles/2014_pca_step_by_step.html"&gt;PCA&lt;/a&gt;). I guess it reveals the internal structure of your clusters. Once you apply PCA to the data you can use k-means much much easier and more accurate.&lt;/p&gt;

&lt;h2&gt;A Serious(!) Solution&lt;/h2&gt;

&lt;p&gt;According to your data I see the generative distribution of different segments are different which is a great chance for you to segment your time series. See &lt;a href="http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2012_KhaleghiRMP12.pdf"&gt;this&lt;/a&gt; which is probably the best and most state-of-the-art solution to your problem. The main idea behind this paper is that if different segments of a time series are generated by different underlying distributions you can find those distributions, set tham as ground truth for your clustering approach and find clusters. &lt;/p&gt;

&lt;p&gt;For example assume a long video in which the first 10 minutes somebody is biking, in the second 10 mins he is running and in the third he is sitting. you can cluster these three different segments (activities) using this approach.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6131, 2, '2015-06-16 11:39:51.88', 5, NULL, NULL, '&lt;p&gt;I like this question because it gets at the politics that exist in every organization. In my view and to a significant degree, expectations about model performance are a function of the org culture and degree to which an organization is "technically literate." One way to make clear what I mean is to consider the differences between the 4 big "data science" entities -- Google, FB, Amazon and Yahoo -- versus the 4 big agency holding entities -- WPP, Omnicon, Interbrand and Publicis. Google, et al, are very technically literate. The agencies, on the other hand, are known to lean towards tech phobia. What''s the evidence for this? First off, the technically literate group was founded or are run by engineers, computer scientists, geeks and people with strong tech backgrounds. Who runs the tech illiterate companies? Marketers who have risen to prominence by virtue of their soft communication and people skills. And not only that, having worked in some of these shops in NYC, I can testify that these organizations systematically punish and/or push out the highly technically literate types as not a "fit" with the culture.
Next, consider their aggregate (stock) market caps, The tech literate group adds up to about 800 billion dollars while the tech illiterate group amounts to 80 billion. Tech literate entities are 10x bigger than the others in market cap. This is a clear statement of the market''s expectations and it''s not high for the illiterates. So, by extrapolation, what kind of hope can you have for challenging the "predictive accuracy" expectations of bozos like these?&lt;/p&gt;

&lt;p&gt;So, given that cultural breakout and depending on where you fall, you should have more or less realistic expectations. Of course, different "tech illiterate" entities will have managers who know what they''re doing, but for the most part, these entities are dominated by the idiocy of the lowest common denominator in tech skills, i.e., people who are at best technical semi-literates (and dangerous) or, more commonly, totally innumerate but don''t know it. Case in point, I worked for a guy who wanted words like "correlation" scrubbed from c-suite decks. This is an extreme case: after all, every secretary knows what a "correlation" is.&lt;/p&gt;

&lt;p&gt;This raises the issue of how one deals with the maddeningly naive and innumerate when they ask a really dumb question like, "Why aren''t you getting 99% predictive accuracy?" One good response is to reply with a question like, "Why would you assume such an unrealistically high PA is even possible?" Another might be, "Because if I actually got 99% PA, I would have assumed that I was doing something wrong." Which is highly likely to be true, even with 90% PA.&lt;/p&gt;

&lt;p&gt;The there''s the more fundamental question of the insistence on PA as the sole criterion for model value. The late Leo Breiman left many footprints on the statistical and predictive modeling community of which PA is one. His primary concern with PA was to address the many criticisms being made in the 90s regarding the instability and error inherent in running a single CART tree. His solution was to motivate ârandom forestsâ as an approximate and provisional method that would maximize accuracy and reduce instability by eliminating tree structure.  He benchmarked the lower MSE from ~1,000 iterative RF âmini-modelsâ against the error from a single logistic regression model. The only problem was that he never bothered to mention the glaring apples to oranges comparison: what might the logistic regression MSE have been if it had also been performed 1,000 times? &lt;/p&gt;

&lt;p&gt;The 2008 Netflix Prize offered a sizeable monetary reward to any statistician or team able to improve upon the MSE of their recommender system. At the time, Netflix was spending $150 million a year on this system, convinced that the costs were more than recovered in customer loyalty and purchase of movies that would otherwise never have been chosen. The eventual winners used a complex ensemble of 107 different models.&lt;/p&gt;

&lt;p&gt;As Netflix learned however, the real problem was that, from a fully loaded cost perspective, the actual improvement in error over their current model was a mere 0.005% reduction in the 5 point ratings. Not to mention that the IT costs in time, heavy-lifting and maintenance of the winning ensemble of 107 models more than nullified any gains from the error reduction. Given this, Netflix eventually abandoned the pursuit of MSE and no more Netflix Prizes have been awarded&lt;/p&gt;

&lt;p&gt;And this is the point: minimizing predictive error can be easily gamed or p-hacked and is prone to analyst fraud (i.e., finding a solution that glorifies the analystâs modeling skills, positively impacting his potential end-of-year bonus). Moreover, it is a completely statistical solution and goal set in an economic and business vacuum. The metric provides little or no consideration of ancillary, collateral costs -- the very real operational consequences evaluated from A to Z that should be an integral part of any fully-loaded, trade-off based decision-making process.&lt;/p&gt;

&lt;p&gt;This has become one of those issues that is embedded in organizations and is very, very difficult to change. In other words, I am fully aware that I am tilting at windmills with this rant about the caveats with the use of PA.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1129, 2, '2014-09-16 16:37:33.357', 2, NULL, NULL, '&lt;p&gt;It''s probably due to the &lt;a href="http://www.ats.ucla.edu/stat/sas/seminars/sas_logistic/logistic1.htm" rel="nofollow"&gt;effect coding&lt;/a&gt; of categorical predictors. Eg, the regression coefficient for CPR = 7 is not zero but -(sum of regression coefficients for the other 6 levels). I guess EM should have an option to switch it to reference coding, then your way of computing the predicted probability should work.&lt;/p&gt;
', NULL, NULL, '''/a'':17C ''/p'':64C ''/stat/sas/seminars/sas_logistic/logistic1.htm'':12C ''6'':39C ''7'':27C ''categor'':19C ''code'':16C,53C ''coeffici'':24C,35C ''comput'':58C ''cpr'':26C ''due'':5C ''effect'':15C ''eg'':21C ''em'':43C ''guess'':42C ''href'':9C ''level'':40C ''nofollow'':14C ''option'':47C ''p'':1C ''predict'':60C ''predictor'':20C ''probabl'':4C,61C ''refer'':52C ''regress'':23C,34C ''rel'':13C ''sum'':32C ''switch'':49C ''way'':56C ''work'':63C ''www.ats.ucla.edu'':11C ''www.ats.ucla.edu/stat/sas/seminars/sas_logistic/logistic1.htm'':10C ''zero'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1132, 1, '2014-09-17 04:49:39.77', 1, 769, 'K-Means clustering for mixed numeric and categorical data implementation in C#', '&lt;p&gt;I am a research scholar in data mining. I''m interested in C# implementation of K-Means clustering algorithm for mixed numeric and categorical data.&lt;/p&gt;
', 'clustering  k-means  beginner  library', NULL, '''/p'':47C ''algorithm'':40C ''beginn'':18B ''c'':13A,33C ''categor'':9A,45C ''cluster'':4A,14B,39C ''data'':10A,27C,46C ''implement'':11A,34C ''interest'':31C ''k'':2A,16B,37C ''k-mean'':1A,15B,36C ''librari'':19B ''m'':30C ''mean'':3A,17B,38C ''mine'':28C ''mix'':6A,42C ''numer'':7A,43C ''p'':20C ''research'':24C ''scholar'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1133, 2, '2014-09-17 10:16:07.1', 1, NULL, NULL, '&lt;p&gt;The solution is described here: &lt;a href="http://datascience.stackexchange.com/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data"&gt;K-Means clustering for mixed numeric and categorical data&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;C# implementation can be found in ALGLIB library, which I strongly recommend: &lt;a href="http://www.alglib.net/translator/man/manual.csharp.html#gs_packages" rel="nofollow"&gt;http://www.alglib.net/translator/man/manual.csharp.html#gs_packages&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':22C ''/p'':23C ''/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data'':11C ''/translator/man/manual.csharp.html#gs_packages'':41C ''/translator/man/manual.csharp.html#gs_packages&lt;/a&gt;&lt;/p&gt;'':46C ''alglib'':31C ''c'':25C ''categor'':20C ''cluster'':15C ''data'':21C ''datascience.stackexchange.com'':10C ''datascience.stackexchange.com/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data'':9C ''describ'':5C ''found'':29C ''href'':8C,38C ''implement'':26C ''k'':13C ''k-mean'':12C ''librari'':32C ''mean'':14C ''mix'':17C ''nofollow'':43C ''numer'':18C ''p'':1C,24C ''recommend'':36C ''rel'':42C ''solut'':3C ''strong'':35C ''www.alglib.net'':40C,45C ''www.alglib.net/translator/man/manual.csharp.html#gs_packages'':39C ''www.alglib.net/translator/man/manual.csharp.html#gs_packages&lt;/a&gt;&lt;/p&gt;'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5369, 2, '2015-03-21 20:58:48.757', 2, NULL, NULL, '&lt;p&gt;You need to &lt;strong&gt;analyze sentence structure&lt;/strong&gt; and extract corresponding &lt;em&gt;syntactic categories&lt;/em&gt; of interest (in this case, I think it would be &lt;strong&gt;noun phrase&lt;/strong&gt;, which is a &lt;em&gt;phrasal category&lt;/em&gt;). For details, see &lt;a href="http://en.wikipedia.org/wiki/Syntactic_category" rel="nofollow"&gt;corresponding Wikipedia article&lt;/a&gt; and &lt;a href="http://www.nltk.org/book/ch08.html" rel="nofollow"&gt;"Analyzing Sentence Structure"&lt;/a&gt; chapter of NLTK book.&lt;/p&gt;

&lt;p&gt;In regard to available &lt;strong&gt;software&lt;/strong&gt; tools for implementing the above-mentioned approach and beyond, I would suggest to consider either &lt;a href="http://www.nltk.org" rel="nofollow"&gt;NLTK&lt;/a&gt; (if you prefer Python), or &lt;a href="http://www-nlp.stanford.edu/software" rel="nofollow"&gt;StanfordNLP software&lt;/a&gt; (if you prefer Java). For many other NLP frameworks, libraries and programming various languages support, see corresponding (NLP) sections in &lt;a href="https://github.com/josephmisiti/awesome-machine-learning" rel="nofollow"&gt;this excellent curated list&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':51C,63C,99C,114C,146C ''/book/ch08.html'':57C ''/em'':16C,37C ''/josephmisiti/awesome-machine-learning'':139C ''/p'':68C,147C ''/software'':109C ''/strong'':9C,30C,76C ''/wiki/syntactic_category'':45C ''above-ment'':81C ''analyz'':6C,60C ''approach'':84C ''articl'':50C ''avail'':73C ''beyond'':86C ''book'':67C ''case'':21C ''categori'':15C,36C ''chapter'':64C ''consid'':91C ''correspond'':12C,48C,131C ''curat'':144C ''detail'':39C ''either'':92C ''em'':13C,34C ''en.wikipedia.org'':44C ''en.wikipedia.org/wiki/syntactic_category'':43C ''excel'':143C ''extract'':11C ''framework'':123C ''github.com'':138C ''github.com/josephmisiti/awesome-machine-learning'':137C ''href'':42C,54C,94C,106C,136C ''implement'':79C ''interest'':18C ''java'':118C ''languag'':128C ''librari'':124C ''list'':145C ''mani'':120C ''mention'':83C ''need'':3C ''nlp'':122C,132C ''nltk'':66C,98C ''nofollow'':47C,59C,97C,111C,141C ''noun'':28C ''p'':1C,69C ''phrasal'':35C ''phrase'':29C ''prefer'':102C,117C ''program'':126C ''python'':103C ''regard'':71C ''rel'':46C,58C,96C,110C,140C ''section'':133C ''see'':40C,130C ''sentenc'':7C,61C ''softwar'':75C,113C ''stanfordnlp'':112C ''strong'':5C,27C,74C ''structur'':8C,62C ''suggest'':89C ''support'':129C ''syntact'':14C ''think'':23C ''tool'':77C ''various'':127C ''wikipedia'':49C ''would'':25C,88C ''www-nlp.stanford.edu'':108C ''www-nlp.stanford.edu/software'':107C ''www.nltk.org'':56C,95C ''www.nltk.org/book/ch08.html'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1140, 2, '2014-09-18 19:56:25.827', 3, NULL, NULL, '&lt;ul&gt;
&lt;li&gt;pre-process your documents (some of the steps may be skipped)
&lt;ul&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Tokenization" rel="nofollow"&gt;tokenize&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Stop_words" rel="nofollow"&gt;remove stop words&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Stemming" rel="nofollow"&gt;stem&lt;/a&gt; or &lt;a href="http://en.wikipedia.org/wiki/Lemmatisation" rel="nofollow"&gt;lemmatize&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;do normalization (e.g. U.S.A. -&gt; USA, mÃ©tÃ©o -&gt; meteo, etc) and orthographic correction &lt;/li&gt;
&lt;li&gt;perform phonetic normalization (e.g. with &lt;a href="http://en.wikipedia.org/wiki/Soundex" rel="nofollow"&gt;Soundex&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;find equivalence classes (using thesauri, e.g. &lt;a href="http://en.wikipedia.org/wiki/WordNet" rel="nofollow"&gt;WordNet&lt;/a&gt;) &lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;use a &lt;a href="http://en.wikipedia.org/wiki/Vector_space_model" rel="nofollow"&gt;Vector Space model&lt;/a&gt; to represent documents (you may use TF, aforementioned &lt;a href="http://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;TF-IDF&lt;/a&gt; or other models)&lt;/li&gt;
&lt;li&gt;do the same with the query: preprocess and represent it in the vector space&lt;/li&gt;
&lt;li&gt;find the most similar documents by computing the vector similarity (e.g. using the &lt;a href="http://en.wikipedia.org/wiki/Cosine_similarity" rel="nofollow"&gt;cosine similarity&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;That''s an outline of the &lt;a href="http://en.wikipedia.org/wiki/Information_retrieval" rel="nofollow"&gt;Information Retrieval&lt;/a&gt; process &lt;/p&gt;

&lt;p&gt;&lt;a href="http://nlp.stanford.edu/IR-book/" rel="nofollow"&gt;Introduction to Information Retrieval&lt;/a&gt; by Christopher D. Manning, Prabhakar Raghavan and Hinrich SchÃ¼tze is a very good book to get started in IR.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Or just use &lt;a href="http://lucene.apache.org/solr/" rel="nofollow"&gt;Apache Solr&lt;/a&gt; to get everything you need out of the box (or &lt;a href="http://lucene.apache.org/core/" rel="nofollow"&gt;Apache Lucene&lt;/a&gt;, that is used by Solr, to build your own application)&lt;/p&gt;
', NULL, NULL, '''/a'':25C,38C,49C,59C,90C,107C,124C,143C,187C,206C,221C,257C,277C ''/core/'':272C ''/ir-book/'':214C ''/li'':26C,39C,60C,75C,91C,108C,110C,147C,163C,188C ''/p'':208C,242C,288C ''/solr/'':252C ''/ul'':109C,189C ''/wiki/cosine_similarity'':182C ''/wiki/information_retrieval'':201C ''/wiki/lemmatisation'':55C ''/wiki/soundex'':86C ''/wiki/stemming'':45C ''/wiki/stop_words'':32C ''/wiki/tf%e2%80%93idf'':137C ''/wiki/tokenization'':21C ''/wiki/vector_space_model'':118C ''/wiki/wordnet'':103C ''aforement'':132C ''apach'':255C,275C ''applic'':287C ''book'':236C ''box'':266C ''build'':284C ''christoph'':223C ''class'':95C ''comput'':171C ''correct'':74C ''cosin'':185C ''d'':224C ''document'':7C,127C,169C ''e.g'':64C,80C,98C,175C ''en.wikipedia.org'':20C,31C,44C,54C,85C,102C,117C,136C,181C,200C ''en.wikipedia.org/wiki/cosine_similarity'':180C ''en.wikipedia.org/wiki/information_retrieval'':199C ''en.wikipedia.org/wiki/lemmatisation'':53C ''en.wikipedia.org/wiki/soundex'':84C ''en.wikipedia.org/wiki/stemming'':43C ''en.wikipedia.org/wiki/stop_words'':30C ''en.wikipedia.org/wiki/tf%e2%80%93idf'':135C ''en.wikipedia.org/wiki/tokenization'':19C ''en.wikipedia.org/wiki/vector_space_model'':116C ''en.wikipedia.org/wiki/wordnet'':101C ''equival'':94C ''etc'':71C ''everyth'':260C ''find'':93C,165C ''get'':238C,259C ''good'':235C ''hinrich'':229C ''hr'':243C ''href'':18C,29C,42C,52C,83C,100C,115C,134C,179C,198C,211C,249C,269C ''idf'':142C ''inform'':204C,219C ''introduct'':217C ''ir'':241C ''lemmat'':58C ''li'':2C,16C,27C,40C,61C,76C,92C,111C,148C,164C ''lucen'':276C ''lucene.apache.org'':251C,271C ''lucene.apache.org/core/'':270C ''lucene.apache.org/solr/'':250C ''man'':225C ''may'':12C,129C ''meteo'':70C ''model'':123C,146C ''mã'':67C ''need'':262C ''nlp.stanford.edu'':213C ''nlp.stanford.edu/ir-book/'':212C ''nofollow'':23C,34C,47C,57C,88C,105C,120C,139C,184C,203C,216C,254C,274C ''normal'':63C,79C ''o'':69C ''orthograph'':73C ''outlin'':194C ''p'':190C,209C,244C ''perform'':77C ''phonet'':78C ''prabhakar'':226C ''pre'':4C ''pre-process'':3C ''preprocess'':155C ''process'':5C,207C ''queri'':154C ''raghavan'':227C ''rel'':22C,33C,46C,56C,87C,104C,119C,138C,183C,202C,215C,253C,273C ''remov'':35C ''repres'':126C,157C ''retriev'':205C,220C ''schã'':230C ''similar'':168C,174C,186C ''skip'':14C ''solr'':256C,282C ''soundex'':89C ''space'':122C,162C ''start'':239C ''stem'':48C ''step'':11C ''stop'':36C ''tf'':131C,141C ''tf-idf'':140C ''thesauri'':97C ''token'':24C ''tze'':231C ''tã'':68C ''u.s.a'':65C ''ul'':1C,15C ''usa'':66C ''use'':96C,112C,130C,176C,247C,280C ''vector'':121C,161C,173C ''word'':37C ''wordnet'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1141, 1, '2014-09-19 02:34:04.093', 5, 170, 'Some suggestion for career in data science or predictive modeling', '&lt;p&gt;I am looking to choose my career in the area of decision science or predictive modeling and I am aware that this is kind of opinion based but I would like to have some suggestion from experts that I can use it to build my career in correct path. What are the tools should I know like R, SAS or any other. What are the thinks I should know to work in a data science or machine learning or predictive modeling. For me I am having problem in identifying steps that I should follow. Please suggest me some steps to follow.&lt;/p&gt;
', 'education  beginner  career', NULL, '''/p'':116C ''area'':24C ''awar'':34C ''base'':41C ''beginn'':12B ''build'':58C ''career'':4A,13B,21C,60C ''choos'':19C ''correct'':62C ''data'':6A,88C ''decis'':26C ''educ'':11B ''expert'':51C ''follow'':108C,115C ''identifi'':103C ''kind'':38C ''know'':70C,83C ''learn'':92C ''like'':45C,71C ''look'':17C ''machin'':91C ''model'':10A,30C,95C ''opinion'':40C ''p'':14C ''path'':63C ''pleas'':109C ''predict'':9A,29C,94C ''problem'':101C ''r'':72C ''sas'':73C ''scienc'':7A,27C,89C ''step'':104C,113C ''suggest'':2A,49C,110C ''think'':80C ''tool'':67C ''use'':55C ''work'':85C ''would'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1142, 2, '2014-09-19 07:15:13.803', 3, NULL, NULL, '&lt;p&gt;First of all I should say you question probably is an off-topic and will be closed soon.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Discussed at this SE site&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Anyway I can target you to similar questions discussed at this SE site already:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/questions/808/statistics-computer-science-data-science"&gt;Statistics + Computer Science = Data Science?&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/questions/739/starting-my-career-as-data-scientist-is-software-engineering-experience-require"&gt;Starting my career as Data Scientist, is Software Engineering experience required?&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Cross Validated SE&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;A set of relevant questions at Cross Validated Stack Exchange:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/questions/tagged/careers"&gt;http://stats.stackexchange.com/questions/tagged/careers&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Data scientist map&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This is good infographics of data science knowledge you might need to start a career (&lt;a href="http://pennlio.files.wordpress.com/2013/09/roadtodatascientist1.png" rel="nofollow"&gt;Link to image&lt;/a&gt;):&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/tJJ2x.png" alt="This is good infographics of data science knowledge you might need to start a career"&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Careers SE&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Also, simple "data scientist" querying of Careers SE site 
&lt;a href="http://careers.stackoverflow.com/jobs?searchTerm=data+scientist&amp;amp;location="&gt;http://careers.stackoverflow.com/jobs?searchTerm=data+scientist&amp;amp;location=&lt;/a&gt; will lead you to the following knowlege "tags":&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://careers.stackoverflow.com/jobs/tag/r"&gt;R&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://careers.stackoverflow.com/jobs/tag/bigdata"&gt;bigdata&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://careers.stackoverflow.com/jobs/tag/data-visualization"&gt;data-visualization&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://careers.stackoverflow.com/jobs/tag/hadoop"&gt;hadoop&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://careers.stackoverflow.com/jobs/tag/mapreduce"&gt;mapreduce&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://careers.stackoverflow.com/jobs/tag/scala"&gt;scala&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://careers.stackoverflow.com/jobs/tag/python"&gt;python&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://careers.stackoverflow.com/jobs/tag/matlab"&gt;matlab&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;etc.&lt;/p&gt;
', NULL, NULL, '''/2013/09/roadtodatascientist1.png'':142C ''/a'':60C,81C,148C,214C,223C,234C,243C,252C,261C,270C,279C ''/jobs/tag/bigdata'':221C ''/jobs/tag/data-visualization'':230C ''/jobs/tag/hadoop'':241C ''/jobs/tag/mapreduce'':250C ''/jobs/tag/matlab'':277C ''/jobs/tag/python'':268C ''/jobs/tag/r'':212C ''/jobs/tag/scala'':259C ''/jobs?searchterm=data+scientist&amp;amp;location='':193C ''/jobs?searchterm=data+scientist&amp;amp;location=&lt;/a&gt;'':196C ''/li'':62C,83C,215C,224C,235C,244C,253C,262C,271C,280C ''/p'':21C,30C,46C,61C,82C,91C,103C,121C,149C,172C,178C,205C,284C ''/questions/739/starting-my-career-as-data-scientist-is-software-engineering-experience-require'':69C ''/questions/808/statistics-computer-science-data-science'':54C ''/questions/tagged/careers'':110C ''/questions/tagged/careers&lt;/a&gt;&lt;/li&gt;'':113C ''/strong'':29C,90C,120C,177C ''/tjj2x.png'':155C ''/ul'':84C,114C,281C ''alreadi'':45C ''also'':180C ''alt'':156C ''anyway'':32C ''bigdata'':222C ''career'':72C,137C,171C,175C,186C ''careers.stackoverflow.com'':192C,195C,211C,220C,229C,240C,249C,258C,267C,276C ''careers.stackoverflow.com/jobs/tag/bigdata'':219C ''careers.stackoverflow.com/jobs/tag/data-visualization'':228C ''careers.stackoverflow.com/jobs/tag/hadoop'':239C ''careers.stackoverflow.com/jobs/tag/mapreduce'':248C ''careers.stackoverflow.com/jobs/tag/matlab'':275C ''careers.stackoverflow.com/jobs/tag/python'':266C ''careers.stackoverflow.com/jobs/tag/r'':210C ''careers.stackoverflow.com/jobs/tag/scala'':257C ''careers.stackoverflow.com/jobs?searchterm=data+scientist&amp;amp;location='':191C ''careers.stackoverflow.com/jobs?searchterm=data+scientist&amp;amp;location=&lt;/a&gt;'':194C ''close'':19C ''comput'':56C ''cross'':87C,99C ''data'':58C,74C,117C,128C,162C,182C,232C ''data-visu'':231C ''datascience.stackexchange.com'':53C,68C ''datascience.stackexchange.com/questions/739/starting-my-career-as-data-scientist-is-software-engineering-experience-require'':67C ''datascience.stackexchange.com/questions/808/statistics-computer-science-data-science'':52C ''discuss'':24C,40C ''engin'':78C ''etc'':283C ''exchang'':102C ''experi'':79C ''first'':2C ''follow'':202C ''good'':125C,159C ''hadoop'':242C ''href'':51C,66C,107C,139C,190C,209C,218C,227C,238C,247C,256C,265C,274C ''i.stack.imgur.com'':154C ''i.stack.imgur.com/tjj2x.png'':153C ''imag'':147C ''img'':151C ''infograph'':126C,160C ''knowledg'':130C,164C ''knowleg'':203C ''lead'':198C ''li'':48C,63C,105C,207C,216C,225C,236C,245C,254C,263C,272C ''link'':145C ''map'':119C ''mapreduc'':251C ''matlab'':278C ''might'':132C,166C ''need'':133C,167C ''nofollow'':144C ''off-top'':13C ''p'':1C,22C,31C,49C,64C,85C,92C,115C,122C,150C,173C,179C,282C ''pennlio.files.wordpress.com'':141C ''pennlio.files.wordpress.com/2013/09/roadtodatascientist1.png'':140C ''probabl'':10C ''python'':269C ''queri'':184C ''question'':9C,39C,97C ''r'':213C ''rel'':143C ''relev'':96C ''requir'':80C ''say'':7C ''scala'':260C ''scienc'':57C,59C,129C,163C ''scientist'':75C,118C,183C ''se'':27C,43C,89C,176C,187C ''set'':94C ''similar'':38C ''simpl'':181C ''site'':28C,44C,188C ''softwar'':77C ''soon'':20C ''src'':152C ''stack'':101C ''start'':70C,135C,169C ''statist'':55C ''stats.stackexchange.com'':109C,112C ''stats.stackexchange.com/questions/tagged/careers'':108C ''stats.stackexchange.com/questions/tagged/careers&lt;/a&gt;&lt;/li&gt;'':111C ''strong'':23C,86C,116C,174C ''tag'':204C ''target'':35C ''topic'':15C ''ul'':47C,104C,206C ''valid'':88C,100C ''visual'':233C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1161, 2, '2014-09-25 15:20:18.66', 1, NULL, NULL, '&lt;p&gt;I would say that trying out the resources online such as the &lt;a href="https://exploredatascience.boozallen.com" rel="nofollow"&gt;Explore Data Science course&lt;/a&gt; and Andrew Ng''s ML Course on Coursera (mentioned in the notes above) are great. However, nothing beats true data that you understand inside and out. After picking up some skills from Explore Data Science, I propose you collect your fuel usage data continuously. Collect the basics: city, state, gallons, total cost, and your vehicles mileage. When you forget to enter it, then you have to learn how to work with missing data. As you feel more comfortable, enter in kaggle contests. The data science community is growing so quickly that the online resources are vast and will help you identify any areas where a text book is what you need. Good Luck!! Have fun!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1143, 1, '2014-09-19 09:08:55.18', 10, 441, 'Machine Learning Algorithms, Depth of Understanding Vs Number of algorithms,', '&lt;p&gt;Recently i was introduced to the field of Data Science (its been 6 months approx),and i started the journey with Machine Learning Course by Andrew Ng and post that started working on the Data Science Specialization by JHU.&lt;/p&gt;

&lt;p&gt;On practical Application front i have been working on building a Predictive Model that would Predict attrition so far i have used glm,bayesglm,rf in an effort to learn and apply these methods,but i find a lot of gap in my understanding of these Algorithms.&lt;/p&gt;

&lt;p&gt;Dilemma:Whether "i should focus more on learning the intricacies of a few algorithms or should i use the approach of knowing a lot of them as and when and as much as required"&lt;/p&gt;

&lt;p&gt;**Please guide me in the right direction,may be by suggesting books or articles or anything that you think would help. &lt;/p&gt;

&lt;p&gt;&lt;em&gt;*&lt;/em&gt; i would be grateful if you would reply with an idea of guiding someone who has just started his career in the field of Data Science, and wants to be a person who solves practical issues for the business world.&lt;/p&gt;

&lt;p&gt;***I would read(as many as possible) resources(books,articles) suggested in this post and would provide a personal feed back on the pros and cons of the same so as to make this a helpful post for people who come across a similar question in future,and i think it would be great if people suggesting these books can do the same.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1144, 2, '2014-09-19 11:38:36.15', 5, NULL, NULL, '&lt;p&gt;Arguably someone calling themself a data scientist ought to know more about the intricacies of the algorithms he uses&amp;mdash;e.g. what affects the convergence rate of the Fisher scoring algorithm in GLM&amp;mdash;than a common or garden statistician&amp;mdash;who might be content just to know that the maximum-likelihood solution will be found (perhaps after they make a cup of coffee). In any case understanding the general concepts of statistics &amp;amp; machine learning is important in addition to familiarity with the methods you do use&amp;mdash;the theory behind them, the assumptions they make, what diagnostic checks you should perform, how to interpret the results. Avoid being &lt;a href="http://stats.stackexchange.com/questions/104500/skills-hard-to-find-in-machine-learners/104507#104507"&gt;this parody&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;You''d probably enjoy reading &lt;a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/" rel="nofollow"&gt;Hastie et al. (2009), &lt;em&gt;The Elements of Statistical Learning&lt;/em&gt;&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1145, 2, '2014-09-19 12:39:23.103', 8, NULL, NULL, '&lt;p&gt;I would recommend limiting yourself to a few tried and trusted algorithms. I would not recommend Elements of statistical learning ( as a first book).  It is too theoretical, aimed  at graduate students, with exercises asking how to prove X or Y...  I think &lt;a href="http://www-bcf.usc.edu/~gareth/ISL/"&gt;ISL&lt;/a&gt; is more appropriate, with more practical advice ( in any case both books are free as pdf downloads).&lt;/p&gt;

&lt;p&gt;Besides statistics, I would make sure you are comfortable with experimental design/AB tests, and with Business Intelligence/Visualisation.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9677, 1, '2016-01-07 11:42:34.537', 1, 503, 'How can one can aim for a career in machine learning?', '&lt;p&gt;I''m interested in various machine learning methods and wonder how can I aim for a career in the field.
What are most basic skills to have?&lt;/p&gt;

&lt;p&gt;I mean the matters strictly connected to the machine learning - not programming or libraries.&lt;/p&gt;
', 'machine-learning  beginner  career', NULL, '''/p'':45C,61C ''aim'':5A,31C ''basic'':41C ''beginn'':15B ''career'':8A,16B,34C ''connect'':52C ''field'':37C ''interest'':20C ''learn'':11A,14B,24C,56C ''librari'':60C ''m'':19C ''machin'':10A,13B,23C,55C ''machine-learn'':12B ''matter'':50C ''mean'':48C ''method'':25C ''one'':3A ''p'':17C,46C ''program'':58C ''skill'':42C ''strict'':51C ''various'':22C ''wonder'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2381, 1, '2014-10-31 08:50:23.16', 2, 68, 'How to find a model for a short discrete time-series?', '&lt;p&gt;I''m examining the activity of customers over the years which have about one event per year. This results is many short time-series for which I found the distributions (hit/miss over 4 years sorted by probability in the data):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;0000 : 0.31834
0001 : 0.17582
0010 : 0.13605
0100 : 0.13554
1000 : 0.12886
0011 : 0.01717
1100 : 0.01650
0110 : 0.01578
0101 : 0.01220
1010 : 0.01117
1001 : 0.00883
0111 : 0.00571
1110 : 0.00565
1111 : 0.00496
1101 : 0.00384
1011 : 0.00351
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Apparently a purely uncorrelated binomial model wouldn''t do, but one can observe that if both, the number of 1''s and 11''s coincide, then the probabilities are approximately equal (apart from a small recency effect of 0001).&lt;/p&gt;

&lt;p&gt;Can you see a way to approach such data to deduce a probabilistic model? Basically where I have only a few probability parameters which roughly explain this distribution?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10172, 1, '2016-02-10 02:34:35.447', 1, 16, 'Content based recommendations using SQL queries on the MovieLens data set', '&lt;p&gt;how can I generate content based recommendations using SQL queries on the MovieLens data set? &lt;/p&gt;
', 'recommender-system', NULL, '''/p'':31C ''base'':2A,21C ''content'':1A,20C ''data'':10A,29C ''generat'':19C ''movielen'':9A,28C ''p'':15C ''queri'':6A,25C ''recommend'':3A,13B,22C ''recommender-system'':12B ''set'':11A,30C ''sql'':5A,24C ''system'':14B ''use'':4A,23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1146, 2, '2014-09-19 22:51:43.883', 2, NULL, NULL, '&lt;p&gt;Since it is general methodological question, let''s assume we have only one text-based variable - total number of words in a sentence. First of all, it''s worth to &lt;strong&gt;visualize&lt;/strong&gt; your data. I will pretend I have following data: &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/KciVJ.png" alt="number of words vs. age"&gt;&lt;/p&gt;

&lt;p&gt;Here we see slight dependency between age and number of words in responses. We may assume that young people (approx. between 12 and 25) tend to use 1-4 words, while people of age 25-35 try to give longer answers. But how do we split these points? I would do it something like this: &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/uect9.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;In 2D plot it looks pretty straightforward, and this is how it works most of the time in practise. However, you asked for splitting data by a single variable - age. That is, something like this: &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/G10GI.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Is it a good split? I don''t know. In fact, it depends on your actual needs and interpretation of the "cut points". That''s why I asked about &lt;em&gt;concrete&lt;/em&gt; task. Anyway, this interpretation is up to you. &lt;/p&gt;

&lt;p&gt;In practise, you will have much more text-based variables. E.g. you can use every word as a feature (don''t forget to &lt;a href="http://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html" rel="nofollow"&gt;stem or lemmatize&lt;/a&gt; it first) with values from zero to a number of occurrences in the response. Visualizing high-dimensional data is not an easy task, so you need a way to discover groups of data without plotting them. &lt;a href="http://en.wikipedia.org/wiki/Cluster_analysis" rel="nofollow"&gt;&lt;strong&gt;Clustering&lt;/strong&gt;&lt;/a&gt; is a general approach for this. Though clustering algorithms may work with data of arbitrary dimensionality, we still have only 2D to plot it, so let''s come back to our example. &lt;/p&gt;

&lt;p&gt;With algorithm like &lt;a href="http://en.wikipedia.org/wiki/K-means_clustering" rel="nofollow"&gt;&lt;strong&gt;k-means&lt;/strong&gt;&lt;/a&gt; you can obtain 2 groups like this: &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Tp4aK.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Two dots - red and blue - show cluster centres, calculated by k-means. You can use coordinates of these points to split your data by any subset of axes, even if you have 10k dimensions. But again, the most important question here is: &lt;em&gt;what linguistic features will provide reasonable grouping of ages&lt;/em&gt;. &lt;/p&gt;
', NULL, NULL, '''-35'':95C ''-4'':88C ''/a'':254C,302C,352C ''/em'':209C,427C ''/g10gi.png'':170C ''/ir-book/html/htmledition/stemming-and-lemmatization-1.html'':248C ''/kcivj.png'':51C ''/p'':45C,58C,115C,127C,164C,176C,218C,335C,360C,372C,428C ''/strong'':35C,301C,351C ''/tp4ak.png'':366C ''/uect9.png'':121C ''/wiki/cluster_analysis'':296C ''/wiki/k-means_clustering'':344C ''1'':87C ''10k'':407C ''12'':81C ''2'':356C ''25'':83C,94C ''2d'':130C,323C ''actual'':193C ''age'':57C,66C,93C,158C,426C ''algorithm'':311C,338C ''alt'':52C,122C,171C,367C ''answer'':100C ''anyway'':211C ''approach'':306C ''approx'':79C ''arbitrari'':317C ''ask'':150C,205C ''assum'':10C,75C ''axe'':402C ''back'':331C ''base'':17C,229C ''blue'':378C ''calcul'':382C ''centr'':381C ''cluster'':300C,310C,380C ''come'':330C ''concret'':208C ''coordin'':390C ''cut'':199C ''data'':37C,44C,153C,273C,288C,315C,397C ''depend'':64C,190C ''descript'':125C,174C,370C ''dimens'':408C ''dimension'':272C,318C ''discov'':285C ''dot'':375C ''e.g'':231C ''easi'':277C ''em'':207C,417C ''en.wikipedia.org'':295C,343C ''en.wikipedia.org/wiki/cluster_analysis'':294C ''en.wikipedia.org/wiki/k-means_clustering'':342C ''enter'':123C,172C,368C ''even'':403C ''everi'':235C ''exampl'':334C ''fact'':188C ''featur'':239C,420C ''first'':26C,256C ''follow'':43C ''forget'':242C ''general'':5C,305C ''give'':98C ''good'':181C ''group'':286C,357C,424C ''high'':271C ''high-dimension'':270C ''howev'':148C ''href'':245C,293C,341C ''i.stack.imgur.com'':50C,120C,169C,365C ''i.stack.imgur.com/g10gi.png'':168C ''i.stack.imgur.com/kcivj.png'':49C ''i.stack.imgur.com/tp4ak.png'':364C ''i.stack.imgur.com/uect9.png'':119C ''imag'':124C,173C,369C ''img'':47C,117C,166C,362C ''import'':413C ''interpret'':196C,213C ''k'':349C,385C ''k-mean'':348C,384C ''know'':186C ''lemmat'':253C ''let'':8C,328C ''like'':113C,162C,339C,358C ''linguist'':419C ''longer'':99C ''look'':133C ''may'':74C,312C ''mean'':350C,386C ''methodolog'':6C ''much'':225C ''need'':194C,281C ''nlp.stanford.edu'':247C ''nlp.stanford.edu/ir-book/html/htmledition/stemming-and-lemmatization-1.html'':246C ''nofollow'':250C,298C,346C ''number'':20C,53C,68C,263C ''obtain'':355C ''occurr'':265C ''one'':14C ''p'':1C,46C,59C,116C,128C,165C,177C,219C,336C,361C,373C ''peopl'':78C,91C ''plot'':131C,290C,325C ''point'':107C,200C,393C ''practis'':147C,221C ''pretend'':40C ''pretti'':134C ''provid'':422C ''question'':7C,414C ''reason'':423C ''red'':376C ''rel'':249C,297C,345C ''respons'':72C,268C ''see'':62C ''sentenc'':25C ''show'':379C ''sinc'':2C ''singl'':156C ''slight'':63C ''someth'':112C,161C ''split'':105C,152C,182C,395C ''src'':48C,118C,167C,363C ''stem'':251C ''still'':320C ''straightforward'':135C ''strong'':33C,299C,347C ''subset'':400C ''task'':210C,278C ''tend'':84C ''text'':16C,228C ''text-bas'':15C,227C ''though'':309C ''time'':145C ''total'':19C ''tri'':96C ''two'':374C ''use'':86C,234C,389C ''valu'':258C ''variabl'':18C,157C,230C ''visual'':34C,269C ''vs'':56C ''way'':283C ''without'':289C ''word'':22C,55C,70C,89C,236C ''work'':141C,313C ''worth'':31C ''would'':109C ''young'':77C ''zero'':260C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9756, 1, '2016-01-12 06:38:51.017', 2, 11, 'Does Apche Kafka support sockets-based communication?', '&lt;p&gt;Does Apche Kafka support sockets-based communication?&lt;/p&gt;

&lt;p&gt;Example: After running both Kafka producer and consumer.if I send a message through putty(TCP/IP client), will the consumer receive it or not?&lt;/p&gt;
', 'apache-kafka', NULL, '''/p'':21C,46C ''apach'':10B ''apache-kafka'':9B ''apch'':2A,14C ''base'':7A,19C ''client'':38C ''communic'':8A,20C ''consum'':41C ''consumer.if'':30C ''exampl'':23C ''kafka'':3A,11B,15C,27C ''messag'':34C ''p'':12C,22C ''produc'':28C ''putti'':36C ''receiv'':42C ''run'':25C ''send'':32C ''socket'':6A,18C ''sockets-bas'':5A,17C ''support'':4A,16C ''tcp/ip'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1147, 1, '2014-09-20 02:22:07.51', 1, 160, 'Invariance Property of Vowpal Wabbit Updates - Explaination', '&lt;p&gt;One of the discussed nice aspects of the procedure that Vowpal Wabbit uses for updates to sgd 
&lt;a href="http://lowrank.net/nikos/pubs/liw.pdf" rel="nofollow"&gt;pdf&lt;/a&gt; is so-called weight invariance, described in the linked as:&lt;/p&gt;

&lt;p&gt;"Among these updates we mainly focus on a novel
set of updates that satisfies an additional invariance
property: for all importance weights of h, the update
is equivalent to two updates with importance weight
h/2. We call these updates importance invariant."&lt;/p&gt;

&lt;p&gt;What does this mean and why is it useful?&lt;/p&gt;
', 'machine-learning  gradient-descent', NULL, '''/a'':40C ''/nikos/pubs/liw.pdf'':36C ''/p'':52C,95C,106C ''addit'':69C ''among'':54C ''aspect'':20C ''call'':44C,90C ''descent'':13B ''describ'':47C ''discuss'':18C ''equival'':81C ''explain'':7A ''focus'':59C ''gradient'':12B ''gradient-desc'':11B ''h'':77C ''h/2'':88C ''href'':33C ''import'':74C,86C,93C ''invari'':1A,46C,70C,94C ''learn'':10B ''link'':50C ''lowrank.net'':35C ''lowrank.net/nikos/pubs/liw.pdf'':34C ''machin'':9B ''machine-learn'':8B ''main'':58C ''mean'':100C ''nice'':19C ''nofollow'':38C ''novel'':62C ''one'':15C ''p'':14C,53C,96C ''pdf'':39C ''procedur'':23C ''properti'':2A,71C ''rel'':37C ''satisfi'':67C ''set'':63C ''sgd'':31C ''so-cal'':42C ''two'':83C ''updat'':6A,29C,56C,65C,79C,84C,92C ''use'':27C,105C ''vowpal'':4A,25C ''wabbit'':5A,26C ''weight'':45C,75C,87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1148, 2, '2014-09-21 06:01:41.097', 1, NULL, NULL, '&lt;p&gt;If you have enough data, use cross validation.&lt;/p&gt;

&lt;p&gt;If you don''t have a lot of data, use cross validation on a similar dataset, and transfer the window size (the UCR archive has a bunch of similar dataset)&lt;/p&gt;

&lt;p&gt;Don''t forget, that the best warping  window size depends on the amount of training data. As you get more data, you can have a smaller warping window, see fig 6 of &lt;a href="http://www.cs.ucr.edu/~eamonn/DTW_myths.pdf" rel="nofollow"&gt;http://www.cs.ucr.edu/~eamonn/DTW_myths.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;eamonn&lt;/p&gt;
', NULL, NULL, '''/p'':10C,42C,88C ''/~eamonn/dtw_myths.pdf'':80C ''/~eamonn/dtw_myths.pdf&lt;/a&gt;&lt;/p&gt;'':85C ''6'':74C ''amount'':56C ''archiv'':35C ''best'':49C ''bunch'':38C ''cross'':8C,22C ''data'':6C,20C,59C,64C ''dataset'':27C,41C ''depend'':53C ''eamonn'':87C ''enough'':5C ''fig'':73C ''forget'':46C ''get'':62C ''href'':77C ''lot'':18C ''nofollow'':82C ''p'':1C,11C,43C,86C ''rel'':81C ''see'':72C ''similar'':26C,40C ''size'':32C,52C ''smaller'':69C ''train'':58C ''transfer'':29C ''ucr'':34C ''use'':7C,21C ''valid'':9C,23C ''warp'':50C,70C ''window'':31C,51C,71C ''www.cs.ucr.edu'':79C,84C ''www.cs.ucr.edu/~eamonn/dtw_myths.pdf'':78C ''www.cs.ucr.edu/~eamonn/dtw_myths.pdf&lt;/a&gt;&lt;/p&gt;'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1149, 1, '2014-09-22 15:48:32.697', 2, 215, 'Text Classification with mixed features in Random Forests', '&lt;p&gt;I am working on a text classification problem on tweets. At the moment I was only considering the content of the tweets as a source of information, and I was using a simple bag of words approach using term frequencies as features, using Random Forests (this is something I cannot change). &lt;/p&gt;

&lt;p&gt;Now my idea is to try to incorporate information present in the URLs used in tweets. Now, not all the tweets have URLs, and if I decide to use the same term frequency representation also for URLs I will have a huge number of features only from URLs. For this reason, I suppose that having a single set of features containing both the tweet term frequencies and the URL term frequencies could be bad. Besides I''ll have to fill some impossible values (like -1) for the URL features for tweets that do not have URLs, and I will probably worsen the classification for this tweets, as I will have a huge number of uninformative features. &lt;/p&gt;

&lt;p&gt;Do you have any suggestions regarding this issue? &lt;/p&gt;
', 'text-mining  random-forest', NULL, '''-1'':153C ''/p'':67C,185C,195C ''also'':103C ''approach'':52C ''bad'':142C ''bag'':49C ''besid'':143C ''cannot'':65C ''chang'':66C ''classif'':2A,22C,171C ''consid'':32C ''contain'':129C ''content'':34C ''could'':140C ''decid'':95C ''featur'':5A,57C,113C,128C,157C,184C ''fill'':148C ''forest'':8A,14B,60C ''frequenc'':55C,101C,134C,139C ''huge'':110C,180C ''idea'':71C ''imposs'':150C ''incorpor'':76C ''inform'':42C,77C ''issu'':194C ''like'':152C ''ll'':145C ''mine'':11B ''mix'':4A ''moment'':28C ''number'':111C,181C ''p'':15C,68C,186C ''present'':78C ''probabl'':168C ''problem'':23C ''random'':7A,13B,59C ''random-forest'':12B ''reason'':119C ''regard'':192C ''represent'':102C ''set'':126C ''simpl'':48C ''singl'':125C ''someth'':63C ''sourc'':40C ''suggest'':191C ''suppos'':121C ''term'':54C,100C,133C,138C ''text'':1A,10B,21C ''text-min'':9B ''tri'':74C ''tweet'':25C,37C,84C,89C,132C,159C,174C ''uninform'':183C ''url'':81C,91C,105C,116C,137C,156C,164C ''use'':46C,53C,58C,82C,97C ''valu'':151C ''word'':51C ''work'':18C ''worsen'':169C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1150, 2, '2014-09-23 02:20:24.67', 2, NULL, NULL, '&lt;p&gt;This has been asked, and answered, on CrossValidated. See &lt;a href="http://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression"&gt;http://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression&lt;/a&gt; and &lt;a href="http://stats.stackexchange.com/questions/45803/logistic-regression-in-r-resulted-in-hauck-donner-phenomenon-now-what"&gt;http://stats.stackexchange.com/questions/45803/logistic-regression-in-r-resulted-in-hauck-donner-phenomenon-now-what&lt;/a&gt; for two related answers (and there are other related questions that you can explore there). That logistic regression can blow up is a known effect in computational statistics.&lt;/p&gt;

&lt;p&gt;Also, in situations where exp(-30) gives you roughly the relative accuracy of the double type, you would want to be extremely careful with accumulation of the round-off errors, as 1+exp(-30)=1: summing the likelihood contributions may run into numerical problems, especially when you start computing numerical derivatives and gradients. For a brief introduction into the issue in application to the typical problems in statistical computing and the specific problems it tends to encounter, see &lt;a href="http://www.stata.com/meeting/nordic-and-baltic14/abstracts/materials/dk14_gould.pdf" rel="nofollow"&gt;http://www.stata.com/meeting/nordic-and-baltic14/abstracts/materials/dk14_gould.pdf&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''-30'':63C,92C ''/meeting/nordic-and-baltic14/abstracts/materials/dk14_gould.pdf'':141C ''/meeting/nordic-and-baltic14/abstracts/materials/dk14_gould.pdf&lt;/a&gt;.&lt;/p&gt;'':146C ''/p'':56C ''/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression'':15C ''/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression&lt;/a&gt;'':18C ''/questions/45803/logistic-regression-in-r-resulted-in-hauck-donner-phenomenon-now-what'':24C ''/questions/45803/logistic-regression-in-r-resulted-in-hauck-donner-phenomenon-now-what&lt;/a&gt;'':27C ''1'':90C,93C ''accumul'':82C ''accuraci'':69C ''also'':58C ''answer'':7C,31C ''applic'':120C ''ask'':5C ''blow'':47C ''brief'':114C ''care'':80C ''comput'':54C,107C,127C ''contribut'':97C ''crossvalid'':9C ''deriv'':109C ''doubl'':72C ''effect'':52C ''encount'':135C ''error'':88C ''especi'':103C ''exp'':62C,91C ''explor'':41C ''extrem'':79C ''give'':64C ''gradient'':111C ''href'':12C,21C,138C ''introduct'':115C ''issu'':118C ''known'':51C ''likelihood'':96C ''logist'':44C ''may'':98C ''nofollow'':143C ''numer'':101C,108C ''p'':1C,57C ''problem'':102C,124C,131C ''question'':37C ''regress'':45C ''rel'':142C ''relat'':30C,36C,68C ''rough'':66C ''round'':86C ''round-off'':85C ''run'':99C ''see'':10C,136C ''situat'':60C ''specif'':130C ''start'':106C ''statist'':55C,126C ''stats.stackexchange.com'':14C,17C,23C,26C ''stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression'':13C ''stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression&lt;/a&gt;'':16C ''stats.stackexchange.com/questions/45803/logistic-regression-in-r-resulted-in-hauck-donner-phenomenon-now-what'':22C ''stats.stackexchange.com/questions/45803/logistic-regression-in-r-resulted-in-hauck-donner-phenomenon-now-what&lt;/a&gt;'':25C ''sum'':94C ''tend'':133C ''two'':29C ''type'':73C ''typic'':123C ''want'':76C ''would'':75C ''www.stata.com'':140C,145C ''www.stata.com/meeting/nordic-and-baltic14/abstracts/materials/dk14_gould.pdf'':139C ''www.stata.com/meeting/nordic-and-baltic14/abstracts/materials/dk14_gould.pdf&lt;/a&gt;.&lt;/p&gt;'':144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1151, 1, '2014-09-23 03:34:35.75', 2, 949, 'Prerequisites for Data Science', '&lt;p&gt;I''m a java developer and I want to pursue career in Data Science and machine learning. Please advise me where and how to begin? What subjects like mathematical/statistical skills are required and so on.&lt;/p&gt;
', 'machine-learning  bigdata', NULL, '''/p'':45C ''advis'':28C ''begin'':34C ''bigdata'':8B ''career'':20C ''data'':3A,22C ''develop'':14C ''java'':13C ''learn'':7B,26C ''like'':37C ''m'':11C ''machin'':6B,25C ''machine-learn'':5B ''mathematical/statistical'':38C ''p'':9C ''pleas'':27C ''prerequisit'':1A ''pursu'':19C ''requir'':41C ''scienc'':4A,23C ''skill'':39C ''subject'':36C ''want'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1152, 2, '2014-09-23 13:51:24.65', 1, NULL, NULL, '&lt;p&gt;Are you using raw term frequencies, or TF-IDF?&lt;/p&gt;

&lt;p&gt;Perhaps you could simply combine the terms in the tweet with the terms in the URL-linked pages (if any) into a single bag of words, calculate TF-IDF, and normalize to avoid bias towards longer documents (i.e., those tweets containing URL links).&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;if I decide to use the same term frequency representation also for URLs I will have a huge number of features only from URLs&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I don''t understand what you mean here. Aren''t your features the terms in your bag of words? So the number of features will be the size of your vocabulary, which I imagine won''t change much whether you include URLs or not.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Besides I''ll have to fill some impossible values (like -1) for the URL features for tweets that do not have URLs, and I will probably worsen the classification for this tweets, as I will have a huge number of uninformative features.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I don''t understand this either. Term-document matrices are virtually always a sparse matrix, since most of the terms in your vocabulary won''t appear in most of your documents. So, the vast majority of values in your TDM will be 0. I don''t know where you''re getting -1 from.&lt;/p&gt;
', NULL, NULL, '''-1'':146C,233C ''/blockquote'':87C,179C ''/p'':12C,59C,86C,133C,178C,235C ''0'':224C ''also'':72C ''alway'':193C ''appear'':207C ''aren'':97C ''avoid'':48C ''bag'':38C,105C ''besid'':136C ''bias'':49C ''blockquot'':60C,134C ''calcul'':41C ''chang'':125C ''classif'':164C ''combin'':18C ''contain'':56C ''could'':16C ''decid'':64C ''document'':52C,189C,212C ''either'':186C ''featur'':82C,100C,112C,150C,177C ''fill'':141C ''frequenc'':7C,70C ''get'':232C ''huge'':79C,173C ''i.e'':53C ''idf'':11C,44C ''imagin'':122C ''imposs'':143C ''includ'':129C ''know'':228C ''like'':145C ''link'':31C,58C ''ll'':138C ''longer'':51C ''major'':216C ''matric'':190C ''matrix'':196C ''mean'':95C ''much'':126C ''normal'':46C ''number'':80C,110C,174C ''p'':1C,13C,61C,88C,135C,180C ''page'':32C ''perhap'':14C ''probabl'':161C ''raw'':5C ''re'':231C ''represent'':71C ''simpli'':17C ''sinc'':197C ''singl'':37C ''size'':116C ''spars'':195C ''tdm'':221C ''term'':6C,20C,26C,69C,102C,188C,201C ''term-docu'':187C ''tf'':10C,43C ''tf-idf'':9C,42C ''toward'':50C ''tweet'':23C,55C,152C,167C ''understand'':92C,184C ''uninform'':176C ''url'':30C,57C,74C,85C,130C,149C,157C ''url-link'':29C ''use'':4C,66C ''valu'':144C,218C ''vast'':215C ''virtual'':192C ''vocabulari'':119C,204C ''whether'':127C ''won'':123C,205C ''word'':40C,107C ''worsen'':162C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1153, 2, '2014-09-23 20:39:29.81', 1, NULL, NULL, '&lt;p&gt;I remember a long time ago playing with Elastic Search (the website is very different now from what I remember). There is some stuff about dealing with human language here : &lt;a href="http://www.elasticsearch.org/guide/en/elasticsearch/guide/current/languages.html" rel="nofollow"&gt;http://www.elasticsearch.org/guide/en/elasticsearch/guide/current/languages.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Be warned that Elastic search is like a big bazooka to your problem. If your problem is very simple, maybe you want to go from scratch. There is some docs in the web about it.&lt;/p&gt;
', NULL, NULL, '''/guide/en/elasticsearch/guide/current/languages.html'':36C ''/guide/en/elasticsearch/guide/current/languages.html&lt;/a&gt;&lt;/p&gt;'':41C ''/p'':78C ''ago'':7C ''bazooka'':52C ''big'':51C ''deal'':27C ''differ'':16C ''doc'':72C ''elast'':10C,46C ''go'':66C ''href'':33C ''human'':29C ''languag'':30C ''like'':49C ''long'':5C ''mayb'':62C ''nofollow'':38C ''p'':1C,42C ''play'':8C ''problem'':55C,58C ''rel'':37C ''rememb'':3C,21C ''scratch'':68C ''search'':11C,47C ''simpl'':61C ''stuff'':25C ''time'':6C ''want'':64C ''warn'':44C ''web'':75C ''websit'':13C ''www.elasticsearch.org'':35C,40C ''www.elasticsearch.org/guide/en/elasticsearch/guide/current/languages.html'':34C ''www.elasticsearch.org/guide/en/elasticsearch/guide/current/languages.html&lt;/a&gt;&lt;/p&gt;'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1154, 1, '2014-09-23 20:58:01.027', 3, 91, 'Trouble representing a problem', '&lt;p&gt;I have a problem and I''m having trouble representing it - first I thought I should use graph theory (nodes and edges) and now I''m not sure.&lt;/p&gt;

&lt;p&gt;My data is some tanks names and it''s volumes, those tanks are connected by pipelines which I have the names and length.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;------(pipeline 1)------.-----(pipeline 2)------.----(pipeline 3)---
  |                     |    |                           |         |
[R tank 1]        [S tank 1] [S tank 2]            (pipeline 4) [S tank 3]
                                                         |
                                                     [S tank 4]

R tank is sink (receiver) and S tank is source (sender)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Problem is the pipe names change doesn''t occur where there is a tank - they change name because historical reasons, size or connections...&lt;/p&gt;

&lt;p&gt;So if I want to graphically show that S tank 2 is connected to pipeline 2 at point X and pipeline 2 connects to pipeline and the content goes to R tank 1, how should I do this? (I think the point X may not be relevant but if I had some way to get the distance travelled would be great).  &lt;/p&gt;
', 'dataset  recommendation  graphs', NULL, '''/code'':99C ''/p'':37C,62C,125C,188C ''/pre'':100C ''1'':66C,73C,76C,159C ''2'':68C,79C,137C,142C,148C ''3'':70C,84C ''4'':81C,87C ''chang'':107C,117C ''code'':64C ''connect'':52C,124C,139C,149C ''content'':154C ''data'':40C ''dataset'':5B ''distanc'':183C ''doesn'':108C ''edg'':30C ''first'':20C ''get'':181C ''goe'':155C ''graph'':7B,26C ''graphic'':132C ''great'':187C ''histor'':120C ''length'':61C ''m'':15C,34C ''may'':170C ''name'':44C,59C,106C,118C ''node'':28C ''occur'':110C ''p'':8C,38C,101C,126C ''pipe'':105C ''pipelin'':54C,65C,67C,69C,80C,141C,147C,151C ''point'':144C,168C ''pre'':63C ''problem'':4A,12C,102C ''r'':71C,88C,157C ''reason'':121C ''receiv'':92C ''recommend'':6B ''relev'':173C ''repres'':2A,18C ''sender'':98C ''show'':133C ''sink'':91C ''size'':122C ''sourc'':97C ''sure'':36C ''tank'':43C,50C,72C,75C,78C,83C,86C,89C,95C,115C,136C,158C ''theori'':27C ''think'':166C ''thought'':22C ''travel'':184C ''troubl'':1A,17C ''use'':25C ''volum'':48C ''want'':130C ''way'':179C ''would'':185C ''x'':145C,169C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1155, 1, '2014-09-24 16:25:48.803', 3, 64, 'Estimating Variance Reduction Resultant from Additional Data', '&lt;p&gt;I couldn''t quite think of how best to title this, so recommendations are welcome. Same goes for the tags (I don''t have the reputation to use the tags that I thought were appropriate). The question is this:&lt;/p&gt;

&lt;p&gt;"Suppose you have N pairs of observations, (x,y), and you have a model with some unknown parameters, B, that estimates the relationship between x and y, F(x,B) -&gt; y. Now suppose you determine B using the method of least-squares (and, implicitly, that all the assumptions of least-squares are satisfied). The parameters, B, are themselves random variables, each with its own variance. Is there any way to estimate the reduction (or increase) in the variance of B that would result from applying the same method of least-squares to N+1 pairs of observations?"&lt;/p&gt;

&lt;p&gt;The question is asked in the context of experimentation. If each data point costs $X, an affirmative answer to the question would go a long way in determining whether or not to continue testing.&lt;/p&gt;
', 'statistics  algorithms  experiments', NULL, '''+1'':149C ''/p'':51C,153C,189C ''addit'':6A ''affirm'':171C ''algorithm'':9B ''answer'':172C ''appli'':139C ''appropri'':46C ''ask'':158C ''assumpt'':101C ''b'':71C,82C,88C,110C,134C ''best'':19C ''context'':161C ''continu'':187C ''cost'':168C ''couldn'':13C ''data'':7A,166C ''determin'':87C,182C ''estim'':1A,73C,125C ''experi'':10B ''experiment'':163C ''f'':80C ''go'':177C ''goe'':28C ''implicit'':97C ''increas'':129C ''least'':94C,104C,145C ''least-squar'':93C,103C,144C ''long'':179C ''method'':91C,142C ''model'':66C ''n'':56C,148C ''observ'':59C,152C ''p'':11C,52C,154C ''pair'':57C,150C ''paramet'':70C,109C ''point'':167C ''question'':48C,156C,175C ''quit'':15C ''random'':113C ''recommend'':24C ''reduct'':3A,127C ''relationship'':75C ''reput'':37C ''result'':4A,137C ''satisfi'':107C ''squar'':95C,105C,146C ''statist'':8B ''suppos'':53C,85C ''tag'':31C,41C ''test'':188C ''think'':16C ''thought'':44C ''titl'':21C ''unknown'':69C ''use'':39C,89C ''variabl'':114C ''varianc'':2A,119C,132C ''way'':123C,180C ''welcom'':26C ''whether'':183C ''would'':136C,176C ''x'':60C,77C,81C,169C ''y'':61C,79C,83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1159, 1, '2014-09-25 08:40:59.467', 9, 2068, 'How to do SVD and PCA with big data?', '&lt;p&gt;I have a large set of data, about 8GB. I want to use machine learning to analyze it. So I think I should do SVD then PCA to reduce the data dimension for efficiency. But MATLAB and Octave cannot load such a large dataset.&lt;/p&gt;

&lt;p&gt;What tools I can use to do SVD with such a large amount of data? &lt;/p&gt;
', 'bigdata  data-mining  dimensionality-reduction', NULL, '''/p'':62C,79C ''8gb'':26C ''amount'':76C ''analyz'':34C ''big'':8A ''bigdata'':10B ''cannot'':56C ''data'':9A,12B,24C,48C,78C ''data-min'':11B ''dataset'':61C ''dimens'':49C ''dimension'':15B ''dimensionality-reduct'':14B ''effici'':51C ''larg'':21C,60C,75C ''learn'':32C ''load'':57C ''machin'':31C ''matlab'':53C ''mine'':13B ''octav'':55C ''p'':17C,63C ''pca'':6A,44C ''reduc'':46C ''reduct'':16B ''set'':22C ''svd'':4A,42C,71C ''think'':38C ''tool'':65C ''use'':30C,68C ''want'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1160, 2, '2014-09-25 15:02:45.533', 1, NULL, NULL, '&lt;p&gt;If I understand you correctly, I would try a few featurization methods to transform the text column to a numeric value. Then you can proceed with analysis as usual. There is a great book on NLP called &lt;a href="http://tamingtext.com/" rel="nofollow"&gt;Taming Text&lt;/a&gt; that would give numerous ways to think about your text variables.&lt;/p&gt;
', NULL, NULL, '''/a'':46C ''/p'':58C ''analysi'':28C ''book'':35C ''call'':38C ''column'':18C ''correct'':6C ''featur'':12C ''give'':49C ''great'':34C ''href'':40C ''method'':13C ''nlp'':37C ''nofollow'':43C ''numer'':21C,50C ''p'':1C ''proceed'':26C ''rel'':42C ''tame'':44C ''tamingtext.com'':41C ''text'':17C,45C,56C ''think'':53C ''transform'':15C ''tri'':9C ''understand'':4C ''usual'':30C ''valu'':22C ''variabl'':57C ''way'':51C ''would'':8C,48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1162, 2, '2014-09-25 15:29:00.877', 2, NULL, NULL, '&lt;p&gt;I think ethics in Data Science is important. There is a fundamental difference in using user data to better their experience and show relevant ads and using user data to trick people into clicking on ads for the sake of monetary profit. Personally I like ads that give me relevant information like deals on things I would buy anyway. However, showing me weight loss ads because I got dumped is creepy and unethical. As my friend Peter always says, "don''t be creepy with data". &lt;/p&gt;
', NULL, NULL, '''/p'':87C ''ad'':26C,37C,47C,66C ''alway'':79C ''anyway'':60C ''better'':20C ''buy'':59C ''click'':35C ''creepi'':72C,84C ''data'':6C,18C,30C,86C ''deal'':54C ''differ'':14C ''dump'':70C ''ethic'':4C ''experi'':22C ''friend'':77C ''fundament'':13C ''give'':49C ''got'':69C ''howev'':61C ''import'':9C ''inform'':52C ''like'':46C,53C ''loss'':65C ''monetari'':42C ''p'':1C ''peopl'':33C ''person'':44C ''peter'':78C ''profit'':43C ''relev'':25C,51C ''sake'':40C ''say'':80C ''scienc'':7C ''show'':24C,62C ''thing'':56C ''think'':3C ''trick'':32C ''uneth'':74C ''use'':16C,28C ''user'':17C,29C ''weight'':64C ''would'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1163, 2, '2014-09-25 17:33:09.617', 4, NULL, NULL, '&lt;p&gt;Don''t bother.&lt;/p&gt;

&lt;p&gt;First rule of programming- which also applies to data science: get everything working on a small test problem.&lt;/p&gt;

&lt;p&gt;so take a random sample of your data of say 100,000 rows. try different algorithms etc.  once you have got everything working to your satisfaction, you can try larger (and larger) data sets - and see how the test error reduces as you add more data.&lt;/p&gt;

&lt;p&gt;furthermore you do not want to apply svd to only 8 columns: you apply it when you have a lot of columns.&lt;/p&gt;
', NULL, NULL, '''/p'':5C,25C,73C,97C ''000'':38C ''100'':37C ''8'':85C ''add'':70C ''algorithm'':42C ''also'':12C ''appli'':13C,81C,88C ''bother'':4C ''column'':86C,96C ''data'':15C,34C,59C,72C ''differ'':41C ''error'':66C ''etc'':43C ''everyth'':18C,48C ''first'':7C ''furthermor'':75C ''get'':17C ''got'':47C ''larger'':56C,58C ''lot'':94C ''p'':1C,6C,26C,74C ''problem'':24C ''program'':10C ''random'':30C ''reduc'':67C ''row'':39C ''rule'':8C ''sampl'':31C ''satisfact'':52C ''say'':36C ''scienc'':16C ''see'':62C ''set'':60C ''small'':22C ''svd'':82C ''take'':28C ''test'':23C,65C ''tri'':40C,55C ''want'':79C ''work'':19C,49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1164, 2, '2014-09-25 18:48:12.303', 1, NULL, NULL, '&lt;p&gt;Although you can probably find some tools that will let you do it on a single machine, you''re getting into the range where it make sense to consider "big data" tools like Spark, especially if you think your data set might grow. Spark has a component called MLlib which supports PCA and SVD. &lt;a href="http://spark.apache.org/docs/latest/mllib-dimensionality-reduction.html" rel="nofollow"&gt;The documentation has examples&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':67C ''/docs/latest/mllib-dimensionality-reduction.html'':60C ''/p'':68C ''although'':2C ''big'':31C ''call'':49C ''compon'':48C ''consid'':30C ''data'':32C,41C ''document'':64C ''especi'':36C ''exampl'':66C ''find'':6C ''get'':21C ''grow'':44C ''href'':57C ''let'':11C ''like'':34C ''machin'':18C ''make'':27C ''might'':43C ''mllib'':50C ''nofollow'':62C ''p'':1C ''pca'':53C ''probabl'':5C ''rang'':24C ''re'':20C ''rel'':61C ''sens'':28C ''set'':42C ''singl'':17C ''spark'':35C,45C ''spark.apache.org'':59C ''spark.apache.org/docs/latest/mllib-dimensionality-reduction.html'':58C ''support'':52C ''svd'':55C ''think'':39C ''tool'':8C,33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2535, 2, '2014-11-25 03:38:33.6', 1, NULL, NULL, '&lt;p&gt;You are right, the covariance matrix should have n^2 elements. However, since cov_{i,j} = cov_{j,i}, there is no need to have a repeated feature cov_{j,i} if cov_{i,j} is already accounted for. Hence there will be only n*(n+1)/2 = 12*13/2 = 78 unique covariances and thus only 78 unique covariance based features (n of those will be variances). &lt;/p&gt;
', NULL, NULL, '''+1'':48C ''/2'':49C ''/p'':69C ''12'':50C ''13/2'':51C ''2'':11C ''78'':52C,58C ''account'':39C ''alreadi'':38C ''base'':61C ''cov'':15C,18C,30C,34C ''covari'':6C,54C,60C ''element'':12C ''featur'':29C,62C ''henc'':41C ''howev'':13C ''j'':17C,19C,31C,36C ''matrix'':7C ''n'':10C,46C,47C,63C ''need'':24C ''p'':1C ''repeat'':28C ''right'':4C ''sinc'':14C ''thus'':56C ''uniqu'':53C,59C ''varianc'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1165, 1, '2014-09-25 20:18:46.88', 4, 1243, 'Looking for a strong Phd Topic in Predictive Analytics in the context of Big Data', '&lt;p&gt;I''m going to start a Computer Science phd this year and for that I need a research topic. I am interested in Predictive Analytics in the context of Big Data. I am interested by the area of Education (MOOCs, Online courses...). In that field, what are the unexplored areas that can help me choose a strong topic? Thanks.&lt;/p&gt;
', 'machine-learning  bigdata  data-mining  statistics  predictive-modeling', NULL, '''/p'':87C ''analyt'':9A,52C ''area'':64C,77C ''big'':14A,57C ''bigdata'':19B ''choos'':82C ''comput'':34C ''context'':12A,55C ''cours'':69C ''data'':15A,21B,58C ''data-min'':20B ''educ'':66C ''field'':72C ''go'':30C ''help'':80C ''interest'':49C,61C ''learn'':18B ''look'':1A ''m'':29C ''machin'':17B ''machine-learn'':16B ''mine'':22B ''model'':26B ''mooc'':67C ''need'':43C ''onlin'':68C ''p'':27C ''phd'':5A,36C ''predict'':8A,25B,51C ''predictive-model'':24B ''research'':45C ''scienc'':35C ''start'':32C ''statist'':23B ''strong'':4A,84C ''thank'':86C ''topic'':6A,46C,85C ''unexplor'':76C ''year'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1166, 1, '2014-09-26 06:56:26.913', 1, 1241, 'A tool like Matlab for NLP?', '&lt;p&gt;Matlab is a great tool for some mathematical experiments, Neural Networks, Image Processing ... &lt;/p&gt;

&lt;p&gt;I would like to know if there is such a comprehensive and strong tool for data manipulation and NLP tasks? such as tokenization, POS tagging, parsing, training, testing .... &lt;/p&gt;

&lt;p&gt;However I am new to NLP and I need a tool which let me experiment, get familiar and progress&lt;/p&gt;
', 'data-mining  nlp', NULL, '''/p'':25C,55C,76C ''comprehens'':37C ''data'':8B,42C ''data-min'':7B ''experi'':20C,71C ''familiar'':73C ''get'':72C ''great'':15C ''howev'':57C ''imag'':23C ''know'':31C ''let'':69C ''like'':3A,29C ''manipul'':43C ''mathemat'':19C ''matlab'':4A,12C ''mine'':9B ''need'':65C ''network'':22C ''neural'':21C ''new'':60C ''nlp'':6A,10B,45C,62C ''p'':11C,26C,56C ''pars'':52C ''pos'':50C ''process'':24C ''progress'':75C ''strong'':39C ''tag'':51C ''task'':46C ''test'':54C ''token'':49C ''tool'':2A,16C,40C,67C ''train'':53C ''would'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1167, 2, '2014-09-26 07:47:45.6', 1, NULL, NULL, '&lt;p&gt;The correct model is binomial, both poisson and normal are just approximations. The binomial pdf is defined on the integers between zero and number of trials. The poisson is defined on the integers between 0 and infinity, and normal is on all real variables between +/- infinity. &lt;/p&gt;

&lt;p&gt;In other words: for a poisson there is a (possibly small) but non zero probability of having more clicks than impressions. For gaussian you can have even negative clicks. Of course, the particular parameters determine how big an impact this has... probably worth plotting the respective pdfs&lt;/p&gt;
', NULL, NULL, '''/p'':48C,97C ''0'':36C ''approxim'':13C ''big'':86C ''binomi'':6C,15C ''click'':68C,78C ''correct'':3C ''cours'':80C ''defin'':18C,31C ''determin'':84C ''even'':76C ''gaussian'':72C ''impact'':88C ''impress'':70C ''infin'':38C,47C ''integ'':21C,34C ''model'':4C ''negat'':77C ''non'':62C ''normal'':10C,40C ''number'':25C ''p'':1C,49C ''paramet'':83C ''particular'':82C ''pdf'':16C ''pdfs'':96C ''plot'':93C ''poisson'':8C,29C,55C ''possibl'':59C ''probabl'':64C,91C ''real'':44C ''respect'':95C ''small'':60C ''trial'':27C ''variabl'':45C ''word'':52C ''worth'':92C ''zero'':23C,63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6472, 1, '2015-07-15 21:06:00.36', 2, 139, 'IPython notebook shortcut to run', '&lt;p&gt;I am currious if there is a shortcut for runing the  selected cell in IPython notebook such as alt+F5 or ctrl+f5 or something simmiliar  that is present in Visual Studio (I don''t recall exactly now ) , cause I found it annoing to go with  the mouse on  the run  button every  time I need to run a cell?&lt;/p&gt;
', 'python', NULL, '''/p'':68C ''alt'':26C ''anno'':50C ''button'':59C ''caus'':46C ''cell'':20C,67C ''ctrl'':29C ''currious'':10C ''everi'':60C ''exact'':44C ''f5'':27C,30C ''found'':48C ''go'':52C ''ipython'':1A,22C ''mous'':55C ''need'':63C ''notebook'':2A,23C ''p'':7C ''present'':36C ''python'':6B ''recal'':43C ''run'':5A,58C,65C ''rune'':17C ''select'':19C ''shortcut'':3A,15C ''simmiliar'':33C ''someth'':32C ''studio'':39C ''time'':61C ''visual'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1172, 1, '2014-09-27 13:14:16.71', 1, 28, 'General programs/libraries for studying user search behavior?', '&lt;p&gt;Are there any general open-source programs or libraries (e.g., a Python library) for analyzing user search behavior?  By "search behavior", I mean a user''s interaction with a search engine, such as querying, clicking relevant results, and spending time on those results.  I''d like something with the following properties - it doesn''t have to be all of them, but the more the merrier:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Models individual user behavior (aggregate and time-based)&lt;/li&gt;
&lt;li&gt;Models group user behavior&lt;/li&gt;
&lt;li&gt;Simulates individual user behavior, given a model&lt;/li&gt;
&lt;li&gt;Is easily extensible (to accept data input formats, user models, document models, etc., that end-users define)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Links are a plus!&lt;/p&gt;
', 'machine-learning  data-mining  search  library', NULL, '''/li'':95C,101C,110C,130C ''/p'':83C,137C ''/ul'':131C ''accept'':116C ''aggreg'':90C ''analyz'':32C ''base'':94C ''behavior'':7A,35C,38C,89C,100C,106C ''click'':52C ''d'':62C ''data'':12B,117C ''data-min'':11B ''defin'':129C ''document'':122C ''doesn'':70C ''e.g'':27C ''easili'':113C ''end'':127C ''end-us'':126C ''engin'':48C ''etc'':124C ''extens'':114C ''follow'':67C ''format'':119C ''general'':1A,20C ''given'':107C ''group'':98C ''individu'':87C,104C ''input'':118C ''interact'':44C ''learn'':10B ''li'':85C,96C,102C,111C ''librari'':15B,26C,30C ''like'':63C ''link'':133C ''machin'':9B ''machine-learn'':8B ''mean'':40C ''merrier'':82C ''mine'':13B ''model'':86C,97C,109C,121C,123C ''open'':22C ''open-sourc'':21C ''p'':16C,132C ''plus'':136C ''program'':24C ''programs/libraries'':2A ''properti'':68C ''python'':29C ''queri'':51C ''relev'':53C ''result'':54C,60C ''search'':6A,14B,34C,37C,47C ''simul'':103C ''someth'':64C ''sourc'':23C ''spend'':56C ''studi'':4A ''time'':57C,93C ''time-bas'':92C ''ul'':84C ''user'':5A,33C,42C,88C,99C,105C,120C,128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1173, 1, '2014-09-27 21:58:42.477', 1, 42, 'Versatile data structure for combined statistics', '&lt;p&gt;Not sure if this is Math, Stats or Data Science, but I figured I would post it here to get the site used.&lt;/p&gt;

&lt;p&gt;As a programmer, when you have a system/component implemented, you might want to allow some performance monitoring. For example to query how often a function call was used, how long it took and so on. So typically you care about count, means/percentile, max/min and similiar statistics. This could be measurements since startup, but also a rolling average or window.&lt;/p&gt;

&lt;p&gt;I wonder if there is a good data structure which can be updated efficiently concurrently which can be used as the source for most of those queries. For example having a ringbuffer of rollup-metrics (count, sum, min, max) over increasing periods of time and a background aggregate process triggered regularly.&lt;/p&gt;

&lt;p&gt;The focus here (for me) is on in-memory data structures with limited memory consumption. (For other things I would use a RRD type of library).&lt;/p&gt;
', 'dataset  statistics  algorithms', NULL, '''/p'':34C,95C,149C,178C ''aggreg'':145C ''algorithm'':9B ''allow'':49C ''also'':89C ''averag'':92C ''background'':144C ''call'':61C ''care'':74C ''combin'':5A ''concurr'':111C ''consumpt'':166C ''could'':83C ''count'':76C,133C ''data'':2A,19C,104C,161C ''dataset'':7B ''effici'':110C ''exampl'':54C,125C ''figur'':23C ''focus'':152C ''function'':60C ''get'':30C ''good'':103C ''implement'':44C ''in-memori'':158C ''increas'':138C ''librari'':177C ''limit'':164C ''long'':65C ''math'':16C ''max'':136C ''max/min'':78C ''means/percentile'':77C ''measur'':85C ''memori'':160C,165C ''metric'':132C ''might'':46C ''min'':135C ''monitor'':52C ''often'':58C ''p'':10C,35C,96C,150C ''perform'':51C ''period'':139C ''post'':26C ''process'':146C ''programm'':38C ''queri'':56C,123C ''regular'':148C ''ringbuff'':128C ''roll'':91C ''rollup'':131C ''rollup-metr'':130C ''rrd'':174C ''scienc'':20C ''similiar'':80C ''sinc'':86C ''site'':32C ''sourc'':118C ''startup'':87C ''stat'':17C ''statist'':6A,8B,81C ''structur'':3A,105C,162C ''sum'':134C ''sure'':12C ''system/component'':43C ''thing'':169C ''time'':141C ''took'':67C ''trigger'':147C ''type'':175C ''typic'':72C ''updat'':109C ''use'':33C,63C,115C,172C ''versatil'':1A ''want'':47C ''window'':94C ''wonder'':98C ''would'':25C,171C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1174, 1, '2014-09-28 12:51:43.823', 1, 325, 'How to classify and cluster this time series data', '&lt;p&gt;I have post already the question few months ago about my project that I''m starting to work on. This post can be see here: 
&lt;a href="http://datascience.stackexchange.com/questions/211/human-activity-recognition-using-smartphone-data-set-problem"&gt;Human activity recognition using smartphone data set problem&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Now, I know this is based around multivariate time series analysis and tasks are to classify and cluster the data. I have gathered some materials (e-books, tutorials etc.) on this but still can''t see a more detailed picture of how even I should start. Here''s the tutorial that looks like it might be helpful but the thing is my data looks differently and I''m not really sure if this can be applied to my work.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://little-book-of-r-for-multivariate-analysis.readthedocs.org/en/latest/src/multivariateanalysis.html#scatterplots-of-the-principal-components" rel="nofollow"&gt;http://little-book-of-r-for-multivariate-analysis.readthedocs.org/en/latest/src/multivariateanalysis.html#scatterplots-of-the-principal-components&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;So basically, my questions are:&lt;/p&gt;

&lt;p&gt;How I can start on some very basic analysis? How to read data so it any meaning for me.
Any tips and advises will be much appreciated!
Note: I''m just the beginner in data science.  &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1175, 2, '2014-09-29 00:20:24.093', 1, NULL, NULL, '&lt;p&gt;Often different data samples have different weighting ( eg the costs of misclassification error  for one group of data  is higher than for other classes).
Most error metrics are of the form $\sum_i e_i$ where e_i is the loss ( eg squared error) on data point $i$. Therefore weightings of the form $\sum_i w_i e_i$ are equivalent to duplicating the data w_i times (eg for w_i integer). &lt;/p&gt;

&lt;p&gt;One simple case is if you have repeated data - rather than keeping all the duplicated data points, you just "weight" your one repeated sample by the number of instances.&lt;/p&gt;

&lt;p&gt;Now whilst this is easy to do in a batch setting, it is hard in vowpal wabbits online big data setting: given that you have a large data set, you do not just want to represent the data n times to deal with the weighting ( because it increases your computational load). Similarly, just multiplying the gradient vector by the weighting - which is correct in batch gradient descent - will cause big problems for stochastic/online gradient descent: essentially you shoot off in one direction ( think of large integer weights) then you shoot off in the other - causing significant instability. SGD essentially relies on all the errors to be of roughly the same order ( so that the learning rate can be set appropriately). So what they propose is to ensure that the update for training sample x_i with weight n is equivalent to presenting training sample x_i n times consecutively.&lt;/p&gt;

&lt;p&gt;The idea being that presenting it consecutively reduces the problem because the error gradient (for that single example $x_i$) reduces for each consecutive presentation and update (as you get closer &amp;amp; closer to the minimum for that specific example). In other words the consecutive updates provides a kind of feedback control. &lt;/p&gt;

&lt;p&gt;To me it sounds like you would still have instabilities (you get to zero error on x_i, then you get to zero error on x_i+1,...). the learning rate will need to be adjusted to take into account the size of the weights.&lt;/p&gt;
', NULL, NULL, '''+1'':340C ''/p'':75C,106C,257C,311C,358C ''account'':352C ''adjust'':348C ''amp'':290C ''appropri'':227C ''batch'':117C,172C ''big'':126C,177C ''case'':79C ''caus'':176C,202C ''class'':25C ''closer'':289C,291C ''comput'':157C ''consecut'':256C,265C,282C,303C ''control'':310C ''correct'':170C ''cost'':11C ''data'':4C,19C,47C,66C,85C,92C,127C,135C,145C ''deal'':149C ''descent'':174C,182C ''differ'':3C,7C ''direct'':189C ''duplic'':64C,91C ''e'':35C,38C,59C ''easi'':112C ''eg'':9C,43C,70C ''ensur'':234C ''equival'':62C,247C ''error'':14C,27C,45C,211C,271C,327C,336C ''essenti'':183C,206C ''exampl'':276C,298C ''feedback'':309C ''form'':32C,54C ''get'':288C,324C,333C ''given'':129C ''gradient'':163C,173C,181C,272C ''group'':17C ''hard'':121C ''higher'':21C ''idea'':260C ''increas'':155C ''instabl'':204C,322C ''instanc'':105C ''integ'':74C,193C ''keep'':88C ''kind'':307C ''larg'':134C,192C ''learn'':222C,342C ''like'':317C ''load'':158C ''loss'':42C ''metric'':28C ''minimum'':294C ''misclassif'':13C ''multipli'':161C ''n'':146C,245C,254C ''need'':345C ''number'':103C ''often'':2C ''one'':16C,77C,98C,188C ''onlin'':125C ''order'':218C ''p'':1C,76C,107C,258C,312C ''point'':48C,93C ''present'':249C,263C,283C ''problem'':178C,268C ''propos'':231C ''provid'':305C ''rate'':223C,343C ''rather'':86C ''reduc'':266C,279C ''reli'':207C ''repeat'':84C,99C ''repres'':143C ''rough'':215C ''sampl'':5C,100C,240C,251C ''set'':118C,128C,136C,226C ''sgd'':205C ''shoot'':185C,197C ''signific'':203C ''similar'':159C ''simpl'':78C ''singl'':275C ''size'':354C ''sound'':316C ''specif'':297C ''squar'':44C ''still'':320C ''stochastic/online'':180C ''sum'':33C,55C ''take'':350C ''therefor'':50C ''think'':190C ''time'':69C,147C,255C ''train'':239C,250C ''updat'':237C,285C,304C ''vector'':164C ''vowpal'':123C ''w'':57C,67C,72C ''wabbit'':124C ''want'':141C ''weight'':8C,51C,96C,152C,167C,194C,244C,357C ''whilst'':109C ''word'':301C ''would'':319C ''x'':241C,252C,277C,329C,338C ''zero'':326C,335C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1176, 2, '2014-09-29 00:28:26.413', 1, NULL, NULL, '&lt;p&gt;PCA is usually implemented by computing SVD on the covariance matrix.&lt;/p&gt;

&lt;p&gt;Computing the covariance matrix is an &lt;strong&gt;embarrassingly parallel&lt;/strong&gt; task, so it scales &lt;em&gt;linear&lt;/em&gt; with the number of records, and is trivial to distribute on multiple machines!&lt;/p&gt;

&lt;p&gt;Just do one pass over your data to compute the means. Then a second pass to compute the covariance matrix. This can be done with map-reduce easily - essentially it''s the same as computing the means again. Sum terms as in covariance are trivial to parallelize! You may only need to pay attention to numerics when summing a lot of values of similar magnitude.&lt;/p&gt;

&lt;p&gt;Things get different when you have a huge number of &lt;em&gt;variables&lt;/em&gt;. But on an 8 GB system, you should be able to run PCA on up to 20.000 dimensions in-memory with the BLAS libraries. But then you may run into the problem that PCA isn''t all that reliable anymore, because it has too many degrees of freedom. In other words: it overfits easily. I''ve seen the recommendation of having at least 10*d*d records (or was it d^3). So for 10000 dimensions, you should have at least a billion records (of 10000 dimensions... that is a lot!) for the result to be statistically reliable.&lt;/p&gt;
', NULL, NULL, '''/em'':31C,127C ''/p'':13C,45C,113C,227C ''/strong'':24C ''10'':192C ''10000'':203C,214C ''20.000'':144C ''3'':200C ''8'':131C ''abl'':137C ''anymor'':168C ''attent'':101C ''billion'':211C ''blas'':151C ''comput'':7C,15C,55C,63C,82C ''covari'':11C,17C,65C,90C ''d'':193C,194C,199C ''data'':53C ''degre'':174C ''differ'':117C ''dimens'':145C,204C,215C ''distribut'':41C ''done'':70C ''easili'':75C,182C ''em'':29C,125C ''embarrass'':22C ''essenti'':76C ''freedom'':176C ''gb'':132C ''get'':116C ''huge'':122C ''implement'':5C ''in-memori'':146C ''isn'':163C ''least'':191C,209C ''librari'':152C ''linear'':30C ''lot'':107C,219C ''machin'':44C ''magnitud'':112C ''mani'':173C ''map'':73C ''map-reduc'':72C ''matrix'':12C,18C,66C ''may'':96C,156C ''mean'':57C,84C ''memori'':148C ''multipl'':43C ''need'':98C ''number'':34C,123C ''numer'':103C ''one'':49C ''overfit'':181C ''p'':1C,14C,46C,114C ''parallel'':23C,94C ''pass'':50C,61C ''pay'':100C ''pca'':2C,140C,162C ''problem'':160C ''recommend'':187C ''record'':36C,195C,212C ''reduc'':74C ''reliabl'':167C,226C ''result'':222C ''run'':139C,157C ''scale'':28C ''second'':60C ''seen'':185C ''similar'':111C ''statist'':225C ''strong'':21C ''sum'':86C,105C ''svd'':8C ''system'':133C ''task'':25C ''term'':87C ''thing'':115C ''trivial'':39C,92C ''usual'':4C ''valu'':109C ''variabl'':126C ''ve'':184C ''word'':179C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1188, 2, '2014-10-01 14:44:35.467', 1, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;How I can start on some very basic analysis?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Take your labeled data and compute histograms of the values for each of the sets. Plot these and visually see if there''s any differences. Also compute the mean and variance of each of the different labeled sets and see if there are differences. &lt;/p&gt;

&lt;p&gt;If it''s timeseries data, take small (overlapping) windows of time and compute various metrics - min, max, variance, mean, for instance - and use that as input to a classifier.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':13C ''/p'':12C,59C,90C ''also'':40C ''analysi'':11C ''basic'':10C ''blockquot'':1C ''classifi'':89C ''comput'':20C,41C,73C ''data'':18C,65C ''differ'':39C,50C,58C ''histogram'':21C ''input'':86C ''instanc'':81C ''label'':17C,51C ''max'':77C ''mean'':43C,79C ''metric'':75C ''min'':76C ''overlap'':68C ''p'':2C,14C,60C ''plot'':30C ''see'':34C,54C ''set'':29C,52C ''small'':67C ''start'':6C ''take'':15C,66C ''time'':71C ''timeseri'':64C ''use'':83C ''valu'':24C ''varianc'':45C,78C ''various'':74C ''visual'':33C ''window'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1177, 1, '2014-09-29 14:52:48.13', 6, 79, 'Distance calculation/vector range significance', '&lt;p&gt;I''m trying to implement item based collaborative filtering. Do any distance calculations allow for weighting of certain ranges of values within each vector? &lt;/p&gt;

&lt;p&gt;For example, I would like to be able to say values 10..22 within each vector are more significant than values within the range 0..10. &lt;/p&gt;

&lt;p&gt;I''ve been experimenting with Pearson, Tanimoto and Euclidean algorithms, but they all seem to assume equal weighting for each value within the vector.&lt;/p&gt;

&lt;p&gt;Am I approaching this problem in the right way, and if not, how do others deal with this problem? &lt;/p&gt;
', 'machine-learning  classification  algorithms  beginner', NULL, '''/p'':36C,64C,90C,111C ''0'':62C ''10'':49C,63C ''22'':50C ''abl'':45C ''algorithm'':9B,75C ''allow'':25C ''approach'':94C ''assum'':81C ''base'':18C ''beginn'':10B ''calcul'':24C ''calculation/vector'':2A ''certain'':29C ''classif'':8B ''collabor'':19C ''deal'':107C ''distanc'':1A,23C ''equal'':82C ''euclidean'':74C ''exampl'':39C ''experi'':69C ''filter'':20C ''implement'':16C ''item'':17C ''learn'':7B ''like'':42C ''m'':13C ''machin'':6B ''machine-learn'':5B ''other'':106C ''p'':11C,37C,65C,91C ''pearson'':71C ''problem'':96C,110C ''rang'':3A,30C,61C ''right'':99C ''say'':47C ''seem'':79C ''signific'':4A,56C ''tanimoto'':72C ''tri'':14C ''valu'':32C,48C,58C,86C ''ve'':67C ''vector'':35C,53C,89C ''way'':100C ''weight'':27C,83C ''within'':33C,51C,59C,87C ''would'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1183, 1, '2014-09-30 19:53:20.163', 2, 25, 'Discovering dis-associations between periods of time-series', '&lt;p&gt;I''m interested in discovering some kind of dis-associations between the periods of a time series based on its data, e.g., find some (unknown number of) periods where the data is not similar with the data from another period.&lt;/p&gt;

&lt;p&gt;Also I would like to compare the same data but over 2 years (something like DTW?).&lt;/p&gt;

&lt;p&gt;I get my data Excel as a two-column list:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;c1=date (one per each day of the year), c2=Data To Analyze
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So, what algorithms could I use and in what software?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Update/Later edit:&lt;/strong&gt;
I''m looking for dates as cut-off points from which the DataToAnalyze could be part of another cluster of consecutive dates. For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;2014-1-1 --&amp;gt; 2014-3-10
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;are part of &lt;em&gt;Cluster_1&lt;/em&gt; based on DataToAnalyze. And:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;2014-3-11 --&amp;gt; 2014-5-2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;are part of &lt;em&gt;Cluster_2&lt;/em&gt; based on DataToAnalyze, and so on. So, clusters of consecutive dates should be automatically determined based on some algorithms, which is what I''m looking for. Which ones (or which software) would be applicable to this problem?&lt;/p&gt;
', 'clustering  time-series', NULL, '''-1'':152C,153C ''-10'':157C ''-11'':177C ''-2'':181C ''-3'':156C,176C ''-5'':180C ''/code'':104C,158C,182C ''/em'':167C,191C ''/p'':57C,75C,88C,117C,148C,172C,229C ''/pre'':105C,159C,183C ''/strong'':122C ''1'':166C ''2'':70C,190C ''2014'':151C,155C,175C,179C ''algorithm'':109C,210C ''also'':59C ''analyz'':103C ''anoth'':55C,141C ''applic'':225C ''associ'':4A,26C ''automat'':205C ''base'':34C,168C,192C,207C ''c1'':91C ''c2'':100C ''cluster'':11B,142C,165C,189C,199C ''code'':90C,150C,174C ''column'':86C ''compar'':64C ''consecut'':144C,201C ''could'':110C,137C ''cut'':130C ''cut-off'':129C ''data'':37C,47C,53C,67C,80C,101C ''datatoanalyz'':136C,170C,194C ''date'':92C,127C,145C,202C ''day'':96C ''determin'':206C ''dis'':3A,25C ''dis-associ'':2A,24C ''discov'':1A,20C ''dtw'':74C ''e.g'':38C ''edit'':121C ''em'':164C,188C ''exampl'':147C ''excel'':81C ''find'':39C ''get'':78C ''gt'':154C,178C ''interest'':18C ''kind'':22C ''like'':62C,73C ''list'':87C ''look'':125C,216C ''m'':17C,124C,215C ''number'':42C ''one'':93C,219C ''p'':15C,58C,76C,106C,118C,160C,184C ''part'':139C,162C,186C ''per'':94C ''period'':6A,29C,44C,56C ''point'':132C ''pre'':89C,149C,173C ''problem'':228C ''seri'':10A,14B,33C ''similar'':50C ''softwar'':116C,222C ''someth'':72C ''strong'':119C ''time'':9A,13B,32C ''time-seri'':8A,12B ''two'':85C ''two-column'':84C ''unknown'':41C ''update/later'':120C ''use'':112C ''would'':61C,223C ''year'':71C,99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1184, 2, '2014-09-30 20:12:32.87', 0, NULL, NULL, '&lt;p&gt;I would reccomend python if you lazily evaluate the file you will have a miniscule memory footprint, and numpy/scipy give you access to all of the tools Octave/Matlab would. &lt;/p&gt;
', NULL, NULL, '''/p'':31C ''access'':23C ''evalu'':9C ''file'':11C ''footprint'':18C ''give'':21C ''lazili'':8C ''memori'':17C ''miniscul'':16C ''numpy/scipy'':20C ''octave/matlab'':29C ''p'':1C ''python'':5C ''reccomend'':4C ''tool'':28C ''would'':3C,30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1178, 2, '2014-09-29 17:46:58.617', 2, NULL, NULL, '&lt;p&gt;The kinds of tools you will use will vary based on the problem you are trying to solve. Social media data is rich and therefore many questions can be asked - and many tools can be used.&lt;/p&gt;

&lt;p&gt;However, there is a general pattern you might keep in mind. Typically, you will have to use the platform''s API to gather data. You will then have to normalized and store the data in a data warehouse. Finally, you will access and analyze the data with the tools you desire. Keeping the end goal in mind, you will have to strategically choose the best technologies for the job you are doing.&lt;/p&gt;

&lt;p&gt;For example, let''s say you wanted to study the relationships between users on a social network - a question like "Who are the mutual friends of the most popular individuals in this social network?" In this case, you would gather data using the social media platform''s API, normalize it into a CSV, import it into a Neo4j database, and then use Cypher to make queries. &lt;/p&gt;
', NULL, NULL, '''/p'':38C,112C,180C ''access'':81C ''analyz'':83C ''api'':60C,161C ''ask'':31C ''base'':11C ''best'':104C ''case'':150C ''choos'':102C ''csv'':166C ''cypher'':176C ''data'':22C,63C,73C,76C,85C,154C ''databas'':172C ''desir'':90C ''end'':93C ''exampl'':115C ''final'':78C ''friend'':138C ''gather'':62C,153C ''general'':44C ''goal'':94C ''howev'':40C ''import'':167C ''individu'':143C ''job'':108C ''keep'':48C,91C ''kind'':3C ''let'':116C ''like'':133C ''make'':178C ''mani'':27C,33C ''media'':21C,158C ''might'':47C ''mind'':50C,96C ''mutual'':137C ''neo4j'':171C ''network'':130C,147C ''normal'':69C,162C ''p'':1C,39C,113C ''pattern'':45C ''platform'':58C,159C ''popular'':142C ''problem'':14C ''queri'':179C ''question'':28C,132C ''relationship'':124C ''rich'':24C ''say'':118C ''social'':20C,129C,146C,157C ''solv'':19C ''store'':71C ''strateg'':101C ''studi'':122C ''technolog'':105C ''therefor'':26C ''tool'':5C,34C,88C ''tri'':17C ''typic'':51C ''use'':8C,37C,56C,155C,175C ''user'':126C ''vari'':10C ''want'':120C ''warehous'':77C ''would'':152C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2382, 1, '2014-10-31 12:51:13.363', 1, 85, 'Data set: Time series forecasting', '&lt;p&gt;I need data set for energy forecasting for my project. Currently I am using east/west wind energy dataset from &lt;a href="http://www.nrel.gov/" rel="nofollow"&gt;http://www.nrel.gov/&lt;/a&gt;. I need one more data set for solar energy. Please let me know where I can find one for free.&lt;/p&gt;
', 'dataset  energy', NULL, '''/&lt;/a&gt;.'':35C ''/p'':56C ''current'':19C ''data'':1A,11C,40C ''dataset'':6B,26C ''east/west'':23C ''energi'':7B,14C,25C,44C ''find'':52C ''forecast'':5A,15C ''free'':55C ''href'':29C ''know'':48C ''let'':46C ''need'':10C,37C ''nofollow'':32C ''one'':38C,53C ''p'':8C ''pleas'':45C ''project'':18C ''rel'':31C ''seri'':4A ''set'':2A,12C,41C ''solar'':43C ''time'':3A ''use'':22C ''wind'':24C ''www.nrel.gov'':30C,34C ''www.nrel.gov/&lt;/a&gt;.'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1179, 2, '2014-09-29 20:31:49.387', 1, NULL, NULL, '&lt;p&gt;I created a scoring system ("Thomas Scoring System") to deal with this problem.  If you treat "distance" as a similarity score, this system should work for you. &lt;a href="http://exploringpossibilityspace.blogspot.com/2014/02/thomas-scoring-system.html" rel="nofollow"&gt;http://exploringpossibilityspace.blogspot.com/2014/02/thomas-scoring-system.html&lt;/a&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Here''s Thomas Scoring System (TSS) in a nutshell: Treat each metric and their values as evidence that weigh for or against specific values in the performance index. This is an inference process and not an arithmetic calculation, as in the Usual Method. The output of TSS is an estimation of the Weight of Evidence for all index values.  The Weight of Evidence can be expressed as a probability distribution, where all the evidence weights sum to one.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;To use your example, each range is a condition: A) 10 &amp;lt;= &lt;em&gt;x&lt;/em&gt; &amp;lt;= 22; and B) &lt;em&gt;x&lt;/em&gt; &amp;lt; 10. If A is true, then it weighs in favor of certain similarity (distance) scores.  If B is true, then it weights in favor of other similarity (distance) scores, but would have less weight compared to other evidence.&lt;/p&gt;
', NULL, NULL, '''/2014/02/thomas-scoring-system.html'':33C ''/2014/02/thomas-scoring-system.html&lt;/a&gt;&lt;/p&gt;'':38C ''/blockquote'':120C ''/em'':136C,143C ''/p'':119C,183C ''10'':132C,145C ''22'':138C ''arithmet'':77C ''b'':140C,161C ''blockquot'':39C ''calcul'':78C ''certain'':156C ''compar'':179C ''condit'':130C ''creat'':3C ''deal'':11C ''distanc'':18C,158C,172C ''distribut'':110C ''em'':134C,141C ''estim'':90C ''evid'':57C,95C,103C,114C,182C ''exampl'':125C ''exploringpossibilityspace.blogspot.com'':32C,37C ''exploringpossibilityspace.blogspot.com/2014/02/thomas-scoring-system.html'':31C ''exploringpossibilityspace.blogspot.com/2014/02/thomas-scoring-system.html&lt;/a&gt;&lt;/p&gt;'':36C ''express'':106C ''favor'':154C,168C ''href'':30C ''index'':68C,98C ''infer'':72C ''less'':177C ''lt'':133C,137C,144C ''method'':83C ''metric'':52C ''nofollow'':35C ''nutshel'':49C ''one'':118C ''output'':85C ''p'':1C,40C,121C ''perform'':67C ''probabl'':109C ''problem'':14C ''process'':73C ''rang'':127C ''rel'':34C ''score'':5C,8C,22C,44C,159C,173C ''similar'':21C,157C,171C ''specif'':63C ''sum'':116C ''system'':6C,9C,24C,45C ''thoma'':7C,43C ''treat'':17C,50C ''true'':149C,163C ''tss'':46C,87C ''use'':123C ''usual'':82C ''valu'':55C,64C,99C ''weigh'':59C,152C ''weight'':93C,101C,115C,166C,178C ''work'':26C ''would'':175C ''x'':135C,142C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1180, 2, '2014-09-29 22:45:38.623', 1, NULL, NULL, '&lt;p&gt;You mention distance metrics but Pearson and Tanimoto are not. For Euclidean distance, simply scale the dimensions in question by some factor. For example doubling a dimension''s values makes its contribution to distance larger and so makes the feature more important to the distance metric. &lt;/p&gt;

&lt;p&gt;On a related note you may wish to look at the Mahalanobis distance which kind of accounts for covariance or ''redundancy'' across dimensions. &lt;/p&gt;
', NULL, NULL, '''/p'':48C,73C ''account'':66C ''across'':71C ''contribut'':33C ''covari'':68C ''dimens'':18C,28C,72C ''distanc'':4C,14C,35C,46C,62C ''doubl'':26C ''euclidean'':13C ''exampl'':25C ''factor'':23C ''featur'':41C ''import'':43C ''kind'':64C ''larger'':36C ''look'':58C ''mahalanobi'':61C ''make'':31C,39C ''may'':55C ''mention'':3C ''metric'':5C,47C ''note'':53C ''p'':1C,49C ''pearson'':7C ''question'':20C ''redund'':70C ''relat'':52C ''scale'':16C ''simpli'':15C ''tanimoto'':9C ''valu'':30C ''wish'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1181, 1, '2014-09-30 09:43:24.153', 1, 93, 'document clustering by semantic similarity based EMD(earth mover distance)', '&lt;p&gt;I want the text-based semantic clustering EMD do.
Is there a better way of using LDA to detect topics in text, there are so provide better results?
I''m going to do my EMD on discovery topics.
Thanks&lt;/p&gt;
', 'text-mining  clustering', NULL, '''/p'':56C ''base'':6A,21C ''better'':29C,43C ''cluster'':2A,14B,23C ''detect'':35C ''discoveri'':53C ''distanc'':10A ''document'':1A ''earth'':8A ''emd'':7A,24C,51C ''go'':47C ''lda'':33C ''m'':46C ''mine'':13B ''mover'':9A ''p'':15C ''provid'':42C ''result'':44C ''semant'':4A,22C ''similar'':5A ''text'':12B,20C,38C ''text-bas'':19C ''text-min'':11B ''thank'':55C ''topic'':36C,54C ''use'':32C ''want'':17C ''way'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1192, 1, '2014-10-02 06:18:11.397', 3, 19, 'Amalgamating multiple datasets with different variables coding', '&lt;p&gt;I have several datasets with thousands of variables. This different datasets have different variables for the same thing. Is there a way to automatically/semi-automatically check compatible variables and make them consistent?&lt;/p&gt;

&lt;p&gt;If there is such thing, that would save me months of tedious work. The data is stored in SPSS format.&lt;/p&gt;
', 'dataset  data-cleaning', NULL, '''/p'':44C,66C ''amalgam'':1A ''automatically/semi-automatically'':36C ''check'':37C ''clean'':11B ''code'':7A ''compat'':38C ''consist'':43C ''data'':10B,60C ''data-clean'':9B ''dataset'':3A,8B,16C,23C ''differ'':5A,22C,25C ''format'':65C ''make'':41C ''month'':55C ''multipl'':2A ''p'':12C,45C ''save'':53C ''sever'':15C ''spss'':64C ''store'':62C ''tedious'':57C ''thing'':30C,50C ''thousand'':18C ''variabl'':6A,20C,26C,39C ''way'':34C ''work'':58C ''would'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1193, 2, '2014-10-02 08:17:03.253', 2, NULL, NULL, '&lt;p&gt;The only reliable way to see how long it takes is to code it up and give it a shot.  Training will take more time, then you can save your model (pickle) to use later.&lt;/p&gt;
', NULL, NULL, '''/p'':37C ''code'':14C ''give'':18C ''later'':36C ''long'':9C ''model'':32C ''p'':1C ''pickl'':33C ''reliabl'':4C ''save'':30C ''see'':7C ''shot'':21C ''take'':11C,24C ''time'':26C ''train'':22C ''use'':35C ''way'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1182, 2, '2014-09-30 14:29:06.307', 1, NULL, NULL, '&lt;p&gt;It sounds like you would like the &lt;a href="http://www.boost.org/doc/libs/1_56_0/doc/html/accumulators.html" rel="nofollow"&gt;Boost Accumulators&lt;/a&gt; library:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Boost.Accumulators is both a library for incremental statistical
  computation as well as an extensible framework for incremental
  calculation in general. The library deals primarily with the concept
  of an accumulator, which is a primitive computational entity that
  accepts data one sample at a time and maintains some internal state.
  These accumulators may offload some of their computations on other
  accumulators, on which they depend. Accumulators are grouped within an
  accumulator set. Boost.Accumulators resolves the inter-dependencies
  between accumulators in a set and ensures that accumulators are
  processed in the proper order.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/a'':18C ''/blockquote'':116C ''/doc/libs/1_56_0/doc/html/accumulators.html'':13C ''/p'':20C,115C ''accept'':60C ''accumul'':17C,52C,73C,82C,87C,92C,101C,108C ''blockquot'':21C ''boost'':16C ''boost.accumulators'':23C,94C ''calcul'':40C ''comput'':31C,57C,79C ''concept'':49C ''data'':61C ''deal'':45C ''depend'':86C,99C ''ensur'':106C ''entiti'':58C ''extens'':36C ''framework'':37C ''general'':42C ''group'':89C ''href'':10C ''increment'':29C,39C ''inter'':98C ''inter-depend'':97C ''intern'':70C ''librari'':19C,27C,44C ''like'':4C,7C ''maintain'':68C ''may'':74C ''nofollow'':15C ''offload'':75C ''one'':62C ''order'':114C ''p'':1C,22C ''primarili'':46C ''primit'':56C ''process'':110C ''proper'':113C ''rel'':14C ''resolv'':95C ''sampl'':63C ''set'':93C,104C ''sound'':3C ''state'':71C ''statist'':30C ''time'':66C ''well'':33C ''within'':90C ''would'':6C ''www.boost.org'':12C ''www.boost.org/doc/libs/1_56_0/doc/html/accumulators.html'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1185, 1, '2014-10-01 10:32:32.96', 2, 50, 'What are some best papers on gradient descent for NN implementation?', '&lt;p&gt;I''m trying to implement GD for standard task of NN training :) The best papers for practioneer I''ve founded so far are:&lt;/p&gt;

&lt;p&gt;1) "Efficient BackProp" by Yann LeCun et al.&lt;/p&gt;

&lt;p&gt;2) "Stochastic Gradient Descent Tricks" by Leon Bottou&lt;/p&gt;

&lt;p&gt;Are there some other must read papers on this topic?&lt;/p&gt;

&lt;p&gt;Thank you!&lt;/p&gt;
', 'neuralnetwork  gradient-descent', NULL, '''/p'':40C,50C,60C,72C,76C ''1'':42C ''2'':52C ''al'':49C ''backprop'':44C ''best'':4A,30C ''bottou'':59C ''descent'':8A,15B,55C ''effici'':43C ''et'':48C ''far'':38C ''found'':36C ''gd'':22C ''gradient'':7A,14B,54C ''gradient-desc'':13B ''implement'':11A,21C ''lecun'':47C ''leon'':58C ''m'':18C ''must'':66C ''neuralnetwork'':12B ''nn'':10A,27C ''p'':16C,41C,51C,61C,73C ''paper'':5A,31C,68C ''praction'':33C ''read'':67C ''standard'':24C ''stochast'':53C ''task'':25C ''thank'':74C ''topic'':71C ''train'':28C ''tri'':19C ''trick'':56C ''ve'':35C ''yann'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1199, 2, '2014-10-03 11:22:55.273', 1, NULL, NULL, '&lt;p&gt;I solved a similar problem not so long ago. Let X be a numerical variable that has missing values. First, we assign the value 0 to those instances in which the value is missing. Then, we add a new categorical variable to our model, X_missing, which domain is {True,False}. We obtain a data model with mixed numerical/categorical variables. You can then apply gradient descent to train a regression model from these variables. &lt;/p&gt;
', NULL, NULL, '''/p'':76C ''0'':26C ''add'':38C ''ago'':10C ''appli'':65C ''assign'':23C ''categor'':41C ''data'':56C ''descent'':67C ''domain'':49C ''fals'':52C ''first'':21C ''gradient'':66C ''instanc'':29C ''let'':11C ''long'':9C ''miss'':19C,35C,47C ''mix'':59C ''model'':45C,57C,72C ''new'':40C ''numer'':15C ''numerical/categorical'':60C ''obtain'':54C ''p'':1C ''problem'':6C ''regress'':71C ''similar'':5C ''solv'':3C ''train'':69C ''true'':51C ''valu'':20C,25C,33C ''variabl'':16C,42C,61C,75C ''x'':12C,46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1186, 1, '2014-10-01 13:26:52.037', 8, 192, 'How much time do scikit classifiers take to classify?', '&lt;p&gt;I am planning to use scikit linear support vector machine (SVM) classifier for text classification on a corpus consisting of 1 million labeled documents. What I am planning to do is, when a user enters some keyword, the classifier will first classify it in a category, and then a subsequent information retrieval query will happen in within the documents of that category catagory. I have a few questions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;How do I confirm that classification will not take much time? I don''t want users to have to spend time waiting for a classification to finish in order to get better results.&lt;/li&gt;
&lt;li&gt;Is using Python''s scikit library for websites/web applications suitable for this?&lt;/li&gt;
&lt;li&gt;Does anyone know how amazon or flipkart perform classification on user queries, or do they use a completely different logic?&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  classification  python  scikit', NULL, '''/li'':121C,135C,157C ''/ol'':158C ''/p'':85C ''1'':37C ''amazon'':141C ''anyon'':138C ''applic'':131C ''better'':119C ''catagori'':79C ''categori'':62C,78C ''classif'':13B,31C,93C,112C,145C ''classifi'':6A,9A,28C,55C,58C ''complet'':154C ''confirm'':91C ''consist'':35C ''corpus'':34C ''differ'':155C ''document'':40C,75C ''enter'':51C ''finish'':114C ''first'':57C ''flipkart'':143C ''get'':118C ''happen'':71C ''inform'':67C ''keyword'':53C ''know'':139C ''label'':39C ''learn'':12B ''li'':87C,122C,136C ''librari'':128C ''linear'':23C ''logic'':156C ''machin'':11B,26C ''machine-learn'':10B ''million'':38C ''much'':2A,97C ''ol'':86C ''order'':116C ''p'':16C ''perform'':144C ''plan'':19C,44C ''python'':14B,125C ''queri'':69C,148C ''question'':84C ''result'':120C ''retriev'':68C ''scikit'':5A,15B,22C,127C ''spend'':107C ''subsequ'':66C ''suitabl'':132C ''support'':24C ''svm'':27C ''take'':7A,96C ''text'':30C ''time'':3A,98C,108C ''use'':21C,124C,152C ''user'':50C,103C,147C ''vector'':25C ''wait'':109C ''want'':102C ''websites/web'':130C ''within'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1187, 1, '2014-10-01 14:03:05.87', 1, 19, 'Which method works best for attribution models for the objectives?', '&lt;p&gt;I have a client that is managing several campaigns. However I''m not clear what percentage should be applied to each channel that bring traffic to my website, when assessing their participation in the objectives. &lt;/p&gt;

&lt;p&gt;For those interested, here I leave the link to my profile on linkedin. &lt;a href="https://www.linkedin.com/pub/mario-mu%C3%B1oz-ahumada/52/287/28b" rel="nofollow"&gt;Specialist online markegin in BogotÃ¡&lt;/a&gt;.&lt;/p&gt;
', 'statistics', NULL, '''/a'':75C ''/p'':48C,76C ''/pub/mario-mu%c3%b1oz-ahumada/52/287/28b'':67C ''appli'':31C ''assess'':42C ''attribut'':6A ''best'':4A ''bogotã'':74C ''bring'':36C ''campaign'':21C ''channel'':34C ''clear'':26C ''client'':16C ''howev'':22C ''href'':64C ''interest'':52C ''leav'':55C ''link'':57C ''linkedin'':62C ''m'':24C ''manag'':19C ''markegin'':72C ''method'':2A ''model'':7A ''nofollow'':69C ''object'':10A,47C ''onlin'':71C ''p'':12C,49C ''particip'':44C ''percentag'':28C ''profil'':60C ''rel'':68C ''sever'':20C ''specialist'':70C ''statist'':11B ''traffic'':37C ''websit'':40C ''work'':3A ''www.linkedin.com'':66C ''www.linkedin.com/pub/mario-mu%c3%b1oz-ahumada/52/287/28b'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1189, 1, '2014-10-01 18:11:26.22', 2, 84, 'How to define confusion matrix for classification?', '&lt;p&gt;Below is the dataset where the response variable is play with two labels (yes, and no), &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;No. outlook temperature humidity    windy   play
1   sunny       hot     high        FALSE   no
2   sunny       hot     high        TRUE    no
3   overcast    hot     high        FALSE   yes
4   rainy       mild    high        FALSE   yes
5   rainy       cool    normal      FALSE   yes
6   rainy       cool    normal      TRUE    no
7   overcast    cool    normal      TRUE    yes
8   sunny       mild    high        FALSE   no
9   sunny       cool    normal      FALSE   yes
10  rainy       mild    normal      FALSE   yes
11  sunny       mild    normal      TRUE    yes
12  overcast    mild    high        TRUE    yes
13  overcast    hot     normal      FALSE   yes
14  rainy       mild    high        TRUE    no
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here are the decisions with their respective classifications: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1: (outlook,overcast) -&amp;gt; (play,yes) 
[Support=0.29 , Confidence=1.00 , Correctly Classify= 3, 7, 12, 13]

2: (humidity,normal), (windy,FALSE) -&amp;gt; (play,yes)
[Support=0.29 , Confidence=1.00 , Correctly Classify= 5, 9, 10]

3: (outlook,sunny), (humidity,high) -&amp;gt; (play,no) 
[Support=0.21 , Confidence=1.00 , Correctly Classify= 1, 2, 8]

4: (outlook,rainy), (windy,FALSE) -&amp;gt; (play,yes) 
[Support=0.21 , Confidence=1.00 , Correctly Classify= 4]

5: (outlook,sunny), (humidity,normal) -&amp;gt; (play,yes) 
[Support=0.14 , Confidence=1.00 , Correctly Classify= 11]

6: (outlook,rainy), (windy,TRUE) -&amp;gt; (play,no) 
[Support=0.14 , Confidence=1.00 , Correctly Classify= 6, 14]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Thanks. &lt;/p&gt;
', 'confusion-matrix', NULL, '''/code'':121C,231C ''/p'':28C,132C,235C ''/pre'':122C,232C ''0.14'':209C,224C ''0.21'':177C,194C ''0.29'':142C,160C ''1'':37C,135C,182C ''1.00'':144C,162C,179C,196C,211C,226C ''10'':91C,167C ''11'':97C,214C ''12'':103C,149C ''13'':109C,150C ''14'':115C,230C ''2'':43C,151C,183C ''3'':49C,147C,168C ''4'':55C,185C,199C ''5'':61C,165C,200C ''6'':67C,215C,229C ''7'':73C,148C ''8'':79C,184C ''9'':85C,166C ''classif'':7A,131C ''classifi'':146C,164C,181C,198C,213C,228C ''code'':30C,134C ''confid'':143C,161C,178C,195C,210C,225C ''confus'':4A,9B ''confusion-matrix'':8B ''cool'':63C,69C,75C,87C ''correct'':145C,163C,180C,197C,212C,227C ''dataset'':15C ''decis'':127C ''defin'':3A ''fals'':41C,53C,59C,65C,83C,89C,95C,113C,155C,189C ''gt'':138C,156C,173C,190C,205C,220C ''high'':40C,46C,52C,58C,82C,106C,118C,172C ''hot'':39C,45C,51C,111C ''humid'':34C,152C,171C,203C ''label'':24C ''matrix'':5A,10B ''mild'':57C,81C,93C,99C,105C,117C ''normal'':64C,70C,76C,88C,94C,100C,112C,153C,204C ''outlook'':32C,136C,169C,186C,201C,216C ''overcast'':50C,74C,104C,110C,137C ''p'':11C,123C,233C ''play'':21C,36C,139C,157C,174C,191C,206C,221C ''pre'':29C,133C ''raini'':56C,62C,68C,92C,116C,187C,217C ''respect'':130C ''respons'':18C ''sunni'':38C,44C,80C,86C,98C,170C,202C ''support'':141C,159C,176C,193C,208C,223C ''temperatur'':33C ''thank'':234C ''true'':47C,71C,77C,101C,107C,119C,219C ''two'':23C ''variabl'':19C ''windi'':35C,154C,188C,218C ''yes'':25C,54C,60C,66C,78C,90C,96C,102C,108C,114C,140C,158C,192C,207C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6473, 2, '2015-07-16 03:12:41.317', 0, NULL, NULL, '&lt;p&gt;IPython notebook Keyboard shortcuts&lt;img src="http://i.stack.imgur.com/VGcXs.png" alt="IPython notebook"&gt;&lt;/p&gt;

&lt;p&gt;![Option the options [2:4] i think is the answer to your question.] &lt;/p&gt;
', NULL, NULL, '''/p'':14C,29C ''/vgcxs.png'':10C ''2'':19C ''4'':20C ''alt'':11C ''answer'':25C ''i.stack.imgur.com'':9C ''i.stack.imgur.com/vgcxs.png'':8C ''img'':6C ''ipython'':2C,12C ''keyboard'':4C ''notebook'':3C,13C ''option'':16C,18C ''p'':1C,15C ''question'':28C ''shortcut'':5C ''src'':7C ''think'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1206, 2, '2014-10-04 15:52:44.37', 1, NULL, NULL, '&lt;p&gt;Suppose your test object is &lt;code&gt;(sunny, hot, normal, TRUE)&lt;/code&gt;. Look through the rules top to bottom and see if any of the conditions are matched. The first rule for example tests the &lt;code&gt;outlook&lt;/code&gt; feature. The value doesn''t match, so the rule isn''t matched. Move on to the next rule. And so on. In this case, rule 5 matches the test case and the classification for the p lay variable is "yes".&lt;/p&gt;

&lt;p&gt;More generally, for any test case, look at the values its features take and find the first rule that those values satisfy. The implication of that rule will be its classification.&lt;/p&gt;
', NULL, NULL, '''/code'':12C,38C ''/p'':79C,112C ''5'':64C ''bottom'':19C ''case'':62C,68C,86C ''classif'':71C,111C ''code'':7C,36C ''condit'':26C ''doesn'':42C ''exampl'':33C ''featur'':39C,92C ''find'':95C ''first'':30C,97C ''general'':82C ''hot'':9C ''implic'':104C ''isn'':48C ''lay'':75C ''look'':13C,87C ''match'':28C,44C,50C,65C ''move'':51C ''next'':55C ''normal'':10C ''object'':5C ''outlook'':37C ''p'':1C,74C,80C ''rule'':16C,31C,47C,56C,63C,98C,107C ''satisfi'':102C ''see'':21C ''sunni'':8C ''suppos'':2C ''take'':93C ''test'':4C,34C,67C,85C ''top'':17C ''true'':11C ''valu'':41C,90C,101C ''variabl'':76C ''yes'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1208, 1, '2014-10-04 19:49:53.543', 3, 41, 'What circumstances causes two different classifiers to classify data exactly like one another', '&lt;p&gt;Okay, here is the background:
I am doing text mining, and my basic flow is like this:
extract feature (n-gram), reduce feature count, score (tf-idf) and classify. for my own sake i am doing comparison between SVM and neural network classifiers. here is the weird part (or am i wrong and this is reasonable?), if i use 2gram the classifiers'' result (accuracy/precision) is different and the SVM is the better one; but when i use 3-gram the results are exactly the same. what causes this? is there any explanation? is it the case of very separable classes?&lt;/p&gt;
', 'text-mining  neuralnetwork  svm', NULL, '''/p'':122C ''2gram'':81C ''3'':99C ''accuracy/precision'':85C ''anoth'':13A ''background'':24C ''basic'':32C ''better'':93C ''case'':117C ''caus'':3A,108C ''circumst'':2A ''class'':121C ''classifi'':6A,8A,50C,64C,83C ''comparison'':58C ''count'':44C ''data'':9A ''differ'':5A,87C ''exact'':10A,104C ''explan'':113C ''extract'':37C ''featur'':38C,43C ''flow'':33C ''gram'':41C,100C ''idf'':48C ''like'':11A,35C ''mine'':16B,29C ''n'':40C ''n-gram'':39C ''network'':63C ''neural'':62C ''neuralnetwork'':17B ''okay'':20C ''one'':12A,94C ''p'':19C ''part'':69C ''reason'':77C ''reduc'':42C ''result'':84C,102C ''sake'':54C ''score'':45C ''separ'':120C ''svm'':18B,60C,90C ''text'':15B,28C ''text-min'':14B ''tf'':47C ''tf-idf'':46C ''two'':4A ''use'':80C,98C ''weird'':68C ''wrong'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1190, 1, '2014-10-01 19:51:21.22', 4, 201, 'Type of regression with nominal, ordinal, interval and ratio data', '&lt;p&gt;Statement of problem: An ambulance is at the hospital dropping off a patient. The goal of the paramedic is to get released from the hospital as soon as possible. I am curious, what are the factors in how long an ambulance off loads a patient at the hospital? Can I predict how long an offload will take given certain variables. And how confident can I be in this model? The Dependent Variable is HospitalTime, it is a ratio type of data and is measured in seconds. The Independent Variables are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Hospital, a nominal type of data recoded into integers, 1 would stand
for Lee Memorial.&lt;/li&gt;
&lt;li&gt;Ambulance, a nominal type of data recoded into integers, 9 would
stand for ambulance #9&lt;/li&gt;
&lt;li&gt;PatientPriority is an ordinal type of data recoded into integers. A 1
is a high priority, 2 is a medium priority and 3 is low acuity.&lt;/li&gt;
&lt;li&gt;MonthOfCall is an interval type of data recoded into integers. A 6
would be June and 12 is December. A 12 (December) is not twice as
much as a 6 (June) in this case.&lt;/li&gt;
&lt;li&gt;HourOfCall is an interval type of data recoded into integers. Once
again, an offload happening at 10:00 pm is not more than something
happening at 10:00 am.&lt;/li&gt;
&lt;li&gt;Officer1 and Officer2 are nominal data and are integers representing
an EMT and a paramedic.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;My question is this: Given this type of data and my goal to predict the off loading time at the hospital, what kind of regression model should I look into?&lt;/p&gt;

&lt;p&gt;I have looked at my statistics books from university days and they are all using ratio data. My data is mixed with nominal, ordinal, interval and ratio.&lt;/p&gt;

&lt;p&gt;I have as much data as you could ask for. I have at least 100,000 observations.&lt;/p&gt;

&lt;p&gt;Can you please push me in the right direction? What kind of model should I use with this type of data?&lt;/p&gt;

&lt;p&gt;Shown below are observations to give you a tiny peek at my data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;IncidentID,HospitalTime,Hospital,Ambulance,PatientPriority,MonthOfCall,HourOfCall,Officer1,Officer2
757620,1849,7,11,2,10,10,234,771,chr(10) 802611,2625,7,11,3,1,18,234,777,chr(10) 
765597,1149,7,12,3,11,2,234,777,chr(10) 770926,1785,7,12,3,11,15,234,777,chr(10) 
771689,3557,7,12,2,11,14,234,777,chr(10) 822758,1073,7,20,3,3,13,777,307,chr(10) 
767249,2570,7,22,2,11,11,560,778,chr(10) 767326,1998,7,22,1,11,18,560,777,chr(10) 
785903,1660,7,22,3,12,12,234,777,chr(10) 787644,2852,7,22,3,12,17,234,777,chr(10) 
760294,1327,7,23,2,10,14,498,735,chr(10) 994677,3653,7,32,2,2,15,181,159,chr(10) 
994677,3653,7,32,2,2,15,181,159,chr(10) 788471,2053,5,9,2,1,3,498,777,chr(10) 
788471,2053,5,9,2,1,3,498,777,chr(10) 759983,1342,5,11,2,10,8,474,777,chr(10)
791243,1635,5,11,2,1,18,234,777,chr(10) 800796,1381,5,11,3,1,11,234,777,chr(10)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;P.S. This question is cross-posted in Stack-Overflow under the same title and author.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1197, 1, '2014-10-02 23:15:27.05', 6, 352, 'How can I classify text considering word order, instead of just using a bag-of-words approach?', '&lt;p&gt;I''m sorry if this is a basic or repeated question. My searches are yielding little fruit. &lt;/p&gt;

&lt;p&gt;I''ve made a Naive Bayes classifier that uses the bag-of-words technique to classify spam posts on a messageboard. It works, but I think I could get much better results if my models considered the word orderings and phrases. (ex: ''girls'' and ''live'' may not trigger a high spam score, even though ''live girls'' is most likely junk). How can I build a model that takes word ordering into account?&lt;/p&gt;

&lt;p&gt;I''ve considered storing n-grams (check-out-these, out-these-live, these-live-girls), but this seems to radically increase the size of the dictionary I keep score in and causes inconsistency as phrases with very similar wording but different order will slip through.&lt;/p&gt;

&lt;p&gt;I''m not tied to Bayesian classification, but I''d like something that someone without a strong background in statistics could grok and implement. &lt;/p&gt;
', 'classification', NULL, '''/p'':38C,113C,164C,190C ''account'':112C ''approach'':18A ''background'':183C ''bag'':15A,51C ''bag-of-word'':14A,50C ''basic'':28C ''bay'':45C ''bayesian'':171C ''better'':71C ''build'':104C ''caus'':150C ''check'':123C ''check-out-thes'':122C ''classif'':19B,172C ''classifi'':4A,46C,56C ''consid'':6A,76C,117C ''could'':68C,186C ''d'':175C ''dictionari'':144C ''differ'':159C ''even'':93C ''ex'':82C ''fruit'':37C ''get'':69C ''girl'':83C,96C,133C ''gram'':121C ''grok'':187C ''high'':90C ''implement'':189C ''inconsist'':151C ''increas'':139C ''instead'':9A ''junk'':100C ''keep'':146C ''like'':99C,176C ''littl'':36C ''live'':85C,95C,129C,132C ''m'':22C,167C ''made'':42C ''may'':86C ''messageboard'':61C ''model'':75C,106C ''much'':70C ''n'':120C ''n-gram'':119C ''naiv'':44C ''order'':8A,79C,110C,160C ''out-these-l'':126C ''p'':20C,39C,114C,165C ''phrase'':81C,153C ''post'':58C ''question'':31C ''radic'':138C ''repeat'':30C ''result'':72C ''score'':92C,147C ''search'':33C ''seem'':136C ''similar'':156C ''size'':141C ''slip'':162C ''someon'':179C ''someth'':177C ''sorri'':23C ''spam'':57C,91C ''statist'':185C ''store'':118C ''strong'':182C ''take'':108C ''techniqu'':54C ''text'':5A ''these-live-girl'':130C ''think'':66C ''though'':94C ''tie'':169C ''trigger'':88C ''use'':12A,48C ''ve'':41C,116C ''without'':180C ''word'':7A,17A,53C,78C,109C,157C ''work'':63C ''yield'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1191, 1, '2014-10-02 03:30:37.93', 2, 258, 'Using SVD for clustering', '&lt;p&gt;The dataset that I am experimenting with is in the form of a table with columns userid and itemid. If there is a row for a given user and a given item, that means the user accessed the item (like in an online store). I am trying to cluster similar items based on this data. If a pair of items is accessed together often, then the items are similar.&lt;/p&gt;

&lt;p&gt;Because this is a case of a high dimensionality (# of users and items will be in 10,000''s) I think I am justified in trying to use SVD as a pre-clustering step and then do some classical clustering. When I tried doing this I got poor clustering results when compared with simple hierarchical clustering. Items that weren''t very similar were being bucketed together in one dimension, while there were available dimensions that weren''t used. The results weren''t completely random, but they were definitely worse than the output from the hierarchical clustering. I attempted the SVD step with Mahaut and Octave and the results were similar. For the hierarchical clustering I used the Jaccard measure.&lt;/p&gt;

&lt;p&gt;At this point I am starting to doubt the notion of SVD as a way to reduce dimensionality. Do you think that SVD cannot be used effectively in this case (and why?) or do you think that I made some mistake along the way?&lt;/p&gt;
', 'clustering', NULL, '''/p'':76C,198C,244C ''000'':95C ''10'':94C ''access'':43C,68C ''along'':241C ''attempt'':176C ''avail'':151C ''base'':58C ''bucket'':143C ''cannot'':223C ''case'':82C,229C ''classic'':117C ''cluster'':4A,5B,55C,111C,118C,127C,134C,174C,192C ''column'':22C ''compar'':130C ''complet'':161C ''data'':61C ''dataset'':8C ''definit'':166C ''dimens'':147C,152C ''dimension'':86C,217C ''doubt'':207C ''effect'':226C ''experi'':12C ''form'':17C ''given'':33C,37C ''got'':125C ''hierarch'':133C,173C,191C ''high'':85C ''item'':38C,45C,57C,66C,73C,90C,135C ''itemid'':25C ''jaccard'':196C ''justifi'':101C ''like'':46C ''made'':238C ''mahaut'':181C ''mean'':40C ''measur'':197C ''mistak'':240C ''notion'':209C ''octav'':183C ''often'':70C ''one'':146C ''onlin'':49C ''output'':170C ''p'':6C,77C,199C ''pair'':64C ''point'':202C ''poor'':126C ''pre'':110C ''pre-clust'':109C ''random'':162C ''reduc'':216C ''result'':128C,158C,186C ''row'':30C ''similar'':56C,75C,140C,188C ''simpl'':132C ''start'':205C ''step'':112C,179C ''store'':50C ''svd'':2A,106C,178C,211C,222C ''tabl'':20C ''think'':98C,220C,235C ''togeth'':69C,144C ''tri'':53C,103C,121C ''use'':1A,105C,156C,194C,225C ''user'':34C,42C,88C ''userid'':23C ''way'':214C,243C ''weren'':137C,154C,159C ''wors'':167C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8973, 4, '2015-11-22 05:41:54.86', 0, NULL, NULL, 'Social network data consists of a collection of "nodes" (which can be any sort of entity - e.g. people, corporations) and "links" (which can be any sort of relationship - e.g. friend, sharing a board member). ', NULL, NULL, '''board'':33C ''collect'':7C ''consist'':4C ''corpor'':19C ''data'':3C ''e.g'':17C,29C ''entiti'':16C ''friend'':30C ''link'':21C ''member'':34C ''network'':2C ''node'':9C ''peopl'':18C ''relationship'':28C ''share'':31C ''social'':1C ''sort'':14C,26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9701, 1, '2016-01-08 11:51:00.473', 0, 32, 'Please list some well tested api''s for arima model', '&lt;p&gt;I am looking for a good python api for timeseries models such as ARIMA. Please list some well tested apis and few more advance models possible for financial time-series analysis.&lt;/p&gt;
', 'python  predictive-modeling  time-series', NULL, '''/p'':51C ''advanc'':42C ''analysi'':50C ''api'':6A,26C,38C ''arima'':9A,32C ''financi'':46C ''good'':24C ''list'':2A,34C ''look'':21C ''model'':10A,14B,29C,43C ''p'':18C ''pleas'':1A,33C ''possibl'':44C ''predict'':13B ''predictive-model'':12B ''python'':11B,25C ''seri'':17B,49C ''test'':5A,37C ''time'':16B,48C ''time-seri'':15B,47C ''timeseri'':28C ''well'':4A,36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8693, 2, '2015-11-02 14:03:03.14', 1, NULL, NULL, '&lt;p&gt;There might be a couple of different things you could do.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(data.table)
d &amp;lt;- data.table(read.csv(''/tmp/x.csv'', sep=''\t'', quote='''', header=T, na.strings=''''))
# bucket start date to decade
d[, start.decade:=10*floor(YearFrom/10)]
# create quantiles to represent the inner quartile range
xy.plot &amp;lt;- d[, .(bg1.lower=quantile(BkgArea1, .25),
       bg1.upper=quantile(BkgArea1, .75),
       bg2.lower=quantile(BkgArea2, .25), 
       bg2.upper=quantile(BkgArea2, .75), 
       bg3.lower=quantile(BkgArea3, .25), 
       bg3.upper=quantile(BkgArea3, .75)), by=start.decade]
plot(xy.plot$start.decade, xy.plot$bg1.lower, col=''red'', type=''l'')
lines(xy.plot$start.decade, xy.plot$bg1.upper, col=''red'')
lines(xy.plot$start.decade, xy.plot$bg2.lower, col=''green'')
lines(xy.plot$start.decade, xy.plot$bg2.upper, col=''green'')
lines(xy.plot$start.decade, xy.plot$bg3.lower, col=''blue'')
lines(xy.plot$start.decade, xy.plot$bg3.upper, col=''blue'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/RKB2Q.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/RKB2Q.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I think this is a little messy. You could just show one pair at a time. &lt;/p&gt;

&lt;p&gt;Mosaic plots are nice for categorical data. The relative area is the density of users in the cross-sectional pair. Changing the numbers to text would make it more clear.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;mosaicplot(BkgArea1 ~ BkgArea3 + BkgArea2, d[start.decade==2010])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/0UU2p.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/0UU2p.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You could have one per decade or visualize the total column against the decade, depending on what you want.&lt;/p&gt;
', NULL, NULL, '''/0uu2p.png'':209C,216C ''/a'':140C,222C ''/code'':120C,202C ''/p'':13C,141C,159C,192C,223C,244C ''/pre'':121C,203C ''/rkb2q.png'':127C,134C ''/tmp/x.csv'':22C ''10'':36C ''2010'':201C ''25'':53C,61C,69C ''75'':57C,65C,73C ''alt'':135C,217C ''area'':170C ''bg1.lower'':50C,80C ''bg1.upper'':54C,89C ''bg2.lower'':58C,96C ''bg2.upper'':62C,103C ''bg3.lower'':66C,110C ''bg3.upper'':70C,117C ''bkgarea1'':52C,56C,196C ''bkgarea2'':60C,64C,198C ''bkgarea3'':68C,72C,197C ''blue'':112C,119C ''bucket'':29C ''categor'':166C ''chang'':182C ''clear'':191C ''code'':15C,194C ''col'':81C,90C,97C,104C,111C,118C ''column'':235C ''could'':11C,151C,226C ''coupl'':6C ''creat'':39C ''cross'':179C ''cross-sect'':178C ''d'':18C,34C,49C,199C ''data'':167C ''data.table'':17C,20C ''date'':31C ''decad'':33C,230C,238C ''densiti'':173C ''depend'':239C ''descript'':138C,220C ''differ'':8C ''enter'':136C,218C ''floor'':37C ''green'':98C,105C ''header'':26C ''href'':124C,206C ''i.stack.imgur.com'':126C,133C,208C,215C ''i.stack.imgur.com/0uu2p.png'':207C,214C ''i.stack.imgur.com/rkb2q.png'':125C,132C ''imag'':137C,219C ''img'':130C,212C ''inner'':44C ''l'':84C ''librari'':16C ''line'':85C,92C,99C,106C,113C ''littl'':148C ''lt'':19C,48C ''make'':188C ''messi'':149C ''might'':3C ''mosaic'':161C ''mosaicplot'':195C ''na.strings'':28C ''nice'':164C ''nofollow'':129C,211C ''number'':184C ''one'':154C,228C ''p'':1C,122C,142C,160C,204C,224C ''pair'':155C,181C ''per'':229C ''plot'':76C,162C ''pre'':14C,193C ''quantil'':40C,51C,55C,59C,63C,67C,71C ''quartil'':45C ''quot'':25C ''rang'':46C ''read.csv'':21C ''red'':82C,91C ''rel'':128C,210C ''relat'':169C ''repres'':42C ''section'':180C ''sep'':23C ''show'':153C ''src'':131C,213C ''start'':30C ''start.decade'':35C,75C,78C,87C,94C,101C,108C,115C,200C ''text'':186C ''thing'':9C ''think'':144C ''time'':158C ''total'':234C ''type'':83C ''user'':175C ''visual'':232C ''want'':243C ''would'':187C ''xy.plot'':47C,77C,79C,86C,88C,93C,95C,100C,102C,107C,109C,114C,116C ''yearfrom/10'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1194, 2, '2014-10-02 08:58:18.34', 1, NULL, NULL, '&lt;p&gt;We are using Singular Value Decomposition in the much same manner as you, except rather than clustering similar items, we are using a reduced-rank matrix to power a recommendation engine based on a term-document matrix in Latent Semantic Indexing.&lt;/p&gt;

&lt;p&gt;From your brief description, your approach seems sound enough. However, I highly recommend reading &lt;a href="http://lsirwww.epfl.ch/courses/dis/2003ws/papers/ut-cs-94-270.pdf" rel="nofollow"&gt;Berry, Dumais &amp;amp; O''Brien''s Using Linear Algebra for Intelligent Information Retrieval&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Key to using SVD is selecting an acceptable rank-k approximation to the original sparse matrix. You should carry out some exploratory analysis to see how much variance can be explained, using the singular values in the diagonal matrix Sigma. This question was brought up in &lt;a href="http://stats.stackexchange.com/questions/49646/for-a-random-matrix-shouldnt-a-svd-explain-nothing-at-all-what-am-i-doing-wro"&gt;this question on CrossValidated&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;A lot of the papers I''ve read suggest anywhere a rank k from 200 to 300 singular values. In a proof-of-concept implementation, we had original sparse matrix of about 10000 rows (unique terms) to about 1000 columns (unique documents), and we were capturing just under 85% of the variance with only 300 singular values.&lt;/p&gt;

&lt;p&gt;However, that really hinges upon the nature of your data, so your mileage may vary.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1195, 1, '2014-10-02 13:06:22.433', 2, 62, 'How to classify test objects?', '&lt;p&gt;I''m coding a program that tests several classifiers over a database weather.arff, I found rules below, I want classify test objects.&lt;/p&gt;

&lt;p&gt;I do not understand how the classification, it is described:
"In classification, let R be the set of generated rules and T the training data. The basic idea of the proposed method is to choose a set of high confidence rules in R to cover T. In classifying a test object, the first rule in the set of rules that matches the test object condition classifies it. This process ensures that only the highest ranked rules classify test objects. "&lt;/p&gt;

&lt;p&gt;How to classify test objects?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;No. outlook temperature humidity    windy   play
1   sunny       hot     high        FALSE   no
2   sunny       hot     high        TRUE    no
3   overcast    hot     high        FALSE   yes
4   rainy       mild    high        FALSE   yes
5   rainy       cool    normal      FALSE   yes
6   rainy       cool    normal      TRUE    no
7   overcast    cool    normal      TRUE    yes
8   sunny       mild    high        FALSE   no
9   sunny       cool    normal      FALSE   yes
10  rainy       mild    normal      FALSE   yes
11  sunny       mild    normal      TRUE    yes
12  overcast    mild    high        TRUE    yes
13  overcast    hot     normal      FALSE   yes
14  rainy       mild    high        TRUE    no
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Rule found:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1: (outlook,overcast) -&amp;gt; (play,yes) 
[Support=0.29 , Confidence=1.00 , Correctly Classify= 3, 7, 12, 13]

2: (humidity,normal), (windy,FALSE) -&amp;gt; (play,yes)
[Support=0.29 , Confidence=1.00 , Correctly Classify= 5, 9, 10]

3: (outlook,sunny), (humidity,high) -&amp;gt; (play,no) 
[Support=0.21 , Confidence=1.00 , Correctly Classify= 1, 2, 8]

4: (outlook,rainy), (windy,FALSE) -&amp;gt; (play,yes) 
[Support=0.21 , Confidence=1.00 , Correctly Classify= 4]

5: (outlook,sunny), (humidity,normal) -&amp;gt; (play,yes) 
[Support=0.14 , Confidence=1.00 , Correctly Classify= 11]

6: (outlook,rainy), (windy,TRUE) -&amp;gt; (play,no) 
[Support=0.14 , Confidence=1.00 , Correctly Classify= 6, 14]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Thanks,
Dung&lt;/p&gt;
', 'classification', NULL, '''/code'':211C,315C ''/p'':30C,111C,118C,216C,320C ''/pre'':212C,316C ''0.14'':293C,308C ''0.21'':261C,278C ''0.29'':226C,244C ''1'':127C,219C,266C ''1.00'':228C,246C,263C,280C,295C,310C ''10'':181C,251C ''11'':187C,298C ''12'':193C,233C ''13'':199C,234C ''14'':205C,314C ''2'':133C,235C,267C ''3'':139C,231C,252C ''4'':145C,269C,283C ''5'':151C,249C,284C ''6'':157C,299C,313C ''7'':163C,232C ''8'':169C,268C ''9'':175C,250C ''basic'':58C ''choos'':66C ''classif'':6B,38C,43C ''classifi'':3A,16C,27C,79C,97C,108C,115C,230C,248C,265C,282C,297C,312C ''code'':10C,120C,218C ''condit'':96C ''confid'':71C,227C,245C,262C,279C,294C,309C ''cool'':153C,159C,165C,177C ''correct'':229C,247C,264C,281C,296C,311C ''cover'':76C ''data'':56C ''databas'':19C ''describ'':41C ''dung'':319C ''ensur'':101C ''fals'':131C,143C,149C,155C,173C,179C,185C,203C,239C,273C ''first'':84C ''found'':22C,215C ''generat'':50C ''gt'':222C,240C,257C,274C,289C,304C ''high'':70C,130C,136C,142C,148C,172C,196C,208C,256C ''highest'':105C ''hot'':129C,135C,141C,201C ''humid'':124C,236C,255C,287C ''idea'':59C ''let'':44C ''m'':9C ''match'':92C ''method'':63C ''mild'':147C,171C,183C,189C,195C,207C ''normal'':154C,160C,166C,178C,184C,190C,202C,237C,288C ''object'':5A,29C,82C,95C,110C,117C ''outlook'':122C,220C,253C,270C,285C,300C ''overcast'':140C,164C,194C,200C,221C ''p'':7C,31C,112C,213C,317C ''play'':126C,223C,241C,258C,275C,290C,305C ''pre'':119C,217C ''process'':100C ''program'':12C ''propos'':62C ''r'':45C,74C ''raini'':146C,152C,158C,182C,206C,271C,301C ''rank'':106C ''rule'':23C,51C,72C,85C,90C,107C,214C ''set'':48C,68C,88C ''sever'':15C ''sunni'':128C,134C,170C,176C,188C,254C,286C ''support'':225C,243C,260C,277C,292C,307C ''temperatur'':123C ''test'':4A,14C,28C,81C,94C,109C,116C ''thank'':318C ''train'':55C ''true'':137C,161C,167C,191C,197C,209C,303C ''understand'':35C ''want'':26C ''weather.arff'':20C ''windi'':125C,238C,272C,302C ''yes'':144C,150C,156C,168C,180C,186C,192C,198C,204C,224C,242C,276C,291C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1207, 2, '2014-10-04 19:37:51.523', 2, NULL, NULL, '&lt;p&gt;There are a bunch of techniques. You have already mentioned n-gram, then there is word combination and others. But the main problem (at least from your point of view) is that as the feature becomes more complex (like n-gram) the feature count increases dramatically. This is manageable. Basically before classification you must score your features and then threshold at a certain score. this way the features (or in your case n-grams) that are scored below a certain level are omitted and the feature count becomes manageable.
as for the scoring. There are numerous ways (which to select is dependent on your application) to score the features. You can begin with "BiNormal separation", "chi square", "Information Gain" and etc.
I don''t know if this answer helps you but if you are interested i can elaborate...&lt;/p&gt;

&lt;p&gt;I forgot, in word combination you put a window of size m on the text and extract each combination of n words. of course n
', NULL, NULL, '''/p'':143C ''alreadi'':10C ''answer'':132C ''applic'':109C ''basic'':53C ''becom'':38C,92C ''begin'':116C ''binorm'':118C ''bunch'':5C ''case'':75C ''certain'':66C,84C ''chi'':120C ''classif'':55C ''combin'':19C,149C,163C ''complex'':40C ''count'':47C,91C ''cours'':168C ''depend'':106C ''dramat'':49C ''elabor'':142C ''etc'':125C ''extract'':161C ''featur'':37C,46C,60C,71C,90C,113C ''forgot'':146C ''gain'':123C ''gram'':14C,44C,78C ''help'':133C ''increas'':48C ''inform'':122C ''interest'':139C ''know'':129C ''least'':27C ''level'':85C ''like'':41C ''m'':156C ''main'':24C ''manag'':52C,93C ''mention'':11C ''must'':57C ''n'':13C,43C,77C,165C,169C ''n-gram'':12C,42C,76C ''numer'':100C ''omit'':87C ''other'':21C ''p'':1C,144C ''point'':30C ''problem'':25C ''put'':151C ''score'':58C,67C,81C,97C,111C ''select'':104C ''separ'':119C ''size'':155C ''squar'':121C ''techniqu'':7C ''text'':159C ''threshold'':63C ''view'':32C ''way'':69C,101C ''window'':153C ''word'':18C,148C,166C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8949, 2, '2015-11-20 22:39:07.593', 1, NULL, NULL, '&lt;p&gt;&lt;a href="http://www.kdd.org/" rel="nofollow"&gt;ACM KDD&lt;/a&gt; is the top conference for both industry and academia. &lt;/p&gt;

&lt;p&gt;Last time it took place in Sydney. Have a look to &lt;a href="http://www.kdd.org/kdd2015/" rel="nofollow"&gt;the program and participants&lt;/a&gt;. &lt;/p&gt;
', NULL, NULL, '''/a'':9C,43C ''/kdd2015/'':36C ''/p'':19C,44C ''academia'':18C ''acm'':7C ''confer'':13C ''href'':3C,33C ''industri'':16C ''kdd'':8C ''last'':21C ''look'':30C ''nofollow'':6C,38C ''p'':1C,20C ''particip'':42C ''place'':25C ''program'':40C ''rel'':5C,37C ''sydney'':27C ''time'':22C ''took'':24C ''top'':12C ''www.kdd.org'':4C,35C ''www.kdd.org/kdd2015/'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1196, 1, '2014-10-02 17:32:42.003', 2, 95, 'Supervised Learning with Necessarily Missing Data', '&lt;p&gt;Many discussions of missing data in supervised (and unsupervised) learning deal with various methods of imputation, like mean values or EM. But in some cases the data will be missing as a necessary consequence of the data generation process.&lt;/p&gt;

&lt;p&gt;For instance, let''s say I''m trying to predict students'' grades, and one of the inputs I want to analyze is the average grades of the student''s siblings. If a particular student is an only child, then that value will be missing, not because we failed to collect the data, but because logically there is no data to collect. This is distinct from cases where the student has siblings, but we can''t find their grades. &lt;/p&gt;

&lt;p&gt;Other examples abound: say we''re in college admissions and we want to include students'' AP exam results, but not all students took AP exams. Or we''re looking at social network data, but not all subjects have facebook and/or twitter accounts.&lt;/p&gt;

&lt;p&gt;These data are missing, but they''re certainly not missing at random. And many algorithms, such as all supervised learning packages in scikit-learn, simply demand that there be no missing values at all in the data set.&lt;/p&gt;

&lt;p&gt;How have people dealt with this in the past, and what off-the-shelf solutions are there? For instance, I believe the gradient boosting algorithm in R uses trees with three possible branches: left, right, and missing. Any other alternatives out there?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':50C,130C,174C,215C,260C ''abound'':134C ''account'':173C ''admiss'':140C ''algorithm'':190C,242C ''altern'':257C ''analyz'':72C ''and/or'':171C ''ap'':147C,155C ''averag'':75C ''believ'':238C ''boost'':241C ''branch'':250C ''case'':35C,117C ''certain'':183C ''child'':89C ''collect'':101C,112C ''colleg'':139C ''consequ'':44C ''data'':6A,15C,37C,47C,103C,110C,164C,177C,213C ''deal'':21C ''dealt'':220C ''demand'':202C ''discuss'':12C ''distinct'':115C ''em'':31C ''exam'':148C,156C ''exampl'':133C ''facebook'':170C ''fail'':99C ''find'':127C ''generat'':48C ''grade'':63C,76C,129C ''gradient'':240C ''imput'':26C ''includ'':145C ''input'':68C ''instanc'':53C,236C ''learn'':2A,9B,20C,195C,200C ''left'':251C ''let'':54C ''like'':27C ''logic'':106C ''look'':160C ''m'':58C ''machin'':8B ''machine-learn'':7B ''mani'':11C,189C ''mean'':28C ''method'':24C ''miss'':5A,14C,40C,95C,179C,185C,207C,254C ''necessari'':43C ''necessarili'':4A ''network'':163C ''off-the-shelf'':228C ''one'':65C ''p'':10C,51C,131C,175C,216C ''packag'':196C ''particular'':84C ''past'':225C ''peopl'':219C ''possibl'':249C ''predict'':61C ''process'':49C ''r'':244C ''random'':187C ''re'':137C,159C,182C ''result'':149C ''right'':252C ''say'':56C,135C ''scikit'':199C ''scikit-learn'':198C ''set'':214C ''shelf'':231C ''sibl'':81C,122C ''simpli'':201C ''social'':162C ''solut'':232C ''student'':62C,79C,85C,120C,146C,153C ''subject'':168C ''supervis'':1A,17C,194C ''three'':248C ''took'':154C ''tree'':246C ''tri'':59C ''twitter'':172C ''unsupervis'':19C ''use'':245C ''valu'':29C,92C,208C ''various'':23C ''want'':70C,143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1198, 2, '2014-10-03 00:25:48.49', 8, NULL, NULL, '&lt;p&gt;As you mentioned, the API is the hard part, not the data.  &lt;a href="http://quandl.com"&gt;Quandl&lt;/a&gt; seems to solve this problem by providing over 10 million publicly available data sets under one easy, RESTful API.  If programming isn''t your strong suit, there is a free tool to make loading data into Excel very easy.  Additionally, if you &lt;em&gt;do&lt;/em&gt; enjoy programming, there are several native libraries in &lt;a href="https://www.quandl.com/help/libraries"&gt;R, Python, Java and more&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':18C,82C ''/em'':63C ''/help/libraries'':76C ''/p'':83C ''10'':27C ''addit'':58C ''api'':6C,37C ''avail'':30C ''data'':13C,31C,53C ''easi'':35C,57C ''em'':61C ''enjoy'':64C ''excel'':55C ''free'':48C ''hard'':9C ''href'':15C,73C ''isn'':40C ''java'':79C ''librari'':70C ''load'':52C ''make'':51C ''mention'':4C ''million'':28C ''nativ'':69C ''one'':34C ''p'':1C ''part'':10C ''problem'':23C ''program'':39C,65C ''provid'':25C ''public'':29C ''python'':78C ''quandl'':17C ''quandl.com'':16C ''r'':77C ''rest'':36C ''seem'':19C ''set'':32C ''sever'':68C ''solv'':21C ''strong'':43C ''suit'':44C ''tool'':49C ''www.quandl.com'':75C ''www.quandl.com/help/libraries'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6176, 1, '2015-06-22 15:26:36.693', 2, 61, 'Debugging Neural Network for (Natural Language) Tagging', '&lt;p&gt;I''ve been coding a Neural Network for recognizing and tagging parts of speech in English (written in Java). The code itself has no ''errors'' or apparent flaws. Nevertheless, it is not learning -- the more I train it does not change its ability to predict the testing data. The following is information about what I''ve done, please ask me to update this post if I left something important out.&lt;/p&gt;

&lt;p&gt;I wrote the neural network and tested it on several different problems to make sure that the network itself worked. I trained it to learn how to double numbers, XOR, cube numbers, and learn the sin function to a decent accuracy. So, I''m fairly confident that the actual algorithm is working.&lt;/p&gt;

&lt;p&gt;The network using using the sigmoid activation function. The learning rate is .3, Momentum is .6. The weights are initialized to rng.nextFloat() -.5) * 4&lt;/p&gt;

&lt;p&gt;I then got the Brown Corpus data-set and simplified the tagset to ''universal'' with NLTK. I used NLTK for generating and saving all the corpus and dictionary data. I cut the last 15,000 sentences out of the corpus for testing purposes. I used the rest of the corpus (about 40,000 sentences of tagged words) for training. &lt;/p&gt;

&lt;p&gt;The neural network layout is as follows: There is an input neuron for each Tag. Output Layer: There is one output neuron for each tag. The network is taking inputs for 3 words: first: the word coming before the word we want to tag, second: the word that needs to be tagged, third: the word that follows the second word. So, total number of inputs are 3x(total number of possible tags). The input values are numbers between 0 and 1. Each of the 3 words being fed into the input layer is searched for in a dictionary (made up by the 40,000 corpus, the same corpus that is used for training). The dictionary holds the number of times that each word has been tagged in the corpus as what part of speech. &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;For instance, the word ''cover'' is tagged as a noun 1 time and a verb 3
  times.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Percentages of being tagged are computed for each part of speech that the word is associated as, and this is what is fed into the network for that particular word. So, the input neuron designated as NOUN would receive .33 and VERB would receive .66. The other input neurons that hold tags for that word receive an input of 0.0. This is done for each of the 3 words to be inputted. If a word is the first word of a sentence, the first group of tags are all 0. If a word is the last word of a sentence, the final group of input neurons that hold the tag probabilities for the following word are left as 0s.
I''ve been using 10 hidden nodes (I''ve read a number of papers and this seems to be a good place to start testing with)&lt;/p&gt;

&lt;p&gt;None of the 15,000 testing sentences were used to make the ''dictionary.'' So, when testing the network with this partial corpus there will be some words the network has never seen. Words that are not recognized have their suffix stripped, and their suffix is searched for in another ''dictionary.'' Whatever is most probable for that word is then used as inputs for it.&lt;/p&gt;

&lt;p&gt;This is my set-up, and I started trying to train the network. I''ve been training the network with all 40,000 sentences. 1 epoch = 1 forward and backpropagation of every word in each sentence of the 40,000 training-set. So, just doing 1 epoch takes quite a few seconds. Just by knowing the word probabilities the network did pretty well, but the more I train it, nothing happens. The numbers that follow the epochs are the number of correctly tagged words divided by the total number of words.&lt;/p&gt;

&lt;p&gt;First run 50 epochs: 0.928218786&lt;/p&gt;

&lt;p&gt;100 epochs:        0.933130661&lt;/p&gt;

&lt;p&gt;500 epochs:       0.928614499 took around 30 minutes to train this                   &lt;/p&gt;

&lt;p&gt;Tried 10 epochs:         0.928953683 &lt;/p&gt;

&lt;p&gt;Using only 1 epoch had results that pretty much varied between .92 and .93&lt;/p&gt;

&lt;p&gt;So, it doesn''t appear to be working...&lt;/p&gt;

&lt;p&gt;I then took 55 sentences from the corpus and used the same dictionary that had probabilities for all 40,000 words. For this one, I trained it in the same way I trained my XOR -- I only used those 55 sentences and I only tested the trained network weights on those 55 sentences. The network was able to learn those 55 sentences quite easily. With 120 epochs (taking a couple seconds) the network went from tagging 3768 incorrectly and 56 correctly (on the first few epochs) to tagging 3772 correctly and 52 incorrectly on the 120th epoch. &lt;/p&gt;

&lt;p&gt;This is where I''m at, I''ve been trying to debug this for over a day now, and haven''t figured anything out.&lt;/p&gt;
', 'machine-learning  neuralnetwork  knowledge-base  java', NULL, '''/blockquote'':386C ''/p'':87C,141C,167C,229C,365C,385C,533C,599C,694C,701C,706C,718C,724C,740C,750C,849C,875C ''0'':309C,477C ''0.0'':447C ''0.928218786'':700C ''0.928614499'':710C ''0.928953683'':723C ''0.933130661'':705C ''000'':204C,222C,334C,539C,624C,641C,771C ''0s'':506C ''1'':311C,378C,626C,628C,648C,728C ''10'':511C,721C ''100'':703C ''120'':817C ''120th'':847C ''15'':203C,538C ''3'':155C,262C,315C,383C,455C ''30'':713C ''33'':427C ''3768'':828C ''3772'':840C ''3x'':297C ''4'':166C ''40'':221C,333C,623C,640C,770C ''5'':165C ''50'':698C ''500'':708C ''52'':843C ''55'':755C,791C,803C,812C ''56'':831C ''6'':158C ''66'':432C ''92'':737C ''93'':739C ''abil'':59C ''abl'':808C ''accuraci'':129C ''activ'':149C ''actual'':137C ''algorithm'':138C ''anoth'':583C ''anyth'':873C ''appar'':43C ''appear'':746C ''around'':712C ''ask'':75C ''associ'':403C ''backpropag'':631C ''base'':14B ''blockquot'':366C ''brown'':173C ''chang'':57C ''code'':20C,37C ''come'':267C ''comput'':393C ''confid'':134C ''corpus'':174C,195C,209C,219C,335C,338C,359C,556C,759C ''correct'':684C,832C,841C ''coupl'':821C ''cover'':372C ''cube'':119C ''cut'':200C ''data'':64C,176C,198C ''data-set'':175C ''day'':867C ''debug'':1A,862C ''decent'':128C ''design'':422C ''dictionari'':197C,328C,345C,547C,584C,764C ''differ'':99C ''divid'':687C ''doesn'':744C ''done'':73C,450C ''doubl'':116C ''easili'':815C ''english'':32C ''epoch'':627C,649C,679C,699C,704C,709C,722C,729C,818C,837C,848C ''error'':41C ''everi'':633C ''fair'':133C ''fed'':318C,410C ''figur'':872C ''final'':489C ''first'':264C,465C,471C,696C,835C ''flaw'':44C ''follow'':66C,237C,287C,501C,677C ''forward'':629C ''function'':125C,150C ''generat'':190C ''good'':527C ''got'':171C ''group'':472C,490C ''happen'':673C ''haven'':870C ''hidden'':512C ''hold'':346C,438C,495C ''import'':85C ''incorrect'':829C,844C ''inform'':68C ''initi'':162C ''input'':241C,260C,295C,304C,321C,420C,435C,445C,459C,492C,596C ''instanc'':369C ''java'':15B,35C ''know'':657C ''knowledg'':13B ''knowledge-bas'':12B ''languag'':6A ''last'':202C,483C ''layer'':247C,322C ''layout'':234C ''learn'':10B,49C,113C,122C,152C,810C ''left'':83C,504C ''m'':132C,855C ''machin'':9B ''machine-learn'':8B ''made'':329C ''make'':102C,545C ''minut'':714C ''momentum'':156C ''much'':734C ''natur'':5A ''need'':279C ''network'':3A,23C,93C,106C,144C,233C,257C,413C,552C,563C,614C,620C,662C,799C,806C,824C ''neural'':2A,22C,92C,232C ''neuralnetwork'':11B ''neuron'':242C,252C,421C,436C,493C ''never'':565C ''nevertheless'':45C ''nltk'':185C,188C ''node'':513C ''none'':535C ''noth'':672C ''noun'':377C,424C ''number'':117C,120C,293C,299C,307C,348C,518C,675C,682C,691C ''one'':250C,775C ''output'':246C,251C ''p'':16C,88C,142C,168C,230C,367C,387C,534C,600C,695C,702C,707C,719C,725C,741C,751C,850C ''paper'':520C ''part'':28C,362C,396C ''partial'':555C ''particular'':416C ''percentag'':388C ''place'':528C ''pleas'':74C ''possibl'':301C ''post'':80C ''predict'':61C ''pretti'':664C,733C ''probabl'':498C,588C,660C,767C ''problem'':100C ''purpos'':212C ''quit'':651C,814C ''rate'':153C ''read'':516C ''receiv'':426C,431C,443C ''recogn'':25C,571C ''rest'':216C ''result'':731C ''rng.nextfloat'':164C ''run'':697C ''save'':192C ''search'':324C,580C ''second'':275C,289C,654C,822C ''seem'':523C ''seen'':566C ''sentenc'':205C,223C,469C,487C,541C,625C,637C,756C,792C,804C,813C ''set'':177C,605C,644C ''set-up'':604C ''sever'':98C ''sigmoid'':148C ''simplifi'':179C ''sin'':124C ''someth'':84C ''speech'':30C,364C,398C ''start'':530C,609C ''strip'':575C ''suffix'':574C,578C ''sure'':103C ''tag'':7A,27C,225C,245C,255C,274C,282C,302C,356C,374C,391C,439C,474C,497C,685C,827C,839C ''tagset'':181C ''take'':259C,650C,819C ''test'':63C,95C,211C,531C,540C,550C,796C ''third'':283C ''time'':350C,379C,384C ''took'':711C,754C ''total'':292C,298C,690C ''train'':53C,110C,228C,343C,612C,618C,643C,670C,716C,777C,784C,798C ''training-set'':642C ''tri'':610C,720C,860C ''univers'':183C ''updat'':78C ''use'':145C,146C,187C,214C,341C,510C,543C,594C,726C,761C,789C ''valu'':305C ''vari'':735C ''ve'':18C,72C,508C,515C,616C,858C ''verb'':382C,429C ''want'':272C ''way'':782C ''weight'':160C,800C ''well'':665C ''went'':825C ''whatev'':585C ''word'':226C,263C,266C,270C,277C,285C,290C,316C,353C,371C,401C,417C,442C,456C,462C,466C,480C,484C,502C,561C,567C,591C,634C,659C,686C,693C,772C ''work'':108C,140C,749C ''would'':425C,430C ''written'':33C ''wrote'':90C ''xor'':118C,786C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1203, 2, '2014-10-03 18:05:13.163', 3, NULL, NULL, '&lt;p&gt;There is a very simple hack to incorporate word order in an existing bag-of-words model implementation. Treat some of the phrases, such as the frequently occurring bi-grams (e.g. New York) as a unit, i.e. a single word instead of treating them as separate entities. This will ensure that "New York" is different from "York New". You could also define higher order word shingles such as for n=3,4 etc.&lt;/p&gt;

&lt;p&gt;You could use the Lucene &lt;a href="http://lucene.apache.org/core/4_6_0/analyzers-common/org/apache/lucene/analysis/shingle/ShingleFilter.html" rel="nofollow"&gt;ShingleFilter&lt;/a&gt; to decompose your document text into shingles as a pre-processing step and then apply the classifier on this decomposed text.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import java.io.*;
import org.apache.lucene.analysis.core.*;
import org.apache.lucene.analysis.*;
import org.apache.lucene.analysis.shingle.ShingleFilter;
import org.apache.lucene.analysis.standard.*;
import org.apache.lucene.util.*;
import org.apache.lucene.analysis.util.*;
import org.apache.lucene.analysis.tokenattributes.CharTermAttribute;
import org.apache.lucene.analysis.charfilter.*;
import org.apache.lucene.analysis.core.WhitespaceTokenizer;

class TestAnalyzer extends Analyzer {

    TestAnalyzer() {
        super();
    }

    protected TokenStreamComponents createComponents( String fieldName, Reader reader ) {
        String token;
        TokenStream result = null;

        Tokenizer source = new WhitespaceTokenizer( Version.LUCENE_CURRENT, reader );
        result = new ShingleFilter(source, 2, 2);

        return new TokenStreamComponents( source, result );

    }
}

public class LuceneTest {

    public static void main(String[] args) throws Exception {

        TestAnalyzer analyzer = new TestAnalyzer();

        try {
            TokenStream stream = analyzer.tokenStream("field", new StringReader("This is a sample sentence."));
            CharTermAttribute termAtt = stream.addAttribute(CharTermAttribute.class);

            stream.reset();

            // print all tokens until stream is exhausted
            while (stream.incrementToken()) {
                System.out.println(termAtt.toString());
            }

            stream.end();
            stream.close();
         }
         catch (Exception ex) {
             ex.printStackTrace();
         }

    }
}
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':92C ''/code'':223C ''/core/4_6_0/analyzers-common/org/apache/lucene/analysis/shingle/shinglefilter.html'':88C ''/p'':77C,115C ''/pre'':224C ''2'':167C,168C ''3'':74C ''4'':75C ''also'':64C ''analyz'':141C,186C ''analyzer.tokenstream'':192C ''appli'':108C ''arg'':182C ''bag'':16C ''bag-of-word'':15C ''bi'':32C ''bi-gram'':31C ''catch'':219C ''chartermattribut'':201C ''chartermattribute.class'':204C ''class'':138C,175C ''classifi'':110C ''code'':117C ''could'':63C,80C ''createcompon'':146C ''current'':161C ''decompos'':94C,113C ''defin'':65C ''differ'':58C ''document'':96C ''e.g'':34C ''ensur'':53C ''entiti'':50C ''etc'':76C ''ex'':221C ''ex.printstacktrace'':222C ''except'':184C,220C ''exhaust'':212C ''exist'':14C ''extend'':140C ''field'':193C ''fieldnam'':148C ''frequent'':29C ''gram'':33C ''hack'':7C ''higher'':66C ''href'':85C ''i.e'':40C ''implement'':20C ''import'':118C,120C,122C,124C,126C,128C,130C,132C,134C,136C ''incorpor'':9C ''instead'':44C ''java.io'':119C ''lucen'':83C ''lucene.apache.org'':87C ''lucene.apache.org/core/4_6_0/analyzers-common/org/apache/lucene/analysis/shingle/shinglefilter.html'':86C ''lucenetest'':176C ''main'':180C ''model'':19C ''n'':73C ''new'':35C,55C,61C,158C,164C,170C,187C,194C ''nofollow'':90C ''null'':155C ''occur'':30C ''order'':11C,67C ''org.apache.lucene.analysis'':123C ''org.apache.lucene.analysis.charfilter'':135C ''org.apache.lucene.analysis.core'':121C ''org.apache.lucene.analysis.core.whitespacetokenizer'':137C ''org.apache.lucene.analysis.shingle.shinglefilter'':125C ''org.apache.lucene.analysis.standard'':127C ''org.apache.lucene.analysis.tokenattributes.chartermattribute'':133C ''org.apache.lucene.analysis.util'':131C ''org.apache.lucene.util'':129C ''p'':1C,78C ''phrase'':25C ''pre'':103C,116C ''pre-process'':102C ''print'':206C ''process'':104C ''protect'':144C ''public'':174C,177C ''reader'':149C,150C,162C ''rel'':89C ''result'':154C,163C,173C ''return'':169C ''sampl'':199C ''sentenc'':200C ''separ'':49C ''shingl'':69C,99C ''shinglefilt'':91C,165C ''simpl'':6C ''singl'':42C ''sourc'':157C,166C,172C ''static'':178C ''step'':105C ''stream'':191C,210C ''stream.addattribute'':203C ''stream.close'':218C ''stream.end'':217C ''stream.incrementtoken'':214C ''stream.reset'':205C ''string'':147C,151C,181C ''stringread'':195C ''super'':143C ''system.out.println'':215C ''termatt'':202C ''termatt.tostring'':216C ''testanalyz'':139C,142C,185C,188C ''text'':97C,114C ''throw'':183C ''token'':152C,156C,208C ''tokenstream'':153C,190C ''tokenstreamcompon'':145C,171C ''treat'':21C,46C ''tri'':189C ''unit'':39C ''use'':81C ''version.lucene'':160C ''void'':179C ''whitespacetoken'':159C ''word'':10C,18C,43C,68C ''york'':36C,56C,60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1204, 1, '2014-10-03 20:12:24.427', -1, 358, 'NLP lab, linux or windows and which programming languages?', '&lt;p&gt;I would like to do some data mining and NLP experiments to do some research&lt;/p&gt;

&lt;p&gt;I have decided to use NLTK or related tools and software&lt;/p&gt;

&lt;p&gt;Which environment or operating system do you suggest for my purpose? I mean doing research on NLP&lt;/p&gt;

&lt;p&gt;Windows or Linux? &lt;/p&gt;

&lt;p&gt;I am a user of Windows but I thought if Linux has better shell and related software for NLP tasks then I switch to Linux&lt;/p&gt;

&lt;p&gt;What is your experience and your preferred OS?&lt;/p&gt;

&lt;p&gt;As NLTK is in Python I thought Python is a good language for my purpose, do you suggest Python too?&lt;/p&gt;
', 'nlp  tools', NULL, '''/p'':28C,41C,60C,65C,92C,102C,124C ''better'':79C ''data'':19C ''decid'':32C ''environ'':44C ''experi'':23C,97C ''good'':114C ''lab'':2A ''languag'':9A,115C ''like'':15C ''linux'':3A,64C,77C,91C ''mean'':55C ''mine'':20C ''nlp'':1A,10B,22C,59C,85C ''nltk'':35C,105C ''oper'':46C ''os'':101C ''p'':12C,29C,42C,61C,66C,93C,103C ''prefer'':100C ''program'':8A ''purpos'':53C,118C ''python'':108C,111C,122C ''relat'':37C,82C ''research'':27C,57C ''shell'':80C ''softwar'':40C,83C ''suggest'':50C,121C ''switch'':89C ''system'':47C ''task'':86C ''thought'':75C,110C ''tool'':11B,38C ''use'':34C ''user'':70C ''window'':5A,62C,72C ''would'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1205, 1, '2014-10-04 07:50:11.633', 1, 278, 'Recommended Language/Framework for Building a New Recommendation Engine', '&lt;p&gt;Next week I''m going to begin prototyping a recommendation engine for work. I''ve implemented/completed the Netflix Challenge in Java before (for college) but have no real idea what to use for a production/enterprise level recommendation engine. Taking into consideration everything from a standalone programming language to things like Apache Mahout and Neo4j, does anyone have any advice on how to proceed?&lt;/p&gt;
', 'recommendation', NULL, '''/p'':74C ''advic'':69C ''anyon'':66C ''apach'':61C ''begin'':17C ''build'':4A ''challeng'':29C ''colleg'':34C ''consider'':51C ''engin'':8A,21C,48C ''everyth'':52C ''go'':15C ''idea'':39C ''implemented/completed'':26C ''java'':31C ''languag'':57C ''language/framework'':2A ''level'':46C ''like'':60C ''m'':14C ''mahout'':62C ''neo4j'':64C ''netflix'':28C ''new'':6A ''next'':11C ''p'':10C ''proceed'':73C ''production/enterprise'':45C ''program'':56C ''prototyp'':18C ''real'':38C ''recommend'':1A,7A,9B,20C,47C ''standalon'':55C ''take'':49C ''thing'':59C ''use'':42C ''ve'':25C ''week'':12C ''work'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1209, 2, '2014-10-04 19:55:31.48', 0, NULL, NULL, '&lt;p&gt;coursera should be a good start. i have seen machine learning on their course list if im not mistaken. once you get the hang of it, a machine learning classic text like "machine learning and pattern recognition" by bishop or other texts can familiarize you with different classes of algorithms.
if you want to go in depth, mathematics is a must, but if you just want to get familiar and use the algorithms there''s absolutely no need for it&lt;/p&gt;
', NULL, NULL, '''/p'':82C ''absolut'':77C ''algorithm'':51C,74C ''bishop'':40C ''class'':49C ''classic'':31C ''cours'':15C ''coursera'':2C ''depth'':58C ''differ'':48C ''familiar'':45C,70C ''get'':23C,69C ''go'':56C ''good'':6C ''hang'':25C ''im'':18C ''learn'':12C,30C,35C ''like'':33C ''list'':16C ''machin'':11C,29C,34C ''mathemat'':59C ''mistaken'':20C ''must'':62C ''need'':79C ''p'':1C ''pattern'':37C ''recognit'':38C ''seen'':10C ''start'':7C ''text'':32C,43C ''use'':72C ''want'':54C,67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1210, 2, '2014-10-04 20:09:17.547', 0, NULL, NULL, '&lt;p&gt;i have seen many papers that are basically what you just described. there is nothing wrong with what you are doing, but there are severe limitations in how this can predict the market.
let me give you an example: suppose that u have some data and you begin to predict. with each set of data, you predict the next datapoint. and then you feed this datapoint back to the system as input and do this on and on .... in most of the times the system would just continue the last trend and the timeseries won''t &lt;strong&gt;break&lt;/strong&gt;. this is not prediction, this is line continuation... only when the system sees the break in real data, will the prediction break, and this is the lag that you are talking about (if i understand your question right).
The first thing that you can do to enhance this is to extract some market indicators from the price. this would really help&lt;/p&gt;
', NULL, NULL, '''/p'':162C ''/strong'':100C ''back'':68C ''basic'':9C ''begin'':49C ''break'':99C,115C,122C ''continu'':89C,108C ''data'':46C,56C,118C ''datapoint'':61C,67C ''describ'':13C ''enhanc'':147C ''exampl'':40C ''extract'':151C ''feed'':65C ''first'':140C ''give'':37C ''help'':161C ''indic'':154C ''input'':73C ''lag'':127C ''last'':91C ''let'':35C ''limit'':27C ''line'':107C ''mani'':5C ''market'':34C,153C ''next'':60C ''noth'':16C ''p'':1C ''paper'':6C ''predict'':32C,51C,58C,104C,121C ''price'':157C ''question'':137C ''real'':117C ''realli'':160C ''right'':138C ''see'':113C ''seen'':4C ''set'':54C ''sever'':26C ''strong'':98C ''suppos'':41C ''system'':71C,86C,112C ''talk'':131C ''thing'':141C ''time'':84C ''timeseri'':95C ''trend'':92C ''u'':43C ''understand'':135C ''won'':96C ''would'':87C,159C ''wrong'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1211, 2, '2014-10-04 20:25:30.527', 0, NULL, NULL, '&lt;p&gt;AAAAh. this was one of my course assignments. this is a simple dataset. almost all simple classifiers work well with this. try to reduce the features with PCA and then use a classifier like K-Nearest Neighbors or Neural Networks. you can skip the feature reduction part but that would probably hurt a little&lt;/p&gt;
', NULL, NULL, '''/p'':57C ''aaaah'':2C ''almost'':15C ''assign'':9C ''classifi'':18C,34C ''cours'':8C ''dataset'':14C ''featur'':27C,47C ''hurt'':54C ''k'':37C ''k-nearest'':36C ''like'':35C ''littl'':56C ''nearest'':38C ''neighbor'':39C ''network'':42C ''neural'':41C ''one'':5C ''p'':1C ''part'':49C ''pca'':29C ''probabl'':53C ''reduc'':25C ''reduct'':48C ''simpl'':13C,17C ''skip'':45C ''tri'':23C ''use'':32C ''well'':20C ''work'':19C ''would'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1212, 2, '2014-10-05 05:01:03.437', 0, NULL, NULL, '&lt;p&gt;If you merely want to scale up a simple collaborative filter (low rank matrix factorization), I''d suggest looking at &lt;a href="http://docs.graphlab.org/collaborative_filtering.html" rel="nofollow"&gt;graphlab&lt;/a&gt;. Another graph-based (or should I say &lt;a href="http://giraph.apache.org/" rel="nofollow"&gt;Giraph&lt;/a&gt;?) solution is &lt;a href="http://grafos.ml/okapi.html#collaborative" rel="nofollow"&gt;Okapi&lt;/a&gt;. Spark''s &lt;a href="http://spark.apache.org/docs/latest/mllib-collaborative-filtering.html" rel="nofollow"&gt;MLLib&lt;/a&gt; is another option (&lt;a href="https://databricks.com/blog/2014/07/23/scalable-collaborative-filtering-with-spark-mllib.html" rel="nofollow"&gt;details&lt;/a&gt;), and it also supports implicit feedback out of the box. Mahout''s behind the curve today; I wouldn''t bother with it until it is &lt;a href="https://www.mapr.com/blog/mahout-spark-what%E2%80%99s-new-recommenders" rel="nofollow"&gt;migrated to Spark&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;If you want to do something that the libraries don''t do, say with regularization, you''ll have to roll your own solution in your general purpose programming language of choice. It''s not hard to get a prototype running, but you might run into scale problems in production; that''s why I recommended solutions that scale easily.&lt;/p&gt;

&lt;p&gt;There are also black box recommender systems from commercial vendors but I have no experience with those.&lt;/p&gt;
', NULL, NULL, '''/a'':30C,45C,56C,67C,79C,115C ''/blog/2014/07/23/scalable-collaborative-filtering-with-spark-mllib.html'':75C ''/blog/mahout-spark-what%e2%80%99s-new-recommenders'':109C ''/collaborative_filtering.html'':26C ''/docs/latest/mllib-collaborative-filtering.html'':63C ''/okapi.html#collaborative'':52C ''/p'':116C,176C,195C ''also'':82C,180C ''anoth'':31C,69C ''base'':34C ''behind'':92C ''black'':181C ''bother'':99C ''box'':89C,182C ''choic'':148C ''collabor'':11C ''commerci'':186C ''curv'':94C ''d'':18C ''databricks.com'':74C ''databricks.com/blog/2014/07/23/scalable-collaborative-filtering-with-spark-mllib.html'':73C ''detail'':78C ''docs.graphlab.org'':25C ''docs.graphlab.org/collaborative_filtering.html'':24C ''easili'':175C ''experi'':192C ''factor'':16C ''feedback'':85C ''filter'':12C ''general'':143C ''get'':154C ''giraph'':44C ''giraph.apache.org'':41C ''grafos.ml'':51C ''grafos.ml/okapi.html#collaborative'':50C ''graph'':33C ''graph-bas'':32C ''graphlab'':29C ''hard'':152C ''href'':23C,40C,49C,60C,72C,106C ''implicit'':84C ''languag'':146C ''librari'':126C ''ll'':134C ''look'':20C ''low'':13C ''mahout'':90C ''matrix'':15C ''mere'':4C ''might'':160C ''migrat'':112C ''mllib'':66C ''nofollow'':28C,43C,54C,65C,77C,111C ''okapi'':55C ''option'':70C ''p'':1C,117C,177C ''problem'':164C ''product'':166C ''program'':145C ''prototyp'':156C ''purpos'':144C ''rank'':14C ''recommend'':171C,183C ''regular'':132C ''rel'':27C,42C,53C,64C,76C,110C ''roll'':137C ''run'':157C,161C ''say'':38C,130C ''scale'':7C,163C,174C ''simpl'':10C ''solut'':46C,140C,172C ''someth'':123C ''spark'':57C,114C ''spark.apache.org'':62C ''spark.apache.org/docs/latest/mllib-collaborative-filtering.html'':61C ''suggest'':19C ''support'':83C ''system'':184C ''today'':95C ''vendor'':187C ''want'':5C,120C ''wouldn'':97C ''www.mapr.com'':108C ''www.mapr.com/blog/mahout-spark-what%e2%80%99s-new-recommenders'':107C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1213, 1, '2014-10-05 05:12:13.633', 3, 171, 'machine learning algorithms for 2d data?', '&lt;p&gt;I''m looking for a supervised learning algorithm that can take 2d data for input and output. As an example of something similar to my data, consider a black image with some sparse white dots. Blur that image using a full range of grayscale. Then create a machine that can take the blurred image as input and produce the original sharp image as output. &lt;/p&gt;

&lt;p&gt;I could make some sample 1D data by taking a region/radius around the original sharp point, but I don''t know the exact radius. It would be significant data duplication and a lot of guessing.&lt;/p&gt;

&lt;p&gt;Any good algorithm suggestions for this problem? Thanks for your time.&lt;/p&gt;
', 'machine-learning  dimensionality-reduction', NULL, '''/p'':78C,115C,128C ''1d'':85C ''2d'':5A,25C ''algorithm'':3A,21C,119C ''around'':91C ''black'':42C ''blur'':49C,66C ''consid'':40C ''could'':81C ''creat'':59C ''data'':6A,26C,39C,86C,108C ''dimension'':11B ''dimensionality-reduct'':10B ''dot'':48C ''duplic'':109C ''exact'':102C ''exampl'':33C ''full'':54C ''good'':118C ''grayscal'':57C ''guess'':114C ''imag'':43C,51C,67C,75C ''input'':28C,69C ''know'':100C ''learn'':2A,9B,20C ''look'':16C ''lot'':112C ''m'':15C ''machin'':1A,8B,61C ''machine-learn'':7B ''make'':82C ''origin'':73C,93C ''output'':30C,77C ''p'':13C,79C,116C ''point'':95C ''problem'':123C ''produc'':71C ''radius'':103C ''rang'':55C ''reduct'':12B ''region/radius'':90C ''sampl'':84C ''sharp'':74C,94C ''signific'':107C ''similar'':36C ''someth'':35C ''spars'':46C ''suggest'':120C ''supervis'':19C ''take'':24C,64C,88C ''thank'':124C ''time'':127C ''use'':52C ''white'':47C ''would'':105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1214, 1, '2014-10-05 06:25:27.303', 7, 56, 'Book keeping of experiment runs and results', '&lt;p&gt;I am a hands on researcher and I like testing out viable solutions, so I tend to run a lot of experiments. For example, if I am calculating a similarity score between documents, I might want to try out many measures. In fact, for each measure I might need to make several runs to test the effect of some parameters. &lt;/p&gt;

&lt;p&gt;So far, I''ve been tracking the runs inputs and their results by writing out the results into  files with as much info about the inputs. The problem is that retrieving a specific result becomes a challenge sometimes, even if I try to add the input info to th filename. I tried using a spreadsheet with links to results but this isn''t making a huge difference.&lt;/p&gt;

&lt;p&gt;What tools/process do you use for the book keeping of your experiments?&lt;/p&gt;
', 'tools  experiments', NULL, '''/p'':71C,140C,154C ''add'':116C ''becom'':107C ''book'':1A,149C ''calcul'':38C ''challeng'':109C ''differ'':139C ''document'':43C ''effect'':67C ''even'':111C ''exampl'':34C ''experi'':4A,9B,32C,153C ''fact'':53C ''far'':74C ''file'':91C ''filenam'':122C ''hand'':14C ''huge'':138C ''info'':95C,119C ''input'':81C,98C,118C ''isn'':134C ''keep'':2A,150C ''like'':19C ''link'':129C ''lot'':30C ''make'':61C,136C ''mani'':50C ''measur'':51C,56C ''might'':45C,58C ''much'':94C ''need'':59C ''p'':10C,72C,141C ''paramet'':70C ''problem'':100C ''research'':16C ''result'':7A,84C,89C,106C,131C ''retriev'':103C ''run'':5A,28C,63C,80C ''score'':41C ''sever'':62C ''similar'':40C ''solut'':23C ''sometim'':110C ''specif'':105C ''spreadsheet'':127C ''tend'':26C ''test'':20C,65C ''th'':121C ''tool'':8B ''tools/process'':143C ''track'':78C ''tri'':48C,114C,124C ''use'':125C,146C ''ve'':76C ''viabl'':22C ''want'':46C ''write'':86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1215, 2, '2014-10-05 12:25:15.91', 0, NULL, NULL, '&lt;p&gt;I vastly prefer to use windows.&lt;/p&gt;

&lt;p&gt;Despite that preference, i would recommend that you build your lab in Linux. Installation of these types of tools is often much easier under Linux. The anaconda distribution (&lt;a href="http://docs.continuum.io/anaconda/pkg-docs.html" rel="nofollow"&gt;http://docs.continuum.io/anaconda/pkg-docs.html&lt;/a&gt;) contains nltk, and is available under Linux (&lt;a href="http://docs.continuum.io/anaconda/install.html" rel="nofollow"&gt;http://docs.continuum.io/anaconda/install.html&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;As one example of an issue we''ve run into with windows, installing theano with cuda drivers is very difficult under windows, and quite simple under Linux.&lt;/p&gt;
', NULL, NULL, '''/anaconda/install.html'':59C ''/anaconda/install.html&lt;/a&gt;).&lt;/p&gt;'':64C ''/anaconda/pkg-docs.html'':42C ''/anaconda/pkg-docs.html&lt;/a&gt;)'':47C ''/p'':8C,93C ''anaconda'':36C ''avail'':52C ''build'':18C ''contain'':48C ''cuda'':81C ''despit'':10C ''difficult'':85C ''distribut'':37C ''docs.continuum.io'':41C,46C,58C,63C ''docs.continuum.io/anaconda/install.html'':57C ''docs.continuum.io/anaconda/install.html&lt;/a&gt;).&lt;/p&gt;'':62C ''docs.continuum.io/anaconda/pkg-docs.html'':40C ''docs.continuum.io/anaconda/pkg-docs.html&lt;/a&gt;)'':45C ''driver'':82C ''easier'':32C ''exampl'':68C ''href'':39C,56C ''instal'':23C,78C ''issu'':71C ''lab'':20C ''linux'':22C,34C,54C,92C ''much'':31C ''nltk'':49C ''nofollow'':44C,61C ''often'':30C ''one'':67C ''p'':1C,9C,65C ''prefer'':4C,12C ''quit'':89C ''recommend'':15C ''rel'':43C,60C ''run'':74C ''simpl'':90C ''theano'':79C ''tool'':28C ''type'':26C ''use'':6C ''vast'':3C ''ve'':73C ''window'':7C,77C,87C ''would'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1216, 1, '2014-10-06 12:00:55.303', 6, 3166, 'Career switch to Big Data Analytics', '&lt;p&gt;I am a 35 year old IT professional who is purely technical. I am good at programming, learning new technologies, understanding them and implementing. I did not like mathematics at school, so I didn''t score well in mathematics. I am very much interested in pursuing a career in Big Data analytics. I am more interested in Analytics rather than Big Data technologies (Hadoop etc.), though I do not dislike it.  However, when I look around in the internet, I see that, people who are good in analytics (Data Scientists) are mainly Mathematics graduates  who have done their PHds and sound like intelligent creatures, who are far far ahead of  me. I get scared sometimes to think whether my decision is correct, because learning advance statistics on your own is very tough and requires a of hard work and time investment. &lt;/p&gt;

&lt;p&gt;I would like to know whether my decision is correct, or should I leave this piece of work to only intellectuals who have spend their life in studying in prestigious colleges and earned their degrees and PHDs.&lt;/p&gt;
', 'career', NULL, '''/p'':150C,189C ''35'':12C ''advanc'':133C ''ahead'':117C ''analyt'':6A,60C,66C,96C ''around'':84C ''big'':4A,58C,69C ''career'':1A,7B,56C ''colleg'':182C ''correct'':130C,161C ''creatur'':112C ''data'':5A,59C,70C,97C ''decis'':128C,159C ''degre'':186C ''didn'':42C ''dislik'':78C ''done'':105C ''earn'':184C ''etc'':73C ''far'':115C,116C ''get'':121C ''good'':23C,94C ''graduat'':102C ''hadoop'':72C ''hard'':145C ''howev'':80C ''implement'':32C ''intellectu'':172C ''intellig'':111C ''interest'':52C,64C ''internet'':87C ''invest'':149C ''know'':156C ''learn'':26C,132C ''leav'':165C ''life'':177C ''like'':36C,110C,154C ''look'':83C ''main'':100C ''mathemat'':37C,47C,101C ''much'':51C ''new'':27C ''old'':14C ''p'':8C,151C ''peopl'':91C ''phds'':107C,188C ''piec'':167C ''prestigi'':181C ''profession'':16C ''program'':25C ''pure'':19C ''pursu'':54C ''rather'':67C ''requir'':142C ''scare'':122C ''school'':39C ''scientist'':98C ''score'':44C ''see'':89C ''sometim'':123C ''sound'':109C ''spend'':175C ''statist'':134C ''studi'':179C ''switch'':2A ''technic'':20C ''technolog'':28C,71C ''think'':125C ''though'':74C ''time'':148C ''tough'':140C ''understand'':29C ''well'':45C ''whether'':126C,157C ''work'':146C,169C ''would'':153C ''year'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1240, 1, '2014-10-10 00:59:46.703', 5, 466, 'Methods for standardizing / normalizing different rank scales', '&lt;p&gt;I know there is the normal &lt;em&gt;subtract the mean and divide by the standard deviation&lt;/em&gt; for standardizing your data, but I''m interested to know if there are more appropriate methods for this kind of discrete data. Consider the following case.&lt;/p&gt;

&lt;p&gt;I have 5 items that have been ranked by customers. First 2 items were ranked on a 1-10 scale. Others are 1-100 and 1-5. To transform everything to a 1 to 10 scale, is there another method better suited for this case?&lt;/p&gt;

&lt;p&gt;If the data has a central tendency, then the standard would work fine, but what about when you have more of a halo effect, or some more exponential distribution?&lt;/p&gt;
', 'statistics', NULL, '''-10'':73C ''-100'':78C ''-5'':81C ''/em'':26C ''/p'':53C,100C,131C ''1'':72C,77C,80C,87C ''10'':89C ''2'':66C ''5'':57C ''anoth'':93C ''appropri'':41C ''better'':95C ''case'':52C,99C ''central'':107C ''consid'':49C ''custom'':64C ''data'':30C,48C,104C ''deviat'':25C ''differ'':5A ''discret'':47C ''distribut'':130C ''divid'':21C ''effect'':125C ''em'':16C ''everyth'':84C ''exponenti'':129C ''fine'':114C ''first'':65C ''follow'':51C ''halo'':124C ''interest'':34C ''item'':58C,67C ''kind'':45C ''know'':11C,36C ''m'':33C ''mean'':19C ''method'':1A,42C,94C ''normal'':4A,15C ''other'':75C ''p'':9C,54C,101C ''rank'':6A,62C,69C ''scale'':7A,74C,90C ''standard'':3A,24C,28C,111C ''statist'':8B ''subtract'':17C ''suit'':96C ''tendenc'':108C ''transform'':83C ''work'':113C ''would'':112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5386, 1, '2015-03-25 09:51:57.847', 0, 67, 'Identity covariance matrix, decorrelated data?', '&lt;p&gt;Why would you want to decorrelated data?&lt;/p&gt;

&lt;p&gt;As I am reading about PCA and whitening on image data for DNN, I wonder what is the purpose of achieving the identity covariance matrix in your data is?&lt;/p&gt;

&lt;p&gt;Is it to remove interaction between variables, thus allow simpler models to express interactions without having to compute x1*x2?&lt;/p&gt;
', 'machine-learning  dataset  statistics  correlation  deep-learning', NULL, '''/p'':23C,54C,76C ''achiev'':45C ''allow'':64C ''comput'':73C ''correl'':11B ''covari'':2A,48C ''data'':5A,22C,35C,52C ''dataset'':9B ''decorrel'':4A,21C ''deep'':13B ''deep-learn'':12B ''dnn'':37C ''express'':68C ''ident'':1A,47C ''imag'':34C ''interact'':60C,69C ''learn'':8B,14B ''machin'':7B ''machine-learn'':6B ''matrix'':3A,49C ''model'':66C ''p'':15C,24C,55C ''pca'':30C ''purpos'':43C ''read'':28C ''remov'':59C ''simpler'':65C ''statist'':10B ''thus'':63C ''variabl'':62C ''want'':19C ''whiten'':32C ''without'':70C ''wonder'':39C ''would'':17C ''x1'':74C ''x2'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1220, 2, '2014-10-06 19:38:00.207', 1, NULL, NULL, '&lt;p&gt;Due to high demand, it is possible to start a career in data science without a formal degree. My experience is that having a degree is often a ''requirement'' in job descriptions, but if the employer is desperate enough, then that won''t matter. In general, it''s harder to get into large corporations with formalized job application processes than smaller companies without them. "Knowing people" can get you a long way, in either case.&lt;/p&gt;

&lt;p&gt;Regardless of your education, no matter how high demand is, you must have the skills to do the job.&lt;/p&gt;

&lt;p&gt;You are correct in noting that advanced statistics and other mathematics are very hard to learn independently. It is a matter of how badly you want to make the career change. While some people do have ''natural talent'' in mathematics, everybody does have to do the work to learn. Some may learn more quickly, but everybody has to take the time to learn. &lt;/p&gt;

&lt;p&gt;What it comes down to is your ability to show potential employers that you have a genuine interest in the field, and that you will be able to learn quickly on the job. The more knowledge you have, the more projects you can share in a portfolio, and the more work experience under your belt, the higher level jobs that will be available to you. You may have to start  in an entry level position first.&lt;/p&gt;

&lt;p&gt;I could suggest ways to study mathematics independently, but that isn''t part of your question. For now, just know that it''s hard, but possible if you are determined to make a career change. Strike while the iron is hot (while demand is high).&lt;/p&gt;
', NULL, NULL, '''/p'':77C,98C,163C,241C,288C ''abil'':172C ''abl'':191C ''advanc'':106C ''applic'':59C ''avail'':227C ''bad'':123C ''belt'':219C ''career'':12C,129C,276C ''case'':76C ''chang'':130C,277C ''come'':167C ''compani'':63C ''corpor'':55C ''correct'':102C ''could'':244C ''data'':14C ''degre'':19C,26C ''demand'':5C,87C,285C ''descript'':33C ''desper'':39C ''determin'':272C ''due'':2C ''educ'':82C ''either'':75C ''employ'':37C,176C ''enough'':40C ''entri'':237C ''everybodi'':140C,155C ''experi'':21C,216C ''field'':185C ''first'':240C ''formal'':18C,57C ''general'':47C ''genuin'':181C ''get'':52C,69C ''hard'':113C,266C ''harder'':50C ''high'':4C,86C,287C ''higher'':221C ''hot'':283C ''independ'':116C,250C ''interest'':182C ''iron'':281C ''isn'':253C ''job'':32C,58C,97C,197C,223C ''know'':66C,262C ''knowledg'':200C ''larg'':54C ''learn'':115C,148C,151C,162C,193C ''level'':222C,238C ''long'':72C ''make'':127C,274C ''mathemat'':110C,139C,249C ''matter'':45C,84C,120C ''may'':150C,231C ''must'':90C ''natur'':136C ''note'':104C ''often'':28C ''p'':1C,78C,99C,164C,242C ''part'':255C ''peopl'':67C,133C ''portfolio'':211C ''posit'':239C ''possibl'':8C,268C ''potenti'':175C ''process'':60C ''project'':205C ''question'':258C ''quick'':153C,194C ''regardless'':79C ''requir'':30C ''scienc'':15C ''share'':208C ''show'':174C ''skill'':93C ''smaller'':62C ''start'':10C,234C ''statist'':107C ''strike'':278C ''studi'':248C ''suggest'':245C ''take'':158C ''talent'':137C ''time'':160C ''want'':125C ''way'':73C,246C ''without'':16C,64C ''won'':43C ''work'':146C,215C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1221, 2, '2014-10-06 19:54:57.197', 1, NULL, NULL, '&lt;p&gt;Python is easy to use and manage in Linux. The Python package manager PIP and the Python environment manager Virtualenv are both fully supported in Linux. See: &lt;a href="http://docs.python-guide.org/en/latest/starting/install/linux/" rel="nofollow"&gt;http://docs.python-guide.org/en/latest/starting/install/linux/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The PyCharm IDE from JetBrains is also available for free in Linux (community edition), and it runs well. This is a great tool for a Python developer.&lt;/p&gt;

&lt;p&gt;In addition, if you need to integrate other tools, they will most likely be best supported in Linux. Need a MapReduce in your app? Hadoop will run in Linux. Need to store data in MySQL? Linux. Most data science tools are open source, and most open source technologies are supported best in Linux.&lt;/p&gt;

&lt;p&gt;There is also no cost for running Linux.&lt;/p&gt;

&lt;p&gt;If you need to keep Windows for your primary work computer (like your laptop), then you can install Linux on a server and use Putty to SSH into the Linux machine. You would then perform your work on the Linux machine, though you would still be running Windows locally.&lt;/p&gt;
', NULL, NULL, '''/en/latest/starting/install/linux/'':33C ''/en/latest/starting/install/linux/&lt;/a&gt;&lt;/p&gt;'':38C ''/p'':67C,122C,132C,183C ''addit'':70C ''also'':46C,126C ''app'':92C ''avail'':47C ''best'':83C,119C ''communiti'':52C ''comput'':144C ''cost'':128C ''data'':101C,106C ''develop'':66C ''docs.python-guide.org'':32C,37C ''docs.python-guide.org/en/latest/starting/install/linux/'':31C ''docs.python-guide.org/en/latest/starting/install/linux/&lt;/a&gt;&lt;/p&gt;'':36C ''easi'':4C ''edit'':53C ''environ'':19C ''free'':49C ''fulli'':24C ''great'':61C ''hadoop'':93C ''href'':30C ''ide'':42C ''instal'':151C ''integr'':75C ''jetbrain'':44C ''keep'':138C ''laptop'':147C ''like'':81C,145C ''linux'':10C,27C,51C,86C,97C,104C,121C,131C,152C,163C,173C ''local'':182C ''machin'':164C,174C ''manag'':8C,14C,20C ''mapreduc'':89C ''mysql'':103C ''need'':73C,87C,98C,136C ''nofollow'':35C ''open'':110C,114C ''p'':1C,39C,68C,123C,133C ''packag'':13C ''perform'':168C ''pip'':15C ''primari'':142C ''putti'':158C ''pycharm'':41C ''python'':2C,12C,18C,65C ''rel'':34C ''run'':56C,95C,130C,180C ''scienc'':107C ''see'':28C ''server'':155C ''sourc'':111C,115C ''ssh'':160C ''still'':178C ''store'':100C ''support'':25C,84C,118C ''technolog'':116C ''though'':175C ''tool'':62C,77C,108C ''use'':6C,157C ''virtualenv'':21C ''well'':57C ''window'':139C,181C ''work'':143C,170C ''would'':166C,177C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1222, 2, '2014-10-06 23:38:17.523', 1, NULL, NULL, '&lt;p&gt;You should look more into the infrastructure side of things if you don''t like maths. The lower you go in the software stack, the further away you get from maths (of the data science sort). In other words, you could build the foundation that others will use to create the tools that will serve analysts. Think of companies like Cloudera, MapR, Databricks, etc. Skills that will come in handy are distributed systems and database design. You are not going to be become a data scientist without maths; that''s a ridiculous notion!&lt;/p&gt;
', NULL, NULL, '''/p'':95C ''analyst'':57C ''away'':28C ''becom'':84C ''build'':43C ''cloudera'':62C ''come'':69C ''compani'':60C ''could'':42C ''creat'':51C ''data'':35C,86C ''databas'':76C ''databrick'':64C ''design'':77C ''distribut'':73C ''etc'':65C ''foundat'':45C ''get'':30C ''go'':21C,81C ''handi'':71C ''infrastructur'':8C ''like'':16C,61C ''look'':4C ''lower'':19C ''mapr'':63C ''math'':17C,32C,89C ''notion'':94C ''other'':47C ''p'':1C ''ridicul'':93C ''scienc'':36C ''scientist'':87C ''serv'':56C ''side'':9C ''skill'':66C ''softwar'':24C ''sort'':37C ''stack'':25C ''system'':74C ''thing'':11C ''think'':58C ''tool'':53C ''use'':49C ''without'':88C ''word'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1230, 2, '2014-10-09 01:57:41.01', 3, NULL, NULL, '&lt;p&gt;For ratings, I think you would need to use &lt;a href="http://mathworld.wolfram.com/SpearmanRankCorrelationCoefficient.html" rel="nofollow"&gt;Spearman''s rank correlation&lt;/a&gt; for your similarity metric.&lt;/p&gt;

&lt;p&gt;Cosine similarity is often used when comparing documents, and perhaps would not be a good fit for rank variables. Euclidean distance is fine for lower dimensions, but comparison of rank variables normally call for Spearman.&lt;/p&gt;

&lt;p&gt;Here''s a &lt;a href="http://stats.stackexchange.com/questions/8071/how-to-choose-between-pearson-and-spearman-correlation"&gt;question on CrossValidated regarding Spearman (vs Pearson)&lt;/a&gt;, which might shed more light for you.&lt;/p&gt;
', NULL, NULL, '''/a'':22C,81C ''/p'':27C,64C,89C ''/questions/8071/how-to-choose-between-pearson-and-spearman-correlation'':73C ''/spearmanrankcorrelationcoefficient.html'':15C ''call'':61C ''compar'':35C ''comparison'':56C ''correl'':21C ''cosin'':29C ''crossvalid'':76C ''dimens'':54C ''distanc'':49C ''document'':36C ''euclidean'':48C ''fine'':51C ''fit'':44C ''good'':43C ''href'':12C,70C ''light'':86C ''lower'':53C ''mathworld.wolfram.com'':14C ''mathworld.wolfram.com/spearmanrankcorrelationcoefficient.html'':13C ''metric'':26C ''might'':83C ''need'':8C ''nofollow'':17C ''normal'':60C ''often'':32C ''p'':1C,28C,65C ''pearson'':80C ''perhap'':38C ''question'':74C ''rank'':20C,46C,58C ''rate'':3C ''regard'':77C ''rel'':16C ''shed'':84C ''similar'':25C,30C ''spearman'':18C,63C,78C ''stats.stackexchange.com'':72C ''stats.stackexchange.com/questions/8071/how-to-choose-between-pearson-and-spearman-correlation'':71C ''think'':5C ''use'':10C,33C ''variabl'':47C,59C ''vs'':79C ''would'':7C,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1223, 1, '2014-10-07 17:27:22.063', 4, 503, 'Scikit Learn Logistic Regression Memory Leak', '&lt;p&gt;I''m curious if anyone else has run into this. I have a data set with about 350k samples, each with 4k sparse features. The sparse fill rate is about 0.5%. The data is stored in a &lt;code&gt;scipy.sparse.csr.csr_matrix&lt;/code&gt; object, with &lt;code&gt;dtype=''numpy.float64''&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;I''m using this as an input to sklearn''s Logistic Regression classifier. The &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html" rel="nofollow"&gt;documentation&lt;/a&gt; indicates that sparse CSR matrices are acceptable inputs to this classifier. However, when I train the classifier, I get extremely bad memory performance; the memory usage of my process explodes from ~150 MB to fill all the available memory and then everything grinds to a halt as memory swapping to disk takes over.&lt;/p&gt;

&lt;p&gt;Does anyone know why this classifier might expand the sparse matrix to a dense matrix? I''m using the default parameters for the classifier at the moment, within an updated anacoda distribution. Thanks!&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;scipy.__version__ = ''0.14.0''
sklearn.__version__ = ''0.15.2''
&lt;/code&gt;&lt;/pre&gt;
', 'efficiency  performance  sklearn', NULL, '''/a'':82C ''/code'':51C,57C,178C ''/p'':58C,136C,171C ''/pre'':179C ''/stable/modules/generated/sklearn.linear_model.logisticregression.html'':78C ''0.14.0'':175C ''0.15.2'':177C ''0.5'':41C ''150'':114C ''350k'':28C ''4k'':32C ''accept'':89C ''anacoda'':168C ''anyon'':15C,139C ''avail'':120C ''bad'':103C ''classifi'':72C,93C,99C,143C,161C ''code'':48C,54C,173C ''csr'':86C ''curious'':13C ''data'':24C,43C ''default'':157C ''dens'':151C ''disk'':133C ''distribut'':169C ''document'':81C ''dtype'':55C ''effici'':7B ''els'':16C ''everyth'':124C ''expand'':145C ''explod'':112C ''extrem'':102C ''featur'':34C ''fill'':37C,117C ''get'':101C ''grind'':125C ''halt'':128C ''howev'':94C ''href'':75C ''indic'':83C ''input'':66C,90C ''know'':140C ''leak'':6A ''learn'':2A ''logist'':3A,70C ''m'':12C,61C,154C ''matric'':87C ''matrix'':50C,148C,152C ''mb'':115C ''memori'':5A,104C,107C,121C,130C ''might'':144C ''moment'':164C ''nofollow'':80C ''numpy.float64'':56C ''object'':52C ''p'':10C,59C,137C ''paramet'':158C ''perform'':8B,105C ''pre'':172C ''process'':111C ''rate'':38C ''regress'':4A,71C ''rel'':79C ''run'':18C ''sampl'':29C ''scikit'':1A ''scikit-learn.org'':77C ''scikit-learn.org/stable/modules/generated/sklearn.linear_model.logisticregression.html'':76C ''scipy.__version__'':174C ''scipy.sparse.csr.csr'':49C ''set'':25C ''sklearn'':9B,68C ''sklearn.__version__'':176C ''spars'':33C,36C,85C,147C ''store'':45C ''swap'':131C ''take'':134C ''thank'':170C ''train'':97C ''updat'':167C ''usag'':108C ''use'':62C,155C ''within'':165C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1224, 2, '2014-10-07 18:09:19.433', 5, NULL, NULL, '&lt;p&gt;Ok, this ended up being an RTFM situation, although in this case it was RTF error message.&lt;/p&gt;

&lt;p&gt;While running this, I kept getting the following error:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I assumed that, since this had to do with the target vector, and since it was a warning only, that it would just silently change my target vector to 1-D.&lt;/p&gt;

&lt;p&gt;However, when I explicitly converted my target vector to 1-D, my memory problems went away. Apparently having the target vector in an incorrect form caused it to convert my input vectors into dense vectors from sparse vectors.&lt;/p&gt;

&lt;p&gt;Lesson learned: &lt;strong&gt;follow the recommendations&lt;/strong&gt; when sklearn ''suggests'' you do something.&lt;/p&gt;
', NULL, NULL, '''/code'':60C ''/p'':19C,30C,94C,134C,149C ''/pre'':61C ''/strong'':142C ''1'':92C,105C ''1d'':43C ''although'':10C ''appar'':112C ''array'':44C ''assum'':64C ''away'':111C ''case'':13C ''caus'':121C ''chang'':48C,87C ''code'':32C ''column'':36C ''column-vector'':35C ''convert'':100C,124C ''d'':93C,106C ''dataconversionwarn'':33C ''dens'':129C ''end'':4C ''error'':17C,29C ''exampl'':57C ''expect'':46C ''explicit'':99C ''follow'':28C,139C ''form'':120C ''get'':26C ''howev'':96C ''incorrect'':119C ''input'':126C ''kept'':25C ''learn'':137C ''lesson'':136C ''memori'':108C ''messag'':18C ''n'':54C ''ok'':2C ''p'':1C,20C,62C,95C,135C ''pass'':40C ''pleas'':47C ''pre'':31C ''problem'':109C ''ravel'':59C ''recommend'':141C ''rtf'':16C ''rtfm'':8C ''run'':22C ''sampl'':55C ''shape'':50C ''silent'':86C ''sinc'':66C,76C ''situat'':9C ''sklearn'':144C ''someth'':148C ''spars'':132C ''strong'':138C ''suggest'':145C ''target'':73C,89C,102C,115C ''use'':58C ''vector'':37C,74C,90C,103C,116C,127C,130C,133C ''warn'':80C ''went'':110C ''would'':84C ''y'':38C,52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1225, 1, '2014-10-08 10:42:41.833', 2, 108, 'data processing, correlation calculation', '&lt;p&gt;I have product purchase count data which looks likes this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;user item1 item2
   a     2     4
   b     1     3
   c     5     6
   ...   ...   ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;These data are imported into &lt;code&gt;python&lt;/code&gt; using &lt;code&gt;numpy.genfromtxt&lt;/code&gt;. Now I want to process it to get the correlation between &lt;code&gt;item1&lt;/code&gt; purchase amount and &lt;code&gt;item2&lt;/code&gt; purchase amount -- basically for each value &lt;code&gt;x&lt;/code&gt; of &lt;code&gt;item1&lt;/code&gt; I want to find all the users who bought &lt;code&gt;item1&lt;/code&gt; in &lt;code&gt;x&lt;/code&gt; quantity then average the &lt;code&gt;item2&lt;/code&gt; over the same users. What is the best way to do this? I can do this by using &lt;code&gt;for&lt;/code&gt; loops but I thought there might be something more efficient than that. Thanks!&lt;/p&gt;
', 'python  correlation', NULL, '''/code'':33C,43C,47C,61C,67C,76C,80C,92C,96C,103C,124C ''/p'':18C,138C ''/pre'':34C ''1'':28C ''2'':25C ''3'':29C ''4'':26C ''5'':31C ''6'':32C ''amount'':63C,69C ''averag'':99C ''b'':27C ''basic'':70C ''best'':111C ''bought'':89C ''c'':30C ''calcul'':4A ''code'':20C,41C,45C,59C,65C,74C,78C,90C,94C,101C,122C ''correl'':3A,6B,57C ''count'':12C ''data'':1A,13C,37C ''effici'':134C ''find'':84C ''get'':55C ''import'':39C ''item1'':22C,60C,79C,91C ''item2'':23C,66C,102C ''like'':16C ''look'':15C ''loop'':125C ''might'':130C ''numpy.genfromtxt'':46C ''p'':7C,35C ''pre'':19C ''process'':2A,52C ''product'':10C ''purchas'':11C,62C,68C ''python'':5B,42C ''quantiti'':97C ''someth'':132C ''thank'':137C ''thought'':128C ''use'':44C,121C ''user'':21C,87C,107C ''valu'':73C ''want'':50C,82C ''way'':112C ''x'':75C,95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1226, 2, '2014-10-08 13:37:27.357', 0, NULL, NULL, '&lt;p&gt;If you want to do NLP stay away from Windows! ( No Offense) Python NLTK stands out in terms of resource and community support for NLP. &lt;/p&gt;

&lt;p&gt;Moreover in Linux you can always leverage the benefit of  awk,sed, grep which are pretty useful tools when it comes to pre-processing. And no worries of funny characters getting introduced when you use a file.&lt;/p&gt;
', NULL, NULL, '''/p'':27C,67C ''alway'':34C ''away'':9C ''awk'':39C ''benefit'':37C ''charact'':59C ''come'':49C ''communiti'':23C ''file'':66C ''funni'':58C ''get'':60C ''grep'':41C ''introduc'':61C ''leverag'':35C ''linux'':31C ''moreov'':29C ''nlp'':7C,26C ''nltk'':15C ''offens'':13C ''p'':1C,28C ''pre'':52C ''pre-process'':51C ''pretti'':44C ''process'':53C ''python'':14C ''resourc'':21C ''sed'':40C ''stand'':16C ''stay'':8C ''support'':24C ''term'':19C ''tool'':46C ''use'':45C,64C ''want'':4C ''window'':11C ''worri'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2280, 2, '2014-10-15 11:37:47.09', 0, NULL, NULL, '&lt;p&gt;In the case you mention, I recommend to keep the changes as a dictionary, for instance in a .csv file. Write a script that replaces the values in the original data based on the translation in your dictionary. That way, you separate the corrections from the script itself.&lt;/p&gt;
', NULL, NULL, '''/p'':50C ''base'':33C ''case'':4C ''chang'':12C ''correct'':45C ''csv'':20C ''data'':32C ''dictionari'':15C,39C ''file'':21C ''instanc'':17C ''keep'':10C ''mention'':6C ''origin'':31C ''p'':1C ''recommend'':8C ''replac'':26C ''script'':24C,48C ''separ'':43C ''translat'':36C ''valu'':28C ''way'':41C ''write'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1227, 1, '2014-10-08 16:17:11.663', 7, 59, 'Union grouping in bipartite graphs?', '&lt;p&gt;I''m trying to figure out a good (and fast) solution to the following problem:&lt;/p&gt;

&lt;p&gt;I have two models I''m working with, let''s call them players and teams. A player can be on multiple teams and a team can have multiple players). I''m working on creating a UI element on a form that allows a user to select multiple teams (checkboxes). As the user is selecting (or deselecting) teams, I''d like to display the teams grouped by the players.&lt;/p&gt;

&lt;p&gt;So for examples:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;If the selected teams have no players that intersect, each team would have its own section. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;If the user selects two teams and they have the same players, there would be one section containing the names of the two teams and all the players.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;If TEAM_A has players [1, 2, 4, 5] and TEAM_B has players [1, 3, 5, 6]. There would be the following sections: SECTION_X = [TEAM_A, TEAM_B, 1, 5], SECTION_Y = [TEAM_A, 2, 3], SECTION _Z = [TEAM_B, 3, 5]&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I hope that''s clear. Essentially, I want to find the teams that players have in common and group by that. I was thinking maybe there is a way to do this by navigating a bipartite graph? Not exactly sure how though and I might be overthinking it. I was hoping to do this by creating some type of data structure on the server and using it on the client. I would love to hear your suggestions and I appreciate any help you can give!&lt;/p&gt;
', 'graphs', NULL, '''/li'':119C,151C,199C ''/ol'':200C ''/p'':23C,93C,98C,118C,150C,198C,287C ''1'':159C,168C,184C ''2'':160C,190C ''3'':169C,191C,196C ''4'':161C ''5'':162C,170C,185C,197C ''6'':171C ''allow'':66C ''appreci'':281C ''b'':165C,183C,195C ''bipartit'':4A,237C ''call'':35C ''checkbox'':73C ''clear'':206C ''client'':271C ''common'':218C ''contain'':139C ''creat'':58C,257C ''d'':83C ''data'':261C ''deselect'':80C ''display'':86C ''element'':61C ''essenti'':207C ''exact'':240C ''exampl'':97C ''fast'':17C ''figur'':12C ''find'':211C ''follow'':21C,176C ''form'':64C ''give'':286C ''good'':15C ''graph'':5A,6B,238C ''group'':2A,89C,220C ''hear'':276C ''help'':283C ''hope'':203C,252C ''intersect'':110C ''let'':33C ''li'':100C,120C,152C ''like'':84C ''love'':274C ''m'':9C,30C,55C ''mayb'':226C ''might'':246C ''model'':28C ''multipl'':45C,52C,71C ''name'':141C ''navig'':235C ''ol'':99C ''one'':137C ''overthink'':248C ''p'':7C,24C,94C,101C,121C,153C,201C ''player'':37C,41C,53C,92C,108C,133C,149C,158C,167C,215C ''problem'':22C ''section'':117C,138C,177C,178C,186C,192C ''select'':70C,78C,104C,125C ''server'':265C ''solut'':18C ''structur'':262C ''suggest'':278C ''sure'':241C ''team'':39C,46C,49C,72C,81C,88C,105C,112C,127C,145C,155C,164C,180C,182C,188C,194C,213C ''think'':225C ''though'':243C ''tri'':10C ''two'':27C,126C,144C ''type'':259C ''ui'':60C ''union'':1A ''use'':267C ''user'':68C,76C,124C ''want'':209C ''way'':230C ''work'':31C,56C ''would'':113C,135C,173C,273C ''x'':179C ''y'':187C ''z'':193C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1229, 1, '2014-10-09 01:41:11.797', 6, 750, 'Cosine Similarity for Ratings Recommendations? Why use it?', '&lt;p&gt;Lets say I have a database of users who rate different products on a scale of 1-5. Our recommendation engine recommends products to users based on the preferences of other users who are highly similar. My first approach to finding similar users was to use Cosine Similarity, and just treat user ratings as vector components. The main problem with this approach is that it just measures vector angles and doesn''t take rating scale or magnitude into consideration.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;My question is this:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Are there any drawbacks to&lt;/strong&gt; &lt;strong&gt;just using the percentage difference between the vector components of two vectors as a measure of similarity&lt;/strong&gt;? What disadvantages, if any, would I encounter if I used that method, instead of Cosine Similarity or Euclidean Distance?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;For Example, why not just do this:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;n = 5 stars
a = (1,4,4)
b = (2,3,4)

similarity(a,b) = 1 - ( (|1-2|/5) + (|4-3|/5) + (|4-4|/5) ) / 3 = .86667
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Instead of Cosine Similarity :&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;a = (1,4,4)
b = (2,3,4)

CosSimilarity(a,b) = 
(1*2)+(4*3)+(4*4) / sqrt( (1^2)+(4^2)+(4^2) ) * sqrt( (2^2)+(3^2)+(4^2) ) = .9697
&lt;/code&gt;&lt;/pre&gt;
', 'machine-learning  recommendation', NULL, '''-2'':177C ''-3'':180C ''-4'':183C ''-5'':30C ''/5'':178C,181C,184C ''/code'':187C,231C ''/p'':92C,100C,147C,158C,196C ''/pre'':188C,232C ''/strong'':99C,108C,127C,157C,195C ''1'':29C,165C,175C,176C,200C,210C,217C ''2'':169C,204C,211C,218C,220C,222C,224C,225C,227C,229C ''3'':170C,185C,205C,213C,226C ''4'':166C,167C,171C,179C,182C,201C,202C,206C,212C,214C,215C,219C,221C,228C ''5'':162C ''86667'':186C ''9697'':230C ''angl'':81C ''approach'':51C,74C ''b'':168C,174C,203C,209C ''base'':38C ''code'':160C,198C ''compon'':68C,118C ''consider'':91C ''cosin'':1A,59C,142C,193C ''cossimilar'':207C ''databas'':18C ''differ'':23C,114C ''disadvantag'':129C ''distanc'':146C ''doesn'':83C ''drawback'':106C ''encount'':134C ''engin'':33C ''euclidean'':145C ''exampl'':151C ''find'':53C ''first'':50C ''high'':47C ''instead'':140C,191C ''learn'':10B ''let'':13C ''machin'':9B ''machine-learn'':8B ''magnitud'':89C ''main'':70C ''measur'':79C,124C ''method'':139C ''n'':161C ''p'':12C,93C,101C,148C,189C ''percentag'':113C ''pre'':159C,197C ''prefer'':41C ''problem'':71C ''product'':24C,35C ''question'':96C ''rate'':4A,22C,65C,86C ''recommend'':5A,11B,32C,34C ''say'':14C ''scale'':27C,87C ''similar'':2A,48C,54C,60C,126C,143C,172C,194C ''sqrt'':216C,223C ''star'':163C ''strong'':94C,102C,109C,149C,190C ''take'':85C ''treat'':63C ''two'':120C ''use'':7A,58C,111C,137C ''user'':20C,37C,44C,55C,64C ''vector'':67C,80C,117C,121C ''would'':132C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1231, 2, '2014-10-09 02:24:07.64', 4, NULL, NULL, '&lt;p&gt;Rating bias and scale can easily be accounted for by standardization. The point of using  Euclidean similarity metrics in vector space co-embeddings is that it reduces the recommendation problem to one of finding the nearest neighbors, which can be done efficiently both exactly and approximately. What you don''t want to do in real-life settings is to have to compare every item/user pair and sort them according to some expensive metric. That just doesn''t scale.&lt;/p&gt;

&lt;p&gt;One trick is to use an approximation to cull the herd to a managable size of tentative recommendations, then to run your expensive ranking on top of that.&lt;/p&gt;

&lt;p&gt;edit: Microsoft Research is presenting a paper that covers this very topic at RecSys right now: &lt;a href="http://www.ulrichpaquet.com/Papers/SpeedUp.pdf" rel="nofollow"&gt;Speeding Up the Xbox Recommender System Using a
Euclidean Transformation for Inner-Product Spaces&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':152C ''/p'':82C,112C,153C ''/papers/speedup.pdf'':134C ''accord'':72C ''account'':9C ''approxim'':48C,90C ''bias'':3C ''co'':24C ''co-embed'':23C ''compar'':65C ''cover'':122C ''cull'':92C ''doesn'':79C ''done'':43C ''easili'':7C ''edit'':114C ''effici'':44C ''embed'':25C ''euclidean'':17C,145C ''everi'':66C ''exact'':46C ''expens'':75C,106C ''find'':36C ''herd'':94C ''href'':131C ''inner'':149C ''inner-product'':148C ''item/user'':67C ''life'':59C ''manag'':97C ''metric'':19C,76C ''microsoft'':115C ''nearest'':38C ''neighbor'':39C ''nofollow'':136C ''one'':34C,84C ''p'':1C,83C,113C ''pair'':68C ''paper'':120C ''point'':14C ''present'':118C ''problem'':32C ''product'':150C ''rank'':107C ''rate'':2C ''real'':58C ''real-lif'':57C ''recommend'':31C,101C,141C ''recsi'':127C ''reduc'':29C ''rel'':135C ''research'':116C ''right'':128C ''run'':104C ''scale'':5C,81C ''set'':60C ''similar'':18C ''size'':98C ''sort'':70C ''space'':22C,151C ''speed'':137C ''standard'':12C ''system'':142C ''tentat'':100C ''top'':109C ''topic'':125C ''transform'':146C ''trick'':85C ''use'':16C,88C,143C ''vector'':21C ''want'':53C ''www.ulrichpaquet.com'':133C ''www.ulrichpaquet.com/papers/speedup.pdf'':132C ''xbox'':140C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1232, 2, '2014-10-09 02:35:24.533', 2, NULL, NULL, '&lt;p&gt;nDCG is a ranking metric and RMSE is not. In the context of recommender systems, you would use a ranking metric when your ratings are implicit (e.g., item skipped vs. item consumed) rather than explicit (the user provides an actual number, a la Netflix).&lt;/p&gt;
', NULL, NULL, '''/p'':46C ''actual'':41C ''consum'':33C ''context'':13C ''e.g'':28C ''explicit'':36C ''implicit'':27C ''item'':29C,32C ''la'':44C ''metric'':6C,22C ''ndcg'':2C ''netflix'':45C ''number'':42C ''p'':1C ''provid'':39C ''rank'':5C,21C ''rate'':25C ''rather'':34C ''recommend'':15C ''rmse'':8C ''skip'':30C ''system'':16C ''use'':19C ''user'':38C ''vs'':31C ''would'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1234, 2, '2014-10-09 10:50:39.197', 1, NULL, NULL, '&lt;p&gt;I am developing a recommendation engine for stack overflow (personal project). Check it on &lt;a href="http://recommender.im" rel="nofollow"&gt;http://recommender.im&lt;/a&gt; . &lt;/p&gt;

&lt;p&gt;It is still a working in progress, but I have a quite functional website working. I am putting there most of the code I used through python notebooks.&lt;/p&gt;

&lt;p&gt;Basically I used:&lt;br&gt;
- Frontend: angularJS&lt;br&gt;
- Website backend: flask + scikit-learn&lt;br&gt;
- machine learning and data preparation: python, pandas, scikit-learn&lt;/p&gt;

&lt;p&gt;I really like python for data science as the community and libraries are really good.&lt;/p&gt;
', NULL, NULL, '''/a'':22C ''/p'':23C,53C,79C,96C ''angularj'':60C ''backend'':63C ''basic'':55C ''br'':58C,61C,68C ''check'':13C ''code'':47C ''communiti'':90C ''data'':72C,86C ''develop'':4C ''engin'':7C ''flask'':64C ''frontend'':59C ''function'':37C ''good'':95C ''href'':17C ''learn'':67C,70C,78C ''librari'':92C ''like'':83C ''machin'':69C ''nofollow'':20C ''notebook'':52C ''overflow'':10C ''p'':1C,24C,54C,80C ''panda'':75C ''person'':11C ''prepar'':73C ''progress'':31C ''project'':12C ''put'':42C ''python'':51C,74C,84C ''quit'':36C ''realli'':82C,94C ''recommend'':6C ''recommender.im'':18C,21C ''rel'':19C ''scienc'':87C ''scikit'':66C,77C ''scikit-learn'':65C,76C ''stack'':9C ''still'':27C ''use'':49C,57C ''websit'':38C,62C ''work'':29C,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1235, 2, '2014-10-09 10:58:02.557', 1, NULL, NULL, '&lt;p&gt;Pandas is the best thing since sliced bread (for data science, at least). &lt;/p&gt;

&lt;p&gt;an example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pd
In [22]: df = pd.read_csv(''yourexample.csv'')

In [23]: df
Out[23]:
   user   item1   item2
0     a        2      4
1     b        1      3
2     c        5      6

In [24]: df.columns
Out[24]: Index([u''user '', u''item1 '', u''item2''], dtype=''object'')

In [25]: df.corr()
Out[25]:
          item1      item2
item1   1.000000  0.995871
item2   0.995871  1.000000

In [26]: df.cov()
Out[26]:
          item1      item2
item1   4.333333  3.166667
item2   3.166667  2.333333
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Bingo!&lt;/p&gt;
', NULL, NULL, '''/code'':90C ''/p'':15C,19C,94C ''/pre'':91C ''0'':38C ''0.995871'':73C,75C ''1'':42C,44C ''1.000000'':72C,76C ''2'':40C,46C ''2.333333'':89C ''22'':25C ''23'':31C,34C ''24'':51C,54C ''25'':65C,68C ''26'':78C,81C ''3'':45C ''3.166667'':86C,88C ''4'':41C ''4.333333'':85C ''5'':48C ''6'':49C ''b'':43C ''best'':5C ''bingo'':93C ''bread'':9C ''c'':47C ''code'':21C ''csv'':28C ''data'':11C ''df'':26C,32C ''df.columns'':52C ''df.corr'':66C ''df.cov'':79C ''dtype'':62C ''exampl'':18C ''import'':22C ''index'':55C ''item1'':36C,59C,69C,71C,82C,84C ''item2'':37C,61C,70C,74C,83C,87C ''least'':14C ''object'':63C ''p'':1C,16C,92C ''panda'':2C ''pd'':23C ''pd.read'':27C ''pre'':20C ''scienc'':12C ''sinc'':7C ''slice'':8C ''thing'':6C ''u'':56C,58C,60C ''user'':35C,57C ''yourexample.csv'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2325, 2, '2014-10-21 02:43:26.79', 4, NULL, NULL, '&lt;p&gt;There''s &lt;a href="http://www.vision.cs.chubu.ac.jp/CV-R/pdf/AmirICCVW2009.pdf" rel="nofollow"&gt;a recent paper on this subject&lt;/a&gt; (&lt;em&gt;On-line Random Forests&lt;/em&gt;), coming from computer vision. &lt;a href="http://www.ymer.org/amir/software/online-random-forests/" rel="nofollow"&gt;Here''s an implementation&lt;/a&gt;, and a presentation: &lt;a href="http://www.slideshare.net/CvilleDataScience/online-random-forest-in-10-minutes" rel="nofollow"&gt;Online random forests in 10 minutes&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':17C,40C,57C ''/amir/software/online-random-forests/'':33C ''/cv-r/pdf/amiriccvw2009.pdf'':8C ''/cvilledatascience/online-random-forest-in-10-minutes'':48C ''/em'':24C ''/p'':58C ''10'':55C ''come'':25C ''comput'':27C ''em'':18C ''forest'':23C,53C ''href'':5C,30C,45C ''implement'':39C ''line'':21C ''minut'':56C ''nofollow'':10C,35C,50C ''on-lin'':19C ''onlin'':51C ''p'':1C ''paper'':13C ''present'':43C ''random'':22C,52C ''recent'':12C ''rel'':9C,34C,49C ''subject'':16C ''vision'':28C ''www.slideshare.net'':47C ''www.slideshare.net/cvilledatascience/online-random-forest-in-10-minutes'':46C ''www.vision.cs.chubu.ac.jp'':7C ''www.vision.cs.chubu.ac.jp/cv-r/pdf/amiriccvw2009.pdf'':6C ''www.ymer.org'':32C ''www.ymer.org/amir/software/online-random-forests/'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10130, 1, '2016-02-07 22:27:42.207', 2, 19, 'User activity representation for Prediction/ML', '&lt;p&gt;I want to predict future user activities (e.g., account cancellation) but I don''t know how I''m supposed to represent the data.&lt;/p&gt;

&lt;p&gt;The raw data is a sequence of all activities by all users:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;2015-01-01T04:04|user1|created account
2015-01-01T05:04|user2|created account
2015-01-01T06:04|user1|changed plan
2015-01-01T07:04|user2|changed plan
2015-01-01T08:04|user1|cancelled submission
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In the simplest prediction I''d like to label a user as 0=Cancelled and 1=Not Cancelled and subsequently would train a classifier for this label.&lt;/p&gt;

&lt;p&gt;The question is how would I prepare the data for a machine learning algorithm (e.g. SVM or logistic regression)?. I now have multiple rows per user which are ordered by the time they happened, but I''m thinking of having only one row per user. How would I represent the activities? Furthermore, the number of activities per user is not limited (1 to &lt;code&gt;n&lt;/code&gt; where &lt;code&gt;n&lt;/code&gt; would hardly ever exceed &lt;code&gt;10&lt;/code&gt;), and the the number of features per activity can be large (up to 100 features).&lt;/p&gt;

&lt;p&gt;I''d really appreciate it if anyone could help me out here :-)&lt;/p&gt;

&lt;p&gt;Thx&lt;/p&gt;
', 'machine-learning  preprocessing', NULL, '''-01'':52C,53C,60C,61C,68C,69C,76C,77C,84C,85C ''/code'':91C,188C,192C,199C ''/p'':34C,48C,121C,215C,229C,232C ''/pre'':92C ''0'':106C ''04'':55C,63C,71C,79C,87C ''1'':109C,184C ''10'':198C ''100'':213C ''2015'':51C,59C,67C,75C,83C ''account'':19C,58C,66C ''activ'':2A,17C,44C,173C,178C,207C ''algorithm'':136C ''anyon'':223C ''appreci'':220C ''cancel'':20C,89C,107C,111C ''chang'':73C,81C ''classifi'':117C ''code'':50C,186C,190C,197C ''could'':224C ''creat'':57C,65C ''d'':99C,218C ''data'':33C,38C,131C ''e.g'':18C,137C ''ever'':195C ''exceed'':196C ''featur'':205C,214C ''furthermor'':174C ''futur'':15C ''happen'':156C ''hard'':194C ''help'':225C ''know'':25C ''label'':102C,120C ''larg'':210C ''learn'':8B,135C ''like'':100C ''limit'':183C ''logist'':140C ''m'':28C,159C ''machin'':7B,134C ''machine-learn'':6B ''multipl'':145C ''n'':187C,191C ''number'':176C,203C ''one'':164C ''order'':151C ''p'':10C,35C,93C,122C,216C,230C ''per'':147C,166C,179C,206C ''plan'':74C,82C ''pre'':49C ''predict'':14C,97C ''prediction/ml'':5A ''prepar'':129C ''preprocess'':9B ''question'':124C ''raw'':37C ''realli'':219C ''regress'':141C ''repres'':31C,171C ''represent'':3A ''row'':146C,165C ''sequenc'':41C ''simplest'':96C ''submiss'':90C ''subsequ'':113C ''suppos'':29C ''svm'':138C ''t04'':54C ''t05'':62C ''t06'':70C ''t07'':78C ''t08'':86C ''think'':160C ''thx'':231C ''time'':154C ''train'':115C ''user'':1A,16C,47C,104C,148C,167C,180C ''user1'':56C,72C,88C ''user2'':64C,80C ''want'':12C ''would'':114C,127C,169C,193C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1241, 1, '2014-10-10 01:12:19.163', 4, 145, 'Collaborative Social Network Visualization', '&lt;p&gt;I do movement building work for Effective Altruism (&lt;a href="http://en.m.wikipedia.org/wiki/Effective_altruism" rel="nofollow"&gt;http://en.m.wikipedia.org/wiki/Effective_altruism&lt;/a&gt;), and would like to level up our growth strategy. It occurred to me that a social network visualization tool which allowed us to strategically find and recruit new influencers/donors would be mega useful. I''d love to find something (preferably free), similar to InMaps, which would allow us to:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Combine all of our social media connections into a single map&lt;/li&gt;
&lt;li&gt;Easily see who the superconnectors are&lt;/li&gt;
&lt;li&gt;Weight each person by their degree of social influence (perhaps some function of things like Klout score * amount of social media connections * number of Google mentions, etc) &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Does such a thing exist? If not, is anyone interested in pro bono work for an amazing cause? =)&lt;/p&gt;

&lt;p&gt;Disclaimer: I am a data science noob, so preferably the solution would be one with a nice GUI and minimal involvement of R or Python.&lt;/p&gt;
', 'social-network-analysis', NULL, '''/li'':91C,99C,128C ''/p'':77C,149C,176C ''/ul'':129C ''/wiki/effective_altruism'':22C ''/wiki/effective_altruism&lt;/a&gt;),'':27C ''allow'':48C,74C ''altruism'':17C ''amaz'':147C ''amount'':118C ''analysi'':8B ''anyon'':139C ''bono'':143C ''build'':13C ''caus'':148C ''collabor'':1A ''combin'':80C ''connect'':86C,122C ''d'':62C ''data'':155C ''degre'':106C ''disclaim'':151C ''easili'':93C ''effect'':16C ''en.m.wikipedia.org'':21C,26C ''en.m.wikipedia.org/wiki/effective_altruism'':20C ''en.m.wikipedia.org/wiki/effective_altruism&lt;/a&gt;),'':25C ''etc'':127C ''exist'':135C ''find'':52C,65C ''free'':68C ''function'':112C ''googl'':125C ''growth'':35C ''gui'':168C ''href'':19C ''influenc'':109C ''influencers/donors'':56C ''inmap'':71C ''interest'':140C ''involv'':171C ''klout'':116C ''level'':32C ''li'':79C,92C,100C ''like'':30C,115C ''love'':63C ''map'':90C ''media'':85C,121C ''mega'':59C ''mention'':126C ''minim'':170C ''movement'':12C ''network'':3A,7B,44C ''new'':55C ''nice'':167C ''nofollow'':24C ''noob'':157C ''number'':123C ''occur'':38C ''one'':164C ''p'':9C,130C,150C ''perhap'':110C ''person'':103C ''prefer'':67C,159C ''pro'':142C ''python'':175C ''r'':173C ''recruit'':54C ''rel'':23C ''scienc'':156C ''score'':117C ''see'':94C ''similar'':69C ''singl'':89C ''social'':2A,6B,43C,84C,108C,120C ''social-network-analysi'':5B ''solut'':161C ''someth'':66C ''strateg'':51C ''strategi'':36C ''superconnector'':97C ''thing'':114C,134C ''tool'':46C ''ul'':78C ''us'':49C,75C ''use'':60C ''visual'':4A,45C ''weight'':101C ''work'':14C,144C ''would'':29C,57C,73C,162C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1242, 2, '2014-10-10 03:33:45.353', 3, NULL, NULL, '&lt;p&gt;I think &lt;a href="https://gephi.github.io/" rel="nofollow"&gt;Gephi&lt;/a&gt;, an open-source visualization tool, would help you a lot. Actually, as I know, the InMaps and its community detection algorithm are same as the Gephi''s.&lt;/p&gt;
', NULL, NULL, '''/a'':10C ''/p'':39C ''actual'':22C ''algorithm'':32C ''communiti'':30C ''detect'':31C ''gephi'':9C,37C ''gephi.github.io'':6C ''help'':18C ''href'':5C ''inmap'':27C ''know'':25C ''lot'':21C ''nofollow'':8C ''open'':13C ''open-sourc'':12C ''p'':1C ''rel'':7C ''sourc'':14C ''think'':3C ''tool'':16C ''visual'':15C ''would'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1243, 1, '2014-10-10 03:45:09.343', 6, 59, 'Can we quantify how position within search results is related to click-through probability?', '&lt;p&gt;Suppose, for example, that the first search result on a page of Google search results is swapped with the second result. How much would this change the click-through probabilities of the two results? How much would its click-through probability drop if the fifth search result was swapped with the sixth? &lt;/p&gt;

&lt;p&gt;Can we say something, with some level of assurance, about how expected click-through probabilities change if we do these types of pairwise swaps within pages of search results?&lt;/p&gt;

&lt;p&gt;What we seek is a measure of the contribution to click-through rates made specifically by position bias.&lt;/p&gt;

&lt;p&gt;Likely, how position ranking would affect the sales in Amazon or other online shopping website? If we cast the sales into two parts, the product quality and its ranking effect.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;
sales = alpha*quality + beta*position + epsilon
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;How can we quantify the beta?&lt;/p&gt;
', 'recommendation  search  information-retrieval  regression', NULL, '''/code'':171C ''/p'':77C,109C,130C,162C,172C,180C ''affect'':137C ''alpha'':166C ''amazon'':141C ''assur'':87C ''beta'':168C,179C ''bias'':129C ''cast'':149C ''chang'':48C,95C ''click'':13A,51C,63C,92C,122C ''click-through'':12A,50C,62C,91C,121C ''code'':164C ''contribut'':119C ''drop'':66C ''effect'':161C ''epsilon'':170C ''exampl'':25C ''expect'':90C ''fifth'':69C ''first'':28C ''googl'':35C ''inform'':19B ''information-retriev'':18B ''level'':85C ''like'':132C ''made'':125C ''measur'':116C ''much'':45C,59C ''onlin'':144C ''p'':22C,78C,110C,131C,163C,173C ''page'':33C,105C ''pairwis'':102C ''part'':154C ''posit'':5A,128C,134C,169C ''probabl'':15A,53C,65C,94C ''product'':156C ''qualiti'':157C,167C ''quantifi'':3A,177C ''rank'':135C,160C ''rate'':124C ''recommend'':16B ''regress'':21B ''relat'':10A ''result'':8A,30C,37C,43C,57C,71C,108C ''retriev'':20B ''sale'':139C,151C,165C ''say'':81C ''search'':7A,17B,29C,36C,70C,107C ''second'':42C ''seek'':113C ''shop'':145C ''sixth'':76C ''someth'':82C ''specif'':126C ''suppos'':23C ''swap'':39C,73C,103C ''two'':56C,153C ''type'':100C ''websit'':146C ''within'':6A,104C ''would'':46C,60C,136C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2262, 2, '2014-10-12 19:52:55.43', 3, NULL, NULL, '&lt;p&gt;you might want to look at &lt;a href="http://deeplearning.net/software/jobman/intro.html" rel="nofollow"&gt;http://deeplearning.net/software/jobman/intro.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;it was designed for deep learning (I guess), but it is application agnostic. It is effectively an API version of SeanEasters approach&lt;/p&gt;
', NULL, NULL, '''/p'':41C ''/software/jobman/intro.html'':12C ''/software/jobman/intro.html&lt;/a&gt;&lt;/p&gt;'':17C ''agnost'':31C ''api'':36C ''applic'':30C ''approach'':40C ''deep'':23C ''deeplearning.net'':11C,16C ''deeplearning.net/software/jobman/intro.html'':10C ''deeplearning.net/software/jobman/intro.html&lt;/a&gt;&lt;/p&gt;'':15C ''design'':21C ''effect'':34C ''guess'':26C ''href'':9C ''learn'':24C ''look'':6C ''might'':3C ''nofollow'':14C ''p'':1C,18C ''rel'':13C ''seaneast'':39C ''version'':37C ''want'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1244, 1, '2014-10-10 03:48:54.66', 9, 1908, 'Hashing Trick - what actually happens', '&lt;p&gt;When ML algorithms, e.g. Vowpal Wabbit or some of the factorization machines winning click through rate competitions (&lt;a href="https://www.kaggle.com/c/criteo-display-ad-challenge/forums/t/10555/3-idiots-solution/55862#post55862"&gt;Kaggle&lt;/a&gt;), mention that features are ''hashed'', what does that actually mean for the model? Lets say there is a variable that represents the ID of an internet add, which takes on values such as ''236BG231''. Then I understand that this feature is hashed to a random integer. But, my question is:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Is the integer now used in the model, as an integer (numeric) OR&lt;/li&gt;
&lt;li&gt;is the hashed value actually still treated like a categorical variable and one-hot-encoded? Thus the hashing trick is just to save space somehow with large data?&lt;/li&gt;
&lt;/ul&gt;
', 'machine-learning  predictive-modeling  kaggle', NULL, '''/a'':37C ''/c/criteo-display-ad-challenge/forums/t/10555/3-idiots-solution/55862#post55862'':35C ''/li'':104C,135C ''/p'':88C ''/ul'':136C ''236bg231'':71C ''actual'':4A,46C,110C ''add'':64C ''algorithm'':16C ''categor'':115C ''click'':27C ''competit'':30C ''data'':134C ''e.g'':17C ''encod'':121C ''factor'':24C ''featur'':40C,77C ''happen'':5A ''hash'':1A,42C,79C,108C,124C ''hot'':120C ''href'':32C ''id'':60C ''integ'':83C,93C,101C ''internet'':63C ''kaggl'':12B,36C ''larg'':133C ''learn'':8B ''let'':51C ''li'':90C,105C ''like'':113C ''machin'':7B,25C ''machine-learn'':6B ''mean'':47C ''mention'':38C ''ml'':15C ''model'':11B,50C,98C ''numer'':102C ''one'':119C ''one-hot-encod'':118C ''p'':13C ''predict'':10B ''predictive-model'':9B ''question'':86C ''random'':82C ''rate'':29C ''repres'':58C ''save'':129C ''say'':52C ''somehow'':131C ''space'':130C ''still'':111C ''take'':66C ''thus'':122C ''treat'':112C ''trick'':2A,125C ''ul'':89C ''understand'':74C ''use'':95C ''valu'':68C,109C ''variabl'':56C,116C ''vowpal'':18C ''wabbit'':19C ''win'':26C ''www.kaggle.com'':34C ''www.kaggle.com/c/criteo-display-ad-challenge/forums/t/10555/3-idiots-solution/55862#post55862'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1246, 1, '2014-10-10 13:34:11.543', 4, 2800, 'Stochastic gradient descent based on vector operations?', '&lt;p&gt;let''s assume that I want to train a stochastic gradient descent regression algorithm using a dataset that has N samples. Since the size of the dataset is fixed, I will reuse the data T times. At each iteration or "epoch", I use each training sample exactly once after randomly reordering the whole training set.&lt;/p&gt;

&lt;p&gt;My implementation is based on Python and Numpy. Therefore, using vector operations can remarkably decrease computation time. Coming up with a vectorized implementation of batch gradient descent is quite straightforward. However, in the case of stochastic gradient descent I can not figure out how to avoid the outer loop that iterates through all the samples at each epoch.&lt;/p&gt;

&lt;p&gt;Does anybody know any vectorized implementation of stochastic gradient descent? &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT&lt;/strong&gt;: I''ve been asked why would I like to use online gradient descent if the size of my dataset is fixed. &lt;/p&gt;

&lt;p&gt;From [1], one can see that online gradient descent converges slower than batch gradient descent to the minimum of the empirical cost. However, it converges faster to the minimum of the expected cost, which measures generalization performance. I''d like to test the impact of these theoretical results in my particular problem, by means of cross validation. Without a vectorized implementation, my online gradient descent code is much slower than the batch gradient descent one. That remarkably increases the time it takes to the cross validation process to be completed.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT&lt;/strong&gt;: I include here the pseudocode of my on-line gradient descent implementation, as requested by ffriend. I am solving a regression problem.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Method: on-line gradient descent (regression)
Input: X (nxp matrix; each line contains a training sample, represented as a length-p vector), Y (length-n vector; output of the training samples)
Output: A (length-p+1 vector of coefficients)

Initialize coefficients (assign value 0 to all coefficients)
Calculate outputs F
prev_error = inf
error = sum((F-Y)^2)/n
it = 0
while abs(error - prev_error)&amp;gt;ERROR_THRESHOLD and it&amp;lt;=MAX_ITERATIONS:
    Randomly shuffle training samples
    for each training sample i:
        Compute error for training sample i
        Update coefficients based on the error above
    prev_error = error
    Calculate outputs F
    error = sum((F-Y)^2)/n
    it = it + 1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;[1] "Large Scale Online Learning", L. Bottou, Y. Le Cunn, NIPS 2003.&lt;/p&gt;
', 'python  gradient-descent  regression', NULL, '''+1'':329C ''/code'':407C ''/n'':353C,403C ''/p'':69C,129C,141C,167C,259C,287C,422C ''/pre'':408C ''/strong'':145C,263C ''0'':337C,355C ''1'':170C,406C,410C ''2'':352C,402C ''2003'':421C ''ab'':357C ''algorithm'':27C ''anybodi'':132C ''ask'':149C ''assign'':335C ''assum'':16C ''avoid'':116C ''base'':4A,74C,386C ''batch'':95C,181C,240C ''bottou'':416C ''calcul'':341C,394C ''case'':104C ''code'':234C,289C ''coeffici'':332C,334C,340C,385C ''come'':88C ''complet'':258C ''comput'':86C,378C ''contain'':303C ''converg'':178C,193C ''cost'':190C,201C ''cross'':224C,253C ''cunn'':419C ''d'':207C ''data'':47C ''dataset'':30C,40C,164C ''decreas'':85C ''descent'':3A,11B,25C,97C,108C,140C,158C,177C,183C,233C,242C,275C,295C ''edit'':144C,262C ''empir'':189C ''epoch'':54C,128C ''error'':345C,347C,358C,360C,362C,379C,389C,392C,393C,397C ''exact'':60C ''expect'':200C ''f'':343C,350C,396C,400C ''f-i'':349C,399C ''faster'':194C ''ffriend'':280C ''figur'':112C ''fix'':42C,166C ''general'':204C ''gradient'':2A,10B,24C,96C,107C,139C,157C,176C,182C,232C,241C,274C,294C ''gradient-desc'':9B ''gt'':361C ''howev'':101C,191C ''impact'':212C ''implement'':72C,93C,136C,229C,276C ''includ'':265C ''increas'':246C ''inf'':346C ''initi'':333C ''input'':297C ''iter'':52C,121C,368C ''know'':133C ''l'':415C ''larg'':411C ''le'':418C ''learn'':414C ''length'':311C,316C,327C ''length-n'':315C ''length-p'':310C,326C ''let'':14C ''like'':153C,208C ''line'':273C,293C,302C ''loop'':119C ''lt'':366C ''matrix'':300C ''max'':367C ''mean'':222C ''measur'':203C ''method'':290C ''minimum'':186C,197C ''much'':236C ''n'':33C,317C ''nip'':420C ''numpi'':78C ''nxp'':299C ''on-lin'':271C,291C ''one'':171C,243C ''onlin'':156C,175C,231C,413C ''oper'':7A,82C ''outer'':118C ''output'':319C,324C,342C,395C ''p'':13C,70C,130C,142C,168C,260C,312C,328C,409C ''particular'':219C ''perform'':205C ''pre'':288C ''prev'':344C,359C,391C ''problem'':220C,286C ''process'':255C ''pseudocod'':268C ''python'':8B,76C ''quit'':99C ''random'':63C,369C ''regress'':12B,26C,285C,296C ''remark'':84C,245C ''reorder'':64C ''repres'':307C ''request'':278C ''result'':216C ''reus'':45C ''sampl'':34C,59C,125C,306C,323C,372C,376C,382C ''scale'':412C ''see'':173C ''set'':68C ''shuffl'':370C ''sinc'':35C ''size'':37C,161C ''slower'':179C,237C ''solv'':283C ''stochast'':1A,23C,106C,138C ''straightforward'':100C ''strong'':143C,261C ''sum'':348C,398C ''take'':250C ''test'':210C ''theoret'':215C ''therefor'':79C ''threshold'':363C ''time'':49C,87C,248C ''train'':21C,58C,67C,305C,322C,371C,375C,381C ''updat'':384C ''use'':28C,56C,80C,155C ''valid'':225C,254C ''valu'':336C ''ve'':147C ''vector'':6A,81C,92C,135C,228C,313C,318C,330C ''want'':19C ''whole'':66C ''without'':226C ''would'':151C ''x'':298C ''y'':314C,351C,401C,417C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3726, 2, '2014-12-19 01:42:36.78', 1, NULL, NULL, '&lt;p&gt;This is the fundamental challenge to all data modeling.  We don''t just want to memorize the the link between a given input and a given output (otherwise you wouldn''t be modeling data, you''d be memorizing 1:1 connections with a dict / hash / relational database table / etc).  We want to capture the underlying pattern in the data from only looking at the training data.&lt;/p&gt;

&lt;p&gt;Let''s expand a little on your gravity example.  You have your 10 training samples showing the start and ending position of an object dropped.  For consistency, let''s say the object was dropped the moment the object''s location was initially recorded and the ending location was recorded at some precise time interval later (but before the object hit the ground).  Let''s also say the model (neural network in this case) managed to precisely learn the expected change in location since it just comes down to subtraction in one axis.  You can show it another 10, 100, 1000 examples that all leverage the connection found and your model will keep performing well.&lt;/p&gt;

&lt;p&gt;Why not keep going to 10k, 100k, or even more samples?  Theoretically, if you managed to isolate the connection and run the experiment the same way each time, your model will always work.  But realistically, something is going to eventually change in the system.  You hire a new lab assistant who tends to press the ''record location'' button well after having dropped the object (giving the object more initial velocity, which you won''t notice having only recorded location).  Maybe you lost your initial ball and had to use something else which is lighter and catches the wind more (so it goes slower).  .... the longer you run the experiment, the more small changes will creep into your system.  Eventually these changes will alter the connection enough to make your initial model wrong.&lt;/p&gt;

&lt;p&gt;When modeling data, we want to capture the underlying patterns and acknowledge that the model only matters as long as those underlying patterns stay relevant.  It''s not really about the number of samples.  It''s about the connections / the model itself.  The number of samples just happens to be one of the better proxies we have - the more samples you use, the more confident you have some underlying pattern.  ''Statistical validity'' is one stab at solving this, though it''s validity is still up for question in the era of big data.  There is &lt;a href="http://ieeexplore.ieee.org/xpl/login.jsp?tp=&amp;amp;arnumber=5726612&amp;amp;url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5726612" rel="nofollow"&gt;plenty of work&lt;/a&gt; done trying to solve for how to gain confidence in good generalization in neural networks specifically, but it''s still very much an open question.&lt;/p&gt;

&lt;p&gt;For a different example, if you''re looking at user behavior, you''ll see differences between day and night; weekdays and weekends; summer and winter; year of a person''s life; culture a person grew up in... even if you prove you found a pattern in your initial sample, the system will eventually change and it''s up to luck whether the connection(s) you found are a part of the system that changed or a part of the system that didn''t.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1247, 2, '2014-10-10 14:35:11.323', 9, NULL, NULL, '&lt;p&gt;I am new to this forum. Chiming in late on this question. I have been maintaining (I am a co-founder of) a catalog of publicly available data portals. There is over 1000 now listed and cover portals at international, federal, state, municipal and academic levels across the globe. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.opengeocode.org/opendata/"&gt;http://www.opengeocode.org/opendata/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/opendata/'':58C ''/opendata/&lt;/a&gt;&lt;/p&gt;'':61C ''/p'':52C ''1000'':35C ''academ'':47C ''across'':49C ''avail'':29C ''catalog'':26C ''chime'':8C ''co'':22C ''co-found'':21C ''cover'':39C ''data'':30C ''feder'':43C ''forum'':7C ''founder'':23C ''globe'':51C ''href'':55C ''intern'':42C ''late'':10C ''level'':48C ''list'':37C ''maintain'':17C ''municip'':45C ''new'':4C ''p'':1C,53C ''portal'':31C,40C ''public'':28C ''question'':13C ''state'':44C ''www.opengeocode.org'':57C,60C ''www.opengeocode.org/opendata/'':56C ''www.opengeocode.org/opendata/&lt;/a&gt;&lt;/p&gt;'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1248, 2, '2014-10-10 14:53:22.5', 1, NULL, NULL, '&lt;p&gt;I am new to this forum. Data cleansing of address data is an area I work in. I agree with the other posters that you should not modify the original data, but add fields for corrected values. I developed a technique in our systems (opengeocode.org) we call ''reduced to common form''. In this method, addresses and geographic names are analyzed for reduction into an unambiguous short form, which is then used for record matching (vs. the original values). For example, the method I use for matching US postal addresses is based on the US Post Office''s published method for matching addresses.&lt;/p&gt;

&lt;p&gt;For geographic names, the method will reduce to short gazetteer form in Romanized script.&lt;/p&gt;

&lt;p&gt;The link below is an article I wrote a couple of years ago that explains how the street address reduction works:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.nwstartups.com/api/doc/middleware.php#streetR" rel="nofollow"&gt;http://www.nwstartups.com/api/doc/middleware.php#streetR&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/api/doc/middleware.php#streetr'':149C ''/api/doc/middleware.php#streetr&lt;/a&gt;&lt;/p&gt;'':154C ''/p'':104C,120C,143C ''add'':34C ''address'':11C,56C,90C,103C,140C ''ago'':134C ''agre'':20C ''analyz'':61C ''area'':15C ''articl'':127C ''base'':92C ''call'':48C ''cleans'':9C ''common'':51C ''correct'':37C ''coupl'':131C ''data'':8C,12C,32C ''develop'':40C ''exampl'':81C ''explain'':136C ''field'':35C ''form'':52C,68C,116C ''forum'':7C ''gazett'':115C ''geograph'':58C,107C ''href'':146C ''link'':123C ''match'':75C,87C,102C ''method'':55C,83C,100C,110C ''modifi'':29C ''name'':59C,108C ''new'':4C ''nofollow'':151C ''offic'':97C ''opengeocode.org'':46C ''origin'':31C,78C ''p'':1C,105C,121C,144C ''post'':96C ''postal'':89C ''poster'':24C ''publish'':99C ''record'':74C ''reduc'':49C,112C ''reduct'':63C,141C ''rel'':150C ''roman'':118C ''script'':119C ''short'':67C,114C ''street'':139C ''system'':45C ''techniqu'':42C ''unambigu'':66C ''us'':88C,95C ''use'':72C,85C ''valu'':38C,79C ''vs'':76C ''work'':17C,142C ''wrote'':129C ''www.nwstartups.com'':148C,153C ''www.nwstartups.com/api/doc/middleware.php#streetr'':147C ''www.nwstartups.com/api/doc/middleware.php#streetr&lt;/a&gt;&lt;/p&gt;'':152C ''year'':133C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1249, 2, '2014-10-10 16:54:45.047', 1, NULL, NULL, '&lt;p&gt;There are several third-party Java APIs for WordNet listed here: &lt;a href="http://wordnet.princeton.edu/wordnet/related-projects/#Java" rel="nofollow"&gt;http://wordnet.princeton.edu/wordnet/related-projects/#Java&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In the past, I''ve used JWNL the most: &lt;a href="http://sourceforge.net/projects/jwordnet/" rel="nofollow"&gt;http://sourceforge.net/projects/jwordnet/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The documentation for JWNL isn''t great, but it should provide the functionality you need.&lt;/p&gt;
', NULL, NULL, '''/p'':60C ''/projects/jwordnet/'':38C ''/projects/jwordnet/&lt;/a&gt;&lt;/p&gt;'':43C ''/wordnet/related-projects/#java'':18C ''/wordnet/related-projects/#java&lt;/a&gt;&lt;/p&gt;'':23C ''api'':9C ''document'':46C ''function'':57C ''great'':51C ''href'':15C,35C ''isn'':49C ''java'':8C ''jwnl'':31C,48C ''list'':12C ''need'':59C ''nofollow'':20C,40C ''p'':1C,24C,44C ''parti'':7C ''past'':27C ''provid'':55C ''rel'':19C,39C ''sever'':4C ''sourceforge.net'':37C,42C ''sourceforge.net/projects/jwordnet/'':36C ''sourceforge.net/projects/jwordnet/&lt;/a&gt;&lt;/p&gt;'':41C ''third'':6C ''third-parti'':5C ''use'':30C ''ve'':29C ''wordnet'':11C ''wordnet.princeton.edu'':17C,22C ''wordnet.princeton.edu/wordnet/related-projects/#java'':16C ''wordnet.princeton.edu/wordnet/related-projects/#java&lt;/a&gt;&lt;/p&gt;'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8975, 4, '2015-11-22 05:42:25.853', 0, NULL, NULL, 'Dimensionality reduction refers to techniques for reducing many variables into a smaller number while keeping as much information as possible. One prominent method is [tag pca]', NULL, NULL, '''dimension'':1C ''inform'':18C ''keep'':15C ''mani'':8C ''method'':23C ''much'':17C ''number'':13C ''one'':21C ''pca'':26C ''possibl'':20C ''promin'':22C ''reduc'':7C ''reduct'':2C ''refer'':3C ''smaller'':12C ''tag'':25C ''techniqu'':5C ''variabl'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2284, 1, '2014-10-16 05:44:40.183', 6, 121, 'Is it possible to identify different queries/questions in sentence?', '&lt;p&gt;I want to identifies different queries in sentences. &lt;/p&gt;

&lt;p&gt;Like - &lt;code&gt;Who is Bill Gates and where he was born?&lt;/code&gt; or &lt;code&gt;Who is Bill Gates, where he was born?&lt;/code&gt; contains two queries &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Who is Bill Gates?&lt;/li&gt;
&lt;li&gt;Where Bill Gates was born&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I worked on Coreference resolution, so I can identify that &lt;code&gt;he&lt;/code&gt; points to &lt;code&gt;Bill Gates&lt;/code&gt; so resolved sentence is "Who is Bill Gates, where Bill Gates was born"&lt;/p&gt;

&lt;p&gt;Like wise&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;MGandhi is good guys, Where he was born?
single query
who is MGandhi and where was he born?
2 queries
who is MGandhi, where he was born and died?
3 quries
India won world cup against Australia, when?
1 query (when India won WC against Auz)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I can perform Coreference resolution (Identifying and converting &lt;code&gt;he&lt;/code&gt; to &lt;code&gt;Gandhi&lt;/code&gt;) but not getting how can I distinguish queries in it. &lt;/p&gt;

&lt;p&gt;How to do this? &lt;/p&gt;

&lt;p&gt;I checked various sentence parser, but as this is pure nlp stuff, sentence parser does not identify it. &lt;/p&gt;

&lt;p&gt;I tried to find "Sentence disambiguation" like "word sense disambiguation", but nothing exist like that.&lt;/p&gt;

&lt;p&gt;Any help or suggestion would be much appreciable. &lt;/p&gt;
', 'machine-learning  data-mining  nlp  social-network-analysis', NULL, '''/code'':43C,54C,87C,93C,160C,173C,177C ''/li'':65C,72C ''/ol'':73C ''/p'':30C,58C,107C,111C,188C,194C,214C,231C,241C ''/pre'':161C ''1'':152C ''2'':132C ''3'':143C ''analysi'':20B ''appreci'':240C ''australia'':150C ''auz'':159C ''bill'':36C,48C,63C,68C,91C,100C,103C ''born'':42C,53C,71C,106C,121C,131C,140C ''check'':197C ''code'':33C,45C,85C,90C,113C,171C,175C ''contain'':55C ''convert'':170C ''corefer'':78C,166C ''cup'':148C ''data'':14B ''data-min'':13B ''die'':142C ''differ'':6A,26C ''disambigu'':221C,225C ''distinguish'':184C ''exist'':228C ''find'':219C ''gandhi'':176C ''gate'':37C,49C,64C,69C,92C,101C,104C ''get'':180C ''good'':116C ''guy'':117C ''help'':234C ''identifi'':5A,25C,83C,168C,212C ''india'':145C,155C ''learn'':12B ''li'':60C,66C ''like'':32C,109C,222C,229C ''machin'':11B ''machine-learn'':10B ''mgandhi'':114C,126C,136C ''mine'':15B ''much'':239C ''network'':19B ''nlp'':16B,206C ''noth'':227C ''ol'':59C ''p'':21C,31C,74C,108C,162C,189C,195C,215C,232C ''parser'':200C,209C ''perform'':165C ''point'':88C ''possibl'':3A ''pre'':112C ''pure'':205C ''queri'':27C,57C,123C,133C,153C,185C ''queries/questions'':7A ''quri'':144C ''resolut'':79C,167C ''resolv'':95C ''sens'':224C ''sentenc'':9A,29C,96C,199C,208C,220C ''singl'':122C ''social'':18B ''social-network-analysi'':17B ''stuff'':207C ''suggest'':236C ''tri'':217C ''two'':56C ''various'':198C ''want'':23C ''wc'':157C ''wise'':110C ''won'':146C,156C ''word'':223C ''work'':76C ''world'':147C ''would'':237C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1250, 2, '2014-10-10 18:10:14.507', 3, NULL, NULL, '&lt;p&gt;I recently ran into a similar problem: How to manage extracting a variety of features from a large dataset, without knowing up front what all of them would be. (Even calculating mean values repeatedly would be computationally expensive.) Further, how would I manage predictions based on different feature sets? Meaning, if I added a new feature, how would I know which models to train on new features? It could quickly snowball into a huge mess.&lt;/p&gt;

&lt;p&gt;My current solution is to track it all in a local NoSQL database (MongoDB). For example, I might have a collection &lt;code&gt;features&lt;/code&gt;, each entry of which has a name, a description of how the feature was was calculated, the python file that ran the extraction, etc.&lt;/p&gt;

&lt;p&gt;Likewise, a collection &lt;code&gt;models&lt;/code&gt; includes models run on the data. Each entry might have a name, a list of features that were used to train the model, its eventual parameters, predicted values on a held-out test set, metrics for how the model performed, etc. &lt;/p&gt;

&lt;p&gt;From my vantage point, this has a number of benefits:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;By saving predictions, I can use them later in ensemble predictions.&lt;/li&gt;
&lt;li&gt;Because I keep track of which features were used, I know which ones need retraining as I extract more features.&lt;/li&gt;
&lt;li&gt;By saving model descriptions, I ensure that I always know what I''ve tried. I never have to wonder, "Have I tried LASSO with regularization parameters set by grid-search CV?" I can always look it up, and see how successful it was.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;From your question, it sounds like you could adapt this approach to your problem''s workflow. Install Mongo or another database of choice, and then save each experimental run, its inputs, its results, and anything else you might wish to track over the course of the project. This should be much easier to query than a spreadsheet, at the least.&lt;/p&gt;
', NULL, NULL, '''/code'':102C,134C ''/li'':203C,225C,271C ''/p'':77C,127C,177C,189C,334C ''/ul'':272C ''ad'':54C ''adapt'':282C ''alway'':235C,261C ''anoth'':293C ''anyth'':308C ''approach'':284C ''base'':46C ''benefit'':188C ''calcul'':32C,118C ''choic'':296C ''code'':100C,132C ''collect'':99C,131C ''comput'':38C ''could'':70C,281C ''cours'':317C ''current'':80C ''cv'':258C ''data'':140C ''databas'':91C,294C ''dataset'':20C ''descript'':111C,230C ''differ'':48C ''easier'':325C ''els'':309C ''ensembl'':201C ''ensur'':232C ''entri'':104C,142C ''etc'':126C,176C ''even'':31C ''eventu'':159C ''exampl'':94C ''expens'':39C ''experiment'':301C ''extract'':12C,125C,222C ''featur'':16C,49C,57C,68C,101C,115C,150C,211C,224C ''file'':121C ''front'':24C ''grid'':256C ''grid-search'':255C ''held'':166C ''held-out'':165C ''huge'':75C ''includ'':135C ''input'':304C ''instal'':290C ''keep'':207C ''know'':22C,61C,215C,236C ''larg'':19C ''lasso'':249C ''later'':199C ''least'':333C ''li'':191C,204C,226C ''like'':279C ''likewis'':129C ''list'':148C ''local'':89C ''look'':262C ''manag'':11C,44C ''mean'':33C,51C ''mess'':76C ''metric'':170C ''might'':96C,143C,311C ''model'':63C,133C,136C,157C,174C,229C ''mongo'':291C ''mongodb'':92C ''much'':324C ''name'':109C,146C ''need'':218C ''never'':242C ''new'':56C,67C ''nosql'':90C ''number'':186C ''one'':217C ''p'':1C,78C,128C,178C,273C ''paramet'':160C,252C ''perform'':175C ''point'':182C ''predict'':45C,161C,194C,202C ''problem'':8C,287C ''project'':320C ''python'':120C ''queri'':327C ''question'':276C ''quick'':71C ''ran'':4C,123C ''recent'':3C ''regular'':251C ''repeat'':35C ''result'':306C ''retrain'':219C ''run'':137C,302C ''save'':193C,228C,299C ''search'':257C ''see'':266C ''set'':50C,169C,253C ''similar'':7C ''snowbal'':72C ''solut'':81C ''sound'':278C ''spreadsheet'':330C ''success'':268C ''test'':168C ''track'':84C,208C,314C ''train'':65C,155C ''tri'':240C,248C ''ul'':190C ''use'':153C,197C,213C ''valu'':34C,162C ''vantag'':181C ''varieti'':14C ''ve'':239C ''wish'':312C ''without'':21C ''wonder'':245C ''workflow'':289C ''would'':29C,36C,42C,59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1253, 1, '2014-10-11 10:24:01.393', 17, 1240, 'Why are NLP and Machine Learning communities interested in deep learning?', '&lt;p&gt;I hope you can help me, as I have some questions on this topic. I''m new in the field of deep learning, and while I did some tutorials, I can''t relate or distinguish concepts from one another.&lt;/p&gt;
', 'machine-learning  data-mining  neuralnetwork  nlp  deep-learning', NULL, '''/p'':63C ''anoth'':62C ''communiti'':7A ''concept'':59C ''data'':16B ''data-min'':15B ''deep'':10A,21B,45C ''deep-learn'':20B ''distinguish'':58C ''field'':43C ''help'':28C ''hope'':25C ''interest'':8A ''learn'':6A,11A,14B,22B,46C ''m'':39C ''machin'':5A,13B ''machine-learn'':12B ''mine'':17B ''neuralnetwork'':18B ''new'':40C ''nlp'':3A,19B ''one'':61C ''p'':23C ''question'':34C ''relat'':56C ''topic'':37C ''tutori'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8056, 2, '2015-09-10 03:19:21.917', 1, NULL, NULL, '&lt;p&gt;Have you checked the following reference out?
Link: &lt;a href="http://www.borgelt.net/docs/apriori.pdf" rel="nofollow"&gt;http://www.borgelt.net/docs/apriori.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The above link has the explanation along with the code. &lt;/p&gt;
', NULL, NULL, '''/docs/apriori.pdf'':14C ''/docs/apriori.pdf&lt;/a&gt;&lt;/p&gt;'':19C ''/p'':31C ''along'':27C ''check'':4C ''code'':30C ''explan'':26C ''follow'':6C ''href'':11C ''link'':9C,23C ''nofollow'':16C ''p'':1C,20C ''refer'':7C ''rel'':15C ''www.borgelt.net'':13C,18C ''www.borgelt.net/docs/apriori.pdf'':12C ''www.borgelt.net/docs/apriori.pdf&lt;/a&gt;&lt;/p&gt;'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1255, 2, '2014-10-11 19:48:20.583', 6, NULL, NULL, '&lt;p&gt;The a second bullet is the value in feature hashing.  Hashing and one hot encoding to sparse data saves space.  Depending on the hash algo you can have varying degrees of collisions which acts as a kind of dimensionality reduction.&lt;/p&gt;

&lt;p&gt;Also, in the specific case of Kaggle feature hashing and one hot encoding help with feature expansion/engineering by taking all possible tuples (usually just second order but sometimes third) of features that are then hashed with collisions that explicitly create interactions that are often predictive whereas the individual features are not.&lt;/p&gt;

&lt;p&gt;In most cases this technique combined with feature selection and elastic net regularization in LR acts very similar to a one hidden layer NN so it performs quite well in competitions.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2255, 1, '2014-10-11 23:26:53.197', 3, 470, 'Single Layer Perceptron with three classes', '&lt;p&gt;I need some help with a single layered perceptron with multiple classes.&lt;/p&gt;

&lt;p&gt;What I need to do is classify a dataset with three different classes, by now I just learnt how to do it with two classes, so I have no really a good clue how to do it with three.&lt;/p&gt;

&lt;p&gt;The dataset have three different classes: Iris-setosa, Iris-versicolor and Iris-versicolor.&lt;/p&gt;

&lt;p&gt;The url with the dataset and the information is in : &lt;a href="http://ftp.ics.uci.edu/pub/machine-learning-databases/iris/iris.data" rel="nofollow"&gt;http://ftp.ics.uci.edu/pub/machine-learning-databases/iris/iris.data&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I really appreciate any help anyone can give to me.&lt;/p&gt;

&lt;p&gt;Thanks a lot!&lt;/p&gt;
', 'dataset', NULL, '''/p'':21C,62C,80C,113C,118C ''/pub/machine-learning-databases/iris/iris.data'':96C ''/pub/machine-learning-databases/iris/iris.data&lt;/a&gt;.&lt;/p&gt;'':101C ''anyon'':108C ''appreci'':105C ''class'':6A,20C,35C,47C,69C ''classifi'':29C ''clue'':55C ''dataset'':7B,31C,65C,86C ''differ'':34C,68C ''ftp.ics.uci.edu'':95C,100C ''ftp.ics.uci.edu/pub/machine-learning-databases/iris/iris.data'':94C ''ftp.ics.uci.edu/pub/machine-learning-databases/iris/iris.data&lt;/a&gt;.&lt;/p&gt;'':99C ''give'':110C ''good'':54C ''help'':12C,107C ''href'':93C ''inform'':89C ''iri'':71C,74C,78C ''iris-setosa'':70C ''iris-versicolor'':73C,77C ''layer'':2A,16C ''learnt'':40C ''lot'':117C ''multipl'':19C ''need'':10C,25C ''nofollow'':98C ''p'':8C,22C,63C,81C,102C,114C ''perceptron'':3A,17C ''realli'':52C,104C ''rel'':97C ''setosa'':72C ''singl'':1A,15C ''thank'':115C ''three'':5A,33C,61C,67C ''two'':46C ''url'':83C ''versicolor'':75C,79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2256, 2, '2014-10-12 02:53:51.65', 2, NULL, NULL, '&lt;p&gt;I don''t think there is a way to build your graph from raw data without using at least basic programming skills. I''m not aware of a drag-and-drop interface for importing and displaying data. Graphs are just a bit too complex. Imagine trying to find the profit of selling a product if all you had was CSVs of receipts dropped into Excel. You''d need labels of the columns, some basic calculations, and so on before you had anything intelligible. Graphs are similar in this regard.&lt;/p&gt;

&lt;p&gt;Thankfully, there are open source solutions, with some elbow grease and a few days of work, you can probably get a nice visualization.&lt;/p&gt;

&lt;p&gt;Cypher queries are relatively simple to write. Using Neo4j and Cypher, you can create a basic visualization of your graph, which is displayed using D3.js&lt;/p&gt;

&lt;p&gt;GraphAlchemist recently open-sourced their project Alchemy.js which specializes in graph visualization. &lt;a href="https://github.com/GraphAlchemist/Alchemy" rel="nofollow"&gt;https://github.com/GraphAlchemist/Alchemy&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/graphalchemist/alchemy'':163C ''/graphalchemist/alchemy&lt;/a&gt;&lt;/p&gt;'':168C ''/p'':92C,117C,144C ''alchemy.js'':153C ''anyth'':84C ''awar'':27C ''basic'':21C,76C,134C ''bit'':44C ''build'':11C ''calcul'':77C ''column'':74C ''complex'':46C ''creat'':132C ''csvs'':62C ''cypher'':119C,129C ''d'':69C ''d3.js'':143C ''data'':16C,39C ''day'':107C ''display'':38C,141C ''drag'':31C ''drag-and-drop'':30C ''drop'':33C,65C ''elbow'':102C ''excel'':67C ''find'':50C ''get'':113C ''github.com'':162C,167C ''github.com/graphalchemist/alchemy'':161C ''github.com/graphalchemist/alchemy&lt;/a&gt;&lt;/p&gt;'':166C ''graph'':13C,40C,86C,138C,157C ''graphalchemist'':146C ''greas'':103C ''href'':160C ''imagin'':47C ''import'':36C ''intellig'':85C ''interfac'':34C ''label'':71C ''least'':20C ''m'':25C ''need'':70C ''neo4j'':127C ''nice'':115C ''nofollow'':165C ''open'':97C,149C ''open-sourc'':148C ''p'':1C,93C,118C,145C ''probabl'':112C ''product'':56C ''profit'':52C ''program'':22C ''project'':152C ''queri'':120C ''raw'':15C ''receipt'':64C ''recent'':147C ''regard'':91C ''rel'':164C ''relat'':122C ''sell'':54C ''similar'':88C ''simpl'':123C ''skill'':23C ''solut'':99C ''sourc'':98C,150C ''special'':155C ''thank'':94C ''think'':5C ''tri'':48C ''use'':18C,126C,142C ''visual'':116C,135C,158C ''way'':9C ''without'':17C ''work'':109C ''write'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2285, 1, '2014-10-16 06:37:09.887', 2, 114, 'what is buyer classification problem?', '&lt;p&gt;Recently in a data analytic job interview for an e-commerce site, they asked me, do i have some knowledge of buyer classification problem. Unfortunately i heard this term for the first time.&lt;br&gt;
After interview i tried to search a lot about it over google but didn''t find something meaningful. Please any one let me know if you have heard this term before and paste some links explaining this concept. Thanks   &lt;/p&gt;
', 'classification', NULL, '''/p'':82C ''analyt'':12C ''ask'':22C ''br'':42C ''buyer'':3A,30C ''classif'':4A,6B,31C ''commerc'':19C ''concept'':80C ''data'':11C ''didn'':56C ''e'':18C ''e-commerc'':17C ''explain'':78C ''find'':58C ''first'':40C ''googl'':54C ''heard'':35C,70C ''interview'':14C,44C ''job'':13C ''know'':66C ''knowledg'':28C ''let'':64C ''link'':77C ''lot'':50C ''meaning'':60C ''one'':63C ''p'':7C ''past'':75C ''pleas'':61C ''problem'':5A,32C ''recent'':8C ''search'':48C ''site'':20C ''someth'':59C ''term'':37C,72C ''thank'':81C ''time'':41C ''tri'':46C ''unfortun'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2257, 1, '2014-10-12 05:27:17.687', 5, 124, 'How to do this complicated data extrapolation, prediction modeling?', '&lt;p&gt;I have some very complicated data about some movie sales online, first for each data entry, I have a key which is a combination of five keys, which are territory, day, etc, and then, for each key I have the sales for a period of time, and other information, like the movie''s box office and genre. &lt;/p&gt;

&lt;p&gt;For each day, there is a delay for the data loading to the database, around ten hours, I try to fill the gap, do some data extrapolations.&lt;/p&gt;

&lt;p&gt;For each movie we sell, there is some decay of selling since the new release of the movie, i.e. usually for each movie, it follows some sales decay pattern.&lt;/p&gt;

&lt;p&gt;For a recent day, I pulled some data, and I found that some decay pattern:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/H0lSJ.png" alt="decay curve 1"&gt;&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/L5DAf.png" alt="decay curve 2"&gt;&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/RJPjK.png" alt="decay curve 3"&gt;&lt;/p&gt;

&lt;p&gt;And for that day, the sales for each key can range from around $150000 to $0. The pic is as follow:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/o7tsq.png" alt="one day sales curve"&gt; &lt;/p&gt;

&lt;p&gt;In the picture, the 15000 means there are around 15000 keys for each day. &lt;/p&gt;

&lt;p&gt;found this article,&lt;/p&gt;

&lt;p&gt;&lt;a href="http://homepage.stat.uiowa.edu/~kcowles/s166_2009/Project_Lee&amp;amp;Pyo.pdf"&gt;http://homepage.stat.uiowa.edu/~kcowles/s166_2009/Project_Lee&amp;amp;Pyo.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I am trying to predict for each key, the sales amount, like for a movie, territory, day etc combination, the sales amount, how much dollars, means for that movie, that territory, that day, how much money we get from selling online. I tried ARIMA time series model, but there is some concerns for that model, seen from the pics, there is some seasonal thing, and decay thing for the movie, so the sales prediction can not be always flat, there may be a pump after a going down, it may happens on a weekend, since there is seasonal thing, and the decay trend, etc, how to capture these things. Thank you for your reply!&lt;/p&gt;

&lt;p&gt;I am not sure whether can be applied, and how to be applied here.&lt;/p&gt;

&lt;p&gt;Thanks a lot in advance. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2271, 2, '2014-10-14 01:19:37.067', 2, NULL, NULL, '&lt;p&gt;You are probably aware that deep learning is all the rage these days, and it has touched NLP too. There is a tutorial on it from a recent conference: &lt;a href="http://nlp.stanford.edu/courses/NAACL2013/" rel="nofollow"&gt;Deep Learning for Natural Language Processing (without Magic)&lt;/a&gt; by Richard Socher and Christopher Manning, who are from Stanford.&lt;/p&gt;
', NULL, NULL, '''/a'':46C ''/courses/naacl2013/'':35C ''/p'':57C ''awar'':5C ''christoph'':51C ''confer'':30C ''day'':14C ''deep'':7C,38C ''href'':32C ''languag'':42C ''learn'':8C,39C ''magic'':45C ''man'':52C ''natur'':41C ''nlp'':19C ''nlp.stanford.edu'':34C ''nlp.stanford.edu/courses/naacl2013/'':33C ''nofollow'':37C ''p'':1C ''probabl'':4C ''process'':43C ''rage'':12C ''recent'':29C ''rel'':36C ''richard'':48C ''socher'':49C ''stanford'':56C ''touch'':18C ''tutori'':24C ''without'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2258, 1, '2014-10-12 11:23:26.493', 6, 317, 'Where to start on neural networks', '&lt;p&gt;First of all I know the question may be not suitable for the website but I''d really appreciate it if you just gave me some pointers.&lt;/p&gt;

&lt;p&gt;I''m a 16 years old programmer, I''ve had experience with many different programming languages, a while ago I started a course at Coursera, titled introduction to machine learning and since that moment i got very motivated to learn about AI, I started reading about neural networks and I made a working perceptron using Java and it was really fun but when i started to do something a little more challenging (building a digit recognition software), I found out that I have to learn a lot of math, I love math but the schools here don''t teach us much, now I happen to know someone who is a math teacher do you think learning math (specifically calculus) is necessary for me to learn AI or should I wait until I learn those stuff at school?&lt;/p&gt;

&lt;p&gt;Also what other things would be helpful in the path of me learning AI and machine learning? do other techniques (like SVM) also require strong math?&lt;/p&gt;

&lt;p&gt;Sorry if my question is long, I''d really appreciate if you could share with me any experience you have had with learning AI.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6014, 2, '2015-06-05 05:10:10.423', 1, NULL, NULL, '&lt;p&gt;As you accept vague answers:
Sranford NLP tools are strong for this kind of stuff. NER, POS Tagger maybe, Parsers, etc. Now for machine learning itself, I would try looking up at WEKA, it''s got a lot of filtering, classifiers and clustering methods, including StringToWordVector filters, that are, in my opinion, fundamental to text classification. Mostly, the tags you should be looking for are Text Categorization, Natural Language Processing and even Sentiment Analysis if you will. &lt;/p&gt;
', NULL, NULL, '''/p'':79C ''accept'':4C ''analysi'':75C ''answer'':6C ''categor'':68C ''classif'':57C ''classifi'':42C ''cluster'':44C ''etc'':22C ''even'':73C ''filter'':41C,48C ''fundament'':54C ''got'':37C ''includ'':46C ''kind'':14C ''languag'':70C ''learn'':26C ''look'':31C,64C ''lot'':39C ''machin'':25C ''mayb'':20C ''method'':45C ''most'':58C ''natur'':69C ''ner'':17C ''nlp'':8C ''opinion'':53C ''p'':1C ''parser'':21C ''pos'':18C ''process'':71C ''sentiment'':74C ''sranford'':7C ''stringtowordvector'':47C ''strong'':11C ''stuff'':16C ''tag'':60C ''tagger'':19C ''text'':56C,67C ''tool'':9C ''tri'':30C ''vagu'':5C ''weka'':34C ''would'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2259, 2, '2014-10-12 11:49:04.48', 2, NULL, NULL, '&lt;p&gt;Well , When it comes to AI I am an absolute beginner but here is my answer to your question based on my understandings :&lt;/p&gt;

&lt;p&gt;a perceptron has only one activation function, therefore it can return only the values of true and false (in most cases true=0 and false=1), so because of that, I don''t think that you will be able to accomplish your goal using only one perceptron but you can absolutely do it using multiple perceptrons which essentially is a neural networks, of course training the network would be a lot harder than calculating the changes of weights as you do in perceptrons, You are gonna have to take advantage of a training algorithm such as backpropagation and a sigmoid activation function.
I hope my answer was helpful.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2260, 2, '2014-10-12 16:35:21.26', 4, NULL, NULL, '&lt;p&gt;The standard way to do this is called ''one versus all''... you train three perceptrons. First 1 target = is class a?, 2nd perceptron target = is class b? 3rd = is class c. You just train each perceptron separately, and then take max of the three perceptrons to decide class&lt;/p&gt;
', NULL, NULL, '''/p'':50C ''1'':18C ''2nd'':23C ''3rd'':29C ''b'':28C ''c'':32C ''call'':9C ''class'':21C,27C,31C,49C ''decid'':48C ''first'':17C ''max'':42C ''one'':10C ''p'':1C ''perceptron'':16C,24C,37C,46C ''separ'':38C ''standard'':3C ''take'':41C ''target'':19C,25C ''three'':15C,45C ''train'':14C,35C ''versus'':11C ''way'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2261, 2, '2014-10-12 19:36:33.027', 4, NULL, NULL, '&lt;p&gt;No, you should go ahead and learn the maths on your own. You will "only" need to learn calculus, statistics, and linear algebra (like the rest of machine learning). The theory of neural networks is pretty primitive at this point -- it more of an art than a science -- so I think you can understand it if you try. Ipso facto, there are a lot of tricks that you need practical experience to learn. There are lot of complicated extensions, but you can worry about them once you get that far.&lt;/p&gt;

&lt;p&gt;Once you can understand the Coursera classes on ML and neural networks (Hinton''s), I suggest getting some practice. You might like &lt;a href="http://karpathy.github.io/neuralnets/" rel="nofollow"&gt;this&lt;/a&gt; introduction.&lt;/p&gt;
', NULL, NULL, '''/a'':124C ''/neuralnets/'':120C ''/p'':92C,126C ''ahead'':6C ''algebra'':24C ''art'':46C ''calculus'':20C ''class'':100C ''complic'':79C ''coursera'':99C ''experi'':72C ''extens'':80C ''facto'':61C ''far'':91C ''get'':89C,110C ''go'':5C ''hinton'':106C ''href'':117C ''introduct'':125C ''ipso'':60C ''karpathy.github.io'':119C ''karpathy.github.io/neuralnets/'':118C ''learn'':8C,19C,30C,74C ''like'':25C,115C ''linear'':23C ''lot'':65C,77C ''machin'':29C ''math'':10C ''might'':114C ''ml'':102C ''need'':17C,70C ''network'':35C,105C ''neural'':34C,104C ''nofollow'':122C ''p'':1C,93C ''point'':41C ''practic'':71C,112C ''pretti'':37C ''primit'':38C ''rel'':121C ''rest'':27C ''scienc'':49C ''statist'':21C ''suggest'':109C ''theori'':32C ''think'':52C ''tri'':59C ''trick'':67C ''understand'':55C,97C ''worri'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2268, 1, '2014-10-13 19:02:24.67', 0, 544, 'What is the state of the art in the field of NLP?', '&lt;p&gt;I am new to Natural Language Processing, I think NLP is a challenging field, the syntax and semantic ambiguities could cause a lot of problems. For example I think for these problems machine translation is a hard task.&lt;/p&gt;

&lt;p&gt;Therefore there are probably many approaches and methods that have been applied to this field. But what are the latest and most promising approaches and methods in the field of NLP?&lt;/p&gt;

&lt;p&gt;Are these techniques highly dependent on the target language?&lt;/p&gt;
', 'nlp', NULL, '''/p'':53C,86C,97C ''ambigu'':33C ''appli'':66C ''approach'':60C,78C ''art'':7A ''caus'':35C ''challeng'':27C ''could'':34C ''depend'':92C ''exampl'':41C ''field'':10A,28C,69C,83C ''hard'':51C ''high'':91C ''languag'':20C,96C ''latest'':74C ''lot'':37C ''machin'':47C ''mani'':59C ''method'':62C,80C ''natur'':19C ''new'':17C ''nlp'':12A,13B,24C,85C ''p'':14C,54C,87C ''probabl'':58C ''problem'':39C,46C ''process'':21C ''promis'':77C ''semant'':32C ''state'':4A ''syntax'':30C ''target'':95C ''task'':52C ''techniqu'':90C ''therefor'':55C ''think'':23C,43C ''translat'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2263, 1, '2014-10-13 11:23:23.483', 1, 73, '"Hadoop" formats for user database: online advertising', '&lt;p&gt;I was wondering if someone could point me to suitable database formats for building up a user database:&lt;/p&gt;

&lt;p&gt;basically I am collecting logs of impressions data, and I want to compile a user database &lt;/p&gt;

&lt;p&gt;which sites user visits, country/gender/..? and other categorisations with the aim of 
a) doing searches: give me all users visiting games sites from france...
b) machine learning: eg clustering users by the sites they visit&lt;/p&gt;

&lt;p&gt;so I am interested in storing info about 100''s of millions of users&lt;/p&gt;

&lt;p&gt;with indexes? on user, sites, geo-location&lt;/p&gt;

&lt;p&gt;and the idea would be that this data would be continually updated ( eg nightly update to user database of new sites visited etc)&lt;/p&gt;

&lt;p&gt;what are suitable database systems. Can someone suggest suitable reading material? 
I was imagining Hbase might be suitable...&lt;/p&gt;
', 'hbase', NULL, '''/p'':28C,46C,83C,99C,109C,134C,154C ''100'':93C ''advertis'':7A ''aim'':58C ''b'':72C ''basic'':30C ''build'':23C ''categoris'':55C ''cluster'':76C ''collect'':33C ''compil'':42C ''continu'':121C ''could'':15C ''country/gender'':52C ''data'':37C,118C ''databas'':5A,20C,27C,45C,128C,139C ''eg'':75C,123C ''etc'':133C ''format'':2A,21C ''franc'':71C ''game'':68C ''geo'':107C ''geo-loc'':106C ''give'':63C ''hadoop'':1A ''hbase'':8B,150C ''idea'':113C ''imagin'':149C ''impress'':36C ''index'':102C ''info'':91C ''interest'':88C ''learn'':74C ''locat'':108C ''log'':34C ''machin'':73C ''materi'':146C ''might'':151C ''million'':96C ''new'':130C ''night'':124C ''onlin'':6A ''p'':9C,29C,47C,84C,100C,110C,135C ''point'':16C ''read'':145C ''search'':62C ''site'':49C,69C,80C,105C,131C ''someon'':14C,142C ''store'':90C ''suggest'':143C ''suitabl'':19C,138C,144C,153C ''system'':140C ''updat'':122C,125C ''user'':4A,26C,44C,50C,66C,77C,98C,104C,127C ''visit'':51C,67C,82C,132C ''want'':40C ''wonder'':12C ''would'':114C,119C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2264, 2, '2014-10-13 13:35:09.443', 2, NULL, NULL, '&lt;p&gt;Perceptrons, strictly speaking, are binary classifiers.&lt;/p&gt;

&lt;p&gt;To make a multi-class classifier, you should switch to a standard feed-forward neural net with a &lt;a href="http://en.wikipedia.org/wiki/Softmax_function" rel="nofollow"&gt;softmax&lt;/a&gt; output layer. Without any hidden layers this is equivalent to multinomial logistic regression.&lt;/p&gt;

&lt;p&gt;You can also do the one-vs-all trick as @seanv507 suggests. This often works well in practice but there''s no strong basis for it in theory, and the true multi-class version is easier conceptually and practically in this case.&lt;/p&gt;
', NULL, NULL, '''/a'':38C ''/p'':8C,52C,98C ''/wiki/softmax_function'':34C ''also'':56C ''basi'':78C ''binari'':6C ''case'':97C ''class'':15C,88C ''classifi'':7C,16C ''conceptu'':92C ''easier'':91C ''en.wikipedia.org'':33C ''en.wikipedia.org/wiki/softmax_function'':32C ''equival'':47C ''feed'':24C ''feed-forward'':23C ''forward'':25C ''hidden'':43C ''href'':31C ''layer'':40C,44C ''logist'':50C ''make'':11C ''multi'':14C,87C ''multi-class'':13C,86C ''multinomi'':49C ''net'':27C ''neural'':26C ''nofollow'':36C ''often'':68C ''one'':60C ''one-vs-al'':59C ''output'':39C ''p'':1C,9C,53C ''perceptron'':2C ''practic'':72C,94C ''regress'':51C ''rel'':35C ''seanv507'':65C ''softmax'':37C ''speak'':4C ''standard'':22C ''strict'':3C ''strong'':77C ''suggest'':66C ''switch'':19C ''theori'':82C ''trick'':63C ''true'':85C ''version'':89C ''vs'':61C ''well'':70C ''without'':41C ''work'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2265, 2, '2014-10-13 13:50:07.08', 2, NULL, NULL, '&lt;p&gt;Check out the partial_fit method of &lt;a href="http://scikit-learn.org/0.15/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" rel="nofollow"&gt;scikit''s SGD classifier&lt;/a&gt;. You have control over what you call with it: you can do it "true" online learning by passing an instance at a time, or you can batch up instances into mini-batches if all your data are available in an array. If they are, you can slice the array to provide the minibatches.&lt;/p&gt;
', NULL, NULL, '''/0.15/modules/generated/sklearn.linear_model.sgdclassifier.html#sklearn.linear_model.sgdclassifier'':13C ''/a'':20C ''/p'':75C ''array'':62C,70C ''avail'':59C ''batch'':47C,53C ''call'':27C ''check'':2C ''classifi'':19C ''control'':23C ''data'':57C ''fit'':6C ''href'':10C ''instanc'':40C,49C ''learn'':36C ''method'':7C ''mini'':52C ''mini-batch'':51C ''minibatch'':74C ''nofollow'':15C ''onlin'':35C ''p'':1C ''partial'':5C ''pass'':38C ''provid'':72C ''rel'':14C ''scikit'':16C ''scikit-learn.org'':12C ''scikit-learn.org/0.15/modules/generated/sklearn.linear_model.sgdclassifier.html#sklearn.linear_model.sgdclassifier'':11C ''sgd'':18C ''slice'':68C ''time'':43C ''true'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2266, 2, '2014-10-13 13:53:36.027', 1, NULL, NULL, '&lt;p&gt;It''s just logistic regression. Get a bunch of data about presentations of search results, along with whether an item was clicked on. An instance is a search result item, with possible features being rank, "quality" (not sure what you mean by this) etc. The what you''re asking about is a question of inference on the parameter related to rank.&lt;/p&gt;
', NULL, NULL, '''/p'':63C ''along'':17C ''ask'':50C ''bunch'':9C ''click'':23C ''data'':11C ''etc'':45C ''featur'':34C ''get'':7C ''infer'':56C ''instanc'':26C ''item'':21C,31C ''logist'':5C ''mean'':42C ''p'':1C ''paramet'':59C ''possibl'':33C ''present'':13C ''qualiti'':37C ''question'':54C ''rank'':36C,62C ''re'':49C ''regress'':6C ''relat'':60C ''result'':16C,30C ''search'':15C,29C ''sure'':39C ''whether'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9135, 2, '2015-11-30 17:02:03.633', 1, NULL, NULL, '&lt;p&gt;&lt;a href="https://github.com/dmlc/xgboost" rel="nofollow"&gt;This&lt;/a&gt; is the repository where the xgboost package resides. That is from where the xgboost library for major languages like Julia, Java, R, etc are forked from.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/dmlc/xgboost/tree/master/python-package" rel="nofollow"&gt;This&lt;/a&gt; is the Python implementation.&lt;/p&gt;

&lt;p&gt;Walking through the source code (as instructed in the "How to contribute") docs would help you understand the intuition behind it.&lt;/p&gt;
', NULL, NULL, '''/a'':10C,47C ''/dmlc/xgboost'':6C ''/dmlc/xgboost/tree/master/python-package'':43C ''/p'':37C,52C,75C ''behind'':73C ''code'':58C ''contribut'':65C ''doc'':66C ''etc'':33C ''fork'':35C ''github.com'':5C,42C ''github.com/dmlc/xgboost'':4C ''github.com/dmlc/xgboost/tree/master/python-package'':41C ''help'':68C ''href'':3C,40C ''implement'':51C ''instruct'':60C ''intuit'':72C ''java'':31C ''julia'':30C ''languag'':28C ''librari'':25C ''like'':29C ''major'':27C ''nofollow'':8C,45C ''p'':1C,38C,53C ''packag'':17C ''python'':50C ''r'':32C ''rel'':7C,44C ''repositori'':13C ''resid'':18C ''sourc'':57C ''understand'':70C ''walk'':54C ''would'':67C ''xgboost'':16C,24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6193, 2, '2015-06-23 20:44:49.51', 1, NULL, NULL, '&lt;p&gt;Take a look at map(),reduce(),filter() functions in Python.&lt;/p&gt;
', NULL, NULL, '''/p'':12C ''filter'':8C ''function'':9C ''look'':4C ''map'':6C ''p'':1C ''python'':11C ''reduc'':7C ''take'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6333, 2, '2015-07-03 12:08:21.97', 0, NULL, NULL, '&lt;p&gt;Let me start with: I don''t know how appropriate this is for Data Science StackExchange. But I can''t comment yet and think I can provide an answer, so here goes.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Putting the values into the formula. The quantities are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;$g_0$: Standard gravity (9.81 m/s2);&lt;/li&gt;
&lt;li&gt;$\Delta Z$: Change in geopotential height over some distance;&lt;/li&gt;
&lt;li&gt;$\Delta n$: The distance over which you calculated $\Delta Z$; and&lt;/li&gt;
&lt;li&gt;$f$: The Coriolis parameter, measuring the rate of revolution at&lt;/li&gt;
&lt;li&gt;$\phi$: The chosen latitude.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You wrote the equation that links the Coriolis parameter $f$ to the latitude and to $\Omega=7.29\times 10^{-5}$ s$^{-1}$. Let''s calculate this part first. I''m assuming that a) you can use numpy along with matplotlib, and b) you were asked to plot the geostrophic wind as a function of latitude. One way to enter this into Python is by writing:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; import numpy as np
&amp;gt; phi = np.arange(10, 91)
&amp;gt; Omega = 7.29e-5
&amp;gt; coriolis = 2 * Omega * np.sin(phi * np.pi/180)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The second line gives you a numpy array of latitudes, in degrees, from 10N to 90N. I left out the tropics, since $f=0$ at the equator; the geostrophic wind isn''t well-defined there. The third line defines Omega, and the last line gives you a numpy array of values for $f$. Note that the argument of np.sin should be in radians, not degrees.&lt;/p&gt;

&lt;p&gt;After doing this, the rest of the values are easy to input. For the order of operations, make sure everything in the numerator is grouped together and same for the denominator. You get:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; grav, dz, dn = 9.81, 60.0, 2.0e5
&amp;gt; VGeo = (grav * dz) / (coriolis * dn)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The first line uses a sequence of values to set grav ($g_0$), dz, and dn. The second line defines a numpy array for $V_g$, with the numerator and denominator grouped.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Plotting out the result. I don''t know how familiar you are with the workings of matplotlib. Let me know if this doesn''t make sense and I can expand on it. Many plots of something that varies in latitude have latitude on the y-axis, since that''s the way globes and maps are oriented. So, when you make a plot of $(x,y)$ values, $y$ should be $\phi$ and $x$ should be $V_g$. A working example with some labels, using pyplot, might be:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; import matplotlib.pyplot as plt
&amp;gt; fig, ax = plt.subplots()
&amp;gt; ax.set_ylim(0,90)
&amp;gt; ax.set_xlim(0,125)
&amp;gt; ax.plot(VGeo, phi, linestyle=''-'', color=''k'')
&amp;gt; ax.set_ylabel(''Latitude $\phi$'')
&amp;gt; ax.set_xlabel(''Wind $V_g$'')
&amp;gt; ax.set_title(''Geostrophic wind $V_g = \frac{g_0 \Delta z}{f \Delta n}$'')
&amp;gt; fig.savefig(''test.png'', format=''png'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m doing several things here. The second line creates a figure and axes using plt.subplots(); without any arguments, this returns a single axis. Next, I''m setting the x- and y-limits so that we see all the data and so that the y-axis starts at the equator (even though we don''t plot values south of 10N, I think it looks better). Then I''m just plotting $(x,y)=(V_g,\phi)$, with a solid black line. The next three lines involve creating labels using TeX formatting. The last line is saving the figure to a local PNG file.&lt;/p&gt;

&lt;p&gt;Let me know how it works out!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2267, 2, '2014-10-13 14:46:54.403', 1, NULL, NULL, '&lt;p&gt;you might want to look at this paper
&lt;a href="http://research.microsoft.com/pubs/68148/predictingclicks.pdf" rel="nofollow"&gt;Predicting Clicks: Estimating the Click-Through Rate for New Ads&lt;/a&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Whenever an ad is displayed on the search results page, it has 
  some chance of being viewed by the user. The farther down the 
  page an ad is displayed, the less likely it is to be viewed. As a 
  simplification, we consider the probability that an ad is clicked on 
  to be dependent on two factors: a) the probability that it is viewed, 
  and b) the probability that it is clicked on, given that it is viewed:&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/a'':28C ''/blockquote'':110C ''/p'':29C,109C ''/pubs/68148/predictingclicks.pdf'':14C ''ad'':27C,34C,58C,78C ''b'':96C ''blockquot'':30C ''chanc'':45C ''click'':18C,22C,80C,102C ''click-through'':21C ''consid'':73C ''depend'':84C ''display'':36C,60C ''estim'':19C ''factor'':87C ''farther'':53C ''given'':104C ''href'':11C ''less'':62C ''like'':63C ''look'':6C ''might'':3C ''new'':26C ''nofollow'':16C ''p'':1C,31C ''page'':41C,56C ''paper'':9C ''predict'':17C ''probabl'':75C,90C,98C ''rate'':24C ''rel'':15C ''research.microsoft.com'':13C ''research.microsoft.com/pubs/68148/predictingclicks.pdf'':12C ''result'':40C ''search'':39C ''simplif'':71C ''two'':86C ''user'':51C ''view'':48C,68C,94C,108C ''want'':4C ''whenev'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2340, 2, '2014-10-23 20:11:18.75', 1, NULL, NULL, '&lt;p&gt;If you just want to get a forecast, use automatic forecasting software like Autobox, ForecastPro, SCA, etc.  &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Comparison of different software: &lt;a href="http://stats.stackexchange.com/questions/68253/expert-forecasting-software-evaluation"&gt;http://stats.stackexchange.com/questions/68253/expert-forecasting-software-evaluation&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Worked example: &lt;a href="http://people.duke.edu/~rnau/autocomp.htm" rel="nofollow"&gt;http://people.duke.edu/~rnau/autocomp.htm&lt;/a&gt;  &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If you are doing research on forecasting techniques, there are some Java packages, for example:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;JMotif &lt;a href="http://code.google.com/p/jmotif/" rel="nofollow"&gt;http://code.google.com/p/jmotif/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;JTSA &lt;a href="http://sourceforge.net/projects/jtsa/" rel="nofollow"&gt;http://sourceforge.net/projects/jtsa/&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;However, Java is not very widely used for this; if you must use Java you will likely have a feeling of being out in the cold.   R and Python would have much more packages, examples, etc.  A tiny sample of what is available in R:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0387886974" rel="nofollow"&gt;http://www.amazon.com/Introductory-Time-Series-Paul-Cowpertwait/dp/0387886974/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://a-little-book-of-r-for-time-series.readthedocs.org/en/latest/" rel="nofollow"&gt;http://a-little-book-of-r-for-time-series.readthedocs.org/en/latest/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.r-bloggers.com/time-series-analysis-and-mining-with-r/" rel="nofollow"&gt;http://www.r-bloggers.com/time-series-analysis-and-mining-with-r/&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2273, 1, '2014-10-14 08:50:53.907', 4, 383, 'What is the best Data Mining algorithm for prediction based on a single variable?', '&lt;p&gt;I have a variable whose value I would like to predict, and I would like to use only one variable as predictor. For instance, predict traffic density based on weather.&lt;/p&gt;

&lt;p&gt;Initially, I thought about using &lt;a href="http://en.wikipedia.org/wiki/Self-organizing_map" rel="nofollow"&gt;Self-Organizing Maps&lt;/a&gt; (SOM), which performs unsupervised clustering + regression. However, since it has an important component of dimensionality reduction, I see it as more appropriated for a large number of variables.&lt;/p&gt;

&lt;p&gt;Does it make sense to use it for a single variable as predictor? Maybe there are more adequate techniques for this &lt;em&gt;simple&lt;/em&gt; case: I used "Data Mining" instead of "machine learning" in the title of my question, because I think maybe a linear regression could do the job...&lt;/p&gt;
', 'clustering  regression  correlation', NULL, '''/a'':67C ''/em'':121C ''/p'':49C,96C,148C ''/wiki/self-organizing_map'':60C ''adequ'':115C ''algorithm'':7A ''appropri'':89C ''base'':10A,46C ''best'':4A ''case'':122C ''cluster'':15B,72C ''compon'':80C ''correl'':17B ''could'':144C ''data'':5A,125C ''densiti'':45C ''dimension'':82C ''em'':119C ''en.wikipedia.org'':59C ''en.wikipedia.org/wiki/self-organizing_map'':58C ''howev'':74C ''href'':57C ''import'':79C ''initi'':51C ''instanc'':42C ''instead'':127C ''job'':147C ''larg'':92C ''learn'':130C ''like'':27C,33C ''linear'':142C ''machin'':129C ''make'':100C ''map'':66C ''mayb'':111C,140C ''mine'':6A,126C ''nofollow'':62C ''number'':93C ''one'':37C ''organ'':65C ''p'':18C,50C,97C ''perform'':70C ''predict'':9A,29C,43C ''predictor'':40C,110C ''question'':136C ''reduct'':83C ''regress'':16B,73C,143C ''rel'':61C ''see'':85C ''self'':64C ''self-organ'':63C ''sens'':101C ''simpl'':120C ''sinc'':75C ''singl'':13A,107C ''som'':68C ''techniqu'':116C ''think'':139C ''thought'':53C ''titl'':133C ''traffic'':44C ''unsupervis'':71C ''use'':35C,55C,103C,124C ''valu'':24C ''variabl'':14A,22C,38C,95C,108C ''weather'':48C ''whose'':23C ''would'':26C,32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2274, 2, '2014-10-14 13:10:39.773', 5, NULL, NULL, '&lt;p&gt;Common rule in machine learning is to &lt;strong&gt;try simple things first&lt;/strong&gt;. For predicting continuous variables there''s nothing more basic than &lt;strong&gt;simple linear regression&lt;/strong&gt;. "Simple" in the name means that there''s only one predictor variable used (+ intercept, of course): &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;y = b0 + x*b1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where &lt;code&gt;b0&lt;/code&gt; is an intercept and &lt;code&gt;b1&lt;/code&gt; is a slope. For example, you may want to predict lemonade consumption in a park based on temperature:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;cons = b0 + temp * b1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Temperature is in well-defined &lt;strong&gt;continuous&lt;/strong&gt; variable. But if we talk about something more abstract like "weather", then it''s harder to understand how we measure and encode it. It''s ok if we say that the weather takes values &lt;code&gt;{terrible, bad, normal, good, excellent}&lt;/code&gt; and assign values numbers from -2 to +2 (implying that "excellent" weather is twice as good as "good"). But what if the weather is given by words &lt;code&gt;{shiny, rainy, cool, ...}&lt;/code&gt;? We can''t give an order to these variables. We call such variables &lt;strong&gt;categorical&lt;/strong&gt;. Since there''s no natural order between different categories, we can''t encode them as a single numerical variable (and linear regression expects numbers only), but we can use so-called &lt;strong&gt;dummy encoding&lt;/strong&gt;:  instead of a single variable &lt;code&gt;weather&lt;/code&gt; we use 3 variables - &lt;code&gt;[weather_shiny, weather_rainy, weather_cool]&lt;/code&gt;, only one of which can take value 1, and others should take value 0. In fact, we will have to drop one variable because of &lt;a href="http://en.wikipedia.org/wiki/Multicollinearity"&gt;collinearity&lt;/a&gt;. So model for predicting traffic from weather may look like this: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;traffic = b0 + weather_shiny * b1 + weather_rainy * b2  # weather_cool dropped
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where either &lt;code&gt;b1&lt;/code&gt; or &lt;code&gt;b2&lt;/code&gt; is 1, or both are 0. &lt;/p&gt;

&lt;p&gt;Note that you can also encounter non-linear dependency between predictor and predicted variables (you can easily check it by plotting &lt;code&gt;(x,y)&lt;/code&gt; pairs). Simplest way to deal with it without refusing linear model is to use polynomial features - simply add polynomials of your feature as new features. E.g. for temperature example (for dummy variables it doesn''t make sense, cause &lt;code&gt;1^n&lt;/code&gt; and &lt;code&gt;0^n&lt;/code&gt; are still 1 and 0 for any &lt;code&gt;n&lt;/code&gt;):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;traffic = b0 + temp * b1 + temp^2 * b2 [+ temp^3 * b3 + ...]
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2313, 1, '2014-10-20 06:38:16.49', 3, 426, 'Machine Learning - Where is the difference between one-class, binary-class and multinominal-class classification?', '&lt;p&gt;Where is the difference between one-class, binary-class and multinominal-class classification?&lt;/p&gt;

&lt;p&gt;If I like to classify text in lets say four classes and also want the system to be able to tell me that none of these classes matches the unknown/untrained test-data. &lt;/p&gt;

&lt;p&gt;Couldn''t I just use all the methods that I mentioned above to reach my goal?
e.g. I could describe C1, C2, C3 and C4 as four different trainings-sets for binary-classification and use the trained models to label an unknow data-set ...&lt;/p&gt;

&lt;p&gt;Just by saying, Training-Set for C1 contains class 1 (all good samples for C1) and class 0 (mix of all C2, C3 and C4 as bad samples for C1).&lt;/p&gt;

&lt;p&gt;Is unlabeled-data C1 -&gt; 1 or 0&lt;/p&gt;

&lt;p&gt;Is unlabeled-data C2 -&gt; 1 or 0
... and so on ...&lt;/p&gt;

&lt;p&gt;For multinominal classification I could just define a training-set containing all good sample data for C1, C2, C3 and C4 in one training-set and then use the one resulting model for classification ...&lt;/p&gt;

&lt;p&gt;But where is the difference between this two methods? (except of that I have to use different algorithms)&lt;/p&gt;

&lt;p&gt;And how would I define a training-set for the described problem of categorizing data in those four classes using one-class classfication (is that even possible)?&lt;/p&gt;

&lt;p&gt;Excuse me if I''m completely wrong in my thinking. Would appreciate an answer that makes the methodology a little bit clearer to me =)&lt;/p&gt;
', 'machine-learning  data-mining  classification  categorical-data', NULL, '''/p'':46C,81C,130C,163C,173C,186C,224C,244C,275C,301C ''0'':150C,172C,182C ''1'':142C,170C,180C ''abl'':66C ''algorithm'':243C ''also'':60C ''answer'':290C ''appreci'':288C ''bad'':159C ''binari'':12A,39C,116C ''binary-class'':11A,38C ''binary-classif'':115C ''bit'':297C ''c1'':103C,139C,147C,162C,169C,205C ''c2'':104C,154C,179C,206C ''c3'':105C,155C,207C ''c4'':107C,157C,209C ''categor'':27B,260C ''categorical-data'':26B ''class'':10A,13A,17A,37C,40C,44C,58C,74C,141C,149C,265C,269C ''classfic'':270C ''classif'':18A,25B,45C,117C,190C,223C ''classifi'':52C ''clearer'':298C ''complet'':282C ''contain'':140C,199C ''could'':101C,192C ''couldn'':83C ''data'':23B,28B,80C,128C,168C,178C,203C,261C ''data-min'':22B ''data-set'':127C ''defin'':194C,250C ''describ'':102C,257C ''differ'':6A,33C,110C,230C,242C ''e.g'':99C ''even'':273C ''except'':235C ''excus'':277C ''four'':57C,109C,264C ''goal'':98C ''good'':144C,201C ''label'':124C ''learn'':2A,21B ''let'':55C ''like'':50C ''littl'':296C ''m'':281C ''machin'':1A,20B ''machine-learn'':19B ''make'':292C ''match'':75C ''mention'':93C ''method'':90C,234C ''methodolog'':294C ''mine'':24B ''mix'':151C ''model'':122C,221C ''multinomin'':16A,43C,189C ''multinominal-class'':15A,42C ''none'':71C ''one'':9A,36C,211C,219C,268C ''one-class'':8A,35C,267C ''p'':29C,47C,82C,131C,164C,174C,187C,225C,245C,276C ''possibl'':274C ''problem'':258C ''reach'':96C ''result'':220C ''sampl'':145C,160C,202C ''say'':56C,134C ''set'':113C,129C,137C,198C,214C,254C ''system'':63C ''tell'':68C ''test'':79C ''test-data'':78C ''text'':53C ''think'':286C ''train'':112C,121C,136C,197C,213C,253C ''training-set'':135C,196C,212C,252C ''trainings-set'':111C ''two'':233C ''unknow'':126C ''unknown/untrained'':77C ''unlabel'':167C,177C ''unlabeled-data'':166C,176C ''use'':87C,119C,217C,241C,266C ''want'':61C ''would'':248C,287C ''wrong'':283C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9231, 1, '2015-12-06 09:17:40.57', 0, 12, 'Missing features for classifier', '&lt;p&gt;If I am given 60 features along with test label and I was to find values of other features what is the best way to do it ?&lt;/p&gt;
', 'machine-learning  parameter', NULL, '''/p'':37C ''60'':14C ''along'':16C ''best'':32C ''classifi'':4A ''featur'':2A,15C,28C ''find'':24C ''given'':13C ''label'':19C ''learn'':7B ''machin'':6B ''machine-learn'':5B ''miss'':1A ''p'':9C ''paramet'':8B ''test'':18C ''valu'':25C ''way'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2383, 2, '2014-10-31 13:05:49.263', 3, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Gather app reviews for single app and try to find out the most important features that you can mine for the app developer&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;try predicting the next word in a sentence based on previous words.Eg. Swiftkey does this.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Get questions data from stack overflow and try to predict tags for every post on stack overflow.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':28C,47C,68C ''/ol'':69C ''/p'':27C,46C,67C ''app'':5C,9C,25C ''base'':39C ''data'':52C ''develop'':26C ''everi'':62C ''featur'':18C ''find'':13C ''gather'':4C ''get'':50C ''import'':17C ''li'':2C,29C,48C ''mine'':22C ''next'':34C ''ol'':1C ''overflow'':55C,66C ''p'':3C,30C,49C ''post'':63C ''predict'':32C,59C ''previous'':41C ''question'':51C ''review'':6C ''sentenc'':38C ''singl'':8C ''stack'':54C,65C ''swiftkey'':43C ''tag'':60C ''tri'':11C,31C,57C ''word'':35C ''words.eg'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2275, 1, '2014-10-14 15:26:01.46', 2, 19, 'what are the parameter estimation methods for the two methods in this paper', '&lt;p&gt;&lt;a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=923635" rel="nofollow"&gt;http://papers.ssrn.com/sol3/papers.cfm?abstract_id=923635&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;in the above paper, page 4, two models, non-adaptive regression model, adaptive regression model, the non-adaptive regression model''s parameter estimation method is "least squares", what is the parameter estimation for the adaptive regression model? is there any package in R to do parameter estimation for this kind of adaptive regression model? If I add more predictors in the adaptive regression model, can R still solve it? and how?&lt;/p&gt;

&lt;p&gt;Thank you!&lt;/p&gt;
', 'statistics', NULL, '''/p'':100C,104C ''/sol3/papers.cfm?abstract_id=923635'':20C ''/sol3/papers.cfm?abstract_id=923635&lt;/a&gt;&lt;/p&gt;'':25C ''4'':32C ''adapt'':37C,40C,46C,63C,80C,90C ''add'':85C ''estim'':5A,51C,60C,75C ''href'':17C ''kind'':78C ''least'':54C ''method'':6A,10A,52C ''model'':34C,39C,42C,48C,65C,82C,92C ''nofollow'':22C ''non'':36C,45C ''non-adapt'':35C,44C ''p'':15C,26C,101C ''packag'':69C ''page'':31C ''paper'':13A,30C ''papers.ssrn.com'':19C,24C ''papers.ssrn.com/sol3/papers.cfm?abstract_id=923635'':18C ''papers.ssrn.com/sol3/papers.cfm?abstract_id=923635&lt;/a&gt;&lt;/p&gt;'':23C ''paramet'':4A,50C,59C,74C ''predictor'':87C ''r'':71C,94C ''regress'':38C,41C,47C,64C,81C,91C ''rel'':21C ''solv'':96C ''squar'':55C ''statist'':14B ''still'':95C ''thank'':102C ''two'':9A,33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2276, 2, '2014-10-14 17:47:02.663', 0, NULL, NULL, '&lt;p&gt;The kind of data you store and analyze is very much dependent upon the kind of data you can gather. So, without knowing what your ''impression data'' looks like, it is very hard to suggest how to normalize and store it.&lt;/p&gt;

&lt;p&gt;Furthermore, the way you store data is also dependent upon how you wish to analyze it. For example, if you want to perform basic analytics like page view counts, how many pages a user visits per session, etc (SQL). . . data needs to be stored differently than if you want to build recommendations based on traffic patterns (graph database).&lt;/p&gt;

&lt;p&gt;Please edit your question to include more detail. Apologies that I cannot simply leave a comment.&lt;/p&gt;
', NULL, NULL, '''/p'':43C,103C,121C ''also'':52C ''analyt'':69C ''analyz'':9C,59C ''apolog'':113C ''base'':97C ''basic'':68C ''build'':95C ''cannot'':116C ''comment'':120C ''count'':73C ''data'':5C,18C,28C,50C,84C ''databas'':102C ''depend'':13C,53C ''detail'':112C ''differ'':89C ''edit'':106C ''etc'':82C ''exampl'':62C ''furthermor'':45C ''gather'':21C ''graph'':101C ''hard'':34C ''impress'':27C ''includ'':110C ''kind'':3C,16C ''know'':24C ''leav'':118C ''like'':30C,70C ''look'':29C ''mani'':75C ''much'':12C ''need'':85C ''normal'':39C ''p'':1C,44C,104C ''page'':71C,76C ''pattern'':100C ''per'':80C ''perform'':67C ''pleas'':105C ''question'':108C ''recommend'':96C ''session'':81C ''simpli'':117C ''sql'':83C ''store'':7C,41C,49C,88C ''suggest'':36C ''traffic'':99C ''upon'':14C,54C ''user'':78C ''view'':72C ''visit'':79C ''want'':65C,93C ''way'':47C ''wish'':57C ''without'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2322, 1, '2014-10-20 22:22:09.66', 4, 137, 'Distributed Scalable Decision Trees', '&lt;p&gt;Are there any good sources that explain how decision trees can be implemented in a scalable way on a distributed computing system.  Where in a given source is this explained?&lt;/p&gt;
', 'bigdata  classification', NULL, '''/p'':38C ''bigdata'':5B ''classif'':6B ''comput'':28C ''decis'':3A,16C ''distribut'':1A,27C ''explain'':14C,37C ''given'':33C ''good'':11C ''implement'':20C ''p'':7C ''scalabl'':2A,23C ''sourc'':12C,34C ''system'':29C ''tree'':4A,17C ''way'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2277, 2, '2014-10-14 18:24:36.88', 1, NULL, NULL, '&lt;p&gt;I am more of an expert on data ETL and combining/aggregating than on the forumulas themselves. I work frequently with weather data. I like to give some suggestions on using weather data in analysis.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Two types of data are reported in US/Canada:&lt;br/&gt;
A. Measurements&lt;br/&gt;
B. Weather Type&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;As far as weather type (sunny, rainy, severe thunderstorm) they are either going to already be reflected in measurements (e.g., sunny, rainy) and are redundant or they are inclement weather conditions and are not necessarily reflected in the measurements. &lt;/p&gt;

&lt;p&gt;For inclement weather types, I would have separate formulae.&lt;/p&gt;

&lt;p&gt;For measurements, there are 7 standard daily measurements for Weather Station reporting in North America.&lt;/p&gt;

&lt;p&gt;Temp Min/Max&lt;br/&gt;
Precipitation&lt;br/&gt;
Average Wind Speed&lt;br/&gt;
Average Cloudiness (percentage)&lt;br/&gt;
Total sunlight (minutes)&lt;br/&gt;
Snowfall&lt;br/&gt;
Snow Depth&lt;br/&gt;&lt;/p&gt;

&lt;p&gt;Not all stations report all 7 daily measurements. Some report only Temp and Precipitation. So you may want to have one formula for Temp/Precipitation and an expanded formulae when all seven measurements are available.&lt;/p&gt;

&lt;p&gt;The two links below are NOAA/NWS weather terms used in their datasets:&lt;/p&gt;

&lt;p&gt;This document is the vocabulary for the annual summaries:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www1.ncdc.noaa.gov/pub/data/cdo/documentation/ANNUAL_documentation.pdf" rel="nofollow"&gt;http://www1.ncdc.noaa.gov/pub/data/cdo/documentation/ANNUAL_documentation.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This document is the vocabulary for the daily summaries &lt;/p&gt;

&lt;p&gt;&lt;a href="http://www1.ncdc.noaa.gov/pub/data/cdo/documentation/GHCND_documentation.pdf" rel="nofollow"&gt;http://www1.ncdc.noaa.gov/pub/data/cdo/documentation/GHCND_documentation.pdf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/li'':54C ''/ol'':55C ''/p'':36C,96C,107C,124C,148C,184C,198C,209C,231C ''/pub/data/cdo/documentation/annual_documentation.pdf'':215C ''/pub/data/cdo/documentation/annual_documentation.pdf&lt;/a&gt;&lt;/p&gt;'':220C ''/pub/data/cdo/documentation/ghcnd_documentation.pdf'':237C ''/pub/data/cdo/documentation/ghcnd_documentation.pdf&lt;/a&gt;&lt;/p&gt;'':242C ''7'':113C,155C ''alreadi'':71C ''america'':123C ''analysi'':35C ''annual'':207C ''avail'':183C ''averag'':131C,135C ''b'':51C ''br'':47C,50C,128C,130C,134C,138C,142C,144C,147C ''cloudi'':136C ''combining/aggregating'':12C ''condit'':87C ''daili'':115C,156C,229C ''data'':9C,23C,33C,42C ''dataset'':197C ''depth'':146C ''document'':201C,223C ''e.g'':76C ''either'':68C ''etl'':10C ''expand'':176C ''expert'':7C ''far'':58C ''formula'':106C,171C,177C ''forumula'':16C ''frequent'':20C ''give'':27C ''go'':69C ''href'':212C,234C ''inclement'':85C,99C ''li'':38C ''like'':25C ''link'':188C ''may'':166C ''measur'':49C,75C,95C,110C,116C,157C,181C ''min/max'':127C ''minut'':141C ''necessarili'':91C ''noaa/nws'':191C ''nofollow'':217C,239C ''north'':122C ''ol'':37C ''one'':170C ''p'':1C,56C,97C,108C,125C,149C,185C,199C,210C,221C,232C ''percentag'':137C ''precipit'':129C,163C ''raini'':63C,78C ''redund'':81C ''reflect'':73C,92C ''rel'':216C,238C ''report'':44C,120C,153C,159C ''separ'':105C ''seven'':180C ''sever'':64C ''snow'':145C ''snowfal'':143C ''speed'':133C ''standard'':114C ''station'':119C,152C ''suggest'':29C ''summari'':208C,230C ''sunlight'':140C ''sunni'':62C,77C ''temp'':126C,161C ''temp/precipitation'':173C ''term'':193C ''thunderstorm'':65C ''total'':139C ''two'':39C,187C ''type'':40C,53C,61C,101C ''us/canada'':46C ''use'':31C,194C ''vocabulari'':204C,226C ''want'':167C ''weather'':22C,32C,52C,60C,86C,100C,118C,192C ''wind'':132C ''work'':19C ''would'':103C ''www1.ncdc.noaa.gov'':214C,219C,236C,241C ''www1.ncdc.noaa.gov/pub/data/cdo/documentation/annual_documentation.pdf'':213C ''www1.ncdc.noaa.gov/pub/data/cdo/documentation/annual_documentation.pdf&lt;/a&gt;&lt;/p&gt;'':218C ''www1.ncdc.noaa.gov/pub/data/cdo/documentation/ghcnd_documentation.pdf'':235C ''www1.ncdc.noaa.gov/pub/data/cdo/documentation/ghcnd_documentation.pdf&lt;/a&gt;&lt;/p&gt;'':240C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2278, 1, '2014-10-15 02:22:09.983', 2, 56, 'Adaptive regression parameter estimation in R', '&lt;p&gt;How would I do parameter estimation and prediction for the adaptive regression model using R, as in the 4th page of the paper linked below?&lt;/p&gt;

&lt;p&gt;&lt;a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=923635" rel="nofollow"&gt;http://papers.ssrn.com/sol3/papers.cfm?abstract_id=923635&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Could anyone clarify this for me?&lt;/p&gt;

&lt;p&gt;If you know adaptive regression models very well, share some useful link, or describe the model/parameter estimation/prediction, that would be very helpful.&lt;/p&gt;

&lt;p&gt;Thank you so much!&lt;/p&gt;
', 'r  statistics  regression', NULL, '''/p'':36C,55C,79C,85C ''/sol3/papers.cfm?abstract_id=923635'':42C ''/sol3/papers.cfm?abstract_id=923635&lt;/a&gt;&lt;/p&gt;'':47C ''4th'':29C ''adapt'':1A,21C,60C ''anyon'':50C ''clarifi'':51C ''could'':49C ''describ'':70C ''estim'':4A,16C ''estimation/prediction'':73C ''help'':78C ''href'':39C ''know'':59C ''link'':34C,68C ''model'':23C,62C ''model/parameter'':72C ''much'':84C ''nofollow'':44C ''p'':10C,37C,48C,56C,80C ''page'':30C ''paper'':33C ''papers.ssrn.com'':41C,46C ''papers.ssrn.com/sol3/papers.cfm?abstract_id=923635'':40C ''papers.ssrn.com/sol3/papers.cfm?abstract_id=923635&lt;/a&gt;&lt;/p&gt;'':45C ''paramet'':3A,15C ''predict'':18C ''r'':6A,7B,25C ''regress'':2A,9B,22C,61C ''rel'':43C ''share'':65C ''statist'':8B ''thank'':81C ''use'':24C,67C ''well'':64C ''would'':12C,75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2279, 2, '2014-10-15 03:08:59.263', 1, NULL, NULL, '&lt;p&gt;Here is some notes for an R package ''earth'' for adaptive regression. This may be useful.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://cran.r-project.org/web/packages/earth/vignettes/earth-notes.pdf" rel="nofollow"&gt;http://cran.r-project.org/web/packages/earth/vignettes/earth-notes.pdf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':18C ''/web/packages/earth/vignettes/earth-notes.pdf'':24C ''/web/packages/earth/vignettes/earth-notes.pdf&lt;/a&gt;&lt;/p&gt;'':29C ''adapt'':12C ''cran.r-project.org'':23C,28C ''cran.r-project.org/web/packages/earth/vignettes/earth-notes.pdf'':22C ''cran.r-project.org/web/packages/earth/vignettes/earth-notes.pdf&lt;/a&gt;&lt;/p&gt;'':27C ''earth'':10C ''href'':21C ''may'':15C ''nofollow'':26C ''note'':5C ''p'':1C,19C ''packag'':9C ''r'':8C ''regress'':13C ''rel'':25C ''use'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6335, 2, '2015-07-04 02:27:33.023', 3, NULL, NULL, '&lt;p&gt;The most logical way to transform hour is into two variables that swing back and forth out of sink. Imagine the position of the end of the hour hand of a 24-hour clock. The &lt;code&gt;x&lt;/code&gt; position swings back and forth out of sink with the &lt;code&gt;y&lt;/code&gt; position. For a 24-hour clock you can accomplish this with &lt;code&gt;x=sin(2pi*hour/24)&lt;/code&gt;,&lt;code&gt;y=cos(2pi*hour/24)&lt;/code&gt;.  &lt;/p&gt;

&lt;p&gt;You need both variables or the proper movement through time is lost.  This is due to the fact that the derivative of either sin or cos changes in time where as the &lt;code&gt;(x,y)&lt;/code&gt; position varies smoothly as it travels around the unit circle.&lt;/p&gt;

&lt;p&gt;Finally, consider whether it is worthwhile to add a third feature to trace linear time, which can be constructed my hours (or minutes or seconds) from the start of the first record or a Unix time stamp or something similar.  These three features then provide proxies for both the cyclic and linear progression of time e.g. you can pull out cyclic phenomenon like sleep cycles in people''s movement and also linear growth like population vs. time.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Adding some relevant example code that I generated for another answer:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Example of if being accomplished:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Enable inline plotting
%matplotlib inline

#Import everything I need...

import numpy as np
import matplotlib as mp

import matplotlib.pyplot as plt
import pandas as pd

# Grab some random times from here: https://www.random.org/clock-times/
# put them into a csv.
from pandas import DataFrame, read_csv
df = read_csv(''/Users/angus/Machine_Learning/ipython_notebooks/times.csv'',delimiter='':'')
df[''hourfloat'']=df.hour+df.minute/60.0
df[''x'']=np.sin(2.*np.pi*df.hourfloat/24.)
df[''y'']=np.cos(2.*np.pi*df.hourfloat/24.)

df
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/1UTYj.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/1UTYj.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def kmeansshow(k,X):

    from sklearn import cluster
    from matplotlib import pyplot
    import numpy as np

    kmeans = cluster.KMeans(n_clusters=k)
    kmeans.fit(X)

    labels = kmeans.labels_
    centroids = kmeans.cluster_centers_
    #print centroids

    for i in range(k):
        # select only data observations with cluster label == i
        ds = X[np.where(labels==i)]
        # plot the data observations
        pyplot.plot(ds[:,0],ds[:,1],''o'')
        # plot the centroids
        lines = pyplot.plot(centroids[i,0],centroids[i,1],''kx'')
        # make the centroid x''s bigger
        pyplot.setp(lines,ms=15.0)
        pyplot.setp(lines,mew=2.0)
    pyplot.show()
    return centroids
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now lets try it out:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;kmeansshow(6,df[[''x'', ''y'']].values)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/MkVNg.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/MkVNg.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You can just barely see that there are some after midnight times included with the before midnight green cluster.  Now lets reduce the number of clusters and show that before and after midnight can be connected in a single cluster in more detail:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;kmeansshow(3,df[[''x'', ''y'']].values)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/bWg2B.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/bWg2B.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;See how the blue cluster contains times that are from before and after midnight that are clustered together in the same cluster...&lt;/p&gt;

&lt;p&gt;QED!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6441, 5, '2015-07-12 19:59:56.35', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6442, 4, '2015-07-12 19:59:56.35', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6593, 2, '2015-07-27 02:12:12.203', 7, NULL, NULL, '&lt;p&gt;Wow, this was bit challenging but I was able to make one of these plots in python.
The two main components are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://stackoverflow.com/questions/24659005/radar-chart-with-multiple-scales-on-multiple-axes"&gt;plotting multiple radial axes&lt;/a&gt; on a polar plot&lt;/li&gt;
&lt;li&gt;&lt;a href="https://stackoverflow.com/questions/12075001/set-radial-axis-on-matplotlib-polar-plots"&gt;remapping radial axes&lt;/a&gt; for variables with reversed scales
&lt;a href="http://i.stack.imgur.com/XQpKQ.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/XQpKQ.png" alt="complex radar plot"&gt;&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="https://gist.github.com/kylerbrown/29ce940165b22b8f25f4" rel="nofollow"&gt;code&lt;/a&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns # improves plot aesthetics


def _invert(x, limits):
    """inverts a value x on a scale from
    limits[0] to limits[1]"""
    return limits[1] - (x - limits[0])

def _scale_data(data, ranges):
    """scales data[1:] to ranges[0],
    inverts if the scale is reversed"""
    for d, (y1, y2) in zip(data[1:], ranges[1:]):
        assert (y1 &amp;lt;= d &amp;lt;= y2) or (y2 &amp;lt;= d &amp;lt;= y1)
    x1, x2 = ranges[0]
    d = data[0]
    if x1 &amp;gt; x2:
        d = _invert(d, (x1, x2))
        x1, x2 = x2, x1
    sdata = [d]
    for d, (y1, y2) in zip(data[1:], ranges[1:]):
        if y1 &amp;gt; y2:
            d = _invert(d, (y1, y2))
            y1, y2 = y2, y1
        sdata.append((d-y1) / (y2-y1) 
                     * (x2 - x1) + x1)
    return sdata

class ComplexRadar():
    def __init__(self, fig, variables, ranges,
                 n_ordinate_levels=6):
        angles = np.arange(0, 360, 360./len(variables))

        axes = [fig.add_axes([0.1,0.1,0.9,0.9],polar=True,
                label = "axes{}".format(i)) 
                for i in range(len(variables))]
        l, text = axes[0].set_thetagrids(angles, 
                                         labels=variables)
        [txt.set_rotation(angle-90) for txt, angle 
             in zip(text, angles)]
        for ax in axes[1:]:
            ax.patch.set_visible(False)
            ax.grid("off")
            ax.xaxis.set_visible(False)
        for i, ax in enumerate(axes):
            grid = np.linspace(*ranges[i], 
                               num=n_ordinate_levels)
            gridlabel = ["{}".format(round(x,2)) 
                         for x in grid]
            if ranges[i][0] &amp;gt; ranges[i][1]:
                grid = grid[::-1] # hack to invert grid
                          # gridlabels aren''t reversed
            gridlabel[0] = "" # clean up origin
            ax.set_rgrids(grid, labels=gridlabel,
                         angle=angles[i])
            #ax.spines["polar"].set_visible(False)
            ax.set_ylim(*ranges[i])
        # variables for plotting
        self.angle = np.deg2rad(np.r_[angles, angles[0]])
        self.ranges = ranges
        self.ax = axes[0]
    def plot(self, data, *args, **kw):
        sdata = _scale_data(data, self.ranges)
        self.ax.plot(self.angle, np.r_[sdata, sdata[0]], *args, **kw)
    def fill(self, data, *args, **kw):
        sdata = _scale_data(data, self.ranges)
        self.ax.fill(self.angle, np.r_[sdata, sdata[0]], *args, **kw)

# example data
variables = ("Normal Scale", "Inverted Scale", "Inverted 2", 
            "Normal Scale 2", "Normal 3", "Normal 4 %", "Inverted 3 %")
data = (1.76, 1.1, 1.2, 
        4.4, 3.4, 86.8, 20)
ranges = [(0.1, 2.3), (1.5, 0.3), (1.3, 0.5),
         (1.7, 4.5), (1.5, 3.7), (70, 87), (100, 10)]            
# plotting
fig1 = plt.figure(figsize=(6, 6))
radar = ComplexRadar(fig1, variables, ranges)
radar.plot(data)
radar.fill(data, alpha=0.2)
plt.show()    
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6613, 5, '2015-07-28 17:54:44.533', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2281, 2, '2014-10-15 20:37:40.633', 2, NULL, NULL, '&lt;p&gt;I would say... it really depends. You may need to: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;em&gt;use&lt;/em&gt; machine learning algorithms: this will be useful for specific applications you may have. In this situation what you need is some programming skills and the taste for testing (practicing will make you strong). Here maths are not so much required I would say. &lt;/li&gt;
&lt;li&gt;be able to &lt;em&gt;modify&lt;/em&gt; existing algorithms. Your specific application may be reticent to regular algorithms, so you may need to adapt them to get maximum efficiency. Here maths come into play. &lt;/li&gt;
&lt;li&gt;understand the theory behind algorithms. Here maths are necessary, and will help you increase your knowledge of the field of machine learning, develop your own algorithms, speak the same langage as your peers... NN theory may be primitive as said by @Emre, but for instance this is not the case for SVM (the theory behind SVM requires e.g. to understand &lt;a href="http://en.wikipedia.org/wiki/Reproducing_kernel_Hilbert_space" rel="nofollow"&gt;reproducing kernel Hilbert spaces&lt;/a&gt;). &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;On the mid term for sure you will need strong maths. But you don''t need to wait for them to come to you, you can start right now with linear algebra, which is beautiful and useful for everything. And in case you encounter (possibly temporary) difficulties of any sort with maths, keep on practicing the way you already do (many people can talk about the perceptron but are not able to make a perceptron in Java), this is very valuable. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2286, 2, '2014-10-16 08:03:15.593', 2, NULL, NULL, '&lt;p&gt;The basic thing, you can do in that situation, is to split your query into N simple sentences each of which should be processed in order to receive YES/NO answer considering if the sentence is a query. That way you will receive following results:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Input: Gandhi is good guys, Where he was born?
-&amp;gt;
Gandhi is good guys - not query
Where he was born?  - query
===
1 query

Input: who is MGandhi and where was he born?
-&amp;gt;
who is MGandhi     - query
where was he born? - query
===
2 queries
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This approach will require anaphora resolution (in order to convert &lt;code&gt;he&lt;/code&gt; into &lt;code&gt;Gandhi&lt;/code&gt; in first example) and a parser to correctly divide complex sentence into simple ones.&lt;/p&gt;
', NULL, NULL, '''/code'':93C,108C,112C ''/p'':46C,127C ''/pre'':94C ''1'':70C ''2'':91C ''anaphora'':100C ''answer'':31C ''approach'':97C ''basic'':3C ''born'':57C,68C,80C,89C ''code'':48C,106C,110C ''complex'':122C ''consid'':32C ''convert'':105C ''correct'':120C ''divid'':121C ''exampl'':115C ''first'':114C ''follow'':44C ''gandhi'':50C,59C,111C ''good'':52C,61C ''gt'':58C,81C ''guy'':53C,62C ''input'':49C,72C ''mgandhi'':75C,84C ''n'':17C ''one'':126C ''order'':27C,103C ''p'':1C,95C ''parser'':118C ''pre'':47C ''process'':25C ''queri'':15C,38C,64C,69C,71C,85C,90C,92C ''receiv'':29C,43C ''requir'':99C ''resolut'':101C ''result'':45C ''sentenc'':19C,35C,123C ''simpl'':18C,125C ''situat'':10C ''split'':13C ''thing'':4C ''way'':40C ''yes/no'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2287, 1, '2014-10-16 12:15:32.017', 2, 246, 'Regression Model for explained model(Details inside)', '&lt;p&gt;I am kind of a newbie on machine learning and I would like to ask some questions based on a problem I have .&lt;/p&gt;

&lt;p&gt;Let''s say I have x y z as variable and I have values of these variables as time progresses like :&lt;/p&gt;

&lt;p&gt;t0  = x0 y0 z0  &lt;br&gt;
t1  = x1 y1 z1  &lt;br&gt;
tn  = xn yn zn  &lt;br&gt;&lt;/p&gt;

&lt;p&gt;Now I want a model that when it''s given 3 values of x , y , z I want a prediction of them like:&lt;/p&gt;

&lt;p&gt;Input : x_test y_test z_test 
Output : x_prediction y_prediction z_prediction&lt;/p&gt;

&lt;p&gt;These values are float numbers. What is the best model for this kind of problem? 
Thanks in advance for all the answers.&lt;/p&gt;

&lt;p&gt;More details:
Ok so let me give some more details about the problems so as to be more specific.&lt;/p&gt;

&lt;p&gt;I have run certain benchmarks and taken values of performance counters from the cores of a system per interval.&lt;/p&gt;

&lt;p&gt;The performance counters are the x , y , z in the above example.They are dependent to each other.Simple example is x = IPC , y  =  Cache misses , z  = Energy at Core.&lt;/p&gt;

&lt;p&gt;So I got this dataset of all these performance counters per interval .What I want to do is create a model that after learning from the training dataset , it will be given a certain state of the core ( the performance counters) and predict the performance counters that the core will have in the next interval.&lt;/p&gt;
', 'machine-learning  logistic-regression  predictive-modeling  regression', NULL, '''/p'':42C,65C,82C,107C,123C,147C,168C,189C,219C,276C ''3'':94C ''advanc'':142C ''answer'':146C ''ask'':33C ''base'':36C ''benchmark'':174C ''best'':133C ''br'':71C,76C,81C ''cach'':213C ''certain'':173C,254C ''core'':183C,218C,258C,269C ''counter'':180C,193C,230C,261C,266C ''creat'':239C ''dataset'':225C,248C ''depend'':204C ''detail'':6A,150C,158C ''energi'':216C ''exampl'':208C ''example.they'':202C ''explain'':4A ''float'':128C ''give'':155C ''given'':93C,252C ''got'':223C ''input'':109C ''insid'':7A ''interv'':188C,232C,275C ''ipc'':211C ''kind'':21C,137C ''learn'':10B,27C,244C ''let'':44C,153C ''like'':31C,64C,106C ''logist'':12B ''logistic-regress'':11B ''machin'':9B,26C ''machine-learn'':8B ''miss'':214C ''model'':2A,5A,16B,88C,134C,241C ''newbi'':24C ''next'':274C ''number'':129C ''ok'':151C ''other.simple'':207C ''output'':116C ''p'':18C,43C,66C,83C,108C,124C,148C,169C,190C,220C ''per'':187C,231C ''perform'':179C,192C,229C,260C,265C ''predict'':15B,103C,118C,120C,122C,263C ''predictive-model'':14B ''problem'':39C,139C,161C ''progress'':63C ''question'':35C ''regress'':1A,13B,17B ''run'':172C ''say'':46C ''specif'':167C ''state'':255C ''system'':186C ''t0'':67C ''t1'':72C ''taken'':176C ''test'':111C,113C,115C ''thank'':140C ''time'':62C ''tn'':77C ''train'':247C ''valu'':57C,95C,126C,177C ''variabl'':53C,60C ''want'':86C,101C,235C ''would'':30C ''x'':49C,97C,110C,117C,196C,210C ''x0'':68C ''x1'':73C ''xn'':78C ''y'':50C,98C,112C,119C,197C,212C ''y0'':69C ''y1'':74C ''yn'':79C ''z'':51C,99C,114C,121C,198C,215C ''z0'':70C ''z1'':75C ''zn'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2288, 2, '2014-10-16 14:34:33.18', 0, NULL, NULL, '&lt;p&gt;AFAIK if you want to predict the value of one variable, you need to have one or more variables as predictors; i.e.: you assume the behaviour of one variable can be explained by the behaviour of other variables.
In your case you have three independent variables whose value you want to predict, and since you don''t mention any other variables, I assume that each variable depends on the others. In that case you could fit three models (for instance, regression models), each of which would predict the value of one variable, based on the others. As an example, to predict x:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;x_prediction=int+cy*y_test+cz*z_test
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;, where int is the intercept and cy, cz, the coefficients of the linear regression. 
Likewise, in order to predict y and z:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;y_prediction=int+cx*x_test+cx*z_test
z_prediction=int+cx*x_test+cy*y_test
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':116C,162C ''/p'':104C,141C ''/pre'':117C,163C ''afaik'':2C ''assum'':25C,64C ''base'':94C ''behaviour'':27C,36C ''case'':42C,74C ''code'':106C,143C ''coeffici'':128C ''could'':76C ''cx'':147C,150C,156C ''cy'':110C,125C,159C ''cz'':113C,126C ''depend'':68C ''exampl'':100C ''explain'':33C ''fit'':77C ''i.e'':23C ''independ'':46C ''instanc'':81C ''int'':109C,120C,146C,155C ''intercept'':123C ''likewis'':133C ''linear'':131C ''mention'':59C ''model'':79C,83C ''need'':14C ''one'':11C,17C,29C,92C ''order'':135C ''other'':71C,97C ''p'':1C,118C ''pre'':105C,142C ''predict'':7C,53C,88C,102C,108C,137C,145C,154C ''predictor'':22C ''regress'':82C,132C ''sinc'':55C ''test'':112C,115C,149C,152C,158C,161C ''three'':45C,78C ''valu'':9C,49C,90C ''variabl'':12C,20C,30C,39C,47C,62C,67C,93C ''want'':5C,51C ''whose'':48C ''would'':87C ''x'':103C,107C,148C,157C ''y'':111C,138C,144C,160C ''z'':114C,140C,151C,153C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2579, 1, '2014-12-02 11:12:06.103', 4, 993, 'Mahout Similarity algorithm comparison', '&lt;p&gt;Which of the following is best (or widely used) for calculating item-item similarity measure in mahout and why ?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Pearson Correlation
Spearman Correlation
Euclidean Distance
Tanimoto Coefficient
LogLikelihood Similarity
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is there any thumb-rule to chose from these set of algorithm also how to differentiate each of them ?&lt;/p&gt;
', 'machine-learning  data-mining  statistics  algorithms  recommendation', NULL, '''/code'':48C ''/p'':35C,71C ''/pre'':49C ''algorithm'':3A,12B,63C ''also'':64C ''best'':20C ''calcul'':25C ''chose'':58C ''code'':37C ''coeffici'':45C ''comparison'':4A ''correl'':39C,41C ''data'':9B ''data-min'':8B ''differenti'':67C ''distanc'':43C ''euclidean'':42C ''follow'':18C ''item'':27C,28C ''item-item'':26C ''learn'':7B ''loglikelihood'':46C ''machin'':6B ''machine-learn'':5B ''mahout'':1A,32C ''measur'':30C ''mine'':10B ''p'':14C,50C ''pearson'':38C ''pre'':36C ''recommend'':13B ''rule'':56C ''set'':61C ''similar'':2A,29C,47C ''spearman'':40C ''statist'':11B ''tanimoto'':44C ''thumb'':55C ''thumb-rul'':54C ''use'':23C ''wide'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2290, 2, '2014-10-16 21:12:09.49', 2, NULL, NULL, '&lt;p&gt;Buyer classification is used to categorize users who purchase groups of items. Buyers are categorized in order to be targeted for advertising. When users buy similar items, they are more likely to buy similar items in the future. This is useful information when pricing items on a website.&lt;/p&gt;

&lt;p&gt;A clear example, a website selling nutritional supplements may want to target different buyers by category. Men in their 20s are unlikely to purchase menopause supplements, and women in their 50s are also unlikely to buy creatine. So, by analyzing user purchase history and categorizing types of buyers, the site can send intelligent promotions - males in their 20s get ads for creatine while women in their 50s get ads for menopause supplements.&lt;/p&gt;

&lt;p&gt;Also, if I want to run a sale to attract customers, I don''t want to offer lower prices on items which are often purchased together. I''d rathe price one low to attract the buyer, and then hope they buy the complimentary item at full price. Categorizing buyers also helps with this problem.&lt;/p&gt;

&lt;p&gt;You may want to read up on shopping cart analysis, which is not a new problem. Department stores have been analyzing shopping carts and classifying buyers long before online shopping was popularized. That''s why you have to use membership cards to get the ''special'' prices.&lt;/p&gt;

&lt;p&gt;Fine tuning these details can increase revenue substantially.&lt;/p&gt;
', NULL, NULL, '''/p'':50C,124C,180C,228C,238C ''20s'':71C,109C ''50s'':82C,118C ''ad'':111C,120C ''advertis'':23C ''also'':84C,126C,175C ''analysi'':191C ''analyz'':91C,202C ''attract'':135C,159C ''buy'':26C,34C,87C,166C ''buyer'':2C,14C,65C,99C,161C,174C,207C ''card'':222C ''cart'':190C,204C ''categor'':7C,16C,96C,173C ''categori'':67C ''classif'':3C ''classifi'':206C ''clear'':53C ''complimentari'':168C ''creatin'':88C,113C ''custom'':136C ''d'':153C ''depart'':198C ''detail'':233C ''differ'':64C ''exampl'':54C ''fine'':230C ''full'':171C ''futur'':39C ''get'':110C,119C,224C ''group'':11C ''help'':176C ''histori'':94C ''hope'':164C ''increas'':235C ''inform'':43C ''intellig'':104C ''item'':13C,28C,36C,46C,146C,169C ''like'':32C ''long'':208C ''low'':157C ''lower'':143C ''male'':106C ''may'':60C,183C ''membership'':221C ''men'':68C ''menopaus'':76C,122C ''new'':196C ''nutrit'':58C ''offer'':142C ''often'':149C ''one'':156C ''onlin'':210C ''order'':18C ''p'':1C,51C,125C,181C,229C ''popular'':213C ''price'':45C,144C,155C,172C,227C ''problem'':179C,197C ''promot'':105C ''purchas'':10C,75C,93C,150C ''rath'':154C ''read'':186C ''revenu'':236C ''run'':131C ''sale'':133C ''sell'':57C ''send'':103C ''shop'':189C,203C,211C ''similar'':27C,35C ''site'':101C ''special'':226C ''store'':199C ''substanti'':237C ''supplement'':59C,77C,123C ''target'':21C,63C ''togeth'':151C ''tune'':231C ''type'':97C ''unlik'':73C,85C ''use'':5C,42C,220C ''user'':8C,25C,92C ''want'':61C,129C,140C,184C ''websit'':49C,56C ''women'':79C,115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2293, 1, '2014-10-17 10:47:13.197', 6, 1056, 'Scalable Outlier/Anomaly Detection', '&lt;p&gt;I am trying to setup a Big Data Infra-Structure using Hadoop, Hive, Elastic Search (amongst others) and I would like to run some algorithms over these datasets. I would like the algorithms themselves, to be scalable, so that excludes using tools such as Weka, R, or even RHadoop.
The &lt;a href="https://mahout.apache.org"&gt;Apache Mahout Library&lt;/a&gt; seems to be a good option, and it features &lt;a href="https://mahout.apache.org/users/basics/algorithms.html"&gt;algorithms for regression and clustering tasks&lt;/a&gt;. What I am struggling to find is a solution for anomaly or outlier detection.
Since Mahout features Hidden Markov Models and a variety of clustering techniques (including K-Means) I was wondering if it would be possible to build a model to detect outliers in time-series, using any of this. I would be grateful if somebody experienced on this could advice me a) if it is possible, and in case it is b) how-to do it, plus c) an estimation of the effort involved and d) accuracy/problems of this approach.&lt;/p&gt;
', 'bigdata  data-mining  algorithms', NULL, '''/a'':67C,88C ''/p'':184C ''/users/basics/algorithms.html'':81C ''accuracy/problems'':180C ''advic'':152C ''algorithm'':8B,35C,43C,82C ''amongst'':26C ''anomali'':99C ''apach'':64C ''approach'':183C ''b'':164C ''big'':16C ''bigdata'':4B ''build'':128C ''c'':171C ''case'':161C ''cluster'':86C,113C ''could'':151C ''d'':179C ''data'':6B,17C ''data-min'':5B ''dataset'':38C ''detect'':3A,102C,132C ''effort'':176C ''elast'':24C ''estim'':173C ''even'':58C ''exclud'':50C ''experienc'':148C ''featur'':76C,105C ''find'':94C ''good'':72C ''grate'':145C ''hadoop'':22C ''hidden'':106C ''hive'':23C ''how-to'':165C ''href'':62C,78C ''includ'':115C ''infra'':19C ''infra-structur'':18C ''involv'':177C ''k'':117C ''k-mean'':116C ''librari'':66C ''like'':31C,41C ''mahout'':65C,104C ''mahout.apache.org'':63C,80C ''mahout.apache.org/users/basics/algorithms.html'':79C ''markov'':107C ''mean'':118C ''mine'':7B ''model'':108C,130C ''option'':73C ''other'':27C ''outlier'':101C,133C ''outlier/anomaly'':2A ''p'':9C ''plus'':170C ''possibl'':126C,158C ''r'':56C ''regress'':84C ''rhadoop'':59C ''run'':33C ''scalabl'':1A,47C ''search'':25C ''seem'':68C ''seri'':137C ''setup'':14C ''sinc'':103C ''solut'':97C ''somebodi'':147C ''structur'':20C ''struggl'':92C ''task'':87C ''techniqu'':114C ''time'':136C ''time-seri'':135C ''tool'':52C ''tri'':12C ''use'':21C,51C,138C ''varieti'':111C ''weka'':55C ''wonder'':121C ''would'':30C,40C,124C,143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2294, 2, '2014-10-17 10:55:04.147', 3, NULL, NULL, '&lt;p&gt;It strongly depends on the environment/company you are working with. In my eyes there is a "big data" hype at the moment and a lot of companies try to enter the field with hadoop based solutions - what makes hadoop also a buzzword but its not always the best solution. &lt;/p&gt;

&lt;p&gt;In my mind, a good Data Scientist should be able to ask the right questions and keep on asking again until its clear whats really needed. Than a good DataScientist - of course - needs to know how to address the problem (or at least know someone who can). Otherwise your stakeholder could be frustrated :-) &lt;/p&gt;

&lt;p&gt;So, i would say its not absolutely necessary to learn Hadoop.&lt;/p&gt;
', NULL, NULL, '''/p'':51C,106C,119C ''abl'':62C ''absolut'':114C ''address'':90C ''also'':41C ''alway'':47C ''ask'':64C,71C ''base'':36C ''best'':49C ''big'':18C ''buzzword'':43C ''clear'':75C ''compani'':28C ''could'':103C ''cours'':84C ''data'':19C,58C ''datascientist'':82C ''depend'':4C ''enter'':31C ''environment/company'':7C ''eye'':14C ''field'':33C ''frustrat'':105C ''good'':57C,81C ''hadoop'':35C,40C,118C ''hype'':20C ''keep'':69C ''know'':87C,96C ''learn'':117C ''least'':95C ''lot'':26C ''make'':39C ''mind'':55C ''moment'':23C ''necessari'':115C ''need'':78C,85C ''otherwis'':100C ''p'':1C,52C,107C ''problem'':92C ''question'':67C ''realli'':77C ''right'':66C ''say'':111C ''scientist'':59C ''solut'':37C,50C ''someon'':97C ''stakehold'':102C ''strong'':3C ''tri'':29C ''what'':76C ''work'':10C ''would'':110C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2296, 2, '2014-10-17 12:50:56.243', 6, NULL, NULL, '&lt;p&gt;I would take a look at &lt;a href="https://github.com/tdunning/t-digest"&gt;t-digest algorithm&lt;/a&gt;. It''s &lt;a href="https://issues.apache.org/jira/browse/MAHOUT-1361"&gt;been merged into mahout&lt;/a&gt; and also a part of some other libraries (github.com/addthis/stream-lib/blob/master/src/main/java/com/clearspring/analytics/stream/quantile/TDigest.java) for big data streaming. You can get more about this algorithm particularly and big data anomaly detection in general in next resources:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Practical machine learning anomaly detection book.(info.mapr.com/rs/mapr/images/Practical_Machine_Learning_Anomaly_Detection.pdf)&lt;/li&gt;
&lt;li&gt;Webinar: Anomaly Detection When You Don''t Know What You Need to Find (youtube.com/watch?v=i-mSV63Q9rA#t=757)&lt;/li&gt;
&lt;li&gt;Anomaly Detection in Elasticsearch (info.prelert.com/anomaly-detection-in-elasticsearch)&lt;/li&gt;
&lt;li&gt;Beating Billion Dollar Fraud Using Anomaly Detection: A Signal Processing Approach using Argyle Data on the Hortonworks Data Platform with Accumulo (oreilly.com/pub/e/3211)&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':17C,29C ''/addthis/stream-lib/blob/master/src/main/java/com/clearspring/analytics/stream/quantile/tdigest.java)'':40C ''/anomaly-detection-in-elasticsearch)&lt;/li&gt;'':99C ''/jira/browse/mahout-1361'':24C ''/ol'':125C ''/p'':63C ''/pub/e/3211)&lt;/li&gt;'':124C ''/rs/mapr/images/practical_machine_learning_anomaly_detection.pdf)&lt;/li&gt;'':74C ''/tdunning/t-digest'':12C ''/watch?v=i-msv63q9ra#t=757)&lt;/li&gt;'':91C ''accumulo'':121C ''algorithm'':16C,51C ''also'':31C ''anomali'':56C,69C,77C,93C,106C ''approach'':111C ''argyl'':113C ''beat'':101C ''big'':42C,54C ''billion'':102C ''book'':71C ''data'':43C,55C,114C,118C ''detect'':57C,70C,78C,94C,107C ''digest'':15C ''dollar'':103C ''elasticsearch'':96C ''find'':88C ''fraud'':104C ''general'':59C ''get'':47C ''github.com'':11C,39C ''github.com/addthis/stream-lib/blob/master/src/main/java/com/clearspring/analytics/stream/quantile/tdigest.java)'':38C ''github.com/tdunning/t-digest'':10C ''hortonwork'':117C ''href'':9C,21C ''info.mapr.com'':73C ''info.mapr.com/rs/mapr/images/practical_machine_learning_anomaly_detection.pdf)&lt;/li&gt;'':72C ''info.prelert.com'':98C ''info.prelert.com/anomaly-detection-in-elasticsearch)&lt;/li&gt;'':97C ''issues.apache.org'':23C ''issues.apache.org/jira/browse/mahout-1361'':22C ''know'':83C ''learn'':68C ''li'':65C,75C,92C,100C ''librari'':37C ''look'':6C ''machin'':67C ''mahout'':28C ''merg'':26C ''need'':86C ''next'':61C ''ol'':64C ''oreilly.com'':123C ''oreilly.com/pub/e/3211)&lt;/li&gt;'':122C ''p'':1C ''part'':33C ''particular'':52C ''platform'':119C ''practic'':66C ''process'':110C ''resourc'':62C ''signal'':109C ''stream'':44C ''t-digest'':13C ''take'':4C ''use'':105C,112C ''webinar'':76C ''would'':3C ''youtube.com'':90C ''youtube.com/watch?v=i-msv63q9ra#t=757)&lt;/li&gt;'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2297, 1, '2014-10-17 13:34:48.54', 1, 189, 'Masters thesis topics in Applied probability and Probabilistic models in Machine Learning', '&lt;p&gt;I''m looking for a topic for my masters thesis. Machine learning is my primary domain and I want to work on probabilistic models and applied probability in Machine Learning. Please suggest some exciting new topics that would make for a good masters thesis subject.
Anything related to Markov chains Monte Carlo, Bayesian methods, Probabilistic graphical models, Markov models and so on in context of machine learning would be great!&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':87C ''anyth'':62C ''appli'':5A,42C ''bayesian'':69C ''carlo'':68C ''chain'':66C ''context'':80C ''domain'':32C ''excit'':50C ''good'':58C ''graphic'':72C ''great'':86C ''learn'':12A,15B,28C,46C,83C ''look'':19C ''m'':18C ''machin'':11A,14B,27C,45C,82C ''machine-learn'':13B ''make'':55C ''markov'':65C,74C ''master'':1A,25C,59C ''method'':70C ''model'':9A,40C,73C,75C ''mont'':67C ''new'':51C ''p'':16C ''pleas'':47C ''primari'':31C ''probabilist'':8A,39C,71C ''probabl'':6A,43C ''relat'':63C ''subject'':61C ''suggest'':48C ''thesi'':2A,26C,60C ''topic'':3A,22C,52C ''want'':35C ''work'':37C ''would'':54C,84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2298, 1, '2014-10-17 13:58:39.353', 2, 100, 'Differences in scoring from PMML model on different platforms', '&lt;p&gt;I''ve built a toy Random Forest model in &lt;code&gt;R&lt;/code&gt; (using the &lt;code&gt;German Credit&lt;/code&gt; dataset from the &lt;code&gt;caret&lt;/code&gt; package), exported it in &lt;code&gt;PMML 4.0&lt;/code&gt; and deployed onto Hadoop, using the &lt;code&gt;Cascading Pattern&lt;/code&gt; library.&lt;/p&gt;

&lt;p&gt;I''ve run into an issue where &lt;code&gt;Cascading Pattern&lt;/code&gt; scores the same data differently (in a binary classification problem) than the same model in &lt;code&gt;R&lt;/code&gt;. Out of 200 observations, 2 are scored differently.&lt;/p&gt;

&lt;p&gt;Why is this? Could it be due to a difference in the implementation of Random Forests? &lt;/p&gt;
', 'machine-learning  r  hadoop  random-forest  predictive-modeling', NULL, '''/code'':33C,39C,45C,53C,63C,77C,95C ''/p'':65C,104C,122C ''2'':100C ''200'':98C ''4.0'':52C ''binari'':85C ''built'':24C ''caret'':44C ''cascad'':61C,75C ''classif'':86C ''code'':31C,36C,43C,50C,60C,74C,93C ''could'':109C ''credit'':38C ''data'':81C ''dataset'':40C ''deploy'':55C ''differ'':1A,8A,82C,103C,115C ''due'':112C ''export'':47C ''forest'':17B,28C,121C ''german'':37C ''hadoop'':14B,57C ''implement'':118C ''issu'':72C ''learn'':12B ''librari'':64C ''machin'':11B ''machine-learn'':10B ''model'':6A,20B,29C,91C ''observ'':99C ''onto'':56C ''p'':21C,66C,105C ''packag'':46C ''pattern'':62C,76C ''platform'':9A ''pmml'':5A,51C ''predict'':19B ''predictive-model'':18B ''problem'':87C ''r'':13B,32C,94C ''random'':16B,27C,120C ''random-forest'':15B ''run'':69C ''score'':3A,78C,102C ''toy'':26C ''use'':34C,58C ''ve'':23C,68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2299, 2, '2014-10-18 03:22:19.06', 2, NULL, NULL, '&lt;p&gt;One of real challenges, I faced with R is different packages compatible with different versions.. quite a lot R packages are not available for latest version of R.. And R quite a few time gives error  due to library or package was written for older version.. &lt;/p&gt;
', NULL, NULL, '''..'':17C,31C,50C ''/p'':51C ''avail'':25C ''challeng'':5C ''compat'':13C ''differ'':11C,15C ''due'':40C ''error'':39C ''face'':7C ''give'':38C ''latest'':27C ''librari'':42C ''lot'':20C ''older'':48C ''one'':2C ''p'':1C ''packag'':12C,22C,44C ''quit'':18C,34C ''r'':9C,21C,30C,33C ''real'':4C ''time'':37C ''version'':16C,28C,49C ''written'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2311, 2, '2014-10-19 19:12:32.94', 5, NULL, NULL, '&lt;p&gt;First we need to understand why we need Deep learning. To build models ML need Test Data with Labels (supervised or unsupervised). In many domains as the data grows maintaining the data with labels is hard. Deep learning networks doesn''t need labeled data. The Deep learning algorithms can figure out the labels. So this obviates the need for domain experts to come out with labels for the data which is very important in the areas of speech recognition, computer vision, and language understanding. Google Cat image recognition is a very interesting experiment. Also it is interesting to know "Geoffrey hinton" the professor who was hired by Google.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.wired.com/2014/01/geoffrey-hinton-deep-learning/" rel="nofollow"&gt;http://www.wired.com/2014/01/geoffrey-hinton-deep-learning/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You may get more insight as you explore in this framework.&lt;/p&gt;
', NULL, NULL, '''/2014/01/geoffrey-hinton-deep-learning/'':116C ''/2014/01/geoffrey-hinton-deep-learning/&lt;/a&gt;&lt;/p&gt;'':121C ''/p'':110C,134C ''algorithm'':49C ''also'':95C ''area'':77C ''build'':13C ''cat'':87C ''come'':64C ''comput'':81C ''data'':18C,29C,33C,45C,70C ''deep'':10C,38C,47C ''doesn'':41C ''domain'':26C,61C ''experi'':94C ''expert'':62C ''explor'':130C ''figur'':51C ''first'':2C ''framework'':133C ''geoffrey'':101C ''get'':125C ''googl'':86C,109C ''grow'':30C ''hard'':37C ''hinton'':102C ''hire'':107C ''href'':113C ''imag'':88C ''import'':74C ''insight'':127C ''interest'':93C,98C ''know'':100C ''label'':20C,35C,44C,54C,67C ''languag'':84C ''learn'':11C,39C,48C ''maintain'':31C ''mani'':25C ''may'':124C ''ml'':15C ''model'':14C ''need'':4C,9C,16C,43C,59C ''network'':40C ''nofollow'':118C ''obviat'':57C ''p'':1C,111C,122C ''professor'':104C ''recognit'':80C,89C ''rel'':117C ''speech'':79C ''supervis'':21C ''test'':17C ''understand'':6C,85C ''unsupervis'':23C ''vision'':82C ''www.wired.com'':115C,120C ''www.wired.com/2014/01/geoffrey-hinton-deep-learning/'':114C ''www.wired.com/2014/01/geoffrey-hinton-deep-learning/&lt;/a&gt;&lt;/p&gt;'':119C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4829, 2, '2015-01-08 00:25:05.277', 1, NULL, NULL, '&lt;p&gt;We faced this problem and analysed the issue.
Cloudera, Hue or Hive didn''t have any error reported but the users via Beeswax were unable to run queries.
It was good to analyze below:
Duplicate PID for Hive (check for all hive servers. If you do have a tool to check the duplicate PID then use that or use the kinit to login to cloudera admin node and analyze it.)
We killed the duplicate PID
AND
restarted hive server. It fixed the issue.&lt;/p&gt;
', NULL, NULL, '''/p'':85C ''admin'':67C ''analys'':7C ''analyz'':34C,70C ''beeswax'':24C ''check'':40C,52C ''cloudera'':10C,66C ''didn'':14C ''duplic'':36C,54C,75C ''error'':18C ''face'':3C ''fix'':82C ''good'':32C ''hive'':13C,39C,43C,79C ''hue'':11C ''issu'':9C,84C ''kill'':73C ''kinit'':62C ''login'':64C ''node'':68C ''p'':1C ''pid'':37C,55C,76C ''problem'':5C ''queri'':29C ''report'':19C ''restart'':78C ''run'':28C ''server'':44C,80C ''tool'':50C ''unabl'':26C ''use'':57C,60C ''user'':22C ''via'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2314, 1, '2014-10-20 08:48:42.167', 7, 356, 'On-line random forests by adding more single Decisions Trees', '&lt;p&gt;A Random Forest (RF) is created by an ensemble of Decision Trees''s (DT). By using bagging, each DT is trained in a different data subset. Hence, &lt;strong&gt;is there any way of implementing an on-line random forest by adding more decision tress on new data?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;For example, we have 10K samples and train 10 DT''s. Then we get 1K samples, and instead of training again the full RF, we add a new DT. The prediction is done now by the Bayesian average of 10+1 DT''s.&lt;/p&gt;

&lt;p&gt;In addition, if we keep all the previous data, the new DT''s can be trained mainly in the new data, where the probability of picking a sample is weighted depending how many times have been already picked.&lt;/p&gt;
', 'random-forest  online-learning', NULL, '''+1'':110C ''/p'':68C,113C,153C ''/strong'':67C ''10'':78C,109C ''10k'':74C ''1k'':84C ''ad'':7A,60C ''add'':95C ''addit'':116C ''alreadi'':151C ''averag'':107C ''bag'':35C ''bayesian'':106C ''creat'':24C ''data'':43C,66C,123C,135C ''decis'':10A,29C,62C ''depend'':145C ''differ'':42C ''done'':102C ''dt'':32C,37C,79C,98C,111C,126C ''ensembl'':27C ''exampl'':71C ''forest'':5A,14B,21C,58C ''full'':92C ''get'':83C ''henc'':45C ''implement'':52C ''instead'':87C ''keep'':119C ''learn'':17B ''line'':3A,56C ''main'':131C ''mani'':147C ''new'':65C,97C,125C,134C ''on-lin'':1A,54C ''onlin'':16B ''online-learn'':15B ''p'':18C,69C,114C ''pick'':140C,152C ''predict'':100C ''previous'':122C ''probabl'':138C ''random'':4A,13B,20C,57C ''random-forest'':12B ''rf'':22C,93C ''sampl'':75C,85C,142C ''singl'':9A ''strong'':46C ''subset'':44C ''time'':148C ''train'':39C,77C,89C,130C ''tree'':11A,30C ''tress'':63C ''use'':34C ''way'':50C ''weight'':144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2302, 1, '2014-10-18 04:58:45.1', 2, 914, 'R Script to generate random dataset in 2d space', '&lt;p&gt;I want to analyze the effectiveness and efficiency of kernel methods for which I would require 3 different data-set in 2 dimensional space for each of the following cases:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;BAD_kmeans: The data set for which the kmeans clustering algorithm
will not perform well.&lt;/li&gt;
&lt;li&gt;BAD_pca: The data set for which the Principal Component Analysis
(PCA) dimension reduction method upon projection of the original
points into 1-dimensional space (i.e., the first eigenvector) will
not perform well.&lt;/li&gt;
&lt;li&gt;BAD_svm: The data set for which the linear Support Vector Machine
(SVM) supervised classification method using two classes of points
(positive and negative)  will not perform well.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Which packages can I use in R to generate the random 2d data-set for each of the above cases ? A sample script in R would help in understanding&lt;/p&gt;
', 'machine-learning  classification  r  clustering', NULL, '''/li'':66C,101C,131C ''/ol'':132C ''/p'':48C,164C ''1'':90C ''2'':39C ''2d'':8A,145C ''3'':33C ''algorithm'':61C ''analysi'':78C ''analyz'':20C ''bad'':51C,68C,103C ''case'':47C,154C ''class'':121C ''classif'':13B,117C ''cluster'':15B,60C ''compon'':77C ''data'':36C,54C,71C,106C,147C ''data-set'':35C,146C ''dataset'':6A ''differ'':34C ''dimens'':80C ''dimension'':40C,91C ''effect'':22C ''effici'':24C ''eigenvector'':96C ''first'':95C ''follow'':46C ''generat'':4A,142C ''help'':161C ''i.e'':93C ''kernel'':26C ''kmean'':52C,59C ''learn'':12B ''li'':50C,67C,102C ''linear'':111C ''machin'':11B,114C ''machine-learn'':10B ''method'':27C,82C,118C ''negat'':126C ''ol'':49C ''origin'':87C ''p'':16C,133C ''packag'':135C ''pca'':69C,79C ''perform'':64C,99C,129C ''point'':88C,123C ''posit'':124C ''princip'':76C ''project'':84C ''r'':1A,14B,140C,159C ''random'':5A,144C ''reduct'':81C ''requir'':32C ''sampl'':156C ''script'':2A,157C ''set'':37C,55C,72C,107C,148C ''space'':9A,41C,92C ''supervis'':116C ''support'':112C ''svm'':104C,115C ''two'':120C ''understand'':163C ''upon'':83C ''use'':119C,138C ''vector'':113C ''want'':18C ''well'':65C,100C,130C ''would'':31C,160C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2580, 2, '2014-12-02 14:35:33.447', 0, NULL, NULL, '&lt;p&gt;The advantage of mahout is that it is scalable, apache license , good community and documentation support. Also Fast-prototyping and evaluation
to evaluate a different configuration of the same algorithm we just need to update a parameter and run again.&lt;/p&gt;
', NULL, NULL, '''/p'':43C ''advantag'':3C ''algorithm'':32C ''also'':18C ''apach'':11C ''communiti'':14C ''configur'':28C ''differ'':27C ''document'':16C ''evalu'':23C,25C ''fast'':20C ''fast-prototyp'':19C ''good'':13C ''licens'':12C ''mahout'':5C ''need'':35C ''p'':1C ''paramet'':39C ''prototyp'':21C ''run'':41C ''scalabl'':10C ''support'':17C ''updat'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6636, 5, '2015-07-30 17:48:36.43', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6688, 2, '2015-08-04 20:22:00.21', 2, NULL, NULL, '&lt;p&gt;A layer in a neural network does not process logic of the kind you present. It cannot "check the input pattern with a simple if else pattern". You could make a model that processed your example inputs to example outputs as you show, but it would not be a neural network.&lt;/p&gt;

&lt;p&gt;In machine-learning, neural networks have a formal, numeric structure. There are a few variations, but essentially in your diagram:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Each circle is a "neuron" which may have a numeric output value, also called its &lt;em&gt;activation&lt;/em&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Each arrow is a connection between neurons that shows where values are passed between neurons. Each connection has a strength or &lt;em&gt;weight&lt;/em&gt; associated with it.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;When you have many arrows going into a neuron, you can calculate its new &lt;em&gt;activation&lt;/em&gt; starting with the sum of &lt;em&gt;weight&lt;/em&gt;s times the activation values of neurons that they are connected from. Call this sum $z$. Then the activation of the neuron you are looking at is $a = f(z)$ where f is an "activation function" (also called "transfer function").&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The above very roughly covers how many neural networks are constructed. As you can see though, there is no room in that for having your if/else statement.&lt;/p&gt;

&lt;p&gt;Instead, neural networks like the one you show (technically a &lt;em&gt;feed forward&lt;/em&gt; network also called a &lt;em&gt;multi-layer perceptron&lt;/em&gt; or MLP) are most often &lt;em&gt;trained by example&lt;/em&gt;. This involves repeatedly giving the network inputs where you know the output you want. To start with the network will give completely incorrect outputs, and you then adjust the weights to try and make the result closer. This is repeated very many times until the network becomes the best model it can for your purpose. A very common algorithm used to train a neural network in this way is &lt;a href="https://en.wikipedia.org/wiki/Backpropagation" rel="nofollow"&gt;backpropagation and gradient descent&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;For your problem as described, a neural network is &lt;em&gt;not&lt;/em&gt; a good model, and would not be used in the real world. That is because you already know the full logic of a simpler model that covers all possible inputs. At best with a neural network you will get an approximation to that already-perfect model.&lt;/p&gt;

&lt;p&gt;However, if this is for the purposes of understanding neural networks, your approach would be roughly this:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Generate some training data with example inputs and outputs. Most example outputs will be the 23,23 default values, but when inputs are the special values 6,7,8 then the training outputs should be 3 and 4.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Build an initial network. Normally you would use an appropriate library - it is important you learn how the library works and understand any limitations. E.g. many activation functions have a maximum output of 1, so cannot learn the values 3,4, or 23.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Train the network to fit the example data. There are very many options there, depending on the library you are using. Usually it is just a matter of selecting parameters for the learning that help it progress smoothly. You may have to run training for a large number of repeats over the whole dataset (called epochs)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Test that your network works as you expect. Even if you have trained it well, the response of the network is never as precise as your problem statement. If you gave it inputs of 5.8,7.2,8.1 (i.e. close to 6,7,8) then it would likely output something like 7.2,8.5 and not 23,23 .  . . it will interpolate between values.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':345C ''/em'':97C,125C,149C,157C,244C,254C,264C,359C ''/li'':99C,130C,199C,469C,516C,576C,641C ''/ol'':642C ''/p'':53C,76C,98C,129C,198C,229C,346C,407C,426C,468C,515C,575C,640C ''/ul'':200C ''/wiki/backpropagation'':338C ''1'':505C ''23'':445C,446C,514C,633C,634C ''3'':465C,511C ''4'':467C,512C ''5.8'':613C ''6'':456C,619C ''7'':457C,620C ''7.2'':614C,629C ''8'':458C,621C ''8.1'':615C ''8.5'':630C ''activ'':96C,148C,161C,176C,192C,498C ''adjust'':292C ''algorithm'':323C ''alreadi'':376C,404C ''already-perfect'':403C ''also'':92C,194C,246C ''approach'':421C ''appropri'':481C ''approxim'':400C ''arrow'':103C,137C ''associ'':126C ''backpropag'':341C ''becom'':311C ''best'':313C,391C ''build'':472C ''calcul'':144C ''call'':93C,170C,195C,247C,573C ''cannot'':18C,507C ''check'':19C ''circl'':81C ''close'':617C ''closer'':301C ''common'':322C ''complet'':286C ''connect'':106C,118C,168C ''construct'':212C ''could'':30C ''cover'':206C,386C ''data'':433C,526C ''dataset'':572C ''default'':447C ''depend'':533C ''descent'':344C ''describ'':352C ''diagram'':75C ''e.g'':496C ''els'':27C ''em'':95C,123C,147C,155C,241C,249C,260C,357C ''en.wikipedia.org'':337C ''en.wikipedia.org/wiki/backpropagation'':336C ''epoch'':574C ''essenti'':72C ''even'':587C ''exampl'':37C,40C,263C,435C,440C,525C ''expect'':586C ''f'':186C,189C ''feed'':242C ''fit'':523C ''formal'':63C ''forward'':243C ''full'':379C ''function'':193C,197C,499C ''gave'':609C ''generat'':430C ''get'':398C ''give'':268C,285C ''go'':138C ''good'':361C ''gradient'':343C ''help'':553C ''howev'':409C ''href'':335C ''i.e'':616C ''if/else'':227C ''import'':485C ''incorrect'':287C ''initi'':474C ''input'':21C,38C,271C,389C,436C,451C,611C ''instead'':231C ''interpol'':637C ''involv'':266C ''kind'':14C ''know'':274C,377C ''larg'':565C ''layer'':3C,252C ''learn'':58C,487C,508C,551C ''li'':78C,100C,131C,428C,470C,517C,577C ''librari'':482C,490C,536C ''like'':234C,625C,628C ''limit'':495C ''logic'':11C,380C ''look'':182C ''machin'':57C ''machine-learn'':56C ''make'':31C,298C ''mani'':136C,208C,306C,497C,530C ''matter'':545C ''maximum'':502C ''may'':86C,558C ''mlp'':256C ''model'':33C,314C,362C,384C,406C ''multi'':251C ''multi-lay'':250C ''network'':7C,52C,60C,210C,233C,245C,270C,283C,310C,329C,355C,395C,419C,475C,521C,582C,598C ''neural'':6C,51C,59C,209C,232C,328C,354C,394C,418C ''neuron'':84C,108C,116C,141C,164C,179C ''never'':600C ''new'':146C ''nofollow'':340C ''normal'':476C ''number'':566C ''numer'':64C,89C ''often'':259C ''ol'':427C ''one'':236C ''option'':531C ''output'':41C,90C,276C,288C,438C,441C,462C,503C,626C ''p'':1C,54C,79C,101C,132C,201C,230C,347C,408C,429C,471C,518C,578C ''paramet'':548C ''pass'':114C ''pattern'':22C,28C ''perceptron'':253C ''perfect'':405C ''possibl'':388C ''precis'':602C ''present'':16C ''problem'':350C,605C ''process'':10C,35C ''progress'':555C ''purpos'':319C,415C ''real'':370C ''rel'':339C ''repeat'':267C,304C,568C ''respons'':595C ''result'':300C ''room'':221C ''rough'':205C,424C ''run'':561C ''see'':216C ''select'':547C ''show'':44C,110C,238C ''simpl'':25C ''simpler'':383C ''smooth'':556C ''someth'':627C ''special'':454C ''start'':150C,280C ''statement'':228C,606C ''strength'':121C ''structur'':65C ''sum'':153C,172C ''technic'':239C ''test'':579C ''though'':217C ''time'':159C,307C ''train'':261C,326C,432C,461C,519C,562C,591C ''transfer'':196C ''tri'':296C ''ul'':77C ''understand'':417C,493C ''use'':324C,367C,479C,539C ''usual'':540C ''valu'':91C,112C,162C,448C,455C,510C,639C ''variat'':70C ''want'':278C ''way'':332C ''weight'':124C,156C,294C ''well'':593C ''whole'':571C ''work'':491C,583C ''world'':371C ''would'':47C,364C,422C,478C,624C ''z'':173C,187C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2303, 1, '2014-10-18 08:08:25.513', 5, 1347, 'Python and R good tutorials?', '&lt;p&gt;I would like to learn both Python and R for usage in data science projects. &lt;/p&gt;

&lt;p&gt;I am currently unemployed, fresh out of university, scouting around for jobs and thought it would be good if I get some Kaggle projects under my profile.&lt;/p&gt;

&lt;p&gt;However, I have very little knowledge in either language. Have used Matlab and C/C++ in the past. But I haven''t produced production quality code or developed an application or software in either language. It has been dirty coding for academic usage all along.&lt;/p&gt;

&lt;p&gt;I have used a little bit of Python, in a university project, but I dont know the fundamentals like what is a package , etc etc. ie havent read the intricacies of the language using a standard Python Textbook etc..&lt;/p&gt;

&lt;p&gt;Have done some amount of coding in C/C++ way back (3-4 years back then switched over to Matlab/Octave).&lt;/p&gt;

&lt;p&gt;I would like to get started in Python Numpy Scipy scikit-learn and pandas etc. but just reading up Wikipedia articles or Python textbooks is going to be infeasible for me.&lt;/p&gt;

&lt;p&gt;And same goes with R, except that I have zero knowledge of R.&lt;/p&gt;

&lt;p&gt;Does anyone have any suggestions?&lt;/p&gt;
', 'machine-learning  r  python', NULL, '''-4'':156C ''/p'':27C,56C,102C,143C,164C,198C,213C,220C ''3'':155C ''academ'':98C ''along'':101C ''amount'':148C ''anyon'':216C ''applic'':86C ''around'':38C ''articl'':187C ''back'':154C,158C ''bit'':109C ''c/c'':71C,152C ''code'':82C,96C,150C ''current'':31C ''data'':24C ''develop'':84C ''dirti'':95C ''done'':146C ''dont'':118C ''either'':65C,90C ''etc'':127C,128C,142C,181C ''except'':205C ''fresh'':33C ''fundament'':121C ''get'':49C,170C ''go'':192C ''goe'':202C ''good'':4A,46C ''haven'':77C ''havent'':130C ''howev'':58C ''ie'':129C ''infeas'':195C ''intricaci'':133C ''job'':40C ''kaggl'':51C ''know'':119C ''knowledg'':63C,210C ''languag'':66C,91C,136C ''learn'':8B,16C,178C ''like'':14C,122C,168C ''littl'':62C,108C ''machin'':7B ''machine-learn'':6B ''matlab'':69C ''matlab/octave'':163C ''numpi'':174C ''p'':11C,28C,57C,103C,144C,165C,199C,214C ''packag'':126C ''panda'':180C ''past'':74C ''produc'':79C ''product'':80C ''profil'':55C ''project'':26C,52C,115C ''python'':1A,10B,18C,111C,140C,173C,189C ''qualiti'':81C ''r'':3A,9B,20C,204C,212C ''read'':131C,184C ''scienc'':25C ''scikit'':177C ''scikit-learn'':176C ''scipi'':175C ''scout'':37C ''softwar'':88C ''standard'':139C ''start'':171C ''suggest'':219C ''switch'':160C ''textbook'':141C,190C ''thought'':42C ''tutori'':5A ''unemploy'':32C ''univers'':36C,114C ''usag'':22C,99C ''use'':68C,106C,137C ''way'':153C ''wikipedia'':186C ''would'':13C,44C,167C ''year'':157C ''zero'':209C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2304, 1, '2014-10-18 08:19:00.607', 1, 351, 'Energy consumption time series forcasting', '&lt;p&gt;Is there a good java library for doing time series energy consumption forecasting based on weather data and other variables?&lt;/p&gt;
', 'time-series  java  energy', NULL, '''/p'':32C ''base'':25C ''consumpt'':2A,23C ''data'':28C ''energi'':1A,10B,22C ''forcast'':5A ''forecast'':24C ''good'':15C ''java'':9B,16C ''librari'':17C ''p'':11C ''seri'':4A,8B,21C ''time'':3A,7B,20C ''time-seri'':6B ''variabl'':31C ''weather'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2305, 2, '2014-10-18 17:32:52.583', 0, NULL, NULL, '&lt;p&gt;I can only recommend &lt;a href="http://adv-r.had.co.nz/" rel="nofollow"&gt;Advanced R&lt;/a&gt; by Hadley Wickham. I think it is at the same time incredibly rich in content and easy to read. You say you have zero knowledge in R, but I believe since you already have programming skills in other languages this book can complement very fruitfully any classical "R beginner manual" (for the latter see &lt;a href="http://cran.r-project.org/other-docs.html#english" rel="nofollow"&gt;here&lt;/a&gt;). &lt;/p&gt;
', NULL, NULL, '''/a'':13C,76C ''/other-docs.html#english'':72C ''/p'':77C ''adv-r.had.co.nz'':8C ''advanc'':11C ''alreadi'':46C ''beginn'':62C ''believ'':43C ''book'':54C ''classic'':60C ''complement'':56C ''content'':28C ''cran.r-project.org'':71C ''cran.r-project.org/other-docs.html#english'':70C ''easi'':30C ''fruit'':58C ''hadley'':15C ''href'':7C,69C ''incred'':25C ''knowledg'':38C ''languag'':52C ''latter'':66C ''manual'':63C ''nofollow'':10C,74C ''p'':1C ''program'':48C ''r'':12C,40C,61C ''read'':32C ''recommend'':5C ''rel'':9C,73C ''rich'':26C ''say'':34C ''see'':67C ''sinc'':44C ''skill'':49C ''think'':18C ''time'':24C ''wickham'':16C ''zero'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2306, 1, '2014-10-18 18:05:26.93', 1, 29, 'Treebank conversion from dependency to constituency?', '&lt;p&gt;Most Treebank conversion which I found in the web are from constituency treebank to dependency treebank, I wonder why there is little jobs in the opposite direction?&lt;/p&gt;
', 'nlp', NULL, '''/p'':36C ''constitu'':6A,20C ''convers'':2A,11C ''depend'':4A,23C ''direct'':35C ''found'':14C ''job'':31C ''littl'':30C ''nlp'':7B ''opposit'':34C ''p'':8C ''treebank'':1A,10C,21C,24C ''web'':17C ''wonder'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2307, 2, '2014-10-19 03:24:22.877', 1, NULL, NULL, '&lt;p&gt;None of the algorithms you mention are good with data that has uniform distribution.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;size &amp;lt;- 20             #length of random number vectors
set.seed(1) 
x &amp;lt;- runif(size)          # generate samples from uniform distribution (0.0, 1.0)
y &amp;lt;-runif(size) 
df &amp;lt;-data.frame(x,y)

# other distributions: rpois, rmvnorm, rnbinom, rbinom, rbeta, rchisq, rexp, rgamma, rlogis, rstab, rt, rgeom, rhyper, rwilcox, rweibull.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;See &lt;a href="http://statistics.ats.ucla.edu/stat/r/modules/prob_dist.htm" rel="nofollow"&gt;this page&lt;/a&gt; for tutorial on generating random samples from distributions.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;For specific set of randomized data sets that are ''hard'' for these methods (e.r. linearly inseparable &lt;em&gt;n&lt;/em&gt;-classes XOR patterns), see this blog post (incl. R code): &lt;a href="http://tjo-en.hatenablog.com/entry/2014/01/06/234155" rel="nofollow"&gt;http://tjo-en.hatenablog.com/entry/2014/01/06/234155&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':79C ''/code'':66C ''/em'':109C ''/entry/2014/01/06/234155'':124C ''/entry/2014/01/06/234155&lt;/a&gt;.&lt;/p&gt;'':129C ''/p'':16C,88C ''/pre'':67C ''/stat/r/modules/prob_dist.htm'':74C ''0.0'':38C ''1'':28C ''1.0'':39C ''20'':21C ''algorithm'':5C ''blog'':115C ''class'':110C ''code'':18C,119C ''data'':11C,96C ''data.frame'':46C ''df'':44C ''distribut'':15C,37C,50C,87C ''e.r'':104C ''em'':107C ''generat'':33C,83C ''good'':9C ''hard'':100C ''hr'':89C ''href'':71C,121C ''incl'':117C ''insepar'':106C ''length'':22C ''linear'':105C ''lt'':20C,30C,41C,45C ''mention'':7C ''method'':103C ''n'':108C ''nofollow'':76C,126C ''none'':2C ''number'':25C ''p'':1C,68C,90C ''page'':78C ''pattern'':112C ''post'':116C ''pre'':17C ''r'':118C ''random'':24C,84C,95C ''rbeta'':55C ''rbinom'':54C ''rchisq'':56C ''rel'':75C,125C ''rexp'':57C ''rgamma'':58C ''rgeom'':62C ''rhyper'':63C ''rlogi'':59C ''rmvnorm'':52C ''rnbinom'':53C ''rpoi'':51C ''rstab'':60C ''rt'':61C ''runif'':31C,42C ''rweibul'':65C ''rwilcox'':64C ''sampl'':34C,85C ''see'':69C,113C ''set'':93C,97C ''set.seed'':27C ''size'':19C,32C,43C ''specif'':92C ''statistics.ats.ucla.edu'':73C ''statistics.ats.ucla.edu/stat/r/modules/prob_dist.htm'':72C ''tjo-en.hatenablog.com'':123C,128C ''tjo-en.hatenablog.com/entry/2014/01/06/234155'':122C ''tjo-en.hatenablog.com/entry/2014/01/06/234155&lt;/a&gt;.&lt;/p&gt;'':127C ''tutori'':81C ''uniform'':14C,36C ''vector'':26C ''x'':29C,47C ''xor'':111C ''y'':40C,48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2308, 1, '2014-10-19 11:02:44.397', 1, 2714, 'Masters thesis topics in big data', '&lt;p&gt;I am looking for a thesis to complete my master M2, I will work on a topic in the big data''s field (creation big data applications), using hadoop/mapReduce and Ecosystem ( visualisation, analysis ...), Please suggest some topics or project that would make for a good masters thesis subject.&lt;br&gt;
I add that I have bases in data warehouses, databases, data mining, good skills in programming, system administration and cryptography ... &lt;br&gt;&lt;/p&gt;

&lt;p&gt;Thanks &lt;/p&gt;
', 'bigdata  hadoop  research', NULL, '''/p'':81C,84C ''add'':61C ''administr'':77C ''analysi'':43C ''applic'':37C ''base'':65C ''big'':5A,30C,35C ''bigdata'':7B ''br'':59C,80C ''complet'':18C ''creation'':34C ''cryptographi'':79C ''data'':6A,31C,36C,67C,70C ''databas'':69C ''ecosystem'':41C ''field'':33C ''good'':55C,72C ''hadoop'':8B ''hadoop/mapreduce'':39C ''look'':13C ''m2'':21C ''make'':52C ''master'':1A,20C,56C ''mine'':71C ''p'':10C,82C ''pleas'':44C ''program'':75C ''project'':49C ''research'':9B ''skill'':73C ''subject'':58C ''suggest'':45C ''system'':76C ''thank'':83C ''thesi'':2A,16C,57C ''topic'':3A,27C,47C ''use'':38C ''visualis'':42C ''warehous'':68C ''work'':24C ''would'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2323, 1, '2014-10-21 00:09:40.597', 2, 1549, 'Field Aware Factorization Machines', '&lt;p&gt;Can anyone explain how field aware factorization machines (FM) compare to standard FM?&lt;/p&gt;

&lt;p&gt;Standard:
&lt;a href="http://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle2010FM.pdf" rel="nofollow"&gt;http://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle2010FM.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;"Field Aware":
&lt;a href="http://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf" rel="nofollow"&gt;http://www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf&lt;/a&gt;&lt;/p&gt;
', 'machine-learning  recommendation', NULL, '''/p'':23C ''/pub/pdfs/rendle2010fm.pdf'':30C ''/pub/pdfs/rendle2010fm.pdf&lt;/a&gt;&lt;/p&gt;'':35C ''/~r01922136/kaggle-2014-criteo.pdf'':43C ''/~r01922136/kaggle-2014-criteo.pdf&lt;/a&gt;&lt;/p&gt;'':48C ''anyon'':11C ''awar'':2A,15C,38C ''compar'':19C ''explain'':12C ''factor'':3A,16C ''field'':1A,14C,37C ''fm'':18C,22C ''href'':27C,40C ''learn'':7B ''machin'':4A,6B,17C ''machine-learn'':5B ''nofollow'':32C,45C ''p'':9C,24C,36C ''recommend'':8B ''rel'':31C,44C ''standard'':21C,25C ''www.csie.ntu.edu.tw'':42C,47C ''www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf'':41C ''www.csie.ntu.edu.tw/~r01922136/kaggle-2014-criteo.pdf&lt;/a&gt;&lt;/p&gt;'':46C ''www.ismll.uni-hildesheim.de'':29C,34C ''www.ismll.uni-hildesheim.de/pub/pdfs/rendle2010fm.pdf'':28C ''www.ismll.uni-hildesheim.de/pub/pdfs/rendle2010fm.pdf&lt;/a&gt;&lt;/p&gt;'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2324, 2, '2014-10-21 01:34:13.407', 1, NULL, NULL, '&lt;p&gt;Standard factorization machines have fields too. The "novelty" here seems to be the use of GBDT features and the application of the hashing tricks. Not to great effect, it seems: check out the minute range in performance on the last slide.&lt;/p&gt;
', NULL, NULL, '''/p'':43C ''applic'':21C ''check'':32C ''effect'':29C ''factor'':3C ''featur'':18C ''field'':6C ''gbdt'':17C ''great'':28C ''hash'':24C ''last'':41C ''machin'':4C ''minut'':35C ''novelti'':9C ''p'':1C ''perform'':38C ''rang'':36C ''seem'':11C,31C ''slide'':42C ''standard'':2C ''trick'':25C ''use'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6606, 2, '2015-07-28 11:13:17.27', 2, NULL, NULL, '&lt;p&gt;$\mathcal{N}$ does indeed denote a (multivariate) normal / Gaussian distribution. $I_p$ is just an identity matrix of dimension $p$. So this a matrix with $\lambda^{-1}$ along the diagonal. Read this as the covariance matrix, so this is a spherical Gaussian (0 covariance between different dimensions) where each variable has variance $\lambda^{-1}$.&lt;/p&gt;
', NULL, NULL, '''-1'':28C,55C ''/p'':56C ''0'':44C ''along'':29C ''covari'':36C,45C ''denot'':6C ''diagon'':31C ''differ'':47C ''dimens'':20C,48C ''distribut'':11C ''gaussian'':10C,43C ''ident'':17C ''inde'':5C ''lambda'':27C,54C ''mathcal'':2C ''matrix'':18C,25C,37C ''multivari'':8C ''n'':3C ''normal'':9C ''p'':1C,13C,21C ''read'':32C ''spheric'':42C ''variabl'':51C ''varianc'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2315, 2, '2014-10-20 09:31:18.65', 9, NULL, NULL, '&lt;p&gt;Every ML algorithm with high complexity can overfit. However, the OP is asking whether an RF will not overfit when increasing the number of trees in the forest.&lt;/p&gt;

&lt;p&gt;In general, ensemble methods reduces the prediction variance to almost nothing, improving the accuracy of the ensemble. If we define the variance of the expected generalization error of an individual randomized model as:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ZUORL.gif" alt=""&gt;&lt;/p&gt;

&lt;p&gt;From &lt;a href="http://arxiv.org/abs/1407.7502" rel="nofollow"&gt;here&lt;/a&gt;, the variance of the expected generalization error of an ensemble corresponds to:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/5Zf9e.gif" alt=""&gt;&lt;/p&gt;

&lt;p&gt;where &lt;code&gt;p(x)&lt;/code&gt; is the Pearsonâs correlation coefficient between the predictions of two randomized models trained on the same data from two independent seeds. If we increase the number of DT''s in the RF, larger &lt;code&gt;M&lt;/code&gt;, the variance of the ensemble decreases when &lt;code&gt;Ï(x)&amp;lt;1&lt;/code&gt;. Therefore, the variance of an ensemble is strictly smaller than the variance of an individual model. &lt;/p&gt;

&lt;p&gt;In a nutshell, increasing the number of individual randomized models in an ensemble will never increase the generalization error.&lt;/p&gt;
', NULL, NULL, '''/5zf9e.gif'':103C ''/a'':84C ''/abs/1407.7502'':80C ''/code'':111C,148C,161C ''/p'':30C,65C,73C,97C,105C,178C,199C ''/zuorl.gif'':71C ''1'':160C ''accuraci'':45C ''algorithm'':4C ''almost'':41C ''alt'':72C,104C ''arxiv.org'':79C ''arxiv.org/abs/1407.7502'':78C ''ask'':14C ''code'':108C,146C,156C ''coeffici'':117C ''complex'':7C ''correl'':116C ''correspond'':95C ''data'':129C ''decreas'':154C ''defin'':51C ''dt'':140C ''ensembl'':34C,48C,94C,153C,167C,192C ''error'':58C,91C,198C ''everi'':2C ''expect'':56C,89C ''forest'':29C ''general'':33C,57C,90C,197C ''high'':6C ''howev'':10C ''href'':77C ''i.stack.imgur.com'':70C,102C ''i.stack.imgur.com/5zf9e.gif'':101C ''i.stack.imgur.com/zuorl.gif'':69C ''img'':67C,99C ''improv'':43C ''increas'':22C,136C,183C,195C ''independ'':132C ''individu'':61C,176C,187C ''larger'':145C ''lt'':159C ''m'':147C ''method'':35C ''ml'':3C ''model'':63C,124C,177C,189C ''never'':194C ''nofollow'':82C ''noth'':42C ''number'':24C,138C,185C ''nutshel'':182C ''op'':12C ''overfit'':9C,20C ''p'':1C,31C,66C,74C,98C,106C,109C,179C ''pearsonâ'':114C ''predict'':38C,120C ''random'':62C,123C,188C ''reduc'':36C ''rel'':81C ''rf'':17C,144C ''seed'':133C ''smaller'':170C ''src'':68C,100C ''strict'':169C ''therefor'':162C ''train'':125C ''tree'':26C ''two'':122C,131C ''varianc'':39C,53C,86C,150C,164C,173C ''whether'':15C ''x'':110C,158C ''ï'':157C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2316, 1, '2014-10-19 21:03:35.39', 1, 29, 'Machine Learning - Where is the difference between one-class, binary-class and multinominal-class classification?', '&lt;p&gt;Where is the difference between one-class, binary-class and multinominal-class classification?&lt;/p&gt;

&lt;p&gt;If I like to classify text in lets say four classes and also want the system to be able to tell me that none of these classes matches the unknown/untrained test-data. &lt;/p&gt;

&lt;p&gt;Couldn''t I just use all the methods that I mentioned above to reach my goal?
e.g. I could describe C1, C2, C3 and C4 as four different trainings-sets for binary-classification and use the trained models to label an unknow data-set ...&lt;/p&gt;

&lt;p&gt;Just by saying, Training-Set for C1 contains class 1 (all good samples for C1) and class 0 (mix of all C2, C3 and C4 as bad samples for C1).&lt;/p&gt;

&lt;p&gt;Is unlabeled-data C1 -&gt; 1 or 0&lt;/p&gt;

&lt;p&gt;Is unlabeled-data C2 -&gt; 1 or 0
... and so on ...&lt;/p&gt;

&lt;p&gt;For multinominal classification I could just define a training-set containing all good sample data for C1, C2, C3 and C4 in one training-set and then use the one resulting model for classification ...&lt;/p&gt;

&lt;p&gt;But where is the difference between this two methods? (except of that I have to use different algorithms)&lt;/p&gt;

&lt;p&gt;And how would I define a training-set for the described problem of categorizing data in those four classes using one-class classfication (is that even possible)?&lt;/p&gt;

&lt;p&gt;Excuse me if I''m completely wrong in my thinking. Would appreciate an answer that makes the methodology a little bit clearer to me =)&lt;/p&gt;
', 'machine-learning  classification  data-mining', NULL, '''/p'':43C,78C,127C,160C,170C,183C,221C,241C,272C,298C ''0'':147C,169C,179C ''1'':139C,167C,177C ''abl'':63C ''algorithm'':240C ''also'':57C ''answer'':287C ''appreci'':285C ''bad'':156C ''binari'':12A,36C,113C ''binary-class'':11A,35C ''binary-classif'':112C ''bit'':294C ''c1'':100C,136C,144C,159C,166C,202C ''c2'':101C,151C,176C,203C ''c3'':102C,152C,204C ''c4'':104C,154C,206C ''categor'':257C ''class'':10A,13A,17A,34C,37C,41C,55C,71C,138C,146C,262C,266C ''classfic'':267C ''classif'':18A,22B,42C,114C,187C,220C ''classifi'':49C ''clearer'':295C ''complet'':279C ''contain'':137C,196C ''could'':98C,189C ''couldn'':80C ''data'':24B,77C,125C,165C,175C,200C,258C ''data-min'':23B ''data-set'':124C ''defin'':191C,247C ''describ'':99C,254C ''differ'':6A,30C,107C,227C,239C ''e.g'':96C ''even'':270C ''except'':232C ''excus'':274C ''four'':54C,106C,261C ''goal'':95C ''good'':141C,198C ''label'':121C ''learn'':2A,21B ''let'':52C ''like'':47C ''littl'':293C ''m'':278C ''machin'':1A,20B ''machine-learn'':19B ''make'':289C ''match'':72C ''mention'':90C ''method'':87C,231C ''methodolog'':291C ''mine'':25B ''mix'':148C ''model'':119C,218C ''multinomin'':16A,40C,186C ''multinominal-class'':15A,39C ''none'':68C ''one'':9A,33C,208C,216C,265C ''one-class'':8A,32C,264C ''p'':26C,44C,79C,128C,161C,171C,184C,222C,242C,273C ''possibl'':271C ''problem'':255C ''reach'':93C ''result'':217C ''sampl'':142C,157C,199C ''say'':53C,131C ''set'':110C,126C,134C,195C,211C,251C ''system'':60C ''tell'':65C ''test'':76C ''test-data'':75C ''text'':50C ''think'':283C ''train'':109C,118C,133C,194C,210C,250C ''training-set'':132C,193C,209C,249C ''trainings-set'':108C ''two'':230C ''unknow'':123C ''unknown/untrained'':74C ''unlabel'':164C,174C ''unlabeled-data'':163C,173C ''use'':84C,116C,214C,238C,263C ''want'':58C ''would'':245C,284C ''wrong'':280C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2317, 2, '2014-10-20 15:32:58.097', 0, NULL, NULL, '&lt;p&gt;&lt;em&gt;The Art of R Programming&lt;/em&gt; by Normal Matloff is a great way to find your way towards being an R user.  I''ve recommended this book to several people navigating the tutorial / book universe and to my knowledge they''ve all stuck with it. &lt;/p&gt;
', NULL, NULL, '''/em'':8C ''/p'':48C ''art'':4C ''book'':29C,36C ''em'':2C ''find'':17C ''great'':14C ''knowledg'':41C ''matloff'':11C ''navig'':33C ''normal'':10C ''p'':1C ''peopl'':32C ''program'':7C ''r'':6C,23C ''recommend'':27C ''sever'':31C ''stuck'':45C ''toward'':20C ''tutori'':35C ''univers'':37C ''user'':24C ''ve'':26C,43C ''way'':15C,19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2318, 2, '2014-10-20 16:12:45.427', 2, NULL, NULL, '&lt;p&gt;There is an online data science "game" that takes you from learning how to use Python for loading a csv and using scikit to machine learning algorithms such as support vector machines. Here is a &lt;a href="http://mentally-physically-spiritually-strong.com/2014/10/18/explore-data-science-game/" rel="nofollow"&gt;blog post&lt;/a&gt; with a demo video and the actual site is &lt;a href="https://exploredatascience.com" rel="nofollow"&gt;Explore Data Science&lt;/a&gt;. Personally, I think its genius. &lt;/p&gt;
', NULL, NULL, '''/2014/10/18/explore-data-science-game/'':41C ''/a'':46C,64C ''/p'':70C ''actual'':53C ''algorithm'':28C ''blog'':44C ''csv'':21C ''data'':6C,62C ''demo'':49C ''explor'':61C ''exploredatascience.com'':58C ''game'':8C ''genius'':69C ''href'':38C,57C ''learn'':13C,27C ''load'':19C ''machin'':26C,33C ''mentally-physically-spiritually-strong.com'':40C ''mentally-physically-spiritually-strong.com/2014/10/18/explore-data-science-game/'':39C ''nofollow'':43C,60C ''onlin'':5C ''p'':1C ''person'':65C ''post'':45C ''python'':17C ''rel'':42C,59C ''scienc'':7C,63C ''scikit'':24C ''site'':54C ''support'':31C ''take'':10C ''think'':67C ''use'':16C,23C ''vector'':32C ''video'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2347, 2, '2014-10-25 23:07:15.843', 1, NULL, NULL, '&lt;p&gt;Here''s some practical advice from my own experiences-&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;The first thing to do is to convince management the the change will be for the better. A mock-up of a sample report can be very useful here.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Even if management agrees, they''ll still want to know why variances have occurred, and hence you''ll need to be able to supply more data.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Although exception reporting is best, management will want to see everything anyway, as doing so makes them feel as though they are doing something useful.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Don''t change everything at once- too big a change at once can cause resistance.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;For how to best present the data, read Edward Tufte''s books, at the very least his first one, "The Visual Display of Quantitative Data".&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Defining what''s an exception can be hard, because the recipients will each have their own  ideas. Using say, a 95% confidence interval is good, but it won''t be universally liked. Some people will consider any change above $X significant, and others will want to see everything that''s more than X% different from the prior period. Have fun with this part :(&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':46C,76C,105C,124C,153C,220C ''/ol'':221C ''/p'':11C,45C,75C,104C,123C,152C,219C ''95'':176C ''abl'':70C ''advic'':6C ''agre'':52C ''although'':79C ''anyway'':90C ''best'':83C,130C ''better'':31C ''big'':115C ''book'':138C ''caus'':121C ''chang'':26C,110C,117C,193C ''confid'':177C ''consid'':191C ''convinc'':22C ''data'':74C,133C,151C ''defin'':156C ''differ'':209C ''display'':148C ''edward'':135C ''even'':49C ''everyth'':89C,111C,203C ''except'':80C,160C ''experi'':10C ''feel'':96C ''first'':16C,144C ''fun'':215C ''good'':180C ''hard'':163C ''henc'':64C ''idea'':172C ''interv'':178C ''know'':58C ''least'':142C ''li'':13C,47C,77C,106C,125C,154C ''like'':187C ''ll'':54C,66C ''make'':94C ''manag'':23C,51C,84C ''mock'':34C ''mock-up'':33C ''need'':67C ''occur'':62C ''ol'':12C ''one'':145C ''other'':198C ''p'':1C,14C,48C,78C,107C,126C,155C ''part'':218C ''peopl'':189C ''period'':213C ''practic'':5C ''present'':131C ''prior'':212C ''quantit'':150C ''read'':134C ''recipi'':166C ''report'':39C,81C ''resist'':122C ''sampl'':38C ''say'':174C ''see'':88C,202C ''signific'':196C ''someth'':102C ''still'':55C ''suppli'':72C ''thing'':17C ''though'':98C ''tuft'':136C ''univers'':186C ''use'':43C,103C,173C ''varianc'':60C ''visual'':147C ''want'':56C,86C,200C ''won'':183C ''x'':195C,208C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2320, 2, '2014-10-20 19:40:34.193', 7, NULL, NULL, '&lt;p&gt;To me Big Data is primarily about the tools (after all, that''s where it started); a "big" dataset is one that''s too big to be handled with conventional tools - in particular, big enough to demand storage and processing on a cluster rather than a single machine. This rules out a conventional RDBMS, and demands new techniques for processing; in particular, various Hadoop-like frameworks make it easy to distribute a computation over a cluster, at the cost of restricting the form of this computation. I''ll second the reference to &lt;a href="http://www.chrisstucchio.com/blog/2013/hadoop_hatred.html"&gt;http://www.chrisstucchio.com/blog/2013/hadoop_hatred.html&lt;/a&gt; ; Big Data techniques are a last resort for datasets which are simply too big to handle any other way. I''d say any dataset for any purpose could qualify if it was big enough - though if the shape of the problem is such that existing "big data" tools aren''t appropriate, then it would probably be better to come up with a new name.&lt;/p&gt;

&lt;p&gt;Of course there is some overlap; when I (briefly) worked at last.fm, we worked on the same 50TB dataset using Hadoop and also in an SQL database on a fairly ridiculous server (I remember it had 1TB RAM, and this is a few years ago). Which in a sense meant it both was and wasn''t big data, depending on which job you were working on. But I think that''s an accurate characterization; the people who worked on the Hadoop jobs found it useful to go to Big Data conferences and websites, while the people who worked on the SQL jobs didn''t.&lt;/p&gt;
', NULL, NULL, '''/blog/2013/hadoop_hatred.html'':99C ''/blog/2013/hadoop_hatred.html&lt;/a&gt;'':102C ''/p'':167C,273C ''1tb'':205C ''50tb'':186C ''accur'':241C ''ago'':213C ''also'':191C ''appropri'':153C ''aren'':151C ''better'':159C ''big'':4C,19C,26C,35C,103C,116C,135C,148C,225C,257C ''briefli'':177C ''character'':242C ''cluster'':44C,78C ''come'':161C ''comput'':75C,88C ''confer'':259C ''convent'':31C,54C ''cost'':81C ''could'':130C ''cours'':170C ''d'':123C ''data'':5C,104C,149C,226C,258C ''databas'':195C ''dataset'':20C,111C,126C,187C ''demand'':38C,57C ''depend'':227C ''didn'':271C ''distribut'':73C ''easi'':71C ''enough'':36C,136C ''exist'':147C ''fair'':198C ''form'':85C ''found'':251C ''framework'':68C ''go'':255C ''hadoop'':66C,189C,249C ''hadoop-lik'':65C ''handl'':29C,118C ''href'':96C ''job'':230C,250C,270C ''last'':108C ''last.fm'':180C ''like'':67C ''ll'':90C ''machin'':49C ''make'':69C ''meant'':218C ''name'':166C ''new'':58C,165C ''one'':22C ''overlap'':174C ''p'':1C,168C ''particular'':34C,63C ''peopl'':244C,264C ''primarili'':7C ''probabl'':157C ''problem'':143C ''process'':41C,61C ''purpos'':129C ''qualifi'':131C ''ram'':206C ''rather'':45C ''rdbms'':55C ''refer'':93C ''rememb'':202C ''resort'':109C ''restrict'':83C ''ridicul'':199C ''rule'':51C ''say'':124C ''second'':91C ''sens'':217C ''server'':200C ''shape'':140C ''simpli'':114C ''singl'':48C ''sql'':194C,269C ''start'':17C ''storag'':39C ''techniqu'':59C,105C ''think'':237C ''though'':137C ''tool'':10C,32C,150C ''use'':188C,253C ''various'':64C ''wasn'':223C ''way'':121C ''websit'':261C ''work'':178C,182C,233C,246C,266C ''would'':156C ''www.chrisstucchio.com'':98C,101C ''www.chrisstucchio.com/blog/2013/hadoop_hatred.html'':97C ''www.chrisstucchio.com/blog/2013/hadoop_hatred.html&lt;/a&gt;'':100C ''year'':212C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2328, 1, '2014-10-21 09:42:51.61', 1, 122, 'Which packages or functions can I use in R to plot 3D data like this?', '&lt;p&gt;There''re many data points, each of which is associated with two coordinates and a numeral value, or three coordinates. And I wish it is coloured.
I checked packages "scatterplot3d" and "plot3D" but I couldn''t find one like the example I give. It is like it has a fitting surface.&lt;/p&gt;

&lt;p&gt;My data is basically like the following. In this way I think this kind of plot is gonna be perfectly suitble for this data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    ki,kt,Top10AverageF1Score
    360,41,0.09371256716549396
    324,41,0.09539634212851525
    360,123,0.09473510831594467
    36,164,0.09773486852645874
    ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But I also may have one more additional variable, which makes it like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    NeighborhoodSize,ki,kt,Top10AverageF1Score
    10,360,41,0.09371256716549396
    15,324,41,0.09539634212851525
    15,360,123,0.09473510831594467
    20,36,164,0.09773486852645874
    ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Do you also have any good idea for visualizing the second case? What kind of plot and which packages and functions, etc.&lt;/strong&gt;
&lt;img src="http://i.stack.imgur.com/5whM7.png" alt="enter image description here"&gt;&lt;/p&gt;
', 'r  visualization', NULL, '''/5whm7.png'':183C ''/code'':113C,152C ''/p'':69C,95C,129C,189C ''/pre'':114C,153C ''/strong'':178C ''0.09371256716549396'':103C,139C ''0.09473510831594467'':109C,147C ''0.09539634212851525'':106C,143C ''0.09773486852645874'':112C,151C ''10'':136C ''123'':108C,146C ''15'':140C,144C ''164'':111C,150C ''20'':148C ''324'':104C,141C ''36'':110C,149C ''360'':101C,107C,137C,145C ''3d'':12A ''41'':102C,105C,138C,142C ''addit'':123C ''also'':118C,158C ''alt'':184C ''associ'':27C ''basic'':74C ''case'':167C ''check'':45C ''code'':97C,131C ''colour'':43C ''coordin'':30C,37C ''couldn'':52C ''data'':13A,21C,72C,94C ''descript'':187C ''enter'':185C ''etc'':177C ''exampl'':58C ''find'':54C ''fit'':67C ''follow'':77C ''function'':4A,176C ''give'':60C ''gonna'':88C ''good'':161C ''i.stack.imgur.com'':182C ''i.stack.imgur.com/5whm7.png'':181C ''idea'':162C ''imag'':186C ''img'':179C ''ki'':98C,133C ''kind'':84C,169C ''kt'':99C,134C ''like'':14A,56C,63C,75C,128C ''make'':126C ''mani'':20C ''may'':119C ''neighborhoods'':132C ''numer'':33C ''one'':55C,121C ''p'':17C,70C,115C,154C ''packag'':2A,46C,174C ''perfect'':90C ''plot'':11A,86C,171C ''plot3d'':49C ''point'':22C ''pre'':96C,130C ''r'':9A,15B ''re'':19C ''scatterplot3d'':47C ''second'':166C ''src'':180C ''strong'':155C ''suitbl'':91C ''surfac'':68C ''think'':82C ''three'':36C ''top10averagef1score'':100C,135C ''two'':29C ''use'':7A ''valu'':34C ''variabl'':124C ''visual'':16B,164C ''way'':80C ''wish'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2321, 1, '2014-10-20 22:13:32.42', 5, 198, 'Bayes Optimal Decision Boundaries for Gaussian Data with Equal Covariance', '&lt;p&gt;I am drawing samples from two classes in the two-dimensional Cartesian space, each of which has the same covariance matrix $[2, 0; 0, 2]$.  One class has a mean of $[1.5, 1]$ and the other has a mean of $[1, 1.5]$.  If the priors are $4/7$ for the former and $3/7$ for the later, how would I derive the equation for the ideal decision boundary?&lt;/p&gt;

&lt;p&gt;If it turns out that misclassifying the second class is twice as expensive as the first class, and the objective is to minimize the expected cost, what equation would I use for the best decision boundary?&lt;/p&gt;
', 'classification  discriminant-analysis', NULL, '''/p'':83C,121C ''0'':39C,40C ''1'':49C,57C ''1.5'':48C,58C ''2'':38C,41C ''3/7'':68C ''4/7'':63C ''analysi'':14B ''bay'':1A ''best'':118C ''boundari'':4A,82C,120C ''cartesian'':28C ''class'':22C,43C,93C,101C ''classif'':11B ''cost'':110C ''covari'':10A,36C ''data'':7A ''decis'':3A,81C,119C ''deriv'':75C ''dimension'':27C ''discrimin'':13B ''discriminant-analysi'':12B ''draw'':18C ''equal'':9A ''equat'':77C,112C ''expect'':109C ''expens'':97C ''first'':100C ''former'':66C ''gaussian'':6A ''ideal'':80C ''later'':71C ''matrix'':37C ''mean'':46C,55C ''minim'':107C ''misclassifi'':90C ''object'':104C ''one'':42C ''optim'':2A ''p'':15C,84C ''prior'':61C ''sampl'':19C ''second'':92C ''space'':29C ''turn'':87C ''twice'':95C ''two'':21C,26C ''two-dimension'':25C ''use'':115C ''would'':73C,113C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2326, 2, '2014-10-21 06:12:51.887', 3, NULL, NULL, '&lt;p&gt;&lt;a href="http://spark.apache.org/" rel="nofollow"&gt;Apache Spark&lt;/a&gt; &lt;a href="http://spark-summit.org/2014/talk/scalable-distributed-decision-trees-in-spark-mllib" rel="nofollow"&gt;can do it&lt;/a&gt;, using the new &lt;a href="http://spark.apache.org/docs/1.1.0/mllib-decision-tree.html" rel="nofollow"&gt;MLLib&lt;/a&gt; library. &lt;a href="https://www.youtube.com/watch?v=3rRrcPXHu98" rel="nofollow"&gt;Here''s a presentation&lt;/a&gt;, and &lt;a href="http://databricks.com/blog/2014/09/29/scalable-decision-trees-in-mllib.html" rel="nofollow"&gt;here are some benchmarks&lt;/a&gt;. Bindings are available for python, scala, and java.&lt;/p&gt;
', NULL, NULL, '''/2014/talk/scalable-distributed-decision-trees-in-spark-mllib'':14C ''/a'':9C,20C,32C,45C,58C ''/blog/2014/09/29/scalable-decision-trees-in-mllib.html'':51C ''/docs/1.1.0/mllib-decision-tree.html'':28C ''/p'':67C ''/watch?v=3rrrcpxhu98'':38C ''apach'':7C ''avail'':61C ''benchmark'':57C ''bind'':59C ''databricks.com'':50C ''databricks.com/blog/2014/09/29/scalable-decision-trees-in-mllib.html'':49C ''href'':3C,11C,25C,35C,48C ''java'':66C ''librari'':33C ''mllib'':31C ''new'':23C ''nofollow'':6C,16C,30C,40C,53C ''p'':1C ''present'':44C ''python'':63C ''rel'':5C,15C,29C,39C,52C ''scala'':64C ''spark'':8C ''spark-summit.org'':13C ''spark-summit.org/2014/talk/scalable-distributed-decision-trees-in-spark-mllib'':12C ''spark.apache.org'':4C,27C ''spark.apache.org/docs/1.1.0/mllib-decision-tree.html'':26C ''use'':21C ''www.youtube.com'':37C ''www.youtube.com/watch?v=3rrrcpxhu98'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2327, 2, '2014-10-21 07:06:21.58', 1, NULL, NULL, '&lt;p&gt;I have found the video tutorial/IPython notebook format really helped me get into the python ecosystem.&lt;/p&gt;

&lt;p&gt;There were two tutorials at SciPy 2013 that cover sklearn (&lt;a href="https://www.youtube.com/watch?v=r4bRUvvlaBw" rel="nofollow"&gt;part 1 of 1st tutorial&lt;/a&gt;, &lt;a href="https://github.com/jakevdp/sklearn_scipy2013" rel="nofollow"&gt;github repo for notebooks&lt;/a&gt;). &lt;/p&gt;

&lt;p&gt;Similar tutorials, from PyCon2012 and PyData2012, are out there for pandas but I don''t have the rep to link searching for &lt;code&gt;pandas tutorial&lt;/code&gt; on youtube should allow you to find them. &lt;/p&gt;

&lt;p&gt;Since you mention Kaggle, I guess you will have seen their getting started with python tutorial for the titanic passenger dataset (I don''t have the rep here to provide a link but searching for &lt;code&gt;Getting Started with Python: Kaggle''s Titanic Competition&lt;/code&gt; should get you there).&lt;/p&gt;
', NULL, NULL, '''/a'':42C,54C ''/code'':82C,137C ''/jakevdp/sklearn_scipy2013'':47C ''/p'':18C,55C,91C,142C ''/watch?v=r4bruvvlabw'':34C ''1'':38C ''1st'':40C ''2013'':26C ''allow'':86C ''code'':79C,128C ''competit'':136C ''cover'':28C ''dataset'':113C ''ecosystem'':17C ''find'':89C ''format'':9C ''found'':4C ''get'':13C,104C,129C,139C ''github'':50C ''github.com'':46C ''github.com/jakevdp/sklearn_scipy2013'':45C ''guess'':98C ''help'':11C ''href'':31C,44C ''kaggl'':96C,133C ''link'':76C,124C ''mention'':95C ''nofollow'':36C,49C ''notebook'':8C,53C ''p'':1C,19C,56C,92C ''panda'':67C,80C ''part'':37C ''passeng'':112C ''provid'':122C ''pycon2012'':60C ''pydata2012'':62C ''python'':16C,107C,132C ''realli'':10C ''rel'':35C,48C ''rep'':74C,119C ''repo'':51C ''scipi'':25C ''search'':77C,126C ''seen'':102C ''similar'':57C ''sinc'':93C ''sklearn'':29C ''start'':105C,130C ''titan'':111C,135C ''tutori'':23C,41C,58C,81C,108C ''tutorial/ipython'':7C ''two'':22C ''video'':6C ''www.youtube.com'':33C ''www.youtube.com/watch?v=r4bruvvlabw'':32C ''youtub'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2334, 1, '2014-10-22 14:53:42.127', 2, 202, 'MovieLens data set', '&lt;p&gt;I want to analyze &lt;a href="http://grouplens.org/datasets/movielens/" rel="nofollow"&gt;MovieLens data set&lt;/a&gt; and load on my machine the M1 file. I combine actually two data files (ratings.dat and movies.dat) and sort the table according &lt;code&gt;''userId''&lt;/code&gt; and &lt;code&gt;''Time''&lt;/code&gt; columns. The head of my DataFrame looks like here (all columns values are corresponding to the original data sets):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;In [36]: df.head(10)
Out[36]: 
        userId  movieId  Rating       Time                         movieName  \
40034        1      150       5  978301777                  Apollo 13 (1995)   
77615        1     1028       5  978301777               Mary Poppins (1964)   
550485       1     2018       4  978301777                      Bambi (1942)   
400889       1     1962       4  978301753         Driving Miss Daisy (1989)   
787274       1     1035       5  978301753        Sound of Music, The (1965)   
128308       1      938       4  978301752                       Gigi (1958)   
497972       1     3105       5  978301713                 Awakenings (1990)   
28417        1     2028       5  978301619        Saving Private Ryan (1998)   
6551         1     1961       5  978301590                   Rain Man (1988)   
35492        1     2692       4  978301570  Run Lola Run (Lola rennt) (1998)   

                            genre  
40034                       Drama  
77615   Children''s|Comedy|Musical  
550485       Animation|Children''s  
400889                      Drama  
787274                    Musical  
128308                    Musical  
497972                      Drama  
28417            Action|Drama|War  
6551                        Drama  
35492        Action|Crime|Romance  

[10 rows x 6 columns]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I can not understand that the same user with user Id 1 see or rated the different movies (Apollo13 (Id:150), Mary Poppins (Id:1028) and Bambi (Id:2018) exactly at the same time (up to the milleseconds). If somebody works already with this data set, please, clear this situation.&lt;/p&gt;
', 'dataset  pandas', NULL, '''/a'':21C ''/code'':46C,50C,203C ''/datasets/movielens/'':15C ''/p'':70C,256C ''/pre'':204C ''1'':85C,93C,101C,108C,117C,127C,134C,141C,150C,158C,217C ''10'':76C,198C ''1028'':94C,230C ''1035'':118C ''128308'':126C,184C ''13'':90C ''150'':86C,226C ''1942'':106C ''1958'':132C ''1961'':151C ''1962'':109C ''1964'':99C ''1965'':125C ''1988'':156C ''1989'':115C ''1990'':139C ''1995'':91C ''1998'':148C,167C ''2018'':102C,234C ''2028'':142C ''2692'':159C ''28417'':140C,188C ''3105'':135C ''35492'':157C,194C ''36'':74C,78C ''4'':103C,110C,129C,160C ''40034'':84C,169C ''400889'':107C,180C ''497972'':133C,186C ''5'':87C,95C,119C,136C,143C,152C ''550485'':100C,176C ''6'':201C ''6551'':149C,192C ''77615'':92C,171C ''787274'':116C,182C ''938'':128C ''978301570'':161C ''978301590'':153C ''978301619'':144C ''978301713'':137C ''978301752'':130C ''978301753'':111C,120C ''978301777'':88C,96C,104C ''accord'':43C ''action'':189C,195C ''actual'':32C ''alreadi'':247C ''analyz'':10C ''anim'':177C ''apollo'':89C ''apollo13'':224C ''awaken'':138C ''bambi'':105C,232C ''children'':172C,178C ''clear'':253C ''code'':44C,48C,72C ''column'':51C,61C,202C ''combin'':31C ''comedi'':174C ''correspond'':64C ''crime'':196C ''daisi'':114C ''data'':2A,19C,34C,68C,250C ''datafram'':56C ''dataset'':4B ''df.head'':75C ''differ'':222C ''drama'':170C,181C,187C,190C,193C ''drive'':112C ''exact'':235C ''file'':29C,35C ''genr'':168C ''gigi'':131C ''grouplens.org'':14C ''grouplens.org/datasets/movielens/'':13C ''head'':53C ''href'':12C ''id'':216C,225C,229C,233C ''like'':58C ''load'':23C ''lola'':163C,165C ''look'':57C ''m1'':28C ''machin'':26C ''man'':155C ''mari'':97C,227C ''millesecond'':243C ''miss'':113C ''movi'':223C ''movieid'':80C ''movielen'':1A,18C ''movienam'':83C ''movies.dat'':38C ''music'':123C,175C,183C,185C ''nofollow'':17C ''origin'':67C ''p'':6C,205C ''panda'':5B ''pleas'':252C ''poppin'':98C,228C ''pre'':71C ''privat'':146C ''rain'':154C ''rate'':81C,220C ''ratings.dat'':36C ''rel'':16C ''rennt'':166C ''romanc'':197C ''row'':199C ''run'':162C,164C ''ryan'':147C ''save'':145C ''see'':218C ''set'':3A,20C,69C,251C ''situat'':255C ''somebodi'':245C ''sort'':40C ''sound'':121C ''tabl'':42C ''time'':49C,82C,239C ''two'':33C ''understand'':209C ''user'':213C,215C ''userid'':45C,79C ''valu'':62C ''want'':8C ''war'':191C ''work'':246C ''x'':200C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2335, 2, '2014-10-22 15:50:43.23', 2, NULL, NULL, '&lt;p&gt;When you enter ratings on movie lens, you get pages with 10 movies or so. You set all the ratings, then submit by clicking "next page" or something. 
So I guess all the ratings for the same page are received at the same time, when you submit the page.&lt;/p&gt;
', NULL, NULL, '''/p'':51C ''10'':13C ''click'':25C ''enter'':4C ''get'':10C ''guess'':32C ''len'':8C ''movi'':7C,14C ''next'':26C ''p'':1C ''page'':11C,27C,39C,50C ''rate'':5C,21C,35C ''receiv'':41C ''set'':18C ''someth'':29C ''submit'':23C,48C ''time'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9143, 2, '2015-12-01 09:26:11.457', 1, NULL, NULL, '&lt;p&gt;Check the ranges of your dimensions and consider scaling if you see a large difference.&lt;/p&gt;

&lt;p&gt;I would interpret your described  behaviour due to much larger range if the index that the two other dimensions.
See also the &lt;a href="http://datascience.stackexchange.com/questions/6715/is-it-necessary-to-standardize-your-data-before-clustering/6722"&gt;question&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':47C ''/p'':17C,48C ''/questions/6715/is-it-necessary-to-standardize-your-data-before-clustering/6722'':45C ''also'':39C ''behaviour'':24C ''check'':2C ''consid'':9C ''datascience.stackexchange.com'':44C ''datascience.stackexchange.com/questions/6715/is-it-necessary-to-standardize-your-data-before-clustering/6722'':43C ''describ'':23C ''differ'':16C ''dimens'':7C,37C ''due'':25C ''href'':42C ''index'':32C ''interpret'':21C ''larg'':15C ''larger'':28C ''much'':27C ''p'':1C,18C ''question'':46C ''rang'':4C,29C ''scale'':10C ''see'':13C,38C ''two'':35C ''would'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2337, 1, '2014-10-23 14:51:57.16', 4, 79, 'Clustering strings inside strings?', '&lt;p&gt;I am not sure whether I formulated the question correctly. Basically, what I want to do is:&lt;/p&gt;

&lt;p&gt;Let''s suppose I have a list of 1000 strings which look like this:&lt;/p&gt;

&lt;p&gt;cvzxcvzx&lt;strong&gt;string&lt;/strong&gt;cvzcxvz&lt;/p&gt;

&lt;p&gt;otortorotr&lt;strong&gt;string&lt;/strong&gt;grptprt&lt;/p&gt;

&lt;p&gt;vmvmvmeop&lt;strong&gt;string2&lt;/strong&gt;vmrprp&lt;/p&gt;

&lt;p&gt;vccermpqp&lt;strong&gt;string2&lt;/strong&gt;rowerm&lt;/p&gt;

&lt;p&gt;proororor&lt;strong&gt;string3&lt;/strong&gt;potrprt&lt;/p&gt;

&lt;p&gt;mprto2435&lt;strong&gt;string3&lt;/strong&gt;famerpaer&lt;/p&gt;

&lt;p&gt;etc.&lt;/p&gt;

&lt;p&gt;I''d like to extract these reoccuring strings that occur on the list. What solution should I use? Does anyone know about algorithm that could do this?&lt;/p&gt;
', 'nlp  text-mining  feature-extraction', NULL, '''/p'':30C,46C,53C,60C,67C,74C,81C,88C,91C,120C ''/strong'':51C,58C,65C,72C,79C,86C ''1000'':40C ''algorithm'':115C ''anyon'':112C ''basic'':23C ''cluster'':1A ''correct'':22C ''could'':117C ''cvzcxvz'':52C ''cvzxcvzx'':48C ''d'':94C ''etc'':90C ''extract'':11B,97C ''famerpa'':87C ''featur'':10B ''feature-extract'':9B ''formul'':19C ''grptprt'':59C ''insid'':3A ''know'':113C ''let'':32C ''like'':44C,95C ''list'':38C,105C ''look'':43C ''mine'':8B ''mprto2435'':83C ''nlp'':5B ''occur'':102C ''otortorotr'':55C ''p'':12C,31C,47C,54C,61C,68C,75C,82C,89C,92C ''potrprt'':80C ''proororor'':76C ''question'':21C ''reoccur'':99C ''rowerm'':73C ''solut'':107C ''string'':2A,4A,41C,50C,57C,100C ''string2'':64C,71C ''string3'':78C,85C ''strong'':49C,56C,63C,70C,77C,84C ''suppos'':34C ''sure'':16C ''text'':7B ''text-min'':6B ''use'':110C ''vccermpqp'':69C ''vmrprp'':66C ''vmvmvmeop'':62C ''want'':26C ''whether'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2338, 2, '2014-10-23 15:50:09.82', 5, NULL, NULL, '&lt;p&gt;In my experience, the answer depends on the project at hand. For pure research, I prefer R for two reasons: 1) broad variety of libraries and 2) much of the data science literature includes R samples.&lt;/p&gt;

&lt;p&gt;If the project requires an interactive interface to be used by laypersons, I''ve found R to be too constrained. Shiny is a great start, but it''s not flexible enough yet. In these cases, I''ll start to look at porting my R work over to Python or js.&lt;/p&gt;
', NULL, NULL, '''/p'':38C,90C ''1'':22C ''2'':28C ''answer'':6C ''broad'':23C ''case'':74C ''constrain'':59C ''data'':32C ''depend'':7C ''enough'':70C ''experi'':4C ''flexibl'':69C ''found'':54C ''great'':63C ''hand'':12C ''includ'':35C ''interact'':45C ''interfac'':46C ''js'':89C ''layperson'':51C ''librari'':26C ''literatur'':34C ''ll'':76C ''look'':79C ''much'':29C ''p'':1C,39C ''port'':81C ''prefer'':17C ''project'':10C,42C ''pure'':14C ''python'':87C ''r'':18C,36C,55C,83C ''reason'':21C ''requir'':43C ''research'':15C ''sampl'':37C ''scienc'':33C ''shini'':60C ''start'':64C,77C ''two'':20C ''use'':49C ''varieti'':24C ''ve'':53C ''work'':84C ''yet'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2352, 1, '2014-10-26 20:11:22.317', 1, 26, 'making logical inference from a simuation generated data', '&lt;p&gt;I have data collected from a computer simulation of football games which seem to have recurring patterns of the following form.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;if madrid plays arsernal and the match ends under 3 goal, then on their next match against each others, madrid will win. if madrid happens to loose and then plays against chelsea next, they will win 90% of the time.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;how do I find such inferences from simulation generated data like this. There are other forms of hidden patterns that I believe exists in the dataset.&lt;/p&gt;
', 'machine-learning  data-mining', NULL, '''/em'':80C ''/p'':37C,81C,109C ''3'':49C ''90'':76C ''arsern'':43C ''believ'':104C ''chelsea'':71C ''collect'':19C ''comput'':22C ''data'':8A,13B,18C,92C ''data-min'':12B ''dataset'':108C ''em'':39C ''end'':47C ''exist'':105C ''find'':86C ''follow'':35C ''footbal'':25C ''form'':36C,98C ''game'':26C ''generat'':7A,91C ''goal'':50C ''happen'':64C ''hidden'':100C ''infer'':3A,88C ''learn'':11B ''like'':93C ''logic'':2A ''loos'':66C ''machin'':10B ''machine-learn'':9B ''madrid'':41C,59C,63C ''make'':1A ''match'':46C,55C ''mine'':14B ''next'':54C,72C ''other'':58C ''p'':15C,38C,82C ''pattern'':32C,101C ''play'':42C,69C ''recur'':31C ''seem'':28C ''simuat'':6A ''simul'':23C,90C ''time'':79C ''win'':61C,75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2339, 2, '2014-10-23 17:10:40.89', 2, NULL, NULL, '&lt;p&gt;Interesting question! I have not encountered it before so here is a solution I just made up, inspired by the approach taken by the word2vec paper:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Define the pair-wise similarity based on the longest common substring (LCS), or the LCS normalized by the products of the string lengths. Cache this in a matrix for any pair of strings considered since it is expensive to calculate. Also consider approximations.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Find a Euclidean (hyperspherical, perhaps?) embedding that minimizes the error (Euclidean distance if using the ball, and the dot product if using the sphere). Assume random initialization, and use a gradient-based optimization method by taking the Jacobian of the error.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Now you have a Hilbert space embedding, so cluster using your algorithm of choice!&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;em&gt;Response to deleted comment asking how to cluster multiple substrings&lt;/em&gt;: The bulk of the complexity lies in the first stage; the calculation of the LCS, so it depends on efficiently you do that. I''ve had luck with genetic algorithms. Anyway, what you''d do in this case is define a similarity &lt;em&gt;vector&lt;/em&gt; rather than a scalar, whose elements are the k-longest pair-wise LCS; see &lt;a href="http://cstheory.stackexchange.com/questions/8361/algorithm-find-the-first-k-longest-substrings-between-two-similar-strings"&gt;this&lt;/a&gt; discussion for algorithms. Then I would define the error by the sum of the errors corresponding to each substring.&lt;/p&gt;

&lt;p&gt;Something I did not address is how to choose the dimensionality of the embedding. The word2vec paper might provide some heuristics; see &lt;a href="https://groups.google.com/forum/#!topic/word2vec-toolkit/HRvNPIqe6mM" rel="nofollow"&gt;this&lt;/a&gt; discussion. I recall they used pretty big spaces, on the order of a 1000 dimensions, but they were optimizing something more complicated, so I suggest you start at R^2 and work your way up. Of course, you will want to use a higher dimensionality for the multiple LCS case.&lt;/p&gt;
', NULL, NULL, '''/a'':223C,275C ''/em'':155C,200C ''/forum/#!topic/word2vec-toolkit/hrvnpiqe6mm'':271C ''/li'':77C,123C,141C ''/ol'':142C ''/p'':28C,76C,122C,140C,243C,326C ''/questions/8361/algorithm-find-the-first-k-longest-substrings-between-two-similar-strings'':221C ''1000'':289C ''2'':305C ''address'':249C ''algorithm'':137C,185C,226C ''also'':73C ''anyway'':186C ''approach'':22C ''approxim'':75C ''ask'':149C ''assum'':104C ''ball'':95C ''base'':38C,112C ''big'':282C ''bulk'':157C ''cach'':56C ''calcul'':72C,167C ''case'':193C,325C ''choic'':139C ''choos'':253C ''cluster'':134C,152C ''comment'':148C ''common'':42C ''complex'':160C ''complic'':297C ''consid'':66C,74C ''correspond'':239C ''cours'':312C ''cstheory.stackexchange.com'':220C ''cstheory.stackexchange.com/questions/8361/algorithm-find-the-first-k-longest-substrings-between-two-similar-strings'':219C ''d'':189C ''defin'':32C,195C,230C ''delet'':147C ''depend'':173C ''dimens'':290C ''dimension'':255C,320C ''discuss'':224C,276C ''distanc'':91C ''dot'':98C ''effici'':175C ''element'':206C ''em'':144C,198C ''embed'':85C,132C,258C ''encount'':7C ''error'':89C,121C,232C,238C ''euclidean'':82C,90C ''expens'':70C ''find'':80C ''first'':164C ''genet'':184C ''gradient'':111C ''gradient-bas'':110C ''groups.google.com'':270C ''groups.google.com/forum/#!topic/word2vec-toolkit/hrvnpiqe6mm'':269C ''heurist'':265C ''higher'':319C ''hilbert'':130C ''href'':218C,268C ''hyperspher'':83C ''initi'':106C ''inspir'':19C ''interest'':2C ''jacobian'':118C ''k'':210C ''k-longest'':209C ''lcs'':44C,47C,170C,215C,324C ''length'':55C ''li'':30C,78C,124C ''lie'':161C ''longest'':41C,211C ''luck'':182C ''made'':17C ''matrix'':60C ''method'':114C ''might'':262C ''minim'':87C ''multipl'':153C,323C ''nofollow'':273C ''normal'':48C ''ol'':29C ''optim'':113C,294C ''order'':286C ''p'':1C,31C,79C,125C,143C,244C ''pair'':35C,63C,213C ''pair-wis'':34C,212C ''paper'':27C,261C ''perhap'':84C ''pretti'':281C ''product'':51C,99C ''provid'':263C ''question'':3C ''r'':304C ''random'':105C ''rather'':201C ''recal'':278C ''rel'':272C ''respons'':145C ''scalar'':204C ''see'':216C,266C ''similar'':37C,197C ''sinc'':67C ''solut'':14C ''someth'':245C,295C ''space'':131C,283C ''sphere'':103C ''stage'':165C ''start'':302C ''string'':54C,65C ''substr'':43C,154C,242C ''suggest'':300C ''sum'':235C ''take'':116C ''taken'':23C ''use'':93C,101C,108C,135C,280C,317C ''ve'':180C ''vector'':199C ''want'':315C ''way'':309C ''whose'':205C ''wise'':36C,214C ''word2vec'':26C,260C ''work'':307C ''would'':229C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2342, 2, '2014-10-23 21:59:32.713', 2, NULL, NULL, '&lt;p&gt;You should read the paper from Google on PLANET, which was their distributed MapReduce-based implementation of random decision forests: &lt;a href="http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36296.pdf" rel="nofollow"&gt;http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36296.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You may or may not like the architecture but there are a number of interesting ideas about scaling up here.&lt;/p&gt;
', NULL, NULL, '''/media/research.google.com/en//pubs/archive/36296.pdf'':27C ''/media/research.google.com/en//pubs/archive/36296.pdf&lt;/a&gt;&lt;/p&gt;'':32C ''/p'':54C ''architectur'':41C ''base'':17C ''decis'':21C ''distribut'':14C ''forest'':22C ''googl'':8C ''href'':24C ''idea'':49C ''implement'':18C ''interest'':48C ''like'':39C ''mapreduc'':16C ''mapreduce-bas'':15C ''may'':35C,37C ''nofollow'':29C ''number'':46C ''p'':1C,33C ''paper'':6C ''planet'':10C ''random'':20C ''read'':4C ''rel'':28C ''scale'':51C ''static.googleusercontent.com'':26C,31C ''static.googleusercontent.com/media/research.google.com/en//pubs/archive/36296.pdf'':25C ''static.googleusercontent.com/media/research.google.com/en//pubs/archive/36296.pdf&lt;/a&gt;&lt;/p&gt;'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2343, 1, '2014-10-24 23:13:07.803', 1, 1866, 'Small project ideas for Machine Learning', '&lt;p&gt;I need some serious help. I am supposed to implement a project (Non-Existing as of now) for my Machine Learning course. I have no basics in AI or Data mining or Machine learning. I have been searching for a while and unable to find something that i can finish implementing in 3-4 weeks time. It carries a huge chunk of my final marks and no matter how much i try i am unable to understand how it works!
Can the machine learning masters please help me out with this. I need a project suggestion to start with. And i want to know how to proceed after gathering the data set. I am totally blank and running out of time for my graduation :(&lt;/p&gt;

&lt;p&gt;Appreciate your suggestions! Thanks in advance.&lt;/p&gt;
', 'machine-learning  bigdata  data-mining  clustering  algorithms', NULL, '''-4'':71C ''/p'':142C,150C ''3'':70C ''advanc'':149C ''ai'':45C ''algorithm'':15B ''appreci'':144C ''basic'':43C ''bigdata'':10B ''blank'':133C ''carri'':75C ''chunk'':78C ''cluster'':14B ''cours'':39C ''data'':12B,47C,128C ''data-min'':11B ''exist'':31C ''final'':81C ''find'':62C ''finish'':67C ''gather'':126C ''graduat'':141C ''help'':21C,104C ''huge'':77C ''idea'':3A ''implement'':26C,68C ''know'':121C ''learn'':6A,9B,38C,51C,101C ''machin'':5A,8B,37C,50C,100C ''machine-learn'':7B ''mark'':82C ''master'':102C ''matter'':85C ''mine'':13B,48C ''much'':87C ''need'':18C,110C ''non'':30C ''non-exist'':29C ''p'':16C,143C ''pleas'':103C ''proceed'':124C ''project'':2A,28C,112C ''run'':135C ''search'':55C ''serious'':20C ''set'':129C ''small'':1A ''someth'':63C ''start'':115C ''suggest'':113C,146C ''suppos'':24C ''thank'':147C ''time'':73C,138C ''total'':132C ''tri'':89C ''unabl'':60C,92C ''understand'':94C ''want'':119C ''week'':72C ''work'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2344, 1, '2014-10-25 01:36:27.483', 1, 240, 'pandas dataframes memory', '&lt;p&gt;I have a question about memory usage. &lt;/p&gt;

&lt;p&gt;I want to do 4 things:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1) make a dataframe from one of several columns from a datasource, say a json string
2) make the third column of the original dataset the index to the dataframe
3) change the name of another column
4) change the series i''ve created to a dataframe
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;My question is about memory efficiency. It seems that for step 1), I am first loading a whole dataframe, then run a concat command to concatenate the columns I want. &lt;/p&gt;

&lt;p&gt;For step 2, I again need to resave the new dataframe as another object.&lt;/p&gt;

&lt;p&gt;For step 3, it seems to stick so nothing there.&lt;/p&gt;

&lt;p&gt;Please advise on a more efficient way to go about this, if that exists.&lt;/p&gt;

&lt;p&gt;Command:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;   df = pd.DataFrame(jsonobject)
   df = df.set_index("columnC")
   df.index.names= ["foo"]
   df1 = df["foo"].map(lambda x:x["id"])
   df2 = pd.DataFrame(df1)
&lt;/code&gt;&lt;/pre&gt;
', 'pandas', NULL, '''/code'':71C,174C ''/p'':13C,21C,104C,120C,132C,148C,151C ''/pre'':72C,175C ''1'':24C,85C ''2'':40C,108C ''3'':54C,124C ''4'':19C,61C ''advis'':135C ''anoth'':59C,118C ''chang'':55C,62C ''code'':23C,153C ''column'':32C,44C,60C,101C ''columnc'':160C ''command'':97C,150C ''concat'':96C ''concaten'':99C ''creat'':67C ''datafram'':2A,27C,53C,70C,92C,116C ''dataset'':48C ''datasourc'':35C ''df'':154C,157C,164C ''df.index.names'':161C ''df.set'':158C ''df1'':163C,173C ''df2'':171C ''effici'':79C,139C ''exist'':147C ''first'':88C ''foo'':162C,165C ''go'':142C ''id'':170C ''index'':50C,159C ''json'':38C ''jsonobject'':156C ''lambda'':167C ''load'':89C ''make'':25C,41C ''map'':166C ''memori'':3A,11C,78C ''name'':57C ''need'':111C ''new'':115C ''noth'':130C ''object'':119C ''one'':29C ''origin'':47C ''p'':5C,14C,73C,105C,121C,133C,149C ''panda'':1A,4B ''pd.dataframe'':155C,172C ''pleas'':134C ''pre'':22C,152C ''question'':9C,75C ''resav'':113C ''run'':94C ''say'':36C ''seem'':81C,126C ''seri'':64C ''sever'':31C ''step'':84C,107C,123C ''stick'':128C ''string'':39C ''thing'':20C ''third'':43C ''usag'':12C ''ve'':66C ''want'':16C,103C ''way'':140C ''whole'':91C ''x'':168C,169C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2345, 2, '2014-10-25 08:40:14.613', 3, NULL, NULL, '&lt;p&gt;Gather million twitter user accounts. Then try to guess their gender based on their avatar, tweets and who they follow.&lt;/p&gt;
', NULL, NULL, '''/p'':22C ''account'':6C ''avatar'':16C ''base'':13C ''follow'':21C ''gather'':2C ''gender'':12C ''guess'':10C ''million'':3C ''p'':1C ''tri'':8C ''tweet'':17C ''twitter'':4C ''user'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2353, 1, '2014-10-26 20:35:39.767', 2, 207, 'MATLAB Perceptron', '&lt;p&gt;I cant seem to figure out why I have a high percentage error. I''m trying to get a perceptron between X1 and X2 which are Gaussian distributed data sets with distinct means and identical covariances. My code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;N=200;
C= [2 1; 1 2]; %Covariance
m1=[0 2];
m2=[1.5 0];%mean
X1 = mvnrnd(m1, C, N/2);
X2 = mvnrnd(m2, C, N/2);

X = [X1; X2];
X = [X ones(N,1)]; %bias
y = [-1*ones(N/2,1); ones(N/2,1)]; %classification

%Split data into training and test 
ii = randperm(N);
Xtr = X(ii(1:N/2),:);
ytr = X(ii(1:N/2),:);
Xts = X(ii(N/2+1:N),:);
yts = y(ii(N/2+1:N),:);
Nts = N/2;

w = randn(3,1);
eta = 0.001;
%learn from training set
for iter=1:500 
j = ceil(rand*N/2);
if( ytr(j)*Xtr(j,:)*w &amp;lt; 0)
w = w + eta*Xtr(j,:)''; 
end
end

%apply what you have learnt to test set
yhts = Xts * w;
disp([yts yhts])
PercentageError = 100*sum(yts .*yhts &amp;lt; 0)/Nts;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Any help would be appreciated. Thank you&lt;/p&gt;
', 'machine-learning  matlab', NULL, '''+1'':115C,121C ''-1'':84C ''/code'':180C ''/nts'':179C ''/p'':46C,190C ''/pre'':181C ''0'':58C,62C,150C,178C ''0.001'':130C ''1'':53C,54C,81C,87C,90C,104C,109C,128C,137C ''1.5'':61C ''100'':173C ''2'':52C,55C,59C ''200'':50C ''3'':127C ''500'':138C ''appli'':158C ''appreci'':187C ''bias'':82C ''c'':51C,67C,72C ''cant'':9C ''ceil'':140C ''classif'':91C ''code'':45C,48C ''covari'':43C,56C ''data'':36C,93C ''disp'':169C ''distinct'':39C ''distribut'':35C ''end'':156C,157C ''error'':20C ''eta'':129C,153C ''figur'':12C ''gaussian'':34C ''get'':25C ''help'':184C ''high'':18C ''ident'':42C ''ii'':98C,103C,108C,113C,119C ''iter'':136C ''j'':139C,145C,147C,155C ''learn'':5B,131C ''learnt'':162C ''lt'':149C,177C ''m'':22C ''m1'':57C,66C ''m2'':60C,71C ''machin'':4B ''machine-learn'':3B ''matlab'':1A,6B ''mean'':40C,63C ''mvnrnd'':65C,70C ''n'':49C,80C,100C,116C,122C ''n/2'':68C,73C,86C,89C,105C,110C,114C,120C,124C,142C ''nts'':123C ''one'':79C,85C,88C ''p'':7C,182C ''percentag'':19C ''percentageerror'':172C ''perceptron'':2A,27C ''pre'':47C ''rand'':141C ''randn'':126C ''randperm'':99C ''seem'':10C ''set'':37C,134C,165C ''split'':92C ''sum'':174C ''test'':97C,164C ''thank'':188C ''train'':95C,133C ''tri'':23C ''w'':125C,148C,151C,152C,168C ''would'':185C ''x'':74C,77C,78C,102C,107C,112C ''x1'':29C,64C,75C ''x2'':31C,69C,76C ''xtr'':101C,146C,154C ''xts'':111C,167C ''y'':83C,118C ''yhts'':166C,171C,176C ''ytr'':106C,144C ''yts'':117C,170C,175C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2346, 1, '2014-10-25 22:37:18.523', 4, 105, 'Business exception reporting', '&lt;p&gt;I''ve looked at all the other sections on stackexchange and haven''t found a better fit for this question. Sorry if it''s misplaced. If it is, feel free to downvote, but could you please also drop a recommendation where the question would be better suited? Thanks!&lt;/p&gt;

&lt;p&gt;I work in an analytical role at a a large financial services firm. We do a ton of daily reporting over metrics that rarely change in a meaningful way from day to day. From this daily reporting, our management is required to extract what was important yesterday and what important trends have developed / are developing over time.&lt;/p&gt;

&lt;p&gt;I want to change this to a model of daily exception reporting and weekly trend reporting. &lt;/p&gt;

&lt;p&gt;Features might include: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;User report consolidation (so there''s only one daily email)&lt;/li&gt;
&lt;li&gt;report ordering based upon level of variance from past performance (see the most important stuff first)&lt;/li&gt;
&lt;li&gt;HTML email support (with my audience, pretty counts)&lt;/li&gt;
&lt;li&gt;web interface to allow preference changes, including LDAP support (make administration easier)&lt;/li&gt;
&lt;li&gt;Unsubscribe feature at the report level&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Here''s what I''d like to know&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;What are the practical problems I might run into?&lt;/li&gt;
&lt;li&gt;What is the best way to display the new reports?&lt;/li&gt;
&lt;li&gt;How should  I define an "exception"? How can I know if my definition is a good one?&lt;/li&gt;
&lt;li&gt;I assume I''d be using a mix of Python, SQL, and powershell. Anything else I should consider, e.g. R? What are some good resources?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Thanks,
Brandon &lt;/p&gt;
', 'recommendation  visualization', NULL, '''/li'':150C,168C,178C,192C,200C,223C,235C,254C,281C ''/p'':55C,114C,132C,137C,211C,286C ''/ul'':201C,282C ''administr'':190C ''allow'':183C ''also'':43C ''analyt'':61C ''anyth'':269C ''assum'':257C ''audienc'':175C ''base'':154C ''best'':228C ''better'':22C,52C ''brandon'':285C ''busi'':1A ''chang'':81C,119C,185C ''consid'':273C ''consolid'':142C ''could'':40C ''count'':177C ''d'':207C,259C ''daili'':75C,92C,125C,148C ''day'':87C,89C ''defin'':240C ''definit'':249C ''develop'':109C,111C ''display'':231C ''downvot'':38C ''drop'':44C ''e.g'':274C ''easier'':191C ''els'':270C ''email'':149C,171C ''except'':2A,126C,242C ''extract'':99C ''featur'':134C,195C ''feel'':35C ''financi'':67C ''firm'':69C ''first'':167C ''fit'':23C ''found'':20C ''free'':36C ''good'':252C,279C ''haven'':18C ''html'':170C ''import'':102C,106C,165C ''includ'':136C,186C ''interfac'':181C ''know'':210C,246C ''larg'':66C ''ldap'':187C ''level'':156C,199C ''li'':139C,151C,169C,179C,193C,213C,224C,236C,255C ''like'':208C ''look'':9C ''make'':189C ''manag'':95C ''meaning'':84C ''metric'':78C ''might'':135C,220C ''misplac'':31C ''mix'':263C ''model'':123C ''new'':233C ''one'':147C,253C ''order'':153C ''p'':6C,56C,115C,133C,202C,283C ''past'':160C ''perform'':161C ''pleas'':42C ''powershel'':268C ''practic'':217C ''prefer'':184C ''pretti'':176C ''problem'':218C ''python'':265C ''question'':26C,49C ''r'':275C ''rare'':80C ''recommend'':4B,46C ''report'':3A,76C,93C,127C,131C,141C,152C,198C,234C ''requir'':97C ''resourc'':280C ''role'':62C ''run'':221C ''section'':14C ''see'':162C ''servic'':68C ''sorri'':27C ''sql'':266C ''stackexchang'':16C ''stuff'':166C ''suit'':53C ''support'':172C,188C ''thank'':54C,284C ''time'':113C ''ton'':73C ''trend'':107C,130C ''ul'':138C,212C ''unsubscrib'':194C ''upon'':155C ''use'':261C ''user'':140C ''varianc'':158C ''ve'':8C ''visual'':5B ''want'':117C ''way'':85C,229C ''web'':180C ''week'':129C ''work'':58C ''would'':50C ''yesterday'':103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2357, 2, '2014-10-27 16:13:21.233', 2, NULL, NULL, '&lt;p&gt;A confusion matrix is a cross tabulation of your predicted values against the true observed values, and (test) accuracy is emperical rate of correct predictions. So in this case you''ll need to &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Predict the ''play'' attribute for your test set.  (Currently you don''t have a method to predict your second test case, so for the sake of argument let''s assume your model would predict yes for the sunny example. &lt;/li&gt;
&lt;li&gt;The following method of keeping track of your predictions is reffered to as a confusion matrix.  The top labels are prediced&lt;/li&gt;
&lt;/ol&gt;

&lt;pre&gt;
               Predicted
         +----------------+
         Â¦     Â¦ yes Â¦ no Â¦
Oserved  Â¦ yes Â¦ 1   Â¦ 1  Â¦
         Â¦ no  Â¦ 0   Â¦ 0  Â¦
         +----------------+
&lt;/pre&gt;

&lt;p&gt;Here the first 1 is from your first test case and the second 1 is from the misclassified second test case.&lt;/p&gt;

&lt;ol start="3"&gt;
&lt;li&gt;Calculate accuracy,&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Accuracy = (# correct predictions)/(# total predictions) = 1 / 2 = .50.&lt;/p&gt;
', NULL, NULL, '''/li'':77C,100C,155C ''/ol'':101C,156C ''/p'':35C,148C,166C ''/pre'':125C ''0'':121C,123C ''1'':114C,116C,130C,140C,163C ''2'':164C ''3'':151C ''50'':165C ''accuraci'':20C,154C,158C ''argument'':64C ''assum'':67C ''attribut'':41C ''calcul'':153C ''case'':30C,58C,136C,147C ''confus'':3C,93C ''correct'':25C,159C ''cross'':7C ''current'':46C ''emper'':22C ''exampl'':76C ''first'':129C,134C ''follow'':80C ''keep'':83C ''label'':97C ''let'':65C ''li'':37C,78C,152C ''ll'':32C ''matrix'':4C,94C ''method'':52C,81C ''misclassifi'':144C ''model'':69C ''need'':33C ''observ'':16C ''ol'':36C,149C ''oserv'':110C ''p'':1C,126C,157C ''play'':40C ''pre'':102C ''predic'':99C ''predict'':11C,26C,38C,54C,71C,87C,103C,160C,162C ''rate'':23C ''reffer'':89C ''sake'':62C ''second'':56C,139C,145C ''set'':45C ''start'':150C ''sunni'':75C ''tabul'':8C ''test'':19C,44C,57C,135C,146C ''top'':96C ''total'':161C ''track'':84C ''true'':15C ''valu'':12C,17C ''would'':70C ''yes'':72C,106C,112C ''â'':104C,105C,107C,109C,111C,113C,115C,117C,118C,120C,122C,124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4846, 2, '2015-01-09 08:22:40.92', 1, NULL, NULL, '&lt;p&gt;There is no ideal configuration, for &lt;code&gt;R&lt;/code&gt; or in general - product selection is always a difficult task and many factors are at play. I think that the solution is rather simple - get the best computer that your budget allows.&lt;/p&gt;

&lt;p&gt;Having said that, since you want to focus on &lt;code&gt;R&lt;/code&gt; development and one of &lt;code&gt;R&lt;/code&gt;''s pressing issues is its &lt;a href="http://stackoverflow.com/q/5171593/2872891"&gt;critical dependence&lt;/a&gt; on the amount of available &lt;em&gt;physical memory&lt;/em&gt; (RAM), I would suggest favoring more RAM to other parameters. The second most important parameter, in my opinion, would be &lt;em&gt;number of cores&lt;/em&gt; (or &lt;em&gt;processors&lt;/em&gt; - see details below), due to your potential &lt;em&gt;multiprocessing&lt;/em&gt; focus. Finally, the two next most important criteria I''d pay attention to would be &lt;em&gt;compatibility&lt;/em&gt; with Linux and system/manufacturer''s &lt;em&gt;quality&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;As far as the &lt;em&gt;storage&lt;/em&gt; goes, I suggest considering &lt;em&gt;solid state drives (SSD)&lt;/em&gt;, if you''d rather prefer to have a bit more more speed than more space (however, if your work will involve intensive disk operations, you might want to investigate the issue of SSD &lt;em&gt;reliability&lt;/em&gt; or consult with people, knowledgeable in this matter). However, I think that for R-focused work, disk operations are much less critical than memory ones, as I''ve mentioned above.&lt;/p&gt;

&lt;p&gt;When choosing a specific &lt;em&gt;Linux distribution&lt;/em&gt;, I suggest using a well-supported one, such as Debian or, even better, Ubuntu (if you care more about support, choose their LTS version). I''d rather not buy parts and assemble custom box, but some people would definitely prefer that route - for that you really need to know hardware well, but potential compatibility could still be an issue. The next paragraph provides some examples for both &lt;em&gt;commercial-off-the-shelf (COTS)&lt;/em&gt; and &lt;em&gt;custom&lt;/em&gt; solutions.&lt;/p&gt;

&lt;p&gt;Should you be interested in the &lt;em&gt;custom system&lt;/em&gt; route, &lt;a href="http://www.tomshardware.com/forum/295552-28-multiprocessor-multicore-system-rendering-workstation" rel="nofollow"&gt;this discussion&lt;/a&gt; might be worth reading, as it contains some interesting pricing numbers (just to get an idea of potential savings) and also sheds some light on &lt;em&gt;multiprocessor vs. multi-core&lt;/em&gt; alternatives (obviously, the &lt;em&gt;context&lt;/em&gt; is different, but nevertheless could be useful). As I said, I would go the COTS route, mainly due to reliability and compatibility issues. In terms of single-processor multi-core systems, your budget is more than enough. However, when we go to multiprocessor workstations (I''m not even talking about servers), even two-processor configurations can go over your budget easily. Some, not far away, such as &lt;a href="http://www8.hp.com/us/en/campaigns/workstations/z820.html" rel="nofollow"&gt;HP Z820 Workstation&lt;/a&gt;. It starts from 2439 USD, but in minimal configuration. When you upgrade it to match your desired specs (if it''s even possible), I''m sure that we''ll be talking about 5K USD price range (extrapolating from the series'' higher-level models). What I like about HP Z820, though, is the fact that this system is &lt;a href="http://www.ubuntu.com/certification/hardware/201011-6754" rel="nofollow"&gt;Ubuntu certified&lt;/a&gt;. Considering system compatibility and assuming your desire to run Ubuntu, the best way to approach your problem is to go through &lt;a href="http://www.ubuntu.com/certification" rel="nofollow"&gt;Ubuntu-certified hardware lists&lt;/a&gt; and shortlist systems that you like. Just for the sake of completeness, take a look at this &lt;a href="http://www.mediaworkstations.net/i-x2.html" rel="nofollow"&gt;interesting multiprocessor system&lt;/a&gt;, which in compatible configuration might cost less than from HP or other major vendors. However, it''s multimedia-oriented as well as it''s reliability and compatibility are unknown, not to mention that it''s way over your specified budget.&lt;/p&gt;

&lt;p&gt;In terms of &lt;code&gt;R&lt;/code&gt; and &lt;code&gt;R&lt;/code&gt;-focused software, I highly recommend you to use &lt;em&gt;RStudio Server&lt;/em&gt; instead of &lt;em&gt;RStudio&lt;/em&gt;, as that will provide you with an opportunity to be able to work from any Internet-enabled location (provided you computer will be running, obviously). Another advice that I have is to keep an eye on &lt;em&gt;alternative &lt;code&gt;R&lt;/code&gt; distributions&lt;/em&gt;. I''m not talking about commercial expensive ones, but about emerging open source projects, such as &lt;code&gt;pqR&lt;/code&gt;: &lt;a href="http://www.pqr-project.org" rel="nofollow"&gt;http://www.pqr-project.org&lt;/a&gt;. Will update as needed. I hope this is helpful.&lt;/p&gt;
', NULL, NULL, '''/a'':76C,350C,469C,537C,571C,599C,739C ''/certification'':563C ''/certification/hardware/201011-6754'':532C ''/code'':10C,56C,63C,648C,652C,711C,732C ''/em'':85C,110C,114C,124C,142C,150C,159C,169C,205C,246C,322C,326C,339C,382C,388C,664C,669C,713C ''/forum/295552-28-multiprocessor-multicore-system-rendering-workstation'':345C ''/i-x2.html'':593C ''/p'':43C,151C,237C,328C,641C,749C ''/q/5171593/2872891'':73C ''/us/en/campaigns/workstations/z820.html'':463C ''2439'':473C ''5k'':502C ''abl'':680C ''advic'':697C ''allow'':42C ''also'':371C ''altern'':383C,708C ''alway'':17C ''amount'':79C ''anoth'':696C ''approach'':552C ''assembl'':279C ''assum'':542C ''attent'':136C ''avail'':81C ''away'':456C ''best'':37C,549C ''better'':260C ''bit'':178C ''box'':281C ''budget'':41C,423C,451C,640C ''buy'':276C ''care'':264C ''certifi'':536C,568C ''choos'':240C,268C ''code'':8C,54C,61C,646C,650C,709C,730C ''commerci'':317C,719C ''commercial-off-the-shelf'':316C 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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2348, 1, '2014-10-25 23:46:14.493', 1, 307, 'How to calculate classification accuracy with confusion matrix?', '&lt;p&gt;I have Train and Test data, how to calculate classification accuracy with confusion matrix ? Thanks&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;@attribute outlook {sunny, overcast, rainy}
@attribute temperature {hot, mild, cool}
@attribute humidity {high, normal}
@attribute windy {TRUE, FALSE}
@attribute play {yes, no}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Train:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1   sunny       hot     high    FALSE   no
2   sunny       hot     high    TRUE    no
3   overcast    hot     high    FALSE   yes
4   rainy       mild    high    FALSE   yes
5   rainy       cool    normal  FALSE   yes
6   rainy       cool    normal  TRUE    no
7   sunny       cool    normal  FALSE   yes
8   rainy       mild    normal  FALSE   yes
9   sunny       mild    normal  TRUE    yes
10  overcast    mild    high    TRUE    yes
11  overcast    hot     normal  FALSE   yes
12  rainy       mild    high    TRUE    no
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Test:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;overcast    cool    normal  TRUE    yes
sunny       mild    high    FALSE   no
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Rules found:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(humidity,normal), (windy,FALSE) -&amp;gt; (play,yes) [Support=0.33 , Confidence=1.00 , Correctly Classify= 4, 8, 9, 12]
(outlook,overcast) -&amp;gt; (play,yes) [Support=0.25 , Confidence=1.00 , Correctly Classify= 2, 11]
(outlook,rainy), (windy,FALSE) -&amp;gt; (play,yes) [Support=0.25 , Confidence=1.00 , Correctly Classify= 3]
(outlook,sunny), (temperature,hot) -&amp;gt; (play,no) [Support=0.17 , Confidence=1.00 , Correctly Classify= 0, 1]
(outlook,sunny), (humidity,normal) -&amp;gt; (play,yes) [Support=0.17 , Confidence=1.00 , Correctly Classify= 10]
(outlook,rainy), (windy,TRUE) -&amp;gt; (play,no) [Support=0.17 , Confidence=1.00 , Correctly Classify= 5, 13]
&lt;/code&gt;&lt;/pre&gt;
', 'classification  accuracy', NULL, '''/code'':52C,131C,148C,244C ''/p'':27C,56C,135C,153C ''/pre'':53C,132C,149C,245C ''0'':213C ''0.17'':208C,223C,237C ''0.25'':179C,194C ''0.33'':164C ''1'':59C,214C ''1.00'':166C,181C,196C,210C,225C,239C ''10'':113C,228C ''11'':119C,185C ''12'':125C,172C ''13'':243C ''2'':65C,184C ''3'':71C,199C ''4'':77C,169C ''5'':83C,242C ''6'':89C ''7'':95C ''8'':101C,170C ''9'':107C,171C ''accuraci'':5A,10B,22C ''attribut'':30C,35C,40C,44C,48C ''calcul'':3A,20C ''classif'':4A,9B,21C ''classifi'':168C,183C,198C,212C,227C,241C ''code'':29C,58C,137C,155C ''confid'':165C,180C,195C,209C,224C,238C ''confus'':7A,24C ''cool'':39C,85C,91C,97C,139C ''correct'':167C,182C,197C,211C,226C,240C ''data'':17C ''fals'':47C,63C,75C,81C,87C,99C,105C,123C,146C,159C,189C ''found'':152C ''gt'':160C,175C,190C,204C,219C,233C ''high'':42C,62C,68C,74C,80C,116C,128C,145C ''hot'':37C,61C,67C,73C,121C,203C ''humid'':41C,156C,217C ''matrix'':8A,25C ''mild'':38C,79C,103C,109C,115C,127C,144C ''normal'':43C,86C,92C,98C,104C,110C,122C,140C,157C,218C ''outlook'':31C,173C,186C,200C,215C,229C ''overcast'':33C,72C,114C,120C,138C,174C ''p'':11C,54C,133C,150C ''play'':49C,161C,176C,191C,205C,220C,234C ''pre'':28C,57C,136C,154C ''raini'':34C,78C,84C,90C,102C,126C,187C,230C ''rule'':151C ''sunni'':32C,60C,66C,96C,108C,143C,201C,216C ''support'':163C,178C,193C,207C,222C,236C ''temperatur'':36C,202C ''test'':16C,134C ''thank'':26C ''train'':14C,55C ''true'':46C,69C,93C,111C,117C,129C,141C,232C ''windi'':45C,158C,188C,231C ''yes'':50C,76C,82C,88C,100C,106C,112C,118C,124C,142C,162C,177C,192C,221C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2349, 1, '2014-10-26 13:12:23.597', 4, 79, 'Rough vs Fuzzy vs Granular Computing', '&lt;p&gt;For my Computational Intelligence class, I''m working on classifying short text. One of the papers that I''ve found makes a lot of use of &lt;em&gt;granular computing&lt;/em&gt;, but I''m struggling to find a decent explanation of what exactly it is.&lt;/p&gt;

&lt;p&gt;From what I can gather from the paper, it sounds to me like granular computing is very similar to fuzzy sets. So, what exactly is the difference. I''m asking about rough sets as well, because I''m curious about them and how they relate to fuzzy sets. If at all.&lt;/p&gt;

&lt;p&gt;Edit: &lt;a href="http://ijcai.org/papers11/Papers/IJCAI11-298.pdf" rel="nofollow"&gt;Here&lt;/a&gt; is the paper I''m referencing.&lt;/p&gt;
', 'machine-learning  data-mining  classification', NULL, '''/a'':123C ''/em'':44C ''/p'':59C,112C,130C ''/papers11/papers/ijcai11-298.pdf'':119C ''ask'':90C ''class'':19C ''classif'':13B ''classifi'':24C ''comput'':6A,17C,43C,75C ''curious'':99C ''data'':11B ''data-min'':10B ''decent'':52C ''differ'':87C ''edit'':114C ''em'':41C ''exact'':56C,84C ''explan'':53C ''find'':50C ''found'':34C ''fuzzi'':3A,80C,107C ''gather'':65C ''granular'':5A,42C,74C ''href'':116C ''ijcai.org'':118C ''ijcai.org/papers11/papers/ijcai11-298.pdf'':117C ''intellig'':18C ''learn'':9B ''like'':73C ''lot'':37C ''m'':21C,47C,89C,98C,128C ''machin'':8B ''machine-learn'':7B ''make'':35C ''mine'':12B ''nofollow'':121C ''one'':27C ''p'':14C,60C,113C ''paper'':30C,68C,126C ''referenc'':129C ''rel'':120C ''relat'':105C ''rough'':1A,92C ''set'':81C,93C,108C ''short'':25C ''similar'':78C ''sound'':70C ''struggl'':48C ''text'':26C ''use'':39C ''ve'':33C ''vs'':2A,4A ''well'':95C ''work'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2350, 2, '2014-10-26 19:36:07.113', 4, NULL, NULL, '&lt;p&gt;"&lt;strong&gt;Granularity&lt;/strong&gt;" refers to the &lt;strong&gt;&lt;em&gt;resolution&lt;/em&gt;&lt;/strong&gt; of the variables under analysis.  If you are analyzing &lt;em&gt;height&lt;/em&gt; of people, you could use &lt;em&gt;course-grained variables&lt;/em&gt; that have only a few possible values -- e.g. "above-average, average, below-average" -- or a &lt;em&gt;fine-grained variable&lt;/em&gt;, with many or an infinite number of values -- e.g. integer values or real number values.&lt;/p&gt;

&lt;p&gt;A measure is "&lt;strong&gt;fuzzy&lt;/strong&gt;" if the distinction between alternative values is not crisp.  In the course-grained variable for &lt;em&gt;height&lt;/em&gt;, a "crisp" measure would mean that any given individual could &lt;em&gt;only&lt;/em&gt; be assigned one value -- e.g. a tall-ish person is either "above-average", or "average".  In contrast, a "fuzzy" measure allows for &lt;em&gt;degrees of membership&lt;/em&gt; for each value, with "membership" taking values from 0 to 1.0.  Thus, a tall-ish person could be a value of "0.5 above-average", "0.5 average", "0.0 below-average".&lt;/p&gt;

&lt;p&gt;Finally, a measure is "&lt;strong&gt;rough&lt;/strong&gt;" when two values are given: upper and lower bounds as an estimate of the "crisp" measure.  In our example of a tall-ish person, the rough measure would be {UPPER = above-average, LOWER = average}.&lt;/p&gt;

&lt;p&gt;Why use granular, fuzzy, or rough measures at all, you might ask?  Why not measure everything in nice, precise real numbers?  Because many real-world phenomena don''t have a good, reliable intrinsic measure and measurement procedure that results in a real number.  If you ask married couples to rate the quality of their marriage on a scale from 1 to 10, or 1.00 to 10.00, they might give you a number (or range of numbers), but how reliable are those reports? Using a course-grained measure (e.g. "happy", "neutral/mixed", "unhappy"), or fuzzy measure, or rough measure can be more reliable and more credible in your analysis.  Generally, it''s much better to use rough/crude measures well than to use precise/fine-grained measures poorly.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2351, 2, '2014-10-26 20:10:06.923', 1, NULL, NULL, '&lt;p&gt;I think the most likely explanation is that the two libraries do not quite support TreeModel in PMML in the same way. Perhaps one only supports a subset of features, and ignores ones it does not understand. This could cause different scoring.&lt;/p&gt;

&lt;p&gt;I''d also double check that upstream parsing code is the same in both cases. Maybe a missing value is treated differently upstream.&lt;/p&gt;
', NULL, NULL, '''/p'':44C,69C ''also'':48C ''case'':60C ''caus'':41C ''check'':50C ''code'':54C ''could'':40C ''d'':47C ''differ'':42C,67C ''doubl'':49C ''explan'':7C ''featur'':31C ''ignor'':33C ''librari'':12C ''like'':6C ''mayb'':61C ''miss'':63C ''one'':25C,34C ''p'':1C,45C ''pars'':53C ''perhap'':24C ''pmml'':19C ''quit'':15C ''score'':43C ''subset'':29C ''support'':16C,27C ''think'':3C ''treat'':66C ''treemodel'':17C ''two'':11C ''understand'':38C ''upstream'':52C,68C ''valu'':64C ''way'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2375, 2, '2014-10-30 07:31:03.903', 2, NULL, NULL, '&lt;p&gt;The problem arises if you want to classify a new example as either spam or not spam. A one-class method will only give you some score of how well a new instance fits to this class, but how do you turn this into a binary prediction without knowing how big the score would be for the other class?&lt;/p&gt;

&lt;p&gt;If you look at the Naive Bayes classifier it essentially trains a "one-class" model for each class and arrives at a prediction by choosing the class with the highest score. But this requires you to have training examples for all classes.&lt;/p&gt;
', NULL, NULL, '''/p'':62C,107C ''aris'':4C ''arriv'':84C ''bay'':70C ''big'':53C ''binari'':48C ''choos'':89C ''class'':22C,39C,61C,78C,82C,91C,106C ''classifi'':9C,71C ''either'':14C ''essenti'':73C ''exampl'':12C,103C ''fit'':36C ''give'':26C ''highest'':94C ''instanc'':35C ''know'':51C ''look'':66C ''method'':23C ''model'':79C ''naiv'':69C ''new'':11C,34C ''one'':21C,77C ''one-class'':20C,76C ''p'':1C,63C ''predict'':49C,87C ''problem'':3C ''requir'':98C ''score'':29C,55C,95C ''spam'':15C,18C ''train'':74C,102C ''turn'':44C ''want'':7C ''well'':32C ''without'':50C ''would'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2355, 1, '2014-10-27 09:55:36.887', 2, 453, 'Graphlab, plots are invisible', '&lt;p&gt;Sorry, if this topic is not connected direct to the Data Science...
I try to understand how the &lt;a href="http://graphlab.com/learn/gallery/index.html" rel="nofollow"&gt;Graphlab tool&lt;/a&gt; works. Firstly I want to execute the toy examples from Gallery site. When I try to execute the example code, everything is ok excepted one command: I can not see the graphlab plot after &lt;code&gt;show()&lt;/code&gt;. The command &lt;code&gt;show()&lt;/code&gt; return me some kind of object in IPython and nothing in IPython Notebook.
If the example code has the plot, which depends direct from matplotlib module, I can produce the real plots and save it on my machine. Consequently I suppose the main error is depended from the graphlab (or object from  its class). 
If somebody already used this tool and got the plot, can he/she tell me, how I can execute the plots command.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;In [8]: import graphlab

In [9]: from IPython.display import display

        from IPython.display import Image

        graphlab.canvas.set_target(''ipynb'')

In [10]:import urllib

        url = ''https://s3.amazonaws.com/GraphLab-Datasets/americanMovies   /freebase_performances.csv''

        urllib.urlretrieve(url, filename=''freebase_performances.csv'')  # downloads an 8MB file to the working directory

Out[10]: (''freebase_performances.csv'', &amp;lt;httplib.HTTPMessage instance at 0x7f44e153cf38&amp;gt;)

In [11]: data = graphlab.SFrame.read_csv(''remote://freebase_performances.csv'', column_type_hints={''year'': int})
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;...
...
...&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;In [15]:data.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;No plot after this line
...
...&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;In [19]:print data.show()

&amp;lt;IPython.core.display.Javascript object at 0x7f44e14c0850&amp;gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The object of graphlab (?) after print command&lt;/p&gt;
', 'graphs', NULL, '''/a'':34C ''/code'':71C,76C,213C,222C,243C ''/freebase_performances.csv'':180C ''/graphlab-datasets/americanmovies'':179C ''/learn/gallery/index.html'':29C ''/p'':152C,216C,230C,253C ''/pre'':214C,223C,244C ''0x7f44e14c0850'':241C ''0x7f44e153cf38'':200C ''10'':173C,194C ''11'':203C ''15'':220C ''19'':234C ''8'':156C ''8mb'':187C ''9'':160C ''alreadi'':133C ''class'':130C ''code'':54C,69C,74C,93C,154C,218C,232C ''column'':208C ''command'':60C,73C,151C,252C ''connect'':13C ''consequ'':115C ''csv'':206C ''data'':17C,204C ''data.show'':221C,236C ''depend'':98C,122C ''direct'':14C,99C ''directori'':192C ''display'':164C ''download'':185C ''error'':120C ''everyth'':55C ''exampl'':43C,53C,92C ''except'':58C ''execut'':40C,51C,148C ''file'':188C ''filenam'':183C ''first'':36C ''freebase_performances.csv'':184C,195C,207C ''galleri'':45C ''got'':138C ''graph'':5B ''graphlab'':1A,32C,66C,125C,158C,249C ''graphlab.canvas.set'':169C ''graphlab.com'':28C ''graphlab.com/learn/gallery/index.html'':27C ''graphlab.sframe.read'':205C ''gt'':201C,242C ''he/she'':142C ''hint'':210C ''href'':26C ''httplib.httpmessage'':197C ''imag'':168C ''import'':157C,163C,167C,174C ''instanc'':198C ''int'':212C ''invis'':4A ''ipynb'':171C ''ipython'':84C,88C ''ipython.core.display.javascript'':238C ''ipython.display'':162C,166C ''kind'':80C ''line'':229C ''lt'':196C,237C ''machin'':114C ''main'':119C ''matplotlib'':101C ''modul'':102C ''nofollow'':31C ''notebook'':89C ''noth'':86C ''object'':82C,127C,239C,247C ''ok'':57C ''one'':59C ''p'':6C,215C,224C,245C ''plot'':2A,67C,96C,108C,140C,150C,226C ''pre'':153C,217C,231C ''print'':235C,251C ''produc'':105C ''real'':107C ''rel'':30C ''return'':77C ''s3.amazonaws.com'':178C ''s3.amazonaws.com/graphlab-datasets/americanmovies'':177C ''save'':110C ''scienc'':18C ''see'':64C ''show'':70C,75C ''site'':46C ''somebodi'':132C ''sorri'':7C ''suppos'':117C ''target'':170C ''tell'':143C ''tool'':33C,136C ''topic'':10C ''toy'':42C ''tri'':20C,49C ''type'':209C ''understand'':22C ''url'':176C,182C ''urllib'':175C ''urllib.urlretrieve'':181C ''use'':134C ''want'':38C ''work'':35C,191C ''year'':211C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2356, 2, '2014-10-27 15:43:00.067', 0, NULL, NULL, '&lt;p&gt;OK, so values at time t-1 predict values at time t. That makes sense. &lt;/p&gt;

&lt;p&gt;First you should decide whether you think these values are independent or not. Do the x predict the y or z at all? And, do you think just the previous 1 value is predictive, or the previous n?&lt;/p&gt;

&lt;p&gt;Either way you could model this as a simple regression problem. What technique is best really depends on what you expect the relationship to be, and what these variables are; I am not sure that''s given here.&lt;/p&gt;

&lt;p&gt;For example if they''re sensor values read fairly rapidly, and the sensor changes slowly, you''d expect some simple model like a moving average to do well. For other types of values this would not be predictive at all.&lt;/p&gt;

&lt;p&gt;This looks like the Markov chain model, so you may look into that, but somehow I think it''s over-general for what I think the problem is.&lt;/p&gt;
', NULL, NULL, '''-1'':8C ''/p'':17C,57C,97C,139C,170C ''1'':49C ''averag'':123C ''best'':73C ''chain'':146C ''chang'':112C ''could'':62C ''d'':115C ''decid'':22C ''depend'':75C ''either'':59C ''exampl'':100C ''expect'':79C,116C ''fair'':107C ''first'':19C ''general'':162C ''given'':95C ''independ'':29C ''like'':120C,143C ''look'':142C,151C ''make'':15C ''markov'':145C ''may'':150C ''model'':63C,119C,147C ''move'':122C ''n'':56C ''ok'':2C ''over-gener'':160C ''p'':1C,18C,58C,98C,140C ''predict'':9C,35C,52C,136C ''previous'':48C,55C ''problem'':69C,168C ''rapid'':108C ''re'':103C ''read'':106C ''realli'':74C ''regress'':68C ''relationship'':81C ''sens'':16C ''sensor'':104C,111C ''simpl'':67C,118C ''slowli'':113C ''somehow'':155C ''sure'':92C ''techniqu'':71C ''think'':25C,45C,157C,166C ''time'':6C,12C ''type'':129C ''valu'':4C,10C,27C,50C,105C,131C ''variabl'':87C ''way'':60C ''well'':126C ''whether'':23C ''would'':133C ''x'':34C ''y'':37C ''z'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2358, 2, '2014-10-27 17:39:18.997', 0, NULL, NULL, '&lt;p&gt;It is classify test objects: "In classification, let R be the set of generated rules and T the training data. The basic idea of the proposed method is to choose a set of high confidence rules in R to cover T. In classifying a test object, the first rule in the set of rules that matches the test object condition classifies it. This process ensures that only the highest ranked rules classify test objects."&lt;/p&gt;

&lt;p&gt;Suppose 1 test case is (overcast, cool, normal, TRUE). Look through the rules top to bottom and see if any of the conditions are matched. The first rule for example tests the outlook feature. The value doesn''t match, so the rule isn''t matched. Move on to the next rule. And so on. In this case, rule 2 matches the test case and the classification for the play variable is "yes". The second test case is misclassified.&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', NULL, NULL, '''/p'':76C,156C,159C ''1'':79C ''2'':136C ''basic'':23C ''bottom'':93C ''case'':81C,134C,140C,153C ''choos'':31C ''classif'':8C,143C ''classifi'':4C,44C,62C,73C ''condit'':61C,100C ''confid'':36C ''cool'':84C ''cover'':41C ''data'':21C ''doesn'':114C ''ensur'':66C ''exampl'':107C ''featur'':111C ''first'':49C,104C ''generat'':15C ''high'':35C ''highest'':70C ''idea'':24C ''isn'':120C ''let'':9C ''look'':87C ''match'':57C,102C,116C,122C,137C ''method'':28C ''misclassifi'':155C ''move'':123C ''next'':127C ''normal'':85C ''object'':6C,47C,60C,75C ''outlook'':110C ''overcast'':83C ''p'':1C,77C,157C ''play'':146C ''process'':65C ''propos'':27C ''r'':10C,39C ''rank'':71C ''rule'':16C,37C,50C,55C,72C,90C,105C,119C,128C,135C ''second'':151C ''see'':95C ''set'':13C,33C,53C ''suppos'':78C ''test'':5C,46C,59C,74C,80C,108C,139C,152C ''thank'':158C ''top'':91C ''train'':20C ''true'':86C ''valu'':113C ''variabl'':147C ''yes'':149C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2394, 1, '2014-11-03 12:41:51.737', -1, 66, 'Best format for recording time stamp and GPS', '&lt;p&gt;In most data acquisition settings it is useful to tag your data with time and location. If I write the data to csv file, what are the best formats that I can use for this two variables if I want to create a heatmap on Google Maps? &lt;/p&gt;
', 'data-formats', NULL, '''/p'':60C ''acquisit'':16C ''best'':1A,40C ''creat'':54C ''csv'':35C ''data'':10B,15C,24C,33C ''data-format'':9B ''file'':36C ''format'':2A,11B,41C ''googl'':58C ''gps'':8A ''heatmap'':56C ''locat'':28C ''map'':59C ''p'':12C ''record'':4A ''set'':17C ''stamp'':6A ''tag'':22C ''time'':5A,26C ''two'':48C ''use'':20C,45C ''variabl'':49C ''want'':52C ''write'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2363, 2, '2014-10-28 06:27:40.09', 1, NULL, NULL, '&lt;p&gt;The difference was, it appears, due to the different implementation of Random Forests in &lt;code&gt;R&lt;/code&gt; and &lt;code&gt;Cascading Pattern&lt;/code&gt; (as well as &lt;code&gt;openscoring&lt;/code&gt; which I tried later) with respect to ties in the tree voting - i.e. when an even number of trees are built (say, 500) and exactly half classify an application as &lt;code&gt;Good&lt;/code&gt;, and the other half as &lt;code&gt;Bad&lt;/code&gt;, the handling of those situations differs. Solved it by growing and odd (501) number of trees.&lt;/p&gt;
', NULL, NULL, '''/code'':18C,23C,29C,62C,70C ''/p'':87C ''500'':52C ''501'':83C ''appear'':6C ''applic'':58C ''bad'':69C ''built'':50C ''cascad'':21C ''classifi'':56C ''code'':16C,20C,27C,60C,68C ''differ'':3C,10C,76C ''due'':7C ''even'':45C ''exact'':54C ''forest'':14C ''good'':61C ''grow'':80C ''half'':55C,66C ''handl'':72C ''i.e'':42C ''implement'':11C ''later'':33C ''number'':46C,84C ''odd'':82C ''openscor'':28C ''p'':1C ''pattern'':22C ''r'':17C ''random'':13C ''respect'':35C ''say'':51C ''situat'':75C ''solv'':77C ''tie'':37C ''tree'':40C,48C,86C ''tri'':32C ''vote'':41C ''well'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9218, 2, '2015-12-04 18:13:24.56', 1, NULL, NULL, '&lt;p&gt;See also &lt;a href="http://stackoverflow.com/questions/1793532/how-do-i-determine-k-when-using-k-means-clustering"&gt;this StackExchange post&lt;/a&gt;. The Schwarz criterion, or BIC, is very general, so the question is, "how does this formula apply the BIC to $k$-means?"&lt;/p&gt;

&lt;p&gt;The &lt;a href="https://en.wikipedia.org/wiki/Bayesian_information_criterion" rel="nofollow"&gt;Wikipedia page on Bayesian Information Criterion&lt;/a&gt; (BIC) uses this definition of BIC:&lt;/p&gt;

&lt;p&gt;$$BIC = -2 \ln \hat{L} + \kappa \ln {n}$$&lt;/p&gt;

&lt;p&gt;where:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;$n$ is the #observations&lt;/li&gt;
&lt;li&gt;$\kappa$ is the #free parameters (I use Greek $\kappa$ to distinguish from your $k$)&lt;/li&gt;
&lt;li&gt;$\hat{L}$ is the maximized likelihood after fitting parameters&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Pelleg and Moore (2000) use $R$ for the #observations.  Assuming your professor does to, that means:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;$\kappa = \lambda m k$, the # free parameters.  &lt;/li&gt;
&lt;li&gt;$W(C) = -2 \ln \hat{L}$&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So, what are $\lambda$ and $m$?  I can''t tell which is which, but it has to come out something like this:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;$k$ is the number of clusters [as you say]&lt;/li&gt;
&lt;li&gt;$\lambda$ is the number of dimensions (columns) in the data&lt;/li&gt;
&lt;li&gt;$m$ is the number of parameters to estimate for each dimension (2 for the usual Gaussian: mean and variance).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It makes sense that $W(C)$, the "within-class scatter" would be measured by log likelihood.  Note, $W(C)$ has to sum the log likelihood for all clusters.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2359, 1, '2014-10-27 22:17:31.497', 2, 58, 'Ranked tag recommendation for university courses', '&lt;p&gt;Our system allows an admin to manage a database of university courses. These courses have multiple fields, like the department, a title, and a description.&lt;/p&gt;

&lt;p&gt;I am adding the ability to add learning objectives to a course. To simplify the problem, let''s say that learning objectives are just tags. Courses can have more than one learning objective associated with them. So a course like CHEM 101 might have "chemistry", "technology", "science", and several others.&lt;/p&gt;

&lt;p&gt;Assuming I can reduce a course to a set of features, (using keywords/stemming/nlp, I suppose?), what kind of problem is this and what algorithm would you suggest? It seems very similar to a classification problem, but I want to provide a sorted list of suggestions with the most relevant at the top.&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/p'':37C,89C,143C ''101'':80C ''abil'':43C ''ad'':41C ''add'':45C ''admin'':16C ''algorithm'':114C ''allow'':14C ''associ'':72C ''assum'':91C ''chem'':79C ''chemistri'':83C ''classif'':10B,124C ''cours'':6A,23C,25C,50C,64C,77C,96C ''databas'':20C ''depart'':31C ''descript'':36C ''featur'':101C ''field'':28C ''keywords/stemming/nlp'':103C ''kind'':107C ''learn'':9B,46C,59C,70C ''let'':55C ''like'':29C,78C ''list'':133C ''machin'':8B ''machine-learn'':7B ''manag'':18C ''might'':81C ''multipl'':27C ''object'':47C,60C,71C ''one'':69C ''other'':88C ''p'':11C,38C,90C ''problem'':54C,109C,125C ''provid'':130C ''rank'':1A ''recommend'':3A ''reduc'':94C ''relev'':139C ''say'':57C ''scienc'':85C ''seem'':119C ''set'':99C ''sever'':87C ''similar'':121C ''simplifi'':52C ''sort'':132C ''suggest'':117C,135C ''suppos'':105C ''system'':13C ''tag'':2A,63C ''technolog'':84C ''titl'':33C ''top'':142C ''univers'':5A,22C ''use'':102C ''want'':128C ''would'':115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2360, 2, '2014-10-27 23:24:46.673', 4, NULL, NULL, '&lt;p&gt;For item-ratings type of data with the restriction that an item''s rating should be between 1 and 10 after transformation, I would suggest using a simple re-scaling, such that the item''s transformed rating $x_t$ is given by:&lt;/p&gt;

&lt;p&gt;$$x_t = 9\left(\frac{x_i - x_{min}}{x_{max} - x_{min}}\right) + 1$$&lt;/p&gt;

&lt;p&gt;where $x_{min}$ and $x_{max}$ are the minimum and maximum possible rating in the specific scale for the item, and $x_i$ is the item rating. &lt;/p&gt;

&lt;p&gt;In the case of the above scaling, the transformation applied is independent of the data. However, in the normalization, the transformation applied is dependent on the data (through mean and standard deviation), and might change as more data becomes available.&lt;/p&gt;

&lt;p&gt;Section 4.3 on page 30 of &lt;a href="http://www.dai-labor.de/fileadmin/files/publications/DiplomaThesisStephanSpiegel.pdf" rel="nofollow"&gt;this document&lt;/a&gt; shows other ways of normalizing in which your restriction (transforming to the same absolute scale) might not be preserved. &lt;/p&gt;
', NULL, NULL, '''/a'':151C ''/fileadmin/files/publications/diplomathesisstephanspiegel.pdf'':146C ''/p'':46C,63C,92C,134C,171C ''1'':20C,62C ''10'':22C ''30'':140C ''4.3'':137C ''9'':50C ''absolut'':165C ''appli'':103C,115C ''avail'':133C ''becom'':132C ''case'':96C ''chang'':128C ''data'':8C,108C,120C,131C ''depend'':117C ''deviat'':125C ''document'':150C ''frac'':52C ''given'':44C ''howev'':109C ''href'':143C ''independ'':105C ''item'':4C,14C,37C,84C,90C ''item-r'':3C ''left'':51C ''max'':58C,70C ''maximum'':75C ''mean'':122C ''might'':127C,167C ''min'':56C,60C,67C ''minimum'':73C ''nofollow'':148C ''normal'':112C,156C ''p'':1C,47C,64C,93C,135C ''page'':139C ''possibl'':76C ''preserv'':170C ''rate'':5C,16C,40C,77C,91C ''re'':32C ''re-scal'':31C ''rel'':147C ''restrict'':11C,160C ''right'':61C ''scale'':33C,81C,100C,166C ''section'':136C ''show'':152C ''simpl'':30C ''specif'':80C ''standard'':124C ''suggest'':27C ''transform'':24C,39C,102C,114C,161C ''type'':6C ''use'':28C ''way'':154C ''would'':26C ''www.dai-labor.de'':145C ''www.dai-labor.de/fileadmin/files/publications/diplomathesisstephanspiegel.pdf'':144C ''x'':41C,48C,53C,55C,57C,59C,66C,69C,86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2361, 2, '2014-10-27 23:56:11.853', 1, NULL, NULL, '&lt;p&gt;This will most likely turn out to be multiple binary classification problems. &lt;/p&gt;

&lt;p&gt;Instead of building just one classifier, you would have to build as many classifiers as there are tags. Each classifier''s task would be to predict whether a certain tag exists or not. A tag specific probability threshold can then be learnt by tuning over the precision-curve where course wise precision recall can be calculated. &lt;/p&gt;

&lt;p&gt;The percentile of the predicted probability for each tag can be used to obtain a ordered (sorted) list of tag suggestions. &lt;/p&gt;

&lt;p&gt;This way of converting a multi-class problem to multiple binary classification problems is known as binary relevance. While it is very efficient, it assumes that the labels themselves are independent. However, picking the probability threshold for each tag in order to optimize the course wise precision recall curve can lead to modeling dependence in the tags. &lt;/p&gt;
', NULL, NULL, '''/p'':14C,73C,96C,156C ''assum'':123C ''binari'':11C,109C,115C ''build'':18C,26C ''calcul'':72C ''certain'':44C ''class'':105C ''classif'':12C,110C ''classifi'':21C,29C,35C ''convert'':101C ''cours'':66C,143C ''curv'':64C,147C ''depend'':152C ''effici'':121C ''exist'':46C ''howev'':130C ''independ'':129C ''instead'':16C ''known'':113C ''label'':126C ''lead'':149C ''learnt'':57C ''like'':5C ''list'':92C ''mani'':28C ''model'':151C ''multi'':104C ''multi-class'':103C ''multipl'':10C,108C ''obtain'':88C ''one'':20C ''optim'':141C ''order'':90C,139C ''p'':1C,15C,74C,97C ''percentil'':76C ''pick'':131C ''precis'':63C,68C,145C ''precision-curv'':62C ''predict'':41C,79C ''probabl'':52C,80C,133C ''problem'':13C,106C,111C ''recal'':69C,146C ''relev'':116C ''sort'':91C ''specif'':51C ''suggest'':95C ''tag'':33C,45C,50C,83C,94C,137C,155C ''task'':37C ''threshold'':53C,134C ''tune'':59C ''turn'':6C ''use'':86C ''way'':99C ''whether'':42C ''wise'':67C,144C ''would'':23C,38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2610, 2, '2014-12-05 11:05:15.257', 1, NULL, NULL, '&lt;p&gt;for hadoop try &lt;a href="http://www.amazon.in/Hadoop-Action-Chuck-Lam/dp/8177228137?tag=googinhydr18418-21" rel="nofollow"&gt;hadoop in action&lt;/a&gt;  or &lt;a href="http://www.amazon.in/Hadoop-The-Definitive-Guide-White/dp/9350237563?tag=googinhydr18418-21" rel="nofollow"&gt;Hadoop: The Definitive Guide&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':15C,28C ''/hadoop-action-chuck-lam/dp/8177228137?tag=googinhydr18418-21'':9C ''/hadoop-the-definitive-guide-white/dp/9350237563?tag=googinhydr18418-21'':21C ''/p'':29C ''action'':14C ''definit'':26C ''guid'':27C ''hadoop'':3C,12C,24C ''href'':6C,18C ''nofollow'':11C,23C ''p'':1C ''rel'':10C,22C ''tri'':4C ''www.amazon.in'':8C,20C ''www.amazon.in/hadoop-action-chuck-lam/dp/8177228137?tag=googinhydr18418-21'':7C ''www.amazon.in/hadoop-the-definitive-guide-white/dp/9350237563?tag=googinhydr18418-21'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2362, 1, '2014-10-28 06:00:49.643', 2, 258, 'Neural Networks getting stuck at local optima', '&lt;p&gt;I''m training a NN with 8 features and 8000 training examples with a single output (0, 1) using the scipy.optimise CG algorithm and the results are somewhat inconsistent. The goal is to get the NN to be as ''precise'' as possible (recall doesn''t really matter too much) so I''ve set the threshold for y value quite high (0.75). Most of the time it gets a precision of around 80%, however sometimes it fails (using exactly the same parameters, lambda etc..) to generate any outputs which are above the 0.75 threshold, meaning the precision equals 0.&lt;/p&gt;

&lt;p&gt;I''ve successfully trained NNs before without these unusual results (albeit the goal was a somewhat more conventional multi-class classifier with many more features).&lt;/p&gt;

&lt;p&gt;I''m wondering if the training NNs with fewer features increases the chances of it getting stuck at a local optima; or getting stuck at local optima has a more significant impact on NNs with fewer features?&lt;/p&gt;

&lt;p&gt;Any thoughts on what''s going on!?&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/p'':111C,139C,178C,187C ''0'':29C,110C ''0.75'':73C,104C ''1'':30C ''8'':19C ''80'':84C ''8000'':22C ''albeit'':123C ''algorithm'':35C ''around'':83C ''cg'':34C ''chanc'':153C ''class'':133C ''classifi'':134C ''convent'':130C ''doesn'':56C ''equal'':109C ''etc'':95C ''exact'':90C ''exampl'':24C ''fail'':88C ''featur'':20C,138C,150C,177C ''fewer'':149C,176C ''generat'':97C ''get'':3A,46C,79C,156C,163C ''go'':185C ''goal'':43C,125C ''high'':72C ''howev'':85C ''impact'':172C ''inconsist'':41C ''increas'':151C ''lambda'':94C ''learn'':10B ''local'':6A,160C,166C ''m'':14C,142C ''machin'':9B ''machine-learn'':8B ''mani'':136C ''matter'':59C ''mean'':106C ''much'':61C ''multi'':132C ''multi-class'':131C ''network'':2A ''neural'':1A ''neuralnetwork'':11B ''nn'':17C,48C ''nns'':117C,147C,174C ''optima'':7A,161C,167C ''output'':28C,99C ''p'':12C,112C,140C,179C ''paramet'':93C ''possibl'':54C ''precis'':52C,81C,108C ''quit'':71C ''realli'':58C ''recal'':55C ''result'':38C,122C ''scipy.optimise'':33C ''set'':65C ''signific'':171C ''singl'':27C ''sometim'':86C ''somewhat'':40C,128C ''stuck'':4A,157C,164C ''success'':115C ''thought'':181C ''threshold'':67C,105C ''time'':77C ''train'':15C,23C,116C,146C ''unusu'':121C ''use'':31C,89C ''valu'':70C ''ve'':64C,114C ''without'':119C ''wonder'':143C ''y'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2433, 1, '2014-11-10 00:10:26.947', 2, 55, 'FUZZY ARTMAP for continuous data', '&lt;p&gt;I was going through an IEEE Research paper which has used Fuzzy ARTMAP for predicting the price of electricity given some highly correlated data. &lt;/p&gt;

&lt;p&gt;As per my basic understanding about Fuzzy ARTMAP it is a classification algorithm, so how will it be able to predict continuous data?  &lt;/p&gt;

&lt;p&gt;The text from research paper is:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;In the architecture of the FA network, the preprocessing stages take
  the input vector and contribute to produce complement coding, which
  avoids category proliferation, i.e., the creation of a relatively
  large number of categories to represent the training data. A sequence
  of input vectors (price and demand) and their respective target
  vectors are introduced to the FA network in order to classify the
  input pattern correctly. The classiï¬ed input patterns are then grouped
  into labels using membership functions.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I was using MATLAB to implement the same, so is there a library in MATLAB to approach towards the solution. &lt;/p&gt;
', 'classification  regression', NULL, '''/blockquote'':149C ''/p'':33C,58C,66C,148C,171C ''abl'':53C ''algorithm'':47C ''approach'':167C ''architectur'':71C ''artmap'':2A,21C,42C ''avoid'':90C ''basic'':38C ''blockquot'':67C ''categori'':91C,102C ''classif'':6B,46C ''classifi'':130C ''classiï'':136C ''code'':88C ''complement'':87C ''continu'':4A,56C ''contribut'':84C ''correct'':134C ''correl'':31C ''creation'':95C ''data'':5A,32C,57C,107C ''demand'':115C ''ed'':137C ''electr'':27C ''fa'':74C,125C ''function'':147C ''fuzzi'':1A,20C,41C ''given'':28C ''go'':11C ''group'':142C ''high'':30C ''i.e'':93C ''ieee'':14C ''implement'':156C ''input'':81C,111C,132C,138C ''introduc'':122C ''label'':144C ''larg'':99C ''librari'':163C ''matlab'':154C,165C ''membership'':146C ''network'':75C,126C ''number'':100C ''order'':128C ''p'':8C,34C,59C,68C,150C ''paper'':16C,64C ''pattern'':133C,139C ''per'':36C ''predict'':23C,55C ''preprocess'':77C ''price'':25C,113C ''produc'':86C ''prolifer'':92C ''regress'':7B ''relat'':98C ''repres'':104C ''research'':15C,63C ''respect'':118C ''sequenc'':109C ''solut'':170C ''stage'':78C ''take'':79C ''target'':119C ''text'':61C ''toward'':168C ''train'':106C ''understand'':39C ''use'':19C,145C,153C ''vector'':82C,112C,120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2364, 2, '2014-10-28 07:00:01.593', 0, NULL, NULL, '&lt;p&gt;Using unsupervised learning to reduce the dimensionality and then using supervised learning to obtain an accurate predictive model is commonly used. See for example &lt;a href="http://faculty.ucmerced.edu/hbhat/BhatZaelit2012.pdf" rel="nofollow"&gt;Bhat and Zaelit, 2012&lt;/a&gt; where they first use PCA to reduce the dimension of a problem from 87 to 35. Then, they use L1 regression to obtain the best predictive model. This method beats non-linear tree based models built on the entire dataset and also its subset. &lt;/p&gt;

&lt;p&gt;If your goal is to create more accurate classification of data into clusters, then a commonly used technique is to use supervised learning as a method to accurately pick the number of clusters &lt;a href="http://jmlr.csail.mit.edu/papers/volume14/pan13a/pan13a.pdf" rel="nofollow"&gt;see Pan et al, 2013&lt;/a&gt; for a recent example. The basic approach here is to choose the number of clusters such that a supervised multi-class method can learn these clusters and predict the clusters with the highest out of sample accuracy. This is one way to convince yourself that the clusters are both meaningful and predictable. &lt;/p&gt;

&lt;p&gt;Another approach, if your goal is to classify documents as being from US/ USA/ Australia or for that matter discussing, soccer/ American football/ Australian football could be to solve three binary classification problems that independently predict if the document talks about soccer, American football or Australian football. Combining the results from these three classifiers (known as binary relevance), you could also have the ability of tagging a document as both soccer or American football or any combination of the above three tags. &lt;/p&gt;
', NULL, NULL, '''/a'':37C,130C ''/hbhat/bhatzaelit2012.pdf'':30C ''/p'':83C,184C,268C ''/papers/volume14/pan13a/pan13a.pdf'':122C ''2012'':36C ''2013'':129C ''35'':53C ''87'':51C ''abil'':249C ''accur'':17C,92C,112C ''accuraci'':168C ''al'':128C ''also'':80C,246C ''american'':207C,228C,258C ''anoth'':186C ''approach'':137C,187C ''australia'':200C ''australian'':209C,231C ''base'':72C ''basic'':136C ''beat'':67C ''best'':62C ''bhat'':33C ''binari'':216C,242C ''built'':74C ''choos'':141C ''class'':152C ''classif'':93C,217C ''classifi'':193C,239C ''cluster'':97C,117C,145C,157C,161C,178C ''combin'':233C,262C ''common'':21C,100C ''convinc'':174C ''could'':211C,245C ''creat'':90C ''data'':95C ''dataset'':78C ''dimens'':46C ''dimension'':8C ''discuss'':205C ''document'':194C,224C,253C ''entir'':77C ''et'':127C ''exampl'':25C,134C ''faculty.ucmerced.edu'':29C ''faculty.ucmerced.edu/hbhat/bhatzaelit2012.pdf'':28C ''first'':40C ''footbal'':208C,210C,229C,232C,259C ''goal'':87C,190C ''highest'':164C ''href'':27C,119C ''independ'':220C ''jmlr.csail.mit.edu'':121C ''jmlr.csail.mit.edu/papers/volume14/pan13a/pan13a.pdf'':120C ''known'':240C ''l1'':57C ''learn'':4C,13C,107C,155C ''linear'':70C ''matter'':204C ''meaning'':181C ''method'':66C,110C,153C ''model'':19C,64C,73C ''multi'':151C ''multi-class'':150C ''nofollow'':32C,124C ''non'':69C ''non-linear'':68C ''number'':115C,143C ''obtain'':15C,60C ''one'':171C ''p'':1C,84C,185C ''pan'':126C ''pca'':42C ''pick'':113C ''predict'':18C,63C,159C,183C,221C ''problem'':49C,218C ''recent'':133C ''reduc'':6C,44C ''regress'':58C ''rel'':31C,123C ''relev'':243C ''result'':235C ''sampl'':167C ''see'':23C,125C ''soccer'':206C,227C,256C ''solv'':214C ''subset'':82C ''supervis'':12C,106C,149C ''tag'':251C,267C ''talk'':225C ''techniqu'':102C ''three'':215C,238C,266C ''tree'':71C ''unsupervis'':3C ''us'':198C ''usa'':199C ''use'':2C,11C,22C,41C,56C,101C,105C ''way'':172C ''zaelit'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2366, 2, '2014-10-28 17:07:32.723', 1, NULL, NULL, '&lt;p&gt;You are altering weights in the wrong direction for the negative cases.&lt;/p&gt;

&lt;p&gt;The line&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;w = w + eta*Xtr(j,:)'';
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;should be&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;w = w + eta*Xtr(j,:)''*ytr(j);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;With that change I got 12% error.&lt;/p&gt;
', NULL, NULL, '''/code'':26C,41C ''/p'':14C,18C,31C,51C ''/pre'':27C,42C ''12'':49C ''alter'':4C ''case'':13C ''chang'':46C ''code'':20C,33C ''direct'':9C ''error'':50C ''eta'':23C,36C ''got'':48C ''j'':25C,38C,40C ''line'':17C ''negat'':12C ''p'':1C,15C,28C,43C ''pre'':19C,32C ''w'':21C,22C,34C,35C ''weight'':5C ''wrong'':8C ''xtr'':24C,37C ''ytr'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2367, 2, '2014-10-29 00:52:06.097', 3, NULL, NULL, '&lt;p&gt;Instead of "recursive neural nets with back propagation" you might consider the approach used by Frantzi, et. al. at National Centre for Text Mining (NaCTeM) at University of Manchester for &lt;em&gt;Termine&lt;/em&gt; (see: &lt;a href="http://www.nactem.ac.uk/index.php" rel="nofollow"&gt;http://www.nactem.ac.uk/index.php&lt;/a&gt; and &lt;a href="http://personalpages.manchester.ac.uk/staff/sophia.ananiadou/IJODL2000.pdf" rel="nofollow"&gt;http://personalpages.manchester.ac.uk/staff/sophia.ananiadou/IJODL2000.pdf&lt;/a&gt;) Instead of deep neural nets, they "combine linguistic and statistical information".&lt;/p&gt;
', NULL, NULL, '''/em'':34C ''/index.php'':40C ''/index.php&lt;/a&gt;'':45C ''/p'':68C ''/staff/sophia.ananiadou/ijodl2000.pdf'':51C ''/staff/sophia.ananiadou/ijodl2000.pdf&lt;/a&gt;)'':56C ''al'':19C ''approach'':14C ''back'':8C ''centr'':22C ''combin'':63C ''consid'':12C ''deep'':59C ''em'':32C ''et'':18C ''frantzi'':17C ''href'':37C,48C ''inform'':67C ''instead'':2C,57C ''linguist'':64C ''manchest'':30C ''might'':11C ''mine'':25C ''nactem'':26C ''nation'':21C ''net'':6C,61C ''neural'':5C,60C ''nofollow'':42C,53C ''p'':1C ''personalpages.manchester.ac.uk'':50C,55C ''personalpages.manchester.ac.uk/staff/sophia.ananiadou/ijodl2000.pdf'':49C ''personalpages.manchester.ac.uk/staff/sophia.ananiadou/ijodl2000.pdf&lt;/a&gt;)'':54C ''propag'':9C ''recurs'':4C ''rel'':41C,52C ''see'':35C ''statist'':66C ''termin'':33C ''text'':24C ''univers'':28C ''use'':15C ''www.nactem.ac.uk'':39C,44C ''www.nactem.ac.uk/index.php'':38C ''www.nactem.ac.uk/index.php&lt;/a&gt;'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2368, 1, '2014-10-29 05:25:55.603', 12, 1157, 'Machine learning - features engineering from date/time data', '&lt;p&gt;What are the common/best practices to handle time data for machine learning application?&lt;/p&gt;

&lt;p&gt;For example, if in data set there is a column with timestamp of event, such as "2014-05-05", how you can extract useful features from this column if any?&lt;/p&gt;

&lt;p&gt;Thanks in advance!&lt;/p&gt;
', 'machine-learning  time-series  feature-selection', NULL, '''-05'':50C,51C ''/p'':31C,63C,68C ''2014'':49C ''advanc'':67C ''applic'':30C ''column'':42C,60C ''common/best'':21C ''data'':7A,26C,37C ''date/time'':6A ''engin'':4A ''event'':46C ''exampl'':34C ''extract'':55C ''featur'':3A,15B,57C ''feature-select'':14B ''handl'':24C ''learn'':2A,10B,29C ''machin'':1A,9B,28C ''machine-learn'':8B ''p'':17C,32C,64C ''practic'':22C ''select'':16B ''seri'':13B ''set'':38C ''thank'':65C ''time'':12B,25C ''time-seri'':11B ''timestamp'':44C ''use'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2369, 2, '2014-10-29 07:52:41.06', 3, NULL, NULL, '&lt;p&gt;In several cases data and events inside a time series are seasonal. In such cases the month and the year of the event matters alot. Hence in such scenarios you can use binary variables to represent if the event is during a given month/year or not.&lt;/p&gt;

&lt;p&gt;Hope this answers your question. If not kindly be a little more specific on what exactly are you trying to achieve  &lt;/p&gt;
', NULL, NULL, '''/p'':48C,71C ''achiev'':70C ''alot'':26C ''answer'':52C ''binari'':34C ''case'':4C,16C ''data'':5C ''event'':7C,24C,40C ''exact'':65C ''given'':44C ''henc'':27C ''hope'':50C ''insid'':8C ''kind'':57C ''littl'':60C ''matter'':25C ''month'':18C ''month/year'':45C ''p'':1C,49C ''question'':54C ''repres'':37C ''scenario'':30C ''season'':13C ''seri'':11C ''sever'':3C ''specif'':62C ''time'':10C ''tri'':68C ''use'':33C ''variabl'':35C ''year'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2426, 1, '2014-11-07 21:36:00.08', 2, 70, 'Validity of data', '&lt;p&gt;I have a data set that is pivoted in to the following format:&lt;/p&gt;

&lt;p&gt;[key] [id] [0] [1] [5] [10] [15] [60] [120] [180],.. [365]&lt;/p&gt;

&lt;p&gt;So key could be&lt;/p&gt;

&lt;p&gt;[Products] [1000] [15,000] [4000]... etc&lt;/p&gt;

&lt;p&gt;Where products is the category of item being reviewed and key is the identifier for the product; the only fields (0, 1,... 180,.. [365]) are individual daily samples identify how many of "x" product were logged as either sold, in-stock etc.&lt;/p&gt;

&lt;p&gt;What I need to do is perform some kind of analysis on an entire slew of products and their inventory levels. i.e. each import of data I need to make sure the incoming data is accurate or predictably accurate and that some human did not typo a stock level. The problem is, using a simple average or rolling average can introduce significant variance and smoothing out the average renders my analysis less reliable. Ideally this analysis would trigger an alarm that someone would have to investigate.&lt;/p&gt;

&lt;p&gt;Is there a better and more accurate way of performing this analysis?&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'machine-learning  statistics', NULL, '''/p'':22C,35C,41C,49C,94C,182C,196C,199C ''0'':26C,71C ''000'':46C ''1'':27C,72C ''10'':29C ''1000'':44C ''120'':32C ''15'':30C,45C ''180'':33C,73C ''365'':34C,74C ''4000'':47C ''5'':28C ''60'':31C ''accur'':131C,134C,190C ''alarm'':175C ''analysi'':106C,166C,171C,195C ''averag'':151C,154C,163C ''better'':187C ''categori'':55C ''could'':39C ''daili'':77C ''data'':3A,12C,121C,129C ''either'':88C ''entir'':109C ''etc'':48C,93C ''field'':70C ''follow'':20C ''format'':21C ''human'':138C ''i.e'':117C ''id'':25C ''ideal'':169C ''identifi'':64C,79C ''import'':119C ''in-stock'':90C ''incom'':128C ''individu'':76C ''introduc'':156C ''inventori'':115C ''investig'':181C ''item'':57C ''key'':24C,38C,61C ''kind'':104C ''learn'':6B ''less'':167C ''level'':116C,144C ''log'':86C ''machin'':5B ''machine-learn'':4B ''make'':125C ''mani'':81C ''need'':98C,123C ''p'':8C,23C,36C,42C,50C,95C,183C,197C ''perform'':102C,193C ''pivot'':16C ''predict'':133C ''problem'':146C ''product'':43C,52C,67C,84C,112C ''reliabl'':168C ''render'':164C ''review'':59C ''roll'':153C ''sampl'':78C ''set'':13C ''signific'':157C ''simpl'':150C ''slew'':110C ''smooth'':160C ''sold'':89C ''someon'':177C ''statist'':7B ''stock'':92C,143C ''sure'':126C ''thank'':198C ''trigger'':173C ''typo'':141C ''use'':148C ''valid'':1A ''varianc'':158C ''way'':191C ''would'':172C,178C ''x'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2370, 2, '2014-10-29 13:54:23.793', 20, NULL, NULL, '&lt;p&gt;I would start by graphing the time variable vs other variables and looking for trends.  &lt;/p&gt;

&lt;h2&gt;For example&lt;/h2&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/QGYUC.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;In this case there is a periodic weekly trend and a long term upwards trend.  So you would want to encode two time variables:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;day_of_week&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;absolute_time&lt;/code&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;h2&gt;In general&lt;/h2&gt;

&lt;p&gt;There are several common time frames that trends occur over:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;absolute_time&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;day_of_year&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;day_of_week&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;month_of_year&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;hour_of_day&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;minute_of_hour&lt;/code&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Look for trends in all of these.  &lt;/p&gt;

&lt;h2&gt;Weird trends&lt;/h2&gt;

&lt;p&gt;Look for weird trends too.  For example you may see rare but persistent time based trends:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;is_easter&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;is_superbowl&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;is_national_emergency&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;etc.&lt;/code&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;These often require that you cross reference your data against some external source that maps events to time.  &lt;/p&gt;

&lt;h2&gt;Why graph?&lt;/h2&gt;

&lt;p&gt;There are two reasons that I think graphing is so important.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;Weird trends&lt;/em&gt;&lt;br&gt;
While the general trends can be automated pretty easily (just add them
every time), weird trends will often require a human eye and knowledge
of the world to find.  This is one reason that graphing is so
important.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;Data errors&lt;/em&gt;&lt;br&gt;
All too often data has serious errors in it.  For example, you may find that the dates were encoded in two formats and only one of them has been correctly loaded into your program.  There are a myriad of such problems and they are surprisingly common.  This is the other reason I think graphing is important, not just for time series, but for any data. &lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/code'':66C,72C,96C,103C,110C,117C,124C,131C,170C,176C,183C,188C ''/em'':234C,281C ''/h2'':21C,78C,146C,214C ''/li'':67C,73C,97C,104C,111C,118C,125C,132C,171C,177C,184C,189C,275C,349C ''/p'':17C,33C,59C,90C,142C,164C,210C,227C,274C,348C ''/qgyuc.png'':27C ''/ul'':74C,133C,190C,350C ''absolut'':70C,94C ''add'':246C ''alt'':28C ''autom'':242C ''base'':162C ''br'':235C,282C ''case'':37C ''code'':62C,69C,93C,99C,106C,113C,120C,127C,167C,173C,179C,186C ''common'':83C,328C ''correct'':312C ''cross'':197C ''data'':200C,279C,286C,347C ''date'':299C ''day'':63C,100C,107C,123C ''descript'':31C ''easili'':244C ''easter'':169C ''em'':231C,278C ''emerg'':182C ''encod'':55C,301C ''enter'':29C ''error'':280C,289C ''etc'':187C ''event'':207C ''everi'':248C ''exampl'':20C,154C,293C ''extern'':203C ''eye'':257C ''find'':264C,296C ''format'':304C ''frame'':85C ''general'':77C,238C ''graph'':6C,213C,223C,270C,336C ''h2'':18C,75C,143C,211C ''hour'':121C,130C ''human'':256C ''i.stack.imgur.com'':26C ''i.stack.imgur.com/qgyuc.png'':25C ''imag'':30C ''img'':23C ''import'':226C,273C,338C ''knowledg'':259C ''li'':61C,68C,92C,98C,105C,112C,119C,126C,166C,172C,178C,185C,229C,276C ''load'':313C ''long'':46C ''look'':14C,135C,148C ''map'':206C ''may'':156C,295C ''minut'':128C ''month'':114C ''myriad'':320C ''nation'':181C ''occur'':88C ''often'':193C,253C,285C ''one'':267C,307C ''p'':1C,22C,34C,79C,134C,147C,191C,215C,230C,277C ''period'':41C ''persist'':160C ''pretti'':243C ''problem'':323C ''program'':316C ''rare'':158C ''reason'':219C,268C,333C ''refer'':198C ''requir'':194C,254C ''see'':157C ''seri'':343C ''serious'':288C ''sever'':82C ''sourc'':204C ''src'':24C ''start'':4C ''superbowl'':175C ''surpris'':327C ''term'':47C ''think'':222C,335C ''time'':8C,57C,71C,84C,95C,161C,209C,249C,342C ''trend'':16C,43C,49C,87C,137C,145C,151C,163C,233C,239C,251C ''two'':56C,218C,303C ''ul'':60C,91C,165C,228C ''upward'':48C ''variabl'':9C,12C,58C ''vs'':10C ''want'':53C ''week'':42C,65C,109C ''weird'':144C,150C,232C,250C ''world'':262C ''would'':3C,52C ''year'':102C,116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2424, 2, '2014-11-07 08:09:40.26', 0, NULL, NULL, '&lt;p&gt;I would recommend those materials:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Python
&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1449319793" rel="nofollow"&gt;Python for Data Analysis&lt;/a&gt; - book which nicely covers Pandas workflow with IPython.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;R
&lt;ul&gt;
&lt;li&gt;&lt;a href="https://www.coursera.org/specialization/jhudatascience/1?utm_medium=listingPage" rel="nofollow"&gt;Coursera Data Science Specialisation&lt;/a&gt; - free nine one month time consuming courses which introduces R step by step from beginning to machine learning.&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.datacamp.com/courses" rel="nofollow"&gt;Data Camp courses&lt;/a&gt; - at least 4 free courses covering topics from data analysis.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':24C,51C,82C ''/amzn/click/1449319793'':17C ''/courses'':76C ''/li'':33C,35C,70C,93C,95C ''/p'':7C ''/specialization/jhudatascience/1?utm_medium=listingpage'':44C ''/ul'':34C,94C,96C ''4'':85C ''analysi'':23C,92C ''begin'':66C ''book'':25C ''camp'':80C ''consum'':57C ''cours'':58C,81C,87C ''coursera'':47C ''cover'':28C,88C ''data'':22C,48C,79C,91C ''free'':52C,86C ''href'':14C,41C,73C ''introduc'':60C ''ipython'':32C ''learn'':69C ''least'':84C ''li'':9C,12C,36C,39C,71C ''machin'':68C ''materi'':6C ''month'':55C ''nice'':27C ''nine'':53C ''nofollow'':19C,46C,78C ''one'':54C ''p'':1C ''panda'':29C ''python'':10C,20C ''r'':37C,61C ''rads.stackoverflow.com'':16C ''rads.stackoverflow.com/amzn/click/1449319793'':15C ''recommend'':4C ''rel'':18C,45C,77C ''scienc'':49C ''specialis'':50C ''step'':62C,64C ''time'':56C ''topic'':89C ''ul'':8C,11C,38C ''workflow'':30C ''would'':3C ''www.coursera.org'':43C ''www.coursera.org/specialization/jhudatascience/1?utm_medium=listingpage'':42C ''www.datacamp.com'':75C ''www.datacamp.com/courses'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2626, 2, '2014-12-05 20:13:17.793', 0, NULL, NULL, '&lt;p&gt;If you are willing to pay for a vendor solution, &lt;a href="http://www.teradata.com/" rel="nofollow"&gt;Teradata&lt;/a&gt; is designed to solve the problem of large scale joins with low latency.&lt;/p&gt;
', NULL, NULL, '''/a'':18C ''/p'':32C ''design'':20C ''href'':13C ''join'':28C ''larg'':26C ''latenc'':31C ''low'':30C ''nofollow'':16C ''p'':1C ''pay'':7C ''problem'':24C ''rel'':15C ''scale'':27C ''solut'':11C ''solv'':22C ''teradata'':17C ''vendor'':10C ''will'':5C ''www.teradata.com'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2371, 2, '2014-10-29 13:58:55.653', 1, NULL, NULL, '&lt;p&gt;I have found some solution and will post it here, because somebody, who works with graphlab, can have the same question.&lt;/p&gt;

&lt;p&gt;We can look at the example here: &lt;a href="http://graphlab.com/learn/gallery/notebooks/graph_analytics_movies.html" rel="nofollow"&gt;Six degrees of Kevin Bacon&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;At te beginning of the program execution you need to run next command:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;graphlab.canvas.set_target(''ipynb'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Exactly this is a key of the whole problem (at least by me:-)&lt;/p&gt;

&lt;p&gt;At the beginning it is important to know, which parameter of &lt;code&gt;set_target()&lt;/code&gt; command you want to execute. You can use two options for argument of this command:  &lt;code&gt;''ipynb''&lt;/code&gt; (which is executed direct in iPython Notebook, like in example) or &lt;code&gt;''browser''&lt;/code&gt; (which open the new window with the plots)
On my machine 64-bit, Ubuntu, I can not use the command &lt;code&gt;''ipynb''&lt;/code&gt;. Only the &lt;code&gt;''browser''&lt;/code&gt;-command get me the plot back. I don''t think, it is necessary to change https to http, but you can do it anyway. We have also the other machine by us (32-bit, Ubuntu) and it executes the other command &lt;code&gt;''ipynb''&lt;/code&gt;, but not &lt;code&gt;''browser''&lt;/code&gt; (without to change https to http)&lt;/p&gt;
', NULL, NULL, '''/a'':44C ''/code'':66C,98C,116C,130C,153C,158C,202C,207C ''/learn/gallery/notebooks/graph_analytics_movies.html'':36C ''/p'':23C,45C,60C,82C,214C ''/pre'':67C ''32'':191C ''64'':142C ''also'':185C ''anyway'':182C ''argument'':110C ''back'':164C ''bacon'':43C ''begin'':49C,86C ''bit'':143C,192C ''browser'':129C,157C,206C ''chang'':173C,210C ''code'':62C,95C,114C,128C,151C,156C,200C,205C ''command'':59C,99C,113C,150C,159C,199C ''degre'':40C ''direct'':120C ''exact'':69C ''exampl'':30C,126C ''execut'':53C,103C,119C,196C ''found'':4C ''get'':160C ''graphlab'':17C ''graphlab.canvas.set'':63C ''graphlab.com'':35C ''graphlab.com/learn/gallery/notebooks/graph_analytics_movies.html'':34C ''href'':33C ''http'':176C,213C ''https'':174C,211C ''import'':89C ''ipynb'':65C,115C,152C,201C ''ipython'':122C ''kevin'':42C ''key'':73C ''know'':91C ''least'':79C ''like'':124C ''look'':27C ''machin'':141C,188C ''necessari'':171C ''need'':55C ''new'':134C ''next'':58C ''nofollow'':38C ''notebook'':123C ''open'':132C ''option'':108C ''p'':1C,24C,46C,68C,83C ''paramet'':93C ''plot'':138C,163C ''post'':9C ''pre'':61C ''problem'':77C ''program'':52C ''question'':22C ''rel'':37C ''run'':57C ''set'':96C ''six'':39C ''solut'':6C ''somebodi'':13C ''target'':64C,97C ''te'':48C ''think'':168C ''two'':107C ''ubuntu'':144C,193C ''us'':190C ''use'':106C,148C ''want'':101C ''whole'':76C ''window'':135C ''without'':208C ''work'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2372, 2, '2014-10-29 14:37:00.91', -1, NULL, NULL, '&lt;p&gt;Decision Trees are unstable learners and very sensitive to changes in the input parameters. &lt;/p&gt;
', NULL, NULL, '''/p'':16C ''chang'':11C ''decis'':2C ''input'':14C ''learner'':6C ''p'':1C ''paramet'':15C ''sensit'':9C ''tree'':3C ''unstabl'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2373, 1, '2014-10-29 21:57:15.603', 3, 121, 'Detecting Spam using Machine Learning', '&lt;p&gt;The most online tutorials like to use a simple example to introduce to machine learning by classify unknown text in spam or not spam. They say that this is a binary-class problem. But why is this a binary-class problem? I think it is a one-class problem! I do only need positive samples of my inbox to learn what is not spam. If I do take a bunch of not spam textes as positiv samples and a bunch of spam-mails as negativ samples, then of course it''s possible to train a binary-classifier and make predictions from unlabeled data, but where is the difference to the onc-class-approach? There I would just define a training-set of all non spam examples and train some one-class classifier. What do you think?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':154C ''approach'':128C ''binari'':41C,50C,110C ''binary-class'':40C,49C ''binary-classifi'':109C ''bunch'':82C,92C ''class'':42C,51C,60C,127C,148C ''classifi'':26C,111C,149C ''cours'':102C ''data'':117C ''defin'':133C ''detect'':1A ''differ'':122C ''exampl'':19C,142C ''inbox'':70C ''introduc'':21C ''learn'':5A,8B,24C,72C ''like'':14C ''machin'':4A,7B,23C ''machine-learn'':6B ''mail'':96C ''make'':113C ''need'':65C ''negativ'':98C ''non'':140C ''onc'':126C ''onc-class-approach'':125C ''one'':59C,147C ''one-class'':58C,146C ''onlin'':12C ''p'':9C ''posit'':66C ''positiv'':88C ''possibl'':105C ''predict'':114C ''problem'':43C,52C,61C ''sampl'':67C,89C,99C ''say'':35C ''set'':137C ''simpl'':18C ''spam'':2A,30C,33C,76C,85C,95C,141C ''spam-mail'':94C ''take'':80C ''text'':28C,86C ''think'':54C,153C ''train'':107C,136C,144C ''training-set'':135C ''tutori'':13C ''unknown'':27C ''unlabel'':116C ''use'':3A,16C ''would'':131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2374, 2, '2014-10-29 22:10:53.57', 0, NULL, NULL, '&lt;p&gt;Your training data needs to be one set of data with samples of all the categories, because you are trying (I think) to create a model that will be fed such data.&lt;/p&gt;

&lt;p&gt;Have you given any thoughts as to what model(s) you might be using? I''m asking because pure classification models will achieve a better fit if the amount of data in each class is pretty uniform in the training data. However, regression models need the data type proportions to match the expected input.&lt;/p&gt;
', NULL, NULL, '''/p'':34C,90C ''achiev'':58C ''amount'':64C ''ask'':52C ''better'':60C ''categori'':17C ''class'':69C ''classif'':55C ''creat'':25C ''data'':4C,11C,33C,66C,76C,82C ''expect'':88C ''fed'':31C ''fit'':61C ''given'':38C ''howev'':77C ''input'':89C ''m'':51C ''match'':86C ''might'':47C ''model'':27C,44C,56C,79C ''need'':5C,80C ''one'':8C ''p'':1C,35C ''pretti'':71C ''proport'':84C ''pure'':54C ''regress'':78C ''sampl'':13C ''set'':9C ''think'':23C ''thought'':40C ''train'':3C,75C ''tri'':21C ''type'':83C ''uniform'':72C ''use'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2376, 2, '2014-10-30 14:48:34.907', 5, NULL, NULL, '&lt;p&gt;Strictly speaking, "one class classification" does not make sense as an idea. If there is only one possible state of a predicted value, then there is no prediction problem. The answer is always the single class.&lt;/p&gt;

&lt;p&gt;Concretely, if you only have spam examples, you would always achieve 100% accuracy by classifying all email as spam. This is clearly wrong, and the only way to know how it is wrong is to know where the classification is wrong -- where emails are not in the spam class.&lt;/p&gt;

&lt;p&gt;So-called &lt;a href="http://en.wikipedia.org/wiki/One-class_classification"&gt;one-class classification&lt;/a&gt; techniques are really anomaly detection approaches. They have an implicit assumption that things unlike the examples are not part of the single class, but, this is just an assumption about data being probably &lt;em&gt;not&lt;/em&gt; within the class. There''s a binary classification problem lurking in there.&lt;/p&gt;

&lt;p&gt;What is wrong with a binary classifier?&lt;/p&gt;
', NULL, NULL, '''/a'':103C ''/em'':139C ''/p'':38C,89C,152C,161C ''/wiki/one-class_classification'':98C ''100'':51C ''accuraci'':52C ''achiev'':50C ''alway'':34C,49C ''anomali'':107C ''answer'':32C ''approach'':109C ''assumpt'':114C,132C ''binari'':146C,159C ''call'':93C ''class'':5C,37C,88C,101C,126C,142C ''classif'':6C,78C,102C,147C ''classifi'':54C,160C ''clear'':61C ''concret'':40C ''data'':134C ''detect'':108C ''em'':137C ''email'':56C,82C ''en.wikipedia.org'':97C ''en.wikipedia.org/wiki/one-class_classification'':96C ''exampl'':46C,119C ''href'':95C ''idea'':13C ''implicit'':113C ''know'':68C,75C ''lurk'':149C ''make'':9C ''one'':4C,18C,100C ''one-class'':99C ''p'':1C,39C,90C,153C ''part'':122C ''possibl'':19C ''predict'':23C,29C ''probabl'':136C ''problem'':30C,148C ''realli'':106C ''sens'':10C ''singl'':36C,125C ''so-cal'':91C ''spam'':45C,58C,87C ''speak'':3C ''state'':20C ''strict'':2C ''techniqu'':104C ''thing'':116C ''unlik'':117C ''valu'':24C ''way'':66C ''within'':140C ''would'':48C ''wrong'':62C,72C,80C,156C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2377, 2, '2014-10-30 14:59:13.243', 1, NULL, NULL, '&lt;p&gt;Basically your machine-learning problem is:
given [day-of-week, weather, departure-time, route], predict arrival time, then obtain travel time. Once your model is solid, you can then predict your travel time for each potential departure time and route, and choose the lowest.&lt;/p&gt;

&lt;p&gt;You don''t really need to factor in the pit stops, if you think they''re a function of the rest, or a random variable altogether.&lt;/p&gt;

&lt;p&gt;If you want a tool that can do the machine-learning aspect ''out of the box'' you should try Weka.
You''ll have to encode the data in the specific format it expects, but other than that you won''t have to do any coding (i.e. you won''t need to code any of the actual machine-learning algorithms).&lt;/p&gt;

&lt;p&gt;I would discretize your departure time to make sure you have enough data.
Weka will let you try out the different algorithms, see which one is best.
Note that it''s a regression problem as opposed to a classification problem, so only a subset of the algorithms apply. 
Once you''ve got the Weka part figured out, you can also call it programmatically, which will allow you to code the interface you want, and potentially include the automatic weather retrieval. &lt;/p&gt;
', NULL, NULL, '''/p'':49C,77C,140C,222C ''actual'':135C ''algorithm'':139C,163C,188C ''allow'':207C ''also'':201C ''altogeth'':76C ''appli'':189C ''arriv'':20C ''aspect'':91C ''automat'':219C ''basic'':2C ''best'':168C ''box'':95C ''call'':202C ''choos'':46C ''classif'':180C ''code'':124C,131C,210C ''data'':106C,154C ''day'':11C ''day-of-week'':10C ''departur'':16C,41C,146C ''departure-tim'':15C ''differ'':162C ''discret'':144C ''encod'':104C ''enough'':153C ''expect'':112C ''factor'':57C ''figur'':197C ''format'':110C ''function'':68C ''given'':9C ''got'':193C ''i.e'':125C ''includ'':217C ''interfac'':212C ''learn'':6C,90C,138C ''let'':157C ''ll'':101C ''lowest'':48C ''machin'':5C,89C,137C ''machine-learn'':4C,88C,136C ''make'':149C ''model'':28C ''need'':55C,129C ''note'':169C ''obtain'':23C ''one'':166C ''oppos'':177C ''p'':1C,50C,78C,141C ''part'':196C ''pit'':60C ''potenti'':40C,216C ''predict'':19C,34C ''problem'':7C,175C,181C ''programmat'':204C ''random'':74C ''re'':66C ''realli'':54C ''regress'':174C ''rest'':71C ''retriev'':221C ''rout'':18C,44C ''see'':164C ''solid'':30C ''specif'':109C ''stop'':61C ''subset'':185C ''sure'':150C ''think'':64C ''time'':17C,21C,25C,37C,42C,147C ''tool'':83C ''travel'':24C,36C ''tri'':98C,159C ''variabl'':75C ''ve'':192C ''want'':81C,214C ''weather'':14C,220C ''week'':13C ''weka'':99C,155C,195C ''won'':118C,127C ''would'':143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2378, 1, '2014-10-30 18:22:18.907', 5, 211, 'Matrix properties and machine learning/data mining', '&lt;p&gt;I''m doing some data analysis in a Statistical Pattern Recognition course using PRML. We analyzed a lot of matrix properties, like eigenvalues, column independence, positive semi-definite matrix, etc. When we are doing, for example, linear regression, we need to calculate some of those properties, and fit them into the equation.&lt;/p&gt;

&lt;p&gt;So my question is, my question is about the intuition behind these matrix properties, and their implications in the ML/DM literature.&lt;/p&gt;

&lt;p&gt;If anyone could answer, can you teach me what is the importance of eigenvalue, positive semi-definite matrix, and column independence for ML/DM. And possibly, other important matrix properties you think important in study the dataset, and why.&lt;/p&gt;

&lt;p&gt;I''d be really appreciated if someone can answer this question.&lt;/p&gt;
', 'machine-learning  data-mining  dataset', NULL, '''/p'':68C,91C,132C,145C ''analysi'':20C ''analyz'':30C ''answer'':96C,142C ''anyon'':94C ''appreci'':138C ''behind'':80C ''calcul'':57C ''column'':38C,113C ''could'':95C ''cours'':26C ''d'':135C ''data'':11B,19C ''data-min'':10B ''dataset'':13B,129C ''definit'':43C,110C ''eigenvalu'':37C,106C ''equat'':67C ''etc'':45C ''exampl'':51C ''fit'':63C ''implic'':86C ''import'':104C,120C,125C ''independ'':39C,114C ''intuit'':79C ''learn'':9B ''learning/data'':5A ''like'':36C ''linear'':52C ''literatur'':90C ''lot'':32C ''m'':16C ''machin'':4A,8B ''machine-learn'':7B ''matrix'':1A,34C,44C,82C,111C,121C ''mine'':6A,12B ''ml/dm'':89C,116C ''need'':55C ''p'':14C,69C,92C,133C ''pattern'':24C ''posit'':40C,107C ''possibl'':118C ''prml'':28C ''properti'':2A,35C,61C,83C,122C ''question'':72C,75C,144C ''realli'':137C ''recognit'':25C ''regress'':53C ''semi'':42C,109C ''semi-definit'':41C,108C ''someon'':140C ''statist'':23C ''studi'':127C ''teach'':99C ''think'':124C ''use'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2425, 2, '2014-11-07 09:04:17.603', 1, NULL, NULL, '&lt;p&gt;Another possibility is the &lt;a href="http://en.wikipedia.org/wiki/Earth_mover&amp;#39;s_distance" rel="nofollow"&gt;Earth Mover''s Distance&lt;/a&gt;. I applied it to Computer Vision, but I think that it may be adapted to your specific problem. &lt;/p&gt;
', NULL, NULL, '''/a'':17C ''/p'':36C ''/wiki/earth_mover&amp;#39;s_distance'':10C ''adapt'':31C ''anoth'':2C ''appli'':19C ''comput'':22C ''distanc'':16C ''earth'':13C ''en.wikipedia.org'':9C ''en.wikipedia.org/wiki/earth_mover&amp;#39;s_distance'':8C ''href'':7C ''may'':29C ''mover'':14C ''nofollow'':12C ''p'':1C ''possibl'':3C ''problem'':35C ''rel'':11C ''specif'':34C ''think'':26C ''vision'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2611, 2, '2014-12-05 11:22:01.423', 1, NULL, NULL, '&lt;p&gt;Here''s a good &lt;a href="http://databricks.gitbooks.io/databricks-spark-reference-applications/" rel="nofollow"&gt;open source book for spark&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':18C ''/databricks-spark-reference-applications/'':10C ''/p'':19C ''book'':15C ''databricks.gitbooks.io'':9C ''databricks.gitbooks.io/databricks-spark-reference-applications/'':8C ''good'':5C ''href'':7C ''nofollow'':12C ''open'':13C ''p'':1C ''rel'':11C ''sourc'':14C ''spark'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6056, 2, '2015-06-09 15:16:41.17', 0, NULL, NULL, '&lt;p&gt;Your question seems vague without the sample data you are using. How does your data-set look like? If there are delimiters within your data, you could get rid of only spaces between all words and then ''big data'' &amp;amp; ''bigdata'' would be the same, if that is what you want to do. &lt;/p&gt;
', NULL, NULL, '''/p'':56C ''amp'':42C ''big'':40C ''bigdata'':43C ''could'':29C ''data'':9C,17C,27C,41C ''data-set'':16C ''delimit'':24C ''get'':30C ''like'':20C ''look'':19C ''p'':1C ''question'':3C ''rid'':31C ''sampl'':8C ''seem'':4C ''set'':18C ''space'':34C ''use'':12C ''vagu'':5C ''want'':53C ''within'':25C ''without'':6C ''word'':37C ''would'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2443, 2, '2014-11-11 08:34:22.21', 1, NULL, NULL, '&lt;p&gt;May be it will be a little offtopic, but I''d like to highly recommend you to go through this MOOC &lt;a href="https://www.coursera.org/course/statistics" rel="nofollow"&gt;https://www.coursera.org/course/statistics&lt;/a&gt;. This is a very good and clear introduction to statistics. It give you a base principles about core field in data science. I hope it will be a good start point for beginning friendship between you and statistics.&lt;/p&gt;
', NULL, NULL, '''/course/statistics'':27C ''/course/statistics&lt;/a&gt;.'':32C ''/p'':71C ''base'':47C ''begin'':65C ''clear'':39C ''core'':50C ''d'':12C ''data'':53C ''field'':51C ''friendship'':66C ''give'':44C ''go'':19C ''good'':37C,61C ''high'':15C ''hope'':56C ''href'':24C ''introduct'':40C ''like'':13C ''littl'':8C ''may'':2C ''mooc'':22C ''nofollow'':29C ''offtop'':9C ''p'':1C ''point'':63C ''principl'':48C ''recommend'':16C ''rel'':28C ''scienc'':54C ''start'':62C ''statist'':42C,70C ''www.coursera.org'':26C,31C ''www.coursera.org/course/statistics'':25C ''www.coursera.org/course/statistics&lt;/a&gt;.'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2384, 1, '2014-10-31 17:55:25.723', 2, 429, 'Text-Classification-Problem, what is the right approach?', '&lt;p&gt;I''m planing to write a classification program that is able to classify unknown text in around 10 different categories and if none of them fits it would be nice to know that. It is also possible that more then one category is right.&lt;/p&gt;

&lt;p&gt;My predefined categories are:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;c1 = "politics"
c2 = "biology"
c3 = "food"
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m thinking about the right approach in how to represent my training-data or what kind of classification is the right one. The first challenge is about finding the right features. If I only have text (250 words each) what method would you recommend to find the right features? My first approach is to remove all stop-words and use the POS-Tagger (&lt;a href="http://nlp.stanford.edu/software/tagger.shtml" rel="nofollow"&gt;Stanford NLP POS-Tagger&lt;/a&gt;) to find nouns, adjective etc. I count them an use all frequently appeared words as features.&lt;/p&gt;

&lt;p&gt;e.g. politics, I''ve around 2.000 text-entities. With the mentioned POS-Tagger I found:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;law:           841
capitalism:    412
president:     397
democracy:     1007
executive:     112
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Would it be right to use only that as features? The trainings-set would then look like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Training set for politics:
feature law         numeric
feature capitalism  numeric
feature president   numeric
feature democracy   numeric
feature executive   numeric
class politics,all_others

sample data:
politics,5,7,1,9,3
politics,14,4,6,7,9
politics,9,9,9,4,2,1
politics,5,8,0,7,6
...
all_others,0,2,4,1,0
all_others,0,0,1,1,1
all_others,7,4,0,0,0
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Would this be a right approach for binary-classification? Or how would I define my sets? Or is multi-class classification the right approach? Then it would look like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Training set for politics:
feature law         numeric
feature capitalism  numeric
feature president   numeric
feature democracy   numeric
feature executive   numeric
feature genetics    numeric
feature muscle      numeric
feature blood       numeric
feature burger      numeric
feature salad       numeric
feature cooking     numeric 
class politics,biology,food

sample data:
politics,5,7,1,9,3,0,0,2,1,0,1
politics,14,4,6,7,9,0,0,0,0,0,1
politics,9,9,9,4,2,1,1,1,1,0,3
politics,5,8,0,7,6,2,2,0,1,0,1
...
biology,0,2,4,1,0,4,19,5,0,2,2
biology,0,0,1,1,1,12,9,9,2,1,1
biology,7,4,0,0,0,10,10,3,0,0,7
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What would you say?&lt;/p&gt;
', 'machine-learning', NULL, '''/a'':155C ''/code'':73C,204C,299C,463C ''/p'':58C,64C,172C,191C,225C,333C,470C ''/pre'':74C,205C,300C,464C ''/software/tagger.shtml'':147C ''0'':275C,280C,284C,287C,288C,296C,297C,298C,385C,386C,389C,397C,398C,399C,400C,401C,413C,418C,423C,425C,428C,432C,436C,440C,441C,454C,455C,456C,460C,461C ''1'':256C,271C,283C,289C,290C,291C,382C,388C,390C,402C,409C,410C,411C,412C,424C,426C,431C,442C,443C,444C,449C,450C ''10'':31C,457C,458C ''1007'':201C ''112'':203C ''12'':445C ''14'':260C,392C ''19'':434C ''2'':270C,281C,387C,408C,421C,422C,429C,437C,438C,448C ''2.000'':179C ''250'':114C ''3'':258C,384C,414C,459C ''397'':199C ''4'':261C,269C,282C,295C,393C,407C,430C,433C,453C ''412'':197C ''5'':254C,273C,380C,416C,435C ''6'':262C,277C,394C,420C ''7'':255C,263C,276C,294C,381C,395C,419C,452C,462C ''8'':274C,417C ''841'':195C ''9'':257C,264C,266C,267C,268C,383C,396C,404C,405C,406C,446C,447C ''abl'':24C ''adject'':159C ''also'':49C ''appear'':168C ''approach'':9A,82C,129C,307C,327C ''around'':30C,178C ''binari'':310C ''binary-classif'':309C ''biolog'':70C,375C,427C,439C,451C ''blood'':362C ''burger'':365C ''c1'':67C ''c2'':69C ''c3'':71C ''capit'':196C,236C,344C ''categori'':33C,55C,62C ''challeng'':102C ''class'':247C,323C,373C ''classif'':3A,20C,95C,311C,324C ''classifi'':26C ''code'':66C,193C,227C,335C ''cook'':371C ''count'':162C ''data'':90C,252C,378C ''defin'':316C ''democraci'':200C,242C,350C ''differ'':32C ''e.g'':174C ''entiti'':182C ''etc'':160C ''execut'':202C,245C,353C ''featur'':108C,126C,171C,216C,232C,235C,238C,241C,244C,340C,343C,346C,349C,352C,355C,358C,361C,364C,367C,370C ''find'':105C,123C,157C ''first'':101C,128C ''fit'':39C ''food'':72C,376C ''found'':190C ''frequent'':167C ''genet'':356C ''href'':144C ''kind'':93C ''know'':45C ''law'':194C,233C,341C ''learn'':12B ''like'':224C,332C ''look'':223C,331C ''m'':15C,77C ''machin'':11B ''machine-learn'':10B ''mention'':185C ''method'':118C ''multi'':322C ''multi-class'':321C ''muscl'':359C ''nice'':43C ''nlp'':151C ''nlp.stanford.edu'':146C ''nlp.stanford.edu/software/tagger.shtml'':145C ''nofollow'':149C ''none'':36C ''noun'':158C ''numer'':234C,237C,240C,243C,246C,342C,345C,348C,351C,354C,357C,360C,363C,366C,369C,372C ''one'':54C,99C ''other'':250C,279C,286C,293C ''p'':13C,59C,75C,173C,206C,301C,465C ''plane'':16C ''polit'':68C,175C,231C,248C,253C,259C,265C,272C,339C,374C,379C,391C,403C,415C ''pos'':141C,153C,187C ''pos-tagg'':140C,152C,186C ''possibl'':50C ''pre'':65C,192C,226C,334C ''predefin'':61C ''presid'':198C,239C,347C ''problem'':4A ''program'':21C ''recommend'':121C ''rel'':148C ''remov'':132C ''repres'':86C ''right'':8A,57C,81C,98C,107C,125C,210C,306C,326C ''salad'':368C ''sampl'':251C,377C ''say'':469C ''set'':220C,229C,318C,337C ''stanford'':150C ''stop'':135C ''stop-word'':134C ''tagger'':142C,154C,188C ''text'':2A,28C,113C,181C ''text-classification-problem'':1A ''text-ent'':180C ''think'':78C ''train'':89C,219C,228C,336C ''training-data'':88C ''trainings-set'':218C ''unknown'':27C ''use'':138C,165C,212C ''ve'':177C ''word'':115C,136C,169C ''would'':41C,119C,207C,221C,302C,314C,330C,467C ''write'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2395, 1, '2014-11-03 16:51:47.467', 6, 144, 'Modelling Unevenly Spaced Time Series', '&lt;p&gt;I have a continuous variable, sampled over a period of a year at irregular intervals. Some days have more than one observation per hour, while other periods have nothing for days. This makes it particularly difficult to detect patterns in the time series, because some months (for instance October) are highly sampled, while others are not.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/7MEXt.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;My question is what would be the best approach to model this time series?&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;I believe most time series analysis techniques (like ARMA) need a fixed frequency. I could aggregate the data, in order to have a constant sample or choose a sub-set of the data that is very detailed. With both options I would be missing some information from the original dataset, that could unveil distinct patterns.&lt;/li&gt;
&lt;li&gt;Instead of decomposing the series in cycles, I could feed the model
with the entire dataset and expect it to pick up the patterns. For
instance, I transformed the hour, weekday and month in categorical
variables and tried a multiple regression with good results (R2=0.71)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I have the idea that machine learning techniques such as ANN can also pick these patterns from uneven time series, but I was wondering if anybody has tried that, and could provide me some advice about the best way of representing time patterns in a Neural network.&lt;/p&gt;
', 'neuralnetwork  time-series  regression', NULL, '''/7mext.png'':74C ''/li'':155C,203C ''/p'':68C,80C,96C,253C ''/ul'':204C ''0.71'':202C ''advic'':240C ''aggreg'':114C ''also'':218C ''alt'':75C ''analysi'':104C ''ann'':216C ''anybodi'':231C ''approach'':90C ''arma'':107C ''believ'':100C ''best'':89C,243C ''categor'':191C ''choos'':125C ''constant'':122C ''continu'':15C ''could'':113C,151C,165C,236C ''cycl'':163C ''data'':116C,132C ''dataset'':149C,172C ''day'':28C,42C ''decompos'':159C ''descript'':78C ''detail'':136C ''detect'':49C ''difficult'':47C ''distinct'':153C ''enter'':76C ''entir'':171C ''expect'':174C ''feed'':166C ''fix'':110C ''frequenc'':111C ''good'':199C ''high'':62C ''hour'':35C,186C ''i.stack.imgur.com'':73C ''i.stack.imgur.com/7mext.png'':72C ''idea'':209C ''imag'':77C ''img'':70C ''inform'':145C ''instanc'':59C,182C ''instead'':157C ''interv'':26C ''irregular'':25C ''learn'':212C ''li'':98C,156C ''like'':106C ''machin'':211C ''make'':44C ''miss'':143C ''model'':1A,92C,168C ''month'':57C,189C ''multipl'':196C ''need'':108C ''network'':252C ''neural'':251C ''neuralnetwork'':6B ''noth'':40C ''observ'':33C ''octob'':60C ''one'':32C ''option'':139C ''order'':118C ''origin'':148C ''other'':65C ''p'':11C,69C,81C,205C ''particular'':46C ''pattern'':50C,154C,180C,221C,248C ''per'':34C ''period'':20C,38C ''pick'':177C,219C ''provid'':237C ''question'':83C ''r2'':201C ''regress'':10B,197C ''repres'':246C ''result'':200C ''sampl'':17C,63C,123C ''seri'':5A,9B,54C,95C,103C,161C,225C ''set'':129C ''space'':3A ''src'':71C ''sub'':128C ''sub-set'':127C ''techniqu'':105C,213C ''time'':4A,8B,53C,94C,102C,224C,247C ''time-seri'':7B ''transform'':184C ''tri'':194C,233C ''ul'':97C ''uneven'':2A,223C ''unveil'':152C ''variabl'':16C,192C ''way'':244C ''weekday'':187C ''wonder'':229C ''would'':86C,141C ''year'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10410, 2, '2016-02-26 15:58:37.93', 2, NULL, NULL, '&lt;p&gt;Just split them 50/50 (or another percentage, depending on what you want). Once you want to test a specific metric, make sure that if you can only generate that for a portion of the A group because of some specific property (ie you need information C for this metric) to compare it only to the people in group B that also have information C to avoid bias.&lt;/p&gt;

&lt;p&gt;Example: I have 200,000 customers. I split them 50/50. I''m testing my new recommendation method against a random advertisement. However my recommender only works if they have searched in the last month. After getting back the results, I will have to check that against the control group filtered to have only people that have searched in the past month. People that have searched in the past month are much more likely to have a stronger kind of connection to you, so if you would check against the whole group you would overestimate the performance of your recommender.&lt;/p&gt;

&lt;p&gt;Alternatively you could already split up the groups knowing whether they have information C or not, but you might want to test different things so splitting before hand can give you extra info, at the small cost that your groups might be slightly unequal, but for a big sample size this is not an issue at all.&lt;/p&gt;
', NULL, NULL, '''/p'':69C,170C,229C ''000'':75C ''200'':74C ''50/50'':5C,80C ''advertis'':91C ''alreadi'':175C ''also'':62C ''altern'':172C ''anoth'':7C ''avoid'':67C ''b'':60C ''back'':107C ''bias'':68C ''big'':219C ''c'':47C,65C,185C ''check'':114C,157C ''compar'':52C ''connect'':150C ''control'':118C ''cost'':208C ''could'':174C ''custom'':76C ''depend'':9C ''differ'':194C ''exampl'':71C ''extra'':203C ''filter'':120C ''generat'':29C ''get'':106C ''give'':201C ''group'':37C,59C,119C,161C,179C,211C ''hand'':199C ''howev'':92C ''ie'':43C ''info'':204C ''inform'':46C,64C,184C ''issu'':226C ''kind'':148C ''know'':180C ''last'':103C ''like'':143C ''m'':82C ''make'':22C ''method'':87C ''metric'':21C,50C ''might'':190C,212C ''month'':104C,131C,139C ''much'':141C ''need'':45C ''new'':85C ''overestim'':164C ''p'':1C,70C,171C ''past'':130C,138C ''peopl'':57C,124C,132C ''percentag'':8C ''perform'':166C ''portion'':33C ''properti'':42C ''random'':90C ''recommend'':86C,94C,169C ''result'':109C ''sampl'':220C ''search'':100C,127C,135C ''size'':221C ''slight'':214C ''small'':207C ''specif'':20C,41C ''split'':3C,78C,176C,197C ''stronger'':147C ''sure'':23C ''test'':18C,83C,193C ''thing'':195C ''unequ'':215C ''want'':13C,16C,191C ''whether'':181C ''whole'':160C ''work'':96C ''would'':156C,163C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2386, 2, '2014-11-01 10:07:09.843', -1, NULL, NULL, '&lt;p&gt;The following great article by Sebastian Raschka on &lt;em&gt;Bayesian approach to text classification&lt;/em&gt; should be very helpful for your task. I also highly recommend his excellent &lt;strong&gt;blog&lt;/strong&gt; on &lt;em&gt;data science&lt;/em&gt; topics, as an additional general reference: &lt;a href="http://sebastianraschka.com/articles.html" rel="nofollow"&gt;http://sebastianraschka.com/articles.html&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;You may also check this &lt;em&gt;educational report&lt;/em&gt; on text classification: &lt;a href="http://www.datasciencecentral.com/profiles/blogs/my-data-science-apprenticeship-project" rel="nofollow"&gt;http://www.datasciencecentral.com/profiles/blogs/my-data-science-apprenticeship-project&lt;/a&gt;. It might provide you with some additional ideas.&lt;/p&gt;
', NULL, NULL, '''/articles.html'':48C ''/articles.html&lt;/a&gt;.&lt;/p&gt;'':53C ''/em'':16C,37C,63C ''/p'':85C ''/profiles/blogs/my-data-science-apprenticeship-project'':71C ''/profiles/blogs/my-data-science-apprenticeship-project&lt;/a&gt;.'':76C ''/strong'':32C ''addit'':41C,83C ''also'':25C,57C ''approach'':12C ''articl'':5C ''bayesian'':11C ''blog'':31C ''check'':58C ''classif'':15C,66C ''data'':35C ''educ'':61C ''em'':10C,34C,60C ''excel'':29C ''follow'':3C ''general'':42C ''great'':4C ''help'':20C ''high'':26C ''href'':45C,68C ''idea'':84C ''may'':56C ''might'':78C ''nofollow'':50C,73C ''p'':1C,54C ''provid'':79C ''raschka'':8C ''recommend'':27C ''refer'':43C ''rel'':49C,72C ''report'':62C ''scienc'':36C ''sebastian'':7C ''sebastianraschka.com'':47C,52C ''sebastianraschka.com/articles.html'':46C ''sebastianraschka.com/articles.html&lt;/a&gt;.&lt;/p&gt;'':51C ''strong'':30C ''task'':23C ''text'':14C,65C ''topic'':38C ''www.datasciencecentral.com'':70C,75C ''www.datasciencecentral.com/profiles/blogs/my-data-science-apprenticeship-project'':69C ''www.datasciencecentral.com/profiles/blogs/my-data-science-apprenticeship-project&lt;/a&gt;.'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2387, 2, '2014-11-01 10:16:55.557', 2, NULL, NULL, '&lt;p&gt;Check &lt;strong&gt;my answer&lt;/strong&gt; on the related question here: &lt;a href="http://datascience.stackexchange.com/a/843/2452"&gt;http://datascience.stackexchange.com/a/843/2452&lt;/a&gt;. However, given your situation, you might need to work on something simpler. For this, it might be a good idea to review &lt;strong&gt;recommended literature&lt;/strong&gt; in the &lt;em&gt;open source data science master''s curriculum&lt;/em&gt;: &lt;a href="http://datasciencemasters.org" rel="nofollow"&gt;http://datasciencemasters.org&lt;/a&gt;. Many referenced there sources are free and easily available online. I''m pretty sure that many of these sources contain examples of simple projects that you could (re)implement, extend or otherwise use for your task.&lt;/p&gt;
', NULL, NULL, '''/a'':63C ''/a/843/2452'':16C ''/a/843/2452&lt;/a&gt;.'':19C ''/em'':56C ''/p'':100C ''/strong'':6C,45C ''answer'':5C ''avail'':72C ''check'':2C ''contain'':83C ''could'':90C ''curriculum'':55C ''data'':51C ''datascience.stackexchange.com'':15C,18C ''datascience.stackexchange.com/a/843/2452'':14C ''datascience.stackexchange.com/a/843/2452&lt;/a&gt;.'':17C ''datasciencemasters.org'':59C,62C ''easili'':71C ''em'':48C ''exampl'':84C ''extend'':93C ''free'':69C ''given'':21C ''good'':38C ''howev'':20C ''href'':13C,58C ''idea'':39C ''implement'':92C ''literatur'':44C ''m'':75C ''mani'':64C,79C ''master'':53C ''might'':25C,35C ''need'':26C ''nofollow'':61C ''onlin'':73C ''open'':49C ''otherwis'':95C ''p'':1C ''pretti'':76C ''project'':87C ''question'':10C ''re'':91C ''recommend'':43C ''referenc'':65C ''rel'':60C ''relat'':9C ''review'':41C ''scienc'':52C ''simpl'':86C ''simpler'':31C ''situat'':23C ''someth'':30C ''sourc'':50C,67C,82C ''strong'':3C,42C ''sure'':77C ''task'':99C ''use'':96C ''work'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2391, 1, '2014-11-02 07:20:32.603', 3, 135, 'Anomaly detection in multiple parameters', '&lt;p&gt;I am a newbie to data science with a typical problem. I have a data set with metric1, metric2 and metric3. All these metrics are interdependent on each other. I want to detect anomalies in metric3. Currently, I am using Nupic from numenta.org for my analysis and it doesn''t seem to be effective. Is there any ML library which can detect anomalies in multiple parameters?&lt;/p&gt;
', 'machine-learning  bigdata  statistics  anomaly-detection', NULL, '''/p'':81C ''analysi'':60C ''anomali'':1A,12B,48C,77C ''anomaly-detect'':11B ''bigdata'':9B ''current'':51C ''data'':20C,29C ''detect'':2A,13B,47C,76C ''doesn'':63C ''effect'':68C ''interdepend'':40C ''learn'':8B ''librari'':73C ''machin'':7B ''machine-learn'':6B ''metric'':38C ''metric1'':32C ''metric2'':33C ''metric3'':35C,50C ''ml'':72C ''multipl'':4A,79C ''newbi'':18C ''numenta.org'':57C ''nupic'':55C ''p'':14C ''paramet'':5A,80C ''problem'':25C ''scienc'':21C ''seem'':65C ''set'':30C ''statist'':10B ''typic'':24C ''use'':54C ''want'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2392, 1, '2014-11-02 11:36:28.143', 1, 48, 'scikit-learn OMP mem error', '&lt;p&gt;I tried to use OMP algorithm available in scikit-learn. My net datasize which includes both target signal and dictionary ~ 1G. However when I ran the code, it exited with mem-error.
The machine has 16G RAM, so I don''t think this should have happened. I tried with some logging where the error came and  found that the data got loaded completely into numpy arrays. And it was the algorithm itself that caused the error. Can someone help me with this
or sugggest more memory efficient algorithm for feature selection, or is subsampling the 
data my only option. Are there some deterministic good subsampling techniques.&lt;/p&gt;

&lt;p&gt;EDIT:
Relevant code piece:&lt;/p&gt;



&lt;pre&gt;&lt;code&gt;n=8;
y=mydata[:,0];
X=mydata[:,[1,2,3,4,5,6,7,8]];
#print y;
#print X;
print "here";
omp = OrthogonalMatchingPursuit(n_nonzero_coefs=5,copy_X = False, normalize=True);
omp.fit(X,y);
coef = omp.coef_;
print omp.coef_;
idx_r, = coef.nonzero();
for id in idx_r:
        print coef[id], vars[id],"\n";
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The error I get:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;File "/usr/local/lib/python2.7/dist-packages/sklearn/base.py", line 324, in score
return r2_score(y, self.predict(X), sample_weight=sample_weight)
File "/usr/local/lib/python2.7/dist-packages/sklearn/metrics/metrics.py", line 2332, in r2_score
numerator = (weight * (y_true - y_pred) ** 2).sum(dtype=np.float64)
MemoryError
&lt;/code&gt;&lt;/pre&gt;
', 'python  bigdata  feature-selection  scikit  scalability', NULL, '''/code'':185C,229C ''/p'':123C,129C,192C ''/pre'':186C,230C ''/usr/local/lib/python2.7/dist-packages/sklearn/base.py'':196C ''/usr/local/lib/python2.7/dist-packages/sklearn/metrics/metrics.py'':212C ''0'':136C ''1'':139C ''16g'':52C ''1g'':36C ''2'':140C,224C ''2332'':214C ''3'':141C ''324'':198C ''4'':142C ''5'':143C,158C ''6'':144C ''7'':145C ''8'':133C,146C ''algorithm'':20C,87C,104C ''array'':82C ''avail'':21C ''bigdata'':8B ''came'':71C ''caus'':90C ''code'':42C,127C,131C,194C ''coef'':157C,167C,180C ''coef.nonzero'':173C ''complet'':79C ''copi'':159C ''data'':76C,112C ''datas'':28C ''determinist'':119C ''dictionari'':35C ''dtype'':226C ''edit'':125C ''effici'':103C ''error'':6A,48C,70C,92C,189C ''exit'':44C ''fals'':161C ''featur'':10B,106C ''feature-select'':9B ''file'':195C,211C ''found'':73C ''get'':191C ''good'':120C ''got'':77C ''happen'':62C ''help'':95C ''howev'':37C ''id'':175C,181C,183C ''idx'':171C,177C ''includ'':30C ''learn'':3A,25C ''line'':197C,213C ''load'':78C ''log'':67C ''machin'':50C ''mem'':5A,47C ''mem-error'':46C ''memori'':102C ''memoryerror'':228C ''mydata'':135C,138C ''n'':132C,155C,184C ''net'':27C ''nonzero'':156C ''normal'':162C ''np.float64'':227C ''numer'':218C ''numpi'':81C ''omp'':4A,19C,153C ''omp.coef'':168C,170C ''omp.fit'':164C ''option'':115C ''orthogonalmatchingpursuit'':154C ''p'':14C,124C,187C ''piec'':128C ''pre'':130C,193C ''pred'':223C ''print'':147C,149C,151C,169C,179C ''python'':7B ''r'':172C,178C ''r2'':202C,216C ''ram'':53C ''ran'':40C ''relev'':126C ''return'':201C ''sampl'':207C,209C ''scalabl'':13B ''scikit'':2A,12B,24C ''scikit-learn'':1A,23C ''score'':200C,203C,217C ''select'':11B,107C ''self.predict'':205C ''signal'':33C ''someon'':94C ''subsampl'':110C,121C ''sugggest'':100C ''sum'':225C ''target'':32C ''techniqu'':122C ''think'':58C ''tri'':16C,64C ''true'':163C,221C ''use'':18C ''var'':182C ''weight'':208C,210C,219C ''x'':137C,150C,160C,165C,206C ''y'':134C,148C,166C,204C,220C,222C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8694, 1, '2015-11-02 15:42:45.377', 4, 129, 'Random Forests with Big Data - number of trees v. number of observations', '&lt;p&gt;I frequently use Random Forest, Regularized Random Forest, Guided Random Forest, and similar tree models.&lt;/p&gt;

&lt;p&gt;The size of the data that I''m dealing with has grown beyond what I can work around using HPC and parallelism. It''s typically large due to row length (observations) not columns (features). The data is also often not normally distributed.&lt;/p&gt;

&lt;p&gt;I have to make a choice between: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Running a small number of trees (i.e. 50 or less) with either complete data or a relatively large and comparative sample&lt;/li&gt;
&lt;li&gt;Running several times the number of trees, but with a correspondingly scaled down sample size&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;There are work-arounds and for any 1 case -- for instance, I can do some ad hoc tests to see which I think will work better, but what I''m wondering &lt;strong&gt;&lt;em&gt;is if there is a good theoretical (or robust empirical) reasoning&lt;/em&gt;&lt;/strong&gt; to either guide the choice of approach over the other or to describe the tradeoff being made?&lt;/p&gt;

&lt;p&gt;In other words, I''m hoping that someone more comfortable with the math, statistics, and theory underlying this (type of) algorithm can offer some generalizable insight.&lt;/p&gt;
', 'bigdata  random-forest', NULL, '''/em'':175C ''/li'':110C,127C ''/ol'':128C ''/p'':33C,77C,86C,194C,222C ''/strong'':176C ''1'':138C ''50'':96C ''ad'':146C ''algorithm'':216C ''also'':72C ''approach'':183C ''around'':52C,134C ''better'':156C ''beyond'':47C ''big'':4A ''bigdata'':13B ''case'':139C ''choic'':84C,181C ''column'':67C ''comfort'':205C ''compar'':108C ''complet'':101C ''correspond'':122C ''data'':5A,39C,70C,102C ''deal'':43C ''describ'':189C ''distribut'':76C ''due'':61C ''either'':100C,178C ''em'':163C ''empir'':173C ''featur'':68C ''forest'':2A,16B,22C,25C,28C ''frequent'':19C ''generaliz'':220C ''good'':169C ''grown'':46C ''guid'':26C,179C ''hoc'':147C ''hope'':201C ''hpc'':54C ''i.e'':95C ''insight'':221C ''instanc'':141C ''larg'':60C,106C ''length'':64C ''less'':98C ''li'':88C,111C ''m'':42C,160C,200C ''made'':193C ''make'':82C ''math'':208C ''model'':32C ''normal'':75C ''number'':6A,10A,92C,116C ''observ'':12A,65C ''offer'':218C ''often'':73C ''ol'':87C ''p'':17C,34C,78C,129C,195C ''parallel'':56C ''random'':1A,15B,21C,24C,27C ''random-forest'':14B ''reason'':174C ''regular'':23C ''relat'':105C ''robust'':172C ''row'':63C ''run'':89C,112C ''sampl'':109C,125C ''scale'':123C ''see'':150C ''sever'':113C ''similar'':30C ''size'':36C,126C ''small'':91C ''someon'':203C ''statist'':209C ''strong'':162C ''test'':148C ''theoret'':170C ''theori'':211C ''think'':153C ''time'':114C ''tradeoff'':191C ''tree'':8A,31C,94C,118C ''type'':214C ''typic'':59C ''under'':212C ''use'':20C,53C ''v'':9A ''wonder'':161C ''word'':198C ''work'':51C,133C,155C ''work-around'':132C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2393, 2, '2014-11-03 03:22:30.393', 3, NULL, NULL, '&lt;p&gt;I think perhaps the first thing to decide that will help clarify some of your other questions is whether you want to perform binary classification or multi-class classification. If you''re interested in classifying each instance in your dataset into more than one class, then this brings up a set of new concerns regarding setting up your data set, the experiments you want to run, and how you plan to evaluate your classifier(s). My hunch is that you could formulate your task as a binary one where you train and test one classifier for each class you want to predict, and simply set up the data matrix so that there are two classes to predict - (1) the one you''re interested in classifying and (2) everything else.&lt;/p&gt;

&lt;p&gt;In that case, instead of your training set looking like this (where each row is a document and columns 1-3 contain features for that document, and the class column is the class to be predicted): &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1           2           3           class
feature1    feature2    feature3    politics
feature1    feature2    feature3    law
feature1    feature2    feature3    president
feature1    feature2    feature3    politics
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;it would look like the following in the case where you''re interested in detecting the politics class against everything else:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1           2           3           class
feature1    feature2    feature3    politics
feature1    feature2    feature3    non-politics
feature1    feature2    feature3    non-politics
feature1    feature2    feature3    politics
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You would need to do this process for each class you''re interested in predicting, and then train and test one classifier per class and evaluate each classifier according to your chosen metrics (usually accuracy, precision, or recall or some variation thereof).&lt;/p&gt;

&lt;p&gt;As far as choosing features, this requires quite a bit of thinking. Features can be highly dependent on the type of text you''re trying to classify, so be sure to explore your dataset and get a sense for how people are writing in each domain. Qualitative investigation isn''t enough to decide once and for all what are good features, but it is a good way to get ideas. Also, look into &lt;a href="http://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;TF-IDF&lt;/a&gt; weighting of terms instead of just using their frequency within each instance of your dataset. This will help you pick up on (a) terms that are prevalent within a document (and possibly a target class) and (b) terms that distinguish a given document from other documents. I hope this helps a little.&lt;/p&gt;
', NULL, NULL, '''-3'':154C ''/a'':374C ''/code'':193C,244C ''/p'':132C,170C,217C,289C,427C ''/pre'':194C,245C ''/wiki/tf%e2%80%93idf'':368C ''1'':120C,153C,173C,220C ''2'':129C,174C,221C ''3'':175C,222C ''accord'':275C ''accuraci'':281C ''also'':361C ''b'':411C ''binari'':25C,89C ''bit'':300C ''bring'':50C ''case'':136C,204C ''choos'':294C ''chosen'':278C ''clarifi'':13C ''class'':30C,47C,100C,117C,162C,166C,176C,213C,223C,256C,270C,409C ''classif'':26C,31C ''classifi'':37C,76C,97C,127C,268C,274C,317C ''code'':172C,219C ''column'':152C,163C ''concern'':56C ''contain'':155C ''could'':83C ''data'':61C,110C ''dataset'':42C,324C,389C ''decid'':9C,343C ''depend'':307C ''detect'':210C ''distinguish'':414C ''document'':150C,159C,404C,417C,420C ''domain'':336C ''els'':131C,216C ''en.wikipedia.org'':367C ''en.wikipedia.org/wiki/tf%e2%80%93idf'':366C ''enough'':341C ''evalu'':74C,272C ''everyth'':130C,215C ''experi'':64C ''explor'':322C ''far'':292C ''featur'':156C,295C,303C,351C ''feature1'':177C,181C,185C,189C,224C,228C,234C,240C ''feature2'':178C,182C,186C,190C,225C,229C,235C,241C ''feature3'':179C,183C,187C,191C,226C,230C,236C,242C ''first'':6C ''follow'':201C ''formul'':84C ''frequenc'':383C ''get'':326C,359C ''given'':416C ''good'':350C,356C ''help'':12C,392C,424C ''high'':306C ''hope'':422C ''href'':365C ''hunch'':79C ''idea'':360C ''idf'':373C ''instanc'':39C,386C ''instead'':137C,378C ''interest'':35C,125C,208C,259C ''investig'':338C ''isn'':339C ''law'':184C ''like'':143C,199C ''littl'':426C ''look'':142C,198C,362C ''matrix'':111C ''metric'':279C ''multi'':29C ''multi-class'':28C ''need'':249C ''new'':55C ''nofollow'':370C ''non'':232C,238C ''non-polit'':231C,237C ''one'':46C,90C,96C,122C,267C ''p'':1C,133C,195C,246C,290C ''peopl'':331C ''per'':269C ''perform'':24C ''perhap'':4C ''pick'':394C ''plan'':72C ''polit'':180C,192C,212C,227C,233C,239C,243C ''possibl'':406C ''pre'':171C,218C ''precis'':282C ''predict'':104C,119C,169C,261C ''presid'':188C ''preval'':401C ''process'':253C ''qualit'':337C ''question'':18C ''quit'':298C ''re'':34C,124C,207C,258C,314C ''recal'':284C ''regard'':57C ''rel'':369C ''requir'':297C ''row'':147C ''run'':68C ''sens'':328C ''set'':53C,58C,62C,107C,141C ''simpli'':106C ''sure'':320C ''target'':408C ''task'':86C ''term'':377C,398C,412C ''test'':95C,266C ''text'':312C ''tf'':372C ''tf-idf'':371C ''thereof'':288C ''thing'':7C ''think'':3C,302C ''train'':93C,140C,264C ''tri'':315C ''two'':116C ''type'':310C ''use'':381C ''usual'':280C ''variat'':287C ''want'':22C,66C,102C ''way'':357C ''weight'':375C ''whether'':20C ''within'':384C,402C ''would'':197C,248C ''write'':333C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2397, 2, '2014-11-03 22:10:13.263', 1, NULL, NULL, '&lt;p&gt;As Spacedman put it, "best" is pretty subjective. However, as we have found, a good format for time is &lt;a href="http://en.wikipedia.org/wiki/Unix_time" rel="nofollow"&gt;Unix time&lt;/a&gt; (aka POSIX time, aka Epoch time). Most databases support it and it is still pretty human readable.&lt;/p&gt;

&lt;p&gt;For location, we like &lt;a href="http://en.wikipedia.org/wiki/Decimal_degrees" rel="nofollow"&gt;decimal degrees&lt;/a&gt; as it is easy to read and stored and is compatible with Google Maps API. It''s also easy to convert to other formats if needed.&lt;/p&gt;
', NULL, NULL, '''/a'':30C,63C ''/p'':48C,90C ''/wiki/decimal_degrees'':58C ''/wiki/unix_time'':25C ''aka'':31C,34C ''also'':81C ''api'':78C ''best'':6C ''compat'':74C ''convert'':84C ''databas'':38C ''decim'':61C ''degre'':62C ''easi'':67C,82C ''en.wikipedia.org'':24C,57C ''en.wikipedia.org/wiki/decimal_degrees'':56C ''en.wikipedia.org/wiki/unix_time'':23C ''epoch'':35C ''format'':17C,87C ''found'':14C ''good'':16C ''googl'':76C ''howev'':10C ''href'':22C,55C ''human'':46C ''like'':53C ''locat'':51C ''map'':77C ''need'':89C ''nofollow'':27C,60C ''p'':1C,49C ''posix'':32C ''pretti'':8C,45C ''put'':4C ''read'':69C ''readabl'':47C ''rel'':26C,59C ''spacedman'':3C ''still'':44C ''store'':71C ''subject'':9C ''support'':39C ''time'':19C,29C,33C,36C ''unix'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2398, 1, '2014-11-04 00:58:00.137', 2, 193, 'Python Machine Learning Experts', '&lt;p&gt;I''d like to apply some of the more complex supervised machine learning techniques in python - deep learning, generalized addative models, proper implementation of regularization, other cool stuff I dont even know about, etc.&lt;/p&gt;

&lt;p&gt;Any recommendations how I could find expert ML folks that would like to collaborate on projects?&lt;/p&gt;
', 'machine-learning  python', NULL, '''/p'':44C,62C ''addat'':29C ''appli'':14C ''collabor'':59C ''complex'':19C ''cool'':36C ''could'':50C ''d'':11C ''deep'':26C ''dont'':39C ''etc'':43C ''even'':40C ''expert'':4A,52C ''find'':51C ''folk'':54C ''general'':28C ''implement'':32C ''know'':41C ''learn'':3A,7B,22C,27C ''like'':12C,57C ''machin'':2A,6B,21C ''machine-learn'':5B ''ml'':53C ''model'':30C ''p'':9C,45C ''project'':61C ''proper'':31C ''python'':1A,8B,25C ''recommend'':47C ''regular'':34C ''stuff'':37C ''supervis'':20C ''techniqu'':23C ''would'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2399, 2, '2014-11-04 04:23:37.107', 1, NULL, NULL, '&lt;p&gt;Check out &lt;a href="http://lightsidelabs.com/what/research/" rel="nofollow"&gt;LightSide&lt;/a&gt; for a GUI introduction to text mining in general, and more specifically for generating features quickly. It was developed (and I believe is still being developed) by researchers at CMU and it''s how I got hooked on text mining. There is quite a bit of functionality right out of the box, and you can quickly import CSV data into the application, extract features, and start running experiments. It also makes use of suites of algorithms from several other prominent, well-regarded open source toolkits like Weka and LibLinear so you know you''re using something credible under the hood. That being said, both of these last mentioned toolkits are definitely worth checking out for added functionality, even if they have a bit of a steeper learning curve. Hope that helps.&lt;/p&gt;
', NULL, NULL, '''/a'':12C ''/p'':145C ''/what/research/'':8C ''ad'':129C ''algorithm'':88C ''also'':82C ''applic'':74C ''believ'':34C ''bit'':57C,136C ''box'':64C ''check'':2C,126C ''cmu'':42C ''credibl'':110C ''csv'':70C ''curv'':141C ''data'':71C ''definit'':124C ''develop'':31C,38C ''even'':131C ''experi'':80C ''extract'':75C ''featur'':27C,76C ''function'':59C,130C ''general'':21C ''generat'':26C ''got'':48C ''gui'':15C ''help'':144C ''hood'':113C ''hook'':49C ''hope'':142C ''href'':5C ''import'':69C ''introduct'':16C ''know'':105C ''last'':120C ''learn'':140C ''liblinear'':102C ''lightsid'':11C ''lightsidelabs.com'':7C ''lightsidelabs.com/what/research/'':6C ''like'':99C ''make'':83C ''mention'':121C ''mine'':19C,52C ''nofollow'':10C ''open'':96C ''p'':1C ''promin'':92C ''quick'':28C,68C ''quit'':55C ''re'':107C ''regard'':95C ''rel'':9C ''research'':40C ''right'':60C ''run'':79C ''said'':116C ''sever'':90C ''someth'':109C ''sourc'':97C ''specif'':24C ''start'':78C ''steeper'':139C ''still'':36C ''suit'':86C ''text'':18C,51C ''toolkit'':98C,122C ''use'':84C,108C ''weka'':100C ''well'':94C ''well-regard'':93C ''worth'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2401, 2, '2014-11-04 13:41:52.24', 2, NULL, NULL, '&lt;p&gt;You could try some competitions from &lt;a href="http://kaggle.com" rel="nofollow"&gt;kaggle&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Data Science courses from Coursera, edX, etc also provide forums for discussion.&lt;/p&gt;

&lt;p&gt;Linkedin or freelance sites could be other possibilities.&lt;/p&gt;
', NULL, NULL, '''/a'':14C ''/p'':15C,29C,39C ''also'':24C ''competit'':6C ''could'':3C,35C ''cours'':19C ''coursera'':21C ''data'':17C ''discuss'':28C ''edx'':22C ''etc'':23C ''forum'':26C ''freelanc'':33C ''href'':9C ''kaggl'':13C ''kaggle.com'':10C ''linkedin'':31C ''nofollow'':12C ''p'':1C,16C,30C ''possibl'':38C ''provid'':25C ''rel'':11C ''scienc'':18C ''site'':34C ''tri'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9726, 1, '2016-01-10 17:10:59.837', 0, 32, 'Predicted features combined with original ones', '&lt;p&gt;I am currious  how good  such  a procedure could be :
I  get some predictions  of some 10  learners  trained   on train set  and predicted on train set also .
Then I am column binding   those predictions  to the original train.
Could this  be  a valid procedure  on improving  learning process?&lt;/p&gt;
', 'predictive-modeling', NULL, '''/p'':60C ''10'':27C ''also'':38C ''bind'':43C ''column'':42C ''combin'':3A ''could'':19C,50C ''currious'':13C ''featur'':2A ''get'':22C ''good'':15C ''improv'':57C ''learn'':58C ''learner'':28C ''model'':9B ''one'':6A ''origin'':5A,48C ''p'':10C ''predict'':1A,8B,24C,34C,45C ''predictive-model'':7B ''procedur'':18C,55C ''process'':59C ''set'':32C,37C ''train'':29C,31C,36C,49C ''valid'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2403, 1, '2014-11-05 09:24:58.003', 2, 300, 'Data science without knowledge of a specific topic, is it worth pursuing as a career?', '&lt;p&gt;I had a conversation with someone recently and mentioned my interest in data analysis and who I intended to learn the necessary skills and tools. They suggested to me that while it is great to learn the tools and build the skills there is little point in doing so unless i have specialized  knowledge in a specific field. &lt;/p&gt;

&lt;p&gt;They basically summed it to that I''d just be like a builder with a pile of tools who could build a few wooden boxes and may be build better things (cabins, cupboards etc), but without knowledge in a specific field I''d never be a builder people would come to for a specific product.&lt;/p&gt;

&lt;p&gt;Has anyone found this or have any input on what to make of this ? It would seem if it was true one would have to learn the data science aspects of things and then learn  a new field just to  become specialized.&lt;/p&gt;
', 'education  beginner  career', NULL, '''/p'':78C,135C,179C ''analysi'':33C ''anyon'':138C ''aspect'':166C ''basic'':81C ''becom'':177C ''beginn'':17B ''better'':109C ''box'':104C ''build'':59C,100C,108C ''builder'':92C,126C ''cabin'':111C ''career'':15A,18B ''come'':129C ''convers'':23C ''could'':99C ''cupboard'':112C ''d'':87C,122C ''data'':1A,32C,164C ''educ'':16B ''etc'':113C ''field'':77C,120C,174C ''found'':139C ''great'':53C ''input'':144C ''intend'':37C ''interest'':30C ''knowledg'':4A,73C,116C ''learn'':39C,55C,162C,171C ''like'':90C ''littl'':64C ''make'':148C ''may'':106C ''mention'':28C ''necessari'':41C ''never'':123C ''new'':173C ''one'':158C ''p'':19C,79C,136C ''peopl'':127C ''pile'':95C ''point'':65C ''product'':134C ''pursu'':12A ''recent'':26C ''scienc'':2A,165C ''seem'':153C ''skill'':42C,61C ''someon'':25C ''special'':72C,178C ''specif'':7A,76C,119C,133C ''suggest'':46C ''sum'':82C ''thing'':110C,168C ''tool'':44C,57C,97C ''topic'':8A ''true'':157C ''unless'':69C ''without'':3A,115C ''wooden'':103C ''worth'':11A ''would'':128C,152C,159C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6956, 2, '2015-08-31 15:18:41.987', 1, NULL, NULL, '&lt;p&gt;You are avoiding the error with sampling purely by chance (the words are common). Simplest way would be to merge training and test, construct a tdm on a joined set, separate into training and test again and then purge some columns (the constant ones in the train set, as they correspond to words occurring in test only =&gt; useless for training).&lt;/p&gt;
', NULL, NULL, '''/p'':62C ''avoid'':4C ''chanc'':11C ''column'':42C ''common'':15C ''constant'':44C ''construct'':25C ''correspond'':52C ''error'':6C ''join'':30C ''merg'':21C ''occur'':55C ''one'':45C ''p'':1C ''pure'':9C ''purg'':40C ''sampl'':8C ''separ'':32C ''set'':31C,49C ''simplest'':16C ''tdm'':27C ''test'':24C,36C,57C ''train'':22C,34C,48C,61C ''useless'':59C ''way'':17C ''word'':13C,54C ''would'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2407, 2, '2014-11-05 17:07:02.563', 1, NULL, NULL, '&lt;p&gt;Another library that I use that has not been mentioned yet is gensim.  Its &lt;a href="http://radimrehurek.com/gensim/corpora/dictionary.html" rel="nofollow"&gt;Dictionary&lt;/a&gt; module allows you to convert a list of words into a list of (id,count) pairs.  It also has an &lt;code&gt;allow_update&lt;/code&gt; variable that updates the dictionary in the case that new words are encountered at runtime.&lt;/p&gt;

&lt;p&gt;It also has built-in support for TF-IDF, LSI, and LDA, among other models.&lt;/p&gt;
', NULL, NULL, '''/a'':24C ''/code'':48C ''/gensim/corpora/dictionary.html'':20C ''/p'':64C,83C ''allow'':26C,46C ''also'':42C,67C ''among'':80C ''anoth'':2C ''built'':70C ''built-in'':69C ''case'':56C ''code'':45C ''convert'':29C ''count'':39C ''dictionari'':23C,53C ''encount'':61C ''gensim'':14C ''href'':17C ''id'':38C ''idf'':76C ''lda'':79C ''librari'':3C ''list'':31C,36C ''lsi'':77C ''mention'':11C ''model'':82C ''modul'':25C ''new'':58C ''nofollow'':22C ''p'':1C,65C ''pair'':40C ''radimrehurek.com'':19C ''radimrehurek.com/gensim/corpora/dictionary.html'':18C ''rel'':21C ''runtim'':63C ''support'':72C ''tf'':75C ''tf-idf'':74C ''updat'':47C,51C ''use'':6C ''variabl'':49C ''word'':33C,59C ''yet'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2410, 2, '2014-11-05 17:22:00.213', 3, NULL, NULL, '&lt;p&gt;ARIMA, Exponential Smoothing and others indeed require evenly spaced sample points. As you write, you could bucketize your data (say into days), but as you also write, you would lose information. In addition, you may end up with missing values, so you would need to impute, since ARIMA is not very good at handling missing values.&lt;/p&gt;

&lt;p&gt;One alternative, as you again write, is to feed time dummies into a regression framework. I personally do not really like categorical dummies, because this implies a sharp cutoff between neighboring categories. This is usually not very natural. So I would rather look at periodic splines with different periodicities. This approach has the advantage of dealing with your uneven sampling and also with missing values.&lt;/p&gt;

&lt;p&gt;Be &lt;em&gt;very&lt;/em&gt; careful about interpreting $R^2$. In-sample fit is notoriously misleading as a measure of out-of-sample forecast accuracy (&lt;a href="https://www.otexts.org/fpp/2/5" rel="nofollow"&gt;see here&lt;/a&gt;). I would argue that this disconnect between in-sample fit and out-of-sample forecast accuracy also means that there is no connection between in-sample fit and how well a model "understood" the data, even if your interest lies not in forecasting, but only in modeling per se. My philosophy is that if you can''t forecast a time series well, you haven''t understood it in any meaningful sense.&lt;/p&gt;

&lt;p&gt;Finally, don''t overdo the modeling. Just from eyeballing your data, it is obvious that &lt;em&gt;something&lt;/em&gt; happened in June, on one day in August and in September/October. I suggest you first find out what this &lt;em&gt;something&lt;/em&gt; was and include this in your model, e.g., as explanatory variables (which you can include in ARIMAX if you want to). What happened there is obviously not seasonality.&lt;/p&gt;
', NULL, NULL, '''/a'':162C ''/em'':130C,256C,278C ''/fpp/2/5'':157C ''/p'':58C,125C,237C,307C ''2'':135C ''accuraci'':152C,180C ''addit'':34C ''advantag'':113C ''also'':27C,121C,181C ''altern'':61C ''approach'':110C ''argu'':165C ''arima'':2C,49C ''arimax'':295C ''august'':264C ''bucket'':18C ''care'':131C ''categor'':81C ''categori'':91C ''connect'':187C ''could'':17C ''cutoff'':88C ''data'':20C,200C,249C ''day'':23C,262C ''deal'':115C ''differ'':107C ''disconnect'':168C ''dummi'':70C,82C ''e.g'':286C ''em'':128C,254C,276C ''end'':37C ''even'':9C,201C ''explanatori'':288C ''exponenti'':3C ''eyebal'':247C ''feed'':68C ''final'':239C ''find'':272C ''first'':271C ''fit'':139C,173C,192C ''forecast'':151C,179C,208C,223C ''framework'':74C ''good'':53C ''handl'':55C ''happen'':257C,301C ''haven'':229C ''href'':154C ''impli'':85C ''imput'':47C ''in-sampl'':136C,170C,189C ''includ'':281C,293C ''inde'':7C ''inform'':32C ''interest'':204C ''interpret'':133C ''june'':259C ''lie'':205C ''like'':80C ''look'':102C ''lose'':31C ''may'':36C ''mean'':182C ''meaning'':235C ''measur'':145C ''mislead'':142C ''miss'':40C,56C,123C ''model'':197C,212C,244C,285C ''natur'':97C ''need'':45C ''neighbor'':90C ''nofollow'':159C ''notori'':141C ''obvious'':252C,304C ''one'':60C,261C ''other'':6C ''out-of-sampl'':147C,175C ''overdo'':242C ''p'':1C,59C,126C,238C ''per'':213C ''period'':104C,108C ''person'':76C ''philosophi'':216C ''point'':12C ''r'':134C ''rather'':101C ''realli'':79C ''regress'':73C ''rel'':158C ''requir'':8C ''sampl'':11C,119C,138C,150C,172C,178C,191C ''say'':21C ''se'':214C ''season'':306C ''see'':160C ''sens'':236C ''september/october'':267C ''seri'':226C ''sharp'':87C ''sinc'':48C ''smooth'':4C ''someth'':255C,277C ''space'':10C ''spline'':105C ''suggest'':269C ''time'':69C,225C ''understood'':198C,231C ''uneven'':118C ''usual'':94C ''valu'':41C,57C,124C ''variabl'':289C ''want'':298C ''well'':195C,227C ''would'':30C,44C,100C,164C ''write'':15C,28C,65C ''www.otexts.org'':156C ''www.otexts.org/fpp/2/5'':155C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2411, 2, '2014-11-05 17:50:39.573', 0, NULL, NULL, '&lt;p&gt;Sure, you can. Companies are clamoring for data scientists. Be careful though that they all interpret the term differently. Depending on the company you might find yourself asked to do anything from statistics to writing production code. Either one is a full-time job in itself and you have to be prepared for both, so asking for deep specialized knowledge on top of that''s not reasonable, in my opinion, and the companies I''ve talked to stressed the other two areas (esp. the programming). However, I found that it helps to be familiar with the types of problems that you might face. Depending on the sector, that could be anomaly detection, recommendation/personalization, prediction, record linkage, etc. These are things you can learn as examples at the same times as maths and programming.&lt;/p&gt;
', NULL, NULL, '''/p'':136C ''anomali'':113C ''anyth'':32C ''area'':84C ''ask'':29C,58C ''care'':12C ''clamor'':7C ''code'':38C ''compani'':5C,24C,75C ''could'':111C ''data'':9C ''deep'':60C ''depend'':21C,106C ''detect'':114C ''differ'':20C ''either'':39C ''esp'':85C ''etc'':119C ''exampl'':127C ''face'':105C ''familiar'':96C ''find'':27C ''found'':90C ''full'':44C ''full-tim'':43C ''help'':93C ''howev'':88C ''interpret'':17C ''job'':46C ''knowledg'':62C ''learn'':125C ''linkag'':118C ''math'':133C ''might'':26C,104C ''one'':40C ''opinion'':72C ''p'':1C ''predict'':116C ''prepar'':54C ''problem'':101C ''product'':37C ''program'':87C,135C ''reason'':69C ''recommendation/personalization'':115C ''record'':117C ''scientist'':10C ''sector'':109C ''special'':61C ''statist'':34C ''stress'':80C ''sure'':2C ''talk'':78C ''term'':19C ''thing'':122C ''though'':13C ''time'':45C,131C ''top'':64C ''two'':83C ''type'':99C ''ve'':77C ''write'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2412, 1, '2014-11-05 18:57:55.037', 1, 103, 'Learning resources for Data Science for applications in road traffic data?', '&lt;h1&gt;My Background&lt;/h1&gt;

&lt;p&gt;I am a graduate student in Civil Engineering. For the analyses of road traffic data (vehicle trajectories as time series) I work with big data sets mostly about a million data points or more.&lt;br&gt;
I started using R language when MS Excel could not open the big data files. Using basic statistics knowledge and R code I developed few algorithms to identify certain patterns in the data which worked for many applications. But I still lack serious programming skills in R.&lt;br&gt;
Now, I am familiar with basic inferential statistics and R packages (plyr, dplyr, ggplot2, etc). Recently I came to know that Machine Learning algorithms also help in defining patterns in the data through supervised/ unsupervised learning and their application might improve the accuracy of prediction of certain ''behaviors'' of drivers using the traffic data.&lt;/p&gt;

&lt;h1&gt;Question&lt;/h1&gt;

&lt;p&gt;Having the basic knowledge of Statistics and R, I want to learn about the data science/ machine learning as a beginner. I know that some concepts in Stats. and ML overlap and that might bridge the gap in my learning of ML. Keeping my background in mind, what resources (books/ online courses) would you recommend me to start learning data science and apply it in my field?&lt;/p&gt;
', 'recommendation', NULL, '''/h1'':16C,158C ''/p'':155C,227C ''accuraci'':143C ''algorithm'':78C,124C ''also'':125C ''analys'':28C ''appli'':222C ''applic'':7A,90C,139C ''background'':15C,204C ''basic'':69C,106C,162C ''beginn'':180C ''behavior'':148C ''big'':41C,65C ''book'':209C ''br'':52C,100C ''bridg'':194C ''came'':118C ''certain'':81C,147C ''civil'':24C ''code'':74C ''concept'':185C ''could'':61C ''cours'':211C ''data'':4A,11A,32C,42C,48C,66C,85C,132C,154C,174C,219C ''defin'':128C ''develop'':76C ''dplyr'':113C ''driver'':150C ''engin'':25C ''etc'':115C ''excel'':60C ''familiar'':104C ''field'':226C ''file'':67C ''gap'':196C ''ggplot2'':114C ''graduat'':21C ''h1'':13C,156C ''help'':126C ''identifi'':80C ''improv'':141C ''inferenti'':107C ''keep'':202C ''know'':120C,182C ''knowledg'':71C,163C ''lack'':94C ''languag'':57C ''learn'':1A,123C,136C,171C,177C,199C,218C ''machin'':122C,176C ''mani'':89C ''might'':140C,193C ''million'':47C ''mind'':206C ''ml'':189C,201C ''most'':44C ''ms'':59C ''onlin'':210C ''open'':63C ''overlap'':190C ''p'':17C,159C ''packag'':111C ''pattern'':82C,129C ''plyr'':112C ''point'':49C ''predict'':145C ''program'':96C ''question'':157C ''r'':56C,73C,99C,110C,167C ''recent'':116C ''recommend'':12B,214C ''resourc'':2A,208C ''road'':9A,30C ''scienc'':5A,175C,220C ''seri'':37C ''serious'':95C ''set'':43C ''skill'':97C ''start'':54C,217C ''stat'':187C ''statist'':70C,108C,165C ''still'':93C ''student'':22C ''supervis'':134C ''time'':36C ''traffic'':10A,31C,153C ''trajectori'':34C ''unsupervis'':135C ''use'':55C,68C,151C ''vehicl'':33C ''want'':169C ''work'':39C,87C ''would'':212C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2413, 2, '2014-11-05 21:22:09.193', 1, NULL, NULL, '&lt;p&gt;One way to use both metric1 and metric2 in order to find anomalies in metric3 is to consider residual analysis. &lt;/p&gt;

&lt;p&gt;In your case, this would require, creating a predictive model with metric1 and metric2 as the predictors and metric3 as the response variable. &lt;/p&gt;

&lt;p&gt;Then, calculate the residuals for metric3 as its predicted value subtracted from its true value. Now, you can report the all members of the lowest decile [or any other percentile] as one kind of an anomaly and all the members of the highest decile [or any other percentile]   as another kind of an anomaly. &lt;/p&gt;
', NULL, NULL, '''/p'':22C,47C,103C ''analysi'':21C ''anomali'':14C,84C,102C ''anoth'':98C ''calcul'':50C ''case'':26C ''consid'':19C ''creat'':30C ''decil'':74C,92C ''find'':13C ''highest'':91C ''kind'':81C,99C ''lowest'':73C ''member'':70C,88C ''metric1'':7C,35C ''metric2'':9C,37C ''metric3'':16C,42C,54C ''model'':33C ''one'':2C,80C ''order'':11C ''p'':1C,23C,48C ''percentil'':78C,96C ''predict'':32C,57C ''predictor'':40C ''report'':67C ''requir'':29C ''residu'':20C,52C ''respons'':45C ''subtract'':59C ''true'':62C ''use'':5C ''valu'':58C,63C ''variabl'':46C ''way'':3C ''would'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2434, 2, '2014-11-10 02:15:27.147', 4, NULL, NULL, '&lt;p&gt;More importantly than the technology is the type of join you are using. For instance if the join keys are sorted, you can use sort merge joins and use join orders to get a better performance. &lt;/p&gt;

&lt;p&gt;That being said, you can use in memory solutions for fastest joins if the size of your intermediate results will not blow up your cluster memory. Look at Spark SQL or Mem-SQL for instance.&lt;/p&gt;
', NULL, NULL, '''/p'':38C,76C ''better'':36C ''blow'':61C ''cluster'':64C ''fastest'':50C ''get'':34C ''import'':3C ''instanc'':16C,75C ''intermedi'':57C ''join'':11C,19C,28C,31C,51C ''key'':20C ''look'':66C ''mem'':72C ''mem-sql'':71C ''memori'':47C,65C ''merg'':27C ''order'':32C ''p'':1C,39C ''perform'':37C ''result'':58C ''said'':42C ''size'':54C ''solut'':48C ''sort'':22C,26C ''spark'':68C ''sql'':69C,73C ''technolog'':6C ''type'':9C ''use'':14C,25C,30C,45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5714, 1, '2015-05-07 19:54:35.19', 1, 26, 'HiveQL stats raw vs totalSize', '&lt;p&gt;When running 
&lt;code&gt;ANALYZE TABLE Table1 PARTITION(units) COMPUTE STATISTICS;&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;I recieve the following:
&lt;code&gt;stats: [numFiles=25, numRows=56351355, totalSize=291119418, rawDataSize=6649459890]&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;What is the difference between &lt;code&gt;totalSize&lt;/code&gt; and &lt;code&gt;rawDataSize&lt;/code&gt;?&lt;/p&gt;

&lt;p&gt;Obviously there are more partitions I assume I just need to add one of these stats for each partition but what is &lt;code&gt;rawDataSize&lt;/code&gt; and &lt;code&gt;totalSize&lt;/code&gt;?&lt;/p&gt;
', 'bigdata', NULL, '''/code'':18C,35C,45C,49C,76C,80C ''/p'':19C,36C,50C,81C ''25'':28C ''291119418'':32C ''56351355'':30C ''6649459890'':34C ''add'':63C ''analyz'':11C ''assum'':58C ''bigdata'':6B ''code'':10C,25C,43C,47C,74C,78C ''comput'':16C ''differ'':41C ''follow'':24C ''hiveql'':1A ''need'':61C ''numfil'':27C ''numrow'':29C ''obvious'':52C ''one'':64C ''p'':7C,20C,37C,51C ''partit'':14C,56C,70C ''raw'':3A ''rawdatas'':33C,48C,75C ''reciev'':22C ''run'':9C ''stat'':2A,26C,67C ''statist'':17C ''tabl'':12C ''table1'':13C ''totals'':5A,31C,44C,79C ''unit'':15C ''vs'':4A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2437, 1, '2014-11-10 11:13:07.987', 2, 73, 'How does the supposed "Unified Architecture for NLP" from Collobert and Weston 2008 really works?', '&lt;p&gt;n this paper (&lt;a href="http://ronan.collobert.com/pub/matos/2008_nlp_icml.pdf" rel="nofollow"&gt;here&lt;/a&gt;)  they suppose a "unified architecture for NLP" with deep neural networks with multitask learning&lt;/p&gt;

&lt;p&gt;My problem is to understand the layer architecture in figure 1, see below:
&lt;img src="http://i.stack.imgur.com/RoU0P.png" alt="unified deep learning architecture for NLP"&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Is someone able to give me a concrete, reproducible example how this architecture processing 3 sentences through their layers?&lt;/li&gt;
&lt;li&gt;What are the outputs after each layer? &lt;/li&gt;
&lt;li&gt;Why they choose which layer?&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Thans in advance!&lt;/p&gt;
', 'nlp  neuralnetwork', NULL, '''/a'':30C ''/li'':94C,103C,110C ''/ol'':111C ''/p'':45C,72C,116C ''/pub/matos/2008_nlp_icml.pdf'':26C ''/rou0p.png'':64C ''1'':57C ''2008'':13A ''3'':89C ''abl'':77C ''advanc'':115C ''alt'':65C ''architectur'':6A,35C,54C,69C,87C ''choos'':107C ''collobert'':10A ''concret'':82C ''deep'':39C,67C ''exampl'':84C ''figur'':56C ''give'':79C ''href'':23C ''i.stack.imgur.com'':63C ''i.stack.imgur.com/rou0p.png'':62C ''img'':60C ''layer'':53C,93C,102C,109C ''learn'':44C,68C ''li'':74C,95C,104C ''multitask'':43C ''n'':19C ''network'':41C ''neural'':40C ''neuralnetwork'':17B ''nlp'':8A,16B,37C,71C ''nofollow'':28C ''ol'':73C ''output'':99C ''p'':18C,46C,112C ''paper'':21C ''problem'':48C ''process'':88C ''realli'':14A ''rel'':27C ''reproduc'':83C ''ronan.collobert.com'':25C ''ronan.collobert.com/pub/matos/2008_nlp_icml.pdf'':24C ''see'':58C ''sentenc'':90C ''someon'':76C ''src'':61C ''suppos'':4A,32C ''than'':113C ''understand'':51C ''unifi'':5A,34C,66C ''weston'':12A ''work'':15A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2414, 2, '2014-11-05 21:55:43.587', 1, NULL, NULL, '&lt;p&gt;The best way to learn data science is through problem solving. I suggest you to head over to Kaggle and work through the for-knowledge problems. &lt;/p&gt;

&lt;p&gt;To get a good start on Machine Learning problems, acclimate yourself with the tree package in R. This will help you understand how decision trees work, and building upon that, how random forests, gradient boosting machines and other sophisticated tree based algorithms work. &lt;/p&gt;

&lt;p&gt;Then, there are SVMs and deep learning models. &lt;/p&gt;

&lt;p&gt;To get an understanding of unsupervised learning problems, learn k-means and employ it for clustering. &lt;/p&gt;

&lt;p&gt;Other general concepts/ ideas to understand are: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;cross-validation &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;overfitting, regularization &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;bias-variance trade off&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;dimensionality reduction/ variable selection&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;generalization error&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;ensemble learning &lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;For books, the most common recommendation to anyone who is familiar with statistics and wants to get into Machine Learning is &lt;a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/" rel="nofollow"&gt;"The Elements of Statistical Learning"&lt;/a&gt; by Hastie, Tibshirani, and Friedman.  &lt;/p&gt;
', NULL, NULL, '''/a'':191C ''/li'':120C,126C,135C,143C,149C,155C ''/ol'':156C ''/p'':29C,74C,84C,103C,112C,119C,125C,134C,142C,148C,154C,197C ''/~tibs/elemstatlearn/'':183C ''acclim'':40C ''algorithm'':72C ''anyon'':165C ''base'':71C ''best'':3C ''bias'':130C ''bias-vari'':129C ''book'':159C ''boost'':65C ''build'':58C ''cluster'':102C ''common'':162C ''concept'':107C ''cross'':117C ''cross-valid'':116C ''data'':7C ''decis'':54C ''deep'':81C ''dimension'':138C ''element'':187C ''employ'':99C ''ensembl'':152C ''error'':147C ''familiar'':168C ''for-knowledg'':25C ''forest'':63C ''friedman'':196C ''general'':106C,146C ''get'':32C,87C,174C ''good'':34C ''gradient'':64C ''hasti'':193C ''head'':17C ''help'':50C ''href'':180C ''idea'':108C ''k'':96C ''k-mean'':95C ''kaggl'':20C ''knowledg'':27C ''learn'':6C,38C,82C,92C,94C,153C,177C,190C ''li'':114C,121C,127C,136C,144C,150C ''machin'':37C,66C,176C ''mean'':97C ''model'':83C ''nofollow'':185C ''ol'':113C ''overfit'':123C ''p'':1C,30C,75C,85C,104C,115C,122C,128C,137C,145C,151C,157C ''packag'':45C ''problem'':11C,28C,39C,93C ''r'':47C ''random'':62C ''recommend'':163C ''reduct'':139C ''regular'':124C ''rel'':184C ''scienc'':8C ''select'':141C ''solv'':12C ''sophist'':69C ''start'':35C ''statist'':170C,189C ''statweb.stanford.edu'':182C ''statweb.stanford.edu/~tibs/elemstatlearn/'':181C ''suggest'':14C ''svms'':79C ''tibshirani'':194C ''trade'':132C ''tree'':44C,55C,70C ''understand'':52C,89C,110C ''unsupervis'':91C ''upon'':59C ''valid'':118C ''variabl'':140C ''varianc'':131C ''want'':172C ''way'':4C ''work'':22C,56C,73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8993, 1, '2015-11-23 16:49:32.403', 1, 30, 'How to select a bunch of optimized data from a larger data set?', '&lt;p&gt;How to select a bunch of optimized data from a larger data set? I have a data set containing the products to be sold to the customers. And the products were sold before, it has some information, like the review, ratings, how many were sold per product from the customers. Now, I want to select a bunch of optimized data from a larger data set based on the number of units sold, ratings, review etc all the information. The purpose is to increase the number of units to be sold in the future. How to do this? What kind of model, method, algorithms should be used here? If it is related to machine learning, what kind of machine learning tools should I use here? Thank you so much. &lt;/p&gt;
', 'optimization  data', NULL, '''/p'':145C ''algorithm'':119C ''base'':82C ''bunch'':5A,21C,73C ''contain'':35C ''custom'':43C,66C ''data'':8A,12A,15B,24C,28C,33C,76C,80C ''etc'':91C ''futur'':109C ''increas'':99C ''inform'':53C,94C ''kind'':115C,132C ''larger'':11A,27C,79C ''learn'':130C,135C ''like'':54C ''machin'':129C,134C ''mani'':59C ''method'':118C ''model'':117C ''much'':144C ''number'':85C,101C ''optim'':7A,14B,23C,75C ''p'':16C ''per'':62C ''product'':37C,46C,63C ''purpos'':96C ''rate'':57C,89C ''relat'':127C ''review'':56C,90C ''select'':3A,19C,71C ''set'':13A,29C,34C,81C ''sold'':40C,48C,61C,88C,106C ''thank'':141C ''tool'':136C ''unit'':87C,103C ''use'':122C,139C ''want'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2416, 1, '2014-11-06 07:38:33.49', 3, 96, 'Similarity measure for ordered binary vectors', '&lt;p&gt;I would like to ask your opinion on how to choose a similarity measure. I have a set of vectors of length N, each element of which can contain either 0 or 1. The vectors are actually ordered sequences, so the position of each element is important. Suppose I have three vectors of length 10, x_1 x2, x3: x1 has three 1 at positions 6,7,8 (indexes start from 1. Both x2 and x3 have an additional 1, but x2 has it in position 9 while x3 has it in position 1. I am looking for a metric according to which x1 is more similar to x2 than to x3, in that the additional 1 is closer to the "bulk" of ones. I guess this is a relatively common problem, but I am confused on the best way to approach it.
Many thanks in advance!&lt;/p&gt;
', 'similarity', NULL, '''/p'':156C ''0'':39C ''1'':41C,65C,71C,80C,88C,102C,125C ''10'':63C ''6'':74C ''7'':75C ''8'':76C ''9'':95C ''accord'':109C ''actual'':45C ''addit'':87C,124C ''advanc'':155C ''approach'':150C ''ask'':13C ''best'':147C ''binari'':5A ''bulk'':130C ''choos'':19C ''closer'':127C ''common'':139C ''confus'':144C ''contain'':37C ''either'':38C ''element'':33C,53C ''guess'':134C ''import'':55C ''index'':77C ''length'':30C,62C ''like'':11C ''look'':105C ''mani'':152C ''measur'':2A,22C ''metric'':108C ''n'':31C ''one'':132C ''opinion'':15C ''order'':4A,46C ''p'':8C ''posit'':50C,73C,94C,101C ''problem'':140C ''relat'':138C ''sequenc'':47C ''set'':26C ''similar'':1A,7B,21C,115C ''start'':78C ''suppos'':56C ''thank'':153C ''three'':59C,70C ''vector'':6A,28C,43C,60C ''way'':148C ''would'':10C ''x'':64C ''x1'':68C,112C ''x2'':66C,82C,90C,117C ''x3'':67C,84C,97C,120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2417, 2, '2014-11-06 08:52:22.617', 3, NULL, NULL, '&lt;p&gt;If speed isn''t a great concern you could use a &lt;a href="http://en.wikipedia.org/wiki/Kernel_density_estimation" rel="nofollow"&gt;KDE&lt;/a&gt; with a high bandwidth to pick up the similarity between neighboring elements, then an appropriate metric like the &lt;a href="http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence" rel="nofollow"&gt;K-L divergence&lt;/a&gt;. Similarity and divergence are complementary, of course, so as a final step you would have to relate them; e.g., &lt;code&gt;sim(A, B) = exp[- KLD(A, B)]&lt;/code&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':21C,51C ''/code'':79C ''/p'':80C ''/wiki/kernel_density_estimation'':17C ''/wiki/kullback%e2%80%93leibler_divergence'':44C ''appropri'':36C ''b'':74C,78C ''bandwidth'':25C ''code'':71C ''complementari'':56C ''concern'':8C ''could'':10C ''cours'':58C ''diverg'':50C,54C ''e.g'':70C ''element'':33C ''en.wikipedia.org'':16C,43C ''en.wikipedia.org/wiki/kernel_density_estimation'':15C ''en.wikipedia.org/wiki/kullback%e2%80%93leibler_divergence'':42C ''exp'':75C ''final'':62C ''great'':7C ''high'':24C ''href'':14C,41C ''isn'':4C ''k'':48C ''k-l'':47C ''kde'':20C ''kld'':76C ''l'':49C ''like'':38C ''metric'':37C ''neighbor'':32C ''nofollow'':19C,46C ''p'':1C ''pick'':27C ''rel'':18C,45C ''relat'':68C ''sim'':72C ''similar'':30C,52C ''speed'':3C ''step'':63C ''use'':11C ''would'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2418, 2, '2014-11-06 13:56:02.497', 0, NULL, NULL, '&lt;p&gt;The &lt;a href="http://en.wikibooks.org/wiki/R_Programming" rel="nofollow"&gt;R Programming Wikibook&lt;/a&gt; is a nice collaborative handbook for R.&lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/p'':21C ''/wiki/r_programming'':7C ''collabor'':17C ''en.wikibooks.org'':6C ''en.wikibooks.org/wiki/r_programming'':5C ''handbook'':18C ''href'':4C ''nice'':16C ''nofollow'':9C ''p'':1C ''program'':11C ''r'':10C,20C ''rel'':8C ''wikibook'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2419, 2, '2014-11-06 15:19:35.017', 3, NULL, NULL, '&lt;p&gt;One thing you could do is fuzzify your vectors: replace each 1 by (for example) 0.4 in its position, 0.2 in the neighbouring positions, and 0.1 in the second position over. Then add up what''s in each position. 
With these fuzzified vectors, you can apply a similarity metric either based on a distance or one like cosines similarity. 
Your example would produce: (showing only first decimal)&lt;/p&gt;

&lt;p&gt;0000011100 -&gt; 0001378731  &lt;/p&gt;

&lt;p&gt;0000011110 -&gt; 0001378873&lt;/p&gt;

&lt;p&gt;1000011100 -&gt; 4211378731&lt;/p&gt;

&lt;p&gt;cos(x1, x2) = 0.9613,  cos(x1,x3) = 0.9469&lt;/p&gt;
', NULL, NULL, '''/p'':69C,73C,77C,81C,91C ''0.1'':27C ''0.2'':21C ''0.4'':17C ''0.9469'':90C ''0.9613'':86C ''0000011100'':71C ''0000011110'':75C ''0001378731'':72C ''0001378873'':76C ''1'':13C ''1000011100'':79C ''4211378731'':80C ''add'':34C ''appli'':47C ''base'':52C ''cos'':83C,87C ''cosin'':59C ''could'':5C ''decim'':68C ''distanc'':55C ''either'':51C ''exampl'':16C,62C ''first'':67C ''fuzzifi'':8C,43C ''like'':58C ''metric'':50C ''neighbour'':24C ''one'':2C,57C ''p'':1C,70C,74C,78C,82C ''posit'':20C,25C,31C,40C ''produc'':64C ''replac'':11C ''second'':30C ''show'':65C ''similar'':49C,60C ''thing'':3C ''vector'':10C,44C ''would'':63C ''x1'':84C,88C ''x2'':85C ''x3'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2420, 2, '2014-11-06 17:56:06.257', 0, NULL, NULL, '&lt;p&gt;Keep in mind that "big data" is an increasingly trendy thing for a company to say they''re involved in.  Higher ups might read an article about it in HBR, and say to themselves, "I''ve got to get me some of that" (not that they''re necessarily wrong). &lt;/p&gt;

&lt;p&gt;What this means for you is that the advanced analytics isn''t as necessary for that company as just getting something up and running might be. &lt;/p&gt;

&lt;p&gt;Luckily for you, most of the components said companies might need are free.  Moreover, I believe both Hortonworks and Cloudera have free "sandbox" virtual machines, which you can run on your PC, to play around with and get your bearings. &lt;/p&gt;

&lt;p&gt;Advanced analytics on big data platforms are valuable, to be sure, but many companies need to learn to crawl before they can run. &lt;/p&gt;
', NULL, NULL, '''/p'':51C,79C,121C,146C ''advanc'':61C,123C ''analyt'':62C,124C ''around'':115C ''articl'':27C ''bear'':120C ''believ'':96C ''big'':6C,126C ''cloudera'':100C ''compani'':15C,69C,89C,136C ''compon'':87C ''crawl'':141C ''data'':7C,127C ''free'':93C,102C ''get'':40C,72C,118C ''got'':38C ''hbr'':31C ''higher'':22C ''hortonwork'':98C ''increas'':10C ''involv'':20C ''isn'':63C ''keep'':2C ''learn'':139C ''luckili'':81C ''machin'':105C ''mani'':135C ''mean'':55C ''might'':24C,77C,90C ''mind'':4C ''moreov'':94C ''necessari'':66C ''necessarili'':49C ''need'':91C,137C ''p'':1C,52C,80C,122C ''pc'':112C ''platform'':128C ''play'':114C ''re'':19C,48C ''read'':25C ''run'':76C,109C,145C ''said'':88C ''sandbox'':103C ''say'':17C,33C ''someth'':73C ''sure'':133C ''thing'':12C ''trendi'':11C ''up'':23C ''valuabl'':130C ''ve'':37C ''virtual'':104C ''wrong'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9062, 2, '2015-11-26 10:40:40.813', 1, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;What criteria should I mainly take into account before selecting a
  tool for analysing big data?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;There are a lot of criterion which are to be taken into account when the tool selection is concerned. The can be:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Structure of the data. (The data model Ex: Hierarchical, tabular, etc)&lt;/li&gt;
&lt;li&gt;Type of data and what is the problem statement.   (time series, or classification, etc)&lt;/li&gt;
&lt;li&gt;Speed&lt;/li&gt;
&lt;li&gt;Security&lt;/li&gt;
&lt;/ul&gt;

&lt;blockquote&gt;
  &lt;p&gt;Aim : To develop a software that analyses the data coming from the
  wind mills and generate reports. The software should be able to
  predict when a wind mill can fail based on the analysis.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Almost all the existing analytics tools like Python, Julia, R, etc can do this.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;And is it also possible that I find an algorithm for predictive
  analysis without knowing what would be the results of the tool after
  analysis.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Yes.&lt;/strong&gt;  The predictive algorithm or technique can be inferred by looking at the data and the contents of the data. &lt;strong&gt;It is not dependant on the tool.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Some points which I would like to include which I believe would be useful to you:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Select the database depending on your data, and it''s type.  According to your data, a &lt;a href="https://en.wikipedia.org/wiki/NoSQL" rel="nofollow"&gt;NoSQL database&lt;/a&gt; would be more relevant and suitable.&lt;/li&gt;
&lt;li&gt;Select the algorithms and techniques only after you have a clear knowledge about the problem statement and takeaways and also after clearly looking at the data for an &lt;a href="https://en.wikipedia.org/wiki/Exploratory_data_analysis" rel="nofollow"&gt;exploratory analysis&lt;/a&gt;.&lt;/li&gt;
&lt;li&gt;If you want more flexibility, then use a tool/programming language like Python, R and Julia. Else, you can use a tool like Knime, Orange (it has a &lt;a href="https://pypi.python.org/pypi/Orange/2.7" rel="nofollow"&gt;Python library&lt;/a&gt; too.), RapidMiner, etc.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':243C,289C,328C ''/blockquote'':20C,119C,164C ''/li'':58C,74C,77C,80C,250C,290C,332C ''/p'':19C,44C,118C,135C,163C,197C,215C ''/pypi/orange/2.7'':323C ''/strong'':168C,196C ''/ul'':81C,333C ''/wiki/exploratory_data_analysis'':284C ''/wiki/nosql'':238C ''abl'':105C ''accord'':229C ''account'':10C,34C ''aim'':84C ''algorithm'':147C,171C,254C ''almost'':121C ''also'':141C,271C ''analys'':16C,90C ''analysi'':117C,150C,162C,288C ''analyt'':125C ''base'':114C ''believ'':209C ''big'':17C ''blockquot'':1C,82C,136C ''classif'':72C ''clear'':262C,273C ''come'':93C ''concern'':40C ''content'':184C ''criteria'':4C ''criterion'':27C ''data'':18C,50C,52C,62C,92C,181C,187C,224C,232C,277C ''databas'':220C,242C ''depend'':192C,221C ''develop'':86C ''els'':307C ''en.wikipedia.org'':237C,283C ''en.wikipedia.org/wiki/exploratory_data_analysis'':282C ''en.wikipedia.org/wiki/nosql'':236C ''etc'':57C,73C,131C,331C ''ex'':54C ''exist'':124C ''exploratori'':287C ''fail'':113C ''find'':145C ''flexibl'':296C ''generat'':99C ''hierarch'':55C ''href'':235C,281C,320C ''includ'':206C ''infer'':176C ''julia'':129C,306C ''knime'':314C ''know'':152C ''knowledg'':263C ''languag'':301C ''li'':46C,59C,75C,78C,217C,251C,291C ''librari'':327C ''like'':127C,204C,302C,313C ''look'':178C,274C ''lot'':25C ''main'':7C ''mill'':97C,111C ''model'':53C ''nofollow'':240C,286C,325C ''nosql'':241C ''orang'':315C ''p'':2C,21C,83C,120C,137C,165C,198C ''point'':200C ''possibl'':142C ''predict'':107C,149C,170C ''problem'':67C,266C ''pypi.python.org'':322C ''pypi.python.org/pypi/orange/2.7'':321C ''python'':128C,303C,326C ''r'':130C,304C ''rapidmin'':330C ''rel'':239C,285C,324C ''relev'':247C ''report'':100C ''result'':157C ''secur'':79C ''select'':12C,38C,218C,252C ''seri'':70C ''softwar'':88C,102C ''speed'':76C ''statement'':68C,267C ''strong'':166C,188C ''structur'':47C ''suitabl'':249C ''tabular'':56C ''take'':8C ''takeaway'':269C ''taken'':32C ''techniqu'':173C,256C ''time'':69C ''tool'':14C,37C,126C,160C,195C,312C ''tool/programming'':300C ''type'':60C,228C ''ul'':45C,216C ''use'':212C,298C,310C ''want'':294C ''wind'':96C,110C ''without'':151C ''would'':154C,203C,210C,244C ''yes'':167C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2421, 2, '2014-11-06 21:36:51.897', 0, NULL, NULL, '&lt;p&gt;This is a really strange question in my opinion. Why you''re going to move in a new direction if you are not sure that you love this new direction or at least find it very interesting? If you do love Big Data, why do you care about the PhD intelligent creatures that are already in the field? The same amount of PhD creatures are in every area of IT. Please have a quick read at this very nice article &lt;a href="http://www.forbes.com/sites/louisefron/2013/09/13/why-you-cant-find-a-job-you-love/" rel="nofollow"&gt;http://www.forbes.com/sites/louisefron/2013/09/13/why-you-cant-find-a-job-you-love/&lt;/a&gt; and then ask yourself if you love Big Data enough and you are ready to add your grain of sand to the mountain of knowledge&lt;/p&gt;
', NULL, NULL, '''/p'':117C ''/sites/louisefron/2013/09/13/why-you-cant-find-a-job-you-love/'':86C ''/sites/louisefron/2013/09/13/why-you-cant-find-a-job-you-love/&lt;/a&gt;'':91C ''add'':107C ''alreadi'':56C ''amount'':62C ''area'':69C ''articl'':81C ''ask'':94C ''big'':43C,99C ''care'':48C ''creatur'':53C,65C ''data'':44C,100C ''direct'':20C,31C ''enough'':101C ''everi'':68C ''field'':59C ''find'':35C ''go'':14C ''grain'':109C ''href'':83C ''intellig'':52C ''interest'':38C ''knowledg'':116C ''least'':34C ''love'':28C,42C,98C ''mountain'':114C ''move'':16C ''new'':19C,30C ''nice'':80C ''nofollow'':88C ''opinion'':10C ''p'':1C ''phd'':51C,64C ''pleas'':72C ''question'':7C ''quick'':75C ''re'':13C ''read'':76C ''readi'':105C ''realli'':5C ''rel'':87C ''sand'':111C ''strang'':6C ''sure'':25C ''www.forbes.com'':85C,90C ''www.forbes.com/sites/louisefron/2013/09/13/why-you-cant-find-a-job-you-love/'':84C ''www.forbes.com/sites/louisefron/2013/09/13/why-you-cant-find-a-job-you-love/&lt;/a&gt;'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2422, 1, '2014-11-07 04:13:44.583', 2, 453, 'Clarification about Octave data size limit', '&lt;p&gt;I''m just starting to work on a relatively large dataset after ML course in Coursera.
Trying to work on &lt;a href="https://archive.ics.uci.edu/ml/datasets/YearPredictionMSD" rel="nofollow"&gt;https://archive.ics.uci.edu/ml/datasets/YearPredictionMSD&lt;/a&gt;.
Got an accuracy of 5.2 in training and test set with linear regression using gradient descent in octave.&lt;/p&gt;

&lt;p&gt;I tried adding all possible quadratic features (515345 instances and 4275 features), but the code just won''t stop executing in my HP Pavilion g6 2320tx, with 4GB RAM in Ubuntu 14.04.&lt;/p&gt;

&lt;p&gt;Is this beyond the data size capacity of Octave ?&lt;/p&gt;
', 'octave', NULL, '''/ml/datasets/yearpredictionmsd'':33C ''/ml/datasets/yearpredictionmsd&lt;/a&gt;.'':38C ''/p'':57C,91C,102C ''14.04'':90C ''2320tx'':84C ''4275'':69C ''4gb'':86C ''5.2'':43C ''515345'':66C ''accuraci'':41C ''ad'':61C ''archive.ics.uci.edu'':32C,37C ''archive.ics.uci.edu/ml/datasets/yearpredictionmsd'':31C ''archive.ics.uci.edu/ml/datasets/yearpredictionmsd&lt;/a&gt;.'':36C ''beyond'':95C ''capac'':99C ''clarif'':1A ''code'':73C ''cours'':22C ''coursera'':24C ''data'':4A,97C ''dataset'':19C ''descent'':54C ''execut'':78C ''featur'':65C,70C ''g6'':83C ''got'':39C ''gradient'':53C ''hp'':81C ''href'':30C ''instanc'':67C ''larg'':18C ''limit'':6A ''linear'':50C ''m'':10C ''ml'':21C ''nofollow'':35C ''octav'':3A,7B,56C,101C ''p'':8C,58C,92C ''pavilion'':82C ''possibl'':63C ''quadrat'':64C ''ram'':87C ''regress'':51C ''rel'':34C ''relat'':17C ''set'':48C ''size'':5A,98C ''start'':12C ''stop'':77C ''test'':47C ''train'':45C ''tri'':25C,60C ''ubuntu'':89C ''use'':52C ''won'':75C ''work'':14C,27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2423, 2, '2014-11-07 05:53:25.6', 2, NULL, NULL, '&lt;p&gt;You have about 4GB of RAM on your machine and Octave is an in memory application. &lt;/p&gt;

&lt;p&gt;If you want to work with 515345 instances and 4275 features, assuming that you are using double precision (i.e. 8 bytes), you would need a memory of 515345*4275*8/1000000/1024 bytes ~ 17.6 GB. Even if you were using 4 bytes for each data point, you would require at least 9 GB for the computation to go through. &lt;/p&gt;

&lt;p&gt;This issue might not be the Octave memory restriction in this case. See &lt;a href="http://stackoverflow.com/questions/565806/is-anyone-using-64-bit-build-of-octave"&gt;here&lt;/a&gt; for further details on Octave''s memory usage. &lt;/p&gt;
', NULL, NULL, '''/a'':98C ''/p'':18C,77C,107C ''/questions/565806/is-anyone-using-64-bit-build-of-octave'':96C ''17.6'':51C ''4'':58C ''4275'':29C,48C ''4gb'':5C ''515345'':26C,47C ''8'':39C ''8/1000000/1024'':49C ''9'':69C ''applic'':17C ''assum'':31C ''byte'':40C,50C,59C ''case'':90C ''comput'':73C ''data'':62C ''detail'':101C ''doubl'':36C ''even'':53C ''featur'':30C ''gb'':52C,70C ''go'':75C ''href'':93C ''i.e'':38C ''instanc'':27C ''issu'':80C ''least'':68C ''machin'':10C ''memori'':16C,45C,86C,105C ''might'':81C ''need'':43C ''octav'':12C,85C,103C ''p'':1C,19C,78C ''point'':63C ''precis'':37C ''ram'':7C ''requir'':66C ''restrict'':87C ''see'':91C ''stackoverflow.com'':95C ''stackoverflow.com/questions/565806/is-anyone-using-64-bit-build-of-octave'':94C ''usag'':106C ''use'':35C,57C ''want'':22C ''work'':24C ''would'':42C,65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2438, 2, '2014-11-10 11:16:07.537', 2, NULL, NULL, '&lt;p&gt;You can easily have an RStudio server installed in Digital Ocean using this package:
&lt;a href="https://github.com/sckott/analogsea" rel="nofollow"&gt;https://github.com/sckott/analogsea&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/sckott/analogsea'':20C ''/sckott/analogsea&lt;/a&gt;&lt;/p&gt;'':25C ''digit'':11C ''easili'':4C ''github.com'':19C,24C ''github.com/sckott/analogsea'':18C ''github.com/sckott/analogsea&lt;/a&gt;&lt;/p&gt;'':23C ''href'':17C ''instal'':9C ''nofollow'':22C ''ocean'':12C ''p'':1C ''packag'':15C ''rel'':21C ''rstudio'':7C ''server'':8C ''use'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2490, 2, '2014-11-18 09:48:19.333', 1, NULL, NULL, '&lt;p&gt;Your dataset can be viewed as a directed graph. The party''s location (latitude and longitude) can be denoted as a node and the directed edge can denote who referred whom. Once the dataset can be viewed as this, the problem boils down to joining co-ordinates with lines. &lt;/p&gt;
', NULL, NULL, '''/p'':52C ''boil'':43C ''co'':48C ''co-ordin'':47C ''dataset'':3C,35C ''denot'':20C,29C ''direct'':9C,26C ''edg'':27C ''graph'':10C ''join'':46C ''latitud'':15C ''line'':51C ''locat'':14C ''longitud'':17C ''node'':23C ''ordin'':49C ''p'':1C ''parti'':12C ''problem'':42C ''refer'':31C ''view'':6C,38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5393, 1, '2015-03-26 00:58:14.003', 0, 41, 'Mahout SSVD, how should I set k and p', '&lt;p&gt;I have been looking at Stochastic Single Value Decomposition in Mahout for implementing a distributed LSA algorithm. However, I am having trouble finding the best way to set k and p such that k+p &amp;lt; min(m,n). Is there an optimal way to set k and p? I know that p should not exceed 10% of k and that k is the rank (typically from 20 to 200 according to the documents). Can I relate it to the number of dependent vectors?&lt;/p&gt;
', 'apache-mahout', NULL, '''/p'':98C ''10'':70C ''20'':81C ''200'':83C ''accord'':84C ''algorithm'':30C ''apach'':11B ''apache-mahout'':10B ''best'':38C ''decomposit'':22C ''depend'':96C ''distribut'':28C ''document'':87C ''exceed'':69C ''find'':36C ''howev'':31C ''implement'':26C ''k'':7A,42C,47C,60C,72C,75C ''know'':64C ''look'':17C ''lsa'':29C ''lt'':49C ''m'':51C ''mahout'':1A,12B,24C ''min'':50C ''n'':52C ''number'':94C ''optim'':56C ''p'':9A,13C,44C,48C,62C,66C ''rank'':78C ''relat'':90C ''set'':6A,41C,59C ''singl'':20C ''ssvd'':2A ''stochast'':19C ''troubl'':35C ''typic'':79C ''valu'':21C ''vector'':97C ''way'':39C,57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2427, 1, '2014-11-08 12:22:17.92', 6, 90, 'The meaning of multi-class classification rules', '&lt;p&gt;The meaning of multi-class classification rules&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Example: I have two classification rules (Refund is a predictor and Cheat is a binary response):
(Refund, No) â (Cheat, No) Support = 0.4, Confidence = 0.57
(Refund, No) â (Cheat, Yes) Support = 0.3, Confidence = 0.43
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;=&gt; multi-class classification rules:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(Refund, No) â (Cheat, No) v (Cheat, Yes)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;When predicted classification for test data, (Cheat, No) will be selected priority so why we need to have (Cheat, Yes) in multi-class classification rules here?&lt;/p&gt;
', 'classification', NULL, '''/code'':56C,75C ''/p'':19C,64C,105C ''/pre'':57C,76C ''0.3'':53C ''0.4'':44C ''0.43'':55C ''0.57'':46C ''binari'':36C ''cheat'':33C,41C,50C,70C,73C,84C,96C ''class'':6A,16C,61C,101C ''classif'':7A,9B,17C,26C,62C,80C,102C ''code'':21C,66C ''confid'':45C,54C ''data'':83C ''exampl'':22C ''mean'':2A,12C ''multi'':5A,15C,60C,100C ''multi-class'':4A,14C,59C,99C ''need'':93C ''p'':10C,58C,77C ''pre'':20C,65C ''predict'':79C ''predictor'':31C ''prioriti'':89C ''refund'':28C,38C,47C,67C ''respons'':37C ''rule'':8A,18C,27C,63C,103C ''select'':88C ''support'':43C,52C ''test'':82C ''two'':25C ''v'':72C ''yes'':51C,74C,97C ''â'':40C,49C,69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2428, 2, '2014-11-08 18:33:37.063', 3, NULL, NULL, '&lt;pre&gt;&lt;code&gt;(Cheat, No) will be selected (cheat will be classified as No) for the rule (Refund, No). 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In a binary response variable like cheat, all the information can be inferred from just the first rule: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(Refund, No) â (Cheat, No) Support = 0.4, Confidence = 0.57 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The other rule is redundant. &lt;/p&gt;

&lt;p&gt;However, in the case of a multi-class response variable, we would like to have all the rules written out so we exactly know the likelihood of the rule implying each of the different classes in the response variable. To keep things consistent, this is also done for the case when the response variable is binary. &lt;/p&gt;
', NULL, NULL, '''/code'':19C,52C ''/p'':40C,60C,119C ''/pre'':20C,53C ''0.4'':49C ''0.57'':51C ''also'':108C ''binari'':24C,118C ''case'':65C,112C ''cheat'':3C,8C,28C,46C ''class'':70C,97C ''classifi'':11C ''code'':2C,42C ''confid'':50C ''consist'':105C ''differ'':96C ''done'':109C ''exact'':85C ''first'':38C ''howev'':62C ''impli'':92C ''infer'':34C ''inform'':31C ''keep'':103C ''know'':86C ''like'':27C,75C ''likelihood'':88C ''multi'':69C ''multi-class'':68C ''p'':21C,54C,61C ''pre'':1C,41C ''redund'':59C ''refund'':17C,43C ''respons'':25C,71C,100C,115C ''rule'':16C,39C,57C,80C,91C ''select'':7C ''support'':48C ''thing'':104C ''variabl'':26C,72C,101C,116C ''would'':74C ''written'':81C ''â'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5715, 1, '2015-05-07 20:08:05.44', 4, 302, 'Performance profiling and tuning in Apache Spark', '&lt;p&gt;Beyond the immediate suspects defined in the &lt;a href="https://spark.apache.org/docs/latest/tuning.html#memory-tuning" rel="nofollow"&gt;spark documentation&lt;/a&gt;, what are some ways to profile, tune and boost performance of an Apache Spark application? &lt;/p&gt;
', 'apache-spark', NULL, '''/a'':28C ''/docs/latest/tuning.html#memory-tuning'':23C ''/p'':44C ''apach'':6A,9B,41C ''apache-spark'':8B ''applic'':43C ''beyond'':12C ''boost'':37C ''defin'':16C ''document'':27C ''href'':20C ''immedi'':14C ''nofollow'':25C ''p'':11C ''perform'':1A,38C ''profil'':2A,34C ''rel'':24C ''spark'':7A,10B,26C,42C ''spark.apache.org'':22C ''spark.apache.org/docs/latest/tuning.html#memory-tuning'':21C ''suspect'':15C ''tune'':4A,35C ''way'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2429, 2, '2014-11-08 20:29:37.377', 1, NULL, NULL, '&lt;p&gt;The first thing you should do is to identify how large an error your analysis can handle. That will make your job much easier because you won''t have to find everything.&lt;/p&gt;

&lt;p&gt;A standard way of identifying "suspicious" data is is Benford''s Law, which predicts the distribution of the first digit of each number. It can also be generalized for for other digits. &lt;a href="http://en.wikipedia.org/wiki/Benford" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Benford&lt;/a&gt;''s_law&lt;/p&gt;

&lt;p&gt;As for finding outliers, I''d probably use boxplots, particularly because you can achieve high data density with them, reducing the time to manually skim them.&lt;/p&gt;

&lt;p&gt;One thing that might be useful is to compare the ratio of one variable to another- in my company we use this method all the time.&lt;/p&gt;
', NULL, NULL, '''/p'':34C,105C,133C ''/wiki/benford'':72C ''/wiki/benford&lt;/a&gt;''''s_law&lt;/p&gt;'':77C ''achiev'':92C ''also'':61C ''analysi'':16C ''anoth'':122C ''benford'':45C ''boxplot'':87C ''compani'':125C ''compar'':115C ''d'':84C ''data'':42C,94C ''densiti'':95C ''digit'':55C,67C ''distribut'':51C ''easier'':25C ''en.wikipedia.org'':71C,76C ''en.wikipedia.org/wiki/benford'':70C ''en.wikipedia.org/wiki/benford&lt;/a&gt;''''s_law&lt;/p&gt;'':75C ''error'':14C ''everyth'':33C ''find'':32C,81C ''first'':3C,54C ''general'':63C ''handl'':18C ''high'':93C ''href'':69C ''identifi'':10C,40C ''job'':23C ''larg'':12C ''law'':47C ''make'':21C ''manual'':102C ''method'':129C ''might'':110C ''much'':24C ''nofollow'':74C ''number'':58C ''one'':107C,119C ''outlier'':82C ''p'':1C,35C,78C,106C ''particular'':88C ''predict'':49C ''probabl'':85C ''ratio'':117C ''reduc'':98C ''rel'':73C ''skim'':103C ''standard'':37C ''suspici'':41C ''thing'':4C,108C ''time'':100C,132C ''use'':86C,112C,127C ''variabl'':120C ''way'':38C ''won'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2431, 2, '2014-11-09 10:36:51.29', 1, NULL, NULL, '&lt;p&gt;In my experience to have a PhD doesn''t mean necessarily be good in the enviroment of data science company, I work as data scientist and I''m just an engineer but I''ve known some universitary teachers who works in collaboration with my company and sometimes I''ve said them that Their point of view was not right because despite of their ideas and reasonings were right they are not applicables to the company activities, so we had to modify some data models to make them usefull for the company and the results lost their value so we had to seek new models. What I mean is that Data Science is a multidisciplinar area so many different people working together is needed so I think that your skills could be very useful in a data scientist team, you only have to find where you fit ;)&lt;/p&gt;
', NULL, NULL, '''/p'':148C ''activ'':77C ''applic'':73C ''area'':116C ''collabor'':43C ''compani'':21C,46C,76C,92C ''could'':131C ''data'':19C,25C,84C,111C,137C ''despit'':62C ''differ'':119C ''doesn'':9C ''engin'':32C ''enviro'':17C ''experi'':4C ''find'':144C ''fit'':147C ''good'':14C ''idea'':65C ''known'':36C ''lost'':96C ''m'':29C ''make'':87C ''mani'':118C ''mean'':11C,108C ''model'':85C,105C ''modifi'':82C ''multidisciplinar'':115C ''necessarili'':12C ''need'':124C ''new'':104C ''p'':1C ''peopl'':120C ''phd'':8C ''point'':55C ''reason'':67C ''result'':95C ''right'':60C,69C ''said'':51C ''scienc'':20C,112C ''scientist'':26C,138C ''seek'':103C ''skill'':130C ''sometim'':48C ''teacher'':39C ''team'':139C ''think'':127C ''togeth'':122C ''universitari'':38C ''use'':134C ''useful'':89C ''valu'':98C ''ve'':35C,50C ''view'':57C ''work'':23C,41C,121C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2432, 1, '2014-11-09 14:11:18.35', 4, 172, 'What technologies are fastest at performing joins on large datasets?', '&lt;p&gt;By "large", I mean in the range of 100m to 10b rows.&lt;/p&gt;

&lt;p&gt;I''m currently using both Hadoop MapReduce and Amazon RedShift. MapReduce has been a little disappointing here. Redshift works very well if the data is distributed well for the given query.&lt;/p&gt;

&lt;p&gt;Are there other technologies that I should be looking at here? If so, what are the trade offs?&lt;/p&gt;
', 'bigdata  performance  map-reduce  aws', NULL, '''/p'':30C,63C,83C ''100m'':26C ''10b'':28C ''amazon'':40C ''aw'':16B ''bigdata'':11B ''current'':34C ''data'':55C ''dataset'':10A ''disappoint'':47C ''distribut'':57C ''fastest'':4A ''given'':61C ''hadoop'':37C ''join'':7A ''larg'':9A,19C ''littl'':46C ''look'':73C ''m'':33C ''map'':14B ''map-reduc'':13B ''mapreduc'':38C,42C ''mean'':21C ''off'':82C ''p'':17C,31C,64C ''perform'':6A,12B ''queri'':62C ''rang'':24C ''redshift'':41C,49C ''reduc'':15B ''row'':29C ''technolog'':2A,68C ''trade'':81C ''use'':35C ''well'':52C,58C ''work'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2435, 2, '2014-11-10 09:40:26.753', 4, NULL, NULL, '&lt;p&gt;If you are working on collaborative filtering you should pose the problem as a low-rank matrix approximation, wherein both the users are items are co-embedded into the same low-dimensionality space. Similarity search will be much simpler then. I recommend using LSH, as you suggested. Another fruitful avenue for dimensionality reduction not yet mentioned is &lt;a href="http://en.wikipedia.org/wiki/Random_projection" rel="nofollow"&gt;random projection&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':71C ''/p'':72C ''/wiki/random_projection'':66C ''anoth'':52C ''approxim'':20C ''avenu'':54C ''co'':29C ''co-embed'':28C ''collabor'':7C ''dimension'':36C,56C ''embed'':30C ''en.wikipedia.org'':65C ''en.wikipedia.org/wiki/random_projection'':64C ''filter'':8C ''fruit'':53C ''href'':63C ''item'':26C ''low'':17C,35C ''low-dimension'':34C ''low-rank'':16C ''lsh'':48C ''matrix'':19C ''mention'':60C ''much'':42C ''nofollow'':68C ''p'':1C ''pose'':11C ''problem'':13C ''project'':70C ''random'':69C ''rank'':18C ''recommend'':46C ''reduct'':57C ''rel'':67C ''search'':39C ''similar'':38C ''simpler'':43C ''space'':37C ''suggest'':51C ''use'':47C ''user'':24C ''wherein'':21C ''work'':5C ''yet'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2436, 1, '2014-11-10 09:45:13.063', 2, 643, 'Large categorical dataset for regression', '&lt;p&gt;I need to collect several large datasets (thousands of samples, dozens of features) for regression with only categorical inputs. I already look for such datasets in the UCI repository, but I did not find any suitable one.&lt;/p&gt;

&lt;p&gt;Does anybody know of any such dataset, or of any additional dataset repository on the Internet?&lt;/p&gt;
', 'dataset  categorical-data', NULL, '''/p'':48C,66C ''addit'':60C ''alreadi'':31C ''anybodi'':51C ''categor'':2A,8B,28C ''categorical-data'':7B ''collect'':14C ''data'':9B ''dataset'':3A,6B,17C,35C,56C,61C ''dozen'':21C ''featur'':23C ''find'':44C ''input'':29C ''internet'':65C ''know'':52C ''larg'':1A,16C ''look'':32C ''need'':12C ''one'':47C ''p'':10C,49C ''regress'':5A,25C ''repositori'':39C,62C ''sampl'':20C ''sever'':15C ''suitabl'':46C ''thousand'':18C ''uci'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6053, 2, '2015-06-09 13:51:35.77', 1, NULL, NULL, '&lt;p&gt;I don''t use Java much but have you looked into machine learning with the Weka Java API?  You might also try Encog, mentioned &lt;a href="http://www.jeffheaton.com/ai/" rel="nofollow"&gt;here&lt;/a&gt;, and if you search for &lt;em&gt;Practical Artificial Intelligence Programming With Java&lt;/em&gt; by Mark Watson, you might still be able to find a PDF version that''s licensed for free non-commercial use.&lt;/p&gt;
', NULL, NULL, '''/a'':34C ''/ai/'':30C ''/em'':47C ''/p'':70C ''abl'':55C ''also'':22C ''api'':19C ''artifici'':42C ''commerci'':68C ''em'':40C ''encog'':24C ''find'':57C ''free'':65C ''href'':27C ''intellig'':43C ''java'':6C,18C,46C ''learn'':14C ''licens'':63C ''look'':11C ''machin'':13C ''mark'':49C ''mention'':25C ''might'':21C,52C ''much'':7C ''nofollow'':32C ''non'':67C ''non-commerci'':66C ''p'':1C ''pdf'':59C ''practic'':41C ''program'':44C ''rel'':31C ''search'':38C ''still'':53C ''tri'':23C ''use'':5C,69C ''version'':60C ''watson'':50C ''weka'':17C ''www.jeffheaton.com'':29C ''www.jeffheaton.com/ai/'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2439, 1, '2014-11-10 16:20:22.647', 4, 85, 'Classification of DNA Sequences', '&lt;p&gt;I have a &lt;a href="https://archive.ics.uci.edu/ml/datasets/Molecular+Biology+%28Splice-junction+Gene+Sequences%29" rel="nofollow"&gt;database&lt;/a&gt; of 3190 instances of DNA consisting of 60 sequential DNA nucleotide positions classified according to 3 types: EI, IE, Other.&lt;/p&gt;

&lt;p&gt;I want to formulate a supervised classifier.&lt;/p&gt;

&lt;p&gt;My present approach is to formulate a 2nd order Markov Transition Matrix for each instance and apply the resulting data to a Neural Network.&lt;/p&gt;

&lt;p&gt;How best to approach this classification problem, given that the Sequence of the data should be relevant? Is there a better approach than the one I came up with?&lt;/p&gt;
', 'sequence', NULL, '''/a'':18C ''/ml/datasets/molecular+biology+%28splice-junction+gene+sequences%29'':14C ''/p'':39C,48C,74C,105C ''2nd'':57C ''3'':34C ''3190'':20C ''60'':26C ''accord'':32C ''appli'':66C ''approach'':52C,79C,97C ''archive.ics.uci.edu'':13C ''archive.ics.uci.edu/ml/datasets/molecular+biology+%28splice-junction+gene+sequences%29'':12C ''best'':77C ''better'':96C ''came'':102C ''classif'':1A,81C ''classifi'':31C,47C ''consist'':24C ''data'':69C,89C ''databas'':17C ''dna'':3A,23C,28C ''ei'':36C ''formul'':44C,55C ''given'':83C ''href'':11C ''ie'':37C ''instanc'':21C,64C ''markov'':59C ''matrix'':61C ''network'':73C ''neural'':72C ''nofollow'':16C ''nucleotid'':29C ''one'':100C ''order'':58C ''p'':6C,40C,49C,75C ''posit'':30C ''present'':51C ''problem'':82C ''rel'':15C ''relev'':92C ''result'':68C ''sequenc'':4A,5B,86C ''sequenti'':27C ''supervis'':46C ''transit'':60C ''type'':35C ''want'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2441, 2, '2014-11-10 18:46:42.993', 0, NULL, NULL, '&lt;p&gt;You should probably start with a very basic approach: bag-of-words representation (vector as long as your vocabulary, 1 if the word is found in the text, 0 if it''s not), and a simple classifier like naive bayes. This works surprisingly well to find topics (a little less for sentiment classification).
For preprocessing you would probably want to do stop-word removal and stemming (in order to reduce the vocabulary) rather than POS tagging. &lt;/p&gt;

&lt;p&gt;The problem with the basic approach is that you would have a n-class classifier, and no "this fits multiple categories" or "this fits 0 categories" answers. If you want to include that aspect, then the best is to design n 2-class classifiers, one for each of your classes, where each classifier decides whether the text fits the class or not.&lt;/p&gt;

&lt;p&gt;But I would try out-of-the box naive bayes first, just to see how it works. You can use Weka, it''s free, open-source, and can be integrated with java. You can also do the preprocessing (stemming) with the Python NLTK. &lt;/p&gt;
', NULL, NULL, '''/p'':80C,145C,192C ''0'':31C,107C ''1'':22C ''2'':124C ''also'':183C ''answer'':109C ''approach'':10C,87C ''aspect'':116C ''bag'':12C ''bag-of-word'':11C ''basic'':9C,86C ''bay'':42C,157C ''best'':119C ''box'':155C ''categori'':103C,108C ''class'':96C,125C,132C,142C ''classif'':55C ''classifi'':39C,97C,126C,135C ''decid'':136C ''design'':122C ''find'':48C ''first'':158C ''fit'':101C,106C,140C ''found'':27C ''free'':171C ''includ'':114C ''integr'':178C ''java'':180C ''less'':52C ''like'':40C ''littl'':51C ''long'':18C ''multipl'':102C ''n'':95C,123C ''n-class'':94C ''naiv'':41C,156C ''nltk'':191C ''one'':127C ''open'':173C ''open-sourc'':172C ''order'':71C ''out-of-th'':151C ''p'':1C,81C,146C ''pos'':78C ''preprocess'':57C,186C ''probabl'':4C,60C ''problem'':83C ''python'':190C ''rather'':76C ''reduc'':73C ''remov'':67C ''represent'':15C ''see'':161C ''sentiment'':54C ''simpl'':38C ''sourc'':174C ''start'':5C ''stem'':69C,187C ''stop'':65C ''stop-word'':64C ''surpris'':45C ''tag'':79C ''text'':30C,139C ''topic'':49C ''tri'':150C ''use'':167C ''vector'':16C ''vocabulari'':21C,75C ''want'':61C,112C ''weka'':168C ''well'':46C ''whether'':137C ''word'':14C,25C,66C ''work'':44C,164C ''would'':59C,91C,149C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2442, 2, '2014-11-10 19:27:03.397', 0, NULL, NULL, '&lt;p&gt;All you need are data sets with enough records and enough features for your purposes. You can simply convert any continuous variables into categorical ones by grouping. Some sources for large sets can be found by a search for "large free data sets". If you are dead set on lots of categorical data, try insurance data (given that I''m an actuary, I should have thought of that earlier). Those tend to be laden with categorical variables, as I well know from first person experience.&lt;/p&gt;
', NULL, NULL, '''/p'':87C ''actuari'':63C ''categor'':25C,53C,77C ''continu'':22C ''convert'':20C ''data'':6C,43C,54C,57C ''dead'':48C ''earlier'':70C ''enough'':9C,12C ''experi'':86C ''featur'':13C ''first'':84C ''found'':36C ''free'':42C ''given'':58C ''group'':28C ''insur'':56C ''know'':82C ''laden'':75C ''larg'':32C,41C ''lot'':51C ''m'':61C ''need'':4C ''one'':26C ''p'':1C ''person'':85C ''purpos'':16C ''record'':10C ''search'':39C ''set'':7C,33C,44C,49C ''simpli'':19C ''sourc'':30C ''tend'':72C ''thought'':67C ''tri'':55C ''variabl'':23C,78C ''well'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9225, 1, '2015-12-05 19:00:20.923', 0, 26, 'Out-of-core learning with recurrent neural networks in Keras or other packages', '&lt;p&gt;Does anyone have experience working with out-of-core data and neural nets in Keras (or any Theano-backed framework)? &lt;/p&gt;

&lt;p&gt;I am working with HPC monitoring data sized in the 100-1000GB per day range (think millions and millions of timesteps across hundreds of nodes; scales to 5TB per day on larger systems) and was hoping to build some large recurrent nets (offline) for sequence / event prediction. Other predictive options exist, but this is still in the research stage and I''d like to explore RNNs for this. I have some nice machines with K80s at my disposal and am fine leaving something to train for a long time, but just wanted to test out the technique as prototypes with small datasets have worked well to predict failures in HPC clusters.&lt;/p&gt;

&lt;p&gt;Has anyone tried to feed Keras data streams and regenerate them for other passes?&lt;/p&gt;
', 'bigdata  neuralnetwork  time-series  deep-learning  theano', NULL, '''-1000'':60C ''/p'':47C,161C,177C ''100'':59C ''5tb'':77C ''across'':71C ''anyon'':26C,164C ''back'':45C ''bigdata'':15B ''build'':87C ''cluster'':160C ''core'':4A,34C ''d'':111C ''data'':35C,55C,169C ''dataset'':151C ''day'':63C,79C ''deep'':21B ''deep-learn'':20B ''dispos'':127C ''event'':95C ''exist'':100C ''experi'':28C ''explor'':114C ''failur'':157C ''feed'':167C ''fine'':130C ''framework'':46C ''gb'':61C ''hope'':85C ''hpc'':53C,159C ''hundr'':72C ''k80s'':124C ''kera'':11A,40C,168C ''larg'':89C ''larger'':81C ''learn'':5A,22B ''leav'':131C ''like'':112C ''long'':137C ''machin'':122C ''million'':66C,68C ''monitor'':54C ''net'':38C,91C ''network'':9A ''neural'':8A,37C ''neuralnetwork'':16B ''nice'':121C ''node'':74C ''offlin'':92C ''option'':99C ''out-of-cor'':1A,31C ''p'':24C,48C,162C ''packag'':14A ''pass'':176C ''per'':62C,78C ''predict'':96C,98C,156C ''prototyp'':148C ''rang'':64C ''recurr'':7A,90C ''regener'':172C ''research'':107C ''rnns'':115C ''scale'':75C ''sequenc'':94C ''seri'':19B ''size'':56C ''small'':150C ''someth'':132C ''stage'':108C ''still'':104C ''stream'':170C ''system'':82C ''techniqu'':146C ''test'':143C ''theano'':23B,44C ''theano-back'':43C ''think'':65C ''time'':18B,138C ''time-seri'':17B ''timestep'':70C ''train'':134C ''tri'':165C ''want'':141C ''well'':154C ''work'':29C,51C,153C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2445, 1, '2014-11-11 15:03:51.68', 2, 83, 'Analyzing mobile usage. What kind of approach should I apply?', '&lt;p&gt;I need to analyse a dataset about mobile phone usage (#calls, #sms, #internetConnections) per each cell and hour in the different days.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[date] [CDR/Position] [#calls] [#sms] [#internetConnections]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;My purpose is detecting similarities in the data (Monday-Tuesday is similar... or Monday night is different...). After this, I''d like to find the reason they are similar/dissimilar.&lt;/p&gt;

&lt;p&gt;What can I apply?&lt;/p&gt;
', 'data-mining  clustering', NULL, '''/code'':46C ''/p'':38C,79C,85C ''/pre'':47C ''analys'':19C ''analyz'':1A ''appli'':10A,84C ''approach'':7A ''call'':26C,43C ''cdr/position'':42C ''cell'':31C ''cluster'':14B ''code'':40C ''d'':70C ''data'':12B,56C ''data-min'':11B ''dataset'':21C ''date'':41C ''day'':37C ''detect'':52C ''differ'':36C,66C ''find'':73C ''hour'':33C ''internetconnect'':28C,45C ''kind'':5A ''like'':71C ''mine'':13B ''mobil'':2A,23C ''monday'':58C,63C ''monday-tuesday'':57C ''need'':17C ''night'':64C ''p'':15C,48C,80C ''per'':29C ''phone'':24C ''pre'':39C ''purpos'':50C ''reason'':75C ''similar'':53C,61C ''similar/dissimilar'':78C ''sms'':27C,44C ''tuesday'':59C ''usag'':3A,25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2446, 1, '2014-11-11 18:34:40.213', 2, 111, 'Opening biosemi bdf data using MNE and biosig using python', '&lt;p&gt;I have a biosemi bdf of EEG data which contains 32 channel. 
I''ve opened it using biosig, everything works great, a first list is channel and inside each list there are eeg data.
But if I open it using MNE it the first list is eeg data, and the second list (inside the list of eeg data) are two list of eeg data.&lt;/p&gt;

&lt;p&gt;this is how I open the data using MNE&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;raw_file=read_raw_edf("E:\eegDATA\\256\s02_reduced.bdf",preload=True,verbose=True)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Am I missing something here?&lt;/p&gt;
', 'python', NULL, '''/code'':104C ''/p'':77C,88C,112C ''/pre'':105C ''256'':98C ''32'':23C ''bdf'':3A,17C ''biosemi'':2A,16C ''biosig'':8A,30C ''channel'':24C,38C ''code'':90C ''contain'':22C ''data'':4A,20C,46C,60C,70C,76C,85C ''e'':96C ''edf'':95C ''eeg'':19C,45C,59C,69C,75C ''eegdata'':97C ''everyth'':31C ''file'':92C ''first'':35C,56C ''great'':33C ''insid'':40C,65C ''list'':36C,42C,57C,64C,67C,73C ''miss'':109C ''mne'':6A,53C,87C ''open'':1A,27C,50C,83C ''p'':12C,78C,106C ''pre'':89C ''preload'':100C ''python'':10A,11B ''raw'':91C,94C ''read'':93C ''s02_reduced.bdf'':99C ''second'':63C ''someth'':110C ''true'':101C,103C ''two'':72C ''use'':5A,9A,29C,52C,86C ''ve'':26C ''verbos'':102C ''work'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2447, 1, '2014-11-11 19:22:00.073', 2, 73, 'How can I create a custom tag in JPMML?', '&lt;p&gt;I''m trying to create a logistic regression model in jpmml, then write the PMML to a file. The problem I''m having, is that I can''t find any way to create a custom tag, such as "shortForm" and "longForm" in the following example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;MapValues outputColumn="longForm"&amp;gt;
  &amp;lt;FieldColumnPair field="gender" column="shortForm"/&amp;gt;
  &amp;lt;InlineTable&amp;gt;
    &amp;lt;row&amp;gt;&amp;lt;shortForm&amp;gt;m&amp;lt;/shortForm&amp;gt;&amp;lt;longForm&amp;gt;male&amp;lt;/longForm&amp;gt;
    &amp;lt;/row&amp;gt;
    &amp;lt;row&amp;gt;&amp;lt;shortForm&amp;gt;f&amp;lt;/shortForm&amp;gt;&amp;lt;longForm&amp;gt;female&amp;lt;/longForm&amp;gt;
    &amp;lt;/row&amp;gt;
  &amp;lt;/InlineTable&amp;gt;
&amp;lt;/MapValues&amp;gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here''s what I have so far:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;MapValues mv = new MapValues("output")
  .withFieldColumnPairs(
        new FieldColumnPair( new FieldName("gender"), "shortForm" )
  ).withInlineTable(
        new InlineTable().withRows(
                new Row().with???( new ??? )
)))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In short, I am asking for an API call I can use to instantiate the "shortForm" element in the example, and attach it to the "row" object. I''ve been all through the API, examples, and Google/SO, and can''t find a thing. &lt;/p&gt;

&lt;p&gt;Thanks for your help!&lt;/p&gt;
', 'logistic-regression  java', NULL, '''/code'':124C,157C ''/inlinetable'':119C ''/longform'':93C,113C ''/mapvalues'':122C ''/p'':60C,134C,203C,209C ''/pre'':125C,158C ''/row'':96C,116C ''/shortform'':86C,106C ''api'':167C,193C ''ask'':164C ''attach'':181C ''call'':168C ''code'':62C,136C ''column'':72C ''creat'':4A,19C,47C ''custom'':6A,49C ''element'':176C ''exampl'':59C,179C,194C ''f'':104C ''far'':133C ''femal'':111C ''field'':70C ''fieldcolumnpair'':69C,144C ''fieldnam'':146C ''file'':32C ''find'':43C,200C ''follow'':58C ''gender'':71C,147C ''google/so'':196C ''gt'':67C,74C,77C,80C,83C,87C,90C,94C,97C,100C,103C,107C,110C,114C,117C,120C,123C ''help'':208C ''inlinet'':76C,151C ''instanti'':173C ''java'':13B ''jpmml'':9A,25C ''logist'':11B,21C ''logistic-regress'':10B ''longform'':55C,66C,89C,109C ''lt'':63C,68C,75C,78C,81C,85C,88C,92C,95C,98C,101C,105C,108C,112C,115C,118C,121C ''m'':16C,36C,84C ''male'':91C ''mapvalu'':64C,137C,140C ''model'':23C ''mv'':138C ''new'':139C,143C,145C,150C,153C,156C ''object'':186C ''output'':141C ''outputcolumn'':65C ''p'':14C,126C,159C,204C ''pmml'':29C ''pre'':61C,135C ''problem'':34C ''regress'':12B,22C ''row'':79C,99C,154C,185C ''short'':161C ''shortform'':53C,73C,82C,102C,148C,175C ''tag'':7A,50C ''thank'':205C ''thing'':202C ''tri'':17C ''use'':171C ''ve'':188C ''way'':45C ''withfieldcolumnpair'':142C ''withinlinet'':149C ''withrow'':152C ''write'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2448, 1, '2014-11-12 01:31:01.377', 1, 22, 'Method to create master product database to validate entries, and enrich data set', '&lt;p&gt;We have a ruby-on-rails platform (w/ postgreSQL db) for people to upload various products to trade. Of course, many of these products listed are the same, while they are described differently by the consumer (either through spelling, case etc.) "lots of duplicates"&lt;/p&gt;

&lt;p&gt;For the purposes of analytics and a better UX, we''re aiming to create an evolving "master product list", or "whitelist", if you will, that will have users select from an existing list of products they are uploading, OR request to add a new one. We also plan to enrich each product entry with additional information from the web, that would be tied to the "master product".&lt;/p&gt;

&lt;p&gt;Here are some methods we''re proposing to solve this problem:&lt;/p&gt;

&lt;p&gt;A) Take all the "items" listed in the website (~90,000), de-dupe as much as possible by running select "distinct" queries (while maintaining a key-map back to original data by generating an array of item keys from each distinct listing in a group-by.)&lt;/p&gt;

&lt;p&gt;THEN&lt;/p&gt;

&lt;p&gt;A1) Running this data through mechanical turk, and asking each turk user to list data in a uniform format.&lt;/p&gt;

&lt;p&gt;OR&lt;/p&gt;

&lt;p&gt;A2) Running each product entry through the Amazon products API and asking the user to identify a match.&lt;/p&gt;

&lt;p&gt;or&lt;/p&gt;

&lt;p&gt;A3) A better method?&lt;/p&gt;
', 'data-cleaning  sql', NULL, '''/p'':64C,133C,146C,197C,200C,221C,224C,244C,247C,253C ''000'':158C ''90'':157C ''a1'':202C ''a2'':226C ''a3'':249C ''add'':107C ''addit'':120C ''aim'':77C ''also'':112C ''amazon'':233C ''analyt'':70C ''api'':235C ''array'':184C ''ask'':210C,237C ''back'':177C ''better'':73C,251C ''case'':59C ''clean'':16B ''consum'':55C ''cours'':39C ''creat'':3A,79C ''data'':12A,15B,180C,205C,216C ''data-clean'':14B ''databas'':6A ''db'':29C ''de'':160C ''de-dup'':159C ''describ'':51C ''differ'':52C ''distinct'':169C,190C ''dupe'':161C ''duplic'':63C ''either'':56C ''enrich'':11A,115C ''entri'':9A,118C,230C ''etc'':60C ''evolv'':81C ''exist'':97C ''format'':220C ''generat'':182C ''group'':195C ''group-bi'':194C ''identifi'':241C ''inform'':121C ''item'':152C,186C ''key'':175C,187C ''key-map'':174C ''list'':44C,84C,98C,153C,191C,215C ''lot'':61C ''maintain'':172C ''mani'':40C ''map'':176C ''master'':4A,82C,131C ''match'':243C ''mechan'':207C ''method'':1A,138C,252C ''much'':163C ''new'':109C ''one'':110C ''origin'':179C ''p'':18C,65C,134C,147C,198C,201C,222C,225C,245C,248C ''peopl'':31C ''plan'':113C ''platform'':26C ''possibl'':165C ''postgresql'':28C ''problem'':145C ''product'':5A,35C,43C,83C,100C,117C,132C,229C,234C ''propos'':141C ''purpos'':68C ''queri'':170C ''rail'':25C ''re'':76C,140C ''request'':105C ''rubi'':23C ''ruby-on-rail'':22C ''run'':167C,203C,227C ''select'':94C,168C ''set'':13A ''solv'':143C ''spell'':58C ''sql'':17B ''take'':149C ''tie'':128C ''trade'':37C ''turk'':208C,212C ''uniform'':219C ''upload'':33C,103C ''user'':93C,213C,239C ''ux'':74C ''valid'':8A ''various'':34C ''w'':27C ''web'':124C ''websit'':156C ''whitelist'':86C ''would'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2651, 1, '2014-12-08 22:37:32.777', 12, 1822, 'Deep learning basics', '&lt;p&gt;I am looking for a paper detailing the very basics of deep learning. Ideally like the Andrew Ng course for deep learning. Do you know where I can find this ?&lt;/p&gt;
', 'machine-learning  deep-learning', NULL, '''/p'':41C ''andrew'':27C ''basic'':3A,20C ''cours'':29C ''deep'':1A,8B,22C,31C ''deep-learn'':7B ''detail'':17C ''find'':39C ''ideal'':24C ''know'':35C ''learn'':2A,6B,9B,23C,32C ''like'':25C ''look'':13C ''machin'':5B ''machine-learn'':4B ''ng'':28C ''p'':10C ''paper'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2455, 2, '2014-11-12 18:22:07.253', 1, NULL, NULL, '&lt;p&gt;NLP is very vast and varied. Here are a few basic tools in NLP:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Sentence splitting: Identifying sentence boundaries in text&lt;/li&gt;
&lt;li&gt;Tokenization: Splitting a sentence into individual words&lt;/li&gt;
&lt;li&gt;Lemmatization: Converting a word to its root form. E.g. says, said,
saying will all map to root form - say &lt;/li&gt;
&lt;li&gt;Stemmer: It is similar to a
lemmatizer, but it stems a word rather than get to the root form.
e.g. laughed, laughing will stem to laugh. However, said, saying
will map to sa - which is not particularly enlightening in terms of
what "sa" means&lt;/li&gt;
&lt;li&gt;POS tagger: Tags a word with the Part of Speech - what is a noun, verb, preposition etc.&lt;/li&gt;
&lt;li&gt;Parser: Links words with POS tags to other words with POS tags. E.g. John ate an apple. Here John and apple are nouns linked by the verb - eat. John is the subject of the verb, and apple is the object of the verb.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;If you are looking for the state of the art for these tools, check out &lt;a href="http://nlp.stanford.edu/software/corenlp.shtml" rel="nofollow"&gt;StanfordCoreNLP&lt;/a&gt;, which has most of these tools and a trained model to identify the above from a document. There is also an online demo to check out stanfordCoreNLP before downloading and using it with your application. &lt;/p&gt;

&lt;p&gt;NLP has several subfields. Here are a few of them: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Machine Translation: Automatic Translation from one language to another&lt;/li&gt;
&lt;li&gt;Information Retrieval: Something like a search engine, that retrieves relevant information from a large set of documents based on a search query&lt;/li&gt;
&lt;li&gt;Information Extraction: Extract concepts and keywords - such as names of people, locations, times, synonyms etc.&lt;/li&gt;
&lt;li&gt;Deep Learning has lately become a new field of NLP, where a system tries to understand a document like a human understands it. &lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':192C ''/li'':26C,35C,56C,102C,121C,166C,252C,276C,293C,318C ''/ol'':167C,319C ''/p'':16C,228C,240C ''/software/corenlp.shtml'':188C ''also'':212C ''anoth'':251C ''appl'':139C,143C,159C ''applic'':227C ''art'':178C ''ate'':137C ''automat'':245C ''base'':271C ''basic'':12C ''becom'':299C ''boundari'':23C ''check'':182C,217C ''concept'':281C ''convert'':38C ''deep'':295C ''demo'':215C ''document'':209C,270C,312C ''download'':221C ''e.g'':45C,77C,135C ''eat'':150C ''engin'':260C ''enlighten'':95C ''etc'':120C,292C ''extract'':279C,280C ''field'':302C ''form'':44C,54C,76C ''get'':72C ''howev'':84C ''href'':185C ''human'':315C ''identifi'':21C,204C ''individu'':33C ''inform'':254C,264C,278C ''john'':136C,141C,151C ''keyword'':283C ''languag'':249C ''larg'':267C ''late'':298C ''laugh'':78C,79C,83C ''learn'':296C ''lemmat'':37C,64C ''li'':18C,27C,36C,57C,103C,122C,242C,253C,277C,294C ''like'':257C,313C ''link'':124C,146C ''locat'':289C ''look'':172C ''machin'':243C ''map'':51C,88C ''mean'':101C ''model'':202C ''name'':286C ''new'':301C ''nlp'':2C,15C,230C,304C ''nlp.stanford.edu'':187C ''nlp.stanford.edu/software/corenlp.shtml'':186C ''nofollow'':190C ''noun'':117C,145C ''object'':162C ''ol'':17C,241C ''one'':248C ''onlin'':214C ''p'':1C,168C,229C ''parser'':123C ''part'':111C ''particular'':94C ''peopl'':288C ''pos'':104C,127C,133C ''preposit'':119C ''queri'':275C ''rather'':70C ''rel'':189C ''relev'':263C ''retriev'':255C,262C ''root'':43C,53C,75C ''sa'':90C,100C ''said'':47C,85C ''say'':46C,48C,55C,86C ''search'':259C,274C ''sentenc'':19C,22C,31C ''set'':268C ''sever'':232C ''similar'':61C ''someth'':256C ''speech'':113C ''split'':20C,29C ''stanfordcorenlp'':191C,219C ''state'':175C ''stem'':67C,81C ''stemmer'':58C ''subfield'':233C ''subject'':154C ''synonym'':291C ''system'':307C ''tag'':106C,128C,134C ''tagger'':105C ''term'':97C ''text'':25C ''time'':290C ''token'':28C ''tool'':13C,181C,198C ''train'':201C ''translat'':244C,246C ''tri'':308C ''understand'':310C,316C ''use'':223C ''vari'':7C ''vast'':5C ''verb'':118C,149C,157C,165C ''word'':34C,40C,69C,108C,125C,131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3817, 2, '2015-01-05 22:49:27.293', 2, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;For known and unknown properties, how should I proceed to go from daily to weekly/monthly data ?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Aggregation.  &lt;/p&gt;

&lt;p&gt;For example, you have the number of time people searched for ''widgets'' every day.  Add up the daily totals for a month to get monthly totals.  I would need to see more specifics about the actual data collected at each granularity to give you a more complete version.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;For known and unknown properties, how should I proceed to go from weekly/monthly to daily data ?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;You can''t.  &lt;/p&gt;

&lt;p&gt;In physics, a comparable idea is the &lt;a href="http://en.wikipedia.org/wiki/Nyquist_frequency" rel="nofollow"&gt;Nyquist frequency&lt;/a&gt;.  The general idea is that you can''t add more information than what you already have present in your data without bringing in more data.  Given only the day someone ran a query, how can you tell what time of day that query was ran?  You may be able to make some inferences, but the only way to answer the question is to directly or indirectly bring in more information to the system.  There are things you can do to make informed guesses at the daily state of monthly variables (as 
gchaks mentioned, interpolation), but your data is still fundamentally monthly data stretched to look daily.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;When given two time series with different time steps, what is better: Using the Lowest or the biggest time step ?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;That totally depends on what you''re trying to answer.  &lt;/p&gt;

&lt;p&gt;The smaller granularity will be more sensitive to noise and other anomalies.  The lager granularity will be able to answer questions more confidently, but loose some of it''s usefulness.  For example, if you''re trying to see when people start looking up venues to weekend plans to know when to launch marketing campaigns for a new night club, you''ll want to be looking at daily data, if not smaller.  If you''re looking at the general trending of night clubs to figure out who you want to invest in, then monthly would probably be better.&lt;/p&gt;
', NULL, NULL, '''/a'':116C ''/blockquote'':20C,93C,247C ''/p'':19C,23C,73C,92C,98C,223C,246C,259C,358C ''/wiki/nyquist_frequency'':111C ''abl'':165C,278C ''actual'':60C ''add'':39C,125C ''aggreg'':22C ''alreadi'':131C ''anomali'':272C ''answer'':175C,258C,280C ''better'':237C,357C ''biggest'':243C ''blockquot'':1C,74C,224C ''bring'':138C,183C ''campaign'':314C ''club'':319C,342C ''collect'':62C ''compar'':103C ''complet'':71C ''confid'':283C ''daili'':15C,42C,90C,202C,222C,327C ''data'':18C,61C,91C,136C,141C,213C,218C,328C ''day'':38C,145C,157C ''depend'':251C ''differ'':232C ''direct'':180C ''en.wikipedia.org'':110C ''en.wikipedia.org/wiki/nyquist_frequency'':109C ''everi'':37C ''exampl'':26C,292C ''figur'':344C ''frequenc'':115C ''fundament'':216C ''gchak'':208C ''general'':118C,338C ''get'':48C ''give'':67C ''given'':142C,227C ''go'':13C,86C ''granular'':65C,263C,275C ''guess'':199C ''href'':108C ''idea'':104C,119C ''indirect'':182C ''infer'':169C ''inform'':127C,186C,198C ''interpol'':210C ''invest'':350C ''know'':309C ''known'':4C,77C ''lager'':274C ''launch'':312C ''ll'':321C ''look'':221C,302C,325C,335C ''loos'':285C ''lowest'':240C ''make'':167C,197C ''market'':313C ''may'':163C ''mention'':209C ''month'':46C,49C,205C,217C,353C ''need'':53C ''new'':317C ''night'':318C,341C ''nofollow'':113C ''nois'':269C ''number'':30C ''nyquist'':114C ''p'':2C,21C,24C,75C,94C,99C,225C,248C,260C ''peopl'':33C,300C ''physic'':101C ''plan'':307C ''present'':133C ''probabl'':355C ''proceed'':11C,84C ''properti'':7C,80C ''queri'':149C,159C ''question'':177C,281C ''ran'':147C,161C ''re'':255C,295C,334C ''rel'':112C ''search'':34C ''see'':55C,298C ''sensit'':267C ''seri'':230C ''smaller'':262C,331C ''someon'':146C ''specif'':57C ''start'':301C ''state'':203C ''step'':234C,245C ''still'':215C ''stretch'':219C ''system'':189C ''tell'':153C ''thing'':192C ''time'':32C,155C,229C,233C,244C ''total'':43C,50C,250C ''trend'':339C ''tri'':256C,296C ''two'':228C ''unknown'':6C,79C ''use'':238C,290C ''variabl'':206C ''venu'':304C ''version'':72C ''want'':322C,348C ''way'':173C ''weekend'':306C ''weekly/monthly'':17C,88C ''widget'':36C ''without'':137C ''would'':52C,354C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2449, 2, '2014-11-12 03:12:11.55', 1, NULL, NULL, '&lt;p&gt;There are two straight forward (vanilla) ways without going for any fancy featurization: &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Clustering&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;Run a clustering algorithm. Something like k-means should work well with this kind of a dataset. While doing this, I would not feed the day_of_week information into the clustering algorithm. &lt;/p&gt;

&lt;p&gt;I would suggest running k-means (after normalizing each of the columns). Choose a small number of clusters that is easy to investigate (or you could use the number of clusters that maximizes the BIC). &lt;/p&gt;

&lt;p&gt;Investigate the clusters to understand membership by day_of_week in each of these clusters. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Multi-class Classification&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;Treat the day_of_week as the response that you would like to predict. Build a decision tree of a fixed depth to predict the day_of_week given the columns. By examining this tree, you can easily tell, which decisions led to a set of leaves being labeled Sunday vs the set of decisions that led to a set of leaves being labeled Monday. These decisions will also help you understand the similarities between different days. &lt;/p&gt;
', NULL, NULL, '''/p'':15C,20C,56C,94C,111C,119C,198C ''/strong'':19C,118C ''algorithm'':25C,55C ''also'':189C ''bic'':93C ''build'':135C ''choos'':71C ''class'':116C ''classif'':117C ''cluster'':18C,24C,54C,76C,89C,98C,110C ''column'':70C,151C ''could'':84C ''dataset'':39C ''day'':48C,103C,123C,146C,197C ''decis'':137C,161C,175C,187C ''depth'':142C ''differ'':196C ''easi'':79C ''easili'':158C ''examin'':153C ''fanci'':13C ''featur'':14C ''feed'':46C ''fix'':141C ''forward'':6C ''given'':149C ''go'':10C ''help'':190C ''inform'':51C ''investig'':81C,96C ''k'':29C,63C ''k-mean'':28C,62C ''kind'':36C ''label'':169C,184C ''leav'':167C,182C ''led'':162C,177C ''like'':27C,132C ''maxim'':91C ''mean'':30C,64C ''membership'':101C ''monday'':185C ''multi'':115C ''multi-class'':114C ''normal'':66C ''number'':74C,87C ''p'':1C,16C,21C,57C,95C,112C,120C ''predict'':134C,144C ''respons'':128C ''run'':22C,61C ''set'':165C,173C,180C ''similar'':194C ''small'':73C ''someth'':26C ''straight'':5C ''strong'':17C,113C ''suggest'':60C ''sunday'':170C ''tell'':159C ''treat'':121C ''tree'':138C,155C ''two'':4C ''understand'':100C,192C ''use'':85C ''vanilla'':7C ''vs'':171C ''way'':8C ''week'':50C,105C,125C,148C ''well'':33C ''without'':9C ''work'':32C ''would'':44C,59C,131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2450, 2, '2014-11-12 06:14:15.09', 0, NULL, NULL, '&lt;p&gt;Try the &lt;a href="https://archive.ics.uci.edu/ml/datasets/KDD+Cup+1998+Data" rel="nofollow"&gt;1998 KDD Cup dataset&lt;/a&gt;. Its a regression problem with categorical and integer predictors. For your task, you could either treat integer predictors as categorical or ignore them completely. &lt;/p&gt;
', NULL, NULL, '''/a'':15C ''/ml/datasets/kdd+cup+1998+data'':8C ''/p'':40C ''1998'':11C ''archive.ics.uci.edu'':7C ''archive.ics.uci.edu/ml/datasets/kdd+cup+1998+data'':6C ''categor'':21C,35C ''complet'':39C ''could'':29C ''cup'':13C ''dataset'':14C ''either'':30C ''href'':5C ''ignor'':37C ''integ'':23C,32C ''kdd'':12C ''nofollow'':10C ''p'':1C ''predictor'':24C,33C ''problem'':19C ''regress'':18C ''rel'':9C ''task'':27C ''treat'':31C ''tri'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2451, 1, '2014-11-12 09:21:19.49', 2, 112, 'Modeling when the response variable has too many 0''s and few continuous values?', '&lt;p&gt;For problems where the data represents online fraud or insurance (where each row represents a transaction), it is typical for the response variable to denote the value of fraud committed in dollars. Such a response value might have less than 5% non-zero values denoting fraudulent transactions. &lt;/p&gt;

&lt;p&gt;I have two questions regarding such a dataset: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;What algorithms can we use to ensure that the model not only predicts the fraudulent transactions accurately, but also predicts the value of fraud associated with these.  &lt;/li&gt;
&lt;li&gt;Assuming that we can quantify the cost involved in each false positive (tagging a non-fraudulent transaction as fraudulent) and cost incurred due to a false negative (tagging a fraudulent transaction as non-fraudulent), how can we optimize the model to maximize savings (or minimize losses)?&lt;/li&gt;
&lt;/ol&gt;
', 'classification  regression  predictive-modeling  unbalanced-classes', NULL, '''/li'':112C,162C ''/ol'':163C ''/p'':72C,82C ''0'':9A ''5'':64C ''accur'':101C ''algorithm'':86C ''also'':103C ''associ'':109C ''assum'':114C ''class'':22B ''classif'':15B ''commit'':53C ''continu'':13A ''cost'':120C,135C ''data'':28C ''dataset'':81C ''denot'':48C,69C ''dollar'':55C ''due'':137C ''ensur'':91C ''fals'':124C,140C ''fraud'':31C,52C,108C ''fraudul'':70C,99C,130C,133C,144C,149C ''incur'':136C ''insur'':33C ''involv'':121C ''less'':62C ''li'':84C,113C ''loss'':161C ''mani'':8A ''maxim'':157C ''might'':60C ''minim'':160C ''model'':1A,19B,94C,155C ''negat'':141C ''non'':66C,129C,148C ''non-fraudul'':128C,147C ''non-zero'':65C ''ol'':83C ''onlin'':30C ''optim'':153C ''p'':23C,73C ''posit'':125C ''predict'':18B,97C,104C ''predictive-model'':17B ''problem'':25C ''quantifi'':118C ''question'':77C ''regard'':78C ''regress'':16B ''repres'':29C,37C ''respons'':4A,45C,58C ''row'':36C ''save'':158C ''tag'':126C,142C ''transact'':39C,71C,100C,131C,145C ''two'':76C ''typic'':42C ''unbalanc'':21B ''unbalanced-class'':20B ''use'':89C ''valu'':14A,50C,59C,68C,106C ''variabl'':5A,46C ''zero'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2452, 2, '2014-11-12 11:08:44.973', 1, NULL, NULL, '&lt;p&gt;You can/should use a generic Java Architecture for XML Binding (JAXB) approach.&lt;/p&gt;

&lt;p&gt;Simply put, call &lt;code&gt;Row#withContent(Object...)&lt;/code&gt; with instances of &lt;code&gt;org.w3c.dom.Element&lt;/code&gt; that represent the desired XML content.&lt;/p&gt;

&lt;p&gt;For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Document document = documentBuilder.newDocument();
Element shortForm = document.createElement("shortForm");
shortForm.setTextContent("m");
Element longForm = document.createElement("longForm");
longForm.setTextContent("male");
row = row.withContent(shortForm, longForm);
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':23C,29C,62C ''/p'':14C,36C,40C ''/pre'':63C ''approach'':13C ''architectur'':8C ''bind'':11C ''call'':18C ''can/should'':3C ''code'':19C,27C,42C ''content'':35C ''desir'':33C ''document'':43C,44C ''document.createelement'':48C,54C ''documentbuilder.newdocument'':45C ''element'':46C,52C ''exampl'':39C ''generic'':6C ''instanc'':25C ''java'':7C ''jaxb'':12C ''longform'':53C,55C,61C ''longform.settextcontent'':56C ''m'':51C ''male'':57C ''object'':22C ''org.w3c.dom.element'':28C ''p'':1C,15C,37C ''pre'':41C ''put'':17C ''repres'':31C ''row'':20C,58C ''row.withcontent'':59C ''shortform'':47C,49C,60C ''shortform.settextcontent'':50C ''simpli'':16C ''use'':4C ''withcont'':21C ''xml'':10C,34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2453, 2, '2014-11-12 14:51:42.213', 0, NULL, NULL, '&lt;p&gt;If you prefer quick hands-on/interactive tutorials, below are my suggestions - &lt;/p&gt;

&lt;p&gt;Python - &lt;a href="http://www.codecademy.com/" rel="nofollow"&gt;codeacademy&lt;/a&gt;, Google Python Class&lt;/p&gt;

&lt;p&gt;R - CodeSchool''s &lt;a href="http://tryr.codeschool.com/" rel="nofollow"&gt;''Try R''&lt;/a&gt; and DataCamp (suggested above)&lt;/p&gt;
', NULL, NULL, '''/a'':24C,40C ''/interactive'':9C ''/p'':15C,28C,45C ''class'':27C ''codeacademi'':23C ''codeschool'':31C ''datacamp'':42C ''googl'':25C ''hand'':7C ''hands-on'':6C ''href'':19C,34C ''nofollow'':22C,37C ''p'':1C,16C,29C ''prefer'':4C ''python'':17C,26C ''quick'':5C ''r'':30C,39C ''rel'':21C,36C ''suggest'':14C,43C ''tri'':38C ''tryr.codeschool.com'':35C ''tutori'':10C ''www.codecademy.com'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2454, 1, '2014-11-12 17:27:57.85', 5, 435, 'Processing data stored in Redshift', '&lt;p&gt;We''re currently using Redshift as our data warehouse, which we''re very happy with. However, we now have a requirement to do machine learning against the data in our warehouse. Given the volume of data involved, ideally I''d want to run the computation in the same location as the data rather than shifting the data around, but this doesn''t seem possible with Redshift. I''ve looked at MADlib, but this is not an option as Redshift does not support UDFs (which MADlib requires). I''m currently looking at shifting the data over to EMR and processing it with the Apache Spark machine learning library (or maybe H20, or Mahout, or whatever). So my questions are: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;is there a better way?&lt;/li&gt;
&lt;li&gt;if not, how should I make the data accessible to Spark? The options I''ve identified so far include: use Sqoop to load it into HDFS, use DBInputFormat, do a Redshift export to S3 and have Spark grab it from there. What are the pros/cons for these different approaches (and any others) when using Spark?&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Note that this is off-line batch learning, but we''d like to be able to do this as quickly as possible so that we can iterate experiments quickly.&lt;/p&gt;
', 'hadoop  aws', NULL, '''/li'':135C,192C ''/ol'':193C ''/p'':127C,225C ''abl'':210C ''access'':145C ''apach'':111C ''approach'':185C ''around'':66C ''aw'':7B ''batch'':202C ''better'':133C ''comput'':53C ''current'':11C,97C ''d'':48C,206C ''data'':2A,16C,36C,44C,60C,65C,102C,144C ''dbinputformat'':164C ''differ'':184C ''doesn'':69C ''emr'':105C ''experi'':223C ''export'':168C ''far'':154C ''given'':40C ''grab'':174C ''h20'':118C ''hadoop'':6B ''happi'':22C ''hdfs'':162C ''howev'':24C ''ideal'':46C ''identifi'':152C ''includ'':155C ''involv'':45C ''iter'':222C ''learn'':33C,114C,203C ''li'':129C,136C ''librari'':115C ''like'':207C ''line'':201C ''load'':159C ''locat'':57C ''look'':77C,98C ''m'':96C ''machin'':32C,113C ''madlib'':79C,93C ''mahout'':120C ''make'':142C ''mayb'':117C ''note'':195C ''off-lin'':199C ''ol'':128C ''option'':85C,149C ''other'':188C ''p'':8C,194C ''possibl'':72C,217C ''process'':1A,107C ''pros/cons'':181C ''question'':125C ''quick'':215C,224C ''rather'':61C ''re'':10C,20C ''redshift'':5A,13C,74C,87C,167C ''requir'':29C,94C ''run'':51C ''s3'':170C ''seem'':71C ''shift'':63C,100C ''spark'':112C,147C,173C,191C ''sqoop'':157C ''store'':3A ''support'':90C ''udf'':91C ''use'':12C,156C,163C,190C ''ve'':76C,151C ''volum'':42C ''want'':49C ''warehous'':17C,39C ''way'':134C ''whatev'':122C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2487, 1, '2014-11-17 11:54:39.933', 1, 46, 'Ontology database', '&lt;p&gt;this is my first ever stack exchange question.&lt;/p&gt;

&lt;p&gt;I''m trying to build a tool right now and one of the features of the tool is the ability to break down a product or service into it''s associated attributes/properties/classes/keywords/entities. (Choose which word best suits, as I have no idea).&lt;/p&gt;

&lt;p&gt;For example if we had a Camera as the product. I would like to be able to generate a breakdown of everything that is associated to a camera. Such as;&lt;/p&gt;

&lt;p&gt;Digital, Film, Optical, LCD, Glass, CCD, CMOS, RGB, Lens, Shutter, Negative, Polaroid, Darkroom, Flash, Resolution, Stabilisation, Batteries, Zoom, Angle, Telephoto, Macro, Filters, Memory, CF, SD&lt;/p&gt;

&lt;p&gt;The list could go on for quite some time, those were jsut a few off the top of my head.&lt;/p&gt;

&lt;p&gt;How on earth could I go about retrieving such attributes automatically? Is there a database out there that has such info? Are there any special tricks anyone has up their sleeve to be able to accumulate datasets such as the example above?&lt;/p&gt;

&lt;p&gt;Very interested in your answers.&lt;/p&gt;

&lt;p&gt;Thanks :)&lt;/p&gt;
', 'data-mining', NULL, '''/p'':15C,59C,91C,118C,140C,184C,191C,194C ''abil'':36C ''abl'':76C,175C ''accumul'':177C ''angl'':111C ''answer'':190C ''anyon'':168C ''associ'':47C,85C ''attribut'':151C ''attributes/properties/classes/keywords/entities'':48C ''automat'':152C ''batteri'':109C ''best'':52C ''break'':38C ''breakdown'':80C ''build'':21C ''camera'':67C,88C ''ccd'':98C ''cf'':116C ''choos'':49C ''cmos'':99C ''could'':122C,145C ''darkroom'':105C ''data'':4B ''data-min'':3B ''databas'':2A,156C ''dataset'':178C ''digit'':93C ''earth'':144C ''ever'':11C ''everyth'':82C ''exampl'':62C,182C ''exchang'':13C ''featur'':30C ''film'':94C ''filter'':114C ''first'':10C ''flash'':106C ''generat'':78C ''glass'':97C ''go'':123C,147C ''head'':139C ''idea'':58C ''info'':162C ''interest'':187C ''jsut'':131C ''lcd'':96C ''len'':101C ''like'':73C ''list'':121C ''m'':18C ''macro'':113C ''memori'':115C ''mine'':5B ''negat'':103C ''one'':27C ''ontolog'':1A ''optic'':95C ''p'':6C,16C,60C,92C,119C,141C,185C,192C ''polaroid'':104C ''product'':41C,70C ''question'':14C ''quit'':126C ''resolut'':107C ''retriev'':149C ''rgb'':100C ''right'':24C ''sd'':117C ''servic'':43C ''shutter'':102C ''sleev'':172C ''special'':166C ''stabilis'':108C ''stack'':12C ''suit'':53C ''telephoto'':112C ''thank'':193C ''time'':128C ''tool'':23C,33C ''top'':136C ''tri'':19C ''trick'':167C ''word'':51C ''would'':72C ''zoom'':110C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2456, 2, '2014-11-12 18:41:33.053', 1, NULL, NULL, '&lt;p&gt;You have a great idea going, and it might work for your specific project. However there are a few considerations you should take into account: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;In your first sentence, Obama in incorrectly classified as an organization, instead of a person. This is because the training model used my NLTK probably does not have enough data to recognize Obama as a PERSON. So, one way would be to update this model by training a new model with a lot of labeled training data. Generating labeled training data is one of the most expensive tasks in NLP - because of all the man hours it takes to tag sentences with the correct Part of Speech as well as semantic role. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;In sentence 2, there are 2 concepts - "Former Vice President", and "Dick Cheney". You can use co-reference to identify the relation between the 2 NNPs. Both the NNP are refering to the same entity, and the same entity could be referenced as - "former vice president" as well as "Dick Cheney". Co-reference is often used to identify the Named entity that pronouns refer to. e.g. "Dick Cheney is the former vice president of USA. He is a Republican". Here the pronoun "he" refers to "Dick Cheney", and it should be identified by a co-reference identification tool. &lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':124C,229C ''/ol'':230C ''/p'':27C,123C,228C ''2'':129C,132C,152C ''account'':26C ''cheney'':139C,178C,196C,215C ''classifi'':38C ''co'':144C,180C,224C ''co-refer'':143C,179C,223C ''concept'':133C ''consider'':21C ''correct'':114C ''could'':167C ''data'':60C,87C,91C ''dick'':138C,177C,195C,214C ''e.g'':194C ''enough'':59C ''entiti'':162C,166C,189C ''expens'':97C ''first'':33C ''former'':134C,171C,199C ''generat'':88C ''go'':7C ''great'':5C ''hour'':106C ''howev'':16C ''idea'':6C ''identif'':226C ''identifi'':147C,186C,220C ''incorrect'':37C ''instead'':42C ''label'':85C,89C ''li'':29C,125C ''lot'':83C ''man'':105C ''might'':10C ''model'':51C,75C,80C ''name'':188C ''new'':79C ''nlp'':100C ''nltk'':54C ''nnp'':156C ''nnps'':153C ''obama'':35C,63C ''often'':183C ''ol'':28C ''one'':68C,93C ''organ'':41C ''p'':1C,30C,126C ''part'':115C ''person'':45C,66C ''presid'':136C,173C,201C ''probabl'':55C ''project'':15C ''pronoun'':191C,210C ''recogn'':62C ''refer'':145C,158C,181C,192C,212C,225C ''referenc'':169C ''relat'':149C ''republican'':207C ''role'':122C ''semant'':121C ''sentenc'':34C,111C,128C ''specif'':14C ''speech'':117C ''tag'':110C ''take'':24C,108C ''task'':98C ''tool'':227C ''train'':50C,77C,86C,90C ''updat'':73C ''usa'':203C ''use'':52C,142C,184C ''vice'':135C,172C,200C ''way'':69C ''well'':119C,175C ''work'':11C ''would'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2466, 1, '2014-11-14 08:53:21.51', 2, 28, 'Dimension reduction for logical arrays', '&lt;p&gt;I have measurements of 4 devices at two different points of time. A measurement basically consists of an array of ones and zeros corresponding to a bit value at the corresponding location:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;whos measurement1_dev1_time1

Name                         Size               Bytes  Class      Attributes

measurement1_dev1_time1      4096x8             32768  logical
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I assume that for a specific device the changes between time 1 and 2 of the measurements are unique. However, since I am dealing with 32768 bits at different locations, it is quite hard to visualize if there is some kind of dependency. &lt;/p&gt;

&lt;p&gt;As every bit at location &lt;code&gt;x&lt;/code&gt;can be regarded as one dimension of an observation I thought to use PCA to reduce the number of dimensions.&lt;/p&gt;

&lt;p&gt;Thus, for every of the 5 devices:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;I randomly sample &lt;code&gt;n&lt;/code&gt; measurements at point &lt;code&gt;t1&lt;/code&gt;and &lt;code&gt;t2&lt;/code&gt; seperatly&lt;/li&gt;
&lt;li&gt;I prepare an array as input for &lt;code&gt;pca()&lt;/code&gt; with &lt;code&gt;m&lt;/code&gt;*n columns (&lt;code&gt;m&lt;/code&gt;&amp;lt; 32768; its a subset of all the observed bits, as the original data might be too big for pca) and 4 rows (one row for each device).&lt;/li&gt;
&lt;li&gt;On this array &lt;code&gt;A&lt;/code&gt; I calculate the pca: ``[coeff score latent] = pca(zscore(A))```&lt;/li&gt;
&lt;li&gt;Then I try to visualize it using &lt;code&gt;biplot&lt;/code&gt;: &lt;code&gt;biplot(coeff(:,1:2), ''score'', score(:,1:2))&lt;/code&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;However, this gives me really strange results. Maybe PCA is not the right approach for this problem? I also modified the input data to do the PCA not on the logical bit array itself. Instead, I created a vector, which holds the indices where there is a ''1'' in the original measurement array. Also this produces strange results.&lt;/p&gt;

&lt;p&gt;As I am completely new to PCA I want to ask you if you either see a flaw in the process or if PCA is just not the right approach for my goal and I better look for other dimension reduction approaches or clustering algorithms.&lt;/p&gt;
', 'dimensionality-reduction', NULL, '''/code'':60C,115C,153C,159C,163C,176C,180C,185C,221C,243C,253C ''/li'':165C,214C,232C,254C ''/ol'':255C ''/p'':42C,106C,136C,145C,315C,362C ''/pre'':61C ''1'':74C,247C,251C,304C ''2'':76C,248C,252C ''32768'':58C,88C,187C ''4'':14C,207C ''4096x8'':57C ''5'':143C ''algorithm'':361C ''also'':275C,310C ''approach'':270C,346C,358C ''array'':5A,28C,170C,218C,289C,309C ''ask'':327C ''assum'':64C ''attribut'':53C ''basic'':24C ''better'':352C ''big'':203C ''biplot'':242C,245C ''bit'':36C,89C,110C,195C,288C ''byte'':51C ''calcul'':223C ''chang'':71C ''class'':52C ''cluster'':360C ''code'':44C,113C,151C,157C,161C,174C,178C,183C,219C,241C,244C ''coeff'':226C,246C ''column'':182C ''complet'':320C ''consist'':25C ''correspond'':33C,40C ''creat'':293C ''data'':199C,279C ''deal'':86C ''depend'':105C ''dev1'':47C,55C ''devic'':15C,69C,144C,213C ''differ'':18C,91C ''dimens'':1A,121C,135C,356C ''dimension'':7B ''dimensionality-reduct'':6B ''either'':331C ''everi'':109C,140C ''flaw'':334C ''give'':259C ''goal'':349C ''hard'':96C ''hold'':297C ''howev'':82C,257C ''indic'':299C ''input'':172C,278C ''instead'':291C ''kind'':103C ''latent'':228C ''li'':147C,166C,215C,233C ''locat'':41C,92C,112C ''logic'':4A,59C,287C ''look'':353C ''lt'':186C ''m'':179C,184C ''mayb'':264C ''measur'':12C,23C,79C,154C,308C ''measurement1'':46C,54C ''might'':200C ''modifi'':276C ''n'':152C,181C ''name'':49C ''new'':321C ''number'':133C ''observ'':124C,194C ''ol'':146C ''one'':30C,120C,209C ''origin'':198C,307C ''p'':9C,62C,107C,137C,256C,316C ''pca'':129C,175C,205C,225C,229C,265C,283C,323C,340C ''point'':19C,156C ''pre'':43C ''prepar'':168C ''problem'':273C ''process'':337C ''produc'':312C ''quit'':95C ''random'':149C ''realli'':261C ''reduc'':131C ''reduct'':2A,8B,357C ''regard'':118C ''result'':263C,314C ''right'':269C,345C ''row'':208C,210C ''sampl'':150C ''score'':227C,249C,250C ''see'':332C ''seperat'':164C ''sinc'':83C ''size'':50C ''specif'':68C ''strang'':262C,313C ''subset'':190C ''t1'':158C ''t2'':162C ''thought'':126C ''thus'':138C ''time'':21C,73C ''time1'':48C,56C ''tri'':236C ''two'':17C ''uniqu'':81C ''use'':128C,240C ''valu'':37C ''vector'':295C ''visual'':98C,238C ''want'':325C ''whos'':45C ''x'':114C ''zero'':32C ''zscore'':230C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2457, 2, '2014-11-12 19:00:05.013', 1, NULL, NULL, '&lt;p&gt;TF IDF will give you the degree of measure of how relevant a document is to your query. owever, to evaluate your IR system you need to use metrics such as - Precision, Recall and F score. &lt;/p&gt;

&lt;p&gt;Precision: Out of all the documents that your system retrieves, which ones are relevant? This measures how much noise there is in the output of your IR system. &lt;/p&gt;

&lt;p&gt;Recall: Out of all the documents that are relevant, which ones did your system retrieve? This measures how much coverage does your IR system have? &lt;/p&gt;

&lt;p&gt;It is possible to get 100% recall all the time by basically retrieving ALL documents from a collection for any query. However, the precision in this case will be very low. &lt;/p&gt;

&lt;p&gt;It is possible to get a very high precision by hand modeling an IR system ti produce very accurate results. However, it would produce a very bad recall as there will not be coverage over all the documents. &lt;/p&gt;

&lt;p&gt;So we need to measure F score- which is the harmonic mean between Precision and Recall
Check out &lt;a href="http://nlp.stanford.edu/IR-book/pdf/08eval.pdf" rel="nofollow"&gt;Chapter 8 of the Stanford IR book&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;If you are looking for datasets only here are a few that are relevant: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://zitnik.si/mediawiki/index.php?title=Datasets" rel="nofollow"&gt;http://zitnik.si/mediawiki/index.php?title=Datasets&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.daviddlewis.com/resources/testcollections/" rel="nofollow"&gt;http://www.daviddlewis.com/resources/testcollections/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://boston.lti.cs.cmu.edu/callan/Data/" rel="nofollow"&gt;http://boston.lti.cs.cmu.edu/callan/Data/&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':202C ''/callan/data/'':248C ''/callan/data/&lt;/a&gt;&lt;/li&gt;'':253C ''/ir-book/pdf/08eval.pdf'':192C ''/mediawiki/index.php?title=datasets'':226C ''/mediawiki/index.php?title=datasets&lt;/a&gt;&lt;/li&gt;'':231C ''/p'':38C,68C,95C,128C,168C,203C,219C ''/resources/testcollections/'':237C ''/resources/testcollections/&lt;/a&gt;&lt;/li&gt;'':242C ''/ul'':254C ''100'':102C ''8'':196C ''accur'':148C ''bad'':156C ''basic'':108C ''book'':201C ''boston.lti.cs.cmu.edu'':247C,252C ''boston.lti.cs.cmu.edu/callan/data/'':246C ''boston.lti.cs.cmu.edu/callan/data/&lt;/a&gt;&lt;/li&gt;'':251C ''case'':123C ''chapter'':195C ''check'':186C ''collect'':114C ''coverag'':89C,163C ''dataset'':210C ''degre'':8C ''document'':15C,45C,75C,111C,167C ''evalu'':22C ''f'':36C,175C ''get'':101C,134C ''give'':5C ''hand'':140C ''harmon'':180C ''high'':137C ''howev'':118C,150C ''href'':189C,223C,234C,245C ''idf'':3C ''ir'':24C,66C,92C,143C,200C ''li'':221C,232C,243C ''look'':208C ''low'':127C ''mean'':181C ''measur'':10C,55C,86C,174C ''metric'':30C ''model'':141C ''much'':57C,88C ''need'':27C,172C ''nlp.stanford.edu'':191C ''nlp.stanford.edu/ir-book/pdf/08eval.pdf'':190C ''nofollow'':194C,228C,239C,250C ''nois'':58C ''one'':51C,80C ''output'':63C ''owev'':20C ''p'':1C,39C,69C,96C,129C,169C,204C ''possibl'':99C,132C ''precis'':33C,40C,120C,138C,183C ''produc'':146C,153C ''queri'':19C,117C ''recal'':34C,70C,103C,157C,185C ''rel'':193C,227C,238C,249C ''relev'':13C,53C,78C,218C ''result'':149C ''retriev'':49C,84C,109C ''score'':37C,176C ''stanford'':199C ''system'':25C,48C,67C,83C,93C,144C ''tf'':2C ''ti'':145C ''time'':106C ''ul'':220C ''use'':29C ''would'':152C ''www.daviddlewis.com'':236C,241C ''www.daviddlewis.com/resources/testcollections/'':235C ''www.daviddlewis.com/resources/testcollections/&lt;/a&gt;&lt;/li&gt;'':240C ''zitnik.si'':225C,230C ''zitnik.si/mediawiki/index.php?title=datasets'':224C ''zitnik.si/mediawiki/index.php?title=datasets&lt;/a&gt;&lt;/li&gt;'':229C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2458, 2, '2014-11-12 19:31:27.17', 1, NULL, NULL, '&lt;p&gt;There are really so many good resources now.  If you want to stay away from textbooks, both O''Reilly Media and Packt Publishing offer much lighter but effective reading on a lot of great topics.  These books are much more applied in practice.  &lt;/p&gt;

&lt;p&gt;As far as learning the languages go, Coursera, Udacity, Code Acadmey, and Code School have great tutorials.  I would recommend taking a look at the following:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Coursera AI and Stats Courses&lt;/strong&gt;
&lt;a href="https://www.coursera.org/courses?orderby=upcoming&amp;amp;lngs=en&amp;amp;cats=stats,cs-ai" rel="nofollow"&gt;https://www.coursera.org/courses?orderby=upcoming&amp;amp;lngs=en&amp;amp;cats=stats,cs-ai&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Udacity Data Science courses&lt;/strong&gt;
&lt;a href="https://www.udacity.com/courses#!/data-science" rel="nofollow"&gt;https://www.udacity.com/courses#!/data-science&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/courses#!/data-science'':103C ''/courses#!/data-science&lt;/a&gt;&lt;/p&gt;'':108C ''/courses?orderby=upcoming&amp;amp;lngs=en&amp;amp;cats=stats,cs-ai'':86C ''/courses?orderby=upcoming&amp;amp;lngs=en&amp;amp;cats=stats,cs-ai&lt;/a&gt;&lt;/p&gt;'':91C ''/p'':45C,73C ''/strong'':81C,98C ''acadmey'':57C ''ai'':77C ''appli'':42C ''away'':15C ''book'':38C ''code'':56C,59C ''cours'':80C,97C ''coursera'':54C,76C ''data'':95C ''effect'':29C ''far'':48C ''follow'':72C ''go'':53C ''good'':7C ''great'':35C,62C ''href'':83C,100C ''languag'':52C ''learn'':50C ''lighter'':27C ''look'':69C ''lot'':33C ''mani'':6C ''media'':21C ''much'':26C,40C ''nofollow'':88C,105C ''o'':19C ''offer'':25C ''p'':1C,46C,74C,92C ''packt'':23C ''practic'':44C ''publish'':24C ''read'':30C ''realli'':4C ''recommend'':66C ''reilli'':20C ''rel'':87C,104C ''resourc'':8C ''school'':60C ''scienc'':96C ''stat'':79C ''stay'':14C ''strong'':75C,93C ''take'':67C ''textbook'':17C ''topic'':36C ''tutori'':63C ''udac'':55C,94C ''want'':12C ''would'':65C ''www.coursera.org'':85C,90C ''www.coursera.org/courses?orderby=upcoming&amp;amp;lngs=en&amp;amp;cats=stats,cs-ai'':84C ''www.coursera.org/courses?orderby=upcoming&amp;amp;lngs=en&amp;amp;cats=stats,cs-ai&lt;/a&gt;&lt;/p&gt;'':89C ''www.udacity.com'':102C,107C ''www.udacity.com/courses#!/data-science'':101C ''www.udacity.com/courses#!/data-science&lt;/a&gt;&lt;/p&gt;'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2459, 2, '2014-11-12 19:35:01.55', 0, NULL, NULL, '&lt;p&gt;Also, &lt;strong&gt;Green Tea Press&lt;/strong&gt; offers free books on related topics such as an intro to Python and using python with Probability and Stats.  &lt;a href="http://www.greenteapress.com/index.html" rel="nofollow"&gt;http://www.greenteapress.com/index.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/index.html'':31C ''/index.html&lt;/a&gt;&lt;/p&gt;'':36C ''/strong'':7C ''also'':2C ''book'':10C ''free'':9C ''green'':4C ''href'':28C ''intro'':17C ''nofollow'':33C ''offer'':8C ''p'':1C ''press'':6C ''probabl'':24C ''python'':19C,22C ''rel'':32C ''relat'':12C ''stat'':26C ''strong'':3C ''tea'':5C ''topic'':13C ''use'':21C ''www.greenteapress.com'':30C,35C ''www.greenteapress.com/index.html'':29C ''www.greenteapress.com/index.html&lt;/a&gt;&lt;/p&gt;'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2462, 2, '2014-11-13 07:17:18.147', 3, NULL, NULL, '&lt;p&gt;One way would be to create 20 features (each feature representing a codon). In this way, you would have a dataset with 3190 instances and 20 categorical features. There is no need to treat the sequence as a Markov chain. &lt;/p&gt;

&lt;p&gt;Once the dataset has been featurized as suggested above, any supervised classifier can work well. I would suggest using a gradient boosting machine as it might be better suited to handle categorical features. &lt;/p&gt;
', NULL, NULL, '''/p'':42C,77C ''20'':8C,27C ''3190'':24C ''better'':71C ''boost'':65C ''categor'':28C,75C ''chain'':41C ''classifi'':55C ''codon'':14C ''creat'':7C ''dataset'':22C,46C ''featur'':9C,11C,29C,49C,76C ''gradient'':64C ''handl'':74C ''instanc'':25C ''machin'':66C ''markov'':40C ''might'':69C ''need'':33C ''one'':2C ''p'':1C,43C ''repres'':12C ''sequenc'':37C ''suggest'':51C,61C ''suit'':72C ''supervis'':54C ''treat'':35C ''use'':62C ''way'':3C,17C ''well'':58C ''work'':57C ''would'':4C,19C,60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6451, 2, '2015-07-14 06:09:27.803', 1, NULL, NULL, '&lt;p&gt;While checking on python &lt;a href="https://github.com/dmlc/xgboost" rel="nofollow"&gt;xgboost&lt;/a&gt; I found the existence of this &lt;a href="https://github.com/dmlc/rabit" rel="nofollow"&gt;open source project&lt;/a&gt; that helps create scalable machine learning program.Should be worth exploring.&lt;/p&gt;
', NULL, NULL, '''/a'':14C,31C ''/dmlc/rabit'':25C ''/dmlc/xgboost'':10C ''/p'':42C ''check'':3C ''creat'':34C ''exist'':18C ''explor'':41C ''found'':16C ''github.com'':9C,24C ''github.com/dmlc/rabit'':23C ''github.com/dmlc/xgboost'':8C ''help'':33C ''href'':7C,22C ''learn'':37C ''machin'':36C ''nofollow'':12C,27C ''open'':28C ''p'':1C ''program.should'':38C ''project'':30C ''python'':5C ''rel'':11C,26C ''scalabl'':35C ''sourc'':29C ''worth'':40C ''xgboost'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2463, 1, '2014-11-13 08:58:25.847', 2, 151, 'Geospatial Social Network Analysis Visualization', '&lt;p&gt;I have a data set that keep tracks of who referred someone to a program and the geo coords of both parties, what will be the best way to visualize this kind of data set. This visualization should also be able to use the geo coordinates to place this entities in the map to form clusters or on a real map.&lt;/p&gt;

&lt;p&gt;Am interested in Algorithm and/or Library to do this preferable Java, Python, Scala or NodeJS lib.  The record can be a big as thousand or hundreds of thousands. &lt;/p&gt;

&lt;p&gt;Thanks.&lt;/p&gt;
', 'algorithms  scalability  social-network-analysis  library', NULL, '''/p'':75C,105C,108C ''abl'':54C ''algorithm'':6B,80C ''also'':52C ''analysi'':4A,11B ''and/or'':81C ''best'':40C ''big'':98C ''cluster'':69C ''coord'':32C ''coordin'':59C ''data'':17C,47C ''entiti'':63C ''form'':68C ''geo'':31C,58C ''geospati'':1A ''hundr'':102C ''interest'':78C ''java'':87C ''keep'':20C ''kind'':45C ''lib'':92C ''librari'':12B,82C ''map'':66C,74C ''network'':3A,10B ''nodej'':91C ''p'':13C,76C,106C ''parti'':35C ''place'':61C ''prefer'':86C ''program'':28C ''python'':88C ''real'':73C ''record'':94C ''refer'':24C ''scala'':89C ''scalabl'':7B ''set'':18C,48C ''social'':2A,9B ''social-network-analysi'':8B ''someon'':25C ''thank'':107C ''thousand'':100C,104C ''track'':21C ''use'':56C ''visual'':5A,43C,50C ''way'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2464, 1, '2014-11-13 09:19:22.797', 3, 640, 'Clustering of documents using the topics derived from Latent Dirichlet Allocation', '&lt;p&gt;I want to use Latent Dirichlet Allocation for a project and I am using Python with the gensim library. After finding the topics I would like to cluster the documents using an algorithm such as k-means(Ideally I would like to use a good one for overlapping clusters so any recommendation is welcomed). I managed to get the topics but they are in the form of:&lt;/p&gt;

&lt;p&gt;0.041*Minister + 0.041*Key + 0.041*moments + 0.041*controversial + 0.041*Prime&lt;/p&gt;

&lt;p&gt;In order to apply a clustering algorithm, and correct me if I''m wrong, I believe I should find a way to represent each word as a number using either tfidf or word2vec.&lt;/p&gt;

&lt;p&gt;Do you have any ideas of how I could "strip" the textual information from e.g. a list, in order to do so and then place them back in order to make the appropriate multiplication?&lt;/p&gt;

&lt;p&gt;For instance the way I see it if the word Minister has a tfidf weight of 0.042 and so on for any other word within the same topic I should be to compute something like:&lt;/p&gt;

&lt;p&gt;0.041*0.42 + ... + 0.041*tfidf(Prime) and get a result that will be later on used in order to cluster the results.&lt;/p&gt;

&lt;p&gt;Thank you for your time.&lt;/p&gt;
', 'python  clustering  lda', NULL, '''/p'':84C,96C,131C,167C,204C,227C,234C ''0.041'':86C,88C,90C,92C,94C,206C,208C ''0.042'':185C ''0.42'':207C ''algorithm'':48C,104C ''alloc'':11A,22C ''appli'':101C ''appropri'':165C ''back'':159C ''believ'':113C ''cluster'':1A,13B,43C,65C,103C,224C ''comput'':201C ''controversi'':93C ''correct'':106C ''could'':141C ''deriv'':7A ''dirichlet'':10A,21C ''document'':3A,45C ''e.g'':147C ''either'':127C ''find'':36C,116C ''form'':82C ''gensim'':33C ''get'':74C,212C ''good'':61C ''idea'':137C ''ideal'':54C ''inform'':145C ''instanc'':170C ''k'':52C ''k-mean'':51C ''key'':89C ''latent'':9A,20C ''later'':218C ''lda'':14B ''librari'':34C ''like'':41C,57C,203C ''list'':149C ''m'':110C ''make'':163C ''manag'':72C ''mean'':53C ''minist'':87C,179C ''moment'':91C ''multipl'':166C ''number'':125C ''one'':62C ''order'':99C,151C,161C,222C ''overlap'':64C ''p'':15C,85C,97C,132C,168C,205C,228C ''place'':157C ''prime'':95C,210C ''project'':25C ''python'':12B,30C ''recommend'':68C ''repres'':120C ''result'':214C,226C ''see'':174C ''someth'':202C ''strip'':142C ''textual'':144C ''tfidf'':128C,182C,209C ''thank'':229C ''time'':233C ''topic'':6A,38C,76C,196C ''use'':4A,19C,29C,46C,59C,126C,220C ''want'':17C ''way'':118C,172C ''weight'':183C ''welcom'':70C ''within'':193C ''word'':122C,178C,192C ''word2vec'':130C ''would'':40C,56C ''wrong'':111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2537, 1, '2014-11-25 09:40:20.04', 3, 30, 'Why isn''t dimension sampling used with gradient boosting machines (GBM)?', '&lt;p&gt;GBMs, like random forests, build each tree on a different sample of the dataset and hence, going by the spirit of ensemble models, produce higher accuracies. However, I have not seen GBM being used with dimension sampling at every split of the tree like is common practice with random forests. &lt;/p&gt;

&lt;p&gt;Are there some tests that show that dimensional sampling with GBM would decrease its accuracy because of which this is avoided, either in literature form or in practical experience? &lt;/p&gt;
', 'random-forest  accuracy  gbm  ensemble-modeling', NULL, '''/p'':71C,102C ''accuraci'':15B,46C,87C ''avoid'':93C ''boost'':9A ''build'':25C ''common'':66C ''dataset'':34C ''decreas'':85C ''differ'':30C ''dimens'':4A,56C ''dimension'':80C ''either'':94C ''ensembl'':18B,42C ''ensemble-model'':17B ''everi'':59C ''experi'':101C ''forest'':14B,24C,70C ''form'':97C ''gbm'':11A,16B,52C,83C ''gbms'':21C ''go'':37C ''gradient'':8A ''henc'':36C ''higher'':45C ''howev'':47C ''isn'':2A ''like'':22C,64C ''literatur'':96C ''machin'':10A ''model'':19B,43C ''p'':20C,72C ''practic'':67C,100C ''produc'':44C ''random'':13B,23C,69C ''random-forest'':12B ''sampl'':5A,31C,57C,81C ''seen'':51C ''show'':78C ''spirit'':40C ''split'':60C ''test'':76C ''tree'':27C,63C ''use'':6A,54C ''would'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2500, 1, '2014-11-19 09:26:58.7', 3, 458, 'Recommendation engine with mahout', '&lt;p&gt;I have a list user data: user name, age, sex, address, location etc., and &lt;/p&gt;

&lt;p&gt;a set of product data: Product name, Cost, description etc.&lt;/p&gt;

&lt;p&gt;Now I would like to build a recommendation engine that will be able to:&lt;/p&gt;

&lt;p&gt;1 Figure out similar products&lt;/p&gt;

&lt;p&gt;eg :&lt;/p&gt;

&lt;p&gt;name  :   category   :  cost    :   ingredients&lt;/p&gt;

&lt;p&gt;x     :     x1   :        15  :       xx1, xx2, xx3&lt;/p&gt;

&lt;p&gt;y     :    y1   :        14   :     yy1, yy2, yy3&lt;/p&gt;

&lt;p&gt;z     :    x1  :          12   :     xx1, xy1 &lt;/p&gt;

&lt;p&gt;here x and z are similar.&lt;/p&gt;

&lt;p&gt;2 Recommend relevant products from the product list to a user&lt;/p&gt;

&lt;p&gt;How can I implement this using mahout?&lt;/p&gt;
', 'machine-learning  data-mining  recommendation', NULL, '''/p'':27C,39C,55C,62C,65C,71C,79C,87C,94C,102C,115C,124C ''1'':57C ''12'':91C ''14'':83C ''15'':75C ''2'':104C ''abl'':53C ''address'':23C ''age'':21C ''build'':46C ''categori'':68C ''cost'':36C,69C ''data'':9B,18C,33C ''data-min'':8B ''descript'':37C ''eg'':64C ''engin'':2A,49C ''etc'':25C,38C ''figur'':58C ''implement'':120C ''ingredi'':70C ''learn'':7B ''like'':44C ''list'':16C,111C ''locat'':24C ''machin'':6B ''machine-learn'':5B ''mahout'':4A,123C ''mine'':10B ''name'':20C,35C,67C ''p'':12C,28C,40C,56C,63C,66C,72C,80C,88C,95C,103C,116C ''product'':32C,34C,61C,107C,110C ''recommend'':1A,11B,48C,105C ''relev'':106C ''set'':30C ''sex'':22C ''similar'':60C,101C ''use'':122C ''user'':17C,19C,114C ''would'':43C ''x'':73C,97C ''x1'':74C,90C ''xx1'':76C,92C ''xx2'':77C ''xx3'':78C ''xy1'':93C ''y'':81C ''y1'':82C ''yy1'':84C ''yy2'':85C ''yy3'':86C ''z'':89C,99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2468, 2, '2014-11-14 15:15:20.617', 2, NULL, NULL, '&lt;h3&gt;Storing user profiles&lt;/h3&gt;

&lt;p&gt;If you just want to store all user profiles... just save them into normal &lt;strong&gt;RDBMS&lt;/strong&gt;. Assuming one user profiles takes 10Kb of storage, you need only ~9.5Gb for every million of users, which is pretty little and gives you all advantages of mature relational databases. &lt;/p&gt;

&lt;p&gt;It makes sense to use &lt;strong&gt;HBase&lt;/strong&gt; only when you have really many users (say, &gt; 1B) or when data is very sparse (most columns are empty). But don''t expect it to be as convenient as good old SQL databases. &lt;/p&gt;

&lt;p&gt;In advertising, and especially in &lt;a href="http://en.wikipedia.org/wiki/Real-time_bidding" rel="nofollow"&gt;real-time bidding&lt;/a&gt;, very fast retrieval of user profiles is needed. &lt;a href="http://www.aerospike.com/" rel="nofollow"&gt;&lt;strong&gt;Aerospike&lt;/strong&gt;&lt;/a&gt; becomes more and more popular for this task.&lt;/p&gt;

&lt;h3&gt;Analysing data slices&lt;/h3&gt;

&lt;p&gt;Common use of business logs is to analyse specific slices of data, e.g. number of users from France that visited sites from "game" category on November 1-14, 2014. Standard way to manage such data efficiently is to organize them into &lt;a href="http://en.wikipedia.org/wiki/Data_cube" rel="nofollow"&gt;&lt;strong&gt;data cubes&lt;/strong&gt;&lt;/a&gt;. You won''t get individual records (e.g. users), but you''ll get aggregated statistics really fast. &lt;/p&gt;

&lt;p&gt;Such cubes may have many different dimensions, but in 99% of cases they have &lt;strong&gt;date field&lt;/strong&gt; that they are &lt;strong&gt;partitioned&lt;/strong&gt; by. It makes great sense, because almost every query includes time period to get data from. &lt;/p&gt;

&lt;p&gt;As for software, &lt;strong&gt;Vertica&lt;/strong&gt; is great for such aggregations. Cheaper* solution from Hadoop world is &lt;strong&gt;Impala&lt;/strong&gt;, which is also great. &lt;/p&gt;

&lt;p&gt;(* - if you count only license price)&lt;/p&gt;

&lt;h3&gt;Machine learning&lt;/h3&gt;

&lt;p&gt;It really depends on concrete tasks and ML toolkit in use. For real-time bidding you would want blazing fast access to user profile vectors and would probably prefer Aerospike. For online learning &lt;strong&gt;Spark Streaming&lt;/strong&gt; may be used as a data source, and no storage used at all. For offline machine learning there''s excellent &lt;a href="https://spark.apache.org/docs/latest/mllib-guide.html" rel="nofollow"&gt;&lt;strong&gt;MLlib&lt;/strong&gt;&lt;/a&gt; from the same Spark project, which works with a variety of sources. &lt;/p&gt;
', NULL, NULL, '''-14'':176C ''/a'':116C,133C,201C,368C ''/docs/latest/mllib-guide.html'':362C ''/h3'':5C,147C,298C ''/p'':55C,98C,142C,218C,260C,286C,294C,381C ''/strong'':23C,64C,132C,200C,237C,243C,267C,281C,337C,367C ''/wiki/data_cube'':194C ''/wiki/real-time_bidding'':109C ''1'':175C ''10kb'':29C ''1b'':73C ''2014'':177C ''9.5'':35C ''99'':229C ''access'':321C ''advantag'':50C ''advertis'':101C ''aerospik'':131C,330C ''aggreg'':214C,272C ''almost'':250C ''also'':284C ''analys'':144C,156C ''assum'':24C ''becom'':134C ''bid'':115C,315C ''blaze'':319C ''busi'':152C ''case'':231C ''categori'':172C ''cheaper'':273C ''column'':81C ''common'':149C ''concret'':304C ''conveni'':92C ''count'':290C ''cube'':199C,221C ''data'':76C,145C,160C,183C,198C,258C,343C ''databas'':54C,97C ''date'':235C ''depend'':302C ''differ'':225C ''dimens'':226C ''e.g'':161C,208C ''effici'':184C ''empti'':83C ''en.wikipedia.org'':108C,193C ''en.wikipedia.org/wiki/data_cube'':192C ''en.wikipedia.org/wiki/real-time_bidding'':107C ''especi'':103C ''everi'':38C,251C ''excel'':357C ''expect'':87C ''fast'':118C,217C,320C ''field'':236C ''franc'':166C ''game'':171C ''gb'':36C ''get'':205C,213C,257C ''give'':47C ''good'':94C ''great'':247C,269C,285C ''h3'':1C,143C,295C ''hadoop'':276C ''hbase'':63C ''href'':106C,126C,191C,359C ''impala'':280C ''includ'':253C ''individu'':206C ''learn'':297C,333C,354C ''licens'':292C ''littl'':45C ''ll'':212C ''log'':153C ''machin'':296C,353C ''make'':58C,246C ''manag'':181C ''mani'':70C,224C ''matur'':52C ''may'':222C,338C ''million'':39C ''ml'':307C ''mllib'':366C ''need'':33C,124C ''nofollow'':111C,129C,196C,364C ''normal'':20C ''novemb'':174C ''number'':162C ''offlin'':352C ''old'':95C ''one'':25C ''onlin'':332C ''organ'':187C ''p'':6C,56C,99C,148C,219C,261C,287C,299C ''partit'':242C ''period'':255C ''popular'':138C ''prefer'':329C ''pretti'':44C ''price'':293C ''probabl'':328C ''profil'':4C,15C,27C,122C,324C ''project'':373C ''queri'':252C ''rdbms'':22C ''real'':113C,313C ''real-tim'':112C,312C ''realli'':69C,216C,301C ''record'':207C ''rel'':110C,128C,195C,363C ''relat'':53C ''retriev'':119C ''save'':17C ''say'':72C ''sens'':59C,248C ''site'':169C ''slice'':146C,158C ''softwar'':264C ''solut'':274C ''sourc'':344C,380C ''spark'':335C,372C ''spark.apache.org'':361C ''spark.apache.org/docs/latest/mllib-guide.html'':360C ''spars'':79C ''specif'':157C ''sql'':96C ''standard'':178C ''statist'':215C ''storag'':31C,347C ''store'':2C,12C ''stream'':336C ''strong'':21C,62C,130C,197C,234C,241C,265C,279C,334C,365C ''take'':28C ''task'':141C,305C ''time'':114C,254C,314C ''toolkit'':308C ''use'':61C,150C,310C,340C,348C ''user'':3C,14C,26C,41C,71C,121C,164C,209C,323C ''varieti'':378C ''vector'':325C ''vertica'':266C ''visit'':168C ''want'':10C,318C ''way'':179C ''won'':203C ''work'':375C ''world'':277C ''would'':317C,327C ''www.aerospike.com'':127C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3719, 1, '2014-12-18 04:36:06.107', 10, 4753, 'Neo4j vs OrientDB vs Titan', '&lt;p&gt;I am working on a data-science project related on social relationship mining and need to store data in some graph databases. Initially I chose Neo4j as the database. But it seams Neo4j doesn''t scale well. The alternative I found out are Titan  and oriebtDB. I have gone through &lt;a href="http://db-engines.com/en/system/Neo4j%3BOrientDB%3BTitan" rel="nofollow"&gt;this&lt;/a&gt; comparison on these three Databases, But I would like to get more details on  these databases. So Could some one help me in choosing the best one. Mainly I would like to compare performance, scaling, on line documentation/tutorials available, Python library support, query language complexity and graph algorithm support of these databases. Also is there any other good database options ?&lt;/p&gt;
', 'data-mining  graphs  databases  social-network-analysis', NULL, '''/a'':75C ''/en/system/neo4j%3borientdb%3btitan'':71C ''/p'':136C ''algorithm'':123C ''also'':128C ''altern'':55C ''analysi'':14B ''avail'':114C ''best'':101C ''choos'':99C ''chose'':41C ''compar'':108C ''comparison'':76C ''complex'':120C ''could'':93C ''data'':7B,22C,34C ''data-min'':6B ''data-sci'':21C ''databas'':10B,38C,45C,80C,91C,127C,134C ''db-engines.com'':70C ''db-engines.com/en/system/neo4j%3borientdb%3btitan'':69C ''detail'':88C ''documentation/tutorials'':113C ''doesn'':50C ''found'':57C ''get'':86C ''gone'':65C ''good'':133C ''graph'':9B,37C,122C ''help'':96C ''href'':68C ''initi'':39C ''languag'':119C ''librari'':116C ''like'':84C,106C ''line'':112C ''main'':103C ''mine'':8B,29C ''need'':31C ''neo4j'':1A,42C,49C ''network'':13B ''nofollow'':73C ''one'':95C,102C ''option'':135C ''oriebtdb'':62C ''orientdb'':3A ''p'':15C ''perform'':109C ''project'':24C ''python'':115C ''queri'':118C ''rel'':72C ''relat'':25C ''relationship'':28C ''scale'':52C,110C ''scienc'':23C ''seam'':48C ''social'':12B,27C ''social-network-analysi'':11B ''store'':33C ''support'':117C,124C ''three'':79C ''titan'':5A,60C ''vs'':2A,4A ''well'':53C ''work'':18C ''would'':83C,105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2469, 1, '2014-11-14 15:20:44.463', 2, 109, 'survey data analysis (discrete data)', '&lt;p&gt;I did small survey and get such data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;|-------------| Yes | No | Dont_Know |  
|-------------|     |    |           |  
| Employee    | 60  | 5  | 5         |  
| Workers     | 17  | 0  | 1         |  
| Businessmen | 71  | 5  | 10        |  
| Jobless     | 4   | 30 | 0         |  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;R code&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dt &amp;lt;- data.frame(workers = c("Employee",
                             "Workers", 
                             "Businessmen", 
                             "Jobless"), 
                 yes = c(60,17,71,4), 
                 no = c(5,0,5,30), 
                 dont_know = c(5,1,10,0)
                )
&lt;/code&gt;&lt;/pre&gt;

&lt;ol&gt;
&lt;li&gt;What kind of test I must do, if I want to show, that the Jobless people are often choosing &lt;strong&gt;No&lt;/strong&gt; answer?   &lt;/li&gt;
&lt;li&gt;Is the difference between Jobless and Businessmen answers significant? &lt;/li&gt;
&lt;li&gt;And what is about other groups?  &lt;/li&gt;
&lt;li&gt;What another information I can get from such data or what kind questions I can ask from such data?&lt;/li&gt;
&lt;/ol&gt;
', 'r', NULL, '''/code'':39C,75C ''/li'':102C,113C,121C,142C ''/ol'':143C ''/p'':16C,44C ''/pre'':40C,76C ''/strong'':100C ''0'':29C,38C,65C,74C ''1'':30C,72C ''10'':34C,73C ''17'':28C,59C ''30'':37C,67C ''4'':36C,61C ''5'':25C,26C,33C,64C,66C,71C ''60'':24C,58C ''71'':32C,60C ''analysi'':3A ''anoth'':124C ''answer'':101C,111C ''ask'':138C ''businessmen'':31C,54C,110C ''c'':51C,57C,63C,70C ''choos'':97C ''code'':18C,43C,46C ''data'':2A,5A,15C,131C,141C ''data.frame'':49C ''differ'':106C ''discret'':4A ''dont'':21C,68C ''dt'':47C ''employe'':23C,52C ''get'':13C,128C ''group'':120C ''inform'':125C ''jobless'':35C,55C,93C,108C ''kind'':80C,134C ''know'':22C,69C ''li'':78C,103C,114C,122C ''lt'':48C ''must'':84C ''often'':96C ''ol'':77C ''p'':7C,41C ''peopl'':94C ''pre'':17C,45C ''question'':135C ''r'':6B,42C ''show'':90C ''signific'':112C ''small'':10C ''strong'':98C ''survey'':1A,11C ''test'':82C ''want'':88C ''worker'':27C,50C,53C ''yes'':19C,56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2470, 1, '2014-11-14 16:39:29.663', 1, 49, 'Non-linear transformations input dataset for support vector machines', '&lt;p&gt;I have two classes (A,B) that I want to classify using a SVM. Say that I have a class C and a function f. Can I do this:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;
A'' =  f(A,C) = |A-C|
B'' =  f(B,C) = |B-C|
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;and then perform the classification on A'' and B'' instead? In the context of my problem A and B are classes where elements are vectors. The f function measures the Mahalanobis distance of each vector with respect to the distribution imposed by C.&lt;/p&gt;
', 'svm', NULL, '''/code'':59C ''/p'':42C,60C,105C ''a-c'':49C ''b'':18C,52C,54C,57C,70C,80C ''b-c'':56C ''c'':33C,48C,51C,55C,58C,104C ''class'':16C,32C,82C ''classif'':66C ''classifi'':23C ''code'':44C ''context'':74C ''dataset'':6A ''distanc'':93C ''distribut'':101C ''element'':84C ''f'':37C,46C,53C,88C ''function'':36C,89C ''impos'':102C ''input'':5A ''instead'':71C ''linear'':3A ''machin'':10A ''mahalanobi'':92C ''measur'':90C ''non'':2A ''non-linear'':1A ''p'':12C,43C,61C ''perform'':64C ''problem'':77C ''respect'':98C ''say'':27C ''support'':8A ''svm'':11B,26C ''transform'':4A ''two'':15C ''use'':24C ''vector'':9A,86C,96C ''want'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2471, 2, '2014-11-14 17:11:13.06', 2, NULL, NULL, '&lt;p&gt;I would definitely use a graph (Though, this clearly depends on the final application, maybe you could add more information) &lt;/p&gt;

&lt;p&gt;For the nodes, you should consider as nodes not only tanks but also points were pipelines change name or bifurcate. For instance, following your example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    e1     e2        e3          e4        e5          e7           
    +---+----------.---------+----------.----------+-----------      
    |          |         |                     |          |      
[R tank 1]  [S tank 1] [S tank 2]              |e6     [S tank 3]
                                               |                 
                                           [S tank 4]            
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now adding the nodes:  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    e1 n2  e2     n4   e3   n6   e4    n8  e5     n9   e7           
n1 +---+----------.---------+----------.----------+----------+      
   |          |         |                     |              |      
  n3         n5        n7                     |e6           n11     
                                              |                 
                                             n10                
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Lastly, you need some kind of mapping. 
Some of the nodes will map to tanks:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[R tank 1]     n3                                                   
[S tank 1]     n5                                                   
[S tank 2]     n7                                                   
[S tank 4]     n10                                                  
[S tank 3]     n11                                                  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And the pipelines will be represented by paths in the graph&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Pipeline 1     e1 e2                                                
Pipeline 2     e3 e4                                                
Pipeline 3     e5 e7                                                
Pipeline 4     e6                                                   
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':74C,102C,143C,175C ''/p'':22C,49C,81C,120C,157C ''/pre'':75C,103C,144C,176C ''1'':60C,63C,125C,129C,161C ''2'':66C,133C,165C ''3'':70C,141C,169C ''4'':73C,137C,173C ''ad'':78C ''add'':19C ''also'':36C ''applic'':15C ''bifurc'':43C ''chang'':40C ''clear'':10C ''code'':51C,83C,122C,159C ''consid'':29C ''could'':18C ''definit'':4C ''depend'':11C ''e1'':52C,84C,162C ''e2'':53C,86C,163C ''e3'':54C,88C,166C ''e4'':55C,90C,167C ''e5'':56C,92C,170C ''e6'':67C,99C,174C ''e7'':57C,94C,171C ''exampl'':48C ''final'':14C ''follow'':46C ''graph'':7C,156C ''inform'':21C ''instanc'':45C ''kind'':109C ''last'':105C ''map'':111C,117C ''mayb'':16C ''n1'':95C ''n10'':101C,138C ''n11'':100C,142C ''n2'':85C ''n3'':96C,126C ''n4'':87C ''n5'':97C,130C ''n6'':89C ''n7'':98C,134C ''n8'':91C ''n9'':93C ''name'':41C ''need'':107C ''node'':26C,31C,80C,115C ''p'':1C,23C,76C,104C,145C ''path'':153C ''pipelin'':39C,148C,160C,164C,168C,172C ''point'':37C ''pre'':50C,82C,121C,158C ''r'':58C,123C ''repres'':151C ''tank'':34C,59C,62C,65C,69C,72C,119C,124C,128C,132C,136C,140C ''though'':8C ''use'':5C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2495, 1, '2014-11-18 14:03:26.76', 3, 269, 'How to run R programs on multicore using doParallel package?', '&lt;p&gt;I am running SVM algorithm in R.It is taking long time to run the algorithm.I have system with 32GB RAM.How can I use that whole RAM memory to speed my process.&lt;/p&gt;
', 'r', NULL, '''/p'':44C ''32gb'':31C ''algorithm'':17C ''algorithm.i'':27C ''doparallel'':9A ''long'':22C ''memori'':39C ''multicor'':7A ''p'':12C ''packag'':10A ''process'':43C ''program'':5A ''r'':4A,11B ''r.it'':19C ''ram'':38C ''ram.how'':32C ''run'':3A,15C,25C ''speed'':41C ''svm'':16C ''system'':29C ''take'':21C ''time'':23C ''use'':8A,35C ''whole'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2472, 2, '2014-11-14 17:32:26.05', 3, NULL, NULL, '&lt;p&gt;You are just predicting if Play = Yes or Play = No.&lt;/p&gt;

&lt;p&gt;The confusion matrix would look like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;             Predicted
          +------+------+
          |  Yes |  No  |
    +-------------------+
A   |     |      |      |
c   | Yes |  TP  |  FP  |
t   |     |      |      |
u   +-------------------+
a   |     |      |      |
l   | No  |  FN  |  TN  |
    |     |      |      |
    +-----+------+------+

TP: True positives
FP: False positives 
FN: False negatives 
TN: True negatives
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The accuracy can then be calculated as (TP + TN)/(TP + FP + TN + FN). &lt;/p&gt;
', NULL, NULL, '''/code'':51C ''/p'':12C,21C,67C ''/pre'':52C ''accuraci'':55C ''c'':28C ''calcul'':59C ''code'':23C ''confus'':15C ''fals'':43C,46C ''fn'':37C,45C,66C ''fp'':31C,42C,64C ''l'':35C ''like'':19C ''look'':18C ''matrix'':16C ''negat'':47C,50C ''p'':1C,13C,53C ''play'':7C,10C ''posit'':41C,44C ''pre'':22C ''predict'':5C,24C ''tn'':38C,48C,62C,65C ''tp'':30C,39C,61C,63C ''true'':40C,49C ''u'':33C ''would'':17C ''yes'':8C,25C,29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2488, 2, '2014-11-18 01:26:51.9', 1, NULL, NULL, '&lt;p&gt;It seems to me as if a good starting point would be to read up on the semantic web, perhaps starting with &lt;a href="http://dbpedia.org/About" rel="nofollow"&gt;DBpedia&lt;/a&gt; and maybe LinkedData. You could go from there and build up your own database.&lt;/p&gt;

&lt;p&gt;Example of a &lt;a href="http://dbpedia.org/snorql/?query=%0D%0Aselect%20%3Flabel%20where%20%7B%0D%0A%20%20%23%3ACamera%20dbpedia-owl%3Aabstract%20%3Fabstract.%0D%0A%20%20%23FILTER%20langMatches%28%20lang%28%3Fabstract%29%2C%20%27en%27%29.%0D%0A%20%20%23%3Fprod%20dbpedia2%3Aproducts%20%3Frelated%20.%0D%0A%20%20%3Fprod%20dbpedia-owl%3Aproduct%20%3ACamera%20.%0D%0A%20%20%3Fprod%20dcterms%3Asubject%20%3Fcategories%20.%0D%0A%20%20%3Fentity%20dcterms%3Asubject%20%3Fcategories.%0D%0A%20%20%3Fentity%20rdf%3Atype%20yago%3APhysicalEntity100001930%20.%0D%0A%20%20%3Fentity%20rdfs%3Alabel%20%3Flabel%20.%0D%0A%20%20filter%20langMatches%28%20lang%28%3Flabel%29%2C%20%27en%27%29.%0D%0A%7D" rel="nofollow"&gt;SPARQL query&lt;/a&gt; starting with the DBpedia page of ''Camera'':&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;select ?label where {
  ?prod dbpedia-owl:product :Camera .
  ?prod dcterms:subject ?categories .
  ?entity dcterms:subject ?categories.
  ?entity rdf:type yago:PhysicalEntity100001930 .
  ?entity rdfs:label ?label .
  filter langMatches( lang(?label), ''en'').
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Generating a lot of words somehow related to ''Camera''.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;...
"Shutter button"@en
"Rangefinder camera"@en
"Still camera"@en
"Lomo LC-A"@en
"Flexaret"@en
"Land Camera"@en
"Robot (camera)"@en
"Speed Graphic"@en
"Ansco Panda"@en
"Image trigger"@en
"Still video camera"@en
"Hidden camera"@en
"Mainichi Shimbun"@en
"Åhiradai Station"@en
"Depth-of-field adapter"@en
"Banquet camera"@en
"Digital versus film photography"@en
"Fernseh"@en
"Remote camera"@en
"Professional video camera"@en
....
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;em&gt;The above result is just an excerpt.&lt;/em&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':32C,61C ''/about'':28C ''/code'':103C,186C ''/em'':197C ''/p'':47C,69C,115C,198C ''/pre'':104C,187C ''/snorql/?query=%0d%0aselect%20%3flabel%20where%20%7b%0d%0a%20%20%23%3acamera%20dbpedia-owl%3aabstract%20%3fabstract.%0d%0a%20%20%23filter%20langmatches%28%20lang%28%3fabstract%29%2c%20%27en%27%29.%0d%0a%20%20%23%3fprod%20dbpedia2%3aproducts%20%3frelated%20.%0d%0a%20%20%3fprod%20dbpedia-owl%3aproduct%20%3acamera%20.%0d%0a%20%20%3fprod%20dcterms%3asubject%20%3fcategories%20.%0d%0a%20%20%3fentity%20dcterms%3asubject%20%3fcategories.%0d%0a%20%20%3fentity%20rdf%3atype%20yago%3aphysicalentity100001930%20.%0d%0a%20%20%3fentity%20rdfs%3alabel%20%3flabel%20.%0d%0a%20%20filter%20langmatches%28%20lang%28%3flabel%29%2c%20%27en%27%29.%0d%0a%7d'':56C ''adapt'':167C ''ansco'':143C ''banquet'':169C ''build'':42C ''button'':119C ''camera'':68C,80C,114C,122C,125C,135C,138C,151C,154C,170C,180C,184C ''categori'':84C,88C ''code'':71C,117C ''could'':37C ''databas'':46C ''dbpedia'':31C,65C,77C ''dbpedia-owl'':76C ''dbpedia.org'':27C,55C ''dbpedia.org/about'':26C ''dbpedia.org/snorql/?query=%0d%0aselect%20%3flabel%20where%20%7b%0d%0a%20%20%23%3acamera%20dbpedia-owl%3aabstract%20%3fabstract.%0d%0a%20%20%23filter%20langmatches%28%20lang%28%3fabstract%29%2c%20%27en%27%29.%0d%0a%20%20%23%3fprod%20dbpedia2%3aproducts%20%3frelated%20.%0d%0a%20%20%3fprod%20dbpedia-owl%3aproduct%20%3acamera%20.%0d%0a%20%20%3fprod%20dcterms%3asubject%20%3fcategories%20.%0d%0a%20%20%3fentity%20dcterms%3asubject%20%3fcategories.%0d%0a%20%20%3fentity%20rdf%3atype%20yago%3aphysicalentity100001930%20.%0d%0a%20%20%3fentity%20rdfs%3alabel%20%3flabel%20.%0d%0a%20%20filter%20langmatches%28%20lang%28%3flabel%29%2c%20%27en%27%29.%0d%0a%7d'':54C ''dcterm'':82C,86C ''depth'':164C ''depth-of-field'':163C ''digit'':172C ''em'':189C ''en'':102C,120C,123C,126C,131C,133C,136C,139C,142C,145C,148C,152C,155C,158C,162C,168C,171C,176C,178C,181C,185C ''entiti'':85C,89C,94C ''exampl'':49C ''excerpt'':196C ''fernseh'':177C ''field'':166C ''film'':174C ''filter'':98C ''flexaret'':132C ''generat'':106C ''go'':38C ''good'':9C ''graphic'':141C ''hidden'':153C ''hiradai'':160C ''href'':25C,53C ''imag'':146C ''label'':73C,96C,97C,101C ''land'':134C ''lang'':100C ''langmatch'':99C ''lc'':129C ''lc-a'':128C ''linkeddata'':35C ''lomo'':127C ''lot'':108C ''mainichi'':156C ''mayb'':34C ''nofollow'':30C,58C ''owl'':78C ''p'':1C,48C,105C,188C ''page'':66C ''panda'':144C ''perhap'':21C ''photographi'':175C ''physicalentity100001930'':93C ''point'':11C ''pre'':70C,116C ''prod'':75C,81C ''product'':79C ''profession'':182C ''queri'':60C ''rangefind'':121C ''rdf'':90C ''rdfs'':95C ''read'':15C ''rel'':29C,57C ''relat'':112C ''remot'':179C ''result'':192C ''robot'':137C ''seem'':3C ''select'':72C ''semant'':19C ''shimbun'':157C ''shutter'':118C ''somehow'':111C ''sparql'':59C ''speed'':140C ''start'':10C,22C,62C ''station'':161C ''still'':124C,149C ''subject'':83C,87C ''trigger'':147C ''type'':91C ''versus'':173C ''video'':150C,183C ''web'':20C ''word'':110C ''would'':12C ''yago'':92C ''å'':159C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2483, 2, '2014-11-16 01:13:21.363', 2, NULL, NULL, '&lt;p&gt;If you have R is quite simple&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Copy the lines into a file, let''s say: "mydata.json"&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Be sure you have installed the rjson package&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; install.packages("rjson")
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Import your data&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; library("rjson")
 json_data &amp;lt;- fromJSON(file="mydata.json")
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/code'':40C,59C ''/li'':24C,42C,61C ''/ol'':62C ''/p'':9C,23C,35C,48C ''/pre'':41C,60C ''code'':37C,50C ''copi'':13C ''data'':47C,54C ''file'':18C,57C ''fromjson'':56C ''import'':45C ''instal'':31C ''install.packages'':38C ''json'':53C ''let'':19C ''li'':11C,25C,43C ''librari'':51C ''line'':15C ''lt'':55C ''mydata.json'':22C,58C ''ol'':10C ''p'':1C,12C,26C,44C ''packag'':34C ''pre'':36C,49C ''quit'':7C ''r'':5C ''rjson'':33C,39C,52C ''say'':21C ''simpl'':8C ''sure'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2473, 1, '2014-11-14 19:03:35.603', 2, 220, 'Choosing the right data mining method to find the effect of each parameter over the target', '&lt;p&gt;I am dealing with a lot of categorical data right now and I would like to use an appropriate data mining method in any tool [preferably R] to find the effect of each parameter [categorical parameters] over my target variable. To give a brief notion about the data that am dealing with, my target variable denotes the product type [say, disposables and non-disposables] and I have parameters like root cause,symptom,customer name, product name etc. As my target can be considered as a binary value, I tried to find the combination of values leading to the desired categories using Apriori but, I have more than 2 categories in that attribute and I want to use all of them and find the effect of the mentioned parameters over each category. I really wanted to try SVM and use hyperplanes to separate the content and get n-dimensional view. But, I do not have enough knowledge to validate the technique, functions am using to do the analysis. Currently I have like 9000 records and each of them represents a complaint from the user. There are lot of columns available in the dataset which is what I am trying to use to determine the target variable [ myForumla &amp;lt;- Target~. ] I tried with just 4 categorical columns too. Not getting a proper result.&lt;/p&gt;

&lt;p&gt;Can just the categorical variables be used to develop a SVM model and get visualization with n hyper planes? Is there any appropriate data mining technique available for dealing with just the categorical data?&lt;/p&gt;
', 'data-mining  classification  r  svm  categorical-data', NULL, '''/p'':250C,286C ''2'':135C ''4'':241C ''9000'':200C ''analysi'':195C ''appropri'':45C,274C ''apriori'':129C ''attribut'':139C ''avail'':217C,278C ''binari'':113C ''brief'':70C ''categor'':24B,34C,61C,242C,255C,284C ''categori'':127C,136C,158C ''categorical-data'':23B ''caus'':98C ''choos'':1A ''classif'':20B ''column'':216C,243C ''combin'':120C ''complaint'':208C ''consid'':110C ''content'':171C ''current'':196C ''custom'':100C ''data'':4A,18B,25B,35C,46C,74C,275C,285C ''data-min'':17B ''dataset'':220C ''deal'':29C,77C,280C ''denot'':82C ''desir'':126C ''determin'':230C ''develop'':260C ''dimension'':176C ''dispos'':87C,91C ''effect'':10A,57C,151C ''enough'':183C ''etc'':104C ''find'':8A,55C,118C,149C ''function'':189C ''get'':173C,246C,265C ''give'':68C ''hyper'':269C ''hyperplan'':167C ''knowledg'':184C ''lead'':123C ''like'':41C,96C,199C ''lot'':32C,214C ''lt'':235C ''mention'':154C ''method'':6A,48C ''mine'':5A,19B,47C,276C ''model'':263C ''myforumla'':234C ''n'':175C,268C ''n-dimension'':174C ''name'':101C,103C ''non'':90C ''non-dispos'':89C ''notion'':71C ''p'':26C,251C ''paramet'':13A,60C,62C,95C,155C ''plane'':270C ''prefer'':52C ''product'':84C,102C ''proper'':248C ''r'':21B,53C ''realli'':160C ''record'':201C ''repres'':206C ''result'':249C ''right'':3A,36C ''root'':97C ''say'':86C ''separ'':169C ''svm'':22B,164C,262C ''symptom'':99C ''target'':16A,65C,80C,107C,232C,236C ''techniqu'':188C,277C ''tool'':51C ''tri'':116C,163C,226C,238C ''type'':85C ''use'':43C,128C,144C,166C,191C,228C,258C ''user'':211C ''valid'':186C ''valu'':114C,122C ''variabl'':66C,81C,233C,256C ''view'':177C ''visual'':266C ''want'':142C,161C ''would'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2474, 1, '2014-11-14 22:48:10.173', 2, 71, 'Parsing data from a string', '&lt;p&gt;I think this is something that experienced programmers do all the time. But, given my limited programming experience, please bear with me.&lt;/p&gt;

&lt;p&gt;I have an excel file which has particular cell entries that read &lt;/p&gt;

&lt;p&gt;&lt;em&gt;[[{"from": "4", "response": true, "value": 20}, {"from": "8", "response": true, "value": 20}, {"from": "9", "response": true, "value": 20}, {"from": "3", "response": true, "value": 20}], [{"from": "14", "response": false, "value": 20}, {"from": "15", "response": true, "value": 20}, {"from": "17", "response": false, "value": 20}, {"from": "13", "response": true, "value": 20}]]&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;Now, for each such entry I want to take the information in each of the curly brackets and make a row of data out of it. Each such row would have 3 columns. For example, the row formed from the first entry within curly brackets should have the entries "4" "true" and "20" respectively. The part I posted should give me 6 such rows, and for n such repetitions I should end up with a matrix of 6n rows, and 4 columns ( an identifier, plus the 3 columns mentioned).&lt;/p&gt;

&lt;p&gt;What would be most efficient way to do this? By "doing this" I mean learning the trick, and then implementing it. I have access to quite a few software packages(Excel, Stata, Matlab, R) in my laboratory, so that should not be an issue.&lt;/p&gt;
', 'parsing', NULL, '''/em'':95C ''/p'':30C,44C,96C,187C,233C ''13'':90C ''14'':72C ''15'':78C ''17'':84C ''20'':52C,58C,64C,70C,76C,82C,88C,94C,150C ''3'':66C,129C,184C ''4'':48C,147C,178C ''6'':159C ''6n'':175C ''8'':54C ''9'':60C ''access'':212C ''bear'':27C ''bracket'':114C,142C ''cell'':40C ''column'':130C,179C,185C ''cur'':113C,141C ''data'':2A,120C ''effici'':193C ''em'':46C ''end'':169C ''entri'':41C,102C,139C,146C ''exampl'':132C ''excel'':35C,219C ''experi'':25C ''experienc'':14C ''fals'':74C,86C ''file'':36C ''first'':138C ''form'':135C ''give'':157C ''given'':21C ''identifi'':181C ''implement'':208C ''inform'':108C ''issu'':232C ''laboratori'':225C ''learn'':203C ''limit'':23C ''make'':116C ''matlab'':221C ''matrix'':173C ''mean'':202C ''mention'':186C ''n'':164C ''p'':7C,31C,45C,97C,188C ''packag'':218C ''pars'':1A,6B ''part'':153C ''particular'':39C ''pleas'':26C ''plus'':182C ''post'':155C ''program'':24C ''programm'':15C ''quit'':214C ''r'':222C ''read'':43C ''repetit'':166C ''respect'':151C ''respons'':49C,55C,61C,67C,73C,79C,85C,91C ''row'':118C,126C,134C,161C,176C ''softwar'':217C ''someth'':12C ''stata'':220C ''string'':5A ''take'':106C ''think'':9C ''time'':19C ''trick'':205C ''true'':50C,56C,62C,68C,80C,92C,148C ''valu'':51C,57C,63C,69C,75C,81C,87C,93C ''want'':104C ''way'':194C ''within'':140C ''would'':127C,190C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2476, 2, '2014-11-15 01:26:16.583', 1, NULL, NULL, '&lt;p&gt;Here are some things to try.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Plot a bar graph. The bar graph will clearly show jobless people are often choosing NO. Try an 1-way ANOVA test. If the p &amp;lt; delta (i.e. delta=0.05), try a post-hoc test (i.e. Tukey''s HSD) to do a pairwise comparison.&lt;/li&gt;
&lt;li&gt;Like I said earlier, try a multiple comparison test (1-way ANOVA) first, if there is a statistically significant difference, you can try a pairwise comparison test (post-hoc test).&lt;/li&gt;
&lt;li&gt;Maybe try a clustering algorithm? Be careful, because the marginal sums (by rows or columns) are not equal. Maybe create a similarity matrix by profession? To me, it seems that Employees and Businessmen are in one group (very similar), while Workers and Jobless are each in their own group. If you turn those frequencies into proportions, then you might just have 2 groups; one for employees + workers + businessmen, and one for jobless.&lt;/li&gt;
&lt;li&gt;Use contingency table analysis to see if the responses (yes/no/don''t know) are associated with profession. &lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2477, 2, '2014-11-15 01:30:00.87', 1, NULL, NULL, '&lt;p&gt;How about&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Ordinary Least Square (OLS) regression? Since you have a class imbalance, you might want to combine that with boosting algorithms.&lt;/li&gt;
&lt;li&gt;If you have a function to quantify the cost involved with FP''s and FN''s, use any optimization technique you can find. My favorite is genetic algorithms. You may also try linear programming. &lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':27C,63C ''/ol'':64C ''/p'':4C ''algorithm'':26C,56C ''also'':59C ''boost'':25C ''class'':16C ''combin'':22C ''cost'':37C ''favorit'':53C ''find'':51C ''fn'':43C ''fp'':40C ''function'':33C ''genet'':55C ''imbal'':17C ''involv'':38C ''least'':8C ''li'':6C,28C ''linear'':61C ''may'':58C ''might'':19C ''ol'':5C,10C ''optim'':47C ''ordinari'':7C ''p'':1C ''program'':62C ''quantifi'':35C ''regress'':11C ''sinc'':12C ''squar'':9C ''techniqu'':48C ''tri'':60C ''use'':45C ''want'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2479, 2, '2014-11-15 09:34:47.653', 1, NULL, NULL, '&lt;p&gt;You can try Bayesian belief networks (BBNs). BBNs can easily handle categorical variables and give you the picture of the multivariable interactions. Furthermore, you may use sensitivity analysis to observe how each variable influences your class variable. &lt;/p&gt;

&lt;p&gt;Once you learn the structure of the BBN, you can identify the Markov blanket of the class variable. The variables in the Markov blanket of the class variable is a subset of all the variables, and you may use optimization techniques to see which combination of values in this Markov blanket maximizes your class prediction.&lt;/p&gt;
', NULL, NULL, '''/p'':39C,96C ''analysi'':29C ''bayesian'':5C ''bbn'':48C ''bbns'':8C,9C ''belief'':6C ''blanket'':54C,64C,91C ''categor'':13C ''class'':37C,57C,67C,94C ''combin'':85C ''easili'':11C ''furthermor'':24C ''give'':16C ''handl'':12C ''identifi'':51C ''influenc'':35C ''interact'':23C ''learn'':43C ''markov'':53C,63C,90C ''maxim'':92C ''may'':26C,78C ''multivari'':22C ''network'':7C ''observ'':31C ''optim'':80C ''p'':1C,40C ''pictur'':19C ''predict'':95C ''see'':83C ''sensit'':28C ''structur'':45C ''subset'':71C ''techniqu'':81C ''tri'':4C ''use'':27C,79C ''valu'':87C ''variabl'':14C,34C,38C,58C,60C,68C,75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2480, 1, '2014-11-15 15:49:07.213', 3, 123, 'Visualization using D3', '&lt;p&gt;I am new to D3 programming (any programming, for that matter). I have protein-protein interaction data in JSON format and csv format. I would like to use that data for network visualization.&lt;/p&gt;

&lt;p&gt;Data attributes: Protein Name, Protein Group, Protein type, Protein Source Node, Protein Target Node&lt;/p&gt;

&lt;p&gt;Can anyone suggest good network visualizations for such data. How does it work with hive plots?&lt;/p&gt;
', 'visualization  javascript', NULL, '''/p'':41C,57C,75C ''anyon'':60C ''attribut'':44C ''csv'':29C ''d3'':3A,11C ''data'':24C,37C,43C,67C ''format'':27C,30C ''good'':62C ''group'':48C ''hive'':73C ''interact'':23C ''javascript'':5B ''json'':26C ''like'':33C ''matter'':17C ''name'':46C ''network'':39C,63C ''new'':9C ''node'':53C,56C ''p'':6C,42C,58C ''plot'':74C ''program'':12C,14C ''protein'':21C,22C,45C,47C,49C,51C,54C ''protein-protein'':20C ''sourc'':52C ''suggest'':61C ''target'':55C ''type'':50C ''use'':2A,35C ''visual'':1A,4B,40C,64C ''work'':71C ''would'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2482, 2, '2014-11-16 01:05:02.603', 1, NULL, NULL, '&lt;p&gt;Why dont you have a look at the following example?
&lt;a href="http://bl.ocks.org/mbostock/2066421" rel="nofollow"&gt;http://bl.ocks.org/mbostock/2066421&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You can also find a fiddle here: &lt;a href="http://jsfiddle.net/boatrokr/rk2s5/" rel="nofollow"&gt;http://jsfiddle.net/boatrokr/rk2s5/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Pay attention to the part where the links are defined.&lt;/p&gt;
', NULL, NULL, '''/boatrokr/rk2s5/'':34C ''/boatrokr/rk2s5/&lt;/a&gt;&lt;/p&gt;'':39C ''/mbostock/2066421'':16C ''/mbostock/2066421&lt;/a&gt;&lt;/p&gt;'':21C ''/p'':51C ''also'':25C ''attent'':42C ''bl.ocks.org'':15C,20C ''bl.ocks.org/mbostock/2066421'':14C ''bl.ocks.org/mbostock/2066421&lt;/a&gt;&lt;/p&gt;'':19C ''defin'':50C ''dont'':3C ''exampl'':11C ''fiddl'':28C ''find'':26C ''follow'':10C ''href'':13C,31C ''jsfiddle.net'':33C,38C ''jsfiddle.net/boatrokr/rk2s5/'':32C ''jsfiddle.net/boatrokr/rk2s5/&lt;/a&gt;&lt;/p&gt;'':37C ''link'':48C ''look'':7C ''nofollow'':18C,36C ''p'':1C,22C,40C ''part'':45C ''pay'':41C ''rel'':17C,35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2486, 1, '2014-11-16 21:03:09.037', 3, 248, 'Visualizing Support Vector Machines (SVM) with Multiple Explanatory Variables', '&lt;p&gt;I was wondering if anyone was aware of any methods for visualizing an SVM model where there are more than three continuous explanatory variables. In my particular situation, my response variable is binomial, with 6 continuous explanatory variables (predictors), one categorical explanatory variable (predictor). I have already reduced the number of predictors and I am primarily using R for my analysis.&lt;/p&gt;

&lt;p&gt;(I am unaware if such a task is possible/ worth pursuing.)&lt;/p&gt;

&lt;p&gt;Thanks for your time.&lt;/p&gt;
', 'machine-learning  classification  r  visualization  svm', NULL, '''/p'':79C,92C,98C ''6'':52C ''alreadi'':64C ''analysi'':78C ''anyon'':22C ''awar'':24C ''binomi'':50C ''categor'':58C ''classif'':13B ''continu'':39C,53C ''explanatori'':8A,40C,54C,59C ''learn'':12B ''machin'':4A,11B ''machine-learn'':10B ''method'':27C ''model'':32C ''multipl'':7A ''number'':67C ''one'':57C ''p'':17C,80C,93C ''particular'':44C ''possibl'':89C ''predictor'':56C,61C,69C ''primarili'':73C ''pursu'':91C ''r'':14B,75C ''reduc'':65C ''respons'':47C ''situat'':45C ''support'':2A ''svm'':5A,16B,31C ''task'':87C ''thank'':94C ''three'':38C ''time'':97C ''unawar'':83C ''use'':74C ''variabl'':9A,41C,48C,55C,60C ''vector'':3A ''visual'':1A,15B,29C ''wonder'':20C ''worth'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5461, 2, '2015-04-06 01:07:35.447', 0, NULL, NULL, '&lt;p&gt;Erin Hartmann who just finished up a PhD in Political Science at UC-Berkley has a paper that uses Obama for America data from 2012.  She is using the paper on the job-market for an American Politics sub-field position  Might want to check out her work, though I''m not sure it will answer your question.  She has a public site here: &lt;a href="http://www.erinhartman.com" rel="nofollow"&gt;http://www.erinhartman.com&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':75C ''/p'':76C ''2012'':27C ''america'':24C ''american'':40C ''answer'':60C ''berkley'':16C ''check'':49C ''data'':25C ''erin'':2C ''field'':44C ''finish'':6C ''hartmann'':3C ''href'':70C ''job'':36C ''job-market'':35C ''m'':55C ''market'':37C ''might'':46C ''nofollow'':73C ''obama'':22C ''p'':1C ''paper'':19C,32C ''phd'':9C ''polit'':11C,41C ''posit'':45C ''public'':66C ''question'':62C ''rel'':72C ''scienc'':12C ''site'':67C ''sub'':43C ''sub-field'':42C ''sure'':57C ''though'':53C ''uc'':15C ''uc-berkley'':14C ''use'':21C,30C ''want'':47C ''work'':52C ''www.erinhartman.com'':71C,74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2489, 1, '2014-11-18 09:07:00.867', 4, 120, 'Reduction of multiple answers to single variable', '&lt;p&gt;The questionnaire for the data is &lt;a href="http://www.cc.gatech.edu/gvu/user_surveys/survey-1997-10/questions/general.html" rel="nofollow"&gt;here&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The first question takes multiple entry for the same question, I want to reduce this to a single variable. How do I do it?&lt;/p&gt;

&lt;p&gt;The clean data is available &lt;a href="http://wikisend.com/download/586046/DataRaw.arff" rel="nofollow"&gt;here&lt;/a&gt;.
NB: The Column CompuPlat has missing values.&lt;/p&gt;

&lt;p&gt;part of dataset&lt;/p&gt;

&lt;p&gt;&lt;code&gt;CMFam CMHobb  CMNone  CMOther CMPol   CMProf  CMRel
0   0   1   0   0   0   0
0   0   0   0   0   0   0
1   1   0   0   0   1   0
0   0   0   1   0   0   0
0   0   0   0   1   1   0
1   0   0   0   0   1   1
Community Membership_Family
Community Membership_Hobbies
Community Membership_None
Community Membership_Other
Community Membership_Political
Community Membership_Professional
Community Membership_Religious
Community Membership_Support
&lt;/code&gt;
I want to club all of them in a variable CM&lt;/p&gt;
', 'dataset  dimensionality-reduction', NULL, '''/a'':27C,69C ''/code'':158C ''/download/586046/dataraw.arff'':65C ''/gvu/user_surveys/survey-1997-10/questions/general.html'':23C ''/p'':28C,54C,77C,82C,170C ''0'':92C,93C,95C,96C,97C,98C,99C,100C,101C,102C,103C,104C,105C,108C,109C,110C,112C,113C,114C,115C,117C,118C,119C,120C,121C,122C,123C,126C,128C,129C,130C,131C ''1'':94C,106C,107C,111C,116C,124C,125C,127C,132C,133C ''answer'':4A ''avail'':60C ''clean'':57C ''club'':162C ''cm'':169C ''cmfam'':85C ''cmhobb'':86C ''cmnone'':87C ''cmother'':88C ''cmpol'':89C ''cmprof'':90C ''cmrel'':91C ''code'':84C ''column'':72C ''communiti'':134C,137C,140C,143C,146C,149C,152C,155C ''compuplat'':73C ''data'':17C,58C ''dataset'':8B,81C ''dimension'':10B ''dimensionality-reduct'':9B ''entri'':35C ''famili'':136C ''first'':31C ''hobbi'':139C ''href'':20C,62C ''membership'':135C,138C,141C,144C,147C,150C,153C,156C ''miss'':75C ''multipl'':3A,34C ''nb'':70C ''nofollow'':25C,67C ''none'':142C ''p'':12C,29C,55C,78C,83C ''part'':79C ''polit'':148C ''profession'':151C ''question'':32C,39C ''questionnair'':14C ''reduc'':43C ''reduct'':1A,11B ''rel'':24C,66C ''religi'':154C ''singl'':6A,47C ''support'':157C ''take'':33C ''valu'':76C ''variabl'':7A,48C,168C ''want'':41C,160C ''wikisend.com'':64C ''wikisend.com/download/586046/dataraw.arff'':63C ''www.cc.gatech.edu'':22C ''www.cc.gatech.edu/gvu/user_surveys/survey-1997-10/questions/general.html'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5070, 2, '2015-02-06 09:06:02.607', 4, NULL, NULL, '&lt;p&gt;First of all, the fact that you have known some Java, even ten years ago, already means that you don''t "know nothing about programming" (I suggest you update the title of your question to reflect that - change "nothing" to "a little"). I''d like to make several points, which I hope will be useful to you.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;In terms of the level of &lt;em&gt;programming proficiency&lt;/em&gt;, which is expected (needed) for a data scientist, the following popular &lt;strong&gt;definition&lt;/strong&gt; says it all:&lt;/li&gt;
&lt;/ul&gt;

&lt;blockquote&gt;
  &lt;p&gt;A &lt;em&gt;data scientist&lt;/em&gt; is someone who is better at statistics than any
  software engineer and better at software engineering than any
  statistician.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;ul&gt;
&lt;li&gt;Another perspective on the role of programming abilities in a data scientist''s skill set can be found in a popular visual representation of data science, using Venn diagrams. The original &lt;strong&gt;data science Venn diagram&lt;/strong&gt; was presented by data scientist Drew Conway (see &lt;a href="http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram" rel="nofollow"&gt;this blog post&lt;/a&gt;):&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ZwWt2.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Since its original introduction, the original diagram was modified by various people for various reasons. The two interesting &lt;strong&gt;adaptations&lt;/strong&gt; are for data science in the social sciences domain (&lt;a href="http://www.datascienceassn.org/content/fourth-bubble-data-science-venn-diagram-social-sciences" rel="nofollow"&gt;http://www.datascienceassn.org/content/fourth-bubble-data-science-venn-diagram-social-sciences&lt;/a&gt;), as well as data science Venn diagram V2.0, where data science is represented not as an intersection of knowledge domains, but as their union (&lt;a href="http://www.anlytcs.com/2014/01/data-science-venn-diagram-v20.html" rel="nofollow"&gt;http://www.anlytcs.com/2014/01/data-science-venn-diagram-v20.html&lt;/a&gt;). Another very interesting and useful visual perspective of data science skill set, also based on Venn diagram, is the following Gartner''s diagram, &lt;strong&gt;mapping&lt;/strong&gt; specific &lt;em&gt;skills&lt;/em&gt; to business intelligence (BI) or business analytics &lt;em&gt;knowledge domains&lt;/em&gt;:&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/XBbDM.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;An alternative perspective for a data scientist''s skill set and domain knowledge is a &lt;strong&gt;taxonomy of data scientists&lt;/strong&gt;, such as &lt;a href="http://www.datasciencecentral.com/profiles/blogs/six-categories-of-data-scientists" rel="nofollow"&gt;this taxonomy&lt;/a&gt;, which classifies data scientists, according to their &lt;em&gt;focus&lt;/em&gt; (or the &lt;em&gt;strongest skill set&lt;/em&gt;): mathematics, data engineering, machine learning, business, software engineering, visualization, spacial data (GIS) or others.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;If you''re curious about the meaning of the &lt;strong&gt;"Danger Zone"&lt;/strong&gt; in the original data science Venn diagram, &lt;a href="http://www.quora.com/In-the-data-science-venn-diagram-why-is-the-common-region-of-Hacking-Skills-and-Substantive-Expertise-considered-as-danger-zone" rel="nofollow"&gt;this Quora discussion&lt;/a&gt;, containing, among other nice answers, also an answer by the original diagram''s author, can be very helpful.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;If you''re interested in learning about a range of &lt;strong&gt;skills&lt;/strong&gt; and &lt;strong&gt;knowledge domains&lt;/strong&gt;, useful for a data scientist, check this open source &lt;em&gt;curriculum&lt;/em&gt; for learning data science: &lt;a href="http://datasciencemasters.org" rel="nofollow"&gt;http://datasciencemasters.org&lt;/a&gt;, or on GitHub: &lt;a href="https://github.com/datasciencemasters/go" rel="nofollow"&gt;https://github.com/datasciencemasters/go&lt;/a&gt;. Of course, popular and research papers, lectures on YouTube, MOOC courses, online and offline bootcamps as well as a wealth of other resources is only an Internet search away.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Finally, a note on &lt;strong&gt;programming languages&lt;/strong&gt; for data science. I think that it is important to understand that this aspect is really of secondary importance. The &lt;strong&gt;focus&lt;/strong&gt; should be on two words, which the term "data science" consists of: &lt;em&gt;data&lt;/em&gt; and &lt;em&gt;science&lt;/em&gt;. &lt;strong&gt;Focus on data&lt;/strong&gt; means that it is important to think about data science (or BI, or analytics) tasks in terms of the corresponding &lt;em&gt;domain knowledge&lt;/em&gt; as well as to pay attention to &lt;em&gt;data quality&lt;/em&gt; and &lt;em&gt;representativeness&lt;/em&gt;. &lt;strong&gt;Focus on science&lt;/strong&gt; means adhering to &lt;em&gt;scientific approaches&lt;/em&gt; to data collection and analysis, of which &lt;em&gt;reproducibility&lt;/em&gt; plays an important role. A &lt;em&gt;programming language&lt;/em&gt; for data science is just a &lt;strong&gt;tool&lt;/strong&gt; and, therefore, should be chosen to match the task at hand. Python and R represent very good and the most popular programming languages and environments for a data scientist, however, you should be aware of other options (&lt;em&gt;tool set&lt;/em&gt;).&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2491, 1, '2014-11-18 09:56:06.157', 1, 545, 'Change aliases of filter items in Tableau', '&lt;p&gt;Data sample contains a single feature: random integer number from 1 to 4.&lt;/p&gt;

&lt;p&gt;Is it possble to change &lt;code&gt;1,2,3,4&lt;/code&gt; representation on the filter card to some custom names, say: &lt;code&gt;Type1,Type2,Type3,Type4&lt;/code&gt;? (not changing data set)&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/4ZPYM.png" alt="enter image description here"&gt;&lt;/p&gt;
', 'visualization  tableau', NULL, '''/4zpym.png'':63C ''/code'':36C,52C ''/p'':24C,57C,69C ''1'':21C,32C ''2'':33C ''3'':34C ''4'':23C,35C ''alias'':2A ''alt'':64C ''card'':41C ''chang'':1A,30C,54C ''code'':31C,47C ''contain'':13C ''custom'':44C ''data'':11C,55C ''descript'':67C ''enter'':65C ''featur'':16C ''filter'':4A,40C ''i.stack.imgur.com'':62C ''i.stack.imgur.com/4zpym.png'':61C ''imag'':66C ''img'':59C ''integ'':18C ''item'':5A ''name'':45C ''number'':19C ''p'':10C,25C,58C ''possbl'':28C ''random'':17C ''represent'':37C ''sampl'':12C ''say'':46C ''set'':56C ''singl'':15C ''src'':60C ''tableau'':7A,9B ''type1'':48C ''type2'':49C ''type3'':50C ''type4'':51C ''visual'':8B');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2492, 2, '2014-11-18 10:19:59.51', 2, NULL, NULL, '&lt;p&gt;The variable represents the answer to the first question. &lt;/p&gt;

&lt;p&gt;One straightforward way is to allow for all possible categories in this variable. For example, if there are 5 options in this answer, you will have to treat it as a categorical variable with 2^5 = 32 categories. &lt;/p&gt;

&lt;p&gt;However, the number of categories increase exponentially with the number of options (check boxes) provided for the answer. In that case, it might be better to restrict the number of categories to, for example, 5. This can be done by leaving the top 4 choices/ options (by count) as they are and treating every other choice as "other".&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2512, 2, '2014-11-21 10:24:18.623', 2, NULL, NULL, '&lt;p&gt;UCL - CSML. It covers computer science, machine learning and statistics.&lt;/p&gt;

&lt;p&gt;Firstly, reputation of the university.
Secondly, you are from Mathematics background, hence I assume you don''t have sufficient programming knowledge.
Thirdly, Statistics and Machine Learning dominates this field. Employers would prefer these 2 before Mathematics.&lt;/p&gt;

&lt;p&gt;In short, this course provides everything that you are lacking. HOWEVER, they don''t teach programming languages like Java, C++,... but Matlab, R, and Mathematica. Hence, it would be essential if you pick up the former from somewhere.&lt;/p&gt;
', NULL, NULL, '''/p'':12C,50C,90C ''2'':47C ''assum'':27C ''background'':24C ''c'':71C ''comput'':6C ''cours'':55C ''cover'':5C ''csml'':3C ''domin'':40C ''employ'':43C ''essenti'':81C ''everyth'':57C ''field'':42C ''first'':14C ''former'':87C ''henc'':25C,77C ''howev'':62C ''java'':70C ''knowledg'':34C ''lack'':61C ''languag'':68C ''learn'':9C,39C ''like'':69C ''machin'':8C,38C ''mathemat'':23C,49C ''mathematica'':76C ''matlab'':73C ''p'':1C,13C,51C ''pick'':84C ''prefer'':45C ''program'':33C,67C ''provid'':56C ''r'':74C ''reput'':15C ''scienc'':7C ''second'':19C ''short'':53C ''somewher'':89C ''statist'':11C,36C ''suffici'':32C ''teach'':66C ''third'':35C ''ucl'':2C ''univers'':18C ''would'':44C,79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2493, 1, '2014-11-18 11:04:28.837', 1, 95, 'Analyze paragraphs using Neuroph', '&lt;p&gt;Currently we are regularly analyzing sets of paragraphs every month. I would like to automate this and split each paragraphs into chunks of data.&lt;/p&gt;

&lt;p&gt;To do this I would like to employ a neural network. However, I am not really very familiar with creating neural networks.&lt;/p&gt;

&lt;p&gt;Any ideas or starting point on how to do this using Neuroph or maybe in other framework/approaches?&lt;/p&gt;

&lt;p&gt;Edit for more info as suggested.&lt;/p&gt;

&lt;p&gt;I have very little experience on neural networks though I have some introduction with it in college. However I am very much familar with java&lt;/p&gt;

&lt;p&gt;The data is around 3 megabytes only and consists of rules and relationships for a single domain. This means that the data is complex but relatively limited though still free-form English language.&lt;/p&gt;
', 'text-mining  clustering  neuralnetwork  java', NULL, '''/p'':36C,60C,79C,87C,114C,150C ''3'':120C ''analyz'':1A,16C ''around'':119C ''autom'':26C ''chunk'':33C ''cluster'':8B ''colleg'':105C ''complex'':139C ''consist'':124C ''creat'':57C ''current'':12C ''data'':35C,117C,137C ''domain'':132C ''edit'':81C ''employ'':45C ''english'':148C ''everi'':20C ''experi'':93C ''familar'':111C ''familiar'':55C ''form'':147C ''framework/approaches'':78C ''free'':146C ''free-form'':145C ''howev'':49C,106C ''idea'':63C ''info'':84C ''introduct'':101C ''java'':10B,113C ''languag'':149C ''like'':24C,43C ''limit'':142C ''littl'':92C ''mayb'':75C ''mean'':134C ''megabyt'':121C ''mine'':7B ''month'':21C ''much'':110C ''network'':48C,59C,96C ''neural'':47C,58C,95C ''neuralnetwork'':9B ''neuroph'':4A,73C ''p'':11C,37C,61C,80C,88C,115C ''paragraph'':2A,19C,31C ''point'':66C ''realli'':53C ''regular'':15C ''relat'':141C ''relationship'':128C ''rule'':126C ''set'':17C ''singl'':131C ''split'':29C ''start'':65C ''still'':144C ''suggest'':86C ''text'':6B ''text-min'':5B ''though'':97C,143C ''use'':3A,72C ''would'':23C,42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2494, 1, '2014-11-18 12:41:54.973', 3, 125, 'What is the best practice to classify category of named entity in sentence', '&lt;p&gt;I have 1-4 gram text data from wikipedia for 14 categories, which I am using for NE classification.
I feed named entity from sentence to lucene indexer which searches named entity from these 14 categories. 
Issue I am facing is, for single entity I get multiple classes as a result with same score.
like while search &lt;code&gt;titanic&lt;/code&gt;, indexer gives this result&lt;/p&gt;

&lt;p&gt;Score    - 11.23
Title    - titanic
Category - Book&lt;/p&gt;

&lt;p&gt;Score    - 11.23
Title    - titanic
Category - Movie&lt;/p&gt;

&lt;p&gt;Score    - 11.23
Title    - titanic
Category - Product&lt;/p&gt;

&lt;p&gt;now problem is which class to be considered?&lt;/p&gt;

&lt;p&gt;I already tried with classifiers (NB,ME in nltk,scikit learn), but as it consider each entity from dataset as feature, it works as indexer only.&lt;/p&gt;

&lt;p&gt;Why lucene?&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Tz8Uy.jpg" alt="enter image description here"&gt;&lt;/p&gt;
', 'machine-learning  data-mining  classification  nlp', NULL, '''-4'':26C ''/code'':82C ''/p'':87C,95C,103C,111C,121C,149C,153C,165C ''/tz8uy.jpg'':159C ''1'':25C ''11.23'':90C,98C,106C ''14'':33C,57C ''alreadi'':124C ''alt'':160C ''best'':4A ''book'':94C ''categori'':8A,34C,58C,93C,101C,109C ''class'':70C,117C ''classif'':20B,41C ''classifi'':7A,127C ''code'':80C ''consid'':120C,137C ''data'':18B,29C ''data-min'':17B ''dataset'':141C ''descript'':163C ''enter'':161C ''entiti'':11A,45C,54C,66C,139C ''face'':62C ''featur'':143C ''feed'':43C ''get'':68C ''give'':84C ''gram'':27C ''i.stack.imgur.com'':158C ''i.stack.imgur.com/tz8uy.jpg'':157C ''imag'':162C ''img'':155C ''index'':50C,83C,147C ''issu'':59C ''learn'':16B,133C ''like'':77C ''lucen'':49C,152C ''machin'':15B ''machine-learn'':14B ''mine'':19B ''movi'':102C ''multipl'':69C ''name'':10A,44C,53C ''nb'':128C ''ne'':40C ''nlp'':21B ''nltk'':131C ''p'':22C,88C,96C,104C,112C,122C,150C,154C ''practic'':5A ''problem'':114C ''product'':110C ''result'':73C,86C ''scikit'':132C ''score'':76C,89C,97C,105C ''search'':52C,79C ''sentenc'':13A,47C ''singl'':65C ''src'':156C ''text'':28C ''titan'':81C,92C,100C,108C ''titl'':91C,99C,107C ''tri'':125C ''use'':38C ''wikipedia'':31C ''work'':145C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2496, 2, '2014-11-18 19:52:54.05', 2, NULL, NULL, '&lt;p&gt;Does it matter that the model is created in the form of SVM?&lt;/p&gt;

&lt;p&gt;If no, I have seen a clever 6-D visualization. Its varieties are becoming popular in medical presentations. 
3 dimensions are shown as usual, in orthographic projection.
Dimension 4 is color (0..255)
Dimension 5 is thickness of the symbol 
Dimension 6 requires animation. It is a frequency of vibration of a dot on the screen. 
In static, printed versions, one can replace frequency of vibration by blur around the point, for a comparable visual perception.&lt;/p&gt;

&lt;p&gt;If yes, and you specifically need to draw separating hyperplanes, and make them look like lines\planes, the previous trick will not produce good results. Multiple 3-D images are better.&lt;/p&gt;
', NULL, NULL, '''/p'':15C,93C,126C ''0'':48C ''255'':49C ''3'':35C,121C ''4'':45C ''5'':51C ''6'':24C,58C ''anim'':60C ''around'':85C ''becom'':30C ''better'':125C ''blur'':84C ''clever'':23C ''color'':47C ''compar'':90C ''creat'':9C ''d'':25C,122C ''dimens'':36C,44C,50C,57C ''dot'':69C ''draw'':102C ''form'':12C ''frequenc'':64C,80C ''good'':118C ''hyperplan'':104C ''imag'':123C ''like'':109C ''line'':110C ''look'':108C ''make'':106C ''matter'':4C ''medic'':33C ''model'':7C ''multipl'':120C ''need'':100C ''one'':77C ''orthograph'':42C ''p'':1C,16C,94C ''percept'':92C ''plane'':111C ''point'':87C ''popular'':31C ''present'':34C ''previous'':113C ''print'':75C ''produc'':117C ''project'':43C ''replac'':79C ''requir'':59C ''result'':119C ''screen'':72C ''seen'':21C ''separ'':103C ''shown'':38C ''specif'':99C ''static'':74C ''svm'':14C ''symbol'':56C ''thick'':53C ''trick'':114C ''usual'':40C ''varieti'':28C ''version'':76C ''vibrat'':66C,82C ''visual'':26C,91C ''yes'':96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2499, 1, '2014-11-19 01:07:10.283', 1, 155, 'API for historical housing prices', '&lt;p&gt;I''m looking for an (ideally free) API that would have time series avg/median housing prices by zip code or city/state. &lt;/p&gt;

&lt;p&gt;Quandl almost fits the bill, but it returns inconsistent results across different zip codes and the data is not as up to date as I''d like (it''s mid November, and the last month is August).&lt;/p&gt;

&lt;p&gt;I also looked at Zillow, but storing their data is against TOS, and at 1,000 calls daily--it would take forever to pull in the necessary data.&lt;/p&gt;

&lt;p&gt;Any suggestions (even if they aren''t free) would be much appreciated!&lt;/p&gt;
', 'dataset', NULL, '''/p'':29C,68C,98C,112C ''000'':85C ''1'':84C ''across'':41C ''almost'':32C ''also'':71C ''api'':1A,15C ''appreci'':111C ''aren'':105C ''august'':67C ''avg/median'':21C ''bill'':35C ''call'':86C ''city/state'':28C ''code'':26C,44C ''d'':56C ''daili'':87C ''data'':47C,78C,97C ''dataset'':6B ''date'':53C ''differ'':42C ''even'':102C ''fit'':33C ''forev'':91C ''free'':14C,107C ''histor'':3A ''hous'':4A,22C ''ideal'':13C ''inconsist'':39C ''last'':64C ''like'':57C ''look'':10C,72C ''m'':9C ''mid'':60C ''month'':65C ''much'':110C ''necessari'':96C ''novemb'':61C ''p'':7C,30C,69C,99C ''price'':5A,23C ''pull'':93C ''quandl'':31C ''result'':40C ''return'':38C ''seri'':20C ''store'':76C ''suggest'':101C ''take'':90C ''time'':19C ''tos'':81C ''would'':17C,89C,108C ''zillow'':74C ''zip'':25C,43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2501, 1, '2014-11-19 12:38:05.597', 4, 107, 'Data scheduling for recommender', '&lt;p&gt;I do at the moment some data experiments with the &lt;a href="http://graphlab.com/products/create/docs/" rel="nofollow"&gt;Graphlab toolkit&lt;/a&gt;. I have at the first next SFrame, with the three columns:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Users Items Rating
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The pair in the same row from every &lt;code&gt;Users&lt;/code&gt; and &lt;code&gt;Items&lt;/code&gt; values build the unique key and the &lt;code&gt;Rating&lt;/code&gt; is the corresponded float value. These values are not normalised. First of all, I do someself next normalisation:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Division of every rating value of specific user by the rating maximum from this user (scale between 0 and 1)&lt;/li&gt;
&lt;li&gt;Take the logarithm by every rating value&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Afterward I create a recommender model and evaluate the basic metrics for it.&lt;/p&gt;

&lt;p&gt;In this topic I invite everybody to discuss another interesting normalisation methods. If anybody could tell some good method for data preparation, it would be great. The results could be evaluated because of the metrics and I can publish it here.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;PS&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;My dataset is comming from some music site, the users rated some tracks. I have approximately 100 000 users and 300 000 tracks. Total number of ratings is over 3 millions (actually the matrix is sparse). This is the most simple data set, which I analyze now. In the future I can (and will) use some additional information about the users and tracks (f.e. duration, year, genre, band etc). At the moment I just interest to collect some methods for rating normalisation without to use additional information (users &amp;amp; items features). My problem is, the data set doesn''t have any &lt;code&gt;Rating&lt;/code&gt; at the first. I create someself the column &lt;code&gt;Rating&lt;/code&gt;, based on the number of events for unique &lt;code&gt;User-Item&lt;/code&gt; pair (I have this information). You can of course understand that some users can hear some tracks many times, and another users only one time. Consequently the dispersion is very high and I want to reduce it (normalise the ratings value).&lt;/p&gt;
', 'recommendation  data-cleaning', NULL, '''/a'':29C ''/code'':47C,60C,64C,74C,294C,305C,318C ''/li'':116C,125C ''/ol'':126C ''/p'':41C,93C,141C,184C,189C,360C ''/pre'':48C ''/products/create/docs/'':24C ''/strong'':188C ''0'':113C ''000'':208C,212C ''1'':115C ''100'':207C ''3'':220C ''300'':211C ''actual'':222C ''addit'':247C,276C ''afterward'':128C ''amp'':279C ''analyz'':236C ''anoth'':151C,339C ''anybodi'':156C ''approxim'':206C ''band'':258C ''base'':306C ''basic'':137C ''build'':66C ''clean'':8B ''code'':43C,58C,62C,72C,292C,303C,314C ''collect'':267C ''column'':40C,302C ''com'':194C ''consequ'':344C ''correspond'':77C ''could'':157C,171C ''cours'':327C ''creat'':130C,299C ''data'':1A,7B,16C,163C,232C,286C ''data-clean'':6B ''dataset'':192C ''discuss'':150C ''dispers'':346C ''divis'':96C ''doesn'':288C ''durat'':255C ''etc'':259C ''evalu'':135C,173C ''event'':311C ''everi'':57C,98C,122C ''everybodi'':148C ''experi'':17C ''f.e'':254C ''featur'':281C ''first'':34C,85C,297C ''float'':78C ''futur'':240C ''genr'':257C ''good'':160C ''graphlab'':27C ''graphlab.com'':23C ''graphlab.com/products/create/docs/'':22C ''great'':168C ''hear'':333C ''high'':349C ''href'':21C ''inform'':248C,277C,323C ''interest'':152C,265C ''invit'':147C ''item'':45C,63C,280C,317C ''key'':69C ''li'':95C,117C ''logarithm'':120C ''mani'':336C ''matrix'':224C ''maximum'':107C ''method'':154C,161C,269C ''metric'':138C,177C ''million'':221C ''model'':133C ''moment'':14C,262C ''music'':197C ''next'':35C,91C ''nofollow'':26C ''normalis'':84C,92C,153C,272C,356C ''number'':215C,309C ''ol'':94C ''one'':342C ''p'':9C,49C,127C,142C,185C,190C ''pair'':51C,319C ''pre'':42C ''prepar'':164C ''problem'':283C ''ps'':187C ''publish'':181C ''rate'':46C,73C,99C,106C,123C,201C,217C,271C,293C,304C,358C ''recommend'':4A,5B,132C ''reduc'':354C ''rel'':25C ''result'':170C ''row'':55C ''scale'':111C ''schedul'':2A ''set'':233C,287C ''sframe'':36C ''simpl'':231C ''site'':198C ''someself'':90C,300C ''spars'':226C ''specif'':102C ''strong'':186C ''take'':118C ''tell'':158C ''three'':39C ''time'':337C,343C ''toolkit'':28C ''topic'':145C ''total'':214C ''track'':203C,213C,253C,335C ''understand'':328C ''uniqu'':68C,313C ''use'':245C,275C ''user'':44C,59C,103C,110C,200C,209C,251C,278C,316C,331C,340C ''user-item'':315C ''valu'':65C,79C,81C,100C,124C,359C ''want'':352C ''without'':273C ''would'':166C ''year'':256C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2502, 1, '2014-11-19 19:25:02.77', 4, 212, 'Good-Turing Smoothing Intuition', '&lt;p&gt;I''m working through the &lt;a href="https://www.coursera.org/course/nlp" rel="nofollow"&gt;Coursera NLP course by Jurafsky &amp;amp; Manning&lt;/a&gt;, and the &lt;a href="https://class.coursera.org/nlp/lecture/32" rel="nofollow"&gt;lecture on Good-Turing smoothing&lt;/a&gt; struck me odd.&lt;/p&gt;

&lt;p&gt;The example given was:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;You are fishing (a scenario from Josh Goodman), and caught:&lt;br&gt;
  10 carp, 3 perch, 2 whitefish, 1 trout, 1 salmon, 1 eel = 18 fish&lt;br&gt;
  ...&lt;br&gt;
  How likely is it that the next species is new (i.e. catfish or bass)&lt;br&gt;
  Let''s use our estimate of things-we-saw-once to estimate the new things.&lt;br&gt;
  3/18 (because N_1=3)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I get the intuition of using the count of uniquely seen items to estimate the number of unseen item types (N = 3), but the next steps seem counterintuitive.&lt;/p&gt;

&lt;p&gt;Why is the denominator left unchanged instead of incremented by the estimate of unseen item types? I.e., I would expect the probabilities to become:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Carp : 10 / 21&lt;br&gt;
  Perch : 3 / 21&lt;br&gt;
  Whitefish : 2 / 21&lt;br&gt;
  Trout : 1 / 21&lt;br&gt;
  Salmon : 1 / 21&lt;br&gt;
  Eel : 1 / 21&lt;br&gt;
  Something new : 3 / 21&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;It seems like the Good-Turing count penalizes seen items too much (trout, salmon, &amp;amp; eel are each taken down to 1/27); coupled with the need to adjust the formula for gaps in the counts (e.g., Perch &amp;amp; Carp would be zeroed out otherwise), it just feels like a bad hack.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2503, 1, '2014-11-20 05:38:27.303', 2, 118, 'Creating Data model for mahout recommendation engine', '&lt;p&gt;I am trying to build an item-item similarity matching recommendation engine with mahout. The data set is as in the following format ( attributes are in text not in numerals format )&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;name : category : cost : ingredients

x : xx1 : 15 : xxx1, xxx2, xxx3

y : yy1 : 14 : yyy1, yyy2, yyy3

z : xx1 : 12 : xxx1, xxy1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So in-order to use this data set for mahout to train, what is the right way to convert this in to numeric (as CSV Boolean data set) format accepted by mahout.&lt;/p&gt;
', 'machine-learning  data-mining  dataset  recommendation', NULL, '''/code'':73C ''/p'':49C,109C ''/pre'':74C ''12'':70C ''14'':64C ''15'':58C ''accept'':106C ''attribut'':41C ''boolean'':102C ''build'':21C ''categori'':53C ''code'':51C ''convert'':95C ''cost'':54C ''creat'':1A ''csv'':101C ''data'':2A,12B,33C,83C,103C ''data-min'':11B ''dataset'':14B ''engin'':7A,29C ''follow'':39C ''format'':40C,48C,105C ''in-ord'':77C ''ingredi'':55C ''item'':24C,25C ''item-item'':23C ''learn'':10B ''machin'':9B ''machine-learn'':8B ''mahout'':5A,31C,86C,108C ''match'':27C ''mine'':13B ''model'':3A ''name'':52C ''numer'':47C,99C ''order'':79C ''p'':16C,75C ''pre'':50C ''recommend'':6A,15B,28C ''right'':92C ''set'':34C,84C,104C ''similar'':26C ''text'':44C ''train'':88C ''tri'':19C ''use'':81C ''way'':93C ''x'':56C ''xx1'':57C,69C ''xxx1'':59C,71C ''xxx2'':60C ''xxx3'':61C ''xxy1'':72C ''y'':62C ''yy1'':63C ''yyy1'':65C ''yyy2'':66C ''yyy3'':67C ''z'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2504, 1, '2014-11-20 06:49:00.357', 9, 1872, 'Deep Learning vs gradient boosting: When to use what?', '&lt;p&gt;I have a big data problem with a large dataset (take for example 50 million rows and 200 columns). The dataset consists of about 100 numerical columns and 100 categorical columns and a response column that represents a binary class problem. The cardinality of each of the categorical columns is less than 50. &lt;/p&gt;

&lt;p&gt;I want to know a priori whether I should go for deep learning methods or ensemble tree based methods (for example gradient boosting, adaboost, or random forests). Are there some exploratory data analysis or some other techniques that can help me decide for one method over the other? &lt;/p&gt;
', 'machine-learning  classification  deep-learning', NULL, '''/p'':70C,120C ''100'':41C,45C ''200'':34C ''50'':30C,69C ''adaboost'':95C ''analysi'':104C ''base'':89C ''big'':20C ''binari'':55C ''boost'':5A,94C ''cardin'':59C ''categor'':46C,64C ''class'':56C ''classif'':12B ''column'':35C,43C,47C,51C,65C ''consist'':38C ''data'':21C,103C ''dataset'':26C,37C ''decid'':113C ''deep'':1A,14B,83C ''deep-learn'':13B ''ensembl'':87C ''exampl'':29C,92C ''exploratori'':102C ''forest'':98C ''go'':81C ''gradient'':4A,93C ''help'':111C ''know'':75C ''larg'':25C ''learn'':2A,11B,15B,84C ''less'':67C ''machin'':10B ''machine-learn'':9B ''method'':85C,90C,116C ''million'':31C ''numer'':42C ''one'':115C ''p'':16C,71C ''priori'':77C ''problem'':22C,57C ''random'':97C ''repres'':53C ''respons'':50C ''row'':32C ''take'':27C ''techniqu'':108C ''tree'':88C ''use'':8A ''vs'':3A ''want'':73C ''whether'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2505, 2, '2014-11-20 09:33:20.32', 3, NULL, NULL, '&lt;p&gt;Have you looked into tourr package in R. This package does hyperplane reduction. In addition it has an optimizer that tries to find the best reduction.&lt;/p&gt;

&lt;p&gt;There is a very nice video in &lt;a href="https://www.youtube.com/watch?v=iSXNfZESR5I" rel="nofollow"&gt;https://www.youtube.com/watch?v=iSXNfZESR5I&lt;/a&gt; That shows what  R is capable even beyound tourr package. &lt;/p&gt;

&lt;p&gt;Also I refer you to &lt;a href="http://stackoverflow.com/questions/8017427/plotting-data-from-an-svm-fit-hyperplane"&gt;http://stackoverflow.com/questions/8017427/plotting-data-from-an-svm-fit-hyperplane&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':28C,57C ''/questions/8017427/plotting-data-from-an-svm-fit-hyperplane'':68C ''/questions/8017427/plotting-data-from-an-svm-fit-hyperplane&lt;/a&gt;&lt;/p&gt;'':71C ''/watch?v=isxnfzesr5i'':41C ''/watch?v=isxnfzesr5i&lt;/a&gt;'':46C ''addit'':16C ''also'':59C ''best'':26C ''beyound'':54C ''capabl'':52C ''even'':53C ''find'':24C ''href'':38C,65C ''hyperplan'':13C ''look'':4C ''nice'':34C ''nofollow'':43C ''optim'':20C ''p'':1C,29C,58C ''packag'':7C,11C,56C ''r'':9C,50C ''reduct'':14C,27C ''refer'':61C ''rel'':42C ''show'':48C ''stackoverflow.com'':67C,70C ''stackoverflow.com/questions/8017427/plotting-data-from-an-svm-fit-hyperplane'':66C ''stackoverflow.com/questions/8017427/plotting-data-from-an-svm-fit-hyperplane&lt;/a&gt;&lt;/p&gt;'':69C ''tourr'':6C,55C ''tri'':22C ''video'':35C ''www.youtube.com'':40C,45C ''www.youtube.com/watch?v=isxnfzesr5i'':39C ''www.youtube.com/watch?v=isxnfzesr5i&lt;/a&gt;'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2506, 2, '2014-11-20 13:07:45.903', 0, NULL, NULL, '&lt;p&gt;Just go through Neo4j (graph data base and will be useful for social network analysis) also.. may be helpful &lt;/p&gt;
', NULL, NULL, '''..'':18C ''/p'':22C ''also'':17C ''analysi'':16C ''base'':8C ''data'':7C ''go'':3C ''graph'':6C ''help'':21C ''may'':19C ''neo4j'':5C ''network'':15C ''p'':1C ''social'':14C ''use'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2507, 1, '2014-11-20 14:26:10.463', 7, 462, 'Log file analysis: extracting information part from value part', '&lt;p&gt;I''m trying to build a data set on several log files of one of our products.&lt;/p&gt;

&lt;p&gt;The different log files have their own layout and own content; I successfully grouped them together, only one step remaining...&lt;/p&gt;

&lt;p&gt;Indeed, the log "messages" are the best information. I don''t have the comprehensive list of all those messages, and it''s a bad idea to hard code based on those because that list can change every day.&lt;/p&gt;

&lt;p&gt;What I would like to do is to separate the indentification text from the value text (for example: "Loaded file XXX" becomes (identification: "Loaded file", value: "XXX")). Unfortunately, this example is simple, and in real world there are different layouts and sometimes multiple values.&lt;/p&gt;

&lt;p&gt;I was thinking about using string kernels, but it is intended for clustering ... and cluseting is not applicable here (I don''t know the number of different types of messages and eventhough, it would be too much).&lt;/p&gt;

&lt;p&gt;Do you have any idea?&lt;/p&gt;

&lt;p&gt;Thanks for your help.&lt;/p&gt;

&lt;p&gt;P.S: For those who programs, this can be easier to understand. Let''s say that the code contains as logs printf("blabla %s", "xxx") -&gt; I would like to have "blabla" and "xxx" seperatated&lt;/p&gt;
', 'text-mining  clustering', NULL, '''/p'':32C,54C,94C,140C,179C,186C,192C,227C ''analysi'':3A ''applic'':159C ''bad'':79C ''base'':84C ''becom'':117C ''best'':62C ''blabla'':215C,223C ''build'':19C ''chang'':91C ''cluset'':156C ''cluster'':13B,154C ''code'':83C,210C ''comprehens'':69C ''contain'':211C ''content'':44C ''data'':21C ''day'':93C ''differ'':35C,134C,168C ''easier'':202C ''eventhough'':173C ''everi'':92C ''exampl'':113C,125C ''extract'':4A ''file'':2A,26C,37C,115C,120C ''group'':47C ''hard'':82C ''help'':191C ''idea'':80C,185C ''identif'':118C ''inde'':56C ''indentif'':106C ''inform'':5A,63C ''intend'':152C ''kernel'':148C ''know'':164C ''layout'':41C,135C ''let'':205C ''like'':99C,220C ''list'':70C,89C ''load'':114C,119C ''log'':1A,25C,36C,58C,213C ''m'':16C ''messag'':59C,74C,171C ''mine'':12B ''much'':178C ''multipl'':138C ''number'':166C ''one'':28C,51C ''p'':14C,33C,55C,95C,141C,180C,187C,193C ''p.s'':194C ''part'':6A,9A ''printf'':214C ''product'':31C ''program'':198C ''real'':130C ''remain'':53C ''say'':207C ''separ'':104C ''seperat'':226C ''set'':22C ''sever'':24C ''simpl'':127C ''sometim'':137C ''step'':52C ''string'':147C ''success'':46C ''text'':11B,107C,111C ''text-min'':10B ''thank'':188C ''think'':144C ''togeth'':49C ''tri'':17C ''type'':169C ''understand'':204C ''unfortun'':123C ''use'':146C ''valu'':8A,110C,121C,139C ''world'':131C ''would'':98C,175C,219C ''xxx'':116C,122C,217C,225C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2508, 1, '2014-11-20 15:02:17.937', 2, 99, 'How to run R scripts without closing X11', '&lt;p&gt;I would like to run an R script using a single command (e.g. bat file or shortcut).&lt;/p&gt;

&lt;p&gt;This R script asks the user to choose a file and then plots information about that file. All is done via dialog boxes.&lt;/p&gt;

&lt;p&gt;I don''t want the user to go inside R - because they don''t know it at all.&lt;/p&gt;

&lt;p&gt;So, I was using r cmd and other similar stuffs, but as soon as the plots are displayed, R exits and closes the plots.&lt;/p&gt;

&lt;p&gt;What can I do?&lt;/p&gt;

&lt;p&gt;Thanks for your help.&lt;/p&gt;
', 'r', NULL, '''/p'':28C,53C,73C,99C,105C,111C ''ask'':33C ''bat'':24C ''box'':52C ''choos'':37C ''close'':7A,96C ''cmd'':80C ''command'':22C ''dialog'':51C ''display'':92C ''done'':49C ''e.g'':23C ''exit'':94C ''file'':25C,39C,46C ''go'':62C ''help'':110C ''inform'':43C ''insid'':63C ''know'':69C ''like'':13C ''p'':10C,29C,54C,74C,100C,106C ''plot'':42C,90C,98C ''r'':4A,9B,17C,31C,64C,79C,93C ''run'':3A,15C ''script'':5A,18C,32C ''shortcut'':27C ''similar'':83C ''singl'':21C ''soon'':87C ''stuff'':84C ''thank'':107C ''use'':19C,78C ''user'':35C,60C ''via'':50C ''want'':58C ''without'':6A ''would'':12C ''x11'':8A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2510, 1, '2014-11-20 17:54:11.99', 1, 351, 'Graduate Degree Choices for Data Science', '&lt;p&gt;I''m currently finishing up a B.S. in mathematics and would like to attend graduate school (a master''s degree for starters, with the possibility of a subsequent Ph.D.) with an eye toward entering the field of data science. I''m also particularly interested in machine learning.&lt;/p&gt;

&lt;p&gt;What are the graduate degree choices that would get me to where I want to go?&lt;/p&gt;

&lt;p&gt;Is there a consensus as to whether a graduate degree in applied mathematics, statistics, or computer science would put me in a better position to enter the field of data science?&lt;/p&gt;

&lt;p&gt;Thank you all for the help, this is a big choice for me and any input is very much appreciated. Typically I ask my questions on Mathematics Stack Exchange, but I thought asking here would give me a broader and better rounded perspective.&lt;/p&gt;
', 'career', NULL, '''/p'':56C,74C,107C,152C ''also'':50C ''appli'':87C ''appreci'':128C ''ask'':131C,141C ''attend'':22C ''b.s'':15C ''better'':98C,149C ''big'':118C ''broader'':147C ''career'':7B ''choic'':3A,63C,119C ''comput'':91C ''consensus'':79C ''current'':11C ''data'':5A,46C,105C ''degre'':2A,28C,62C,85C ''enter'':42C,101C ''exchang'':137C ''eye'':40C ''field'':44C,103C ''finish'':12C ''get'':66C ''give'':144C ''go'':73C ''graduat'':1A,23C,61C,84C ''help'':114C ''input'':124C ''interest'':52C ''learn'':55C ''like'':20C ''m'':10C,49C ''machin'':54C ''master'':26C ''mathemat'':17C,88C,135C ''much'':127C ''p'':8C,57C,75C,108C ''particular'':51C ''perspect'':151C ''ph.d'':37C ''posit'':99C ''possibl'':33C ''put'':94C ''question'':133C ''round'':150C ''school'':24C ''scienc'':6A,47C,92C,106C ''stack'':136C ''starter'':30C ''statist'':89C ''subsequ'':36C ''thank'':109C ''thought'':140C ''toward'':41C ''typic'':129C ''want'':71C ''whether'':82C ''would'':19C,65C,93C,143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2637, 2, '2014-12-07 13:37:40.373', 4, NULL, NULL, '&lt;p&gt;So if 4GB of RAM isn''t sufficient, 1GB isn''t going to be. That is really too little to run an HDFS namenode, a datanode, YARN, Spark driver alone, let alone leaving room for your workers.&lt;/p&gt;

&lt;p&gt;Much more reasonable is to simply run Spark locally on that instance without Hadoop at all.&lt;/p&gt;

&lt;p&gt;But I would question whether Spark is the right choice if you are definitely limited to such a small machine.&lt;/p&gt;
', NULL, NULL, '''/p'':39C,57C,79C ''1gb'':10C ''4gb'':4C ''alon'':31C,33C ''choic'':68C ''datanod'':27C ''definit'':72C ''driver'':30C ''go'':13C ''hadoop'':54C ''hdfs'':24C ''instanc'':52C ''isn'':7C,11C ''leav'':34C ''let'':32C ''limit'':73C ''littl'':20C ''local'':49C ''machin'':78C ''much'':41C ''namenod'':25C ''p'':1C,40C,58C ''question'':62C ''ram'':6C ''realli'':18C ''reason'':43C ''right'':67C ''room'':35C ''run'':22C,47C ''simpli'':46C ''small'':77C ''spark'':29C,48C,64C ''suffici'':9C ''whether'':63C ''without'':53C ''worker'':38C ''would'':61C ''yarn'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2513, 1, '2014-11-21 10:34:31.673', 5, 68, 'Please enlighten me with Platt''s SMO algorithm (for SVM)', '&lt;p&gt;From &lt;a href="http://nshorter.com/ResearchPapers/MachineLearning/A_Roadmap_to_SVM_SMO.pdf"&gt;A_Roadmap_to_SVM_SMO.pdf&lt;/a&gt;, pg 12.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://s13.postimg.org/9dx9t4w47/whatwhat.png" alt="a busy cat"&gt;&lt;/p&gt;

&lt;p&gt;Assume I am using linear kernel, how will I be able to get both the first and second inner product?&lt;/p&gt;

&lt;p&gt;My guess, inner product of datapoint with datapoint j labelled class A for the first inner product of the equation and inner product of datapoint j with datapoints labelled class B for second inner product?&lt;/p&gt;
', 'svm', NULL, '''/9dx9t4w47/whatwhat.png'':29C ''/a'':20C ''/p'':23C,34C,56C,93C ''/researchpapers/machinelearning/a_roadmap_to_svm_smo.pdf'':18C ''12'':22C ''a_roadmap_to_svm_smo.pdf'':19C ''abl'':46C ''algorithm'':8A ''alt'':30C ''assum'':36C ''b'':88C ''busi'':32C ''cat'':33C ''class'':68C,87C ''datapoint'':63C,65C,82C,85C ''enlighten'':2A ''equat'':77C ''first'':51C,72C ''get'':48C ''guess'':59C ''href'':15C ''img'':25C ''inner'':54C,60C,73C,79C,91C ''j'':66C,83C ''kernel'':41C ''label'':67C,86C ''linear'':40C ''nshorter.com'':17C ''nshorter.com/researchpapers/machinelearning/a_roadmap_to_svm_smo.pdf'':16C ''p'':12C,24C,35C,57C ''pg'':21C ''platt'':5A ''pleas'':1A ''product'':55C,61C,74C,80C,92C ''s13.postimg.org'':28C ''s13.postimg.org/9dx9t4w47/whatwhat.png'':27C ''second'':53C,90C ''smo'':7A ''src'':26C ''svm'':10A,11B ''use'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2514, 2, '2014-11-21 10:44:06.783', 3, NULL, NULL, '&lt;p&gt;There are no &lt;em&gt;unseen&lt;/em&gt; item types in the given data, by definition. 3 is the count of items seen &lt;em&gt;once&lt;/em&gt;, and they are already included in the denominator 18. If the next item were &lt;em&gt;previously&lt;/em&gt; unseen, it would become seen &lt;em&gt;once&lt;/em&gt; when it appears. Since 3-of-18 examples were seen-once items, this is an estimate of the probability that the next item will be seen-once too on its first appearance.&lt;/p&gt;

&lt;p&gt;It is certainly a heuristic. There is no way to know whether there are 0 or 1000 other types out there.&lt;/p&gt;
', NULL, NULL, '''-18'':57C ''/em'':7C,25C,42C,50C ''/p'':85C,108C ''0'':101C ''1000'':103C ''18'':34C ''3'':16C,55C ''alreadi'':29C ''appear'':53C,84C ''becom'':46C ''certain'':89C ''count'':19C ''data'':13C ''definit'':15C ''denomin'':33C ''em'':5C,23C,40C,48C ''estim'':67C ''exampl'':58C ''first'':83C ''given'':12C ''heurist'':91C ''includ'':30C ''item'':8C,21C,38C,63C,74C ''know'':97C ''next'':37C,73C ''p'':1C,86C ''previous'':41C ''probabl'':70C ''seen'':22C,47C,61C,78C ''seen-onc'':60C,77C ''sinc'':54C ''type'':9C,105C ''unseen'':6C,43C ''way'':95C ''whether'':98C ''would'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2515, 2, '2014-11-21 11:50:47.717', 5, NULL, NULL, '&lt;p&gt;First of all, word "sample" is normally used to describe &lt;a href="http://en.wikipedia.org/wiki/Sample_%28statistics%29"&gt;&lt;em&gt;subset&lt;/em&gt; of population&lt;/a&gt;, so I will refer to the same thing as "example". &lt;/p&gt;

&lt;p&gt;Your SGD implementation is slow because of this line: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for each training example i:
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here you explicitly use exactly one example for each update of model parameters. By definition, vectorization is a technique for converting operations on one element into operations on a vector of such elements. Thus, no, you cannot process examples one by one and still use vectorization. &lt;/p&gt;

&lt;p&gt;You can, however, approximate true SGD by using &lt;strong&gt;mini-batches&lt;/strong&gt;. Mini-batch is a small subset of original dataset (say, 100 examples). You calculate error and parameter updates based on mini-batches, but you still iterate over many of them without global optimization, making the process stochastic. So, to make your implementation much faster it''s enough to change previous line to: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;batches = split dataset into mini-batches
for batch in batches: 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and calculate error from batch, not from a single example. &lt;/p&gt;

&lt;p&gt;Though pretty obvious, I should also mention vectorization on per-example level. That is, instead of something like this: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;theta = np.array([...])  # parameter vector
x = np.array([...])      # example
y = 0                    # predicted response
for i in range(len(example)):
    y += x[i] * theta[i]
error = (true_y - y) ** 2  # true_y - true value of response
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;you should definitely do something like this: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;error = (true_y - sum(np.dot(x, theta))) ** 2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;which, again, easy to generalize for mini-batches:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;true_y = np.array([...])     # vector of response values
X = np.array([[...], [...]]) # mini-batch
errors = true_y - sum(np.dot(X, theta), 1)
error = sum(e ** 2 for e in errors)
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2516, 1, '2014-11-21 15:23:37.36', 5, 62, 'Relation mining of multivariant categorical timeseries without excluding the temporal nature', '&lt;p&gt;To all:&lt;/p&gt;

&lt;p&gt;I have been wracking my brain at this for a while and thought maybe someone here would know of a package or algorithm to handle the following:&lt;/p&gt;

&lt;p&gt;I have nominal multivariant timeseries that look like the following:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;          Time Var1 Var2 Var3 Var4 Var5 ... VarN
             0     A     A   B    C    A   ... H
             1     A     A   B    D    D   ... H
             2     B     A   C    D    D   ... H
             ..
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And so on from times 0 to 1,000,000. What I would like to do is search the time series for rules of the type:&lt;/p&gt;

&lt;p&gt;Given Var3 is in state B in the previous step and Var5 is in state D in the previous step, than Var1 will be in state B. What I want to do is have the rules that include the time interval explicitly. A simpler case of interest would simply be to reduce the time series to &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;               Time    Var1 Var2 Var3 Var4 Var5 ... VarN
                0        0    0    0     0   0   ... 0
                1        0    0    0     1   1   ... 0
                2        1    0    1     0   0   ... 0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Where the the variable is 1 if its state is different from the previous step and zero otherwise. Then I just want to have rules that say something like:&lt;/p&gt;

&lt;p&gt;If Var4 and Var5 changed in the previous step than Var1 will change in the current step. Which would be easy for a lag of one, as I could just make the data into something like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;   Var1 Var2 Var3 Var4 Var5 ... VarN Var1_t-1 Var2_t-1 Var3_t-1 ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and then do sequence mining, but if I want to have rules that aren''t just a single lag but could be lags from 1 to 500 than my data set begins to be a little difficult to work with. &lt;/p&gt;

&lt;p&gt;Any help would be greatly appreciated. &lt;/p&gt;

&lt;p&gt;Edit to respond to comment:
Each column could be in one of 7 different states. As far as a target, it is non-specific, any rules between the columns would be of interest. However, predicting columns 30-40 and 62-75 would be particularly interesting.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2517, 2, '2014-11-21 18:55:07.86', 2, NULL, NULL, '&lt;p&gt;Dimension reduction (like PCA) is an excellent way to visualize the results of classification on a high-dimensional feature space.&lt;/p&gt;

&lt;p&gt;The simplest approach is to project the features to some low-d (usually 2-d) space and plot them. Then either project the decision boundary onto the space and plot it as well, or simply color/label the points according to their predicted class. You can even use, say, shape to represent ground-truth class, and color to represent predicted class.&lt;/p&gt;

&lt;p&gt;This is true for any categorical classifier, but here''s an SVM-specific example: &lt;a href="http://www.ece.umn.edu/users/cherkass/predictive_learning/Resources/Visualization%20and%20Interpretation%20of%20SVM%20Classifiers.pdf" rel="nofollow"&gt;http://www.ece.umn.edu/users/cherkass/predictive_learning/Resources/Visualization%20and%20Interpretation%20of%20SVM%20Classifiers.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In particular, see figures 1a and 2a.&lt;/p&gt;
', NULL, NULL, '''/p'':23C,87C,122C ''/users/cherkass/predictive_learning/resources/visualization%20and%20interpretation%20of%20svm%20classifiers.pdf'':108C ''/users/cherkass/predictive_learning/resources/visualization%20and%20interpretation%20of%20svm%20classifiers.pdf&lt;/a&gt;&lt;/p&gt;'':113C ''1a'':119C ''2'':39C ''2a'':121C ''accord'':64C ''approach'':27C ''boundari'':50C ''categor'':94C ''class'':68C,80C,86C ''classif'':15C ''classifi'':95C ''color'':82C ''color/label'':61C ''d'':37C,40C ''decis'':49C ''dimens'':2C ''dimension'':20C ''either'':46C ''even'':71C ''exampl'':103C ''excel'':8C ''featur'':21C,32C ''figur'':118C ''ground'':78C ''ground-truth'':77C ''high'':19C ''high-dimension'':18C ''href'':105C ''like'':4C ''low'':36C ''low-d'':35C ''nofollow'':110C ''onto'':51C ''p'':1C,24C,88C,114C ''particular'':116C ''pca'':5C ''plot'':43C,55C ''point'':63C ''predict'':67C,85C ''project'':30C,47C ''reduct'':3C ''rel'':109C ''repres'':76C,84C ''result'':13C ''say'':73C ''see'':117C ''shape'':74C ''simplest'':26C ''simpli'':60C ''space'':22C,41C,53C ''specif'':102C ''svm'':101C ''svm-specif'':100C ''true'':91C ''truth'':79C ''use'':72C ''usual'':38C ''visual'':11C ''way'':9C ''well'':58C ''www.ece.umn.edu'':107C,112C ''www.ece.umn.edu/users/cherkass/predictive_learning/resources/visualization%20and%20interpretation%20of%20svm%20classifiers.pdf'':106C ''www.ece.umn.edu/users/cherkass/predictive_learning/resources/visualization%20and%20interpretation%20of%20svm%20classifiers.pdf&lt;/a&gt;&lt;/p&gt;'':111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2518, 2, '2014-11-21 21:33:06.777', 1, NULL, NULL, '&lt;p&gt;Try using the &lt;strong&gt;item-based similarity&lt;/strong&gt; algorithm available under &lt;em&gt;Apache Mahout&lt;/em&gt;. It is easy to implement and you will have a good sense how the recommendation system for your data set will work. You could provide ingredients and category as the major inputs to get the similar products.As a neophyte to this field, I would say that this approach is an easy way for all the neophytes to get a good heads up of what kind of a result one can expect from building a recommendation system of their own.   &lt;/p&gt;
', NULL, NULL, '''/em'':17C ''/p'':97C ''/strong'':10C ''algorithm'':11C ''apach'':15C ''approach'':65C ''avail'':12C ''base'':8C ''build'':90C ''categori'':45C ''could'':41C ''data'':36C ''easi'':20C,68C ''em'':14C ''expect'':88C ''field'':59C ''get'':51C,75C ''good'':28C,77C ''head'':78C ''implement'':22C ''ingredi'':43C ''input'':49C ''item'':7C ''item-bas'':6C ''kind'':82C ''mahout'':16C ''major'':48C ''neophyt'':56C,73C ''one'':86C ''p'':1C ''products.as'':54C ''provid'':42C ''recommend'':32C,92C ''result'':85C ''say'':62C ''sens'':29C ''set'':37C ''similar'':9C,53C ''strong'':5C ''system'':33C,93C ''tri'':2C ''use'':3C ''way'':69C ''work'':39C ''would'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2519, 2, '2014-11-21 21:39:29.617', 2, NULL, NULL, '&lt;p&gt;&lt;a href="http://stackoverflow.com/questions/24220676/r-script-using-x11-window-only-opens-for-a-second"&gt;This&lt;/a&gt; looks like a similar kind of a problem.&lt;/p&gt;

&lt;p&gt;Solutions: (taken from above source)&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Just sleep via  Sys.sleep(10) which would wait ten seconds.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Wait for user input via readLines(stdin()) or something like that [untested]&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Use the tcltk package which comes with R and is available on all platforms to pop up a window the user has to click to make the click disappear. That solution has been posted a few times over the years on r-help.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;2nd option is better to use for the user.&lt;/p&gt;

&lt;p&gt;P.S. Since I did not come up with the answer myself, I tried to put it in comment but my reputation is too low for that.&lt;/p&gt;
', NULL, NULL, '''/a'':8C ''/li'':39C,55C,103C ''/ol'':104C ''/p'':17C,24C,38C,54C,102C,115C,143C ''/questions/24220676/r-script-using-x11-window-only-opens-for-a-second'':6C ''10'':32C ''2nd'':106C ''answer'':126C ''avail'':68C ''better'':109C ''click'':81C,85C ''come'':63C,122C ''comment'':134C ''disappear'':86C ''help'':101C ''href'':3C ''input'':45C ''kind'':13C ''li'':26C,40C,56C ''like'':10C,51C ''look'':9C ''low'':140C ''make'':83C ''ol'':25C ''option'':107C ''p'':1C,18C,27C,41C,57C,105C,116C ''p.s'':117C ''packag'':61C ''platform'':71C ''pop'':73C ''post'':91C ''problem'':16C ''put'':131C ''r'':65C,100C ''r-help'':99C ''readlin'':47C ''reput'':137C ''second'':37C ''similar'':12C ''sinc'':118C ''sleep'':29C ''solut'':19C,88C ''someth'':50C ''sourc'':23C ''stackoverflow.com'':5C ''stackoverflow.com/questions/24220676/r-script-using-x11-window-only-opens-for-a-second'':4C ''stdin'':48C ''sys.sleep'':31C ''taken'':20C ''tcltk'':60C ''ten'':36C ''time'':94C ''tri'':129C ''untest'':53C ''use'':58C,111C ''user'':44C,78C,114C ''via'':30C,46C ''wait'':35C,42C ''window'':76C ''would'':34C ''year'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2520, 2, '2014-11-22 08:10:40.48', 3, NULL, NULL, '&lt;p&gt;Every field has their own variation of "data science," so I would suggest choosing a subject that interest you and going from there.  &lt;/p&gt;

&lt;p&gt;I can''t offer what the go to subject is for your particular interest.  A graduate degree that would "get you where you want to go" is quite a personal understanding, so I can'' answer that.  But what I will say is, from my own personal experience, when I graduated with my undergrad degree in economics, I was really interested in data science, and economics allowed me to use data science in a field I''m really interested in.  So I applied to Ph.D programs to further my knowledge and am using data science extensively in many different forms of analysis.  &lt;/p&gt;

&lt;p&gt;My suggestion is to apply to graduate degrees that have interesting subject matter to you and will allow you to use data science as understanding.  You would fit well in an economics degree because of your background :)&lt;/p&gt;
', NULL, NULL, '''/p'':25C,128C,167C ''allow'':92C,147C ''analysi'':127C ''answer'':61C ''appli'':108C,134C ''background'':166C ''choos'':15C ''data'':9C,88C,96C,119C,151C ''degre'':43C,80C,137C,162C ''differ'':124C ''econom'':82C,91C,161C ''everi'':2C ''experi'':73C ''extens'':121C ''field'':3C,100C ''fit'':157C ''form'':125C ''get'':46C ''go'':22C,33C,52C ''graduat'':42C,76C,136C ''interest'':19C,40C,86C,104C,140C ''knowledg'':115C ''m'':102C ''mani'':123C ''matter'':142C ''offer'':30C ''p'':1C,26C,129C ''particular'':39C ''person'':56C,72C ''ph.d'':110C ''program'':111C ''quit'':54C ''realli'':85C,103C ''say'':67C ''scienc'':10C,89C,97C,120C,152C ''subject'':17C,35C,141C ''suggest'':14C,131C ''undergrad'':79C ''understand'':57C,154C ''use'':95C,118C,150C ''variat'':7C ''want'':50C ''well'':158C ''would'':13C,45C,156C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2521, 1, '2014-11-22 11:54:54.78', 1, 117, 'DBPedia as Table not having all the properties', '&lt;p&gt;I browsed a sample for available data at &lt;a href="http://dbpedia.org/page/Sachin_Tendulkar" rel="nofollow"&gt;http://dbpedia.org/page/Sachin_Tendulkar&lt;/a&gt;. I wanted these properties as columns, so I downloaded the CSV files from &lt;a href="http://wiki.dbpedia.org/DBpediaAsTables" rel="nofollow"&gt;http://wiki.dbpedia.org/DBpediaAsTables&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Now, when I browse the data for the same entity "Sachin_Tendulkar", I find that many of the properties are not available. e.g. the property "dbpprop:bestBowling" is not present.&lt;/p&gt;

&lt;p&gt;How can I get all the properties that I can browse through the direct resource page.&lt;/p&gt;
', 'dataset', NULL, '''/dbpediaastables'':46C ''/dbpediaastables&lt;/a&gt;.&lt;/p&gt;'':51C ''/p'':83C,101C ''/page/sachin_tendulkar'':23C ''/page/sachin_tendulkar&lt;/a&gt;.'':28C ''avail'':16C,74C ''bestbowl'':79C ''brows'':12C,56C,95C ''column'':34C ''csv'':39C ''data'':17C,58C ''dataset'':9B ''dbpedia'':1A ''dbpedia.org'':22C,27C ''dbpedia.org/page/sachin_tendulkar'':21C ''dbpedia.org/page/sachin_tendulkar&lt;/a&gt;.'':26C ''dbpprop'':78C ''direct'':98C ''download'':37C ''e.g'':75C ''entiti'':62C ''file'':40C ''find'':66C ''get'':88C ''href'':20C,43C ''mani'':68C ''nofollow'':25C,48C ''p'':10C,52C,84C ''page'':100C ''present'':82C ''properti'':8A,32C,71C,77C,91C ''rel'':24C,47C ''resourc'':99C ''sachin'':63C ''sampl'':14C ''tabl'':3A ''tendulkar'':64C ''want'':30C ''wiki.dbpedia.org'':45C,50C ''wiki.dbpedia.org/dbpediaastables'':44C ''wiki.dbpedia.org/dbpediaastables&lt;/a&gt;.&lt;/p&gt;'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2526, 2, '2014-11-23 02:27:31.947', 0, NULL, NULL, '&lt;p&gt;The question was already answered on the DBpedia-discussion mailing list, by Daniel:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Hi Abhay,&lt;/p&gt;
  
  &lt;p&gt;the DBpediaAsTables dataset only contains the properties in the
  dbpedia-owl namespace (mapping-based infobox data) and not those from
  the dbpprop (raw infobox properties) namespace (regarding the
  differences see [1]).&lt;/p&gt;
  
  &lt;p&gt;However, as you are only interested in the data about specific
  entities, take a look at the CSV link at the bottom of the entity''s
  description page, e.g., for your example this link is [2].&lt;/p&gt;
  
  &lt;p&gt;Cheers, 
  Daniel&lt;/p&gt;
  
  &lt;p&gt;[1] wiki.dbpedia.org/Datasets#h434-10&lt;/p&gt;
  
  &lt;p&gt;[2] dbpedia.org/sparql?default-graph-uri=http%3A%2F%2Fdbpedia.org&amp;amp;query=DESCRIBE+%3Chttp://dbpedia.org/resource/Sachin_Tendulkar%3E&amp;amp;format=text%2Fcsv&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;On the &lt;a href="http://wiki.dbpedia.org/DBpediaAsTables" rel="nofollow"&gt;DBpediaAsTables web page&lt;/a&gt;, you can find out which datasets were used to generate the tables: instance_types_en, labels, short_abstracts_en, mappingbased_properties_en, geo_coordinates_en. 
Also, I want to clarify that DBpediaAsTables contains all instances from DBpedia 2014, and with "we provide some of the core DBpedia data" we want to say that not all datasets are included in the tables (but only the 5 I stated before) &lt;/p&gt;

&lt;p&gt;If you want to generate your own tables that will contain custom properties, please refer to the section &lt;a href="http://wiki.dbpedia.org/DBpediaAsTables#h347-4" rel="nofollow"&gt;Generate your own Custom Tables&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Cheers,&lt;/p&gt;

&lt;p&gt;Petar&lt;/p&gt;
', NULL, NULL, '''/a'':123C,224C ''/blockquote'':109C ''/datasets#h434-10&lt;/p&gt;'':103C ''/dbpediaastables'':117C ''/dbpediaastables#h347-4'':216C ''/p'':16C,21C,56C,94C,98C,192C,225C,228C,231C ''/sparql?default-graph-uri=http%3a%2f%2fdbpedia.org&amp;amp;query=describe+%3chttp://dbpedia.org/resource/sachin_tendulkar%3e&amp;amp;format=text%2fcsv&lt;/p&gt;'':108C ''1'':55C,100C ''2'':93C,105C ''2014'':161C ''5'':188C ''abhay'':20C ''abstract'':141C ''alreadi'':5C ''also'':149C ''answer'':6C ''base'':38C ''blockquot'':17C ''bottom'':79C ''cheer'':96C,227C ''clarifi'':153C ''contain'':27C,156C,204C ''coordin'':147C ''core'':169C ''csv'':75C ''custom'':205C,222C ''daniel'':15C,97C ''data'':40C,66C,171C ''dataset'':25C,129C,179C ''dbpedia'':10C,33C,160C,170C ''dbpedia-discuss'':9C ''dbpedia-owl'':32C ''dbpedia.org'':107C ''dbpedia.org/sparql?default-graph-uri=http%3a%2f%2fdbpedia.org&amp;amp;query=describe+%3chttp://dbpedia.org/resource/sachin_tendulkar%3e&amp;amp;format=text%2fcsv&lt;/p&gt;'':106C ''dbpediaast'':24C,120C,155C ''dbpprop'':46C ''descript'':84C ''differ'':53C ''discuss'':11C ''e.g'':86C ''en'':138C,142C,145C,148C ''entiti'':69C,82C ''exampl'':89C ''find'':126C ''generat'':133C,198C,219C ''geo'':146C ''hi'':19C ''howev'':58C ''href'':114C,213C ''includ'':181C ''infobox'':39C,48C ''instanc'':136C,158C ''interest'':63C ''label'':139C ''link'':76C,91C ''list'':13C ''look'':72C ''mail'':12C ''map'':37C ''mapping-bas'':36C ''mappingbas'':143C ''namespac'':35C,50C ''nofollow'':119C,218C ''owl'':34C ''p'':1C,18C,22C,57C,95C,99C,104C,110C,193C,226C,229C ''page'':85C,122C ''petar'':230C ''pleas'':207C ''properti'':29C,49C,144C,206C ''provid'':165C ''question'':3C ''raw'':47C ''refer'':208C ''regard'':51C ''rel'':118C,217C ''say'':175C ''section'':211C ''see'':54C ''short'':140C ''specif'':68C ''state'':190C ''tabl'':135C,184C,201C,223C ''take'':70C ''type'':137C ''use'':131C ''want'':151C,173C,196C ''web'':121C ''wiki.dbpedia.org'':102C,116C,215C ''wiki.dbpedia.org/datasets#h434-10&lt;/p&gt;'':101C ''wiki.dbpedia.org/dbpediaastables'':115C ''wiki.dbpedia.org/dbpediaastables#h347-4'':214C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2522, 2, '2014-11-22 16:08:49.147', 3, NULL, NULL, '&lt;p&gt;Why not do an MSc in ooh... &lt;strong&gt;Data Science&lt;/strong&gt;?&lt;/p&gt;

&lt;p&gt;I wrote a quick review of &lt;a href="http://barry.rowlingson.com/blog/uk-data-science-masters.html" rel="nofollow"&gt;UK Data Science Masters&lt;/a&gt;'' offerings recently. That should help you get an idea what is offered. Mostly they are mashups of stats and computing, but there are specialisms (health, finance for example) that might interest you.&lt;/p&gt;

&lt;p&gt;Note that list was compiled for courses that have already started, so some of those courses might not be available for starting next October, or have different syllabus contents.&lt;/p&gt;
', NULL, NULL, '''/a'':32C ''/blog/uk-data-science-masters.html'':25C ''/p'':13C,65C,96C ''/strong'':12C ''alreadi'':76C ''avail'':86C ''barry.rowlingson.com'':24C ''barry.rowlingson.com/blog/uk-data-science-masters.html'':23C ''compil'':71C ''comput'':52C ''content'':95C ''cours'':73C,82C ''data'':10C,29C ''differ'':93C ''exampl'':60C ''financ'':58C ''get'':39C ''health'':57C ''help'':37C ''href'':22C ''idea'':41C ''interest'':63C ''list'':69C ''mashup'':48C ''master'':31C ''might'':62C,83C ''most'':45C ''msc'':6C ''next'':89C ''nofollow'':27C ''note'':67C ''octob'':90C ''offer'':33C,44C ''ooh'':8C ''p'':1C,14C,66C ''quick'':18C ''recent'':34C ''rel'':26C ''review'':19C ''scienc'':11C,30C ''special'':56C ''start'':77C,88C ''stat'':50C ''strong'':9C ''syllabus'':94C ''uk'':28C ''wrote'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2523, 1, '2014-11-23 01:05:06.473', 8, 352, 'Relationship between KS, AUROC, and Gini', '&lt;p&gt;Common model validation statistics like the &lt;a href="https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test" rel="nofollow"&gt;KolmogorovâSmirnov test&lt;/a&gt; (KS), &lt;a href="https://en.wikipedia.org/wiki/Receiver_operating_characteristic" rel="nofollow"&gt;AUROC&lt;/a&gt;, and &lt;a href="https://en.wikipedia.org/wiki/Gini_coefficient" rel="nofollow"&gt;Gini coefficient&lt;/a&gt; are all functionally related. However, my question has to do with proving how these are all related. I am curious if anyone can help me prove these relationships. I haven''t been able to find anything online, but I am just genuinely interested how the proofs work. For example, I know Gini=2AUROC-1, but my best proof involves pointing at a graph. I am interested in formal proofs. Any help would be greatly appreciated!&lt;/p&gt;
', 'data-mining  statistics  predictive-modeling  accuracy', NULL, '''-1'':107C ''/a'':32C,42C,53C ''/p'':129C ''/wiki/gini_coefficient'':48C ''/wiki/kolmogorov%e2%80%93smirnov_test'':26C ''/wiki/receiver_operating_characteristic'':38C ''2auroc'':106C ''abl'':86C ''accuraci'':14B ''anyon'':75C ''anyth'':89C ''appreci'':128C ''auroc'':4A,41C ''best'':110C ''coeffici'':52C ''common'':16C ''curious'':73C ''data'':8B ''data-min'':7B ''en.wikipedia.org'':25C,37C,47C ''en.wikipedia.org/wiki/gini_coefficient'':46C ''en.wikipedia.org/wiki/kolmogorov%e2%80%93smirnov_test'':24C ''en.wikipedia.org/wiki/receiver_operating_characteristic'':36C ''exampl'':102C ''find'':88C ''formal'':121C ''function'':56C ''genuin'':95C ''gini'':6A,51C,105C ''graph'':116C ''great'':127C ''haven'':83C ''help'':77C,124C ''howev'':58C ''href'':23C,35C,45C ''interest'':96C,119C ''involv'':112C ''know'':104C ''kolmogorovâ'':29C ''ks'':3A,33C ''like'':20C ''mine'':9B ''model'':13B,17C ''nofollow'':28C,40C,50C ''onlin'':90C ''p'':15C ''point'':113C ''predict'':12B ''predictive-model'':11B ''proof'':99C,111C,122C ''prove'':65C,79C ''question'':60C ''rel'':27C,39C,49C ''relat'':57C,70C ''relationship'':1A,81C ''smirnov'':30C ''statist'':10B,19C ''test'':31C ''valid'':18C ''work'':100C ''would'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2524, 2, '2014-11-23 02:26:42.47', 0, NULL, NULL, '&lt;p&gt;The official answer:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Date: Sun, 23 Nov 2014 03:08:19 +0100
From: Petar Ristoski &amp;lt;petar.ristoski@informatik.uni-mannheim.de&amp;gt;
To: ''Barry Carter'' &amp;lt;carter.barry@gmail.com&amp;gt;
Subject: RE: CSV tables don''t have all properties?

Hi Carter,

The question was already answered on the DBpedia mailing list, but I will
try to clarify it again. On the DBpedia as Tables web page says that "For
each class in the DBpedia ontology (such as Person, Radio Station, Ice
Hockey Player, or Band) we provide a single CSV/JSON file which contains all
instances of this class. Each instance is described by its URI, an English
label and a short abstract, the MAPPING-BASED INFOBOX data describing the
instance (extracted from the English edition of Wikipedia), geo-coordinates,
and external links." As you can see we only provide the mapping-based
infobox properties (dbpedia-owl namespace), while the properties from the
dbpprop (raw infobox properties) namespace are completely ignored.
Therefore, dbpprop:bestBowling is missing from the file.

Also, there is a section "Generating your own Custom Tables" [1], where we
explain how to generate your own tables that will contain the properties you
need.

Regards,

Petar

[1] http://wiki.dbpedia.org/DBpediaAsTables#h347-4 
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''+0100'':16C ''/code'':204C ''/dbpediaastables#h347-4'':203C ''/p'':5C ''/pre'':205C ''03'':13C ''08'':14C ''1'':181C,200C ''19'':15C ''2014'':12C ''23'':10C ''abstract'':110C ''alreadi'':43C ''also'':171C ''answer'':4C,44C ''band'':83C ''barri'':24C ''base'':114C,143C ''bestbowl'':165C ''carter'':25C,39C ''carter.barry@gmail.com'':27C ''clarifi'':55C ''class'':69C,96C ''code'':7C ''complet'':161C ''contain'':91C,193C ''coordin'':129C ''csv'':31C ''csv/json'':88C ''custom'':179C ''data'':116C ''date'':8C ''dbpedia'':47C,60C,72C,147C ''dbpedia-owl'':146C ''dbpprop'':155C,164C ''describ'':100C,117C ''edit'':124C ''english'':105C,123C ''explain'':184C ''extern'':131C ''extract'':120C ''file'':89C,170C ''generat'':176C,187C ''geo'':128C ''geo-coordin'':127C ''gt'':22C,28C ''hi'':38C ''hockey'':80C ''ice'':79C ''ignor'':162C ''infobox'':115C,144C,157C ''instanc'':93C,98C,119C ''label'':106C ''link'':132C ''list'':49C ''lt'':20C,26C ''mail'':48C ''map'':113C,142C ''mapping-bas'':112C,141C ''miss'':167C ''namespac'':149C,159C ''need'':197C ''nov'':11C ''offici'':3C ''ontolog'':73C ''owl'':148C ''p'':1C ''page'':64C ''person'':76C ''petar'':18C,199C ''petar.ristoski@informatik.uni-mannheim.de'':21C ''player'':81C ''pre'':6C ''properti'':37C,145C,152C,158C,195C ''provid'':85C,139C ''question'':41C ''radio'':77C ''raw'':156C ''re'':30C ''regard'':198C ''ristoski'':19C ''say'':65C ''section'':175C ''see'':136C ''short'':109C ''singl'':87C ''station'':78C ''subject'':29C ''sun'':9C ''tabl'':32C,62C,180C,190C ''therefor'':163C ''tri'':53C ''uri'':103C ''web'':63C ''wiki.dbpedia.org'':202C ''wiki.dbpedia.org/dbpediaastables#h347-4'':201C ''wikipedia'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2525, 1, '2014-11-23 02:27:10.67', 3, 59, 'Can some one explain how PCA is relevant in extracting parameters of Gaussian Mixture Models', '&lt;p&gt;I am having some difficulty in seeing connection between PCA on second order moment matrix in estimating parameters of Gaussian Mixture Models. Can anyone connect the above??&lt;/p&gt;
', 'clustering', NULL, '''/p'':45C ''anyon'':41C ''cluster'':16B ''connect'':25C,42C ''difficulti'':22C ''estim'':34C ''explain'':4A ''extract'':10A ''gaussian'':13A,37C ''matrix'':32C ''mixtur'':14A,38C ''model'':15A,39C ''moment'':31C ''one'':3A ''order'':30C ''p'':17C ''paramet'':11A,35C ''pca'':6A,27C ''relev'':8A ''second'':29C ''see'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2538, 1, '2014-11-25 10:30:17.567', 4, 99, 'Error analysis for better accuracy', '&lt;p&gt;I have historic error of time series.  I want to analyze error series to improve forecast series. Are there any methods to do this?&lt;/p&gt;
', 'time-series  forecast', NULL, '''/p'':35C ''accuraci'':5A ''analysi'':2A ''analyz'':21C ''better'':4A ''error'':1A,14C,22C ''forecast'':9B,26C ''histor'':13C ''improv'':25C ''method'':31C ''p'':10C ''seri'':8B,17C,23C,27C ''time'':7B,16C ''time-seri'':6B ''want'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2559, 2, '2014-11-28 06:02:59.21', 1, NULL, NULL, '&lt;p&gt;Data analysis is always driven by the request. It could be: "I want to find out this, so I need to collect those data first. Then I would use this model to analyze". If you just want to practice, by reviewing your data set, there is one:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Task&lt;/strong&gt;: Which issue affects the "number of check in " most? &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Data set&lt;/strong&gt;: what you have&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Model&lt;/strong&gt;: Correlation (e.g. Spearman, which is nonparametric measure of statistical dependence between two variables)&lt;/p&gt;
', NULL, NULL, '''/p'':49C,63C,72C,90C ''/strong'':53C,68C,76C ''affect'':56C ''alway'':5C ''analysi'':3C ''analyz'':34C ''check'':60C ''collect'':23C ''correl'':77C ''could'':11C ''data'':2C,25C,44C,66C ''depend'':86C ''driven'':6C ''e.g'':78C ''find'':16C ''first'':26C ''issu'':55C ''measur'':83C ''model'':32C,75C ''need'':21C ''nonparametr'':82C ''number'':58C ''one'':48C ''p'':1C,50C,64C,73C ''practic'':40C ''request'':9C ''review'':42C ''set'':45C,67C ''spearman'':79C ''statist'':85C ''strong'':51C,65C,74C ''task'':52C ''two'':88C ''use'':30C ''variabl'':89C ''want'':14C,38C ''would'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2561, 2, '2014-11-28 06:17:34.48', 2, NULL, NULL, '&lt;p&gt;Cannot say it is the best one, but Latent Semantic Analysis could be one option. Basically it is based on co-occurrence, you need to weight it first.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Latent_semantic_analysis" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Latent_semantic_analysis&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://lsa.colorado.edu/papers/dp1.LSAintro.pdf" rel="nofollow"&gt;http://lsa.colorado.edu/papers/dp1.LSAintro.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The problem is that LSA does not have firm statistic support.&lt;/p&gt;

&lt;p&gt;Have fun&lt;/p&gt;
', NULL, NULL, '''/p'':31C,66C,70C ''/papers/dp1.lsaintro.pdf'':48C ''/papers/dp1.lsaintro.pdf&lt;/a&gt;&lt;/p&gt;'':53C ''/wiki/latent_semantic_analysis'':37C ''/wiki/latent_semantic_analysis&lt;/a&gt;&lt;/p&gt;'':42C ''analysi'':12C ''base'':20C ''basic'':17C ''best'':7C ''cannot'':2C ''co'':23C ''co-occurr'':22C ''could'':13C ''en.wikipedia.org'':36C,41C ''en.wikipedia.org/wiki/latent_semantic_analysis'':35C ''en.wikipedia.org/wiki/latent_semantic_analysis&lt;/a&gt;&lt;/p&gt;'':40C ''firm'':63C ''first'':30C ''fun'':69C ''href'':34C,45C ''latent'':10C ''lsa'':59C ''lsa.colorado.edu'':47C,52C ''lsa.colorado.edu/papers/dp1.lsaintro.pdf'':46C ''lsa.colorado.edu/papers/dp1.lsaintro.pdf&lt;/a&gt;&lt;/p&gt;'':51C ''need'':26C ''nofollow'':39C,50C ''occurr'':24C ''one'':8C,15C ''option'':16C ''p'':1C,32C,43C,54C,67C ''problem'':56C ''rel'':38C,49C ''say'':3C ''semant'':11C ''statist'':64C ''support'':65C ''weight'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2567, 1, '2014-11-29 16:05:08.81', 8, 209, 'What regression to use to calculate the result of election in a multiparty system?', '&lt;p&gt;I want to make a prediction for the result of the parliamentary elections. My output will be the % each party receives. There is more than 2 parties so logistic regression is not a viable option. I could make a separate regression for each party but in that case the results would be in some manner independent from each other. It would not ensure that the sum of the results would be 100%.&lt;/p&gt;

&lt;p&gt;What regression (or other method) should I use? Is it possible to use this method in R or Python via a specific library?&lt;/p&gt;
', 'classification  r  python  regression  predictive-modeling', NULL, '''/p'':95C,120C ''100'':94C ''2'':48C ''calcul'':6A ''case'':70C ''classif'':15B ''could'':59C ''elect'':10A,35C ''ensur'':85C ''independ'':78C ''librari'':119C ''logist'':51C ''make'':26C,60C ''manner'':77C ''method'':101C,111C ''model'':21B ''multiparti'':13A ''option'':57C ''output'':37C ''p'':22C,96C ''parliamentari'':34C ''parti'':42C,49C,66C ''possibl'':107C ''predict'':20B,28C ''predictive-model'':19B ''python'':17B,115C ''r'':16B,113C ''receiv'':43C ''regress'':2A,18B,52C,63C,98C ''result'':8A,31C,72C,91C ''separ'':62C ''specif'':118C ''sum'':88C ''system'':14A ''use'':4A,104C,109C ''via'':116C ''viabl'':56C ''want'':24C ''would'':73C,83C,92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2527, 1, '2014-11-23 14:58:34.127', 6, 224, 'Using Clustering in text processing', '&lt;p&gt;Hi this is my first question in the Data Science stack. I want to create an algorithm for text classification. Suppose i have a large set of text and articles. Lets say around 5000 plain texts. I first use a simple function to determine the frequency of all the four and above character words. I then use this as the feature of each training sample. Now i want my algorithm to be able to cluster the training sets to according to their features, which here is the frequency of each word in the article. (Note that in this example, each article would have its own unique feature since each article has a different feature, for example an article has 10 "water and 23 "pure" and another has 8 "politics" and 14 "leverage"). Can you suggest the best possible clustering algorithm for this example?&lt;/p&gt;
', 'text-mining  clustering', NULL, '''/p'':154C ''10'':130C ''14'':141C ''23'':133C ''5000'':44C ''8'':138C ''abl'':83C ''accord'':90C ''algorithm'':27C,80C,150C ''anoth'':136C ''around'':43C ''articl'':40C,104C,111C,120C,128C ''best'':147C ''charact'':63C ''classif'':30C ''cluster'':2A,9B,85C,149C ''creat'':25C ''data'':19C ''determin'':54C ''differ'':123C ''exampl'':109C,126C,153C ''featur'':71C,93C,117C,124C ''first'':15C,48C ''four'':60C ''frequenc'':56C,98C ''function'':52C ''hi'':11C ''larg'':35C ''let'':41C ''leverag'':142C ''mine'':8B ''note'':105C ''p'':10C ''plain'':45C ''polit'':139C ''possibl'':148C ''process'':5A ''pure'':134C ''question'':16C ''sampl'':75C ''say'':42C ''scienc'':20C ''set'':36C,88C ''simpl'':51C ''sinc'':118C ''stack'':21C ''suggest'':145C ''suppos'':31C ''text'':4A,7B,29C,38C,46C ''text-min'':6B ''train'':74C,87C ''uniqu'':116C ''use'':1A,49C,67C ''want'':23C,78C ''water'':131C ''word'':64C,101C ''would'':112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2528, 2, '2014-11-23 17:49:04.487', 5, NULL, NULL, '&lt;p&gt;If you want to proceed on your existing path I suggest normalizing each term''s frequency by its popularity in the entire corpus, so rare and hence predictive words are promoted. Then use random projections to reduce the dimensionality of these very long vectors down to size so your clustering algorithm will work better (you don''t want to cluster in high dimensional spaces).&lt;/p&gt;

&lt;p&gt;But there are other ways of topic modeling. Read &lt;a href="http://www.cs.princeton.edu/~blei/papers/Blei2012.pdf"&gt;this&lt;/a&gt; tutorial to learn more.&lt;/p&gt;
', NULL, NULL, '''/a'':83C ''/p'':66C,88C ''/~blei/papers/blei2012.pdf'':81C ''algorithm'':52C ''better'':55C ''cluster'':51C,61C ''corpus'':24C ''dimension'':40C,64C ''entir'':23C ''exist'':9C ''frequenc'':17C ''henc'':28C ''high'':63C ''href'':78C ''learn'':86C ''long'':44C ''model'':75C ''normal'':13C ''p'':1C,67C ''path'':10C ''popular'':20C ''predict'':29C ''proceed'':6C ''project'':36C ''promot'':32C ''random'':35C ''rare'':26C ''read'':76C ''reduc'':38C ''size'':48C ''space'':65C ''suggest'':12C ''term'':15C ''topic'':74C ''tutori'':84C ''use'':34C ''vector'':45C ''want'':4C,59C ''way'':72C ''word'':30C ''work'':54C ''www.cs.princeton.edu'':80C ''www.cs.princeton.edu/~blei/papers/blei2012.pdf'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2529, 2, '2014-11-23 23:00:30.007', 1, NULL, NULL, '&lt;p&gt;Store the edges (relations) in your server:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(TeamID, playerID)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;When you want to find common elements just filter all edges where:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;TeamID="TeamA" or TeamID="TeamB"
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;(You could use indexes to speed is up, etc)&lt;/p&gt;

&lt;p&gt;Then group by playerID and check how many items are in each group. The groups with two items belong to both teams and are shared.&lt;/p&gt;
', NULL, NULL, '''/code'':14C,37C ''/p'':9C,29C,49C,76C ''/pre'':15C,38C ''belong'':69C ''check'':56C ''code'':11C,31C ''common'':22C ''could'':41C ''edg'':4C,27C ''element'':23C ''etc'':48C ''filter'':25C ''find'':21C ''group'':52C,63C,65C ''index'':43C ''item'':59C,68C ''mani'':58C ''p'':1C,16C,39C,50C ''playerid'':13C,54C ''pre'':10C,30C ''relat'':5C ''server'':8C ''share'':75C ''speed'':45C ''store'':2C ''team'':72C ''teama'':33C ''teamb'':36C ''teamid'':12C,32C,35C ''two'':67C ''use'':42C ''want'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2530, 2, '2014-11-24 09:20:47.543', 3, NULL, NULL, '&lt;p&gt;I recommend you to take a look to Oryx (&lt;a href="https://github.com/cloudera/oryx" rel="nofollow"&gt;https://github.com/cloudera/oryx&lt;/a&gt;). Oryx is based on Apache Mahout (actually one of the creators of Mahout Sean Owen built it) and provides recommendation using collaborative filtering. Oryx is a very practical tool for implementing recommendation. I have used it in several projects: recommending products in retail stores (small businesses), building an e-commerce recommender and user similarity from mobile app interaction. &lt;/p&gt;

&lt;p&gt;You just have to represent data in the form:
UserId ItemId Value&lt;/p&gt;

&lt;p&gt;Where value is a measure (subjective) of the &lt;em&gt;importance&lt;/em&gt; or &lt;em&gt;influence&lt;/em&gt; of the interaction between that user and the item. User and item can be anything actually, and the same procedure can be used for tagging. For example, for recommending songs, finding similar songs and bands, and finding similar users according to their music tastes you can represent data as&lt;/p&gt;

&lt;p&gt;UserId SongId NumberOfPlays&lt;/p&gt;

&lt;p&gt;Where NumberOfPlays is the amount of times a song has been played by user (in an online music service for example). This exampl was given in Myrrix the predecessor of Oryx. They also show how to recommend tags to questions in StackOverflow.&lt;/p&gt;

&lt;p&gt;The github site is not that well documented but it will be enough to get it running (and working :))&lt;/p&gt;
', NULL, NULL, '''/cloudera/oryx'':15C ''/cloudera/oryx&lt;/a&gt;).'':20C ''/em'':106C,110C ''/p'':80C,94C,160C,165C,209C,230C ''accord'':150C ''actual'':27C,126C ''also'':199C ''amount'':171C ''anyth'':125C ''apach'':25C ''app'':78C ''band'':145C ''base'':23C ''build'':67C ''built'':36C ''busi'':66C ''collabor'':42C ''commerc'':71C ''creator'':31C ''data'':87C,158C ''document'':218C ''e'':70C ''e-commerc'':69C ''em'':104C,108C ''enough'':223C ''exampl'':137C,187C,189C ''filter'':43C ''find'':141C,147C ''form'':90C ''get'':225C ''github'':212C ''github.com'':14C,19C ''github.com/cloudera/oryx'':13C ''github.com/cloudera/oryx&lt;/a&gt;).'':18C ''given'':191C ''href'':12C ''implement'':51C ''import'':105C ''influenc'':109C ''interact'':79C,113C ''item'':119C,122C ''itemid'':92C ''look'':8C ''mahout'':26C,33C ''measur'':100C ''mobil'':77C ''music'':153C,184C ''myrrix'':193C ''nofollow'':17C ''numberofplay'':164C,168C ''one'':28C ''onlin'':183C ''oryx'':10C,21C,44C,197C ''owen'':35C ''p'':1C,81C,95C,161C,166C,210C ''play'':178C ''practic'':48C ''predecessor'':195C ''procedur'':130C ''product'':61C ''project'':59C ''provid'':39C ''question'':206C ''recommend'':3C,40C,52C,60C,72C,139C,203C ''rel'':16C ''repres'':86C,157C ''retail'':63C ''run'':227C ''sean'':34C ''servic'':185C ''sever'':58C ''show'':200C ''similar'':75C,142C,148C ''site'':213C ''small'':65C ''song'':140C,143C,175C ''songid'':163C ''stackoverflow'':208C ''store'':64C ''subject'':101C ''tag'':135C,204C ''take'':6C ''tast'':154C ''time'':173C ''tool'':49C ''use'':41C,55C,133C ''user'':74C,116C,120C,149C,180C ''userid'':91C,162C ''valu'':93C,97C ''well'':217C ''work'':229C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2534, 1, '2014-11-25 03:08:34.843', 2, 42, 'Confused about description of YearPrediction Dataset', '&lt;p&gt;&lt;a href="https://archive.ics.uci.edu/ml/datasets/YearPredictionMSD" rel="nofollow"&gt;https://archive.ics.uci.edu/ml/datasets/YearPredictionMSD&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;According to the description given in the above link, 
the Attribute information specifies "average and covariance over all ''segments'', each segment being described by a 12-dimensional timbre vector". So the covariance matrix should have 12*12 = 144 elements. But why is the number of timbre covariance features only 78 ?&lt;/p&gt;
', 'dataset', NULL, '''/ml/datasets/yearpredictionmsd'':13C ''/ml/datasets/yearpredictionmsd&lt;/a&gt;&lt;/p&gt;'':18C ''/p'':70C ''12'':45C,55C,56C ''144'':57C ''78'':69C ''accord'':20C ''archive.ics.uci.edu'':12C,17C ''archive.ics.uci.edu/ml/datasets/yearpredictionmsd'':11C ''archive.ics.uci.edu/ml/datasets/yearpredictionmsd&lt;/a&gt;&lt;/p&gt;'':16C ''attribut'':30C ''averag'':33C ''confus'':1A ''covari'':35C,51C,66C ''dataset'':6A,7B ''describ'':42C ''descript'':3A,23C ''dimension'':46C ''element'':58C ''featur'':67C ''given'':24C ''href'':10C ''inform'':31C ''link'':28C ''matrix'':52C ''nofollow'':15C ''number'':63C ''p'':8C,19C ''rel'':14C ''segment'':38C,40C ''specifi'':32C ''timbr'':47C,65C ''vector'':48C ''yearpredict'':5A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5133, 2, '2015-02-13 05:57:36.84', 1, NULL, NULL, '&lt;p&gt;The answer to both data sets is an OCR application with some post-processing, but a more specialized program than a generic low-quality or an open source OCR. Essentially the harder the problem, the more capable and advanced tools need to be used to solve it.&lt;/p&gt;

&lt;p&gt;There will be two major stages in this task: digitizing the data (image to text, i.e. OCR) and processing the data (performing the actual count). Look at them separately in order to select the best method for each stage.&lt;/p&gt;

&lt;p&gt;The main challenges in these images and generic OCR are:&lt;/p&gt;

&lt;p&gt;a) images have low resolution. For example the # 1 image has resolution of about 72 dpi. Suggested resolution for such text quality is to scan at 300 to 400 dpi, but it is clear that re-scanning or controlling scan resolution is not applicable now. Thatâs why one option is to clean and increase the size using image pre-processing tools. This is what the original #1 image snippet looks like after adaptive binarization and zoomed at 300%. It is clear that each character has too few pixels and characters can be easily misread.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/oNqPE.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;b) GIF format in #1 is not supported by many OCR applications. Images need to be batch-converted to a different format, such as PNG or TIF.&lt;/p&gt;

&lt;p&gt;c) in these scans the backgrounds and bleed-through (shadow from the text on the other side of the paper) is visible. Good binarization needs to be used to remove background and bleed-through, but not remove vital parts of actual characters.&lt;/p&gt;

&lt;p&gt;After implementing specific pre-processing solutions for the items listed above, and then using a high quality OCR system, such as www.ocr-it.com API, highest possible results can be achieved.  Result is far from perfect, but it is as high accuracy as it could be achieved with a modern OCR engine on these images.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/v5biL.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Luckily for this project, the data needs to be counted, so the second stage has all necessary data for reliable data post-processing analysis. Contrary to other basic OCR engines, the OCR provided by &lt;a href="http://www.ocr-it.com" rel="nofollow"&gt;www.ocr-it.com&lt;/a&gt; API, which I used to produce the above recognition.  OCR-IT API is free to develop with, and very inexpensive to use per page, so that may be a very economical but powerful solution for this project.  It returns formatted text layout, including preserving line breaks and overall format structure.  This makes text post-processing easier.  &lt;/p&gt;

&lt;p&gt;A simple algorithm can be run to count the number of lines, resulting in the necessary for the research count.&lt;/p&gt;

&lt;p&gt;The above describes a two-stage approach: getting best possible OCR result, and using an applicable method to process data for the required task&lt;/p&gt;

&lt;p&gt;Bat wait, there is moreâ¦&lt;/p&gt;

&lt;p&gt;There is a second option to use an even more specialized OCR application called &lt;a href="http://www.wisetrend.com/abbyy-flexicapture/" rel="nofollow"&gt;FlexiCapture&lt;/a&gt; with FlexiLayout technology.  This powerful and intelligent data capture technology has built-in high-accuracy OCR, and it has a powerful rules and data analytics engine to perform very specialized user-defined chains of actions and tasks.&lt;/p&gt;

&lt;p&gt;The implementation of this method using FlexiCapture with FlexiLayout takes the following logical steps.&lt;/p&gt;

&lt;p&gt;First, full page OCR is performed and all objects are extracted, including characters, noise, black horizontal and vertical lines, white gaps, and objects (which could be pictures, logos, handwriting, etc.).  This produces objects upon which we can apply our search criteria.&lt;/p&gt;

&lt;p&gt;Next, the following constraints are applied to the post-OCR data analysis and search criteria: separate image into three vertical columns and run the following logic per column, use line-start as individual count, skip header/footer/indented lines (county names), assume each name to have at least three characters, find recursively every name starting from top to bottom in every column, exclude previously found lines.&lt;/p&gt;

&lt;p&gt;While the above logic sounds complex to setup, the actual setup takes just a few minutes and requires minimal work through user interface (UI) environment.  No coding or programming is necessary.   The following search elements and criteria have been created.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/MOPHu.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;RepeatingGroup consisting of a CharacterString search object.&lt;/p&gt;

&lt;p&gt;This setup produces the following search result for the first column of data:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/KzpKF.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;As the last step, FlexiCapture is instructed to return the number of total found elements that fit our search criteria, effectively producing the necessary data for the research task.&lt;/p&gt;

&lt;p&gt;There are other logic alternatives that can be setup in FlexiCapture, such as finding the number of white spaces between lines, or searching for the fixed-length fixed-placement 3-letter combinations at the end of every column linen.&lt;/p&gt;

&lt;p&gt;In conclusion, there are several options (which is always nice) how this task can be achieved with relative ease in effort and high quality, but the success depends on the quality of tools used and necessary knowledge how to use them.&lt;/p&gt;

&lt;p&gt;If you believe some of these tools and processes can be beneficial to your project, please contact my directly.  I specialize in these workflows.  Ilya @ WiseTREND.  My company may be able to help with the setup or guidance.  We have participated in various research initiatives, some through donations to a good cause.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4991, 2, '2015-01-30 10:44:58.467', 1, NULL, NULL, '&lt;p&gt;Is this article good enough?
&lt;a href="http://www.succeed-project.eu/wiki/index.php/NLTK#Input_format_for_training" rel="nofollow"&gt;http://www.succeed-project.eu/wiki/index.php/NLTK#Input_format_for_training&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;There is explanation about how corpus should look like.&lt;/p&gt;

&lt;p&gt;Your data needs to be in IOB format (word tag chunktag) to make it work.&lt;br/&gt;
Eric NNP B-PERSON&lt;br/&gt;
is VB O&lt;br/&gt;
the AT B-NP&lt;br/&gt;
CEO NN I-NP&lt;br/&gt;
of IN O&lt;br/&gt;
Google NNP B-ORGANIZATION &lt;br/&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':27C,77C ''/wiki/index.php/nltk#input_format_for_training'':11C ''/wiki/index.php/nltk#input_format_for_training&lt;/a&gt;&lt;/p&gt;'':16C ''articl'':4C ''b'':48C,58C,74C ''b-np'':57C ''b-organ'':73C ''b-person'':47C ''br'':44C,50C,54C,60C,66C,70C,76C ''ceo'':61C ''chunktag'':39C ''corpus'':23C ''data'':30C ''enough'':6C ''eric'':45C ''explan'':20C ''format'':36C ''good'':5C ''googl'':71C ''href'':8C ''i-np'':63C ''iob'':35C ''like'':26C ''look'':25C ''make'':41C ''need'':31C ''nn'':62C ''nnp'':46C,72C ''nofollow'':13C ''np'':59C,65C ''o'':53C,69C ''organ'':75C ''p'':1C,17C,28C ''person'':49C ''rel'':12C ''tag'':38C ''vb'':52C ''word'':37C ''work'':43C ''www.succeed-project.eu'':10C,15C ''www.succeed-project.eu/wiki/index.php/nltk#input_format_for_training'':9C ''www.succeed-project.eu/wiki/index.php/nltk#input_format_for_training&lt;/a&gt;&lt;/p&gt;'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2536, 2, '2014-11-25 09:17:45.02', 2, NULL, NULL, '&lt;p&gt;This problem is one of estimating the lag. Once that is estimated, you could create additional features representing the lagged values and move forward with "sequence mining" as you have already suggested in the question itself. &lt;/p&gt;

&lt;p&gt;For each variable, Var_i, you will have to estimate its lag l_i. This lag can be calculated by estimating the order of a Markov chain with seven symbols (you could use either &lt;a href="http://en.wikipedia.org/wiki/Bayesian_information_criterion" rel="nofollow"&gt;BIC&lt;/a&gt; or &lt;a href="http://en.wikipedia.org/wiki/Akaike_information_criterion" rel="nofollow"&gt;AIC&lt;/a&gt; to estimate this order; both would require calculating likelihood of candidate orders and pick the order that maximizes either of these criteria). Once you are done calculating the order of the Markov chain for each of the variables, then you could represent your dataset such that each row will have the current value of Var_i and its preceding values, all the way back to its estimated lag l_i. While this methodology is laborious, it pays rich dividends as its automated and parsimonious way of representing the necessary information. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2540, 2, '2014-11-26 02:07:06.46', 1, NULL, NULL, '&lt;p&gt;I have just finished my Ph.D. and have used some NLP in it. My university didn''t offer any NLP courses. So I ended up teaching myself NLP. I used the following book:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.nltk.org/book_1ed/" rel="nofollow"&gt;http://www.nltk.org/book_1ed/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Which serves as a great introduction to NLP using NLTK (Natural Language Tool Kit). It gives a good introduction into programming with Python. So handy if you''ve never programmed in Python before too.&lt;/p&gt;

&lt;p&gt;I would highly suggest using nltk from nltk.org (sorry can''t post more than two links)&lt;/p&gt;

&lt;p&gt;The book I used is now out of date as NLTK is now on version 3.0, the book mentioned previously is for NLTK 2.x. But you''re in luck. The Authors are working on a new version of the book for NLTK 3.X You can view the unfinished book over at&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.nltk.org/book/" rel="nofollow"&gt;http://www.nltk.org/book/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I would highly suggest using NLTK and if you''re new to natural language processing. I would highly suggest you try and get yourself a copy of the following book: &lt;/p&gt;

&lt;p&gt;Foundations of Statistical Natural Language Processing by Manning and Shutze&lt;/p&gt;

&lt;p&gt;Even though it doesn''t contain any code, it servers a great introduction to natural language processing.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2541, 1, '2014-11-26 04:40:17.84', 2, 90, 'Evaluating Recommendation engines', '&lt;p&gt;What is the standard way for evaluating and comparing different algorithms while developing recommendation system? Whether we need to have a predetermined annotated ranked dataset and then compare with precision/recall/F measure of  different algorithms ? Is this the best way for evaluation ? Or is there any other way to compare results of various recommendation algorithms ?&lt;/p&gt;
', 'machine-learning  data-mining  dataset  statistics  recommendation', NULL, '''/p'':68C ''algorithm'':24C,47C,67C ''annot'':36C ''best'':51C ''compar'':22C,41C,62C ''data'':8B ''data-min'':7B ''dataset'':10B,38C ''develop'':26C ''differ'':23C,46C ''engin'':3A ''evalu'':1A,20C,54C ''learn'':6B ''machin'':5B ''machine-learn'':4B ''measur'':44C ''mine'':9B ''need'':31C ''p'':13C ''precision/recall/f'':43C ''predetermin'':35C ''rank'':37C ''recommend'':2A,12B,27C,66C ''result'':63C ''standard'':17C ''statist'':11B ''system'':28C ''various'':65C ''way'':18C,52C,60C ''whether'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2543, 1, '2014-11-26 08:47:49.65', 5, 317, 'Can Machine Learning be applied in software developement', '&lt;p&gt;I''m from programming background. I''m now learning Analytics. I''m learning concepts from basic statistics to model building like linear regression, logistic regression, time-series analysis, etc.,&lt;/p&gt;

&lt;p&gt;As my previous experience is completely on programming, I would like to do some analysis on the data which programmer has.&lt;/p&gt;

&lt;p&gt;Say, Lets have the details below(I''m using SVN repository)&lt;/p&gt;

&lt;p&gt;personname, code check-in date, file checked-in, number of times checkedin, branch, check-in date and time, build version, Number of defects, defect date, file that has defect, build version, defect fix date, defect fix hours, (please feel free to add/remove how many ever variables needed)&lt;/p&gt;

&lt;p&gt;I Just need a trigger/ starting point on what can be done with these data. can I bring any insights with this data.&lt;/p&gt;

&lt;p&gt;or can you provide any links that has information about similar type of work done.&lt;/p&gt;
', 'machine-learning  recommendation  predictive-modeling  software-development', NULL, '''/p'':50C,73C,86C,138C,163C,180C ''add/remove'':132C ''analysi'':48C,66C ''analyt'':29C ''appli'':5A ''background'':24C ''basic'':35C ''branch'':102C ''bring'':157C ''build'':39C,109C,120C ''check'':91C,96C,104C ''check-in'':90C,103C ''checked-in'':95C ''checkedin'':101C ''code'':89C ''complet'':57C ''concept'':33C ''data'':69C,154C,162C ''date'':93C,106C,115C,124C ''defect'':113C,114C,119C,122C,125C ''detail'':79C ''develop'':8A,18B ''done'':151C,179C ''etc'':49C ''ever'':135C ''experi'':55C ''feel'':129C ''file'':94C,116C ''fix'':123C,126C ''free'':130C ''hour'':127C ''inform'':173C ''insight'':159C ''learn'':3A,11B,28C,32C ''let'':76C ''like'':40C,62C ''linear'':41C ''link'':170C ''logist'':43C ''m'':21C,26C,31C,82C ''machin'':2A,10B ''machine-learn'':9B ''mani'':134C ''model'':15B,38C ''need'':137C,142C ''number'':98C,111C ''p'':19C,51C,74C,87C,139C,164C ''personnam'':88C ''pleas'':128C ''point'':146C ''predict'':14B ''predictive-model'':13B ''previous'':54C ''program'':23C,59C ''programm'':71C ''provid'':168C ''recommend'':12B ''regress'':42C,44C ''repositori'':85C ''say'':75C ''seri'':47C ''similar'':175C ''softwar'':7A,17B ''software-develop'':16B ''start'':145C ''statist'':36C ''svn'':84C ''time'':46C,100C,108C ''time-seri'':45C ''trigger'':144C ''type'':176C ''use'':83C ''variabl'':136C ''version'':110C,121C ''work'':178C ''would'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2544, 2, '2014-11-26 09:37:43.97', 3, NULL, NULL, '&lt;p&gt;Definately - Yes.
Good question. 
Was thinking about it myself.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;(1) Collect the data&lt;/strong&gt;.
The first problem you have: gather enough data. 
All the attributes you mentioned (date, name, check-in title/comment, N of deffects etc) are potentially useful - gather as much as possible.
As soon as you have a big project, a number of developers, many branches, frequent commits and you have started collecting all the data, you a ready to go further.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;(2) Ask good questions&lt;/strong&gt;.
The next question you should ask yourself: what effect are you going to measure, estimate and maybe predict.
Frequency of possible bugs? Tracking inaccurate "committers"? Risky branches? Want to see some groups of users/bugs/commits according to some metrics?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;(3) Select the model&lt;/strong&gt;.
As soon as you have the questions formulated, you should follow the general approach in data science - extract needed features in your data, select appropriate model, train you model and test it, apply it. This is too broad process to discuss it this thread, so please use this site to get right answers.&lt;/p&gt;
', NULL, NULL, '''/p'':11C,80C,126C,187C ''/strong'':18C,87C,133C ''1'':14C ''2'':83C ''3'':129C ''accord'':122C ''answer'':186C ''appli'':166C ''approach'':147C ''appropri'':158C ''ask'':84C,93C ''attribut'':29C ''big'':56C ''branch'':63C,114C ''broad'':171C ''bug'':109C ''check'':35C ''check-in'':34C ''collect'':15C,70C ''commit'':65C ''committ'':112C ''data'':17C,26C,73C,149C,156C ''date'':32C ''deffect'':40C ''defin'':2C ''develop'':61C ''discuss'':174C ''effect'':96C ''enough'':25C ''estim'':102C ''etc'':41C ''extract'':151C ''featur'':153C ''first'':20C ''follow'':144C ''formul'':141C ''frequenc'':106C ''frequent'':64C ''gather'':24C,45C ''general'':146C ''get'':184C ''go'':78C,99C ''good'':4C,85C ''group'':119C ''inaccur'':111C ''mani'':62C ''mayb'':104C ''measur'':101C ''mention'':31C ''metric'':125C ''model'':132C,159C,162C ''much'':47C ''n'':38C ''name'':33C ''need'':152C ''next'':89C ''number'':59C ''p'':1C,12C,81C,127C ''pleas'':179C ''possibl'':49C,108C ''potenti'':43C ''predict'':105C ''problem'':21C ''process'':172C ''project'':57C ''question'':5C,86C,90C,140C ''readi'':76C ''right'':185C ''riski'':113C ''scienc'':150C ''see'':117C ''select'':130C,157C ''site'':182C ''soon'':51C,135C ''start'':69C ''strong'':13C,82C,128C ''test'':164C ''think'':7C ''thread'':177C ''title/comment'':37C ''track'':110C ''train'':160C ''use'':44C,180C ''users/bugs/commits'':121C ''want'':115C ''yes'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2545, 2, '2014-11-26 09:57:00.047', 1, NULL, NULL, '&lt;p&gt;As you are also looking for examples, then github is a good place to check out.&lt;/p&gt;

&lt;p&gt;I took a random repository and went to "Graphs" on the right hand side, which opens up &lt;a href="https://github.com/mbostock/d3/graphs/contributors" rel="nofollow"&gt;contribution frequency graph&lt;/a&gt;. There''s several tabs next to it that display other aspects of a repository and commit history graphically - commits, code frequency, punch card, etc.&lt;/p&gt;
', NULL, NULL, '''/a'':47C ''/mbostock/d3/graphs/contributors'':41C ''/p'':18C,72C ''also'':5C ''aspect'':58C ''card'':70C ''check'':16C ''code'':67C ''commit'':63C,66C ''contribut'':44C ''display'':56C ''etc'':71C ''exampl'':8C ''frequenc'':45C,68C ''github'':10C ''github.com'':40C ''github.com/mbostock/d3/graphs/contributors'':39C ''good'':13C ''graph'':28C,46C ''graphic'':65C ''hand'':32C ''histori'':64C ''href'':38C ''look'':6C ''next'':52C ''nofollow'':43C ''open'':35C ''p'':1C,19C ''place'':14C ''punch'':69C ''random'':23C ''rel'':42C ''repositori'':24C,61C ''right'':31C ''sever'':50C ''side'':33C ''tab'':51C ''took'':21C ''went'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2663, 2, '2014-12-09 22:33:38.64', 2, NULL, NULL, '&lt;p&gt;Gaussian mixture modeling can - if your data is nicely gaussian-like - be used for outlier detection. Points with a low density in &lt;em&gt;every&lt;/em&gt; cluster are likely to be outliers.&lt;/p&gt;

&lt;p&gt;Works well in idealistic scenarios.&lt;/p&gt;
', NULL, NULL, '''/em'':27C ''/p'':34C,41C ''cluster'':28C ''data'':8C ''densiti'':23C ''detect'':18C ''em'':25C ''everi'':26C ''gaussian'':2C,12C ''gaussian-lik'':11C ''idealist'':39C ''like'':13C,30C ''low'':22C ''mixtur'':3C ''model'':4C ''nice'':10C ''outlier'':17C,33C ''p'':1C,35C ''point'':19C ''scenario'':40C ''use'':15C ''well'':37C ''work'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2546, 2, '2014-11-26 11:18:13.227', 1, NULL, NULL, '&lt;p&gt;If you''re simply trying to separate textual and numeric information then there is a solution based on regular expressions or even just string splitting.&lt;/p&gt;

&lt;p&gt;You could even do something like finding the first numeric character and split the text in half right before that.&lt;/p&gt;

&lt;p&gt;With regular expressions you can match all numeric characters that follow eachother. The pattern would be &lt;code&gt;([0-9]+)&lt;/code&gt; with a global flag. It would match all the groups of numbers and you can do whatever you with with them afterwards.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.regextester.com/index.html" rel="nofollow"&gt;Regex Tester&lt;/a&gt; is good for playing around with that stuff.&lt;/p&gt;
', NULL, NULL, '''-9'':69C ''/a'':104C ''/code'':70C ''/index.html'':99C ''/p'':27C,49C,93C,113C ''0'':68C ''afterward'':92C ''around'':109C ''base'':18C ''charact'':39C,59C ''code'':67C ''could'':30C ''eachoth'':62C ''even'':23C,31C ''express'':21C,53C ''find'':35C ''first'':37C ''flag'':74C ''follow'':61C ''global'':73C ''good'':106C ''group'':80C ''half'':45C ''href'':96C ''inform'':12C ''like'':34C ''match'':56C,77C ''nofollow'':101C ''number'':82C ''numer'':11C,38C,58C ''p'':1C,28C,50C,94C ''pattern'':64C ''play'':108C ''re'':4C ''regex'':102C ''regular'':20C,52C ''rel'':100C ''right'':46C ''separ'':8C ''simpli'':5C ''solut'':17C ''someth'':33C ''split'':26C,41C ''string'':25C ''stuff'':112C ''tester'':103C ''text'':43C ''textual'':9C ''tri'':6C ''whatev'':87C ''would'':65C,76C ''www.regextester.com'':98C ''www.regextester.com/index.html'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5004, 2, '2015-01-31 01:18:10.307', 2, NULL, NULL, '&lt;p&gt;A standard trick is to estimate the logarithm of the desired quantity, then take its exponential, which is always positive. The drawback is that the error is optimized for the log, which treats differences in order of magnitude as equal. Another option is to do your regression as usual then project onto the feasible set (use the positive part of the output; $max(0, \cdot)$)&lt;/p&gt;
', NULL, NULL, '''/p'':67C ''0'':65C ''alway'':20C ''anoth'':42C ''cdot'':66C ''desir'':12C ''differ'':35C ''drawback'':23C ''equal'':41C ''error'':27C ''estim'':7C ''exponenti'':17C ''feasibl'':55C ''log'':32C ''logarithm'':9C ''magnitud'':39C ''max'':64C ''onto'':53C ''optim'':29C ''option'':43C ''order'':37C ''output'':63C ''p'':1C ''part'':60C ''posit'':21C,59C ''project'':52C ''quantiti'':13C ''regress'':48C ''set'':56C ''standard'':3C ''take'':15C ''treat'':34C ''trick'':4C ''use'':57C ''usual'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2547, 1, '2014-11-26 20:01:23.187', 2, 60, 'Statistical comparison of 2 small data sets for 2X increase in the population mean', '&lt;p&gt;I am trying to determine whether or not we are 90% confident that the mean of a &lt;em&gt;proposed&lt;/em&gt; &lt;em&gt;population&lt;/em&gt; is at least 2 times that of the mean of the &lt;em&gt;incumbant&lt;/em&gt; &lt;em&gt;population&lt;/em&gt; based on samples from each population which is all the data I have right now. Here are the data.&lt;/p&gt;

&lt;p&gt;incumbantvalues = (7.3, 8.4, 8.4, 8.5, 8.7, 9.1, 9.8, 11.0, 11.1, 11.9)&lt;/p&gt;

&lt;p&gt;proposedvalues =  (17.3, 17.9, 19.2, 20.3, 20.5, 20.6, 21.1, 21.2, 21.3, 21.7)&lt;/p&gt;

&lt;p&gt;I have no idea if either population is or will be normal.&lt;/p&gt;

&lt;p&gt;The ratio of the &lt;em&gt;sample&lt;/em&gt; means does exceed 2.0 but how does that translate to confidence that the proposed population mean will be at least twice that of the mean of the incumbant population with 90% confidence ?&lt;/p&gt;

&lt;p&gt;Can re-sampling (bootstrapping with replacement) help answer this question ?&lt;/p&gt;
', 'statistics  sampling', NULL, '''/em'':37C,40C,54C,57C,125C ''/p'':77C,90C,103C,117C,158C,171C ''11.0'':87C ''11.1'':88C ''11.9'':89C ''17.3'':93C ''17.9'':94C ''19.2'':95C ''2'':4A,44C ''2.0'':129C ''20.3'':96C ''20.5'':97C ''20.6'':98C ''21.1'':99C ''21.2'':100C ''21.3'':101C ''21.7'':102C ''2x'':9A ''7.3'':80C ''8.4'':81C,82C ''8.5'':83C ''8.7'':84C ''9.1'':85C ''9.8'':86C ''90'':28C,156C ''answer'':168C ''base'':58C ''bootstrap'':164C ''comparison'':2A ''confid'':29C,136C,157C ''data'':6A,68C,76C ''determin'':22C ''either'':110C ''em'':35C,38C,52C,55C,123C ''exceed'':128C ''help'':167C ''idea'':108C ''increas'':10A ''incumb'':53C,153C ''incumbantvalu'':79C ''least'':43C,145C ''mean'':14A,32C,49C,126C,141C,150C ''normal'':116C ''p'':17C,78C,91C,104C,118C,159C ''popul'':13A,39C,56C,63C,111C,140C,154C ''propos'':36C,139C ''proposedvalu'':92C ''question'':170C ''ratio'':120C ''re'':162C ''re-sampl'':161C ''replac'':166C ''right'':71C ''sampl'':16B,60C,124C,163C ''set'':7A ''small'':5A ''statist'':1A,15B ''time'':45C ''translat'':134C ''tri'':20C ''twice'':146C ''whether'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2558, 1, '2014-11-28 03:02:44.323', 2, 39, 'Smoothing Proportions :: Massive User Database', '&lt;p&gt;What are some possible techniques for smoothing proportions across very large categories, in order to take into account the sample size? The application of interest here is to use the proportions as input into a predictive model, but I am wary of using the raw proportions in cases where there is little evidence and I don''t want to overfit.&lt;/p&gt;

&lt;p&gt;Here is an example, where the ID denotes a customer and impressions and clicks are the number of ads shown and clicks the customer has made, respectively.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/3oHzQ.jpg" alt="enter image description here"&gt;&lt;/p&gt;
', 'machine-learning  predictive-modeling  feature-extraction', NULL, '''/3ohzq.jpg'':111C ''/p'':76C,105C,117C ''account'':33C ''across'':24C ''ad'':96C ''alt'':112C ''applic'':38C ''case'':63C ''categori'':27C ''click'':91C,99C ''custom'':87C,101C ''databas'':5A ''denot'':85C ''descript'':115C ''enter'':113C ''evid'':68C ''exampl'':81C ''extract'':14B ''featur'':13B ''feature-extract'':12B ''i.stack.imgur.com'':110C ''i.stack.imgur.com/3ohzq.jpg'':109C ''id'':84C ''imag'':114C ''img'':107C ''impress'':89C ''input'':48C ''interest'':40C ''larg'':26C ''learn'':8B ''littl'':67C ''machin'':7B ''machine-learn'':6B ''made'':103C ''massiv'':3A ''model'':11B,52C ''number'':94C ''order'':29C ''overfit'':75C ''p'':15C,77C,106C ''possibl'':19C ''predict'':10B,51C ''predictive-model'':9B ''proport'':2A,23C,46C,61C ''raw'':60C ''respect'':104C ''sampl'':35C ''shown'':97C ''size'':36C ''smooth'':1A,22C ''src'':108C ''take'':31C ''techniqu'':20C ''use'':44C,58C ''user'':4A ''want'':73C ''wari'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2549, 2, '2014-11-26 21:20:52.973', 3, NULL, NULL, '&lt;p&gt;Without a doubt you can. The key is to have a set of hypotheses (i.e. assumptions \ scenarios that you want to evaluate) and wrangle the data together to prove \ disprove what you thought is true. &lt;/p&gt;

&lt;p&gt;Here are a few things to watch-out for:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Be ready for Disappointments:&lt;/strong&gt; Often times, once you have invested time and energy in building these models, analysts tend to get biased towards publishing results (publication bias). Treat this as an exploration that with a lot of dead-ends and the goal should be to find the ones that are not.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Know your Data:&lt;/strong&gt; You cannot will your data into doing things magically without truly understanding it. Ensure that you know the different attributes (predictors and dependents) very well. Knowing your data well will allow you to cleanse it and think about appropriate models. All models don''t work equally well on all data - data that has a lot of categorical variables might require creative solutions like Dimension reduction before it can be modeled.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Know the "Operational" Processes:&lt;/strong&gt; Knowing how things operate within your firm will help you refine the set of hypothesis that you want to test. For e.g. in your scenario above, knowing how developers work with your change management software and what types of administrative setups have been done will help you figure out why the data is coming in the way it is. Some developers might only be focused on certain modules that are more mature than others, might work only on certain shifts and that might limit how many lines of code are checked in, how many bugs are found etc.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Having said that here are some scenarios you might want to test:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Developer Effectiveness : 
How different developers working on same modules overtime has resulted into increase or decrease in bugs. 
Does more line of code results in more bugs? Maybe this might be an indicator that the programs need to be split further into smaller components
Folks might be more productive during certain times of day than others - does time of day affect bug introductions?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Module Maturity: 
Which Modules have the most number of issues? 
Are they worked upon by more developers or less?
Do defects keep aging for a long time before they are fixed?&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Of course, these questions will change depending on what you are working on. &lt;/p&gt;

&lt;p&gt;Hope this helps.&lt;/p&gt;
', NULL, NULL, '''/li'':108C,187C,295C,378C,413C ''/p'':37C,49C,107C,186C,294C,310C,377C,412C,429C,434C ''/strong'':58C,115C,195C ''/ul'':296C,414C ''administr'':235C ''affect'':374C ''age'':403C ''allow'':146C ''analyst'':72C ''appropri'':154C ''assumpt'':17C ''attribut'':135C ''bias'':76C,81C ''bug'':290C,331C,340C,375C ''build'':69C ''cannot'':117C ''categor'':172C ''certain'':262C,274C,364C ''chang'':228C,421C ''check'':286C ''cleans'':149C ''code'':284C,336C ''come'':249C ''compon'':357C ''cours'':417C ''creativ'':176C ''data'':27C,114C,120C,143C,165C,166C,247C ''day'':367C,373C ''dead'':93C ''dead-end'':92C ''decreas'':329C ''defect'':401C ''depend'':138C,422C ''develop'':224C,256C,314C,318C,397C ''differ'':134C,317C ''dimens'':179C ''disappoint'':57C ''disprov'':31C ''done'':239C ''doubt'':4C ''e.g'':217C ''effect'':315C ''end'':94C ''energi'':67C ''ensur'':129C ''equal'':161C ''etc'':293C ''evalu'':23C ''explor'':86C ''figur'':243C ''find'':101C ''firm'':202C ''fix'':411C ''focus'':260C ''folk'':358C ''found'':292C ''get'':75C ''goal'':97C ''help'':204C,241C,433C ''hope'':431C ''hypothes'':15C ''hypothesi'':210C ''i.e'':16C ''increas'':327C ''indic'':346C ''introduct'':376C ''invest'':64C ''issu'':390C ''keep'':402C ''key'':8C ''know'':112C,132C,141C,191C,196C,222C ''less'':399C ''li'':51C,109C,188C,312C,379C ''like'':178C ''limit'':279C ''line'':282C,334C ''long'':406C ''lot'':90C,170C ''magic'':124C ''manag'':229C ''mani'':281C,289C ''matur'':267C,382C ''mayb'':341C ''might'':174C,257C,270C,278C,306C,343C,359C ''model'':71C,155C,157C,185C ''modul'':263C,322C,381C,384C ''need'':350C ''number'':388C ''often'':59C ''one'':103C ''oper'':193C,199C ''other'':269C,369C ''overtim'':323C ''p'':1C,38C,52C,110C,189C,297C,313C,380C,415C,430C ''predictor'':136C ''process'':194C ''product'':362C ''program'':349C ''prove'':30C ''public'':80C ''publish'':78C ''question'':419C ''readi'':55C ''reduct'':180C ''refin'':206C ''requir'':175C ''result'':79C,325C,337C ''said'':299C ''scenario'':18C,220C,304C ''set'':13C,208C ''setup'':236C ''shift'':275C ''smaller'':356C ''softwar'':230C ''solut'':177C ''split'':353C ''strong'':53C,111C,190C ''tend'':73C ''test'':215C,309C ''thing'':43C,123C,198C ''think'':152C ''thought'':34C ''time'':60C,65C,365C,371C,407C ''togeth'':28C ''toward'':77C ''treat'':82C ''true'':36C ''truli'':126C ''type'':233C ''ul'':50C,311C ''understand'':127C ''upon'':394C ''variabl'':173C ''want'':21C,213C,307C ''watch'':46C ''watch-out'':45C ''way'':252C ''well'':140C,144C,162C ''within'':200C ''without'':2C,125C ''work'':160C,225C,271C,319C,393C,427C ''wrangl'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2578, 1, '2014-12-02 10:58:30.95', 1, 177, 'Graphlab vs Mahout', '&lt;p&gt;I have some question regarding to the choice of the better implementation. I would know the differences and advantages of &lt;a href="https://mahout.apache.org/" rel="nofollow"&gt;Mahout Apache&lt;/a&gt; (Java implementation) versus &lt;a href="http://graphlab.com/index.html" rel="nofollow"&gt;Graphlab&lt;/a&gt; (Python implementation) in the area of the data sciences. Specially in the area of recommenders and classifiers. Can anybody here get some (qualified) feedback about both possibilities?&lt;/p&gt;
', 'bigdata  classification  python  recommendation  java', NULL, '''/a'':37C,49C ''/index.html'':45C ''/p'':77C ''advantag'':28C ''anybodi'':68C ''apach'':36C ''area'':54C,62C ''better'':20C ''bigdata'':4B ''choic'':17C ''classif'':5B ''classifi'':66C ''data'':57C ''differ'':26C ''feedback'':73C ''get'':70C ''graphlab'':1A,48C ''graphlab.com'':44C ''graphlab.com/index.html'':43C ''href'':31C,42C ''implement'':21C,39C,51C ''java'':8B,38C ''know'':24C ''mahout'':3A,35C ''mahout.apache.org'':32C ''nofollow'':34C,47C ''p'':9C ''possibl'':76C ''python'':6B,50C ''qualifi'':72C ''question'':13C ''recommend'':7B,64C ''regard'':14C ''rel'':33C,46C ''scienc'':58C ''special'':59C ''versus'':40C ''vs'':2A ''would'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2550, 1, '2014-11-27 03:21:11.11', 3, 102, 'How to ensemble classifier incorporating all features in python?', '&lt;p&gt;I am doing a text classification task(5000 essays evenly distributed by 10 labels). I explored &lt;code&gt;LinearSVC&lt;/code&gt; and got an accuracy of 80%. Now I guess whether accuracy could be raised by using &lt;code&gt;ensemble&lt;/code&gt; classifier with &lt;code&gt;SVM&lt;/code&gt; as base estimator?&lt;/p&gt;

&lt;p&gt;However, I do not know how to employ an &lt;code&gt;ensemble&lt;/code&gt; classifier incorporating all the features? Please note that I do not want to combine the different features directly in a single vector.&lt;/p&gt;

&lt;p&gt;Therefore, &lt;strong&gt;My first question:&lt;/strong&gt; in order to improve the current accuracy, is it possible to use &lt;code&gt;ensemble&lt;/code&gt; classifier with &lt;code&gt;svm&lt;/code&gt; as base estimator?
&lt;strong&gt;My second question&lt;/strong&gt; How to employ an &lt;code&gt;ensemble&lt;/code&gt; classifier incorporating all features?&lt;/p&gt;
', 'machine-learning  python  nlp  sklearn  ensemble-modeling', NULL, '''/code'':38C,57C,62C,79C,124C,129C,144C ''/p'':66C,102C,149C ''/strong'':109C,137C ''10'':32C ''5000'':27C ''80'':44C ''accuraci'':42C,49C,116C ''base'':64C,131C ''classif'':25C ''classifi'':4A,58C,80C,125C,145C ''code'':36C,55C,60C,77C,122C,127C,142C ''combin'':93C ''could'':50C ''current'':115C ''differ'':95C ''direct'':97C ''distribut'':30C ''employ'':75C,140C ''ensembl'':3A,17B,56C,78C,123C,143C ''ensemble-model'':16B ''essay'':28C ''estim'':65C,132C ''even'':29C ''explor'':35C ''featur'':7A,84C,96C,148C ''first'':107C ''got'':40C ''guess'':47C ''howev'':68C ''improv'':113C ''incorpor'':5A,81C,146C ''know'':72C ''label'':33C ''learn'':12B ''linearsvc'':37C ''machin'':11B ''machine-learn'':10B ''model'':18B ''nlp'':14B ''note'':86C ''order'':111C ''p'':19C,67C,103C ''pleas'':85C ''possibl'':119C ''python'':9A,13B ''question'':108C,136C ''rais'':52C ''second'':135C ''singl'':100C ''sklearn'':15B ''strong'':105C,133C ''svm'':61C,128C ''task'':26C ''text'':24C ''therefor'':104C ''use'':54C,121C ''vector'':101C ''want'':91C ''whether'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2551, 2, '2014-11-27 03:27:46.193', 0, NULL, NULL, '&lt;p&gt;If these data are available in the actual excel spreadsheet cells (ie, before you export them to the JSON format provided in your question), you can use the following to get them into R:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;highlight the region of interest within excel&lt;/li&gt;
&lt;li&gt;copy it to the clipboard (eg. Ctrl-C)&lt;/li&gt;
&lt;li&gt;&lt;p&gt;At an R prompt type:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;d &amp;lt;- read.delim(''clipboard'')
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The data will now be available as a data.frame in R. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;d
  from response value
1    4     TRUE    20
2    8     TRUE    20
3    9     TRUE    20
4    3     TRUE    20
5   14    FALSE    20
6   15     TRUE    20
7   17    FALSE    20
8   13     TRUE    20  
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':72C,127C ''/li'':46C,57C,74C ''/ol'':75C ''/p'':36C,65C,88C ''/pre'':73C,128C ''1'':95C ''13'':124C ''14'':112C ''15'':116C ''17'':120C ''2'':99C ''20'':98C,102C,106C,110C,114C,118C,122C,126C ''3'':103C,108C ''4'':96C,107C ''5'':111C ''6'':115C ''7'':119C ''8'':100C,123C ''9'':104C ''actual'':9C ''avail'':6C,82C ''c'':56C ''cell'':12C ''clipboard'':52C,71C ''code'':67C,90C ''copi'':48C ''ctrl'':55C ''ctrl-c'':54C ''d'':68C,91C ''data'':4C,78C ''data.frame'':85C ''eg'':53C ''excel'':10C,45C ''export'':16C ''fals'':113C,121C ''follow'':30C ''format'':21C ''get'':32C ''highlight'':39C ''ie'':13C ''interest'':43C ''json'':20C ''li'':38C,47C,58C ''lt'':69C ''ol'':37C ''p'':1C,59C,76C ''pre'':66C,89C ''prompt'':63C ''provid'':22C ''question'':25C ''r'':35C,62C,87C ''read.delim'':70C ''region'':41C ''respons'':93C ''spreadsheet'':11C ''true'':97C,101C,105C,109C,117C,125C ''type'':64C ''use'':28C ''valu'':94C ''within'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2569, 2, '2014-11-30 20:01:23.93', 5, NULL, NULL, '&lt;p&gt;Robert is right, multinomial logistic regression is the best tool to use. Although you would need to have a integer value representing the party as the dependent variable, for example:&lt;/p&gt;

&lt;p&gt;1= Conservative majority, 2= Labour majority, 3= Liberal majority....(and so on)&lt;/p&gt;

&lt;p&gt;You can perform this in R using the nnet package. Here is a good place to quickly run through how to use it: &lt;a href="http://www.ats.ucla.edu/stat/r/dae/mlogit.htm"&gt;http://www.ats.ucla.edu/stat/r/dae/mlogit.htm&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Hope this helps.&lt;/p&gt;
', NULL, NULL, '''/p'':32C,46C,83C ''/stat/r/dae/mlogit.htm'':75C ''/stat/r/dae/mlogit.htm&lt;/a&gt;&lt;/p&gt;'':78C ''1'':34C ''2'':37C ''3'':40C ''although'':14C ''best'':10C ''conserv'':35C ''depend'':28C ''exampl'':31C ''good'':61C ''help'':82C ''hope'':80C ''href'':72C ''integ'':21C ''labour'':38C ''liber'':41C ''logist'':6C ''major'':36C,39C,42C ''multinomi'':5C ''need'':17C ''nnet'':56C ''p'':1C,33C,47C,79C ''packag'':57C ''parti'':25C ''perform'':50C ''place'':62C ''quick'':64C ''r'':53C ''regress'':7C ''repres'':23C ''right'':4C ''robert'':2C ''run'':65C ''tool'':11C ''use'':13C,54C,69C ''valu'':22C ''variabl'':29C ''would'':16C ''www.ats.ucla.edu'':74C,77C ''www.ats.ucla.edu/stat/r/dae/mlogit.htm'':73C ''www.ats.ucla.edu/stat/r/dae/mlogit.htm&lt;/a&gt;&lt;/p&gt;'':76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2552, 2, '2014-11-27 03:37:08.01', 1, NULL, NULL, '&lt;p&gt;The standard way to evaluate a recommendation engine is by using the &lt;strong&gt;&lt;a href="http://en.wikipedia.org/wiki/Root-mean-square_deviation" rel="nofollow"&gt;RMSE (root mean square error)&lt;/a&gt;&lt;/strong&gt; of the predicted values and the ground truth.&lt;/p&gt;

&lt;p&gt;It is almost a SOP that, after finishing developing a recommendation engine, we will evaluate this engine by comparing its RMSE with other famous, common recommendation algorithms like &lt;strong&gt;&lt;a href="http://en.wikipedia.org/wiki/Singular_value_decomposition" rel="nofollow"&gt;SVD&lt;/a&gt;&lt;/strong&gt;, &lt;strong&gt;&lt;a href="http://en.wikipedia.org/wiki/Collaborative_filtering" rel="nofollow"&gt;tranditional CF&lt;/a&gt;&lt;/strong&gt;, even &lt;a href="http://en.wikipedia.org/wiki/Restricted_Boltzmann_machine" rel="nofollow"&gt;RBM&lt;/a&gt;, etc.&lt;/p&gt;

&lt;p&gt;Some terms mentioned above do not seem to be related with recommendation, but you can easily find on the internet how these techniques can be used in this topic.&lt;/p&gt;
', NULL, NULL, '''/a'':27C,76C,88C,99C ''/p'':37C,101C,132C ''/strong'':28C,77C,89C ''/wiki/collaborative_filtering'':83C ''/wiki/restricted_boltzmann_machine'':95C ''/wiki/root-mean-square_deviation'':19C ''/wiki/singular_value_decomposition'':72C ''algorithm'':65C ''almost'':41C ''cf'':87C ''common'':63C ''compar'':57C ''develop'':47C ''easili'':118C ''en.wikipedia.org'':18C,71C,82C,94C ''en.wikipedia.org/wiki/collaborative_filtering'':81C ''en.wikipedia.org/wiki/restricted_boltzmann_machine'':93C ''en.wikipedia.org/wiki/root-mean-square_deviation'':17C ''en.wikipedia.org/wiki/singular_value_decomposition'':70C ''engin'':9C,50C,55C ''error'':26C ''etc'':100C ''evalu'':6C,53C ''even'':90C ''famous'':62C ''find'':119C ''finish'':46C ''ground'':35C ''href'':16C,69C,80C,92C ''internet'':122C ''like'':66C ''mean'':24C ''mention'':105C ''nofollow'':21C,74C,85C,97C ''p'':1C,38C,102C ''predict'':31C ''rbm'':98C ''recommend'':8C,49C,64C,114C ''rel'':20C,73C,84C,96C ''relat'':112C ''rmse'':22C,59C ''root'':23C ''seem'':109C ''sop'':43C ''squar'':25C ''standard'':3C ''strong'':14C,67C,78C ''svd'':75C ''techniqu'':125C ''term'':104C ''topic'':131C ''trandit'':86C ''truth'':36C ''use'':12C,128C ''valu'':32C ''way'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5018, 2, '2015-02-02 17:00:51.307', 2, NULL, NULL, '&lt;p&gt;Yes, you can execute multiple HQL''s using Hue as long as each individual HQL is separated by a semi colon (;)&lt;/p&gt;
', NULL, NULL, '''/p'':23C ''colon'':22C ''execut'':5C ''hql'':7C,16C ''hue'':10C ''individu'':15C ''long'':12C ''multipl'':6C ''p'':1C ''semi'':21C ''separ'':18C ''use'':9C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2553, 2, '2014-11-27 04:23:31.5', 2, NULL, NULL, '&lt;p&gt;Yes, in principle, resampling can help answer this question. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;incumbent &amp;lt;- c(7.3, 8.4, 8.4, 8.5, 8.7, 9.1, 9.8, 11.0, 11.1, 11.9)
proposed  &amp;lt;- c(17.3, 17.9, 19.2, 20.3, 20.5, 20.6, 21.1, 21.2, 21.3, 21.7)

set.seed(42)

M  &amp;lt;- 2000
rs &amp;lt;- double(M)

for (i in 1:M) {
    rs[i] &amp;lt;- mean(sample(proposed, replace=T)) - 2 * mean(sample(incumbent, replace=T))
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;To make the assessment, you should choose &lt;em&gt;one&lt;/em&gt; (not both) of the following:  &lt;/p&gt;

&lt;p&gt;A. The (two-tailed) 90% confidence interval for the difference in the (weighted) means using Hall''s method is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ci.hall &amp;lt;- 2 * (mean(proposed)-2*mean(incumbent)) - rev(quantile(rs,prob=c(0.05, 0.95)))
names(ci.hall) &amp;lt;- rev(names(ci.hall))
ci.hall

   5%   95% 
-0.29  2.95 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This is appropriate if you have any concern about missing the possibility that mean(proposed) might actually be less than 2 * mean(incumbent).&lt;/p&gt;

&lt;p&gt;B. The proportion of resample means &gt;= 0 provides the (one-tailed) estimate that mean(proposed) is at least twice mean(incumbent):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;sum(rs&amp;gt;=0)/M

[1] 0.8915
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The problem is that the samples are really rather small and resampling estimates
can be unstable for small n. The same issue applies if you want to assess normality and go with parametric comparisons. &lt;/p&gt;

&lt;p&gt;If you can get to, say, n &gt;= 30, the approach described here should be fine.&lt;/p&gt;
', NULL, NULL, '''-0.29'':135C ''-2'':116C ''/code'':68C,137C,197C ''/em'':80C ''/m'':194C ''/p'':11C,86C,108C,163C,187C,234C,251C ''/pre'':69C,138C,198C ''0'':171C,193C ''0.05'':124C ''0.8915'':196C ''0.95'':125C ''1'':52C,195C ''11.0'':24C ''11.1'':25C ''11.9'':26C ''17.3'':30C ''17.9'':31C ''19.2'':32C ''2'':62C,113C,160C ''2.95'':136C ''20.3'':33C ''20.5'':34C ''20.6'':35C ''2000'':44C ''21.1'':36C ''21.2'':37C ''21.3'':38C ''21.7'':39C ''30'':243C ''42'':41C ''5'':133C ''7.3'':17C ''8.4'':18C,19C ''8.5'':20C ''8.7'':21C ''9.1'':22C ''9.8'':23C ''90'':93C ''95'':134C ''actual'':156C ''answer'':8C ''appli'':222C ''approach'':245C ''appropri'':142C ''assess'':74C,227C ''b'':165C ''c'':16C,29C,123C ''choos'':77C ''ci.hall'':111C,127C,131C,132C ''code'':13C,110C,189C ''comparison'':233C ''concern'':147C ''confid'':94C ''describ'':246C ''differ'':98C ''doubl'':47C ''em'':78C ''estim'':177C,212C ''fine'':250C ''follow'':85C ''get'':239C ''go'':230C ''gt'':192C ''hall'':104C ''help'':7C ''incumb'':14C,65C,118C,162C,186C ''interv'':95C ''issu'':221C ''least'':183C ''less'':158C ''lt'':15C,28C,43C,46C,56C,112C,128C ''m'':42C,48C,53C ''make'':72C ''mean'':57C,63C,102C,114C,117C,153C,161C,170C,179C,185C ''method'':106C ''might'':155C ''miss'':149C ''n'':218C,242C ''name'':126C,130C ''normal'':228C ''one'':79C,175C ''one-tail'':174C ''p'':1C,70C,87C,139C,164C,199C,235C ''parametr'':232C ''possibl'':151C ''pre'':12C,109C,188C ''principl'':4C ''prob'':122C ''problem'':201C ''proport'':167C ''propos'':27C,59C,115C,154C,180C ''provid'':172C ''quantil'':120C ''question'':10C ''rather'':208C ''realli'':207C ''replac'':60C,66C ''resampl'':5C,169C,211C ''rev'':119C,129C ''rs'':45C,54C,121C,191C ''sampl'':58C,64C,205C ''say'':241C ''set.seed'':40C ''small'':209C,217C ''sum'':190C ''tail'':92C,176C ''twice'':184C ''two'':91C ''two-tail'':90C ''unstabl'':215C ''use'':103C ''want'':225C ''weight'':101C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2555, 2, '2014-11-27 07:21:56.277', 0, NULL, NULL, '&lt;p&gt;Check these :&lt;/p&gt;

&lt;p&gt;Repository of Test Domains for Information Extraction : &lt;a href="http://www.isi.edu/info-agents/RISE/repository.html" rel="nofollow"&gt;http://www.isi.edu/info-agents/RISE/repository.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;DBpedia : &lt;a href="http://wiki.dbpedia.org/Downloads32" rel="nofollow"&gt;http://wiki.dbpedia.org/Downloads32&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/downloads32'':29C ''/downloads32&lt;/a&gt;&lt;/p&gt;'':34C ''/info-agents/rise/repository.html'':17C ''/info-agents/rise/repository.html&lt;/a&gt;&lt;/p&gt;'':22C ''/p'':4C ''check'':2C ''dbpedia'':24C ''domain'':9C ''extract'':12C ''href'':14C,26C ''inform'':11C ''nofollow'':19C,31C ''p'':1C,5C,23C ''rel'':18C,30C ''repositori'':6C ''test'':8C ''wiki.dbpedia.org'':28C,33C ''wiki.dbpedia.org/downloads32'':27C ''wiki.dbpedia.org/downloads32&lt;/a&gt;&lt;/p&gt;'':32C ''www.isi.edu'':16C,21C ''www.isi.edu/info-agents/rise/repository.html'':15C ''www.isi.edu/info-agents/rise/repository.html&lt;/a&gt;&lt;/p&gt;'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2557, 2, '2014-11-27 21:58:15.113', 0, NULL, NULL, '&lt;p&gt;Here is what I programmed within a loop.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;randomly take 10 values (with replacement) from the incumbant sample, determine its mean&lt;/li&gt;
&lt;li&gt;randomly take 10 values (with replacement) from the proposed sample, determine its mean&lt;/li&gt;
&lt;li&gt;form the ratio of the above two means and append it to a master list&lt;/li&gt;
&lt;li&gt;repeat steps 1 thru 3 many times (I chose 1 million)&lt;/li&gt;
&lt;li&gt;% Confidence=(number of ratios that equal or exceed 2.0/1000000)*100&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Results:
Exactly 897450 ratios were found to be greater than or equal to 2.0, producing a confidence of 89.745%.&lt;/p&gt;

&lt;p&gt;Conclusion:
We are less than 90% confident that the proposed population will have a mean at least twice that of the incumbant population.&lt;/p&gt;
', NULL, NULL, '''/1000000'':82C ''/li'':26C,41C,58C,71C,84C ''/ol'':85C ''/p'':10C,106C,131C ''1'':62C,69C ''10'':15C,30C ''100'':83C ''2.0'':81C,100C ''3'':64C ''89.745'':105C ''897450'':89C ''90'':113C ''append'':52C ''chose'':68C ''conclus'':108C ''confid'':73C,103C,114C ''determin'':23C,38C ''equal'':78C,98C ''exact'':88C ''exceed'':80C ''form'':43C ''found'':92C ''greater'':95C ''incumb'':21C,129C ''least'':124C ''less'':111C ''li'':12C,27C,42C,59C,72C ''list'':57C ''loop'':9C ''mani'':65C ''master'':56C ''mean'':25C,40C,50C,122C ''million'':70C ''number'':74C ''ol'':11C ''p'':1C,86C,107C ''popul'':118C,130C ''produc'':101C ''program'':6C ''propos'':36C,117C ''random'':13C,28C ''ratio'':45C,76C,90C ''repeat'':60C ''replac'':18C,33C ''result'':87C ''sampl'':22C,37C ''step'':61C ''take'':14C,29C ''thru'':63C ''time'':66C ''twice'':125C ''two'':49C ''valu'':16C,31C ''within'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2568, 1, '2014-11-30 00:42:28.237', -1, 417, 'Sentiment analysis using python', '&lt;p&gt;I have some text files containing moview reviews I need to find out whether the review is good or bad. I tried the following code but its not working:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import nltk
with open("c:/users/user/desktop/datascience/moviesr/movies-1-32.txt", ''r'') as m11:
    mov_rev = m11.read()
mov_review1=nltk.word_tokenize(mov_rev)
bon="crap aweful horrible terrible bad bland trite sucks unpleasant boring dull moronic dreadful disgusting distasteful flawed ordinary slow senseless unoriginal weak wacky uninteresting unpretentious "
bag_of_negative_words=nltk.word_tokenize(bon)
bop="Absorbing Big-Budget Brilliant Brutal Charismatic Charming Clever Comical Dazzling Dramatic Enjoyable Entertaining Excellent Exciting  Expensive Fascinating Fast-Moving First-Rate Funny Highly-Charged Hilarious Imaginative Insightful Inspirational Intriguing Juvenile Lasting Legendary Pleasant Powerful Ripping Riveting Romantic Sad  Satirical Sensitive  Sentimental Surprising Suspenseful Tender Thought Provoking Tragic Uplifting Uproarious"
bop.lower()
bag_of_positive_words=nltk.word_tokenize(bop)
vec=[]
for i in bag_of_negative_words:
    if i in mov_review1:
        vec.append(1)
    else:
        for w in bag_of_positive_words:
            if w in moview_review1:
                vec.append(5)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;so i am trying to check whether the review contains a positive word or a negative word. If it contains negative word then a value 1 will be assigned to the vector vec else a value of 5 will be assigned.
but the output i am getting is an empty vector.&lt;/p&gt;

&lt;p&gt;please help..
Also please suggest others way of solving this problem.
thanks in advance&lt;/p&gt;
', 'python  nlp', NULL, '''..'':240C ''/code'':182C ''/p'':37C,236C,253C ''/pre'':183C ''/users/user/desktop/datascience/moviesr/movies-1-32.txt'':45C ''1'':166C,210C ''5'':181C,222C ''absorb'':91C ''advanc'':252C ''also'':241C ''analysi'':2A ''assign'':213C,225C ''awe'':60C ''bad'':27C,63C ''bag'':83C,145C,156C,171C ''big'':93C ''big-budget'':92C ''bland'':64C ''bon'':58C,89C ''bop'':90C,151C ''bop.lower'':144C ''bore'':68C ''brilliant'':95C ''brutal'':96C ''budget'':94C ''c'':44C ''charg'':118C ''charismat'':97C ''charm'':98C ''check'':190C ''clever'':99C ''code'':32C,39C ''comic'':100C ''contain'':13C,194C,204C ''crap'':59C ''dazzl'':101C ''disgust'':72C ''distast'':73C ''dramat'':102C ''dread'':71C ''dull'':69C ''els'':167C,218C ''empti'':234C ''enjoy'':103C ''entertain'':104C ''excel'':105C ''excit'':106C ''expens'':107C ''fascin'':108C ''fast'':110C ''fast-mov'':109C ''file'':12C ''find'':19C ''first'':113C ''first-rat'':112C ''flaw'':74C ''follow'':31C ''funni'':115C ''get'':231C ''good'':25C ''help'':239C ''high'':117C ''highly-charg'':116C ''hilari'':119C ''horribl'':61C ''imagin'':120C ''import'':40C ''insight'':121C ''inspir'':122C ''intrigu'':123C ''juvenil'':124C ''last'':125C ''legendari'':126C ''m11'':48C ''m11.read'':51C ''moron'':70C ''mov'':49C,52C,56C,163C ''move'':111C ''moview'':14C,178C ''need'':17C ''negat'':85C,158C,200C,205C ''nlp'':6B ''nltk'':41C ''nltk.word'':54C,87C,149C ''open'':43C ''ordinari'':75C ''other'':244C ''output'':228C ''p'':7C,184C,237C ''pleas'':238C,242C ''pleasant'':127C ''posit'':147C,173C,196C ''power'':128C ''pre'':38C ''problem'':249C ''provok'':140C ''python'':4A,5B ''r'':46C ''rate'':114C ''rev'':50C,57C ''review'':15C,23C,193C ''review1'':53C,164C,179C ''rip'':129C ''rivet'':130C ''romant'':131C ''sad'':132C ''satir'':133C ''senseless'':77C ''sensit'':134C ''sentiment'':1A,135C ''slow'':76C ''solv'':247C ''suck'':66C ''suggest'':243C ''surpris'':136C ''suspens'':137C ''tender'':138C ''terribl'':62C ''text'':11C ''thank'':250C ''thought'':139C ''token'':55C,88C,150C ''tragic'':141C ''tri'':29C,188C ''trite'':65C ''uninterest'':81C ''unorigin'':78C ''unpleas'':67C ''unpretenti'':82C ''uplift'':142C ''uproari'':143C ''use'':3A ''valu'':209C,220C ''vec'':152C,217C ''vec.append'':165C,180C ''vector'':216C,235C ''w'':169C,176C ''wacki'':80C ''way'':245C ''weak'':79C ''whether'':21C,191C ''word'':86C,148C,159C,174C,197C,201C,206C ''work'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2562, 2, '2014-11-28 08:55:25.813', 2, NULL, NULL, '&lt;p&gt;I don''t know if you ever read SenseCluster by Ted Pedersen : &lt;a href="http://senseclusters.sourceforge.net/" rel="nofollow"&gt;http://senseclusters.sourceforge.net/&lt;/a&gt;. Very good paper for sense clustering. &lt;/p&gt;

&lt;p&gt;Also, when you analyze words, think that "computer", "computers", "computering", ... represent one concept, so only one feature. Very important for a correct analysis.&lt;/p&gt;

&lt;p&gt;To speak about the clustering algorithm, you could use a &lt;a href="http://en.wikipedia.org/wiki/Hierarchical_clustering" rel="nofollow"&gt;hierarchical clustering&lt;/a&gt;. At each step of the algo, you merge the 2 most similar texts according to their features (using a measure of dissimilarity, euclidean distance for example). With that measure of dissimilarity, you are able to find the best number of clusters and so, the best clustering for your texts and articles.&lt;/p&gt;

&lt;p&gt;Good luck :)&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;.'':21C ''/a'':74C ''/p'':28C,53C,126C,130C ''/wiki/hierarchical_clustering'':69C ''2'':84C ''abl'':108C ''accord'':88C ''algo'':80C ''algorithm'':60C ''also'':30C ''analysi'':52C ''analyz'':33C ''articl'':125C ''best'':112C,119C ''cluster'':27C,59C,73C,115C,120C ''comput'':37C,38C,39C ''concept'':42C ''correct'':51C ''could'':62C ''dissimilar'':96C,105C ''distanc'':98C ''en.wikipedia.org'':68C ''en.wikipedia.org/wiki/hierarchical_clustering'':67C ''euclidean'':97C ''ever'':8C ''exampl'':100C ''featur'':46C,91C ''find'':110C ''good'':23C,128C ''hierarch'':72C ''href'':15C,66C ''import'':48C ''know'':5C ''luck'':129C ''measur'':94C,103C ''merg'':82C ''nofollow'':18C,71C ''number'':113C ''one'':41C,45C ''p'':1C,29C,54C,127C ''paper'':24C ''pedersen'':13C ''read'':9C ''rel'':17C,70C ''repres'':40C ''sens'':26C ''senseclust'':10C ''senseclusters.sourceforge.net'':16C,20C ''senseclusters.sourceforge.net/&lt;/a&gt;.'':19C ''similar'':86C ''speak'':56C ''step'':77C ''ted'':12C ''text'':87C,123C ''think'':35C ''use'':63C,92C ''word'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2563, 1, '2014-11-28 09:19:32.867', 2, 65, 'Visualization of three-dimensional report', '&lt;p&gt;I have a visualization problem.&lt;/p&gt;

&lt;p&gt;Creating a comparison report of PR event efficiency. Say, show or exhibition.&lt;/p&gt;

&lt;p&gt;There are two dimensions of comparison:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;compare vs the same event performance in the past years &lt;/li&gt;
&lt;li&gt;compare vs another type of analogical/competitive events &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;There is also a number of comparison aspects:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Audience&lt;/li&gt;
&lt;li&gt;Media Coverage&lt;/li&gt;
&lt;li&gt;Social Buzz&lt;/li&gt;
&lt;li&gt;ROI&lt;/li&gt;
&lt;li&gt;.... etc&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Each aspect is a set of some final KPI-s (just numbers, which can be compared vs another "dimensions"), plus maybe some descriptive text and pictures (which couldn''t be a metric but should be attached to the report).&lt;/p&gt;

&lt;p&gt;So finaly it looks like a three-dimensional coube:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Years&lt;/li&gt;
&lt;li&gt;Another Events&lt;/li&gt;
&lt;li&gt;Aspects&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;If I put it in plain Word or PPT it will look like a document with dozen of slides/papers and linear structure.&lt;/p&gt;

&lt;p&gt;Any ideas how to compile an elegant user-friendly report?&lt;/p&gt;
', 'marketing  infographics  visualization', NULL, '''/li'':51C,60C,75C,79C,83C,86C,89C,148C,152C,155C ''/ol'':156C ''/p'':16C,30C,38C,71C,132C,144C,180C,193C ''/ul'':61C,90C ''also'':65C ''analogical/competitive'':58C ''anoth'':55C,110C,150C ''aspect'':70C,93C,154C ''attach'':128C ''audienc'':74C ''buzz'':82C ''compar'':41C,53C,108C ''comparison'':20C,37C,69C ''compil'':186C ''coub'':143C ''couldn'':120C ''coverag'':78C ''creat'':18C ''descript'':115C ''dimens'':35C,111C ''dimension'':5A,142C ''document'':172C ''dozen'':174C ''effici'':25C ''eleg'':188C ''etc'':88C ''event'':24C,45C,59C,151C ''exhibit'':29C ''final'':99C ''finali'':135C ''friend'':191C ''idea'':183C ''infograph'':8B ''kpi'':101C ''kpi-'':100C ''li'':40C,52C,73C,76C,80C,84C,87C,146C,149C,153C ''like'':138C,170C ''linear'':178C ''look'':137C,169C ''market'':7B ''mayb'':113C ''media'':77C ''metric'':124C ''number'':67C,104C ''ol'':145C ''p'':10C,17C,31C,62C,91C,133C,157C,181C ''past'':49C ''perform'':46C ''pictur'':118C ''plain'':163C ''plus'':112C ''ppt'':166C ''pr'':23C ''problem'':15C ''put'':160C ''report'':6A,21C,131C,192C ''roi'':85C ''say'':26C ''set'':96C ''show'':27C ''slides/papers'':176C ''social'':81C ''structur'':179C ''text'':116C ''three'':4A,141C ''three-dimension'':3A,140C ''two'':34C ''type'':56C ''ul'':39C,72C ''user'':190C ''user-friend'':189C ''visual'':1A,9B,14C ''vs'':42C,54C,109C ''word'':164C ''year'':50C,147C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2564, 1, '2014-11-28 11:39:09.537', 2, 134, 'Using the Datumbox Machine Learning Framework for website classification - guidelines?', '&lt;p&gt;A short while ago, I came across this ML framework that has implemented several different algorithms ready for use. The site also provides a handy API that you can access with an API key.&lt;/p&gt;

&lt;p&gt;I have need of the framework to solve a website classification problem where I basically need to categorize several thousand websites based on their HTML content. As I don''t want to be bound to their existing API, I wanted to use the framework to implement my own.&lt;/p&gt;

&lt;p&gt;However, besides some introductory-level data mining courses and associated reading, I know very little as to what exactly I would need to use. Specifically, I''m at a loss as to what exactly I need to do to train the classifier and then model the data.&lt;/p&gt;

&lt;p&gt;The framework already includes some classification algorithms like NaiveBayes, which I know is well suited to the task of text classification, but I''m not exactly sure how to apply it to the problem.&lt;/p&gt;

&lt;p&gt;Can anyone give me a rough guidelines as to what exactly I would need to do to accomplish this task?&lt;/p&gt;
', 'machine-learning  classification  java', NULL, '''/p'':51C,101C,151C,187C,209C ''access'':46C ''accomplish'':206C ''across'':23C ''ago'':20C ''algorithm'':32C,159C ''alreadi'':155C ''also'':38C ''anyon'':190C ''api'':42C,49C,90C ''appli'':182C ''associ'':113C ''base'':74C ''basic'':67C ''besid'':104C ''bound'':86C ''came'':22C ''categor'':70C ''classif'':9A,14B,63C,158C,173C ''classifi'':145C ''content'':78C ''cours'':111C ''data'':109C,150C ''datumbox'':3A ''differ'':31C ''exact'':122C,137C,178C,199C ''exist'':89C ''framework'':6A,26C,58C,96C,154C ''give'':191C ''guidelin'':10A,195C ''handi'':41C ''howev'':103C ''html'':77C ''implement'':29C,98C ''includ'':156C ''introductori'':107C ''introductory-level'':106C ''java'':15B ''key'':50C ''know'':116C,164C ''learn'':5A,13B ''level'':108C ''like'':160C ''littl'':118C ''loss'':133C ''m'':130C,176C ''machin'':4A,12B ''machine-learn'':11B ''mine'':110C ''ml'':25C ''model'':148C ''naivebay'':161C ''need'':55C,68C,125C,139C,202C ''p'':16C,52C,102C,152C,188C ''problem'':64C,186C ''provid'':39C ''read'':114C ''readi'':33C ''rough'':194C ''sever'':30C,71C ''short'':18C ''site'':37C ''solv'':60C ''specif'':128C ''suit'':167C ''sure'':179C ''task'':170C,208C ''text'':172C ''thousand'':72C ''train'':143C ''use'':1A,35C,94C,127C ''want'':83C,92C ''websit'':8A,62C,73C ''well'':166C ''would'':124C,201C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2576, 1, '2014-12-01 20:10:29.967', 4, 136, 'API for Company Data Enrichment Suggestions', '&lt;p&gt;I''m looking for API suggestions for enriching data on companies. Currently I use the Crunchbase API to look up a company''s name or domain and I am trying to gather the domain/name (if I don''t already have both), contact email (this one is a long shot), and the location of their headquarters. &lt;/p&gt;

&lt;p&gt;This works incredibly well if Crunchbase has the company in their API, but I''d say this only happens about 25% of the time.&lt;/p&gt;

&lt;p&gt;I''d love to get some suggestions on some free APIs that I could use along with Crunchbase. I''d also love to see if anyone has had positive or negative experiences with paid APIs! &lt;/p&gt;
', 'dataset', NULL, '''/p'':64C,90C,127C ''25'':86C ''along'':107C ''alreadi'':47C ''also'':112C ''anyon'':117C ''api'':1A,13C,25C,77C,102C,126C ''compani'':3A,19C,30C,74C ''contact'':50C ''could'':105C ''crunchbas'':24C,71C,109C ''current'':20C ''d'':80C,93C,111C ''data'':4A,17C ''dataset'':7B ''domain'':34C ''domain/name'':42C ''email'':51C ''enrich'':5A,16C ''experi'':123C ''free'':101C ''gather'':40C ''get'':96C ''happen'':84C ''headquart'':63C ''incred'':68C ''locat'':60C ''long'':56C ''look'':11C,27C ''love'':94C,113C ''m'':10C ''name'':32C ''negat'':122C ''one'':53C ''p'':8C,65C,91C ''paid'':125C ''posit'':120C ''say'':81C ''see'':115C ''shot'':57C ''suggest'':6A,14C,98C ''time'':89C ''tri'':38C ''use'':22C,106C ''well'':69C ''work'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2565, 2, '2014-11-28 12:48:14.443', 4, NULL, NULL, '&lt;p&gt;AUC and accuracy are fairly different things. AUC applies to binary classifiers that have some notion of a decision threshold internally. For example logistic regression returns positive/negative depending on whether the logistic function is greater/smaller than a threshold, usually 0.5 by default. When you choose your threshold, you have a classifier. You have to choose one.&lt;/p&gt;

&lt;p&gt;For a given choice of threshold, you can compute accuracy, which is the proportion of true positives and negatives in the whole data set.&lt;/p&gt;

&lt;p&gt;AUC measures how true positive rate (recall) and false positive rate trade off, so in that sense it is already measuring something else. More importantly, AUC is not a function of threshold. It is an evaluation of the classifier as threshold varies over all possible values. It is in a sense a broader metric, testing the quality of the internal value that the classifier generates and then compares to a threshold. It is not testing the quality of a particular choice of threshold.&lt;/p&gt;

&lt;p&gt;AUC has a different interpretation, and that is that it''s also the probability that a randomly chosen positive example is ranked above a randomly chosen negative example, according to the classifier''s internal value for the examples.&lt;/p&gt;

&lt;p&gt;AUC is computable even if you have an algorithm that only produces a ranking on examples. AUC is not computable if you truly only have a black-box classifier, and not one with an internal threshold. These would usually dictate which of the two is even available to a problem at hand.&lt;/p&gt;

&lt;p&gt;AUC is, I think, a more comprehensive measure, although applicable in fewer situations. It''s not strictly better than accuracy; it''s different. It depends in part on whether you care more about true positives, false negatives, etc.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;F-measure is more like accuracy in the sense that it''s a function of a classifier and its threshold setting. But it measures precision vs recall (true positive rate), which is not the same as either above.&lt;/em&gt;&lt;/p&gt;
', NULL, NULL, '''/em'':346C ''/p'':58C,84C,169C,209C,264C,304C,347C ''0.5'':41C ''accord'':199C ''accuraci'':4C,69C,285C,313C ''algorithm'':219C ''alreadi'':105C ''also'':182C ''although'':274C ''appli'':10C ''applic'':275C ''auc'':2C,9C,86C,111C,171C,211C,227C,266C ''avail'':258C ''better'':283C ''binari'':12C ''black'':238C ''black-box'':237C ''box'':239C ''broader'':138C ''care'':296C ''choic'':63C,166C ''choos'':46C,56C ''chosen'':188C,196C ''classifi'':13C,52C,124C,149C,202C,240C,324C ''compar'':153C ''comprehens'':272C ''comput'':68C,213C,230C ''data'':82C ''decis'':20C ''default'':43C ''depend'':29C,290C ''dictat'':251C ''differ'':7C,174C,288C ''either'':344C ''els'':108C ''em'':306C ''etc'':303C ''evalu'':121C ''even'':214C,257C ''exampl'':24C,190C,198C,208C,226C ''f'':308C ''f-measur'':307C ''fair'':6C ''fals'':94C,301C ''fewer'':277C ''function'':34C,115C,321C ''generat'':150C ''given'':62C ''greater/smaller'':36C ''hand'':263C ''import'':110C ''intern'':22C,145C,204C,246C ''interpret'':175C ''like'':312C ''logist'':25C,33C ''measur'':87C,106C,273C,309C,331C ''metric'':139C ''negat'':78C,197C,302C ''notion'':17C ''one'':57C,243C ''p'':1C,59C,85C,170C,210C,265C,305C ''part'':292C ''particular'':165C ''posit'':76C,90C,95C,189C,300C,336C ''positive/negative'':28C ''possibl'':130C ''precis'':332C ''probabl'':184C ''problem'':261C ''produc'':222C ''proport'':73C ''qualiti'':142C,162C ''random'':187C,195C ''rank'':192C,224C ''rate'':91C,96C,337C ''recal'':92C,334C ''regress'':26C ''return'':27C ''sens'':102C,136C,316C ''set'':83C,328C ''situat'':278C ''someth'':107C ''strict'':282C ''test'':140C,160C ''thing'':8C ''think'':269C ''threshold'':21C,39C,48C,65C,117C,126C,156C,168C,247C,327C ''trade'':97C ''true'':75C,89C,299C,335C ''truli'':233C ''two'':255C ''usual'':40C,250C ''valu'':131C,146C,205C ''vari'':127C ''vs'':333C ''whether'':31C,294C ''whole'':81C ''would'':249C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2566, 2, '2014-11-28 13:16:02.52', 3, NULL, NULL, '&lt;p&gt;NARMAX Methodology and Residual analysis both address this issue. Search for the following articles:(Error = Residual = Noise)&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Chaotic Time Series Prediction with residual Analysis Method Using Hybrid ElmanâNARX Neural Networks, Muhammad Ardalani-Farsa (2010)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Orthogonal Least Squares Methods and their Application to Non-Linear System Identification, S. Chen, S. A. Billings, W. Luo (1989)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Any article working on NARMAX, NARMA and Residual Analysis. Remember in NARX and NAR there is no error estimation and analysis.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Notice in general you can follow this steps:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Estimate a time series and calculate Error or Residuals using any .&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Consider errors or residuals as a new time series. Try to estimate Error-Time-Series. Now you can add this estimations to your initial model.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;You can do this residual analysis as many times as you need. In practice 2 or 3 times suffices. Remember in practice, residual time series are noisy and SNR in this time series is so small. So you should use some Noise-Robust methods for residual analysis.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':43C,68C,93C,120C,150C,202C ''/ol'':94C,203C ''/p'':19C,42C,67C,92C,104C,119C,149C,201C ''1989'':66C ''2'':167C ''2010'':41C ''3'':169C ''add'':142C ''address'':8C ''analysi'':6C,29C,79C,91C,158C,200C ''applic'':52C ''ardalani'':39C ''ardalani-farsa'':38C ''articl'':15C,72C ''bill'':63C ''calcul'':113C ''chaotic'':23C ''chen'':60C ''consid'':123C ''elmanâ'':33C ''error'':16C,88C,114C,124C,136C ''error-time-seri'':135C ''estim'':89C,108C,134C,144C ''farsa'':40C ''follow'':14C,101C ''general'':98C ''hybrid'':32C ''identif'':58C ''initi'':147C ''issu'':10C ''least'':47C ''li'':21C,44C,69C,106C,121C,151C ''linear'':56C ''luo'':65C ''mani'':160C ''method'':30C,49C,197C ''methodolog'':3C ''model'':148C ''muhammad'':37C ''nar'':84C ''narma'':76C ''narmax'':2C,75C ''narx'':34C,82C ''need'':164C ''network'':36C ''neural'':35C ''new'':129C ''nois'':18C,195C ''noise-robust'':194C ''noisi'':179C ''non'':55C ''non-linear'':54C ''notic'':96C ''ol'':20C,105C ''orthogon'':46C ''p'':1C,22C,45C,70C,95C,107C,122C,152C ''practic'':166C,174C ''predict'':26C ''rememb'':80C,172C ''residu'':5C,17C,28C,78C,116C,126C,157C,175C,199C ''robust'':196C ''search'':11C ''seri'':25C,111C,131C,138C,177C,185C ''small'':188C ''snr'':181C ''squar'':48C ''step'':103C ''suffic'':171C ''system'':57C ''time'':24C,110C,130C,137C,161C,170C,176C,184C ''tri'':132C ''use'':31C,117C,192C ''w'':64C ''work'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2570, 2, '2014-11-30 20:14:15.56', 2, NULL, NULL, '&lt;p&gt;On what do you want to base your prediction? I''ve tried to predict multiparty election results for my thesis based on previous years and then using results for some polling stations from this year predict the results in all other polling stations. For this the linear model with which I compared estimated the number of votes each party would obtain by regressing over the votes from previous years. If you have the estimated number of votes for all parties you can calculate the percentage from that. See &lt;a href="http://amstat.tandfonline.com.proxy.ubn.ru.nl/doi/abs/10.1198/016214504000001835#.VHt5tDGG-OM" rel="nofollow"&gt;http://amstat.tandfonline.com.proxy.ubn.ru.nl/doi/abs/10.1198/016214504000001835#.VHt5tDGG-OM&lt;/a&gt; for the relevant paper, which extends the linear model.&lt;/p&gt;
', NULL, NULL, '''/doi/abs/10.1198/016214504000001835#.vht5tdgg-om'':94C ''/doi/abs/10.1198/016214504000001835#.vht5tdgg-om&lt;/a&gt;'':99C ''/p'':109C ''amstat.tandfonline.com.proxy.ubn.ru.nl'':93C,98C ''amstat.tandfonline.com.proxy.ubn.ru.nl/doi/abs/10.1198/016214504000001835#.vht5tdgg-om'':92C ''amstat.tandfonline.com.proxy.ubn.ru.nl/doi/abs/10.1198/016214504000001835#.vht5tdgg-om&lt;/a&gt;'':97C ''base'':8C,22C ''calcul'':84C ''compar'':53C ''elect'':17C ''estim'':54C,75C ''extend'':105C ''href'':91C ''linear'':48C,107C ''model'':49C,108C ''multiparti'':16C ''nofollow'':96C ''number'':56C,76C ''obtain'':62C ''p'':1C ''paper'':103C ''parti'':60C,81C ''percentag'':86C ''poll'':32C,43C ''predict'':10C,15C,37C ''previous'':24C,69C ''regress'':64C ''rel'':95C ''relev'':102C ''result'':18C,29C,39C ''see'':89C ''station'':33C,44C ''thesi'':21C ''tri'':13C ''use'':28C ''ve'':12C ''vote'':58C,67C,78C ''want'':6C ''would'':61C ''year'':25C,36C,70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2571, 2, '2014-12-01 00:23:30.58', 1, NULL, NULL, '&lt;p&gt;The United States Census Bureau has many free housing datasets (some of which are updated more than once every 10 years). There is an &lt;a href="http://www.census.gov/data/developers/data-sets/acs-survey-1-year-data.html" rel="nofollow"&gt;API for American Community Survey 1 Year Data&lt;/a&gt; that includes housing data. There are raw data sets at &lt;a href="http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t" rel="nofollow"&gt;American Fact Finder&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':41C,62C ''/data/developers/data-sets/acs-survey-1-year-data.html'':30C ''/faces/nav/jsf/pages/searchresults.xhtml?refresh=t'':56C ''/p'':63C ''1'':38C ''10'':21C ''american'':35C,59C ''api'':33C ''bureau'':6C ''census'':5C ''communiti'':36C ''data'':40C,45C,49C ''dataset'':11C ''everi'':20C ''fact'':60C ''factfinder2.census.gov'':55C ''factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t'':54C ''finder'':61C ''free'':9C ''hous'':10C,44C ''href'':27C,53C ''includ'':43C ''mani'':8C ''nofollow'':32C,58C ''p'':1C ''raw'':48C ''rel'':31C,57C ''set'':50C ''state'':4C ''survey'':37C ''unit'':3C ''updat'':16C ''www.census.gov'':29C ''www.census.gov/data/developers/data-sets/acs-survey-1-year-data.html'':28C ''year'':22C,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2572, 2, '2014-12-01 00:26:39.927', 1, NULL, NULL, '&lt;p&gt;There is real estate data for sale at &lt;a href="http://www.dataquick.com/" rel="nofollow"&gt;DataQuick&lt;/a&gt; or &lt;a href="http://www.realquest.com/" rel="nofollow"&gt;Real Quest&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':16C,25C ''/p'':26C ''data'':6C ''dataquick'':15C ''estat'':5C ''href'':11C,19C ''nofollow'':14C,22C ''p'':1C ''quest'':24C ''real'':4C,23C ''rel'':13C,21C ''sale'':8C ''www.dataquick.com'':12C ''www.realquest.com'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2573, 2, '2014-12-01 09:58:00.28', 0, NULL, NULL, '&lt;p&gt;try&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;vec =[]

for word in bag_of_negative_words:
    if word in mov_review1:
        vec.append(1)

for word in bag_of_positive_words:
    if word in moview_review1:
         vec.append(5)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':35C ''/p'':3C ''/pre'':36C ''1'':20C ''5'':34C ''bag'':10C,24C ''code'':5C ''mov'':17C ''moview'':31C ''negat'':12C ''p'':1C ''posit'':26C ''pre'':4C ''review1'':18C,32C ''tri'':2C ''vec'':6C ''vec.append'':19C,33C ''word'':8C,13C,15C,22C,27C,29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2575, 1, '2014-12-01 14:52:31.827', 3, 235, 'Hive: How to calculate the Kendall coefficient of correlation of a pair of a numeric columns in the group?', '&lt;p&gt;In this &lt;a href="https://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF" rel="nofollow"&gt;wiki page&lt;/a&gt; there is a function &lt;code&gt;corr()&lt;/code&gt; that calculates the Pearson coefficient of correlation, but my question is that: is there any function in Hive that enables to calculate the &lt;a href="http://en.wikipedia.org/wiki/Kendall%27s_W" rel="nofollow"&gt;Kendall coefficient&lt;/a&gt; of correlation of a pair of a numeric columns in the group?&lt;/p&gt;
', 'hadoop  correlation  hive', NULL, '''/a'':35C,75C ''/code'':42C ''/confluence/display/hive/languagemanual+udf'':30C ''/p'':88C ''/wiki/kendall%27s_w'':70C ''calcul'':4A,44C,64C ''code'':40C ''coeffici'':7A,47C,74C ''column'':16A,84C ''corr'':41C ''correl'':9A,21B,49C,77C ''cwiki.apache.org'':29C ''cwiki.apache.org/confluence/display/hive/languagemanual+udf'':28C ''en.wikipedia.org'':69C ''en.wikipedia.org/wiki/kendall%27s_w'':68C ''enabl'':62C ''function'':39C,58C ''group'':19A,87C ''hadoop'':20B ''hive'':1A,22B,60C ''href'':27C,67C ''kendal'':6A,73C ''nofollow'':32C,72C ''numer'':15A,83C ''p'':23C ''page'':34C ''pair'':12A,80C ''pearson'':46C ''question'':52C ''rel'':31C,71C ''wiki'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2577, 2, '2014-12-01 21:59:47.54', 2, NULL, NULL, '&lt;p&gt;This is not a regression but a multi-class classification problem. The output is typically the probabilities of all classes for any given test instance (test row). So in your case, the output for any given test row from the trained model will be of the form:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;prob_1, prob_2, prob_3,..., prob_k
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where prob_i denotes the probability of the i-th class (in your case i-th party), assuming  there are k classes in the response variable. Note that the sum of these k probabilities is going to be 1. The class prediction in this case is going to be the class that has the maximum probability. &lt;/p&gt;

&lt;p&gt;There are many classifiers in R that do multi-class classification. You could use logistic regression with multi-class support through the &lt;a href="http://cran.r-project.org/web/packages/nnet/nnet.pdf" rel="nofollow"&gt;nnet&lt;/a&gt; package in R and invoking the &lt;code&gt;multinom&lt;/code&gt; command. &lt;/p&gt;

&lt;p&gt;As an alternative, you could also use the &lt;a href="http://cran.r-project.org/web/packages/gbm/gbm.pdf" rel="nofollow"&gt;gbm&lt;/a&gt; package in R and invoke the &lt;code&gt;gbm&lt;/code&gt; command. To create a multi-class classifier, just use &lt;code&gt;distribution="multinomial" while using the&lt;/code&gt;gbm` function. &lt;/p&gt;
', NULL, NULL, '''/a'':156C,185C ''/code'':61C,165C,194C,211C ''/p'':50C,122C,167C,214C ''/pre'':62C ''/web/packages/gbm/gbm.pdf'':181C ''/web/packages/nnet/nnet.pdf'':152C ''1'':54C,104C ''2'':56C ''3'':58C ''also'':174C ''altern'':171C ''assum'':83C ''case'':33C,78C,110C ''class'':11C,22C,75C,87C,106C,116C,134C,144C,201C ''classif'':12C,135C ''classifi'':127C,202C ''code'':52C,163C,192C,205C ''command'':166C,195C ''could'':137C,173C ''cran.r-project.org'':151C,180C ''cran.r-project.org/web/packages/gbm/gbm.pdf'':179C ''cran.r-project.org/web/packages/nnet/nnet.pdf'':150C ''creat'':197C ''denot'':67C ''distribut'':206C ''form'':49C ''function'':213C ''gbm'':184C,193C,212C ''given'':25C,38C ''go'':101C,112C ''href'':149C,178C ''i-th'':72C,79C ''instanc'':27C ''invok'':161C,190C ''k'':60C,86C,98C ''logist'':139C ''mani'':126C ''maximum'':120C ''model'':44C ''multi'':10C,133C,143C,200C ''multi-class'':9C,132C,142C,199C ''multinom'':164C ''multinomi'':207C ''nnet'':155C ''nofollow'':154C,183C ''note'':92C ''output'':15C,35C ''p'':1C,63C,123C,168C ''packag'':157C,186C ''parti'':82C ''pre'':51C ''predict'':107C ''prob'':53C,55C,57C,59C,65C ''probabl'':19C,69C,99C,121C ''problem'':13C ''r'':129C,159C,188C ''regress'':6C,140C ''rel'':153C,182C ''respons'':90C ''row'':29C,40C ''sum'':95C ''support'':145C ''test'':26C,28C,39C ''th'':74C,81C ''train'':43C ''typic'':17C ''use'':138C,175C,204C,209C ''variabl'':91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2581, 1, '2014-12-02 16:09:35.333', 4, 348, 'Attributes extraction from unstructured product descriptions', '&lt;p&gt;I am trying to match new product description with the existing ones. Product description looks like this: ï¿¼Panasonic DMC-FX07EB digital camera silver. These are steps to be performed:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Tokenize description and recognize attributes: Panasonic =&gt; Brand, DMC-FX07EB =&gt; Model, etc.&lt;br/&gt;&lt;/li&gt;
&lt;li&gt;Get few candidates with similar features&lt;br/&gt;&lt;/li&gt;
&lt;li&gt;Get the best candidate.&lt;br/&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I am having problem with the first step (1). In order to get ''Panasonic =&gt; Brand'', DMC-FX07EB =&gt; Model, silver =&gt; color, I need to have index where each token of the product description correspond to certain attribute name (Brand, model, color, etc.) in the existing database. The problem is that in my database product descriptions are presented as one atomic attribute e.g. ''description'' (no separated product attributes).&lt;/p&gt;

&lt;p&gt;Basically I don''t have training data, so I am trying to build index of all product attributes so I can build training data. So far I have attributes from bestbuy.com and semantics3.com APIs, but both sources lack most of attributes or contain irrelevant ones. Any suggestions for better APIs to get product attributes? Better approach to do this? &lt;/p&gt;

&lt;p&gt;P.S. For every product there is a matched product description in the Database, which is as well in a form of one atomic attribute. I have checked this &lt;a href="http://stackoverflow.com/questions/18496925/how-to-parse-product-titles-unstructured-into-structured-data"&gt;question on SO&lt;/a&gt;, it helped me and it seems we have same approach but I am still trying to get training data. &lt;/p&gt;
', 'machine-learning  nlp  feature-extraction', NULL, '''/a'':247C ''/li'':62C,71C,78C ''/ol'':79C ''/p'':46C,148C,209C,267C ''/questions/18496925/how-to-parse-product-titles-unstructured-into-structured-data'':243C ''1'':89C ''api'':183C,199C ''approach'':205C,257C ''atom'':140C,233C ''attribut'':1A,53C,117C,141C,147C,167C,178C,190C,203C,234C ''basic'':150C ''best'':75C ''bestbuy.com'':180C ''better'':198C,204C ''br'':61C,70C,77C ''brand'':55C,95C,119C ''build'':162C,171C ''camera'':38C ''candid'':66C,76C ''certain'':116C ''check'':237C ''color'':101C,121C ''contain'':192C ''correspond'':114C ''data'':156C,173C,266C ''databas'':126C,133C,223C ''descript'':6A,22C,28C,50C,113C,135C,143C,220C ''digit'':37C ''dmc'':35C,57C,97C ''dmc-fx07eb'':34C,56C,96C ''e.g'':142C ''etc'':60C,122C ''everi'':213C ''exist'':25C,125C ''extract'':2A,13B ''far'':175C ''featur'':12B,69C ''feature-extract'':11B ''first'':87C ''form'':230C ''fx07eb'':36C,58C,98C ''get'':64C,73C,93C,201C,264C ''help'':249C ''href'':240C ''index'':106C,163C ''irrelev'':193C ''lack'':187C ''learn'':9B ''li'':48C,63C,72C ''like'':30C ''look'':29C ''machin'':8B ''machine-learn'':7B ''match'':19C,218C ''model'':59C,99C,120C ''name'':118C ''need'':103C ''new'':20C ''nlp'':10B ''ol'':47C ''one'':26C,139C,194C,232C ''order'':91C ''p'':14C,80C,149C,210C ''p.s'':211C ''panason'':33C,54C,94C ''perform'':45C ''present'':137C ''problem'':84C,128C ''product'':5A,21C,27C,112C,134C,146C,166C,202C,214C,219C ''question'':244C ''recogn'':52C ''seem'':253C ''semantics3.com'':182C ''separ'':145C ''silver'':39C,100C ''similar'':68C ''sourc'':186C ''stackoverflow.com'':242C ''stackoverflow.com/questions/18496925/how-to-parse-product-titles-unstructured-into-structured-data'':241C ''step'':42C,88C ''still'':261C ''suggest'':196C ''token'':49C,109C ''train'':155C,172C,265C ''tri'':17C,160C,262C ''unstructur'':4A ''well'':227C ''ï'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2582, 1, '2014-12-02 16:19:19.043', 4, 154, 'Consequence of Feature Scaling', '&lt;p&gt;I am currently using SVM and scaling my training features to the range of [0,1].
I first fit/transform my training set and then apply the &lt;strong&gt;same&lt;/strong&gt; transformation to my testing set. For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    ### Configure transformation and apply to training set
    min_max_scaler = MinMaxScaler(feature_range=(0, 1))
    X_train = min_max_scaler.fit_transform(X_train)

    ### Perform transformation on testing set
    X_test = min_max_scaler.transform(X_test)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Let assume that a given feature in the training set has a range of [0,100], and that same feature in the testing set has a range of [-10,120]. In the training set that feature will be scaled appropriately to [0,1], while in the testing set that feature will be scaled to a range outside of that first specified, something like [-0.1,1.2].&lt;/p&gt;

&lt;p&gt;I was wondering what the consequences of the testing set features being out of range of those being used to train the model? Is this a problem?&lt;/p&gt;
', 'machine-learning  svm  feature-scaling', NULL, '''-0.1'':149C ''-10'':114C ''/code'':83C ''/p'':49C,151C,180C ''/pre'':84C ''/strong'':41C ''0'':27C,65C,100C,127C ''1'':28C,66C,128C ''1.2'':150C ''100'':101C ''120'':115C ''appli'':37C,55C ''appropri'':125C ''assum'':87C ''code'':51C ''configur'':52C ''consequ'':1A,158C ''current'':15C ''exampl'':48C ''featur'':3A,10B,22C,63C,91C,105C,121C,135C,163C ''feature-sc'':9B ''first'':30C,145C ''fit/transform'':31C ''given'':90C ''learn'':7B ''let'':86C ''like'':148C ''machin'':6B ''machine-learn'':5B ''max'':60C ''min'':59C ''min_max_scaler.fit'':69C ''min_max_scaler.transform'':80C ''minmaxscal'':62C ''model'':175C ''outsid'':142C ''p'':12C,85C,152C ''perform'':73C ''pre'':50C ''problem'':179C ''rang'':25C,64C,98C,112C,141C,167C ''scale'':4A,11B,19C,124C,138C ''scaler'':61C ''set'':34C,46C,58C,77C,95C,109C,119C,133C,162C ''someth'':147C ''specifi'':146C ''strong'':39C ''svm'':8B,17C ''test'':45C,76C,79C,82C,108C,132C,161C ''train'':21C,33C,57C,68C,72C,94C,118C,173C ''transform'':42C,53C,70C,74C ''use'':16C,171C ''wonder'':155C ''x'':67C,71C,78C,81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2583, 2, '2014-12-02 20:07:14.887', 1, NULL, NULL, '&lt;p&gt;Try to search from the database''s of officials "bad words" that google publish in this link &lt;a href="http://fffff.at/googles-official-list-of-bad-words/" rel="nofollow"&gt;Goolgles official list of bad words...&lt;/a&gt;
Also here is the link for the good words &lt;a href="http://www.enchantedlearning.com/wordlist/positivewords.shtml" rel="nofollow"&gt;Not official list of good words...&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;For the code i would do it like this...&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;textArray = file(''dir_to_your_text'',''r'').read().split()

#Bad words should be listed like this for the split function to work
# "*** ****** **** ****" the stars are for the cenzuration :P
badArray = file(''dir_to_your_bad_word_file).read().split()
goodArray = file(''dir_to_your_good_word_file).read().split()

#Than you use maching algoritem from difflib on good and bad word for ewery word in array of words
import difflib

goodMachingCouter = 0;
badMacihngCouter = 0;


for iGood in range(0, len(goodArray)):
    for iWord in range(0, len(textArray)):
        goodMachingCounter += difflib.SequenceMatcher(None, goodArray[iGood], textArray[iWord]).ratio()

for iBad in range(0, len(badArray)):
    for iWord in range(0, len(textArray)):
        badMachingCounter += difflib.SequenceMatcher(None, badArray[ibad], textArray[iWgoodord]).ratio()

goodMachingCouter *= 100/(len(goodArray)*len(textArray))
badMacihngCouter *= 100/(len(badArray)*len(textArray))

print(''Show the good measurment of the text in %: ''+goodMachingCouter)
print(''Show the bad measurment of the text in %: ''+badMacihngCouter)
print(''Show the hootnes of the text: '' + len(textArray)*goodMachingCounter)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The code will be slow but accurate :) I didn''t run and test it pls do it for me and post the correct code :) because i wana test it too :)&lt;/p&gt;
', NULL, NULL, '''/a'':32C,55C ''/code'':230C ''/googles-official-list-of-bad-words/'':23C ''/p'':56C,67C,263C ''/pre'':231C ''/wordlist/positivewords.shtml'':46C ''0'':141C,143C,148C,155C,170C,177C ''100'':189C,195C ''accur'':239C ''algoritem'':123C ''also'':33C ''array'':135C ''bad'':11C,30C,79C,104C,129C,213C ''badarray'':99C,172C,183C,197C ''badmachingcount'':180C ''badmacihngcout'':142C,194C,219C ''cenzur'':97C ''code'':60C,69C,234C,256C ''correct'':255C ''databas'':7C ''didn'':241C ''difflib'':125C,139C ''difflib.sequencematcher'':159C,181C ''dir'':72C,101C,111C ''eweri'':132C ''fffff.at'':22C ''fffff.at/googles-official-list-of-bad-words/'':21C ''file'':71C,100C,106C,110C,116C ''function'':89C ''good'':40C,53C,114C,127C,203C ''goodarray'':109C,150C,161C,191C ''goodmachingcount'':158C,229C ''goodmachingcout'':140C,188C,209C ''googl'':14C ''goolgl'':26C ''hootn'':223C ''href'':20C,43C ''ibad'':167C,184C ''igood'':145C,162C ''import'':138C ''iwgoodord'':186C ''iword'':152C,164C,174C ''len'':149C,156C,171C,178C,190C,192C,196C,198C,227C ''like'':65C,84C ''link'':18C,37C ''list'':28C,51C,83C ''mach'':122C ''measur'':204C,214C ''nofollow'':25C,48C ''none'':160C,182C ''offici'':10C,27C,50C ''p'':1C,57C,98C,232C ''pls'':247C ''post'':253C ''pre'':68C ''print'':200C,210C,220C ''publish'':15C ''r'':76C ''rang'':147C,154C,169C,176C ''ratio'':165C,187C ''read'':77C,107C,117C ''rel'':24C,47C ''run'':243C ''search'':4C ''show'':201C,211C,221C ''slow'':237C ''split'':78C,88C,108C,118C ''star'':93C ''test'':245C,260C ''text'':75C,207C,217C,226C ''textarray'':70C,157C,163C,179C,185C,193C,199C,228C ''tri'':2C ''use'':121C ''wana'':259C ''word'':12C,31C,41C,54C,80C,105C,115C,130C,133C,137C ''work'':91C ''would'':62C ''www.enchantedlearning.com'':45C ''www.enchantedlearning.com/wordlist/positivewords.shtml'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2602, 2, '2014-12-04 15:12:57.327', 8, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Item Based Algorithm&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for every item i that u has no preference for yet

  for every item j that u has a preference for

    compute a similarity s between i and j

    add u''s preference for j, weighted by s, to a running average

 return the top items, ranked by weighted average
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;User Based Algorithm&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for every item i that u has no preference for yet

 for every other user v that has a preference for i

   compute a similarity s between u and v

   add v''s preference for i, weighted by s, to a running average

 return the top items, ranked by weighted average
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Item vs User based:&lt;/p&gt;

&lt;p&gt;1) Recommenders scale with the number of items or users they must deal with, so there are scenarios in which each type can perform better than the other&lt;/p&gt;

&lt;p&gt;2) Similarity estimates between items are more likely to converge over time than similarities between users&lt;/p&gt;

&lt;p&gt;3) We can compute and cache similarities that converge, which can give item based recommenders a performance advantage&lt;/p&gt;

&lt;p&gt;4) Item based recommenders begin with a list of a user''s preferred items and therefore do not need a nearest item neighborhood as user based recommenders do&lt;/p&gt;
', NULL, NULL, '''/code'':60C,122C ''/p'':7C,68C,129C,159C,177C,197C,227C ''/pre'':61C,123C ''/strong'':6C,67C ''1'':131C ''2'':161C ''3'':179C ''4'':199C ''add'':39C,101C ''advantag'':196C ''algorithm'':5C,66C ''averag'':51C,59C,113C,121C ''base'':4C,65C,128C,192C,201C,224C ''begin'':203C ''better'':155C ''cach'':184C ''code'':9C,70C ''comput'':31C,93C,182C ''converg'':170C,187C ''deal'':143C ''estim'':163C ''everi'':11C,22C,72C,83C ''give'':190C ''item'':3C,12C,23C,55C,73C,117C,125C,138C,165C,191C,200C,212C,220C ''j'':24C,38C,44C ''like'':168C ''list'':206C ''must'':142C ''nearest'':219C ''need'':217C ''neighborhood'':221C ''number'':136C ''p'':1C,62C,124C,130C,160C,178C,198C ''perform'':154C,195C ''pre'':8C,69C ''prefer'':18C,29C,42C,79C,90C,104C,211C ''rank'':56C,118C ''recommend'':132C,193C,202C,225C ''return'':52C,114C ''run'':50C,112C ''scale'':133C ''scenario'':148C ''similar'':33C,95C,162C,174C,185C ''strong'':2C,63C ''therefor'':214C ''time'':172C ''top'':54C,116C ''type'':152C ''u'':15C,26C,40C,76C,98C ''user'':64C,85C,127C,140C,176C,209C,223C ''v'':86C,100C,102C ''vs'':126C ''weight'':45C,58C,107C,120C ''yet'':20C,81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2584, 2, '2014-12-02 22:23:33.7', 3, NULL, NULL, '&lt;p&gt;Left you a quick response on SO. The gist is that you can collect a lot of information from electronics shops and manufacturers'' web sites, and lots you can annotate manually. If your goal is to only get training data, that''s all you need:&lt;/p&gt;

&lt;p&gt;My answer form the cross-post:
"Having developed a commercial analyzer of this kind, I can tell you that there is no easy solution for this problem. But there are multiple shortcuts, especially if your domain is limited to cameras/electronics.&lt;/p&gt;

&lt;p&gt;Firstly, you should look at more sites. Many have product brand annotated in the page (proper html annotations, bold font, all caps in the beginning of the name). Some sites have entire pages with brand selectors for search purposes. This way you can create a pretty good starter dictionary of brand names. Same with product line names and even with models. Alphanumeric models can be extracted in bulk by regular expressions and filtered pretty quickly.&lt;/p&gt;

&lt;p&gt;There are plenty of other tricks, but I''ll try to be brief. Just a piece of advice here: there is always a trade-off between manual work and algorithms. Always keep in mind that both approaches can be mixed and both have return-on-invested-time curves, which people tend to forget. If your goal is not to create an automatic algorithm to extract product brands and models, this problem should have limited time budget in your plan. You can realistically create a dictionary of 1000 brands in a day, and for decent performance on known data source of electronic goods (we are not talking Amazon here or are we?) a dictionary of 4000 brands may be all you need for your work. So do the math before you invest weeks into the latest neural network named entity recognizer."&lt;/p&gt;
', NULL, NULL, '''/p'':47C,90C,167C,311C ''1000'':257C ''4000'':285C ''advic'':186C ''algorithm'':199C,233C ''alphanumer'':153C ''alway'':190C,200C ''amazon'':277C ''analyz'':60C ''annot'':31C,103C,109C ''answer'':50C ''approach'':206C ''automat'':232C ''begin'':116C ''bold'':110C ''brand'':102C,126C,142C,237C,258C,286C ''brief'':181C ''budget'':246C ''bulk'':159C ''cameras/electronics'':89C ''cap'':113C ''collect'':15C ''commerci'':59C ''creat'':135C,230C,253C ''cross'':54C ''cross-post'':53C ''curv'':218C ''data'':41C,268C ''day'':261C ''decent'':264C ''develop'':57C ''dictionari'':140C,255C,283C ''domain'':85C ''easi'':72C ''electron'':21C,271C ''entir'':123C ''entiti'':309C ''especi'':82C ''even'':150C ''express'':162C ''extract'':157C,235C ''filter'':164C ''first'':92C ''font'':111C ''forget'':223C ''form'':51C ''get'':39C ''gist'':10C ''goal'':35C,226C ''good'':138C,272C ''html'':108C ''inform'':19C ''invest'':216C,301C ''keep'':201C ''kind'':63C ''known'':267C ''latest'':305C ''left'':2C ''limit'':87C,244C ''line'':147C ''ll'':177C ''look'':95C ''lot'':17C,28C ''mani'':99C ''manual'':32C,196C ''manufactur'':24C ''math'':298C ''may'':287C ''mind'':203C ''mix'':209C ''model'':152C,154C,239C ''multipl'':80C ''name'':119C,143C,148C,308C ''need'':46C,291C ''network'':307C ''neural'':306C ''p'':1C,48C,91C,168C ''page'':106C,124C ''peopl'':220C ''perform'':265C ''piec'':184C ''plan'':249C ''plenti'':171C ''post'':55C ''pretti'':137C,165C ''problem'':76C,241C ''product'':101C,146C,236C ''proper'':107C ''purpos'':130C ''quick'':5C,166C ''realist'':252C ''recogn'':310C ''regular'':161C ''respons'':6C ''return'':214C ''return-on-invested-tim'':213C ''search'':129C ''selector'':127C ''shop'':22C ''shortcut'':81C ''site'':26C,98C,121C ''solut'':73C ''sourc'':269C ''starter'':139C ''talk'':276C ''tell'':66C ''tend'':221C ''time'':217C,245C ''trade'':193C ''trade-off'':192C ''train'':40C ''tri'':178C ''trick'':174C ''way'':132C ''web'':25C ''week'':302C ''work'':197C,294C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2585, 2, '2014-12-03 05:00:30.5', 3, NULL, NULL, '&lt;p&gt;This was meant as a comment but it is too long.&lt;/p&gt;

&lt;p&gt;The fact that your test set has a different range &lt;strong&gt;might&lt;/strong&gt; be a sign that the training set is not a good representation of the test set. However, if the difference is really small as in your example, it is likely that it won''t affect your predictions. Unfortunately, I don''t think I have a good reason to think it won''t affect a SVM in any circumstance.&lt;/p&gt;

&lt;p&gt;Notice that the rationale for using MinMaxScalar is (according to the documentation): &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The motivation to use this scaling include robustness to very small
  standard deviations of features and preserving zero entries in sparse
  data.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Therefore, it is important for you to make sure that your data fits that case.&lt;/p&gt;

&lt;p&gt;If you are really concerned about having a difference range, you should use a regular standardization (such as &lt;code&gt;preprocessing.scale&lt;/code&gt;) instead.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':126C ''/code'':165C ''/p'':13C,86C,100C,125C,143C,167C ''/strong'':27C ''accord'':96C ''affect'':62C,80C ''blockquot'':101C ''case'':142C ''circumst'':85C ''code'':163C ''comment'':7C ''concern'':149C ''data'':124C,139C ''deviat'':115C ''differ'':23C,47C,153C ''document'':99C ''entri'':121C ''exampl'':54C ''fact'':16C ''featur'':117C ''fit'':140C ''good'':38C,73C ''howev'':44C ''import'':131C ''includ'':109C ''instead'':166C ''like'':57C ''long'':12C ''make'':135C ''meant'':4C ''might'':26C ''minmaxscalar'':94C ''motiv'':104C ''notic'':88C ''p'':1C,14C,87C,102C,127C,144C ''predict'':64C ''preprocessing.scale'':164C ''preserv'':119C ''rang'':24C,154C ''rational'':91C ''realli'':49C,148C ''reason'':74C ''regular'':159C ''represent'':39C ''robust'':110C ''scale'':108C ''set'':20C,34C,43C ''sign'':30C ''small'':50C,113C ''spars'':123C ''standard'':114C,160C ''strong'':25C ''sure'':136C ''svm'':82C ''test'':19C,42C ''therefor'':128C ''think'':69C,76C ''train'':33C ''unfortun'':65C ''use'':93C,106C,157C ''won'':60C,78C ''zero'':120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2586, 1, '2014-12-03 07:46:30.967', 1, 101, 'Can data analytics be a basis for artificial intelligence?', '&lt;p&gt;I''m very passionate about how computers can be made able to think intelligently and independently (in our favour, of course!). I''m currently studying Bachelors science of information technology at UTS (University of Technology:Sydney). I have two months before I start my second year, and have not yet been able to decide on which major should I select that can lead myself towards dedicated study of Artificial Intelligence (which I love with my life).&lt;/p&gt;

&lt;p&gt;I have the following majors available:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Internetworking and Applications &lt;/li&gt;
&lt;li&gt;Data Analytics&lt;/li&gt;
&lt;li&gt;(there are other two as well, but business oriented).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="http://uts.edu.au" rel="nofollow"&gt;Here&lt;/a&gt; is the link to my subjects. I believe that being able to play with data is a sign of intelligence (I may be wrong too!). Will one of these subjects form me a good foundation for my further study in A.I.? Or should I jump into Engineering? Or Pure Science?&lt;/p&gt;
', 'bigdata  career', NULL, '''/a'':127C ''/li'':103C,107C,118C ''/p'':89C,97C,178C ''/ul'':119C ''a.i'':168C ''abl'':23C,64C,138C ''analyt'':3A,106C ''applic'':102C ''artifici'':8A,81C ''avail'':96C ''bachelor'':38C ''basi'':6A ''believ'':135C ''bigdata'':10B ''busi'':116C ''career'':11B ''comput'':19C ''cours'':33C ''current'':36C ''data'':2A,105C,142C ''decid'':66C ''dedic'':78C ''engin'':174C ''favour'':31C ''follow'':94C ''form'':158C ''foundat'':162C ''good'':161C ''href'':122C ''independ'':28C ''inform'':41C ''intellig'':9A,26C,82C,147C ''internetwork'':100C ''jump'':172C ''lead'':75C ''li'':99C,104C,108C ''life'':88C ''link'':130C ''love'':85C ''m'':14C,35C ''made'':22C ''major'':69C,95C ''may'':149C ''month'':52C ''nofollow'':125C ''one'':154C ''orient'':117C ''p'':12C,90C,120C ''passion'':16C ''play'':140C ''pure'':176C ''rel'':124C ''scienc'':39C,177C ''second'':57C ''select'':72C ''sign'':145C ''start'':55C ''studi'':37C,79C,166C ''subject'':133C,157C ''sydney'':48C ''technolog'':42C,47C ''think'':25C ''toward'':77C ''two'':51C,112C ''ul'':98C ''univers'':45C ''ut'':44C ''uts.edu.au'':123C ''well'':114C ''wrong'':151C ''year'':58C ''yet'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2588, 2, '2014-12-03 13:21:34.153', 3, NULL, NULL, '&lt;p&gt;The difference between these methods is the assumptions they make about the task.  &lt;a href="http://en.wikipedia.org/wiki/Multiclass_classification" rel="nofollow"&gt;Multi-class classification&lt;/a&gt; assumes that each document has exactly one label.  So a document can either be about sports or weather, not both.
&lt;a href="http://en.wikipedia.org/wiki/Multi-label_classification" rel="nofollow"&gt;Multi-label classification&lt;/a&gt; allows a document to have any combination of labels, including none.  So a document can be about only sports, only weather, sports AND weather, or neither.&lt;/p&gt;

&lt;p&gt;You could train a multi-label classifier with data where each document has exactly one label, but there is no guarantee that the predictions made at test time will have only one label.  Also you are forcing the classifier to do more work (and potentially make more errors) by considering more possible labelings than it needs to.
Therefore, if the multi-class assumption makes sense for your problem, you are better off with a multi-class classifier.&lt;/p&gt;

&lt;p&gt;The method that you describe for training individual binary classifiers corresponds to multi-label classification.  The binary classifiers that you use could each be trained from one-class data or two-class data.  However, this is only one of the many ways to do multi-label classification (see the wikipedia page above for more).&lt;/p&gt;

&lt;p&gt;Unfortunately, the problem that you describe does not cleanly fit into either multi-class or multi-label classification, since you want each document to have &lt;em&gt;at most&lt;/em&gt; one label.&lt;/p&gt;
', NULL, NULL, '''/a'':26C,58C ''/em'':259C ''/p'':85C,167C,227C,262C ''/wiki/multi-label_classification'':51C ''/wiki/multiclass_classification'':19C ''allow'':59C ''also'':121C ''assum'':27C ''assumpt'':9C,151C ''better'':159C ''binari'':177C,186C ''class'':24C,150C,165C,198C,203C,243C ''classif'':25C,57C,184C,219C,248C ''classifi'':94C,126C,166C,178C,187C ''clean'':237C ''combin'':65C ''consid'':137C ''correspond'':179C ''could'':88C,191C ''data'':96C,199C,204C ''describ'':173C,234C ''differ'':3C ''document'':30C,37C,61C,72C,99C,253C ''either'':39C,240C ''em'':256C ''en.wikipedia.org'':18C,50C ''en.wikipedia.org/wiki/multi-label_classification'':49C ''en.wikipedia.org/wiki/multiclass_classification'':17C ''error'':135C ''exact'':32C,101C ''fit'':238C ''forc'':124C ''guarante'':108C ''howev'':205C ''href'':16C,48C ''includ'':68C ''individu'':176C ''label'':34C,56C,67C,93C,103C,120C,140C,183C,218C,247C,261C ''made'':112C ''make'':11C,133C,152C ''mani'':212C ''method'':6C,170C ''multi'':23C,55C,92C,149C,164C,182C,217C,242C,246C ''multi-class'':22C,148C,163C,241C ''multi-label'':54C,91C,181C,216C,245C ''need'':143C ''neither'':84C ''nofollow'':21C,53C ''none'':69C ''one'':33C,102C,119C,197C,209C,260C ''one-class'':196C ''p'':1C,86C,168C,228C ''page'':223C ''possibl'':139C ''potenti'':132C ''predict'':111C ''problem'':156C,231C ''rel'':20C,52C ''see'':220C ''sens'':153C ''sinc'':249C ''sport'':42C,77C,80C ''task'':14C ''test'':114C ''therefor'':145C ''time'':115C ''train'':89C,175C,194C ''two'':202C ''two-class'':201C ''unfortun'':229C ''use'':190C ''want'':251C ''way'':213C ''weather'':44C,79C,82C ''wikipedia'':222C ''work'':130C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2589, 2, '2014-12-03 13:55:16.15', 4, NULL, NULL, '&lt;p&gt;I believe the claim that you are referring to is that the &lt;em&gt;maximum-likelihood estimate&lt;/em&gt; of the component means in a GMM must lie in the span of the eigenvectors of the second moment matrix.  This follows from two steps:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Each component mean in the maximum-likelihood estimate is a linear combination of the data points.  (You can show this by setting the gradient of the log-likelihood function to zero.)&lt;/li&gt;
&lt;li&gt;Any linear combination of the data points must lie in the span of the eigenvectors of the second moment matrix.  (You can show this by first showing that any individual data point must lie in the span, and therefore any linear combination must also be in the span.)&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/em'':19C ''/li'':81C,131C ''/ol'':132C ''/p'':45C ''also'':126C ''believ'':3C ''claim'':5C ''combin'':60C,85C,124C ''compon'':22C,49C ''data'':63C,88C,113C ''eigenvector'':34C,97C ''em'':14C ''estim'':18C,56C ''first'':108C ''follow'':41C ''function'':78C ''gmm'':26C ''gradient'':72C ''individu'':112C ''li'':47C,82C ''lie'':28C,91C,116C ''likelihood'':17C,55C,77C ''linear'':59C,84C,123C ''log'':76C ''log-likelihood'':75C ''matrix'':39C,102C ''maximum'':16C,54C ''maximum-likelihood'':15C,53C ''mean'':23C,50C ''moment'':38C,101C ''must'':27C,90C,115C,125C ''ol'':46C ''p'':1C ''point'':64C,89C,114C ''refer'':9C ''second'':37C,100C ''set'':70C ''show'':67C,105C,109C ''span'':31C,94C,119C,130C ''step'':44C ''therefor'':121C ''two'':43C ''zero'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2604, 2, '2014-12-04 18:36:57.947', 11, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Custom Google Search&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;You can use the Custom Google Search for datasets:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.google.com/cse/publicurl?cx=002720237717066476899:v2wv26idk7m"&gt;Google Custom Search: Datasets&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;It includes 230 sources and meta-sources of datasets, including all mentioned in this question. Please, feel free to exclude .gov and any other websites from results by adding " -.gov" or " -site.com" to the search line. Other Google Search Operators work.&lt;/p&gt;

&lt;p&gt;Don''t hesitate to contact me if you have ideas what websites to add.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;IOGDS&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The following service categorizes more than 1,000,000 public datasets:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://logd.tw.rpi.edu/node/9903"&gt;IOGDS: International Open Government Dataset Search&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':29C,121C ''/cse/publicurl?cx=002720237717066476899:v2wv26idk7m'':24C ''/node/9903'':114C ''/p'':7C,18C,30C,74C,90C,95C,108C,122C ''/strong'':6C,94C ''000'':104C,105C ''1'':103C ''230'':34C ''ad'':61C ''add'':89C ''categor'':100C ''contact'':80C ''custom'':3C,13C,26C ''dataset'':17C,28C,41C,107C,119C ''exclud'':52C ''feel'':49C ''follow'':98C ''free'':50C ''googl'':4C,14C,25C,70C ''gov'':53C,62C ''govern'':118C ''hesit'':78C ''href'':21C,111C ''idea'':85C ''includ'':33C,42C ''intern'':116C ''iogd'':93C,115C ''line'':68C ''logd.tw.rpi.edu'':113C ''logd.tw.rpi.edu/node/9903'':112C ''mention'':44C ''meta'':38C ''meta-sourc'':37C ''open'':117C ''oper'':72C ''p'':1C,8C,19C,31C,75C,91C,96C,109C ''pleas'':48C ''public'':106C ''question'':47C ''result'':59C ''search'':5C,15C,27C,67C,71C,120C ''servic'':99C ''site.com'':64C ''sourc'':35C,39C ''strong'':2C,92C ''use'':11C ''websit'':57C,87C ''work'':73C ''www.google.com'':23C ''www.google.com/cse/publicurl?cx=002720237717066476899:v2wv26idk7m'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2590, 1, '2014-12-03 14:18:34.003', 1, 28, 'Cross-sell models and additional holders', '&lt;p&gt;I would like to pose a question about how to treat additional holders in the propensity-to-buy models of banking products.&lt;/p&gt;

&lt;p&gt;Up to now I was only taking into considerations the clients as first holders. 
For example, if a client â1â appears as the first holder of a saving account âAâ with a balance at the end of the month of 100â¬ and as an additional holder of a saving account âBâ with a balance at the end of the month of 50â¬, the saving balance at the end of the month for the client is considered to be just 100â¬.
Moreover, if a client only appears as an additional holder (and he/she is not a first account holder of ANY product), he/she is dismissed by the model.&lt;/p&gt;

&lt;p&gt;However I have been told to include additional holders in the models (additional holders have the same rights of the first holders).&lt;/p&gt;

&lt;p&gt;One possibility is to recalculate all the variables summing up the position as first and additional holder (in the previous example, the balance at the end of the month of client â1â would be 150â¬). Together with this, I would create some variable that represents the maximum degree of intervention of the client in the account (ex. ''first holder'', ''second holder'').&lt;/p&gt;

&lt;p&gt;Another possibility would be to âdoubleâ all the variables, considering the client as first and additional holder (in the example, we would create two variables:  the balance at the end of the month as FH =100â¬, :  the balance at the end of the month as AH =50â¬).&lt;/p&gt;

&lt;p&gt;Did any of you encounter a similar problem?It would be very helpful to understand how you solved it.&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'data-mining  dataset  predictive-modeling  logistic-regression  data-cleaning', NULL, '''/p'':45C,157C,181C,245C,295C,316C,319C ''100â'':89C,129C,283C ''150â'':218C ''1â'':67C,215C ''50â'':111C,294C ''account'':76C,98C,146C,239C ''addit'':6A,33C,93C,138C,166C,171C,198C,263C ''ah'':293C ''anoth'':247C ''appear'':68C,135C ''aâ'':78C ''balanc'':81C,103C,114C,205C,274C,285C ''bank'':43C ''buy'':40C ''bâ'':100C ''clean'':20B ''client'':57C,65C,123C,133C,213C,236C,259C ''consid'':125C,257C ''consider'':55C ''creat'':224C,270C ''cross'':2A ''cross-sel'':1A ''data'':9B,19B ''data-clean'':18B ''data-min'':8B ''dataset'':11B ''degre'':231C ''dismiss'':153C ''doubleâ'':253C ''encount'':301C ''end'':84C,106C,117C,208C,277C,288C ''ex'':240C ''exampl'':62C,203C,267C ''fh'':282C ''first'':59C,71C,145C,179C,196C,241C,261C ''he/she'':141C,151C ''help'':309C ''holder'':7A,34C,60C,72C,94C,139C,147C,167C,172C,180C,199C,242C,244C,264C ''howev'':159C ''includ'':165C ''intervent'':233C ''like'':24C ''logist'':16B ''logistic-regress'':15B ''maximum'':230C ''mine'':10B ''model'':4A,14B,41C,156C,170C ''month'':87C,109C,120C,211C,280C,291C ''moreov'':130C ''one'':183C ''p'':21C,46C,158C,182C,246C,296C,317C ''pose'':26C ''posit'':194C ''possibl'':184C,248C ''predict'':13B ''predictive-model'':12B ''previous'':202C ''problem'':304C ''product'':44C,150C ''propens'':38C ''propensity-to-buy'':37C ''question'':28C ''recalcul'':187C ''regress'':17B ''repres'':228C ''right'':176C ''save'':75C,97C,113C ''second'':243C ''sell'':3A ''similar'':303C ''solv'':314C ''sum'':191C ''take'':53C ''thank'':318C ''togeth'':219C ''told'':163C ''treat'':32C ''two'':271C ''understand'':311C ''variabl'':190C,226C,256C,272C ''would'':23C,216C,223C,249C,269C,306C ''â'':66C,77C,99C,214C,252C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2591, 1, '2014-12-03 14:23:38.83', 2, 47, 'Decision trees, categorizacion and oversampling', '&lt;p&gt;I want to create a model to predict the propensity to buy a certain product. As my proportion of 1''s is very low, I decided to apply oversampling (to get a 10% of 1''s and a 90% of 0''s). Now, I want to discretize some of the variables. To do so I run a tree for each variable against the target. My question is...shall I define the prior probabilities when I do this (run the trees), or it doesn''t matter and I can use the over-sampled dataset just like that?&lt;/p&gt;

&lt;p&gt;Thanks.&lt;/p&gt;
', 'classification  predictive-modeling  sampling', NULL, '''/p'':109C,112C ''0'':52C ''1'':31C,46C ''10'':44C ''90'':50C ''appli'':39C ''buy'':23C ''categorizacion'':3A ''certain'':25C ''classif'':6B ''creat'':15C ''dataset'':105C ''decid'':37C ''decis'':1A ''defin'':81C ''discret'':58C ''doesn'':94C ''get'':42C ''like'':107C ''low'':35C ''matter'':96C ''model'':9B,17C ''over-sampl'':102C ''oversampl'':5A,40C ''p'':11C,110C ''predict'':8B,19C ''predictive-model'':7B ''prior'':83C ''probabl'':84C ''product'':26C ''propens'':21C ''proport'':29C ''question'':77C ''run'':67C,89C ''sampl'':10B,104C ''shall'':79C ''target'':75C ''thank'':111C ''tree'':2A,69C,91C ''use'':100C ''variabl'':62C,72C ''want'':13C,56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2593, 1, '2014-12-03 15:56:50.687', 6, 88, 'How to connect data-mining with machine learner process', '&lt;p&gt;I want to write a data-mining service in &lt;a href="http://golang.org"&gt;Google Go&lt;/a&gt; which collects data through scraping and APIs.&lt;/p&gt;

&lt;p&gt;However as Go lacks good ML support I would like to do the ML stuff in Python.&lt;/p&gt;

&lt;p&gt;Having a web background I would connect both services with something like RPC but as I believe that this is a common problem in data science I think that there is some better solution.&lt;/p&gt;

&lt;p&gt;For example most (web) protocols lack at:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;buffering between processes&lt;/li&gt;
&lt;li&gt;clustering over multiple instances&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;So what (type of libraries) do data scientists use to connect different languages/processes?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Bodo&lt;/p&gt;
', 'machine-learning  data-mining', NULL, '''/a'':33C ''/li'':111C,117C ''/p'':41C,60C,96C,105C,135C,138C ''/strong'':134C ''/ul'':118C ''api'':40C ''background'':65C ''believ'':78C ''better'':94C ''bodo'':137C ''buffer'':108C ''cluster'':113C ''collect'':35C ''common'':83C ''connect'':3A,68C,131C ''data'':5A,15B,24C,36C,86C,127C ''data-min'':4A,14B,23C ''differ'':132C ''exampl'':99C ''go'':32C,45C ''golang.org'':30C ''good'':47C ''googl'':31C ''howev'':43C ''href'':29C ''instanc'':116C ''lack'':46C,103C ''languages/processes'':133C ''learn'':13B ''learner'':9A ''li'':107C,112C ''librari'':125C ''like'':52C,73C ''machin'':8A,12B ''machine-learn'':11B ''mine'':6A,16B,25C ''ml'':48C,56C ''multipl'':115C ''p'':17C,42C,61C,97C,119C,136C ''problem'':84C ''process'':10A,110C ''protocol'':102C ''python'':59C ''rpc'':74C ''scienc'':87C ''scientist'':128C ''scrape'':38C ''servic'':26C,70C ''solut'':95C ''someth'':72C ''strong'':120C ''stuff'':57C ''support'':49C ''think'':89C ''type'':123C ''ul'':106C ''use'':129C ''want'':19C ''web'':64C,101C ''would'':51C,67C ''write'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6057, 2, '2015-06-09 15:21:27.99', 3, NULL, NULL, '&lt;p&gt;Try using both regression and decision trees. Compare the efficiency of each technique by using a 10 fold cross validation. Stick to the one with higher efficiency. It would be difficult to judge which method would be a better fit just by knowing that your dataset is continuous and, or categorical.  &lt;/p&gt;
', NULL, NULL, '''/p'':53C ''10'':18C ''better'':40C ''categor'':52C ''compar'':9C ''continu'':49C ''cross'':20C ''dataset'':47C ''decis'':7C ''difficult'':32C ''effici'':11C,28C ''fit'':41C ''fold'':19C ''higher'':27C ''judg'':34C ''know'':44C ''method'':36C ''one'':25C ''p'':1C ''regress'':5C ''stick'':22C ''techniqu'':14C ''tree'':8C ''tri'':2C ''use'':3C,16C ''valid'':21C ''would'':30C,37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2594, 2, '2014-12-03 18:22:29.333', 3, NULL, NULL, '&lt;p&gt;For those not familiar, item-item recommenders calculate similarities between items, as opposed to user-user (or user-based) recommenders, which calculate similarities between users. Although some algorithms can be used for both, this question is in regard to item-item algorithms (thanks for being specific in your question).&lt;/p&gt;

&lt;p&gt;Accuracy or effectiveness of recommenders is evaluated based on comparing recommendations to a previously collected data set (training set). For example, I have shopping cart data from the last six months; I''ll use the first 5 months as training data, then run my various algorithms, and compare the quality against what really happened during the 6th month.&lt;/p&gt;

&lt;p&gt;The reason Mahout ships with so many algorithms is because different algorithms are more or less effective in each data set you may work with. So, ideally, you do some testing as I described with many algorithms and compare the accuracy, then choose the winner.&lt;/p&gt;

&lt;p&gt;Interestingly, you can also take other factors into account, such as the need to minimize the data set (for performance reasons), and run your tests only with a certain portion of the training data available. In such a case, one algorithm may work better with the smaller data set, but another may work with the complete set. Then, you get to weigh performance VS accuracy VS challenge of implementation (such as deploying on a Hadoop cluster).&lt;/p&gt;

&lt;p&gt;Therefore, different algorithms are suited for different project. However, there are some general rules:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;All algorithms always do better with unreduced data sets (more data is better).&lt;/li&gt;
&lt;li&gt;More complex algorithms aren''t necessarily better.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I suggest starting with a simple algorithm and ensuring you have high quality data. If you have additional time, you can implement more complex algorithms and create a comparison which is unique to your data set. &lt;/p&gt;

&lt;p&gt;Most of my info comes from &lt;a href="http://ai.arizona.edu/intranet/papers/comparative.ieeeis.pdf" rel="nofollow"&gt;This study&lt;/a&gt;. You''ll find lots of detail about implementation there.&lt;/p&gt;
', NULL, NULL, '''/a'':337C ''/intranet/papers/comparative.ieeeis.pdf'':332C ''/li'':272C,281C ''/ol'':282C ''/p'':55C,115C,162C,240C,256C,320C,347C ''5'':93C ''6th'':113C ''account'':172C ''accuraci'':57C,157C,228C ''addit'':301C ''ai.arizona.edu'':331C ''ai.arizona.edu/intranet/papers/comparative.ieeeis.pdf'':330C ''algorithm'':32C,47C,102C,124C,128C,153C,204C,244C,260C,276C,290C,308C ''also'':167C ''although'':30C ''alway'':261C ''anoth'':214C ''aren'':277C ''avail'':198C ''base'':23C,64C ''better'':207C,263C,271C,280C ''calcul'':10C,26C ''cart'':81C ''case'':202C ''certain'':192C ''challeng'':230C ''choos'':159C ''cluster'':239C ''collect'':71C ''come'':326C ''compar'':66C,104C,155C ''comparison'':312C ''complet'':219C ''complex'':275C,307C ''creat'':310C ''data'':72C,82C,97C,136C,180C,197C,211C,266C,269C,297C,318C ''deploy'':235C ''describ'':150C ''detail'':343C ''differ'':127C,243C,248C ''effect'':59C,133C ''ensur'':292C ''evalu'':63C ''exampl'':77C ''factor'':170C ''familiar'':5C ''find'':340C ''first'':92C ''general'':254C ''get'':223C ''hadoop'':238C ''happen'':110C ''high'':295C ''howev'':250C ''href'':329C ''ideal'':143C ''implement'':232C,305C,345C ''info'':325C ''interest'':164C ''item'':7C,8C,13C,45C,46C ''item-item'':6C,44C ''last'':85C ''less'':132C ''li'':258C,273C ''ll'':89C,339C ''lot'':341C ''mahout'':119C ''mani'':123C,152C ''may'':139C,205C,215C ''minim'':178C ''month'':87C,94C,114C ''necessarili'':279C ''need'':176C ''nofollow'':334C ''ol'':257C ''one'':203C ''oppos'':15C ''p'':1C,56C,116C,163C,241C,283C,321C ''perform'':183C,226C ''portion'':193C ''previous'':70C ''project'':249C ''qualiti'':106C,296C ''question'':39C,54C ''realli'':109C ''reason'':118C,184C ''recommend'':9C,24C,61C,67C ''regard'':42C ''rel'':333C ''rule'':255C ''run'':99C,186C ''set'':73C,75C,137C,181C,212C,220C,267C,319C ''ship'':120C ''shop'':80C ''similar'':11C,27C ''simpl'':289C ''six'':86C ''smaller'':210C ''specif'':51C ''start'':286C ''studi'':336C ''suggest'':285C ''suit'':246C ''take'':168C ''test'':147C,188C ''thank'':48C ''therefor'':242C ''time'':302C ''train'':74C,96C,196C ''uniqu'':315C ''unreduc'':265C ''use'':35C,90C ''user'':18C,19C,22C,29C ''user-bas'':21C ''user-us'':17C ''various'':101C ''vs'':227C,229C ''weigh'':225C ''winner'':161C ''work'':140C,206C,216C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2595, 2, '2014-12-03 18:36:00.88', 2, NULL, NULL, '&lt;p&gt;Within each class, you''ll have distributions of values for the features. That in itself is not a reason for concern. &lt;/p&gt;

&lt;p&gt;From a slightly theoretical point of view, you can ask yourself why you should scale your features and why you should scale them in exactly the chosen way.&lt;br&gt;
One reason may be that your particular training algorithm is known to converge faster (better) with values around 0 - 1 than with features which cover other orders of magnitude. In that case, you''re probably fine. My guess is that your SVM is fine: you want to avoid too large numbers because of the inner product, but a max of 1.2 vs. a max of 1.0 won''t make much of a difference.&lt;br&gt;
(OTOH, if you e.g. knew your algorithm to not accept negative values you''d obviously be in trouble. )&lt;/p&gt;

&lt;p&gt;The practical question is whether your model performs well for cases that are slightly out of the range covered by training. This I believe can best and possibly only be answered by testing with such cases / inspecting test results for performance drop for cases outside the training domain. It is a valid concern and looking into this would be part of the validation of your model. &lt;/p&gt;

&lt;p&gt;Observing differences of the size you describe is IMHO a reason to have a pretty close look at model stability. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2633, 1, '2014-12-07 04:40:53.677', 0, 301, 'Can hadoop with Spark be configured with 1GB RAM', '&lt;p&gt;I''m trying to set up a cluster (1 namenode, 1 datanode) on AWS.
I''m using free one year trial period of AWS, but the challenge is, instance is created with 1GB of RAM.&lt;/p&gt;

&lt;p&gt;As I''m a student, I cannot afford much. Can anyone please suggest me some solution?&lt;/p&gt;

&lt;p&gt;Also, it would be great if you could provide any links for setting up multi cluster hadoop with spark on AWS.&lt;/p&gt;

&lt;p&gt;Note: I cannot try in GCE as my trial period is exhausted. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6967, 2, '2015-09-01 04:24:16.963', 0, NULL, NULL, '&lt;p&gt;In machine learning, we usually call "correctly" and "wrongly" as sensitivity, specificity, prediction accuracy etc. Please &lt;a href="https://en.wikipedia.org/wiki/Sensitivity_and_specificity" rel="nofollow"&gt;read&lt;/a&gt; this Wikipedia page for more details.&lt;/p&gt;

&lt;p&gt;You can use hold a validation subset by cross-validation to validate your model.&lt;/p&gt;
', NULL, NULL, '''/a'':26C ''/p'':33C,50C ''/wiki/sensitivity_and_specificity'':22C ''accuraci'':15C ''call'':7C ''correct'':8C ''cross'':44C ''cross-valid'':43C ''detail'':32C ''en.wikipedia.org'':21C ''en.wikipedia.org/wiki/sensitivity_and_specificity'':20C ''etc'':16C ''hold'':38C ''href'':19C ''learn'':4C ''machin'':3C ''model'':49C ''nofollow'':24C ''p'':1C,34C ''page'':29C ''pleas'':17C ''predict'':14C ''read'':25C ''rel'':23C ''sensit'':12C ''specif'':13C ''subset'':41C ''use'':37C ''usual'':6C ''valid'':40C,45C,47C ''wikipedia'':28C ''wrong'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2596, 1, '2014-12-03 21:47:34.907', 4, 81, 'Method for solving problem with variable number of predictors', '&lt;p&gt;I''ve been toying with this idea for a while. I think there is probably some method in the text mining literature, but I haven''t come across anything just right...&lt;/p&gt;

&lt;p&gt;What is/are some methods for tackling a problem where the number of variables it its self a variable.  This is not a missing data problem, but one where the nature of the problem fundamentally changes. Consider the following example:&lt;/p&gt;

&lt;p&gt;Suppose I want to predict who will win a race, a simple multinomial classification problem. I have lots of past data on races, plenty to train on.  Lets further suppose I have observed each contestant run multiple races. The problem however is that the number or racers is variable. Sometimes there are only 2 racers, sometimes there are as many as 100 racers.&lt;/p&gt;

&lt;p&gt;One solution might be to train a separate model for each number or racers, resulting in 99 models in this case, using any method I choose.  E.g. I could have 100 random forests. &lt;/p&gt;

&lt;p&gt;Another solution might be to include an additional variable called ''number_of_contestants'' and have input field for 100 racers and simply leave them blank when no racer is present.  Intuitively, it seems that this method would have difficulties predicting the outcome of a 100 contestant race if the number of racers follows a Poisson distribution (which I didn''t originally specify in the problem, but I am saying it here).    &lt;/p&gt;

&lt;p&gt;Thoughts?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2597, 2, '2014-12-04 03:42:48.357', 2, NULL, NULL, '&lt;p&gt;I don''t see the problem. All you need is a learner to map a bit string as long as the &lt;em&gt;total number of contestants&lt;/em&gt;, representing the subset who are taking part, to another bit string (with only one bit set) representing the winner, or a ranked list, if you want them all (assuming you have the whole list in your training data). In the latter case you would have a learning-to-rank problem.&lt;/p&gt;

&lt;p&gt;If the contestant landscape can change it would help to find a vector space embedding for them so you can use the previous embeddings as an initial guess and rank anyone, even hypothetical, given their vector representation. As the number of users increases the embedding should stabilize and retraining should become less costly. The question is how to find the embedding, of course. If you have a lot of training data, you could probably find a randomized one along with the ranking function. If you don''t, you would have to generate the embedding by some algorithm and estimate only the ranking function. I have not faced your problem before so I can''t direct you to a particular paper, but the recent NLP literature should give you some inspiration, e.g. &lt;a href="http://jmlr.org/papers/volume13/shalit12a/shalit12a.pdf" rel="nofollow"&gt;this&lt;/a&gt;. I still think it is feasible.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2598, 1, '2014-12-04 05:18:03.72', 5, 2514, 'Item based and user based recommendation difference in Mahout', '&lt;p&gt;I would like to know how exactly mahout user based and item based recommendation differ from each other.&lt;/p&gt;

&lt;p&gt;It defines that&lt;/p&gt;

&lt;p&gt;&lt;a href="https://mahout.apache.org/users/recommender/userbased-5-minutes.html"&gt;User-based&lt;/a&gt;: Recommend items by finding similar users. This is often harder to scale because of the dynamic nature of users.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://mahout.apache.org/users/recommender/intro-itembased-hadoop.html"&gt;Item-based&lt;/a&gt;: Calculate similarity between items and make recommendations. Items usually don''t change much, so this often can be computed off line.&lt;/p&gt;

&lt;p&gt;But though there are two kind of recommendation available, what I understand is that both these will take some data model ( say 1,2 or 1,2,.5 as item1,item2,value or user1,user2,value where value is not mandatory) and will perform all calculation as the similarity measure and recommender build-in function we chose and we can run both user/item based recommendation on the same data ( is this a correct assumption ?? ).&lt;/p&gt;

&lt;p&gt;So I would like to know how exactly and in which all aspects these two type of algorithm differ. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3820, 2, '2015-01-06 01:26:41.587', 0, NULL, NULL, '&lt;p&gt;You don''t need a tool and I don''t recommend you use one.&lt;/p&gt;

&lt;p&gt;Convert the html to well-formed XML (XHTML) - I recommend the tagsoup.&lt;/p&gt;

&lt;p&gt;Once you''ve done that the data is just another XML feed and you can write an XSLT transformation (or XQuery) to access and pull out the data you want in the format you want. &lt;/p&gt;

&lt;p&gt;That might mean learning  XSLT/XQuery if you don''t already know it but you will be learning skills that (unlike scraping tools) have multiple rather than just than one useful application.&lt;/p&gt;
', NULL, NULL, '''/p'':16C,31C,68C,101C ''access'':55C ''alreadi'':79C ''anoth'':42C ''applic'':100C ''convert'':18C ''data'':39C,60C ''done'':36C ''feed'':44C ''form'':24C ''format'':65C ''html'':20C ''know'':80C ''learn'':73C,86C ''mean'':72C ''might'':71C ''multipl'':93C ''need'':5C ''one'':15C,98C ''p'':1C,17C,32C,69C ''pull'':57C ''rather'':94C ''recommend'':12C,28C ''scrape'':90C ''skill'':87C ''tagsoup'':30C ''tool'':7C,91C ''transform'':51C ''unlik'':89C ''use'':14C,99C ''ve'':35C ''want'':62C,67C ''well'':23C ''well-form'':22C ''write'':48C ''xhtml'':26C ''xml'':25C,43C ''xqueri'':53C ''xslt'':50C ''xslt/xquery'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2599, 2, '2014-12-04 06:08:25', 3, NULL, NULL, '&lt;p&gt;You are correct that both models work on the same data without any problem. Both items operate on a matrix of user-item ratings.&lt;/p&gt;

&lt;p&gt;In the user-based approach the algorithm produces a rating for an item &lt;code&gt;i&lt;/code&gt; by a user &lt;code&gt;u&lt;/code&gt; by combining the ratings of other users &lt;code&gt;u''&lt;/code&gt; that are similar to &lt;code&gt;u&lt;/code&gt;. Similar here means that the two user''s ratings have a high Pearson correlation or cosine similarity or something similar.&lt;/p&gt;

&lt;p&gt;In the item-based approach we produce a rating for &lt;code&gt;i&lt;/code&gt; by &lt;code&gt;u&lt;/code&gt; by looking at the set of items &lt;code&gt;i''&lt;/code&gt; that are similar to &lt;code&gt;i&lt;/code&gt; (in the same sense as above except now we''d be looking at the ratings that items have received from users) that &lt;code&gt;u&lt;/code&gt; has rated and then combines the ratings by &lt;code&gt;u&lt;/code&gt; of &lt;code&gt;i''&lt;/code&gt; into a predicted rating  by &lt;code&gt;u&lt;/code&gt; for &lt;code&gt;i&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;The item-based approach was invented at Amazon (&lt;a href="http://dl.acm.org/citation.cfm?id=642471" rel="nofollow"&gt;http://dl.acm.org/citation.cfm?id=642471&lt;/a&gt;) to address their scale challenges with user-based filtering. The number of things they sell is much less and much less dynamic than the number of users so the item-item similarities can be computed offline and accessed when needed.&lt;/p&gt;
', NULL, NULL, '''/citation.cfm?id=642471'':193C ''/citation.cfm?id=642471&lt;/a&gt;)'':198C ''/code'':45C,51C,61C,68C,104C,108C,118C,125C,150C,161C,165C,173C,177C ''/p'':27C,89C,178C,241C ''access'':238C ''address'':200C ''algorithm'':36C ''amazon'':188C ''approach'':34C,96C,184C ''base'':33C,95C,183C,207C ''challeng'':203C ''code'':43C,49C,59C,66C,102C,106C,116C,123C,148C,159C,163C,171C,175C ''combin'':53C,155C ''comput'':235C ''correct'':4C ''correl'':82C ''cosin'':84C ''d'':135C ''data'':12C ''dl.acm.org'':192C,197C ''dl.acm.org/citation.cfm?id=642471'':191C ''dl.acm.org/citation.cfm?id=642471&lt;/a&gt;)'':196C ''dynam'':221C ''except'':132C ''filter'':208C ''high'':80C ''href'':190C ''invent'':186C ''item'':17C,25C,42C,94C,115C,142C,182C,230C,231C ''item-bas'':93C,181C ''item-item'':229C ''less'':217C,220C ''look'':110C,137C ''matrix'':21C ''mean'':71C ''model'':7C ''much'':216C,219C ''need'':240C ''nofollow'':195C ''number'':210C,224C ''offlin'':236C ''oper'':18C ''p'':1C,28C,90C,179C ''pearson'':81C ''predict'':168C ''problem'':15C ''produc'':37C,98C ''rate'':26C,39C,55C,77C,100C,140C,152C,157C,169C ''receiv'':144C ''rel'':194C ''scale'':202C ''sell'':214C ''sens'':129C ''set'':113C ''similar'':64C,69C,85C,88C,121C,232C ''someth'':87C ''thing'':212C ''two'':74C ''u'':50C,60C,67C,107C,149C,160C,172C ''user'':24C,32C,48C,58C,75C,146C,206C,226C ''user-bas'':31C,205C ''user-item'':23C ''without'':13C ''work'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2600, 1, '2014-12-04 07:47:32.74', 1, 77, 'How does one feed graph optimization problems into Python''s anneal function in SciPy?', '&lt;p&gt;I am interested in graph problems like 2-color, max-clique, stable sets, etc but the documentation for scipy.optimize.anneal seems to be for ordinary functions. How would one apply this library towards graph formulations?&lt;/p&gt;
', 'python  graphs  optimization', NULL, '''/p'':54C ''2'':26C ''anneal'':11A ''appli'':48C ''cliqu'':30C ''color'':27C ''document'':36C ''etc'':33C ''feed'':4A ''formul'':53C ''function'':12A,44C ''graph'':5A,16B,23C,52C ''interest'':21C ''librari'':50C ''like'':25C ''max'':29C ''max-cliqu'':28C ''one'':3A,47C ''optim'':6A,17B ''ordinari'':43C ''p'':18C ''problem'':7A,24C ''python'':9A,15B ''scipi'':14A ''scipy.optimize.anneal'':38C ''seem'':39C ''set'':32C ''stabl'':31C ''toward'':51C ''would'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2601, 1, '2014-12-04 08:54:32.863', 3, 317, 'How to use "sann" function in R to solve graph problems?', '&lt;p&gt;I came across a package in R which has a function called &lt;code&gt;sann&lt;/code&gt; for simulated annealing. &lt;/p&gt;

&lt;p&gt;&lt;code&gt;sann&lt;/code&gt; uses parameters &lt;code&gt;fn&lt;/code&gt; and &lt;code&gt;gr&lt;/code&gt; to optimize and to select new points, respectively. &lt;/p&gt;

&lt;p&gt;For something like the max-clique or max-stable set problems, &lt;code&gt;fn&lt;/code&gt; would be a summing function, but it''s less clear how one would formulate &lt;code&gt;gr&lt;/code&gt; to fix these graph computations. In these cases, how would &lt;code&gt;gr&lt;/code&gt; "select"?&lt;/p&gt;
', 'r  graphs', NULL, '''/code'':29C,37C,42C,46C,72C,89C,102C ''/p'':33C,55C,104C ''across'':17C ''anneal'':32C ''call'':26C ''came'':16C ''case'':97C ''clear'':82C ''cliqu'':63C ''code'':27C,35C,40C,44C,70C,87C,100C ''comput'':94C ''fix'':91C ''fn'':41C,71C ''formul'':86C ''function'':5A,25C,77C ''gr'':45C,88C,101C ''graph'':10A,13B,93C ''less'':81C ''like'':59C ''max'':62C,66C ''max-cliqu'':61C ''max-stabl'':65C ''new'':52C ''one'':84C ''optim'':48C ''p'':14C,34C,56C ''packag'':19C ''paramet'':39C ''point'':53C ''problem'':11A,69C ''r'':7A,12B,21C ''respect'':54C ''sann'':4A,28C,36C ''select'':51C,103C ''set'':68C ''simul'':31C ''solv'':9A ''someth'':58C ''stabl'':67C ''sum'':76C ''use'':3A,38C ''would'':73C,85C,99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2606, 2, '2014-12-04 22:13:30.747', 2, NULL, NULL, '&lt;p&gt;Classes related to Artificial Intelligence are typically taught in Computer Science departments. Looking at the &lt;a href="https://my.feit.uts.edu.au/pages/course/undergraduate/it_project_subjects" rel="nofollow"&gt;IT Project Subjects offered by your university&lt;/a&gt;, I suspect Data Analytics would indeed be more relevant to AI than Internetworking and Applications.&lt;/p&gt;

&lt;p&gt;Looking at the courses offered by your department, the following likely involve aspects of AI:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Image Processing and Pattern Recognition&lt;/li&gt;
&lt;li&gt;Intelligent Agents&lt;/li&gt;
&lt;li&gt;Building Intelligent Agents&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;For self-directed study in AI, I recommend starting with Russell &amp;amp; Norvig''s essential textbook &lt;a href="http://aima.cs.berkeley.edu/" rel="nofollow"&gt;Artificial Intelligence: A Modern Approach&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;As to what it will take to create a human-like strong AI, I recommend this collection of essays: &lt;a href="http://rads.stackoverflow.com/amzn/click/0198248547" rel="nofollow"&gt;The Philosophy of Artificial Intelligence&lt;/a&gt;... even though the material is getting a bit out-of-date by now.&lt;/p&gt;

&lt;p&gt;Good luck!&lt;/p&gt;
', NULL, NULL, '''/a'':31C,111C,146C ''/amzn/click/0198248547'':138C ''/li'':72C,76C,81C ''/p'':47C,64C,112C,161C,165C ''/pages/course/undergraduate/it_project_subjects'':21C ''/ul'':82C ''agent'':75C,80C ''ai'':42C,63C,90C,127C ''aima.cs.berkeley.edu'':103C ''amp'':96C ''analyt'':35C ''applic'':46C ''approach'':110C ''artifici'':5C,106C,144C ''aspect'':61C ''bit'':154C ''build'':78C ''class'':2C ''collect'':131C ''comput'':11C ''cours'':52C ''creat'':121C ''data'':34C ''date'':158C ''depart'':13C,56C ''direct'':87C ''essay'':133C ''essenti'':99C ''even'':147C ''follow'':58C ''get'':152C ''good'':163C ''href'':18C,102C,135C ''human'':124C ''human-lik'':123C ''imag'':67C ''inde'':37C ''intellig'':6C,74C,79C,107C,145C ''internetwork'':44C ''involv'':60C ''li'':66C,73C,77C ''like'':59C,125C ''look'':14C,49C ''luck'':164C ''materi'':150C ''modern'':109C ''my.feit.uts.edu.au'':20C ''my.feit.uts.edu.au/pages/course/undergraduate/it_project_subjects'':19C ''nofollow'':23C,105C,140C ''norvig'':97C ''offer'':27C,53C ''out-of-d'':155C ''p'':1C,48C,83C,113C,162C ''pattern'':70C ''philosophi'':142C ''process'':68C ''project'':25C ''rads.stackoverflow.com'':137C ''rads.stackoverflow.com/amzn/click/0198248547'':136C ''recognit'':71C ''recommend'':92C,129C ''rel'':22C,104C,139C ''relat'':3C ''relev'':40C ''russel'':95C ''scienc'':12C ''self'':86C ''self-direct'':85C ''start'':93C ''strong'':126C ''studi'':88C ''subject'':26C ''suspect'':33C ''take'':119C ''taught'':9C ''textbook'':100C ''though'':148C ''typic'':8C ''ul'':65C ''univers'':30C ''would'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2607, 2, '2014-12-04 23:26:15.803', 1, NULL, NULL, '&lt;p&gt;Would it be possible to use Approximate Bayesian computation (ABC)? If you assume a distribution for the number of competitors (e.g. Poisson), select a subset of competitors each iteration and simulate your data using multinomial distributions with probabilities based on competitors'' features, after discarding parameters that don''t match your training data, you should be able to obtain parameters for each competitor (that is, posterior distributions) and generate more races. &lt;/p&gt;

&lt;p&gt;This might not work if the number of competitors is so important that it affects the coefficients of features for each competitor.&lt;/p&gt;
', NULL, NULL, '''/p'':72C,96C ''abc'':11C ''abl'':57C ''affect'':88C ''approxim'':8C ''assum'':14C ''base'':40C ''bayesian'':9C ''coeffici'':90C ''competitor'':21C,28C,42C,63C,82C,95C ''comput'':10C ''data'':34C,53C ''discard'':45C ''distribut'':16C,37C,67C ''e.g'':22C ''featur'':43C,92C ''generat'':69C ''import'':85C ''iter'':30C ''match'':50C ''might'':75C ''multinomi'':36C ''number'':19C,80C ''obtain'':59C ''p'':1C,73C ''paramet'':46C,60C ''poisson'':23C ''possibl'':5C ''posterior'':66C ''probabl'':39C ''race'':71C ''select'':24C ''simul'':32C ''subset'':26C ''train'':52C ''use'':7C,35C ''work'':77C ''would'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2608, 1, '2014-12-05 00:26:12.983', 1, 57, 'Simple Excel Question: VLookup Error', '&lt;p&gt;My data looks like this: &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/9LgwU.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Why is this error showing up?&lt;/p&gt;
', 'data-cleaning', NULL, '''/9lgwu.png'':21C ''/p'':15C,27C,35C ''alt'':22C ''clean'':8B ''data'':7B,11C ''data-clean'':6B ''descript'':25C ''enter'':23C ''error'':5A,32C ''excel'':2A ''i.stack.imgur.com'':20C ''i.stack.imgur.com/9lgwu.png'':19C ''imag'':24C ''img'':17C ''like'':13C ''look'':12C ''p'':9C,16C,28C ''question'':3A ''show'':33C ''simpl'':1A ''src'':18C ''vlookup'':4A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2609, 1, '2014-12-05 05:50:29.903', 6, 1462, 'Good books for Hadoop, Spark, and Spark Streaming', '&lt;p&gt;Can anyone suggest any good books to learn hadoop and map reduce basics?&lt;/p&gt;

&lt;p&gt;Also something for Spark, and Spark Streaming?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'hadoop', NULL, '''/p'':24C,33C,36C ''also'':26C ''anyon'':12C ''basic'':23C ''book'':2A,16C ''good'':1A,15C ''hadoop'':4A,9B,19C ''learn'':18C ''map'':21C ''p'':10C,25C,34C ''reduc'':22C ''someth'':27C ''spark'':5A,7A,29C,31C ''stream'':8A,32C ''suggest'':13C ''thank'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3808, 1, '2015-01-04 11:22:28.817', 1, 59, 'Compare Neural Network generalization results', '&lt;p&gt;I''m trying to develop my neural network with both early stopping and bayesian regularization (matlab implementation, lm algorithm is used for both).
Since in bayesian regularization I have not the validation set, how can I compare the generalization capability of the networks obtained with the two methodologies? 
Thanks&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/p'':60C ''algorithm'':29C ''bayesian'':24C,36C ''capabl'':50C ''compar'':1A,47C ''develop'':15C ''earli'':21C ''general'':4A,49C ''implement'':27C ''learn'':8B ''lm'':28C ''m'':12C ''machin'':7B ''machine-learn'':6B ''matlab'':26C ''methodolog'':58C ''network'':3A,18C,53C ''neural'':2A,17C ''neuralnetwork'':9B ''obtain'':54C ''p'':10C ''regular'':25C,37C ''result'':5A ''set'':43C ''sinc'':34C ''stop'':22C ''thank'':59C ''tri'':13C ''two'':57C ''use'':31C ''valid'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2612, 1, '2014-12-05 14:03:58.777', 5, 143, 'Name Anonymization Software', '&lt;p&gt;Although I have seen a few good questions asked about data anonymization, I was wondering if there were answers to this more specific variant.&lt;/p&gt;

&lt;p&gt;I am seeking a tool (or to design one) that will anonymize human names from a specific country: particularly first names in unstructured text. Many of the tools that I have seen have considered the wider dimensions of data anonymization; with an equal focus on dates of birth, addresses, etc.&lt;/p&gt;

&lt;p&gt;An imperative aspect is that it needs to have near absolute recall. The major pitfalls, as far as I can see, are diminutive variants ("Tommy" instead of "Thomas", "Ben" instead of "Benjamin", etc.) and typos. These two factors prevent a simple regex based on a database of names (based on censuses, etc.)&lt;/p&gt;
', 'text-mining  anonymization', NULL, '''/p'':33C,85C,139C ''absolut'':97C ''address'':83C ''although'':9C ''anonym'':2A,7B,20C,46C,74C ''answer'':27C ''ask'':17C ''aspect'':89C ''base'':129C,135C ''ben'':115C ''benjamin'':118C ''birth'':82C ''census'':137C ''consid'':68C ''countri'':52C ''data'':19C,73C ''databas'':132C ''date'':80C ''design'':42C ''dimens'':71C ''diminut'':109C ''equal'':77C ''etc'':84C,119C,138C ''factor'':124C ''far'':103C ''first'':54C ''focus'':78C ''good'':15C ''human'':47C ''imper'':88C ''instead'':112C,116C ''major'':100C ''mani'':59C ''mine'':6B ''name'':1A,48C,55C,134C ''near'':96C ''need'':93C ''one'':43C ''p'':8C,34C,86C ''particular'':53C ''pitfal'':101C ''prevent'':125C ''question'':16C ''recal'':98C ''regex'':128C ''see'':107C ''seek'':37C ''seen'':12C,66C ''simpl'':127C ''softwar'':3A ''specif'':31C,51C ''text'':5B,58C ''text-min'':4B ''thoma'':114C ''tommi'':111C ''tool'':39C,62C ''two'':123C ''typo'':121C ''unstructur'':57C ''variant'':32C,110C ''wider'':70C ''wonder'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2613, 2, '2014-12-05 15:28:39.64', 0, NULL, NULL, '&lt;p&gt;Ruby together with &lt;a href="http://www.nokogiri.org/" rel="nofollow"&gt;Nokogiri&lt;/a&gt; allows to access HTML and XML documents via XPath and CSS selectors. Here is a &lt;a href="http://thaiwood.io/screen-scraping-with-a-saw-a-nokogiri-tutorial-with-examples/" rel="nofollow"&gt;tutorial&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':11C,35C ''/p'':36C ''/screen-scraping-with-a-saw-a-nokogiri-tutorial-with-examples/'':31C ''access'':14C ''allow'':12C ''css'':22C ''document'':18C ''href'':6C,28C ''html'':15C ''nofollow'':9C,33C ''nokogiri'':10C ''p'':1C ''rel'':8C,32C ''rubi'':2C ''selector'':23C ''thaiwood.io'':30C ''thaiwood.io/screen-scraping-with-a-saw-a-nokogiri-tutorial-with-examples/'':29C ''togeth'':3C ''tutori'':34C ''via'':19C ''www.nokogiri.org'':7C ''xml'':17C ''xpath'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10519, 1, '2016-03-04 05:59:23.453', 0, 18, 'Resource for reinforcement learning using ANN?', '&lt;p&gt;I recently started learning the reinforcement learning using ANN. And I am a beginner in reinforcement learning. I wish to apply reinforcement learning principles for problems similar to a bot for 2048. So please suggest me some sources to learn reinforcement learning using ANN.&lt;/p&gt;
', 'machine-learning  neuralnetwork  reinforcement-learning', NULL, '''/p'':59C ''2048'':46C ''ann'':6A,23C,58C ''appli'':35C ''beginn'':28C ''bot'':44C ''learn'':4A,9B,13B,18C,21C,31C,37C,54C,56C ''machin'':8B ''machine-learn'':7B ''neuralnetwork'':10B ''p'':14C ''pleas'':48C ''principl'':38C ''problem'':40C ''recent'':16C ''reinforc'':3A,12B,20C,30C,36C,55C ''reinforcement-learn'':11B ''resourc'':1A ''similar'':41C ''sourc'':52C ''start'':17C ''suggest'':49C ''use'':5A,22C,57C ''wish'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2614, 2, '2014-12-05 15:29:04.743', 2, NULL, NULL, '&lt;p&gt;I don''t think you really need some special software, but rather to employee existing tools, such as encryption algorithms.&lt;/p&gt;

&lt;p&gt;Why not just encrypt the names with any key-based algorithm and store the key securely?&lt;/p&gt;

&lt;p&gt;If you didn''t need to be able to recover the names, but just to identify variation to the level of differences in diminutives, then you could simply use hashing rather than encryption.&lt;/p&gt;

&lt;p&gt;I''m not sure what environment you want to carry this out it, but any language such as &lt;code&gt;R&lt;/code&gt; or SQL/NoSQL database could easily carry this out programmatically.&lt;/p&gt;
', NULL, NULL, '''/code'':98C ''/p'':22C,41C,76C,108C ''abl'':50C ''algorithm'':21C,35C ''base'':34C ''carri'':87C,104C ''code'':96C ''could'':69C,102C ''databas'':101C ''didn'':45C ''differ'':64C ''diminut'':66C ''easili'':103C ''employe'':15C ''encrypt'':20C,27C,75C ''environ'':83C ''exist'':16C ''hash'':72C ''identifi'':58C ''key'':33C,39C ''key-bas'':32C ''languag'':93C ''level'':62C ''m'':79C ''name'':29C,54C ''need'':8C,47C ''p'':1C,23C,42C,77C ''programmat'':107C ''r'':97C ''rather'':13C,73C ''realli'':7C ''recov'':52C ''secur'':40C ''simpli'':70C ''softwar'':11C ''special'':10C ''sql/nosql'':100C ''store'':37C ''sure'':81C ''think'':5C ''tool'':17C ''use'':71C ''variat'':59C ''want'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2615, 2, '2014-12-05 15:31:06.383', 1, NULL, NULL, '&lt;p&gt;This is a common result of imprecise matching, such as with whitespace problems. &lt;/p&gt;

&lt;p&gt;For example, if you imagine that the grey shading used in codeblocks on StackExchange represents the whitespaces in your Excel cells, then 
  &lt;code&gt;AK&lt;/code&gt; 
is technically the same as &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;AK  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You can trim and/or use wildcard chars to allow matching to skip spaces. For example:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;=VLOOKUP(CONCATENATE("*",TRIM(A2),"*"), $E$2:$F$5, 2,FALSE)&lt;/code&gt;&lt;/p&gt;
', NULL, NULL, '''/code'':41C,51C,82C ''/p'':15C,47C,69C,83C ''/pre'':52C ''2'':77C,80C ''5'':79C ''a2'':75C ''ak'':40C,50C ''allow'':62C ''and/or'':57C ''cell'':37C ''char'':60C ''code'':39C,49C,71C ''codeblock'':28C ''common'':5C ''concaten'':73C ''e'':76C ''exampl'':18C,68C ''excel'':36C ''f'':78C ''fals'':81C ''grey'':24C ''imagin'':21C ''imprecis'':8C ''match'':9C,63C ''p'':1C,16C,53C,70C ''pre'':48C ''problem'':14C ''repres'':31C ''result'':6C ''shade'':25C ''skip'':65C ''space'':66C ''stackexchang'':30C ''technic'':43C ''trim'':56C,74C ''use'':26C,58C ''vlookup'':72C ''whitespac'':13C,33C ''wildcard'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2616, 2, '2014-12-05 15:38:09.867', 3, NULL, NULL, '&lt;p&gt;There''s such an overwhelming amount of literature that with programming, databases, and Big Data I like to stick to the O''reilly series as my go-to source. O''reilly books are extremely popular in the industry and I''ve been very satisfied.&lt;/p&gt;

&lt;p&gt;A current version of &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="http://shop.oreilly.com/product/0636920021773.do" rel="nofollow"&gt;Hadoop: The Definitive Guide&lt;/a&gt;, &lt;/li&gt;
&lt;li&gt;&lt;a href="http://shop.oreilly.com/product/0636920025122.do" rel="nofollow"&gt;MapReduce Design Patterns&lt;/a&gt;, and &lt;/li&gt;
&lt;li&gt;&lt;a href="http://shop.oreilly.com/product/0636920028512.do" rel="nofollow"&gt;Learning Spark&lt;/a&gt; &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;might suit your needs by providing high quality, immediately useful information and avoiding information overload -- all are published by O''reilly.&lt;/p&gt;

&lt;p&gt;Spark Streaming is covered in Chapter 13 of "Learning Spark".&lt;/p&gt;
', NULL, NULL, '''/a'':67C,80C,93C ''/li'':68C,82C,94C ''/ol'':95C ''/p'':47C,53C,118C,130C ''/product/0636920021773.do'':60C ''/product/0636920025122.do'':74C ''/product/0636920028512.do'':88C ''13'':126C ''amount'':7C ''avoid'':109C ''big'':15C ''book'':34C ''chapter'':125C ''cover'':123C ''current'':50C ''data'':16C ''databas'':13C ''definit'':65C ''design'':78C ''extrem'':36C ''go'':29C ''go-to'':28C ''guid'':66C ''hadoop'':63C ''high'':103C ''href'':57C,71C,85C ''immedi'':105C ''industri'':40C ''inform'':107C,110C ''learn'':91C,128C ''li'':55C,69C,83C ''like'':18C ''literatur'':9C ''mapreduc'':77C ''might'':97C ''need'':100C ''nofollow'':62C,76C,90C ''o'':23C,32C,116C ''ol'':54C ''overload'':111C ''overwhelm'':6C ''p'':1C,48C,96C,119C ''pattern'':79C ''popular'':37C ''program'':12C ''provid'':102C ''publish'':114C ''qualiti'':104C ''reilli'':24C,33C,117C ''rel'':61C,75C,89C ''satisfi'':46C ''seri'':25C ''shop.oreilly.com'':59C,73C,87C ''shop.oreilly.com/product/0636920021773.do'':58C ''shop.oreilly.com/product/0636920025122.do'':72C ''shop.oreilly.com/product/0636920028512.do'':86C ''sourc'':31C ''spark'':92C,120C,129C ''stick'':20C ''stream'':121C ''suit'':98C ''use'':106C ''ve'':43C ''version'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3812, 2, '2015-01-04 22:29:14.98', 0, NULL, NULL, '&lt;p&gt;Don''t forget about MLlib (the hot kid on the block), cloudera-ml, cloudera oryx, cloudera oryx2, and a myriad of other efforts to make old algorithms like k-means solve nonexistant problems on wannabe big data... &lt;/p&gt;

&lt;h1&gt;There is no one size fits all.&lt;/h1&gt;

&lt;p&gt;Thus, there is no answer to your question, in particular no concise answer. &lt;/p&gt;
', NULL, NULL, '''/h1'':50C ''/p'':41C,65C ''algorithm'':29C ''answer'':56C,64C ''big'':39C ''block'':12C ''cloudera'':14C,16C,18C ''cloudera-ml'':13C ''concis'':63C ''data'':40C ''effort'':25C ''fit'':48C ''forget'':4C ''h1'':42C ''hot'':8C ''k'':32C ''k-mean'':31C ''kid'':9C ''like'':30C ''make'':27C ''mean'':33C ''ml'':15C ''mllib'':6C ''myriad'':22C ''nonexist'':35C ''old'':28C ''one'':46C ''oryx'':17C ''oryx2'':19C ''p'':1C,51C ''particular'':61C ''problem'':36C ''question'':59C ''size'':47C ''solv'':34C ''thus'':52C ''wannab'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2655, 2, '2014-12-09 08:26:57.997', 11, NULL, NULL, '&lt;p&gt;Neural Networks and Deep Learning by Michael Nielsen. The book is still in progress, but it looks quite interesting and promising. And it''s free! Here''s the link: &lt;a href="http://neuralnetworksanddeeplearning.com/" rel="nofollow"&gt;http://neuralnetworksanddeeplearning.com/&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;There are only 5 chapters so far, and the most of them talk about usual neural networks, but it''s still worth having a look. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Update:&lt;/strong&gt; the book has been finished!&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':38C ''/p'':39C,66C,76C ''/strong'':70C ''5'':44C ''book'':11C,72C ''chapter'':45C ''deep'':5C ''far'':47C ''finish'':75C ''free'':26C ''href'':32C ''interest'':20C ''learn'':6C ''link'':30C ''look'':18C,65C ''michael'':8C ''network'':3C,57C ''neural'':2C,56C ''neuralnetworksanddeeplearning.com'':33C,37C ''neuralnetworksanddeeplearning.com/&lt;/a&gt;'':36C ''nielsen'':9C ''nofollow'':35C ''p'':1C,40C,67C ''progress'':15C ''promis'':22C ''quit'':19C ''rel'':34C ''still'':13C,61C ''strong'':68C ''talk'':53C ''updat'':69C ''usual'':55C ''worth'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2617, 2, '2014-12-05 15:43:40.72', 1, NULL, NULL, '&lt;p&gt;In terms of open source NLG components, I''m most familiar with Mumble and FUF/SURGE. They''ve got both similarities and differences, so it''s hard to say which is better...&lt;/p&gt;

&lt;p&gt;&lt;a href="https://code.google.com/p/sparser/source/browse/#svn%2Ftrunk%2FMumble" rel="nofollow"&gt;Mumble&lt;/a&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;written in Lisp&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.eclipse.org/legal/epl-v10.html" rel="nofollow"&gt;EPL license&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;based on tree-adjoining grammar&lt;/li&gt;
&lt;li&gt;focuses on linguistic message planning&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="http://www.cs.bgu.ac.il/surge/index.html" rel="nofollow"&gt;FUF/SURGE&lt;/a&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;written in Lisp&lt;/li&gt;
&lt;li&gt;GPL license&lt;/li&gt;
&lt;li&gt;based on functional unification grammar&lt;/li&gt;
&lt;li&gt;focuses on syntactic realization&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Since it sounds like you''re interested in abstractive summarization (which is much harder than traditional extractive summarization), I''d recommend the following academic papers:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.nist.gov/tac/publications/2010/participant.papers/Rali.proceedings.pdf" rel="nofollow"&gt;Text Generation for Abstractive Summarization&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.aclweb.org/anthology/W11-1608" rel="nofollow"&gt;Framework for Abstractive Summarization using Text-to-Text Generation&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://dl.acm.org/citation.cfm?id=2000990" rel="nofollow"&gt;Towards a Framework for Abstractive Summarization of Multimodal Documents&lt;/a&gt; -- full disclosure: I''m the author of this one&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Also, consider checking out this textbook to get started: &lt;a href="http://rads.stackoverflow.com/amzn/click/0521620368" rel="nofollow"&gt;Building Natural Language Generation Systems&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':43C,61C,88C,155C,175C,194C,228C ''/amzn/click/0521620368'':220C ''/anthology/w11-1608'':162C ''/citation.cfm?id=2000990'':182C ''/legal/epl-v10.html'':56C ''/li'':50C,62C,70C,77C,95C,99C,106C,112C,156C,176C,204C ''/p'':33C,44C,89C,140C,229C ''/p/sparser/source/browse/#svn%2ftrunk%2fmumble'':39C ''/surge/index.html'':84C ''/tac/publications/2010/participant.papers/rali.proceedings.pdf'':147C ''/ul'':78C,113C,205C ''abstract'':123C,153C,167C,189C ''academ'':138C ''adjoin'':68C ''also'':207C ''author'':200C ''base'':64C,101C ''better'':32C ''build'':223C ''check'':209C ''code.google.com'':38C ''code.google.com/p/sparser/source/browse/#svn%2ftrunk%2fmumble'':37C ''compon'':8C ''consid'':208C ''d'':134C ''differ'':23C ''disclosur'':196C ''dl.acm.org'':181C ''dl.acm.org/citation.cfm?id=2000990'':180C ''document'':193C ''epl'':59C ''extract'':131C ''familiar'':12C ''focus'':72C,108C ''follow'':137C ''framework'':165C,187C ''fuf/surge'':16C,87C ''full'':195C ''function'':103C ''generat'':151C,174C,226C ''get'':214C ''got'':19C ''gpl'':97C ''grammar'':69C,105C ''hard'':27C ''harder'':128C ''href'':36C,53C,81C,144C,159C,179C,217C ''interest'':121C ''languag'':225C ''li'':46C,51C,63C,71C,91C,96C,100C,107C,142C,157C,177C ''licens'':60C,98C ''like'':118C ''linguist'':74C ''lisp'':49C,94C ''m'':10C,198C ''messag'':75C ''much'':127C ''multimod'':192C ''mumbl'':14C,42C ''natur'':224C ''nlg'':7C ''nofollow'':41C,58C,86C,149C,164C,184C,222C ''one'':203C ''open'':5C ''p'':1C,34C,79C,114C,206C ''paper'':139C ''plan'':76C ''rads.stackoverflow.com'':219C ''rads.stackoverflow.com/amzn/click/0521620368'':218C ''re'':120C ''realiz'':111C ''recommend'':135C ''rel'':40C,57C,85C,148C,163C,183C,221C ''say'':29C ''similar'':21C ''sinc'':115C ''sound'':117C ''sourc'':6C ''start'':215C ''summar'':124C,132C,154C,168C,190C ''syntact'':110C ''system'':227C ''term'':3C ''text'':150C,171C,173C ''text-to-text'':170C ''textbook'':212C ''toward'':185C ''tradit'':130C ''tree'':67C ''tree-adjoin'':66C ''ul'':45C,90C,141C ''unif'':104C ''use'':169C ''ve'':18C ''written'':47C,92C ''www.aclweb.org'':161C ''www.aclweb.org/anthology/w11-1608'':160C ''www.cs.bgu.ac.il'':83C ''www.cs.bgu.ac.il/surge/index.html'':82C ''www.eclipse.org'':55C ''www.eclipse.org/legal/epl-v10.html'':54C ''www.nist.gov'':146C ''www.nist.gov/tac/publications/2010/participant.papers/rali.proceedings.pdf'':145C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5261, 1, '2015-03-01 14:28:22.123', 4, 265, 'Vectorization of for loop in sentiment analysis', '&lt;p&gt;I''m struggling with for loop in R. I have a following data frame with sentences and two dictionaries with pos and neg words:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(stringr)
library(plyr)
library(dplyr)
library(stringi)
library(qdap)
library(qdapRegex)
library(reshape2)
library(zoo)

# Create data.frame with sentences
sent &amp;lt;- data.frame(words = c("great just great right size and i love this notebook", "benefits great laptop at the top",
                         "wouldnt bad notebook and very good", "very good quality", "bad orgtop but great",
                         "great improvement for that great improvement bad product but overall is not good", "notebook is not good but i love batterytop"), user = c(1,2,3,4,5,6,7),
               number = c(1,1,1,1,1,1,1), stringsAsFactors=F)

# Create pos/negWords
posWords &amp;lt;- c("great","improvement","love","great improvement","very good","good","right","very","benefits",
          "extra","benefit","top","extraordinarily","extraordinary","super","benefits super","good","benefits great",
          "wouldnt bad")
negWords &amp;lt;- c("hate","bad","not good","horrible")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And now I''m gonna to create replication of origin data frame for big data simulation:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Replicate original data.frame - big data simulation (700.000 rows of sentences)
df.expanded &amp;lt;- as.data.frame(replicate(100000,sent$words))
    sent &amp;lt;- coredata(sent)[rep(seq(nrow(sent)),100000),]
    sent$words &amp;lt;- paste(c(""), sent$words, c(""), collapse = NULL)
rownames(sent) &amp;lt;- NULL
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For my further approach, I''ll have to do descending ordering of words in dictionaries with their sentiment score (pos word = 1 and neg word = -1).&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Ordering words in pos/negWords
wordsDF &amp;lt;- data.frame(words = posWords, value = 1,stringsAsFactors=F)
wordsDF &amp;lt;- rbind(wordsDF,data.frame(words = negWords, value = -1))
wordsDF$lengths &amp;lt;- unlist(lapply(wordsDF$words, nchar))
wordsDF &amp;lt;- wordsDF[order(-wordsDF[,3]),]
wordsDF$words &amp;lt;- paste(c(""), wordsDF$words, c(""), collapse = NULL)
rownames(wordsDF) &amp;lt;- NULL
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then I have a following function with for loop. 1) matching exact words 2) count them 3) compute score 4) remove matched words from sentence for another iteration:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;scoreSentence_new &amp;lt;- function(sentence){
  score &amp;lt;- 0
  for(x in 1:nrow(wordsDF)){
    sd &amp;lt;- function(text) {stri_count(text, regex=wordsDF[x,1])} # count matched words
    results &amp;lt;- sapply(sentence, sd, USE.NAMES=F) # count matched words
    score &amp;lt;- (score + (results * wordsDF[x,2])) # compute score
    sentence &amp;lt;- str_replace_all(sentence, wordsDF[x,1], " ") # remove matched words from sentence for next iteration
  }
  score
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;When I call that function&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;SentimentScore_new &amp;lt;- scoreSentence_new(sent$words)
    sent_new &amp;lt;- cbind(sent, SentimentScore_new)
    sent_new$words &amp;lt;- str_trim(sent_new$words, side = "both")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;it resulted into desired output:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;                                                                             words user     SentimentScore_new
                             great just great right size and i love this notebook    1                  4
                                                 benefits great laptop at the top    2                  2
                                               wouldnt bad notebook and very good    3                  2
                                                                very good quality    4                  1
                                                             bad orgtop but great    5                  0
 great improvement for that great improvement bad product but overall is not good    6                  0
                                       notebook is not good but i love batterytop    7                  0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In real I''m using dictionaries with pos/neg words about 7.000 words and I have 200.000 sentences. When I used my approach for 1.000 sentences it takes 45 mins. Please, could you anyone help me with some faster approach using of vectorization or parallel solution. Because of my beginner R programming skills I''m in the end of my efforts :-( Thank you very much in advance for any of your advice or solution&lt;/p&gt;

&lt;p&gt;I was wondering about something like that:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;n &amp;lt;- 1:nrow(wordsDF)
score &amp;lt;- 0

try_1 &amp;lt;- function(ttt) {
sd &amp;lt;- function(text) {stri_count(text, regex=wordsDF[ttt,1])}
results &amp;lt;- sapply(sent$words, sd, USE.NAMES=F)
    score &amp;lt;- (score + (results * wordsDF[ttt,2])) # compute score (count * sentValue)
    sent$words &amp;lt;- str_replace_all(sent$words, wordsDF[ttt,1], " ")
score
}

a &amp;lt;- unlist(sapply(n, try_1))
apply(a,1,sum)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But doesn''t work :-(&lt;/p&gt;
', 'r', NULL, '''-1'':259C,284C ''/code'':169C,231C,313C,412C,448C,527C,681C ''/p'':34C,188C,260C,344C,420C,456C,603C,612C,688C ''/pre'':170C,232C,314C,413C,449C,528C,682C ''0'':354C,501C,516C,526C,622C ''1'':114C,123C,124C,125C,126C,127C,128C,129C,255C,273C,325C,358C,371C,402C,473C,495C,617C,624C,638C,668C,676C,679C ''1.000'':553C ''100000'':205C,216C ''2'':115C,329C,391C,481C,482C,490C,653C ''200.000'':545C ''3'':116C,298C,332C,489C ''4'':117C,335C,474C,494C ''45'':557C ''5'':118C,500C ''6'':119C,515C ''7'':120C,525C ''7.000'':540C ''700.000'':197C ''advanc'':595C ''advic'':600C ''analysi'':7A ''anoth'':342C ''anyon'':562C ''appli'':677C ''approach'':237C,551C,568C ''as.data.frame'':203C ''bad'':79C,87C,97C,160C,165C,484C,496C,508C ''batterytop'':111C,524C ''beginn'':578C ''benefit'':72C,147C,149C,154C,157C,475C ''big'':185C,194C ''c'':61C,113C,122C,136C,163C,221C,224C,303C,306C ''call'':417C ''cbind'':433C ''code'':36C,190C,262C,346C,422C,458C,614C ''collaps'':225C,307C ''comput'':333C,392C,654C ''coredata'':210C ''could'':560C ''count'':330C,366C,372C,382C,633C,656C ''creat'':53C,132C,178C ''data'':22C,182C,186C,195C ''data.frame'':54C,59C,193C,269C,280C ''descend'':243C ''desir'':454C ''df.expanded'':201C ''dictionari'':28C,248C,535C ''doesn'':685C ''dplyr'':42C ''effort'':589C ''end'':586C ''exact'':327C ''extra'':148C ''extraordinari'':152C ''extraordinarili'':151C ''f'':131C,275C,381C,646C ''faster'':567C ''follow'':21C,320C ''frame'':23C,183C ''function'':321C,350C,363C,419C,626C,630C ''gonna'':176C ''good'':83C,85C,103C,107C,143C,144C,156C,167C,488C,492C,514C,520C ''great'':62C,64C,73C,90C,91C,95C,137C,140C,158C,463C,465C,476C,499C,502C,506C ''hate'':164C ''help'':563C ''horribl'':168C ''improv'':92C,96C,138C,141C,503C,507C ''iter'':343C,410C ''lappli'':289C ''laptop'':74C,477C ''length'':286C ''librari'':37C,39C,41C,43C,45C,47C,49C,51C ''like'':610C ''ll'':239C ''loop'':4A,15C,324C ''love'':69C,110C,139C,470C,523C ''lt'':58C,135C,162C,202C,209C,219C,229C,268C,277C,287C,294C,301C,311C,349C,353C,362C,376C,386C,395C,425C,432C,440C,616C,621C,625C,629C,640C,648C,660C,671C ''m'':11C,175C,533C,583C ''match'':326C,337C,373C,383C,404C ''min'':558C ''much'':593C ''n'':615C,674C ''nchar'':292C ''neg'':32C,257C ''negword'':161C,282C ''new'':348C,424C,427C,431C,436C,438C,444C,462C ''next'':409C ''notebook'':71C,80C,104C,472C,485C,517C ''nrow'':214C,359C,618C ''null'':226C,230C,308C,312C ''number'':121C ''order'':244C,263C,296C ''orgtop'':88C,497C ''origin'':181C,192C ''output'':455C ''overal'':100C,511C ''p'':9C,171C,233C,315C,414C,450C,529C,604C,683C ''parallel'':573C ''past'':220C,302C ''pleas'':559C ''plyr'':40C ''pos'':30C,253C ''pos/neg'':537C ''pos/negwords'':133C,266C ''posword'':134C,271C ''pre'':35C,189C,261C,345C,421C,457C,613C ''product'':98C,509C ''program'':580C ''qdap'':46C ''qdapregex'':48C ''qualiti'':86C,493C ''r'':8B,17C,579C ''rbind'':278C ''real'':531C ''regex'':368C,635C ''remov'':336C,403C ''rep'':212C ''replac'':397C,662C ''replic'':179C,191C,204C ''reshape2'':50C ''result'':375C,388C,452C,639C,650C ''right'':65C,145C,466C ''row'':198C ''rownam'':227C,309C ''sappli'':377C,641C,673C ''score'':252C,334C,352C,385C,387C,393C,411C,620C,647C,649C,655C,669C ''scoresent'':347C,426C ''sd'':361C,379C,628C,644C ''sent'':57C,206C,208C,211C,215C,217C,222C,228C,428C,430C,434C,437C,443C,642C,658C,664C ''sentenc'':25C,56C,200C,340C,351C,378C,394C,399C,407C,546C,554C ''sentiment'':6A,251C ''sentimentscor'':423C,435C,461C ''sentvalu'':657C ''seq'':213C ''side'':446C ''simul'':187C,196C ''size'':66C,467C ''skill'':581C ''solut'':574C,602C ''someth'':609C ''str'':396C,441C,661C ''stri'':365C,632C ''stringi'':44C ''stringr'':38C ''stringsasfactor'':130C,274C ''struggl'':12C ''sum'':680C ''super'':153C,155C ''take'':556C ''text'':364C,367C,631C,634C ''thank'':590C ''top'':77C,150C,480C ''tri'':623C,675C ''trim'':442C ''ttt'':627C,637C,652C,667C ''two'':27C ''unlist'':288C,672C ''use'':534C,549C,569C ''use.names'':380C,645C ''user'':112C,460C ''valu'':272C,283C ''vector'':1A,571C ''wonder'':607C ''word'':33C,60C,207C,218C,223C,246C,254C,258C,264C,270C,281C,291C,300C,305C,328C,338C,374C,384C,405C,429C,439C,445C,459C,538C,541C,643C,659C,665C ''wordsdf'':267C,276C,279C,285C,290C,293C,295C,297C,299C,304C,310C,360C,369C,389C,400C,619C,636C,651C,666C ''work'':687C ''wouldnt'':78C,159C,483C ''x'':356C,370C,390C,401C ''zoo'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2618, 1, '2014-12-05 15:51:54.69', 10, 222, 'Ethically and Cost-effectively Scaling Data Scrapes', '&lt;p&gt;Few things in life give me pleasure like scraping structured and unstructured data from the Internet and making use of it in my models. &lt;/p&gt;

&lt;p&gt;For instance, the Data Science Toolkit (or &lt;code&gt;RDSTK&lt;/code&gt; for R programmers) allows me to pull lots of good location-based data using IP''s or addresses and the &lt;code&gt;tm.webmining.plugin&lt;/code&gt; for R''s &lt;code&gt;tm&lt;/code&gt; package makes scraping financial and news data straightfoward. When going beyond such (semi-) structured data I tend to use &lt;code&gt;XPath&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;However, I''m constantly getting throttled by limits on the number of queries you''re allowed to make. I think Google limits me to about 50,000 requests per 24 hours, which is a problem for Big Data.&lt;/p&gt;

&lt;p&gt;From a &lt;em&gt;technical&lt;/em&gt; perspective getting around these limits is easy -- just switch IP addresses and purge other identifiers from your environment. However, this presents both ethical and financial concerns (I think?).&lt;/p&gt;

&lt;p&gt;Is there a solution that I''m overlooking?&lt;/p&gt;
', 'text-mining  scraping', NULL, '''/code'':49C,73C,79C,101C ''/em'':148C ''/p'':38C,102C,142C,177C,187C ''000'':130C ''24'':133C ''50'':129C ''address'':68C,159C ''allow'':53C,119C ''around'':151C ''base'':62C ''beyond'':90C ''big'':140C ''code'':47C,71C,77C,99C ''concern'':174C ''constant'':107C ''cost'':4A ''cost-effect'':3A ''data'':7A,26C,43C,63C,86C,94C,141C ''easi'':155C ''effect'':5A ''em'':146C ''environ'':166C ''ethic'':1A,171C ''financi'':83C,173C ''get'':108C,150C ''give'':18C ''go'':89C ''good'':59C ''googl'':124C ''hour'':134C ''howev'':104C,167C ''identifi'':163C ''instanc'':41C ''internet'':29C ''ip'':65C,158C ''life'':17C ''like'':21C ''limit'':111C,125C,153C ''locat'':61C ''location-bas'':60C ''lot'':57C ''m'':106C,185C ''make'':31C,81C,121C ''mine'':11B ''model'':37C ''news'':85C ''number'':114C ''overlook'':186C ''p'':13C,39C,103C,143C,178C ''packag'':80C ''per'':132C ''perspect'':149C ''pleasur'':20C ''present'':169C ''problem'':138C ''programm'':52C ''pull'':56C ''purg'':161C ''queri'':116C ''r'':51C,75C ''rdstk'':48C ''re'':118C ''request'':131C ''scale'':6A ''scienc'':44C ''scrape'':8A,12B,22C,82C ''semi'':92C ''solut'':182C ''straightfoward'':87C ''structur'':23C,93C ''switch'':157C ''technic'':147C ''tend'':96C ''text'':10B ''text-min'':9B ''thing'':15C ''think'':123C,176C ''throttl'':109C ''tm'':78C ''tm.webmining.plugin'':72C ''toolkit'':45C ''unstructur'':25C ''use'':32C,64C,98C ''xpath'':100C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2619, 2, '2014-12-05 15:57:07.737', 1, NULL, NULL, '&lt;p&gt;I''m not sure I fully understand your question, but it seems to me that you''re trying to determine the category of the string/entity "titanic" out of context. Your data tells you that "titanic" could be a book, a movie, or a product, and you want to figure out which one is correct -- is that what you''re trying to do?&lt;/p&gt;

&lt;p&gt;If so, the problem is that you''ve dropped the context in which the string/entity "titanic" appears in your original text. For example...&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;In the sentence "I couldn''t stop reading Titanic," the word "titanic" refers to a book.&lt;/li&gt;
&lt;li&gt;In the sentence "Titanic was one of the highest-grossing films of all time," the word "titanic" refers to a movie.&lt;/li&gt;
&lt;li&gt;In the sentence "The Titanic was the world''s largest ocean liner," the word "titanic" refers to a product.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Without that context, there''s no way to know which is the correct category. I''d suggest looking into how named entity recognition tools like &lt;a href="http://nlp.stanford.edu/software/CRF-NER.shtml" rel="nofollow"&gt;Stanford NER&lt;/a&gt; work -- that will help you better understand how to do something like this. You''ll see that the input to an NER tool generally needs to be a sentence, in order to take advantage of the context to properly categorize the extracted entities.&lt;/p&gt;
', NULL, NULL, '''/a'':190C ''/li'':108C,132C,153C ''/p'':64C,89C,234C ''/software/crf-ner.shtml'':185C ''/ul'':154C ''advantag'':224C ''appear'':82C ''better'':196C ''book'':40C,107C ''categor'':230C ''categori'':23C,169C ''context'':30C,76C,158C,227C ''correct'':55C,168C ''could'':37C ''couldn'':96C ''d'':171C ''data'':32C ''determin'':21C ''drop'':74C ''entiti'':177C,233C ''exampl'':88C ''extract'':232C ''figur'':50C ''film'':121C ''fulli'':7C ''general'':214C ''gross'':120C ''help'':194C ''highest'':119C ''highest-gross'':118C ''href'':182C ''input'':209C ''know'':164C ''largest'':143C ''li'':91C,109C,133C ''like'':180C,202C ''liner'':145C ''ll'':205C ''look'':173C ''m'':3C ''movi'':42C,131C ''name'':176C ''need'':215C ''ner'':189C,212C ''nlp.stanford.edu'':184C ''nlp.stanford.edu/software/crf-ner.shtml'':183C ''nofollow'':187C ''ocean'':144C ''one'':53C,115C ''order'':221C ''origin'':85C ''p'':1C,65C,155C ''problem'':69C ''product'':45C,152C ''proper'':229C ''question'':10C ''re'':18C,60C ''read'':99C ''recognit'':178C ''refer'':104C,128C,149C ''rel'':186C ''see'':206C ''seem'':13C ''sentenc'':94C,112C,136C,219C ''someth'':201C ''stanford'':188C ''stop'':98C ''string/entity'':26C,80C ''suggest'':172C ''sure'':5C ''take'':223C ''tell'':33C ''text'':86C ''time'':124C ''titan'':27C,36C,81C,100C,103C,113C,127C,138C,148C ''tool'':179C,213C ''tri'':19C,61C ''ul'':90C ''understand'':8C,197C ''ve'':73C ''want'':48C ''way'':162C ''without'':156C ''word'':102C,126C,147C ''work'':191C ''world'':141C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6058, 2, '2015-06-09 15:34:12.437', 1, NULL, NULL, '&lt;p&gt;To use Decision Tree, you should transform the continuous variable into categorical.&lt;/p&gt;

&lt;p&gt;One more thing, Logistic Regression is usually used to predict result according to the probability.&lt;/p&gt;
', NULL, NULL, '''/p'':14C,31C ''accord'':27C ''categor'':13C ''continu'':10C ''decis'':4C ''logist'':19C ''one'':16C ''p'':1C,15C ''predict'':25C ''probabl'':30C ''regress'':20C ''result'':26C ''thing'':18C ''transform'':8C ''tree'':5C ''use'':3C,23C ''usual'':22C ''variabl'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2620, 2, '2014-12-05 16:14:01.097', 1, NULL, NULL, '&lt;p&gt;From an older version of the &lt;a href="http://opennlp.sourceforge.net/README.html" rel="nofollow"&gt;OpenNLP README&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;strong&gt;Training the Tools&lt;/strong&gt;&lt;/p&gt;
  
  &lt;p&gt;There are training tools for all components expect the coref component. Please consult the help message of the tool and the javadoc to figure out how to train the tools.&lt;/p&gt;
  
  &lt;p&gt;The tutorials in our wiki might also be helpful.&lt;/p&gt;
  
  &lt;p&gt;The following modules currently support training via the WordFreak opennlp.plugin v1.4 (&lt;a href="http://wordfreak.sourceforge.net/plugins.html" rel="nofollow"&gt;http://wordfreak.sourceforge.net/plugins.html&lt;/a&gt;).&lt;/p&gt;
  
  &lt;ul&gt;
  &lt;li&gt;coreference: org.annotation.opennlp.OpenNlpCoreferenceAnnotator (use opennlp 1.4.3 for training, models are compatible)&lt;/li&gt;
  &lt;/ul&gt;
  
  &lt;p&gt;&lt;strong&gt;Note:&lt;/strong&gt; In order to train a model you need all the training data. There is not currently a mechanism to update the models distributed with the project with additional data.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;As you can see, OpenNLP does not provide training tools for the coreference component. However, it seems at one point it was possible to train new models for OpenNLP''s coref component using the third-party WordFreak plugin... however, it hasn''t been updated in over a decade, so your mileage may vary.&lt;/p&gt;
', NULL, NULL, '''/a'':17C ''/blockquote'':140C ''/li'':104C ''/p'':18C,26C,58C,69C,139C,196C ''/plugins.html'':86C ''/plugins.html&lt;/a&gt;).&lt;/p&gt;'':91C ''/readme.html'':12C ''/strong'':25C,109C ''/ul'':105C ''1.4.3'':98C ''addit'':137C ''also'':66C ''blockquot'':19C ''compat'':103C ''compon'':34C,38C,155C,173C ''consult'':40C ''coref'':37C,172C ''corefer'':94C,154C ''current'':74C,125C ''data'':121C,138C ''decad'':190C ''distribut'':132C ''expect'':35C ''figur'':51C ''follow'':72C ''hasn'':183C ''help'':42C,68C ''howev'':156C,181C ''href'':9C,83C ''javadoc'':49C ''li'':93C ''may'':194C ''mechan'':127C ''messag'':43C ''might'':65C ''mileag'':193C ''model'':101C,115C,131C,168C ''modul'':73C ''need'':117C ''new'':167C ''nofollow'':14C,88C ''note'':108C ''older'':4C ''one'':160C ''opennlp'':15C,97C,146C,170C ''opennlp.plugin'':80C ''opennlp.sourceforge.net'':11C ''opennlp.sourceforge.net/readme.html'':10C ''order'':111C ''org.annotation.opennlp.opennlpcoreferenceannotator'':95C ''p'':1C,20C,27C,59C,70C,106C,141C ''parti'':178C ''pleas'':39C ''plugin'':180C ''point'':161C ''possibl'':164C ''project'':135C ''provid'':149C ''readm'':16C ''rel'':13C,87C ''see'':145C ''seem'':158C ''strong'':21C,107C ''support'':75C ''third'':177C ''third-parti'':176C ''tool'':24C,31C,46C,57C,151C ''train'':22C,30C,55C,76C,100C,113C,120C,150C,166C ''tutori'':61C ''ul'':92C ''updat'':129C,186C ''use'':96C,174C ''v1.4'':81C ''vari'':195C ''version'':5C ''via'':77C ''wiki'':64C ''wordfreak'':79C,179C ''wordfreak.sourceforge.net'':85C,90C ''wordfreak.sourceforge.net/plugins.html'':84C ''wordfreak.sourceforge.net/plugins.html&lt;/a&gt;).&lt;/p&gt;'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2621, 2, '2014-12-05 16:35:42.91', 1, NULL, NULL, '&lt;p&gt;First, some clarification on terminology.&lt;/p&gt;

&lt;p&gt;A &lt;em&gt;package&lt;/em&gt; in R is a collection of R functions, data, and compiled code in a well-defined format. &lt;/p&gt;

&lt;p&gt;SANN (&lt;code&gt;sann&lt;/code&gt;) is not a package. Depending on which package you''re using, &lt;code&gt;sann&lt;/code&gt; is either a function or, more often, a &lt;em&gt;method&lt;/em&gt; used within an optimization function.&lt;/p&gt;

&lt;p&gt;Packages containing &lt;code&gt;sann&lt;/code&gt; include &lt;code&gt;optim&lt;/code&gt;, &lt;code&gt;trustOptim&lt;/code&gt;, &lt;code&gt;consPlan&lt;/code&gt;, and &lt;code&gt;constrOptim&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;In the package &lt;code&gt;optim&lt;/code&gt;, the &lt;code&gt;sann&lt;/code&gt; method is implemented as:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; func &amp;lt;- function(x){
+   out &amp;lt;- (x[1]-2)^2 + (x[2]-1)^2
+   return &amp;lt;- out
+   }&amp;gt; 
&amp;gt; optim(par=c(0,0), fn=func, gr = NULL,
+       method = c("SANN"),
+       lower = -Inf, upper = Inf,
+       control = list(), hessian = T)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As you said, for the "SANN" (&lt;code&gt;sann&lt;/code&gt;) method &lt;code&gt;gr&lt;/code&gt; is used to generate a new candidate point. If it is NULL a default Gaussian Markov kernel is used.&lt;/p&gt;

&lt;p&gt;Now in your use case -- the case of a graph -- what you probably want to do is to &lt;a href="http://www.inside-r.org/r-doc/graphics/par" rel="nofollow"&gt;use &lt;code&gt;par&lt;/code&gt; and &lt;code&gt;value&lt;/code&gt; to pass values to &lt;code&gt;fn&lt;/code&gt; and &lt;code&gt;gr&lt;/code&gt;&lt;/a&gt;. This is a nice feature of this implementation of SANN in &lt;code&gt;optim&lt;/code&gt; which is covered a little more than half way  through this &lt;a href="http://www.inside-r.org/r-doc/stats/optim" rel="nofollow"&gt;documentation page&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;par&lt;/code&gt; is the best set of parameters found and &lt;code&gt;value&lt;/code&gt; is the value of &lt;code&gt;fn&lt;/code&gt; corresponding to &lt;code&gt;par&lt;/code&gt;.&lt;/p&gt;
', NULL, NULL, '''-1'':120C ''-2'':116C ''/a'':228C,263C ''/code'':36C,50C,73C,77C,80C,83C,87C,95C,99C,147C,158C,162C,212C,216C,223C,227C,242C,268C,279C,286C,291C ''/em'':12C,61C ''/p'':7C,31C,67C,88C,104C,182C,264C,292C ''/pre'':148C ''/r-doc/graphics/par'':206C ''/r-doc/stats/optim'':258C ''0'':130C,131C ''1'':115C ''2'':117C,119C,121C ''best'':271C ''c'':129C,137C ''candid'':169C ''case'':188C,190C ''clarif'':4C ''code'':24C,34C,48C,71C,75C,78C,81C,85C,93C,97C,106C,156C,160C,210C,214C,221C,225C,240C,266C,277C,284C,289C ''collect'':17C ''compil'':23C ''consplan'':82C ''constroptim'':86C ''contain'':70C ''control'':143C ''correspond'':287C ''cover'':245C ''data'':21C ''default'':176C ''defin'':29C ''depend'':41C ''document'':261C ''either'':52C ''em'':10C,59C ''featur'':233C ''first'':2C ''fn'':132C,222C,285C ''format'':30C ''found'':275C ''func'':108C,133C ''function'':20C,54C,66C,110C ''gaussian'':177C ''generat'':166C ''gr'':134C,161C,226C ''graph'':193C ''gt'':107C,125C,126C ''half'':250C ''hessian'':145C ''href'':203C,255C ''implement'':102C,236C ''includ'':74C ''inf'':140C,142C ''kernel'':179C ''list'':144C ''littl'':247C ''lower'':139C ''lt'':109C,113C,123C ''markov'':178C ''method'':60C,100C,136C,159C ''new'':168C ''nice'':232C ''nofollow'':208C,260C ''null'':135C,174C ''often'':57C ''optim'':65C,76C,94C,127C,241C ''p'':1C,8C,32C,68C,89C,149C,183C,265C ''packag'':11C,40C,44C,69C,92C ''page'':262C ''par'':128C,211C,267C,290C ''paramet'':274C ''pass'':218C ''point'':170C ''pre'':105C ''probabl'':196C ''r'':14C,19C ''re'':46C ''rel'':207C,259C ''return'':122C ''said'':152C ''sann'':33C,35C,49C,72C,98C,138C,155C,157C,238C ''set'':272C ''terminolog'':6C ''trustoptim'':79C ''upper'':141C ''use'':47C,62C,164C,181C,187C,209C ''valu'':215C,219C,278C,282C ''want'':197C ''way'':251C ''well'':28C ''well-defin'':27C ''within'':63C ''www.inside-r.org'':205C,257C ''www.inside-r.org/r-doc/graphics/par'':204C ''www.inside-r.org/r-doc/stats/optim'':256C ''x'':111C,114C,118C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2622, 2, '2014-12-05 16:37:54.327', 4, NULL, NULL, '&lt;p&gt;The &lt;code&gt;Data Science Toolkit&lt;/code&gt; is &lt;a href="http://www.datasciencetoolkit.org/" rel="nofollow"&gt;a powerful library&lt;/a&gt; (or collection of libraries, technically) which are available in a number of languages. For instance, I use the implementation called &lt;code&gt;RDSTK&lt;/code&gt; in R.&lt;/p&gt;

&lt;p&gt;In the case of your preferred language, Google Go, there''s &lt;a href="https://code.google.com/p/go-wiki/wiki/Projects#Web_Libraries" rel="nofollow"&gt;a list&lt;/a&gt; of web-related libraries here which looks very useful.&lt;/p&gt;
', NULL, NULL, '''/a'':17C,65C ''/code'':7C,40C ''/p'':43C,76C ''/p/go-wiki/wiki/projects#web_libraries'':60C ''avail'':25C ''call'':37C ''case'':47C ''code'':3C,38C ''code.google.com'':59C ''code.google.com/p/go-wiki/wiki/projects#web_libraries'':58C ''collect'':19C ''data'':4C ''go'':53C ''googl'':52C ''href'':10C,57C ''implement'':36C ''instanc'':32C ''languag'':30C,51C ''librari'':16C,21C,70C ''list'':64C ''look'':73C ''nofollow'':13C,62C ''number'':28C ''p'':1C,44C ''power'':15C ''prefer'':50C ''r'':42C ''rdstk'':39C ''rel'':12C,61C ''relat'':69C ''scienc'':5C ''technic'':22C ''toolkit'':6C ''use'':34C,75C ''web'':68C ''web-rel'':67C ''www.datasciencetoolkit.org'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10426, 2, '2016-02-27 11:29:31.56', 1, NULL, NULL, '&lt;p&gt;Look at the NLTK library for Python, there are functions to facilitate tokenizing sentences, see &lt;a href="http://www.nltk.org/_modules/nltk/tokenize.html" rel="nofollow"&gt;http://www.nltk.org/_modules/nltk/tokenize.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/_modules/nltk/tokenize.html'':21C ''/_modules/nltk/tokenize.html&lt;/a&gt;&lt;/p&gt;'':26C ''facilit'':13C ''function'':11C ''href'':18C ''librari'':6C ''look'':2C ''nltk'':5C ''nofollow'':23C ''p'':1C ''python'':8C ''rel'':22C ''see'':16C ''sentenc'':15C ''token'':14C ''www.nltk.org'':20C,25C ''www.nltk.org/_modules/nltk/tokenize.html'':19C ''www.nltk.org/_modules/nltk/tokenize.html&lt;/a&gt;&lt;/p&gt;'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2623, 1, '2014-12-05 16:54:13.967', 1, 133, 'How to Interpret Multinomial Specification in R''s `mnlogit` package', '&lt;p&gt;The &lt;code&gt;mnlogit&lt;/code&gt; package in R allows for the fast estimation of multinomial logit models.&lt;/p&gt;

&lt;p&gt;The specification of forumlas is a bit different from most other regression models/packages in R, however. &lt;/p&gt;

&lt;p&gt;Using the &lt;code&gt;Fish&lt;/code&gt; dataset as a reproducible example, &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; require(mnlogit)
Loading required package: mnlogit

Package: mnlogit
Version: 1.1.1
Multinomial Logit Choice Models.
Scientific Computing Group, Sentrana Inc, 2013.

&amp;gt; data(Fish, package =''mnlogit'')
&amp;gt; head(Fish)
           mode   income     alt   price  catch chid
1.beach   FALSE 7083.332   beach 157.930 0.0678    1
1.boat    FALSE 7083.332    boat 157.930 0.2601    1
1.charter  TRUE 7083.332 charter 182.930 0.5391    1
1.pier    FALSE 7083.332    pier 157.930 0.0503    1
2.beach   FALSE 1250.000   beach  15.114 0.1049    2
2.boat    FALSE 1250.000    boat  10.534 0.1574    2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m trying to understand the difference between the model specification of &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;fm &amp;lt;- formula(mode ~ 0 + price | income | catch)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;fm &amp;lt;- formula(mode ~ 0 + price | income + catch)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;while the documentation covers the detail of such changes  in the general coeffcient area of the forumla (i.e. where &lt;code&gt;price&lt;/code&gt; is), I don''t see an explanation of how operators like &lt;code&gt;+&lt;/code&gt; affect the alternative-specific area of the formula/code, relative to &lt;code&gt;|&lt;/code&gt;.&lt;/p&gt;
', 'r  logistic-regression  regression', NULL, '''/code'':20C,57C,143C,169C,184C,208C,221C,234C ''/p'':33C,51C,63C,158C,173C,235C ''/pre'':144C,170C,185C ''0'':165C,180C ''0.0503'':127C ''0.0678'':106C ''0.1049'':134C ''0.1574'':141C ''0.2601'':113C ''0.5391'':120C ''1'':107C,114C,121C,128C ''1.1.1'':76C ''1.beach'':101C ''1.boat'':108C ''1.charter'':115C ''1.pier'':122C ''10.534'':140C ''1250.000'':131C,138C ''15.114'':133C ''157.930'':105C,112C,126C ''182.930'':119C ''2'':135C,142C ''2.beach'':129C ''2.boat'':136C ''2013'':86C ''7083.332'':103C,110C,117C,124C ''affect'':222C ''allow'':24C ''alt'':97C ''altern'':225C ''alternative-specif'':224C ''area'':200C,227C ''beach'':104C,132C ''bit'':41C ''boat'':111C,139C ''catch'':99C,168C,183C ''chang'':195C ''charter'':118C ''chid'':100C ''choic'':79C ''code'':18C,55C,65C,160C,175C,206C,220C,233C ''coeffcient'':199C ''comput'':82C ''cover'':190C ''data'':88C ''dataset'':58C ''detail'':192C ''differ'':42C,152C ''document'':189C ''estim'':28C ''exampl'':62C ''explan'':215C ''fals'':102C,109C,123C,130C,137C ''fast'':27C ''fish'':56C,89C,94C ''fm'':161C,176C ''formula'':163C,178C ''formula/code'':230C ''forumla'':38C,203C ''general'':198C ''group'':83C ''gt'':66C,87C,92C ''head'':93C ''howev'':50C ''i.e'':204C ''inc'':85C ''incom'':96C,167C,182C ''interpret'':3A ''like'':219C ''load'':69C ''logist'':13B ''logistic-regress'':12B ''logit'':31C,78C ''lt'':162C,177C ''m'':147C ''mnlogit'':9A,19C,68C,72C,74C,91C ''mode'':95C,164C,179C ''model'':32C,80C,155C ''models/packages'':47C ''multinomi'':4A,30C,77C ''oper'':218C ''p'':16C,34C,52C,145C,171C,186C ''packag'':10A,21C,71C,73C,90C ''pier'':125C ''pre'':64C,159C,174C ''price'':98C,166C,181C,207C ''r'':7A,11B,23C,49C ''regress'':14B,15B,46C ''relat'':231C ''reproduc'':61C ''requir'':67C,70C ''scientif'':81C ''see'':213C ''sentrana'':84C ''specif'':5A,36C,156C,226C ''tri'':148C ''true'':116C ''understand'':150C ''use'':53C ''version'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2624, 2, '2014-12-05 17:13:14.073', 5, NULL, NULL, '&lt;p&gt;For many APIs (most I''ve seen) ratelimiting is a function of your API Key or OAuth credentials. (Google, Twitter, NOAA, Yahoo, Facebook, etc.) The good news is you won''t need to spoof your IP, you just need to swap out credentials as they hit there rate limit. &lt;/p&gt;

&lt;p&gt;A bit of shameless self promotion here but I wrote a python package specifically for handling this problem.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/rawkintrevo/angemilner" rel="nofollow"&gt;https://github.com/rawkintrevo/angemilner&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://pypi.python.org/pypi/angemilner/0.2.0" rel="nofollow"&gt;https://pypi.python.org/pypi/angemilner/0.2.0&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;It requires a mongodb daemon and basically you make a page for each one of your keys. So you have 4 email addresses each with a separate key assigned. When you load the key in you specify the maximum calls per day and minimum time between uses. &lt;/p&gt;

&lt;p&gt;Load keys:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from angemilner import APIKeyLibrarian
l= APIKeyLibrarian()
l.new_api_key("your_assigned_key1", ''noaa'', 1000, .2)
l.new_api_key("your_assigned_key2", ''noaa'', 1000, .2)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then when you run your scraper for instance the NOAA api:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;url= ''http://www.ncdc.noaa.gov/cdo-web/api/v2/stations'' 
payload= {  ''limit'': 1000,
        ''datasetid'':  ''GHCND'', 
        ''startdate'': ''1999-01-01'' }

r = requests.get(url, params=payload, headers= {''token'': ''your_assigned_key''})
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;becomes:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;url= ''http://www.ncdc.noaa.gov/cdo-web/api/v2/stations''
payload= {  ''limit'': 1000,
            ''datasetid'':  ''GHCND'',
            ''startdate'': ''1999-01-01'' }

r = requests.get(url, params=payload, headers= {''token'': l.check_out_api_key(''noaa'')[''key'']})
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;so if you have 5 keys, &lt;code&gt;l.check_out_api_key&lt;/code&gt; returns the key that has the least uses and waits until enough time has elapsed for it to be used again. &lt;/p&gt;

&lt;p&gt;Finally to see how often your keys have been used / remaining useage available:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;pprint(l.summary())
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I didn''t write this for R because most scraping is done in python (most of MY scraping).  It could be easily ported. &lt;/p&gt;

&lt;p&gt;Thats how you can &lt;em&gt;technically&lt;/em&gt; get around rate limiting.  &lt;em&gt;Ethically&lt;/em&gt; ...&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE&lt;/strong&gt; The example uses Google Places API &lt;a href="https://github.com/rawkintrevo/angemilner/blob/master/example2.py" rel="nofollow"&gt;here&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''-01'':201C,202C,231C,232C ''/a'':363C ''/cdo-web/api/v2/stations'''''':193C,223C ''/code'':173C,213C,246C,260C,302C ''/em'':336C,343C ''/p'':51C,71C,142C,146C,187C,217C,282C,297C,328C,344C,364C ''/pre'':174C,214C,247C,303C ''/pypi/angemilner/0.2.0'':88C ''/pypi/angemilner/0.2.0&lt;/a&gt;&lt;/p&gt;'':93C ''/rawkintrevo/angemilner'':77C ''/rawkintrevo/angemilner&lt;/a&gt;&lt;/p&gt;'':82C ''/rawkintrevo/angemilner/blob/master/example2.py'':359C ''/strong'':348C ''1000'':162C,171C,196C,226C ''1999'':200C,230C ''2'':163C,172C ''4'':115C ''5'':253C ''address'':117C ''angemiln'':150C ''api'':4C,15C,156C,165C,186C,242C,258C,354C ''apikeylibrarian'':152C,154C ''around'':338C ''assign'':123C,159C,168C,211C ''avail'':296C ''basic'':101C ''becom'':216C ''bit'':54C ''call'':134C ''code'':148C,189C,219C,255C,299C ''could'':324C ''credenti'':19C,44C ''daemon'':99C ''datasetid'':197C,227C ''day'':136C ''didn'':306C ''done'':316C ''easili'':326C ''elaps'':275C ''em'':334C,341C ''email'':116C ''enough'':272C ''etc'':25C ''ethic'':342C ''exampl'':350C ''facebook'':24C ''final'':284C ''function'':12C ''get'':337C ''ghcnd'':198C,228C ''github.com'':76C,81C,358C ''github.com/rawkintrevo/angemilner'':75C ''github.com/rawkintrevo/angemilner&lt;/a&gt;&lt;/p&gt;'':80C ''github.com/rawkintrevo/angemilner/blob/master/example2.py'':357C ''good'':27C ''googl'':20C,352C ''handl'':68C ''header'':208C,238C ''hit'':47C ''href'':74C,85C,356C ''import'':151C ''instanc'':183C ''ip'':37C ''key'':16C,111C,122C,128C,145C,157C,166C,212C,243C,245C,254C,259C,263C,290C ''key1'':160C ''key2'':169C ''l'':153C ''l.check'':240C,256C ''l.new'':155C,164C ''l.summary'':301C ''least'':267C ''limit'':50C,195C,225C,340C ''load'':126C,144C ''make'':103C ''mani'':3C ''maximum'':133C ''minimum'':138C ''mongodb'':98C ''need'':33C,40C ''news'':28C ''noaa'':22C,161C,170C,185C,244C ''nofollow'':79C,90C,361C ''oauth'':18C ''often'':288C ''one'':108C ''p'':1C,52C,72C,83C,94C,143C,175C,215C,248C,283C,304C,329C,345C ''packag'':65C ''page'':105C ''param'':206C,236C ''payload'':194C,207C,224C,237C ''per'':135C ''place'':353C ''port'':327C ''pprint'':300C ''pre'':147C,188C,218C,298C ''problem'':70C ''promot'':58C ''pypi.python.org'':87C,92C ''pypi.python.org/pypi/angemilner/0.2.0'':86C ''pypi.python.org/pypi/angemilner/0.2.0&lt;/a&gt;&lt;/p&gt;'':91C ''python'':64C,318C ''r'':203C,233C,311C ''rate'':49C,339C ''ratelimit'':9C ''rel'':78C,89C,360C ''remain'':294C ''requests.get'':204C,234C ''requir'':96C ''return'':261C ''run'':179C ''scrape'':314C,322C ''scraper'':181C ''see'':286C ''seen'':8C ''self'':57C ''separ'':121C ''shameless'':56C ''specif'':66C ''specifi'':131C ''spoof'':35C ''startdat'':199C,229C ''strong'':346C ''swap'':42C ''technic'':335C ''that'':330C ''time'':139C,273C ''token'':209C,239C ''twitter'':21C ''updat'':347C ''url'':190C,205C,220C,235C ''use'':141C,268C,280C,293C,351C ''useag'':295C ''ve'':7C ''wait'':270C ''won'':31C ''write'':308C ''wrote'':62C ''www.ncdc.noaa.gov'':192C,222C ''www.ncdc.noaa.gov/cdo-web/api/v2/stations'''''':191C,221C ''yahoo'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2625, 2, '2014-12-05 18:09:27.67', 2, NULL, NULL, '&lt;p&gt;If your only motivation for using Google Go is webscraping, and you want to do you ML in python, I would recommend the following stack:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://docs.python-requests.org/en/latest/" rel="nofollow"&gt;Python requests for scraping data&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.mongodb.org/" rel="nofollow"&gt;MongoDB for caching data&lt;/a&gt; (MongoDB''s page oriented format makes it a natural home for storing JSON objects commonly returned by APIs)&lt;/p&gt;

&lt;p&gt;&lt;a href="http://api.mongodb.org/python/current/" rel="nofollow"&gt;pymongo for interfacing python and mongodb&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/" rel="nofollow"&gt;scikit-learn for doing your machine learning&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This all happens in python and you can extend it multiple processors with 
&lt;a href="https://docs.python.org/2/library/multiprocessing.html" rel="nofollow"&gt;multiprocessing&lt;/a&gt; or to multiple nodes with django&lt;/p&gt;
', NULL, NULL, '''/2/library/multiprocessing.html'':125C ''/a'':41C,53C,87C,105C,129C ''/en/latest/'':33C ''/p'':27C,42C,72C,88C,106C,136C ''/python/current/'':78C ''/stable/'':94C ''api'':71C ''api.mongodb.org'':77C ''api.mongodb.org/python/current/'':76C ''cach'':51C ''common'':68C ''data'':40C,52C ''django'':135C ''docs.python-requests.org'':32C ''docs.python-requests.org/en/latest/'':31C ''docs.python.org'':124C ''docs.python.org/2/library/multiprocessing.html'':123C ''extend'':116C ''follow'':25C ''format'':58C ''go'':9C ''googl'':8C ''happen'':110C ''home'':63C ''href'':30C,45C,75C,91C,122C ''interfac'':83C ''json'':66C ''learn'':99C,104C ''machin'':103C ''make'':59C ''ml'':18C ''mongodb'':49C,54C,86C ''motiv'':5C ''multipl'':118C,132C ''multiprocess'':128C ''natur'':62C ''node'':133C ''nofollow'':35C,48C,80C,96C,127C ''object'':67C ''orient'':57C ''p'':1C,28C,43C,73C,89C,107C ''page'':56C ''processor'':119C ''pymongo'':81C ''python'':20C,36C,84C,112C ''recommend'':23C ''rel'':34C,47C,79C,95C,126C ''request'':37C ''return'':69C ''scikit'':98C ''scikit-learn'':97C ''scikit-learn.org'':93C ''scikit-learn.org/stable/'':92C ''scrape'':39C ''stack'':26C ''store'':65C ''use'':7C ''want'':14C ''webscrap'':11C ''would'':22C ''www.mongodb.org'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2627, 1, '2014-12-06 00:41:24.933', 1, 34, '3D map using leaflet', '&lt;p&gt;I''m trying to create 3D bars on this map. Can anyone please advise if this is possible, and how?&lt;/p&gt;

&lt;p&gt;&lt;a href="http://leafletjs.com/examples/choropleth.html" rel="nofollow"&gt;http://leafletjs.com/examples/choropleth.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;My data: UFO sightings in the USA (location wise).
Count of these sightings per location will be the height of the 3D bar.
Base map is a choropleth with US population density values.&lt;/p&gt;

&lt;p&gt;I don''t mind integrating Javascript or d3.js into the code to create the 3D bars.&lt;/p&gt;
', 'javascript  visualization', NULL, '''/examples/choropleth.html'':34C ''/examples/choropleth.html&lt;/a&gt;&lt;/p&gt;'':39C ''/p'':28C,74C,92C ''3d'':1A,13C,62C,90C ''advis'':21C ''anyon'':19C ''bar'':14C,63C,91C ''base'':64C ''choropleth'':68C ''code'':86C ''count'':50C ''creat'':12C,88C ''d3.js'':83C ''data'':42C ''densiti'':72C ''height'':59C ''href'':31C ''integr'':80C ''javascript'':5B,81C ''leaflet'':4A ''leafletjs.com'':33C,38C ''leafletjs.com/examples/choropleth.html'':32C ''leafletjs.com/examples/choropleth.html&lt;/a&gt;&lt;/p&gt;'':37C ''locat'':48C,55C ''m'':9C ''map'':2A,17C,65C ''mind'':79C ''nofollow'':36C ''p'':7C,29C,40C,75C ''per'':54C ''pleas'':20C ''popul'':71C ''possibl'':25C ''rel'':35C ''sight'':44C,53C ''tri'':10C ''ufo'':43C ''us'':70C ''usa'':47C ''use'':3A ''valu'':73C ''visual'':6B ''wise'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2628, 1, '2014-12-06 01:10:30.817', 1, 84, 'SAS Nested Likelihood Ratio Test for a Logistic Model', '&lt;p&gt;Using SAS Studio (online, student version)...
Need to do a "nested likelihood ratio test" for a logistic regression. 
Entirety of instructions are: "Perform a nested likelihood ratio test comparing your full model (all predictors included)to a reduced model of interest."
The two models I have are:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Proc Logistic Data=Project_C;
Model Dem (event=''1'') = VEP TIF Income NonCit Unemployed Swing;
Run;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Proc Logistic Data=Project_C;
Model Dem (Event=''1'') = VEP TIF Income / clodds=Wald clparm=Wald expb rsquare;
Run;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I honestly have no idea where to even start. 
Any suggestions would be appreciated. 
Thanks!&lt;/p&gt;
', 'logistic-regression', NULL, '''/code'':80C,106C ''/p'':61C,84C,124C ''/pre'':81C,107C ''1'':72C,95C ''appreci'':122C ''c'':68C,91C ''clodd'':99C ''clparm'':101C ''code'':63C,86C ''compar'':42C ''data'':66C,89C ''dem'':70C,93C ''entireti'':32C ''even'':116C ''event'':71C,94C ''expb'':103C ''full'':44C ''honest'':110C ''idea'':113C ''includ'':48C ''incom'':75C,98C ''instruct'':34C ''interest'':54C ''likelihood'':3A,25C,39C ''logist'':8A,11B,30C,65C,88C ''logistic-regress'':10B ''model'':9A,45C,52C,57C,69C,92C ''need'':20C ''nest'':2A,24C,38C ''noncit'':76C ''onlin'':17C ''p'':13C,82C,108C ''perform'':36C ''pre'':62C,85C ''predictor'':47C ''proc'':64C,87C ''project'':67C,90C ''ratio'':4A,26C,40C ''reduc'':51C ''regress'':12B,31C ''rsquar'':104C ''run'':79C,105C ''sas'':1A,15C ''start'':117C ''student'':18C ''studio'':16C ''suggest'':119C ''swing'':78C ''test'':5A,27C,41C ''thank'':123C ''tif'':74C,97C ''two'':56C ''unemploy'':77C ''use'':14C ''vep'':73C,96C ''version'':19C ''wald'':100C,102C ''would'':120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3818, 2, '2015-01-05 23:09:25.8', 1, NULL, NULL, '&lt;p&gt;It is possible that the other variables you''re feeding into the NN are simply bad at predicting sales.  Sell prediction is a notoriously hard problem.&lt;/p&gt;

&lt;p&gt;Specifically the addressing of mapping a multi-state categorical variable to the NN''s {0,1} input range:  Another idea is to change that one, 5-state variable into five boolean variables.  Rather than {0,0.25,0.5,0.75,1.0} on your one variable, make each of the five boolean variables represent a single day and make [1,0,0,0,0] equal Monday, [0,1,0,0,0] equal Tuesday, etc.  I''ve personally had more success both with training good networks and introspecting the network itself when spreading out states of classes like that.&lt;/p&gt;

&lt;p&gt;Other hacks you can try:&lt;br&gt;
 * Take out the the ''day'' column all together and see if any of the other variables get used.&lt;br&gt;
 * Plot the distribution of spend as a function of day.  Even if nothing else comes of this current model, it sounds like you''ve found one interesting insight already.&lt;br&gt;
 * Consider also trying different models.&lt;/p&gt;
', NULL, NULL, '''/p'':28C,126C,188C ''0'':45C,65C,88C,89C,90C,91C,94C,96C,97C,98C ''0.25'':66C ''0.5'':67C ''0.75'':68C ''1'':46C,87C,95C ''1.0'':69C ''5'':56C ''address'':32C ''alreadi'':181C ''also'':184C ''anoth'':49C ''bad'':17C ''boolean'':61C,79C ''br'':133C,152C,182C ''categor'':39C ''chang'':53C ''class'':123C ''column'':139C ''come'':167C ''consid'':183C ''current'':170C ''day'':84C,138C,162C ''differ'':186C ''distribut'':155C ''els'':166C ''equal'':92C,99C ''etc'':101C ''even'':163C ''feed'':11C ''five'':60C,78C ''found'':177C ''function'':160C ''get'':150C ''good'':111C ''hack'':129C ''hard'':26C ''idea'':50C ''input'':47C ''insight'':180C ''interest'':179C ''introspect'':114C ''like'':124C,174C ''make'':74C,86C ''map'':34C ''model'':171C,187C ''monday'':93C ''multi'':37C ''multi-st'':36C ''network'':112C,116C ''nn'':14C,43C ''noth'':165C ''notori'':25C ''one'':55C,72C,178C ''p'':1C,29C,127C ''person'':104C ''plot'':153C ''possibl'':4C ''predict'':19C,22C ''problem'':27C ''rang'':48C ''rather'':63C ''re'':10C ''repres'':81C ''sale'':20C ''see'':143C ''sell'':21C ''simpli'':16C ''singl'':83C ''sound'':173C ''specif'':30C ''spend'':157C ''spread'':119C ''state'':38C,57C,121C ''success'':107C ''take'':134C ''togeth'':141C ''train'':110C ''tri'':132C,185C ''tuesday'':100C ''use'':151C ''variabl'':8C,40C,58C,62C,73C,80C,149C ''ve'':103C,176C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2629, 1, '2014-12-06 06:53:14.617', 2, 94, 'What is "data science"?', '&lt;p&gt;In recent years, the term "data" seems to have become a term widely used without specific definition. Everyone seems to use the phrase. Even people as technology-impaired as my grandparents use the term and seem to understand words like "data breach." But I don''t understand what makes "data science" a new discipline. Data has been the foundation of science for centuries. Without data, there would be no Mendel, no SchrÃ¶dinger, etc. You can''t have science without interpreting  and analyzing data. &lt;/p&gt;

&lt;p&gt;But clearly it means something. Everyone is talking about it.  So what exactly do people mean by data when they use terms like "big data" and why has this become a discipline in itself? Also, if it is an emerging discipline, where can I find more serious/in-depth information so I can better educate myself? &lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'bigdata  definitions', NULL, '''/p'':93C,149C,152C ''also'':129C ''analyz'':91C ''becom'':17C,124C ''better'':146C ''big'':118C ''bigdata'':5B ''breach'':50C ''centuri'':71C ''clear'':96C ''data'':3A,13C,49C,58C,63C,73C,92C,112C,119C ''definit'':6B,24C ''dinger'':81C ''disciplin'':62C,126C,135C ''educ'':147C ''emerg'':134C ''etc'':82C ''even'':31C ''everyon'':25C,100C ''exact'':107C ''find'':139C ''foundat'':67C ''grandpar'':39C ''impair'':36C ''inform'':142C ''interpret'':89C ''like'':48C,117C ''make'':57C ''mean'':98C,110C ''mendel'':78C ''new'':61C ''p'':7C,94C,150C ''peopl'':32C,109C ''phrase'':30C ''recent'':9C ''schrã'':80C ''scienc'':4A,59C,69C,87C ''seem'':14C,26C,44C ''serious/in-depth'':141C ''someth'':99C ''specif'':23C ''talk'':102C ''technolog'':35C ''technology-impair'':34C ''term'':12C,19C,42C,116C ''thank'':151C ''understand'':46C,55C ''use'':21C,28C,40C,115C ''wide'':20C ''without'':22C,72C,88C ''word'':47C ''would'':75C ''year'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2631, 1, '2014-12-06 15:04:03.823', 6, 126, 'What would be a good way to use clustering for outlier detection?', '&lt;p&gt;For simplicity let''s assume the feature space is the XY plane.&lt;/p&gt;
', 'machine-learning  data-mining  clustering  anomaly-detection', NULL, '''/p'':36C ''anomali'':21B ''anomaly-detect'':20B ''assum'':28C ''cluster'':9A,19B ''data'':17B ''data-min'':16B ''detect'':12A,22B ''featur'':30C ''good'':5A ''learn'':15B ''let'':26C ''machin'':14B ''machine-learn'':13B ''mine'':18B ''outlier'':11A ''p'':23C ''plane'':35C ''simplic'':25C ''space'':31C ''use'':8A ''way'':6A ''would'':2A ''xy'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2632, 1, '2014-12-06 17:56:53.157', 2, 26, 'Timing sequence in MapReduce', '&lt;p&gt;I''m running a test on MapReduce algorithm in different environments, like Hadoop and MongoDB, and using different types of data. What are the different methods or techniques to find out the execution time of a query.&lt;/p&gt;

&lt;p&gt;If I''m inserting a huge amount of data, consider it to be 2-3GB, what are the methods to find out the time for the process to be completed.&lt;/p&gt;
', 'efficiency  map-reduce  performance  experiments', NULL, '''-3'':65C ''/p'':49C,82C ''2'':64C ''algorithm'':19C ''amount'':57C ''complet'':81C ''consid'':60C ''data'':32C,59C ''differ'':21C,29C,36C ''effici'':5B ''environ'':22C ''execut'':44C ''experi'':10B ''find'':41C,72C ''gb'':66C ''hadoop'':24C ''huge'':56C ''insert'':54C ''like'':23C ''m'':13C,53C ''map'':7B ''map-reduc'':6B ''mapreduc'':4A,18C ''method'':37C,70C ''mongodb'':26C ''p'':11C,50C ''perform'':9B ''process'':78C ''queri'':48C ''reduc'':8B ''run'':14C ''sequenc'':2A ''techniqu'':39C ''test'':16C ''time'':1A,45C,75C ''type'':30C ''use'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2634, 1, '2014-12-07 10:43:01.697', 2, 45, 'Linear combination of weak estimators over fuzzy classifiers?', '&lt;p&gt;Having:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;a set of soft fuzzy classifiers (classification onto overlapping sets) $C_i(x) \to [0,1]$;&lt;/li&gt;
&lt;li&gt;a corresponding set of weak estimators $R_i(z)$ of the form $R_i(z) = \mathit{EX}(y\mid z)$.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The estimators $R_i$ are just some kind of regression, Kalman or particle filters. The classifiers $C_i$ are fixed and static. How to make a strong estimator out of a weighted combination of the form:&lt;/p&gt;

&lt;p&gt;$$L(x, z) = \sum_{i}C_i(x)R_i(z)Q_i$$&lt;/p&gt;

&lt;p&gt;In other words how to choose the weights $Q_i$? Is there some kind of online approach to this problem? &lt;/p&gt;

&lt;p&gt;Here is brief description of a practical application. When an event $E$ is registered, multiple measurements are made. Based on these measurements, the classifiers $C_i$ make a soft assignment of the event to multiple overlapping categories. What we get is fit ratios for the soft clusters.&lt;/p&gt;

&lt;p&gt;Now there is some chance that event $E$ may trigger a subsequent event $D$, depending on another variable $z$ -- independent from the event $E$. We know that all the soft cluster "memberships" may influence the probability of event $D$ being triggered.&lt;/p&gt;

&lt;p&gt;We want to estimate the probability that $E$ triggers $D$, given the $C_i$ fitness ratios and value of $z$.&lt;/p&gt;
', 'machine-learning  regression  online-learning', NULL, '''/li'':37C,59C ''/p'':18C,98C,113C,135C,184C,227C,249C ''/ul'':60C ''0'':35C ''1'':36C ''anoth'':202C ''applic'':144C ''approach'':131C ''assign'':166C ''base'':155C ''brief'':139C ''c'':31C,78C,105C,161C,241C ''categori'':173C ''chanc'':190C ''choos'':120C ''classif'':27C ''classifi'':8A,26C,77C,160C ''cluster'':183C,216C ''combin'':2A,94C ''correspond'':40C ''d'':199C,224C,238C ''depend'':200C ''descript'':140C ''e'':148C,193C,209C,236C ''estim'':5A,44C,63C,89C,232C ''event'':147C,169C,192C,198C,208C,223C ''ex'':55C ''filter'':75C ''fit'':178C,243C ''fix'':81C ''form'':50C,97C ''fuzzi'':7A,25C ''get'':176C ''given'':239C ''independ'':205C ''influenc'':219C ''kalman'':72C ''kind'':69C,128C ''know'':211C ''l'':100C ''learn'':11B,15B ''li'':20C,38C ''linear'':1A ''machin'':10B ''machine-learn'':9B ''made'':154C ''make'':86C,163C ''mathit'':54C ''may'':194C,218C ''measur'':152C,158C ''membership'':217C ''mid'':57C ''multipl'':151C,171C ''onlin'':14B,130C ''online-learn'':13B ''onto'':28C ''overlap'':29C,172C ''p'':16C,61C,99C,114C,136C,185C,228C ''particl'':74C ''practic'':143C ''probabl'':221C,234C ''problem'':134C ''q'':111C,123C ''r'':45C,51C,64C,108C ''ratio'':179C,244C ''regist'':150C ''regress'':12B,71C ''set'':22C,30C,41C ''soft'':24C,165C,182C,215C ''static'':83C ''strong'':88C ''subsequ'':197C ''sum'':103C ''trigger'':195C,226C,237C ''ul'':19C ''valu'':246C ''variabl'':203C ''want'':230C ''weak'':4A,43C ''weight'':93C,122C ''word'':117C ''x'':33C,101C,107C ''y'':56C ''z'':47C,53C,58C,102C,110C,204C,248C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2635, 2, '2014-12-07 11:29:14.057', 1, NULL, NULL, '&lt;p&gt;I am not 100% if a message queue library will be the right tool for this job but so far it looks to me so.&lt;/p&gt;

&lt;p&gt;With a messaging library like:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://nsq.io" rel="nofollow"&gt;nsq&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://zeromq.org" rel="nofollow"&gt;zeromq&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://mqtt.org" rel="nofollow"&gt;mqtt&lt;/a&gt; (?)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You can connect different processes operating on different environment through a TCP based protocol. As these systems run distributed it is possible to connect multiple nodes.&lt;/p&gt;

&lt;p&gt;For &lt;strong&gt;nsq&lt;/strong&gt; we even have a library in Python &lt;strong&gt;and&lt;/strong&gt; Go!&lt;/p&gt;
', NULL, NULL, '''/a'':43C,52C,61C ''/li'':44C,53C,62C ''/p'':27C,34C,91C,108C ''/strong'':96C,106C ''/ul'':63C ''100'':5C ''base'':77C ''connect'':67C,88C ''differ'':68C,72C ''distribut'':83C ''environ'':73C ''even'':98C ''far'':21C ''go'':107C ''href'':38C,47C,56C ''job'':18C ''li'':36C,45C,54C ''librari'':10C,32C,101C ''like'':33C ''look'':23C ''messag'':8C,31C ''mqtt'':60C ''mqtt.org'':57C ''multipl'':89C ''node'':90C ''nofollow'':41C,50C,59C ''nsq'':42C,95C ''nsq.io'':39C ''oper'':70C ''p'':1C,28C,64C,92C ''possibl'':86C ''process'':69C ''protocol'':78C ''python'':103C ''queue'':9C ''rel'':40C,49C,58C ''right'':14C ''run'':82C ''strong'':94C,104C ''system'':81C ''tcp'':76C ''tool'':15C ''ul'':35C ''zeromq'':51C ''zeromq.org'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2636, 2, '2014-12-07 12:16:10.207', 1, NULL, NULL, '&lt;p&gt;Perhaps you could cluster the items, then those items with the furthest distance from the midpoint of any cluster would be candidates for outliers.&lt;/p&gt;
', NULL, NULL, '''/p'':26C ''candid'':23C ''cluster'':5C,20C ''could'':4C ''distanc'':14C ''furthest'':13C ''item'':7C,10C ''midpoint'':17C ''outlier'':25C ''p'':1C ''perhap'':2C ''would'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2645, 1, '2014-12-08 10:47:52.34', 1, 107, 'Assign new point to a class using spectral clustering', '&lt;p&gt;Say I used spectral clustering to cluster a data-set $D$ of points $X_0 - X_n$ into a number $C$ of clusters. How can I efficiently assign a new single point $X_{n+1}$ to his convenient cluster?&lt;/p&gt;

&lt;p&gt;Do I have to do the classification from the beginning (destroy all the clusters and apply the algorithm to the data-set $X_0 - X_{n+1}$), or is there an optimized way to extend to the point $X_{n+1}$?&lt;/p&gt;
', 'machine-learning  data-mining  clustering', NULL, '''+1'':53C,87C,101C ''/p'':58C,102C ''0'':33C,84C ''algorithm'':77C ''appli'':75C ''assign'':1A,46C ''begin'':69C ''c'':39C ''class'':6A ''classif'':66C ''cluster'':9A,16B,22C,24C,41C,57C,73C ''conveni'':56C ''d'':29C ''data'':14B,27C,81C ''data-min'':13B ''data-set'':26C,80C ''destroy'':70C ''effici'':45C ''extend'':95C ''learn'':12B ''machin'':11B ''machine-learn'':10B ''mine'':15B ''n'':35C,52C,86C,100C ''new'':2A,48C ''number'':38C ''optim'':92C ''p'':17C,59C ''point'':3A,31C,50C,98C ''say'':18C ''set'':28C,82C ''singl'':49C ''spectral'':8A,21C ''use'':7A,20C ''way'':93C ''x'':32C,34C,51C,83C,85C,99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4922, 2, '2015-01-22 10:44:37.24', 3, NULL, NULL, '&lt;p&gt;I would suggest Kaggle learning projects - &lt;a href="http://www.kaggle.com/competitions" rel="nofollow"&gt;http://www.kaggle.com/competitions&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Look for the ones in the 101 section that offer knowledge. There''s many pre-made solutions ready, which you can ingest and try variations of.&lt;/p&gt;

&lt;p&gt;Also, I have bookmarked a &lt;a href="http://www.analyticsvidhya.com/blog/learning-path-data-science-python/" rel="nofollow"&gt;Comprehensive learning path â Data Science in Python&lt;/a&gt;, which among other things gives a few answers to your specific question.&lt;/p&gt;
', NULL, NULL, '''/a'':68C ''/blog/learning-path-data-science-python/'':57C ''/competitions'':12C ''/competitions&lt;/a&gt;&lt;/p&gt;'':17C ''/p'':46C,81C ''101'':25C ''also'':48C ''among'':70C ''answer'':76C ''bookmark'':51C ''comprehens'':60C ''data'':64C ''give'':73C ''href'':9C,54C ''ingest'':41C ''kaggl'':5C ''knowledg'':29C ''learn'':6C,61C ''look'':19C ''made'':35C ''mani'':32C ''nofollow'':14C,59C ''offer'':28C ''one'':22C ''p'':1C,18C,47C ''path'':62C ''pre'':34C ''pre-mad'':33C ''project'':7C ''python'':67C ''question'':80C ''readi'':37C ''rel'':13C,58C ''scienc'':65C ''section'':26C ''solut'':36C ''specif'':79C ''suggest'':4C ''thing'':72C ''tri'':43C ''variat'':44C ''would'':3C ''www.analyticsvidhya.com'':56C ''www.analyticsvidhya.com/blog/learning-path-data-science-python/'':55C ''www.kaggle.com'':11C,16C ''www.kaggle.com/competitions'':10C ''www.kaggle.com/competitions&lt;/a&gt;&lt;/p&gt;'':15C ''â'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2639, 2, '2014-12-07 15:39:00.947', 1, NULL, NULL, '&lt;p&gt;You might find a solution for this by checking out Viola &amp;amp; Jones face detection algorithm (and object detection in general) &lt;a href="http://www.cs.ubc.ca/~lowe/425/slides/13-ViolaJones.pdf" rel="nofollow"&gt;http://www.cs.ubc.ca/~lowe/425/slides/13-ViolaJones.pdf&lt;/a&gt;. Particularly the AdaBoost algorithm for building a strong classifier from weak classifiers. &lt;a href="https://www.cs.princeton.edu/~schapire/papers/explaining-adaboost.pdf" rel="nofollow"&gt;https://www.cs.princeton.edu/~schapire/papers/explaining-adaboost.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In this algorithm this is used particularly for feature selection, where it is wanted to select just the features that grouped together classify better, discarding the other as noisy features.&lt;/p&gt;

&lt;p&gt;The approach to obtain this strong classifier is having a set of examples X, a set of weights W and a set of expected results (classifications) Y. For a two-class example (e.g. face o no face image) the first weak classifier is selected and then the classification error is found between this classifier and the expected output. For the samples that were misclassified their weights are incremented, so the next weak classifier to find is more biased to neglect this wrong classification.&lt;/p&gt;

&lt;p&gt;The algorithm (AdaBoost) converges when the sign of the sum of classifications for each sample outputs the correct classification.&lt;/p&gt;

&lt;p&gt;For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Y = +1 -1 +1 +1

WC1=+1 +1 +1 +1
WC2=-1 -1 -1 -1
WC3=+1 -1 +1 +1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So the strong classifier is WC1+WC2+WC3:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;SC=+1 -1 +1 +1 == Y
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Hope this solves your question.&lt;/p&gt;
', NULL, NULL, '''+1'':201C,203C,204C,206C,207C,208C,209C,216C,218C,219C,235C,237C,238C ''-1'':202C,211C,212C,213C,214C,217C,236C ''/code'':220C,240C ''/p'':86C,172C,193C,197C,231C,248C ''/pre'':221C,241C ''/~lowe/425/slides/13-violajones.pdf'':27C ''/~lowe/425/slides/13-violajones.pdf&lt;/a&gt;.'':32C ''/~schapire/papers/explaining-adaboost.pdf'':49C ''/~schapire/papers/explaining-adaboost.pdf&lt;/a&gt;&lt;/p&gt;'':54C ''adaboost'':35C,176C ''algorithm'':17C,36C,58C,175C ''amp'':13C ''approach'':89C ''better'':79C ''bias'':166C ''build'':38C ''check'':10C ''class'':119C ''classif'':113C,136C,171C,185C,192C ''classifi'':41C,44C,78C,94C,130C,142C,161C,226C ''code'':199C,233C ''converg'':177C ''correct'':191C ''detect'':16C,20C ''discard'':80C ''e.g'':121C ''error'':137C ''exampl'':100C,120C,196C ''expect'':111C,145C ''face'':15C,122C,125C ''featur'':64C,74C,85C ''find'':4C,163C ''first'':128C ''found'':139C ''general'':22C ''group'':76C ''hope'':243C ''href'':24C,46C ''imag'':126C ''increment'':156C ''jone'':14C ''might'':3C ''misclassifi'':152C ''neglect'':168C ''next'':159C ''nofollow'':29C,51C ''noisi'':84C ''o'':123C ''object'':19C ''obtain'':91C ''output'':146C,189C ''p'':1C,55C,87C,173C,194C,222C,242C ''particular'':33C,62C ''pre'':198C,232C ''question'':247C ''rel'':28C,50C ''result'':112C ''sampl'':149C,188C ''sc'':234C ''select'':65C,71C,132C ''set'':98C,103C,109C ''sign'':180C ''solut'':6C ''solv'':245C ''strong'':40C,93C,225C ''sum'':183C ''togeth'':77C ''two'':118C ''two-class'':117C ''use'':61C ''viola'':12C ''w'':106C ''want'':69C ''wc1'':205C,228C ''wc2'':210C,229C ''wc3'':215C,230C ''weak'':43C,129C,160C ''weight'':105C,154C ''wrong'':170C ''www.cs.princeton.edu'':48C,53C ''www.cs.princeton.edu/~schapire/papers/explaining-adaboost.pdf'':47C ''www.cs.princeton.edu/~schapire/papers/explaining-adaboost.pdf&lt;/a&gt;&lt;/p&gt;'':52C ''www.cs.ubc.ca'':26C,31C ''www.cs.ubc.ca/~lowe/425/slides/13-violajones.pdf'':25C ''www.cs.ubc.ca/~lowe/425/slides/13-violajones.pdf&lt;/a&gt;.'':30C ''x'':101C ''y'':114C,200C,239C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2640, 2, '2014-12-07 15:46:40.67', 5, NULL, NULL, '&lt;p&gt;A very robust clustering algorithm against outliers is PFCM from Bezdek &lt;a href="http://www.comp.ita.br/~forster/CC-222/material/fuzzyclust/fuzzy01492404.pdf"&gt;http://www.comp.ita.br/~forster/CC-222/material/fuzzyclust/fuzzy01492404.pdf&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In this paper Bezdek proposes Possibilistic-Fuzzy-C-Means which is an improvement of the different variations of fuzzy posibilistic clustering. This algorithm is particularly good at detecting outliers and avoiding them to influence the clusterization. So using PFCM you could find which points are identified as outliers and at the same time have a very robust fuzzy clustering of your data.&lt;/p&gt;
', NULL, NULL, '''/p'':85C ''/~forster/cc-222/material/fuzzyclust/fuzzy01492404.pdf'':17C ''/~forster/cc-222/material/fuzzyclust/fuzzy01492404.pdf&lt;/a&gt;.&lt;/p&gt;'':20C ''algorithm'':6C,45C ''avoid'':53C ''bezdek'':12C,25C ''c'':30C ''cluster'':5C,43C,58C,81C ''could'':63C ''data'':84C ''detect'':50C ''differ'':38C ''find'':64C ''fuzzi'':29C,41C,80C ''good'':48C ''href'':14C ''identifi'':68C ''improv'':35C ''influenc'':56C ''mean'':31C ''outlier'':8C,51C,70C ''p'':1C,21C ''paper'':24C ''particular'':47C ''pfcm'':10C,61C ''point'':66C ''posibilist'':42C ''possibilist'':28C ''possibilistic-fuzzy-c-mean'':27C ''propos'':26C ''robust'':4C,79C ''time'':75C ''use'':60C ''variat'':39C ''www.comp.ita.br'':16C,19C ''www.comp.ita.br/~forster/cc-222/material/fuzzyclust/fuzzy01492404.pdf'':15C ''www.comp.ita.br/~forster/cc-222/material/fuzzyclust/fuzzy01492404.pdf&lt;/a&gt;.&lt;/p&gt;'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2641, 2, '2014-12-07 16:17:20.613', 1, NULL, NULL, '&lt;p&gt;Following link contains a list of positive and negative polarised emotions on the scale of [-5, 5]. Just try to count up the scores based on the word matches and you can get overall movie review score. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010" rel="nofollow"&gt;http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''-5'':17C ''/p'':39C ''/pubdb/views/publication_details.php?id=6010'':45C ''/pubdb/views/publication_details.php?id=6010&lt;/a&gt;&lt;/p&gt;'':50C ''5'':18C ''base'':26C ''contain'':4C ''count'':22C ''emot'':12C ''follow'':2C ''get'':34C ''href'':42C ''link'':3C ''list'':6C ''match'':30C ''movi'':36C ''negat'':10C ''nofollow'':47C ''overal'':35C ''p'':1C,40C ''polaris'':11C ''posit'':8C ''rel'':46C ''review'':37C ''scale'':15C ''score'':25C,38C ''tri'':20C ''word'':29C ''www2.imm.dtu.dk'':44C,49C ''www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010'':43C ''www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010&lt;/a&gt;&lt;/p&gt;'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2642, 1, '2014-12-07 16:24:43.997', 4, 860, 'Machine Learning &amp; Partial Differential Equations', '&lt;p&gt;Are there any algorithms which were developed using partial differential equations for tackling some of the machine learning problems? Most works I see online are in the field of computer vision and a few bizarre ones in topic modelling. But just curious if someone has used or seen it being used for some decision making process or classification problems?&lt;/p&gt;
', 'machine-learning  algorithms', NULL, '''/p'':70C ''algorithm'':9B,14C ''bizarr'':45C ''classif'':68C ''comput'':40C ''curious'':52C ''decis'':64C ''develop'':17C ''differenti'':4A,20C ''equat'':5A,21C ''field'':38C ''learn'':2A,8B,28C ''machin'':1A,7B,27C ''machine-learn'':6B ''make'':65C ''model'':49C ''one'':46C ''onlin'':34C ''p'':10C ''partial'':3A,19C ''problem'':29C,69C ''process'':66C ''see'':33C ''seen'':58C ''someon'':54C ''tackl'':23C ''topic'':48C ''use'':18C,56C,61C ''vision'':41C ''work'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2643, 1, '2014-12-07 16:31:57.913', 4, 446, 'How to set up multi cluster spark without hadoop on Google Compute engine', '&lt;p&gt;I''m new to apache spark. &lt;/p&gt;

&lt;p&gt;Is it possible to configure multi cluster spark without hadoop?&lt;/p&gt;

&lt;p&gt;If so, can you please provide the steps. 
I would like to create clusters on Google Compute Engine  (1-master, 1-worker)&lt;/p&gt;
', 'bigdata  hadoop  scala', NULL, '''/p'':24C,36C,60C ''1'':56C,58C ''apach'':22C ''bigdata'':14B ''cluster'':6A,32C,51C ''comput'':12A,54C ''configur'':30C ''creat'':50C ''engin'':13A,55C ''googl'':11A,53C ''hadoop'':9A,15B,35C ''like'':48C ''m'':19C ''master'':57C ''multi'':5A,31C ''new'':20C ''p'':17C,25C,37C ''pleas'':42C ''possibl'':28C ''provid'':43C ''scala'':16B ''set'':3A ''spark'':7A,23C,33C ''step'':45C ''without'':8A,34C ''worker'':59C ''would'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3701, 2, '2014-12-13 20:37:07.843', 4, NULL, NULL, '&lt;p&gt;I suspected you were using the names as identifiers. You shouldn''t; they''re not unique and they raise this privacy issue. Use instead their student numbers, which you can verify from their IDs, stored in hashed form. Use the student''s last name as a salt, for good measure (form the string to be hashed by concatenating the ID number and the last name).&lt;/p&gt;
', NULL, NULL, '''/p'':67C ''concaten'':59C ''form'':39C,52C ''good'':50C ''hash'':38C,57C ''id'':35C,61C ''identifi'':10C ''instead'':25C ''issu'':23C ''last'':44C,65C ''measur'':51C ''name'':8C,45C,66C ''number'':28C,62C ''p'':1C ''privaci'':22C ''rais'':20C ''re'':15C ''salt'':48C ''shouldn'':12C ''store'':36C ''string'':54C ''student'':27C,42C ''suspect'':3C ''uniqu'':17C ''use'':6C,24C,40C ''verifi'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2646, 1, '2014-12-08 14:51:27.613', 5, 399, 'Extract most informative parts of text from documents', '&lt;p&gt;Are there any articles or discussions about extracting part of text that holds the most of information about current document.&lt;/p&gt;

&lt;p&gt;For example, I have a large corpus of documents from the same domain. There are parts of text that hold the key information what single document talks about. I want to extract some of those parts and use them as kind of a summary of the text. Is there any useful documentation about how to achieve something like this. &lt;/p&gt;

&lt;p&gt;It would be really helpful if someone could point me into the right direction what I should search for or read to get some insight in work that might have already been done in this field of Natural language processing.&lt;/p&gt;
', 'nlp  text-mining', NULL, '''/p'':34C,95C,137C ''achiev'':91C ''alreadi'':127C ''articl'':17C ''corpus'':42C ''could'':104C ''current'':32C ''direct'':110C ''discuss'':19C ''document'':8A,33C,44C,61C,87C ''domain'':48C ''done'':129C ''exampl'':37C ''extract'':1A,21C,67C ''field'':132C ''get'':119C ''help'':101C ''hold'':26C,55C ''inform'':3A,30C,58C ''insight'':121C ''key'':57C ''kind'':76C ''languag'':135C ''larg'':41C ''like'':93C ''might'':125C ''mine'':12B ''natur'':134C ''nlp'':9B ''p'':13C,35C,96C ''part'':4A,22C,51C,71C ''point'':105C ''process'':136C ''read'':117C ''realli'':100C ''right'':109C ''search'':114C ''singl'':60C ''someon'':103C ''someth'':92C ''summari'':79C ''talk'':62C ''text'':6A,11B,24C,53C,82C ''text-min'':10B ''use'':73C,86C ''want'':65C ''work'':123C ''would'':98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2648, 2, '2014-12-08 15:48:25.647', 6, NULL, NULL, '&lt;p&gt;What you''re describing is often achieved using a simple combination of &lt;a href="http://en.wikipedia.org/wiki/Tf%E2%80%93idf"&gt;TF-IDF&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/Automatic_summarization#Extraction-based_summarization"&gt;extractive summarization&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In a nutshell, TF-IDF tells you the relative importance of each word in each document, in comparison to the rest of your corpus. At this point, you have a score for each word in each document approximating its "importance." Then you can use these individual word scores to compute a composite score for each sentence by summing the scores of each word in each sentence. Finally, simply take the top-N scoring sentences from each document as its summary.&lt;/p&gt;

&lt;p&gt;Earlier this year, I put together an iPython Notebook that culminates with an implementation of this in Python using NLTK and Scikit-learn: &lt;a href="https://github.com/charlieg/A-Smattering-of-NLP-in-Python"&gt;A Smattering of NLP in Python&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':22C,31C,153C ''/charlieg/a-smattering-of-nlp-in-python'':146C ''/p'':32C,116C,154C ''/wiki/automatic_summarization#extraction-based_summarization'':28C ''/wiki/tf%e2%80%93idf'':18C ''achiev'':8C ''approxim'':72C ''combin'':12C ''comparison'':52C ''composit'':86C ''comput'':84C ''corpus'':58C ''culmin'':128C ''describ'':5C ''document'':50C,71C,112C ''earlier'':118C ''en.wikipedia.org'':17C,27C ''en.wikipedia.org/wiki/automatic_summarization#extraction-based_summarization'':26C ''en.wikipedia.org/wiki/tf%e2%80%93idf'':16C ''extract'':29C ''final'':101C ''github.com'':145C ''github.com/charlieg/a-smattering-of-nlp-in-python'':144C ''href'':15C,25C,143C ''idf'':21C,39C ''implement'':131C ''import'':44C,74C ''individu'':80C ''ipython'':125C ''learn'':141C ''n'':107C ''nlp'':150C ''nltk'':137C ''notebook'':126C ''nutshel'':36C ''often'':7C ''p'':1C,33C,117C ''point'':61C ''put'':122C ''python'':135C,152C ''re'':4C ''relat'':43C ''rest'':55C ''scikit'':140C ''scikit-learn'':139C ''score'':65C,82C,87C,94C,108C ''sentenc'':90C,100C,109C ''simpl'':11C ''simpli'':102C ''smatter'':148C ''sum'':92C ''summar'':30C ''summari'':115C ''take'':103C ''tell'':40C ''tf'':20C,38C ''tf-idf'':19C,37C ''togeth'':123C ''top'':106C ''top-n'':105C ''use'':9C,78C,136C ''word'':47C,68C,81C,97C ''year'':120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2649, 2, '2014-12-08 20:55:55.677', 1, NULL, NULL, '&lt;p&gt;I think you do not, since I was able to find several papers that proposed algorithms for the same:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.ifp.illinois.edu/~hning2/papers/incremental_spectral_clustering.pdf" rel="nofollow"&gt;Incremental spectral clustering by efficiently updating the eigen-system&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.sciencedirect.com/science/article/pii/S0925231214004433" rel="nofollow"&gt;Incremental kernel spectral clustering for online learning of non-stationary data&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://dx.doi.org/10.1109/PDCAT.2011.4" rel="nofollow"&gt;A Fast Incremental Spectral Clustering for Large Data Sets&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Such algorithms are called &lt;em&gt;sequential&lt;/em&gt;, &lt;em&gt;incremental&lt;/em&gt;, &lt;em&gt;streaming&lt;/em&gt;, or &lt;em&gt;online&lt;/em&gt;. Armed with this knowledge, you can find more papers on your own.&lt;/p&gt;
', NULL, NULL, '''/10.1109/pdcat.2011.4'':70C ''/a'':41C,63C,82C ''/em'':92C,95C,98C,102C ''/li'':42C,64C,83C ''/p'':21C,115C ''/science/article/pii/s0925231214004433'':48C ''/ul'':84C ''/~hning2/papers/incremental_spectral_clustering.pdf'':28C ''abl'':10C ''algorithm'':17C,87C ''arm'':103C ''call'':89C ''cluster'':33C,54C,77C ''data'':62C,80C ''dx.doi.org'':69C ''dx.doi.org/10.1109/pdcat.2011.4'':68C ''effici'':35C ''eigen'':39C ''eigen-system'':38C ''em'':90C,93C,96C,100C ''fast'':74C ''find'':12C,109C ''href'':25C,45C,67C ''increment'':31C,51C,75C,94C ''kernel'':52C ''knowledg'':106C ''larg'':79C ''learn'':57C ''li'':23C,43C,65C ''nofollow'':30C,50C,72C ''non'':60C ''non-stationari'':59C ''onlin'':56C,101C ''p'':1C,85C ''paper'':14C,111C ''propos'':16C ''rel'':29C,49C,71C ''sequenti'':91C ''set'':81C ''sever'':13C ''sinc'':7C ''spectral'':32C,53C,76C ''stationari'':61C ''stream'':97C ''system'':40C ''think'':3C ''ul'':22C ''updat'':36C ''www.ifp.illinois.edu'':27C ''www.ifp.illinois.edu/~hning2/papers/incremental_spectral_clustering.pdf'':26C ''www.sciencedirect.com'':47C ''www.sciencedirect.com/science/article/pii/s0925231214004433'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4923, 1, '2015-01-22 13:36:24.967', 2, 108, 'Bag of Words creation in image', '&lt;p&gt;In SIFT feature extraction how the key points will be generated and how the features will be stored in the database. In image will the bag of visual words be images or text words?&lt;/p&gt;
', 'feature-extraction', NULL, '''/p'':45C ''bag'':1A,36C ''creation'':4A ''databas'':31C ''extract'':9B,14C ''featur'':8B,13C,25C ''feature-extract'':7B ''generat'':21C ''imag'':6A,33C,41C ''key'':17C ''p'':10C ''point'':18C ''sift'':12C ''store'':28C ''text'':43C ''visual'':38C ''word'':3A,39C,44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2671, 2, '2014-12-10 13:54:43.77', 2, NULL, NULL, '&lt;p&gt;[Since you''ve updated your question to refer to a different algorithm (changed k-means to kNN), I''m adding this as a separate answer specifically for kNN.]&lt;/p&gt;

&lt;p&gt;It appears you may still be confusing &lt;code&gt;kNN&lt;/code&gt; with &lt;code&gt;k-means&lt;/code&gt;. &lt;code&gt;kNN&lt;/code&gt; does not have multiple stages of refinement, nor does it require computing centroids. It is a &lt;a href="http://en.wikipedia.org/wiki/Lazy_learning" rel="nofollow"&gt;lazy learner&lt;/a&gt; that classifies a new observation by examining the &lt;code&gt;k&lt;/code&gt; training observations that are closest to the new observation and picks whichever class is most prevalent among those &lt;code&gt;k&lt;/code&gt; training observations. Note that the distances are &lt;em&gt;not&lt;/em&gt; relative to a cluster centroid. You don''t have to worry about there being more than &lt;code&gt;k&lt;/code&gt; training observations in the neighborhood of the new observation because the neighborhood isn''t based on a threshold distance - it is defined simply by the &lt;code&gt;k&lt;/code&gt; points nearest to the new observation (i.e., it changes for each new observation being evaluated).&lt;/p&gt;

&lt;p&gt;A possible pathological case is when multiple training observations lie at &lt;em&gt;exactly&lt;/em&gt; the same distance from the new observation, which would require you to evaluate more than &lt;code&gt;k&lt;/code&gt; neighbors. But you would still simply pick the class that is most prevalent among that group of observations.&lt;/p&gt;
', NULL, NULL, '''/a'':77C ''/code'':42C,48C,51C,88C,109C,138C,166C,215C ''/em'':119C,197C ''/p'':31C,182C,234C ''/wiki/lazy_learning'':72C ''ad'':22C ''algorithm'':13C ''among'':105C,229C ''answer'':27C ''appear'':34C ''base'':153C ''case'':187C ''centroid'':64C,124C ''chang'':14C,175C ''class'':101C,224C ''classifi'':79C ''closest'':93C ''cluster'':123C ''code'':40C,44C,49C,86C,107C,136C,164C,213C ''comput'':63C ''confus'':39C ''defin'':160C ''differ'':12C ''distanc'':115C,157C,200C ''em'':117C,195C ''en.wikipedia.org'':71C ''en.wikipedia.org/wiki/lazy_learning'':70C ''evalu'':181C,210C ''exact'':196C ''examin'':84C ''group'':231C ''href'':69C ''i.e'':173C ''isn'':151C ''k'':16C,46C,87C,108C,137C,165C,214C ''k-mean'':15C,45C ''knn'':19C,30C,41C,50C ''lazi'':75C ''learner'':76C ''lie'':193C ''m'':21C ''may'':36C ''mean'':17C,47C ''multipl'':55C,190C ''nearest'':168C ''neighbor'':216C ''neighborhood'':143C,150C ''new'':81C,96C,146C,171C,178C,203C ''nofollow'':74C ''note'':112C ''observ'':82C,90C,97C,111C,140C,147C,172C,179C,192C,204C,233C ''p'':1C,32C,183C ''patholog'':186C ''pick'':99C,222C ''point'':167C ''possibl'':185C ''preval'':104C,228C ''question'':7C ''refer'':9C ''refin'':58C ''rel'':73C ''relat'':120C ''requir'':62C,207C ''separ'':26C ''simpli'':161C,221C ''sinc'':2C ''specif'':28C ''stage'':56C ''still'':37C,220C ''threshold'':156C ''train'':89C,110C,139C,191C ''updat'':5C ''ve'':4C ''whichev'':100C ''worri'':130C ''would'':206C,219C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2659, 1, '2014-12-09 21:13:10.797', 1, 148, 'kNN - what happens if more than K observation have the same distance to the centroid of the cluster', '&lt;p&gt;&lt;strong&gt;EDIT&lt;/strong&gt; It was pointed out in the Answers-section that I am confusing k-means and kNN. Indeed I was thinking about kNN but wrote k-means since I''m still new to this topic and confuse the terms quite often. So here is the changed question.&lt;/p&gt;

&lt;p&gt;I was looking at kNN today and something struck me as odd or - to be more precise - something that I was unable to find information about namely the following situation.&lt;/p&gt;

&lt;p&gt;Imagine that we pick kNN for some dataset. I want to remain as general as possible, thus $k$ will not be specified here. Further we select, at some point, an observation where the number of neighbors that fulfill the requirement to be in the neighbourhood are actually more than the specified $k$.&lt;/p&gt;

&lt;p&gt;What criterion/criteria should be applied here if we are restricted to use the specific K and thus cannot alter the structure of the neighborhood (number of neighbors). Which observations will be left out and why? Also is this a problem that occurs often, or is it something of an anomaly?&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/p'':76C,108C,162C,214C ''/strong'':26C ''actual'':156C ''also'':199C ''alter'':182C ''anomali'':213C ''answer'':34C ''answers-sect'':33C ''appli'':168C ''cannot'':181C ''centroid'':15A ''chang'':74C ''classif'':22B ''cluster'':18A ''confus'':39C,65C ''criterion/criteria'':165C ''dataset'':117C ''distanc'':12A ''edit'':25C ''find'':101C ''follow'':106C ''fulfil'':147C ''general'':123C ''happen'':3A ''imagin'':110C ''inde'':45C ''inform'':102C ''k'':7A,41C,54C,127C,161C,178C ''k-mean'':40C,53C ''knn'':1A,44C,50C,82C,114C ''learn'':21B ''left'':195C ''look'':80C ''m'':58C ''machin'':20B ''machine-learn'':19B ''mean'':42C,55C ''name'':104C ''neighbor'':145C,190C ''neighborhood'':187C ''neighbourhood'':154C ''new'':60C ''number'':143C,188C ''observ'':8A,140C,192C ''occur'':205C ''odd'':89C ''often'':69C,206C ''p'':23C,77C,109C,163C ''pick'':113C ''point'':29C,138C ''possibl'':125C ''precis'':94C ''problem'':203C ''question'':75C ''quit'':68C ''remain'':121C ''requir'':149C ''restrict'':173C ''section'':35C ''select'':135C ''sinc'':56C ''situat'':107C ''someth'':85C,95C,210C ''specif'':177C ''specifi'':131C,160C ''still'':59C ''strong'':24C ''struck'':86C ''structur'':184C ''term'':67C ''think'':48C ''thus'':126C,180C ''today'':83C ''topic'':63C ''unabl'':99C ''use'':175C ''want'':119C ''wrote'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2652, 1, '2014-12-09 00:58:03.057', 2, 1107, 'java.lang.NoClassDefFoundError: org/apache/hadoop/fs/FSDataInputStream', '&lt;p&gt;&lt;br&gt;
Im traying to integrate Hadoop and R, I was install the pachages rJava and Rhipe in R, I do this steps to start Hadoop and R:&lt;br&gt;
-starting Hadoop services.,&lt;br&gt;
-loading rJava and Rhipe packages by library function.&lt;br&gt;
-Calling rhinit() to initialize Rhipe.&lt;br&gt;
the problem here is when I call rhinit() funtion, it show this error:&lt;br&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;blockquote&gt;
    &lt;p&gt;Initializing Rhipe v0.73&lt;br&gt;
    Error in .jnew("org/godhuli/rhipe/PersonalServer") : 
     java.lang.NoClassDefFoundError: org/apache/hadoop/fs/FSDataInputStream''&lt;/p&gt;
  &lt;/blockquote&gt;
&lt;/blockquote&gt;

&lt;p&gt;please some helps to fixe this problem.&lt;/p&gt;
', 'r  hadoop', NULL, '''/blockquote'':82C,83C ''/p'':67C,81C,92C ''blockquot'':68C,69C ''br'':6C,33C,37C,46C,52C,66C,74C ''call'':47C,59C ''error'':65C,75C ''fix'':89C ''function'':45C ''funtion'':61C ''hadoop'':4B,11C,30C,35C ''help'':87C ''im'':7C ''initi'':50C,71C ''instal'':16C ''integr'':10C ''java.lang.noclassdeffounderror'':1A,79C ''jnew'':77C ''librari'':44C ''load'':38C ''org/apache/hadoop/fs/fsdatainputstream'':2A,80C ''org/godhuli/rhipe/personalserver'':78C ''p'':5C,70C,84C ''pachag'':18C ''packag'':42C ''pleas'':85C ''problem'':54C,91C ''r'':3B,13C,23C,32C ''rhinit'':48C,60C ''rhipe'':21C,41C,51C,72C ''rjava'':19C,39C ''servic'':36C ''show'':63C ''start'':29C,34C ''step'':27C ''tray'':8C ''v0.73'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2653, 2, '2014-12-09 01:19:32.29', 14, NULL, NULL, '&lt;p&gt;The subject is new so most of the wisdom is scattered in papers, but here are two recent books:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.iro.umontreal.ca/~bengioy/dlbook/"&gt;Deep Learning&lt;/a&gt;, Yoshua Bengio, Ian J. Goodfellow, Aaron Courville.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf"&gt;Deep Learning: Methods and Applications&lt;/a&gt;, Li Deng and Dong Yu.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;And some practical material: &lt;a href="http://deeplearning.net/tutorial/"&gt;http://deeplearning.net/tutorial/&lt;/a&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;ACL 2012 + NAACL 2013 Tutorial: &lt;a href="http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial"&gt;Deep Learning for NLP (without Magic)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':31C,51C,90C ''/index.php/deeplearningtutorial/deeplearningtutorial'':83C ''/li'':39C,57C,91C ''/p'':21C ''/pubs/209355/deeplearning-nowpublishing-vol7-sig-039.pdf'':45C ''/tutorial/'':68C ''/tutorial/&lt;/a&gt;&lt;/p&gt;'':71C ''/ul'':58C,92C ''/~bengioy/dlbook/'':28C ''2012'':75C ''2013'':77C ''aaron'':37C ''acl'':74C ''applic'':50C ''bengio'':33C ''book'':20C ''courvill'':38C ''deep'':29C,46C,84C ''deeplearning.net'':67C,70C ''deeplearning.net/tutorial/'':66C ''deeplearning.net/tutorial/&lt;/a&gt;&lt;/p&gt;'':69C ''deng'':53C ''dong'':55C ''goodfellow'':36C ''href'':25C,42C,65C,80C ''ian'':34C ''j'':35C ''learn'':30C,47C,85C ''li'':23C,40C,52C,73C ''magic'':89C ''materi'':63C ''method'':48C ''naacl'':76C ''new'':5C ''nlp'':87C ''p'':1C,59C ''paper'':14C ''practic'':62C ''recent'':19C ''research.microsoft.com'':44C ''research.microsoft.com/pubs/209355/deeplearning-nowpublishing-vol7-sig-039.pdf'':43C ''scatter'':12C ''subject'':3C ''tutori'':78C ''two'':18C ''ul'':22C,72C ''wisdom'':10C ''without'':88C ''www.iro.umontreal.ca'':27C ''www.iro.umontreal.ca/~bengioy/dlbook/'':26C ''www.socher.org'':82C ''www.socher.org/index.php/deeplearningtutorial/deeplearningtutorial'':81C ''yoshua'':32C ''yu'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2654, 1, '2014-12-09 02:28:51.43', 1, 86, 'Web Framework Built for Recommendations', '&lt;p&gt;I''m wondering if there is a web framework well suited for placing recommendations on content.&lt;/p&gt;

&lt;p&gt;In most cases, a data scientist goes through after the fact and builds (or uses) a completely different tool to create recommendations. This involves analyzing traffic logs, a history of shopping cart data, ratings, and so forth. It usually comes from multiples sources (the web server, the application''s database, Google Analytics, etc) and then has to be cleaned up and processed, THEN delivered back to the application in way it understands.&lt;/p&gt;

&lt;p&gt;Is there a web framework on the market which handles collecting this data up front, as to minimize the retrospective data wrangling?&lt;/p&gt;
', 'predictive-modeling  data-cleaning', NULL, '''/p'':29C,103C,127C ''analyt'':82C ''analyz'':55C ''applic'':78C,98C ''back'':95C ''build'':43C ''built'':3A ''cart'':62C ''case'':33C ''clean'':11B,89C ''collect'':115C ''come'':70C ''complet'':47C ''content'':28C ''creat'':51C ''data'':10B,35C,63C,117C,125C ''data-clean'':9B ''databas'':80C ''deliv'':94C ''differ'':48C ''etc'':83C ''fact'':41C ''forth'':67C ''framework'':2A,21C,109C ''front'':119C ''goe'':37C ''googl'':81C ''handl'':114C ''histori'':59C ''involv'':54C ''log'':57C ''m'':14C ''market'':112C ''minim'':122C ''model'':8B ''multipl'':72C ''p'':12C,30C,104C ''place'':25C ''predict'':7B ''predictive-model'':6B ''process'':92C ''rate'':64C ''recommend'':5A,26C,52C ''retrospect'':124C ''scientist'':36C ''server'':76C ''shop'':61C ''sourc'':73C ''suit'':23C ''tool'':49C ''traffic'':56C ''understand'':102C ''use'':45C ''usual'':69C ''way'':100C ''web'':1A,20C,75C,108C ''well'':22C ''wonder'':15C ''wrangl'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2656, 2, '2014-12-09 08:30:28', 0, NULL, NULL, '&lt;p&gt;Found it myself.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Go to context menu right clicking to the dimension field.&lt;/li&gt;
&lt;li&gt;Go to &lt;strong&gt;Aliases...&lt;/strong&gt; and change the labels.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/qKq73.png" alt="enter image description here"&gt;&lt;/p&gt;
', NULL, NULL, '''/li'':18C,29C ''/p'':5C,42C ''/qkq73.png'':36C ''/strong'':24C ''/ul'':30C ''alias'':23C ''alt'':37C ''chang'':26C ''click'':13C ''context'':10C ''descript'':40C ''dimens'':16C ''enter'':38C ''field'':17C ''found'':2C ''go'':8C,20C ''i.stack.imgur.com'':35C ''i.stack.imgur.com/qkq73.png'':34C ''imag'':39C ''img'':32C ''label'':28C ''li'':7C,19C ''menu'':11C ''p'':1C,31C ''right'':12C ''src'':33C ''strong'':22C ''ul'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2658, 1, '2014-12-09 20:49:37.093', 6, 222, 'Using NLP to automate the categorization of user description', '&lt;p&gt;I have a huge file of customer complaints about the products my company owns and I would like to do a data analysis on those descriptions and tag a category to each of them. &lt;/p&gt;

&lt;p&gt;For example: I need to figure out the number of complaints on &lt;strong&gt;&lt;em&gt;Software&lt;/em&gt;&lt;/strong&gt; and &lt;strong&gt;&lt;em&gt;Hardware&lt;/em&gt;&lt;/strong&gt; side of my product from the customer complaints. Currently, I am using excel to do the data analysis which do seek a significant amount of manual work to get a tag name to the complaints.&lt;/p&gt;

&lt;p&gt;Is there a way in NLP to build and train a model to automate this process? I have been reading stuffs about NLP for the past couple of days and it looks like NLP has a lot of good features to get a head start in addressing this issue. Could someone please guide me with the way I should use NLP to address this issue?&lt;/p&gt;
', 'data-mining  classification  nlp  categorical-data', NULL, '''/em'':70C,76C ''/p'':53C,113C,180C ''/strong'':71C,77C ''address'':161C,177C ''amount'':101C ''analysi'':41C,95C ''autom'':4A,128C ''build'':122C ''categor'':6A,16B ''categori'':48C ''categorical-data'':15B ''classif'':13B ''compani'':31C ''complaint'':26C,65C,85C,112C ''could'':164C ''coupl'':141C ''current'':86C ''custom'':25C,84C ''data'':11B,17B,40C,94C ''data-min'':10B ''day'':143C ''descript'':9A,44C ''em'':68C,74C ''exampl'':56C ''excel'':90C ''featur'':154C ''figur'':60C ''file'':23C ''get'':106C,156C ''good'':153C ''guid'':167C ''hardwar'':75C ''head'':158C ''huge'':22C ''issu'':163C,179C ''like'':36C,147C ''look'':146C ''lot'':151C ''manual'':103C ''mine'':12B ''model'':126C ''name'':109C ''need'':58C ''nlp'':2A,14B,120C,137C,148C,175C ''number'':63C ''own'':32C ''p'':18C,54C,114C ''past'':140C ''pleas'':166C ''process'':130C ''product'':29C,81C ''read'':134C ''seek'':98C ''side'':78C ''signific'':100C ''softwar'':69C ''someon'':165C ''start'':159C ''strong'':67C,73C ''stuff'':135C ''tag'':46C,108C ''train'':124C ''use'':1A,89C,174C ''user'':8A ''way'':118C,171C ''work'':104C ''would'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2660, 2, '2014-12-09 21:19:23.747', 6, NULL, NULL, '&lt;p&gt;One way to handle this is to use ''supervised classification''. In this model, you manually classify a subset of the data and use it to train your algorithm. Then, you feed the remaining data into your software to classify it.&lt;/p&gt;

&lt;p&gt;This is accomplished with NLTK for Python (nltk.org).&lt;/p&gt;

&lt;p&gt;If you are simply looking for strings like "hardware" and "software", this is a simple use case, and you will likely get decent results using a ''feature extractor'', which informs your classifier which phrases in the document are relevant. &lt;/p&gt;

&lt;p&gt;While it''s possible to implement an automated method for finding the keywords, it sounds like you have a list in mind already, so you can skip that step and just use the tags you are aware of. (If your results aren''t satisfactory the first time, this is something you might try later on).&lt;/p&gt;

&lt;p&gt;That''s an overview for getting started. If you are unhappy with the initial results, you can refine your classifier by introducing more complex methods, such as sentence segmentation, identification of dialogue act types, and decision trees. The sky is the limit (or more likely, your time is the limit)!&lt;/p&gt;

&lt;p&gt;More info at:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.nltk.org/book/ch06.html"&gt;http://www.nltk.org/book/ch06.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/book/ch06.html'':213C ''/book/ch06.html&lt;/a&gt;&lt;/p&gt;'':216C ''/p'':42C,52C,93C,150C,202C,207C ''accomplish'':46C ''act'':184C ''algorithm'':29C ''alreadi'':117C ''aren'':136C ''autom'':102C ''awar'':131C ''case'':70C ''classif'':11C ''classifi'':17C,40C,85C,171C ''complex'':175C ''data'':22C,35C ''decent'':76C ''decis'':187C ''dialogu'':183C ''document'':90C ''extractor'':81C ''featur'':80C ''feed'':32C ''find'':105C ''first'':140C ''get'':75C,157C ''handl'':5C ''hardwar'':62C ''href'':210C ''identif'':181C ''implement'':100C ''info'':205C ''inform'':83C ''initi'':165C ''introduc'':173C ''keyword'':107C ''later'':148C ''like'':61C,74C,110C,196C ''limit'':193C,201C ''list'':114C ''look'':58C ''manual'':16C ''method'':103C,176C ''might'':146C ''mind'':116C ''model'':14C ''nltk'':48C ''nltk.org'':51C ''one'':2C ''overview'':155C ''p'':1C,43C,53C,94C,151C,203C,208C ''phrase'':87C ''possibl'':98C ''python'':50C ''refin'':169C ''relev'':92C ''remain'':34C ''result'':77C,135C,166C ''satisfactori'':138C ''segment'':180C ''sentenc'':179C ''simpl'':68C ''simpli'':57C ''skip'':121C ''sky'':190C ''softwar'':38C,64C ''someth'':144C ''sound'':109C ''start'':158C ''step'':123C ''string'':60C ''subset'':19C ''supervis'':10C ''tag'':128C ''time'':141C,198C ''train'':27C ''tree'':188C ''tri'':147C ''type'':185C ''unhappi'':162C ''use'':9C,24C,69C,78C,126C ''way'':3C ''www.nltk.org'':212C,215C ''www.nltk.org/book/ch06.html'':211C ''www.nltk.org/book/ch06.html&lt;/a&gt;&lt;/p&gt;'':214C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6781, 2, '2015-08-13 04:21:31.877', 1, NULL, NULL, '&lt;p&gt;aggregate() should work, as the previous answer suggests. Another option is with the &lt;a href="http://plyr.had.co.nz/" rel="nofollow"&gt;plyr package&lt;/a&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;count(yourDF,c(''id''))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Using more columns in the vector with ''id'' will subdivide the count. &lt;/p&gt;

&lt;p&gt;I believe ddply() (also part of plyr) has a summarize argument which can also do this, similar to aggregate().&lt;/p&gt;
', NULL, NULL, '''/a'':22C ''/code'':30C ''/p'':23C,45C,66C ''/pre'':31C ''aggreg'':2C,65C ''also'':50C,60C ''anoth'':10C ''answer'':8C ''argument'':57C ''believ'':48C ''c'':28C ''code'':25C ''column'':35C ''count'':26C,44C ''ddpli'':49C ''href'':16C ''id'':29C,40C ''nofollow'':19C ''option'':11C ''p'':1C,32C,46C ''packag'':21C ''part'':51C ''plyr'':20C,53C ''plyr.had.co.nz'':17C ''pre'':24C ''previous'':7C ''rel'':18C ''similar'':63C ''subdivid'':42C ''suggest'':9C ''summar'':56C ''use'':33C ''vector'':38C ''work'':4C ''yourdf'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2661, 2, '2014-12-09 21:33:31.2', 3, NULL, NULL, '&lt;p&gt;K-means does not make an assumption regarding how many observations &lt;strong&gt;should be&lt;/strong&gt; assigned to each cluster. &lt;code&gt;K&lt;/code&gt; is simply the number of clusters one chooses to generate. During each iteration, each observation is assigned to the cluster having the nearest mean. There is no assumption that all clusters should have a comparable number of observations assigned (i.e., for &lt;code&gt;N&lt;/code&gt; observations, there is no expectation that each cluster should have ~ &lt;code&gt;N/K&lt;/code&gt; observations assigned).&lt;/p&gt;

&lt;p&gt;It is quite possible that the numbers of observations in the various clusters are highly imbalanced. This can be due to the distribution of the data, the number of clusters chosen (&lt;code&gt;K&lt;/code&gt;), or even how the cluster means are initialized.&lt;/p&gt;
', NULL, NULL, '''/code'':24C,68C,81C,119C ''/p'':84C,128C ''/strong'':17C ''assign'':18C,41C,63C,83C ''assumpt'':9C,52C ''choos'':32C ''chosen'':116C ''cluster'':21C,30C,44C,55C,76C,98C,115C,124C ''code'':22C,66C,79C,117C ''compar'':59C ''data'':111C ''distribut'':108C ''due'':105C ''even'':121C ''expect'':73C ''generat'':34C ''high'':100C ''i.e'':64C ''imbalanc'':101C ''initi'':127C ''iter'':37C ''k'':3C,23C,118C ''k-mean'':2C ''make'':7C ''mani'':12C ''mean'':4C,48C,125C ''n'':67C ''n/k'':80C ''nearest'':47C ''number'':28C,60C,92C,113C ''observ'':13C,39C,62C,69C,82C,94C ''one'':31C ''p'':1C,85C ''possibl'':89C ''quit'':88C ''regard'':10C ''simpli'':26C ''strong'':14C ''various'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2662, 2, '2014-12-09 22:32:22.66', 3, NULL, NULL, '&lt;p&gt;You are mixing up kNN classification and k-means.&lt;/p&gt;

&lt;p&gt;There is nothing wrong with having more than k observations near a center in k-means. In fact, this it the usual case; you shouldn''t choose k too large. If you have 1 million points, a k of 100 may be okay. &lt;em&gt;K-means does not guarantee clusters of a particular size&lt;/em&gt;. Worst case, clusters in k-means can have only one element (outliers) or even disappear.&lt;/p&gt;

&lt;p&gt;What you probably meant to write, but got mixed up, is &lt;strong&gt;what to do if a point is at the same distance to two centers&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;From a statistical point of view, it doesn''t matter. Both have the same squared error.&lt;/p&gt;

&lt;p&gt;From an implementation point of view, choose any &lt;em&gt;deterministic&lt;/em&gt; rule, so that your algorithm converges and doesn''t go into an infinite loop of reassignment.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Update:&lt;/strong&gt; with respect to kNN classification:&lt;/p&gt;

&lt;p&gt;There are many ways to resolve this, that will surprisingly often work just as good as the other, without a clear advantage of one over the other:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;randomly choose a winner from the tied objects&lt;/li&gt;
&lt;li&gt;take all into account with equal weighting&lt;/li&gt;
&lt;li&gt;if you have m objects at the same distance where you expected only r, then put a weight of r/k on each of them.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;E.g. k=5.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;distance   label   weight
    0        A       1
    1        B       1
    1        A       1
    2        A      2/3
    2        B      2/3
    2        B      2/3
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;yields A=2.66, B=2.33&lt;/p&gt;

&lt;p&gt;The reason that randomly choosing works just as good as the others is that usually, the majority decision in kNN will not be changed by contributions with a weight of less than 1; in particular when k is larger than say 10.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2675, 2, '2014-12-10 15:37:41.853', 1, NULL, NULL, '&lt;p&gt;Sheldon is correct, this sounds like a fairly typical use case for supervised classification. If all of your customer complaints are either software or hardware (i.e., zero individual complaints cover both categories, and zero are outside these two classes), then all you need is a binary classifier, which makes things simpler than they otherwise could be.&lt;/p&gt;

&lt;p&gt;If you''re looking for a Java-based NLP toolkit that supports something like this, you should check out the Stanford Classifier: &lt;a href="http://nlp.stanford.edu/software/classifier.shtml" rel="nofollow"&gt;http://nlp.stanford.edu/software/classifier.shtml&lt;/a&gt; -- it''s licensed as open source software under the GPL.&lt;/p&gt;

&lt;p&gt;Their wiki page should help you get started using the classifier: &lt;a href="http://www-nlp.stanford.edu/wiki/Software/Classifier" rel="nofollow"&gt;http://www-nlp.stanford.edu/wiki/Software/Classifier&lt;/a&gt; -- keep in mind that you''ll need to manually annotate a large sample of your data as a training set, as Sheldon mentioned.&lt;/p&gt;
', NULL, NULL, '''/p'':58C,103C,149C ''/software/classifier.shtml'':87C ''/software/classifier.shtml&lt;/a&gt;'':92C ''/wiki/software/classifier'':120C ''/wiki/software/classifier&lt;/a&gt;'':125C ''annot'':135C ''base'':68C ''binari'':47C ''case'':12C ''categori'':33C ''check'':78C ''class'':40C ''classif'':15C ''classifi'':48C,82C,115C ''complaint'':21C,30C ''correct'':4C ''could'':56C ''cover'':31C ''custom'':20C ''data'':141C ''either'':23C ''fair'':9C ''get'':111C ''gpl'':102C ''hardwar'':26C ''help'':109C ''href'':84C,117C ''i.e'':27C ''individu'':29C ''java'':67C ''java-bas'':66C ''keep'':126C ''larg'':137C ''licens'':95C ''like'':7C,74C ''ll'':131C ''look'':63C ''make'':50C ''manual'':134C ''mention'':148C ''mind'':128C ''need'':44C,132C ''nlp'':69C ''nlp.stanford.edu'':86C,91C ''nlp.stanford.edu/software/classifier.shtml'':85C ''nlp.stanford.edu/software/classifier.shtml&lt;/a&gt;'':90C ''nofollow'':89C,122C ''open'':97C ''otherwis'':55C ''outsid'':37C ''p'':1C,59C,104C ''page'':107C ''re'':62C ''rel'':88C,121C ''sampl'':138C ''set'':145C ''sheldon'':2C,147C ''simpler'':52C ''softwar'':24C,99C ''someth'':73C ''sound'':6C ''sourc'':98C ''stanford'':81C ''start'':112C ''supervis'':14C ''support'':72C ''thing'':51C ''toolkit'':70C ''train'':144C ''two'':39C ''typic'':10C ''use'':11C,113C ''wiki'':106C ''www-nlp.stanford.edu'':119C,124C ''www-nlp.stanford.edu/wiki/software/classifier'':118C ''www-nlp.stanford.edu/wiki/software/classifier&lt;/a&gt;'':123C ''zero'':28C,35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2664, 2, '2014-12-10 01:59:48.7', 1, NULL, NULL, '&lt;p&gt;I haven''t seen anything like that and very much doubt that such frameworks exist, at least, as complete frameworks. The reason for this is IMHO the fact that data &lt;em&gt;transformation&lt;/em&gt; and &lt;em&gt;cleaning&lt;/em&gt; is very &lt;strong&gt;domain-&lt;/strong&gt; and &lt;strong&gt;project-specific&lt;/strong&gt;. Having said that, there are multiple tools that can help with these activities in terms of &lt;em&gt;partial automation&lt;/em&gt; and &lt;em&gt;integration&lt;/em&gt; with and between existing statistical and Web frameworks.&lt;/p&gt;

&lt;p&gt;For example, for &lt;strong&gt;Python&lt;/strong&gt;, the use of &lt;em&gt;data manipulation&lt;/em&gt; library &lt;code&gt;pandas&lt;/code&gt; as well as &lt;em&gt;machine learning&lt;/em&gt; library &lt;code&gt;scikit-learn&lt;/code&gt; can be easily integrated with Web frameworks (especially Python-based, but not necessarily), as these libraries are also Python-based. These and other Python data science tools that might be of interest can be found here: &lt;a href="http://pydata.org/downloads" rel="nofollow"&gt;http://pydata.org/downloads&lt;/a&gt;. Specifically, for cleaning and pre-processing tasks, which you asked about, &lt;code&gt;pandas&lt;/code&gt; seem to be the first tool to explore. Again, for Python, the following discussion on StackOverflow on methods and approaches might be helpful: &lt;a href="http://stackoverflow.com/q/14262433/2872891"&gt;http://stackoverflow.com/q/14262433/2872891&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Consider an example of &lt;strong&gt;another platform&lt;/strong&gt;. The use of &lt;code&gt;pandas&lt;/code&gt; for data transformation and cleaning is rather &lt;em&gt;low-level&lt;/em&gt;. The platform that I like very much and currently use as the platform of choice for data science tasks is &lt;code&gt;R&lt;/code&gt;. Rich &lt;em&gt;ecosystem of R packages&lt;/em&gt; especially shines in the area of data transformation and cleaning. This is because, in addition to very flexible low-level methods of performing these tasks, there are some R packages, which take a &lt;em&gt;higher-level approach&lt;/em&gt; to the problem, which may potentially improve developer''s productivity and decrease the amount of defects. In particular, I''m talking about two packages, which I find very promising: &lt;code&gt;editrules&lt;/code&gt; and &lt;code&gt;deducorrect&lt;/code&gt;. You can find more detailed information about these and other R packages for data transformation and cleaning in my another answer here on &lt;em&gt;Data Science StackExchange&lt;/em&gt; (paper that I reference in the last link there could be especially useful, as it presents an &lt;em&gt;approach&lt;/em&gt; to data transformation and cleaning that is &lt;em&gt;generic&lt;/em&gt; enough, so that could be used as a &lt;strong&gt;framework&lt;/strong&gt; for this on any decent platform): &lt;a href="http://datascience.stackexchange.com/a/722/2452"&gt;http://datascience.stackexchange.com/a/722/2452&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE&lt;/strong&gt;: On the topic of &lt;em&gt;recommender systems&lt;/em&gt; and their integration with data wrangling tools and Web frameworks, you may find my other answer here on DS SE useful: &lt;a href="http://datascience.stackexchange.com/a/836/2452"&gt;http://datascience.stackexchange.com/a/836/2452&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2667, 1, '2014-12-10 04:33:31.35', 3, 1083, 'Python interface to Titan Database', '&lt;p&gt;How can I connect to Titan database from Python ? &lt;/p&gt;

&lt;p&gt;What I understand is that Titan (Graph database) provides an interface (Blueprint) to Cassandra (Column Store) and 
bulb is a python interface to graph DB.&lt;/p&gt;

&lt;p&gt;Now how can I start programming in python to connect with titan DB?
Is there any good documentation/tutorial available ?&lt;/p&gt;
', 'python  databases  nosql', NULL, '''/p'':19C,46C,67C ''avail'':66C ''blueprint'':32C ''bulb'':38C ''cassandra'':34C ''column'':35C ''connect'':13C,57C ''databas'':5A,7B,16C,28C ''db'':45C,60C ''documentation/tutorial'':65C ''good'':64C ''graph'':27C,44C ''interfac'':2A,31C,42C ''nosql'':8B ''p'':9C,20C,47C ''program'':53C ''provid'':29C ''python'':1A,6B,18C,41C,55C ''start'':52C ''store'':36C ''titan'':4A,15C,26C,59C ''understand'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2668, 1, '2014-12-10 06:37:17.193', 12, 301, 'Data Science Tools Using Scala', '&lt;p&gt;I know that Spark is fully integrated with Scala. It''s use case is specifically for large data sets. Which other tools have good Scala support? Is Scala best suited for larger data sets? Or is it also suited for smaller data sets? &lt;/p&gt;
', 'scalability  scala', NULL, '''/p'':52C ''also'':46C ''best'':37C ''case'':21C ''data'':1A,26C,41C,50C ''fulli'':14C ''good'':32C ''integr'':15C ''know'':10C ''larg'':25C ''larger'':40C ''p'':8C ''scala'':5A,7B,17C,33C,36C ''scalabl'':6B ''scienc'':2A ''set'':27C,42C,51C ''smaller'':49C ''spark'':12C ''specif'':23C ''suit'':38C,47C ''support'':34C ''tool'':3A,30C ''use'':4A,20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2669, 2, '2014-12-10 07:12:33.64', 0, NULL, NULL, '&lt;p&gt;It is important to see all the rules if one of the states of target column is more important than others. For example, if you are predicting fraudulent transactions, you might want to flag something as fraud even if is has 5% probability.&lt;/p&gt;
', NULL, NULL, '''/p'':45C ''5'':43C ''column'':17C ''even'':39C ''exampl'':24C ''flag'':35C ''fraud'':38C ''fraudul'':29C ''import'':4C,20C ''might'':32C ''one'':11C ''other'':22C ''p'':1C ''predict'':28C ''probabl'':44C ''rule'':9C ''see'':6C ''someth'':36C ''state'':14C ''target'':16C ''transact'':30C ''want'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2670, 1, '2014-12-10 10:15:00.94', 9, 941, 'Visualizing deep neural network training', '&lt;p&gt;I''m trying to find an equivalent of Hinton Diagrams for multilayer networks to plot the weights during training. &lt;/p&gt;

&lt;p&gt;The trained network is somewhat similar to a Deep SRN, i.e. it has a high number of multiple weight matrices which would make the simultaneous plot of several Hinton Diagrams visually confusing. &lt;/p&gt;

&lt;p&gt;Does anyone know of a good way to visualize the weight update process for recurrent networks with multiple layers? &lt;/p&gt;

&lt;p&gt;I haven''t found much papers on the topic. I was thinking to display time-related information on the weights per layer instead if I can''t come up with something. E.g. the weight-delta over time for each layer (omitting the use of every single connection). PCA is another possibility, though I''d like to not produce much additional computations, since the visualization is done online during training.&lt;/p&gt;
', 'machine-learning  neuralnetwork  visualization  deep-learning', NULL, '''/p'':34C,68C,89C,162C ''addit'':152C ''anoth'':142C ''anyon'':71C ''come'':119C ''comput'':153C ''confus'':67C ''connect'':139C ''d'':146C ''deep'':2A,12B,44C ''deep-learn'':11B ''delta'':127C ''diagram'':24C,65C ''display'':104C ''done'':158C ''e.g'':123C ''equival'':21C ''everi'':137C ''find'':19C ''found'':94C ''good'':75C ''haven'':92C ''high'':50C ''hinton'':23C,64C ''i.e'':46C ''inform'':108C ''instead'':114C ''know'':72C ''layer'':88C,113C,132C ''learn'':8B,13B ''like'':147C ''m'':16C ''machin'':7B ''machine-learn'':6B ''make'':58C ''matric'':55C ''much'':95C,151C ''multilay'':26C ''multipl'':53C,87C ''network'':4A,27C,38C,85C ''neural'':3A ''neuralnetwork'':9B ''number'':51C ''omit'':133C ''onlin'':159C ''p'':14C,35C,69C,90C ''paper'':96C ''pca'':140C ''per'':112C ''plot'':29C,61C ''possibl'':143C ''process'':82C ''produc'':150C ''recurr'':84C ''relat'':107C ''sever'':63C ''similar'':41C ''simultan'':60C ''sinc'':154C ''singl'':138C ''someth'':122C ''somewhat'':40C ''srn'':45C ''think'':102C ''though'':144C ''time'':106C,129C ''time-rel'':105C ''topic'':99C ''train'':5A,33C,37C,161C ''tri'':17C ''updat'':81C ''use'':135C ''visual'':1A,10B,66C,78C,156C ''way'':76C ''weight'':31C,54C,80C,111C,126C ''weight-delta'':125C ''would'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2678, 2, '2014-12-10 17:36:20.55', 7, NULL, NULL, '&lt;p&gt;As you''ve described it, Step 4 is where you want to use TF-IDF. Essentially, TD-IDF will count each term in each document, and assign a score given the relative frequency across the collection of documents.&lt;/p&gt;

&lt;p&gt;There''s one big step missing from your process, however: annotating a training set. Before you train your classifier, you''ll need to manually annotate a sample of your data with the labels you want to be able to apply automatically using the classifier.&lt;/p&gt;

&lt;p&gt;To make all of this easier, you might want to consider using the &lt;a href="http://nlp.stanford.edu/software/classifier.shtml"&gt;Stanford Classifier&lt;/a&gt;. It will perform the feature extraction and build the classifier model (supporting several different machine learning algorithms), but you''ll still need to annotate the training data by hand.&lt;/p&gt;
', NULL, NULL, '''/a'':110C ''/p'':42C,88C,140C ''/software/classifier.shtml'':107C ''4'':8C ''abl'':81C ''across'':37C ''algorithm'':127C ''annot'':54C,68C,134C ''appli'':83C ''assign'':30C ''automat'':84C ''big'':47C ''build'':118C ''classifi'':62C,87C,109C,120C ''collect'':39C ''consid'':100C ''count'':23C ''data'':73C,137C ''describ'':5C ''differ'':124C ''document'':28C,41C ''easier'':95C ''essenti'':18C ''extract'':116C ''featur'':115C ''frequenc'':36C ''given'':33C ''hand'':139C ''howev'':53C ''href'':104C ''idf'':17C,21C ''label'':76C ''learn'':126C ''ll'':64C,130C ''machin'':125C ''make'':91C ''manual'':67C ''might'':97C ''miss'':49C ''model'':121C ''need'':65C,132C ''nlp.stanford.edu'':106C ''nlp.stanford.edu/software/classifier.shtml'':105C ''one'':46C ''p'':1C,43C,89C ''perform'':113C ''process'':52C ''relat'':35C ''sampl'':70C ''score'':32C ''set'':57C ''sever'':123C ''stanford'':108C ''step'':7C,48C ''still'':131C ''support'':122C ''td'':20C ''td-idf'':19C ''term'':25C ''tf'':16C ''tf-idf'':15C ''train'':56C,60C,136C ''use'':14C,85C,101C ''ve'':4C ''want'':12C,78C,98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8976, 5, '2015-11-22 05:42:59.207', 0, NULL, NULL, '&lt;p&gt;For analysis, categorical values are considered as abstract entities without any mathematical structure such as an order or a topology, regardless of how they are coded and stored.  For more, see &lt;a href="http://en.wikipedia.org/wiki/Categorical_variable" rel="nofollow"&gt;Wikipedia&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/a'':41C ''/p'':42C ''/wiki/categorical_variable'':37C ''abstract'':9C ''analysi'':3C ''categor'':4C ''code'':27C ''consid'':7C ''en.wikipedia.org'':36C ''en.wikipedia.org/wiki/categorical_variable'':35C ''entiti'':10C ''href'':34C ''mathemat'':13C ''nofollow'':39C ''order'':18C ''p'':1C ''regardless'':22C ''rel'':38C ''see'':32C ''store'':29C ''structur'':14C ''topolog'':21C ''valu'':5C ''wikipedia'':40C ''without'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2673, 1, '2014-12-10 14:18:36.933', 2, 174, 'Training Neural Networks with unknown length of input', '&lt;p&gt;I''m currently going into the world of machine learning and Neural Networks, thanks to &lt;a href="https://github.com/cazala/synaptic" rel="nofollow"&gt;synaptic (js)&lt;/a&gt; that interests me a lot.&lt;/p&gt;

&lt;p&gt;So I read a lot, wikipedia links and &lt;a href="https://github.com/cazala/synaptic/wiki/Neural-Networks-101" rel="nofollow"&gt;synaptic''s NN 101&lt;/a&gt;, but there''s a lot of basics questions that I don''t understand (but I''d like to) in the use of machine learning (NN) and the point of these technologies.&lt;/p&gt;

&lt;p&gt;Let''s say, I wan''t my network to (kind of) learn (something like) gravity, so to train it I set in input 10 objects with a mass, and a position x, y (and z) and I set output the new x, y (and z) of each objects.
I guess I should give it several configurations and everything but &lt;strong&gt;here is the question; can it, then, be able to compute the interactions between 10000, 100000 objects?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;At this stage in my learning, what I don''t clearly get is what is the point of teaching/training neurons to compute XOR like it''s shown in &lt;a href="https://github.com/cazala/synaptic#documentation" rel="nofollow"&gt;synaptic''s documentation&lt;/a&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;var trainingSet = [
    {
        input: [0,0],
        output: [0]
    },
    {
        input: [0,1],
        output: [1]
    },
    {
        input: [1,0],
        output: [1]
    },
    {
        input: [1,1],
        output: [0]
    },
];

var trainer = new Trainer(myNetwork);
trainer.train(trainingSet);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Were we just give it all the possible inputs and outputs to a XOR.&lt;/p&gt;

&lt;p&gt;Well, as I''m all new to the technologies I think my questions are full of non-sense and everything but thanks for reading and help you might bring :)&lt;/p&gt;
', 'neuralnetwork', NULL, '''/a'':35C,62C,214C ''/cazala/synaptic'':30C ''/cazala/synaptic#documentation'':208C ''/cazala/synaptic/wiki/neural-networks-101'':55C ''/code'':247C ''/p'':41C,94C,174C,215C,264C,296C ''/pre'':248C ''/strong'':173C ''0'':221C,222C,224C,226C,232C,239C ''1'':227C,229C,231C,234C,236C,237C ''10'':119C ''10000'':170C ''100000'':171C ''101'':61C ''abl'':164C ''basic'':69C ''bring'':295C ''clear'':186C ''code'':217C ''comput'':166C,197C ''configur'':151C ''current'':13C ''d'':78C ''document'':213C ''everyth'':153C,286C ''full'':280C ''get'':187C ''github.com'':29C,54C,207C ''github.com/cazala/synaptic'':28C ''github.com/cazala/synaptic#documentation'':206C ''github.com/cazala/synaptic/wiki/neural-networks-101'':53C ''give'':148C,253C ''go'':14C ''graviti'':110C ''guess'':145C ''help'':292C ''href'':27C,52C,205C ''input'':8A,118C,220C,225C,230C,235C,258C ''interact'':168C ''interest'':37C ''js'':34C ''kind'':105C ''learn'':20C,86C,107C,181C ''length'':6A ''let'':96C ''like'':79C,109C,199C ''link'':49C ''lot'':40C,47C,67C ''m'':12C,269C ''machin'':19C,85C ''mass'':123C ''might'':294C ''mynetwork'':244C ''network'':3A,23C,103C ''neural'':2A,22C ''neuralnetwork'':9B ''neuron'':195C ''new'':136C,242C,271C ''nn'':60C,87C ''nofollow'':32C,57C,210C ''non'':283C ''non-sens'':282C ''object'':120C,143C,172C ''output'':134C,223C,228C,233C,238C,260C ''p'':10C,42C,95C,175C,249C,265C ''point'':90C,192C ''posit'':126C ''possibl'':257C ''pre'':216C ''question'':70C,159C,278C ''read'':45C,290C ''rel'':31C,56C,209C ''say'':98C ''sens'':284C ''set'':116C,133C ''sever'':150C ''shown'':202C ''someth'':108C ''stage'':178C ''strong'':155C ''synapt'':33C,58C,211C ''teaching/training'':194C ''technolog'':93C,274C ''thank'':24C,288C ''think'':276C ''train'':1A,113C ''trainer'':241C,243C ''trainer.train'':245C ''trainingset'':219C,246C ''understand'':75C ''unknown'':5A ''use'':83C ''var'':218C,240C ''wan'':100C ''well'':266C ''wikipedia'':48C ''world'':17C ''x'':127C,137C ''xor'':198C,263C ''y'':128C,138C ''z'':130C,140C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3804, 1, '2015-01-03 22:38:39.097', 1, 173, 'Format for storing textual data', '&lt;p&gt;For an upcoming project, I''m mining textual posts from an online forum, using Scrapy. What is the best way to store this text data? I''m thinking of simply exporting it into a JSON file, but is there a better format? Or does it not matter?&lt;/p&gt;
', 'text-mining  crawling', NULL, '''/p'':58C ''best'':29C ''better'':51C ''crawl'':9B ''data'':5A,35C ''export'':41C ''file'':46C ''format'':1A,52C ''forum'':23C ''json'':45C ''m'':16C,37C ''matter'':57C ''mine'':8B,17C ''onlin'':22C ''p'':10C ''post'':19C ''project'':14C ''scrapi'':25C ''simpli'':40C ''store'':3A,32C ''text'':7B,34C ''text-min'':6B ''textual'':4A,18C ''think'':38C ''upcom'':13C ''use'':24C ''way'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3729, 2, '2014-12-19 16:02:06.33', 1, NULL, NULL, '&lt;p&gt;I''m pretty sure that the software you''re referring to is a some kind of &lt;strong&gt;internal&lt;/strong&gt; &lt;em&gt;research project&lt;/em&gt; software, developed by Enlitic (&lt;a href="http://www.enlitic.com" rel="nofollow"&gt;http://www.enlitic.com&lt;/a&gt;), where Jeremy Howard works as a founder and CEO. By "internal research project software" I mean either a &lt;em&gt;proof-of-concept&lt;/em&gt; software, or a &lt;em&gt;prototype&lt;/em&gt; software.&lt;/p&gt;
', NULL, NULL, '''/a'':35C ''/em'':24C,59C,65C ''/p'':67C ''/strong'':20C ''ceo'':44C ''concept'':58C ''develop'':26C ''either'':52C ''em'':21C,54C,63C ''enlit'':28C ''founder'':42C ''howard'':38C ''href'':30C ''intern'':19C,46C ''jeremi'':37C ''kind'':16C ''m'':3C ''mean'':51C ''nofollow'':33C ''p'':1C ''pretti'':4C ''project'':23C,48C ''proof'':56C ''proof-of-concept'':55C ''prototyp'':64C ''re'':10C ''refer'':11C ''rel'':32C ''research'':22C,47C ''softwar'':8C,25C,49C,60C,66C ''strong'':18C ''sure'':5C ''work'':39C ''www.enlitic.com'':31C,34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2676, 2, '2014-12-10 15:41:03.987', 1, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Gravity&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Basically you''d like to find a function that maps an input (an object with a mass and a location) to an output (new location). It''s not necessary to have one set of input neurons for each different object. It''s sufficient to encode the input variables generically for all objects.&lt;/p&gt;

&lt;p&gt;Like a function: &lt;/p&gt;

&lt;p&gt;$$f( x, y, z, mass ) \to ( x_n, y_n, z_n )$$&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;XOR&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Training the XOR function is special as the early neural networks, the perceptrons, were unable to learn the XOR function (because it is not linear separable). Multi-Layer Perceptrons however are able to learn the XOR function. This is just to demonstrate that the NN implementation of Synaptic is capable of learning problems that are not linear separable&lt;/p&gt;
', NULL, NULL, '''/p'':5C,59C,64C,78C,83C,144C ''/strong'':4C,82C ''abl'':117C ''basic'':7C ''capabl'':135C ''d'':9C ''demonstr'':127C ''differ'':45C ''earli'':93C ''encod'':51C ''f'':66C ''find'':12C ''function'':14C,63C,88C,104C,122C ''generic'':55C ''graviti'':3C ''howev'':115C ''implement'':131C ''input'':18C,41C,53C ''layer'':113C ''learn'':101C,119C,137C ''like'':10C,61C ''linear'':109C,142C ''locat'':26C,31C ''map'':16C ''mass'':23C,70C ''multi'':112C ''multi-lay'':111C ''n'':73C,75C,77C ''necessari'':35C ''network'':95C ''neural'':94C ''neuron'':42C ''new'':30C ''nn'':130C ''object'':20C,46C,58C ''one'':38C ''output'':29C ''p'':1C,6C,60C,65C,79C,84C ''perceptron'':97C,114C ''problem'':138C ''separ'':110C,143C ''set'':39C ''special'':90C ''strong'':2C,80C ''suffici'':49C ''synapt'':133C ''train'':85C ''unabl'':99C ''variabl'':54C ''x'':67C,72C ''xor'':81C,87C,103C,121C ''y'':68C,74C ''z'':69C,76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2677, 1, '2014-12-10 16:08:03.537', 5, 368, 'Document classification: tf-idf prior to or after feature filtering?', '&lt;p&gt;I have a document classification project where I am getting site content and then assigning one of numerous labels to the website according to content.&lt;/p&gt;

&lt;p&gt;I found out that &lt;a href="http://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;tf-idf&lt;/a&gt; could be very useful for this. However, I was unsure as to &lt;em&gt;when&lt;/em&gt; exactly to use it.&lt;/p&gt;

&lt;p&gt;Assumming a website that is concerned with a specific topic makes repeated mention of it, this was my current process:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Retrieve site content, parse for plain text &lt;/li&gt;
&lt;li&gt;Normalize and stem content &lt;/li&gt;
&lt;li&gt;Tokenize into unigrams (maybe bigrams too)&lt;/li&gt;
&lt;li&gt;Retrieve a count of each unigram for the given document, filtering low length and low occurrence words&lt;/li&gt;
&lt;li&gt;Train a classifier such as NaiveBayes on the resulting set&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;My question is the following: &lt;strong&gt;Where would tf-idf fit in here&lt;/strong&gt;? Before normalizing/stemming? After normalizing but before tokenizing? After tokenizing? &lt;/p&gt;

&lt;p&gt;Any insight would be greatly appreciated.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Edit:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Upon closer inspection, I think I may have run into a misunderstanding at to how TF-IDF operates. At the above step 4 that I describe, would I have to feed the &lt;em&gt;entirety&lt;/em&gt; of my data into TF-IDF at once? If, for example, my data is as follows: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[({tokenized_content_site1}, category_string_site1), 
 ({tokenized_content_site2}, category_string_site2), 
...
 ({tokenized_content_siten}, category_string_siten)}]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here, the outermost structure is a list, containing tuples, containing a dictionary (or hashmap) and a string.&lt;/p&gt;

&lt;p&gt;Would I have to feed the &lt;em&gt;entirety&lt;/em&gt; of that data into the TF-IDF calculator at once to achieve the desired effect? Specifically, I have been looking at the &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html" rel="nofollow"&gt;scikit-learn&lt;/a&gt; TfidfVectorizer to do this, but I am a bit unsure as to its use as examples are pretty sparse.&lt;/p&gt;
', 'classification  feature-selection  feature-extraction', NULL, '''/a'':61C,339C ''/code'':275C ''/em'':76C,236C,305C ''/li'':113C,119C,127C,146C,158C ''/ol'':159C ''/p'':45C,81C,103C,185C,193C,199C,254C,295C,359C ''/pre'':276C ''/stable/modules/generated/sklearn.feature_extraction.text.tfidfvectorizer.html'':333C ''/strong'':175C,198C ''/wiki/tf%e2%80%93idf'':55C ''4'':224C ''accord'':42C ''achiev'':318C ''appreci'':192C ''assign'':34C ''assum'':83C ''bigram'':125C ''bit'':348C ''calcul'':314C ''categori'':260C,266C,272C ''classif'':2A,12B,24C ''classifi'':150C ''closer'':202C ''code'':256C ''concern'':88C ''contain'':285C,287C ''content'':31C,44C,108C,118C,258C,264C,270C ''could'':62C ''count'':131C ''current'':101C ''data'':239C,250C,308C ''describ'':227C ''desir'':320C ''dictionari'':289C ''document'':1A,23C,138C ''edit'':197C ''effect'':321C ''em'':74C,234C,303C ''en.wikipedia.org'':54C ''en.wikipedia.org/wiki/tf%e2%80%93idf'':53C ''entireti'':235C,304C ''exact'':77C ''exampl'':248C,355C ''extract'':18B ''featur'':10A,14B,17B ''feature-extract'':16B ''feature-select'':13B ''feed'':232C,301C ''filter'':11A,139C ''fit'':172C ''follow'':165C,253C ''found'':48C ''get'':29C ''given'':137C ''great'':191C ''hashmap'':291C ''howev'':68C ''hr'':194C ''href'':52C,330C ''idf'':5A,60C,171C,218C,243C,313C ''insight'':188C ''inspect'':203C ''label'':38C ''learn'':338C ''length'':141C ''li'':105C,114C,120C,128C,147C ''list'':284C ''look'':326C ''low'':140C,143C ''make'':93C ''may'':207C ''mayb'':124C ''mention'':95C ''misunderstand'':212C ''naivebay'':153C ''nofollow'':57C,335C ''normal'':115C,179C ''normalizing/stemming'':177C ''numer'':37C ''occurr'':144C ''ol'':104C ''one'':35C ''oper'':219C ''outermost'':280C ''p'':19C,46C,82C,160C,186C,195C,200C,277C,296C ''pars'':109C ''plain'':111C ''pre'':255C ''pretti'':357C ''prior'':6A ''process'':102C ''project'':25C ''question'':162C ''rel'':56C,334C ''repeat'':94C ''result'':156C ''retriev'':106C,129C ''run'':209C ''scikit'':337C ''scikit-learn'':336C ''scikit-learn.org'':332C ''scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.tfidfvectorizer.html'':331C ''select'':15B ''set'':157C ''site'':30C,107C ''site1'':259C,262C ''site2'':265C,268C ''siten'':271C,274C ''spars'':358C ''specif'':91C,322C ''stem'':117C ''step'':223C ''string'':261C,267C,273C,294C ''strong'':166C,196C ''structur'':281C ''text'':112C ''tf'':4A,59C,170C,217C,242C,312C ''tf-idf'':3A,58C,169C,216C,241C,311C ''tfidfvector'':340C ''think'':205C ''token'':121C,182C,184C,257C,263C,269C ''topic'':92C ''train'':148C ''tupl'':286C ''unigram'':123C,134C ''unsur'':71C,349C ''upon'':201C ''use'':65C,79C,353C ''websit'':41C,85C ''word'':145C ''would'':168C,189C,228C,297C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4924, 2, '2015-01-22 17:25:40.69', 2, NULL, NULL, '&lt;p&gt;You can use some public data sets to play around with. &lt;/p&gt;

&lt;p&gt;The iris flower data set is great for classification problems. As an example you can try to classify the flower species with k-nearest neighbor model or a decision tree (CART) model.  &lt;/p&gt;

&lt;p&gt;You can find this and other sample machine learning datasets here: &lt;a href="http://archive.ics.uci.edu/ml/datasets.html" rel="nofollow"&gt;http://archive.ics.uci.edu/ml/datasets.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This list identifies what type of machine learning tasks can be performed with each dataset. &lt;/p&gt;
', NULL, NULL, '''/ml/datasets.html'':65C ''/ml/datasets.html&lt;/a&gt;&lt;/p&gt;'':70C ''/p'':13C,48C,87C ''archive.ics.uci.edu'':64C,69C ''archive.ics.uci.edu/ml/datasets.html'':63C ''archive.ics.uci.edu/ml/datasets.html&lt;/a&gt;&lt;/p&gt;'':68C ''around'':11C ''cart'':46C ''classif'':23C ''classifi'':32C ''data'':7C,18C ''dataset'':59C,86C ''decis'':44C ''exampl'':27C ''find'':52C ''flower'':17C,34C ''great'':21C ''href'':62C ''identifi'':74C ''iri'':16C ''k'':38C ''k-nearest'':37C ''learn'':58C,79C ''list'':73C ''machin'':57C,78C ''model'':41C,47C ''nearest'':39C ''neighbor'':40C ''nofollow'':67C ''p'':1C,14C,49C,71C ''perform'':83C ''play'':10C ''problem'':24C ''public'':6C ''rel'':66C ''sampl'':56C ''set'':8C,19C ''speci'':35C ''task'':80C ''tree'':45C ''tri'':30C ''type'':76C ''use'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3702, 1, '2014-12-13 23:08:53.93', 4, 120, 'K nearest neighbour', '&lt;p&gt;Is the k-nearest neighbour algorithm a discriminative or a generative classifier? My first thought on this was that it was generative, because it actually used Bayes''s theorem to compute the posterior. Searching further for this, it seems like it is a discriminative model, but I couldn''t find the explanation.&lt;/p&gt;

&lt;p&gt;So is KNN discriminative first of all? And if it is, is that because it doesn''t model the the priors or the likelihood?&lt;/p&gt;
', 'classification', NULL, '''/p'':59C,85C ''actual'':31C ''algorithm'':12C ''bay'':33C ''classif'':4B ''classifi'':18C ''comput'':37C ''couldn'':54C ''discrimin'':14C,50C,64C ''doesn'':76C ''explan'':58C ''find'':56C ''first'':20C,65C ''generat'':17C,28C ''k'':1A,9C ''k-nearest'':8C ''knn'':63C ''like'':46C ''likelihood'':84C ''model'':51C,78C ''nearest'':2A,10C ''neighbour'':3A,11C ''p'':5C,60C ''posterior'':39C ''prior'':81C ''search'':40C ''seem'':45C ''theorem'':35C ''thought'':21C ''use'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3700, 1, '2014-12-13 19:27:56.213', 0, 58, 'Choice of weights for the Laplacian Eigenmaps algorithm', '&lt;p&gt;In &lt;a href="http://web.cse.ohio-state.edu/~mbelkin/papers/PLM_UCTHESIS_03.pdf" rel="nofollow" title="Belkin&amp;#39;s thesis about Laplacian Eigenmaps"&gt;his thesis&lt;/a&gt; (section 2.3.3) Belkin uses the heat equation to derive an approximation for $\mathcal{L}f$:&lt;/p&gt;

&lt;p&gt;$$\mathcal{L}f(x_i)\approx \frac{1}{t}\Big(f(x_i)-\alpha \sum_{x_j, ||x_i-x_j||&amp;lt;\epsilon}e^{-\frac{||x_i-x_j||^2}{4t}}f(x_j)\Big)$$
where $$\alpha=\Big(\sum_{x_j, ||x_i-x_j||&amp;lt;\epsilon}e^{-\frac{||x_i-x_j||^2}{4t}}\Big)^{-1}$$.&lt;/p&gt;

&lt;p&gt;However, I''m not sure how these considerations lead to this choice of weights for the weight matrix (which will be used to construct the Laplacian):&lt;/p&gt;

&lt;p&gt;$$W_{ij} =
\begin{cases} 
e^{-\frac{||x_i-x_j||^2}{4t}} &amp;amp; if\ ||x_i-x_j||&amp;lt;\epsilon \\ 
0 &amp;amp; otherwise 
\end{cases}$$&lt;/p&gt;

&lt;p&gt;A very vague idea of mine was that the factors $\alpha$ and $\frac{1}{t}$ don''t change for a given $x_i$ so if one choses the weights like above the resulting discrete Laplacian would (let aside those two constants) converge to the continuous version.&lt;/p&gt;

&lt;p&gt;Any ideas or tips what I''d have to read up to in order to get a better understanding?&lt;/p&gt;
', 'machine-learning', NULL, '''-1'':109C ''/a'':31C ''/p'':47C,110C,138C,167C,215C,236C ''/~mbelkin/papers/plm_ucthesis_03.pdf'':18C ''0'':162C ''1'':56C,182C ''2'':80C,106C,151C ''2.3.3'':33C ''39'':23C ''4t'':81C,107C,152C ''algorithm'':8A ''alpha'':62C,87C,179C ''amp'':153C,163C ''approx'':54C ''approxim'':42C ''asid'':206C ''begin'':142C ''belkin'':22C,34C ''better'':234C ''big'':58C,85C,88C,108C ''case'':143C,166C ''chang'':186C ''choic'':1A,123C ''chose'':195C ''consider'':119C ''constant'':209C ''construct'':135C ''continu'':213C ''converg'':210C ''d'':223C ''deriv'':40C ''discret'':202C ''e'':73C,99C,144C ''eigenmap'':7A,28C ''end'':165C ''epsilon'':72C,98C,161C ''equat'':38C ''f'':46C,51C,59C,82C ''factor'':178C ''frac'':55C,74C,100C,145C,181C ''get'':232C ''given'':189C ''heat'':37C ''howev'':112C ''href'':15C ''i-x'':67C,76C,93C,102C,147C,156C ''idea'':172C,218C ''ij'':141C ''j'':65C,70C,79C,84C,91C,96C,105C,150C,159C ''l'':45C,50C ''laplacian'':6A,27C,137C,203C ''lead'':120C ''learn'':11B ''let'':205C ''like'':198C ''lt'':71C,97C,160C ''m'':114C ''machin'':10B ''machine-learn'':9B ''mathcal'':44C,49C ''matrix'':129C ''mine'':174C ''nofollow'':20C ''one'':194C ''order'':230C ''otherwis'':164C ''p'':12C,48C,111C,139C,168C,216C ''read'':226C ''rel'':19C ''result'':201C ''section'':32C ''sum'':63C,89C ''sure'':116C ''thesi'':25C,30C ''tip'':220C ''titl'':21C ''two'':208C ''understand'':235C ''use'':35C,133C ''vagu'':171C ''version'':214C ''w'':140C ''web.cse.ohio-state.edu'':17C ''web.cse.ohio-state.edu/~mbelkin/papers/plm_ucthesis_03.pdf'':16C ''weight'':3A,125C,128C,197C ''would'':204C ''x'':52C,60C,64C,66C,69C,75C,78C,83C,90C,92C,95C,101C,104C,146C,149C,155C,158C,190C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2679, 2, '2014-12-10 19:50:17.797', 9, NULL, NULL, '&lt;p&gt;The closes thing I know is &lt;a href="http://cs.stanford.edu/people/karpathy/convnetjs/"&gt;ConvNetJS&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;ConvNetJS is a Javascript library for training Deep Learning models (mainly Neural Networks) entirely in your browser. Open a tab and you''re training. No software requirements, no compilers, no installations, no GPUs, no sweat.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Demos on this site plot weighs and how do they change with time (bear in mind, its many parameters, as practical networks do have a lot of neurons). Moreover, if you are not satisfied with their plotting, there is access to networks parameters and you can plot as you wish (since it is JavaScript).&lt;/p&gt;
', NULL, NULL, '''/a'':14C ''/blockquote'':54C ''/p'':15C,53C,110C ''/people/karpathy/convnetjs/'':12C ''access'':95C ''bear'':69C ''blockquot'':16C ''browser'':34C ''chang'':66C ''close'':3C ''compil'':46C ''convnetj'':13C,18C ''cs.stanford.edu'':11C ''cs.stanford.edu/people/karpathy/convnetjs/'':10C ''deep'':25C ''demo'':56C ''entir'':31C ''gpus'':50C ''href'':9C ''instal'':48C ''javascript'':21C,109C ''know'':6C ''learn'':26C ''librari'':22C ''lot'':81C ''main'':28C ''mani'':73C ''mind'':71C ''model'':27C ''moreov'':84C ''network'':30C,77C,97C ''neural'':29C ''neuron'':83C ''open'':35C ''p'':1C,17C,55C ''paramet'':74C,98C ''plot'':60C,92C,102C ''practic'':76C ''re'':40C ''requir'':44C ''satisfi'':89C ''sinc'':106C ''site'':59C ''softwar'':43C ''sweat'':52C ''tab'':37C ''thing'':4C ''time'':68C ''train'':24C,41C ''weigh'':61C ''wish'':105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2682, 1, '2014-12-10 22:47:49.88', 3, 1807, 'Use Cases of Neo4J and Spark GraphX', '&lt;p&gt;I have used Neo4J to implement a content recommendation engine. I like Cypher, and find graph databases to be intuitive.&lt;/p&gt;

&lt;p&gt;Looking at scaling to a larger data set, I am not confident No4J + Cypher will be performant. Spark has the GraphX project, which I have not used in the past.&lt;/p&gt;

&lt;p&gt;Has anybody switched from Neo4J to Spark GraphX? Do the use cases overlap, aside from scalability? Or, does GraphX address a completely different problem set than Neo4J?&lt;/p&gt;
', 'scalability  graphs  neo4j', NULL, '''/p'':32C,64C,93C ''address'':85C ''anybodi'':67C ''asid'':79C ''case'':2A,77C ''complet'':87C ''confid'':45C ''content'':19C ''cypher'':24C,47C ''data'':40C ''databas'':28C ''differ'':88C ''engin'':21C ''find'':26C ''graph'':9B,27C ''graphx'':7A,54C,73C,84C ''implement'':17C ''intuit'':31C ''larger'':39C ''like'':23C ''look'':34C ''neo4j'':4A,10B,15C,70C,92C ''no4j'':46C ''overlap'':78C ''p'':11C,33C,65C ''past'':63C ''perform'':50C ''problem'':89C ''project'':55C ''recommend'':20C ''scalabl'':8B,81C ''scale'':36C ''set'':41C,90C ''spark'':6A,51C,72C ''switch'':68C ''use'':1A,14C,60C,76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3685, 2, '2014-12-11 07:08:00.54', 4, NULL, NULL, '&lt;p&gt;Based on my cursory understanding of the topics, associated with your question, I think that &lt;strong&gt;Gephi&lt;/strong&gt; (&lt;a href="https://gephi.github.io" rel="nofollow"&gt;https://gephi.github.io&lt;/a&gt;; the original gephi.org link redirects there) should be able to handle &lt;em&gt;neural network dynamic visualization&lt;/em&gt;. It seems that, in order to achieve your goal, you need to &lt;strong&gt;stream&lt;/strong&gt; your graph(s) with corresponding weights (&lt;a href="https://forum.gephi.org/viewtopic.php?t=1875" rel="nofollow"&gt;https://forum.gephi.org/viewtopic.php?t=1875&lt;/a&gt;). For &lt;em&gt;streaming&lt;/em&gt;, you most likely will need this &lt;em&gt;plug-in&lt;/em&gt;: &lt;a href="https://marketplace.gephi.org/plugin/graph-streaming" rel="nofollow"&gt;https://marketplace.gephi.org/plugin/graph-streaming&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE&lt;/strong&gt;: You may also find useful SoNIA software: &lt;a href="http://web.stanford.edu/group/sonia" rel="nofollow"&gt;http://web.stanford.edu/group/sonia&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':26C ''/em'':43C,78C,89C ''/group/sonia'':115C ''/group/sonia&lt;/a&gt;.&lt;/p&gt;'':120C ''/plugin/graph-streaming'':94C ''/plugin/graph-streaming&lt;/a&gt;.&lt;/p&gt;'':99C ''/strong'':19C,58C,103C ''/viewtopic.php?t=1875'':69C ''/viewtopic.php?t=1875&lt;/a&gt;).'':74C ''abl'':35C ''achiev'':50C ''also'':106C ''associ'':10C ''base'':2C ''correspond'':63C ''cursori'':5C ''dynam'':41C ''em'':38C,76C,85C ''find'':107C ''forum.gephi.org'':68C,73C ''forum.gephi.org/viewtopic.php?t=1875'':67C ''forum.gephi.org/viewtopic.php?t=1875&lt;/a&gt;).'':72C ''gephi'':18C ''gephi.github.io'':22C,25C ''gephi.org'':29C ''goal'':52C ''graph'':60C ''handl'':37C ''href'':21C,66C,91C,112C ''like'':81C ''link'':30C ''marketplace.gephi.org'':93C,98C ''marketplace.gephi.org/plugin/graph-streaming'':92C ''marketplace.gephi.org/plugin/graph-streaming&lt;/a&gt;.&lt;/p&gt;'':97C ''may'':105C ''need'':54C,83C ''network'':40C ''neural'':39C ''nofollow'':24C,71C,96C,117C ''order'':48C ''origin'':28C ''p'':1C,100C ''plug'':87C ''plug-in'':86C ''question'':13C ''redirect'':31C ''rel'':23C,70C,95C,116C ''seem'':45C ''softwar'':110C ''sonia'':109C ''stream'':57C,77C ''strong'':17C,56C,101C ''think'':15C ''topic'':9C ''understand'':6C ''updat'':102C ''use'':108C ''visual'':42C ''web.stanford.edu'':114C,119C ''web.stanford.edu/group/sonia'':113C ''web.stanford.edu/group/sonia&lt;/a&gt;.&lt;/p&gt;'':118C ''weight'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3687, 1, '2014-12-11 13:37:29.43', 1, 146, 'R-Cassandra connection with RJDBC', '&lt;p&gt;I am trying to connect with cassandra in R using &lt;a href="http://cran.r-project.org/web/packages/RJDBC/index.html" rel="nofollow"&gt;RJDBC&lt;/a&gt;. When I execute &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;casscon &amp;lt;- dbConnect(cassdrv, "jdbc:cassandra://ipaddrs:9160/demodb")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am getting&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Error in .jcall(drv@jdrv, "Ljava/sql/Connection;", "connect", as.character(url)[1],
: java.lang.StringIndexOutOfBoundsException: String index out of range: -1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I can''t figure out the problem. I need a solution for this.&lt;/p&gt;
', 'r  databases', NULL, '''-1'':67C ''/a'':28C ''/code'':42C,68C ''/p'':32C,48C,84C ''/pre'':43C,69C ''/web/packages/rjdbc/index.html'':24C ''1'':60C ''9160/demodb'':41C ''as.character'':58C ''cassandra'':3A,16C ''casscon'':35C ''cassdrv'':38C ''code'':34C,50C ''connect'':4A,14C,57C ''cran.r-project.org'':23C ''cran.r-project.org/web/packages/rjdbc/index.html'':22C ''databas'':8B ''dbconnect'':37C ''drv'':54C ''error'':51C ''execut'':31C ''figur'':74C ''get'':47C ''href'':21C ''index'':63C ''ipaddr'':40C ''java.lang.stringindexoutofboundsexception'':61C ''jcall'':53C ''jdbc'':39C ''jdrv'':55C ''ljava/sql/connection'':56C ''lt'':36C ''need'':79C ''nofollow'':26C ''p'':9C,44C,70C ''pre'':33C,49C ''problem'':77C ''r'':2A,7B,18C ''r-cassandra'':1A ''rang'':66C ''rel'':25C ''rjdbc'':6A,27C ''solut'':81C ''string'':62C ''tri'':12C ''url'':59C ''use'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4898, 2, '2015-01-17 16:53:08.033', 9, NULL, NULL, '&lt;p&gt;Late answer, but here is an eclectic list of &lt;strong&gt;&lt;a href="http://rs.io/100-interesting-data-sets-for-statistics/"&gt;100+ Interesting Data Sets&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The blog post is fun and easy to read through (I have no affiliation). It''s worth to scan through, and to scrape a few from the top:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Last words of every Texas inmate executed since 1984 &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;10,000 annotated images of cats&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;2.2 million chess matches&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/100-interesting-data-sets-for-statistics/'':16C ''/a'':21C ''/li'':67C,77C,85C ''/p'':23C,53C,66C,76C,84C ''/strong'':22C ''/ul'':86C ''000'':71C ''10'':70C ''100'':17C ''1984'':65C ''2.2'':80C ''affili'':38C ''annot'':72C ''answer'':3C ''blog'':26C ''cat'':75C ''chess'':82C ''data'':19C ''easi'':31C ''eclect'':8C ''everi'':60C ''execut'':63C ''fun'':29C ''href'':13C ''imag'':73C ''inmat'':62C ''interest'':18C ''last'':57C ''late'':2C ''li'':55C,68C,78C ''list'':9C ''match'':83C ''million'':81C ''p'':1C,24C,56C,69C,79C ''post'':27C ''read'':33C ''rs.io'':15C ''rs.io/100-interesting-data-sets-for-statistics/'':14C ''scan'':43C ''scrape'':47C ''set'':20C ''sinc'':64C ''strong'':11C ''texa'':61C ''top'':52C ''ul'':54C ''word'':58C ''worth'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3688, 1, '2014-12-11 19:23:55.907', 7, 236, 'Multiple labels in supervised learning algorithm', '&lt;p&gt;I have a corpus of text with a corresponding topics. For example &lt;code&gt;"A rapper Tupac was shot in LA"&lt;/code&gt; and it was labelled as &lt;code&gt;["celebrity", "murder"]&lt;/code&gt;. So basically each vector of features can have many labels (not the same amount. The first feature vector can have 3 labels, second 1, third 5).&lt;/p&gt;

&lt;p&gt;If I would have just one label corresponded to each text, I would try a &lt;a href="http://en.wikipedia.org/wiki/Naive_Bayes_classifier"&gt;Naive Bayes classifier&lt;/a&gt;, but I do not really know how should I proceed if I can have many labels.&lt;/p&gt;

&lt;p&gt;Is there any way to transform Naive Bayes into multi label classification problem (if there is a better approach - please let me know)?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;P.S.&lt;/strong&gt; few things about the data I have.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;approximately 10.000 elements in the dataset&lt;/li&gt;
&lt;li&gt;text is approximately 2-3 sentences&lt;/li&gt;
&lt;li&gt;maximum 7 labels per text&lt;/li&gt;
&lt;/ul&gt;
', 'machine-learning  text-mining', NULL, '''-3'':164C ''/a'':95C ''/code'':34C,43C ''/li'':158C,166C,173C ''/p'':70C,112C,137C,149C ''/strong'':141C ''/ul'':174C ''/wiki/naive_bayes_classifier'':91C ''1'':67C ''10.000'':153C ''2'':163C ''3'':64C ''5'':69C ''7'':169C ''algorithm'':6A ''amount'':57C ''approach'':132C ''approxim'':152C,162C ''basic'':45C ''bay'':93C,121C ''better'':131C ''celebr'':41C ''classif'':125C ''classifi'':94C ''code'':26C,40C ''corpus'':17C ''correspond'':22C,79C ''data'':146C ''dataset'':157C ''element'':154C ''en.wikipedia.org'':90C ''en.wikipedia.org/wiki/naive_bayes_classifier'':89C ''exampl'':25C ''featur'':49C,60C ''first'':59C ''href'':88C ''know'':101C,136C ''la'':33C ''label'':2A,38C,53C,65C,78C,111C,124C,170C ''learn'':5A,9B ''let'':134C ''li'':151C,159C,167C ''machin'':8B ''machine-learn'':7B ''mani'':52C,110C ''maximum'':168C ''mine'':12B ''multi'':123C ''multipl'':1A ''murder'':42C ''naiv'':92C,120C ''one'':77C ''p'':13C,71C,113C,138C ''p.s'':140C ''per'':171C ''pleas'':133C ''problem'':126C ''proceed'':105C ''rapper'':28C ''realli'':100C ''second'':66C ''sentenc'':165C ''shot'':31C ''strong'':139C ''supervis'':4A ''text'':11B,19C,82C,160C,172C ''text-min'':10B ''thing'':143C ''third'':68C ''topic'':23C ''transform'':119C ''tri'':85C ''tupac'':29C ''ul'':150C ''vector'':47C,61C ''way'':117C ''would'':74C,84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3805, 2, '2015-01-03 23:08:17.697', 0, NULL, NULL, '&lt;p&gt;for python, the standard tool is pandas. It was specifically designed to deal with financial data timeseries.
&lt;a href="http://pandas.pydata.org/pandas-docs/stable/timeseries.html" rel="nofollow"&gt;pandas timeseries&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':28C ''/p'':29C ''/pandas-docs/stable/timeseries.html'':23C ''data'':17C ''deal'':14C ''design'':12C ''financi'':16C ''href'':20C ''nofollow'':25C ''p'':1C ''panda'':8C,26C ''pandas.pydata.org'':22C ''pandas.pydata.org/pandas-docs/stable/timeseries.html'':21C ''python'':3C ''rel'':24C ''specif'':11C ''standard'':5C ''timeseri'':18C,27C ''tool'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5368, 2, '2015-03-21 20:09:53.157', 5, NULL, NULL, '&lt;p&gt;In my opinion, ideally, to be a more &lt;strong&gt;well-rounded&lt;/strong&gt; professional, it would be nice to know &lt;strong&gt;at least&lt;/strong&gt; one programming language for the most popular &lt;strong&gt;programming paradigms&lt;/strong&gt; (&lt;em&gt;procedural&lt;/em&gt;, &lt;em&gt;object-oriented&lt;/em&gt;, &lt;em&gt;functional&lt;/em&gt;). Certainly, I consider &lt;em&gt;R&lt;/em&gt; and &lt;em&gt;Python&lt;/em&gt; as the two most popular programming languages and environments for &lt;em&gt;data science&lt;/em&gt; and, therefore, &lt;strong&gt;primary&lt;/strong&gt; data science tools.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Julia&lt;/em&gt; is impressive in certain aspects, but it tries to catch up with those two and establish itself as a major data science tool. However, I don''t see this happening any time soon, simply due to &lt;em&gt;R/Python&lt;/em&gt;''s &lt;em&gt;popularity&lt;/em&gt;, very large &lt;em&gt;communities&lt;/em&gt; as well as enormous &lt;em&gt;ecosystems&lt;/em&gt; of existing and newly developed &lt;em&gt;packages/libraries&lt;/em&gt;, covering an very wide range of domains / fields of study.&lt;/p&gt;

&lt;p&gt;Having said that, many packages and libraries, focused on data science, ML and AI areas, are &lt;strong&gt;implemented&lt;/strong&gt; and/or provide &lt;strong&gt;APIs&lt;/strong&gt; in languages other than R or Python (for the proof, see &lt;a href="https://github.com/josephmisiti/awesome-machine-learning"&gt;this curated list&lt;/a&gt; and &lt;a href="https://github.com/onurakpolat/awesome-bigdata"&gt;this curated list&lt;/a&gt;, both of which are excellent and give a solid perspective about the variety in the field). This is especially true for &lt;em&gt;performance-oriented&lt;/em&gt; or &lt;em&gt;specialized&lt;/em&gt; software. For that software, I''ve seen projects with implementation and/or APIs mostly in Java, C and C++ (Java is especially popular in the &lt;strong&gt;big data&lt;/strong&gt; segment of data science - due to its closeness to &lt;em&gt;Hadoop&lt;/em&gt; and its ecosystem - and in the &lt;strong&gt;NLP&lt;/strong&gt; segment), but other options are available, albeit to a much more limited, domain-based, extent. Neither of these languages is a waste of time, however you have to &lt;strong&gt;prioritize&lt;/strong&gt; mastering any or all of them with your current work situation, projects and interests. So, to answer your question about viability of C/C++ (and Java), I would say that they are all &lt;strong&gt;viable&lt;/strong&gt;, however not as &lt;em&gt;primary&lt;/em&gt; data science tools, but as &lt;strong&gt;secondary&lt;/strong&gt; ones.&lt;/p&gt;

&lt;p&gt;Answering your questions on 1) &lt;em&gt;C&lt;/em&gt; as a &lt;em&gt;potential data science tool&lt;/em&gt; and 2) its &lt;em&gt;efficiency&lt;/em&gt;, I would say that: 1) while it''s possible to use &lt;em&gt;C&lt;/em&gt; for data science, I &lt;strong&gt;would recommend against&lt;/strong&gt; doing it, because you''d have a very hard time finding corresponding libraries or, even more so, trying to implement corresponding algorithms by yourself; 2) you shouldn''t worry about efficiency, as many performance-critical segments of code are implemented in low-level languages like C, plus, there are options to interface popular data science languages with, say, C (for example, &lt;code&gt;Rcpp&lt;/code&gt; package for integration R with C/C++: &lt;a href="http://dirk.eddelbuettel.com/code/rcpp.html"&gt;http://dirk.eddelbuettel.com/code/rcpp.html&lt;/a&gt;). This is in addition to simpler, but often rather effective, approaches to performance, such as consistent use of vectorization in R as well as using various parallel programming frameworks, packages and libraries. For R ecosystem examples, see &lt;a href="http://cran.r-project.org/web/views/HighPerformanceComputing.html"&gt;CRAN Task View "High-Performance and Parallel Computing with R"&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Speaking about &lt;em&gt;data science&lt;/em&gt;, I think that it makes quite a lot of sense to mention the importance of &lt;strong&gt;reproducible research&lt;/strong&gt; approach as well as the availability of various &lt;strong&gt;tools&lt;/strong&gt;, supporting this concept (for more details, please see &lt;a href="http://datascience.stackexchange.com/a/759/2452"&gt;my relevant answer&lt;/a&gt;). I hope that my answer is helpful.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3689, 2, '2014-12-11 20:27:27.847', 4, NULL, NULL, '&lt;p&gt;From listening to presentations by Martin Odersky, the creator of Scala, it is especially well suited for building highly scalable systems by leveraging functional programming constructs in conjuction with object orientation and flelxible syntax. It is also useful for development of small systems and rapid prototyping because it takes less lines of code than some other languages and it has an interactive mode for fast feedback. One notable Scala framework is Akka which uses the actor model of concurrent computation. Many of Odersky''s presentations are on YouTube and there is a list of tools implemented with Scala on wiki.scala-lang.org.&lt;/p&gt;

&lt;p&gt;An implicit point is that tools and frameworks written in Scala inherently have Scala integration and usually a Scala API. Then other APIs may be added to support other languages beginning with Java since Scala is already integrated and in fact critically depends on Java.  If a tool or framework is not written in Scala, it is unlikely that it offers any support for Scala.  That is why in answer to your question I have pointed towards tools and frameworks written in Scala and Spark is one example.  However, Scala currently has a minor share of the market but its adoption rate is growing and the high growth rate of Spark will enhance that. The reason I use Scala is because Spark''s API for Scala is richer than the Java and Python APIs. &lt;/p&gt;

&lt;p&gt;The main reasons I prefer Scala generally is because it is much more expressive than Java because it allows and facilitates the use of functions as objects and values while retaining object oriented modularity, which enables development of complex and correct programs with far less code than Java which I had preferred because of widespread use, clarity and excellent documentation.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3690, 1, '2014-12-11 23:56:03.023', 0, 678, 'Exporting R model to OpenCV''s Machine Learning Library', '&lt;p&gt;I''m wonder if it''s possible to export a model trained in R, to OpenCV''s Machine Learning (ML) library format? The latter appears to save/read models in &lt;a href="http://docs.opencv.org/modules/ml/doc/statistical_models.html#cvstatmodel-load" rel="nofollow"&gt;XML/YAML&lt;/a&gt;, whereas the former might be exportable via &lt;a href="http://cran.r-project.org/web/packages/pmml/index.html" rel="nofollow"&gt;PMML&lt;/a&gt;. Specifically, I''m working with Random Forests, which are classifiers available both in R and OpenCV''s ML library.&lt;/p&gt;

&lt;p&gt;Any advice on how I can get the two to share models would be greatly appreciated.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3733, 1, '2014-12-20 03:37:21.82', 3, 106, 'Which Optimization method to use?', '&lt;p&gt;I have a non-function (not in closed form) that takes in a few parameters (about 20) and returns a real value. A few of these parameters are discrete while others are continuous. Some of these parameters can only be chosen from a finite space of values. &lt;/p&gt;

&lt;p&gt;Since I don''t have the function in closed form, I cannot use any gradient based methods. However, the discrete nature and the boxed constraints on a few of those parameters restrict even the number of derivative free optimization techniques at my disposal. I am wondering what are the options in terms of optimization methods that I can use. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3691, 2, '2014-12-12 00:13:10.68', 5, NULL, NULL, '&lt;p&gt;For starters, Naive Bayes is probably not appropriate here.  It assumes independence among the inputs (hence the "Naive") and words in a sentence are very dependent.&lt;/p&gt;

&lt;p&gt;But, assuming you really want to run with NB as an algorithm to start your experimentation, there are two options I''d consider:&lt;/p&gt;

&lt;h2&gt;Ungraceful: Lots of NB classifiers&lt;/h2&gt;

&lt;p&gt;This would be an alternative approach.  Make a corupus of all the words observed as your vector of inputs.  Make a corpus off all the tags that are observed as your vector of outputs.  An NB classifier with multiple outputs is the equivalent of having multiple NB classifiers with one output each (so do whichever is easier to implement in whatever software framework you''re using).  Treat each element as a training sample where a given input (a word) is a &lt;code&gt;1&lt;/code&gt; if that word is present and a &lt;code&gt;0&lt;/code&gt; if that word isn''t.  Use the same binary scheme for the output.&lt;/p&gt;

&lt;p&gt;This brute forces the application of the NB Classifier to your data, and leaves you to find meaning by still haivng to mine the huge set of classifiers you''ll be left with.&lt;/p&gt;

&lt;h2&gt;More Graceful: Process your data&lt;/h2&gt;

&lt;p&gt;This is the approach I''d recommend if you want to run with one multiple-class NB Classifier.  &lt;/p&gt;

&lt;p&gt;Your goal here is to figure out how to map each set of tags to a single class.  I''m sure there is some sort of clustering scheme or network analysis (perhaps ["celebrity"] linked to ["murder"] could become a the segment ["debauchery"]) that will sensibly map your tags to one single cluster.  If you treat tags as nodes and two given tags together as links, then you''ll want to look into community detection algorithms (which is where I''d start).  But, if you just want something working, then some sort of hack on the tags that converts a list of tags to only the tag that''s most commonly seen in your dataset would be enough.&lt;/p&gt;

&lt;p&gt;This method front-loads the work of cleaning your data and would make the NB Classifier''s output easier to understand.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3692, 2, '2014-12-12 01:30:26.723', 2, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;Apply your clustering algorithm&lt;/li&gt;
&lt;li&gt;Calculate distance from all data points to its assigned cluster&lt;/li&gt;
&lt;li&gt;Label the data points furthest from a center as an outlier&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Randomly generating 100 data points from three gaussians, clustering them with k-means, and marking the 10 ''furthest from a center'' data points gave the following graph:
&lt;img src="http://i.stack.imgur.com/TrSDZ.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;see &lt;a href="http://nbviewer.ipython.org/github/TheGrimmScientist/Snippets/blob/master/OutlierDetectionWithClustering/OutlierDetectionWithClustering.ipynb" rel="nofollow"&gt;this notebook&lt;/a&gt; for the full example&lt;/p&gt;

&lt;p&gt;The burden of solving what "distance" means will already have to be solved for you to run a clustering algorithm.  It will still be up to you to pick off what distance means an outlier.  In this example, I just picked the N most distant data point, though you''ll probably want to pick any number of data points over a certain number of standard deviations from a center.&lt;/p&gt;
', NULL, NULL, '''/a'':85C ''/github/thegrimmscientist/snippets/blob/master/outlierdetectionwithclustering/outlierdetectionwithclustering.ipynb'':80C ''/li'':7C,19C,32C ''/ol'':33C ''/p'':73C,90C,161C ''/trsdz.png'':67C ''10'':52C ''100'':37C ''algorithm'':6C,111C ''alreadi'':100C ''alt'':68C ''appli'':3C ''assign'':17C ''burden'':93C ''calcul'':9C ''center'':28C,56C,160C ''certain'':153C ''cluster'':5C,18C,43C,110C ''data'':13C,23C,38C,57C,137C,149C ''descript'':71C ''deviat'':157C ''distanc'':10C,97C,123C ''distant'':136C ''enter'':69C ''exampl'':89C,129C ''follow'':61C ''full'':88C ''furthest'':25C,53C ''gaussian'':42C ''gave'':59C ''generat'':36C ''graph'':62C ''href'':77C ''i.stack.imgur.com'':66C ''i.stack.imgur.com/trsdz.png'':65C ''imag'':70C ''img'':63C ''k'':47C ''k-mean'':46C ''label'':21C ''li'':2C,8C,20C ''ll'':141C ''mark'':50C ''mean'':48C,98C,124C ''n'':134C ''nbviewer.ipython.org'':79C ''nbviewer.ipython.org/github/thegrimmscientist/snippets/blob/master/outlierdetectionwithclustering/outlierdetectionwithclustering.ipynb'':78C ''nofollow'':82C ''notebook'':84C ''number'':147C,154C ''ol'':1C ''outlier'':31C,126C ''p'':34C,74C,91C ''pick'':120C,132C,145C ''point'':14C,24C,39C,58C,138C,150C ''probabl'':142C ''random'':35C ''rel'':81C ''run'':108C ''see'':75C ''solv'':95C,104C ''src'':64C ''standard'':156C ''still'':114C ''though'':139C ''three'':41C ''want'':143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4899, 2, '2015-01-18 10:22:35.777', 0, NULL, NULL, '&lt;p&gt;I may still misunderstand what you mean, but the general simple formulation is to minimize sum of loss over all training examples. Converting to your formulation, that ''assumes'' the joint distribution of the input and output is just the empirical distribution found in the data. That''s the best assumption you can make without additional information. If you had reason to assume something else, you would. &lt;/p&gt;
', NULL, NULL, '''/p'':68C ''addit'':56C ''assum'':29C,63C ''assumpt'':51C ''best'':50C ''convert'':24C ''data'':46C ''distribut'':32C,42C ''els'':65C ''empir'':41C ''exampl'':23C ''formul'':13C,27C ''found'':43C ''general'':11C ''inform'':57C ''input'':35C ''joint'':31C ''loss'':19C ''make'':54C ''may'':3C ''mean'':8C ''minim'':16C ''misunderstand'':5C ''output'':37C ''p'':1C ''reason'':61C ''simpl'':12C ''someth'':64C ''still'':4C ''sum'':17C ''train'':22C ''without'':55C ''would'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3693, 1, '2014-12-12 03:00:57.507', 6, 425, 'What are the best practices to anonymize user names in data?', '&lt;p&gt;I''m working on a project which asks fellow students to share their original text data for further analysis using data mining techniques, and, I think it would be appropriate to anonymize student names with their submissions.  &lt;/p&gt;

&lt;p&gt;Setting aside the better solutions of a url where students submit their work and a backend script inserts the anonymized ID, &lt;strong&gt;What sort of solutions could I direct students to implement on their own to anonymized their own names?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I''m still a noob in this area. I don''t know what are the norms. I was thinking the solution could be a hashing algorithm. That sounds like a better solution than making up a fake name as two people could pick the same fake name.possible people could pick the same fake name. &lt;strong&gt;What are some of the concerns I should be aware of?&lt;/strong&gt; &lt;/p&gt;
', 'machine-learning  data-cleaning', NULL, '''/p'':56C,99C,168C ''/strong'':98C,167C ''algorithm'':126C ''analysi'':37C ''anonym'':7A,50C,77C,94C ''appropri'':48C ''area'':108C ''asid'':59C ''ask'':26C ''awar'':165C ''backend'':73C ''best'':4A ''better'':61C,131C ''clean'':17B ''concern'':161C ''could'':84C,122C,142C,149C ''data'':11A,16B,34C,39C ''data-clean'':15B ''direct'':86C ''fake'':137C,146C,153C ''fellow'':27C ''hash'':125C ''id'':78C ''implement'':89C ''insert'':75C ''know'':112C ''learn'':14B ''like'':129C ''m'':20C,102C ''machin'':13B ''machine-learn'':12B ''make'':134C ''mine'':40C ''name'':9A,52C,97C,138C,154C ''name.possible'':147C ''noob'':105C ''norm'':116C ''origin'':32C ''p'':18C,57C,100C ''peopl'':141C,148C ''pick'':143C,150C ''practic'':5A ''project'':24C ''script'':74C ''set'':58C ''share'':30C ''solut'':62C,83C,121C,132C ''sort'':81C ''sound'':128C ''still'':103C ''strong'':79C,155C ''student'':28C,51C,67C,87C ''submiss'':55C ''submit'':68C ''techniqu'':41C ''text'':33C ''think'':44C,119C ''two'':140C ''url'':65C ''use'':38C ''user'':8A ''work'':21C,70C ''would'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3694, 2, '2014-12-12 09:02:16.35', 2, NULL, NULL, '&lt;p&gt;A standard practice in psychology (where you want to code participants in order to link different measurements together) is to have participants choose their mother''s maiden name initials and birthdate, e.g., in the format XX-YYMMDD.&lt;/p&gt;

&lt;p&gt;This if course can still run into conflicts. Then again, I don''t think there is &lt;em&gt;any&lt;/em&gt; surefire conflict-free anonymization algorithm your students could do &lt;em&gt;without knowing all the other students&lt;/em&gt;. Mothers'' names and birthdates could be identical, own birthdates could be identical, shoe sizes could be, favorite superhero characters... The only thing I could think of would be (US) Social Security numbers, but you &lt;a href="http://www.ssa.gov/pubs/EN-05-10064.pdf" rel="nofollow"&gt;&lt;em&gt;really&lt;/em&gt; don''t want to use them&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Bottom line: anonymize on the backend. Or, as &lt;a href="http://datascience.stackexchange.com/questions/3693/what-are-the-best-practices-to-anonymize-user-names-in-data#comment3919_3693"&gt;@Emre suggests&lt;/a&gt;, think about whether you really need an identifier at all. Maybe the DB-generated index is enough?&lt;/p&gt;
', NULL, NULL, '''/a'':129C,147C ''/em'':60C,78C,122C ''/p'':40C,130C,166C ''/pubs/en-05-10064.pdf'':117C ''/questions/3693/what-are-the-best-practices-to-anonymize-user-names-in-data#comment3919_3693'':144C ''algorithm'':66C ''anonym'':65C,134C ''backend'':137C ''birthdat'':32C,82C,87C ''bottom'':132C ''charact'':97C ''choos'':24C ''code'':11C ''conflict'':49C,63C ''conflict-fre'':62C ''could'':69C,83C,88C,93C,102C ''cours'':44C ''datascience.stackexchange.com'':143C ''datascience.stackexchange.com/questions/3693/what-are-the-best-practices-to-anonymize-user-names-in-data#comment3919_3693'':142C ''db'':161C ''db-gener'':160C ''differ'':17C ''e.g'':33C ''em'':58C,71C,120C ''emr'':145C ''enough'':165C ''favorit'':95C ''format'':36C ''free'':64C ''generat'':162C ''href'':114C,141C ''ident'':85C,90C ''identifi'':155C ''index'':163C ''initi'':30C ''know'':73C ''line'':133C ''link'':16C ''maiden'':28C ''mayb'':158C ''measur'':18C ''mother'':26C,79C ''name'':29C,80C ''need'':153C ''nofollow'':119C ''number'':110C ''order'':14C ''p'':1C,41C,131C ''particip'':12C,23C ''practic'':4C ''psycholog'':6C ''realli'':121C,152C ''rel'':118C ''run'':47C ''secur'':109C ''shoe'':91C ''size'':92C ''social'':108C ''standard'':3C ''still'':46C ''student'':68C,77C ''suggest'':146C ''superhero'':96C ''surefir'':61C ''thing'':100C ''think'':55C,103C,148C ''togeth'':19C ''us'':107C ''use'':127C ''want'':9C,125C ''whether'':150C ''without'':72C ''would'':105C ''www.ssa.gov'':116C ''www.ssa.gov/pubs/en-05-10064.pdf'':115C ''xx'':38C ''xx-yymmdd'':37C ''yymmdd'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3696, 2, '2014-12-12 22:39:58.47', 10, NULL, NULL, '&lt;h2&gt;Re: size of data&lt;/h2&gt;

&lt;h3&gt;The short answer&lt;/h3&gt;

&lt;p&gt;Scala works for both small and large data, but its creation and development is motivated by needing something scalable.  &lt;a href="http://www.scala-lang.org/what-is-scala.html"&gt;Scala is an acronym for âScalable Languageâ&lt;/a&gt;.&lt;/p&gt;

&lt;h3&gt;The long answer&lt;/h3&gt;

&lt;p&gt;Scala is a &lt;a href="http://en.wikipedia.org/wiki/Functional_programming"&gt;functional programming language&lt;/a&gt; that runs on the &lt;a href="http://en.wikipedia.org/wiki/Java_virtual_machine"&gt;jvm&lt;/a&gt;.  The ''functional'' part of this is a fundamental difference in the language that makes you think differently about programming.  If you like that way of thinking, it lets you quickly work with small data.  Whether you like it or not, functional languages are fundamentally easier to massively scale.  The jvm piece is also important because the jvm is basically everywhere and, thus, Scala code can run basically everywhere.  (Note there are plenty of other &lt;a href="http://en.wikipedia.org/wiki/List_of_JVM_languages"&gt;languages written on the jvm&lt;/a&gt; and plenty of other &lt;a href="http://en.wikipedia.org/wiki/List_of_programming_languages_by_type#Functional_languages"&gt;functional programming languages&lt;/a&gt;, and languages beyond Scala do appear in both lists.)&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.youtube.com/watch?v=3jg1AheF4n0"&gt;This talk&lt;/a&gt; give a good overview of the motivation behind Scala.&lt;/p&gt;

&lt;h2&gt;Re: other tools that have good Scala support:&lt;/h2&gt;

&lt;p&gt;As you mentioned, Spark (distributable batch processing better at iteratative algorithms than its counterpart) is a big one.  With Spark comes its libraries &lt;a href="https://spark.apache.org/mllib/"&gt;Mllib&lt;/a&gt; for machine learning and &lt;a href="https://spark.apache.org/graphx/"&gt;GraphX&lt;/a&gt; for graphs.  As mentioned by  Erik Allik and Tris Nefzger, &lt;a href="http://akka.io"&gt;Akka&lt;/a&gt; and &lt;a href="https://github.com/factorie/factorie"&gt;Factorie&lt;/a&gt; exist.  There is also &lt;a href="https://www.playframework.com/"&gt;Play&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Generally,  I can''t tell if there is a specific use case you''re digging for (if so, make that a part of your question), or just want a survey of big data tools and happen to know Scala a bit and want to start there.&lt;/p&gt;
', NULL, NULL, '''/a'':45C,64C,75C,160C,173C,192C,243C,254C,269C,277C,286C ''/factorie/factorie'':275C ''/graphx/'':252C ''/h2'':6C,212C ''/h3'':11C,51C ''/mllib/'':241C ''/p'':46C,183C,202C,287C,335C ''/watch?v=3jg1ahef4n0'':189C ''/what-is-scala.html'':36C ''/wiki/functional_programming'':60C ''/wiki/java_virtual_machine'':73C ''/wiki/list_of_jvm_languages'':154C ''/wiki/list_of_programming_languages_by_type#functional_languages'':169C ''acronym'':40C ''akka'':268C ''akka.io'':267C ''algorithm'':224C ''allik'':261C ''also'':128C,281C ''answer'':10C,50C ''appear'':179C ''basic'':134C,142C ''batch'':219C ''behind'':200C ''better'':221C ''beyond'':176C ''big'':230C,320C ''bit'':329C ''case'':300C ''code'':139C ''come'':234C ''counterpart'':227C ''creation'':23C ''data'':5C,20C,109C,321C ''develop'':25C ''differ'':84C,92C ''dig'':303C ''distribut'':218C ''easier'':120C ''en.wikipedia.org'':59C,72C,153C,168C ''en.wikipedia.org/wiki/functional_programming'':58C ''en.wikipedia.org/wiki/java_virtual_machine'':71C ''en.wikipedia.org/wiki/list_of_jvm_languages'':152C ''en.wikipedia.org/wiki/list_of_programming_languages_by_type#functional_languages'':167C ''erik'':260C ''everywher'':135C,143C ''exist'':278C ''factori'':276C ''function'':61C,77C,116C,170C ''fundament'':83C,119C ''general'':289C ''github.com'':274C ''github.com/factorie/factorie'':273C ''give'':193C ''good'':195C,209C ''graph'':256C ''graphx'':253C ''h2'':1C,203C ''h3'':7C,47C ''happen'':324C ''href'':33C,57C,70C,151C,166C,186C,238C,249C,266C,272C,283C ''import'':129C ''iterat'':223C ''jvm'':74C,125C,132C,159C ''know'':326C ''languag'':63C,87C,117C,155C,172C,175C ''languageâ'':44C ''larg'':19C ''learn'':246C ''let'':103C ''librari'':236C ''like'':97C,112C ''list'':182C ''long'':49C ''machin'':245C ''make'':89C,307C ''massiv'':122C ''mention'':216C,258C ''mllib'':242C ''motiv'':27C,199C ''need'':29C ''nefzger'':264C ''note'':144C ''one'':231C ''overview'':196C ''p'':12C,52C,184C,213C,288C ''part'':78C,310C ''piec'':126C ''play'':285C ''plenti'':147C,162C ''process'':220C ''program'':62C,94C,171C ''question'':313C ''quick'':105C ''re'':2C,204C,302C ''run'':66C,141C ''scala'':13C,37C,53C,138C,177C,201C,210C,327C ''scalabl'':31C,43C ''scale'':123C ''short'':9C ''size'':3C ''small'':17C,108C ''someth'':30C ''spark'':217C,233C ''spark.apache.org'':240C,251C ''spark.apache.org/graphx/'':250C ''spark.apache.org/mllib/'':239C ''specif'':298C ''start'':333C ''support'':211C ''survey'':318C ''talk'':191C ''tell'':293C ''think'':91C,101C ''thus'':137C ''tool'':206C,322C ''tris'':263C ''use'':299C ''want'':316C,331C ''way'':99C ''whether'':110C ''work'':14C,106C ''written'':156C ''www.playframework.com'':284C ''www.scala-lang.org'':35C ''www.scala-lang.org/what-is-scala.html'':34C ''www.youtube.com'':188C ''www.youtube.com/watch?v=3jg1ahef4n0'':187C ''â'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3697, 2, '2014-12-12 22:48:41.97', 6, NULL, NULL, '&lt;p&gt;Data becomes "big" when a single &lt;a href="http://en.wikipedia.org/wiki/Commodity_computing"&gt;commodity computer&lt;/a&gt; can no longer handle the amount of data you have.  It denotes the point at which you need to start thinking about building supercomputers or using clusters to process your data.&lt;/p&gt;
', NULL, NULL, '''/a'':15C ''/p'':47C ''/wiki/commodity_computing'':12C ''amount'':21C ''becom'':3C ''big'':4C ''build'':38C ''cluster'':42C ''commod'':13C ''comput'':14C ''data'':2C,23C,46C ''denot'':27C ''en.wikipedia.org'':11C ''en.wikipedia.org/wiki/commodity_computing'':10C ''handl'':19C ''href'':9C ''longer'':18C ''need'':33C ''p'':1C ''point'':29C ''process'':44C ''singl'':7C ''start'':35C ''supercomput'':39C ''think'':36C ''use'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3698, 2, '2014-12-13 05:28:04.013', 3, NULL, NULL, '&lt;p&gt;If your Data points are dense and noise points are away from the dense region, you can try DBSCAN algorithm. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/DBSCAN" rel="nofollow"&gt;http://en.wikipedia.org/wiki/DBSCAN&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/gdFlG.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Tweak its parameters until u get a best fit. &lt;/p&gt;
', NULL, NULL, '''/gdflg.png'':39C ''/p'':22C,45C,56C ''/wiki/dbscan'':28C ''/wiki/dbscan&lt;/a&gt;&lt;/p&gt;'':33C ''algorithm'':21C ''alt'':40C ''away'':12C ''best'':54C ''data'':4C ''dbscan'':20C ''dens'':7C,15C ''descript'':43C ''en.wikipedia.org'':27C,32C ''en.wikipedia.org/wiki/dbscan'':26C ''en.wikipedia.org/wiki/dbscan&lt;/a&gt;&lt;/p&gt;'':31C ''enter'':41C ''fit'':55C ''get'':52C ''href'':25C ''i.stack.imgur.com'':38C ''i.stack.imgur.com/gdflg.png'':37C ''imag'':42C ''img'':35C ''nofollow'':30C ''nois'':9C ''p'':1C,23C,34C,46C ''paramet'':49C ''point'':5C,10C ''region'':16C ''rel'':29C ''src'':36C ''tri'':19C ''tweak'':47C ''u'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3699, 1, '2014-12-13 17:42:54.927', 2, 285, 'How to optimize the parameters of a classifier on scikit-learn?', '
For an imbalanced set of data is it better to choose an L1 or L2 regularization?&lt;br&gt;
Is there any special preprocessing to improve the model score (log_loss in particular)?&lt;br&gt;&lt;br&gt;
&lt;b&gt;Note&lt;/b&gt;: I''m a beginner in Machine Learning and I''m learning through examples and competitions on Kaggle.  

', 'classification  logistic-regression  scikit', NULL, '''/b'':53C ''b'':51C ''beginn'':57C ''better'':26C ''br'':34C,49C,50C ''choos'':28C ''classif'':13B ''classifi'':8A ''competit'':68C ''data'':23C ''exampl'':66C ''imbalanc'':20C ''improv'':41C ''kaggl'':70C ''l1'':30C ''l2'':32C ''learn'':12A,60C,64C ''log'':45C ''logist'':15B ''logistic-regress'':14B ''loss'':46C ''m'':55C,63C ''machin'':59C ''model'':43C ''note'':52C ''optim'':3A ''paramet'':5A ''particular'':48C ''preprocess'':39C ''regress'':16B ''regular'':33C ''scikit'':11A,17B ''scikit-learn'':10A ''score'':44C ''set'':21C ''special'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5389, 2, '2015-03-25 19:45:31.32', 19, NULL, NULL, '&lt;p&gt;&lt;a href="http://tensorflow.org/"&gt;Tensor Flow&lt;/a&gt; (&lt;a href="http://www.tensorflow.org/api_docs/python/nn.html#neural-network"&gt;docs&lt;/a&gt;) by Google is another nice framework which has automatic differentiation. I''ve written down some &lt;a href="http://martin-thoma.com/tensor-flow-quick/"&gt;quick thoughts about Google Tensor Flow&lt;/a&gt; on my blog, together with the MNIST example which they have in their tutorial.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/benanne/Lasagne"&gt;Lasagne&lt;/a&gt; (&lt;a href="http://lasagne.readthedocs.org/en/latest/"&gt;docs&lt;/a&gt;) is very nice, as it uses theano (â you can use the GPU) and makes it simpler to use. The author of lasagne won the Kaggle Galaxy challenge, as far as I know. It is nice with &lt;a href="http://pythonhosted.org/nolearn/"&gt;nolearn&lt;/a&gt;. Here is an MNIST example network:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;#!/usr/bin/env python

import lasagne
from lasagne import layers
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet

import sys
import os
import gzip
import pickle
import numpy


PY2 = sys.version_info[0] == 2

if PY2:
    from urllib import urlretrieve

    def pickle_load(f, encoding):
        return pickle.load(f)
else:
    from urllib.request import urlretrieve

    def pickle_load(f, encoding):
        return pickle.load(f, encoding=encoding)

DATA_URL = ''http://deeplearning.net/data/mnist/mnist.pkl.gz''
DATA_FILENAME = ''mnist.pkl.gz''


def _load_data(url=DATA_URL, filename=DATA_FILENAME):
    """Load data from `url` and store the result in `filename`."""
    if not os.path.exists(filename):
        print("Downloading MNIST dataset")
        urlretrieve(url, filename)

    with gzip.open(filename, ''rb'') as f:
        return pickle_load(f, encoding=''latin-1'')


def load_data():
    """Get data with labels, split into training, validation and test set."""
    data = _load_data()
    X_train, y_train = data[0]
    X_valid, y_valid = data[1]
    X_test, y_test = data[2]
    y_train = numpy.asarray(y_train, dtype=numpy.int32)
    y_valid = numpy.asarray(y_valid, dtype=numpy.int32)
    y_test = numpy.asarray(y_test, dtype=numpy.int32)

    return dict(
        X_train=X_train,
        y_train=y_train,
        X_valid=X_valid,
        y_valid=y_valid,
        X_test=X_test,
        y_test=y_test,
        num_examples_train=X_train.shape[0],
        num_examples_valid=X_valid.shape[0],
        num_examples_test=X_test.shape[0],
        input_dim=X_train.shape[1],
        output_dim=10,
    )


def nn_example(data):
    net1 = NeuralNet(
        layers=[(''input'', layers.InputLayer),
                (''hidden'', layers.DenseLayer),
                (''output'', layers.DenseLayer),
                ],
        # layer parameters:
        input_shape=(None, 28*28),
        hidden_num_units=100,  # number of units in ''hidden'' layer
        output_nonlinearity=lasagne.nonlinearities.softmax,
        output_num_units=10,  # 10 target values for the digits 0, 1, 2, ..., 9

        # optimization method:
        update=nesterov_momentum,
        update_learning_rate=0.01,
        update_momentum=0.9,

        max_epochs=10,
        verbose=1,
        )

    # Train the network
    net1.fit(data[''X_train''], data[''y_train''])

    # Try the network on new data
    print("Feature vector (100-110): %s" % data[''X_test''][0][100:110])
    print("Label: %s" % str(data[''y_test''][0]))
    print("Predicted: %s" % str(net1.predict([data[''X_test''][0]])))


def main():
    data = load_data()
    print("Got %i testing datasets." % len(data[''X_train'']))
    nn_example(data)

if __name__ == ''__main__'':
    main()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://caffe.berkeleyvision.org/"&gt;Caffe&lt;/a&gt; is a C++ library, but has Python bindings. You can do most stuff by configuration files (prototxt). It has a lot of options and can also make use of the GPU.&lt;/p&gt;
', NULL, NULL, '''-1'':236C ''-110'':425C ''/a'':7C,14C,41C,64C,71C,115C,478C ''/api_docs/python/nn.html#neural-network'':12C ''/benanne/lasagne'':62C ''/code'':471C ''/data/mnist/mnist.pkl.gz'''''':190C ''/en/latest/'':69C ''/nolearn/'':113C ''/p'':56C,122C,510C ''/pre'':472C ''/tensor-flow-quick/'':34C ''/usr/bin/env'':125C ''0'':155C,259C,323C,328C,333C,384C,430C,440C,449C ''0.01'':396C ''0.9'':399C ''1'':265C,337C,385C,404C ''10'':340C,377C,378C,402C ''100'':364C,424C,431C ''110'':432C ''2'':156C,271C,386C ''28'':359C,360C ''9'':387C ''also'':504C ''anoth'':18C ''author'':92C ''automat'':23C ''bind'':486C ''blog'':44C ''c'':481C ''caff'':477C ''caffe.berkeleyvision.org'':476C ''challeng'':99C ''code'':124C ''configur'':493C ''data'':186C,191C,196C,198C,201C,204C,239C,241C,251C,253C,258C,264C,270C,344C,409C,412C,420C,427C,437C,446C,452C,454C,461C,466C ''dataset'':220C,459C ''deeplearning.net'':189C ''deeplearning.net/data/mnist/mnist.pkl.gz'''''':188C ''def'':163C,176C,194C,237C,341C,450C ''dict'':294C ''differenti'':24C ''digit'':383C ''dim'':335C,339C ''doc'':13C,70C ''download'':218C ''dtype'':277C,284C,291C ''els'':171C ''encod'':167C,180C,184C,185C,234C ''epoch'':401C ''exampl'':49C,120C,320C,325C,330C,343C,465C ''f'':166C,170C,179C,183C,229C,233C ''far'':101C ''featur'':422C ''file'':494C ''filenam'':192C,200C,202C,212C,216C,223C,226C ''flow'':6C,40C ''framework'':20C ''galaxi'':98C ''get'':240C ''github.com'':61C ''github.com/benanne/lasagne'':60C ''googl'':16C,38C ''got'':456C ''gpu'':84C,509C ''gzip'':147C ''gzip.open'':225C ''hidden'':350C,361C,369C ''href'':3C,9C,31C,59C,66C,110C,475C ''import'':127C,131C,135C,140C,142C,144C,146C,148C,150C,161C,174C ''info'':154C ''input'':334C,348C,356C ''kaggl'':97C ''know'':104C ''label'':243C,434C ''lasagn'':63C,94C,128C,130C ''lasagne.nonlinearities.softmax'':373C ''lasagne.readthedocs.org'':68C ''lasagne.readthedocs.org/en/latest/'':67C ''lasagne.updates'':134C ''latin'':235C ''layer'':132C,347C,354C,370C ''layers.denselayer'':351C,353C ''layers.inputlayer'':349C ''learn'':394C ''len'':460C ''librari'':482C ''load'':165C,178C,195C,203C,232C,238C,252C,453C ''lot'':499C ''main'':451C,469C,470C ''make'':86C,505C ''martin-thoma.com'':33C ''martin-thoma.com/tensor-flow-quick/'':32C ''max'':400C ''method'':389C ''mnist'':48C,119C,219C ''mnist.pkl.gz'':193C ''momentum'':137C,392C,398C ''name'':468C ''nesterov'':136C,391C ''net1'':345C ''net1.fit'':408C ''net1.predict'':445C ''network'':121C,407C,417C ''neuralnet'':141C,346C ''new'':419C ''nice'':19C,74C,107C ''nn'':342C,464C ''nolearn'':114C ''nolearn.lasagne'':139C ''none'':358C ''nonlinear'':372C ''num'':319C,324C,329C,362C,375C ''number'':365C ''numpi'':151C ''numpy.asarray'':274C,281C,288C ''numpy.int32'':278C,285C,292C ''optim'':388C ''option'':501C ''os'':145C ''os.path.exists'':215C ''output'':338C,352C,371C,374C ''p'':1C,57C,473C ''paramet'':355C ''pickl'':149C,164C,177C,231C ''pickle.load'':169C,182C ''pre'':123C ''predict'':442C ''print'':217C,421C,433C,441C,455C ''prototxt'':495C ''py2'':152C,158C ''python'':126C,485C ''pythonhosted.org'':112C ''pythonhosted.org/nolearn/'':111C ''quick'':35C ''rate'':395C ''rb'':227C ''result'':210C ''return'':168C,181C,230C,293C ''set'':250C ''shape'':357C ''simpler'':88C ''split'':244C ''store'':208C ''str'':436C,444C ''stuff'':491C ''sys'':143C ''sys.version'':153C ''target'':379C ''tensor'':5C,39C ''tensorflow.org'':4C ''test'':249C,267C,269C,287C,290C,312C,314C,316C,318C,331C,429C,439C,448C,458C ''theano'':78C ''thought'':36C ''togeth'':45C ''train'':246C,255C,257C,273C,276C,296C,298C,300C,302C,321C,405C,411C,414C,463C ''tri'':415C ''tutori'':55C ''unit'':363C,367C,376C ''updat'':390C,393C,397C ''url'':187C,197C,199C,206C,222C ''urllib'':160C ''urllib.request'':173C ''urlretriev'':162C,175C,221C ''use'':77C,82C,90C,506C ''valid'':247C,261C,263C,280C,283C,304C,306C,308C,310C,326C ''valu'':380C ''ve'':26C ''vector'':423C ''verbos'':403C ''won'':95C ''written'':27C ''www.tensorflow.org'':11C ''www.tensorflow.org/api_docs/python/nn.html#neural-network'':10C ''x'':254C,260C,266C,295C,297C,303C,305C,311C,313C,410C,428C,447C,462C ''x_test.shape'':332C ''x_train.shape'':322C,336C ''x_valid.shape'':327C ''y'':256C,262C,268C,272C,275C,279C,282C,286C,289C,299C,301C,307C,309C,315C,317C,413C,438C ''â'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3730, 1, '2014-12-19 16:02:48.693', 5, 816, 'R random forest on Amazon ec2 Error: cannot allocate vector of size 5.4 Gb', '&lt;p&gt;I''m training random forest models in R using randomForest() with 1000 trees and data.frames with about 20 predictors and 600K rows. On my laptop everything works fine but when I move to amazon ec2 to run the same thing i get:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Error: cannot allocate vector of size 5.4 Gb
Execution halted
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m using the c3.4xlarge instance type so it''s pretty beefy... does anyone know a workaround for this to get it to run on this instance? I would love to know the memory nuances that causes this problem only on the ec2 instance and not on my laptop (OS X 10.9.5 Processor  2.7 GHz Intel Core i7; Memory  16 GB 1600 MHz DDR3)&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'r  random-forest', NULL, '''/code'':75C ''/p'':62C,142C,145C ''/pre'':76C ''10.9.5'':129C ''1000'':31C ''16'':137C ''1600'':139C ''2.7'':131C ''20'':37C ''5.4'':13A,71C ''600k'':40C ''alloc'':9A,67C ''amazon'':5A,53C ''anyon'':91C ''beefi'':89C ''c3.4xlarge'':82C ''cannot'':8A,66C ''caus'':114C ''code'':64C ''core'':134C ''data.frames'':34C ''ddr3'':141C ''ec2'':6A,54C,120C ''error'':7A,65C ''everyth'':45C ''execut'':73C ''fine'':47C ''forest'':3A,18B,24C ''gb'':14A,72C,138C ''get'':61C,98C ''ghz'':132C ''halt'':74C ''i7'':135C ''instanc'':83C,104C,121C ''intel'':133C ''know'':92C,109C ''laptop'':44C,126C ''love'':107C ''m'':21C,79C ''memori'':111C,136C ''mhz'':140C ''model'':25C ''move'':51C ''nuanc'':112C ''os'':127C ''p'':19C,77C,143C ''pre'':63C ''predictor'':38C ''pretti'':88C ''problem'':116C ''processor'':130C ''r'':1A,15B,27C ''random'':2A,17B,23C ''random-forest'':16B ''randomforest'':29C ''row'':41C ''run'':56C,101C ''size'':12A,70C ''thank'':144C ''thing'':59C ''train'':22C ''tree'':32C ''type'':84C ''use'':28C,80C ''vector'':10A,68C ''work'':46C ''workaround'':94C ''would'':106C ''x'':128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3705, 1, '2014-12-16 01:19:12.477', 3, 274, 'Sci-kit Pipeline and GridsearchCV returns indexError: too many indices for array', '&lt;p&gt;I''m trying to get to grips with sci-kit learn for some simple machine learning projects but I''m coming unstuck with Pipelines and wonder what I''ve done wrong...&lt;/p&gt;

&lt;p&gt;I''m trying to work through a &lt;a href="http://www.kaggle.com/c/data-science-london-scikit-learn/data" rel="nofollow"&gt;tutorial on Kaggle&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Here''s my code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas as pd

train = pd.read_csv(local path to training data) train_labels =
pd.read_csv(local path to labels)


from sklearn.decomposition import PCA
from sklearn.svm import LinearSVC
from sklearn.grid_search import GridSearchCV

pca = PCA()
clf = LinearSVC()

n_components = arange(1, 39)
loss = [''l1'',''l2'']
penalty = [''l1'',''l2'']
C = arange(0, 1, .1)
whiten = [True, False]

from sklearn.pipeline import Pipeline

#set up pipeline
pipe = Pipeline(steps=[(''pca'', pca), (''clf'', clf)])

#set up GridsearchCV
estimator = GridSearchCV(pipe, dict(pca__n_components = n_components, pca__whiten = whiten, clf__loss = loss, clf__penalty = penalty, clf__C = C)) 

&amp;gt; estimator
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Returns:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;GridSearchCV(cv=None,
       estimator=Pipeline(steps=[(''pca'', PCA(copy=True, n_components=None, whiten=False)), (''clf'', LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
     intercept_scaling=1, loss=''l2'', multi_class=''ovr'', penalty=''l2'',
     random_state=None, tol=0.0001, verbose=0))]),
       fit_params={}, iid=True, loss_func=None, n_jobs=1,
       param_grid={''clf__penalty'': [''l1'', ''l2''], ''clf__loss'': [''l1'', ''l2''], ''clf__C'': array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9]), ''pca__n_components'': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,
       35, 36, 37, 38]), ''pca__whiten'': [True, False]},
       pre_dispatch=''2*n_jobs'', refit=True, score_func=None, scoring=None,
       verbose=0)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But when I try to train data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;estimator.fit(train, train_labels)
The error is:

    428         for test_fold_idx, per_label_splits in enumerate(zip(*per_label_cvs)):
    429             for label, (_, test_split) in zip(unique_labels, per_label_splits):
--&amp;gt; 430                 label_test_folds = test_folds[y == label]
    431                 # the test split can be too big because we used
    432                 # KFold(max(c, self.n_folds), self.n_folds) instead of

IndexError: too many indices for array
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Can anyone point me in the right direction?&lt;/p&gt;
', 'scikit  sklearn', NULL, '''/a'':68C ''/c/data-science-london-scikit-learn/data'':62C ''/code'':174C,318C,398C ''/p'':49C,69C,75C,178C,328C,409C ''/pre'':175C,319C,399C ''0'':128C,224C,248C,317C ''0.0001'':222C ''0.1'':249C ''0.2'':250C ''0.3'':251C ''0.4'':252C ''0.5'':253C ''0.6'':254C ''0.7'':255C ''0.8'':256C ''0.9'':257C ''1'':118C,129C,130C,210C,234C,262C ''1.0'':199C ''10'':271C ''11'':272C ''12'':273C ''13'':274C ''14'':275C ''15'':276C ''16'':277C ''17'':278C ''18'':279C ''19'':280C ''2'':263C,306C ''20'':281C ''21'':282C ''22'':283C ''23'':284C ''24'':285C ''25'':286C ''26'':287C ''27'':288C ''28'':289C ''29'':290C ''3'':264C ''30'':291C ''31'':292C ''32'':293C ''33'':294C ''34'':295C ''35'':296C ''36'':297C ''37'':298C ''38'':299C ''39'':119C ''4'':265C ''428'':338C ''429'':352C ''430'':365C ''431'':373C ''432'':384C ''5'':266C ''6'':267C ''7'':268C ''8'':269C ''9'':270C ''anyon'':402C ''arang'':117C,127C ''array'':13A,247C,261C,397C ''big'':380C ''c'':126C,170C,171C,198C,246C,387C ''class'':200C,214C ''clf'':113C,146C,147C,163C,166C,169C,196C,237C,241C,245C ''code'':74C,77C,180C,330C ''come'':38C ''compon'':116C,157C,159C,192C,260C ''copi'':189C ''csv'':84C,93C ''cv'':182C ''cvs'':351C ''data'':89C,327C ''dict'':154C ''direct'':408C ''dispatch'':305C ''done'':47C ''dual'':203C ''enumer'':347C ''error'':336C ''estim'':151C,173C,184C ''estimator.fit'':331C ''fals'':133C,195C,303C ''fit'':205C,225C ''fold'':341C,368C,370C ''func'':230C,312C ''get'':21C ''grid'':236C ''gridsearchcv'':6A,110C,150C,152C,181C ''grip'':23C ''gt'':172C,364C ''href'':59C ''idx'':342C ''iid'':227C ''import'':78C,100C,104C,109C,136C ''indexerror'':8A,392C ''indic'':11A,395C ''instead'':390C ''intercept'':206C,208C ''job'':233C,308C ''kaggl'':67C ''kfold'':385C ''kit'':3A,27C ''l1'':121C,124C,239C,243C ''l2'':122C,125C,212C,217C,240C,244C ''label'':91C,97C,334C,344C,350C,354C,360C,362C,366C,372C ''learn'':28C,33C ''linearsvc'':105C,114C,197C ''local'':85C,94C ''loss'':120C,164C,165C,211C,229C,242C ''m'':18C,37C,52C ''machin'':32C ''mani'':10A,394C ''max'':386C ''multi'':213C ''n'':115C,156C,158C,191C,232C,259C,307C ''nofollow'':64C ''none'':183C,193C,202C,220C,231C,313C,315C ''ovr'':215C ''p'':16C,50C,70C,176C,320C,400C ''panda'':79C ''param'':226C,235C ''path'':86C,95C ''pca'':101C,111C,112C,144C,145C,155C,160C,187C,188C,258C,300C ''pd'':81C ''pd.read'':83C,92C ''penalti'':123C,167C,168C,216C,238C ''per'':343C,349C,361C ''pipe'':141C,153C ''pipelin'':4A,41C,137C,140C,142C,185C ''point'':403C ''pre'':76C,179C,304C,329C ''project'':34C ''random'':218C ''refit'':309C ''rel'':63C ''return'':7A,177C ''right'':407C ''scale'':209C ''sci'':2A,26C ''sci-kit'':1A,25C ''scikit'':14B ''score'':311C,314C ''search'':108C ''self.n_folds'':388C,389C ''set'':138C,148C ''simpl'':31C ''sklearn'':15B ''sklearn.decomposition'':99C ''sklearn.grid'':107C ''sklearn.pipeline'':135C ''sklearn.svm'':103C ''split'':345C,356C,363C,376C ''state'':219C ''step'':143C,186C ''test'':340C,355C,367C,369C,375C ''tol'':221C ''train'':82C,88C,90C,326C,332C,333C ''tri'':19C,53C,324C ''true'':132C,190C,204C,207C,228C,302C,310C ''tutori'':65C ''uniqu'':359C ''unstuck'':39C ''use'':383C ''ve'':46C ''verbos'':223C,316C ''weight'':201C ''whiten'':131C,161C,162C,194C,301C ''wonder'':43C ''work'':55C ''wrong'':48C ''www.kaggle.com'':61C ''www.kaggle.com/c/data-science-london-scikit-learn/data'':60C ''y'':371C ''zip'':348C,358C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3706, 2, '2014-12-16 02:44:28.95', 1, NULL, NULL, '&lt;p&gt;It turns out that the Pandas dataframe is the wrong shape.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;estimator.fit(train.values, train_labels[0].values)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;works, although I also had to drop the penalty term.&lt;/p&gt;
', NULL, NULL, '''/code'':22C ''/p'':13C,35C ''/pre'':23C ''0'':20C ''also'':28C ''although'':26C ''code'':15C ''datafram'':8C ''drop'':31C ''estimator.fit'':16C ''label'':19C ''p'':1C,24C ''panda'':7C ''penalti'':33C ''pre'':14C ''shape'':12C ''term'':34C ''train'':18C ''train.values'':17C ''turn'':3C ''valu'':21C ''work'':25C ''wrong'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3707, 2, '2014-12-16 06:12:35.423', 2, NULL, NULL, '&lt;p&gt;There''s a lot of online tutorial. Especially in youtube, but if you will want accurate website you can see from here &lt;a href="http://ttic.uchicago.edu/~shai/icml08tutorial/" rel="nofollow"&gt;http://ttic.uchicago.edu/~shai/icml08tutorial/&lt;/a&gt; or &lt;a href="http://cs229.stanford.edu/materials.html" rel="nofollow"&gt;http://cs229.stanford.edu/materials.html&lt;/a&gt;. you can visit them now.&lt;/p&gt;
', NULL, NULL, '''/materials.html'':39C ''/materials.html&lt;/a&gt;.'':44C ''/p'':50C ''/~shai/icml08tutorial/'':28C ''/~shai/icml08tutorial/&lt;/a&gt;'':33C ''accur'':17C ''cs229.stanford.edu'':38C,43C ''cs229.stanford.edu/materials.html'':37C ''cs229.stanford.edu/materials.html&lt;/a&gt;.'':42C ''especi'':9C ''href'':25C,36C ''lot'':5C ''nofollow'':30C,41C ''onlin'':7C ''p'':1C ''rel'':29C,40C ''see'':21C ''ttic.uchicago.edu'':27C,32C ''ttic.uchicago.edu/~shai/icml08tutorial/'':26C ''ttic.uchicago.edu/~shai/icml08tutorial/&lt;/a&gt;'':31C ''tutori'':8C ''visit'':47C ''want'':16C ''websit'':18C ''youtub'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3708, 2, '2014-12-16 08:51:30.987', 1, NULL, NULL, '&lt;p&gt;You can see if you can mix Spark streaming (&lt;a href="https://spark.apache.org/docs/1.1.0/streaming-programming-guide.html" rel="nofollow"&gt;https://spark.apache.org/docs/1.1.0/streaming-programming-guide.html&lt;/a&gt;) and Spark ML Library (&lt;a href="https://spark.apache.org/docs/1.1.0/mllib-guide.html" rel="nofollow"&gt;https://spark.apache.org/docs/1.1.0/mllib-guide.html&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;Spark Streaming permits to process live data streams and Spark ML Library is a Machine Learning Library for Spark. So maybe you can do something good! &lt;/p&gt;

&lt;p&gt;But this is a very interesting subject, I am working on it. It can be good to create a Google Community on it (&lt;a href="https://plus.google.com/communities" rel="nofollow"&gt;https://plus.google.com/communities&lt;/a&gt;)?&lt;/p&gt;
', NULL, NULL, '''/communities'':91C ''/communities&lt;/a&gt;)?&lt;/p&gt;'':96C ''/docs/1.1.0/mllib-guide.html'':29C ''/docs/1.1.0/mllib-guide.html&lt;/a&gt;).&lt;/p&gt;'':34C ''/docs/1.1.0/streaming-programming-guide.html'':15C ''/docs/1.1.0/streaming-programming-guide.html&lt;/a&gt;)'':20C ''/p'':62C ''communiti'':84C ''creat'':81C ''data'':42C ''good'':61C,79C ''googl'':83C ''href'':12C,26C,88C ''interest'':69C ''learn'':51C ''librari'':24C,47C,52C ''live'':41C ''machin'':50C ''mayb'':56C ''mix'':8C ''ml'':23C,46C ''nofollow'':17C,31C,93C ''p'':1C,35C,63C ''permit'':38C ''plus.google.com'':90C,95C ''plus.google.com/communities'':89C ''plus.google.com/communities&lt;/a&gt;)?&lt;/p&gt;'':94C ''process'':40C ''rel'':16C,30C,92C ''see'':4C ''someth'':60C ''spark'':9C,22C,36C,45C,54C ''spark.apache.org'':14C,19C,28C,33C ''spark.apache.org/docs/1.1.0/mllib-guide.html'':27C ''spark.apache.org/docs/1.1.0/mllib-guide.html&lt;/a&gt;).&lt;/p&gt;'':32C ''spark.apache.org/docs/1.1.0/streaming-programming-guide.html'':13C ''spark.apache.org/docs/1.1.0/streaming-programming-guide.html&lt;/a&gt;)'':18C ''stream'':10C,37C,43C ''subject'':70C ''work'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8688, 1, '2015-11-02 10:29:59.463', 2, 204, 'How to visualise multidimensional categorical data with additional time dimension', '&lt;p&gt;I am trying to visualize a dataset that registers the career of people in an organisation and their backgrounds before starting in that particular organisation. I ''d like to show how individuals are positioned in the space based on their background areas and show the evolution through time in the beginning of each decade. I have seen the answers &lt;a href="http://datascience.stackexchange.com/questions/8070/how-to-visualize-categorical-data-over-time"&gt;here&lt;/a&gt; but my data are much more categorical and also, ideally, &lt;em&gt;I ''d like to display all three background categories (see below) at the same time&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;A sample of my data follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Person  BkgArea1    BkgArea2    BkgArea3    TotalSize   YearFrom    YearTo
P1  3   2   0   5   1959    1965
P2  0   2   1   3   1959    1967
P3  3   0   2   5   1991    2008
P4  3   0   0   3   1959    1980
P5  3   0   0   3   1981    1998
P6  0   2   1   3   2006    2014
P7  2   2   2   6   2008    2014
P8  0   0   3   3   1991    1996
P9  3   3   1   7   1966    1972
P10 3   0   2   5   1975    1991
P11 3   2   0   5   1998    2013
P12 3   1   0   4   2004    2013
P13 0   2   0   2   1998    2010
P14 0   3   0   3   2003    2008
P15 1   2   3   6   1998    2008
P16 3   0   2   5   1973    1977
P17 1   3   0   4   1998    2012
P18 3   0   2   5   1996    2008
P19 0   3   1   4   1998    2011
P20 3   2   0   5   1998    2006
P21 3   0   0   3   1986    1989
P22 0   3   2   5   1996    2014
P23 3   2   3   8   1959    1976
P24 3   2   0   5   1998    2001
P25 0   0   3   3   1998    2011
P26 3   3   2   8   1965    1992
P27 2   3   2   7   2010    2014
P28 3   2   2   7   1986    1998
P29 3   3   0   6   2013    2014
P30 0   3   2   5   1976    1977
P31 3   3   0   6   1986    1987
P32 0   2   2   4   1980    1990
P33 3   0   2   5   1975    1986
P34 0   3   3   6   1977    1991
P35 3   2   2   7   1963    1974
P36 3   3   0   6   1998    2001
P37 0   2   3   5   1998    2004
P38 0   3   2   5   1974    1980
P39 3   2   3   8   1989    1998
P40 0   3   3   6   1991    1998
P41 0   1   2   3   1998    2001
P42 0   2   0   2   2003    2012
P43 3   3   3   9   1973    1986
P44 3   2   1   6   1978    1986
P45 0   2   0   2   2002    2012
P46 0   3   3   6   1982    1988
P47 0   3   0   3   1992    1998
P48 3   2   2   7   1998    2004
P49 2   2   0   4   2012    2014
P50 3   2   2   7   2008    2014
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The values in columns &lt;code&gt;BkgArea1-3&lt;/code&gt; are essentially categorical values, that indicate the strength of an individual in a specific category. It is thus
&lt;code&gt;0: no background, 1: weak background, 2: average background, 3: strong background&lt;/code&gt;. The &lt;code&gt;TotalSize&lt;/code&gt; column is the sum of the scores of the individuals&lt;/p&gt;

&lt;p&gt;The initial idea was to assign basic colors to BkgAreas 1-3, namely Red, Green, Blue, and then colour the individuals according to the mixture of their backgrounds, weighing each color with the strength of each area, i.e. &lt;code&gt;2/3 * Red&lt;/code&gt; if an individual has score 2 in BkgArea1, etc. I then tried to display the area as 3d scatter plot but it looks very complicated and hard to interpret.&lt;/p&gt;

&lt;p&gt;I also tried to apply &lt;em&gt;Multiple Correspondence Analysis&lt;/em&gt; but I haven''t got very satisfying results. Do you have any ideas as of how to represent this dataset?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;After people having posted various useful answers but not 100% covering the representation needs, I summarize what I would like to achieve:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Group/cluster the individuals according to their backgrounds and relative to the three background areas. if someone has scores in more than one of them, they should be displayed somewhere in the middle.&lt;/li&gt;
&lt;li&gt;Show, visually if possible, the number of individuals and the &lt;code&gt;TotalSize&lt;/code&gt; of the individuals. This could be a cloud &lt;/li&gt;
&lt;li&gt;Show the development of the organization. I think this might be easy to do with stacked column charts or histograms&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;a href="http://pandas.pydata.org/pandas-docs/stable/visualization.html#visualization-radviz" rel="nofollow"&gt;RadViz&lt;/a&gt; can be a method to solve 1 and 2, or at least I am inclined toward that representation. The problem in my data is that individuals under RadViz will be placed at very specific points so we lose somewhat the track of how many individuals have specific background characteristics&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5051, 2, '2015-02-05 02:25:51.627', 1, NULL, NULL, '&lt;p&gt;Your (frequency within good documents / total frequency) seems reasonable to me.
It could be that most words that occur in many good documents, simply also occur in many bad documents.&lt;/p&gt;

&lt;p&gt;How about you make a list of all the words appearing in the good documents.
Then you count their appearance in the good documents and their appearance in the bad documents and compare those two numbers.
The words that appear more often in the good ones, with a difference higher than a certain threshold are the ones of interest to you (if they exist).&lt;/p&gt;
', NULL, NULL, '''/p'':32C,98C ''also'':26C ''appear'':44C,53C,60C,73C ''bad'':30C,63C ''certain'':86C ''compar'':66C ''could'':14C ''count'':51C ''differ'':82C ''document'':6C,24C,31C,48C,57C,64C ''exist'':97C ''frequenc'':3C,8C ''good'':5C,23C,47C,56C,78C ''higher'':83C ''interest'':92C ''list'':39C ''make'':37C ''mani'':22C,29C ''number'':69C ''occur'':20C,27C ''often'':75C ''one'':79C,90C ''p'':1C,33C ''reason'':10C ''seem'':9C ''simpli'':25C ''threshold'':87C ''total'':7C ''two'':68C ''within'':4C ''word'':18C,43C,71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3711, 1, '2014-12-16 13:07:55.063', 4, 1243, 'How does the naive Bayes classifier handle missing data in training?', '&lt;p&gt;Naive Bayes apparently handles missing data differently, depending on whether they exist in training or testing/classification instances.&lt;/p&gt;

&lt;p&gt;When classifying instances, the attribute with the missing value is simply not included in the probability calculation (&lt;a href="http://www.inf.ed.ac.uk/teaching/courses/iaml/slides/naive-2x2.pdf" rel="nofollow"&gt;http://www.inf.ed.ac.uk/teaching/courses/iaml/slides/naive-2x2.pdf&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;In training, "the instance [with the missing data] is not included in frequency count for attribute value-class combination." (&lt;a href="http://www.csee.wvu.edu/~timm/cs591o/old/BasicMethods.html" rel="nofollow"&gt;http://www.csee.wvu.edu/~timm/cs591o/old/BasicMethods.html&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;Does that mean that particular training record simply isn''t included in the training phase? Or does it mean something else?&lt;/p&gt;
', 'machine-learning  data-mining  classification  naive-bayes-classifier', NULL, '''/p'':41C,123C ''/teaching/courses/iaml/slides/naive-2x2.pdf'':64C ''/teaching/courses/iaml/slides/naive-2x2.pdf&lt;/a&gt;)&lt;/p&gt;'':69C ''/~timm/cs591o/old/basicmethods.html'':95C ''/~timm/cs591o/old/basicmethods.html&lt;/a&gt;)&lt;/p&gt;'':100C ''appar'':26C ''attribut'':47C,86C ''bay'':5A,21B,25C ''calcul'':59C ''class'':89C ''classif'':18B ''classifi'':6A,22B,44C ''combin'':90C ''count'':84C ''data'':9A,16B,29C,78C ''data-min'':15B ''depend'':31C ''differ'':30C ''els'':122C ''exist'':35C ''frequenc'':83C ''handl'':7A,27C ''href'':61C,92C ''includ'':55C,81C,112C ''instanc'':40C,45C,74C ''isn'':110C ''learn'':14B ''machin'':13B ''machine-learn'':12B ''mean'':104C,120C ''mine'':17B ''miss'':8A,28C,50C,77C ''naiv'':4A,20B,24C ''naive-bayes-classifi'':19B ''nofollow'':66C,97C ''p'':23C,42C,70C,101C ''particular'':106C ''phase'':116C ''probabl'':58C ''record'':108C ''rel'':65C,96C ''simpli'':53C,109C ''someth'':121C ''testing/classification'':39C ''train'':11A,37C,72C,107C,115C ''valu'':51C,88C ''value-class'':87C ''whether'':33C ''www.csee.wvu.edu'':94C,99C ''www.csee.wvu.edu/~timm/cs591o/old/basicmethods.html'':93C ''www.csee.wvu.edu/~timm/cs591o/old/basicmethods.html&lt;/a&gt;)&lt;/p&gt;'':98C ''www.inf.ed.ac.uk'':63C,68C ''www.inf.ed.ac.uk/teaching/courses/iaml/slides/naive-2x2.pdf'':62C ''www.inf.ed.ac.uk/teaching/courses/iaml/slides/naive-2x2.pdf&lt;/a&gt;)&lt;/p&gt;'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3713, 2, '2014-12-17 02:58:06.057', 4, NULL, NULL, '&lt;p&gt;See a similar answer &lt;a href="http://stats.stackexchange.com/questions/105979/is-knn-a-discriminative-learning-algorithm"&gt;here&lt;/a&gt;. To clarify, k nearest neighbor is a discriminative classifier. &lt;/p&gt;

&lt;p&gt;The difference between a generative and a discriminative classifier is that the former models the joint probability where as the latter models the conditional probability (the posterior) starting from the prior. &lt;/p&gt;

&lt;p&gt;In the case of nearest neighbors, the conditional probability of a class given a data point is modeled. To do this, one starts with the prior probability on the classes.  &lt;/p&gt;
', NULL, NULL, '''/a'':12C ''/p'':22C,55C,87C ''/questions/105979/is-knn-a-discriminative-learning-algorithm'':10C ''answer'':5C ''case'':59C ''clarifi'':14C ''class'':68C,86C ''classifi'':21C,32C ''condit'':47C,64C ''data'':71C ''differ'':25C ''discrimin'':20C,31C ''former'':36C ''generat'':28C ''given'':69C ''href'':7C ''joint'':39C ''k'':15C ''latter'':44C ''model'':37C,45C,74C ''nearest'':16C,61C ''neighbor'':17C,62C ''one'':78C ''p'':1C,23C,56C ''point'':72C ''posterior'':50C ''prior'':54C,82C ''probabl'':40C,48C,65C,83C ''see'':2C ''similar'':4C ''start'':51C,79C ''stats.stackexchange.com'':9C ''stats.stackexchange.com/questions/105979/is-knn-a-discriminative-learning-algorithm'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3715, 2, '2014-12-17 09:07:11.32', 2, NULL, NULL, '&lt;p&gt;This seems to be a standard regression problem in which there are two goals:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Obtain a predictive model that can be used for prediction.&lt;/li&gt;
&lt;li&gt;Which variables seem to be the most important ones to be used. &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;For both the above problems use an ensemble model. Consider both a random forest and a gradient boosted machine. Both these models will use the independent variables and predict the Hospital time. Additionally, through variable importances, you can obtain which variables are the most important ones and have the most impact in predicting the output. &lt;/p&gt;
', NULL, NULL, '''/li'':29C,43C ''/ol'':44C ''/p'':16C,101C ''addit'':78C ''boost'':63C ''consid'':55C ''ensembl'':53C ''forest'':59C ''goal'':15C ''gradient'':62C ''hospit'':76C ''impact'':96C ''import'':38C,81C,90C ''independ'':71C ''li'':18C,30C ''machin'':64C ''model'':22C,54C,67C ''obtain'':19C,84C ''ol'':17C ''one'':39C,91C ''output'':100C ''p'':1C,45C ''predict'':21C,28C,74C,98C ''problem'':9C,50C ''random'':58C ''regress'':8C ''seem'':3C,33C ''standard'':7C ''time'':77C ''two'':14C ''use'':26C,42C,51C,69C ''variabl'':32C,72C,80C,86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3716, 2, '2014-12-17 09:32:53.703', 1, NULL, NULL, '&lt;p&gt;In the way that you''ve defined or set up the problem, i.e.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;sales = alpha*quality + beta*position + epsilon
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;We can easily quantify &lt;code&gt;beta&lt;/code&gt; given that your model is correct. You just need to run it through linear regression and it will give you the coefficient for &lt;code&gt;beta&lt;/code&gt;*.&lt;/p&gt;

&lt;p&gt;If you would like to model click through rates, you would have to train a classifier. So you would have to fit a logistic model that models: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;clicks ~ alpha*quality + beta*position + epsilon
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;*I believe you would have to restrict the training set to contain results where all impressions were obtained on the first page otherwise your model will not hold (I would guess that &lt;code&gt;beta&lt;/code&gt; is going to be strongly dependent on the page). &lt;/p&gt;
', NULL, NULL, '''/code'':24C,33C,59C,98C,135C ''/p'':15C,60C,89C,145C ''/pre'':25C,99C ''alpha'':19C,93C ''believ'':102C ''beta'':21C,32C,58C,95C,134C ''classifi'':77C ''click'':68C,92C ''code'':17C,31C,57C,91C,133C ''coeffici'':55C ''contain'':112C ''correct'':39C ''defin'':8C ''depend'':141C ''easili'':29C ''epsilon'':23C,97C ''first'':121C ''fit'':83C ''give'':52C ''given'':34C ''go'':137C ''guess'':131C ''hold'':128C ''i.e'':14C ''impress'':116C ''like'':65C ''linear'':47C ''logist'':85C ''model'':37C,67C,86C,88C,125C ''need'':42C ''obtain'':118C ''otherwis'':123C ''p'':1C,26C,61C,100C ''page'':122C,144C ''posit'':22C,96C ''pre'':16C,90C ''problem'':13C ''qualiti'':20C,94C ''quantifi'':30C ''rate'':70C ''regress'':48C ''restrict'':107C ''result'':113C ''run'':44C ''sale'':18C ''set'':10C,110C ''strong'':140C ''train'':75C,109C ''ve'':7C ''way'':4C ''would'':64C,72C,80C,104C,130C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9156, 1, '2015-12-01 21:26:44.943', 2, 76, 'Forecasting an individual based on a representative group', '&lt;p&gt;Iâm trying to create a model to forecast demand/expenditure for an individual based on historical data of many individuals over time, but Iâm having trouble finding examples of this. &lt;/p&gt;

&lt;p&gt;To give a simple example of what Iâm trying to do:&lt;/p&gt;

&lt;p&gt;Letâs say thereâs an ice-cream store that children in the surrounding area visit every week. Weâve got the history of all purchases at the store and details of who made the purchase, so we can track the purchase history of each individual. We also have details about each individual, e.g. age, gender, school, no. of siblings etc. &lt;/p&gt;

&lt;p&gt;We can assume that a child will buy from the store once a week until they âleaveâ for good and stop purchasing.&lt;/p&gt;

&lt;p&gt;What I want to do is create purchase forecasts for each child over a few weeks. The bit Iâm struggling with is where a new child, say Timmy, has only been purchasing for a few weeks and so doesn''t have enough of a history to forecast based solely on his own data (but we do have his details). &lt;/p&gt;

&lt;p&gt;My assumption is that demand can be better predicted by taking a child''s details into account, so I''d like to find a group in the historical data that best represents Timmy, and leverage their data in some way when creating Timmy''s forecast. Are there any standard methods/practices for this type of forecasting? &lt;/p&gt;

&lt;p&gt;Any advice or guidance would be much appreciated.&lt;/p&gt;
', 'machine-learning  predictive-modeling  forecast', NULL, '''/p'':48C,62C,126C,151C,213C,270C,280C ''account'':231C ''advic'':273C ''age'':119C ''also'':112C ''appreci'':279C ''area'':79C ''assum'':130C ''assumpt'':216C ''base'':4A,30C,201C ''best'':245C ''better'':222C ''bit'':170C ''buy'':135C ''child'':133C,164C,179C,227C ''children'':75C ''cream'':72C ''creat'':21C,159C,256C ''d'':234C ''data'':33C,206C,243C,251C ''demand'':219C ''demand/expenditure'':26C ''detail'':95C,114C,212C,229C ''doesn'':192C ''e.g'':118C ''enough'':195C ''etc'':125C ''everi'':81C ''exampl'':45C,54C ''find'':44C,237C ''forecast'':1A,15B,25C,161C,200C,259C,269C ''gender'':120C ''give'':51C ''good'':147C ''got'':85C ''group'':8A,239C ''guidanc'':275C ''histor'':32C,242C ''histori'':87C,107C,198C ''ice'':71C ''ice-cream'':70C ''individu'':3A,29C,36C,110C,117C ''iâ'':17C,40C,57C,171C ''learn'':11B ''leaveâ'':145C ''letâ'':64C ''leverag'':249C ''like'':235C ''m'':18C,41C,58C,172C ''machin'':10B ''machine-learn'':9B ''made'':98C ''mani'':35C ''methods/practices'':264C ''model'':14B,23C ''much'':278C ''new'':178C ''p'':16C,49C,63C,127C,152C,214C,271C ''predict'':13B,223C ''predictive-model'':12B ''purchas'':90C,100C,106C,150C,160C,185C ''repres'':7A,246C ''say'':66C,180C ''school'':121C ''sibl'':124C ''simpl'':53C ''sole'':202C ''standard'':263C ''stop'':149C ''store'':73C,93C,138C ''struggl'':173C ''surround'':78C ''take'':225C ''thereâ'':67C ''time'':38C ''timmi'':181C,247C,257C ''track'':104C ''tri'':19C,59C ''troubl'':43C ''type'':267C ''ve'':84C ''visit'':80C ''want'':155C ''way'':254C ''week'':82C,141C,168C,189C ''weâ'':83C ''would'':276C ''â'':144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3717, 2, '2014-12-17 18:15:42.133', 1, NULL, NULL, '&lt;p&gt;In general, you have a choice when handling missing values hen training a naive Bayes classifier. You can either choose to either&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Omit records with any missing values,&lt;/li&gt;
&lt;li&gt;Omit only the missing attributes.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I''ll use the example linked to above to demonstrate these two approaches.  Suppose we add one more training record to that example. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Outlook  Temperature  Humidity   Windy   Play
-------  -----------  --------   -----   ----
rainy    cool        normal    TRUE    no
rainy    mild        high      TRUE    no
sunny    hot         high      FALSE   no
sunny    hot         high      TRUE    no
sunny    mild        high      FALSE   no
overcast cool        normal    TRUE    yes
overcast hot         high      FALSE   yes
overcast hot         normal    FALSE   yes
overcast mild        high      TRUE    yes
rainy    cool        normal    FALSE   yes
rainy    mild        high      FALSE   yes
rainy    mild        normal    FALSE   yes
sunny    cool        normal    FALSE   yes
sunny    mild        normal    TRUE    yes
NA       hot         normal    FALSE   yes
&lt;/code&gt;&lt;/pre&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;If we decide to omit the last record due to the missing &lt;code&gt;outlook&lt;/code&gt; value, we would have the exact same trained model as discussed in the link. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;We could also choose to use all of the information available from this record.  We could choose to simply omit the attribute &lt;code&gt;outlook&lt;/code&gt; from this record. This would yield the following updated table. &lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;pre&gt;
           Outlook            Temperature           Humidity   
====================   =================   =================  
          Yes    No            Yes   No            Yes    No 
Sunny       2     3     Hot     3     2    High      3     4
Overcast    4     0     Mild    4     2    Normal    7     1 
Rainy       3     2     Cool    3     1
          -----------         ---------            ---------- 
Sunny     2/9   3/5     Hot   3/10   2/5    High    3/10   4/5 
Overcast  4/9   0/5     Mild  4/10   2/5    Normal  7/10   1/5
Rainy     3/9   2/5     Cool  3/10   1/5


            Windy        Play
=================    ========
      Yes     No     Yes   No
False 7      2       10     5
True  3      3
      ----------   ----------
False  7/10    2/5   10/15  5/15
True   3/10    3/5
&lt;/pre&gt;

&lt;p&gt;Notice there are 15 observations for each attribute &lt;strong&gt;except&lt;/strong&gt; &lt;code&gt;Outlook&lt;/code&gt;, which has only 14. This is since that value was unavailable for the last record. All further development would continue as discussed in the linked article.  &lt;/p&gt;

&lt;p&gt;For example in the R package &lt;code&gt;e1071&lt;/code&gt; naiveBayes implementation has the option &lt;code&gt;na.action&lt;/code&gt; which can be set to na.omit or na.pass.&lt;/p&gt;
', NULL, NULL, '''/code'':149C,168C,211C,320C,357C,365C ''/li'':33C,40C,184C,223C ''/ol'':41C,224C ''/p'':24C,66C,183C,222C,347C,374C ''/pre'':150C,305C ''/strong'':317C ''0'':246C ''0/5'':270C ''1'':252C,258C ''1/5'':276C,282C ''10'':292C ''10/15'':300C ''14'':324C ''15'':310C ''2'':236C,240C,249C,255C,291C ''2/5'':264C,273C,279C,299C ''2/9'':260C ''3'':237C,239C,242C,254C,257C,295C,296C ''3/10'':263C,266C,281C,303C ''3/5'':261C,304C ''3/9'':278C ''4'':243C,245C,248C ''4/10'':272C ''4/5'':267C ''4/9'':269C ''5'':293C ''5/15'':301C ''7'':251C,290C ''7/10'':275C,298C ''add'':58C ''also'':189C ''approach'':55C ''articl'':346C ''attribut'':39C,208C,314C ''avail'':197C ''bay'':16C ''choic'':7C ''choos'':21C,190C,203C ''classifi'':17C ''code'':68C,166C,209C,318C,355C,363C ''continu'':340C ''cool'':75C,100C,120C,135C,256C,280C ''could'':188C,202C ''decid'':156C ''demonstr'':52C ''develop'':338C ''discuss'':179C,342C ''due'':162C ''e1071'':356C ''either'':20C,23C ''exact'':174C ''exampl'':47C,65C,350C ''except'':316C ''fals'':87C,97C,107C,112C,122C,127C,132C,137C,147C,289C,297C ''follow'':219C ''general'':3C ''handl'':9C ''hen'':12C ''high'':81C,86C,91C,96C,106C,116C,126C,241C,265C ''hot'':85C,90C,105C,110C,145C,238C,262C ''humid'':71C,228C ''implement'':359C ''inform'':196C ''last'':160C,334C ''li'':26C,34C,152C,185C ''link'':48C,182C,345C ''ll'':44C ''mild'':80C,95C,115C,125C,130C,140C,247C,271C ''miss'':10C,31C,38C,165C ''model'':177C ''na'':144C ''na.action'':364C ''na.omit'':371C ''na.pass'':373C ''naiv'':15C ''naivebay'':358C ''normal'':76C,101C,111C,121C,131C,136C,141C,146C,250C,274C ''notic'':307C ''observ'':311C ''ol'':25C,151C ''omit'':27C,35C,158C,206C ''one'':59C ''option'':362C ''outlook'':69C,167C,210C,226C,319C ''overcast'':99C,104C,109C,114C,244C,268C ''p'':1C,42C,153C,186C,306C,348C ''packag'':354C ''play'':73C,284C ''pre'':67C,225C ''r'':353C ''raini'':74C,79C,119C,124C,129C,253C,277C ''record'':28C,62C,161C,200C,214C,335C ''set'':369C ''simpli'':205C ''sinc'':327C ''strong'':315C ''sunni'':84C,89C,94C,134C,139C,235C,259C ''suppos'':56C ''tabl'':221C ''temperatur'':70C,227C ''train'':13C,61C,176C ''true'':77C,82C,92C,102C,117C,142C,294C,302C ''two'':54C ''unavail'':331C ''updat'':220C ''use'':45C,192C ''valu'':11C,32C,169C,329C ''windi'':72C,283C ''would'':171C,216C,339C ''yes'':103C,108C,113C,118C,123C,128C,133C,138C,143C,148C,229C,231C,233C,285C,287C ''yield'':217C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3718, 1, '2014-12-17 21:17:13.34', 4, 304, 'Interactive Graphing while logging data', '&lt;p&gt;I''m looking to graph and interactively explore live/continuously measured data. There are quite a few options out there, with plot.ly being the most user-friendly. Plot.ly has a fantastic and easy to use UI (easily scalable, pannable, easily zoomable/fit to screen), but cannot handle the large sets of data I''m collecting. Does anyone know of any alternatives?&lt;/p&gt;

&lt;p&gt;I have MATLAB, but don''t have enough licenses to simultaneously run this and do development at the same time. I know that LabVIEW would be a great option, but it is currently cost-prohibitive.&lt;/p&gt;

&lt;p&gt;Thanks in advance!&lt;/p&gt;
', 'dataset  visualization', NULL, '''/p'':69C,107C,112C ''advanc'':111C ''altern'':68C ''anyon'':64C ''cannot'':53C ''collect'':62C ''cost'':105C ''cost-prohibit'':104C ''current'':103C ''data'':5A,19C,59C ''dataset'':6B ''develop'':86C ''easi'':41C ''easili'':45C,48C ''enough'':78C ''explor'':16C ''fantast'':39C ''friend'':35C ''graph'':2A,13C ''great'':98C ''handl'':54C ''interact'':1A,15C ''know'':65C,92C ''labview'':94C ''larg'':56C ''licens'':79C ''live/continuously'':17C ''log'':4A ''look'':11C ''m'':10C,61C ''matlab'':73C ''measur'':18C ''option'':25C,99C ''p'':8C,70C,108C ''pannabl'':47C ''plot.ly'':29C,36C ''prohibit'':106C ''quit'':22C ''run'':82C ''scalabl'':46C ''screen'':51C ''set'':57C ''simultan'':81C ''thank'':109C ''time'':90C ''ui'':44C ''use'':43C ''user'':34C ''user-friend'':33C ''visual'':7B ''would'':95C ''zoomable/fit'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3720, 2, '2014-12-18 04:41:33.493', 1, NULL, NULL, '&lt;p&gt;Visualizing large datasets is a long standing problem. One of the issues is to understand how we can show over a million points on a screen that has only about ~ million pixels. &lt;/p&gt;

&lt;p&gt;Having said that, here are a few tools that can handle big data: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Tableau: you could use their free desktop tool. &lt;/li&gt;
&lt;li&gt;Tabplot: built on top of ggplot2 in R to handle larger datasets. &lt;/li&gt;
&lt;li&gt;See &lt;a href="http://www.techrepublic.com/blog/five-apps/five-web-based-apps-to-help-you-visualize-big-data/" rel="nofollow"&gt;this&lt;/a&gt; review for 5 other products that can help you do your job. &lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':85C ''/blog/five-apps/five-web-based-apps-to-help-you-visualize-big-data/'':81C ''/li'':60C,74C,98C ''/ol'':99C ''/p'':34C,49C ''5'':88C ''big'':47C ''built'':63C ''could'':54C ''data'':48C ''dataset'':4C,73C ''desktop'':58C ''free'':57C ''ggplot2'':67C ''handl'':46C,71C ''help'':93C ''href'':78C ''issu'':13C ''job'':97C ''larg'':3C ''larger'':72C ''li'':51C,61C,75C ''long'':7C ''million'':23C,32C ''nofollow'':83C ''ol'':50C ''one'':10C ''p'':1C,35C ''pixel'':33C ''point'':24C ''problem'':9C ''product'':90C ''r'':69C ''rel'':82C ''review'':86C ''said'':37C ''screen'':27C ''see'':76C ''show'':20C ''stand'':8C ''tableau'':52C ''tabplot'':62C ''tool'':43C,59C ''top'':65C ''understand'':16C ''use'':55C ''visual'':2C ''www.techrepublic.com'':80C ''www.techrepublic.com/blog/five-apps/five-web-based-apps-to-help-you-visualize-big-data/'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6096, 2, '2015-06-12 12:57:10.24', 1, NULL, NULL, '&lt;p&gt;There are many resources online where you can learn more about deep learning. Specially &lt;a href="https://github.com/ChristosChristofidis/awesome-deep-learning" rel="nofollow"&gt;this link&lt;/a&gt; has a great collection. I recently forked this repository. You should go through the resources that interest you. Implementing is the best way to learn, where you get to see the results. As @Christopher mentioned, Pyhton is the best to learn for a beginner. Cheers!&lt;/p&gt;
', NULL, NULL, '''/a'':25C ''/christoschristofidis/awesome-deep-learning'':20C ''/p'':71C ''beginn'':69C ''best'':47C,64C ''cheer'':70C ''christoph'':59C ''collect'':29C ''deep'':13C ''fork'':32C ''get'':53C ''github.com'':19C ''github.com/christoschristofidis/awesome-deep-learning'':18C ''go'':37C ''great'':28C ''href'':17C ''implement'':44C ''interest'':42C ''learn'':10C,14C,50C,66C ''link'':24C ''mani'':4C ''mention'':60C ''nofollow'':22C ''onlin'':6C ''p'':1C ''pyhton'':61C ''recent'':31C ''rel'':21C ''repositori'':34C ''resourc'':5C,40C ''result'':57C ''see'':55C ''special'':15C ''way'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3721, 1, '2014-12-18 04:48:49.82', 2, 417, 'Machine Learning for hedging/ portfolio optimization?', '&lt;p&gt;With increasingly sophisticated methods that work on large scale datasets, financial applications are obvious. I am aware of machine learning being employed on financial services to detect fraud and flag fraudulent activities but I have a lesser understanding of how it helps to predict the price of the stock the next day and how many stocks of a particular company to buy. &lt;/p&gt;

&lt;p&gt;Do the hedge funds still employ portfolio optimization techniques that are right out of the mathematical finance literature or have they started to use machine learning to hedge their bets? More importantly, what are the features that are used by these hedge funds and what is a representative problem set up? &lt;/p&gt;
', 'machine-learning  feature-selection  optimization', NULL, '''/p'':77C,130C ''activ'':46C ''applic'':26C ''awar'':31C ''bet'':108C ''buy'':76C ''compani'':74C ''dataset'':24C ''day'':66C ''detect'':41C ''employ'':36C,84C ''featur'':11B,114C ''feature-select'':10B ''financ'':95C ''financi'':25C,38C ''flag'':44C ''fraud'':42C ''fraudul'':45C ''fund'':82C,121C ''hedg'':4A,81C,106C,120C ''help'':56C ''import'':110C ''increas'':16C ''larg'':22C ''learn'':2A,9B,34C,104C ''lesser'':51C ''literatur'':96C ''machin'':1A,8B,33C,103C ''machine-learn'':7B ''mani'':69C ''mathemat'':94C ''method'':18C ''next'':65C ''obvious'':28C ''optim'':6A,13B,86C ''p'':14C,78C ''particular'':73C ''portfolio'':5A,85C ''predict'':58C ''price'':60C ''problem'':127C ''repres'':126C ''right'':90C ''scale'':23C ''select'':12B ''servic'':39C ''set'':128C ''sophist'':17C ''start'':100C ''still'':83C ''stock'':63C,70C ''techniqu'':87C ''understand'':52C ''use'':102C,117C ''work'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3722, 2, '2014-12-18 08:10:00.63', 2, NULL, NULL, '&lt;p&gt;That is a rather broad question, and there is tons of literature about quantitative analysis and stock market prediction using machine learning.&lt;/p&gt;

&lt;p&gt;The most classical example of predicting the stock market is employing neural networks; you can use whatever feature you think might be relevant for your prediction, for example the unemployment rate, the oil price, the gold price, the interest rates, and the timeseries itself, i. e. the volatility, the change in the last 2,3,7,..., days etc. - a more classical approach is the input-output-analysis in econometrics, or the autoregression analysis, but all of it can be modeled using neural networks or any other function approximator / regression in a very natural way.&lt;/p&gt;

&lt;p&gt;But, as said, there are tons of other possibilities to model the market, to name a few: Ant Colony Optimization (ACO), Classical regression analysis, genetic algorithms, decision trees, reinforcement learning etc. you name it, almost EVERYTHING has probably been applied to the stock market prediction problem.&lt;/p&gt;

&lt;p&gt;There are different fond manager types on the markets. There are still the Quants which are doing a quantitative analysis using classical financial maths and maths borrowed from the physics to describe the market movements. There are still the most conservative ones which do a long-term, fundamental analysis of the corporation, that is, looking in how the corporation earns money and where it spends money. Or the tactical analysts who just look for immediate signals to buy / sell a stock in the short term. And those quantitative guys who employ machine learning amongst other methods.&lt;/p&gt;
', NULL, NULL, '''/p'':24C,121C,169C,268C ''2'':79C ''3'':80C ''7'':81C ''aco'':143C ''algorithm'':148C ''almost'':157C ''amongst'':265C ''analysi'':16C,93C,99C,146C,190C,220C ''analyst'':241C ''ant'':140C ''appli'':162C ''approach'':87C ''approxim'':114C ''autoregress'':98C ''borrow'':197C ''broad'':6C ''buy'':249C ''chang'':75C ''classic'':28C,86C,144C,192C ''coloni'':141C ''conserv'':211C ''corpor'':223C,230C ''day'':82C ''decis'':149C ''describ'':202C ''differ'':173C ''e'':71C ''earn'':231C ''econometr'':95C ''employ'':36C,262C ''etc'':83C,153C ''everyth'':158C ''exampl'':29C,53C ''featur'':43C ''financi'':193C ''fond'':174C ''function'':113C ''fundament'':219C ''genet'':147C ''gold'':61C ''guy'':260C ''immedi'':246C ''input'':91C ''input-output-analysi'':90C ''interest'':64C ''last'':78C ''learn'':23C,152C,264C ''literatur'':13C ''long'':217C ''long-term'':216C ''look'':226C,244C ''machin'':22C,263C ''manag'':175C ''market'':19C,34C,135C,166C,179C,204C ''math'':194C,196C ''method'':267C ''might'':46C ''model'':106C,133C ''money'':232C,237C ''movement'':205C ''name'':137C,155C ''natur'':119C ''network'':38C,109C ''neural'':37C,108C ''oil'':58C ''one'':212C ''optim'':142C ''output'':92C ''p'':1C,25C,122C,170C ''physic'':200C ''possibl'':131C ''predict'':20C,31C,51C,167C ''price'':59C,62C ''probabl'':160C ''problem'':168C ''quant'':184C ''quantit'':15C,189C,259C ''question'':7C ''rate'':56C,65C ''rather'':5C ''regress'':115C,145C ''reinforc'':151C ''relev'':48C ''said'':125C ''sell'':250C ''short'':255C ''signal'':247C ''spend'':236C ''still'':182C,208C ''stock'':18C,33C,165C,252C ''tactic'':240C ''term'':218C,256C ''think'':45C ''timeseri'':68C ''ton'':11C,128C ''tree'':150C ''type'':176C ''unemploy'':55C ''use'':21C,41C,107C,191C ''volatil'':73C ''way'':120C ''whatev'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3724, 2, '2014-12-18 18:07:18.17', 5, NULL, NULL, '&lt;p&gt;ScalaNLP is a suite of machine learning and numerical computing libraries with support for common natural language processing tasks. &lt;a href="http://www.scalanlp.org/"&gt;http://www.scalanlp.org/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':26C ''common'':16C ''comput'':11C ''href'':22C ''languag'':18C ''learn'':8C ''librari'':12C ''machin'':7C ''natur'':17C ''numer'':10C ''p'':1C ''process'':19C ''scalanlp'':2C ''suit'':5C ''support'':14C ''task'':20C ''www.scalanlp.org'':23C,25C ''www.scalanlp.org/&lt;/a&gt;&lt;/p&gt;'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3725, 2, '2014-12-19 01:20:39.907', 1, NULL, NULL, '&lt;p&gt;Recall the definition he makes for the graph Laplacian earlier, $L = D -W $. Now consider the map in the RHS parentheses which I''ll call $L^*$,
$$ L^*f(x_i) := f(x_i) - \alpha \sum_{x_j, ||x_i-x_j||&amp;lt;\epsilon}e^{-\frac{||x_i-x_j||^2}{4t}}f(x_j).$$
The suggested weight matrix definition is natural because it lets us write
$$ L^* := I - D^{-1}W. $$ Here''s &lt;a href="http://www.cs.yale.edu/homes/singer/publications/laplacian_ACHA.pdf" rel="nofollow"&gt;a reference&lt;/a&gt; to a related paper with some easy to read exposition. Hope this helps!&lt;/p&gt;
', NULL, NULL, '''-1'':73C ''/a'':86C ''/homes/singer/publications/laplacian_acha.pdf'':81C ''/p'':100C ''2'':53C ''4t'':54C ''alpha'':35C ''call'':26C ''consid'':16C ''d'':13C,72C ''definit'':4C,62C ''e'':46C ''earlier'':11C ''easi'':93C ''epsilon'':45C ''exposit'':96C ''f'':29C,32C,55C ''frac'':47C ''graph'':9C ''help'':99C ''hope'':97C ''href'':78C ''i-x'':40C,49C ''j'':38C,43C,52C,57C ''l'':12C,27C,28C,70C ''laplacian'':10C ''let'':67C ''ll'':25C ''lt'':44C ''make'':6C ''map'':18C ''matrix'':61C ''natur'':64C ''nofollow'':83C ''p'':1C ''paper'':90C ''parenthes'':22C ''read'':95C ''recal'':2C ''refer'':85C ''rel'':82C ''relat'':89C ''rhs'':21C ''suggest'':59C ''sum'':36C ''us'':68C ''w'':14C,74C ''weight'':60C ''write'':69C ''www.cs.yale.edu'':80C ''www.cs.yale.edu/homes/singer/publications/laplacian_acha.pdf'':79C ''x'':30C,33C,37C,39C,42C,48C,51C,56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3728, 1, '2014-12-19 11:42:04.547', 1, 138, 'What software is being used in this image recognition system?', '&lt;p&gt;I was wondering if anyone knew which piece of software is being used in this video? It is an image recognition system that makes the training process very simple.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn#t-775098" rel="nofollow"&gt;http://www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn#t-775098&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The example is with car images, though the video should start at the right spot.&lt;/p&gt;
', 'classification', NULL, '''/p'':42C,70C ''/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn#t-775098'':48C ''/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn#t-775098&lt;/a&gt;&lt;/p&gt;'':53C ''anyon'':17C ''car'':59C ''classif'':11B ''exampl'':56C ''href'':45C ''imag'':8A,32C,60C ''knew'':18C ''make'':36C ''nofollow'':50C ''p'':12C,43C,54C ''piec'':20C ''process'':39C ''recognit'':9A,33C ''rel'':49C ''right'':68C ''simpl'':41C ''softwar'':2A,22C ''spot'':69C ''start'':65C ''system'':10A,34C ''though'':61C ''train'':38C ''use'':5A,25C ''video'':28C,63C ''wonder'':15C ''www.ted.com'':47C,52C ''www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn#t-775098'':46C ''www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn#t-775098&lt;/a&gt;&lt;/p&gt;'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3731, 2, '2014-12-19 16:33:44.423', 4, NULL, NULL, '&lt;p&gt;Here''s some advice (use at your own risk!):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;make sure that your R environment on EC2 is identical to the one on your laptop&lt;/li&gt;
&lt;li&gt;make sure you''re using 64-bit images for your virtual machine instance&lt;/li&gt;
&lt;li&gt;try to create/enable swap space: &lt;a href="http://stackoverflow.com/a/22247782/2872891"&gt;http://stackoverflow.com/a/22247782/2872891&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;see this discussion: &lt;a href="http://stackoverflow.com/q/5171593/2872891"&gt;http://stackoverflow.com/q/5171593/2872891&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;see this discussion: &lt;a href="http://stackoverflow.com/q/12322959/2872891"&gt;http://stackoverflow.com/q/12322959/2872891&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;see this discussion: &lt;a href="http://stackoverflow.com/q/1358003/2872891"&gt;http://stackoverflow.com/q/1358003/2872891&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If the above-mentioned simpler measures don''t help OR you want to achieve more &lt;strong&gt;scalability&lt;/strong&gt; and/or &lt;strong&gt;performance&lt;/strong&gt;, including an ability to &lt;em&gt;parallelize&lt;/em&gt; the process on a single machine or across multiple machines, consider using &lt;code&gt;bigrf&lt;/code&gt; R package: &lt;a href="http://cran.r-project.org/web/packages/bigrf" rel="nofollow"&gt;http://cran.r-project.org/web/packages/bigrf&lt;/a&gt;. Also see this discussion: &lt;a href="http://stackoverflow.com/q/1358003/2872891"&gt;http://stackoverflow.com/q/1358003/2872891&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a/22247782/2872891'':56C ''/a/22247782/2872891&lt;/a&gt;&lt;/li&gt;'':59C ''/code'':142C ''/em'':127C ''/li'':30C,45C ''/p'':11C ''/q/12322959/2872891'':80C ''/q/12322959/2872891&lt;/a&gt;&lt;/li&gt;'':83C ''/q/1358003/2872891'':92C,163C ''/q/1358003/2872891&lt;/a&gt;&lt;/li&gt;'':95C ''/q/1358003/2872891&lt;/a&gt;.&lt;/p&gt;'':166C ''/q/5171593/2872891'':68C ''/q/5171593/2872891&lt;/a&gt;&lt;/li&gt;'':71C ''/strong'':116C,120C ''/ul'':96C ''/web/packages/bigrf'':149C ''/web/packages/bigrf&lt;/a&gt;.'':154C ''64'':37C ''abil'':123C ''above-ment'':100C ''achiev'':112C ''across'':135C ''advic'':5C ''also'':155C ''and/or'':117C ''bigrf'':141C ''bit'':38C ''code'':140C ''consid'':138C ''cran.r-project.org'':148C,153C ''cran.r-project.org/web/packages/bigrf'':147C ''cran.r-project.org/web/packages/bigrf&lt;/a&gt;.'':152C ''create/enable'':49C ''discuss'':63C,75C,87C,158C ''ec2'':21C ''em'':125C ''environ'':19C ''help'':107C ''href'':53C,65C,77C,89C,146C,160C ''ident'':23C ''imag'':39C ''includ'':121C ''instanc'':44C ''laptop'':29C ''li'':13C,31C,46C,60C,72C,84C ''machin'':43C,133C,137C ''make'':14C,32C ''measur'':104C ''mention'':102C ''multipl'':136C ''nofollow'':151C ''one'':26C ''p'':1C,97C ''packag'':144C ''parallel'':126C ''perform'':119C ''process'':129C ''r'':18C,143C ''re'':35C ''rel'':150C ''risk'':10C ''scalabl'':115C ''see'':61C,73C,85C,156C ''simpler'':103C ''singl'':132C ''space'':51C ''stackoverflow.com'':55C,58C,67C,70C,79C,82C,91C,94C,162C,165C ''stackoverflow.com/a/22247782/2872891'':54C ''stackoverflow.com/a/22247782/2872891&lt;/a&gt;&lt;/li&gt;'':57C ''stackoverflow.com/q/12322959/2872891'':78C ''stackoverflow.com/q/12322959/2872891&lt;/a&gt;&lt;/li&gt;'':81C ''stackoverflow.com/q/1358003/2872891'':90C,161C ''stackoverflow.com/q/1358003/2872891&lt;/a&gt;&lt;/li&gt;'':93C ''stackoverflow.com/q/1358003/2872891&lt;/a&gt;.&lt;/p&gt;'':164C ''stackoverflow.com/q/5171593/2872891'':66C ''stackoverflow.com/q/5171593/2872891&lt;/a&gt;&lt;/li&gt;'':69C ''strong'':114C,118C ''sure'':15C,33C ''swap'':50C ''tri'':47C ''ul'':12C ''use'':6C,36C,139C ''virtual'':42C ''want'':110C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3732, 2, '2014-12-19 23:24:55.017', 2, NULL, NULL, '&lt;p&gt;Additional to other ideas: reduce your data until you figure out what you &lt;strong&gt;can&lt;/strong&gt; run on the Amazon instance. If it can''t do 100k rows then something is very wrong, if it fails at 590k rows then its marginal.&lt;/p&gt;

&lt;p&gt;The c3.4xlarge instance has 30Gb of RAM, so yes it should be enough.&lt;/p&gt;
', NULL, NULL, '''/p'':44C,59C ''/strong'':17C ''100k'':28C ''30gb'':50C ''590k'':39C ''addit'':2C ''amazon'':21C ''c3.4xlarge'':47C ''data'':8C ''enough'':58C ''fail'':37C ''figur'':11C ''idea'':5C ''instanc'':22C,48C ''margin'':43C ''p'':1C,45C ''ram'':52C ''reduc'':6C ''row'':29C,40C ''run'':18C ''someth'':31C ''strong'':15C ''wrong'':34C ''yes'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3740, 1, '2014-12-22 09:31:31.747', 2, 243, 'Improving Naive Bayes accuracy for text classification', '&lt;p&gt;I am performing document (text) classification on the category of websites, and use the website content (tokenized, stemmed and lowercased).&lt;/p&gt;

&lt;p&gt;My problem is that I have an over-represented category which has vastly more data points than any other (roughly 70% or 4000~ of my data points are of his one category, while about 20 other categories make up the last 30%, some of which have fewer than 50 data points).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;My first question:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;What could I do to improve the accuracy of my classifier in this case of sparse data for some of the labels? Should I simply discard a certain proportion of the data points in the category which is over-represented? Should I use something other than Gaussian Naive Bayes with tf-idf?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;My second question:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;After I perform the classification, I save the tfidf vector as well as the classifier to disk. However, when I re-rerun the classification on the same data, I sometimes get different results from what I initially got (for example, if previously a data point was classified as "Entertainment", it might receive "News" now). Is this indicative of an error in my implementation, or expected?&lt;/p&gt;
', 'machine-learning  classification  accuracy  naive-bayes-classifier', NULL, '''/p'':38C,92C,99C,155C,162C,230C ''/strong'':98C,161C ''20'':75C ''30'':82C ''4000'':63C ''50'':89C ''70'':61C ''accuraci'':4A,12B,108C ''bay'':3A,15B,150C ''case'':114C ''categori'':26C,50C,72C,77C,136C ''certain'':128C ''classif'':7A,11B,23C,168C,188C ''classifi'':16B,111C,178C,211C ''content'':33C ''could'':102C ''data'':55C,66C,90C,117C,132C,192C,208C ''differ'':196C ''discard'':126C ''disk'':180C ''document'':21C ''entertain'':213C ''error'':224C ''exampl'':204C ''expect'':229C ''fewer'':87C ''first'':96C ''gaussian'':148C ''get'':195C ''got'':202C ''howev'':181C ''idf'':154C ''implement'':227C ''improv'':1A,106C ''indic'':221C ''initi'':201C ''label'':122C ''last'':81C ''learn'':10B ''lowercas'':37C ''machin'':9B ''machine-learn'':8B ''make'':78C ''might'':215C ''naiv'':2A,14B,149C ''naive-bayes-classifi'':13B ''news'':217C ''one'':71C ''over-repres'':47C,139C ''p'':17C,39C,93C,100C,156C,163C ''perform'':20C,166C ''point'':56C,67C,91C,133C,209C ''previous'':206C ''problem'':41C ''proport'':129C ''question'':97C,160C ''re'':185C ''re-rerun'':184C ''receiv'':216C ''repres'':49C,141C ''rerun'':186C ''result'':197C ''rough'':60C ''save'':170C ''second'':159C ''simpli'':125C ''someth'':145C ''sometim'':194C ''spars'':116C ''stem'':35C ''strong'':94C,157C ''text'':6A,22C ''tf'':153C ''tf-idf'':152C ''tfidf'':172C ''token'':34C ''use'':30C,144C ''vast'':53C ''vector'':173C ''websit'':28C,32C ''well'':175C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4843, 2, '2015-01-09 00:25:16.067', 1, NULL, NULL, '&lt;p&gt;Looks like lots of marketing around a specific case of what everyone was already doing to sound bigger than it is.  So, are people implementing mathematical frameworks in software to extract and use the structure within data?  Absolutely.  Are people using the word ''Correlithm'' when doing it?  Not as far as I''ve seen.&lt;/p&gt;
', NULL, NULL, '''/p'':56C ''absolut'':39C ''alreadi'':15C ''around'':7C ''bigger'':19C ''case'':10C ''correlithm'':45C ''data'':38C ''everyon'':13C ''extract'':32C ''far'':51C ''framework'':28C ''implement'':26C ''like'':3C ''look'':2C ''lot'':4C ''market'':6C ''mathemat'':27C ''p'':1C ''peopl'':25C,41C ''seen'':55C ''softwar'':30C ''sound'':18C ''specif'':9C ''structur'':36C ''use'':34C,42C ''ve'':54C ''within'':37C ''word'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3741, 1, '2014-12-22 13:04:38.577', 2, 76, 'Stochastic gradient descent in matrix factorization, sensitive to label''s scale?', '&lt;p&gt;I''m trying to figure out a strange phenomenon, when I use matrix factorization (the Netflix Prize solution) for a rating matrix: &lt;/p&gt;

&lt;p&gt;$R = P^T * Q + B_u + B_i$&lt;/p&gt;

&lt;p&gt;with ratings ranging from 1 to 10.&lt;/p&gt;

&lt;p&gt;Then I evaluate the model by each label''s absolute mean average error in test set, the first column is origin_score, the second(we don''t transform the data, then train and its  prediction error), the third(we transform the data all by dividing 2, train, and when I use this model to make prediction, firstly reconstruct the matrix and then just multiply 2 and make it back to the same scale)&lt;/p&gt;

&lt;p&gt;As you see, in grade 3-4 (most samples are label from 3-4), it''s more precise while in high score range(like 9 and 10, just 2% of the whole traiing set), it''s worse.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;
+----------------------+--------------------+--------------------+&lt;br&gt;
| rounded_origin_score | abs_mean_avg_error | abs_mean_avg_error  | 
+----------------------+--------------------+---------------------+&lt;br&gt;
| 1.0                  | 2.185225396100167  |  2.559125413626183  |
| 2.0                  | 1.4072212825108161 |  1.5290497332538155 |
| 3.0                  | 0.7606073396581479 |  0.6285151230269825 |
| 4.0                  | 0.7823491986435621 |  0.6419077576969795 |
| 5.0                  | 1.2734369551159568 |  1.256590210555053  |
| 6.0                  | 1.9546560495715863 |  2.0461809588933835 |
| 7.0                  | 2.707229888048017  |  2.8866856489147494 |
| 8.0                  | 3.5084244741417137 |  3.7212155956153796 |
| 9.0                  | 4.357185793060213  |  4.590550124054919  |
| 10.0                 | 5.180752400467891  |  5.468600926567884  |
+----------------------+--------------------+---------------------+
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;I''ve re-train the model several times, and got same result, so I think it''s not effect by randomness.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3734, 1, '2014-12-20 07:03:57.837', 2, 113, 'Approaches to high dimension pattern matching problem', '&lt;p&gt;My apologies in advance as I am new to this. I have searched the internet and tried various processes and nothing seems to work or address this situation. &lt;/p&gt;

&lt;p&gt;I have a dataset of 30,000 transactions and 500,000 items. Average item size for a transaction is 50. The dataset is sparse, so the support number must be set quite low. Furthermore, the rules become more valuable the larger the number of items in the rule. &lt;/p&gt;

&lt;p&gt;I have tried running this in arules and the tests fail after exceeding 64 gb of RAM (the limit of the machine). I have tried reducing items and transactions to smaller subsets, but still hit this memory limit.&lt;/p&gt;

&lt;p&gt;Ultimately, I am looking for ways to cluster large groups of similar accounts by selection of items and generate confidence and lift of various next items selected from those clusters.&lt;/p&gt;

&lt;p&gt;My question: are there alternative, more efficient ways to do this, or other approaches to consider?&lt;/p&gt;

&lt;p&gt;Thank you.&lt;/p&gt;
', 'r', NULL, '''/p'':38C,88C,128C,160C,178C,182C ''000'':46C,50C ''30'':45C ''50'':59C ''500'':49C ''64'':103C ''account'':142C ''address'':35C ''advanc'':13C ''altern'':166C ''apolog'':11C ''approach'':1A,175C ''arul'':96C ''averag'':52C ''becom'':76C ''cluster'':137C,159C ''confid'':149C ''consid'':177C ''dataset'':43C,61C ''dimens'':4A ''effici'':168C ''exceed'':102C ''fail'':100C ''furthermor'':73C ''gb'':104C ''generat'':148C ''group'':139C ''high'':3A ''hit'':124C ''internet'':24C ''item'':51C,53C,84C,116C,146C,155C ''larg'':138C ''larger'':80C ''lift'':151C ''limit'':108C,127C ''look'':133C ''low'':72C ''machin'':111C ''match'':6A ''memori'':126C ''must'':68C ''new'':17C ''next'':154C ''noth'':30C ''number'':67C,82C ''p'':9C,39C,89C,129C,161C,179C ''pattern'':5A ''problem'':7A ''process'':28C ''question'':163C ''quit'':71C ''r'':8B ''ram'':106C ''reduc'':115C ''rule'':75C,87C ''run'':93C ''search'':22C ''seem'':31C ''select'':144C,156C ''set'':70C ''similar'':141C ''situat'':37C ''size'':54C ''smaller'':120C ''spars'':63C ''still'':123C ''subset'':121C ''support'':66C ''test'':99C ''thank'':180C ''transact'':47C,57C,118C ''tri'':26C,92C,114C ''ultim'':130C ''valuabl'':78C ''various'':27C,153C ''way'':135C,169C ''work'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3735, 2, '2014-12-20 08:06:13.077', 1, NULL, NULL, '&lt;p&gt;According to the following discussion on &lt;em&gt;StackOverflow&lt;/em&gt;, a situation like that you''ve described can occur, when one of the variables in a data set is of &lt;strong&gt;unexpected type&lt;/strong&gt; (for example, a &lt;code&gt;factor&lt;/code&gt; instead of a &lt;code&gt;character&lt;/code&gt;): &lt;a href="http://stackoverflow.com/q/7246412/2872891"&gt;http://stackoverflow.com/q/7246412/2872891&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Also, consider using package &lt;code&gt;bigmemory&lt;/code&gt;, recommended in the accepted answer, or similar packages for &lt;em&gt;big data&lt;/em&gt; analysis. For the latter, please see section &lt;strong&gt;"Large memory and out-of-memory data"&lt;/strong&gt; in &lt;em&gt;CRAN Task View&lt;/em&gt; &lt;a href="http://cran.r-project.org/web/views/HighPerformanceComputing.html" rel="nofollow"&gt;High-Performance and Parallel Computing with R&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Finally, an additional note. There is an &lt;strong&gt;ecosystem&lt;/strong&gt; of R packages, built around the &lt;code&gt;arules&lt;/code&gt; package, which includes supporting packages for algorithms (&lt;code&gt;arulesNBMiner&lt;/code&gt;), applications (&lt;code&gt;arulesSequences&lt;/code&gt;, &lt;code&gt;arulesClassify&lt;/code&gt;) and visualization (&lt;code&gt;arulesViz&lt;/code&gt;). You are likely aware of &lt;a href="http://www.jmlr.org/papers/volume12/hahsler11a/hahsler11a.pdf" rel="nofollow"&gt;that&lt;/a&gt;, but I have decided to include this good-to-know fact just in case and for the sake of completeness.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3736, 2, '2014-12-21 00:40:12.337', 1, NULL, NULL, '&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Bayesian_optimization" rel="nofollow"&gt;Bayesian optimization&lt;/a&gt; is a principled way of sequentially finding the extremum of black-box functions. What''s more, there a numerous software packages that make it easy, such as &lt;a href="http://rmcantin.bitbucket.org/html/" rel="nofollow"&gt;BayesOpt&lt;/a&gt; and &lt;a href="http://engineeringblog.yelp.com/2014/07/introducing-moe-metric-optimization-engine-a-new-open-source-machine-learning-service-for-optimal-ex.html" rel="nofollow"&gt;MOE&lt;/a&gt;. Another flexible Bayesian framework that you can use for optimization is &lt;a href="http://en.wikipedia.org/wiki/Gaussian_process" rel="nofollow"&gt;Gaussian processes&lt;/a&gt;: &lt;a href="http://ml.dcs.shef.ac.uk/gpss/gpss13/talks/Sheffield-GPSS2013-Osborne.pdf" rel="nofollow"&gt;Global Optimisation with Gaussian Processes&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3738, 1, '2014-12-22 03:30:45.673', 7, 557, 'How to deal with time series which change in seasonality or other patterns?', '&lt;h1&gt;Background&lt;/h1&gt;

&lt;p&gt;I''m working on a time series data set of energy meter readings. The length of the series varies by meter - for some I have several years, others only a few months, etc. Many display significant seasonality, and often multiple layers - within the day, week, or year.&lt;/p&gt;

&lt;p&gt;One of the things I''ve been working on is clustering of these time series. My work is academic for the moment, and while I''m doing other analysis of the data as well, I have a specific goal to carry out some clustering.&lt;/p&gt;

&lt;p&gt;I did some initial work where I calculated various features (percentage used on weekends vs. weekday, percentage used in different time blocks, etc.). I then moved on to looking at using Dynamic Time Warping (DTW) to obtain the distance between different series, and clustering based on the difference values, and I''ve found several papers related to this.&lt;/p&gt;

&lt;h1&gt;Question&lt;/h1&gt;

&lt;p&gt;Will the seasonality in a specific series changing cause my clustering to be incorrect? And if so, how do I deal with it?&lt;/p&gt;

&lt;p&gt;My concern is that the distances obtained by DTW could be misleading in the cases where the pattern in a time series has changed. This could lead to incorrect clustering.&lt;/p&gt;

&lt;p&gt;In case the above is unclear, consider these examples:&lt;/p&gt;

&lt;h2&gt;Example 1&lt;/h2&gt;

&lt;p&gt;A meter has low readings from midnight until 8AM, the readings then increase sharply for the next hour and stay high from 9AM until 5PM, then decrease sharply over the next hour and then stay low from 6PM until midnight. The meter continues this pattern consistently every day for several months, but then changes to a pattern where readings simply stay at a consistent level throughout the day.&lt;/p&gt;

&lt;h2&gt;Example 2&lt;/h2&gt;

&lt;p&gt;A meter shows approximately the same amount of energy being consumed each month. After several years, it changes to a pattern where energy usage is higher during the summer months before returning to the usual amount.&lt;/p&gt;

&lt;h1&gt;Possible Directions&lt;/h1&gt;

&lt;ul&gt;
&lt;li&gt;I''ve wondered whether I can continue to compare whole time series, but split them and consider them as a separate series if the pattern changes considerably. However, to do this I''d need to be able to detect such changes. Also, I just don''t know if this is a suitable way or working with the data.&lt;/li&gt;
&lt;li&gt;I''ve also considered splitting the data and considering it as many separate time series. For instance, I could consider every day/meter combination as a separate series. However, I''d then need to do similarly if I wanted to consider the weekly/monthly/yearly patterns. I &lt;em&gt;think&lt;/em&gt; this would work, but it''s potentially quite onerous and I''d hate to go down this path if there''s a better way that I''m missing.&lt;/li&gt;
&lt;/ul&gt;

&lt;h1&gt;Further Notes&lt;/h1&gt;

&lt;p&gt;These are things that have come up in comments, or things I''ve thought of due to comments, which might be relevant. I''m putting them here so people don''t have to read through everything to get relevant information.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;I''m working in Python, but have rpy for those places where R is more suitable. I''m not necessarily looking for a Python answer though - if someone has a practical answer of what should be done I''m happy to figure out implementation details myself.&lt;/li&gt;
&lt;li&gt;I have a lot of working "rough draft" code - I''ve done some DTW runs, I''ve done a couple of different types of clustering, etc. I think I largely understand the direction I''m taking, and what I''m really looking for is related to how I process my data before finding distances, running clustering, etc. Given this, I suspect the answer would be the same whether the distances between series are calculated via DTW or a simpler Euclidean Distance (ED).&lt;/li&gt;
&lt;li&gt;I have found these papers especially informative on time series and DTW and they may be helpful if some background is needed to the topic area: &lt;a href="http://www.cs.ucr.edu/~eamonn/selected_publications.htm" rel="nofollow"&gt;http://www.cs.ucr.edu/~eamonn/selected_publications.htm&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;hr&gt;

&lt;p&gt;P.S. - I can''t post in Meta or even a comment yet, so sorry for the off-topic bit, but: I love this site and hope it picks up steam! I was so happy when I found it, I immediately found topics which weren''t covered at all or as helpfully at SO and CV. I feel a lot more comfortable asking this question here because it''s not a pure statistics question or a programming question. I would feel like I was asking something potentially off-topic at either of those sites, and I feel like I''m more likely to get an answer which really covers my question and which I can understand by asking here.&lt;/p&gt;
', 'data-mining  clustering  time-series  beginner', NULL, '''/em'':479C ''/h1'':24C,182C,370C,513C ''/h2'':254C,328C ''/li'':431C,508C,604C,688C ''/p'':73C,119C,179C,207C,239C,250C,324C,366C,555C,849C ''/ul'':509C,726C ''/~eamonn/selected_publications.htm'':720C ''/~eamonn/selected_publications.htm&lt;/a&gt;&lt;/li&gt;'':725C ''1'':253C ''2'':327C ''5pm'':280C ''6pm'':293C ''8am'':264C ''9am'':278C ''abl'':409C ''academ'':93C ''also'':414C,435C ''amount'':336C,365C ''analysi'':103C ''answer'':582C,589C,668C,835C ''approxim'':333C ''area'':715C ''ask'':791C,813C,847C ''background'':23C,709C ''base'':165C ''beginn'':21B ''better'':502C ''bit'':748C ''block'':142C ''calcul'':128C,679C ''carri'':115C ''case'':223C,242C ''caus'':192C ''chang'':8A,191C,232C,309C,347C,398C,413C ''cluster'':17B,85C,118C,164C,194C,238C,630C,661C ''code'':614C ''combin'':455C ''come'':520C ''comfort'':790C ''comment'':523C,532C,739C ''compar'':381C ''concern'':210C ''consid'':247C,389C,436C,441C,452C,472C ''consider'':399C ''consist'':301C,319C ''consum'':340C ''continu'':298C,379C ''could'':218C,234C,451C ''coupl'':625C ''cover'':775C,838C ''cv'':784C ''d'':405C,462C,491C ''data'':15B,33C,106C,430C,439C,656C ''data-min'':14B ''day'':69C,303C,323C ''day/meter'':454C ''deal'':3A,204C ''decreas'':282C ''detail'':602C ''detect'':411C ''differ'':140C,161C,168C,627C ''direct'':369C,638C ''display'':60C ''distanc'':159C,214C,659C,675C,686C ''done'':594C,617C,623C ''draft'':613C ''dtw'':155C,217C,619C,681C,701C ''due'':530C ''dynam'':152C ''ed'':687C ''either'':820C ''em'':477C ''energi'':36C,338C,352C ''especi'':695C ''etc'':58C,143C,631C,662C ''euclidean'':685C ''even'':737C ''everi'':302C,453C ''everyth'':550C ''exampl'':249C,252C,326C ''featur'':130C ''feel'':786C,809C,826C ''figur'':599C ''find'':658C ''found'':173C,692C,766C,770C ''get'':552C,833C ''given'':663C ''go'':494C ''goal'':113C ''h1'':22C,180C,367C,510C ''h2'':251C,325C ''happi'':597C,763C ''hate'':492C ''help'':706C,780C ''high'':276C ''higher'':355C ''hope'':755C ''hour'':273C,287C ''howev'':400C,460C ''hr'':727C ''href'':717C ''immedi'':769C ''implement'':601C ''incorrect'':197C,237C ''increas'':268C ''inform'':554C,696C ''initi'':124C ''instanc'':449C ''know'':419C ''larg'':635C ''layer'':66C ''lead'':235C ''length'':40C ''level'':320C ''li'':372C,432C,557C,605C,689C ''like'':810C,827C,831C ''look'':149C,578C,647C ''lot'':609C,788C ''love'':751C ''low'':259C,291C ''m'':27C,100C,506C,538C,559C,575C,596C,640C,645C,829C ''mani'':59C,444C ''may'':704C ''meta'':735C ''meter'':37C,46C,257C,297C,331C ''midnight'':262C,295C ''might'':534C ''mine'':16B ''mislead'':220C ''miss'':507C ''moment'':96C ''month'':57C,306C,342C,359C ''move'':146C ''multipl'':65C ''necessarili'':577C ''need'':406C,464C,711C ''next'':272C,286C ''nofollow'':722C ''note'':512C ''obtain'':157C,215C ''off-top'':745C,816C ''often'':64C ''one'':75C ''oner'':488C ''other'':53C ''p'':25C,74C,120C,183C,208C,240C,255C,329C,514C,728C ''p.s'':729C ''paper'':175C,694C ''path'':497C ''pattern'':13A,226C,300C,312C,350C,397C,475C ''peopl'':543C ''percentag'':131C,137C ''pick'':757C ''place'':568C ''possibl'':368C ''post'':733C ''potenti'':486C,815C ''practic'':588C ''process'':654C ''program'':805C ''pure'':800C ''put'':539C ''python'':562C,581C ''question'':181C,793C,802C,806C,840C ''quit'':487C ''r'':570C ''read'':38C,260C,266C,314C,548C ''realli'':646C,837C ''rel'':721C ''relat'':176C,650C ''relev'':536C,553C ''return'':361C ''rough'':612C ''rpi'':565C ''run'':620C,660C ''season'':10A,62C,186C ''separ'':393C,445C,458C ''seri'':6A,20B,32C,43C,89C,162C,190C,230C,384C,394C,447C,459C,677C,699C ''set'':34C ''sever'':51C,174C,305C,344C ''sharpli'':269C,283C ''show'':332C ''signific'':61C ''similar'':467C ''simpler'':684C ''simpli'':315C ''site'':753C,823C ''someon'':585C ''someth'':814C ''sorri'':742C ''specif'':112C,189C ''split'':386C,437C ''statist'':801C ''stay'':275C,290C,316C ''steam'':759C ''suitabl'':424C,573C ''summer'':358C ''suspect'':666C ''take'':641C ''thing'':78C,517C,525C ''think'':478C,633C ''though'':583C ''thought'':528C ''throughout'':321C ''time'':5A,19B,31C,88C,141C,153C,229C,383C,446C,698C ''time-seri'':18B ''topic'':714C,747C,771C,818C ''type'':628C ''ul'':371C,556C ''unclear'':246C ''understand'':636C,845C ''usag'':353C ''use'':132C,138C,151C ''usual'':364C ''valu'':169C ''vari'':44C ''various'':129C ''ve'':80C,172C,374C,434C,527C,616C,622C ''via'':680C ''vs'':135C ''want'':470C ''warp'':154C ''way'':425C,503C ''week'':70C ''weekday'':136C ''weekend'':134C ''weekly/monthly/yearly'':474C ''well'':108C ''weren'':773C ''whether'':376C,673C ''whole'':382C ''within'':67C ''wonder'':375C ''work'':28C,82C,91C,125C,427C,482C,560C,611C ''would'':481C,669C,808C ''www.cs.ucr.edu'':719C,724C ''www.cs.ucr.edu/~eamonn/selected_publications.htm'':718C ''www.cs.ucr.edu/~eamonn/selected_publications.htm&lt;/a&gt;&lt;/li&gt;'':723C ''year'':52C,72C,345C ''yet'':740C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3739, 2, '2014-12-22 05:14:16.13', 1, NULL, NULL, '&lt;p&gt;If you want to just mine for seasonal patterns, then look into &lt;a href="http://en.wikipedia.org/wiki/Autocorrelation" rel="nofollow"&gt;autocorrelation&lt;/a&gt;.  If you''re looking for a model that can learn seasonal patterns and make forecasts from it, then &lt;a href="https://stat.ethz.ch/R-manual/R-patched/library/stats/html/HoltWinters.html" rel="nofollow"&gt;Holt-Winters&lt;/a&gt; is a good start, and &lt;a href="http://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average" rel="nofollow"&gt;ARIMA&lt;/a&gt; would be a good thing to follow up with.  &lt;a href="http://a-little-book-of-r-for-time-series.readthedocs.org/en/latest/" rel="nofollow"&gt;Here&lt;/a&gt;&lt;a href="https://media.readthedocs.org/pdf/a-little-book-of-r-for-time-series/latest/a-little-book-of-r-for-time-series.pdf" rel="nofollow"&gt;[pdf]&lt;/a&gt; is the tutorial that got me off the ground.&lt;/p&gt;
', NULL, NULL, '''/a'':22C,51C,65C,83C,92C ''/en/latest/'':79C ''/p'':102C ''/pdf/a-little-book-of-r-for-time-series/latest/a-little-book-of-r-for-time-series.pdf'':88C ''/r-manual/r-patched/library/stats/html/holtwinters.html'':45C ''/wiki/autocorrelation'':18C ''/wiki/autoregressive_integrated_moving_average'':61C ''a-little-book-of-r-for-time-series.readthedocs.org'':78C ''a-little-book-of-r-for-time-series.readthedocs.org/en/latest/'':77C ''arima'':64C ''autocorrel'':21C ''en.wikipedia.org'':17C,60C ''en.wikipedia.org/wiki/autocorrelation'':16C ''en.wikipedia.org/wiki/autoregressive_integrated_moving_average'':59C ''follow'':72C ''forecast'':37C ''good'':54C,69C ''got'':97C ''ground'':101C ''holt'':49C ''holt-wint'':48C ''href'':15C,42C,58C,76C,85C ''learn'':32C ''look'':12C,26C ''make'':36C ''media.readthedocs.org'':87C ''media.readthedocs.org/pdf/a-little-book-of-r-for-time-series/latest/a-little-book-of-r-for-time-series.pdf'':86C ''mine'':7C ''model'':29C ''nofollow'':20C,47C,63C,81C,90C ''p'':1C ''pattern'':10C,34C ''pdf'':91C ''re'':25C ''rel'':19C,46C,62C,80C,89C ''season'':9C,33C ''start'':55C ''stat.ethz.ch'':44C ''stat.ethz.ch/r-manual/r-patched/library/stats/html/holtwinters.html'':43C ''thing'':70C ''tutori'':95C ''want'':4C ''winter'':50C ''would'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3748, 2, '2014-12-22 19:14:26.313', 7, NULL, NULL, '&lt;p&gt;There''s also Richard Socher''s recent PhD dissertation on intersection of NLP and deep learning: &lt;a href="http://nlp.stanford.edu/~socherr/thesis.pdf"&gt;Recursive Deep Learning for Natural Language Processing and Computer Vision&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':33C ''/p'':34C ''/~socherr/thesis.pdf'':22C ''also'':4C ''comput'':31C ''deep'':16C,24C ''dissert'':10C ''href'':19C ''intersect'':12C ''languag'':28C ''learn'':17C,25C ''natur'':27C ''nlp'':14C ''nlp.stanford.edu'':21C ''nlp.stanford.edu/~socherr/thesis.pdf'':20C ''p'':1C ''phd'':9C ''process'':29C ''recent'':8C ''recurs'':23C ''richard'':5C ''socher'':6C ''vision'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3742, 1, '2014-12-22 14:06:45.61', 0, 72, 'Data transposition code in R', '&lt;p&gt;I''ve been working in SAS for a few years but as my time as a student with a no-cost-to-me license comes to an end, I want to learn R.&lt;/p&gt;

&lt;p&gt;Is it possible to transpose a data set so that all the observations for a single ID are on the same line?  (I have 2-8 observations per unique individual but they are currently arranged vertically rather than horizontally.)  In SAS, I had been using PROC SQL and PROC TRANSPOSE depending on my analysis aims.&lt;/p&gt;

&lt;p&gt;Example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ID    date        timeframe  fruit_amt   veg_amt &amp;lt;br/&amp;gt; 
4352  05/23/2013  before     0.25        0.75 &amp;lt;br/&amp;gt; 
5002  05/24/2014  after      0.06        0.25 &amp;lt;br/&amp;gt; 
4352  04/16/2014  after      0           0 &amp;lt;br/&amp;gt; 
4352  05/23/2013  after      0.06        0.25 &amp;lt;br/&amp;gt; 
5002  05/24/2014  before     0.75        0.25 &amp;lt;br/&amp;gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Desired:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ID    B_fr05/23/2013   B_veg05/23/2013  A_fr05/23/2013  A_veg05/23/2013   B_fr05/24/2014   B_veg05/24/2014   (etc)  &amp;lt;br/&amp;gt;
4352  0.25             0.75             0.06            0.25              .                .  &amp;lt;br/&amp;gt;
5002  .                .                .               .                 0.75             0.25 &amp;lt;br/&amp;gt;
&lt;/code&gt;&lt;/pre&gt;
', 'data-mining  r  dataset  beginner', NULL, '''-8'':74C ''/code'':160C,198C ''/p'':47C,104C,107C,164C ''/pre'':161C,199C ''0'':139C,140C ''0.06'':131C,147C,187C ''0.25'':123C,132C,148C,156C,185C,188C,194C ''0.75'':124C,155C,186C,193C ''04/16/2014'':137C ''05/23/2013'':121C,145C ''05/24/2014'':129C,153C ''2'':73C ''4352'':120C,136C,144C,184C ''5002'':128C,152C,192C ''aim'':103C ''amt'':114C,116C ''analysi'':102C ''arrang'':83C ''b'':168C,170C,176C,178C ''beginn'':11B ''br'':118C,126C,134C,142C,150C,158C,182C,190C,196C ''code'':3A,109C,166C ''come'':38C ''cost'':34C ''current'':82C ''data'':1A,7B,55C ''data-min'':6B ''dataset'':10B ''date'':111C ''depend'':99C ''desir'':163C ''end'':41C ''etc'':180C ''exampl'':106C ''fr05/23/2013'':169C,173C ''fr05/24/2014'':177C ''fruit'':113C ''gt'':119C,127C,135C,143C,151C,159C,183C,191C,197C ''horizont'':87C ''id'':65C,110C,167C ''individu'':78C ''learn'':45C ''licens'':37C ''line'':70C ''lt'':117C,125C,133C,141C,149C,157C,181C,189C,195C ''mine'':8B ''no-cost-to-m'':32C ''observ'':61C,75C ''p'':12C,48C,105C,162C ''per'':76C ''possibl'':51C ''pre'':108C,165C ''proc'':94C,97C ''r'':5A,9B,46C ''rather'':85C ''sas'':18C,89C ''set'':56C ''singl'':64C ''sql'':95C ''student'':29C ''time'':26C ''timefram'':112C ''transpos'':53C,98C ''transposit'':2A ''uniqu'':77C ''use'':93C ''ve'':14C ''veg'':115C ''veg05/23/2013'':171C,175C ''veg05/24/2014'':179C ''vertic'':84C ''want'':43C ''work'':16C ''year'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3743, 1, '2014-12-22 15:52:12.133', 2, 131, 'What does ''contextual'' mean in ''contextual bandits''?', '&lt;p&gt;I recently read a lot about the &lt;strong&gt;n-armed bandit problem&lt;/strong&gt; and its solution with various algorithms, for example for webscale content optimization. Some discussions were referring to ''&lt;strong&gt;contextual bandits&lt;/strong&gt;'', I couldn''t find a clear definition what the word ''contextual'' should mean here. Does anyone know what is meant by that, in contrast to ''usual'' bandits?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':74C ''/strong'':25C,46C ''algorithm'':31C ''anyon'':62C ''arm'':22C ''bandit'':7A,23C,45C,73C ''clear'':52C ''content'':36C ''contextu'':3A,6A,44C,57C ''contrast'':70C ''couldn'':48C ''definit'':53C ''discuss'':39C ''exampl'':33C ''find'':50C ''know'':63C ''learn'':10B ''lot'':16C ''machin'':9B ''machine-learn'':8B ''mean'':4A,59C ''meant'':66C ''n'':21C ''n-arm'':20C ''optim'':37C ''p'':11C ''problem'':24C ''read'':14C ''recent'':13C ''refer'':41C ''solut'':28C ''strong'':19C,43C ''usual'':72C ''various'':30C ''webscal'':35C ''word'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3745, 1, '2014-12-22 18:33:35.363', 1, 104, 'Bayesian Decision Tree', '&lt;p&gt;I was looking to learn about Bayesian theory in decision tree and how it avoids overfitting but couldn''t find any tutorials for someone just starting. Do you know any resources to learn about it?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':43C ''avoid'':22C ''bayesian'':1A,14C ''couldn'':25C ''decis'':2A,17C ''find'':27C ''know'':36C ''learn'':6B,12C,40C ''look'':10C ''machin'':5B ''machine-learn'':4B ''overfit'':23C ''p'':7C ''resourc'':38C ''someon'':31C ''start'':33C ''theori'':15C ''tree'':3A,18C ''tutori'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3746, 2, '2014-12-22 18:44:18.983', 2, NULL, NULL, '&lt;p&gt;A contextual bandit algorithm not only adapts to the user-click feedback as the algorithm progresses, it also utilizes pre-existing information about the user''s (and similar users) browsing patterns to select which content to display.&lt;/p&gt;

&lt;p&gt;So, rather than starting with no prediction (cold start) with what the user will click (traditional bandit and also traditional A/B testing), it takes other data into account (warm start) to help predict which content to display during the bandit test.&lt;/p&gt;

&lt;p&gt;See: &lt;a href="http://www.research.rutgers.edu/~lihong/pub/Li10Contextual.pdf" rel="nofollow"&gt;http://www.research.rutgers.edu/~lihong/pub/Li10Contextual.pdf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':41C,84C ''/~lihong/pub/li10contextual.pdf'':91C ''/~lihong/pub/li10contextual.pdf&lt;/a&gt;&lt;/p&gt;'':96C ''a/b'':63C ''account'':70C ''adapt'':8C ''algorithm'':5C,17C ''also'':20C,61C ''bandit'':4C,59C,82C ''brows'':33C ''click'':13C,57C ''cold'':50C ''content'':38C,77C ''contextu'':3C ''data'':68C ''display'':40C,79C ''exist'':24C ''feedback'':14C ''help'':74C ''href'':88C ''inform'':25C ''nofollow'':93C ''p'':1C,42C,85C ''pattern'':34C ''pre'':23C ''pre-exist'':22C ''predict'':49C,75C ''progress'':18C ''rather'':44C ''rel'':92C ''see'':86C ''select'':36C ''similar'':31C ''start'':46C,51C,72C ''take'':66C ''test'':64C,83C ''tradit'':58C,62C ''user'':12C,28C,32C,55C ''user-click'':11C ''util'':21C ''warm'':71C ''www.research.rutgers.edu'':90C,95C ''www.research.rutgers.edu/~lihong/pub/li10contextual.pdf'':89C ''www.research.rutgers.edu/~lihong/pub/li10contextual.pdf&lt;/a&gt;&lt;/p&gt;'':94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3747, 2, '2014-12-22 19:12:53.943', 3, NULL, NULL, '&lt;p&gt;Regarding your first question...&lt;/p&gt;

&lt;p&gt;Do you anticipate the majority category to be similarly over-represented in real-world data as it is in your training data? If so, perhaps you could perform two-step classification:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Train a binary classifier (on all your training data) to predict membership (yes/no) in the majority class.&lt;/li&gt;
&lt;li&gt;Train a multi-class classifier (on the rest of the training data) to predict membership in the remaining minority classes.&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':62C,85C ''/ol'':86C ''/p'':6C,42C ''anticip'':10C ''binari'':47C ''categori'':13C ''class'':61C,68C,84C ''classif'':41C ''classifi'':48C,69C ''could'':36C ''data'':24C,31C,53C,76C ''first'':4C ''li'':44C,63C ''major'':12C,60C ''membership'':56C,79C ''minor'':83C ''multi'':67C ''multi-class'':66C ''ol'':43C ''over-repres'':17C ''p'':1C,7C ''perform'':37C ''perhap'':34C ''predict'':55C,78C ''question'':5C ''real'':22C ''real-world'':21C ''regard'':2C ''remain'':82C ''repres'':19C ''rest'':72C ''similar'':16C ''step'':40C ''train'':30C,45C,52C,64C,75C ''two'':39C ''two-step'':38C ''world'':23C ''yes/no'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3761, 2, '2014-12-25 15:40:43.283', 2, NULL, NULL, '&lt;p&gt;A rule of thumb approach is:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;start with a number of hidden neurons equal (or little higher) that the number of features. &lt;/li&gt;
&lt;li&gt;In your case it would be 9. My suggestion is to start with 9*2 = 18 to cover a wider range of possibilities.&lt;/li&gt;
&lt;li&gt;Be sure your test and validation sets are selected "fairly": a random selection and varying the seed some number of times to test different configurations would be ok.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In general, a number of neurons equal to the number of features will tend to make each hidden neuron try to learn &lt;em&gt;that special thing&lt;/em&gt; that each feature is adding, so will could say it is "learning each feature" separately. Although this sounds good it might tend to overfitting. &lt;/p&gt;

&lt;p&gt;Since your number of inputs and your dataset size is small its ok to start with a hidden layer size of the double (18) and start lowering down. When the training error and test error stabilize in a difference lower than a threshold then you could have found a better generalizing model.&lt;/p&gt;

&lt;p&gt;Neural networks are very good at finding local optima by exploring deeply a solution from a starting point. However, the starting point it is also very important. If you are not getting a good generalization you might try to find good initial starting points with methods of Hybrid Neural Networks. A common one, for example, is using genetic algorithms to find an initial combination of weights and then start the neural from that point. Given that your search space would be better covered (in case your problem actually needs that).&lt;/p&gt;

&lt;p&gt;As for every problem in machine learning is very important to clean your data before introducing it to the NN. Try to be very detailed in order to avoid the NN to learn things you already know. For example if you know how two features are correlated improve the input data by making this correlation explicit so less workload is given to the NN (that might actually get you in trouble).&lt;/p&gt;
', NULL, NULL, '''/em'':111C ''/li'':27C,52C,82C ''/p'':8C,136C,190C,282C,355C ''/ul'':83C ''18'':44C,161C ''2'':43C ''9'':35C,42C ''actual'':279C,350C ''ad'':116C ''algorithm'':250C ''alreadi'':319C ''also'':216C ''although'':127C ''approach'':6C ''avoid'':312C ''better'':187C,273C ''case'':31C,276C ''clean'':295C ''combin'':255C ''common'':243C ''configur'':78C ''correl'':330C,338C ''could'':119C,183C ''cover'':46C,274C ''data'':297C,334C ''dataset'':145C ''deepli'':203C ''detail'':308C ''differ'':77C,176C ''doubl'':160C ''em'':107C ''equal'':18C,91C ''error'':169C,172C ''everi'':286C ''exampl'':246C,322C ''explicit'':339C ''explor'':202C ''fair'':63C ''featur'':26C,96C,114C,125C,328C ''find'':198C,231C,252C ''found'':185C ''general'':86C,188C,226C ''genet'':249C ''get'':223C,351C ''given'':266C,344C ''good'':130C,196C,225C,232C ''hidden'':16C,102C,155C ''higher'':21C ''howev'':210C ''hybrid'':239C ''import'':218C,293C ''improv'':331C ''initi'':233C,254C ''input'':142C,333C ''introduc'':299C ''know'':320C,325C ''layer'':156C ''learn'':106C,123C,290C,316C ''less'':341C ''li'':10C,28C,53C ''littl'':20C ''local'':199C ''lower'':164C,177C ''machin'':289C ''make'':100C,336C ''method'':237C ''might'':132C,228C,349C ''model'':189C ''need'':280C ''network'':193C,241C ''neural'':192C,240C,262C ''neuron'':17C,90C,103C ''nn'':303C,314C,347C ''number'':14C,24C,72C,88C,94C,140C ''ok'':81C,150C ''one'':244C ''optima'':200C ''order'':310C ''overfit'':135C ''p'':1C,84C,137C,191C,283C ''point'':209C,213C,235C,265C ''possibl'':51C ''problem'':278C,287C ''random'':65C ''rang'':49C ''rule'':3C ''say'':120C ''search'':269C ''seed'':70C ''select'':62C,66C ''separ'':126C ''set'':60C ''sinc'':138C ''size'':146C,157C ''small'':148C ''solut'':205C ''sound'':129C ''space'':270C ''special'':109C ''stabil'':173C ''start'':11C,40C,152C,163C,208C,212C,234C,260C ''suggest'':37C ''sure'':55C ''tend'':98C,133C ''test'':57C,76C,171C ''thing'':110C,317C ''threshold'':180C ''thumb'':5C ''time'':74C ''train'':168C ''tri'':104C,229C,304C ''troubl'':354C ''two'':327C ''ul'':9C ''use'':248C ''valid'':59C ''vari'':68C ''weight'':257C ''wider'':48C ''workload'':342C ''would'':33C,79C,271C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3752, 2, '2014-12-23 17:10:10.777', 1, NULL, NULL, '&lt;p&gt;Regarding the being new to decision trees and wanting to get off the ground, I wrote &lt;a href="http://thegrimmscientist.com/2014/10/23/tutorial-decision-trees/" rel="nofollow"&gt;a tutorial on decision trees&lt;/a&gt; that will help.&lt;/p&gt;

&lt;p&gt;Regarding methods to avoid overfitting:  The game for any model is to limit its complexity to what is reasonable given the data you have.  Complexity in decision trees is manifested as adding new decision boundaries, so any limit in complexity is a limit in the decision boundaries it can draw.  Two common ways to do this is to place constraints on when a new decision can be created (a minimum of data in a leaf, significant increase in information, etc) or more simply to limit the max depth of the tree.&lt;/p&gt;
', NULL, NULL, '''/2014/10/23/tutorial-decision-trees/'':22C ''/a'':30C ''/p'':34C,127C ''ad'':67C ''avoid'':39C ''boundari'':70C,82C ''common'':87C ''complex'':50C,60C,75C ''constraint'':95C ''creat'':103C ''data'':57C,107C ''decis'':7C,28C,62C,69C,81C,100C ''depth'':123C ''draw'':85C ''etc'':115C ''game'':42C ''get'':12C ''given'':55C ''ground'':15C ''help'':33C ''href'':19C ''increas'':112C ''inform'':114C ''leaf'':110C ''limit'':48C,73C,78C,120C ''manifest'':65C ''max'':122C ''method'':37C ''minimum'':105C ''model'':45C ''new'':5C,68C,99C ''nofollow'':24C ''overfit'':40C ''p'':1C,35C ''place'':94C ''reason'':54C ''regard'':2C,36C ''rel'':23C ''signific'':111C ''simpli'':118C ''thegrimmscientist.com'':21C ''thegrimmscientist.com/2014/10/23/tutorial-decision-trees/'':20C ''tree'':8C,29C,63C,126C ''tutori'':26C ''two'':86C ''want'':10C ''way'':88C ''wrote'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4900, 2, '2015-01-18 19:14:41.42', 1, NULL, NULL, '&lt;p&gt;If I''ve understood your question correctly, one easy solution would be to concatenate the bits together and make a 7-bit binary sequence then convert it to integer. So, for your sample dataset quoted in the question you would get:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;
CM
16
0
98
8
6
67
&lt;/code&gt;&lt;/p&gt;
', NULL, NULL, '''/code'':53C ''/p'':43C,54C ''0'':48C ''16'':47C ''6'':51C ''67'':52C ''7'':22C ''8'':50C ''98'':49C ''binari'':24C ''bit'':17C,23C ''cm'':46C ''code'':45C ''concaten'':15C ''convert'':27C ''correct'':8C ''dataset'':35C ''easi'':10C ''get'':42C ''integ'':30C ''make'':20C ''one'':9C ''p'':1C,44C ''question'':7C,39C ''quot'':36C ''sampl'':34C ''sequenc'':25C ''solut'':11C ''togeth'':18C ''understood'':5C ''ve'':4C ''would'':12C,41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3753, 2, '2014-12-23 18:11:24.117', 0, NULL, NULL, '&lt;p&gt;You can use the &lt;code&gt;reshape2&lt;/code&gt; package for this task.&lt;/p&gt;

&lt;p&gt;First, transform the data to the long format with &lt;code&gt;melt&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(reshape2)
dat_m &amp;lt;- melt(dat, measure.vars = c("fruit_amt", "veg_amt"))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where &lt;code&gt;dat&lt;/code&gt; is the name of your data frame.&lt;/p&gt;

&lt;p&gt;Second, cast to the wide format:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dcast(dat_m, ID ~ timeframe + variable + date)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The result:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    ID after_fruit_amt_04/16/2014 after_fruit_amt_05/23/2013 after_fruit_amt_05/24/2014 after_veg_amt_04/16/2014
1 4352                          0                       0.06                         NA                        0
2 5002                         NA                         NA                       0.06                       NA
  after_veg_amt_05/23/2013 after_veg_amt_05/24/2014 before_fruit_amt_05/23/2013 before_fruit_amt_05/24/2014
1                     0.25                       NA                        0.25                          NA
2                       NA                     0.25                          NA                        0.75
  before_veg_amt_05/23/2013 before_veg_amt_05/24/2014
1                      0.75                        NA
2                        NA                      0.25
&amp;gt; 
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':8C,26C,43C,49C,75C,153C ''/p'':13C,27C,57C,65C,80C ''/pre'':44C,76C,154C ''0'':102C,105C ''0.06'':103C,110C ''0.25'':129C,131C,135C,151C ''0.75'':137C,147C ''04/16/2014'':87C,99C ''05/23/2013'':91C,115C,123C,141C ''05/24/2014'':95C,119C,127C,145C ''1'':100C,128C,146C ''2'':106C,133C,149C ''4352'':101C ''5002'':107C ''amt'':40C,42C,86C,90C,94C,98C,114C,118C,122C,126C,140C,144C ''c'':38C ''cast'':60C ''code'':6C,24C,29C,47C,67C,82C ''dat'':32C,36C,48C,69C ''data'':18C,55C ''date'':74C ''dcast'':68C ''first'':15C ''format'':22C,64C ''frame'':56C ''fruit'':39C,85C,89C,93C,121C,125C ''gt'':152C ''id'':71C,83C ''librari'':30C ''long'':21C ''lt'':34C ''m'':33C,70C ''measure.vars'':37C ''melt'':25C,35C ''na'':104C,108C,109C,111C,130C,132C,134C,136C,148C,150C ''name'':52C ''p'':1C,14C,45C,58C,77C ''packag'':9C ''pre'':28C,66C,81C ''reshape2'':7C,31C ''result'':79C ''second'':59C ''task'':12C ''timefram'':72C ''transform'':16C ''use'':4C ''variabl'':73C ''veg'':41C,97C,113C,117C,139C,143C ''wide'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9063, 1, '2015-11-26 13:07:02.697', 2, 82, 'How to extract important phrases (which may contain company name) from resume?', '&lt;p&gt;I have thousands of CV / resumes with me. We want to build a parser which can extract company names from resume. &lt;/p&gt;

&lt;p&gt;So far we have tried&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Maintained a list of common words present in companies (Eg. Org, Ltd, Limited, Technologies etc.) and use them to identify probable companies. But this list is limited and many times many companies don''t get extracted.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Using HTML of CV we have tried to give more score to probable companies which have a certain feature (like Bold, Italics) &lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Since CV is not only text and we always have some structural information along with it. There should be better ways to extract information. Maybe training some model which could predict companies mentioned in the resume. We are open to any better approaches/suggestions we could incorporate into our system for better accuracy. The precision so far is really bad (less than 45%). &lt;/p&gt;

&lt;p&gt;We have already done the segmentation of work experience in CV. So we are able to extract the segment containing work experience with very high precision.&lt;/p&gt;

&lt;p&gt;We also have a comprehensive list of companies (millions). Although it contains duplicates and needs significant amount of cleaning. But yes we have a lot of data&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Other approaches we are trying - 
We try to predict the important phrases inside text using N-Grams and then mark them as probable companies. Then we do a search on companies corpus with us to find any match. How useful is this technique ? Any better approaches ?&lt;/p&gt;
', 'machine-learning  data-mining  nlp  parsing', NULL, '''/li'':91C,117C ''/ol'':118C ''/p'':43C,50C,90C,116C,182C,210C,239C,244C,293C ''/strong'':243C ''45'':181C ''abl'':198C ''accuraci'':171C ''along'':133C ''alreadi'':186C ''also'':213C ''although'':221C ''alway'':128C ''amount'':228C ''approach'':247C,292C ''approaches/suggestions'':162C ''bad'':178C ''better'':139C,161C,170C,291C ''bold'':114C ''build'':33C ''certain'':111C ''clean'':230C ''common'':58C ''compani'':9A,39C,62C,75C,85C,107C,151C,219C,270C,277C ''comprehens'':216C ''contain'':8A,203C,223C ''corpus'':278C ''could'':149C,164C ''cv'':26C,97C,121C,194C ''data'':17B,238C ''data-min'':16B ''done'':187C ''duplic'':224C ''edit'':242C ''eg'':63C ''etc'':68C ''experi'':192C,205C ''extract'':3A,38C,89C,142C,200C ''far'':46C,175C ''featur'':112C ''find'':282C ''get'':88C ''give'':102C ''gram'':263C ''high'':208C ''html'':95C ''identifi'':73C ''import'':4A,256C ''incorpor'':165C ''inform'':132C,143C ''insid'':258C ''ital'':115C ''learn'':15B ''less'':179C ''li'':52C,92C ''like'':113C ''limit'':66C,80C ''list'':56C,78C,217C ''lot'':236C ''ltd'':65C ''machin'':14B ''machine-learn'':13B ''maintain'':54C ''mani'':82C,84C ''mark'':266C ''match'':284C ''may'':7A ''mayb'':144C ''mention'':152C ''million'':220C ''mine'':18B ''model'':147C ''n'':262C ''n-gram'':261C ''name'':10A,40C ''need'':226C ''nlp'':19B ''ol'':51C ''open'':158C ''org'':64C ''p'':21C,44C,53C,93C,119C,183C,211C,240C,245C ''pars'':20B ''parser'':35C ''phrase'':5A,257C ''precis'':173C,209C ''predict'':150C,254C ''present'':60C ''probabl'':74C,106C,269C ''realli'':177C ''resum'':12A,27C,42C,155C ''score'':104C ''search'':275C ''segment'':189C,202C ''signific'':227C ''sinc'':120C ''strong'':241C ''structur'':131C ''system'':168C ''techniqu'':289C ''technolog'':67C ''text'':125C,259C ''thousand'':24C ''time'':83C ''train'':145C ''tri'':49C,100C,250C,252C ''us'':280C ''use'':70C,94C,260C,286C ''want'':31C ''way'':140C ''word'':59C ''work'':191C,204C ''yes'':232C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4830, 2, '2015-01-08 07:55:31.067', 0, NULL, NULL, '&lt;p&gt;I think one has to be very careful when storing textual data. If they are user comments then, for security concerns it''s better if it is encoded in some format before storage.
A protobuf object can then be defined to resolve the encoding. &lt;/p&gt;

&lt;p&gt;Depending on the query pattern, and accepted latency in retrieval of the data, DB should be decided. Just a recommendation, if the idea is to store comments over a period of time for each user, consider HBase or Cassandra. They are optimized for time range queries.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Recommend read:&lt;/strong&gt; &lt;a href="http://info.mapr.com/rs/mapr/images/Time_Series_Databases.pdf" rel="nofollow"&gt;http://info.mapr.com/rs/mapr/images/Time_Series_Databases.pdf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':46C,94C ''/rs/mapr/images/time_series_databases.pdf'':104C ''/rs/mapr/images/time_series_databases.pdf&lt;/a&gt;&lt;/p&gt;'':109C ''/strong'':99C ''accept'':54C ''better'':25C ''care'':9C ''cassandra'':86C ''comment'':18C,74C ''concern'':22C ''consid'':83C ''data'':13C,60C ''db'':61C ''decid'':64C ''defin'':41C ''depend'':48C ''encod'':29C,45C ''format'':32C ''hbase'':84C ''href'':101C ''idea'':70C ''info.mapr.com'':103C,108C ''info.mapr.com/rs/mapr/images/time_series_databases.pdf'':102C ''info.mapr.com/rs/mapr/images/time_series_databases.pdf&lt;/a&gt;&lt;/p&gt;'':107C ''latenc'':55C ''nofollow'':106C ''object'':37C ''one'':4C ''optim'':89C ''p'':1C,47C,95C ''pattern'':52C ''period'':77C ''protobuf'':36C ''queri'':51C,93C ''rang'':92C ''read'':98C ''recommend'':67C,97C ''rel'':105C ''resolv'':43C ''retriev'':57C ''secur'':21C ''storag'':34C ''store'':11C,73C ''strong'':96C ''textual'':12C ''think'':3C ''time'':79C,91C ''user'':17C,82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3754, 1, '2014-12-23 19:46:57.267', 1, 108, 'Hadoop/Pig Aggregate Data', '&lt;p&gt;I am working on a project with two data sets. A time vs. speed data set (let''s call it traffic), and a time vs. weather data set (called weather). &lt;/p&gt;

&lt;p&gt;I am looking to find a correlation between these two sets using Pig. However the traffic data set has the time field, D/M/Y hr:min:sec, and the weather data set has the time field, D/M/Y. &lt;/p&gt;

&lt;p&gt;Due to this I would like to average the speed per day and put it into a single D/M/Y value inside the traffic file.&lt;/p&gt;

&lt;p&gt;I then plan to use:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data = JOIN speed BY day, JOIN weather BY day with ''merge''
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I will then find the correlation using: (I am borrowing this code from elsewhere)&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;set = LOAD ''data.txt'' AS (speed:double, weather:double)
rel = GROUP set ALL
cor = FOREACH rel GENERATE COR(set.speed, set.weather)
dump cor;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This is my first experience with Pig (I''ve never even used SQL), so I would like to know a few things:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1. How can I merge the rows of my traffic file (ie. average D/M/Y hr:min:sec into D/M/Y)?
2. Is there a better way to find a correlation between the fields of different datasets?
3. Are the JOIN BY and the COR() functions used appropriately in my above code?  
&lt;/code&gt;&lt;/pre&gt;
', 'hadoop  beginner  correlation  apache-pig', NULL, '''/code'':126C,167C,245C ''/p'':41C,79C,105C,112C,143C,192C ''/pre'':127C,168C,246C ''1'':195C ''2'':214C ''3'':230C ''aggreg'':2A ''apach'':8B ''apache-pig'':7B ''appropri'':240C ''averag'':88C,207C ''beginn'':5B ''better'':218C ''borrow'':138C ''call'':29C,39C ''code'':114C,140C,145C,194C,244C ''cor'':158C,162C,166C,237C ''correl'':6B,49C,134C,223C ''d/m/y'':65C,78C,99C,208C,213C ''data'':3A,19C,25C,37C,59C,72C,115C ''data.txt'':148C ''dataset'':229C ''day'':92C,119C,123C ''differ'':228C ''doubl'':151C,153C ''due'':81C ''dump'':165C ''elsewher'':142C ''even'':180C ''experi'':174C ''field'':64C,77C,226C ''file'':104C,205C ''find'':47C,132C,221C ''first'':173C ''foreach'':159C ''function'':238C ''generat'':161C ''group'':155C ''hadoop'':4B ''hadoop/pig'':1A ''howev'':56C ''hr'':66C,209C ''ie'':206C ''insid'':101C ''join'':116C,120C,233C ''know'':188C ''let'':27C ''like'':86C,186C ''load'':147C ''look'':45C ''merg'':125C,199C ''min'':67C,210C ''never'':179C ''p'':10C,42C,80C,106C,128C,169C ''per'':91C ''pig'':9B,55C,176C ''plan'':109C ''pre'':113C,144C,193C ''project'':16C ''put'':94C ''rel'':154C,160C ''row'':201C ''sec'':68C,211C ''set'':20C,26C,38C,53C,60C,73C,146C,156C ''set.speed'':163C ''set.weather'':164C ''singl'':98C ''speed'':24C,90C,117C,150C ''sql'':182C ''thing'':191C ''time'':22C,34C,63C,76C ''traffic'':31C,58C,103C,204C ''two'':18C,52C ''use'':54C,111C,135C,181C,239C ''valu'':100C ''ve'':178C ''vs'':23C,35C ''way'':219C ''weather'':36C,40C,71C,121C,152C ''work'':13C ''would'':85C,185C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3762, 2, '2014-12-25 18:10:13.447', 0, NULL, NULL, '&lt;h2&gt;Top level:&lt;/h2&gt;

&lt;p&gt;The rule is to chose the most simple network that can perform satisfactorily.  See &lt;a href="http://ieeexplore.ieee.org/xpl/login.jsp?reload=true&amp;amp;tp=&amp;amp;arnumber=5726612&amp;amp;url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5726612" rel="nofollow"&gt;this publication&lt;/a&gt; and it''s &lt;a href="http://www.pdx.edu/sites/www.pdx.edu.sysc/files/EvaluatingANNs.pdf" rel="nofollow"&gt;pdf&lt;/a&gt;.  &lt;/p&gt;

&lt;h2&gt;The Methodology:&lt;/h2&gt;

&lt;p&gt;So do your proposed test (training many networks at each number of hidden nodes) and plot the results.  At the minimum number of nodes, you''ll see the worst performance.  As you increase the number of nodes, you''ll see an increase in performance (reduction of error).  At some point N, you''ll see the performance seems to hit an upper limit and increasing nodes beyond this will stop giving significant performance gains.  Further increases may start to hurt performance a little as training gets more difficult).  That point N is the number of nodes you want.&lt;/p&gt;

&lt;h1&gt;How it worked for me:&lt;/h1&gt;

&lt;p&gt;The first time I used this methodology, it created a beautiful almost-sigmoid-looking function with a very clear number of nodes that were needed to achieve good results.  I hope this works for you as well as it worked for me.&lt;/p&gt;
', NULL, NULL, '''/a'':29C,41C ''/h1'':152C ''/h2'':4C,46C ''/p'':42C,145C,197C ''/sites/www.pdx.edu.sysc/files/evaluatinganns.pdf'':37C ''/xpl/login.jsp?reload=true&amp;amp;tp=&amp;amp;arnumber=5726612&amp;amp;url=http%3a%2f%2fieeexplore.ieee.org%2fxpls%2fabs_all.jsp%3farnumber%3d5726612'':24C ''achiev'':181C ''almost'':166C ''almost-sigmoid-look'':165C ''beauti'':164C ''beyond'':113C ''chose'':10C ''clear'':173C ''creat'':162C ''difficult'':134C ''error'':94C ''first'':155C ''function'':169C ''gain'':120C ''get'':132C ''give'':117C ''good'':182C ''h1'':146C ''h2'':1C,43C ''hidden'':60C ''hit'':106C ''hope'':185C ''href'':21C,34C ''hurt'':126C ''ieeexplore.ieee.org'':23C ''ieeexplore.ieee.org/xpl/login.jsp?reload=true&amp;amp;tp=&amp;amp;arnumber=5726612&amp;amp;url=http%3a%2f%2fieeexplore.ieee.org%2fxpls%2fabs_all.jsp%3farnumber%3d5726612'':22C ''increas'':80C,89C,111C,122C ''level'':3C ''limit'':109C ''littl'':129C ''ll'':73C,86C,100C ''look'':168C ''mani'':54C ''may'':123C ''methodolog'':45C,160C ''minimum'':68C ''n'':98C,137C ''need'':179C ''network'':14C,55C ''node'':61C,71C,84C,112C,142C,176C ''nofollow'':26C,39C ''number'':58C,69C,82C,140C,174C ''p'':5C,47C,153C ''pdf'':40C ''perform'':17C,77C,91C,103C,119C,127C ''plot'':63C ''point'':97C,136C ''propos'':51C ''public'':28C ''reduct'':92C ''rel'':25C,38C ''result'':65C,183C ''rule'':7C ''satisfactorili'':18C ''see'':19C,74C,87C,101C ''seem'':104C ''sigmoid'':167C ''signific'':118C ''simpl'':13C ''start'':124C ''stop'':116C ''test'':52C ''time'':156C ''top'':2C ''train'':53C,131C ''upper'':108C ''use'':158C ''want'':144C ''well'':191C ''work'':149C,187C,194C ''worst'':76C ''www.pdx.edu'':36C ''www.pdx.edu/sites/www.pdx.edu.sysc/files/evaluatinganns.pdf'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3757, 2, '2014-12-24 13:46:47.367', 2, NULL, NULL, '&lt;p&gt;Apart from the fancier methods you could try the Bayes formula&lt;/p&gt;

&lt;p&gt;P(I | p1 ... pn) =   P(p1 ... pn | I) P(I) / sum_i (P(p1 ... pn | i) P(i))&lt;/p&gt;

&lt;p&gt;P(I | p1 ... pn) is the probability that a user belongs to age group I if he liked p1, .., pn&lt;/p&gt;

&lt;p&gt;P(i) is the probability that a user belongs to age group i&lt;/p&gt;

&lt;p&gt;P(p1 .. pn | i) is the probability that a user liked p1, .., pn if he belongs to age group i.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;You already have the estimates for P(i) from your data: this is just the proportion of users in age group I. &lt;/li&gt;
&lt;li&gt;&lt;p&gt;To estimate P(p1 ... pn |i), for each age group i estimate the probability (frequency) p_ij to like a page j. To have p_ij non-zero for all j, you can mix in the frequency for the whole population with a small weight.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Then log P(p1...pn| i) = sum(log p_ij, i = p1, .., pn), the sum over all pages that a new user likes. This formula would be approximately true assuming that a user likes the pages in his age group independently. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;Theoretically, you should also add log (1-p_ij) for all i that he hasn''t liked, but in practice you should find that the sum of log (1-p_ij) will be irrelevantly small, so you won''t need too much memory.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If you or someone else has tried this, please comment about the result.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3758, 2, '2014-12-24 20:02:42.213', 2, NULL, NULL, '&lt;p&gt;The larger your target scores, the larger latent variables should be (well, it''s not only a magnitude that matters, but also a variance, but it still applies to your case). There''s no problem with larger coefficients of latent vectors unless you use regularization (and, likely, you do). In case of regularization your optimal solution will tend towards smaller values, and''d sometimes prefer to sacrifice some accuracy for lower regularization penalty.&lt;/p&gt;

&lt;p&gt;Gradient Descent doesn''t suffer from problem of large coefficients (unless you run into some sort of numerical issues): if the learning rate is tuned properly (there are lots of stuff on it, google), it should arrive to equivalent parameters. Otherwise nobody guarantees you convergence :-)&lt;/p&gt;

&lt;p&gt;The common rule of thumb when doing regression (and your instance of matrix factorization is a kind of regression) is to standardize your data: make it having zero mean and unit variance.&lt;/p&gt;
', NULL, NULL, '''/p'':75C,122C,156C ''accuraci'':70C ''also'':23C ''appli'':29C ''arriv'':113C ''case'':32C,52C ''coeffici'':39C,86C ''common'':125C ''converg'':121C ''d'':64C ''data'':147C ''descent'':78C ''doesn'':79C ''equival'':115C ''factor'':137C ''googl'':110C ''gradient'':77C ''guarante'':119C ''instanc'':134C ''issu'':95C ''kind'':140C ''larg'':85C ''larger'':3C,8C,38C ''latent'':9C,41C ''learn'':98C ''like'':48C ''lot'':105C ''lower'':72C ''magnitud'':19C ''make'':148C ''matrix'':136C ''matter'':21C ''mean'':152C ''nobodi'':118C ''numer'':94C ''optim'':56C ''otherwis'':117C ''p'':1C,76C,123C ''paramet'':116C ''penalti'':74C ''prefer'':66C ''problem'':36C,83C ''proper'':102C ''rate'':99C ''regress'':131C,142C ''regular'':46C,54C,73C ''rule'':126C ''run'':89C ''sacrific'':68C ''score'':6C ''smaller'':61C ''solut'':57C ''sometim'':65C ''sort'':92C ''standard'':145C ''still'':28C ''stuff'':107C ''suffer'':81C ''target'':5C ''tend'':59C ''thumb'':128C ''toward'':60C ''tune'':101C ''unit'':154C ''unless'':43C,87C ''use'':45C ''valu'':62C ''variabl'':10C ''varianc'':25C,155C ''vector'':42C ''well'':13C ''zero'':151C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3759, 1, '2014-12-25 05:24:55.59', 1, 42, 'How are the findings learnt from the data set are generalized compared to Statistics?', '&lt;p&gt;I am new to data science/ machine learning world. I know that in Statistics we assume that a certain event/ process has some particular distribution and the samples of that random process are part of some sampling distribution. The findings from the data could then be generalized by using confidence intervals and significance levels.&lt;/p&gt;

&lt;p&gt;How do we generalize our findings once we "learn" the patterns in the data set? What is the alternative to confidence levels here?&lt;/p&gt;
', 'machine-learning  statistics', NULL, '''/p'':74C,99C ''altern'':94C ''assum'':35C ''certain'':38C ''compar'':12A ''confid'':69C,96C ''could'':63C ''data'':8A,24C,62C,89C ''distribut'':44C,57C ''event'':39C ''find'':4A,59C,81C ''general'':11A,66C,79C ''interv'':70C ''know'':30C ''learn'':17B,27C,84C ''learnt'':5A ''level'':73C,97C ''machin'':16B,26C ''machine-learn'':15B ''new'':22C ''p'':19C,75C ''part'':53C ''particular'':43C ''pattern'':86C ''process'':40C,51C ''random'':50C ''sampl'':47C,56C ''scienc'':25C ''set'':9A,90C ''signific'':72C ''statist'':14A,18B,33C ''use'':68C ''world'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5092, 1, '2015-02-09 23:22:05.547', 1, 66, 'design pattern for extracting features', '&lt;p&gt;I am looking for a design pattern that is relevant to a module that extracts features.
I want to define a certain number of features over my data points, and then according to the performance and the feature selection, I may want to remove some of them and add others, and also I may want to consider any subsets of them to test.&lt;/p&gt;

&lt;p&gt;What is a good design pattern to do that? Did I miss something obvious? I am neither an engineer nor a developer, so I never study such things but I understand that it could help me a lot!&lt;/p&gt;

&lt;p&gt;Thanks for any help,&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3760, 1, '2014-12-25 10:13:16.273', 1, 134, 'Neural Network Hidden Neuron Selection Strategy', '&lt;p&gt;I''m trying to determine what is the best number of hidden neurons for my MATLAB neural network. I was thinking to adopt the following strategy:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Loop for some values of hidden neurons, e.g. 1 to 40;&lt;/li&gt;
&lt;li&gt;For each NN with a fixed number of hidden neurons, perform a certain number of   training (e.g. 40, limiting the number of epoch for time reasons: I was thinking to doing this because the network seems to be hard to train, the MSE after some epochs is very high)&lt;/li&gt;
&lt;li&gt;Store the MSE obtained with all the nets with different number of hidden neurons&lt;/li&gt;
&lt;li&gt;Perform the previous procedure more than 1 time, e.g. 4, to take into account the initial random weight, and take the average of the MSEs&lt;/li&gt;
&lt;li&gt;Select and perform the "real" training on a NN with a number of hidden neurons such that the MSE previously calculated is minimized&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The MSE that I''m referring is the validation MSE: my samples splitting in trainining, testing and validation to avoid overfitting is 70%, 15% and 15% respectively)&lt;/p&gt;

&lt;p&gt;Other informations related to my problem are:&lt;br&gt;
fitting problem&lt;br&gt;
9 input neurons&lt;br&gt;
2 output neurons&lt;br&gt;
1630 samples  &lt;/p&gt;

&lt;p&gt;This strategy could be work? Is there any better criterion to adopt? Thank you&lt;/p&gt;

&lt;p&gt;Edit: Test done, so the result suggest me to adopt 12 neurons? (low validation MSE and  number of neurons lower than 2*numberOfInputNeurons? but also 18 could be good...
&lt;img src="http://i.stack.imgur.com/qxOSV.png" alt="enter image description here"&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3771, 1, '2014-12-28 14:42:52.403', 0, 98, 'Time series: variations as a feature', '&lt;p&gt;I am trying to predict clients comportement from market rates. &lt;/p&gt;

&lt;p&gt;The value of the products depends on the actual rate but this is not enough. The comportement of the client also depends on their awareness wich depends on the evolution of rates. I''ve added this in model using past 6 month rates as features in polynomial regression. &lt;/p&gt;

&lt;p&gt;In fact media coverage of rate mostly depends on rate variations and I wanted to add that in my model.  The idea would be to add a derivative/variation of rate as a feature. But I anticipated something wrong, example with only two month , my variation will be of the form $x_n - x_{n-1}$ that is a simple linear combination of actual and past rates. So for a 1d polynomial regression i will have:&lt;/p&gt;

&lt;p&gt;$$ x_{n+1} = a * x_{n} +  b * x_{n-1} + c * (x_{n} - x_{n-1})$$ &lt;/p&gt;

&lt;p&gt;instead of: &lt;/p&gt;

&lt;p&gt;$$ x_{n+1} = a_0 * x_{n} +  b_0 * x_{n-1}$$&lt;/p&gt;

&lt;p&gt;wich is strictly equivalent with $ a + c = a_0 $ and $b-c= b_0$. Higher polynomial degree results in a more or less equivalent result. &lt;/p&gt;

&lt;p&gt;I am thinking about a way to include derivative information but it seems not possible. So I am wondering if all the information is included in my curve. Is this a general idea ? all information is somewhat directly contained in data and modifications of features will result in higher order objective function ?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3763, 1, '2014-12-26 06:53:29.67', 4, 120, 'How to apply AdaBoost to more "complex" (non-binary) classifications/data fitting?', '&lt;p&gt;In &lt;a href="http://www.wired.com/2014/01/how-to-hack-okcupid/all/" rel="nofollow"&gt;this&lt;/a&gt; article, Chris McKinlay says he used AdaBoost to choose the proper "importances" of questions he answered on okcupid.&lt;/p&gt;

&lt;p&gt;If you haven''t read and don''t want to read the article, or are unfamiliar with okcupid and the question system, here''s the data and problem he had:
The goal is to "match" as highly as possible with as many users as possible, each of whom may have answered an arbitrary number of questions. These questions may have between 2 and 4 answers each, and for the sake of simplicity, let''s pretend that the formula for a match% $\ M $ between you and another user is given by&lt;/p&gt;

&lt;p&gt;$\ M = Q_a/Q_c $&lt;/p&gt;

&lt;p&gt;Where $\ Q_c $ is the number of questions you and the other user have in common, and&lt;/p&gt;

&lt;p&gt;$\ Q_a $ is the number of questions you both answered with the same value.&lt;/p&gt;

&lt;p&gt;The real formula is slightly more complex, but the approach would be the same regarding "picking" a correct answer (he actually used boosting to find the ideal "importance" to place on a given question, rather than the right answer).
In any case, the point is you want to pick a certain value for each question, such that you maximize your match% with as many users as possible - something you might quantify by the sum of $\ M $ over all users.&lt;/p&gt;

&lt;p&gt;Now I''ve watched the MIT course on AI up to and including the lecture on boosting, but I don''t understand how you would apply it to a problem like this. Honestly I don''t even know where to begin with choosing rules for the weak learners. I don''t have any "rules" about what values to choose for each question (if the user is under 5''5, choose A, etc) - I''m just trying to fit the data I have.&lt;/p&gt;

&lt;p&gt;Is this not the way boosting is supposed to be used? Is there likely some other optimization left out of how he figured this out?&lt;/p&gt;
', 'machine-learning  bigdata  optimization', NULL, '''/2014/01/how-to-hack-okcupid/all/'':24C ''/a'':28C ''/p'':47C,139C,144C,163C,179C,260C,344C,371C ''2'':110C ''4'':112C ''5'':329C,330C ''a/q_c'':143C ''actual'':201C ''adaboost'':4A,35C ''ai'':270C ''anoth'':134C ''answer'':44C,99C,113C,174C,199C,219C ''appli'':3A,287C ''approach'':190C ''arbitrari'':101C ''articl'':29C,61C ''begin'':302C ''bigdata'':16B ''binari'':10A ''boost'':203C,278C,351C ''c'':148C ''case'':222C ''certain'':231C ''choos'':37C,304C,320C,331C ''chris'':30C ''classifications/data'':11A ''common'':161C ''complex'':7A,187C ''correct'':198C ''cours'':268C ''data'':74C,341C ''etc'':333C ''even'':298C ''figur'':368C ''find'':205C ''fit'':12A,339C ''formula'':126C,183C ''given'':137C,213C ''goal'':80C ''haven'':51C ''high'':85C ''honest'':294C ''href'':21C ''ideal'':207C ''import'':40C,208C ''includ'':274C ''know'':299C ''learn'':15B ''learner'':309C ''lectur'':276C ''left'':363C ''let'':121C ''like'':292C,359C ''m'':130C,141C,256C,335C ''machin'':14B ''machine-learn'':13B ''mani'':90C,244C ''match'':83C,129C,241C ''maxim'':239C ''may'':97C,107C ''mckinlay'':31C ''might'':250C ''mit'':267C ''nofollow'':26C ''non'':9A ''non-binari'':8A ''number'':102C,151C,169C ''okcupid'':46C,66C ''optim'':17B,362C ''p'':18C,48C,140C,145C,164C,180C,261C,345C ''pick'':196C,229C ''place'':210C ''point'':224C ''possibl'':87C,93C,247C ''pretend'':123C ''problem'':76C,291C ''proper'':39C ''q'':142C,147C,165C ''quantifi'':251C ''question'':42C,69C,104C,106C,153C,171C,214C,235C,323C ''rather'':215C ''read'':53C,59C ''real'':182C ''regard'':195C ''rel'':25C ''right'':218C ''rule'':305C,315C ''sake'':118C ''say'':32C ''simplic'':120C ''slight'':185C ''someth'':248C ''sum'':254C ''suppos'':353C ''system'':70C ''tri'':337C ''understand'':283C ''unfamiliar'':64C ''use'':34C,202C,356C ''user'':91C,135C,158C,245C,259C,326C ''valu'':178C,232C,318C ''ve'':264C ''want'':57C,227C ''watch'':265C ''way'':350C ''weak'':308C ''would'':191C,286C ''www.wired.com'':23C ''www.wired.com/2014/01/how-to-hack-okcupid/all/'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3764, 2, '2014-12-26 10:01:46.5', 3, NULL, NULL, '&lt;p&gt;After reading your question, I became curious about the topic of &lt;em&gt;time series clustering&lt;/em&gt; and &lt;em&gt;dynamic time warping (DTW)&lt;/em&gt;. So, I have performed a limited search and came up with basic understanding (for me) and the following set of IMHO &lt;em&gt;relevant references&lt;/em&gt; (for you). I hope that you''ll find this useful, but keep in mind that I have intentionally skipped research papers, as I was more interested in &lt;em&gt;practical aspects&lt;/em&gt; of the topic.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Resources:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;UCR Time Series Classification/Clustering&lt;/strong&gt;: &lt;a href="http://www.cs.ucr.edu/~eamonn/time_series_data" rel="nofollow"&gt;main page&lt;/a&gt;, &lt;a href="http://www.cs.ucr.edu/~eamonn/UCRsuite.html" rel="nofollow"&gt;software page&lt;/a&gt; and &lt;a href="http://www.cs.ucr.edu/~eamonn/SIGKDD_trillion.pdf" rel="nofollow"&gt;corresponding paper&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Time Series Classification and Clustering with Python&lt;/strong&gt;: &lt;a href="http://alexminnaar.com/2014/04/06/time-series-classification-and-clustering-with-python" rel="nofollow"&gt;a blog post&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Capital Bikeshare: Time Series Clustering&lt;/strong&gt;: &lt;a href="http://ofdataandscience.blogspot.com/2013/03/capital-bikeshare-time-series-clustering.html" rel="nofollow"&gt;another blog post&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Time Series Classification and Clustering&lt;/strong&gt;: &lt;a href="http://nbviewer.ipython.org/github/alexminnaar/time-series-classification-and-clustering/blob/master/Time%20Series%20Classification%20and%20Clustering.ipynb" rel="nofollow"&gt;ipython notebook&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Dynamic Time Warping using rpy and Python&lt;/strong&gt;: &lt;a href="https://nipunbatra.wordpress.com/2013/06/09/dynamic-time-warping-using-rpy-and-python" rel="nofollow"&gt;another blog post&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Mining Time-series with Trillions of Points: Dynamic Time Warping at Scale&lt;/strong&gt;: &lt;a href="http://practicalquant.blogspot.com/2012/10/mining-time-series-with-trillions-of.html" rel="nofollow"&gt;another blog post&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Time Series Analysis and Mining in R&lt;/strong&gt; (to add R to the mix): &lt;a href="http://rdatamining.wordpress.com/2011/08/23/time-series-analysis-and-mining-with-r" rel="nofollow"&gt;yet another blog post&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;And, finally, two &lt;strong&gt;tools implementing/supporting DTW&lt;/strong&gt;, to top it off: &lt;a href="http://rdatamining.wordpress.com/2011/08/23/time-series-analysis-and-mining-with-r" rel="nofollow"&gt;R package&lt;/a&gt; and &lt;a href="http://mlpy.sourceforge.net" rel="nofollow"&gt;Python module&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9075, 2, '2015-11-27 09:11:51.69', 1, NULL, NULL, '&lt;p&gt;&lt;a href="http://www.r-podcast.org/" rel="nofollow"&gt;The R-Podcast&lt;/a&gt; is a podcast about data analysis in R. Other cases are &lt;a href="https://itunes.apple.com/us/podcast/oreilly-data-show/id944929220?intcmp=il-data-na-na-na_20150826_radar_data_show_podcast_post_top_subscribe_cta" rel="nofollow"&gt;O''Reilly Data Show&lt;/a&gt; and &lt;a href="http://radar.oreilly.com/tag/podcast" rel="nofollow"&gt;O''Reilly Radar Podcasts&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':11C,34C,47C ''/p'':48C ''/tag/podcast'':40C ''/us/podcast/oreilly-data-show/id944929220?intcmp=il-data-na-na-na_20150826_radar_data_show_podcast_post_top_subscribe_cta'':27C ''analysi'':17C ''case'':21C ''data'':16C,32C ''href'':3C,24C,37C ''itunes.apple.com'':26C ''itunes.apple.com/us/podcast/oreilly-data-show/id944929220?intcmp=il-data-na-na-na_20150826_radar_data_show_podcast_post_top_subscribe_cta'':25C ''nofollow'':6C,29C,42C ''o'':30C,43C ''p'':1C ''podcast'':10C,14C,46C ''r'':9C,19C ''r-podcast'':8C ''radar'':45C ''radar.oreilly.com'':39C ''radar.oreilly.com/tag/podcast'':38C ''reilli'':31C,44C ''rel'':5C,28C,41C ''show'':33C ''www.r-podcast.org'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3766, 1, '2014-12-26 16:25:27.373', 2, 195, 'predict with Multinomial Logistic Regression', '&lt;p&gt;If I execute the following code I have no problem:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;require(foreign)
require(nnet)
require(ggplot2)
require(reshape2)

ml &amp;lt;- read.dta("http://www.ats.ucla.edu/stat/data/hsbdemo.dta")
ml$prog2 &amp;lt;- relevel(ml$prog, ref = "academic")
test &amp;lt;- multinom(prog2 ~ ses + write, data = ml)
predict(test, newdata = dses, "probs")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;but if I try:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;require(caret)
ml &amp;lt;- read.dta("http://www.ats.ucla.edu/stat/data/hsbdemo.dta")
ml$prog2 &amp;lt;- relevel(ml$prog, ref = "academic")
test &amp;lt;- train(prog2 ~ ses + write,method="multinom" ,data = ml)
predict(test$finalModel, newdata = dses, "probs")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;it returns &lt;code&gt;Error in eval(expr, envir, enclos) : object ''sesmiddle'' not found&lt;/code&gt;, why?&lt;/p&gt;
', 'machine-learning  r  logistic-regression  predictive-modeling', NULL, '''/code'':65C,107C,123C ''/p'':27C,72C,125C ''/pre'':66C,108C ''/stat/data/hsbdemo.dta'':43C,82C ''academ'':51C,90C ''caret'':76C ''code'':22C,29C,74C,112C ''data'':58C,99C ''dses'':63C,105C ''enclo'':118C ''envir'':117C ''error'':113C ''eval'':115C ''execut'':19C ''expr'':116C ''finalmodel'':103C ''follow'':21C ''foreign'':31C ''found'':122C ''ggplot2'':35C ''learn'':8B ''logist'':4A,11B ''logistic-regress'':10B ''lt'':39C,46C,53C,78C,85C,92C ''machin'':7B ''machine-learn'':6B ''method'':97C ''ml'':38C,44C,48C,59C,77C,83C,87C,100C ''model'':15B ''multinom'':54C,98C ''multinomi'':3A ''newdata'':62C,104C ''nnet'':33C ''object'':119C ''p'':16C,67C,109C ''pre'':28C,73C ''predict'':1A,14B,60C,101C ''predictive-model'':13B ''prob'':64C,106C ''problem'':26C ''prog'':49C,88C ''prog2'':45C,55C,84C,94C ''r'':9B ''read.dta'':40C,79C ''ref'':50C,89C ''regress'':5A,12B ''relevel'':47C,86C ''requir'':30C,32C,34C,36C,75C ''reshape2'':37C ''return'':111C ''ses'':56C,95C ''sesmiddl'':120C ''test'':52C,61C,91C,102C ''train'':93C ''tri'':71C ''write'':57C,96C ''www.ats.ucla.edu'':42C,81C ''www.ats.ucla.edu/stat/data/hsbdemo.dta'':41C,80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3767, 2, '2014-12-26 17:26:02.033', 2, NULL, NULL, '&lt;p&gt;That''s not the error I get and I''m thinking that you left out some code. I get:&lt;/p&gt;

&lt;pre&gt;
predict(test$finalModel, newdata = dses, "probs")
Error in as.data.frame(newdata) : object ''dses'' not found
&lt;/pre&gt;

&lt;p&gt;I don''t know why I see this so much, but you should avoid using the &lt;code&gt;finalModel&lt;/code&gt; object for prediction. &lt;code&gt;train&lt;/code&gt; is doing some things that the model from &lt;code&gt;multinom&lt;/code&gt; sin''t aware of (such as expanding dummy variables). Try using &lt;/p&gt;

&lt;pre&gt;
predict(test, newdata = dses, "probs")
&lt;/pre&gt;

&lt;p&gt;and never use &lt;code&gt;predict(test$finalModel)&lt;/code&gt; again...&lt;/p&gt;

&lt;p&gt;Max&lt;/p&gt;
', NULL, NULL, '''/code'':57C,63C,74C,102C ''/p'':21C,86C,104C,107C ''/pre'':37C,93C ''as.data.frame'':31C ''avoid'':52C ''awar'':77C ''code'':18C,55C,61C,72C,98C ''dses'':27C,34C,91C ''dummi'':82C ''error'':6C,29C ''expand'':81C ''finalmodel'':25C,56C,101C ''found'':36C ''get'':8C,20C ''know'':42C ''left'':15C ''m'':11C ''max'':106C ''model'':70C ''much'':48C ''multinom'':73C ''never'':96C ''newdata'':26C,32C,90C ''object'':33C,58C ''p'':1C,38C,94C,105C ''pre'':22C,87C ''predict'':23C,60C,88C,99C ''prob'':28C,92C ''see'':45C ''sin'':75C ''test'':24C,89C,100C ''thing'':67C ''think'':12C ''train'':62C ''tri'':84C ''use'':53C,85C,97C ''variabl'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3770, 1, '2014-12-28 11:29:14.24', 6, 1678, 'How to merge monthly, daily and weekly data?', '&lt;p&gt;Google Trends returns weekly data so I have to find a way to merge them with my daily/monthly data. &lt;/p&gt;

&lt;p&gt;What I have done so far is to break each serie into daily data, for exemple: &lt;/p&gt;

&lt;p&gt;from:&lt;/p&gt;

&lt;p&gt;2013-03-03 - 2013-03-09 37&lt;/p&gt;

&lt;p&gt;to:&lt;/p&gt;

&lt;p&gt;2013-03-03 37 
2013-03-04 37
2013-03-05 37
2013-03-06 37
2013-03-07 37
2013-03-08 37
2013-03-09 37&lt;/p&gt;

&lt;p&gt;But this is adding a lot of complexity to my problem. I was trying to predict google searchs from the last 6 months values, or 6 values in monthly data. Daily data would imply a work on 180 past values. (I have 10 years of data so 120 points in monthly data / 500+ in weekly data/ 3500+ in daily data)&lt;/p&gt;

&lt;p&gt;The other approach would be to "merge" daily data in weekly/monthly data. But some questions arise from this process. Some data can be averaged because their sum represent something. Rainfall for example, the amount of rain in a given week will be the sum of the amounts for each days composing the weeks. &lt;/p&gt;

&lt;p&gt;In my case I am dealing with prices, financial rates and other things. For the prices it is common in my field to take volume exchanged into account, so the weekly data would be a weighted average. For financial rates it is a bit more complex a some formulas are involved to build weekly rates from daily rates.  For the other things i don''t know the underlying properties. I think those properties are important to avoid meaningless indicators (an average of fiancial rates would be a non-sense for example). &lt;/p&gt;

&lt;p&gt;So three questions: &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;For known and unknown properties, how should I proceed to go from daily to weekly/monthly data ?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I feel like breaking weekly/monthly data into daily data like i''ve done is somewhat wrong because I am introducing quantities that have no sense in real life. So almost the same question: &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;For known and unknown properties, how should I proceed to go from weekly/monthly to daily data ?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Last but not least: &lt;strong&gt;when given two time series with different time steps, what is better: Using the Lowest or the biggest time step ?&lt;/strong&gt; I think this is a compromise between the number of data and the complexity of the model but I can''t see any strong argument to choose between those options. &lt;/p&gt;

&lt;p&gt;Edit: if you know a tool (in R Python even Excel) to do it easily it would be very appreciated.&lt;/p&gt;
', 'time-series', NULL, '''-03'':56C,57C,59C,68C,69C,72C,76C,80C,84C,88C,92C ''-04'':73C ''-05'':77C ''-06'':81C ''-07'':85C ''-08'':89C ''-09'':60C,93C ''/p'':32C,50C,53C,62C,65C,95C,157C,212C,306C,311C,331C,366C,386C,444C,466C ''/strong'':330C,385C,413C ''10'':139C ''120'':144C ''180'':134C ''2013'':55C,58C,67C,71C,75C,79C,83C,87C,91C ''3500'':153C ''37'':61C,70C,74C,78C,82C,86C,90C,94C ''500'':149C ''6'':118C,122C ''account'':241C ''ad'':100C ''almost'':362C ''amount'':192C,205C ''appreci'':465C ''approach'':161C ''argument'':438C ''aris'':174C ''averag'':182C,250C,294C ''avoid'':290C ''better'':404C ''biggest'':410C ''bit'':257C ''break'':42C,336C ''build'':266C ''case'':216C ''choos'':440C ''common'':232C ''complex'':104C,259C,427C ''compos'':209C ''compromis'':419C ''daili'':5A,46C,127C,155C,166C,270C,326C,340C,383C ''daily/monthly'':30C ''data'':8A,17C,31C,47C,126C,128C,142C,148C,152C,156C,167C,170C,179C,245C,329C,338C,341C,384C,424C ''day'':208C ''deal'':219C ''differ'':399C ''done'':37C,345C ''easili'':460C ''edit'':446C ''even'':455C ''exampl'':190C,305C ''excel'':456C ''exchang'':239C ''exempl'':49C ''far'':39C ''feel'':334C ''fiancial'':296C ''field'':235C ''financi'':222C,252C ''find'':22C ''formula'':262C ''given'':197C,394C ''go'':324C,379C ''googl'':13C,113C ''impli'':130C ''import'':288C ''indic'':292C ''introduc'':352C ''involv'':264C ''know'':279C,449C ''known'':315C,370C ''last'':117C,388C ''least'':391C ''life'':360C ''like'':335C,342C ''lot'':102C ''lowest'':407C ''meaningless'':291C ''merg'':3A,26C,165C ''model'':430C ''month'':4A,119C,125C,147C ''non'':302C ''non-sens'':301C ''number'':422C ''option'':443C ''p'':12C,33C,51C,54C,63C,66C,96C,158C,213C,307C,312C,332C,367C,387C,445C ''past'':135C ''point'':145C ''predict'':112C ''price'':221C,229C ''problem'':107C ''proceed'':322C,377C ''process'':177C ''properti'':282C,286C,318C,373C ''python'':454C ''quantiti'':353C ''question'':173C,310C,365C ''r'':453C ''rain'':194C ''rainfal'':188C ''rate'':223C,253C,268C,271C,297C ''real'':359C ''repres'':186C ''return'':15C ''search'':114C ''see'':435C ''sens'':303C,357C ''seri'':11B,44C,397C ''someth'':187C ''somewhat'':347C ''step'':401C,412C ''strong'':313C,368C,392C,437C ''sum'':185C,202C ''take'':237C ''thing'':226C,275C ''think'':284C,415C ''three'':309C ''time'':10B,396C,400C,411C ''time-seri'':9B ''tool'':451C ''trend'':14C ''tri'':110C ''two'':395C ''under'':281C ''unknown'':317C,372C ''use'':405C ''valu'':120C,123C,136C ''ve'':344C ''volum'':238C ''way'':24C ''week'':7A,16C,151C,198C,211C,244C,267C ''weekly/monthly'':169C,328C,337C,381C ''weight'':249C ''work'':132C ''would'':129C,162C,246C,298C,462C ''wrong'':348C ''year'':140C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6112, 2, '2015-06-14 08:56:13.25', 0, NULL, NULL, '&lt;p&gt;I would say Teradata should be good choice. However, when you make join you have to take careful consideration of choosing right joining keys.&lt;/p&gt;
', NULL, NULL, '''/p'':26C ''care'':19C ''choic'':9C ''choos'':22C ''consider'':20C ''good'':8C ''howev'':10C ''join'':14C,24C ''key'':25C ''make'':13C ''p'':1C ''right'':23C ''say'':4C ''take'':18C ''teradata'':5C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3772, 1, '2014-12-28 14:58:45.32', 4, 313, 'Time series prediction', '&lt;p&gt;I am trying to predict a time serie from another one. My approach is based on a moving windows. I predict the output value of the serie from the following features: the previous value and the 6 past values of the source serie. &lt;/p&gt;

&lt;p&gt;Is it usefull to add the previous value of the time serie ? &lt;/p&gt;

&lt;p&gt;I feel like I don''t use all the information contained in the curve to predict futures values. But I don''t see how it would be possible to use all previous data to predict a value (first, the number of features would be growing trough time...).&lt;/p&gt;

&lt;p&gt;What are the caveats of a 6 month time-window approach ?&lt;/p&gt;

&lt;p&gt;Is there any paper about differents method of feature selection for time-series ? &lt;/p&gt;
', 'time-series  feature-selection', NULL, '''/p'':54C,68C,117C,131C,147C ''6'':47C,125C ''add'':60C ''anoth'':20C ''approach'':23C,130C ''base'':25C ''caveat'':122C ''contain'':80C ''curv'':83C ''data'':102C ''differ'':138C ''featur'':8B,41C,111C,141C ''feature-select'':7B ''feel'':71C ''first'':107C ''follow'':40C ''futur'':86C ''grow'':114C ''inform'':79C ''like'':72C ''method'':139C ''month'':126C ''move'':28C ''number'':109C ''one'':21C ''output'':33C ''p'':10C,55C,69C,118C,132C ''paper'':136C ''past'':48C ''possibl'':97C ''predict'':3A,15C,31C,85C,104C ''previous'':43C,62C,101C ''see'':92C ''select'':9B,142C ''seri'':2A,6B,18C,37C,53C,67C,146C ''sourc'':52C ''time'':1A,5B,17C,66C,116C,128C,145C ''time-seri'':4B,144C ''time-window'':127C ''tri'':13C ''trough'':115C ''use'':76C,99C ''useful'':58C ''valu'':34C,44C,49C,63C,87C,106C ''window'':29C,129C ''would'':95C,112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3781, 1, '2014-12-30 10:33:08.24', 2, 686, 'Scikit Learn: KMeans Clustering 3D data over a time period (dimentionality reduction?)', '&lt;p&gt;I have a &lt;strong&gt;dataset of xyz coordinates with a date component&lt;/strong&gt; in a pandas dataframe&lt;/p&gt;

&lt;p&gt;ex:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;date1: $[x_1,y_1,z_1]$,&lt;/li&gt;
&lt;li&gt;date2: $[x_2,y_2,z_2]$,&lt;/li&gt;
&lt;li&gt;date3: $[x_3,y_3,z_3]$,
..&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I would like to &lt;strong&gt;classify a sample of object positions over the period of a week&lt;/strong&gt;
(using indexes to re-map the classification label back to the date), like this:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Week 1: $[x_1,y_1,z_1], [x_2, y_2, z_2], [x_3,y_3,z_3], [x_4,y_4,z_4], [x_5,y_5,z_5], [x_6,y_6,z_6], [x_7,y_7,z_7]$,&lt;/li&gt;
&lt;li&gt;Week 2: $[x_8,y_8,z_8],[x_9,y_9,z_9],[x_{10},y_{10},z_{10}],[x_{11},y_{11},z_{11}],[x_{12},y_{12},z_{12}],[x_{13},y_{13},z_{13}],[x_{14},y_{14},z_{14}]$,&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;When I try to run KMeans it returns&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;k_means = KMeans(n_clusters=cclasses)
k_means.fit(process_set.hpc)
date_classes = k_means.labels_

ValueError: Found array with dim 3. Expected &amp;lt;= 2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Questions:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Do I have to run it through Principal Component Analysis (PCA) first? if so, how do I maintain date mapping to the classification created?&lt;/li&gt;
&lt;li&gt;Are there any other methods I could use?&lt;/li&gt;
&lt;li&gt;Am I doing everything completely backwards and should consider a different approach, any thoughts?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3773, 2, '2014-12-28 20:10:11.3', 2, NULL, NULL, '&lt;p&gt;I''m fairly new to this myself, but have spent a lot of time recently learning about time series and hope that I can help fellow learners. If I had the reputation to comment I''d ask you a few things first, but I can''t. I''ll happily do further work and edit this response if you respond or make edits to your question. With those caveats out of the way:&lt;/p&gt;

&lt;h1&gt;Is it useful to use the previous value as a feature?&lt;/h1&gt;

&lt;p&gt;One of the first things I would say is that the correct aspects to be looking at in your data very much depends on the nature of the data, as well as what you''re trying to do with it:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;It sounds like you have monthly values, but it''s not clear how far into the future you''re wanting to predict, or how much historic data you have access to.&lt;/li&gt;
&lt;li&gt;We also don''t know what these two series represent, or why one time series is being used to predict the other - and without that, I don''t think anyone will be able to tell you whether the previous value of the series to be predicted is valuable information or not.&lt;/li&gt;
&lt;/ul&gt;

&lt;h1&gt;Any caveats to using a 6 month time window?&lt;/h1&gt;

&lt;p&gt;One obvious caveat to only using the last 6 months is that if there''s any seasonality over the year-long period then you''re going to miss it.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;If you''re not sure: if you have multiple years of information, try plotting the series you want to predict over multiple years. You may well be able to see whether the series generally increases or decreases at certain times of year. If you can share this plot here, it might help people answer your questions in more depth.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;As far as caveats about this time-window approach, I''m not too clear from your post what algorithm you''re using to predict values. More information on that would be helpful; it''s possible that rather than questioning what features to select, you should be questioning what methodology to use for forecasting.&lt;/p&gt;

&lt;h1&gt;Helpful further reading?&lt;/h1&gt;

&lt;p&gt;Once you''ve provided more information I''ll be happy to tackle your last question on suitable reading if I''m able to. For now, I will say that there''s a &lt;em&gt;lot&lt;/em&gt; of information available, but a lot of it is academic in nature. Quite frequently papers aren''t easy to digest, seem to contradict one another, or are only relevant to specific situations. This is a rapidly growing and changing field, so it''s sometimes difficult to find a clear best practice or consensus opinion.&lt;/p&gt;

&lt;p&gt;That said, it might be worth looking at some of the free, online courses available to see if any would help you understand the area you''re interested in:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://www.coursera.org/courses?query=data%20science" rel="nofollow"&gt;Coursera "data science" related courses&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3774, 2, '2014-12-29 06:53:32.2', 2, NULL, NULL, '&lt;p&gt;Let me give you a few simple approaches in time series analysis.&lt;/p&gt;

&lt;p&gt;The first approach consists in using previous values of your time series $Y_{t}$ as in $Y_{t} = \phi_{1}Y_{t-1} + ... + \phi_{n}Y_{t-n}$. In case you don''t know, these models are called autoregressive (AR) models. This answers your first question. Of course it is useful to include the previous value of your time series. There is a whole set of models based on that idea.&lt;/p&gt;

&lt;p&gt;The second approach is taking a window and extracting some features to describe the time series at each point in time. Then you use a conventional machine learning technique to predict future values as typically done. This is more common in a classification or regression setting but future values can be thought of as classifying future values. This technique has the advantage of dramatically reducing the number of features, although you usually lose characteristics associated with time. This addresses your second concern.&lt;/p&gt;

&lt;p&gt;Another model that could be helpful in your case is the vector autoregressive model (VAR) (using &lt;a href="http://en.wikipedia.org/wiki/Vector_autoregression" rel="nofollow"&gt;Wikipedia&lt;/a&gt;''s notation):&lt;/p&gt;

&lt;p&gt;$$\left( \begin{array}{ccc}
y_{1,t} \\
y_{2,t} 
\end{array}\right) = \left( \begin{array}{ccc}c_{1} \\ c_{2}\end{array}\right) + \left( \begin{array}{ccc}A_{1,1} &amp;amp; A_{1,2} \\ A_{2,1} &amp;amp; A_{2,2}\end{array}\right)\left( \begin{array}{ccc}
y_{1,t-1} \\
y_{2,t-1} 
\end{array}\right) + \left( \begin{array}{ccc}
e_{1,t} \\
e_{2,t} 
\end{array}\right)$$&lt;/p&gt;

&lt;p&gt;Here you can see that $y_{1,t}$ has a contribution from its previous value $t_{1,t-1}$ but also includes the value of the other series $y_{2,t-1}$ in a linear combination. As usual, the purpose is to find the elements of $A_{i,j}$ that minimize some measure of error between observed values and estimated values.&lt;/p&gt;

&lt;p&gt;A general suggestion: The first thing you need to do is to test the autocorrelation of your first series in order to confirm that an autoregressive approach is suitable and then test the cross correlation between both series to support the idea that using the second series to improve your predictions is appropriate.&lt;/p&gt;
', NULL, NULL, '''-1'':38C,255C,259C,296C,309C ''/a'':198C ''/p'':14C,88C,172C,201C,276C,339C,394C ''/wiki/vector_autoregression'':194C ''1'':35C,208C,221C,232C,233C,236C,240C,253C,268C,284C,294C ''2'':211C,223C,237C,239C,243C,244C,257C,271C,307C ''address'':168C ''advantag'':151C ''also'':298C ''although'':159C ''amp'':234C,241C ''analysi'':13C ''anoth'':174C ''answer'':59C ''approach'':9C,18C,92C,367C ''appropri'':393C ''ar'':56C ''array'':205C,214C,218C,225C,229C,246C,250C,261C,265C,274C ''associ'':164C ''autocorrel'':355C ''autoregress'':55C,186C,366C ''base'':84C ''begin'':204C,217C,228C,249C,264C ''c'':220C,222C ''call'':54C ''case'':46C,182C ''ccc'':206C,219C,230C,251C,266C ''characterist'':163C ''classif'':132C ''classifi'':144C ''combin'':313C ''common'':129C ''concern'':171C ''confirm'':363C ''consist'':19C ''contribut'':288C ''convent'':115C ''correl'':375C ''could'':177C ''cours'':64C ''cross'':374C ''describ'':102C ''done'':125C ''dramat'':153C ''e'':267C,270C ''element'':322C ''en.wikipedia.org'':193C ''en.wikipedia.org/wiki/vector_autoregression'':192C ''end'':213C,224C,245C,260C,273C ''error'':332C ''estim'':337C ''extract'':98C ''featur'':100C,158C ''find'':320C ''first'':17C,61C,345C,358C ''futur'':121C,137C,145C ''general'':342C ''give'':4C ''help'':179C ''href'':191C ''idea'':87C,382C ''improv'':389C ''includ'':69C,299C ''j'':326C ''know'':50C ''learn'':117C ''left'':203C,216C,227C,248C,263C ''let'':2C ''linear'':312C ''lose'':162C ''machin'':116C ''measur'':330C ''minim'':328C ''model'':52C,57C,83C,175C,187C ''n'':40C,44C ''need'':348C ''nofollow'':196C ''notat'':200C ''number'':156C ''observ'':334C ''order'':361C ''p'':1C,15C,89C,173C,202C,277C,340C ''phi'':34C,39C ''point'':108C ''predict'':120C,391C ''previous'':22C,71C,291C ''purpos'':317C ''question'':62C ''reduc'':154C ''regress'':134C ''rel'':195C ''right'':215C,226C,247C,262C,275C ''second'':91C,170C,386C ''see'':281C ''seri'':12C,27C,76C,105C,305C,359C,378C,387C ''set'':81C,135C ''simpl'':8C ''suggest'':343C ''suitabl'':369C ''support'':380C ''t-n'':42C ''take'':94C ''techniqu'':118C,148C ''test'':353C,372C ''thing'':346C ''thought'':141C ''time'':11C,26C,75C,104C,110C,166C ''typic'':124C ''use'':21C,67C,113C,189C,384C ''usual'':161C,315C ''valu'':23C,72C,122C,138C,146C,292C,301C,335C,338C ''var'':188C ''vector'':185C ''whole'':80C ''wikipedia'':197C ''window'':96C ''y'':28C,32C,36C,41C,207C,210C,252C,256C,283C,306C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3775, 2, '2014-12-29 08:38:44.673', 2, NULL, NULL, '&lt;p&gt;This does not seem a Data Science problem. However there are very nice tools to do exactly that, checkout: logstash, flume and fluentd. Actually if you want to be able to filter in fast and "smart" way checkout Kibana from the guys of ElastichSearch (&lt;a href="http://www.elasticsearch.org/overview/kibana" rel="nofollow"&gt;http://www.elasticsearch.org/overview/kibana&lt;/a&gt;). Those tools are enough to solve your problem in a very efficient way.&lt;/p&gt;
', NULL, NULL, '''/overview/kibana'':50C ''/overview/kibana&lt;/a&gt;).'':55C ''/p'':69C ''abl'':31C ''actual'':25C ''checkout'':20C,39C ''data'':7C ''effici'':67C ''elastichsearch'':45C ''enough'':59C ''exact'':18C ''fast'':35C ''filter'':33C ''fluentd'':24C ''flume'':22C ''guy'':43C ''howev'':10C ''href'':47C ''kibana'':40C ''logstash'':21C ''nice'':14C ''nofollow'':52C ''p'':1C ''problem'':9C,63C ''rel'':51C ''scienc'':8C ''seem'':5C ''smart'':37C ''solv'':61C ''tool'':15C,57C ''want'':28C ''way'':38C,68C ''www.elasticsearch.org'':49C,54C ''www.elasticsearch.org/overview/kibana'':48C ''www.elasticsearch.org/overview/kibana&lt;/a&gt;).'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3776, 2, '2014-12-29 09:03:08.853', 1, NULL, NULL, '&lt;p&gt;Bias-Variance decomposition is one way, and V-C dimension/bound is another...&lt;/p&gt;

&lt;p&gt;Both of these are metrics you can use to get a feeling of how confident you should be that your training results will generalize to out-of-sample.&lt;/p&gt;

&lt;p&gt;V-C dimension focuses on the results of &lt;em&gt;this&lt;/em&gt; learning algorithm outcome.
Bias-Variance focuses on the expected outcome of the algorithm itself.&lt;/p&gt;

&lt;p&gt;Pick your poison - I sincerely hope that this was helpful.&lt;/p&gt;
', NULL, NULL, '''/em'':60C ''/p'':16C,47C,76C,88C ''algorithm'':62C,74C ''anoth'':15C ''bias'':3C,65C ''bias-vari'':2C,64C ''c'':12C,51C ''confid'':32C ''decomposit'':5C ''dimens'':52C ''dimension/bound'':13C ''em'':58C ''expect'':70C ''feel'':29C ''focus'':53C,67C ''general'':41C ''get'':27C ''help'':87C ''hope'':83C ''learn'':61C ''metric'':22C ''one'':7C ''out-of-sampl'':43C ''outcom'':63C,71C ''p'':1C,17C,48C,77C ''pick'':78C ''poison'':80C ''result'':39C,56C ''sampl'':46C ''sincer'':82C ''train'':38C ''use'':25C ''v'':11C,50C ''v-c'':10C,49C ''varianc'':4C,66C ''way'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6148, 2, '2015-06-18 20:36:47.597', 5, NULL, NULL, '&lt;p&gt;Find a way to make your program write to disk periodically. Keep count of the number of tweets you grab and save after that number is high. I don''t write R but psuedocode might look like:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;$tweets = get_tweets();
$count = 0;
$tweet_array = array();
for each ($tweets as $tweet) {
  $tweet_array += $tweet;
  $count++;
  if ($count &amp;gt; 10000) {
    append_to_file($tweet_array, ''file_name.txt'');
    clear_array($tweet_array);
  }
}&lt;/code&gt;&lt;/p&gt;
', NULL, NULL, '''/code'':73C ''/p'':39C,74C ''0'':46C ''10000'':62C ''append'':63C ''array'':48C,49C,56C,67C,70C,72C ''clear'':69C ''code'':41C ''count'':14C,45C,58C,60C ''disk'':11C ''file'':65C ''file_name.txt'':68C ''find'':2C ''get'':43C ''grab'':21C ''gt'':61C ''high'':28C ''keep'':13C ''like'':38C ''look'':37C ''make'':6C ''might'':36C ''number'':17C,26C ''p'':1C,40C ''period'':12C ''program'':8C ''psuedocod'':35C ''r'':33C ''save'':23C ''tweet'':19C,42C,44C,47C,52C,54C,55C,57C,66C,71C ''way'':4C ''write'':9C,32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9076, 1, '2015-11-27 09:26:20.967', 1, 37, 'removing words based on a predefined vector', '&lt;p&gt;I have the dataset test_stopword and I want to remove some words from the dataset based on a vector. How can I do that in R?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;texts &amp;lt;- c("This is the first document.", 
       "Is this a text?", 
       "This is the second file.", 
       "This is the third text.", 
       "File is not this.") 

test_stopword &amp;lt;- as.data.frame(texts)
ordinal_stopwords  &amp;lt;- c("first","primary","second","secondary","third")
&lt;/code&gt;&lt;/pre&gt;
', 'r  data-cleaning', NULL, '''/code'':83C ''/p'':40C ''/pre'':84C ''as.data.frame'':72C ''base'':3A,29C ''c'':45C,77C ''clean'':11B ''code'':42C ''data'':10B ''data-clean'':9B ''dataset'':16C,28C ''document'':50C ''file'':59C,65C ''first'':49C,78C ''lt'':44C,71C,76C ''ordin'':74C ''p'':12C ''pre'':41C ''predefin'':6A ''primari'':79C ''r'':8B,39C ''remov'':1A,23C ''second'':58C,80C ''secondari'':81C ''stopword'':18C,70C,75C ''test'':17C,69C ''text'':43C,54C,64C,73C ''third'':63C,82C ''vector'':7A,32C ''want'':21C ''word'':2A,25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3779, 1, '2014-12-29 17:27:59.763', 2, 122, 'Which Graph Properties are Useful for Predictive Analytics?', '&lt;p&gt;Let''s assume I''m building a content recommendation engine for online content. I have web log data which I can import into a graph, containing a user ID, the page they viewed, and a timestamp for when they viewed it. Essentially, it''s a history of which pages each user has viewed. &lt;/p&gt;

&lt;p&gt;I''ve used Neo4J and Cypher to write a simple traversal algorithm. For each page (node) I want to build recommendations for, I find which pages are most popular amongst other users who have also visited this page. That seems to give decent results. But, I''d like to explore alternatives to see which method gives the most relevant recommendations.&lt;/p&gt;

&lt;p&gt;In addition to my simple traversal, I''m curious if there are graph-level properties I can utilize to build another set of recommendations with this data set. I''ve looked at &lt;a href="http://snap.stanford.edu/" rel="nofollow"&gt;SNAP&lt;/a&gt;, it has a good library for algorithms like Page Rank, Clauset-Newman-Moore community detection, Girvan-Newman community detection, betweenness centrality, K core, and so on. &lt;/p&gt;

&lt;p&gt;There are many algorithms to choose from. Which algorithms have you had success with? Which would you consider trying?&lt;/p&gt;
', 'recommendation  predictive-modeling  graphs', NULL, '''/a'':171C ''/p'':68C,130C,200C,221C ''addit'':133C ''algorithm'':81C,178C,205C,210C ''also'':104C ''altern'':120C ''amongst'':99C ''analyt'':8A ''anoth'':153C ''assum'':17C ''between'':193C ''build'':20C,89C,152C ''central'':194C ''choos'':207C ''clauset'':183C ''clauset-newman-moor'':182C ''communiti'':186C,191C ''consid'':219C ''contain'':40C ''content'':22C,27C ''core'':196C ''curious'':140C ''cypher'':75C ''d'':116C ''data'':32C,159C ''decent'':112C ''detect'':187C,192C ''engin'':24C ''essenti'':56C ''explor'':119C ''find'':93C ''girvan'':189C ''girvan-newman'':188C ''give'':111C,125C ''good'':175C ''graph'':2A,13B,39C,145C ''graph-level'':144C ''histori'':60C ''href'':166C ''id'':43C ''import'':36C ''k'':195C ''let'':15C ''level'':146C ''librari'':176C ''like'':117C,179C ''log'':31C ''look'':163C ''m'':19C,139C ''mani'':204C ''method'':124C ''model'':12B ''moor'':185C ''neo4j'':73C ''newman'':184C,190C ''node'':85C ''nofollow'':169C ''onlin'':26C ''p'':14C,69C,131C,201C ''page'':45C,63C,84C,95C,107C,180C ''popular'':98C ''predict'':7A,11B ''predictive-model'':10B ''properti'':3A,147C ''rank'':181C ''recommend'':9B,23C,90C,129C,156C ''rel'':168C ''relev'':128C ''result'':113C ''see'':122C ''seem'':109C ''set'':154C,160C ''simpl'':79C,136C ''snap'':170C ''snap.stanford.edu'':167C ''success'':214C ''timestamp'':50C ''travers'':80C,137C ''tri'':220C ''use'':5A,72C ''user'':42C,65C,101C ''util'':150C ''ve'':71C,162C ''view'':47C,54C,67C ''visit'':105C ''want'':87C ''web'':30C ''would'':217C ''write'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3780, 1, '2014-12-30 06:43:24.34', 1, 37, 'How to transform one graph to a spectrum?', '&lt;p&gt;Recently, I studied a paper called "What Does Your Chair Know About Your Stress Level?&lt;/p&gt;

&lt;p&gt;It can be download at the link below.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;a href="http://www.barnrich.ch/wiki/data/media/pub/2010_what_does_your_chair_know_about_your_stress_level.pdf" rel="nofollow"&gt;http://www.barnrich.ch/wiki/data/media/pub/2010_what_does_your_chair_know_about_your_stress_level.pdf&lt;/a&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;at page 210, Fig.5 (picture 5) mentioned that&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Spectra of the norm of the CoP vector during the experiment (stages 3â7) for the same subject used in Fig. 4.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;How did they do to transform Fig.4 to Fig.5? And what do x-axis and y-axis mean in Fig.5?&lt;/p&gt;

&lt;p&gt;Fig4
&lt;img src="http://i.stack.imgur.com/Iq7qc.png" alt="Fig4"&gt;&lt;/p&gt;

&lt;p&gt;Fig5
&lt;img src="http://i.stack.imgur.com/7HPxu.png" alt="Fig5"&gt;&lt;/p&gt;
', 'machine-learning', NULL, '''/7hpxu.png'':128C ''/blockquote'':51C,87C ''/iq7qc.png'':118C ''/p'':28C,38C,61C,86C,111C,121C,131C ''/wiki/data/media/pub/2010_what_does_your_chair_know_about_your_stress_level.pdf'':45C ''/wiki/data/media/pub/2010_what_does_your_chair_know_about_your_stress_level.pdf&lt;/a&gt;&lt;/p&gt;'':50C ''210'':55C ''3â'':76C ''4'':85C ''5'':58C ''7'':77C ''alt'':119C,129C ''axi'':103C,107C ''blockquot'':39C,62C ''call'':18C ''chair'':22C ''cop'':70C ''download'':33C ''experi'':74C ''fig'':84C ''fig.4'':95C ''fig.5'':56C,97C,110C ''fig4'':113C,120C ''fig5'':123C,130C ''graph'':5A ''href'':42C ''i.stack.imgur.com'':117C,127C ''i.stack.imgur.com/7hpxu.png'':126C ''i.stack.imgur.com/iq7qc.png'':116C ''img'':114C,124C ''know'':23C ''learn'':11B ''level'':27C ''link'':36C ''machin'':10B ''machine-learn'':9B ''mean'':108C ''mention'':59C ''nofollow'':47C ''norm'':67C ''one'':4A ''p'':12C,29C,40C,52C,63C,88C,112C,122C ''page'':54C ''paper'':17C ''pictur'':57C ''recent'':13C ''rel'':46C ''spectra'':64C ''spectrum'':8A ''src'':115C,125C ''stage'':75C ''stress'':26C ''studi'':15C ''subject'':81C ''transform'':3A,94C ''use'':82C ''vector'':71C ''www.barnrich.ch'':44C,49C ''www.barnrich.ch/wiki/data/media/pub/2010_what_does_your_chair_know_about_your_stress_level.pdf'':43C ''www.barnrich.ch/wiki/data/media/pub/2010_what_does_your_chair_know_about_your_stress_level.pdf&lt;/a&gt;&lt;/p&gt;'':48C ''x'':102C ''x-axi'':101C ''y'':106C ''y-axi'':105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3783, 1, '2014-12-30 12:55:06.247', 0, 149, 'How to model this "un predicatability" problem?', '&lt;p&gt;Imagine modeling the "&lt;em&gt;input(plaintext) - output(ciphertext)"&lt;/em&gt; pairs of an encryption algorithm as a data science problem. Very informally, the strength of an encryption scheme is measured by the randomness (unpredictability, or entropy) of the output. This is counter-intuitive to classic regression problems, which are frequently used for prediction. Say, informally, the strength of an encryption scheme is determined by the number of such input-output pairs needed beyond which it becomes predictable. &lt;/p&gt;

&lt;p&gt;How do we model this as data science problem? Given all pairs of two different encryption schemes, can we determine which is stronger just by using the input-output pairs of both the schemes? Is there any other way apart from regression to solve this problem?&lt;/p&gt;
', 'machine-learning  regression', NULL, '''/em'':21C ''/p'':91C,140C ''algorithm'':26C ''apart'':133C ''becom'':89C ''beyond'':86C ''ciphertext'':20C ''classic'':57C ''counter'':54C ''counter-intuit'':53C ''data'':29C,99C ''determin'':75C,112C ''differ'':107C ''em'':16C ''encrypt'':25C,38C,72C,108C ''entropi'':47C ''frequent'':62C ''given'':102C ''imagin'':13C ''inform'':33C,67C ''input'':17C,82C,121C ''input-output'':81C,120C ''intuit'':55C ''learn'':10B ''machin'':9B ''machine-learn'':8B ''measur'':41C ''model'':3A,14C,96C ''need'':85C ''number'':78C ''output'':19C,50C,83C,122C ''p'':12C,92C ''pair'':22C,84C,104C,123C ''plaintext'':18C ''predicat'':6A ''predict'':65C,90C ''problem'':7A,31C,59C,101C,139C ''random'':44C ''regress'':11B,58C,135C ''say'':66C ''scheme'':39C,73C,109C,127C ''scienc'':30C,100C ''solv'':137C ''strength'':35C,69C ''stronger'':115C ''two'':106C ''un'':5A ''unpredict'':45C ''use'':63C,118C ''way'':132C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3784, 1, '2014-12-30 14:23:58.323', 2, 39, 'Does a NB wrapper consider feature subset size?', '&lt;p&gt;while comparing two different algorithms to feature selection I stumbled upon the follwing question: &lt;/p&gt;

&lt;p&gt;For a given dataset with a discrete class variable we want to train a naive bayes classifier. We decide to conduct feature selection during preprocessing using naive bayes in a wrapper approach. 
Does this method of feature selection consider the size of the used feature subsets? &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/2Vh2Q.png" alt="Naive Bayes Classification"&gt;&lt;/p&gt;

&lt;p&gt;When considering how NB classifies a given instance, the size of the feature subset being used for classification only influences the number of parts that the product of the conditional dependencies has but that does not make a difference, or does it?&lt;/p&gt;

&lt;p&gt;It''d be great if someone could offer a solid explanation since for me it''s more of a gut feeling at the moment.&lt;/p&gt;
', 'classification  feature-selection', NULL, '''/2vh2q.png'':82C ''/p'':28C,76C,87C,131C,157C ''algorithm'':18C ''alt'':83C ''approach'':61C ''bay'':45C,57C,85C ''class'':37C ''classif'':9B,86C,106C ''classifi'':46C,93C ''compar'':15C ''condit'':118C ''conduct'':50C ''consid'':5A,68C,90C ''could'':139C ''d'':134C ''dataset'':33C ''decid'':48C ''depend'':119C ''differ'':17C,127C ''discret'':36C ''explan'':143C ''featur'':6A,11B,20C,51C,66C,74C,101C ''feature-select'':10B ''feel'':153C ''follw'':26C ''given'':32C,95C ''great'':136C ''gut'':152C ''i.stack.imgur.com'':81C ''i.stack.imgur.com/2vh2q.png'':80C ''img'':78C ''influenc'':108C ''instanc'':96C ''make'':125C ''method'':64C ''moment'':156C ''naiv'':44C,56C,84C ''nb'':3A,92C ''number'':110C ''offer'':140C ''p'':13C,29C,77C,88C,132C ''part'':112C ''preprocess'':54C ''product'':115C ''question'':27C ''select'':12B,21C,52C,67C ''sinc'':144C ''size'':8A,70C,98C ''solid'':142C ''someon'':138C ''src'':79C ''stumbl'':23C ''subset'':7A,75C,102C ''train'':42C ''two'':16C ''upon'':24C ''use'':55C,73C,104C ''variabl'':38C ''want'':40C ''wrapper'':4A,60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3792, 1, '2014-12-31 17:57:48.243', 2, 79, 'Object Recognition for classification, is it being used in industry?', '&lt;p&gt;I''m wondering if e-commerce companies where products are offered by users, such as EBay, are using Object Recognition to ensure that an uploaded image corresponds to an specific type of object (clothing, shoes, glasses, etc) either to classify automatically or more importantly to filter undesired images (such as non related or even illegal types).&lt;/p&gt;

&lt;p&gt;If so, which algorithms and/or open platforms could be use for doing so? From what I''ve looked it seems that HOG+Exemplar SVM might be one of the most accurate methods developed so far (&lt;a href="http://www.cs.cmu.edu/~efros/exemplarsvm-iccv11.pdf" rel="nofollow"&gt;http://www.cs.cmu.edu/~efros/exemplarsvm-iccv11.pdf&lt;/a&gt;), even having couple of public repo''s with Matlab implementations (&lt;a href="https://github.com/quantombone/exemplarsvm" rel="nofollow"&gt;https://github.com/quantombone/exemplarsvm&lt;/a&gt;), but I''m still wondering if this is being used in industry.&lt;/p&gt;
', 'classification  object-recognition  hog  computer-vision', NULL, '''/p'':77C,156C ''/quantombone/exemplarsvm'':138C ''/quantombone/exemplarsvm&lt;/a&gt;),'':143C ''/~efros/exemplarsvm-iccv11.pdf'':118C ''/~efros/exemplarsvm-iccv11.pdf&lt;/a&gt;),'':123C ''accur'':109C ''algorithm'':82C ''and/or'':83C ''automat'':61C ''classif'':4A,11B ''classifi'':60C ''cloth'':54C ''commerc'':26C ''compani'':27C ''comput'':17B ''computer-vis'':16B ''correspond'':47C ''could'':86C ''coupl'':126C ''develop'':111C ''e'':25C ''e-commerc'':24C ''ebay'':36C ''either'':58C ''ensur'':42C ''etc'':57C ''even'':74C,124C ''exemplar'':101C ''far'':113C ''filter'':66C ''github.com'':137C,142C ''github.com/quantombone/exemplarsvm'':136C ''github.com/quantombone/exemplarsvm&lt;/a&gt;),'':141C ''glass'':56C ''hog'':15B,100C ''href'':115C,135C ''illeg'':75C ''imag'':46C,68C ''implement'':133C ''import'':64C ''industri'':10A,155C ''look'':96C ''m'':21C,146C ''matlab'':132C ''method'':110C ''might'':103C ''nofollow'':120C,140C ''non'':71C ''object'':1A,13B,39C,53C ''object-recognit'':12B ''offer'':31C ''one'':105C ''open'':84C ''p'':19C,78C ''platform'':85C ''product'':29C ''public'':128C ''recognit'':2A,14B,40C ''rel'':119C,139C ''relat'':72C ''repo'':129C ''seem'':98C ''shoe'':55C ''specif'':50C ''still'':147C ''svm'':102C ''type'':51C,76C ''undesir'':67C ''upload'':45C ''use'':8A,38C,88C,153C ''user'':33C ''ve'':95C ''vision'':18B ''wonder'':22C,148C ''www.cs.cmu.edu'':117C,122C ''www.cs.cmu.edu/~efros/exemplarsvm-iccv11.pdf'':116C ''www.cs.cmu.edu/~efros/exemplarsvm-iccv11.pdf&lt;/a&gt;),'':121C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6149, 2, '2015-06-18 21:50:08.29', 0, NULL, NULL, '&lt;p&gt;Use &lt;code&gt;subset&lt;/code&gt; instead i.e. &lt;code&gt;subset(testdf, testdf$list2=="Ø¨")&lt;/code&gt; results in what you want.  &lt;/p&gt;
', NULL, NULL, '''/code'':5C,14C ''/p'':20C ''code'':3C,8C ''i.e'':7C ''instead'':6C ''list2'':12C ''p'':1C ''result'':15C ''subset'':4C,9C ''testdf'':10C,11C ''use'':2C ''want'':19C ''ø'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8977, 4, '2015-11-22 05:42:59.207', 0, NULL, NULL, 'Categorical data can take on a limited (usually fixed) number of possible values called categories. Categorical values "label", they do not "measure". Nominal and dichotomous/binary scale types are categorical. Some people consider ordinal scale categorical too.', NULL, NULL, '''call'':14C ''categor'':1C,16C,29C,35C ''categori'':15C ''consid'':32C ''data'':2C ''dichotomous/binary'':25C ''fix'':9C ''label'':18C ''limit'':7C ''measur'':22C ''nomin'':23C ''number'':10C ''ordin'':33C ''peopl'':31C ''possibl'':12C ''scale'':26C,34C ''take'':4C ''type'':27C ''usual'':8C ''valu'':13C,17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3785, 2, '2014-12-30 19:45:57.283', 3, NULL, NULL, '&lt;p&gt;The correct term for what you''re describing here is ''class imbalance'' or ''class imbalance problem'' . It''d be great if you could include that in the title to have a more meaningful title.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Concerning your first question:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Have you plotted a confusion matrix of the resulting classifications? Is the reason why the accuracy is not satisfying really that instances are wrongly classified as the most common class?&lt;/p&gt;

&lt;p&gt;Given your context of application it seems that you could use a certain degree of oversampling. To what extent this can be applied should depend on the frequency of each underrepresented class.&lt;/p&gt;

&lt;p&gt;If there is a reasonably high variation in the value distributions on the underrepresented class instances then one could argue that this should be considered when applying the oversampling.&lt;/p&gt;

&lt;p&gt;Also, the approach Charlie suggested in his answer could be considered, given that the instances of the underrepresented classes would form a dataset that is suitable for classification.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Edit:&lt;/em&gt; I haven''t used naive bayes for text classification yet, so I''m not too sure how your attributes look like exactly. Do you just use the frequency of the terms that scored best with tfidf? More general, do you have discrete or continously valued attributes? &lt;/p&gt;

&lt;p&gt;If the latter is the case you should consider using some kind of discretisation.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Regarding your second question:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Are you splitting the dataset in any way? Normally, given that the classifier has been trained on the same data, the outcome for the same instance should also be the same.&lt;/p&gt;
', NULL, NULL, '''/em'':175C ''/p'':36C,44C,76C,110C,141C,171C,222C,238C,246C,279C ''/strong'':43C,245C ''accuraci'':61C ''also'':143C,275C ''answer'':150C ''appli'':100C,138C ''applic'':82C ''approach'':145C ''argu'':131C ''attribut'':194C,221C ''bay'':181C ''best'':209C ''case'':229C ''certain'':90C ''charli'':146C ''class'':12C,15C,75C,109C,126C,161C ''classif'':55C,170C,184C ''classifi'':70C,260C ''common'':74C ''concern'':39C ''confus'':50C ''consid'':136C,153C,232C ''context'':80C ''contin'':219C ''correct'':3C ''could'':24C,87C,130C,151C ''d'':19C ''data'':267C ''dataset'':165C,252C ''degre'':91C ''depend'':102C ''describ'':9C ''discret'':217C ''discretis'':237C ''distribut'':122C ''edit'':174C ''em'':173C ''exact'':197C ''extent'':96C ''first'':41C ''form'':163C ''frequenc'':105C,203C ''general'':213C ''given'':78C,154C,257C ''great'':21C ''haven'':177C ''high'':117C ''imbal'':13C,16C ''includ'':25C ''instanc'':67C,127C,157C,273C ''kind'':235C ''latter'':226C ''like'':196C ''look'':195C ''m'':188C ''matrix'':51C ''meaning'':34C ''naiv'':180C ''normal'':256C ''one'':129C ''outcom'':269C ''oversampl'':93C,140C ''p'':1C,37C,45C,77C,111C,142C,172C,223C,239C,247C ''plot'':48C ''problem'':17C ''question'':42C,244C ''re'':8C ''realli'':65C ''reason'':58C,116C ''regard'':241C ''result'':54C ''satisfi'':64C ''score'':208C ''second'':243C ''seem'':84C ''split'':250C ''strong'':38C,240C ''suggest'':147C ''suitabl'':168C ''sure'':191C ''term'':4C,206C ''text'':183C ''tfidf'':211C ''titl'':29C,35C ''train'':263C ''underrepres'':108C,125C,160C ''use'':88C,179C,201C,233C ''valu'':121C,220C ''variat'':118C ''way'':255C ''would'':162C ''wrong'':69C ''yet'':185C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3786, 1, '2014-12-30 21:45:13.76', 1, 68, 'How to create separate nodes for each column in a spreadsheet using Neo4J?', '&lt;p&gt;I have an excel file containing lot of columns. I want to create a graph database in Neo4J with all these columns as a separate node so that I can establish relationship between them and play around with the cypher query to get to know my data quite well. I did write a cypher query to make each column as a node, but when I run the relationship query it shows me the name of one node and gives a random number for all the related nodes despite changing the &lt;strong&gt;captions in the properties&lt;/strong&gt; section.&lt;/p&gt;

&lt;p&gt;Sample Create statement:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;LOAD CSV WITH HEADERS FROM "File Location" AS row CREATE (:NodeName {DATE_OCCURED: row.Date});
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Query to visualize the relationship between nodes:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;MATCH (a)-[:`REL NAME`]-&amp;gt;(b) RETURN a,b LIMIT 25;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This gives me the values the node "a" and random numbers for all the node "b". I don''t know where I am going wrong. I thought it has something to do with the way the data has been set up in my file. So I created a file for each column of my main file and wrote the create statements to get the nodes. I still don''t get the actual values of the related nodes.  &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/xxKVi.png" alt="Sample output for the relationship query"&gt;&lt;/p&gt;
', 'neo4j  csv', NULL, '''/code'':136C,160C ''/p'':114C,119C,146C,240C,254C ''/pre'':137C,161C ''/strong'':112C ''/xxkvi.png'':246C ''25'':159C ''actual'':234C ''alt'':247C ''around'':53C ''b'':154C,157C,178C ''caption'':108C ''chang'':105C ''code'':121C,148C ''column'':8A,25C,38C,75C,214C ''contain'':22C ''creat'':3A,29C,117C,131C,209C,222C ''csv'':15B,123C ''cypher'':56C,70C ''data'':63C,199C ''databas'':32C ''date'':133C ''despit'':104C ''establish'':47C ''excel'':20C ''file'':21C,127C,206C,211C,218C ''get'':59C,225C,232C ''give'':95C,164C ''go'':186C ''graph'':31C ''gt'':153C ''header'':125C ''i.stack.imgur.com'':245C ''i.stack.imgur.com/xxkvi.png'':244C ''img'':242C ''know'':61C,182C ''limit'':158C ''load'':122C ''locat'':128C ''lot'':23C ''main'':217C ''make'':73C ''match'':149C ''name'':90C,152C ''neo4j'':13A,14B,34C ''node'':5A,42C,78C,93C,103C,145C,169C,177C,227C,239C ''nodenam'':132C ''number'':98C,173C ''occur'':134C ''one'':92C ''output'':249C ''p'':16C,115C,138C,162C,241C ''play'':52C ''pre'':120C,147C ''properti'':111C ''queri'':57C,71C,85C,139C,253C ''quit'':64C ''random'':97C,172C ''rel'':151C ''relat'':102C,238C ''relationship'':48C,84C,143C,252C ''return'':155C ''row'':130C ''row.date'':135C ''run'':82C ''sampl'':116C,248C ''section'':113C ''separ'':4A,41C ''set'':202C ''show'':87C ''someth'':192C ''spreadsheet'':11A ''src'':243C ''statement'':118C,223C ''still'':229C ''strong'':107C ''thought'':189C ''use'':12A ''valu'':167C,235C ''visual'':141C ''want'':27C ''way'':197C ''well'':65C ''write'':68C ''wrong'':187C ''wrote'':220C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5093, 1, '2015-02-10 01:04:15.963', 2, 138, 'InterquartileRange takes up most instances in data set', '&lt;p&gt;I''m very new to this community, so please overlook my noobness.&lt;/p&gt;

&lt;p&gt;I have a data set with 2948 instances and I tried to remove outliers using InterquartileRange filter in Weka. The issue is that the number of ''YES'' instances in ExtremeValues and Outliers takes up to 2947 and 2946 respectively. In other words, all my data are considered outliers. &lt;/p&gt;

&lt;p&gt;What does this say about my data set? Or am I not meant to perform IQR on this data, if so, is there other algorithms to identify outliers other than IQR?  And how would one perform regression on such a data set?&lt;/p&gt;

&lt;p&gt;Thank you.&lt;/p&gt;
', 'dataset  data-cleaning', NULL, '''/p'':26C,76C,120C,124C ''2946'':65C ''2947'':63C ''2948'':34C ''algorithm'':102C ''clean'':12B ''communiti'':20C ''consid'':74C ''data'':7A,11B,31C,72C,84C,96C,118C ''data-clean'':10B ''dataset'':9B ''extremevalu'':57C ''filter'':44C ''identifi'':104C ''instanc'':5A,35C,55C ''interquartilerang'':1A,43C ''iqr'':93C,108C ''issu'':48C ''m'':15C ''meant'':90C ''new'':17C ''noob'':25C ''number'':52C ''one'':112C ''outlier'':41C,59C,75C,105C ''overlook'':23C ''p'':13C,27C,77C,121C ''perform'':92C,113C ''pleas'':22C ''regress'':114C ''remov'':40C ''respect'':66C ''say'':81C ''set'':8A,32C,85C,119C ''take'':2A,60C ''thank'':122C ''tri'':38C ''use'':42C ''weka'':46C ''word'':69C ''would'':111C ''yes'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3787, 2, '2014-12-31 00:09:35.943', 1, NULL, NULL, '&lt;h1&gt;Short Version&lt;/h1&gt;

&lt;p&gt;The difference is in the trade-off between complexity of model and how hard it will be to train.  The more states your variable can take, the much more data you''ll need to train it.  It sounds like you''re reducing the feature space through some front-end processing.&lt;/p&gt;

&lt;h1&gt;Example&lt;/h1&gt;

&lt;h3&gt;Context:&lt;/h3&gt;

&lt;p&gt;Say you''re using NBC to model if you go outside given the high temperature that day.  Let''s say temperature is given to you as an int in Celsius which just happened to range between 0 and 40 degrees.  Over one year of running the experiment - recording the temperature and if you went outside - you''ll have 365 data points.  &lt;/p&gt;

&lt;h3&gt;Internal Representation:&lt;/h3&gt;

&lt;p&gt;The internal structure the NBC uses will be a matrix with 82 values (41 values for your one input variable * 2 because you have two states in your output class).  That means each bin will have an average of &lt;code&gt;365/82 = 8.9ish&lt;/code&gt; samples.  In practice, you''ll probably see a few bins with lots more samples than this and a many bins with 0 samples.  &lt;/p&gt;

&lt;h3&gt;A Pitfall&lt;/h3&gt;

&lt;p&gt;Say you saw 8 cases at a temperature of 5 C (all of which you stayed inside) and nothing at a temp of 3 or 4 C.  If, after the model is built, you ''ask it'' what class a temp of 4 C should be in.  It won''t know.  Intuitively, we''d say "stay inside", but the model won''t.  One way to fix this is to bin the classes 0,1,2,... into larger groups of temperatures (i.e., class 0 for temp 0-3, class 1 for temp 4-7, etc).  The extreme case of this would be two temperature states "high" and "low".  The actual cut-off should depend more on the data observed, but one such scheme is converting temperatures within &lt;code&gt;0-20&lt;/code&gt; to "low" and &lt;code&gt;21-40&lt;/code&gt; is "high".  &lt;/p&gt;

&lt;h3&gt;Discussion&lt;/h3&gt;

&lt;p&gt;This front end processing seems to be what you''re talking about as the "wrapper" around the NBC.  The result of our "high" and "low" binning scheme will result in a much smaller model (2 input states and 2 output classes gives you a &lt;code&gt;2*2 = 4&lt;/code&gt; number of classes.  This will be way easier to process and will take way less data to confidently train at the expense of complexity.  One example of a drawback of such a scheme is:  Say the actual pattern is that you love spring and fall weather and only go outside when it''s not too hot or not too cold.  So your outside adventures are evenly split across the upper part of the "low" class and the lower part of the "high" class giving you a really useless model.  If this were the case, a 3-class bin scheme of "high", "medium", and "low" would be best.&lt;/p&gt;
', NULL, NULL, '''-20'':334C ''-3'':291C ''-40'':341C ''-7'':297C ''/code'':176C,335C,342C,398C ''/h1'':4C,61C ''/h3'':64C,131C,204C,348C ''/p'':58C,127C,200C,345C,506C ''0'':102C,198C,277C,287C,290C,333C ''1'':278C,293C ''2'':153C,279C,384C,388C,395C,396C ''21'':340C ''3'':229C,494C ''365'':124C ''365/82'':173C ''4'':231C,247C,296C,397C ''40'':104C ''41'':146C ''5'':215C ''8'':209C ''8.9'':174C ''82'':144C ''across'':466C ''actual'':313C,435C ''adventur'':462C ''around'':365C ''ask'':240C ''averag'':170C ''best'':505C ''bin'':166C,186C,196C,274C,375C,496C ''built'':238C ''c'':216C,232C,248C ''case'':210C,301C,492C ''celsius'':95C ''class'':162C,243C,276C,286C,292C,390C,401C,473C,481C,495C ''code'':172C,332C,339C,394C ''cold'':458C ''complex'':15C,422C ''confid'':416C ''context'':63C ''convert'':329C ''cut'':315C ''cut-off'':314C ''d'':258C ''data'':36C,125C,322C,414C ''day'':82C ''degre'':105C ''depend'':318C ''differ'':7C ''discuss'':347C ''drawback'':427C ''easier'':406C ''end'':56C,352C ''etc'':298C ''even'':464C ''exampl'':60C,424C ''expens'':420C ''experi'':112C ''extrem'':300C ''fall'':443C ''featur'':50C ''fix'':270C ''front'':55C,351C ''front-end'':54C ''give'':391C,482C ''given'':77C,88C ''go'':75C,447C ''group'':282C ''h1'':1C,59C ''h3'':62C,128C,201C,346C ''happen'':98C ''hard'':20C ''high'':79C,309C,344C,372C,480C,499C ''hot'':454C ''i.e'':285C ''input'':151C,385C ''insid'':222C,261C ''int'':93C ''intern'':129C,134C ''intuit'':256C ''ish'':175C ''know'':255C ''larger'':281C ''less'':413C ''let'':83C ''like'':45C ''ll'':38C,122C,181C ''lot'':188C ''love'':440C ''low'':311C,337C,374C,472C,502C ''lower'':476C ''mani'':195C ''matrix'':142C ''mean'':164C ''medium'':500C ''model'':17C,72C,236C,264C,383C,487C ''much'':34C,381C ''nbc'':70C,137C,367C ''need'':39C ''noth'':224C ''number'':399C ''observ'':323C ''one'':107C,150C,267C,325C,423C ''output'':161C,389C ''outsid'':76C,120C,448C,461C ''p'':5C,65C,132C,205C,349C ''part'':469C,477C ''pattern'':436C ''pitfal'':203C ''point'':126C ''practic'':179C ''probabl'':182C ''process'':57C,353C,408C ''rang'':100C ''re'':47C,68C,359C ''realli'':485C ''record'':113C ''reduc'':48C ''represent'':130C ''result'':369C,378C ''run'':110C ''sampl'':177C,190C,199C ''saw'':208C ''say'':66C,85C,206C,259C,433C ''scheme'':327C,376C,431C,497C ''see'':183C ''seem'':354C ''short'':2C ''smaller'':382C ''sound'':44C ''space'':51C ''split'':465C ''spring'':441C ''state'':28C,158C,308C,386C ''stay'':221C,260C ''structur'':135C ''take'':32C,411C ''talk'':360C ''temp'':227C,245C,289C,295C ''temperatur'':80C,86C,115C,213C,284C,307C,330C ''trade'':12C ''trade-off'':11C ''train'':25C,41C,417C ''two'':157C,306C ''upper'':468C ''use'':69C,138C ''useless'':486C ''valu'':145C,147C ''variabl'':30C,152C ''version'':3C ''way'':268C,405C,412C ''weather'':444C ''went'':119C ''within'':331C ''won'':253C,265C ''would'':304C,503C ''wrapper'':364C ''year'':108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3788, 1, '2014-12-31 02:50:32.863', 1, 118, 'Creating a Graph from Address Data', '&lt;p&gt;What''s the best / easiest way to create a graph from address data?  For example, if I have 100 houses from all across a city is there any easy way to determine the shortest distance between two houses and all that good stuff?  Would this require changing the data into coordinates and using GIS software or can I get away with using Python or R?&lt;/p&gt;
', 'python  graphs', NULL, '''/p'':75C ''100'':28C ''across'':32C ''address'':5A,21C ''away'':69C ''best'':13C ''chang'':56C ''citi'':34C ''coordin'':60C ''creat'':1A,17C ''data'':6A,22C,58C ''determin'':41C ''distanc'':44C ''easi'':38C ''easiest'':14C ''exampl'':24C ''get'':68C ''gis'':63C ''good'':51C ''graph'':3A,8B,19C ''hous'':29C,47C ''p'':9C ''python'':7B,72C ''r'':74C ''requir'':55C ''shortest'':43C ''softwar'':64C ''stuff'':52C ''two'':46C ''use'':62C,71C ''way'':15C,39C ''would'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3790, 4, '2014-12-31 15:34:17.397', 0, NULL, NULL, 'Statistics is a scientific approach to inductive inference based on probabilistic models associated with the data at hand. By extension, it covers the design of experiments and surveys to gather data to this purpose.', NULL, NULL, '''approach'':5C ''associ'':13C ''base'':9C ''cover'':22C ''data'':16C,31C ''design'':24C ''experi'':26C ''extens'':20C ''gather'':30C ''hand'':18C ''induct'':7C ''infer'':8C ''model'':12C ''probabilist'':11C ''purpos'':34C ''scientif'':4C ''statist'':1C ''survey'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3791, 2, '2014-12-31 16:18:21.237', 0, NULL, NULL, '&lt;p&gt;If you have latitude/longitude coordinate data, there should be no problem accomplishing this using great circle calculations which could indeed be accomplished in Python, R, or essentially any other language.&lt;/p&gt;

&lt;p&gt;Here is an article on several methods and calculations for this:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.movable-type.co.uk/scripts/latlong.html" rel="nofollow"&gt;Calculate distance, bearing and more between Latitude/Longitude points&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The main issue with street address data alone is of course the lack of contextual information regarding the physical layout of the streets. Given a sufficiently complete scale map of the relevant area, you could also calculate a distance. That said, the haversine formula discussed in the article above would have a greater accuracy unless the scale map mentioned was also plotted as the surface of a sphere.&lt;/p&gt;
', NULL, NULL, '''/a'':62C ''/p'':32C,45C,63C,133C ''/scripts/latlong.html'':51C ''accomplish'':13C,23C ''accuraci'':118C ''address'':70C ''alon'':72C ''also'':100C,125C ''area'':97C ''articl'':37C,112C ''bear'':56C ''calcul'':18C,42C,54C,101C ''circl'':17C ''complet'':91C ''contextu'':79C ''coordin'':6C ''could'':20C,99C ''cours'':75C ''data'':7C,71C ''discuss'':109C ''distanc'':55C,103C ''essenti'':28C ''formula'':108C ''given'':88C ''great'':16C ''greater'':117C ''haversin'':107C ''href'':48C ''inde'':21C ''inform'':80C ''issu'':67C ''lack'':77C ''languag'':31C ''latitude/longitude'':5C,60C ''layout'':84C ''main'':66C ''map'':93C,122C ''mention'':123C ''method'':40C ''nofollow'':53C ''p'':1C,33C,46C,64C ''physic'':83C ''plot'':126C ''point'':61C ''problem'':12C ''python'':25C ''r'':26C ''regard'':81C ''rel'':52C ''relev'':96C ''said'':105C ''scale'':92C,121C ''sever'':39C ''sphere'':132C ''street'':69C,87C ''suffici'':90C ''surfac'':129C ''unless'':119C ''use'':15C ''would'':114C ''www.movable-type.co.uk'':50C ''www.movable-type.co.uk/scripts/latlong.html'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4879, 1, '2015-01-15 00:48:48.383', 0, 38, 'Training a function that maps n-dim to n-dim', '&lt;p&gt;As an example, say the input is an array of numbers representing an audio snippet and the output is a transformed/filtered version of it.&lt;/p&gt;

&lt;p&gt;What would be the proper name for that? Which are examples of algorithms for the job?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT:&lt;/strong&gt;
More specifically, I want to train audio source separation. The input is a mixed sound (spectrogram) and the output is the sound with some energy removed in certain frequencies. The function needs to recognize some pattern in the input and decide what to remove.&lt;/p&gt;
', 'algorithms', NULL, '''/p'':39C,57C,106C ''/strong'':61C ''algorithm'':13B,53C ''array'':23C ''audio'':28C,68C ''certain'':89C ''decid'':102C ''dim'':8A,12A ''edit'':60C ''energi'':86C ''exampl'':17C,51C ''frequenc'':90C ''function'':3A,92C ''input'':20C,72C,100C ''job'':56C ''map'':5A ''mix'':75C ''n'':7A,11A ''n-dim'':6A,10A ''name'':46C ''need'':93C ''number'':25C ''output'':32C,80C ''p'':14C,40C,58C ''pattern'':97C ''proper'':45C ''recogn'':95C ''remov'':87C,105C ''repres'':26C ''say'':18C ''separ'':70C ''snippet'':29C ''sound'':76C,83C ''sourc'':69C ''specif'':63C ''spectrogram'':77C ''strong'':59C ''train'':1A,67C ''transformed/filtered'':35C ''version'':36C ''want'':65C ''would'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3793, 2, '2014-12-31 21:47:37.49', 0, NULL, NULL, '&lt;p&gt;Here''s a simple solution using the R package &lt;code&gt;ggmap&lt;/code&gt;. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;start &amp;lt;- ''95 clark st, new haven, ct''
end &amp;lt;- ''maison mathis, new haven, ct''
legs &amp;lt;- route(start,end, alternatives = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Will find routes from &lt;code&gt;start&lt;/code&gt; to &lt;code&gt;end&lt;/code&gt;. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;      m    km     miles seconds   minutes       hours  startLon startLat    endLon   endLat leg route
1    60 0.060 0.0372840       7 0.1166667 0.001944444 -72.91617 41.31511 -72.91678 41.31541   1     A
2   718 0.718 0.4461652      95 1.5833333 0.026388889 -72.91678 41.31541 -72.92100 41.30979   2     A
3   436 0.436 0.2709304      64 1.0666667 0.017777778 -72.92100 41.30979 -72.92555 41.31171   3     A
4   431 0.431 0.2678234      68 1.1333333 0.018888889 -72.92555 41.31171 -72.92792 41.30829   4     A
5    60 0.060 0.0372840       7 0.1166667 0.001944444 -72.91617 41.31511 -72.91678 41.31541   1     B
6  1276 1.276 0.7929064     179 2.9833333 0.049722222 -72.91678 41.31541 -72.92430 41.30543   2     B
7   421 0.421 0.2616094      62 1.0333333 0.017222222 -72.92430 41.30543 -72.92869 41.30729   3     B
8   129 0.129 0.0801606      28 0.4666667 0.007777778 -72.92869 41.30729 -72.92792 41.30829   4     B
9    60 0.060 0.0372840       7 0.1166667 0.001944444 -72.91617 41.31511 -72.91678 41.31541   1     C
10  421 0.421 0.2616094      58 0.9666667 0.016111111 -72.91678 41.31541 -72.91924 41.31211   2     C
11  522 0.522 0.3243708     101 1.6833333 0.028055556 -72.91924 41.31211 -72.92502 41.31382   3     C
12  240 0.240 0.1491360      33 0.5500000 0.009166667 -72.92502 41.31382 -72.92555 41.31171   4     C
13  431 0.431 0.2678234      68 1.1333333 0.018888889 -72.92555 41.31171 -72.92792 41.30829   5     C
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And then we can find the length of time estimated to walk there with something like this. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;tapply(legs$minutes, legs$route, max)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''-72.91617'':75C,127C,179C ''-72.91678'':77C,88C,129C,140C,181C,192C ''-72.91924'':194C,205C ''-72.92100'':90C,101C ''-72.92430'':142C,153C ''-72.92502'':207C,218C ''-72.92555'':103C,114C,220C,231C ''-72.92792'':116C,168C,233C ''-72.92869'':155C,166C ''/code'':13C,39C,48C,52C,237C,266C ''/p'':14C,53C,257C,272C ''/pre'':40C,238C,267C ''0.001944444'':74C,126C,178C ''0.007777778'':165C ''0.009166667'':217C ''0.016111111'':191C ''0.017222222'':152C ''0.017777778'':100C ''0.018888889'':113C,230C ''0.026388889'':87C ''0.028055556'':204C ''0.0372840'':71C,123C,175C ''0.049722222'':139C ''0.060'':70C,122C,174C ''0.0801606'':162C ''0.1166667'':73C,125C,177C ''0.129'':161C ''0.1491360'':214C ''0.240'':213C ''0.2616094'':149C,188C ''0.2678234'':110C,227C ''0.2709304'':97C ''0.3243708'':201C ''0.421'':148C,187C ''0.431'':109C,226C ''0.436'':96C ''0.4461652'':84C ''0.4666667'':164C ''0.522'':200C ''0.5500000'':216C ''0.718'':83C ''0.7929064'':136C ''0.9666667'':190C ''1'':68C,79C,131C,183C ''1.0333333'':151C ''1.0666667'':99C ''1.1333333'':112C,229C ''1.276'':135C ''1.5833333'':86C ''1.6833333'':203C ''10'':185C ''101'':202C ''11'':198C ''12'':211C ''1276'':134C ''129'':160C ''13'':224C ''179'':137C ''2'':81C,92C,144C,196C ''2.9833333'':138C ''240'':212C ''28'':163C ''3'':94C,105C,157C,209C ''33'':215C ''4'':107C,118C,170C,222C ''41.30543'':143C,154C ''41.30729'':156C,167C ''41.30829'':117C,169C,234C ''41.30979'':91C,102C ''41.31171'':104C,115C,221C,232C ''41.31211'':195C,206C ''41.31382'':208C,219C ''41.31511'':76C,128C,180C ''41.31541'':78C,89C,130C,141C,182C,193C ''421'':147C,186C ''431'':108C,225C ''436'':95C ''5'':120C,235C ''522'':199C ''58'':189C ''6'':133C ''60'':69C,121C,173C ''62'':150C ''64'':98C ''68'':111C,228C ''7'':72C,124C,146C,176C ''718'':82C ''8'':159C ''9'':172C ''95'':19C,85C ''altern'':37C ''b'':132C,145C,158C,171C ''c'':184C,197C,210C,223C,236C ''clark'':20C ''code'':11C,16C,46C,50C,55C,259C ''ct'':24C,31C ''end'':25C,36C,51C ''endlat'':65C ''endlon'':64C ''estim'':249C ''find'':43C,244C ''ggmap'':12C ''haven'':23C,30C ''help'':271C ''hope'':269C ''hour'':61C ''km'':57C ''leg'':32C,66C,261C,263C ''length'':246C ''like'':255C ''lt'':18C,26C,33C ''m'':56C ''maison'':27C ''mathi'':28C ''max'':265C ''mile'':58C ''minut'':60C,262C ''new'':22C,29C ''p'':1C,41C,239C,268C ''packag'':10C ''pre'':15C,54C,258C ''r'':9C ''rout'':34C,44C,67C,264C ''second'':59C ''simpl'':5C ''solut'':6C ''someth'':254C ''st'':21C ''start'':17C,35C,47C ''startlat'':63C ''startlon'':62C ''tappli'':260C ''time'':248C ''true'':38C ''use'':7C ''walk'':251C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3795, 1, '2015-01-02 05:36:54.783', 0, 32, 'Are Correlithm Objects used for anything in the industry?', '&lt;p&gt;This refers to a system described in a book by Nick Lawrence titled "&lt;a href="http://rads.stackoverflow.com/amzn/click/0975276107" rel="nofollow"&gt;Correlithm Object Technology&lt;/a&gt;". The author coined the term "correlithm" as a combination of "correlation" and "algorithm".&lt;/p&gt;

&lt;p&gt;The Correlithm Objects ("COs") described in the book are, in the author''s view, "primary data tokens" in biological neural systems and are central to "all high-level data representation, storage, and manipulation". In addition to this the author describes the COs as "important mathematical objects of the statistics of bounded, high-dimensional spaces".&lt;/p&gt;

&lt;p&gt;Considering these descriptions it seems like these COs could be useful for AI. What I was curious about is whether they are actually used anywhere in the industry, and if so, in what kind of situations?&lt;/p&gt;
', 'machine-learning  algorithms  correlation', NULL, '''/a'':39C ''/amzn/click/0975276107'':33C ''/p'':53C,111C,149C ''actual'':135C ''addit'':90C ''ai'':125C ''algorithm'':13B,52C ''anyth'':6A ''anywher'':137C ''author'':41C,66C,94C ''biolog'':73C ''book'':24C,62C ''bound'':106C ''central'':78C ''coin'':42C ''combin'':48C ''consid'':113C ''correl'':14B,50C ''correlithm'':2A,36C,45C,56C ''cos'':58C,97C,120C ''could'':121C ''curious'':129C ''data'':70C,84C ''describ'':21C,59C,95C ''descript'':115C ''dimension'':109C ''high'':82C,108C ''high-dimension'':107C ''high-level'':81C ''href'':30C ''import'':99C ''industri'':9A,140C ''kind'':146C ''lawrenc'':27C ''learn'':12B ''level'':83C ''like'':118C ''machin'':11B ''machine-learn'':10B ''manipul'':88C ''mathemat'':100C ''neural'':74C ''nick'':26C ''nofollow'':35C ''object'':3A,37C,57C,101C ''p'':15C,54C,112C ''primari'':69C ''rads.stackoverflow.com'':32C ''rads.stackoverflow.com/amzn/click/0975276107'':31C ''refer'':17C ''rel'':34C ''represent'':85C ''seem'':117C ''situat'':148C ''space'':110C ''statist'':104C ''storag'':86C ''system'':20C,75C ''technolog'':38C ''term'':44C ''titl'':28C ''token'':71C ''use'':4A,123C,136C ''view'':68C ''whether'':132C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3800, 2, '2015-01-03 03:15:08.96', 2, NULL, NULL, '&lt;p&gt;This won''t be a very satisfying answer, but here''s my take...&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;strong&gt;For known and unknown properties, how should I proceed to go from daily to weekly/monthly data ?&lt;/strong&gt;&lt;/p&gt;
  
  &lt;p&gt;&lt;strong&gt;For known and unknown properties, how should I proceed to go from weekly/monthly to daily data ?&lt;/strong&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Same answer for both: you can''t do this for unknown properties, and for known properties it will depend on how the values were computed.&lt;/p&gt;

&lt;p&gt;As you alluded to:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;(an average of fiancial rates would be a non-sense for example)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;There is no single transformation that will be appropriate in all cases, whether the properties/values are known or unknown. Even with known properties, you''ll likely need a unique transformation for each type: mean, median, mode, min, max, boolean, etc.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;strong&gt;when given two time series with different time steps, what is better: Using the Lowest or the biggest time step ?&lt;/strong&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Whenever possible, try to preserve the full granularity of the smallest possible step. Assuming you know how to transform the values, you can always roll-up the steps (e.g., day to month, month to year)... but you won''t necessarily be able to reconstruct smaller steps from larger ones following a lossy conversion.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':57C,107C,175C ''/p'':15C,36C,56C,84C,90C,106C,149C,174C,231C ''/strong'':35C,55C,173C ''abl'':219C ''allud'':88C ''alway'':200C ''answer'':9C,60C ''appropri'':117C ''assum'':190C ''averag'':94C ''better'':164C ''biggest'':170C ''blockquot'':16C,91C,150C ''boolean'':147C ''case'':120C ''comput'':83C ''convers'':230C ''daili'':31C,53C ''data'':34C,54C ''day'':207C ''depend'':77C ''differ'':159C ''e.g'':206C ''etc'':148C ''even'':128C ''exampl'':105C ''fiancial'':96C ''follow'':227C ''full'':183C ''given'':154C ''go'':29C,49C ''granular'':184C ''know'':192C ''known'':20C,40C,73C,125C,130C ''larger'':225C ''like'':134C ''ll'':133C ''lossi'':229C ''lowest'':167C ''max'':146C ''mean'':142C ''median'':143C ''min'':145C ''mode'':144C ''month'':209C,210C ''necessarili'':217C ''need'':135C ''non'':102C ''non-sens'':101C ''one'':226C ''p'':1C,17C,37C,58C,85C,92C,108C,151C,176C ''possibl'':178C,188C ''preserv'':181C ''proceed'':27C,47C ''properti'':23C,43C,70C,74C,131C ''properties/values'':123C ''rate'':97C ''reconstruct'':221C ''roll'':202C ''roll-up'':201C ''satisfi'':8C ''sens'':103C ''seri'':157C ''singl'':112C ''smaller'':222C ''smallest'':187C ''step'':161C,172C,189C,205C,223C ''strong'':18C,38C,152C ''take'':14C ''time'':156C,160C,171C ''transform'':113C,138C,195C ''tri'':179C ''two'':155C ''type'':141C ''uniqu'':137C ''unknown'':22C,42C,69C,127C ''use'':165C ''valu'':81C,197C ''weekly/monthly'':33C,51C ''whenev'':177C ''whether'':121C ''won'':3C,215C ''would'':98C ''year'':212C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3796, 2, '2015-01-02 10:58:38.187', 1, NULL, NULL, '&lt;p&gt;As far as I am aware, object recognition is not extensively used in industry yet. Google''s image search, for instance, is based on exploiting the text on the web pages rather than the images themselves. I have seen several start-up companies that market prototypes based on object recognition, but these products are either prototypes or not widely used.&lt;/p&gt;

&lt;p&gt;Regarding state-of-the-art techniques, you should take a look to recent literature on deep learning and deep neural networks applied to object recognition, along with more specific techniques like convolutional networks. &lt;/p&gt;
', NULL, NULL, '''/p'':63C,99C ''along'':91C ''appli'':87C ''art'':70C ''awar'':7C ''base'':24C,49C ''compani'':45C ''convolut'':97C ''deep'':81C,84C ''either'':57C ''exploit'':26C ''extens'':12C ''far'':3C ''googl'':17C ''imag'':19C,36C ''industri'':15C ''instanc'':22C ''learn'':82C ''like'':96C ''literatur'':79C ''look'':76C ''market'':47C ''network'':86C,98C ''neural'':85C ''object'':8C,51C,89C ''p'':1C,64C ''page'':32C ''product'':55C ''prototyp'':48C,58C ''rather'':33C ''recent'':78C ''recognit'':9C,52C,90C ''regard'':65C ''search'':20C ''seen'':40C ''sever'':41C ''specif'':94C ''start'':43C ''start-up'':42C ''state'':67C ''state-of-the-art'':66C ''take'':74C ''techniqu'':71C,95C ''text'':28C ''use'':13C,62C ''web'':31C ''wide'':61C ''yet'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4864, 1, '2015-01-12 09:27:15.25', 0, 316, 'What is the underlying pattern, or algorithm(s) used in these numbers?', '&lt;p&gt;I have 11 lottery tickets (used) and I have discovered that in each ticket, the 3rd digit''s value is +1 of the value of the 6th digit. I have 11 tickets, each ticket is composed of 16 digits. Would someone (anyone) help me find the logic used here, in creating the other digits and their interrelation? I am a complete noob in data analysis, and any help would be greatly appreciated. For those who would like to know, I incidentally discovered the certain pattern, and made me realize these numbers are not totally random and if I could find the underlying pattern I''d be able to predict. The excel file is here &lt;a href="http://s000.tinyupload.com/index.php?file_id=39010250377074241779" rel="nofollow"&gt;Excel file&lt;/a&gt;&lt;/p&gt;
', 'data-mining  algorithms', NULL, '''+1'':38C ''/a'':141C ''/index.php?file_id=39010250377074241779'':136C ''/p'':142C ''11'':20C,48C ''16'':55C ''3rd'':33C ''6th'':44C ''abl'':124C ''algorithm'':7A,16B ''analysi'':82C ''anyon'':59C ''appreci'':89C ''certain'':101C ''complet'':78C ''compos'':53C ''could'':116C ''creat'':68C ''d'':122C ''data'':14B,81C ''data-min'':13B ''digit'':34C,45C,56C,71C ''discov'':27C,99C ''excel'':128C,139C ''file'':129C,140C ''find'':62C,117C ''great'':88C ''help'':60C,85C ''href'':133C ''incident'':98C ''interrel'':74C ''know'':96C ''like'':94C ''logic'':64C ''lotteri'':21C ''made'':104C ''mine'':15B ''nofollow'':138C ''noob'':79C ''number'':12A,108C ''p'':17C ''pattern'':5A,102C,120C ''predict'':126C ''random'':112C ''realiz'':106C ''rel'':137C ''s000.tinyupload.com'':135C ''s000.tinyupload.com/index.php?file_id=39010250377074241779'':134C ''someon'':58C ''ticket'':22C,31C,49C,51C ''total'':111C ''under'':4A,119C ''use'':9A,23C,65C ''valu'':36C,41C ''would'':57C,86C,93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3813, 2, '2015-01-04 23:57:20.15', 1, NULL, NULL, '&lt;p&gt;In general, use a storage method that allows you to quickly query it. If your collection is huge, you might need something Lucene-based, like ElasticSearch. If you are a SQL crack and your favorite DB supports it, a full-text index might do the trick. For small sizes like the 5000 documents, even Linux'' LocateDb+grep or OSX'' spotlight could be enough.&lt;/p&gt;

&lt;p&gt;The important point is to be able to quickly verify assumptions about the content of your data - how many documents contain X and Y, does any document contain W but not V, etc.&lt;/p&gt;

&lt;p&gt;This will be useful at both the whole set level as well as for analyzing your topic clusters. Finally, a few GNU tools or SQL mastery can also help you profile your document sets more efficiently (n-gram counts/ranks, collocations, concordances, etc)&lt;/p&gt;

&lt;p&gt;Edit: that means, for the above reasons and given your collection size, good old plain text (in a file system or a database) might be more efficient than any "fancy" format.&lt;/p&gt;
', NULL, NULL, '''/p'':67C,102C,147C,180C ''5000'':55C ''abl'':75C ''allow'':9C ''also'':131C ''analyz'':118C ''assumpt'':79C ''base'':26C ''cluster'':121C ''collect'':17C,159C ''colloc'':144C ''concord'':145C ''contain'':89C,96C ''content'':82C ''could'':64C ''counts/ranks'':143C ''crack'':34C ''data'':85C ''databas'':171C ''db'':38C ''document'':56C,88C,95C,136C ''edit'':149C ''effici'':139C,175C ''elasticsearch'':28C ''enough'':66C ''etc'':101C,146C ''even'':57C ''fanci'':178C ''favorit'':37C ''file'':167C ''final'':122C ''format'':179C ''full'':43C ''full-text'':42C ''general'':3C ''given'':157C ''gnu'':125C ''good'':161C ''gram'':142C ''grep'':60C ''help'':132C ''huge'':19C ''import'':70C ''index'':45C ''level'':113C ''like'':27C,53C ''linux'':58C ''locatedb'':59C ''lucen'':25C ''lucene-bas'':24C ''mani'':87C ''masteri'':129C ''mean'':151C ''method'':7C ''might'':21C,46C,172C ''n'':141C ''n-gram'':140C ''need'':22C ''old'':162C ''osx'':62C ''p'':1C,68C,103C,148C ''plain'':163C ''point'':71C ''profil'':134C ''queri'':13C ''quick'':12C,77C ''reason'':155C ''set'':112C,137C ''size'':52C,160C ''small'':51C ''someth'':23C ''spotlight'':63C ''sql'':33C,128C ''storag'':6C ''support'':39C ''system'':168C ''text'':44C,164C ''tool'':126C ''topic'':120C ''trick'':49C ''use'':4C,107C ''v'':100C ''verifi'':78C ''w'':97C ''well'':115C ''whole'':111C ''x'':90C ''y'':92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3797, 1, '2015-01-02 20:03:59.95', 3, 1212, 'Lambda Architecture - How to implement the Merge Layer / Query Layer', '&lt;p&gt;I am reading up about lambda architecture.&lt;/p&gt;

&lt;p&gt;It makes sense. we have queue based data ingestion. we have an in-memory store for data which is very new and we have HDFS for old data.&lt;/p&gt;

&lt;p&gt;So we have our entire data set. in our system. very good.&lt;/p&gt;

&lt;p&gt;but the architecture diagram shows that the merge layer is able to query both the batch layer and the speed layer in one shot.&lt;/p&gt;

&lt;p&gt;How to do that?&lt;/p&gt;

&lt;p&gt;Your batch layer is probably a map reduce job or a HIVE query. The speed layer query is probably a scala program which is execution on the spark.&lt;/p&gt;

&lt;p&gt;Now how will you merge these?&lt;/p&gt;

&lt;p&gt;Is there any guidance.&lt;/p&gt;
', 'bigdata  hadoop', NULL, '''/p'':21C,52C,66C,92C,98C,128C,136C,142C ''abl'':78C ''architectur'':2A,20C,70C ''base'':29C ''batch'':83C,101C ''bigdata'':11B ''data'':30C,40C,51C,59C ''diagram'':71C ''entir'':58C ''execut'':124C ''good'':65C ''guidanc'':141C ''hadoop'':12B ''hdfs'':48C ''hive'':111C ''implement'':5A ''in-memori'':35C ''ingest'':31C ''job'':108C ''lambda'':1A,19C ''layer'':8A,10A,76C,84C,88C,102C,115C ''make'':24C ''map'':106C ''memori'':37C ''merg'':7A,75C,134C ''new'':44C ''old'':50C ''one'':90C ''p'':13C,22C,53C,67C,93C,99C,129C,137C ''probabl'':104C,118C ''program'':121C ''queri'':9A,80C,112C,116C ''queue'':28C ''read'':16C ''reduc'':107C ''scala'':120C ''sens'':25C ''set'':60C ''shot'':91C ''show'':72C ''spark'':127C ''speed'':87C,114C ''store'':38C ''system'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3798, 2, '2015-01-03 01:07:15.08', 3, NULL, NULL, '&lt;p&gt;Try exploring the rich field of "Anomaly Detection in Time Series". Control charts and CUSUMs (or cumulative sum control charts) might help you.&lt;/p&gt;

&lt;p&gt;Simple &lt;a href="http://en.wikipedia.org/wiki/Bullet_graph" rel="nofollow"&gt;Bullet Graphs&lt;/a&gt; might be all you need. Based on historical data and domain knowledge, define normal variance. Then make it clear to stakeholders when the current value is outside of predefined ranges.&lt;/p&gt;

&lt;p&gt;Stephen Few is an expert in business dashboards. Any of his books will help you.&lt;/p&gt;

&lt;p&gt;If you are open to R, try &lt;a href="http://shiny.rstudio.com/" rel="nofollow"&gt;Shiny&lt;/a&gt; to create simple interactive web applications (It is very straightforward). There is also an open source package for &lt;a href="https://github.com/twitter/AnomalyDetection" rel="nofollow"&gt;anomaly detection&lt;/a&gt;, both local and global.  &lt;/p&gt;

&lt;p&gt;Create quick prototypes and get feedback!&lt;/p&gt;
', NULL, NULL, '''/a'':37C,100C,128C ''/p'':25C,68C,85C,133C,141C ''/twitter/anomalydetection'':123C ''/wiki/bullet_graph'':32C ''also'':113C ''anomali'':8C,126C ''applic'':106C ''base'':43C ''book'':81C ''bullet'':35C ''busi'':76C ''chart'':14C,21C ''clear'':56C ''control'':13C,20C ''creat'':102C,135C ''cumul'':18C ''current'':61C ''cusum'':16C ''dashboard'':77C ''data'':46C ''defin'':50C ''detect'':9C,127C ''domain'':48C ''en.wikipedia.org'':31C ''en.wikipedia.org/wiki/bullet_graph'':30C ''expert'':74C ''explor'':3C ''feedback'':140C ''field'':6C ''get'':139C ''github.com'':122C ''github.com/twitter/anomalydetection'':121C ''global'':132C ''graph'':36C ''help'':23C,83C ''histor'':45C ''href'':29C,95C,120C ''interact'':104C ''knowledg'':49C ''local'':130C ''make'':54C ''might'':22C,38C ''need'':42C ''nofollow'':34C,98C,125C ''normal'':51C ''open'':90C,115C ''outsid'':64C ''p'':1C,26C,69C,86C,134C ''packag'':117C ''predefin'':66C ''prototyp'':137C ''quick'':136C ''r'':92C ''rang'':67C ''rel'':33C,97C,124C ''rich'':5C ''seri'':12C ''shini'':99C ''shiny.rstudio.com'':96C ''simpl'':27C,103C ''sourc'':116C ''stakehold'':58C ''stephen'':70C ''straightforward'':110C ''sum'':19C ''time'':11C ''tri'':2C,93C ''valu'':62C ''varianc'':52C ''web'':105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3799, 2, '2015-01-03 01:17:47.957', 0, NULL, NULL, '&lt;p&gt;I have shared a number of resources on &lt;em&gt;time series classification and clustering&lt;/em&gt; in one of my recent answers here on &lt;em&gt;Data Science StackExchange&lt;/em&gt;: &lt;a href="http://datascience.stackexchange.com/a/3764/2452"&gt;http://datascience.stackexchange.com/a/3764/2452&lt;/a&gt;. Hopefully, you will find them relevant to this question and useful.&lt;/p&gt;
', NULL, NULL, '''/a/3764/2452'':34C ''/a/3764/2452&lt;/a&gt;.'':37C ''/em'':16C,29C ''/p'':49C ''answer'':22C ''classif'':13C ''cluster'':15C ''data'':26C ''datascience.stackexchange.com'':33C,36C ''datascience.stackexchange.com/a/3764/2452'':32C ''datascience.stackexchange.com/a/3764/2452&lt;/a&gt;.'':35C ''em'':10C,25C ''find'':41C ''hope'':38C ''href'':31C ''number'':6C ''one'':18C ''p'':1C ''question'':46C ''recent'':21C ''relev'':43C ''resourc'':8C ''scienc'':27C ''seri'':12C ''share'':4C ''stackexchang'':28C ''time'':11C ''use'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9077, 2, '2015-11-27 09:48:24.633', 2, NULL, NULL, '&lt;pre&gt;&lt;code&gt;texts &amp;lt;- c("This is the first document.", 
       "Is this a text?", 
       "This is the second file.", 
       "This is the third text.", 
       "File is not this.") 

test_stopword &amp;lt;- as.data.frame(texts)
ordinal_stopwords  &amp;lt;- c("first","primary","second","secondary","third")

(newdata &amp;lt;- as.data.frame(sapply(texts, function(x)   gsub(paste(ordinal_stopwords, collapse = ''|''), '''', x))))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The output is getting skewed when added in a code block (&lt;a href="http://meta.stackexchange.com/q/270069/302377"&gt;maybe a bug in SE&lt;/a&gt;). But, you would get the desired output.&lt;/p&gt;
', NULL, NULL, '''/a'':80C ''/code'':56C ''/p'':88C ''/pre'':57C ''/q/270069/302377'':74C ''ad'':65C ''as.data.frame'':32C,45C ''block'':69C ''bug'':77C ''c'':5C,37C ''code'':2C,68C ''collaps'':54C ''desir'':86C ''document'':10C ''file'':19C,25C ''first'':9C,38C ''function'':48C ''get'':62C,84C ''gsub'':50C ''href'':71C ''lt'':4C,31C,36C,44C ''mayb'':75C ''meta.stackexchange.com'':73C ''meta.stackexchange.com/q/270069/302377'':72C ''newdata'':43C ''ordin'':34C,52C ''output'':60C,87C ''p'':58C ''past'':51C ''pre'':1C ''primari'':39C ''sappli'':46C ''se'':79C ''second'':18C,40C ''secondari'':41C ''skew'':63C ''stopword'':30C,35C,53C ''test'':29C ''text'':3C,14C,24C,33C,47C ''third'':23C,42C ''would'':83C ''x'':49C,55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3811, 1, '2015-01-04 21:52:38.977', 1, 48, 'normalize identification values properly', '&lt;p&gt;I''m building a neural network to analyze a business'' sales. I''m normalizing all input values to the range &lt;code&gt;{0,1}&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;I''m struggling with the day of the week column. Business days are identified by a number ranging &lt;code&gt;{1-5}&lt;/code&gt; (1=Monday). Normalizing these values to the range &lt;code&gt;{0,1}&lt;/code&gt; is straightforward, but results in a major bias in the final output.&lt;/p&gt;

&lt;p&gt;The reason is that the full range of normalized values for the business day column is explored with every week worth of data, whereas other price-related column explore their full range of normalized values infrequently.&lt;/p&gt;

&lt;p&gt;The business day column ends up being the largest contributor to the final output.&lt;/p&gt;

&lt;p&gt;How can I normalize it to make its contribution more in tune with the rest of the inputs?&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''-5'':56C ''/code'':33C,57C,69C ''/p'':34C,82C,121C,137C,157C ''0'':31C,67C ''1'':32C,55C,58C,68C ''analyz'':17C ''bias'':77C ''build'':12C ''busi'':19C,46C,96C,124C ''code'':30C,54C,66C ''column'':45C,98C,112C,126C ''contribut'':147C ''contributor'':132C ''data'':106C ''day'':41C,47C,97C,125C ''end'':127C ''everi'':102C ''explor'':100C,113C ''final'':80C,135C ''full'':89C,115C ''identif'':2A ''identifi'':49C ''infrequ'':120C ''input'':25C,156C ''largest'':131C ''learn'':7B ''m'':11C,22C,37C ''machin'':6B ''machine-learn'':5B ''major'':76C ''make'':145C ''monday'':59C ''network'':15C ''neural'':14C ''neuralnetwork'':8B ''normal'':1A,23C,60C,92C,118C,142C ''number'':52C ''output'':81C,136C ''p'':9C,35C,83C,122C,138C ''price'':110C ''price-rel'':109C ''proper'':4A ''rang'':29C,53C,65C,90C,116C ''reason'':85C ''relat'':111C ''rest'':153C ''result'':73C ''sale'':20C ''straightforward'':71C ''struggl'':38C ''tune'':150C ''valu'':3A,26C,62C,93C,119C ''week'':44C,103C ''wherea'':107C ''worth'':104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3810, 2, '2015-01-04 19:29:48.93', 2, NULL, NULL, '&lt;p&gt;SÃ¸ren HÃ¸jsgaard has many quality resources for graphical models in R. He has a tutorial &lt;a href="http://people.math.aau.dk/~sorenh/misc/2012-Oslo-GMwR/GMwR-notes.pdf" rel="nofollow"&gt;"Graphical Models with R"&lt;/a&gt; and a &lt;a href="http://cran.r-project.org/web/views/gR.html" rel="nofollow"&gt;list of CRAN packages&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Additionally, &lt;a href="http://cran.r-project.org/web/packages/mclust/index.html" rel="nofollow"&gt;mclust&lt;/a&gt; is one of the best clustering packages in R.&lt;/p&gt;
', NULL, NULL, '''/a'':30C,44C,56C ''/p'':45C,66C ''/web/packages/mclust/index.html'':52C ''/web/views/gr.html'':37C ''/~sorenh/misc/2012-oslo-gmwr/gmwr-notes.pdf'':23C ''addit'':47C ''best'':61C ''cluster'':62C ''cran'':42C ''cran.r-project.org'':36C,51C ''cran.r-project.org/web/packages/mclust/index.html'':50C ''cran.r-project.org/web/views/gr.html'':35C ''graphic'':11C,26C ''href'':20C,34C,49C ''hã'':4C ''jsgaard'':5C ''list'':40C ''mani'':7C ''mclust'':55C ''model'':12C,27C ''nofollow'':25C,39C,54C ''one'':58C ''p'':1C,46C ''packag'':43C,63C ''people.math.aau.dk'':22C ''people.math.aau.dk/~sorenh/misc/2012-oslo-gmwr/gmwr-notes.pdf'':21C ''qualiti'':8C ''r'':14C,29C,65C ''rel'':24C,38C,53C ''ren'':3C ''resourc'':9C ''sã'':2C ''tutori'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4842, 2, '2015-01-09 00:18:05.037', 3, NULL, NULL, '&lt;p&gt;You have a few problems here.  The first is cleaning your data.  That''s a whole separate issue form anonymization and belongs in another question if you''re still having problems with it.&lt;/p&gt;

&lt;p&gt;The second is your anonymization.  After you have some sort of identifier you''re satisfied with (sounds like you''re using people''s real names), try hashing their names to generate a new id.  This id is useful because you''ll always be able to take the original name and figure out what id it is but won''t be able to derive the real names from just the hashed id (providing your hashing algorithm is good).&lt;/p&gt;

&lt;p&gt;Further reading:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://security.stackexchange.com/a/61878"&gt;http://security.stackexchange.com/a/61878&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stackoverflow.com/a/21563966/4435034"&gt;http://stackoverflow.com/a/21563966/4435034&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a/21563966/4435034'':134C ''/a/21563966/4435034&lt;/a&gt;&lt;/li&gt;'':137C ''/a/61878'':125C ''/a/61878&lt;/a&gt;&lt;/li&gt;'':128C ''/p'':35C,114C,118C ''/ul'':138C ''abl'':80C,97C ''algorithm'':111C ''alway'':78C ''anonym'':21C,41C ''anoth'':25C ''belong'':23C ''clean'':11C ''data'':13C ''deriv'':99C ''figur'':87C ''first'':9C ''form'':20C ''generat'':67C ''good'':113C ''hash'':63C,106C,110C ''href'':122C,131C ''id'':70C,72C,90C,107C ''identifi'':48C ''issu'':19C ''li'':120C,129C ''like'':54C ''ll'':77C ''name'':61C,65C,85C,102C ''new'':69C ''origin'':84C ''p'':1C,36C,115C ''peopl'':58C ''problem'':6C,32C ''provid'':108C ''question'':26C ''re'':29C,50C,56C ''read'':117C ''real'':60C,101C ''satisfi'':51C ''second'':38C ''security.stackexchange.com'':124C,127C ''security.stackexchange.com/a/61878'':123C ''security.stackexchange.com/a/61878&lt;/a&gt;&lt;/li&gt;'':126C ''separ'':18C ''sort'':46C ''sound'':53C ''stackoverflow.com'':133C,136C ''stackoverflow.com/a/21563966/4435034'':132C ''stackoverflow.com/a/21563966/4435034&lt;/a&gt;&lt;/li&gt;'':135C ''still'':30C ''take'':82C ''tri'':62C ''ul'':119C ''use'':57C,74C ''whole'':17C ''won'':94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3801, 2, '2015-01-03 07:58:10.663', 4, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;&lt;strong&gt;when given two time series with different time steps, what is better: Using the Lowest or the biggest time step ?&lt;/strong&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;For your timeseries analysis you should do both: get to the highest granularity possible with the daily dataset, and also repeat the analysis with the monthly dataset. With the monthly dataset you have 120 data points, which is sufficient to get a timeseries model even with seasonality in your data.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;strong&gt;For known and unknown properties, how should I proceed to go from daily to weekly/monthly data ?&lt;/strong&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;To obtain say weekly or monthly data from daily data, you can use smoothing functions. For financial data, you can use moving average or exponential smoothing, but if those do not work for your data, then you can use the spline smoothing function "smooth.spline" in R: &lt;a href="https://stat.ethz.ch/R-manual/R-patched/library/stats/html/smooth.spline.html" rel="nofollow"&gt;https://stat.ethz.ch/R-manual/R-patched/library/stats/html/smooth.spline.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The model returned will have less noise than the original daily dataset, and you can get values for the desired time points. Finally, these data points can be used in your timeseries analysis. &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;strong&gt;For known and unknown properties, how should I proceed to go from weekly/monthly to daily data ?&lt;/strong&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;To obtain daily data when you have monthly or weekly data, you can use interpolation. First, you should find an equation to describe the data. In order to do this you should plot the data (e.g. price over time). When factors are known to you, this equation should be influenced by those factors. When factors are unknown, you can use a best fit equation. The simplest would be a linear function or piecewise linear function, but for financial data this won''t work well. In that case, you should consider piecewise cubic spline interpolation. This link goes into more detail on possible interpolation functions: &lt;a href="http://people.math.gatech.edu/~meyer/MA6635/chap2.pdf" rel="nofollow"&gt;http://people.math.gatech.edu/~meyer/MA6635/chap2.pdf&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;In R, there is a method for doing interpolation of timeseries data. Here you would create a vector with say weekly values and NAs in the gaps for the daily values, and then use the "interpNA" function to get the interpolated values for the NAs. However, this function uses the "approx" function to get the interpolated values, which applies either a linear or constant interpolation. To perform cubic spline interpolation in R, you should use the "splinefun" function instead.&lt;/p&gt;

&lt;p&gt;Something to be aware of is that timeseries models typically do some sort of averaging to forecast future values whether you are looking at exponential smoothing or Auto-Regressive Integrated Moving Average (ARIMA) methods amongst others. So a timeseries model to forecast daily values may not be the best choice, but the weekly or monthly models may be better. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3802, 2, '2015-01-03 08:48:51.07', 3, NULL, NULL, '&lt;p&gt;I''m not an expert in this area, but I believe that your question is concerned with &lt;em&gt;time series aggregation and disaggregation&lt;/em&gt;. If that is the case, here are some hopefully relevant resources, which might be helpful in solving your problem (first five items are main, but representative, and last two are supplementary):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www4.ncsu.edu/~jjseater/tempaggecontimeseries.pdf" rel="nofollow"&gt;Temporal Aggregation and Economic Time Series&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://journal.r-project.org/archive/2013-2/sax-steiner.pdf" rel="nofollow"&gt;Temporal Disaggregation of Time Series&lt;/a&gt; (IMHO, an excellent overview paper)&lt;/li&gt;
&lt;li&gt;&lt;a href="http://cran.r-project.org/web/views/TimeSeries.html" rel="nofollow"&gt;CRAN Task View: Time Series Analysis&lt;/a&gt; (R-focused)&lt;/li&gt;
&lt;li&gt;&lt;a href="http://people.su.se/~lundh/reproduce/introduction_ts.pdf" rel="nofollow"&gt;Introduction to R''s Time Series Facilities&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://faculty.washington.edu/ezivot/econ424/Working%20with%20Time%20Series%20Data%20in%20R.pdf" rel="nofollow"&gt;Working with Financial Time Series Data in R&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.scausa.com/tsfbook.php" rel="nofollow"&gt;Notes on chapters contents for the book "Time Series Analysis and Forecasting"&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/q/121599/31372"&gt;Discussion on Cross Validated&lt;/a&gt; on daily to monthly data conversion (Python-focused)&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5588, 2, '2015-04-22 16:48:21.76', 4, NULL, NULL, '&lt;p&gt;The type of ML problem you are trying to solve is a regression problem.  Essentially, given your attributes, predict a continuous variable.&lt;/p&gt;

&lt;p&gt;There are multiple ways to solve your problem, but (assuming attributes are numerical) they all boil down to "plot your data points, then draw a line through them".  The different methods calculate the placement of this predictor based on various different measures of error; the most basic probably being least squares, which aims to minimize the sum of squared errors.  &lt;/p&gt;

&lt;p&gt;Metacademy is a great website for learning ML related stuff online, because they provide roadmaps that tell you what you need to learn before learning ______, as well as how to learn those prerequisites.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.metacademy.org/graphs/concepts/linear_regression" rel="nofollow"&gt;Here&lt;/a&gt; is a link to the metacademy page for linear regression, which is where you''re going to want to start.  I''d recommend watching the coursera videos from Andrew Ng.  If you''re just getting started with ML, his course is a wonderful introduction.  However, a common complaint for the coursera class is it''s too shallow, and is to be considered as an overview of machine learning.  If you consider yourself a little more mathematically savvy, I hear the Stanford course (also taught by him) may be a more in depth resource for you.&lt;/p&gt;

&lt;p&gt;You should implement your own basic linear regression at first, just to get a deeper understanding of how it works.  Afterwords, you can just use a python ML library called scikit learn to perform the regression (&lt;a href="http://scikit-learn.org/stable/modules/linear_model.html#ordinary-least-squares" rel="nofollow"&gt;link here&lt;/a&gt;).  I strongly recommend you take this approach for all ML algorithms; strive to understand at least how the algorithms work at a basic level.  It is far too easy for someone just starting to learn about ML to see all the implemented algorithms in scikit learn go on to use the algorithms as black boxes without understanding what the algorithm is actually doing.&lt;/p&gt;

&lt;p&gt;Lastly, in order to evaluate the different methods, you should split your dataset into two different parts; a training set with which to train your models on, then a testing set with which to test how accurate the predictions are.  The idea here being that your data is representative of the "wild" data that you''ll see, so by purposely hiding some data, you can effectively simulate how the algorithm will perform in "real world" conditions.  &lt;/p&gt;

&lt;p&gt;Generally, people go with something along the lines of 70% of your data being used to train, and 30% of it being used to test.  Later on, as you get into complicated models with multiple parameters, it will help to split your data into 3 parts: training, used to train your model, validation, used to adjust model parameters, and finally, a test set, used to measure how accurate the model is.&lt;/p&gt;

&lt;p&gt;Good luck!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3806, 2, '2015-01-04 05:22:48.67', 2, NULL, NULL, '&lt;p&gt;The &lt;em&gt;optimal&lt;/em&gt; solution very much depends on multiple factors, including your (current and future) business and IT processes, stakeholders'' needs and preferences, and, in general, business and IT architectures. So, IMHO it''s difficult to answer this wide and not well-defined question. Having said that, I hope that you will find the following earlier &lt;strong&gt;answers&lt;/strong&gt; of mine here on &lt;em&gt;Data Science StackExchange&lt;/em&gt; relevant and useful.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;On &lt;strong&gt;data analysis workflow&lt;/strong&gt; (introductory): &lt;a href="http://datascience.stackexchange.com/a/1006/2452"&gt;http://datascience.stackexchange.com/a/1006/2452&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;On &lt;strong&gt;data analysis workflow&lt;/strong&gt; (more detailed): &lt;a href="http://datascience.stackexchange.com/a/759/2452"&gt;http://datascience.stackexchange.com/a/759/2452&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;On &lt;strong&gt;dashboard visualization&lt;/strong&gt;: &lt;a href="http://datascience.stackexchange.com/a/907/2452"&gt;http://datascience.stackexchange.com/a/907/2452&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;On &lt;strong&gt;big data visualization&lt;/strong&gt;: &lt;a href="http://datascience.stackexchange.com/a/3723/2452"&gt;http://datascience.stackexchange.com/a/3723/2452&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3807, 1, '2015-01-04 10:59:42.973', 1, 104, 'How i can generate the probabilistic graph for my dataset?', '&lt;p&gt;Im doing my academic project. im having the base paper for reference  the paper is IEEE paper "effective and efficient clustering methods for correlated probabilistic graph". i wish to do this in R tool. in this paper two algorithm are implemented. i like to implement the peedr algorithm in the paper. how can i give the input for that algorithm.? suggest the packgages in R tool
 the paper can be found here&lt;br&gt;
&lt;a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6570474" rel="nofollow"&gt;http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6570474&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3814, 1, '2015-01-05 18:34:55.92', 1, 68, 'Communicating clearly about "samples"', '&lt;p&gt;It is often pointed out that &lt;em&gt;sample&lt;/em&gt; is an overloaded term in statistics and the sciences being supported by statistics.  In my field (geological sciences) as in most other sciences, the process of collecting meaningful data is critical and discussions about the traps and pitfalls in that process talk about &lt;em&gt;sampling&lt;/em&gt;.  Not far down the road from that, particularly when lab results are back, conversations involving statisticians, data scientists, geomathematicians, GIS analysts and even &lt;em&gt;normal&lt;/em&gt; geologists are likely to attempt to include multiple meanings of &lt;em&gt;sample&lt;/em&gt; in the same sentence!&lt;/p&gt;

&lt;p&gt;Q: Have any data scientists (or statisticians) found practical ways to communicate these different meanings?  &lt;/p&gt;

&lt;p&gt;One way is to always add soil, rock, statistical and so on before &lt;em&gt;sample&lt;/em&gt;.  But I was curious if there are any other approaches to effective communication that are in use.&lt;/p&gt;
', 'statistics  sampling', NULL, '''/em'':16C,62C,88C,101C,140C ''/p'':106C,123C,158C ''add'':130C ''alway'':129C ''analyst'':83C ''approach'':150C ''attempt'':93C ''back'':75C ''clear'':2A ''collect'':43C ''communic'':1A,119C,153C ''convers'':76C ''critic'':47C ''curious'':144C ''data'':45C,79C,111C ''differ'':121C ''discuss'':49C ''effect'':152C ''em'':14C,60C,86C,99C,138C ''even'':85C ''far'':64C ''field'':32C ''found'':115C ''geolog'':33C ''geologist'':89C ''geomathematician'':81C ''gis'':82C ''includ'':95C ''involv'':77C ''lab'':72C ''like'':91C ''mean'':97C,122C ''meaning'':44C ''multipl'':96C ''normal'':87C ''often'':10C ''one'':125C ''overload'':19C ''p'':7C,107C,124C ''particular'':70C ''pitfal'':54C ''point'':11C ''practic'':116C ''process'':41C,57C ''q'':108C ''result'':73C ''road'':67C ''rock'':132C ''sampl'':4A,6B,15C,61C,100C,139C ''scienc'':25C,34C,39C ''scientist'':80C,112C ''sentenc'':105C ''soil'':131C ''statist'':5B,22C,29C,133C ''statistician'':78C,114C ''support'':27C ''talk'':58C ''term'':20C ''trap'':52C ''use'':157C ''way'':117C,126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4884, 1, '2015-01-15 18:32:36.883', 0, 103, 'What are the current killing machine learning methods?', '&lt;p&gt;I was wondering whether we could list machine learning winning methods to apply in many fields of interest: NLP, image, vision, medical, deep package inspection, etc. I mean, if someone will get started a new ML project, what are the ML methods that cannot be forgotten?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':59C ''appli'':25C ''cannot'':56C ''could'':18C ''current'':4A ''deep'':35C ''etc'':38C ''field'':28C ''forgotten'':58C ''get'':44C ''imag'':32C ''inspect'':37C ''interest'':30C ''kill'':5A ''learn'':7A,11B,21C ''list'':19C ''machin'':6A,10B,20C ''machine-learn'':9B ''mani'':27C ''mean'':40C ''medic'':34C ''method'':8A,23C,54C ''ml'':48C,53C ''new'':47C ''nlp'':31C ''p'':12C ''packag'':36C ''project'':49C ''someon'':42C ''start'':45C ''vision'':33C ''whether'':16C ''win'':22C ''wonder'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3815, 2, '2015-01-05 22:16:06.093', 1, NULL, NULL, '&lt;p&gt;"Sample" as a noun usually refers to a single data point.  "Sample" as a verb is the act of extracting a subset of data points from some larger body (reality or a larger dataset).  The only way to be less ambiguous is use more specific words than "data" or "sample".&lt;/p&gt;

&lt;h2&gt;Example:&lt;/h2&gt;

&lt;p&gt;Say you collect 1MM data points from four different sensors in the field giving you four sets of 250k data points.  Say this data is too big for some demo of a model you''re testing or an analysis you''re running, so you pick 100k data points evenly split across the four sensors (giving four sets of 25k data points).  &lt;/p&gt;

&lt;p&gt;In this example, you''re sampling twice.  First, to gather your 1MM data points sampling from reality.  Second, you sample again to decrease the size of your data set to something more manageable.  ''Data'' or ''sample'' could refer to reality, the 1MM dataset, the 100k dataset, or any of the sensor-specific subsets.  To make it less ambiguous, establish a unique name as soon as possible for each possible definition you''ll be working with.  (''reality'' for the set of all possibly observed samples.  ''the complete dataset'', something derived from the source of the dataset, or even X for the full 1MM dataset.  ''our trial dataset'', or even Y for the small 100k dataset.&lt;/p&gt;

&lt;p&gt;What you actually do comes down to context and what''s appropriate for your intended audience, but the general answer is to use more specific words.&lt;/p&gt;
', NULL, NULL, '''/h2'':55C ''/p'':52C,118C,235C,263C ''100k'':102C,164C,233C ''1mm'':60C,131C,161C,222C ''250k'':75C ''25k'':115C ''across'':107C ''act'':19C ''actual'':239C ''ambigu'':42C,178C ''analysi'':95C ''answer'':256C ''appropri'':248C ''audienc'':252C ''big'':83C ''bodi'':30C ''collect'':59C ''come'':241C ''complet'':206C ''context'':244C ''could'':156C ''data'':11C,25C,49C,61C,76C,80C,103C,116C,132C,147C,153C ''dataset'':35C,162C,165C,207C,215C,223C,226C,234C ''decreas'':142C ''definit'':190C ''demo'':86C ''deriv'':209C ''differ'':65C ''establish'':179C ''even'':105C,217C,228C ''exampl'':54C,122C ''extract'':21C ''field'':69C ''first'':127C ''four'':64C,72C,109C,112C ''full'':221C ''gather'':129C ''general'':255C ''give'':70C,111C ''h2'':53C ''intend'':251C ''larger'':29C,34C ''less'':41C,177C ''ll'':192C ''make'':175C ''manag'':152C ''model'':89C ''name'':182C ''noun'':5C ''observ'':203C ''p'':1C,56C,119C,236C ''pick'':101C ''point'':12C,26C,62C,77C,104C,117C,133C ''possibl'':186C,189C,202C ''re'':91C,97C,124C ''realiti'':31C,136C,159C,196C ''refer'':7C,157C ''run'':98C ''sampl'':2C,13C,51C,125C,134C,139C,155C,204C ''say'':57C,78C ''second'':137C ''sensor'':66C,110C,171C ''sensor-specif'':170C ''set'':73C,113C,148C,199C ''singl'':10C ''size'':144C ''small'':232C ''someth'':150C,208C ''soon'':184C ''sourc'':212C ''specif'':46C,172C,261C ''split'':106C ''subset'':23C,173C ''test'':92C ''trial'':225C ''twice'':126C ''uniqu'':181C ''use'':44C,259C ''usual'':6C ''verb'':16C ''way'':38C ''word'':47C,262C ''work'':194C ''x'':218C ''y'':229C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3816, 2, '2015-01-05 22:33:02.01', 2, NULL, NULL, '&lt;p&gt;As Neil said in the comments, split out a &lt;a href="http://en.wikipedia.org/wiki/Test_set" rel="nofollow"&gt;test set&lt;/a&gt; (data you don''t use in training either model) and see how each trained model performs on the test set.&lt;/p&gt;
', NULL, NULL, '''/a'':20C ''/p'':41C ''/wiki/test_set'':15C ''comment'':7C ''data'':21C ''either'':28C ''en.wikipedia.org'':14C ''en.wikipedia.org/wiki/test_set'':13C ''href'':12C ''model'':29C,35C ''neil'':3C ''nofollow'':17C ''p'':1C ''perform'':36C ''rel'':16C ''said'':4C ''see'':31C ''set'':19C,40C ''split'':8C ''test'':18C,39C ''train'':27C,34C ''use'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3819, 2, '2015-01-06 01:00:54.457', 2, NULL, NULL, '&lt;p&gt;Being able to aggregate data based on date segments is a piece of cake using Tableau software. You would simply plug your data into the tool, and then you can drag and drop both the metric and date dimensions onto a report body space. Tableau will instantaneously whip up aggregate visualizations and/or detail data tables, on the fly. You can group/sum by year, quarter, month, week, day, hour, etc. (standard, built in, out of the box functionality offered by the tool)&lt;/p&gt;

&lt;p&gt;Also, if you wanted to incorporate additional data later on (which I assume is what you meant by ''unknown properties''), you can import another data set and easily append it onto the first one, as long as it also offers dates.&lt;/p&gt;

&lt;p&gt;I would recommend checking out the free version, which I believe is called Tableau Public. &lt;/p&gt;
', NULL, NULL, '''/p'':83C,126C,143C ''abl'':3C ''addit'':91C ''aggreg'':5C,51C ''also'':85C,123C ''and/or'':53C ''anoth'':108C ''append'':113C ''assum'':97C ''base'':7C ''believ'':138C ''bodi'':44C ''box'':77C ''built'':72C ''cake'':15C ''call'':140C ''check'':131C ''data'':6C,24C,55C,92C,109C ''date'':9C,39C,125C ''day'':68C ''detail'':54C ''dimens'':40C ''drag'':32C ''drop'':34C ''easili'':112C ''etc'':70C ''first'':117C ''fli'':59C ''free'':134C ''function'':78C ''group/sum'':62C ''hour'':69C ''import'':107C ''incorpor'':90C ''instantan'':48C ''later'':93C ''long'':120C ''meant'':101C ''metric'':37C ''month'':66C ''offer'':79C,124C ''one'':118C ''onto'':41C,115C ''p'':1C,84C,127C ''piec'':13C ''plug'':22C ''properti'':104C ''public'':142C ''quarter'':65C ''recommend'':130C ''report'':43C ''segment'':10C ''set'':110C ''simpli'':21C ''softwar'':18C ''space'':45C ''standard'':71C ''tabl'':56C ''tableau'':17C,46C,141C ''tool'':27C,82C ''unknown'':103C ''use'':16C ''version'':135C ''visual'':52C ''want'':88C ''week'':67C ''whip'':49C ''would'':20C,129C ''year'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3821, 1, '2015-01-06 04:34:09.967', 5, 169, 'Finding unpredictability or uncertainty in a time series', '&lt;p&gt;I am interested in finding a statistic that tracks the unpredictability of a time series. For simplicity sake, assume that each value in the time series is either 1 or 0. So for example, the following two time series are entirely predictable
TS1: 1 1 1 1 1 1 1 1
TS2: 0 1 0 1 0 1 0 1 0 1 0 1&lt;/p&gt;

&lt;p&gt;However, the following time series is not that predictable:
TS3: 1 1 0 1 0 0 1 0 0 0 0 0 1 1 0 1 1 1&lt;/p&gt;

&lt;p&gt;I am looking for a statistic that given a time series, would return a number between 0 and 1 with 0 indicating that the series is completely predictable and 1 indicating the series in completely unpredictable.&lt;/p&gt;

&lt;p&gt;I looked at some entropy measures like Kolmogorov Complexity and Shannon entropy, but neither seem to fit my requirement. In Kolmogorov complexity, the statistic value changes depending on the length of the time series (as in "1 0 1 0 1" and "1 0 1 0" have different complexities, so its not possible to compare predictability of two time series with differing number of observations). In Shannon entropy, the order of observations didn''t seem to matter. &lt;/p&gt;

&lt;p&gt;Any pointers on what would be a good statistic for my requirement?&lt;/p&gt;
', 'time-series', NULL, '''/p'':77C,107C,145C,224C,238C ''0'':43C,65C,67C,69C,71C,73C,75C,91C,93C,94C,96C,97C,98C,99C,100C,103C,125C,129C,184C,186C,190C,192C ''1'':41C,56C,57C,58C,59C,60C,61C,62C,63C,66C,68C,70C,72C,74C,76C,89C,90C,92C,95C,101C,102C,104C,105C,106C,127C,138C,183C,185C,187C,189C,191C ''assum'':31C ''chang'':172C ''compar'':201C ''complet'':135C,143C ''complex'':155C,168C,195C ''depend'':173C ''didn'':219C ''differ'':194C,208C ''either'':40C ''entir'':53C ''entropi'':151C,158C,214C ''exampl'':46C ''find'':1A,17C ''fit'':163C ''follow'':48C,81C ''given'':116C ''good'':233C ''howev'':79C ''indic'':130C,139C ''interest'':15C ''kolmogorov'':154C,167C ''length'':176C ''like'':153C ''look'':111C,148C ''matter'':223C ''measur'':152C ''neither'':160C ''number'':123C,209C ''observ'':211C,218C ''order'':216C ''p'':12C,78C,108C,146C,225C ''pointer'':227C ''possibl'':199C ''predict'':54C,87C,136C,202C ''requir'':165C,237C ''return'':121C ''sake'':30C ''seem'':161C,221C ''seri'':8A,11B,27C,38C,51C,83C,119C,133C,141C,180C,206C ''shannon'':157C,213C ''simplic'':29C ''statist'':19C,114C,170C,234C ''time'':7A,10B,26C,37C,50C,82C,118C,179C,205C ''time-seri'':9B ''track'':21C ''ts1'':55C ''ts2'':64C ''ts3'':88C ''two'':49C,204C ''uncertainti'':4A ''unpredict'':2A,23C,144C ''valu'':34C,171C ''would'':120C,230C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3823, 2, '2015-01-06 07:40:12.223', 4, NULL, NULL, '&lt;p&gt;Since you have looked at Kolmogorov-Smirnov and Shannon entropy measures, I would like to suggest some other hopefully relevant options. First of all, you could take a look at the so-called &lt;a href="http://en.wikipedia.org/wiki/Approximate_entropy" rel="nofollow"&gt;approximate entropy $ApEn$&lt;/a&gt;. Other potential statistics include &lt;em&gt;block entropy&lt;/em&gt;, &lt;em&gt;T-complexity&lt;/em&gt; (&lt;em&gt;T-entropy&lt;/em&gt;) as well as &lt;em&gt;Tsallis entropy&lt;/em&gt;: &lt;a href="http://members.noa.gr/anastasi/papers/B29.pdf" rel="nofollow"&gt;http://members.noa.gr/anastasi/papers/B29.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In addition to the above-mentioned potential measures, I would like to suggest to have a look at available statistics in &lt;em&gt;Bayesian inference-based&lt;/em&gt; model of &lt;em&gt;stochastic volatility&lt;/em&gt; in time series, implemented in &lt;code&gt;R&lt;/code&gt; package &lt;code&gt;stochvol&lt;/code&gt;: &lt;a href="http://cran.r-project.org/web/packages/stochvol" rel="nofollow"&gt;http://cran.r-project.org/web/packages/stochvol&lt;/a&gt; (see detailed &lt;a href="http://cran.r-project.org/web/packages/stochvol/vignettes/article.pdf" rel="nofollow"&gt;vignette&lt;/a&gt;). Such statistics of uncertainty include &lt;em&gt;overall level of volatility&lt;/em&gt; $\mu$, &lt;em&gt;persistence&lt;/em&gt; $\phi$ and &lt;em&gt;volatility of volatility&lt;/em&gt; $\sigma$: &lt;a href="http://simpsonm.public.iastate.edu/BlogPosts/btcvol/KastnerFruwhirthSchnatterASISstochvol.pdf" rel="nofollow"&gt;http://simpsonm.public.iastate.edu/BlogPosts/btcvol/KastnerFruwhirthSchnatterASISstochvol.pdf&lt;/a&gt;. A &lt;strong&gt;comprehensive&lt;/strong&gt; &lt;em&gt;example&lt;/em&gt; of using stochastic volatility model approach and &lt;code&gt;stochvol&lt;/code&gt; package can be found in the excellent blog post &lt;a href="http://www.themattsimpson.com/2014/02/22/exactly-how-volatile-is-bitcoin" rel="nofollow"&gt;"Exactly how volatile is bitcoin?"&lt;/a&gt; by Matt Simpson.&lt;/p&gt;
', NULL, NULL, '''/2014/02/22/exactly-how-volatile-is-bitcoin'':214C ''/a'':47C,150C,222C ''/anastasi/papers/b29.pdf'':77C ''/anastasi/papers/b29.pdf&lt;/a&gt;&lt;/p&gt;'':82C ''/blogposts/btcvol/kastnerfruwhirthschnatterasisstochvol.pdf'':178C ''/blogposts/btcvol/kastnerfruwhirthschnatterasisstochvol.pdf&lt;/a&gt;.'':183C ''/code'':125C,129C,200C ''/em'':55C,60C,65C,72C,111C,117C,161C,165C,172C,190C ''/p'':226C ''/strong'':187C ''/web/packages/stochvol'':134C ''/web/packages/stochvol&lt;/a&gt;'':139C ''/web/packages/stochvol/vignettes/article.pdf'':146C ''/wiki/approximate_entropy'':41C ''above-ment'':88C ''addit'':85C ''apen'':46C ''approach'':196C ''approxim'':44C ''avail'':103C ''base'':110C ''bayesian'':107C ''bitcoin'':221C ''block'':53C ''blog'':208C ''call'':36C ''code'':123C,127C,198C ''complex'':59C ''comprehens'':186C ''could'':28C ''cran.r-project.org'':133C,138C,145C ''cran.r-project.org/web/packages/stochvol'':132C ''cran.r-project.org/web/packages/stochvol&lt;/a&gt;'':137C ''cran.r-project.org/web/packages/stochvol/vignettes/article.pdf'':144C ''detail'':141C ''em'':52C,56C,61C,69C,106C,114C,156C,163C,168C,188C ''en.wikipedia.org'':40C ''en.wikipedia.org/wiki/approximate_entropy'':39C ''entropi'':12C,45C,54C,64C,71C ''exact'':217C ''exampl'':189C ''excel'':207C ''first'':24C ''found'':204C ''hope'':21C ''href'':38C,74C,131C,143C,175C,211C ''implement'':121C ''includ'':51C,155C ''infer'':109C ''inference-bas'':108C ''kolmogorov'':8C ''kolmogorov-smirnov'':7C ''level'':158C ''like'':16C,95C ''look'':5C,31C,101C ''matt'':224C ''measur'':13C,92C ''members.noa.gr'':76C,81C ''members.noa.gr/anastasi/papers/b29.pdf'':75C ''members.noa.gr/anastasi/papers/b29.pdf&lt;/a&gt;&lt;/p&gt;'':80C ''mention'':90C ''model'':112C,195C ''mu'':162C ''nofollow'':43C,79C,136C,148C,180C,216C ''option'':23C ''overal'':157C ''p'':1C,83C ''packag'':126C,201C ''persist'':164C ''phi'':166C ''post'':209C ''potenti'':49C,91C ''r'':124C ''rel'':42C,78C,135C,147C,179C,215C ''relev'':22C ''see'':140C ''seri'':120C ''shannon'':11C ''sigma'':173C ''simpson'':225C ''simpsonm.public.iastate.edu'':177C,182C ''simpsonm.public.iastate.edu/blogposts/btcvol/kastnerfruwhirthschnatterasisstochvol.pdf'':176C ''simpsonm.public.iastate.edu/blogposts/btcvol/kastnerfruwhirthschnatterasisstochvol.pdf&lt;/a&gt;.'':181C ''sinc'':2C ''smirnov'':9C ''so-cal'':34C ''statist'':50C,104C,152C ''stochast'':115C,193C ''stochvol'':128C,199C ''strong'':185C ''suggest'':18C,97C ''t-complex'':57C ''t-entropi'':62C ''take'':29C ''time'':119C ''tsalli'':70C ''uncertainti'':154C ''use'':192C ''vignett'':149C ''volatil'':116C,160C,169C,171C,194C,219C ''well'':67C ''would'':15C,94C ''www.themattsimpson.com'':213C ''www.themattsimpson.com/2014/02/22/exactly-how-volatile-is-bitcoin'':212C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3825, 1, '2015-01-06 14:08:35.533', 1, 62, 'Mimic a Mahout like system', '&lt;p&gt;I have a data set, in excel format, with &lt;strong&gt;account names, reported symptoms, a determined root cause and a date in month year format&lt;/strong&gt; for each row. I am trying to implement a mahout like system with a purpose of determining the likelihood symptoms an account can report by doing a user based similarity kind of a thing. Technically, I am just hoping to tweak the recommendation system into a deterministic system to spot out the probable symptoms an account can report on. Instead of ratings, I can get the frequency of symptoms by accounts. Is it possible to use a programming language or any other software to build such system?&lt;/p&gt;

&lt;p&gt;Here is an example:&lt;/p&gt;

&lt;p&gt;Account : &lt;em&gt;X&lt;/em&gt; Symptoms : &lt;em&gt;AB, AD, AB, AB&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;Account : &lt;em&gt;Y&lt;/em&gt;  Symptoms : &lt;em&gt;AE, AE, AB, AB, EA&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;For the sake of this example, let''s assume that all the dates are this month. &lt;/p&gt;

&lt;p&gt;O/P: Account : X Symptom: AE&lt;/p&gt;

&lt;p&gt;Here both of them have reported &lt;em&gt;AB&lt;/em&gt; 2 or more times. I could fix such number as a threshold to look for probable symptoms. &lt;/p&gt;
', 'recommendation  similarity  apache-mahout', NULL, '''/em'':136C,143C,149C,157C,193C ''/p'':125C,131C,144C,158C,176C,183C,211C ''/strong'':37C ''2'':194C ''ab'':139C,141C,142C,154C,155C,192C ''account'':22C,59C,93C,108C,133C,146C,179C ''ad'':140C ''ae'':152C,153C,182C ''apach'':9B ''apache-mahout'':8B ''assum'':168C ''base'':66C ''build'':122C ''caus'':29C ''could'':199C ''data'':15C ''date'':32C,172C ''determin'':27C,54C ''determinist'':84C ''ea'':156C ''em'':134C,138C,147C,151C,191C ''exampl'':130C,165C ''excel'':18C ''fix'':200C ''format'':19C,36C ''frequenc'':104C ''get'':102C ''hope'':76C ''implement'':45C ''instead'':97C ''kind'':68C ''languag'':116C ''let'':166C ''like'':4A,48C ''likelihood'':56C ''look'':207C ''mahout'':3A,10B,47C ''mimic'':1A ''month'':34C,175C ''name'':23C ''number'':202C ''o/p'':178C ''p'':11C,126C,132C,145C,159C,177C,184C ''possibl'':111C ''probabl'':90C,209C ''program'':115C ''purpos'':52C ''rate'':99C ''recommend'':6B,80C ''report'':24C,61C,95C,190C ''root'':28C ''row'':40C ''sake'':162C ''set'':16C ''similar'':7B,67C ''softwar'':120C ''spot'':87C ''strong'':21C ''symptom'':25C,57C,91C,106C,137C,150C,181C,210C ''system'':5A,49C,81C,85C,124C ''technic'':72C ''thing'':71C ''threshold'':205C ''time'':197C ''tri'':43C ''tweak'':78C ''use'':113C ''user'':65C ''x'':135C,180C ''y'':148C ''year'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3826, 1, '2015-01-06 20:43:35.48', 1, 40, 'Correlation threshold for Neural Network features selection', '&lt;p&gt;I''m trying to do a correlation analysis between inputs and outputs inspecting the data in order to understand which input variables to include. What could be a threshold in the correlation value to consider a variable eligible to be an input for my Neural Network?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':58C ''analysi'':19C ''consid'':46C ''correl'':1A,18C,43C ''could'':37C ''data'':26C ''elig'':49C ''featur'':6A ''includ'':35C ''input'':21C,32C,53C ''inspect'':24C ''learn'':10B ''m'':13C ''machin'':9B ''machine-learn'':8B ''network'':5A,57C ''neural'':4A,56C ''order'':28C ''output'':23C ''p'':11C ''select'':7A ''threshold'':2A,40C ''tri'':14C ''understand'':30C ''valu'':44C ''variabl'':33C,48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3827, 2, '2015-01-07 01:26:17.56', 1, NULL, NULL, '&lt;p&gt;Given non-linearity of neural networks, I believe correlation analysis isn''t a good way to estimate importance of variables. For example, imagine that you have 2 input variables - &lt;code&gt;x1&lt;/code&gt; and &lt;code&gt;x2&lt;/code&gt; - and following conditions hold: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;cor(x2, y) = 1&lt;/code&gt; if &lt;code&gt;x1 = 1&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;cor(x2, y) = 0&lt;/code&gt; otherwise&lt;/li&gt;
&lt;li&gt;&lt;code&gt;x1 = 1&lt;/code&gt; in 10% of cases&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;That is, &lt;code&gt;x2&lt;/code&gt; is a very good predictor for &lt;code&gt;y&lt;/code&gt;, but only given that &lt;code&gt;x1 = 1&lt;/code&gt;, which is the case only in 10% of data. Taking into account correlations of &lt;code&gt;x1&lt;/code&gt; and &lt;code&gt;x2&lt;/code&gt; separately won''t expose this dependency, and you will most likely drop out both variables. &lt;/p&gt;

&lt;p&gt;There are other ways to perform feature selection, however. Simplest one is to train your model with all possible sets of variables and check the best subset. This is pretty inefficient with many variables, though, so many ways to improve it exist. For a good introduction in best subset selection see chapter 6.1 of &lt;a href="http://www-bcf.usc.edu/~gareth/ISL/" rel="nofollow"&gt;Introduction to Statistical Learning&lt;/a&gt;. &lt;/p&gt;
', NULL, NULL, '''/a'':204C ''/code'':34C,38C,51C,56C,64C,71C,83C,92C,100C,117C,121C ''/li'':57C,66C,76C ''/p'':43C,137C,205C ''/ul'':77C ''/~gareth/isl/'':197C ''0'':63C ''1'':50C,55C,70C,99C ''10'':73C,107C ''2'':29C ''6.1'':191C ''account'':112C ''analysi'':12C ''believ'':10C ''best'':164C,186C ''case'':75C,104C ''chapter'':190C ''check'':162C ''code'':32C,36C,46C,53C,59C,68C,81C,90C,97C,115C,119C ''condit'':41C ''cor'':47C,60C ''correl'':11C,113C ''data'':109C ''depend'':127C ''drop'':133C ''estim'':19C ''exampl'':24C ''exist'':180C ''expos'':125C ''featur'':145C ''follow'':40C ''given'':2C,95C ''good'':16C,87C,183C ''hold'':42C ''howev'':147C ''href'':194C ''imagin'':25C ''import'':20C ''improv'':178C ''ineffici'':169C ''input'':30C ''introduct'':184C,200C ''isn'':13C ''learn'':203C ''li'':45C,58C,67C ''like'':132C ''linear'':5C ''mani'':171C,175C ''model'':154C ''network'':8C ''neural'':7C ''nofollow'':199C ''non'':4C ''non-linear'':3C ''one'':149C ''otherwis'':65C ''p'':1C,78C,138C ''perform'':144C ''possibl'':157C ''predictor'':88C ''pretti'':168C ''rel'':198C ''see'':189C ''select'':146C,188C ''separ'':122C ''set'':158C ''simplest'':148C ''statist'':202C ''subset'':165C,187C ''take'':110C ''though'':173C ''train'':152C ''ul'':44C ''variabl'':22C,31C,136C,160C,172C ''way'':17C,142C,176C ''won'':123C ''www-bcf.usc.edu'':196C ''www-bcf.usc.edu/~gareth/isl/'':195C ''x1'':33C,54C,69C,98C,116C ''x2'':37C,48C,61C,82C,120C ''y'':49C,62C,91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4826, 2, '2015-01-07 07:39:04.027', 1, NULL, NULL, '&lt;p&gt;Suppose that a forum post has, on average, 2000 characters, which is more or less the equivalent of a page of text, than the total memory needed is 10MB if text is ASCII. Even if the text is Unicode encoded in an Asian language it will take 40MB. &lt;/p&gt;

&lt;p&gt;This is far too little for modern computers, so a simple text format is the best since it can be parsed in the fastest way, and loaded into RAM all at once.&lt;/p&gt;
', NULL, NULL, '''/p'':50C,84C ''10mb'':30C ''2000'':10C ''40mb'':49C ''ascii'':34C ''asian'':44C ''averag'':9C ''best'':67C ''charact'':11C ''comput'':59C ''encod'':41C ''equival'':18C ''even'':35C ''far'':54C ''fastest'':75C ''format'':64C ''forum'':5C ''languag'':45C ''less'':16C ''littl'':56C ''load'':78C ''memori'':27C ''modern'':58C ''need'':28C ''p'':1C,51C ''page'':21C ''pars'':72C ''post'':6C ''ram'':80C ''simpl'':62C ''sinc'':68C ''suppos'':2C ''take'':48C ''text'':23C,32C,38C,63C ''total'':26C ''unicod'':40C ''way'':76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4880, 1, '2015-01-15 01:57:38.387', 2, 91, 'Generate tags for live chat transcripts', '&lt;p&gt;I''m wondering if there''s a way to automatically generate a list of tags for live chat transcripts without domain knowledge.  I''ve tried applying NLP chunking to the chat transcripts and keep only the noun phrases as tag candidates.  However, this approach would generate too many useless noun phrases.  I could use some rules to prune out some of them, but it would be hard to generalize the rules.&lt;/p&gt;
', 'machine-learning  nlp', NULL, '''/p'':83C ''appli'':37C ''approach'':55C ''automat'':21C ''candid'':52C ''chat'':5A,29C,42C ''chunk'':39C ''could'':64C ''domain'':32C ''general'':80C ''generat'':1A,22C,57C ''hard'':78C ''howev'':53C ''keep'':45C ''knowledg'':33C ''learn'':9B ''list'':24C ''live'':4A,28C ''m'':13C ''machin'':8B ''machine-learn'':7B ''mani'':59C ''nlp'':10B,38C ''noun'':48C,61C ''p'':11C ''phrase'':49C,62C ''prune'':69C ''rule'':67C,82C ''tag'':2A,26C,51C ''transcript'':6A,30C,43C ''tri'':36C ''use'':65C ''useless'':60C ''ve'':35C ''way'':19C ''without'':31C ''wonder'':14C ''would'':56C,76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4831, 1, '2015-01-08 10:09:32.01', 3, 525, 'Similarity measure based on multiple classes from a hierarchical taxonomy?', '&lt;p&gt;Could anyone recommend a good similarity measure for objects which have multiple classes, where each class is part of a hierarchy?&lt;/p&gt;

&lt;p&gt;For example, let''s say the classes look like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1 Produce
  1.1 Eggs
    1.1.1 Duck eggs
    1.1.2 Chicken eggs
  1.2 Milk
    1.2.1 Cow milk
    1.2.2 Goat milk
2 Baked goods
  2.1 Cakes
    2.1.1 Cheesecake
    2.1.2 Chocolate
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;An object might be tagged with items from the above at any level, e.g.:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Omelette: eggs, milk (1.1, 1.2)
Duck egg omelette: duck eggs, milk (1.1.1, 1.2)
Goat milk chocolate cheesecake: goat milk, cheesecake, chocolate (1.2.2, 2.1.1, 2.1.2)
Beef: produce (1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If the classes weren''t part of a hierarchy, I''d probably I''d look at cosine similarity (or equivalent) between classes assigned to an object, but I''d like to use the fact that different classes with the same parents also have some similarity value (e.g. in the example above, beef has some small similarity to omelette, since they both have items from the class ''1 produce'').&lt;/p&gt;

&lt;p&gt;If it helps, the hierarchy has ~200k classes, with a maximum depth of 5.&lt;/p&gt;
', 'similarity', NULL, '''/code'':75C,122C ''/p'':34C,45C,92C,193C,209C ''/pre'':76C,123C ''1'':48C,121C,191C ''1.1'':50C,98C ''1.1.1'':52C,106C ''1.1.2'':55C ''1.2'':58C,99C,107C ''1.2.1'':60C ''1.2.2'':63C,116C ''2'':66C ''2.1'':69C ''2.1.1'':71C,117C ''2.1.2'':73C,118C ''200k'':201C ''5'':208C ''also'':166C ''anyon'':14C ''assign'':147C ''bake'':67C ''base'':3A ''beef'':119C,176C ''cake'':70C ''cheesecak'':72C,111C,114C ''chicken'':56C ''chocol'':74C,110C,115C ''class'':6A,25C,28C,42C,127C,146C,161C,190C,202C ''code'':47C,94C ''cosin'':141C ''could'':13C ''cow'':61C ''d'':135C,138C,153C ''depth'':206C ''differ'':160C ''duck'':53C,100C,103C ''e.g'':91C,171C ''egg'':51C,54C,57C,96C,101C,104C ''equival'':144C ''exampl'':37C,174C ''fact'':158C ''goat'':64C,108C,112C ''good'':17C,68C ''help'':197C ''hierarch'':9A ''hierarchi'':33C,133C,199C ''item'':84C,187C ''let'':38C ''level'':90C ''like'':44C,154C ''look'':43C,139C ''maximum'':205C ''measur'':2A,19C ''might'':80C ''milk'':59C,62C,65C,97C,105C,109C,113C ''multipl'':5A,24C ''object'':21C,79C,150C ''omelett'':95C,102C,182C ''p'':12C,35C,77C,124C,194C ''parent'':165C ''part'':30C,130C ''pre'':46C,93C ''probabl'':136C ''produc'':49C,120C,192C ''recommend'':15C ''say'':40C ''similar'':1A,11B,18C,142C,169C,180C ''sinc'':183C ''small'':179C ''tag'':82C ''taxonomi'':10A ''use'':156C ''valu'':170C ''weren'':128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4941, 2, '2015-01-25 14:43:21.033', 6, NULL, NULL, '&lt;p&gt;Neo4j and Spark GraphX are meant for solving problem at different level and they are complimentary to each other.&lt;/p&gt;

&lt;p&gt;They can be connected by Neo4j''s Mazerunner extension:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Mazerunner is a Neo4j unmanaged extension and distributed graph
  processing platform that extends Neo4j to do big data graph processing
  jobs while persisting the results back to Neo4j.&lt;/p&gt;
  
  &lt;p&gt;Mazerunner uses a message broker to distribute graph processing jobs
  to Apache Spark''s GraphX module. When an agent job is dispatched, a
  subgraph is exported from Neo4j and written to Apache Hadoop HDFS.&lt;/p&gt;
  
  &lt;p&gt;After Neo4j exports a subgraph to HDFS, a separate Mazerunner service
  for Spark is notified to begin processing that data. The Mazerunner
  service will then start a distributed graph processing algorithm using
  Scala and Spark''s GraphX module. The GraphX algorithm is serialized
  and dispatched to Apache Spark for processing.&lt;/p&gt;
  
  &lt;p&gt;Once the Apache Spark job completes, the results are written back to
  HDFS as a Key-Value list of property updates to be applied back to
  Neo4j.&lt;/p&gt;
  
  &lt;p&gt;Neo4j is then notified that a property update list is available from
  Apache Spark on HDFS. Neo4j batch imports the results and applies the
  updates back to the original graph.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Check out this tutorial to get an idea on how to combine the two:
&lt;a href="http://www.kennybastani.com/2014/11/using-apache-spark-and-neo4j-for-big.html" rel="nofollow"&gt;http://www.kennybastani.com/2014/11/using-apache-spark-and-neo4j-for-big.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/2014/11/using-apache-spark-and-neo4j-for-big.html'':234C ''/2014/11/using-apache-spark-and-neo4j-for-big.html&lt;/a&gt;&lt;/p&gt;'':239C ''/blockquote'':214C ''/p'':21C,32C,63C,99C,151C,181C,213C ''agent'':83C ''algorithm'':131C,141C ''apach'':76C,96C,147C,155C,195C ''appli'':177C,205C ''avail'':193C ''back'':60C,163C,178C,208C ''batch'':200C ''begin'':117C ''big'':51C ''blockquot'':33C ''broker'':69C ''check'':216C ''combin'':227C ''complet'':158C ''complimentari'':17C ''connect'':26C ''data'':52C,120C ''differ'':12C ''dispatch'':86C,145C ''distribut'':42C,71C,128C ''export'':90C,103C ''extend'':47C ''extens'':31C,40C ''get'':221C ''graph'':43C,53C,72C,129C,212C ''graphx'':5C,79C,137C,140C ''hadoop'':97C ''hdfs'':98C,107C,165C,198C ''href'':231C ''idea'':223C ''import'':201C ''job'':55C,74C,84C,157C ''key'':169C ''key-valu'':168C ''level'':13C ''list'':171C,191C ''mazerunn'':30C,35C,65C,110C,122C ''meant'':7C ''messag'':68C ''modul'':80C,138C ''neo4j'':2C,28C,38C,48C,62C,92C,102C,180C,183C,199C ''nofollow'':236C ''notifi'':115C,186C ''origin'':211C ''p'':1C,22C,34C,64C,100C,152C,182C,215C ''persist'':57C ''platform'':45C ''problem'':10C ''process'':44C,54C,73C,118C,130C,150C ''properti'':173C,189C ''rel'':235C ''result'':59C,160C,203C ''scala'':133C ''separ'':109C ''serial'':143C ''servic'':111C,123C ''solv'':9C ''spark'':4C,77C,113C,135C,148C,156C,196C ''start'':126C ''subgraph'':88C,105C ''tutori'':219C ''two'':229C ''unmanag'':39C ''updat'':174C,190C,207C ''use'':66C,132C ''valu'':170C ''written'':94C,162C ''www.kennybastani.com'':233C,238C ''www.kennybastani.com/2014/11/using-apache-spark-and-neo4j-for-big.html'':232C ''www.kennybastani.com/2014/11/using-apache-spark-and-neo4j-for-big.html&lt;/a&gt;&lt;/p&gt;'':237C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4833, 2, '2015-01-08 12:08:43.113', 5, NULL, NULL, '&lt;p&gt;While I don''t have enough expertise to advise you on selection of the best &lt;em&gt;similarity measure&lt;/em&gt;, I''ve seen a number of them in various papers. The following &lt;strong&gt;collection&lt;/strong&gt; of research papers hopefully will be useful to you in determining the optimal measure for your research. Please note that I intentionally included papers, using both &lt;em&gt;frequentist&lt;/em&gt; and &lt;em&gt;Bayesian&lt;/em&gt; approaches to hierarchical classification, including class information, for the sake of more &lt;em&gt;comprehensive&lt;/em&gt; coverage.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Frequentist approach:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://arxiv.org/pdf/cmp-lg/9709008.pdf"&gt;Semantic similarity based on corpus statistics and lexical taxonomy&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.sciencedirect.com/science/article/pii/S0048733313000115"&gt;Canât see the forest for the leaves: Similarity and distance measures for hierarchical taxonomies with a patent classification example&lt;/a&gt; (also see &lt;a href="http://mcnameephd.pbworks.com/w/page/11389167/Research%20Policy%20-%20Similarity%20and%20distance%20measures%20for%20hierarchical%20taxonomies"&gt;additional results and data&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;a href="http://research.microsoft.com/pubs/192155/hier_embd.pdf"&gt;Learning hierarchical similarity metrics&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://arxiv.org/ftp/arxiv/papers/1211/1211.4709.pdf"&gt;A new similarity measure for taxonomy based on edge counting&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.siam.org/meetings/sdm01/pdf/sdm01_22.pdf"&gt;Hierarchical classification of real life documents&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://users.cis.fiu.edu/~taoli/pub/Li-jiis.pdf"&gt;Hierarchical document classification using automatically generated hierarchy&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.cs.ucla.edu/~weiwang/paper/SSDBM09.pdf"&gt;Split-Order distance for clustering and classification hierarchies&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://ccis2k.org/iajit/PDF/vol.8,no.3/791.pdf"&gt;A hierarchical k-NN classifier for textual data&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Bayesian approach:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://ba.stat.cmu.edu/journal/2007/vol02/issue01/shahbaba.pdf"&gt;Improving classification when a class hierarchy is available using a hierarchy-based prior&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://gfx.cs.princeton.edu/pubs/DeCoro_2007_BAF/bayesgenre.pdf"&gt;Bayesian aggregation for hierarchical genre classification&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://oro.open.ac.uk/12991/1/Hierarchical_Classification.pdf"&gt;Hierarchical classification for multiple, distributed web databases&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/12991/1/hierarchical_classification.pdf'':292C ''/a'':108C,136C,148C,160C,178C,192C,207C,224C,241C,271C,285C,300C ''/em'':20C,64C,68C,83C ''/ftp/arxiv/papers/1211/1211.4709.pdf'':167C ''/iajit/pdf/vol.8,no.3/791.pdf'':231C ''/journal/2007/vol02/issue01/shahbaba.pdf'':256C ''/li'':109C,149C,161C,179C,193C,208C,225C,242C,272C,286C,301C ''/meetings/sdm01/pdf/sdm01_22.pdf'':185C ''/p'':85C,91C,249C ''/pdf/cmp-lg/9709008.pdf'':98C ''/pubs/192155/hier_embd.pdf'':155C ''/pubs/decoro_2007_baf/bayesgenre.pdf'':278C ''/science/article/pii/s0048733313000115'':115C ''/strong'':35C,90C,248C ''/ul'':243C,302C ''/w/page/11389167/research%20policy%20-%20similarity%20and%20distance%20measures%20for%20hierarchical%20taxonomies'':143C ''/~taoli/pub/li-jiis.pdf'':199C ''/~weiwang/paper/ssdbm09.pdf'':214C ''addit'':144C ''advis'':10C ''aggreg'':280C ''also'':137C ''approach'':69C,89C,247C ''arxiv.org'':97C,166C ''arxiv.org/ftp/arxiv/papers/1211/1211.4709.pdf'':165C ''arxiv.org/pdf/cmp-lg/9709008.pdf'':96C ''automat'':204C ''avail'':264C ''ba.stat.cmu.edu'':255C ''ba.stat.cmu.edu/journal/2007/vol02/issue01/shahbaba.pdf'':254C ''base'':101C,174C,269C ''bayesian'':67C,246C,279C ''best'':16C ''canâ'':116C ''ccis2k.org'':230C ''ccis2k.org/iajit/pdf/vol.8,no.3/791.pdf'':229C ''class'':74C,261C ''classif'':72C,134C,187C,202C,222C,258C,284C,294C ''classifi'':237C ''cluster'':220C ''collect'':34C ''comprehens'':82C ''corpus'':103C ''count'':177C ''coverag'':84C ''data'':147C,240C ''databas'':299C ''determin'':46C ''distanc'':126C,218C ''distribut'':297C ''document'':191C,201C ''edg'':176C ''em'':17C,62C,66C,81C ''enough'':7C ''exampl'':135C ''expertis'':8C ''follow'':32C ''forest'':120C ''frequentist'':63C,88C ''generat'':205C ''genr'':283C ''gfx.cs.princeton.edu'':277C ''gfx.cs.princeton.edu/pubs/decoro_2007_baf/bayesgenre.pdf'':276C ''hierarch'':71C,129C,157C,186C,200C,233C,282C,293C ''hierarchi'':206C,223C,262C,268C ''hierarchy-bas'':267C ''hope'':39C ''href'':95C,112C,140C,152C,164C,182C,196C,211C,228C,253C,275C,289C ''improv'':257C ''includ'':58C,73C ''inform'':75C ''intent'':57C ''k'':235C ''k-nn'':234C ''learn'':156C ''leav'':123C ''lexic'':106C ''li'':93C,110C,150C,162C,180C,194C,209C,226C,251C,273C,287C ''life'':190C ''mcnameephd.pbworks.com'':142C ''mcnameephd.pbworks.com/w/page/11389167/research%20policy%20-%20similarity%20and%20distance%20measures%20for%20hierarchical%20taxonomies'':141C ''measur'':19C,49C,127C,171C ''metric'':159C ''multipl'':296C ''new'':169C ''nn'':236C ''note'':54C ''number'':25C ''optim'':48C ''order'':217C ''oro.open.ac.uk'':291C ''oro.open.ac.uk/12991/1/hierarchical_classification.pdf'':290C ''p'':1C,86C,244C ''paper'':30C,38C,59C ''patent'':133C ''pleas'':53C ''prior'':270C ''real'':189C ''research'':37C,52C ''research.microsoft.com'':154C ''research.microsoft.com/pubs/192155/hier_embd.pdf'':153C ''result'':145C ''sake'':78C ''see'':118C,138C ''seen'':23C ''select'':13C ''semant'':99C ''similar'':18C,100C,124C,158C,170C ''split'':216C ''split-ord'':215C ''statist'':104C ''strong'':33C,87C,245C ''taxonomi'':107C,130C,173C ''textual'':239C ''ul'':92C,250C ''use'':42C,60C,203C,265C ''users.cis.fiu.edu'':198C ''users.cis.fiu.edu/~taoli/pub/li-jiis.pdf'':197C ''various'':29C ''ve'':22C ''web'':298C ''www.cs.ucla.edu'':213C ''www.cs.ucla.edu/~weiwang/paper/ssdbm09.pdf'':212C ''www.sciencedirect.com'':114C ''www.sciencedirect.com/science/article/pii/s0048733313000115'':113C ''www.siam.org'':184C ''www.siam.org/meetings/sdm01/pdf/sdm01_22.pdf'':183C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4897, 2, '2015-01-17 05:30:01.813', 3, NULL, NULL, '&lt;p&gt;You may want to consider gradient boosted trees rather than random forests. They''re also an ensemble tree-based method, but since this method doesn''t sample dimensions, it won''t run in to the problem of not having a useful predictor available to split on at any particular time. &lt;/p&gt;

&lt;p&gt;Different implementations of GBDT have different ways of handling missing values, which will make a big difference in your case; I believe R does ternary splits which is likely to work fine. &lt;/p&gt;
', NULL, NULL, '''/p'':53C,87C ''also'':16C ''avail'':45C ''base'':21C ''believ'':76C ''big'':70C ''boost'':8C ''case'':74C ''consid'':6C ''differ'':55C,60C,71C ''dimens'':30C ''doesn'':27C ''ensembl'':18C ''fine'':86C ''forest'':13C ''gbdt'':58C ''gradient'':7C ''handl'':63C ''implement'':56C ''like'':83C ''make'':68C ''may'':3C ''method'':22C,26C ''miss'':64C ''p'':1C,54C ''particular'':51C ''predictor'':44C ''problem'':38C ''r'':77C ''random'':12C ''rather'':10C ''re'':15C ''run'':34C ''sampl'':29C ''sinc'':24C ''split'':47C,80C ''ternari'':79C ''time'':52C ''tree'':9C,20C ''tree-bas'':19C ''use'':43C ''valu'':65C ''want'':4C ''way'':61C ''won'':32C ''work'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4834, 1, '2015-01-08 13:44:20.16', 1, 26, 'Correcting Datasets with artificially low starting values', '&lt;p&gt;I am working on a project where we would like to take the ratio of two measurements A/B and subject these ratios to a ranking algorithm. The ratio is normalized prior to ranking (though the ranking/normalization are not that import to my question).&lt;/p&gt;

&lt;p&gt;In most cases measurement A (the starting measurement) is a count with values greater than 1000.  We expect an increase for measurement B for positive effects and a decrease in measurement B for negative effects.  &lt;/p&gt;

&lt;p&gt;Here is the issue, some of our starting counts are nearly zero which we believe is an artifact of experimental preparation.  This of course leads to some really high ratios/scaling issues for these data points.  &lt;/p&gt;

&lt;p&gt;What is the best way to adjust these values in order to better understand the real role in our experiment?  &lt;/p&gt;

&lt;p&gt;One suggestion we received was to add 1000 to all counts (from measurement A and B) to scale the values and remove the bias of such a low starting count,  is this a viable option?  Thank you in advance for your assistance, let me know if I am not being clear enough.&lt;/p&gt;
', 'statistics  data-cleaning', NULL, '''/p'':56C,93C,130C,152C,206C ''1000'':73C,161C ''a/b'':30C ''add'':160C ''adjust'':138C ''advanc'':192C ''algorithm'':38C ''artifact'':112C ''artifici'':4A ''assist'':195C ''b'':80C,89C,169C ''believ'':109C ''best'':135C ''better'':144C ''bias'':177C ''case'':60C ''clean'':11B ''clear'':204C ''correct'':1A ''count'':68C,103C,164C,183C ''cours'':118C ''data'':10B,128C ''data-clean'':9B ''dataset'':2A ''decreas'':86C ''effect'':83C,92C ''enough'':205C ''expect'':75C ''experi'':151C ''experiment'':114C ''greater'':71C ''high'':123C ''import'':52C ''increas'':77C ''issu'':98C,125C ''know'':198C ''lead'':119C ''let'':196C ''like'':22C ''low'':5A,181C ''measur'':29C,61C,65C,79C,88C,166C ''near'':105C ''negat'':91C ''normal'':42C ''one'':154C ''option'':188C ''order'':142C ''p'':12C,57C,94C,131C,153C ''point'':129C ''posit'':82C ''prepar'':115C ''prior'':43C ''project'':18C ''question'':55C ''rank'':37C,45C ''ranking/normalization'':48C ''ratio'':26C,34C,40C ''ratios/scaling'':124C ''real'':147C ''realli'':122C ''receiv'':157C ''remov'':175C ''role'':148C ''scale'':171C ''start'':6A,64C,102C,182C ''statist'':8B ''subject'':32C ''suggest'':155C ''take'':24C ''thank'':189C ''though'':46C ''two'':28C ''understand'':145C ''valu'':7A,70C,140C,173C ''viabl'':187C ''way'':136C ''work'':15C ''would'':21C ''zero'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4835, 2, '2015-01-08 14:22:33.347', 1, NULL, NULL, '&lt;p&gt;Yes, the general idea is to add a baseline small count to every category. The technical term for this is &lt;a href="http://en.wikipedia.org/wiki/Additive_smoothing" rel="nofollow"&gt;Laplace smoothing&lt;/a&gt;. Really it''s not so much of a hack, as encoding the idea that you think there is some (uniform?) prior distribution of the events occurring.&lt;/p&gt;
', NULL, NULL, '''/a'':31C ''/p'':58C ''/wiki/additive_smoothing'':26C ''add'':8C ''baselin'':10C ''categori'':15C ''count'':12C ''distribut'':53C ''en.wikipedia.org'':25C ''en.wikipedia.org/wiki/additive_smoothing'':24C ''encod'':42C ''event'':56C ''everi'':14C ''general'':4C ''hack'':40C ''href'':23C ''idea'':5C,44C ''laplac'':29C ''much'':37C ''nofollow'':28C ''occur'':57C ''p'':1C ''prior'':52C ''realli'':32C ''rel'':27C ''small'':11C ''smooth'':30C ''technic'':17C ''term'':18C ''think'':47C ''uniform'':51C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4836, 1, '2015-01-08 15:47:34.103', 2, 2733, 'Tutorials on topic models and LDA', '&lt;p&gt;I would like to know if you people have some good tutorials (fast and straightforward) about topic models and LDA, teaching intuitively how to set some parameters, what they mean and if possible, with some real examples.&lt;/p&gt;
', 'topic-model  lda', NULL, '''/p'':49C ''exampl'':48C ''fast'':24C ''good'':22C ''intuit'':33C ''know'':16C ''lda'':6A,10B,31C ''like'':14C ''mean'':41C ''model'':4A,9B,29C ''p'':11C ''paramet'':38C ''peopl'':19C ''possibl'':44C ''real'':47C ''set'':36C ''straightforward'':26C ''teach'':32C ''topic'':3A,8B,28C ''topic-model'':7B ''tutori'':1A,23C ''would'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4844, 1, '2015-01-09 03:46:04.837', 1, 1804, 'Hardware requirements for Linux server to run R &amp; RStudio', '&lt;p&gt;I want to build a home server/workstation to run my R projects. Based on what I have gathered, it should probably be Linux based. I want to buy the hardware now, but I am confused with the many available options for processors/ram/motherboards. I want to be able to use parallel processing, at least 64GB? of memory and enough storage space (~10TB?). Software wise, Ubuntu?, R, RStudio, PostgreSQL, some NOSQL database, probably Hadoop. I do a lot of text/geospatial/network analytics that are resource intensive. Budget ~$3000US.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;My Questions:&lt;/strong&gt;&lt;BR&gt;
What could an ideal configuration look like? (Hardware + Software)&lt;br&gt;
What type of processor?&lt;BR&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Notes:&lt;/strong&gt;&lt;BR&gt;
No, I don''t want to use a cloud solution.&lt;BR&gt;
I know it is a vague question, but any thoughts will help, please?&lt;BR&gt;
If it is off-topic or too vague, I will gladly delete.&lt;BR&gt;&lt;/p&gt;

&lt;p&gt;Cheers B&lt;/p&gt;
', 'r', NULL, '''/p'':97C,119C,164C,168C ''/strong'':102C,123C ''10tb'':72C ''3000us'':96C ''64gb'':65C ''abl'':58C ''analyt'':90C ''avail'':50C ''b'':167C ''base'':24C,35C ''br'':103C,113C,118C,124C,135C,149C,163C ''budget'':95C ''build'':15C ''buy'':39C ''cheer'':166C ''cloud'':133C ''configur'':108C ''confus'':46C ''could'':105C ''databas'':81C ''delet'':162C ''enough'':69C ''gather'':29C ''glad'':161C ''hadoop'':83C ''hardwar'':1A,41C,111C ''help'':147C ''home'':17C ''ideal'':107C ''intens'':94C ''know'':137C ''least'':64C ''like'':110C ''linux'':4A,34C ''look'':109C ''lot'':87C ''mani'':49C ''memori'':67C ''nosql'':80C ''note'':122C ''off-top'':153C ''option'':51C ''p'':11C,98C,120C,165C ''parallel'':61C ''pleas'':148C ''postgresql'':78C ''probabl'':32C,82C ''process'':62C ''processor'':117C ''processors/ram/motherboards'':53C ''project'':23C ''question'':101C,142C ''r'':8A,10B,22C,76C ''requir'':2A ''resourc'':93C ''rstudio'':9A,77C ''run'':7A,20C ''server'':5A ''server/workstation'':18C ''softwar'':73C,112C ''solut'':134C ''space'':71C ''storag'':70C ''strong'':99C,121C ''text/geospatial/network'':89C ''thought'':145C ''topic'':155C ''type'':115C ''ubuntu'':75C ''use'':60C,131C ''vagu'':141C,158C ''want'':13C,37C,55C,129C ''wise'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4847, 2, '2015-01-09 10:02:08.437', 1, NULL, NULL, '&lt;p&gt;There is a worked out example &lt;a href="http://en.wikipedia.org/wiki/Simple_linear_regression#Numerical_example" rel="nofollow"&gt;in the Wikipedia page for "simple linear regression"&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Just for the sake of it, let me plug in your example into the formulas:&lt;/p&gt;

&lt;p&gt;The fitted model should be a straight line with parameters $\alpha$ (value at $x = 0$) and $\beta$ (the slope):&lt;/p&gt;

&lt;p&gt;$$f(x) = \alpha + \beta x$$&lt;/p&gt;

&lt;p&gt;The values for these parameters that minimize the distance between line and data points are called $\hat{\alpha}$ and $\hat{\beta}$.  They can be computed out of the data point values by using these formulae, &lt;a href="http://en.wikipedia.org/wiki/Simple_linear_regression#Fitting_the_regression_line" rel="nofollow"&gt;derived here&lt;/a&gt;:&lt;/p&gt;

&lt;p&gt;$$\begin{align}
\hat{\beta} &amp;amp; = \frac{ \overline{xy} - \bar{x}\bar{y} }{ \overline{x^2} - \bar{x}^2 } , 
\\
\\
\hat{\alpha} &amp;amp; = \bar{y} - \hat{\beta}\bar{x}
\end{align}
$$&lt;/p&gt;

&lt;p&gt;where an expression with an overline $\overline{xy}$ means the sample average of that expression: $\overline{xy} = \tfrac{1}{n} \sum_{i=1}^n{x_iy_i}$. &lt;/p&gt;

&lt;p&gt;Here are the values I find for the datapoints you have listed in your question:&lt;/p&gt;

&lt;p&gt;$$
\begin{align}
\overline{xy} &amp;amp;= \frac{1}{4} \sum{&amp;lt;(1 \times 4000), (2 \times 10000), (3 \times 22000), (4 \times 30000)&amp;gt;} \\
              &amp;amp;= \frac{1}{4}(4000 + 20000 + 66000 + 120000) = 52500, \\
\overline{x} &amp;amp;= \frac{1}{4} \sum{&amp;lt;1, 2, 3, 4&amp;gt;} = 2.5 ,\\
\overline{y} &amp;amp;= \frac{1}{4} \sum{&amp;lt;4000, 10000, 22000, 30000&amp;gt;} = 16500 , \\
\overline{x^2} &amp;amp;= \frac{1}{4} \sum{&amp;lt;1^2, 2^2, 3^2, 4^2&amp;gt;} = 7.5 , \\
\overline{x}^2 &amp;amp;= 2.5^2 = 6.25
\end{align}
$$&lt;/p&gt;

&lt;p&gt;and the fitted line should be:&lt;/p&gt;

&lt;p&gt;$$
\begin{align}\
\hat{\beta} &amp;amp;= \frac{52500 - 2.5 \times 16500}{7.5 - 6.25} = \frac{41250}{1.25} = 33000 , \\
\hat{\alpha} &amp;amp;= 16500 - 33000 \times 2.5 = - 66000 , \\
\Rightarrow f(x) &amp;amp;= - 66000 + 33000 x 
\end{align}
$$&lt;/p&gt;

&lt;p&gt;Therefore, the model would predict, for a house with 10 rooms, a rent of:&lt;/p&gt;

&lt;p&gt;$$ f(x) = -66000 + 33000 \times 10 = 264000 $$&lt;/p&gt;
', NULL, NULL, '''-66000'':346C ''/a'':23C,115C ''/p'':24C,41C,62C,69C,116C,147C,176C,193C,283C,291C,326C,342C,351C ''/wiki/simple_linear_regression#fitting_the_regression_line'':110C ''/wiki/simple_linear_regression#numerical_example'':12C ''0'':57C ''1'':167C,171C,201C,205C,220C,231C,235C,245C,260C,264C ''1.25'':307C ''10'':337C,349C ''10000'':210C,250C ''120000'':225C ''16500'':254C,302C,312C ''2'':132C,135C,208C,236C,257C,265C,266C,267C,269C,271C,276C,279C ''2.5'':240C,278C,300C,315C ''20000'':223C ''22000'':213C,251C ''264000'':350C ''3'':211C,237C,268C ''30000'':216C,252C ''33000'':308C,313C,322C,347C ''4'':202C,214C,221C,232C,238C,246C,261C,270C ''4000'':207C,222C,249C ''41250'':306C ''52500'':226C,299C ''6.25'':280C,304C ''66000'':224C,316C,321C ''7.5'':273C,303C ''align'':119C,146C,196C,282C,294C,325C ''alpha'':53C,66C,88C,137C,310C ''amp'':122C,138C,199C,218C,229C,243C,258C,277C,297C,311C,320C ''averag'':160C ''bar'':126C,128C,133C,139C,143C ''begin'':118C,195C,293C ''beta'':59C,67C,91C,121C,142C,296C ''call'':86C ''comput'':95C ''data'':83C,99C ''datapoint'':186C ''deriv'':113C ''distanc'':79C ''en.wikipedia.org'':11C,109C ''en.wikipedia.org/wiki/simple_linear_regression#fitting_the_regression_line'':108C ''en.wikipedia.org/wiki/simple_linear_regression#numerical_example'':10C ''end'':145C,281C,324C ''exampl'':7C,37C ''express'':151C,163C ''f'':64C,318C,344C ''find'':183C ''fit'':44C,287C ''formula'':40C,105C ''frac'':123C,200C,219C,230C,244C,259C,298C,305C ''gt'':217C,239C,253C,272C ''hat'':87C,90C,120C,136C,141C,295C,309C ''hous'':335C ''href'':9C,107C ''iy'':174C ''let'':32C ''line'':50C,81C,288C ''linear'':21C ''list'':189C ''lt'':204C,234C,248C,263C ''mean'':157C ''minim'':77C ''model'':45C,330C ''n'':168C,172C ''nofollow'':14C,112C ''overlin'':124C,130C,154C,155C,164C,197C,227C,241C,255C,274C ''p'':1C,25C,42C,63C,70C,117C,148C,177C,194C,284C,292C,327C,343C ''page'':18C ''paramet'':52C,75C ''plug'':34C ''point'':84C,100C ''predict'':332C ''question'':192C ''regress'':22C ''rel'':13C,111C ''rent'':340C ''rightarrow'':317C ''room'':338C ''sake'':29C ''sampl'':159C ''simpl'':20C ''slope'':61C ''straight'':49C ''sum'':169C,203C,233C,247C,262C ''tfrac'':166C ''therefor'':328C ''time'':206C,209C,212C,215C,301C,314C,348C ''use'':103C ''valu'':54C,72C,101C,181C ''wikipedia'':17C ''work'':5C ''would'':331C ''x'':56C,65C,68C,127C,131C,134C,144C,173C,228C,256C,275C,319C,323C,345C ''xy'':125C,156C,165C,198C ''y'':129C,140C,242C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4837, 1, '2015-01-08 16:13:36.613', 0, 169, 'Learning cost function for linear regression', '&lt;p&gt;(Me: Never learned calculus or advanced math and I started Stanford openclasses for machine learning. I know basic matrix calculations.)&lt;/p&gt;

&lt;p&gt;One chapter of my course is about cost function. I have been trying to find any example calculation of it with numbers. Googling only finds the same formula everytime, and also on Octave. But I want to do the same thing first with pen+paper and without it, I cannot understand. Please give me a very simple example of using the formula with numbers. Thanks a lot.&lt;/p&gt;

&lt;p&gt;I require a cost function calculation example for following sample dataset:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;#Rooms = Rent
1 = 4000
2 = 10000
3 = 22000
4 = 30000
&lt;/code&gt;&lt;/pre&gt;
', 'machine-learning', NULL, '''/code'':126C ''/p'':31C,100C,113C ''/pre'':127C ''1'':118C ''10000'':121C ''2'':120C ''22000'':123C ''3'':122C ''30000'':125C ''4'':124C ''4000'':119C ''advanc'':16C ''also'':63C ''basic'':28C ''calcul'':30C,50C,107C ''calculus'':14C ''cannot'':82C ''chapter'':34C ''code'':115C ''cost'':2A,40C,105C ''cours'':37C ''dataset'':112C ''everytim'':61C ''exampl'':49C,90C,108C ''find'':47C,57C ''first'':74C ''follow'':110C ''formula'':60C,94C ''function'':3A,41C,106C ''give'':85C ''googl'':55C ''know'':27C ''learn'':1A,9B,13C,25C ''linear'':5A ''lot'':99C ''machin'':8B,24C ''machine-learn'':7B ''math'':17C ''matrix'':29C ''never'':12C ''number'':54C,96C ''octav'':65C ''one'':33C ''openclass'':22C ''p'':10C,32C,101C ''paper'':77C ''pen'':76C ''pleas'':84C ''pre'':114C ''regress'':6A ''rent'':117C ''requir'':103C ''room'':116C ''sampl'':111C ''simpl'':89C ''stanford'':21C ''start'':20C ''thank'':97C ''thing'':73C ''tri'':45C ''understand'':83C ''use'':92C ''want'':68C ''without'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4838, 2, '2015-01-08 20:58:44.547', 4, NULL, NULL, '&lt;p&gt;I highly recommend this tutorial: &lt;strong&gt;&lt;a href="http://programminghistorian.org/lessons/topic-modeling-and-mallet" rel="nofollow"&gt;Getting Started with Topic Modeling and MALLET&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Here are some additional links to help you get started...&lt;/p&gt;

&lt;p&gt;Good introductory materials (including links to research papers): &lt;a href="http://www.cs.princeton.edu/~blei/topicmodeling.html" rel="nofollow"&gt;http://www.cs.princeton.edu/~blei/topicmodeling.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Software:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;MALLET (Java): &lt;a href="http://mallet.cs.umass.edu/topics.php" rel="nofollow"&gt;http://mallet.cs.umass.edu/topics.php&lt;/a&gt;
&lt;ul&gt;
&lt;li&gt;topic modeling developer''s guide: &lt;a href="http://mallet.cs.umass.edu/topics-devel.php" rel="nofollow"&gt;http://mallet.cs.umass.edu/topics-devel.php&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;gensim (Python): &lt;a href="http://radimrehurek.com/gensim/" rel="nofollow"&gt;http://radimrehurek.com/gensim/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;topicmodels (R): &lt;a href="http://cran.r-project.org/web/packages/topicmodels/index.html" rel="nofollow"&gt;http://cran.r-project.org/web/packages/topicmodels/index.html&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Stanford Topic Modeling Toolbox (designed for use by social scientists): &lt;a href="http://www-nlp.stanford.edu/software/tmt/tmt-0.4/" rel="nofollow"&gt;http://www-nlp.stanford.edu/software/tmt/tmt-0.4/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Mr.LDA (scalable topic modeling using MapReduce): &lt;a href="http://lintool.github.io/Mr.LDA/" rel="nofollow"&gt;http://lintool.github.io/Mr.LDA/&lt;/a&gt;
&lt;ul&gt;
&lt;li&gt;If you''re working with &lt;em&gt;massive&lt;/em&gt; amounts of input text, you might want to consider using Mr.LDA to build your topics models -- its MapReduce-based approach might be more efficient when working with lots of data.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Even more here on the Biased Estimates blog: &lt;a href="http://www.biasedestimates.com/p/topic-models-reading-list.html" rel="nofollow"&gt;Topic Models Reading List&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4881, 1, '2015-01-15 11:35:08.843', 1, 150, 'Probability distribution in input-output pairs', '&lt;p&gt;This question might sound silly. But I have been wondering why do we assume that there is
a hidden probability distribution between input-output pairs in machine learning setup ?&lt;/p&gt;

&lt;p&gt;For example, if we want to learn a function $f: \mathcal{X} \rightarrow \mathcal{Y}$, we generally tend to assume a probability distribution $\rho(x,y)$ on $Z=\mathcal{X} \times \mathcal{Y} $ and try to minimize the error 
$$
\mathcal{E}(f) = \int (f(x)-y)^2 \ d\rho(x,y)
$$&lt;/p&gt;

&lt;p&gt;Is the probability distribtution $\rho$ inherent to the very nature of $Z$ or depends on $f$ ?&lt;/p&gt;

&lt;p&gt;Can anyone please provide a good intuitive explanation for this ?&lt;/p&gt;
', 'machine-learning  statistics', NULL, '''/p'':43C,96C,114C,126C ''2'':91C ''anyon'':117C ''assum'':26C,64C ''d'':92C ''depend'':111C ''distribtut'':101C ''distribut'':2A,33C,67C ''e'':85C ''error'':83C ''exampl'':46C ''explan'':123C ''f'':54C,86C,88C,113C ''function'':53C ''general'':61C ''good'':121C ''hidden'':31C ''inher'':103C ''input'':5A,36C ''input-output'':4A,35C ''int'':87C ''intuit'':122C ''learn'':10B,41C,51C ''machin'':9B,40C ''machine-learn'':8B ''mathcal'':55C,58C,73C,76C,84C ''might'':15C ''minim'':81C ''natur'':107C ''output'':6A,37C ''p'':12C,44C,97C,115C ''pair'':7A,38C ''pleas'':118C ''probabl'':1A,32C,66C,100C ''provid'':119C ''question'':14C ''rho'':68C,93C,102C ''rightarrow'':57C ''setup'':42C ''silli'':17C ''sound'':16C ''statist'':11B ''tend'':62C ''time'':75C ''tri'':79C ''want'':49C ''wonder'':22C ''x'':56C,69C,74C,89C,94C ''y'':59C,70C,77C,90C,95C ''z'':72C,109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4839, 2, '2015-01-08 21:07:57.643', 0, NULL, NULL, '&lt;p&gt;Let me assume you intend to use Python libraries to analyze the data, since you are using Scrapy to gather the data.&lt;/p&gt;

&lt;p&gt;If this is true, then a factor to consider for storage would be compatibility with other Python libraries.  Of course, plain text is compatible with anything.  But e.g. Pandas has a &lt;a href="http://pandas.pydata.org/pandas-docs/stable/io.html" rel="nofollow"&gt;host of IO tools&lt;/a&gt; that simplifies reading from certain formats.  If you intend to use &lt;code&gt;scikit-learn&lt;/code&gt; for modeling, then Pandas can still read the data in for you, if you then cast it from a DataFrame to a Numpy array as an intermediate step.&lt;/p&gt;

&lt;p&gt;These tools allow you to read CSV and JSON, but also HDF5 ... particularly, I would draw your attention to the &lt;a href="http://pandas.pydata.org/pandas-docs/stable/io.html#io-msgpack" rel="nofollow"&gt;experimental support for msgpack&lt;/a&gt;, which seems to be a &lt;a href="http://msgpack.org" rel="nofollow"&gt;binary version of JSON&lt;/a&gt;.  Binary means here that the stored files will be smaller and therefore faster to read and write.  A somewhat similar alternative is &lt;a href="http://bsonspec.org" rel="nofollow"&gt;BSON&lt;/a&gt;, which has a Python implementation  &amp;mdash; no Pandas or Numpy involved.&lt;/p&gt;

&lt;p&gt;Considering these formats only makes sense if you intend to give &lt;em&gt;at least some formatting&lt;/em&gt; to the stored text, e.g. storing the post title separately from the post content, or storing all posts in a thread in order, or storing the timestamp ... If you considered JSON at all, then I suppose this is what you intended.  If you just intend to store the plain post contents, then use plain text. &lt;/p&gt;
', NULL, NULL, '''/a'':68C,146C,161C,190C ''/code'':84C ''/em'':220C ''/p'':24C,113C,202C,276C ''/pandas-docs/stable/io.html'':61C ''/pandas-docs/stable/io.html#io-msgpack'':139C ''allow'':117C ''also'':125C ''altern'':182C ''analyz'':12C ''anyth'':51C ''array'':108C ''assum'':4C ''attent'':132C ''binari'':157C,162C ''bson'':189C ''bsonspec.org'':186C ''cast'':100C ''certain'':73C ''code'':80C ''compat'':39C,49C ''consid'':34C,204C,250C ''content'':234C,271C ''cours'':45C ''csv'':121C ''data'':14C,23C,93C ''datafram'':104C ''draw'':130C ''e.g'':53C,225C ''em'':215C ''experiment'':142C ''factor'':32C ''faster'':174C ''file'':168C ''format'':74C,206C,219C ''gather'':21C ''give'':214C ''hdf5'':126C ''host'':64C ''href'':58C,136C,153C,185C ''implement'':195C ''intend'':6C,77C,212C,261C,265C ''intermedi'':111C ''involv'':201C ''io'':66C ''json'':123C,160C,251C ''learn'':83C ''least'':217C ''let'':2C ''librari'':10C,43C ''make'':208C ''mdash'':196C ''mean'':163C ''model'':86C ''msgpack'':145C ''msgpack.org'':154C ''nofollow'':63C,141C,156C,188C ''numpi'':107C,200C ''order'':243C ''p'':1C,25C,114C,203C ''panda'':54C,88C,198C ''pandas.pydata.org'':60C,138C ''pandas.pydata.org/pandas-docs/stable/io.html'':59C ''pandas.pydata.org/pandas-docs/stable/io.html#io-msgpack'':137C ''particular'':127C ''plain'':46C,269C,274C ''post'':228C,233C,238C,270C ''python'':9C,42C,194C ''read'':71C,91C,120C,176C ''rel'':62C,140C,155C,187C ''scikit'':82C ''scikit-learn'':81C ''scrapi'':19C ''seem'':148C ''sens'':209C ''separ'':230C ''similar'':181C ''simplifi'':70C ''sinc'':15C ''smaller'':171C ''somewhat'':180C ''step'':112C ''still'':90C ''storag'':36C ''store'':167C,223C,226C,236C,245C,267C ''support'':143C ''suppos'':256C ''text'':47C,224C,275C ''therefor'':173C ''thread'':241C ''timestamp'':247C ''titl'':229C ''tool'':67C,116C ''true'':29C ''use'':8C,18C,79C,273C ''version'':158C ''would'':37C,129C ''write'':178C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4845, 1, '2015-01-09 05:42:54.587', 1, 208, 'Approaches to Bag-Of-Words Information Retrieval', '&lt;p&gt;I''m interested in an overview of the modern/state-of-the-art approaches to bag-of-words information retrieval, where you have a single query $q$ and a set of documents which you hope to rank by relevance $d_1,...,d_n$.  &lt;/p&gt;

&lt;p&gt;I''m specifically interested in approaches which require absolutely no linguistic knowledge and rely on no outside linguistic or lexical resources for boosting performance (such as thesauri or prebuilt word-nets and the like).  Thus where a ranking is produced entirely by evaluating query-document similarity and where the problems of synonymy and polysemy are overcome by exploiting inter-document word co-occurence.&lt;/p&gt;

&lt;p&gt;I''ve spent some amount of time in the literature (reading papers/tutorials), however there is so much information out there it''s hard to get a bird''s eye view.  The best I can make out is that modern approaches involve some combination of a weighted vector space model (such as a generalized vector space model, LSI, or a topic-based vector space model using LDA), in conjunction with pseudo-relevance feedback (using either Rocchio or some more advanced approach).&lt;/p&gt;

&lt;p&gt;All these vector space models tend to use cosine-similarity as the similarity function, however I have seen some literature discussing similarity functions which are more exotic.&lt;/p&gt;

&lt;p&gt;Is this basically where we currently are in attacking this particular type of problem? &lt;/p&gt;
', 'information-retrieval', NULL, '''/p'':53C,122C,206C,236C,252C ''1'':50C ''absolut'':63C ''advanc'':204C ''amount'':128C ''approach'':1A,22C,60C,163C,205C ''attack'':246C ''bag'':4A,25C ''bag-of-word'':3A,24C ''base'':185C ''basic'':240C ''best'':155C ''bird'':150C ''boost'':77C ''co'':120C ''co-occur'':119C ''combin'':166C ''conjunct'':192C ''cosin'':217C ''cosine-similar'':216C ''current'':243C ''d'':49C,51C ''discuss'':229C ''document'':41C,101C,117C ''either'':199C ''entir'':96C ''evalu'':98C ''exot'':235C ''exploit'':114C ''eye'':152C ''feedback'':197C ''function'':222C,231C ''general'':176C ''get'':148C ''hard'':146C ''hope'':44C ''howev'':136C,223C ''inform'':7A,10B,28C,141C ''information-retriev'':9B ''inter'':116C ''inter-docu'':115C ''interest'':15C,58C ''involv'':164C ''knowledg'':66C ''lda'':190C ''lexic'':74C ''like'':89C ''linguist'':65C,72C ''literatur'':133C,228C ''lsi'':180C ''m'':14C,56C ''make'':158C ''model'':172C,179C,188C,212C ''modern'':162C ''modern/state-of-the-art'':21C ''much'':140C ''n'':52C ''net'':86C ''occur'':121C ''outsid'':71C ''overcom'':112C ''overview'':18C ''p'':12C,54C,123C,207C,237C ''papers/tutorials'':135C ''particular'':248C ''perform'':78C ''polysemi'':110C ''prebuilt'':83C ''problem'':106C,251C ''produc'':95C ''pseudo'':195C ''pseudo-relev'':194C ''q'':36C ''queri'':35C,100C ''query-docu'':99C ''rank'':46C,93C ''read'':134C ''relev'':48C,196C ''reli'':68C ''requir'':62C ''resourc'':75C ''retriev'':8A,11B,29C ''rocchio'':200C ''seen'':226C ''set'':39C ''similar'':102C,218C,221C,230C ''singl'':34C ''space'':171C,178C,187C,211C ''specif'':57C ''spent'':126C ''synonymi'':108C ''tend'':213C ''thesauri'':81C ''thus'':90C ''time'':130C ''topic'':184C ''topic-bas'':183C ''type'':249C ''use'':189C,198C,215C ''ve'':125C ''vector'':170C,177C,186C,210C ''view'':153C ''weight'':169C ''word'':6A,27C,85C,118C ''word-net'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4960, 2, '2015-01-28 04:34:30.777', 0, NULL, NULL, '&lt;p&gt;&lt;a href="http://www.cs.waikato.ac.nz/ml/weka/" rel="nofollow"&gt;Weka&lt;/a&gt; can import CSV files, and allows you to choose which columns and rows you want to use in your analysis.  It''s not an "add-in" for Excel per-se, but it might work for you.&lt;/p&gt;
', NULL, NULL, '''/a'':10C ''/ml/weka/'':6C ''/p'':49C ''add'':36C ''add-in'':35C ''allow'':16C ''analysi'':30C ''choos'':19C ''column'':21C ''csv'':13C ''excel'':39C ''file'':14C ''href'':3C ''import'':12C ''might'':45C ''nofollow'':8C ''p'':1C ''per'':41C ''per-s'':40C ''rel'':7C ''row'':23C ''se'':42C ''use'':27C ''want'':25C ''weka'':9C ''work'':46C ''www.cs.waikato.ac.nz'':5C ''www.cs.waikato.ac.nz/ml/weka/'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5160, 1, '2015-02-17 02:39:20.92', 0, 30, 'Where the following propagation model arise in practice', '&lt;p&gt;This is in continuation to a previos question. I would like to know where in practice will the following M-ary tree-structured social network propagation model arise. I am looking for some concrete examples in social media (twitter and youtube etc.). Eventually, I want to do some statistical analysis on such social networks.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/x14St.jpg" alt="Example of a binary tree-structured social network"&gt;&lt;/p&gt;
', 'statistics  social-network-analysis', NULL, '''/p'':71C,88C ''/x14st.jpg'':77C ''alt'':78C ''analysi'':13B,66C ''ari'':36C ''aris'':6A,44C ''binari'':82C ''concret'':50C ''continu'':18C ''etc'':58C ''eventu'':59C ''exampl'':51C,79C ''follow'':3A,33C ''i.stack.imgur.com'':76C ''i.stack.imgur.com/x14st.jpg'':75C ''img'':73C ''know'':27C ''like'':25C ''look'':47C ''m'':35C ''m-ari'':34C ''media'':54C ''model'':5A,43C ''network'':12B,41C,70C,87C ''p'':14C,72C ''practic'':8A,30C ''previo'':21C ''propag'':4A,42C ''question'':22C ''social'':11B,40C,53C,69C,86C ''social-network-analysi'':10B ''src'':74C ''statist'':9B,65C ''structur'':39C,85C ''tree'':38C,84C ''tree-structur'':37C,83C ''twitter'':55C ''want'':61C ''would'':24C ''youtub'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4848, 2, '2015-01-09 11:25:11.623', 2, NULL, NULL, '&lt;p&gt;What you are asking about is, in my view, the main problem of implementing a lambda architecture.  Here are some suggestions on how to solve it.&lt;/p&gt;

&lt;p&gt;The combination of Spark and &lt;a href="https://spark.apache.org/docs/1.1.1/streaming-programming-guide.html" rel="nofollow"&gt;Spark Streaming&lt;/a&gt; largely supersedes the original lambda architecture (which usually involved Hadoop and Storm).  &lt;a href="http://blog.cloudera.com/blog/2014/08/building-lambda-architecture-with-spark-streaming/" rel="nofollow"&gt;Read here&lt;/a&gt; an example of how to use a &lt;code&gt;SparkContext&lt;/code&gt; and a separate &lt;code&gt;StreamingContext&lt;/code&gt; to produce &lt;em&gt;different&lt;/em&gt; &lt;code&gt;RDD&lt;/code&gt;s, one for batch processed results and another for real-time results.&lt;/p&gt;

&lt;p&gt;Once you have replicated that in your system, you still have to think about &lt;em&gt;how to query&lt;/em&gt; both kind of &lt;code&gt;RDD&lt;/code&gt;s.  The trivial case would be to just &lt;code&gt;union&lt;/code&gt; both of them:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;scala&amp;gt; rdd1.union(rdd2).collect
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Or maybe you can create a new &lt;code&gt;DStream&lt;/code&gt;, similar to &lt;code&gt;stateStream&lt;/code&gt; in the linked example, where some keys are kept for real-time results, and others for batch results. &lt;/p&gt;
', NULL, NULL, '''/a'':44C,66C ''/blog/2014/08/building-lambda-architecture-with-spark-streaming/'':61C ''/code'':76C,82C,90C,130C,141C,153C,165C,170C ''/docs/1.1.1/streaming-programming-guide.html'':39C ''/em'':87C,124C ''/p'':28C,104C,145C,190C ''/pre'':154C ''anoth'':98C ''architectur'':18C,50C ''ask'':5C ''batch'':94C,188C ''blog.cloudera.com'':60C ''blog.cloudera.com/blog/2014/08/building-lambda-architecture-with-spark-streaming/'':59C ''case'':134C ''code'':74C,80C,88C,128C,139C,147C,163C,168C ''collect'':152C ''combin'':31C ''creat'':160C ''differ'':86C ''dstream'':164C ''em'':85C,120C ''exampl'':68C,174C ''gt'':149C ''hadoop'':54C ''href'':36C,58C ''implement'':15C ''involv'':53C ''kept'':179C ''key'':177C ''kind'':126C ''lambda'':17C,49C ''larg'':45C ''link'':173C ''main'':12C ''mayb'':157C ''new'':162C ''nofollow'':41C,63C ''one'':92C ''origin'':48C ''other'':186C ''p'':1C,29C,105C,155C ''pre'':146C ''problem'':13C ''process'':95C ''produc'':84C ''queri'':123C ''rdd'':89C,129C ''rdd1.union'':150C ''rdd2'':151C ''read'':64C ''real'':101C,182C ''real-tim'':100C,181C ''rel'':40C,62C ''replic'':109C ''result'':96C,103C,184C,189C ''scala'':148C ''separ'':79C ''similar'':166C ''solv'':26C ''spark'':33C,42C ''spark.apache.org'':38C ''spark.apache.org/docs/1.1.1/streaming-programming-guide.html'':37C ''sparkcontext'':75C ''statestream'':169C ''still'':115C ''storm'':56C ''stream'':43C ''streamingcontext'':81C ''suggest'':22C ''supersed'':46C ''system'':113C ''think'':118C ''time'':102C,183C ''trivial'':133C ''union'':140C ''use'':72C ''usual'':52C ''view'':10C ''would'':135C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4850, 2, '2015-01-09 18:26:08.933', 2, NULL, NULL, '&lt;p&gt;I think this point is the core of your question:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;ul&gt;
  &lt;li&gt;How should I define an "exception"? How can I know if my definition is a good one?&lt;/li&gt;
  &lt;/ul&gt;
&lt;/blockquote&gt;

&lt;p&gt;I would go about it as follows: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Go back to the managers who receive the current reporting and ask them to give you examples of reported exceptions that were actually exceptions, i.e. they were acted upon and the action yielded some benefit.&lt;/li&gt;
&lt;li&gt;Ask them also if there were any features in that report that were not plainly viewable, but led them to think it was an exception, e.g. the month-on-month difference was not reported, but it could be computed from the week-on-week difference.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Treat the examples from the first as labels for training; you want to learn "what is an exception" and you need to have an expert answer that question for you.&lt;/p&gt;

&lt;p&gt;Treat the suggested features as new features for your classification.&lt;/p&gt;

&lt;p&gt;If you cannot get the experts to answer the questions you make, then try to infer them from their interaction with your reports: how many of those reports were downloaded, how many times are they mentioned, how are they ever used in decision-making ...  Try to separate the important ones from the uninteresting ones, and there you have your labels.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':35C ''/li'':33C,82C,131C ''/p'':12C,44C,164C,176C,239C ''/ul'':34C,132C ''act'':74C ''action'':78C ''actual'':69C ''also'':86C ''answer'':159C,185C ''ask'':58C,84C ''back'':48C ''benefit'':81C ''blockquot'':13C ''cannot'':180C ''classif'':175C ''comput'':123C ''core'':8C ''could'':121C ''current'':55C ''decis'':221C ''decision-mak'':220C ''defin'':19C ''definit'':28C ''differ'':115C,130C ''download'':207C ''e.g'':109C ''ever'':217C ''exampl'':63C,136C ''except'':21C,66C,70C,108C,151C ''expert'':158C,183C ''featur'':91C,169C,172C ''first'':139C ''follow'':43C ''get'':181C ''give'':61C ''go'':39C,47C ''good'':31C ''i.e'':71C ''import'':227C ''infer'':193C ''interact'':197C ''know'':25C ''label'':141C,238C ''learn'':147C ''led'':101C ''li'':15C,46C,83C ''make'':189C,222C ''manag'':51C ''mani'':202C,209C ''mention'':213C ''month'':112C,114C ''month-on-month'':111C ''need'':154C ''new'':171C ''one'':32C,228C,232C ''p'':1C,36C,133C,165C,177C ''plain'':98C ''point'':5C ''question'':11C,161C,187C ''receiv'':53C ''report'':56C,65C,94C,118C,200C,205C ''separ'':225C ''suggest'':168C ''think'':3C,104C ''time'':210C ''train'':143C ''treat'':134C,166C ''tri'':191C,223C ''ul'':14C,45C ''uninterest'':231C ''upon'':75C ''use'':218C ''viewabl'':99C ''want'':145C ''week'':127C,129C ''week-on-week'':126C ''would'':38C ''yield'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4863, 2, '2015-01-12 07:58:39.78', 1, NULL, NULL, '&lt;p&gt;I would like to recommend to check the following &lt;strong&gt;open data&lt;/strong&gt; &lt;em&gt;repositories&lt;/em&gt; and &lt;em&gt;meta-repositories&lt;/em&gt; (they are not focused on &lt;em&gt;categorical data&lt;/em&gt;, but I''m sure that many data sets, listed there, contain such data):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.kdnuggets.com/datasets" rel="nofollow"&gt;http://www.kdnuggets.com/datasets&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.data.gov" rel="nofollow"&gt;http://www.data.gov&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.datasciencecentral.com/profiles/blogs/big-data-sets-available-for-free" rel="nofollow"&gt;http://www.datasciencecentral.com/profiles/blogs/big-data-sets-available-for-free&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Also check built-in data sets in the open source software &lt;em&gt;Parallel Sets&lt;/em&gt;, which is focused on the categorical data visualization: &lt;a href="https://eagereyes.org/parallel-sets" rel="nofollow"&gt;https://eagereyes.org/parallel-sets&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':66C ''/datasets'':53C ''/datasets&lt;/a&gt;&lt;/li&gt;'':58C ''/em'':17C,23C,32C,96C ''/li'':67C ''/p'':46C ''/parallel-sets'':109C ''/parallel-sets&lt;/a&gt;.&lt;/p&gt;'':114C ''/profiles/blogs/big-data-sets-available-for-free'':73C ''/profiles/blogs/big-data-sets-available-for-free&lt;/a&gt;&lt;/li&gt;'':78C ''/strong'':14C ''/ul'':79C ''also'':81C ''built'':84C ''built-in'':83C ''categor'':30C,102C ''check'':8C,82C ''contain'':43C ''data'':13C,31C,39C,45C,86C,103C ''eagereyes.org'':108C,113C ''eagereyes.org/parallel-sets'':107C ''eagereyes.org/parallel-sets&lt;/a&gt;.&lt;/p&gt;'':112C ''em'':15C,19C,29C,93C ''focus'':27C,99C ''follow'':10C ''href'':50C,61C,70C,106C ''li'':48C,59C,68C ''like'':4C ''list'':41C ''m'':35C ''mani'':38C ''meta'':21C ''meta-repositori'':20C ''nofollow'':55C,64C,75C,111C ''open'':12C,90C ''p'':1C,80C ''parallel'':94C ''recommend'':6C ''rel'':54C,63C,74C,110C ''repositori'':16C,22C ''set'':40C,87C,95C ''softwar'':92C ''sourc'':91C ''strong'':11C ''sure'':36C ''ul'':47C ''visual'':104C ''would'':3C ''www.data.gov'':62C,65C ''www.datasciencecentral.com'':72C,77C ''www.datasciencecentral.com/profiles/blogs/big-data-sets-available-for-free'':71C ''www.datasciencecentral.com/profiles/blogs/big-data-sets-available-for-free&lt;/a&gt;&lt;/li&gt;'':76C ''www.kdnuggets.com'':52C,57C ''www.kdnuggets.com/datasets'':51C ''www.kdnuggets.com/datasets&lt;/a&gt;&lt;/li&gt;'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4851, 2, '2015-01-09 19:05:38.04', 1, NULL, NULL, '&lt;p&gt;I don''t have enough reputation to ask questions in comment for clarification before answering it, so I''m going to do both here. &lt;/p&gt;

&lt;h3&gt;Here are the things that would help answering this question for now:&lt;/h3&gt;

&lt;p&gt;Can you post part of the &lt;code&gt;process_set.hpc&lt;/code&gt;? &lt;/p&gt;

&lt;p&gt;What''s its format? Is it a numpy array? Is it a Pandas dataframe?&lt;/p&gt;

&lt;p&gt;What''s the value of &lt;code&gt;cclasses&lt;/code&gt;?&lt;/p&gt;

&lt;h3&gt;And now the answer:&lt;/h3&gt;

&lt;p&gt;First of all, &lt;code&gt;k-means&lt;/code&gt; algorithm is able to find clusters in any n-dimensional data. If &lt;code&gt;n&lt;/code&gt; is too big, it is better to use PCA but for &lt;code&gt;n=3&lt;/code&gt; that wouldn''t necessarily add any value. &lt;/p&gt;

&lt;p&gt;The second thing that looks suspicious to me is that in the documentation for &lt;code&gt;kmeans&lt;/code&gt; in &lt;code&gt;scikit-learn&lt;/code&gt;, there is no &lt;code&gt;compute_labels&lt;/code&gt; option, as seen &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html" rel="nofollow"&gt;here&lt;/a&gt;. However, that option exists for &lt;code&gt;MiniBatchKMeans&lt;/code&gt; as seen &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.cluster.MiniBatchKMeans.html" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Also, if you make your data in the form of a pandas dataframe (if it is not already so), things would be much easier to track and you won''t have to reattach the timing information to your data afterwards.&lt;/p&gt;

&lt;p&gt;I may be able to give you a more thorough answer if I know a bit more about the format of the data.&lt;/p&gt;

&lt;p&gt;Good luck!&lt;/p&gt;
', NULL, NULL, '''/a'':174C,193C ''/code'':50C,76C,92C,108C,123C,149C,155C,162C,182C ''/h3'':40C,83C ''/p'':26C,51C,67C,77C,131C,194C,236C,261C,265C ''/stable/modules/generated/sklearn.cluster.kmeans.html'':170C ''/stable/modules/generated/sklearn.cluster.minibatchkmeans.html'':189C ''3'':122C ''abl'':95C,241C ''add'':128C ''afterward'':235C ''algorithm'':93C ''alreadi'':213C ''also'':196C ''answer'':16C,35C,82C,248C ''array'':61C ''ask'':9C ''better'':114C ''big'':111C ''bit'':253C ''cclass'':75C ''clarif'':14C ''cluster'':98C ''code'':48C,74C,88C,106C,120C,147C,151C,159C,180C ''comment'':12C ''comput'':160C ''data'':104C,201C,234C,260C ''datafram'':66C,208C ''dimension'':103C ''document'':145C ''easier'':219C ''enough'':6C ''exist'':178C ''find'':97C ''first'':85C ''form'':204C ''format'':56C,257C ''give'':243C ''go'':21C ''good'':263C ''h3'':27C,78C ''help'':34C ''howev'':175C ''href'':167C,186C ''inform'':231C ''k'':90C ''k-mean'':89C ''kmean'':148C ''know'':251C ''label'':161C ''learn'':154C ''look'':137C ''luck'':264C ''m'':20C ''make'':199C ''may'':239C ''mean'':91C ''minibatchkmean'':181C ''much'':218C ''n'':102C,107C,121C ''n-dimension'':101C ''necessarili'':127C ''nofollow'':172C,191C ''numpi'':60C ''option'':163C,177C ''p'':1C,41C,52C,68C,84C,132C,195C,237C,262C ''panda'':65C,207C ''part'':45C ''pca'':117C ''post'':44C ''process_set.hpc'':49C ''question'':10C,37C ''reattach'':228C ''rel'':171C,190C ''reput'':7C ''scikit'':153C ''scikit-learn'':152C ''scikit-learn.org'':169C,188C ''scikit-learn.org/stable/modules/generated/sklearn.cluster.kmeans.html'':168C ''scikit-learn.org/stable/modules/generated/sklearn.cluster.minibatchkmeans.html'':187C ''second'':134C ''seen'':165C,184C ''suspici'':138C ''thing'':31C,135C,215C ''thorough'':247C ''time'':230C ''track'':221C ''use'':116C ''valu'':72C,130C ''won'':224C ''would'':33C,216C ''wouldn'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4870, 2, '2015-01-13 17:00:04.647', 2, NULL, NULL, '&lt;p&gt;Aleksandar Blekh has given some really nice links about the big picture of how to do sentiment analysis; I''ll try to provide some links to software and talk about the nitty-gritty of how to make it work. I''ll point you to the example using scikit-learn (&lt;a href="http://scikit-learn.org/stable/" rel="nofollow"&gt;http://scikit-learn.org/stable/&lt;/a&gt; ), a machine learning library in Python. &lt;/p&gt;

&lt;p&gt;You would first want to take your dataset and load it into scikit-learn in a sparse format. This link (&lt;a href="http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html" rel="nofollow"&gt;http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html&lt;/a&gt; ) gives examples of how to load text in a bag-of-words representation, and the same module (scikit-learn.feature_extraction.text) can also count n-grams. It then describes how to run Naive Bayes and SVM on that dataset. You can take that example and start playing with it. &lt;/p&gt;
', NULL, NULL, '''/p'':69C,150C ''/stable/'':57C ''/stable/&lt;/a&gt;'':62C ''/stable/tutorial/text_analytics/working_with_text_data.html'':96C ''/stable/tutorial/text_analytics/working_with_text_data.html&lt;/a&gt;'':101C ''aleksandar'':2C ''also'':122C ''analysi'':19C ''bag'':112C ''bag-of-word'':111C ''bay'':134C ''big'':12C ''blekh'':3C ''count'':123C ''dataset'':78C,139C ''describ'':129C ''exampl'':48C,103C,144C ''first'':73C ''format'':89C ''give'':102C ''given'':5C ''gram'':126C ''gritti'':35C ''href'':54C,93C ''learn'':52C,65C,85C ''librari'':66C ''link'':9C,26C,91C ''ll'':21C,43C ''load'':80C,107C ''machin'':64C ''make'':39C ''modul'':119C ''n'':125C ''n-gram'':124C ''naiv'':133C ''nice'':8C ''nitti'':34C ''nitty-gritti'':33C ''nofollow'':59C,98C ''p'':1C,70C ''pictur'':13C ''play'':147C ''point'':44C ''provid'':24C ''python'':68C ''realli'':7C ''rel'':58C,97C ''represent'':115C ''run'':132C ''scikit'':51C,84C ''scikit-learn'':50C,83C ''scikit-learn.feature_extraction.text'':120C ''scikit-learn.org'':56C,61C,95C,100C ''scikit-learn.org/stable/'':55C ''scikit-learn.org/stable/&lt;/a&gt;'':60C ''scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html'':94C ''scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html&lt;/a&gt;'':99C ''sentiment'':18C ''softwar'':28C ''spars'':88C ''start'':146C ''svm'':136C ''take'':76C,142C ''talk'':30C ''text'':108C ''tri'':22C ''use'':49C ''want'':74C ''word'':114C ''work'':41C ''would'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5149, 2, '2015-02-15 17:46:34.887', 1, NULL, NULL, '&lt;p&gt;Alex''s answer is spot on in how you would go about figuring out similarity. One more step required, to answer your question, is to come up with a threshold of similarity. I.e. some similarity threshold beyond which you can say that the discrepancies are probably errors.&lt;/p&gt;

&lt;p&gt;If you are looking for resources to learn more about this, I''d recommend &lt;a href="http://rads.stackoverflow.com/amzn/click/0123814790" rel="nofollow"&gt;Data Mining Concepts and Techniques&lt;/a&gt; by Han et all&lt;/p&gt;
', NULL, NULL, '''/a'':77C ''/amzn/click/0123814790'':69C ''/p'':49C,82C ''alex'':2C ''answer'':4C,22C ''beyond'':38C ''come'':27C ''concept'':74C ''d'':63C ''data'':72C ''discrep'':45C ''error'':48C ''et'':80C ''figur'':14C ''go'':12C ''han'':79C ''href'':66C ''i.e'':34C ''learn'':58C ''look'':54C ''mine'':73C ''nofollow'':71C ''one'':17C ''p'':1C,50C ''probabl'':47C ''question'':24C ''rads.stackoverflow.com'':68C ''rads.stackoverflow.com/amzn/click/0123814790'':67C ''recommend'':64C ''rel'':70C ''requir'':20C ''resourc'':56C ''say'':42C ''similar'':16C,33C,36C ''spot'':6C ''step'':19C ''techniqu'':76C ''threshold'':31C,37C ''would'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4852, 1, '2015-01-10 00:15:45.99', 0, 703, 'Storing Big Matrix in DataBase', '&lt;p&gt;I have a mysql database with the following format:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;id      string
1        foo1...
2        foo2...
..       ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;There are &gt;100k entries in this db.&lt;/p&gt;

&lt;p&gt;What I want to do is for each string, compare it to each other string and store some metric of the comparison.  Doing this will essentially yield a 2D matrix of size &lt;code&gt;NxN&lt;/code&gt; where &lt;code&gt;N&lt;/code&gt; is the number of row in the db.&lt;/p&gt;

&lt;p&gt;My initial thought was creating another db where each index corresponds to the string of the index in the first db and each column is the value from comparing the two strings. For example, id 1 column 2 in the second db would be the value outputted from comparing id1 and id2 in the first db.&lt;/p&gt;

&lt;p&gt;The format of the second db:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;id    col1    col2    col3    ....
1       1      0.4     0.5    .....
...    ...     ...      ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This way of creating the second db would result in 100k rows x 100k columns, which is the issue at hand.  What is the best way to handle large data sets like this?  Is storing the data in a text file more efficient (say each text file corresponds to one row in the second db.)&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4853, 2, '2015-01-10 04:33:10.407', 1, NULL, NULL, '&lt;p&gt;It is usually more practical to compute the distances on the fly rather than storing $N^2$ values. If possible, you will want to use a heuristic to select only the items could be sufficiently similar so you don''t waste time computing distance to irrelevant items. PostgreSQL can create an &lt;a href="http://www.postgresql.org/docs/9.1/static/pgtrgm.html" rel="nofollow"&gt;index on character trigrams&lt;/a&gt; in strings that enables efficient querying for other strings that are sufficiently similar (measured by the Jaccard similarity coefficient).&lt;/p&gt;

&lt;p&gt;Storing an $N$ column wide table is not possible because the maximum number of columns allowed in MySQL is 4096. Even if that were not the case, using an RDBMS in such a way is considered bad practice and such a table would be difficult to work with.&lt;/p&gt;

&lt;p&gt;If for some reason you must store a dense distance matrix, a format such as &lt;a href="http://www.hdfgroup.org/HDF5/" rel="nofollow"&gt;HDF5&lt;/a&gt; would be more efficient than an RDBMS.&lt;/p&gt;
', NULL, NULL, '''/a'':64C,159C ''/docs/9.1/static/pgtrgm.html'':57C ''/hdf5/'':155C ''/p'':84C,134C,167C ''2'':18C ''4096'':105C ''allow'':101C ''bad'':122C ''case'':112C ''charact'':62C ''coeffici'':83C ''column'':89C,100C ''comput'':8C,44C ''consid'':121C ''could'':34C ''creat'':51C ''dens'':144C ''difficult'':130C ''distanc'':10C,45C,145C ''effici'':69C,163C ''enabl'':68C ''even'':106C ''fli'':13C ''format'':148C ''hdf5'':158C ''heurist'':28C ''href'':54C,152C ''index'':60C ''irrelev'':47C ''item'':33C,48C ''jaccard'':81C ''matrix'':146C ''maximum'':97C ''measur'':78C ''must'':141C ''mysql'':103C ''n'':17C,88C ''nofollow'':59C,157C ''number'':98C ''p'':1C,85C,135C ''possibl'':21C,94C ''postgresql'':49C ''practic'':6C,123C ''queri'':70C ''rather'':14C ''rdbms'':115C,166C ''reason'':139C ''rel'':58C,156C ''select'':30C ''similar'':37C,77C,82C ''store'':16C,86C,142C ''string'':66C,73C ''suffici'':36C,76C ''tabl'':91C,127C ''time'':43C ''trigram'':63C ''use'':26C,113C ''usual'':4C ''valu'':19C ''want'':24C ''wast'':42C ''way'':119C ''wide'':90C ''work'':132C ''would'':128C,160C ''www.hdfgroup.org'':154C ''www.hdfgroup.org/hdf5/'':153C ''www.postgresql.org'':56C ''www.postgresql.org/docs/9.1/static/pgtrgm.html'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5064, 1, '2015-02-05 23:57:05.757', 0, 44, 'Where in practice can we see following information propagation model', '&lt;p&gt;I am not certain, if this is the right place to ask the following question. I am looking for some practical scenarios in social networks where the following information propagation model can arise:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/mERQh.jpg" alt="A toy example of the desired information propagation model"&gt;&lt;/p&gt;

&lt;p&gt;Basically, I have a source node and some information propagating radially from it and each recipient receives the information from a single sender.&lt;/p&gt;
', 'social-network-analysis', NULL, '''/merqh.jpg'':55C ''/p'':49C,66C,91C ''alt'':56C ''analysi'':14B ''aris'':48C ''ask'':27C ''basic'':68C ''certain'':19C ''desir'':62C ''exampl'':59C ''follow'':7A,29C,43C ''i.stack.imgur.com'':54C ''i.stack.imgur.com/merqh.jpg'':53C ''img'':51C ''inform'':8A,44C,63C,76C,86C ''look'':33C ''model'':10A,46C,65C ''network'':13B,40C ''node'':73C ''p'':15C,50C,67C ''place'':25C ''practic'':3A,36C ''propag'':9A,45C,64C,77C ''question'':30C ''radial'':78C ''receiv'':84C ''recipi'':83C ''right'':24C ''scenario'':37C ''see'':6A ''sender'':90C ''singl'':89C ''social'':12B,39C ''social-network-analysi'':11B ''sourc'':72C ''src'':52C ''toy'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8833, 2, '2015-11-13 13:40:09.927', 0, NULL, NULL, '&lt;p&gt;This can be thought of as an &lt;a href="https://en.m.wikipedia.org/wiki/Active_learning_(machine_learning)" rel="nofollow"&gt;active learning&lt;/a&gt; problem. You want feedback from the learning process to influence what kind of input examples you next train on.  Active learning is still a difficult problem in most settings. &lt;/p&gt;

&lt;p&gt;As @cohoz points out, hill climbing is an intuitive option in this case, but only if you always want to increase or decrease your measure of frustration. If instead the goal is to explore frustration as a function of as much of the music parameter feature space as possible, you will want to read about active learning and uncertainty.&lt;/p&gt;
', NULL, NULL, '''/a'':18C ''/p'':48C,109C ''/wiki/active_learning_(machine_learning)'':13C ''activ'':16C,38C,105C ''alway'':67C ''case'':62C ''climb'':55C ''cohoz'':51C ''decreas'':72C ''difficult'':43C ''en.m.wikipedia.org'':12C ''en.m.wikipedia.org/wiki/active_learning_(machine_learning)'':11C ''exampl'':33C ''explor'':83C ''featur'':95C ''feedback'':22C ''frustrat'':76C,84C ''function'':87C ''goal'':80C ''hill'':54C ''href'':10C ''increas'':70C ''influenc'':28C ''input'':32C ''instead'':78C ''intuit'':58C ''kind'':30C ''learn'':17C,25C,39C,106C ''measur'':74C ''much'':90C ''music'':93C ''next'':35C ''nofollow'':15C ''option'':59C ''p'':1C,49C ''paramet'':94C ''point'':52C ''possibl'':98C ''problem'':19C,44C ''process'':26C ''read'':103C ''rel'':14C ''set'':47C ''space'':96C ''still'':41C ''thought'':5C ''train'':36C ''uncertainti'':108C ''want'':21C,68C,101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5635, 1, '2015-04-28 12:58:33.92', 1, 21, 'large database operation. check for relatedness between entities', '&lt;p&gt;I have one small list of entities, such as:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Russia
Vladimir
Moscow
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then I have a massive database of JSON indices. For each entry there are multiple alpha-numeric identifiers.&lt;/p&gt;

&lt;p&gt;So for instance, for &lt;code&gt;Russia&lt;/code&gt; there might only be one. But for &lt;code&gt;Vladimir&lt;/code&gt; maybe there will be 100. &lt;/p&gt;

&lt;p&gt;They''re stored in JSON but I read them into my java program like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;        // GET JSON DATA
        File f = new File("/home/matthias/Workbench/SUTD/nytimes_corpus/wdtk-parent/wdtk-examples/JSON_Output/user666.json");
        String jsonTxt = null;

        if (f.exists())
        {
            InputStream is = new FileInputStream("/home/matthias/Workbench/SUTD/nytimes_corpus/wdtk-parent/wdtk-examples/JSON_Output/user666.json");
            jsonTxt = IOUtils.toString(is);
        }
        //reformat
        jsonTxt = ( jsonTxt.substring(1, jsonTxt.length()-1) ).replace("\\","");

        Gson json = new Gson();
        Map&amp;lt;String, HashSet&amp;lt;String&amp;gt;&amp;gt; mast_Q_storage_map = new HashMap&amp;lt;String, HashSet&amp;lt;String&amp;gt;&amp;gt;();
        mast_Q_storage_map = (Map&amp;lt;String, HashSet&amp;lt;String&amp;gt;&amp;gt;) json.fromJson(jsonTxt, mast_Q_storage_map.getClass());
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to get all of the values associated with the entities from the small list. &lt;/p&gt;

&lt;p&gt;So I need to search the big list and retrieve their values. &lt;/p&gt;

&lt;p&gt;Then I want to try to determine if there is a relationship between any of the entities in the sentence, as in I want to check if &lt;/p&gt;

&lt;p&gt;Russia   X Vladimir
Russia   X Moscow
Moscow   X Vladimir
Moscow   X Russia
Vladimir X Russia
Vladimir X Moscow&lt;/p&gt;

&lt;p&gt;will result in a relationship, I''ve been trying to do it like this, but I''m running into big problems:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;        // Read in all the sentences, that are in files, in this folder
        final File folder = new File("/home/matthias/Workbench/SUTD/nytimes_corpus/wdtk-parent/wdtk-examples/JSON_Output/");

        for (final File fileEntry : folder.listFiles()) 
        {
            BufferedReader br = new BufferedReader(new FileReader(fileEntry));
            try 
            {
                //Store the filename
                //System.out.println(fileEntry.getName());
                StringBuilder sb = new StringBuilder();
                String line = br.readLine();

                while (line != null) 
                {   
                    sb.append(line);
                    sb.append(System.lineSeparator());
                    line = br.readLine();
                }
                String everything = sb.toString();
                //System.out.println(everything);

                Document doc = Jsoup.parse(everything);

                Elements contents = doc.getElementsByTag("sentence");
                for (Element content : contents) 
                {
                    //store the sentence number
                    String number = content.select("sentence").text();
                    number = number.substring(0, number.indexOf(" ")); 
                    System.out.println(number);

                    //get all the entities in this sentence
                    Elements pers = content.select("PERSON");
                    Elements locs = content.select("LOCATION");
                    Elements orgs = content.select("ORGANIZATION");

                    //collect all the elements to a list, all the elements of one sentence
                    List&amp;lt;String&amp;gt; list = new ArrayList&amp;lt;String&amp;gt;();

                    for (Element per : pers) 
                    {
                        list.add(per.text().trim());
                    }
                    for (Element loc : locs) 
                    {
                        list.add(loc.text().trim());
                    }
                    for (Element org : orgs) 
                    {
                        list.add(org.text().trim());
                    }


                    System.out.println();
                    System.out.println();
                    System.out.println();
                    System.out.println("This is list of sentence elements:");
                    for (String s : list)
                        System.out.println(s);
                    System.out.println();
                    System.out.println();
                    System.out.println();


                    List&amp;lt;String&amp;gt; Q_value_list = new ArrayList&amp;lt;String&amp;gt;();

                    // for the list of Q values to keys
                    for (Entry&amp;lt;String, HashSet&amp;lt;String&amp;gt;&amp;gt; e : mast_Q_storage_map.entrySet()) 
                    {
                        for (String s : list)
                        {
                            if (e.getKey().contains(s)) 
                            {
                                //List&amp;lt;String&amp;gt; Q_value_list = new ArrayList&amp;lt;String&amp;gt;(e.getValue());

                                System.out.println(e.getKey() + " :: " + Q_value_list.toString());
                            }
                        }
                    }



                                //czeher
                                for (String home:Q_value_list) 
                                {
                                  for (String away:Q_value_list) 
                                  {


                                    String URL_czech = "http://milenio.dcc.uchile.cl/sparql?default-graph-uri=&amp;amp;query=PREFIX+%3A+%3Chttp%3A%2F%2Fwww.wikidata.org%2Fentity%2F%3E%0D%0ASELECT+*+WHERE+%7B%0D%0A+++%3A" 
                                                       + home + "+%3FsimpleProperty+%3A" 
                                                       + away + "%0D%0A%7D%0D%0A&amp;amp;format=text%2Fhtml&amp;amp;timeout=0&amp;amp;debug=on";


                                    URL wikidata_page = new URL(URL_czech);
                                    HttpURLConnection wiki_connection = (HttpURLConnection)wikidata_page.openConnection();
                                    InputStream wikiInputStream = null;


                                        try 
                                        {
                                            // try to connect and use the input stream
                                            wiki_connection.connect();
                                            wikiInputStream = wiki_connection.getInputStream();
                                        } 
                                        catch(IOException error) 
                                        {
                                            // failed, try using the error stream
                                            wikiInputStream = wiki_connection.getErrorStream();
                                        }
                                    // parse the input stream using Jsoup
                                    Document docx = Jsoup.parse(wikiInputStream, null, wikidata_page.getProtocol()+"://"+wikidata_page.getHost()+"/");



                                    Elements link_text = docx.select("table.sparql &amp;gt; tbody &amp;gt; tr:nth-child(2) &amp;gt; td &amp;gt; a");
                                    //link_text.text();
                                    for (Element l : link_text) 
                                    {
                                        String output = l.text();
                                        System.out.println( output );
                                    }


                                  }






                    }

                }
            }
            finally 
            {
                br.close();
            }

        }
&lt;/code&gt;&lt;/pre&gt;
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''br.readline'':312C,321C ''bufferedread'':293C,296C ''catch'':556C ''check'':4A,222C ''child'':591C ''code'':24C,56C,66C,93C,269C ''connect'':538C,547C ''contain'':473C ''content'':332C,337C,338C ''content.select'':345C,363C,367C,371C ''czech'':506C,535C ''data'':96C ''databas'':2A,36C ''debug'':527C ''determin'':203C ''doc'':328C ''doc.getelementsbytag'':333C ''document'':327C,573C ''docx'':574C ''docx.select'':583C ''e'':465C ''e.getkey'':472C,489C ''e.getvalue'':487C ''element'':331C,336C,361C,365C,369C,376C,382C,397C,404C,411C,426C,580C,599C ''entiti'':8A,19C,176C,213C,357C ''entri'':42C,457C ''error'':558C,563C ''everyth'':323C,326C,330C ''f'':98C ''f.exists'':106C ''fail'':559C ''file'':97C,100C,278C,283C,286C,290C ''fileentri'':291C,299C ''fileentry.getname'':305C ''fileinputstream'':110C ''filenam'':303C ''fileread'':298C ''final'':282C,289C,608C ''folder'':281C,284C ''folder.listfiles'':292C ''format'':520C ''get'':94C,168C ''gson'':122C,125C ''gt'':132C,133C,145C,146C,157C,158C,389C,395C,439C,447C,463C,464C,478C,486C,585C,587C,593C,595C ''hashmap'':139C ''hashset'':129C,142C,154C,460C ''home'':494C,510C ''httpurlconnect'':536C,539C ''identifi'':49C ''indic'':39C ''input'':551C,569C ''inputstream'':107C,541C ''instanc'':54C ''ioexcept'':557C ''ioutils.tostring'':113C ''java'':11B,87C ''json'':38C,80C,95C,123C ''json.fromjson'':159C ''jsontxt'':103C,112C,116C,160C ''jsontxt.length'':119C ''jsontxt.substring'':117C ''jsoup'':572C ''jsoup.parse'':329C,575C ''key'':455C ''l'':600C ''l.text'':605C ''larg'':1A ''like'':89C,258C ''line'':311C,314C,317C,320C ''link'':581C,601C ''list'':17C,180C,190C,379C,386C,390C,423C,430C,436C,442C,450C,470C,481C,497C,503C ''list.add'':400C,407C,414C ''loc'':366C,405C,406C ''loc.text'':408C ''locat'':368C ''lt'':127C,130C,140C,143C,152C,155C,387C,393C,437C,445C,458C,461C,476C,484C ''m'':262C ''map'':126C,137C,150C,151C ''massiv'':35C ''mast'':134C,147C ''mast_q_storage_map.entryset'':466C ''mast_q_storage_map.getclass'':161C ''mayb'':69C ''might'':60C ''milenio.dcc.uchile.cl'':508C ''milenio.dcc.uchile.cl/sparql?default-graph-uri=&amp;amp;query=prefix+%3a+%3chttp%3a%2f%2fwww.wikidata.org%2fentity%2f%3e%0d%0aselect+*+where+%7b%0d%0a+++%3a'':507C ''moscow'':27C,231C,232C,235C,243C ''multipl'':45C ''need'':185C ''new'':99C,109C,124C,138C,285C,295C,297C,308C,391C,443C,482C,532C ''nth'':590C ''nth-child'':589C ''null'':104C,315C,543C,577C ''number'':342C,344C,348C,353C ''number.indexof'':351C ''number.substring'':349C ''numer'':48C ''one'':15C,63C,384C ''oper'':3A ''org'':370C,412C,413C ''org.text'':415C ''organ'':372C ''output'':604C,607C ''p'':12C,30C,51C,75C,164C,182C,196C,225C,245C ''page'':531C ''pars'':10B,567C ''per'':362C,398C,399C ''per.text'':401C ''person'':364C ''pre'':23C,92C,268C ''problem'':266C ''program'':88C ''q'':135C,148C,440C,452C,479C,495C,501C ''q_value_list.tostring'':490C ''re'':77C ''read'':83C,270C ''related'':6A ''relationship'':208C,250C 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''wiki_connection.geterrorstream'':566C ''wiki_connection.getinputstream'':555C ''wikidata'':530C ''wikidata_page.gethost'':579C ''wikidata_page.getprotocol'':578C ''wikidata_page.openconnection'':540C ''wikiinputstream'':542C,554C,565C,576C ''x'':227C,230C,233C,236C,239C,242C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4855, 1, '2015-01-10 22:53:14.353', 1, 21, 'Shifting dataPoints up by a constant (Is there an issue with too many 0''s for features?)', '&lt;p&gt;I am currently collecting second by second data regarding buyer vs seller initiated trades for different financial instruments (securities mostly). If there are more buyer initiated trades in a given second, then that second''s data point would contain a positive value in the pertinent feature. If there are more seller initiated trades, then there would be a negative value. And if either there is an equal amount of buy vs seller initiated trades OR if there are simply not any trades in a given second, there will be a 0 for the feature in that data point. Along with this feature, there are several other features that are based on what occurred in the preceding seconds (eg if the value discussed above was 12 for the data point immediately preceding the current point, then the second feature for the current data point would be 12 - please let me know if this is not clear) After much troubleshooting, I have concluded that if there are too many data points with too many 0''s for features, the classifier simply wont work. When I print out the probabilities of evaluation data points falling into different classes, I simply get&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;0:NaN,1:NaN
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;for all model evaluation points I try to classify. (I am using logistic regression from apache-mahout. In total have 183 features, but over 40million data points. There are three categories to which the data point can be classified)&lt;/p&gt;

&lt;p&gt;I have found that if I set the default value to 1, then I no longer encounter this error, e.g. if there are no trades, the value will be a 1, if there is one seller initiated trade, the value will be 0.&lt;/p&gt;

&lt;p&gt;So with all this in mind, I have two related questions:&lt;/p&gt;

&lt;p&gt;1) Has anyone else encountered this issue? e.g. if you have a vector with x features, and for a majority of the data points, a majority of the features contain 0''s, is this know to give issues?&lt;/p&gt;

&lt;p&gt;2) Is shifting all values up by a constant (such as 1) a valid fix to this issue? I assume that if this constant is applied to all values, then it shouldn''t skew the data, but I figure it won''t hurt to check with the experts.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Also, I''m new to this, so if you believe that my question could use more info please let me know, and if you could give me ideas of what information to include, it would be greatly appreciated.&lt;/p&gt;

&lt;p&gt;thanks in advance&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4856, 2, '2015-01-11 01:57:49.49', 1, NULL, NULL, '&lt;p&gt;The derivative is a linear transform, and you''re using a linear model. As you''ve demonstrated, nothing is gained by adding linear combination of other features (like the derivative) as a new feature in a linear model.&lt;/p&gt;

&lt;p&gt;You may want a derivative coefficient to help interpret the model. You could use the normal model $x_{n+1} = a * x_n + b * n_{n-1}$ and then report the value $a-b$ to the client. A more general approach is to examine the &lt;a href="http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.signal.freqz.html" rel="nofollow"&gt;frequency response&lt;/a&gt; of the model (the derivative is a high-pass filter, your model is a FIR filter).&lt;/p&gt;

&lt;p&gt;Alternately you can rotate the basis of the input such that the derivative is a feature and the dimensions are still independent as follows:&lt;/p&gt;

&lt;p&gt;You can think of the independent variables $x_n$ and $x_{n-1}$ as forming a &lt;a href="https://en.wikipedia.org/wiki/Standard_basis" rel="nofollow"&gt;standard basis&lt;/a&gt; for the two dimensions of your model''s input, that is $\langle 1, 0\rangle$ and $\langle 0, 1\rangle$. 
Neither $a\langle 1, 0 \rangle$ or $b \langle 0, 1 \rangle$ are orthogonal to  c$\langle1, -1\rangle$.&lt;/p&gt;

&lt;p&gt;Therefore, &lt;strong&gt;if you want to use $c(x_n - x_{n-1})$ as a feature, I would also use the orthogonal feature $d(x_n + x_{n-1})$&lt;/strong&gt;. That is,&lt;/p&gt;

&lt;p&gt;$$ x_{n+1} = c(x_n - x_{n-1}) + d(x_n + x_{n-1}) $$.&lt;/p&gt;

&lt;p&gt;You could think of this model as the derivative plus the integral. &lt;/p&gt;

&lt;p&gt;The same logic applies to the choice of scaling function for the Haar wavelet.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4859, 1, '2015-01-11 11:43:38.253', 1, 641, 'Aspect based sentiment analysis using machine learning approach', '&lt;p&gt;I am very new in machine learning. I have annotated data with category, aspect, opinion word and sentiment. for example, for the bellow text&lt;/p&gt;

&lt;p&gt;"The apple was really tasty"&lt;/p&gt;

&lt;p&gt;I have category-&gt;food, aspect-&gt; apple, opinion word -&gt;tasty and sentiment-&gt;positive. I have training data like this format.&lt;/p&gt;

&lt;p&gt;How can I train a SVM classifier using this format of training set?
How to extract features like n-gram, POS and sentiment word to train the classifier?
Could you please suggest any beginning step for this aspect based sentiment analysis using machine learning algorithms?&lt;/p&gt;
', 'machine-learning  svm  feature-extraction', NULL, '''/p'':41C,48C,69C,117C ''algorithm'':116C ''analysi'':4A,112C ''annot'':26C ''appl'':44C,55C ''approach'':8A ''aspect'':1A,30C,54C,109C ''base'':2A,110C ''begin'':105C ''bellow'':39C ''categori'':29C,52C ''classifi'':77C,99C ''could'':100C ''data'':27C,65C ''exampl'':36C ''extract'':15B,86C ''featur'':14B,87C ''feature-extract'':13B ''food'':53C ''format'':68C,80C ''gram'':91C ''learn'':7A,11B,23C,115C ''like'':66C,88C ''machin'':6A,10B,22C,114C ''machine-learn'':9B ''n'':90C ''n-gram'':89C ''new'':20C ''opinion'':31C,56C ''p'':16C,42C,49C,70C ''pleas'':102C ''pos'':92C ''posit'':61C ''realli'':46C ''sentiment'':3A,34C,60C,94C,111C ''set'':83C ''step'':106C ''suggest'':103C ''svm'':12B,76C ''tasti'':47C,58C ''text'':40C ''train'':64C,74C,82C,97C ''use'':5A,78C,113C ''word'':32C,57C,95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4860, 2, '2015-01-11 13:45:04.36', 1, NULL, NULL, '&lt;p&gt;I would recommend you to start from reading the draft of the introductory book &lt;a href="http://rads.stackoverflow.com/amzn/click/1608458849" rel="nofollow"&gt;"Sentiment analysis and opinion mining"&lt;/a&gt; by Bing Liu. The draft in a PDF document format is available for free &lt;a href="http://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdf" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;More details about the new upcoming book of this author, as well as comprehensive information on the topic of aspect-based sentiment analysis, with references and links to data sets, are available at this page: &lt;a href="http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html" rel="nofollow"&gt;http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Another interesting resource is a survey book &lt;a href="http://www.cs.cornell.edu/home/llee/opinion-mining-sentiment-analysis-survey.html" rel="nofollow"&gt;"Opinion mining and sentiment analysis&lt;/a&gt; by Bo Pang and Lillian Lee. The book is available in print and as a downloadable PDF e-book in a &lt;a href="http://www.cs.cornell.edu/home/llee/omsa/omsa-published.pdf" rel="nofollow"&gt;published version&lt;/a&gt; or an &lt;a href="http://www.cs.cornell.edu/home/llee/omsa/omsa.pdf" rel="nofollow"&gt;author-formatted version&lt;/a&gt;, which are almost identical in terms of contents.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4861, 2, '2015-01-11 17:38:19.187', 1, NULL, NULL, '&lt;p&gt;While reading blogs and papers is helpful to identify the latest and greatest, having a solid foundation helps a lot, too. But I assume you already have gone over Manning''s great (and free in e-book form) book on IR, right?&lt;/p&gt;

&lt;p&gt;&lt;a href="http://nlp.stanford.edu/IR-book/" rel="nofollow"&gt;http://nlp.stanford.edu/IR-book/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;It contains information on creating your own thesaurus from your document collection to solve synonymy problems, LSA for polysemy, etc..&lt;/p&gt;

&lt;p&gt;As for similarity measures, you will see there that Okapi BM25 (Robertson et al.) is considered superior to cosine similarity (but more expensive to implement and run).
Regarding the current state of the art, there was a small emergence of Bayesian Network-based classifiers in the early nineties (starting with Turtle &amp;amp; Croft), but that went quiet for a while.
However, right now, using BNs for IR is again finding some revival, particularly in  biomedical IR.
In that respect, I think most ongoing work is directed towards using Bayesian models incl. topic models and deep learning for word-sense disambiguation (WSD) and semantic similarity.
Here is a pointer to a recent paper with good references on the topic.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://arxiv.org/abs/1412.6629" rel="nofollow"&gt;http://arxiv.org/abs/1412.6629&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5648, 1, '2015-04-29 20:39:45.52', 1, 161, 'Where is the error in the following code?', '&lt;p&gt;I am trying to analyze the movie database using python, downloaded from imdb. While trying to generate some plots, I am running into errors which confuses me.
I am trying to generate a matrix of small figures which can show me any hidden pattern etc. Here is the code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;fig, axes = plt.subplots(nrows=4, ncols=6, figsize=(12, 8), 
                         tight_layout=True)

bins = np.arange(1950,2012,3)
for ax, genre in zip(axes.ravel(), movieGenre):
    ax.hist(movieDF[movieDF[''%s''%genre]==1].year, bins=bins, histtype=''stepfilled'', normed=True, color=''r'', alpha=.3, ec=''None'')
    ax.hist(movieDF.year, bins=bins, histtype=''stepfilled'', ec=''None'', normed=True, zorder=0, color=''grey'')
    ax.annotate(genre, xy=(1955, 3e-2), fontsize=14)
    ax.xaxis.set_ticks(np.arange(1950, 2013, 30))
    ax.set_yticks([])
    ax.set_xlabel(''Year'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The first &lt;code&gt;hist&lt;/code&gt; isn''t working, but the second one is working when I am commenting out the first one. Here is the traceback:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; KeyError                                  Traceback (most recent call last)
&amp;lt;ipython-input-158-c2e7c2737372&amp;gt; in &amp;lt;module&amp;gt;()
      4 bins = np.arange(1950,2012,3)
      5 for ax, genre in zip(axes.ravel(), movieGenre):
----&amp;gt; 6     ax.hist(movieDF[movieDF[''%s''%genre]==1].year, bins=bins, histtype=''stepfilled'', normed=True, color=''r'', alpha=.3, ec=''None'')
      7     ax.hist(movieDF.year, bins=bins, histtype=''stepfilled'', ec=''None'', normed=True, zorder=0, color=''grey'')
      8     ax.annotate(genre, xy=(1955, 3e-2), fontsize=14)

/Users/dt/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/matplotlib/axes.pyc in hist(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)
   8247         # Massage ''x'' for processing.
   8248         # NOTE: Be sure any changes here is also done below to ''weights''
-&amp;gt; 8249         if isinstance(x, np.ndarray) or not iterable(x[0]):
   8250             # TODO: support masked arrays;
   8251             x = np.asarray(x)

/Users/dt/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/series.pyc in __getitem__(self, key)
    477     def __getitem__(self, key):
    478         try:
--&amp;gt; 479             result = self.index.get_value(self, key)
    480 
    481             if not np.isscalar(result):

/Users/dt/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/pandas/core/index.pyc in get_value(self, series, key)
   1169 
   1170         try:
-&amp;gt; 1171             return self._engine.get_value(s, k)
   1172         except KeyError as e1:
   1173             if len(self) &amp;gt; 0 and self.inferred_type == ''integer'':



KeyError: 0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here is the first few columns of the data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;     imdbID     title                      rating    vote      runtime  year    genre
0   tt0111161   The Shawshank Redemption    9.3      1,439,277  142    1994 [Crime, Drama]
1   tt0468569   The Dark Knight             9.0      1,410,124  152    2008 [Action, Crime, Drama]
2   tt1375666   Inception                   8.8      1,209,159  148    2010 [Action, Mystery, Sci-Fi, Thriller]
3   tt0137523   Fight Club                  8.9      1,123,462  139    1999 [Drama]
4   tt0110912   Pulp Fiction                8.9      1,117,193  154    1994 [Crime, Drama]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;movieGenre is basically collecting all the different genres from ''genre'' column with duplicates removed: &lt;code&gt;movieGenre = set(movieDF.genre.sum())&lt;/code&gt; . I then added a single column to movieDF data frame for each genre such that if a particular movie belong to that genre, then that cell is &lt;code&gt;True&lt;/code&gt; otherwise it is &lt;code&gt;False&lt;/code&gt;.  So for example, for the movie Inception, the &lt;code&gt;Action&lt;/code&gt; column is marked &lt;code&gt;True&lt;/code&gt; but &lt;code&gt;Crime&lt;/code&gt; column is marked &lt;code&gt;False&lt;/code&gt; and so forth.&lt;/p&gt;

&lt;p&gt;Thanks.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4925, 1, '2015-01-22 21:34:57.443', 16, 897, 'VM image for data science projects', '&lt;p&gt;As there are numerous tools available for data science tasks, and it''s cumbersome to install everything and build up a perfect system. Is there a linux/Mac OS image with Python, R and other open-source data science tools installed available for people to use? Ideally an Ubuntu or a light weight OS with latest version of python , R (IDEs too), and other opensource data visualization tools installed will be ideal.  I haven''t come across one in my quick search on google. Please let me know if there are any or if someone of you have created one for yourself? I assume some universities might have their own VM images. Please share the links. I thank everyone of you out there for your active support in this regard. &lt;/p&gt;
', 'r  python  visualization', NULL, '''/p'':141C ''across'':87C ''activ'':136C ''assum'':114C ''avail'':16C,52C ''build'':29C ''come'':86C ''creat'':109C ''cumbersom'':24C ''data'':4A,18C,48C,76C ''everyon'':129C ''everyth'':27C ''googl'':94C ''haven'':84C ''ide'':71C ''ideal'':57C,82C ''imag'':2A,39C,122C ''instal'':26C,51C,79C ''know'':98C ''latest'':66C ''let'':96C ''light'':62C ''link'':126C ''linux/mac'':37C ''might'':117C ''numer'':14C ''one'':88C,110C ''open'':46C ''open-sourc'':45C ''opensourc'':75C ''os'':38C,64C ''p'':10C ''peopl'':54C ''perfect'':32C ''pleas'':95C,123C ''project'':6A ''python'':8B,41C,69C ''quick'':91C ''r'':7B,42C,70C ''regard'':140C ''scienc'':5A,19C,49C ''search'':92C ''share'':124C ''someon'':105C ''sourc'':47C ''support'':137C ''system'':33C ''task'':20C ''thank'':128C ''tool'':15C,50C,78C ''ubuntu'':59C ''univers'':116C ''use'':56C ''version'':67C ''visual'':9B,77C ''vm'':1A,121C ''weight'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4862, 2, '2015-01-12 05:47:37.127', 0, NULL, NULL, '&lt;p&gt;If you can provide more details about the processing you''re doing to the data, I think the responses would be a little more helpful.&lt;/p&gt;

&lt;p&gt;Here are a few things to consider:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;What''s the shape of your data going into k-means? Are you aggregating up to the week level? If so, then your data won''t look like the example you posted, since that seems like daily data.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Try using .&lt;a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.shape.html#numpy.ndarray.shape" rel="nofollow"&gt;shape&lt;/a&gt; on your output data set. Does the # of rows match the data set you put into it? (This ties in to question #1. You''ll be unable to join back directly if your original data is daily and the k-means data is weekly.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;I''m not sure if this is what you''re attempting, but just declaring &lt;code&gt;data_classes&lt;/code&gt; as the labels isn''t going to add them back in to your original data. Like oxtay suggested, using pandas is a good choice because it will allow you to &lt;a href="http://pandas.pydata.org/pandas-docs/stable/merging.html" rel="nofollow"&gt;join&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4987, 2, '2015-01-29 17:28:51.8', 1, NULL, NULL, '&lt;p&gt;I would find the unit variance of the all the weeks and then divide by that. Scikit can do this for you using &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html" rel="nofollow"&gt;scale&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':33C ''/p'':34C ''/stable/modules/generated/sklearn.preprocessing.scale.html'':29C ''divid'':15C ''find'':4C ''href'':26C ''nofollow'':31C ''p'':1C ''rel'':30C ''scale'':32C ''scikit'':18C ''scikit-learn.org'':28C ''scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html'':27C ''unit'':6C ''use'':24C ''varianc'':7C ''week'':12C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6081, 2, '2015-06-11 12:14:38.347', 1, NULL, NULL, '&lt;p&gt;I am not sure about what you are trying to do. But generally speaking, you can look at &lt;a href="http://ranger.uta.edu/~chqding/papers/KmeansPCA1.pdf" rel="nofollow"&gt;this paper&lt;/a&gt; for the relation between k-means clustering and PCA, especially Theorem 3.3.&lt;/p&gt;
', NULL, NULL, '''/a'':29C ''/p'':43C ''/~chqding/papers/kmeanspca1.pdf'':24C ''3.3'':42C ''cluster'':37C ''especi'':40C ''general'':14C ''href'':21C ''k'':35C ''k-mean'':34C ''look'':18C ''mean'':36C ''nofollow'':26C ''p'':1C ''paper'':28C ''pca'':39C ''ranger.uta.edu'':23C ''ranger.uta.edu/~chqding/papers/kmeanspca1.pdf'':22C ''rel'':25C ''relat'':32C ''speak'':15C ''sure'':5C ''theorem'':41C ''tri'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4866, 2, '2015-01-12 10:37:27.507', 6, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;&lt;p&gt;11 instances of data is insufficient for discovering the behavior (even finding patterns) for such presumably complex systems. You need at least thousands of them.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;These numbers are encoded versions of numbers generated by Serial Key Generation Algorithms, which in my experience are usually implemented by Python. So it''s not at all strange that there may be a correlation between some digits of them. As it is necessary for the system to recognize its created numbers from the numbers created by other lotteries.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Even if there was a correlation or correlation between all the instances of lottery numbers, &lt;em&gt;correlation doesn''t imply causation&lt;/em&gt; and these patterns, if found any, couldn''t really give any information about the key generation system itself.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;If you are really serious about it. you could go to this page and enter your data as a sequence it would show you the pattern, if there is any:
&lt;a href="http://oeis.org/" rel="nofollow"&gt;The On-Line Encyclopedia of Integer Sequences&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4867, 2, '2015-01-12 10:41:57.83', 1, NULL, NULL, '&lt;p&gt;From your question I am not sure how you are using logistic regression. The "vanilla" version of logistic regression gives a binary classifier, but your target has three values: a positive value, a negative value, and zero. The Apache site, on the page for logistic regression refers to the thesis of Paul Komarek, in which "logistic regression" refers to the binary classifier. So your problem might be that there your target variable has 3 values.&lt;/p&gt;

&lt;p&gt;Assuming that that is the case, you have many options, for example:
 - a) use "multi-nomial logistic", 
 - b) use ordinary logistic regression 3 times to fit 3 models, one per level, to distinguish it from the other two combined  &lt;/p&gt;
', NULL, NULL, '''/p'':77C,119C ''3'':75C,102C,106C ''apach'':40C ''assum'':79C ''b'':97C ''binari'':23C,62C ''case'':84C ''classifi'':24C,63C ''combin'':118C ''distinguish'':112C ''exampl'':90C ''fit'':105C ''give'':21C ''komarek'':54C ''level'':110C ''logist'':13C,19C,46C,57C,96C,100C ''mani'':87C ''might'':67C ''model'':107C ''multi'':94C ''multi-nomi'':93C ''negat'':35C ''nomial'':95C ''one'':108C ''option'':88C ''ordinari'':99C ''p'':1C,78C ''page'':44C ''paul'':53C ''per'':109C ''posit'':32C ''problem'':66C ''question'':4C ''refer'':48C,59C ''regress'':14C,20C,47C,58C,101C ''site'':41C ''sure'':8C ''target'':27C,72C ''thesi'':51C ''three'':29C ''time'':103C ''two'':117C ''use'':12C,92C,98C ''valu'':30C,33C,36C,76C ''vanilla'':16C ''variabl'':73C ''version'':17C ''zero'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4868, 2, '2015-01-12 16:39:40.64', 0, NULL, NULL, '&lt;p&gt;The similarity function at the core of the method will define all the values for your distances $d_1,d_2, \ldots, d_n$. The initial query should have some words as a reference point to compare to the words in the document. Not knowing whether the query is a sentence or arbitrary list, you are restricted to a method that does some kind of histogram comparison of the frequency of the words matching in the documents. You can perform naive summations of keyword mappings counts, look at keyword likelihoods in the normalized distributions, or give a distribution of weighting based on the strongest matches. More exotic functions will be based on your prior belief of how the words should be compared. Working within a Bayesian Framework you can see your prior assumptions explicitly. Cosine similarity or any other vector based measure will be slightly arbitrary without knowing the desired nature of comparison between query and document.&lt;/p&gt;

&lt;p&gt;There is not much more you can do without looking at some type of features, or attempt to cross compare the documents together, or use the initial query''s structure. In short, my answer is to use normalized frequency similarities of the document to the queries and produce a ranking, and with more specific goals in mind to apply measures like cosine similarity on test datasets to search for the best measure. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4872, 2, '2015-01-14 01:08:04.997', 1, NULL, NULL, '&lt;p&gt;Using derivatives as features is &lt;strong&gt;almost the same&lt;/strong&gt; as using past values, as both reconstruct phase or state space for dynamic system behind the time series. but they differ in some points, like noise amplification and how they carry information.
(see &lt;strong&gt;Ref&lt;/strong&gt;: State space reconstruction in the presence of noise; Martin Casdagli; Physica D - 1991 - section 2)&lt;/p&gt;

&lt;p&gt;Notice all information is embedded in time series, but using derivatives is going to reinterpret this information, which may be useful or useless.&lt;/p&gt;

&lt;p&gt;In your case, if you use all parameters and terms, i believe there is no use in it. but in case of using some algorithms like orthogonal forward regression (OFR) it may be beneficial. (see &lt;strong&gt;Ref&lt;/strong&gt;: Orthogonal least squares methods and their application to non-linear
system identification; S. CHEN, S. A. BILLINGS; INT. J. CONTROL, 1989)&lt;/p&gt;
', NULL, NULL, '''/p'':63C,88C,150C ''/strong'':11C,47C,127C ''1989'':149C ''1991'':60C ''2'':62C ''algorithm'':114C ''almost'':8C ''amplif'':38C ''applic'':134C ''behind'':26C ''believ'':101C ''benefici'':123C ''bill'':145C ''carri'':42C ''casdag'':57C ''case'':92C,110C ''chen'':142C ''control'':148C ''d'':59C ''deriv'':3C,75C ''differ'':32C ''dynam'':24C ''embed'':69C ''featur'':5C ''forward'':117C ''go'':77C ''identif'':140C ''inform'':43C,67C,81C ''int'':146C ''j'':147C ''least'':129C ''like'':36C,115C ''linear'':138C ''martin'':56C ''may'':83C,121C ''method'':131C ''nois'':37C,55C ''non'':137C ''non-linear'':136C ''notic'':65C ''ofr'':119C ''orthogon'':116C,128C ''p'':1C,64C,89C ''paramet'':97C ''past'':14C ''phase'':19C ''physica'':58C ''point'':35C ''presenc'':53C ''reconstruct'':18C,50C ''ref'':46C,126C ''regress'':118C ''reinterpret'':79C ''section'':61C ''see'':44C,124C ''seri'':29C,72C ''space'':22C,49C ''squar'':130C ''state'':21C,48C ''strong'':7C,45C,125C ''system'':25C,139C ''term'':99C ''time'':28C,71C ''use'':2C,13C,74C,85C,95C,105C,112C ''useless'':87C ''valu'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4886, 2, '2015-01-15 23:27:20.597', 0, NULL, NULL, '&lt;p&gt;I am just 5 months late but with CRFSuite you can actually use those float features as numbers, not as strings. For this, you just need to invent an unique label for each dimension then add a ":" followed by the value.&lt;/p&gt;

&lt;p&gt;For example, a word "jungle" is represented in 5 dimensions:
0.1 0.4 0.8 0.2 0.9&lt;/p&gt;

&lt;p&gt;Then CRFSuite would take that word + feature as:&lt;/p&gt;

&lt;p&gt;LABEL f1:0.1 f2:0.4 f3:0.8 f4:0.2 f5:0.9&lt;/p&gt;

&lt;p&gt;where of course you replace ``LABEL'''' by an actual string and you separate all spaces with tabs (that''s the format for CRFSuite).&lt;/p&gt;

&lt;p&gt;Not sure though for other packages.&lt;/p&gt;
', NULL, NULL, '''/p'':43C,60C,70C,83C,108C,116C ''0.1'':55C,74C ''0.2'':58C,80C ''0.4'':56C,76C ''0.8'':57C,78C ''0.9'':59C,82C ''5'':5C,53C ''actual'':13C,93C ''add'':37C ''cours'':87C ''crfsuit'':10C,63C,107C ''dimens'':35C,54C ''exampl'':46C ''f1'':73C ''f2'':75C ''f3'':77C ''f4'':79C ''f5'':81C ''featur'':17C,68C ''float'':16C ''follow'':39C ''format'':105C ''invent'':29C ''jungl'':49C ''label'':32C,72C,90C ''late'':7C ''month'':6C ''need'':27C ''number'':19C ''p'':1C,44C,61C,71C,84C,109C ''packag'':115C ''replac'':89C ''repres'':51C ''separ'':97C ''space'':99C ''string'':22C,94C ''sure'':111C ''tab'':101C ''take'':65C ''though'':112C ''uniqu'':31C ''use'':14C ''valu'':42C ''word'':48C,67C ''would'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4873, 1, '2015-01-14 09:08:00.483', 1, 1110, 'Querying DBpedia from Python', '&lt;p&gt;How can I get information about an entity from DBpedia using Python?&lt;/p&gt;

&lt;p&gt;Eg: I need to get all DBpedia information about USA  (&lt;a href="http://dbpedia.org/page/United_States" rel="nofollow"&gt;http://dbpedia.org/page/United_States&lt;/a&gt;) .  So I need to write the query from python (SPARQL) and need to get all attributes on USA as result.&lt;/p&gt;

&lt;p&gt;I tried :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;PREFIX db: &amp;lt;http://dbpedia.org/resource/&amp;gt;
SELECT ?p ?o
WHERE { db:United_States ?p ?o }
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But here all DBpedia information is not displaying.&lt;/p&gt;

&lt;p&gt;How can I do this and which all are the possible plugins/api available for python to connect with DBpedia ?&lt;/p&gt;

&lt;p&gt;Also what will be the SPARQL query for generating the above problem result?&lt;/p&gt;
', 'python', NULL, '''/code'':83C ''/p'':19C,61C,65C,94C,115C,130C ''/page/united_states'':35C ''/page/united_states&lt;/a&gt;)'':40C ''/pre'':84C ''/resource/&amp;gt;'':73C ''also'':117C ''attribut'':56C ''avail'':108C ''code'':67C ''connect'':112C ''db'':69C,78C ''dbpedia'':2A,16C,27C,89C,114C ''dbpedia.org'':34C,39C,72C ''dbpedia.org/page/united_states'':33C ''dbpedia.org/page/united_states&lt;/a&gt;)'':38C ''dbpedia.org/resource/&amp;gt;'':71C ''display'':93C ''eg'':21C ''entiti'':14C ''generat'':125C ''get'':10C,25C,54C ''href'':32C ''inform'':11C,28C,90C ''lt'':70C ''need'':23C,43C,52C ''nofollow'':37C ''o'':76C,82C ''p'':6C,20C,62C,75C,81C,85C,95C,116C ''plugins/api'':107C ''possibl'':106C ''pre'':66C ''prefix'':68C ''problem'':128C ''python'':4A,5B,18C,49C,110C ''queri'':1A,47C,123C ''rel'':36C ''result'':60C,129C ''select'':74C ''sparql'':50C,122C ''state'':80C ''tri'':64C ''unit'':79C ''usa'':30C,58C ''use'':17C ''write'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8698, 1, '2015-11-03 13:09:48.553', 0, 133, 'how to disable query from beeline results', '&lt;p&gt;I am occuring a strange &lt;code&gt;hive&lt;/code&gt;-client &lt;code&gt;beeline&lt;/code&gt; behavior. 
In the outputed file with query results there is also a queary at the beggining and at the end. Is there any option to disable such behavior? I can''t see such option in the &lt;code&gt;beeline -help&lt;/code&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;-bash-4.2$ beeline -help
Usage: java org.apache.hive.cli.beeline.BeeLine 
   -u &amp;lt;database url&amp;gt;               the JDBC URL to connect to
   -n &amp;lt;username&amp;gt;                   the username to connect as
   -p &amp;lt;password&amp;gt;                   the password to connect as
   -d &amp;lt;driver class&amp;gt;               the driver class to use
   -i &amp;lt;init file&amp;gt;                  script file for initialization
   -e &amp;lt;query&amp;gt;                      query that should be executed
   -f &amp;lt;exec file&amp;gt;                  script file that should be executed
   --hiveconf property=value       Use value for given property
   --hivevar name=value            hive variable name and value
                                   This is Hive specific settings in which variables
                                   can be set at session level and referenced in Hive
                                   commands or queries.
   --color=[true/false]            control whether color is used for display
   --showHeader=[true/false]       show column names in query results
   --headerInterval=ROWS;          the interval between which heades are displayed
   --fastConnect=[true/false]      skip building table/column list for tab-completion
   --autoCommit=[true/false]       enable/disable automatic transaction commit
   --verbose=[true/false]          show verbose error messages and debug info
   --showWarnings=[true/false]     display connection warnings
   --showNestedErrs=[true/false]   display nested errors
   --numberFormat=[pattern]        format numbers using DecimalFormat pattern
   --force=[true/false]            continue running script even after errors
   --maxWidth=MAXWIDTH             the maximum width of the terminal
   --maxColumnWidth=MAXCOLWIDTH    the maximum width to use when displaying columns
   --silent=[true/false]           be more silent
   --autosave=[true/false]         automatically save preferences
   --outputformat=[table/vertical/csv2/tsv2/dsv/csv/tsv]  format mode for result display
                                   Note that csv, and tsv are deprecated - use csv2, tsv2 instead
  --truncateTable=[true/false]    truncate table column when it exceeds length
   --delimiterForDSV=DELIMITER     specify the delimiter for delimiter-separated values output format (default: |)
   --isolation=LEVEL               set the transaction isolation level
   --nullemptystring=[true/false]  set to true to get historic behavior of printing null as empty string
   --help                          display this message
&lt;/code&gt;&lt;/pre&gt;
', 'hadoop  hive', NULL, '''-4.2'':67C ''/code'':18C,22C,62C,348C ''/p'':63C ''/pre'':349C ''also'':33C ''autocommit'':214C ''automat'':217C,279C ''autosav'':277C ''bash'':66C ''beelin'':6A,21C,60C,68C ''beggin'':38C ''behavior'':23C,50C,337C ''build'':207C ''class'':105C,109C ''client'':19C ''code'':16C,20C,59C,65C ''color'':178C,182C ''column'':190C,271C,304C ''command'':175C ''commit'':219C ''complet'':213C ''connect'':82C,91C,100C,232C ''continu'':248C ''control'':180C ''csv'':291C ''csv2'':297C ''d'':102C ''databas'':75C ''debug'':227C ''decimalformat'':244C ''default'':321C ''delimit'':310C,313C,316C ''delimiter-separ'':315C ''delimiterfordsv'':309C ''deprec'':295C ''disabl'':3A,48C ''display'':186C,203C,231C,236C,270C,288C,345C ''driver'':104C,108C ''e'':121C ''empti'':342C ''enable/disable'':216C ''end'':42C ''error'':224C,238C,253C ''even'':251C ''exceed'':307C ''exec'':132C ''execut'':129C,140C ''f'':130C ''fastconnect'':204C ''file'':27C,115C,118C,133C,136C ''forc'':246C ''format'':241C,284C,320C ''get'':335C ''given'':147C ''gt'':77C,87C,96C,106C,116C,124C,134C ''hadoop'':8B ''head'':201C ''headerinterv'':195C ''help'':61C,69C,344C ''histor'':336C ''hive'':9B,17C,152C,159C,174C ''hiveconf'':141C ''hivevar'':149C ''info'':228C ''init'':114C ''initi'':120C ''instead'':299C ''interv'':198C ''isol'':322C,327C ''java'':71C ''jdbc'':79C ''length'':308C ''level'':170C,323C,328C ''list'':209C ''lt'':74C,85C,94C,103C,113C,122C,131C ''maxcolumnwidth'':262C ''maxcolwidth'':263C ''maximum'':257C,265C ''maxwidth'':254C,255C ''messag'':225C,347C ''mode'':285C ''n'':84C ''name'':150C,154C,191C ''nest'':237C ''note'':289C ''null'':340C ''nullemptystr'':329C ''number'':242C ''numberformat'':239C ''occur'':13C ''option'':46C,56C ''org.apache.hive.cli.beeline.beeline'':72C ''output'':26C,319C ''outputformat'':282C ''p'':10C,93C ''password'':95C,98C ''pattern'':240C,245C ''pre'':64C ''prefer'':281C ''print'':339C ''properti'':142C,148C ''queari'':35C ''queri'':4A,29C,123C,125C,177C,193C ''referenc'':172C ''result'':7A,30C,194C,287C ''row'':196C ''run'':249C ''save'':280C ''script'':117C,135C,250C ''see'':54C ''separ'':317C ''session'':169C ''set'':161C,167C,324C,331C ''show'':189C,222C ''showhead'':187C ''shownestederr'':234C ''showwarn'':229C ''silent'':272C,276C ''skip'':206C ''specif'':160C ''specifi'':311C ''strang'':15C ''string'':343C ''tab'':212C ''tab-complet'':211C ''tabl'':303C ''table/column'':208C ''table/vertical/csv2/tsv2/dsv/csv/tsv'':283C ''termin'':261C ''transact'':218C,326C ''true'':333C ''true/false'':179C,188C,205C,215C,221C,230C,235C,247C,273C,278C,301C,330C ''truncat'':302C ''truncatet'':300C ''tsv'':293C ''tsv2'':298C ''u'':73C ''url'':76C,80C ''usag'':70C ''use'':111C,144C,184C,243C,268C,296C ''usernam'':86C,89C ''valu'':143C,145C,151C,156C,318C ''variabl'':153C,164C ''verbos'':220C,223C ''warn'':233C ''whether'':181C ''width'':258C,266C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4874, 2, '2015-01-14 12:46:41.357', 2, NULL, NULL, '&lt;p&gt;You do not need a wrapper for DBPedia, you need a library that can issue a SPARQL query to its SPARQL endpoint.  Here is &lt;a href="https://pypi.python.org/pypi/SPARQLWrapper/1.6.4" rel="nofollow"&gt;an option for the library&lt;/a&gt; and here is the URL to point it to: &lt;a href="http://dbpedia.org/sparql" rel="nofollow"&gt;http://dbpedia.org/sparql&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You need to issue a DESCRIBE query on the United_States resource page:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;PREFIX dbres: &amp;lt;http://dbpedia.org/resource/&amp;gt;

DESCRIBE dbres:United_States
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Please note this is a &lt;strong&gt;huge&lt;/strong&gt; download of resulting triplets.&lt;/p&gt;

&lt;p&gt;Here is how you would issue the query:&lt;/p&gt;



&lt;pre&gt;&lt;code&gt;from SPARQLWrapper import SPARQLWrapper, JSON

def get_country_description():
    sparql = SPARQLWrapper("http://dbpedia.org/sparql")
    sparql.setReturnFormat(JSON)

    sparql.setQuery(query)  # the previous query as a literal string

    return sparql.query().convert()
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':38C ''/code'':85C,141C ''/p'':72C,100C,110C ''/pre'':86C,142C ''/pypi/sparqlwrapper/1.6.4'':30C ''/resource/&amp;gt;'':80C ''/sparql'':52C,126C ''/sparql&lt;/a&gt;&lt;/p&gt;'':57C ''/strong'':95C ''code'':74C,112C ''convert'':140C ''countri'':120C ''dbpedia'':9C ''dbpedia.org'':51C,56C,79C,125C ''dbpedia.org/resource/&amp;gt;'':78C ''dbpedia.org/sparql'':50C,124C ''dbpedia.org/sparql&lt;/a&gt;&lt;/p&gt;'':55C ''dbres'':76C,82C ''def'':118C ''describ'':64C,81C ''descript'':121C ''download'':96C ''endpoint'':23C ''get'':119C ''href'':27C,49C ''huge'':94C ''import'':115C ''issu'':16C,62C,107C ''json'':117C,128C ''librari'':13C,37C ''liter'':136C ''lt'':77C ''need'':5C,11C,60C ''nofollow'':32C,54C ''note'':89C ''option'':34C ''p'':1C,58C,87C,101C ''page'':71C ''pleas'':88C ''point'':45C ''pre'':73C,111C ''prefix'':75C ''previous'':132C ''pypi.python.org'':29C ''pypi.python.org/pypi/sparqlwrapper/1.6.4'':28C ''queri'':19C,65C,109C,130C,133C ''rel'':31C,53C ''resourc'':70C ''result'':98C ''return'':138C ''sparql'':18C,22C,122C ''sparql.query'':139C ''sparql.setquery'':129C ''sparql.setreturnformat'':127C ''sparqlwrapp'':114C,116C,123C ''state'':69C,84C ''string'':137C ''strong'':93C ''triplet'':99C ''unit'':68C,83C ''url'':43C ''would'':106C ''wrapper'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6674, 1, '2015-08-04 03:18:22.52', 2, 93, 'How to calculate a specific time complexity of inverse calculation of matrix?', '&lt;p&gt;I am a green-hand in calculating the time complexity. Given a calculation as follows:&lt;/p&gt;

&lt;p&gt;\begin{equation}
\mathbf{x}=\mathbf{A^T}(\mathbf{AA^T}+\lambda\mathbf{I}_n)^{-1}\mathbf{b}
\end{equation}
where $\mathbf{A}\in R^{n\times p}$, $\mathbf{I}_n\in R^{n\times n}$, $\mathbf{b}\in R^{n}$ and $\lambda\in R$.&lt;/p&gt;

&lt;p&gt;Could some one kindly give me the complexity of the above formula in details? Thanks a lot. &lt;/p&gt;
', 'data-mining  algorithms', NULL, '''-1'':50C ''/p'':34C,80C,99C ''aa'':44C ''algorithm'':16B ''b'':52C,72C ''begin'':36C ''calcul'':3A,10A,25C,31C ''complex'':7A,28C,89C ''could'':82C ''data'':14B ''data-min'':13B ''detail'':95C ''end'':53C ''equat'':37C,54C ''follow'':33C ''formula'':93C ''give'':86C ''given'':29C ''green'':22C ''green-hand'':21C ''hand'':23C ''invers'':9A ''kind'':85C ''lambda'':46C,77C ''lot'':98C ''mathbf'':38C,40C,43C,47C,51C,56C,63C,71C ''matrix'':12A ''mine'':15B ''n'':49C,60C,65C,68C,70C,75C ''one'':84C ''p'':17C,35C,62C,81C ''r'':59C,67C,74C,79C ''specif'':5A ''thank'':96C ''time'':6A,27C,61C,69C ''x'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4875, 1, '2015-01-14 15:42:48.013', 3, 103, 'Determine highly correlated segments', '&lt;p&gt;Given a dataset that has a binary (0/1) dependent variable and a large collection of continuous and categorical independent variables, is there a process and ideally a R package that can find combinations/subsets/segments of the IVs that are highly correlated with the DV?&lt;/p&gt;

&lt;p&gt;Simple example: 
DV: college education (0/1), and IVs: age (20 to 120), income (0 to 1 million), race (white, black, hispanic etc), gender (0/1), state, etc.&lt;/p&gt;

&lt;p&gt;Then finding correlations combining IVs and subsets of IVs (e.g. women between 30 and 50, with incomes over 100k are highly positively correlated with the DV), and then being able to compare the combinations (e.g. to find out women between 30 and 40, with incomes over 100k have a higher correlation than women between 40 and 50, with incomes over 100k)&lt;/p&gt;
', 'machine-learning  r  correlation', NULL, '''/p'':54C,82C,145C ''0'':69C ''0/1'':18C,61C,79C ''1'':71C ''100k'':102C,130C,144C ''120'':67C ''20'':65C ''30'':96C,124C ''40'':126C,138C ''50'':98C,140C ''abl'':113C ''age'':64C ''binari'':17C ''black'':75C ''categor'':28C ''collect'':24C ''colleg'':59C ''combin'':87C,117C ''combinations/subsets/segments'':43C ''compar'':115C ''continu'':26C ''correl'':3A,9B,50C,86C,106C,134C ''dataset'':13C ''depend'':19C ''determin'':1A ''dv'':53C,58C,109C ''e.g'':93C,118C ''educ'':60C ''etc'':77C,81C ''exampl'':57C ''find'':42C,85C,120C ''gender'':78C ''given'':11C ''high'':2A,49C,104C ''higher'':133C ''hispan'':76C ''ideal'':36C ''incom'':68C,100C,128C,142C ''independ'':29C ''iv'':46C,63C,88C,92C ''larg'':23C ''learn'':7B ''machin'':6B ''machine-learn'':5B ''million'':72C ''p'':10C,55C,83C ''packag'':39C ''posit'':105C ''process'':34C ''r'':8B,38C ''race'':73C ''segment'':4A ''simpl'':56C ''state'':80C ''subset'':90C ''variabl'':20C,30C ''white'':74C ''women'':94C,122C,136C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4876, 1, '2015-01-14 15:52:10.633', 1, 160, 'Appropriate way to store data in R', '&lt;p&gt;I have data, which looks like this:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/EqH9I.jpg" alt="Data table"&gt;&lt;/p&gt;

&lt;p&gt;These data are only for one subject. I will have a lot more.&lt;br&gt;
These data will be analyzed in R.&lt;/p&gt;

&lt;p&gt;Now I''m storing them like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;subject &amp;lt;- rep(1, times = 24)
measurement &amp;lt;- factor(x = rep(x = 1:3, each = 8), 
                      labels = c("Distance", "Frequency", "Energy"))
speed &amp;lt;- factor(x = rep(x = 1:2, each = 4, times = 3), 
                labels = c("speed1", "speed2"))
condition &amp;lt;- factor(x = rep(x = 1:2, each = 2, times = 6), 
                    labels = c("Control", "Experm"))
Try &amp;lt;- factor(x = rep(x = 1:2, times = 12), 
              labels = c("Try1", "Try2"))
result &amp;lt;- c(1:8, 
            11:18, 
            21:28)

dt &amp;lt;- data.frame(subject, measurement, speed, condition, Try, result)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What is the appropriate way to store these data in R (in a data frame)? &lt;/p&gt;
', 'r  dataset  data-formats', NULL, '''/code'':151C ''/eqh9i.jpg'':27C ''/p'':21C,31C,54C,63C,169C ''/pre'':152C ''1'':69C,78C,93C,109C,125C,136C ''11'':138C ''12'':128C ''18'':139C ''2'':94C,110C,112C,126C ''21'':140C ''24'':71C ''28'':141C ''3'':79C,98C ''4'':96C ''6'':114C ''8'':81C,137C ''alt'':28C ''analyz'':51C ''appropri'':1A,157C ''br'':46C ''c'':83C,100C,116C,130C,135C ''code'':65C ''condit'':103C,148C ''control'':117C ''data'':5A,11B,16C,29C,34C,48C,162C,167C ''data-format'':10B ''data.frame'':144C ''dataset'':9B ''distanc'':84C ''dt'':142C ''energi'':86C ''experm'':118C ''factor'':74C,89C,105C,121C ''format'':12B ''frame'':168C ''frequenc'':85C ''i.stack.imgur.com'':26C ''i.stack.imgur.com/eqh9i.jpg'':25C ''img'':23C ''label'':82C,99C,115C,129C ''like'':19C,61C ''look'':18C ''lot'':44C ''lt'':67C,73C,88C,104C,120C,134C,143C ''m'':58C ''measur'':72C,146C ''one'':38C ''p'':13C,22C,32C,55C,153C ''pre'':64C ''r'':7A,8B,53C,164C ''rep'':68C,76C,91C,107C,123C ''result'':133C,150C ''speed'':87C,147C ''speed1'':101C ''speed2'':102C ''src'':24C ''store'':4A,59C,160C ''subject'':39C,66C,145C ''tabl'':30C ''time'':70C,97C,113C,127C ''tri'':119C,149C ''try1'':131C ''try2'':132C ''way'':2A,158C ''x'':75C,77C,90C,92C,106C,108C,122C,124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4937, 2, '2015-01-24 16:51:33.3', 1, NULL, NULL, '&lt;p&gt;I think the idea is that you have &lt;em&gt;all&lt;/em&gt; data in HDFS, and query it with Impala, and also keep &lt;em&gt;some&lt;/em&gt; small amount of data in your data warehouse. That is, keep all 10 years in Hadoop, and also 2 years in an EDW. The cost of also having the 2 years in Hadoop is small.&lt;/p&gt;
', NULL, NULL, '''/em'':12C,26C ''/p'':62C ''10'':39C ''2'':45C,56C ''also'':22C,44C,53C ''amount'':28C ''cost'':51C ''data'':13C,30C,33C ''edw'':49C ''em'':10C,24C ''hadoop'':42C,59C ''hdfs'':15C ''idea'':5C ''impala'':20C ''keep'':23C,37C ''p'':1C ''queri'':17C ''small'':27C,61C ''think'':3C ''warehous'':34C ''year'':40C,46C,57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8699, 1, '2015-11-03 14:46:10.123', 1, 40, 'Method of finding threshold in Decision tree for continuous data', '&lt;p&gt;I am using decision tree in Weka and I have some continuous data, so when I use Weka it automatically find the threshold for me but for some reason I want to implement Decision Tree by myself so I need to know what approach to use to find the threshold to discretize my continuous data?  &lt;/p&gt;
', 'machine-learning', NULL, '''/p'':70C ''approach'':58C ''automat'':34C ''continu'':9A,26C,68C ''data'':10A,27C,69C ''decis'':6A,18C,48C ''discret'':66C ''find'':3A,35C,62C ''implement'':47C ''know'':56C ''learn'':13B ''machin'':12B ''machine-learn'':11B ''method'':1A ''need'':54C ''p'':14C ''reason'':43C ''threshold'':4A,37C,64C ''tree'':7A,19C,49C ''use'':17C,31C,60C ''want'':45C ''weka'':21C,32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10165, 1, '2016-02-09 19:10:00.017', 1, 25, 'What''s the best way to use binned data in a tree-based model?', '&lt;p&gt;I have some numeric data that has come ''binned'', but the bins are not of equal sizes in terms of scale or quantile&lt;/p&gt;

&lt;p&gt;For example, an age variable that is [0-16), [16-21), [21-30), [30-45), [45-65), [65, ]&lt;/p&gt;

&lt;p&gt;If I leave it as a categorical variable, a tree will treat each category separately and discount the ordered relationship between the factors.&lt;/p&gt;

&lt;p&gt;If I change it to an ordinal variable, e.g. [0, 1, 2, 3, 4, 5] and keep an array of labels for later reporting, the tree might split on, e.g. &amp;lt;2.5, which seems more natural to me, but then again the distance between 1 and 2 is not the same as the distance between 2 and 3 by any measure.&lt;/p&gt;

&lt;p&gt;I''m leaning towards the second solution, but I would love some input!&lt;/p&gt;
', 'random-forest  feature-extraction  categorical-data  decision-trees', NULL, '''-16'':62C ''-21'':64C ''-30'':66C ''-45'':68C ''-65'':70C ''/p'':52C,72C,97C,160C,175C ''0'':61C,108C ''1'':109C,143C ''16'':63C ''2'':110C,145C,154C ''2.5'':130C ''21'':65C ''3'':111C,156C ''30'':67C ''4'':112C ''45'':69C ''5'':113C ''65'':71C ''age'':57C ''array'':117C ''base'':14A ''best'':4A ''bin'':8A,37C,40C ''categor'':23B,80C ''categori'':87C ''categorical-data'':22B ''chang'':101C ''come'':36C ''data'':9A,24B,33C ''decis'':26B ''decision-tre'':25B ''discount'':90C ''distanc'':141C,152C ''e.g'':107C,128C ''equal'':44C ''exampl'':55C ''extract'':21B ''factor'':96C ''featur'':20B ''feature-extract'':19B ''forest'':18B ''input'':174C ''keep'':115C ''label'':119C ''later'':121C ''lean'':164C ''leav'':76C ''love'':172C ''lt'':129C ''m'':163C ''measur'':159C ''might'':125C ''model'':15A ''natur'':134C ''numer'':32C ''order'':92C ''ordin'':105C ''p'':28C,53C,73C,98C,161C ''quantil'':51C ''random'':17B ''random-forest'':16B ''relationship'':93C ''report'':122C ''scale'':49C ''second'':167C ''seem'':132C ''separ'':88C ''size'':45C ''solut'':168C ''split'':126C ''term'':47C ''toward'':165C ''treat'':85C ''tree'':13A,27B,83C,124C ''tree-bas'':12A ''use'':7A ''variabl'':58C,81C,106C ''way'':5A ''would'':171C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5275, 2, '2015-03-04 04:39:35.38', 4, NULL, NULL, '&lt;p&gt;I do think it is new job, basically data scientist has to apply mathematical algorithms on data with considerable constraint in terms 1) Run time of the application 2) Resource use of the application. If these constraints are not present, I would not call the job data science. Moreover, these algorithms are often need to be ran on distributed systems, which is another dimension of the problem.&lt;/p&gt;

&lt;p&gt;Of course, this has been done before, in some combination of statistics, mathematics and programming, but it was not wide spread to give rise to the new term. The real rise of data science is from the ability to gather large amounts of data, thus need to need to process it. &lt;/p&gt;
', NULL, NULL, '''/p'':69C,122C ''1'':24C ''2'':30C ''abil'':108C ''algorithm'':16C,52C ''amount'':112C ''anoth'':64C ''appli'':14C ''applic'':29C,35C ''basic'':9C ''call'':45C ''combin'':80C ''consider'':20C ''constraint'':21C,38C ''cours'':72C ''data'':10C,18C,48C,103C,114C ''dimens'':65C ''distribut'':60C ''done'':76C ''gather'':110C ''give'':93C ''job'':8C,47C ''larg'':111C ''mathemat'':15C,83C ''moreov'':50C ''need'':55C,116C,118C ''new'':7C,97C ''often'':54C ''p'':1C,70C ''present'':41C ''problem'':68C ''process'':120C ''program'':85C ''ran'':58C ''real'':100C ''resourc'':31C ''rise'':94C,101C ''run'':25C ''scienc'':49C,104C ''scientist'':11C ''spread'':91C ''statist'':82C ''system'':61C ''term'':23C,98C ''think'':4C ''thus'':115C ''time'':26C ''use'':32C ''wide'':90C ''would'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4877, 2, '2015-01-14 17:06:58.75', 1, NULL, NULL, '&lt;p&gt;I am no expert in that particular case, but doing a bit of research, it seems that the measure you want to construct is called "&lt;a href="https://en.wikipedia.org/wiki/Point-biserial_correlation_coefficient" rel="nofollow"&gt;Point-biserial correlation coefficient&lt;/a&gt;", i.e. the inferred correlation between a continuous variable $X$ and a categorical variable $Y$, e.g. $Yâ\{â1,0,1\}$.  See a related question on &lt;a href="http://stats.stackexchange.com/questions/74116/pearson-correlation-between-discrete-variable-thats-mostly-0-and-a-standard-nor"&gt;Cross Validated SE&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;And yes, &lt;a href="http://cran.r-project.org/web/packages/polycor/index.html" rel="nofollow"&gt;there is an R package for that&lt;/a&gt; :)&lt;/p&gt;
', NULL, NULL, '''/a'':39C,73C,92C ''/p'':74C,93C ''/questions/74116/pearson-correlation-between-discrete-variable-thats-mostly-0-and-a-standard-nor'':69C ''/web/packages/polycor/index.html'':82C ''/wiki/point-biserial_correlation_coefficient'':31C ''0'':58C ''1'':57C,59C ''biseri'':36C ''bit'':13C ''call'':26C ''case'':9C ''categor'':51C ''coeffici'':38C ''construct'':24C ''continu'':46C ''correl'':37C,43C ''cran.r-project.org'':81C ''cran.r-project.org/web/packages/polycor/index.html'':80C ''cross'':70C ''e.g'':54C ''en.wikipedia.org'':30C ''en.wikipedia.org/wiki/point-biserial_correlation_coefficient'':29C ''expert'':5C ''href'':28C,66C,79C ''i.e'':40C ''infer'':42C ''measur'':20C ''nofollow'':33C,84C ''p'':1C,75C ''packag'':89C ''particular'':8C ''point'':35C ''point-biseri'':34C ''question'':63C ''r'':88C ''rel'':32C,83C ''relat'':62C ''research'':15C ''se'':72C ''see'':60C ''seem'':17C ''stats.stackexchange.com'':68C ''stats.stackexchange.com/questions/74116/pearson-correlation-between-discrete-variable-thats-mostly-0-and-a-standard-nor'':67C ''valid'':71C ''variabl'':47C,52C ''want'':22C ''x'':48C ''y'':53C ''yes'':77C ''yâ'':55C ''â'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4878, 2, '2015-01-14 17:56:58.563', 3, NULL, NULL, '&lt;p&gt;Without more information all I can say is that:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;the say you''re storing it is fine in general &lt;/li&gt;
&lt;li&gt;you can further transform/store your data depending on your use case&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;To expand on #2, if I want to study Distance vs Energy across all subjects, then I would format my data like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; library(reshape2)
&amp;gt; dt2 &amp;lt;- dt[dt$measurement %in% c(''Distance'',''Energy''),]
&amp;gt; dt_cast &amp;lt;- dcast(dt2, subject+Try~measurement+speed+condition, value.var=''result'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The transformed data (dt_cast) would then look like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  subject  Try Distance_speed1_Control Distance_speed1_Experm Distance_speed2_Control
1       1 Try1                       1                      3                       5
2       1 Try2                       2                      4                       6
  Distance_speed2_Experm Energy_speed1_Control Energy_speed1_Experm Energy_speed2_Control
1                      7                    21                   23                    25
2                      8                    22                   24                    26
  Energy_speed2_Experm
1                   27
2                   28
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Allowing me to, for example, look at the relationship between the Distance_speed1_Control vs Energy_speed1_Control columns.&lt;/p&gt;

&lt;p&gt;Basically subset/aggregate your data and then use the dcast to get the rows and columns the computer needs.&lt;/p&gt;
', NULL, NULL, '''/code'':92C,159C ''/li'':24C,37C ''/ol'':38C ''/p'':11C,63C,104C,181C,201C ''/pre'':93C,160C ''1'':118C,119C,121C,125C,142C,155C ''2'':43C,124C,127C,147C,157C ''21'':144C ''22'':149C ''23'':145C ''24'':150C ''25'':146C ''26'':151C ''27'':156C ''28'':158C ''3'':122C ''4'':128C ''5'':123C ''6'':129C ''7'':143C ''8'':148C ''across'':52C ''allow'':162C ''basic'':183C ''c'':76C ''case'':36C ''cast'':81C,99C ''code'':65C,106C ''column'':180C,197C ''comput'':199C ''condit'':89C ''control'':111C,117C,135C,141C,175C,179C ''data'':31C,60C,97C,186C ''dcast'':83C,191C ''depend'':32C ''distanc'':49C,77C,109C,112C,115C,130C,173C ''dt'':72C,73C,80C,98C ''dt2'':70C,84C ''energi'':51C,78C,133C,136C,139C,152C,177C ''exampl'':166C ''expand'':41C ''experm'':114C,132C,138C,154C ''fine'':21C ''format'':58C ''general'':23C ''get'':193C ''gt'':66C,69C,79C ''inform'':4C ''li'':13C,25C ''librari'':67C ''like'':61C,103C ''look'':102C,167C ''lt'':71C,82C ''measur'':74C ''need'':200C ''ol'':12C ''p'':1C,39C,94C,161C,182C ''pre'':64C,105C ''re'':17C ''relationship'':170C ''reshape2'':68C ''result'':91C ''row'':195C ''say'':8C,15C ''speed'':88C ''speed1'':110C,113C,134C,137C,174C,178C ''speed2'':116C,131C,140C,153C ''store'':18C ''studi'':48C ''subject'':54C,85C,107C ''subset/aggregate'':184C ''transform'':96C ''transform/store'':29C ''tri'':86C,108C ''try1'':120C ''try2'':126C ''use'':35C,189C ''value.var'':90C ''vs'':50C,176C ''want'':46C ''without'':2C ''would'':57C,100C ''~measurement'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4882, 1, '2015-01-15 14:15:51.923', 1, 94, 'Tools for generative models', '&lt;p&gt;There are quite a number of generative models &lt;a href="http://en.wikipedia.org/wiki/Generative_model" rel="nofollow"&gt;here&lt;/a&gt;, e.g.:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Gaussian mixture model and other types of mixture model
Hidden Markov model
Probabilistic context-free grammar
Naive Bayes
Averaged one-dependence estimators
Latent Dirichlet allocation
Restricted Boltzmann machine
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What I want to do is, given a set of strings learnt by each of the generative model, I want to make use of the learnt model to output a set of strings in descending order of their probabilities. &lt;/p&gt;

&lt;p&gt;Is there any readily available tools (preferably in Python, other are also ok) that support for generative model that allows me to achieve this?&lt;/p&gt;
', 'machine-learning  r  tools  sklearn', NULL, '''/a'':28C ''/code'':63C ''/p'':30C,105C,131C ''/pre'':64C ''/wiki/generative_model'':24C ''achiev'':129C ''alloc'':59C ''allow'':126C ''also'':118C ''avail'':111C ''averag'':52C ''bay'':51C ''boltzmann'':61C ''code'':32C ''context'':47C ''context-fre'':46C ''depend'':55C ''descend'':100C ''dirichlet'':58C ''e.g'':29C ''en.wikipedia.org'':23C ''en.wikipedia.org/wiki/generative_model'':22C ''estim'':56C ''free'':48C ''gaussian'':33C ''generat'':3A,18C,82C,123C ''given'':72C ''grammar'':49C ''hidden'':42C ''href'':21C ''latent'':57C ''learn'':7B ''learnt'':77C,91C ''machin'':6B,62C ''machine-learn'':5B ''make'':87C ''markov'':43C ''mixtur'':34C,40C ''model'':4A,19C,35C,41C,44C,83C,92C,124C ''naiv'':50C ''nofollow'':26C ''number'':16C ''ok'':119C ''one'':54C ''one-depend'':53C ''order'':101C ''output'':94C ''p'':11C,65C,106C ''pre'':31C ''prefer'':113C ''probabilist'':45C ''probabl'':104C ''python'':115C ''quit'':14C ''r'':8B ''readili'':110C ''rel'':25C ''restrict'':60C ''set'':74C,96C ''sklearn'':10B ''string'':76C,98C ''support'':121C ''tool'':1A,9B,112C ''type'':38C ''use'':88C ''want'':68C,85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4883, 1, '2015-01-15 15:13:54.713', 3, 49, 'What kind of data is not appropriate using CF to do recommendation?', '&lt;p&gt;I am currently working on a recommendation system for daily news. At first, I evaluated all the recommender algorithms and their corresponding settings (e.g., similarities, factorizers, ...etc) implemented in Mahout. Since we want to recommend daily news for users, we use the reading behavior of each user collected two days ago as training set, data of the next day as the testing set. The evaluated RMSE is good, the best recommender is SVD+SGD, so we implemented the recommender on our system for several days of trial run.&lt;/p&gt;

&lt;p&gt;However, the result, the actually recommended news, seems to be not very attractive for real users ("not attractive" here means, the users feel like "why you recommend this to me?"). So we decided another approach: use the tags and categories and their relationship to do the main job of recommendation, the result from CF is for just supporting.&lt;/p&gt;

&lt;p&gt;This makes me wonder if CF if not appropriate for some kind of content. Because I also worked on movie and music recommendation, CF is a good tool. But for news, it seems not the case.&lt;/p&gt;

&lt;p&gt;Can anyone explain why this happening, and also give some guideline about how to choose appropriate recommendation methods? Thanks:)&lt;/p&gt;
', 'recommendation', NULL, '''/p'':103C,163C,201C,222C ''actual'':109C ''ago'':65C ''algorithm'':33C ''also'':181C,210C ''anoth'':138C ''anyon'':204C ''approach'':139C ''appropri'':7A,173C,218C ''attract'':117C,122C ''behavior'':58C ''best'':84C ''case'':200C ''categori'':144C ''cf'':9A,158C,170C,188C ''choos'':217C ''collect'':62C ''content'':178C ''correspond'':36C ''current'':17C ''daili'':24C,50C ''data'':4A,69C ''day'':64C,73C,99C ''decid'':137C ''e.g'':38C ''etc'':41C ''evalu'':29C,79C ''explain'':205C ''factor'':40C ''feel'':127C ''first'':27C ''give'':211C ''good'':82C,191C ''guidelin'':213C ''happen'':208C ''howev'':105C ''implement'':42C,91C ''job'':152C ''kind'':2A,176C ''like'':128C ''mahout'':44C ''main'':151C ''make'':166C ''mean'':124C ''method'':220C ''movi'':184C ''music'':186C ''news'':25C,51C,111C,195C ''next'':72C ''p'':14C,104C,164C,202C ''read'':57C ''real'':119C ''recommend'':12A,13B,21C,32C,49C,85C,93C,110C,131C,154C,187C,219C ''relationship'':147C ''result'':107C,156C ''rmse'':80C ''run'':102C ''seem'':112C,197C ''set'':37C,68C,77C ''sever'':98C ''sgd'':88C ''similar'':39C ''sinc'':45C ''support'':162C ''svd'':87C ''system'':22C,96C ''tag'':142C ''test'':76C ''thank'':221C ''tool'':192C ''train'':67C ''trial'':101C ''two'':63C ''use'':8A,55C,140C ''user'':53C,61C,120C,126C ''want'':47C ''wonder'':168C ''work'':18C,182C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4885, 2, '2015-01-15 20:07:51.903', 4, NULL, NULL, '&lt;p&gt;If you''re working in R, Carson Sievert''s tutorial on using LDA to model topics in movie reviews is an excellent starting point:  &lt;/p&gt;

&lt;p&gt;&lt;a href="http://cpsievert.github.io/LDAvis/reviews/reviews.html" rel="nofollow"&gt;http://cpsievert.github.io/LDAvis/reviews/reviews.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This tutorial makes use of LDAvis, an interactive visualization of topic and word distributions that can really aid intuition.&lt;/p&gt;

&lt;p&gt;Also, although not short, David M. Blei''s lectures on topic models are a great resource for understanding the meaning behind the parameters: &lt;a href="http://videolectures.net/mlss09uk_blei_tm/" rel="nofollow"&gt;http://videolectures.net/mlss09uk_blei_tm/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/ldavis/reviews/reviews.html'':32C ''/ldavis/reviews/reviews.html&lt;/a&gt;&lt;/p&gt;'':37C ''/mlss09uk_blei_tm/'':87C ''/mlss09uk_blei_tm/&lt;/a&gt;&lt;/p&gt;'':92C ''/p'':26C,58C ''aid'':56C ''also'':60C ''although'':61C ''behind'':80C ''blei'':66C ''carson'':8C ''cpsievert.github.io'':31C,36C ''cpsievert.github.io/ldavis/reviews/reviews.html'':30C ''cpsievert.github.io/ldavis/reviews/reviews.html&lt;/a&gt;&lt;/p&gt;'':35C ''david'':64C ''distribut'':52C ''excel'':23C ''great'':74C ''href'':29C,84C ''interact'':46C ''intuit'':57C ''lda'':14C ''ldavi'':44C ''lectur'':68C ''m'':65C ''make'':41C ''mean'':79C ''model'':16C,71C ''movi'':19C ''nofollow'':34C,89C ''p'':1C,27C,38C,59C ''paramet'':82C ''point'':25C ''r'':7C ''re'':4C ''realli'':55C ''rel'':33C,88C ''resourc'':75C ''review'':20C ''short'':63C ''sievert'':9C ''start'':24C ''topic'':17C,49C,70C ''tutori'':11C,40C ''understand'':77C ''use'':13C,42C ''videolectures.net'':86C,91C ''videolectures.net/mlss09uk_blei_tm/'':85C ''videolectures.net/mlss09uk_blei_tm/&lt;/a&gt;&lt;/p&gt;'':90C ''visual'':47C ''word'':51C ''work'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4887, 2, '2015-01-16 01:00:45.89', 0, NULL, NULL, '&lt;p&gt;The question is very general. However, there are some studies being conducted to test which algorithms perform relatively well in a broad range of problems (I''ll add link to papers later), concerning regression and classification.&lt;/p&gt;

&lt;p&gt;Lately Random Decision Forests, Support Vector Machines and certain variations of Neural Networks are being said to achieve the best results for very broad variety of problems.&lt;/p&gt;

&lt;p&gt;This does not mean that these are "the best algorithms" for any problem, that does not exist, and actually is not very realistic to pursue. Also it must be observed that both RDF and SVM are rather-easy methods to initially grasp and obtain good results, so they are becoming really popular. NN have been used intensively since couple of decades (after they revived), so they appear often in implementations.&lt;/p&gt;

&lt;p&gt;If you are interested in learning further you should look for an specific area and deal with a problem that can be solved nicely by machine learning to understand the main idea (and why is impossible to find  &lt;strong&gt;the method&lt;/strong&gt;).&lt;/p&gt;

&lt;p&gt;You will find common the task to try to predict the expected behavior of something given some known or observable characteristics (to learn the function that models the problem given input data), the issues related to dealing with data in high-dimensional spaces, the need for good quality data, the notable improvements that can give data pre-processing, and many others.&lt;/p&gt;
', NULL, NULL, '''/p'':38C,67C,140C,184C,249C ''/strong'':183C ''achiev'':57C ''actual'':87C ''add'':29C ''algorithm'':17C,78C ''also'':94C ''appear'':136C ''area'':155C ''becom'':119C ''behavior'':198C ''best'':59C,77C ''broad'':23C,63C ''certain'':48C ''characterist'':206C ''classif'':37C ''common'':189C ''concern'':34C ''conduct'':13C ''coupl'':128C ''data'':217C,224C,235C,242C ''deal'':157C,222C ''decad'':130C ''decis'':42C ''dimension'':228C ''easi'':107C ''exist'':85C ''expect'':197C ''find'':179C,188C ''forest'':43C ''function'':210C ''general'':6C ''give'':241C ''given'':201C,215C ''good'':114C,233C ''grasp'':111C ''high'':227C ''high-dimension'':226C ''howev'':7C ''idea'':173C ''implement'':139C ''imposs'':177C ''improv'':238C ''initi'':110C ''input'':216C ''intens'':126C ''interest'':145C ''issu'':219C ''known'':203C ''late'':40C ''later'':33C ''learn'':147C,168C,208C ''link'':30C ''ll'':28C ''look'':151C ''machin'':46C,167C ''main'':172C ''mani'':247C ''mean'':72C ''method'':108C,182C ''model'':212C ''must'':96C ''need'':231C ''network'':52C ''neural'':51C ''nice'':165C ''nn'':122C ''notabl'':237C ''observ'':98C,205C ''obtain'':113C ''often'':137C ''other'':248C ''p'':1C,39C,68C,141C,185C ''paper'':32C ''perform'':18C ''popular'':121C ''pre'':244C ''pre-process'':243C ''predict'':195C ''problem'':26C,66C,81C,160C,214C ''process'':245C ''pursu'':93C ''qualiti'':234C ''question'':3C ''random'':41C ''rang'':24C ''rather'':106C ''rather-easi'':105C ''rdf'':101C ''realist'':91C ''realli'':120C ''regress'':35C ''relat'':19C,220C ''result'':60C,115C ''reviv'':133C ''said'':55C ''sinc'':127C ''solv'':164C ''someth'':200C ''space'':229C ''specif'':154C ''strong'':180C ''studi'':11C ''support'':44C ''svm'':103C ''task'':191C ''test'':15C ''tri'':193C ''understand'':170C ''use'':125C ''variat'':49C ''varieti'':64C ''vector'':45C ''well'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4890, 2, '2015-01-16 04:54:40.55', 5, NULL, NULL, '&lt;p&gt;The key is establishing a proper validation metric.&lt;/p&gt;

&lt;p&gt;I notice you talk about how you tried different recommendation algorithms, but at the end of the day you evaluated them all with RMSE. But there''s no particular reason to believe that minimizing RMSE generates a "subjectively better" recommendation experience for the user - it just happens to be convenient, and happens to work well in some industries, but there is no real reason why it must.&lt;/p&gt;

&lt;p&gt;RMSE is measuring how well your recommender algorithm is predicting user behavior. But that''s not the same as measuring recommendation quality. Maybe users value something else - familiarity, or serendipity, or some other quality of the item being recommended. Users don''t really care about being predicted. &lt;/p&gt;

&lt;p&gt;Given your results, if you want to understand your users further, I''d focus my efforts in coming up with a mathematical metric that more closely matches the target you care about - user satisfaction - rather than RMSE. Once you know what metric you''re trying to optimize, the algorithm to optimize it is much easier to select! &lt;/p&gt;
', NULL, NULL, '''/p'':10C,79C,128C,187C ''algorithm'':22C,88C,178C ''behavior'':92C ''believ'':43C ''better'':50C ''care'':124C,160C ''close'':155C ''come'':147C ''conveni'':61C ''d'':142C ''day'':29C ''differ'':20C ''easier'':184C ''effort'':145C ''els'':107C ''end'':26C ''establish'':5C ''evalu'':31C ''experi'':52C ''familiar'':108C ''focus'':143C ''generat'':47C ''given'':130C ''happen'':58C,63C ''industri'':69C ''item'':117C ''key'':3C ''know'':169C ''match'':156C ''mathemat'':151C ''mayb'':103C ''measur'':83C,100C ''metric'':9C,152C,171C ''minim'':45C ''much'':183C ''must'':78C ''notic'':13C ''optim'':176C,180C ''p'':1C,11C,80C,129C ''particular'':40C ''predict'':90C,127C ''proper'':7C ''qualiti'':102C,114C ''rather'':164C ''re'':173C ''real'':74C ''realli'':123C ''reason'':41C,75C ''recommend'':21C,51C,87C,101C,119C ''result'':132C ''rmse'':35C,46C,81C,166C ''satisfact'':163C ''select'':186C ''serendip'':110C ''someth'':106C ''subject'':49C ''talk'':15C ''target'':158C ''tri'':19C,174C ''understand'':137C ''user'':55C,91C,104C,120C,139C,162C ''valid'':8C ''valu'':105C ''want'':135C ''well'':66C,85C ''work'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4891, 2, '2015-01-16 05:30:35.787', 0, NULL, NULL, '&lt;p&gt;Machine learning is a good example of a problem type where Spark-based solutions are light-years ahead of mapreduce-based solutions, despite the young age of spark-on-yarn. &lt;/p&gt;
', NULL, NULL, '''/p'':36C ''age'':30C ''ahead'':21C ''base'':15C,25C ''despit'':27C ''exampl'':7C ''good'':6C ''learn'':3C ''light'':19C ''light-year'':18C ''machin'':2C ''mapreduc'':24C ''mapreduce-bas'':23C ''p'':1C ''problem'':10C ''solut'':16C,26C ''spark'':14C,33C ''spark-bas'':13C ''spark-on-yarn'':32C ''type'':11C ''yarn'':35C ''year'':20C ''young'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4892, 2, '2015-01-16 05:44:41.907', 2, NULL, NULL, '&lt;p&gt;You can try RAKE(Rapid Automatic Keyword Extraction) and there is a python implementation here: &lt;a href="https://github.com/aneesha/RAKE" rel="nofollow"&gt;https://github.com/aneesha/RAKE&lt;/a&gt;. RAKE is an document-oriented keyword extraction algorithm and also language-independent(theoretically, since RAKE use a generated stop word list to partition candidate keywords, and considering different languages, we need to find a better way to generated stop word list.). However, about English documents, RAKE can extract keywords(or tags) in a acceptable precision and recall. RAKE is also efficient, because to use it we don''t have to training a whole corpus, RAKE can generate a keyword list by calculating the word''s degree and frequency then comes up a score for every candidate keyword then pick the top N words.&lt;/p&gt;

&lt;p&gt;Hope this answer helps you or lead you a direction for your next step investigation.&lt;/p&gt;
', NULL, NULL, '''/aneesha/rake'':21C ''/aneesha/rake&lt;/a&gt;.'':26C ''/p'':132C,149C ''accept'':82C ''algorithm'':35C ''also'':37C,88C ''answer'':136C ''automat'':7C ''better'':63C ''calcul'':110C ''candid'':52C,124C ''come'':118C ''consid'':55C ''corpus'':102C ''degre'':114C ''differ'':56C ''direct'':143C ''document'':31C,73C ''document-ori'':30C ''effici'':89C ''english'':72C ''everi'':123C ''extract'':9C,34C,76C ''find'':61C ''frequenc'':116C ''generat'':46C,66C,105C ''github.com'':20C,25C ''github.com/aneesha/rake'':19C ''github.com/aneesha/rake&lt;/a&gt;.'':24C ''help'':137C ''hope'':134C ''howev'':70C ''href'':18C ''implement'':15C ''independ'':40C ''investig'':148C ''keyword'':8C,33C,53C,77C,107C,125C ''languag'':39C,57C ''language-independ'':38C ''lead'':140C ''list'':49C,69C,108C ''n'':130C ''need'':59C ''next'':146C ''nofollow'':23C ''orient'':32C ''p'':1C,133C ''partit'':51C ''pick'':127C ''precis'':83C ''python'':14C ''rake'':5C,27C,43C,74C,86C,103C ''rapid'':6C ''recal'':85C ''rel'':22C ''score'':121C ''sinc'':42C ''step'':147C ''stop'':47C,67C ''tag'':79C ''theoret'':41C ''top'':129C ''train'':99C ''tri'':4C ''use'':44C,92C ''way'':64C ''whole'':101C ''word'':48C,68C,112C,131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4894, 2, '2015-01-17 04:29:24.393', 1, NULL, NULL, '&lt;p&gt;AdaBoost is a supervised learning method; it starts with a table of ''correct'' answers and generates a predictive model for a target, which is known. It is then possible to inspect this model to figure out how it works, what it judged was more important. With that in mind, here is my guess for what he did:&lt;/p&gt;

&lt;p&gt;First, he created a training dataset. The dataset was created, according to the article, like this: &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Now heâd do the same for love. First heâd need data. While his
  dissertation work continued to run on the side, he set up 12 fake
  OkCupid accounts and wrote a Python script to manage them. The script
  would search his target demographic (heterosexual and bisexual women
  between the ages of 25 and 45), visit their pages, and scrape their
  profiles for every scrap of available information: ethnicity, height,
  smoker or nonsmoker, astrological signââall that crap,â he says.&lt;/p&gt;
  
  &lt;p&gt;To find the survey answers, he had to do a bit of extra sleuthing.
  OkCupid lets users see the responses of others, but only to questions
  theyâve answered themselves. McKinlay set up his bots to simply answer
  each question randomlyâhe wasnât using the dummy profiles to attract
  any of the women, so the answers didnât matÂ­terâthen scooped the
  womenâs answers into a database. "&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;So he used python scripts to collect lots of information! At the end of this, he had a table of data, where each row had three pieces of information:
- A bot''s answer to all the questions, and their weights
- A woman''s answer to all the questions
- Their match percentage. &lt;/p&gt;

&lt;p&gt;On this, he could use AdaBoost to create a predictive model, predicting the match percentage from the available information. The weak learners were probably decision stumps, greedily choosing one variable at a time to split on, that''s the standard that people refer to when talking about AdaBoost. &lt;/p&gt;

&lt;p&gt;Once the predictive model, was in place, it could be used for optimization - determining which weights to put on the questions when holding all other variables constant, maximizing the average match percentage to women in his target audience. &lt;/p&gt;

&lt;p&gt;Of course, this is just a guess. The article doesn''t have much detail. But it''s a potential way to use AdaBoost for that purpose. &lt;/p&gt;
', NULL, NULL, '''/blockquote'':236C ''/p'':59C,77C,163C,235C,289C,339C,379C,407C ''12'':106C ''25'':133C ''45'':135C ''accord'':71C ''account'':109C ''adaboost'':2C,296C,338C,403C ''age'':131C ''answer'':15C,169C,193C,202C,221C,231C,270C,281C ''articl'':74C,389C ''astrolog'':154C ''attract'':214C ''audienc'':378C ''avail'':147C,308C ''averag'':370C ''bisexu'':127C ''bit'':175C ''blockquot'':78C ''bot'':199C,268C ''choos'':318C ''collect'':244C ''constant'':367C ''continu'':97C ''correct'':14C ''could'':294C,349C ''cours'':382C ''crap'':159C ''creat'':63C,70C,298C ''d'':82C,90C ''data'':92C,258C ''databas'':234C ''dataset'':66C,68C ''decis'':315C ''demograph'':124C ''detail'':394C ''determin'':354C ''didnâ'':222C ''dissert'':95C ''doesn'':390C ''dummi'':211C ''end'':250C ''ethnic'':149C ''everi'':144C ''extra'':177C ''fake'':107C ''figur'':36C ''find'':166C ''first'':61C,88C ''generat'':17C ''greedili'':317C ''guess'':54C,387C ''height'':150C ''heterosexu'':125C ''heâ'':81C,89C ''hold'':363C ''import'':46C ''inform'':148C,247C,266C,309C ''inspect'':32C ''judg'':43C ''known'':26C ''learn'':6C ''learner'':312C ''let'':180C ''like'':75C ''lot'':245C ''love'':87C ''manag'':116C ''match'':287C,304C,371C ''matâ'':224C ''maxim'':368C ''mckinlay'':195C ''method'':7C ''mind'':50C ''model'':20C,34C,301C,344C ''much'':393C ''need'':91C ''nonsmok'':153C ''okcupid'':108C,179C ''one'':319C ''optim'':353C ''other'':186C ''p'':1C,60C,79C,164C,237C,290C,340C,380C ''page'':138C ''peopl'':332C ''percentag'':288C,305C,372C ''piec'':264C ''place'':347C ''possibl'':30C ''potenti'':399C ''predict'':19C,300C,302C,343C ''probabl'':314C ''profil'':142C,212C ''purpos'':406C ''put'':358C ''python'':113C,241C ''question'':190C,204C,274C,285C,361C ''randomlyâ'':205C ''refer'':333C ''respons'':184C ''row'':261C ''run'':99C ''say'':162C ''scoop'':227C ''scrap'':145C ''scrape'':140C ''script'':114C,119C,242C ''search'':121C ''see'':182C ''set'':104C,196C ''side'':102C ''signâ'':155C ''simpli'':201C ''sleuth'':178C ''smoker'':151C ''split'':325C ''standard'':330C ''start'':9C ''stump'':316C ''supervis'':5C ''survey'':168C ''tabl'':12C,256C ''talk'':336C ''target'':23C,123C,377C ''terâ'':225C ''theyâ'':191C ''three'':263C ''time'':323C ''train'':65C ''use'':209C,240C,295C,351C,402C ''user'':181C ''variabl'':320C,366C ''ve'':192C ''visit'':136C ''wasnâ'':207C ''way'':400C ''weak'':311C ''weight'':277C,356C ''woman'':279C ''women'':128C,218C,374C ''womenâ'':229C ''work'':40C,96C ''would'':120C ''wrote'':111C ''â'':156C,160C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8700, 2, '2015-11-03 15:03:29.3', 1, NULL, NULL, '&lt;p&gt;No offense, but let me guess, you work for a large company where there is no person with a full understanding of the process overseeing this? I say this because it sounds as if the testers want you to manually and intentionally over-fit your models. There is no such thing as a perfectly accurate model.&lt;/p&gt;

&lt;p&gt;Yes, I would say that there is bias in your model building. New model input should be a random selection not by finding and labeling similar instances to ones that were misclassified.&lt;/p&gt;

&lt;p&gt;Your production model will degrade over time (the distribution in the population has changed). You should have a sense of a threshold, an error rate at which is no longer exceptable and you will need to retrain. To retrain, you could add a random selection of new labeled data to your existing data, then train, validate, select model, deploy, and monitor. &lt;/p&gt;

&lt;p&gt;You may "fix" an error report with a new retraining, but it is an unrealistic expectation that this will also cover all previous error reports. Your model needs to generalize. See here:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Test_set" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Test_set&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Look at the image at the bottom. In essence you are being asked to create the orange model were you want to create the green model, which generalizes better to unseen data. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4895, 2, '2015-01-17 04:42:47.53', 0, NULL, NULL, '&lt;p&gt;Your results are reasonable. Your data brings several ideas to mind: &lt;/p&gt;

&lt;p&gt;1) It is quite reasonable that as you change the available features, this will change the relative performance of machine learning methods. This happens quite a lot. Which machine learning method performs best often depends on the features, so as you change the features the best method changes.&lt;/p&gt;

&lt;p&gt;2) It is reasonable that in some cases, disparate models will reach the exact same results. This is most likely in the case where the number of data points is low enough or the data is separable enough that both models reach the exact same conclusions for all test points. &lt;/p&gt;
', NULL, NULL, '''/p'':13C,63C,115C ''1'':15C ''2'':65C ''avail'':25C ''best'':47C,60C ''bring'':8C ''case'':72C,87C ''chang'':23C,29C,56C,62C ''conclus'':110C ''data'':7C,92C,99C ''depend'':49C ''dispar'':73C ''enough'':96C,102C ''exact'':78C,108C ''featur'':26C,52C,58C ''happen'':38C ''idea'':10C ''learn'':35C,44C ''like'':84C ''lot'':41C ''low'':95C ''machin'':34C,43C ''method'':36C,45C,61C ''mind'':12C ''model'':74C,105C ''number'':90C ''often'':48C ''p'':1C,14C,64C ''perform'':32C,46C ''point'':93C,114C ''quit'':18C,39C ''reach'':76C,106C ''reason'':5C,19C,68C ''relat'':31C ''result'':3C,80C ''separ'':101C ''sever'':9C ''test'':113C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4896, 2, '2015-01-17 05:20:32.763', 1, NULL, NULL, '&lt;p&gt;A simple way would be to consider Laplace Smoothing (&lt;a href="http://en.wikipedia.org/wiki/Additive_smoothing" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Additive_smoothing&lt;/a&gt; ) or something like it. &lt;/p&gt;

&lt;p&gt;Basically, instead of calculating your response rate as (Clicks)/(Impressions) you calculate (Clicks + X)/(Impressions + Y), with X and Y chosen, for example, so that X/Y is the global average of clicks/impressions. &lt;/p&gt;

&lt;p&gt;When Clicks and Impressions are both high, this smoothed response rate is basically equal to the true response rate (signal dominates the prior). When Clicks and Impressions are both low, the this smoothed response rate will be close to the global average response rate - a good guess when you have little data and don''t want to put much weight on it!&lt;/p&gt;

&lt;p&gt;The absolute scale of X and Y will determine how many data points you consider "enough data". It''s been argued that the right thing to do is set X to 1, and Y appropriately given that. &lt;/p&gt;
', NULL, NULL, '''/p'':25C,59C,123C,162C ''/wiki/additive_smoothing'':15C ''/wiki/additive_smoothing&lt;/a&gt;'':20C ''1'':156C ''absolut'':126C ''appropri'':159C ''argu'':145C ''averag'':56C,102C ''basic'':27C,73C ''calcul'':30C,38C ''chosen'':47C ''click'':35C,39C,62C,85C ''clicks/impressions'':58C ''close'':98C ''consid'':8C,139C ''data'':112C,136C,141C ''determin'':133C ''domin'':81C ''en.wikipedia.org'':14C,19C ''en.wikipedia.org/wiki/additive_smoothing'':13C ''en.wikipedia.org/wiki/additive_smoothing&lt;/a&gt;'':18C ''enough'':140C ''equal'':74C ''exampl'':49C ''given'':160C ''global'':55C,101C ''good'':106C ''guess'':107C ''high'':67C ''href'':12C ''impress'':36C,41C,64C,87C ''instead'':28C ''laplac'':9C ''like'':23C ''littl'':111C ''low'':90C ''mani'':135C ''much'':119C ''nofollow'':17C ''p'':1C,26C,60C,124C ''point'':137C ''prior'':83C ''put'':118C ''rate'':33C,71C,79C,95C,104C ''rel'':16C ''respons'':32C,70C,78C,94C,103C ''right'':148C ''scale'':127C ''set'':153C ''signal'':80C ''simpl'':3C ''smooth'':10C,69C,93C ''someth'':22C ''thing'':149C ''true'':77C ''want'':116C ''way'':4C ''weight'':120C ''would'':5C ''x'':40C,44C,129C,154C ''x/y'':52C ''y'':42C,46C,131C,158C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4908, 5, '2015-01-19 19:17:36.82', 0, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Overview&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;From The Discipline of Machine Learning by Tom Mitchell:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The field of Machine Learning seeks to answer the question "How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?" This question covers a broad range of learning tasks, such as how to design autonomous mobile robots that learn to navigate from their own experience, how to data mine historical medical records to learn which future patients will respond best to which treatments, and how to build search engines that automatically customize to their user''s interests. To be more precise, we say that a machine learns with respect to a particular task T, performance metric P, and type of experience E, if the system reliably improves its performance P at task T, following experience E. Depending on how we specify T, P, and E, the learning task might also be called by names such as data mining, autonomous discovery, database updating, programming by example, etc.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The following journals are dedicated to research in Machine Learning:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://jmlr.org/" rel="nofollow"&gt;Journal of Machine Learning Research&lt;/a&gt; (Open Access)&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.springer.com/computer/ai/journal/10994" rel="nofollow"&gt;Machine Learning&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.springer.com/engineering/computational+intelligence+and+complexity/journal/13042" rel="nofollow"&gt;International Journal of Machine Learning and Cybernetics&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.ijmla.net/index.php/ijmla" rel="nofollow"&gt;International Journal of Machine Learning and Computing&lt;/a&gt; (Open Access)&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.ijmlc.org/" rel="nofollow"&gt;International Journal of Machine Learning and Computing&lt;/a&gt; (Open Access)&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':203C,217C,234C,251C,268C ''/blockquote'':178C ''/computer/ai/journal/10994'':212C ''/engineering/computational+intelligence+and+complexity/journal/13042'':224C ''/index.php/ijmla'':241C ''/li'':206C,218C,235C,254C,271C ''/p'':5C,16C,177C,190C ''/strong'':4C ''/ul'':272C ''access'':205C,253C,270C ''also'':160C ''answer'':26C ''automat'':36C,101C ''autonom'':65C,169C ''best'':90C ''blockquot'':17C ''broad'':55C ''build'':32C,97C ''call'':162C ''comput'':33C,250C,267C ''cover'':53C ''custom'':102C ''cybernet'':233C ''data'':78C,167C ''databas'':171C ''dedic'':184C ''depend'':147C ''design'':64C ''disciplin'':9C ''discoveri'':170C ''e'':132C,146C,155C ''engin'':99C ''etc'':176C ''exampl'':175C ''experi'':39C,75C,131C,145C ''field'':20C ''follow'':144C,181C ''fundament'':44C ''futur'':86C ''govern'':47C ''histor'':80C ''href'':194C,209C,221C,238C,257C ''improv'':37C,137C ''interest'':107C ''intern'':227C,244C,261C ''jmlr.org'':195C ''journal'':182C,198C,228C,245C,262C ''law'':45C ''learn'':12C,23C,49C,58C,69C,84C,117C,157C,189C,201C,216C,231C,248C,265C ''li'':192C,207C,219C,236C,255C ''machin'':11C,22C,116C,188C,200C,215C,230C,247C,264C ''medic'':81C ''metric'':126C ''might'':159C ''mine'':79C,168C ''mitchel'':15C ''mobil'':66C ''name'':164C ''navig'':71C ''nofollow'':197C,214C,226C,243C,260C ''open'':204C,252C,269C ''overview'':3C ''p'':1C,6C,18C,127C,140C,153C,179C ''particular'':122C ''patient'':87C ''perform'':125C,139C ''precis'':111C ''process'':50C ''program'':173C ''question'':28C,52C ''rang'':56C ''record'':82C ''rel'':196C,213C,225C,242C,259C ''reliabl'':136C ''research'':186C,202C ''respect'':119C ''respond'':89C ''robot'':67C ''say'':113C ''search'':98C ''seek'':24C ''specifi'':151C ''strong'':2C ''system'':34C,135C ''task'':59C,123C,142C,158C ''tom'':14C ''treatment'':93C ''type'':129C ''ul'':191C ''updat'':172C ''user'':105C ''www.ijmla.net'':240C ''www.ijmla.net/index.php/ijmla'':239C ''www.ijmlc.org'':258C ''www.springer.com'':211C,223C ''www.springer.com/computer/ai/journal/10994'':210C ''www.springer.com/engineering/computational+intelligence+and+complexity/journal/13042'':222C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4901, 1, '2015-01-19 09:37:56.34', 1, 79, 'Does high error rate in regression imply the data set is unpredictable?', '&lt;p&gt;I have a data set of video watching records in a 3G network. In this data set, 2 different kind of features are included: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;user-side information, e.g., age, gender, data plan and etc; &lt;/li&gt;
&lt;li&gt;Video watching records of these users, each of  which associated with a download ratio and some detailed network condition metrics, say, download speed, RTT, and something similar.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Under the scenario of internet streaming, a video is divided into several chunks and downloaded to end device one by one, so we have download ratio = download bytes / file size in bytes&lt;/p&gt;

&lt;p&gt;Now, Given this data set, I want to predict the download ratio of each video.&lt;/p&gt;

&lt;p&gt;Since it is a regression problem, so I use &lt;em&gt;gradient boosting regression tree&lt;/em&gt; as model and run 10-fold cross validation.&lt;/p&gt;

&lt;p&gt;However, I have tried different model parameter configurations and even different models (linear regression, decision regress tree), the best root-mean-square error I can get is 0.3790, which is quite high, because if I don''t use any complex models and just use the mean value of known labels as prediction values, then I can still have an RMSE of 0.3890. There is not obvious difference.&lt;/p&gt;

&lt;p&gt;For this problem, I have some questions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Does this high error rate imply that the label in data set is unpredictable? &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Apart from the feature problem, is there any other possibilities? If yes, how can I validate them?&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', 'feature-selection  regression', NULL, '''/em'':153C ''/li'':56C,85C,260C,281C ''/ol'':282C ''/p'':42C,120C,137C,162C,232C,241C,259C,280C ''/ul'':86C ''0.3790'':192C ''0.3890'':226C ''10'':158C ''2'':35C ''3g'':29C ''age'':50C ''apart'':263C ''associ'':67C ''best'':182C ''boost'':150C ''byte'':115C,119C ''chunk'':100C ''complex'':204C ''condit'':76C ''configur'':171C ''cross'':160C ''data'':9A,21C,33C,52C,125C,255C ''decis'':178C ''detail'':74C ''devic'':105C ''differ'':36C,168C,174C,231C ''divid'':97C ''download'':70C,79C,102C,112C,114C,132C ''e.g'':49C ''em'':148C ''end'':104C ''error'':3A,187C,248C ''etc'':55C ''even'':173C ''featur'':14B,39C,266C ''feature-select'':13B ''file'':116C ''fold'':159C ''gender'':51C ''get'':190C ''given'':123C ''gradient'':149C ''high'':2A,196C,247C ''howev'':164C ''impli'':7A,250C ''includ'':41C ''inform'':48C ''internet'':92C ''kind'':37C ''known'':213C ''label'':214C,253C ''li'':44C,57C,243C,261C ''linear'':176C ''mean'':185C,210C ''metric'':77C ''model'':155C,169C,175C,205C ''network'':30C,75C ''obvious'':230C ''ol'':242C ''one'':106C,108C ''p'':17C,87C,121C,138C,163C,233C,244C,262C ''paramet'':170C ''plan'':53C ''possibl'':272C ''predict'':130C,216C ''problem'':144C,236C,267C ''question'':240C ''quit'':195C ''rate'':4A,249C ''ratio'':71C,113C,133C ''record'':26C,60C ''regress'':6A,16B,143C,151C,177C,179C ''rmse'':224C ''root'':184C ''root-mean-squar'':183C ''rtt'':81C ''run'':157C ''say'':78C ''scenario'':90C ''select'':15B ''set'':10A,22C,34C,126C,256C ''sever'':99C ''side'':47C ''similar'':84C ''sinc'':139C ''size'':117C ''someth'':83C ''speed'':80C ''squar'':186C ''still'':221C ''stream'':93C ''tree'':152C,180C ''tri'':167C ''ul'':43C ''unpredict'':12A,258C ''use'':147C,202C,208C ''user'':46C,63C ''user-sid'':45C ''valid'':161C,278C ''valu'':211C,217C ''video'':24C,58C,95C,136C ''want'':128C ''watch'':25C,59C ''yes'':274C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4902, 2, '2015-01-19 10:07:11.27', 2, NULL, NULL, '&lt;p&gt;It is always helpful to just Google the exact error that you are seeing, excluding specifics like the actual memory of the vector.  For me, the first hit was an interesting documentation called "&lt;a href="http://stat.ethz.ch/R-manual/R-devel/library/base/html/Memory-limits.html" rel="nofollow"&gt;R: Memory limits of R&lt;/a&gt;", where, under "Unix", one can read:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The address-space limit is system-specific: 32-bit OSes imposes a limit of no more than 4Gb: it is often 3Gb. Running 32-bit executables on a 64-bit OS will have similar limits: 64-bit executables will have an essentially infinite system-specific limit (e.g., 128Tb for Linux on x86_64 cpus).&lt;/p&gt;
  
  &lt;p&gt;See the OS/shell''s help on commands such as limit or ulimit for how to impose limitations on the resources available to a single process. For example a bash user could use&lt;/p&gt;
  
  &lt;p&gt;&lt;code&gt;ulimit -t 600 -v 4000000&lt;/code&gt;&lt;/p&gt;
  
  &lt;p&gt;whereas a csh user might use&lt;/p&gt;
  
  &lt;p&gt;&lt;code&gt;limit cputime 10m&lt;/code&gt;
  &lt;code&gt;limit vmemoryuse 4096m&lt;/code&gt;&lt;/p&gt;
  
  &lt;p&gt;to limit a process to 10 minutes of CPU time and (around) 4Gb of virtual memory. (There are other options to set the RAM in use, but they are not generally honoured.)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;So, you should check&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="http://stackoverflow.com/questions/11565649/how-to-find-if-my-amazon-ec2-instance-is-32-bit-or-64-bit"&gt;What type of OS are you running on your EC2 instance&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;What type of R build are you running on that OS, and &lt;a href="http://randyzwitch.com/r-amazon-ec2/" rel="nofollow"&gt;make sure you run a 64bit version&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;If both are already 64bit, then use &lt;code&gt;ulimit&lt;/code&gt; to set memory to e.g. 8Gb: &lt;code&gt;ulimit -v 8000000&lt;/code&gt; &lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4903, 1, '2015-01-19 14:26:57.117', 5, 1249, 'What is the difference between feature generation and feature extraction?', '&lt;p&gt;Can anybody tell me what is the purpose of feature generation? and why feature space enrichment is needed before classifying an image? Is it a necessary step?&lt;/p&gt;

&lt;p&gt;Is there any method to enrich feature space?&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/p'':43C,53C ''anybodi'':17C ''classif'':14B ''classifi'':35C ''differ'':4A ''enrich'':31C,50C ''extract'':10A ''featur'':6A,9A,25C,29C,51C ''generat'':7A,26C ''imag'':37C ''learn'':13B ''machin'':12B ''machine-learn'':11B ''method'':48C ''necessari'':41C ''need'':33C ''p'':15C,44C ''purpos'':23C ''space'':30C,52C ''step'':42C ''tell'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4904, 2, '2015-01-19 14:55:25.58', 5, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Feature Generation&lt;/strong&gt; -- This is the process of taking raw, unstructured data and defining features (i.e. variables) for potential use in your statistical analysis. For instance, in the case of text mining you may begin with a raw log of thousands of text messages (e.g. SMS, email, social network messages, etc) and generate features by removing low-value words (i.e. stopwords), using certain size blocks of words (i.e. n-grams) or applying other rules.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Feature Extraction&lt;/strong&gt; -- After generating features, it is often necessary to test transformations of the original features and select a subset of this pool of potential original and derived features for use in your model (i.e. feature extraction and selection). Testing derived values is a common step because the data may contain important information which has a non-linear pattern or relationship with your outcome, thus the importance of the data element may only be apparent in its transformed state (e.g. higher order derivatives). Using too many features can result in multiply colinearity or otherwise confound statistical models, whereas extracting the minimum number of features to suit the purpose of your analysis follows the principal of parsimony.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Enhancing your feature space&lt;/em&gt; in this way is often a necessary step in classification of images or other data objects because the raw feature space is typically filled with an overwhelming amount of unstructured and irrelevant data that comprises what''s often referred to as "noise" in the paradigm of a "signal" and "noise" (which is to say that some data has predictive value and other data does not). By enhancing the feature space you can better identify the important data which has predictive or other value in your analysis (i.e. the "signal") while removing confounding information (i.e. "noise").&lt;/p&gt;
', NULL, NULL, '''/em'':207C ''/p'':79C,200C,303C ''/strong'':5C,84C ''amount'':235C ''analysi'':26C,194C,293C ''appar'':158C ''appli'':76C ''begin'':37C ''better'':280C ''block'':68C ''case'':31C ''certain'':66C ''classif'':217C ''colinear'':175C ''common'':127C ''compris'':242C ''confound'':178C,299C ''contain'':133C ''data'':14C,131C,153C,222C,240C,264C,270C,284C ''defin'':16C ''deriv'':110C,123C,166C ''e.g'':47C,163C ''element'':154C ''em'':202C ''email'':49C ''enhanc'':203C,274C ''etc'':53C ''extract'':83C,119C,182C ''featur'':3C,17C,56C,82C,87C,98C,111C,118C,170C,187C,205C,227C,276C ''fill'':231C ''follow'':195C ''generat'':4C,55C,86C ''gram'':74C ''higher'':164C ''i.e'':18C,63C,71C,117C,294C,301C ''identifi'':281C ''imag'':219C ''import'':134C,150C,283C ''inform'':135C,300C ''instanc'':28C ''irrelev'':239C ''linear'':141C ''log'':41C ''low'':60C ''low-valu'':59C ''mani'':169C ''may'':36C,132C,155C ''messag'':46C,52C ''mine'':34C ''minimum'':184C ''model'':116C,180C ''multipli'':174C ''n'':73C ''n-gram'':72C ''necessari'':91C,214C ''network'':51C ''nois'':249C,257C,302C ''non'':140C ''non-linear'':139C ''number'':185C ''object'':223C ''often'':90C,212C,245C ''order'':165C ''origin'':97C,108C ''otherwis'':177C ''outcom'':147C ''overwhelm'':234C ''p'':1C,80C,201C ''paradigm'':252C ''parsimoni'':199C ''pattern'':142C ''pool'':105C ''potenti'':21C,107C ''predict'':266C,287C ''princip'':197C ''process'':9C ''purpos'':191C ''raw'':12C,40C,226C ''refer'':246C ''relationship'':144C ''remov'':58C,298C ''result'':172C ''rule'':78C ''say'':261C ''select'':100C,121C ''signal'':255C,296C ''size'':67C ''sms'':48C ''social'':50C ''space'':206C,228C,277C ''state'':162C ''statist'':25C,179C ''step'':128C,215C ''stopword'':64C ''strong'':2C,81C ''subset'':102C ''suit'':189C ''take'':11C ''test'':93C,122C ''text'':33C,45C ''thousand'':43C ''thus'':148C ''transform'':94C,161C ''typic'':230C ''unstructur'':13C,237C ''use'':22C,65C,113C,167C ''valu'':61C,124C,267C,290C ''variabl'':19C ''way'':210C ''wherea'':181C ''word'':62C,70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4934, 2, '2015-01-23 23:16:54.22', 1, NULL, NULL, '&lt;p&gt;What information should be shown on the charts are based on what the viewer wants. From your sample charts, the x axis indicates ''date''. For time serial analysis, people usually want to know the trending of the metrics and to predict the performance in the future. &lt;/p&gt;

&lt;p&gt;Here are 2 suggestions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;People may want to know how it will perform in the future. Add a trendline. Trendline could help people to predict the performance in the future.
&lt;a href="http://onlinehelp.tableausoftware.com/current/pro/online/mac/en-us/trendlines.html" rel="nofollow"&gt;http://onlinehelp.tableausoftware.com/current/pro/online/mac/en-us/trendlines.html&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;People may want to know how far away from goal. When people mouseover on the points on the metric line, you can try to show how far from current metric to the goal.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Try to understand what information people want to know and visualise them. Hope my answer could help you.&lt;/p&gt;
', NULL, NULL, '''/current/pro/online/mac/en-us/trendlines.html'':87C ''/current/pro/online/mac/en-us/trendlines.html&lt;/a&gt;&lt;/li&gt;'':92C ''/li'':127C ''/ol'':128C ''/p'':48C,54C,148C ''2'':52C ''add'':69C ''analysi'':29C ''answer'':144C ''away'':101C ''axi'':23C ''base'':11C ''chart'':9C,20C ''could'':73C,145C ''current'':122C ''date'':25C ''far'':100C,120C ''futur'':47C,68C,82C ''goal'':103C,126C ''help'':74C,146C ''hope'':142C ''href'':84C ''indic'':24C ''inform'':3C,134C ''know'':34C,61C,98C,138C ''li'':56C,93C ''line'':113C ''may'':58C,95C ''metric'':39C,112C,123C ''mouseov'':106C ''nofollow'':89C ''ol'':55C ''onlinehelp.tableausoftware.com'':86C,91C ''onlinehelp.tableausoftware.com/current/pro/online/mac/en-us/trendlines.html'':85C ''onlinehelp.tableausoftware.com/current/pro/online/mac/en-us/trendlines.html&lt;/a&gt;&lt;/li&gt;'':90C ''p'':1C,49C,129C ''peopl'':30C,57C,75C,94C,105C,135C ''perform'':44C,65C,79C ''point'':109C ''predict'':42C,77C ''rel'':88C ''sampl'':19C ''serial'':28C ''show'':118C ''shown'':6C ''suggest'':53C ''time'':27C ''trend'':36C ''trendlin'':71C,72C ''tri'':116C,130C ''understand'':132C ''usual'':31C ''viewer'':15C ''visualis'':140C ''want'':16C,32C,59C,96C,136C ''x'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4905, 2, '2015-01-19 18:03:17.467', 1, NULL, NULL, '&lt;p&gt;Some notes:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;In the case of supervised learning you assume there is a function $f:\mathcal{X} \rightarrow \mathcal{Y}$, which means there is a connection somehow between inputs and outputs and you want to use it in order to predict. But how this function might look? Without considering the model itself, usually what happens is that there is some noise. And this noise can be in $Y$ and also can be in $X$. This noise gives use the probabilistic setup of the problem, because without it we would have only to solve some equations. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Now is important to understand that the noise defines the distribution. So a random variable can be imagined as a function having something fixed and well defined and something not fixed, but taking values according with a distribution. If the variable part does not exist, that you would not have a random variable, right, it would be a simple formula. But its not. Now the $P(X)$ incorporates what happens in $X$ alone, and $P(Y)$ what is in $Y$ alone. When you predict the decision theory says that you are interested in saying which is the most probable value $y_i$ given some input value $x_i$. So you are interested in finding $P(Y|X)$.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;A joint probability is not always completely described by marginal probabilities. In fact, is completely described only when marginal probabilities are independent. Which means for r.v. $X, Y$ knowing $P(X)$ and $P(Y)$ does not put you in position to know the joint density $P(X, Y)$ (thought for independence you have  $P(X,Y)=P(X)P(Y)$). &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;From here you can go directly and try to estimate $P(Y|X)$. In fact if your only interest in only in prediction, this might be a fair bet. A lot of supervised learning algorithms tries to estimate directly this probability. They are called discriminant classifiers. The reason is because they are used to classify something to  the class with maximal conditional probability, you discriminate (choose) the maximum value. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Now arriving at your question. Notice the obvious $P(X,Y) = P(Y|X)P(X)$ than you see that by trying learning the joint probability, you also learn the conditional (what you need for prediction). This kind of approach is called generative, because knowing the joint probability you can not only predict, but you can generate new data for your problem. More than that, knowing the join probability can give you more insights related with how what are you model works. You can find such kind of information which is not contained only in marginal distributions. &lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Some final notes:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Technically you do not minimize the error function, but it''s expectation. The error function remains as it is.&lt;/li&gt;
&lt;li&gt;$\mathcal{Z}$ is only a domain it''s impossible to describe a probability only by it''s domain. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It''s late, I hope I was not completely incoherent.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4910, 5, '2015-01-19 19:22:11.877', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4911, 4, '2015-01-19 19:22:11.877', 0, NULL, NULL, 'An instance of supervised learning that identifies the category or categories which a new instance of dataset belongs.', NULL, NULL, '''belong'':18C ''categori'':9C,11C ''dataset'':17C ''identifi'':7C ''instanc'':2C,15C ''learn'':5C ''new'':14C ''supervis'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4912, 2, '2015-01-19 21:18:54.347', 1, NULL, NULL, '&lt;p&gt;It''s a little hasty to make too many conclusions about your data based on what you presented here. At the end of the day, all the information you have right now is that "GBT did not work well for this prediction problem and this metric", summed up by a single RMSE comparison. This isn''t very much information - it could be that this is a bad dataset for GBT and some 
other model would work, it could be that the label can''t be predicted from these features with any model, or there could be some error in model setup/validation. &lt;/p&gt;

&lt;p&gt;I''d recommend checking the following hypotheses: &lt;/p&gt;

&lt;p&gt;1) Maybe, with your dataset size and the features you have, GBT isn''t a very high-performance model. Try something completely different - maybe just a simple linear regression! Or a random forest. Or GBDT with very different parameter settings. Or something else. This will help you diagnose whether it''s an issue with choice of models or with something else; if a few very different approaches give you roughly similar results, you''ll know that it''s not the model choice that is causing these results, and if one of those models behaves differently, then that gives you additional information to help diagnose the issue. &lt;/p&gt;

&lt;p&gt;2) Maybe there''s some issue with model setup and validation? I would recommend doing some exploration to get some intuition as to whether the RMSE you''re getting is reasonable or whether you should expect better. Your post contained very little detail about what the data actually represents, what you know about the features and labels, etc. Perhaps you know those things but didn''t include them here, but if not, you should go back and try to get additional understanding of the data before continuing. Look at some random data points, plot the columns against the target, look at the histograms of your features and labels, that sort of thing. There''s no substitute for looking at the data. &lt;/p&gt;

&lt;p&gt;3) Maybe there just aren''t enough data points to justify complex models. When you have low numbers of data points (&amp;lt; 100), a simpler parametric model built with domain expertise and knowledge of what the features are may very well outperform a nonparametric model.  &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4914, 1, '2015-01-20 15:27:38.16', 18, 687, 'When to use what - Machine Learning', '&lt;p&gt;Recently in a Machine Learning class from professor Oriol Pujol at UPC/Barcelona he described the most common algorithms, principles and concepts to use for a wide range of machine learning related task. Here I share them with you and ask you: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;is there any comprehensive framework matching tasks with approaches or methods related to different types of machine learning related problems?&lt;/li&gt;
&lt;/ul&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;How do I learn a simple Gaussian?&lt;/strong&gt; 
Probability, random variables, distributions; estimation, convergence and asymptotics, confidence interval.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I learn a mixture of Gaussians (MoG)?&lt;/strong&gt; Likelihood, Expectation-Maximization (EM); generalization, model selection, cross-validation; k-means, hidden markov models (HMM)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I learn any density?&lt;/strong&gt; Parametric vs. non-Parametric estimation, Sobolev and other functional spaces; l  Ì 2 error; Kernel density estimation (KDE), optimal kernel, KDE theory&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I predict a continuous variable (regression)?&lt;/strong&gt; Linear regression, regularization, ridge regression, and LASSO; local linear regression; conditional density estimation.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I predict a discrete variable (classification)?&lt;/strong&gt; Bayes classifier, naive Bayes, generative vs. discriminative; perceptron, weight decay, linear support vector machine; nearest neighbor classifier and theory&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Which loss function should I use?&lt;/strong&gt; Maximum likelihood estimation theory; l -2 estimation; Bayessian estimation; minimax and decision theory, Bayesianism vs frequentism&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Which model should I use?&lt;/strong&gt; AIC and BIC; Vapnik-Chervonenskis theory; cross-validation theory; bootstrapping; Probably Approximately Correct (PAC) theory; Hoeffding-derived bounds&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How can I learn fancier (combined) models?&lt;/strong&gt; Ensemble learning theory; boosting; bagging; stacking&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How can I learn fancier (nonlinear) models?&lt;/strong&gt; Generalized linear models, logistic regression; Kolmogorov theorem, generalized additive models; kernelization, reproducing kernel Hilbert spaces, non-linear SVM, Gaussian process regression&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How can I learn fancier (compositional) models?&lt;/strong&gt; Recursive models, decision trees, hierarchical clustering; neural networks, back propagation, deep belief networks; graphical models, mixtures of HMMs, conditional random fields, max-margin Markov networks; log-linear models; grammars&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I reduce or relate features?&lt;/strong&gt; Feature selection vs dimensionality reduction, wrapper methods for feature selection; causality vs correlation, partial correlation, Bayes net structure learning&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I create new features?&lt;/strong&gt; principal component analysis (PCA), independent component analysis (ICA), multidimensional scaling, manifold learning, supervised dimensionality reduction, metric learning&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I reduce or relate the data?&lt;/strong&gt; Clustering, bi-clustering, constrained clustering; association rules and market basket analysis; ranking/ordinal regression; link analysis; relational data&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I treat time series?&lt;/strong&gt; ARMA; Kalman filter and stat-space models, particle filter; functional data analysis; change-point detection; cross-validation for time series&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I treat non-ideal data?&lt;/strong&gt; covariate shift; class imbalance; missing data, irregularly sampled data, measurement errors; anomaly detection, robustness&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I optimize the parameters?&lt;/strong&gt; Unconstrained vs constrained/Convex optimization, derivative-free methods, first- and second-order methods, backfitting; natural gradient; bound optimization and EM&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I optimize linear functions?&lt;/strong&gt; computational linear algebra, matrix inversion for regression, singular value decomposition (SVD) for dimensionality reduction&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I optimize with constraints?&lt;/strong&gt; Convexity, Lagrange multipliers, Karush-Kuhn-Tucker conditions, interior point methods, SMO algorithm for SVM&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I evaluate deeply-nested sums?&lt;/strong&gt; Exact graphical model inference, variational bounds on sums, approximate graphical model inference, expectation propagation&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I evaluate large sums and searches?&lt;/strong&gt; Generalized N-body problems (GNP), hierarchical data structures, nearest neighbor search, fast multiple method; Monte Carlo integration, Markov Chain Monte Carlo, Monte Carlo SVD&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I treat even larger problems?&lt;/strong&gt; Parallel/distributed EM, parallel/distributed GNP; stochastic subgradient methods, online learning&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do I apply all this in the real world?&lt;/strong&gt; Overview of the parts of the ML, choosing between the methods to use for each task, prior knowledge and assumptions; exploratory data analysis and information visualization; evaluation and interpretation, using confidence intervals and hypothesis test, ROC curves; where the research problems in ML are&lt;/p&gt;
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''neighbor'':215C,641C ''nest'':602C ''net'':394C ''network'':343C,348C,361C ''neural'':342C ''new'':404C ''non'':143C,319C,495C ''non-id'':494C ''non-linear'':318C ''non-parametr'':142C ''nonlinear'':300C ''onlin'':674C ''optim'':159C,519C,526C,541C,550C,574C ''order'':535C ''oriol'':20C ''overview'':690C ''p'':11C,79C,100C,131C,164C,189C,220C,246C,276C,293C,326C,368C,398C,425C,455C,488C,514C,545C,569C,594C,620C,657C,677C ''pac'':269C ''parallel/distributed'':667C,669C ''paramet'':521C ''parametr'':140C,144C ''part'':693C ''partial'':391C ''particl'':472C ''pca'':410C ''perceptron'':207C ''point'':479C,587C ''predict'':169C,194C ''princip'':407C ''principl'':30C ''prior'':706C ''probabl'':89C,266C ''problem'':75C,635C,665C,730C ''process'':323C ''professor'':19C ''propag'':345C,618C ''pujol'':21C ''random'':90C,355C ''rang'':38C ''ranking/ordinal'':448C ''real'':687C ''recent'':12C ''recurs'':336C ''reduc'':373C,430C ''reduct'':382C,421C,567C 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''upc/barcelona'':23C ''use'':3A,34C,227C,252C,702C,719C ''valid'':122C,263C,483C ''valu'':562C ''vapnik'':258C ''vapnik-chervonenski'':257C ''variabl'':91C,172C,197C ''variat'':609C ''vector'':212C ''visual'':715C ''vs'':141C,205C,243C,380C,389C,524C ''weight'':208C ''wide'':37C ''world'':688C ''wrapper'':383C ''ì'':152C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6969, 2, '2015-09-01 09:08:25.243', 0, NULL, NULL, '&lt;p&gt;One interesting algorithm that I''ve once tested is called TopMine: &lt;a href="http://web.engr.illinois.edu/~elkishk2/" rel="nofollow"&gt;http://web.engr.illinois.edu/~elkishk2/&lt;/a&gt; (under Code and Datasets). It is able to extract bi-grams that could be used as key words and it can also assign them into topics.&lt;/p&gt;
', NULL, NULL, '''/p'':50C ''/~elkishk2/'':17C ''/~elkishk2/&lt;/a&gt;'':22C ''abl'':29C ''algorithm'':4C ''also'':45C ''assign'':46C ''bi'':33C ''bi-gram'':32C ''call'':11C ''code'':24C ''could'':36C ''dataset'':26C ''extract'':31C ''gram'':34C ''href'':14C ''interest'':3C ''key'':40C ''nofollow'':19C ''one'':2C ''p'':1C ''rel'':18C ''test'':9C ''topic'':49C ''topmin'':12C ''use'':38C ''ve'':7C ''web.engr.illinois.edu'':16C,21C ''web.engr.illinois.edu/~elkishk2/'':15C ''web.engr.illinois.edu/~elkishk2/&lt;/a&gt;'':20C ''word'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4915, 2, '2015-01-21 03:06:39.243', 2, NULL, NULL, '&lt;p&gt;That''s a good list covering a lot.  I''ve used some of these methods since before anything was called machine learning, and I think you will see some of the methods you list coming in and out of use over time.  If a method has been out of favour for too long, it might be time for a revisit.  Some methods can obfuscate behind different names resulting from different fields of study.  &lt;/p&gt;

&lt;p&gt;One of the main areas I have used these methods is in mineral potential modelling, which is geospatial and to support that you could add some additional categories relating to spatial and oriented data methods.&lt;/p&gt;

&lt;p&gt;Taking your broad question to specific fields will probably be where you find more examples of methods not in your comprehensive list.  For example, two methods I''ve seen in mineral potential have been backward stepwise regression and weights of evidence modelling.  I''m not a statistician; perhaps these would be considered covered in the list under linear regression and Bayesian methods.&lt;/p&gt;
', NULL, NULL, '''/p'':75C,112C,176C ''add'':101C ''addit'':103C ''anyth'':19C ''area'':81C ''backward'':148C ''bayesian'':174C ''behind'':66C ''broad'':116C ''call'':21C ''categori'':104C ''come'':36C ''comprehens'':134C ''consid'':165C ''could'':100C ''cover'':7C,166C ''data'':110C ''differ'':67C,71C ''evid'':154C ''exampl'':128C,137C ''favour'':51C ''field'':72C,120C ''find'':126C ''geospati'':94C ''good'':5C ''learn'':23C ''linear'':171C ''list'':6C,35C,135C,169C ''long'':54C ''lot'':9C ''m'':157C ''machin'':22C ''main'':80C ''method'':16C,33C,46C,63C,86C,111C,130C,139C,175C ''might'':56C ''miner'':89C,144C ''model'':91C,155C ''name'':68C ''obfusc'':65C ''one'':77C ''orient'':109C ''p'':1C,76C,113C ''perhap'':161C ''potenti'':90C,145C ''probabl'':122C ''question'':117C ''regress'':150C,172C ''relat'':105C ''result'':69C ''revisit'':61C ''see'':29C ''seen'':142C ''sinc'':17C ''spatial'':107C ''specif'':119C ''statistician'':160C ''stepwis'':149C ''studi'':74C ''support'':97C ''take'':114C ''think'':26C ''time'':43C,58C ''two'':138C ''use'':12C,41C,84C ''ve'':11C,141C ''weight'':152C ''would'':163C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4916, 2, '2015-01-21 07:50:53.953', 4, NULL, NULL, '&lt;p&gt;I agree with @geogaffer. This is a very good list, indeed. However, I see some &lt;em&gt;issues&lt;/em&gt; with this list as it is currently formulated. For example, one issue is that suggested solutions are of different &lt;strong&gt;granularity levels&lt;/strong&gt; - some of them represent &lt;em&gt;approaches&lt;/em&gt;, some - &lt;em&gt;methods&lt;/em&gt;, some - &lt;em&gt;algorithms&lt;/em&gt;, and some other - just &lt;em&gt;concepts&lt;/em&gt; (in other words, &lt;em&gt;terms&lt;/em&gt; within a topic''s domain terminology). In addition, - and I believe that this is much more important than the above - I think that it would be much valuable, if all those solutions in the list were arranged within a &lt;strong&gt;unified thematic statistical framework&lt;/strong&gt;. This idea was inspired by reading an excellent book by Lisa Harlow "The essence of multivariate thinking". Hence, recently I''ve initiated a corresponding, albeit currently somewhat limited, &lt;a href="http://stats.stackexchange.com/q/131320/31372"&gt;discussion&lt;/a&gt; on the StackExchange''s &lt;em&gt;Cross Validated&lt;/em&gt; site. Don''t let the title confuse you - my implied intention and hope is for building a &lt;em&gt;unified framework&lt;/em&gt;, as mentioned above.&lt;/p&gt;
', NULL, NULL, '''/a'':150C ''/em'':19C,49C,53C,57C,64C,70C,158C,179C ''/p'':183C ''/q/131320/31372'':148C ''/strong'':42C,115C ''addit'':78C ''agre'':3C ''albeit'':140C ''algorithm'':56C ''approach'':48C ''arrang'':107C ''believ'':81C ''book'':124C ''build'':174C ''concept'':63C ''confus'':165C ''correspond'':139C ''cross'':156C ''current'':26C,141C ''differ'':38C ''discuss'':149C ''domain'':75C ''em'':17C,47C,51C,55C,62C,68C,155C,176C ''essenc'':129C ''exampl'':29C ''excel'':123C ''formul'':27C ''framework'':114C,178C ''geogaff'':5C ''good'':10C ''granular'':40C ''harlow'':127C ''henc'':133C ''hope'':171C ''howev'':13C ''href'':145C ''idea'':117C ''impli'':168C ''import'':87C ''inde'':12C ''initi'':137C ''inspir'':119C ''intent'':169C ''issu'':18C,31C ''let'':162C ''level'':41C ''limit'':143C ''lisa'':126C ''list'':11C,22C,105C ''mention'':181C ''method'':52C ''much'':85C,97C ''multivari'':131C ''one'':30C ''p'':1C ''read'':121C ''recent'':134C ''repres'':46C ''see'':15C ''site'':159C ''solut'':35C,102C ''somewhat'':142C ''stackexchang'':153C ''statist'':113C ''stats.stackexchange.com'':147C ''stats.stackexchange.com/q/131320/31372'':146C ''strong'':39C,110C ''suggest'':34C ''term'':69C ''terminolog'':76C ''themat'':112C ''think'':92C,132C ''titl'':164C ''topic'':73C ''unifi'':111C,177C ''valid'':157C ''valuabl'':98C ''ve'':136C ''within'':71C,108C ''word'':67C ''would'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4917, 1, '2015-01-21 09:15:54.75', 0, 77, 'Visualize performance, % of goal implementation', '&lt;p&gt;I want to visualize goal achievment progress.
This is my first idea:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;use area chart to show progress in current metric&lt;/li&gt;
&lt;li&gt;use horizontal band to show the goal value&lt;/li&gt;
&lt;li&gt;colorize areas under/above the band into "positive" and "negative" colors&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/h7fUa.png" alt="enter image description here"&gt;
&lt;img src="http://i.stack.imgur.com/EIhmz.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is this approach informative enough? Are there better choises?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Additional info:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;charts made in Tableau&lt;/li&gt;
&lt;li&gt;two data sources: metric progress &amp;amp; goals&lt;/li&gt;
&lt;/ul&gt;
', 'visualization  marketing  tableau', NULL, '''/eihmz.png'':73C ''/h7fua.png'':63C ''/li'':34C,44C,56C,103C,112C ''/p'':22C,79C,92C,96C ''/strong'':91C ''/ul'':57C,113C ''achiev'':15C ''addit'':94C ''alt'':64C,74C ''amp'':110C ''approach'':84C ''area'':26C,47C ''band'':38C,50C ''better'':89C ''chart'':27C,99C ''chois'':90C ''color'':46C,55C ''current'':32C ''data'':106C ''descript'':67C,77C ''enough'':86C ''enter'':65C,75C ''first'':20C ''goal'':4A,14C,42C,111C ''horizont'':37C ''i.stack.imgur.com'':62C,72C ''i.stack.imgur.com/eihmz.png'':71C ''i.stack.imgur.com/h7fua.png'':61C ''idea'':21C ''imag'':66C,76C ''img'':59C,69C ''implement'':5A ''info'':95C ''inform'':85C ''li'':24C,35C,45C,98C,104C ''made'':100C ''market'':7B ''metric'':33C,108C ''negat'':54C ''p'':9C,58C,80C,93C ''perform'':2A ''posit'':52C ''progress'':16C,30C,109C ''show'':29C,40C ''sourc'':107C ''src'':60C,70C ''strong'':81C ''tableau'':8B,102C ''two'':105C ''ul'':23C,97C ''under/above'':48C ''use'':25C,36C ''valu'':43C ''visual'':1A,6B,13C ''want'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5581, 1, '2015-04-22 03:44:02.667', 0, 107, 'what can be done with the following data?', '&lt;p&gt;I have scraped the rating that a customer gave in the following categories namely overall rating score, value for money, seat comfort,staff service, catering and entertainment from an airline forum. I would like to know what quality information I can deduce from such ratings about an airline. I have rating of nearly 300 customers who traveled in an airline.  &lt;/p&gt;
', 'data-mining', NULL, '''/p'':73C ''300'':66C ''airlin'':42C,60C,72C ''categori'':25C ''cater'':37C ''comfort'':34C ''custom'':20C,67C ''data'':8A,10B ''data-min'':9B ''deduc'':54C ''done'':4A ''entertain'':39C ''follow'':7A,24C ''forum'':43C ''gave'':21C ''inform'':51C ''know'':48C ''like'':46C ''mine'':11B ''money'':32C ''name'':26C ''near'':65C ''overal'':27C ''p'':12C ''qualiti'':50C ''rate'':17C,28C,57C,63C ''score'':29C ''scrape'':15C ''seat'':33C ''servic'':36C ''staff'':35C ''travel'':69C ''valu'':30C ''would'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4920, 2, '2015-01-21 14:17:17.97', 0, NULL, NULL, '&lt;p&gt;I think your approach is a little backwards.&lt;/p&gt;

&lt;p&gt;"What is the mean of a Gaussian distribution fitted to this data?" is never the problem statement, so "how do I fit a Gaussian?" is never the problem you actually want to solve.&lt;/p&gt;

&lt;p&gt;The difference is more than semantic. Consider the question of "how do I construct new features?" If your goal is to develop an index, you might use some type of factor analysis. If your goal is to simply reduce the feature space before fitting a linear model, you might skip the step entirely and use elastic net regression instead.&lt;/p&gt;

&lt;p&gt;A better approach would be to compile a list of &lt;em&gt;actual data analysis tasks you would like to be able to tackle&lt;/em&gt;. Questions like:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;How do I predict whether customers will return to my shopping website?&lt;/p&gt;
  
  &lt;p&gt;How do I learn how many "major" consumer shopping patterns there are, and what are they?&lt;/p&gt;
  
  &lt;p&gt;How do I construct an index of "volatility" for different items in my online store?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Also your list right now includes an enormous amount of material; far too much to "review" and gain more than a surface-level understanding. Having an actual purpose in mind can help you sort out your priorities.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':185C ''/em'':131C ''/p'':10C,45C,106C,134C,149C,167C,184C,225C ''abl'':128C ''actual'':41C,119C,214C ''also'':187C ''amount'':195C ''analysi'':78C,121C ''approach'':5C,110C ''backward'':9C ''better'':109C ''blockquot'':135C ''compil'':114C ''consid'':53C ''construct'':60C,172C ''consum'':158C ''custom'':142C ''data'':23C,120C ''develop'':68C ''differ'':48C,178C ''distribut'':19C ''elast'':102C ''em'':118C ''enorm'':194C ''entir'':99C ''factor'':77C ''far'':198C ''featur'':62C,87C ''fit'':20C,33C,90C ''gain'':204C ''gaussian'':18C,35C ''goal'':65C,81C ''help'':219C ''includ'':192C ''index'':70C,174C ''instead'':105C ''item'':179C ''learn'':154C ''level'':210C ''like'':125C,133C ''linear'':92C ''list'':116C,189C ''littl'':8C ''major'':157C ''mani'':156C ''materi'':197C ''mean'':15C ''might'':72C,95C ''mind'':217C ''model'':93C ''much'':200C ''net'':103C ''never'':25C,37C ''new'':61C ''onlin'':182C ''p'':1C,11C,46C,107C,136C,150C,168C,186C ''pattern'':160C ''predict'':140C ''prioriti'':224C ''problem'':27C,39C ''purpos'':215C ''question'':55C,132C ''reduc'':85C ''regress'':104C ''return'':144C ''review'':202C ''right'':190C ''semant'':52C ''shop'':147C,159C ''simpli'':84C ''skip'':96C ''solv'':44C ''sort'':221C ''space'':88C ''statement'':28C ''step'':98C ''store'':183C ''surfac'':209C ''surface-level'':208C ''tackl'':130C ''task'':122C ''think'':3C ''type'':75C ''understand'':211C ''use'':73C,101C ''volatil'':176C ''want'':42C ''websit'':148C ''whether'':141C ''would'':111C,124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9228, 1, '2015-12-05 22:24:29.063', 1, 131, 'Decision tree vs. KNN', '&lt;p&gt;In which cases is it better to use a Decision tree and other cases a KNN?&lt;/p&gt;

&lt;p&gt;Why use one of them in certain cases? And the other in different cases? (By looking at its functionality, not at the algorithm)&lt;/p&gt;

&lt;p&gt;Anyone have some explanations or references about this?&lt;/p&gt;
', 'machine-learning  data-mining  decision-trees', NULL, '''/p'':31C,56C,66C ''algorithm'':55C ''anyon'':58C ''better'':20C ''case'':17C,28C,40C,46C ''certain'':39C ''data'':9B ''data-min'':8B ''decis'':1A,12B,24C ''decision-tre'':11B ''differ'':45C ''explan'':61C ''function'':51C ''knn'':4A,30C ''learn'':7B ''look'':48C ''machin'':6B ''machine-learn'':5B ''mine'':10B ''one'':35C ''p'':14C,32C,57C ''refer'':63C ''tree'':2A,13B,25C ''use'':22C,34C ''vs'':3A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4921, 1, '2015-01-22 00:52:51.71', 2, 94, 'Could someone please offer me some guidance on some kind of particular, SPECIFIC project that I could attemp, to "get my feet wet, so to speak"', '&lt;p&gt;I am COMPLETELY new to the field of Data Science, mainly because every employer I have worked for, simply COULDN''T sell any customers anything that would use techniques learned in this field.&lt;/p&gt;

&lt;p&gt;Of particular interest to me is machine learning/Predictive Analysis.&lt;/p&gt;

&lt;p&gt;I have attempted many "test projects" myself, but I seem to NEED some sort of outside "catalyst" to tell me a specific goal, and a specific set of guidelines, when I am trying to learn something.
Otherwise, I tend to lose focus, and jump from one interesting topic to the next, without ever gaining any experience.&lt;/p&gt;

&lt;p&gt;Thank you!!&lt;/p&gt;
', 'machine-learning  data-mining  bigdata  predictive-modeling', NULL, '''/p'':71C,82C,140C,144C ''analysi'':81C ''anyth'':62C ''attemp'':18A ''attempt'':86C ''bigdata'':33B ''catalyst'':100C ''complet'':40C ''could'':1A,17A ''couldn'':57C ''custom'':61C ''data'':31B,46C ''data-min'':30B ''employ'':51C ''ever'':136C ''everi'':50C ''experi'':139C ''feet'':22A ''field'':44C,70C ''focus'':125C ''gain'':137C ''get'':20A ''goal'':106C ''guidanc'':7A ''guidelin'':112C ''interest'':75C,130C ''jump'':127C ''kind'':10A ''learn'':29B,67C,118C ''learning/predictive'':80C ''lose'':124C ''machin'':28B,79C ''machine-learn'':27B ''main'':48C ''mani'':87C ''mine'':32B ''model'':36B ''need'':95C ''new'':41C ''next'':134C ''offer'':4A ''one'':129C ''otherwis'':120C ''outsid'':99C ''p'':37C,72C,83C,141C ''particular'':12A,74C ''pleas'':3A ''predict'':35B ''predictive-model'':34B ''project'':14A,89C ''scienc'':47C ''seem'':93C ''sell'':59C ''set'':110C ''simpli'':56C ''someon'':2A ''someth'':119C ''sort'':97C ''speak'':26A ''specif'':13A,105C,109C ''techniqu'':66C ''tell'':102C ''tend'':122C ''test'':88C ''thank'':142C ''topic'':131C ''tri'':116C ''use'':65C ''wet'':23A ''without'':135C ''work'':54C ''would'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5161, 2, '2015-02-17 06:43:03.82', 2, NULL, NULL, '&lt;p&gt;&lt;a href="http://www.rstudio.com/products/rstudio/#Server" rel="nofollow"&gt;RStudio Server&lt;/a&gt; is definately one of the options, meant exactly for this. I''ve thought about using it with a cloud virtual machine, but haven''t had the need yet. But when I (probably) need to prepare an intro data analysis class for the fall semester, then Rstudio Server is the first option I''ll be trying out.&lt;/p&gt;
', NULL, NULL, '''/a'':11C ''/p'':68C ''/products/rstudio/#server'':6C ''analysi'':50C ''class'':51C ''cloud'':30C ''data'':49C ''defin'':13C ''exact'':19C ''fall'':54C ''first'':61C ''haven'':34C ''href'':3C ''intro'':48C ''ll'':64C ''machin'':32C ''meant'':18C ''need'':38C,44C ''nofollow'':8C ''one'':14C ''option'':17C,62C ''p'':1C ''prepar'':46C ''probabl'':43C ''rel'':7C ''rstudio'':9C,57C ''semest'':55C ''server'':10C,58C ''thought'':24C ''tri'':66C ''use'':26C ''ve'':23C ''virtual'':31C ''www.rstudio.com'':5C ''www.rstudio.com/products/rstudio/#server'':4C ''yet'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4926, 2, '2015-01-23 02:08:59.95', 11, NULL, NULL, '&lt;p&gt;There is another choice which popular recently: docker(&lt;a href="https://www.docker.com"&gt;https://www.docker.com&lt;/a&gt;). Docker is a container and let you create/maintain a working environment very easily and fast. &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;install essential tools for data science in python 
&lt;ul&gt;
&lt;li&gt;&lt;a href="https://registry.hub.docker.com/u/ceshine/python-datascience/"&gt;https://registry.hub.docker.com/u/ceshine/python-datascience/&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;use r language to do data science
&lt;ul&gt;
&lt;li&gt;&lt;a href="https://github.com/rocker-org/rocker"&gt;https://github.com/rocker-org/rocker&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Hope that would help you.&lt;/p&gt;
', NULL, NULL, '''/a'':14C ''/li'':52C,72C ''/p'':30C,80C ''/rocker-org/rocker'':67C ''/rocker-org/rocker&lt;/a&gt;&lt;/li&gt;'':70C ''/u/ceshine/python-datascience/'':47C ''/u/ceshine/python-datascience/&lt;/a&gt;&lt;/li&gt;'':50C ''/ul'':51C,71C,73C ''anoth'':4C ''choic'':5C ''contain'':18C ''create/maintain'':22C ''data'':37C,59C ''docker'':9C,15C ''easili'':27C ''environ'':25C ''essenti'':34C ''fast'':29C ''github.com'':66C,69C ''github.com/rocker-org/rocker'':65C ''github.com/rocker-org/rocker&lt;/a&gt;&lt;/li&gt;'':68C ''help'':78C ''hope'':75C ''href'':11C,44C,64C ''instal'':33C ''languag'':56C ''let'':20C ''li'':32C,42C,53C,62C ''p'':1C,74C ''popular'':7C ''python'':40C ''r'':55C ''recent'':8C ''registry.hub.docker.com'':46C,49C ''registry.hub.docker.com/u/ceshine/python-datascience/'':45C ''registry.hub.docker.com/u/ceshine/python-datascience/&lt;/a&gt;&lt;/li&gt;'':48C ''scienc'':38C,60C ''tool'':35C ''ul'':31C,41C,61C ''use'':54C ''work'':24C ''would'':77C ''www.docker.com'':12C,13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4927, 2, '2015-01-23 02:26:23.773', 7, NULL, NULL, '&lt;p&gt;While &lt;em&gt;Docker&lt;/em&gt; images are now more trendy, I personally find &lt;em&gt;Docker&lt;/em&gt; technology not user-friendly, even for advanced users. If you are OK with using &lt;em&gt;non-local&lt;/em&gt; VM images and can use &lt;em&gt;Amazon Web Services (AWS) EC2&lt;/em&gt;, consider R-focused images for data science projects, pre-built by Louis Aslett. The images contain very recent, if not the latest, versions of &lt;em&gt;Ubuntu LTS&lt;/em&gt;, &lt;em&gt;R&lt;/em&gt; and &lt;em&gt;RStudio Server&lt;/em&gt;. You can access them &lt;a href="http://www.louisaslett.com/RStudio_AMI"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Besides main components I''ve listed above, the images contain many useful data science tools built-in as well. For example, the images support LaTeX, ODBC, OpenGL, Git, optimized numeric libraries and more.&lt;/p&gt;
', NULL, NULL, '''/a'':97C ''/em'':5C,16C,36C,48C,78C,81C,86C ''/p'':98C,134C ''/rstudio_ami'':95C ''access'':89C ''advanc'':24C ''amazon'':43C ''aslett'':63C ''aw'':46C ''besid'':100C ''built'':60C,116C ''built-in'':115C ''compon'':102C ''consid'':49C ''contain'':66C,109C ''data'':55C,112C ''docker'':4C,15C ''ec2'':47C ''em'':3C,14C,32C,42C,75C,79C,83C ''even'':22C ''exampl'':121C ''find'':13C ''focus'':52C ''friend'':21C ''git'':128C ''href'':92C ''imag'':6C,38C,53C,65C,108C,123C ''latest'':72C ''latex'':125C ''librari'':131C ''list'':105C ''local'':35C ''loui'':62C ''lts'':77C ''main'':101C ''mani'':110C ''non'':34C ''non-loc'':33C ''numer'':130C ''odbc'':126C ''ok'':29C ''opengl'':127C ''optim'':129C ''p'':1C,99C ''person'':12C ''pre'':59C ''pre-built'':58C ''project'':57C ''r'':51C,80C ''r-focus'':50C ''recent'':68C ''rstudio'':84C ''scienc'':56C,113C ''server'':85C ''servic'':45C ''support'':124C ''technolog'':17C ''tool'':114C ''trendi'':10C ''ubuntu'':76C ''use'':31C,41C,111C ''user'':20C,25C ''user-friend'':19C ''ve'':104C ''version'':73C ''vm'':37C ''web'':44C ''well'':119C ''www.louisaslett.com'':94C ''www.louisaslett.com/rstudio_ami'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4938, 2, '2015-01-25 01:29:53.47', 5, NULL, NULL, '&lt;p&gt;About your question, I think basic support vector machine means hard-margin SVM. At first, let''s review what is hard-margin SVM.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;What is hard-margin SVM&lt;/strong&gt;: 
In shortly, we want to find a hyperplane with the largest margin which be able to separate all observations correctly in our training sample space. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;The optimisation problem in hard-margin SVM&lt;/strong&gt;: Now, let''s look at the above definition again and what is the optimisation problem which we need to solve. &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;the largest margin hyperplane: We want &lt;strong&gt;&lt;code&gt;max(margin)&lt;/code&gt;&lt;/strong&gt;&lt;/li&gt;
&lt;li&gt;be able to separate all observations correctly: We need to optimise &lt;strong&gt;&lt;code&gt;margin&lt;/code&gt;&lt;/strong&gt; and also satisfy the constrain: &lt;strong&gt;No in-sample errors&lt;/strong&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Back to your question, since you mentioned the training data set is non-linearly separable, by using hard-margin SVM without feature transformations, &lt;strong&gt;it''s impossible to find any hyperplane which satisfy "No in-sample errors"&lt;/strong&gt;. Normally, we solve SVM optimisation problem by Quadratic Programming, because it can do optimisation tasks with constrains. If you use Gradient Descent or other optimisation algorithms which without satisfying the constrains of hard-margin SVM, you should still get a result, but that''s not a hard-margin SVM hyperplane.&lt;/p&gt;

&lt;p&gt;By the way, with non-linearly separable data, usually we choose &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;hard-margin SVM + feature transformations&lt;/li&gt;
&lt;li&gt;use soft-margin SVM directly(In practical, soft-margin SVM usually get good results)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Thank you for your question, and hope I do answer your question.&lt;/p&gt;
', NULL, NULL, '''/code'':107C,125C ''/li'':109C,139C,258C,276C ''/ol'':140C ''/p'':27C,62C,94C,235C,249C,291C ''/strong'':36C,73C,108C,126C,138C,182C ''/ul'':277C ''abl'':51C,112C ''algorithm'':208C ''also'':128C ''answer'':288C ''back'':142C ''basic'':7C ''choos'':248C ''code'':104C,123C ''constrain'':131C,199C,213C ''correct'':56C,117C ''data'':151C,245C ''definit'':81C ''descent'':204C ''direct'':265C ''error'':137C,181C ''featur'':165C,256C ''find'':42C,172C ''first'':17C ''get'':222C,273C ''good'':274C ''gradient'':203C ''hard'':13C,24C,33C,70C,161C,216C,231C,253C ''hard-margin'':12C,23C,32C,69C,160C,215C,230C,252C ''hope'':285C ''hyperplan'':44C,100C,174C,234C ''imposs'':170C ''in-sampl'':134C,178C ''largest'':47C,98C ''let'':18C,75C ''li'':96C,110C,251C,259C ''linear'':156C,243C ''look'':77C ''machin'':10C ''margin'':14C,25C,34C,48C,71C,99C,106C,124C,162C,217C,232C,254C,263C,270C ''max'':105C ''mean'':11C ''mention'':148C ''need'':91C,119C ''non'':155C,242C ''non-linear'':154C,241C ''normal'':183C ''observ'':55C,116C ''ol'':95C ''optimis'':66C,87C,121C,187C,196C,207C ''p'':1C,28C,63C,141C,236C,278C ''practic'':267C ''problem'':67C,88C,188C ''program'':191C ''quadrat'':190C ''question'':4C,145C,283C,290C ''result'':224C,275C ''review'':20C ''sampl'':60C,136C,180C ''satisfi'':129C,176C,211C ''separ'':53C,114C,157C,244C ''set'':152C ''short'':38C ''sinc'':146C ''soft'':262C,269C ''soft-margin'':261C,268C ''solv'':93C,185C ''space'':61C ''still'':221C ''strong'':29C,64C,103C,122C,132C,167C ''support'':8C ''svm'':15C,26C,35C,72C,163C,186C,218C,233C,255C,264C,271C ''task'':197C ''thank'':279C ''think'':6C ''train'':59C,150C ''transform'':166C,257C ''ul'':250C ''use'':159C,202C,260C ''usual'':246C,272C ''vector'':9C ''want'':40C,102C ''way'':239C ''without'':164C,210C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4928, 2, '2015-01-23 06:10:37.417', 2, NULL, NULL, '&lt;p&gt;How to calculate key points:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Take your image $I(x,y)$ and convolve it using a Gaussian $G(x,y,k\sigma$) at different scales $k \sigma$ to obtain $L(x,y,k\sigma)$. This produces several versions (one for each scale $k\sigma$) of your image $I(x,y)$ with different degrees of blurring.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Separate your blurred images according to octaves (an octave is usually taken as $2\sigma$). Within a given octave, take the difference between adjacent blurred images $L(x,y,k_{i}\sigma)$ and  $L(x,y,k_{j}\sigma)$. This difference is called a Difference of Gaussians (DoG) $D(x,y,\sigma) = L(x,y,k_{i}\sigma) - L(x,y,k_{j}\sigma)$. At this point, an image will be helpful (source: &lt;a href="http://docs.opencv.org/master/doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.html" rel="nofollow"&gt;opencv&lt;/a&gt;):&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/yLVAi.jpg" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;As you can see, you have the blurred/convolved/filtered images on the left and the differences between adjacent images on the right.&lt;/p&gt;

&lt;ol start="3"&gt;
&lt;li&gt;Then, take one pixel on a DoG and compare it to its 8 neighbors in the same DoG, as well as to the 9 equivalent pixels of the DoG located in the next scale  and to the other 9 pixels in the previous DoG. In the drawing, this corresponds to the DoG''s located above and below. If this pixel is a local extrema, then you have a candidate keypoint. Keep in mind that candidates can be discarded based on other criteria.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Once you have these keypoints, you proceed to generate descriptors. For each keypoint, calculate an image gradient. As you should know, gradients tell you the direction of maximum rate of change. Therefore, you can construct a grid around each keypoint that is oriented according to the dominant gradient around that point. This grid has subregions (usually 16 subregions) and for each subregion, calculate an 8-bin histogram. Finally, concatenate every histogram you obtained from every subregion in your grid and that is the feature vector for that keypoint (actually, the full feature vector also includes the location and rotation angle.) A helpful illustration (from Solem''s book Programming Computer Vision with Python):&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Ws829.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;At this point, you have a feature vector with 132 values for each keypoint. As for your second question, I''m not sure about the best approach to store an array in a database. Maybe others can expand on this point. Of course, there are several options:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Create an array datatype to store your data&lt;/li&gt;
&lt;li&gt;Simply use a VARCHAR field&lt;/li&gt;
&lt;li&gt;Store it as a binary file&lt;/li&gt;
&lt;li&gt;Use a database specifically designed to handle arrays.&lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4930, 2, '2015-01-23 14:00:52.367', 5, NULL, NULL, '&lt;p&gt;Did you try Cloudera''s QuickStart VM?:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.cloudera.com/content/cloudera/en/downloads/quickstart_vms/cdh-5-3-x.html"&gt;http://www.cloudera.com/content/cloudera/en/downloads/quickstart_vms/cdh-5-3-x.html&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I found it very easy to run it and it includes open source software such as &lt;a href="http://mahout.apache.org/"&gt;Mahout&lt;/a&gt; and &lt;a href="https://spark.apache.org/"&gt;Spark&lt;/a&gt;. &lt;/p&gt;
', NULL, NULL, '''/a'':42C,48C ''/content/cloudera/en/downloads/quickstart_vms/cdh-5-3-x.html'':16C ''/content/cloudera/en/downloads/quickstart_vms/cdh-5-3-x.html&lt;/a&gt;&lt;/li&gt;'':19C ''/p'':9C,49C ''/ul'':20C ''cloudera'':5C ''easi'':26C ''found'':23C ''href'':13C,39C,45C ''includ'':32C ''li'':11C ''mahout'':41C ''mahout.apache.org'':40C ''open'':33C ''p'':1C,21C ''quickstart'':7C ''run'':28C ''softwar'':35C ''sourc'':34C ''spark'':47C ''spark.apache.org'':46C ''tri'':4C ''ul'':10C ''vm'':8C ''www.cloudera.com'':15C,18C ''www.cloudera.com/content/cloudera/en/downloads/quickstart_vms/cdh-5-3-x.html'':14C ''www.cloudera.com/content/cloudera/en/downloads/quickstart_vms/cdh-5-3-x.html&lt;/a&gt;&lt;/li&gt;'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4931, 2, '2015-01-23 15:34:07.57', 5, NULL, NULL, '&lt;p&gt;Today i used this repository from &lt;a href="https://github.com/sequenceiq/docker-spark"&gt;https://github.com/sequenceiq/docker-spark&lt;/a&gt; and build it with docker. it is a docker image building spark based on hadoop image of the same owner. if you to use spark, it has a python api called pyspark &lt;a href="http://spark.apache.org/docs/latest/api/python/"&gt;http://spark.apache.org/docs/latest/api/python/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/docs/latest/api/python/'':52C ''/docs/latest/api/python/&lt;/a&gt;&lt;/p&gt;'':55C ''/sequenceiq/docker-spark'':12C ''/sequenceiq/docker-spark&lt;/a&gt;'':15C ''api'':45C ''base'':28C ''build'':17C,26C ''call'':46C ''docker'':20C,24C ''github.com'':11C,14C ''github.com/sequenceiq/docker-spark'':10C ''github.com/sequenceiq/docker-spark&lt;/a&gt;'':13C ''hadoop'':30C ''href'':9C,49C ''imag'':25C,31C ''owner'':35C ''p'':1C ''pyspark'':47C ''python'':44C ''repositori'':6C ''spark'':27C,40C ''spark.apache.org'':51C,54C ''spark.apache.org/docs/latest/api/python/'':50C ''spark.apache.org/docs/latest/api/python/&lt;/a&gt;&lt;/p&gt;'':53C ''today'':2C ''use'':4C,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4935, 1, '2015-01-24 15:15:27.777', 0, 58, 'Questions on "Active Archive"', '&lt;p&gt;In the big data world there is lot of talk about implementing an "active archive". I see cloudera talk about it a lot. &lt;/p&gt;

&lt;p&gt;The value prop is that you move the low value (and less used) data from EDW to Hadoop and then save on expensive EDW storage by using Hadoop.&lt;/p&gt;

&lt;p&gt;So in my company say we keep 10 years of data on EDW and say we don''t use anything below 2 years very actively. So I move 8 years of data from EDW to Hadoop.&lt;/p&gt;

&lt;p&gt;I can setup an Impala (or equivalent) product to query the 8 years of data as well.&lt;/p&gt;

&lt;p&gt;But the problem is how do you order, sort a query which requires some data form EDW and some data from Impala?&lt;/p&gt;

&lt;p&gt;because this kind of grouping, sorting ordering etc will have to be done in memory and the apps which queried the EDW were not written for operations and don''t have the capacity as well to sort, group and process so much of data.&lt;/p&gt;

&lt;p&gt;So how are people implementing the Active Archive?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4936, 1, '2015-01-24 15:52:28.727', 5, 209, 'What happens when we train a linear SVM on non-linearly separable data?', '&lt;p&gt;What happens when we train a basic support vector machine (linear kernel and no soft-margin) on non-linearly separable data? The optimisation problem is not feasible, so what does the minimisation algorithm return?&lt;/p&gt;
', 'machine-learning  svm', NULL, '''/p'':56C ''algorithm'':54C ''basic'':26C ''data'':14A,42C ''feasibl'':48C ''happen'':2A,21C ''kernel'':31C ''learn'':17B ''linear'':7A,12A,30C,40C ''machin'':16B,29C ''machine-learn'':15B ''margin'':36C ''minimis'':53C ''non'':11A,39C ''non-linear'':10A,38C ''optimis'':44C ''p'':19C ''problem'':45C ''return'':55C ''separ'':13A,41C ''soft'':35C ''soft-margin'':34C ''support'':27C ''svm'':8A,18B ''train'':5A,24C ''vector'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4942, 1, '2015-01-25 22:52:23.437', 7, 591, 'High-dimensional data: What are useful techniques to know?', '&lt;p&gt;Due to various &lt;a href="http://en.wikipedia.org/wiki/Curse_of_dimensionality"&gt;curses of dimensionality&lt;/a&gt;, the accuracy and speed of many of the common predictive techniques degrade on high dimensional data. What are some of the most useful techniques/tricks/heuristics that help deal with high-dimensional data effectively? For example,&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Do certain statistical/modeling methods perform well on high-dimensional datasets?&lt;/li&gt;
&lt;li&gt;Can we improve the performance of our predictive models on high-dimensional data by using certain (that define alternative notions of distance) or &lt;a href="http://en.wikipedia.org/wiki/Kernel_method"&gt;kernels&lt;/a&gt; (that define alternative notions of dot product)?&lt;/li&gt;
&lt;li&gt;What are the most useful techniques of dimensionality reduction for high-dimensional data?&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4943, 1, '2015-01-25 23:03:02.297', 4, 322, 'Intuition for the regularization parameter in SVM', '&lt;p&gt;How does varying the regularization parameter in an SVM change the decision boundary for a non-separable dataset? A visual answer and/or some commentary on the limiting behaviors (for large and small regularization) would be very helpful.&lt;/p&gt;
', 'svm', NULL, '''/p'':48C ''and/or'':32C ''answer'':31C ''behavior'':38C ''boundari'':22C ''chang'':19C ''commentari'':34C ''dataset'':28C ''decis'':21C ''help'':47C ''intuit'':1A ''larg'':40C ''limit'':37C ''non'':26C ''non-separ'':25C ''p'':9C ''paramet'':5A,15C ''regular'':4A,14C,43C ''separ'':27C ''small'':42C ''svm'':7A,8B,18C ''vari'':12C ''visual'':30C ''would'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4944, 1, '2015-01-25 23:09:22.863', 1, 152, 'How to learn a classifier from a dataset with high imbalance', '&lt;p&gt;What are the most useful techniques for learning a binary classifier from a dataset with a high degree of imbalance (i.e., a dataset with the "target" class being much rarer than the "background" class)? For example,&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Should one first down-sample the majority/background class to reduce its frequency and then readjust the probabilities reported by the learning algorithm? How should one do the readjustment?&lt;/li&gt;
&lt;li&gt;Should one use different approaches for different learning algorithms, i.e., are there different techniques for dealing with imbalance in SVM, random forests, logistic regression, etc.?&lt;/li&gt;
&lt;/ul&gt;
', 'machine-learning  classification  unbalanced-classes', NULL, '''/li'':88C,115C ''/p'':56C ''/ul'':116C ''algorithm'':81C,98C ''approach'':94C ''background'':52C ''binari'':29C ''class'':18B,46C,53C,67C ''classif'':15B ''classifi'':5A,30C ''dataset'':8A,33C,42C ''deal'':105C ''degre'':37C ''differ'':93C,96C,102C ''down-sampl'':62C ''etc'':114C ''exampl'':55C ''first'':61C ''forest'':111C ''frequenc'':71C ''high'':10A,36C ''i.e'':40C,99C ''imbal'':11A,39C,107C ''learn'':3A,14B,27C,80C,97C ''li'':58C,89C ''logist'':112C ''machin'':13B ''machine-learn'':12B ''majority/background'':66C ''much'':48C ''one'':60C,84C,91C ''p'':19C ''probabl'':76C ''random'':110C ''rarer'':49C ''readjust'':74C,87C ''reduc'':69C ''regress'':113C ''report'':77C ''sampl'':64C ''svm'':109C ''target'':45C ''techniqu'':25C,103C ''ul'':57C ''unbalanc'':17B ''unbalanced-class'':16B ''use'':24C,92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4945, 2, '2015-01-26 01:37:05.64', 6, NULL, NULL, '&lt;p&gt;The regularization parameter (lambda) serves as a degree of importance that is given to miss-classifications. SVM pose a quadratic optimization problem that looks for maximizing the margin between both classes and minimizing the amount of miss-classifications. However, for non-separable problems, in order to find a solution, the miss-classification constraint must be relaxed, and this is done by setting the mentioned "regularization".&lt;/p&gt;

&lt;p&gt;So, intuitively, as lambda grows larger they less the wrongly classified examples are allowed (or the highest the price the pay in the loss function). Then when lambda tends to infinite the solution tends to the hard-margin (allow no miss-classification). When lambda tends to 0 (without being 0) the more the miss-classifications are allowed.&lt;/p&gt;

&lt;p&gt;There is definitely a tradeoff between this two and normally smaller lambdas but not too small use to generalize well. Below three examples for linear SVM classification (binary).&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/NbNCS.png" alt="Linear SVM Lambda = 0.1"&gt;
&lt;img src="http://i.stack.imgur.com/xanGh.png" alt="Linear SVM Lambda = 1"&gt;
&lt;img src="http://i.stack.imgur.com/R6fHT.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;For non-linear-kernel SVM the idea is the similar.
Given this, for higher values of lambda there is a higher possibility of overfitting, while for lower values of lambda there is higher possibilities of underfitting. &lt;/p&gt;

&lt;p&gt;The images below show the behavior for RBF Kernel, letting the sigma parameter fixed on 1 and trying lambda = 0.01 and lambda = 10&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/8KIpI.png" alt="RBF Kernel SVM lambda = 0.01"&gt;
&lt;img src="http://i.stack.imgur.com/6Vmqt.png" alt="RBF Kernel SVM lambda = 10"&gt;&lt;/p&gt;

&lt;p&gt;You can say the first figure where lambda is lower is more "relaxed" than the second figure where data is intended to be fitted more precisely.&lt;/p&gt;

&lt;p&gt;(Slides from Prof. Oriol Pujol. Universitat de Barcelona)&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4981, 2, '2015-01-29 12:42:05.757', 1, NULL, NULL, '&lt;p&gt;If I understand your question correctly. Yes, SVM can take multiple inputs. My suggestion for handling a vector as a feature would be to expand it out. For example,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;x0 = (1,2)       x0 = 1
x1 = .4  -----&amp;gt;  x1 = 2
x2 = 0           x2 = .4
                 x3 = 0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If this does not capture all of the characteristics of the vector that are important, then you may want to add other features (like magnitude of the vector) as well. &lt;/p&gt;
', NULL, NULL, '''/code'':50C ''/p'':31C,83C ''/pre'':51C ''0'':45C,49C ''1'':35C,38C ''2'':36C,43C ''4'':40C,47C ''add'':73C ''captur'':57C ''characterist'':61C ''code'':33C ''correct'':7C ''exampl'':30C ''expand'':26C ''featur'':22C,75C ''gt'':41C ''handl'':17C ''import'':67C ''input'':13C ''like'':76C ''magnitud'':77C ''may'':70C ''multipl'':12C ''p'':1C,52C ''pre'':32C ''question'':6C ''suggest'':15C ''svm'':9C ''take'':11C ''understand'':4C ''vector'':19C,64C,80C ''want'':71C ''well'':82C ''would'':23C ''x0'':34C,37C ''x1'':39C,42C ''x2'':44C,46C ''x3'':48C ''yes'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4952, 2, '2015-01-26 19:24:51.783', 2, NULL, NULL, '&lt;p&gt;There is nothing necessarily wrong with this.  If you have no better information, then using past performance (i.e., prior probabilities) can work pretty well, particularly when your classes are very unevenly distributed.&lt;/p&gt;

&lt;p&gt;Example methods using class priors are &lt;a href="http://en.wikipedia.org/wiki/Maximum_likelihood" rel="nofollow"&gt;Gaussian Maximum Likelihood&lt;/a&gt; classification and &lt;a href="http://en.wikipedia.org/wiki/Naive_Bayes_classifier" rel="nofollow"&gt;NaÃ¯ve Bayes&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;[UPDATE]&lt;/p&gt;

&lt;p&gt;Since you''ve added additional details to the question...&lt;/p&gt;

&lt;p&gt;Suppose you are doing 10-fold cross-validation (holding out 10% of the data for validating each of the 10 subsets). If you use the entire data set to establish the priors (including the 10% of validation data), then yes, it is "cheating" since each of the 10 subset models uses information from the corresponding validation set (i.e., it is not truly a blind test). However, if the priors are recomputed for each fold using only the 90% of data used for that fold, then it is a "fair" validation.&lt;/p&gt;

&lt;p&gt;An example of the effect of this "cheating" is if you have a single, extreme outlier in your data. Normally, with k-fold cross-validation, there would be one fold where the outlier is in the validation data and not the training data. When applying the corresponding classifier to the outlier during validation, it would likely perform poorly. However, if the training data for that fold included global statistics (from the entire data set), then the outlier would influence the statistics (priors) for that fold, potentially resulting in artificially favorable performance.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4947, 2, '2015-01-26 09:39:41.527', 2, NULL, NULL, '&lt;p&gt;A common strategy for dealing with imbalance is to penalize harder the missclassifications that select the class with higher frequency. &lt;/p&gt;

&lt;p&gt;In a binary classification problem you could penalize by dividing 1/n where n is the number of examples of the opposite class.&lt;/p&gt;

&lt;p&gt;See the following from Prof. Jordi VitriÃ¡&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/fTosz.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;This is the loss function for structured output SVM. &lt;/p&gt;

&lt;p&gt;The problem you mention is common in object recognition and object classification in images where much more background images are used than images containing the object. A stronger case happens with exemplar SVM''s where just a single image of the object is used.&lt;/p&gt;
', NULL, NULL, '''/ftosz.png'':61C ''/p'':22C,46C,55C,67C,78C,124C ''1/n'':34C ''alt'':62C ''background'':97C ''binari'':26C ''case'':108C ''class'':18C,45C ''classif'':27C,91C ''common'':3C,85C ''contain'':103C ''could'':30C ''deal'':6C ''descript'':65C ''divid'':33C ''enter'':63C ''exampl'':41C ''exemplar'':111C ''follow'':50C ''frequenc'':21C ''function'':73C ''happen'':109C ''harder'':12C ''higher'':20C ''i.stack.imgur.com'':60C ''i.stack.imgur.com/ftosz.png'':59C ''imag'':64C,93C,98C,102C,118C ''imbal'':8C ''img'':57C ''jordi'':53C ''loss'':72C ''mention'':83C ''missclassif'':14C ''much'':95C ''n'':36C ''number'':39C ''object'':87C,90C,105C,121C ''opposit'':44C ''output'':76C ''p'':1C,23C,47C,56C,68C,79C ''penal'':11C,31C ''problem'':28C,81C ''prof'':52C ''recognit'':88C ''see'':48C ''select'':16C ''singl'':117C ''src'':58C ''strategi'':4C ''stronger'':107C ''structur'':75C ''svm'':77C,112C ''use'':100C,123C ''vitriã'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4948, 2, '2015-01-26 14:45:43.053', 2, NULL, NULL, '&lt;p&gt;Some good answers have already been posted at this site:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/1107/quick-guide-into-training-highly-imbalanced-data-sets"&gt;Quick guide into training highly imbalanced data sets&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;And on Stats SE:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/questions/81111/classification-problem-using-imbalanced-dataset"&gt;http://stats.stackexchange.com/questions/81111/classification-problem-using-imbalanced-dataset&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/questions/16050/how-to-handle-data-imbalance-in-classification"&gt;http://stats.stackexchange.com/questions/16050/how-to-handle-data-imbalance-in-classification&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/questions/60180/testing-classification-on-oversampled-imbalance-data?rq=1"&gt;http://stats.stackexchange.com/questions/60180/testing-classification-on-oversampled-imbalance-data?rq=1&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':28C ''/li'':29C ''/p'':12C,36C ''/questions/1107/quick-guide-into-training-highly-imbalanced-data-sets'':19C ''/questions/16050/how-to-handle-data-imbalance-in-classification'':52C ''/questions/16050/how-to-handle-data-imbalance-in-classification&lt;/a&gt;&lt;/li&gt;'':55C ''/questions/60180/testing-classification-on-oversampled-imbalance-data?rq=1'':61C ''/questions/60180/testing-classification-on-oversampled-imbalance-data?rq=1&lt;/a&gt;&lt;/li&gt;'':64C ''/questions/81111/classification-problem-using-imbalanced-dataset'':43C ''/questions/81111/classification-problem-using-imbalanced-dataset&lt;/a&gt;&lt;/li&gt;'':46C ''/ul'':30C,65C ''alreadi'':6C ''answer'':4C ''data'':26C ''datascience.stackexchange.com'':18C ''datascience.stackexchange.com/questions/1107/quick-guide-into-training-highly-imbalanced-data-sets'':17C ''good'':3C ''guid'':21C ''high'':24C ''href'':16C,40C,49C,58C ''imbalanc'':25C ''li'':14C,38C,47C,56C ''p'':1C,31C ''post'':8C ''quick'':20C ''se'':35C ''set'':27C ''site'':11C ''stat'':34C ''stats.stackexchange.com'':42C,45C,51C,54C,60C,63C ''stats.stackexchange.com/questions/16050/how-to-handle-data-imbalance-in-classification'':50C ''stats.stackexchange.com/questions/16050/how-to-handle-data-imbalance-in-classification&lt;/a&gt;&lt;/li&gt;'':53C ''stats.stackexchange.com/questions/60180/testing-classification-on-oversampled-imbalance-data?rq=1'':59C ''stats.stackexchange.com/questions/60180/testing-classification-on-oversampled-imbalance-data?rq=1&lt;/a&gt;&lt;/li&gt;'':62C ''stats.stackexchange.com/questions/81111/classification-problem-using-imbalanced-dataset'':41C ''stats.stackexchange.com/questions/81111/classification-problem-using-imbalanced-dataset&lt;/a&gt;&lt;/li&gt;'':44C ''train'':23C ''ul'':13C,37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4949, 1, '2015-01-26 15:45:51.317', 4, 135, 'Where did this NY Times op-ed get his Google Search data?', '&lt;p&gt;I hope this is a question appropriate for SO.&lt;/p&gt;

&lt;p&gt;The article in question: &lt;a href="http://www.nytimes.com/2015/01/25/opinion/sunday/seth-stephens-davidowitz-searching-for-sex.html" rel="nofollow"&gt;http://www.nytimes.com/2015/01/25/opinion/sunday/seth-stephens-davidowitz-searching-for-sex.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;As far as I can tell, the only publicly available data from Google Search is through their Trends API.  The help page states that&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The numbers on the graph reflect how many searches have been done for a particular term, relative to the total number of searches done on Google over time. They don''t represent absolute search volume numbers, because the data is normalized and presented on a scale from 0-100. &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;However in the article, the author reports (absolute) "average monthly searches".  The source is stated as:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;All monthly search numbers are approximate and derived from anonymous and aggregate web activity.&lt;/p&gt;
  
  &lt;p&gt;Source: analysis of Google data by (author)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;So, how did he get this "anonymous and aggregate web activity"?&lt;/p&gt;
', 'dataset  search  google', NULL, '''-100'':119C ''/2015/01/25/opinion/sunday/seth-stephens-davidowitz-searching-for-sex.html'':37C ''/2015/01/25/opinion/sunday/seth-stephens-davidowitz-searching-for-sex.html&lt;/a&gt;&lt;/p&gt;'':42C ''/blockquote'':121C,166C ''/p'':27C,68C,120C,139C,156C,165C,179C ''0'':118C ''absolut'':103C,130C ''activ'':155C,178C ''aggreg'':153C,176C ''analysi'':159C ''anonym'':151C,174C ''api'':62C ''appropri'':24C ''approxim'':147C ''articl'':30C,126C ''author'':128C,164C ''avail'':53C ''averag'':131C ''blockquot'':69C,140C ''data'':13A,54C,109C,162C ''dataset'':14B ''deriv'':149C ''done'':82C,94C ''ed'':8A ''far'':45C ''get'':9A,172C ''googl'':11A,16B,56C,96C,161C ''graph'':75C ''help'':64C ''hope'':19C ''howev'':123C ''href'':34C ''mani'':78C ''month'':132C,143C ''nofollow'':39C ''normal'':111C ''number'':72C,91C,106C,145C ''ny'':4A ''op'':7A ''op-'':6A ''p'':17C,28C,43C,70C,122C,141C,157C,167C ''page'':65C ''particular'':85C ''present'':113C ''public'':52C ''question'':23C,32C ''reflect'':76C ''rel'':38C ''relat'':87C ''report'':129C ''repres'':102C ''scale'':116C ''search'':12A,15B,57C,79C,93C,104C,133C,144C ''sourc'':135C,158C ''state'':66C,137C ''tell'':49C ''term'':86C ''time'':5A,98C ''total'':90C ''trend'':61C ''volum'':105C ''web'':154C,177C ''www.nytimes.com'':36C,41C ''www.nytimes.com/2015/01/25/opinion/sunday/seth-stephens-davidowitz-searching-for-sex.html'':35C ''www.nytimes.com/2015/01/25/opinion/sunday/seth-stephens-davidowitz-searching-for-sex.html&lt;/a&gt;&lt;/p&gt;'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4950, 1, '2015-01-26 17:45:02.203', 4, 176, 'semi-structured text parsing using machine learning', '&lt;p&gt;I am looking for a method to parse semi-structured textual data, i.e. data poorly formatted but usually having a visual structure of a matrix which may vary a lot in content and number of items in it, which may have headers or not, which may be interpreted sometimes column-wise or row-wise, and so on.&lt;/p&gt;

&lt;p&gt;I have read about the WHISK information extraction paper : &lt;a href="https://homes.cs.washington.edu/~soderlan/soderland_ml99.pdf" rel="nofollow"&gt;https://homes.cs.washington.edu/~soderlan/soderland_ml99.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;but unfortunately, it is not very detailed and I have not been able to find a real-system implementing it, or even snippets of code.&lt;/p&gt;

&lt;p&gt;Has anybody have an idea where I can find such help? Or suggest an alternative approach which may be suited to my problem?&lt;/p&gt;

&lt;p&gt;Thank you in advance for your reply!&lt;/p&gt;
', 'text-mining  information-retrieval  parsing', NULL, '''/p'':77C,125C,150C,159C ''/~soderlan/soderland_ml99.pdf'':92C ''/~soderlan/soderland_ml99.pdf&lt;/a&gt;&lt;/p&gt;'':97C ''abl'':111C ''advanc'':155C ''altern'':141C ''anybodi'':128C ''approach'':142C ''code'':124C ''column'':68C ''column-wis'':67C ''content'':49C ''data'':29C,31C ''detail'':105C ''even'':121C ''extract'':86C ''find'':113C,135C ''format'':33C ''header'':59C ''help'':137C ''homes.cs.washington.edu'':91C,96C ''homes.cs.washington.edu/~soderlan/soderland_ml99.pdf'':90C ''homes.cs.washington.edu/~soderlan/soderland_ml99.pdf&lt;/a&gt;&lt;/p&gt;'':95C ''href'':89C ''i.e'':30C ''idea'':131C ''implement'':118C ''inform'':13B,85C ''information-retriev'':12B ''interpret'':65C ''item'':53C ''learn'':8A ''look'':19C ''lot'':47C ''machin'':7A ''matrix'':42C ''may'':44C,57C,63C,144C ''method'':22C ''mine'':11B ''nofollow'':94C ''number'':51C ''p'':16C,78C,98C,126C,151C ''paper'':87C ''pars'':5A,15B,24C ''poor'':32C ''problem'':149C ''read'':81C ''real'':116C ''real-system'':115C ''rel'':93C ''repli'':158C ''retriev'':14B ''row'':72C ''row-wis'':71C ''semi'':2A,26C ''semi-structur'':1A,25C ''snippet'':122C ''sometim'':66C ''structur'':3A,27C,39C ''suggest'':139C ''suit'':146C ''system'':117C ''text'':4A,10B ''text-min'':9B ''textual'':28C ''thank'':152C ''unfortun'':100C ''use'':6A ''usual'':35C ''vari'':45C ''visual'':38C ''whisk'':84C ''wise'':69C,73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4951, 1, '2015-01-26 18:20:58.203', 0, 46, 'Can distribution values of a target variable be used as features in cross-validation?', '&lt;p&gt;I came across an SVM predictive model where the author used the probabilistic distribution value of the target variable as a feature in the feature set. For example:&lt;/p&gt;

&lt;p&gt;The author built a model for each gesture of each player to guess which gesture would be played next. Calculating over 1000 games played the distribution may look like (20%, 10%, 70%). These numbers were then used as feature variables to predict the target variable for cross-fold validation.&lt;/p&gt;

&lt;p&gt;Is that legitimate? That seems like cheating. I would think you would have to exclude the target variables from your test set when calculating features in order to not "cheat".&lt;/p&gt;
', 'accuracy  methods', NULL, '''/p'':47C,99C,131C ''10'':79C ''1000'':70C ''20'':78C ''70'':80C ''accuraci'':16B ''across'':21C ''author'':28C,50C ''built'':51C ''calcul'':68C,124C ''came'':20C ''cheat'':107C,130C ''cross'':14A,96C ''cross-fold'':95C ''cross-valid'':13A ''distribut'':2A,32C,74C ''exampl'':46C ''exclud'':115C ''featur'':11A,40C,43C,87C,125C ''fold'':97C ''game'':71C ''gestur'':56C,63C ''guess'':61C ''legitim'':103C ''like'':77C,106C ''look'':76C ''may'':75C ''method'':17B ''model'':25C,53C ''next'':67C ''number'':82C ''order'':127C ''p'':18C,48C,100C ''play'':66C,72C ''player'':59C ''predict'':24C,90C ''probabilist'':31C ''seem'':105C ''set'':44C,122C ''svm'':23C ''target'':6A,36C,92C,117C ''test'':121C ''think'':110C ''use'':9A,29C,85C ''valid'':15A,98C ''valu'':3A,33C ''variabl'':7A,37C,88C,93C,118C ''would'':64C,109C,112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9421, 2, '2015-12-17 07:07:04.163', 0, NULL, NULL, '&lt;p&gt;Found the issue, a simple bug in my code. I wrote:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for index in result:
    if result[index] != 0:
        error += 1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;while it should have been something like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for index in range(N):
    if result[index] != 0:
        error += 1
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':26C,50C ''/p'':13C,36C ''/pre'':27C,51C ''0'':23C,47C ''1'':25C,49C ''bug'':7C ''code'':10C,15C,38C ''error'':24C,48C ''found'':2C ''index'':17C,22C,40C,46C ''issu'':4C ''like'':35C ''n'':43C ''p'':1C,28C ''pre'':14C,37C ''rang'':42C ''result'':19C,21C,45C ''simpl'':6C ''someth'':34C ''wrote'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4982, 2, '2015-01-29 12:48:41.62', 2, NULL, NULL, '&lt;p&gt;It really depends on what you are looking to do. I love Apache Spark, but Storm has some history. I am sure as the streaming capability in Spark is built out that it will become a competitive solution. However, until Spark has some heavy hitting users (for streaming) there will remain unknown bugs. &lt;/p&gt;

&lt;p&gt;You can also consider the community. Spark has a great community. I am not sure the level of the Storm community as I am usually the one receiving the data not handling the ingest. I can say we have used Storm on projects and I have been impressed with the real-time analysis and volumes of streaming data.&lt;/p&gt;
', NULL, NULL, '''/p'':55C,116C ''also'':59C ''analysi'':110C ''apach'':14C ''becom'':36C ''bug'':54C ''built'':31C ''capabl'':27C ''communiti'':62C,67C,77C ''competit'':38C ''consid'':60C ''data'':86C,115C ''depend'':4C ''great'':66C ''handl'':88C ''heavi'':45C ''histori'':20C ''hit'':46C ''howev'':40C ''impress'':104C ''ingest'':90C ''level'':73C ''look'':9C ''love'':13C ''one'':83C ''p'':1C,56C ''project'':99C ''real'':108C ''real-tim'':107C ''realli'':3C ''receiv'':84C ''remain'':52C ''say'':93C ''solut'':39C ''spark'':15C,29C,42C,63C ''storm'':17C,76C,97C ''stream'':26C,49C,114C ''sure'':23C,71C ''time'':109C ''unknown'':53C ''use'':96C ''user'':47C ''usual'':81C ''volum'':112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4953, 2, '2015-01-26 21:09:16.85', 0, NULL, NULL, '&lt;p&gt;I would also suggest you to try an idea of ''Anomaly detection'' using Gaussian distribution. In some cases it works really good - especially if you have a VERY skewed classes (say, among a million of examples only 10-20 are ''1'' (in class) and all the rest a 0''s).  You may look up it in this video by prof. Andrew Ng.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.youtube.com/watch?v=h5iVXB9mczo" rel="nofollow"&gt;http://www.youtube.com/watch?v=h5iVXB9mczo&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Or in text:
&lt;a href="http://www.holehouse.org/mlclass/15_Anomaly_Detection.html" rel="nofollow"&gt;http://www.holehouse.org/mlclass/15_Anomaly_Detection.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Notice, that this is not a classification problem, it is not using a classification algorithm.&lt;/p&gt;
', NULL, NULL, '''-20'':40C ''/mlclass/15_anomaly_detection.html'':84C ''/mlclass/15_anomaly_detection.html&lt;/a&gt;&lt;/p&gt;'':89C ''/p'':64C,106C ''/watch?v=h5ivxb9mczo'':70C ''/watch?v=h5ivxb9mczo&lt;/a&gt;&lt;/p&gt;'':75C ''0'':50C ''1'':42C ''10'':39C ''algorithm'':105C ''also'':4C ''among'':33C ''andrew'':62C ''anomali'':12C ''case'':19C ''class'':31C,44C ''classif'':97C,104C ''detect'':13C ''distribut'':16C ''especi'':24C ''exampl'':37C ''gaussian'':15C ''good'':23C ''href'':67C,81C ''idea'':10C ''look'':54C ''may'':53C ''million'':35C ''ng'':63C ''nofollow'':72C,86C ''notic'':91C ''p'':1C,65C,76C,90C ''problem'':98C ''prof'':61C ''realli'':22C ''rel'':71C,85C ''rest'':48C ''say'':32C ''skew'':30C ''suggest'':5C ''text'':79C ''tri'':8C ''use'':14C,102C ''video'':59C ''work'':21C ''would'':3C ''www.holehouse.org'':83C,88C ''www.holehouse.org/mlclass/15_anomaly_detection.html'':82C ''www.holehouse.org/mlclass/15_anomaly_detection.html&lt;/a&gt;&lt;/p&gt;'':87C ''www.youtube.com'':69C,74C ''www.youtube.com/watch?v=h5ivxb9mczo'':68C ''www.youtube.com/watch?v=h5ivxb9mczo&lt;/a&gt;&lt;/p&gt;'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4955, 1, '2015-01-27 10:31:10.233', 6, 410, 'How to extract features and classify alert emails coming from monitoring tools into proper category?', '&lt;p&gt;My company provides managed services to a lot of its clients. Our customers typically uses following monitoring tools to monitor their servers/webapps:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;OpsView&lt;/li&gt;
&lt;li&gt;Nagios&lt;/li&gt;
&lt;li&gt;Pingdom&lt;/li&gt;
&lt;li&gt;Custom shell scripts&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Whenever any issue is found, an alert mail comes to our Ops team so that they act upon rectifying the issue.&lt;/p&gt;

&lt;p&gt;As we manage thousands of servers, our Ops teams'' inbox is flooded with email alerts all the time. Even a single issue which has a cascading effect, can trigger 20-30 emails.&lt;/p&gt;

&lt;p&gt;Now, what I want to do is to implement a system which will be able to extract important features out of an alert email - like server IP address, type of problem, severity of problem etc. and also classify the emails into proper category, like &lt;code&gt;CPU-Load-Customer1-Server2, MySQL-Replication-Customer2-DBServer3&lt;/code&gt; etc. We will then have a pre-defined set of debugging steps for each category, in order to help the Ops team to rectify the problem faster. Also, the feature extractor will provide input data to the team for a problem.&lt;/p&gt;

&lt;p&gt;So far I have been able to train &lt;em&gt;NaiveBayesClassifier&lt;/em&gt; with supervised learning techniques i.e. labeled training data(cluster data), and able to classify new unseen emails into its proper cluster/category. As the emails are based on certain templates, the accuracy of the classifier is very high. But we also get alert emails from custom scripts, which may not follow the templates. So, instead of doing supervised learning, I want to try out unsupervised learning for the same. I am looking into &lt;em&gt;KMeans clustering&lt;/em&gt;. But again the problem is, we won''t know the number of clusters beforehand. &lt;strong&gt;So, which algorithm will be best for this use case?&lt;/strong&gt; Right now I am using Python''s TextBlob library for classification.&lt;/p&gt;

&lt;p&gt;Also, for feature extraction out of an alert email, I am looking into NLTK (&lt;a href="http://www.nltk.org/book/ch07.html" rel="nofollow"&gt;http://www.nltk.org/book/ch07.html&lt;/a&gt;) library. I tried it out, but it seems to work on proper English paragraphs/texts well, however, for alert emails, it extracted a lot of unnecessary features. &lt;strong&gt;Is there already any existing solution for the same? If not, what will be the best way to implement the same? Which library, which algorithm?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;PS: I am not a Data Scientist.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Sample emails:&lt;/strong&gt;    &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;PROBLEM: CRITICAL - Customer1_PROD - Customer1_PROD_SLAVE_DB_01 -  CPU Load Avg     Service: CPU Load Avg  Host: Customer1_PROD_SLAVE_DB_01  Alias: Customer1_PROD_SLAVE_DB_01  Address: 10.10.0.100  Host Group Hierarchy: Opsview &amp;gt; Customer1  - BIG C &amp;gt; Customer1_PROD  State: CRITICAL  Date &amp;amp; Time: Sat Oct 4 07:02:06 UTC 2014    Additional Information:     CRITICAL - load average: 41.46, 40.69, 37.91
RECOVERY: OK - Customer1_PROD - Customer1_PROD_SLAVE_DB_01 -  CPU Load Avg     Service: CPU Load Avg  Host: Customer1_PROD_SLAVE_DB_01  Alias: Customer1_PROD_SLAVE_DB_01  Address: 10.1.1.100  Host Group Hierarchy: Opsview &amp;gt; Customer1  - BIG C &amp;gt; Customer1_PROD  State: OK  Date &amp;amp; Time: Sat Oct 4 07:52:05 UTC 2014    Additional Information:     OK - load average: 0.36, 0.23, 4.83
PROBLEM: CRITICAL - Customer1_PROD - Customer1_PROD_SLAVE_DB_01 -  CPU Load Avg     Service: CPU Load Avg  Host: Customer1_PROD_SLAVE_DB_01  Alias: Customer1_PROD_SLAVE_DB_01  Address: 10.100.10.10  Host Group Hierarchy: Opsview &amp;gt; Customer1  - BIG C &amp;gt; Customer1_PROD  State: CRITICAL  Date &amp;amp; Time: Sat Oct 4 09:29:05 UTC 2014    Additional Information:     CRITICAL - load average: 29.59, 26.50, 18.49
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Classifier code:(format of csv - email, &amp;lt;disk/cpu/memory/mysql&amp;gt;)&lt;/strong&gt;    &lt;/p&gt;



&lt;pre&gt;&lt;code&gt;from textblob import TextBlob
from textblob.classifiers import NaiveBayesClassifier
import csv
train = []
with open(''cpu.txt'', ''r'') as csvfile:
    reader = csv.reader(csvfile, delimiter='','', quotechar=''"'')
    for row in reader:
        tup = unicode(row[0], "ISO-8859-1"), row[1]
        train.append(tup)
// this can be done in a loop, but for the time being let it be
with open(''memory.txt'', ''r'') as csvfile:
    reader = csv.reader(csvfile, delimiter='','', quotechar=''"'')
    for row in reader:
        tup = unicode(row[0], "ISO-8859-1"), row[1]
        train.append(tup)

with open(''disk.txt'', ''r'') as csvfile:
    reader = csv.reader(csvfile, delimiter='','', quotechar=''"'')
    for row in reader:
        tup = unicode(row[0], "ISO-8859-1"), row[1]
        train.append(tup)

with open(''mysql.txt'', ''r'') as csvfile:
    reader = csv.reader(csvfile, delimiter='','', quotechar=''"'')
    for row in reader:
        tup = unicode(row[0], "ISO-8859-1"), row[1]
        train.append(tup)

cl = NaiveBayesClassifier(train)
cl.classify(email)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Feature extractor code taken from:&lt;/strong&gt; &lt;a href="https://gist.github.com/shlomibabluki/5539628" rel="nofollow"&gt;https://gist.github.com/shlomibabluki/5539628&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Please let me know if any more information is required here.&lt;/p&gt;

&lt;p&gt;Thanks in advance.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4956, 2, '2015-01-27 11:15:04.25', 8, NULL, NULL, '&lt;p&gt;To add to a possibly never ending list:&lt;/p&gt;

&lt;p&gt;as mentioned by cyndd, there is &lt;a href="http://www.wikidata.org/wiki/Wikidata:Main_Page"&gt;Wikidata&lt;/a&gt;,&lt;/p&gt;

&lt;p&gt;and for curated structured knowledge, &lt;a href="http://www.wolframalpha.com/"&gt;Wolfram Alpha&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':24C,37C ''/p'':10C,25C,38C ''/wiki/wikidata:main_page'':22C ''add'':3C ''alpha'':36C ''curat'':29C ''cyndd'':15C ''end'':8C ''href'':19C,33C ''knowledg'':31C ''list'':9C ''mention'':13C ''never'':7C ''p'':1C,11C,26C ''possibl'':6C ''structur'':30C ''wikidata'':23C ''wolfram'':35C ''www.wikidata.org'':21C ''www.wikidata.org/wiki/wikidata:main_page'':20C ''www.wolframalpha.com'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4957, 1, '2015-01-27 15:13:09.157', 2, 1894, 'Machine learning toolkit for Excel', '&lt;p&gt;Do you know of any machine learning add-ins that I could use within Excel? For example I would like to be able to select a range of data and use that for training purposes and then use another sheet for getting the results of different learning algorithms.&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/p'':60C ''abl'':34C ''add'':19C ''add-in'':18C ''algorithm'':59C ''anoth'':50C ''could'':23C ''data'':40C ''differ'':57C ''exampl'':28C ''excel'':5A,26C ''get'':53C ''in'':20C ''know'':13C ''learn'':2A,8B,17C,58C ''like'':31C ''machin'':1A,7B,16C ''machine-learn'':6B ''neuralnetwork'':9B ''p'':10C ''purpos'':46C ''rang'':38C ''result'':55C ''select'':36C ''sheet'':51C ''toolkit'':3A ''train'':45C ''use'':24C,42C,49C ''within'':25C ''would'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4958, 2, '2015-01-27 22:01:26.237', 0, NULL, NULL, '&lt;p&gt;Recently I am working at a similar analysis. I wrote some functions to test any possible combinations between variables, however it is specifically used for my own data set which definitely is different from your one.&lt;/p&gt;

&lt;p&gt;This is a fairly small job so I can not say any package dealing with such tests. And you have already worked out some combinations. Just keep going for a ideal function, maybe will be done in a couple of days.&lt;/p&gt;

&lt;p&gt;I add a link here, which partially answers your question and code is included: &lt;a href="http://stats.stackexchange.com/questions/4040/r-compute-correlation-by-group"&gt;http://stats.stackexchange.com/questions/4040/r-compute-correlation-by-group&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/p'':38C,81C,105C ''/questions/4040/r-compute-correlation-by-group'':101C ''/questions/4040/r-compute-correlation-by-group&lt;/a&gt;'':104C ''add'':84C ''alreadi'':60C ''analysi'':9C ''answer'':90C ''code'':94C ''combin'':18C,64C ''coupl'':78C ''data'':29C ''day'':80C ''deal'':53C ''definit'':32C ''differ'':34C ''done'':75C ''fair'':43C ''function'':13C,71C ''go'':67C ''howev'':21C ''href'':98C ''ideal'':70C ''includ'':96C ''job'':45C ''keep'':66C ''link'':86C ''mayb'':72C ''one'':37C ''p'':1C,39C,82C ''packag'':52C ''partial'':89C ''possibl'':17C ''question'':92C ''recent'':2C ''say'':50C ''set'':30C ''similar'':8C ''small'':44C ''specif'':24C ''stats.stackexchange.com'':100C,103C ''stats.stackexchange.com/questions/4040/r-compute-correlation-by-group'':99C ''stats.stackexchange.com/questions/4040/r-compute-correlation-by-group&lt;/a&gt;'':102C ''test'':15C,56C ''use'':25C ''variabl'':20C ''work'':5C,61C ''wrote'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4959, 2, '2015-01-28 03:55:58.367', 3, NULL, NULL, '&lt;p&gt;The idea you have in mind is called "feature selection" or "attribute selection". The fact that you have a categorical dependent variable and continuous independent variables is mostly irrelevant because you''re expected to use an algorithm or statistical method that is suitable for your requirements.&lt;/p&gt;

&lt;p&gt;As for feature selection methods, there are several options:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Find the subset of features that achieves better performance (usually in cross validation)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Find the subset of features that correlates highly with the target variable and low with each other (although other criteria can be used)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Use an algorithm that includes a built-in feature selection mechanism (e.g. decision trees, hierarchical bayesian methods)&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Furthermore, there are several methods aimed at obtaining a good compromise between a thorough search and a reasonable time execution (e.g. best first, steepest ascent search, etc)&lt;/p&gt;

&lt;p&gt;This &lt;a href="http://stats.stackexchange.com/questions/56092/feature-selection-packages-in-r"&gt;question&lt;/a&gt; in particular provides very good suggestions for R packages. &lt;/p&gt;
', NULL, NULL, '''/a'':165C ''/li'':77C,104C,126C ''/ol'':127C ''/p'':48C,59C,76C,103C,125C,156C,175C ''/questions/56092/feature-selection-packages-in-r'':163C ''achiev'':69C ''aim'':134C ''algorithm'':38C,109C ''although'':97C ''ascent'':153C ''attribut'':13C ''bayesian'':123C ''best'':150C ''better'':70C ''built'':114C ''built-in'':113C ''call'':9C ''categor'':21C ''compromis'':139C ''continu'':25C ''correl'':86C ''criteria'':99C ''cross'':74C ''decis'':120C ''depend'':22C ''e.g'':119C,149C ''etc'':155C ''execut'':148C ''expect'':34C ''fact'':16C ''featur'':10C,52C,67C,84C,116C ''find'':63C,80C ''first'':151C ''furthermor'':129C ''good'':138C,170C ''hierarch'':122C ''high'':87C ''href'':160C ''idea'':3C ''includ'':111C ''independ'':26C ''irrelev'':30C ''li'':61C,78C,105C ''low'':93C ''mechan'':118C ''method'':41C,54C,124C,133C ''mind'':7C ''most'':29C ''obtain'':136C ''ol'':60C ''option'':58C ''p'':1C,49C,62C,79C,106C,128C,157C ''packag'':174C ''particular'':167C ''perform'':71C ''provid'':168C ''question'':164C ''r'':173C ''re'':33C ''reason'':146C ''requir'':47C ''search'':143C,154C ''select'':11C,14C,53C,117C ''sever'':57C,132C ''statist'':40C ''stats.stackexchange.com'':162C ''stats.stackexchange.com/questions/56092/feature-selection-packages-in-r'':161C ''steepest'':152C ''subset'':65C,82C ''suggest'':171C ''suitabl'':44C ''target'':90C ''thorough'':142C ''time'':147C ''tree'':121C ''use'':36C,102C,107C ''usual'':72C ''valid'':75C ''variabl'':23C,27C,91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4975, 2, '2015-01-29 00:57:39.603', 2, NULL, NULL, '&lt;p&gt;Even a simple Internet search reveals numerous papers on &lt;em&gt;graph clustering&lt;/em&gt; approaches and algorithms. &lt;a href="http://dollar.biz.uiowa.edu/~street/graphClustering.pdf" rel="nofollow"&gt;This paper&lt;/a&gt; is most likely the best starting point, as it presents a rather &lt;strong&gt;comprehensive overview&lt;/strong&gt; of the topic in terms of the problem as well as &lt;em&gt;approaches&lt;/em&gt;, &lt;em&gt;methods&lt;/em&gt; and &lt;em&gt;algorithms&lt;/em&gt; for solutions. The rest you can find easily via online search. In regard to &lt;em&gt;graph clustering visualization&lt;/em&gt;, I recommend you to check &lt;a href="http://datascience.stackexchange.com/a/814/2452"&gt;my relevant answer&lt;/a&gt; - I''m pretty sure that the tools I reference there are able to visualize graph clusters as well.&lt;/p&gt;
', NULL, NULL, '''/a'':27C,97C ''/a/814/2452'':93C ''/em'':14C,57C,60C,64C,83C ''/p'':116C ''/strong'':43C ''/~street/graphclustering.pdf'':22C ''abl'':109C ''algorithm'':17C,63C ''answer'':96C ''approach'':15C,56C ''best'':32C ''check'':88C ''cluster'':13C,81C,113C ''comprehens'':41C ''datascience.stackexchange.com'':92C ''datascience.stackexchange.com/a/814/2452'':91C ''dollar.biz.uiowa.edu'':21C ''dollar.biz.uiowa.edu/~street/graphclustering.pdf'':20C ''easili'':72C ''em'':11C,55C,58C,62C,79C ''even'':2C ''find'':71C ''graph'':12C,80C,112C ''href'':19C,90C ''internet'':5C ''like'':30C ''m'':99C ''method'':59C ''nofollow'':24C ''numer'':8C ''onlin'':74C ''overview'':42C ''p'':1C ''paper'':9C,26C ''point'':34C ''present'':37C ''pretti'':100C ''problem'':51C ''rather'':39C ''recommend'':85C ''refer'':106C ''regard'':77C ''rel'':23C ''relev'':95C ''rest'':68C ''reveal'':7C ''search'':6C,75C ''simpl'':4C ''solut'':66C ''start'':33C ''strong'':40C ''sure'':101C ''term'':48C ''tool'':104C ''topic'':46C ''via'':73C ''visual'':82C,111C ''well'':53C,115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4961, 2, '2015-01-28 04:42:19.78', 2, NULL, NULL, '&lt;p&gt;First of all, let me tell you that Excel shouldn''t be used for machine learning or any data analysis complicated enough that you wouldn''t be comfortable doing it on paper. Why? Here is a list of resources to tell you why:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="http://lemire.me/blog/archives/2014/05/23/you-shouldnt-use-a-spreadsheet-for-important-work-i-mean-it/" rel="nofollow"&gt;You shouldnât use a spreadsheet for important work (I mean it)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://randyzwitch.com/excel-destroys-data/" rel="nofollow"&gt;Destroy Your Data Using Excel With This One Weird Trick!&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://people.umass.edu/evagold/excel.html" rel="nofollow"&gt;Using Excel for Statistical Data Analysis - Caveats&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://biostat.mc.vanderbilt.edu/wiki/Main/ExcelProblems" rel="nofollow"&gt;Problems with Excel&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.burns-stat.com/documents/tutorials/spreadsheet-addiction/" rel="nofollow"&gt;Spreadsheet Addiction&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Now, if you really really want to do heavy calculations without exporting your data, I suggest using &lt;a href="http://xlwings.org/" rel="nofollow"&gt;xlwings&lt;/a&gt;. Basically, this allows two-way communication between Excel and Python. Watch the video in the homepage for a quick introduction. In this way, you would be able to use numpy, pandas and scikit-learn (or other machine learning library that you may prefer) without exporting your data first.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4962, 2, '2015-01-28 05:53:28.083', 1, NULL, NULL, '&lt;p&gt;Nobody does serious machine learning in Excel; that''s not what it''s for. Fortunately, you can directly import Excel files into better platforms like python. In particular, there''s a great package called &lt;code&gt;pandas&lt;/code&gt;, which makes work very pleasant. &lt;a href="https://www.youtube.com/watch?v=_JZFSFR6Yeo" rel="nofollow"&gt;Here''s a demo&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':55C ''/code'':38C ''/p'':56C ''/watch?v=_jzfsfr6yeo'':48C ''better'':24C ''call'':35C ''code'':36C ''demo'':54C ''direct'':19C ''excel'':8C,21C ''file'':22C ''fortun'':16C ''great'':33C ''href'':45C ''import'':20C ''learn'':6C ''like'':26C ''machin'':5C ''make'':40C ''nobodi'':2C ''nofollow'':50C ''p'':1C ''packag'':34C ''panda'':37C ''particular'':29C ''platform'':25C ''pleasant'':43C ''python'':27C ''rel'':49C ''serious'':4C ''work'':41C ''www.youtube.com'':47C ''www.youtube.com/watch?v=_jzfsfr6yeo'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4963, 1, '2015-01-28 05:57:56.09', 0, 215, 'What is advantage of using Dryad istead of Spark?', '&lt;p&gt;I found that Apache-Spark very powerful in Big-Data processing. but I want to know about Dryad (Microsoft) benefits. Is there any advantage for this framework than Spark?
Why we must use Dryad instead of Spark?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'bigdata', NULL, '''/p'':51C,54C ''advantag'':3A,37C ''apach'':16C ''apache-spark'':15C ''benefit'':33C ''big'':22C ''big-data'':21C ''bigdata'':10B ''data'':23C ''dryad'':6A,31C,47C ''found'':13C ''framework'':40C ''instead'':48C ''istead'':7A ''know'':29C ''microsoft'':32C ''must'':45C ''p'':11C,52C ''power'':19C ''process'':24C ''spark'':9A,17C,42C,50C ''thank'':53C ''use'':5A,46C ''want'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4983, 2, '2015-01-29 12:51:45.28', 0, NULL, NULL, '&lt;p&gt;I agree that there is nothing wrong with using these type of features.  I have used for inter-arrival times for example in modeling work.  I have noticed however that many of these kind of features have "interesting" covariance relationships with each other, so you have to be really careful about using multiple distribution features in a model.&lt;/p&gt;
', NULL, NULL, '''/p'':61C ''agre'':3C ''arriv'':21C ''care'':52C ''covari'':41C ''distribut'':56C ''exampl'':24C ''featur'':14C,38C,57C ''howev'':31C ''inter'':20C ''inter-arriv'':19C ''interest'':40C ''kind'':36C ''mani'':33C ''model'':26C,60C ''multipl'':55C ''noth'':7C ''notic'':30C ''p'':1C ''realli'':51C ''relationship'':42C ''time'':22C ''type'':12C ''use'':10C,17C,54C ''work'':27C ''wrong'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4965, 2, '2015-01-28 09:24:14.097', 2, NULL, NULL, '&lt;p&gt;I would suggest to consider using &lt;em&gt;latent variable modeling (LVM)&lt;/em&gt; or similar &lt;em&gt;structural equation modeling (SEM)&lt;/em&gt; as an approach to this problem. Using this approach is based on recognizing and analyzing &lt;strong&gt;latent variables&lt;/strong&gt; - constructs (&lt;em&gt;factors&lt;/em&gt;), measured not directly, but through sets of measured variables (&lt;em&gt;indicators&lt;/em&gt;). Note that a closely related term &lt;em&gt;latent feature&lt;/em&gt; is frequently used within the &lt;em&gt;machine learning&lt;/em&gt; domain. It seems to me that latent variables resemble what you call "combinations/subsets/segments of the IVs".&lt;/p&gt;

&lt;p&gt;By hypothesizing - usually, based on theory or domain knowledge - the &lt;strong&gt;latent structure&lt;/strong&gt; of factors, LVM or SEM are able to automatically confirm or decline those hypotheses. This is done by using a combination of &lt;em&gt;exploratory factor analysis (EFA)&lt;/em&gt; and &lt;em&gt;confirmatory factor analysis (CFA)&lt;/em&gt; (see my answer ). While EFA is frequently performed independently (and maybe that''s enough for your purposes), doing it along with CFA represents a large part of LVM/SEM methodology, which is usually completed by performing &lt;em&gt;path analysis&lt;/em&gt;, which is concerned about relationships between latent variables.&lt;/p&gt;

&lt;p&gt;The &lt;code&gt;R&lt;/code&gt; &lt;em&gt;ecosystem&lt;/em&gt; offers a variety of &lt;strong&gt;packages&lt;/strong&gt; for performing LVM/SEM in its entirety or for performing EFA, CFA and path analysis. The most popular ones for EFA are &lt;code&gt;psych&lt;/code&gt;, &lt;code&gt;GPArotation&lt;/code&gt; and &lt;code&gt;Hmisc&lt;/code&gt;. The most popular packages for CFA, path analysis and LVM are &lt;code&gt;sem&lt;/code&gt; (the first R package for SEM), &lt;code&gt;lavaan&lt;/code&gt;, &lt;code&gt;OpenMx&lt;/code&gt;, &lt;code&gt;semPLS&lt;/code&gt;, &lt;code&gt;plspm&lt;/code&gt;. Various supplementary SEM-focused packages are &lt;a href="http://pairach.com/2011/08/13/r-packages-for-structural-equation-model" rel="nofollow"&gt;also available&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4966, 2, '2015-01-28 10:33:38.88', 5, NULL, NULL, '&lt;p&gt;As far as I know, currently there not that many projects and products that allow to perform serious &lt;em&gt;machine learning (ML)&lt;/em&gt; work from within Excel. However, the situation seems to be changing rapidly due to active Microsoft''s efforts in popularizing its ML cloud platform &lt;em&gt;Azure ML&lt;/em&gt; (along with &lt;em&gt;ML Studio&lt;/em&gt;). The &lt;a href="http://blogs.microsoft.com/blog/2015/01/23/microsoft-acquire-revolution-analytics-help-customers-find-big-data-value-advanced-statistical-analysis"&gt;recent acquisition&lt;/a&gt; of R-focused company Revolution Analytics by Microsoft (which appears to me as more of &lt;em&gt;acqui-hiring&lt;/em&gt; to a large extent) is an example of the company''s aggressive data science &lt;strong&gt;market strategy&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;In regard to &lt;em&gt;ML toolkits for Excel&lt;/em&gt;, as a confirmation that we should expect most Excel-enabled ML projects and products to be &lt;strong&gt;Azure ML-focused&lt;/strong&gt;, consider the following two &lt;em&gt;projects&lt;/em&gt; (the latter is an open source):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Excel DataScope&lt;/strong&gt; (Microsoft Research): &lt;a href="http://research.microsoft.com/en-us/projects/exceldatascope"&gt;http://research.microsoft.com/en-us/projects/exceldatascope&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Azure ML Excel Add-In&lt;/strong&gt; (seems to be Microsoft sponsored): &lt;a href="https://azuremlexcel.codeplex.com"&gt;https://azuremlexcel.codeplex.com&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4976, 2, '2015-01-29 04:19:16.007', 3, NULL, NULL, '&lt;p&gt;Flink is the Apache renaming of the &lt;a href="http://stratosphere.eu/" rel="nofollow"&gt;Stratosphere project from several universities in Berlin&lt;/a&gt;. It doesn''t have the same industrial foothold and momentum that the Spark project has, but it seems nice, and more mature than, say, Dryad. I''d say it''s worth investigating, at least for personal or academic use, but for industrial deployment I''d still prefer Spark, which at this point is battle tested. For a more technical discussion, see &lt;a href="https://www.quora.com/Are-Spark-and-Stratosphere-competitors-Do-they-cover-the-same-set-of-use-cases" rel="nofollow"&gt;this Quora post by committers on both projects&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4977, 1, '2015-01-29 07:32:19.207', 2, 1153, 'What is the best Big-Data framework for stream processing?', '&lt;p&gt;I found that Apache-Storm, Apache-Spark, Apache-Flink and TIBCO StreamBase are some powerful frameworks for stream processing. but I don''t know which one of them has the best performance and capabilities.&lt;/p&gt;

&lt;p&gt;I know Apache-Spark and Apache-Flink are two general purpose frameworks that support stream processing. but Apache-Storm and TIBCO StreamBase are built for stream processing specially. Is there any considerable advantage between these frameworks?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'bigdata', NULL, '''/p'':51C,92C,95C ''advantag'':88C ''apach'':18C,21C,24C,56C,60C,73C ''apache-flink'':23C,59C ''apache-spark'':20C,55C ''apache-storm'':17C,72C ''best'':4A,47C ''big'':6A ''big-data'':5A ''bigdata'':12B ''built'':79C ''capabl'':50C ''consider'':87C ''data'':7A ''flink'':25C,61C ''found'':15C ''framework'':8A,32C,66C,91C ''general'':64C ''know'':40C,54C ''one'':42C ''p'':13C,52C,93C ''perform'':48C ''power'':31C ''process'':11A,35C,70C,82C ''purpos'':65C ''spark'':22C,57C ''special'':83C ''storm'':19C,74C ''stream'':10A,34C,69C,81C ''streambas'':28C,77C ''support'':68C ''thank'':94C ''tibco'':27C,76C ''two'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4967, 1, '2015-01-28 13:42:15.43', 1, 127, 'Quasi-categorical variables - any ideas?', '&lt;p&gt;Let''s say I''m trying to predict a person''s electricity consumption, using the time of day as a predictor (hours 00-23), and further assume I have a hefty but finite amount of historical measurements.&lt;/p&gt;

&lt;p&gt;Now, I''m trying to set up a linear model akin to &lt;/p&gt;

&lt;p&gt;power_used = hr_of_day * alpha_hr + temperature * beta&lt;/p&gt;

&lt;p&gt;Problem: using the hr_of_day as a numerical value is a very bad idea for many reasons, the fact that 23 and 0 are actually quite close values is one problem that can be solved with a simple transformation [1]. The fact that electrical consumption is often bi-modal is another problem which isn''t solved by a simple transformation. &lt;/p&gt;

&lt;p&gt;A possible solution that works rather well is to treat the time of day as a categorical variable. That does the trick, but it suffers from a significant drawback in that there''s no information sharing between neighbouring hours.&lt;/p&gt;

&lt;p&gt;So what I''m asking is this: does anyone know of a "soft" version of categorical values? I''m suggesting something quite loosely defined: Ideally I would have some parameter alpha that reduces the regression to numerical regression where alpha = 1 and reduces to categorical regression where alpha = 0, and behaves "in between" if it''s some other number. &lt;/p&gt;

&lt;p&gt;Right now the only answer I can think of is to alter the weights in the regression in such a way that they tend towards zero the further away the quasi-categorical value is from the desired value. Surely there are other approaches?&lt;/p&gt;

&lt;p&gt;[1] 
introduce the hour variable as two new variables: Cos(time_of_day/24) and Sin(time_of_day/24)&lt;/p&gt;
', 'regression  categorical-data', NULL, '''-23'':35C ''/p'':49C,63C,74C,138C,179C,240C,286C,306C ''0'':99C,229C ''00'':34C ''1'':116C,221C,288C ''23'':97C ''actual'':101C ''akin'':61C ''alpha'':70C,211C,220C,228C ''alter'':253C ''amount'':45C ''anoth'':128C ''answer'':246C ''anyon'':189C ''approach'':285C ''ask'':185C ''assum'':38C ''away'':270C ''bad'':89C ''behav'':231C ''beta'':73C ''bi'':125C ''bi-mod'':124C ''categor'':3A,9B,156C,196C,225C,274C ''categorical-data'':8B ''close'':103C ''consumpt'':24C,121C ''cos'':297C ''data'':10B ''day'':29C,69C,81C,153C ''day/24'':300C,305C ''defin'':204C ''desir'':279C ''drawback'':168C ''electr'':23C,120C ''fact'':95C,118C ''finit'':44C ''hefti'':42C ''histor'':47C ''hour'':33C,178C,291C ''hr'':67C,71C,79C ''idea'':6A,90C ''ideal'':205C ''inform'':174C ''introduc'':289C ''isn'':131C ''know'':190C ''let'':12C ''linear'':59C ''loos'':203C ''m'':16C,53C,184C,199C ''mani'':92C ''measur'':48C ''modal'':126C ''model'':60C ''neighbour'':177C ''new'':295C ''number'':239C ''numer'':84C,217C ''often'':123C ''one'':106C ''p'':11C,50C,64C,75C,139C,180C,241C,287C ''paramet'':210C ''person'':21C ''possibl'':141C ''power'':65C ''predict'':19C ''predictor'':32C ''problem'':76C,107C,129C ''quasi'':2A,273C ''quasi-categor'':1A,272C ''quit'':102C,202C ''rather'':145C ''reason'':93C ''reduc'':213C,223C ''regress'':7B,215C,218C,226C,258C ''right'':242C ''say'':14C ''set'':56C ''share'':175C ''signific'':167C ''simpl'':114C,136C ''sin'':302C ''soft'':193C ''solut'':142C ''solv'':111C,133C ''someth'':201C ''suffer'':164C ''suggest'':200C ''sure'':281C ''temperatur'':72C ''tend'':265C ''think'':249C ''time'':27C,151C,298C,303C ''toward'':266C ''transform'':115C,137C ''treat'':149C ''tri'':17C,54C ''trick'':161C ''two'':294C ''use'':25C,66C,77C ''valu'':85C,104C,197C,275C,280C ''variabl'':4A,157C,292C,296C ''version'':194C ''way'':262C ''weight'':255C ''well'':146C ''work'':144C ''would'':207C ''zero'':267C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4968, 1, '2015-01-28 17:40:32.45', 3, 2279, 'What are the differences between Apache Spark and Apache Flink?', '&lt;p&gt;Both Apache-Spark and Apache-Flink projects claim pretty much similar capabilities.&lt;/p&gt;

&lt;p&gt;what is the difference between these projects. Is there any advantage in either Spark or Flink?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'bigdata', NULL, '''/p'':27C,45C,48C ''advantag'':39C ''apach'':6A,9A,15C,19C ''apache-flink'':18C ''apache-spark'':14C ''bigdata'':11B ''capabl'':26C ''claim'':22C ''differ'':4A,32C ''either'':41C ''flink'':10A,20C,44C ''much'':24C ''p'':12C,28C,46C ''pretti'':23C ''project'':21C,35C ''similar'':25C ''spark'':7A,16C,42C ''thank'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4969, 2, '2015-01-28 18:42:06.763', 4, NULL, NULL, '&lt;p&gt;Current size limit for Amazon Redshift is 128 nodes or 2 PBs of compressed data. Might be circa 6PB uncompressed though mileage varies for compression. You can always let us know if you need more. anurag@aws (I run Amazon Redshift and Amazon EMR)&lt;/p&gt;
', NULL, NULL, '''/p'':46C ''128'':9C ''2'':12C ''6pb'':20C ''alway'':29C ''amazon'':6C,41C,44C ''anurag'':37C ''aw'':38C ''circa'':19C ''compress'':15C,26C ''current'':2C ''data'':16C ''emr'':45C ''know'':32C ''let'':30C ''limit'':4C ''might'':17C ''mileag'':23C ''need'':35C ''node'':10C ''p'':1C ''pbs'':13C ''redshift'':7C,42C ''run'':40C ''size'':3C ''though'':22C ''uncompress'':21C ''us'':31C ''vari'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4970, 2, '2015-01-28 19:02:15.203', 0, NULL, NULL, '&lt;p&gt;If you really want to use an Excel add-in, the XLMiner package is a solid option, although expensive:
&lt;a href="http://www.solver.com/xlminer-data-mining" rel="nofollow"&gt;http://www.solver.com/xlminer-data-mining&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/xlminer-data-mining'':26C ''/xlminer-data-mining&lt;/a&gt;&lt;/p&gt;'':31C ''add'':11C ''add-in'':10C ''although'':20C ''excel'':9C ''expens'':21C ''href'':23C ''nofollow'':28C ''option'':19C ''p'':1C ''packag'':15C ''realli'':4C ''rel'':27C ''solid'':18C ''use'':7C ''want'':5C ''www.solver.com'':25C,30C ''www.solver.com/xlminer-data-mining'':24C ''www.solver.com/xlminer-data-mining&lt;/a&gt;&lt;/p&gt;'':29C ''xlminer'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4971, 1, '2015-01-28 19:24:07.543', 4, 259, 'How to access maximum volume of tweets using Twitter Streaming API, without firehose access?', '&lt;p&gt;Twitter is a popular source of data for many applications, especially involving sentiment analysis and the like.  I have some things I''m interested in doing with Twitter data, but here''s the issue:  To get all Tweets, you have to get special permission from Twitter (which, as I understand it, is never granted) or pay big bucks to Gnip or the like.&lt;/p&gt;

&lt;p&gt;OTOH, &lt;a href="https://dev.twitter.com/streaming/firehose" rel="nofollow"&gt;Twitter''s API documentation&lt;/a&gt; says:&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Few applications require this level of access. Creative use of a combination of other resources and various access levels can satisfy nearly every application use case.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;Using the filter api with keyword tracking seems like something that would be a big part of this, but you obviously can''t enumerate every keyword.  Using a User stream on many User accounts that follow a lot of people might be an option as well, and I''m not sure if it makes sense to think about using the search API in addition.  &lt;/p&gt;

&lt;p&gt;So here''s the question "What combination of other resources and access levels is the best way to get the maximum amount of data from Twitter"?&lt;/p&gt;
', 'software-development', NULL, '''/a'':96C ''/em'':127C ''/p'':82C,98C,128C,194C,222C ''/streaming/firehose'':89C ''access'':3A,14A,107C,118C,207C ''account'':163C ''addit'':193C ''amount'':217C ''analysi'':32C ''api'':11A,94C,133C,191C ''applic'':28C,102C,124C ''best'':211C ''big'':75C,144C ''buck'':76C ''case'':126C ''combin'':112C,202C ''creativ'':108C ''data'':25C,47C,219C ''dev.twitter.com'':88C ''dev.twitter.com/streaming/firehose'':87C ''develop'':17B ''document'':95C ''em'':100C ''enumer'':153C ''especi'':29C ''everi'':123C,154C ''filter'':132C ''firehos'':13A ''follow'':165C ''get'':54C,60C,214C ''gnip'':78C ''grant'':72C ''href'':86C ''interest'':42C ''involv'':30C ''issu'':52C ''keyword'':135C,155C ''level'':105C,119C,208C ''like'':35C,81C,138C ''lot'':167C ''m'':41C,178C ''make'':183C ''mani'':27C,161C ''maximum'':4A,216C ''might'':170C ''near'':122C ''never'':71C ''nofollow'':91C ''obvious'':150C ''option'':173C ''otoh'':84C ''p'':18C,83C,99C,129C,195C ''part'':145C ''pay'':74C ''peopl'':169C ''permiss'':62C ''popular'':22C ''question'':200C ''rel'':90C ''requir'':103C ''resourc'':115C,205C ''satisfi'':121C ''say'':97C ''search'':190C ''seem'':137C ''sens'':184C ''sentiment'':31C ''softwar'':16B ''software-develop'':15B ''someth'':139C ''sourc'':23C ''special'':61C ''stream'':10A,159C ''sure'':180C ''thing'':39C ''think'':186C ''track'':136C ''tweet'':7A,56C ''twitter'':9A,19C,46C,64C,92C,221C ''understand'':68C ''use'':8A,109C,125C,130C,156C,188C ''user'':158C,162C ''various'':117C ''volum'':5A ''way'':212C ''well'':175C ''without'':12A ''would'':141C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4984, 2, '2015-01-29 12:58:28.257', 4, NULL, NULL, '&lt;p&gt;R is great for a lot of analysis. As mentioned about, there are newer adaptations for big data like MapR, RHadoop, and scalable versions of RStudio.&lt;/p&gt;

&lt;p&gt;However, if your concern is libraries, keep your eye on Spark. Spark was created for big data and is MUCH faster than Hadoop alone. It has vastly growing machine learning, SQL, streaming, and graph libraries. Thus allowing much if not all of the analysis to be done within the framework (with multiple language APIs, I prefer Scala) without having to shuffle between languages/tools.&lt;/p&gt;
', NULL, NULL, '''/p'':28C,93C ''adapt'':16C ''allow'':66C ''alon'':53C ''analysi'':9C,73C ''api'':83C ''big'':18C,45C ''concern'':33C ''creat'':43C ''data'':19C,46C ''done'':76C ''eye'':38C ''faster'':50C ''framework'':79C ''graph'':63C ''great'':4C ''grow'':57C ''hadoop'':52C ''howev'':30C ''keep'':36C ''languag'':82C ''languages/tools'':92C ''learn'':59C ''librari'':35C,64C ''like'':20C ''lot'':7C ''machin'':58C ''mapr'':21C ''mention'':11C ''much'':49C,67C ''multipl'':81C ''newer'':15C ''p'':1C,29C ''prefer'':85C ''r'':2C ''rhadoop'':22C ''rstudio'':27C ''scala'':86C ''scalabl'':24C ''shuffl'':90C ''spark'':40C,41C ''sql'':60C ''stream'':61C ''thus'':65C ''vast'':56C ''version'':25C ''within'':77C ''without'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4972, 2, '2015-01-28 21:56:23.98', 2, NULL, NULL, '&lt;p&gt;I would suggest you to use the idea of so-called ''fuzzy clustering'', where you put each of your hours of the day value into several clusters at the same time. Details in paper: &lt;a href="http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/cmeans.html" rel="nofollow"&gt;http://home.deib.polimi.it/matteucc/Clustering/tutorial_html/cmeans.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The idea is trivial:&lt;/p&gt;

&lt;p&gt;You decide how many clusters you want to have. For example, 4 (so you divide your day hours into 4 cathegories). Instead of computing just 1 number (which defines cluster membership) for each of your day hours you compute 4 numbers which represent the degree of membership to each of 4 clusters. So for example if you 4 clusters will contain periods 12 AM-6 AM, 6 AM- 12 PM, 12 PM - 6 PM and 6 PM - 12 AM then you would replace for example 4 AM hour in original data with vector of 4 numbers, first one is the biggest, second is smaller, third one is the smallest one etc.&lt;/p&gt;

&lt;p&gt;Then you could use these 4 numbers in your model to fit a regression line.&lt;/p&gt;

&lt;p&gt;Of course, if you want you could use 24 clusters and in such case each your day of hour would have a high ''relation'' with nearby hours and almost 0 with the distant hours.&lt;/p&gt;
', NULL, NULL, '''-6'':118C ''/matteucc/clustering/tutorial_html/cmeans.html'':41C ''/matteucc/clustering/tutorial_html/cmeans.html&lt;/a&gt;&lt;/p&gt;'':46C ''/p'':52C,165C,182C,218C ''0'':213C ''1'':79C ''12'':116C,122C,124C,131C ''24'':192C ''4'':65C,73C,93C,104C,111C,139C,148C,172C ''6'':120C,126C,129C ''almost'':212C ''biggest'':154C ''call'':13C ''case'':197C ''cathegori'':74C ''cluster'':15C,29C,58C,83C,105C,112C,193C ''comput'':77C,92C ''contain'':114C ''could'':169C,190C ''cours'':185C ''data'':144C ''day'':25C,70C,89C,200C ''decid'':55C ''defin'':82C ''degre'':98C ''detail'':34C ''distant'':216C ''divid'':68C ''etc'':164C ''exampl'':64C,108C,138C ''first'':150C ''fit'':178C ''fuzzi'':14C ''high'':206C ''home.deib.polimi.it'':40C,45C ''home.deib.polimi.it/matteucc/clustering/tutorial_html/cmeans.html'':39C ''home.deib.polimi.it/matteucc/clustering/tutorial_html/cmeans.html&lt;/a&gt;&lt;/p&gt;'':44C ''hour'':22C,71C,90C,141C,202C,210C,217C ''href'':38C ''idea'':9C,49C ''instead'':75C ''line'':181C ''mani'':57C ''membership'':84C,100C ''model'':176C ''nearbi'':209C ''nofollow'':43C ''number'':80C,94C,149C,173C ''one'':151C,159C,163C ''origin'':143C ''p'':1C,47C,53C,166C,183C ''paper'':36C ''period'':115C ''pm'':123C,125C,127C,130C ''put'':18C ''regress'':180C ''rel'':42C ''relat'':207C ''replac'':136C ''repres'':96C ''second'':155C ''sever'':28C ''smaller'':157C ''smallest'':162C ''so-cal'':11C ''suggest'':4C ''third'':158C ''time'':33C ''trivial'':51C ''use'':7C,170C,191C ''valu'':26C ''vector'':146C ''want'':60C,188C ''would'':3C,135C,203C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4973, 2, '2015-01-28 22:27:18.587', 9, NULL, NULL, '&lt;p&gt;Did you know about the PUMA Benchmarks and dataset downloads? 
&lt;a href="https://sites.google.com/site/farazahmad/pumadatasets"&gt;https://sites.google.com/site/farazahmad/pumadatasets&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;It does include the following:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;TeraSort&lt;/li&gt;
&lt;li&gt;Wikipedia&lt;/li&gt;
&lt;li&gt;List item&lt;/li&gt;
&lt;li&gt;Self-Join&lt;/li&gt;
&lt;li&gt;Adjacency-List&lt;/li&gt;
&lt;li&gt;Movies-database&lt;/li&gt;
&lt;li&gt;Ranked-Inverted-Index &lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':31C,34C,38C,43C,48C,53C,59C ''/ol'':60C ''/p'':20C,27C ''/site/farazahmad/pumadatasets'':16C ''/site/farazahmad/pumadatasets&lt;/a&gt;'':19C ''adjac'':46C ''adjacency-list'':45C ''benchmark'':8C ''databas'':52C ''dataset'':10C ''download'':11C ''follow'':26C ''href'':13C ''includ'':24C ''index'':58C ''invert'':57C ''item'':37C ''join'':42C ''know'':4C ''li'':29C,32C,35C,39C,44C,49C,54C ''list'':36C,47C ''movi'':51C ''movies-databas'':50C ''ol'':28C ''p'':1C,21C ''puma'':7C ''rank'':56C ''ranked-inverted-index'':55C ''self'':41C ''self-join'':40C ''sites.google.com'':15C,18C ''sites.google.com/site/farazahmad/pumadatasets'':14C ''sites.google.com/site/farazahmad/pumadatasets&lt;/a&gt;'':17C ''terasort'':30C ''wikipedia'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4974, 1, '2015-01-28 23:19:37.187', 2, 64, 'Partitioning Weighted Undirected Graph', '&lt;p&gt;I have an array of edges and weights:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[[''a'', ''b'', 4],
 [''a'', ''c'', 3],
 [''c'', ''a'', 2], 
 ...]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have about 100,000 edges and weights are between 1 and 700, most around 100.&lt;/p&gt;

&lt;p&gt;I am thinking of using Markov Cluster Algorithm however wanted to reach out to see if this is the best to use. What about Affinity Propagation? In either case, what is the workflow? Do you typically have a way to measure how well clustered the results. Is there an equivalent to a silhouette score? Is there a way to visualize the clusters?&lt;/p&gt;
', 'clustering  graphs', NULL, '''/code'':28C ''/p'':16C,47C,111C ''/pre'':29C ''000'':35C ''1'':41C ''100'':34C,46C ''2'':27C ''3'':24C ''4'':21C ''700'':43C ''affin'':73C ''algorithm'':56C ''around'':45C ''array'':11C ''b'':20C ''best'':68C ''c'':23C,25C ''case'':77C ''cluster'':5B,55C,92C,110C ''code'':18C ''edg'':13C,36C ''either'':76C ''equival'':98C ''graph'':4A,6B ''howev'':57C ''markov'':54C ''measur'':89C ''p'':7C,30C,48C ''partit'':1A ''pre'':17C ''propag'':74C ''reach'':60C ''result'':94C ''score'':102C ''see'':63C ''silhouett'':101C ''think'':51C ''typic'':84C ''undirect'':3A ''use'':53C,70C ''visual'':108C ''want'':58C ''way'':87C,106C ''weight'':2A,15C,38C ''well'':91C ''workflow'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4978, 1, '2015-01-29 09:04:56.19', 0, 42, 'Can 2 dimensional input be applied to SVM?', '&lt;p&gt;When considering Support Vector Machine, in an take in multiple inputs. Can each of these inputs be a vector??
What i am trying to say is, can the input be a 2 dimensional vector??&lt;/p&gt;
', 'machine-learning  svm', NULL, '''/p'':48C ''2'':2A,45C ''appli'':6A ''consid'':15C ''dimension'':3A,46C ''input'':4A,24C,29C,42C ''learn'':11B ''machin'':10B,18C ''machine-learn'':9B ''multipl'':23C ''p'':13C ''say'':38C ''support'':16C ''svm'':8A,12B ''take'':21C ''tri'':36C ''vector'':17C,32C,47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4999, 2, '2015-01-30 21:14:25.563', 0, NULL, NULL, '&lt;p&gt;N_dimension input - n_dimension output is a too general description. You could think of it as a regression problem where you predict multi-dimensional output. &lt;/p&gt;

&lt;p&gt;But also it could be the case that you are solving multiclass-classification problem:&lt;/p&gt;

&lt;p&gt;input: n features&lt;/p&gt;

&lt;p&gt;output: vector which defines class membership - either 0''s and 1''s or the real value which defines degree of membership to the class&lt;/p&gt;

&lt;p&gt;Or you could also think of it as of multilabel classification problem:&lt;/p&gt;

&lt;p&gt;input: n features&lt;/p&gt;

&lt;p&gt;output: vector of 0 and 1 which define which labels are associated with the input.&lt;/p&gt;

&lt;p&gt;So in general multi-dimensional output is not telling anything about the matter of task.&lt;/p&gt;

&lt;p&gt;You could try 2 approaches to solve the task which involves multi-dimensional output:&lt;/p&gt;

&lt;p&gt;1) One-vs-rest or one-vs-one strategies (or their variations) where for each ''part'' (dimension) of the output you train separate classifier or separate regressor.&lt;/p&gt;

&lt;p&gt;2) Neural network with multiple output neurons. I would suggest to try it after trying #1, neural networks are complicated, computing-expensive and maybe somewhat clumsy - so far, I wasn''t able to construct neural network which would outperform other models in specific tasks I tried to solve.  But of course, this is my personal opinion about NN. In your case they may really shine.&lt;/p&gt;
', NULL, NULL, '''/p'':29C,46C,51C,77C,91C,96C,113C,131C,148C,179C,247C ''0'':60C,101C ''1'':63C,103C,150C,196C ''2'':136C,181C ''abl'':213C ''also'':32C,82C ''anyth'':125C ''approach'':137C ''associ'':109C ''case'':37C,242C ''class'':57C,76C ''classif'':44C,89C ''classifi'':175C ''clumsi'':207C ''complic'':200C ''comput'':202C ''computing-expens'':201C ''construct'':215C ''could'':14C,34C,81C,134C ''cours'':232C ''defin'':56C,70C,105C ''degre'':71C ''descript'':12C ''dimens'':3C,6C,168C ''dimension'':27C,120C,146C ''either'':59C ''expens'':203C ''far'':209C ''featur'':50C,95C ''general'':11C,117C ''input'':4C,48C,93C,112C ''involv'':143C ''label'':107C ''matter'':128C ''may'':244C ''mayb'':205C ''membership'':58C,73C ''model'':222C ''multi'':26C,119C,145C ''multi-dimension'':25C,118C,144C ''multiclass'':43C ''multiclass-classif'':42C ''multilabel'':88C ''multipl'':185C ''n'':2C,5C,49C,94C ''network'':183C,198C,217C ''neural'':182C,197C,216C ''neuron'':187C ''nn'':239C ''one'':152C,157C,159C ''one-vs-on'':156C ''one-vs-rest'':151C ''opinion'':237C ''outperform'':220C ''output'':7C,28C,53C,98C,121C,147C,171C,186C ''p'':1C,30C,47C,52C,78C,92C,97C,114C,132C,149C,180C ''part'':167C ''person'':236C ''predict'':24C ''problem'':21C,45C,90C ''real'':67C ''realli'':245C ''regress'':20C ''regressor'':178C ''rest'':154C ''separ'':174C,177C ''shine'':246C ''solv'':41C,139C,229C ''somewhat'':206C ''specif'':224C ''strategi'':160C ''suggest'':190C ''task'':130C,141C,225C ''tell'':124C ''think'':15C,83C ''train'':173C ''tri'':135C,192C,195C,227C ''valu'':68C ''variat'':163C ''vector'':54C,99C ''vs'':153C,158C ''wasn'':211C ''would'':189C,219C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8701, 1, '2015-11-03 16:24:33.707', 1, 21, 'Making sense of blocks python package output', '&lt;p&gt;I''ve modified the &lt;code&gt;Blocks&lt;/code&gt; &lt;a href="http://blocks.readthedocs.org/en/latest/tutorial.html" rel="nofollow"&gt;tutorial&lt;/a&gt;, to train an MLP neural net with a dataset provided for an assignment in my ML course.&lt;/p&gt;

&lt;p&gt;I''d like to evaluate the accuracy of the network with varying parameterization, but I am not sure how to obtain the accuracy in first place.&lt;/p&gt;

&lt;p&gt;Inspecting the &lt;code&gt;main_loop&lt;/code&gt; object useing &lt;code&gt;dir()&lt;/code&gt; &amp;amp; &lt;code&gt;vars()&lt;/code&gt;, I''m not coming across anything other than &lt;code&gt;the test_cost_with_regularization&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;It is possible to record means of different &lt;code&gt;Theano&lt;/code&gt; variables via the monitor classes, so perhaps the answer lies there within?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Final Output&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;---------------------------------------------------------
TRAINING HAS BEEN FINISHED:
---------------------------------------------------------
Training status:
     batch_interrupt_received: False
     epoch_interrupt_received: False
     epoch_started: False
     epochs_done: 1
     iterations_done: 16
     received_first_batch: True
     resumed_from: None
     training_started: True
Log records from the iteration 16:
     test_cost_with_regularization: 2.3032071347
     training_finish_requested: True
     training_finished: True
&lt;/code&gt;&lt;/pre&gt;
', 'python  neuralnetwork  accuracy', NULL, '''/a'':27C ''/code'':18C,80C,85C,89C,104C,117C,190C ''/en/latest/tutorial.html'':23C ''/p'':45C,73C,105C,130C,136C ''/pre'':191C ''/strong'':135C ''1'':158C ''16'':161C,177C ''2.3032071347'':182C ''accuraci'':10B,53C,69C ''across'':94C ''amp'':86C ''answer'':126C ''anyth'':95C ''assign'':40C ''batch'':145C,164C ''block'':4A,17C ''blocks.readthedocs.org'':22C ''blocks.readthedocs.org/en/latest/tutorial.html'':21C ''class'':122C ''code'':16C,77C,83C,87C,98C,115C,138C ''come'':93C ''cost'':101C,179C ''cours'':44C ''d'':48C ''dataset'':36C ''differ'':114C ''dir'':84C ''done'':157C,160C ''epoch'':149C,153C,156C ''evalu'':51C ''fals'':148C,152C,155C ''final'':133C ''finish'':142C,184C,188C ''first'':71C,163C ''href'':20C ''inspect'':75C ''interrupt'':146C,150C ''iter'':159C,176C ''lie'':127C ''like'':49C ''log'':172C ''loop'':79C ''m'':91C ''main'':78C ''make'':1A ''mean'':112C ''ml'':43C ''mlp'':31C ''modifi'':14C ''monitor'':121C ''net'':33C ''network'':56C ''neural'':32C ''neuralnetwork'':9B ''nofollow'':25C ''none'':168C ''object'':81C ''obtain'':67C ''output'':7A,134C ''p'':11C,46C,74C,106C,131C ''packag'':6A ''parameter'':59C ''perhap'':124C ''place'':72C ''possibl'':109C ''pre'':137C ''provid'':37C ''python'':5A,8B ''receiv'':147C,151C,162C ''record'':111C,173C ''regular'':103C,181C ''rel'':24C ''request'':185C ''resum'':166C ''sens'':2A ''start'':154C,170C ''status'':144C ''strong'':132C ''sure'':64C ''test'':100C,178C ''theano'':116C ''train'':29C,139C,143C,169C,183C,187C ''true'':165C,171C,186C,189C ''tutori'':26C ''use'':82C ''var'':88C ''vari'':58C ''variabl'':118C ''ve'':13C ''via'':119C ''within'':129C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4979, 1, '2015-01-29 11:35:07.657', 0, 44, 'Matlab simulation through FIS and Markov Process', '&lt;p&gt;I need to simulate for an academical project how the traffic fluxes (input/output with respect to a monitored area, measured in number of cars) of a city area evolves in correspondence of an event (i.e. the opening of a restricted traffic area to decongest the traffic).
I have some simulated sensors that provide the data: I was thinking to use a combination of a fuzzy system (to assign a membership function to each type of data, e.g. PM10 value and CO2 value) and a markov process: I would need to modify the probability to decrement the number of car in the monitored area (simulating that a car is going out the congested area, towards the new opened area) basing on decisions made by means of a fuzzy system.
So my questions are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;It is a good way to interpret the problem or there are better ideas that I have not taken into account yet?&lt;/li&gt;
&lt;li&gt;How to implement such a combination of markov chain and fuzzy systems in matlab?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'markov-process  matlab  simulation', NULL, '''/li'':171C,187C ''/p'':146C,191C ''/ul'':188C ''academ'':20C ''account'':169C ''area'':32C,41C,55C,116C,126C,131C ''assign'':81C ''base'':132C ''better'':161C ''car'':37C,112C,120C ''chain'':181C ''citi'':40C ''co2'':94C ''combin'':75C,178C ''congest'':125C ''correspond'':44C ''data'':68C,89C ''decis'':134C ''decongest'':57C ''decrement'':108C ''e.g'':90C ''event'':47C ''evolv'':42C ''fis'':4A ''flux'':25C ''function'':84C ''fuzzi'':78C,140C,183C ''go'':122C ''good'':152C ''i.e'':48C ''idea'':162C ''implement'':175C ''input/output'':26C ''interpret'':155C ''li'':148C,172C ''made'':135C ''markov'':6A,9B,98C,180C ''markov-process'':8B ''matlab'':1A,11B,186C ''mean'':137C ''measur'':33C ''membership'':83C ''modifi'':104C ''monitor'':31C,115C ''need'':15C,102C ''new'':129C ''number'':35C,110C ''open'':50C,130C ''p'':13C,189C ''pm10'':91C ''probabl'':106C ''problem'':157C ''process'':7A,10B,99C ''project'':21C ''provid'':66C ''question'':144C ''respect'':28C ''restrict'':53C ''sensor'':64C ''simul'':2A,12B,17C,63C,117C ''system'':79C,141C,184C ''taken'':167C ''thank'':190C ''think'':71C ''toward'':127C ''traffic'':24C,54C,59C ''type'':87C ''ul'':147C ''use'':73C ''valu'':92C,95C ''way'':153C ''would'':101C ''yet'':170C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4980, 1, '2015-01-29 12:13:01.677', 6, 565, 'Help regarding NER in NLTK', '&lt;p&gt;I have been working in NLTK for a while using Python. The problem I am facing is that their is no help available on training NER in NLTK with my custom data. They have used MaxEnt and trained it on ACE corpus. I have searched on the web a lot but I could not find any way that can be used to train NLTK''s NER. &lt;/p&gt;

&lt;p&gt;If anyone can provide me with any link/article/blog etc which can direct me to Training Datasets Format used in training NLTK''s NER so I can prepare my Datasets on that particular format. And if I am directed to any link/article/blog etc which can help me TRAIN NLTK''s NER for my own data.&lt;/p&gt;

&lt;p&gt;This is a question widely searched and least answered. Might be helpful for someone in the future whose working with NER.&lt;/p&gt;
', 'machine-learning  python  nlp', NULL, '''/p'':78C,134C,157C ''ace'':52C ''answer'':144C ''anyon'':81C ''avail'':34C ''corpus'':53C ''could'':64C ''custom'':42C ''data'':43C,133C ''dataset'':95C,108C ''direct'':91C,117C ''etc'':88C,121C ''face'':27C ''find'':66C ''format'':96C,112C ''futur'':152C ''help'':1A,33C,124C,147C ''learn'':8B ''least'':143C ''link/article/blog'':87C,120C ''lot'':61C ''machin'':7B ''machine-learn'':6B ''maxent'':47C ''might'':145C ''ner'':3A,37C,77C,102C,129C,156C ''nlp'':10B ''nltk'':5A,17C,39C,75C,100C,127C ''p'':11C,79C,135C ''particular'':111C ''prepar'':106C ''problem'':24C ''provid'':83C ''python'':9B,22C ''question'':139C ''regard'':2A ''search'':56C,141C ''someon'':149C ''train'':36C,49C,74C,94C,99C,126C ''use'':21C,46C,72C,97C ''way'':68C ''web'':59C ''whose'':153C ''wide'':140C ''work'':15C,154C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8335, 2, '2015-10-06 21:02:32.477', 4, NULL, NULL, '&lt;p&gt;They will of course still learn some best decision boundary. We know it will be meaningless, but there will still be better and best coefficients for the algorithm to learn when fitting to this particular instance of data from this random process. It may produce better than 50% accuracy on the data set, but of course this is purely due to overfitting whatever the data happens to be. It will not predict future outcomes with more than 50% accuracy.&lt;/p&gt;
', NULL, NULL, '''/p'':81C ''50'':49C,79C ''accuraci'':50C,80C ''algorithm'':29C ''best'':9C,25C ''better'':23C,47C ''boundari'':11C ''coeffici'':26C ''cours'':5C,57C ''data'':39C,53C,66C ''decis'':10C ''due'':61C ''fit'':33C ''futur'':74C ''happen'':67C ''instanc'':37C ''know'':13C ''learn'':7C,31C ''may'':45C ''meaningless'':17C ''outcom'':75C ''overfit'':63C ''p'':1C ''particular'':36C ''predict'':73C ''process'':43C ''produc'':46C ''pure'':60C ''random'':42C ''set'':54C ''still'':6C,21C ''whatev'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4985, 1, '2015-01-29 13:54:24.94', 3, 236, 'Pre-processing (center, scale, impute) among training sets (different forms) and the test set - what is a good approach?', '&lt;p&gt;I am currently working on a multi-class classification problem with a large training set. However, it has some specific characteristics, which induced me to experiment with it, resulting in few versions of the training set (as a result of re-sampling, removing observations, etc).&lt;/p&gt;

&lt;p&gt;I want to perform pre-processing of the data, that is to scale, center and impute (not much imputation though) values. This is the point where I''ve started to get confused.&lt;/p&gt;

&lt;p&gt;I''ve been taught that you should always pre-process the test set in the same way you''ve pre-processed the training set, that is (for scaling and centering) to measure the mean and standard deviation on the training set and apply those values to the test set. This seems reasonably to me. &lt;/p&gt;

&lt;p&gt;But what to do in case when you have shrinked/resampled training set? Should one focus on characteristics of the data that is actually feeding the model (that is what would ''train'' function in R''s caret package suggest, as you can put the pre-processing object in there directly) and apply these to the test set, or maybe one should capture the real characteristics of the data (from the whole untouched training set) and apply these? If the second option is better, maybe it would be worth it to capture the characteristics of the data by merging the training and test data together just for pre-processing step to get as accurate estimates as possible (I''ve actually never heard of anyone doing that though)?&lt;/p&gt;

&lt;p&gt;I know I can simply test some of the approaches specified here, and I surely will, but are there any suggestions based on theory or your intuition/experience on how to tackle this problem?&lt;/p&gt;

&lt;p&gt;I also have one additional and optional question. Does it make sense to center but NOT scale the data (or the other way around) in any case? Can anyone present any example where that approach would be reasonable?&lt;/p&gt;

&lt;p&gt;Thank you very much in advance.&lt;/p&gt;
', 'machine-learning  data-mining  dataset  processing  feature-scaling', NULL, '''/p'':80C,115C,173C,302C,337C,377C,385C ''accur'':288C ''actual'':197C,294C ''addit'':343C ''advanc'':384C ''also'':340C ''alway'':124C ''among'':7A ''anyon'':298C,367C ''appli'':161C,226C,250C ''approach'':20A,313C,373C ''around'':362C ''base'':325C ''better'':257C ''captur'':236C,265C ''caret'':210C ''case'':180C,365C ''center'':4A,96C,148C,352C ''characterist'':54C,191C,239C,267C ''class'':41C ''classif'':42C ''confus'':114C ''current'':35C ''data'':25B,91C,194C,242C,270C,277C,357C ''data-min'':24B ''dataset'':27B ''deviat'':155C ''differ'':10A ''direct'':224C ''estim'':289C ''etc'':79C ''exampl'':370C ''experi'':59C ''featur'':30B ''feature-sc'':29B ''feed'':198C ''focus'':189C ''form'':11A ''function'':206C ''get'':113C,286C ''good'':19A ''heard'':296C ''howev'':49C ''imput'':6A,98C,101C ''induc'':56C ''intuition/experience'':330C ''know'':305C ''larg'':46C ''learn'':23B ''machin'':22B ''machine-learn'':21B ''make'':349C ''mayb'':233C,258C ''mean'':152C ''measur'':150C ''merg'':272C ''mine'':26B ''model'':200C ''much'':100C,382C ''multi'':40C ''multi-class'':39C ''never'':295C ''object'':221C ''observ'':78C ''one'':188C,234C,342C ''option'':255C,345C ''p'':32C,81C,116C,174C,303C,338C,378C ''packag'':211C ''perform'':85C ''point'':107C ''possibl'':291C ''pre'':2A,87C,126C,138C,219C,282C ''pre-process'':1A,86C,125C,137C,218C,281C ''present'':368C ''problem'':43C,336C ''process'':3A,28B,88C,127C,139C,220C,283C ''put'':216C ''question'':346C ''r'':208C ''re'':75C ''re-sampl'':74C ''real'':238C ''reason'':170C,376C ''remov'':77C ''result'':62C,72C ''sampl'':76C ''scale'':5A,31B,95C,146C,355C ''second'':254C ''seem'':169C ''sens'':350C ''set'':9A,15A,48C,69C,130C,142C,159C,167C,186C,231C,248C ''shrinked/resampled'':184C ''simpli'':308C ''specif'':53C ''specifi'':314C ''standard'':154C ''start'':111C ''step'':284C ''suggest'':212C,324C ''sure'':318C ''tackl'':334C ''taught'':120C ''test'':14A,129C,166C,230C,276C,309C ''thank'':379C ''theori'':327C ''though'':102C,301C ''togeth'':278C ''train'':8A,47C,68C,141C,158C,185C,205C,247C,274C ''untouch'':246C ''valu'':103C,163C ''ve'':110C,118C,136C,293C ''version'':65C ''want'':83C ''way'':134C,361C ''whole'':245C ''work'':36C ''worth'':262C ''would'':204C,260C,374C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4986, 1, '2015-01-29 17:21:07.07', 2, 275, 'Normalize weekly data - Python', '&lt;p&gt;I have a weekly dataset and I have to normalize this data. 
Data is something like this : &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1. week   50
2. week   51
3. week   50
4. week   54
5. week   150
6. week   155
7. week   ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The important thing is, the difference between week 3 and week 4 (50-54) is not same with week 5 and week 6. And also there is a huge different between week 4 and week 5. &lt;/p&gt;

&lt;p&gt;My question is how can i handle all of this things ?&lt;/p&gt;

&lt;p&gt;Is the standard normalization functions(for example scikit normalization) can do it for me and should I normalize this data 0-1 or -1 to 1 ? &lt;/p&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html" rel="nofollow"&gt;Sklearn normalization page&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;NOTE&lt;/strong&gt; I am working with python and generally scikit-learn library.&lt;/p&gt;

&lt;p&gt;Any help is appreciated.&lt;/p&gt;
', 'python  sklearn  scikit', NULL, '''-1'':124C,126C ''-54'':65C ''/a'':141C ''/code'':49C ''/p'':26C,88C,101C,129C,142C,158C,164C ''/pre'':50C ''/stable/modules/generated/sklearn.preprocessing.normalize.html'':135C ''/strong'':146C ''0'':123C ''1'':29C,128C ''150'':43C ''155'':46C ''2'':32C ''3'':35C,60C ''4'':38C,63C,84C ''5'':41C,71C,87C ''50'':31C,37C,64C ''51'':34C ''54'':40C ''6'':44C,74C ''7'':47C ''also'':76C ''appreci'':163C ''code'':28C ''data'':3A,20C,21C,122C ''dataset'':13C ''differ'':57C,81C ''exampl'':109C ''function'':107C ''general'':153C ''handl'':96C ''help'':161C ''href'':132C ''huge'':80C ''import'':53C ''learn'':156C ''librari'':157C ''like'':24C ''nofollow'':137C ''normal'':1A,18C,106C,111C,120C,139C ''note'':145C ''p'':8C,51C,89C,102C,130C,143C,159C ''page'':140C ''pre'':27C ''python'':4A,5B,151C ''question'':91C ''rel'':136C ''scikit'':7B,110C,155C ''scikit-learn'':154C ''scikit-learn.org'':134C ''scikit-learn.org/stable/modules/generated/sklearn.preprocessing.normalize.html'':133C ''sklearn'':6B,138C ''someth'':23C ''standard'':105C ''strong'':144C ''thing'':54C,100C ''week'':2A,12C,30C,33C,36C,39C,42C,45C,48C,59C,62C,70C,73C,83C,86C ''work'':149C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4988, 2, '2015-01-29 20:36:25.567', 3, NULL, NULL, '&lt;p&gt;This looks a well structured dataset. You can read more about database design in &lt;a href="http://en.wikipedia.org/wiki/Database_design#Normalization" rel="nofollow"&gt;this section of wikipedia&lt;/a&gt;. Your data are well structured so querying is easy. As Jake C says, you''ll want to transform it for specific tasks. Packages like dplr and reshape2 are excellent for this. You could also consider writing your data to a specific database. This is particularly useful if your dataset is so large that R runs out of RAM. I''ve written an example with SQLite here: &lt;a href="https://scottishsnow.wordpress.com/2014/08/14/writing-to-a-database-r-and-sqlite/" rel="nofollow"&gt;https://scottishsnow.wordpress.com/2014/08/14/writing-to-a-database-r-and-sqlite/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/2014/08/14/writing-to-a-database-r-and-sqlite/'':98C ''/2014/08/14/writing-to-a-database-r-and-sqlite/&lt;/a&gt;&lt;/p&gt;'':103C ''/a'':27C ''/wiki/database_design#normalization'':20C ''also'':61C ''c'':39C ''consid'':62C ''could'':60C ''data'':29C,65C ''databas'':13C,69C ''dataset'':7C,76C ''design'':14C ''dplr'':52C ''easi'':36C ''en.wikipedia.org'':19C ''en.wikipedia.org/wiki/database_design#normalization'':18C ''exampl'':90C ''excel'':56C ''href'':17C,95C ''jake'':38C ''larg'':79C ''like'':51C ''ll'':42C ''look'':3C ''nofollow'':22C,100C ''p'':1C ''packag'':50C ''particular'':72C ''queri'':34C ''r'':81C ''ram'':85C ''read'':10C ''rel'':21C,99C ''reshape2'':54C ''run'':82C ''say'':40C ''scottishsnow.wordpress.com'':97C,102C ''scottishsnow.wordpress.com/2014/08/14/writing-to-a-database-r-and-sqlite/'':96C ''scottishsnow.wordpress.com/2014/08/14/writing-to-a-database-r-and-sqlite/&lt;/a&gt;&lt;/p&gt;'':101C ''section'':24C ''specif'':48C,68C ''sqlite'':92C ''structur'':6C,32C ''task'':49C ''transform'':45C ''use'':73C ''ve'':87C ''want'':43C ''well'':5C,31C ''wikipedia'':26C ''write'':63C ''written'':88C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4989, 2, '2015-01-29 21:03:27.097', 1, NULL, NULL, '&lt;p&gt;As other answers have noted, R can be used along with Hadoop and other distributed computing platforms to scale it up to the "Big Data" level.  However, if you''re not wedded to R specifically, but are willing to use an "R-like" environment, &lt;a href="http://www.incanter.org" rel="nofollow"&gt;Incanter&lt;/a&gt; is a project that might work well for you, as it is native to the JVM (based on Clojure) and doesn''t have the "impedance mismatch" between itself and Hadop that R has.  That is to say, from Incanter, you can invoke Java native Hadoop / HDFS APIs without needing to go through a JNI bridge or anything.&lt;/p&gt;
', NULL, NULL, '''/a'':53C ''/p'':111C ''along'':11C ''answer'':4C ''anyth'':110C ''api'':100C ''base'':70C ''big'':25C ''bridg'':108C ''clojur'':72C ''comput'':17C ''data'':26C ''distribut'':16C ''doesn'':74C ''environ'':46C ''go'':104C ''hadoop'':13C,98C ''hadop'':83C ''hdfs'':99C ''howev'':28C ''href'':48C ''imped'':78C ''incant'':52C,92C ''invok'':95C ''java'':96C ''jni'':107C ''jvm'':69C ''level'':27C ''like'':45C ''might'':58C ''mismatch'':79C ''nativ'':66C,97C ''need'':102C ''nofollow'':51C ''note'':6C ''p'':1C ''platform'':18C ''project'':56C ''r'':7C,35C,44C,85C ''r-like'':43C ''re'':31C ''rel'':50C ''say'':90C ''scale'':20C ''specif'':36C ''use'':10C,41C ''wed'':33C ''well'':60C ''will'':39C ''without'':101C ''work'':59C ''www.incanter.org'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4990, 2, '2015-01-29 21:31:50.28', 0, NULL, NULL, '&lt;p&gt;As bogatron and Paul already said, there is nothing wrong with using the prediction from one classifier as a feature in another classifier. Actually, so-called "Cascading classifiers" work that way. From &lt;a href="http://en.wikipedia.org/wiki/Cascading_classifiers" rel="nofollow"&gt;Wikipedia&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Cascading is a particular case of ensemble learning based on the concatenation of several classifiers, using all information collected from the output from a given classifier as additional information for the next classifier in the cascade.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This can be helpful not only to inform posterior classifiers using new features but also as an optimization measure. In the Viola-Jones object detection framework, a set of weak classifiers is used sequentially in order to reduce the amount of computation in the object recognition task. If one of the weak classifiers fails to recognize an object of interest, others classifiers don''t need to be computed.&lt;/p&gt;
', NULL, NULL, '''/a'':43C ''/blockquote'':84C ''/p'':44C,83C,155C ''/wiki/cascading_classifiers'':39C ''actual'':25C ''addit'':74C ''alreadi'':6C ''also'':100C ''amount'':126C ''anoth'':23C ''base'':55C ''blockquot'':45C ''bogatron'':3C ''call'':28C ''cascad'':29C,47C,82C ''case'':51C ''classifi'':18C,24C,30C,61C,72C,79C,95C,117C,139C,148C ''collect'':65C ''comput'':128C,154C ''concaten'':58C ''detect'':111C ''en.wikipedia.org'':38C ''en.wikipedia.org/wiki/cascading_classifiers'':37C ''ensembl'':53C ''fail'':140C ''featur'':21C,98C ''framework'':112C ''given'':71C ''help'':89C ''href'':36C ''inform'':64C,75C,93C ''interest'':146C ''jone'':109C ''learn'':54C ''measur'':104C ''need'':151C ''new'':97C ''next'':78C ''nofollow'':41C ''noth'':10C ''object'':110C,131C,144C ''one'':17C,135C ''optim'':103C ''order'':122C ''other'':147C ''output'':68C ''p'':1C,46C,85C ''particular'':50C ''paul'':5C ''posterior'':94C ''predict'':15C ''recogn'':142C ''recognit'':132C ''reduc'':124C ''rel'':40C ''said'':7C ''sequenti'':120C ''set'':114C ''sever'':60C ''so-cal'':26C ''task'':133C ''use'':13C,62C,96C,119C ''viola'':108C ''viola-jon'':107C ''way'':33C ''weak'':116C,138C ''wikipedia'':42C ''work'':31C ''wrong'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4992, 1, '2015-01-30 11:08:37.28', 6, 88, 'Amplifying a Locality Sensitive Hash', '&lt;p&gt;I''m trying to build a cosine locality sensitive hash so I can find candidate similar pairs of items without having to compare every possible pair. I have it basically working, but most of the pairs in my data seem to have cosine similarity in the -0.2 to +0.2 range so I''m trying to dice it quite finely and pick things with cosine similarity 0.1 and above.&lt;/p&gt;

&lt;p&gt;I''ve been reading Mining Massive Datasets chapter 3. This talks about increasing the accuracy of candidate pair selection by Amplifying a Locality-Sensitive Family. I think I just about understand the mathematical explanation, but I''m struggling to see how I implement this practically.&lt;/p&gt;

&lt;p&gt;What I have so far is as follows&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;I have say 1000 movies each with ratings from some selection of 1M users. Each movie is represented by a sparse vector of user scores (row number = user ID, value = user''s score)&lt;/li&gt;
&lt;li&gt;I build N random vectors. The vector length matches the length of the movie vectors (i.e. the number of users). The vector values are +1 or -1. I actually encode these vectors as binary to save space, with +1 mapped to 1 and -1 mapped to 0&lt;/li&gt;
&lt;li&gt;I build sketch vectors for each movie by taking the dot product of the movie and each of the N random vectors (or rather, if I create a matrix R by laying the N random vectors horizontally and layering them on top of each other then the sketch for movie m is R*m), then taking the sign of each element in the resulting vector, so I end with a sketch vector for each movie of +1s and -1s, which again I encode as binary. Each vector is length N bits.&lt;/li&gt;
&lt;li&gt;Next I look for similar sketches by doing the following
&lt;ol&gt;
&lt;li&gt;I split the sketch vector into b bands of r bits&lt;/li&gt;
&lt;li&gt;Each band of r bits is a number. I combine that number with the band number and add the movie to a hash bucket under that number. Each movie can be added to more than one bucket.&lt;/li&gt;
&lt;li&gt;I then look in each bucket. Any movies that are in the same bucket are candidate pairs.&lt;/li&gt;
&lt;/ol&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Comparing this to 3.6.3 of mmds, my AND step is when I look at bands of r bits - a pair of movies pass the AND step if the r bits have the same value. My OR step happens in the buckets: movies are candidate pairs if they are both in any of the buckets.&lt;/p&gt;

&lt;p&gt;The book suggests I can "amplify" my results by adding more AND and OR steps, but I''m at a loss for how to do this practically as the explanation of the construction process for further layers is in terms of checking pairwise equality rather than coming up with bucket numbers.&lt;/p&gt;

&lt;p&gt;Can anyone help me understand how to do this?&lt;/p&gt;
', 'machine-learning', NULL, '''+0.2'':58C ''+1'':198C,212C,299C ''-0.2'':56C ''-1'':200C,217C,302C ''/li'':172C,221C,316C,341C,380C,399C,401C ''/ol'':400C,402C ''/p'':78C,126C,136C,458C,511C,522C ''0'':220C ''0.1'':75C ''1'':215C ''1000'':142C ''1m'':151C ''3'':88C ''3.6.3'':407C ''accuraci'':94C ''actual'':202C ''ad'':374C,469C ''add'':360C ''amplifi'':1A,100C,465C ''anyon'':514C ''b'':336C ''band'':337C,344C,357C,418C ''basic'':39C ''binari'':207C,309C ''bit'':315C,340C,347C,421C,433C ''book'':461C ''bucket'':366C,379C,387C,395C,444C,457C,509C ''build'':14C,175C,224C ''candid'':24C,96C,397C,447C ''chapter'':87C ''check'':501C ''combin'':352C ''come'':506C ''compar'':32C,404C ''construct'':492C ''cosin'':16C,52C,73C ''creat'':249C ''data'':48C ''dataset'':86C ''dice'':65C ''dot'':233C ''element'':283C ''encod'':203C,307C ''end'':290C ''equal'':503C ''everi'':33C ''explan'':114C,489C ''famili'':105C ''far'':132C ''find'':23C ''fine'':68C ''follow'':135C,327C ''happen'':441C ''hash'':5A,19C,365C ''help'':515C ''horizont'':259C ''i.e'':189C ''id'':167C ''implement'':123C ''increas'':92C ''item'':28C ''lay'':254C ''layer'':261C,496C ''learn'':8B ''length'':181C,184C,313C ''li'':138C,173C,222C,317C,329C,342C,381C ''local'':3A,17C,103C ''locality-sensit'':102C ''look'':320C,384C,416C ''loss'':480C ''m'':11C,62C,117C,273C,276C,477C ''machin'':7B ''machine-learn'':6B ''map'':213C,218C ''massiv'':85C ''match'':182C ''mathemat'':113C ''matrix'':251C ''mine'':84C ''mmds'':409C ''movi'':143C,154C,187C,229C,237C,272C,297C,362C,371C,389C,425C,445C ''n'':176C,242C,256C,314C ''next'':318C ''number'':165C,191C,350C,354C,358C,369C,510C ''ol'':137C,328C ''one'':378C ''p'':9C,79C,127C,403C,459C,512C ''pair'':26C,35C,45C,97C,398C,423C,448C ''pairwis'':502C ''pass'':426C ''pick'':70C ''possibl'':34C ''practic'':125C,486C ''process'':493C ''product'':234C ''quit'':67C ''r'':252C,275C,339C,346C,420C,432C ''random'':177C,243C,257C ''rang'':59C ''rate'':146C ''rather'':246C,504C ''read'':83C ''repres'':156C ''result'':286C,467C ''row'':164C ''save'':209C ''say'':141C ''score'':163C,171C ''see'':120C ''seem'':49C ''select'':98C,149C ''sensit'':4A,18C,104C ''sign'':280C ''similar'':25C,53C,74C,322C ''sketch'':225C,270C,293C,323C,333C ''space'':210C ''spars'':159C ''split'':331C ''step'':412C,429C,440C,474C ''struggl'':118C ''suggest'':462C ''take'':231C,278C ''talk'':90C ''term'':499C ''thing'':71C ''think'':107C ''top'':264C ''tri'':12C,63C ''understand'':111C,517C ''user'':152C,162C,166C,169C,193C ''valu'':168C,196C,437C ''ve'':81C ''vector'':160C,178C,180C,188C,195C,205C,226C,244C,258C,287C,294C,311C,334C ''without'':29C ''work'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5023, 1, '2015-02-03 08:52:42.133', 1, 222, 'What is a discrimination threshold of binary classifier?', '&lt;p&gt;With respect to &lt;a href="http://en.wikipedia.org/wiki/Receiver_operating_characteristic" rel="nofollow"&gt;ROC&lt;/a&gt; can anyone please tell me what the phrase "discrimination threshold of binary classifier system" means? I know what a binary classifier is.&lt;/p&gt;
', 'classification  graphs', NULL, '''/a'':23C ''/p'':46C ''/wiki/receiver_operating_characteristic'':19C ''anyon'':25C ''binari'':7A,35C,43C ''classif'':9B ''classifi'':8A,36C,44C ''discrimin'':4A,32C ''en.wikipedia.org'':18C ''en.wikipedia.org/wiki/receiver_operating_characteristic'':17C ''graph'':10B ''href'':16C ''know'':40C ''mean'':38C ''nofollow'':21C ''p'':11C ''phrase'':31C ''pleas'':26C ''rel'':20C ''respect'':13C ''roc'':22C ''system'':37C ''tell'':27C ''threshold'':5A,33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4993, 2, '2015-01-30 12:11:41.293', 0, NULL, NULL, '&lt;p&gt;After speaking with some experienced statisticians, this is what I got.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;As for technical issues regarding the paper, I''d be worried about data leakage or using future information in the current model. This can also occur in cross validation. You should make sure each model trains only on past data, and predicts on future data. I wasn''t sure exactly how they conducted CV, but it definitely matters. It''s also non-trivial to prevent all sources of leakage. They do claim unseen examples but it''s not explicit exactly what code they wrote here. I''m not saying they are leaking for sure, but I''m saying it could happen.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/blockquote'':119C ''/p'':13C,118C ''also'':40C,76C ''blockquot'':14C ''claim'':88C ''code'':98C ''conduct'':68C ''could'':116C ''cross'':43C ''current'':36C ''cv'':69C ''d'':24C ''data'':28C,55C,60C ''definit'':72C ''exact'':65C,96C ''exampl'':90C ''experienc'':6C ''explicit'':95C ''futur'':32C,59C ''got'':12C ''happen'':117C ''inform'':33C ''issu'':19C ''leak'':108C ''leakag'':29C,85C ''m'':103C,113C ''make'':47C ''matter'':73C ''model'':37C,50C ''non'':78C ''non-trivi'':77C ''occur'':41C ''p'':1C,15C ''paper'':22C ''past'':54C ''predict'':57C ''prevent'':81C ''regard'':20C ''say'':105C,114C ''sourc'':83C ''speak'':3C ''statistician'':7C ''sure'':48C,64C,110C ''technic'':18C ''train'':51C ''trivial'':79C ''unseen'':89C ''use'':31C ''valid'':44C ''wasn'':62C ''worri'':26C ''wrote'':100C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4994, 2, '2015-01-30 16:33:23.683', 0, NULL, NULL, '&lt;p&gt;I would call a mapping between N dimensional input and N dimensional output a regression problem. &lt;/p&gt;

&lt;p&gt;If you add more constraints about the relation between the input and output it might be called different names: linear filtering, nonlinear filtering, etc...&lt;/p&gt;

&lt;p&gt;some examples on common techniques for that would be: neural networks, regression trees, regularised regressions...&lt;/p&gt;
', NULL, NULL, '''/p'':18C,44C,61C ''add'':22C ''call'':4C,36C ''common'':49C ''constraint'':24C ''differ'':37C ''dimension'':9C,13C ''etc'':43C ''exampl'':47C ''filter'':40C,42C ''input'':10C,30C ''linear'':39C ''map'':6C ''might'':34C ''n'':8C,12C ''name'':38C ''network'':56C ''neural'':55C ''nonlinear'':41C ''output'':14C,32C ''p'':1C,19C,45C ''problem'':17C ''regress'':16C,57C,60C ''regularis'':59C ''relat'':27C ''techniqu'':50C ''tree'':58C ''would'':3C,53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4995, 1, '2015-01-30 18:55:46.173', 3, 1722, 'Can we access HDFS file system and YARN scheduler in Apache Spark?', '&lt;p&gt;We can access HDFS file system and YARN scheduler In the Apache-Hadoop. But Spark has a higher level of coding. Is it possible to access HDFS and YARN in Apache-Spark too?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'bigdata  hadoop', NULL, '''/p'':51C,54C ''access'':3A,18C,42C ''apach'':11A,28C,48C ''apache-hadoop'':27C ''apache-spark'':47C ''bigdata'':13B ''code'':37C ''file'':5A,20C ''hadoop'':14B,29C ''hdfs'':4A,19C,43C ''higher'':34C ''level'':35C ''p'':15C,52C ''possibl'':40C ''schedul'':9A,24C ''spark'':12A,31C,49C ''system'':6A,21C ''thank'':53C ''yarn'':8A,23C,45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4996, 2, '2015-01-30 20:15:09.003', 3, NULL, NULL, '&lt;p&gt;Apache Storm and Apache Spark are more popular than the other ones, there are already many discussions on Quora(&lt;a href="http://www.quora.com/What-is-the-difference-between-Apache-Storm-and-Apache-Spark" rel="nofollow"&gt;Storm vs Spark&lt;/a&gt;, &lt;a href="http://www.quora.com/Are-there-any-use-cases-for-a-comparison-between-Storm-and-Spark-Streaming" rel="nofollow"&gt;Use cases for comparison&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;Personally, I think Spark is a better choice.&lt;/p&gt;
', NULL, NULL, '''/a'':31C,43C ''/are-there-any-use-cases-for-a-comparison-between-storm-and-spark-streaming'':36C ''/p'':44C,54C ''/what-is-the-difference-between-apache-storm-and-apache-spark'':25C ''alreadi'':16C ''apach'':2C,5C ''better'':52C ''case'':40C ''choic'':53C ''comparison'':42C ''discuss'':18C ''href'':22C,33C ''mani'':17C ''nofollow'':27C,38C ''one'':13C ''p'':1C,45C ''person'':46C ''popular'':9C ''quora'':20C ''rel'':26C,37C ''spark'':6C,30C,49C ''storm'':3C,28C ''think'':48C ''use'':39C ''vs'':29C ''www.quora.com'':24C,35C ''www.quora.com/are-there-any-use-cases-for-a-comparison-between-storm-and-spark-streaming'':34C ''www.quora.com/what-is-the-difference-between-apache-storm-and-apache-spark'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4997, 2, '2015-01-30 20:26:42.293', 6, NULL, NULL, '&lt;h2&gt;Data Sets&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://academictorrents.com/" rel="nofollow"&gt;Academic Torrents&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public" rel="nofollow"&gt;Quora&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html" rel="nofollow"&gt;hadoopilluminated.com&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://catalog.data.gov/dataset" rel="nofollow"&gt;data.gov&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.quandl.com/" rel="nofollow"&gt;Quandl&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.freebase.com/" rel="nofollow"&gt;freebase.com&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://usgovxml.com/" rel="nofollow"&gt;usgovxml.com&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://enigma.io/" rel="nofollow"&gt;enigma.io&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datahub.io/" rel="nofollow"&gt;datahub.io&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://aws.amazon.com/datasets" rel="nofollow"&gt;aws.amazon.com/datasets&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://databib.org/" rel="nofollow"&gt;databib.org&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.datacite.org" rel="nofollow"&gt;datacite.org&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.quandl.com/" rel="nofollow"&gt;quandl.com&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://figshare.com/" rel="nofollow"&gt;figshare.com&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://dev.maxmind.com/geoip/legacy/geolite/" rel="nofollow"&gt;GeoLite Legacy Downloadable Databases&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public" rel="nofollow"&gt;Quora''s Big Datasets Answer&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://hadoopilluminated.com/hadoop_illuminated/Public_Bigdata_Sets.html" rel="nofollow"&gt;Public Big Data Sets&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://data.ohouston.org/" rel="nofollow"&gt;Houston Data Portal&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.kaggle.com/wiki/DataSources" rel="nofollow"&gt;Kaggle Data Sources&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.1000genomes.org/data" rel="nofollow"&gt;A Deep Catalog of Human Genetic Variation&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.freebase.com/" rel="nofollow"&gt;A community-curated database of well-known people, places, and things&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.google.com/publicdata/directory" rel="nofollow"&gt;Google Public Data&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://data.worldbank.org/" rel="nofollow"&gt;World Bank Data&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://nyctaxi.herokuapp.com/" rel="nofollow"&gt;NYC Taxi data&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.opendataphilly.org/" rel="nofollow"&gt;Open Data Philly&lt;/a&gt; Connecting people with data for Philadelphia&lt;/li&gt;
&lt;li&gt;&lt;a href="http://networkrepository.com" rel="nofollow"&gt;Network Repository&lt;/a&gt; An interactive data repository with over 600+ networks  in 20+ collections; from large-scale social networks, web graphs, biological networks, communication and technological networks, etc.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://ahmetkurnaz.net/en/statistical-data-sources/" rel="nofollow"&gt;A list of useful sources&lt;/a&gt; A blog post includes many data set databases&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="https://github.com/okulbilisim/awesome-datascience#data-sets" rel="nofollow"&gt;Data Sets&lt;/a&gt; From &lt;a href="https://github.com/okulbilisim/awesome-datascience" rel="nofollow"&gt;awesome-datascience&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':14C,25C,36C,47C,56C,65C,74C,83C,92C,112C,121C,130C,139C,153C,168C,182C,193C,206C,223C,244C,257C,268C,279C,290C,306C,347C,368C,380C ''/data'':213C ''/dataset'':43C ''/datasets'':99C ''/datasets&lt;/a&gt;&lt;/li&gt;'':104C ''/en/statistical-data-sources/'':339C ''/geoip/legacy/geolite/'':146C ''/h2'':4C ''/hadoop_illuminated/public_bigdata_sets.html'':32C,175C ''/li'':15C,26C,37C,48C,57C,66C,75C,84C,93C,113C,122C,131C,140C,154C,169C,183C,194C,207C,224C,245C,258C,269C,280C,297C,333C,356C ''/okulbilisim/awesome-datascience'':374C ''/okulbilisim/awesome-datascience#data-sets'':363C ''/p'':381C ''/publicdata/directory'':251C ''/ul'':357C ''/where-can-i-find-large-datasets-open-to-the-public'':21C,160C ''/wiki/datasources'':200C ''20'':316C ''600'':313C ''academ'':12C ''academictorrents.com'':9C ''ahmetkurnaz.net'':338C ''ahmetkurnaz.net/en/statistical-data-sources/'':337C ''answer'':167C ''awesom'':378C ''awesome-datasci'':377C ''aws.amazon.com'':98C,103C ''aws.amazon.com/datasets'':97C ''aws.amazon.com/datasets&lt;/a&gt;&lt;/li&gt;'':102C ''bank'':266C ''big'':165C,179C ''biolog'':326C ''blog'':349C ''catalog'':218C ''catalog.data.gov'':42C ''catalog.data.gov/dataset'':41C ''collect'':317C ''communic'':328C ''communiti'':233C ''community-cur'':232C ''connect'':291C ''curat'':234C ''data'':2C,180C,191C,204C,256C,267C,278C,288C,294C,309C,353C,366C ''data.gov'':46C ''data.ohouston.org'':187C ''data.worldbank.org'':262C ''databas'':152C,235C,355C ''databib.org'':108C,111C ''datacite.org'':120C ''datahub.io'':88C,91C ''datasci'':379C ''dataset'':166C ''deep'':217C ''dev.maxmind.com'':145C ''dev.maxmind.com/geoip/legacy/geolite/'':144C ''download'':151C ''enigma.io'':79C,82C ''etc'':332C ''figshare.com'':135C,138C ''freebase.com'':64C ''genet'':221C ''geolit'':149C ''github.com'':362C,373C ''github.com/okulbilisim/awesome-datascience'':372C ''github.com/okulbilisim/awesome-datascience#data-sets'':361C ''googl'':254C ''graph'':325C ''h2'':1C ''hadoopilluminated.com'':31C,35C,174C ''hadoopilluminated.com/hadoop_illuminated/public_bigdata_sets.html'':30C,173C ''houston'':190C ''href'':8C,18C,29C,40C,51C,60C,69C,78C,87C,96C,107C,116C,125C,134C,143C,157C,172C,186C,197C,210C,227C,248C,261C,272C,283C,300C,336C,360C,371C ''human'':220C ''includ'':351C ''interact'':308C ''kaggl'':203C ''known'':239C ''larg'':320C ''large-scal'':319C ''legaci'':150C ''li'':6C,16C,27C,38C,49C,58C,67C,76C,85C,94C,105C,114C,123C,132C,141C,155C,170C,184C,195C,208C,225C,246C,259C,270C,281C,298C,334C ''list'':343C ''mani'':352C ''network'':304C,314C,323C,327C,331C ''networkrepository.com'':301C ''nofollow'':11C,23C,34C,45C,54C,63C,72C,81C,90C,101C,110C,119C,128C,137C,148C,162C,177C,189C,202C,215C,230C,253C,264C,275C,286C,303C,341C,365C,376C ''nyc'':276C ''nyctaxi.herokuapp.com'':273C ''open'':287C ''p'':358C ''peopl'':240C,292C ''philadelphia'':296C ''philli'':289C ''place'':241C ''portal'':192C ''post'':350C ''public'':178C,255C ''quandl'':55C ''quandl.com'':129C ''quora'':24C,163C ''rel'':10C,22C,33C,44C,53C,62C,71C,80C,89C,100C,109C,118C,127C,136C,147C,161C,176C,188C,201C,214C,229C,252C,263C,274C,285C,302C,340C,364C,375C ''repositori'':305C,310C ''scale'':321C ''set'':3C,181C,354C,367C ''social'':322C ''sourc'':205C,346C ''taxi'':277C ''technolog'':330C ''thing'':243C ''torrent'':13C ''ul'':5C ''use'':345C ''usgovxml.com'':70C,73C ''variat'':222C ''web'':324C ''well'':238C ''well-known'':237C ''world'':265C ''www.1000genomes.org'':212C ''www.1000genomes.org/data'':211C ''www.datacite.org'':117C ''www.freebase.com'':61C,228C ''www.google.com'':250C ''www.google.com/publicdata/directory'':249C ''www.kaggle.com'':199C ''www.kaggle.com/wiki/datasources'':198C ''www.opendataphilly.org'':284C ''www.quandl.com'':52C,126C ''www.quora.com'':20C,159C ''www.quora.com/where-can-i-find-large-datasets-open-to-the-public'':19C,158C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4998, 1, '2015-01-30 21:04:30.173', 2, 168, 'Which Big-Data Frameworks have most simple interfaces?', '&lt;p&gt;I found that Apache-Spark has pretty much simple interface and easy to use. But I want to know about other interfaces.&lt;/p&gt;

&lt;p&gt;Can anyone give me a ranking of Big-Data frameworks in base of simplicity of their interfaces. also this is useful to express most simple and complex interfaces in base of your experiences.&lt;/p&gt;

&lt;p&gt;Definitely this question is about some frameworks with same tasks. For example a selection between Flink and Spark just in your opinion. Detailed comparison is so lengthy and this is not my purpose. Just a selection or ranking on your opinions is sufficient.&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'bigdata', NULL, '''/p'':35C,71C,116C,119C ''also'':55C ''anyon'':38C ''apach'':16C ''apache-spark'':15C ''base'':49C,67C ''big'':3A,45C ''big-data'':2A,44C ''bigdata'':10B ''comparison'':96C ''complex'':64C ''data'':4A,46C ''definit'':73C ''detail'':95C ''easi'':24C ''exampl'':84C ''experi'':70C ''express'':60C ''flink'':88C ''found'':13C ''framework'':5A,47C,79C ''give'':39C ''interfac'':9A,22C,34C,54C,65C ''know'':31C ''lengthi'':99C ''much'':20C ''opinion'':94C,113C ''p'':11C,36C,72C,117C ''pretti'':19C ''purpos'':105C ''question'':75C ''rank'':42C,110C ''select'':86C,108C ''simpl'':8A,21C,62C ''simplic'':51C ''spark'':17C,90C ''suffici'':115C ''task'':82C ''thank'':118C ''use'':26C,58C ''want'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5058, 2, '2015-02-05 17:22:04.123', 3, NULL, NULL, '&lt;p&gt;Ok, in this case, time is your dependent variables, and all the other ones are your features.&lt;/p&gt;

&lt;p&gt;You should use linear regression (since more complex stuff needs more expertise), any machine learning library has that implemented.&lt;/p&gt;

&lt;p&gt;Do not use the date as a feature, those are usually lousy estimators.&lt;/p&gt;
', NULL, NULL, '''/p'':19C,40C,55C ''case'':5C ''complex'':28C ''date'':46C ''depend'':9C ''estim'':54C ''expertis'':32C ''featur'':18C,49C ''implement'':39C ''learn'':35C ''librari'':36C ''linear'':24C ''lousi'':53C ''machin'':34C ''need'':30C ''ok'':2C ''one'':15C ''p'':1C,20C,41C ''regress'':25C ''sinc'':26C ''stuff'':29C ''time'':6C ''use'':23C,44C ''usual'':52C ''variabl'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5000, 1, '2015-01-30 21:30:18.077', 3, 133, 'Proper way of fighting negative outputs of a regression algorithms where output must be positive all the way', '&lt;p&gt;Maybe it is a bit general question. I am trying to solve various regression tasks and I try various algorithms for them. For example, multivariate linear regression or an SVR. I know that the output can''t be negative and I never have negative output values in my training set, though I could have 0''s in it (for example, I predict ''amount of cars on the road'' - it can''t be negative but can be 0). Rather often I face a problem that I am able to train relatively good algorithm (maybe fit a good regression line to my data) and I have relatively small average squared error on training set. But when I try to run my regression algorithm against new data I sometimes get a negative output. Obviously, I can''t accept negative output since it is not a valid value. The question is - what is the proper way of working with such output? Should I think of negative output as a 0 output? Is there any general advice for such cases?&lt;/p&gt;
', 'machine-learning  regression', NULL, '''/p'':199C ''0'':78C,100C,189C ''abl'':110C ''accept'':158C ''advic'':195C ''algorithm'':10A,43C,115C,144C ''amount'':86C ''averag'':130C ''bit'':28C ''car'':88C ''case'':198C ''could'':76C ''data'':124C,147C ''error'':132C ''exampl'':47C,83C ''face'':104C ''fight'':4A ''fit'':117C ''general'':29C,194C ''get'':150C ''good'':114C,119C ''know'':55C ''learn'':21B ''line'':121C ''linear'':49C ''machin'':20B ''machine-learn'':19B ''mayb'':24C,116C ''multivari'':48C ''must'':13A ''negat'':5A,62C,67C,96C,152C,159C,185C ''never'':65C ''new'':146C ''obvious'':154C ''often'':102C ''output'':6A,12A,58C,68C,153C,160C,180C,186C,190C ''p'':23C ''posit'':15A ''predict'':85C ''problem'':106C ''proper'':1A,174C ''question'':30C,169C ''rather'':101C ''regress'':9A,22B,37C,50C,120C,143C ''relat'':113C,128C ''road'':91C ''run'':141C ''set'':73C,135C ''sinc'':161C ''small'':129C ''solv'':35C ''sometim'':149C ''squar'':131C ''svr'':53C ''task'':38C ''think'':183C ''though'':74C ''train'':72C,112C,134C ''tri'':33C,41C,139C ''valid'':166C ''valu'':69C,167C ''various'':36C,42C ''way'':2A,18A,175C ''work'':177C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5001, 1, '2015-01-30 23:39:04.687', 4, 119, 'Data available from industry', '&lt;p&gt;I''m going to start my degree thesis and I want to do a fault detector system using machine learning techniques. I need datasets for my thesis but I don''t know where I can get that data. I''m looking for historical operation/maintenance/fault  datasets of any kind of machine in the oil&amp;amp;gas industry (Drills, steam injectors etc) or electrical companies (transformators, generators etc). &lt;/p&gt;

&lt;p&gt;Thank you for your help.&lt;/p&gt;
', 'dataset  open-source  freebase', NULL, '''/p'':77C,84C ''amp'':64C ''avail'':2A ''compani'':73C ''data'':1A,48C ''dataset'':5B,34C,55C ''degre'':17C ''detector'':26C ''drill'':67C ''electr'':72C ''etc'':70C,76C ''fault'':25C ''freebas'':9B ''gas'':65C ''generat'':75C ''get'':46C ''go'':13C ''help'':83C ''histor'':53C ''industri'':4A,66C ''injector'':69C ''kind'':58C ''know'':42C ''learn'':30C ''look'':51C ''m'':12C,50C ''machin'':29C,60C ''need'':33C ''oil'':63C ''open'':7B ''open-sourc'':6B ''operation/maintenance/fault'':54C ''p'':10C,78C ''sourc'':8B ''start'':15C ''steam'':68C ''system'':27C ''techniqu'':31C ''thank'':79C ''thesi'':18C,37C ''transform'':74C ''use'':28C ''want'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8336, 1, '2015-10-06 21:08:00.613', 0, 33, 'Linear Mixed Effects Model in C', '&lt;p&gt;I am looking for a &lt;code&gt;C&lt;/code&gt; implementation of a linear mixed effects model. The purpose is to be faster than the R implementation of &lt;code&gt;lmer()&lt;/code&gt; in the &lt;code&gt;lme4&lt;/code&gt; package.&lt;/p&gt;
', 'machine-learning  r  statistics  c', NULL, '''/code'':21C,42C,47C ''/p'':49C ''c'':6A,12B,20C ''code'':19C,40C,45C ''effect'':3A,27C ''faster'':34C ''implement'':22C,38C ''learn'':9B ''linear'':1A,25C ''lme4'':46C ''lmer'':41C ''look'':16C ''machin'':8B ''machine-learn'':7B ''mix'':2A,26C ''model'':4A,28C ''p'':13C ''packag'':48C ''purpos'':30C ''r'':10B,37C ''statist'':11B');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5002, 2, '2015-01-30 23:44:42.127', 5, NULL, NULL, '&lt;p&gt;I think that it is impossible to answer this question &lt;em&gt;comprehensively&lt;/em&gt;, at least for the following &lt;strong&gt;reasons&lt;/strong&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;big data frameworks have different &lt;strong&gt;goals&lt;/strong&gt; and target different &lt;strong&gt;knowledge domains&lt;/strong&gt;, so the comparison simply doesn''t make much sense;&lt;/li&gt;
&lt;li&gt;most big data frameworks (and other programming frameworks, for that matter) have &lt;em&gt;multiple interfaces&lt;/em&gt;, and frequently those &lt;strong&gt;sets of interfaces&lt;/strong&gt; are significantly different (the &lt;em&gt;intersection&lt;/em&gt; is small), so there is a risk of comparing apples and oranges;&lt;/li&gt;
&lt;li&gt;trying to compare anything (in this case, interfaces), using &lt;em&gt;simplicity&lt;/em&gt; as a criterion, involves a significant amount of &lt;strong&gt;subjectivity&lt;/strong&gt; - what one person &lt;em&gt;perceive&lt;/em&gt; as very simple, another person might find quite complex;&lt;/li&gt;
&lt;li&gt;the &lt;strong&gt;variety&lt;/strong&gt; and the &lt;strong&gt;number&lt;/strong&gt; of &lt;em&gt;big data frameworks&lt;/em&gt; is mind-boggling (for example, see &lt;a href="https://github.com/onurakpolat/awesome-bigdata" rel="nofollow"&gt;https://github.com/onurakpolat/awesome-bigdata&lt;/a&gt;); the same applies to a related topic of &lt;em&gt;machine learning frameworks&lt;/em&gt; (for example, see &lt;a href="https://github.com/josephmisiti/awesome-machine-learning" rel="nofollow"&gt;https://github.com/josephmisiti/awesome-machine-learning&lt;/a&gt;);&lt;/li&gt;
&lt;li&gt;corollary from the points above: a &lt;strong&gt;comprehensive&lt;/strong&gt; comparison (considering all the above-mentioned issues) would go far beyond the &lt;strong&gt;scope&lt;/strong&gt; of a single answer on this site, in volume and effort - it would be more like a long research paper, a book chapter or even a book.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/em'':14C,67C,82C,108C,125C,151C,181C ''/josephmisiti/awesome-machine-learning'':189C ''/josephmisiti/awesome-machine-learning&lt;/a&gt;);&lt;/li&gt;'':194C ''/li'':50C,95C,135C,248C ''/onurakpolat/awesome-bigdata'':163C ''/onurakpolat/awesome-bigdata&lt;/a&gt;);'':168C ''/p'':23C ''/strong'':22C,33C,40C,75C,119C,140C,145C,204C,220C ''/ul'':249C ''above-ment'':209C ''amount'':115C ''anoth'':129C ''answer'':9C,224C ''anyth'':100C ''appl'':92C ''appli'':171C ''beyond'':216C ''big'':26C,53C,148C ''boggl'':155C ''book'':242C,247C ''case'':103C ''chapter'':243C ''compar'':91C,99C ''comparison'':43C,205C ''complex'':134C ''comprehens'':13C,203C ''consid'':206C ''corollari'':196C ''criterion'':111C ''data'':27C,54C,149C ''differ'':30C,36C,78C ''doesn'':45C ''domain'':39C ''effort'':231C ''em'':12C,64C,80C,106C,123C,147C,177C ''even'':245C ''exampl'':157C,183C ''far'':215C ''find'':132C ''follow'':19C ''framework'':28C,55C,59C,150C,180C ''frequent'':69C ''github.com'':162C,167C,188C,193C ''github.com/josephmisiti/awesome-machine-learning'':187C ''github.com/josephmisiti/awesome-machine-learning&lt;/a&gt;);&lt;/li&gt;'':192C ''github.com/onurakpolat/awesome-bigdata'':161C ''github.com/onurakpolat/awesome-bigdata&lt;/a&gt;);'':166C ''go'':214C ''goal'':32C ''href'':160C,186C ''imposs'':7C ''interfac'':66C,74C,104C ''intersect'':81C ''involv'':112C ''issu'':212C ''knowledg'':38C ''learn'':179C ''least'':16C ''li'':25C,51C,96C,136C,195C ''like'':236C ''long'':238C ''machin'':178C ''make'':47C ''matter'':62C ''mention'':211C ''might'':131C ''mind'':154C ''mind-boggl'':153C ''much'':48C ''multipl'':65C ''nofollow'':165C,191C ''number'':144C ''one'':121C ''orang'':94C ''p'':1C ''paper'':240C ''perceiv'':124C ''person'':122C,130C ''point'':199C ''program'':58C ''question'':11C ''quit'':133C ''reason'':21C ''rel'':164C,190C ''relat'':174C ''research'':239C ''risk'':89C ''scope'':219C ''see'':158C,184C ''sens'':49C ''set'':72C ''signific'':77C,114C ''simpl'':128C ''simpli'':44C ''simplic'':107C ''singl'':223C ''site'':227C ''small'':84C ''strong'':20C,31C,37C,71C,117C,138C,143C,202C,218C ''subject'':118C ''target'':35C ''think'':3C ''topic'':175C ''tri'':97C ''ul'':24C ''use'':105C ''varieti'':139C ''volum'':229C ''would'':213C,233C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5017, 2, '2015-02-02 16:08:50.58', 1, NULL, NULL, '&lt;p&gt;I don''t really get why would you mix Fuzziness and Probabilities. HMMs already can give you probabilities without the need of adding Fuzzy systems into the mix.&lt;/p&gt;

&lt;p&gt;I would just do a random walk with probabilities of transitions defined by the state of the lights.&lt;/p&gt;
', NULL, NULL, '''/p'':30C,50C ''ad'':24C ''alreadi'':15C ''defin'':43C ''fuzzi'':11C,25C ''get'':6C ''give'':17C ''hmms'':14C ''light'':49C ''mix'':10C,29C ''need'':22C ''p'':1C,31C ''probabl'':13C,19C,40C ''random'':37C ''realli'':5C ''state'':46C ''system'':26C ''transit'':42C ''walk'':38C ''without'':20C ''would'':8C,33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5005, 2, '2015-01-31 02:22:36', 3, NULL, NULL, '&lt;p&gt;Yes.&lt;/p&gt;

&lt;p&gt;There are examples on spark official document: &lt;a href="https://spark.apache.org/examples.html" rel="nofollow"&gt;https://spark.apache.org/examples.html&lt;/a&gt;
Just put your HDFS file uri in your input file path as below (scala syntax).&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;val file = spark.textFile("hdfs://train_data")
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':45C ''/examples.html'':16C ''/examples.html&lt;/a&gt;'':21C ''/p'':3C,37C ''/pre'':46C ''code'':39C ''data'':44C ''document'':11C ''exampl'':7C ''file'':26C,31C,41C ''hdfs'':25C ''href'':13C ''input'':30C ''nofollow'':18C ''offici'':10C ''p'':1C,4C ''path'':32C ''pre'':38C ''put'':23C ''rel'':17C ''scala'':35C ''spark'':9C ''spark.apache.org'':15C,20C ''spark.apache.org/examples.html'':14C ''spark.apache.org/examples.html&lt;/a&gt;'':19C ''spark.textfile'':42C ''syntax'':36C ''train'':43C ''uri'':27C ''val'':40C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5020, 2, '2015-02-02 20:58:47.97', 2, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;I want to try out unsupervised learning for the same. I am looking into KMeans clustering. But again the problem is, we won''t know the number of clusters beforehand. So, which algorithm will be best for this use case?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;When you don''t know the number of clusters beforehand, it is still possible to do unsupervised learning using a &lt;a href="http://en.wikipedia.org/wiki/Dirichlet_process" rel="nofollow"&gt;Dirichlet process&lt;/a&gt; to sample parameters associated to clusters/groups and then cluster your tokens according to those parameters. The general idea is to use a Dirichlet distribution to generate probabilities over words for each cluster and a Dirichlet process uses these probabilities to assign a cluster to each word in your vocabulary. If you want to share clusters between emails, then you use Hierarchical Dirichlet Processes. &lt;a href="http://blog.echen.me/2012/03/20/infinite-mixture-models-with-nonparametric-bayes-and-the-dirichlet-process/" rel="nofollow"&gt;Here&lt;/a&gt; you can find a nice blog post of how this works.&lt;/p&gt;

&lt;p&gt;The most popular library for clustering is &lt;a href="http://radimrehurek.com/gensim/tut2.html" rel="nofollow"&gt;gensim&lt;/a&gt;, but notice their warning regarding the Hierarchical Dirichlet Process implementation:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;gensim uses a fast, online implementation based on [3]. The HDP model is a new addition to gensim, and still rough around its academic edges â use with care.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;As for feature extraction, your question doesn''t say exactly what kind of unnecessary features you are getting, but if that''s the case, you need to filter your tokens before or after processing them with NLTK. In general, you can''t expect excellent results for very specific applications.&lt;/p&gt;
', NULL, NULL, '''/2012/03/20/infinite-mixture-models-with-nonparametric-bayes-and-the-dirichlet-process/'':143C ''/a'':75C,147C,176C ''/blockquote'':44C,220C ''/gensim/tut2.html'':172C ''/p'':43C,159C,187C,219C,271C ''/wiki/dirichlet_process'':70C ''3'':198C ''academ'':213C ''accord'':87C ''addit'':205C ''algorithm'':35C ''applic'':270C ''around'':211C ''assign'':116C ''associ'':79C ''base'':196C ''beforehand'':32C,55C ''best'':38C ''blockquot'':1C,188C ''blog'':153C ''blog.echen.me'':142C ''blog.echen.me/2012/03/20/infinite-mixture-models-with-nonparametric-bayes-and-the-dirichlet-process/'':141C ''care'':218C ''case'':42C,245C ''cluster'':18C,31C,54C,84C,107C,118C,130C,166C ''clusters/groups'':81C ''dirichlet'':73C,98C,110C,137C,184C ''distribut'':99C ''doesn'':228C ''edg'':214C ''email'':132C ''en.wikipedia.org'':69C ''en.wikipedia.org/wiki/dirichlet_process'':68C ''exact'':231C ''excel'':265C ''expect'':264C ''extract'':225C ''fast'':193C ''featur'':224C,236C ''filter'':249C ''find'':150C ''general'':92C,260C ''generat'':101C ''gensim'':175C,190C,207C ''get'':239C ''hdp'':200C ''hierarch'':136C,183C ''href'':67C,140C,169C ''idea'':93C ''implement'':186C,195C ''kind'':233C ''kmean'':17C ''know'':27C,50C ''learn'':9C,63C ''librari'':164C ''look'':15C ''model'':201C ''need'':247C ''new'':204C ''nice'':152C ''nltk'':258C ''nofollow'':72C,145C,174C ''notic'':178C ''number'':29C,52C ''onlin'':194C ''p'':2C,45C,160C,189C,221C ''paramet'':78C,90C ''popular'':163C ''possibl'':59C ''post'':154C ''probabl'':102C,114C ''problem'':22C ''process'':74C,111C,138C,185C,255C ''question'':227C ''radimrehurek.com'':171C ''radimrehurek.com/gensim/tut2.html'':170C ''regard'':181C ''rel'':71C,144C,173C ''result'':266C ''rough'':210C ''sampl'':77C ''say'':230C ''share'':129C ''specif'':269C ''still'':58C,209C ''token'':86C,251C ''tri'':6C ''unnecessari'':235C ''unsupervis'':8C,62C ''use'':41C,64C,96C,112C,135C,191C,216C ''vocabulari'':124C ''want'':4C,127C ''warn'':180C ''won'':25C ''word'':104C,121C ''work'':158C ''â'':215C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5006, 2, '2015-01-31 12:29:35.733', 5, NULL, NULL, '&lt;p&gt;&lt;strong&gt;HDFS&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Spark was built as an alternative to MapReduce and thus supports most of its functionality. In particular, it means that "Spark can create distributed datasets from any storage source supported by Hadoop, including your local file system, HDFS, Cassandra, HBase, Amazon S3, etc."&lt;a href="http://spark.apache.org/docs/1.2.0/programming-guide.html#external-datasets"&gt;1&lt;/a&gt;. For most common data sources (like HDFS or S3) Spark automatically recognizes schema, e.g.: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;val sc = SparkContext(...)
val localRDD = sc.textFile("file://...")
val hdfsRDD  = sc.textFile("hdfs://...")
val s3RDD    = sc.textFile("s3://...")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For more complicated cases you may need to work with lower-level functions like &lt;code&gt;newAPIHadoopFile&lt;/code&gt;: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;val hBaseRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat], 
      classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable],
      classOf[org.apache.hadoop.hbase.client.Result])
val customRDD = sc.newAPIHadoopRDD(conf, classOf[MyCustomInputFormat], 
      classOf[MyCustomKeyClass],
      classOf[MyCustomValueClass]) 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But general rule is that if some data source is available for MapReduce, it can be easily reused in Spark.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;YARN&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Currently Spark supports 3 cluster managers / modes: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Standalone&lt;/strong&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Mesos&lt;/strong&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;YARN&lt;/strong&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Standalone mode uses Spark''s own master server and works for Spark only, while YARN and Mesos modes aim to share same set of system resources between several frameworks (e.g. Spark, MapReduce, Impala, etc.). Comparison of YARN and Mesos may be found &lt;a href="http://www.quora.com/How-does-YARN-compare-to-Mesos"&gt;here&lt;/a&gt;, and detailed description of Spark on YARN &lt;a href="http://blog.cloudera.com/blog/2014/05/apache-spark-resource-management-and-yarn-app-models/"&gt;here&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;And, in best traditions of Spark, you can switch between different modes simply by changing &lt;a href="https://spark.apache.org/docs/1.2.0/submitting-applications.html"&gt;master URL&lt;/a&gt;. &lt;/p&gt;
', NULL, NULL, '''/a'':56C,235C,249C,274C ''/blog/2014/05/apache-spark-resource-management-and-yarn-app-models/'':247C ''/code'':87C,107C,131C ''/docs/1.2.0/programming-guide.html#external-datasets'':54C ''/docs/1.2.0/submitting-applications.html'':271C ''/how-does-yarn-compare-to-mesos'':233C ''/li'':174C,179C,184C ''/p'':5C,71C,108C,154C,159C,168C,250C,275C ''/pre'':88C,132C ''/strong'':4C,158C,173C,178C,183C ''/ul'':185C ''1'':55C ''3'':164C ''aim'':205C ''altern'':12C ''amazon'':47C ''automat'':67C ''avail'':144C ''best'':254C ''blog.cloudera.com'':246C ''blog.cloudera.com/blog/2014/05/apache-spark-resource-management-and-yarn-app-models/'':245C ''built'':9C ''case'':93C ''cassandra'':45C ''chang'':266C ''classof'':115C,117C,119C,125C,127C,129C ''cluster'':165C ''code'':73C,105C,110C ''common'':59C ''comparison'':221C ''complic'':92C ''conf'':114C,124C ''creat'':29C ''current'':161C ''customrdd'':122C ''data'':60C,141C ''dataset'':31C ''descript'':238C ''detail'':237C ''differ'':262C ''distribut'':30C ''e.g'':70C,216C ''easili'':150C ''etc'':49C,220C ''file'':42C ''found'':228C ''framework'':215C ''function'':21C,103C ''general'':135C ''hadoop'':38C ''hbase'':46C ''hbaserdd'':112C ''hdfs'':3C,44C,63C ''hdfsrdd'':81C ''href'':51C,230C,244C,268C ''impala'':219C ''includ'':39C ''level'':102C ''li'':170C,175C,180C ''like'':62C,104C ''local'':41C ''localrdd'':78C ''lower'':101C ''lower-level'':100C ''manag'':166C ''mapreduc'':14C,146C,218C ''master'':193C,272C ''may'':95C,226C ''mean'':25C ''meso'':177C,203C,225C ''mode'':167C,188C,204C,263C ''mycustominputformat'':126C ''mycustomkeyclass'':128C ''mycustomvalueclass'':130C ''need'':96C ''newapihadoopfil'':106C ''org.apache.hadoop.hbase.client.result'':120C ''org.apache.hadoop.hbase.io.immutablebyteswritable'':118C ''p'':1C,6C,89C,133C,155C,160C,186C,251C ''particular'':23C ''pre'':72C,109C ''recogn'':68C ''resourc'':212C ''reus'':151C ''rule'':136C ''s3'':48C,65C,86C ''s3rdd'':84C ''sc'':75C ''sc.newapihadooprdd'':113C,123C ''sc.textfile'':79C,82C,85C ''schema'':69C ''server'':194C ''set'':209C ''sever'':214C ''share'':207C ''simpli'':264C ''sourc'':35C,61C,142C ''spark'':7C,27C,66C,153C,162C,190C,198C,217C,240C,257C ''spark.apache.org'':53C,270C ''spark.apache.org/docs/1.2.0/programming-guide.html#external-datasets'':52C ''spark.apache.org/docs/1.2.0/submitting-applications.html'':269C ''sparkcontext'':76C ''standalon'':172C,187C ''storag'':34C ''strong'':2C,156C,171C,176C,181C ''support'':17C,36C,163C ''switch'':260C ''system'':43C,211C ''tableinputformat'':116C ''thus'':16C ''tradit'':255C ''ul'':169C ''url'':273C ''use'':189C ''val'':74C,77C,80C,83C,111C,121C ''work'':98C,196C ''www.quora.com'':232C ''www.quora.com/how-does-yarn-compare-to-mesos'':231C ''yarn'':157C,182C,201C,223C,242C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5007, 1, '2015-01-31 13:28:45.59', 0, 34, 'How to proceed 2 executions in 1 step in hive?', '&lt;p&gt;I am wondering if there is a way to proceed 2 exectuions in 1 step in hive.
For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;SELECT * FROM TABLE1
SELECT * FROM TABLE2
;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Do this in one window, and do not have to open 2 hive windows to execute each line separetly.&lt;/p&gt;

&lt;p&gt;Can it be done on HUE?&lt;/p&gt;
', 'hive', NULL, '''/code'':41C ''/p'':32C,63C,71C ''/pre'':42C ''1'':7A,26C ''2'':4A,23C,55C ''code'':34C ''done'':68C ''exampl'':31C ''exectuion'':24C ''execut'':5A,59C ''hive'':10A,11B,29C,56C ''hue'':70C ''line'':61C ''one'':47C ''open'':54C ''p'':12C,43C,64C ''pre'':33C ''proceed'':3A,22C ''select'':35C,38C ''separet'':62C ''step'':8A,27C ''table1'':37C ''table2'':40C ''way'':20C ''window'':48C,57C ''wonder'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5008, 2, '2015-01-31 14:27:15.657', 3, NULL, NULL, '&lt;p&gt;A huge list of open data sets is listed here:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/155/publicly-available-datasets"&gt;Publicly available datasets&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Including Amazon, KDnuggets, Stanford, Twitter, Freebase, Google Public and more.&lt;/p&gt;
', NULL, NULL, '''/a'':23C ''/li'':24C ''/p'':12C,37C ''/questions/155/publicly-available-datasets'':19C ''/ul'':25C ''amazon'':28C ''avail'':21C ''data'':7C ''datascience.stackexchange.com'':18C ''datascience.stackexchange.com/questions/155/publicly-available-datasets'':17C ''dataset'':22C ''freebas'':32C ''googl'':33C ''href'':16C ''huge'':3C ''includ'':27C ''kdnugget'':29C ''li'':14C ''list'':4C,10C ''open'':6C ''p'':1C,26C ''public'':20C,34C ''set'':8C ''stanford'':30C ''twitter'':31C ''ul'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5009, 2, '2015-01-31 16:55:53.35', 2, NULL, NULL, '&lt;p&gt;You can use HiveCLI Tool to run HiveQL with a given sql file.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;$HIVE_HOME/bin/hive -f /home/my/hive-script.sql&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Please see official document: &lt;a href="https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Cli" rel="nofollow"&gt;https://cwiki.apache.org/confluence/display/Hive/LanguageManual+Cli&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;What you need to do is to&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Put your HiveQLs in a file as below&lt;br&gt;
&lt;code&gt;SELECT * FROM TABLE1;
SELECT * FROM TABLE2;&lt;/code&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Use HiveCLI and run with above file&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/blockquote'':23C ''/code'':67C ''/confluence/display/hive/languagemanual+cli'':33C ''/confluence/display/hive/languagemanual+cli&lt;/a&gt;&lt;/p&gt;'':38C ''/home/my/hive-script.sql'':21C ''/li'':69C,80C ''/ol'':81C ''/p'':15C,22C,47C,68C,79C ''blockquot'':16C ''br'':59C ''code'':60C ''cwiki.apache.org'':32C,37C ''cwiki.apache.org/confluence/display/hive/languagemanual+cli'':31C ''cwiki.apache.org/confluence/display/hive/languagemanual+cli&lt;/a&gt;&lt;/p&gt;'':36C ''document'':28C ''f'':20C ''file'':14C,56C,78C ''given'':12C ''hive'':18C ''hivec'':5C,73C ''hiveql'':9C,53C ''home/bin/hive'':19C ''href'':30C ''li'':49C,70C ''need'':42C ''nofollow'':35C ''offici'':27C ''ol'':48C ''p'':1C,17C,24C,39C,50C,71C ''pleas'':25C ''put'':51C ''rel'':34C ''run'':8C,75C ''see'':26C ''select'':61C,64C ''sql'':13C ''table1'':63C ''table2'':66C ''tool'':6C ''use'':4C,72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5010, 2, '2015-01-31 17:31:09.967', 1, NULL, NULL, '&lt;p&gt;You can separate each query with a semi colon (;)&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;select column1 from table1;
select column2 from table2;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This command can be executed from command line via inline queries or a file. Usage of Hive CLI is not recommended. You must use beeline to execute queries configured via hive server 2 so that all/any underlying security control measures are honored.&lt;/p&gt;

&lt;p&gt;you may invoke beeline with the command:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;beeline
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':22C,80C ''/p'':11C,67C,76C ''/pre'':23C,81C ''2'':57C ''all/any'':60C ''beelin'':49C,72C,79C ''cli'':42C ''code'':13C,78C ''colon'':10C ''column1'':15C ''column2'':19C ''command'':26C,31C,75C ''configur'':53C ''control'':63C ''execut'':29C,51C ''file'':38C ''hive'':41C,55C ''honor'':66C ''inlin'':34C ''invok'':71C ''line'':32C ''may'':70C ''measur'':64C ''must'':47C ''p'':1C,24C,68C ''pre'':12C,77C ''queri'':6C,35C,52C ''recommend'':45C ''secur'':62C ''select'':14C,18C ''semi'':9C ''separ'':4C ''server'':56C ''table1'':17C ''table2'':21C ''under'':61C ''usag'':39C ''use'':48C ''via'':33C,54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5011, 2, '2015-02-01 01:34:13.597', 1, NULL, NULL, '&lt;p&gt;I thought about it this way: the training and test sets are both a sample of the unknown population. We assume that the training set is representative of the population we''re studying. That is, whatever transformations we make to the training set are what we would make to the overall population. In addition, whatever subset of the training data we use, we assume that this subset represents the training set, which represents the population.&lt;/p&gt;

&lt;p&gt;So in response to your first question, it''s fine to use that shrinked/resmpled training as long as you feel it''s still representative of that population. That''s assuming your untouched training set captures the "real characteristics" in the first place :)&lt;/p&gt;

&lt;p&gt;As for your second question, don''t merge the training and testing set. The testing set is there to act as future unknown observations. If you build these into the model then you won''t know if the model wrong or not, because you used up the data you were going to test it with.&lt;/p&gt;
', NULL, NULL, '''/p'':77C,121C,178C ''act'':142C ''addit'':55C ''assum'':22C,65C,108C ''build'':149C ''captur'':113C ''characterist'':116C ''data'':61C,170C ''feel'':98C ''fine'':88C ''first'':84C,119C ''futur'':144C ''go'':173C ''know'':158C ''long'':95C ''make'':40C,49C ''merg'':130C ''model'':153C,161C ''observ'':146C ''overal'':52C ''p'':1C,78C,122C ''place'':120C ''popul'':20C,31C,53C,76C,105C ''question'':85C,127C ''re'':33C ''real'':115C ''repres'':28C,69C,74C,102C ''respons'':81C ''sampl'':16C ''second'':126C ''set'':12C,26C,44C,72C,112C,135C,138C ''shrinked/resmpled'':92C ''still'':101C ''studi'':34C ''subset'':57C,68C ''test'':11C,134C,137C,175C ''thought'':3C ''train'':9C,25C,43C,60C,71C,93C,111C,132C ''transform'':38C ''unknown'':19C,145C ''untouch'':110C ''use'':63C,90C,167C ''way'':7C ''whatev'':37C,56C ''won'':156C ''would'':48C ''wrong'':162C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5013, 1, '2015-02-01 09:35:16.113', 0, 284, 'Machine learning for Point Clouds Lidar data', '&lt;p&gt;Our main use case is object detection in 3d lidar point clouds i.e. data is not in RGB-D format. We are planning to use CNN for this purpose using theano. Hardware limitations are CPU: 32 GB RAM Intel 47XX 4th Gen core i7 and GPU: Nvidia quadro k1100M 2GB. Kindly help me with recommendation for architecture. &lt;/p&gt;

&lt;p&gt;I am thinking in the lines of 27000 input neurons on basis of 30x30x30 voxel grid but can''t tell in advance if this is a good option.&lt;/p&gt;

&lt;p&gt;Additional Note: Dataset has 4500 points on average per view per point cloud&lt;/p&gt;
', 'machine-learning  dataset  recommendation', NULL, '''/p'':72C,102C,117C ''27000'':81C ''2gb'':64C ''30x30x30'':87C ''32'':50C ''3d'':22C ''4500'':108C ''47xx'':54C ''4th'':55C ''addit'':104C ''advanc'':95C ''architectur'':71C ''averag'':111C ''basi'':85C ''case'':17C ''cloud'':5A,25C,116C ''cnn'':40C ''core'':57C ''cpu'':49C ''d'':33C ''data'':7A,27C ''dataset'':11B,106C ''detect'':20C ''format'':34C ''gb'':51C ''gen'':56C ''good'':100C ''gpu'':60C ''grid'':89C ''hardwar'':46C ''help'':66C ''i.e'':26C ''i7'':58C ''input'':82C ''intel'':53C ''k1100m'':63C ''kind'':65C ''learn'':2A,10B ''lidar'':6A,23C ''limit'':47C ''line'':79C ''machin'':1A,9B ''machine-learn'':8B ''main'':15C ''neuron'':83C ''note'':105C ''nvidia'':61C ''object'':19C ''option'':101C ''p'':13C,73C,103C ''per'':112C,114C ''plan'':37C ''point'':4A,24C,109C,115C ''purpos'':43C ''quadro'':62C ''ram'':52C ''recommend'':12B,69C ''rgb'':32C ''rgb-d'':31C ''tell'':93C ''theano'':45C ''think'':76C ''use'':16C,39C,44C ''view'':113C ''voxel'':88C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5014, 1, '2015-02-02 14:53:51.81', 1, 57, 'How to compute F1 score?', '&lt;p&gt;Recently I read about path ranking algorithm in a paper (source: &lt;a href="https://www.cs.cmu.edu/~nlao/publication/2014.kdd.pdf" rel="nofollow"&gt;Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion&lt;/a&gt;). &lt;/p&gt;

&lt;p&gt;In this paper was a table (Table 3) with facts and I tried to understand how they were calculated.&lt;/p&gt;

&lt;p&gt;F1 (harmonic mean of precision and recall) = 0.04&lt;br/&gt;
P (precision) = 0.03&lt;br/&gt;
R (recall) = 0.33&lt;br/&gt;
W (weight given to this feature by logistic regression)&lt;/p&gt;

&lt;p&gt;I found a formula for F1 via Google which is&lt;/p&gt;

&lt;p&gt;$F1 = 2 * \frac{precision * recall}{precision + recall}$&lt;/p&gt;

&lt;p&gt;The problem is that I get the result of 0.055 with this formula, but not the expected result of 0.04.
Can someone help me to get this part?
Also, does someone know how ''W'' can be calculated?
Thanks.&lt;/p&gt;
', 'machine-learning', NULL, '''/a'':39C ''/p'':40C,61C,89C,101C,110C,150C ''/~nlao/publication/2014.kdd.pdf'':25C ''0.03'':74C ''0.04'':70C,131C ''0.055'':121C ''0.33'':78C ''2'':104C ''3'':49C ''algorithm'':16C ''also'':140C ''approach'':34C ''br'':71C,75C,79C ''calcul'':60C,148C ''comput'':3A ''expect'':128C ''f1'':4A,63C,96C,103C ''fact'':51C ''featur'':85C ''formula'':94C,124C ''found'':92C ''frac'':105C ''fusion'':38C ''get'':117C,137C ''given'':82C ''googl'':98C ''harmon'':64C ''help'':134C ''href'':22C ''know'':143C ''knowledg'':28C,37C ''learn'':8B ''logist'':87C ''machin'':7B ''machine-learn'':6B ''mean'':65C ''nofollow'':27C ''p'':9C,41C,62C,72C,90C,102C,111C ''paper'':19C,44C ''part'':139C ''path'':14C ''precis'':67C,73C,106C,108C ''probabilist'':36C ''problem'':113C ''r'':76C ''rank'':15C ''read'':12C ''recal'':69C,77C,107C,109C ''recent'':10C ''regress'':88C ''rel'':26C ''result'':119C,129C ''scale'':33C ''score'':5A ''someon'':133C,142C ''sourc'':20C ''tabl'':47C,48C ''thank'':149C ''tri'':54C ''understand'':56C ''vault'':29C ''via'':97C ''w'':80C,145C ''web'':32C ''web-scal'':31C ''weight'':81C ''www.cs.cmu.edu'':24C ''www.cs.cmu.edu/~nlao/publication/2014.kdd.pdf'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5015, 2, '2015-02-02 15:55:49.363', 1, NULL, NULL, '&lt;p&gt;First you need to learn about Logistic Regression, it is an algorithm that will assign weights to different features given some training data. Read the wiki intro, is quite helpful, basically the Betas there are the same as the Ws in the paper. &lt;/p&gt;

&lt;p&gt;The formula you have is correct, and those value do seem off. It also depends on the number of significant figures you have, perhaps they are making their calculations with more than the ones they are reporting.&lt;/p&gt;

&lt;p&gt;But honestly, you can''t understand much of the paper unless you understand LR&lt;/p&gt;
', NULL, NULL, '''/p'':45C,84C,100C ''algorithm'':13C ''also'':60C ''assign'':16C ''basic'':32C ''beta'':34C ''calcul'':75C ''correct'':52C ''data'':24C ''depend'':61C ''differ'':19C ''featur'':20C ''figur'':67C ''first'':2C ''formula'':48C ''given'':21C ''help'':31C ''honest'':87C ''intro'':28C ''learn'':6C ''logist'':8C ''lr'':99C ''make'':73C ''much'':92C ''need'':4C ''number'':64C ''one'':80C ''p'':1C,46C,85C ''paper'':44C,95C ''perhap'':70C ''quit'':30C ''read'':25C ''regress'':9C ''report'':83C ''seem'':57C ''signific'':66C ''train'':23C ''understand'':91C,98C ''unless'':96C ''valu'':55C ''weight'':17C ''wiki'':27C ''ws'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5016, 2, '2015-02-02 15:59:40.643', 0, NULL, NULL, '&lt;p&gt;First, CNNs are great for image recognition, where you usually take sub sampled windows of about 80 by 80 pixels, 27,000 input neurons is too large and it will take you forever to train a CNN on that.&lt;/p&gt;

&lt;p&gt;Furthermore, why did you choose CNN? Why don''t you try some more down to earth algorithms fisrst? Like SVMs, or Logistic regressions.&lt;/p&gt;

&lt;p&gt;4500 Data points and 27000 features seems unrealistic to me, and very prone to over fitting.&lt;/p&gt;

&lt;p&gt;Check this first.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/tutorial/machine_learning_map/" rel="nofollow"&gt;http://scikit-learn.org/stable/tutorial/machine_learning_map/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':41C,66C,84C,89C ''/stable/tutorial/machine_learning_map/'':95C ''/stable/tutorial/machine_learning_map/&lt;/a&gt;&lt;/p&gt;'':100C ''000'':23C ''27'':22C ''27000'':72C ''4500'':68C ''80'':18C,20C ''algorithm'':59C ''check'':86C ''choos'':47C ''cnn'':38C,48C ''cnns'':3C ''data'':69C ''earth'':58C ''featur'':73C ''first'':2C,88C ''fisrst'':60C ''fit'':83C ''forev'':34C ''furthermor'':43C ''great'':5C ''href'':92C ''imag'':7C ''input'':24C ''larg'':28C ''like'':61C ''logist'':64C ''neuron'':25C ''nofollow'':97C ''p'':1C,42C,67C,85C,90C ''pixel'':21C ''point'':70C ''prone'':80C ''recognit'':8C ''regress'':65C ''rel'':96C ''sampl'':14C ''scikit-learn.org'':94C,99C ''scikit-learn.org/stable/tutorial/machine_learning_map/'':93C ''scikit-learn.org/stable/tutorial/machine_learning_map/&lt;/a&gt;&lt;/p&gt;'':98C ''seem'':74C ''sub'':13C ''svms'':62C ''take'':12C,32C ''train'':36C ''tri'':53C ''unrealist'':75C ''usual'':11C ''window'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5019, 2, '2015-02-02 20:20:08.503', 4, NULL, NULL, '&lt;p&gt;The problem is your model choice, as you seem to recognize. In the case of linear regression, there is no restriction on your outputs.  Often this is fine when predictions need to be non-negative so long as they are far enough away from zero.  However, since many of your training examples are zero-valued, this isn''t the case.&lt;/p&gt;

&lt;p&gt;If your data is non-negative and discrete (as in the case with number of cars on the road), you could model using a generalized linear model (GLM) with a log link function.  This is known as Poisson regression and is helpful for modeling discrete non-negative counts such as the problem you described. The Poisson distribution is parameterized by a single value $\lambda$, which describes both the expected value and the variance of the distribution.&lt;/p&gt;

&lt;p&gt;This results in an approach similar to the one described by Emre in that you are attempting to fit a linear model to the log of your observations.&lt;/p&gt;
', NULL, NULL, '''/p'':64C,144C,174C ''approach'':150C ''attempt'':162C ''away'':45C ''car'':82C ''case'':15C,63C,78C ''choic'':7C ''could'':87C ''count'':115C ''data'':68C ''describ'':121C,133C,155C ''discret'':74C,111C ''distribut'':124C,143C ''emr'':157C ''enough'':44C ''exampl'':54C ''expect'':136C ''far'':43C ''fine'':29C ''fit'':164C ''function'':99C ''general'':91C ''glm'':94C ''help'':108C ''howev'':48C ''isn'':60C ''known'':102C ''lambda'':131C ''linear'':17C,92C,166C ''link'':98C ''log'':97C,170C ''long'':39C ''mani'':50C ''model'':6C,88C,93C,110C,167C ''need'':32C ''negat'':37C,72C,114C ''non'':36C,71C,113C ''non-neg'':35C,70C,112C ''number'':80C ''observ'':173C ''often'':26C ''one'':154C ''output'':25C ''p'':1C,65C,145C ''parameter'':126C ''poisson'':104C,123C ''predict'':31C ''problem'':3C,119C ''recogn'':12C ''regress'':18C,105C ''restrict'':22C ''result'':147C ''road'':85C ''seem'':10C ''similar'':151C ''sinc'':49C ''singl'':129C ''train'':53C ''use'':89C ''valu'':58C,130C,137C ''varianc'':140C ''zero'':47C,57C ''zero-valu'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5318, 1, '2015-03-13 22:43:42.76', 2, 103, 'Probability of similarity of two clusters', '&lt;p&gt;For example, there are 4 objects. Each object has some elements. There are 3 clusters. Each element of each object belongs to some cluster. Clustering was done before, and we know only how many elements of each object belong to some cluster.
How we can calculate the porbability of similarity of two objects?&lt;/p&gt;

&lt;pre&gt;
         Cluster1  Cluster2  Cluster3
Object1    500       300        200
Object2    200       200        100
Object3    250       2500       1250       
Object4    190       210        300
&lt;/pre&gt;
', 'clustering  similarity', NULL, '''/p'':63C ''/pre'':84C ''100'':75C ''1250'':79C ''190'':81C ''200'':71C,73C,74C ''210'':82C ''250'':77C ''2500'':78C ''3'':23C ''300'':70C,83C ''4'':14C ''500'':69C ''belong'':30C,48C ''calcul'':55C ''cluster'':6A,7B,24C,33C,34C,51C ''cluster1'':65C ''cluster2'':66C ''cluster3'':67C ''done'':36C ''element'':20C,26C,44C ''exampl'':11C ''know'':40C ''mani'':43C ''object'':15C,17C,29C,47C,62C ''object1'':68C ''object2'':72C ''object3'':76C ''object4'':80C ''p'':9C ''porbabl'':57C ''pre'':64C ''probabl'':1A ''similar'':3A,8B,59C ''two'':5A,61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5021, 1, '2015-02-03 04:23:37.463', 1, 74, 'Rank players of any given sport', '&lt;p&gt;I''ve recently become interested in possibly of developing some sort of method for ranking athletes of sports such as American football and determining which players are better than others in terms of specific statistics. &lt;/p&gt;

&lt;p&gt;My thoughts are that there are two ways to go about doing this. The first would be some sort of mathematical formula which would take in the statistics of a given player and provide some sort of standardized score which could be compared with other players to determine which is better.&lt;/p&gt;

&lt;p&gt;My other idea would be to have some machine learning algorithm go through historical data and determine the patterns which indicate how well a certain combination of statistics would perform in the following week of play by using the patterns it recognizes over time.&lt;/p&gt;

&lt;p&gt;I''m not sure which approach would be more effective and so I''m hoping that someone has an idea or any advice as to which would be best to look into. Thanks!&lt;/p&gt;
', 'predictive-modeling  scoring  ranking  sports', NULL, '''/p'':49C,102C,148C,183C ''advic'':172C ''algorithm'':114C ''american'':34C ''approach'':155C ''athlet'':29C ''becom'':17C ''best'':178C ''better'':41C,101C ''certain'':128C ''combin'':129C ''compar'':93C ''could'':91C ''data'':118C ''determin'':37C,98C,120C ''develop'':22C ''effect'':159C ''first'':65C ''follow'':136C ''footbal'':35C ''formula'':72C ''given'':5A,81C ''go'':60C,115C ''histor'':117C ''hope'':164C ''idea'':106C,169C ''indic'':124C ''interest'':18C ''learn'':113C ''look'':180C ''m'':151C,163C ''machin'':112C ''mathemat'':71C ''method'':26C ''model'':9B ''other'':43C ''p'':13C,50C,103C,149C ''pattern'':122C,143C ''perform'':133C ''play'':139C ''player'':2A,39C,82C,96C ''possibl'':20C ''predict'':8B ''predictive-model'':7B ''provid'':84C ''rank'':1A,11B,28C ''recent'':16C ''recogn'':145C ''score'':10B,89C ''someon'':166C ''sort'':24C,69C,86C ''specif'':47C ''sport'':6A,12B,31C ''standard'':88C ''statist'':48C,78C,131C ''sure'':153C ''take'':75C ''term'':45C ''thank'':182C ''thought'':52C ''time'':147C ''two'':57C ''use'':141C ''ve'':15C ''way'':58C ''week'':137C ''well'':126C ''would'':66C,74C,107C,132C,156C,176C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5033, 1, '2015-02-03 23:49:30.013', -1, 98, 'Format of CSV file', '&lt;p&gt;I''am trying t create a regression based prediction (like booking website): predict the number of clicks for each hotel.
I have to generate a .csv file containing two columns: hotel_id, predicted_number_of_clicks for all hotel_ids.
My first question question is : should I put the Id_hotel as feature in the predictive model. I think that I must to drop it no?
Second question is : how can write in the csv file only this 2 columns "hotel_id", "predicted_number_of_clicks" if I drop hotel_id from the model feature?
Thank you very much for your reply in advance&lt;/p&gt;
', 'regression  predictive-modeling', NULL, '''/p'':114C ''2'':88C ''advanc'':113C ''base'':17C ''book'':20C ''click'':26C,45C,95C ''column'':39C,89C ''contain'':37C ''creat'':14C ''csv'':3A,35C,84C ''drop'':73C,98C ''featur'':62C,104C ''file'':4A,36C,85C ''first'':51C ''format'':1A ''generat'':33C ''hotel'':29C,40C,48C,60C,90C,99C ''id'':41C,49C,59C,91C,100C ''like'':19C ''model'':8B,66C,103C ''much'':108C ''must'':71C ''number'':24C,43C,93C ''p'':9C ''predict'':7B,18C,22C,42C,65C,92C ''predictive-model'':6B ''put'':57C ''question'':52C,53C,77C ''regress'':5B,16C ''repli'':111C ''second'':76C ''thank'':105C ''think'':68C ''tri'':12C ''two'':38C ''websit'':21C ''write'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5028, 2, '2015-02-03 19:53:47.233', 1, NULL, NULL, '&lt;p&gt;From what I understand of the objectives of the lambda architecture your point:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Your batch layer is probably a map reduce job or a HIVE query. &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Is not what was intended. The batch layer is not meant to be directly queried against, but rather feeds a serving layer, possibly a simple key-value store, for low latency queries.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/jtQ1b.png" alt="lambda architecture diagram"&gt;&lt;/p&gt;

&lt;p&gt;Check out &lt;a href="http://lambda-architecture.net/" rel="nofollow"&gt;http://lambda-architecture.net/&lt;/a&gt; for a more full explanation.&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':89C ''/blockquote'':32C ''/jtq1b.png'':73C ''/p'':15C,31C,67C,78C,95C ''alt'':74C ''architectur'':12C,76C ''batch'':19C,40C ''blockquot'':16C ''check'':80C ''diagram'':77C ''direct'':47C ''explan'':94C ''feed'':52C ''full'':93C ''hive'':29C ''href'':83C ''i.stack.imgur.com'':72C ''i.stack.imgur.com/jtq1b.png'':71C ''img'':69C ''intend'':38C ''job'':26C ''key'':60C ''key-valu'':59C ''lambda'':11C,75C ''lambda-architecture.net'':84C,88C ''lambda-architecture.net/&lt;/a&gt;'':87C ''latenc'':65C ''layer'':20C,41C,55C ''low'':64C ''map'':24C ''meant'':44C ''nofollow'':86C ''object'':8C ''p'':1C,17C,33C,68C,79C ''point'':14C ''possibl'':56C ''probabl'':22C ''queri'':30C,48C,66C ''rather'':51C ''reduc'':25C ''rel'':85C ''serv'':54C ''simpl'':58C ''src'':70C ''store'':62C ''understand'':5C ''valu'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5022, 2, '2015-02-03 08:09:44.953', 1, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Prediction&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;If the main goal is predicting anything, say, the statisitcs of the player in the next game, game result, then I would not recommend to do any scoring. Better way to go is using the pure statistics data as an input to the model. Any scoring/rankning - is information loss.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Ranking&lt;/strong&gt; &lt;/p&gt;

&lt;p&gt;If the goal is ranking itself, than you still need to have some target variable to predict. As you may want to check real predictive value of those ranks. That could be, again, playser stats in the &lt;em&gt;next&lt;/em&gt; game or game result itself.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;em&gt;References&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/questions/265/can-machine-learning-algorithms-predict-sports-scores-or-plays/269#269"&gt;Sport scores prediction&lt;/a&gt; and &lt;a href="http://datascience.stackexchange.com/questions/1119/predictive-modeling-based-on-rfm-scoring-indicators"&gt;RFM scoring&lt;/a&gt; are probably the next directions for you to look at.&lt;/p&gt;
', NULL, NULL, '''/a'':124C,133C ''/em'':102C,113C ''/p'':5C,56C,61C,108C,114C,144C ''/questions/1119/predictive-modeling-based-on-rfm-scoring-indicators'':130C ''/questions/265/can-machine-learning-algorithms-predict-sports-scores-or-plays/269#269'':120C ''/strong'':4C,60C ''anyth'':13C ''better'':35C ''check'':85C ''could'':93C ''data'':44C ''datascience.stackexchange.com'':119C,129C ''datascience.stackexchange.com/questions/1119/predictive-modeling-based-on-rfm-scoring-indicators'':128C ''datascience.stackexchange.com/questions/265/can-machine-learning-algorithms-predict-sports-scores-or-plays/269#269'':118C ''direct'':138C ''em'':100C,111C ''game'':23C,24C,103C,105C ''go'':38C ''goal'':10C,65C ''hr'':109C ''href'':117C,127C ''inform'':54C ''input'':47C ''look'':142C ''loss'':55C ''main'':9C ''may'':82C ''model'':50C ''need'':72C ''next'':22C,101C,137C ''p'':1C,6C,57C,62C,110C,115C ''player'':19C ''playser'':96C ''predict'':3C,12C,79C,87C,123C ''probabl'':135C ''pure'':42C ''rank'':59C,67C,91C ''real'':86C ''recommend'':30C ''refer'':112C ''result'':25C,106C ''rfm'':131C ''say'':14C ''score'':34C,122C,132C ''scoring/rankning'':52C ''sport'':121C ''stat'':97C ''statisitc'':16C ''statist'':43C ''still'':71C ''strong'':2C,58C ''target'':76C ''use'':40C ''valu'':88C ''variabl'':77C ''want'':83C ''way'':36C ''would'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5024, 1, '2015-02-03 10:14:53.543', 4, 2070, 'Is the GA R package the best Genetic Algorithm package?', '&lt;p&gt;I am learning about Data Science and I love the Healthcare part. That''s why I have started a blog and my third entry is about using Genetic Algorithm for solving NP-Problems. 
This post is &lt;a href="https://datasciencecgp.wordpress.com/2015/01/31/the-amazing-genetic-algorithms/" rel="nofollow"&gt;https://datasciencecgp.wordpress.com/2015/01/31/the-amazing-genetic-algorithms/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I have some expertise with GA package solving problems like the TSP, but do you know any most powerful R package?&lt;/p&gt;

&lt;p&gt;Thanks so much!&lt;/p&gt;
', 'r  algorithms  genetic', NULL, '''/2015/01/31/the-amazing-genetic-algorithms/'':56C ''/2015/01/31/the-amazing-genetic-algorithms/&lt;/a&gt;&lt;/p&gt;'':61C ''/p'':84C,89C ''algorithm'':9A,12B,43C ''best'':7A ''blog'':34C ''data'':19C ''datasciencecgp.wordpress.com'':55C,60C ''datasciencecgp.wordpress.com/2015/01/31/the-amazing-genetic-algorithms/'':54C ''datasciencecgp.wordpress.com/2015/01/31/the-amazing-genetic-algorithms/&lt;/a&gt;&lt;/p&gt;'':59C ''entri'':38C ''expertis'':66C ''ga'':3A,68C ''genet'':8A,13B,42C ''healthcar'':25C ''href'':53C ''know'':78C ''learn'':17C ''like'':72C ''love'':23C ''much'':88C ''nofollow'':58C ''np'':47C ''np-problem'':46C ''p'':14C,62C,85C ''packag'':5A,10A,69C,83C ''part'':26C ''post'':50C ''power'':81C ''problem'':48C,71C ''r'':4A,11B,82C ''rel'':57C ''scienc'':20C ''solv'':45C,70C ''start'':32C ''thank'':86C ''third'':37C ''tsp'':74C ''use'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5025, 2, '2015-02-03 11:37:06.76', 5, NULL, NULL, '&lt;p&gt;For such questions, I like to go to the &lt;a href="http://cran.r-project.org/web/views/"&gt;Task Views&lt;/a&gt; on CRAN, since the packages noted there are, to a degree, pre-vetted by the R community. I''d trust those a tiny bit more than just googling myself.&lt;/p&gt;

&lt;p&gt;The &lt;a href="http://cran.r-project.org/web/views/MachineLearning.html"&gt;Machine Learning Task View at CRAN&lt;/a&gt; says:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Packages &lt;a href="http://cran.r-project.org/web/packages/rgp/index.html"&gt;rgp&lt;/a&gt;
  and
  &lt;a href="http://cran.r-project.org/web/packages/rgenoud/index.html"&gt;rgenoud&lt;/a&gt;
  offer optimization routines based on genetic algorithms. The package
  &lt;a href="http://cran.r-project.org/web/packages/Rmalschains/index.html"&gt;Rmalschains&lt;/a&gt;
  implements memetic algorithms with local search chains, which are a
  special type of evolutionary algorithms, combining a steady state
  genetic algorithm with local search for real-valued parameter
  optimization.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/a'':18C,63C,75C,83C,99C ''/blockquote'':131C ''/p'':49C,65C,130C ''/web/packages/rgenoud/index.html'':81C ''/web/packages/rgp/index.html'':73C ''/web/packages/rmalschains/index.html'':97C ''/web/views/'':15C ''/web/views/machinelearning.html'':56C ''algorithm'':90C,102C,114C,120C ''base'':87C ''bit'':43C ''blockquot'':66C ''chain'':106C ''combin'':115C ''communiti'':36C ''cran'':20C,62C ''cran.r-project.org'':14C,55C,72C,80C,96C ''cran.r-project.org/web/packages/rgenoud/index.html'':79C ''cran.r-project.org/web/packages/rgp/index.html'':71C ''cran.r-project.org/web/packages/rmalschains/index.html'':95C ''cran.r-project.org/web/views/'':13C ''cran.r-project.org/web/views/machinelearning.html'':54C ''d'':38C ''degre'':29C ''evolutionari'':113C ''genet'':89C,119C ''go'':8C ''googl'':47C ''href'':12C,53C,70C,78C,94C ''implement'':100C ''learn'':58C ''like'':6C ''local'':104C,122C ''machin'':57C ''memet'':101C ''note'':24C ''offer'':84C ''optim'':85C,129C ''p'':1C,50C,67C ''packag'':23C,68C,92C ''paramet'':128C ''pre'':31C ''pre-vet'':30C ''question'':4C ''r'':35C ''real'':126C ''real-valu'':125C ''rgenoud'':82C ''rgp'':74C ''rmalschain'':98C ''routin'':86C ''say'':64C ''search'':105C,123C ''sinc'':21C ''special'':110C ''state'':118C ''steadi'':117C ''task'':16C,59C ''tini'':42C ''trust'':39C ''type'':111C ''valu'':127C ''vet'':32C ''view'':17C,60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5026, 2, '2015-02-03 16:00:05.673', 3, NULL, NULL, '&lt;p&gt;Classifiers often return probabilities of belonging to a class. For example in logistic regression the predicted values are the predicted probability of belonging to the non-reference class or $\text{Pr}(Y = 1)$. The discrimination threshold is just the cutoff imposed on the predicted probabilities for assigning observations to each class.&lt;/p&gt;
', NULL, NULL, '''/p'':54C ''1'':35C ''assign'':49C ''belong'':7C,24C ''class'':10C,30C,53C ''classifi'':2C ''cutoff'':42C ''discrimin'':37C ''exampl'':12C ''impos'':43C ''logist'':14C ''non'':28C ''non-refer'':27C ''observ'':50C ''often'':3C ''p'':1C ''pr'':33C ''predict'':17C,21C,46C ''probabl'':5C,22C,47C ''refer'':29C ''regress'':15C ''return'':4C ''text'':32C ''threshold'':38C ''valu'':18C ''y'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5029, 2, '2015-02-03 20:05:59.917', 6, NULL, NULL, '&lt;p&gt;One other data source I didn''t see listed is &lt;a href="http://gdeltproject.org/"&gt;The GDELT Project&lt;/a&gt;. From the site:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;GDELT Project monitors the world''s broadcast, print, and web news from nearly every corner of every country in over 100 languages and identifies the people, locations, organizations, counts, themes, sources, and events driving our global society every second of every day, creating a free open platform for computing on the entire world.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/a'':18C ''/blockquote'':79C ''/p'':22C,78C ''100'':45C ''blockquot'':23C ''broadcast'':31C ''comput'':73C ''corner'':39C ''count'':53C ''countri'':42C ''creat'':67C ''data'':4C ''day'':66C ''didn'':7C ''drive'':58C ''entir'':76C ''event'':57C ''everi'':38C,41C,62C,65C ''free'':69C ''gdelt'':16C,25C ''gdeltproject.org'':14C ''global'':60C ''href'':13C ''identifi'':48C ''languag'':46C ''list'':10C ''locat'':51C ''monitor'':27C ''near'':37C ''news'':35C ''one'':2C ''open'':70C ''organ'':52C ''p'':1C,24C ''peopl'':50C ''platform'':71C ''print'':32C ''project'':17C,26C ''second'':63C ''see'':9C ''site'':21C ''societi'':61C ''sourc'':5C,55C ''theme'':54C ''web'':34C ''world'':29C,77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5030, 2, '2015-02-03 20:24:04.8', 1, NULL, NULL, '&lt;p&gt;As &lt;a href="http://datascience.stackexchange.com/users/1131/sidhha"&gt;@Sidhha&lt;/a&gt; mentioned GridSearchCV is a great way to estimate parameters. Here is an &lt;a href="http://scikit-learn.org/stable/auto_examples/grid_search_digits.html#example-grid-search-digits-py" rel="nofollow"&gt;example&lt;/a&gt; showing how to use it. Good luck.&lt;/p&gt;
', NULL, NULL, '''/a'':9C,30C ''/p'':38C ''/stable/auto_examples/grid_search_digits.html#example-grid-search-digits-py'':26C ''/users/1131/sidhha'':7C ''datascience.stackexchange.com'':6C ''datascience.stackexchange.com/users/1131/sidhha'':5C ''estim'':17C ''exampl'':29C ''good'':36C ''great'':14C ''gridsearchcv'':11C ''href'':4C,23C ''luck'':37C ''mention'':10C ''nofollow'':28C ''p'':1C ''paramet'':18C ''rel'':27C ''scikit-learn.org'':25C ''scikit-learn.org/stable/auto_examples/grid_search_digits.html#example-grid-search-digits-py'':24C ''show'':31C ''sidhha'':8C ''use'':34C ''way'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5089, 2, '2015-02-09 00:34:26.52', 1, NULL, NULL, '&lt;p&gt;The issue was with column addressing, heres the correct working code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;grpd = GROUP features BY featureid;
temp = FOREACH grpd GENERATE group as featureid, MAX(features.featurevalue) as val;
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':32C ''/p'':13C ''/pre'':33C ''address'':7C ''code'':12C,15C ''column'':6C ''correct'':10C ''featur'':18C ''featureid'':20C,27C ''features.featurevalue'':29C ''foreach'':22C ''generat'':24C ''group'':17C,25C ''grpd'':16C,23C ''here'':8C ''issu'':3C ''max'':28C ''p'':1C ''pre'':14C ''temp'':21C ''val'':31C ''work'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5853, 2, '2015-05-21 12:51:35.033', 7, NULL, NULL, '&lt;p&gt;Word of warning from a former airline Revenue Management analyst: you might be barking up the wrong tree with this approach. Apologies for the wall of text that follows, but this data is a lot more complex and noisy than might appear at first glance, so wanted to provide a short description of how it''s generated; forewarned is forearmed.&lt;/p&gt;

&lt;p&gt;Airline fares have two components to them: all the actual fares (complete with fare rules and what have you) that an airline has available for a certain route, most of which are published the Airline Tariff Publishing Company (a few special-use ones are not, but those are the exception rather than the rule) and the actual inventory management performed by the airline on a day-to-day basis.&lt;/p&gt;

&lt;p&gt;Fares can be submitted to ATPCO four times a day, at set intervals, and when airlines do so, it will usually consist of a mixture of additions, deletions, and modifications of existing fares. When an airline initiates a pricing action (assuming their competitors aren''t trying to make their own moves here), they usually have to wait until the next update to see if their competitors follow/respond. The converse goes when a competitor initiates a pricing action, as the airline has to wait until the next update before they can respond.&lt;/p&gt;

&lt;p&gt;Now, this is all well and good with respect to fares, but the problem is that, because this is all getting published in ATPCO, fares are the next best thing to public information... all your competitors get to see what you''ve got in your arsenal, so attempts to obfuscate are not unheard of, such as publishing fares that will never actually be assigned any inventory, listing all the fares as day-of-departure, etc. &lt;/p&gt;

&lt;p&gt;In many ways, the secret sauce comes down to the actual inventory allocation, i.e. how many seats on each flight will you be willing to sell for a given fare, and this information is not publicly available. You can get some glimpses by scraping web info, but the potential combinations of departure time/date and fare rules are quite numerous and may quickly escalate beyond your ability to easily keep track of. &lt;/p&gt;

&lt;p&gt;Typically an airline will only be willing to sell a handful of seats for a very low fare and the people who snag those have to book quite far in advance lest the fare rules lock them out, or other travelers simply beat them to the punch. The airline will be willing to sell a few more seats for a higher fare, and so on and so forth. They will be quite happy to sell all of the seats for the highest fare they''ve got published, but this is not usually feasible.&lt;/p&gt;

&lt;p&gt;What you''re seeing with fares getting higher the closer you get to the day of departure is simply the natural process of having the cheap seats get booked farther out, while the remaining inventory gradually gets more expensive. Of course, there are some caveats here. The RM process is actively managed and human intervention is quite common as the RM team generally strives to meet its revenue goals and maximize revenue on each flight. As such, flights that fill up quickly may be "tightened up" by closing out low fares. Flights that are booking slowly may be "loosened up" by allocating more seats to lower fares.&lt;/p&gt;

&lt;p&gt;There is a constant interplay and competition between airlines in this area, but you are not very likely to capture the actual dynamics just from scraping fares. Don''t get me wrong, we had such tools at our disposal, and, despite their limitations, they were quite valuable, but they were just one data source that fed into the decision-making process. You''d need access to the hundreds, if not thousands of operational decisions made by RM teams on a daily basis, as well as state-of-the-world information as they see it at the time. If you cannot find an airline partner to work with in order to get this data, you might need to consider alternate data sources.&lt;/p&gt;

&lt;p&gt;I''d recommend looking into getting access to O&amp;amp;D fare data from the Official Airline Guide (or one of their competitors) and try to use that for your analysis. It''s sample-based (about 10% of all tickets sold) and aggregated at a higher level than would be ideal so careful route selection is imperative (I''d recommend something with plenty of airlines, flying non-stop multiple times a day, with large aircraft), but you may be able to get a better picture of what was actually sold (average fare) and how much of it was sold (load factor), vs. merely what is available for sale at a given point in time. Using that information you might be in better position to at least explore the outcomes of the airlines'' pricing strategy, and make your inferences from there.&lt;/p&gt;
', NULL, NULL, '''/p'':62C,135C,228C,306C,379C,474C,583C,708C,852C ''10'':747C ''abil'':373C ''abl'':791C ''access'':650C,716C ''action'':176C,213C ''activ'':526C ''actual'':73C,121C,291C,318C,606C,800C ''addit'':163C ''advanc'':411C ''aggreg'':753C ''aircraft'':786C ''airlin'':8C,64C,85C,98C,127C,152C,172C,216C,383C,429C,593C,689C,726C,775C,843C ''alloc'':320C,577C ''altern'':705C ''amp'':719C ''analysi'':740C ''analyst'':11C ''apolog'':23C ''appear'':43C ''approach'':22C ''area'':596C ''aren'':180C ''arsenal'':275C ''assign'':293C ''assum'':177C ''atpco'':142C,253C ''attempt'':277C ''avail'':87C,344C,817C ''averag'':802C ''bark'':15C ''base'':745C ''basi'':134C,667C ''beat'':423C ''best'':258C ''better'':795C,833C ''beyond'':371C ''book'':407C,504C,570C ''cannot'':686C ''captur'':604C ''care'':763C ''caveat'':520C ''certain'':90C ''cheap'':501C ''close'':563C ''closer'':485C ''combin'':357C ''come'':314C ''common'':533C ''compani'':101C ''competit'':591C ''competitor'':179C,202C,209C,265C,732C ''complet'':75C ''complex'':38C ''compon'':68C ''consid'':704C ''consist'':158C ''constant'':588C ''convers'':205C ''cours'':516C ''d'':648C,711C,720C,769C ''daili'':666C ''data'':33C,637C,699C,706C,722C ''day'':131C,133C,146C,302C,490C,783C ''day-of-departur'':301C ''day-to-day'':130C ''decis'':644C,659C ''decision-mak'':643C ''delet'':164C ''departur'':304C,359C,492C ''descript'':53C ''despit'':625C ''dispos'':623C ''dynam'':607C ''easili'':375C ''escal'':370C ''etc'':305C ''except'':114C ''exist'':168C ''expens'':514C ''explor'':838C ''factor'':812C ''far'':409C ''fare'':65C,74C,77C,137C,169C,240C,254C,287C,299C,337C,362C,398C,414C,442C,463C,481C,566C,582C,611C,721C,803C ''farther'':505C ''feasibl'':473C ''fed'':640C ''fill'':555C ''find'':687C ''first'':45C ''fli'':776C ''flight'':327C,550C,553C,567C ''follow'':30C ''follow/respond'':203C ''forearm'':61C ''forewarn'':59C ''former'':7C ''forth'':448C ''four'':143C ''general'':538C ''generat'':58C ''get'':250C,266C,347C,482C,487C,503C,512C,614C,697C,715C,793C ''given'':336C,822C ''glanc'':46C ''glimps'':349C ''goal'':544C ''goe'':206C ''good'':236C ''got'':272C,466C ''gradual'':511C ''guid'':727C ''hand'':391C ''happi'':453C ''higher'':441C,483C,756C ''highest'':462C ''human'':529C ''hundr'':653C ''i.e'':321C ''ideal'':761C ''imper'':767C ''infer'':849C ''info'':353C ''inform'':262C,340C,676C,828C ''initi'':173C,210C ''interplay'':589C ''interv'':149C ''intervent'':530C ''inventori'':122C,295C,319C,510C ''keep'':376C ''larg'':785C ''least'':837C ''lest'':412C ''level'':757C ''like'':602C ''limit'':627C ''list'':296C ''load'':811C ''lock'':416C ''look'':713C ''loosen'':574C ''lot'':36C ''low'':397C,565C ''lower'':581C ''made'':660C ''make'':184C,645C,847C ''manag'':10C,123C,527C ''mani'':309C,323C ''maxim'':546C ''may'':368C,558C,572C,789C ''meet'':541C ''mere'':814C ''might'':13C,42C,701C,830C ''mixtur'':161C ''modif'':166C ''move'':187C ''much'':806C ''multipl'':780C ''natur'':496C ''need'':649C,702C ''never'':290C ''next'':196C,222C,257C ''noisi'':40C ''non'':778C ''non-stop'':777C ''numer'':366C ''o'':718C ''obfusc'':279C ''offici'':725C ''one'':107C,636C,729C ''oper'':658C ''order'':695C ''outcom'':840C ''p'':1C,63C,136C,229C,307C,380C,475C,584C,709C ''partner'':690C ''peopl'':401C ''perform'':124C ''pictur'':796C ''plenti'':773C ''point'':823C ''posit'':834C ''potenti'':356C ''price'':175C,212C,844C ''problem'':243C ''process'':497C,524C,646C ''provid'':50C ''public'':261C,343C ''publish'':96C,100C,251C,286C,467C ''punch'':427C ''quick'':369C,557C ''quit'':365C,408C,452C,532C,630C ''rather'':115C ''re'':478C ''recommend'':712C,770C ''remain'':509C ''respect'':238C ''respond'':227C ''revenu'':9C,543C,547C ''rm'':523C,536C,662C ''rout'':91C,764C ''rule'':78C,118C,363C,415C ''sale'':819C ''sampl'':744C ''sample-bas'':743C ''sauc'':313C ''scrape'':351C,610C ''seat'':324C,393C,438C,459C,502C,579C ''secret'':312C ''see'':199C,268C,479C,679C ''select'':765C ''sell'':333C,389C,434C,455C ''set'':148C ''short'':52C ''simpli'':422C,494C ''slowli'':571C ''snag'':403C ''sold'':751C,801C,810C ''someth'':771C ''sourc'':638C,707C ''special'':105C ''special-us'':104C ''state'':672C ''state-of-the-world'':671C ''stop'':779C ''strategi'':845C ''strive'':539C ''submit'':140C ''tariff'':99C ''team'':537C,663C ''text'':28C ''thing'':259C ''thousand'':656C ''ticket'':750C ''tighten'':560C ''time'':144C,683C,781C,825C ''time/date'':360C ''tool'':620C ''track'':377C ''travel'':421C ''tree'':19C ''tri'':182C,734C ''two'':67C ''typic'':381C ''unheard'':282C ''updat'':197C,223C ''use'':106C,736C,826C ''usual'':157C,190C,472C ''valuabl'':631C ''ve'':271C,465C ''vs'':813C ''wait'':193C,219C ''wall'':26C ''want'':48C ''warn'':4C ''way'':310C ''web'':352C ''well'':234C,669C ''will'':331C,387C,432C ''word'':2C ''work'':692C ''world'':675C ''would'':759C ''wrong'':18C,616C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5031, 2, '2015-02-03 22:51:57.083', 1, NULL, NULL, '&lt;p&gt;If you have an imbalanced dataset you usually want to make it balanced to begin with, since that will artificially affect your scores.&lt;/p&gt;

&lt;p&gt;Now, you want to be measuring precision and recall, since those can capture a bit better the imbalanced dataset biases.&lt;/p&gt;

&lt;p&gt;L1 or L2 won''t perform particularly better in a balanced or unbalanced dataset, what you want to do is call elastic nets (which is a combination of the two) and do cross validation over the coefficients of each of the regularizers. &lt;/p&gt;

&lt;p&gt;Also, doing grid search is very odd, you are better using just cross validation and see what parameters work better.&lt;/p&gt;

&lt;p&gt;They even have ElasticNetCV, which does that part for you (&lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNetCV.html#sklearn.linear_model.ElasticNetCV" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNetCV.html#sklearn.linear_model.ElasticNetCV&lt;/a&gt;)&lt;/p&gt;
', NULL, NULL, '''/p'':25C,47C,91C,113C ''/stable/modules/generated/sklearn.linear_model.elasticnetcv.html#sklearn.linear_model.elasticnetcv'':129C ''/stable/modules/generated/sklearn.linear_model.elasticnetcv.html#sklearn.linear_model.elasticnetcv&lt;/a&gt;)&lt;/p&gt;'':134C ''affect'':22C ''also'':93C ''artifici'':21C ''balanc'':14C,59C ''begin'':16C ''better'':42C,56C,102C,112C ''bias'':46C ''bit'':41C ''call'':69C ''captur'':39C ''coeffici'':85C ''combin'':75C ''cross'':81C,105C ''dataset'':7C,45C,62C ''elast'':70C ''elasticnetcv'':118C ''even'':116C ''grid'':95C ''href'':126C ''imbalanc'':6C,44C ''l1'':49C ''l2'':51C ''make'':12C ''measur'':32C ''net'':71C ''nofollow'':131C ''odd'':99C ''p'':1C,26C,48C,92C,114C ''paramet'':110C ''part'':122C ''particular'':55C ''perform'':54C ''precis'':33C ''recal'':35C ''regular'':90C ''rel'':130C ''scikit-learn.org'':128C,133C ''scikit-learn.org/stable/modules/generated/sklearn.linear_model.elasticnetcv.html#sklearn.linear_model.elasticnetcv'':127C ''scikit-learn.org/stable/modules/generated/sklearn.linear_model.elasticnetcv.html#sklearn.linear_model.elasticnetcv&lt;/a&gt;)&lt;/p&gt;'':132C ''score'':24C ''search'':96C ''see'':108C ''sinc'':18C,36C ''two'':78C ''unbalanc'':61C ''use'':103C ''usual'':9C ''valid'':82C,106C ''want'':10C,29C,65C ''won'':52C ''work'':111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5032, 2, '2015-02-03 22:56:04.747', 2, NULL, NULL, '&lt;p&gt;Just to add a bit.&lt;/p&gt;

&lt;p&gt;Like it was mentioned before, if you have a classifier (probabilistic) your output is a probability (a number between 0 and 1), ideally you want to say that everything larger than 0.5 is part of one class and anything less than 0.5 is the other class.&lt;/p&gt;

&lt;p&gt;But if you are classifying cancer rates, you are deeply concerned with false negatives (telling some he does not have cancer, when he does) while a false positive (telling someone he does have cancer when he doesn''t) is not as critical. So you might artificially move that threshold from 0.5 to higher or lower values, to change the sensitivity of the model in general.&lt;/p&gt;

&lt;p&gt;By doing this, you can generate the ROC plot for different thresholds.&lt;/p&gt;
', NULL, NULL, '''/p'':7C,55C,122C,136C ''0'':28C ''0.5'':40C,50C,107C ''1'':30C ''add'':4C ''anyth'':47C ''artifici'':102C ''bit'':6C ''cancer'':62C,77C,90C ''chang'':114C ''class'':45C,54C ''classifi'':18C,61C ''concern'':67C ''critic'':98C ''deepli'':66C ''differ'':134C ''doesn'':93C ''everyth'':37C ''fals'':69C,83C ''general'':121C ''generat'':129C ''higher'':109C ''ideal'':31C ''larger'':38C ''less'':48C ''like'':9C ''lower'':111C ''mention'':12C ''might'':101C ''model'':119C ''move'':103C ''negat'':70C ''number'':26C ''one'':44C ''output'':21C ''p'':1C,8C,56C,123C ''part'':42C ''plot'':132C ''posit'':84C ''probabilist'':19C ''probabl'':24C ''rate'':63C ''roc'':131C ''say'':35C ''sensit'':116C ''someon'':86C ''tell'':71C,85C ''threshold'':105C,135C ''valu'':112C ''want'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5038, 1, '2015-02-04 04:51:28.063', 0, 94, 'minimization with a negative cost function: works in MATLAB, not in Python', '&lt;p&gt;I''m trying to use a particular cost function (based on doubling rate of wealth) for a classification problem, and the solution works well in MATLAB. See &lt;a href="https://github.com/acmyers/compareCostFXs" rel="nofollow"&gt;https://github.com/acmyers/compareCostFXs&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;When I try to do this in Python 2.7.6 I don''t get any errors, but it only returns zeros for the theta values.&lt;/p&gt;

&lt;p&gt;Here is the cost function and optimization method I''ve used in Python:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def costFunctionDRW(theta, X, y):

    # Initialize useful values
    m = len(y)
    # Marginal probability of acceptance
    marg_pA = sum(y)/m
    # Marginal probability of rejection
    marg_pR = 1 - marg_pA

    # =============================================================
    pred = sigmoid(np.dot(X,theta))
    final_wealth_individual = (pred/marg_pA)*y + ((1-pred)/marg_pR)*(1-y)
    final_wealth = np.prod(final_wealth_individual)
    final_wealth = -final_wealth

    return final_wealth

result = scipy.optimize.fmin(costFunctionDRW, x0=initial_theta, \
                   args=(X_array, y_array), maxiter=1000, disp=False, full_output=True )
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Any advice would be much appreciated!&lt;/p&gt;
', 'classification  python', NULL, '''/acmyers/comparecostfxs'':47C ''/acmyers/comparecostfxs&lt;/a&gt;&lt;/p&gt;'':52C ''/code'':171C ''/m'':115C ''/marg_pr'':137C ''/p'':78C,93C,180C ''/pre'':172C ''1'':122C,135C,138C ''1000'':165C ''2.7.6'':62C ''accept'':110C ''advic'':175C ''appreci'':179C ''arg'':159C ''array'':161C,163C ''base'':25C ''classif'':13B,33C ''code'':95C ''cost'':5A,23C,83C ''costfunctiondrw'':97C,155C ''def'':96C ''disp'':166C ''doubl'':27C ''error'':68C ''fals'':167C ''final'':130C,140C,143C,146C,148C,151C ''full'':168C ''function'':6A,24C,84C ''get'':66C ''github.com'':46C,51C ''github.com/acmyers/comparecostfxs'':45C ''github.com/acmyers/comparecostfxs&lt;/a&gt;&lt;/p&gt;'':50C ''href'':44C ''individu'':132C,145C ''initi'':101C,157C ''len'':105C ''m'':17C,104C ''marg'':111C,120C,123C ''margin'':107C,116C ''matlab'':9A,41C ''maxit'':164C ''method'':87C ''minim'':1A ''much'':178C ''negat'':4A ''nofollow'':49C ''np.dot'':127C ''np.prod'':142C ''optim'':86C ''output'':169C ''p'':15C,53C,79C,173C ''pa'':112C,124C ''particular'':22C ''pr'':121C ''pre'':94C ''pred'':125C,136C ''pred/marg_pa'':133C ''probabl'':108C,117C ''problem'':34C ''python'':12A,14B,61C,92C ''rate'':28C ''reject'':119C ''rel'':48C ''result'':153C ''return'':72C,150C ''scipy.optimize.fmin'':154C ''see'':42C ''sigmoid'':126C ''solut'':37C ''sum'':113C ''theta'':76C,98C,129C,158C ''tri'':18C,56C ''true'':170C ''use'':20C,90C,102C ''valu'':77C,103C ''ve'':89C ''wealth'':30C,131C,141C,144C,147C,149C,152C ''well'':39C ''work'':7A,38C ''would'':176C ''x'':99C,128C,160C ''x0'':156C ''y'':100C,106C,114C,134C,139C,162C ''zero'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5099, 2, '2015-02-10 16:26:19.457', 5, NULL, NULL, '&lt;p&gt;I think the key here is what you believe to be dependent variables. You mentioned, for instance, a three month rest. Encoding the rest between races is likely to be a better idea than simply encoding the date of the rest as a lot of the date is redundant to what is actually being said. As with any machine learning algorithm, representation of the data is key and in many ways, the actual algorithm applied is less important.  &lt;/p&gt;
', NULL, NULL, '''/p'':80C ''actual'':54C,74C ''algorithm'':62C,75C ''appli'':76C ''believ'':10C ''better'':33C ''data'':66C ''date'':39C,48C ''depend'':13C ''encod'':23C,37C ''idea'':34C ''import'':79C ''instanc'':18C ''key'':5C,68C ''learn'':61C ''less'':78C ''like'':29C ''lot'':45C ''machin'':60C ''mani'':71C ''mention'':16C ''month'':21C ''p'':1C ''race'':27C ''redund'':50C ''represent'':63C ''rest'':22C,25C,42C ''said'':56C ''simpli'':36C ''think'':3C ''three'':20C ''variabl'':14C ''way'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5039, 2, '2015-02-04 05:33:59.907', 0, NULL, NULL, '&lt;p&gt;If your number of cases is large, you may be running into a problem of numerical underflow in the line &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;final_wealth = np.prod(final_wealth_individual)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;If each value of &lt;code&gt;final_wealth_individual&lt;/code&gt; is between 0 and 1, multiplying them all together can lead to a result that is too small to represent as a floating point number, resulting in a value of 0.&lt;/p&gt;

&lt;p&gt;To address this issue, take the log of &lt;code&gt;final_wealth_individual&lt;/code&gt; and add them together instead of multiplying.  Note that this will cause &lt;code&gt;final_wealth&lt;/code&gt; to be negative, so you will not need to multiply it by -1 as you are currently.&lt;/p&gt;
', NULL, NULL, '''-1'':117C ''/blockquote'':32C ''/code'':42C,88C,104C ''/p'':22C,31C,74C,122C ''0'':45C,73C ''1'':47C ''add'':90C ''address'':77C ''blockquot'':23C ''case'':6C ''caus'':100C ''code'':38C,84C,101C ''current'':121C ''final'':25C,28C,39C,85C,102C ''float'':65C ''individu'':30C,41C,87C ''instead'':93C ''issu'':79C ''larg'':8C ''lead'':53C ''line'':21C ''log'':82C ''may'':10C ''multipli'':48C,95C,114C ''need'':112C ''negat'':107C ''note'':96C ''np.prod'':27C ''number'':4C,67C ''numer'':17C ''p'':1C,24C,33C,75C ''point'':66C ''problem'':15C ''repres'':62C ''result'':56C,68C ''run'':12C ''small'':60C ''take'':80C ''togeth'':51C,92C ''underflow'':18C ''valu'':36C,71C ''wealth'':26C,29C,40C,86C,103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5428, 1, '2015-04-01 20:56:22.337', 0, 135, 'Canonical VS Graph Isomorphism', '&lt;p&gt;I''m having a having a hard time understanding the difference between an isomorphism in graphs and canonical graphs. I have read through the Wikipedia articles, but it still isn''t clicking.&lt;/p&gt;

&lt;p&gt;Can somebody explain the difference, perhaps with an example?&lt;/p&gt;
', 'graphs  terminology', NULL, '''/p'':40C,51C ''articl'':33C ''canon'':1A,25C ''click'':39C ''differ'':18C,46C ''exampl'':50C ''explain'':44C ''graph'':3A,5B,23C,26C ''hard'':14C ''isn'':37C ''isomorph'':4A,21C ''m'':9C ''p'':7C,41C ''perhap'':47C ''read'':29C ''somebodi'':43C ''still'':36C ''terminolog'':6B ''time'':15C ''understand'':16C ''vs'':2A ''wikipedia'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5040, 1, '2015-02-04 06:10:34.893', 0, 73, 'How to determine whether a bad performance is caused by data quality?', '&lt;p&gt;I''m using a set of features, says $X_1, X_2, ..., X_m $, to predict a target value $Y$, which is a continuous value from zero to one.&lt;/p&gt;

&lt;p&gt;At first, I try to use a linear regression model to do the prediction, but it does not perform well. The root-mean-squared error is about 0.35, which is quite high for prediction of a value from 0 to 1.&lt;/p&gt;

&lt;p&gt;Then, I have tried different models, &lt;em&gt;e.g.,&lt;/em&gt; decision-tree-based regression, random-forest-based regression, gradient boosting tree regression and &lt;em&gt;etc.&lt;/em&gt; However, all of these models also do not perform well. (RMSE $\approx $0.35, there is not significant difference with linear regression)&lt;/p&gt;

&lt;p&gt;I understand there are many possible reasons for this problem, such as: feature selection or choice of model, but maybe more fundamentally, the quality of data set is not good.&lt;/p&gt;

&lt;p&gt;My question is: &lt;strong&gt;how can I examine whether it is caused by bad data quality?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;BTW, for the size of data set, there are more than 10K data points, each of which associated with 105 features.&lt;/p&gt;

&lt;p&gt;I have also tried to investigate importance of each feature by using decision-tree-based regression, it turns out that, only one feature (which should not be the most outstanding feature in my knowledge to this problem) have an importance of 0.2, while the rest of them only have an importance less than 0.1.&lt;/p&gt;
', 'machine-learning', NULL, '''/em'':100C,118C ''/p'':46C,90C,140C,172C,191C,214C,271C ''/strong'':190C ''0'':87C ''0.1'':270C ''0.2'':258C ''0.35'':76C,131C ''1'':26C,89C ''105'':212C ''10k'':204C ''2'':28C ''also'':124C,218C ''approx'':130C ''associ'':210C ''bad'':6A,187C ''base'':104C,109C,231C ''boost'':112C ''btw'':193C ''caus'':9A,185C ''choic'':157C ''continu'':40C ''data'':11A,167C,188C,198C,205C ''decis'':102C,229C ''decision-tree-bas'':101C,228C ''determin'':3A ''differ'':96C,136C ''e.g'':99C ''em'':98C,116C ''error'':73C ''etc'':117C ''examin'':181C ''featur'':23C,154C,213C,225C,239C,247C ''first'':49C ''forest'':108C ''fundament'':163C ''good'':171C ''gradient'':111C ''high'':80C ''howev'':119C ''import'':222C,256C,267C ''investig'':221C ''knowledg'':250C ''learn'':15B ''less'':268C ''linear'':55C,138C ''m'':18C,30C ''machin'':14B ''machine-learn'':13B ''mani'':146C ''mayb'':161C ''mean'':71C ''model'':57C,97C,123C,159C ''one'':45C,238C ''outstand'':246C ''p'':16C,47C,91C,141C,173C,192C,215C ''perform'':7A,66C,127C ''point'':206C ''possibl'':147C ''predict'':32C,61C,82C ''problem'':151C,253C ''qualiti'':12A,165C,189C ''question'':175C ''quit'':79C ''random'':107C ''random-forest-bas'':106C ''reason'':148C ''regress'':56C,105C,110C,114C,139C,232C ''rest'':261C ''rmse'':129C ''root'':70C ''root-mean-squar'':69C ''say'':24C ''select'':155C ''set'':21C,168C,199C ''signific'':135C ''size'':196C ''squar'':72C ''strong'':177C ''target'':34C ''tree'':103C,113C,230C ''tri'':51C,95C,219C ''turn'':234C ''understand'':143C ''use'':19C,53C,227C ''valu'':35C,41C,85C ''well'':67C,128C ''whether'':4A,182C ''x'':25C,27C,29C ''y'':36C ''zero'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5041, 2, '2015-02-04 06:15:52.873', 1, NULL, NULL, '&lt;p&gt;How many features do you have?&lt;/p&gt;

&lt;p&gt;Is quite unlikely that ALL the features are bad. So you could regress with a different number of features.&lt;/p&gt;

&lt;p&gt;For example, do one pass with all the features, then take one out (usually X_m) so you have m-1 features. Keep doing this so you can take out uninformative features.&lt;/p&gt;

&lt;p&gt;Also, I would recommend you calculating P-Values to see whether your regessors are significative are informative.&lt;/p&gt;
', NULL, NULL, '''-1'':51C ''/p'':8C,29C,63C,83C ''also'':65C ''bad'':18C ''calcul'':70C ''could'':21C ''differ'':25C ''exampl'':32C ''featur'':4C,16C,28C,39C,52C,62C ''inform'':82C ''keep'':53C ''m'':46C,50C ''mani'':3C ''number'':26C ''one'':34C,42C ''p'':1C,9C,30C,64C,72C ''p-valu'':71C ''pass'':35C ''quit'':11C ''recommend'':68C ''regessor'':78C ''regress'':22C ''see'':75C ''signif'':80C ''take'':41C,59C ''uninform'':61C ''unlik'':12C ''usual'':44C ''valu'':73C ''whether'':76C ''would'':67C ''x'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5042, 2, '2015-02-04 06:18:04.207', 1, NULL, NULL, '&lt;p&gt;Trying to classify using hotel ID is the same as trying to determine if a student is going to perform well on a test based on their last name. You should get additional things, like number of rooms, amenities, location, staff, etc. Those are informative features that you can use in a classifier&lt;/p&gt;
', NULL, NULL, '''/p'':55C ''addit'':34C ''amen'':40C ''base'':26C ''classifi'':4C,54C ''determin'':14C ''etc'':43C ''featur'':47C ''get'':33C ''go'':19C ''hotel'':6C ''id'':7C ''inform'':46C ''last'':29C ''like'':36C ''locat'':41C ''name'':30C ''number'':37C ''p'':1C ''perform'':21C ''room'':39C ''staff'':42C ''student'':17C ''test'':25C ''thing'':35C ''tri'':2C,12C ''use'':5C,51C ''well'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5427, 1, '2015-04-01 15:23:17.997', 7, 580, 'How to generate synthetic dataset using machine learning model learnt with original dataset?', '&lt;p&gt;Generally, the machine learning model is built on datasets. I''d like to know if there is any way to generate synthetic dataset using such trained machine learning model preserving original dataset characteristics ?  &lt;/p&gt;

&lt;p&gt;[original data --&gt; build machine learning model --&gt; use ml model to generate synthetic data....!!!]&lt;/p&gt;

&lt;p&gt;Is it possible ? Please point me to related resource if possible.&lt;/p&gt;
', 'machine-learning  dataset', NULL, '''/p'':52C,67C,80C ''build'':56C ''built'':25C ''characterist'':51C ''d'':29C ''data'':55C,66C ''dataset'':5A,13A,17B,27C,41C,50C ''general'':19C ''generat'':3A,39C,64C ''know'':32C ''learn'':8A,16B,22C,46C,58C ''learnt'':10A ''like'':30C ''machin'':7A,15B,21C,45C,57C ''machine-learn'':14B ''ml'':61C ''model'':9A,23C,47C,59C,62C ''origin'':12A,49C,54C ''p'':18C,53C,68C ''pleas'':72C ''point'':73C ''possibl'':71C,79C ''preserv'':48C ''relat'':76C ''resourc'':77C ''synthet'':4A,40C,65C ''train'':44C ''use'':6A,42C,60C ''way'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5043, 2, '2015-02-04 06:22:23.377', 2, NULL, NULL, '&lt;p&gt;First, it sounds like your choice of model selection is a problem here.  Your outputs are binary-valued, not continuous. Specifically you may have a classification problem on your hands rather than a traditional regression problem.  My first recommendation would be to try a simple classification approach such as logistic regression or linear discriminant analysis.&lt;/p&gt;

&lt;p&gt;Regarding your suspicions of &lt;em&gt;bad data&lt;/em&gt;, what would bad data look like in this situation? Do you have reason to suspect that your $X$ values are noisy or that your $y$ values are mislabeled? It is also possible that there is not a strong relationship between any of your features and your targets.  Since your targets are binary, you should look at histograms of each of your features to get a rough sense of the class conditional distributions, i.e. $p(X_1|y=1)$ vs $p(X_1|y=0)$. In general though, you will need to be more specific about what "bad data" means to you.&lt;/p&gt;
', NULL, NULL, '''/em'':67C ''/p'':58C,169C ''0'':151C ''1'':143C,145C,149C ''also'':98C ''analysi'':57C ''approach'':49C ''bad'':65C,70C,164C ''binari'':19C,119C ''binary-valu'':18C ''choic'':7C ''class'':137C ''classif'':28C,48C ''condit'':138C ''continu'':22C ''data'':66C,71C,165C ''discrimin'':56C ''distribut'':139C ''em'':64C ''featur'':111C,129C ''first'':2C,40C ''general'':153C ''get'':131C ''hand'':32C ''histogram'':124C ''i.e'':140C ''like'':5C,73C ''linear'':55C ''logist'':52C ''look'':72C,122C ''may'':25C ''mean'':166C ''mislabel'':95C ''model'':9C ''need'':157C ''noisi'':88C ''output'':16C ''p'':1C,59C,141C,147C ''possibl'':99C ''problem'':13C,29C,38C ''rather'':33C ''reason'':80C ''recommend'':41C ''regard'':60C ''regress'':37C,53C ''relationship'':106C ''rough'':133C ''select'':10C ''sens'':134C ''simpl'':47C ''sinc'':115C ''situat'':76C ''sound'':4C ''specif'':23C,161C ''strong'':105C ''suspect'':82C ''suspicion'':62C ''target'':114C,117C ''though'':154C ''tradit'':36C ''tri'':45C ''valu'':20C,86C,93C ''vs'':146C ''would'':42C,69C ''x'':85C,142C,148C ''y'':92C,144C,150C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5044, 2, '2015-02-04 09:28:16.91', 0, NULL, NULL, '&lt;p&gt;When training the model you don''t need to use the hotel id. The model needs to learn from examples. It only needs feature values and number of clicks, so it can learn the relationship between these.&lt;/p&gt;

&lt;p&gt;Once you''ve trained your model, you can use it for unseen examples.
These would be hotels for which you have both an id and a set of feature values.
Your model should take the id and the feature values as input, but it should only use the feature values for the prediction. The id should just be kept on the side, so it can print it together with the prediction to the output csv file.&lt;/p&gt;

&lt;p&gt;I hope this helps!&lt;/p&gt;
', NULL, NULL, '''/p'':39C,117C,123C ''click'':30C ''csv'':115C ''exampl'':21C,53C ''featur'':25C,69C,79C,89C ''file'':116C ''help'':122C ''hope'':120C ''hotel'':13C,57C ''id'':14C,64C,76C,95C ''input'':82C ''kept'':99C ''learn'':19C,34C ''model'':5C,16C,46C,72C ''need'':9C,17C,24C ''number'':28C ''output'':114C ''p'':1C,40C,118C ''predict'':93C,111C ''print'':106C ''relationship'':36C ''set'':67C ''side'':102C ''take'':74C ''togeth'':108C ''train'':3C,44C ''unseen'':52C ''use'':11C,49C,87C ''valu'':26C,70C,80C,90C ''ve'':43C ''would'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5056, 2, '2015-02-05 10:02:08.827', 3, NULL, NULL, '&lt;p&gt;By working with your features you could make the ML algorithm (maybe regression or SVR or whatever) to learn this fact (that sequental races are increasing the performance of athlete). To do this you may want to drop out the date column and introduce some new column, maybe ''race number'' with 0 for first race, 1 for second, 2 for third etc.&lt;/p&gt;

&lt;p&gt;In such case regression model will be able to learn what you say ''that the second race had an effect on the athlete, which meant he performed better in the third race''. It is all about feature selection.&lt;/p&gt;
', NULL, NULL, '''/p'':64C,104C ''0'':53C ''1'':57C ''2'':60C ''abl'':73C ''algorithm'':12C ''athlet'':31C,88C ''better'':93C ''case'':68C ''column'':43C,48C ''could'':8C ''date'':42C ''drop'':39C ''effect'':85C ''etc'':63C ''fact'':22C ''featur'':6C,102C ''first'':55C ''increas'':27C ''introduc'':45C ''learn'':20C,75C ''make'':9C ''may'':36C ''mayb'':13C,49C ''meant'':90C ''ml'':11C ''model'':70C ''new'':47C ''number'':51C ''p'':1C,65C ''perform'':29C,92C ''race'':25C,50C,56C,82C,97C ''regress'':14C,69C ''say'':78C ''second'':59C,81C ''select'':103C ''sequent'':24C ''svr'':16C ''third'':62C,96C ''want'':37C ''whatev'':18C ''work'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5074, 1, '2015-02-06 18:13:16.21', 0, 117, 'MPI, MapReduce, or Spark for complex datasets and processing', '&lt;p&gt;I have 2 data files: the first one is a database, potentially very large; the second one contains queries I want to answer. My program pipeline is processing the database to get some information first, and then use that same information to answer the queries. Although the number of queries is not big, processing each query takes a long time. So I want to give each worker some queries to answer, then combine all the answers together into one.&lt;/p&gt;

&lt;p&gt;This sounds like a MapReduce job. But to answer each query, the worker also needs to use the processed information from the database, and I''m not sure how to do this with MapReduce. I''m new to parallel programming and just heard of MPI and Spark.&lt;/p&gt;

&lt;p&gt;Can you help me to choose an appropriate one?&lt;/p&gt;

&lt;p&gt;Please ask if the description is not clear.&lt;/p&gt;
', 'map-reduce  parallel', NULL, '''/p'':94C,143C,154C,164C ''2'':17C ''also'':109C ''although'':60C ''answer'':37C,57C,85C,90C,104C ''appropri'':152C ''ask'':157C ''big'':67C ''choos'':150C ''clear'':163C ''combin'':87C ''complex'':6A ''contain'':32C ''data'':18C ''databas'':25C,44C,118C ''dataset'':7A ''descript'':160C ''file'':19C ''first'':21C,49C ''get'':46C ''give'':79C ''heard'':138C ''help'':147C ''inform'':48C,55C,115C ''job'':101C ''larg'':28C ''like'':98C ''long'':73C ''m'':121C,131C ''map'':11B ''map-reduc'':10B ''mapreduc'':2A,100C,129C ''mpi'':1A,140C ''need'':110C ''new'':132C ''number'':62C ''one'':22C,31C,93C,153C ''p'':14C,95C,144C,155C ''parallel'':13B,134C ''pipelin'':40C ''pleas'':156C ''potenti'':26C ''process'':9A,42C,68C,114C ''program'':39C,135C ''queri'':33C,59C,64C,70C,83C,106C ''reduc'':12B ''second'':30C ''sound'':97C ''spark'':4A,142C ''sure'':123C ''take'':71C ''time'':74C ''togeth'':91C ''use'':52C,112C ''want'':35C,77C ''worker'':81C,108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5045, 2, '2015-02-04 11:22:53.063', 1, NULL, NULL, '&lt;p&gt;Supervised learning should try to ''understand'' what makes a hotel to have more clicks than other. As a consequence learning tries to define which are the characteristics of some given hotels which make them attractive or not. So it uses some kind of similarities, because it is supposed that similar hotels behaves in a similar way. &lt;/p&gt;

&lt;p&gt;Now if you restrict the similarity to identity than you learn nothing new because hotels are unique. In fact such kind of learner exists and is called Rote learner, and it consists of one-to-one mapping from inputs to outputs. It is also called memoisation. And this happens if you will add hotel_id in the features. However I think you hope to use that to predict the number of clicks for new hotels (which does have a different hotel_id than any from training set). &lt;/p&gt;

&lt;p&gt;On the other hand, in order to use hotel_id to store prediction you only have to save a copy of the original data set. At learning time you have a train data set from which you remove hotel_id, and use that for learning. &lt;/p&gt;

&lt;p&gt;At prediction time you make a copy of the data set for later use. From the original data set remove order_id, use that for prediction and get the results. Now the predicted results have the same order of instances as the copied data set. This happens for sure in python (scikit learn), java (weka), R. In fact I am not aware of a system which does not preserve positions.&lt;/p&gt;

&lt;p&gt;Now using positions from the copy of the original and prediction you can associate each hotel_id to each prediction with no problem. &lt;/p&gt;
', NULL, NULL, '''/p'':58C,148C,195C,267C,292C ''add'':113C ''also'':104C ''associ'':282C ''attract'':36C ''awar'':258C ''behav'':53C ''call'':86C,105C ''characterist'':28C ''click'':15C,132C ''consequ'':20C ''consist'':91C ''copi'':169C,203C,239C,274C ''data'':173C,182C,206C,214C,240C ''defin'':24C ''differ'':140C ''exist'':83C ''fact'':78C,254C ''featur'':118C ''get'':224C ''given'':31C ''hand'':153C ''happen'':109C,243C ''hope'':123C ''hotel'':11C,32C,52C,74C,114C,135C,141C,158C,188C,284C ''howev'':119C ''id'':115C,142C,159C,189C,218C,285C ''ident'':67C ''input'':99C ''instanc'':236C ''java'':250C ''kind'':43C,80C ''later'':209C ''learn'':3C,21C,70C,176C,194C,249C ''learner'':82C,88C ''make'':9C,34C,201C ''map'':97C ''memois'':106C ''new'':72C,134C ''noth'':71C ''number'':130C ''one'':94C,96C ''one-to-on'':93C ''order'':155C,217C,234C ''origin'':172C,213C,277C ''output'':101C ''p'':1C,59C,149C,196C,268C ''posit'':266C,271C ''predict'':128C,162C,198C,222C,229C,279C,288C ''preserv'':265C ''problem'':291C ''python'':247C ''r'':252C ''remov'':187C,216C ''restrict'':63C ''result'':226C,230C ''rote'':87C ''save'':167C ''scikit'':248C ''set'':147C,174C,183C,207C,215C,241C ''similar'':45C,51C,56C,65C ''store'':161C ''supervis'':2C ''suppos'':49C ''sure'':245C ''system'':261C ''think'':121C ''time'':177C,199C ''train'':146C,181C ''tri'':5C,22C ''understand'':7C ''uniqu'':76C ''use'':41C,125C,157C,191C,210C,219C,270C ''way'':57C ''weka'':251C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5046, 2, '2015-02-04 12:51:31.943', 2, NULL, NULL, '&lt;p&gt;Your understanding is correct. The point is that equation (8)
$$y_i(&amp;lt;\textbf{w}, \phi_i&amp;gt; + b) - 1 = 0$$
is not exactly an equation, but a system of equations, one for each $i$ index of the support vectors (those for each $0&amp;lt;\alpha_i&amp;lt;C$. &lt;/p&gt;

&lt;p&gt;The point is that you cannot compute $b$ during the optimization of the dual problem since it does not matter for optimization, you have to go back and compute $b$ from all the other equations you have (one possible way is (8)). &lt;/p&gt;

&lt;p&gt;Vapnick suggestion is to not use only one of those equations, but two of them, specifically one support vector for a negative observation and one for a positive observation. In other words two support vectors which have opposite signs for $y_i$. &lt;/p&gt;

&lt;p&gt;Let''s name $A$ the index of one support vector and $B$ the index of a suport vector of opposite side, baiscally you select from the system of equations at (8) only two of them. Evaluate both of them and take the mean.&lt;/p&gt;

&lt;p&gt;From:
$$y_A(&amp;lt;\textbf{w},\phi_A&amp;gt;+b)=1$$
$$y_B(&amp;lt;\textbf{w}, \phi_B&amp;gt;+b)=1$$
We get:
$$b_A=\frac{1}{y_A}-&amp;lt;\textbf{w},\phi_A&amp;gt;$$
$$b_B=\frac{1}{y_B}-&amp;lt;\textbf{w},\phi_B&amp;gt;$$
Where $b_A$ and $b_B$ are two estimations, then the mean is
$$b = (b_A+b_B)/2 = -\frac{1}{2}(&amp;lt;\textbf{w},\phi_A&amp;gt;+&amp;lt;\textbf{w},\phi_B&amp;gt;)=-\frac{1}{2}\sum_{i=1}^{n}y_i\alpha_i(&amp;lt;\phi(x_i),\phi(x_A)&amp;gt;+&amp;lt;\phi(x_i),\phi(x_B)&amp;gt;)$$&lt;/p&gt;
', NULL, NULL, '''/2'':251C ''/p'':51C,95C,139C,184C,294C ''0'':22C,45C ''1'':21C,196C,206C,212C,224C,253C,268C,272C ''2'':254C,269C ''8'':11C,94C,171C ''alpha'':47C,276C ''b'':20C,60C,82C,152C,195C,198C,203C,205C,209C,221C,222C,226C,231C,234C,237C,238C,246C,247C,249C,250C,265C,292C ''back'':79C ''baiscal'':162C ''c'':50C ''cannot'':58C ''comput'':59C,81C ''correct'':5C ''dual'':66C ''equat'':10C,27C,32C,87C,107C,169C ''estim'':241C ''evalu'':176C ''exact'':25C ''frac'':211C,223C,252C,267C ''get'':208C ''go'':78C ''gt'':19C,194C,204C,220C,232C,260C,266C,285C,293C ''index'':37C,146C,154C ''let'':141C ''lt'':14C,46C,49C,189C,199C,215C,227C,255C,261C,278C,286C ''matter'':72C ''mean'':183C,244C ''n'':273C ''name'':143C ''negat'':118C ''observ'':119C,125C ''one'':33C,90C,104C,113C,121C,148C ''opposit'':134C,160C ''optim'':63C,74C ''p'':1C,52C,96C,140C,185C ''phi'':17C,192C,202C,218C,230C,258C,264C,279C,282C,287C,290C ''point'':7C,54C ''posit'':124C ''possibl'':91C ''problem'':67C ''select'':164C ''side'':161C ''sign'':135C ''sinc'':68C ''specif'':112C ''suggest'':98C ''sum'':270C ''suport'':157C ''support'':40C,114C,130C,149C ''system'':30C,167C ''take'':181C ''textbf'':15C,190C,200C,216C,228C,256C,262C ''two'':109C,129C,173C,240C ''understand'':3C ''use'':102C ''vapnick'':97C ''vector'':41C,115C,131C,150C,158C ''w'':16C,191C,201C,217C,229C,257C,263C ''way'':92C ''word'':128C ''x'':280C,283C,288C,291C ''y'':12C,137C,187C,197C,213C,225C,274C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5055, 1, '2015-02-05 07:41:10.443', 3, 279, 'machine learning on athlete performances to predict the time in a future race', '&lt;p&gt;&lt;strong&gt;Example Data&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I have a dataset (in &lt;code&gt;R&lt;/code&gt; as a &lt;code&gt;data frame&lt;/code&gt;) of race results for athletes.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;athlete racedistance time    location tracktype       date    coach
A          100       10.0       UK     typeA       2014-01-01  carlos
A          200       20.0       US     typeB       2014-02-01  carla
A          100        9.5      AUS     typeC       2014-03-01  chris
B          100       11.0       UK     typeA       2014-01-01  carla
B          200       21.0       US     typeB       2014-02-01  carlos
B          400       61.0      AUS     typeC       2014-03-01  carla
B          100       10.5      GER     typeA       2014-04-01  clive
C          100        9.5       UK     typeA       2014-01-01  clive
C          200       21.5       US     typeB       2014-02-01  chris
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Question&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Is there an appropriate machine learning algorithm or method that can use the previous results of each athlete as a feature, when trying to predict the &lt;code&gt;time&lt;/code&gt; for an athlete in a future race? &lt;/p&gt;

&lt;p&gt;For example, &lt;code&gt;athlete A&lt;/code&gt; has three races, with one month rest between them. In the third race he performs slightly better than the first race over the same distance. &lt;/p&gt;

&lt;p&gt;Can an algorithm learn that the second race had an effect on the athlete, which meant he performed better in the third race?&lt;/p&gt;

&lt;p&gt;From what I''ve read on the subject and the training examples I''ve completed it would appear that each ''row'' of data should be independent, is this the case for all ML algorithms? Is there another prediction technique I should be considering to solve this type of problem?&lt;/p&gt;
', 'machine-learning  r  predictive-modeling', NULL, '''-01'':63C,64C,73C,82C,90C,91C,100C,109C,118C,126C,127C,136C ''-02'':72C,99C,135C ''-03'':81C,108C ''-04'':117C ''/code'':35C,41C,138C,174C,189C ''/p'':26C,47C,144C,182C,215C,240C,291C ''/pre'':139C ''/strong'':25C,143C ''10.0'':59C ''10.5'':113C ''100'':58C,76C,85C,112C,121C ''11.0'':86C ''20.0'':68C ''200'':67C,94C,130C ''2014'':62C,71C,80C,89C,98C,107C,116C,125C,134C ''21.0'':95C ''21.5'':131C ''400'':103C ''61.0'':104C ''9.5'':77C,122C ''algorithm'':152C,219C,275C ''anoth'':278C ''appear'':259C ''appropri'':149C ''athlet'':4A,46C,50C,163C,177C,187C,230C ''aus'':78C,105C ''b'':84C,93C,102C,111C ''better'':206C,235C ''c'':120C,129C ''carla'':74C,92C,110C ''carlo'':65C,101C ''case'':271C ''chris'':83C,137C ''clive'':119C,128C ''coach'':56C ''code'':33C,38C,49C,172C,186C ''complet'':256C ''consid'':284C ''data'':24C,39C,264C ''dataset'':31C ''date'':55C ''distanc'':214C ''effect'':227C ''exampl'':23C,185C,253C ''featur'':166C ''first'':209C ''frame'':40C ''futur'':12A,180C ''ger'':114C ''independ'':267C ''learn'':2A,16B,151C,220C ''locat'':53C ''machin'':1A,15B,150C ''machine-learn'':14B ''meant'':232C ''method'':154C ''ml'':274C ''model'':20B ''month'':195C ''one'':194C ''p'':21C,27C,140C,145C,183C,216C,241C ''perform'':5A,204C,234C ''pre'':48C ''predict'':7A,19B,170C,279C ''predictive-model'':18B ''previous'':159C ''problem'':290C ''question'':142C ''r'':17B,34C ''race'':13A,43C,181C,192C,202C,210C,224C,239C ''racedist'':51C ''read'':246C ''rest'':196C ''result'':44C,160C ''row'':262C ''second'':223C ''slight'':205C ''solv'':286C ''strong'':22C,141C ''subject'':249C ''techniqu'':280C ''third'':201C,238C ''three'':191C ''time'':9A,52C,173C ''tracktyp'':54C ''train'':252C ''tri'':168C ''type'':288C ''typea'':61C,88C,115C,124C ''typeb'':70C,97C,133C ''typec'':79C,106C ''uk'':60C,87C,123C ''us'':69C,96C,132C ''use'':157C ''ve'':245C,255C ''would'':258C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5047, 1, '2015-02-04 22:03:48.507', 1, 229, 'OCR / Text Recognition and Recovery Problem', '&lt;p&gt;I am working on a research project that deals with American military casualties during WWII. Specifically, I am attempting to construct a count of casualties for each service at the county level. There are two sources of data here, each presenting their own challenges.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;1. Army and Air Force data.&lt;/strong&gt; The National Archives hosts lists of Army and Air Force servicemen killed in action by state and county. There are .gif images of the report available online. &lt;a href="http://media.nara.gov/media/images/29/19/29-1891a.gif" rel="nofollow"&gt;Here&lt;/a&gt; is a sample for several counties in Texas. &lt;/p&gt;

&lt;p&gt;I DO NOT need to recover the names or any other information. I simply need to count the number of names (each on its own line, and listed in groups of five) under each County. There are hundreds of these images (50 states - 30-100 for each state). &lt;/p&gt;

&lt;p&gt;I have been unable to find an OCR program that can tackle this problem adequately. How would you suggest I approach this challenge? (I have some programming expertise in Python and Java, but would prefer to use any off-the-shelf solutions that may exist).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;2. Navy and Marine Core data.&lt;/strong&gt; This data is organized differently. Each state has long lists of casualties with the address of their next of kin. &lt;a href="http://media.nara.gov/media/images/27/31/27-3023a.gif" rel="nofollow"&gt;Here&lt;/a&gt; is a sample for Texas again. For these images, I need to BOTH count the number of dead and recover their hometown, which is typically the last word in each entry. I can then match these hometowns to counties and merge with database 1.&lt;/p&gt;

&lt;p&gt;Again, the usual OCR programs have proved inadequate. Any help on this (admittedly more difficult) problem would be very much appreciated.&lt;/p&gt;

&lt;p&gt;Thank you in advance experts!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5048, 2, '2015-02-04 23:58:53.323', 0, NULL, NULL, '&lt;p&gt;In what way have the usual OCR programs proved inadequate?
Do you have some example output that you find you can''t work with?&lt;/p&gt;

&lt;p&gt;I can see how the columns complicate things.&lt;/p&gt;

&lt;p&gt;I''d say for data set 1:
OCR the images, then read the files line by line and match on for instance a sequence of at least five numbers. So you get 0, 1, 2 or 3 per row.
You may miss a couple due to the OCR accidentally recognizing a number as a letter for instance, but I expect this to work reasonably well.
How precise do you have to be?&lt;/p&gt;

&lt;p&gt;Data set 2 seems more difficult.
Maybe counting can be done by matching on capitalized sequences followed by a comma.
Placename... very tricky. Once again, do you have some OCR output we can look at?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5049, 2, '2015-02-05 00:49:11.713', 10, NULL, NULL, '&lt;p&gt;I''m surprised one has not mentioned this, as it seems fairly obvious: &lt;a href="http://www.kaggle.com"&gt;http://www.kaggle.com&lt;/a&gt; consistently has new and very interesting datasets. Information is considered an asset, so often companies don''t want to release that data (plus privacy concerns). Kaggle gives you data and they hope you solve business problems with it in exchange.&lt;/p&gt;
', NULL, NULL, '''/a'':19C ''/p'':60C ''asset'':31C ''busi'':54C ''compani'':34C ''concern'':44C ''consid'':29C ''consist'':20C ''data'':41C,48C ''dataset'':26C ''exchang'':59C ''fair'':13C ''give'':46C ''hope'':51C ''href'':16C ''inform'':27C ''interest'':25C ''kaggl'':45C ''m'':3C ''mention'':8C ''new'':22C ''obvious'':14C ''often'':33C ''one'':5C ''p'':1C ''plus'':42C ''privaci'':43C ''problem'':55C ''releas'':39C ''seem'':12C ''solv'':53C ''surpris'':4C ''want'':37C ''www.kaggle.com'':17C,18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5050, 1, '2015-02-05 01:29:00.997', 3, 211, 'Word Frequency Analysis of Document Sets', '&lt;p&gt;I''m doing some work trying to extract commonly occurring words from a set of human classified documents and had a couple questions for anyone who might know something about NLP or statistical analysis of text.&lt;/p&gt;

&lt;p&gt;We have a set of a bunch of documents, and users have classified them as either good or bad. What I''d like to do is figure out what words are common to the good documents, but not necessarily the other ones.&lt;/p&gt;

&lt;p&gt;I could, for example, use the (frequency within good documents / total frequency) which would essentially normalize the effect of a word being generally common. This, unfortunately, gives very high precedence to words that occur in only a few good documents &amp;amp; not at all in the other documents. I could add some kind of minimum threshold for # of occurrences in good docs before evaluating the total frequency, but it seems kind of hacky.&lt;/p&gt;

&lt;p&gt;Does anyone know what the best practice equation or model to use in this case is? I''ve done a lot of searching and found a lot of references to TF-IDF but that seems more applicable for assessing the value of a term on a single document against the whole set of docs. Here I''m dealing with a set of docs that is a subset of the larger collection.&lt;/p&gt;

&lt;p&gt;In other words, I''d like to identify which words are uniquely or more important to the class of good documents.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5053, 2, '2015-02-05 06:54:16.023', 2, NULL, NULL, '&lt;p&gt;I guess what you are looking for is Differential Word Usage. This method takes two text corpora as input and you can get the list of words which are being used more in one text corpus over the other.&lt;/p&gt;

&lt;p&gt;Basically what you need to do is to build a common Term Document Matrix for the corpora you are using and then divide this TDM into two TDMs such that all the document columns from corpus 1 fall in one TDM and all the documents columns form corpus 2 fall in the second TDM.  For example, you have 2 corpora, the first one containing 10 documents and the second one containing 15 documents. You first, combine both these corpora and form 25 document corpus and then form the TDM, where terms become the rows (let''s say there are 300 terms) and the 25 documents become the 25 columns. Here the first 10 columns represent the documents of first corpus and the remaining 15 belong to the second corpus. So, you divide this TDM of dimensions &lt;code&gt;300 x 25&lt;/code&gt; to two TDMs of dimensions &lt;code&gt;300 x 10&lt;/code&gt; and &lt;code&gt;300 x 15&lt;/code&gt;. Then you can use &lt;code&gt;Chi-square difference&lt;/code&gt; over these TDMs to determine which words are occurring more in one corpus than the other.&lt;/p&gt;

&lt;p&gt;A wonderful example has been given regarding this approach by Vik in his blog using Wikileaks corpus and &lt;code&gt;R&lt;/code&gt; here: &lt;a href="http://www.vikparuchuri.com/blog/finding-word-use-patterns-in-wikileaks/" rel="nofollow"&gt;http://www.vikparuchuri.com/blog/finding-word-use-patterns-in-wikileaks/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/blog/finding-word-use-patterns-in-wikileaks/'':254C ''/blog/finding-word-use-patterns-in-wikileaks/&lt;/a&gt;&lt;/p&gt;'':259C ''/code'':183C,193C,199C,209C,248C ''/p'':41C,226C ''1'':79C ''10'':107C,155C,192C ''15'':114C,166C,198C ''2'':91C,101C ''25'':124C,146C,150C,182C ''300'':142C,180C,190C,196C ''approach'':236C ''basic'':43C ''becom'':134C,148C ''belong'':167C ''blog'':241C ''build'':51C ''chi'':206C ''chi-squar'':205C ''code'':179C,189C,195C,204C,246C ''column'':76C,88C,151C,156C ''combin'':118C ''common'':53C ''contain'':106C,113C ''corpora'':18C,59C,102C,121C ''corpus'':37C,78C,90C,126C,162C,171C,222C,244C ''determin'':214C ''differ'':208C ''differenti'':10C ''dimens'':178C,188C ''divid'':65C,174C ''document'':55C,75C,87C,108C,115C,125C,147C,159C ''exampl'':98C,230C ''fall'':80C,92C ''first'':104C,117C,154C,161C ''form'':89C,123C,129C ''get'':24C ''given'':233C ''guess'':3C ''href'':251C ''input'':20C ''let'':137C ''list'':26C ''look'':7C ''matrix'':56C ''method'':14C ''need'':46C ''nofollow'':256C ''occur'':218C ''one'':35C,82C,105C,112C,221C ''p'':1C,42C,227C ''r'':247C ''regard'':234C ''rel'':255C ''remain'':165C ''repres'':157C ''row'':136C ''say'':139C ''second'':95C,111C,170C ''squar'':207C ''take'':15C ''tdm'':67C,83C,96C,131C,176C ''tdms'':70C,186C,212C ''term'':54C,133C,143C ''text'':17C,36C ''two'':16C,69C,185C ''usag'':12C ''use'':32C,62C,203C,242C ''vik'':238C ''wikileak'':243C ''wonder'':229C ''word'':11C,28C,216C ''www.vikparuchuri.com'':253C,258C ''www.vikparuchuri.com/blog/finding-word-use-patterns-in-wikileaks/'':252C ''www.vikparuchuri.com/blog/finding-word-use-patterns-in-wikileaks/&lt;/a&gt;&lt;/p&gt;'':257C ''x'':181C,191C,197C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5054, 2, '2015-02-05 07:26:04.347', 4, NULL, NULL, '&lt;p&gt;There are many algorithm to do classification: NaÃ¯ve Bayes, logistic regression, SVM, decision tree..etc. My suggestion is to try NaÃ¯ve Bayes first by calculating below probabilities which a new document belongs to $class_{good}$ or $class_{bad}$. (&lt;a href="https://web.stanford.edu/class/cs124/lec/naivebayes.pdf" rel="nofollow"&gt;https://web.stanford.edu/class/cs124/lec/naivebayes.pdf&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;$$
P(Class_{good} \vert document_{new}) = \frac{P(document_{new} \vert Class_{good}) \cdot P(Class_{good}) }{P(document_{new})}
$$&lt;/p&gt;

&lt;p&gt;$$
P(Class_{bad} \vert document_{new}) = \frac{P(document_{new} \vert Class_{bad}) \cdot P(Class_{bad}) }{P(document_{new})}
$$&lt;/p&gt;

&lt;p&gt;And generally, when we are doing text mining questions, we will do several preprocess on one document: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Tokenization(1-gram/bigram/...etc)&lt;/li&gt;
&lt;li&gt;remove stop words (''a'', ''the'' ''at'', ... etc)&lt;/li&gt;
&lt;li&gt;Stemming: transforms a word into its root form. (studied =&gt; study)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;My suggestion is to do above preprocesses and try more features not just the words in one document, if there are some metadataes.&lt;/p&gt;
', NULL, NULL, '''/class/cs124/lec/naivebayes.pdf'':46C ''/class/cs124/lec/naivebayes.pdf&lt;/a&gt;)&lt;/p&gt;'':51C ''/li'':121C,130C,142C ''/p'':73C,95C,114C,168C ''/ul'':143C ''1'':118C ''algorithm'':5C ''bad'':41C,77C,87C,91C ''bay'':11C,25C ''belong'':35C ''calcul'':28C ''cdot'':66C,88C ''class'':37C,40C,54C,64C,68C,76C,86C,90C ''classif'':8C ''decis'':15C ''document'':34C,57C,61C,71C,79C,83C,93C,113C,162C ''etc'':17C,120C,129C ''featur'':155C ''first'':26C ''form'':139C ''frac'':59C,81C ''general'':98C ''good'':38C,55C,65C,69C ''gram/bigram'':119C ''href'':43C ''li'':116C,122C,131C ''logist'':12C ''mani'':4C ''metadata'':167C ''mine'':104C ''naã'':9C,23C ''new'':33C,58C,62C,72C,80C,84C,94C ''nofollow'':48C ''one'':112C,161C ''p'':1C,52C,53C,60C,67C,70C,74C,75C,82C,89C,92C,96C,144C ''preprocess'':110C,151C ''probabl'':30C ''question'':105C ''regress'':13C ''rel'':47C ''remov'':123C ''root'':138C ''sever'':109C ''stem'':132C ''stop'':124C ''studi'':140C,141C ''suggest'':19C,146C ''svm'':14C ''text'':103C ''token'':117C ''transform'':133C ''tree'':16C ''tri'':22C,153C ''ul'':115C ''ve'':10C,24C ''vert'':56C,63C,78C,85C ''web.stanford.edu'':45C,50C ''web.stanford.edu/class/cs124/lec/naivebayes.pdf'':44C ''web.stanford.edu/class/cs124/lec/naivebayes.pdf&lt;/a&gt;)&lt;/p&gt;'':49C ''word'':125C,135C,159C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5104, 2, '2015-02-11 06:13:12.223', 2, NULL, NULL, '&lt;p&gt;Without a sample of your data, it''s unclear what''s the structure of your data and what tool is suitable to process it.&lt;/p&gt;

&lt;p&gt;Here are some blind recommendations based on my experience:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;If you just need some flexibilty parsing the text record, such as variable repeat number of certain field, or conditional parsing of fields, then you should check out this python library: &lt;a href="http://construct.readthedocs.org/en/latest/" rel="nofollow"&gt;http://construct.readthedocs.org/en/latest/&lt;/a&gt;
it allows you to first define a hirachcal structure of your data, and then apply this structure to parse information from a text file. It''s especial useful when parsing binary files.&lt;/li&gt;
&lt;li&gt;If you''re looking for an algorithm that can actually "understand" your text data and "infer" the structure in a smart way. Then you might want to try graph based approach: 
&lt;a href="http://kavita-ganesan.com/opinosis" rel="nofollow"&gt;http://kavita-ganesan.com/opinosis&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/en/latest/'':75C ''/en/latest/&lt;/a&gt;'':80C ''/li'':113C ''/opinosis'':150C ''/opinosis&lt;/a&gt;&lt;/li&gt;'':155C ''/p'':26C,37C ''/ul'':156C ''actual'':124C ''algorithm'':121C ''allow'':82C ''appli'':95C ''approach'':145C ''base'':33C,144C ''binari'':111C ''blind'':31C ''certain'':56C ''check'':66C ''condit'':59C ''construct.readthedocs.org'':74C,79C ''construct.readthedocs.org/en/latest/'':73C ''construct.readthedocs.org/en/latest/&lt;/a&gt;'':78C ''data'':7C,17C,92C,128C ''defin'':86C ''especi'':107C ''experi'':36C ''field'':57C,62C ''file'':104C,112C ''first'':85C ''flexibilti'':45C ''graph'':143C ''hirachc'':88C ''href'':72C,147C ''infer'':130C ''inform'':100C ''kavita-ganesan.com'':149C,154C ''kavita-ganesan.com/opinosis'':148C ''kavita-ganesan.com/opinosis&lt;/a&gt;&lt;/li&gt;'':153C ''li'':39C,114C ''librari'':70C ''look'':118C ''might'':139C ''need'':43C ''nofollow'':77C,152C ''number'':54C ''p'':1C,27C ''pars'':46C,60C,99C,110C ''process'':24C ''python'':69C ''re'':117C ''recommend'':32C ''record'':49C ''rel'':76C,151C ''repeat'':53C ''sampl'':4C ''smart'':135C ''structur'':14C,89C,97C,132C ''suitabl'':22C ''text'':48C,103C,127C ''tool'':20C ''tri'':142C ''ul'':38C ''unclear'':10C ''understand'':125C ''use'':108C ''variabl'':52C ''want'':140C ''way'':136C ''without'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5060, 2, '2015-02-05 20:40:51.363', 2, NULL, NULL, '&lt;p&gt;I think I''ve worked something out. Basically I''m looking for an approach that works in a map/reduce type environment and I think this approach does it.&lt;/p&gt;

&lt;p&gt;So,&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;suppose I have b bands of r rows and I want to add another AND stage, say another c ANDs.&lt;/li&gt;
&lt;li&gt;so instead of b * r bits I need hashes of b * r * c bits&lt;/li&gt;
&lt;li&gt;and I run my previous procedure c times, each time on b * r bits&lt;/li&gt;
&lt;li&gt;If x and y are found to be a candidate pair by any of these procedures it emits a key value pair ((x, y), 1), with the tuple of IDs (x,y) as the key and the value 1&lt;/li&gt;
&lt;li&gt;At the end of the c procedures I group these pairs by key and sum&lt;/li&gt;
&lt;li&gt;Any pair (x,y) with a sum equal to c was a candidate pair in each of the c rounds, and so is a candidate pair of the entire procedure.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So now I have a workable solution, and all I need to do is work out whether using 3 steps like this will actually help me get a better result with fewer overall hash bits or better overall performance...&lt;/p&gt;
', NULL, NULL, '''/li'':56C,72C,88C,129C,146C,178C ''/p'':30C,33C,220C ''/ul'':179C ''1'':114C,128C ''3'':199C ''actual'':204C ''add'':48C ''and'':55C ''anoth'':49C,53C ''approach'':15C,27C ''b'':39C,61C,68C,85C ''band'':40C ''basic'':9C ''better'':209C,217C ''bit'':63C,71C,87C,215C ''c'':54C,70C,80C,136C,157C,166C ''candid'':99C,160C,172C ''emit'':107C ''end'':133C ''entir'':176C ''environ'':22C ''equal'':155C ''fewer'':212C ''found'':95C ''get'':207C ''group'':139C ''hash'':66C,214C ''help'':205C ''id'':119C ''instead'':59C ''key'':109C,124C,143C ''li'':35C,57C,73C,89C,130C,147C ''like'':201C ''look'':12C ''m'':11C ''map/reduce'':20C ''need'':65C,191C ''overal'':213C,218C ''p'':1C,31C,180C ''pair'':100C,111C,141C,149C,161C,173C ''perform'':219C ''previous'':78C ''procedur'':79C,105C,137C,177C ''r'':42C,62C,69C,86C ''result'':210C ''round'':167C ''row'':43C ''run'':76C ''say'':52C ''solut'':187C ''someth'':7C ''stage'':51C ''step'':200C ''sum'':145C,154C ''suppos'':36C ''think'':3C,25C ''time'':81C,83C ''tupl'':117C ''type'':21C ''ul'':34C ''use'':198C ''valu'':110C,127C ''ve'':5C ''want'':46C ''whether'':197C ''work'':6C,17C,195C ''workabl'':186C ''x'':91C,112C,120C,150C ''y'':93C,113C,121C,151C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5061, 1, '2015-02-05 21:18:01.527', 0, 921, 'How much of a background in programming is necessary to become a data scientist?', '&lt;p&gt;I once knew some Java, but that was close to 10 years ago.  Assuming I can learn a language to get into data analytics.... what language do you recommend?  &lt;/p&gt;
', 'bigdata  beginner', NULL, '''/p'':47C ''10'':28C ''ago'':30C ''analyt'':41C ''assum'':31C ''background'':5A ''becom'':11A ''beginn'':16B ''bigdata'':15B ''close'':26C ''data'':13A,40C ''get'':38C ''java'':22C ''knew'':20C ''languag'':36C,43C ''learn'':34C ''much'':2A ''necessari'':9A ''p'':17C ''program'':7A ''recommend'':46C ''scientist'':14A ''year'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5062, 2, '2015-02-05 23:22:08.8', 1, NULL, NULL, '&lt;p&gt;Based on this infographic and other things I''ve read, it sounds like you need to know some coding to be a true data scientist. &lt;a href="http://blog.datacamp.com/how-to-become-a-data-scientist/" rel="nofollow"&gt;http://blog.datacamp.com/how-to-become-a-data-scientist/&lt;/a&gt; But you could still be a data &lt;strong&gt;analyst&lt;/strong&gt; without compsci - basically a statistician. &lt;/p&gt;
', NULL, NULL, '''/how-to-become-a-data-scientist/'':31C ''/how-to-become-a-data-scientist/&lt;/a&gt;'':36C ''/p'':52C ''/strong'':46C ''analyst'':45C ''base'':2C ''basic'':49C ''blog.datacamp.com'':30C,35C ''blog.datacamp.com/how-to-become-a-data-scientist/'':29C ''blog.datacamp.com/how-to-become-a-data-scientist/&lt;/a&gt;'':34C ''code'':20C ''compsci'':48C ''could'':39C ''data'':25C,43C ''href'':28C ''infograph'':5C ''know'':18C ''like'':14C ''need'':16C ''nofollow'':33C ''p'':1C ''read'':11C ''rel'':32C ''scientist'':26C ''sound'':13C ''statistician'':51C ''still'':40C ''strong'':44C ''thing'':8C ''true'':24C ''ve'':10C ''without'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5063, 2, '2015-02-05 23:23:04.18', 0, NULL, NULL, '&lt;p&gt;The R language is the best place to start. Grab some open datasets, and start programming with r. R has many different analytical functions that you can learn a lot with it.&lt;/p&gt;
', NULL, NULL, '''/p'':34C ''analyt'':24C ''best'':7C ''dataset'':14C ''differ'':23C ''function'':25C ''grab'':11C ''languag'':4C ''learn'':29C ''lot'':31C ''mani'':22C ''open'':13C ''p'':1C ''place'':8C ''program'':17C ''r'':3C,19C,20C ''start'':10C,16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6189, 1, '2015-06-23 16:36:41.873', 3, 95, 'Bigdata cluster compatible distributed predictive model', '&lt;p&gt;I might be asking a dumb question but my question is can I write a python program (lets say a classifier) using some library that scales in hadoop (not only using a simple parallel processing).The reason I am confused is 
1)The scikit learn python codes don''t scale in big data.
2)The spark mlib is written in scala and not in python.
3)Although h2o is open sourced I don''t think it is written in python.&lt;/p&gt;
', 'machine-learning  python', NULL, '''/p'':92C ''1'':53C ''2'':65C ''3'':77C ''although'':78C ''ask'':15C ''big'':63C ''bigdata'':1A ''classifi'':32C ''cluster'':2A ''code'':58C ''compat'':3A ''confus'':51C ''data'':64C ''distribut'':4A ''dumb'':17C ''h2o'':79C ''hadoop'':39C ''learn'':9B,56C ''let'':29C ''librari'':35C ''machin'':8B ''machine-learn'':7B ''might'':13C ''mlib'':68C ''model'':6A ''open'':81C ''p'':11C ''parallel'':45C ''predict'':5A ''process'':46C ''program'':28C ''python'':10B,27C,57C,76C,91C ''question'':18C,21C ''reason'':48C ''say'':30C ''scala'':72C ''scale'':37C,61C ''scikit'':55C ''simpl'':44C ''sourc'':82C ''spark'':67C ''think'':86C ''use'':33C,42C ''write'':25C ''written'':70C,89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5065, 2, '2015-02-06 00:05:02.313', 2, NULL, NULL, '&lt;p&gt;Data Scientists code every day. However, just because you don''t have background doesn''t mean you can''t pick it up! The level of programming you need to know to start doing Data Science isn''t very high, but you will at least need:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;the logical mindset to phrase the solution to your problem in
procedural code  &lt;/li&gt;
&lt;li&gt;to know the programming language, functions, and libraries needed in this field.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;1st point is the most difficult of the two. Hopefully you have taken enough math and physics by now to wire your mind to think programmatically. If so then yes, you absolutely can learn a language! There are guides out there that teach out the syntax and functions. For example:  &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;R - &lt;a href="http://tryr.codeschool.com/" rel="nofollow"&gt;http://tryr.codeschool.com/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;General Python - &lt;a href="http://www.codecademy.com/en/tracks/python" rel="nofollow"&gt;http://www.codecademy.com/en/tracks/python&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;DataSci Python - &lt;a href="https://www.kaggle.com/c/titanic-gettingStarted/details/getting-started-with-python" rel="nofollow"&gt;https://www.kaggle.com/c/titanic-gettingStarted/details/getting-started-with-python&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Personally I would recommend Python first. To me the language places more emphasis on readability and cleanliness, making it a great first language. It''s also a general purpose language so it''s good to know. I did start with R though and it''s also good, but is more function-over-form IMO. Try both out and see which feels best first, since you''ll likely have to pick up both if you delve into this field anyways.&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/li&gt;'':141C ''/c/titanic-gettingstarted/details/getting-started-with-python'':162C ''/c/titanic-gettingstarted/details/getting-started-with-python&lt;/a&gt;&lt;/li&gt;'':167C ''/en/tracks/python'':149C ''/en/tracks/python&lt;/a&gt;&lt;/li&gt;'':154C ''/li'':63C,77C ''/ol'':78C ''/p'':47C,130C,250C ''/ul'':168C ''1st'':80C ''absolut'':111C ''also'':195C,215C ''anyway'':249C ''background'':14C ''best'':232C ''cleanli'':186C ''code'':4C,62C ''data'':2C,35C ''datasci'':156C ''day'':6C ''delv'':245C ''difficult'':85C ''doesn'':15C ''emphasi'':182C ''enough'':93C ''everi'':5C ''exampl'':129C ''feel'':231C ''field'':76C,248C ''first'':175C,191C,233C ''form'':223C ''function'':70C,127C,221C ''function-over-form'':220C ''general'':143C,197C ''good'':203C,216C ''great'':190C ''guid'':118C ''high'':40C ''hope'':89C ''howev'':7C ''href'':135C,146C,159C ''imo'':224C ''isn'':37C ''know'':31C,66C,205C ''languag'':69C,115C,179C,192C,199C ''learn'':113C ''least'':45C ''level'':25C ''li'':49C,64C,132C,142C,155C ''librari'':72C ''like'':237C ''ll'':236C ''logic'':51C ''make'':187C ''math'':94C ''mean'':17C ''mind'':102C ''mindset'':52C ''need'':29C,46C,73C ''nofollow'':138C,151C,164C ''ol'':48C ''p'':1C,79C,169C ''person'':170C ''phrase'':54C ''physic'':96C ''pick'':21C,240C ''place'':180C ''point'':81C ''problem'':59C ''procedur'':61C ''program'':27C,68C ''programmat'':105C ''purpos'':198C ''python'':144C,157C,174C ''r'':133C,210C ''readabl'':184C ''recommend'':173C ''rel'':137C,150C,163C ''scienc'':36C ''scientist'':3C ''see'':229C ''sinc'':234C ''solut'':56C ''start'':33C,208C ''syntax'':125C ''taken'':92C ''teach'':122C ''think'':104C ''though'':211C ''tri'':225C ''tryr.codeschool.com'':136C,140C ''tryr.codeschool.com/&lt;/a&gt;&lt;/li&gt;'':139C ''two'':88C ''ul'':131C ''wire'':100C ''would'':172C ''www.codecademy.com'':148C,153C ''www.codecademy.com/en/tracks/python'':147C ''www.codecademy.com/en/tracks/python&lt;/a&gt;&lt;/li&gt;'':152C ''www.kaggle.com'':161C,166C ''www.kaggle.com/c/titanic-gettingstarted/details/getting-started-with-python'':160C ''www.kaggle.com/c/titanic-gettingstarted/details/getting-started-with-python&lt;/a&gt;&lt;/li&gt;'':165C ''yes'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5066, 2, '2015-02-06 00:47:49.187', 2, NULL, NULL, '&lt;p&gt;It sounds to me like you have a binary classification problem (classifying good vs. bad documents for some definitions of good and bad) and the words are being used as features or "signals" for what predicts good vs. bad documents. One thing you might try is to measure some type of correlation statistic between unigrams and each class you''re interested in. This preserves your requirement of measuring occurrences of words given a target class over &lt;em&gt;groups&lt;/em&gt; of documents. &lt;/p&gt;

&lt;p&gt;So, to be a bit more concrete, you could split your documents into two sets (good and bad), and then tokenize your documents to obtain individual terms. From here you could really choose whichever term weighting scheme you like (TF, TF normalized against the length of the document, TF-IDF) and measure your correlation statistic between &lt;em&gt;all these unigrams&lt;/em&gt; and the class of interest. You could then produce a ranking based on the correlation coefficients for each term, and take the top-&lt;em&gt;k&lt;/em&gt; terms. Some correlation statistics you might try could be &lt;a href="http://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test" rel="nofollow"&gt;Chi-squared&lt;/a&gt; (which would measure "lack of independence" between terms and a class). There''s also a nice implementation of Chi-squared test for feature selection in &lt;a href="http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection" rel="nofollow"&gt;Python''s Scikit-Learn&lt;/a&gt; machine learning library that may be a place to start for this task. Hopefully, that helps!&lt;/p&gt;
', NULL, NULL, '''/a'':192C,231C ''/em'':80C,146C,172C ''/p'':83C,248C ''/stable/modules/feature_selection.html#univariate-feature-selection'':223C ''/wiki/pearson%27s_chi-squared_test'':186C ''also'':206C ''bad'':16C,24C,40C,102C ''base'':158C ''binari'':10C ''bit'':89C ''chi'':190C,212C ''chi-squar'':189C,211C ''choos'':117C ''class'':59C,76C,149C,203C ''classif'':11C ''classifi'':13C ''coeffici'':162C ''concret'':91C ''correl'':53C,139C,161C,175C ''could'':93C,115C,153C,180C ''definit'':20C ''document'':17C,41C,82C,96C,107C,132C ''em'':78C,142C,170C ''en.wikipedia.org'':185C ''en.wikipedia.org/wiki/pearson%27s_chi-squared_test'':184C ''featur'':32C,216C ''given'':73C ''good'':14C,22C,38C,100C ''group'':79C ''help'':247C ''hope'':245C ''href'':183C,220C ''idf'':135C ''implement'':209C ''independ'':198C ''individu'':110C ''interest'':62C,151C ''k'':171C ''lack'':196C ''learn'':230C,233C ''length'':129C ''librari'':234C ''like'':6C,123C ''machin'':232C ''may'':236C ''measur'':49C,69C,137C,195C ''might'':45C,178C ''nice'':208C ''nofollow'':188C,225C ''normal'':126C ''obtain'':109C ''occurr'':70C ''one'':42C ''p'':1C,84C ''place'':239C ''predict'':37C ''preserv'':65C ''problem'':12C ''produc'':155C ''python'':226C ''rank'':157C ''re'':61C ''realli'':116C ''rel'':187C,224C ''requir'':67C ''scheme'':121C ''scikit'':229C ''scikit-learn'':228C ''scikit-learn.org'':222C ''scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection'':221C ''select'':217C ''set'':99C ''signal'':34C ''sound'':3C ''split'':94C ''squar'':191C,213C ''start'':241C ''statist'':54C,140C,176C ''take'':167C ''target'':75C ''task'':244C ''term'':111C,119C,165C,173C,200C ''test'':214C ''tf'':124C,125C,134C ''tf-idf'':133C ''thing'':43C ''token'':105C ''top'':169C ''tri'':46C,179C ''two'':98C ''type'':51C ''unigram'':56C,145C ''use'':30C ''vs'':15C,39C ''weight'':120C ''whichev'':118C ''word'':27C,72C ''would'':194C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5067, 2, '2015-02-06 05:51:56.09', 0, NULL, NULL, '&lt;p&gt;Johns Hopkins University as a set of course on Coursea that is gear on Data Science. Here is the link to the classes &lt;a href="https://www.coursera.org/specialization/jhudatascience/1?utm_medium=courseDescripTop" rel="nofollow"&gt;https://www.coursera.org/specialization/jhudatascience/1?utm_medium=courseDescripTop&lt;/a&gt;. You can also take the classes for free.&lt;/p&gt;

&lt;p&gt;This is a set of 9 classes that would give you a good foundation to build-on and to start a career.&lt;/p&gt;
', NULL, NULL, '''/p'':43C,68C ''/specialization/jhudatascience/1?utm_medium=coursedescriptop'':29C ''/specialization/jhudatascience/1?utm_medium=coursedescriptop&lt;/a&gt;.'':34C ''9'':50C ''also'':37C ''build'':61C ''build-on'':60C ''career'':67C ''class'':24C,40C,51C ''cours'':9C ''coursea'':11C ''data'':16C ''foundat'':58C ''free'':42C ''gear'':14C ''give'':54C ''good'':57C ''hopkin'':3C ''href'':26C ''john'':2C ''link'':21C ''nofollow'':31C ''p'':1C,44C ''rel'':30C ''scienc'':17C ''set'':7C,48C ''start'':65C ''take'':38C ''univers'':4C ''would'':53C ''www.coursera.org'':28C,33C ''www.coursera.org/specialization/jhudatascience/1?utm_medium=coursedescriptop'':27C ''www.coursera.org/specialization/jhudatascience/1?utm_medium=coursedescriptop&lt;/a&gt;.'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5068, 2, '2015-02-06 06:11:35.397', 0, NULL, NULL, '&lt;p&gt;Data science, being a new term, covers a broad spectrum of jobs. At one end you are expected to write production code. At the other end you do statistics in packaged software. They also call such people statisticians or analysts. So decide what you enjoy doing before you leap. If you just want to analyze data, you could definitely get by with R or python as long as you''re mathematically proficient. I find that in these kind of jobs, your communication and social skills matter too, since you have to explain the data to executives and the like.&lt;/p&gt;
', NULL, NULL, '''/p'':101C ''also'':35C ''analyst'':41C ''analyz'':56C ''broad'':10C ''call'':36C ''code'':23C ''communic'':83C ''could'':59C ''cover'':8C ''data'':2C,57C,95C ''decid'':43C ''definit'':60C ''end'':16C,27C ''enjoy'':46C ''execut'':97C ''expect'':19C ''explain'':93C ''find'':75C ''get'':61C ''job'':13C,81C ''kind'':79C ''leap'':50C ''like'':100C ''long'':68C ''mathemat'':72C ''matter'':87C ''new'':6C ''one'':15C ''p'':1C ''packag'':32C ''peopl'':38C ''product'':22C ''profici'':73C ''python'':66C ''r'':64C ''re'':71C ''scienc'':3C ''sinc'':89C ''skill'':86C ''social'':85C ''softwar'':33C ''spectrum'':11C ''statist'':30C ''statistician'':39C ''term'':7C ''want'':54C ''write'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5071, 2, '2015-02-06 09:41:54.033', 3, NULL, NULL, '&lt;p&gt;The problem is with division on python 2.x.  In python 2.x, division involving two integers produces an integer result.  So &lt;code&gt;1/2==0&lt;/code&gt;.  Python 3.x does not have this problem, &lt;code&gt;1/2==.5&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;There are two ways to avoid this.  First, you can always convert one value you a float.  So &lt;code&gt;1./2==0.5&lt;/code&gt; and &lt;code&gt;1/2.==0.5&lt;/code&gt;.  However, you have to remember to do this everywhere, and if you forget it can lead to hard-to-find errors.&lt;/p&gt;

&lt;p&gt;The more reliable method is to always put this at the top of your code: &lt;code&gt;from __future__ import division&lt;/code&gt;.  This will switch python 2.x to the python 3 behavior, so &lt;code&gt;1/2==.5&lt;/code&gt;.  In python 3.x it does nothing, so it also makes your code python 3.x compatible in this regard.&lt;/p&gt;
', NULL, NULL, '''./2'':62C ''/code'':27C,39C,64C,69C,114C,130C ''/p'':40C,92C,151C ''0'':26C ''0.5'':63C,68C ''1'':61C ''1/2'':25C,37C,67C,128C ''2'':9C,13C,119C ''3'':29C,124C,133C,145C ''5'':38C,129C ''also'':140C ''alway'':52C,100C ''avoid'':47C ''behavior'':125C ''code'':24C,36C,60C,66C,108C,109C,127C,143C ''compat'':147C ''convert'':53C ''divis'':6C,15C,113C ''error'':91C ''everywher'':78C ''find'':90C ''first'':49C ''float'':58C ''forget'':82C ''futur'':111C ''hard'':88C ''hard-to-find'':87C ''howev'':70C ''import'':112C ''integ'':18C,21C ''involv'':16C ''lead'':85C ''make'':141C ''method'':97C ''noth'':137C ''one'':54C ''p'':1C,41C,93C ''problem'':3C,35C ''produc'':19C ''put'':101C ''python'':8C,12C,28C,118C,123C,132C,144C ''regard'':150C ''reliabl'':96C ''rememb'':74C ''result'':22C ''switch'':117C ''top'':105C ''two'':17C,44C ''valu'':55C ''way'':45C ''x'':10C,14C,30C,120C,134C,146C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5073, 1, '2015-02-06 17:52:05.363', 2, 284, 'Question and Answer Chatbot for Customer Support', '&lt;p&gt;I want to build a chatbot that serves as a first line customer support on a  retail website. I have a large log of chat sessions between customers and support professionals that I can use. I am wondering what is the best way to go about building this chatbot. &lt;/p&gt;

&lt;p&gt;Here are some ideas I have looked into;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;The first thing I did was to breakdown the chat logs into Q&amp;amp;A pairs. Treat each question as a document, compute a term-document matrix and use it to retrieve the most similar question when a customer asks sometihng. The idea was to then simply pick the answer given to the question by the human (with some modifications). However, this gives really abysmal results and does not match to previous questions very well. Even if I get this approach to work well, we would be limited to the questions that already been asked. &lt;/li&gt;
&lt;li&gt;Another thing I thought about was to modify something like ALICE and write some AIML for customer support related QA. However, that would take a lot of very precise AIML writint to work well. This solution would not be able to scale to other languages, which is something I want to do.&lt;/li&gt;
&lt;li&gt;Another idea I have is to try and cluster the answers given by the customer support staff (they are more likely to be aligned compared to the questions asked by customers). This would give me a sense of what questions are similar. Then I can use something like LSA on the questions and fall back to the first approach.&lt;/li&gt;
&lt;li&gt;Yet another way is to build an ontology of the domain specific knowledge and given a question decide which category the question falls into. The questions from the chat logs can then be mapped to the ontology and I could train classifiers for mapping questions to different knowledge areas. Once I reach the leaf nodes, I can give back pre-defined answers. However, my reservation is that mapping chat logs to the ontology would be very tedious. Is there a good way to map the existing QA to the ontology?&lt;/li&gt;
&lt;/ul&gt;
', 'machine-learning  nlp', NULL, '''/li'':171C,225C,286C,379C ''/p'':62C,72C ''/ul'':380C ''abl'':212C ''abysm'':140C ''aiml'':187C,202C ''alic'':183C ''align'':250C ''alreadi'':168C ''amp'':88C ''anoth'':173C,227C,289C ''answer'':3A,125C,237C,350C ''approach'':156C,285C ''area'':336C ''ask'':115C,170C,255C ''back'':281C,346C ''best'':54C ''breakdown'':82C ''build'':16C,59C,293C ''categori'':307C ''chat'':37C,84C,316C,357C ''chatbot'':4A,18C,61C ''classifi'':329C ''cluster'':235C ''compar'':251C ''comput'':97C ''could'':327C ''custom'':6A,25C,40C,114C,189C,241C,257C ''decid'':305C ''defin'':349C ''differ'':334C ''document'':96C,101C ''domain'':298C ''even'':151C ''exist'':374C ''fall'':280C,310C ''first'':23C,76C,284C ''get'':154C ''give'':138C,260C,345C ''given'':126C,238C,302C ''go'':57C ''good'':369C ''howev'':136C,193C,351C ''human'':132C ''idea'':67C,118C,228C ''knowledg'':300C,335C ''languag'':217C ''larg'':34C ''leaf'':341C ''learn'':10B ''li'':74C,172C,226C,287C ''like'':182C,247C,274C ''limit'':163C ''line'':24C ''log'':35C,85C,317C,358C ''look'':70C ''lot'':198C ''lsa'':275C ''machin'':9B ''machine-learn'':8B ''map'':321C,331C,356C,372C ''match'':145C ''matrix'':102C ''modif'':135C ''modifi'':180C ''nlp'':11B ''node'':342C ''ontolog'':295C,324C,361C,378C ''p'':12C,63C ''pair'':90C ''pick'':123C ''pre'':348C ''pre-defin'':347C ''precis'':201C ''previous'':147C ''profession'':43C ''q'':87C ''qa'':192C,375C ''question'':1A,93C,111C,129C,148C,166C,254C,266C,278C,304C,309C,313C,332C ''reach'':339C ''realli'':139C ''relat'':191C ''reserv'':353C ''result'':141C ''retail'':29C ''retriev'':107C ''scale'':214C ''sens'':263C ''serv'':20C ''session'':38C ''similar'':110C,268C ''simpli'':122C ''solut'':208C ''someth'':181C,220C,273C ''sometihng'':116C ''specif'':299C ''staff'':243C ''support'':7A,26C,42C,190C,242C ''take'':196C ''tedious'':365C ''term'':100C ''term-docu'':99C ''thing'':77C,174C ''thought'':176C ''train'':328C ''treat'':91C ''tri'':233C ''ul'':73C ''use'':47C,104C,272C ''want'':14C,222C ''way'':55C,290C,370C ''websit'':30C ''well'':150C,159C,206C ''wonder'':50C ''work'':158C,205C ''would'':161C,195C,209C,259C,362C ''write'':185C ''writint'':203C ''yet'':288C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5124, 2, '2015-02-12 11:10:53.967', 1, NULL, NULL, '&lt;p&gt;What you are looking for is a machine learning algorithm. Although the easiest way would be to take the average scores and use that, there are much more accurate ways to make predictive models.&lt;/p&gt;

&lt;p&gt;This was the first data science tutorial I did. It''s perfect for getting started. Here is it in &lt;a href="https://www.kaggle.com/c/titanic-gettingStarted/details/new-getting-started-with-r" rel="nofollow"&gt;R&lt;/a&gt; and in &lt;a href="https://www.kaggle.com/c/titanic-gettingStarted/details/getting-started-with-python-ii" rel="nofollow"&gt;Python&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;If you''re looking for a short answer, something you can just look up how to implement, I''d check out Random Forests.&lt;/p&gt;
', NULL, NULL, '''/a'':65C,76C ''/c/titanic-gettingstarted/details/getting-started-with-python-ii'':72C ''/c/titanic-gettingstarted/details/new-getting-started-with-r'':61C ''/p'':36C,77C,102C ''accur'':30C ''algorithm'':11C ''although'':12C ''answer'':86C ''averag'':21C ''check'':98C ''d'':97C ''data'':42C ''easiest'':14C ''first'':41C ''forest'':101C ''get'':51C ''href'':58C,69C ''implement'':95C ''learn'':10C ''look'':5C,82C,91C ''machin'':9C ''make'':33C ''model'':35C ''much'':28C ''nofollow'':63C,74C ''p'':1C,37C,78C ''perfect'':49C ''predict'':34C ''python'':75C ''r'':64C ''random'':100C ''re'':81C ''rel'':62C,73C ''scienc'':43C ''score'':22C ''short'':85C ''someth'':87C ''start'':52C ''take'':19C ''tutori'':44C ''use'':24C ''way'':15C,31C ''would'':16C ''www.kaggle.com'':60C,71C ''www.kaggle.com/c/titanic-gettingstarted/details/getting-started-with-python-ii'':70C ''www.kaggle.com/c/titanic-gettingstarted/details/new-getting-started-with-r'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5932, 1, '2015-05-28 07:38:38.79', -1, 1234, 'getting error:-Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/hadoop/io/Writable', '&lt;p&gt;I am trying to connect to hive from java but getting error. I searched in google but not got any helpfull solution. I have added all jars also.&lt;/p&gt;

&lt;p&gt;The code is:-&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;package mypackage;
import java.sql.SQLException;
import java.sql.Connection;
import java.sql.ResultSet;
import java.sql.Statement;
import java.sql.DriverManager;

/**
*
* @author dsri
*/
public class HiveJdbcClient {


private static String driver = "org.apache.hadoop.hive.jdbc.HiveDriver";

public static void main(String[] args) throws SQLException, 
       ClassNotFoundException {
 Class.forName("org.apache.hadoop.hive.jdbc.HiveDriver");
 try {
 Class.forName(driver);
 } catch (ClassNotFoundException e) {
 e.printStackTrace();
  System.exit(1);
}

Connection connect = DriverManager.getConnection("jdbc:hive://master:10000 /default", "", "");
Statement state = connect.createStatement();
String tableName = "mytable";
state.executeQuery("drop table " + tableName);
ResultSet res=state.executeQuery("ADD JAR /home/dsri/hadoop_home/hive/lib /hive-serdes-1.0-SNAPSHOT.jar");
res = state.executeQuery("create table tweets (id BIGINT,created_at     STRING,source STRING,favorited BOOLEAN,retweet_count INT,retweeted_status STRUCT&amp;lt;text:STRING,user:STRUCT&amp;lt;screen_name:STRING,name:STRING&amp;gt;&amp;gt;,entities STRUCT&amp;lt;urls:ARRAY&amp;lt;STRUCT&amp;lt;expanded_url:STRING&amp;gt;&amp;gt;,user_mentions:ARRAY&amp;lt;STRUCT&amp;lt;screen_name:STRING,name:STRING&amp;gt;&amp;gt;,hashtags:ARRAY&amp;lt;STRUCT&amp;lt;text:STRING&amp;gt;&amp;gt;&amp;gt;,text STRING,user  STRUCT&amp;lt;screen_name:STRING,name:STRING,friends_count:INT,followers_count:INT,statuses_count:INT,verified:BOOLEAN,utc_offset:INT,time_zone:STRING&amp;gt;,in_reply_to_screen_name STRING) ROW FORMAT SERDE ''com.cloudera.hive.serde.JSONSerDe'' LOCATION ''/user/flume/tweets''");


String show = "show tables";
System.out.println("Running show");
res = state.executeQuery(show);
if (res.next()) {
  System.out.println(res.getString(1));
}


String describe = "describe " + tableName;
System.out.println("Running describe");
res = state.executeQuery(describe);
while (res.next()) {
  System.out.println(res.getString(1) + "\t" + res.getString(2));
}

}
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am getting these errors:-&lt;/p&gt;

&lt;p&gt;run:
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/home/dsri/hadoop/hive/lib/slf4j-log4j12-1.6.1.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/dsri/hadoop/lib/slf4j-log4j12-1.4.3.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/home/dsri/GlassFish_Server/glassfish/modules/weld-osgi-bundle.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See &lt;a href="http://www.slf4j.org/codes.html#multiple_bindings" rel="nofollow"&gt;http://www.slf4j.org/codes.html#multiple_bindings&lt;/a&gt; for an explanation.
Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/hadoop/io/Writable
    at org.apache.hadoop.hive.jdbc.HiveStatement.executeQuery(HiveStatement.java:198)
    at org.apache.hadoop.hive.jdbc.HiveStatement.execute(HiveStatement.java:132)
    at org.apache.hadoop.hive.jdbc.HiveConnection.configureConnection(HiveConnection.java:133)
    at org.apache.hadoop.hive.jdbc.HiveConnection.(HiveConnection.java:122)
    at org.apache.hadoop.hive.jdbc.HiveDriver.connect(HiveDriver.java:106)
    at java.sql.DriverManager.getConnection(DriverManager.java:571)
    at java.sql.DriverManager.getConnection(DriverManager.java:215)
    at dp.HiveJdbcClient.main(HiveJdbcClient.java:35)
Caused by: java.lang.ClassNotFoundException: org.apache.hadoop.io.Writable
    at java.net.URLClassLoader$1.run(URLClassLoader.java:366)
 at java.net.URLClassLoader$1.run(URLClassLoader.java:355)
    at java.security.AccessController.doPrivileged(Native Method)
    at java.net.URLClassLoader.findClass(URLClassLoader.java:354)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:425)
    at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:308)
    at java.lang.ClassLoader.loadClass(ClassLoader.java:358)
    ... 8 more
Java Result: 1&lt;/p&gt;
', 'hadoop  java  hive', NULL, '''/code'':257C ''/codes.html#multiple_bindings'':305C ''/codes.html#multiple_bindings&lt;/a&gt;'':310C ''/default'':97C ''/hive-serdes-1.0-snapshot.jar'':114C ''/home/dsri/glassfish_server/glassfish/modules/weld-osgi-bundle.jar'':297C ''/home/dsri/hadoop/hive/lib/slf4j-log4j12-1.6.1.jar'':281C ''/home/dsri/hadoop/lib/slf4j-log4j12-1.4.3.jar'':289C ''/home/dsri/hadoop_home/hive/lib'':113C ''/org/slf4j/impl/staticloggerbinder.class'':282C,290C,298C ''/p'':41C,46C,265C,378C ''/pre'':258C ''/user/flume/tweets'':223C ''1'':90C,238C,253C,377C ''1.run'':350C,354C ''10000'':96C ''2'':256C ''8'':373C ''ad'':37C ''add'':111C ''also'':40C ''appclassloader.loadclass'':368C ''arg'':76C ''array'':152C,163C,175C ''author'':61C ''bigint'':121C ''bind'':274C,277C,285C,293C ''boolean'':128C,204C ''catch'':85C ''caus'':344C ''class'':64C,269C ''class.forname'':80C,83C ''classloader.java:358'':372C ''classloader.java:425'':365C ''classnotfoundexcept'':79C,86C ''code'':44C,48C ''com.cloudera.hive.serde.jsonserde'':221C ''connect'':17C,91C,92C ''connect.createstatement'':100C ''contain'':271C ''count'':130C,195C,198C,201C ''creat'':117C,122C ''describ'':240C,241C,245C,248C ''dp.hivejdbcclient.main'':342C ''driver'':69C,84C ''drivermanager.getconnection'':93C ''drivermanager.java:215'':340C ''drivermanager.java:571'':337C ''drop'':105C ''dsri'':62C ''e'':87C ''e.printstacktrace'':88C ''entiti'':148C ''error'':2A,24C,264C ''except'':3A,314C ''expand'':156C ''explan'':313C ''favorit'':127C ''file'':280C,288C,296C ''follow'':197C ''format'':219C ''found'':276C,284C,292C ''friend'':194C ''get'':1A,23C,262C ''googl'':28C ''got'':31C ''gt'':146C,147C,159C,160C,172C,173C,181C,182C,183C,211C ''hadoop'':9B ''hashtag'':174C ''helpful'':33C ''hive'':11B,19C ''hiveconnection.java:122'':331C ''hiveconnection.java:133'':328C ''hivedriver.java:106'':334C ''hivejdbccli'':65C ''hivejdbcclient.java:35'':343C ''hivestatement.java:132'':325C ''hivestatement.java:198'':322C ''href'':302C ''id'':120C ''import'':51C,53C,55C,57C,59C ''int'':131C,196C,199C,202C,207C ''jar'':39C,112C,279C,287C,295C ''java'':10B,21C,375C ''java.lang.classloader.loadclass'':364C,371C ''java.lang.classnotfoundexception'':346C ''java.lang.noclassdeffounderror'':7A,318C ''java.net.urlclassloader'':349C,353C ''java.net.urlclassloader.findclass'':361C ''java.security.accesscontroller.doprivileged'':357C ''java.sql.connection'':54C ''java.sql.drivermanager'':60C ''java.sql.drivermanager.getconnection'':336C,339C ''java.sql.resultset'':56C ''java.sql.sqlexception'':52C ''java.sql.statement'':58C ''jdbc'':94C ''launcher.java:308'':369C ''locat'':222C ''lt'':135C,140C,150C,153C,155C,164C,166C,176C,178C,188C ''main'':6A,74C,317C ''master'':95C ''mention'':162C ''method'':359C ''multipl'':272C ''mypackag'':50C ''mytabl'':103C ''name'':142C,144C,168C,170C,190C,192C,216C ''nativ'':358C ''nofollow'':307C ''offset'':206C ''org.apache.hadoop.hive.jdbc.hiveconnection'':330C ''org.apache.hadoop.hive.jdbc.hiveconnection.configureconnection'':327C ''org.apache.hadoop.hive.jdbc.hivedriver'':70C,81C ''org.apache.hadoop.hive.jdbc.hivedriver.connect'':333C ''org.apache.hadoop.hive.jdbc.hivestatement.execute'':324C ''org.apache.hadoop.hive.jdbc.hivestatement.executequery'':321C ''org.apache.hadoop.io.writable'':347C ''org/apache/hadoop/io/writable'':8A,319C ''p'':12C,42C,259C,266C ''packag'':49C ''path'':270C ''pre'':47C ''privat'':66C ''public'':63C,71C ''rel'':306C ''repli'':213C ''res'':109C,115C,231C,246C ''res.getstring'':237C,252C,255C ''res.next'':235C,250C ''result'':376C ''resultset'':108C ''retweet'':129C,132C ''row'':218C ''run'':229C,244C,267C ''screen'':141C,167C,189C,215C ''search'':26C ''see'':300C ''serd'':220C ''show'':225C,226C,230C,233C ''slf4j'':268C,273C,275C,283C,291C,299C ''solut'':34C ''sourc'':125C ''sqlexcept'':78C ''state'':99C ''state.executequery'':104C,110C,116C,232C,247C ''statement'':98C ''static'':67C,72C ''status'':133C,200C ''string'':68C,75C,101C,124C,126C,137C,143C,145C,158C,169C,171C,180C,185C,191C,193C,210C,217C,224C,239C ''struct'':134C,139C,149C,154C,165C,177C,187C ''sun.misc.launcher'':367C ''system.exit'':89C ''system.out.println'':228C,236C,243C,251C ''tabl'':106C,118C,227C ''tablenam'':102C,107C,242C ''text'':136C,179C,184C ''thread'':5A,316C ''throw'':77C ''time'':208C ''tri'':15C,82C ''tweet'':119C ''url'':151C,157C ''urlclassloader.java:354'':362C ''urlclassloader.java:355'':355C ''urlclassloader.java:366'':351C ''user'':138C,161C,186C ''utc'':205C ''verifi'':203C ''void'':73C ''www.slf4j.org'':304C,309C ''www.slf4j.org/codes.html#multiple_bindings'':303C ''www.slf4j.org/codes.html#multiple_bindings&lt;/a&gt;'':308C ''zone'':209C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5077, 2, '2015-02-07 02:41:06.783', 1, NULL, NULL, '&lt;p&gt;If I understand your question correctly. What you want to do is &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Write a sql query against to a database to get the processed/aggregated results first.&lt;/li&gt;
&lt;li&gt;Execute several queries in parallel.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The first thing first, if you want to run MapReduce programs on your data, you need to distribute your data. In addition, MapReduce doesn''t run your queries to different nodes separately. If your $query_{A}$ runs on $data_{A}$, what we do here is to divide $data_{A}$ to different nodes and run $query_{A}$ on these nodes on $PartOfData_{A}$ in parallel. By the way, you can use Sqoop to migrate the data from your DB to HDFS(it is used to store data separately).&lt;/p&gt;

&lt;p&gt;Here are steps for you:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;migrate all/part of your data to HDFS&lt;/li&gt;
&lt;li&gt;transform your queries to MapReduce version program&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':31C,38C,145C,154C ''/ol'':39C,155C ''/p'':14C,128C,135C ''addit'':62C ''all/part'':139C ''correct'':7C ''data'':54C,60C,79C,88C,115C,126C,142C ''databas'':24C ''db'':118C ''differ'':70C,91C ''distribut'':58C ''divid'':87C ''doesn'':64C ''execut'':33C ''first'':30C,42C,44C ''get'':26C ''hdfs'':120C,144C ''li'':16C,32C,137C,146C ''mapreduc'':50C,63C,151C ''migrat'':113C,138C ''need'':56C ''node'':71C,92C,99C ''ol'':15C,136C ''p'':1C,40C,129C ''parallel'':37C,104C ''partofdata'':101C ''processed/aggregated'':28C ''program'':51C,153C ''queri'':20C,35C,68C,75C,95C,149C ''question'':6C ''result'':29C ''run'':49C,66C,77C,94C ''separ'':72C,127C ''sever'':34C ''sql'':19C ''sqoop'':111C ''step'':132C ''store'':125C ''thing'':43C ''transform'':147C ''understand'':4C ''use'':110C,123C ''version'':152C ''want'':10C,47C ''way'':107C ''write'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5079, 1, '2015-02-07 14:21:42.543', 5, 227, 'Item based recommender using SVD', '&lt;p&gt;I have an item-item similarity matrix. e.g. (the matrix is symmetric, and much bigger):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1.00 0.88 0.96 0.99 
0.88 1.00 0.99 0.96 
0.96 0.99 1.00 0.86 
0.99 0.96 0.86 1.00 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I need to implement recommender which, for a set of items, recommends a new set of items.&lt;/p&gt;

&lt;p&gt;I was thinking about using SVD to reduce the items to n-dimensional space, let''s say 50-dimensional space, so each item is represented with a vector 50 numbers, and similarity between two items is calculated by cosine similarity between two 50-dimensional vectors.&lt;/p&gt;

&lt;p&gt;For a base set of items (which can get quite big), I hope I could calculate an average of their vectors, and use it for search.&lt;/p&gt;

&lt;p&gt;Is this a good idea? What is this procedure called? And can it be done in Mahout?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;EDIT: &lt;/p&gt;

&lt;p&gt;This is my code so far:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ItemSimilarity similarity = new LogLikelihoodSimilarity(model);
Matrix m = new DenseMatrix(NUM_ITEMS, NUM_ITEMS);
// copy similarities to a matrix
for (int i = 0; i &amp;lt; NUM_ITEMS; i++) {
        double[] similar = similarity.itemSimilarities(i, range(NUM_ITEMS));
        for (int j = 0; j &amp;lt; NUM_ITEMS; j++) {
            m.setQuick(i, j, similar[j]);
    }
}
Matrix v = new SingularValueDecomposition(m).getV();
Matrix reduced = v.viewPart(0, NUM_ITEMS, 0, 50);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The problem is, SVD is taking forever for NUM_ITEMS &gt; 30. I don''t know if there is an issue with data, or with SVD implementation I''m using. The matrix m is symmetrical, could that be an issue? I tried googling "demean matrix mahout" with no results. How should I preprocess it for SVD to work faster? I will need NUM_ITEMS to be about 20.000 - 40.000 in the future. Is this reasonable size for SVD?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;EDIT 2:&lt;/p&gt;

&lt;p&gt;The problem was the matrix contained a few NaN values, that''s why SVD was taking infinite time. After replacing these with 0.0 it works fine for 1000 x 1000 matrix. And my recommendations are working like a charm. I''ll still need compute SVD of 20x more rows and columns. If anyone knows what''s the easiest way to compute (approximate) SVD of 20.000 x 20.000 dense matrix, probably through some cloud parallel service (?), please let me know.&lt;/p&gt;

&lt;p&gt;PS. Thanks for help!&lt;/p&gt;
', 'recommendation  apache-mahout', NULL, '''/code'':46C,238C ''/p'':27C,66C,114C,142C,161C,165C,173C,318C,323C,404C,410C ''/pre'':47C,239C ''0'':197C,213C,233C,236C ''0.0'':347C ''0.86'':41C,44C ''0.88'':31C,34C ''0.96'':32C,37C,38C,43C ''0.99'':33C,36C,39C,42C ''1.00'':30C,35C,40C,45C ''1000'':352C,354C ''2'':322C ''20.000'':307C,389C,391C ''20x'':371C ''30'':251C ''40.000'':308C ''50'':86C,97C,111C,237C ''anyon'':377C ''apach'':8B ''apache-mahout'':7B ''approxim'':386C ''averag'':133C ''base'':2A,118C ''big'':126C ''bigger'':26C ''calcul'':105C,131C ''call'':153C ''charm'':363C ''cloud'':397C ''code'':29C,170C,175C ''column'':375C ''comput'':368C,385C ''contain'':330C ''copi'':189C ''cosin'':107C ''could'':130C,275C ''data'':262C ''demean'':283C ''dens'':392C ''densematrix'':184C ''dimension'':81C,87C,112C ''done'':158C ''doubl'':203C ''e.g'':19C ''easiest'':382C ''edit'':164C,321C ''far'':172C ''faster'':298C ''fine'':350C ''forev'':247C ''futur'':311C ''get'':124C ''getv'':229C ''good'':147C ''googl'':282C ''help'':409C ''hope'':128C ''hr'':162C,319C ''idea'':148C ''implement'':52C,266C ''infinit'':341C ''int'':195C,211C ''issu'':260C,279C ''item'':1A,15C,16C,59C,65C,77C,91C,103C,121C,186C,188C,201C,209C,217C,235C,250C,303C ''item-item'':14C ''itemsimilar'':176C ''j'':212C,214C,218C,221C,223C ''know'':255C,378C,403C ''let'':83C,401C ''like'':361C ''ll'':365C ''loglikelihoodsimilar'':179C ''lt'':199C,215C ''m'':182C,228C,268C,272C ''m.setquick'':219C ''mahout'':9B,160C,285C ''matrix'':18C,21C,181C,193C,224C,230C,271C,284C,329C,355C,393C ''model'':180C ''much'':25C ''n'':80C ''n-dimension'':79C ''nan'':333C ''need'':50C,301C,367C ''new'':62C,178C,183C,226C ''num'':185C,187C,200C,208C,216C,234C,249C,302C ''number'':98C ''p'':10C,48C,67C,115C,143C,163C,166C,240C,320C,324C,405C ''parallel'':398C ''pleas'':400C ''pre'':28C,174C ''preprocess'':292C ''probabl'':394C ''problem'':242C,326C ''procedur'':152C ''ps'':406C ''quit'':125C ''rang'':207C ''reason'':314C ''recommend'':3A,6B,53C,60C,358C ''reduc'':75C,231C ''replac'':344C ''repres'':93C ''result'':288C ''row'':373C ''say'':85C ''search'':141C ''servic'':399C ''set'':57C,63C,119C ''similar'':17C,100C,108C,177C,190C,204C,222C ''similarity.itemsimilarities'':205C ''singularvaluedecomposit'':227C ''size'':315C ''space'':82C,88C ''still'':366C ''svd'':5A,73C,244C,265C,295C,317C,338C,369C,387C ''symmetr'':23C,274C ''take'':246C,340C ''thank'':407C ''think'':70C ''time'':342C ''tri'':281C ''two'':102C,110C ''use'':4A,72C,138C,269C ''v'':225C ''v.viewpart'':232C ''valu'':334C ''vector'':96C,113C,136C ''way'':383C ''work'':297C,349C,360C ''x'':353C,390C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5080, 1, '2015-02-07 15:37:47.327', 3, 914, 'Working as data scientist for a nonprofit company', '&lt;p&gt;Hello I work as data scientist for a private company.
I am interested in working for a nonprofit company, such as a research institute (public or private) or a company that takes care of  issues such as  environment, public health,  social improvements. Even an internet company like Wikipedia can be interesting.
Does anybody know if nonprofit companies hire data scientists?&lt;/p&gt;
', 'career', NULL, '''/p'':71C ''anybodi'':63C ''care'':43C ''career'':9B ''compani'':8A,20C,29C,40C,56C,67C ''data'':3A,15C,69C ''environ'':48C ''even'':53C ''health'':50C ''hello'':11C ''hire'':68C ''improv'':52C ''institut'':34C ''interest'':23C,61C ''internet'':55C ''issu'':45C ''know'':64C ''like'':57C ''nonprofit'':7A,28C,66C ''p'':10C ''privat'':19C,37C ''public'':35C,49C ''research'':33C ''scientist'':4A,16C,70C ''social'':51C ''take'':42C ''wikipedia'':58C ''work'':1A,13C,25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5091, 1, '2015-02-09 17:28:54.39', 0, 262, 'Extract company names/job titles from free text', '&lt;p&gt;I have a complete Hadoop platform with &lt;em&gt;HDFS&lt;/em&gt;, &lt;em&gt;MR&lt;/em&gt;, &lt;em&gt;Hive&lt;/em&gt;, &lt;em&gt;PIG&lt;/em&gt;, &lt;em&gt;Hbase&lt;/em&gt;, etc., &lt;em&gt;Python&lt;/em&gt;, &lt;em&gt;R&lt;/em&gt;, &lt;em&gt;Java&lt;/em&gt;. All data sets have a large size.&lt;/p&gt;

&lt;p&gt;The data set A, describing the jobs of people working in a company, is composed of the following fields:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Id Person: a unique alphanumeric identifier per person.&lt;/li&gt;
&lt;li&gt;Start Date: a date format iso entry in the post  &lt;/li&gt;
&lt;li&gt;&lt;p&gt;End Date: iso size release date of the position. If the date is not given, it is the current position&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Job Title: a text field containing the title and the name of the company. The text is free, non-standardized, French and / or English and can contain typos. Ex: Director Big Data Analytics with &lt;strong&gt;Google&lt;/strong&gt;, Commercial Manager at [missing text] , Manager at &lt;strong&gt;googole&lt;/strong&gt; ...&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;My question is; how can I create a feature to easily process the name of company of the job (jobtitle)?&lt;/p&gt;

&lt;p&gt;Thank you in advance&lt;/p&gt;
', 'hadoop  apache-pig  hbase', NULL, '''/em'':23C,26C,29C,32C,35C,39C,42C,45C ''/li'':85C,97C,121C,173C ''/p'':53C,74C,120C,172C,196C,202C ''/strong'':161C,171C ''/ul'':174C ''advanc'':201C ''alphanumer'':81C ''analyt'':157C ''apach'':10B ''apache-pig'':9B ''big'':155C ''commerci'':162C ''compani'':2A,67C,137C,191C ''complet'':17C ''compos'':69C ''contain'':129C,151C ''creat'':182C ''current'':118C ''data'':47C,56C,156C ''date'':88C,90C,101C,105C,111C ''describ'':59C ''director'':154C ''easili'':186C ''em'':21C,24C,27C,30C,33C,37C,40C,43C ''end'':100C ''english'':148C ''entri'':93C ''etc'':36C ''ex'':153C ''extract'':1A ''featur'':184C ''field'':73C,128C ''follow'':72C ''format'':91C ''free'':6A,141C ''french'':145C ''given'':114C ''googl'':160C ''googol'':170C ''hadoop'':8B,18C ''hbase'':12B,34C ''hdfs'':22C ''hive'':28C ''id'':77C ''identifi'':82C ''iso'':92C,102C ''java'':44C ''job'':61C,124C,194C ''jobtitl'':195C ''larg'':51C ''li'':76C,86C,98C,122C ''manag'':163C,167C ''miss'':165C ''mr'':25C ''name'':134C,189C ''names/job'':3A ''non'':143C ''non-standard'':142C ''p'':13C,54C,99C,123C,175C,197C ''peopl'':63C ''per'':83C ''person'':78C,84C ''pig'':11B,31C ''platform'':19C ''posit'':108C,119C ''post'':96C ''process'':187C ''python'':38C ''question'':177C ''r'':41C ''releas'':104C ''set'':48C,57C ''size'':52C,103C ''standard'':144C ''start'':87C ''strong'':159C,169C ''text'':7A,127C,139C,166C ''thank'':198C ''titl'':4A,125C,131C ''typo'':152C ''ul'':75C ''uniqu'':80C ''work'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5081, 2, '2015-02-08 01:47:57.963', 3, NULL, NULL, '&lt;p&gt;I see at least five ways to approach this problem of finding a data scientist position/work specifically at non-profit, non-governmental or similar organizations, as I describe below. I hope that this is helpful.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;First, and the most obvious, way is to search &lt;strong&gt;major job portals&lt;/strong&gt;, such as indeed.com, dice.com, monster.com, CareerBuilder, Glassdoor and others, for &lt;em&gt;data scientist&lt;/em&gt; or similar positions, such as &lt;em&gt;data analyst&lt;/em&gt;, &lt;em&gt;data engineer&lt;/em&gt;, &lt;em&gt;quantitative analyst&lt;/em&gt;, &lt;em&gt;statistical modeler&lt;/em&gt;, or even &lt;em&gt;market researcher&lt;/em&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;The second and also obvious way is to tap your &lt;strong&gt;professional social networks&lt;/strong&gt; and research or inquire your contacts for any potential data science work opportunities in the areas of your interests.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;The third way is to search web sites, &lt;strong&gt;focused&lt;/strong&gt; on the non-profit and related topics (they usually have a job listings or similar section) as well as &lt;strong&gt;specialized non-profit job portals&lt;/strong&gt;. For example: &lt;a href="http://encore.org" rel="nofollow"&gt;http://encore.org&lt;/a&gt;, &lt;a href="http://idealist.org" rel="nofollow"&gt;http://idealist.org&lt;/a&gt;, &lt;a href="http://bridgespan.org" rel="nofollow"&gt;http://bridgespan.org&lt;/a&gt;, &lt;a href="http://cgcareers.org" rel="nofollow"&gt;http://cgcareers.org&lt;/a&gt;, &lt;a href="http://opportunityknocks.org" rel="nofollow"&gt;http://opportunityknocks.org&lt;/a&gt;, &lt;a href="http://foundationcenter.org" rel="nofollow"&gt;http://foundationcenter.org&lt;/a&gt;, &lt;a href="http://thenonprofittimes.com" rel="nofollow"&gt;http://thenonprofittimes.com&lt;/a&gt;, &lt;a href="http://philanthropy.com" rel="nofollow"&gt;http://philanthropy.com&lt;/a&gt;, &lt;a href="http://ynpn.org" rel="nofollow"&gt;http://ynpn.org&lt;/a&gt;, &lt;a href="http://philanthropyjournal.org" rel="nofollow"&gt;http://philanthropyjournal.org&lt;/a&gt;, &lt;a href="http://nonprofit-jobs.org" rel="nofollow"&gt;http://nonprofit-jobs.org&lt;/a&gt;, &lt;a href="http://jobs.cof.org/home/index.cfm?site_id=11690" rel="nofollow"&gt;CoF Jobs&lt;/a&gt; (section isn''t easy to find), &lt;a href="http://careers.councilofnonprofits.org" rel="nofollow"&gt;http://careers.councilofnonprofits.org&lt;/a&gt;, &lt;a href="http://nonprofittalentmatch.com" rel="nofollow"&gt;http://nonprofittalentmatch.com&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;The fourth way is to perform &lt;strong&gt;Internet search and research&lt;/strong&gt; on non-profit or similar organizations that you might be interested in working with (criteria might vary from organization''s size to industry focus or geographical locations). Based on the information presented  on their websites, make notes and then approach those organizations, &lt;strong&gt;directly&lt;/strong&gt; inquiring or applying for positions of interest.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;The fifth way is to consider various non-profit, for-profit and otherwise social good-themed &lt;em&gt;data science-focused&lt;/em&gt; &lt;strong&gt;organizations, initiatives and Kaggle-like competitions&lt;/strong&gt;, such as &lt;a href="http://www.datakind.org" rel="nofollow"&gt;DataKind&lt;/a&gt;, &lt;a href="http://www.drivendata.org" rel="nofollow"&gt;DrivenData&lt;/a&gt;, &lt;a href="http://datalook.io" rel="nofollow"&gt;DataLook&lt;/a&gt; and &lt;a href="http://dssg.io" rel="nofollow"&gt;Data Science for Social Good&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':187C,194C,201C,208C,215C,222C,229C,236C,243C,250C,257C,267C,280C,287C,403C,410C,417C,429C ''/em'':70C,79C,83C,87C,91C,97C,385C ''/home/index.cfm?site_id=11690'':262C ''/li'':99C,137C,289C,358C,431C ''/p'':39C,98C,136C,288C,357C,430C ''/strong'':56C,116C,150C,178C,303C,349C,394C ''/ul'':432C ''also'':105C ''analyst'':78C,86C ''appli'':352C ''approach'':9C,344C ''area'':132C ''base'':332C ''bridgespan.org'':197C,200C ''careerbuild'':62C ''careers.councilofnonprofits.org'':276C,279C ''cgcareers.org'':204C,207C ''cof'':265C ''competit'':393C ''consid'':366C ''contact'':122C ''criteria'':319C ''data'':15C,68C,77C,81C,126C,381C,424C ''datakind'':402C ''datalook'':416C ''datalook.io'':413C ''describ'':31C ''dice.com'':60C ''direct'':348C ''drivendata'':409C ''dssg.io'':421C ''easi'':271C ''em'':67C,76C,80C,84C,88C,94C,380C ''encore.org'':183C,186C ''engin'':82C ''even'':93C ''exampl'':180C ''fifth'':362C ''find'':13C,273C ''first'':43C ''five'':6C ''focus'':149C,328C,384C ''for-profit'':371C ''foundationcenter.org'':218C,221C ''fourth'':293C ''geograph'':330C ''glassdoor'':63C ''good'':378C,428C ''good-them'':377C ''government'':25C ''help'':38C ''hope'':34C ''href'':182C,189C,196C,203C,210C,217C,224C,231C,238C,245C,252C,259C,275C,282C,398C,405C,412C,420C ''idealist.org'':190C,193C ''indeed.com'':59C ''industri'':327C ''inform'':335C ''initi'':388C ''inquir'':120C,350C ''interest'':135C,315C,356C ''internet'':299C ''isn'':269C ''job'':54C,163C,176C,266C ''jobs.cof.org'':261C ''jobs.cof.org/home/index.cfm?site_id=11690'':260C ''kaggl'':391C ''kaggle-lik'':390C ''least'':5C ''li'':41C,100C,138C,290C,359C ''like'':392C ''list'':164C ''locat'':331C ''major'':53C ''make'':340C ''market'':95C ''might'':313C,320C ''model'':90C ''monster.com'':61C ''network'':115C ''nofollow'':185C,192C,199C,206C,213C,220C,227C,234C,241C,248C,255C,264C,278C,285C,401C,408C,415C,423C ''non'':21C,24C,154C,174C,306C,369C ''non-government'':23C ''non-profit'':20C,153C,173C,305C,368C ''nonprofit-jobs.org'':253C,256C ''nonprofittalentmatch.com'':283C,286C ''note'':341C ''obvious'':47C,106C ''opportun'':129C ''opportunityknocks.org'':211C,214C ''organ'':28C,310C,323C,346C,387C ''other'':65C ''otherwis'':375C ''p'':1C,42C,101C,139C,291C,360C ''perform'':297C ''philanthropy.com'':232C,235C ''philanthropyjournal.org'':246C,249C ''portal'':55C,177C ''posit'':73C,354C ''position/work'':17C ''potenti'':125C ''present'':336C ''problem'':11C ''profession'':113C ''profit'':22C,155C,175C,307C,370C,373C ''quantit'':85C ''rel'':184C,191C,198C,205C,212C,219C,226C,233C,240C,247C,254C,263C,277C,284C,400C,407C,414C,422C ''relat'':157C ''research'':96C,118C,302C ''scienc'':127C,383C,425C ''science-focus'':382C ''scientist'':16C,69C ''search'':51C,145C,300C ''second'':103C ''section'':167C,268C ''see'':3C ''similar'':27C,72C,166C,309C ''site'':147C ''size'':325C ''social'':114C,376C,427C ''special'':172C ''specif'':18C ''statist'':89C ''strong'':52C,112C,148C,171C,298C,347C,386C ''tap'':110C ''theme'':379C ''thenonprofittimes.com'':225C,228C ''third'':141C ''topic'':158C ''ul'':40C ''usual'':160C ''vari'':321C ''various'':367C ''way'':7C,48C,107C,142C,294C,363C ''web'':146C ''websit'':339C ''well'':169C ''work'':128C,317C ''www.datakind.org'':399C ''www.drivendata.org'':406C ''ynpn.org'':239C,242C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8366, 2, '2015-10-09 13:01:23.863', 1, NULL, NULL, '&lt;p&gt;The maximum depth of a forest is a parameter which you set yourself. If you''re asking how do you find the optimal depth of a tree given a set of features then this is through cross-validation. For example, create 5 rf''s with 5 different tree depths and see which one performs the best on the validation set. &lt;/p&gt;
', NULL, NULL, '''/p'':63C ''5'':44C,48C ''ask'':18C ''best'':58C ''creat'':43C ''cross'':39C ''cross-valid'':38C ''depth'':4C,25C,51C ''differ'':49C ''exampl'':42C ''featur'':33C ''find'':22C ''forest'':7C ''given'':29C ''maximum'':3C ''one'':55C ''optim'':24C ''p'':1C ''paramet'':10C ''perform'':56C ''re'':17C ''rf'':45C ''see'':53C ''set'':13C,31C,62C ''tree'':28C,50C ''valid'':40C,61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5082, 2, '2015-02-08 03:29:06.623', 3, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;I need to implement recommender which, for a set of items, recommends a new set of items.&lt;/p&gt;
  
  &lt;p&gt;Is this a good idea?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Have you looked into &lt;a href="http://en.wikipedia.org/wiki/Association_rule_learning" rel="nofollow"&gt;Association Rules Mining&lt;/a&gt;? If you''re open to other procedures this one is first one that came to my mind for recommenders based on sets of items. For those not familiar, this is simple method for retail shops to determine "75% of customers who bought A, B also bought C". Within these algos, the &lt;a href="http://en.wikipedia.org/wiki/Apriori_algorithm" rel="nofollow"&gt;Apriori algo&lt;/a&gt; is straight-forward, easy to implement, and may get you what you need.&lt;/p&gt;
', NULL, NULL, '''/a'':44C,105C ''/blockquote'':28C ''/p'':20C,27C,120C ''/wiki/apriori_algorithm'':100C ''/wiki/association_rule_learning'':38C ''75'':82C ''algo'':94C,104C ''also'':89C ''apriori'':103C ''associ'':41C ''b'':88C ''base'':64C ''blockquot'':1C ''bought'':86C,90C ''c'':91C ''came'':58C ''custom'':84C ''determin'':81C ''easi'':110C ''en.wikipedia.org'':37C,99C ''en.wikipedia.org/wiki/apriori_algorithm'':98C ''en.wikipedia.org/wiki/association_rule_learning'':36C ''familiar'':72C ''first'':55C ''forward'':109C ''get'':115C ''good'':25C ''href'':35C,97C ''idea'':26C ''implement'':6C,112C ''item'':13C,19C,68C ''look'':32C ''may'':114C ''method'':76C ''mind'':61C ''mine'':43C ''need'':4C,119C ''new'':16C ''nofollow'':40C,102C ''one'':53C,56C ''open'':48C ''p'':2C,21C,29C ''procedur'':51C ''re'':47C ''recommend'':7C,14C,63C ''rel'':39C,101C ''retail'':78C ''rule'':42C ''set'':11C,17C,66C ''shop'':79C ''simpl'':75C ''straight'':108C ''straight-forward'':107C ''within'':92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5084, 2, '2015-02-08 16:05:58.517', 1, NULL, NULL, '&lt;p&gt;Assuming the central node is at level 0 (root), this graph becomes a tree. Now it is easy reason about the different types of trees that can be formed and different types of social networks that can be modelled. &lt;/p&gt;

&lt;p&gt;In order for the graph to be a tree, communication between nodes has to be restricted, such that the tree structure is not violated (e.g. two nodes on the same level cannot communicate). I can''t think of any social network where communication is naturally restricted in this way, but such a social network can be created artificially.&lt;/p&gt;

&lt;p&gt;Examples of such artificial social networks would commonly follow a chain-of-command structure. For example in a military setting, the official information flows would have a tree structure.   &lt;/p&gt;
', NULL, NULL, '''/p'':41C,101C,133C ''0'':9C ''artifici'':100C,106C ''assum'':2C ''becom'':13C ''cannot'':74C ''central'':4C ''chain'':114C ''chain-of-command'':113C ''command'':116C ''common'':110C ''communic'':52C,75C,85C ''creat'':99C ''differ'':23C,32C ''e.g'':67C ''easi'':19C ''exampl'':103C,119C ''flow'':127C ''follow'':111C ''form'':30C ''graph'':12C,47C ''inform'':126C ''level'':8C,73C ''militari'':122C ''model'':40C ''natur'':87C ''network'':36C,83C,96C,108C ''node'':5C,54C,69C ''offici'':125C ''order'':44C ''p'':1C,42C,102C ''reason'':20C ''restrict'':58C,88C ''root'':10C ''set'':123C ''social'':35C,82C,95C,107C ''structur'':63C,117C,132C ''think'':79C ''tree'':15C,26C,51C,62C,131C ''two'':68C ''type'':24C,33C ''violat'':66C ''way'':91C ''would'':109C,128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5085, 1, '2015-02-08 18:05:34.02', 0, 36, 'Should I use epochs &gt; 1 when training data is unlimited?', '&lt;p&gt;If I have virtually endless training data (it''s synthesized) is there still purpose in having epochs? I.e. training on the same samples multiple times?&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/p'':41C ''1'':5A ''data'':8A,22C ''endless'':20C ''epoch'':4A,32C ''i.e'':33C ''learn'':13B ''machin'':12B ''machine-learn'':11B ''multipl'':39C ''neuralnetwork'':14B ''p'':15C ''purpos'':29C ''sampl'':38C ''still'':28C ''synthes'':25C ''time'':40C ''train'':7A,21C,34C ''unlimit'':10A ''use'':3A ''virtual'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5086, 2, '2015-02-08 21:03:52.657', 1, NULL, NULL, '&lt;p&gt;If the data is unlimited, how would you have an epoch to begin with? For example, if you are analyzing tweets, you could never finish an epoch will &lt;em&gt;all&lt;/em&gt; the tweets, since there will be an endless supply of new tweets. A much better approach will be to do some online or streaming learning.&lt;/p&gt;

&lt;p&gt;Would it make sense to create a subset by ignoring new incoming tweets or data in general?&lt;/p&gt;

&lt;p&gt;That really depends on your problem. For example if you have a datastream of tweets, or any other where new data is being produced in real time, you would miss out any trends or patterns that emerged after you sampled from the stream. Are those missed patterns relevant to your problem? They may or may not be.    &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5087, 2, '2015-02-08 21:17:50.99', 2, NULL, NULL, '&lt;p&gt;This was intended as a comment.&lt;/p&gt;

&lt;p&gt;I think this question is not receiving enough attention because it is too difficult to answer without trying several approaches first. &lt;/p&gt;

&lt;p&gt;Your third idea is a nice one, but LSA is most likely not going to be able to help you choose clusters with the necessary granularity. For example, you may get a cluster of "broken things" but not to the specificity that is required to be helpful to the customer. A similar approach would be using Hierarchical Dirichlet Processes, but again, you may not get the clusters that you want.&lt;/p&gt;

&lt;p&gt;I wouldn''t be so eager to try your fourth option because it requires a lot of work and I''m not sure you''re going to get great results unless your customer support deals with very well-defined questions and in that case, you''re going to have to build your own ontology from scratch.&lt;/p&gt;

&lt;p&gt;In your situation, I would be curious if I can get information about the inner workings of Watson (obviously, the purpose is only to have a general idea of some steps that are sensible enough to try.) Here is a descriptive image that could be helpful from &lt;a href="http://en.wikipedia.org/wiki/Watson_%28computer%29" rel="nofollow"&gt;Wikipedia&lt;/a&gt;:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Anu0t.jpg" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;The main idea is to segment questions into its components (using a regular chunking), then come up with several possibilities (hypothesis generation) of what the question might refer to (maybe this step can be implemented as a clustering). The third stage is rating the previous hypothesis based on available information. Finally, you choose the more likely hypothesis. There is a feedback involved in this process using sample questions and their answers. It would be interesting to see if the rating stage can be modeled in terms of a Bayesian or an incremental approach to allow for a feedback.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5088, 1, '2015-02-09 00:18:46.427', 2, 245, 'Error when using MAX in Apache Pig (Hadoop)', '&lt;p&gt;I am trying to calculate maximum values for different groups in a relation in Pig. The relation has three columns patientid, featureid and featurevalue (all int). 
I group the relation based on featureid and want to calculate the max feature value of each group, heres the code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;grpd = GROUP features BY featureid;
DUMP grpd;
temp = FOREACH grpd GENERATE $0 as featureid, MAX($1.featurevalue) as val;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Its  giving me  &lt;strong&gt;Invalid scalar projection: grpd&lt;/strong&gt; Exception. I read on different forums that MAX takes in a "bag" format for such functions, but when I take the dump of grpd, it shows me a bag format. Here''s a small part of the output from the dump:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(5662,{(22579,5662,1)})
(5663,{(28331,5663,1),(2624,5663,1)})
(5664,{(27591,5664,1)})
(5665,{(30217,5665,1),(31526,5665,1)})
(5666,{(27783,5666,1),(30983,5666,1),(32424,5666,1),(28064,5666,1),(28932,5666,1)})
(5667,{(31257,5667,1),(27281,5667,1)})
(5669,{(31041,5669,1)})
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Whats the issue ?&lt;/p&gt;
', 'hadoop  apache-pig', NULL, '''/code'':82C,187C ''/p'':61C,135C,193C ''/pre'':83C,188C ''/strong'':93C ''0'':75C ''1'':141C,145C,148C,152C,156C,159C,163C,166C,169C,172C,175C,179C,182C,186C ''1.featurevalue'':79C ''22579'':139C ''2624'':146C ''27281'':180C ''27591'':150C ''27783'':161C ''28064'':170C ''28331'':143C ''28932'':173C ''30217'':154C ''30983'':164C ''31041'':184C ''31257'':177C ''31526'':157C ''32424'':167C ''5662'':138C,140C ''5663'':142C,144C,147C ''5664'':149C,151C ''5665'':153C,155C,158C ''5666'':160C,162C,165C,168C,171C,174C ''5667'':176C,178C,181C ''5669'':183C,185C ''apach'':6A,11B ''apache-pig'':10B ''bag'':105C,122C ''base'':44C ''calcul'':18C,50C ''code'':60C,63C,137C ''column'':33C ''differ'':22C,98C ''dump'':69C,115C,134C ''error'':1A ''except'':94C ''featur'':53C,66C ''featureid'':35C,46C,68C,77C ''featurevalu'':37C ''foreach'':72C ''format'':106C,123C ''forum'':99C ''function'':109C ''generat'':74C ''give'':86C ''group'':23C,41C,57C,65C ''grpd'':64C,70C,73C,92C,117C ''hadoop'':8A,9B ''here'':58C ''int'':39C ''invalid'':89C ''issu'':192C ''max'':4A,52C,78C,101C ''maximum'':19C ''output'':131C ''p'':13C,84C,189C ''part'':128C ''patientid'':34C ''pig'':7A,12B,28C ''pre'':62C,136C ''project'':91C ''read'':96C ''relat'':26C,30C,43C ''scalar'':90C ''show'':119C ''small'':127C ''strong'':88C ''take'':102C,113C ''temp'':71C ''three'':32C ''tri'':16C ''use'':3A ''val'':81C ''valu'':20C,54C ''want'':48C ''what'':190C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5094, 2, '2015-02-10 02:06:19.957', 2, NULL, NULL, '&lt;p&gt;I think what you want is to extract company names from "Job Title". In natural language process, we call this kind of research as "Name Entity Recognition(NER)". You can try to use  Stanford Named Entity Recognizer (NER)[&lt;a href="http://nlp.stanford.edu/software/CRF-NER.shtml]" rel="nofollow"&gt;http://nlp.stanford.edu/software/CRF-NER.shtml]&lt;/a&gt;. Stanford NER performs very well on English contents and there are lots packages for many programming language:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;UIMA: Florian Laws made a Stanford NER UIMA annotator using a modified version of Stanford NER, which is available on his homepage. [Old version.]&lt;/p&gt;
  
  &lt;p&gt;Perl: Kieren Diment has written Text-NLP-Stanford-EntityExtract, a Perl module that provides an interface to Stanford NER running as a server.&lt;/p&gt;
  
  &lt;p&gt;Ruby: tiendung has written a Ruby Binding for the Stanford POS tagger and Named Entity Recognizer.&lt;/p&gt;
  
  &lt;p&gt;Python: Dat Hoang wrote pyner, a Python interface to Stanford NER. [Old version.] NLTK (2.0+) contains an interface to Stanford NER written by Nitin Madnani: documentation (note: set the character encoding or you get ASCII by default!), code, on Github.&lt;/p&gt;
  
  &lt;p&gt;F#/C#/.NET: Sergey Tihon has ported Stanford NER to F# (and other .NET languages, such as C#), using IKVM. See also pages on: GitHub and NuGet.&lt;/p&gt;
  
  &lt;p&gt;PHP: PHP-Stanford-NLP. Supports POS Tagger, NER, Parser. By Anthony Gentile (agentile).&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;If you are not satisfied with the performance of Stanford NER, you can also train you own models to extract company names by crawl company names from several popular sites with company names, such as Linkedin/Facebook/Glassdoor...etc&lt;/p&gt;
', NULL, NULL, '''/.net'':184C ''/blockquote'':226C ''/c'':183C ''/p'':67C,94C,120C,138C,180C,209C,225C,265C ''/software/crf-ner.shtml]'':44C ''/software/crf-ner.shtml]&lt;/a&gt;.'':49C ''2.0'':154C ''agentil'':224C ''also'':203C,241C ''annot'':78C ''anthoni'':222C ''ascii'':174C ''avail'':88C ''bind'':128C ''blockquot'':68C ''c'':199C ''call'':20C ''charact'':169C ''code'':177C ''compani'':10C,248C,252C,259C ''contain'':155C ''content'':57C ''crawl'':251C ''dat'':141C ''default'':176C ''diment'':98C ''document'':165C ''encod'':170C ''english'':56C ''entiti'':27C,37C,136C ''entityextract'':105C ''etc'':264C ''extract'':9C,247C ''f'':182C,192C ''florian'':71C ''gentil'':223C ''get'':173C ''github'':179C,206C ''hoang'':142C ''homepag'':91C ''href'':41C ''ikvm'':201C ''interfac'':112C,147C,157C ''job'':13C ''kieren'':97C ''kind'':22C ''languag'':17C,66C,196C ''law'':72C ''linkedin/facebook/glassdoor'':263C ''lot'':61C ''made'':73C ''madnani'':164C ''mani'':64C ''model'':245C ''modifi'':81C ''modul'':108C ''name'':11C,26C,36C,135C,249C,253C,260C ''natur'':16C ''ner'':29C,39C,51C,76C,85C,115C,150C,160C,190C,219C,238C ''net'':195C ''nitin'':163C ''nlp'':103C,215C ''nlp.stanford.edu'':43C,48C ''nlp.stanford.edu/software/crf-ner.shtml]'':42C ''nlp.stanford.edu/software/crf-ner.shtml]&lt;/a&gt;.'':47C ''nltk'':153C ''nofollow'':46C ''note'':166C ''nuget'':208C ''old'':92C,151C ''p'':1C,69C,95C,121C,139C,181C,210C,227C ''packag'':62C ''page'':204C ''parser'':220C ''perform'':52C,235C ''perl'':96C,107C ''php'':211C,213C ''php-stanford-nlp'':212C ''popular'':256C ''port'':188C ''pos'':132C,217C ''process'':18C ''program'':65C ''provid'':110C ''pyner'':144C ''python'':140C,146C ''recogn'':38C,137C ''recognit'':28C ''rel'':45C ''research'':24C ''rubi'':122C,127C ''run'':116C ''satisfi'':232C ''see'':202C ''sergey'':185C ''server'':119C ''set'':167C ''sever'':255C ''site'':257C ''stanford'':35C,50C,75C,84C,104C,114C,131C,149C,159C,189C,214C,237C ''support'':216C ''tagger'':133C,218C ''text'':102C ''text-nlp-stanford-entityextract'':101C ''think'':3C ''tiendung'':123C ''tihon'':186C ''titl'':14C ''train'':242C ''tri'':32C ''uima'':70C,77C ''use'':34C,79C,200C ''version'':82C,93C,152C ''want'':6C ''well'':54C ''written'':100C,125C,161C ''wrote'':143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5096, 2, '2015-02-10 07:09:28.237', 2, NULL, NULL, '&lt;p&gt;When I think about how to implement a certain ML or data mining process in an OOP (like Java), I usually go to see how smarter people than me designed their system.
In this case, I''d see how Weka, RapidMiner, JAVA ML or others decided to tackle this problem.&lt;/p&gt;

&lt;p&gt;In your case of feature/attribute selection, i''m adding a link to Weka''s API.
If you want to better understand how it''s done, you should download Weka and play with the source code.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://weka.sourceforge.net/doc.dev/weka/classifiers/meta/AttributeSelectedClassifier.html" rel="nofollow"&gt;http://weka.sourceforge.net/doc.dev/weka/classifiers/meta/AttributeSelectedClassifier.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Without getting into specific implementations, I think that a linked list of any sort would do the trick, since you want to select a subset of all features.&lt;/p&gt;
', NULL, NULL, '''/doc.dev/weka/classifiers/meta/attributeselectedclassifier.html'':95C ''/doc.dev/weka/classifiers/meta/attributeselectedclassifier.html&lt;/a&gt;&lt;/p&gt;'':100C ''/p'':52C,89C,130C ''ad'':62C ''api'':68C ''better'':73C ''case'':36C,56C ''certain'':10C ''code'':88C ''d'':38C ''data'':13C ''decid'':47C ''design'':31C ''done'':78C ''download'':81C ''featur'':129C ''feature/attribute'':58C ''get'':103C ''go'':23C ''href'':92C ''implement'':8C,106C ''java'':20C,43C ''like'':19C ''link'':64C,111C ''list'':112C ''m'':61C ''mine'':14C ''ml'':11C,44C ''nofollow'':97C ''oop'':18C ''other'':46C ''p'':1C,53C,90C,101C ''peopl'':28C ''play'':84C ''problem'':51C ''process'':15C ''rapidmin'':42C ''rel'':96C ''see'':25C,39C ''select'':59C,124C ''sinc'':120C ''smarter'':27C ''sort'':115C ''sourc'':87C ''specif'':105C ''subset'':126C ''system'':33C ''tackl'':49C ''think'':4C,108C ''trick'':119C ''understand'':74C ''usual'':22C ''want'':71C,122C ''weka'':41C,66C,82C ''weka.sourceforge.net'':94C,99C ''weka.sourceforge.net/doc.dev/weka/classifiers/meta/attributeselectedclassifier.html'':93C ''weka.sourceforge.net/doc.dev/weka/classifiers/meta/attributeselectedclassifier.html&lt;/a&gt;&lt;/p&gt;'':98C ''without'':102C ''would'':116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5097, 1, '2015-02-10 12:36:48.25', 0, 61, 'Python for data analytics', '&lt;p&gt;What are some data analytic package &amp;amp; feature in python which helps do data analytic?&lt;/p&gt;
', 'data-mining  python', NULL, '''/p'':25C ''amp'':16C ''analyt'':4A,14C,24C ''data'':3A,6B,13C,23C ''data-min'':5B ''featur'':17C ''help'':21C ''mine'':7B ''p'':9C ''packag'':15C ''python'':1A,8B,19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5098, 2, '2015-02-10 15:59:19.01', 0, NULL, NULL, '&lt;p&gt;You''re looking for this answer: &lt;a href="https://www.quora.com/Why-is-Python-a-language-of-choice-for-data-scientists" rel="nofollow"&gt;https://www.quora.com/Why-is-Python-a-language-of-choice-for-data-scientists&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/why-is-python-a-language-of-choice-for-data-scientists'':12C ''/why-is-python-a-language-of-choice-for-data-scientists&lt;/a&gt;&lt;/p&gt;'':17C ''answer'':7C ''href'':9C ''look'':4C ''nofollow'':14C ''p'':1C ''re'':3C ''rel'':13C ''www.quora.com'':11C,16C ''www.quora.com/why-is-python-a-language-of-choice-for-data-scientists'':10C ''www.quora.com/why-is-python-a-language-of-choice-for-data-scientists&lt;/a&gt;&lt;/p&gt;'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5414, 2, '2015-03-29 11:33:26.277', 0, NULL, NULL, '&lt;p&gt;Looking at existing challenges around and their data format (for example &lt;a href="http://www.kaggle.com/c/datasciencebowl/data" rel="nofollow"&gt;http://www.kaggle.com/c/datasciencebowl/data&lt;/a&gt;) I would say put the images in a folder per class. You can use the file names for the index.&lt;/p&gt;
', NULL, NULL, '''/c/datasciencebowl/data'':17C ''/c/datasciencebowl/data&lt;/a&gt;)'':22C ''/p'':43C ''around'':6C ''challeng'':5C ''class'':33C ''data'':9C ''exampl'':12C ''exist'':4C ''file'':38C ''folder'':31C ''format'':10C ''href'':14C ''imag'':28C ''index'':42C ''look'':2C ''name'':39C ''nofollow'':19C ''p'':1C ''per'':32C ''put'':26C ''rel'':18C ''say'':25C ''use'':36C ''would'':24C ''www.kaggle.com'':16C,21C ''www.kaggle.com/c/datasciencebowl/data'':15C ''www.kaggle.com/c/datasciencebowl/data&lt;/a&gt;)'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5101, 2, '2015-02-10 21:13:37.107', 1, NULL, NULL, '&lt;p&gt;It might be a good idea to start with voluntary work and see if that leads to payed position. &lt;a href="http://www.datakind.org/howitworks/datachapters/datakind-uk/" rel="nofollow"&gt;DataKind&lt;/a&gt;, that has been mentioned above, is where I would start, especially if you live in England, since you can register your interest online and even do work with them as a data scientist for a weekend in a meetup event. &lt;/p&gt;

&lt;p&gt;There are also various &lt;a href="https://www.google.com/search?client=safari&amp;amp;rls=en&amp;amp;q=data%20science%20site:meetup.com&amp;amp;ie=UTF-8&amp;amp;oe=UTF-8&amp;amp;gfe_rd=cr&amp;amp;oq=data%20science&amp;amp;gs_l=navquery.3..0l4j0i5.1459.4969.0.5279.12.12.0.0.0.0.82.718.12.12.0....0....1.61.navquery..0.12.717.-odm8NeyBww&amp;amp;ved=0CCkQ2wE&amp;amp;bav=on.2,or.r_cp.r_qf.&amp;amp;bvm=bv.85464276,d.d2s&amp;amp;biw=1240&amp;amp;bih=658&amp;amp;ech=1&amp;amp;psi=yHDaVNOsH9LnasblgZAI.1423601865135.3&amp;amp;ei=yHDaVNOsH9LnasblgZAI&amp;amp;emsg=NCSR&amp;amp;noj=1" rel="nofollow"&gt;meetups&lt;/a&gt; in many cities where it''s likely to find people working on nonprofit companies and expand your network. &lt;/p&gt;
', NULL, NULL, '''/a'':29C,84C ''/howitworks/datachapters/datakind-uk/'':25C ''/p'':70C,103C ''/search?client=safari&amp;amp;rls=en&amp;amp;q=data%20science%20site:meetup.com&amp;amp;ie=utf-8&amp;amp;oe=utf-8&amp;amp;gfe_rd=cr&amp;amp;oq=data%20science&amp;amp;gs_l=navquery.3..0l4j0i5.1459.4969.0.5279.12.12.0.0.0.0.82.718.12.12.0....0....1.61.navquery..0.12.717.-odm8neybww&amp;amp;ved=0cckq2we&amp;amp;bav=on.2,or.r_cp.r_qf.&amp;amp;bvm=bv.85464276,d.d2s&amp;amp;biw=1240&amp;amp;bih=658&amp;amp;ech=1&amp;amp;psi=yhdavnosh9lnasblgzai.1423601865135.3&amp;amp;ei=yhdavnosh9lnasblgzai&amp;amp;emsg=ncsr&amp;amp;noj=1'':80C ''also'':74C ''citi'':87C ''compani'':98C ''data'':61C ''datakind'':28C ''england'':45C ''especi'':40C ''even'':54C ''event'':69C ''expand'':100C ''find'':93C ''good'':6C ''href'':22C,77C ''idea'':7C ''interest'':51C ''lead'':17C ''like'':91C ''live'':43C ''mani'':86C ''meetup'':68C,83C ''mention'':33C ''might'':3C ''network'':102C ''nofollow'':27C,82C ''nonprofit'':97C ''onlin'':52C ''p'':1C,71C ''pay'':19C ''peopl'':94C ''posit'':20C ''regist'':49C ''rel'':26C,81C ''scientist'':62C ''see'':14C ''sinc'':46C ''start'':9C,39C ''various'':75C ''voluntari'':11C ''weekend'':65C ''work'':12C,56C,95C ''would'':38C ''www.datakind.org'':24C ''www.datakind.org/howitworks/datachapters/datakind-uk/'':23C ''www.google.com'':79C ''www.google.com/search?client=safari&amp;amp;rls=en&amp;amp;q=data%20science%20site:meetup.com&amp;amp;ie=utf-8&amp;amp;oe=utf-8&amp;amp;gfe_rd=cr&amp;amp;oq=data%20science&amp;amp;gs_l=navquery.3..0l4j0i5.1459.4969.0.5279.12.12.0.0.0.0.82.718.12.12.0....0....1.61.navquery..0.12.717.-odm8neybww&amp;amp;ved=0cckq2we&amp;amp;bav=on.2,or.r_cp.r_qf.&amp;amp;bvm=bv.85464276,d.d2s&amp;amp;biw=1240&amp;amp;bih=658&amp;amp;ech=1&amp;amp;psi=yhdavnosh9lnasblgzai.1423601865135.3&amp;amp;ei=yhdavnosh9lnasblgzai&amp;amp;emsg=ncsr&amp;amp;noj=1'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5103, 2, '2015-02-10 23:18:18.657', 2, NULL, NULL, '&lt;p&gt;Both Storm and Spark are great tools. It depends on your use-case. &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Do you want to quickly parse huge stream of data and store it into a database? Use Storm (e.g. counting tweets).&lt;/li&gt;
&lt;li&gt;Training a classifier on a stream of data would be a task suitable for Spark. There''s a data window on which you''re working and it will take a while.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I haven''t tried Flink, but it looks more similar to Spark. Spark has general concept of RDD (Resilient Distributed Datasets) that can be used also for graphs, huge matrices etc.&lt;/p&gt;

&lt;p&gt;If you want to write a word count, you any of them. But who wants a word count (except from hello world tutorials)? &lt;/p&gt;
', NULL, NULL, '''/li'':40C,73C ''/p'':16C,107C,132C ''/ul'':74C ''also'':101C ''case'':15C ''classifi'':44C ''concept'':91C ''count'':38C,116C,126C ''data'':28C,49C,60C ''databas'':34C ''dataset'':96C ''depend'':10C ''distribut'':95C ''e.g'':37C ''etc'':106C ''except'':127C ''flink'':80C ''general'':90C ''graph'':103C ''great'':7C ''haven'':77C ''hello'':129C ''huge'':25C,104C ''li'':18C,41C ''look'':83C ''matric'':105C ''p'':1C,75C,108C ''pars'':24C ''quick'':23C ''rdd'':93C ''re'':65C ''resili'':94C ''similar'':85C ''spark'':5C,56C,87C,88C ''store'':30C ''storm'':3C,36C ''stream'':26C,47C ''suitabl'':54C ''take'':70C ''task'':53C ''tool'':8C ''train'':42C ''tri'':79C ''tutori'':131C ''tweet'':39C ''ul'':17C ''use'':14C,35C,100C ''use-cas'':13C ''want'':21C,111C,123C ''window'':61C ''word'':115C,125C ''work'':66C ''world'':130C ''would'':50C ''write'':113C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5106, 2, '2015-02-11 07:03:08.857', 0, NULL, NULL, '&lt;p&gt;I am also a newbie to data analytic, though I have done some analytic with weka. I can suggest you some things.&lt;/p&gt;

&lt;p&gt;1) Have you checked your data distribution for the field you are applying IQR for? If the distribution is normal then only IQR will be effective.&lt;/p&gt;

&lt;p&gt;2) You haven''t explained about your data. Such as whether it''s labelled or unlabelled. If it''s unlabelled you can apply clustering &amp;amp; see what data lie outside.&lt;/p&gt;

&lt;p&gt;I hope it helps :).&lt;/p&gt;
', NULL, NULL, '''/p'':24C,52C,84C,90C ''1'':26C ''2'':54C ''also'':4C ''amp'':78C ''analyt'':9C,15C ''appli'':38C,76C ''check'':29C ''cluster'':77C ''data'':8C,31C,61C,81C ''distribut'':32C,43C ''done'':13C ''effect'':51C ''explain'':58C ''field'':35C ''haven'':56C ''help'':89C ''hope'':87C ''iqr'':39C,48C ''label'':67C ''lie'':82C ''newbi'':6C ''normal'':45C ''outsid'':83C ''p'':1C,25C,53C,85C ''see'':79C ''suggest'':20C ''thing'':23C ''though'':10C ''unlabel'':69C,73C ''weka'':17C ''whether'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5107, 2, '2015-02-11 07:56:48.717', 0, NULL, NULL, '&lt;p&gt;No, no purpose other than saving data. A fresh sample is always better than a used one.&lt;/p&gt;

&lt;p&gt;The only situation I can think of in which having epochs would make sense would be if the synthesis process would be really time consuming per example.&lt;/p&gt;
', NULL, NULL, '''/p'':19C,48C ''alway'':13C ''better'':14C ''consum'':45C ''data'':8C ''epoch'':31C ''exampl'':47C ''fresh'':10C ''make'':33C ''one'':18C ''p'':1C,20C ''per'':46C ''process'':40C ''purpos'':4C ''realli'':43C ''sampl'':11C ''save'':7C ''sens'':34C ''situat'':23C ''synthesi'':39C ''think'':26C ''time'':44C ''use'':17C ''would'':32C,35C,41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5114, 2, '2015-02-11 13:56:30.847', 4, NULL, NULL, '&lt;p&gt;If this is about splitting your data into training and testing data, then 80/20 is a common rule of thumb. An "optimal" split (which would need to be operationalized) would likely depend on your sample size, distributions and relationships between your variables.&lt;/p&gt;

&lt;p&gt;It is also common to split your data &lt;em&gt;three&lt;/em&gt; ways (e.g., 60/20/20 - again rules of thumb), into a training set that you train your models on and a test set which you test your model on. You will iterate training and testing until you like the result. Then, &lt;em&gt;and only then&lt;/em&gt; you apply the final model (trained on both the training and test set) on the third validation set. This avoids "overfitting on the test set".&lt;/p&gt;

&lt;p&gt;However, &lt;a href="http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29" rel="nofollow"&gt;cross-validation&lt;/a&gt; is much better than a simple data split. Your textbook should also cover cross-validation. If it doesn''t, get a better textbook.&lt;/p&gt;
', NULL, NULL, '''/a'':139C ''/em'':56C,100C ''/p'':44C,126C,164C ''/wiki/cross-validation_%28statistics%29'':133C ''60/20/20'':59C ''80/20'':15C ''also'':48C,151C ''appli'':102C ''avoid'':120C ''better'':142C,162C ''common'':18C,49C ''cover'':152C ''cross'':137C,154C ''cross-valid'':136C,153C ''data'':8C,13C,53C,146C ''depend'':33C ''distribut'':38C ''doesn'':158C ''e.g'':58C ''em'':54C,96C ''en.wikipedia.org'':132C ''en.wikipedia.org/wiki/cross-validation_%28statistics%29'':131C ''final'':104C ''get'':160C ''howev'':128C ''href'':130C ''iter'':86C ''like'':32C,92C ''model'':72C,82C,105C ''much'':141C ''need'':27C ''nofollow'':135C ''operation'':30C ''optim'':23C ''overfit'':121C ''p'':1C,45C,127C ''rel'':134C ''relationship'':40C ''result'':94C ''rule'':19C,61C ''sampl'':36C ''set'':67C,77C,113C,118C,125C ''simpl'':145C ''size'':37C ''split'':6C,24C,51C,147C ''test'':12C,76C,80C,89C,112C,124C ''textbook'':149C,163C ''third'':116C ''three'':55C ''thumb'':21C,63C ''train'':10C,66C,70C,87C,106C,110C ''valid'':117C,138C,155C ''variabl'':43C ''way'':57C ''would'':26C,31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5630, 2, '2015-04-27 20:50:57.117', 3, NULL, NULL, '&lt;p&gt;One thing you could do is use an associations rule approach, in the sense of finding the most frequent patterns associated with being passive, or &lt;em&gt;over-associated&lt;/em&gt; with being passive:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;set a frequency threshold&lt;/li&gt;
&lt;li&gt;figure out what kind of patterns you''re looking for, for example frequent sequences. It''s good if you can partially order them, similarly to set inclusion in the &lt;a href="http://en.wikipedia.org/wiki/Apriori_algorithm" rel="nofollow"&gt;a priori algorithm&lt;/a&gt;. For example, look for sequences, and order them by "is subsequence of".&lt;/li&gt;
&lt;li&gt;Starting with a reasonably small list of minimal patterns that you''re looking for, go through your data counting the occurrences of these patterns.  &lt;/li&gt;
&lt;li&gt;Keep the most frequent patterns (above your threshold), this is your step-1 set of frequent patterns.&lt;/li&gt;
&lt;li&gt;Generate candidates for step-&lt;em&gt;i+1&lt;/em&gt; patterns by adding something to your step-i patterns.&lt;/li&gt;
&lt;li&gt;Count how many times the candidates occur. Go to step 4 until you have no more frequent patterns for step &lt;em&gt;i&lt;/em&gt;.&lt;/li&gt;
&lt;li&gt;You then have a bunch of rule candidates, and you want to use association rules metrics to find the patterns that best predict being passive. &lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''+1'':149C ''-1'':136C ''/a'':83C ''/em'':31C,150C,185C ''/li'':42C,96C,122C,141C,161C,186C,213C ''/ol'':214C ''/p'':35C ''/wiki/apriori_algorithm'':77C ''4'':173C ''ad'':153C ''algorithm'':82C ''approach'':12C ''associ'':10C,22C,30C,201C ''best'':209C ''bunch'':192C ''candid'':144C,168C,195C ''could'':5C ''count'':116C,163C ''data'':115C ''em'':27C,147C,183C ''en.wikipedia.org'':76C ''en.wikipedia.org/wiki/apriori_algorithm'':75C ''exampl'':55C,85C ''figur'':44C ''find'':17C,205C ''frequenc'':40C ''frequent'':20C,56C,127C,139C,179C ''generat'':143C ''go'':112C,170C ''good'':60C ''href'':74C ''inclus'':70C ''keep'':124C ''kind'':47C ''li'':37C,43C,97C,123C,142C,162C,187C ''list'':103C ''look'':52C,86C,110C ''mani'':165C ''metric'':203C ''minim'':105C ''nofollow'':79C ''occur'':169C ''occurr'':118C ''ol'':36C ''one'':2C ''order'':65C,90C ''over-associ'':28C ''p'':1C ''partial'':64C ''passiv'':25C,34C,212C ''pattern'':21C,49C,106C,121C,128C,140C,151C,160C,180C,207C ''predict'':210C ''priori'':81C ''re'':51C,109C ''reason'':101C ''rel'':78C ''rule'':11C,194C,202C ''sens'':15C ''sequenc'':57C,88C ''set'':38C,69C,137C ''similar'':67C ''small'':102C ''someth'':154C ''start'':98C ''step'':135C,146C,158C,172C,182C ''step-i'':157C ''subsequ'':94C ''thing'':3C ''threshold'':41C,131C ''time'':166C ''use'':8C,200C ''want'':198C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5125, 2, '2015-02-12 11:23:23.487', 4, NULL, NULL, '&lt;p&gt;&lt;a href="http://statsmodels.sourceforge.net/devel/" rel="nofollow"&gt;statsmodels&lt;/a&gt; is a good, and fairly standard, package to statistics.&lt;/p&gt;

&lt;p&gt;For Bayesian interference you can go with &lt;a href="http://pymc-devs.github.io/pymc/" rel="nofollow"&gt;PyMC&lt;/a&gt; - see as in &lt;a href="http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/" rel="nofollow"&gt;Cam Davidson-Pilon, Probabilistic Programming &amp;amp; Bayesian Methods for Hackers&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':10C,37C,59C ''/devel/'':6C ''/p'':20C,60C ''/probabilistic-programming-and-bayesian-methods-for-hackers/'':45C ''/pymc/'':33C ''amp'':54C ''bayesian'':23C,55C ''cam'':48C ''camdavidsonpilon.github.io'':44C ''camdavidsonpilon.github.io/probabilistic-programming-and-bayesian-methods-for-hackers/'':43C ''davidson'':50C ''davidson-pilon'':49C ''fair'':15C ''go'':27C ''good'':13C ''hacker'':58C ''href'':3C,30C,42C ''interfer'':24C ''method'':56C ''nofollow'':8C,35C,47C ''p'':1C,21C ''packag'':17C ''pilon'':51C ''probabilist'':52C ''program'':53C ''pymc'':36C ''pymc-devs.github.io'':32C ''pymc-devs.github.io/pymc/'':31C ''rel'':7C,34C,46C ''see'':38C ''standard'':16C ''statist'':19C ''statsmodel'':9C ''statsmodels.sourceforge.net'':5C ''statsmodels.sourceforge.net/devel/'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5108, 1, '2015-02-11 08:00:52.377', 1, 57, 'Reasons and prevention of trivial (and less trivial) misclassification errors?', '&lt;p&gt;I was not sure about posting this question with mentioning the name of the company, which I quite respect and admire. However, I''ve figured that a wider exposure might help the team to fix this and similar problems faster as well as increase the quality of the &lt;em&gt;machine learning (ML)&lt;/em&gt; engine of their website.&lt;/p&gt;

&lt;p&gt;The problem exposes itself by too many occurrences of a quite trivial &lt;em&gt;misclassification error&lt;/em&gt; on Amazon''s book categories &lt;strong&gt;classification&lt;/strong&gt; (which I''m a frequent visitor of). In the following example, the underlying reason of such behavior is quite clear, but in other cases the reasons might be different. I am curious about what could be other potential &lt;em&gt;reasons&lt;/em&gt; for misclassification and what are the &lt;em&gt;strategies/approaches&lt;/em&gt; to avoiding such problems. Without much further ado, here''s how the problem appears in real life.&lt;/p&gt;

&lt;p&gt;I was reviewing some books, related to transitioning from graduate programs (Ph.D., in particular) to work environment in academia. Among several other books, I ran across the following one:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/4LSzV.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;So far, so good. However, let''s scroll down a bit further to see the the books ratings in relevant categories. We should expect Amazon to figure out &lt;em&gt;categories&lt;/em&gt;, &lt;strong&gt;relevant&lt;/strong&gt; to the book''s &lt;em&gt;discipline, topic and contents&lt;/em&gt;. How surprised was I (and that''s an understatement!) to see the following result of Amazon.com''s sophisticated ML engine and algorithms:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/KXrFh.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Clearly, the only fuzzy fact that connects this book with the subject "Audiology and Speech Pathology" (!) is IMHO the author''s last name (Boice), which, is &lt;strong&gt;close to&lt;/strong&gt; the word "voice". If my guess is correct, Amazon''s ML engine, for some reason, decided to take into account the book''s lexicographical attribute instead of the book''s most important and &lt;strong&gt;most relevant attributes&lt;/strong&gt;, such as title, topic and contents. I''ve seen multiple occurrences of similar absolutely incorrect ML-based decision making on Amazon.com and some other websites. So, hopefully my question makes sense as well as interesting and important enough to spark a discussion: &lt;strong&gt;What could be other potential reasons for misclassification and what are the strategies/approaches to avoiding such problems?&lt;/strong&gt; (Any related thoughts will also be appreciated.)&lt;/p&gt;
', 'machine-learning  classification  topic-model', NULL, '''/4lszv.png'':206C ''/em'':71C,93C,142C,151C,244C,257C ''/kxrfh.png'':286C ''/p'':76C,169C,200C,212C,280C,292C,431C ''/strong'':101C,247C,323C,361C,423C ''absolut'':375C ''academia'':189C ''account'':343C ''across'':196C ''admir'':39C ''ado'':159C ''algorithm'':279C ''also'':428C ''alt'':207C,287C ''amazon'':95C,238C,332C ''amazon.com'':273C,383C ''among'':190C ''appear'':165C ''appreci'':430C ''attribut'':348C,360C ''audiolog'':306C ''author'':313C ''avoid'':153C,420C ''base'':379C ''behavior'':118C ''bit'':224C ''boic'':317C ''book'':97C,175C,193C,230C,250C,302C,345C,352C ''case'':125C ''categori'':98C,234C,243C ''classif'':14B,100C ''clear'':121C,294C ''close'':321C ''compani'':33C ''connect'':300C ''content'':256C,367C ''correct'':331C ''could'':136C,407C ''curious'':133C ''decid'':339C ''decis'':380C ''descript'':210C,290C ''differ'':130C ''disciplin'':253C ''discuss'':404C ''em'':67C,90C,140C,149C,242C,252C ''engin'':72C,277C,335C ''enough'':400C ''enter'':208C,288C ''environ'':187C ''error'':10A,92C ''exampl'':112C ''expect'':237C ''expos'':80C ''exposur'':47C ''fact'':298C ''far'':215C ''faster'':58C ''figur'':43C,240C ''fix'':53C ''follow'':111C,198C,270C ''frequent'':106C ''fuzzi'':297C ''good'':217C ''graduat'':180C ''guess'':329C ''help'':49C ''hope'':389C ''howev'':40C,218C ''i.stack.imgur.com'':205C,285C ''i.stack.imgur.com/4lszv.png'':204C ''i.stack.imgur.com/kxrfh.png'':284C ''imag'':209C,289C ''img'':202C,282C ''imho'':311C ''import'':355C,399C ''incorrect'':376C ''increas'':62C ''instead'':349C ''interest'':397C ''last'':315C ''learn'':13B,69C ''less'':7A ''let'':219C ''lexicograph'':347C ''life'':168C ''m'':104C ''machin'':12B,68C ''machine-learn'':11B ''make'':381C,392C ''mani'':84C ''mention'':28C ''might'':48C,128C ''misclassif'':9A,91C,144C,413C ''ml'':70C,276C,334C,378C ''ml-base'':377C ''model'':17B ''much'':157C ''multipl'':371C ''name'':30C,316C ''occurr'':85C,372C ''one'':199C ''p'':18C,77C,170C,201C,213C,281C,293C ''particular'':184C ''patholog'':309C ''ph.d'':182C ''post'':24C ''potenti'':139C,410C ''prevent'':3A ''problem'':57C,79C,155C,164C,422C ''program'':181C ''qualiti'':64C ''question'':26C,391C ''quit'':36C,88C,120C ''ran'':195C ''rate'':231C ''real'':167C ''reason'':1A,115C,127C,141C,338C,411C ''relat'':176C,425C ''relev'':233C,246C,359C ''respect'':37C ''result'':271C ''review'':173C ''scroll'':221C ''see'':227C,268C ''seen'':370C ''sens'':393C ''sever'':191C ''similar'':56C,374C ''sophist'':275C ''spark'':402C ''speech'':308C ''src'':203C,283C ''strategies/approaches'':150C,418C ''strong'':99C,245C,320C,357C,405C ''subject'':305C ''sure'':22C ''surpris'':259C ''take'':341C ''team'':51C ''thought'':426C ''titl'':364C ''topic'':16B,254C,365C ''topic-model'':15B ''transit'':178C ''trivial'':5A,8A,89C ''under'':114C ''understat'':266C ''ve'':42C,369C ''visitor'':107C ''voic'':326C ''websit'':75C,387C ''well'':60C,395C ''wider'':46C ''without'':156C ''word'':325C ''work'':186C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5109, 1, '2015-02-11 10:48:51.903', 3, 1986, 'Machine Learning on financial big data', '&lt;p&gt;&lt;em&gt;Disclaimer:&lt;/em&gt; although I know some things about big data and am currently learning some other things about machine learning, the specific area that I wish to study is vague, or at least appears vague to me now. I''ll do my best to describe it, but this question could still be categorised as too vague or not really a question. Hopefully, I''ll be able to reword it more precisely once I get a reaction.&lt;/p&gt;

&lt;p&gt;So,&lt;/p&gt;

&lt;p&gt;I have some experience with Hadoop and the Hadoop stack (gained via using CDH), and I''m reading a book about Mahout, which is a collection of machine learning libraries. I also think I know enough statistics to be able to comprehend the math behind the machine learning algorithms, and I have some experience with R.
My ultimate goal is making a setup that would make trading predictions and deal with financial data in real time.&lt;/p&gt;

&lt;p&gt;I wonder if there''re any materials that I can further read to help me understand ways of managing that problem; books, video tutorials and exercises with example datasets are all welcome.&lt;/p&gt;
', 'machine-learning  bigdata  finance', NULL, '''/em'':15C ''/p'':90C,93C,171C,205C ''abl'':79C,134C ''algorithm'':143C ''also'':126C ''although'':16C ''appear'':47C ''area'':36C ''behind'':139C ''best'':56C ''big'':5A,22C ''bigdata'':10B ''book'':114C,194C ''categoris'':66C ''cdh'':108C ''collect'':120C ''comprehend'':136C ''could'':63C ''current'':26C ''data'':6A,23C,167C ''dataset'':201C ''deal'':164C ''describ'':58C ''disclaim'':14C ''em'':13C ''enough'':130C ''exampl'':200C ''exercis'':198C ''experi'':98C,148C ''financ'':11B ''financi'':4A,166C ''gain'':105C ''get'':87C ''goal'':153C ''hadoop'':100C,103C ''help'':186C ''hope'':75C ''know'':18C,129C ''learn'':2A,9B,27C,33C,123C,142C ''least'':46C ''librari'':124C ''ll'':53C,77C ''m'':111C ''machin'':1A,8B,32C,122C,141C ''machine-learn'':7B ''mahout'':116C ''make'':155C,160C ''manag'':191C ''materi'':179C ''math'':138C ''p'':12C,91C,94C,172C ''precis'':84C ''predict'':162C ''problem'':193C ''question'':62C,74C ''r'':150C ''re'':177C ''reaction'':89C ''read'':112C,184C ''real'':169C ''realli'':72C ''reword'':81C ''setup'':157C ''specif'':35C ''stack'':104C ''statist'':131C ''still'':64C ''studi'':41C ''thing'':20C,30C ''think'':127C ''time'':170C ''trade'':161C ''tutori'':196C ''ultim'':152C ''understand'':188C ''use'':107C ''vagu'':43C,48C,69C ''via'':106C ''video'':195C ''way'':189C ''welcom'':204C ''wish'':39C ''wonder'':174C ''would'':159C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5110, 1, '2015-02-11 11:53:30.89', 1, 50, 'Graph layout for a network of molecules', '&lt;p&gt;I am looking for a suitable graph representation where the nodes/vertices are molecules with 2 main variables: structural characteristic and a real number property (for example biological potency). The network is build up by molecules by matching structural properties and it can be rather complex with many clusters of high degree vertices (ie molecules that are structurally similar to each other). However the representation should give a clear picture of potencies, although high detail accuracy is not required: some sort of classification is adequate. I played with JUNG with a graph having edge weights as the change in potency and the ISOM layout thinking that it reserves (as much as possible) the edge weights but with not good results. Something like radial layout could be ideal, as it gives a clear picture of potency distribution, but this obviously would suffer from crowding of vertices that are closer to the center.  As, obviously, my knowledge is restricted, is there some layout that you would suggest and is practical for a non expert to implement? &lt;/p&gt;
', 'visualization  graphs', NULL, '''/p'':184C ''2'':25C ''accuraci'':85C ''adequ'':94C ''although'':82C ''biolog'':37C ''build'':42C ''center'':160C ''chang'':107C ''characterist'':29C ''classif'':92C ''clear'':78C,141C ''closer'':157C ''cluster'':58C ''complex'':55C ''could'':134C ''crowd'':152C ''degre'':61C ''detail'':84C ''distribut'':145C ''edg'':103C,123C ''exampl'':36C ''expert'':181C ''give'':76C,139C ''good'':128C ''graph'':1A,9B,17C,101C ''high'':60C,83C ''howev'':72C ''ideal'':136C ''ie'':63C ''implement'':183C ''isom'':112C ''jung'':98C ''knowledg'':164C ''layout'':2A,113C,133C,170C ''like'':131C ''look'':13C ''main'':26C ''mani'':57C ''match'':47C ''molecul'':7A,23C,45C,64C ''much'':119C ''network'':5A,40C ''nodes/vertices'':21C ''non'':180C ''number'':33C ''obvious'':148C,162C ''p'':10C ''pictur'':79C,142C ''play'':96C ''possibl'':121C ''potenc'':38C,81C,109C,144C ''practic'':177C ''properti'':34C,49C ''radial'':132C ''rather'':54C ''real'':32C ''represent'':18C,74C ''requir'':88C ''reserv'':117C ''restrict'':166C ''result'':129C ''similar'':68C ''someth'':130C ''sort'':90C ''structur'':28C,48C,67C ''suffer'':150C ''suggest'':174C ''suitabl'':16C ''think'':114C ''variabl'':27C ''vertic'':62C,154C ''visual'':8B ''weight'':104C,124C ''would'':149C,173C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6612, 1, '2015-07-28 17:54:23.927', 1, 38, 'Reducing the dimensionality of word embeddings', '&lt;p&gt;I trained word embeddings with 300 dimensions. Now, I would like to have word embeddings with 50 dimensions: is it better to retrain the word embeddings with 50 dimensions, or can I use some dimensionality reduction method to scale the word embeddings with 300 dimensions down to 50 dimensions?&lt;/p&gt;
', 'nlp  dimensionality-reduction  word-embeddings', NULL, '''/p'':64C ''300'':20C,58C ''50'':31C,42C,62C ''better'':35C ''dimens'':21C,32C,43C,59C,63C ''dimension'':3A,9B,49C ''dimensionality-reduct'':8B ''embed'':6A,13B,18C,29C,40C,56C ''like'':25C ''method'':51C ''nlp'':7B ''p'':14C ''reduc'':1A ''reduct'':10B,50C ''retrain'':37C ''scale'':53C ''train'':16C ''use'':47C ''word'':5A,12B,17C,28C,39C,55C ''word-embed'':11B ''would'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5111, 2, '2015-02-11 12:25:31.077', 9, NULL, NULL, '&lt;p&gt;There are tons of materials on financial (big) data analysis that you can read and peruse. I''m not an expert in finance, but am curious about the field, especially in the context of data science and R. Therefore, the following are selected relevant resource suggestions that I have for you. I hope that they will be useful.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Books: Financial analysis (general / non-R)&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.springer.com/statistics/business,+economics+%26+finance/book/978-0-387-20270-9" rel="nofollow"&gt;Statistics and Finance: An Introduction&lt;/a&gt;;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.springer.com/economics/public+finance/book/978-0-387-77826-6" rel="nofollow"&gt;Statistical Models and Methods for Financial Markets&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Books: Machine Learning in Finance&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.worldscientific.com/worldscibooks/10.1142/p818" rel="nofollow"&gt;Machine Learning for Financial Engineering&lt;/a&gt; (!) - seems to be an edited collection of papers;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.elsevier.com/books/neural-networks-in-finance/mcnelis/978-0-12-485967-8#description" rel="nofollow"&gt;Neural Networks in Finance: Gaining Predictive Edge in the Market&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Books: Financial analysis with R&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.springer.com/statistics/business,+economics+%26+finance/book/978-1-4614-8787-6" rel="nofollow"&gt;Statistical Analysis of Financial Data in R&lt;/a&gt;;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.springer.com/statistics/business,+economics+%26+finance/book/978-1-4419-7786-1" rel="nofollow"&gt;Statistics and Data Analysis for Financial Engineering&lt;/a&gt;;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0470978708" rel="nofollow"&gt;Financial Risk Modelling and Portfolio Optimization with R&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.springer.com/statistics/business%2C+economics+%26+finance/book/978-3-642-16520-7" rel="nofollow"&gt;Statistics of Financial Markets: An Introduction&lt;/a&gt; (code in R and MATLAB).&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Academic Journals&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://algorithmicfinance.org" rel="nofollow"&gt;Algorithmic Finance&lt;/a&gt; (open access)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Web sites&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://www.rmetrics.org" rel="nofollow"&gt;RMetrics&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://quant.stackexchange.com"&gt;Quantitative Finance on StackExchange&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;R Packages&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;the above-mentioned &lt;em&gt;RMetrics&lt;/em&gt; site (see &lt;a href="https://www.rmetrics.org/about" rel="nofollow"&gt;this page&lt;/a&gt; for general description);&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;CRAN Task Views&lt;/em&gt;, including &lt;a href="http://cran.r-project.org/web/views/Finance.html" rel="nofollow"&gt;Finance&lt;/a&gt;, &lt;a href="http://cran.r-project.org/web/views/Econometrics.html" rel="nofollow"&gt;Econometrics&lt;/a&gt; and several other Task Views.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Competitions&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.modeloff.com" rel="nofollow"&gt;MODELOFF (The Financial Modeling World Championships)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Educational Programs&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://ieor.columbia.edu/ms-financial-engineering" rel="nofollow"&gt;MS in Financial Engineering - Columbia University&lt;/a&gt;;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www3.cs.stonybrook.edu/~skiena/691" rel="nofollow"&gt;Computational Finance - Hong Kong University&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Blogs (Finance/R)&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://timelyportfolio.blogspot.com" rel="nofollow"&gt;Timely Portfolio&lt;/a&gt;;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://systematicinvestor.wordpress.com" rel="nofollow"&gt;Systematic Investor&lt;/a&gt;;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://moneymakingmankind.wordpress.com" rel="nofollow"&gt;Money-making Mankind&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5112, 2, '2015-02-11 13:25:28.473', 2, NULL, NULL, '&lt;p&gt;By looking at the ratings of the book, it looks like they are doing some sort of hierarchical clustering.
First of all it''s a book, then a text book, then in medicine &amp;amp; health science.&lt;/p&gt;

&lt;p&gt;This is a pretty difficult problem because:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;You don''t know how many and which topics you have, and the topics are constantly changing.&lt;/li&gt;
&lt;li&gt;Since this is a clustering problem (or a semi-supervised problem in the better case), a closed feedback loop for these errors cannot be applied. Clustering is always more difficult than a supervised learning problem.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Apparently they''re doing a pretty good job in general. This book in particular is a tough one...&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5113, 1, '2015-02-11 13:26:27.753', 1, 140, 'Dividing percentage', '&lt;p&gt;A book I''m now reading, "Apache Mahout Cookbook" by Pierro Giacomelli, states that &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;To avoid [this], you need to divide the vector files into two sets called the 80-20 split &amp;lt;...&gt;
  A good dividing percentage is shown to be 80% and 20%.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Is there a strict statistical proof of this being the best percentage, or is it a euristic result?&lt;/p&gt;
', 'machine-learning  statistics', NULL, '''-20'':41C ''/blockquote'':56C ''/p'':22C,55C,76C ''20'':54C ''80'':40C,52C ''apach'':14C ''avoid'':26C ''best'':68C ''blockquot'':23C ''book'':9C ''call'':38C ''cookbook'':16C ''divid'':1A,31C,46C ''eurist'':74C ''file'':34C ''giacomelli'':19C ''good'':45C ''learn'':5B ''lt'':43C ''m'':11C ''machin'':4B ''machine-learn'':3B ''mahout'':15C ''need'':29C ''p'':7C,24C,57C ''percentag'':2A,47C,69C ''pierro'':18C ''proof'':63C ''read'':13C ''result'':75C ''set'':37C ''shown'':49C ''split'':42C ''state'':20C ''statist'':6B,62C ''strict'':61C ''two'':36C ''vector'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5115, 1, '2015-02-11 16:46:38.8', 0, 80, 'Calculating entropies of attributes', '&lt;p&gt;Can you please show the step by step calculation of Entropy(Ssun)?  I do not understand how 0.918 is arrived at.&lt;/p&gt;

&lt;p&gt;I tried but I get the values as 0.521089678, 0.528771238, 0.521089678 for Sunny, Windy, Rainy.&lt;/p&gt;

&lt;p&gt;I was able to calculate the target entropy (Decision) correctly as = -(6/10)*log(6/10) + -(2/10)log(2/10) + -(1/10)log(1/10) + -(1/10)log(1/10) = 1.570950594&lt;/p&gt;

&lt;p&gt;I am totally stuck at the next step.  Request your help.&lt;/p&gt;

&lt;p&gt;Reference: &lt;a href="http://www.doc.ic.ac.uk/~sgc/teaching/pre2012/v231/lecture11.html" rel="nofollow"&gt;http://www.doc.ic.ac.uk/~sgc/teaching/pre2012/v231/lecture11.html&lt;/a&gt;
Please search for "The first thing we need to do is work out which attribute will be put into the node at the top of our tree:" to reach the line I am referring to.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5116, 1, '2015-02-11 18:31:31.193', 1, 173, 'How do I use Hierarchical Dirichlet Process (HDP) implementations (hdpfaster by C.Wang or hca by W.Buntine at mloss.org) to discover number of topics?', '&lt;p&gt;Context of question:&lt;/p&gt;

&lt;p&gt;I want to find semantically similar documents in corpora. For that, I''m first trying Latent Dirichlet Allocation (LDA) with divergences (Hellinger, Kullback-Leibler, Jensen-Shannon) on the per document topic distributions. However, to find # of topics for my corpus ( a 948 document dataset, extracted from larger collection, where docs about the same story are humanly annotated), it was suggested to use HDP. Unfortunately, after MANY tries, I''m still unsure I''m using the packages correctly.&lt;/p&gt;

&lt;p&gt;More detailed:&lt;/p&gt;

&lt;p&gt;1) HDPFASTER continually increases the number of topics (in train.log file).  What is the stopping criterion (e.g. difference in avg. likelihood between successive iterations smaller than x? which x?) and/or how many cycles should I let it run? Afterwords, do I take as the correct # of topics the last line of "train.log" OR should I also test for minimum perplexity (It has also been suggested that Bayesian Information Criterion (BIC) might be a better measure) and choose the smallest? If the latter, should I test for ALL iterations?!  Is HDPFASTER''s test feature the appropriate tool for this? Last, should I --sample_hyper? Do I just use a(alpha) as input to LDA''s prior alpha, perhaps divided by a number (e.g. # of topics)? What about LDA''s prior beta? &lt;/p&gt;

&lt;p&gt;2) HCA continually decreases  the number of topics (in *.log file). When to stop? Do I accept the final # of topics in .log ( exp.ent =number0) OR do I search for the minimum perplexity of test set (as I assume it appears as number2 in "log_2(perp)=number1,number2" in .log throughout iterations), which ALWAYS appears VERY close to my initial max number of topics? Hyperparameters alpha,beta: do I sample using -D , -E? Which do I put as input to LDA''s priors? Is it generally worth it?&lt;/p&gt;

&lt;p&gt;Generally: Is it possible that my dataset is just too small and/or ''biased/fragmented/incomplete'', in the sense that each story has only 1-3 examples, while the diversity of the topics these stories discuss can be quite large? Should I just try and augment it with a larger ''homogeneous'' dataset? &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8541, 2, '2015-10-21 12:26:12.9', 0, NULL, NULL, '&lt;p&gt;Your requirements suggest you might want to use &lt;a href="https://en.wikipedia.org/wiki/Uplift_modelling" rel="nofollow"&gt;uplift Modelling&lt;/a&gt;. This has a neat R package references in the wiki article.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stochasticsolutions.com/pdf/sig-based-up-trees.pdf" rel="nofollow"&gt;This&lt;/a&gt; looks like a practical paper.&lt;/p&gt;
', NULL, NULL, '''/a'':19C,41C ''/p'':31C,47C ''/pdf/sig-based-up-trees.pdf'':37C ''/wiki/uplift_modelling'':14C ''articl'':30C ''en.wikipedia.org'':13C ''en.wikipedia.org/wiki/uplift_modelling'':12C ''href'':11C,34C ''like'':43C ''look'':42C ''might'':6C ''model'':18C ''neat'':23C ''nofollow'':16C,39C ''p'':1C,32C ''packag'':25C ''paper'':46C ''practic'':45C ''r'':24C ''refer'':26C ''rel'':15C,38C ''requir'':3C ''stochasticsolutions.com'':36C ''stochasticsolutions.com/pdf/sig-based-up-trees.pdf'':35C ''suggest'':4C ''uplift'':17C ''use'':9C ''want'':7C ''wiki'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5150, 2, '2015-02-15 23:07:23.753', 0, NULL, NULL, '&lt;p&gt;I''m currently writing a book about Data Science in Higher Education, and the following methodologies are the ones I am including:&lt;/p&gt;

&lt;p&gt;For &lt;em&gt;regression&lt;/em&gt;, we have:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Simple Linear Regression&lt;/li&gt;
&lt;li&gt;Multiple Linear Regression&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;For &lt;em&gt;classification&lt;/em&gt;, we have:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Naive Bayes Classifier&lt;/li&gt;
&lt;li&gt;Decision Tree Induction&lt;/li&gt;
&lt;li&gt;K-Nearest Neighbor&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;These are some of the more elementary topics in statistical analysis (which you could argue is predictive analytics which you could argue is data science), and thus I would suspect they are also the more common. &lt;/p&gt;
', NULL, NULL, '''/em'':29C,49C ''/li'':38C,43C,58C,63C,69C ''/p'':24C,32C,52C,108C ''/ul'':44C,70C ''also'':104C ''analysi'':82C ''analyt'':89C ''argu'':86C,93C ''bay'':56C ''book'':7C ''classif'':48C ''classifi'':57C ''common'':107C ''could'':85C,92C ''current'':4C ''data'':9C,95C ''decis'':60C ''educ'':13C ''elementari'':78C ''em'':27C,47C ''follow'':16C ''higher'':12C ''includ'':23C ''induct'':62C ''k'':66C ''k-nearest'':65C ''li'':34C,39C,54C,59C,64C ''linear'':36C,41C ''m'':3C ''methodolog'':17C ''multipl'':40C ''naiv'':55C ''nearest'':67C ''neighbor'':68C ''one'':20C ''p'':1C,25C,45C,71C ''predict'':88C ''regress'':28C,37C,42C ''scienc'':10C,96C ''simpl'':35C ''statist'':81C ''suspect'':101C ''thus'':98C ''topic'':79C ''tree'':61C ''ul'':33C,53C ''would'':100C ''write'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6614, 4, '2015-07-28 17:54:44.533', 0, NULL, NULL, 'Word embedding is the collective name for a set of language modeling and feature learning techniques in NLP where words are mapped to vectors of real numbers in a low dimensional space, relative to the vocabulary size.', NULL, NULL, '''collect'':5C ''dimension'':31C ''embed'':2C ''featur'':14C ''languag'':11C ''learn'':15C ''low'':30C ''map'':22C ''model'':12C ''name'':6C ''nlp'':18C ''number'':27C ''real'':26C ''relat'':33C ''set'':9C ''size'':37C ''space'':32C ''techniqu'':16C ''vector'':24C ''vocabulari'':36C ''word'':1C,20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5134, 2, '2015-02-13 06:03:41.233', 1, NULL, NULL, '&lt;p&gt;The MOOCs like UDACITY, COURSERA, UDEMY and EDX  are a great place to learn high quality R programming courses free. You can also search on the MOOCTIVITY siter which is a MOOC aggregator. &lt;/p&gt;

&lt;p&gt;I have personally found great courses on R programming at  UDACITY, COURSERA and UDEMY and also EDX.&lt;/p&gt;

&lt;p&gt;Good Luck !!&lt;/p&gt;

&lt;p&gt;Gopinath SUbbegowda&lt;/p&gt;
', NULL, NULL, '''/p'':35C,54C,58C,62C ''aggreg'':34C ''also'':24C,52C ''cours'':20C,42C ''coursera'':6C,48C ''edx'':9C,53C ''found'':40C ''free'':21C ''good'':56C ''gopinath'':60C ''great'':12C,41C ''high'':16C ''learn'':15C ''like'':4C ''luck'':57C ''mooc'':3C,33C ''mooctiv'':28C ''p'':1C,36C,55C,59C ''person'':39C ''place'':13C ''program'':19C,45C ''qualiti'':17C ''r'':18C,44C ''search'':25C ''siter'':29C ''subbegowda'':61C ''udac'':5C,47C ''udemi'':7C,50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5117, 2, '2015-02-11 18:32:14.23', 2, NULL, NULL, '&lt;p&gt;Consider the formula for Entropy:&lt;/p&gt;

&lt;p&gt;$E(S) = \sum_\limits{i=1}^{n}-p_{i}\log_2p_{i}$&lt;/p&gt;

&lt;p&gt;Expanding that summation for the four concept decision attributes for your problem gives&lt;/p&gt;

&lt;p&gt;$E(S) = -p_{cinema}\log_2\left(p_{cinema}\right) - p_{tennis}\log_2\left(p_{tennis}\right) - p_{stayin}\log_2\left(p_{stayin}\right) - p_{shopping}\log_2\left(p_{shopping}\right)$&lt;/p&gt;

&lt;p&gt;There are three observations where &lt;code&gt;Weather=Sunny&lt;/code&gt;. Of those three, one has &lt;code&gt;Decision=Cinema&lt;/code&gt; and two have &lt;code&gt;Decision=Tennis&lt;/code&gt;. So for &lt;code&gt;Weather=Sunny&lt;/code&gt; you have $p_{cinema}=\frac{1}{3}$, $p_{tennis}=\frac{2}{3}$, and other decision probabilities are zero. Plugging those values into the equation above gives&lt;/p&gt;

&lt;p&gt;$
\begin{align}
E(S_{Sun}) &amp;amp;= -\frac{1}{3}\log_2\left(\frac{1}{3}\right)-\frac{2}{3}\log_2\left(\frac{2}{3}\right) - \left(0\right)\cdot\log_2\left(0\right) - \left(0\right)\cdot\log_2\left(0\right) \\
           &amp;amp;= 0.38998 + 0.52832 + 0 + 0 \\
           &amp;amp;= 0.918
\end{align}$&lt;/p&gt;
', NULL, NULL, '''/code'':82C,91C,98C,104C ''/p'':7C,21C,36C,72C,131C,186C ''0'':160C,166C,169C,175C,180C,181C ''0.38998'':178C ''0.52832'':179C ''0.918'':183C ''1'':14C,110C,140C,146C ''2'':43C,51C,59C,67C,115C,143C,150C,153C,156C,164C,173C ''2p'':19C ''3'':111C,116C,141C,147C,151C,157C ''align'':134C,185C ''amp'':138C,177C,182C ''attribut'':31C ''begin'':133C ''cdot'':162C,171C ''cinema'':41C,46C,90C,108C ''code'':79C,88C,95C,101C ''concept'':29C ''consid'':2C ''decis'':30C,89C,96C,119C ''e'':9C,38C,135C ''end'':184C ''entropi'':6C ''equat'':128C ''expand'':23C ''formula'':4C ''four'':28C ''frac'':109C,114C,139C,145C,149C,155C ''give'':35C,130C ''left'':44C,52C,60C,68C,144C,154C,159C,165C,168C,174C ''limit'':12C ''log'':18C,42C,50C,58C,66C,142C,152C,163C,172C ''n'':15C ''observ'':77C ''one'':86C ''p'':1C,8C,16C,22C,37C,40C,45C,48C,53C,56C,61C,64C,69C,73C,107C,112C,132C ''plug'':123C ''probabl'':120C ''problem'':34C ''right'':47C,55C,63C,71C,148C,158C,161C,167C,170C,176C ''shop'':65C,70C ''stayin'':57C,62C ''sum'':11C ''summat'':25C ''sun'':137C ''sunni'':81C,103C ''tenni'':49C,54C,97C,113C ''three'':76C,85C ''two'':93C ''valu'':125C ''weather'':80C,102C ''zero'':122C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5118, 2, '2015-02-11 23:31:20.127', 2, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Let me first explain the concept of entropy for decision trees:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Entropy is a so called impurity measure for a set of elements. Impurity - being the opposite of purity - is referring to the distribution of the decision categories (or class labels) within the set. Initially, each row in the table is one element and your set of elements are all rows of your table. This set is called pure if it just consists of the same class label and it is called impure if it contains all different class labels in the same proportion. Translating into the values of entropy (remember being an impurity measure, the set has in the latter case the highest possible value and in the former case (just the same class label) the lowest value. The lowest possible value for entropy is 0.   &lt;/p&gt;

&lt;p&gt;So let''s choose a visual respresentation to illustrate what really counts and what does not matter for the entropy. &lt;/p&gt;

&lt;p&gt;1) You start off with the initial table which contains the following class labels:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/CbjbT.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Please note that for computing the entropy none of the remaining attributes, attribute values matter, also the order of the rows don''t matter (entropy is a measure for a set of elements)&lt;/p&gt;

&lt;p&gt;2) You convert the different class labels to different colors&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/A4mux.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;3) So instead of this table with the colored class labeled, you just draw a circle ("ball") for each class label (=element)&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ufvDS.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Now you throw all the balls in a bag. There you go! Your elements of a set are represented as balls in a bag.  If you open the bag and look into it, you might see &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;just balls of the same color --&gt; one pure color. The entropy says, it''s not impure at all, I give it a zero! entropy = 0&lt;/li&gt;
&lt;li&gt;the same amount of balls in different colors --&gt; that doesn''t look pure at all, it''s impure! The entropy assigns the highest possible value to this set (bag). The highest possible entropy value depends on the number of class labels. If you have just two, the maximum entropy is 1. In your case, you have 4 different class labels, so the maximum entropy would be e.g. if you had 12 balls and 3 balls of each color. The maximum entropy of a 4 class set is 2.&lt;/li&gt;
&lt;li&gt;none of the above. Then your entropy is between the two values. If one color is dominant then the entropy will be close to 0, if the colors are very mixed up, then it is close to the maximum (2 in your case).&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;How does a decision tree use the entropy?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Well, first you calculate the entropy of the whole set. That impurity is your reference. What your decision tree tries to achieve is to reduce the impurity of the whole set. So the information Gain for a given attribute is computed by taking the entropy of the whole set and subtracting it with the entropies of sets that are obtained by breaking the whole set into one piece per attribute category. To make sure a set with just a few elements don''t get the same weight as a set with many elements the entropies of sets are multiplied by their relative frequency.&lt;/p&gt;

&lt;p&gt;Coming back to the example, for computing the Gain (S, parents) you need to compute the weighted entropies of the two sets: set 1 = all rows with category(parent) = yes and set 2 = all rows with category(parent) = no).&lt;/p&gt;

&lt;p&gt;Set 1 refers to five rows, all having the decision category = cinema(just 5 orange balls!)
So the weight is 5 rows out of 10 rows = 5/10 = 0.5. And the entropy is 0 (totally pure) or by applying the formula:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/dDMBf.jpg" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;You have a sum over all class labels so n = 4. So you assign a class label to each of the i''s. E.g.&lt;/p&gt;

&lt;p&gt;For i = 1 being cinema: - (5/5 * log(3/3) = - (1 * log(1)) = - (1*0) = 0&lt;/p&gt;

&lt;p&gt;For i = 2 being tennis: - (0/5 * log(0/3) = - (0 * log(0)) = 0&lt;/p&gt;

&lt;p&gt;For i = 3 being stay in: - (0/5 * log(0/3) = - (0 * log(0)) = 0&lt;/p&gt;

&lt;p&gt;For i = 4 being shopping: - (0/5 * log(0/3) = - (0 * log(0)) = 0&lt;/p&gt;

&lt;p&gt;So the sum of all four is 0. 0 times the weight 0.5 equals 0.&lt;/p&gt;

&lt;p&gt;Set 2 also refers to five rows, having the decision categories = 1*cinema, 2*tennis, 1*stay in, 1*shopping
So the weight is also 5 rows out of 10 rows = 5/10 = 0.5. Applying the formula:&lt;/p&gt;

&lt;p&gt;For i = 1 being cinema: - (1/5 * log(1/5) = - (0.2 * -2,32) = 0.46&lt;/p&gt;

&lt;p&gt;For i = 2 being tennis: - (2/5 * log(2/5) = - (0.4 * -1,32) = 0.53&lt;/p&gt;

&lt;p&gt;For i = 3 being stay in: - (1/5 * log(1/5) = - (0.2 * -2,32) = 0.46&lt;/p&gt;

&lt;p&gt;For i = 4 being shopping: - (1/5 * log(1/5) = - (0.2 * -2,32) = 0.46&lt;/p&gt;

&lt;p&gt;So the sum of all four is 1.92. 1.92 times the weight 0.5 = 0.96.
Thus, the Gain for this attribute is: 
Initial entropy - weighted entropies for all attribute categories:
1.57 - 0 - 0.96 = 0.61.&lt;/p&gt;

&lt;p&gt;For the other attributes you do the same calculation.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5119, 1, '2015-02-12 07:00:38.16', 0, 424, 'Kaggle Titanic Survival Table an example of Naive Bayes?', '&lt;p&gt;is the survival table classification method on the Kaggle Titanic dataset an example of an implementation of Naive Bayes ? I am asking because I am reading up on Naive Bayes and the basic idea is as follows:
"Find out the probability of the previously unseen instance
belonging to each class, then simply pick the most probable class"&lt;/p&gt;

&lt;p&gt;The survival table 
(&lt;a href="http://www.markhneedham.com/blog/tag/kaggle/" rel="nofollow"&gt;http://www.markhneedham.com/blog/tag/kaggle/&lt;/a&gt;)
seems like an evaluation of the possibilities of survival given possible combinations of values of the chosen features and I''m wondering if it could be an example of Naive Bayes in another name. Can someone shed light on this ?&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/blog/tag/kaggle/'':81C ''/blog/tag/kaggle/&lt;/a&gt;)'':86C ''/p'':72C,127C ''anoth'':119C ''ask'':36C ''basic'':47C ''bay'':9A,33C,44C,117C ''belong'':61C ''chosen'':103C ''class'':64C,71C ''classif'':13B,19C ''combin'':98C ''could'':111C ''dataset'':25C ''evalu'':90C ''exampl'':6A,27C,114C ''featur'':104C ''find'':52C ''follow'':51C ''given'':96C ''href'':78C ''idea'':48C ''implement'':30C ''instanc'':60C ''kaggl'':1A,23C ''learn'':12B ''light'':124C ''like'':88C ''m'':107C ''machin'':11B ''machine-learn'':10B ''method'':20C ''naiv'':8A,32C,43C,116C ''name'':120C ''nofollow'':83C ''p'':14C,73C ''pick'':67C ''possibl'':93C,97C ''previous'':58C ''probabl'':55C,70C ''read'':40C ''rel'':82C ''seem'':87C ''shed'':123C ''simpli'':66C ''someon'':122C ''surviv'':3A,17C,75C,95C ''tabl'':4A,18C,76C ''titan'':2A,24C ''unseen'':59C ''valu'':100C ''wonder'':108C ''www.markhneedham.com'':80C,85C ''www.markhneedham.com/blog/tag/kaggle/'':79C ''www.markhneedham.com/blog/tag/kaggle/&lt;/a&gt;)'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5120, 1, '2015-02-12 07:07:56.257', 1, 84, 'How to start the process of coming up with the predicted math score?', '&lt;p&gt;I am working on a problem(non competition) from hacker rank &lt;a href="https://www.hackerrank.com/challenges/predict-missing-grade" rel="nofollow"&gt;https://www.hackerrank.com/challenges/predict-missing-grade&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Basically you''re given test data of a bunch of students of their scores in other subjects but math and you are to predict their score in math based off all their other test scores. Say you were passed data of&lt;/p&gt;

&lt;p&gt;{"SerialNumber":1,"English":1,"Physics":2,"Chemistry":3,"ComputerScience":2}&lt;/p&gt;

&lt;p&gt;How would you go about generating that student''s score in mathematics or coming up with a prediction engine to generate the math score? I know that''s the whole point of this question but can someone give me a hint or a resource to go to so I can have a chance of figuring this out and actually get started? I really want to learn.&lt;/p&gt;
', 'machine-learning  data-mining  statistics  predictive-modeling', NULL, '''/challenges/predict-missing-grade'':40C ''/challenges/predict-missing-grade&lt;/a&gt;&lt;/p&gt;'':45C ''/p'':88C,100C,168C ''1'':91C,93C ''2'':95C,99C ''3'':97C ''actual'':160C ''base'':75C ''basic'':47C ''bunch'':55C ''chanc'':154C ''chemistri'':96C ''come'':7A,115C ''competit'':32C ''computersci'':98C ''data'':18B,52C,86C ''data-min'':17B ''engin'':120C ''english'':92C ''figur'':156C ''generat'':107C,122C ''get'':161C ''give'':139C ''given'':50C ''go'':105C,147C ''hacker'':34C ''hint'':142C ''href'':37C ''know'':127C ''learn'':16B,167C ''machin'':15B ''machine-learn'':14B ''math'':12A,65C,74C,124C ''mathemat'':113C ''mine'':19B ''model'':23B ''nofollow'':42C ''non'':31C ''p'':24C,46C,89C,101C ''pass'':85C ''physic'':94C ''point'':132C ''predict'':11A,22B,70C,119C ''predictive-model'':21B ''problem'':30C ''process'':5A ''question'':135C ''rank'':35C ''re'':49C ''realli'':164C ''rel'':41C ''resourc'':145C ''say'':82C ''score'':13A,60C,72C,81C,111C,125C ''serialnumb'':90C ''someon'':138C ''start'':3A,162C ''statist'':20B ''student'':57C,109C ''subject'':63C ''test'':51C,80C ''want'':165C ''whole'':131C ''work'':27C ''would'':103C ''www.hackerrank.com'':39C,44C ''www.hackerrank.com/challenges/predict-missing-grade'':38C ''www.hackerrank.com/challenges/predict-missing-grade&lt;/a&gt;&lt;/p&gt;'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5121, 1, '2015-02-12 07:08:16.537', 4, 1421, 'Applications and differences for Jaccard similarity and Cosine Similarity', '&lt;p&gt;&lt;strong&gt;Jaccard similarity&lt;/strong&gt; and &lt;strong&gt;cosine similarity&lt;/strong&gt; are two very common measurements while comparing item similarities. However, I am not very clear in what situation which one should be preferable than another.&lt;/p&gt;

&lt;p&gt;Can somebody help clarify the differences of these two measurements (the difference in concept or principle, not the definition or computation) and their preferable applications?&lt;/p&gt;
', 'similarity', NULL, '''/p'':46C,73C ''/strong'':15C,20C ''anoth'':45C ''applic'':1A,72C ''clarifi'':51C ''clear'':35C ''common'':24C ''compar'':27C ''comput'':68C ''concept'':61C ''cosin'':8A,18C ''definit'':66C ''differ'':3A,53C,59C ''help'':50C ''howev'':30C ''item'':28C ''jaccard'':5A,13C ''measur'':25C,57C ''one'':40C ''p'':11C,47C ''prefer'':43C,71C ''principl'':63C ''similar'':6A,9A,10B,14C,19C,29C ''situat'':38C ''somebodi'':49C ''strong'':12C,17C ''two'':22C,56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5122, 1, '2015-02-12 10:46:41.28', 2, 234, 'Best Python library for statistical inference', '&lt;p&gt;I''m curious if anyone has Python library suggestions for inferential statistics. I''m currently reading &lt;a href="http://www-bcf.usc.edu/~gareth/ISL/" rel="nofollow"&gt;An Introduction to Statistical Learning&lt;/a&gt;, which uses R for the example code, but ideally I''d like to use Python as well. &lt;/p&gt;

&lt;p&gt;Most of my data experience is with Pandas, Matplotlib, and Sklearn doing predictive modeling. &lt;/p&gt;

&lt;p&gt;So far I''ve found &lt;a href="https://pypi.python.org/pypi/statsmodels" rel="nofollow"&gt;statsmodels&lt;/a&gt;. Is this what is recommended or is there something else?&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'python  statistics', NULL, '''/a'':38C,87C ''/p'':56C,72C,98C,101C ''/pypi/statsmodels'':83C ''/~gareth/isl/'':30C ''anyon'':14C ''best'':1A ''code'':45C ''curious'':12C ''current'':24C ''d'':49C ''data'':61C ''els'':97C ''exampl'':44C ''experi'':62C ''far'':75C ''found'':78C ''href'':27C,80C ''ideal'':47C ''infer'':6A ''inferenti'':20C ''introduct'':34C ''learn'':37C ''librari'':3A,17C ''like'':50C ''m'':11C,23C ''matplotlib'':66C ''model'':71C ''nofollow'':32C,85C ''p'':9C,57C,73C,99C ''panda'':65C ''predict'':70C ''pypi.python.org'':82C ''pypi.python.org/pypi/statsmodels'':81C ''python'':2A,7B,16C,53C ''r'':41C ''read'':25C ''recommend'':92C ''rel'':31C,84C ''sklearn'':68C ''someth'':96C ''statist'':5A,8B,21C,36C ''statsmodel'':86C ''suggest'':18C ''thank'':100C ''use'':40C,52C ''ve'':77C ''well'':55C ''www-bcf.usc.edu'':29C ''www-bcf.usc.edu/~gareth/isl/'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6324, 2, '2015-07-02 18:51:51.377', 0, NULL, NULL, '&lt;p&gt;One place you might find some interesting step-by-step explainations is the &lt;a href="http://blog.kaggle.com/category/dojo/" rel="nofollow"&gt;Kaggle tutorial and winner''s interviews&lt;/a&gt;. Often people will post a detailed summary of their approach.&lt;/p&gt;
', NULL, NULL, '''/a'':29C ''/category/dojo/'':20C ''/p'':40C ''approach'':39C ''blog.kaggle.com'':19C ''blog.kaggle.com/category/dojo/'':18C ''detail'':35C ''explain'':13C ''find'':6C ''href'':17C ''interest'':8C ''interview'':28C ''kaggl'':23C ''might'':5C ''nofollow'':22C ''often'':30C ''one'':2C ''p'':1C ''peopl'':31C ''place'':3C ''post'':33C ''rel'':21C ''step'':10C,12C ''step-by-step'':9C ''summari'':36C ''tutori'':24C ''winner'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5128, 2, '2015-02-12 15:47:15.277', 5, NULL, NULL, '&lt;p&gt;Jaccard Similarity is given by 
$s_{ij} = \frac{p}{p+q+r}$&lt;/p&gt;

&lt;p&gt;where,&lt;/p&gt;

&lt;p&gt;p = # of attributes positive for both objects &lt;br&gt;
q = # of attributes 1 for i and 0 for j &lt;br&gt;
r = # of attributes 0 for i and 1 for j &lt;br&gt;&lt;/p&gt;

&lt;p&gt;Whereas, cosine similarity = $\frac{A \cdot B}{\|A\|\|B\|}$ where A and B are object vectors.&lt;/p&gt;

&lt;p&gt;Simply put, in cosine similarity, the number of common attributes is divided by the total number of possible attributes. Whereas in Jaccard Similarity, the number of common attributes is divided by the number of attributes that exists in at least one of the two objects. &lt;/p&gt;

&lt;p&gt;And there are many other measures of similarity, each with its own eccentricities. When deciding which one to use, try to think of a few representative cases and work out which index would give the most usable results to achieve your objective.&lt;/p&gt;

&lt;p&gt;For example, if you have two objects both with 10 attributes, out of a possible 100 attributes. Further they have all 10 attributes in common. In this case, the Jaccard index will be 1 and the cosine index will be 0.001. Now consider another scenario where object A has 10 attributes, and object B has 50 attributes, but B has all 10 attributes that A has. Here, Jaccard index will be 0.2 and cosine index will still be 0.001. So the key question is if that extra bit of information reflected, in this case, in the Jaccard index useful or does it hurt or does it not matter. Your choice will depend on which is the best solution for your problem. &lt;/p&gt;

&lt;p&gt;The Cosine index could be used to identify plagiarism, but will not be a good index to identify mirror sites on the internet. Whereas the Jaccard index, will be a good index to identify mirror sites, but not so great at catching copy pasta plagiarism (within a larger document). &lt;/p&gt;

&lt;p&gt;Of course, these are toy examples to illustrate a point. When applying these indices, you must think about your problem thoroughly and figure out how to define similarity. Once you have a definition in mind, you can go about shopping for an index.    &lt;/p&gt;
', NULL, NULL, '''/p'':14C,17C,49C,67C,114C,158C,281C,332C,377C ''0'':34C,41C ''0.001'':200C,238C ''0.2'':231C ''1'':30C,45C,193C ''10'':169C,181C,209C,221C ''100'':175C ''50'':215C ''achiev'':155C ''anoth'':203C ''appli'':345C ''attribut'':21C,29C,40C,78C,87C,96C,103C,170C,176C,182C,210C,216C,222C ''b'':57C,59C,63C,213C,218C ''best'':276C ''bit'':247C ''br'':26C,37C,48C ''case'':142C,187C,253C ''catch'':324C ''cdot'':56C ''choic'':269C ''common'':77C,95C,184C ''consid'':202C ''copi'':325C ''cosin'':52C,72C,196C,233C,284C ''could'':286C ''cours'':335C ''decid'':130C ''defin'':360C ''definit'':366C ''depend'':271C ''divid'':80C,98C ''document'':331C ''eccentr'':128C ''exampl'':161C,339C ''exist'':105C ''extra'':246C ''figur'':356C ''frac'':9C,54C ''give'':149C ''given'':5C ''go'':371C ''good'':297C,313C ''great'':322C ''hurt'':262C ''identifi'':290C,300C,316C ''ij'':8C ''illustr'':341C ''index'':147C,190C,197C,228C,234C,257C,285C,298C,309C,314C,376C ''indic'':347C ''inform'':249C ''internet'':305C ''j'':36C,47C ''jaccard'':2C,90C,189C,227C,256C,308C ''key'':241C ''larger'':330C ''least'':108C ''mani'':119C ''matter'':267C ''measur'':121C ''mind'':368C ''mirror'':301C,317C ''must'':349C ''number'':75C,84C,93C,101C ''object'':25C,65C,113C,157C,166C,206C,212C ''one'':109C,132C ''p'':1C,10C,11C,15C,18C,19C,50C,68C,115C,159C,282C,333C ''pasta'':326C ''plagiar'':291C,327C ''point'':343C ''posit'':22C ''possibl'':86C,174C ''problem'':280C,353C ''put'':70C ''q'':12C,27C ''question'':242C ''r'':13C,38C ''reflect'':250C ''repres'':141C ''result'':153C ''scenario'':204C ''shop'':373C ''similar'':3C,53C,73C,91C,123C,361C ''simpli'':69C ''site'':302C,318C ''solut'':277C ''still'':236C ''think'':137C,350C ''thorough'':354C ''total'':83C ''toy'':338C ''tri'':135C ''two'':112C,165C ''usabl'':152C ''use'':134C,258C,288C ''vector'':66C ''wherea'':51C,88C,306C ''within'':328C ''work'':144C ''would'':148C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5129, 1, '2015-02-12 16:11:30.087', 0, 138, 'R Programing beginner', '&lt;p&gt;Hi I am new to Data analytics.I am planning to learn R by doing some real time projects. How should I stream line (set goals) my time in learning R and also I have not learnt statistics till data. I am planning to learn both side by side. I am mid level data warehouse engineer who has experience in DBMS Data-Integration. I am planning to learn R so that I can bring out useful analysis from the Integrated data. &lt;/p&gt;

&lt;p&gt;If I be specific, I am beginning in R, so what are the basic statistical concepts I should know and implement it in R. If I want to be an expert or above average person in R how should I plan strategically to become one. Say if I can spend 2 hrs a day for 1 year what level I should reach. FYI am working for a SaaS company. What are the way s in which I can utilize R knowledge in a SaaS environment &lt;/p&gt;
', 'r  statistics', NULL, '''/p'':88C,175C ''1'':145C ''2'':140C ''also'':38C ''analysi'':83C ''analytics.i'':13C ''averag'':123C ''basic'':103C ''becom'':133C ''begin'':96C ''beginn'':3A ''bring'':80C ''compani'':158C ''concept'':105C ''data'':12C,45C,59C,68C,87C ''data-integr'':67C ''day'':143C ''dbms'':66C ''engin'':61C ''environ'':174C ''experi'':64C ''expert'':120C ''fyi'':152C ''goal'':31C ''hi'':7C ''hrs'':141C ''implement'':110C ''integr'':69C,86C ''know'':108C ''knowledg'':170C ''learn'':17C,35C,50C,74C ''learnt'':42C ''level'':58C,148C ''line'':29C ''mid'':57C ''new'':10C ''one'':134C ''p'':6C,89C ''person'':124C ''plan'':15C,48C,72C,130C ''program'':2A ''project'':24C ''r'':1A,4B,18C,36C,75C,98C,113C,126C,169C ''reach'':151C ''real'':22C ''saa'':157C,173C ''say'':135C ''set'':30C ''side'':52C,54C ''specif'':93C ''spend'':139C ''statist'':5B,43C,104C ''strateg'':131C ''stream'':28C ''till'':44C ''time'':23C,33C ''use'':82C ''util'':168C ''want'':116C ''warehous'':60C ''way'':162C ''work'':154C ''year'':146C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9168, 2, '2015-12-02 12:19:27.893', 1, NULL, NULL, '&lt;p&gt;The root-mean-square error (RMSE) is a frequently used measure of the differences between values (sample and population values) predicted by a model and the values actually observed. The RMSE for your training and your test sets should be very similar if you have built a good model. If the RMSE for the test set is much higher than that of the training set, it is likely that you''ve badly over fit the data.&lt;/p&gt;
', NULL, NULL, '''/p'':79C ''actual'':30C ''bad'':74C ''built'':48C ''data'':78C ''differ'':16C ''error'':7C ''fit'':76C ''frequent'':11C ''good'':50C ''higher'':61C ''like'':70C ''mean'':5C ''measur'':13C ''model'':26C,51C ''much'':60C ''observ'':31C ''p'':1C ''popul'':21C ''predict'':23C ''rmse'':8C,33C,54C ''root'':4C ''root-mean-squar'':3C ''sampl'':19C ''set'':40C,58C,67C ''similar'':44C ''squar'':6C ''test'':39C,57C ''train'':36C,66C ''use'':12C ''valu'':18C,22C,29C ''ve'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5167, 2, '2015-02-17 22:50:19.067', 1, NULL, NULL, '&lt;p&gt;It''s an economic calculation, really.  When you have a computing "problem" (in the most general possible sense) that you can''t solve with one computer, it makes sense to use a cluster of commodity machines when doing so A. allows you to solve the problem, and B. is cheaper than a forklift upgrade to a bigger computer, or upgrading to specialized hardware.&lt;/p&gt;

&lt;p&gt;When those things are true, and you are going the "commodity cluster" route, Hadoop makes a lot of sense, especially if the nature of the problem maps (no pun intended) well to MapReduce.  If it doesn''t, one shouldn''t be scared to consider "older" cluster approaches like a Beowulf cluster using MPI or OpenMP.  &lt;/p&gt;

&lt;p&gt;That said, the newer YARN based Hadoop does support a form of MPI, so those worlds are starting to move closer together.&lt;/p&gt;
', NULL, NULL, '''/p'':65C,122C,146C ''allow'':42C ''approach'':113C ''b'':49C ''base'':129C ''beowulf'':116C ''bigger'':58C ''calcul'':6C ''cheaper'':51C ''closer'':144C ''cluster'':34C,78C,112C,117C ''commod'':36C,77C ''comput'':12C,27C,59C ''consid'':110C ''doesn'':102C ''econom'':5C ''especi'':86C ''forklift'':54C ''form'':134C ''general'':17C ''go'':75C ''hadoop'':80C,130C ''hardwar'':64C ''intend'':96C ''like'':114C ''lot'':83C ''machin'':37C ''make'':29C,81C ''map'':93C ''mapreduc'':99C ''move'':143C ''mpi'':119C,136C ''natur'':89C ''newer'':127C ''older'':111C ''one'':26C,104C ''openmp'':121C ''p'':1C,66C,123C ''possibl'':18C ''problem'':13C,47C,92C ''pun'':95C ''realli'':7C ''rout'':79C ''said'':125C ''scare'':108C ''sens'':19C,30C,85C ''shouldn'':105C ''solv'':24C,45C ''special'':63C ''start'':141C ''support'':132C ''thing'':69C ''togeth'':145C ''true'':71C ''upgrad'':55C,61C ''use'':32C,118C ''well'':97C ''world'':139C ''yarn'':128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5130, 2, '2015-02-12 16:46:29.453', 1, NULL, NULL, '&lt;p&gt;I would start off with &lt;a href="http://www.amazon.ca/All-Statistics-Concise-Statistical-Inference/dp/0387402721" rel="nofollow"&gt;All Of Statistics by Larry Wasserman&lt;/a&gt;. This quickly gets you upto speed with statistics assuming you have some mathematical background. I think all it needs is introductory calculus and linear algebra. &lt;/p&gt;

&lt;p&gt;R is pretty straightforward to pick up and there are a number of resources you can use. The &lt;a href="https://www.coursera.org/course/rprog" rel="nofollow"&gt;R Programming&lt;/a&gt; course on coursera is an excellent short course to get you familiarized with R. Besides that there are a number of books and tutorials on the subject.&lt;/p&gt;

&lt;p&gt;I would recommend you start working through All of Statistics and start playing around in R along with that book.   &lt;/p&gt;

&lt;p&gt;If you are at a SaaS company, there are a number of data sciency roles and responsibilities that should be available. Most SaaS companies will have analytical tools to provide basic insight. Once you start learning statistics and data science, you will be able to identify the gaps in the pre baked tools and ways to improve them. I would also read &lt;a href="http://www.boozallen.com/insights/2013/11/data-science-field-guide" rel="nofollow"&gt;The Field Guide to Data Science&lt;/a&gt;, it''s a short book that will give you a high level idea of data science and its utility. &lt;/p&gt;
', NULL, NULL, '''/a'':20C,75C,202C ''/all-statistics-concise-statistical-inference/dp/0387402721'':11C ''/course/rprog'':70C ''/insights/2013/11/data-science-field-guide'':193C ''/p'':46C,103C,125C,222C ''abl'':170C ''algebra'':45C ''along'':121C ''also'':187C ''analyt'':153C ''around'':118C ''assum'':29C ''avail'':147C ''background'':34C ''bake'':178C ''basic'':157C ''besid'':90C ''book'':97C,124C,207C ''calculus'':42C ''compani'':133C,150C ''cours'':76C,83C ''coursera'':78C ''data'':139C,165C,200C,217C ''excel'':81C ''familiar'':87C ''field'':197C ''gap'':174C ''get'':23C,85C ''give'':210C ''guid'':198C ''high'':213C ''href'':8C,67C,190C ''idea'':215C ''identifi'':172C ''improv'':183C ''insight'':158C ''introductori'':41C ''larri'':18C ''learn'':162C ''level'':214C ''linear'':44C ''mathemat'':33C ''need'':39C ''nofollow'':13C,72C,195C ''number'':59C,95C,137C ''p'':1C,47C,104C,126C ''pick'':53C ''play'':117C ''pre'':177C ''pretti'':50C ''program'':74C ''provid'':156C ''quick'':22C ''r'':48C,73C,89C,120C ''read'':188C ''recommend'':107C ''rel'':12C,71C,194C ''resourc'':61C ''respons'':143C ''role'':141C ''saa'':132C,149C ''scienc'':166C,201C,218C ''scienci'':140C ''short'':82C,206C ''speed'':26C ''start'':4C,109C,116C,161C ''statist'':16C,28C,114C,163C ''straightforward'':51C ''subject'':102C ''think'':36C ''tool'':154C,179C ''tutori'':99C ''upto'':25C ''use'':64C ''util'':221C ''wasserman'':19C ''way'':181C ''work'':110C ''would'':3C,106C,186C ''www.amazon.ca'':10C ''www.amazon.ca/all-statistics-concise-statistical-inference/dp/0387402721'':9C ''www.boozallen.com'':192C ''www.boozallen.com/insights/2013/11/data-science-field-guide'':191C ''www.coursera.org'':69C ''www.coursera.org/course/rprog'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5131, 2, '2015-02-12 19:09:05.9', 3, NULL, NULL, '&lt;p&gt;No one seems to have posted the XKCD overfitting comic yet. &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/wSN2q.png" alt="enter image description here"&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':13C,25C ''/wsn2q.png'':19C ''alt'':20C ''comic'':11C ''descript'':23C ''enter'':21C ''i.stack.imgur.com'':18C ''i.stack.imgur.com/wsn2q.png'':17C ''imag'':22C ''img'':15C ''one'':3C ''overfit'':10C ''p'':1C,14C ''post'':7C ''seem'':4C ''src'':16C ''xkcd'':9C ''yet'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5132, 2, '2015-02-13 00:33:59.857', 1, NULL, NULL, '&lt;p&gt;This issue is caused because the following environment variables are not set correctly
PKG_CONFIG_PATH and LD_LIBRARY_PATH
for my configuration, i should execute this:&lt;/p&gt;

&lt;p&gt;export PKG_CONFIG_PATH = /usr/local/lib &lt;br&gt;
export LD_LIBRARY_PATH = /usr/local/lib&lt;/p&gt;

&lt;p&gt;also set this commands on R console: &lt;/p&gt;

&lt;p&gt;Sys.setenv(HADOOP_HOME="/usr/local/hadoop/")&lt;br&gt;
Sys.setenv(HADOOP_BIN="/usr/local/hadoop/bin")&lt;br&gt;
Sys.setenv(HADOOP_CONF_DIR="/usr/local/hadoop/conf")&lt;br&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':29C,42C,51C,69C ''/usr/local/hadoop'':56C ''/usr/local/hadoop/bin'':61C ''/usr/local/hadoop/conf'':67C ''/usr/local/lib'':35C,41C ''also'':44C ''bin'':60C ''br'':36C,57C,62C,68C ''caus'':5C ''command'':47C ''conf'':65C ''config'':16C,33C ''configur'':24C ''consol'':50C ''correct'':14C ''dir'':66C ''environ'':9C ''execut'':27C ''export'':31C,37C ''follow'':8C ''hadoop'':54C,59C,64C ''home'':55C ''issu'':3C ''ld'':19C,38C ''librari'':20C,39C ''p'':1C,30C,43C,52C ''path'':17C,21C,34C,40C ''pkg'':15C,32C ''r'':49C ''set'':13C,45C ''sys.setenv'':53C,58C,63C ''variabl'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5135, 2, '2015-02-13 09:06:33.07', 1, NULL, NULL, '&lt;p&gt;Naive Bayes is just one of the several approaches that you may apply in order to solve the Titanic''s problem. The aim of the Kaggle''s Titanic problem is to build a &lt;strong&gt;classification&lt;/strong&gt; system that is able to predict one outcome (whether one person survived or not) given some input data. The survival table is a &lt;strong&gt;training dataset&lt;/strong&gt;, that is, a table containing a set of examples to train your system with. &lt;/p&gt;

&lt;p&gt;As I mentioned before, you could apply Naive Bayes to build your classification system to solve the Titanic problem. Naive Bayes is one of the simplest classification algorithms out there. It assumes that the data in your dataset has a very specific structure. Sometimes Naive Bayes can provide you with results that are good enough. Even if that is not the case, Naive Bayes may be useful as a first step; the information you obtain by analyzing Naive Bayes'' results, and by further data analysis, will help you to choose which classification algorithm you could try next. Other examples of classification methods are k-nearest neighbours, neural networks, and logistic regression, but this is just a short list. &lt;/p&gt;

&lt;p&gt;If you are new to Machine Learning, I recommend you to take a look to this course from Stanford: &lt;a href="https://www.coursera.org/course/ml" rel="nofollow"&gt;https://www.coursera.org/course/ml&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/course/ml'':225C ''/course/ml&lt;/a&gt;&lt;/p&gt;'':230C ''/p'':79C,200C ''/strong'':37C,64C ''abl'':41C ''aim'':24C ''algorithm'':108C,173C ''analysi'':165C ''analyz'':157C ''appli'':14C,87C ''approach'':10C ''assum'':112C ''bay'':3C,89C,101C,126C,144C,159C ''build'':33C,91C ''case'':142C ''choos'':170C ''classif'':36C,93C,107C,172C,181C ''contain'':69C ''could'':86C,175C ''cours'':218C ''data'':55C,115C,164C ''dataset'':63C,118C ''enough'':135C ''even'':136C ''exampl'':73C,179C ''first'':150C ''given'':52C ''good'':134C ''help'':167C ''href'':222C ''inform'':153C ''input'':54C ''k'':185C ''k-nearest'':184C ''kaggl'':27C ''learn'':208C ''list'':199C ''logist'':191C ''look'':215C ''machin'':207C ''may'':13C,145C ''mention'':83C ''method'':182C ''naiv'':2C,88C,100C,125C,143C,158C ''nearest'':186C ''neighbour'':187C ''network'':189C ''neural'':188C ''new'':205C ''next'':177C ''nofollow'':227C ''obtain'':155C ''one'':6C,44C,47C,103C ''order'':16C ''outcom'':45C ''p'':1C,80C,201C ''person'':48C ''predict'':43C ''problem'':22C,30C,99C ''provid'':128C ''recommend'':210C ''regress'':192C ''rel'':226C ''result'':131C,160C ''set'':71C ''sever'':9C ''short'':198C ''simplest'':106C ''solv'':18C,96C ''sometim'':124C ''specif'':122C ''stanford'':220C ''step'':151C ''strong'':35C,61C ''structur'':123C ''surviv'':49C,57C ''system'':38C,77C,94C ''tabl'':58C,68C ''take'':213C ''titan'':20C,29C,98C ''train'':62C,75C ''tri'':176C ''use'':147C ''whether'':46C ''www.coursera.org'':224C,229C ''www.coursera.org/course/ml'':223C ''www.coursera.org/course/ml&lt;/a&gt;&lt;/p&gt;'':228C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5136, 2, '2015-02-13 15:04:24.963', 1, NULL, NULL, '&lt;p&gt;I second saq7 and Gopinath, the R courses on Coursera are excellent. I really rate the Johns Hopkins ones: &lt;a href="https://www.coursera.org/specialization/jhudatascience/1/courses" rel="nofollow"&gt;https://www.coursera.org/specialization/jhudatascience/1/courses&lt;/a&gt;. You should also keep an eye on the &lt;a href="http://software-carpentry.org/" rel="nofollow"&gt;software carpentry&lt;/a&gt; site for courses they run in your area. If you can''t wait, all the software carpentry &lt;a href="http://swcarpentry.github.io/r-novice-inflammation/" rel="nofollow"&gt;learning material is online&lt;/a&gt; so you can follow it yourself.&lt;/p&gt;
', NULL, NULL, '''/a'':46C,75C ''/p'':82C ''/r-novice-inflammation/'':68C ''/specialization/jhudatascience/1/courses'':25C ''/specialization/jhudatascience/1/courses&lt;/a&gt;.'':30C ''also'':33C ''area'':54C ''carpentri'':45C,63C ''cours'':9C,49C ''coursera'':11C ''excel'':13C ''eye'':36C ''follow'':79C ''gopinath'':6C ''hopkin'':19C ''href'':22C,40C,65C ''john'':18C ''keep'':34C ''learn'':71C ''materi'':72C ''nofollow'':27C,43C,70C ''one'':20C ''onlin'':74C ''p'':1C ''r'':8C ''rate'':16C ''realli'':15C ''rel'':26C,42C,69C ''run'':51C ''saq7'':4C ''second'':3C ''site'':47C ''softwar'':44C,62C ''software-carpentry.org'':41C ''swcarpentry.github.io'':67C ''swcarpentry.github.io/r-novice-inflammation/'':66C ''wait'':59C ''www.coursera.org'':24C,29C ''www.coursera.org/specialization/jhudatascience/1/courses'':23C ''www.coursera.org/specialization/jhudatascience/1/courses&lt;/a&gt;.'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6226, 1, '2015-06-26 12:19:16.267', 2, 89, 'How to combine two different random forest models into one in R?', '&lt;p&gt;I have two different dataset with same variables. I have built training models separately using random forest. Now i want to combine both these models. Could anyone tell me how can i this be achieved? Do we have something called combine() function in R?&lt;/p&gt;

&lt;p&gt;Regards,
Arun&lt;/p&gt;
', 'machine-learning  r  random-forest', NULL, '''/p'':65C,69C ''achiev'':55C ''anyon'':47C ''arun'':68C ''built'':31C ''call'':60C ''combin'':3A,42C,61C ''could'':46C ''dataset'':25C ''differ'':5A,24C ''forest'':7A,19B,37C ''function'':62C ''learn'':15B ''machin'':14B ''machine-learn'':13B ''model'':8A,33C,45C ''one'':10A ''p'':20C,66C ''r'':12A,16B,64C ''random'':6A,18B,36C ''random-forest'':17B ''regard'':67C ''separ'':34C ''someth'':59C ''tell'':48C ''train'':32C ''two'':4A,23C ''use'':35C ''variabl'':28C ''want'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5137, 1, '2015-02-14 07:09:01.527', -5, 166, 'Will demand for data scientist decrease because of AYASDI?', '&lt;p&gt;I am pursing Data Analyst course at Udacity. I came across this video : &lt;a href="https://www.youtube.com/watch?v=3Z73Wd2T1xE" rel="nofollow"&gt;https://www.youtube.com/watch?v=3Z73Wd2T1xE&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;Watching it lead me to wonder if &lt;a href="http://www.ayasdi.com/" rel="nofollow"&gt;Ayasdi&lt;/a&gt; products would reduce the demand for data scientists. I wish to compete in Kaggle contests but after watching the video, I feel that many of those problems can be easily solved using their platform and that I will be at disadvantage since I do not have access to their tools. Also I feel such tools would reduce the need of experts in data sciences. Now I am worried if I should continue with Nano Degree in Data Analyst at Udacity.&lt;/p&gt;
', 'data-mining  bigdata', NULL, '''/a'':53C ''/p'':38C,134C ''/watch?v=3z73wd2t1xe'':32C ''/watch?v=3z73wd2t1xe&lt;/a&gt;'':37C ''access'':100C ''across'':25C ''also'':104C ''analyst'':19C,131C ''ayasdi'':9A,52C ''bigdata'':13B ''came'':24C ''compet'':65C ''contest'':68C ''continu'':125C ''cours'':20C ''data'':4A,11B,18C,60C,116C,130C ''data-min'':10B ''decreas'':6A ''degre'':128C ''demand'':2A,58C ''disadvantag'':94C ''easili'':83C ''expert'':114C ''feel'':75C,106C ''href'':29C,48C ''kaggl'':67C ''lead'':42C ''mani'':77C ''mine'':12B ''nano'':127C ''need'':112C ''nofollow'':34C,51C ''p'':14C,39C ''platform'':87C ''problem'':80C ''product'':54C ''purs'':17C ''reduc'':56C,110C ''rel'':33C,50C ''scienc'':117C ''scientist'':5A,61C ''sinc'':95C ''solv'':84C ''tool'':103C,108C ''udac'':22C,133C ''use'':85C ''video'':27C,73C ''watch'':40C,71C ''wish'':63C ''wonder'':45C ''worri'':121C ''would'':55C,109C ''www.ayasdi.com'':49C ''www.youtube.com'':31C,36C ''www.youtube.com/watch?v=3z73wd2t1xe'':30C ''www.youtube.com/watch?v=3z73wd2t1xe&lt;/a&gt;'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5138, 1, '2015-02-14 17:13:31.473', 1, 95, 'Definition of likelihood function', '&lt;p&gt;I am confused by the definition of the likelihood function in different contexts.&lt;/p&gt;

&lt;p&gt;In the case of linear and logistic regression, it is defined as y given x&lt;/p&gt;

&lt;p&gt;In the case naive bayes and LDA, it is defined over x and y&lt;/p&gt;

&lt;p&gt;In the case of the EM algorithm (with observed variable x and unobserved variable z), it is defined over x&lt;/p&gt;

&lt;p&gt;How do we know over what it should be defined?&lt;/p&gt;
', 'machine-learning  statistics', NULL, '''/p'':23C,40C,56C,78C,90C ''algorithm'':64C ''bay'':46C ''case'':27C,44C,60C ''confus'':12C ''context'':22C ''defin'':35C,51C,75C,89C ''definit'':1A,15C ''differ'':21C ''em'':63C ''function'':4A,19C ''given'':38C ''know'':83C ''lda'':48C ''learn'':7B ''likelihood'':3A,18C ''linear'':29C ''logist'':31C ''machin'':6B ''machine-learn'':5B ''naiv'':45C ''observ'':66C ''p'':9C,24C,41C,57C,79C ''regress'':32C ''statist'':8B ''unobserv'':70C ''variabl'':67C,71C ''x'':39C,53C,68C,77C ''y'':37C,55C ''z'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5184, 2, '2015-02-19 12:06:08.173', 1, NULL, NULL, '&lt;p&gt;Finally solved the issue.
You should go to:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Data â&amp;gt; your_data_source â&amp;gt; Edit Aliases â&amp;gt; Measure Names
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And see full mapping between variables/aggregations and aliases.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/uSoHC.png" alt="enter image description here"&gt;&lt;/p&gt;
', NULL, NULL, '''/code'':27C ''/p'':10C,38C,50C ''/pre'':28C ''/usohc.png'':44C ''alias'':22C,37C ''alt'':45C ''code'':12C ''data'':13C,17C ''descript'':48C ''edit'':21C ''enter'':46C ''final'':2C ''full'':32C ''go'':8C ''gt'':15C,20C,24C ''i.stack.imgur.com'':43C ''i.stack.imgur.com/usohc.png'':42C ''imag'':47C ''img'':40C ''issu'':5C ''map'':33C ''measur'':25C ''name'':26C ''p'':1C,29C,39C ''pre'':11C ''see'':31C ''solv'':3C ''sourc'':18C ''src'':41C ''variables/aggregations'':35C ''â'':14C,19C,23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5140, 2, '2015-02-14 21:45:05.987', 2, NULL, NULL, '&lt;p&gt;I also found confusing the definition of likelihood the first time I encountered it. The only thing you need to remember is that likelihood refers to the probability of your data given a parameter. That is,&lt;/p&gt;

&lt;p&gt;$$L(\theta| X) = P(X|\theta)$$&lt;/p&gt;

&lt;p&gt;As you can see, the likelihood focuses on the parameter, so you''re asking "What is the likelihood of this parameter given some observations?" In this case, the phrase "given some observation" assumes the observations $X$ are fixed and the parameter $\theta$ is not fixed. On the other hand, if you describe the same in terms of probability, you''re trying to measure the probability of some observations $X$ given a fixed parameter $\theta$. It is only a matter of usage. &lt;/p&gt;

&lt;p&gt;In the case of logistic regression, the likelihood function is described as:&lt;/p&gt;

&lt;p&gt;$$L(\beta| y) = \prod_{i}^{N} \pi_{i}^{y_{i}}(1-\pi_{i})^{n_{i} - y_{i}}$$&lt;/p&gt;

&lt;p&gt;where $\beta$ is the vector of parameters $\{\pi_{i}| i=1,...,N \}$and $y$ is the vector of Bernoulli variables. As you can see, this is the same (by definition) as $P(y|\beta)$ and that is what you have in the equation above: The product of the probabilities for each observation $y_{i}$ given its parameter $\pi_{i}$. As usual, each probability $\pi_{i}$ is equal to the inverse of the logit function.&lt;/p&gt;

&lt;p&gt;In Naive Bayes, you have the same basic idea. The likelihood is defined as $L(p_{k}|x) = \prod_{i}^{N}p_{ki}^{x_{i}}(1-p_{ki})^{(1-x_{i})}$ where $p_{k}$ is a vector of parameters describing the probabilities of a class $k$ generating a word $x_{i}$).&lt;/p&gt;

&lt;p&gt;In the EM algorithm, the likelihood is again $L(\theta|X,Z)$, where $\theta$ are your parameters, $X$ your observations and $Z$ are latent variables. The fact that you have a latent variables shouldn''t confuse you, because this variable will be "integrated out"  in the expectation step of the algorithm.&lt;/p&gt;

&lt;p&gt;In the previous equations, the calculation you need to do is self-evident if you think about it starting from your observations: First, I have some observations. Then I want to calculate the probability of obtaining these observation using some model. Most models require parameters to be fitted. So I want to calculate the probability of these observations given those parameters. That is, $p(X|\theta)$. Now, if you need to talk about likeliihood, then you simply write $L(\theta|X)$.&lt;/p&gt;
', NULL, NULL, '''/p'':38C,46C,129C,143C,162C,238C,291C,344C,428C ''1'':155C,174C,265C,268C ''algorithm'':296C,343C ''also'':3C ''ask'':61C ''assum'':80C ''basic'':247C ''bay'':242C ''bernoulli'':182C ''beta'':146C,165C,197C ''calcul'':351C,378C,399C ''case'':74C,133C ''class'':284C ''confus'':5C,328C ''data'':32C ''defin'':252C ''definit'':7C,193C ''describ'':99C,141C,279C ''em'':295C ''encount'':14C ''equal'':230C ''equat'':206C,349C ''evid'':359C ''expect'':339C ''fact'':319C ''first'':11C,369C ''fit'':394C ''fix'':85C,92C,119C ''focus'':54C ''found'':4C ''function'':139C,237C ''generat'':286C ''given'':33C,69C,77C,117C,218C,405C ''hand'':96C ''idea'':248C ''integr'':335C ''invers'':233C ''k'':256C,273C,285C ''ki'':262C,267C ''l'':40C,145C,254C,301C,425C ''latent'':316C,324C ''likelihood'':9C,25C,53C,65C,138C,250C,298C ''likeliihood'':420C ''logist'':135C ''logit'':236C ''matter'':126C ''measur'':110C ''model'':387C,389C ''n'':150C,158C,175C,260C ''naiv'':241C ''need'':20C,353C,416C ''observ'':71C,79C,82C,115C,215C,312C,368C,373C,384C,404C ''obtain'':382C ''p'':1C,39C,43C,47C,130C,144C,163C,195C,239C,255C,261C,266C,272C,292C,345C,410C ''paramet'':35C,57C,68C,88C,120C,170C,220C,278C,309C,391C,407C ''phrase'':76C ''pi'':151C,156C,171C,221C,227C ''previous'':348C ''probabl'':29C,105C,112C,212C,226C,281C,380C,401C ''prod'':148C,258C ''product'':209C ''re'':60C,107C ''refer'':26C ''regress'':136C ''rememb'':22C ''requir'':390C ''see'':51C,187C ''self'':358C ''self-evid'':357C ''shouldn'':326C ''simpli'':423C ''start'':365C ''step'':340C ''talk'':418C ''term'':103C ''theta'':41C,45C,89C,121C,302C,306C,412C,426C ''thing'':18C ''think'':362C ''time'':12C ''tri'':108C ''usag'':128C ''use'':385C ''usual'':224C ''variabl'':183C,317C,325C,332C ''vector'':168C,180C,276C ''want'':376C,397C ''word'':288C ''write'':424C ''x'':42C,44C,83C,116C,257C,263C,269C,289C,303C,310C,411C,427C ''y'':147C,153C,160C,177C,196C,216C ''z'':304C,314C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5153, 2, '2015-02-16 03:03:43.883', 2, NULL, NULL, '&lt;p&gt;I''m doing some similar research, and have found PluralSight, &lt;a href="http://pluralsight.com" rel="nofollow"&gt;http://pluralsight.com&lt;/a&gt;, to be an invaluable resource.  They have video courses on Machine Learning, AWS, Azure, Hadoop, Big Data, etc.  Personally, I find that these video courses allow me to learn the material much faster and more easily than books.&lt;/p&gt;
', NULL, NULL, '''/a'':18C ''/p'':57C ''allow'':44C ''aw'':31C ''azur'':32C ''big'':34C ''book'':56C ''cours'':27C,43C ''data'':35C ''easili'':54C ''etc'':36C ''faster'':51C ''find'':39C ''found'':10C ''hadoop'':33C ''href'':13C ''invalu'':22C ''learn'':30C,47C ''m'':3C ''machin'':29C ''materi'':49C ''much'':50C ''nofollow'':16C ''p'':1C ''person'':37C ''pluralsight'':11C ''pluralsight.com'':14C,17C ''rel'':15C ''research'':7C ''resourc'':23C ''similar'':6C ''video'':26C,42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3751, 1, '2014-12-23 07:15:34.5', 3, 179, 'Route to picking up Deep learning', '&lt;p&gt;I would like to pick up on the topic of deep learning. Should I begin from the topic of AI before working my way into Deep learning?&lt;/p&gt;
', 'beginner  deep-learning', NULL, '''/p'':39C ''ai'':31C ''begin'':26C ''beginn'':7B ''deep'':5A,9B,22C,37C ''deep-learn'':8B ''learn'':6A,10B,23C,38C ''like'':14C ''p'':11C ''pick'':3A,16C ''rout'':1A ''topic'':20C,29C ''way'':35C ''work'':33C ''would'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5141, 2, '2015-02-14 22:45:50.003', 3, NULL, NULL, '&lt;p&gt;What got me to understand the problem about overfitting was by imagining what the most overfit model possible would be. Essentially, it would be a simple look-up table.&lt;/p&gt;

&lt;p&gt;You tell the model what attributes each piece of data has and it simply remembers it and does nothing more with it. If you give it a piece of data that it''s seen before, it looks it up and simply regurgitates what you told it earlier. If you give it data it &lt;em&gt;hasn''t&lt;/em&gt; seen before, the outcome is unpredictable or random. But the point of machine learning isn''t to tell you what happened, it''s to understand the patterns and use those patterns to predict what''s going on.&lt;/p&gt;

&lt;p&gt;So think of a decision tree. If you keep growing your decision tree bigger and bigger, eventually you''ll wind up with a tree in which every leaf node is based on exactly one data point. You''ve just found a backdoor way of creating a look-up table.&lt;/p&gt;

&lt;p&gt;In order to generalize your results to figure out what might happen in the future, you must create a model that generalizes what''s going on in your training set. Overfit models do a great job of describing the data you already have, but descriptive models are not necessarily predictive models.&lt;/p&gt;

&lt;p&gt;The No Free Lunch Theorem says that no model can outperform any other model on the set of all &lt;em&gt;possible&lt;/em&gt; instances. If you want to predict what will come next in the sequence of numbers "2, 4, 16, 32" you can''t build a model more accurate than any other if you don''t make the assumption that there''s an underlying pattern. A model that''s overfit isn''t really evaluating the patterns - it''s simply modeling what it knows is possible and giving you the observations. You get predictive power by assuming that there is some underlying function and that if you can determine what that function is, you can predict the outcome of events. But if there really is no pattern, then you''re out of luck and all you can hope for is a look-up table to tell you what you know is possible.&lt;/p&gt;
', NULL, NULL, '''/em'':90C,256C ''/p'':32C,128C,180C,233C,387C ''16'':274C ''2'':272C ''32'':275C ''4'':273C ''accur'':283C ''alreadi'':223C ''assum'':330C ''assumpt'':293C ''attribut'':39C ''backdoor'':171C ''base'':160C ''bigger'':143C,145C ''build'':279C ''come'':265C ''creat'':174C,199C ''data'':43C,63C,85C,164C,221C ''decis'':134C,141C ''describ'':219C ''descript'':226C ''determin'':342C ''earlier'':80C ''em'':87C,254C ''essenti'':22C ''evalu'':308C ''event'':353C ''eventu'':146C ''everi'':156C ''exact'':162C ''figur'':189C ''found'':169C ''free'':237C ''function'':336C,345C ''futur'':196C ''general'':185C,203C ''get'':326C ''give'':58C,83C,321C ''go'':126C,206C ''got'':3C ''great'':216C ''grow'':139C ''happen'':111C,193C ''hasn'':88C ''hope'':371C ''imagin'':13C ''instanc'':257C ''isn'':105C,305C ''job'':217C ''keep'':138C ''know'':317C,384C ''leaf'':157C ''learn'':104C ''ll'':148C ''look'':29C,70C,177C,376C ''look-up'':28C,176C,375C ''luck'':366C ''lunch'':238C ''machin'':103C ''make'':291C ''might'':192C ''model'':18C,37C,201C,213C,227C,232C,243C,248C,281C,301C,314C ''must'':198C ''necessarili'':230C ''next'':266C ''node'':158C ''noth'':52C ''number'':271C ''observ'':324C ''one'':163C ''order'':183C ''outcom'':94C,351C ''outperform'':245C ''overfit'':10C,17C,212C,304C ''p'':1C,33C,129C,181C,234C ''pattern'':117C,121C,299C,310C,360C ''piec'':41C,61C ''point'':101C,165C ''possibl'':19C,255C,319C,386C ''power'':328C ''predict'':123C,231C,262C,327C,349C ''problem'':8C ''random'':98C ''re'':363C ''realli'':307C,357C ''regurgit'':75C ''rememb'':48C ''result'':187C ''say'':240C ''seen'':67C,91C ''sequenc'':269C ''set'':211C,251C ''simpl'':27C ''simpli'':47C,74C,313C ''tabl'':31C,179C,378C ''tell'':35C,108C,380C ''theorem'':239C ''think'':131C ''told'':78C ''train'':210C ''tree'':135C,142C,153C ''under'':298C,335C ''understand'':6C,115C ''unpredict'':96C ''use'':119C ''ve'':167C ''want'':260C ''way'':172C ''wind'':149C ''would'':20C,24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5142, 1, '2015-02-15 04:51:21.167', 4, 201, 'Local Development for Apache Spark', '&lt;p&gt;I''m wondering how other developers are setting up their local environments for working on Spark projects. Do you configure a ''local'' cluster using a tool like Vagrant? Or, is it most common to SSH into a cloud environment, such as a cluster on AWS? Perhaps there are many tasks where a single-node cluster is adequate, and can be run locally more easily.&lt;/p&gt;
', 'apache-spark', NULL, '''/p'':75C ''adequ'':67C ''apach'':4A,7B ''apache-spark'':6B ''aw'':54C ''cloud'':47C ''cluster'':32C,52C,65C ''common'':42C ''configur'':29C ''develop'':2A,15C ''easili'':74C ''environ'':21C,48C ''like'':36C ''local'':1A,20C,31C,72C ''m'':11C ''mani'':58C ''node'':64C ''p'':9C ''perhap'':55C ''project'':26C ''run'':71C ''set'':17C ''singl'':63C ''single-nod'':62C ''spark'':5A,8B,25C ''ssh'':44C ''task'':59C ''tool'':35C ''use'':33C ''vagrant'':37C ''wonder'':12C ''work'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5145, 1, '2015-02-15 14:00:31.457', 0, 158, 'Comparing two ordered lists', '&lt;p&gt;Hi I would appreciate it if someone can point me in the right direction.  I''m looking for an algorithm or mathematical theory which I would use to compute the similarity between two ordered lists, where each list element can have n sub-elements.  I will explain with an example:&lt;/p&gt;

&lt;p&gt;Suppose I go to a baseball game and I record the sequence of strikes and balls for each of the first 30 players at bat.  My list looks like this, where P is a player, S is a strike and B is a ball. Order matters. &lt;/p&gt;

&lt;p&gt;L1: {P1=(S,S,S)}, {P2=(B,B,S)}, {P3=(B,B,S,S)}, ... &lt;/p&gt;

&lt;p&gt;My friend goes to a baseball game and does the same thing.  Later, we meet up and compare our lists.  We find that our lists are almost identical except that I recorded a strike for player 16 where my friend recorded a ball.  What are the chances we were at the same game and one of us made a mistake at player 16?&lt;/p&gt;

&lt;p&gt;Thanks in advance...&lt;/p&gt;
', 'data-mining  statistics', NULL, '''/p'':61C,109C,125C,190C,195C ''16'':163C,189C ''30'':84C ''advanc'':194C ''algorithm'':29C ''almost'':153C ''appreci'':13C ''b'':103C,117C,118C,121C,122C ''ball'':78C,106C,169C ''basebal'':68C,132C ''bat'':87C ''chanc'':173C ''compar'':1A,144C ''comput'':38C ''data'':6B ''data-min'':5B ''direct'':23C ''element'':48C,54C ''exampl'':60C ''except'':155C ''explain'':57C ''find'':148C ''first'':83C ''friend'':128C,166C ''game'':69C,133C,179C ''go'':65C ''goe'':129C ''hi'':10C ''ident'':154C ''l1'':111C ''later'':139C ''like'':91C ''list'':4A,44C,47C,89C,146C,151C ''look'':26C,90C ''m'':25C ''made'':184C ''mathemat'':31C ''matter'':108C ''meet'':141C ''mine'':7B ''mistak'':186C ''n'':51C ''one'':181C ''order'':3A,43C,107C ''p'':9C,62C,94C,110C,126C,191C ''p1'':112C ''p2'':116C ''p3'':120C ''player'':85C,97C,162C,188C ''point'':18C ''record'':72C,158C,167C ''right'':22C ''sequenc'':74C ''similar'':40C ''someon'':16C ''statist'':8B ''strike'':76C,101C,160C ''sub'':53C ''sub-el'':52C ''suppos'':63C ''thank'':192C ''theori'':32C ''thing'':138C ''two'':2A,42C ''us'':183C ''use'':36C ''would'':12C,35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5146, 2, '2015-02-15 14:14:47.11', 2, NULL, NULL, '&lt;p&gt;What about a model stating that you have two vectors of size N (where N is the total number of players, maybe unknown to us) where each element belongs to a space of {B,S} sequences, maybe empty. If you then define a distance function between two arbitrary sequences (say, normalized Levenshtein distance for two non-empty ones and some fixed cost when one is missing), you can define cosine similarity between the vectors.&lt;/p&gt;

&lt;p&gt;(Obviously, you just consider your sequence a compact representation of sparse vector in this case.)&lt;/p&gt;
', NULL, NULL, '''/p'':77C,94C ''arbitrari'':49C ''b'':35C ''belong'':30C ''case'':93C ''compact'':86C ''consid'':82C ''cosin'':72C ''cost'':64C ''defin'':43C,71C ''distanc'':45C,54C ''element'':29C ''empti'':39C,59C ''fix'':63C ''function'':46C ''levenshtein'':53C ''mayb'':23C,38C ''miss'':68C ''model'':5C ''n'':14C,16C ''non'':58C ''non-empti'':57C ''normal'':52C ''number'':20C ''obvious'':79C ''one'':60C,66C ''p'':1C,78C ''player'':22C ''represent'':87C ''say'':51C ''sequenc'':37C,50C,84C ''similar'':73C ''size'':13C ''space'':33C ''spars'':89C ''state'':6C ''total'':19C ''two'':10C,48C,56C ''unknown'':24C ''us'':26C ''vector'':11C,76C,90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5147, 1, '2015-02-15 17:01:50.72', 2, 194, 'Data Science Methodologies', '&lt;p&gt;What are the best known Data Science Methodologies today? By methodology I mean a step-by-step phased process that can be used for framing guidance, although I will be grateful for something close too.&lt;/p&gt;

&lt;p&gt;To help clarify, there are methodologies in the programming world, like Extreme Programming, Feature Driven Development, Unified Process, and many more. I am looking for their equivalents, if they exist.&lt;/p&gt;

&lt;p&gt;A google search did not turn up much, but I find it hard to believe there is nothing out there. Any ideas? &lt;/p&gt;
', 'methods', NULL, '''/p'':42C,74C,98C ''although'':33C ''believ'':90C ''best'':9C ''clarifi'':46C ''close'':40C ''data'':1A,11C ''develop'':59C ''driven'':58C ''equival'':70C ''exist'':73C ''extrem'':55C ''featur'':57C ''find'':86C ''frame'':31C ''googl'':77C ''grate'':37C ''guidanc'':32C ''hard'':88C ''help'':45C ''idea'':97C ''known'':10C ''like'':54C ''look'':67C ''mani'':63C ''mean'':18C ''method'':4B ''methodolog'':3A,13C,16C,49C ''much'':83C ''noth'':93C ''p'':5C,43C,75C ''phase'':24C ''process'':25C,61C ''program'':52C,56C ''scienc'':2A,12C ''search'':78C ''someth'':39C ''step'':21C,23C ''step-by-step'':20C ''today'':14C ''turn'':81C ''unifi'':60C ''use'':29C ''world'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6763, 4, '2015-08-11 12:59:07.74', 0, NULL, NULL, 'Latent Dirichlet Allocation (LDA) is an algorithm in the field of topic modeling.', NULL, NULL, '''algorithm'':7C ''alloc'':3C ''dirichlet'':2C ''field'':10C ''latent'':1C ''lda'':4C ''model'':13C ''topic'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5158, 2, '2015-02-16 20:14:41.513', 5, NULL, NULL, '&lt;p&gt;While I have only had a brief look at it I think &lt;a href="https://cloud.sagemath.com/"&gt;SageMathCloud&lt;/a&gt; looks quite promising. I have recommended it to at least one person previously, and they seemed to be quite happy with it. Bayond R support you also get access to Python, SAGE (as the name indicates) and a few other things. &lt;/p&gt;

&lt;p&gt;EDIT: Make sure to check the &lt;a href="https://github.com/sagemath/cloud/wiki/FAQ#useR"&gt;documentation&lt;/a&gt; on how to get an R (as opposed to a Python) session in a worksheet.&lt;/p&gt;
', NULL, NULL, '''/a'':18C,74C ''/p'':60C,90C ''/sagemath/cloud/wiki/faq#user'':72C ''access'':47C ''also'':45C ''bayond'':41C ''brief'':8C ''check'':66C ''cloud.sagemath.com'':16C ''document'':73C ''edit'':62C ''get'':46C,78C ''github.com'':71C ''github.com/sagemath/cloud/wiki/faq#user'':70C ''happi'':38C ''href'':15C,69C ''indic'':54C ''least'':28C ''look'':9C,19C ''make'':63C ''name'':53C ''one'':29C ''oppos'':82C ''p'':1C,61C ''person'':30C ''previous'':31C ''promis'':21C ''python'':49C,85C ''quit'':20C,37C ''r'':42C,80C ''recommend'':24C ''sage'':50C ''sagemathcloud'':17C ''seem'':34C ''session'':86C ''support'':43C ''sure'':64C ''thing'':59C ''think'':13C ''worksheet'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5148, 2, '2015-02-15 17:35:11.93', 2, NULL, NULL, '&lt;p&gt;Can you elaborate what you mean by ''methodologies''?&lt;/p&gt;

&lt;p&gt;In the meantime, take a look at &lt;a href="http://www.boozallen.com/insights/2013/11/data-science-field-guide" rel="nofollow"&gt;The Field Guide To Data Science&lt;/a&gt; by Booz Allen Hamilton. This guide talks about data science processes and frameworks.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.mosaicdatascience.com/resources/data-science-design-patterns/" rel="nofollow"&gt;Data Science Design Patterns&lt;/a&gt; by Mosaic talks about, you guessed it, data science design patterns. This is quite useful to get a sense of common design patterns. They are also working on releasing a book on the same subject. &lt;/p&gt;

&lt;p&gt;Then there are several resources out there that will come up as results to more targeted searches, such as machine learning paradigms, recommender systems paradigms, etc. Data Science is a large and varied field, and you''ll find many resources out there for each subsection of it. As far as I know, there isn''t one book that covers it all. &lt;/p&gt;
', NULL, NULL, '''/a'':32C,59C ''/insights/2013/11/data-science-field-guide'':23C ''/p'':10C,46C,95C,158C ''/resources/data-science-design-patterns/'':52C ''allen'':35C ''also'':85C ''book'':90C,153C ''booz'':34C ''come'':106C ''common'':80C ''cover'':155C ''data'':30C,41C,55C,67C,123C ''design'':57C,69C,81C ''elabor'':4C ''etc'':122C ''far'':145C ''field'':27C,130C ''find'':134C ''framework'':45C ''get'':76C ''guess'':65C ''guid'':28C,38C ''hamilton'':36C ''href'':20C,49C ''isn'':150C ''know'':148C ''larg'':127C ''learn'':117C ''ll'':133C ''look'':17C ''machin'':116C ''mani'':135C ''mean'':7C ''meantim'':14C ''methodolog'':9C ''mosaic'':61C ''nofollow'':25C,54C ''one'':152C ''p'':1C,11C,47C,96C ''paradigm'':118C,121C ''pattern'':58C,70C,82C ''process'':43C ''quit'':73C ''recommend'':119C ''rel'':24C,53C ''releas'':88C ''resourc'':101C,136C ''result'':109C ''scienc'':31C,42C,56C,68C,124C ''search'':113C ''sens'':78C ''sever'':100C ''subject'':94C ''subsect'':141C ''system'':120C ''take'':15C ''talk'':39C,62C ''target'':112C ''use'':74C ''vari'':129C ''work'':86C ''www.boozallen.com'':22C ''www.boozallen.com/insights/2013/11/data-science-field-guide'':21C ''www.mosaicdatascience.com'':51C ''www.mosaicdatascience.com/resources/data-science-design-patterns/'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5151, 2, '2015-02-15 23:16:47.21', 6, NULL, NULL, '&lt;p&gt;I will assume that you have not been working in statistics that long since you are talking about jumping ship because of a new software that''s coming out. Let me give you a bit of reassurance: &lt;/p&gt;

&lt;p&gt;For the past fifty or so years computers have been solving complex mathematical problems for statisticians and mathematicians, and now with the type of computing power we have today we are able to process terabytes of data per second and develop extremely sophisticated predictive models in a matter of seconds. Software has constantly evolved and constantly made people''s lives easier. Has that changed the fact that we still need people to interpret the data? The model? The results? No, it hasn''t. &lt;/p&gt;

&lt;p&gt;However, you should know that when software comes out that eliminates a lot of the grunt work, well, you no longer need to employ undergrads/post-baccs to complete those grunt tasks. Now you can use a software to do it, and higher people of a stronger caliber to perform more complex sets of analyses. The more software evolves, the more training a data scientist will require before breaking into the field. &lt;/p&gt;

&lt;p&gt;So you see, it''s not that completing the nano degree alone will or will not give you a competitive edge in the data science job market, it''s that this nano degree is &lt;em&gt;the first step&lt;/em&gt; in your journey toward becoming a data scientist. &lt;/p&gt;

&lt;p&gt;If anything, software like this should encourage all of us to hit the books and ensure that we''re still up to date on our game. No amount of bangs and whistles will teach a computer judgement. Prediction? Yes. But not judgement. We don''t have Skynet yet. &lt;/p&gt;
', NULL, NULL, '''/em'':236C ''/p'':39C,124C,196C,245C,295C ''abl'':72C ''alon'':209C ''amount'':274C ''analys'':178C ''anyth'':248C ''assum'':4C ''bang'':276C ''becom'':241C ''bit'':36C ''book'':260C ''break'':192C ''calib'':171C ''chang'':104C ''come'':29C,133C ''competit'':217C ''complet'':152C,205C ''complex'':52C,175C ''comput'':48C,65C,282C ''constant'':93C,96C ''data'':77C,115C,187C,221C,243C ''date'':269C ''degre'':208C,230C ''develop'':81C ''easier'':101C ''edg'':218C ''elimin'':136C ''em'':232C ''employ'':149C ''encourag'':253C ''ensur'':262C ''evolv'':94C,182C ''extrem'':82C ''fact'':106C ''field'':195C ''fifti'':44C ''first'':234C ''game'':272C ''give'':33C,214C ''grunt'':141C,154C ''hasn'':122C ''higher'':166C ''hit'':258C ''howev'':126C ''interpret'':113C ''job'':223C ''journey'':239C ''judgement'':283C,288C ''jump'':20C ''know'':129C ''let'':31C ''like'':250C ''live'':100C ''long'':14C ''longer'':146C ''lot'':138C ''made'':97C ''market'':224C ''mathemat'':53C ''mathematician'':58C ''matter'':88C ''model'':85C,117C ''nano'':207C,229C ''need'':110C,147C ''new'':25C ''p'':1C,40C,125C,197C,246C ''past'':43C ''peopl'':98C,111C,167C ''per'':78C ''perform'':173C ''power'':66C ''predict'':84C,284C ''problem'':54C ''process'':74C ''re'':265C ''reassur'':38C ''requir'':190C ''result'':119C ''scienc'':222C ''scientist'':188C,244C ''second'':79C,90C ''see'':200C ''set'':176C ''ship'':21C ''sinc'':15C ''skynet'':293C ''softwar'':26C,91C,132C,161C,181C,249C ''solv'':51C ''sophist'':83C ''statist'':12C ''statistician'':56C ''step'':235C ''still'':109C,266C ''stronger'':170C ''talk'':18C ''task'':155C ''teach'':280C ''terabyt'':75C ''today'':69C ''toward'':240C ''train'':185C ''type'':63C ''undergrads/post-baccs'':150C ''us'':256C ''use'':159C ''well'':143C ''whistl'':278C ''work'':10C,142C ''year'':47C ''yes'':285C ''yet'':294C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5152, 2, '2015-02-16 02:32:21.373', 5, NULL, NULL, '&lt;p&gt;Why restrict yourself to those two approaches? Because they''re cool? I would always start with a simple linear classifier \ regressor. So in this case a Linear SVM or Logistic Regression, preferably with an algorithm implementation that can take advantage of sparsity due to the size of the data. It will take a long time to run a DL algorithm on that dataset, and I would only normally try deep learning on specialist problems where there''s some hierarchical structure in the data, such as images or text. It''s overkill for a lot of simpler learning problems, and takes a lot of time and expertise to learn and also DL algorithms are very slow to train. Additionally, just because you have 50M rows, doesn''t mean you need to use the entire dataset to get good results. Depending on the data, you may get good results with a sample of a few 100,000 rows or a few million. I would start simple, with a small sample and a linear classifier, and get more complicated from there if the results are not satisfactory. At least that way you''ll get a baseline. We''ve often found simple linear models to out perform more sophisticated models on most tasks, so you want to always start there.&lt;/p&gt;
', NULL, NULL, '''/p'':218C ''000'':156C ''100'':155C ''50m'':124C ''addit'':119C ''advantag'':41C ''algorithm'':36C,61C,113C ''also'':111C ''alway'':15C,215C ''approach'':8C ''baselin'':194C ''case'':26C ''classifi'':21C,173C ''complic'':177C ''cool'':12C ''data'':50C,84C,143C ''dataset'':64C,135C ''deep'':71C ''depend'':140C ''dl'':60C,112C ''doesn'':126C ''due'':44C ''entir'':134C ''expertis'':107C ''found'':198C ''get'':137C,146C,175C,192C ''good'':138C,147C ''hierarch'':80C ''imag'':87C ''implement'':37C ''learn'':72C,98C,109C ''least'':187C ''linear'':20C,28C,172C,200C ''ll'':191C ''logist'':31C ''long'':55C ''lot'':95C,103C ''may'':145C ''mean'':128C ''million'':161C ''model'':201C,207C ''need'':130C ''normal'':69C ''often'':197C ''overkil'':92C ''p'':1C ''perform'':204C ''prefer'':33C ''problem'':75C,99C ''re'':11C ''regress'':32C ''regressor'':22C ''restrict'':3C ''result'':139C,148C,182C ''row'':125C,157C ''run'':58C ''sampl'':151C,169C ''satisfactori'':185C ''simpl'':19C,165C,199C ''simpler'':97C ''size'':47C ''slow'':116C ''small'':168C ''sophist'':206C ''sparsiti'':43C ''specialist'':74C ''start'':16C,164C,216C ''structur'':81C ''svm'':29C ''take'':40C,53C,101C ''task'':210C ''text'':89C ''time'':56C,105C ''train'':118C ''tri'':70C ''two'':7C ''use'':132C ''ve'':196C ''want'':213C ''way'':189C ''would'':14C,67C,163C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5554, 2, '2015-04-19 12:17:00.21', -1, NULL, NULL, '&lt;p&gt;I also asked it in lasagne-users forum and Oliver Duerr helped me a lot with code sample:
&lt;a href="https://groups.google.com/forum/#!topic/lasagne-users/8ZA7hr2wKfM" rel="nofollow"&gt;https://groups.google.com/forum/#!topic/lasagne-users/8ZA7hr2wKfM&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/forum/#!topic/lasagne-users/8za7hr2wkfm'':25C ''/forum/#!topic/lasagne-users/8za7hr2wkfm&lt;/a&gt;&lt;/p&gt;'':30C ''also'':3C ''ask'':4C ''code'':19C ''duerr'':13C ''forum'':10C ''groups.google.com'':24C,29C ''groups.google.com/forum/#!topic/lasagne-users/8za7hr2wkfm'':23C ''groups.google.com/forum/#!topic/lasagne-users/8za7hr2wkfm&lt;/a&gt;&lt;/p&gt;'':28C ''help'':14C ''href'':22C ''lasagn'':8C ''lasagne-us'':7C ''lot'':17C ''nofollow'':27C ''oliv'':12C ''p'':1C ''rel'':26C ''sampl'':20C ''user'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5155, 1, '2015-02-16 12:16:26.923', 1, 42, 'Assigning new items to existing similarity based clustering', '&lt;p&gt;Given some clusters created from similarity measures between items, is there a recommended way to assign a new item to an existing cluster based on similarity alone? (i.e. avoiding re-clustering)&lt;/p&gt;

&lt;p&gt;Measuring the similarity of a new item to all other items is fairly cheap, so I''m looking for a way of using this to assign it to the cluster it''s most likely to belong to. It''s also important for it to take cluster size into account (i.e. doesn''t unfairly weight towards or against larger clusters).&lt;/p&gt;

&lt;p&gt;Basically, I''m trying to sacrifice some clustering accuracy in exchange for avoiding a complete re-clustering when the occasional new item is added.&lt;/p&gt;
', 'clustering  similarity  online-learning', NULL, '''/p'':47C,108C,135C ''account'':97C ''accuraci'':118C ''ad'':134C ''alon'':41C ''also'':88C ''assign'':1A,30C,74C ''avoid'':43C,122C ''base'':7A,38C ''basic'':110C ''belong'':84C ''cheap'':62C ''cluster'':8A,9B,17C,37C,46C,78C,94C,107C,117C,127C ''complet'':124C ''creat'':18C ''doesn'':99C ''exchang'':120C ''exist'':5A,36C ''fair'':61C ''given'':15C ''i.e'':42C,98C ''import'':89C ''item'':3A,23C,33C,55C,59C,132C ''larger'':106C ''learn'':13B ''like'':82C ''look'':66C ''m'':65C,112C ''measur'':21C,49C ''new'':2A,32C,54C,131C ''occasion'':130C ''onlin'':12B ''online-learn'':11B ''p'':14C,48C,109C ''re'':45C,126C ''re-clust'':44C,125C ''recommend'':27C ''sacrific'':115C ''similar'':6A,10B,20C,40C,51C ''size'':95C ''take'':93C ''toward'':103C ''tri'':113C ''unfair'':101C ''use'':71C ''way'':28C,69C ''weight'':102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5156, 2, '2015-02-16 13:50:32.2', 2, NULL, NULL, '&lt;p&gt;I suggest you think about this in terms of "data set" and "training set" (technically, it is also recommended to have a separate test set). Once you have your clusters defined on the training set, your can start using them to classify any amount of new data without recalculating, by simply measuring similarity to cluster centroids, for example.&lt;/p&gt;

&lt;p&gt;(This doesn''t prevent you from deciding to enlarge your training set and data set later, just try to not do that selectively to avoiding overfitting.)&lt;/p&gt;
', NULL, NULL, '''/p'':60C,88C ''also'':19C ''amount'':45C ''avoid'':86C ''centroid'':57C ''classifi'':43C ''cluster'':31C,56C ''data'':11C,48C,75C ''decid'':68C ''defin'':32C ''doesn'':63C ''enlarg'':70C ''exampl'':59C ''later'':77C ''measur'':53C ''new'':47C ''overfit'':87C ''p'':1C,61C ''prevent'':65C ''recalcul'':50C ''recommend'':20C ''select'':84C ''separ'':24C ''set'':12C,15C,26C,36C,73C,76C ''similar'':54C ''simpli'':52C ''start'':39C ''suggest'':3C ''technic'':16C ''term'':9C ''test'':25C ''think'':5C ''train'':14C,35C,72C ''tri'':79C ''use'':40C ''without'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8606, 1, '2015-10-26 11:50:41.147', 1, 486, 'What are some nice algorithms/techniques for optimizing and predicting Click Through Rates (CTR)?', '&lt;p&gt;Definition of Click Through Rates:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;CTR is the number of clicks that your ad receives divided by the
  number of times your ad is shown expressed as a percentage (clicks Ã·
  impressions = CTR).&lt;/p&gt;
&lt;/blockquote&gt;
', 'r  python  optimization', NULL, '''/blockquote'':55C ''/p'':23C,54C ''ad'':34C,43C ''algorithms/techniques'':5A ''blockquot'':24C ''click'':10A,20C,31C,50C ''ctr'':13A,26C,53C ''definit'':18C ''divid'':36C ''express'':46C ''impress'':52C ''nice'':4A ''number'':29C,39C ''optim'':7A,16B ''p'':17C,25C ''percentag'':49C ''predict'':9A ''python'':15B ''r'':14B ''rate'':12A,22C ''receiv'':35C ''shown'':45C ''time'':41C ''ã'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5162, 2, '2015-02-17 07:02:21.497', 2, NULL, NULL, '&lt;p&gt;In addition to other answers (and there''s some good link in the comments) it depends on what the problem is or what kinds of questions you want to answer. As I can only suggest based on my own experience, then in case of a classification task, the possible methods can be severely limited based on class balance in dataset. &lt;/p&gt;

&lt;p&gt;Once you go to a larger than around 1:10 class imbalance, then most classification methods just stop working. You''ll be left with methods based on random forest and maybe neural nets (haven''t tried yet). I work with the class balance in the range of 1:500 to 1:1000 and have found that neither down- or upsampling works. Luckily my dataset is "only" 6mln observations by 200 variables and I''m able to run boosted trees on the whole set in reasonable time.&lt;/p&gt;

&lt;p&gt;So to directly answer your question:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;you should come up with a bunch of questions you would want to answer and in case of classification then check the class balances of the target variables.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;you should check the distribution (not in mathematical sense) of missing values in all of your data and document what you find. Some ML methods are fine with missing values while others are not and you need to look into data imputation (which has its own set of rules and guidelines and problems).&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':191C,248C ''/p'':62C,150C,158C,190C,247C ''/ul'':249C ''1'':72C,111C,114C ''10'':73C ''1000'':115C ''200'':133C ''500'':112C ''6mln'':130C ''abl'':138C ''addit'':3C ''answer'':6C,31C,155C,175C ''around'':71C ''balanc'':59C,106C,185C ''base'':37C,56C,89C ''boost'':141C ''bunch'':168C ''case'':44C,178C ''check'':182C,196C ''class'':58C,74C,105C,184C ''classif'':47C,78C,180C ''come'':164C ''comment'':15C ''data'':210C,234C ''dataset'':61C,127C ''depend'':17C ''direct'':154C ''distribut'':198C ''document'':212C ''experi'':41C ''find'':215C ''fine'':220C ''forest'':92C ''found'':118C ''go'':66C ''good'':11C ''guidelin'':244C ''haven'':97C ''imbal'':75C ''imput'':235C ''kind'':25C ''larger'':69C ''left'':86C ''li'':160C,192C ''limit'':55C ''link'':12C ''ll'':84C ''look'':232C ''luckili'':125C ''m'':137C ''mathemat'':201C ''mayb'':94C ''method'':51C,79C,88C,218C ''miss'':204C,222C ''ml'':217C ''need'':230C ''neither'':120C ''net'':96C ''neural'':95C ''observ'':131C ''other'':225C ''p'':1C,63C,151C,161C,193C ''possibl'':50C ''problem'':21C,246C ''question'':27C,157C,170C ''random'':91C ''rang'':109C ''reason'':148C ''rule'':242C ''run'':140C ''sens'':202C ''set'':146C,240C ''sever'':54C ''stop'':81C ''suggest'':36C ''target'':188C ''task'':48C ''time'':149C ''tree'':142C ''tri'':99C ''ul'':159C ''upsampl'':123C ''valu'':205C,223C ''variabl'':134C,189C ''want'':29C,173C ''whole'':145C ''work'':82C,102C,124C ''would'':172C ''yet'':100C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5164, 1, '2015-02-17 18:07:28.333', 0, 108, 'Getting Scikit-Learn RandomForestClassifier to output Top N results', '&lt;p&gt;I''d like to see the top N results for a RandomForestClassifier prediction, ordered by descending probability.&lt;/p&gt;

&lt;p&gt;The answer may be predict_proba, but I have no idea how to interpret the results.&lt;/p&gt;

&lt;p&gt;Help appreciated!&lt;/p&gt;
', 'random-forest  scikit', NULL, '''/p'':33C,51C,55C ''answer'':36C ''appreci'':54C ''d'':17C ''descend'':31C ''forest'':13B ''get'':1A ''help'':53C ''idea'':45C ''interpret'':48C ''learn'':4A ''like'':18C ''may'':37C ''n'':9A,23C ''order'':29C ''output'':7A ''p'':15C,34C,52C ''predict'':28C,39C ''proba'':40C ''probabl'':32C ''random'':12B ''random-forest'':11B ''randomforestclassifi'':5A,27C ''result'':10A,24C,50C ''scikit'':3A,14B ''scikit-learn'':2A ''see'':20C ''top'':8A,22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5165, 1, '2015-02-17 19:10:05.547', 0, 106, 'When it is time to use Hadoop?', '&lt;p&gt;&lt;em&gt;Hadoop&lt;/em&gt; is a buzzword now. A lot of start-ups use it (or just say, that they use it), a lot of widely known companies use it. But when and what is the border? When person can say: "Better to solve it without Hadoop"?&lt;/p&gt;
', 'bigdata  hadoop  efficiency  scalability  performance', NULL, '''/em'':16C ''/p'':61C ''better'':55C ''bigdata'':8B ''border'':50C ''buzzword'':19C ''compani'':41C ''effici'':10B ''em'':14C ''hadoop'':7A,9B,15C,60C ''known'':40C ''lot'':22C,37C ''p'':13C ''perform'':12B ''person'':52C ''say'':31C,54C ''scalabl'':11B ''solv'':57C ''start'':25C ''start-up'':24C ''time'':4A ''up'':26C ''use'':6A,27C,34C,42C ''wide'':39C ''without'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5166, 1, '2015-02-17 21:50:10.21', 1, 88, 'training neural net with multiple sets of time-series data', '&lt;p&gt;I have the following data&lt;/p&gt;

&lt;p&gt;($x^1_i$, $y^1_i$) for $i=1,2,...N_1$&lt;/p&gt;

&lt;p&gt;($x^2_i$, $y^2_i$) for $i=1,2,...N_2$&lt;/p&gt;

&lt;p&gt;...&lt;/p&gt;

&lt;p&gt;($x^m_i$, $y^m_i$) for $i=1,2,...N_m$&lt;/p&gt;

&lt;p&gt;Is it possible to train a neural net to produce some $y_k$ where $k&amp;lt;=min(N)$ given a input ${x_1, x_2, ..., x_{k-1}}$?&lt;/p&gt;

&lt;p&gt;If so any suggestion of documentation/ library I can look at (preferably python)?&lt;/p&gt;
', 'machine-learning  dataset  neuralnetwork  time-series  regression', NULL, '''-1'':100C ''/p'':27C,41C,55C,57C,71C,101C,116C ''1'':30C,33C,37C,40C,51C,67C,95C ''2'':38C,44C,47C,52C,54C,68C,97C ''data'':11A,26C ''dataset'':15B ''document'':108C ''follow'':25C ''given'':91C ''input'':93C ''k'':85C,87C,99C ''learn'':14B ''librari'':109C ''look'':112C ''lt'':88C ''m'':60C,63C,70C ''machin'':13B ''machine-learn'':12B ''min'':89C ''multipl'':5A ''n'':39C,53C,69C,90C ''net'':3A,80C ''neural'':2A,79C ''neuralnetwork'':16B ''p'':21C,28C,42C,56C,58C,72C,102C ''possibl'':75C ''prefer'':114C ''produc'':82C ''python'':115C ''regress'':20B ''seri'':10A,19B ''set'':6A ''suggest'':106C ''time'':9A,18B ''time-seri'':8A,17B ''train'':1A,77C ''x'':29C,43C,59C,94C,96C,98C ''y'':32C,46C,62C,84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6768, 1, '2015-08-11 22:21:42.453', 6, 159, 'sklearn - overfitting problem', '&lt;p&gt;I''m looking for recommendations as to the best way forward for my current machine learning problem&lt;/p&gt;

&lt;p&gt;The outline of the problem and what I''ve done is as follows:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;I have 900+ trials of EEG data, where each trial is 1 second long. The ground truth is known for each and classifies state 0 and state 1 (40-60% split)&lt;/li&gt;
&lt;li&gt;Each trial goes through preprocessing where I filter and extract power of certain frequency bands, and these make up a set of features (feature matrix: 913x32)&lt;/li&gt;
&lt;li&gt;Then I use sklearn to train the model. cross_validation is used where I use a test size of 0.2. Classifier is set to SVC with rbf kernel, C = 1, gamma = 1 (I''ve tried a number of different values)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You can find a shortened version of the code here: &lt;a href="http://pastebin.com/Xu13ciL4"&gt;http://pastebin.com/Xu13ciL4&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;My issues:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;When I use the classifier to predict labels for my test set, every prediction is 0&lt;/li&gt;
&lt;li&gt;train accuracy is 1, while test set accuracy is around 0.56&lt;/li&gt;
&lt;li&gt;my learning curve plot looks like this:&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/lkUnN.png"&gt;&lt;img src="http://i.stack.imgur.com/lkUnN.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Now, this seems like a classic case of overfitting here. However, overfitting here is unlikely to be caused by a disproportionate number of features to samples (32 features, 900 samples). I''ve tried a number of things to alleviate this problem:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;I''ve tried using dimensionality reduction (PCA) in case it is because I have too many features for the number of samples, but accuracy scores and learning curve plot looks the same as above. Unless I set the number of components to below 10, at which point train accuracy begins to drop, but is this not somewhat expected given you''re beginning to lose information?&lt;/li&gt;
&lt;li&gt;I have tried normalizing and standardizing the data. Standardizing (SD = 1) does nothing to change train or accuracy scores. Normalizing (0-1) drops my training accuracy to 0.6.&lt;/li&gt;
&lt;li&gt;I''ve tried a variety of C and gamma settings for SVC, but they don''t change either score&lt;/li&gt;
&lt;li&gt;Tried using other estimators like GaussianNB, even ensemble methods like adaboost. No change&lt;/li&gt;
&lt;li&gt;Tried explcitly setting a regularization method using linearSVC but didn''t improve the situation&lt;/li&gt;
&lt;li&gt;I tried running the same features through a neural net using theano and my train accuracy is around 0.6, test is around 0.5&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I''m happy to keep thinking about the problem but at this point I''m looking for a nudge in the right direction. Where might my problem be and what could I do to solve it?&lt;/p&gt;

&lt;p&gt;It''s entirely possible that my set of features just don''t distinguish between the 2 categories, but I''d like to try some other options before jumping to this conclusion. Furthermore, if my features don''t distinguish then that would explain the low test set scores, but how do you get a perfect training set score in that case? Is that possible?&lt;/p&gt;
', 'machine-learning  python  svm  sklearn', NULL, '''-1'':365C ''-60'':75C ''/a'':230C ''/li'':77C,105C,147C,190C,203C,212C,342C,372C,393C,408C,424C,449C ''/lkunn.png'':219C,224C ''/p'':28C,43C,171C,231C,274C,488C,553C ''/ul'':148C,213C,450C ''/xu13cil4'':164C ''/xu13cil4&lt;/a&gt;&lt;/p&gt;'':167C ''0'':70C,189C,364C ''0.2'':126C ''0.5'':448C ''0.56'':202C ''0.6'':371C,444C ''1'':57C,73C,136C,138C,195C,354C ''10'':320C ''2'':505C ''32'':259C ''40'':74C ''900'':48C,261C ''913x32'':104C ''accuraci'':193C,199C,300C,325C,361C,369C,441C ''adaboost'':405C ''allevi'':271C ''alt'':225C ''around'':201C,443C,447C ''band'':93C ''begin'':326C,338C ''best'':19C ''c'':135C,380C ''case'':239C,285C,549C ''categori'':506C ''caus'':250C ''certain'':91C ''chang'':358C,390C,407C ''classic'':238C ''classifi'':68C,127C,178C ''code'':158C ''compon'':317C ''conclus'':520C ''could'':482C ''cross'':115C ''current'':24C ''curv'':207C,304C ''d'':509C ''data'':52C,351C ''descript'':228C ''didn'':419C ''differ'':145C ''dimension'':281C ''direct'':474C ''disproportion'':253C ''distinguish'':502C,527C ''done'':39C ''drop'':328C,366C ''eeg'':51C ''either'':391C ''ensembl'':402C ''enter'':226C ''entir'':492C ''estim'':398C ''even'':401C ''everi'':186C ''expect'':334C ''explain'':531C ''explcit'':411C ''extract'':88C ''featur'':101C,102C,256C,260C,293C,431C,498C,524C ''filter'':86C ''find'':152C ''follow'':42C ''forward'':21C ''frequenc'':92C ''furthermor'':521C ''gamma'':137C,382C ''gaussiannb'':400C ''get'':541C ''given'':335C ''goe'':81C ''ground'':61C ''happi'':454C ''howev'':243C ''href'':161C,216C ''i.stack.imgur.com'':218C,223C ''i.stack.imgur.com/lkunn.png'':217C,222C ''imag'':227C ''img'':220C ''improv'':421C ''inform'':341C ''issu'':170C ''jump'':517C ''keep'':456C ''kernel'':134C ''known'':64C ''label'':181C ''learn'':6B,26C,206C,303C ''li'':45C,78C,106C,173C,191C,204C,276C,343C,373C,394C,409C,425C ''like'':210C,236C,399C,404C,510C ''linearsvc'':417C ''long'':59C ''look'':13C,209C,306C,467C ''lose'':340C ''low'':533C ''m'':12C,453C,466C ''machin'':5B,25C ''machine-learn'':4B ''make'':96C ''mani'':292C ''matrix'':103C ''method'':403C,415C ''might'':476C ''model'':114C ''net'':435C ''neural'':434C ''normal'':347C,363C ''noth'':356C ''nudg'':470C ''number'':143C,254C,267C,296C,315C ''option'':515C ''outlin'':31C ''overfit'':2A,241C,244C ''p'':10C,29C,149C,168C,214C,232C,451C,489C ''pastebin.com'':163C,166C ''pastebin.com/xu13cil4'':162C ''pastebin.com/xu13cil4&lt;/a&gt;&lt;/p&gt;'':165C ''pca'':283C ''perfect'':543C ''plot'':208C,305C ''point'':323C,464C ''possibl'':493C,552C ''power'':89C ''predict'':180C,187C ''preprocess'':83C ''problem'':3A,27C,34C,273C,460C,478C ''python'':7B ''rbf'':133C ''re'':337C ''recommend'':15C ''reduct'':282C ''regular'':414C ''right'':473C ''run'':428C ''sampl'':258C,262C,298C ''score'':301C,362C,392C,536C,546C ''sd'':353C ''second'':58C ''seem'':235C ''set'':99C,129C,185C,198C,313C,383C,412C,496C,535C,545C ''shorten'':154C ''situat'':423C ''size'':124C ''sklearn'':1A,9B,110C ''solv'':486C ''somewhat'':333C ''split'':76C ''src'':221C ''standard'':349C,352C ''state'':69C,72C ''svc'':131C,385C ''svm'':8B ''test'':123C,184C,197C,445C,534C ''theano'':437C ''thing'':269C ''think'':457C ''train'':112C,192C,324C,359C,368C,440C,544C ''tri'':141C,265C,279C,346C,376C,395C,410C,427C,512C ''trial'':49C,55C,80C ''truth'':62C ''ul'':44C,172C,275C ''unless'':311C ''unlik'':247C ''use'':109C,118C,121C,176C,280C,396C,416C,436C ''valid'':116C ''valu'':146C ''varieti'':378C ''ve'':38C,140C,264C,278C,375C ''version'':155C ''way'':20C ''would'':530C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5168, 1, '2015-02-17 22:55:26.3', 3, 67, 'What are the possible ways to handle class unbalance in a large scale image recognition problem with Deep Neural Nets?', '&lt;p&gt;I have 22 classes of objects but they have very skewed distributions where max class has 100.000 images and the min class has 1600 images. In that setting I would like to hear some possible solutions to this balance problem.&lt;/p&gt;

&lt;p&gt;I have tried followings so far;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;multiply number of instances in the lower classes up to the max class by replicating the instances, possibly adding some noise as well.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;changing the learning regarding the class distribution in the given minibatch of the next epoch. (no implemented but in my mind)&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;What are your suggestions?&lt;/p&gt;
', 'classification  unbalanced-classes  deep-learning  object-recognition', NULL, '''/li'':108C,133C ''/ol'':134C ''/p'':72C,80C,107C,132C,140C ''100.000'':48C ''1600'':55C ''22'':34C ''ad'':102C ''balanc'':70C ''chang'':111C ''class'':8A,24B,35C,46C,53C,91C,96C,116C ''classif'':21B ''deep'':18A,26B ''deep-learn'':25B ''distribut'':43C,117C ''epoch'':125C ''far'':79C ''follow'':77C ''given'':120C ''handl'':7A ''hear'':64C ''imag'':14A,49C,56C ''implement'':127C ''instanc'':87C,100C ''larg'':12A ''learn'':27B,113C ''li'':82C,109C ''like'':62C ''lower'':90C ''max'':45C,95C ''min'':52C ''mind'':131C ''minibatch'':121C ''multipli'':84C ''net'':20A ''neural'':19A ''next'':124C ''nois'':104C ''number'':85C ''object'':29B,37C ''object-recognit'':28B ''ol'':81C ''p'':31C,73C,83C,110C,135C ''possibl'':4A,66C,101C ''problem'':16A,71C ''recognit'':15A,30B ''regard'':114C ''replic'':98C ''scale'':13A ''set'':59C ''skew'':42C ''solut'':67C ''suggest'':139C ''tri'':76C ''unbal'':9A ''unbalanc'':23B ''unbalanced-class'':22B ''way'':5A ''well'':106C ''would'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5169, 2, '2015-02-17 23:04:07.277', 2, NULL, NULL, '&lt;p&gt;From my perspective, for 5 million instances you need lots of trees to get a good generalization bound (a good model in the layman term). If this is not a problem then go for it,even the exact answer is relying on the nature of your problem. GBT is a good method especially if you have mixed feature types like categorical, numerical and such. In addition, compared to Neural Networks it has lower number of hyperparameters to be tuned. Therefore, it is faster to have a best setting model. One more thing is the alternative of parallel training. You can train multiple trees at the same time with a good CPU. If you are not satisfied with the results then go for Neural Nets since that means your model should be more extensive and should learn higher order information through your data. That is the due of NNs compared to other learning algorithms. &lt;/p&gt;
', NULL, NULL, '''/p'':155C ''5'':6C ''addit'':67C ''algorithm'':154C ''altern'':96C ''answer'':40C ''best'':88C ''bound'':19C ''categor'':62C ''compar'':68C,150C ''cpu'':112C ''data'':143C ''due'':147C ''especi'':54C ''even'':37C ''exact'':39C ''extens'':134C ''faster'':84C ''featur'':59C ''gbt'':49C ''general'':18C ''get'':15C ''go'':34C,122C ''good'':17C,21C,52C,111C ''higher'':138C ''hyperparamet'':77C ''inform'':140C ''instanc'':8C ''layman'':25C ''learn'':137C,153C ''like'':61C ''lot'':11C ''lower'':74C ''mean'':128C ''method'':53C ''million'':7C ''mix'':58C ''model'':22C,90C,130C ''multipl'':103C ''natur'':45C ''need'':10C ''net'':125C ''network'':71C ''neural'':70C,124C ''nns'':149C ''number'':75C ''numer'':63C ''one'':91C ''order'':139C ''p'':1C ''parallel'':98C ''perspect'':4C ''problem'':32C,48C ''reli'':42C ''result'':120C ''satisfi'':117C ''set'':89C ''sinc'':126C ''term'':26C ''therefor'':81C ''thing'':93C ''time'':108C ''train'':99C,102C ''tree'':13C,104C ''tune'':80C ''type'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5170, 1, '2015-02-18 00:08:32.8', 0, 51, 'depth/complexity of decision trees', '&lt;p&gt;I have used the same methods/parameters to create two decision trees. The trees classify the presence or absence of a medical condition using the presence or absence of various symptoms. There is a tree for Medical Condition #1 and another tree for Medical Condition #2. Both trees are based on the same set of symptoms, rated by patients. If Medical Condition #1 resulted in a much simpler tree than Medical Condition #2, can that suggest Medical Condition #2 is a more complex disease? If so, can anyone point me to a reference that states the complexity/depth of a tree can be representative of a complex condition?&lt;/p&gt;
', 'classification  statistics', NULL, '''/p'':114C ''1'':45C,69C ''2'':52C,79C,85C ''absenc'':25C,34C ''anoth'':47C ''anyon'':94C ''base'':56C ''classif'':5B ''classifi'':21C ''complex'':89C,112C ''complexity/depth'':103C ''condit'':29C,44C,51C,68C,78C,84C,113C ''creat'':15C ''decis'':3A,17C ''depth/complexity'':1A ''diseas'':90C ''medic'':28C,43C,50C,67C,77C,83C ''methods/parameters'':13C ''much'':73C ''p'':7C ''patient'':65C ''point'':95C ''presenc'':23C,32C ''rate'':63C ''refer'':99C ''repres'':109C ''result'':70C ''set'':60C ''simpler'':74C ''state'':101C ''statist'':6B ''suggest'':82C ''symptom'':37C,62C ''tree'':4A,18C,20C,41C,48C,54C,75C,106C ''two'':16C ''use'':10C,30C ''various'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5182, 2, '2015-02-19 06:30:45.19', 6, NULL, NULL, '&lt;p&gt;I have used &lt;a href="http://docs.aws.amazon.com/AmazonS3/latest/dev/Versioning.html"&gt;Versioning on Amazon S3 buckets&lt;/a&gt; to manage 10-100GB in 10-100 files. Transfer can be slow, so it has helped to compress and transfer in parallel, or just run computations on EC2. The &lt;a href="https://github.com/boto/boto"&gt;boto&lt;/a&gt; library provides a nice python interface.&lt;/p&gt;
', NULL, NULL, '''-100'':19C,23C ''/a'':15C,52C ''/amazons3/latest/dev/versioning.html'':9C ''/boto/boto'':50C ''/p'':59C ''10'':18C,22C ''amazon'':12C ''boto'':51C ''bucket'':14C ''compress'':34C ''comput'':42C ''docs.aws.amazon.com'':8C ''docs.aws.amazon.com/amazons3/latest/dev/versioning.html'':7C ''ec2'':44C ''file'':24C ''gb'':20C ''github.com'':49C ''github.com/boto/boto'':48C ''help'':32C ''href'':6C,47C ''interfac'':58C ''librari'':53C ''manag'':17C ''nice'':56C ''p'':1C ''parallel'':38C ''provid'':54C ''python'':57C ''run'':41C ''s3'':13C ''slow'':28C ''transfer'':25C,36C ''use'':4C ''version'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5171, 1, '2015-02-18 00:33:51.823', 0, 157, 'How to appropriately set weights for weighted KNN', '&lt;p&gt;I''m currently trying to predict a continuous variable using KNN. Instead of treating each neighbor equally I would like to use the weights to create a weighted average. The weights by themselves are not ideal, as the closer a neighbor the more I would like that neighbor to influence the final results. &lt;/p&gt;

&lt;p&gt;This lead me to consider the inverse of each of the distances, but this doesn''t handle the case where an instance is the exact same -&gt; with a distance of 0.&lt;/p&gt;

&lt;p&gt;Any recommendations on how to properly set the weights of each neighbor relative to their distance? Similar to how the inverse would handle this, but one that allows for 0 values.&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':66C,99C,132C ''0'':98C,130C ''allow'':128C ''appropri'':3A ''averag'':41C ''case'':86C ''closer'':51C ''consid'':72C ''continu'':20C ''creat'':38C ''current'':15C ''distanc'':79C,96C,116C ''doesn'':82C ''equal'':29C ''exact'':92C ''final'':64C ''handl'':84C,123C ''ideal'':48C ''influenc'':62C ''instanc'':89C ''instead'':24C ''invers'':74C,121C ''knn'':8A,23C ''lead'':69C ''learn'':11B ''like'':32C,58C ''m'':14C ''machin'':10B ''machine-learn'':9B ''neighbor'':28C,53C,60C,112C ''one'':126C ''p'':12C,67C,100C ''predict'':18C ''proper'':106C ''recommend'':102C ''relat'':113C ''result'':65C ''set'':4A,107C ''similar'':117C ''treat'':26C ''tri'':16C ''use'':22C,34C ''valu'':131C ''variabl'':21C ''weight'':5A,7A,36C,40C,43C,109C ''would'':31C,57C,122C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6227, 1, '2015-06-26 12:56:55.41', 3, 46, 'Traversing trees in SQL: JOINs vs imperative algorithm', '&lt;p&gt;I have a table representing posts on a message board. Posts may or may not have parents.&lt;/p&gt;

&lt;p&gt;What is the most common way to get all posts starting from a given post, or to find the root post of any given post? &lt;/p&gt;

&lt;p&gt;I can think of using JOINs to join parents and children, but how can I know how many joins I need before I stop?&lt;/p&gt;
', 'graphs  sql', NULL, '''/p'':29C,56C,82C ''algorithm'':8A ''board'':21C ''children'':68C ''common'':35C ''find'':48C ''get'':38C ''given'':44C,54C ''graph'':9B ''imper'':7A ''join'':5A,63C,65C,76C ''know'':73C ''mani'':75C ''may'':23C,25C ''messag'':20C ''need'':78C ''p'':11C,30C,57C ''parent'':28C,66C ''post'':17C,22C,40C,45C,51C,55C ''repres'':16C ''root'':50C ''sql'':4A,10B ''start'':41C ''stop'':81C ''tabl'':15C ''think'':60C ''travers'':1A ''tree'':2A ''use'':62C ''vs'':6A ''way'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5172, 1, '2015-02-18 04:24:51.153', 1, 28, 'perceptron implementation, preocedure for post-feature vecor: bag-of-words model', '&lt;p&gt;I have code to generate feature vectors in the style of the bag-of-words model, you can see it on my &lt;a href="https://github.com/h1395010/perceptron/blob/master/src/file_dict_createur/FileDictCreateur.java" rel="nofollow"&gt;github page&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;It renders output of the form:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;/data/train/politics/p_0.txt, [0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]
/data/train/science/s_0.txt, [1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0]
/data/train/atheism/a_0.txt, [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
/data/train/sports/s_1.txt, [0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to move on to the next step of the perceptron but I''m confused about what to do next. I guess the next thing I need to do is generate a feature vector for the ''test'' data, isn''t it?&lt;/p&gt;

&lt;p&gt;For instance, in &lt;a href="http://en.literateprograms.org/index.php?title=Special:DownloadCode/Perceptron_(Java)&amp;amp;oldid=19184" rel="nofollow"&gt;this example&lt;/a&gt; what is ''teachingOutput'' supposed to be, training data vectors? Such as the ones I have above?&lt;/p&gt;

&lt;p&gt;I also tried poking around in &lt;a href="http://stackoverflow.com/questions/21738277/perceptron-learning-most-important-feature"&gt;this implementation&lt;/a&gt; in an attempt to figure out what to do but, alas, to no avail. &lt;/p&gt;
', 'machine-learning', NULL, '''/a'':50C,249C,281C ''/code'':190C ''/data/train/atheism/a_0.txt'':126C ''/data/train/politics/p_0.txt'':62C ''/data/train/science/s_0.txt'':94C ''/data/train/sports/s_1.txt'':158C ''/h1395010/perceptron/blob/master/src/file_dict_createur/filedictcreateur.java'':45C ''/index.php?title=special:downloadcode/perceptron_(java)&amp;amp;oldid=19184'':244C ''/p'':51C,59C,235C,266C,296C ''/pre'':191C ''/questions/21738277/perceptron-learning-most-important-feature'':278C ''0'':63C,64C,65C,66C,69C,70C,71C,72C,73C,74C,75C,77C,78C,79C,81C,82C,83C,87C,88C,89C,90C,91C,92C,93C,96C,97C,98C,99C,100C,102C,104C,107C,108C,110C,112C,113C,116C,117C,118C,123C,125C,127C,129C,130C,131C,132C,133C,135C,136C,137C,138C,139C,140C,141C,142C,143C,144C,146C,147C,148C,149C,150C,151C,152C,153C,154C,155C,156C,157C,159C,160C,163C,164C,165C,166C,167C,169C,172C,173C,175C,176C,177C,178C,179C,180C,181C,182C,184C,185C,186C,188C ''1'':67C,76C,80C,84C,85C,86C,95C,101C,103C,105C,106C,109C,111C,114C,115C,119C,120C,121C,122C,124C,128C,134C,145C,161C,162C,168C,170C,171C,174C,183C,187C,189C ''2'':68C ''ala'':292C ''also'':269C ''around'':272C ''attempt'':284C ''avail'':295C ''bag'':10A,31C ''bag-of-word'':9A,30C ''code'':20C,61C ''confus'':208C ''data'':231C,257C ''en.literateprograms.org'':243C ''en.literateprograms.org/index.php?title=special:downloadcode/perceptron_(java)&amp;amp;oldid=19184'':242C ''exampl'':248C ''featur'':7A,23C,226C ''figur'':286C ''form'':58C ''generat'':22C,224C ''github'':48C ''github.com'':44C ''github.com/h1395010/perceptron/blob/master/src/file_dict_createur/filedictcreateur.java'':43C ''guess'':215C ''href'':42C,241C,275C ''implement'':2A,280C ''instanc'':238C ''isn'':232C ''learn'':16B ''m'':207C ''machin'':15B ''machine-learn'':14B ''model'':13A,34C ''move'':196C ''need'':220C ''next'':200C,213C,217C ''nofollow'':47C,246C ''one'':262C ''output'':55C ''p'':17C,52C,192C,236C,267C ''page'':49C ''perceptron'':1A,204C ''poke'':271C ''post'':6A ''post-featur'':5A ''pre'':60C ''preocedur'':3A ''rel'':46C,245C ''render'':54C ''see'':37C ''stackoverflow.com'':277C ''stackoverflow.com/questions/21738277/perceptron-learning-most-important-feature'':276C ''step'':201C ''style'':27C ''suppos'':253C ''teachingoutput'':252C ''test'':230C ''thing'':218C ''train'':256C ''tri'':270C ''vecor'':8A ''vector'':24C,227C,258C ''want'':194C ''word'':12A,33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5173, 2, '2015-02-18 09:04:27.77', 1, NULL, NULL, '&lt;p&gt;Technically speaking the inverse of the distance should not pose any problem. Considering the case when you have to classify an observation (instance) whih is identical with one observation from the ''learned'' data set, than the inverse distance is not defined by a direct formula since you have to divide by zero. But considering the math behind, inverse of the zero distance is positive infinity. Which means that, the weight for the identical point would dominate all other weights of the data instances. Thus the classification for that specific point could be taken as the class of the identical observation from the data set. &lt;/p&gt;

&lt;p&gt;On the other hand the inverse of the euclidean distance is only one type of distance. You can always apply a kernel function on that to weight non-uniformly the contributions. For example you can choose a normal density with mean equals zero and variance equals 1. Than, your weight would be the value of density function for the euclidean distance. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5175, 1, '2015-02-18 10:03:13.913', 1, 946, 'List of used aliases in Tableau', '&lt;p&gt;I often get the problem when this or that alias name is already used somewhere, and I can''t easily find that variable or aggregation to release the name.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/kK9mM.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Is there some place in Tableau where I can view/edit/reset full list of aliases?&lt;/p&gt;
', 'visualization  tools  tableau', NULL, '''/kk9mm.png'':46C ''/p'':40C,52C,68C ''aggreg'':35C ''alia'':20C ''alias'':4A,67C ''alreadi'':23C ''alt'':47C ''descript'':50C ''easili'':30C ''enter'':48C ''find'':31C ''full'':64C ''get'':13C ''i.stack.imgur.com'':45C ''i.stack.imgur.com/kk9mm.png'':44C ''imag'':49C ''img'':42C ''list'':1A,65C ''name'':21C,39C ''often'':12C ''p'':10C,41C,53C ''place'':57C ''problem'':15C ''releas'':37C ''somewher'':25C ''src'':43C ''tableau'':6A,9B,59C ''tool'':8B ''use'':3A,24C ''variabl'':33C ''view/edit/reset'':63C ''visual'':7B');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5178, 1, '2015-02-13 10:09:25.177', 19, 1394, 'How to deal with version control of large amounts of (binary) data', '&lt;p&gt;I am a PhD student of Geophysics and work with large amounts of image data  (hundreds of GB, tens of thousands of files). I know &lt;code&gt;svn&lt;/code&gt; and &lt;code&gt;git&lt;/code&gt; fairly well and come to value a project history, combined with the ability to easily work together and have protection against disk corruption. I find &lt;code&gt;git&lt;/code&gt; also extremely helpful for having consistent backups but I know that git cannot handle large amounts of binary data efficiently.&lt;/p&gt;

&lt;p&gt;In my masters studies I worked on data sets of similar size (also images) and had a lot of problems keeping track of different version on different servers/devices. Diffing 100GB over the network really isn''t fun, and cost me a lot of time and effort.&lt;/p&gt;

&lt;p&gt;I know that others in science seem to have similar problems, yet I couldn''t find a good solution.&lt;/p&gt;

&lt;p&gt;I want to use the storage facilities of my institute, so I need something that can use a "dumb" server. I also would like to have an additional backup on a portable hard disk, because I would like to avoid transferring hundreds of GB over the network wherever possible. So, I need a tool that can handle more than one remote location.&lt;/p&gt;

&lt;p&gt;Lastly, I really need something that other researcher can use, so it does not need to be super simple, but should be learnable in a few hours.&lt;/p&gt;

&lt;p&gt;I have evaluated a lot of different solutions, but none seem to fit the bill:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://subversion.apache.org/"&gt;svn&lt;/a&gt; is somewhat inefficient and needs a smart server&lt;/li&gt;
&lt;li&gt;hg &lt;a href="http://mercurial.selenic.com/wiki/BigfilesExtension"&gt;bigfile&lt;/a&gt;/&lt;a href="http://mercurial.selenic.com/wiki/LargefilesExtension"&gt;largefile&lt;/a&gt; can only use one remote&lt;/li&gt;
&lt;li&gt;git &lt;a href="https://github.com/beenje/git-bigfile"&gt;bigfile&lt;/a&gt;/&lt;a href="https://github.com/alebedev/git-media"&gt;media&lt;/a&gt; can also use only one remote, but is also not very efficient&lt;/li&gt;
&lt;li&gt;&lt;a href="https://attic-backup.org/"&gt;attic&lt;/a&gt; doesn''t seem to have a log, or diffing capabilities&lt;/li&gt;
&lt;li&gt;&lt;a href="https://bup.github.io/"&gt;bup&lt;/a&gt; looks really good, but needs a "smart" server to work&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I''ve tried &lt;code&gt;git-annex&lt;/code&gt;, which does everything I need it to do (and much more), but it is very difficult to use and not well documented. I''ve used it for several days and couldn''t get my head around it, so I doubt any other coworker would be interested.&lt;/p&gt;

&lt;p&gt;How do researchers deal with large datasets, and what are other research groups using?&lt;/p&gt;

&lt;p&gt;To be clear, I am primarily interested in how other researchers deal with this situation, not just this specific dataset. It seems to me that almost everyone should have this problem, yet I don''t know anyone who has solved it. Should I just keep a backup of the original data and forget all this version control stuff? Is that what everyone else is doing?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6807, 2, '2015-08-17 05:18:34.563', 4, NULL, NULL, '&lt;p&gt;This is a huge topic, so I will just give you a high-level overview and some pointers to more info.&lt;/p&gt;

&lt;p&gt;Yes, there are definitely ways to work with video that are different from working with individual still images.  &lt;/p&gt;

&lt;p&gt;At the simplest level, it could be running an object detector such as HoG (or a sliding-window convnet) on each frame, and then some means of assigning nearby detections in adjacent frames to be the same object, and discarding detections which do not seem to have continuity in time.  Many of the algorithms in this field seem to look at a single frame as a building block to looking at the whole sequence, where perhaps the data from adjacent frames is combined, aggregated, and/or used to disambiguate the current frame. &lt;/p&gt;

&lt;p&gt;Another approach is to first estimate object motion between frames (using optical flow, phase correlation, pyramid block matching or another method) and then treat areas of multiple frames that are collocated after accounting for motion as being the same object.  This is very powerful but limited by the accuracy of the motion estimation.&lt;/p&gt;

&lt;p&gt;In newer research, there is a back-and-forth between finding where things are (detection) and how things move (tracking), where each task can help the other one, for example (Kalal 2010) or (Andriluka 2008), to the point that the two parts of the algorithm are no longer separable.  Kalal''s &lt;a href="http://kahlan.eps.surrey.ac.uk/featurespace/tld/" rel="nofollow"&gt;TLD&lt;/a&gt; algorithm is a recently famous version of this.&lt;/p&gt;

&lt;p&gt;There are also algorithms which work directly in the spatiotemporal (or sometimes just temporal) domain.  An example of purely temporal would be detecting vehicles by the periodicity of variation of their wheel spokes.&lt;/p&gt;

&lt;p&gt;Some of the frequently studied model problems are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;People, vehicles or other objects detection and tracking throughout a video sequence.  For example, using the &lt;a href="http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/" rel="nofollow"&gt;CalTech Pedestrians&lt;/a&gt; dataset or other standard benchmarks.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Hand gesture recognition, either for games, user interface, or sometimes sign language recognition.  Often using depth data, for example Kinect video.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Activity recognition, for example walking vs standing.  Due to the nature of this problem, it is much more common to see pure spatiotemporal algorithms here, for example (Sadanand and Corso 2012).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Simultaneous location and mapping (SLAM).  Often found in robotics, this problem is basically to build a 3D model of the environment from video of a single camera (or stereo or depth video) which is moving around; the environment is often assumed to be static.  This is often done by running some kind of feature detector such as SIFT or SURF, matching up features in successive frames, and then building point clouds from the relative 3D motion implied by features.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Here are some more references:&lt;/p&gt;

&lt;p&gt;Shah, Mubarak, and Ramesh Jain, eds. Motion-based recognition. Vol. 9. Springer Science &amp;amp; Business Media, 2013.&lt;/p&gt;

&lt;p&gt;Turk, Matthew. "Gesture recognition." &lt;a href="http://www.springer.com/us/book/9780387307718" rel="nofollow"&gt;Computer Vision: A Reference Guide&lt;/a&gt; (2014): 346-349.&lt;/p&gt;

&lt;p&gt;Rosenfeld, Azriel, David Doermann, and Daniel DeMenthon, eds. Video mining. Vol. 6. Springer Science &amp;amp; Business Media, 2013.&lt;/p&gt;

&lt;p&gt;Kalal, Zdenek, Krystian Mikolajczyk, and Jiri Matas. "&lt;a href="http://vision.stanford.edu/teaching/cs231b_spring1415/papers/Kalal-PAMI.pdf" rel="nofollow"&gt;Tracking-learning-detection.&lt;/a&gt;" Pattern Analysis and Machine Intelligence, IEEE Transactions on 34.7 (2012): 1409-1422.&lt;/p&gt;

&lt;p&gt;M. Andriluka, S. Roth, B. Schiele. &lt;a href="https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/people-detection-pose-estimation-and-tracking/people-tracking-by-detection-and-people-detection-by-tracking/" rel="nofollow"&gt;People-Tracking-by-Detection and People-Detection-by-Tracking&lt;/a&gt;. Computer Vision and Pattern Recognition (CVPR) 2008&lt;/p&gt;

&lt;p&gt;Sadanand, Sreemanananth, and Jason J. Corso. "Action bank: A high-level representation of activity in video." Computer Vision and Pattern Recognition (CVPR), 2012&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8009, 1, '2015-09-04 21:24:25.313', 2, 430, 'Implementing RMSProp, but finding differences between reference versions', '&lt;p&gt;I am researching to implement RMSProp in a neural network project I am writing.&lt;/p&gt;

&lt;p&gt;I have not found any published paper to refer for a canonical version - I first stumbled across the idea from a &lt;a href="https://www.coursera.org/course/neuralnets" rel="nofollow"&gt;Coursera class presented by Geoffrey Hinton&lt;/a&gt; (lecture 6 I think). I don''t think the approach has ever been formally published, despite many gradient-descent optimisation libraries having an option called "RMSProp". In addition, my searches are showing up a few variations of the original idea, and it is not clear why they differ, or whether there is a clear reason to use one version over another.&lt;/p&gt;

&lt;p&gt;The general idea behind RMSProp is to scale learning rates by a moving average of current gradient magnitude. On each update step, the existing squared gradients are averaged into a running average (which is "decayed" by a factor) and when the network weight params are updated, the updates are divided by the square roots of these averaged squared gradients. This seems to work by stochastically "feeling out" the second order derivatives of the cost function.&lt;/p&gt;

&lt;p&gt;Naively, I would implement this as follows:&lt;/p&gt;

&lt;p&gt;Params:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;$\gamma$ geometric rate for averaging in [0,1]&lt;/li&gt;
&lt;li&gt;$\iota$ numerical stability/smoothing term to prevent divide-by-zero, usually small e.g 1e-6&lt;/li&gt;
&lt;li&gt;$\epsilon$ learning rate&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Terms:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;$W$ network weights&lt;/li&gt;
&lt;li&gt;$\Delta$ gradients of weights i.e. $\frac{\partial E}{\partial W}$ for a specific mini-batch&lt;/li&gt;
&lt;li&gt;$R$ RMSProp matrix of running average squared weights&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Initialise:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;$R \leftarrow 1$ (i.e. all matrix cells set to 1)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;For each mini-batch:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;$R \leftarrow (1-\gamma)R + \gamma \Delta^2$ (element-wise square, not matrix multiply)&lt;/li&gt;
&lt;li&gt;$W = W - \epsilon \frac{\Delta}{\sqrt{R + \iota}}$ (all element-wise)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I have implemented and used a version similar to this before, but that time around I did something different. Instead of updating $R$ with a single $\Delta^2$ from the mini-batch (i.e. gradients summed across the mini-batch, then squared), I summed up each individual example gradient squared from the mini-batch. Reading up on this again, I''m guessing that''s wrong. But it worked reasonably well, better than simple momentum. Probably not a good idea though, because of all those extra element-wise squares and sums needed, it will be less efficient if not required.&lt;/p&gt;

&lt;p&gt;So now I am discovering further variations that seem to work. They  call themselves RMSProp, and none seems to come with much rationale beyond "this works". For example, the Python &lt;code&gt;climin&lt;/code&gt; library &lt;a href="http://climin.readthedocs.org/en/latest/rmsprop.html" rel="nofollow"&gt;seems to implement what I suggest above&lt;/a&gt;, but then suggests a further combination with momentum with the teaser "In some cases, adding a momentum term Î² is beneficial", with a partial explanation about adaptable step rates - I guess I''d need to get more involved in that library before fully understanding what they are. In another example the &lt;a href="http://downhill.readthedocs.org/en/stable/generated/downhill.adaptive.RMSProp.html" rel="nofollow"&gt;downhill library''s RMSProp implementation&lt;/a&gt; combines two moving averages - one is the same as above, but then another, the average of gradients without squaring is also tracked (it is squared and taken away from the average of squared weights).&lt;/p&gt;

&lt;p&gt;I''d really like to understand more about these alternative RMSProp versions. Where have they come from, where is the theory or intuition that suggests the alternative formulations, and why do these libraries use them? Is there any evidence of better performance?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6072, 1, '2015-06-10 18:53:25.013', 0, 127, 'CSV to PKL for Theano', '&lt;p&gt;I am trying to make a pkl file to be loaded into theano from a csv starting point&lt;/p&gt;

&lt;pre&gt;

    import numpy as np
    import csv
    import gzip, cPickle
    from numpy import genfromtxt
    import theano
    import theano.tensor as T

    #Open csv file and read in data
    csvFile = "filename.csv"
    my_data = genfromtxt(csvFile, delimiter='','', skip_header=1)
    data_shape = "There are " + repr(my_data.shape[0]) + " samples of vector length " + repr(my_data.shape[1])

    num_rows = my_data.shape[0] # Number of data samples
    num_cols = my_data.shape[1] # Length of Data Vector

    total_size = (num_cols-1) * num_rows


    data = np.arange(total_size)
    data = data.reshape(num_rows, num_cols-1) # 2D Matrix of data points
    data = data.astype(''float32'')

    label = np.arange(num_rows)
    print label.shape
    #label = label.reshape(num_rows, 1) # 2D Matrix of data points
    label = label.astype(''float32'')

    print data.shape

    #Read through data file, assume label is in last col
    for i in range(my_data.shape[0]):
        label[i] = my_data[i][num_cols-1]

        for j in range(num_cols-1):
            data[i][j] = my_data[i][j]


    #Split data in terms of 70% train, 10% val, 20% test

    train_num = int(num_rows * 0.7)
    val_num = int(num_rows * 0.1)
    test_num = int(num_rows * 0.2)

    DataSetState = "This dataset has " + repr(data.shape[0]) + " samples of length " + repr(data.shape[1]) + ". The number of training examples is " + repr(train_num)
    print DataSetState



    train_set_x = data[:train_num]
    train_set_y = label[:train_num]

    val_set_x = data[train_num+1:train_num+val_num]
    val_set_y = label[train_num+1:train_num+val_num]

    test_set_x = data[train_num+val_num+1:]
    test_set_y = label[train_num+val_num+1:]


    # Divided dataset into 3 parts. split by percentage.

    train_set = train_set_x, train_set_y
    val_set = val_set_x, val_set_y
    test_set = test_set_x, val_set_y


    dataset = [train_set, val_set, test_set]

    f = gzip.open(csvFile+''.pkl.gz'',''wb'')
    cPickle.dump(dataset, f, protocol=2)
    f.close()

&lt;/pre&gt;

&lt;p&gt;When I run the resulting pkl file through Thenao, (as a DBN or SdA) it pretrains just fine, which makes me think the data is stored correctly. &lt;/p&gt;

&lt;p&gt;However when it comes to finetune I get the following error:&lt;/p&gt;

&lt;pre&gt;

        epoch 1, minibatch 2775/2775, validation error 0.000000 %

    Traceback (most recent call last):
      File "SdA_custom.py", line 489, in 
        test_SdA()
      File "SdA_custom.py", line 463, in test_SdA
        test_losses = test_model()
      File "SdA_custom.py", line 321, in test_score
        return [test_score_i(i) for i in xrange(n_test_batches)]

      File "/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py", line 606, in __call__
        storage_map=self.fn.storage_map)
      File "/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py", line 595, in __call__
        outputs = self.fn()
    ValueError: Input dimension mis-match. (input[0].shape[0] = 10, input[1].shape[0] = 3)
    Apply node that caused the error: Elemwise{neq,no_inplace}(argmax, Subtensor{int64:int64:}.0)
    Inputs types: [TensorType(int64, vector), TensorType(int32, vector)]
    Inputs shapes: [(10,), (3,)]
    Inputs strides: [(8,), (4,)]
    Inputs values: [''not shown'', array([0, 0, 0], dtype=int32)]

    Backtrace when the node is created:
      File "/home/dean/Documents/DeepLearningRepo/DeepLearningTutorials-master/code/logistic_sgd.py", line 164, in errors
        return T.mean(T.neq(self.y_pred, y))

    HINT: Use the Theano flag ''exception_verbosity=high'' for a debugprint and storage map footprint of this apply node.

&lt;/pre&gt;

&lt;p&gt;10 is the size of my batch, if I change to a batch size of 1 I get the following:&lt;/p&gt;

&lt;pre&gt;

    ValueError: Input dimension mis-match. (input[0].shape[0] = 1, input[1].shape[0] = 0)

&lt;/pre&gt;

&lt;p&gt;I think I am storing the labels wrong when I make a pkl, but I can''t seem to spot what is happening or why changing the batch alters the error &lt;/p&gt;

&lt;p&gt;Hope you can help!&lt;/p&gt;
', 'machine-learning  csv', NULL, '''+1'':253C,264C,277C,286C ''-1'':101C,114C,167C,174C ''/home/dean/documents/deeplearningrepo/deeplearningtutorials-master/code/logistic_sgd.py'':512C ''/p'':29C,366C,379C,563C,614C,620C ''/pre'':337C,541C,581C ''/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py'':431C,441C ''0'':73C,84C,159C,217C,455C,457C,462C,478C,500C,501C,502C,572C,574C,579C,580C ''0.000000'':387C ''0.1'':204C ''0.2'':210C ''0.7'':198C ''1'':66C,80C,92C,133C,223C,382C,460C,558C,575C,577C ''10'':189C,458C,489C,543C ''164'':514C ''2'':335C ''20'':191C ''2775/2775'':384C ''2d'':115C,134C ''3'':290C,463C,490C ''321'':414C ''4'':494C ''463'':403C ''489'':396C ''595'':443C ''606'':433C ''70'':187C ''8'':493C ''alter'':611C ''appli'':464C,539C ''argmax'':474C ''array'':499C ''assum'':148C ''backtrac'':505C ''batch'':429C,549C,555C,610C ''call'':391C,435C,445C ''caus'':467C ''chang'':552C,608C ''col'':90C,100C,113C,153C,166C,173C ''come'':371C ''correct'':365C ''cpickl'':39C ''cpickle.dump'':331C ''creat'':510C ''csv'':1A,9B,26C,36C,51C ''csvfile'':57C,62C,328C ''data'':56C,60C,67C,87C,95C,104C,108C,118C,120C,137C,146C,163C,175C,179C,183C,238C,250C,272C,362C ''data.astype'':121C ''data.reshape'':109C ''data.shape'':143C,216C,222C ''dataset'':213C,288C,319C,332C ''datasetst'':211C,234C ''dbn'':350C ''debugprint'':532C ''delimit'':63C ''dimens'':450C,567C ''divid'':287C ''dtype'':503C ''elemwis'':470C ''epoch'':381C ''error'':378C,386C,469C,516C,613C ''exampl'':228C ''except'':527C ''f'':326C,333C ''f.close'':336C ''file'':18C,52C,147C,345C,393C,400C,411C,430C,440C,511C ''filename.csv'':58C ''fine'':356C ''finetun'':373C ''flag'':526C ''float32'':122C,141C ''follow'':377C,562C ''footprint'':536C ''genfromtxt'':43C,61C ''get'':375C,560C ''gzip'':38C ''gzip.open'':327C ''happen'':605C ''header'':65C ''help'':619C ''high'':529C ''hint'':522C ''hope'':616C ''howev'':368C ''import'':31C,35C,37C,42C,44C,46C ''inplac'':473C ''input'':449C,454C,459C,479C,487C,491C,495C,566C,571C,576C ''int'':195C,201C,207C ''int32'':485C,504C ''int64'':476C,477C,482C ''j'':169C,177C,181C ''label'':123C,129C,139C,149C,160C,244C,261C,281C,589C ''label.astype'':140C ''label.reshape'':130C ''label.shape'':128C ''last'':152C,392C ''learn'':8B ''length'':77C,93C,220C ''line'':395C,402C,413C,432C,442C,513C ''load'':21C ''loss'':408C ''machin'':7B ''machine-learn'':6B ''make'':15C,358C,593C ''map'':437C,439C,535C ''match'':453C,570C ''matrix'':116C,135C ''minibatch'':383C ''mis'':452C,569C ''mis-match'':451C,568C ''model'':410C ''my_data.shape'':72C,79C,83C,91C,158C ''n'':427C ''neq'':471C ''node'':465C,508C,540C ''np'':34C ''np.arange'':105C,124C ''num'':81C,89C,99C,102C,110C,112C,125C,131C,165C,172C,194C,196C,200C,202C,206C,208C,232C,240C,246C,252C,255C,257C,263C,266C,268C,274C,276C,283C,285C ''number'':85C,225C ''numpi'':32C,41C ''open'':50C ''output'':446C ''p'':10C,338C,367C,542C,582C,615C ''part'':291C ''percentag'':294C ''pkl'':3A,17C,344C,595C ''pkl.gz'':329C ''point'':28C,119C,138C ''pre'':30C,380C,564C ''pretrain'':354C ''print'':127C,142C,233C ''protocol'':334C ''rang'':157C,171C ''read'':54C,144C ''recent'':390C ''repr'':71C,78C,215C,221C,230C ''result'':343C ''return'':418C,517C ''row'':82C,103C,111C,126C,132C,197C,203C,209C ''run'':341C ''sampl'':74C,88C,218C ''score'':417C,420C ''sda'':352C,399C,406C ''sda_custom.py'':394C,401C,412C ''seem'':600C ''self.fn'':447C ''self.fn.storage'':438C ''self.y_pred'':520C ''set'':236C,242C,248C,259C,270C,279C,296C,298C,301C,304C,306C,309C,312C,314C,317C,321C,323C,325C ''shape'':68C,456C,461C,488C,573C,578C ''shown'':498C ''size'':98C,107C,546C,556C ''skip'':64C ''split'':182C,292C ''spot'':602C ''start'':27C ''storag'':436C,534C ''store'':364C,587C ''stride'':492C ''subtensor'':475C ''t.mean'':518C ''t.neq'':519C ''tensortyp'':481C,484C ''term'':185C ''test'':192C,205C,269C,278C,311C,313C,324C,398C,405C,407C,409C,416C,419C,428C ''theano'':5A,23C,45C,525C ''theano.tensor'':47C ''thenao'':347C ''think'':360C,584C ''total'':97C,106C ''traceback'':388C ''train'':188C,193C,227C,231C,235C,239C,241C,245C,251C,254C,262C,265C,273C,282C,295C,297C,300C,320C ''tri'':13C ''type'':480C ''use'':523C ''val'':190C,199C,247C,256C,258C,267C,275C,284C,303C,305C,308C,316C,322C ''valid'':385C ''valu'':496C ''valueerror'':448C,565C ''vector'':76C,96C,483C,486C ''verbos'':528C ''wb'':330C ''wrong'':590C ''x'':237C,249C,271C,299C,307C,315C ''xrang'':426C ''y'':243C,260C,280C,302C,310C,318C,521C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5238, 2, '2015-02-26 12:10:17.283', 0, NULL, NULL, '&lt;p&gt;I think those two maybe help you:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.coursera.org/course/datasci?from_restricted_preview=1&amp;amp;course_id=346&amp;amp;r=https%3A%2F%2Fclass.coursera.org%2Fdatasci-001" rel="nofollow"&gt;Introduction to Data Science&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://dataquest.io/" rel="nofollow"&gt;Dataquest.io&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':22C,31C ''/course/datasci?from_restricted_preview=1&amp;amp;course_id=346&amp;amp;r=https%3a%2f%2fclass.coursera.org%2fdatasci-001'':15C ''/p'':9C,23C,32C ''data'':20C ''dataquest.io'':27C,30C ''help'':7C ''href'':12C,26C ''introduct'':18C ''mayb'':6C ''nofollow'':17C,29C ''p'':1C,10C,24C ''rel'':16C,28C ''scienc'':21C ''think'':3C ''two'':5C ''www.coursera.org'':14C ''www.coursera.org/course/datasci?from_restricted_preview=1&amp;amp;course_id=346&amp;amp;r=https%3a%2f%2fclass.coursera.org%2fdatasci-001'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5179, 2, '2015-02-13 13:38:56.797', 7, NULL, NULL, '&lt;p&gt;I have dealt with similar problems with very large synthetic biology datasets, where we have many, many GB of &lt;a href="http://en.wikipedia.org/wiki/Flow_cytometry"&gt;flow cytometry&lt;/a&gt; data spread across many, many thousands of files, and need to maintain them consistently between collaborating groups at (multiple) different institutions.  &lt;/p&gt;

&lt;p&gt;Typical version control like svn and git is not practical for this circumstance, because it''s just not designed for this type of dataset.  Instead, we have fallen to using "cloud storage" solutions, particularly &lt;a href="https://www.dropbox.com/"&gt;DropBox&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/BitTorrent_Sync"&gt;Bittorrent Sync&lt;/a&gt;.  DropBox has the advantage that it does do at least some primitive logging and version control and manages the servers for you, but the disadvantage that it''s a commercial service, you have to pay for large storage, and you''re putting your unpublished data on a commercial storage; you don''t have to pay much, though, so it''s a viable option.  Bittorrent Sync has a very similar interface, but you run it yourself on your own storage servers and it doesn''t have any version control.  Both of them hurt my programmer soul, but they''re the best solutions my collaborators and I have found so far.&lt;/p&gt;
', NULL, NULL, '''/a'':28C,90C,99C ''/p'':50C,209C ''/wiki/bittorrent_sync'':96C ''/wiki/flow_cytometry'':25C ''across'':31C ''advantag'':103C ''best'':199C ''biolog'':12C ''bittorr'':97C,163C ''circumst'':64C ''cloud'':82C ''collabor'':44C,202C ''commerci'':129C,147C ''consist'':42C ''control'':54C,115C,187C ''cytometri'':27C ''data'':29C,144C ''dataset'':13C,75C ''dealt'':4C ''design'':70C ''differ'':48C ''disadvantag'':124C ''doesn'':182C ''dropbox'':89C,100C ''en.wikipedia.org'':24C,95C ''en.wikipedia.org/wiki/bittorrent_sync'':94C ''en.wikipedia.org/wiki/flow_cytometry'':23C ''fallen'':79C ''far'':208C ''file'':36C ''flow'':26C ''found'':206C ''gb'':19C ''git'':58C ''group'':45C ''href'':22C,87C,93C ''hurt'':191C ''instead'':76C ''institut'':49C ''interfac'':169C ''larg'':10C,136C ''least'':109C ''like'':55C ''log'':112C ''maintain'':40C ''manag'':117C ''mani'':17C,18C,32C,33C ''much'':155C ''multipl'':47C ''need'':38C ''option'':162C ''p'':1C,51C ''particular'':85C ''pay'':134C,154C ''practic'':61C ''primit'':111C ''problem'':7C ''programm'':193C ''put'':141C ''re'':140C,197C ''run'':172C ''server'':119C,179C ''servic'':130C ''similar'':6C,168C ''solut'':84C,200C ''soul'':194C ''spread'':30C ''storag'':83C,137C,148C,178C ''svn'':56C ''sync'':98C,164C ''synthet'':11C ''though'':156C ''thousand'':34C ''type'':73C ''typic'':52C ''unpublish'':143C ''use'':81C ''version'':53C,114C,186C ''viabl'':161C ''www.dropbox.com'':88C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5180, 2, '2015-02-18 14:07:39.487', 1, NULL, NULL, '&lt;p&gt;As there isn''t too much code/context you''re giving us I assume that you''re working with a binary classification problem:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
X = ... # input for classification, shape (n_samples, n_features)
y_pred = rf.predict_proba(X)[:, 1] # index slicing to retrieve a 1d-array of probabilities
y_pred
# array([  0.18, 0.21, 0.1, 0.2, 0.3])

# Now lets see what the 2 biggest probabilities are
np.argsort(y_pred)
# [2, 0, 3, 1, 4] =&amp;gt; lowest probability is at index 2 (the 3rd probability as classified by the RandomForestClassifier)
# Now lets see what the top 5 samples look like (remember, argsort sorts in ascending order, so we take the last 5 indices of argsort):
X[np.argsort(y_pred)[-5:]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now you should see the rows in X with the 5 most confident predictions.&lt;/p&gt;
', NULL, NULL, '''-5'':125C ''/code'':126C ''/p'':24C,143C ''/pre'':127C ''0'':77C ''0.1'':61C ''0.18'':59C ''0.2'':62C ''0.21'':60C ''0.3'':63C ''1'':45C,79C ''1d'':52C ''1d-array'':51C ''2'':69C,76C,87C ''3'':78C ''3rd'':89C ''4'':80C ''5'':102C,117C,139C ''argsort'':107C,120C ''array'':53C,58C ''ascend'':110C ''assum'':14C ''biggest'':70C ''binari'':21C ''classif'':22C,34C ''classifi'':92C ''code'':26C ''code/context'':8C ''confid'':141C ''featur'':39C ''give'':11C ''gt'':81C ''import'':27C ''index'':46C,86C ''indic'':118C ''input'':32C ''isn'':4C ''last'':116C ''let'':65C,97C ''like'':105C ''look'':104C ''lowest'':82C ''much'':7C ''n'':36C,38C ''np'':30C ''np.argsort'':73C,122C ''numpi'':28C ''order'':111C ''p'':1C,128C ''pre'':25C ''pred'':41C,57C,75C,124C ''predict'':142C ''proba'':43C ''probabl'':55C,71C,83C,90C ''problem'':23C ''randomforestclassifi'':95C ''re'':10C,17C ''rememb'':106C ''retriev'':49C ''rf.predict'':42C ''row'':134C ''sampl'':37C,103C ''see'':66C,98C,132C ''shape'':35C ''slice'':47C ''sort'':108C ''take'':114C ''top'':101C ''us'':12C ''work'':18C ''x'':31C,44C,121C,136C ''y'':40C,56C,74C,123C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6229, 2, '2015-06-26 13:33:43.82', 1, NULL, NULL, '&lt;p&gt;This question has already been answered &lt;a href="http://stackoverflow.com/questions/19170130/combining-random-forests-built-with-different-training-sets-in-r"&gt;here&lt;/a&gt;. It addresses the problem in hand. &lt;/p&gt;
', NULL, NULL, '''/a'':14C ''/p'':21C ''/questions/19170130/combining-random-forests-built-with-different-training-sets-in-r'':12C ''address'':16C ''alreadi'':5C ''answer'':7C ''hand'':20C ''href'':9C ''p'':1C ''problem'':18C ''question'':3C ''stackoverflow.com'':11C ''stackoverflow.com/questions/19170130/combining-random-forests-built-with-different-training-sets-in-r'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5181, 2, '2015-02-18 23:14:02.563', 1, NULL, NULL, '&lt;p&gt;Imagine each node in your graph as a user, and each edge as an action such as ''share''. It may also be a bidirectional relationship ''share'' and ''view''.&lt;/p&gt;

&lt;p&gt;Some social scientists and engineers estimate the probability that a message is ''shared'' and ''viewed'' given that a particular user decides to share it. This process is called "information diffusion", "information propagation", "cascading", etc.&lt;/p&gt;

&lt;p&gt;If you are interested in the details of how these calculations are performed, check out these papers:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://cs.stanford.edu/people/jure/pubs/netrate-netsci14.pdf" rel="nofollow"&gt;http://cs.stanford.edu/people/jure/pubs/netrate-netsci14.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://cs.stanford.edu/people/jure/pubs/cascades-www14.pdf" rel="nofollow"&gt;http://cs.stanford.edu/people/jure/pubs/cascades-www14.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://cs.stanford.edu/people/jure/pubs/infopath-wsdm13.pdf" rel="nofollow"&gt;http://cs.stanford.edu/people/jure/pubs/infopath-wsdm13.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Similar topics:
&lt;a href="http://snap.stanford.edu/papers.html" rel="nofollow"&gt;http://snap.stanford.edu/papers.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':30C,66C,85C ''/papers.html'':126C ''/papers.html&lt;/a&gt;&lt;/p&gt;'':131C ''/people/jure/pubs/cascades-www14.pdf'':102C ''/people/jure/pubs/cascades-www14.pdf&lt;/a&gt;&lt;/p&gt;'':107C ''/people/jure/pubs/infopath-wsdm13.pdf'':113C ''/people/jure/pubs/infopath-wsdm13.pdf&lt;/a&gt;&lt;/p&gt;'':118C ''/people/jure/pubs/netrate-netsci14.pdf'':91C ''/people/jure/pubs/netrate-netsci14.pdf&lt;/a&gt;&lt;/p&gt;'':96C ''action'':16C ''also'':22C ''bidirect'':25C ''calcul'':78C ''call'':59C ''cascad'':64C ''check'':81C ''cs.stanford.edu'':90C,95C,101C,106C,112C,117C ''cs.stanford.edu/people/jure/pubs/cascades-www14.pdf'':100C ''cs.stanford.edu/people/jure/pubs/cascades-www14.pdf&lt;/a&gt;&lt;/p&gt;'':105C ''cs.stanford.edu/people/jure/pubs/infopath-wsdm13.pdf'':111C ''cs.stanford.edu/people/jure/pubs/infopath-wsdm13.pdf&lt;/a&gt;&lt;/p&gt;'':116C ''cs.stanford.edu/people/jure/pubs/netrate-netsci14.pdf'':89C ''cs.stanford.edu/people/jure/pubs/netrate-netsci14.pdf&lt;/a&gt;&lt;/p&gt;'':94C ''decid'':52C ''detail'':74C ''diffus'':61C ''edg'':13C ''engin'':36C ''estim'':37C ''etc'':65C ''given'':47C ''graph'':7C ''href'':88C,99C,110C,123C ''imagin'':2C ''inform'':60C,62C ''interest'':71C ''may'':21C ''messag'':42C ''node'':4C ''nofollow'':93C,104C,115C,128C ''p'':1C,31C,67C,86C,97C,108C,119C ''paper'':84C ''particular'':50C ''perform'':80C ''probabl'':39C ''process'':57C ''propag'':63C ''rel'':92C,103C,114C,127C ''relationship'':26C ''scientist'':34C ''share'':19C,27C,44C,54C ''similar'':120C ''snap.stanford.edu'':125C,130C ''snap.stanford.edu/papers.html'':124C ''snap.stanford.edu/papers.html&lt;/a&gt;&lt;/p&gt;'':129C ''social'':33C ''topic'':121C ''user'':10C,51C ''view'':29C,46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5185, 2, '2015-02-19 14:59:05.007', 0, NULL, NULL, '&lt;p&gt;A decaying exponential of the form $e^{-\alpha x}$ (where $x$ is the distance from the observation) is convenient in this situation. It has the nice feature that the weight is equal to $1$ when the observation lies exactly at one of your training points and decays to zero as $x\rightarrow \infty$.&lt;/p&gt;

&lt;p&gt;What you need to decide is the scaling factor, $\alpha$, which can greatly affect your results, since the decaying exponential is nonlinear. If $\alpha$ is too small or large, then all $k$ points will be weighted nearly equally (assuming none of the $k$ points have a distance very close to zero). One approach is to pick a global value of $\alpha$ that is suitable for your data set. Another approach is to compute a new value of $\alpha$ for each set of $k$ neighbors under consideration, which will guarantee variability in the weights (as long as all $k$ neighbors are not equidistant).&lt;/p&gt;
', NULL, NULL, '''/p'':55C,159C ''1'':35C ''affect'':70C ''alpha'':9C,66C,80C,117C,134C ''anoth'':125C ''approach'':109C,126C ''assum'':95C ''close'':105C ''comput'':129C ''consider'':142C ''conveni'':20C ''data'':123C ''decay'':3C,48C,75C ''decid'':61C ''distanc'':15C,103C ''e'':8C ''equal'':33C,94C ''equidist'':158C ''exact'':40C ''exponenti'':4C,76C ''factor'':65C ''featur'':28C ''form'':7C ''global'':114C ''great'':69C ''guarante'':145C ''infti'':54C ''k'':88C,99C,139C,154C ''larg'':85C ''lie'':39C ''long'':151C ''near'':93C ''need'':59C ''neighbor'':140C,155C ''new'':131C ''nice'':27C ''none'':96C ''nonlinear'':78C ''observ'':18C,38C ''one'':42C,108C ''p'':1C,56C ''pick'':112C ''point'':46C,89C,100C ''result'':72C ''rightarrow'':53C ''scale'':64C ''set'':124C,137C ''sinc'':73C ''situat'':23C ''small'':83C ''suitabl'':120C ''train'':45C ''valu'':115C,132C ''variabl'':146C ''weight'':31C,92C,149C ''x'':10C,12C,52C ''zero'':50C,107C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6270, 1, '2015-06-30 03:22:59.463', 0, 78, 'I want to extract name from CV', '&lt;p&gt;I want to extract name from CV. 
I need high level if accuracy more than 95 %. I have started with taking assumptions that it is highly likely to be found in 10% top lines or if not there then in some section similar to Personal details.
Can u plz  provide any help/guidance ?&lt;/p&gt;
', 'text-mining', NULL, '''/p'':64C ''10'':43C ''95'':27C ''accuraci'':24C ''assumpt'':33C ''cv'':7A,18C ''detail'':57C ''extract'':4A,15C ''found'':41C ''help/guidance'':63C ''high'':21C,37C ''level'':22C ''like'':38C ''line'':45C ''mine'':10B ''name'':5A,16C ''need'':20C ''p'':11C ''person'':56C ''plz'':60C ''provid'':61C ''section'':53C ''similar'':54C ''start'':30C ''take'':32C ''text'':9B ''text-min'':8B ''top'':44C ''u'':59C ''want'':2A,13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5204, 1, '2015-02-21 14:02:39.17', 1, 109, 'Matrix factorization for like/dislike/unknown data', '&lt;p&gt;Most literature focus on either explicit rating data or implicit (like/unknown) data. Are there any good publications to handle like/dislike/unknown data? That is, in the data matrix there are three values, and I''d like to recommend from unknown entries.&lt;/p&gt;

&lt;p&gt;And are there any good open source implementations on this?&lt;/p&gt;

&lt;p&gt;Thanks.&lt;/p&gt;
', 'machine-learning  recommendation', NULL, '''/p'':51C,63C,66C ''d'':44C ''data'':5A,18C,22C,31C,36C ''either'':15C ''entri'':50C ''explicit'':16C ''factor'':2A ''focus'':13C ''good'':26C,57C ''handl'':29C ''implement'':60C ''implicit'':20C ''learn'':8B ''like'':45C ''like/dislike/unknown'':4A,30C ''like/unknown'':21C ''literatur'':12C ''machin'':7B ''machine-learn'':6B ''matrix'':1A,37C ''open'':58C ''p'':10C,52C,64C ''public'':27C ''rate'':17C ''recommend'':9B,47C ''sourc'':59C ''thank'':65C ''three'':40C ''unknown'':49C ''valu'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5652, 1, '2015-04-30 10:06:11.107', 1, 49, 'Gradient descent parameter estimation Package for R', '&lt;p&gt;I am looking for a package that does gradient descent parameter estimation in R, maybe with some bootstrapping to get confidence intervals. I wonder if people call it something different here as I get almost nothing on my searches, and the one article I found was from someone who rolled their own. &lt;/p&gt;

&lt;p&gt;It is not that hard to implement, but I would prefer to use something standard.&lt;/p&gt;
', 'r  gradient-descent', NULL, '''/p'':65C,82C ''almost'':47C ''articl'':55C ''bootstrap'':30C ''call'':39C ''confid'':33C ''descent'':2A,11B,22C ''differ'':42C ''estim'':4A,24C ''found'':57C ''get'':32C,46C ''gradient'':1A,10B,21C ''gradient-desc'':9B ''hard'':71C ''implement'':73C ''interv'':34C ''look'':15C ''mayb'':27C ''noth'':48C ''one'':54C ''p'':12C,66C ''packag'':5A,18C ''paramet'':3A,23C ''peopl'':38C ''prefer'':77C ''r'':7A,8B,26C ''roll'':62C ''search'':51C ''someon'':60C ''someth'':41C,80C ''standard'':81C ''use'':79C ''wonder'':36C ''would'':76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5186, 1, '2015-02-19 16:23:15.093', 1, 51, 'Decision Tree Bayes rules / Maximax / Maximin', '&lt;p&gt;I need to draw a decision tree about this subject :&lt;/p&gt;

&lt;p&gt;The research and development manager in an old oil company, which is considering making some changes, lists the following courses of action for the company:&lt;/p&gt;

&lt;p&gt;(i) Adopt a process developed by another oil company. This would cost â¬7 million in royalties and yield a net â¬20 million profit (before paying the royalty).&lt;/p&gt;

&lt;p&gt;(ii) Carry out one or two (not simultaneously) alternative research projects :&lt;/p&gt;

&lt;p&gt;(R1) the more expensive one has a 0.8 chance of success; net profit â¬16 million
and a further â¬6 million in royalties. If it fails there will be a net loss of â¬10 million. &lt;/p&gt;

&lt;p&gt;(R2) the alternative research programme is less expensive but only has a 0.7
chance of success with a net profit of â¬15 million and a further â¬5 million in royalties. If it fails a net loss of â¬6 million will be incurred.&lt;/p&gt;

&lt;p&gt;(iii) Make no changes. After meeting current operating costs the company expects to make a net â¬15 million profit from its existing process.
Failure of one research program would still leave open all remaining courses of action (including the other research programme).&lt;/p&gt;

&lt;p&gt;I need also to indicate the different payoffs. This is what I''ve done so far :
&lt;img src="http://i.stack.imgur.com/BLTJ6.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;I would like to be sure that I''m going in the right direction since I''m a beginner with decision trees. And then I need to decide the best course of action using Bayes, Maximax and Maximin rules.&lt;/p&gt;
', 'statistics', NULL, '''/bltj6.png'':243C ''/p'':19C,46C,77C,90C,129C,176C,221C,249C,290C ''0.7'':143C ''0.8'':99C ''10'':127C ''15'':153C,195C ''16'':106C ''20'':70C ''5'':159C ''6'':112C,171C ''7'':61C ''action'':42C,215C,283C ''adopt'':49C ''also'':225C ''alt'':244C ''altern'':87C,133C ''anoth'':54C ''bay'':3A,285C ''beginn'':269C ''best'':280C ''carri'':80C ''chanc'':100C,144C ''chang'':36C,181C ''compani'':30C,45C,56C,188C ''consid'':33C ''cost'':59C,186C ''cours'':40C,213C,281C ''current'':184C ''decid'':278C ''decis'':1A,14C,271C ''descript'':247C ''develop'':24C,52C ''differ'':229C ''direct'':264C ''done'':236C ''draw'':12C ''enter'':245C ''exist'':200C ''expect'':189C ''expens'':95C,138C ''fail'':118C,165C ''failur'':202C ''far'':238C ''follow'':39C ''go'':260C ''i.stack.imgur.com'':242C ''i.stack.imgur.com/bltj6.png'':241C ''ii'':79C ''iii'':178C ''imag'':246C ''img'':239C ''includ'':216C ''incur'':175C ''indic'':227C ''leav'':209C ''less'':137C ''like'':253C ''list'':37C ''loss'':124C,168C ''m'':259C,267C ''make'':34C,179C,191C ''manag'':25C ''maximax'':5A,286C ''maximin'':6A,288C ''meet'':183C ''million'':62C,71C,107C,113C,128C,154C,160C,172C,196C ''need'':10C,224C,276C ''net'':68C,103C,123C,149C,167C,193C ''oil'':29C,55C ''old'':28C ''one'':82C,96C,204C ''open'':210C ''oper'':185C ''p'':8C,20C,47C,78C,91C,130C,177C,222C,250C ''pay'':74C ''payoff'':230C ''process'':51C,201C ''profit'':72C,104C,150C,197C ''program'':206C ''programm'':135C,220C ''project'':89C ''r1'':92C ''r2'':131C ''remain'':212C ''research'':22C,88C,134C,205C,219C ''right'':263C ''royalti'':64C,76C,115C,162C ''rule'':4A,289C ''simultan'':86C ''sinc'':265C ''src'':240C ''statist'':7B ''still'':208C ''subject'':18C ''success'':102C,146C ''sure'':256C ''tree'':2A,15C,272C ''two'':84C ''use'':284C ''ve'':235C ''would'':58C,207C,252C ''yield'':66C ''â'':60C,69C,105C,111C,126C,152C,158C,170C,194C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5188, 2, '2015-02-19 22:17:45.773', 1, NULL, NULL, '&lt;p&gt;No, you can''t infer that.
I assume you have the same training set with the same predictors (symptoms). The only difference in the training set is the binary class label for each patient.&lt;/p&gt;

&lt;p&gt;The smaller tree just means:
- with the given symptoms, it might be easier to distinguish between the people who have the condition 1 and the ones not having it. (since you have the same certainty with fewer information)
This distinction seems harder with medical condition 2, that''s way you have to consider more symptoms to be pretty sure about your classification. So if condition 2 is a very mild condition where it is hard to diagnose even for an expert if someone has it then it would result in a deep tree.&lt;/p&gt;
', NULL, NULL, '''/p'':36C,131C ''1'':60C ''2'':83C,103C ''assum'':9C ''binari'':30C ''certainti'':72C ''class'':31C ''classif'':99C ''condit'':59C,82C,102C,108C ''consid'':90C ''deep'':129C ''diagnos'':114C ''differ'':23C ''distinct'':77C ''distinguish'':52C ''easier'':50C ''even'':115C ''expert'':118C ''fewer'':74C ''given'':45C ''hard'':112C ''harder'':79C ''infer'':6C ''inform'':75C ''label'':32C ''mean'':42C ''medic'':81C ''might'':48C ''mild'':107C ''one'':63C ''p'':1C,37C ''patient'':35C ''peopl'':55C ''predictor'':19C ''pretti'':95C ''result'':126C ''seem'':78C ''set'':15C,27C ''sinc'':67C ''smaller'':39C ''someon'':120C ''sure'':96C ''symptom'':20C,46C,92C ''train'':14C,26C ''tree'':40C,130C ''way'':86C ''would'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5192, 1, '2015-02-20 12:21:31.56', 2, 84, 'Where can I find resources and papers regarding Data Science in the area of Public Health', '&lt;p&gt;I''m quite new to Data Science, but I would like to do a project to learn more about it.
My subject will be Data Understanding in Public Health.
So I want to do some introductory research to public health.
I would like to visualize some data with the use of a tool like Tableau.&lt;/p&gt;

&lt;p&gt;Which path would you take to develop a good understanding of Data Science? I imagine taking some online courses, eg. Udacity courses on data science, but which courses would you recommend?
Where can I get real data (secondary Dummy Data) to work with?
And are there any good resources on research papers done in Data Science area with the subject of Public Health?&lt;/p&gt;

&lt;p&gt;Any suggestions and comments are welcome.&lt;/p&gt;
', 'visualization  tableau  research', NULL, '''/p'':76C,141C,149C ''area'':13A,134C ''comment'':146C ''cours'':96C,99C,105C ''data'':9A,26C,45C,67C,89C,101C,114C,117C,132C ''develop'':84C ''done'':130C ''dummi'':116C ''eg'':97C ''find'':4A ''get'':112C ''good'':86C,125C ''health'':16A,49C,60C,140C ''imagin'':92C ''introductori'':56C ''learn'':37C ''like'':31C,63C,74C ''m'':22C ''new'':24C ''onlin'':95C ''p'':20C,77C,142C ''paper'':7A,129C ''path'':79C ''project'':35C ''public'':15A,48C,59C,139C ''quit'':23C ''real'':113C ''recommend'':108C ''regard'':8A ''research'':19B,57C,128C ''resourc'':5A,126C ''scienc'':10A,27C,90C,102C,133C ''secondari'':115C ''subject'':42C,137C ''suggest'':144C ''tableau'':18B,75C ''take'':82C,93C ''tool'':73C ''udac'':98C ''understand'':46C,87C ''use'':70C ''visual'':17B,65C ''want'':52C ''welcom'':148C ''work'':119C ''would'':30C,62C,80C,106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5193, 1, '2015-02-20 14:09:23.483', 4, 1645, 'Python or R for implementing machine learning algorithms for fraud detection', '&lt;p&gt;I was wondering which language can I use: R or Python, for my internship in fraud detection in an online banking system: I have to build machine learning algorithms (NN, etc.) that predict transaction frauds.
Thank you very much for your answer.&lt;/p&gt;
', 'machine-learning  r  python', NULL, '''/p'':60C ''algorithm'':8A,46C ''answer'':59C ''bank'':38C ''build'':43C ''detect'':11A,34C ''etc'':48C ''fraud'':10A,33C,52C ''implement'':5A ''internship'':31C ''languag'':22C ''learn'':7A,14B,45C ''machin'':6A,13B,44C ''machine-learn'':12B ''much'':56C ''nn'':47C ''onlin'':37C ''p'':17C ''predict'':50C ''python'':1A,16B,28C ''r'':3A,15B,26C ''system'':39C ''thank'':53C ''transact'':51C ''use'':25C ''wonder'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6281, 1, '2015-06-30 14:48:40.6', 0, 36, 'Is it possible to use central imputation in R for categorical variable?', '&lt;p&gt;Could you please let me know how can i use the centralImputation in R for categorical variable? What value should the empty values should be replaced ideally while handling missing values?&lt;/p&gt;
', 'machine-learning  r  sampling', NULL, '''/p'':50C ''categor'':11A,34C ''central'':6A ''centralimput'':30C ''could'':19C ''empti'':40C ''handl'':47C ''ideal'':45C ''imput'':7A ''know'':24C ''learn'':15B ''let'':22C ''machin'':14B ''machine-learn'':13B ''miss'':48C ''p'':18C ''pleas'':21C ''possibl'':3A ''r'':9A,16B,32C ''replac'':44C ''sampl'':17B ''use'':5A,28C ''valu'':37C,41C,49C ''variabl'':12A,35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5289, 2, '2015-03-06 06:11:10.997', 2, NULL, NULL, '&lt;p&gt;The only such existing "dataset" is a 1-by-1 matrix with an arbitrary value of its sole item.&lt;/p&gt;

&lt;p&gt;If the eigenvectors, being the columns of matrix $V$, are "the same" (precisely up to a scalar constant factor), then matrix $V$ is singular (its columns are linearly dependent; its determinant is zero), and, hence, non-invertible $\Rightarrow$ such an eigendecomposition $V \cdot \Lambda \cdot V^{-1}$ cannot exist.&lt;/p&gt;
', NULL, NULL, '''-1'':11C,70C ''/p'':21C,73C ''1'':9C ''arbitrari'':15C ''cannot'':71C ''cdot'':66C,68C ''column'':28C,48C ''constant'':40C ''dataset'':6C ''depend'':51C ''determin'':53C ''eigendecomposit'':64C ''eigenvector'':25C ''exist'':5C,72C ''factor'':41C ''henc'':57C ''invert'':60C ''item'':20C ''lambda'':67C ''linear'':50C ''matrix'':12C,30C,43C ''non'':59C ''non-invert'':58C ''p'':1C,22C ''precis'':35C ''rightarrow'':61C ''scalar'':39C ''singular'':46C ''sole'':19C ''v'':31C,44C,65C,69C ''valu'':16C ''zero'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5236, 2, '2015-02-26 11:19:35.9', 1, NULL, NULL, '&lt;p&gt;I am not sure there is a clear answer to this, especially as the problem does not seem to be well-defined right now - your "figure" seems to indicate edge weights but you then mention node weights, something significantly different.&lt;/p&gt;

&lt;p&gt;If the question is whether you can find a way to split a graph into two smaller modules, then you might want to look into applying &lt;a href="http://en.wikipedia.org/wiki/Cut_%28graph_theory%29#Sparsest_cut" rel="nofollow"&gt;Sparsest Cut&lt;/a&gt; techniques - a cut with low cost would imply (?) high modularity. I believe these can be easily modified to account for either unlabeled, edge-labeled or node-labeled graphs.&lt;/p&gt;
', NULL, NULL, '''/a'':80C ''/p'':43C,111C ''/wiki/cut_%28graph_theory%29#sparsest_cut'':75C ''account'':99C ''answer'':10C ''appli'':70C ''believ'':92C ''clear'':9C ''cost'':86C ''cut'':79C,83C ''defin'':24C ''differ'':42C ''easili'':96C ''edg'':32C,104C ''edge-label'':103C ''either'':101C ''en.wikipedia.org'':74C ''en.wikipedia.org/wiki/cut_%28graph_theory%29#sparsest_cut'':73C ''especi'':13C ''figur'':28C ''find'':52C ''graph'':58C,110C ''high'':89C ''href'':72C ''impli'':88C ''indic'':31C ''label'':105C,109C ''look'':68C ''low'':85C ''mention'':37C ''might'':65C ''modifi'':97C ''modul'':62C ''modular'':90C ''node'':38C,108C ''node-label'':107C ''nofollow'':77C ''p'':1C,44C ''problem'':16C ''question'':47C ''rel'':76C ''right'':25C ''seem'':19C,29C ''signific'':41C ''smaller'':61C ''someth'':40C ''sparsest'':78C ''split'':56C ''sure'':5C ''techniqu'':81C ''two'':60C ''unlabel'':102C ''want'':66C ''way'':54C ''weight'':33C,39C ''well'':23C ''well-defin'':22C ''whether'':49C ''would'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5196, 1, '2015-02-20 21:05:16.223', 0, 72, 'Scaling thousands of automated forecasts in R', '&lt;p&gt;Hello and thanks in advance!  I''d like some advice on a scalability issue and the best way to resolve.  I''m writing an algorithm in R to produce forecasts for several thousand entities.  One entity takes about 43 seconds to generate a forecast and upload the data to my database.  That equates to about 80+ hours for the entire set of entities and that''s much too long.&lt;/p&gt;

&lt;p&gt;I thought about running several R processes in parallel, possibly many on a few different servers, each performing forecasts for a portion of total entities.  Though that would work, is there a better way?  Can Hadoop help at all?  I have little experience with Hadoop so don''t really know if it can apply.  Thanks again!&lt;/p&gt;
', 'r  scalability  forecast', NULL, '''/p'':81C,139C ''43'':50C ''80'':67C ''advanc'':16C ''advic'':21C ''algorithm'':36C ''appli'':136C ''autom'':4A ''best'':28C ''better'':115C ''d'':18C ''data'':59C ''databas'':62C ''differ'':97C ''entir'':71C ''entiti'':45C,47C,74C,107C ''equat'':64C ''experi'':125C ''forecast'':5A,10B,41C,55C,101C ''generat'':53C ''hadoop'':118C,127C ''hello'':12C ''help'':119C ''hour'':68C ''issu'':25C ''know'':132C ''like'':19C ''littl'':124C ''long'':80C ''m'':33C ''mani'':93C ''much'':78C ''one'':46C ''p'':11C,82C ''parallel'':91C ''perform'':100C ''portion'':104C ''possibl'':92C ''process'':89C ''produc'':40C ''r'':7A,8B,38C,88C ''realli'':131C ''resolv'':31C ''run'':86C ''scalabl'':9B,24C ''scale'':1A ''second'':51C ''server'':98C ''set'':72C ''sever'':43C,87C ''take'':48C ''thank'':14C,137C ''though'':108C ''thought'':84C ''thousand'':2A,44C ''total'':106C ''upload'':57C ''way'':29C,116C ''work'':111C ''would'':110C ''write'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5197, 1, '2015-02-20 21:56:45.69', 1, 121, 'Null-invariant measures of association in R', '&lt;p&gt;A usual way to find association rules in R is the "arules" package, which easily let''s use calculate some rules based on the apriori algorithm. &lt;/p&gt;

&lt;p&gt;However, for the data i''m using, I have a lot of NULL cases (baskets where no product A o B is present). This means that I need to calculate some null-invariant measures (kulcynski, for instance). &lt;/p&gt;

&lt;p&gt;Does anyone know of any package or workable code that let''s me implement this as opposed to writing from scratch the entire algorithm?&lt;/p&gt;
', 'data-mining  r', NULL, '''/p'':40C,80C,106C ''algorithm'':39C,105C ''anyon'':83C ''apriori'':38C ''arul'':25C ''associ'':6A,19C ''b'':62C ''base'':35C ''basket'':56C ''calcul'':32C,71C ''case'':55C ''code'':90C ''data'':10B,45C ''data-min'':9B ''easili'':28C ''entir'':104C ''find'':18C ''howev'':42C ''implement'':95C ''instanc'':79C ''invari'':3A,75C ''know'':84C ''kulcynski'':77C ''let'':29C,92C ''lot'':52C ''m'':47C ''mean'':66C ''measur'':4A,76C ''mine'':11B ''need'':69C ''null'':2A,54C,74C ''null-invari'':1A,73C ''o'':61C ''oppos'':98C ''p'':13C,41C,81C ''packag'':26C,87C ''present'':64C ''product'':59C ''r'':8A,12B,22C ''rule'':20C,34C ''scratch'':102C ''use'':31C,48C ''usual'':15C ''way'':16C ''workabl'':89C ''write'':100C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5198, 1, '2015-02-20 23:16:18.767', 2, 95, 'How to visualize multivariate regression results', '&lt;p&gt;Are there commonly accepted ways to visualize the results of a multivariate regression for a non-quantitative audience? In particular, I''m asking how one should present data on coefficients and T statistics (or p-values) for a regression with around 5 independent variables.&lt;/p&gt;
', 'visualization  regression  linear-regression', NULL, '''/p'':59C ''5'':56C ''accept'':16C ''around'':55C ''ask'':36C ''audienc'':31C ''coeffici'':43C ''common'':15C ''data'':41C ''independ'':57C ''linear'':10B ''linear-regress'':9B ''m'':35C ''multivari'':4A,24C ''non'':29C ''non-quantit'':28C ''one'':38C ''p'':12C,49C ''p-valu'':48C ''particular'':33C ''present'':40C ''quantit'':30C ''regress'':5A,8B,11B,25C,53C ''result'':6A,21C ''statist'':46C ''valu'':50C ''variabl'':58C ''visual'':3A,7B,19C ''way'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5199, 1, '2015-02-21 01:16:34.387', 1, 135, 'What is a good hardware setup for using Python across multiple users', '&lt;p&gt;This question is likely somewhat naive.  I know I (and my colleagues) can install and use Python on local machines. But is that really a best practice?  I have no idea.&lt;/p&gt;

&lt;p&gt;Is there value in setting up a Python "server"?  A box on the network where we develop our data science related Python code.  If so, what are the hardware requirements for such a box?  Do I need to be concerned about any specific packages or conflicts between projects?&lt;/p&gt;
', 'python', NULL, '''/p'':46C,96C ''across'':10A ''best'':40C ''box'':58C,81C ''code'':70C ''colleagu'':26C ''concern'':87C ''conflict'':93C ''data'':66C ''develop'':64C ''good'':4A ''hardwar'':5A,76C ''idea'':45C ''instal'':28C ''know'':22C ''like'':18C ''local'':33C ''machin'':34C ''multipl'':11A ''naiv'':20C ''need'':84C ''network'':61C ''p'':14C,47C ''packag'':91C ''practic'':41C ''project'':95C ''python'':9A,13B,31C,55C,69C ''question'':16C ''realli'':38C ''relat'':68C ''requir'':77C ''scienc'':67C ''server'':56C ''set'':52C ''setup'':6A ''somewhat'':19C ''specif'':90C ''use'':8A,30C ''user'':12A ''valu'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5212, 1, '2015-02-23 12:03:47.773', 1, 169, 'Big data and data mining for CRM?', '&lt;p&gt;We are currently developing a customer relationship management software for SME''s. What I''d like to structure for our future CRM is developing CRM with a social-based approach (Social CRM). Therefore we will provide our users (SME''s) to integrate their CRM into their social network accounts. Also CRM will be enhance intercorporate communication of owner company.&lt;/p&gt;

&lt;p&gt;All these processes I''ve just indicated above will certainly generate lots of unstructured data. &lt;/p&gt;

&lt;p&gt;I am wondering how can we integrate &lt;i&gt;big data&lt;/i&gt; and &lt;i&gt;data-mining&lt;/i&gt; contepts for our project; especially for the datas generated by social network? I am not the expert of these topics but I really want to start from somewhere.&lt;/p&gt;

&lt;h3&gt;Basic capabilities of CRM (Modules)&lt;/h3&gt;

&lt;p&gt;-Contacts: People who you have a business relationship.&lt;/p&gt;

&lt;p&gt;-Accounts: Clients who you''ve done a business before.&lt;/p&gt;

&lt;p&gt;-Leads: Accounts who are your potential customers.&lt;/p&gt;

&lt;p&gt;-Oppurtunites: Any business opportunity for an account or a lead.&lt;/p&gt;

&lt;p&gt;-Sales Orders&lt;/p&gt;

&lt;p&gt;-Calendar&lt;/p&gt;

&lt;p&gt;-Tasks&lt;/p&gt;

&lt;p&gt;What kind of unstructured data or the ways (ideas) could be useful for the modules I''ve just wrote above? If you need more specific information please write in comments.&lt;/p&gt;
', 'data-mining  bigdata  software-development', NULL, '''/h3'':147C ''/i'':105C,111C ''/p'':76C,93C,140C,157C,168C,177C,189C,193C,196C,199C,231C ''account'':65C,159C,171C,185C ''also'':66C ''approach'':46C ''base'':45C ''basic'':142C ''big'':1A,103C ''bigdata'':11B ''busi'':155C,166C,181C ''calendar'':195C ''capabl'':143C ''certain'':87C ''client'':160C ''comment'':230C ''communic'':72C ''compani'':75C ''contact'':149C ''contept'':112C ''could'':210C ''crm'':7A,37C,40C,48C,60C,67C,145C ''current'':18C ''custom'':21C,176C ''d'':30C ''data'':2A,4A,9B,92C,104C,109C,119C,205C ''data-min'':8B,108C ''develop'':14B,19C,39C ''done'':164C ''enhanc'':70C ''especi'':116C ''expert'':128C ''futur'':36C ''generat'':88C,120C ''h3'':141C ''idea'':209C ''indic'':84C ''inform'':226C ''integr'':58C,101C ''intercorpor'':71C ''kind'':202C ''lead'':170C,188C ''like'':31C ''lot'':89C ''manag'':23C ''mine'':5A,10B,110C ''modul'':146C,215C ''need'':223C ''network'':64C,123C ''opportun'':182C ''oppurtunit'':179C ''order'':192C ''owner'':74C ''p'':15C,77C,94C,148C,158C,169C,178C,190C,194C,197C,200C ''peopl'':150C ''pleas'':227C ''potenti'':175C ''process'':80C ''project'':115C ''provid'':52C ''realli'':134C ''relationship'':22C,156C ''sale'':191C ''sme'':26C,55C ''social'':44C,47C,63C,122C ''social-bas'':43C ''softwar'':13B,24C ''software-develop'':12B ''somewher'':139C ''specif'':225C ''start'':137C ''structur'':33C ''task'':198C ''therefor'':49C ''topic'':131C ''unstructur'':91C,204C ''use'':212C ''user'':54C ''ve'':82C,163C,217C ''want'':135C ''way'':208C ''wonder'':97C ''write'':228C ''wrote'':219C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5200, 2, '2015-02-21 10:08:39.98', 6, NULL, NULL, '&lt;p&gt;I would say that it is your call and purely depends on your comfort with (or desire to learn) the language. Both languages have extensive ecosystems of packages/libraries, including some, which could be used for fraud detection. I would consider &lt;strong&gt;anomaly detection&lt;/strong&gt; as the main theme for the topic. Therefore, the following resources illustrate the variety of approaches, methods and tools for the task in each ecosystem.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Python Ecosystem&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;scikit-learn&lt;/code&gt; library: for example, see &lt;a href="http://scikit-learn.org/stable/modules/outlier_detection.html" rel="nofollow"&gt;this page&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;LSAnomaly&lt;/code&gt;, a Python module, improving &lt;code&gt;OneClassSVM&lt;/code&gt; (a drop-in replacement): see &lt;a href="http://www.cit.mak.ac.ug/staff/jquinn/software/lsanomaly.html" rel="nofollow"&gt;this page&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;Skyline&lt;/code&gt;: an open source example of implementation, see &lt;a href="https://github.com/etsy/skyline" rel="nofollow"&gt;its GitHub repo&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;A &lt;a href="http://stackoverflow.com/q/18970171/2872891"&gt;relevant discussion&lt;/a&gt; on &lt;em&gt;StackOverflow&lt;/em&gt;;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://github.com/nicolasmiller/pyculiarity" rel="nofollow"&gt;pyculiarity&lt;/a&gt;, a Python port of Twitter''s AnomalyDetection R Package (as mentioned in 2nd bullet of R Ecosystem below "Twitter''s Anomaly Detection package").&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;R Ecosystem&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://cran.r-project.org/web/views" rel="nofollow"&gt;CRAN Task Views&lt;/a&gt;, in particular, &lt;em&gt;Time Series&lt;/em&gt;, &lt;em&gt;Multivariate&lt;/em&gt; and &lt;em&gt;Machine Learning&lt;/em&gt;;&lt;/li&gt;
&lt;li&gt;Twitter''s &lt;a href="https://github.com/twitter/AnomalyDetection" rel="nofollow"&gt;Anomaly Detection package&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.early-warning-signals.org" rel="nofollow"&gt;Early Warning Signals (EWS) Toolbox&lt;/a&gt;, which includes &lt;code&gt;earlywarnings&lt;/code&gt; &lt;a href="http://cran.r-project.org/web/packages/earlywarnings" rel="nofollow"&gt;package&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://h2o.ai" rel="nofollow"&gt;h2o&lt;/a&gt; machine learning platform (interfaces with R) uses &lt;a href="http://learn.h2o.ai/content/hands-on_training/anomaly_detection.html" rel="nofollow"&gt;deep learning for anomaly detection&lt;/a&gt;.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Additional General Information&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;A &lt;a href="http://www.autonlab.org/tutorials/biosurv01.pdf" rel="nofollow"&gt;set of slides&lt;/a&gt;, mentioning a variety of methods for anomaly detection (AD);&lt;/li&gt;
&lt;li&gt;A &lt;a href="http://stats.stackexchange.com/q/26688/31372"&gt;relevant discussion&lt;/a&gt; on &lt;em&gt;Cross Validated&lt;/em&gt;;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://elki.dbs.ifi.lmu.de" rel="nofollow"&gt;ELKI Data Mining Framework&lt;/a&gt;, implementing a large number of AD (and other) algorithms.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':98C,126C,149C,160C,175C,219C,248C,261C,275C,284C,304C,327C,347C,364C ''/code'':84C,103C,110C,131C,266C ''/content/hands-on_training/anomaly_detection.html'':296C ''/em'':164C,225C,228C,233C,352C ''/etsy/skyline'':143C ''/li'':99C,127C,150C,165C,199C,234C,249C,276C,305C,337C,353C,374C ''/nicolasmiller/pyculiarity'':171C ''/p'':71C,77C,206C,313C ''/q/18970171/2872891'':157C ''/q/26688/31372'':344C ''/stable/modules/outlier_detection.html'':93C ''/staff/jquinn/software/lsanomaly.html'':121C ''/strong'':45C,76C,205C,312C ''/tutorials/biosurv01.pdf'':321C ''/twitter/anomalydetection'':242C ''/ul'':200C,306C,375C ''/web/packages/earlywarnings'':271C ''/web/views'':213C ''2nd'':188C ''ad'':336C,370C ''addit'':309C ''algorithm'':373C ''anomali'':43C,196C,245C,302C,334C ''anomalydetect'':182C ''approach'':61C ''bullet'':189C ''call'':9C ''code'':80C,101C,108C,129C,264C ''comfort'':15C ''consid'':41C ''could'':33C ''cran'':216C ''cran.r-project.org'':212C,270C ''cran.r-project.org/web/packages/earlywarnings'':269C ''cran.r-project.org/web/views'':211C ''cross'':350C ''data'':361C ''deep'':299C ''depend'':12C ''desir'':18C ''detect'':38C,44C,197C,246C,303C,335C ''discuss'':159C,346C ''drop'':113C ''drop-in'':112C ''earli'':256C ''earlywarn'':265C ''ecosystem'':27C,70C,75C,192C,204C ''elki'':360C ''elki.dbs.ifi.lmu.de'':357C ''em'':162C,222C,226C,230C,349C ''ew'':259C ''exampl'':87C,135C ''extens'':26C ''follow'':55C ''framework'':363C ''fraud'':37C ''general'':310C ''github'':147C ''github.com'':142C,170C,241C ''github.com/etsy/skyline'':141C ''github.com/nicolasmiller/pyculiarity'':169C ''github.com/twitter/anomalydetection'':240C ''h2o'':283C ''h2o.ai'':280C ''href'':90C,118C,140C,154C,168C,210C,239C,252C,268C,279C,293C,318C,341C,356C ''illustr'':57C ''implement'':137C,365C ''improv'':107C ''includ'':30C,263C ''inform'':311C ''interfac'':288C ''languag'':22C,24C ''larg'':367C ''learn'':20C,83C,232C,286C,300C ''learn.h2o.ai'':295C ''learn.h2o.ai/content/hands-on_training/anomaly_detection.html'':294C ''li'':79C,100C,128C,151C,166C,208C,235C,250C,277C,315C,338C,354C ''librari'':85C ''lsanomali'':102C ''machin'':231C,285C ''main'':48C ''mention'':186C,328C ''method'':62C,332C ''mine'':362C ''modul'':106C ''multivari'':227C ''nofollow'':95C,123C,145C,173C,215C,244C,255C,273C,282C,298C,323C,359C ''number'':368C ''oneclasssvm'':109C ''open'':133C ''p'':1C,72C,201C,307C ''packag'':184C,198C,247C,274C ''packages/libraries'':29C ''page'':97C,125C ''particular'':221C ''platform'':287C ''port'':178C ''pure'':11C ''pyculiar'':174C ''python'':74C,105C,177C ''r'':183C,191C,203C,290C ''rel'':94C,122C,144C,172C,214C,243C,254C,272C,281C,297C,322C,358C ''relev'':158C,345C ''replac'':115C ''repo'':148C ''resourc'':56C ''say'':4C ''scikit'':82C ''scikit-learn'':81C ''scikit-learn.org'':92C ''scikit-learn.org/stable/modules/outlier_detection.html'':91C ''see'':88C,116C,138C ''seri'':224C ''set'':324C ''signal'':258C ''skylin'':130C ''slide'':326C ''sourc'':134C ''stackoverflow'':163C ''stackoverflow.com'':156C ''stackoverflow.com/q/18970171/2872891'':155C ''stats.stackexchange.com'':343C ''stats.stackexchange.com/q/26688/31372'':342C ''strong'':42C,73C,202C,308C ''task'':67C,217C ''theme'':49C ''therefor'':53C ''time'':223C ''tool'':64C ''toolbox'':260C ''topic'':52C ''twitter'':180C,194C,236C ''ul'':78C,207C,314C ''use'':35C,291C ''valid'':351C ''varieti'':59C,330C ''view'':218C ''warn'':257C ''would'':3C,40C ''www.autonlab.org'':320C ''www.autonlab.org/tutorials/biosurv01.pdf'':319C ''www.cit.mak.ac.ug'':120C ''www.cit.mak.ac.ug/staff/jquinn/software/lsanomaly.html'':119C ''www.early-warning-signals.org'':253C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8705, 1, '2015-11-04 05:18:41.42', 0, 75, 'collaborative filtering using graph and machine learning', '&lt;p&gt;What are the advantages and disadvantages of using &lt;a href="https://en.wikipedia.org/wiki/Collaborative_filtering" rel="nofollow"&gt;Collaborative filtering&lt;/a&gt; based recommendation using machine learning approach and graph based approach ? &lt;/p&gt;

&lt;p&gt;Say I have user purchase data (user_name, user_location, user_company_name, product_name, product_price, product_ingredients) and would like to recommend product for user based on what other user from the same location, company are buying, based on product price, ingredients etc.&lt;/p&gt;

&lt;p&gt;How to decide on which of them is suitable for a given use case?
I would like to evaluate Neo4j (Graph database) and Mahout (Machine learning) for Collaborative filtering.&lt;/p&gt;
', 'machine-learning  graphs  apache-mahout  neo4j', NULL, '''/a'':34C ''/p'':45C,92C,123C ''/wiki/collaborative_filtering'':29C ''advantag'':20C ''apach'':13B ''apache-mahout'':12B ''approach'':40C,44C ''base'':35C,43C,74C,86C ''buy'':85C ''case'':107C ''collabor'':1A,32C,121C ''compani'':58C,83C ''data'':52C ''databas'':115C ''decid'':96C ''disadvantag'':22C ''en.wikipedia.org'':28C ''en.wikipedia.org/wiki/collaborative_filtering'':27C ''etc'':91C ''evalu'':112C ''filter'':2A,33C,122C ''given'':105C ''graph'':4A,11B,42C,114C ''href'':26C ''ingredi'':65C,90C ''learn'':7A,10B,39C,119C ''like'':68C,110C ''locat'':56C,82C ''machin'':6A,9B,38C,118C ''machine-learn'':8B ''mahout'':14B,117C ''name'':54C,59C,61C ''neo4j'':15B,113C ''nofollow'':31C ''p'':16C,46C,93C ''price'':63C,89C ''product'':60C,62C,64C,71C,88C ''purchas'':51C ''recommend'':36C,70C ''rel'':30C ''say'':47C ''suitabl'':102C ''use'':3A,24C,37C,106C ''user'':50C,53C,55C,57C,73C,78C ''would'':67C,109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5201, 2, '2015-02-21 11:59:31.86', 0, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Is installing Python locally a good practice?&lt;/strong&gt;  Yes, if you are going to develop in Python, it is always a good idea to have a local environment where you can break things safely.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is there value in setting up a Python "server"?&lt;/strong&gt;  Yes, but before doing so, be sure to be able to share your code with your colleagues using a &lt;a href="http://en.wikipedia.org/wiki/Revision_control" rel="nofollow"&gt;version control system&lt;/a&gt;.  My reasoning would be that, before you move things to a server, you can move a great deal forward by being able to test several different versions in the local environment mentioned above.  Examples of VCS are &lt;a href="http://git-scm.com" rel="nofollow"&gt;git&lt;/a&gt;, &lt;a href="https://subversion.apache.org" rel="nofollow"&gt;svn&lt;/a&gt;, and for the deep nerds, &lt;a href="http://darcs.net" rel="nofollow"&gt;darcs&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Furthermore, a "Python server" where you can deploy your software once it is integrated into a releasable version is something usually called "&lt;a href="http://en.wikipedia.org/wiki/Staging_site" rel="nofollow"&gt;staging server&lt;/a&gt;".  There is a whole philosophy in software engineering &amp;mdash; &lt;a href="http://en.wikipedia.org/wiki/Continuous_integration" rel="nofollow"&gt;Continuous Integration&lt;/a&gt; &amp;mdash; that advocates staging whatever you have in VCS daily or even on each change.  In the end, this means that some automated program, running on the staging server, checks out your code, sees that it compiles, runs all defined tests and maybe outputs a package with a version number.  Examples of such programs are &lt;a href="http://jenkins-ci.org" rel="nofollow"&gt;Jenkins&lt;/a&gt;, &lt;a href="http://buildbot.net" rel="nofollow"&gt;Buildbot&lt;/a&gt; (this one is Python-specific), and &lt;a href="https://travis-ci.org/recent" rel="nofollow"&gt;Travis&lt;/a&gt; (for cloud-hosted projects).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;What are the hardware requirements for such a box?&lt;/strong&gt; None, as far as I can tell.  Whenever it runs out of disk space, you will have to clean up.  Having more CPU speed and memory will make concurrent builds easier, but there is no real minimum.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Do I need to be concerned about any specific packages or conflicts between projects?&lt;/strong&gt;  Yes, this has been identified as a problem, not only in Python, but in many other systems (see &lt;a href="http://en.wikipedia.org/wiki/Dependency_hell" rel="nofollow"&gt;Dependency hell&lt;/a&gt;).  The established practice is to keep projects isolated from each other as far as their dependencies are concerned.  This means, avoid installing dependencies on the system Python interpreter, even locally; always define a &lt;a href="http://virtualenv.readthedocs.org/en/latest/" rel="nofollow"&gt;virtual environment&lt;/a&gt; and install dependencies there.  Many of the aforementioned CI servers will do that for you anyway.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5202, 2, '2015-02-21 12:19:34.107', 0, NULL, NULL, '&lt;p&gt;This is largely a subjective question.  Trying to list some criteria that seem objective to me:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;the important advantage of Python is that it is a general-purpose language.  If you will need to do anything else than statistics with your program (generate a web interface, integrate it with a reporting system, pass it on to other developers for maintenance) you are far better off with Python.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;the important advantage of R is that it is a specialized language.  If you already know that there is a technique you want to use, and it is not a usual suspect (like NN), then you probably will find a library in CRAN that makes life easier for you.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;And here is another, more subjective advice:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;both languages are not really performance-oriented.  If you need to process large quantities of data, or process very fast, or process in parallel, you will run into trouble ... but it is far easier to run into such trouble with R than with Python.  In my experience, you find the limits of R within some weeks, and the way to push them is quite arcane; while you can use Python for years, never really missing the speed that C developers always mention as the Holy Grail, and even when you do, you can use Cython to make up for the difference.  The only real trouble is concurrency, but for statistics, it is hardly ever an issue.&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5203, 2, '2015-02-21 12:33:53.923', 2, NULL, NULL, '&lt;p&gt;If you''re working with R language, I would suggest first to try use &lt;strong&gt;R ecosystem&lt;/strong&gt;''s abilities to &lt;em&gt;parallelize&lt;/em&gt; the processing, if possible. For example, take a look at packages, mentioned in &lt;a href="http://cran.r-project.org/web/views/HighPerformanceComputing.html" rel="nofollow"&gt;this CRAN Task View&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Alternatively&lt;/strong&gt;, if you''re not comfortable or satisfied with the approaches, implemented by the above-referred packages, you can try some other approaches, such as Hadoop or something else. I think that a &lt;em&gt;Hadoop&lt;/em&gt; solution would be an &lt;strong&gt;overkill&lt;/strong&gt; for such problem, considering the &lt;em&gt;learning curve&lt;/em&gt;, associated with it, as well as the fact that, as far as I understand, Hadoop or other MapReduce frameworks/architectures target &lt;em&gt;long-running processes&lt;/em&gt; (an average task is ~ 2 hours, I read somewhere recently). Hope this helps.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5205, 1, '2015-02-22 11:57:57.84', 0, 427, 'Split columns by finding "Â£" and then converting to minvalue, maxvalue', '&lt;p&gt;I have a pandas dataframe with a salary column which contains values like:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Â£36,000 - Â£40,000 per year plus excellent bene...,
  Â£26,658 to Â£32,547 etc&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I isolated this column and split it with the view to recombining into the data frame later via a column bind in pandas.&lt;/p&gt;

&lt;p&gt;I now have an object with columns like the below. The columns I split the original data frame column I think are blank because I didn''t specify them (I called &lt;code&gt;df[''salary'']=df[''salary''].astype(str).str.split()&lt;/code&gt;
)&lt;/p&gt;

&lt;p&gt;So my new object contains this type of information:&lt;/p&gt;

&lt;p&gt;[Â£26,658, to, Â£32,547],
[Competitive, with, Excellent, Benefits]&lt;/p&gt;

&lt;p&gt;What I want to do is:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Create three columns called minvalue and maxvalue and realvalue&lt;/li&gt;
&lt;li&gt;List items starting with Â£ (something to do with &lt;code&gt;"^Â£"&lt;/code&gt;?&lt;/li&gt;
&lt;li&gt;Take till the end of the items found ignoring the Â£ (get the number out) (something to do with &lt;code&gt;(substr(x,2,nchar(x)))&lt;/code&gt;?&lt;/li&gt;
&lt;li&gt;If there are two such items found, call the first number "minvalue" and call the second number "maxvalue" and put it below the right column. If there is only one value in the row, put it below the realvalue column.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I am very new to pandas and programming in general, but keen on learning, your help would be appreciated. &lt;/p&gt;
', 'dataset  statistics  pandas', NULL, '''/blockquote'':52C ''/code'':118C,176C,204C ''/li'':163C,177C,205C,247C ''/ol'':248C ''/p'':29C,51C,77C,119C,130C,143C,151C,269C ''000'':34C,37C ''2'':201C ''26'':44C,133C ''32'':48C,137C ''36'':33C ''40'':36C ''547'':49C,138C ''658'':45C,134C ''appreci'':268C ''astyp'':115C ''bene'':42C ''benefit'':142C ''bind'':74C ''blank'':101C ''blockquot'':30C ''call'':109C,157C,214C,220C ''code'':110C,174C,198C ''column'':2A,24C,57C,73C,85C,90C,97C,156C,231C,246C ''competit'':139C ''contain'':26C,125C ''convert'':8A ''creat'':154C ''data'':68C,95C ''datafram'':20C ''dataset'':12B ''df'':111C,113C ''didn'':104C ''end'':182C ''etc'':50C ''excel'':41C,141C ''find'':4A ''first'':216C ''found'':186C,213C ''frame'':69C,96C ''general'':259C ''get'':190C ''help'':265C ''ignor'':187C ''inform'':129C ''isol'':55C ''item'':166C,185C,212C ''keen'':261C ''later'':70C ''learn'':263C ''li'':153C,164C,178C,206C ''like'':28C,86C ''list'':165C ''maxvalu'':11A,160C,224C ''minvalu'':10A,158C,218C ''nchar'':202C ''new'':123C,253C ''number'':192C,217C,223C ''object'':83C,124C ''ol'':152C ''one'':236C ''origin'':94C ''p'':15C,31C,53C,78C,120C,131C,144C,249C ''panda'':14B,19C,76C,255C ''per'':38C ''plus'':40C ''program'':257C ''put'':226C,241C ''realvalu'':162C,245C ''recombin'':65C ''right'':230C ''row'':240C ''salari'':23C,112C,114C ''second'':222C ''someth'':170C,194C ''specifi'':106C ''split'':1A,59C,92C ''start'':167C ''statist'':13B ''str'':116C ''str.split'':117C ''substr'':199C ''take'':179C ''think'':99C ''three'':155C ''till'':180C ''two'':210C ''type'':127C ''valu'':27C,237C ''via'':71C ''view'':63C ''want'':147C ''would'':266C ''x'':200C,203C ''year'':39C ''â'':5A,32C,35C,43C,47C,132C,136C,169C,175C,189C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5206, 2, '2015-02-22 13:14:36.2', 3, NULL, NULL, '&lt;p&gt;This is more of a general regex question, rather than pandas specific.&lt;/p&gt;

&lt;p&gt;I would first create a function that extracts the numbers you need from strings, and then use the &lt;code&gt;pandas.DataFrame.apply&lt;/code&gt; function to apply it on the pandas column containing the strings. Here is what I would do:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import re
def parseNumbers(salary_txt):
    return [int(item.replace('','','''')) for item in re.findall(''Â£([\d,]+)'',salary_txt)]

#testing if this works
testcases = [''Â£23,000 to Â£100,000'',''Â£34,000'',''Â£10000'']
for testcase in testcases:
    print testcase,parseNumbers(testcase)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here, I just used &lt;code&gt;re.findall&lt;/code&gt;, which finds all patterns that look like &lt;code&gt;Â£([\d,]+)&lt;/code&gt;. This is anything that starts with Â£ and is followed by an arbitrary sequence of digits and commas. The parenthesis tells python to extract only the bit after the Â£ sign. The last thing I do is I remove commas, and parse the remaining string into an integer. You could be more elegant about this I guess, but it works.&lt;/p&gt;

&lt;h1&gt;using this function in pandas&lt;/h1&gt;

&lt;pre&gt;&lt;code&gt;df[''salary_list''] = df[''salary''].apply(parseNumbers)
df[''minsalary''] = df[''salary''].apply(parseNumbers).apply(min)
df[''maxsalary''] = df[''salary''].apply(parseNumbers).apply(max)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Checking if this all works:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas
df = pandas.DataFrame(testcases,columns = [''salary''])
df[''minsalary''] = df[''salary''].apply(parseNumbers).apply(min)
df[''maxsalary''] = df[''salary''].apply(parseNumbers).apply(max)
df

    salary  minsalary   maxsalary
0   Â£23,000 to Â£100,000 23000   100000
1   Â£34,000 34000   34000
2   Â£10000  10000   10000
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The advantages of moving the parsing logic to a separate function is that:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;it may be reusable in other code&lt;/li&gt;
&lt;li&gt;it is easier to read for others, even if they aren''t pandas experts&lt;/li&gt;
&lt;li&gt;it''s easier to develop and test the parsing functionality in isolation&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/code'':36C,99C,108C,119C,213C,272C ''/h1'':187C ''/li'':298C,314C,328C ''/ol'':329C ''/p'':14C,54C,180C,221C,288C ''/pre'':100C,214C,273C ''0'':251C ''000'':81C,85C,88C,254C,258C,264C ''1'':261C ''100'':84C,257C ''10000'':90C,269C,270C,271C ''100000'':260C ''2'':267C ''23'':80C,253C ''23000'':259C ''34'':87C,263C ''34000'':265C,266C ''advantag'':276C ''anyth'':122C ''appli'':39C,195C,201C,203C,209C,211C,235C,237C,243C,245C ''arbitrari'':132C ''aren'':310C ''bit'':146C ''check'':216C ''code'':34C,56C,106C,116C,189C,223C,297C ''column'':44C,229C ''comma'':137C,159C ''contain'':45C ''could'':169C ''creat'':19C ''d'':71C,118C ''def'':59C ''develop'':320C ''df'':190C,193C,197C,199C,205C,207C,226C,231C,233C,239C,241C,247C ''digit'':135C ''easier'':302C,318C ''eleg'':172C ''even'':307C ''expert'':313C ''extract'':23C,143C ''find'':110C ''first'':18C ''follow'':129C ''function'':21C,37C,184C,285C,325C ''general'':7C ''guess'':176C ''h1'':181C ''import'':57C,224C ''int'':64C ''integ'':167C ''isol'':327C ''item'':67C ''item.replace'':65C ''last'':152C ''li'':290C,299C,315C ''like'':115C ''list'':192C ''logic'':281C ''look'':114C ''max'':212C,246C ''maxsalari'':206C,240C,250C ''may'':292C ''min'':204C,238C ''minsalari'':198C,232C,249C ''move'':278C ''need'':27C ''number'':25C ''ol'':289C ''other'':306C ''p'':1C,15C,101C,215C,274C ''panda'':12C,43C,186C,225C,312C ''pandas.dataframe'':227C ''pandas.dataframe.apply'':35C ''parenthesi'':139C ''pars'':161C,280C,324C ''parsenumb'':60C,97C,196C,202C,210C,236C,244C ''pattern'':112C ''pre'':55C,188C,222C ''print'':95C ''python'':141C ''question'':9C ''rather'':10C ''re'':58C ''re.findall'':69C,107C ''read'':304C ''regex'':8C ''remain'':163C ''remov'':158C ''return'':63C ''reusabl'':294C ''salari'':61C,72C,191C,194C,200C,208C,230C,234C,242C,248C ''separ'':284C ''sequenc'':133C ''sign'':150C ''specif'':13C ''start'':124C ''string'':29C,47C,164C ''tell'':140C ''test'':74C,322C ''testcas'':78C,92C,94C,96C,98C,228C ''thing'':153C ''txt'':62C,73C ''use'':32C,105C,182C ''work'':77C,179C,220C ''would'':17C,52C ''â'':70C,79C,83C,86C,89C,117C,126C,149C,252C,256C,262C,268C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5207, 2, '2015-02-22 15:44:39.55', 0, NULL, NULL, '&lt;p&gt;The goal of my analysis is separate from this question. I am trying to figure out all the conclusions I can draw or suggest from my analysis. Yes, I am interested in saying something about the complexity of condition A to B. 
When is a condition complex ? When there are many symptoms as the disease is diagnosed by the symptoms.
If it''s hard to diagnose ? YES
if the symptoms are severe ? NO
can s.o. have both conditions ? NO&lt;/p&gt;
', NULL, NULL, '''/p'':81C ''analysi'':6C,28C ''b'':43C ''complex'':38C,48C ''conclus'':20C ''condit'':40C,47C,79C ''diagnos'':58C,67C ''diseas'':56C ''draw'':23C ''figur'':16C ''goal'':3C ''hard'':65C ''interest'':32C ''mani'':52C ''p'':1C ''question'':11C ''s.o'':76C ''say'':34C ''separ'':8C ''sever'':73C ''someth'':35C ''suggest'':25C ''symptom'':53C,61C,71C ''tri'':14C ''yes'':29C,68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5208, 1, '2015-02-22 20:30:30.63', 1, 46, 'Splitting binary classification into smaller susbsets', '&lt;p&gt;As an example. If you are tying to classify humans from dogs. Is it possible to approach this problem by classifying different kinds of animals (birds, fish, reptiles, mammals, ...) or even smaller subsets (dogs, cats, whales, lions, ...) &lt;/p&gt;

&lt;p&gt;Then when you try to classify a new data set, anything that did not fall into one of those classes can be considered a human.&lt;/p&gt;

&lt;p&gt;If this is possible, are there any benefits into breaking a binary class problem into several classes (or perhaps labels)? &lt;/p&gt;

&lt;p&gt;Benefits I am looking into are: accuracy/precision of the classifier, parallel learning.&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/p'':49C,76C,98C,112C ''accuracy/precision'':106C ''anim'':36C ''anyth'':61C ''approach'':28C ''benefit'':85C,100C ''binari'':2A,89C ''bird'':37C ''break'':87C ''cat'':46C ''class'':70C,90C,94C ''classif'':3A,10B ''classifi'':20C,32C,56C,109C ''consid'':73C ''data'':59C ''differ'':33C ''dog'':23C,45C ''even'':42C ''exampl'':14C ''fall'':65C ''fish'':38C ''human'':21C,75C ''kind'':34C ''label'':97C ''learn'':9B,111C ''lion'':48C ''look'':103C ''machin'':8B ''machine-learn'':7B ''mammal'':40C ''new'':58C ''one'':67C ''p'':11C,50C,77C,99C ''parallel'':110C ''perhap'':96C ''possibl'':26C,81C ''problem'':30C,91C ''reptil'':39C ''set'':60C ''sever'':93C ''smaller'':5A,43C ''split'':1A ''subset'':44C ''susbset'':6A ''tie'':18C ''tri'':54C ''whale'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5209, 1, '2015-02-23 08:00:03.36', 4, 188, 'Accuracy of Stanford NER', '&lt;p&gt;I am performing Named Entity Recognition using Stanford NER. I have successfully trained and tested my model. Now I want to know:&lt;/p&gt;

&lt;p&gt;1) What is the general way of measuring accuracy of NER model ?? For example what techniques or approaches are used ??&lt;/p&gt;

&lt;p&gt;2) Is there any built-in method in STANFORD NER for evaluating the accuracy ??&lt;/p&gt;
', 'nlp  performance', NULL, '''/p'':30C,52C,69C ''1'':32C ''2'':54C ''accuraci'':1A,40C,68C ''approach'':49C ''built'':59C ''built-in'':58C ''entiti'':12C ''evalu'':66C ''exampl'':45C ''general'':36C ''know'':29C ''measur'':39C ''method'':61C ''model'':24C,43C ''name'':11C ''ner'':4A,16C,42C,64C ''nlp'':5B ''p'':7C,31C,53C ''perform'':6B,10C ''recognit'':13C ''stanford'':3A,15C,63C ''success'':19C ''techniqu'':47C ''test'':22C ''train'':20C ''use'':14C,51C ''want'':27C ''way'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5210, 2, '2015-02-23 08:17:30.12', 2, NULL, NULL, '&lt;p&gt;I personally like dotcharts of standardized regression coefficients, possibly with standard error bars to denote uncertainty. Make sure to standardize coefficients (and SEs!) appropriately so they "mean" something to your non-quantitative audience: "As you see, an increase of 1 unit in Z is associated with an increase of 0.3 units in X."&lt;/p&gt;

&lt;p&gt;In R (without standardization):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;set.seed(1)
foo &amp;lt;- data.frame(X=rnorm(30),Y=rnorm(30),Z=rnorm(30))
model &amp;lt;- lm(X~Y+Z,foo)

coefs &amp;lt;- coefficients(model)
std.errs &amp;lt;- summary(model)$coefficients[,2]

dotchart(coefs,pch=19,xlim=range(c(coefs+std.errs,coefs-std.errs)))
lines(rbind(coefs+std.errs,coefs-std.errs,NA),rbind(1:3,1:3,NA))
abline(v=0,lty=2)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/0AwtB.png" alt="regression visualization"&gt;&lt;/p&gt;
', NULL, NULL, '''/0awtb.png'':130C ''/code'':123C ''/p'':56C,62C,134C ''/pre'':124C ''0'':120C ''0.3'':52C ''1'':42C,66C,113C,115C ''19'':99C ''2'':95C,122C ''3'':114C,116C ''30'':72C,75C,78C ''ablin'':118C ''alt'':131C ''appropri'':25C ''associ'':47C ''audienc'':35C ''bar'':14C ''c'':102C ''code'':64C ''coef'':86C,97C,103C,108C ''coeffici'':9C,22C,88C,94C ''coefs-std.errs'':105C,110C ''data.frame'':69C ''denot'':16C ''dotchart'':5C,96C ''error'':13C ''foo'':67C,85C ''i.stack.imgur.com'':129C ''i.stack.imgur.com/0awtb.png'':128C ''img'':126C ''increas'':40C,50C ''like'':4C ''line'':106C ''lm'':81C ''lt'':68C,80C,87C,91C ''lti'':121C ''make'':18C ''mean'':28C ''model'':79C,89C,93C ''na'':111C,117C ''non'':33C ''non-quantit'':32C ''p'':1C,57C,125C ''pch'':98C ''person'':3C ''possibl'':10C ''pre'':63C ''quantit'':34C ''r'':59C ''rang'':101C ''rbind'':107C,112C ''regress'':8C,132C ''rnorm'':71C,74C,77C ''see'':38C ''ses'':24C ''set.seed'':65C ''someth'':29C ''src'':127C ''standard'':7C,12C,21C,61C ''std.errs'':90C,104C,109C ''summari'':92C ''sure'':19C ''uncertainti'':17C ''unit'':43C,53C ''v'':119C ''visual'':133C ''without'':60C ''x'':55C,70C,82C ''xlim'':100C ''y'':73C ''z'':45C,76C,84C ''~y'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8030, 2, '2015-09-07 16:18:51.34', 2, NULL, NULL, '&lt;p&gt;"Reinforcement Learning" means the result of the learning algorithm is a policy; a function that takes a set of inputs and returns a decision. "Supervised Learning", in contrast, learns a function that returns a prediction. They are different types of task.&lt;br&gt;
Multi-Layer Perceptron and Support Vector Machine are architectures i.e. forms for the learnt function in either case.&lt;br&gt;
There is no reason not to try different architectures on different tasks.&lt;/p&gt;
', NULL, NULL, '''/p'':76C ''algorithm'':10C ''architectur'':53C,72C ''br'':43C,63C ''case'':62C ''contrast'':29C ''decis'':25C ''differ'':39C,71C,74C ''either'':61C ''form'':55C ''function'':15C,32C,59C ''i.e'':54C ''input'':21C ''layer'':46C ''learn'':3C,9C,27C,30C ''learnt'':58C ''machin'':51C ''mean'':4C ''multi'':45C ''multi-lay'':44C ''p'':1C ''perceptron'':47C ''polici'':13C ''predict'':36C ''reason'':67C ''reinforc'':2C ''result'':6C ''return'':23C,34C ''set'':19C ''supervis'':26C ''support'':49C ''take'':17C ''task'':42C,75C ''tri'':70C ''type'':40C ''vector'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6213, 1, '2015-06-25 12:50:03.597', 2, 31, 'How can I show the relations between travel destinations?', '&lt;p&gt;I''m trying to do a project about email marketing. I''m working on a tourism company and I want to make a best destination suggestion for the clients. But I need to see the relations between destinations.&lt;/p&gt;

&lt;p&gt;Example: How many people visited Dublin and then visited London?&lt;/p&gt;

&lt;p&gt;My question: How can I best analyse this relation between the cities, given data about traveler itineraries?&lt;/p&gt;

&lt;p&gt;I want to send email offers to clients who went to London and didn''t go to Dublin (assuming a strong relation between London and Dublin).&lt;/p&gt;
', 'statistics', NULL, '''/p'':50C,62C,81C,109C ''analys'':70C ''assum'':101C ''best'':35C,69C ''citi'':75C ''client'':40C,90C ''compani'':28C ''data'':77C ''destin'':9A,36C,49C ''didn'':96C ''dublin'':57C,100C,108C ''email'':20C,87C ''exampl'':52C ''given'':76C ''go'':98C ''itinerari'':80C ''london'':61C,94C,106C ''m'':13C,23C ''make'':33C ''mani'':54C ''market'':21C ''need'':43C ''offer'':88C ''p'':11C,51C,63C,82C ''peopl'':55C ''project'':18C ''question'':65C ''relat'':6A,47C,72C,104C ''see'':45C ''send'':86C ''show'':4A ''statist'':10B ''strong'':103C ''suggest'':37C ''tourism'':27C ''travel'':8A,79C ''tri'':14C ''visit'':56C,60C ''want'':31C,84C ''went'':92C ''work'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5221, 2, '2015-02-24 04:07:29.86', 0, NULL, NULL, '&lt;p&gt;My advice is to begin with a thorough grounding in Statistics, for which a lot of classic and unambiguous material is available online. Topics that you should have a firm grasp on include regression, correlation, hypothesis testing and the bias-variance tradeoff. You don''t have to go into too much theoretical depth into any of these topics but you should know what these are before you start studying machine learning. Your study in machine learning should include developing an understanding of hypothesis spaces, Bayesian analysis (can you explain the difference between MLE, MAP and optimal Bayes?), Expectation Maximization, logistic regression, clustering (especially k-means), max-margin classifiers (SVMs), overfitting (can you explain what it is in terms of bias and variance?) and feature selection. Linear algebra is helpful but can be done without in many cases.   &lt;/p&gt;

&lt;p&gt;As someone who does research in data mining and has worked closely with several companies, I can tell you that if you seriously want to go into machine learning, it is NOT enough to simply know how to code something using Weka or R. Those are easy enough to use once you know the concepts. When companies hire data scientists, they want someone who can take the raw data and do something useful with it. A good grasp of fundamentals is obviously essential, since each company''s data has its own quirks (and will typically be too big for you to try ''everything''). Good luck!&lt;/p&gt;
', NULL, NULL, '''/p'':142C,249C ''advic'':3C ''algebra'':131C ''analysi'':88C ''avail'':23C ''bay'':99C ''bayesian'':87C ''begin'':6C ''bias'':42C,124C ''bias-vari'':41C ''big'':241C ''case'':141C ''classic'':18C ''classifi'':112C ''close'':155C ''cluster'':104C ''code'':182C ''compani'':158C,200C,229C ''concept'':198C ''correl'':36C ''data'':150C,202C,212C,231C ''depth'':55C ''develop'':81C ''differ'':93C ''done'':137C ''easi'':190C ''enough'':176C,191C ''especi'':105C ''essenti'':226C ''everyth'':246C ''expect'':100C ''explain'':91C,117C ''featur'':128C ''firm'':31C ''fundament'':223C ''go'':50C,169C ''good'':220C,247C ''grasp'':32C,221C ''ground'':10C ''help'':133C ''hire'':201C ''hypothesi'':37C,85C ''includ'':34C,80C ''k'':107C ''k-mean'':106C ''know'':64C,179C,196C ''learn'':73C,78C,172C ''linear'':130C ''logist'':102C ''lot'':16C ''luck'':248C ''machin'':72C,77C,171C ''mani'':140C ''map'':96C ''margin'':111C ''materi'':21C ''max'':110C ''max-margin'':109C ''maxim'':101C ''mean'':108C ''mine'':151C ''mle'':95C ''much'':53C ''obvious'':225C ''onlin'':24C ''optim'':98C ''overfit'':114C ''p'':1C,143C ''quirk'':235C ''r'':187C ''raw'':211C ''regress'':35C,103C ''research'':148C ''scientist'':203C ''select'':129C ''serious'':166C ''sever'':157C ''simpli'':178C ''sinc'':227C ''someon'':145C,206C ''someth'':183C,215C ''space'':86C ''start'':70C ''statist'':12C ''studi'':71C,75C ''svms'':113C ''take'':209C ''tell'':161C ''term'':122C ''test'':38C ''theoret'':54C ''thorough'':9C ''topic'':25C,60C ''tradeoff'':44C ''tri'':245C ''typic'':238C ''unambigu'':20C ''understand'':83C ''use'':184C,193C,216C ''varianc'':43C,126C ''want'':167C,205C ''weka'':185C ''without'':138C ''work'':154C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5211, 2, '2015-02-23 11:43:11.833', 1, NULL, NULL, '&lt;p&gt;If you try to get the best accuracy, etc... for a given question you should always learn on a training set that is labeled exactly according to your questions. You shouldn''t expect to get better results if you are using more granular class labels. The classifier then would then try to pick up the differences in the classes and try to separate them apart. Since in practice your variables in the training set will not perfectly explain the more granular classification question you shouldn''t expect to get a better answer for your less granular classification problem.&lt;/p&gt;

&lt;p&gt;If you are not happy with the accuracy of your model you try the following instead:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;review the explanatory variables. Think about what might influence the classification problem. Maybe there us a clever way to construct new variables (from your existing ones) that helps. It''s nowpossible to give a general advise on that since you have to consider the properties of your classifier&lt;/li&gt;
&lt;li&gt;if your class distribution is very skewed you might consider over/undersampling &lt;/li&gt;
&lt;li&gt;you might run more different classifiers and then classify based on the majority vote. Note that you will most likely sacrifice explainability of your model.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Also you seem to have some missunderstanding, when you write ''you would assign it to human if it doesn''t fall into any of the granular classes''. Note that you always try to pick class labels covering the whole universe (all possible classes). This can be always defined as the complement of the other classes. Also you will have to have instances for each class in your training set.&lt;/p&gt;
', NULL, NULL, '''/li'':169C,182C,209C ''/ol'':210C ''/p'':100C,118C,281C ''accord'':27C ''accuraci'':9C,109C ''advis'':156C ''also'':212C,267C ''alway'':17C,242C,258C ''answer'':93C ''apart'':66C ''assign'':224C ''base'':193C ''best'':8C ''better'':37C,92C ''class'':45C,60C,173C,238C,246C,254C,266C,276C ''classif'':83C,98C,131C ''classifi'':48C,168C,189C,192C ''clever'':137C ''complement'':262C ''consid'':163C,180C ''construct'':140C ''cover'':248C ''defin'':259C ''differ'':57C,188C ''distribut'':174C ''doesn'':230C ''etc'':10C ''exact'':26C ''exist'':145C ''expect'':34C,88C ''explain'':79C,205C ''explanatori'':123C ''fall'':232C ''follow'':116C ''general'':155C ''get'':6C,36C,90C ''give'':153C ''given'':13C ''granular'':44C,82C,97C,237C ''happi'':106C ''help'':148C ''human'':227C ''influenc'':129C ''instanc'':273C ''instead'':117C ''label'':25C,46C,247C ''learn'':18C ''less'':96C ''li'':120C,170C,183C ''like'':203C ''major'':196C ''mayb'':133C ''might'':128C,179C,185C ''missunderstand'':218C ''model'':112C,208C ''new'':141C ''note'':198C,239C ''nowposs'':151C ''ol'':119C ''one'':146C ''over/undersampling'':181C ''p'':1C,101C,211C ''perfect'':78C ''pick'':54C,245C ''possibl'':253C ''practic'':69C ''problem'':99C,132C ''properti'':165C ''question'':14C,30C,84C ''result'':38C ''review'':121C ''run'':186C ''sacrific'':204C ''seem'':214C ''separ'':64C ''set'':22C,75C,280C ''shouldn'':32C,86C ''sinc'':67C,159C ''skew'':177C ''think'':125C ''train'':21C,74C,279C ''tri'':4C,52C,62C,114C,243C ''univers'':251C ''us'':135C ''use'':42C ''variabl'':71C,124C,142C ''vote'':197C ''way'':138C ''whole'':250C ''would'':50C,223C ''write'':221C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5214, 1, '2015-02-23 15:45:21.7', 0, 44, 'Going from report to feature matrix', '&lt;p&gt;I am starting to play around in datamining / machine learning and I am stuck on a problem that''s probably easy.&lt;/p&gt;

&lt;p&gt;So I have a report that lists the url and the number of visits a person did. So a combination of ip and url result in an amount of visits.&lt;/p&gt;

&lt;p&gt;Now I want to run the k-means clustering algorithm on this so I thought I could approach it like this:&lt;/p&gt;

&lt;p&gt;This is my data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;url      ip    visits

abc.be   123   5
abc.be/a 123   2
abc.be/b 123   2
abc.be/b 321   4
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And I would turn in into a feature vector/matrix like so:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;abc.be  abc.be/a   abc.be/b   impressions
   1       0          0          5
   0       1          0          2
   0       0          1          2
   0       0          1          4
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But I am stuck on how to transform my data set to a feature matrix. Any help would be appreciated.&lt;/p&gt;
', 'feature-extraction  k-means', NULL, '''/a'':108C,141C ''/b'':113C,118C,144C ''/code'':121C,162C ''/p'':35C,67C,91C,97C,135C,185C ''/pre'':122C,163C ''0'':147C,148C,150C,152C,154C,155C,158C,159C ''1'':146C,151C,156C,160C ''123'':104C,109C,114C ''2'':110C,115C,153C,157C ''321'':119C ''4'':120C,161C ''5'':105C,149C ''abc.be'':103C,107C,112C,117C,138C,140C,143C ''abc.be/a'':106C,139C ''abc.be/b'':111C,116C,142C ''algorithm'':79C ''amount'':64C ''appreci'':184C ''approach'':87C ''around'':19C ''cluster'':78C ''code'':99C,137C ''combin'':56C ''could'':86C ''data'':96C,174C ''datamin'':21C ''easi'':34C ''extract'':9B ''featur'':5A,8B,131C,178C ''feature-extract'':7B ''go'':1A ''help'':181C ''impress'':145C ''ip'':58C,101C ''k'':11B,76C ''k-mean'':10B,75C ''learn'':23C ''like'':89C,133C ''list'':43C ''machin'':22C ''matrix'':6A,179C ''mean'':12B,77C ''number'':48C ''p'':13C,36C,68C,92C,123C,164C ''person'':52C ''play'':18C ''pre'':98C,136C ''probabl'':33C ''problem'':30C ''report'':3A,41C ''result'':61C ''run'':73C ''set'':175C ''start'':16C ''stuck'':27C,168C ''thought'':84C ''transform'':172C ''turn'':127C ''url'':45C,60C,100C ''vector/matrix'':132C ''visit'':50C,66C,102C ''want'':71C ''would'':126C,182C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5215, 2, '2015-02-23 21:57:39.677', 0, NULL, NULL, '&lt;p&gt;You can check the data types of your columns by doing df.dtypes, and if ''salary'' isn''t a string, you can convert it using df[''salary''] = df[''salary''].astype(str). This is what you were already doing before splitting. From there, Ferenc''s method should work!&lt;/p&gt;
', NULL, NULL, '''/p'':48C ''alreadi'':37C ''astyp'':30C ''check'':4C ''column'':10C ''convert'':23C ''data'':6C ''df'':26C,28C ''df.dtypes'':13C ''ferenc'':43C ''isn'':17C ''method'':45C ''p'':1C ''salari'':16C,27C,29C ''split'':40C ''str'':31C ''string'':20C ''type'':7C ''use'':25C ''work'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5216, 2, '2015-02-23 22:39:53.29', 1, NULL, NULL, '&lt;p&gt;I don''t understand what you mean by &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;So I have a report that lists the url and the number of visits a person did. &lt;strong&gt;So a combination of ip and url result in an amount of visits.&lt;/strong&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Assuming that you equate an IP with a user, and you wish to cluster users by their URL visitation frequencies, your matrix, &lt;code&gt;M&lt;/code&gt;, would have &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;One row per IP (user)&lt;/li&gt;
&lt;li&gt;One column for each URL that you are tracking (your features)&lt;/li&gt;
&lt;li&gt;and the entries in &lt;code&gt;M&lt;/code&gt; would be "visits" of a given URL by a particular IP&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Given these assumptions, and your report, &lt;code&gt;M&lt;/code&gt; would be:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    abc.be  abc.be/a  abc.be/b
123   5        2         2
321   0        0         4
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':136C ''/b'':139C ''/blockquote'':46C ''/code'':72C,104C,127C,148C ''/li'':83C,96C,116C ''/p'':10C,45C,75C,130C ''/pre'':149C ''/strong'':44C ''/ul'':117C ''0'':145C,146C ''123'':140C ''2'':142C,143C ''321'':144C ''4'':147C ''5'':141C ''abc.be'':133C,135C,138C ''abc.be/a'':134C ''abc.be/b'':137C ''amount'':41C ''assum'':48C ''assumpt'':121C ''blockquot'':11C ''cluster'':61C ''code'':70C,102C,125C,132C ''column'':86C ''combin'':33C ''entri'':100C ''equat'':51C ''featur'':95C ''frequenc'':67C ''given'':110C,119C ''ip'':35C,53C,81C,115C ''li'':77C,84C,97C ''list'':19C ''m'':71C,103C,126C ''matrix'':69C ''mean'':8C ''number'':24C ''one'':78C,85C ''p'':1C,12C,47C,118C ''particular'':114C ''per'':80C ''person'':28C ''pre'':131C ''report'':17C,124C ''result'':38C ''row'':79C ''strong'':30C ''track'':93C ''ul'':76C ''understand'':5C ''url'':21C,37C,65C,89C,111C ''user'':56C,62C,82C ''visit'':26C,43C,66C,107C ''wish'':59C ''would'':73C,105C,128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5217, 2, '2015-02-24 02:11:45.787', 1, NULL, NULL, '&lt;p&gt;&lt;a href="https://www.codeschool.com/" rel="nofollow"&gt;https://www.codeschool.com/&lt;/a&gt; is very similar to &lt;a href="https://www.datacamp.com/" rel="nofollow"&gt;https://www.datacamp.com/&lt;/a&gt;
when I tried it I fell in love with R and then found datacamp.
www.codecademy.com  is also console-based but R is not yet available.&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':9C,21C ''/p'':48C ''also'':38C ''avail'':47C ''base'':41C ''consol'':40C ''console-bas'':39C ''datacamp'':35C ''fell'':27C ''found'':34C ''href'':3C,15C ''love'':29C ''nofollow'':6C,18C ''p'':1C ''r'':31C,43C ''rel'':5C,17C ''similar'':12C ''tri'':24C ''www.codecademy.com'':36C ''www.codeschool.com'':4C,8C ''www.codeschool.com/&lt;/a&gt;'':7C ''www.datacamp.com'':16C,20C ''www.datacamp.com/&lt;/a&gt;'':19C ''yet'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5220, 2, '2015-02-24 03:51:15.133', 2, NULL, NULL, '&lt;p&gt;The two modules where you can really harness data mining and big data techniques are probably Leads and Opportunities. The reason is that, as you''ve written yourself, both contain ''potential'' information that you can harness (through predictive algorithms) to get more customers. Taking Leads as an example, you can use a variety of machine learning algorithms to assign a probability to each account, based on that account''s potential for becoming your customer in the near future. Since you already have an Accounts module which gives you information about your current customers, you can use this information to train your machine learning algorithms. This is all at a very high level but hopefully, you''re getting the gist of what I''m saying.&lt;/p&gt;
', NULL, NULL, '''/p'':126C ''account'':65C,69C,85C ''algorithm'':40C,58C,105C ''alreadi'':82C ''assign'':60C ''base'':66C ''becom'':73C ''big'':13C ''contain'':31C ''current'':93C ''custom'':44C,75C,94C ''data'':10C,14C ''exampl'':49C ''futur'':79C ''get'':42C,118C ''gist'':120C ''give'':88C ''har'':9C,37C ''high'':112C ''hope'':115C ''inform'':33C,90C,99C ''lead'':18C,46C ''learn'':57C,104C ''level'':113C ''m'':124C ''machin'':56C,103C ''mine'':11C ''modul'':4C,86C ''near'':78C ''opportun'':20C ''p'':1C ''potenti'':32C,71C ''predict'':39C ''probabl'':17C,62C ''re'':117C ''realli'':8C ''reason'':22C ''say'':125C ''sinc'':80C ''take'':45C ''techniqu'':15C ''train'':101C ''two'':3C ''use'':52C,97C ''varieti'':54C ''ve'':27C ''written'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5225, 2, '2015-02-24 22:13:46.327', 0, NULL, NULL, '&lt;p&gt;Data science is a new somewhat vague terminology.  You will find much more information by using keywords such as: epidemiology, population health, public health surveillance, public health, statistics, evidence based medicine, biostatistics, statistical epidemiology, clinical decision making, interventions etc.&lt;/p&gt;

&lt;p&gt;The question as posted is overly broad. On the other hand you''re in luck since there is a substantial amount of information on this topic.  Many of the R/Data Science courses on Coursera from John Hopkins have a public health flavor. Also, R and S-PLUS before it were primarily used in health settings.&lt;/p&gt;
', NULL, NULL, '''/p'':41C,99C ''also'':85C ''amount'':63C ''base'':31C ''biostatist'':33C ''broad'':49C ''clinic'':36C ''cours'':74C ''coursera'':76C ''data'':2C ''decis'':37C ''epidemiolog'':21C,35C ''etc'':40C ''evid'':30C ''find'':12C ''flavor'':84C ''hand'':53C ''health'':23C,25C,28C,83C,97C ''hopkin'':79C ''inform'':15C,65C ''intervent'':39C ''john'':78C ''keyword'':18C ''luck'':57C ''make'':38C ''mani'':69C ''medicin'':32C ''much'':13C ''new'':6C ''over'':48C ''p'':1C,42C ''plus'':90C ''popul'':22C ''post'':46C ''primarili'':94C ''public'':24C,27C,82C ''question'':44C ''r'':86C ''r/data'':72C ''re'':55C ''s-plus'':88C ''scienc'':3C,73C ''set'':98C ''sinc'':58C ''somewhat'':7C ''statist'':29C,34C ''substanti'':62C ''surveil'':26C ''terminolog'':9C ''topic'':68C ''use'':17C,95C ''vagu'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5237, 2, '2015-02-26 11:31:04.87', 4, NULL, NULL, '&lt;p&gt;This is very similar to the netflix problem, most matrix factorization methods can be adapted so that the error function is only evaluated at known points. For instance, you can take the gradient descent approach to SVD (minimizing the frobenius norm) but only evaluate the error and calculate the gradient at known points. I believe you can easily find code for this.&lt;/p&gt;

&lt;p&gt;Another option would be exploiting the binary nature of your matrix and adapting binary matrix factorization tools in order to enforce binary factors (if you require them). I''m sure you can adapt one of the methods described &lt;a href="http://www.hongliangjie.com/2011/03/15/reviews-on-binary-matrix-decomposition/" rel="nofollow"&gt;here&lt;/a&gt; to work with unknown data using a similar trick as the one above.&lt;/p&gt;
', NULL, NULL, '''/2011/03/15/reviews-on-binary-matrix-decomposition/'':108C ''/a'':112C ''/p'':64C,126C ''adapt'':16C,78C,98C ''anoth'':66C ''approach'':36C ''believ'':56C ''binari'':72C,79C,87C ''calcul'':49C ''code'':61C ''data'':117C ''descent'':35C ''describ'':103C ''easili'':59C ''enforc'':86C ''error'':20C,47C ''evalu'':24C,45C ''exploit'':70C ''factor'':12C,81C,88C ''find'':60C ''frobenius'':41C ''function'':21C ''gradient'':34C,51C ''href'':105C ''instanc'':29C ''known'':26C,53C ''m'':94C ''matrix'':11C,76C,80C ''method'':13C,102C ''minim'':39C ''natur'':73C ''netflix'':8C ''nofollow'':110C ''norm'':42C ''one'':99C,124C ''option'':67C ''order'':84C ''p'':1C,65C ''point'':27C,54C ''problem'':9C ''rel'':109C ''requir'':91C ''similar'':5C,120C ''sure'':95C ''svd'':38C ''take'':32C ''tool'':82C ''trick'':121C ''unknown'':116C ''use'':118C ''work'':114C ''would'':68C ''www.hongliangjie.com'':107C ''www.hongliangjie.com/2011/03/15/reviews-on-binary-matrix-decomposition/'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5264, 1, '2015-03-02 05:10:01.897', 1, 124, 'Does reinforcement learning always work on grid world?', '&lt;p&gt;Does reinforcement learning always need a grid world problem to be applied to?&lt;/p&gt;

&lt;p&gt;Can anyone give me any other example of how reinforcement learning can be applied to something which does not have a grid world scenario?&lt;/p&gt;
', 'machine-learning  reinforcement-learning', NULL, '''/p'':29C,55C ''alway'':4A,19C ''anyon'':32C ''appli'':27C,44C ''exampl'':37C ''give'':33C ''grid'':7A,22C,52C ''learn'':3A,11B,14B,18C,41C ''machin'':10B ''machine-learn'':9B ''need'':20C ''p'':15C,30C ''problem'':24C ''reinforc'':2A,13B,17C,40C ''reinforcement-learn'':12B ''scenario'':54C ''someth'':46C ''work'':5A ''world'':8A,23C,53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5222, 2, '2015-02-24 08:40:05.083', 0, NULL, NULL, '&lt;p&gt;So far the answers have focused on learning particular methods. They are fine, but they won''t make you a Data Scientist. Being a Data Scientist is not solely or even primarily about having mastery of particular data analysis methods (ML or others).&lt;/p&gt;

&lt;p&gt;Most fundamental is &lt;em&gt;problem solving&lt;/em&gt; and &lt;em&gt;decision support&lt;/em&gt;. What ever data you collect, what ever analysis methods you apply, and however you improve those methods over time, these must support the over-arching goals of solving problems or making better decisions.&lt;/p&gt;

&lt;p&gt;You need to start getting first-hand experience with data in your field.  I don''t mean Kaggle data (i.e. already cleaned).  I mean raw data or nearly raw. A good 50% of a data scientist''s time is spent wrangling raw data and cleaning it to the point where it''s usable in analysis.  You need to learn how to deal with missing data, erroneous data, ambiguous data, misformatted data, and so on.&lt;/p&gt;

&lt;p&gt;You should also get some experience with decisions that do not map neatly on to the data.  Recommender systems are easy in this regard.  For example, you might take on the challenge of evaluating software vulnerabilities to guide vulnerability management decisions.&lt;/p&gt;
', NULL, NULL, '''/em'':53C,58C ''/p'':45C,93C,170C,213C ''50'':127C ''alreadi'':116C ''also'':174C ''ambigu'':163C ''analysi'':40C,66C,150C ''answer'':5C ''appli'':69C ''arch'':84C ''better'':91C ''challeng'':203C ''clean'':117C,140C ''collect'':63C ''data'':22C,26C,39C,61C,105C,114C,121C,130C,138C,160C,162C,164C,166C,188C ''deal'':157C ''decis'':56C,92C,179C,212C ''easi'':192C ''em'':50C,55C ''erron'':161C ''evalu'':205C ''even'':32C ''ever'':60C,65C ''exampl'':197C ''experi'':103C,177C ''far'':3C ''field'':108C ''fine'':14C ''first'':101C ''first-hand'':100C ''focus'':7C ''fundament'':48C ''get'':99C,175C ''goal'':85C ''good'':126C ''guid'':209C ''hand'':102C ''howev'':71C ''i.e'':115C ''improv'':73C ''kaggl'':113C ''learn'':9C,154C ''make'':19C,90C ''manag'':211C ''map'':183C ''masteri'':36C ''mean'':112C,119C ''method'':11C,41C,67C,75C ''might'':199C ''misformat'':165C ''miss'':159C ''ml'':42C ''must'':79C ''near'':123C ''neat'':184C ''need'':96C,152C ''other'':44C ''over-arch'':82C ''p'':1C,46C,94C,171C ''particular'':10C,38C ''point'':144C ''primarili'':33C ''problem'':51C,88C ''raw'':120C,124C,137C ''recommend'':189C ''regard'':195C ''scientist'':23C,27C,131C ''softwar'':206C ''sole'':30C ''solv'':52C,87C ''spent'':135C ''start'':98C ''support'':57C,80C ''system'':190C ''take'':200C ''time'':77C,133C ''usabl'':148C ''vulner'':207C,210C ''won'':17C ''wrangl'':136C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5277, 1, '2015-03-04 08:05:45.003', 5, 203, 'Do you have to normalize data when building decision trees using R?', '&lt;p&gt;So, our data set this week has 14 attributes and each column has very different values. One column has values below 1 while another column has values that go from three to four whole digits.&lt;/p&gt;

&lt;p&gt;We learned normalization last week and it seems like you''re supposed to normalize data when they have very different values. For decision trees, is the case the same?&lt;/p&gt;

&lt;p&gt;I''m not sure about this but would normalization affect the resulting decision tree from the same data set? It doesn''t seem like it should but...&lt;/p&gt;
', 'r  beginner', NULL, '''/p'':51C,82C,111C ''1'':37C ''14'':23C ''affect'':93C ''anoth'':39C ''attribut'':24C ''beginn'':14B ''build'':8A ''case'':79C ''column'':27C,33C,40C ''data'':6A,18C,67C,101C ''decis'':9A,75C,96C ''differ'':30C,72C ''digit'':50C ''doesn'':104C ''four'':48C ''go'':44C ''last'':56C ''learn'':54C ''like'':61C,107C ''m'':85C ''normal'':5A,55C,66C,92C ''one'':32C ''p'':15C,52C,83C ''r'':12A,13B ''re'':63C ''result'':95C ''seem'':60C,106C ''set'':19C,102C ''suppos'':64C ''sure'':87C ''three'':46C ''tree'':10A,76C,97C ''use'':11A ''valu'':31C,35C,42C,73C ''week'':21C,57C ''whole'':49C ''would'':91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5223, 2, '2015-02-24 11:52:40.977', 2, NULL, NULL, '&lt;p&gt;Yes, this is a straightforward application for neural networks.  In this case yk are the outputs of the last layer ("classifier"); xk is a feature vector and yk is what it gets classified into.  For simplicity prepare your data so that N is the same for all.  The problem you have is perhaps that in the case of time series you won''t have &lt;em&gt;enough&lt;/em&gt; data: you need (ideally) many 1000''s of examples to train a network, which in this case means time series, not points.  Look at the specialized literature on neural networks for time series prediction for ideas on network architecture.&lt;/p&gt;

&lt;p&gt;Library: try Pylearn2 at &lt;a href="http://deeplearning.net/software/pylearn2/" rel="nofollow"&gt;http://deeplearning.net/software/pylearn2/&lt;/a&gt;  It''s not the only good option but it should serve you well.&lt;/p&gt;
', NULL, NULL, '''/em'':68C ''/p'':108C,137C ''/software/pylearn2/'':118C ''/software/pylearn2/&lt;/a&gt;'':123C ''1000'':74C ''applic'':7C ''architectur'':107C ''case'':13C,58C,85C ''classifi'':22C,34C ''data'':40C,69C ''deeplearning.net'':117C,122C ''deeplearning.net/software/pylearn2/'':116C ''deeplearning.net/software/pylearn2/&lt;/a&gt;'':121C ''em'':66C ''enough'':67C ''exampl'':77C ''featur'':26C ''get'':33C ''good'':129C ''href'':115C ''idea'':104C ''ideal'':72C ''last'':20C ''layer'':21C ''librari'':110C ''literatur'':95C ''look'':91C ''mani'':73C ''mean'':86C ''n'':43C ''need'':71C ''network'':10C,81C,98C,106C ''neural'':9C,97C ''nofollow'':120C ''option'':130C ''output'':17C ''p'':1C,109C ''perhap'':54C ''point'':90C ''predict'':102C ''prepar'':38C ''problem'':50C ''pylearn2'':112C ''rel'':119C ''seri'':61C,88C,101C ''serv'':134C ''simplic'':37C ''special'':94C ''straightforward'':6C ''time'':60C,87C,100C ''train'':79C ''tri'':111C ''vector'':27C ''well'':136C ''won'':63C ''xk'':23C ''yes'':2C ''yk'':14C,29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5224, 1, '2015-02-24 11:59:36.033', 9, 225, 'How to prepare images for neural network?', '&lt;p&gt;I would like to use a neural network for image classification.  I''ll start with pre-trained CaffeNet and train it for my application.  &lt;/p&gt;

&lt;p&gt;My question is: how to prepare the input images?  In this case, all the images are of the same object but with variations (think: quality control).  They are at somewhat different scales/resolutions/distances/lighting conditions (and in many cases I don''t know the scale).  Also, in each image there is an area (known) around the object of interest that should be ignored by the network.&lt;/p&gt;

&lt;p&gt;I could (for example) crop the center of each image, which is guaranteed to contain a portion of the object of interest and none of the ignored area; but that seems like it would throw away information, and also the results wouldn''t be really the same scale (maybe 1.5x variation).&lt;/p&gt;

&lt;p&gt;Also: I''ve heard of creating more training data by random crop/mirror/etc, is there a standard method for this?  Any results on how much improvement it produces to classifier accuracy?&lt;/p&gt;
', 'neuralnetwork  convnet  image-classification', NULL, '''/p'':39C,105C,159C,191C ''1.5'':156C ''accuraci'':190C ''also'':84C,145C,161C ''applic'':38C ''area'':91C,134C ''around'':93C ''away'':142C ''caffenet'':32C ''case'':52C,77C ''center'':113C ''classif'':12B,24C ''classifi'':189C ''condit'':73C ''contain'':121C ''control'':66C ''convnet'':9B ''could'':108C ''creat'':166C ''crop'':111C ''crop/mirror/etc'':172C ''data'':169C ''differ'':71C ''exampl'':110C ''guarante'':119C ''heard'':164C ''ignor'':101C,133C ''imag'':4A,11B,23C,49C,55C,87C,116C ''image-classif'':10B ''improv'':185C ''inform'':143C ''input'':48C ''interest'':97C,128C ''know'':81C ''known'':92C ''like'':16C,138C ''ll'':26C ''mani'':76C ''mayb'':155C ''method'':177C ''much'':184C ''network'':7A,21C,104C ''neural'':6A,20C ''neuralnetwork'':8B ''none'':130C ''object'':60C,95C,126C ''p'':13C,40C,106C,160C ''portion'':123C ''pre'':30C ''pre-train'':29C ''prepar'':3A,46C ''produc'':187C ''qualiti'':65C ''question'':42C ''random'':171C ''realli'':151C ''result'':147C,181C ''scale'':83C,154C ''scales/resolutions/distances/lighting'':72C ''seem'':137C ''somewhat'':70C ''standard'':176C ''start'':27C ''think'':64C ''throw'':141C ''train'':31C,34C,168C ''use'':18C ''variat'':63C,158C ''ve'':163C ''would'':15C,140C ''wouldn'':148C ''x'':157C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5226, 1, '2015-02-25 01:07:14.717', 8, 2553, 'strings as features in decision tree/random forest', '&lt;p&gt;I am new to machine learning!&lt;/p&gt;

&lt;p&gt;Right now I am doing some problems on application of decision tree/random forest. I am trying to fit a problem which has numbers as well as strings (such as country name) as features. Now the library, scikit-learn takes only numbers as parameters, but I want to inject the strings as well as they carry significant amount of knowledge.&lt;/p&gt;

&lt;p&gt;How do I handle such scenario, I can convert string to numbers by some mechanism such as hashing in python. But I would like to know the best practice on how strings are handled in decision tree problems.&lt;/p&gt;

&lt;p&gt;Thanks for your support!&lt;/p&gt;
', 'python  random-forest  scikit', NULL, '''/p'':20C,82C,122C,128C ''amount'':79C ''applic'':30C ''best'':111C ''carri'':77C ''convert'':92C ''countri'':51C ''decis'':5A,32C,119C ''featur'':3A,54C ''fit'':39C ''forest'':7A,11B,34C ''handl'':87C,117C ''hash'':101C ''inject'':70C ''know'':109C ''knowledg'':81C ''learn'':19C,60C ''librari'':57C ''like'':107C ''machin'':18C ''mechan'':98C ''name'':52C ''new'':16C ''number'':44C,63C,95C ''p'':13C,21C,83C,123C ''paramet'':65C ''practic'':112C ''problem'':28C,41C,121C ''python'':8B,103C ''random'':10B ''random-forest'':9B ''right'':22C ''scenario'':89C ''scikit'':12B,59C ''scikit-learn'':58C ''signific'':78C ''string'':1A,48C,72C,93C,115C ''support'':127C ''take'':61C ''thank'':124C ''tree'':120C ''tree/random'':6A,33C ''tri'':37C ''want'':68C ''well'':46C,74C ''would'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5235, 2, '2015-02-26 05:08:31.993', 2, NULL, NULL, '&lt;p&gt;Do you know if the scheduler has a memory?&lt;/p&gt;

&lt;p&gt;Let us assume for a moment that the scheduler has no memory.  This is a straightforward classification (supervised learning) problem: the inputs are X, the outputs are the schedules (N-&gt;M maps).  Actually, if &lt;em&gt;every&lt;/em&gt; N gets scheduled and the only question is which M it gets, the outputs are lists which channel (or none) is scheduled to each block, and there is only a certain possible number of those, so you can model them as discrete outputs (classes) with their own probabilities.  Use whatever you like (AdaBoost, Naive Bayes, RBF SVM, Random Forest...) as a classifier.  I think you will quickly learn about the general behavior of the scheduler.&lt;/p&gt;

&lt;p&gt;If the scheduler has a memory, then things get complicated.  I think you might approach that as a hidden Markov model: but the number of individual states may be quite large, and so it may be essentially impossible to build a complete map of transition probabilities.&lt;/p&gt;
', NULL, NULL, '''/em'':49C ''/p'':11C,125C,173C ''actual'':45C ''adaboost'':102C ''approach'':141C ''assum'':15C ''bay'':104C ''behavior'':121C ''block'':74C ''build'':166C ''certain'':80C ''channel'':67C ''class'':93C ''classif'':29C ''classifi'':111C ''complet'':168C ''complic'':136C ''discret'':91C ''em'':47C ''essenti'':163C ''everi'':48C ''forest'':108C ''general'':120C ''get'':51C,61C,135C ''hidden'':145C ''imposs'':164C ''individu'':152C ''input'':34C ''know'':4C ''larg'':157C ''learn'':31C,117C ''let'':13C ''like'':101C ''list'':65C ''m'':43C,59C ''map'':44C,169C ''markov'':146C ''may'':154C,161C ''memori'':10C,24C,132C ''might'':140C ''model'':88C,147C ''moment'':18C ''n'':42C,50C ''naiv'':103C ''none'':69C ''number'':82C,150C ''output'':38C,63C,92C ''p'':1C,12C,126C ''possibl'':81C ''probabl'':97C,172C ''problem'':32C ''question'':56C ''quick'':116C ''quit'':156C ''random'':107C ''rbf'':105C ''schedul'':7C,21C,41C,52C,71C,124C,129C ''state'':153C ''straightforward'':28C ''supervis'':30C ''svm'':106C ''thing'':134C ''think'':113C,138C ''transit'':171C ''us'':14C ''use'':98C ''whatev'':99C ''x'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5227, 1, '2015-02-25 03:24:20.45', 1, 68, 'problem of choosing right statistical method for scheduler prediction', '&lt;p&gt;I am struggling to choose a right data prediction method for the following problem.
Essentially I am trying to model a scheduler operation, trying to predict its scheduling without knowing the scheduling mechanism and having incomplete data. &lt;/p&gt;

&lt;p&gt;(1) There are M available resource blocks that can carry data, N data channels that must be scheduled every time instance i&lt;/p&gt;

&lt;p&gt;(2) Inputs into the scheduler:  &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Matrix $X_i$ size M by N, consisting of N column vectors from each data source.    Each of M elements is index from 1 to 32 carrying information about quality of data channel for particular resource block. 1 - really bad quality, 32 - excellent quality.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Data which contains type of data to be carried (voice/internet etc)&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Scheduler prioritizes number of resource blocks occupied by each channel every time instant i.&lt;/p&gt;

&lt;p&gt;Given that &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;I CAN see resource allocation map every time instant&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;I DO have access to matrix $X_i$  &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;I DON''T know the algorithm of scheduler and&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;I dont have access to the type of data to be scheduled. &lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I want to have a best guess (prediction) how the data will be scheduled based on this incomplete information i.e, which resource block will be occupied by which data channel. What is the best choice of prediction/modelling algorithm?
Any help appreciated!&lt;/p&gt;
', 'predictive-modeling', NULL, '''/li'':131C,146C,181C,193C,206C,222C ''/p'':51C,75C,82C,130C,145C,163C,167C,180C,192C,205C,221C,266C ''/ul'':147C,223C ''1'':53C,109C,123C ''2'':77C ''32'':111C,127C ''access'':187C,212C ''algorithm'':201C,262C ''alloc'':175C ''appreci'':265C ''avail'':57C ''bad'':125C ''base'':239C ''best'':230C,258C ''block'':59C,122C,154C,247C ''carri'':62C,112C,142C ''channel'':66C,118C,158C,254C ''choic'':259C ''choos'':3A,18C ''column'':96C ''consist'':93C ''contain'':136C ''data'':21C,50C,63C,65C,100C,117C,134C,139C,217C,235C,253C ''dont'':210C ''element'':105C ''essenti'':28C ''etc'':144C ''everi'':71C,159C,177C ''excel'':128C ''follow'':26C ''given'':165C ''guess'':231C ''help'':264C ''i.e'':244C ''incomplet'':49C,242C ''index'':107C ''inform'':113C,243C ''input'':78C ''instanc'':73C ''instant'':161C,179C ''know'':43C,199C ''li'':84C,132C,169C,182C,194C,207C ''m'':56C,90C,104C ''map'':176C ''matrix'':86C,189C ''mechan'':46C ''method'':6A,23C ''model'':12B,33C ''must'':68C ''n'':64C,92C,95C ''number'':151C ''occupi'':155C,250C ''oper'':36C ''p'':13C,52C,76C,85C,133C,148C,164C,170C,183C,195C,208C,224C ''particular'':120C ''predict'':9A,11B,22C,39C,232C ''prediction/modelling'':261C ''predictive-model'':10B ''priorit'':150C ''problem'':1A,27C ''qualiti'':115C,126C,129C ''realli'':124C ''resourc'':58C,121C,153C,174C,246C ''right'':4A,20C ''schedul'':8A,35C,41C,45C,70C,81C,149C,203C,220C,238C ''see'':173C ''size'':89C ''sourc'':101C ''statist'':5A ''struggl'':16C ''time'':72C,160C,178C ''tri'':31C,37C ''type'':137C,215C ''ul'':83C,168C ''vector'':97C ''voice/internet'':143C ''want'':226C ''without'':42C ''x'':87C,190C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5228, 1, '2015-02-25 10:07:24.563', 2, 59, 'Graph modularity measure', '&lt;p&gt;There are several metrics for the quality of a graph clustering, e.g. Newman modularity. These enable you to compare two candidate clusterings of the same graph. &lt;/p&gt;

&lt;p&gt;Does anyone know a metric that will answer the question "how modular is this graph"? For example the first of these two graphs is more modular than the second:
    o===o-----o====o    o----o===o-----o&lt;/p&gt;

&lt;p&gt;It would be possible to choose a clustering algorithm, run it, and compute your preferred modularity metric for the best clustering found. But this is only a lower bound, so it doesn''t seem very satisfactory.&lt;/p&gt;

&lt;p&gt;The question matters. For example, the work of life scientists will be easier if the molecular organisation of life is modular than if it is not. It would be good to have a robust test - some of the discussion so far seems to involve wishful thinking.&lt;/p&gt;

&lt;p&gt;My best attempt at this is:
- a tree is more modular if the edges near leaves are higher weight
- the modularity of a graph is the modularity of its min cut spanning tree
Does anyone know of an established answer to this question?&lt;/p&gt;
', 'clustering', NULL, '''/p'':32C,71C,109C,157C,202C ''algorithm'':81C ''answer'':41C,198C ''anyon'':35C,193C ''attempt'':161C ''best'':92C,160C ''bound'':101C ''candid'':26C ''choos'':78C ''cluster'':4B,16C,27C,80C,93C ''compar'':24C ''comput'':85C ''cut'':189C ''discuss'':149C ''doesn'':104C ''e.g'':17C ''easier'':123C ''edg'':172C ''enabl'':21C ''establish'':197C ''exampl'':50C,115C ''far'':151C ''first'':52C ''found'':94C ''good'':140C ''graph'':1A,15C,31C,48C,56C,182C ''higher'':176C ''involv'':154C ''know'':36C,194C ''leav'':174C ''life'':119C,129C ''lower'':100C ''matter'':113C ''measur'':3A ''metric'':9C,38C,89C ''min'':188C ''modular'':2A,19C,45C,59C,88C,131C,169C,179C,185C ''molecular'':126C ''near'':173C ''newman'':18C ''o'':63C,64C,65C,66C,67C,68C,69C,70C ''organis'':127C ''p'':5C,33C,72C,110C,158C ''possibl'':76C ''prefer'':87C ''qualiti'':12C ''question'':43C,112C,201C ''robust'':144C ''run'':82C ''satisfactori'':108C ''scientist'':120C ''second'':62C ''seem'':106C,152C ''sever'':8C ''span'':190C ''test'':145C ''think'':156C ''tree'':166C,191C ''two'':25C,55C ''weight'':177C ''wish'':155C ''work'':117C ''would'':74C,138C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5229, 2, '2015-02-25 10:10:55.683', 6, NULL, NULL, '&lt;p&gt;In most of the well established machine learning systems categorical variables are handled naturally. For example in R you would use factors, in weka you would use nominal variables. This is not the care in scikit learn. The decision trees implemented in sklearn uses only numerical features and these features are interpreted always as continuous numeric variables. &lt;/p&gt;

&lt;p&gt;Thus, simply replacing the strings with a hash code should be avoided, because being considered as a continuous numerical feature any coding you will use will induce an order which simply does not exists in your data. &lt;/p&gt;

&lt;p&gt;One example is to code [''red'',''green'',''blue''] with [1,2,3], would produce weird things like ''red'' is lower than ''blue'', and if you average a ''red'' and a ''blue'' you will get a ''green''. Another more sublte example maigh happen when you code [''low'', ''medium'', ''high''] with [1,2,3]. In the latter case it might happen to have an ordering which makes sense, however some sublte inconsistencies might happen when ''medium'' in not in the middle of ''low'' and ''high''.&lt;/p&gt;

&lt;p&gt;Finally the answer to your question lies in coding the categorical feature into multiple binary features. For example you might code [''red'',''green'',''blue''] with 3 columns, one for each category, having 1 when the category match and 0 otherwise. This is called one-hot-encoding, binary encoding, one-of-k-encoding or whatever. You can check documentation here for &lt;a href="http://scikit-learn.org/stable/modules/preprocessing.html"&gt;encoding categorical features&lt;/a&gt; and &lt;a href="http://scikit-learn.org/stable/modules/feature_extraction.html#dict-feature-extraction"&gt;feature extraction - hashing and dicts&lt;/a&gt;. Obviously one-hot-encoding will expand your space requirements and sometimes it hurts the performance as well. &lt;/p&gt;
', NULL, NULL, '''/a'':255C,267C ''/p'':59C,98C,183C,286C ''/stable/modules/feature_extraction.html#dict-feature-extraction'':261C ''/stable/modules/preprocessing.html'':251C ''0'':223C ''1'':109C,149C,217C ''2'':110C,150C ''3'':111C,151C,210C ''alway'':54C ''anoth'':136C ''answer'':187C ''averag'':125C ''avoid'':72C ''binari'':199C,232C ''blue'':107C,121C,130C,208C ''call'':227C ''care'':35C ''case'':155C ''categor'':11C,195C,253C ''categori'':215C,220C ''check'':243C ''code'':69C,82C,104C,144C,193C,205C ''column'':211C ''consid'':75C ''continu'':56C,78C ''data'':97C ''decis'':40C ''dict'':266C ''document'':244C ''encod'':231C,233C,238C,252C,272C ''establish'':7C ''exampl'':17C,101C,139C,202C ''exist'':94C ''expand'':274C ''extract'':263C ''factor'':23C ''featur'':48C,51C,80C,196C,200C,254C,262C ''final'':185C ''get'':133C ''green'':106C,135C,207C ''handl'':14C ''happen'':141C,158C,171C ''hash'':68C,264C ''high'':147C,182C ''hot'':230C,271C ''howev'':166C ''href'':248C,258C ''hurt'':281C ''implement'':42C ''inconsist'':169C ''induc'':87C ''interpret'':53C ''k'':237C ''latter'':154C ''learn'':9C,38C ''lie'':191C ''like'':116C ''low'':145C,180C ''lower'':119C ''machin'':8C ''maigh'':140C ''make'':164C ''match'':221C ''medium'':146C,173C ''middl'':178C ''might'':157C,170C,204C ''multipl'':198C ''natur'':15C ''nomin'':29C ''numer'':47C,57C,79C ''obvious'':268C ''one'':100C,212C,229C,235C,270C ''one-hot-encod'':228C,269C ''one-of-k-encod'':234C ''order'':89C,162C ''otherwis'':224C ''p'':1C,60C,99C,184C ''perform'':283C ''produc'':113C ''question'':190C ''r'':19C ''red'':105C,117C,127C,206C ''replac'':63C ''requir'':277C ''scikit'':37C ''scikit-learn.org'':250C,260C ''scikit-learn.org/stable/modules/feature_extraction.html#dict-feature-extraction'':259C ''scikit-learn.org/stable/modules/preprocessing.html'':249C ''sens'':165C ''simpli'':62C,91C ''sklearn'':44C ''sometim'':279C ''space'':276C ''string'':65C ''sublt'':138C,168C ''system'':10C ''thing'':115C ''thus'':61C ''tree'':41C ''use'':22C,28C,45C,85C ''variabl'':12C,30C,58C ''weird'':114C ''weka'':25C ''well'':6C,285C ''whatev'':240C ''would'':21C,27C,112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6162, 2, '2015-06-20 06:12:08.537', 1, NULL, NULL, '&lt;p&gt;I think you are in a better position to train your own NER model. You can start with CRFSuite as a package.&lt;/p&gt;
', NULL, NULL, '''/p'':24C ''better'':8C ''crfsuit'':20C ''model'':15C ''ner'':14C ''p'':1C ''packag'':23C ''posit'':9C ''start'':18C ''think'':3C ''train'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5230, 2, '2015-02-25 11:18:26.313', 2, NULL, NULL, '&lt;p&gt;Have you heard about the &lt;a href="http://en.wikipedia.org/wiki/Intersection_graph" rel="nofollow"&gt;intersection graph&lt;/a&gt;? You can try to draw players as points, connections (team mates) as edges and teams as transparent coloured blobs on top.&lt;/p&gt;

&lt;p&gt;As for your original question, I cannot understand your goal. I think your formulation is not well defined / incomplete. Suppose you have teams A [1,2] B [2,3] and C [1,3]. What do you want to display? Do you want to list the parts of the Venn diagram? I think that for more than 3 sets this can become more cumbersome than the bipartite graph itself = simple listing of team compositions.&lt;/p&gt;
', NULL, NULL, '''/a'':16C ''/p'':38C,113C ''/wiki/intersection_graph'':11C ''1'':64C,71C ''2'':65C,67C ''3'':68C,72C,96C ''b'':66C ''becom'':100C ''bipartit'':105C ''blob'':35C ''c'':70C ''cannot'':46C ''colour'':34C ''composit'':112C ''connect'':25C ''cumbersom'':102C ''defin'':57C ''diagram'':89C ''display'':78C ''draw'':21C ''edg'':29C ''en.wikipedia.org'':10C ''en.wikipedia.org/wiki/intersection_graph'':9C ''formul'':53C ''goal'':49C ''graph'':15C,106C ''heard'':4C ''href'':8C ''incomplet'':58C ''intersect'':14C ''list'':83C,109C ''mate'':27C ''nofollow'':13C ''origin'':43C ''p'':1C,39C ''part'':85C ''player'':22C ''point'':24C ''question'':44C ''rel'':12C ''set'':97C ''simpl'':108C ''suppos'':59C ''team'':26C,31C,62C,111C ''think'':51C,91C ''top'':37C ''transpar'':33C ''tri'':19C ''understand'':47C ''venn'':88C ''want'':76C,81C ''well'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5231, 1, '2015-02-25 14:10:04.777', 0, 64, 'How to test/validate unlabeled data in association rules in R?', '&lt;p&gt;I produced association rules by using the arules package (apriori). I''m left with +/- 250 rules. I would like to test/validate the rules that I have, like answering the question: How do I know that these association rules are true? How can I validate them? What are common practice to test it?&lt;/p&gt;

&lt;p&gt;I thought about cross validation (with training data and test data) as I read that it''s not impossible to use it on unsupervised learning methods..but I''m not sure if it makes sense since I don''t use labeled data.&lt;/p&gt;

&lt;p&gt;If someone has a clue, even if it''s not specifically about association rules (but testing other unsupervised learning methods), that would also be helpful to me.&lt;/p&gt;

&lt;p&gt;I uploaded an example of the data that I use here in case it''s relevant: &lt;a href="https://www.mediafire.com/?4b1zqpkbjf15iuy" rel="nofollow"&gt;https://www.mediafire.com/?4b1zqpkbjf15iuy&lt;/a&gt;&lt;/p&gt;
', 'machine-learning  r  cross-validation', NULL, '''/?4b1zqpkbjf15iuy'':166C ''/?4b1zqpkbjf15iuy&lt;/a&gt;&lt;/p&gt;'':171C ''/p'':71C,115C,144C ''250'':33C ''also'':139C ''answer'':46C ''apriori'':28C ''arul'':26C ''associ'':7A,21C,55C,129C ''case'':158C ''clue'':121C ''common'':66C ''cross'':16B,76C ''cross-valid'':15B ''data'':5A,80C,83C,114C,152C ''even'':122C ''exampl'':149C ''help'':141C ''href'':163C ''imposs'':91C ''know'':52C ''label'':113C ''learn'':13B,97C,135C ''left'':31C ''like'':37C,45C ''m'':30C,101C ''machin'':12B ''machine-learn'':11B ''make'':106C ''method'':98C,136C ''nofollow'':168C ''p'':18C,72C,116C,145C ''packag'':27C ''practic'':67C ''produc'':20C ''question'':48C ''r'':10A,14B ''read'':86C ''rel'':167C ''relev'':161C ''rule'':8A,22C,34C,41C,56C,130C ''sens'':107C ''sinc'':108C ''someon'':118C ''specif'':127C ''sure'':103C ''test'':69C,82C,132C ''test/validate'':3A,39C ''thought'':74C ''train'':79C ''true'':58C ''unlabel'':4A ''unsupervis'':96C,134C ''upload'':147C ''use'':24C,93C,112C,155C ''valid'':17B,62C,77C ''would'':36C,138C ''www.mediafire.com'':165C,170C ''www.mediafire.com/?4b1zqpkbjf15iuy'':164C ''www.mediafire.com/?4b1zqpkbjf15iuy&lt;/a&gt;&lt;/p&gt;'':169C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5233, 1, '2015-02-25 18:28:27.903', 2, 190, 'training one SVM model for predicting more than one response variable', '&lt;p&gt;Please I want to know if there is any SVM R package that can handle more than one response variable (y) at a time. that is to train one model for predicting more than one response variable. it could be regression or multi class classification problem.&lt;/p&gt;

&lt;p&gt;Thanks for your help&lt;/p&gt;
', 'machine-learning  r  algorithms', NULL, '''/p'':64C,70C ''algorithm'':16B ''class'':61C ''classif'':62C ''could'':56C ''handl'':32C ''help'':69C ''know'':22C ''learn'':14B ''machin'':13B ''machine-learn'':12B ''model'':4A,47C ''multi'':60C ''one'':2A,9A,35C,46C,52C ''p'':17C,65C ''packag'':29C ''pleas'':18C ''predict'':6A,49C ''problem'':63C ''r'':15B,28C ''regress'':58C ''respons'':10A,36C,53C ''svm'':3A,27C ''thank'':66C ''time'':41C ''train'':1A,45C ''variabl'':11A,37C,54C ''want'':20C ''y'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5239, 2, '2015-02-27 00:45:39.74', 0, NULL, NULL, '&lt;p&gt;You may want to consider using your own &lt;code&gt;APparameter&lt;/code&gt; object to put "significance" constraints on the rules learned by Apriori. See page 13 of the &lt;a href="http://cran.r-project.org/web/packages/arules/arules.pdf" rel="nofollow"&gt;arules documentation&lt;/a&gt;. This could reduce the number of uninteresting rules returned in your run.&lt;/p&gt;

&lt;p&gt;In lieu of gold standard data for your domain, consider bootstrap resampling as a form of validation, as described &lt;a href="http://eprints.pascal-network.org/archive/00003198/01/lal.pdf" rel="nofollow"&gt;in this article&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':38C,82C ''/archive/00003198/01/lal.pdf'':76C ''/code'':12C ''/p'':51C,83C ''/web/packages/arules/arules.pdf'':33C ''13'':26C ''apparamet'':11C ''apriori'':23C ''articl'':81C ''arul'':36C ''bootstrap'':63C ''code'':10C ''consid'':6C,62C ''constraint'':17C ''could'':40C ''cran.r-project.org'':32C ''cran.r-project.org/web/packages/arules/arules.pdf'':31C ''data'':58C ''describ'':71C ''document'':37C ''domain'':61C ''eprints.pascal-network.org'':75C ''eprints.pascal-network.org/archive/00003198/01/lal.pdf'':74C ''form'':67C ''gold'':56C ''href'':30C,73C ''learn'':21C ''lieu'':54C ''may'':3C ''nofollow'':35C,78C ''number'':43C ''object'':13C ''p'':1C,52C ''page'':25C ''put'':15C ''reduc'':41C ''rel'':34C,77C ''resampl'':64C ''return'':47C ''rule'':20C,46C ''run'':50C ''see'':24C ''signific'':16C ''standard'':57C ''uninterest'':45C ''use'':7C ''valid'':69C ''want'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5241, 2, '2015-02-27 05:28:25.457', 0, NULL, NULL, '&lt;p&gt;I think this line is wrong:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ytr = X(ii(1:N/2),:);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;ytr should be the label of the training data. In this case, it should be&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ytr = y(ii(1:N/2),:);
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':16C,42C ''/p'':8C,34C ''/pre'':17C,43C ''1'':14C,40C ''case'':30C ''code'':10C,36C ''data'':27C ''ii'':13C,39C ''label'':23C ''line'':5C ''n/2'':15C,41C ''p'':1C,18C ''pre'':9C,35C ''think'':3C ''train'':26C ''wrong'':7C ''x'':12C ''y'':38C ''ytr'':11C,19C,37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5243, 1, '2015-02-27 10:22:59.577', 0, 146, 'Interested in Mathematical Statistics... where to start from?', '&lt;p&gt;I have been working in the last years with statistics and have gone pretty deep in programming with R. I have however always felt that I wasn''t completely grasping what I was doing, still understanding all passages and procedures conceptually.&lt;/p&gt;

&lt;p&gt;I wanted to get a bit deeper into the math behind it all. I''ve been looking online for texts and tips, but all texts start with a very high level. Any suggestions on where to start?&lt;/p&gt;

&lt;p&gt;To be more precise, I''m not looking for an exaustive list of statistical models and how they work, I kind of get those. I was looking for something like "Basics of statistical modelling"&lt;/p&gt;
', 'statistics  predictive-modeling', NULL, '''/p'':54C,93C,129C ''alway'':35C ''basic'':125C ''behind'':66C ''bit'':61C ''complet'':41C ''conceptu'':53C ''deep'':27C ''deeper'':62C ''exaust'':105C ''felt'':36C ''get'':59C,117C ''gone'':25C ''grasp'':42C ''high'':85C ''howev'':34C ''interest'':1A ''kind'':115C ''last'':19C ''level'':86C ''like'':124C ''list'':106C ''look'':72C,102C,121C ''m'':100C ''math'':65C ''mathemat'':3A ''model'':11B,109C,128C ''onlin'':73C ''p'':12C,55C,94C ''passag'':50C ''precis'':98C ''predict'':10B ''predictive-model'':9B ''pretti'':26C ''procedur'':52C ''program'':29C ''r'':31C ''someth'':123C ''start'':7A,81C,92C ''statist'':4A,8B,22C,108C,127C ''still'':47C ''suggest'':88C ''text'':75C,80C ''tip'':77C ''understand'':48C ''ve'':70C ''want'':57C ''wasn'':39C ''work'':16C,113C ''year'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5244, 1, '2015-02-27 10:42:35.407', 2, 335, 'R in production', '&lt;p&gt;Many of us are very familiar with using R in reproducible, but very much targeted, ad-hoc analysis. Given that R is currently the best collection of cutting-edge scientific methods from world-class experts in each particular field, and given that plenty of libraries exist for data io in R, it seems very natural to extend its applications into production environments for live decision making.&lt;/p&gt;

&lt;p&gt;Therefore my questions are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;did someone of you go into production with pure R (I know of shiny, yhat etc, but would be very interesting to hear of pure R);&lt;/li&gt;
&lt;li&gt;is there a good book/guide/article on the topic of building R into some serious live decision-making pipelines (such as e.g. credit scoring);&lt;/li&gt;
&lt;li&gt;I would like to hear also if you think it''s not a good idea at all;&lt;/li&gt;
&lt;/ul&gt;
', 'r  predictive-modeling  scoring', NULL, '''/li'':114C,140C,159C ''/p'':79C,85C ''/ul'':160C ''ad'':26C ''ad-hoc'':25C ''also'':147C ''analysi'':28C ''applic'':71C ''best'':35C ''book/guide/article'':120C ''build'':125C ''class'':46C ''collect'':36C ''credit'':138C ''current'':33C ''cut'':39C ''cutting-edg'':38C ''data'':60C ''decis'':77C,132C ''decision-mak'':131C ''e.g'':137C ''edg'':40C ''environ'':74C ''etc'':103C ''exist'':58C ''expert'':47C ''extend'':69C ''familiar'':15C ''field'':51C ''given'':29C,53C ''go'':92C ''good'':119C,155C ''hear'':110C,146C ''hoc'':27C ''idea'':156C ''interest'':108C ''io'':61C ''know'':99C ''li'':87C,115C,141C ''librari'':57C ''like'':144C ''live'':76C,130C ''make'':78C,133C ''mani'':10C ''method'':42C ''model'':7B ''much'':23C ''natur'':67C ''p'':9C,80C ''particular'':50C ''pipelin'':134C ''plenti'':55C ''predict'':6B ''predictive-model'':5B ''product'':3A,73C,94C ''pure'':96C,112C ''question'':83C ''r'':1A,4B,18C,31C,63C,97C,113C,126C ''reproduc'':20C ''scientif'':41C ''score'':8B,139C ''seem'':65C ''serious'':129C ''shini'':101C ''someon'':89C ''target'':24C ''therefor'':81C ''think'':150C ''topic'':123C ''ul'':86C ''us'':12C ''use'':17C ''world'':45C ''world-class'':44C ''would'':105C,143C ''yhat'':102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5248, 2, '2015-02-27 19:38:22.287', 3, NULL, NULL, '&lt;p&gt;In Hive itself? Unfortunately, the answer is simply no -- as the language definition manual shows, that statistic is simply not built in. In addition to the language manual, you can get more information on statistics in development in Hive &lt;a href="https://cwiki.apache.org/confluence/display/Hive/StatsDev" rel="nofollow"&gt;here&lt;/a&gt; and &lt;a href="https://cwiki.apache.org/confluence/display/Hive/StatisticsAndDataMining" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Having said that, there are plenty of ways to calculate Kendall''s W on data that''s in Hive. &lt;/p&gt;

&lt;p&gt;You could write out the data to a file or query it into R or a statistical package such as  SAS, Stat, MATLAB, Excel, etc then run your calculation and, if necessary, write your results back to Hive.&lt;/p&gt;

&lt;p&gt;In R, for instance, you could do something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;install.packages("RODBC")
require(RODBC)
db   &amp;lt;- odbcConnect("Hive_DB")
hql  &amp;lt;- "select * from table A"
data &amp;lt;- sqlQuery(db , hql)
kenw &amp;lt;- cor(x = data$a, y = data$b, method="kendall")
sqlSave(db, kenw, tablename = "new_table_of_kendall_coef")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;or (if using Linux or Unix) then you could use &lt;code&gt;RHive&lt;/code&gt; without needing to use an ODBC name.&lt;/p&gt;

&lt;p&gt;Another way to go about it would be to take the functions that do exist in Hive (which you linked to) and calculate Kendall''s coefficient yourself with a custom function. As to how to specifically implement that, well you''d probably want to post on Cross Validated (stats.stackexchange.com).&lt;/p&gt;
', NULL, NULL, '''/a'':49C,59C ''/code'':176C,191C ''/confluence/display/hive/statisticsanddatamining'':55C ''/confluence/display/hive/statsdev'':45C ''/p'':60C,81C,121C,133C,199C,250C ''/pre'':177C ''addit'':25C ''anoth'':201C ''answer'':7C ''b'':164C ''back'':118C ''built'':22C ''calcul'':71C,111C,223C ''code'':135C,189C ''coef'':175C ''coeffici'':226C ''cor'':158C ''could'':84C,128C,187C ''cross'':247C ''custom'':230C ''cwiki.apache.org'':44C,54C ''cwiki.apache.org/confluence/display/hive/statisticsanddatamining'':53C ''cwiki.apache.org/confluence/display/hive/statsdev'':43C ''d'':241C ''data'':76C,88C,151C,160C,163C ''db'':140C,144C,154C,168C ''definit'':14C ''develop'':38C ''etc'':107C ''excel'':106C ''exist'':215C ''file'':91C ''function'':212C,231C ''get'':32C ''go'':204C ''hive'':3C,40C,80C,120C,143C,217C ''hql'':145C,155C ''href'':42C,52C ''implement'':237C ''inform'':34C ''install.packages'':136C ''instanc'':126C ''kendal'':72C,166C,174C,224C ''kenw'':156C,169C ''languag'':13C,28C ''like'':131C ''link'':220C ''linux'':182C ''lt'':141C,146C,152C,157C ''manual'':15C,29C ''matlab'':105C ''method'':165C ''name'':198C ''necessari'':114C ''need'':193C ''new'':171C ''nofollow'':47C,57C ''odbc'':197C ''odbcconnect'':142C ''p'':1C,61C,82C,122C,178C,200C ''packag'':100C ''plenti'':67C ''post'':245C ''pre'':134C ''probabl'':242C ''queri'':93C ''r'':96C,124C ''rel'':46C,56C ''requir'':138C ''result'':117C ''rhive'':190C ''rodbc'':137C,139C ''run'':109C ''said'':63C ''sas'':103C ''select'':147C ''show'':16C ''simpli'':9C,20C ''someth'':130C ''specif'':236C ''sqlqueri'':153C ''sqlsave'':167C ''stat'':104C ''statist'':18C,36C,99C ''stats.stackexchange.com'':249C ''tabl'':149C,172C ''tablenam'':170C ''take'':210C ''unfortun'':5C ''unix'':184C ''use'':181C,188C,195C ''valid'':248C ''w'':74C ''want'':243C ''way'':69C,202C ''well'':239C ''without'':192C ''would'':207C ''write'':85C,115C ''x'':159C ''y'':162C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5249, 1, '2015-02-28 00:44:04.353', 1, 69, 'Combining data sets without using row.name', '&lt;p&gt;I start with a data.frame (or a data_frame) containing my dependent Y variable for analysis, my independent X variables, and some "Z" variables -- extra columns that I don''t need for my modeling exercise.&lt;/p&gt;

&lt;p&gt;What I would like to do is:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Create an analysis data set without the Z variables;&lt;/li&gt;
&lt;li&gt;Break this data set into random training and test sets;&lt;/li&gt;
&lt;li&gt;Find my best model;&lt;/li&gt;
&lt;li&gt;Predict on both the training and test sets using this model;&lt;/li&gt;
&lt;li&gt;Recombine the training and test sets by rows; and finally&lt;/li&gt;
&lt;li&gt;Recombine these data with the Z variables, by column.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;It''s the last step, of course, that presents the problem -- how do I make sure that the rows in the recombined training and test sets match the rows in the original data set? We might try to use the row.names variable from the original set, but I agree with Hadley that this is an error-prone kludge (my words, not his) -- why have a special column that''s treated differently from all other data columns?&lt;/p&gt;

&lt;p&gt;One alternative is to create an ID column that uniquely identifies each row, and then keep this column around when dividing into the train and test sets (but excluding it from all modeling formulas, of course). This seems clumsy as well, and would make all my formulas harder to read.&lt;/p&gt;

&lt;p&gt;This must be a solved problem -- could people tell me how they deal with this? Especially using the plyr/dplyr/tidyr package framework?&lt;/p&gt;
', 'machine-learning  r  predictive-modeling', NULL, '''/li'':71C,83C,89C,102C,114C,125C ''/ol'':126C ''/p'':50C,59C,205C,257C,280C ''agre'':176C ''altern'':208C ''analysi'':30C,64C ''around'':225C ''best'':87C ''break'':73C ''clumsi'':245C ''column'':40C,124C,195C,204C,214C,224C ''combin'':1A ''contain'':24C ''could'':265C ''cours'':134C,242C ''creat'':62C,211C ''data'':2A,22C,65C,75C,118C,160C,203C ''data.frame'':19C ''deal'':271C ''depend'':26C ''differ'':199C ''divid'':227C ''error'':184C ''error-pron'':183C ''especi'':274C ''exclud'':235C ''exercis'':49C ''extra'':39C ''final'':113C ''find'':85C ''formula'':240C,253C ''frame'':23C ''framework'':279C ''hadley'':178C ''harder'':254C ''id'':213C ''identifi'':217C ''independ'':32C ''keep'':222C ''kludg'':186C ''last'':131C ''learn'':9B ''li'':61C,72C,84C,90C,103C,115C ''like'':55C ''machin'':8B ''machine-learn'':7B ''make'':142C,250C ''match'':154C ''might'':163C ''model'':13B,48C,88C,101C,239C ''must'':260C ''need'':45C ''ol'':60C ''one'':207C ''origin'':159C,172C ''p'':14C,51C,127C,206C,258C ''packag'':278C ''peopl'':266C ''plyr/dplyr/tidyr'':277C ''predict'':12B,91C ''predictive-model'':11B ''present'':136C ''problem'':138C,264C ''prone'':185C ''r'':10B ''random'':78C ''read'':256C ''recombin'':104C,116C,149C ''row'':111C,146C,156C,219C ''row.name'':6A ''row.names'':168C ''seem'':244C ''set'':3A,66C,76C,82C,98C,109C,153C,161C,173C,233C ''solv'':263C ''special'':194C ''start'':16C ''step'':132C ''sure'':143C ''tell'':267C ''test'':81C,97C,108C,152C,232C ''train'':79C,95C,106C,150C,230C ''treat'':198C ''tri'':164C ''uniqu'':216C ''use'':5A,99C,166C,275C ''variabl'':28C,34C,38C,70C,122C,169C ''well'':247C ''without'':4A,67C ''word'':188C ''would'':54C,249C ''x'':33C ''y'':27C ''z'':37C,69C,121C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5763, 2, '2015-05-12 09:30:09.273', 2, NULL, NULL, '&lt;p&gt;It seems to be working well on my configuration:&lt;/p&gt;

&lt;p&gt;Ubuntu Vivid and R:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; sessionInfo()
R version 3.1.2 (2014-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] twitteR_1.1.8

loaded via a namespace (and not attached):
 [1] bit_1.1-12     bit64_0.9-4    bitops_1.0-6   DBI_0.3.1      httr_0.6.1    
 [6] magrittr_1.5   RCurl_1.95-4.6 rjson_0.2.15   stringi_0.4-1  stringr_1.0.0 
[11] tools_3.1.2  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Maybe you should update packages versions?&lt;/p&gt;
', NULL, NULL, '''-1'':135C ''-10'':26C ''-12'':114C ''-31'':27C ''-4'':117C ''-4.6'':130C ''-6'':120C ''-8'':42C,50C,54C,59C,63C,68C,83C ''/code'':141C ''/p'':11C,17C,150C ''/pre'':142C ''0.2.15'':132C ''0.3.1'':122C ''0.4'':134C ''0.6.1'':124C ''0.9'':116C ''1'':38C,90C,101C,111C ''1.0'':119C ''1.0.0'':137C ''1.1'':113C ''1.1.8'':103C ''1.5'':127C ''1.95'':129C ''11'':79C,138C ''2014'':25C ''3'':46C ''3.1.2'':24C,140C ''5'':55C ''6'':125C ''64'':30C,35C ''7'':64C ''9'':72C ''address'':74C ''attach'':87C,99C,110C ''base'':88C,97C ''bit'':36C,112C ''bit64'':115C ''bitop'':118C ''c'':45C,71C,75C,78C,86C ''code'':19C ''collat'':52C ''configur'':10C ''ctype'':40C ''dataset'':95C ''dbi'':121C ''en_us.utf'':41C,49C,53C,58C,62C,67C,82C ''gnu'':34C ''graphic'':92C ''grdevic'':93C ''gt'':20C ''httr'':123C ''identif'':85C ''lc'':39C,43C,47C,51C,56C,60C,65C,69C,73C,76C,80C,84C ''linux'':33C ''load'':104C ''local'':37C ''magrittr'':126C ''mayb'':144C ''measur'':81C ''messag'':61C ''method'':96C ''monetari'':57C ''name'':70C ''namespac'':107C ''numer'':44C ''p'':1C,12C,143C ''packag'':89C,100C,148C ''paper'':66C ''pc'':32C ''pc-linux-gnu'':31C ''platform'':28C ''pre'':18C ''r'':16C,22C ''rcurl'':128C ''rjson'':131C ''seem'':3C ''sessioninfo'':21C ''stat'':91C ''stringi'':133C ''stringr'':136C ''telephon'':77C ''time'':48C ''tool'':139C ''twitter'':102C ''ubuntu'':13C ''updat'':147C ''util'':94C ''version'':23C,149C ''via'':105C ''vivid'':14C ''well'':7C ''work'':6C ''x86'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5270, 2, '2015-03-03 05:59:08.82', 2, NULL, NULL, '&lt;p&gt;Training a model, related to &lt;em&gt;information extraction&lt;/em&gt;, in general, and &lt;em&gt;named entity recognition/resolution (NER)&lt;/em&gt;, in particular, is described in detail in &lt;strong&gt;Chapter 7&lt;/strong&gt; of the &lt;a href="http://www.nltk.org/book" rel="nofollow"&gt;NLTK Book&lt;/a&gt;, available online at this URL: &lt;a href="http://www.nltk.org/book/ch07.html" rel="nofollow"&gt;http://www.nltk.org/book/ch07.html&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Additionally, I think that you might find useful my &lt;a href="http://stats.stackexchange.com/a/136760/31372"&gt;related answer&lt;/a&gt; on &lt;em&gt;Cross Validated&lt;/em&gt; site. It has a lot of references to relevant &lt;em&gt;sources&lt;/em&gt; on NER and related topics as well as to various related &lt;em&gt;software&lt;/em&gt; tools.&lt;/p&gt;
', NULL, NULL, '''/a'':42C,75C ''/a/136760/31372'':72C ''/book'':37C ''/book/ch07.html'':52C ''/book/ch07.html&lt;/a&gt;.&lt;/p&gt;'':57C ''/em'':10C,19C,80C,92C,106C ''/p'':108C ''/strong'':30C ''7'':29C ''addit'':59C ''answer'':74C ''avail'':43C ''book'':41C ''chapter'':28C ''cross'':78C ''describ'':23C ''detail'':25C ''em'':7C,14C,77C,90C,104C ''entiti'':16C ''extract'':9C ''find'':65C ''general'':12C ''href'':34C,49C,69C ''inform'':8C ''lot'':85C ''might'':64C ''model'':4C ''name'':15C ''ner'':18C,94C ''nltk'':40C ''nofollow'':39C,54C ''onlin'':44C ''p'':1C,58C ''particular'':21C ''recognition/resolution'':17C ''refer'':87C ''rel'':38C,53C ''relat'':5C,73C,96C,103C ''relev'':89C ''site'':81C ''softwar'':105C ''sourc'':91C ''stats.stackexchange.com'':71C ''stats.stackexchange.com/a/136760/31372'':70C ''strong'':27C ''think'':61C ''tool'':107C ''topic'':97C ''train'':2C ''url'':47C ''use'':66C ''valid'':79C ''various'':102C ''well'':99C ''www.nltk.org'':36C,51C,56C ''www.nltk.org/book'':35C ''www.nltk.org/book/ch07.html'':50C ''www.nltk.org/book/ch07.html&lt;/a&gt;.&lt;/p&gt;'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8607, 2, '2015-10-26 12:09:50.823', 3, NULL, NULL, '&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Logistic_function" rel="nofollow"&gt;Logistic regression&lt;/a&gt; is very common and efficient.&lt;/p&gt;

&lt;p&gt;This will provide you with a probability of click for a given impression. You will likely need additional information for optimization, like keywords or A/B testing.&lt;/p&gt;
', NULL, NULL, '''/a'':11C ''/p'':17C,45C ''/wiki/logistic_function'':6C ''a/b'':43C ''addit'':36C ''click'':27C ''common'':14C ''effici'':16C ''en.wikipedia.org'':5C ''en.wikipedia.org/wiki/logistic_function'':4C ''given'':30C ''href'':3C ''impress'':31C ''inform'':37C ''keyword'':41C ''like'':34C,40C ''logist'':9C ''need'':35C ''nofollow'':8C ''optim'':39C ''p'':1C,18C ''probabl'':25C ''provid'':21C ''regress'':10C ''rel'':7C ''test'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5251, 2, '2015-02-28 17:10:41.147', 1, NULL, NULL, '&lt;p&gt;R and most of its CRAN modules are licensed using the GPL.&lt;/p&gt;

&lt;p&gt;In many companies, legal departments go &lt;em&gt;crazy&lt;/em&gt; if you propose to use anything that is GPL in production... It''s not reasonable, but you''ll see they love Apache, and hate GPL. &lt;strong&gt;Before going into production, make sure it''s okay with the legal department.&lt;/strong&gt; (IMHO you are safe to &lt;em&gt;use&lt;/em&gt; your modified code for &lt;em&gt;internal&lt;/em&gt; products. Integrating R into your commercial product and handing this out to others is very different. But unfortunately, many legal departments try to ban all use of GPL whatsoever.)&lt;/p&gt;

&lt;p&gt;Other than that, R is often really slooow unless calling Fortran code hidden inside. It''s nice when you are still trying to figure out what to do. But for production, you may want maximum performance, and full integration with your services. &lt;em&gt;Benchmark&lt;/em&gt; yourself, if R is the best choice for your use case.&lt;/p&gt;

&lt;p&gt;On the performance issues with R (I know R advocates are going to downvote me for saying so ...):&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Morandat, F., Hill, B., Osvald, L., &amp;amp; Vitek, J. (2012). Evaluating the design of the R language. In ECOOP 2012âObject-Oriented Programming (pp. 104-131). Springer Berlin Heidelberg.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;(by the TraceR/ProfileR/ReactoR people from purdue, who are now working on fastR which tries to execute R code on the JVM?) states:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;On those benchmarks, &lt;strong&gt;R is on average 501 slower than C and 43 times slower Python.&lt;/strong&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;and:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Observations. &lt;strong&gt;R is clearly slow and memory inefficient.&lt;/strong&gt; Much more so than other dynamic languages. This is largely due to the combination of language features (call-by-value, extreme dynamism, lazy evaluation) and the lack of efficient built-in types. We believe that with some effort it should be possible to improve both time and space usage, but this would likely &lt;strong&gt;require a full rewrite of the implementation&lt;/strong&gt;.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Sorry to break the news. It''s now my research, but it aligns with my observations.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5305, 1, '2015-03-11 05:25:47.053', 1, 621, 'How do I get Twitter Dataset for Visualization', '&lt;p&gt;I am a newbie. I would like to do visualization on twitter data : top trends based on country (over map) and time variations (for each months in 1 year or for each year) . Can someone tell me where can I get the twitter data set and any advice on how to start proceeding would be really help full.   &lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'data-mining  clustering  data-cleaning  visualization', NULL, '''/p'':76C,79C ''1'':45C ''advic'':65C ''base'':33C ''clean'':15B ''cluster'':12B ''countri'':35C ''data'':10B,14B,30C,61C ''data-clean'':13B ''data-min'':9B ''dataset'':6A ''full'':75C ''get'':4A,58C ''help'':74C ''like'':24C ''map'':37C ''mine'':11B ''month'':43C ''newbi'':21C ''p'':17C,77C ''proceed'':70C ''realli'':73C ''set'':62C ''someon'':52C ''start'':69C ''tell'':53C ''thank'':78C ''time'':39C ''top'':31C ''trend'':32C ''twitter'':5A,29C,60C ''variat'':40C ''visual'':8A,16B,27C ''would'':23C,71C ''year'':46C,50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5252, 2, '2015-02-28 17:15:39.423', 3, NULL, NULL, '&lt;p&gt;As a fellow CS Ph.D. defending my dissertation in a Big Data-esque topic this year (I started in 2012), the best piece of material I can give you is in a link:
&lt;a href="http://www.rpajournal.com/dev/wp-content/uploads/2014/10/A3.pdf" rel="nofollow"&gt;http://www.rpajournal.com/dev/wp-content/uploads/2014/10/A3.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This is an article written by two Ph.D.s from MIT who have talked about Big Data and MOOCs. Probably, you will find this a good starting point. BTW, along this note, if you really want to come up with a valid topic (that a committee and your adviser will let you propose, research and defend) you need to read LOTS and LOTS of papers. The majority of Ph.D. students make the fatal error of thinking that some ''idea'' they have is new, when it''s not and has already been done. You''ll have to do something truly original to earn your Ph.D. Rather than actually focus on forming an idea right now, you should do a good literature survey and the ideas will ''suggest themselves''. Good luck! It''s an exciting time for you.&lt;/p&gt;
', NULL, NULL, '''/dev/wp-content/uploads/2014/10/a3.pdf'':40C ''/dev/wp-content/uploads/2014/10/a3.pdf&lt;/a&gt;&lt;/p&gt;'':45C ''/p'':182C ''2012'':22C ''actual'':152C ''advis'':94C ''along'':75C ''alreadi'':135C ''articl'':50C ''best'':24C ''big'':12C,61C ''btw'':74C ''come'':83C ''committe'':91C ''cs'':5C ''data'':14C,62C ''data-esqu'':13C ''defend'':7C,101C ''dissert'':9C ''done'':137C ''earn'':147C ''error'':119C ''esqu'':15C ''excit'':178C ''fatal'':118C ''fellow'':4C ''find'':68C ''focus'':153C ''form'':155C ''give'':30C ''good'':71C,164C,173C ''href'':37C ''idea'':124C,157C,169C ''let'':96C ''link'':35C ''literatur'':165C ''ll'':139C ''lot'':106C,108C ''luck'':174C ''major'':112C ''make'':116C ''materi'':27C ''mit'':56C ''mooc'':64C ''need'':103C ''new'':128C ''nofollow'':42C ''note'':77C ''origin'':145C ''p'':1C,46C ''paper'':110C ''ph.d'':6C,114C,149C ''ph.d.s'':54C ''piec'':25C ''point'':73C ''probabl'':65C ''propos'':98C ''rather'':150C ''read'':105C ''realli'':80C ''rel'':41C ''research'':99C ''right'':158C ''someth'':143C ''start'':20C,72C ''student'':115C ''suggest'':171C ''survey'':166C ''talk'':59C ''think'':121C ''time'':179C ''topic'':16C,88C ''truli'':144C ''two'':53C ''valid'':87C ''want'':81C ''written'':51C ''www.rpajournal.com'':39C,44C ''www.rpajournal.com/dev/wp-content/uploads/2014/10/a3.pdf'':38C ''www.rpajournal.com/dev/wp-content/uploads/2014/10/a3.pdf&lt;/a&gt;&lt;/p&gt;'':43C ''year'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5253, 2, '2015-02-28 17:21:46.3', 1, NULL, NULL, '&lt;p&gt;I would actually try out regression. Also, don''t make the mistake of using the serial number in your machine learning algorithms! The reason why I''m suggesting regression as opposed to ''better'' machine learning algorithms is because you said you wanted to learn, and it''s important to understand the algorithms (for the long run, and to truly be good at this stuff) that you''re using. Regression is the easiest tool in the book that works quite well! Weka is so easy to use that you''ll be able to plug and play different machine learning algorithms just for the sake of it. Another pointer that''s won me several competitions is to do some feature selection before using regression/machine learning. For example, in your case, it is reasonable to assume that a student who scores high in Physics probably has a better chance of scoring high in Math as opposed to someone who scores high in English (but not necessarily Physics). If you have enough data, the algorithm itself will be able to deduce these positive/negative correlations and train the model accordingly. Sometimes, there isn''t enough data, and you have to do some feature selection. Good luck! I''m a regular participant on Kaggle myself, and I think it''s great that you''re taking the ''hacker'' route to learn more. It''s the best way to get your hands dirty on real data and engineering problems. &lt;/p&gt;
', NULL, NULL, '''/p'':243C ''abl'':92C,176C ''accord'':186C ''actual'':4C ''algorithm'':23C,37C,53C,100C,172C ''also'':8C ''anoth'':107C ''assum'':134C ''best'':230C ''better'':34C,146C ''book'':77C ''case'':129C ''chanc'':147C ''competit'':114C ''correl'':181C ''data'':170C,192C,239C ''deduc'':178C ''differ'':97C ''dirti'':236C ''easi'':85C ''easiest'':73C ''engin'':241C ''english'':161C ''enough'':169C,191C ''exampl'':126C ''featur'':119C,199C ''get'':233C ''good'':62C,201C ''great'':216C ''hacker'':222C ''hand'':235C ''high'':140C,150C,159C ''import'':49C ''isn'':189C ''kaggl'':209C ''learn'':22C,36C,45C,99C,124C,225C ''ll'':90C ''long'':56C ''luck'':202C ''m'':28C,204C ''machin'':21C,35C,98C ''make'':11C ''math'':152C ''mistak'':13C ''model'':185C ''necessarili'':164C ''number'':18C ''oppos'':32C,154C ''p'':1C ''particip'':207C ''physic'':142C,165C ''play'':96C ''plug'':94C ''pointer'':108C ''positive/negative'':180C ''probabl'':143C ''problem'':242C ''quit'':80C ''re'':68C,219C ''real'':238C ''reason'':25C,132C ''regress'':7C,30C,70C ''regression/machine'':123C ''regular'':206C ''rout'':223C ''run'':57C ''said'':41C ''sake'':104C ''score'':139C,149C,158C ''select'':120C,200C ''serial'':17C ''sever'':113C ''someon'':156C ''sometim'':187C ''student'':137C ''stuff'':65C ''suggest'':29C ''take'':220C ''think'':213C ''tool'':74C ''train'':183C ''tri'':5C ''truli'':60C ''understand'':51C ''use'':15C,69C,87C,122C ''want'':43C ''way'':231C ''weka'':82C ''well'':81C ''won'':111C ''work'':79C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5254, 2, '2015-02-28 21:42:31.383', 0, NULL, NULL, '&lt;p&gt;If you know your approach is working, you can try to implement it more efficiently. Identify the crucial points, try to &lt;em&gt;vectorize&lt;/em&gt; them better, for example.&lt;/p&gt;

&lt;p&gt;The R interpreter isn''t the fastest. There are some efforts underway, but they are not yet ready. Vectorization (which means less interpreter, more low-level code) often yields a factor of 2x-5x, but you can sometimes get a factor of 100x by implementing it e.g. in C. (And the R advocates are going to hate me for saying this...)
Once you know that your approach is working, this may be worth the effort.&lt;/p&gt;
', NULL, NULL, '''/em'':25C ''/p'':30C,109C ''100x'':76C ''2x'':66C ''2x-5x'':65C ''5x'':67C ''advoc'':86C ''approach'':6C,100C ''better'':27C ''c'':82C ''code'':59C ''crucial'':19C ''e.g'':80C ''effici'':16C ''effort'':42C,108C ''em'':23C ''exampl'':29C ''factor'':63C,74C ''fastest'':38C ''get'':72C ''go'':88C ''hate'':90C ''identifi'':17C ''implement'':13C,78C ''interpret'':34C,54C ''isn'':35C ''know'':4C,97C ''less'':53C ''level'':58C ''low'':57C ''low-level'':56C ''may'':104C ''mean'':52C ''often'':60C ''p'':1C,31C ''point'':20C ''r'':33C,85C ''readi'':49C ''say'':93C ''sometim'':71C ''tri'':11C,21C ''underway'':43C ''vector'':24C,50C ''work'':8C,102C ''worth'':106C ''yet'':48C ''yield'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6195, 2, '2015-06-23 22:52:02.133', 6, NULL, NULL, '&lt;p&gt;"Raw data" is what we say in NLP.&lt;/p&gt;
', NULL, NULL, '''/p'':10C ''data'':3C ''nlp'':9C ''p'':1C ''raw'':2C ''say'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5255, 1, '2015-02-28 22:10:58.473', 9, 411, 'What is an ''old name'' of data scientist?', '&lt;p&gt;Terms like ''data science'' and ''data scientist'' are increasingly used these days.
Many companies are hiring ''data scientist''. But I don''t think it''s a completely new job.
Data have existed from the past and someone had to deal with data. 
I guess the term ''data scientist'' becomes more popular because it sounds more fancy and ''sexy''
How were data scientists called in the past?&lt;/p&gt;
', 'bigdata', NULL, '''/p'':77C ''becom'':59C ''bigdata'':9B ''call'':73C ''compani'':24C ''complet'':37C ''data'':7A,13C,16C,27C,40C,52C,57C,71C ''day'':22C ''deal'':50C ''exist'':42C ''fanci'':66C ''guess'':54C ''hire'':26C ''increas'':19C ''job'':39C ''like'':12C ''mani'':23C ''name'':5A ''new'':38C ''old'':4A ''p'':10C ''past'':45C,76C ''popular'':61C ''scienc'':14C ''scientist'':8A,17C,28C,58C,72C ''sexi'':68C ''someon'':47C ''sound'':64C ''term'':11C,56C ''think'':33C ''use'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5256, 2, '2015-02-28 23:21:40.43', 12, NULL, NULL, '&lt;p&gt;In reverse chronological order: data miner, statistician, (applied) mathematician.&lt;/p&gt;
', NULL, NULL, '''/p'':11C ''appli'':9C ''chronolog'':4C ''data'':6C ''mathematician'':10C ''miner'':7C ''order'':5C ''p'':1C ''revers'':3C ''statistician'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5257, 1, '2015-03-01 02:56:05.59', 1, 74, 'Good formula for turning star reviews into upvotes', '&lt;p&gt;I want to turn reviews of up to 5 stars and the number of reviews into upvotes. What''s a good algorithm for doing this?&lt;/p&gt;

&lt;p&gt;A venue with 10 reviews total with a 5-star average rating should obviously get more upvotes than a venue with 10 reviews total with a 3-star average rating. Also, a venue with 60 ratings and a 4-star rating should probably get more upvotes than the one with 10 reviews and a 5-star rating.&lt;/p&gt;

&lt;p&gt;I need this rating to be based off of the total number of reviews and the average star rating, but I would also like the number to stay below a variable number (for example, say upvotes stay below 100, but I can also plug in 200 and it would stay below 200).&lt;/p&gt;
', 'algorithms', NULL, '''/p'':36C,95C,149C ''10'':41C,59C,88C ''100'':135C ''200'':142C,148C ''3'':64C ''4'':76C ''5'':19C,46C,92C ''60'':72C ''algorithm'':9B,32C ''also'':68C,119C,139C ''averag'':48C,66C,113C ''base'':103C ''exampl'':130C ''formula'':2A ''get'':52C,81C ''good'':1A,31C ''like'':120C ''need'':98C ''number'':23C,108C,122C,128C ''obvious'':51C ''one'':86C ''p'':10C,37C,96C ''plug'':140C ''probabl'':80C ''rate'':49C,67C,73C,78C,94C,100C,115C ''review'':6A,15C,25C,42C,60C,89C,110C ''say'':131C ''star'':5A,20C,47C,65C,77C,93C,114C ''stay'':124C,133C,146C ''total'':43C,61C,107C ''turn'':4A,14C ''upvot'':8A,27C,54C,83C,132C ''variabl'':127C ''venu'':39C,57C,70C ''want'':12C ''would'':118C,145C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5259, 2, '2015-03-01 07:16:51.44', 1, NULL, NULL, '&lt;p&gt;You need neither to use the row names or to create an additonal ID column. Here is an approach based on the indices of the training set.&lt;/p&gt;

&lt;p&gt;An example data set:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;set.seed(1)
dat &amp;lt;- data.frame(Y = rnorm(10),
                  X1 = rnorm(10),
                  X2 = rnorm(10),
                  Z1 = rnorm(10),
                  Z2 = rnorm(10))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now, your steps:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Create an analysis data set without the &lt;code&gt;Z&lt;/code&gt; variables&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dat2 &amp;lt;- dat[grep("Z", names(dat), invert = TRUE)]
dat2
#             Y          X1          X2
# 1  -0.6264538  1.51178117  0.91897737
# 2   0.1836433  0.38984324  0.78213630
# 3  -0.8356286 -0.62124058  0.07456498
# 4   1.5952808 -2.21469989 -1.98935170
# 5   0.3295078  1.12493092  0.61982575
# 6  -0.8204684 -0.04493361 -0.05612874
# 7   0.4874291 -0.01619026 -0.15579551
# 8   0.7383247  0.94383621 -1.47075238
# 9   0.5757814  0.82122120 -0.47815006
# 10 -0.3053884  0.59390132  0.41794156
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Break this data set into random training and test sets&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;train_idx &amp;lt;- sample(nrow(dat2), 0.8 * nrow(dat2))
train_idx
# [1]  7  4  3 10  9  2  1  5

train &amp;lt;- dat2[train_idx, ]
train
#             Y          X1          X2
# 7   0.4874291 -0.01619026 -0.15579551
# 4   1.5952808 -2.21469989 -1.98935170
# 3  -0.8356286 -0.62124058  0.07456498
# 10 -0.3053884  0.59390132  0.41794156
# 9   0.5757814  0.82122120 -0.47815006
# 2   0.1836433  0.38984324  0.78213630
# 1  -0.6264538  1.51178117  0.91897737
# 5   0.3295078  1.12493092  0.61982575

test_idx &amp;lt;- setdiff(seq(nrow(dat2)), train_idx)
test_idx
# [1] 6 8

test &amp;lt;- dat2[test_idx, ]
test
#            Y          X1          X2
# 6 -0.8204684 -0.04493361 -0.05612874
# 8  0.7383247  0.94383621 -1.47075238
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Find my best model&lt;/p&gt;

&lt;p&gt;...&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Predict on both the training and test sets using this model&lt;/p&gt;

&lt;p&gt;...&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Recombine the training and test sets by rows&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;idx &amp;lt;- order(c(train_idx, test_idx))
dat3 &amp;lt;- rbind(train, test)[idx, ]
identical(dat3, dat2)
# [1] TRUE
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Recombine these data with the &lt;code&gt;Z&lt;/code&gt; variables, by column&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dat4 &amp;lt;- cbind(dat3, dat[grep("Z", names(dat))])
identical(dat, dat4)
# [1] TRUE
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;In summary, we can use the indices of the training and test data to combine the data in the rows in the original order.&lt;/p&gt;
', NULL, NULL, '''-0.01619026'':121C,184C ''-0.04493361'':117C,239C ''-0.05612874'':118C,240C ''-0.15579551'':122C,185C ''-0.3053884'':132C,195C ''-0.47815006'':130C,201C ''-0.62124058'':105C,192C ''-0.6264538'':96C,207C ''-0.8204684'':116C,238C ''-0.8356286'':104C,191C ''-1.47075238'':126C,244C ''-1.98935170'':110C,189C ''-2.21469989'':109C,188C ''/code'':58C,77C,135C,245C,307C,319C,340C ''/li'':137C,247C,257C,274C,309C,342C ''/ol'':343C ''/p'':29C,35C,64C,79C,150C,254C,256C,271C,273C,285C,323C,369C ''/pre'':59C,136C,246C,308C,341C ''0.07456498'':106C,193C ''0.1836433'':100C,203C ''0.3295078'':112C,211C ''0.38984324'':101C,204C ''0.41794156'':134C,197C ''0.4874291'':120C,183C ''0.5757814'':128C,199C ''0.59390132'':133C,196C ''0.61982575'':114C,213C ''0.7383247'':124C,242C ''0.78213630'':102C,205C ''0.8'':159C ''0.82122120'':129C,200C ''0.91897737'':98C,209C ''0.94383621'':125C,243C ''1'':39C,95C,164C,171C,206C,225C,305C,338C ''1.12493092'':113C,212C ''1.51178117'':97C,208C ''1.5952808'':108C,187C ''10'':45C,48C,51C,54C,57C,131C,168C,194C ''2'':99C,170C,202C ''3'':103C,167C,190C ''4'':107C,166C,186C ''5'':111C,172C,210C ''6'':115C,226C,237C ''7'':119C,165C,182C ''8'':123C,227C,241C ''9'':127C,169C,198C ''additon'':14C ''analysi'':70C ''approach'':20C ''base'':21C ''best'':252C ''break'':140C ''c'':291C ''cbind'':328C ''code'':37C,75C,81C,152C,287C,317C,325C ''column'':16C,322C ''combin'':359C ''creat'':12C,68C ''dat'':40C,84C,88C,330C,334C,336C ''dat2'':82C,91C,158C,161C,175C,220C,230C,304C ''dat3'':296C,303C,329C ''dat4'':326C,337C ''data'':33C,71C,142C,314C,357C,361C ''data.frame'':42C ''exampl'':32C ''find'':250C ''grep'':85C,331C ''id'':15C ''ident'':302C,335C ''idx'':154C,163C,177C,215C,222C,224C,232C,288C,293C,295C,301C ''indic'':24C,351C ''invert'':89C ''li'':66C,138C,248C,258C,275C,310C ''lt'':41C,83C,155C,174C,216C,229C,289C,297C,327C ''model'':253C,270C ''name'':9C,87C,333C ''need'':3C ''neither'':4C ''nrow'':157C,160C,219C ''ol'':65C ''order'':290C,368C ''origin'':367C ''p'':1C,30C,60C,67C,139C,249C,255C,259C,272C,276C,311C,344C ''pre'':36C,80C,151C,286C,324C ''predict'':260C ''random'':145C ''rbind'':298C ''recombin'':277C,312C ''rnorm'':44C,47C,50C,53C,56C ''row'':8C,284C,364C ''sampl'':156C ''seq'':218C ''set'':28C,34C,72C,143C,149C,267C,282C ''set.seed'':38C ''setdiff'':217C ''step'':63C ''summari'':346C ''test'':148C,214C,223C,228C,231C,233C,266C,281C,294C,300C,356C ''train'':27C,146C,153C,162C,173C,176C,178C,221C,264C,279C,292C,299C,354C ''true'':90C,306C,339C ''use'':6C,268C,349C ''variabl'':78C,320C ''without'':73C ''x1'':46C,93C,180C,235C ''x2'':49C,94C,181C,236C ''y'':43C,92C,179C,234C ''z'':76C,86C,318C,332C ''z1'':52C ''z2'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5263, 2, '2015-03-02 05:05:55.497', 3, NULL, NULL, '&lt;p&gt;Try using Earth Mover''s Distance (&lt;a href="http://ai.stanford.edu/~rubner/papers/rubnerIjcv00.pdf" rel="nofollow"&gt;http://ai.stanford.edu/~rubner/papers/rubnerIjcv00.pdf&lt;/a&gt;). It measures how much it "costs" to optimally reshape one histogram into another, where an elementary transform (using the terminology of edit distance-like distance measures) is moving a unit of mass from a bin to a bin. If you have an implementation of EMD, then your distance is dist = EMD(H1, H2, D), where&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; H1 = [ 14, 13, 40, 31 ]; -- histogram #1 (first image)
 H2 = [ 82, 8, 7 ]; -- histogram #2 (second image)
 D  = [ D11, D12, D13; ... ; D41, D42, D43 ]; -- (cross-bin) ground distance
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Dij is a distance from the i''th bin of the first histogram to the j''th bin of the second histogram. For example, D21 is a distance between ''White'' and ''Pink''. It may be defined differently, based on which color space you want to use, and further, based on how you define what it means for two colors to be similar. If I use RGB, and I do not care much as to how human eye perceives colors, then D21 can be the \ell_2 norm of the difference [1, 1, 1] - [1, 0.05, 0.7]. (As far as I remember, the paper I mentioned uses LAB instead of RGB.)&lt;/p&gt;

&lt;p&gt;P.S. Besides color, you may include spatial information into comparison. EMD can handle it well, as long as you properly combine similarity of colors with spatial similarity in the definition of ground distance D.&lt;/p&gt;
', NULL, NULL, '''/code'':106C ''/p'':74C,219C,255C ''/pre'':107C ''/~rubner/papers/rubnerijcv00.pdf'':12C ''/~rubner/papers/rubnerijcv00.pdf&lt;/a&gt;).'':17C ''0.05'':203C ''0.7'':204C ''1'':83C,199C,200C,201C,202C ''13'':79C ''14'':78C ''2'':91C,194C ''31'':81C ''40'':80C ''7'':89C ''8'':88C ''82'':87C ''ai.stanford.edu'':11C,16C ''ai.stanford.edu/~rubner/papers/rubnerijcv00.pdf'':10C ''ai.stanford.edu/~rubner/papers/rubnerijcv00.pdf&lt;/a&gt;).'':15C ''anoth'':30C ''base'':146C,157C ''besid'':222C ''bin'':53C,56C,103C,117C,126C ''care'':179C ''code'':76C ''color'':149C,167C,187C,223C,244C ''combin'':241C ''comparison'':230C ''cost'':23C ''cross'':102C ''cross-bin'':101C ''d'':72C,94C,254C ''d11'':95C ''d12'':96C ''d13'':97C ''d21'':133C,189C ''d41'':98C ''d42'':99C ''d43'':100C ''defin'':144C,161C ''definit'':250C ''differ'':145C,198C ''dij'':109C ''dist'':68C ''distanc'':7C,41C,43C,66C,105C,112C,136C,253C ''distance-lik'':40C ''earth'':4C ''edit'':39C ''elementari'':33C ''ell'':193C ''emd'':63C,69C,231C ''exampl'':132C ''eye'':185C ''far'':206C ''first'':84C,120C ''ground'':104C,252C ''h1'':70C,77C ''h2'':71C,86C ''handl'':233C ''histogram'':28C,82C,90C,121C,130C ''href'':9C ''human'':184C ''imag'':85C,93C ''implement'':61C ''includ'':226C ''inform'':228C ''instead'':216C ''j'':124C ''lab'':215C ''like'':42C ''long'':237C ''mass'':50C ''may'':142C,225C ''mean'':164C ''measur'':19C,44C ''mention'':213C ''move'':46C ''mover'':5C ''much'':21C,180C ''nofollow'':14C ''norm'':195C ''one'':27C ''optim'':25C ''p'':1C,108C,220C ''p.s'':221C ''paper'':211C ''perceiv'':186C ''pink'':140C ''pre'':75C ''proper'':240C ''rel'':13C ''rememb'':209C ''reshap'':26C ''rgb'':174C,218C ''second'':92C,129C ''similar'':170C,242C,247C ''space'':150C ''spatial'':227C,246C ''terminolog'':37C ''th'':116C,125C ''transform'':34C ''tri'':2C ''two'':166C ''unit'':48C ''use'':3C,35C,154C,173C,214C ''want'':152C ''well'':235C ''white'':138C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8597, 1, '2015-10-25 20:32:16.883', 2, 9, 'Number of SURF features normally used?', '&lt;p&gt;I am interesting in knowing an approximate amount of &lt;a href="https://en.wikipedia.org/wiki/Speeded_up_robust_features" rel="nofollow"&gt;SURF features&lt;/a&gt; used to do e.g. image-recognition on 224x224x3 images. Is there a standard amount of features used? Or perhaps a well-acclaimed article using SURF features that I could look at? I am not going to work with the features, but I am just trying to understand some aspects of their usage.&lt;/p&gt;
', 'image-classification', NULL, '''/a'':29C ''/p'':84C ''/wiki/speeded_up_robust_features'':24C ''224x224x3'':38C ''acclaim'':53C ''amount'':18C,44C ''approxim'':17C ''articl'':54C ''aspect'':80C ''classif'':9B ''could'':60C ''e.g'':33C ''en.wikipedia.org'':23C ''en.wikipedia.org/wiki/speeded_up_robust_features'':22C ''featur'':4A,28C,46C,57C,71C ''go'':66C ''href'':21C ''imag'':8B,35C,39C ''image-classif'':7B ''image-recognit'':34C ''interest'':13C ''know'':15C ''look'':61C ''nofollow'':26C ''normal'':5A ''number'':1A ''p'':10C ''perhap'':49C ''recognit'':36C ''rel'':25C ''standard'':43C ''surf'':3A,27C,56C ''tri'':76C ''understand'':78C ''usag'':83C ''use'':6A,30C,47C,55C ''well'':52C ''well-acclaim'':51C ''work'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5265, 2, '2015-03-02 10:06:27.483', 3, NULL, NULL, '&lt;p&gt;Reinforcement learning does not depend on a grid world.  It can be applied to any space of possibilities where there is a "fitness function" that maps between points in the space to a fitness metric.&lt;/p&gt;

&lt;p&gt;Topological spaces have a formally-defined "neighborhoods" but do not necessarily conform to a grid or any dimensional representation.  In a topological space, the only way to get from "here" to "there" is via some "paths" which are sets of contiguous neighborhoods. Continuous fitness functions can be defined over topological spaces.&lt;/p&gt;

&lt;p&gt;For what it is worth, reinforcement learning is not the be-all-end-all (family of) learning algorithms in fitness landscapes.  In a sufficiently rugged fitness landscape, other learning algorithms can perform better.  Also, if there are regions of the space where there are no well-defined fitness function at a given point in time, it may be indeterminate as to what learning algorithms are optimal, if any.&lt;/p&gt;
', NULL, NULL, '''/p'':37C,91C,163C ''algorithm'':111C,123C,158C ''also'':127C ''appli'':14C ''be-all-end-al'':103C ''better'':126C ''conform'':51C ''contigu'':80C ''continu'':82C ''defin'':45C,87C,141C ''depend'':6C ''dimension'':57C ''end'':106C ''famili'':108C ''fit'':24C,35C,83C,113C,119C,142C ''formal'':44C ''formally-defin'':43C ''function'':25C,84C,143C ''get'':67C ''given'':146C ''grid'':9C,54C ''indetermin'':153C ''landscap'':114C,120C ''learn'':3C,99C,110C,122C,157C ''map'':27C ''may'':151C ''metric'':36C ''necessarili'':50C ''neighborhood'':46C,81C ''optim'':160C ''p'':1C,38C,92C ''path'':75C ''perform'':125C ''point'':29C,147C ''possibl'':19C ''region'':131C ''reinforc'':2C,98C ''represent'':58C ''rug'':118C ''set'':78C ''space'':17C,32C,40C,62C,90C,134C ''suffici'':117C ''time'':149C ''topolog'':39C,61C,89C ''via'':73C ''way'':65C ''well'':140C ''well-defin'':139C ''world'':10C ''worth'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5267, 1, '2015-03-02 21:15:34.48', 3, 479, 'Predicting Soccer: guessing which matches a model will predict correcly', '&lt;p&gt;I took on a project to predict the outcome of soccer matches but it turned out to be a very challenging task.
I tried out different models but I only got 50-54% accuracy on my test dataset. Some of the models were created in such
a way that a certain model would predict if a team will win, draw, or loose a match. That same model would also predict if
the opponent of that team will win, draw, or loose the match. Each model predicting with an accuracy of about 50% on each team distinctively. The second set of models I tried, takes the combination of data from both teams and predicts which class the match belongs to (home win, away win, draw). In the system,
only 10 matches are given everyday to be predicted. Meaning, if I predict the 10 matches using the second model, I have a chance of predicting 5 
correctly. In this project, I only need to predict 3 matches correctly out of the 10 matches given in a day. Is there a system
of knowing the 3 matches which my models have the best chance of predicting correctly? I only need to get 3 correct, I usually
get 5 correctly but I don''t know how to select my 3 best matches. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Note:&lt;/strong&gt; The first type of models use about 50 features for prediction while the second uses 101. I''ve tried ensembles, they still give 
me ~50% accuracy. I''m still about to setup a system that selects matches where the prediction for the home team does not 
contradict the prediction for the away team using the first type of model.&lt;/p&gt;
', 'machine-learning  predictive-modeling', NULL, '''-54'':50C ''/p'':236C,300C ''/strong'':240C ''10'':146C,159C,187C ''101'':256C ''3'':181C,200C,217C,233C ''5'':171C,222C ''50'':49C,109C,248C,265C ''accuraci'':51C,106C,266C ''also'':86C ''away'':139C,292C ''belong'':135C ''best'':207C,234C ''certain'':68C ''challeng'':38C ''chanc'':168C,208C ''class'':132C ''combin'':123C ''contradict'':287C ''correc'':10A ''correct'':172C,183C,211C,218C,223C ''creat'':61C ''data'':125C ''dataset'':55C ''day'':192C ''differ'':43C ''distinct'':113C ''draw'':77C,96C,141C ''ensembl'':260C ''everyday'':150C ''featur'':249C ''first'':242C,296C ''get'':216C,221C ''give'':263C ''given'':149C,189C ''got'':48C ''guess'':3A ''home'':137C,283C ''know'':198C,228C ''learn'':13B ''loos'':79C,98C ''m'':268C ''machin'':12B ''machine-learn'':11B ''match'':5A,29C,81C,100C,134C,147C,160C,182C,188C,201C,235C,277C ''mean'':154C ''model'':7A,16B,44C,59C,69C,84C,102C,118C,164C,204C,245C,299C ''need'':178C,214C ''note'':239C ''oppon'':90C ''outcom'':26C ''p'':17C,237C ''predict'':1A,9A,15B,24C,71C,87C,103C,130C,153C,157C,170C,180C,210C,251C,280C,289C ''predictive-model'':14B ''project'':22C,175C ''second'':115C,163C,254C ''select'':231C,276C ''set'':116C ''setup'':272C ''soccer'':2A,28C ''still'':262C,269C ''strong'':238C ''system'':144C,196C,274C ''take'':121C ''task'':39C ''team'':74C,93C,112C,128C,284C,293C ''test'':54C ''took'':19C ''tri'':41C,120C,259C ''turn'':32C ''type'':243C,297C ''use'':161C,246C,255C,294C ''usual'':220C ''ve'':258C ''way'':65C ''win'':76C,95C,138C,140C ''would'':70C,85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5268, 1, '2015-03-02 23:02:45.583', 3, 162, 'How to detect overfitting of a stock screener', '&lt;p&gt;The project I am working on allows users to create Stock Screeners based on both technical and fundamental criteria.  Stock Screeners are then "backtested" by simulating the results of applying in over the last 10 years using Point-in-Time data. I get back the list of trades and overall graph of performance.
(If that is unclear, I have an overview &lt;a href="https://www.equitieslab.com/features/stock-screener/" rel="nofollow"&gt;here&lt;/a&gt; and &lt;a href="https://www.equitieslab.com/wiki/QuickStart/StockScreener" rel="nofollow"&gt;there&lt;/a&gt; with more details).&lt;/p&gt;

&lt;p&gt;Now a common problem is that users create overfitted stock screeners. I would love to give them a warning when the screen is likely to be over-fitted.&lt;/p&gt;

&lt;p&gt;Fields I have to work with&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;All trades made by the Stock Screener
&lt;ul&gt;
&lt;li&gt;Stock, Start Date, Start Price, End Date, End Price&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;S&amp;amp;P 500 performance for the same time frame&lt;/li&gt;
&lt;li&gt;Market Cap, Sector, and Industry of each Stock&lt;/li&gt;
&lt;/ul&gt;
', 'machine-learning  data-mining  classification  bigdata  statistics', NULL, '''/a'':89C,99C ''/features/stock-screener/'':85C ''/li'':163C,165C,177C,187C ''/p'':103C,134C,142C ''/ul'':164C,188C ''/wiki/quickstart/stockscreener'':95C ''10'':53C ''500'':170C ''allow'':25C ''amp'':168C ''appli'':48C ''back'':63C ''backtest'':42C ''base'':31C ''bigdata'':16B ''cap'':180C ''classif'':15B ''common'':107C ''creat'':28C,112C ''criteria'':37C ''data'':13B,60C ''data-min'':12B ''date'':156C,160C ''detail'':102C ''detect'':3A ''end'':159C,161C ''field'':136C ''fit'':133C ''frame'':176C ''fundament'':36C ''get'':62C ''give'':120C ''graph'':70C ''href'':82C,92C ''industri'':183C ''last'':52C ''learn'':11B ''li'':144C,153C,166C,178C ''like'':128C ''list'':65C ''love'':118C ''machin'':10B ''machine-learn'':9B ''made'':147C ''market'':179C ''mine'':14B ''nofollow'':87C,97C ''over-fit'':131C ''overal'':69C ''overfit'':4A,113C ''overview'':80C ''p'':18C,104C,135C,169C ''perform'':72C,171C ''point'':57C ''point-in-tim'':56C ''price'':158C,162C ''problem'':108C ''project'':20C ''rel'':86C,96C ''result'':46C ''screen'':126C ''screener'':8A,30C,39C,115C,151C ''sector'':181C ''simul'':44C ''start'':155C,157C ''statist'':17B ''stock'':7A,29C,38C,114C,150C,154C,186C ''technic'':34C ''time'':59C,175C ''trade'':67C,146C ''ul'':143C,152C ''unclear'':76C ''use'':55C ''user'':26C,111C ''warn'':123C ''work'':23C,140C ''would'':117C ''www.equitieslab.com'':84C,94C ''www.equitieslab.com/features/stock-screener/'':83C ''www.equitieslab.com/wiki/quickstart/stockscreener'':93C ''year'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5269, 2, '2015-03-03 01:35:31.727', 4, NULL, NULL, '&lt;p&gt;The short answer is no! Reinforcement Learning is not limited to discrete spaces. But most of the introductory literature does deal with discrete spaces.&lt;/p&gt;

&lt;p&gt;As you might know by now that there are three important components in any Reinforcement Learning problem: Rewards, States and Actions. The first is a scalar quantity and theoretically the latter two can either be discrete or continuous. The convergence proofs and analyses of the various algorithms are easier to understand for the discrete case and also the corresponding algorithms are easier to code. That is one of the reasons, most introductory material focuses on them.&lt;/p&gt;

&lt;p&gt;Having said that, it should be interesting to note that the early research on Reinforcement Learning actually focussed on continuous state representations. It was only in the the 90s since the literature started representing all the standard algorithms for discrete spaces as we had a lot of proofs for them.&lt;/p&gt;

&lt;p&gt;Finally, if you noticed carefully, I said continuous states only. Mapping continuous states and continuous actions is hard. Nevertheless, we do have some solutions for now. But it is an active area of Research in RL. &lt;/p&gt;

&lt;p&gt;This &lt;a href="http://webdocs.cs.ualberta.ca/~sutton/papers/SSR-98.pdf" rel="nofollow"&gt;paper by Sutton&lt;/a&gt; from ''98 should be a good start for your exploration!&lt;/p&gt;
', NULL, NULL, '''/a'':207C ''/p'':26C,104C,156C,194C,218C ''/~sutton/papers/ssr-98.pdf'':201C ''90s'':134C ''98'':209C ''action'':48C,173C ''activ'':188C ''actual'':122C ''algorithm'':74C,87C,143C ''also'':84C ''analys'':70C ''answer'':4C ''area'':189C ''care'':162C ''case'':82C ''code'':91C ''compon'':39C ''continu'':65C,125C,165C,169C,172C ''converg'':67C ''correspond'':86C ''deal'':22C ''discret'':13C,24C,63C,81C,145C ''earli'':117C ''easier'':76C,89C ''either'':61C ''explor'':217C ''final'':158C ''first'':50C ''focus'':101C ''focuss'':123C ''good'':213C ''hard'':175C ''href'':198C ''import'':38C ''interest'':112C ''introductori'':19C,99C ''know'':31C ''latter'':58C ''learn'':8C,43C,121C ''limit'':11C ''literatur'':20C,137C ''lot'':151C ''map'':168C ''materi'':100C ''might'':30C ''nevertheless'':176C ''nofollow'':203C ''note'':114C ''notic'':161C ''one'':94C ''p'':1C,27C,105C,157C,195C ''paper'':204C ''problem'':44C ''proof'':68C,153C ''quantiti'':54C ''reason'':97C ''reinforc'':7C,42C,120C ''rel'':202C ''repres'':139C ''represent'':127C ''research'':118C,191C ''reward'':45C ''rl'':193C ''said'':107C,164C ''scalar'':53C ''short'':3C ''sinc'':135C ''solut'':181C ''space'':14C,25C,146C ''standard'':142C ''start'':138C,214C ''state'':46C,126C,166C,170C ''sutton'':206C ''theoret'':56C ''three'':37C ''two'':59C ''understand'':78C ''various'':73C ''webdocs.cs.ualberta.ca'':200C ''webdocs.cs.ualberta.ca/~sutton/papers/ssr-98.pdf'':199C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8978, 1, '2015-11-22 16:18:47.337', 2, 66, 'How to place XGBoost in a full stack for ML?', '&lt;p&gt;Is XGBoost complete by itself for prod-strength machine learning?  If not, with which other tools or libs is it typically combined, and how?&lt;/p&gt;

&lt;p&gt;(I recently read a description of a stack that included ca 5 pieces, including XGBoost and Keras.)&lt;/p&gt;
', 'machine-learning  python  tools  xgboost', NULL, '''/p'':43C,62C ''5'':56C ''ca'':55C ''combin'':40C ''complet'':20C ''descript'':49C ''full'':7A ''includ'':54C,58C ''kera'':61C ''learn'':13B,28C ''lib'':36C ''machin'':12B,27C ''machine-learn'':11B ''ml'':10A ''p'':17C,44C ''piec'':57C ''place'':3A ''prod'':25C ''prod-strength'':24C ''python'':14B ''read'':47C ''recent'':46C ''stack'':8A,52C ''strength'':26C ''tool'':15B,34C ''typic'':39C ''xgboost'':4A,16B,19C,59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5273, 1, '2015-03-03 16:12:13.86', 6, 186, 'How many possible hypotheses', '&lt;p&gt;The training set contains $p$ papers. Each paper is annotated as &lt;em&gt;research&lt;/em&gt; or &lt;em&gt;non-research&lt;/em&gt;. To develop the research paper filter, we consider the $W$ most frequent phrases in a paper. The research paper filter will use the presence/absence of these $W$ phrases to decide if the paper is indeed a research paper or not.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;How many possible hypothesis are there?&lt;/p&gt;

&lt;p&gt;I have $2^W$ possible hypothesis, but if we are including conjunctive hypothesis symbols, then I would say $4^W$. Is my logic correct?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;How many of them are consistent?&lt;/p&gt;

&lt;p&gt;For this question, I have no idea where to begin. Any insight?&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  classification', NULL, '''/em'':23C,29C ''/li'':107C,131C ''/ol'':132C ''/p'':70C,80C,106C,116C,130C ''2'':84C ''4'':100C ''annot'':19C ''begin'':127C ''classif'':8B ''conjunct'':93C ''consid'':37C ''consist'':115C ''contain'':13C ''correct'':105C ''decid'':59C ''develop'':31C ''em'':21C,25C ''filter'':35C,49C ''frequent'':41C ''hypothes'':4A ''hypothesi'':77C,87C,94C ''idea'':124C ''includ'':92C ''inde'':64C ''insight'':129C ''learn'':7B ''li'':72C,108C ''logic'':104C ''machin'':6B ''machine-learn'':5B ''mani'':2A,75C,111C ''non'':27C ''non-research'':26C ''ol'':71C ''p'':9C,14C,73C,81C,109C,117C ''paper'':15C,17C,34C,45C,48C,62C,67C ''phrase'':42C,57C ''possibl'':3A,76C,86C ''presence/absence'':53C ''question'':120C ''research'':22C,28C,33C,47C,66C ''say'':99C ''set'':12C ''symbol'':95C ''train'':11C ''use'':51C ''w'':39C,56C,85C,101C ''would'':98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5278, 2, '2015-03-04 12:15:10.117', 4, NULL, NULL, '&lt;p&gt;Most common types of decision trees you encounter are not affected by any monotonic transformation. So, as long as you preserve orde, the decision trees are the same (obviously by the same tree here I understand the same decision structure, not the same values for each test in each node of the tree). &lt;/p&gt;

&lt;p&gt;The reason why it happens is because how usual impurity functions works. In order to find the best split it searches on each dimension (attribute) a split point which is basically an if clause which groups target values corresponding to instances which has test value less than split value, and on the right the values greater than equal. This happens for numerical attributes (which I think is your case because I do not know how to normalize a nominal attribute). Now you might note that the criteria is less than or greater than. Which means that the real information from the attributes in order to find the split (and the whole tree) is only the order of the values. Which means that as long as you transform your attributes in such a way that the original ordering is reserved, you will get the same tree. &lt;/p&gt;

&lt;p&gt;Not all models are insensitive to such kind of transformation. For example linear regression models give the same results if you multiply an attribute with something different than zero. You will get different regression coefficients, but the predicted value will be the same. This is not the case when you take a log of that transformation. So for linear regression, for example, normalizing is useless since it will provide the same result. &lt;/p&gt;

&lt;p&gt;However this is not the case with a penalized linear regression, like ridge regression. In penalized linear regressions a constraint is applied to coefficients. The idea is that the constraint is applied to the sum  of a function of coefficients. Now if you inflate an attribute, the coefficient will be deflated, which means that in the end the penalization for that coefficient it will be artificially modified. In such kind of situation, you normalize attributes in order that each coefficient to be constraint ''fairly''.&lt;/p&gt;

&lt;p&gt;Hope it helps&lt;/p&gt;
', NULL, NULL, '''/p'':55C,202C,276C,362C,367C ''affect'':12C ''appli'':299C,309C ''artifici'':343C ''attribut'':81C,119C,136C,158C,185C,227C,323C,352C ''basic'':87C ''best'':74C ''case'':125C,251C,283C ''claus'':90C ''coeffici'':238C,301C,317C,325C,339C,357C ''common'':3C ''constraint'':297C,307C,360C ''correspond'':95C ''criteria'':143C ''decis'':6C,25C,40C ''deflat'':328C ''differ'':230C,236C ''dimens'':80C ''encount'':9C ''end'':334C ''equal'':114C ''exampl'':215C,265C ''fair'':361C ''find'':72C,162C ''function'':67C,315C ''get'':198C,235C ''give'':219C ''greater'':112C,148C ''group'':92C ''happen'':61C,116C ''help'':366C ''hope'':364C ''howev'':278C ''idea'':303C ''impur'':66C ''inflat'':321C ''inform'':155C ''insensit'':208C ''instanc'':97C ''kind'':211C,347C ''know'':130C ''less'':102C,145C ''like'':289C ''linear'':216C,262C,287C,294C ''log'':256C ''long'':19C,180C ''mean'':151C,177C,330C ''might'':139C ''model'':206C,218C ''modifi'':344C ''monoton'':15C ''multipli'':225C ''node'':51C ''nomin'':135C ''normal'':133C,266C,351C ''note'':140C ''numer'':118C ''obvious'':30C ''ord'':23C ''order'':70C,160C,172C,193C,354C ''origin'':192C ''p'':1C,56C,203C,277C,363C ''penal'':286C,293C,336C ''point'':84C ''predict'':241C ''preserv'':22C ''provid'':272C ''real'':154C ''reason'':58C ''regress'':217C,237C,263C,288C,291C,295C ''reserv'':195C ''result'':222C,275C ''ridg'':290C ''right'':109C ''search'':77C ''sinc'':269C ''situat'':349C ''someth'':229C ''split'':75C,83C,104C,164C ''structur'':41C ''sum'':312C ''take'':254C ''target'':93C ''test'':48C,100C ''think'':122C ''transform'':16C,183C,213C,259C ''tree'':7C,26C,34C,54C,168C,201C ''type'':4C ''understand'':37C ''useless'':268C ''usual'':65C ''valu'':45C,94C,101C,105C,111C,175C,242C ''way'':189C ''whole'':167C ''work'':68C ''zero'':232C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5279, 1, '2015-03-04 14:19:00.713', 4, 114, 'How to select topology for neural network?', '&lt;p&gt;I was given a target function to design neural network and train: (y = (x1 â§ x2) â¨ (x3 â§ x4))&lt;/p&gt;

&lt;p&gt;The number of input and number of output seems obvious (4 and 1). And the training data can use truth table.&lt;/p&gt;

&lt;p&gt;However, in order to train as a multilayer artificial neural network, I need to choose number of hidden units. May I know where can I find some general guideline for this?&lt;/p&gt;

&lt;p&gt;Thank you!&lt;/p&gt;
', 'neuralnetwork', NULL, '''/p'':30C,53C,86C,90C ''1'':44C ''4'':42C ''artifici'':63C ''choos'':69C ''data'':48C ''design'':17C ''find'':80C ''function'':15C ''general'':82C ''given'':12C ''guidelin'':83C ''hidden'':72C ''howev'':55C ''input'':35C ''know'':76C ''may'':74C ''multilay'':62C ''need'':67C ''network'':7A,19C,65C ''neural'':6A,18C,64C ''neuralnetwork'':8B ''number'':33C,37C,70C ''obvious'':41C ''order'':57C ''output'':39C ''p'':9C,31C,54C,87C ''seem'':40C ''select'':3A ''tabl'':52C ''target'':14C ''thank'':88C ''topolog'':4A ''train'':21C,47C,59C ''truth'':51C ''unit'':73C ''use'':50C ''x1'':23C ''x2'':25C ''x3'':27C ''x4'':29C ''y'':22C ''â'':24C,26C,28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5280, 1, '2015-03-04 14:34:51.013', 2, 52, 'Computation of a column-stochastic matrix with target row sums', '&lt;p&gt;I want to generate an $N\times N$ matrix $A$ so as to target an $N$ vector of row sums and simultaneously all column sums should sum to 1. In addition to this, I have a prefixed number of elements which are set to zero. 
For example, beginning with:
$$
  \left[\begin{array}{rrr}
    0 &amp;amp; 1 &amp;amp; 1 \\
    1 &amp;amp; 0 &amp;amp; 0 \\
    1 &amp;amp; 1 &amp;amp; 0
  \end{array}\right]
$$
and the row sum vector $[1.5, 0.25, 1]^{T}$, I want to end up with
$$
  \left[\begin{array}{rrr}
    0 &amp;amp; a_{12} &amp;amp; a_{13} \\
    a_{21} &amp;amp; 0 &amp;amp; 0 \\
    a_{31} &amp;amp; a_{32} &amp;amp; 0
  \end{array}\right]
$$
under the following conditions:&lt;/p&gt;

&lt;p&gt;$a_{12} + a_{13}  = 1.5$&lt;/p&gt;

&lt;p&gt;$ a_{21} = 0.25$&lt;/p&gt;

&lt;p&gt;$a_{31}+a_{32} = 1$&lt;/p&gt;

&lt;p&gt;$a_{21}+a_{31} = 1$&lt;/p&gt;

&lt;p&gt;$a_{12}+a_{32} = 1$&lt;/p&gt;

&lt;p&gt;$a_{13} = 1$&lt;/p&gt;

&lt;p&gt;While this is simplistic, in general, I have $2N$ equations in $N^{2}-Z$ unknowns, where $Z$ is the number of elements fixed to zero. So, this system of equations could be overdetermined or underdetermined, but I would like to be able to generate matrices like this such that all nonzero elements lie in $(0,1]$.&lt;/p&gt;
', 'statistics  algorithms  graphs  social-network-analysis', NULL, '''/p'':137C,144C,149C,156C,163C,170C,175C,233C ''0'':73C,80C,82C,87C,110C,120C,122C,129C,231C ''0.25'':97C,148C ''1'':48C,75C,77C,78C,83C,85C,98C,155C,162C,169C,174C,232C ''1.5'':96C,143C ''12'':113C,140C,166C ''13'':116C,142C,173C ''2'':189C ''21'':118C,147C,159C ''2n'':185C ''31'':124C,152C,161C ''32'':127C,154C,168C ''abl'':218C ''addit'':50C ''algorithm'':13B ''amp'':74C,76C,79C,81C,84C,86C,111C,114C,119C,121C,125C,128C ''analysi'':18B ''array'':71C,89C,108C,131C ''begin'':67C,70C,107C ''column'':5A,43C ''column-stochast'':4A ''comput'':1A ''condit'':136C ''could'':207C ''element'':59C,198C,228C ''end'':88C,103C,130C ''equat'':186C,206C ''exampl'':66C ''fix'':199C ''follow'':135C ''general'':182C ''generat'':23C,220C ''graph'':14B ''left'':69C,106C ''lie'':229C ''like'':215C,222C ''matric'':221C ''matrix'':7A,28C ''n'':25C,27C,35C,188C ''network'':17B ''nonzero'':227C ''number'':57C,196C ''overdetermin'':209C ''p'':19C,138C,145C,150C,157C,164C,171C,176C ''prefix'':56C ''right'':90C,132C ''row'':10A,38C,93C ''rrr'':72C,109C ''set'':62C ''simplist'':180C ''simultan'':41C ''social'':16B ''social-network-analysi'':15B ''statist'':12B ''stochast'':6A ''sum'':11A,39C,44C,46C,94C ''system'':204C ''target'':9A,33C ''time'':26C ''underdetermin'':211C ''unknown'':191C ''vector'':36C,95C ''want'':21C,101C ''would'':214C ''z'':190C,193C ''zero'':64C,201C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5281, 2, '2015-03-04 15:53:46.53', 1, NULL, NULL, '&lt;p&gt;Speed of code execution is rarely an issue.  The important speed in business is almost always the speed of designing, deploying, and maintaining the application.  An experienced programmer can optimize where necessary to get code execution fast enough.  In these cases, R can make a lot of sense in production. &lt;/p&gt;

&lt;p&gt;In cases where speed of execution IS an issue, you are already going to find an optimized C++ or some such real-time decision engine.  So your choices are integrate an R process, or add the bits you need to the engine.  The latter is probably the only option, not because of the speed of R, but because you don''t have the time to incorporate any external process.  If the company has nothing to start with, I can''t imagine everyone saying "let''s build our time critical real-time engine in R because of the great statistical libraries".  &lt;/p&gt;

&lt;p&gt;I''ll give a few examples from my corporate experiences, where I use R in production:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Delivering Shiny applications dealing with data that is not/ not yet institutionalized.  I will generally load already-processed data frames and use Shiny to display different graphs and charts.  Computation is minimal.   &lt;/li&gt;
&lt;li&gt;Decision making analysis that requires heavy use of advanced libraries (mcclust, machine learning) but done on a daily or longer time-scale.  In this case there is no reason to use any other language.  I''ve already done the prototyping in R, so my fastest and best option is to keep things there.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I did not use R for production when integrating with a real-time C++ decision engine.  Issues:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;An additional layer of complication to spawn R processes and integrate the results&lt;/li&gt;
&lt;li&gt;A suitable machine-learning library (Waffles) was available in C++&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The caveat in the latter case: I still use R to generate the training files.&lt;/p&gt;
', NULL, NULL, '''/li'':210C,266C,303C,316C ''/p'':52C,156C,174C,287C,334C ''/ul'':267C,317C ''add'':89C ''addit'':291C ''advanc'':220C ''almost'':16C ''alreadi'':65C,194C,249C ''already-process'':193C ''alway'':17C ''analysi'':214C ''applic'':26C,179C ''avail'':313C ''best'':259C ''bit'':91C ''build'':140C ''busi'':14C ''c'':71C,283C,315C ''case'':42C,55C,237C,324C ''caveat'':320C ''chart'':206C ''choic'':82C ''code'':4C,36C ''compani'':126C ''complic'':294C ''comput'':207C ''corpor'':166C ''critic'':143C ''daili'':229C ''data'':182C,196C ''deal'':180C ''decis'':78C,212C,284C ''deliv'':177C ''deploy'':22C ''design'':21C ''differ'':203C ''display'':202C ''done'':226C,250C ''engin'':79C,96C,147C,285C ''enough'':39C ''everyon'':136C ''exampl'':163C ''execut'':5C,37C,59C ''experi'':167C ''experienc'':28C ''extern'':122C ''fast'':38C ''fastest'':257C ''file'':333C ''find'':68C ''frame'':197C ''general'':191C ''generat'':330C ''get'':35C ''give'':160C ''go'':66C ''graph'':204C ''great'':153C ''heavi'':217C ''imagin'':135C ''import'':11C ''incorpor'':120C ''institution'':188C ''integr'':84C,277C,300C ''issu'':9C,62C,286C ''keep'':263C ''languag'':246C ''latter'':98C,323C ''layer'':292C ''learn'':224C,309C ''let'':138C ''li'':176C,211C,289C,304C ''librari'':155C,221C,310C ''ll'':159C ''load'':192C ''longer'':231C ''lot'':47C ''machin'':223C,308C ''machine-learn'':307C ''maintain'':24C ''make'':45C,213C ''mcclust'':222C ''minim'':209C ''necessari'':33C ''need'':93C ''noth'':128C ''optim'':31C,70C ''option'':103C,260C ''p'':1C,53C,157C,268C,318C ''probabl'':100C ''process'':87C,123C,195C,298C ''product'':51C,173C,275C ''programm'':29C ''prototyp'':252C ''r'':43C,86C,110C,149C,171C,254C,273C,297C,328C ''rare'':7C ''real'':76C,145C,281C ''real-tim'':75C,144C,280C ''reason'':241C ''requir'':216C ''result'':302C ''say'':137C ''scale'':234C ''sens'':49C ''shini'':178C,200C ''spawn'':296C ''speed'':2C,12C,19C,57C,108C ''start'':130C ''statist'':154C ''still'':326C ''suitabl'':306C ''thing'':264C ''time'':77C,118C,142C,146C,233C,282C ''time-scal'':232C ''train'':332C ''ul'':175C,288C ''use'':170C,199C,218C,243C,272C,327C ''ve'':248C ''waffl'':311C ''yet'':187C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5282, 2, '2015-03-04 21:47:35.453', 1, NULL, NULL, '&lt;p&gt;You problem is linearly separable so you can use a single layer perceptron. 
Hidden units are necessary only for non linear problems (xor is a classic example). &lt;/p&gt;

&lt;p&gt;As for general guidelines, I don''t think anyone has ever needed more than one hidden layer. Also the number of neurons in the hidden layer doesn''t need to exceed the number of inputs. &lt;/p&gt;
', NULL, NULL, '''/p'':29C,66C ''also'':48C ''anyon'':39C ''classic'':27C ''doesn'':57C ''ever'':41C ''exampl'':28C ''exceed'':61C ''general'':33C ''guidelin'':34C ''hidden'':15C,46C,55C ''input'':65C ''layer'':13C,47C,56C ''linear'':5C,22C ''necessari'':18C ''need'':42C,59C ''neuron'':52C ''non'':21C ''number'':50C,63C ''one'':45C ''p'':1C,30C ''perceptron'':14C ''problem'':3C,23C ''separ'':6C ''singl'':12C ''think'':38C ''unit'':16C ''use'':10C ''xor'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5322, 1, '2015-03-14 19:18:11.747', 1, 105, 'Cost function for support vector regression', '&lt;p&gt;The optimisation problem for support vector regression is (see &lt;a href="http://alex.smola.org/papers/2003/SmoSch03b.pdf" rel="nofollow"&gt;http://alex.smola.org/papers/2003/SmoSch03b.pdf&lt;/a&gt;):&lt;/p&gt;

&lt;p&gt;minimise:
\begin{align*}
C\sum_{i=0}^{l}(\xi_{i} +\xi^{*}_{i})+ \frac{1}{2}\lVert w \rVert^{2}
\end{align*}&lt;/p&gt;

&lt;p&gt;subject to the constraints:&lt;/p&gt;

&lt;p&gt;\begin{align*}
&amp;amp; y_{i} - &amp;lt;w,x_{i}&amp;gt; - b \leq \epsilon + \xi_{i} \\
&amp;amp; &amp;lt;w,x_{i}&amp;gt; + b - y_{i} \leq \epsilon + \xi^{*}_{i} \\
&amp;amp; \xi_{i}, \xi^{*}_{i} \geq 0
\end{align*}&lt;/p&gt;

&lt;p&gt;I do not understand where the $\lVert w \rVert^{2}$ comes from. I understand how the $\lVert w \rVert^{2}$ is derived in the case of support vector classification (by maximizing the margin), but not in the case of regression.&lt;/p&gt;

&lt;p&gt;The paper says that the "goal is to find a function $f(x)$ that has at most $\epsilon$ deviation from the actually obtained targets $y_{i}$ for all the training data, and at the same time is as flat as possible. In other words, we do not care about errors as long as they are less than $\epsilon$, but will not accept any deviation larger than this."&lt;/p&gt;

&lt;p&gt;But I am not sure what "flat" means.&lt;/p&gt;

&lt;p&gt;Does someone know?&lt;/p&gt;
', 'machine-learning  regression  svm', NULL, '''/p'':54C,60C,99C,141C,210C,220C,225C ''/papers/2003/smosch03b.pdf'':26C ''/papers/2003/smosch03b.pdf&lt;/a&gt;):&lt;/p&gt;'':31C ''0'':39C,96C ''1'':46C ''2'':47C,51C,110C,120C ''accept'':204C ''actual'':164C ''alex.smola.org'':25C,30C ''alex.smola.org/papers/2003/smosch03b.pdf'':24C ''alex.smola.org/papers/2003/smosch03b.pdf&lt;/a&gt;):&lt;/p&gt;'':29C ''align'':35C,53C,63C,98C ''amp'':64C,77C,90C ''b'':72C,83C ''begin'':34C,62C ''c'':36C ''care'':190C ''case'':125C,138C ''classif'':129C ''come'':111C ''constraint'':59C ''cost'':1A ''data'':173C ''deriv'':122C ''deviat'':161C,206C ''end'':52C,97C ''epsilon'':74C,87C,160C,200C ''error'':192C ''f'':154C ''find'':151C ''flat'':181C,218C ''frac'':45C ''function'':2A,153C ''geq'':95C ''goal'':148C ''gt'':71C,82C ''href'':23C ''know'':224C ''l'':40C ''larger'':207C ''learn'':9B ''leq'':73C,86C ''less'':198C ''long'':194C ''lt'':67C,78C ''lvert'':48C,107C,117C ''machin'':8B ''machine-learn'':7B ''margin'':133C ''maxim'':131C ''mean'':219C ''minimis'':33C ''nofollow'':28C ''obtain'':165C ''optimis'':14C ''p'':12C,32C,55C,61C,100C,142C,211C,221C ''paper'':144C ''possibl'':183C ''problem'':15C ''regress'':6A,10B,19C,140C ''rel'':27C ''rvert'':50C,109C,119C ''say'':145C ''see'':21C ''someon'':223C ''subject'':56C ''sum'':37C ''support'':4A,17C,127C ''sure'':216C ''svm'':11B ''target'':166C ''time'':178C ''train'':172C ''understand'':104C,114C ''vector'':5A,18C,128C ''w'':49C,68C,79C,108C,118C ''word'':186C ''x'':69C,80C,155C ''xi'':41C,43C,75C,88C,91C,93C ''y'':65C,84C,167C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5290, 2, '2015-03-06 07:56:19.903', 3, NULL, NULL, '&lt;p&gt;I will restrict myself to the finite-dimensional situation. &lt;/p&gt;

&lt;p&gt;First, if we are talking about &lt;em&gt;normal&lt;/em&gt; eigenvalues and not generalized ones, we need a square matrix. If $M\in\mathbb{R}^{n\times n}$ (or $\mathbb{C}^{n\times n}$) is a &lt;a href="http://en.wikipedia.org/wiki/Diagonalizable_matrix" rel="nofollow"&gt;diagonizable matrix&lt;/a&gt;, it has an &lt;a href="http://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix#Eigendecomposition_of_a_matrix" rel="nofollow"&gt;eigendecomposition&lt;/a&gt;
\begin{equation}
M = V\Lambda V^{-1}
\textrm{,}
\end{equation}
with $V:=\begin{bmatrix}v_{1},v_{2},\ldots,v_{n}\end{bmatrix}$ the matrix with its $n$ eigenvectors $v_{i}$ as columns and $\Lambda :=\operatorname{diag}\left(\lambda_{1},\lambda_{2},\ldots,\lambda_{n}\right)$ the diagonal matrix with the corresponding eigenvalues on its main diagonal.&lt;/p&gt;

&lt;p&gt;As you can see from this equation if you want to get matrices with the same eigenvectors, i.e. $V$ fixed, you can only change the eigenvalues $\lambda_{i}$. In general the relationship between the eigenvalues and the entries of the original matrix $M$ is non-trivial. &lt;/p&gt;

&lt;p&gt;The easiest situation arises if the eigenvectors $v_{i}$ form the standard basis, i.e. only $1$ at the $i$th position, $0$ otherwise. Then $M=I\Lambda I^{-1}=\Lambda $ ($I$ the identity matrix) and you can change every entry on the main diagonal without changing any eigenvector. &lt;/p&gt;

&lt;h2&gt;Edit&lt;/h2&gt;

&lt;p&gt;I understood the question differently than victor. I thought the question asks what kind of different datasets can be described by the same eigenvectors. &lt;/p&gt;
', NULL, NULL, '''-1'':77C,207C ''/a'':58C,70C ''/em'':22C ''/h2'':230C ''/p'':12C,128C,177C,227C,256C ''/wiki/diagonalizable_matrix'':53C ''/wiki/eigendecomposition_of_a_matrix#eigendecomposition_of_a_matrix'':66C ''0'':200C ''1'':86C,110C,194C ''2'':88C,112C ''aris'':182C ''ask'':243C ''basi'':191C ''begin'':71C,83C ''bmatrix'':84C,93C ''c'':43C ''chang'':153C,216C,224C ''column'':103C ''correspond'':122C ''dataset'':248C ''describ'':251C ''diag'':107C ''diagon'':118C,127C,222C ''diagoniz'':56C ''differ'':236C,247C ''dimension'':10C ''easiest'':180C ''edit'':229C ''eigendecomposit'':69C ''eigenvalu'':23C,123C,155C,164C ''eigenvector'':99C,146C,185C,226C,255C ''em'':20C ''en.wikipedia.org'':52C,65C ''en.wikipedia.org/wiki/diagonalizable_matrix'':51C ''en.wikipedia.org/wiki/eigendecomposition_of_a_matrix#eigendecomposition_of_a_matrix'':64C ''end'':79C,92C ''entri'':167C,218C ''equat'':72C,80C,136C ''everi'':217C ''finit'':9C ''finite-dimension'':8C ''first'':14C ''fix'':149C ''form'':188C ''general'':26C,159C ''get'':141C ''h2'':228C ''href'':50C,63C ''i.e'':147C,192C ''ident'':211C ''kind'':245C ''lambda'':75C,105C,109C,111C,114C,156C,205C,208C ''ldot'':89C,113C ''left'':108C ''m'':34C,73C,172C,203C ''main'':126C,221C ''mathbb'':36C,42C ''matric'':142C ''matrix'':32C,57C,95C,119C,171C,212C ''n'':38C,40C,44C,46C,91C,98C,115C ''need'':29C ''nofollow'':55C,68C ''non'':175C ''non-trivi'':174C ''normal'':21C ''one'':27C ''operatornam'':106C ''origin'':170C ''otherwis'':201C ''p'':1C,13C,129C,178C,231C ''posit'':199C ''question'':235C,242C ''r'':37C ''rel'':54C,67C ''relationship'':161C ''restrict'':4C ''right'':116C ''see'':133C ''situat'':11C,181C ''squar'':31C ''standard'':190C ''talk'':18C ''textrm'':78C ''th'':198C ''thought'':240C ''time'':39C,45C ''trivial'':176C ''understood'':233C ''v'':74C,76C,82C,85C,87C,90C,100C,148C,186C ''victor'':238C ''want'':139C ''without'':223C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5284, 1, '2015-03-05 18:28:43.367', 2, 213, 'How append works in hdfs? Where the newly created instance of file is placed?', '&lt;p&gt;I''m a Newbie to Hadoop!&lt;/p&gt;

&lt;p&gt;By web search and after going through hadoop guides, it is clear that hdfs doesn''t allow us to edit the file but to append some data to existing file, it will be creating a new instance temporarily and append the new data to it.&lt;/p&gt;

&lt;p&gt;That being said,&lt;/p&gt;

&lt;p&gt;1.would like to know whether the &lt;em&gt;new file&lt;/em&gt; or &lt;em&gt;temp file&lt;/em&gt; is created in the &lt;em&gt;same block&lt;/em&gt; or in a &lt;em&gt;different block&lt;/em&gt;?&lt;/p&gt;

&lt;p&gt;2.What will happen if the revised file &lt;strong&gt;exceeds&lt;/strong&gt; the previous  allocated block size?&lt;/p&gt;

&lt;p&gt;Any help would be greatly appreciated!&lt;/p&gt;
', 'data-mining  bigdata  hadoop', NULL, '''/em'':90C,95C,103C,110C ''/p'':27C,74C,79C,111C,128C,136C ''/strong'':122C ''1.would'':81C ''2.what'':113C ''alloc'':125C ''allow'':45C ''append'':2A,53C,68C ''appreci'':135C ''bigdata'':18B ''block'':102C,109C,126C ''clear'':40C ''creat'':9A,62C,97C ''data'':16B,55C,71C ''data-min'':15B ''differ'':108C ''doesn'':43C ''edit'':48C ''em'':87C,92C,100C,107C ''exceed'':121C ''exist'':57C ''file'':12A,50C,58C,89C,94C,119C ''go'':34C ''great'':134C ''guid'':37C ''hadoop'':19B,26C,36C ''happen'':115C ''hdfs'':5A,42C ''help'':131C ''instanc'':10A,65C ''know'':84C ''like'':82C ''m'':22C ''mine'':17B ''new'':64C,70C,88C ''newbi'':24C ''newli'':8A ''p'':20C,28C,75C,80C,112C,129C ''place'':14A ''previous'':124C ''revis'':118C ''said'':78C ''search'':31C ''size'':127C ''strong'':120C ''temp'':93C ''temporarili'':66C ''us'':46C ''web'':30C ''whether'':85C ''work'':3A ''would'':132C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5285, 2, '2015-03-06 02:43:38.727', 2, NULL, NULL, '&lt;p&gt;Here are two ideas that I was thinking about.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Bug Prediction&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Based on the previous activity future bugs can be predicted. There are a number of papers available on the net. I remember there was a paper about bug prediction from SVN data. ( Just google for software bug prediction )&lt;/p&gt;

&lt;ol start="2"&gt;
&lt;li&gt;Error position from software traces&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Errors may be like rare events or anomalies in the trace output. May be you can classify the error and pinpoint the procedure that caused the error. I am currently thinking about a system like that now ( Not sure about its success though ). I asked a question here: &lt;a href="http://stats.stackexchange.com/questions/140232/error-position-in-software-trace-file"&gt;http://stats.stackexchange.com/questions/140232/error-position-in-software-trace-file&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/li'':16C,67C ''/ol'':17C,68C ''/p'':11C,57C ''/questions/140232/error-position-in-software-trace-file'':122C ''/questions/140232/error-position-in-software-trace-file&lt;/a&gt;&lt;/p&gt;'':125C ''2'':60C ''activ'':23C ''anomali'':77C ''ask'':114C ''avail'':35C ''base'':19C ''bug'':14C,25C,46C,55C ''caus'':94C ''classifi'':86C ''current'':99C ''data'':50C ''error'':62C,70C,88C,96C ''event'':75C ''futur'':24C ''googl'':52C ''href'':119C ''idea'':5C ''li'':13C,61C ''like'':73C,104C ''may'':71C,82C ''net'':38C ''number'':32C ''ol'':12C,58C ''output'':81C ''p'':1C,18C,69C ''paper'':34C,44C ''pinpoint'':90C ''posit'':63C ''predict'':15C,28C,47C,56C ''previous'':22C ''procedur'':92C ''question'':116C ''rare'':74C ''rememb'':40C ''softwar'':54C,65C ''start'':59C ''stats.stackexchange.com'':121C,124C ''stats.stackexchange.com/questions/140232/error-position-in-software-trace-file'':120C ''stats.stackexchange.com/questions/140232/error-position-in-software-trace-file&lt;/a&gt;&lt;/p&gt;'':123C ''success'':111C ''sure'':108C ''svn'':49C ''system'':103C ''think'':9C,100C ''though'':112C ''trace'':66C,80C ''two'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5286, 2, '2015-03-06 03:46:41.693', 1, NULL, NULL, '&lt;p&gt;When looking for texts to learn advanced topics, I start with a web search for relevant grad courses and textbooks, or background tech/math books like those from Dover.&lt;/p&gt;

&lt;p&gt;To wit, Theoretical Statistics by Keener looks relevant:
&lt;a href="http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-93838-7" rel="nofollow"&gt;http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-93838-7&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;And this:
"Looking for a good Mathematical Statistics self-study book (I''m a physics student and my class &amp;amp; current book are useless to me)"
&lt;a href="http://www.reddit.com/r/statistics/comments/1n6o19/looking_for_a_good_mathematical_statistics/" rel="nofollow"&gt;http://www.reddit.com/r/statistics/comments/1n6o19/looking_for_a_good_mathematical_statistics/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':30C ''/r/statistics/comments/1n6o19/looking_for_a_good_mathematical_statistics/'':82C ''/r/statistics/comments/1n6o19/looking_for_a_good_mathematical_statistics/&lt;/a&gt;&lt;/p&gt;'':87C ''/statistics/statistical+theory+and+methods/book/978-0-387-93838-7'':44C ''/statistics/statistical+theory+and+methods/book/978-0-387-93838-7&lt;/a&gt;&lt;/p&gt;'':49C ''advanc'':8C ''amp'':71C ''background'':23C ''book'':25C,62C,73C ''class'':70C ''cours'':19C ''current'':72C ''dover'':29C ''good'':56C ''grad'':18C ''href'':41C,79C ''keener'':37C ''learn'':7C ''like'':26C ''look'':3C,38C,53C ''m'':64C ''mathemat'':57C ''nofollow'':46C,84C ''p'':1C,31C,50C ''physic'':66C ''rel'':45C,83C ''relev'':17C,39C ''search'':15C ''self'':60C ''self-studi'':59C ''start'':11C ''statist'':35C,58C ''student'':67C ''studi'':61C ''tech/math'':24C ''text'':5C ''textbook'':21C ''theoret'':34C ''topic'':9C ''useless'':75C ''web'':14C ''wit'':33C ''www.reddit.com'':81C,86C ''www.reddit.com/r/statistics/comments/1n6o19/looking_for_a_good_mathematical_statistics/'':80C ''www.reddit.com/r/statistics/comments/1n6o19/looking_for_a_good_mathematical_statistics/&lt;/a&gt;&lt;/p&gt;'':85C ''www.springer.com'':43C,48C ''www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-93838-7'':42C ''www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-93838-7&lt;/a&gt;&lt;/p&gt;'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5287, 1, '2015-03-06 04:09:56.03', 4, 64, 'Dataset to give same eigenvectors?', '&lt;p&gt;Any suggestion on what kind of dataset lets say $n \times d$ ($n$ rows, $d$ columns) would give me same eigenvectors?.&lt;/p&gt;

&lt;p&gt;I believe it should be the one with same absolute values in each cell. Like alternating +1 and -1. But it seems to work otherwise. &lt;/p&gt;

&lt;p&gt;Any pointers?&lt;/p&gt;
', 'machine-learning  data-mining  python', NULL, '''+1'':53C ''-1'':55C ''/p'':35C,62C,66C ''absolut'':46C ''altern'':52C ''believ'':38C ''cell'':50C ''column'':29C ''d'':25C,28C ''data'':10B ''data-min'':9B ''dataset'':1A,20C ''eigenvector'':5A,34C ''give'':3A,31C ''kind'':18C ''learn'':8B ''let'':21C ''like'':51C ''machin'':7B ''machine-learn'':6B ''mine'':11B ''n'':23C,26C ''one'':43C ''otherwis'':61C ''p'':13C,36C,63C ''pointer'':65C ''python'':12B ''row'':27C ''say'':22C ''seem'':58C ''suggest'':15C ''time'':24C ''valu'':47C ''work'':60C ''would'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5288, 2, '2015-03-06 05:45:03.72', 1, NULL, NULL, '&lt;p&gt;This problem, being more of a combinatorial rather than statistical nature, can be though of as a &lt;em&gt;max-flow problem in a bipartite network&lt;/em&gt;. Each column of your matrix corresponds to a "source node" in the left part of the bipartite graph $BP(L, R)$; each row of the matrix corresponds to a "destination node" in the right part $R$. Capacity of each source node is the corresponding column''s sum; similarly, capacities are defined for the destinations via row sums. Value $a_{ij}$ in the matrix describes flow of mass from $j$''th source to $i$''th destination along an existing edge $(j \to i)$ of $BP$. Your initially given 0-1 matrix describes which edges are present (e.g., the matrix in your example prescribes the edge $(3, 2)$ to be absent from the network, along with all self-loops). If you denote $F$ to be the sum of all column sums (or, alternatively, the total capacity of all sources), then your problem narrows down to finding a max-flow from $L$ to $R$ in $BP$ and checking whether the volume of the discovered flow equals $F$.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Note 1 (Feasibility):&lt;/em&gt; Before plunging into finding best flows, you may want to check your row sums: a necessary condition for your problem to have a solution is that the sum of all column sums (which is $N$ in your particular case of a column-stochastic matrix) should be equal the sum of row sums, as both numbers describe the same -- the sum of all elements of the target matrix. (For example, for $N = 3$ and the vector of row sums from your example, the problem is unfeasible.)&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Note 2 (Upper bound):&lt;/em&gt; Since $a_{ij}$ describes the flow from $j$''th source, and the capacities of all sources are 1 in your setting, then each source cannot send more than 1 unit of mass to a single destination. Thus, each $a_{ij}$ will not exceed 1.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Note 3 (Lower bound):&lt;/em&gt; To make $a_{ij}$ for each existing edge $(j \to i)$ strictly positive, you can augment the max-flow problem with lower bounds on edge capacities. In a general setting, edge capacity bounds are $0$ and $+\infty$. You may want to replace the lower bound with some positive number, small enough not to interfere with the discovery of the best flow and large enough to satisfy your edge saturation needs.&lt;/p&gt;
', NULL, NULL, '''-1'':117C ''/em'':28C,201C,295C,347C ''/p'':195C,288C,340C,419C ''0'':116C,383C ''1'':199C,313C,324C,339C ''2'':134C,292C ''3'':133C,274C,344C ''absent'':137C ''along'':104C,141C ''altern'':160C ''augment'':363C ''best'':206C,408C ''bipartit'':26C,45C ''bound'':294C,346C,371C,381C,393C ''bp'':47C,112C,183C ''cannot'':320C ''capac'':65C,77C,163C,308C,374C,380C ''case'':240C ''check'':185C,212C ''column'':30C,73C,157C,232C,244C ''column-stochast'':243C ''combinatori'':8C ''condit'':218C ''correspond'':34C,55C,72C ''defin'':79C ''denot'':149C ''describ'':92C,119C,258C,299C ''destin'':58C,82C,103C,331C ''discov'':191C ''discoveri'':405C ''e.g'':124C ''edg'':107C,121C,132C,355C,373C,379C,416C ''element'':265C ''em'':19C,197C,290C,342C ''enough'':399C,412C ''equal'':193C,249C ''exampl'':129C,271C,283C ''exceed'':338C ''exist'':106C,354C ''f'':150C,194C ''feasibl'':200C ''find'':173C,205C ''flow'':22C,93C,177C,192C,207C,301C,367C,409C ''general'':377C ''given'':115C ''graph'':46C ''ij'':88C,298C,335C,351C ''infti'':385C ''initi'':114C ''interfer'':402C ''j'':97C,108C,303C,356C ''l'':48C,179C ''larg'':411C ''left'':41C ''loop'':146C ''lower'':345C,370C,392C ''make'':349C ''mass'':95C,327C ''matrix'':33C,54C,91C,118C,126C,246C,269C ''max'':21C,176C,366C ''max-flow'':20C,175C,365C ''may'':209C,387C ''n'':236C,273C ''narrow'':170C ''natur'':12C ''necessari'':217C ''need'':418C ''network'':27C,140C ''node'':38C,59C,69C ''note'':198C,291C,343C ''number'':257C,397C ''p'':1C,196C,289C,341C ''part'':42C,63C ''particular'':239C ''plung'':203C ''posit'':360C,396C ''prescrib'':130C ''present'':123C ''problem'':3C,23C,169C,221C,285C,368C ''r'':49C,64C,181C ''rather'':9C ''replac'':390C ''right'':62C ''row'':51C,84C,214C,253C,279C ''satisfi'':414C ''satur'':417C ''self'':145C ''self-loop'':144C ''send'':321C ''set'':316C,378C ''similar'':76C ''sinc'':296C ''singl'':330C ''small'':398C ''solut'':225C ''sourc'':37C,68C,99C,166C,305C,311C,319C ''statist'':11C ''stochast'':245C ''strict'':359C ''sum'':75C,85C,154C,158C,215C,229C,233C,251C,254C,262C,280C ''target'':268C ''th'':98C,102C,304C ''though'':15C ''thus'':332C ''total'':162C ''unfeas'':287C ''unit'':325C ''upper'':293C ''valu'':86C ''vector'':277C ''via'':83C ''volum'':188C ''want'':210C,388C ''whether'':186C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5291, 1, '2015-03-06 10:11:02.133', 1, 120, 'Error using twitter R package''s userTimlien', '&lt;p&gt;I am using twitteR package to retrievie timeline data. My request looks as follows:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;tweets &amp;lt;- try(userTimeline(user , n=50),silent=TRUE)&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;and this worked quite well for a time, but now I receive this error message:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Error in function (type, msg, asError = TRUE)  : easy handle already used in multi handle
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In a related question on Stackoverflow one answer is to use Rcurl directly but this does not seem to work with twitteR package. Anybody got an idea on this?&lt;/p&gt;
', 'data-mining  r', NULL, '''/code'':39C,74C ''/p'':27C,40C,57C,106C ''/pre'':75C ''50'':36C ''alreadi'':69C ''answer'':84C ''anybodi'':100C ''aserror'':65C ''code'':29C,59C ''data'':9B,21C ''data-min'':8B ''direct'':89C ''easi'':67C ''error'':1A,55C,60C ''follow'':26C ''function'':62C ''got'':101C ''handl'':68C,73C ''idea'':103C ''look'':24C ''lt'':31C ''messag'':56C ''mine'':10B ''msg'':64C ''multi'':72C ''n'':35C ''one'':83C ''p'':12C,28C,41C,76C ''packag'':5A,17C,99C ''pre'':58C ''question'':80C ''quit'':45C ''r'':4A,11B ''rcurl'':88C ''receiv'':53C ''relat'':79C ''request'':23C ''retrievi'':19C ''seem'':94C ''silent'':37C ''stackoverflow'':82C ''time'':49C ''timelin'':20C ''tri'':32C ''true'':38C,66C ''tweet'':30C ''twitter'':3A,16C,98C ''type'':63C ''use'':2A,15C,70C,87C ''user'':34C ''usertimelin'':33C ''usertimlien'':7A ''well'':46C ''work'':44C,96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5292, 1, '2015-03-06 10:28:04.483', 2, 56, 'Generic strategy for object detection', '&lt;p&gt;I have a huge collection of objects from which only a tiny fraction are in a class of interest. The collection is initially unlabelled, but labels can be added using an expensive operation (for example, by human).&lt;/p&gt;

&lt;p&gt;Currently I use the simple generic machine learning strategy:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Use hand-crafted rules to select a smaller subset of objects (thus leaving out a fraction of interesting ones).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Label part of the smaller subset, and use these for training and choosing a classification algorithm and its parameters.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Classify the remaining objects in the smaller set (and also perhaps in the big set).&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;This has two drawbacks:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;The labeller still needs to see a huge number of uninteresting objects, and therefore is able to label only a very small fraction of interesting ones. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;The objects not in the smaller set are completely ignored in the learning phase, resulting in a loss of some information (the classification algorithm might not work well on this complement).&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;It seems that it would be better to use online learning: i.e., select the objects to show to the labeller based on the previous labels. But then it becomes no longer obvious that the result of classification algorithm retains the nice theoretical properties (i.e., statistical consistency).&lt;/p&gt;

&lt;p&gt;Is there a general framework for active object detection which works either theoretically or practically (or both)? I could not get the complete picture from the Wikipedia article &lt;a href="http://en.wikipedia.org/wiki/Active_learning_%28machine_learning%29" rel="nofollow"&gt;active learning&lt;/a&gt;. &lt;/p&gt;
', 'machine-learning  classification  object-recognition', NULL, '''/a'':290C ''/li'':87C,110C,129C,167C,202C ''/ol'':130C,203C ''/p'':51C,62C,86C,109C,128C,136C,166C,201C,251C,291C ''/wiki/active_learning_%28machine_learning%29'':285C ''abl'':155C ''activ'':259C,288C ''ad'':42C ''algorithm'':105C,193C,242C ''also'':122C ''articl'':280C ''base'':225C ''becom'':233C ''better'':211C ''big'':126C ''choos'':102C ''class'':30C ''classif'':9B,104C,192C,241C ''classifi'':113C ''collect'':18C,34C ''complement'':200C ''complet'':178C,275C ''consist'':250C ''could'':271C ''craft'':69C ''current'':53C ''detect'':5A,261C ''drawback'':135C ''either'':264C ''en.wikipedia.org'':284C ''en.wikipedia.org/wiki/active_learning_%28machine_learning%29'':283C ''exampl'':48C ''expens'':45C ''fraction'':26C,82C,162C ''framework'':257C ''general'':256C ''generic'':1A,58C ''get'':273C ''hand'':68C ''hand-craft'':67C ''href'':282C ''huge'':17C,147C ''human'':50C ''i.e'':216C,248C ''ignor'':179C ''inform'':190C ''initi'':36C ''interest'':32C,84C,164C ''label'':39C,90C,141C,157C,224C,229C ''learn'':8B,60C,182C,215C,289C ''leav'':79C ''li'':64C,88C,111C,138C,168C ''longer'':235C ''loss'':187C ''machin'':7B,59C ''machine-learn'':6B ''might'':194C ''need'':143C ''nice'':245C ''nofollow'':287C ''number'':148C ''object'':4A,11B,20C,77C,116C,151C,171C,219C,260C ''object-recognit'':10B ''obvious'':236C ''ol'':63C,137C ''one'':85C,165C ''onlin'':214C ''oper'':46C ''p'':13C,52C,65C,89C,112C,131C,139C,169C,204C,252C ''paramet'':108C ''part'':91C ''perhap'':123C ''phase'':183C ''pictur'':276C ''practic'':267C ''previous'':228C ''properti'':247C ''recognit'':12B ''rel'':286C ''remain'':115C ''result'':184C,239C ''retain'':243C ''rule'':70C ''see'':145C ''seem'':206C ''select'':72C,217C ''set'':120C,127C,176C ''show'':221C ''simpl'':57C ''small'':161C ''smaller'':74C,94C,119C,175C ''statist'':249C ''still'':142C ''strategi'':2A,61C ''subset'':75C,95C ''theoret'':246C,265C ''therefor'':153C ''thus'':78C ''tini'':25C ''train'':100C ''two'':134C ''uninterest'':150C ''unlabel'':37C ''use'':43C,55C,66C,97C,213C ''well'':197C ''wikipedia'':279C ''work'':196C,263C ''would'':209C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5294, 1, '2015-03-09 16:53:10.93', 4, 203, 'How are neural nets related to Fourier transforms?', '&lt;p&gt;This is an interview question&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;How are neural nets related to Fourier transforms?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I could find papers that talk about methods to process the Discrete Fourier Transform (DFT) by a single-layer neural network with a linear transfer function. Is there some other correlation that I''m missing?&lt;/p&gt;
', 'neuralnetwork', NULL, '''/blockquote'':28C ''/p'':16C,27C,66C ''blockquot'':17C ''correl'':61C ''could'':31C ''dft'':44C ''discret'':41C ''find'':32C ''fourier'':7A,25C,42C ''function'':56C ''interview'':14C ''layer'':49C ''linear'':54C ''m'':64C ''method'':37C ''miss'':65C ''net'':4A,22C ''network'':51C ''neural'':3A,21C,50C ''neuralnetwork'':9B ''p'':10C,18C,29C ''paper'':33C ''process'':39C ''question'':15C ''relat'':5A,23C ''singl'':48C ''single-lay'':47C ''talk'':35C ''transfer'':55C ''transform'':8A,26C,43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5295, 2, '2015-03-09 18:21:32.663', 6, NULL, NULL, '&lt;p&gt;Python has some very good tools for working with big data:&lt;/p&gt;

&lt;h2&gt;numpy&lt;/h2&gt;

&lt;p&gt;Numpy''s memmory-mapped arrays let you access a file saved on disk as though it were an array.  Only the parts of the array you are actively working with need to be loaded into memory.  It can be used pretty much the same as an ordinary array.&lt;/p&gt;

&lt;h2&gt;h5py and pytables&lt;/h2&gt;

&lt;p&gt;These two libraries provide access to HDF5 files.  These files allow access to just part of the data. Further, thanks to the underlying libraries used to access the data, many mathematical operations and other manipulations of the data can be done without loading it into a python data structure.  Massive, highly structured files are possible, much bigger than 5 TB.  It also allows seamless, lossless compression.&lt;/p&gt;

&lt;h2&gt;databases&lt;/h2&gt;

&lt;p&gt;There are various types of databases that allow you to store big data sets and load just the parts you need.  Many databases allow you to do manipulations without loading the data into a python data structure at all.&lt;/p&gt;

&lt;h2&gt;pandas&lt;/h2&gt;

&lt;p&gt;This allows higher-level access to various types of data, including HDF5 data, csv files, databases, even websites.  For big data, it provides wrappers around HDF5 file access that makes it easier to do analysis on big data sets.&lt;/p&gt;

&lt;h2&gt;mpi4py&lt;/h2&gt;

&lt;p&gt;This is a tool for running your python code in a distributed way across multiple processors or even multiple computers.  This allows you to work on parts of your data simultaneously.&lt;/p&gt;

&lt;h2&gt;dask&lt;/h2&gt;

&lt;p&gt;It provides a version of the normal numpy array that supports many of the normal numpy operations in a multi-core manner that can work on data too large to fit into memory. &lt;/p&gt;

&lt;h2&gt;blaze&lt;/h2&gt;

&lt;p&gt;A tool specifically designed for big data.  It is basically a wrapper around the above libraries, providing consistent interfaces to a variety of different methods of storing large amounts of data (such as HDF5 or databases) and tools to make it easy to manipulate, do mathematical operations on, and analyze data that is too big to fit into memory.&lt;/p&gt;
', NULL, NULL, '''/h2'':16C,72C,143C,187C,232C,268C,307C ''/p'':13C,67C,140C,184C,229C,265C,304C,368C ''5'':132C ''access'':26C,78C,85C,100C,194C,217C ''across'':247C ''activ'':46C ''allow'':84C,136C,152C,168C,190C,255C ''also'':135C ''amount'':337C ''analysi'':224C ''analyz'':358C ''around'':214C,321C ''array'':23C,37C,43C,66C,278C ''basic'':318C ''big'':11C,156C,209C,226C,314C,363C ''bigger'':130C ''blaze'':306C ''code'':242C ''compress'':139C ''comput'':253C ''consist'':326C ''core'':291C ''csv'':203C ''dask'':267C ''data'':12C,91C,102C,111C,121C,157C,176C,180C,199C,202C,210C,227C,263C,297C,315C,339C,359C ''databas'':142C,150C,167C,205C,344C ''design'':312C ''differ'':332C ''disk'':31C ''distribut'':245C ''done'':114C ''easi'':350C ''easier'':221C ''even'':206C,251C ''file'':28C,81C,83C,126C,204C,216C ''fit'':301C,365C ''good'':6C ''h2'':14C,68C,141C,185C,230C,266C,305C ''h5py'':69C ''hdf5'':80C,201C,215C,342C ''high'':124C ''higher'':192C ''higher-level'':191C ''includ'':200C ''interfac'':327C ''larg'':299C,336C ''let'':24C ''level'':193C ''librari'':76C,97C,324C ''load'':52C,116C,160C,174C ''lossless'':138C ''make'':219C,348C ''mani'':103C,166C,281C ''manipul'':108C,172C,352C ''manner'':292C ''map'':22C ''massiv'':123C ''mathemat'':104C,354C ''memmori'':21C ''memmory-map'':20C ''memori'':54C,303C,367C ''method'':333C ''mpi4py'':231C ''much'':60C,129C ''multi'':290C ''multi-cor'':289C ''multipl'':248C,252C ''need'':49C,165C ''normal'':276C,284C ''numpi'':15C,18C,277C,285C ''oper'':105C,286C,355C ''ordinari'':65C ''p'':1C,17C,73C,144C,188C,233C,269C,308C ''panda'':186C ''part'':40C,88C,163C,260C ''possibl'':128C ''pretti'':59C ''processor'':249C ''provid'':77C,212C,271C,325C ''pytabl'':71C ''python'':2C,120C,179C,241C ''run'':239C ''save'':29C ''seamless'':137C ''set'':158C,228C ''simultan'':264C ''specif'':311C ''store'':155C,335C ''structur'':122C,125C,181C ''support'':280C ''tb'':133C ''thank'':93C ''though'':33C ''tool'':7C,237C,310C,346C ''two'':75C ''type'':148C,197C ''under'':96C ''use'':58C,98C ''varieti'':330C ''various'':147C,196C ''version'':273C ''way'':246C ''websit'':207C ''without'':115C,173C ''work'':9C,47C,258C,295C ''wrapper'':213C,320C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5304, 1, '2015-03-11 04:18:21.61', 1, 94, 'How to make an effective sampling from a database of text documents?', '&lt;p&gt;Problem: I want to know methods to perform an effective sampling from a database.
The size of the database is about &lt;code&gt;250K&lt;/code&gt; text documents and in this case each text document is related to some majors (Electrical Engineering, Medicine and so on). So far I have seen some simple techniques like &lt;a href="http://en.wikipedia.org/wiki/Simple_random_sample" rel="nofollow"&gt;Simple random sample&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/Stratified_sampling" rel="nofollow"&gt;Stratified sampling&lt;/a&gt;; however, I don''t think it''s a good idea to apply them for the following reasons:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;In the case of simple random sample, for instance, there are a few documents in the database that talk about majors like Naval Engineering or Arts. Therefore I think they are less likely to be sampled with this method but I would like to have some samples of every major as possible.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;In the case of stratified sampling, most of the documents talk about more than one major so I cannot divide the database in subgroups because they will not be mutually exclusive, at least for the case in which each major is a subgroup.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Finally, I cannot use the whole database due to expensive computational cost processing. So I would really appreciate any suggestions on other sampling methods. Thanks for any help in advance.&lt;/p&gt;
', 'dataset  databases  sampling', NULL, '''/a'':80C,91C ''/code'':40C ''/li'':166C,213C ''/p'':109C,165C,212C,246C ''/ul'':214C ''/wiki/simple_random_sample'':74C ''/wiki/stratified_sampling'':86C ''250k'':39C ''advanc'':245C ''appli'':103C ''appreci'':233C ''art'':138C ''cannot'':187C,218C ''case'':46C,115C,171C,204C ''code'':38C ''comput'':226C ''cost'':227C ''databas'':9A,14B,30C,35C,129C,190C,222C ''dataset'':13B ''divid'':188C ''document'':12A,42C,49C,126C,178C ''due'':223C ''effect'':5A,26C ''electr'':55C ''en.wikipedia.org'':73C,85C ''en.wikipedia.org/wiki/simple_random_sample'':72C ''en.wikipedia.org/wiki/stratified_sampling'':84C ''engin'':56C,136C ''everi'':161C ''exclus'':199C ''expens'':225C ''far'':62C ''final'':216C ''follow'':107C ''good'':100C ''help'':243C ''howev'':92C ''href'':71C,83C ''idea'':101C ''instanc'':121C ''know'':21C ''least'':201C ''less'':144C ''li'':111C,167C ''like'':69C,134C,145C,155C ''major'':54C,133C,162C,184C,208C ''make'':3A ''medicin'':57C ''method'':22C,151C,239C ''mutual'':198C ''naval'':135C ''nofollow'':76C,88C ''one'':183C ''p'':16C,112C,168C,215C ''perform'':24C ''possibl'':164C ''problem'':17C ''process'':228C ''random'':78C,118C ''realli'':232C ''reason'':108C ''rel'':75C,87C ''relat'':51C ''sampl'':6A,15B,27C,79C,90C,119C,148C,159C,174C,238C ''seen'':65C ''simpl'':67C,77C,117C ''size'':32C ''stratifi'':89C,173C ''subgroup'':192C,211C ''suggest'':235C ''talk'':131C,179C ''techniqu'':68C ''text'':11A,41C,48C ''thank'':240C ''therefor'':139C ''think'':96C,141C ''ul'':110C ''use'':219C ''want'':19C ''whole'':221C ''would'':154C,231C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5301, 1, '2015-03-10 10:20:22.51', 1, 125, 'Algorithm for multiple extended string matching', '&lt;p&gt;I need to implement an algorithm for multiple extended string matching in text. Algorithms to match regular expression would be perhaps too slow.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Extended&lt;/strong&gt; means the presence of wildcards (any number of characters instead of a star), for example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;abc*def //matches abcdef, abcpppppdef etc.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Multiple&lt;/strong&gt; means that the search is going on simultaneously for multiple string patterns (not a separate search for each pattern), for example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;abc*def
abc
whatever
some*string
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;QUESTION:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;What is the fast algorithm that can do multiple extended string matching?&lt;/p&gt;

&lt;p&gt;Preferably, optimized for SIMD instructions and multicore implementation. Open source implementation (C/C++/Python) would be great as well. I''m interested in 10 Gbps+ performance on a single core of a modern CPU.&lt;/p&gt;

&lt;p&gt;Thank you&lt;/p&gt;
', 'algorithms  search', NULL, '''/code'':62C,98C ''/matches'':58C ''/p'':33C,53C,89C,104C,118C,153C,157C ''/pre'':63C,99C ''/python'':132C ''/strong'':37C,67C,103C ''10'':142C ''abc'':56C,92C,94C ''abcdef'':59C ''abcpppppdef'':60C ''algorithm'':1A,7B,15C,23C,110C ''c/c'':131C ''charact'':46C ''code'':55C,91C ''core'':148C ''cpu'':152C ''def'':57C,93C ''etc'':61C ''exampl'':52C,88C ''express'':27C ''extend'':4A,18C,36C,115C ''fast'':109C ''gbps'':143C ''go'':73C ''great'':135C ''implement'':13C,127C,130C ''instead'':47C ''instruct'':124C ''interest'':140C ''m'':139C ''match'':6A,20C,25C,117C ''mean'':38C,68C ''modern'':151C ''multicor'':126C ''multipl'':3A,17C,66C,77C,114C ''need'':11C ''number'':44C ''open'':128C ''optim'':121C ''p'':9C,34C,64C,100C,105C,119C,154C ''pattern'':79C,86C ''perform'':144C ''perhap'':30C ''pre'':54C,90C ''prefer'':120C ''presenc'':40C ''question'':102C ''regular'':26C ''search'':8B,71C,83C ''separ'':82C ''simd'':123C ''simultan'':75C ''singl'':147C ''slow'':32C ''sourc'':129C ''star'':50C ''string'':5A,19C,78C,97C,116C ''strong'':35C,65C,101C ''text'':22C ''thank'':155C ''well'':137C ''whatev'':95C ''wildcard'':42C ''would'':28C,133C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5302, 1, '2015-03-10 18:03:32.227', 1, 114, 'Transposing Every nth row to column in a large dataset', '&lt;p&gt;I am attempting to work with a very large data-set (~1.5mil lines) for the first time in SAS and I am having some difficulty. The data-set I have is formatted as a "long" .txt file as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;''cat1/: Topic1_Variable1''
''cat2/: Topic1_Variable2''
''cat3/: Topic1_Variable3''
''cat4/: Topic1_Variable4''

''cat1/: Topic2_Variable1''
''cat2/: Topic2_Variable2''
''cat3/: Topic2_Variable3''
''cat4/: Topic2_Variable4''

''cat1/: Topic3_Variable1''
''cat2/: Topic3_Variable2''
''cat3/: Topic3_Variable3''
''cat4/: Topic3_Variable4''
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Fro analysis and sharing with others, I really would like to see it formatted as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;cat1              cat2              cat3              cat4
Topic1_Variable1  Topic1_Variable2  Topic1_Variable3  Topic1_Variable4
Topic2_Variable1  Topic2_Variable2  Topic2_Variable3  Topic2_Variable4
Topic3_Variable1  Topic3_Variable2  Topic3_Variable3  Topic3_Variable4
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I think that this may be easier in R, but I honestly am drawing a complete blank in SAS. I''ve even played with MS Access to try to get it to look the way I want but the program crashes every time (due to the size?). At any rate, I have looked into some of the statements in PROC TRANSPOSE and PROC SQL but it seems that most functions within those procedures are utilized to combine duplicate ''Topics''. In the data I have been provided, each "group" represents an individual response to a question with several thousand individuals repeated, I want to retain the independence of each occurrence and not perform a UNION as defined in PROC SQL. At this point, I feel like I am over-thinking this but I just can''t get around the mental block and actually do what I am working toward. 
Any help or guidance is much appreciated. I''m open to trying all suggestions or ideas, I think I have access to most statistical computing programs. &lt;/p&gt;
', 'bigdata  dataset  data-formats', NULL, '''/code'':98C,148C ''/p'':59C,117C,326C ''/pre'':99C,149C ''1.5'':29C ''access'':176C,320C ''actual'':293C ''analysi'':102C ''appreci'':306C ''around'':288C ''attempt'':19C ''bigdata'':11B ''blank'':167C ''block'':291C ''cat1'':62C,74C,86C,120C ''cat2'':65C,77C,89C,121C ''cat3'':68C,80C,92C,122C ''cat4'':71C,83C,95C,123C ''code'':61C,119C ''column'':6A ''combin'':227C ''complet'':166C ''comput'':324C ''crash'':191C ''data'':14B,27C,46C,232C ''data-format'':13B ''data-set'':26C,45C ''dataset'':10A,12B ''defin'':266C ''difficulti'':43C ''draw'':164C ''due'':194C ''duplic'':228C ''easier'':157C ''even'':172C ''everi'':2A,192C ''feel'':274C ''file'':56C ''first'':34C ''follow'':58C,116C ''format'':15B,51C,114C ''fro'':101C ''function'':220C ''get'':180C,287C ''group'':238C ''guidanc'':303C ''help'':301C ''honest'':162C ''idea'':315C ''independ'':256C ''individu'':241C,249C ''larg'':9A,25C ''like'':110C,275C ''line'':31C ''long'':54C ''look'':183C,203C ''m'':308C ''may'':155C ''mental'':290C ''mil'':30C ''ms'':175C ''much'':305C ''nth'':3A ''occurr'':259C ''open'':309C ''other'':106C ''over-think'':278C ''p'':16C,100C,150C ''perform'':262C ''play'':173C ''point'':272C ''pre'':60C,118C ''proc'':210C,213C,268C ''procedur'':223C ''program'':190C,325C ''provid'':236C ''question'':245C ''r'':159C ''rate'':200C ''realli'':108C ''repeat'':250C ''repres'':239C ''respons'':242C ''retain'':254C ''row'':4A ''sas'':37C,169C ''see'':112C ''seem'':217C ''set'':28C,47C ''sever'':247C ''share'':104C ''size'':197C ''sql'':214C,269C ''statement'':208C ''statist'':323C ''suggest'':313C ''think'':152C,280C,317C ''thousand'':248C ''time'':35C,193C ''topic'':229C ''topic1'':63C,66C,69C,72C,124C,126C,128C,130C ''topic2'':75C,78C,81C,84C,132C,134C,136C,138C ''topic3'':87C,90C,93C,96C,140C,142C,144C,146C ''toward'':299C ''transpos'':1A,211C ''tri'':178C,311C ''txt'':55C ''union'':264C ''util'':225C ''variable1'':64C,76C,88C,125C,133C,141C ''variable2'':67C,79C,91C,127C,135C,143C ''variable3'':70C,82C,94C,129C,137C,145C ''variable4'':73C,85C,97C,131C,139C,147C ''ve'':171C ''want'':187C,252C ''way'':185C ''within'':221C ''work'':21C,298C ''would'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5309, 1, '2015-03-12 11:08:43.067', 0, 77, 'How can the performance of a neural network vary considerably without changing any parameters?', '&lt;p&gt;I am training a neural network with 1 sigmoid hidden layer and a linear output layer. The network simply approximates a cosine function. The weights are initiliazed according to Nguyen-Widrow initialization and the biases are initialized to 1. I am using MATLAB as a platform.&lt;/p&gt;

&lt;p&gt;Running the network a number of times without changing any parameters, I am getting results (mean squared error) which range from 0.5 to 0.5*10^-6. I cannot understand how the results can even vary that much, I''d imagine there would at least be a narrower and more consistent window of errors.&lt;/p&gt;

&lt;p&gt;What could be causing such a large variance?&lt;/p&gt;
', 'neuralnetwork  parameter  matlab', NULL, '''-6'':93C ''/p'':66C,121C,131C ''0.5'':89C,91C ''1'':26C,58C ''10'':92C ''accord'':46C ''approxim'':38C ''bias'':54C ''cannot'':95C ''caus'':126C ''chang'':12A,76C ''consider'':10A ''consist'':117C ''cosin'':40C ''could'':124C ''d'':106C ''error'':85C,120C ''even'':101C ''function'':41C ''get'':81C ''hidden'':28C ''imagin'':107C ''initi'':51C,56C ''initiliaz'':45C ''larg'':129C ''layer'':29C,34C ''least'':111C ''linear'':32C ''matlab'':17B,62C ''mean'':83C ''much'':104C ''narrow'':114C ''network'':8A,24C,36C,70C ''neural'':7A,23C ''neuralnetwork'':15B ''nguyen'':49C ''nguyen-widrow'':48C ''number'':72C ''output'':33C ''p'':18C,67C,122C ''paramet'':14A,16B,78C ''perform'':4A ''platform'':65C ''rang'':87C ''result'':82C,99C ''run'':68C ''sigmoid'':27C ''simpli'':37C ''squar'':84C ''time'':74C ''train'':21C ''understand'':96C ''use'':61C ''vari'':9A,102C ''varianc'':130C ''weight'':43C ''widrow'':50C ''window'':118C ''without'':11A,75C ''would'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5310, 2, '2015-03-12 13:18:03.737', 2, NULL, NULL, '&lt;p&gt;In general, there is no guarantee that ANNs such as a multi-layer Perceptron network will converge to the global minimum squared error (MSE) solution. The final state of the network can be heavily dependent on how the network weights are initialized. Since most initialization schemes (including Nguyen-Widrow) use random numbers to generate the initial weights, it is quite possible that some initial states will result in convergence on local minima, whereas others will converge on the MSE solution.&lt;/p&gt;
', NULL, NULL, '''/p'':84C ''ann'':9C ''converg'':19C,72C,79C ''depend'':37C ''error'':25C ''final'':29C ''general'':3C ''generat'':57C ''global'':22C ''guarante'':7C ''heavili'':36C ''includ'':49C ''initi'':44C,47C,59C,67C ''layer'':15C ''local'':74C ''minima'':75C ''minimum'':23C ''mse'':26C,82C ''multi'':14C ''multi-lay'':13C ''network'':17C,33C,41C ''nguyen'':51C ''nguyen-widrow'':50C ''number'':55C ''other'':77C ''p'':1C ''perceptron'':16C ''possibl'':64C ''quit'':63C ''random'':54C ''result'':70C ''scheme'':48C ''sinc'':45C ''solut'':27C,83C ''squar'':24C ''state'':30C,68C ''use'':53C ''weight'':42C,60C ''wherea'':76C ''widrow'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5303, 1, '2015-03-10 19:51:59.277', 5, 141, 'Couple PCA plot and clusters to labels', '&lt;p&gt;I am trying my first ''project'' concerning machine learning and I am a bit stuck.
However, I am not sure if it''s even possible but here goes my question.&lt;/p&gt;

&lt;p&gt;What I want to achieve is clustering user groups based on the amount of visits a user does on a certain website.
So I started out with this feature matrix:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;USER    abc.be  abc.be/a    abc.be/b    xyz.be  xyz.be/a
123      0        0           0            0      1
456      1        0           1            0      0
789      2        3           1            0      0
321      1        0           1            0      1
654      1        1           1            1      1
987      0        1           0            3      0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So I got in this example 5 features (my 5 different websites).
So then I used PCA to come to 2 dimensions, so I could plot it and see how it went.&lt;/p&gt;

&lt;p&gt;My feature matrix (in my example) is 5 columns * 6 rows.&lt;/p&gt;

&lt;p&gt;My PCA matrix is 2 columns * 6 rows.&lt;/p&gt;

&lt;p&gt;I came to this plot (please note that this plot uses different data then the example but the idea is the same)&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Tl0Qv.png" alt="PCA points and k-means centroids"&gt;&lt;/p&gt;

&lt;p&gt;The green points are my PCA points
The red circles are my K-Means centroids.&lt;/p&gt;

&lt;p&gt;But the part I am struggling with is this: so I got my clusters (red circles) but how can I use this to say:"Looks like most users go to either site A or site B)?&lt;/p&gt;

&lt;p&gt;So how can I couple my clusters to a feature label from my feature matrix?&lt;/p&gt;

&lt;p&gt;Or how does one approach this?&lt;/p&gt;

&lt;p&gt;Any help is appreciated :)&lt;/p&gt;
', 'machine-learning  feature-extraction  k-means', NULL, '''/a'':87C,94C ''/b'':90C ''/code'':131C ''/p'':48C,80C,166C,179C,189C,213C,228C,246C,284C,301C,309C,315C ''/pre'':132C ''/tl0qv.png'':219C ''0'':96C,97C,98C,99C,103C,105C,106C,111C,112C,115C,117C,126C,128C,130C ''1'':100C,102C,104C,110C,114C,116C,118C,120C,121C,122C,123C,124C,127C ''123'':95C ''2'':108C,154C,185C ''3'':109C,129C ''321'':113C ''456'':101C ''5'':140C,143C,175C ''6'':177C,187C ''654'':119C ''789'':107C ''987'':125C ''abc.be'':84C,86C,89C ''abc.be/a'':85C ''abc.be/b'':88C ''achiev'':54C ''alt'':220C ''amount'':62C ''appreci'':314C ''approach'':307C ''b'':283C ''base'':59C ''bit'':31C ''came'':192C ''centroid'':227C,245C ''certain'':70C ''circl'':239C,263C ''cluster'':5A,56C,261C,292C ''code'':82C ''column'':176C,186C ''come'':152C ''concern'':24C ''could'':158C ''coupl'':1A,290C ''data'':203C ''differ'':144C,202C ''dimens'':155C ''either'':278C ''even'':41C ''exampl'':139C,173C,206C ''extract'':13B ''featur'':12B,78C,141C,169C,295C,299C ''feature-extract'':11B ''first'':22C ''go'':276C ''goe'':45C ''got'':136C,259C ''green'':231C ''group'':58C ''help'':312C ''howev'':33C ''i.stack.imgur.com'':218C ''i.stack.imgur.com/tl0qv.png'':217C ''idea'':209C ''img'':215C ''k'':15B,225C,243C ''k-mean'':14B,224C,242C ''label'':7A,296C ''learn'':10B,26C ''like'':273C ''look'':272C ''machin'':9B,25C ''machine-learn'':8B ''matrix'':79C,170C,183C,300C ''mean'':16B,226C,244C ''note'':197C ''one'':306C ''p'':17C,49C,133C,167C,180C,190C,214C,229C,247C,285C,302C,310C ''part'':250C ''pca'':2A,150C,182C,221C,235C ''pleas'':196C ''plot'':3A,159C,195C,200C ''point'':222C,232C,236C ''possibl'':42C ''pre'':81C ''project'':23C ''question'':47C ''red'':238C,262C ''row'':178C,188C ''say'':271C ''see'':162C ''site'':279C,282C ''src'':216C ''start'':74C ''struggl'':253C ''stuck'':32C ''sure'':37C ''tri'':20C ''use'':149C,201C,268C ''user'':57C,66C,83C,275C ''visit'':64C ''want'':52C ''websit'':71C,145C ''went'':165C ''xyz.be'':91C,93C ''xyz.be/a'':92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9684, 2, '2016-01-07 17:32:32.017', 0, NULL, NULL, '&lt;p&gt;I can suggest several papers on this topic:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Automatic Labelling of Topic Models&lt;/li&gt;
&lt;li&gt;Automatic Labeling Hierarchical Topics&lt;/li&gt;
&lt;li&gt;Representing Topics Labels for Exploring Digital Libraries&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You can find more by looking at their citations.&lt;/p&gt;
', NULL, NULL, '''/li'':18C,24C,33C ''/p'':10C,45C ''/ul'':34C ''automat'':13C,20C ''citat'':44C ''digit'':31C ''explor'':30C ''find'':38C ''hierarch'':22C ''label'':14C,21C,28C ''li'':12C,19C,25C ''librari'':32C ''look'':41C ''model'':17C ''p'':1C,35C ''paper'':6C ''repres'':26C ''sever'':5C ''suggest'':4C ''topic'':9C,16C,23C,27C ''ul'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5306, 2, '2015-03-11 06:52:39.44', 3, NULL, NULL, '&lt;p&gt;(Fresh and complete) Twitter data is not easy to get nowadays, as Twitter is actively monetizing it (through GNIP). There is still a large Twitter graph collected by Kwak et al. for their "What is Twitter, a social network or a news media?" WWW''10 &lt;a href="http://an.kaist.ac.kr/traces/WWW2010.html" rel="nofollow"&gt;publicly available&lt;/a&gt;, but as to tweets, most nice datasets have disappeared from the Internet due to complaints from Twitter. There are still tiny datasets of tweets available online (&lt;a href="http://www.sananalytics.com/lab/twitter-sentiment/" rel="nofollow"&gt;e.g.&lt;/a&gt;), but they are small and stale. Thus, I see two "legal" ways to go about getting tweets now: 1) crawl Twitter using the new version of Twitter API (the obvious limitation of this approach is the limit on the number of requests you can send; this approach is unlikely to give you a representative picture of what is trending if you collect raw tweets, just because you won''t have enough tweets); 2) buy data from Twitter (GNIP), which will cost an awful lot of money. Alternatively, you can 3) study Twitter API for other means of getting "summarized" information from it, not tweets directly. For example, they may provide a method to retrieve, say, 10 most popular hash tags at the moment, maybe, even filtered by geo-location -- you cannot do much data science with the result, but at least you can use it in your visualization application.&lt;/p&gt;
', NULL, NULL, '''/a'':56C,91C ''/lab/twitter-sentiment/'':87C ''/p'':242C ''/traces/www2010.html'':51C ''1'':110C ''10'':46C,207C ''2'':164C ''3'':181C ''activ'':16C ''al'':32C ''altern'':178C ''an.kaist.ac.kr'':50C ''an.kaist.ac.kr/traces/www2010.html'':49C ''api'':119C,184C ''applic'':241C ''approach'':125C,138C ''avail'':55C,81C ''aw'':174C ''buy'':165C ''cannot'':223C ''collect'':28C,153C ''complaint'':71C ''complet'':4C ''cost'':172C ''crawl'':111C ''data'':6C,166C,226C ''dataset'':63C,78C ''direct'':196C ''disappear'':65C ''due'':69C ''e.g'':90C ''easi'':9C ''enough'':162C ''et'':31C ''even'':216C ''exampl'':198C ''filter'':217C ''fresh'':2C ''geo'':220C ''geo-loc'':219C ''get'':11C,107C,189C ''give'':142C ''gnip'':20C,169C ''go'':105C ''graph'':27C ''hash'':210C ''href'':48C,84C ''inform'':191C ''internet'':68C ''kwak'':30C ''larg'':25C ''least'':233C ''legal'':102C ''limit'':122C,128C ''locat'':221C ''lot'':175C ''may'':200C ''mayb'':215C ''mean'':187C ''media'':44C ''method'':203C ''moment'':214C ''monet'':17C ''money'':177C ''much'':225C ''network'':40C ''new'':115C ''news'':43C ''nice'':62C ''nofollow'':53C,89C ''nowaday'':12C ''number'':131C ''obvious'':121C ''onlin'':82C ''p'':1C ''pictur'':146C ''popular'':209C ''provid'':201C ''public'':54C ''raw'':154C ''rel'':52C,88C ''repres'':145C ''request'':133C ''result'':230C ''retriev'':205C ''say'':206C ''scienc'':227C ''see'':100C ''send'':136C ''small'':95C ''social'':39C ''stale'':97C ''still'':23C,76C ''studi'':182C ''summar'':190C ''tag'':211C ''thus'':98C ''tini'':77C ''trend'':150C ''tweet'':60C,80C,108C,155C,163C,195C ''twitter'':5C,14C,26C,37C,73C,112C,118C,168C,183C ''two'':101C ''unlik'':140C ''use'':113C,236C ''version'':116C ''visual'':240C ''way'':103C ''won'':159C ''www'':45C ''www.sananalytics.com'':86C ''www.sananalytics.com/lab/twitter-sentiment/'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5308, 2, '2015-03-11 11:44:01.873', 1, NULL, NULL, '&lt;p&gt;Off the top of my head, I would consider, at least, two &lt;strong&gt;approaches&lt;/strong&gt;, as follows.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Graph sampling.&lt;/strong&gt; If you can (and it makes sense to) model your &lt;em&gt;population&lt;/em&gt; (database of text documents) as a graph, then consider &lt;em&gt;graph sampling&lt;/em&gt;. Check &lt;a href="http://stats.stackexchange.com/a/128661/31372"&gt;this relevant answer&lt;/a&gt; of mine on the topic on &lt;em&gt;Cross Validated&lt;/em&gt; site (contains many references - I would start with &lt;a href="http://arxiv.org/abs/1308.5865" rel="nofollow"&gt;this nice overview paper&lt;/a&gt;).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Topic modeling.&lt;/strong&gt; Another alternative is topic modeling (also referred to as topic models). See &lt;a href="http://www.cs.princeton.edu/~blei/papers/Blei2012.pdf" rel="nofollow"&gt;this introductory paper&lt;/a&gt; as well as &lt;a href="http://www.cs.princeton.edu/~blei/papers/BleiLafferty2009.pdf" rel="nofollow"&gt;this more detailed paper&lt;/a&gt; on the subject. Also, take a look at &lt;a href="http://people.cs.umass.edu/~lmyao/papers/fast-topic-model10.pdf" rel="nofollow"&gt;this interesting paper&lt;/a&gt; on topic modeling of &lt;em&gt;streaming&lt;/em&gt; documents (or other situations, such as large databases, when unsupervised solutions make more sense). Finally, speaking about &lt;strong&gt;software&lt;/strong&gt; for topic modeling, while many options do exist, I suggest investigating &lt;a href="http://mallet.cs.umass.edu" rel="nofollow"&gt;MALLET&lt;/a&gt;, which is an interesting Java-based software package for &lt;em&gt;"statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text"&lt;/em&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':62C,92C,123C,138C,157C,202C ''/a/128661/31372'':58C ''/abs/1308.5865'':85C ''/em'':39C,52C,72C,164C,232C ''/li'':94C,234C ''/p'':19C,93C,233C ''/strong'':16C,26C,100C,184C ''/ul'':235C ''/~blei/papers/blei2012.pdf'':117C ''/~blei/papers/bleilafferty2009.pdf'':131C ''/~lmyao/papers/fast-topic-model10.pdf'':151C ''also'':106C,142C ''altern'':102C ''anoth'':101C ''answer'':61C ''applic'':229C ''approach'':15C ''arxiv.org'':84C ''arxiv.org/abs/1308.5865'':83C ''base'':209C ''check'':53C ''classif'':219C ''cluster'':220C ''consid'':10C,48C ''contain'':74C ''cross'':70C ''databas'':40C,172C ''detail'':136C ''document'':43C,165C,218C ''em'':37C,49C,69C,162C,213C ''exist'':192C ''extract'':224C ''final'':179C ''follow'':18C ''graph'':24C,46C,50C ''head'':7C ''href'':55C,82C,114C,128C,148C,197C ''inform'':223C ''interest'':155C,206C ''introductori'':121C ''investig'':195C ''java'':208C ''java-bas'':207C ''languag'':216C ''larg'':171C ''learn'':228C ''least'':12C ''li'':21C,95C ''look'':145C ''machin'':227C ''make'':32C,176C ''mallet'':201C ''mallet.cs.umass.edu'':198C ''mani'':75C,189C ''mine'':64C ''model'':35C,99C,105C,111C,160C,187C,222C ''natur'':215C ''nice'':89C ''nofollow'':87C,119C,133C,153C,200C ''option'':190C ''overview'':90C ''p'':1C,22C,96C ''packag'':211C ''paper'':91C,122C,137C,156C ''people.cs.umass.edu'':150C ''people.cs.umass.edu/~lmyao/papers/fast-topic-model10.pdf'':149C ''popul'':38C ''process'':217C ''refer'':76C,107C ''rel'':86C,118C,132C,152C,199C ''relev'':60C ''sampl'':25C,51C ''see'':112C ''sens'':33C,178C ''site'':73C ''situat'':168C ''softwar'':183C,210C ''solut'':175C ''speak'':180C ''start'':79C ''statist'':214C ''stats.stackexchange.com'':57C ''stats.stackexchange.com/a/128661/31372'':56C ''stream'':163C ''strong'':14C,23C,97C,182C ''subject'':141C ''suggest'':194C ''take'':143C ''text'':42C,231C ''top'':4C ''topic'':67C,98C,104C,110C,159C,186C,221C ''two'':13C ''ul'':20C ''unsupervis'':174C ''valid'':71C ''well'':125C ''would'':9C,78C ''www.cs.princeton.edu'':116C,130C ''www.cs.princeton.edu/~blei/papers/blei2012.pdf'':115C ''www.cs.princeton.edu/~blei/papers/bleilafferty2009.pdf'':129C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9083, 1, '2015-11-27 16:18:14.92', 0, 70, 'Spark vs Map reduce', '&lt;p&gt;I know that Spark enhances performance relative to mapreduce by doing in-memory computations. But, since the caching is explicitly decided by the programmer, one can also proceed without doing that. I believe even in such cases, Spark is 10x faster than map reduce. What features in the framework make this possible?&lt;/p&gt;
', 'apache-spark  map-reduce', NULL, '''/p'':65C ''10x'':52C ''also'':39C ''apach'':6B ''apache-spark'':5B ''believ'':45C ''cach'':30C ''case'':49C ''comput'':26C ''decid'':33C ''enhanc'':16C ''even'':46C ''explicit'':32C ''faster'':53C ''featur'':58C ''framework'':61C ''in-memori'':23C ''know'':13C ''make'':62C ''map'':3A,9B,55C ''map-reduc'':8B ''mapreduc'':20C ''memori'':25C ''one'':37C ''p'':11C ''perform'':17C ''possibl'':64C ''proceed'':40C ''programm'':36C ''reduc'':4A,10B,56C ''relat'':18C ''sinc'':28C ''spark'':1A,7B,15C,50C ''vs'':2A ''without'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5311, 1, '2015-03-13 02:32:55.357', 0, 23, 'Post Hoc Analysis in Mixed linear models', '&lt;p&gt;I tested whether different version-styles of a loading screen (hourglass vs. progress bar) in different progression patterns (linear, accelerate, decelerate, irregular, binary)
affect time estimations within subjects. &lt;/p&gt;

&lt;p&gt;By analyzing the data with a binomial linear mixed effects model I have found significant results for the interaction effect of "versionStyle x DisplayDuration x progressionPattern" 
I would like to run a post hoc analysis to test across which condition and Display duration the time estimation was significantly affected by the version Style"&lt;/p&gt;

&lt;p&gt;Is there some command that I can use in R?&lt;/p&gt;

&lt;p&gt;This is the code I used for my analysis:&lt;/p&gt;

&lt;p&gt;(data1 &amp;lt;- glmer(Long ~ DisplayDur * Pattern * Proggression+ (1 + DisplayDur + Pattern+ Progression| Subjects),
                dat=anadat,family="binomial",control=glmerControl(optimizer="bobyqa")))&lt;/p&gt;
', 'r', NULL, '''/p'':39C,94C,106C,117C,139C ''1'':126C ''acceler'':30C ''across'':78C ''affect'':34C,89C ''anadat'':132C ''analysi'':3A,75C,116C ''analyz'':42C ''bar'':24C ''binari'':33C ''binomi'':47C,134C ''bobyqa'':138C ''code'':111C ''command'':99C ''condit'':80C ''control'':135C ''dat'':131C ''data'':44C ''data1'':119C ''deceler'':31C ''differ'':13C,26C ''display'':82C ''displaydur'':64C,123C,127C ''durat'':83C ''effect'':50C,60C ''estim'':36C,86C ''famili'':133C ''found'':54C ''glmer'':121C ''glmercontrol'':136C ''hoc'':2A,74C ''hourglass'':21C ''interact'':59C ''irregular'':32C ''like'':69C ''linear'':6A,29C,48C ''load'':19C ''long'':122C ''lt'':120C ''mix'':5A,49C ''model'':7A,51C ''optim'':137C ''p'':9C,40C,95C,107C,118C ''pattern'':28C,124C,128C ''post'':1A,73C ''proggress'':125C ''progress'':23C,27C,129C ''progressionpattern'':66C ''r'':8B,105C ''result'':56C ''run'':71C ''screen'':20C ''signific'':55C,88C ''style'':16C,93C ''subject'':38C,130C ''test'':11C,77C ''time'':35C,85C ''use'':103C,113C ''version'':15C,92C ''version-styl'':14C ''versionstyl'':62C ''vs'':22C ''whether'':12C ''within'':37C ''would'':68C ''x'':63C,65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5312, 2, '2015-03-13 12:16:11.643', 1, NULL, NULL, '&lt;p&gt;There''s a tweet collection (comprising of 16 million tweets) that you can get from NIST. This collection is used for the ad hoc search task on micro-blogs. 
&lt;a href="http://trec.nist.gov/data/tweets/" rel="nofollow"&gt;TREC 2011 Microblog collection&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':43C ''/data/tweets/'':36C ''/p'':44C ''16'':9C ''2011'':40C ''ad'':24C ''blog'':31C ''collect'':6C,19C,42C ''compris'':7C ''get'':15C ''hoc'':25C ''href'':33C ''micro'':30C ''micro-blog'':29C ''microblog'':41C ''million'':10C ''nist'':17C ''nofollow'':38C ''p'':1C ''rel'':37C ''search'':26C ''task'':27C ''trec'':39C ''trec.nist.gov'':35C ''trec.nist.gov/data/tweets/'':34C ''tweet'':5C,11C ''use'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5313, 1, '2015-03-13 14:10:11.143', 0, 77, 'Forecasting sales and creating model', '&lt;p&gt;In a assignment we are given macro economic indicators like GDP, Consumer price index, Producer Price index and Industrial production index. Also we are given Crude oil, Sugar prices and FM-CG Sales. We are required to forecast future quarter sales and give a model. As I''m new to this subject, I don''t know where to start with it, or what to read. Can anyone provide me with some examples of what to do, or any PDFs which might be helpful. &lt;/p&gt;
', 'predictive-modeling  forecast', NULL, '''/p'':95C ''also'':32C ''anyon'':78C ''assign'':13C ''cg'':43C ''consum'':22C ''creat'':4A ''crude'':36C ''econom'':18C ''exampl'':83C ''fm'':42C ''fm-cg'':41C ''forecast'':1A,9B,49C ''futur'':50C ''gdp'':21C ''give'':54C ''given'':16C,35C ''help'':94C ''index'':24C,27C,31C ''indic'':19C ''industri'':29C ''know'':67C ''like'':20C ''m'':59C ''macro'':17C ''might'':92C ''model'':5A,8B,56C ''new'':60C ''oil'':37C ''p'':10C ''pdfs'':90C ''predict'':7B ''predictive-model'':6B ''price'':23C,26C,39C ''produc'':25C ''product'':30C ''provid'':79C ''quarter'':51C ''read'':76C ''requir'':47C ''sale'':2A,44C,52C ''start'':70C ''subject'':63C ''sugar'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5314, 2, '2015-03-13 15:24:57.223', 1, NULL, NULL, '&lt;p&gt;If you are new to forecasting, I would recommend starting with a very simple model for your sales, e.g., Exponential Smoothing, which will use &lt;em&gt;only&lt;/em&gt; the sales data and no external information, but will capture trend and seasonality. &lt;a href="https://www.otexts.org/fpp/" rel="nofollow"&gt;Here is an extremely good free online textbook.&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Once you are comfortable with your simple model, you can look at more complex ones that do include external causal variables. For instance, you could run ARIMAX models (the X stands for eXternal or eXplanatory variables). For this, look at &lt;a href="https://www.otexts.org/fpp/8" rel="nofollow"&gt;chapter 8 in the online textbook&lt;/a&gt; and check the &lt;code&gt;auto.arima()&lt;/code&gt; function in the &lt;code&gt;forecast&lt;/code&gt; package for R, where you can specify external variables using the &lt;code&gt;xreg&lt;/code&gt; parameter.&lt;/p&gt;

&lt;p&gt;Alternatively, you could look at other methods like Neural Networks, or possibly even Vector Autoregression. To learn more, you could look into standard econometrics textbooks.&lt;/p&gt;

&lt;p&gt;Whatever you do, keep two things in mind:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;If you include explanatory variables in your forecast, you will need to forecast those variables themselves. In an assignment-type situation, you may be able to work with &lt;em&gt;actual&lt;/em&gt; future macroeconomic variables, but of course in a "real" forecasting situation, you don''t know next month''s sugar prices yet. Forecasting these to feed the sugar price forecast into your sales forecast model adds an additional bit of uncertainty.&lt;/li&gt;
&lt;li&gt;You may just find out that your fancy model with lots of additional explanatory variables does not do a much better job at forecasting than your original simple model. This would not at all be surprising. &lt;a href="http://stats.stackexchange.com/questions/124955/is-it-unusual-for-mean-to-outperform-arima/124956#124956"&gt;Simple methods very often outperform complex ones in forecasting.&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':57C,113C,305C ''/code'':119C,125C,139C ''/em'':28C,212C ''/fpp/'':46C ''/fpp/8'':104C ''/li'':253C,306C ''/ol'':307C ''/p'':58C,141C,168C,178C ''/questions/124955/is-it-unusual-for-mean-to-outperform-arima/124956#124956'':295C ''8'':108C ''abl'':206C ''actual'':211C ''add'':247C ''addit'':249C,267C ''altern'':143C ''arimax'':86C ''assign'':200C ''assignment-typ'':199C ''auto.arima'':118C ''autoregress'':157C ''better'':275C ''bit'':250C ''captur'':38C ''causal'':79C ''chapter'':107C ''check'':115C ''code'':117C,123C,137C ''comfort'':63C ''complex'':73C,301C ''could'':84C,145C,162C ''cours'':218C ''data'':31C ''e.g'':20C ''econometr'':166C ''em'':26C,210C ''even'':155C ''explanatori'':94C,184C,268C ''exponenti'':21C ''extern'':34C,78C,92C,133C ''extrem'':52C ''fanci'':262C ''feed'':237C ''find'':258C ''forecast'':7C,124C,188C,193C,222C,234C,241C,245C,278C,304C ''free'':54C ''function'':120C ''futur'':213C ''good'':53C ''href'':43C,101C,292C ''includ'':77C,183C ''inform'':35C ''instanc'':82C ''job'':276C ''keep'':173C ''know'':227C ''learn'':159C ''li'':180C,254C ''like'':150C ''look'':70C,98C,146C,163C ''lot'':265C ''macroeconom'':214C ''may'':204C,256C ''method'':149C,297C ''mind'':177C ''model'':16C,67C,87C,246C,263C,283C ''month'':229C ''much'':274C ''need'':191C ''network'':152C ''neural'':151C ''new'':5C ''next'':228C ''nofollow'':48C,106C ''often'':299C ''ol'':179C ''one'':74C,302C ''onlin'':55C,111C ''origin'':281C ''outperform'':300C ''p'':1C,59C,142C,169C ''packag'':126C ''paramet'':140C ''possibl'':154C ''price'':232C,240C ''r'':128C ''real'':221C ''recommend'':10C ''rel'':47C,105C ''run'':85C ''sale'':19C,30C,244C ''season'':41C ''simpl'':15C,66C,282C,296C ''situat'':202C,223C ''smooth'':22C ''specifi'':132C ''stand'':90C ''standard'':165C ''start'':11C ''stats.stackexchange.com'':294C ''stats.stackexchange.com/questions/124955/is-it-unusual-for-mean-to-outperform-arima/124956#124956'':293C ''sugar'':231C,239C ''surpris'':290C ''textbook'':56C,112C,167C ''thing'':175C ''trend'':39C ''two'':174C ''type'':201C ''uncertainti'':252C ''use'':25C,135C ''variabl'':80C,95C,134C,185C,195C,215C,269C ''vector'':156C ''whatev'':170C ''work'':208C ''would'':9C,285C ''www.otexts.org'':45C,103C ''www.otexts.org/fpp/'':44C ''www.otexts.org/fpp/8'':102C ''x'':89C ''xreg'':138C ''yet'':233C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5315, 2, '2015-03-13 15:41:35.577', 0, NULL, NULL, '&lt;p&gt;You''re going to need to do simple random sampling, but maintain counts of the labels you''ve seen thus far. When you reach quota for a label, you''ll need to cull all documents with that label from the pool, and sample from the remaining documents without that label. &lt;/p&gt;

&lt;p&gt;I &lt;em&gt;think&lt;/em&gt; this will be a fair way of getting a stratified sample when your labels are overlapping.&lt;/p&gt;
', NULL, NULL, '''/em'':57C ''/p'':52C,74C ''count'':14C ''cull'':34C ''document'':36C,48C ''em'':55C ''fair'':62C ''far'':22C ''get'':65C ''go'':4C ''label'':17C,29C,39C,51C,71C ''ll'':31C ''maintain'':13C ''need'':6C,32C ''overlap'':73C ''p'':1C,53C ''pool'':42C ''quota'':26C ''random'':10C ''re'':3C ''reach'':25C ''remain'':47C ''sampl'':11C,44C,68C ''seen'':20C ''simpl'':9C ''stratifi'':67C ''think'':56C ''thus'':21C ''ve'':19C ''way'':63C ''without'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5319, 2, '2015-03-14 13:02:21.53', 0, NULL, NULL, '&lt;p&gt;I asked a similar question: &lt;/p&gt;

&lt;p&gt;&lt;a href="http://stats.stackexchange.com/q/140406/70282"&gt;http://stats.stackexchange.com/q/140406/70282&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;What I ended up doing is having a while loop repeatably create NNs of a small size looking for the best root mean square error.&lt;/p&gt;

&lt;p&gt;Essentially:
For hidden layer size of 1 to 10 do
For trials of 1 to 10:
Create NN and test for rmse with test data.
Remember NN and best rmse for particular hidden layer size.
Done for.
Done for.
Review results choosing the smallest hidden layer size which had a small rmse.&lt;/p&gt;

&lt;p&gt;By separating your data into training and tedtinh data, you don''t pick an over fit NN.&lt;/p&gt;
', NULL, NULL, '''/p'':7C,43C,97C,116C ''/q/140406/70282'':13C ''/q/140406/70282&lt;/a&gt;&lt;/p&gt;'':16C ''1'':51C,58C ''10'':53C,60C ''ask'':3C ''best'':38C,73C ''choos'':86C ''creat'':29C,61C ''data'':69C,102C,107C ''done'':80C,82C ''end'':20C ''error'':42C ''essenti'':45C ''fit'':114C ''hidden'':47C,77C,89C ''href'':10C ''layer'':48C,78C,90C ''look'':35C ''loop'':27C ''mean'':40C ''nn'':62C,71C,115C ''nns'':30C ''p'':1C,8C,17C,44C,98C ''particular'':76C ''pick'':111C ''question'':6C ''rememb'':70C ''repeat'':28C ''result'':85C ''review'':84C ''rmse'':66C,74C,96C ''root'':39C ''separ'':100C ''similar'':5C ''size'':34C,49C,79C,91C ''small'':33C,95C ''smallest'':88C ''squar'':41C ''stats.stackexchange.com'':12C,15C ''stats.stackexchange.com/q/140406/70282'':11C ''stats.stackexchange.com/q/140406/70282&lt;/a&gt;&lt;/p&gt;'':14C ''tedtinh'':106C ''test'':64C,68C ''train'':104C ''trial'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5320, 1, '2015-03-14 14:48:12.233', 0, 71, 'Need help with python code as part of a data analysis project', '&lt;p&gt;I am new to python programming. As part of a data analysis project, I am trying to get a scatter plot of Salary vs Wins for each year of 4 consecutive years (so I am trying to get 4 different scatter plots, one for each year). I am using the following code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;teamName = ''OAK''
years = np.arange(2000,2004)
for yr in years:
    df = joined[joined[''yearID'']==yr]
    plt.scatter(df[''salary'']/1e6,df[''W''])
    plt.title(''Wins vs Salaries in year'' + str(yr))
    plt.xlabel(''Total Salary (in millions)'')
    plt.ylabel(''Wins'')
    plt.xlim(0,180)
    plt.ylim(30,130)
    plt.grid()
    plt.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However, I am only getting one plot corresponding to 2003. &lt;/p&gt;

&lt;p&gt;Can anyone point out the mistake ?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'python', NULL, '''/1e6'':88C ''/code'':114C ''/p'':67C,127C,135C,138C ''/pre'':115C ''0'':107C ''130'':111C ''180'':108C ''2000'':74C ''2003'':126C ''2004'':75C ''30'':110C ''4'':44C,53C ''analysi'':11A,26C ''anyon'':130C ''code'':5A,66C,69C ''consecut'':45C ''correspond'':124C ''data'':10A,25C ''df'':80C,86C,89C ''differ'':54C ''follow'':65C ''get'':32C,52C,121C ''help'':2A ''howev'':117C ''join'':81C,82C ''million'':103C ''mistak'':134C ''need'':1A ''new'':17C ''np.arange'':73C ''oak'':71C ''one'':57C,122C ''p'':14C,116C,128C,136C ''part'':7A,22C ''plot'':35C,56C,123C ''plt.grid'':112C ''plt.scatter'':85C ''plt.show'':113C ''plt.title'':91C ''plt.xlabel'':99C ''plt.xlim'':106C ''plt.ylabel'':104C ''plt.ylim'':109C ''point'':131C ''pre'':68C ''program'':20C ''project'':12A,27C ''python'':4A,13B,19C ''salari'':37C,87C,94C,101C ''scatter'':34C,55C ''str'':97C ''teamnam'':70C ''thank'':137C ''total'':100C ''tri'':30C,50C ''use'':63C ''vs'':38C,93C ''w'':90C ''win'':39C,92C,105C ''year'':42C,46C,60C,72C,79C,96C ''yearid'':83C ''yr'':77C,84C,98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10244, 2, '2016-02-15 02:05:23.76', 2, NULL, NULL, '&lt;p&gt;The University of Washington has released a project template for small scientific python projects (including data science projects) called &lt;code&gt;shablona&lt;/code&gt;. Is that more or less what you''re looking for?&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/uwescience/shablona" rel="nofollow"&gt;https://github.com/uwescience/shablona&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/code'':23C ''/p'':34C ''/uwescience/shablona'':40C ''/uwescience/shablona&lt;/a&gt;&lt;/p&gt;'':45C ''call'':20C ''code'':21C ''data'':17C ''github.com'':39C,44C ''github.com/uwescience/shablona'':38C ''github.com/uwescience/shablona&lt;/a&gt;&lt;/p&gt;'':43C ''href'':37C ''includ'':16C ''less'':28C ''look'':32C ''nofollow'':42C ''p'':1C,35C ''project'':9C,15C,19C ''python'':14C ''re'':31C ''rel'':41C ''releas'':7C ''scienc'':18C ''scientif'':13C ''shablona'':22C ''small'':12C ''templat'':10C ''univers'':3C ''washington'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5331, 1, '2015-03-17 02:44:16.263', 1, 55, 'Better approach for handwriting recognition?', '&lt;p&gt;I am trying to write an ANN in python for handwriting recognition by mouse movements. ( like identify characters we draw in paint app n convert it to text) &lt;/p&gt;

&lt;p&gt;The question might seem that I haven''t done my homework, but I am *not * looking for image datasets for handwritten characters.( at least that''s what I''ve figured out so far)&lt;/p&gt;

&lt;p&gt;Is there any database for such (array or co-ordinates corresponding to each character) inputs? If not, how can I create this database from existing ones like MNIST?&lt;/p&gt;

&lt;p&gt;Currently, I have written a script to generate my own database because I could not find any. But it seems a very tedious task to generate a good database. 
I am a beginner in ML. Could somebody point me in &lt;em&gt;right direction&lt;/em&gt; upon how else can we do it?&lt;/p&gt;
', 'machine-learning  python  neuralnetwork', NULL, '''/em'':151C ''/p'':40C,75C,106C,159C ''ann'':18C ''app'':34C ''approach'':2A ''array'':83C ''beginn'':140C ''better'':1A ''charact'':29C,63C,91C ''co'':86C ''co-ordin'':85C ''convert'':36C ''correspond'':88C ''could'':121C,143C ''creat'':98C ''current'':108C ''databas'':80C,100C,118C,136C ''dataset'':60C ''direct'':150C ''done'':50C ''draw'':31C ''els'':154C ''em'':148C ''exist'':102C ''far'':74C ''figur'':71C ''find'':123C ''generat'':115C,133C ''good'':135C ''handwrit'':4A,22C ''handwritten'':62C ''haven'':48C ''homework'':52C ''identifi'':28C ''imag'':59C ''input'':92C ''learn'':8B ''least'':65C ''like'':27C,104C ''look'':57C ''machin'':7B ''machine-learn'':6B ''might'':44C ''ml'':142C ''mnist'':105C ''mous'':25C ''movement'':26C ''n'':35C ''neuralnetwork'':10B ''one'':103C ''ordin'':87C ''p'':11C,41C,76C,107C ''paint'':33C ''point'':145C ''python'':9B,20C ''question'':43C ''recognit'':5A,23C ''right'':149C ''script'':113C ''seem'':45C,127C ''somebodi'':144C ''task'':131C ''tedious'':130C ''text'':39C ''tri'':14C ''upon'':152C ''ve'':70C ''write'':16C ''written'':111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5323, 1, '2015-03-15 11:47:55.003', 0, 158, 'Implementation of Association Rules in Javascript', '&lt;p&gt;I have implemented an interactive visualization with d3.js, javascript to explore the frequency and various combinations of co-occurring item sets. I want to complement the interactive exploration with some automated options.&lt;/p&gt;

&lt;p&gt;Does someone know an efficient javascript implementation of the association rules mining ? &lt;/p&gt;

&lt;p&gt;My typical scenario will have just up to 30 different items.
There are some good web site with implementations of frequent item set mining (improvements from the initial apriori algorithm): &lt;a href="http://www.borgelt.net/apriori.html" rel="nofollow"&gt;http://www.borgelt.net/apriori.html&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;Any help is greatly appreciated.&lt;/p&gt;
', 'efficiency  javascript', NULL, '''/apriori.html'':93C ''/apriori.html&lt;/a&gt;'':98C ''/p'':43C,57C,99C,106C ''30'':67C ''algorithm'':88C ''appreci'':105C ''apriori'':87C ''associ'':3A,54C ''autom'':41C ''co'':28C ''co-occur'':27C ''combin'':25C ''complement'':35C ''d3.js'':17C ''differ'':68C ''effici'':7B,49C ''explor'':20C,38C ''frequenc'':22C ''frequent'':79C ''good'':73C ''great'':104C ''help'':102C ''href'':90C ''implement'':1A,12C,51C,77C ''improv'':83C ''initi'':86C ''interact'':14C,37C ''item'':30C,69C,80C ''javascript'':6A,8B,18C,50C ''know'':47C ''mine'':56C,82C ''nofollow'':95C ''occur'':29C ''option'':42C ''p'':9C,44C,58C,100C ''rel'':94C ''rule'':4A,55C ''scenario'':61C ''set'':31C,81C ''site'':75C ''someon'':46C ''typic'':60C ''various'':24C ''visual'':15C ''want'':33C ''web'':74C ''www.borgelt.net'':92C,97C ''www.borgelt.net/apriori.html'':91C ''www.borgelt.net/apriori.html&lt;/a&gt;'':96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5324, 2, '2015-03-15 18:37:55.84', 0, NULL, NULL, '&lt;p&gt;I don''t know how efficient they are, but I did find some implementations:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://github.com/dmarges/apriori" rel="nofollow"&gt;https://github.com/dmarges/apriori&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://github.com/seratch/apriori.js" rel="nofollow"&gt;https://github.com/seratch/apriori.js&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/dmarges/apriori'':23C ''/dmarges/apriori&lt;/a&gt;&lt;/li&gt;'':28C ''/p'':16C ''/seratch/apriori.js'':34C ''/seratch/apriori.js&lt;/a&gt;&lt;/li&gt;'':39C ''/ul'':40C ''effici'':7C ''find'':13C ''github.com'':22C,27C,33C,38C ''github.com/dmarges/apriori'':21C ''github.com/dmarges/apriori&lt;/a&gt;&lt;/li&gt;'':26C ''github.com/seratch/apriori.js'':32C ''github.com/seratch/apriori.js&lt;/a&gt;&lt;/li&gt;'':37C ''href'':20C,31C ''implement'':15C ''know'':5C ''li'':18C,29C ''nofollow'':25C,36C ''p'':1C ''rel'':24C,35C ''ul'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5325, 2, '2015-03-15 18:53:08.857', 2, NULL, NULL, '&lt;p&gt;Flat means parallel to the x axis; having a small slope. The smaller w is, the closer f(x) is to b; recall that $f(x) \equiv \left&amp;lt; w, x \right&amp;gt; + b$. One way to think about this is as a form of regularization; the flatter the function, the simpler or more parsimonious it is. This applies to both classification and regression.&lt;/p&gt;
', NULL, NULL, '''/p'':66C ''appli'':60C ''axi'':8C ''b'':23C,35C ''classif'':63C ''closer'':18C ''equiv'':28C ''f'':19C,26C ''flat'':2C ''flatter'':49C ''form'':45C ''function'':51C ''gt'':34C ''left'':29C ''lt'':30C ''mean'':3C ''one'':36C ''p'':1C ''parallel'':4C ''parsimoni'':56C ''recal'':24C ''regress'':65C ''regular'':47C ''right'':33C ''simpler'':53C ''slope'':12C ''small'':11C ''smaller'':14C ''think'':39C ''w'':15C,31C ''way'':37C ''x'':7C,20C,27C,32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5336, 1, '2015-03-17 12:24:24.303', 1, 85, 'Time series change rate calculations for displaying trend line chart', '&lt;p&gt;I''m struggling to find a solution to produce a line chart which shows a trend from a Time Series metric. &lt;/p&gt;

&lt;p&gt;I try to make my line chart look similar to the following:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/hZaa2.png" alt="Time series"&gt;&lt;/p&gt;

&lt;p&gt;Basically, I want to make use of relative change rates, but I struggle to find a calculation which makes a visualization as seen above possible. The graph should always be relative to a specific time window, meaning that I can query my data dynamically with a start and end timestamp.&lt;/p&gt;

&lt;p&gt;My (incomplete) sample data set is the following:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;timestamp,value,change_rate,change_rate_absolute_sum,change_rate_delta,change_rate_sum
1426587900000,778;0.00129,0.00129;0.00129,0.00129
1426588200000,778;0,0.00129;-0.00129,0
1426588500000,1189;0.52828,0.52957;0.52828,0.52828
1426588800000,1195;0.00505,0.53462;-0.52323,0.00505
1426589100000,1195;0,0.53462;-0.00505,0
1426589400000,1196;0.00084,0.53546;0.00084,0.00084
1426589700000,1286;0.07525,0.61071;0.07441,0.07525
1426590000000,1290;0.00311,0.61382;-0.07214,0.00311
1426590300000,1294;0.0031,0.61692;-0.00001,0.0031
1426590600000,1296;0.00155,0.61847;-0.00155,0.00155
1426590900000,1356;0.0463,0.66477;0.04475,0.0463
1426591200000,1358;0.00147,0.66624;-0.04483,0.00147
1426591500000,1358;0,0.66624;-0.00147,0
1426591800000,1360;0.00147,0.66771;0.00147,0.00147
1426592100000,1408;0.03529,0.703;0.03382,0.03529
1426592400000,1390;-0.01278,0.69022;-0.04807,-0.01278
1426592700000,1391;0.00072,0.69094;0.0135,0.00072
1426593000000,1410;0.01366,0.7046;0.01294,0.01366
1426593300000,1414;0.00284,0.70744;-0.01082,0.00284
1426593600000,1410;-0.00283,0.70461;-0.00567,-0.00283
1426593900000,1414;0.00284,0.70745;0.00567,0.00284
1426594200000,1420;0.00424,0.71169;0.0014,0.00424
1426594500000,1417;-0.00211,0.70958;-0.00635,-0.00211
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Any ideas warmly appreciated. Thanks a lot in advance!&lt;/p&gt;
', 'dataset  time-series  infographics', NULL, '''-0.00001'':193C ''-0.00129'':151C ''-0.00147'':217C ''-0.00155'':199C ''-0.00211'':275C,278C ''-0.00283'':257C,260C ''-0.00505'':169C ''-0.00567'':259C ''-0.00635'':277C ''-0.01082'':253C ''-0.01278'':233C,236C ''-0.04483'':211C ''-0.04807'':235C ''-0.07214'':187C ''-0.52323'':163C ''/code'':279C ''/hzaa2.png'':58C ''/p'':38C,52C,62C,114C,124C,291C ''/pre'':280C ''0'':149C,152C,167C,170C,215C,218C ''0.00072'':239C,242C ''0.00084'':173C,175C,176C ''0.00129'':143C,144C,145C,146C,150C ''0.0014'':271C ''0.00147'':209C,212C,221C,223C,224C ''0.00155'':197C,200C ''0.00284'':251C,254C,263C,266C ''0.0031'':191C,194C ''0.00311'':185C,188C ''0.00424'':269C,272C ''0.00505'':161C,164C ''0.00567'':265C ''0.01294'':247C ''0.0135'':241C ''0.01366'':245C,248C ''0.03382'':229C ''0.03529'':227C,230C ''0.04475'':205C ''0.0463'':203C,206C ''0.07441'':181C ''0.07525'':179C,182C ''0.52828'':155C,157C,158C ''0.52957'':156C ''0.53462'':162C,168C ''0.53546'':174C ''0.61071'':180C ''0.61382'':186C ''0.61692'':192C ''0.61847'':198C ''0.66477'':204C ''0.66624'':210C,216C ''0.66771'':222C ''0.69022'':234C ''0.69094'':240C ''0.703'':228C ''0.7046'':246C ''0.70461'':258C ''0.70744'':252C ''0.70745'':264C ''0.70958'':276C ''0.71169'':270C ''1189'':154C ''1195'':160C,166C ''1196'':172C ''1286'':178C ''1290'':184C ''1294'':190C ''1296'':196C ''1356'':202C ''1358'':208C,214C ''1360'':220C ''1390'':232C ''1391'':238C ''1408'':226C ''1410'':244C,256C ''1414'':250C,262C ''1417'':274C ''1420'':268C ''1426587900000'':141C ''1426588200000'':147C ''1426588500000'':153C ''1426588800000'':159C ''1426589100000'':165C ''1426589400000'':171C ''1426589700000'':177C ''1426590000000'':183C ''1426590300000'':189C ''1426590600000'':195C ''1426590900000'':201C ''1426591200000'':207C ''1426591500000'':213C ''1426591800000'':219C ''1426592100000'':225C ''1426592400000'':231C ''1426592700000'':237C ''1426593000000'':243C ''1426593300000'':249C ''1426593600000'':255C ''1426593900000'':261C ''1426594200000'':267C ''1426594500000'':273C ''778'':142C,148C ''absolut'':133C ''advanc'':290C ''alt'':59C ''alway'':92C ''appreci'':285C ''basic'':64C ''calcul'':5A,80C ''chang'':3A,72C,129C,131C,135C,138C ''chart'':10A,28C,46C ''code'':126C ''data'':106C,119C ''dataset'':11B ''delta'':137C ''display'':7A ''dynam'':107C ''end'':112C ''find'':21C,78C ''follow'':51C,123C ''graph'':90C ''i.stack.imgur.com'':57C ''i.stack.imgur.com/hzaa2.png'':56C ''idea'':283C ''img'':54C ''incomplet'':117C ''infograph'':15B ''line'':9A,27C,45C ''look'':47C ''lot'':288C ''m'':18C ''make'':43C,68C,82C ''mean'':100C ''metric'':37C ''p'':16C,39C,53C,63C,115C,281C ''possibl'':88C ''pre'':125C ''produc'':25C ''queri'':104C ''rate'':4A,73C,130C,132C,136C,139C ''relat'':71C,94C ''sampl'':118C ''seen'':86C ''seri'':2A,14B,36C,61C ''set'':120C ''show'':30C ''similar'':48C ''solut'':23C ''specif'':97C ''src'':55C ''start'':110C ''struggl'':19C,76C ''sum'':134C,140C ''thank'':286C ''time'':1A,13B,35C,60C,98C ''time-seri'':12B ''timestamp'':113C,127C ''trend'':8A,32C ''tri'':41C ''use'':69C ''valu'':128C ''visual'':84C ''want'':66C ''warm'':284C ''window'':99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9349, 1, '2015-12-12 13:35:11.297', 5, 64, 'What is the difference between affinity matrix eigenvectors and graph Laplacian eigenvectors in the context of spectral clustering?', '&lt;p&gt;In spectral clustering, it''s standard practice to solve the eigenvector problem&lt;/p&gt;

&lt;p&gt;$$L v = \lambda v$$&lt;/p&gt;

&lt;p&gt;where $L$ is the graph Laplacian, $v$ is the eigenvector related to eigenvalue $\lambda$.&lt;/p&gt;

&lt;p&gt;My question: why bother taking the graph Laplacian? Couldn''t I just solve the eigenvector problem for the graph (affinity matrix) itself, &lt;a href="https://www.youtube.com/watch?v=P-LEH-AFovE"&gt;like the guy did in this video&lt;/a&gt;?&lt;/p&gt;

&lt;p&gt;PS: I made &lt;a href="http://stats.stackexchange.com/questions/183227/what-is-the-difference-between-affinity-matrix-eigenvectors-and-graph-laplacian"&gt;this same question&lt;/a&gt; in CrossValidated but I think this is a more appropriate channel. Forgive me if I''m wrong.&lt;/p&gt;
', 'machine-learning  clustering  graphs', NULL, '''/a'':95C,109C ''/p'':37C,43C,59C,96C,127C ''/questions/183227/what-is-the-difference-between-affinity-matrix-eigenvectors-and-graph-laplacian'':105C ''/watch?v=p-leh-afove'':87C ''affin'':6A,80C ''appropri'':119C ''bother'':64C ''channel'':120C ''cluster'':18A,22B,27C ''context'':15A ''couldn'':69C ''crossvalid'':111C ''differ'':4A ''eigenvalu'':57C ''eigenvector'':8A,12A,35C,54C,75C ''forgiv'':121C ''graph'':10A,23B,49C,67C,79C ''guy'':90C ''href'':84C,102C ''l'':39C,46C ''lambda'':41C,58C ''laplacian'':11A,50C,68C ''learn'':21B ''like'':88C ''m'':125C ''machin'':20B ''machine-learn'':19B ''made'':100C ''matrix'':7A,81C ''p'':24C,38C,44C,60C,97C ''practic'':31C ''problem'':36C,76C ''ps'':98C ''question'':62C,108C ''relat'':55C ''solv'':33C,73C ''spectral'':17A,26C ''standard'':30C ''stats.stackexchange.com'':104C ''stats.stackexchange.com/questions/183227/what-is-the-difference-between-affinity-matrix-eigenvectors-and-graph-laplacian'':103C ''take'':65C ''think'':114C ''v'':40C,42C,51C ''video'':94C ''wrong'':126C ''www.youtube.com'':86C ''www.youtube.com/watch?v=p-leh-afove'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5582, 1, '2015-04-22 06:05:29.73', 1, 208, 'Basic ML based Prediction model', '&lt;p&gt;I had this basic query on ML and would like to get basic ideas on modelling prediction models using ML and Python.&lt;/p&gt;

&lt;p&gt;Say I have a training data of 1000 items as&lt;/p&gt;

&lt;p&gt;Item_name, Attrib_1, Attrib_2, Attrib_3,....Attrib_N, Cost&lt;/p&gt;

&lt;p&gt;And my aim is to create a model to predict cost for a new item given the attributes.&lt;/p&gt;

&lt;p&gt;So where should I start and what are the different ways to prediction and solve this problem ? Also how to evaluate different methods ?&lt;/p&gt;
', 'python  regression', NULL, '''/p'':31C,43C,56C,76C,101C ''1'':48C ''1000'':40C ''2'':50C ''3'':52C ''aim'':60C ''also'':95C ''attrib'':47C,49C,51C,53C ''attribut'':75C ''base'':3A ''basic'':1A,12C,21C ''cost'':55C,68C ''creat'':63C ''data'':38C ''differ'':87C,99C ''evalu'':98C ''get'':20C ''given'':73C ''idea'':22C ''item'':41C,45C,72C ''like'':18C ''method'':100C ''ml'':2A,15C,28C ''model'':5A,24C,26C,65C ''n'':54C ''name'':46C ''new'':71C ''p'':8C,32C,44C,57C,77C ''predict'':4A,25C,67C,90C ''problem'':94C ''python'':6B,30C ''queri'':13C ''regress'':7B ''say'':33C ''solv'':92C ''start'':82C ''train'':37C ''use'':27C ''way'':88C ''would'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5326, 1, '2015-03-15 18:54:51.473', 3, 1354, 'How to read file from user in Shiny and assign it to a variable in global.r?', '&lt;p&gt;I want to read a csv file as input from user in Shiny and assign it to a variable in global.r file.The code I have in ui.R is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;fileInput(''file1'', ''Choose CSV File'',
                     accept=c(''text/csv'', ''text/comma-separated-values,text/plain'', .csv''))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The code I have in main Panel of server.R is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;textOutput(''contents'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The code I have currently in server.R is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;output$contents &amp;lt;- renderText({    
      if(is.null(input$file1))return()
      inFile &amp;lt;- input$file1
      data&amp;lt;-read.csv(inFile$datapath)
  print(summary(data))

 })
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to assign this input$file1 to a variable called ''data'' in global.r. Please let me know if this is possible. 
Thanks&lt;/p&gt;
', 'r  csv', NULL, '''/code'':62C,80C,115C ''/p'':48C,75C,91C,141C ''/pre'':63C,81C,116C ''accept'':56C ''assign'':10A,34C,121C ''c'':57C ''call'':128C ''choos'':53C ''code'':42C,50C,66C,77C,84C,93C ''content'':79C,95C ''csv'':18B,25C,54C,61C ''current'':87C ''data'':107C,114C,129C ''datapath'':111C ''file'':4A,26C,55C ''file.the'':41C ''file1'':52C,101C,106C,124C ''fileinput'':51C ''global.r'':16A,40C,131C ''infil'':103C,110C ''input'':28C,100C,105C,123C ''is.null'':99C ''know'':135C ''let'':133C ''lt'':96C,104C,108C ''main'':70C ''output'':94C ''p'':19C,64C,82C,117C ''panel'':71C ''pleas'':132C ''possibl'':139C ''pre'':49C,76C,92C ''print'':112C ''r'':17B ''read'':3A,23C ''read.csv'':109C ''rendertext'':97C ''return'':102C ''server.r'':73C,89C ''shini'':8A,32C ''summari'':113C ''text/comma-separated-values'':59C ''text/csv'':58C ''text/plain'':60C ''textoutput'':78C ''thank'':140C ''ui.r'':46C ''user'':6A,30C ''variabl'':14A,38C,127C ''want'':21C,119C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5327, 2, '2015-03-15 19:42:16.513', 0, NULL, NULL, '&lt;p&gt;All of your plots are appearing on top of each other.  You need to invoke plt.subplot(xxx) before you create each plot.  For info on how the xxx command works, go to &lt;a href="http://www.mathworks.com/help/matlab/ref/subplot.html" rel="nofollow"&gt;the MATLAB documentation&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;You might end up with multiple figures - see &lt;a href="http://stackoverflow.com/questions/21321764/matplotlib-multiple-plots-on-one-figure"&gt;this page&lt;/a&gt; for info about that.&lt;/p&gt;
', NULL, NULL, '''/a'':44C,62C ''/help/matlab/ref/subplot.html'':38C ''/p'':45C,67C ''/questions/21321764/matplotlib-multiple-plots-on-one-figure'':59C ''appear'':7C ''command'':30C ''creat'':21C ''document'':43C ''end'':49C ''figur'':53C ''go'':32C ''href'':35C,56C ''info'':25C,64C ''invok'':16C ''matlab'':42C ''might'':48C ''multipl'':52C ''need'':14C ''nofollow'':40C ''p'':1C,46C ''page'':61C ''plot'':5C,23C ''plt.subplot'':17C ''rel'':39C ''see'':54C ''stackoverflow.com'':58C ''stackoverflow.com/questions/21321764/matplotlib-multiple-plots-on-one-figure'':57C ''top'':9C ''work'':31C ''www.mathworks.com'':37C ''www.mathworks.com/help/matlab/ref/subplot.html'':36C ''xxx'':18C,29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5328, 1, '2015-03-16 11:20:08.417', 1, 56, 'use classification to improve clustering', '&lt;p&gt;I am from information security field, and have some introductory level understanding of machine learning field. My problem is to identify behavioural patterns from network traffic. If I use supervised learning ( classification ) the results are very promising but I encounter of a problem of missing out a pattern with slight change in behaviour. And If I use clustering then i need to do a much work on manual tuning of centroids position to get better clusters. &lt;/p&gt;

&lt;p&gt;I am thinking to ensemble classification and clustering and use classification output to tune cluster centroids. Is this is a good idea to address the problem?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':86C,114C ''address'':111C ''behaviour'':31C,62C ''better'':84C ''centroid'':80C,103C ''chang'':60C ''classif'':2A,41C,93C,98C ''cluster'':5A,67C,85C,95C,102C ''encount'':49C ''ensembl'':92C ''field'':15C,25C ''get'':83C ''good'':108C ''idea'':109C ''identifi'':30C ''improv'':4A ''inform'':13C ''introductori'':19C ''learn'':8B,24C,40C ''level'':20C ''machin'':7B,23C ''machine-learn'':6B ''manual'':77C ''miss'':54C ''much'':74C ''need'':70C ''network'':34C ''output'':99C ''p'':9C,87C ''pattern'':32C,57C ''posit'':81C ''problem'':27C,52C,113C ''promis'':46C ''result'':43C ''secur'':14C ''slight'':59C ''supervis'':39C ''think'':90C ''traffic'':35C ''tune'':78C,101C ''understand'':21C ''use'':1A,38C,66C,97C ''work'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5329, 1, '2015-03-16 11:33:30.593', 1, 90, 'Measuring similarity for sets with same cardinality', '&lt;p&gt;The &lt;a href="http://en.wikipedia.org/wiki/Jaccard_index" rel="nofollow"&gt;Jaccard coefficient&lt;/a&gt; measures similarity between finite sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets.&lt;/p&gt;

&lt;p&gt;I had 100 sets all of same cardinality. By mistake I calculated the similarity measures as the ratio of intersection with total elements in a set (i.e 100).&lt;/p&gt;

&lt;p&gt;This gives different similarity values than the original Jaccard formula.&lt;/p&gt;

&lt;p&gt;I was wondering if the original formula has considered the union of two sets to handle cases where there might be sets with different cardinalities.&lt;/p&gt;

&lt;p&gt;I think though numerically my values are different, they repersent the same idea.&lt;/p&gt;

&lt;p&gt;If anybody could verify/disverify what I am trying to do ?&lt;/p&gt;
', 'dataset  clustering  similarity', NULL, '''/a'':22C ''/p'':49C,79C,91C,118C,133C,145C ''/wiki/jaccard_index'':17C ''100'':53C,78C ''anybodi'':136C ''calcul'':62C ''cardin'':7A,58C,117C ''case'':109C ''cluster'':9B ''coeffici'':21C ''consid'':101C ''could'':137C ''dataset'':8B ''defin'':31C ''differ'':83C,116C,127C ''divid'':38C ''element'':73C ''en.wikipedia.org'':16C ''en.wikipedia.org/wiki/jaccard_index'':15C ''finit'':26C ''formula'':90C,99C ''give'':82C ''handl'':108C ''href'':14C ''i.e'':77C ''idea'':132C ''intersect'':37C,70C ''jaccard'':20C,89C ''measur'':1A,23C,65C ''might'':112C ''mistak'':60C ''nofollow'':19C ''numer'':123C ''origin'':88C,98C ''p'':11C,50C,80C,92C,119C,134C ''ratio'':68C ''rel'':18C ''repers'':129C ''sampl'':27C,47C ''set'':4A,28C,48C,54C,76C,106C,114C ''similar'':2A,10B,24C,64C,84C ''size'':34C,41C ''think'':121C ''though'':122C ''total'':72C ''tri'':142C ''two'':105C ''union'':44C,103C ''valu'':85C,125C ''verify/disverify'':138C ''wonder'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5330, 1, '2015-03-16 19:53:31.303', 0, 122, 'subsetting R data frame', '&lt;p&gt;I am subsetting some data frames and am sure there is a better way.&lt;/p&gt;

&lt;p&gt;Essentially, I have two data frames.  The first is the actual data.  The second has some meta data and importantly a flag on whether or not the row is in the subset I am interested in.  All I would like to do is pull out the subset and write a file.&lt;/p&gt;

&lt;p&gt;For instance, here is my subset data frame:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;     head(temp[,c(1,4:8)])
     ensembl_gene_id   FGF Secreted  SigP Receptors    TF
1 ENSMUSG00000000001 FALSE    FALSE FALSE      TRUE FALSE
2 ENSMUSG00000000001 FALSE    FALSE FALSE      TRUE FALSE
3 ENSMUSG00000000001 FALSE    FALSE FALSE      TRUE FALSE
4 ENSMUSG00000000001 FALSE    FALSE FALSE      TRUE FALSE
5 ENSMUSG00000000001 FALSE    FALSE FALSE      TRUE FALSE
6 ENSMUSG00000000001 FALSE    FALSE FALSE      TRUE FALSE
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here is my actual data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Expt1_C1_EXONS_dxd_res_wDesc[1:5,1:5]
                                   groupID featureID exonBaseMean dispersion         stat
ENSMUSG00000000001:E001 ENSMUSG00000000001      E001    624.80240 0.04271781  1.255734504
ENSMUSG00000000001:E002 ENSMUSG00000000001      E002     30.92281 0.02036015  0.514038911
ENSMUSG00000000001:E003 ENSMUSG00000000001      E003     41.61413 0.01546023 10.105615831
ENSMUSG00000000001:E004 ENSMUSG00000000001      E004    137.47209 0.03975305  0.281105120
ENSMUSG00000000001:E005 ENSMUSG00000000001      E005     85.97116 0.05753662  0.005482149`
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What I was doing is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;write.table(Expt1_C1_EXONS_dxd_res_wDesc[temp$FGF,],
            "Expt1_C1_EXONS_dxd_res_wDesc_FGF.tab",col.names=T,row.names=F,quote=F)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This is taking 20+ min per subset just to subset and write the data.  The data frame is 370,000 rows by 27 variables, so large but not huge.  I have about 30 of these to do.  Is there a more efficient way?  Note, the groupID does NOT equal the first column in my subset data frame.  In some instances the groupID contains a concatenated set of ensembl ids.  So I have preprocessed to get the temp data frame to have what I want in the same row order.&lt;/p&gt;

&lt;p&gt;Thanks,
Bob&lt;/p&gt;
', 'r', NULL, '''/code'':143C,204C,231C ''/p'':21C,74C,84C,151C,212C,323C,327C ''/pre'':144C,205C,232C ''0.005482149'':203C ''0.01546023'':188C ''0.02036015'':181C ''0.03975305'':195C ''0.04271781'':174C ''0.05753662'':202C ''0.281105120'':196C ''0.514038911'':182C ''000'':253C ''1'':90C,101C,160C,162C ''1.255734504'':175C ''10.105615831'':189C ''137.47209'':194C ''2'':108C ''20'':237C ''27'':256C ''3'':115C ''30'':266C ''30.92281'':180C ''370'':252C ''4'':91C,122C ''41.61413'':187C ''5'':129C,161C,163C ''6'':136C ''624.80240'':173C ''8'':92C ''85.97116'':201C ''actual'':33C,149C ''better'':19C ''bob'':326C ''c'':89C ''c1'':155C,217C ''code'':86C,153C,214C ''col.names'':225C ''column'':285C ''concaten'':298C ''contain'':296C ''data'':3A,11C,27C,34C,40C,82C,150C,247C,249C,289C,311C ''dispers'':167C ''dxd'':157C,219C ''e001'':170C,172C ''e002'':177C,179C ''e003'':184C,186C ''e004'':191C,193C ''e005'':198C,200C ''effici'':275C ''ensembl'':93C,301C ''ensmusg00000000001'':102C,109C,116C,123C,130C,137C,169C,171C,176C,178C,183C,185C,190C,192C,197C,199C ''equal'':282C ''essenti'':23C ''exon'':156C,218C ''exonbasemean'':166C ''expt1'':154C,216C ''expt1_c1_exons_dxd_res_wdesc_fgf.tab'':224C ''f'':228C,230C ''fals'':103C,104C,105C,107C,110C,111C,112C,114C,117C,118C,119C,121C,124C,125C,126C,128C,131C,132C,133C,135C,138C,139C,140C,142C ''featureid'':165C ''fgf'':96C,223C ''file'':73C ''first'':30C,284C ''flag'':44C ''frame'':4A,12C,28C,83C,250C,290C,312C ''gene'':94C ''get'':308C ''groupid'':164C,279C,295C ''head'':87C ''huge'':262C ''id'':95C,302C ''import'':42C ''instanc'':77C,293C ''interest'':57C ''larg'':259C ''like'':62C ''meta'':39C ''min'':238C ''note'':277C ''order'':322C ''p'':6C,22C,75C,145C,206C,233C,324C ''per'':239C ''pre'':85C,152C,213C ''preprocess'':306C ''pull'':66C ''quot'':229C ''r'':2A,5B ''receptor'':99C ''res'':158C,220C ''row'':50C,254C,321C ''row.names'':227C ''second'':36C ''secret'':97C ''set'':299C ''sigp'':98C ''stat'':168C ''subset'':1A,9C,54C,69C,81C,240C,243C,288C ''sure'':15C ''take'':236C ''temp'':88C,222C,310C ''tf'':100C ''thank'':325C ''true'':106C,113C,120C,127C,134C,141C ''two'':26C ''variabl'':257C ''want'':317C ''way'':20C,276C ''wdesc'':159C,221C ''whether'':46C ''would'':61C ''write'':71C,245C ''write.table'':215C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5337, 2, '2015-03-17 14:44:22.403', 1, NULL, NULL, '&lt;p&gt;Yes, the Jaccard similarity score is normalized by the union to deal with sets of different cardinality. Without this normalization (if you used just the intersection), very small sets would always have very low scores. &lt;/p&gt;

&lt;p&gt;When the cardinalities of all your sets are the same, the union of any two sets will be a straightforward function of the intersection (this is easy to visualize - as the two sets intersect more and more, their unions get smaller and smaller). The formula is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;union = 2 * cardinality - intersection
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So the Jaccard score in your case would be:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;intersection / (200 - intersection)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If you plot this, you''ll see it''s monotonically the same as what you did. &lt;/p&gt;
', NULL, NULL, '''/code'':92C,110C ''/p'':37C,85C,104C,129C ''/pre'':93C,111C ''2'':89C ''200'':108C ''alway'':32C ''cardin'':18C,41C,90C ''case'':101C ''code'':87C,106C ''deal'':13C ''differ'':17C ''easi'':65C ''formula'':83C ''function'':59C ''get'':78C ''intersect'':27C,62C,72C,91C,107C,109C ''jaccard'':4C,97C ''ll'':118C ''low'':35C ''monoton'':122C ''normal'':8C,21C ''p'':1C,38C,94C,112C ''plot'':115C ''pre'':86C,105C ''score'':6C,36C,98C ''see'':119C ''set'':15C,30C,45C,54C,71C ''similar'':5C ''small'':29C ''smaller'':79C,81C ''straightforward'':58C ''two'':53C,70C ''union'':11C,50C,77C,88C ''use'':24C ''visual'':67C ''without'':19C ''would'':31C,102C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9690, 2, '2016-01-07 21:46:15.24', 3, NULL, NULL, '&lt;p&gt;If you are considering having data science career you should start developing background on statistical theory, software engineering principles, database, data structure, algorithms development and statistical computing.&lt;/p&gt;
', NULL, NULL, '''/p'':29C ''algorithm'':24C ''background'':14C ''career'':9C ''comput'':28C ''consid'':5C ''data'':7C,22C ''databas'':21C ''develop'':13C,25C ''engin'':19C ''p'':1C ''principl'':20C ''scienc'':8C ''softwar'':18C ''start'':12C ''statist'':16C,27C ''structur'':23C ''theori'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5338, 2, '2015-03-17 14:48:29.08', 1, NULL, NULL, '&lt;p&gt;While it is not very clear to me what specific relationships between your data you want to display, I can give some general advice. I think that this time series visualization calls for so-called &lt;a href="http://geog.uoregon.edu/datagraphics/color_scales.htm" rel="nofollow"&gt;diverging color scheme&lt;/a&gt; (as opposed to &lt;em&gt;sequential&lt;/em&gt;, &lt;em&gt;categorical&lt;/em&gt; and other ones). &lt;a href="http://colorbrewer2.org" rel="nofollow"&gt;ColorBrewer&lt;/a&gt; is nice online tool for selecting an appropriate &lt;em&gt;color scheme&lt;/em&gt; and other parameters (note that &lt;em&gt;ColorBrewer&lt;/em&gt;''s color schemes have built-in support in major data analysis and visualization software, such as R, Python, d3.js, plot.ly and others).&lt;/p&gt;

&lt;p&gt;If you would use &lt;code&gt;R&lt;/code&gt; environment, it would be quite easy to produce the time series &lt;em&gt;trend line chart&lt;/em&gt; that you want by using &lt;code&gt;ggplot2&lt;/code&gt; package and its &lt;code&gt;scale_color_gradient2()&lt;/code&gt; function:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;... + scale_color_gradient2(midpoint=midValue, low="blue", mid="white", high="orange" )&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;A quick glance at your profile suggested me that you would prefer a JavaScript solution. In that case, I would advise to use &lt;code&gt;d3.js&lt;/code&gt; library or one of multiple other &lt;em&gt;JavaScript visualization&lt;/em&gt; libraries or packages. For more details, check &lt;a href="http://datascience.stackexchange.com/a/3723/2452"&gt;this relevant answer&lt;/a&gt; of mine.&lt;/p&gt;
', NULL, NULL, '''/a'':48C,67C,221C ''/a/3723/2452'':217C ''/code'':119C,143C,151C,167C,195C ''/datagraphics/color_scales.htm'':42C ''/em'':54C,57C,79C,87C,135C,205C ''/p'':111C,153C,168C,224C ''advic'':25C ''advis'':190C ''analysi'':99C ''answer'':220C ''appropri'':75C ''blue'':162C ''built'':93C ''built-in'':92C ''call'':33C,37C ''case'':187C ''categor'':56C ''chart'':134C ''check'':212C ''clear'':7C ''code'':117C,141C,147C,155C,193C ''color'':46C,77C,89C,149C,157C ''colorbrew'':66C,86C ''colorbrewer2.org'':63C ''d3.js'':107C,194C ''data'':15C,98C ''datascience.stackexchange.com'':216C ''datascience.stackexchange.com/a/3723/2452'':215C ''detail'':211C ''display'':19C ''diverg'':45C ''easi'':125C ''em'':52C,55C,76C,85C,131C,202C ''environ'':120C ''function'':152C ''general'':24C ''geog.uoregon.edu'':41C ''geog.uoregon.edu/datagraphics/color_scales.htm'':40C ''ggplot2'':142C ''give'':22C ''glanc'':172C ''gradient2'':150C,158C ''high'':165C ''href'':39C,62C,214C ''javascript'':183C,203C ''librari'':196C,206C ''line'':133C ''low'':161C ''major'':97C ''mid'':163C ''midpoint'':159C ''midvalu'':160C ''mine'':223C ''multipl'':200C ''nice'':69C ''nofollow'':44C,65C ''note'':83C ''one'':60C,198C ''onlin'':70C ''oppos'':50C ''orang'':166C ''other'':110C ''p'':1C,112C,154C,169C ''packag'':144C,208C ''paramet'':82C ''plot.ly'':108C ''prefer'':181C ''produc'':127C ''profil'':175C ''python'':106C ''quick'':171C ''quit'':124C ''r'':105C,118C ''rel'':43C,64C ''relationship'':12C ''relev'':219C ''scale'':148C,156C ''scheme'':47C,78C,90C ''select'':73C ''sequenti'':53C ''seri'':31C,130C ''so-cal'':35C ''softwar'':102C ''solut'':184C ''specif'':11C ''suggest'':176C ''support'':95C ''think'':27C ''time'':30C,129C ''tool'':71C ''trend'':132C ''use'':116C,140C,192C ''visual'':32C,101C,204C ''want'':17C,138C ''white'':164C ''would'':115C,122C,180C,189C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5600, 1, '2015-04-23 21:59:23.603', 2, 43, 'Using clustering and Lasso with cv', '&lt;p&gt;I used clustering on my dataset. Now when I''m trying to use a LASSO with cv to predict a response, one of the variables it takes into consideration is which cluster a new point is classified into.(I included the cluster variable as a predictor to see if being in a particular group affects the response) 
Since the information on all variables is already captured by the cluster variable,using it again in the Lasso model with some other variables,does it become redundant/biased?&lt;/p&gt;
', 'machine-learning  clustering  predictive-modeling', NULL, '''/p'':100C ''affect'':69C ''alreadi'':79C ''becom'':98C ''captur'':80C ''classifi'':51C ''cluster'':2A,10B,17C,46C,56C,83C ''consider'':43C ''cv'':6A,31C ''dataset'':20C ''group'':68C ''includ'':54C ''inform'':74C ''lasso'':4A,29C,90C ''learn'':9B ''m'':24C ''machin'':8B ''machine-learn'':7B ''model'':13B,91C ''new'':48C ''one'':36C ''p'':14C ''particular'':67C ''point'':49C ''predict'':12B,33C ''predictive-model'':11B ''predictor'':60C ''redundant/biased'':99C ''respons'':35C,71C ''see'':62C ''sinc'':72C ''take'':41C ''tri'':25C ''use'':1A,16C,27C,85C ''variabl'':39C,57C,77C,84C,95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5341, 1, '2015-03-17 19:34:26.72', 0, 82, 'PCA: How to Extract and Weight Variables', '&lt;p&gt;&lt;strong&gt;Background:&lt;/strong&gt; I''m working with log returns for about 400 tech stocks. I want to use PCA to reduce these into principal components (Internet companies, software developers, circuit board manufacturers, etc), which then are to be used as sector-related indices. Parallel analysis is my method of determining how many factors to extract in the first place. Rotation is done via Oblimin with Kaiser Normalization in SPSS.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Questions:&lt;/strong&gt; How do I determine which variables need to be trimmed? Those with low communality? Or should I look at the pattern matrix and start cutting variables which happen to load on multiple factors? After that, can I use the figures in the pattern matrix to weight each stock''s contribution to each component''s variance? For example, if the rotation yields .71 for Stock A and .10 for Stock B, I could multiply the return for each stock in each period, T, by its respective weight. &lt;/p&gt;
', 'statistics  dimensionality-reduction', NULL, '''/p'':83C,174C ''/strong'':15C,87C ''10'':154C ''400'':24C ''71'':149C ''analysi'':58C ''b'':157C ''background'':14C ''board'':43C ''circuit'':42C ''communal'':101C ''compani'':39C ''compon'':37C,140C ''contribut'':137C ''could'':159C ''cut'':112C ''determin'':63C,91C ''develop'':41C ''dimension'':10B ''dimensionality-reduct'':9B ''done'':75C ''etc'':45C ''exampl'':144C ''extract'':4A,68C ''factor'':66C,120C ''figur'':127C ''first'':71C ''happen'':115C ''indic'':56C ''internet'':38C ''kaiser'':79C ''load'':117C ''log'':20C ''look'':105C ''low'':100C ''m'':17C ''mani'':65C ''manufactur'':44C ''matrix'':109C,131C ''method'':61C ''multipl'':119C ''multipli'':160C ''need'':94C ''normal'':80C ''oblimin'':77C ''p'':12C,84C ''parallel'':57C ''pattern'':108C,130C ''pca'':1A,31C ''period'':168C ''place'':72C ''princip'':36C ''question'':86C ''reduc'':33C ''reduct'':11B ''relat'':55C ''respect'':172C ''return'':21C,162C ''rotat'':73C,147C ''sector'':54C ''sector-rel'':53C ''softwar'':40C ''spss'':82C ''start'':111C ''statist'':8B ''stock'':26C,135C,151C,156C,165C ''strong'':13C,85C ''tech'':25C ''trim'':97C ''use'':30C,51C,125C ''variabl'':7A,93C,113C ''varianc'':142C ''via'':76C ''want'':28C ''weight'':6A,133C,173C ''work'':18C ''yield'':148C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5342, 2, '2015-03-17 20:13:00.767', 0, NULL, NULL, '&lt;p&gt;If you label metric 3 as $x_3 = \{1,0\}$, where $1$ means it is an anomaly, this becomes a  &lt;a href="http://en.wikipedia.org/wiki/Logistic_regression" rel="nofollow"&gt;logistic regression&lt;/a&gt; problem where $\mathbb{P}(X_3 = 1) = logit(\beta_0 + \beta_1 x_1 + \beta_2 x_2)$.&lt;/p&gt;
', NULL, NULL, '''/a'':31C ''/p'':50C ''/wiki/logistic_regression'':26C ''0'':11C,41C ''1'':10C,13C,38C,43C,45C ''2'':47C,49C ''3'':6C,9C,37C ''anomali'':18C ''becom'':20C ''beta'':40C,42C,46C ''en.wikipedia.org'':25C ''en.wikipedia.org/wiki/logistic_regression'':24C ''href'':23C ''label'':4C ''logist'':29C ''logit'':39C ''mathbb'':34C ''mean'':14C ''metric'':5C ''nofollow'':28C ''p'':1C,35C ''problem'':32C ''regress'':30C ''rel'':27C ''x'':8C,36C,44C,48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9986, 4, '2016-01-27 08:06:17.083', 0, NULL, NULL, 'The hinge loss function is defined as l(y, t) = max(0, 1 - t â y)', NULL, NULL, '''0'':12C ''1'':13C ''defin'':6C ''function'':4C ''hing'':2C ''l'':8C ''loss'':3C ''max'':11C ''y'':9C,16C ''â'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5343, 2, '2015-03-18 01:34:33.133', 1, NULL, NULL, '&lt;p&gt;Do you change the patterns often? If not, then you can use Aho-Corasick method, whose idea is, first, to build a finite automaton based on your patterns and, then, to make a single pass over the text with this automaton to find matches (if the automaton visits a "matching" state, then there is a match). The complexity of the automata building should be linear in the length of the patterns (# of patters * max pattern length), and the matching phase should be linear in the size of the text you search in.&lt;/p&gt;
', NULL, NULL, '''/p'':95C ''aho'':15C ''aho-corasick'':14C ''automata'':63C ''automaton'':26C,43C,49C ''base'':27C ''build'':23C,64C ''chang'':4C ''complex'':60C ''corasick'':16C ''find'':45C ''finit'':25C ''first'':21C ''idea'':19C ''length'':70C,78C ''linear'':67C,85C ''make'':34C ''match'':46C,52C,58C,81C ''max'':76C ''method'':17C ''often'':7C ''p'':1C ''pass'':37C ''patter'':75C ''pattern'':6C,30C,73C,77C ''phase'':82C ''search'':93C ''singl'':36C ''size'':88C ''state'':53C ''text'':40C,91C ''use'':13C ''visit'':50C ''whose'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5344, 1, '2015-03-18 08:10:08.943', 1, 50, 'procedure for gradient descent', '&lt;p&gt;I''m going to describe the procedure for gradient descent using the language of English:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;1)&lt;/strong&gt; For all of the files (e.g. documents) in our training corpus.&lt;/p&gt;

&lt;p&gt;--&gt; commence, now we are considering our first document&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;2)&lt;/strong&gt; For all the features in the feature vector associated with that training example (document)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;3)&lt;/strong&gt; Take the dot product of the vector of weight values * and the vector of features specific to the document under consideration&lt;/p&gt;

&lt;p&gt;* &lt;em&gt;vector of weight values is applicable to all features, though all features will not be present in all documents, indeed only some features will be present in each individual document&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;4)&lt;/strong&gt; Take the value resulting from step 3 and apply this to our loss function. I''m using the Hinge Loss function, i.e. the formula&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/tVvmW.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Is that equation saying that the hinge loss objective function should be the sum of the misclassification error for every file in our training set?&lt;/p&gt;

&lt;p&gt;So is that equivalent to the java code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Math.max(0, 1 - y * value_from_step_three)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;With &lt;code&gt;y = {-1, 1}&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;^ THIS IS MY FIRST POINT OF CONFUSION&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I guess that is inaccurate but I''m not sure, according to my mental model it should be right. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;5)&lt;/strong&gt; For every weight, update it as:&lt;/p&gt;

&lt;p&gt;new weight value&lt;sub&gt;j&lt;/sub&gt; = old weight value&lt;sub&gt;j&lt;/sub&gt; - learning rate * &lt;em&gt;value from step 4&lt;/em&gt; * value of feature&lt;sub&gt;j&lt;/sub&gt;.&lt;/p&gt;

&lt;p&gt;I''m a bit confused about what &lt;em&gt;value of feature&lt;sub&gt;j&lt;/sub&gt;&lt;/em&gt; should be, since I guess this would correspond to a vector, that has values for every weight under consideration, i.e.&lt;/p&gt;

&lt;p&gt;Example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Document 1 = ["I", "am", "awesome"]
Document 2 = ["I", "am", "great", "great"]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Dictionary is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;["I", "am", "awesome", "great"]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So the documents as a vector would look like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Document 1 = [1, 1, 1, 0]
Document 2 = [1, 1, 0, 2]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;There will be weights:&lt;/p&gt;

&lt;p&gt;[Weight&lt;sub&gt;"I"&lt;/sub&gt;, Weight&lt;sub&gt;"am"&lt;/sub&gt;, Weight&lt;sub&gt;"awesome"&lt;/sub&gt;, Weight&lt;sub&gt;"great"&lt;/sub&gt;]&lt;/p&gt;
', 'gradient-descent', NULL, '''-1'':224C ''/code'':218C,226C,352C,364C,391C ''/em'':128C,291C,314C ''/p'':24C,40C,50C,69C,95C,129C,158C,170C,197C,207C,227C,238C,259C,270C,298C,335C,338C,357C,376C,398C,416C ''/pre'':219C,353C,365C,392C ''/strong'':28C,54C,73C,133C,237C,263C ''/sub'':277C,283C,297C,313C,403C,407C,411C,415C ''/tvvmw.png'':164C ''0'':211C,384C,389C ''1'':27C,212C,225C,342C,380C,381C,382C,383C,387C,388C ''2'':53C,347C,386C,390C ''3'':72C,140C ''4'':132C,290C ''5'':262C ''accord'':250C ''alt'':165C ''appli'':142C ''applic'':103C ''associ'':63C ''awesom'':345C,362C,410C ''bit'':303C ''code'':206C,209C,222C,340C,359C,378C ''commenc'':42C ''confus'':236C,304C ''consid'':46C ''consider'':94C,333C ''corpus'':39C ''correspond'':322C ''descent'':4A,7B,18C ''describ'':13C ''descript'':168C ''dictionari'':355C ''document'':35C,49C,68C,92C,116C,127C,341C,346C,369C,379C,385C ''dot'':76C ''e.g'':34C ''em'':97C,286C,307C ''english'':23C ''enter'':166C ''equat'':174C ''equival'':202C ''error'':189C ''everi'':191C,265C,330C ''exampl'':67C,337C ''featur'':58C,61C,88C,106C,109C,120C,294C,310C ''file'':33C,192C ''first'':48C,233C ''formula'':157C ''function'':147C,154C,181C ''go'':11C ''gradient'':3A,6B,17C ''gradient-desc'':5B ''great'':350C,351C,363C,414C ''guess'':241C,319C ''hing'':152C,178C ''i.e'':155C,334C ''i.stack.imgur.com'':163C ''i.stack.imgur.com/tvvmw.png'':162C ''imag'':167C ''img'':160C ''inaccur'':244C ''inde'':117C ''individu'':126C ''j'':276C,282C,296C,312C ''java'':205C ''languag'':21C ''learn'':284C ''like'':375C ''look'':374C ''loss'':146C,153C,179C ''m'':10C,149C,247C,301C ''math.max'':210C ''mental'':253C ''misclassif'':188C ''model'':254C ''new'':272C ''object'':180C ''old'':278C ''p'':8C,25C,41C,51C,70C,96C,130C,159C,171C,198C,220C,228C,239C,260C,271C,299C,336C,354C,366C,393C,399C ''point'':234C ''pre'':208C,339C,358C,377C ''present'':113C,123C ''procedur'':1A,15C ''product'':77C ''rate'':285C ''result'':137C ''right'':258C ''say'':175C ''set'':196C ''sinc'':317C ''specif'':89C ''src'':161C ''step'':139C,216C,289C ''strong'':26C,52C,71C,131C,229C,261C ''sub'':275C,281C,295C,311C,401C,405C,409C,413C ''sum'':185C ''sure'':249C ''take'':74C,134C ''though'':107C ''three'':217C ''train'':38C,66C,195C ''updat'':267C ''use'':19C,150C ''valu'':83C,101C,136C,214C,274C,280C,287C,292C,308C,328C ''vector'':62C,80C,86C,98C,325C,372C ''weight'':82C,100C,266C,273C,279C,331C,397C,400C,404C,408C,412C ''would'':321C,373C ''y'':213C,223C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5430, 2, '2015-04-02 01:33:37.173', 6, NULL, NULL, '&lt;p&gt;If I were you, I would pick anyone of the frameworks I am comfortable with and implement the use-case. 
Spark-Streaming + MLlib should work and would be my choice since its user base is on the rise and it is one of the most popular project under the Apache Umbrella with good enterprise business plan. Both Cloudera and Hortonworks provide enterprise level support. 
Now, in theory Spark-Streaming lacks behind Storm in stream processing, but the framework is cool in a way that it provides you the option to do streaming, common map and reduce, graph processing and SQL under the same framework. So once you have the pipeline to convert your data to RDD you are good for most of the common jobs related to Data Analysis. It''s written from scratch in Scala which is a very powerful language and provides huge scalability in a distributed setup when handling concurrency. 
Hope this helps, feel free to reach out to me with any questions you have. &lt;/p&gt;
', NULL, NULL, '''/p'':173C ''analysi'':133C ''anyon'':9C ''apach'':53C ''base'':37C ''behind'':75C ''busi'':58C ''case'':22C ''choic'':33C ''cloudera'':61C ''comfort'':15C ''common'':97C,128C ''concurr'':157C ''convert'':116C ''cool'':84C ''data'':118C,132C ''distribut'':153C ''enterpris'':57C,65C ''feel'':161C ''framework'':12C,82C,108C ''free'':162C ''good'':56C,123C ''graph'':101C ''handl'':156C ''help'':160C ''hope'':158C ''hortonwork'':63C ''huge'':149C ''implement'':18C ''job'':129C ''lack'':74C ''languag'':146C ''level'':66C ''map'':98C ''mllib'':26C ''one'':45C ''option'':93C ''p'':1C ''pick'':8C ''pipelin'':114C ''plan'':59C ''popular'':49C ''power'':145C ''process'':79C,102C ''project'':50C ''provid'':64C,90C,148C ''question'':170C ''rdd'':120C ''reach'':164C ''reduc'':100C ''relat'':130C ''rise'':41C ''scala'':140C ''scalabl'':150C ''scratch'':138C ''setup'':154C ''sinc'':34C ''spark'':24C,72C ''spark-stream'':23C,71C ''sql'':104C ''storm'':76C ''stream'':25C,73C,78C,96C ''support'':67C ''theori'':70C ''umbrella'':54C ''use'':21C ''use-cas'':20C ''user'':36C ''way'':87C ''work'':28C ''would'':7C,30C ''written'':136C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5345, 1, '2015-03-18 11:39:40.353', 6, 8799, 'IDE alternatives for R programming (RStudio, IntelliJ IDEA, Eclipse, Visual Studio)', '&lt;p&gt;I use RStudio for R programming. I remember about solid IDE-s from other technology stacks, like Visual Studio or Eclipse.&lt;/p&gt;

&lt;p&gt;I have two questions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;What other IDE-s than RStudio are used (please consider providing some brief description on them).&lt;/li&gt;
&lt;li&gt;Does any of them have noticeable advantages over RStudio?&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I mostly mean debug/build/deploy features, besides coding itself (so text editors are probably not a solution).&lt;/p&gt;
', 'r  tools  rstudio  programming', NULL, '''/li'':65C,76C ''/ol'':77C ''/p'':39C,45C,95C ''advantag'':73C ''altern'':2A ''besid'':84C ''brief'':61C ''code'':85C ''consid'':58C ''debug/build/deploy'':82C ''descript'':62C ''eclips'':9A,38C ''editor'':89C ''featur'':83C ''ide'':1A,28C,51C ''ide-'':27C,50C ''idea'':8A ''intellij'':7A ''li'':47C,66C ''like'':34C ''mean'':81C ''most'':80C ''notic'':72C ''ol'':46C ''p'':16C,40C,78C ''pleas'':57C ''probabl'':91C ''program'':5A,15B,22C ''provid'':59C ''question'':44C ''r'':4A,12B,21C ''rememb'':24C ''rstudio'':6A,14B,19C,54C,75C ''solid'':26C ''solut'':94C ''stack'':33C ''studio'':11A,36C ''technolog'':32C ''text'':88C ''tool'':13B ''two'':43C ''use'':18C,56C ''visual'':10A,35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5347, 2, '2015-03-18 12:47:38.797', 0, NULL, NULL, '&lt;p&gt;Without looking into it too deeply (i don''t have access to R on this computer), it seems...&lt;/p&gt;

&lt;p&gt;&lt;code&gt;temp&lt;/code&gt; is a data frame with x rows, &lt;code&gt;Expt1_C1_EXONS_dxd_res_wDesc&lt;/code&gt; is a data frame with y rows. &lt;code&gt;temp$FGF&lt;/code&gt; is therefore a vector of length x. Since temp is a subset, x &amp;lt; y&lt;/p&gt;

&lt;p&gt;When you write &lt;code&gt;Expt1_C1_EXONS_dxd_res_wDesc[temp$FGF,]&lt;/code&gt; you might be getting errors (NA''s) due to the mismatch in length. It may also be because you are not specifying which rows you want, the vector &lt;code&gt;temp$FGF&lt;/code&gt; contains TRUE and FALSE. It doesn''t contain row indices. &lt;code&gt;temp$FGF==TRUE&lt;/code&gt; returns (or at least selects based on) indices.&lt;/p&gt;

&lt;p&gt;(As i say, i don''t have R in front of me to check.)&lt;/p&gt;

&lt;p&gt;Also, as far as i remember, you need to specify whether you want TRUE or FALSE entries to be included. R does not automatically assume you want TRUE.&lt;/p&gt;

&lt;p&gt;(Again, i don''t have R in front of me to check.)&lt;/p&gt;
', NULL, NULL, '''/code'':24C,39C,50C,80C,112C,127C ''/p'':20C,66C,136C,152C,182C,196C ''access'':12C ''also'':96C,154C ''assum'':178C ''automat'':177C ''base'':133C ''c1'':34C,73C ''check'':151C,195C ''code'':22C,32C,47C,71C,109C,123C ''comput'':17C ''contain'':113C,120C ''data'':27C,42C ''deepli'':7C ''doesn'':118C ''due'':88C ''dxd'':36C,75C ''entri'':170C ''error'':85C ''exon'':35C,74C ''expt1'':33C,72C ''fals'':116C,169C ''far'':156C ''fgf'':49C,79C,111C,125C ''frame'':28C,43C ''front'':147C,191C ''get'':84C ''includ'':173C ''indic'':122C,135C ''least'':131C ''length'':56C,93C ''look'':3C ''lt'':64C ''may'':95C ''might'':82C ''mismatch'':91C ''na'':86C ''need'':161C ''p'':1C,21C,67C,137C,153C,183C ''r'':14C,145C,174C,189C ''rememb'':159C ''res'':37C,76C ''return'':128C ''row'':31C,46C,104C,121C ''say'':140C ''seem'':19C ''select'':132C ''sinc'':58C ''specifi'':102C,163C ''subset'':62C ''temp'':23C,48C,59C,78C,110C,124C ''therefor'':52C ''true'':114C,126C,167C,181C ''vector'':54C,108C ''want'':106C,166C,180C ''wdesc'':38C,77C ''whether'':164C ''without'':2C ''write'':70C ''x'':30C,57C,63C ''y'':45C,65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5348, 1, '2015-03-18 14:01:37.68', 0, 16, 'Extracting the attribute values of centroids when doing MOA clustering', '&lt;p&gt;I am using MOA and I would like to obtain the values of centroids. Do you know how I can do it? Should I do it from the visualization interface?&lt;/p&gt;

&lt;p&gt;Thanks,&lt;/p&gt;

&lt;p&gt;Laia&lt;/p&gt;
', 'data-mining  bigdata  clustering', NULL, '''/p'':47C,50C,53C ''attribut'':3A ''bigdata'':14B ''centroid'':6A,30C ''cluster'':10A,15B ''data'':12B ''data-min'':11B ''extract'':1A ''interfac'':46C ''know'':33C ''laia'':52C ''like'':24C ''mine'':13B ''moa'':9A,20C ''obtain'':26C ''p'':16C,48C,51C ''thank'':49C ''use'':19C ''valu'':4A,28C ''visual'':45C ''would'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5349, 1, '2015-03-18 17:25:32.46', 4, 73, 'Terminology: SOMs, batch learning, online learning, and stochastic gradient descent', '&lt;p&gt;I''m not sure which word to use to differentiate a self-organizing map (SOM) training procedure in which updates for the entire data set are aggregated before they are applied to the network from a training procedure in which the network is updated with each data point individually.&lt;/p&gt;

&lt;p&gt;In the case of other algorithms I''d say &lt;code&gt;stochastic gradient descent&lt;/code&gt;, but I''m not sure &lt;code&gt;gradient descent&lt;/code&gt; is correct for SOM learning.  To my knowledge, SOM learning does not follow any energy function exactly, so that should mean it doesn''t do gradient descent exactly, right?&lt;/p&gt;

&lt;p&gt;Another term would be &lt;code&gt;online learning,&lt;/code&gt; but `online'' sort of implies I train my SOM in the real world with data points streaming in, eg. from a set of sensors.  It may also imply that I use every data point only once, which is not what I want to say.&lt;/p&gt;
', 'machine-learning  gradient-descent', NULL, '''/code'':83C,92C,131C ''/p'':68C,122C,176C ''aggreg'':45C ''algorithm'':75C ''also'':158C ''anoth'':124C ''appli'':49C ''batch'':3A ''case'':72C ''code'':79C,89C,128C ''correct'':94C ''d'':77C ''data'':42C,65C,146C,164C ''descent'':10A,16B,82C,91C,119C ''differenti'':27C ''doesn'':115C ''eg'':150C ''energi'':107C ''entir'':41C ''everi'':163C ''exact'':109C,120C ''follow'':105C ''function'':108C ''gradient'':9A,15B,81C,90C,118C ''gradient-desc'':14B ''impli'':136C,159C ''individu'':67C ''knowledg'':100C ''learn'':4A,6A,13B,97C,102C,130C ''m'':19C,86C ''machin'':12B ''machine-learn'':11B ''map'':32C ''may'':157C ''mean'':113C ''network'':52C,60C ''onlin'':5A,129C,133C ''organ'':31C ''p'':17C,69C,123C ''point'':66C,147C,165C ''procedur'':35C,56C ''real'':143C ''right'':121C ''say'':78C,175C ''self'':30C ''self-organ'':29C ''sensor'':155C ''set'':43C,153C ''som'':2A,33C,96C,101C,140C ''sort'':134C ''stochast'':8A,80C ''stream'':148C ''sure'':21C,88C ''term'':125C ''terminolog'':1A ''train'':34C,55C,138C ''updat'':38C,62C ''use'':25C,162C ''want'':173C ''word'':23C ''world'':144C ''would'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5382, 2, '2015-03-24 15:07:07.623', 0, NULL, NULL, '&lt;p&gt;Good to hear from the author. As he said, it appears to be possible through the Google Adwords Keyword Planner. Specifically, the "Search for new keyword and ad group ideas" option.&lt;/p&gt;

&lt;p&gt;I am unable to get Google to actually generate his list, but I imagine it is just a matter of picking the correct seed words and settings. I get the same order-of-magnitude results when I directly search his terms ("sexless marriage", "unhappy marriage", etc).&lt;/p&gt;

&lt;p&gt;Another option for generating his list would be to search against a dictionary of negative terms (" marriage") and then do some sorting and filtering. You could probably automate this through Google''s API.&lt;/p&gt;
', NULL, NULL, '''/p'':33C,82C,117C ''actual'':42C ''ad'':29C ''adword'':19C ''anoth'':84C ''api'':116C ''appear'':12C ''author'':7C ''autom'':111C ''correct'':57C ''could'':109C ''dictionari'':96C ''direct'':73C ''etc'':81C ''filter'':107C ''generat'':43C,87C ''get'':39C,63C ''good'':2C ''googl'':18C,40C,114C ''group'':30C ''hear'':4C ''idea'':31C ''imagin'':48C ''keyword'':20C,27C ''list'':45C,89C ''magnitud'':69C ''marriag'':78C,80C,100C ''matter'':53C ''negat'':98C ''new'':26C ''option'':32C,85C ''order'':67C ''order-of-magnitud'':66C ''p'':1C,34C,83C ''pick'':55C ''planner'':21C ''possibl'':15C ''probabl'':110C ''result'':70C ''said'':10C ''search'':24C,74C,93C ''seed'':58C ''set'':61C ''sexless'':77C ''sort'':105C ''specif'':22C ''term'':76C,99C ''unabl'':37C ''unhappi'':79C ''word'':59C ''would'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5601, 1, '2015-04-24 07:50:31.95', 1, 129, 'How to classify whether text answer is relevant to an initial text question', '&lt;p&gt;I have a text classification problem in which i need to classify an answer to a message as either relevant or not. &lt;/p&gt;

&lt;p&gt;In the first phase of my calculations, I have already used a SVM to determine if the original message was relevant or not, deciding whether a message contains a hint or question if somebody''s twitter account has been hacked.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;example:
"Hey @foobar, have you been hacked?"   &amp;lt;-- relevant
"My bank account has just been hacked" &amp;lt;-- not relevant
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However, when I want to classify whether the answer is relevant, I would want to have both the original message and the answer as input, right? An answer is relevant in my case if it, in any way, responds to the original message. Is this approach possible using a SVM or any other machine learning tool? I''m using python with the scikit-learn library.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;example:
"Hey @foobar, have you been hacked?"
"@barfoo it seems so, thx for suggesting" &amp;lt;-- relevant

"Hey @foobar, have you been hacked?"
"Lose 20 pounds quickly! http://blabla.com" &amp;lt;-- not relevant
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m not very experienced in this field, so any input would be very appreciated.&lt;/p&gt;
', 'classification  python  svm', NULL, '''/code'':104C,206C ''/p'':40C,82C,173C,224C ''/pre'':105C,207C ''20'':199C ''account'':78C,96C ''alreadi'':51C ''answer'':6A,31C,115C,129C,134C ''appreci'':223C ''approach'':152C ''bank'':95C ''barfoo'':183C ''blabla.com'':202C ''calcul'':48C ''case'':139C ''classif'':14B,22C ''classifi'':3A,29C,112C ''code'':84C,175C ''contain'':69C ''decid'':65C ''determin'':56C ''either'':36C ''exampl'':85C,176C ''experienc'':213C ''field'':216C ''first'':44C ''foobar'':87C,178C,193C ''hack'':81C,91C,100C,182C,197C ''hey'':86C,177C,192C ''hint'':71C ''howev'':107C ''initi'':11A ''input'':131C,219C ''learn'':161C,171C ''librari'':172C ''lose'':198C ''lt'':92C,101C,190C,203C ''m'':164C,210C ''machin'':160C ''messag'':34C,60C,68C,126C,149C ''need'':27C ''origin'':59C,125C,148C ''p'':17C,41C,106C,208C ''phase'':45C ''possibl'':153C ''pound'':200C ''pre'':83C,174C ''problem'':23C ''python'':15B,166C ''question'':13A,73C ''quick'':201C ''relev'':8A,37C,62C,93C,103C,117C,136C,191C,205C ''respond'':145C ''right'':132C ''scikit'':170C ''scikit-learn'':169C ''seem'':185C ''somebodi'':75C ''suggest'':189C ''svm'':16B,54C,156C ''text'':5A,12A,21C ''thx'':187C ''tool'':162C ''twitter'':77C ''use'':52C,154C,165C ''want'':110C,120C ''way'':144C ''whether'':4A,66C,113C ''would'':119C,220C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5350, 2, '2015-03-18 22:58:40.487', 1, NULL, NULL, '&lt;p&gt;My best guess:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;After three weeks heâd harvested 6 million questions and answers from 20,000 women all over the country. &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I assume that we could collect both what the women answered, what answers they would accept and the importance they attribute to the question. This is the &lt;strong&gt;training set&lt;/strong&gt;.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;He picked out the 500 questions that were most popular with both clusters.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This is the &lt;strong&gt;feature selection&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;The &lt;strong&gt;loss function&lt;/strong&gt; to be minimized is could be something like the negative sum of (logarithms of) the &lt;a href="http://www.okcupid.com/help/match-percentages" rel="nofollow"&gt;match percentages&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;$m(x,s,w, y_i, t_i, v_i)$ &lt;/p&gt;

&lt;p&gt;between his answers $x$ (a vector of length 500), the answers he accepts $s$, the importances $w$ he attributes to each questions, the $i$''th woman''s answers $y_i$, the answers $t_i$ she would accept and the importances she assigns to them $v_i$. &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Heâd already decided he would fill out his answers honestly&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;meaning that $x$ and $s$ are fixed, only $w$ is adjusted to minimize the goal function. &lt;/p&gt;

&lt;p&gt;If the match percentage were something simple like ''number of questions which both answered the same way times your importance assigned to the question'', a simple ''give most importance to the question you answered the same way as women in your preferred cluster and zero importance to other questions'' should maximize this. However, the match percentage function is more complicated. &lt;/p&gt;

&lt;p&gt;One would start with equal importance assigned to all questions. 
Then calculate the contribution of each question to the overall loss function: questions $j$ for which his answers do not match many of the women''s answers will get a reduced weight in the next round and the weight of well matching questions will be increased (I would have to think about how the weight update would look in detail though).&lt;/p&gt;

&lt;p&gt;Note that the question relevances in the vector $w$ can only take on a discrete set of values (0, 1, 10, 50, 250) making it a discrete optimization problem and thus potentially NP-hard. In practice, I would allow the weights $w$ take on any real value between 0 and 250 and then round to the nearest allowed value in the end.  &lt;/p&gt;

&lt;p&gt;(Disclaimer: I have never used OkCupid)&lt;/p&gt;
', NULL, NULL, '''/a'':118C ''/blockquote'':28C,78C,194C ''/help/match-percentages'':113C ''/p'':5C,27C,61C,77C,87C,119C,131C,179C,193C,212C,274C,347C,412C,420C ''/strong'':60C,86C,93C ''0'':367C,398C ''000'':21C ''1'':368C ''10'':369C ''20'':20C ''250'':371C,400C ''50'':370C ''500'':68C,141C ''6'':14C ''accept'':45C,145C,169C ''adjust'':206C ''allow'':388C,407C ''alreadi'':184C ''answer'':18C,40C,42C,135C,143C,160C,164C,191C,227C,247C,303C,312C ''assign'':174C,234C,282C ''assum'':31C ''attribut'':50C,151C ''best'':3C ''blockquot'':6C,62C,180C ''calcul'':287C ''cluster'':76C,256C ''collect'':35C ''complic'':273C ''contribut'':289C ''could'':34C,98C ''countri'':26C ''d'':12C,183C ''decid'':185C ''detail'':345C ''disclaim'':414C ''discret'':363C,375C ''end'':411C ''equal'':280C ''featur'':84C ''fill'':188C ''fix'':202C ''function'':92C,211C,270C,297C ''get'':314C ''give'':240C ''goal'':210C ''guess'':4C ''hard'':383C ''harvest'':13C ''heâ'':11C,182C ''honest'':192C ''howev'':266C ''href'':110C ''import'':48C,148C,172C,233C,242C,259C,281C ''increas'':331C ''j'':299C ''length'':140C ''like'':101C,221C ''logarithm'':106C ''look'':343C ''loss'':91C,296C ''m'':121C ''make'':372C ''mani'':307C ''match'':116C,216C,268C,306C,327C ''maxim'':264C ''mean'':196C ''million'':15C ''minim'':96C,208C ''nearest'':406C ''negat'':103C ''never'':417C ''next'':320C ''nofollow'':115C ''note'':349C ''np'':382C ''np-hard'':381C ''number'':222C ''okcupid'':419C ''one'':276C ''optim'':376C ''overal'':295C ''p'':1C,7C,29C,63C,79C,88C,120C,132C,181C,195C,213C,275C,348C,413C ''percentag'':117C,217C,269C ''pick'':65C ''popular'':73C ''potenti'':380C ''practic'':385C ''prefer'':255C ''problem'':377C ''question'':16C,53C,69C,154C,224C,237C,245C,262C,285C,292C,298C,328C,352C ''real'':395C ''reduc'':316C ''rel'':114C ''relev'':353C ''round'':321C,403C ''select'':85C ''set'':59C,364C ''simpl'':220C,239C ''someth'':100C,219C ''start'':278C ''strong'':57C,83C,90C ''sum'':104C ''take'':360C,392C ''th'':157C ''think'':336C ''though'':346C ''three'':9C ''thus'':379C ''time'':231C ''train'':58C ''updat'':341C ''use'':418C ''v'':129C,177C ''valu'':366C,396C,408C ''vector'':138C,356C ''w'':124C,149C,204C,357C,391C ''way'':230C,250C ''week'':10C ''weight'':317C,324C,340C,390C ''well'':326C ''woman'':158C ''women'':22C,39C,252C,310C ''would'':44C,168C,187C,277C,333C,342C,387C ''www.okcupid.com'':112C ''www.okcupid.com/help/match-percentages'':111C ''x'':122C,136C,198C ''y'':125C,161C ''zero'':258C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5351, 2, '2015-03-19 00:21:43.773', 3, NULL, NULL, '&lt;p&gt;Unfortunately, &lt;em&gt;IntelliJ IDEA&lt;/em&gt; currently doesn''t support R - neither via the &lt;em&gt;Ultimate edition&lt;/em&gt;, nor via &lt;em&gt;plug-ins&lt;/em&gt;: &lt;a href="https://www.jetbrains.com/idea/features/editions_comparison_matrix.html" rel="nofollow"&gt;https://www.jetbrains.com/idea/features/editions_comparison_matrix.html&lt;/a&gt;. Having said that, if you have desire, time and good Java knowledge, you can &lt;a href="https://confluence.jetbrains.com/display/IDEADEV/Developing+Custom+Language+Plugins+for+IntelliJ+IDEA" rel="nofollow"&gt;develop an R plug-in yourself&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In my opinion, a better option would be &lt;em&gt;Eclipse&lt;/em&gt;, which offers R support via &lt;em&gt;StatET&lt;/em&gt; IDE: &lt;a href="http://www.walware.de/goto/statet" rel="nofollow"&gt;http://www.walware.de/goto/statet&lt;/a&gt;. However, I find &lt;em&gt;Eclipse&lt;/em&gt; IDE too heavyweight. Therefore, my preferred option is &lt;strong&gt;RStudio&lt;/strong&gt; IDE - I don''t know why one would prefer other options. I especially like &lt;em&gt;RStudio&lt;/em&gt;''s ability of online access to the full development environment via &lt;strong&gt;RStudio Server&lt;/strong&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':64C ''/display/ideadev/developing+custom+language+plugins+for+intellij+idea'':54C ''/em'':6C,18C,25C,77C,85C,102C,130C ''/goto/statet'':91C ''/goto/statet&lt;/a&gt;.'':96C ''/idea/features/editions_comparison_matrix.html'':30C ''/idea/features/editions_comparison_matrix.html&lt;/a&gt;.'':35C ''/p'':65C,146C ''/strong'':113C,145C ''abil'':132C ''access'':135C ''better'':71C ''confluence.jetbrains.com'':53C ''confluence.jetbrains.com/display/ideadev/developing+custom+language+plugins+for+intellij+idea'':52C ''current'':7C ''desir'':42C ''develop'':57C,139C ''doesn'':8C ''eclips'':76C,101C ''edit'':17C ''em'':3C,15C,21C,75C,83C,100C,128C ''environ'':140C ''especi'':126C ''find'':99C ''full'':138C ''good'':45C ''heavyweight'':105C ''howev'':97C ''href'':27C,51C,88C ''ide'':86C,103C,114C ''idea'':5C ''in'':24C ''intellij'':4C ''java'':46C ''know'':118C ''knowledg'':47C ''like'':127C ''neither'':12C ''nofollow'':32C,56C,93C ''offer'':79C ''one'':120C ''onlin'':134C ''opinion'':69C ''option'':72C,109C,124C ''p'':1C,66C ''plug'':23C,61C ''plug-in'':22C,60C ''prefer'':108C,122C ''r'':11C,59C,80C ''rel'':31C,55C,92C ''rstudio'':112C,129C,143C ''said'':37C ''server'':144C ''statet'':84C ''strong'':111C,142C ''support'':10C,81C ''therefor'':106C ''time'':43C ''ultim'':16C ''unfortun'':2C ''via'':13C,20C,82C,141C ''would'':73C,121C ''www.jetbrains.com'':29C,34C ''www.jetbrains.com/idea/features/editions_comparison_matrix.html'':28C ''www.jetbrains.com/idea/features/editions_comparison_matrix.html&lt;/a&gt;.'':33C ''www.walware.de'':90C,95C ''www.walware.de/goto/statet'':89C ''www.walware.de/goto/statet&lt;/a&gt;.'':94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5352, 2, '2015-03-19 07:34:00.85', 6, NULL, NULL, '&lt;p&gt;VisualStudio added syntax highlighting for R a few days ago: &lt;a href="https://www.visualstudio.com/news/2015-mar-10-vso"&gt;https://www.visualstudio.com/news/2015-mar-10-vso&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The current RStudio preview is pretty cool as well - you can switch to a dark theme, code completion is working well, you can filter in the viewer, etc.&lt;/p&gt;
', NULL, NULL, '''/news/2015-mar-10-vso'':16C ''/news/2015-mar-10-vso&lt;/a&gt;&lt;/p&gt;'':19C ''/p'':49C ''ad'':3C ''ago'':11C ''code'':37C ''complet'':38C ''cool'':27C ''current'':22C ''dark'':35C ''day'':10C ''etc'':48C ''filter'':44C ''highlight'':5C ''href'':13C ''p'':1C,20C ''pretti'':26C ''preview'':24C ''r'':7C ''rstudio'':23C ''switch'':32C ''syntax'':4C ''theme'':36C ''viewer'':47C ''visualstudio'':2C ''well'':29C,41C ''work'':40C ''www.visualstudio.com'':15C,18C ''www.visualstudio.com/news/2015-mar-10-vso'':14C ''www.visualstudio.com/news/2015-mar-10-vso&lt;/a&gt;&lt;/p&gt;'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5353, 2, '2015-03-19 07:38:57.917', 1, NULL, NULL, '&lt;p&gt;I would recommend regexp pattern matching. I know that usual implementations are slow but you have to study Thompson''s construction algorithm for nondeterministic automaton. See the wikipedia dedicated &lt;a href="http://en.wikipedia.org/wiki/Thompson%27s_construction_algorithm" rel="nofollow"&gt;article&lt;/a&gt;. However here the wikipedia fails to present this treasure properly. I would strongly recommend to study carefuly this blog article: &lt;a href="http://swtch.com/~rsc/regexp/regexp1.html" rel="nofollow"&gt;Regular expressions can be simple and fast&lt;/a&gt;. For implementations you have pointers in the given article (for example awk and grep uses this implementation). &lt;/p&gt;
', NULL, NULL, '''/a'':39C,74C ''/p'':92C ''/wiki/thompson%27s_construction_algorithm'':35C ''/~rsc/regexp/regexp1.html'':64C ''algorithm'':23C ''articl'':38C,59C,83C ''automaton'':26C ''awk'':86C ''blog'':58C ''carefuli'':56C ''construct'':22C ''dedic'':30C ''en.wikipedia.org'':34C ''en.wikipedia.org/wiki/thompson%27s_construction_algorithm'':33C ''exampl'':85C ''express'':68C ''fail'':44C ''fast'':73C ''given'':82C ''grep'':88C ''howev'':40C ''href'':32C,61C ''implement'':12C,76C,91C ''know'':9C ''match'':7C ''nofollow'':37C,66C ''nondeterminist'':25C ''p'':1C ''pattern'':6C ''pointer'':79C ''present'':46C ''proper'':49C ''recommend'':4C,53C ''regexp'':5C ''regular'':67C ''rel'':36C,65C ''see'':27C ''simpl'':71C ''slow'':14C ''strong'':52C ''studi'':19C,55C ''swtch.com'':63C ''swtch.com/~rsc/regexp/regexp1.html'':62C ''thompson'':20C ''treasur'':48C ''use'':89C ''usual'':11C ''wikipedia'':29C,43C ''would'':3C,51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5354, 2, '2015-03-19 09:51:34.48', 2, NULL, NULL, '&lt;p&gt;I did not use shiny but I tried in my gui code which I wrote using gwidgets to make a ariable global I used &lt;code&gt;data&amp;lt;&amp;lt;-read.csv(inFile$datapath)&lt;/code&gt;
you can try this.&lt;/p&gt;
', NULL, NULL, '''/code'':33C ''/p'':38C ''ariabl'':22C ''code'':13C,26C ''data'':27C ''datapath'':32C ''global'':23C ''gui'':12C ''gwidget'':18C ''infil'':31C ''lt'':28C,29C ''make'':20C ''p'':1C ''read.csv'':30C ''shini'':6C ''tri'':9C,36C ''use'':5C,17C,25C ''wrote'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5356, 1, '2015-03-19 19:38:12.417', 0, 40, '(Cross Posting) Categorizing short sentences into a taxonomy', '&lt;p&gt;I feel like I might be in the wrong exchange, but I do not see a NLP exchange, so I figured this was the best bet. My current task is to take short sentences and try to categorize them based on some taxonomy. I''ll try to be as detailed as possible in this question, but if you need any more details, I''ll do my best to provide them.&lt;/p&gt;

&lt;p&gt;Now there are two main issues with this:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Dealing with a single sentence, versus having a whole article or document, makes categorizing difficult (as far as I know)&lt;/li&gt;
&lt;li&gt;I do not know what taxonomy I should be trying to use (i.e. a small set of topics, or something larger). I imagine this will be determined based on how accurate I can get this model to work&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;To give some examples of what I am thinking about:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;"Barack Obama is a Socialist"  --&gt; This should be categorized as "Politics", or "US Politics"&lt;/li&gt;
&lt;li&gt;"Global Warming is a Hoax" --&gt; This should be categorized as "global warming", or "global warming debate"&lt;/li&gt;
&lt;li&gt;"Charlie Seen''s latest rant about Obama is stupid" --&gt; This should be categorized as "Entertainment", "Hollywood", maybe "Politics".&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Research I have done shows that categorizing with short sentences (and not a large article) makes things difficult, and a common way of dealing with it is to utilize wikipedia / dbpedia. What I would like is this:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Research papers which have tried to categorize sentences, tweets, sm posts, etc into a set of topics&lt;/li&gt;
&lt;li&gt;Common methods for dealing with small amounts of text&lt;/li&gt;
&lt;li&gt;Links to software, apis etc that may help with categorizing (most APIs I have seen work better with documents)&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;For the record, I know this is a quite a bit vague. I''ll try to provide any information needed to help figure out what strategies would work well. Any insights, links and thoughts would be highly appreciated.&lt;/p&gt;
', 'classification  nlp', NULL, '''/li'':115C,155C,185C,203C,223C,283C,294C,315C ''/ol'':156C,224C,316C ''/p'':82C,92C,168C,263C,356C ''accur'':147C ''amount'':291C ''api'':299C,307C ''appreci'':355C ''articl'':104C,240C ''barack'':171C ''base'':51C,144C ''best'':36C,78C ''bet'':37C ''better'':312C ''bit'':328C ''categor'':3A,49C,108C,179C,195C,217C,232C,272C,305C ''charli'':205C ''classif'':9B ''common'':246C,285C ''cross'':1A ''current'':39C ''dbpedia'':256C ''deal'':95C,249C,288C ''debat'':202C ''detail'':61C,73C ''determin'':143C ''difficult'':109C,243C ''document'':106C,314C ''done'':229C ''entertain'':219C ''etc'':277C,300C ''exampl'':161C ''exchang'':21C,29C ''far'':111C ''feel'':13C ''figur'':32C,340C ''get'':150C ''give'':159C ''global'':187C,197C,200C ''help'':303C,339C ''high'':354C ''hoax'':191C ''hollywood'':220C ''i.e'':129C ''imagin'':139C ''inform'':336C ''insight'':348C ''issu'':89C ''know'':114C,120C,322C ''larg'':239C ''larger'':137C ''latest'':208C ''li'':94C,116C,170C,186C,204C,265C,284C,295C ''like'':14C,260C ''link'':296C,349C ''ll'':56C,75C,331C ''main'':88C ''make'':107C,241C ''may'':302C ''mayb'':221C ''method'':286C ''might'':16C ''model'':152C ''need'':70C,337C ''nlp'':10B,28C ''obama'':172C,211C ''ol'':93C,169C,264C ''p'':11C,83C,157C,225C,317C ''paper'':267C ''polit'':181C,184C,222C ''possibl'':63C ''post'':2A,276C ''provid'':80C,334C ''question'':66C ''quit'':326C ''rant'':209C ''record'':320C ''research'':226C,266C ''see'':26C ''seen'':206C,310C ''sentenc'':5A,45C,99C,235C,273C ''set'':132C,280C ''short'':4A,44C,234C ''show'':230C ''singl'':98C ''sm'':275C ''small'':131C,290C ''socialist'':175C ''softwar'':298C ''someth'':136C ''strategi'':343C ''stupid'':213C ''take'':43C ''task'':40C ''taxonomi'':8A,54C,122C ''text'':293C ''thing'':242C ''think'':166C ''thought'':351C ''topic'':134C,282C ''tri'':47C,57C,126C,270C,332C ''tweet'':274C ''two'':87C ''us'':183C ''use'':128C ''util'':254C ''vagu'':329C ''versus'':100C ''warm'':188C,198C,201C ''way'':247C ''well'':346C ''whole'':103C ''wikipedia'':255C ''work'':154C,311C,345C ''would'':259C,344C,352C ''wrong'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6569, 2, '2015-07-24 13:15:31.04', 0, NULL, NULL, '&lt;p&gt;You can refer to my response related to h2o R or Python anomaly detection method in &lt;a href="http://datascience.stackexchange.com/questions/6547/open-source-anomaly-detection-in-python"&gt;stackexchange&lt;/a&gt;,since that is scalable too.&lt;/p&gt;
', NULL, NULL, '''/a'':24C ''/p'':30C ''/questions/6547/open-source-anomaly-detection-in-python'':22C ''anomali'':14C ''datascience.stackexchange.com'':21C ''datascience.stackexchange.com/questions/6547/open-source-anomaly-detection-in-python'':20C ''detect'':15C ''h2o'':10C ''href'':19C ''method'':16C ''p'':1C ''python'':13C ''r'':11C ''refer'':4C ''relat'':8C ''respons'':7C ''scalabl'':28C ''sinc'':25C ''stackexchang'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5357, 1, '2015-03-20 14:56:23.42', 19, 4239, 'Data Science in C (or C++)', '&lt;p&gt;I''m an &lt;code&gt;R&lt;/code&gt; language programmer. I''m also in the group of people who are considered Data Scientists but who come from academic disciplines other than CS.&lt;/p&gt;

&lt;p&gt;This works out well in my role as a Data Scientist, however by starting my career in &lt;code&gt;R&lt;/code&gt; and only having basic knowledge of other scripting/web languages, I''ve felt somewhat inadequate in 2 key areas:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Lack of a solid knowledge of programming theory&lt;/li&gt;
&lt;li&gt;Lack of a competitive level of skill in faster and more widely used languages like &lt;code&gt;C&lt;/code&gt;, &lt;code&gt;C++&lt;/code&gt; and &lt;code&gt;Java&lt;/code&gt;, which could be utilizes to increase the speed of the pipeline and Big Data computations as well as to create DS/data products which can be more readily developed into fast back-end scripts or standalone applications &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The solution is simple of course -- go learn about programming, which is what I''ve been doing by enrolling in some classes (currently C programming). &lt;/p&gt;

&lt;p&gt;However, now that I''m starting to address problems #1 and #2 above, I''m left asking myself "&lt;em&gt;Just how viable are languages like &lt;code&gt;C&lt;/code&gt; and &lt;code&gt;C++&lt;/code&gt; for Data Science?&lt;/em&gt;".&lt;/p&gt;

&lt;p&gt;For instance, I can move data around very quickly and interact with users just fine, but what about advanced regression, Machine Learning, text mining and other more advanced statistical operations? &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;So. can &lt;code&gt;C&lt;/code&gt; do the job -- what tools are available for advanced statistics, ML, AI, and other areas of Data Science?&lt;/strong&gt; Or must I loose most of the efficiency gained by programming in &lt;code&gt;C&lt;/code&gt; by calling on &lt;code&gt;R&lt;/code&gt; scripts or other languages?&lt;/p&gt;

&lt;p&gt;The best resource I''ve found thusfar in C is a library called &lt;a href="http://image.diku.dk/shark/sphinx_pages/build/html/index.html"&gt;Shark&lt;/a&gt;, which gives &lt;code&gt;C&lt;/code&gt;/&lt;code&gt;C++&lt;/code&gt; the ability to use Support Vector Machines, linear regression (not non-linear and other advanced regression like multinomial probit, etc) and a shortlist of other (great but) statistical functions.&lt;/p&gt;
', 'machine-learning  bigdata  statistics  programming', NULL, '''/a'':330C ''/code'':19C,65C,114C,117C,121C,216C,220C,264C,298C,304C,335C,338C ''/em'':224C ''/li'':95C,159C ''/ol'':160C ''/p'':44C,84C,187C,225C,257C,309C,369C ''/shark/sphinx_pages/build/html/index.html'':328C ''/strong'':283C ''1'':198C ''2'':81C,200C ''abil'':340C ''academ'':39C ''address'':196C ''advanc'':245C,254C,273C,354C ''ai'':276C ''also'':24C ''applic'':158C ''area'':83C,279C ''around'':233C ''ask'':205C ''avail'':271C ''back'':153C ''back-end'':152C ''basic'':69C ''best'':312C ''big'':134C ''bigdata'':10B ''c'':4A,6A,113C,116C,185C,215C,219C,263C,297C,319C,334C,337C ''call'':300C,323C ''career'':61C ''class'':183C ''code'':17C,63C,112C,115C,119C,214C,218C,262C,296C,302C,333C,336C ''come'':37C ''competit'':100C ''comput'':136C ''consid'':32C ''could'':123C ''cours'':167C ''creat'':141C ''cs'':43C ''current'':184C ''data'':1A,33C,55C,135C,222C,232C,281C ''develop'':149C ''disciplin'':40C ''ds/data'':142C ''effici'':291C ''em'':207C ''end'':154C ''enrol'':180C ''etc'':359C ''fast'':151C ''faster'':105C ''felt'':77C ''fine'':241C ''found'':316C ''function'':368C ''gain'':292C ''give'':332C ''go'':168C ''great'':365C ''group'':27C ''howev'':57C,189C ''href'':325C ''image.diku.dk'':327C ''image.diku.dk/shark/sphinx_pages/build/html/index.html'':326C ''inadequ'':79C ''increas'':127C ''instanc'':228C ''interact'':237C ''java'':120C ''job'':267C ''key'':82C ''knowledg'':70C,91C ''lack'':87C,97C ''languag'':20C,74C,110C,212C,308C ''learn'':9B,169C,248C ''left'':204C ''level'':101C ''li'':86C,96C ''librari'':322C ''like'':111C,213C,356C ''linear'':346C,351C ''loos'':287C ''m'':15C,23C,193C,203C ''machin'':8B,247C,345C ''machine-learn'':7B ''mine'':250C ''ml'':275C ''move'':231C ''multinomi'':357C ''must'':285C ''non'':350C ''non-linear'':349C ''ol'':85C ''oper'':256C ''p'':13C,45C,161C,188C,226C,258C,310C ''peopl'':29C ''pipelin'':132C ''probit'':358C ''problem'':197C ''product'':143C ''program'':12B,93C,171C,186C,294C ''programm'':21C ''quick'':235C ''r'':18C,64C,303C ''readili'':148C ''regress'':246C,347C,355C ''resourc'':313C ''role'':52C ''scienc'':2A,223C,282C ''scientist'':34C,56C ''script'':155C,305C ''scripting/web'':73C ''shark'':329C ''shortlist'':362C ''simpl'':165C ''skill'':103C ''solid'':90C ''solut'':163C ''somewhat'':78C ''speed'':129C ''standalon'':157C ''start'':59C,194C ''statist'':11B,255C,274C,367C ''strong'':259C ''support'':343C ''text'':249C ''theori'':94C ''thusfar'':317C ''tool'':269C ''use'':109C,342C ''user'':239C ''util'':125C ''ve'':76C,176C,315C ''vector'':344C ''viabl'':210C ''well'':49C,138C ''wide'':108C ''work'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6197, 2, '2015-06-24 01:29:39.12', 2, NULL, NULL, '&lt;p&gt;Partition the time axis (the horizontal axis) into regions, and for each region compute the variation of the function value (or its deviation from the mean) as well as its finite differences (e.g., 1st and 2nd). The time points corresponding to "anomalies" in each of the obtained series are what you are looking for. If the number of points does not satisfy you, repeat the process hierarchically, altering the time granularity.&lt;/p&gt;
', NULL, NULL, '''/p'':73C ''1st'':35C ''2nd'':37C ''alter'':69C ''anomali'':43C ''axi'':5C,8C ''comput'':15C ''correspond'':41C ''deviat'':24C ''differ'':33C ''e.g'':34C ''finit'':32C ''function'':20C ''granular'':72C ''hierarch'':68C ''horizont'':7C ''look'':54C ''mean'':27C ''number'':58C ''obtain'':48C ''p'':1C ''partit'':2C ''point'':40C,60C ''process'':67C ''region'':10C,14C ''repeat'':65C ''satisfi'':63C ''seri'':49C ''time'':4C,39C,71C ''valu'':21C ''variat'':17C ''well'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9369, 1, '2015-12-14 04:36:30.757', 1, 23, 'Latent Semantic Indexing False Positive Detection', '&lt;p&gt;I am using the Gensim &lt;a href="http://radimrehurek.com/gensim/models/lsimodel.html" rel="nofollow"&gt;LsiModel&lt;/a&gt;. I have a set of documents and fixed set of topics. Some of the documents are already categorized, others are not. The goal is to categorize the uncategorized documents with the most relevant category. I am using a similarity search as described here.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://radimrehurek.com/gensim/tut3.html" rel="nofollow"&gt;http://radimrehurek.com/gensim/tut3.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;So, I am comparing each uncategorized document to the categorized corpus to find the most relevant category. I am seeing very good performance on documents which have an appropriate category. However, it is to be expected that some documents will not have an relevant category, e.g. it is in Spanish, it is spam, or it just does not fit into an existing category. With this model every document is categorized and the best fit is the category with the highest similarity score. My question is, &lt;strong&gt;how can I determine when there is not relevant category&lt;/strong&gt;? My assumption is that the similarity measures for the documents should all be low, but this is not always true. This also seems to be an arbitrary measure. Are there better ways to say a particular document does not fit well into existing categories?&lt;/p&gt;
', 'gensim  lsi', NULL, '''/a'':23C ''/gensim/models/lsimodel.html'':19C ''/gensim/tut3.html'':73C ''/gensim/tut3.html&lt;/a&gt;&lt;/p&gt;'':78C ''/p'':67C,221C ''/strong'':176C ''alreadi'':40C ''also'':198C ''alway'':195C ''appropri'':108C ''arbitrari'':203C ''assumpt'':178C ''best'':152C ''better'':207C ''categor'':41C,49C,89C,149C ''categori'':57C,96C,109C,124C,142C,156C,175C,220C ''compar'':83C ''corpus'':90C ''describ'':65C ''detect'':6A ''determin'':169C ''document'':29C,38C,52C,86C,104C,118C,147C,186C,213C ''e.g'':125C ''everi'':146C ''exist'':141C,219C ''expect'':115C ''fals'':4A ''find'':92C ''fit'':138C,153C,216C ''fix'':31C ''gensim'':7B,14C ''goal'':46C ''good'':101C ''highest'':159C ''howev'':110C ''href'':16C,70C ''index'':3A ''latent'':1A ''low'':190C ''lsi'':8B ''lsimodel'':22C ''measur'':183C,204C ''model'':145C ''nofollow'':21C,75C ''other'':42C ''p'':9C,68C,79C ''particular'':212C ''perform'':102C ''posit'':5A ''question'':163C ''radimrehurek.com'':18C,72C,77C ''radimrehurek.com/gensim/models/lsimodel.html'':17C ''radimrehurek.com/gensim/tut3.html'':71C ''radimrehurek.com/gensim/tut3.html&lt;/a&gt;&lt;/p&gt;'':76C ''rel'':20C,74C ''relev'':56C,95C,123C,174C ''say'':210C ''score'':161C ''search'':63C ''see'':99C ''seem'':199C ''semant'':2A ''set'':27C,32C ''similar'':62C,160C,182C ''spam'':132C ''spanish'':129C ''strong'':165C ''topic'':34C ''true'':196C ''uncategor'':51C,85C ''use'':12C,60C ''way'':208C ''well'':217C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5358, 1, '2015-03-20 15:29:12.54', 0, 28, 'What approaches are there to not display a search result that a user has no permission to see?', '&lt;p&gt;&lt;strong&gt;Question&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;What approaches are there to not display a search result that a user is not supposed to see?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Example&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Suppose we have the following situation.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;High-level view&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;In an enterprise, there are different repositories like websites, databases, filesystem folder etc. And there is a search engine that crawls all those repositories and creates an index. The user can then use the search engine UI to perform searches.&lt;/p&gt;

&lt;p&gt;Let''s say the search engine was &lt;a href="https://lucene.apache.org/core/" rel="nofollow"&gt;Apache Lucene&lt;/a&gt; and the encompassing indexing and retrieval system &lt;a href="http://lucene.apache.org/solr/" rel="nofollow"&gt;Apache Solr&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;User permissions are managed via &lt;a href="http://en.wikipedia.org/wiki/Active_Directory" rel="nofollow"&gt;Microsoft Active Directory&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The content in the different repositories are managed with all kinds of applications like MS Word, custom-made applications, database management tools etc.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Search-index and user permissions&lt;/em&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;The index contains the documents A1, A2, B1 and B2.&lt;/li&gt;
&lt;li&gt;User A has only permission to see documents of type A*.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;em&gt;Performing a search&lt;/em&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Now, user A performs a search for &lt;code&gt;A* AND B*&lt;/code&gt;.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;em&gt;Displaying the results&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;A system administrator with permission to see everything should see all four documents in the search results: A1, A2, B1 and B2.&lt;/p&gt;

&lt;p&gt;User A however, who only has permissions to see documents of type A*, should/will only see two documents in the search results: A1 and A2. So user A shouldn''t even know that documents B1 and B2 exist.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Elaboration on the question&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;What ways are there to implement that requirement that users only see documents they are allowed to see?&lt;/p&gt;

&lt;p&gt;I suppose it would be a bad idea to add the information to the index whether a user may see a document. I think that because of this use case: suppose all of a sudden user A may only see document A1 and not A2 anymore; those permissions are now set for example on the filesystem. Now it takes a while until the permissions in the index are updated and during that time user A would still be able to see document A2 in the search results even if he can''t click on the result and access it anymore.&lt;/p&gt;

&lt;p&gt;Also, the above approach probably would break down when there are hundreds of millions of documents and thousands of employees.&lt;/p&gt;

&lt;p&gt;Where and how would the security aspect be implemented in a content indexing and retrieval system in the above scenario?&lt;/p&gt;
', 'search  information-retrieval', NULL, '''/a'':128C,145C,163C ''/code'':248C ''/core/'':123C ''/em'':67C,199C,233C,256C ''/li'':213C,226C,249C ''/p'':27C,47C,52C,60C,68C,110C,146C,164C,190C,200C,234C,257C,281C,321C,329C,349C,451C,473C,495C ''/solr/'':140C ''/strong'':26C,51C,328C ''/ul'':227C,250C ''/wiki/active_directory'':157C ''a1'':208C,276C,305C,392C ''a2'':209C,277C,307C,395C,433C ''abl'':429C ''access'':448C ''activ'':161C ''add'':360C ''administr'':261C ''allow'':346C ''also'':453C ''anymor'':396C,450C ''apach'':126C,143C ''applic'':178C,185C ''approach'':2A,30C,456C ''aspect'':481C ''b'':247C ''b1'':210C,278C,317C ''b2'':212C,280C,319C ''bad'':357C ''break'':459C ''case'':380C ''click'':443C ''code'':244C ''contain'':205C ''content'':167C,486C ''crawl'':90C ''creat'':95C ''custom'':183C ''custom-mad'':182C ''databas'':79C,186C ''differ'':75C,170C ''directori'':162C ''display'':7A,35C,253C ''document'':207C,222C,271C,292C,300C,316C,343C,372C,391C,432C,468C ''elabor'':324C ''em'':62C,192C,229C,252C ''employe'':472C ''en.wikipedia.org'':156C ''en.wikipedia.org/wiki/active_directory'':155C ''encompass'':131C ''engin'':88C,105C,117C ''enterpris'':72C ''etc'':82C,189C ''even'':313C,438C ''everyth'':266C ''exampl'':50C,403C ''exist'':320C ''filesystem'':80C,406C ''folder'':81C ''follow'':58C ''four'':270C ''high'':64C ''high-level'':63C ''howev'':285C ''href'':120C,137C,154C ''hundr'':464C ''idea'':358C ''implement'':336C,483C ''index'':97C,132C,195C,204C,365C,417C,487C ''inform'':21B,362C ''information-retriev'':20B ''kind'':176C ''know'':314C ''let'':112C ''level'':65C ''li'':202C,214C,236C ''like'':77C,179C ''lucen'':127C ''lucene.apache.org'':122C,139C ''lucene.apache.org/core/'':121C ''lucene.apache.org/solr/'':138C ''made'':184C ''manag'':151C,173C,187C ''may'':369C,388C ''microsoft'':160C ''million'':466C ''ms'':180C ''nofollow'':125C,142C,159C ''p'':23C,28C,48C,53C,61C,69C,111C,147C,165C,191C,228C,251C,258C,282C,322C,330C,350C,452C,474C ''perform'':108C,230C,240C ''permiss'':16A,149C,198C,219C,263C,289C,398C,414C ''probabl'':457C ''question'':25C,327C ''rel'':124C,141C,158C ''repositori'':76C,93C,171C ''requir'':338C ''result'':10A,38C,255C,275C,304C,437C,446C ''retriev'':22B,134C,489C ''say'':114C ''scenario'':494C ''search'':9A,19B,37C,87C,104C,109C,116C,194C,232C,242C,274C,303C,436C ''search-index'':193C ''secur'':480C ''see'':18A,46C,221C,265C,268C,291C,298C,342C,348C,370C,390C,431C ''set'':401C ''should/will'':296C ''shouldn'':311C ''situat'':59C ''solr'':144C ''still'':427C ''strong'':24C,49C,323C ''sudden'':385C ''suppos'':44C,54C,352C,381C ''system'':135C,260C,490C ''take'':409C ''think'':374C ''thousand'':470C ''time'':423C ''tool'':188C ''two'':299C ''type'':224C,294C ''ui'':106C ''ul'':201C,235C ''updat'':419C ''use'':102C,379C ''user'':13A,41C,99C,148C,197C,215C,238C,283C,309C,340C,368C,386C,424C ''via'':152C ''view'':66C ''way'':332C ''websit'':78C ''whether'':366C ''word'':181C ''would'':354C,426C,458C,478C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5359, 2, '2015-03-20 17:08:05.33', 3, NULL, NULL, '&lt;p&gt;I have added the following code to global.R file&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data &amp;lt;- reactiveValues()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I used assign function in server.R to assign values to data in global.r&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;output$contents &amp;lt;- renderText({    
      if(is.null(input$file1))return()
      inFile &amp;lt;- input$file1
      data2&amp;lt;-read.csv(inFile$datapath)
  assign(''data'',data2,envir=.GlobalEnv)
  print(summary(data))
 })
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':17C,62C ''/p'':11C,33C ''/pre'':18C,63C ''ad'':4C ''assign'':22C,27C,54C ''code'':7C,13C,35C ''content'':37C ''data'':14C,30C,55C,61C ''data2'':49C,56C ''datapath'':53C ''envir'':57C ''file'':10C ''file1'':43C,48C ''follow'':6C ''function'':23C ''global.r'':9C,32C ''globalenv'':58C ''infil'':45C,52C ''input'':42C,47C ''is.null'':41C ''lt'':15C,38C,46C,50C ''output'':36C ''p'':1C,19C ''pre'':12C,34C ''print'':59C ''reactivevalu'':16C ''read.csv'':51C ''rendertext'':39C ''return'':44C ''server.r'':25C ''summari'':60C ''use'':21C ''valu'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5360, 2, '2015-03-20 17:41:43.97', 1, NULL, NULL, '&lt;p&gt;Since you aren''t sure which software you would like to use, I can explain this concept only in general, by example. The clearest example is a content management system like Drupal or Wordpress (not necessarily a data science tool but the idea is the same).&lt;/p&gt;

&lt;p&gt;In a content management system, there are different types of content which can be accessed. In your case, there would be two types - document type A and document type B. There can be an unlimited number of documents, each assigned one or more types. &lt;/p&gt;

&lt;p&gt;The software has a permission system that gives access to each document type. This is built with roles. In your example, there would be a system administrator role, and a general user role.&lt;/p&gt;

&lt;p&gt;Each role has permissions associated with it. You would configure system administrator to be allowed to view document types A and B, but you would configure general users to only be able to see document type A.&lt;/p&gt;

&lt;p&gt;Each user registers an account in the software system. A site administrator assigns each account one or more roles. So a user with the system administrator role would be able to see all documents of type A and B. However, if a user only has the general user role, they would only be able to see documents of type A. Also noteworthy is the fact that each user can have multiple roles and will get all permissions associated with every role they have.&lt;/p&gt;

&lt;p&gt;USER -has-&gt; ROLE(s)&lt;/p&gt;

&lt;p&gt;ROLE -has-&gt; PERMISSION(s)&lt;/p&gt;

&lt;p&gt;CONTENT TYPE -requires-&gt; PERMISSION(s)&lt;/p&gt;

&lt;p&gt;Every time the content is requested, the software system ensures that the user making the request has the permission to access the content.&lt;/p&gt;

&lt;p&gt;When a search is run, the software system has to validate every potential result individually before displaying the search results to the user. The user must have the permission to see each piece of content that is be delivered back from the search. &lt;/p&gt;
', NULL, NULL, '''/p'':48C,94C,129C,168C,253C,259C,265C,272C,297C,342C ''abl'':162C,199C,223C ''access'':64C,104C,294C ''account'':174C,184C ''administr'':122C,142C,181C,195C ''allow'':145C ''also'':230C ''aren'':4C ''assign'':89C,182C ''associ'':135C,247C ''b'':79C,152C,208C ''back'':338C ''built'':111C ''case'':67C ''clearest'':25C ''concept'':18C ''configur'':140C,156C ''content'':29C,52C,60C,267C,277C,296C,333C ''data'':39C ''deliv'':337C ''differ'':57C ''display'':315C ''document'':73C,77C,87C,107C,148C,165C,203C,226C ''drupal'':33C ''ensur'':283C ''everi'':249C,274C,310C ''exampl'':23C,26C,116C ''explain'':16C ''fact'':234C ''general'':21C,126C,157C,216C ''get'':244C ''give'':103C ''howev'':209C ''idea'':44C ''individu'':313C ''like'':11C,32C ''make'':287C ''manag'':30C,53C ''multipl'':240C ''must'':324C ''necessarili'':37C ''noteworthi'':231C ''number'':85C ''one'':90C,185C ''p'':1C,49C,95C,130C,169C,254C,260C,266C,273C,298C ''permiss'':100C,134C,246C,263C,270C,292C,327C ''piec'':331C ''potenti'':311C ''regist'':172C ''request'':279C,289C ''requir'':269C ''result'':312C,318C ''role'':113C,123C,128C,132C,188C,196C,218C,241C,250C,257C,261C ''run'':303C ''scienc'':40C ''search'':301C,317C,341C ''see'':164C,201C,225C,329C ''sinc'':2C ''site'':180C ''softwar'':8C,97C,177C,281C,305C ''sure'':6C ''system'':31C,54C,101C,121C,141C,178C,194C,282C,306C ''time'':275C ''tool'':41C ''two'':71C ''type'':58C,72C,74C,78C,93C,108C,149C,166C,205C,228C,268C ''unlimit'':84C ''use'':13C ''user'':127C,158C,171C,191C,212C,217C,237C,255C,286C,321C,323C ''valid'':309C ''view'':147C ''wordpress'':35C ''would'':10C,69C,118C,139C,155C,197C,220C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5383, 1, '2015-03-24 15:25:02.27', 5, 93, 'Recommendation - item based vs user based', '&lt;p&gt;I have one clarification - &lt;/p&gt;

&lt;p&gt;First the definitions- &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;User-based&lt;/strong&gt;: Recommend items by finding similar users. This is often harder to scale because of the dynamic nature of users.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Item-based&lt;/strong&gt;: Calculate similarity between items and make recommendations. Items usually don''t change much, so this often can be computed offline.&lt;/p&gt;

&lt;p&gt;Now the question - in item based CF the similarity between items are tracked via user behavior - since user behavior is changing will it not impact the similarity between items?&lt;/p&gt;
', 'recommendation  beginner', NULL, '''/p'':14C,19C,45C,72C,103C ''/strong'':25C,51C ''base'':3A,6A,24C,50C,79C ''beginn'':8B ''behavior'':89C,92C ''calcul'':52C ''cf'':80C ''chang'':63C,94C ''clarif'':13C ''comput'':70C ''definit'':18C ''dynam'':41C ''find'':29C ''first'':16C ''harder'':35C ''impact'':98C ''item'':2A,27C,49C,55C,59C,78C,84C,102C ''item-bas'':48C ''make'':57C ''much'':64C ''natur'':42C ''offlin'':71C ''often'':34C,67C ''one'':12C ''p'':9C,15C,20C,46C,73C ''question'':76C ''recommend'':1A,7B,26C,58C ''scale'':37C ''similar'':30C,53C,82C,100C ''sinc'':90C ''strong'':21C,47C ''track'':86C ''user'':5A,23C,31C,44C,88C,91C ''user-bas'':22C ''usual'':60C ''via'':87C ''vs'':4A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5361, 2, '2015-03-20 21:24:40.41', 5, NULL, NULL, '&lt;p&gt;R is one of the key tool for data scientist, what ever you do don''t stop using it. &lt;/p&gt;

&lt;p&gt;Now talking about C, C++ or even Java.  They are good popular languages. Wether you need them or will need them depend on the type of job or projects you have.  From personal experience, there are so many tools out there for data scientist that you will always feel like you constantly need to be learning.  &lt;/p&gt;

&lt;p&gt;You can add Python or Matlab to things to learn if you want and keep adding.  The best way to learn is to take on a work project using other tools that you are not comfortable with.  If I were you, I would learn Python before C.  It is more used in the community than C.  But learning C is not a waste of your time. &lt;/p&gt;
', NULL, NULL, '''/p'':21C,79C,147C ''ad'':96C ''add'':83C ''alway'':70C ''best'':98C ''c'':26C,27C,127C,136C,139C ''comfort'':116C ''communiti'':134C ''constant'':74C ''data'':10C,65C ''depend'':44C ''even'':29C ''ever'':13C ''experi'':56C ''feel'':71C ''good'':33C ''java'':30C ''job'':49C ''keep'':95C ''key'':7C ''languag'':35C ''learn'':78C,90C,101C,124C,138C ''like'':72C ''mani'':60C ''matlab'':86C ''need'':38C,42C,75C ''one'':4C ''p'':1C,22C,80C ''person'':55C ''popular'':34C ''project'':51C,108C ''python'':84C,125C ''r'':2C ''scientist'':11C,66C ''stop'':18C ''take'':104C ''talk'':24C ''thing'':88C ''time'':146C ''tool'':8C,61C,111C ''type'':47C ''use'':19C,109C,131C ''want'':93C ''wast'':143C ''way'':99C ''wether'':36C ''work'':107C ''would'':123C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5362, 2, '2015-03-21 01:33:11.087', 4, NULL, NULL, '&lt;p&gt;The vim-r-plugin is surprisingly good. You can send lines and paragraphs of code from vim into a tmux session running R in a similar manner to R-Studio. It has &lt;a href="http://www.lepem.ufc.br/jaa/r-plugin.html#r-plugin-use" rel="nofollow"&gt;these commands&lt;/a&gt; if you want to check out what functionality it adds to vim. Of course I use all my other normal vim plugins - auto-complete, folding, etc.&lt;/p&gt;
', NULL, NULL, '''/a'':45C ''/jaa/r-plugin.html#r-plugin-use'':40C ''/p'':73C ''add'':55C ''auto'':69C ''auto-complet'':68C ''check'':50C ''code'':17C ''command'':44C ''complet'':70C ''cours'':59C ''etc'':72C ''fold'':71C ''function'':53C ''good'':9C ''href'':37C ''line'':13C ''manner'':29C ''nofollow'':42C ''normal'':65C ''p'':1C ''paragraph'':15C ''plugin'':6C,67C ''r'':5C,25C,32C ''r-studio'':31C ''rel'':41C ''run'':24C ''send'':12C ''session'':23C ''similar'':28C ''studio'':33C ''surpris'':8C ''tmux'':22C ''use'':61C ''vim'':4C,19C,57C,66C ''vim-r-plugin'':3C ''want'':48C ''www.lepem.ufc.br'':39C ''www.lepem.ufc.br/jaa/r-plugin.html#r-plugin-use'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5365, 2, '2015-03-21 13:04:43.81', 2, NULL, NULL, '&lt;p&gt;I would be keen to understand why you would need another language (apart form Python) if your goal is " but what about advanced regression, Machine Learning, text mining and other more advanced statistical operations".&lt;br&gt;
For that kind of thing, C is a waste of time. It''s a good tool to have but in the ~20 years since Java came out, I''ve rarely coded C.&lt;br&gt;
If you prefer the more functional-programming side of R, learn Scala before you get into too many procedural bad habits coding with C.&lt;br&gt;
Lastly learn to use Hadley Wickham''s libraries - they''ll save you a lot of time doing data manipulation.&lt;/p&gt;
', NULL, NULL, '''/p'':115C ''20'':58C ''advanc'':24C,33C ''anoth'':12C ''apart'':14C ''bad'':90C ''br'':36C,69C,95C ''c'':42C,68C,94C ''came'':62C ''code'':67C,92C ''data'':113C ''form'':15C ''function'':76C ''functional-program'':75C ''get'':85C ''goal'':19C ''good'':51C ''habit'':91C ''hadley'':100C ''java'':61C ''keen'':5C ''kind'':39C ''languag'':13C ''last'':96C ''learn'':27C,81C,97C ''librari'':103C ''ll'':105C ''lot'':109C ''machin'':26C ''mani'':88C ''manipul'':114C ''mine'':29C ''need'':11C ''oper'':35C ''p'':1C ''prefer'':72C ''procedur'':89C ''program'':77C ''python'':16C ''r'':80C ''rare'':66C ''regress'':25C ''save'':106C ''scala'':82C ''side'':78C ''sinc'':60C ''statist'':34C ''text'':28C ''thing'':41C ''time'':47C,111C ''tool'':52C ''understand'':7C ''use'':99C ''ve'':65C ''wast'':45C ''wickham'':101C ''would'':3C,10C ''year'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5366, 2, '2015-03-21 13:23:35.003', -1, NULL, NULL, '&lt;p&gt;If you''re a R user, there is a lot of good practical information at &lt;a href="http://www.rdatamining.com" rel="nofollow"&gt;http://www.rdatamining.com&lt;/a&gt;. Look at their text mining examples.&lt;br&gt;
Also, take a look at the tm package.&lt;br&gt;
This is also a good aggregation site- &lt;a href="http://www.tapor.ca/" rel="nofollow"&gt;http://www.tapor.ca/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':54C ''/a'':23C ''aggreg'':45C ''also'':31C,42C ''br'':30C,39C ''exampl'':29C ''good'':13C,44C ''href'':18C,48C ''inform'':15C ''look'':24C,34C ''lot'':11C ''mine'':28C ''nofollow'':21C,51C ''p'':1C ''packag'':38C ''practic'':14C ''r'':6C ''re'':4C ''rel'':20C,50C ''site'':46C ''take'':32C ''text'':27C ''tm'':37C ''user'':7C ''www.rdatamining.com'':19C,22C ''www.tapor.ca'':49C,53C ''www.tapor.ca/&lt;/a&gt;&lt;/p&gt;'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5370, 2, '2015-03-21 21:58:34.687', 21, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;Or must I loose most of the efficiency gained by programming in C by calling on R scripts or other languages?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Do the opposite: learn C/C++ to write R extensions. Use C/C++ only for the performance critical sections of your new algorithms, use R to build your analysis, import data, make plots etc.&lt;/p&gt;

&lt;p&gt;If you want to go beyond R, I''d recommend learning python. There are many libraries available such as &lt;a href="http://scikit-learn.org/stable/"&gt;scikit-learn&lt;/a&gt; for machine learning algorithms or &lt;a href="http://pybrain.org/"&gt;PyBrain&lt;/a&gt; for building Neural Networks etc. (and use pylab/&lt;a href="http://matplotlib.org/"&gt;matplotlib&lt;/a&gt; for plotting and &lt;a href="http://ipython.org/notebook.html"&gt;iPython notebooks&lt;/a&gt; to develop your analyses). Again, C/C++ is useful to implement time critical algorithms as python extensions.&lt;/p&gt;
', NULL, NULL, '''/a'':88C,98C,111C,122C ''/blockquote'':25C ''/notebook.html'':119C ''/p'':24C,59C,139C ''/stable/'':84C ''algorithm'':47C,92C,135C ''analys'':126C ''analysi'':53C ''avail'':77C ''beyond'':66C ''blockquot'':1C ''build'':51C,100C ''c'':15C ''c/c'':31C,37C,128C ''call'':17C ''critic'':42C,134C ''d'':69C ''data'':55C ''develop'':124C ''effici'':10C ''etc'':58C,103C ''extens'':35C,138C ''gain'':11C ''go'':65C ''href'':81C,95C,108C,116C ''implement'':132C ''import'':54C ''ipython'':120C ''ipython.org'':118C ''ipython.org/notebook.html'':117C ''languag'':23C ''learn'':30C,71C,87C,91C ''librari'':76C ''loos'':6C ''machin'':90C ''make'':56C ''mani'':75C ''matplotlib'':110C ''matplotlib.org'':109C ''must'':4C ''network'':102C ''neural'':101C ''new'':46C ''notebook'':121C ''opposit'':29C ''p'':2C,26C,60C ''perform'':41C ''plot'':57C,113C ''program'':13C ''pybrain'':97C ''pybrain.org'':96C ''pylab'':106C ''python'':72C,137C ''r'':19C,34C,49C,67C ''recommend'':70C ''scikit'':86C ''scikit-learn'':85C ''scikit-learn.org'':83C ''scikit-learn.org/stable/'':82C ''script'':20C ''section'':43C ''time'':133C ''use'':36C,48C,105C,130C ''want'':63C ''write'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5371, 1, '2015-03-22 18:53:39.82', 2, 306, 'R and Python, memory differences yielding performance differences', '&lt;p&gt;I think there are numerous posts regarding which one to use: R or Python. However, I''m curious about how their architecture differences yield differences in speed performance, &lt;strong&gt;not&lt;/strong&gt; which one to use.&lt;/p&gt;

&lt;p&gt;This &lt;a href="http://alstatr.blogspot.com/2014/01/python-and-r-is-python-really-faster.html" rel="nofollow"&gt;blog post&lt;/a&gt; performs a small test between R and python to show that the (optimized) python code was 2x faster than R code.* And I''ve read in &lt;a href="http://datascience.stackexchange.com/questions/59/what-are-rs-memory-constraints"&gt;this post&lt;/a&gt; that R tends to put everything in memory, which is why computations on large datasets is generally slow.&lt;/p&gt;

&lt;p&gt;But what makes python''s low level memory management so much different than R, which helps it yield these benchmarks?&lt;/p&gt;

&lt;p&gt;*Though python was 2x faster in this test than R, I''m not saying that python is generally 2x faster than R.&lt;/p&gt;
', 'r  python  performance', NULL, '''/2014/01/python-and-r-is-python-really-faster.html'':55C ''/a'':60C,94C ''/p'':48C,113C,135C,159C ''/questions/59/what-are-rs-memory-constraints'':91C ''/strong'':43C ''2x'':77C,140C,155C ''alstatr.blogspot.com'':54C ''alstatr.blogspot.com/2014/01/python-and-r-is-python-really-faster.html'':53C ''architectur'':34C ''benchmark'':134C ''blog'':58C ''code'':75C,81C ''comput'':106C ''curious'':30C ''datascience.stackexchange.com'':90C ''datascience.stackexchange.com/questions/59/what-are-rs-memory-constraints'':89C ''dataset'':109C ''differ'':5A,8A,35C,37C,126C ''everyth'':100C ''faster'':78C,141C,156C ''general'':111C,154C ''help'':130C ''howev'':27C ''href'':52C,88C ''larg'':108C ''level'':121C ''low'':120C ''m'':29C,148C ''make'':117C ''manag'':123C ''memori'':4A,102C,122C ''much'':125C ''nofollow'':57C ''numer'':17C ''one'':21C,45C ''optim'':73C ''p'':12C,49C,114C,136C ''perform'':7A,11B,40C,61C ''post'':18C,59C,93C ''put'':99C ''python'':3A,10B,26C,68C,74C,118C,138C,152C ''r'':1A,9B,24C,66C,80C,96C,128C,146C,158C ''read'':85C ''regard'':19C ''rel'':56C ''say'':150C ''show'':70C ''slow'':112C ''small'':63C ''speed'':39C ''strong'':41C ''tend'':97C ''test'':64C,144C ''think'':14C ''though'':137C ''use'':23C,47C ''ve'':84C ''yield'':6A,36C,132C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5373, 1, '2015-03-23 14:00:02.72', 2, 149, 'sk-learn - ValueError: array is too big.', '&lt;p&gt;I have a large dataset with characters and 90000 intances and I have the error &lt;em&gt;ValueError: array is too big&lt;/em&gt; when I have the following code before the plot_kmeans_digits.py code:&lt;/p&gt;

&lt;p&gt;data2=list(csv.DictReader(open(''C:\diabeticdata.csv'', ''rU'')))
vec = DictVectorizer()
data = vec.fit_transform(data2).toarray()&lt;/p&gt;

&lt;p&gt;Do you know how I can solve this error?&lt;/p&gt;

&lt;p&gt;Thanks in advance. &lt;/p&gt;
', 'bigdata  sklearn', NULL, '''/em'':33C ''/p'':44C,60C,71C,76C ''90000'':20C ''advanc'':75C ''array'':5A,29C ''big'':8A,32C ''bigdata'':9B ''c'':50C ''charact'':18C ''code'':39C,43C ''csv.dictreader'':48C ''data'':55C ''data2'':46C,58C ''dataset'':16C ''diabeticdata.csv'':51C ''dictvector'':54C ''em'':27C ''error'':26C,70C ''follow'':38C ''intanc'':21C ''know'':64C ''larg'':15C ''learn'':3A ''list'':47C ''open'':49C ''p'':11C,45C,61C,72C ''plot_kmeans_digits.py'':42C ''ru'':52C ''sk'':2A ''sk-learn'':1A ''sklearn'':10B ''solv'':68C ''thank'':73C ''toarray'':59C ''transform'':57C ''valueerror'':4A,28C ''vec'':53C ''vec.fit'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8706, 2, '2015-11-04 05:25:52.27', 1, NULL, NULL, '&lt;p&gt;As you ask specifically for the Cosine Similarity technique, it has magnitude and direction, and similar to a vector which is used in Physics, as Cosine Similarity deals with vectors in an inner product space. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/RxFDJ.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/RxFDJ.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;So, the magnitude of vectors is exactly the same as the formula in Physics (summating over the squares of the vector elements.)&lt;/p&gt;
', NULL, NULL, '''/a'':56C ''/p'':37C,57C,81C ''/rxfdj.png'':43C,50C ''alt'':51C ''ask'':4C ''cosin'':8C,27C ''deal'':29C ''descript'':54C ''direct'':15C ''element'':80C ''enter'':52C ''exact'':65C ''formula'':70C ''href'':40C ''i.stack.imgur.com'':42C,49C ''i.stack.imgur.com/rxfdj.png'':41C,48C ''imag'':53C ''img'':46C ''inner'':34C ''magnitud'':13C,61C ''nofollow'':45C ''p'':1C,38C,58C ''physic'':25C,72C ''product'':35C ''rel'':44C ''similar'':9C,17C,28C ''space'':36C ''specif'':5C ''squar'':76C ''src'':47C ''summat'':73C ''techniqu'':10C ''use'':23C ''vector'':20C,31C,63C,79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5374, 1, '2015-03-23 15:48:18.683', 0, 70, 'Feature scaling', '&lt;p&gt;I am struggling with a conceptual problem related to feature scaling.&lt;/p&gt;

&lt;p&gt;Let''s assume I am building a classifier (e.g., a NN) and let''s assume I rely on future scaling for the input features of my model.&lt;/p&gt;

&lt;p&gt;In this context I will normalise the training set using its mean and its std and I would do the same with the testing set using the testing mean and std.&lt;/p&gt;

&lt;p&gt;Let us also assume I succeed in building my classifier and I move to production where I try to classify new inputs.  However for such new inputs the mean and std are unknown! How can I scale them appropriately before processing with my model? May be I could use the mean and std from training+testing.....&lt;/p&gt;

&lt;p&gt;I really don''t know which is the correct practice here....any hint?&lt;/p&gt;

&lt;p&gt;Thank you for your help!&lt;/p&gt;
', 'machine-learning  feature-scaling', NULL, '''/p'':21C,50C,83C,141C,156C,163C ''also'':87C ''appropri'':123C ''assum'':25C,37C,88C ''build'':28C,92C ''classifi'':30C,94C,104C ''conceptu'':15C ''context'':54C ''correct'':151C ''could'':132C ''e.g'':31C ''featur'':1A,7B,19C,46C ''feature-sc'':6B ''futur'':41C ''help'':162C ''hint'':155C ''howev'':107C ''input'':45C,106C,111C ''know'':147C ''learn'':5B ''let'':23C,35C,85C ''machin'':4B ''machine-learn'':3B ''may'':129C ''mean'':63C,80C,113C,135C ''model'':49C,128C ''move'':97C ''new'':105C,110C ''nn'':33C ''normalis'':57C ''p'':9C,22C,51C,84C,142C,157C ''practic'':152C ''problem'':16C ''process'':125C ''product'':99C ''realli'':144C ''relat'':17C ''reli'':39C ''scale'':2A,8B,20C,42C,121C ''set'':60C,76C ''std'':66C,82C,115C,137C ''struggl'':12C ''succeed'':90C ''test'':75C,79C,140C ''thank'':158C ''train'':59C,139C ''tri'':102C ''unknown'':117C ''us'':86C ''use'':61C,77C,133C ''would'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5375, 1, '2015-03-23 17:36:03.68', 2, 134, 'Which one is easier to debug between theano and caffe?', '&lt;p&gt;I am investigating whether to use theano or caffe for convnets. I would like to know which one provides a better debug environment. In caffe, it seems you don''t write any code just a config in protobuf - how do you debug the convnet then?&lt;/p&gt;
', 'deep-learning', NULL, '''/p'':60C ''better'':35C ''caff'':10A,23C,39C ''code'':47C ''config'':50C ''convnet'':25C,58C ''debug'':6A,36C,56C ''deep'':12B ''deep-learn'':11B ''easier'':4A ''environ'':37C ''investig'':17C ''know'':30C ''learn'':13B ''like'':28C ''one'':2A,32C ''p'':14C ''protobuf'':52C ''provid'':33C ''seem'':41C ''theano'':8A,21C ''use'':20C ''whether'':18C ''would'':27C ''write'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5376, 2, '2015-03-23 21:48:30.313', 2, NULL, NULL, '&lt;p&gt;You should apply the normalization only on your training dataset. Your test set should be kept completely separate and should be used only when your final model has been chosen. If you use include the testing set in the normalization, it can be seen as using the testing set in the training procedure. This is called data snooping.   &lt;/p&gt;

&lt;p&gt;You should pre-process training dataset and use the obtained mean and std  when processing the testing set afterwards. Note that the testing dataset transformations will likely be imperfect (it will not have zero mean or unity standard deviation) but this testing dataset can safely be used since it has not affected any step of the learning process.&lt;/p&gt;
', NULL, NULL, '''/p'':60C,121C ''affect'':114C ''afterward'':81C ''appli'':4C ''call'':57C ''chosen'':31C ''complet'':18C ''data'':58C ''dataset'':11C,68C,86C,105C ''deviat'':101C ''final'':27C ''imperfect'':91C ''includ'':35C ''kept'':17C ''learn'':119C ''like'':89C ''mean'':73C,97C ''model'':28C ''normal'':6C,41C ''note'':82C ''obtain'':72C ''p'':1C,61C ''pre'':65C ''pre-process'':64C ''procedur'':54C ''process'':66C,77C,120C ''safe'':107C ''seen'':45C ''separ'':19C ''set'':14C,38C,50C,80C ''sinc'':110C ''snoop'':59C ''standard'':100C ''std'':75C ''step'':116C ''test'':13C,37C,49C,79C,85C,104C ''train'':10C,53C,67C ''transform'':87C ''uniti'':99C ''use'':23C,34C,47C,70C,109C ''zero'':96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5379, 1, '2015-03-24 06:01:50.617', 1, 201, 'Tools to preprocess a big data for dashboards?', '&lt;p&gt;I have a complex dataset with more than 16M rows coming from pharmaceutical industry. Regarding the data, it is saved in a sql server with multiple (more than 400) relational tables. Data got several levels of hierachies like province, city, postal code, person, and antigens measures, etc. I would like to create many dashboards in order to observe the changes &amp;amp; trends happening. I can use Pentaho, R (shiny) or Tableau for this purpose. But the problem is data is so huge, and it take so long to process it with dashboard softwares. I have a choice of making cube and connect it to dashboard. My question here is whether there are  any other solutions that I can use instead of making a cube? I don''t want to go through the hassle of making &amp;amp; maintaining a cube. I would like to use a software where I specify relationships between tables, so the aggregation/amalgamation happens smoothly and output processed tables that can connect  to dashboards. I hear Alteryx is one software that can do it for you (I haven''t tried it myself, and it is an expensive one!). I understand this task needs two or more softwares/tools. Please share your input &amp;amp; experience. Please mention what tools do you use, size of your data, and how fast/efficient is the entire system, and other necessary details. &lt;/p&gt;
', 'bigdata  open-source  tableau', NULL, '''/p'':242C ''16m'':23C ''400'':43C ''aggregation/amalgamation'':169C ''alteryx'':183C ''amp'':75C,150C,218C ''antigen'':59C ''big'':5A ''bigdata'':9B ''chang'':74C ''choic'':111C ''citi'':54C ''code'':56C ''come'':25C ''complex'':18C ''connect'':116C,178C ''creat'':66C ''cube'':114C,138C,153C ''dashboard'':8A,68C,106C,119C,180C ''data'':6A,31C,46C,93C,230C ''dataset'':19C ''detail'':241C ''entir'':236C ''etc'':61C ''expens'':203C ''experi'':219C ''fast/efficient'':233C ''go'':144C ''got'':47C ''happen'':77C,170C ''hassl'':147C ''haven'':194C ''hear'':182C ''hierachi'':51C ''huge'':96C ''industri'':28C ''input'':217C ''instead'':134C ''level'':49C ''like'':52C,64C,156C ''long'':101C ''maintain'':151C ''make'':113C,136C,149C ''mani'':67C ''measur'':60C ''mention'':221C ''multipl'':40C ''necessari'':240C ''need'':209C ''observ'':72C ''one'':185C,204C ''open'':11B ''open-sourc'':10B ''order'':70C ''output'':173C ''p'':14C ''pentaho'':81C ''person'':57C ''pharmaceut'':27C ''pleas'':214C,220C ''postal'':55C ''preprocess'':3A ''problem'':91C ''process'':103C,174C ''provinc'':53C ''purpos'':88C ''question'':121C ''r'':82C ''regard'':29C ''relat'':44C ''relationship'':164C ''row'':24C ''save'':34C ''server'':38C ''sever'':48C ''share'':215C ''shini'':83C ''size'':227C ''smooth'':171C ''softwar'':107C,160C,186C ''softwares/tools'':213C ''solut'':129C ''sourc'':12B ''specifi'':163C ''sql'':37C ''system'':237C ''tabl'':45C,166C,175C ''tableau'':13B,85C ''take'':99C ''task'':208C ''tool'':1A,223C ''trend'':76C ''tri'':196C ''two'':210C ''understand'':206C ''use'':80C,133C,158C,226C ''want'':142C ''whether'':124C ''would'':63C,155C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5380, 1, '2015-03-24 08:12:53.14', 2, 74, 'In a SVD with user/video bias, why is the UV contribute so small?', '&lt;p&gt;I''m testing a SVD-based collaborative filter on my data set, in which the label, $r_{ij}$, is a real value from 0 to 1.&lt;/p&gt;

&lt;p&gt;Like the many papers suggested, to have a better performance, instead of using $ \hat{R} = U \cdot V^T $ directly, I use $\hat{R} = \mu + B_u + B_v + U \cdot V^T $, where $\mu$ is the average rating, $B_u$ is the bias of user, and $B_v$ is the bias of item.&lt;/p&gt;

&lt;p&gt;Thus, this model corresponds to a loss function:
$\min_{B_u, B_v, U, V}  = ||I\circ(R-\mu-Bu-B_v-U\cdot V^T)||_F^2 + \lambda (||B_u||_F^2 + ||B_v||_F^2 + ||U||_F^2 + ||V||_F^2)$&lt;/p&gt;

&lt;p&gt;where I is the masking matrix in which $I_{ij} = 1$ if $R_{ij}$ is known, and $||\cdot||_F$ is the frobenius norm.&lt;/p&gt;

&lt;p&gt;Then, I solve this by gradient descent, it seems to work fine, and the test RMSE is 0.25. &lt;/p&gt;

&lt;p&gt;However, when I investigate the contribution of each part in predict function $\hat{R} = \mu + B_u + B_v + U \cdot V^T $, I notice that, $\mu$ is about 0.5, $b_u$ and $b_i$ are about $\pm0.3$, but the part of $ U \cdot V^T $ is quite small, normally about $\pm 0.01$.&lt;/p&gt;

&lt;p&gt;Why does this part contribute so small? Since this part is the actual part where the collaborative filter works, I expect it to contribute more in prediction.&lt;/p&gt;
', 'recommendation', NULL, '''/p'':43C,99C,146C,171C,191C,246C,275C ''0'':40C ''0.01'':245C ''0.25'':190C ''0.5'':222C ''1'':42C,158C ''2'':130C,135C,139C,142C,145C ''actual'':260C ''averag'':82C ''b'':70C,72C,84C,92C,110C,112C,122C,132C,136C,208C,210C,223C,226C ''base'':22C ''better'':53C ''bias'':6A,88C,96C ''bu'':121C ''cdot'':61C,75C,126C,165C,213C,236C ''circ'':117C ''collabor'':23C,264C ''contribut'':11A,198C,252C,271C ''correspond'':104C ''data'':27C ''descent'':179C ''direct'':64C ''expect'':268C ''f'':129C,134C,138C,141C,144C,166C ''filter'':24C,265C ''fine'':184C ''frobenius'':169C ''function'':108C,204C ''gradient'':178C ''hat'':58C,67C,205C ''howev'':193C ''ij'':34C,157C,161C ''instead'':55C ''investig'':196C ''item'':98C ''known'':163C ''label'':32C ''lambda'':131C ''like'':45C ''loss'':107C ''m'':17C ''mani'':47C ''mask'':152C ''matrix'':153C ''min'':109C ''model'':103C ''mu'':69C,79C,120C,207C,219C ''mu-bu-b'':119C ''norm'':170C ''normal'':242C ''notic'':217C ''p'':15C,44C,100C,147C,172C,192C,247C ''paper'':48C ''part'':201C,233C,251C,257C,261C ''perform'':54C ''pm'':244C ''pm0.3'':230C ''predict'':203C,274C ''quit'':240C ''r'':33C,59C,68C,118C,160C,206C ''rate'':83C ''real'':37C ''recommend'':14B ''rmse'':188C ''seem'':181C ''set'':28C ''sinc'':255C ''small'':13A,241C,254C ''solv'':175C ''suggest'':49C ''svd'':3A,21C ''svd-base'':20C ''test'':18C,187C ''thus'':101C ''u'':60C,71C,74C,85C,111C,114C,125C,133C,140C,209C,212C,224C,235C ''use'':57C,66C ''user'':90C ''user/video'':5A ''uv'':10A ''v'':62C,73C,76C,93C,113C,115C,124C,127C,137C,143C,211C,214C,237C ''v-u'':123C ''valu'':38C ''work'':183C,266C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5381, 2, '2015-03-24 11:16:07.133', 0, NULL, NULL, '&lt;p&gt;This site lists historical market capitalizations and enterprise values for S&amp;amp;P 100 and NASDAQ-100 companies for the past 10 years. You can export the data sets to Excel.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://marketcapitalizations.com/historical-data/historical-data-categories/valuations/" rel="nofollow"&gt;http://marketcapitalizations.com/historical-data/historical-data-categories/valuations/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You can also try to contact them for data for a longer period of time.&lt;/p&gt;
', NULL, NULL, '''-100'':18C ''/historical-data/historical-data-categories/valuations/'':39C ''/historical-data/historical-data-categories/valuations/&lt;/a&gt;&lt;/p&gt;'':44C ''/p'':33C,61C ''10'':23C ''100'':15C ''also'':48C ''amp'':13C ''capit'':7C ''compani'':19C ''contact'':51C ''data'':29C,54C ''enterpris'':9C ''excel'':32C ''export'':27C ''histor'':5C ''href'':36C ''list'':4C ''longer'':57C ''market'':6C ''marketcapitalizations.com'':38C,43C ''marketcapitalizations.com/historical-data/historical-data-categories/valuations/'':37C ''marketcapitalizations.com/historical-data/historical-data-categories/valuations/&lt;/a&gt;&lt;/p&gt;'':42C ''nasdaq'':17C ''nofollow'':41C ''p'':1C,14C,34C,45C ''past'':22C ''period'':58C ''rel'':40C ''set'':30C ''site'':3C ''time'':60C ''tri'':49C ''valu'':10C ''year'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6707, 2, '2015-08-06 01:25:40.61', 3, NULL, NULL, '&lt;p&gt;1- You should set a threshold (a hyper-param) that will allow you to quit the loop.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Let V the values for all state s and V'' the new values after value iteration.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;if $\sum_s|V(s) - Vâ(s)| \le threshold$, quit&lt;/p&gt;

&lt;p&gt;2 - V is a function for every cell in the grid yes because you need to update every cell. &lt;/p&gt;

&lt;p&gt;Hope it helps.&lt;/p&gt;
', NULL, NULL, '''/em'':39C ''/p'':20C,40C,52C,73C,78C ''1'':2C ''2'':54C ''allow'':14C ''cell'':61C,72C ''em'':22C ''everi'':60C,71C ''function'':58C ''grid'':64C ''help'':77C ''hope'':75C ''hyper'':10C ''hyper-param'':9C ''iter'':38C ''le'':49C ''let'':23C ''loop'':19C ''need'':68C ''new'':34C ''p'':1C,21C,41C,53C,74C ''param'':11C ''quit'':17C,51C ''set'':5C ''state'':29C ''sum'':43C ''threshold'':7C,50C ''updat'':70C ''v'':24C,32C,45C,55C ''valu'':26C,35C,37C ''vâ'':47C ''yes'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5384, 2, '2015-03-24 15:35:16.227', 1, NULL, NULL, '&lt;p&gt;This problem looks like a lot with the problem of ranking college football teams. I have never worked on this ranking problem, but I believe you can borrow some tools used there to build your model.&lt;/p&gt;

&lt;p&gt;Here goes a couple of references:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.colleyrankings.com/matrate.pdf" rel="nofollow"&gt;Colley Matrix Rankings&lt;/a&gt; - This was one of computer rankings used by the BCS. It is also the only one that shared their methodology.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://public.gettysburg.edu/~cwessell/RankingPage/colley.pdf" rel="nofollow"&gt;An example of the Colley Matrix Rankings&lt;/a&gt; - An easy example to follow.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://umdrive.memphis.edu/ccrousse/public/MATH%207375/PERRON.pdf" rel="nofollow"&gt;The Perron-Frobenius Theorem and the Ranking of Football Teams&lt;/a&gt; - A well known reference that presents some ranking methods. This paper also shows how to assess the probability of winning a game based on the rankings of the teams.&lt;/p&gt;
', NULL, NULL, '''/a'':58C,95C,121C ''/ccrousse/public/math%207375/perron.pdf'':107C ''/matrate.pdf'':52C ''/p'':38C,46C,79C,101C,151C ''/~cwessell/rankingpage/colley.pdf'':85C ''also'':71C,133C ''assess'':137C ''base'':144C ''bcs'':68C ''believ'':26C ''borrow'':29C ''build'':35C ''colleg'':13C ''colley'':55C,92C ''comput'':63C ''coupl'':43C ''easi'':97C ''exampl'':89C,98C ''follow'':100C ''footbal'':14C,119C ''frobenius'':113C ''game'':143C ''goe'':41C ''href'':49C,82C,104C ''known'':124C ''like'':5C ''look'':4C ''lot'':7C ''matrix'':56C,93C ''method'':130C ''methodolog'':78C ''model'':37C ''never'':18C ''nofollow'':54C,87C,109C ''one'':61C,74C ''p'':1C,39C,47C,80C,102C ''paper'':132C ''perron'':112C ''perron-frobenius'':111C ''present'':127C ''probabl'':139C ''problem'':3C,10C,23C ''public.gettysburg.edu'':84C ''public.gettysburg.edu/~cwessell/rankingpage/colley.pdf'':83C ''rank'':12C,22C,57C,64C,94C,117C,129C,147C ''refer'':45C,125C ''rel'':53C,86C,108C ''share'':76C ''show'':134C ''team'':15C,120C,150C ''theorem'':114C ''tool'':31C ''umdrive.memphis.edu'':106C ''umdrive.memphis.edu/ccrousse/public/math%207375/perron.pdf'':105C ''use'':32C,65C ''well'':123C ''win'':141C ''work'':19C ''www.colleyrankings.com'':51C ''www.colleyrankings.com/matrate.pdf'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5385, 2, '2015-03-24 20:47:36.317', 5, NULL, NULL, '&lt;p&gt;As a data scientist the other languages (C++/Java) come in handy when you need incorporate machine learning into an existing production engine.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://waffles.sourceforge.net/" rel="nofollow"&gt;Waffles&lt;/a&gt; is both a well-maintained C++ class library and command-line analysis package.  It''s got supervised and unsupervised learning, tons of data manipulation tools, sparse data tools, and other things such as audio processing.  Since it''s also a class library, you can extend it as you need.  Even if you are not the one developing the C++ engine (chances are you won''t be), this will allow you to prototype, test, and hand something over to the developers.    &lt;/p&gt;

&lt;p&gt;Most importantly, I believe my knowledge of C++ &amp;amp; Java really help me understand how Python and R work.  Any language is only used properly when you understand a little about what is going on underneath.  By learning the differences between languages you can learn to exploit the strengths of your main language.  &lt;/p&gt;

&lt;p&gt;Update&lt;/p&gt;

&lt;p&gt;For commercial applications with large data sets, Apache Spark - MLLib is important.  Here you can use Scala, Java, or Python.  &lt;/p&gt;
', NULL, NULL, '''/a'':33C ''/java'':10C ''/p'':25C,116C,171C,174C,196C ''allow'':104C ''also'':74C ''amp'':126C ''analysi'':47C ''apach'':183C ''applic'':178C ''audio'':69C ''believ'':121C ''c'':9C,40C,94C,125C ''chanc'':96C ''class'':41C,76C ''come'':11C ''command'':45C ''command-lin'':44C ''commerci'':177C ''data'':4C,58C,62C,181C ''develop'':92C,115C ''differ'':157C ''engin'':24C,95C ''even'':85C ''exist'':22C ''exploit'':164C ''extend'':80C ''go'':151C ''got'':51C ''hand'':110C ''handi'':13C ''help'':129C ''href'':28C ''import'':119C,187C ''incorpor'':17C ''java'':127C,193C ''knowledg'':123C ''languag'':8C,138C,159C,170C ''larg'':180C ''learn'':19C,55C,155C,162C ''librari'':42C,77C ''line'':46C ''littl'':147C ''machin'':18C ''main'':169C ''maintain'':39C ''manipul'':59C ''mllib'':185C ''need'':16C,84C ''nofollow'':31C ''one'':91C ''p'':1C,26C,117C,172C,175C ''packag'':48C ''process'':70C ''product'':23C ''proper'':142C ''prototyp'':107C ''python'':133C,195C ''r'':135C ''realli'':128C ''rel'':30C ''scala'':192C ''scientist'':5C ''set'':182C ''sinc'':71C ''someth'':111C ''spark'':184C ''spars'':61C ''strength'':166C ''supervis'':52C ''test'':108C ''thing'':66C ''ton'':56C ''tool'':60C,63C ''underneath'':153C ''understand'':131C,145C ''unsupervis'':54C ''updat'':173C ''use'':141C,191C ''waffl'':32C ''waffles.sourceforge.net'':29C ''well'':38C ''well-maintain'':37C ''won'':99C ''work'':136C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8686, 1, '2015-11-02 06:36:09.557', 2, 142, 'Building Recommendation engine with Python', '&lt;p&gt;Which all are the equivalent or advanced libraries in Python for building recommendation systems like &lt;a href="https://mahout.apache.org/" rel="nofollow"&gt;Mahout&lt;/a&gt; for  Collaborative Filtering and Content Based Filtering ?&lt;/p&gt;

&lt;p&gt;Also is there a way to integrate Mahout with Python?&lt;/p&gt;
', 'python  apache-mahout  software-recommendation', NULL, '''/a'':35C ''/p'':43C,55C ''advanc'':20C ''also'':45C ''apach'':8B ''apache-mahout'':7B ''base'':41C ''build'':1A,25C ''collabor'':37C ''content'':40C ''engin'':3A ''equival'':18C ''filter'':38C,42C ''href'':30C ''integr'':51C ''librari'':21C ''like'':28C ''mahout'':9B,34C,52C ''mahout.apache.org'':31C ''nofollow'':33C ''p'':13C,44C ''python'':5A,6B,23C,54C ''recommend'':2A,12B,26C ''rel'':32C ''softwar'':11B ''software-recommend'':10B ''system'':27C ''way'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5387, 2, '2015-03-25 10:16:41.007', 1, NULL, NULL, '&lt;p&gt;We have dashboards that show information about some processes, which have billions of rows in the database. It''s not queried directly though, but instead from pre-aggregated data.&lt;/p&gt;

&lt;p&gt;We have automated scripts running in database that populate the aggregated data tables specifically for dashboards. In most extreme cases the raw data is so large and comes in at such a high pace that there''s a two-tier aggregation in place. First tier will de-normalize the data and 2nd tier will do the actual sum/count type of aggregation.&lt;/p&gt;

&lt;p&gt;So you don''t need two tools since you can do aggregation purely in database.&lt;/p&gt;

&lt;p&gt;edit (answering question from comment): Our dashboards are in Tableau. Data is all in PostgreSQL databases. Automation is done with unix cronjob executing a database function. The function in turn queries which tasks it has to run and runs them. There is a few moving pieces to the whole system, but the architecture isn''t too complicated. Write down your ideas and go talk to some architects / engineers - they will know the best way to approach this.&lt;/p&gt;
', NULL, NULL, '''/p'':32C,97C,114C,193C ''2nd'':87C ''actual'':92C ''aggreg'':30C,44C,75C,96C,110C ''answer'':117C ''approach'':191C ''architect'':182C ''architectur'':168C ''autom'':36C,132C ''best'':188C ''billion'':13C ''case'':53C ''come'':61C ''comment'':120C ''complic'':172C ''cronjob'':137C ''dashboard'':4C,49C,122C ''data'':31C,45C,56C,85C,126C ''databas'':18C,40C,113C,131C,140C ''de'':82C ''de-norm'':81C ''direct'':23C ''done'':134C ''edit'':116C ''engin'':183C ''execut'':138C ''extrem'':52C ''first'':78C ''function'':141C,143C ''go'':178C ''high'':66C ''idea'':176C ''inform'':7C ''instead'':26C ''isn'':169C ''know'':186C ''larg'':59C ''move'':160C ''need'':103C ''normal'':83C ''p'':1C,33C,98C,115C ''pace'':67C ''piec'':161C ''place'':77C ''popul'':42C ''postgresql'':130C ''pre'':29C ''pre-aggreg'':28C ''process'':10C ''pure'':111C ''queri'':22C,146C ''question'':118C ''raw'':55C ''row'':15C ''run'':38C,152C,154C ''script'':37C ''show'':6C ''sinc'':106C ''specif'':47C ''sum/count'':93C ''system'':165C ''tabl'':46C ''tableau'':125C ''talk'':179C ''task'':148C ''though'':24C ''tier'':74C,79C,88C ''tool'':105C ''turn'':145C ''two'':73C,104C ''two-tier'':72C ''type'':94C ''unix'':136C ''way'':189C ''whole'':164C ''write'':173C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5391, 2, '2015-03-25 21:33:45.957', 1, NULL, NULL, '&lt;p&gt;Having only taken a few courses on this stuff, I''m going to offer my understanding on this.&lt;/p&gt;

&lt;p&gt;The main reason seems to be simplifying, as you assume. It manifests in different areas though. One is mathematical - if your co-variance matrix is identity matrix then the math becomes much easier. The one directly following from this is computational - inverting a matrix is very costly, but identity matrix is its own inverse, so that''s perfect.&lt;/p&gt;

&lt;p&gt;Another part of the simplicity is definitely the ease of interpretation. Independent variables are easy to explain to a non-technical person, but as soon as you get into covariances, that goes out the window.&lt;/p&gt;
', NULL, NULL, '''/p'':20C,81C,119C ''anoth'':83C ''area'':36C ''assum'':31C ''becom'':53C ''co'':44C ''co-vari'':43C ''comput'':63C ''cost'':69C ''cours'':7C ''covari'':113C ''definit'':89C ''differ'':35C ''direct'':58C ''eas'':91C ''easi'':97C ''easier'':55C ''explain'':99C ''follow'':59C ''get'':111C ''go'':13C ''goe'':115C ''ident'':48C,71C ''independ'':94C ''interpret'':93C ''invers'':76C ''invert'':64C ''m'':12C ''main'':23C ''manifest'':33C ''math'':52C ''mathemat'':40C ''matrix'':46C,49C,66C,72C ''much'':54C ''non'':103C ''non-techn'':102C ''offer'':15C ''one'':38C,57C ''p'':1C,21C,82C ''part'':84C ''perfect'':80C ''person'':105C ''reason'':24C ''seem'':25C ''simplic'':87C ''simplifi'':28C ''soon'':108C ''stuff'':10C ''taken'':4C ''technic'':104C ''though'':37C ''understand'':17C ''variabl'':95C ''varianc'':45C ''window'':118C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5413, 1, '2015-03-28 22:35:01.27', 1, 66, 'How to create and format an image dataset from scratch for machine learning?', '&lt;p&gt;I''ve only worked with ML with .csv formats. I''ve worked with image formats too but only premade imagesets (MNIST,etc). If I were to create an imageset from scratch, how are the class labels typically formated? Would I have to manually title the image of a jpeg? &lt;/p&gt;

&lt;p&gt;Best,
Jeremy&lt;/p&gt;
', 'machine-learning  computer-vision', NULL, '''/p'':70C,74C ''best'':72C ''class'':55C ''comput'':18B ''computer-vis'':17B ''creat'':3A,47C ''csv'':28C ''dataset'':8A ''etc'':42C ''format'':5A,29C,35C,58C ''imag'':7A,34C,66C ''imageset'':40C,49C ''jeremi'':73C ''jpeg'':69C ''label'':56C ''learn'':13A,16B ''machin'':12A,15B ''machine-learn'':14B ''manual'':63C ''ml'':26C ''mnist'':41C ''p'':20C,71C ''premad'':39C ''scratch'':10A,51C ''titl'':64C ''typic'':57C ''ve'':22C,31C ''vision'':19B ''work'':24C,32C ''would'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5394, 2, '2015-03-26 03:15:05.417', 1, NULL, NULL, '&lt;p&gt;I think that depends on the pattern of your data, its skewness and sparsity. 
Because it would press these two bias term, which is designed to detect the difference with global mean. &lt;/p&gt;

&lt;p&gt;For example, you can plot mean and std of the user''s bias: ($R_u$ - global_mean) then make a histogram of std. Another way, just make up a rating matrix $R''$ according to the $\mu + B_u + B_v$ you''ve got. And run the solver again and see what happens. These parts play its own role, even your change on global_mean would make a difference to the final RMSE, I think the role of that latent factors are born to capture the &lt;strong&gt;&lt;em&gt;REST&lt;/em&gt;&lt;/strong&gt; non-linear pattern in user &amp;amp; item''s interaction.&lt;/p&gt;

&lt;p&gt;So, as for your question:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Why does this part contributes so small?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Again, you can simply using $R = UV^T$, in this time, I think it contributes quite a lot, right? The contribution of each part is hard to say.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':154C ''/em'':124C ''/p'':34C,136C,143C,153C,184C ''/strong'':125C ''accord'':68C ''amp'':132C ''anoth'':59C ''b'':72C,74C ''bias'':22C,48C ''blockquot'':144C ''born'':117C ''captur'':119C ''chang'':96C ''contribut'':150C,170C,176C ''data'':11C ''depend'':5C ''design'':26C ''detect'':28C ''differ'':30C,103C ''em'':122C ''even'':94C ''exampl'':37C ''factor'':115C ''final'':106C ''global'':32C,51C,98C ''got'':78C ''happen'':87C ''hard'':181C ''histogram'':56C ''interact'':135C ''item'':133C ''latent'':114C ''linear'':128C ''lot'':173C ''make'':54C,62C,101C ''matrix'':66C ''mean'':33C,41C,52C,99C ''mu'':71C ''non'':127C ''non-linear'':126C ''p'':1C,35C,137C,145C,155C ''part'':89C,149C,179C ''pattern'':8C,129C ''play'':90C ''plot'':40C ''press'':19C ''question'':142C ''quit'':171C ''r'':49C,67C,161C ''rate'':65C ''rest'':123C ''right'':174C ''rmse'':107C ''role'':93C,111C ''run'':80C ''say'':183C ''see'':85C ''simpli'':159C ''skew'':13C ''small'':152C ''solver'':82C ''sparsiti'':15C ''std'':43C,58C ''strong'':121C ''term'':23C ''think'':3C,109C,168C ''time'':166C ''two'':21C ''u'':50C,73C ''use'':160C ''user'':46C,131C ''uv'':162C ''v'':75C ''ve'':77C ''way'':60C ''would'':18C,100C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5395, 2, '2015-03-26 03:20:39.833', 0, NULL, NULL, '&lt;p&gt;Sure, as @LaruiK said. &lt;/p&gt;

&lt;p&gt;You''ve notice the factor which would change the similarity measure of the ''co-view'' data, a simple way it to integrate a time decay function into your model.&lt;/p&gt;
', NULL, NULL, '''/p'':6C,38C ''chang'':15C ''co'':22C ''co-view'':21C ''data'':24C ''decay'':33C ''factor'':12C ''function'':34C ''integr'':30C ''laruik'':4C ''measur'':18C ''model'':37C ''notic'':10C ''p'':1C,7C ''said'':5C ''similar'':17C ''simpl'':26C ''sure'':2C ''time'':32C ''ve'':9C ''view'':23C ''way'':27C ''would'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5396, 2, '2015-03-26 03:39:33.163', 1, NULL, NULL, '&lt;p&gt;Now you face the problem of scalability.&lt;/p&gt;

&lt;p&gt;Have you ever heard of random projection? Here is my suggestion:&lt;/p&gt;

&lt;p&gt;let''s say $S$ is your item-item relation matrix, its dimension is too large to decompose, now you can multiply an smaller matrix $U$(randomly generated) and get $R = SU$. And $R$ is small also.&lt;/p&gt;

&lt;p&gt;Then just do SVD of $R$, if $U$ is invertible, and the difference (l2-norm or any norm you like) between $R$ and $S$ is small, it might helps.&lt;/p&gt;

&lt;p&gt;As for the second issue:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;NaN value&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;If it''s dense, try to complete the matrix(using mean?).&lt;/p&gt;
', NULL, NULL, '''/blockquote'':106C ''/p'':9C,22C,61C,93C,100C,105C,119C ''also'':60C ''blockquot'':101C ''complet'':114C ''decompos'':41C ''dens'':111C ''differ'':75C ''dimens'':36C ''ever'':13C ''face'':4C ''generat'':51C ''get'':53C ''heard'':14C ''help'':92C ''invert'':72C ''issu'':99C ''item'':31C,32C ''item-item'':30C ''l2'':77C ''l2-norm'':76C ''larg'':39C ''let'':24C ''like'':83C ''matrix'':34C,48C,116C ''mean'':118C ''might'':91C ''multipli'':45C ''nan'':103C ''norm'':78C,81C ''p'':1C,10C,23C,62C,94C,102C,107C ''problem'':6C ''project'':17C ''r'':54C,57C,68C,85C ''random'':16C,50C ''relat'':33C ''say'':26C ''scalabl'':8C ''second'':98C ''small'':59C,89C ''smaller'':47C ''su'':55C ''suggest'':21C ''svd'':66C ''tri'':112C ''u'':49C,70C ''use'':117C ''valu'':104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5397, 2, '2015-03-26 12:44:44.903', 1, NULL, NULL, '&lt;p&gt;As I understand it, these are the properties that you''re seeking in a sample dataset:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Text data&lt;/li&gt;
&lt;li&gt;It should be informal, i.e. have typos, slang, and basically something not professionally edited &lt;/li&gt;
&lt;li&gt;Something other than Twitter (I don''t blame you, Twitter is a useful yet way overused example datasource in text mining)&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Here are some recommendations:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Emails from the &lt;a href="https://spamassassin.apache.org/publiccorpus/" rel="nofollow"&gt;SpamAssassin corpus&lt;/a&gt; -- note that both "ham" (non-spam) and spam datasets are available&lt;/li&gt;
&lt;li&gt;&lt;a href="http://archive.ics.uci.edu/ml/datasets/microblogPCU" rel="nofollow"&gt;microblogPCU data set&lt;/a&gt; from UCI, which is data scraped from the microblogs of Sina Weibo users -- note, the raw text data is a mix of Chinese and English (you could perform machine translation of the Chinese, filter to only English, or use it as-is)&lt;/li&gt;
&lt;li&gt;&lt;a href="https://archive.ics.uci.edu/ml/datasets/Amazon+Commerce+reviews+set" rel="nofollow"&gt;Amazon Commerce reviews&lt;/a&gt; dataset from UCI&lt;/li&gt;
&lt;li&gt;Within the &lt;a href="https://archive.ics.uci.edu/ml/datasets/Bag+of+Words" rel="nofollow"&gt;bag-o-words dataset&lt;/a&gt;, try using the Enron emails&lt;/li&gt;
&lt;li&gt;The &lt;a href="https://archive.ics.uci.edu/ml/datasets/Twenty+Newsgroups" rel="nofollow"&gt;Twenty Newsgroups&lt;/a&gt; dataset&lt;/li&gt;
&lt;li&gt;This nice collection of &lt;a href="https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection" rel="nofollow"&gt;SMS spam&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;You can always scrape (extract) your own text data from the Internet; I''m not sure which language or statistical package you''re using, but XPath-based packages are available in R (&lt;a href="https://github.com/hadley/rvest" rel="nofollow"&gt;&lt;code&gt;rvest&lt;/code&gt;&lt;/a&gt;, &lt;code&gt;scrapeR&lt;/code&gt;, etc) and Python to accomplish this&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':84C,109C,165C,185C,203C,220C,266C ''/code'':265C,269C ''/hadley/rvest'':260C ''/li'':23C,39C,62C,97C,153C,169C,191C,205C,221C,276C ''/ml/datasets/amazon+commerce+reviews+set'':159C ''/ml/datasets/bag+of+words'':177C ''/ml/datasets/microblogpcu'':103C ''/ml/datasets/sms+spam+collection'':215C ''/ml/datasets/twenty+newsgroups'':198C ''/ol'':63C,277C ''/p'':18C,69C ''/publiccorpus/'':79C ''accomplish'':274C ''alway'':225C ''amazon'':162C ''archive.ics.uci.edu'':102C,158C,176C,197C,214C ''archive.ics.uci.edu/ml/datasets/amazon+commerce+reviews+set'':157C ''archive.ics.uci.edu/ml/datasets/bag+of+words'':175C ''archive.ics.uci.edu/ml/datasets/microblogpcu'':101C ''archive.ics.uci.edu/ml/datasets/sms+spam+collection'':213C ''archive.ics.uci.edu/ml/datasets/twenty+newsgroups'':196C ''as-i'':150C ''avail'':96C,253C ''bag'':181C ''bag-o-word'':180C ''base'':250C ''basic'':34C ''blame'':48C ''chines'':132C,142C ''code'':263C,267C ''collect'':209C ''commerc'':163C ''corpus'':83C ''could'':136C ''data'':22C,107C,114C,127C,231C ''dataset'':17C,94C,166C,184C,204C ''datasourc'':58C ''edit'':38C ''email'':72C,190C ''english'':134C,146C ''enron'':189C ''etc'':270C ''exampl'':57C ''extract'':227C ''filter'':143C ''github.com'':259C ''github.com/hadley/rvest'':258C ''ham'':88C ''href'':76C,100C,156C,174C,195C,212C,257C ''i.e'':29C ''inform'':28C ''internet'':234C ''languag'':240C ''li'':20C,24C,40C,71C,98C,154C,170C,192C,206C,222C ''m'':236C ''machin'':138C ''microblog'':118C ''microblogpcu'':106C ''mine'':61C ''mix'':130C ''newsgroup'':202C ''nice'':208C ''nofollow'':81C,105C,161C,179C,200C,217C,262C ''non'':90C ''non-spam'':89C ''note'':85C,123C ''o'':182C ''ol'':19C,70C ''overus'':56C ''p'':1C,64C ''packag'':243C,251C ''perform'':137C ''profession'':37C ''properti'':9C ''python'':272C ''r'':255C ''raw'':125C ''re'':12C,245C ''recommend'':68C ''rel'':80C,104C,160C,178C,199C,216C,261C ''review'':164C ''rvest'':264C ''sampl'':16C ''scrape'':115C,226C ''scraper'':268C ''seek'':13C ''set'':108C ''sina'':120C ''slang'':32C ''sms'':218C ''someth'':35C,41C ''spam'':91C,93C,219C ''spamassassin'':82C ''spamassassin.apache.org'':78C ''spamassassin.apache.org/publiccorpus/'':77C ''statist'':242C ''sure'':238C ''text'':21C,60C,126C,230C ''translat'':139C ''tri'':186C ''twenti'':201C ''twitter'':44C,50C ''typo'':31C ''uci'':111C,168C ''understand'':4C ''use'':53C,148C,187C,246C ''user'':122C ''way'':55C ''weibo'':121C ''within'':171C ''word'':183C ''xpath'':249C ''xpath-bas'':248C ''yet'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5410, 2, '2015-03-27 15:41:42.213', 2, NULL, NULL, '&lt;p&gt;A couple of ways to improve your design:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Consider a different normalization: The sigmoid function will attenuate large moves.  It is likely precisely these large non-linear moves that attracted you to using neural networks in the first place.  Why remove them?  A simple &lt;code&gt;whitening&lt;/code&gt; of the data may be better&lt;/li&gt;
&lt;li&gt;As pointed out by Nima, your model can only predict what is knowable from the data you give it.  If you are only fitting on data using historical prices, it will only give you what is predictable from them.  Things such as news events / earnings surprises / holidays / option market flows will not be fed into your model.  Consider adding these series as well.&lt;/li&gt;
&lt;li&gt;Lots more historical data.  Neural networks often require very large sample sizes because they are trying to estimate a very large parameter space.  Although, more data doesn''t always imply more information, this will still likely help.&lt;/li&gt;
&lt;li&gt;Experiment with different network architectures.  The number of layers / size of the layers / different gradient decent algorithms / different activation functions / dropout etc..&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/code'':52C ''/li'':59C,124C,163C,187C ''/ol'':188C ''/p'':10C ''activ'':183C ''ad'':119C ''algorithm'':181C ''although'':149C ''alway'':154C ''architectur'':169C ''attenu'':21C ''attract'':35C ''better'':58C ''code'':50C ''consid'':13C,118C ''coupl'':3C ''data'':55C,76C,86C,129C,151C ''decent'':180C ''design'':9C ''differ'':15C,167C,178C,182C ''doesn'':152C ''dropout'':185C ''earn'':105C ''estim'':143C ''etc'':186C ''event'':104C ''experi'':165C ''fed'':114C ''first'':43C ''fit'':84C ''flow'':110C ''function'':19C,184C ''give'':78C,93C ''gradient'':179C ''help'':162C ''histor'':88C,128C ''holiday'':107C ''impli'':155C ''improv'':7C ''inform'':157C ''knowabl'':73C ''larg'':22C,29C,135C,146C ''layer'':173C,177C ''li'':12C,60C,125C,164C ''like'':26C,161C ''linear'':32C ''lot'':126C ''market'':109C ''may'':56C ''model'':67C,117C ''move'':23C,33C ''network'':40C,131C,168C ''neural'':39C,130C ''news'':103C ''nima'':65C ''non'':31C ''non-linear'':30C ''normal'':16C ''number'':171C ''often'':132C ''ol'':11C ''option'':108C ''p'':1C ''paramet'':147C ''place'':44C ''point'':62C ''precis'':27C ''predict'':70C,97C ''price'':89C ''remov'':46C ''requir'':133C ''sampl'':136C ''seri'':121C ''sigmoid'':18C ''simpl'':49C ''size'':137C,174C ''space'':148C ''still'':160C ''surpris'':106C ''thing'':100C ''tri'':141C ''use'':38C,87C ''way'':5C ''well'':123C ''whiten'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5399, 2, '2015-03-26 18:12:19.117', 2, NULL, NULL, '&lt;p&gt;Sorry for the short answer, but if you use shiny with rmarkdown, it can be done as shown in this question:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stackoverflow.com/questions/29253481/data-specific-selectinput-choices-in-rmd-shiny/29255723#29255723"&gt;http://stackoverflow.com/questions/29253481/data-specific-selectinput-choices-in-rmd-shiny/29255723#29255723&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Create a reactive function that reads the file in!&lt;/p&gt;
', NULL, NULL, '''/p'':23C,43C ''/questions/29253481/data-specific-selectinput-choices-in-rmd-shiny/29255723#29255723'':29C ''/questions/29253481/data-specific-selectinput-choices-in-rmd-shiny/29255723#29255723&lt;/a&gt;&lt;/p&gt;'':32C ''answer'':6C ''creat'':34C ''done'':17C ''file'':41C ''function'':37C ''href'':26C ''p'':1C,24C,33C ''question'':22C ''reactiv'':36C ''read'':39C ''rmarkdown'':13C ''shini'':11C ''short'':5C ''shown'':19C ''sorri'':2C ''stackoverflow.com'':28C,31C ''stackoverflow.com/questions/29253481/data-specific-selectinput-choices-in-rmd-shiny/29255723#29255723'':27C ''stackoverflow.com/questions/29253481/data-specific-selectinput-choices-in-rmd-shiny/29255723#29255723&lt;/a&gt;&lt;/p&gt;'':30C ''use'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5400, 2, '2015-03-26 20:00:57.663', 4, NULL, NULL, '&lt;p&gt;It depends on what data you are using to calculate similarity between your items.&lt;/p&gt;

&lt;p&gt;If you are using data from a user interaction with an item - like viewing a web page or buying an item - then each time there is a user interaction with an item, the similarity between it and other items will change. If you are doing this calculation offline, then your model won''t take that change into account until the next time the data set is updated and your recommendations are recalculated.&lt;/p&gt;

&lt;p&gt;However, if you are using item meta-data to calculate similarity, then user behavior won''t make any difference. For example, if you took the number of shared tags between two items, or the number of shared inbound links, etc. &lt;/p&gt;

&lt;p&gt;I have always used user browsing data to build content recommendation engines, but it is certainly possible to calculate similarity in many different ways. Have fun!&lt;/p&gt;
', NULL, NULL, '''/p'':16C,90C,133C,161C ''account'':75C ''alway'':137C ''behavior'':106C ''brows'':140C ''build'':143C ''buy'':36C ''calcul'':11C,64C,102C,153C ''certain'':150C ''chang'':58C,73C ''content'':144C ''data'':6C,22C,81C,100C,141C ''depend'':3C ''differ'':111C,157C ''engin'':146C ''etc'':132C ''exampl'':113C ''fun'':160C ''howev'':92C ''inbound'':130C ''interact'':26C,46C ''item'':15C,29C,38C,49C,56C,97C,124C ''like'':30C ''link'':131C ''make'':109C ''mani'':156C ''meta'':99C ''meta-data'':98C ''model'':68C ''next'':78C ''number'':118C,127C ''offlin'':65C ''p'':1C,17C,91C,134C ''page'':34C ''possibl'':151C ''recalcul'':89C ''recommend'':87C,145C ''set'':82C ''share'':120C,129C ''similar'':12C,51C,103C,154C ''tag'':121C ''take'':71C ''time'':41C,79C ''took'':116C ''two'':123C ''updat'':84C ''use'':9C,21C,96C,138C ''user'':25C,45C,105C,139C ''view'':31C ''way'':158C ''web'':33C ''won'':69C,107C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5401, 1, '2015-03-27 07:20:28.74', 0, 229, 'Which book is the best for introduction to analysis social network using python3', '&lt;p&gt;I am a beginner studying &lt;strong&gt;social network analysis&lt;/strong&gt;.
I installed python 3 just 2 weeks ago.
There are a lot of books for python and social network analysis.&lt;/p&gt;

&lt;p&gt;I couldn''t choose one of them.
I found one book named "Mining the Social Web (Analyzing Data from Facebook, Twitter, Linkedln, and Other Social Media Sites)" written by Matthhew A. Russell.
This book looks very interesting and fits in my purpose, but it is based on python 2. &lt;/p&gt;

&lt;p&gt;Is there any good books with &lt;strong&gt;python 3&lt;/strong&gt;? I usually use &lt;strong&gt;Twitter, Facebook, or Blog data&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;In addition, could you recommend any good book for &lt;strong&gt;nodeXL and UCINET?&lt;/strong&gt;  &lt;/p&gt;
', 'data-mining  python  social-network-analysis', NULL, '''/p'':53C,104C,126C,142C ''/strong'':32C,115C,125C,141C ''2'':38C,103C ''3'':36C,114C ''addit'':129C ''ago'':40C ''analysi'':9A,21B,31C,52C ''analyz'':71C ''base'':100C ''beginn'':26C ''best'':5A ''blog'':123C ''book'':2A,46C,65C,88C,110C,135C ''choos'':58C ''could'':130C ''couldn'':56C ''data'':15B,72C,124C ''data-min'':14B ''facebook'':74C,121C ''fit'':93C ''found'':63C ''good'':109C,134C ''instal'':34C ''interest'':91C ''introduct'':7A ''linkedln'':76C ''look'':89C ''lot'':44C ''matthhew'':84C ''media'':80C ''mine'':16B,67C ''name'':66C ''network'':11A,20B,30C,51C ''nodexl'':138C ''one'':59C,64C ''p'':22C,54C,105C,127C ''purpos'':96C ''python'':17B,35C,48C,102C,113C ''python3'':13A ''recommend'':132C ''russel'':86C ''site'':81C ''social'':10A,19B,29C,50C,69C,79C ''social-network-analysi'':18B ''strong'':28C,112C,119C,137C ''studi'':27C ''twitter'':75C,120C ''ucinet'':140C ''use'':12A,118C ''usual'':117C ''web'':70C ''week'':39C ''written'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5402, 2, '2015-03-27 08:00:09.137', 1, NULL, NULL, '&lt;p&gt;Russell''s book is fine. You might also like &lt;a href="http://shop.oreilly.com/product/0636920020424.do" rel="nofollow"&gt;Social Network Analysis for Startups&lt;/a&gt;. All the examples are in python. You can do all your analysis in that using packages like &lt;code&gt;networkx&lt;/code&gt;. NodeXL is for the Excel crowd. Definitely not the ideal tool for the job; I would shy away from it.&lt;/p&gt;

&lt;p&gt;The obvious book for NodeXL is &lt;a href="http://www.elsevier.com/books/analyzing-social-media-networks-with-nodexl/hansen/978-0-12-382229-1" rel="nofollow"&gt;Analyzing Social Media Networks with NodeXL&lt;/a&gt;, which is written by the authors of NodeXL.&lt;/p&gt;
', NULL, NULL, '''/a'':23C,85C ''/books/analyzing-social-media-networks-with-nodexl/hansen/978-0-12-382229-1'':76C ''/code'':43C ''/p'':64C,94C ''/product/0636920020424.do'':15C ''also'':9C ''analysi'':20C,35C ''analyz'':79C ''author'':91C ''away'':61C ''book'':4C,68C ''code'':41C ''crowd'':49C ''definit'':50C ''exampl'':26C ''excel'':48C ''fine'':6C ''href'':12C,73C ''ideal'':53C ''job'':57C ''like'':10C,40C ''media'':81C ''might'':8C ''network'':19C,82C ''networkx'':42C ''nodexl'':44C,70C,84C,93C ''nofollow'':17C,78C ''obvious'':67C ''p'':1C,65C ''packag'':39C ''python'':29C ''rel'':16C,77C ''russel'':2C ''shi'':60C ''shop.oreilly.com'':14C ''shop.oreilly.com/product/0636920020424.do'':13C ''social'':18C,80C ''startup'':22C ''tool'':54C ''use'':38C ''would'':59C ''written'':88C ''www.elsevier.com'':75C ''www.elsevier.com/books/analyzing-social-media-networks-with-nodexl/hansen/978-0-12-382229-1'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5403, 2, '2015-03-27 08:02:29.943', 1, NULL, NULL, '&lt;p&gt;What you are asking for is usually called &lt;strong&gt;basket analysis&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;I think you should get maximum value from the data you have by using both of them: user &amp;amp; items.&lt;/p&gt;

&lt;p&gt;What you said about "item-based" approach means something like recommendation based on item tags or categories. It isn''t a recommendation system in the full sense, because it uses &lt;strong&gt;your&lt;/strong&gt; categorization/tagging. In other words, you will never place diapers and beer into a single category, but it is still legendary buying pattern :). &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Item-based&lt;/strong&gt; data is used typically in basket analysis algorithms, such as frequent pattern mining. In couple words: you search for most frequent item sets (items bought together or coherently) and make suggestions based on them.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;User-based&lt;/strong&gt; data shouldn''t be ignored either. Clustering approach works here: you can find some groups of customers which buying attitude to item sets (found above) is different from the average.&lt;/p&gt;

&lt;p&gt;You can find more at statistics stack exchage site. 
Questions like &lt;strong&gt;&lt;a href="http://stats.stackexchange.com/questions/95351/what-is-the-best-way-to-analyze-additional-attributes-in-a-market-basket-analysi"&gt;this&lt;/a&gt;&lt;/strong&gt;.
And &lt;strong&gt;&lt;a href="http://stats.stackexchange.com/search?q=basket%20analysis"&gt;more&lt;/a&gt;&lt;/strong&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':192C,202C ''/p'':14C,36C,94C,135C,172C,204C ''/questions/95351/what-is-the-best-way-to-analyze-additional-attributes-in-a-market-basket-analysi'':190C ''/search?q=basket%20analysis'':200C ''/strong'':13C,71C,100C,141C,193C,203C ''algorithm'':108C ''amp'':34C ''analysi'':12C,107C ''approach'':45C,149C ''ask'':5C ''attitud'':161C ''averag'':171C ''base'':44C,50C,99C,132C,140C ''basket'':11C,106C ''beer'':82C ''bought'':125C ''buy'':92C,160C ''call'':9C ''categori'':55C,86C ''categorization/tagging'':72C ''cluster'':148C ''coher'':128C ''coupl'':115C ''custom'':158C ''data'':25C,101C,142C ''diaper'':80C ''differ'':168C ''either'':147C ''exchag'':181C ''find'':154C,176C ''found'':165C ''frequent'':111C,121C ''full'':64C ''get'':20C ''group'':156C ''href'':187C,197C ''ignor'':146C ''isn'':57C ''item'':35C,43C,52C,98C,122C,124C,163C ''item-bas'':42C,97C ''legendari'':91C ''like'':48C,184C ''make'':130C ''maximum'':21C ''mean'':46C ''mine'':113C ''never'':78C ''p'':1C,15C,37C,95C,136C,173C ''pattern'':93C,112C ''place'':79C ''question'':183C ''recommend'':49C,60C ''said'':40C ''search'':118C ''sens'':65C ''set'':123C,164C ''shouldn'':143C ''singl'':85C ''site'':182C ''someth'':47C ''stack'':180C ''statist'':179C ''stats.stackexchange.com'':189C,199C ''stats.stackexchange.com/questions/95351/what-is-the-best-way-to-analyze-additional-attributes-in-a-market-basket-analysi'':188C ''stats.stackexchange.com/search?q=basket%20analysis'':198C ''still'':90C ''strong'':10C,69C,96C,137C,185C,195C ''suggest'':131C ''system'':61C ''tag'':53C ''think'':17C ''togeth'':126C ''typic'':104C ''use'':29C,68C,103C ''user'':33C,139C ''user-bas'':138C ''usual'':8C ''valu'':22C ''word'':75C,116C ''work'':150C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8707, 1, '2015-11-04 07:34:21.203', 5, 236, 'Text-Classification-Problem: Is Word2Vec/NN the best approach?', '&lt;p&gt;I am looking to design a system that given a paragraph of text will be able to categorize it and identify the context:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Is trained with user generated text paragraphs (like comments/questions/answers)&lt;/li&gt;
&lt;li&gt;Each item in the training set will be tagged with . So for e.g. ("category 1", , "text paragraph")&lt;/li&gt;
&lt;li&gt;There will be hundreds of categories&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;What would be the best approach to build such a system? I have been looking at a few different options and the following is a list of possible solutions. Is Word2Vec/NN the best solution at the moment? &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Recursive Neural Tensor Network fed with averaged Word2Vec data &lt;/li&gt;
&lt;li&gt;RNTN and The Paragraph Vector (&lt;a href="https://cs.stanford.edu/~quocle/paragraph_vector.pdf"&gt;https://cs.stanford.edu/~quocle/paragraph_vector.pdf&lt;/a&gt;)?&lt;/li&gt;
&lt;li&gt;TF-IDF used in a Deep Belief Network&lt;/li&gt;
&lt;li&gt;TF-IDF and Logistic Regression&lt;/li&gt;
&lt;li&gt;Bag of words and Naive Bayes classification&lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5406, 1, '2015-03-27 11:22:16.703', 0, 39, 'need explanation about the 2 condition before applying logestic regression', '&lt;p&gt;I want to apply LR to a classification problem, the otto competition in kaggle. And I see a post talks about 2 conditions needed to be checked before applying LR.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;whether the regressors are normally distributed within the classes.&lt;/li&gt;
&lt;li&gt;whether the variance-covariance matrices are equal for all classes.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I have the data say X with 1 dimension represents label, and p dimension represent feature.&lt;/p&gt;

&lt;p&gt;Here is my understanding and confusion.
On condition 1, I can plot each regressor to see whether it is normal. But is there any easy way?&lt;/p&gt;

&lt;p&gt;On condition 2, I can compute the variance-covariance matrices, but why they should be equal? Is this caused by the assumption that we made that $X=(x_1,x_2,...,x_p)$ is draw from a multi-variate Gaussian(or multivariate normal) distribution, with a class-specific mean vector and a common covariance matrix?&lt;/p&gt;
', 'logistic-regression', NULL, '''/li'':57C,70C ''/ol'':71C ''/p'':45C,89C,117C,177C ''1'':80C,99C,148C ''2'':5A,36C,121C,150C ''appli'':8A,18C,43C ''assumpt'':141C ''caus'':138C ''check'':41C ''class'':56C,69C,168C ''class-specif'':167C ''classif'':22C ''common'':174C ''competit'':26C ''comput'':124C ''condit'':6A,37C,98C,120C ''confus'':96C ''covari'':63C,128C,175C ''data'':76C ''dimens'':81C,86C ''distribut'':53C,164C ''draw'':154C ''easi'':115C ''equal'':66C,135C ''explan'':2A ''featur'':88C ''gaussian'':160C ''kaggl'':28C ''label'':83C ''li'':47C,58C ''logest'':9A ''logist'':12B ''logistic-regress'':11B ''lr'':19C,44C ''made'':144C ''matric'':64C,129C ''matrix'':176C ''mean'':170C ''multi'':158C ''multi-vari'':157C ''multivari'':162C ''need'':1A,38C ''normal'':52C,110C,163C ''ol'':46C ''otto'':25C ''p'':14C,72C,85C,90C,118C,152C ''plot'':102C ''post'':33C ''problem'':23C ''regress'':10A,13B ''regressor'':50C,104C ''repres'':82C,87C ''say'':77C ''see'':31C,106C ''specif'':169C ''talk'':34C ''understand'':94C ''varianc'':62C,127C ''variance-covari'':61C,126C ''variat'':159C ''vector'':171C ''want'':16C ''way'':116C ''whether'':48C,59C,107C ''within'':54C ''x'':78C,146C,147C,149C,151C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5407, 2, '2015-03-27 11:22:23.797', 1, NULL, NULL, '&lt;p&gt;In my opinion, it seems that SSAS makes &lt;strong&gt;more sense&lt;/strong&gt; for someone who:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;has significantly invested in Microsoft''s &lt;em&gt;technology stack&lt;/em&gt; and &lt;em&gt;platform&lt;/em&gt;;&lt;/li&gt;
&lt;li&gt;prefer point-and-click &lt;em&gt;interface&lt;/em&gt; (GUI) to command line;&lt;/li&gt;
&lt;li&gt;focus on &lt;em&gt;data warehousing&lt;/em&gt; (OLAP cubes, etc.);&lt;/li&gt;
&lt;li&gt;has limited needs in terms of &lt;em&gt;statistical methods and algorithms variety&lt;/em&gt;;&lt;/li&gt;
&lt;li&gt;has limited needs in &lt;em&gt;cross-language integration&lt;/em&gt;;&lt;/li&gt;
&lt;li&gt;doesn''t care much about &lt;em&gt;openness&lt;/em&gt;, &lt;em&gt;cross-platform integration&lt;/em&gt; and &lt;em&gt;vendor lock-in&lt;/em&gt;.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You can find useful &lt;a href="http://blog.samibadawi.com/2010/04/r-rapidminer-statistica-ssas-or-weka.html" rel="nofollow"&gt;this blog post&lt;/a&gt; by &lt;a href="http://twitter.com/Sami_Badawi" rel="nofollow"&gt;Sami Badawi&lt;/a&gt;. However, note that the post is &lt;em&gt;not recent&lt;/em&gt;, so some information might be outdated. Plus, the post contains an &lt;em&gt;initial&lt;/em&gt; review, which might be not very accurate or comprehensive. If you''re thinking about data science, while considering staying within Microsoft ecosystem, I suggest you to take a look at Microsoft''s own machine learning platform Azure ML. &lt;a href="https://dbolab.com/microsoft-azure-ml-vs-ssas-mining-structures" rel="nofollow"&gt;This blog post&lt;/a&gt; presents a brief comparison of (early) Azure ML and SSAS.&lt;/p&gt;
', NULL, NULL, '''/2010/04/r-rapidminer-statistica-ssas-or-weka.html'':120C ''/a'':126C,137C,210C ''/em'':29C,33C,43C,55C,73C,85C,95C,101C,108C,147C,161C ''/li'':34C,48C,59C,74C,86C,109C ''/microsoft-azure-ml-vs-ssas-mining-structures'':204C ''/p'':17C,221C ''/sami_badawi'':132C ''/strong'':13C ''/ul'':110C ''accur'':168C ''algorithm'':71C ''azur'':198C,217C ''badawi'':136C ''blog'':124C,208C ''blog.samibadawi.com'':119C ''blog.samibadawi.com/2010/04/r-rapidminer-statistica-ssas-or-weka.html'':118C ''brief'':213C ''care'':90C ''click'':40C ''command'':46C ''comparison'':214C ''comprehens'':170C ''consid'':179C ''contain'':157C ''cross'':82C,98C ''cross-languag'':81C ''cross-platform'':97C ''cube'':57C ''data'':53C,176C ''dbolab.com'':203C ''dbolab.com/microsoft-azure-ml-vs-ssas-mining-structures'':202C ''doesn'':88C ''earli'':216C ''ecosystem'':183C ''em'':26C,31C,41C,52C,67C,80C,93C,96C,103C,144C,159C ''etc'':58C ''find'':114C ''focus'':50C ''gui'':44C ''howev'':138C ''href'':117C,129C,201C ''inform'':150C ''initi'':160C ''integr'':84C,100C ''interfac'':42C ''invest'':22C ''languag'':83C ''learn'':196C ''li'':19C,35C,49C,60C,75C,87C ''limit'':62C,77C ''line'':47C ''lock'':106C ''lock-in'':105C ''look'':190C ''machin'':195C ''make'':9C ''method'':69C ''microsoft'':24C,182C,192C ''might'':151C,164C ''ml'':199C,218C ''much'':91C ''need'':63C,78C ''nofollow'':122C,134C,206C ''note'':139C ''olap'':56C ''open'':94C ''opinion'':4C ''outdat'':153C ''p'':1C,111C ''platform'':32C,99C,197C ''plus'':154C ''point'':38C ''point-and-click'':37C ''post'':125C,142C,156C,209C ''prefer'':36C ''present'':211C ''re'':173C ''recent'':146C ''rel'':121C,133C,205C ''review'':162C ''sami'':135C ''scienc'':177C ''seem'':6C ''sens'':12C ''signific'':21C ''someon'':15C ''ssas'':8C,220C ''stack'':28C ''statist'':68C ''stay'':180C ''strong'':10C ''suggest'':185C ''take'':188C ''technolog'':27C ''term'':65C ''think'':174C ''twitter.com'':131C ''twitter.com/sami_badawi'':130C ''ul'':18C ''use'':115C ''varieti'':72C ''vendor'':104C ''wareh'':54C ''within'':181C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5408, 1, '2015-03-27 12:36:11.577', 2, 76, 'Clustering not producing even clusters', '&lt;p&gt;I''m using k-means clustering to processes running on machines. &lt;/p&gt;

&lt;p&gt;Dataset sample : &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;machine name, process
m1,java
m2,tomcat
m1,word
m3,excel
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Build a matrix of associated counts : &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;   java,tomcat,word,excel
m1,1,0,1,0
m2,0,1,0,0
m3,0,0,0,1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I then run k-means against this dataset (have tried Euclidean and Manhattan distance functions) 
The dataset is extremely sparse which I think is causing the generated clusters to not make much sense as many machines get grouped into the same cluster(as they are very similar) &lt;/p&gt;

&lt;p&gt;How to achieve clusters where each cluster contains approx equal number of points ? Or perhaps this is not possible due to the sparseness of the data and instead I should try to cluster on a different attributes of dataset ?&lt;/p&gt;
', 'clustering  k-means', NULL, '''/code'':41C,72C ''/p'':23C,27C,50C,123C,164C ''/pre'':42C,73C ''0'':59C,61C,63C,65C,66C,68C,69C,70C ''1'':58C,60C,64C,71C ''achiev'':127C ''approx'':133C ''associ'':48C ''attribut'':161C ''build'':44C ''caus'':100C ''cluster'':1A,5A,6B,17C,103C,117C,128C,131C,157C ''code'':29C,52C ''contain'':132C ''count'':49C ''data'':150C ''dataset'':25C,83C,92C,163C ''differ'':160C ''distanc'':89C ''due'':144C ''equal'':134C ''euclidean'':86C ''even'':4A ''excel'':40C,56C ''extrem'':94C ''function'':90C ''generat'':102C ''get'':112C ''group'':113C ''instead'':152C ''java'':34C,53C ''k'':8B,15C,79C ''k-mean'':7B,14C,78C ''m'':12C ''m1'':33C,37C,57C ''m2'':35C,62C ''m3'':39C,67C ''machin'':22C,30C,111C ''make'':106C ''manhattan'':88C ''mani'':110C ''matrix'':46C ''mean'':9B,16C,80C ''much'':107C ''name'':31C ''number'':135C ''p'':10C,24C,43C,74C,124C ''perhap'':139C ''point'':137C ''possibl'':143C ''pre'':28C,51C ''process'':19C,32C ''produc'':3A ''run'':20C,77C ''sampl'':26C ''sens'':108C ''similar'':122C ''spars'':95C,147C ''think'':98C ''tomcat'':36C,54C ''tri'':85C,155C ''use'':13C ''word'':38C,55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6291, 2, '2015-07-01 01:45:12.26', 1, NULL, NULL, '&lt;p&gt;Why not use the same statistics to denormalize the results? If the test data is good enough to train a neural network then why would its statistics not be good enough to denormalize the results?&lt;/p&gt;

&lt;p&gt;However, I would suggest not using min and max as the scale factors. The min and max can be very sensitive. I suggest using &lt;a href="https://en.wikipedia.org/wiki/Robust_statistics" rel="nofollow"&gt;robust estimates&lt;/a&gt; of the mean and standard deviation to normalize. &lt;/p&gt;
', NULL, NULL, '''/a'':72C ''/p'':37C,81C ''/wiki/robust_statistics'':67C ''data'':15C ''denorm'':9C,34C ''deviat'':78C ''en.wikipedia.org'':66C ''en.wikipedia.org/wiki/robust_statistics'':65C ''enough'':18C,32C ''estim'':71C ''factor'':51C ''good'':17C,31C ''howev'':39C ''href'':64C ''max'':47C,55C ''mean'':75C ''min'':45C,53C ''network'':23C ''neural'':22C ''nofollow'':69C ''normal'':80C ''p'':1C,38C ''rel'':68C ''result'':11C,36C ''robust'':70C ''scale'':50C ''sensit'':59C ''standard'':77C ''statist'':7C,28C ''suggest'':42C,61C ''test'':14C ''train'':20C ''use'':4C,44C,62C ''would'':26C,41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5409, 1, '2015-03-27 15:31:49.09', 1, 46, 'Huge discrepancies in Logistic Regression and SVM using HOG features to identify an Object', '&lt;p&gt;I am doing some research on Logistic regression and SVM using different parameters using HOG features. I am facing a bit of problem while understanding each classifier with combination of different parameters and different HOG features.&lt;/p&gt;

&lt;p&gt;My findings and confusions are given below, &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;For Hog:    orientations=18, pixelsPerCell=(6,6), cellsPerBlock=(1,1)
Classifier: SVC(C=1000.0,  gamma=0.1, kernel=''rbf''),
Output:     Total dataset=216, Correct prediction=210,Wrong Prediction=6
Classifier: SVC(C=100.0,  gamma=0.1, kernel=''rbf'')
Output:     Total dataset=216, Correct prediction=210,Wrong Prediction=6
Classifier: SVC(C=1.0,  gamma=0.1, kernel=''rbf'')
Output:     Total dataset=216, Correct prediction=209,Wrong Prediction=7
    , 

For Hog:    orientations=9, pixelsPerCell=(6,6), cellsPerBlock=(1,1)
Classifier: SVC(C=1000.0,  gamma=0.1, kernel=''rbf''),
Output:     Total dataset=216, Correct prediction=211,Wrong Prediction=5
Classifier: SVC(C=100.0,  gamma=0.1, kernel=''rbf'')
Output:     Total dataset=216, Correct prediction=211,Wrong Prediction=5
Classifier: SVC(C=1.0,  gamma=0.1, kernel=''rbf'')
Output:     Total dataset=216, Correct prediction=210,Wrong Prediction=6            

-------------------------------------------------------------------------            

For Hog:    orientations=9, pixelsPerCell=(9,9), cellsPerBlock=(3,3)
Classifier: SVC(C=1000.0,  gamma=1, kernel=''rbf''),
Output:     Total dataset=216, Correct prediction=212,Wrong Prediction=4
Classifier: SVC(C=100.0,  gamma=1, kernel=''rbf'')
Output:     Total dataset=216, Correct prediction=212,Wrong Prediction=4
Classifier: SVC(C=1.0,  gamma=1, kernel=''rbf'')
Output:     Total dataset=216, Correct prediction=213,Wrong Prediction=3



For Hog:    orientations=9, pixelsPerCell=(6,6), cellsPerBlock=(3,3)
Classifier: SVC(C=1000.0,  gamma=1, kernel=''rbf''),
Output:     Total dataset=216, Correct prediction=212,Wrong Prediction=4
Classifier: SVC(C=100.0,  gamma=1, kernel=''rbf'')
Output:     Total dataset=216, Correct prediction=210,Wrong Prediction=6
Classifier: SVC(C=1.0,  gamma=1, kernel=''rbf'')
Output:     Total dataset=216, Correct prediction=212,Wrong Prediction=4


For Hog:    orientations=18, pixelsPerCell=(9,9), cellsPerBlock=(3,3)
Classifier: SVC(C=1000.0,  gamma=1, kernel=''rbf''),
Output:     Total dataset=216, Correct prediction=212,Wrong Prediction=4
Classifier: SVC(C=100.0,  gamma=1, kernel=''rbf'')
Output:     Total dataset=216, Correct prediction=212,Wrong Prediction=4
Classifier: SVC(C=1.0,  gamma=1, kernel=''rbf'')
Output:     Total dataset=216, Correct prediction=212,Wrong Prediction=3


For Hog:    orientations=18, pixelsPerCell=(6,6), cellsPerBlock=(3,3)
Classifier: SVC(C=1000.0,  gamma=1, kernel=''rbf''),
Output:     Total dataset=216, Correct prediction=212,Wrong Prediction=4
Classifier: SVC(C=100.0,  gamma=1, kernel=''rbf'')
Output:     Total dataset=216, Correct prediction=212,Wrong Prediction=4
Classifier: SVC(C=1.0,  gamma=1, kernel=''rbf'')
Output:     Total dataset=216, Correct prediction=212,Wrong Prediction=4
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For the above results we can conclude that "C" of SVM and orientation of HOG doesn''t matter much for the combinations, so we do not have the issue of highbias or variance and bin size. What matters is the value of gamma and combination of HOG with "cellsPerBlock".&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;From my analysis, I found
1. Using "rbf" SVM with HOG "cellsPerBlock=(1,1)" doesn''t give proper    outcome.
2. The "rbf" SVM with HOG "cellsPerBlock=(1,1)" works best with gamma=0.1
3. The "rbf" SVM with HOG "cellsPerBlock=(3,3)" and different combination of "pixelspercells" works best with gamma=1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''ve also tried pixelspercell = (3,3) but all the different combinations of "rbf" kernels fails to classify properly.&lt;/p&gt;

&lt;p&gt;"" The problem I face is understanding this discrepancy""&lt;/p&gt;

&lt;p&gt;My Confusion even eggravates when I use Logistic regression for the Combinations of HOG surprisingly Regularized Logistic Regression works better with cellsPerBlock=(1,1)&lt;/p&gt;

&lt;p&gt;Below are my findings with 2nd order Regularized Logistic Regression: I used 0.9 as my threshold level, i.e below 0.9 would be classifies as 0 and above 0.9 would be classified as 1&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Q:   Why did I put 0.9 as threshold- 
Sol: Because the test data that had the class(labels)=1, 99% of them output probability &amp;gt;0.9
     However the test data that had the class(labels)=0, many instances outputprobability &amp;gt;0.5
   So inorder to filter out many test data instances that gave probability &amp;gt;0.5 A threshold of 0.9 was used.

1.  orientations=9,    
pixelsPerCell=(6,6)
cellsPerBlock=(3,3)
Output:     Total dataset=216, Correct prediction=187, Wrong Prediction=29

2.  orientations=9,    
pixelsPerCell=(9,9)
    cellsPerBlock=(3,3)
    Output:     Total dataset=216, Correct prediction=202, Wrong Prediction=14

3.  orientations=9,    
pixelsPerCell=(9,9)
    cellsPerBlock=(1,1)
    Output:     Total dataset=216, Correct prediction=211, Wrong Prediction=5

4.  orientations=9,    
pixelsPerCell=(6,6)
    cellsPerBlock=(1,1)
    Output:     Total dataset=216, Correct prediction=206, Wrong Prediction=10

5.  orientations=9,    
pixelsPerCell=(3,3)
    cellsPerBlock=(1,1)
    Output:     Total dataset=216, Correct prediction=203, Wrong Prediction=13
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5412, 1, '2015-03-28 03:18:48.543', 1, 56, 'parsing data by NLP using just offsets of words', '&lt;p&gt;E.g 1: "The speed of the car is 35 km/h and speed limit is 50 km/h" &lt;/p&gt;

&lt;p&gt;E.g 2: "The car is crossing the current speed limit by driving at 90 km/h".&lt;/p&gt;

&lt;p&gt;E.g.3: "Driving at 50 km/h your car will hit the speed limit."  &lt;/p&gt;

&lt;p&gt;I very new in neural networks and in NLP too. Good in perl and in writing parsers for web pages. NLP is asking me my code to learn english and word meanings. I wish to try differently.&lt;/p&gt;

&lt;p&gt;I want to train my first neural network classification algo using offsets to words. I can train that to parse speed: in e.g1 the 35 is true and 50 is false. That in e.g2 90 is true. and e.g3 50 is false.
Suppose I have a collection of lot of such sentences, so I can use my offset based training for my algorithm. There will be very confusing sentences too.&lt;/p&gt;

&lt;p&gt;The way my algo would work is to use offets of all words which are near the text which is to be classified. In sentence one it would output 11 parameters, is = -1, km/h=+1, car=-2, and=+2,the=-3,speed=+3,of=-4,limit=+4,speed=-5,is=+5,The=-6,km/h=+6. Same thing would be done for all sentences. the algo will train with these offsets and the result. There are too many words and they all will become parameters.&lt;/p&gt;

&lt;p&gt;Can you please suggest how durable is my proposed learning algorithm to parse out actual car driven speed from sentences? &lt;/p&gt;
', 'machine-learning  nlp  neuralnetwork  parsing', NULL, '''+1'':211C ''+2'':215C ''+3'':219C ''+4'':223C ''+5'':227C ''+6'':231C ''-1'':209C ''-2'':213C ''-3'':217C ''-4'':221C ''-5'':225C ''-6'':229C ''/p'':33C,50C,64C,103C,175C,261C,283C ''1'':18C ''11'':206C ''2'':36C ''35'':25C,129C ''50'':31C,55C,133C,144C ''90'':48C,139C ''actual'':277C ''algo'':114C,180C,241C ''algorithm'':167C,273C ''ask'':88C ''base'':163C ''becom'':259C ''car'':23C,38C,58C,212C,278C ''classif'':113C ''classifi'':199C ''code'':91C ''collect'':151C ''confus'':172C ''cross'':40C ''current'':42C ''data'':2A ''differ'':102C ''done'':236C ''drive'':46C,53C ''driven'':279C ''durabl'':268C ''e.g'':17C,35C ''e.g.3'':52C ''e.g1'':127C ''e.g2'':138C ''e.g3'':143C ''english'':94C ''fals'':135C,146C ''first'':110C ''good'':76C ''hit'':60C ''km/h'':26C,32C,49C,56C,210C,230C ''learn'':12B,93C,272C ''limit'':29C,44C,63C,222C ''lot'':153C ''machin'':11B ''machine-learn'':10B ''mani'':253C ''mean'':97C ''near'':192C ''network'':71C,112C ''neural'':70C,111C ''neuralnetwork'':14B ''new'':68C ''nlp'':4A,13B,74C,86C ''offet'':186C ''offset'':7A,116C,162C,246C ''one'':202C ''output'':205C ''p'':16C,34C,51C,65C,104C,176C,262C ''page'':85C ''paramet'':207C,260C ''pars'':1A,15B,124C,275C ''parser'':82C ''perl'':78C ''pleas'':265C ''propos'':271C ''result'':249C ''sentenc'':156C,173C,201C,239C,282C ''speed'':20C,28C,43C,62C,125C,218C,224C,280C ''suggest'':266C ''suppos'':147C ''text'':194C ''thing'':233C ''train'':108C,121C,164C,243C ''tri'':101C ''true'':131C,141C ''use'':5A,115C,160C,185C ''want'':106C ''way'':178C ''web'':84C ''wish'':99C ''word'':9A,96C,118C,189C,254C ''work'':182C ''would'':181C,204C,234C ''write'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5416, 1, '2015-03-30 04:07:36.377', 0, 131, 'Difference between Spark MLlib kmeans &amp; Spark MLlib StreamingKmean implementation? Is it possible to implement Streaming Naive Bayes easily?', '&lt;p&gt;I am new to Spark and MLlib. I was looking at the Kmeans (&lt;a href="http://spark.apache.org/docs/latest/mllib-clustering.html#k-means" rel="nofollow"&gt;http://spark.apache.org/docs/latest/mllib-clustering.html#k-means&lt;/a&gt;) and StreamingKmeans implementation in Spark MLlib (&lt;a href="http://spark.apache.org/docs/latest/mllib-clustering.html#streaming-k-means" rel="nofollow"&gt;http://spark.apache.org/docs/latest/mllib-clustering.html#streaming-k-means&lt;/a&gt;). What I am confused about is, is StreamingKmeans derived from Kmeans implementation in MLlib? Is it possible to write similar code for Naive Bayes (&lt;a href="http://spark.apache.org/docs/latest/mllib-naive-bayes.html" rel="nofollow"&gt;http://spark.apache.org/docs/latest/mllib-naive-bayes.html&lt;/a&gt;) if I want to convert Naive Bayes to Streaming. If so, how can i achieve this? Any pointers and help will be really appreciated.&lt;/p&gt;
', 'machine-learning  classification  data-stream-mining  apache-spark', NULL, '''/docs/latest/mllib-clustering.html#k-means'':48C ''/docs/latest/mllib-clustering.html#k-means&lt;/a&gt;)'':53C ''/docs/latest/mllib-clustering.html#streaming-k-means'':64C ''/docs/latest/mllib-clustering.html#streaming-k-means&lt;/a&gt;).'':69C ''/docs/latest/mllib-naive-bayes.html'':98C ''/docs/latest/mllib-naive-bayes.html&lt;/a&gt;)'':103C ''/p'':128C ''achiev'':118C ''apach'':28B ''apache-spark'':27B ''appreci'':127C ''bay'':17A,93C,110C ''classif'':22B ''code'':90C ''confus'':73C ''convert'':108C ''data'':24B ''data-stream-min'':23B ''deriv'':78C ''differ'':1A ''easili'':18A ''help'':123C ''href'':45C,61C,95C ''implement'':9A,14A,56C,81C ''kmean'':5A,43C,80C ''learn'':21B ''look'':40C ''machin'':20B ''machine-learn'':19B ''mine'':26B ''mllib'':4A,7A,37C,59C,83C ''naiv'':16A,92C,109C ''new'':33C ''nofollow'':50C,66C,100C ''p'':30C ''pointer'':121C ''possibl'':12A,86C ''realli'':126C ''rel'':49C,65C,99C ''similar'':89C ''spark'':3A,6A,29B,35C,58C ''spark.apache.org'':47C,52C,63C,68C,97C,102C ''spark.apache.org/docs/latest/mllib-clustering.html#k-means'':46C ''spark.apache.org/docs/latest/mllib-clustering.html#k-means&lt;/a&gt;)'':51C ''spark.apache.org/docs/latest/mllib-clustering.html#streaming-k-means'':62C ''spark.apache.org/docs/latest/mllib-clustering.html#streaming-k-means&lt;/a&gt;).'':67C ''spark.apache.org/docs/latest/mllib-naive-bayes.html'':96C ''spark.apache.org/docs/latest/mllib-naive-bayes.html&lt;/a&gt;)'':101C ''stream'':15A,25B,112C ''streamingkmean'':8A,55C,77C ''want'':106C ''write'':88C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5417, 1, '2015-03-30 04:35:58.667', 3, 706, 'Choosing between Storm+Trident-ML, Storm+SAMOA or Spark Streaming+MLlib', '&lt;p&gt;I want to implement Streaming Naive Bayes in a distributed system. What are the best approach to choose framework. Should I choose:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Storm alone and implement streaming naive bayes on my own in storm topology.&lt;/li&gt;
&lt;li&gt;Storm + TridentML&lt;/li&gt;
&lt;li&gt;Storm + SAMOA&lt;/li&gt;
&lt;li&gt;Spark Streaming + MLlib&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;What is the best framework set to choose and start working on. Any suggestion will be of great help.&lt;/p&gt;
', 'machine-learning  classification  distributed  data-stream-mining  apache-spark', NULL, '''/li'':64C,68C,72C,77C ''/ol'':78C ''/p'':48C,99C ''alon'':52C ''apach'':23B ''apache-spark'':22B ''approach'':41C ''bay'':32C,57C ''best'':40C,83C ''choos'':1A,43C,47C,87C ''classif'':16B ''data'':19B ''data-stream-min'':18B ''distribut'':17B,35C ''framework'':44C,84C ''great'':97C ''help'':98C ''implement'':29C,54C ''learn'':15B ''li'':50C,65C,69C,73C ''machin'':14B ''machine-learn'':13B ''mine'':21B ''ml'':6A ''mllib'':12A,76C ''naiv'':31C,56C ''ol'':49C ''p'':25C,79C ''samoa'':8A,71C ''set'':85C ''spark'':10A,24B,74C ''start'':89C ''storm'':3A,7A,51C,62C,66C,70C ''stream'':11A,20B,30C,55C,75C ''suggest'':93C ''system'':36C ''topolog'':63C ''trident'':5A ''trident-ml'':4A ''tridentml'':67C ''want'':27C ''work'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5418, 2, '2015-03-30 04:46:43.637', 1, NULL, NULL, '&lt;p&gt;It means the average predictions of the user across all items, and the average of each item across all users accurately predicts the ratings -- you have an easy data set.&lt;/p&gt;
', NULL, NULL, '''/p'':32C ''accur'':22C ''across'':10C,19C ''averag'':5C,15C ''data'':30C ''easi'':29C ''item'':12C,18C ''mean'':3C ''p'':1C ''predict'':6C,23C ''rate'':25C ''set'':31C ''user'':9C,21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5419, 2, '2015-03-30 14:10:48.077', 1, NULL, NULL, '&lt;p&gt;I can''t comment due to reputation, but you really need to tell us what version of SQL Server you are running, maybe some more information about how the data is structured and how you''re pulling the data into these dashboard. Maybe even how long it''s taking and what resources you have available that actually know what they are doing.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;OLTP&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;That said, it sounds like you have a OLTP database with lots of tables. As I don''t know the relationships of these tables or how you are pulling the data from these tables, I can only assume you are pulling data from many of these tables. If optimizing the tables is not helping due to how many joins and records you are pulling, then:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;SQL Server Analysis Services&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;It sounds like you need to create a multidimensional database that can be used for reporting. SQL Server Analysis Services helps you do that by allowing you to define OLAP Cubes in many different structures from MOLAP to ROLAP.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Multidimensional Database (Data Warehousing)&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Another way is creating a new database that is going to be the foundation for your multidimensional data. Therefore, you would need to create a complex ETL System within SQL Server that converts those 400 tables into facts and dimensions automatically on a daily basis and pushes it into your new database. This is a similar process to what SSAS is going to do for you when you define cubes in SQL Server.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Preaggregate Tables or Views&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;If you can''t do that yourself, then another way is just building new tables in your database that are just preaggregates of the 400 tables that will be used for reporting. Basically established how you are reading the data for your dashboards and find ways to preaggregate that data into fewer tables before you actually pull it into a report.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Automation &amp;amp; Tools&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This (as well building multidimensional data) is accomplished simply by creating stored procedures or SSIS packages and automating the process every day. Then Tableau, SSRS or whatever queries the new tables rather than the previous 400 tables that may be slowing the process.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Hire Someone, You Already Have The Tools&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The last and final way is finding a tool that does the ETL for you. There are plenty of ETL vendors out there that can possibly address this problem. But keep in mind, you likely have all the tools you need to do this. You just need to hire the talent to do it either temporarily on contract or full-time.&lt;/p&gt;

&lt;p&gt;If I had no knowledge of what I was doing in SQL, I would contract a ETL Developer, SQL Developer or BI Developer to help me. Because why buy another tool box when you already have a good tool box available to you?&lt;/p&gt;
', NULL, NULL, '''/p'':64C,69C,136C,144C,185C,193C,268C,276C,339C,346C,391C,402C,466C,511C ''/strong'':68C,143C,192C,275C,345C,401C ''400'':229C,302C,383C ''accomplish'':355C ''actual'':58C,333C ''address'':430C ''allow'':171C ''alreadi'':397C,502C ''amp'':343C ''analysi'':141C,164C ''anoth'':195C,286C,497C ''assum'':108C ''autom'':342C,365C ''automat'':235C ''avail'':56C,508C ''basi'':239C ''basic'':310C ''bi'':489C ''box'':499C,507C ''build'':290C,351C ''buy'':496C ''comment'':5C ''complex'':220C ''contract'':461C,482C ''convert'':227C ''creat'':152C,198C,218C,358C ''cube'':176C,264C ''daili'':238C ''dashboard'':43C,320C ''data'':31C,40C,101C,112C,190C,212C,317C,327C,353C ''databas'':80C,155C,189C,201C,246C,295C ''day'':369C ''defin'':174C,263C ''develop'':485C,487C,490C ''differ'':179C ''dimens'':234C ''due'':6C,125C ''either'':458C ''establish'':311C ''etl'':221C,416C,423C,484C ''even'':45C ''everi'':368C ''fact'':232C ''fewer'':329C ''final'':407C ''find'':322C,410C ''foundat'':208C ''full'':464C ''full-tim'':463C ''go'':204C,256C ''good'':505C ''help'':124C,166C,492C ''hire'':394C,452C ''inform'':27C ''join'':129C ''keep'':434C ''know'':59C,89C ''knowledg'':472C ''last'':405C ''like'':75C,148C,438C ''long'':47C ''lot'':82C ''mani'':114C,128C,178C ''may'':386C ''mayb'':24C,44C ''mind'':436C ''molap'':182C ''multidimension'':154C,188C,211C,352C ''need'':12C,150C,216C,444C,450C ''new'':200C,245C,291C,377C ''olap'':175C ''oltp'':67C,79C ''optim'':119C ''p'':1C,65C,70C,137C,145C,186C,194C,269C,277C,340C,347C,392C,403C,467C ''packag'':363C ''plenti'':421C ''possibl'':429C ''preaggreg'':271C,299C,325C ''previous'':382C ''problem'':432C ''procedur'':360C ''process'':251C,367C,390C ''pull'':38C,99C,111C,134C,334C ''push'':241C ''queri'':375C ''rather'':379C ''re'':37C ''read'':315C ''realli'':11C ''record'':131C ''relationship'':91C ''report'':161C,309C,338C ''reput'':8C ''resourc'':53C ''rolap'':184C ''run'':23C ''said'':72C ''server'':20C,140C,163C,225C,267C ''servic'':142C,165C ''similar'':250C ''simpli'':356C ''slow'':388C ''someon'':395C ''sound'':74C,147C ''sql'':19C,139C,162C,224C,266C,479C,486C ''ssas'':254C ''ssis'':362C ''ssrs'':372C ''store'':359C ''strong'':66C,138C,187C,270C,341C,393C ''structur'':33C,180C ''system'':222C ''tabl'':84C,94C,104C,117C,121C,230C,272C,292C,303C,330C,378C,384C ''tableau'':371C ''take'':50C ''talent'':454C ''tell'':14C ''temporarili'':459C ''therefor'':213C ''time'':465C ''tool'':344C,400C,412C,442C,498C,506C ''us'':15C ''use'':159C,307C ''vendor'':424C ''version'':17C ''view'':274C ''wareh'':191C ''way'':196C,287C,323C,408C ''well'':350C ''whatev'':374C ''within'':223C ''would'':215C,481C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5420, 1, '2015-03-30 15:31:17.133', 0, 68, 'Http Server Using Torch7', '&lt;p&gt;I''m starting to learn Torch7 to get into the machine learning/ deep learning field and I''m finding it fascinating (and very complicated haha). My main concern, however, is if I can turn this learning into an application - mainly can I turn my Torch7 Lua scripts into a server that an app can use to perform machine learning calculations? And if it''s possible, how?&lt;/p&gt;

&lt;p&gt;Thank you&lt;/p&gt;
', 'neuralnetwork  deep-learning', NULL, '''/p'':76C,80C ''app'':62C ''applic'':48C ''calcul'':69C ''complic'':33C ''concern'':37C ''deep'':7B,22C ''deep-learn'':6B ''fascin'':30C ''field'':24C ''find'':28C ''get'':17C ''haha'':34C ''howev'':38C ''http'':1A ''learn'':8B,14C,21C,23C,45C,68C ''lua'':55C ''m'':11C,27C ''machin'':20C,67C ''main'':36C,49C ''neuralnetwork'':5B ''p'':9C,77C ''perform'':66C ''possibl'':74C ''script'':56C ''server'':2A,59C ''start'':12C ''thank'':78C ''torch7'':4A,15C,54C ''turn'':43C,52C ''use'':3A,64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9691, 2, '2016-01-07 21:47:52.183', 1, NULL, NULL, '&lt;p&gt;For a testing package similar to Hypothesis and based on Haskell''s quickcheck, there''s the R package from Revolution Analytics called &lt;a href="https://github.com/RevolutionAnalytics/quickcheck" rel="nofollow"&gt;quickcheck&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':32C ''/p'':33C ''/revolutionanalytics/quickcheck'':28C ''analyt'':22C ''base'':10C ''call'':23C ''github.com'':27C ''github.com/revolutionanalytics/quickcheck'':26C ''haskel'':12C ''href'':25C ''hypothesi'':8C ''nofollow'':30C ''p'':1C ''packag'':5C,19C ''quickcheck'':14C,31C ''r'':18C ''rel'':29C ''revolut'':21C ''similar'':6C ''test'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4827, 1, '2015-01-07 16:50:18.803', 0, 147, 'Can we use package "dplyr" on R base 3.0.2?', '&lt;p&gt;Is there any way to use package "dplyr" on RStudio having R base 3.0.2 ?  I am not interested  in "plyr" package.&lt;/p&gt;

&lt;p&gt;Thanks
Navin&lt;/p&gt;
', 'data-mining', NULL, '''/p'':35C,39C ''3.0.2'':9A,27C ''base'':8A,26C ''data'':11B ''data-min'':10B ''dplyr'':5A,21C ''interest'':31C ''mine'':12B ''navin'':38C ''p'':13C,36C ''packag'':4A,20C,34C ''plyr'':33C ''r'':7A,25C ''rstudio'':23C ''thank'':37C ''use'':3A,19C ''way'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5435, 1, '2015-04-02 20:17:19.077', 0, 49, 'How does the algorithm with the complexity of O(N**3) work?', '&lt;p&gt;I have watched an explanation of Big O Notations from this eloquent YouTube video: &lt;a href="https://www.youtube.com/watch?v=V6mKVRU1evU" rel="nofollow"&gt;https://www.youtube.com/watch?v=V6mKVRU1evU&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;However, he does not mention algorithms with two asterisk signs. How does the algorithm with the complexity of &lt;code&gt;O(N**3)&lt;/code&gt; work?&lt;/p&gt;
', 'algorithms  notation', NULL, '''/code'':63C ''/p'':65C ''/watch?v=v6mkvru1evu'':34C ''/watch?v=v6mkvru1evu&lt;/a&gt;&lt;/p&gt;'':39C ''3'':11A,62C ''algorithm'':4A,13B,46C,54C ''asterisk'':49C ''big'':22C ''code'':59C ''complex'':7A,57C ''eloqu'':27C ''explan'':20C ''howev'':41C ''href'':31C ''mention'':45C ''n'':10A,61C ''nofollow'':36C ''notat'':14B,24C ''o'':9A,23C,60C ''p'':15C,40C ''rel'':35C ''sign'':50C ''two'':48C ''video'':29C ''watch'':18C ''work'':12A,64C ''www.youtube.com'':33C,38C ''www.youtube.com/watch?v=v6mkvru1evu'':32C ''www.youtube.com/watch?v=v6mkvru1evu&lt;/a&gt;&lt;/p&gt;'':37C ''youtub'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5424, 1, '2015-03-31 16:26:21.11', 0, 47, 'Resampling the Validation Sample', '&lt;p&gt;I have a large data set where training is quite (time) costly but whose result is rather volatile. I split the original data into a training / validation / test sample and I am considering getting a notion for the volatility by re-sampling the validation sample:&lt;/p&gt;

&lt;p&gt;Bootstrap sample the validation sample &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;For each bootstrap sample,
score the data with the model created from the training data &lt;/li&gt;
&lt;li&gt;Plot / average the performance measures from each bootstrap sample.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Of course, bootstrapping the training data, building a new model on each sample and scoring the out of sample data with each is the original bootstrap idea, but requires retraining a model many times. &lt;/p&gt;

&lt;p&gt;Has anyone used the procedure I mention and/ or find serious issue with it?  &lt;/p&gt;
', 'machine-learning  predictive-modeling  cross-validation  evaluation', NULL, '''/li'':87C,98C ''/p'':62C,69C,135C,151C ''/ul'':99C ''anyon'':138C ''averag'':90C ''bootstrap'':64C,74C,96C,103C,126C ''build'':107C ''consid'':48C ''cost'':27C ''cours'':102C ''creat'':82C ''cross'':12B ''cross-valid'':11B ''data'':20C,38C,78C,86C,106C,120C ''evalu'':14B ''find'':146C ''get'':49C ''idea'':127C ''issu'':148C ''larg'':19C ''learn'':7B ''li'':71C,88C ''machin'':6B ''machine-learn'':5B ''mani'':133C ''measur'':93C ''mention'':143C ''model'':10B,81C,110C,132C ''new'':109C ''notion'':51C ''origin'':37C,125C ''p'':15C,63C,100C,136C ''perform'':92C ''plot'':89C ''predict'':9B ''predictive-model'':8B ''procedur'':141C ''quit'':25C ''rather'':32C ''re'':57C ''re-sampl'':56C ''requir'':129C ''resampl'':1A ''result'':30C ''retrain'':130C ''sampl'':4A,44C,58C,61C,65C,68C,75C,97C,113C,119C ''score'':76C,115C ''serious'':147C ''set'':21C ''split'':35C ''test'':43C ''time'':26C,134C ''train'':23C,41C,85C,105C ''ul'':70C ''use'':139C ''valid'':3A,13B,42C,60C,67C ''volatil'':33C,54C ''whose'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5431, 2, '2015-04-02 10:50:12.953', 0, NULL, NULL, '&lt;p&gt;Problem is resolved. I created the folders in terminal.&lt;/p&gt;

&lt;p&gt;6b.  hdfs dfs -mkdir /user
hdfs dfs -mkdir /user/hduser&lt;/p&gt;

&lt;ol start="7"&gt;
&lt;li&gt;Put this file to HDFS:
hadoop fs -put file1.txt /user/hduser/file1.txt
hadoop fs -put file.txt file.txt&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':42C ''/ol'':43C ''/p'':11C,22C ''/user'':17C ''/user/hduser'':21C ''/user/hduser/file1.txt'':36C ''6b'':13C ''7'':25C ''creat'':6C ''dfs'':15C,19C ''file'':29C ''file.txt'':40C,41C ''file1.txt'':35C ''folder'':8C ''fs'':33C,38C ''hadoop'':32C,37C ''hdfs'':14C,18C,31C ''li'':26C ''mkdir'':16C,20C ''ol'':23C ''p'':1C,12C ''problem'':2C ''put'':27C,34C,39C ''resolv'':4C ''start'':24C ''termin'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5433, 1, '2015-04-02 14:17:54.813', 0, 46, 'Word normalization and dataset structure for multi-contextual text analysis', '&lt;p&gt;I''d like to experiment basic text analysis: word frequency, distances, etc. But, my long term goal is to produce concept map of texts and corpus. So I am more interested in concepts than in words. &lt;/p&gt;

&lt;p&gt;Which method of word standardization (tokenizing, stemming, ...) would be more appropriate for that purpose? &lt;/p&gt;

&lt;p&gt;What are the possible approach to manage many overlapping word-sets (for multi-contextual analysis).&lt;/p&gt;
', 'nlp  clustering  text-mining  algorithms  social-network-analysis', NULL, '''/p'':59C,75C,94C ''algorithm'':17B ''analysi'':11A,21B,30C,93C ''approach'':81C ''appropri'':71C ''basic'':28C ''cluster'':13B ''concept'':43C,55C ''contextu'':9A,92C ''corpus'':48C ''d'':24C ''dataset'':4A ''distanc'':33C ''etc'':34C ''experi'':27C ''frequenc'':32C ''goal'':39C ''interest'':53C ''like'':25C ''long'':37C ''manag'':83C ''mani'':84C ''map'':44C ''method'':62C ''mine'':16B ''multi'':8A,91C ''multi-contextu'':7A,90C ''network'':20B ''nlp'':12B ''normal'':2A ''overlap'':85C ''p'':22C,60C,76C ''possibl'':80C ''produc'':42C ''purpos'':74C ''set'':88C ''social'':19B ''social-network-analysi'':18B ''standard'':65C ''stem'':67C ''structur'':5A ''term'':38C ''text'':10A,15B,29C,46C ''text-min'':14B ''token'':66C ''word'':1A,31C,58C,64C,87C ''word-set'':86C ''would'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5436, 1, '2015-04-02 23:00:20.687', 1, 47, 'how to modify sparse survey dataset with empty data points?', '&lt;p&gt;I am working on a data set where the categorical variables have lots of empty spaces (not "NA" but ""). For example, one variable has 14587 empty spaces out of 14644 observations. There are many such variables where most of the observations are empty.In fact it is a survey dataset where the participant just chose to ignore a particular question.&lt;/p&gt;

&lt;p&gt;I have never handled similar dataset. I am looking for advise as to how best to handle such datasets before any modeling is done. Deleting the rows or the variables with lots of empty spaces doesn''t seem feasible. &lt;/p&gt;

&lt;p&gt;Thanks a lot.&lt;/p&gt;
', 'data-mining', NULL, '''/p'':74C,115C,120C ''14587'':39C ''14644'':44C ''advis'':86C ''best'':90C ''categor'':24C ''chose'':68C ''data'':9A,12B,20C ''data-min'':11B ''dataset'':6A,63C,81C,94C ''delet'':100C ''doesn'':111C ''done'':99C ''empti'':8A,29C,40C,109C ''empty.in'':57C ''exampl'':35C ''fact'':58C ''feasibl'':114C ''handl'':79C,92C ''ignor'':70C ''look'':84C ''lot'':27C,107C,119C ''mani'':48C ''mine'':13B ''model'':97C ''modifi'':3A ''na'':32C ''never'':78C ''observ'':45C,55C ''one'':36C ''p'':14C,75C,116C ''particip'':66C ''particular'':72C ''point'':10A ''question'':73C ''row'':102C ''seem'':113C ''set'':21C ''similar'':80C ''space'':30C,41C,110C ''spars'':4A ''survey'':5A,62C ''thank'':117C ''variabl'':25C,37C,50C,105C ''work'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5437, 2, '2015-04-02 23:38:56.127', 0, NULL, NULL, '&lt;p&gt;I would consider approaching this situation from the following two &lt;strong&gt;perspectives&lt;/strong&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Missing data analysis&lt;/strong&gt;. Despite formally the values in question are empty and not NA, I think that effectively &lt;em&gt;incomplete&lt;/em&gt; data can (and should) be considered as &lt;em&gt;missing&lt;/em&gt;. If that is the case, you need to automatically recode those values and then apply standard &lt;em&gt;missing data handling&lt;/em&gt; approaches, such as &lt;em&gt;multiple imputation&lt;/em&gt;. If you use &lt;code&gt;R&lt;/code&gt;, you can use packages &lt;code&gt;Amelia&lt;/code&gt; (if the data is multivariate normal), &lt;code&gt;mice&lt;/code&gt; (supports non-normal data) or some others. For a nice &lt;strong&gt;overview&lt;/strong&gt; of &lt;em&gt;approaches&lt;/em&gt;, &lt;em&gt;methods&lt;/em&gt; and &lt;em&gt;software&lt;/em&gt; for multiple imputation of data with missing values, see the 2007 excellent article by Nicholas Horton and Ken Kleinman &lt;a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1839993" rel="nofollow"&gt;"Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models"&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Sparse data analysis&lt;/strong&gt;, such as &lt;em&gt;sparse regression&lt;/em&gt;. I''m not too sure how well this approach would work for variables with high levels of sparsity, but you can find a lot of corresponding information in &lt;a href="http://datascience.stackexchange.com/a/918/2452"&gt;my relevant answer&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':171C,223C ''/a/918/2452'':219C ''/code'':85C,92C,101C ''/em'':41C,51C,72C,79C,119C,122C,126C,186C ''/li'':173C,225C ''/p'':15C,172C,224C ''/pmc/articles/pmc1839993'':150C ''/strong'':14C,23C,115C,180C ''/ul'':226C ''2007'':137C ''ado'':154C ''amelia'':91C ''analysi'':22C,179C ''answer'':222C ''appli'':66C ''approach'':5C,73C,118C,195C ''articl'':139C ''automat'':60C ''case'':56C ''code'':83C,90C,99C ''comparison'':158C ''consid'':4C,47C ''correspond'':212C ''data'':21C,42C,70C,95C,106C,131C,161C,168C,178C ''datascience.stackexchange.com'':218C ''datascience.stackexchange.com/a/918/2452'':217C ''despit'':24C ''effect'':38C ''em'':39C,49C,68C,76C,117C,120C,124C,183C ''empti'':31C ''excel'':138C ''find'':208C ''fit'':166C ''follow'':10C ''formal'':25C ''handl'':71C ''high'':201C ''horton'':142C ''href'':147C,216C ''imput'':78C,129C ''incomplet'':40C,167C ''inform'':213C ''ken'':144C ''kleinman'':145C ''level'':202C ''li'':17C,174C ''lot'':210C ''m'':188C ''method'':121C,162C ''mice'':100C ''miss'':20C,50C,69C,133C,160C ''model'':170C ''much'':153C ''multipl'':77C,128C ''multivari'':97C ''na'':34C ''need'':58C ''nice'':112C ''nichola'':141C ''nofollow'':152C ''non'':104C ''non-norm'':103C ''normal'':98C,105C ''noth'':156C ''other'':109C ''overview'':114C ''p'':1C,18C,175C ''packag'':89C ''perspect'':13C ''question'':29C ''r'':84C ''recod'':61C ''regress'':169C,185C ''rel'':151C ''relev'':221C ''see'':135C ''situat'':7C ''softwar'':125C,164C ''spars'':177C,184C ''sparsiti'':204C ''standard'':67C ''strong'':12C,19C,113C,176C ''support'':102C ''sure'':191C ''think'':36C ''two'':11C ''ul'':16C ''use'':82C,88C ''valu'':27C,63C,134C ''variabl'':199C ''well'':193C ''work'':197C ''would'':3C,196C ''www.ncbi.nlm.nih.gov'':149C ''www.ncbi.nlm.nih.gov/pmc/articles/pmc1839993'':148C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5438, 1, '2015-04-02 23:52:02.953', 6, 152, 'What is the term for when a model acts on the thing being modeled and thus changes the concept?', '&lt;p&gt;I''m trying to see if there is a conventional term for this concept to help me in my literature research and writing.  When a machine learning model causes an action to be taken in the real world that affects future instances, what is that called?  &lt;/p&gt;

&lt;p&gt;I''m thinking about something like a recommender system that recommends one given product and doesn''t recommend another given product.  Then, you''ve increased the likelihood that someone is going to buy the first product and decreased the likelihood that someone is going to buy the second product.  So then those sales numbers will eventually become training instances, creating a sort of feedback loop.&lt;/p&gt;

&lt;p&gt;Is there a term for this?&lt;/p&gt;
', 'machine-learning  terminology', NULL, '''/p'':71C,138C,146C ''act'':9A ''action'':55C ''affect'':64C ''anoth'':91C ''becom'':129C ''buy'':105C,118C ''call'':70C ''caus'':53C ''chang'':17A ''concept'':19A,38C ''convent'':34C ''creat'':132C ''decreas'':110C ''doesn'':88C ''eventu'':128C ''feedback'':136C ''first'':107C ''futur'':65C ''given'':85C,92C ''go'':103C,116C ''help'':40C ''increas'':97C ''instanc'':66C,131C ''learn'':22B,51C ''like'':78C ''likelihood'':99C,112C ''literatur'':44C ''loop'':137C ''m'':26C,74C ''machin'':21B,50C ''machine-learn'':20B ''model'':8A,14A,52C ''number'':126C ''one'':84C ''p'':24C,72C,139C ''product'':86C,93C,108C,121C ''real'':61C ''recommend'':80C,83C,90C ''research'':45C ''sale'':125C ''second'':120C ''see'':29C ''someon'':101C,114C ''someth'':77C ''sort'':134C ''system'':81C ''taken'':58C ''term'':4A,35C,143C ''terminolog'':23B ''thing'':12A ''think'':75C ''thus'':16A ''train'':130C ''tri'':27C ''ve'':96C ''world'':62C ''write'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5440, 2, '2015-04-03 00:51:29.8', 3, NULL, NULL, '&lt;p&gt;Though it is not specifically a term, focused on &lt;em&gt;machine learning&lt;/em&gt;, but I would refer to such behavior of a statistical model, using a general term &lt;strong&gt;side effect&lt;/strong&gt; (while adding some &lt;em&gt;clarifying adjectives&lt;/em&gt;, such as expected or unexpected, desired or undesired, and similar). &lt;strong&gt;Modeling outcome&lt;/strong&gt; or &lt;strong&gt;transitive feedback loop outcome&lt;/strong&gt; might be some of the &lt;em&gt;alternative&lt;/em&gt; terms.&lt;/p&gt;
', NULL, NULL, '''/em'':14C,40C,69C ''/p'':71C ''/strong'':33C,54C,61C ''ad'':35C ''adject'':39C ''altern'':68C ''behavior'':21C ''clarifi'':38C ''desir'':46C ''effect'':32C ''em'':11C,37C,67C ''expect'':43C ''feedback'':58C ''focus'':9C ''general'':28C ''learn'':13C ''loop'':59C ''machin'':12C ''might'':62C ''model'':25C,52C ''outcom'':53C,60C ''p'':1C ''refer'':18C ''side'':31C ''similar'':50C ''specif'':6C ''statist'':24C ''strong'':30C,51C,56C ''term'':8C,29C,70C ''though'':2C ''transit'':57C ''undesir'':48C ''unexpect'':45C ''use'':26C ''would'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5441, 2, '2015-04-03 01:44:18.45', 5, NULL, NULL, '&lt;p&gt;There are three terms from social science that apply to your situation:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Reflexivity_%28social_theory%29"&gt;Reflexivity&lt;/a&gt; - refers to circular relationships between cause and effect.  In particular, you could use the definition of the term adopted by George Soros to refer to reverse causal loop between share prices (i.e. present value of fundamentals) and business fundamentals.  In a way, the share price is a "model" of the fundamental business processes.  Usually, people assume that causality is one-way, from fundamentals to share price.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Performativity"&gt;Performativity&lt;/a&gt; - As used by Donald MacKenzie (e.g. &lt;a href="http://www.lse.ac.uk/accounting/CARR/pdf/MacKenzie.pdf"&gt;here&lt;/a&gt;), many economic models are not "cameras" -- taking pictures of economic reality -- but in fact are "engines" -- an integral part of the construction of economic reality. He has a book of that title: &lt;em&gt;&lt;a href="http://mitpress.mit.edu/books/engine-not-camera"&gt;An Engine, Not a Camera&lt;/a&gt;&lt;/em&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Self-fulfilling_prophecy"&gt;Self-fulfilling Prophecy&lt;/a&gt; - a prediction that directly or indirectly causes itself to become true, by the very terms of the prophecy itself, due to positive feedback between belief and behavior. This is the broadest term, and least specific to the situation you describe.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Of the three terms, I suggest that MacKenzie''s "performativity" is the best fit to your situation.  He claims, among other things, that the validity of the economic models (e.g. Black-Scholes option pricing) has been improved by its very use by market participants, and therefore how it reflects in options pricing and trading patterns.&lt;/p&gt;
', NULL, NULL, '''/a'':24C,102C,115C,159C,174C ''/accounting/carr/pdf/mackenzie.pdf'':113C ''/books/engine-not-camera'':153C ''/em'':160C ''/li'':93C,162C,216C ''/ol'':217C ''/p'':14C,92C,161C,215C,275C ''/wiki/performativity'':100C ''/wiki/reflexivity_%28social_theory%29'':22C ''/wiki/self-fulfilling_prophecy'':169C ''adopt'':43C ''among'':238C ''appli'':10C ''assum'':80C ''becom'':184C ''behavior'':201C ''belief'':199C ''best'':231C ''black'':250C ''black-schol'':249C ''book'':144C ''broadest'':205C ''busi'':62C,76C ''camera'':121C,158C ''caus'':30C,181C ''causal'':51C,82C ''circular'':27C ''claim'':237C ''construct'':137C ''could'':36C ''definit'':39C ''describ'':214C ''direct'':178C ''donald'':106C ''due'':194C ''e.g'':108C,248C ''econom'':117C,125C,139C,246C ''effect'':32C ''em'':148C ''en.wikipedia.org'':21C,99C,168C ''en.wikipedia.org/wiki/performativity'':98C ''en.wikipedia.org/wiki/reflexivity_%28social_theory%29'':20C ''en.wikipedia.org/wiki/self-fulfilling_prophecy'':167C ''engin'':131C,155C ''fact'':129C ''feedback'':197C ''fit'':232C ''fulfil'':172C ''fundament'':60C,63C,75C,88C ''georg'':45C ''href'':19C,97C,110C,150C,166C ''i.e'':56C ''improv'':256C ''indirect'':180C ''integr'':133C ''least'':208C ''li'':16C,94C,163C ''loop'':52C ''mackenzi'':107C,226C ''mani'':116C ''market'':262C ''mitpress.mit.edu'':152C ''mitpress.mit.edu/books/engine-not-camera'':151C ''model'':72C,118C,247C ''ol'':15C ''one'':85C ''one-way'':84C ''option'':252C,270C ''p'':1C,17C,95C,164C,218C ''part'':134C ''particip'':263C ''particular'':34C ''pattern'':274C ''peopl'':79C ''perform'':101C,228C ''pictur'':123C ''posit'':196C ''predict'':176C ''present'':57C ''price'':55C,69C,91C,253C,271C ''process'':77C ''propheci'':173C,192C ''realiti'':126C,140C ''refer'':25C,48C ''reflect'':268C ''reflex'':23C ''relationship'':28C ''revers'':50C ''schole'':251C ''scienc'':8C ''self'':171C ''self-fulfil'':170C ''share'':54C,68C,90C ''situat'':13C,212C,235C ''social'':7C ''soro'':46C ''specif'':209C ''suggest'':224C ''take'':122C ''term'':5C,42C,189C,206C,222C ''therefor'':265C ''thing'':240C ''three'':4C,221C ''titl'':147C ''trade'':273C ''true'':185C ''use'':37C,104C,260C ''usual'':78C ''valid'':243C ''valu'':58C ''way'':66C,86C ''www.lse.ac.uk'':112C ''www.lse.ac.uk/accounting/carr/pdf/mackenzie.pdf'':111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5442, 1, '2015-04-03 03:07:50.493', 1, 524, 'Learning resources for data science to win political campaigns?', '&lt;p&gt;Does anyone know, where I can learn about applying data science to win a political campaign? I know the Obama campaign had 12 data scientists in 2008 and 165 data scientists in 2012. In 2012, they ran over 65,000 simulations every night, for 14 months. They correctly predicted every state within 0.5% and Florida within 0.05%. How did they do this? And where can I find the data they used?&lt;/p&gt;
', 'bigdata', NULL, '''/p'':83C ''0.05'':68C ''0.5'':64C ''000'':51C ''12'':34C ''14'':56C ''165'':40C ''2008'':38C ''2012'':44C,46C ''65'':50C ''anyon'':13C ''appli'':20C ''bigdata'':10B ''campaign'':9A,27C,32C ''correct'':59C ''data'':4A,21C,35C,41C,80C ''everi'':53C,61C ''find'':78C ''florida'':66C ''know'':14C,29C ''learn'':1A,18C ''month'':57C ''night'':54C ''obama'':31C ''p'':11C ''polit'':8A,26C ''predict'':60C ''ran'':48C ''resourc'':2A ''scienc'':5A,22C ''scientist'':36C,42C ''simul'':52C ''state'':62C ''use'':82C ''win'':7A,24C ''within'':63C,67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5443, 1, '2015-04-03 20:28:36.71', 10, 2067, 'Do data scientists use Excel?', '&lt;p&gt;I would consider myself a journeyman data scientist.  Like most (I think), I made my first charts and did my first aggregations in high school and college, using Excel.  As I went through college, grad school and ~7 years of work experience, I quickly picked up what I consider to be more advanced tools, like SQL, R, Python, Hadoop, LaTeX, etc.&lt;/p&gt;

&lt;p&gt;We are interviewing for a data scientist position and one candidate advertises himself as a "senior data scientist" (a very buzzy term these days) with 15+ years experience.  When asked what his preferred toolset was, he responded that it was Excel.&lt;/p&gt;

&lt;p&gt;I took this as evidence that he was not as experienced as his resume would claim, but wasn''t sure.  After all, just because it''s not my preferred tool, doesn''t mean it''s not other people''s. &lt;strong&gt;Do experienced data scientists use Excel?  Can you assume a lack of experience from someone who does primarily use Excel?&lt;/strong&gt;&lt;/p&gt;
', 'tools  career  excel', NULL, '''/p'':71C,114C,177C ''/strong'':176C ''15'':98C ''7'':47C ''advanc'':62C ''advertis'':84C ''aggreg'':31C ''ask'':102C ''assum'':164C ''buzzi'':93C ''candid'':83C ''career'':7B ''chart'':26C ''claim'':131C ''colleg'':36C,43C ''consid'':12C,58C ''data'':2A,16C,78C,89C,158C ''day'':96C ''doesn'':146C ''etc'':70C ''evid'':120C ''excel'':5A,8B,38C,113C,161C,175C ''experi'':51C,100C,168C ''experienc'':126C,157C ''first'':25C,30C ''grad'':44C ''hadoop'':68C ''high'':33C ''interview'':75C ''journeyman'':15C ''lack'':166C ''latex'':69C ''like'':18C,64C ''made'':23C ''mean'':148C ''one'':82C ''p'':9C,72C,115C ''peopl'':153C ''pick'':54C ''posit'':80C ''prefer'':105C,144C ''primarili'':173C ''python'':67C ''quick'':53C ''r'':66C ''respond'':109C ''resum'':129C ''school'':34C,45C ''scientist'':3A,17C,79C,90C,159C ''senior'':88C ''someon'':170C ''sql'':65C ''strong'':155C ''sure'':135C ''term'':94C ''think'':21C ''took'':117C ''tool'':6B,63C,145C ''toolset'':106C ''use'':4A,37C,160C,174C ''wasn'':133C ''went'':41C ''work'':50C ''would'':11C,130C ''year'':48C,99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4828, 2, '2015-01-07 22:03:37.357', 3, NULL, NULL, '&lt;p&gt;dplyr 0.3 requires R 3.1+. If you''re stuck on R 3.0.x, you have to use dplyr 0.2.x. &lt;/p&gt;
', NULL, NULL, '''/p'':22C ''0.2'':20C ''0.3'':3C ''3.0'':13C ''3.1'':6C ''dplyr'':2C,19C ''p'':1C ''r'':5C,12C ''re'':9C ''requir'':4C ''stuck'':10C ''use'':18C ''x'':14C,21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5502, 1, '2015-04-11 17:45:26.6', 0, 73, 'Possibility of working on KDDCup data in local system', '&lt;p&gt;I''m trying to apply classification algorithms to KDD Cup 2012 track2 data using R
&lt;a href="http://www.kddcup2012.org/c/kddcup2012-track2" rel="nofollow"&gt;http://www.kddcup2012.org/c/kddcup2012-track2&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;It seems not possible to work with this 10GB training data on my local system with 4GB RAM.
Can anyone work on this data using this kind of a local system ? Or is using a cluster the norm ?&lt;br&gt;
It would be great if anyone could provide me with any guidance on how to get started with working on a cluster and the normally used type of cluster for such tasks&lt;/p&gt;
', 'distributed  state-of-the-art', NULL, '''/c/kddcup2012-track2'':36C ''/c/kddcup2012-track2&lt;/a&gt;&lt;/p&gt;'':41C ''/p'':114C ''10gb'':51C ''2012'':27C ''4gb'':59C ''algorithm'':23C ''anyon'':62C,87C ''appli'':21C ''art'':15B ''br'':81C ''classif'':22C ''cluster'':78C,103C,110C ''could'':88C ''cup'':26C ''data'':6A,29C,53C,66C ''distribut'':10B ''get'':97C ''great'':85C ''guidanc'':93C ''href'':33C ''kdd'':25C ''kddcup'':5A ''kind'':69C ''local'':8A,56C,72C ''m'':18C ''nofollow'':38C ''norm'':80C ''normal'':106C ''p'':16C,42C ''possibl'':1A,46C ''provid'':89C ''r'':31C ''ram'':60C ''rel'':37C ''seem'':44C ''start'':98C ''state'':12B ''state-of-the-art'':11B ''system'':9A,57C,73C ''task'':113C ''track2'':28C ''train'':52C ''tri'':19C ''type'':108C ''use'':30C,67C,76C,107C ''work'':3A,48C,63C,100C ''would'':83C ''www.kddcup2012.org'':35C,40C ''www.kddcup2012.org/c/kddcup2012-track2'':34C ''www.kddcup2012.org/c/kddcup2012-track2&lt;/a&gt;&lt;/p&gt;'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5444, 2, '2015-04-03 22:37:22.08', 7, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;Do experienced data scientists use Excel?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I''ve seen some experienced data scientists, who use Excel - either due to their preference, or due to their workplace''s business and IT environment specifics (for example, many financial institutions use Excel as their major tool, at least, for modeling). However, I think that most experienced data scientists recognize the need to use tools, which are optimal for particular tasks, and adhere to this approach.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Can you assume a lack of experience from someone who does primarily
  use Excel?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;No, you cannot. This is the corollary from my above-mentioned thoughts. Data science does not automatically imply big data - there is plenty of data science work that Excel can handle quite well. Having said that, if a data scientist (even experienced one) does not have knowledge (at least, basic) of modern data science tools, including big data-focused ones, it is somewhat disturbing. This is because experimentation is deeply ingrained into the nature of data science due to exploratory data analysis being a essential and, even, a crucial part of it. Therefore, a person, who does not have an urge to explore other tools within their domain, could rank lower among candidates in the overall fit for a data science position (of course, this is quite fuzzy, as some people are very quick in learning new material, plus, people might have not had an opportunity to satisfy their interest in other tools due to various personal or workplace reasons).&lt;/p&gt;

&lt;p&gt;Therefore, in conclusion, I think that the best answer an experienced data scientist might have to a question in regard to their preferred tool is the following: &lt;strong&gt;My preferred tool is the optimal one, that is the one that best fits the task at hand.&lt;/strong&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5445, 1, '2015-04-03 23:19:27.283', 0, 51, 'Logistic Regression Cost Function Error', '&lt;p&gt;with regards to the Logistic Regression cost function of:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://www.holehouse.org/mlclass/06_Logistic_Regression_files/Image%20%5B16%5D.png" alt="Logistic Regression Cost Function"&gt;&lt;/p&gt;

&lt;p&gt;And hypothesis:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://www.holehouse.org/mlclass/06_Logistic_Regression_files/Image%20%5B17%5D.png" alt="Hypothesis"&gt;&lt;/p&gt;

&lt;p&gt;Is there a way to tell the +/- of the error for how "confident" the hypothesis is?&lt;/p&gt;

&lt;p&gt;E.g. if the +/- of the error was 0.1, I would know that if my hypothesis predicted 0.4 it could be 0.1 greater (0.5) or 0.1 less (0.3)&lt;/p&gt;

&lt;p&gt;This is for binary classification&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5446, 2, '2015-04-04 06:57:47.793', 3, NULL, NULL, '&lt;p&gt;This is an interesting and &lt;a href="http://www.datanami.com/2014/11/04/data-analytics-shaped-election-day-2014" rel="nofollow"&gt;relevant question&lt;/a&gt;. I think that from &lt;em&gt;data science perspective&lt;/em&gt;, it should not be, in principle, any different from any other similar data science tasks, such as prediction, forecasting or other analyses. Similarly to any data science work, the quality of applying data science to politics very much depends on understanding not only data science &lt;strong&gt;approaches, methods and tools&lt;/strong&gt;, but, first and foremost, the &lt;strong&gt;domain&lt;/strong&gt; being analyzed, that is politics domain.&lt;/p&gt;

&lt;p&gt;Rapidly rising popularity of data science and machine learning (ML), in general, certainly has a significant impact on particular verticals and politics is not an exception. This impact can be seen not only in increased &lt;strong&gt;research interest&lt;/strong&gt; in applying data science and ML to political science (for example, see &lt;a href="http://imai.princeton.edu/talk/files/SML11.pdf" rel="nofollow"&gt;this presentation&lt;/a&gt;, &lt;a href="http://www.stat.columbia.edu/~jakulin/Politics" rel="nofollow"&gt;this paper&lt;/a&gt;, &lt;a href="http://stanford.edu/~jgrimmer/bd_2.pdf" rel="nofollow"&gt;this overview paper&lt;/a&gt; and &lt;a href="http://www.oxfordjournals.org/our_journals/polana/virtualissue4.html" rel="nofollow"&gt;this whole virtual/open issue in a prominent Oxford journal&lt;/a&gt;), but in &lt;strong&gt;practical applications&lt;/strong&gt;. Moreover, a new term - &lt;strong&gt;political informatics&lt;/strong&gt; or &lt;em&gt;poliInformatics&lt;/em&gt; or &lt;em&gt;poli-informatics&lt;/em&gt; - has been coined to name an &lt;a href="http://poliinformatics.org" rel="nofollow"&gt;interdisciplinary field&lt;/a&gt;, which stated goal is to study and use data science, big data and ML in the government and politics domains. As I''ve said earlier, the interest in applying data science to politics goes beyond research and often results in politics-focused startups, such as &lt;a href="https://gigaom.com/2012/06/25/can-a-big-data-product-level-the-playing-field-in-politics" rel="nofollow"&gt;PoliticIt&lt;/a&gt; or &lt;a href="http://www.parabellumlabs.com" rel="nofollow"&gt;Para Bellum Labs&lt;/a&gt;. Following the unfortunate, but established trend in startup ecosystem, many of those ventures fail. For example, read &lt;a href="http://www.poliana.com" rel="nofollow"&gt;the story&lt;/a&gt; of one of such startups.&lt;/p&gt;

&lt;p&gt;I am pretty sure that you will be able to find neither &lt;em&gt;proprietary algorithms&lt;/em&gt; that political startups or election data science teams used and use, nor the their &lt;strong&gt;data sets&lt;/strong&gt;. However, I am rather positive that you can get some understanding about typical data sets as well as data collection and analysis methods via the resources that I have referenced above. Hope this helps.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5447, 2, '2015-04-04 07:32:19.127', 1, NULL, NULL, '&lt;p&gt;Logistic regression is just a generalized linear model, so there is a linear regression lurking in here whose errors are supposed to be normally distributed, and from which you can get confidence intervals in the usual way. &lt;/p&gt;

&lt;p&gt;That is you have a linear predictor (the -theta'' * x part), transformed by the inverse of a link function (the logistic function). You can transform prediction +/- confidence interval using this inverse link to get your +/- in probability terms. It won''t be symmetric.&lt;/p&gt;

&lt;p&gt;Nice writeup of how to do this in R: &lt;a href="http://stackoverflow.com/a/14424417/64174"&gt;http://stackoverflow.com/a/14424417/64174&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5448, 2, '2015-04-04 08:30:27.967', 4, NULL, NULL, '&lt;p&gt;Excel allows only very small data and doesn''t have anything that is sufficiently useful and flexible for machine learning or even just plotting. All I would do in Excel, is stare at a subset of the data for a first glance over the values to make sure I don''t miss anything visible by eye.&lt;/p&gt;

&lt;p&gt;So, if his favourite tool is Excel, this might suggest he rarely deals with machine learning, statistics, larger data sizes or any advanced plotting. Someone like this I wouldn''t call a Data Scientist. Of course titles don''t matter and it depends a lot on your requirements.&lt;/p&gt;

&lt;p&gt;In any case, don''t make a judgement by statements of experience or CV. I''ve seen CVs and known the people behind it.&lt;/p&gt;

&lt;p&gt;Don''t assume. &lt;strong&gt;Test him!&lt;/strong&gt; You should be good enough to set up a test. It has been shown that interviews alone are close to useless to determine skills (they only show personality). Set up a very simple supervised learning test and let him use any tool he wants.&lt;/p&gt;

&lt;p&gt;And if you want to screen people at an interview first, then ask him about very basic but important insights about statistics or machine learning. Something that every single of your current employees knows.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5462, 1, '2015-04-06 04:18:57.317', 1, 23, 'Correct tagging but wrong parse tree', '&lt;p&gt;If I parse following sentence:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;He is playing cricket in ground with grass.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;with stanford parser, the result is: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(ROOT
  (S
    (NP (PRP He))
    (VP (VBZ is)
      (VP (VBG playing)
        (NP
          (NP (NN cricket))
          (PP (IN in)
            (NP (NN ground))))
        (PP (IN with)
          (NP (NN grass)))))
    (. .)))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is there any parser who can correct the result on the basis of probability as the probability of appearing grass with ground is higher than cricket?&lt;/p&gt;

&lt;p&gt;Or parser which do chunking before parsing like this:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;He is playing cricket |in ground| |with grass|.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;and calculate probability with all combinations before generating the parse tree.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;He is playing cricket |in ground| |with grass|.&lt;/li&gt;
&lt;li&gt;He is playing cricket |with grass| |in ground|.&lt;/li&gt;
&lt;/ol&gt;
', 'nlp  parsing', NULL, '''/blockquote'':27C,117C ''/code'':65C ''/li'':141C,151C ''/ol'':152C ''/p'':15C,26C,35C,94C,105C,116C,130C ''/pre'':66C ''appear'':86C ''basi'':79C ''blockquot'':16C,106C ''calcul'':120C ''chunk'':100C ''code'':37C ''combin'':124C ''correct'':1A,74C ''cricket'':21C,52C,93C,111C,136C,146C ''follow'':13C ''generat'':126C ''grass'':25C,64C,87C,115C,140C,148C ''ground'':23C,58C,89C,113C,138C,150C ''higher'':91C ''li'':132C,142C ''like'':103C ''nlp'':7B ''nn'':51C,57C,63C ''np'':40C,49C,50C,56C,62C ''ol'':131C ''p'':9C,17C,28C,67C,95C,107C,118C ''pars'':5A,8B,12C,102C,128C ''parser'':31C,71C,97C ''play'':20C,48C,110C,135C,145C ''pp'':53C,59C ''pre'':36C ''probabl'':81C,84C,121C ''prp'':41C ''result'':33C,76C ''root'':38C ''sentenc'':14C ''stanford'':30C ''tag'':2A ''tree'':6A,129C ''vbg'':47C ''vbz'':44C ''vp'':43C,46C ''wrong'':4A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5449, 2, '2015-04-04 12:53:13.217', 2, NULL, NULL, '&lt;p&gt;Let me first clarify that I am starting my journey into data science from a programmer and database developer standpoint. I am not a 10-year data science expert nor a statistical god. However, I do work data scientist and large datasets for a company that works with rather large clients worldwide.&lt;/p&gt;

&lt;p&gt;From my experience, &lt;strong&gt;data scientist use whatever tools they need to get the job done.&lt;/strong&gt; Excel, R, SAS, Python and more are all tools in a toolbox for good data scientist. The best can use a wide variety of tools to analyze and crunch data.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Therefore, if you find yourself comparing R to Python, then you''re likely doing it all wrong in the data science world.&lt;/strong&gt; Good data scientist use both when it makes sense to use one over the other. This also applies to Excel.&lt;/p&gt;

&lt;p&gt;I think that it''s rather hard to find anyone that is going to have experience in so many different tools and languages while been great at everything. I also think it''s going to be hard to find data scientist specifically that can not only program complex algorithms but also know how to use them from a statistical standpoint too. &lt;/p&gt;

&lt;p&gt;Most of the data scientist I''ve worked with come in about 2 flavors. Those that can program and those that can''t. I rarely work with data scientist that can pull data in Python, manipulate it with something like Pandas, fit a model to the data in R and then present it to management at the end of the week.&lt;/p&gt;

&lt;p&gt;I mean, I know they exist. I''ve read many data science blogs from guys developing web scrappers, pushing it into Hadoop, pulling it back out in Python, programming complex things and running it through R to boot. They exist. They''re out there. I just haven''t ran into too many that can do all of that. Maybe it''s just my area though?&lt;/p&gt;

&lt;p&gt;So, does that mean only specializing in one thing bad? No. Plenty of my friends specialize in just one main language and kill it. I know plenty of data guys who only know R and kill it. &lt;strong&gt;I also know plenty of people who just use Excel to analyze data because that''s the only thing most non-data scientist can open and use (especially in B2B companies).&lt;/strong&gt; The question you really need to answer is if this one thing is the ONE thing you need for this position? And most importantly, can they learn new things?&lt;/p&gt;

&lt;p&gt;P.S&lt;/p&gt;

&lt;p&gt;Data Science is not just restricted to "BIG DATA" or NoSQL.&lt;/p&gt;
', NULL, NULL, '''/p'':54C,103C,149C,212C,275C,342C,444C,447C,460C ''/strong'':72C,128C,414C ''10'':26C ''2'':226C ''algorithm'':199C ''also'':145C,180C,201C,383C ''analyz'':99C,393C ''answer'':421C ''anyon'':160C ''appli'':146C ''area'':340C ''b2b'':412C ''back'':301C ''bad'':353C ''best'':90C ''big'':456C ''blog'':289C ''boot'':314C ''clarifi'':5C ''client'':52C ''come'':223C ''compani'':46C,413C ''compar'':111C ''complex'':198C,306C ''crunch'':101C ''data'':13C,28C,39C,60C,87C,102C,125C,130C,190C,217C,241C,246C,260C,287C,372C,394C,404C,449C,457C ''databas'':19C ''dataset'':43C ''develop'':20C,292C ''differ'':170C ''done'':71C ''end'':271C ''especi'':410C ''everyth'':178C ''excel'':73C,148C,391C ''exist'':282C,316C ''experi'':58C,166C ''expert'':30C ''find'':109C,159C,189C ''first'':4C ''fit'':255C ''flavor'':227C ''friend'':358C ''get'':68C ''go'':163C,184C ''god'':34C ''good'':86C,129C ''great'':176C ''guy'':291C,373C ''hadoop'':298C ''hard'':157C,187C ''haven'':323C ''howev'':35C ''import'':438C ''job'':70C ''journey'':11C ''kill'':366C,379C ''know'':202C,280C,369C,376C,384C ''languag'':173C,364C ''larg'':42C,51C ''learn'':441C ''let'':2C ''like'':118C,253C ''main'':363C ''make'':136C ''manag'':268C ''mani'':169C,286C,328C ''manipul'':249C ''mayb'':335C ''mean'':278C,347C ''model'':257C ''need'':66C,419C,432C ''new'':442C ''non'':403C ''non-data'':402C ''nosql'':459C ''one'':140C,351C,362C,425C,429C ''open'':407C ''p'':1C,55C,104C,150C,213C,276C,343C,445C,448C ''p.s'':446C ''panda'':254C ''peopl'':387C ''plenti'':355C,370C,385C ''posit'':435C ''present'':265C ''program'':197C,231C,305C ''programm'':17C ''pull'':245C,299C ''push'':295C ''python'':76C,114C,248C,304C ''question'':416C ''r'':74C,112C,262C,312C,377C ''ran'':325C ''rare'':238C ''rather'':50C,156C ''re'':117C,318C ''read'':285C ''realli'':418C ''restrict'':454C ''run'':309C ''sas'':75C ''scienc'':14C,29C,126C,288C,450C ''scientist'':40C,61C,88C,131C,191C,218C,242C,405C ''scrapper'':294C ''sens'':137C ''someth'':252C ''special'':349C,359C ''specif'':192C ''standpoint'':21C,210C ''start'':9C ''statist'':33C,209C ''strong'':59C,105C,381C ''therefor'':106C ''thing'':307C,352C,400C,426C,430C,443C ''think'':152C,181C ''though'':341C ''tool'':64C,81C,97C,171C ''toolbox'':84C ''use'':62C,92C,132C,139C,205C,390C,409C ''varieti'':95C ''ve'':220C,284C ''web'':293C ''week'':274C ''whatev'':63C ''wide'':94C ''work'':38C,48C,221C,239C ''world'':127C ''worldwid'':53C ''wrong'':122C ''year'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5450, 2, '2015-04-04 20:37:55.33', 9, NULL, NULL, '&lt;p&gt;Most non-technical people often use Excel as a database replacement. I think that''s wrong but tolerable. However, someone who is supposedly experienced in data analysis simply can not use Excel as his main tool (excluding the obvious task of looking at the data for the first time). That''s because Excel was never intended for that kind of analysis and as a consequence of this, it is incredibly easy to make mistakes in Excel (that''s not to say that it is not incredibly easy to make another type of mistakes when using other tools, but Excel aggravates the situation even more.)&lt;/p&gt;

&lt;p&gt;To summarize what Excel doesn''t have and is a must for any analysis:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Reproducibility. A data analysis needs to be reproducible.&lt;/li&gt;
&lt;li&gt;Version control. Good for collaboration and also good for reproducibility. Instead of using xls, use csv (still very complex and has lots of edge cases, but csv parsers are fairly good nowadays.) &lt;/li&gt;
&lt;li&gt;Testing. If you don''t have tests, your code is broken. If your code is broken, your analysis is worse than useless. &lt;/li&gt;
&lt;li&gt;Maintainability.&lt;/li&gt;
&lt;li&gt;Accuracy. Numerical accuracy, accurate date parsing, among others are really lacking in Excel.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;More resources:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.eusprig.org/horror-stories.htm" rel="nofollow"&gt;European Spreadsheet Risks Interest Group - Horror Stories&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://lemire.me/blog/archives/2014/05/23/you-shouldnt-use-a-spreadsheet-for-important-work-i-mean-it/" rel="nofollow"&gt;You shouldnât use a spreadsheet for important work (I mean it)&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.forbes.com/sites/timworstall/2013/02/13/microsofts-excel-might-be-the-most-dangerous-software-on-the-planet/" rel="nofollow"&gt;Microsoft''s Excel Might Be The Most Dangerous Software On The Planet&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://randyzwitch.com/excel-destroys-data/" rel="nofollow"&gt;Destroy Your Data Using Excel With This One Weird Trick!&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.win-vector.com/blog/2014/11/excel-spreadsheets-are-hard-to-get-right/" rel="nofollow"&gt;Excel spreadsheets are hard to get right&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':231C,253C,275C,295C,312C ''/blog/2014/11/excel-spreadsheets-are-hard-to-get-right/'':302C ''/blog/archives/2014/05/23/you-shouldnt-use-a-spreadsheet-for-important-work-i-mean-it/'':238C ''/excel-destroys-data/'':282C ''/horror-stories.htm'':221C ''/li'':134C,168C,192C,195C,210C ''/ol'':211C ''/p'':107C,123C,215C,232C,254C,276C,296C,313C ''/sites/timworstall/2013/02/13/microsofts-excel-might-be-the-most-dangerous-software-on-the-planet/'':260C ''accur'':200C ''accuraci'':197C,199C ''aggrav'':102C ''also'':142C ''among'':203C ''analysi'':29C,63C,122C,129C,187C ''anoth'':92C ''broken'':180C,185C ''case'':160C ''code'':178C,183C ''collabor'':140C ''complex'':154C ''consequ'':67C ''control'':137C ''csv'':151C,162C ''danger'':270C ''data'':28C,47C,128C,287C ''databas'':12C ''date'':201C ''destroy'':285C ''doesn'':113C ''easi'':73C,89C ''edg'':159C ''european'':224C ''even'':105C ''excel'':9C,34C,55C,78C,101C,112C,209C,265C,289C,305C ''exclud'':39C ''experienc'':26C ''fair'':165C ''first'':50C ''get'':310C ''good'':138C,143C,166C ''group'':228C ''hard'':308C ''horror'':229C ''howev'':21C ''href'':218C,235C,257C,279C,299C ''import'':248C ''incred'':72C,88C ''instead'':146C ''intend'':58C ''interest'':227C ''kind'':61C ''lack'':207C ''lemire.me'':237C ''lemire.me/blog/archives/2014/05/23/you-shouldnt-use-a-spreadsheet-for-important-work-i-mean-it/'':236C ''li'':125C,135C,169C,193C,196C ''look'':44C ''lot'':157C ''main'':37C ''maintain'':194C ''make'':75C,91C ''mean'':251C ''microsoft'':263C ''might'':266C ''mistak'':76C,95C ''must'':119C ''need'':130C ''never'':57C ''nofollow'':223C,240C,262C,284C,304C ''non'':4C ''non-techn'':3C ''nowaday'':167C ''numer'':198C ''obvious'':41C ''often'':7C ''ol'':124C ''one'':292C ''other'':204C ''p'':1C,108C,212C,216C,233C,255C,277C,297C ''pars'':202C ''parser'':163C ''peopl'':6C ''planet'':274C ''randyzwitch.com'':281C ''randyzwitch.com/excel-destroys-data/'':280C ''realli'':206C ''rel'':222C,239C,261C,283C,303C ''replac'':13C ''reproduc'':126C,133C,145C ''resourc'':214C ''right'':311C ''risk'':226C ''say'':83C ''shouldnâ'':242C ''simpli'':30C ''situat'':104C ''softwar'':271C ''someon'':22C ''spreadsheet'':225C,246C,306C ''still'':152C ''stori'':230C ''summar'':110C ''suppos'':25C ''task'':42C ''technic'':5C ''test'':170C,176C ''think'':15C ''time'':51C ''toler'':20C ''tool'':38C,99C ''trick'':294C ''type'':93C ''use'':8C,33C,97C,148C,150C,244C,288C ''useless'':191C ''version'':136C ''weird'':293C ''work'':249C ''wors'':189C ''wrong'':18C ''www.eusprig.org'':220C ''www.eusprig.org/horror-stories.htm'':219C ''www.forbes.com'':259C ''www.forbes.com/sites/timworstall/2013/02/13/microsofts-excel-might-be-the-most-dangerous-software-on-the-planet/'':258C ''www.win-vector.com'':301C ''www.win-vector.com/blog/2014/11/excel-spreadsheets-are-hard-to-get-right/'':300C ''xls'':149C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6480, 1, '2015-07-16 13:08:07.277', 0, 27, 'Implementing GLM for a poisson model using matlab function fminunc', '&lt;p&gt;I am trying to simulate a linear non-linear poisson model. The problem is that as far as I know, the log likelihood function in this case should be a convex one, but that does not seem to be true, because I receive different function values for the same initial guess using fminunc.&lt;/p&gt;

&lt;p&gt;BTW, I calculated the likelihood function below, let me know if there is anything wrong with it: 
&lt;img src="http://i.stack.imgur.com/ZwDId.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Leaving that aside, the algorithm does not seem to work, since the weights(B) are very far from what they should be (all should be near -1). Since some of coefficients are positive, the mean becomes large and thus the counts are also very different from what I expect them to be.&lt;/p&gt;

&lt;p&gt;I tried to manually force the program to return only all negative component B for me, but it doesn''t converge after giving it ample time.What should I do?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;            %Description: This program implements a simple generalized linear model
        %approch to neural encoding. We assume that the stimulus is a binary on off
        %4 pixel.We also assume that the training data comes from experimentally
        %recording the response of a single neuron to 10 different 4pixel stimulus.
        %we deploy the canonical link function and use maximum likelihood method to
        %estimate the weights that predicts the number of spikes given a stimulus.
        %Date:7/5/2015 Author: Mani Sotoodeh
        clc;clear;close;
        [B,fval]=Weightestimate(); % creating trainging data and finding optimum weights
        b0=B(1);b1=B(2);b2=B(3);b3=B(4);b4=B(5);
        % pre allocating to create test data
        X=zeros(100,4);
        Mu=zeros(100,1);% process parameter
        diff=zeros(100,1);% difference of predicted and actual count for each training data
        Mu2=zeros(100,1);% predicted process parameter
        T=zeros(100,1);% spike times
        T2=zeros(100,1);%predicted spike times
        Y=zeros(100,1);%number of spikes
        Y2=zeros(100,1);%predicted number of spikes
        Eta=zeros(100,1);% linear predictor
        Eta2=zeros(100,1);% linear predictor
        for i=1:100 % on trials
            for j=1:4 % on pixels
                X(i,j)=randi(2,1)-1;
            end
            Eta(i)=-1+-1*X(i,1)+-1*X(i,2)+-1*X(i,3)+-1*X(i,4);
            Mu(i)=exp(Eta(i));
            [T,Y(i)]=ppsample(Mu(i),20);
            Eta2(i)=b0+b1*X(i,1)+b2*X(i,2)+b3*X(i,3)+b4*X(i,4);
            Mu2(i)=exp(Eta2(i));
            [T2,Y2(i)]=ppsample(Mu2(i),20);
            diff(i)=abs(Y(i)-Y2(i)); 
        end

        difference=sum(diff);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here''s weightestimate function:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;        function [B,fval] = Weightestimate(~)
        %Description: this function initialized a trianing data and returns the
        %appropriate weights using the maximum likelihood method using fminunc
        clc;clear;close all;
        % pre allocating
        x=zeros(10,4);
        mu=zeros(10,1);% process parameter
        T=zeros(10,1);% spike times
        y=zeros(10,1);%number of spikes
        eta=zeros(10,1);% linear predictor
        for i=1:10 % on trials
            for j=1:4 % on pixels
                x(i,j)=randi(2,1)-0.8;
            end
            eta(i)=-0.8+-0.8*x(i,1)+-0.8*x(i,2)+-0.8*x(i,3)+-0.8*x(i,4);
            mu(i)=exp(eta(i));
            [~,y(i)]=ppsample(mu(i),20);
            syms b0;syms b1;syms b2;syms b3;syms b4;syms objfu;syms b
            o(i)=(((b0+b1*x(i,1)+b2*x(i,2)+b3*x(i,3)+b4*x(i,4))*y(i))-exp(b0+b1*x(i,1)+b2*x(i,2)+b3*x(i,3)+b4*x(i,4)));
        end
        %%finding optimum weights
        objfu=-1*sum(o);
        objfu=matlabFunction(objfu);
        b = zeros(1,5);
        function f = ofun(b)
        b0=b(1);b1=b(2);b2=b(3);b3=b(4);b4=b(5);
        f = objfu(b0,b1,b2,b3,b4);   
        end

        [B,fval] = fminunc(@ofun,b);
        C=B&amp;lt;0;
        k=0;

        %ensuring an all negative coefficients
        while (sum(C==logical([1,1,1,1,1]))~=5)
        b=rand(1)*zeros(1,5);
        [B,fval] = fminunc(@ofun,b);
        k=k+1;
        end
        B
        fval
        k
        end
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I don''t think it has anything to do with ppsample, but just for reference:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;            %Description: This function implements a simple poisoon process with a given rate in a specified time period and shows the result using stem
        % the function gets tmax and rate of process (landa) and creates and shows
        % a poisson process with given parameters.

        function [T,nzT]=ppsample(landa,Tmax)
        clc;
            T(1)=random(''exponential'',1/landa);
            i=1;
            while T(i)&amp;lt; Tmax
                T(i+1)=T(i)+random(''exponential'',1/landa);
                i=i+1;
            end
            ST=numel(T);% number of spikes 
            for j=1:ST % zeroing arrival times that has surpassed tmax
            if T(j)&amp;gt;Tmax
                T(j)=0;
            end    
            end
            nzT=max(size(nonzeros(T)));
            if isempty(T)==0
            one=0.1*ones(1,nzT);
            else 
                one=0.1*ones(1,0);
            end

            T=T(1:nzT);
        %     stem(T(1:nzT),one)
        %     axis([0 Tmax 0 .11]);
        %     axis normal
        %     tit=strcat(''a possible outcome of poisson process with parameters landa='',num2str(landa),''and Tmax='',num2str(Tmax));
        %     title(tit,''color'',''r'')
        %     xlabel(''Time'')
        end
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6485, 2, '2015-07-16 23:55:59.507', 5, NULL, NULL, '&lt;p&gt;First off, ignore the haters.  I started working on ML in Music a long time ago and got several degrees using that work. When I started I was asking people the same kind of questions you are. It is a fascinating field and there is always room for someone new. We all have to start somewhere.&lt;/p&gt;

&lt;p&gt;The areas of study you are inquiring about are Music Information Retrieval (&lt;a href="https://en.wikipedia.org/wiki/Music_information_retrieval" rel="nofollow"&gt;Wiki Link&lt;/a&gt;) and Computer Music (&lt;a href="https://en.wikipedia.org/wiki/Computer_music" rel="nofollow"&gt;Wiki Link&lt;/a&gt;) .  You have made a good choice in narrowing your problem to a single instrument (monophonic music) as polyphonic music increases the difficulty greatly.  &lt;/p&gt;

&lt;p&gt;You''re trying to solve two problems really:&lt;/p&gt;

&lt;p&gt;1) Automatic Transcription of Monophonic Music (&lt;a href="https://scholar.google.com/scholar?q=Automatic+Transcription+of+Monophonic+Music" rel="nofollow"&gt;More Readings&lt;/a&gt;) which is the problem of extracting the notes from a single instrument musical piece.&lt;/p&gt;

&lt;p&gt;2) Algorithmic Composition (&lt;a href="https://scholar.google.com/scholar?q=Algorithmic%20Composition" rel="nofollow"&gt;More Readings&lt;/a&gt;) which is the problem of generating new music using a corpus of transcribed music.&lt;/p&gt;

&lt;p&gt;To answer your questions directly:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;I think that this would be an unsupervised learning problem, but I am
  not really sure.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Since there are two learning problems here there are two answers. For the Automatic Transcription you will probably want to follow a supervised learning approach, where your classification are the notes you are trying to extract.  For the Algorithmic Composition problem it can actually go either way. Some reading in both areas will clear this up a lot.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;What features from the sound wave I should extract so that the output music is melodious?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;There are a lot of features used commonly in MIR. @abhnj listed MFCC''s in his answer but there are a lot more.  Feature analysis in MIR takes place in several domains and there are features for each.  Some Domains are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;The Frequency Domain (these are the values we hear played through a speaker)&lt;/li&gt;
&lt;li&gt;The Spectral Domain (This domain is calculated via the Fourier function (&lt;a href="https://en.wikipedia.org/wiki/Fast_Fourier_transform" rel="nofollow"&gt;Read about the Fast Fourier Transform&lt;/a&gt;) and can be transformed using several functions (Magnitude, Power, Log Magnitude, Log Power)&lt;/li&gt;
&lt;li&gt;The Peak Domain (A domain of amplitude and spectral peaks over the spectral domain)&lt;/li&gt;
&lt;li&gt;The Harmonic Domain&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;One of the first problems you will face is how to segment or "cut up" your music signal so that you can extract features.  This is the problem of Segmentation (&lt;a href="https://scholar.google.com/scholar?hl=en&amp;amp;q=music%20segmentation" rel="nofollow"&gt;Some Readings&lt;/a&gt;) which is complex in itself.  Once you have cut your sound source up you can apply various functions to your segments before extracting features from them. Some of these functions (called window functions) are the: Rectangular, Hamming, Hann, Bartlett, Triangular, Bartlett_hann, Blackman, and Blackman_harris.&lt;/p&gt;

&lt;p&gt;Once you have your segments cut from your domain you can then extract features to represent those segments.  Some of these will depend on the domain you selected.  A few example of features are: Your normal statistical features (Mean, Variance, Skewness, etc.), ZCR, RMS, Spectral Centroid, Spectral Irregularity, Spectral Flatness, Spectral Tonality, Spectral Crest, Spectral Slope, Spectral Rolloff, Spectral Loudness, Spectral Pitch, Harmonic Odd Even Ratio, MFCC''s and Bark Scale. There are many more but these are some good basics.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Is it possible, with recurrent neural networks, to output a vector of sequenced musical notes (ABCDEF)?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Yes it is. There have been several works to do this already. (&lt;a href="https://scholar.google.com/scholar?q=Algorithmic%20Composition%20with%20Neural%20Networks" rel="nofollow"&gt;Here are several readings&lt;/a&gt;)&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Any smart way I can feed in the features of the soundwaves as well as sequence of musical notes?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The standard method is to use the explanation I made above (Domain, Segment, Feature Extract) etc.  To save yourself some work I highly recommend starting with a MIR framework such as MARSYAS (&lt;a href="http://marsyas.info/" rel="nofollow"&gt;Marsyas&lt;/a&gt;).  They will provide you with all the basics of feature extraction.  There are many frameworks so just find one that uses a language you are comfortable in.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5452, 2, '2015-04-04 23:43:50.157', 1, NULL, NULL, '&lt;p&gt;I think most people are answering without having a good knowledge of excel. Excel (since 2010) has an in memory columnar [multi table] database , called power pivot (which allows input from csv/databases etc), allowing it to store millions of rows (it doesn''t have to be loaded on a spreadsheet).
It also has an ETL tool called power query allowing you to read the data from a variety of sources (including hadoop). And it has a visualisation tool (power view &amp;amp; power map). A lot of Data Science is doing aggregation and top-n analysis at which power pivot excels.  Add to this the interactive nature of these tools - any user can easily drag and drop a dimension on which to break up the results adn I hope you can see the benefits. So yes you can''t do machine learning, but I would question how much machine learning is done by data scientists day to day: eg when I want to analyse the prediction errors made in machine learning program I find it easiest to slice and dice the errors with excel. &lt;/p&gt;
', NULL, NULL, '''/p'':186C ''2010'':17C ''add'':103C ''adn'':128C ''aggreg'':92C ''allow'':30C,35C,61C ''also'':53C ''amp'':82C ''analys'':165C ''analysi'':97C ''answer'':7C ''benefit'':135C ''break'':124C ''call'':26C,58C ''columnar'':22C ''csv/databases'':33C ''data'':66C,88C,155C ''databas'':25C ''day'':157C,159C ''dice'':181C ''dimens'':120C ''doesn'':43C ''done'':153C ''drag'':116C ''drop'':118C ''easiest'':177C ''easili'':115C ''eg'':160C ''error'':168C,183C ''etc'':34C ''etl'':56C ''excel'':14C,15C,102C,185C ''find'':175C ''good'':11C ''hadoop'':73C ''hope'':130C ''includ'':72C ''input'':31C ''interact'':107C ''knowledg'':12C ''learn'':143C,151C,172C ''load'':48C ''lot'':86C ''machin'':142C,150C,171C ''made'':169C ''map'':84C ''memori'':21C ''million'':39C ''much'':149C ''multi'':23C ''n'':96C ''natur'':108C ''p'':1C ''peopl'':5C ''pivot'':28C,101C ''power'':27C,59C,80C,83C,100C ''predict'':167C ''program'':173C ''queri'':60C ''question'':147C ''read'':64C ''result'':127C ''row'':41C ''scienc'':89C ''scientist'':156C ''see'':133C ''sinc'':16C ''slice'':179C ''sourc'':71C ''spreadsheet'':51C ''store'':38C ''tabl'':24C ''think'':3C ''tool'':57C,79C,111C ''top'':95C ''top-n'':94C ''user'':113C ''varieti'':69C ''view'':81C ''visualis'':78C ''want'':163C ''without'':8C ''would'':146C ''yes'':137C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5457, 2, '2015-04-05 07:57:05.907', 0, NULL, NULL, '&lt;p&gt;I think in the literature the overarching term often used is  active learning  &lt;a href="http://en.wikipedia.org/wiki/Active_learning_%28machine_learning%29" rel="nofollow"&gt;Active Learning&lt;/a&gt; ...in particular 
&lt;a href="http://en.wikipedia.org/wiki/Multi-armed_bandit#Contextual_Bandit" rel="nofollow"&gt;Multi-Armed Contextual Bandit&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':24C,39C ''/p'':40C ''/wiki/active_learning_%28machine_learning%29'':19C ''/wiki/multi-armed_bandit#contextual_bandit'':31C ''activ'':13C,22C ''arm'':36C ''bandit'':38C ''contextu'':37C ''en.wikipedia.org'':18C,30C ''en.wikipedia.org/wiki/active_learning_%28machine_learning%29'':17C ''en.wikipedia.org/wiki/multi-armed_bandit#contextual_bandit'':29C ''href'':16C,28C ''learn'':14C,23C ''literatur'':6C ''multi'':35C ''multi-arm'':34C ''nofollow'':21C,33C ''often'':10C ''overarch'':8C ''p'':1C ''particular'':26C ''rel'':20C,32C ''term'':9C ''think'':3C ''use'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10357, 1, '2016-02-23 14:28:29.563', 1, 36, 'How do linear learning systems classify datapoints that fall on the hyperplane', '&lt;p&gt;How do linear learning systems, such as the simple "closest to the class average" algorithm or SVMs, classify datapoints that fall on the hyperplane?&lt;/p&gt;
', 'machine-learning  classification  algorithms  svm  supervised-learning', NULL, '''/p'':47C ''algorithm'':17B,37C ''averag'':36C ''class'':35C ''classif'':16B ''classifi'':6A,40C ''closest'':32C ''datapoint'':7A,41C ''fall'':9A,43C ''hyperplan'':12A,46C ''learn'':4A,15B,21B,26C ''linear'':3A,25C ''machin'':14B ''machine-learn'':13B ''p'':22C ''simpl'':31C ''supervis'':20B ''supervised-learn'':19B ''svm'':18B ''svms'':39C ''system'':5A,27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5458, 1, '2015-04-05 10:00:43.167', 1, 128, 'Classification of skills based on job ads', '&lt;p&gt;I have around 1,000 job ads in the filed of IT (in excel file). I want to find the skills which are mentioned in each of ads. and then find the similar jobs based on skills.&lt;/p&gt;

&lt;p&gt;My method: I created 12 categories Such as programming skills,  testing skills,  communication skills, network skills, ... . Each advertisement may belong to 3-4 categories. In this case, some said multi-variate classification or Multi label classification is useful. But I don''t know how to do this kind of classification in RapidMiner.&lt;/p&gt;

&lt;p&gt;1- Does anyone know how to do multi-variate classification or Multi label classification in RapidMiner? or is there another way?&lt;/p&gt;

&lt;p&gt;2- Do you recommend "classification" in order to analysis required job skills? or another technique? &lt;/p&gt;

&lt;p&gt;3- Is there any better way to classify the skills which are stated in job ads?&lt;/p&gt;

&lt;p&gt;I''m new in the field of text mining. Please let me know if you have any idea. Thanks&lt;/p&gt;
', 'machine-learning  classification  nlp  text-mining', NULL, '''-4'':78C ''/p'':54C,109C,133C,150C,168C,189C ''000'':21C ''1'':20C,111C ''12'':60C ''2'':135C ''3'':77C,152C ''ad'':7A,23C,44C,167C ''advertis'':73C ''analysi'':143C ''anoth'':131C,148C ''anyon'':113C ''around'':19C ''base'':4A,51C ''belong'':75C ''better'':156C ''case'':82C ''categori'':61C,79C ''classif'':1A,11B,88C,92C,106C,121C,125C,139C ''classifi'':159C ''communic'':68C ''creat'':59C ''excel'':30C ''field'':175C ''file'':26C,31C ''find'':35C,47C ''idea'':187C ''job'':6A,22C,50C,145C,166C ''kind'':104C ''know'':99C,114C,182C ''label'':91C,124C ''learn'':10B ''let'':180C ''m'':171C ''machin'':9B ''machine-learn'':8B ''may'':74C ''mention'':40C ''method'':57C ''mine'':15B,178C ''multi'':86C,90C,119C,123C ''multi-vari'':85C,118C ''network'':70C ''new'':172C ''nlp'':12B ''order'':141C ''p'':16C,55C,110C,134C,151C,169C ''pleas'':179C ''program'':64C ''rapidmin'':108C,127C ''recommend'':138C ''requir'':144C ''said'':84C ''similar'':49C ''skill'':3A,37C,53C,65C,67C,69C,71C,146C,161C ''state'':164C ''techniqu'':149C ''test'':66C ''text'':14B,177C ''text-min'':13B ''thank'':188C ''use'':94C ''variat'':87C,120C ''want'':33C ''way'':132C,157C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5459, 1, '2015-04-05 14:37:45.787', 0, 47, 'SAS. How to write "OR"', '&lt;p&gt;How to write "OR" in this example?&lt;/p&gt;

&lt;p&gt;DATA a1;
set a;
if var1=1 OR 2;
run;&lt;/p&gt;

&lt;p&gt;P.S. va1 is the categorial (with categories: 1, 2, 3)&lt;/p&gt;
', 'sampling  sas', NULL, '''/p'':15C,27C,39C ''1'':23C,36C ''2'':25C,37C ''3'':38C ''a1'':18C ''categori'':33C,35C ''data'':17C ''exampl'':14C ''p'':7C,16C,28C ''p.s'':29C ''run'':26C ''sampl'':5B ''sas'':1A,6B ''set'':19C ''va1'':30C ''var1'':22C ''write'':4A,10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5460, 2, '2015-04-05 17:42:10.457', 0, NULL, NULL, '&lt;p&gt;i''m assuming this will be migrated to stack overflow, but instead of trying to do &lt;code&gt;if var1=1 or 2&lt;/code&gt; wouldn''t it be better to use &lt;code&gt;if var1 in (1, 2)&lt;/code&gt;?&lt;/p&gt;

&lt;p&gt;...and somebody with enough reputation should probably create an sas tag (and an spss tag while you''re at it) unless data scientists only use open source languages like &lt;code&gt;r&lt;/code&gt; and &lt;code&gt;python&lt;/code&gt; now...&lt;/p&gt;
', NULL, NULL, '''/code'':24C,38C,72C,76C ''/p'':39C,78C ''1'':21C,36C ''2'':23C,37C ''assum'':4C ''better'':29C ''code'':18C,32C,70C,74C ''creat'':48C ''data'':62C ''enough'':44C ''instead'':13C ''languag'':68C ''like'':69C ''m'':3C ''migrat'':8C ''open'':66C ''overflow'':11C ''p'':1C,40C ''probabl'':47C ''python'':75C ''r'':71C ''re'':58C ''reput'':45C ''sas'':50C ''scientist'':63C ''somebodi'':42C ''sourc'':67C ''spss'':54C ''stack'':10C ''tag'':51C,55C ''tri'':15C ''unless'':61C ''use'':31C,65C ''var1'':20C,34C ''wouldn'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8708, 2, '2015-11-04 08:11:37.63', 0, NULL, NULL, '&lt;p&gt;I think that big data starts at the point where the size prevents you from doing what you want to.
In most scenarios, there is a limit on the running time that is considered feasible.
In some cases it is an hour, in some cases it might be few weeks.
As long as the data is not big enough that only O(n) algorithms can run in the feasible time frame, you didn''t reach big data.&lt;/p&gt;

&lt;p&gt;I like this definition since it is agnostic to volume, technology level and specific algorithms. It is not agnostic to resources so a grad student will reach the point of big data way before Google.&lt;/p&gt;

&lt;p&gt;In order to be able to quantify how big is the data, I like to consider the time needed to backup it. Since the technology advances, volumes that were considered big some years ago are now moderate. Backup time improves, as the technology improves, just as the running time of the learning algorithms.
I feel it is more sensible to talk about a dataset it takes X hours to backup and not of a dataset of Y bytes.&lt;/p&gt;

&lt;p&gt;PS.&lt;/p&gt;

&lt;p&gt;It is important to note that even if you reached the big data point and you can not run algorithms of complexity more than O(n) in the straight forward way, there is plenty you can do in order to still benefit from such algorithms.&lt;/p&gt;

&lt;p&gt;For example, Feature selection can reduce the number of features that many algorithms running time depends on.
In many long tail distribution focusing in the few items in the head might be of benefit.
You can use a sample and run on it the slower algorithms.&lt;/p&gt;
', NULL, NULL, '''/p'':79C,116C,196C,199C,246C,294C ''abl'':122C ''advanc'':143C ''agnost'':88C,99C ''ago'':151C ''algorithm'':65C,95C,170C,220C,245C,260C,293C ''backup'':138C,155C,187C ''benefit'':242C,281C ''big'':5C,59C,77C,111C,126C,148C,212C ''byte'':195C ''case'':39C,46C ''complex'':222C ''consid'':35C,133C,147C ''data'':6C,56C,78C,112C,129C,213C ''dataset'':181C,192C ''definit'':84C ''depend'':263C ''didn'':74C ''distribut'':269C ''enough'':60C ''even'':207C ''exampl'':249C ''feasibl'':36C,70C ''featur'':250C,257C ''feel'':172C ''focus'':270C ''forward'':230C ''frame'':72C ''googl'':115C ''grad'':104C ''head'':277C ''hour'':43C,185C ''import'':203C ''improv'':157C,161C ''item'':274C ''learn'':169C ''level'':92C ''like'':82C,131C ''limit'':28C ''long'':53C,267C ''mani'':259C,266C ''might'':48C,278C ''moder'':154C ''n'':64C,226C ''need'':136C ''note'':205C ''number'':255C ''o'':63C,225C ''order'':119C,239C ''p'':1C,80C,117C,197C,200C,247C ''plenti'':234C ''point'':10C,109C,214C ''prevent'':14C ''ps'':198C ''quantifi'':124C ''reach'':76C,107C,210C ''reduc'':253C ''resourc'':101C ''run'':31C,67C,165C,219C,261C,288C ''sampl'':286C ''scenario'':24C ''select'':251C ''sensibl'':176C ''sinc'':85C,140C ''size'':13C ''slower'':292C ''specif'':94C ''start'':7C ''still'':241C ''straight'':229C ''student'':105C ''tail'':268C ''take'':183C ''talk'':178C ''technolog'':91C,142C,160C ''think'':3C ''time'':32C,71C,135C,156C,166C,262C ''use'':284C ''volum'':90C,144C ''want'':20C ''way'':113C,231C ''week'':51C ''x'':184C ''y'':194C ''year'':150C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5683, 2, '2015-05-04 04:29:06.83', 3, NULL, NULL, '&lt;p&gt;If you''re looking for something where A and B are equally represented, consider trying something like Z score normalization (or &lt;a href="http://en.wikipedia.org/wiki/Standard_score" rel="nofollow"&gt;standard score&lt;/a&gt;):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;c = (a-u_a)/sigma_a + (b-u_b)/sigma_b
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;That score equally represents the two, but would be on a smaller scale.  It really shouldn''t matter since the numbers are arbitrary, however, if you need to scale it up, you could do something like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;c2 = (sigma_a+sigma_b)*(c) + u_a + u_b
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':32C ''/code'':47C,99C ''/p'':33C,86C ''/pre'':48C,100C ''/sigma_a'':41C ''/sigma_b'':46C ''/wiki/standard_score'':27C ''a-u'':37C ''arbitrari'':72C ''b'':11C,43C,45C,93C,98C ''b-u'':42C ''c'':36C,94C ''c2'':89C ''code'':35C,88C ''consid'':15C ''could'':82C ''en.wikipedia.org'':26C ''en.wikipedia.org/wiki/standard_score'':25C ''equal'':13C,52C ''howev'':73C ''href'':24C ''like'':18C,85C ''look'':5C ''matter'':67C ''need'':76C ''nofollow'':29C ''normal'':21C ''number'':70C ''p'':1C,49C ''pre'':34C,87C ''re'':4C ''realli'':64C ''rel'':28C ''repres'':14C,53C ''scale'':62C,78C ''score'':20C,31C,51C ''shouldn'':65C ''sigma'':90C,92C ''sinc'':68C ''smaller'':61C ''someth'':7C,17C,84C ''standard'':30C ''tri'':16C ''two'':55C ''u'':39C,44C,95C,97C ''would'':57C ''z'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5464, 1, '2015-04-06 17:36:22.86', 1, 114, 'Is it ethical to claim experience with big data when the data isn''t a part of the new advertising/social media/retail fad?', '&lt;p&gt;Obviously most employers, when hiring a data scientist, would prefer experience with big data and/or data science.  But what can one safely assume they will acknowledge as experience?&lt;/p&gt;

&lt;p&gt;Let''s say someone often launches software on a computing cluster that typically generates an amount of data.  I''m not sure what the best measure of this data is for data science.  I''ll call it one or two thousand rows, 200k or 300k points per row...certainly under 500k.  Then for each point, let''s call it 25 or 30 values.  This amounts to 30 or 40 gig of data.  300 or 400 times of this and you can call it a study - maybe one or two studies per year.  I''m under the impression that this is much smaller than a data scientist at Google or Facebook would be used to, but it''s certainly too big for my home computing systems.&lt;/p&gt;

&lt;p&gt;If someone''s been working with this for years (some people at this company have been doing it since before &lt;em&gt;data science&lt;/em&gt; was coined/before social media existed), is it fair for them to claim big data experience?  According to &lt;a href="http://datascience.stackexchange.com/a/37/8953"&gt;this answer&lt;/a&gt;, it''s not the amount of data but what needs to be done with the data that matters - is that a universally accepted opinion?&lt;/p&gt;

&lt;p&gt;For what it''s worth, working with this data entails manipulation/cleaning of the data with some proprietary languages, shell scripts, and a lot of Python.  A little bit of R but that''s a more recent thing.  It involves tons of data visualization, drawing conclusions, and presenting to management/convincing decision makers.  Some of it involves trend determination, extrapolation, and comparisons made between data sets that aren''t related in a straightforward way, so it sounds data science-ish to me.  But I will be the first to admit that I have a limited understanding of what data science currently is.&lt;/p&gt;

&lt;p&gt;...bonus points if you can tell me whether or not this is an Easter egg or is the actual answer total for this site at the moment:
&lt;img src="http://i.stack.imgur.com/w6haD.png" alt="1337"&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I''ll try to clarify.  What do data science employers acknowledge as experience with big data/data science?  Does the size of the data above qualify experience with it?  Or is it universally accepted among those in the field that it''s not at all the size of the data, but what you need to do with the data?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5473, 2, '2015-04-07 14:46:19.027', 1, NULL, NULL, '&lt;p&gt;Data can be big in various ways. It can be large N (observations) and small P (variables per observation), like access logs for example. It can be large P and small N, like in biostatistics where you have tens of thousands of gene expressions from tens or hundreds of people. Or it can be both N and P, like in Facebook data. Data can also be made big, by normalizing every single detail of it, meaning that you''ll be able to make a massive relational database schema that actually represents simple data.&lt;/p&gt;

&lt;p&gt;All kinds of big data needs different approaches and companies mostly care about their own version of big data. That''s why the answer isn''t black and white.&lt;/p&gt;

&lt;p&gt;To answer the question you posed in title then I believe it''s ethical to claim experience with big data as long as you can put it into context of the business or domain where you encountered it and can describe some situations where a different set of tools or skills was needed than is used for smaller toy examples.&lt;/p&gt;
', NULL, NULL, '''/p'':95C,126C,188C ''abl'':82C ''access'':22C ''actual'':91C ''also'':66C ''answer'':120C,129C ''approach'':104C ''believ'':138C ''big'':5C,69C,100C,114C,146C ''biostatist'':36C ''black'':123C ''busi'':159C ''care'':108C ''claim'':143C ''compani'':106C ''context'':156C ''data'':2C,63C,64C,94C,101C,115C,147C ''databas'':88C ''describ'':168C ''detail'':74C ''differ'':103C,173C ''domain'':161C ''encount'':164C ''ethic'':141C ''everi'':72C ''exampl'':25C,187C ''experi'':144C ''express'':45C ''facebook'':62C ''gene'':44C ''hundr'':49C ''isn'':121C ''kind'':98C ''larg'':12C,29C ''like'':21C,34C,60C ''ll'':80C ''log'':23C ''long'':149C ''made'':68C ''make'':84C ''massiv'':86C ''mean'':77C ''most'':107C ''n'':13C,33C,57C ''need'':102C,180C ''normal'':71C ''observ'':14C,20C ''p'':1C,17C,30C,59C,96C,127C ''peopl'':51C ''per'':19C ''pose'':133C ''put'':153C ''question'':131C ''relat'':87C ''repres'':92C ''schema'':89C ''set'':174C ''simpl'':93C ''singl'':73C ''situat'':170C ''skill'':178C ''small'':16C,32C ''smaller'':185C ''ten'':40C,47C ''thousand'':42C ''titl'':135C ''tool'':176C ''toy'':186C ''use'':183C ''variabl'':18C ''various'':7C ''version'':112C ''way'':8C ''white'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5466, 2, '2015-04-06 19:20:18.873', 2, NULL, NULL, '&lt;p&gt;I''ve been using Anaconda Python 3.4 and Pandas to search 10M row database to match 20K of login credentials. Takes about a minute. The pandas internals make great use of memory. That said, truly big data requires a processing architecture matched to the problem. Pandas is just the glue (logic) in this equation, and other tools can do this as well. R, Scala, Haskell, SAS, etc. can replicate some of the logic - perhaps just enough to answer questions faster. But python makes a good (best?) general-purpose tool. You can run R code in python, as well as most other languages. Although interpretive, there are high performance techniques and tools such as pypy that can make python run almost as fast as benchmark tools with only slightly more effort. And python has many libraries that do just about everything - see above list.&lt;/p&gt;

&lt;p&gt;If you are asking if you should learn and use python, my answer is yes Articles indicate that python is used more than R among people who use both. But few data science problems are solved by a single tool. It may become your go-to tool, but its only that - a tool. And just as no sane person builds a house with just a hammer, no sane Data Scientist uses just one tool.&lt;/p&gt;
', NULL, NULL, '''/p'':146C,223C ''10m'':13C ''20k'':18C ''3.4'':8C ''almost'':122C ''although'':105C ''among'':172C ''anaconda'':6C ''answer'':79C,160C ''architectur'':42C ''articl'':163C ''ask'':151C ''becom'':190C ''benchmark'':126C ''best'':87C ''big'':37C ''build'':208C ''code'':96C ''credenti'':21C ''data'':38C,179C,217C ''databas'':15C ''effort'':132C ''enough'':77C ''equat'':55C ''etc'':68C ''everyth'':142C ''fast'':124C ''faster'':81C ''general'':89C ''general-purpos'':88C ''glue'':51C ''go'':193C ''go-to'':192C ''good'':86C ''great'':30C ''hammer'':214C ''haskel'':66C ''high'':109C ''hous'':210C ''indic'':164C ''intern'':28C ''interpret'':106C ''languag'':104C ''learn'':155C ''librari'':137C ''list'':145C ''logic'':52C,74C ''login'':20C ''make'':29C,84C,119C ''mani'':136C ''match'':17C,43C ''may'':189C ''memori'':33C ''minut'':25C ''one'':221C ''p'':1C,147C ''panda'':10C,27C,47C ''peopl'':173C ''perform'':110C ''perhap'':75C ''person'':207C ''problem'':46C,181C ''process'':41C ''purpos'':90C ''pypi'':116C ''python'':7C,83C,98C,120C,134C,158C,166C ''question'':80C ''r'':64C,95C,171C ''replic'':70C ''requir'':39C ''row'':14C ''run'':94C,121C ''said'':35C ''sane'':206C,216C ''sas'':67C ''scala'':65C ''scienc'':180C ''scientist'':218C ''search'':12C ''see'':143C ''singl'':186C ''slight'':130C ''solv'':183C ''take'':22C ''techniqu'':111C ''tool'':58C,91C,113C,127C,187C,195C,201C,222C ''truli'':36C ''use'':5C,31C,157C,168C,175C,219C ''ve'':3C ''well'':63C,100C ''yes'':162C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5469, 2, '2015-04-06 22:07:18.737', 1, NULL, NULL, '&lt;h1&gt;looooops&lt;/h1&gt;

&lt;p&gt;I can imagine it will run slow :)
avoid when possible very long for-loops in R. If so, keep the iterations as simple as possible, be careful using slow search functions and do not subset or edit large data structures.&lt;/p&gt;

&lt;h1&gt;restructure your data&lt;/h1&gt;

&lt;p&gt;I was talking to a guy, at a party, doing similar analysis. He said they arranged the data in a matrix of nrow sentences(200.000) and ncol(7000) words. Any element count how many times a given word occoured in a given sentence. If you need to do more analysis on the data set later, any operations can be done really fast. The result of your sentiment analysis would be the inner product of any row to a scoring vector(+1 +2 -1, score for word). The matrix would take some ~2Gb space if you use the bigmemory package and choose shorts(max 255 counts) instead of integers. bigmemory goes smoothly with multi-core(foreach, doMC, etc.) and elements can be modified individually much faster than for R matrices. The matrix can be saved to shared memort or HDD. bigmemory is using a C++ data structure which makes it possible to do some really fast operations with Rcpp. Steap laerning curve though.&lt;/p&gt;

&lt;p&gt;Sparse matrix would also be a good idea.
bigmemory package do not support windows&lt;/p&gt;

&lt;p&gt;Maybe get some inspiration here:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stackoverflow.com/questions/10233087/sentiment-analysis-using-r"&gt;http://stackoverflow.com/questions/10233087/sentiment-analysis-using-r&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''+1'':133C ''+2'':134C ''-1'':135C ''/h1'':3C,51C ''/p'':46C,216C,232C,239C ''/questions/10233087/sentiment-analysis-using-r'':245C ''/questions/10233087/sentiment-analysis-using-r&lt;/a&gt;&lt;/p&gt;'':248C ''200.000'':77C ''255'':156C ''2gb'':144C ''7000'':80C ''also'':221C ''analysi'':64C,102C,120C ''arrang'':68C ''avoid'':12C ''bigmemori'':150C,161C,193C,226C ''c'':197C ''care'':32C ''choos'':153C ''core'':167C ''count'':84C,157C ''curv'':214C ''data'':44C,50C,70C,105C,198C ''domc'':169C ''done'':112C ''edit'':42C ''element'':83C,172C ''etc'':170C ''fast'':114C,208C ''faster'':178C ''for-loop'':17C ''foreach'':168C ''function'':36C ''get'':235C ''given'':89C,94C ''goe'':162C ''good'':224C ''guy'':58C ''h1'':1C,47C ''hdd'':192C ''href'':242C ''idea'':225C ''imagin'':7C ''individu'':176C ''inner'':124C ''inspir'':237C ''instead'':158C ''integ'':160C ''iter'':26C ''keep'':24C ''laern'':213C ''larg'':43C ''later'':107C ''long'':16C ''looooop'':2C ''loop'':19C ''make'':201C ''mani'':86C ''matric'':182C ''matrix'':73C,140C,184C,219C ''max'':155C ''mayb'':234C ''memort'':190C ''modifi'':175C ''much'':177C ''multi'':166C ''multi-cor'':165C ''ncol'':79C ''need'':98C ''nrow'':75C ''occour'':91C ''oper'':109C,209C ''p'':4C,52C,217C,233C,240C ''packag'':151C,227C ''parti'':61C ''possibl'':14C,30C,203C ''product'':125C ''r'':21C,181C ''rcpp'':211C ''realli'':113C,207C ''restructur'':48C ''result'':116C ''row'':128C ''run'':10C ''said'':66C ''save'':187C ''score'':131C,136C ''search'':35C ''sentenc'':76C,95C ''sentiment'':119C ''set'':106C ''share'':189C ''short'':154C ''similar'':63C ''simpl'':28C ''slow'':11C,34C ''smooth'':163C ''space'':145C ''spars'':218C ''stackoverflow.com'':244C,247C ''stackoverflow.com/questions/10233087/sentiment-analysis-using-r'':243C ''stackoverflow.com/questions/10233087/sentiment-analysis-using-r&lt;/a&gt;&lt;/p&gt;'':246C ''steap'':212C ''structur'':45C,199C ''subset'':40C ''support'':230C ''take'':142C ''talk'':55C ''though'':215C ''time'':87C ''use'':33C,148C,195C ''vector'':132C ''window'':231C ''word'':81C,90C,138C ''would'':121C,141C,220C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8722, 1, '2015-11-04 17:10:29.887', 0, 47, 'Spark 1.5.1: Train many binary classifiers, save them, then use them on new data', '&lt;p&gt;I have a DataFrame representing an annotated dataset with 300 labels.&lt;/p&gt;

&lt;p&gt;The DataFrame looks like follow (the first row is just to explain the columns):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;binary Vector column for labeling, feature column
[0, 1, 1, 0, 0 , 0.... ], featureVec
[0, 0, 1, 0, 1 , 0.... ], featureVec
......
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The labelling column, represent the presence or the absence of each label as an annotation of a feature vector (which we can see in the second column).&lt;/p&gt;

&lt;p&gt;Please correct me if I am wrong: &lt;strong&gt;I can''t train one multi-class classifier because the labels are not exclusives, so I would like to train one binary classifier per label.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;As I have 300 labels, then I need to train and optimise 300 classifier (say logistic regression classifier for example).&lt;/p&gt;

&lt;p&gt;What is the best way and best practices to train my classifiers with N-cross validation for parameter optimisation? any example code or reference is highly appreciated.&lt;/p&gt;

&lt;p&gt;Once the classifiers are optimised, what is the best way and best practices to save the best models and use them to classify the new data?&lt;/p&gt;
', 'classification  multiclass-classification  multilabel-classification', NULL, '''/code'':74C ''/p'':34C,50C,104C,141C,163C,193C,221C ''/pre'':75C ''/strong'':140C ''0'':60C,63C,64C,65C,67C,68C,70C,72C ''1'':61C,62C,69C,71C ''1.5.1'':2A ''300'':32C,146C,155C ''absenc'':85C ''annot'':29C,91C ''appreci'':192C ''best'':168C,171C,203C,206C,211C ''binari'':5A,53C,136C ''class'':121C ''classif'':15B,18B,21B ''classifi'':6A,122C,137C,156C,160C,176C,197C,217C ''code'':52C,187C ''column'':49C,55C,59C,79C,103C ''correct'':107C ''cross'':180C ''data'':14A,220C ''datafram'':26C,37C ''dataset'':30C ''exampl'':162C,186C ''exclus'':128C ''explain'':47C ''featur'':58C,94C ''featurevec'':66C,73C ''first'':42C ''follow'':40C ''high'':191C ''label'':33C,57C,78C,88C,125C,139C,147C ''like'':39C,132C ''logist'':158C ''look'':38C ''mani'':4A ''model'':212C ''multi'':120C ''multi-class'':119C ''multiclass'':17B ''multiclass-classif'':16B ''multilabel'':20B ''multilabel-classif'':19B ''n'':179C ''n-cross'':178C ''need'':150C ''new'':13A,219C ''one'':118C,135C ''optimis'':154C,184C,199C ''p'':22C,35C,76C,105C,142C,164C,194C ''paramet'':183C ''per'':138C ''pleas'':106C ''practic'':172C,207C ''pre'':51C ''presenc'':82C ''refer'':189C ''regress'':159C ''repres'':27C,80C ''row'':43C ''save'':7A,209C ''say'':157C ''second'':102C ''see'':99C ''spark'':1A ''strong'':113C ''train'':3A,117C,134C,152C,174C ''use'':10A,214C ''valid'':181C ''vector'':54C,95C ''way'':169C,204C ''would'':131C ''wrong'':112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8150, 1, '2015-09-22 01:35:33.447', 4, 96, 'How to create clusters of position data?', '&lt;p&gt;I am asking this question because the &lt;a href="http://datascience.stackexchange.com/questions/8018/are-there-any-machine-learning-techniques-to-identify-points-on-plots-images"&gt;previous&lt;/a&gt; one wasn''t very helpful and I asked about a different solution for the same problem.&lt;/p&gt;

&lt;h1&gt;The Problem&lt;/h1&gt;

&lt;p&gt;I have lateral positions, &lt;code&gt;xcoord&lt;/code&gt;, of vehicles over time which were recorded as the distances from the right edge of the road. This can be seen for one vehicle in the following plot:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/s8BNW.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/s8BNW.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Each point on the plot represents the position of the front center of the vehicle. When the vehicle changes the lane (lane numbers not shown) there is a drastic change in the position as seen after the ''Start of Lane Change'' on the plot.&lt;br&gt;
The data behind this plot are like below:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  Vehicle.ID Frame.ID   xcoord Lane
1          2       13 16.46700    2
2          2       14 16.44669    2
3          2       15 16.42600    2
4          2       16 16.40540    2
5          2       17 16.38486    2
6          2       18 16.36433    2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to identify the start and end data points of a lane change by clustering the data as shown in the plot. The data points in the plot circled in red are more similar to each other because the variation between them is smaller compared to the data points in the middle which see large variation in position (&lt;code&gt;xcoord&lt;/code&gt;).&lt;br&gt;
My questions are: Is it possible to apply any clustering technique to segment these data so that I could identify the start and end point of a lane change? If yes, which technique would be most suitable?&lt;br&gt;
I use R. I have tried Hierarchical clustering before but don''t know how to apply it in this context. Please help. &lt;/p&gt;
', 'r  clustering', NULL, '''/a'':24C,101C ''/code'':53C,194C,258C ''/h1'':45C ''/p'':41C,82C,102C,157C,320C ''/pre'':195C ''/questions/8018/are-there-any-machine-learning-techniques-to-identify-points-on-plots-images'':22C ''/s8bnw.png'':88C,95C ''1'':164C ''13'':166C ''14'':171C ''15'':176C ''16'':181C ''16.36433'':192C ''16.38486'':187C ''16.40540'':182C ''16.42600'':177C ''16.44669'':172C ''16.46700'':167C ''17'':186C ''18'':191C ''2'':165C,168C,169C,170C,173C,175C,178C,180C,183C,185C,188C,190C,193C ''3'':174C ''4'':179C ''5'':184C ''6'':189C ''alt'':96C ''appli'':267C,313C ''ask'':13C,32C ''behind'':151C ''br'':148C,259C,297C ''center'':115C ''chang'':122C,133C,144C,210C,288C ''circl'':226C ''cluster'':4A,9B,212C,269C,305C ''code'':51C,159C,256C ''compar'':242C ''context'':317C ''could'':278C ''creat'':3A ''data'':7A,150C,205C,214C,221C,245C,274C ''datascience.stackexchange.com'':21C ''datascience.stackexchange.com/questions/8018/are-there-any-machine-learning-techniques-to-identify-points-on-plots-images'':20C ''descript'':99C ''differ'':35C ''distanc'':63C ''drastic'':132C ''edg'':67C ''end'':204C,283C ''enter'':97C ''follow'':80C ''frame.id'':161C ''front'':114C ''h1'':42C ''help'':29C,319C ''hierarch'':304C ''href'':19C,85C ''i.stack.imgur.com'':87C,94C ''i.stack.imgur.com/s8bnw.png'':86C,93C ''identifi'':200C,279C ''imag'':98C ''img'':91C ''know'':310C ''lane'':124C,125C,143C,163C,209C,287C ''larg'':252C ''later'':49C ''like'':155C ''middl'':249C ''nofollow'':90C ''number'':126C ''one'':25C,76C ''p'':10C,46C,83C,103C,196C ''pleas'':318C ''plot'':81C,108C,147C,153C,219C,225C ''point'':105C,206C,222C,246C,284C ''posit'':6A,50C,111C,136C,255C ''possibl'':265C ''pre'':158C ''previous'':23C ''problem'':40C,44C ''question'':15C,261C ''r'':8B,300C ''record'':60C ''red'':228C ''rel'':89C ''repres'':109C ''right'':66C ''road'':70C ''see'':251C ''seen'':74C,138C ''segment'':272C ''shown'':128C,216C ''similar'':231C ''smaller'':241C ''solut'':36C ''src'':92C ''start'':141C,202C,281C ''suitabl'':296C ''techniqu'':270C,292C ''time'':57C ''tri'':303C ''use'':299C ''variat'':237C,253C ''vehicl'':55C,77C,118C,121C ''vehicle.id'':160C ''want'':198C ''wasn'':26C ''would'':293C ''xcoord'':52C,162C,257C ''yes'':290C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5470, 1, '2015-04-07 07:18:19.127', 0, 156, 'Display more than two variables on the bar chart in Tableau', '&lt;p&gt;I am trying to put more than several variables on the bar chart in Tableau.
Display two of them works fine: use Dual Axes chart type + select Syncronize axes option.&lt;/p&gt;

&lt;p&gt;Var1 and Var2:
&lt;img src="http://i.stack.imgur.com/Wy3Zo.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Var2 and Var3:
&lt;img src="http://i.stack.imgur.com/oHulO.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;I want to add Var3 somehow to the first chart.
As a bars, or may be circles. But I want them to be on the same scale as the first two variables, and all start from zero.
&lt;img src="http://i.stack.imgur.com/RIxY3.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;After dragging Var3 to the first chart I got the stacked bars...
&lt;img src="http://i.stack.imgur.com/21K2K.png" alt="enter image description here"&gt;&lt;/p&gt;
', 'visualization  tableau  barplot', NULL, '''/21k2k.png'':143C ''/ohulo.png'':70C ''/p'':46C,61C,76C,125C,149C ''/rixy3.png'':119C ''/wy3zo.png'':55C ''add'':81C ''alt'':56C,71C,120C,144C ''axe'':39C,44C ''bar'':8A,27C,90C,138C ''barplot'':14B ''chart'':9A,28C,40C,87C,133C ''circl'':94C ''descript'':59C,74C,123C,147C ''display'':1A,31C ''drag'':128C ''dual'':38C ''enter'':57C,72C,121C,145C ''fine'':36C ''first'':86C,107C,132C ''got'':135C ''i.stack.imgur.com'':54C,69C,118C,142C ''i.stack.imgur.com/21k2k.png'':141C ''i.stack.imgur.com/ohulo.png'':68C ''i.stack.imgur.com/rixy3.png'':117C ''i.stack.imgur.com/wy3zo.png'':53C ''imag'':58C,73C,122C,146C ''img'':51C,66C,115C,139C ''may'':92C ''option'':45C ''p'':15C,47C,62C,77C,126C ''put'':20C ''scale'':104C ''select'':42C ''sever'':23C ''somehow'':83C ''src'':52C,67C,116C,140C ''stack'':137C ''start'':112C ''syncron'':43C ''tableau'':11A,13B,30C ''tri'':18C ''two'':4A,32C,108C ''type'':41C ''use'':37C ''var1'':48C ''var2'':50C,63C ''var3'':65C,82C,129C ''variabl'':5A,24C,109C ''visual'':12B ''want'':79C,97C ''work'':35C ''zero'':114C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5471, 1, '2015-04-07 14:05:28.01', 1, 228, 'Building a static local website using Rmarkdown: step by step procedure', '&lt;p&gt;I am trying to understand the procedure of building a static local website using &lt;a href="http://www.r-project.org/" rel="nofollow"&gt;R&lt;/a&gt; and &lt;code&gt;Rmarkdown&lt;/code&gt;. I am aware of a &lt;a href="http://rmarkdown.rstudio.com/html_document_format.html#creating-a-website" rel="nofollow"&gt;Rmarkdown&lt;/a&gt; website where the procedure is outlined, but unfortunately I do not understand the steps. &lt;/p&gt;

&lt;p&gt;Does anybody here have some experience in building a static local website and would be so kind as to describe the procedure in more detail?   &lt;/p&gt;
', 'r', NULL, '''/a'':34C,52C ''/code'':38C ''/html_document_format.html#creating-a-website'':48C ''/p'':67C,94C ''anybodi'':70C ''awar'':41C ''build'':1A,22C,76C ''code'':36C ''describ'':88C ''detail'':93C ''experi'':74C ''href'':29C,45C ''kind'':85C ''local'':4A,25C,79C ''nofollow'':32C,50C ''outlin'':58C ''p'':13C,68C ''procedur'':11A,20C,56C,90C ''r'':12B,33C ''rel'':31C,49C ''rmarkdown'':7A,37C,51C ''rmarkdown.rstudio.com'':47C ''rmarkdown.rstudio.com/html_document_format.html#creating-a-website'':46C ''static'':3A,24C,78C ''step'':8A,10A,66C ''tri'':16C ''understand'':18C,64C ''unfortun'':60C ''use'':6A,27C ''websit'':5A,26C,53C,80C ''would'':82C ''www.r-project.org'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5978, 2, '2015-06-01 16:32:03.14', 2, NULL, NULL, '&lt;p&gt;There is a basic introduction to the Bayesian method for spam detection in the book "Doing Data Science - Straight Talk from the Frontline" by Cathy O''Neil, Rachel Schutt.&lt;/p&gt;

&lt;p&gt;The chapter is good, because it explains why other common data science models don''t work for spam classifiers. The whole book uses R throughout, so only pick it up if you are interested in working with R.&lt;/p&gt;

&lt;p&gt;It uses the Enron email set as training data, since it has emails divided into spam/not spam already. &lt;/p&gt;
', NULL, NULL, '''/p'':31C,71C,91C ''alreadi'':90C ''basic'':5C ''bayesian'':9C ''book'':16C,54C ''cathi'':26C ''chapter'':34C ''classifi'':51C ''common'':42C ''data'':18C,43C,81C ''detect'':13C ''divid'':86C ''email'':77C,85C ''enron'':76C ''explain'':39C ''frontlin'':24C ''good'':36C ''interest'':66C ''introduct'':6C ''method'':10C ''model'':45C ''neil'':28C ''o'':27C ''p'':1C,32C,72C ''pick'':60C ''r'':56C,70C ''rachel'':29C ''schutt'':30C ''scienc'':19C,44C ''set'':78C ''sinc'':82C ''spam'':12C,50C,89C ''spam/not'':88C ''straight'':20C ''talk'':21C ''throughout'':57C ''train'':80C ''use'':55C,74C ''whole'':53C ''work'':48C,68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5662, 2, '2015-05-01 14:06:08.007', 6, NULL, NULL, '&lt;p&gt;An approach that yields more consistent results is &lt;a href="http://en.wikipedia.org/wiki/K-means%2B%2B"&gt;K-means++&lt;/a&gt;.  This approach acknowledges that there is probably a better choice of initial centroid locations than simple random assignment. Specifically, K-means tends to perform better when centroids are seeded in such a way that doesn''t clump them together in space. &lt;/p&gt;

&lt;p&gt;In short, the method is as follows:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Choose one of your data points at random as an initial centroid.&lt;/li&gt;
&lt;li&gt;Calculate $D(x)$, the distance between your initial centroid and all other data points, $x$.&lt;/li&gt;
&lt;li&gt;Choose your next centroid from the remaining datapoints with probability proportional to $D(x)^2$&lt;/li&gt;
&lt;li&gt;Repeat until all centroids have been assigned.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Note: $D(x)$ should be updated as more centroids are added.  It should be set to be the distance between a data point and the nearest centroid.&lt;/p&gt;

&lt;p&gt;You may also be interested to read &lt;a href="http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf"&gt;this paper&lt;/a&gt; that proposes the method and describes its overall expected performance.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5477, 2, '2015-04-08 03:47:26.333', 2, NULL, NULL, '&lt;p&gt;In most things, related to &lt;strong&gt;R&lt;/strong&gt;, there are &lt;strong&gt;many approaches&lt;/strong&gt; to solve a problem, sometimes too many, I would say. The task of building a static website, using &lt;em&gt;RMarkdown&lt;/em&gt;, is not an exception.&lt;/p&gt;

&lt;p&gt;One of the best, albeit somewhat brief, sets of workflows on the topic include the following one by Daniel Wollschlaeger, which includes &lt;a href="http://www.uni-kiel.de/psychologie/rexrepos/posts/rerWorkflowJN.html" rel="nofollow"&gt;this workflow&lt;/a&gt;, based on &lt;em&gt;R&lt;/em&gt;, &lt;em&gt;nanoc&lt;/em&gt; and &lt;em&gt;Jekyll&lt;/em&gt;, as well as &lt;a href="http://www.uni-kiel.de/psychologie/rexrepos/posts/rerWorkflowWP.html" rel="nofollow"&gt;this workflow&lt;/a&gt;, based on &lt;em&gt;R&lt;/em&gt; and &lt;em&gt;WordPress&lt;/em&gt;. Another good workflow is &lt;a href="http://jason.bryer.org/posts/2012-12-10/Markdown_Jekyll_R_for_Blogging.html" rel="nofollow"&gt;this one&lt;/a&gt; by Jason Bryer, which is focused on &lt;em&gt;R(Markdown)&lt;/em&gt;, &lt;em&gt;Jekyll&lt;/em&gt; and &lt;em&gt;GitHub Pages&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Not everyone likes &lt;em&gt;GitHub Pages&lt;/em&gt;, &lt;em&gt;Jekyll&lt;/em&gt;, &lt;em&gt;Octopress&lt;/em&gt; and &lt;em&gt;Ruby&lt;/em&gt;, so some people came up with &lt;em&gt;alternative solutions&lt;/em&gt;. For example, &lt;a href="http://www.znmeb.mobi/stories/blogging-with-rstudio-and-nikola.html" rel="nofollow"&gt;this workflow&lt;/a&gt; by Edward Borasky is based on &lt;em&gt;R&lt;/em&gt; and, for a static website generator, on &lt;em&gt;Python&lt;/em&gt;-based &lt;em&gt;Nicola&lt;/em&gt; (instead of &lt;em&gt;Ruby&lt;/em&gt;-based &lt;em&gt;Jekyll&lt;/em&gt; or &lt;em&gt;nanoc&lt;/em&gt;). Speaking about &lt;strong&gt;static website generators&lt;/strong&gt;, there are tons of them, in various programming languages, so, if you want to experiment, check &lt;a href="http://staticsitegenerators.net" rel="nofollow"&gt;this amazing website&lt;/a&gt;, listing almost all of them. Almost, because some are missing - for example, &lt;em&gt;Samantha&lt;/em&gt; and &lt;em&gt;Ghost&lt;/em&gt;, listed &lt;a href="http://ropensci.github.io/reproducibility-guide/sections/tools" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Some other interesting workflows include &lt;a href="http://joshualande.com/jekyll-github-pages-poole" rel="nofollow"&gt;this one&lt;/a&gt; by Joshua Lande, which is based on Jekyll and GitHub Pages, but includes some nice examples of customization for integrating a website with Disqus, Google Analytics and Twitter as well as getting custom URL for the site and more.&lt;/p&gt;

&lt;p&gt;Those who want a &lt;strong&gt;pure R-based static site solution&lt;/strong&gt;, now have some options, including &lt;code&gt;rsmith&lt;/code&gt; (&lt;a href="https://github.com/hadley/rsmith" rel="nofollow"&gt;https://github.com/hadley/rsmith&lt;/a&gt;), a static site generator by Hadley Wickham, and &lt;code&gt;Poirot&lt;/code&gt; (&lt;a href="https://github.com/ramnathv/poirot" rel="nofollow"&gt;https://github.com/ramnathv/poirot&lt;/a&gt;), a static site generator by Ramnath Vaidyanathan.&lt;/p&gt;

&lt;p&gt;Finally, I would like to mention an &lt;a href="http://notebook.madsenlab.org/labnotebook.html" rel="nofollow"&gt;interesting project&lt;/a&gt; (from an &lt;strong&gt;open science&lt;/strong&gt; perspective) that I recently ran across - an open source software by Mark Madsen for a &lt;em&gt;lab notebook static site&lt;/em&gt;, which is based on &lt;em&gt;GitHub Pages&lt;/em&gt; and &lt;em&gt;Jekyll&lt;/em&gt;, but also supports &lt;em&gt;pandoc&lt;/em&gt;, &lt;em&gt;R&lt;/em&gt;, &lt;em&gt;RMarkdown&lt;/em&gt; and &lt;em&gt;knitr&lt;/em&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':74C,99C,122C,182C,250C,279C,296C,415C ''/code'':358C,379C ''/em'':36C,79C,82C,86C,104C,108C,133C,136C,141C,150C,153C,156C,160C,170C,191C,201C,205C,210C,214C,218C,265C,269C,442C,450C,454C,460C,463C,466C,470C ''/hadley/rsmith'':363C ''/hadley/rsmith&lt;/a&gt;),'':368C ''/jekyll-github-pages-poole'':291C ''/labnotebook.html'':410C ''/p'':41C,142C,280C,336C,397C,471C ''/posts/2012-12-10/markdown_jekyll_r_for_blogging.html'':117C ''/psychologie/rexrepos/posts/rerworkflowjn.html'':69C ''/psychologie/rexrepos/posts/rerworkflowwp.html'':94C ''/ramnathv/poirot'':384C ''/ramnathv/poirot&lt;/a&gt;),'':389C ''/reproducibility-guide/sections/tools'':275C ''/stories/blogging-with-rstudio-and-nikola.html'':177C ''/strong'':9C,15C,225C,350C,421C ''across'':427C ''albeit'':47C ''almost'':252C,256C ''also'':456C ''altern'':168C ''amaz'':248C ''analyt'':322C ''anoth'':109C ''approach'':14C ''base'':75C,100C,187C,202C,211C,302C,346C,445C ''best'':46C ''boraski'':185C ''brief'':49C ''bryer'':125C ''build'':29C ''came'':164C ''check'':241C ''code'':356C,377C ''custom'':314C,329C ''daniel'':61C ''disqus'':320C ''edward'':184C ''em'':34C,77C,80C,84C,102C,106C,130C,134C,138C,147C,151C,154C,158C,167C,189C,199C,203C,208C,212C,216C,263C,267C,437C,447C,452C,458C,461C,464C,468C ''everyon'':145C ''exampl'':172C,262C,312C ''except'':40C ''experi'':240C ''final'':399C ''focus'':128C ''follow'':58C ''generat'':197C,224C,372C,393C ''get'':328C ''ghost'':268C ''github'':139C,148C,306C,448C ''github.com'':362C,367C,383C,388C ''github.com/hadley/rsmith'':361C ''github.com/hadley/rsmith&lt;/a&gt;),'':366C ''github.com/ramnathv/poirot'':382C ''github.com/ramnathv/poirot&lt;/a&gt;),'':387C ''good'':110C ''googl'':321C ''hadley'':374C ''href'':66C,91C,114C,174C,243C,272C,288C,360C,381C,407C ''includ'':56C,64C,286C,309C,355C ''instead'':206C ''integr'':316C ''interest'':284C,413C ''jason'':124C ''jason.bryer.org'':116C ''jason.bryer.org/posts/2012-12-10/markdown_jekyll_r_for_blogging.html'':115C ''jekyl'':85C,135C,152C,213C,304C,453C ''joshua'':298C ''joshualande.com'':290C ''joshualande.com/jekyll-github-pages-poole'':289C ''knitr'':469C ''lab'':438C ''land'':299C ''languag'':234C ''like'':146C,402C ''list'':251C,270C ''madsen'':434C ''mani'':13C,22C ''mark'':433C ''markdown'':132C ''mention'':404C ''miss'':260C ''nanoc'':81C,217C ''nice'':311C ''nicola'':204C ''nofollow'':71C,96C,119C,179C,246C,277C,293C,365C,386C,412C ''notebook'':439C ''notebook.madsenlab.org'':409C ''notebook.madsenlab.org/labnotebook.html'':408C ''octopress'':155C ''one'':43C,59C,121C,295C ''open'':419C,429C ''option'':354C ''p'':1C,42C,143C,281C,337C,398C ''page'':140C,149C,307C,449C ''pandoc'':459C ''peopl'':163C ''perspect'':422C ''poirot'':378C ''problem'':19C ''program'':233C ''project'':414C ''pure'':343C ''python'':200C ''r'':8C,78C,103C,131C,190C,345C,462C ''r-base'':344C ''ramnath'':395C ''ran'':426C ''recent'':425C ''rel'':70C,95C,118C,178C,245C,276C,292C,364C,385C,411C ''relat'':5C ''rmarkdown'':35C,465C ''ropensci.github.io'':274C ''ropensci.github.io/reproducibility-guide/sections/tools'':273C ''rsmith'':357C ''rubi'':159C,209C ''samantha'':264C ''say'':25C ''scienc'':420C ''set'':50C ''site'':333C,348C,371C,392C,441C ''softwar'':431C ''solut'':169C,349C ''solv'':17C ''sometim'':20C ''somewhat'':48C ''sourc'':430C ''speak'':219C ''static'':31C,195C,222C,347C,370C,391C,440C ''staticsitegenerators.net'':244C ''strong'':7C,12C,221C,342C,418C ''support'':457C ''task'':27C ''thing'':4C ''ton'':228C ''topic'':55C ''twitter'':324C ''url'':330C ''use'':33C ''vaidyanathan'':396C ''various'':232C ''want'':238C,340C ''websit'':32C,196C,223C,249C,318C ''well'':88C,326C ''wickham'':375C ''wollschlaeg'':62C ''wordpress'':107C ''workflow'':52C,73C,98C,111C,181C,285C ''would'':24C,401C ''www.uni-kiel.de'':68C,93C ''www.uni-kiel.de/psychologie/rexrepos/posts/rerworkflowjn.html'':67C ''www.uni-kiel.de/psychologie/rexrepos/posts/rerworkflowwp.html'':92C ''www.znmeb.mobi'':176C ''www.znmeb.mobi/stories/blogging-with-rstudio-and-nikola.html'':175C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8712, 2, '2015-11-04 11:17:44.937', 0, NULL, NULL, '&lt;p&gt;For my research on medical data, I am very pleased with the features RapidMiner offers. &lt;a href="http://rapidminer.com" rel="nofollow"&gt;Rapidminer&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/a'':23C ''/p'':24C ''data'':7C ''featur'':14C ''href'':18C ''medic'':6C ''nofollow'':21C ''offer'':16C ''p'':1C ''pleas'':11C ''rapidmin'':15C,22C ''rapidminer.com'':19C ''rel'':20C ''research'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5479, 1, '2015-04-08 07:17:40.713', 1, 86, 'LFR as synthesized data for dynamic community detection methods', '&lt;p&gt;In recent methods for community detection in dynamic networks, LFR benchmark is used as dynamic dataset generator, but I thought it is for static community based data generation. For example in paper &lt;a href="http://www.cise.ufl.edu/~mythai/files/mobicom_CS.pdf" rel="nofollow"&gt;Overlapping Communities in Dynamic Networks: Their Detection and Mobile Applications&lt;/a&gt;, LFR is used. But I don''t know how this dataset is generated. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://pages.towson.edu/npnguyen/Databases/AFOCS.zip" rel="nofollow"&gt;Here&lt;/a&gt; is the code of this paper and ground truth included is only for one snapshot.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Question: How is the dataset generated?&lt;/strong&gt;&lt;/p&gt;
', 'dataset', NULL, '''/a'':61C,85C ''/npnguyen/databases/afocs.zip'':81C ''/p'':75C,101C,111C ''/strong'':110C ''/~mythai/files/mobicom_cs.pdf'':48C ''applic'':60C ''base'':37C ''benchmark'':22C ''code'':88C ''communiti'':7A,16C,36C,52C ''data'':4A,38C ''dataset'':10B,27C,72C,108C ''detect'':8A,17C,57C ''dynam'':6A,19C,26C,54C ''exampl'':41C ''generat'':28C,39C,74C,109C ''ground'':93C ''href'':45C,78C ''includ'':95C ''know'':69C ''lfr'':1A,21C,62C ''method'':9A,14C ''mobil'':59C ''network'':20C,55C ''nofollow'':50C,83C ''one'':99C ''overlap'':51C ''p'':11C,76C,102C ''pages.towson.edu'':80C ''pages.towson.edu/npnguyen/databases/afocs.zip'':79C ''paper'':43C,91C ''question'':104C ''recent'':13C ''rel'':49C,82C ''snapshot'':100C ''static'':35C ''strong'':103C ''synthes'':3A ''thought'':31C ''truth'':94C ''use'':24C,64C ''www.cise.ufl.edu'':47C ''www.cise.ufl.edu/~mythai/files/mobicom_cs.pdf'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5480, 2, '2015-04-08 07:20:29.317', 3, NULL, NULL, '&lt;p&gt;In his book Data Smart, John Foreman solves common data science problems (clustering, naive bayes, ensemble methods,...) using Excel. Indeed it''s always good to have some knowledge of Python or R but I guess Excel can still get most of the job done !&lt;/p&gt;
', NULL, NULL, '''/p'':46C ''alway'':24C ''bay'':16C ''book'':4C ''cluster'':14C ''common'':10C ''data'':5C,11C ''done'':45C ''ensembl'':17C ''excel'':20C,37C ''foreman'':8C ''get'':40C ''good'':25C ''guess'':36C ''inde'':21C ''job'':44C ''john'':7C ''knowledg'':29C ''method'':18C ''naiv'':15C ''p'':1C ''problem'':13C ''python'':31C ''r'':33C ''scienc'':12C ''smart'':6C ''solv'':9C ''still'':39C ''use'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5481, 1, '2015-04-08 16:51:01.88', 4, 59, 'Data store for testing data products?', '&lt;p&gt;Is there a recommended approach for storing processed data for testing new data products?  &lt;/p&gt;

&lt;p&gt;Basically, I''d like to have a system where a data scientist or an analyst could think of a new data product to present to users, do the data processing to create it, and then put it in a data store that our application can then access easily.&lt;/p&gt;

&lt;p&gt;What I''m not sure about is what kind of data store would be good for this type of "testing" use case.  Since it would need to be flexible enough to handle different types of data products, like aggregates, windowed data, etc.  And ideally it wouldn''t require a huge instrumentation process to try out new things.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5771, 1, '2015-05-12 18:34:44.01', 2, 27, 'Ranking Bias in Learning to Rank', '&lt;p&gt;Users tend to click on results ranked highly by search engines much more often than those ranked lower. How do you train a search engine using click data / search logs without this bias? I.e. you don''t want to teach the search engine that the results that are currently ranked highly should necessarily continue to be ranked highly just because they were frequently clicked.&lt;/p&gt;
', 'machine-learning  search  ranking', NULL, '''/p'':77C ''bias'':2A,45C ''click'':16C,39C,76C ''continu'':66C ''current'':61C ''data'':40C ''engin'':23C,37C,55C ''frequent'':75C ''high'':20C,63C,70C ''i.e'':46C ''learn'':4A,9B ''log'':42C ''lower'':30C ''machin'':8B ''machine-learn'':7B ''much'':24C ''necessarili'':65C ''often'':26C ''p'':12C ''rank'':1A,6A,11B,19C,29C,62C,69C ''result'':18C,58C ''search'':10B,22C,36C,41C,54C ''teach'':52C ''tend'':14C ''train'':34C ''use'':38C ''user'':13C ''want'':50C ''without'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5503, 1, '2015-04-11 21:35:14.517', 4, 100, 'Neural Network Golf: smallest network for a certain level of performance', '&lt;p&gt;I am interested in any data, publications, etc about what is the smallest neural network that can achieve a certain level of classification performance.  By small I mean &lt;em&gt;few parameters&lt;/em&gt;, not &lt;em&gt;few arithmetic operations&lt;/em&gt; (=fast).  I am interested primarily in convolutional neural networks for vision applications, using something simple like CIFAR-10 without augmentation as the benchmark.  Top-performing networks on CIFAR in recent years have had anywhere between 100 million and 0.7 million parameters (!!), so clearly small size is not (always) a bad thing.  Small networks are also in general faster to train and overfit less.  Moreover, recent work on &lt;a href="http://arxiv.org/abs/1503.02531" rel="nofollow"&gt;Knowledge Distillation&lt;/a&gt;, &lt;a href="http://arxiv.org/abs/1412.6550" rel="nofollow"&gt;FitNets&lt;/a&gt;, etc show ways of making smaller networks from large networks while preserving most of the performance.&lt;/p&gt;

&lt;p&gt;Another question is, what is the best performance achievable with a network no larger than a fixed size?&lt;/p&gt;

&lt;p&gt;Examples of especially small networks that get good performance (100k parameters with 10% on CIFAR, anyone?) or systematic studies of the size vs performance tradeoff would be appreciated.&lt;/p&gt;
', 'neuralnetwork  convnet  hyperparameter', NULL, '''-10'':71C ''/a'':131C,140C ''/abs/1412.6550'':136C ''/abs/1503.02531'':126C ''/em'':47C,53C ''/p'':157C,177C,207C ''0.7'':93C ''10'':191C ''100'':90C ''100k'':188C ''achiev'':33C,167C ''also'':109C ''alway'':102C ''anoth'':159C ''anyon'':194C ''anywher'':88C ''applic'':65C ''appreci'':206C ''arithmet'':51C ''arxiv.org'':125C,135C ''arxiv.org/abs/1412.6550'':134C ''arxiv.org/abs/1503.02531'':124C ''augment'':73C ''bad'':104C ''benchmark'':76C ''best'':165C ''certain'':8A,35C ''cifar'':70C,82C,193C ''classif'':38C ''clear'':97C ''convnet'':13B ''convolut'':60C ''data'':21C ''distil'':130C ''em'':44C,49C ''especi'':181C ''etc'':23C,141C ''exampl'':179C ''fast'':54C ''faster'':112C ''fitnet'':139C ''fix'':175C ''general'':111C ''get'':185C ''golf'':3A ''good'':186C ''href'':123C,133C ''hyperparamet'':14B ''interest'':18C,57C ''knowledg'':129C ''larg'':149C ''larger'':172C ''less'':117C ''level'':9A,36C ''like'':69C ''make'':145C ''mean'':43C ''million'':91C,94C ''moreov'':118C ''network'':2A,5A,30C,62C,80C,107C,147C,150C,170C,183C ''neural'':1A,29C,61C ''neuralnetwork'':12B ''nofollow'':128C,138C ''oper'':52C ''overfit'':116C ''p'':15C,158C,178C ''paramet'':46C,95C,189C ''perform'':11A,39C,79C,156C,166C,187C,202C ''preserv'':152C ''primarili'':58C ''public'':22C ''question'':160C ''recent'':84C,119C ''rel'':127C,137C ''show'':142C ''simpl'':68C ''size'':99C,176C,200C ''small'':41C,98C,106C,182C ''smaller'':146C ''smallest'':4A,28C ''someth'':67C ''studi'':197C ''systemat'':196C ''thing'':105C ''top'':78C ''top-perform'':77C ''tradeoff'':203C ''train'':114C ''use'':66C ''vision'':64C ''vs'':201C ''way'':143C ''without'':72C ''work'':120C ''would'':204C ''year'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5483, 2, '2015-04-08 19:21:29.893', 3, NULL, NULL, '&lt;p&gt;Try looking at &lt;a href="https://git-lfs.github.com/" rel="nofollow"&gt;Git Large File Storage (LFS)&lt;/a&gt;. It is new, but might be the thing worth looking at.&lt;/p&gt;

&lt;p&gt;As I see, &lt;a href="https://news.ycombinator.com/item?id=9343021" rel="nofollow"&gt;a discussion on Hacker News&lt;/a&gt; mentions a few other ways to deal with large files:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://git-annex.branchable.com/git-annex/" rel="nofollow"&gt;git-annex&lt;/a&gt; (and e.g. &lt;a href="http://git-annex.branchable.com/tips/using_Amazon_S3/" rel="nofollow"&gt;using it with Amazon S3&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;a href="http://mercurial.selenic.com/wiki/LargefilesExtension" rel="nofollow"&gt;Mercurual Largefiles extension&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':15C,44C,68C,83C,96C ''/git-annex/'':62C ''/item?id=9343021'':36C ''/li'':84C,97C ''/p'':27C,55C ''/tips/using_amazon_s3/'':75C ''/ul'':98C ''/wiki/largefilesextension'':90C ''amazon'':81C ''annex'':67C ''deal'':51C ''discuss'':40C ''e.g'':70C ''extens'':95C ''file'':12C,54C ''git'':10C,66C ''git-annex'':65C ''git-annex.branchable.com'':61C,74C ''git-annex.branchable.com/git-annex/'':60C ''git-annex.branchable.com/tips/using_amazon_s3/'':73C ''git-lfs.github.com'':7C ''hacker'':42C ''href'':6C,33C,59C,72C,87C ''larg'':11C,53C ''largefil'':94C ''lfs'':14C ''li'':57C,85C ''look'':3C,25C ''mention'':45C ''mercurial.selenic.com'':89C ''mercurial.selenic.com/wiki/largefilesextension'':88C ''mercuru'':93C ''might'':20C ''new'':18C ''news'':43C ''news.ycombinator.com'':35C ''news.ycombinator.com/item?id=9343021'':34C ''nofollow'':9C,38C,64C,77C,92C ''p'':1C,28C ''rel'':8C,37C,63C,76C,91C ''s3'':82C ''see'':31C ''storag'':13C ''thing'':23C ''tri'':2C ''ul'':56C ''use'':78C ''way'':49C ''worth'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5485, 1, '2015-04-09 08:24:04.6', 1, 87, 'Correlations - Get values in the way we want', '&lt;p&gt;I have :&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;a matrix X with N lines&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;a vector Y&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I''ve computed the Euclidean distance with Y for each line of X.&lt;/p&gt;

&lt;p&gt;What I get is a vector of distances.&lt;/p&gt;

&lt;p&gt;What I want is a vector of scores between 0 and 1, 1 meaning "very" high correlation, 0 meaning "no" correlation.&lt;/p&gt;

&lt;p&gt;Here what I did :&lt;/p&gt;

&lt;p&gt;I divided the vector of distances by the max distance inside it.
I get vector D. &lt;/p&gt;

&lt;p&gt;&lt;em&gt;1 - D&lt;/em&gt; is the final result with values between 0 and 1.&lt;/p&gt;

&lt;p&gt;The problem is that I get many values (75%) too close to 1.
Do you think what I did is correct ?&lt;/p&gt;

&lt;p&gt;How would you get a better result ?
(Between 0 and 1 but not everything too close to 1)&lt;/p&gt;

&lt;p&gt;For now, I tried to take the square of the result. (To stay between 0 and 1 but to minimize the values)&lt;/p&gt;

&lt;p&gt;Here a picture of the distance values I want to turn in a score
&lt;img src="http://i.stack.imgur.com/36LDh.png" alt="distance values I want to turn in a score"&gt;&lt;/p&gt;
', 'machine-learning  data-mining  statistics  python  correlation', NULL, '''/36ldh.png'':215C ''/em'':117C ''/li'':32C,39C ''/p'':21C,31C,38C,55C,65C,88C,94C,112C,128C,151C,171C,195C,226C ''/ul'':40C ''0'':76C,84C,125C,161C,187C ''1'':78C,79C,115C,127C,142C,163C,170C,189C ''75'':138C ''alt'':216C ''better'':158C ''close'':140C,168C ''comput'':44C ''correct'':150C ''correl'':1A,17B,83C,87C ''d'':111C,116C ''data'':13B ''data-min'':12B ''distanc'':47C,64C,101C,105C,202C,217C ''divid'':97C ''em'':114C ''euclidean'':46C ''everyth'':166C ''final'':120C ''get'':2A,59C,109C,135C,156C ''high'':82C ''i.stack.imgur.com'':214C ''i.stack.imgur.com/36ldh.png'':213C ''img'':211C ''insid'':106C ''learn'':11B ''li'':23C,33C ''line'':30C,52C ''machin'':10B ''machine-learn'':9B ''mani'':136C ''matrix'':26C ''max'':104C ''mean'':80C,85C ''mine'':14B ''minim'':192C ''n'':29C ''p'':18C,24C,34C,41C,56C,66C,89C,95C,113C,129C,152C,172C,196C ''pictur'':199C ''problem'':131C ''python'':16B ''result'':121C,159C,183C ''score'':74C,210C,225C ''squar'':180C ''src'':212C ''statist'':15B ''stay'':185C ''take'':178C ''think'':145C ''tri'':176C ''turn'':207C,222C ''ul'':22C ''valu'':3A,123C,137C,194C,203C,218C ''ve'':43C ''vector'':36C,62C,72C,99C,110C ''want'':8A,69C,205C,220C ''way'':6A ''would'':154C ''x'':27C,54C ''y'':37C,49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5488, 2, '2015-04-09 12:21:08.427', 7, NULL, NULL, '&lt;p&gt;As Andre Holzner has said, extending R with C/C++ extension is a very good way to take advantage of the best of both sides. Also you can try the inverse , working with C++ and ocasionally calling function of R with the RInside package  o R. Here you can find how&lt;/p&gt;

&lt;p&gt;&lt;a href="http://cran.r-project.org/web/packages/RInside/index.html"&gt;http://cran.r-project.org/web/packages/RInside/index.html&lt;/a&gt;
&lt;a href="http://dirk.eddelbuettel.com/code/rinside.html"&gt;http://dirk.eddelbuettel.com/code/rinside.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Once you''re working in C++ you have many libraries , many of them built up for specific problems, other more general&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.shogun-toolbox.org/page/features/"&gt;http://www.shogun-toolbox.org/page/features/&lt;/a&gt;
&lt;a href="http://image.diku.dk/shark/sphinx_pages/build/html/index.html"&gt;http://image.diku.dk/shark/sphinx_pages/build/html/index.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://mlpack.org/"&gt;http://mlpack.org/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':116C ''/code/rinside.html'':66C ''/code/rinside.html&lt;/a&gt;&lt;/p&gt;'':69C ''/p'':52C,92C ''/page/features/'':98C ''/page/features/&lt;/a&gt;'':101C ''/shark/sphinx_pages/build/html/index.html'':106C ''/shark/sphinx_pages/build/html/index.html&lt;/a&gt;&lt;/p&gt;'':109C ''/web/packages/rinside/index.html'':58C ''/web/packages/rinside/index.html&lt;/a&gt;'':61C ''advantag'':19C ''also'':26C ''andr'':3C ''best'':22C ''built'':84C ''c'':34C,76C ''c/c'':10C ''call'':37C ''cran.r-project.org'':57C,60C ''cran.r-project.org/web/packages/rinside/index.html'':56C ''cran.r-project.org/web/packages/rinside/index.html&lt;/a&gt;'':59C ''dirk.eddelbuettel.com'':65C,68C ''dirk.eddelbuettel.com/code/rinside.html'':64C ''dirk.eddelbuettel.com/code/rinside.html&lt;/a&gt;&lt;/p&gt;'':67C ''extend'':7C ''extens'':11C ''find'':50C ''function'':38C ''general'':91C ''good'':15C ''holzner'':4C ''href'':55C,63C,95C,103C,112C ''image.diku.dk'':105C,108C ''image.diku.dk/shark/sphinx_pages/build/html/index.html'':104C ''image.diku.dk/shark/sphinx_pages/build/html/index.html&lt;/a&gt;&lt;/p&gt;'':107C ''invers'':31C ''librari'':80C ''mani'':79C,81C ''mlpack.org'':113C,115C ''mlpack.org/&lt;/a&gt;&lt;/p&gt;'':114C ''o'':45C ''ocasion'':36C ''p'':1C,53C,70C,93C,110C ''packag'':44C ''problem'':88C ''r'':8C,40C,46C ''re'':73C ''rinsid'':43C ''said'':6C ''side'':25C ''specif'':87C ''take'':18C ''tri'':29C ''way'':16C ''work'':32C,74C ''www.shogun-toolbox.org'':97C,100C ''www.shogun-toolbox.org/page/features/'':96C ''www.shogun-toolbox.org/page/features/&lt;/a&gt;'':99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5761, 1, '2015-05-11 21:40:07.77', 1, 44, 'computational time and precision trade off', '&lt;p&gt;I know that there are a number of predictive models (generized linear ones, trees, neural network, support vector machines, knn, Naive Bayes, ...) that have been proposed to perform various analytical tasks. Now I am striving to find appropriate references about their performance when the data becames "Big". In other words, how is their performance when the data becames really big. Does the training time increase more than linear? Is there any comparative benchmark between computational time and precision when the data becames high (for the various predictive models).&lt;/p&gt;
', 'data-mining  bigdata', NULL, '''/p'':99C ''analyt'':40C ''appropri'':48C ''bay'':32C ''becam'':56C,68C,92C ''benchmark'':83C ''big'':57C,70C ''bigdata'':9B ''compar'':82C ''comput'':1A,85C ''data'':7B,55C,67C,91C ''data-min'':6B ''find'':47C ''generiz'':21C ''high'':93C ''increas'':75C ''knn'':30C ''know'':12C ''linear'':22C,78C ''machin'':29C ''mine'':8B ''model'':20C,98C ''naiv'':31C ''network'':26C ''neural'':25C ''number'':17C ''one'':23C ''p'':10C ''perform'':38C,52C,64C ''precis'':4A,88C ''predict'':19C,97C ''propos'':36C ''realli'':69C ''refer'':49C ''strive'':45C ''support'':27C ''task'':41C ''time'':2A,74C,86C ''trade'':5A ''train'':73C ''tree'':24C ''various'':39C,96C ''vector'':28C ''word'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5501, 1, '2015-04-11 15:14:18.35', 0, 51, 'Finding AUC using scoreKDD for KDD Cup 2012 track2 data', '&lt;p&gt;I''m trying to apply classification algorithms to KDD Cup 2012 track2 data &lt;a href="http://www.kddcup2012.org/c/kddcup2012-track2" rel="nofollow"&gt;http://www.kddcup2012.org/c/kddcup2012-track2&lt;/a&gt;
The training data is 10 GB. As I''m working on my local Ubuntu 14.04 system with 4GB RAM, loading it is not possible. So I''m using VowpalWabbit(it being out-of -core). I''ve obtained the predictions for linear regression using this data. 
To find AUC, they have provided scorekdd.py : &lt;a href="http://www.kddcup2012.org/c/kddcup2012-track2/data" rel="nofollow"&gt;http://www.kddcup2012.org/c/kddcup2012-track2/data&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;But execution of scorekdd.py on this complete data gives message "process killed", maybe because python tries to load this data into primary memory.
scorekdd.py expects the training data file as first input and predictions as second input file. 
So as first input file to scorekdd.py I gave only the first 2 columns(scorekdd uses only those 2). This input file and the prediction file are each around 1GB.&lt;/p&gt;

&lt;p&gt;Execution of these in scorekdd took place for about 8 hours and then laptop screen went dark.
I think still some process is taking place since the laptop is heated up. The process has been taking place for about 13 hours for now. 
Can I conclude that it is not possible to run scorekdd on this data on my local machine ?
I had tried using perf &lt;a href="http://osmot.cs.cornell.edu/kddcup/software.html" rel="nofollow"&gt;http://osmot.cs.cornell.edu/kddcup/software.html&lt;/a&gt; for finding AUC but when I used it on a subset of the data it gives "Aborting.  Exceeded 500000 items."
Are there better ways to find AUC ? Or does anyone know that KDDCup expects us to use just this scorekdd.py ?&lt;/p&gt;
', 'tools', NULL, '''/c/kddcup2012-track2'':30C ''/c/kddcup2012-track2&lt;/a&gt;'':35C ''/c/kddcup2012-track2/data'':93C ''/c/kddcup2012-track2/data&lt;/a&gt;&lt;/p&gt;'':98C ''/kddcup/software.html'':240C ''/kddcup/software.html&lt;/a&gt;'':245C ''/p'':168C,286C ''10'':40C ''13'':209C ''14.04'':50C ''1gb'':167C ''2'':150C,156C ''2012'':8A,23C ''4gb'':53C ''500000'':264C ''8'':179C ''abort'':262C ''algorithm'':19C ''anyon'':275C ''appli'':17C ''around'':166C ''auc'':2A,84C,248C,272C ''better'':268C ''classif'':18C ''column'':151C ''complet'':106C ''conclud'':215C ''core'':70C ''cup'':7A,22C ''dark'':186C ''data'':10A,25C,38C,81C,107C,119C,127C,226C,259C ''exceed'':263C ''execut'':101C,170C ''expect'':124C,279C ''file'':128C,137C,142C,159C,163C ''find'':1A,83C,247C,271C ''first'':130C,140C,149C ''gave'':146C ''gb'':41C ''give'':108C,261C ''heat'':199C ''hour'':180C,210C ''href'':27C,90C,237C ''input'':131C,136C,141C,158C ''item'':265C ''kdd'':6A,21C ''kddcup'':278C ''kill'':111C ''know'':276C ''laptop'':183C,197C ''linear'':77C ''load'':55C,117C ''local'':48C,229C ''m'':14C,44C,62C ''machin'':230C ''mayb'':112C ''memori'':122C ''messag'':109C ''nofollow'':32C,95C,242C ''obtain'':73C ''osmot.cs.cornell.edu'':239C,244C ''osmot.cs.cornell.edu/kddcup/software.html'':238C ''osmot.cs.cornell.edu/kddcup/software.html&lt;/a&gt;'':243C ''out-of'':67C ''p'':12C,99C,169C ''perf'':235C ''place'':176C,194C,206C ''possibl'':59C,220C ''predict'':75C,133C,162C ''primari'':121C ''process'':110C,191C,202C ''provid'':87C ''python'':114C ''ram'':54C ''regress'':78C ''rel'':31C,94C,241C ''run'':222C ''scorekdd'':4A,152C,174C,223C ''scorekdd.py'':88C,103C,123C,144C,285C ''screen'':184C ''second'':135C ''sinc'':195C ''still'':189C ''subset'':256C ''system'':51C ''take'':193C,205C ''think'':188C ''took'':175C ''tool'':11B ''track2'':9A,24C ''train'':37C,126C ''tri'':15C,115C,233C ''ubuntu'':49C ''us'':280C ''use'':3A,63C,79C,153C,234C,252C,282C ''ve'':72C ''vowpalwabbit'':64C ''way'':269C ''went'':185C ''work'':45C ''www.kddcup2012.org'':29C,34C,92C,97C ''www.kddcup2012.org/c/kddcup2012-track2'':28C ''www.kddcup2012.org/c/kddcup2012-track2&lt;/a&gt;'':33C ''www.kddcup2012.org/c/kddcup2012-track2/data'':91C ''www.kddcup2012.org/c/kddcup2012-track2/data&lt;/a&gt;&lt;/p&gt;'':96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5489, 1, '2015-04-09 14:27:36.663', 1, 595, 'Extracting model equation and other data from ''glm'' function in R', '&lt;p&gt;I''ve made a logistic regression to combine two independent variables in &lt;code&gt;R&lt;/code&gt;, using &lt;code&gt;pROC&lt;/code&gt; package and I obtain this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; summary(fit)

Call: glm(formula = Case ~ X + Y, family = "binomial", data = data)

Deviance Residuals: 
  Min       1Q     Median     3Q      Max  
-1.5751  -0.8277  -0.6095   1.0701   2.3080  

Coefficients:
             Estimate  Std. Error z value Pr(&amp;gt;|z|)    
(Intercept) -0.153731   0.538511  -0.285 0.775281    
X           -0.048843   0.012856  -3.799 0.000145 ***
Y            0.028364   0.009077   3.125 0.001780 ** 
---
Signif. codes:  0 â***â 0.001 â**â 0.01 â*â 0.05 â.â 0.1 â â 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 287.44  on 241  degrees of freedom
Residual deviance: 260.34  on 239  degrees of freedom
AIC: 266.34

Number of Fisher Scoring iterations: 4

&amp;gt;     fit

Call:  glm(formula = Case ~ X + Y, family = "binomial", data = data)

Coefficients:
  (Intercept)       X            Y  
   -0.15373     -0.04884      0.02836  

Degrees of Freedom: 241 Total (i.e. Null);  239 Residual
Null Deviance:      287.4 
Residual Deviance:  260.3        AIC: 266.3
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now I need to extract some information from this data and I''m not sure about how to do it. First, I need the model equation: suppose that fit is a combined predictor called &lt;code&gt;CP&lt;/code&gt;; could it be &lt;code&gt;CP=-0.15-0.05X+0.03Y&lt;/code&gt;?&lt;/p&gt;

&lt;p&gt;Then, the resulting combined predictor from the regression should present a median value, so that I can compare median from the two groups &lt;code&gt;Case&lt;/code&gt; and &lt;code&gt;Controls&lt;/code&gt; which I used to make the regression (in other words, my &lt;code&gt;X&lt;/code&gt; and &lt;code&gt;Y&lt;/code&gt; variables are N-dimensional with &lt;code&gt;N = N1+N2&lt;/code&gt;, where &lt;code&gt;N1 = Number of Controls&lt;/code&gt;, for which &lt;code&gt;Case=0&lt;/code&gt;, and &lt;code&gt;N2 = Number of Cases&lt;/code&gt;, for which &lt;code&gt;Case=1&lt;/code&gt;).&lt;/p&gt;
', 'r  logistic-regression  glm', NULL, '''+0.03'':228C ''-0.04884'':161C ''-0.048843'':84C ''-0.05'':226C ''-0.15'':225C ''-0.15373'':160C ''-0.153731'':79C ''-0.285'':81C ''-0.6095'':66C ''-0.8277'':65C ''-1.5751'':64C ''-3.799'':86C ''/code'':32C,36C,180C,219C,230C,258C,262C,276C,280C,291C,298C,304C,311C,317C ''/p'':42C,231C,318C ''/pre'':181C ''0'':95C,303C ''0.000145'':87C ''0.001'':98C ''0.001780'':92C ''0.009077'':90C ''0.01'':101C ''0.012856'':85C ''0.02836'':162C ''0.028364'':89C ''0.05'':104C ''0.1'':107C ''0.538511'':80C ''0.775281'':82C ''1'':110C,119C,316C ''1.0701'':67C ''1q'':60C ''2.3080'':68C ''239'':132C,170C ''241'':124C,166C ''260.3'':177C ''260.34'':130C ''266.3'':179C ''266.34'':137C ''287.4'':174C ''287.44'':122C ''3.125'':91C ''3q'':62C ''4'':143C ''aic'':136C,178C ''binomi'':54C,114C,153C ''call'':47C,146C,216C ''case'':50C,149C,257C,302C,310C,315C ''code'':30C,34C,44C,94C,217C,223C,256C,260C,274C,278C,287C,293C,301C,306C,314C ''coeffici'':69C,156C ''combin'':25C,214C,236C ''compar'':250C ''control'':261C,297C ''could'':220C ''cp'':218C,224C ''data'':6A,55C,56C,154C,155C,192C ''degre'':125C,133C,163C ''devianc'':57C,121C,129C,173C,176C ''dimension'':285C ''dispers'':111C ''equat'':3A,208C ''error'':72C ''estim'':70C ''extract'':1A,187C ''famili'':53C,115C,152C ''first'':203C ''fisher'':140C ''fit'':46C,145C,211C ''formula'':49C,148C ''freedom'':127C,135C,165C ''function'':9A ''glm'':8A,16B,48C,147C ''group'':255C ''gt'':76C,144C ''i.e'':168C ''independ'':27C ''inform'':189C ''intercept'':78C,157C ''iter'':142C ''logist'':14B,22C ''logistic-regress'':13B ''m'':195C ''made'':20C ''make'':267C ''max'':63C ''median'':61C,244C,251C ''min'':59C ''model'':2A,207C ''n'':284C,288C ''n-dimension'':283C ''n1'':289C,294C ''n2'':290C,307C ''need'':185C,205C ''null'':120C,169C,172C ''number'':138C,295C,308C ''obtain'':40C ''p'':17C,182C,232C ''packag'':37C ''paramet'':112C ''pr'':75C ''pre'':43C ''predictor'':215C,237C ''present'':242C ''proc'':35C ''r'':11A,12B,31C ''regress'':15B,23C,240C,269C ''residu'':58C,128C,171C,175C ''result'':235C ''score'':141C ''signif'':93C ''std'':71C ''summari'':45C ''suppos'':209C ''sure'':197C ''taken'':116C ''total'':167C ''two'':26C,254C ''use'':33C,265C ''valu'':74C,245C ''variabl'':28C,281C ''ve'':19C ''word'':272C ''x'':51C,83C,150C,158C,227C,275C ''y'':52C,88C,151C,159C,229C,279C ''z'':73C,77C ''â'':96C,97C,99C,100C,102C,103C,105C,106C,108C,109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5490, 2, '2015-04-09 15:27:53.033', 2, NULL, NULL, '&lt;p&gt;In order to extract some data from the fitted &lt;code&gt;glm&lt;/code&gt; model object, you need to figure out where that data resides (use documentation and &lt;code&gt;str()&lt;/code&gt; for that). Some data might be available from the &lt;code&gt;summary.glm&lt;/code&gt; object, while more detailed data is available from the &lt;code&gt;glm&lt;/code&gt; object itself. For extracting model parameters, you can use &lt;code&gt;coef()&lt;/code&gt; function or direct access to the structure.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;From Princeton''s* introduction to R course''s website, &lt;a href="http://data.princeton.edu/R/glms.html" rel="nofollow"&gt;GLM section&lt;/a&gt; - see for details &amp;amp; examples:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The functions that can be used to extract results from the fit
  include&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;- ''residuals'' or ''resid'', for the deviance residuals
- ''fitted'' or ''fitted.values'', for the fitted values (estimated probabilities)
- ''predict'', for the linear predictor (estimated logits)
- ''coef'' or ''coefficients'', for the coefficients, and
- ''deviance'', for the deviance. 
&lt;/code&gt;&lt;/pre&gt;
  
  &lt;p&gt;Some of these functions have optional arguments; for example, you can
  extract five different types of residuals, called "deviance",
  "pearson", "response" (response - fitted value), "working" (the
  working dependent variable in the IRLS algorithm - linear predictor),
  and "partial" (a matrix of working residuals formed by omitting each
  term in the model). You specify the one you want using the type
  argument, for example residuals(lrfit,type="pearson").&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;*) More accurately, this website is by GermÃ¡n RodrÃ­guez from Princeton University.&lt;/p&gt;
', NULL, NULL, '''/a'':99C ''/blockquote'':228C ''/code'':13C,30C,42C,54C,66C,158C ''/p'':74C,79C,105C,121C,227C,243C ''/pre'':159C ''/r/glms.html'':94C ''/strong'':78C ''access'':70C ''accur'':231C ''algorithm'':193C ''amp'':103C ''argument'':167C,220C ''avail'':37C,49C ''blockquot'':106C ''call'':178C ''code'':11C,28C,40C,52C,64C,123C ''coef'':65C,147C ''coeffici'':149C,152C ''cours'':87C ''data'':7C,23C,34C,47C ''data.princeton.edu'':93C ''data.princeton.edu/r/glms.html'':92C ''depend'':188C ''detail'':46C,102C ''devianc'':129C,154C,157C,179C ''differ'':174C ''direct'':69C ''document'':26C ''estim'':138C,145C ''exampl'':104C,169C,222C ''extract'':5C,58C,115C,172C ''figur'':19C ''fit'':10C,119C,131C,136C,183C ''fitted.values'':133C ''five'':173C ''form'':203C ''function'':67C,109C,164C ''germã'':236C ''glm'':12C,53C,97C ''guez'':239C ''href'':91C ''includ'':120C ''introduct'':84C ''irl'':192C ''linear'':143C,194C ''logit'':146C ''lrfit'':224C ''matrix'':199C ''might'':35C ''model'':14C,59C,210C ''n'':237C ''need'':17C ''nofollow'':96C ''object'':15C,43C,55C ''omit'':205C ''one'':214C ''option'':166C ''order'':3C ''p'':1C,75C,80C,107C,160C,229C ''paramet'':60C ''partial'':197C ''pearson'':180C,226C ''pre'':122C ''predict'':140C ''predictor'':144C,195C ''princeton'':82C,241C ''probabl'':139C ''r'':86C ''rel'':95C ''resid'':24C,126C ''residu'':124C,130C,177C,202C,223C ''respons'':181C,182C ''result'':116C ''rodrã'':238C ''section'':98C ''see'':100C ''specifi'':212C ''str'':29C ''strong'':76C ''structur'':73C ''summary.glm'':41C ''term'':207C ''type'':175C,219C,225C ''univers'':242C ''updat'':77C ''use'':25C,63C,113C,217C ''valu'':137C,184C ''variabl'':189C ''want'':216C ''websit'':89C,233C ''work'':185C,187C,201C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5491, 2, '2015-04-10 08:37:18.263', 2, NULL, NULL, '&lt;p&gt;I haven''t used them but there was a similar discussion in a finance group&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.nuclearphynance.com/Show%20Post.aspx?PostIDKey=175835" rel="nofollow"&gt;data repository software&lt;/a&gt;
suggestions 
&lt;a href="http://paradigm4.com/forum/viewtopic.php?f=16&amp;amp;t=1596" rel="nofollow"&gt;scidb&lt;/a&gt;, zfs, &lt;a href="http://www.urbackup.org/" rel="nofollow"&gt;http://www.urbackup.org/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':48C ''/a'':29C,39C ''/forum/viewtopic.php?f=16&amp;amp;t=1596'':35C ''/p'':17C ''/show%20post.aspx?postidkey=175835'':23C ''data'':26C ''discuss'':12C ''financ'':15C ''group'':16C ''haven'':3C ''href'':20C,32C,42C ''nofollow'':25C,37C,45C ''p'':1C,18C ''paradigm4.com'':34C ''paradigm4.com/forum/viewtopic.php?f=16&amp;amp;t=1596'':33C ''rel'':24C,36C,44C ''repositori'':27C ''scidb'':38C ''similar'':11C ''softwar'':28C ''suggest'':30C ''use'':5C ''www.nuclearphynance.com'':22C ''www.nuclearphynance.com/show%20post.aspx?postidkey=175835'':21C ''www.urbackup.org'':43C,47C ''www.urbackup.org/&lt;/a&gt;&lt;/p&gt;'':46C ''zfs'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5492, 1, '2015-04-10 12:12:55.5', 1, 108, 'Predictive Model for Customer Payment Pattern', '&lt;p&gt;I am working for a logistics firm and there are approx. 750+ customers who avail our services. I am in the process of building and generating some insight for the business based on the payments made by these customers for the last 1 year. Some make payment on time whereas some are late &amp;amp; some are extremely late. Could you please advice which modeling technique or statistical approach would be best in this case. 
I can think of creating clusters based on the payment history and highlights &amp;amp; placing all defaulters in one cluster where our company can focus. 
PLease suggest. &lt;/p&gt;
', 'data-mining  statistics  clustering  predictive-modeling', NULL, '''/p'':117C ''1'':58C ''750'':27C ''advic'':77C ''amp'':69C,103C ''approach'':83C ''approx'':26C ''avail'':30C ''base'':47C,96C ''best'':86C ''build'':39C ''busi'':46C ''case'':89C ''cluster'':11B,95C,109C ''compani'':112C ''could'':74C ''creat'':94C ''custom'':4A,28C,54C ''data'':8B ''data-min'':7B ''default'':106C ''extrem'':72C ''firm'':22C ''focus'':114C ''generat'':41C ''highlight'':102C ''histori'':100C ''insight'':43C ''last'':57C ''late'':68C,73C ''logist'':21C ''made'':51C ''make'':61C ''mine'':9B ''model'':2A,14B,79C ''one'':108C ''p'':15C ''pattern'':6A ''payment'':5A,50C,62C,99C ''place'':104C ''pleas'':76C,115C ''predict'':1A,13B ''predictive-model'':12B ''process'':37C ''servic'':32C ''statist'':10B,82C ''suggest'':116C ''techniqu'':80C ''think'':92C ''time'':64C ''wherea'':65C ''work'':18C ''would'':84C ''year'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5493, 1, '2015-04-10 12:42:13.517', 2, 102, 'Post processing with Random Forest', '&lt;p&gt;I understand that by filtering out the instances with labels that Random Forest trees are uncertain upon with their decisions, and model these with another classifier could give a better overall result. My question is, how can I "combine" two (or more) classifiers'' classification on a single unlabeled dataset?&lt;/p&gt;
', 'classification  random-forest', NULL, '''/p'':60C ''anoth'':35C ''better'':40C ''classif'':6B,54C ''classifi'':36C,53C ''combin'':49C ''could'':37C ''dataset'':59C ''decis'':30C ''filter'':15C ''forest'':5A,9B,23C ''give'':38C ''instanc'':18C ''label'':20C ''model'':32C ''overal'':41C ''p'':10C ''post'':1A ''process'':2A ''question'':44C ''random'':4A,8B,22C ''random-forest'':7B ''result'':42C ''singl'':57C ''tree'':24C ''two'':50C ''uncertain'':26C ''understand'':12C ''unlabel'':58C ''upon'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5496, 2, '2015-04-10 17:31:30.327', 1, NULL, NULL, '&lt;p&gt;Here are some things that come to mind: &lt;/p&gt;

&lt;p&gt;1) ARMA/ARIMA models: These may show if there is any seasonal trends in their payment history.&lt;/p&gt;

&lt;p&gt;2) &lt;a href="http://www.stat.cmu.edu/~cshalizi/uADA/12/lectures/ch12.pdf" rel="nofollow"&gt;Logistic Regression&lt;/a&gt;: Will allow you model the conditional probability of a missed payments based off of number of previous missed payments. &lt;/p&gt;

&lt;p&gt;From here you need to determine what the threshold for risk is since that''s a little more subjective.&lt;/p&gt;
', NULL, NULL, '''/a'':40C ''/p'':10C,28C,60C,81C ''/~cshalizi/uada/12/lectures/ch12.pdf'':35C ''1'':12C ''2'':30C ''allow'':42C ''arma/arima'':13C ''base'':52C ''come'':7C ''condit'':46C ''determin'':67C ''histori'':27C ''href'':32C ''littl'':78C ''logist'':38C ''may'':16C ''mind'':9C ''miss'':50C,58C ''model'':14C,44C ''need'':65C ''nofollow'':37C ''number'':55C ''p'':1C,11C,29C,61C ''payment'':26C,51C,59C ''previous'':57C ''probabl'':47C ''regress'':39C ''rel'':36C ''risk'':72C ''season'':22C ''show'':17C ''sinc'':74C ''subject'':80C ''thing'':5C ''threshold'':70C ''trend'':23C ''www.stat.cmu.edu'':34C ''www.stat.cmu.edu/~cshalizi/uada/12/lectures/ch12.pdf'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5515, 2, '2015-04-13 08:59:00.397', 0, NULL, NULL, '&lt;p&gt;Just run &lt;/p&gt;

&lt;p&gt;PROC means data=d mean; var a; run;&lt;/p&gt;

&lt;p&gt;for overall data&lt;/p&gt;
', NULL, NULL, '''/p'':4C,14C,19C ''d'':9C ''data'':8C,18C ''mean'':7C,10C ''overal'':17C ''p'':1C,5C,15C ''proc'':6C ''run'':3C,13C ''var'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9525, 1, '2015-12-26 03:47:48.8', 3, 70, 'How word2vec can be used to identify unseen words and relate them to already trained data', '&lt;p&gt;I was working on word2vec gensim model and found it really interesting.
I am intersted in finding how a unknown/unseen word when checked with the model will be able to get similar terms from the trained model.&lt;/p&gt;

&lt;p&gt;Is this possible?
Can word2vec be tweaked for this?
Or the training corpus needs to have all the words of which i want to find similarity.&lt;/p&gt;
', 'nlp  deep-learning  word-embeddings  unsupervised-learning', NULL, '''/p'':65C,93C ''abl'':56C ''alreadi'':14A ''check'':50C ''corpus'':79C ''data'':16A ''deep'':19B ''deep-learn'':18B ''embed'':23B ''find'':44C,91C ''found'':36C ''gensim'':33C ''get'':58C ''identifi'':7A ''interest'':39C ''interst'':42C ''learn'':20B,26B ''model'':34C,53C,64C ''need'':80C ''nlp'':17B ''p'':27C,66C ''possibl'':69C ''realli'':38C ''relat'':11A ''similar'':59C,92C ''term'':60C ''train'':15A,63C,78C ''tweak'':73C ''unknown/unseen'':47C ''unseen'':8A ''unsupervis'':25B ''unsupervised-learn'':24B ''use'':5A ''want'':89C ''word'':9A,22B,48C,85C ''word-embed'':21B ''word2vec'':2A,32C,71C ''work'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5504, 1, '2015-04-12 02:30:05.37', 2, 158, 'Data mining- Clustering techniques', '&lt;p&gt;I have a project for comparison between clustering techniques using the data set of SSA for birth names from 1910-2013 years for the different states.&lt;/p&gt;

&lt;p&gt;I have finished applying my clustering techniques on my data set and the output of the clusters were the clusters of the states for each year.&lt;/p&gt;

&lt;p&gt;Now I can know from my results; which states are close to each other with the birth names and which were not. By looking &lt;/p&gt;

&lt;p&gt;I would like to have interesting results to make my project report interesting, Which states are similar in birth names are not enough to make the read be excited about my project. &lt;/p&gt;

&lt;p&gt;My questions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;any ideas of what can be learned from my project?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;How can I show the comparison between the clustering techniques? anyway other than seeing how Homogeneous the clusters are?&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', 'data-mining  clustering', NULL, '''-2013'':30C ''/li'':143C,166C ''/ol'':167C ''/p'':36C,64C,90C,124C,128C,142C,165C ''1910'':29C ''anyway'':156C ''appli'':41C ''birth'':26C,82C,110C ''close'':76C ''cluster'':3A,8B,17C,43C,54C,57C,154C,163C ''comparison'':15C,151C ''data'':1A,6B,21C,47C ''data-min'':5B ''differ'':34C ''enough'':114C ''excit'':120C ''finish'':40C ''homogen'':161C ''idea'':133C ''interest'':97C,104C ''know'':69C ''learn'':138C ''li'':130C,144C ''like'':94C ''look'':89C ''make'':100C,116C ''mine'':2A,7B ''name'':27C,83C,111C ''ol'':129C ''output'':51C ''p'':9C,37C,65C,91C,125C,131C,145C ''project'':13C,102C,123C,141C ''question'':127C ''read'':118C ''report'':103C ''result'':72C,98C ''see'':159C ''set'':22C,48C ''show'':149C ''similar'':108C ''ssa'':24C ''state'':35C,60C,74C,106C ''techniqu'':4A,18C,44C,155C ''use'':19C ''would'':93C ''year'':31C,63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5505, 2, '2015-04-12 05:23:13.703', 1, NULL, NULL, '&lt;p&gt;I think that you have, at least, the following &lt;strong&gt;major options&lt;/strong&gt; for your &lt;em&gt;data analysis scenario&lt;/em&gt;:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Use &lt;em&gt;big data-enabling&lt;/em&gt; &lt;code&gt;R&lt;/code&gt; packages on your &lt;strong&gt;local system&lt;/strong&gt;. You can find most of them via the corresponding &lt;em&gt;CRAN Task View&lt;/em&gt; that I reference in &lt;a href="http://datascience.stackexchange.com/a/780/2452"&gt;this answer&lt;/a&gt; (see point #3).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Use the same packages on a &lt;strong&gt;public cloud&lt;/strong&gt; infrastructure, such as &lt;em&gt;Amazon Web Services (AWS) EC2&lt;/em&gt;. If your analysis is non-critical and tolerant to potential restarts, consider using &lt;em&gt;AWS Spot Instances&lt;/em&gt;, as their pricing allows for significant financial savings.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Use the above mention &lt;strong&gt;public cloud&lt;/strong&gt; option with &lt;code&gt;R&lt;/code&gt; &lt;em&gt;standard platform&lt;/em&gt;, but on &lt;strong&gt;more powerful instances&lt;/strong&gt; (for example, on AWS you can opt for memory-optimized EC2 instances or general purpose on-demand instances with more memory).&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;In some cases, it is possible to &lt;strong&gt;tune&lt;/strong&gt; a &lt;em&gt;local system&lt;/em&gt; (or a &lt;em&gt;cloud on-demand instance&lt;/em&gt;) to enable &lt;code&gt;R&lt;/code&gt; to work with big(ger) data sets. For some help in this regard, see &lt;a href="http://datascience.stackexchange.com/a/3731/2452"&gt;my relevant answer&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;For both above-mentioned cloud (AWS) options, you can find more convenient to use &lt;code&gt;R&lt;/code&gt;-focused &lt;strong&gt;pre-built VM images&lt;/strong&gt;. See &lt;a href="http://datascience.stackexchange.com/a/4927/2452"&gt;my relevant answer&lt;/a&gt; for details. You may also find useful &lt;a href="https://github.com/onurakpolat/awesome-bigdata" rel="nofollow"&gt;this excellent comprehensive list of big data frameworks&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':68C,229C,267C,290C ''/a/3731/2452'':225C ''/a/4927/2452'':263C ''/a/780/2452'':65C ''/code'':35C,139C,206C,249C ''/em'':21C,32C,56C,95C,114C,143C,192C,201C ''/li'':73C,124C,175C ''/ol'':176C ''/onurakpolat/awesome-bigdata'':279C ''/p'':22C,72C,123C,174C,230C,291C ''/strong'':14C,42C,85C,134C,150C,187C,257C ''3'':71C ''above-ment'':234C ''allow'':118C ''also'':272C ''amazon'':90C ''analysi'':19C,98C ''answer'':67C,228C,266C ''aw'':93C,111C,154C,238C ''big'':28C,210C,287C ''built'':254C ''case'':180C ''cloud'':84C,133C,196C,237C ''code'':33C,137C,204C,247C ''comprehens'':284C ''consid'':108C ''conveni'':244C ''correspond'':51C ''cran'':53C ''critic'':102C ''data'':18C,30C,212C,288C ''data-en'':29C ''datascience.stackexchange.com'':64C,224C,262C ''datascience.stackexchange.com/a/3731/2452'':223C ''datascience.stackexchange.com/a/4927/2452'':261C ''datascience.stackexchange.com/a/780/2452'':63C ''demand'':169C,199C ''detail'':269C ''ec2'':94C,162C ''em'':17C,27C,52C,89C,110C,140C,189C,195C ''enabl'':31C,203C ''exampl'':152C ''excel'':283C ''financi'':121C ''find'':45C,242C,273C ''focus'':250C ''follow'':10C ''framework'':289C ''general'':165C ''ger'':211C ''github.com'':278C ''github.com/onurakpolat/awesome-bigdata'':277C ''help'':216C ''href'':62C,222C,260C,276C ''imag'':256C ''infrastructur'':86C ''instanc'':113C,149C,163C,170C,200C ''least'':8C ''li'':24C,74C,125C ''list'':285C ''local'':40C,190C ''major'':12C ''may'':271C ''memori'':160C,173C ''memory-optim'':159C ''mention'':130C,236C ''nofollow'':281C ''non'':101C ''non-crit'':100C ''ol'':23C ''on-demand'':167C,197C ''opt'':157C ''optim'':161C ''option'':13C,135C,239C ''p'':1C,25C,75C,126C,177C,231C ''packag'':36C,79C ''platform'':142C ''point'':70C ''possibl'':183C ''potenti'':106C ''power'':148C ''pre'':253C ''pre-built'':252C ''price'':117C ''public'':83C,132C ''purpos'':166C ''r'':34C,138C,205C,248C ''refer'':59C ''regard'':219C ''rel'':280C ''relev'':227C,265C ''restart'':107C ''save'':122C ''scenario'':20C ''see'':69C,220C,258C ''servic'':92C ''set'':213C ''signific'':120C ''spot'':112C ''standard'':141C ''strong'':11C,39C,82C,131C,146C,185C,251C ''system'':41C,191C ''task'':54C ''think'':3C ''toler'':104C ''tune'':186C ''use'':26C,76C,109C,127C,246C,274C ''via'':49C ''view'':55C ''vm'':255C ''web'':91C ''work'':208C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5506, 2, '2015-04-12 15:36:25.557', 2, NULL, NULL, '&lt;p&gt;It''s common to perform clustering on certain features, and then use characteristics such as mean differences between other features (not used in clustering) to show how clusters differ.  It''s not always meaningful to say that clusters differ merely by lower distances between cluster centers and objects assigned to them (as in k-means).&lt;/p&gt;

&lt;p&gt;As an example, you might acquire other features (variables) for the states and then average each feature among objects assigned to each cluster.  Then show bar charts of averages for those variables among all the clusters to assist in explaining how the clusters differ.  You might also test for significantly different means of the supplemental features across clusters. &lt;/p&gt;
', NULL, NULL, '''/p'':58C,118C ''acquir'':65C ''across'':116C ''also'':106C ''alway'':34C ''among'':77C,92C ''assign'':50C,79C ''assist'':97C ''averag'':74C,88C ''bar'':85C ''center'':47C ''certain'':9C ''characterist'':14C ''chart'':86C ''cluster'':7C,25C,29C,39C,46C,82C,95C,102C,117C ''common'':4C ''differ'':18C,30C,40C,103C,110C ''distanc'':44C ''exampl'':62C ''explain'':99C ''featur'':10C,21C,67C,76C,115C ''k'':56C ''k-mean'':55C ''lower'':43C ''mean'':17C,57C,111C ''meaning'':35C ''mere'':41C ''might'':64C,105C ''object'':49C,78C ''p'':1C,59C ''perform'':6C ''say'':37C ''show'':27C,84C ''signific'':109C ''state'':71C ''supplement'':114C ''test'':107C ''use'':13C,23C ''variabl'':68C,91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5507, 2, '2015-04-12 16:01:40.447', 2, NULL, NULL, '&lt;p&gt;Several kernel functions can serve as similarity functions (=scores). See a list, for example, &lt;a href="https://www.otexts.org/1560" rel="nofollow"&gt;here&lt;/a&gt;. You can try several of them and see which suits you the best.&lt;/p&gt;

&lt;p&gt;You need something that drops fast at low distances. You can try
$$ score = 1/(1+distance)^2$$
and adjust coefficient in front of distance so that the score fits between 0 and 1
&lt;img src="http://i.stack.imgur.com/oufxf.png" alt="enter image description here"&gt;  &lt;/p&gt;

&lt;p&gt;About your picture: what are axis labels? and what are x-ticks?&lt;/p&gt;
', NULL, NULL, '''/1560'':20C ''/a'':24C ''/oufxf.png'':77C ''/p'':38C,83C,98C ''0'':70C ''1'':53C,54C,72C ''2'':56C ''adjust'':58C ''alt'':78C ''axi'':90C ''best'':37C ''coeffici'':59C ''descript'':81C ''distanc'':48C,55C,63C ''drop'':44C ''enter'':79C ''exampl'':15C ''fast'':45C ''fit'':68C ''front'':61C ''function'':4C,9C ''href'':17C ''i.stack.imgur.com'':76C ''i.stack.imgur.com/oufxf.png'':75C ''imag'':80C ''img'':73C ''kernel'':3C ''label'':91C ''list'':13C ''low'':47C ''need'':41C ''nofollow'':22C ''p'':1C,39C,84C ''pictur'':87C ''rel'':21C ''score'':10C,52C,67C ''see'':11C,32C ''serv'':6C ''sever'':2C,28C ''similar'':8C ''someth'':42C ''src'':74C ''suit'':34C ''tick'':97C ''tri'':27C,51C ''www.otexts.org'':19C ''www.otexts.org/1560'':18C ''x'':96C ''x-tick'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5519, 1, '2015-04-13 16:23:14.553', 0, 64, 'about predict the class for a new datapoint', '&lt;p&gt;I have a new data point and want to classify it into the existing classes.I can calculate pairwise distance for the new point to all existing points(in the existing classes). I know using KNN would be a straightforward to classify this point. Is there a way I could randomly sampling existing classes and then correlated the new point to a potential classes without calculating all pairwise distances? &lt;/p&gt;
', 'data-mining  classification  clustering', NULL, '''/p'':83C ''calcul'':31C,79C ''class'':4A,45C,67C,77C ''classes.i'':29C ''classif'':12B ''classifi'':24C,55C ''cluster'':13B ''correl'':70C ''could'':63C ''data'':10B,19C ''data-min'':9B ''datapoint'':8A ''distanc'':33C,82C ''exist'':28C,40C,44C,66C ''knn'':49C ''know'':47C ''mine'':11B ''new'':7A,18C,36C,72C ''p'':14C ''pairwis'':32C,81C ''point'':20C,37C,41C,57C,73C ''potenti'':76C ''predict'':2A ''random'':64C ''sampl'':65C ''straightforward'':53C ''use'':48C ''want'':22C ''way'':61C ''without'':78C ''would'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9451, 1, '2015-12-20 09:54:24.217', 1, 33, 'Sentiment Analysis algorithm in Weka', '&lt;p&gt;I have an assignment, i have to collect movie reviews and classify them with poz neg notr tags, and turn them info arff files. I did all of this stuff with regex etc.&lt;/p&gt;

&lt;p&gt;But for experiment i have to choose ten classify algorithm. I did some research but couldn''t figure out which algortihm should i use.&lt;/p&gt;

&lt;p&gt;Can you please help?
Thanks. &lt;/p&gt;
', 'classification  weka', NULL, '''/p'':42C,68C,75C ''algorithm'':3A,53C ''algortihm'':64C ''analysi'':2A ''arff'':31C ''assign'':12C ''choos'':50C ''classif'':6B ''classifi'':20C,52C ''collect'':16C ''couldn'':59C ''etc'':41C ''experi'':46C ''figur'':61C ''file'':32C ''help'':73C ''info'':30C ''movi'':17C ''neg'':24C ''notr'':25C ''p'':8C,43C,69C ''pleas'':72C ''poz'':23C ''regex'':40C ''research'':57C ''review'':18C ''sentiment'':1A ''stuff'':38C ''tag'':26C ''ten'':51C ''thank'':74C ''turn'':28C ''use'':67C ''weka'':5A,7B');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6556, 2, '2015-07-23 05:25:56.953', 1, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Stability-Plasticity Dilemma, Learning Rates, and Forgetting Algorithms:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;First, let me say that this is a really great question and is the type of thought provoking stuff that really improves one''s understanding of ML algorithms.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Does this "problem" have a name that it can be referred to?&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;This is generally referred to as "stability".  What''s funny is that stability is actually a useful concept in regular clustering i.e. not online. The "stability" of the algorithm is often chosen as a selection criterion for whether the right number of clusters have been selected.  More specifically, the online clustering stability issue that you have described is referred to as the &lt;code&gt;stability-plasticity dilemma&lt;/code&gt;.&lt;/p&gt;

&lt;ol start="2"&gt;
&lt;li&gt;Are there "standard" solutions to this and ...&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;First, the big picture answer is that many online clustering algorithm are surprisingly stable when they have been well trained with a large cohort of initial data.  However, its still a problem if you want to really nail down the cluster identities of points while allowing the algorithm to react to new data.  The trickiness of you point is briefly addressed in &lt;a href="http://rads.stackoverflow.com/amzn/click/026201243X" rel="nofollow"&gt;&lt;em&gt;Introduction to Machine Learning&lt;/em&gt;&lt;/a&gt; By Ethem Alpaydin.  On &lt;a href="https://books.google.com/books?id=NP5bBAAAQBAJ&amp;amp;pg=PA319&amp;amp;lpg=PA319&amp;amp;dq=online+mini+batch+k-means+stability&amp;amp;source=bl&amp;amp;ots=HCI8IK47SX&amp;amp;sig=nJIyST9HJOnsXZr6GGNy1yBQ6uc&amp;amp;hl=en&amp;amp;sa=X&amp;amp;ved=0CEUQ6AEwBWoVChMIjuqesLvwxgIVCVyICh0_yALu#v=onepage&amp;amp;q=online%20mini%20batch%20k-means%20stability&amp;amp;f=false" rel="nofollow"&gt;page 319&lt;/a&gt; he derives the online k-means algorithm through the application of stochastic gradient descent, but mentions that the &lt;code&gt;stability-plasticity dilemma&lt;/code&gt; arises when choosing a value for the learning rate.  A small learning rate results in stability, but the system looses adaptability where as a larger learning rate gains adaptability, but looses cluster stability.&lt;/p&gt;

&lt;p&gt;I believe the best path forward is to choose an implementation of online clustering which allows you to control the stochastic gradient descent algorithm and then choose the learning rate so that you maximize stability and adaptability as best as you can using a sound cross-validation procedure.&lt;/p&gt;

&lt;p&gt;Another method that I''ve seen employed is some sort of forgetting algorithm e.g. forgetting older points as the data stream matures.  This allows for a fairly stable system on fast time scales and allows for evolution on slower time scales.  &lt;code&gt;Adaptive Resonance Theory&lt;/code&gt; was created to try to solve the &lt;code&gt;stability-plasticity dilemma&lt;/code&gt;.  You might find &lt;a href="http://techlab.bu.edu/files/resources/articles_tt/Soft-clustering%20and%20improved%20stability%20for%20adaptive%20resonance%20theory%20neural%20networks.pdf" rel="nofollow"&gt;this article&lt;/a&gt; interesting. &lt;/p&gt;

&lt;p&gt;I''m not well-versed enough in R to suggest an algorithm, but I suggest you look for a &lt;code&gt;mini-batch k-means&lt;/code&gt; algorithm that allows you to control the learning rate in its stochastic gradient descent algorithm.&lt;/p&gt;

&lt;p&gt;I hope this helps!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5508, 2, '2015-04-12 16:03:49.887', 1, NULL, NULL, '&lt;p&gt;Regarding the approach, SVM with an RBF kernel does a good job, but SVMs can be slowed down by large object sizes, unless you are employing CV with e.g. one tenth of the data randomly assigned to each fold.  However, did you ask yourself why you are employing SVMs in the first place?  &lt;/p&gt;

&lt;p&gt;Have you tried multivariate linear regression, $\mathbf{Y}=\mathbf{X}\boldsymbol{\beta}$, where each record of $\mathbf{Y}$ is coded $y_{ij}=+1$ if the $i$th object is in class $j$, and $y_{ij}=-1$ otherwise?  If the classification accuracy is appreciably high using linear regression, then your data are linearly separable, and more complex methods such as SVMs and ANNs aren''t needed.  Step 2 would be to show that k-nearest neighbor, naive Bayes, linear (Fisher) discriminant analysis, polytomous logistic regression, etc., break down and fail. &lt;/p&gt;

&lt;p&gt;For terminology, you might couch the issue of having more class weights in the context of "lower proportions of objects in certain classes," or "near-zero class size."  Skew tends to be used for describing the distribution of a feature''s values, as in skewness, fat tails, etc.     &lt;/p&gt;

&lt;p&gt;How many features do you have?  Did you try unsupervised clustering (class discovery) on the 100,000 objects before trying supervised classification (class prediction) with SVM?  Maybe the 100,000 objects can be grouped into fewer classes than 50, for which the new class membership could be used as the target class during classification analysis.  This may alleviate the problem of having near-zero class size.&lt;/p&gt;
', NULL, NULL, '''+1'':79C ''-1'':92C ''/p'':55C,147C,198C,267C ''000'':216C,229C ''100'':215C,228C ''2'':123C ''50'':238C ''accuraci'':97C ''allevi'':257C ''analysi'':138C,254C ''ann'':118C ''appreci'':99C ''approach'':4C ''aren'':119C ''ask'':44C ''assign'':37C ''bay'':134C ''beta'':68C ''boldsymbol'':67C ''break'':143C ''certain'':170C ''class'':87C,159C,171C,176C,211C,222C,236C,243C,251C,265C ''classif'':96C,221C,253C ''cluster'':210C ''code'':76C ''complex'':112C ''context'':163C ''couch'':153C ''could'':245C ''cv'':28C ''data'':35C,106C ''describ'':184C ''discoveri'':212C ''discrimin'':137C ''distribut'':186C ''e.g'':30C ''employ'':27C,49C ''etc'':142C,197C ''fail'':146C ''fat'':195C ''featur'':189C,202C ''fewer'':235C ''first'':53C ''fisher'':136C ''fold'':40C ''good'':12C ''group'':233C ''high'':100C ''howev'':41C ''ij'':78C,91C ''issu'':155C ''j'':88C ''job'':13C ''k'':130C ''k-nearest'':129C ''kernel'':9C ''larg'':21C ''linear'':61C,102C,108C,135C ''logist'':140C ''lower'':165C ''mani'':201C ''mathbf'':63C,65C,73C ''may'':256C ''mayb'':226C ''membership'':244C ''method'':113C ''might'':152C ''multivari'':60C ''naiv'':133C ''near'':174C,263C ''near-zero'':173C,262C ''nearest'':131C ''need'':121C ''neighbor'':132C ''new'':242C ''object'':22C,84C,168C,217C,230C ''one'':31C ''otherwis'':93C ''p'':1C,56C,148C,199C ''place'':54C ''polytom'':139C ''predict'':223C ''problem'':259C ''proport'':166C ''random'':36C ''rbf'':8C ''record'':71C ''regard'':2C ''regress'':62C,103C,141C ''separ'':109C ''show'':127C ''size'':23C,177C,266C ''skew'':178C,194C ''slow'':18C ''step'':122C ''supervis'':220C ''svm'':5C,225C ''svms'':15C,50C,116C ''tail'':196C ''target'':250C ''tend'':179C ''tenth'':32C ''terminolog'':150C ''th'':83C ''tri'':59C,208C,219C ''unless'':24C ''unsupervis'':209C ''use'':101C,182C,247C ''valu'':191C ''weight'':160C ''would'':124C ''x'':66C ''y'':64C,74C,77C,90C ''zero'':175C,264C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5509, 2, '2015-04-12 16:08:13.467', 1, NULL, NULL, '&lt;p&gt;Probably kNN and naive Bayes classifier.  kNN is very fast, but NBC can break down a lot.   Linear regression is also one-step solution that does not involve gradient-based learning, so that might help.  LDA is your next choice. &lt;/p&gt;
', NULL, NULL, '''/p'':44C ''also'':22C ''base'':33C ''bay'':6C ''break'':15C ''choic'':43C ''classifi'':7C ''fast'':11C ''gradient'':32C ''gradient-bas'':31C ''help'':38C ''involv'':30C ''knn'':3C,8C ''lda'':39C ''learn'':34C ''linear'':19C ''lot'':18C ''might'':37C ''naiv'':5C ''nbc'':13C ''next'':42C ''one'':24C ''one-step'':23C ''p'':1C ''probabl'':2C ''regress'':20C ''solut'':26C ''step'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5510, 2, '2015-04-12 16:12:39.063', 2, NULL, NULL, '&lt;p&gt;Wouldn''t recommend use of complex methods first.  Use faster simple approaches initially (kNN, NBC, etc.), then progress through linear regression, logistic regression, LDA, CART(RF), KREG, and then to least squares SVM, gradient ascent SVM, ANNs, and then metaheurustics (greedy heuristic hill climbing with GAs, swarm intelligence, ant colony optimization, etc.)&lt;/p&gt;
', NULL, NULL, '''/p'':54C ''ann'':38C ''ant'':50C ''approach'':13C ''ascent'':36C ''cart'':26C ''climb'':45C ''coloni'':51C ''complex'':7C ''etc'':17C,53C ''faster'':11C ''first'':9C ''gas'':47C ''gradient'':35C ''greedi'':42C ''heurist'':43C ''hill'':44C ''initi'':14C ''intellig'':49C ''knn'':15C ''kreg'':28C ''lda'':25C ''least'':32C ''linear'':21C ''logist'':23C ''metaheurust'':41C ''method'':8C ''nbc'':16C ''optim'':52C ''p'':1C ''progress'':19C ''recommend'':4C ''regress'':22C,24C ''rf'':27C ''simpl'':12C ''squar'':33C ''svm'':34C,37C ''swarm'':48C ''use'':5C,10C ''wouldn'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5511, 2, '2015-04-12 16:24:28.79', 1, NULL, NULL, '&lt;p&gt;There are three approaches you could take: APP, OAA, AAO.  APP is discrimination between objects based on all possible pairs of classes.  OAA is use of one-against all (remaining classes), and AAO is all at once such as use of an F-test for multiple classes simultaneously (or Hotelling''s test for MVN).  APP and AAO are actually multiple binary classification runs but with more than two of your original classes.  Each of these approaches yields different results for various classifiers employed.&lt;/p&gt;

&lt;p&gt;Random sampling is a good technique.   You might also try to cluster all of the objects into centers using k-means, and then use the centers as new objects.  Either way, linear and non-linear dimension reduction methods might help get away from the large sample size.     &lt;/p&gt;
', NULL, NULL, '''/p'':87C,138C ''aao'':11C,35C,60C ''actual'':62C ''also'':97C ''app'':9C,12C,58C ''approach'':5C,79C ''away'':132C ''base'':17C ''binari'':64C ''center'':106C,115C ''class'':23C,33C,50C,75C ''classif'':65C ''classifi'':85C ''cluster'':100C ''could'':7C ''differ'':81C ''dimens'':126C ''discrimin'':14C ''either'':119C ''employ'':86C ''f'':46C ''f-test'':45C ''get'':131C ''good'':93C ''help'':130C ''hotel'':53C ''k'':109C ''k-mean'':108C ''larg'':135C ''linear'':121C,125C ''mean'':110C ''method'':128C ''might'':96C,129C ''multipl'':49C,63C ''mvn'':57C ''new'':117C ''non'':124C ''non-linear'':123C ''oaa'':10C,24C ''object'':16C,104C,118C ''one'':29C ''one-against'':28C ''origin'':74C ''p'':1C,88C ''pair'':21C ''possibl'':20C ''random'':89C ''reduct'':127C ''remain'':32C ''result'':82C ''run'':66C ''sampl'':90C,136C ''simultan'':51C ''size'':137C ''take'':8C ''techniqu'':94C ''test'':47C,55C ''three'':4C ''tri'':98C ''two'':71C ''use'':26C,42C,107C,113C ''various'':84C ''way'':120C ''yield'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5513, 2, '2015-04-13 00:22:32.793', 2, NULL, NULL, '&lt;p&gt;You might try Azure Table Storage.  Since you can''t lock yourself down to a specific schema (since one data product might be aggregates whereas another might be time series or something else), Azure Table storage would give you the flexibility of storing data from multiple sources, each having their own format.&lt;/p&gt;

&lt;p&gt;This would also lend itself to making a system highly scalable, as you could use Azure Service Bus in conjunction with Azure Table Storage.&lt;/p&gt;

&lt;p&gt;You might check out this tutorial at Pluralsight, &lt;a href="http://www.pluralsight.com/courses/applied-windows-azure" rel="nofollow"&gt;Applied Windows Azure&lt;/a&gt;, as it shows a number of examples, one using Table Storage and Service Bus, another using Hadoop, and I suspect that some of these might match the extensibility you are looking for.&lt;/p&gt;
', NULL, NULL, '''/a'':100C ''/courses/applied-windows-azure'':94C ''/p'':54C,80C,133C ''aggreg'':25C ''also'':58C ''anoth'':27C,115C ''appli'':97C ''azur'':5C,35C,71C,77C,99C ''bus'':73C,114C ''check'':84C ''conjunct'':75C ''could'':69C ''data'':21C,45C ''els'':34C ''exampl'':107C ''extens'':128C ''flexibl'':42C ''format'':53C ''give'':39C ''hadoop'':117C ''high'':65C ''href'':91C ''lend'':59C ''lock'':12C ''look'':131C ''make'':62C ''match'':126C ''might'':3C,23C,28C,83C,125C ''multipl'':47C ''nofollow'':96C ''number'':105C ''one'':20C,108C ''p'':1C,55C,81C ''pluralsight'':89C ''product'':22C ''rel'':95C ''scalabl'':66C ''schema'':18C ''seri'':31C ''servic'':72C,113C ''show'':103C ''sinc'':8C,19C ''someth'':33C ''sourc'':48C ''specif'':17C ''storag'':7C,37C,79C,111C ''store'':44C ''suspect'':120C ''system'':64C ''tabl'':6C,36C,78C,110C ''time'':30C ''tri'':4C ''tutori'':87C ''use'':70C,109C,116C ''wherea'':26C ''window'':98C ''would'':38C,57C ''www.pluralsight.com'':93C ''www.pluralsight.com/courses/applied-windows-azure'':92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9826, 1, '2016-01-18 05:37:40.897', 2, 59, 'Can we compare word2vec with doc2vec?', '&lt;p&gt;I have set of categories and I want to compare a document vector with word vector of categories to find best matching category. &lt;/p&gt;

&lt;p&gt;Is it possible to compare a word vector with document vector? If yes, is there any literature which gives proof of concept for this?&lt;/p&gt;
', 'deep-learning  information-retrieval  word-embeddings  word2vec', NULL, '''/p'':41C,67C ''best'':38C ''categori'':22C,35C,40C ''compar'':3A,27C,47C ''concept'':64C ''deep'':8B ''deep-learn'':7B ''doc2vec'':6A ''document'':29C,52C ''embed'':15B ''find'':37C ''give'':61C ''inform'':11B ''information-retriev'':10B ''learn'':9B ''literatur'':59C ''match'':39C ''p'':17C,42C ''possibl'':45C ''proof'':62C ''retriev'':12B ''set'':20C ''vector'':30C,33C,50C,53C ''want'':25C ''word'':14B,32C,49C ''word-embed'':13B ''word2vec'':4A,16B ''yes'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5514, 1, '2015-04-13 08:45:07.773', 0, 28, 'SAS PROC means (two variants together)', '&lt;p&gt;PROC means data=d mean; 
var a;
class b; var a;
run; &lt;/p&gt;

&lt;p&gt;I want to perform the "PROC means" for continuous "var a":
1) in general and
2) by classes.
But it performed by the classes only.
How to make procedure for "var a" here in general too?&lt;/p&gt;

&lt;p&gt;P.S. SAS WARNING: Analysis variable "a" was defined in a previous statement, duplicate definition will be ignored.&lt;/p&gt;
', 'processing', NULL, '''/p'':21C,59C,78C ''1'':34C ''2'':38C ''analysi'':64C ''b'':17C ''class'':16C,40C,46C ''continu'':31C ''d'':12C ''data'':11C ''defin'':68C ''definit'':74C ''duplic'':73C ''general'':36C,57C ''ignor'':77C ''make'':50C ''mean'':3A,10C,13C,29C ''p'':8C,22C,60C ''p.s'':61C ''perform'':26C,43C ''previous'':71C ''proc'':2A,9C,28C ''procedur'':51C ''process'':7B ''run'':20C ''sas'':1A,62C ''statement'':72C ''togeth'':6A ''two'':4A ''var'':14C,18C,32C,53C ''variabl'':65C ''variant'':5A ''want'':24C ''warn'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5522, 5, '2015-04-14 01:54:02.727', 0, NULL, NULL, '&lt;p&gt;&lt;a href="https://www.python.org/" rel="nofollow"&gt;Python&lt;/a&gt; is a general-purpose, dynamic, strongly typed language with many 3rd-party libraries for data science applications. There are two versions currently in wide use: 2 and 3. Python 2 is the "old" version, with no new versions being released beyond 2.7, save bugfixes. Python 3 is the "new" version, with active development. &lt;/p&gt;

&lt;p&gt;Python syntax is relatively easy to comprehend compared to other languages. For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;numbers = [1, 2, 5, 8, 9]
for number in numbers:
    print("Hello world #", number)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Python has a clean look due to its regulatory approach to &lt;a href="http://en.wikipedia.org/wiki/Whitespace_%28programming_language%29" rel="nofollow"&gt;whitespace&lt;/a&gt;. While seemingly restrictive, it allows all Python code to look similar, which makes inspecting code much more predictable. All loops and conditionals (&lt;code&gt;for&lt;/code&gt;, &lt;code&gt;while&lt;/code&gt;, &lt;code&gt;if&lt;/code&gt;, etc) must be indented for the code block that follows.&lt;/p&gt;

&lt;p&gt;Popular scientific and data science packages include:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.numpy.org/" rel="nofollow"&gt;Numpy&lt;/a&gt; - A fast, N-dimensional array library; the foundation for
all things scientific Python.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.scipy.org/" rel="nofollow"&gt;Scipy&lt;/a&gt; - Numerical analysis built on Numpy. Allows for optimization, linear algebra, Fourier Transforms and much else. &lt;/li&gt;
&lt;li&gt;&lt;a href="http://pandas.pydata.org/" rel="nofollow"&gt;Pandas&lt;/a&gt; (PANel DAta) - A fast and extremely flexible package that is very useful for data exploration. It handles NaN data well as well as fast indexing. Handles a wide variety of external data types and file formats.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':8C,118C,178C,201C,225C ''/code'':96C,143C,146C,149C ''/li'':193C,217C,262C ''/p'':64C,79C,160C,169C ''/pre'':97C ''/ul'':263C ''/wiki/whitespace_%28programming_language%29'':114C ''1'':83C ''2'':36C,40C,84C ''2.7'':52C ''3'':38C,56C ''3rd'':21C ''3rd-party'':20C ''5'':85C ''8'':86C ''9'':87C ''activ'':62C ''algebra'':211C ''allow'':123C,207C ''analysi'':203C ''applic'':27C ''approach'':108C ''array'':184C ''beyond'':51C ''block'':157C ''bugfix'':54C ''built'':204C ''clean'':102C ''code'':81C,126C,133C,141C,144C,147C,156C ''compar'':73C ''comprehend'':72C ''condit'':140C ''current'':32C ''data'':25C,165C,227C,239C,244C,257C ''develop'':63C ''dimension'':183C ''due'':104C ''dynam'':14C ''easi'':70C ''els'':216C ''en.wikipedia.org'':113C ''en.wikipedia.org/wiki/whitespace_%28programming_language%29'':112C ''etc'':150C ''exampl'':78C ''explor'':240C ''extern'':256C ''extrem'':231C ''fast'':180C,229C,249C ''file'':260C ''flexibl'':232C ''follow'':159C ''format'':261C ''foundat'':187C ''fourier'':212C ''general'':12C ''general-purpos'':11C ''handl'':242C,251C ''hello'':93C ''href'':3C,111C,173C,196C,220C ''includ'':168C ''indent'':153C ''index'':250C ''inspect'':132C ''languag'':17C,76C ''li'':171C,194C,218C ''librari'':23C,185C ''linear'':210C ''look'':103C,128C ''loop'':138C ''make'':131C ''mani'':19C ''much'':134C,215C ''must'':151C ''n'':182C ''n-dimension'':181C ''nan'':243C ''new'':47C,59C ''nofollow'':6C,116C,176C,199C,223C ''number'':82C,89C,91C,95C ''numer'':202C ''numpi'':177C,206C ''old'':43C ''optim'':209C ''p'':1C,65C,98C,161C ''packag'':167C,233C ''panda'':224C ''pandas.pydata.org'':221C ''panel'':226C ''parti'':22C ''popular'':162C ''pre'':80C ''predict'':136C ''print'':92C ''purpos'':13C ''python'':7C,39C,55C,66C,99C,125C,192C ''regulatori'':107C ''rel'':5C,115C,175C,198C,222C ''relat'':69C ''releas'':50C ''restrict'':121C ''save'':53C ''scienc'':26C,166C ''scientif'':163C,191C ''scipi'':200C ''seem'':120C ''similar'':129C ''strong'':15C ''syntax'':67C ''thing'':190C ''transform'':213C ''two'':30C ''type'':16C,258C ''ul'':170C ''use'':35C,237C ''varieti'':254C ''version'':31C,44C,48C,60C ''well'':245C,247C ''whitespac'':117C ''wide'':34C,253C ''world'':94C ''www.numpy.org'':174C ''www.python.org'':4C ''www.scipy.org'':197C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5523, 4, '2015-04-14 01:54:02.727', 0, NULL, NULL, 'Python is a general-purpose, dynamic, strongly typed language with many 3rd-party libraries for data science applications.', NULL, NULL, '''3rd'':14C ''3rd-party'':13C ''applic'':20C ''data'':18C ''dynam'':7C ''general'':5C ''general-purpos'':4C ''languag'':10C ''librari'':16C ''mani'':12C ''parti'':15C ''purpos'':6C ''python'':1C ''scienc'':19C ''strong'':8C ''type'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5524, 1, '2015-04-14 07:17:08.857', -2, 68, 'Understanding of minbucket function in CART model using R', '&lt;p&gt;Assume the training data is &lt;code&gt;fruit&lt;/code&gt;, which I am going to use for prediction in a CART model in R:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;fruit = data.frame(color=c("red", "red", "red", "yellow", "yellow",
                           "orange", "green", "pink", "red", "red"),
                   isApple=c(TRUE, TRUE, FALSE, FALSE, FALSE,
                             FALSE, FALSE, FALSE, TRUE, FALSE))
mod = rpart(isApple ~ color, data=fruit, method="class", minbucket=1)
prp(mod)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Could anyone explain what is exactly the role of &lt;code&gt;minbucket&lt;/code&gt; in plotting CART tree for this example if we are going to use &lt;code&gt;minbucket = 2, 3, 4, 5&lt;/code&gt;?&lt;/p&gt;

&lt;p&gt;Say &lt;code&gt;fruit&lt;/code&gt; is my data frame, I''m finding whether the outcome is apple or not? I have 5 red apples (4 TRUE, 1 FALSE), one FALSE value is tomato here, so what ever is red need not be an apple. But if I give &lt;code&gt;minbucket&lt;/code&gt;=5 or 4 here, there is no split at all. Only for &lt;code&gt;minbucket&lt;/code&gt; 1 to 3 there is a split beyond 3 there is no split. But I have more than 3 observation in my leaf node.&lt;/p&gt;
', 'r', NULL, '''/code'':19C,75C,89C,109C,115C,161C,176C ''/p'':34C,110C,201C ''/pre'':76C ''1'':72C,137C,177C ''2'':105C ''3'':106C,179C,185C,195C ''4'':107C,135C,164C ''5'':108C,132C,162C ''anyon'':79C ''appl'':127C,134C,154C ''assum'':12C ''beyond'':184C ''c'':40C,52C ''cart'':6A,30C,92C ''class'':70C ''code'':17C,36C,87C,103C,113C,159C,174C ''color'':39C,66C ''could'':78C ''data'':15C,67C,118C ''data.frame'':38C ''ever'':147C ''exact'':83C ''exampl'':96C ''explain'':80C ''fals'':55C,56C,57C,58C,59C,60C,62C,138C,140C ''find'':122C ''frame'':119C ''fruit'':18C,37C,68C,114C ''function'':4A ''give'':158C ''go'':23C,100C ''green'':47C ''isappl'':51C,65C ''leaf'':199C ''m'':121C ''method'':69C ''minbucket'':3A,71C,88C,104C,160C,175C ''mod'':63C,74C ''model'':7A,31C ''need'':150C ''node'':200C ''observ'':196C ''one'':139C ''orang'':46C ''outcom'':125C ''p'':11C,77C,111C ''pink'':48C ''plot'':91C ''pre'':35C ''predict'':27C ''prp'':73C ''r'':9A,10B,33C ''red'':41C,42C,43C,49C,50C,133C,149C ''role'':85C ''rpart'':64C ''say'':112C ''split'':169C,183C,189C ''tomato'':143C ''train'':14C ''tree'':93C ''true'':53C,54C,61C,136C ''understand'':1A ''use'':8A,25C,102C ''valu'':141C ''whether'':123C ''yellow'':44C,45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5525, 2, '2015-04-14 08:13:07.223', 0, NULL, NULL, '&lt;p&gt;Geop Solutions write blogs related to bigdata and CRM on weekly basis. You can read their blog at &lt;a href="https://geopsolutions.wordpress.com/" rel="nofollow"&gt;https://geopsolutions.wordpress.com/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':27C ''basi'':13C ''bigdata'':8C ''blog'':5C,18C ''crm'':10C ''geop'':2C ''geopsolutions.wordpress.com'':22C,26C ''geopsolutions.wordpress.com/&lt;/a&gt;&lt;/p&gt;'':25C ''href'':21C ''nofollow'':24C ''p'':1C ''read'':16C ''rel'':23C ''relat'':6C ''solut'':3C ''week'':12C ''write'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5526, 2, '2015-04-14 15:50:49.573', 0, NULL, NULL, '&lt;p&gt;Reading the documentation:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;minbucket: the minimum number of observations in any terminal â&amp;lt;leaf&amp;gt;â
          node.  If only one of âminbucketâ or âminsplitâ is specified,
          the code either sets âminsplitâ to âminbucket*3â or
          âminbucketâ to âminsplit/3â, as appropriate.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So if you set &lt;code&gt;minbucket&lt;/code&gt; to 4, then &lt;code&gt;minsplit&lt;/code&gt; is 12, and you''ve only got 10 observations so it refuses to split. Because:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;minsplit: the minimum number of observations that must exist in a node
          in order for a split to be attempted.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Its all there in the documentation.&lt;/p&gt;
', NULL, NULL, '''/code'':53C,62C,68C,107C ''/p'':5C,84C,116C ''/pre'':54C,108C ''10'':76C ''12'':70C ''3â'':43C ''4'':64C ''appropri'':52C ''attempt'':106C ''code'':7C,35C,60C,66C,86C ''document'':4C,115C ''either'':36C ''exist'':95C ''got'':75C ''gt'':20C ''leaf'':19C ''lt'':18C ''minbucket'':8C,42C,61C ''minbucketâ'':28C,46C ''minimum'':10C,89C ''minsplit'':67C,87C ''minsplit/3'':49C ''minsplitâ'':31C,39C ''must'':94C ''node'':22C,98C ''number'':11C,90C ''observ'':13C,77C,92C ''one'':25C ''order'':100C ''p'':1C,55C,109C ''pre'':6C,85C ''read'':2C ''refus'':80C ''set'':37C,59C ''specifi'':33C ''split'':82C,103C ''termin'':16C ''ve'':73C ''â'':17C,21C,27C,30C,38C,41C,45C,48C,50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9158, 2, '2015-12-01 23:35:37.437', 1, NULL, NULL, '&lt;p&gt;I think you are doing a supervised classification problem.  You have labelled data â previous customers, which you could label as different groups (e.g. high spend / low spend) along with predictors about the customer â their details (e.g. age).  &lt;/p&gt;

&lt;p&gt;You could think of each of these customer details representing dimensions on a plot, wth your task being to find out where different groups of customers are clustered along these dimensions.  Once you know that you can predict what group a new customer will likely belong to, based on their details.&lt;/p&gt;

&lt;p&gt;There are many methods for solving this sort of problem, including but not limited to support &lt;em&gt;vector machines&lt;/em&gt;, &lt;em&gt;gradient descent&lt;/em&gt; and &lt;em&gt;nearest neighbour&lt;/em&gt;.  Which one you choose will depend on the amount and sort of data you have.  A really good guide if you are starting out is &lt;a href="http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html" rel="nofollow"&gt;this flowchart&lt;/a&gt; in the SciKit learn documentation, which isn''t exhaustive, but gives a useful overview.&lt;/p&gt;
', NULL, NULL, '''/a'':159C ''/em'':115C,119C,124C ''/p'':41C,94C,174C ''/stable/tutorial/machine_learning_map/index.html'':154C ''age'':40C ''along'':30C,71C ''amount'':133C ''base'':90C ''belong'':88C ''choos'':128C ''classif'':9C ''cluster'':70C ''could'':20C,44C ''custom'':17C,35C,50C,68C,85C ''data'':14C,137C ''depend'':130C ''descent'':118C ''detail'':38C,51C,93C ''differ'':23C,65C ''dimens'':53C,73C ''document'':164C ''e.g'':25C,39C ''em'':112C,116C,121C ''exhaust'':168C ''find'':62C ''flowchart'':158C ''give'':170C ''good'':142C ''gradient'':117C ''group'':24C,66C,82C ''guid'':143C ''high'':26C ''href'':151C ''includ'':106C ''isn'':166C ''know'':76C ''label'':13C,21C ''learn'':163C ''like'':87C ''limit'':109C ''low'':28C ''machin'':114C ''mani'':98C ''method'':99C ''nearest'':122C ''neighbour'':123C ''new'':84C ''nofollow'':156C ''one'':126C ''overview'':173C ''p'':1C,42C,95C ''plot'':56C ''predict'':80C ''predictor'':32C ''previous'':16C ''problem'':10C,105C ''realli'':141C ''rel'':155C ''repres'':52C ''scikit'':162C ''scikit-learn.org'':153C ''scikit-learn.org/stable/tutorial/machine_learning_map/index.html'':152C ''solv'':101C ''sort'':103C,135C ''spend'':27C,29C ''start'':147C ''supervis'':8C ''support'':111C ''task'':59C ''think'':3C,45C ''use'':172C ''vector'':113C ''wth'':57C ''â'':15C,36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5527, 2, '2015-04-14 17:17:57.647', 0, NULL, NULL, '&lt;p&gt;I think you need to take a step back and figure out what you''re trying to do at a higher level.&lt;/p&gt;

&lt;p&gt;How were the existing classes built? If they were built by clustering unlabeled data, then with this new data point you''re continuing with the clustering process.&lt;/p&gt;

&lt;p&gt;If the existing classes are labeled data, then k-NN is one possible classification method, and there are plenty more (decision trees, naive bayes, neural networks, etc.). &lt;/p&gt;

&lt;p&gt;If you''re doing clustering, then there are several ways of assigning a point to a cluster, among measuring the distances from the point to cluster centroids is one. There''s also single-linkage (distance is min of distances from point to points of cluster), complete-linkage (max of distances). These different methods will give clusters with different shapes and there''s no universally best approach. You could test them with points that are already in clusters... but then if you''re certain of what classes they re in, then you have a classification problem.&lt;/p&gt;

&lt;p&gt;So... if it''s classification, then you can use k-NN, that''s similar to the idea of assigning a point to a cluster according to distance. But it''s not defined as finding the nearest cluster, it''s defined as finding the classes of the k nearest points, then applying a vote or something. 1-NN is basically like single-linkage clustering. kNN does require finding the most similar (training) data points to your new data point. Sampling is definitely sub-optimal, but it may be good enough if you classes are well separated. If the cost of calculating distances is high, then one way of reducing the cost of calculation is the idea of skyline clustering: use a cheap distance metric to determine a subset of points that are likely to be among the k nearest neighbours, then compute these neighbours using the more expensive distance metric.&lt;/p&gt;

&lt;p&gt;Finally, if you will be classifying many points and not updating your model, it may be worth training a model (e.g. a decision tree) on the existing classes.&lt;/p&gt;
', NULL, NULL, '''/p'':24C,53C,83C,182C,336C,366C ''1'':240C ''accord'':209C ''alreadi'':161C ''also'':116C ''among'':102C,321C ''appli'':235C ''approach'':152C ''assign'':96C,203C ''back'':10C ''basic'':243C ''bay'':79C ''best'':151C ''built'':31C,35C ''calcul'':286C,298C ''centroid'':111C ''certain'':169C ''cheap'':307C ''class'':30C,58C,172C,228C,278C,365C ''classif'':69C,180C,188C ''classifi'':343C ''cluster'':37C,51C,89C,101C,110C,130C,142C,163C,208C,221C,248C,304C ''complet'':132C ''complete-linkag'':131C ''comput'':327C ''continu'':48C ''cost'':284C,296C ''could'':154C ''data'':39C,44C,61C,257C,262C ''decis'':76C,360C ''defin'':216C,224C ''definit'':266C ''determin'':311C ''differ'':138C,144C ''distanc'':105C,120C,124C,136C,211C,287C,308C,334C ''e.g'':358C ''enough'':275C ''etc'':82C ''exist'':29C,57C,364C ''expens'':333C ''figur'':12C ''final'':338C ''find'':218C,226C,252C ''give'':141C ''good'':274C ''high'':289C ''higher'':22C ''idea'':201C,301C ''k'':64C,194C,231C,323C ''k-nn'':63C,193C ''knn'':249C ''label'':60C ''level'':23C ''like'':244C,318C ''linkag'':119C,133C,247C ''mani'':344C ''max'':134C ''may'':272C,352C ''measur'':103C ''method'':70C,139C ''metric'':309C,335C ''min'':122C ''model'':350C,357C ''naiv'':78C ''nearest'':220C,232C,324C ''need'':5C ''neighbour'':325C,329C ''network'':81C ''neural'':80C ''new'':43C,261C ''nn'':65C,195C,241C ''one'':67C,113C,291C ''optim'':269C ''p'':1C,25C,54C,84C,183C,337C ''plenti'':74C ''point'':45C,98C,108C,126C,128C,158C,205C,233C,258C,263C,315C,345C ''possibl'':68C ''problem'':181C ''process'':52C ''re'':16C,47C,87C,168C,174C ''reduc'':294C ''requir'':251C ''sampl'':264C ''separ'':281C ''sever'':93C ''shape'':145C ''similar'':198C,255C ''singl'':118C,246C ''single-linkag'':117C,245C ''skylin'':303C ''someth'':239C ''step'':9C ''sub'':268C ''sub-optim'':267C ''subset'':313C ''take'':7C ''test'':155C ''think'':3C ''train'':256C,355C ''tree'':77C,361C ''tri'':17C ''univers'':150C ''unlabel'':38C ''updat'':348C ''use'':192C,305C,330C ''vote'':237C ''way'':94C,292C ''well'':280C ''worth'':354C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5770, 2, '2015-05-12 17:23:52.323', 2, NULL, NULL, '&lt;p&gt;For question 1, I feel there are a great deal of things you can look into with this.  The basic conclusion is which states are culturally similar to one another (physical proximity would lead to such similarity).  Having a ''cultural similarity score'' from year to year, you could determine the connectedness of states over time, see if major events effect how connected the country is..... there is a lot of potential here, and reenforces the notion that you need to know what question you''re trying to answer before you get too far into an analysis.&lt;/p&gt;

&lt;p&gt;As for two, this is a bit trickier.  The visual I see is each state getting a node at it''s center, and like states having color coded connectors.  The visual I''m seeing is similar to the graph they use for the scikit learn site for affinity propagation:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/auto_examples/cluster/plot_affinity_propagation.html" rel="nofollow"&gt;http://scikit-learn.org/stable/auto_examples/cluster/plot_affinity_propagation.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':98C,149C ''/stable/auto_examples/cluster/plot_affinity_propagation.html'':155C ''/stable/auto_examples/cluster/plot_affinity_propagation.html&lt;/a&gt;&lt;/p&gt;'':160C ''1'':4C ''affin'':147C ''analysi'':97C ''anoth'':31C ''answer'':89C ''basic'':21C ''bit'':106C ''center'':121C ''code'':127C ''color'':126C ''conclus'':22C ''connect'':63C ''connected'':52C ''connector'':128C ''could'':49C ''countri'':65C ''cultur'':27C,41C ''deal'':11C ''determin'':50C ''effect'':61C ''event'':60C ''far'':94C ''feel'':6C ''get'':92C,115C ''graph'':138C ''great'':10C ''href'':152C ''know'':82C ''lead'':35C ''learn'':144C ''like'':123C ''look'':16C ''lot'':70C ''m'':132C ''major'':59C ''need'':80C ''node'':117C ''nofollow'':157C ''notion'':77C ''one'':30C ''p'':1C,99C,150C ''physic'':32C ''potenti'':72C ''propag'':148C ''proxim'':33C ''question'':3C,84C ''re'':86C ''reenforc'':75C ''rel'':156C ''scikit'':143C ''scikit-learn.org'':154C,159C ''scikit-learn.org/stable/auto_examples/cluster/plot_affinity_propagation.html'':153C ''scikit-learn.org/stable/auto_examples/cluster/plot_affinity_propagation.html&lt;/a&gt;&lt;/p&gt;'':158C ''score'':43C ''see'':57C,111C,133C ''similar'':28C,38C,42C,135C ''site'':145C ''state'':25C,54C,114C,124C ''thing'':13C ''time'':56C ''tri'':87C ''trickier'':107C ''two'':102C ''use'':140C ''visual'':109C,130C ''would'':34C ''year'':45C,47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5528, 1, '2015-04-14 20:44:56.423', 1, 41, 'Complete link clustering', '&lt;p&gt;I''m conjecturing that with &lt;a href="http://en.wikipedia.org/wiki/Complete-linkage_clustering" rel="nofollow"&gt;Complete-linkage clustering&lt;/a&gt; two elements from the same cluster will always be closer to each other some other element from another cluster.&lt;/p&gt;

&lt;p&gt;In more formal terms:&lt;/p&gt;

&lt;p&gt;Let $C$ be a clustering.
 $\not\exists z \in C_j$ s.t. $\bigtriangleup(x, z) &amp;lt; \bigtriangleup(x, y)$ where $x,y \in C_i$, $C_i \neq C_j$ and $C_i, C_j \in C$.&lt;/p&gt;

&lt;p&gt;I haven''t been able to prove the conjecture yet, thus I''m wondering whether I''m right or wrong. If this is indeed the case, I would much appreciate a sketch a proof. I''m pretty sure I can work my way from there.&lt;/p&gt;

&lt;p&gt;On a side note (not that I think it makes a difference), I''ll be applying the clustering algorithm on a one-dimensinal dataset.&lt;/p&gt;

&lt;p&gt;Your input is much appreciated.&lt;/p&gt;
', 'machine-learning  clustering  algorithms  hierarchical-data-format', NULL, '''/a'':30C ''/p'':50C,56C,95C,142C,169C,176C ''/wiki/complete-linkage_clustering'':23C ''abl'':101C ''algorithm'':8B,162C ''alway'':38C ''anoth'':48C ''appli'':159C ''appreci'':126C,175C ''bigtriangleup'':70C,74C ''c'':59C,67C,81C,83C,86C,89C,91C,94C ''case'':122C ''closer'':40C ''cluster'':3A,7B,29C,36C,49C,62C,161C ''complet'':1A,27C ''complete-linkag'':26C ''conjectur'':16C,105C ''data'':11B ''dataset'':168C ''differ'':155C ''dimensin'':167C ''element'':32C,46C ''en.wikipedia.org'':22C ''en.wikipedia.org/wiki/complete-linkage_clustering'':21C ''exist'':64C ''formal'':54C ''format'':12B ''haven'':98C ''hierarch'':10B ''hierarchical-data-format'':9B ''href'':20C ''inde'':120C ''input'':172C ''j'':68C,87C,92C ''learn'':6B ''let'':58C ''link'':2A ''linkag'':28C ''ll'':157C ''lt'':73C ''m'':15C,109C,113C,132C ''machin'':5B ''machine-learn'':4B ''make'':153C ''much'':125C,174C ''neq'':85C ''nofollow'':25C ''note'':147C ''one'':166C ''one-dimensin'':165C ''p'':13C,51C,57C,96C,143C,170C ''pretti'':133C ''proof'':130C ''prove'':103C ''rel'':24C ''right'':114C ''s.t'':69C ''side'':146C ''sketch'':128C ''sure'':134C ''term'':55C ''think'':151C ''thus'':107C ''two'':31C ''way'':139C ''whether'':111C ''wonder'':110C ''work'':137C ''would'':124C ''wrong'':116C ''x'':71C,75C,78C ''y'':76C,79C ''yet'':106C ''z'':65C,72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5529, 1, '2015-04-15 11:47:44.013', 4, 374, 'Pandas time series optimization problem: add year', '&lt;p&gt;I have a pandas DataFrame containing a time series column. The years are shifted in the past, so that I have to add a constant number of years to every element of that column.&lt;/p&gt;

&lt;p&gt;The best way I found is to iterate through all the records and use&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;x.replace(year=x.year + years)  # x = current element, years = years to add
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;It is cythonized as below, but still very slow (proofing)&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;cdef list _addYearsToTimestamps(list elts, int years):
    cdef cpdatetime x
    cdef int i
    for (i, x) in enumerate(elts):
        try:
            elts[i] = x.replace(year=x.year + years)
        except Exception as e:
            logError(None, "Cannot replace year of %s - leaving value as this: %s" % (str(x), repr(e)))
    return elts

def fixYear(data):
    data.loc[:, ''timestamp''] = _addYearsToTimestamps(list(data.loc[:, ''timestamp'']), REAL_YEAR-(list(data[-1:][''timestamp''])[0].year))
    return data
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m pretty sure that there is a way to change the year without iterating, by using Pandas''s Timestamp features. Unfortunately, I don''t find how. Could someone elaborate?&lt;/p&gt;
', 'python  pandas', NULL, '''-1'':152C ''/code'':75C,158C ''/p'':45C,61C,88C,191C ''/pre'':76C,159C ''0'':154C ''add'':6A,33C,74C ''addyearstotimestamp'':93C,144C ''best'':48C ''cannot'':123C ''cdef'':91C,98C,101C ''chang'':171C ''code'':63C,90C ''column'':20C,44C ''constant'':35C ''contain'':16C ''could'':188C ''cpdatetim'':99C ''current'':69C ''cython'':80C ''data'':141C,151C,157C ''data.loc'':142C,146C ''datafram'':15C ''def'':139C ''e'':120C,136C ''elabor'':190C ''element'':41C,70C ''elt'':95C,109C,111C,138C ''enumer'':108C ''everi'':40C ''except'':117C,118C ''featur'':181C ''find'':186C ''fixyear'':140C ''found'':51C ''int'':96C,102C ''iter'':54C,175C ''leav'':128C ''list'':92C,94C,145C,150C ''logerror'':121C ''m'':162C ''none'':122C ''number'':36C ''optim'':4A ''p'':10C,46C,77C,160C ''panda'':1A,9B,14C,178C ''past'':27C ''pre'':62C,89C ''pretti'':163C ''problem'':5A ''proof'':87C ''python'':8B ''real'':148C ''record'':58C ''replac'':124C ''repr'':135C ''return'':137C,156C ''seri'':3A,19C ''shift'':24C ''slow'':86C ''someon'':189C ''still'':84C ''str'':133C ''sure'':164C ''time'':2A,18C ''timestamp'':143C,147C,153C,180C ''tri'':110C ''unfortun'':182C ''use'':60C,177C ''valu'':129C ''way'':49C,169C ''without'':174C ''x'':68C,100C,106C,134C ''x.replace'':64C,113C ''x.year'':66C,115C ''year'':7A,22C,38C,65C,67C,71C,72C,97C,114C,116C,125C,149C,155C,173C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5530, 1, '2015-04-15 13:10:08.47', 1, 60, 'Abstract data type?', '&lt;p&gt;I don''t know, if I can ask this here, but I''m interested to know what kind of &lt;em&gt;abstract data type (ADT)&lt;/em&gt; does Twitter use to &lt;strong&gt;model relations&lt;/strong&gt; between profiles and why. I''m just starting to learn ADTs, so I would like to learn how they work in the real world as well as their applications.&lt;/p&gt;
', 'social-network-analysis', NULL, '''/em'':33C ''/p'':71C ''/strong'':41C ''abstract'':1A,29C ''adt'':32C,52C ''analysi'':7B ''applic'':70C ''ask'':16C ''data'':2A,30C ''em'':28C ''interest'':22C ''kind'':26C ''know'':12C,24C ''learn'':51C,58C ''like'':56C ''m'':21C,47C ''model'':39C ''network'':6B ''p'':8C ''profil'':43C ''real'':64C ''relat'':40C ''social'':5B ''social-network-analysi'':4B ''start'':49C ''strong'':38C ''twitter'':35C ''type'':3A,31C ''use'':36C ''well'':67C ''work'':61C ''world'':65C ''would'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5531, 2, '2015-04-15 13:37:52.47', 2, NULL, NULL, '&lt;p&gt;Make a pandas Timedelta object then add with the += operator:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;x = pandas.Timedelta(days=365)
mydataframe.timestampcolumn += x
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So the key is to store your time series as timestamps.  To do that, use the pandas &lt;code&gt;to_datetime&lt;/code&gt; function:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;mydataframe[''timestampcolumn''] = pandas.to_datetime(x[''epoch''], unit=''s'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;assuming you have your timestamps as epoch seconds in the dataframe &lt;code&gt;x&lt;/code&gt;.  That''s not a requirement of course; see the &lt;a href="http://pandas.pydata.org/pandas-docs/dev/generated/pandas.tseries.tools.to_datetime.html" rel="nofollow"&gt;to_datetime&lt;/a&gt; documentation for converting other formats.&lt;/p&gt;
', NULL, NULL, '''/a'':92C ''/code'':21C,44C,57C,73C ''/p'':12C,46C,98C ''/pandas-docs/dev/generated/pandas.tseries.tools.to_datetime.html'':87C ''/pre'':22C,58C ''365'':18C ''add'':8C ''assum'':60C ''code'':14C,41C,48C,71C ''convert'':95C ''cours'':80C ''datafram'':70C ''datetim'':43C,52C,91C ''day'':17C ''document'':93C ''epoch'':54C,66C ''format'':97C ''function'':45C ''href'':84C ''key'':26C ''make'':2C ''mydatafram'':49C ''mydataframe.timestampcolumn'':19C ''nofollow'':89C ''object'':6C ''oper'':11C ''p'':1C,23C,59C ''panda'':4C,40C ''pandas.pydata.org'':86C ''pandas.pydata.org/pandas-docs/dev/generated/pandas.tseries.tools.to_datetime.html'':85C ''pandas.timedelta'':16C ''pandas.to'':51C ''pre'':13C,47C ''rel'':88C ''requir'':78C ''second'':67C ''see'':81C ''seri'':32C ''store'':29C ''time'':31C ''timedelta'':5C ''timestamp'':34C,64C ''timestampcolumn'':50C ''unit'':55C ''use'':38C ''x'':15C,20C,53C,72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10222, 2, '2016-02-12 13:42:10.46', 2, NULL, NULL, '&lt;p&gt;$ R^2 $ shows what variation of your purpose variable is described by independent variables. So their synergetic effect could give you good better answer than their correlation. Better use $ R^2 adjusted $. Look at p-values of your variables and think about their real correlation. Are they important in real life. If they have any adequate relationship, so your regression is right.&lt;/p&gt;

&lt;p&gt;Hope it will help.  &lt;/p&gt;
', NULL, NULL, '''/p'':65C,71C ''2'':3C,32C ''adequ'':58C ''adjust'':33C ''answer'':25C ''better'':24C,29C ''correl'':28C,47C ''could'':20C ''describ'':12C ''effect'':19C ''give'':21C ''good'':23C ''help'':70C ''hope'':67C ''import'':50C ''independ'':14C ''life'':53C ''look'':34C ''p'':1C,37C,66C ''p-valu'':36C ''purpos'':9C ''r'':2C,31C ''real'':46C,52C ''regress'':62C ''relationship'':59C ''right'':64C ''show'':4C ''synerget'':18C ''think'':43C ''use'':30C ''valu'':38C ''variabl'':10C,15C,41C ''variat'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5533, 2, '2015-04-15 18:04:28.93', 2, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Any&lt;/strong&gt; platform, focused on &lt;em&gt;social networking&lt;/em&gt; (not necessarily Twitter), at its &lt;em&gt;core&lt;/em&gt; uses the most appropriate and natural &lt;em&gt;abstract data type (ADT)&lt;/em&gt; for such domain - a &lt;a href="http://en.wikipedia.org/wiki/Graph_%28abstract_data_type%29" rel="nofollow"&gt;graph data structure&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;If you use Python, you can check nice &lt;a href="http://networkx.github.io" rel="nofollow"&gt;NetworkX package&lt;/a&gt;, used for "the creation, manipulation, and study of the structure, dynamics, and functions of complex networks". Of course, there are many other software tools for various programming languages for building, using and analyzing &lt;em&gt;network structures&lt;/em&gt;. You might also find useful the relevant book &lt;a href="http://shop.oreilly.com/product/0636920020424.do" rel="nofollow"&gt;"Social Network Analysis for Startups: Finding connections on the social web"&lt;/a&gt;, which provides a nice introduction into the &lt;em&gt;social network analysis (SNA)&lt;/em&gt; and uses the above-mentioned &lt;em&gt;NetworkX&lt;/em&gt; software for SNA examples. P.S. I have no affiliation whatsoever with &lt;em&gt;NetworkX&lt;/em&gt; open source project or the book''s authors.&lt;/p&gt;
', NULL, NULL, '''/a'':46C,64C,128C ''/em'':11C,19C,31C,101C,141C,150C,164C ''/p'':47C,173C ''/product/0636920020424.do'':114C ''/strong'':4C ''/wiki/graph_%28abstract_data_type%29'':40C ''above-ment'':145C ''abstract'':27C ''adt'':30C ''affili'':159C ''also'':104C ''analysi'':119C,139C ''analyz'':97C ''appropri'':23C ''author'':172C ''book'':109C,170C ''build'':94C ''check'':55C ''complex'':79C ''connect'':123C ''core'':18C ''cours'':82C ''creation'':68C ''data'':28C,44C ''domain'':34C ''dynam'':75C ''em'':8C,17C,26C,98C,136C,148C,162C ''en.wikipedia.org'':39C ''en.wikipedia.org/wiki/graph_%28abstract_data_type%29'':38C ''exampl'':154C ''find'':105C,122C ''focus'':6C ''function'':77C ''graph'':43C ''href'':37C,58C,111C ''introduct'':133C ''languag'':92C ''mani'':85C ''manipul'':69C ''mention'':147C ''might'':103C ''natur'':25C ''necessarili'':13C ''network'':10C,80C,99C,118C,138C ''networkx'':62C,149C,163C ''networkx.github.io'':59C ''nice'':56C,132C ''nofollow'':42C,61C,116C ''open'':165C ''p'':1C,48C ''p.s'':155C ''packag'':63C ''platform'':5C ''program'':91C ''project'':167C ''provid'':130C ''python'':52C ''rel'':41C,60C,115C ''relev'':108C ''shop.oreilly.com'':113C ''shop.oreilly.com/product/0636920020424.do'':112C ''sna'':140C,153C ''social'':9C,117C,126C,137C ''softwar'':87C,151C ''sourc'':166C ''startup'':121C ''strong'':2C ''structur'':45C,74C,100C ''studi'':71C ''tool'':88C ''twitter'':14C ''type'':29C ''use'':20C,51C,65C,95C,106C,143C ''various'':90C ''web'':127C ''whatsoev'':160C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8221, 1, '2015-09-28 08:24:50.84', 0, 12, 'Does Multiclass prediction gives alternate classes in vowpal wabbit?', '&lt;p&gt;Does the multiclass task in vowpal wabbit allow to get a list of probable classes for a featureset. i.e instead of getting only the predicted label for a featureset , it is required that a list of labels be returned for that featureset sorted by probabilities. Is this possible in vowpal wabbit?&lt;/p&gt;
', 'multiclass-classification', NULL, '''/p'':65C ''allow'':21C ''altern'':5A ''class'':6A,28C ''classif'':12B ''featureset'':31C,42C,55C ''get'':23C,35C ''give'':4A ''i.e'':32C ''instead'':33C ''label'':39C,50C ''list'':25C,48C ''multiclass'':2A,11B,16C ''multiclass-classif'':10B ''p'':13C ''possibl'':61C ''predict'':3A,38C ''probabl'':27C,58C ''requir'':45C ''return'':52C ''sort'':56C ''task'':17C ''vowpal'':8A,19C,63C ''wabbit'':9A,20C,64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5534, 1, '2015-04-15 23:53:13.957', 4, 1820, 'How to scrape imdb webpage?', '&lt;p&gt;I am trying to learn web scraping using Python by myself as part of an effort to learn data analysis. I am trying to scrape imdb webpage whose url is the following: &lt;a href="http://www.imdb.com/search/title?sort=num_votes,desc&amp;amp;start=1&amp;amp;title_type=feature&amp;amp;year=1950,2012" rel="nofollow"&gt;http://www.imdb.com/search/title?sort=num_votes,desc&amp;amp;start=1&amp;amp;title_type=feature&amp;amp;year=1950,2012&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;I am using BeautifulSoup module. Following is the code I am using:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;r = requests.get(url) # where url is the above url    
bs = BeautifulSoup(r.text)
for movie in bs.findAll(''td'',''title''):
    title = movie.find(''a'').contents[0]
    genres = movie.find(''span'',''genre'').findAll(''a'')
    genres = [g.contents[0] for g in genres]
    runtime = movie.find(''span'',''runtime'').contents[0]
    year = movie.find(''span'',''year_type'').contents[0]
    print title, genres,runtime, rating, year
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am getting the following outputs:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;The Shawshank Redemption [u''Crime'', u''Drama''] 142 mins. (1994)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Using this code, I could scrape title, genre, runtime,and year but I couldn''t scrape the imdb movie id,nor the rating. After inspecting the elements (in chrome browser), I am not being able to find a pattern which will let me use similar code as above.&lt;/p&gt;

&lt;p&gt;Can anybody help me write the piece of code that will let me scrape the movie id and ratings ? &lt;/p&gt;
', 'data-mining  python  scraping', NULL, '''/code'':126C,148C ''/p'':54C,68C,135C,199C,220C ''/pre'':127C,149C ''/search/title?sort=num_votes,desc&amp;amp;start=1&amp;amp;title_type=feature&amp;amp;year=1950,2012'':48C ''/search/title?sort=num_votes,desc&amp;amp;start=1&amp;amp;title_type=feature&amp;amp;year=1950,2012&lt;/a&gt;'':53C ''0'':93C,102C,112C,119C ''142'':145C ''1994'':147C ''abl'':185C ''analysi'':31C ''anybodi'':202C ''beautifulsoup'':59C,81C ''browser'':180C ''bs'':80C ''bs.findall'':86C ''chrome'':179C ''code'':64C,70C,137C,153C,196C,209C ''content'':92C,111C,118C ''could'':155C ''couldn'':164C ''crime'':142C ''data'':7B,30C ''data-min'':6B ''drama'':144C ''effort'':27C ''element'':177C ''find'':187C ''findal'':98C ''follow'':43C,61C,133C ''g'':104C ''g.contents'':101C ''genr'':94C,97C,100C,106C,122C,158C ''get'':131C ''help'':203C ''href'':45C ''id'':170C,217C ''imdb'':4A,37C,168C ''inspect'':175C ''learn'':16C,29C ''let'':192C,212C ''min'':146C ''mine'':8B ''modul'':60C ''movi'':84C,169C,216C ''movie.find'':90C,95C,108C,114C ''nofollow'':50C ''output'':134C ''p'':11C,55C,128C,150C,200C ''part'':24C ''pattern'':189C ''piec'':207C ''pre'':69C,136C ''print'':120C ''python'':9B,20C ''r'':71C ''r.text'':82C ''rate'':124C,173C,219C ''redempt'':140C ''rel'':49C ''requests.get'':72C ''runtim'':107C,110C,123C,159C ''scrape'':3A,10B,18C,36C,156C,166C,214C ''shawshank'':139C ''similar'':195C ''span'':96C,109C,115C ''td'':87C ''titl'':88C,89C,121C,157C ''tri'':14C,34C ''type'':117C ''u'':141C,143C ''url'':40C,73C,75C,79C ''use'':19C,58C,67C,151C,194C ''web'':17C ''webpag'':5A,38C ''whose'':39C ''write'':205C ''www.imdb.com'':47C,52C ''www.imdb.com/search/title?sort=num_votes,desc&amp;amp;start=1&amp;amp;title_type=feature&amp;amp;year=1950,2012'':46C ''www.imdb.com/search/title?sort=num_votes,desc&amp;amp;start=1&amp;amp;title_type=feature&amp;amp;year=1950,2012&lt;/a&gt;'':51C ''year'':113C,116C,125C,161C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5535, 2, '2015-04-16 07:38:38.36', 2, NULL, NULL, '&lt;p&gt;Adapted from Pete''s answer, here''s an implementation of the solution, and the demonstration.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;#!/usr/bin/env python3

import random
import pandas
import time
import datetime

def getRandomDates(n):
    tsMin = time.mktime(time.strptime("1980-01-01 00:00:00", "%Y-%m-%d %H:%M:%S"))
    tsMax = time.mktime(time.strptime("2005-12-31 23:59:59", "%Y-%m-%d %H:%M:%S"))
    return pandas.Series([datetime.datetime.fromtimestamp(tsMin + random.random() * (tsMax - tsMin)) for x in range(0, n)])

def setMaxYear(tss, target):
    maxYearBefore = tss.max().to_datetime().year
    # timedelta cannot be given in years, so we compute the number of days to add in the next line
    deltaDays = (datetime.date(target, 1, 1) - datetime.date(maxYearBefore, 1, 1)).days
    return tss + pandas.Timedelta(days=deltaDays)

data = pandas.DataFrame({''t1'': getRandomDates(1000)})
data[''t2''] = setMaxYear(data[''t1''], 2015)
data[''delta''] = data[''t2''] - data[''t1'']
print(data)
print("delta min: %s" % str(min(data[''delta''])))
print("delta max: %s" % str(max(data[''delta''])))
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''-01'':37C,38C ''-12'':52C ''-31'':53C ''/code'':154C ''/p'':17C ''/pre'':155C ''/usr/bin/env'':20C ''0'':74C ''00'':39C,40C,41C ''1'':107C,108C,111C,112C ''1000'':123C ''1980'':36C ''2005'':51C ''2015'':129C ''23'':54C ''59'':55C,56C ''adapt'':2C ''add'':99C ''answer'':6C ''cannot'':86C ''code'':19C ''comput'':93C ''d'':44C,59C ''data'':119C,124C,127C,130C,132C,134C,137C,144C,152C ''datetim'':29C,83C ''datetime.date'':105C,109C ''datetime.datetime.fromtimestamp'':65C ''day'':97C,113C,117C ''def'':30C,76C ''delta'':131C,139C,145C,147C,153C ''deltaday'':104C,118C ''demonstr'':16C ''getrandomd'':31C,122C ''given'':88C ''h'':45C,60C ''implement'':10C ''import'':22C,24C,26C,28C ''line'':103C ''m'':43C,46C,58C,61C ''max'':148C,151C ''maxyearbefor'':80C,110C ''min'':140C,143C ''n'':32C,75C ''next'':102C ''number'':95C ''p'':1C ''panda'':25C ''pandas.dataframe'':120C ''pandas.series'':64C ''pandas.timedelta'':116C ''pete'':4C ''pre'':18C ''print'':136C,138C,146C ''python3'':21C ''random'':23C ''random.random'':67C ''rang'':73C ''return'':63C,114C ''setmaxyear'':77C,126C ''solut'':13C ''str'':142C,150C ''t1'':121C,128C,135C ''t2'':125C,133C ''target'':79C,106C ''time'':27C ''time.mktime'':34C,49C ''time.strptime'':35C,50C ''timedelta'':85C ''tsmax'':48C,68C ''tsmin'':33C,66C,69C ''tss'':78C,115C ''tss.max'':81C ''x'':71C ''y'':42C,57C ''year'':84C,90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5536, 2, '2015-04-16 10:07:48.187', 2, NULL, NULL, '&lt;p&gt;You can get everything from div with class="rating rating-list"&lt;/p&gt;

&lt;p&gt;All you need to do is retrive attribute id: [id="tt1345836|imdb|8.5|8.5|advsearch"]
When you have this content, you split this string by ''|'', and you get:
1. parameter: movie id
3. parameter: movie score&lt;/p&gt;
', NULL, NULL, '''/p'':14C,52C ''1'':44C ''3'':48C ''8.5'':28C,29C ''advsearch'':30C ''attribut'':23C ''class'':9C ''content'':35C ''div'':7C ''everyth'':5C ''get'':4C,43C ''id'':24C,25C,47C ''imdb'':27C ''list'':13C ''movi'':46C,50C ''need'':18C ''p'':1C,15C ''paramet'':45C,49C ''rate'':10C,12C ''rating-list'':11C ''retriv'':22C ''score'':51C ''split'':37C ''string'':39C ''tt1345836'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5537, 1, '2015-04-16 10:12:56.22', 0, 84, 'Finding predictions using biglm without finding errors', '&lt;p&gt;I''m using the &lt;a href="http://cran.r-project.org/web/packages/biglm/biglm.pdf" rel="nofollow"&gt;biglm&lt;/a&gt; R package for linear regression. &lt;/p&gt;

&lt;p&gt;click/impression is the output required. But the test data does not contain click and impression. 
The &lt;code&gt;predict&lt;/code&gt; function of &lt;code&gt;biglm&lt;/code&gt; gives the error&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Error in eval(expr, envir, enclos) : object ''click'' not found&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I assume this is because &lt;code&gt;predict&lt;/code&gt; tries to compute the standard errors also.&lt;/p&gt;

&lt;p&gt;Is there a method to just obtain the predictions ? I tried assigning values to &lt;code&gt;se.fit&lt;/code&gt; and &lt;code&gt;type&lt;/code&gt; attributes, but I get the same error. &lt;/p&gt;
', 'r', NULL, '''/a'':22C ''/blockquote'':71C ''/code'':48C,53C,80C,106C,110C ''/p'':28C,57C,70C,88C,118C ''/web/packages/biglm/biglm.pdf'':18C ''also'':87C ''assign'':101C ''assum'':74C ''attribut'':111C ''biglm'':4A,21C,52C ''blockquot'':58C ''click'':42C,67C ''click/impression'':30C ''code'':46C,51C,78C,104C,108C ''comput'':83C ''contain'':41C ''cran.r-project.org'':17C ''cran.r-project.org/web/packages/biglm/biglm.pdf'':16C ''data'':38C ''enclo'':65C ''envir'':64C ''error'':7A,56C,60C,86C,117C ''eval'':62C ''expr'':63C ''find'':1A,6A ''found'':69C ''function'':49C ''get'':114C ''give'':54C ''href'':15C ''impress'':44C ''linear'':26C ''m'':11C ''method'':93C ''nofollow'':20C ''object'':66C ''obtain'':96C ''output'':33C ''p'':9C,29C,59C,72C,89C ''packag'':24C ''predict'':2A,47C,79C,98C ''r'':8B,23C ''regress'':27C ''rel'':19C ''requir'':34C ''se.fit'':105C ''standard'':85C ''test'':37C ''tri'':81C,100C ''type'':109C ''use'':3A,12C ''valu'':102C ''without'':5A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5538, 2, '2015-04-16 14:34:23.55', 2, NULL, NULL, '&lt;p&gt;biglm calls model.frame which "all the variables in the formula are included in the data frame" see documentation for model.frame. This is the issue that comes up when predict is called on the biglm class. It looks for those values in the predict function. To get around this you can just create a variable and encode it with 0. See below...&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data(trees)
ff&amp;lt;-log(Volume)~log(Girth)+log(Height)

chunk1&amp;lt;-trees[1:10,]
chunk2&amp;lt;-trees[11:20,]
chunk3&amp;lt;-trees[21:31,]

a &amp;lt;- biglm(ff,chunk1)
summary(a)

#produces same error
chunk2 &amp;lt;- select(chunk2, -Volume)
predict(a, chunk2)

#Fixed
chunk2$Volume &amp;lt;- 0
predict(a, chunk2)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':117C ''/p'':63C ''/pre'':118C ''0'':60C,113C ''1'':79C ''10'':80C ''11'':84C ''20'':85C ''21'':89C ''31'':90C ''around'':48C ''biglm'':2C,35C,93C ''call'':3C,32C ''chunk1'':76C,95C ''chunk2'':81C,101C,104C,108C,110C,116C ''chunk3'':86C ''class'':36C ''code'':65C ''come'':27C ''creat'':53C ''data'':16C,66C ''document'':19C ''encod'':57C ''error'':100C ''ff'':68C,94C ''fix'':109C ''formula'':11C ''frame'':17C ''function'':45C ''get'':47C ''girth'':73C ''height'':75C ''includ'':13C ''issu'':25C ''log'':70C,72C,74C ''look'':38C ''lt'':69C,77C,82C,87C,92C,102C,112C ''model.frame'':4C,21C ''p'':1C ''pre'':64C ''predict'':30C,44C,106C,114C ''produc'':98C ''see'':18C,61C ''select'':103C ''summari'':96C ''tree'':67C,78C,83C,88C ''valu'':41C ''variabl'':8C,55C ''volum'':71C,105C,111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9513, 2, '2015-12-25 09:56:21.907', 3, NULL, NULL, '&lt;p&gt;Yes, one famous example are boosting techniques like &lt;a href="https://en.wikipedia.org/wiki/AdaBoost" rel="nofollow"&gt;Adaboost&lt;/a&gt;. It uses small classifiers to create a big one.&lt;/p&gt;

&lt;p&gt;Here you can find more info about &lt;a href="https://en.wikipedia.org/wiki/Margin_classifier" rel="nofollow"&gt;margin classifiers&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':18C,46C ''/p'':28C,47C ''/wiki/adaboost'':14C ''/wiki/margin_classifier'':41C ''adaboost'':17C ''big'':26C ''boost'':7C ''classifi'':22C,45C ''creat'':24C ''en.wikipedia.org'':13C,40C ''en.wikipedia.org/wiki/adaboost'':12C ''en.wikipedia.org/wiki/margin_classifier'':39C ''exampl'':5C ''famous'':4C ''find'':33C ''href'':11C,38C ''info'':35C ''like'':9C ''margin'':44C ''nofollow'':16C,43C ''one'':3C,27C ''p'':1C,29C ''rel'':15C,42C ''small'':21C ''techniqu'':8C ''use'':20C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5907, 2, '2015-05-25 18:06:22.79', 2, NULL, NULL, '&lt;p&gt;You may want to consider preprocessing - convert different wording for the same type of talent to same wording. For example, a talent in machine learning is called data scientist at Coursera job site, data engineer at Udacity job site, or data analyst. This preprocessing is similar to &lt;a href="http://en.wikipedia.org/wiki/Stemming" rel="nofollow"&gt;stemming&lt;/a&gt; in concept.&lt;/p&gt;
', NULL, NULL, '''/a'':57C ''/p'':60C ''/wiki/stemming'':53C ''analyst'':43C ''call'':28C ''concept'':59C ''consid'':6C ''convert'':8C ''coursera'':32C ''data'':29C,35C,42C ''differ'':9C ''en.wikipedia.org'':52C ''en.wikipedia.org/wiki/stemming'':51C ''engin'':36C ''exampl'':21C ''href'':50C ''job'':33C,39C ''learn'':26C ''machin'':25C ''may'':3C ''nofollow'':55C ''p'':1C ''preprocess'':7C,45C ''rel'':54C ''scientist'':30C ''similar'':47C ''site'':34C,40C ''stem'':56C ''talent'':16C,23C ''type'':14C ''udac'':38C ''want'':4C ''word'':10C,19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5880, 2, '2015-05-23 21:13:32.363', 2, NULL, NULL, '&lt;p&gt;Take a look at this free class &lt;a href="https://www.udacity.com/course/intro-to-artificial-intelligence--cs271" rel="nofollow"&gt;Intro to Artificial Intelligence&lt;/a&gt; from Udacity. One of the instructors is Norvig. The class is more suitable for beginners than Norvig''s book.&lt;/p&gt;

&lt;p&gt;Even though this class doesn''t have programming exercise, it explains concepts so well.&lt;/p&gt;

&lt;p&gt;Its follow up class &lt;a href="https://www.udacity.com/course/artificial-intelligence-for-robotics--cs373" rel="nofollow"&gt;Artificial Intelligence for Robotics&lt;/a&gt; has programming exercises and does a fantastic job in explaining Partical Filters.&lt;/p&gt;
', NULL, NULL, '''/a'':20C,73C ''/course/artificial-intelligence-for-robotics--cs373'':66C ''/course/intro-to-artificial-intelligence--cs271'':13C ''/p'':40C,56C,86C ''artifici'':18C,69C ''beginn'':35C ''book'':39C ''class'':8C,30C,45C,61C ''concept'':53C ''doesn'':46C ''even'':42C ''exercis'':50C,76C ''explain'':52C,83C ''fantast'':80C ''filter'':85C ''follow'':59C ''free'':7C ''href'':10C,63C ''instructor'':26C ''intellig'':19C,70C ''intro'':16C ''job'':81C ''look'':4C ''nofollow'':15C,68C ''norvig'':28C,37C ''one'':23C ''p'':1C,41C,57C ''partic'':84C ''program'':49C,75C ''rel'':14C,67C ''robot'':72C ''suitabl'':33C ''take'':2C ''though'':43C ''udac'':22C ''well'':55C ''www.udacity.com'':12C,65C ''www.udacity.com/course/artificial-intelligence-for-robotics--cs373'':64C ''www.udacity.com/course/intro-to-artificial-intelligence--cs271'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5539, 2, '2015-04-16 15:57:01.27', 5, NULL, NULL, '&lt;p&gt;I have been able to figure out a solution. I thought of posting just in case if it is of any help to anyone or if somebody wants to suggest something different. All suggestions are welcome. Thanks.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;bs = BeautifulSoup(r.text)
for movie in bs.findAll(''td'',''title''):
    title = movie.find(''a'').contents[0]
    genres = movie.find(''span'',''genre'').findAll(''a'')
    genres = [g.contents[0] for g in genres]
    runtime = movie.find(''span'',''runtime'').contents[0]
    rating = movie.find(''span'',''value'').contents[0]
    year = movie.find(''span'',''year_type'').contents[0]
    imdbID = movie.find(''span'',''rating-cancel'').a[''href''].split(''/'')[2]
    print title, genres,runtime, rating, year, imdvid
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The output looks like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;The Shawshank Redemption [u''Crime'', u''Drama''] 142 mins. 9.3 (1994) tt0111161
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':105C,128C ''/p'':39C,113C ''/pre'':106C,129C ''0'':55C,64C,74C,80C,87C ''142'':123C ''1994'':126C ''2'':97C ''9.3'':125C ''abl'':5C ''anyon'':25C ''beautifulsoup'':43C ''bs'':42C ''bs.findall'':48C ''cancel'':93C ''case'':17C ''code'':41C,115C ''content'':54C,73C,79C,86C ''crime'':120C ''differ'':33C ''drama'':122C ''figur'':7C ''findal'':60C ''g'':66C ''g.contents'':63C ''genr'':56C,59C,62C,68C,100C ''help'':23C ''href'':95C ''imdbid'':88C ''imdvid'':104C ''like'':111C ''look'':110C ''min'':124C ''movi'':46C ''movie.find'':52C,57C,70C,76C,82C,89C ''output'':109C ''p'':1C,107C ''post'':14C ''pre'':40C,114C ''print'':98C ''r.text'':44C ''rate'':75C,92C,102C ''rating-cancel'':91C ''redempt'':118C ''runtim'':69C,72C,101C ''shawshank'':117C ''solut'':10C ''somebodi'':28C ''someth'':32C ''span'':58C,71C,77C,83C,90C ''split'':96C ''suggest'':31C,35C ''td'':49C ''thank'':38C ''thought'':12C ''titl'':50C,51C,99C ''tt0111161'':127C ''type'':85C ''u'':119C,121C ''valu'':78C ''want'':29C ''welcom'':37C ''year'':81C,84C,103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9177, 1, '2015-12-02 22:16:52.963', 2, 34, 'How is the Schwarz Criterion defined?', '&lt;p&gt;I am currently reading slides about the $k$-means algorithm. In the analysis, the professor writes&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Minimize Schwarz Criterion: $W(C) + \lambda m k \log R$&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;$W(C)$ is Within-class scatter. I guess $\lambda$ is a weighting factor which has to be chosen by the developer and $k$ is the number of clusters. But what is $m$ and what is $R$?&lt;/p&gt;
', 'k-means', NULL, '''/blockquote'':41C ''/p'':27C,40C,80C ''algorithm'':20C ''analysi'':23C ''blockquot'':28C ''c'':34C,44C ''chosen'':61C ''class'':48C ''cluster'':71C ''criterion'':5A,32C ''current'':13C ''defin'':6A ''develop'':64C ''factor'':56C ''guess'':51C ''k'':8B,18C,37C,66C ''k-mean'':7B ''lambda'':35C,52C ''log'':38C ''m'':36C,75C ''mean'':9B,19C ''minim'':30C ''number'':69C ''p'':10C,29C,42C ''professor'':25C ''r'':39C,79C ''read'':14C ''scatter'':49C ''schwarz'':4A,31C ''slide'':15C ''w'':33C,43C ''weight'':55C ''within'':47C ''within-class'':46C ''write'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5541, 2, '2015-04-16 19:02:45.04', 1, NULL, NULL, '&lt;p&gt;The Wikipedia entry for &lt;a href="https://en.wikipedia.org/wiki/Receiver_operating_characteristic" rel="nofollow"&gt;Receiver operating characteristic&lt;/a&gt; references this paper for the Gini=2AUROC-1 result: Hand, David J.; and Till, Robert J. (2001); A simple generalization of the area under the ROC curve for multiple class classification problems, Machine Learning, 45, 171â186.  But I''m afraid I don''t have easy access to it to see how close it comes to what you want.&lt;/p&gt;
', NULL, NULL, '''-1'':24C ''/a'':16C ''/p'':76C ''/wiki/receiver_operating_characteristic'':10C ''171â'':52C ''186'':53C ''2001'':33C ''2auroc'':23C ''45'':51C ''access'':63C ''afraid'':57C ''area'':39C ''characterist'':15C ''class'':46C ''classif'':47C ''close'':69C ''come'':71C ''curv'':43C ''david'':27C ''easi'':62C ''en.wikipedia.org'':9C ''en.wikipedia.org/wiki/receiver_operating_characteristic'':8C ''entri'':4C ''general'':36C ''gini'':22C ''hand'':26C ''href'':7C ''j'':28C,32C ''learn'':50C ''m'':56C ''machin'':49C ''multipl'':45C ''nofollow'':12C ''oper'':14C ''p'':1C ''paper'':19C ''problem'':48C ''receiv'':13C ''refer'':17C ''rel'':11C ''result'':25C ''robert'':31C ''roc'':42C ''see'':67C ''simpl'':35C ''till'':30C ''want'':75C ''wikipedia'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5542, 2, '2015-04-16 19:24:55.447', 1, NULL, NULL, '&lt;p&gt;As explained in the comments by @Emre, there is a python script on github which you can use to set up a cluster of whatever size you like: &lt;a href="https://greenido.wordpress.com/2014/05/13/spark-cluster-on-google-compute-engine/" rel="nofollow"&gt;Spark Cluster on Google Compute Engine | Ido Green&lt;/a&gt;.  It allows you to specify the instance type to use for the workers (slaves) and separately what to use for the master.&lt;/p&gt;

&lt;p&gt;Note that it uses gcutil, which is deprecated in favor of &lt;a href="https://cloud.google.com/compute/docs/gcloud-compute/" rel="nofollow"&gt;gcloud compute&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/2014/05/13/spark-cluster-on-google-compute-engine/'':34C ''/a'':45C,90C ''/compute/docs/gcloud-compute/'':85C ''/p'':68C,91C ''allow'':47C ''cloud.google.com'':84C ''cloud.google.com/compute/docs/gcloud-compute/'':83C ''cluster'':24C,38C ''comment'':6C ''comput'':41C,89C ''deprec'':77C ''emr'':8C ''engin'':42C ''explain'':3C ''favor'':79C ''gcloud'':88C ''gcutil'':74C ''github'':15C ''googl'':40C ''green'':44C ''greenido.wordpress.com'':33C ''greenido.wordpress.com/2014/05/13/spark-cluster-on-google-compute-engine/'':32C ''href'':31C,82C ''ido'':43C ''instanc'':52C ''like'':29C ''master'':67C ''nofollow'':36C,87C ''note'':70C ''p'':1C,69C ''python'':12C ''rel'':35C,86C ''script'':13C ''separ'':61C ''set'':21C ''size'':27C ''slave'':59C ''spark'':37C ''specifi'':50C ''type'':53C ''use'':19C,55C,64C,73C ''whatev'':26C ''worker'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5772, 2, '2015-05-12 20:08:04.033', 2, NULL, NULL, '&lt;p&gt;Have you seen this paper?
&lt;a href="http://www.cs.cornell.edu/people/tj/publications/joachims_02c.pdf" rel="nofollow"&gt;Optimizing Search Engines using Clickthrough Data&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I stumbled upon this the other day, and I''m still reading through it, but the author attempts to deal with the problem you describe. You may also find &lt;a href="http://research.microsoft.com/en-us/um/people/sdumais/sigir2006-fp345-ranking-agichtein.pdf" rel="nofollow"&gt;Improving Web Search Ranking by Incorporating User Behavior Information&lt;/a&gt; useful.&lt;/p&gt;
', NULL, NULL, '''/a'':20C,68C ''/en-us/um/people/sdumais/sigir2006-fp345-ranking-agichtein.pdf'':56C ''/p'':21C,70C ''/people/tj/publications/joachims_02c.pdf'':11C ''also'':50C ''attempt'':40C ''author'':39C ''behavior'':66C ''clickthrough'':18C ''data'':19C ''day'':29C ''deal'':42C ''describ'':47C ''engin'':16C ''find'':51C ''href'':8C,53C ''improv'':59C ''incorpor'':64C ''inform'':67C ''m'':32C ''may'':49C ''nofollow'':13C,58C ''optim'':14C ''p'':1C,22C ''paper'':6C ''problem'':45C ''rank'':62C ''read'':34C ''rel'':12C,57C ''research.microsoft.com'':55C ''research.microsoft.com/en-us/um/people/sdumais/sigir2006-fp345-ranking-agichtein.pdf'':54C ''search'':15C,61C ''seen'':4C ''still'':33C ''stumbl'':24C ''upon'':25C ''use'':17C,69C ''user'':65C ''web'':60C ''www.cs.cornell.edu'':10C ''www.cs.cornell.edu/people/tj/publications/joachims_02c.pdf'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5543, 1, '2015-04-16 21:11:14.103', 1, 99, 'R lm(log(y)~x,data) models and predict, need to remember the exp. R2 differences', '&lt;p&gt;I noticed a difference between R and Excel when it comes to trend lines.  Basically I am automating a simple process that someone does.  He/she takes web traffic and finds the best trend line by looking at the R squared to predict what the web traffic will be a few months from now to make sure we will have the servers to handle it.&lt;/p&gt;

&lt;p&gt;In R, one can do: lm(log(y)~x,data) but you must "remember" to do &lt;b&gt;exp&lt;/b&gt;(predict(...)).  Is there a better predict function that remembers the left hand operations?&lt;/p&gt;

&lt;p&gt;Also the R-squared reported for log(y)~x is the r squared for x vs that y''s log.  The R-squared against the original y was better.  Is there a nifty function that calculates that?&lt;/p&gt;

&lt;p&gt;As you can see from this output, what summary reports as the rsquared is not "correct" while my rsq data accurately shows the best rmse. (See source code below.)&lt;/p&gt;

&lt;pre&gt;
[1] "rmse is 305221.416535481 rsq is 0.749525713697497 summary$r.squared 0.705953722113025"
[1] "rmse is 304961.752311906 rsq is 0.749815047530537 summary$r.squared 0.706163612870978"
[1] "rmse is 318083.254498832 rsq is 0.723564406971294 summary$r.squared 0.723564406971402"
[1] "rmse is 317352.614485029 rsq is 0.724832898371528 summary$r.squared 0.724832898371531"
&lt;/pre&gt;

&lt;p&gt;I want to learn the best way to do this in R...&lt;/p&gt;

&lt;pre&gt;

rmse=function(x,y){
  return( sqrt(sum((x-y)^2)/length(x)));
}
rsq=function(actual,pred){    
  return( cor(actual,pred,method=c("pearson"))^2);
}


findBestFormula=function(data,yvar,xvar){
  #This figures out the best Excel-like trend function based on lowest rmse
  data$y=data[,yvar];
  data$x=data[,xvar];  
  models=list(lm(log(y)~log(x),data),
              lm(log(y)~x,data),
             lm(y~log(x),data),
              lm(y~x,data));
  preds=list(exp(predict(models[[1]],data)),
    exp(predict(models[[2]],data)),
    (predict(models[[3]],data)),
    (predict(models[[4]],data)));
  rs=c();
  for(i in (1:4)){
    #say(names(models[[i]]))
    rs[i]=rmse(data$y,preds[[i]]);
    print(paste("rmse is",rs[i],"rsq is",rsq(data$y,preds[[i]]),
                "summary$r.squared",summary(models[[i]])$r.squared));
  }
  best=min(rs)
  #say(best);
  for(i in (1:4)){
    if(best==rs[i]){
      return(list(model=models[[i]],pred=preds[[i]],modelNum=i,rmse=best));  
    }
  }

}


summary(findBestFormula(weekDayDf,''daily'',''d'')$model)

&lt;/pre&gt;

&lt;p&gt;Any tips on niftier code to write the above?&lt;/p&gt;
', 'r  forecast', NULL, '''/b'':104C ''/length'':259C ''/p'':84C,118C,159C,190C,246C,422C ''/pre'':232C,411C ''0.705953722113025'':201C ''0.706163612870978'':211C ''0.723564406971294'':218C ''0.723564406971402'':221C ''0.724832898371528'':228C ''0.724832898371531'':231C ''0.749525713697497'':198C ''0.749815047530537'':208C ''1'':192C,202C,212C,222C,327C,347C,387C ''2'':258C,272C,332C ''3'':336C ''304961.752311906'':205C ''305221.416535481'':195C ''317352.614485029'':225C ''318083.254498832'':215C ''4'':340C,348C,388C ''accur'':181C ''actual'':263C,267C ''also'':120C ''autom'':37C ''b'':102C ''base'':288C ''basic'':34C ''best'':51C,184C,239C,282C,379C,383C,390C,404C ''better'':109C,150C ''c'':270C,343C ''calcul'':157C ''code'':188C,417C ''come'':30C ''cor'':266C ''correct'':176C ''d'':409C ''daili'':408C ''data'':6A,95C,180C,275C,292C,294C,296C,298C,307C,312C,317C,321C,328C,333C,337C,341C,356C,369C ''differ'':16A,23C ''excel'':27C,284C ''excel-lik'':283C ''exp'':14A,103C,324C,329C ''figur'':279C ''find'':49C ''findbestformula'':273C,406C ''forecast'':18B ''function'':111C,155C,249C,262C,274C,287C ''hand'':116C ''handl'':82C ''he/she'':44C ''learn'':237C ''left'':115C ''like'':285C ''line'':33C,53C ''list'':301C,323C,394C ''lm'':2A,91C,302C,308C,313C,318C ''log'':3A,92C,127C,140C,303C,305C,309C ''look'':55C ''lowest'':290C ''make'':74C ''method'':269C ''min'':380C ''model'':7A,300C,326C,331C,335C,339C,351C,376C,395C,396C,410C ''modelnum'':401C ''month'':70C ''must'':98C ''name'':350C ''need'':10A ''nifti'':154C ''niftier'':416C ''notic'':21C ''one'':88C ''oper'':117C ''origin'':147C ''output'':167C ''p'':19C,85C,119C,160C,233C,412C ''past'':361C ''pearson'':271C ''pre'':191C,247C ''pred'':264C,268C,322C,358C,371C,398C,399C ''predict'':9A,61C,105C,110C,325C,330C,334C,338C ''print'':360C ''process'':40C ''r'':1A,17B,25C,58C,87C,123C,132C,143C,245C ''r-squar'':122C,142C ''r.squared'':200C,210C,220C,230C,374C,378C ''r2'':15A ''rememb'':12A,99C,113C ''report'':125C,170C ''return'':252C,265C,393C ''rmse'':185C,193C,203C,213C,223C,248C,291C,355C,362C,403C ''rs'':342C,353C,364C,381C,391C ''rsq'':179C,196C,206C,216C,226C,261C,366C,368C ''rsquar'':173C ''say'':349C,382C ''see'':164C,186C ''server'':80C ''show'':182C ''simpl'':39C ''someon'':42C ''sourc'':187C ''sqrt'':253C ''squar'':59C,124C,133C,144C ''sum'':254C ''summari'':169C,199C,209C,219C,229C,373C,375C,405C ''sure'':75C ''take'':45C ''tip'':414C ''traffic'':47C,65C ''trend'':32C,52C,286C ''vs'':136C ''want'':235C ''way'':240C ''web'':46C,64C ''weekdaydf'':407C ''write'':419C ''x'':5A,94C,129C,135C,250C,256C,260C,297C,306C,311C,316C ''x-i'':255C ''xvar'':277C,299C ''y'':4A,93C,128C,138C,148C,251C,257C,293C,304C,310C,314C,319C,357C,370C ''yvar'':276C,295C ''~log'':315C ''~x'':320C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5544, 1, '2015-04-16 23:09:45.397', 0, 94, 'Difference between OLS(statsmodel) and Scikit Linear Regression', '&lt;p&gt;I have a question about two different methods from different libraries which seems doing same job. I am trying to make linear regression model.&lt;/p&gt;

&lt;p&gt;Here is the code which I using statsmodel library with OLS :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;X_train, X_test, y_train, y_test = cross_validation.train_test_split(x, y, test_size=0.3, random_state=1)

x_train = sm.add_constant(X_train)
model = sm.OLS(y_train, x_train)
results = model.fit()

print "GFT + Wiki / GT  R-squared", results.rsquared
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This print out &lt;strong&gt;GFT + Wiki / GT  R-squared 0.981434611923&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;and the second one is scikit learn library Linear model method:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;model = LinearRegression()
model.fit(X_train, y_train)

predictions = model.predict(X_test)

print ''GFT + Wiki / GT R-squared: %.4f'' % model.score(X_test, y_test)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This print out &lt;strong&gt;GFT + Wiki / GT R-squared: 0.8543&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;So my question is the both method prints our R^2 result but one is print out 0.98 and the other one is 0.85. &lt;/p&gt;

&lt;p&gt;From my understanding, OLS works with training dataset. So my questions, &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Is there a way that work with test data set with OLS ?&lt;/li&gt;
&lt;li&gt;Is the traning data set score gives us any meaning(In OLS we didn''t use test data set)? From my past knowledge we have to work with test data.&lt;/li&gt;
&lt;li&gt;What is the difference between OLS and scikit linear regression. Which one we use for calculating the score of the model ? &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Thanks for any help.&lt;/p&gt;
', 'machine-learning  scikit  linear-regression', NULL, '''/code'':98C,153C ''/li'':222C,254C,277C ''/p'':41C,54C,113C,126C,168C,194C,207C,284C ''/pre'':99C,154C ''/strong'':112C,167C ''/ul'':278C ''0.3'':72C ''0.85'':193C ''0.8543'':166C ''0.98'':187C ''0.981434611923'':111C ''1'':75C ''2'':180C ''4f'':147C ''calcul'':271C ''code'':46C,56C,128C ''constant'':79C ''cross_validation.train'':65C ''data'':218C,227C,241C,253C ''dataset'':203C ''didn'':237C ''differ'':1A,23C,26C,259C ''gft'':91C,105C,141C,160C ''give'':230C ''gt'':93C,107C,143C,162C ''help'':283C ''job'':32C ''knowledg'':246C ''learn'':11B,121C ''li'':209C,223C,255C ''librari'':27C,51C,122C ''linear'':7A,14B,38C,123C,264C ''linear-regress'':13B ''linearregress'':130C ''machin'':10B ''machine-learn'':9B ''make'':37C ''mean'':233C ''method'':24C,125C,176C ''model'':40C,82C,124C,129C,276C ''model.fit'':89C,131C ''model.predict'':137C ''model.score'':148C ''ol'':3A,53C,199C,221C,235C,261C ''one'':118C,183C,191C,267C ''p'':16C,42C,100C,114C,155C,169C,195C,279C ''past'':245C ''pre'':55C,127C ''predict'':136C ''print'':90C,102C,140C,157C,177C,185C ''question'':20C,172C,206C ''r'':95C,109C,145C,164C,179C ''r-squar'':94C,108C,144C,163C ''random'':73C ''regress'':8A,15B,39C,265C ''result'':88C,181C ''results.rsquared'':97C ''scikit'':6A,12B,120C,263C ''score'':229C,273C ''second'':117C ''seem'':29C ''set'':219C,228C,242C ''size'':71C ''sm.add'':78C ''sm.ols'':83C ''split'':67C ''squar'':96C,110C,146C,165C ''state'':74C ''statsmodel'':4A,50C ''strong'':104C,159C ''test'':60C,64C,66C,70C,139C,150C,152C,217C,240C,252C ''thank'':280C ''train'':58C,62C,77C,81C,85C,87C,133C,135C,202C ''trane'':226C ''tri'':35C ''two'':22C ''ul'':208C ''understand'':198C ''us'':231C ''use'':49C,239C,269C ''way'':213C ''wiki'':92C,106C,142C,161C ''work'':200C,215C,250C ''x'':57C,59C,68C,76C,80C,86C,132C,138C,149C ''y'':61C,63C,69C,84C,134C,151C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6779, 5, '2015-08-12 23:15:20.783', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5545, 1, '2015-04-17 13:48:39.73', 0, 69, 'Big Data Analytics with Microsoft Azure', '&lt;p&gt;I am studying Microsoft Azure Machine Learning Studio with HDInsights for big data analytics. Having been worked with R and Python in the field, ML Studio seems a bit rudimentary at first glance (similar to Rapidminer, but quite less elaborated). The only real advantage seems to be the flawless (?) cooperation with Azure''s HDInsights Hadoop version (and other Microsoft cloud services), which is comfortable.&lt;/p&gt;

&lt;p&gt;Is it worth the effort to learn? ML Studio processes could be extended by R scripts, as far as I see (but how far?), maybe this is a good way to combine the two.&lt;/p&gt;

&lt;p&gt;Has anybody deeper experience with Azure for machine learning? Perhaps any better alternative?&lt;/p&gt;
', 'bigdata', NULL, '''/p'':73C,109C,124C ''advantag'':52C ''altern'':123C ''analyt'':3A,22C ''anybodi'':112C ''azur'':6A,13C,60C,116C ''better'':122C ''big'':1A,20C ''bigdata'':7B ''bit'':37C ''cloud'':68C ''combin'':106C ''comfort'':72C ''cooper'':58C ''could'':85C ''data'':2A,21C ''deeper'':113C ''effort'':79C ''elabor'':48C ''experi'':114C ''extend'':87C ''far'':92C,98C ''field'':32C ''first'':40C ''flawless'':57C ''glanc'':41C ''good'':103C ''hadoop'':63C ''hdinsight'':18C,62C ''learn'':15C,81C,119C ''less'':47C ''machin'':14C,118C ''mayb'':99C ''microsoft'':5A,12C,67C ''ml'':33C,82C ''p'':8C,74C,110C ''perhap'':120C ''process'':84C ''python'':29C ''quit'':46C ''r'':27C,89C ''rapidmin'':44C ''real'':51C ''rudimentari'':38C ''script'':90C ''see'':95C ''seem'':35C,53C ''servic'':69C ''similar'':42C ''studi'':11C ''studio'':16C,34C,83C ''two'':108C ''version'':64C ''way'':104C ''work'':25C ''worth'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5972, 1, '2015-06-01 12:36:23.203', 6, 723, 'How to learn spam email detection?', '&lt;p&gt;I want to learn how a spam email detector is done. I''m not trying to build a commercial product, it''ll be a serious learning exercise for me. Therefore, I''m looking for resources, such as existing projects, source code, articles, papers etc that I can follow. I want to learn by examples, I don''t think I am good enough to do it from scratch. Ideally, I''d like to get my hand dirty in Bayesian.&lt;/p&gt;

&lt;p&gt;Is there anything like that that? Programming language isn''t a problem for me.&lt;/p&gt;
', 'machine-learning  classification  text-mining', NULL, '''/p'':93C,109C ''anyth'':97C ''articl'':56C ''bayesian'':92C ''build'':31C ''classif'':10B ''code'':55C ''commerci'':33C ''d'':84C ''detect'':6A ''detector'':23C ''dirti'':90C ''done'':25C ''email'':5A,22C ''enough'':76C ''etc'':58C ''exampl'':68C ''exercis'':41C ''exist'':52C ''follow'':62C ''get'':87C ''good'':75C ''hand'':89C ''ideal'':82C ''isn'':103C ''languag'':102C ''learn'':3A,9B,18C,40C,66C ''like'':85C,98C ''ll'':36C ''look'':47C ''m'':27C,46C ''machin'':8B ''machine-learn'':7B ''mine'':13B ''p'':14C,94C ''paper'':57C ''problem'':106C ''product'':34C ''program'':101C ''project'':53C ''resourc'':49C ''scratch'':81C ''serious'':39C ''sourc'':54C ''spam'':4A,21C ''text'':12B ''text-min'':11B ''therefor'':44C ''think'':72C ''tri'':29C ''want'':16C,64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5547, 1, '2015-04-17 18:56:36.563', 1, 1709, 'Theano/Lasagne/Nolearn Neural Network Image Input', '&lt;p&gt;I am working on image classification tasks and decided to use Lasagne + Nolearn for neural networks prototype.
All standard examples like MNIST numbers classification run well, but problems appear when I try to work with my own images.&lt;/p&gt;

&lt;p&gt;I want to use 3-channel images, not grayscale.
And there is the code where I''m trying to get arrays from images:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; img = Image.open(item)
 img = ImageOps.fit(img, (256, 256), Image.ANTIALIAS)
 img = np.asarray(img, dtype = ''float64'') / 255.
 img = img.transpose(2,0,1).reshape(3, 256, 256)   
 X.append(img)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here is the code of NN and its fitting:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;X, y = simple_load("new")

X = np.array(X)
y = np.array(y)


net1 = NeuralNet(
    layers=[  # three layers: one hidden layer
        (''input'', layers.InputLayer),
        (''hidden'', layers.DenseLayer),
        (''output'', layers.DenseLayer),
        ],
    # layer parameters:
    input_shape=(None, 65536),  # 96x96 input pixels per batch
    hidden_num_units=100,  # number of units in hidden layer
    output_nonlinearity=None,  # output layer uses identity function
    output_num_units=len(y),  # 30 target values

    # optimization method:
    update=nesterov_momentum,
    update_learning_rate=0.01,
    update_momentum=0.9,

    regression=True,  # flag to indicate we''re dealing with regression problem


       max_epochs=400,  # we want to train this many epochs
        verbose=1,
        )

  net1.fit(X, y)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I recieve exceptions like this one:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Traceback (most recent call last):
  File "las_mnist.py", line 39, in &amp;lt;module&amp;gt;
    net1.fit(X[i], y[i])
  File "/usr/local/lib/python2.7/dist-packages/nolearn/lasagne.py", line 266, in fit
    self.train_loop(X, y)
  File "/usr/local/lib/python2.7/dist-packages/nolearn/lasagne.py", line 273, in train_loop
    X, y, self.eval_size)
  File "/usr/local/lib/python2.7/dist-packages/nolearn/lasagne.py", line 377, in train_test_split
    kf = KFold(y.shape[0], round(1. / eval_size))
IndexError: tuple index out of range
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;So, in which format do you "feed" your networks with image data?&lt;/strong&gt;
Thanks for answers or any tips!&lt;/p&gt;
', 'classification  python  neuralnetwork  image-classification', NULL, '''/code'':105C,220C,293C ''/p'':51C,76C,117C,229C,316C ''/pre'':106C,221C,294C ''/strong'':309C ''/usr/local/lib/python2.7/dist-packages/nolearn/lasagne.py'':251C,261C,272C ''0'':97C,282C ''0.01'':190C ''0.9'':193C ''1'':98C,216C,284C ''100'':159C ''2'':96C ''255'':93C ''256'':85C,86C,101C,102C ''266'':253C ''273'':263C ''3'':57C,100C ''30'':179C ''377'':274C ''39'':240C ''400'':207C ''65536'':150C ''96x96'':151C ''answer'':312C ''appear'':41C ''array'':73C ''batch'':155C ''call'':235C ''channel'':58C ''classif'':6B,11B,18C,36C ''code'':66C,78C,111C,119C,231C ''data'':308C ''deal'':201C ''decid'':21C ''dtype'':91C ''epoch'':206C,214C ''eval'':285C ''exampl'':32C ''except'':225C ''feed'':303C ''file'':237C,250C,260C,271C ''fit'':116C,255C ''flag'':196C ''float64'':92C ''format'':300C ''function'':173C ''get'':72C ''grayscal'':61C ''gt'':244C ''hidden'':137C,141C,156C,164C ''ident'':172C ''imag'':4A,10B,17C,50C,59C,75C,307C ''image-classif'':9B ''image.antialias'':87C ''image.open'':80C ''imageops.fit'':83C ''img'':79C,82C,84C,88C,90C,94C,104C ''img.transpose'':95C ''index'':289C ''indexerror'':287C ''indic'':198C ''input'':5A,139C,147C,152C ''item'':81C ''kf'':279C ''kfold'':280C ''las_mnist.py'':238C ''lasagn'':24C ''last'':236C ''layer'':133C,135C,138C,145C,165C,170C ''layers.denselayer'':142C,144C ''layers.inputlayer'':140C ''learn'':188C ''len'':177C ''like'':33C,226C ''line'':239C,252C,262C,273C ''load'':123C ''loop'':257C,266C ''lt'':242C ''m'':69C ''mani'':213C ''max'':205C ''method'':183C ''mnist'':34C ''modul'':243C ''momentum'':186C,192C ''nesterov'':185C ''net1'':131C ''net1.fit'':217C,245C ''network'':3A,28C,305C ''neural'':2A,27C ''neuralnet'':132C ''neuralnetwork'':8B ''new'':124C ''nn'':113C ''nolearn'':25C ''none'':149C,168C ''nonlinear'':167C ''np.array'':126C,129C ''np.asarray'':89C ''num'':157C,175C ''number'':35C,160C ''one'':136C,228C ''optim'':182C ''output'':143C,166C,169C,174C ''p'':12C,52C,107C,222C,295C ''paramet'':146C ''per'':154C ''pixel'':153C ''pre'':77C,118C,230C ''problem'':40C,204C ''prototyp'':29C ''python'':7B ''rang'':292C ''rate'':189C ''re'':200C ''recent'':234C ''reciev'':224C ''regress'':194C,203C ''reshap'':99C ''round'':283C ''run'':37C ''self.eval'':269C ''self.train'':256C ''shape'':148C ''simpl'':122C ''size'':270C,286C ''split'':278C ''standard'':31C ''strong'':296C ''target'':180C ''task'':19C ''test'':277C ''thank'':310C ''theano/lasagne/nolearn'':1A ''three'':134C ''tip'':315C ''traceback'':232C ''train'':211C,265C,276C ''tri'':44C,70C ''true'':195C ''tupl'':288C ''unit'':158C,162C,176C ''updat'':184C,187C,191C ''use'':23C,56C,171C ''valu'':181C ''verbos'':215C ''want'':54C,209C ''well'':38C ''work'':15C,46C ''x'':120C,125C,127C,218C,246C,258C,267C ''x.append'':103C ''y'':121C,128C,130C,178C,219C,248C,259C,268C ''y.shape'':281C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5548, 1, '2015-04-17 19:03:34.93', 2, 56, 'Algorithms to find average distance between data maxima', '&lt;p&gt;I''m having trouble figuring out a way to analyze some simple data. When graphed, the data I have make a somewhat sinusoidal curve. What I want to do is to find the x-values of the maximum peaks of the sinusoidal curve. I then want to subtract each of these x-values from the last peak found and average these differences to obtain an average distance between peaks. Is there an easy way to do this with Excel, Mathematica, or MatLab (the programs I have available to me?).&lt;/p&gt;

&lt;p&gt;Thanks in advance!!!&lt;/p&gt;
', 'algorithms', NULL, '''/p'':102C,107C ''advanc'':106C ''algorithm'':1A,9B ''analyz'':20C ''avail'':99C ''averag'':4A,72C,78C ''curv'':34C,54C ''data'':7A,23C,27C ''differ'':74C ''distanc'':5A,79C ''easi'':85C ''excel'':91C ''figur'':15C ''find'':3A,42C ''found'':70C ''graph'':25C ''last'':68C ''m'':12C ''make'':30C ''mathematica'':92C ''matlab'':94C ''maxima'':8A ''maximum'':49C ''obtain'':76C ''p'':10C,103C ''peak'':50C,69C,81C ''program'':96C ''simpl'':22C ''sinusoid'':33C,53C ''somewhat'':32C ''subtract'':59C ''thank'':104C ''troubl'':14C ''valu'':46C,65C ''want'':37C,57C ''way'':18C,86C ''x'':45C,64C ''x-valu'':44C,63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5549, 1, '2015-04-17 19:14:49.843', 2, 109, 'What are some good sources to learn fraud/anomaly detection in normal/time-series data?', '&lt;p&gt;I would like to know more on fraud/anomaly detection. I am looking for good source or survey article/book etc out there which will give me some preliminary idea of the area. &lt;/p&gt;

&lt;p&gt;Any suggestion is greatly appreciated.&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'data-mining  time-series  anomaly-detection', NULL, '''/p'':54C,61C,64C ''anomali'':20B ''anomaly-detect'':19B ''appreci'':60C ''area'':53C ''article/book'':40C ''data'':12A,14B ''data-min'':13B ''detect'':9A,21B,31C ''etc'':41C ''fraud/anomaly'':8A,30C ''give'':46C ''good'':4A,36C ''great'':59C ''idea'':50C ''know'':27C ''learn'':7A ''like'':25C ''look'':34C ''mine'':15B ''normal/time-series'':11A ''p'':22C,55C,62C ''preliminari'':49C ''seri'':18B ''sourc'':5A,37C ''suggest'':57C ''survey'':39C ''thank'':63C ''time'':17B ''time-seri'':16B ''would'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5550, 2, '2015-04-18 08:23:52.907', 1, NULL, NULL, '&lt;p&gt;Usually this is done as outliers analysis (fraud is an outlier vs normal usage). For this aspect, you can find more info in the &lt;a href="http://www.sciencedirect.com/science/book/9780123814791" rel="nofollow"&gt;data mining: concepts and techniques&lt;/a&gt; book, even if general purpose book.&lt;/p&gt;

&lt;p&gt;I am convinced that learning this kind of basis is needed to understanding the domain specific methods.&lt;/p&gt;
', NULL, NULL, '''/a'':38C ''/p'':45C,64C ''/science/book/9780123814791'':30C ''analysi'':8C ''aspect'':18C ''basi'':55C ''book'':39C,44C ''concept'':35C ''convinc'':49C ''data'':33C ''domain'':61C ''done'':5C ''even'':40C ''find'':21C ''fraud'':9C ''general'':42C ''href'':27C ''info'':23C ''kind'':53C ''learn'':51C ''method'':63C ''mine'':34C ''need'':57C ''nofollow'':32C ''normal'':14C ''outlier'':7C,12C ''p'':1C,46C ''purpos'':43C ''rel'':31C ''specif'':62C ''techniqu'':37C ''understand'':59C ''usag'':15C ''usual'':2C ''vs'':13C ''www.sciencedirect.com'':29C ''www.sciencedirect.com/science/book/9780123814791'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5551, 1, '2015-04-18 16:40:30.9', 1, 34, 'What algorithmic approach for selecting similar, relevancy based documents', '&lt;p&gt;I have an application that tracks people making mentions of various topics. We''ve used a Bayes algorithm to do some simple classification (users give a thumbs up/thumbs down) to pick the people that they believe are the best fit for their project. &lt;/p&gt;

&lt;p&gt;Our intention was to use this data to help us sort and order the influencers based on "fit" to the customer''s needs.  &lt;/p&gt;

&lt;p&gt;However, we''ve got some trained data, and now all we can do is say are they similar to the "thumbs up" group, or the "thumbs down" group.  &lt;/p&gt;

&lt;p&gt;What algorithm should we have used for this instead? &lt;/p&gt;

&lt;p&gt;Basically, the ideal is to have a score.. and the biggest, smallest based on the trained data is the one that gets shown first. &lt;/p&gt;

&lt;p&gt;Thoughts? &lt;/p&gt;

&lt;p&gt;Even better if its in Ruby. &lt;/p&gt;
', 'predictive-modeling', NULL, '''..'':133C ''/p'':57C,82C,112C,123C,150C,153C,161C ''algorithm'':2A,31C,115C ''applic'':17C ''approach'':3A ''base'':8A,74C,138C ''basic'':125C ''bay'':30C ''believ'':49C ''best'':52C ''better'':156C ''biggest'':136C ''classif'':36C ''custom'':79C ''data'':65C,90C,142C ''document'':9A ''even'':155C ''first'':149C ''fit'':53C,76C ''get'':147C ''give'':38C ''got'':87C ''group'':106C,111C ''help'':67C ''howev'':84C ''ideal'':127C ''influenc'':73C ''instead'':122C ''intent'':60C ''make'':21C ''mention'':22C ''model'':12B ''need'':81C ''one'':145C ''order'':71C ''p'':13C,58C,83C,113C,124C,151C,154C ''peopl'':20C,46C ''pick'':44C ''predict'':11B ''predictive-model'':10B ''project'':56C ''relev'':7A ''rubi'':160C ''say'':98C ''score'':132C ''select'':5A ''shown'':148C ''similar'':6A,101C ''simpl'':35C ''smallest'':137C ''sort'':69C ''thought'':152C ''thumb'':40C,104C,109C ''topic'':25C ''track'':19C ''train'':89C,141C ''up/thumbs'':41C ''us'':68C ''use'':28C,63C,119C ''user'':37C ''various'':24C ''ve'':27C,86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5552, 2, '2015-04-18 19:45:50.017', 1, NULL, NULL, '&lt;p&gt;This sounds like an interesting project. I recently worked on an almost case study. In order to get only 3 most accurate predictions, I think you may wanna sort those correctly predicted 5 matches by probability of event (win, draw or loose) and then select the first three matches.I hope your models are able to give you probabilities of events. 
I hope this helps :-)&lt;/p&gt;
', NULL, NULL, '''/p'':66C ''3'':21C ''5'':34C ''abl'':55C ''accur'':23C ''almost'':13C ''case'':14C ''correct'':32C ''draw'':41C ''event'':39C,61C ''first'':48C ''get'':19C ''give'':57C ''help'':65C ''hope'':51C,63C ''interest'':6C ''like'':4C ''loos'':43C ''match'':35C ''matches.i'':50C ''may'':28C ''model'':53C ''order'':17C ''p'':1C ''predict'':24C,33C ''probabl'':37C,59C ''project'':7C ''recent'':9C ''select'':46C ''sort'':30C ''sound'':3C ''studi'':15C ''think'':26C ''three'':49C ''wanna'':29C ''win'':40C ''work'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5553, 2, '2015-04-19 02:46:50.28', 0, NULL, NULL, '&lt;p&gt;I teach a Business Analytics course that includes SQL and Excel. I teach in a business school so my students aren''t the most technically capable, which is why I didn''t use something like R, Pandas, or Weka. That being said, Excel is a powerful enough tool to use for some data analysis. It gets most of this power from its ability to act as a front end to SQL Server Analysis Services (a component in SQL Server for data analysis) using the Data Mining Add-In.&lt;/p&gt;

&lt;p&gt;SSAS lets you construct decision trees, perform linear and logistic regressions, and even make bayesian or neural networks. I''ve found that using Excel as a front-end is a less threatening approach to doing these kinds of analyses since they''ve all used Excel before. The way to use SSAS without Excel is through a specialized version of Visual Studio and that isn''t the most user friendly tool out there. When you combine it with a few other Excel tools like Power Query and Power Pivot, you''re able to do some fairly sophisticated analysis of data.&lt;/p&gt;

&lt;p&gt;Full Disclosure, I''m probably not going to use it again when I teach the new version of the course next year (we''re splitting it into two courses so one can focus more heavily on data analysis). But that''s just because the university was able to get enough licenses for Alteryx which is even easier to use and more powerful but is $4-85k/user/year if you can''t get it free somehow. Say what you will about Excel, but it beats that price point.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5555, 1, '2015-04-19 19:31:12.25', 1, 49, 'conversion rate optimization', '&lt;p&gt;I am working on a conversion rate optimization assignment.&lt;/p&gt;

&lt;p&gt;The way I thought about it is to use machine learning to select the landing / conversion page that offers the highest probability of conversion given the features.&lt;/p&gt;

&lt;p&gt;But the general question that I have is, besides improving conversion given the prospect profile, how much improvement can you get by focusing on other, non-machine learning areas, such as creating a better conversion funnel or designing better conversion pages in the first place?&lt;/p&gt;

&lt;p&gt;Thanks a lot&lt;/p&gt;
', 'logistic-regression', NULL, '''/p'':17C,46C,94C,99C ''area'':77C ''assign'':16C ''besid'':56C ''better'':82C,87C ''convers'':1A,13C,34C,42C,58C,83C,88C ''creat'':80C ''design'':86C ''featur'':45C ''first'':92C ''focus'':70C ''funnel'':84C ''general'':50C ''get'':68C ''given'':43C,59C ''highest'':39C ''improv'':57C,65C ''land'':33C ''learn'':29C,76C ''logist'':5B ''logistic-regress'':4B ''lot'':98C ''machin'':28C,75C ''much'':64C ''non'':74C ''non-machin'':73C ''offer'':37C ''optim'':3A,15C ''p'':7C,18C,47C,95C ''page'':35C,89C ''place'':93C ''probabl'':40C ''profil'':62C ''prospect'':61C ''question'':51C ''rate'':2A,14C ''regress'':6B ''select'':31C ''thank'':96C ''thought'':22C ''use'':27C ''way'':20C ''work'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5557, 1, '2015-04-20 11:44:39.87', 0, 26, 'percentage of confidance on desion trees results', '&lt;p&gt;I am looking towards a solution where classification algorithms produce output with some confidence value. but I am confused whether classification algorithms are able to produce results with percentage of confidence? 
Thanks&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':44C ''abl'':35C ''algorithm'':20C,33C ''classif'':19C,32C ''confid'':3A,25C,42C ''confus'':30C ''desion'':5A ''learn'':10B ''look'':14C ''machin'':9B ''machine-learn'':8B ''output'':22C ''p'':11C ''percentag'':1A,40C ''produc'':21C,37C ''result'':7A,38C ''solut'':17C ''thank'':43C ''toward'':15C ''tree'':6A ''valu'':26C ''whether'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6627, 2, '2015-07-30 01:36:23.517', 1, NULL, NULL, '&lt;p&gt;This sounds like a fun problem but it very open ended!  I will provide some links from the &lt;a href="http://scikit-learn.org/stable/user_guide.html" rel="nofollow"&gt;Scikit-Learn User Guide&lt;/a&gt;.  There are many more reading options, but the SKL Userguide has lots of examples and usually links to academic publications for more in-depth reading.  Another great resource is: &lt;a href="http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf" rel="nofollow"&gt;Introduction to Statistical Learning&lt;/a&gt;, and if you are good at math: &lt;a href="http://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLII_print4.pdf" rel="nofollow"&gt;Elements of Statistical Learning&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Model Selection&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The first big question is whether the settings you are trying to predict are ordinal values or continuous values?  Continuous value will allow you to use regression methods, where as ordinal values will give you a choice between regression methods and classification schemes.  I would suggest employing a couple of different models to start and then selecting the best one moving forward.  Some possible candidates are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="http://scikit-learn.org/stable/modules/linear_model.html" rel="nofollow"&gt;Linear regression&lt;/a&gt; (with nonlinear features as appropriate) because linear models are usually insightful, &lt;/li&gt;
&lt;li&gt;Support Vector Classification/Regression (&lt;a href="http://scikit-learn.org/stable/modules/svm.html" rel="nofollow"&gt;SVC&lt;/a&gt;/&lt;a href="http://scikit-learn.org/stable/modules/svm.html#regression" rel="nofollow"&gt;SVR&lt;/a&gt;) as these are often very accurate classifiers. and either &lt;/li&gt;
&lt;li&gt;&lt;a href="http://scikit-learn.org/stable/modules/naive_bayes.html" rel="nofollow"&gt;Naive Bayes&lt;/a&gt; or &lt;a href="http://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees" rel="nofollow"&gt;Random Forests&lt;/a&gt; as these often give good results where other models fail.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;Initial Training Data&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;You will need to prime your solution with some deterministic data or data from another dataset that you have adapted to fit your model.  I suggest priming the system with common sense values and then employing some sort of &lt;a href="http://arxiv.org/ftp/arxiv/papers/0709/0709.3965.pdf" rel="nofollow"&gt;algorithm to forget those values&lt;/a&gt; as the system becomes well-trained.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Moving Forward&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;You will have to decide whether each user''s model will use other user''s data or not.  This is sometimes solved via &lt;a href="http://scikit-learn.org/stable/modules/clustering.html" rel="nofollow"&gt;clustering methods&lt;/a&gt; like &lt;a href="http://scikit-learn.org/stable/modules/clustering.html#k-means" rel="nofollow"&gt;k-means&lt;/a&gt; or &lt;a href="http://scikit-learn.org/stable/modules/clustering.html#dbscan" rel="nofollow"&gt;DBSCAN&lt;/a&gt;), where by users are clustered into groups each cluster has a different model associated with it.  I would suggest retraining (fitting) the model on regular time frames initially, and then investigating the possibility of moving to an &lt;a href="http://scikit-learn.org/stable/modules/scaling_strategies.html#incremental-learning" rel="nofollow"&gt;online learning&lt;/a&gt; system.&lt;/p&gt;

&lt;p&gt;I absolutely suggest using a well known library for your problem. Scikit-Learn is great for the reasons mentioned above and for prototyping, but doesn''t scale particularly well. &lt;a href="http://h2o.ai/" rel="nofollow"&gt;H20&lt;/a&gt; and &lt;a href="http://mahout.apache.org/" rel="nofollow"&gt;Mahout&lt;/a&gt; are high quality scalable libraries that I would recommend for a big data production system.  &lt;a href="http://www.manning.com/owen/" rel="nofollow"&gt;Mahout in Action&lt;/a&gt; is a great book to learn from, also.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5558, 1, '2015-04-20 15:18:38.84', 0, 55, 'Finding frequencies in a noisy, "uneven" dataset', '&lt;p&gt;I''m working on a problem where frequency analysis applies (decomposition of a signal into frequencies, that is), but it''s noisy and the samples are unevenly spaced.&lt;/p&gt;

&lt;p&gt;Specifically: given a list of items purchased at a bar/restaurant, try to estimate the number of guests on the check based on distinct "frequencies" of purchase. The logic is that if there are N guests on a check, then it''s reasonable to see N frequencies of drinks being purchased, one person buying every 10 minutes, another every 15, etc. (Plenty of other properties of the check should be included, but here I''m focusing specifically on estimating distinct frequencies).&lt;/p&gt;

&lt;p&gt;So more formally: given a noisy, unevenly spaced time series, find the smallest number of frequencies which reproduce the signal while minimizing the error (... for some sensible definition of how to minimize both the error and the number of frequencies simultaneously).&lt;/p&gt;

&lt;p&gt;This is more a machine learning problem than signal processing. I realize it''s also an open question, but can anyone point me in the right direction? Is there a particular method or algorithm that applies here?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5559, 2, '2015-04-20 15:23:01.027', 2, NULL, NULL, '&lt;p&gt;The first technique that comes to mind is &lt;a href="http://en.wikipedia.org/wiki/Spline_interpolation" rel="nofollow"&gt;cubic spline interpolation&lt;/a&gt;, which is good at approximating sinusoidal functions without suffering from &lt;a href="http://en.wikipedia.org/wiki/Runge%27s_phenomenon" rel="nofollow"&gt;Runge''s phenomenon&lt;/a&gt;. Here''s a visual of cubic splines applied to a similar problem:&lt;/p&gt;

&lt;p&gt;&lt;img src="https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcTAmsixio8YnpQTT2zL_i602MkIa72JM3b9E1iw-YlNsrahv9w_" alt="Example of cubic spline interpolation"&gt; &lt;/p&gt;

&lt;p&gt;Matlab has an implementation documented &lt;a href="http://www.mathworks.com/help/matlab/ref/spline.html" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Once you have a representation for the underlying function you should be able to find the local maxima easily and take the differences you are looking for.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5587, 2, '2015-04-22 15:45:51.943', 0, NULL, NULL, '&lt;p&gt;This individual works with ''Big Data'' and primarily uses Excel? Seriously?!?! Excel only handles up to 1, 048, 576 rows of data in a a single spreadsheet. For data sets beyond that it needs a plugin. Also pivot tables in Excel have severe restrictions on the analysis that can be performed using them. &lt;/p&gt;

&lt;p&gt;What types of data analytics tasks would need to be performed in the job for which you are recruiting?&lt;/p&gt;

&lt;p&gt;I suggest you conduct interviews that include tests of the sort of tasks that will need to be done in the job under consideration. Without violating confidentiality, privacy or data protection, the programming or data analysis task set as part of the interview should include a (pseudonymized) subset of a dataset relevant to the post being interviewed for. Otherwise you might end up recruiting someone that is articulate in a conversation-based interview but is not actually competent in carrying out the actual job.&lt;/p&gt;
', NULL, NULL, '''/p'':55C,76C,163C ''048'':19C ''1'':18C ''576'':20C ''actual'':155C,161C ''also'':38C ''analysi'':48C,113C ''analyt'':61C ''articul'':145C ''base'':150C ''beyond'':32C ''big'':6C ''carri'':158C ''compet'':156C ''conduct'':81C ''confidenti'':104C ''consider'':101C ''convers'':149C ''conversation-bas'':148C ''data'':7C,23C,30C,60C,107C,112C ''dataset'':128C ''done'':96C ''end'':139C ''excel'':11C,13C,42C ''handl'':15C ''includ'':84C,122C ''individu'':3C ''interview'':82C,120C,134C,151C ''job'':70C,99C,162C ''might'':138C ''need'':35C,64C,93C ''otherwis'':136C ''p'':1C,56C,77C ''part'':117C ''perform'':52C,67C ''pivot'':39C ''plugin'':37C ''post'':132C ''primarili'':9C ''privaci'':105C ''program'':110C ''protect'':108C ''pseudonym'':124C ''recruit'':75C,141C ''relev'':129C ''restrict'':45C ''row'':21C ''serious'':12C ''set'':31C,115C ''sever'':44C ''singl'':27C ''someon'':142C ''sort'':88C ''spreadsheet'':28C ''subset'':125C ''suggest'':79C ''tabl'':40C ''task'':62C,90C,114C ''test'':85C ''type'':58C ''use'':10C,53C ''violat'':103C ''without'':102C ''work'':4C ''would'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5560, 2, '2015-04-20 16:09:13.293', 0, NULL, NULL, '&lt;p&gt;Some classification algorithms can indeed return a probability distribution over the considered classes (see Wikipedia on &lt;a href="http://en.wikipedia.org/wiki/Probabilistic_classification" rel="nofollow"&gt;probabilistic classification&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;In the topic of your question you''re asking about Decision Trees. Well, these have their limitations in terms of providing probability estimates (see this &lt;a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.324.132&amp;amp;rep=rep1&amp;amp;type=pdf" rel="nofollow"&gt;paper on probability estimates from decision trees&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;In case you would like to play around with this, it''s very easy to start with scikit-learn:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
from sklearn.tree import DecisionTreeClassifier

dtc = DecisionTreeClassifier()

# training samples
X = np.array([[1, 1, 0],
              [1, 1, 0],
              [0, 0, 1],
              [0, 0, 1]])

# target values for training samples
y = np.array([0, 0, 0, 1])

dtc.fit(X, y)

print ''Class probabilities for training samples:''
print dtc.predict_proba(X)
print ''Probabilities for previously unseen samples:''
for sample in ((1, 0, 1), (0, 0, 1), (1, 1, 1), (0, 0, 0)):
     print ''Sample {}. Result: {}''.format(sample, dtc.predict_proba(sample))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This code returns following results:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Class probabilities for training samples:
[[ 1.   0. ]
 [ 1.   0. ]
 [ 0.5  0.5]
 [ 0.5  0.5]]
Probabilities for previously unseen samples:
Sample (1, 0, 1). Result: [[ 0.5  0.5]]
Sample (0, 0, 1). Result: [[ 0.5  0.5]]
Sample (1, 1, 1). Result: [[ 0.5  0.5]]
Sample (0, 0, 0). Result: [[ 1.  0.]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;At this scale the results are easily interpretable:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;a sample of features (1, 1, 0) is classified as 0 with 100% probability&lt;/li&gt;
&lt;li&gt;a sample of features (0, 0, 1) is classified 50/50 as 0 or 1. etc.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;This brings us to an important question of &lt;a href="http://stats.stackexchange.com/a/34936/41317"&gt;how accurate is your model&lt;/a&gt;?&lt;/p&gt;
', NULL, NULL, '''/a'':27C,69C,297C ''/a/34936/41317'':291C ''/code'':174C,231C ''/li'':259C,276C ''/p'':28C,70C,92C,182C,242C,298C ''/pre'':175C,232C ''/ul'':277C ''/viewdoc/download?doi=10.1.1.324.132&amp;amp;rep=rep1&amp;amp;type=pdf'':59C ''/wiki/probabilistic_classification'':22C ''0'':111C,114C,115C,116C,118C,119C,128C,129C,130C,155C,157C,158C,163C,164C,165C,191C,193C,205C,211C,212C,225C,226C,227C,230C,251C,255C,265C,266C,272C ''0.5'':194C,195C,196C,197C,208C,209C,215C,216C,222C,223C ''1'':109C,110C,112C,113C,117C,120C,131C,154C,156C,159C,160C,161C,162C,190C,192C,204C,206C,213C,218C,219C,220C,229C,249C,250C,267C,274C ''100'':257C ''50/50'':270C ''accur'':293C ''algorithm'':4C ''around'':79C ''ask'':38C ''bring'':280C ''case'':73C ''citeseerx.ist.psu.edu'':58C ''citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.324.132&amp;amp;rep=rep1&amp;amp;type=pdf'':57C ''class'':14C,136C,185C ''classif'':3C,26C ''classifi'':253C,269C ''code'':94C,178C,184C ''consid'':13C ''decis'':40C,67C ''decisiontreeclassifi'':102C,104C ''distribut'':10C ''dtc'':103C ''dtc.fit'':132C ''dtc.predict'':142C,171C ''easi'':85C ''easili'':240C ''en.wikipedia.org'':21C ''en.wikipedia.org/wiki/probabilistic_classification'':20C ''estim'':52C,65C ''etc'':275C ''featur'':248C,264C ''follow'':180C ''format'':169C ''href'':19C,56C,288C ''import'':95C,101C,284C ''inde'':6C ''interpret'':241C ''learn'':91C ''li'':244C,260C ''like'':76C ''limit'':46C ''model'':296C ''nofollow'':24C,61C ''np'':98C ''np.array'':108C,127C ''numpi'':96C ''p'':1C,29C,71C,176C,233C,278C ''paper'':62C ''play'':78C ''pre'':93C,183C ''previous'':148C,200C ''print'':135C,141C,145C,166C ''proba'':143C,172C ''probabilist'':25C ''probabl'':9C,51C,64C,137C,146C,186C,198C,258C ''provid'':50C ''question'':35C,285C ''re'':37C ''rel'':23C,60C ''result'':168C,181C,207C,214C,221C,228C,238C ''return'':7C,179C ''sampl'':106C,125C,140C,150C,152C,167C,170C,173C,189C,202C,203C,210C,217C,224C,246C,262C ''scale'':236C ''scikit'':90C ''scikit-learn'':89C ''see'':15C,53C ''sklearn.tree'':100C ''start'':87C ''stats.stackexchange.com'':290C ''stats.stackexchange.com/a/34936/41317'':289C ''target'':121C ''term'':48C ''topic'':32C ''train'':105C,124C,139C,188C ''tree'':41C,68C ''ul'':243C ''unseen'':149C,201C ''us'':281C ''valu'':122C ''well'':42C ''wikipedia'':16C ''would'':75C ''x'':107C,133C,144C ''y'':126C,134C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5886, 2, '2015-05-24 03:12:06.02', 2, NULL, NULL, '&lt;p&gt;Find the largest positive number and the smallest (most negative) number in the array.
Add the absolute value of the smallest (most negative) number to every value in the array.
Divide each result by the difference between the largest and the smallest number.&lt;/p&gt;
', NULL, NULL, '''/p'':45C ''absolut'':18C ''add'':16C ''array'':15C,31C ''differ'':37C ''divid'':32C ''everi'':27C ''find'':2C ''largest'':4C,40C ''negat'':11C,24C ''number'':6C,12C,25C,44C ''p'':1C ''posit'':5C ''result'':34C ''smallest'':9C,22C,43C ''valu'':19C,28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5561, 1, '2015-04-20 17:04:42.813', 0, 211, 'Can someone explain the following error in my python code?', '&lt;p&gt;I am analyzing a dataset in Python for strictly learning purpose.
In the code below that I wrote, I am getting some errors which I cannot get rid off. Here is the code first:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;plt.plot(decade_mean.index, decade_mean.values, ''o-'',color=''r'',lw=3,label = ''Decade Average'')
plt.scatter(movieDF.year, movieDF.rating, color=''k'', alpha = 0.3, lw=2)
plt.xlabel(''Year'')
plt.ylabel(''Rating'')
remove_border()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am getting the following errors:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1. TypeError: ''str'' object is not callable
2. NameError: name ''remove_border'' is not defined
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Also, the label=''Decade Average'' is not showing up in the plot.&lt;/p&gt;

&lt;p&gt;What confuses me most is the fact that in a separate  code snippet for plots (see below), I didn''t get the 1st error above, although &lt;code&gt;remove_border&lt;/code&gt; was still a problem.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;plt.hist(movieDF.rating, bins = 5, color = ''blue'', alpha = 0.3)
plt.xlabel(''Rating'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Any explanations of all or some of the errors would be greatly appreciated. Thanks&lt;/p&gt;

&lt;p&gt;Following the comments, I am posting the data and the traceback below:
decade_mean is given below.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;year
1970    8.925000
1980    8.650000
1990    8.615789
2000    8.378947
2010    8.233333
Name: rating, dtype: float64
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;traceback:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;TypeError                                 Traceback (most recent call last)
&amp;lt;ipython-input-361-a6efc7e46c45&amp;gt; in &amp;lt;module&amp;gt;()
      1 plt.plot(decade_mean.index, decade_mean.values, ''o-'',color=''r'',lw=3,label = ''Decade Average'')
      2 plt.scatter(movieDF.year, movieDF.rating, color=''k'', alpha = 0.3, lw=2)
----&amp;gt; 3 plt.xlabel(''Year'')
      4 plt.ylabel(''Rating'')
      5 remove_border()

TypeError: ''str'' object is not callable
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have solved remove_border problem. It was a stupid mistake I made. But I couldn''t figure out the problem with the ''str''.&lt;/p&gt;
', 'data-mining  python', NULL, '''/code'':79C,106C,152C,170C,224C,286C ''/p'':50C,88C,121C,157C,187C,206C,228C,313C ''/pre'':80C,107C,171C,225C,287C ''0.3'':70C,167C,267C ''1'':91C,248C ''1970'':210C ''1980'':212C ''1990'':214C ''1st'':145C ''2'':72C,98C,260C,269C ''2000'':216C ''2010'':218C ''3'':60C,256C,271C ''361'':241C ''4'':274C ''5'':163C,277C ''8.233333'':219C ''8.378947'':217C ''8.615789'':215C ''8.650000'':213C ''8.925000'':211C ''a6efc7e46c45'':242C ''alpha'':69C,166C,266C ''also'':109C ''although'':148C ''analyz'':18C ''appreci'':185C ''averag'':63C,113C,259C ''bin'':162C ''blue'':165C ''border'':78C,102C,151C,279C,293C ''call'':235C ''callabl'':97C,285C ''cannot'':41C ''code'':10A,29C,48C,52C,90C,134C,149C,159C,208C,230C ''color'':57C,67C,164C,253C,264C ''comment'':191C ''confus'':124C ''couldn'':304C ''data'':12B,196C ''data-min'':11B ''dataset'':20C ''decad'':62C,112C,201C,258C ''decade_mean.index'':54C,250C ''decade_mean.values'':55C,251C ''defin'':105C ''didn'':141C ''dtype'':222C ''error'':6A,38C,87C,146C,181C ''explain'':3A ''explan'':174C ''fact'':129C ''figur'':306C ''first'':49C ''float64'':223C ''follow'':5A,86C,189C ''get'':36C,42C,84C,143C ''given'':204C ''great'':184C ''gt'':243C,247C,270C ''input'':240C ''ipython'':239C ''ipython-input'':238C ''k'':68C,265C ''label'':61C,111C,257C ''last'':236C ''learn'':25C ''lt'':237C,245C ''lw'':59C,71C,255C,268C ''made'':301C ''mean'':202C ''mine'':13B ''mistak'':299C ''modul'':246C ''moviedf.rating'':66C,161C,263C ''moviedf.year'':65C,262C ''name'':100C,220C ''nameerror'':99C ''o'':56C,252C ''object'':94C,282C ''p'':15C,81C,108C,122C,172C,188C,226C,288C ''plot'':120C,137C ''plt.hist'':160C ''plt.plot'':53C,249C ''plt.scatter'':64C,261C ''plt.xlabel'':73C,168C,272C ''plt.ylabel'':75C,275C ''post'':194C ''pre'':51C,89C,158C,207C,229C ''problem'':156C,294C,309C ''purpos'':26C ''python'':9A,14B,22C ''r'':58C,254C ''rate'':76C,169C,221C,276C ''recent'':234C ''remov'':77C,101C,150C,278C,292C ''rid'':43C ''see'':138C ''separ'':133C ''show'':116C ''snippet'':135C ''solv'':291C ''someon'':2A ''still'':154C ''str'':93C,281C,312C ''strict'':24C ''stupid'':298C ''thank'':186C ''traceback'':199C,227C,232C ''typeerror'':92C,231C,280C ''would'':182C ''wrote'':33C ''year'':74C,209C,273C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5577, 2, '2015-04-21 20:36:48.103', 1, NULL, NULL, '&lt;p&gt;There are almost too many possible uses for data science in the nonprofit sector to count. You might want to look at the question from the other way around--find a cause that interests you and see how data science would apply. &lt;/p&gt;

&lt;p&gt;For example:&lt;/p&gt;

&lt;p&gt;Disaster relief efforts need better modeling and forecasting in everything from anticipating possible disasters to optimizing relief efforts. How can we identify the population most at risk from natural or human-created disasters, what are the most effective ways to mitigate the risks, and during a disaster, what are the most effective ways to deliver relief?&lt;/p&gt;

&lt;p&gt;In education, what methods work best? In a country or region with low female literacy, what would be the economic impact of increasing female literacy by a certain percentage? &lt;/p&gt;

&lt;p&gt;For poverty relief, based on a particular area''s demographics and economy, what is the most affordable way to reduce poverty? How could a funding organization recognize sooner whether an ongoing project is likely to succeed or fail?&lt;/p&gt;

&lt;p&gt;Or if you''re interested as much or more in the human aspects, you could look at ways for communities or countries to build reliable data banks, getting all of the government, nonprofit, educational, and other organizations to share and use data effectively. &lt;/p&gt;

&lt;p&gt;I hope this helps!&lt;/p&gt;
', NULL, NULL, '''/p'':44C,48C,107C,138C,178C,223C,229C ''afford'':156C ''almost'':4C ''anticip'':61C ''appli'':43C ''area'':147C ''around'':30C ''aspect'':192C ''bank'':206C ''base'':143C ''best'':114C ''better'':54C ''build'':203C ''caus'':33C ''certain'':136C ''communiti'':199C ''could'':162C,194C ''count'':17C ''countri'':117C,201C ''creat'':82C ''data'':10C,40C,205C,221C ''deliv'':105C ''demograph'':149C ''disast'':50C,63C,83C,97C ''econom'':128C ''economi'':151C ''educ'':110C,213C ''effect'':88C,102C,222C ''effort'':52C,67C ''everyth'':59C ''exampl'':47C ''fail'':177C ''femal'':122C,132C ''find'':31C ''forecast'':57C ''fund'':164C ''get'':207C ''govern'':211C ''help'':228C ''hope'':226C ''human'':81C,191C ''human-cr'':80C ''identifi'':71C ''impact'':129C ''increas'':131C ''interest'':35C,184C ''like'':173C ''literaci'':123C,133C ''look'':22C,195C ''low'':121C ''mani'':6C ''method'':112C ''might'':19C ''mitig'':91C ''model'':55C ''much'':186C ''natur'':78C ''need'':53C ''nonprofit'':14C,212C ''ongo'':170C ''optim'':65C ''organ'':165C,216C ''p'':1C,45C,49C,108C,139C,179C,224C ''particular'':146C ''percentag'':137C ''popul'':73C ''possibl'':7C,62C ''poverti'':141C,160C ''project'':171C ''question'':25C ''re'':183C ''recogn'':166C ''reduc'':159C ''region'':119C ''reliabl'':204C ''relief'':51C,66C,106C,142C ''risk'':76C,93C ''scienc'':11C,41C ''sector'':15C ''see'':38C ''share'':218C ''sooner'':167C ''succeed'':175C ''use'':8C,220C ''want'':20C ''way'':29C,89C,103C,157C,197C ''whether'':168C ''work'':113C ''would'':42C,125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5562, 2, '2015-04-20 17:25:20.867', 0, NULL, NULL, '&lt;p&gt;Just to point you in one possible direction: you could treat this problem as one of probabilistic mixture modeling.&lt;/p&gt;

&lt;p&gt;Imagine that each person''s drink ordering is governed by a probability distribution. That distribution may be characterized by the time since their last drink order.  As time passes, the probability that they will order another drink increases until eventually they order another drink and the time resets.&lt;/p&gt;

&lt;p&gt;One possible model for the time between drink orders for a single person is the &lt;a href="http://en.wikipedia.org/wiki/Exponential_distribution" rel="nofollow"&gt;exponential distribution&lt;/a&gt;.  If you consider many people ordering, the resultant times would likely be a mixture of exponentials. The problem then comes down to fitting a mixture of exponentials to the table''s drink data.  You would likely have to supply some additional prior data as well to get a meaningful model (otherwise it''s difficult to tell the difference between 5 people ordering 2 drinks a piece or 1 person ordering 10 drinks).&lt;/p&gt;
', NULL, NULL, '''/a'':97C ''/p'':21C,71C,170C ''/wiki/exponential_distribution'':92C ''1'':165C ''10'':168C ''2'':160C ''5'':157C ''addit'':138C ''anoth'':58C,65C ''character'':40C ''come'':117C ''consid'':100C ''could'':11C ''data'':130C,140C ''differ'':155C ''difficult'':151C ''direct'':9C ''distribut'':35C,37C,96C ''drink'':28C,47C,59C,66C,80C,129C,161C,169C ''en.wikipedia.org'':91C ''en.wikipedia.org/wiki/exponential_distribution'':90C ''eventu'':62C ''exponenti'':95C,113C,124C ''fit'':120C ''get'':144C ''govern'':31C ''href'':89C ''imagin'':23C ''increas'':60C ''last'':46C ''like'':108C,133C ''mani'':101C ''may'':38C ''meaning'':146C ''mixtur'':19C,111C,122C ''model'':20C,75C,147C ''nofollow'':94C ''one'':7C,16C,73C ''order'':29C,48C,57C,64C,81C,103C,159C,167C ''otherwis'':148C ''p'':1C,22C,72C ''pass'':51C ''peopl'':102C,158C ''person'':26C,85C,166C ''piec'':163C ''point'':4C ''possibl'':8C,74C ''prior'':139C ''probabilist'':18C ''probabl'':34C,53C ''problem'':14C,115C ''rel'':93C ''reset'':70C ''result'':105C ''sinc'':44C ''singl'':84C ''suppli'':136C ''tabl'':127C ''tell'':153C ''time'':43C,50C,69C,78C,106C ''treat'':12C ''well'':142C ''would'':107C,132C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5563, 2, '2015-04-20 20:26:15.337', 1, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;So my question is the both method prints our R^2 result but one is print out 0.98 and the other one is 0.85.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;There is a lot of information missing here, but it looks like the first example could be finding the correlation between the predicted and actual values of data from your training set and the second example is finding the correlation between the predicted and actual values of your testing set. This would also explain why the correlation in the second result is lower. The performance of a model on novel (testing) data should nearly always be poorer than on the training dataset that was used to fit the model.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Is there a way that work with test data set with OLS ?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://statsmodels.sourceforge.net/0.5.0/generated/statsmodels.regression.linear_model.OLS.html" rel="nofollow"&gt;The statsmodels documentation&lt;/a&gt; describes a &lt;code&gt;predict()&lt;/code&gt; method that can be used to make predictions on inputs from a design matrix.  You should be able to use this method on your test data once you have fit the model.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Is the traning data set score gives us any meaning(In OLS we didn''t use test data set)? From my past knowledge we have to work with test data.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Evaluation on training data can often be used as a quick check for "best case" generalization performance.  However, this is often only used as a diagnostic to ensure your model has fit properly and should not be taken to be a true indicator of generalization.&lt;/p&gt;
', NULL, NULL, '''/0.5.0/generated/statsmodels.regression.linear_model.ols.html'':141C ''/a'':147C ''/blockquote'':28C,135C,219C ''/code'':152C ''/p'':27C,119C,134C,185C,218C,266C ''0.85'':26C ''0.98'':20C ''2'':13C ''abl'':170C ''actual'':54C,74C ''also'':82C ''alway'':104C ''best'':234C ''blockquot'':1C,120C,186C ''case'':235C ''check'':232C ''code'':150C ''correl'':49C,69C,86C ''could'':45C ''data'':57C,101C,130C,178C,191C,205C,217C,224C ''dataset'':111C ''describ'':148C ''design'':165C ''diagnost'':246C ''didn'':201C ''document'':146C ''ensur'':248C ''evalu'':221C ''exampl'':44C,65C ''explain'':83C ''find'':47C,67C ''first'':43C ''fit'':116C,182C,252C ''general'':236C,265C ''give'':194C ''howev'':238C ''href'':138C ''indic'':263C ''inform'':35C ''input'':162C ''knowledg'':210C ''like'':41C ''look'':40C ''lot'':33C ''lower'':92C ''make'':159C ''matrix'':166C ''mean'':197C ''method'':9C,153C,174C ''miss'':36C ''model'':97C,118C,184C,250C ''near'':103C ''nofollow'':143C ''novel'':99C ''often'':226C,241C ''ol'':133C,199C ''one'':16C,24C ''p'':2C,29C,121C,136C,187C,220C ''past'':209C ''perform'':94C,237C ''poorer'':106C ''predict'':52C,72C,151C,160C ''print'':10C,18C ''proper'':253C ''question'':5C ''quick'':231C ''r'':12C ''rel'':142C ''result'':14C,90C ''score'':193C ''second'':64C,89C ''set'':61C,79C,131C,192C,206C ''statsmodel'':145C ''statsmodels.sourceforge.net'':140C ''statsmodels.sourceforge.net/0.5.0/generated/statsmodels.regression.linear_model.ols.html'':139C ''taken'':258C ''test'':78C,100C,129C,177C,204C,216C ''train'':60C,110C,223C ''trane'':190C ''true'':262C ''us'':195C ''use'':114C,157C,172C,203C,228C,243C ''valu'':55C,75C ''way'':125C ''work'':127C,214C ''would'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5564, 2, '2015-04-21 01:47:50.237', 0, NULL, NULL, '&lt;p&gt;Seems to me that &lt;code&gt;remove_border()&lt;/code&gt; is not being recognized as a function. Seeing how you haven''t listed a package it belongs to, did you implement this yourself? Python is guessing that &lt;code&gt;remove_border&lt;/code&gt; is a string and you''re trying to "call it" - treat it like a function. The 2nd error is saying the same thing, the function &lt;code&gt;remove_border&lt;/code&gt; isn''t defined anywhere that Python can find it.&lt;/p&gt;
', NULL, NULL, '''/code'':9C,40C,69C ''/p'':79C ''2nd'':57C ''anywher'':73C ''belong'':26C ''border'':8C,39C,68C ''call'':49C ''code'':6C,37C,66C ''defin'':72C ''error'':58C ''find'':77C ''function'':16C,55C,65C ''guess'':35C ''haven'':20C ''implement'':30C ''isn'':70C ''like'':53C ''list'':22C ''p'':1C ''packag'':24C ''python'':33C,75C ''re'':46C ''recogn'':13C ''remov'':7C,38C,67C ''say'':60C ''see'':17C ''seem'':2C ''string'':43C ''thing'':63C ''treat'':51C ''tri'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5565, 1, '2015-04-21 05:38:55.83', 3, 172, 'Using EM (Expectation Maximization) algorithm for Training Logistic Regression', '&lt;p&gt;Is it possible to learn the weights for a logistic regression classifier using EM (Expectation Maximization)algorithm? Is there any instance reference?&lt;/p&gt;
', 'machine-learning  classification  statistics  logistic-regression  expectation-maximization', NULL, '''/p'':44C ''algorithm'':5A,38C ''classif'':13B ''classifi'':33C ''em'':2A,35C ''expect'':3A,19B,36C ''expectation-maxim'':18B ''instanc'':42C ''learn'':12B,26C ''logist'':8A,16B,31C ''logistic-regress'':15B ''machin'':11B ''machine-learn'':10B ''maxim'':4A,20B,37C ''p'':21C ''possibl'':24C ''refer'':43C ''regress'':9A,17B,32C ''statist'':14B ''train'':7A ''use'':1A,34C ''weight'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8739, 2, '2015-11-06 13:17:22.93', 1, NULL, NULL, '&lt;p&gt;There are a few thing that you may be doing, but worth mentioning. &lt;/p&gt;

&lt;p&gt;Did you "whiten" your data---mean center and normalize by the standard deviation? This step may be implementation-dependent.
&lt;a href="http://deeplearning.stanford.edu/wiki/index.php/PCA" rel="nofollow"&gt;http://deeplearning.stanford.edu/wiki/index.php/PCA&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Did you check to see how much variablility was explained by the number of chosen parameters? For images +80% is common. &lt;/p&gt;

&lt;p&gt;Make sure you understand the implementation of the PCA you are using. It always confuses me by the terminology different implementations uses, e.g. score, rotation, etc. I suggest following a tutorial and matching their output with your implementation to get a deeper understanding.
&lt;a href="http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html" rel="nofollow"&gt;http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Your model is now built on transformed data. To predict make sure you are using these same transformations on the new input data.&lt;/p&gt;
', NULL, NULL, '''+80'':66C ''/articles/2015_pca_in_3_steps.html'':118C ''/articles/2015_pca_in_3_steps.html&lt;/a&gt;&lt;/p&gt;'':123C ''/p'':15C,69C,148C ''/wiki/index.php/pca'':41C ''/wiki/index.php/pca&lt;/a&gt;&lt;/p&gt;'':46C ''alway'':84C ''built'':129C ''center'':23C ''check'':50C ''chosen'':62C ''common'':68C ''confus'':85C ''data'':21C,132C,147C ''deeper'':112C ''deeplearning.stanford.edu'':40C,45C ''deeplearning.stanford.edu/wiki/index.php/pca'':39C ''deeplearning.stanford.edu/wiki/index.php/pca&lt;/a&gt;&lt;/p&gt;'':44C ''depend'':36C ''deviat'':29C ''differ'':90C ''e.g'':93C ''etc'':96C ''explain'':57C ''follow'':99C ''get'':110C ''href'':38C,115C ''imag'':65C ''implement'':35C,76C,91C,108C ''implementation-depend'':34C ''input'':146C ''make'':71C,135C ''match'':103C ''may'':9C,32C ''mean'':22C ''mention'':14C ''model'':126C ''much'':54C ''new'':145C ''nofollow'':43C,120C ''normal'':25C ''number'':60C ''output'':105C ''p'':1C,16C,47C,70C,124C ''paramet'':63C ''pca'':79C ''predict'':134C ''rel'':42C,119C ''rotat'':95C ''score'':94C ''sebastianraschka.com'':117C,122C ''sebastianraschka.com/articles/2015_pca_in_3_steps.html'':116C ''sebastianraschka.com/articles/2015_pca_in_3_steps.html&lt;/a&gt;&lt;/p&gt;'':121C ''see'':52C ''standard'':28C ''step'':31C ''suggest'':98C ''sure'':72C,136C ''terminolog'':89C ''thing'':6C ''transform'':131C,142C ''tutori'':101C ''understand'':74C,113C ''use'':82C,92C,139C ''variablil'':55C ''whiten'':19C ''worth'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8834, 1, '2015-11-13 17:10:42.153', 2, 57, 'Estimating probability using boltzmann machine', '&lt;p&gt;I am reading about Boltzmann machines and according the formulas the joint probability of the states of all units is $$ P(X = x) = \frac{1}{Z} e^{-\frac{1}{2T} \sum_i\sum_j {x_i x_j w_{ij}}} $$
$$ Z = \sum_x e^{-\frac{1}{2T}\sum_i\sum_j x_i x_j w_{ij}} $$
In thse formulas it is assumed that $ w_{ii} = 0$. However it is infeasible to calculate the quantity $Z$ directly. Therefore people use Gibbs sampling to sample different states. I don''t understand how exactly the sampling is used to estimate probabilities.&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':109C ''0'':76C ''1'':34C,38C,55C ''2t'':39C,56C ''accord'':17C ''assum'':72C ''boltzmann'':4A,14C ''calcul'':82C ''differ'':94C ''direct'':86C ''e'':36C,53C ''estim'':1A,107C ''exact'':101C ''formula'':19C,69C ''frac'':33C,37C,54C ''gibb'':90C ''howev'':77C ''ii'':75C ''ij'':49C,66C ''infeas'':80C ''j'':43C,47C,60C,64C ''joint'':21C ''learn'':8B ''machin'':5A,7B,15C ''machine-learn'':6B ''p'':9C,30C ''peopl'':88C ''probabl'':2A,22C,108C ''quantiti'':84C ''read'':12C ''sampl'':91C,93C,103C ''state'':25C,95C ''sum'':40C,42C,51C,57C,59C ''therefor'':87C ''thse'':68C ''understand'':99C ''unit'':28C ''use'':3A,89C,105C ''w'':48C,65C,74C ''x'':31C,32C,44C,46C,52C,61C,63C ''z'':35C,50C,85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5566, 1, '2015-04-21 05:55:07.347', 3, 201, 'How can I use Data Science to profoundly contribute to Humanity?', '&lt;p&gt;Undergraduate researcher here. I worked at many traditional scientific research labs, ranging from cancer biology, to radiation medicine, and to supercapacitors. I''m thinking of switching to Statistics and Computer Science from Computational Biology to join the exciting field of Data Science. &lt;/p&gt;

&lt;p&gt;What ways can I contribute to Humanity without a technical background in basic science? I don''t know how I will feel about making better clickbaits. I''ve thought about continuing in scientific research but I will lack the science background. I''ve thought about working for the United Nations. What other ways can I contribute? Inspire me!&lt;/p&gt;
', 'career', NULL, '''/p'':56C,116C ''background'':68C,98C ''basic'':70C ''better'':82C ''biolog'':28C,47C ''cancer'':27C ''career'':12B ''clickbait'':83C ''comput'':43C,46C ''continu'':88C ''contribut'':9A,62C,113C ''data'':5A,54C ''excit'':51C ''feel'':79C ''field'':52C ''human'':11A,64C ''inspir'':114C ''join'':49C ''know'':75C ''lab'':24C ''lack'':95C ''m'':36C ''make'':81C ''mani'':20C ''medicin'':31C ''nation'':107C ''p'':13C,57C ''profound'':8A ''radiat'':30C ''rang'':25C ''research'':15C,23C,91C ''scienc'':6A,44C,55C,71C,97C ''scientif'':22C,90C ''statist'':41C ''supercapacitor'':34C ''switch'':39C ''technic'':67C ''think'':37C ''thought'':86C,101C ''tradit'':21C ''undergradu'':14C ''unit'':106C ''use'':4A ''ve'':85C,100C ''way'':59C,110C ''without'':65C ''work'':18C,103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5589, 1, '2015-04-22 18:00:27.083', 2, 216, 'Downloading a large dataset on the web directly into AWS S3', '&lt;p&gt;Does anyone know if it''s possible to import a large dataset into Amazon S3 from a url?  &lt;/p&gt;

&lt;p&gt;Basically, I want to avoid downloading a huge file and then reuploading it to S3 through the web portal.  I just want to supply the download url to S3 and wait for them to download it to their filesystem.  It seems like an easy thing to do, but I just can''t find the documentation on it.&lt;/p&gt;

&lt;p&gt;Thanks,
Will&lt;/p&gt;
', 'dataset  aws', NULL, '''/p'':33C,92C,96C ''amazon'':28C ''anyon'':16C ''avoid'':39C ''aw'':10A,13B ''basic'':35C ''dataset'':4A,12B,26C ''direct'':8A ''document'':89C ''download'':1A,40C,60C,69C ''easi'':78C ''file'':43C ''filesystem'':73C ''find'':87C ''huge'':42C ''import'':23C ''know'':17C ''larg'':3A,25C ''like'':76C ''p'':14C,34C,93C ''portal'':53C ''possibl'':21C ''reupload'':46C ''s3'':11A,29C,49C,63C ''seem'':75C ''suppli'':58C ''thank'':94C ''thing'':79C ''url'':32C,61C ''wait'':65C ''want'':37C,56C ''web'':7A,52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5622, 2, '2015-04-27 07:29:06.11', 1, NULL, NULL, '&lt;p&gt;Use sigmoidal functions to get the best correlational value. Use Octave/MATLAB for processing your matrix using the function.&lt;/p&gt;
', NULL, NULL, '''/p'':20C ''best'':8C ''correl'':9C ''function'':4C,19C ''get'':6C ''matrix'':16C ''octave/matlab'':12C ''p'':1C ''process'':14C ''sigmoid'':3C ''use'':2C,11C,17C ''valu'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5567, 1, '2015-04-21 07:00:39.54', 1, 32, 'Prove Reccurrent Neural Network can exhibit oscillatory behavior', '&lt;p&gt;I understand how recurrent neural networks work, however I''m trying to build a deep intuitive understanding of their behavior which is difficult for me because they exhibit such complex behaviors.&lt;/p&gt;

&lt;p&gt;The most difficult thing for me to understand is how a Reccurrent Neural Network can exhibit an oscillatory behavior along with the notion of exploding weights. For one, I''m guessing that oscillatory behaviors are only possible for certain activation functions and configurations. Here are my following questions:&lt;/p&gt;

&lt;p&gt;1) Can sigmoid activation functions exhibit oscillatory behavior? I''ve convinced myself that they don''t since they have a positive range between 0 and 1 which doesn''t allow for negative derivatives, but maybe I''m wrong. Is there a formal proof out there for whether it can or cannot?&lt;/p&gt;

&lt;p&gt;2) Which activation functions can exhibit oscillatory behaviors and are there proofs out there for them? I believe tanh might have this behavior but I''m not sure.&lt;/p&gt;

&lt;p&gt;3) What are exploding weight derivatives and how do they occur? There is polar opposite of exploding weight''s which seems to be where the weights do not learn and stay stagnate. What causes these issues? I imagine this is dependent upon the activation function as well.&lt;/p&gt;

&lt;p&gt;Lastly, I began reading this article:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.google.com/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=2&amp;amp;ved=0CCsQFjAB&amp;amp;url=http%3A%2F%2Fwww.researchgate.net%2Fprofile%2FChristian_OReilly%2Fpublication%2F52004955_Permanent_oscillations_in_a_3-node_recurrent_neural_network_model%2Flinks%2F0c96052464be171d68000000.pdf&amp;amp;ei=X_E1Vf7ULNGsogSo9YHIBQ&amp;amp;usg=AFQjCNE0fex0s7hY2w_upMNkmaxIseUKww&amp;amp;bvm=bv.91386359,d.cGU" rel="nofollow"&gt;http://www.google.com/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=2&amp;amp;ved=0CCsQFjAB&amp;amp;url=http%3A%2F%2Fwww.researchgate.net%2Fprofile%2FChristian_OReilly%2Fpublication%2F52004955_Permanent_oscillations_in_a_3-node_recurrent_neural_network_model%2Flinks%2F0c96052464be171d68000000.pdf&amp;amp;ei=X_E1Vf7ULNGsogSo9YHIBQ&amp;amp;usg=AFQjCNE0fex0s7hY2w_upMNkmaxIseUKww&amp;amp;bvm=bv.91386359,d.cGU&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;but got confused around page 10 because the author omit''s explaining opaque assumptions such as why we are defining the matrix measure as is and so forth(it''s a good paper nonetheless). Is there a more transparent paper or lecture out there that can shed light on this subject, or maybe simple explanation for what the author is trying to get at?&lt;/p&gt;
', 'machine-learning  neuralnetwork  deep-learning', NULL, '''/p'':48C,98C,151C,181C,230C,238C,315C ''/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=2&amp;amp;ved=0ccsqfjab&amp;amp;url=http%3a%2f%2fwww.researchgate.net%2fprofile%2fchristian_oreilly%2fpublication%2f52004955_permanent_oscillations_in_a_3-node_recurrent_neural_network_model%2flinks%2f0c96052464be171d68000000.pdf&amp;amp;ei=x_e1vf7ulngsogso9yhibq&amp;amp;usg=afqjcne0fex0s7hy2w_upmnkmaxiseukww&amp;amp;bvm=bv.91386359,d.cgu'':244C ''/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=2&amp;amp;ved=0ccsqfjab&amp;amp;url=http%3a%2f%2fwww.researchgate.net%2fprofile%2fchristian_oreilly%2fpublication%2f52004955_permanent_oscillations_in_a_3-node_recurrent_neural_network_model%2flinks%2f0c96052464be171d68000000.pdf&amp;amp;ei=x_e1vf7ulngsogso9yhibq&amp;amp;usg=afqjcne0fex0s7hy2w_upmnkmaxiseukww&amp;amp;bvm=bv.91386359,d.cgu&lt;/a&gt;&lt;/p&gt;'':249C ''0'':123C ''1'':100C,125C ''10'':256C ''2'':153C ''3'':183C ''activ'':89C,103C,155C,226C ''allow'':129C ''along'':69C ''around'':254C ''articl'':237C ''assumpt'':264C ''author'':259C,309C ''began'':234C ''behavior'':8A,36C,47C,68C,83C,107C,160C,175C ''believ'':170C ''build'':29C ''cannot'':150C ''caus'':216C ''certain'':88C ''complex'':46C ''configur'':92C ''confus'':253C ''convinc'':110C ''deep'':14B,31C ''deep-learn'':13B ''defin'':270C ''depend'':223C ''deriv'':132C,188C ''difficult'':39C,52C ''doesn'':127C ''exhibit'':6A,44C,65C,105C,158C ''explain'':262C ''explan'':305C ''explod'':74C,186C,199C ''follow'':96C ''formal'':141C ''forth'':278C ''function'':90C,104C,156C,227C ''get'':313C ''good'':282C ''got'':252C ''guess'':80C ''howev'':24C ''href'':241C ''imagin'':220C ''intuit'':32C ''issu'':218C ''last'':232C ''learn'':11B,15B,211C ''lectur'':292C ''light'':298C ''m'':26C,79C,136C,178C ''machin'':10B ''machine-learn'':9B ''matrix'':272C ''mayb'':134C,303C ''measur'':273C ''might'':172C ''negat'':131C ''network'':4A,22C,63C ''neural'':3A,21C,62C ''neuralnetwork'':12B ''nofollow'':246C ''nonetheless'':284C ''notion'':72C ''occur'':193C ''omit'':260C ''one'':77C ''opaqu'':263C ''opposit'':197C ''oscillatori'':7A,67C,82C,106C,159C ''p'':16C,49C,99C,152C,182C,231C,239C,250C ''page'':255C ''paper'':283C,290C ''polar'':196C ''posit'':120C ''possibl'':86C ''proof'':142C,164C ''prove'':1A ''question'':97C ''rang'':121C ''read'':235C ''reccurr'':2A,61C ''recurr'':20C ''rel'':245C ''seem'':203C ''shed'':297C ''sigmoid'':102C ''simpl'':304C ''sinc'':116C ''stagnat'':214C ''stay'':213C ''subject'':301C ''sure'':180C ''tanh'':171C ''thing'':53C ''transpar'':289C ''tri'':27C,311C ''understand'':18C,33C,57C ''upon'':224C ''ve'':109C ''weight'':75C,187C,200C,208C ''well'':229C ''whether'':146C ''work'':23C ''wrong'':137C ''www.google.com'':243C,248C ''www.google.com/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=2&amp;amp;ved=0ccsqfjab&amp;amp;url=http%3a%2f%2fwww.researchgate.net%2fprofile%2fchristian_oreilly%2fpublication%2f52004955_permanent_oscillations_in_a_3-node_recurrent_neural_network_model%2flinks%2f0c96052464be171d68000000.pdf&amp;amp;ei=x_e1vf7ulngsogso9yhibq&amp;amp;usg=afqjcne0fex0s7hy2w_upmnkmaxiseukww&amp;amp;bvm=bv.91386359,d.cgu'':242C ''www.google.com/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=2&amp;amp;ved=0ccsqfjab&amp;amp;url=http%3a%2f%2fwww.researchgate.net%2fprofile%2fchristian_oreilly%2fpublication%2f52004955_permanent_oscillations_in_a_3-node_recurrent_neural_network_model%2flinks%2f0c96052464be171d68000000.pdf&amp;amp;ei=x_e1vf7ulngsogso9yhibq&amp;amp;usg=afqjcne0fex0s7hy2w_upmnkmaxiseukww&amp;amp;bvm=bv.91386359,d.cgu&lt;/a&gt;&lt;/p&gt;'':247C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5571, 2, '2015-04-21 09:30:38.253', 0, NULL, NULL, '&lt;p&gt;Seems that &lt;code&gt;remove border&lt;/code&gt; is not defined. You have to define the function before used. &lt;/p&gt;

&lt;p&gt;I do not know where the string error comes, is not clear to me. If you post the full traceback it will be clearer. &lt;/p&gt;

&lt;p&gt;Finally your label is not show because you have to call the method &lt;code&gt;plt.legend()&lt;/code&gt;&lt;/p&gt;
', NULL, NULL, '''/code'':7C,62C ''/p'':19C,45C,63C ''border'':6C ''call'':57C ''clear'':32C ''clearer'':44C ''code'':4C,60C ''come'':29C ''defin'':10C,14C ''error'':28C ''final'':47C ''full'':39C ''function'':16C ''know'':24C ''label'':49C ''method'':59C ''p'':1C,20C,46C ''plt.legend'':61C ''post'':37C ''remov'':5C ''seem'':2C ''show'':52C ''string'':27C ''traceback'':40C ''use'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5572, 1, '2015-04-21 09:47:05.917', 1, 66, 'Finding parameters with extreme values (classification with scikit-learn)', '&lt;p&gt;I am currently working with the forest cover type prediction from Kaggle, using classification models with scikit-learn. My main purpose is learning about the different models, so I don''t pretend to discuss about which one is better.&lt;/p&gt;

&lt;p&gt;When working with logistic regression, I wonder if I need the ''penalty'' parameter (where I can choose L1 or L2 regularization). Based on what I found, these regularization terms are useful to avoid over-fitting, specially when the parameter values are extreme (by extreme I understand the range of some parameter values are very large compared to other parameters, Correct me if I am wrong. In this case, wouldn''t it be enough to apply log-scale or normalization to these values?).&lt;/p&gt;

&lt;p&gt;The main questions are: as the number of parameters is large, are there visualization techniques and tools in scikit-learn which can help me to find parameters with extreme values? is there any statistical function/tool which returns how extreme the values of parameters are?&lt;/p&gt;
', 'classification  logistic-regression  scikit  parameter  kaggle', NULL, '''/p'':59C,145C,192C ''appli'':136C ''avoid'':93C ''base'':82C ''better'':58C ''case'':129C ''choos'':77C ''classif'':6A,11B,32C ''compar'':117C ''correct'':121C ''cover'':26C ''current'':21C ''differ'':45C ''discuss'':53C ''enough'':134C ''extrem'':4A,103C,105C,176C,186C ''find'':1A,173C ''fit'':96C ''forest'':25C ''found'':86C ''function/tool'':182C ''help'':170C ''kaggl'':17B,30C ''l1'':78C ''l2'':80C ''larg'':116C,157C ''learn'':10A,37C,42C,167C ''log'':138C ''log-scal'':137C ''logist'':13B,64C ''logistic-regress'':12B ''main'':39C,148C ''model'':33C,46C ''need'':70C ''normal'':141C ''number'':153C ''one'':56C ''over-fit'':94C ''p'':18C,60C,146C ''paramet'':2A,16B,73C,100C,112C,120C,155C,174C,190C ''penalti'':72C ''predict'':28C ''pretend'':51C ''purpos'':40C ''question'':149C ''rang'':109C ''regress'':14B,65C ''regular'':81C,88C ''return'':184C ''scale'':139C ''scikit'':9A,15B,36C,166C ''scikit-learn'':8A,35C,165C ''special'':97C ''statist'':181C ''techniqu'':161C ''term'':89C ''tool'':163C ''type'':27C ''understand'':107C ''use'':31C,91C ''valu'':5A,101C,113C,144C,177C,188C ''visual'':160C ''wonder'':67C ''work'':22C,62C ''wouldn'':130C ''wrong'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5602, 1, '2015-04-24 14:50:51.29', 1, 224, 'Training Dataset required for Classifier', '&lt;p&gt;I am currently trying to develop a classifier in python using Naive Bayes technique. I need a dataset so that I can train it. My classifier would classify a new document given to it into one of the four categories : Science and technology, Sports, politics, Entertainment. Can anybody please help me find a dataset for this. I''ve been stuck on this problem for quite some time now. Any help would be greatly appreciated.&lt;/p&gt;
', 'classification  python', NULL, '''/p'':83C ''anybodi'':56C ''appreci'':82C ''bay'':21C ''categori'':48C ''classif'':6B ''classifi'':5A,16C,34C,36C ''current'':11C ''dataset'':2A,26C,62C ''develop'':14C ''document'':39C ''entertain'':54C ''find'':60C ''four'':47C ''given'':40C ''great'':81C ''help'':58C,78C ''naiv'':20C ''need'':24C ''new'':38C ''one'':44C ''p'':8C ''pleas'':57C ''polit'':53C ''problem'':71C ''python'':7B,18C ''quit'':73C ''requir'':3A ''scienc'':49C ''sport'':52C ''stuck'':68C ''techniqu'':22C ''technolog'':51C ''time'':75C ''train'':1A,31C ''tri'':12C ''use'':19C ''ve'':66C ''would'':35C,79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5573, 2, '2015-04-21 12:28:13.413', 1, NULL, NULL, '&lt;p&gt;If by "parameters" you mean features (called "Data Fields" at Kaggle), then, yes, you can log-scale those. To visualize them you can just use histograms.
To do it for all features in python, for example, you can put your data in pandas DataFrame (let us call it "data") and then use data.hist()
This has nothing to do with the regularization in any model.&lt;/p&gt;

&lt;p&gt;If by "parameters" you mean the &lt;em&gt;coefficients&lt;/em&gt; obtained after fitting the logistic regression, then one uses regularization. This has, however, is not directly related to log-transform. How you list/visualize your coefficients depends on the programming tool you use for logistic regression (or other model)&lt;/p&gt;
', NULL, NULL, '''/em'':77C ''/p'':67C,117C ''call'':8C,49C ''coeffici'':76C,103C ''data'':9C,43C,51C ''data.hist'':55C ''datafram'':46C ''depend'':104C ''direct'':93C ''em'':75C ''exampl'':38C ''featur'':7C,34C ''field'':10C ''fit'':80C ''histogram'':28C ''howev'':90C ''kaggl'':12C ''let'':47C ''list/visualize'':101C ''log'':18C,97C ''log-scal'':17C ''log-transform'':96C ''logist'':82C,112C ''mean'':6C,73C ''model'':66C,116C ''noth'':58C ''obtain'':78C ''one'':85C ''p'':1C,68C ''panda'':45C ''paramet'':4C,71C ''program'':107C ''put'':41C ''python'':36C ''regress'':83C,113C ''regular'':63C,87C ''relat'':94C ''scale'':19C ''tool'':108C ''transform'':98C ''us'':48C ''use'':27C,54C,86C,110C ''visual'':22C ''yes'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5574, 1, '2015-04-21 13:18:17.537', 1, 129, 'Use of Nash-Equilibrium in big data environments', '&lt;p&gt;Has there been a successful implementation of Nash-Equilibrium in big data problems like suggesting a best buy in a stock market, in traffic monitoring systems or crowd control systems.
All the above mentioned scenarios have competitive environments and one needs to get the best possible solution in them, which should suit well for Nash-Equilibrium cases.&lt;/p&gt;
', 'bigdata  social-network-analysis  computer-vision  finance', NULL, '''/p'':79C ''analysi'':14B ''best'':37C,65C ''big'':7A,31C ''bigdata'':10B ''buy'':38C ''case'':78C ''competit'':57C ''comput'':16B ''computer-vis'':15B ''control'':49C ''crowd'':48C ''data'':8A,32C ''environ'':9A,58C ''equilibrium'':5A,29C,77C ''financ'':18B ''get'':63C ''implement'':25C ''like'':34C ''market'':42C ''mention'':54C ''monitor'':45C ''nash'':4A,28C,76C ''nash-equilibrium'':3A,27C,75C ''need'':61C ''network'':13B ''one'':60C ''p'':19C ''possibl'':66C ''problem'':33C ''scenario'':55C ''social'':12B ''social-network-analysi'':11B ''solut'':67C ''stock'':41C ''success'':24C ''suggest'':35C ''suit'':72C ''system'':46C,50C ''traffic'':44C ''use'':1A ''vision'':17B ''well'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5576, 2, '2015-04-21 19:31:39.673', 1, NULL, NULL, '&lt;p&gt;Check out term frequency-inverse document frequency (TF-IDF), if you''re parsing large chunks of text for mentioned topics. The metric measures importance of topics bounded by how often they appear across a corpus of documents, which helps to weed out topics that may be very common, but are very common to all discussions, and therefore not very useful.&lt;/p&gt;

&lt;p&gt;It''s a simple calculation, but there is a ruby gem that calculates it directly: &lt;code&gt;tf-idf-similarity&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Hope this helps.&lt;/p&gt;
', NULL, NULL, '''/code'':86C ''/p'':64C,87C,92C ''across'':36C ''appear'':35C ''bound'':30C ''calcul'':70C,78C ''check'':2C ''chunk'':18C ''code'':81C ''common'':51C,55C ''corpus'':38C ''direct'':80C ''discuss'':58C ''document'':8C,40C ''frequenc'':6C,9C ''frequency-invers'':5C ''gem'':76C ''help'':42C,91C ''hope'':89C ''idf'':12C,84C ''import'':27C ''invers'':7C ''larg'':17C ''may'':48C ''measur'':26C ''mention'':22C ''metric'':25C ''often'':33C ''p'':1C,65C,88C ''pars'':16C ''re'':15C ''rubi'':75C ''similar'':85C ''simpl'':69C ''term'':4C ''text'':20C ''tf'':11C,83C ''tf-idf'':10C ''tf-idf-similar'':82C ''therefor'':60C ''topic'':23C,29C,46C ''use'':63C ''weed'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5590, 1, '2015-04-22 21:26:38.7', 1, 109, 'What is the best way to scale a numerical dataset', '&lt;p&gt;I have a dataset with differents attributes which don''t have the same range on their values which is a problem when we need to compute distance beetween objects. After some research i found that i can do the regularisation job with this formula : (value-min)/(max-min)  where min and max are respectively the minimum and maximum value in the domain of val attribute.
The question is that one, does it exist other ways ?&lt;/p&gt;

&lt;p&gt;Thank you for your help.&lt;/p&gt;
', 'data-mining  dataset  beginner  feature-scaling', NULL, '''/p'':97C,104C ''attribut'':26C,86C ''beetween'':47C ''beginn'':15B ''best'':4A ''comput'':45C ''data'':12B ''data-min'':11B ''dataset'':10A,14B,23C ''differ'':25C ''distanc'':46C ''domain'':83C ''exist'':94C ''featur'':17B ''feature-sc'':16B ''formula'':63C ''found'':53C ''help'':103C ''job'':60C ''max'':68C,73C ''max-min'':67C ''maximum'':79C ''min'':66C,69C,71C ''mine'':13B ''minimum'':77C ''need'':43C ''numer'':9A ''object'':48C ''one'':91C ''p'':19C,98C ''problem'':40C ''question'':88C ''rang'':33C ''regularis'':59C ''research'':51C ''respect'':75C ''scale'':7A,18B ''thank'':99C ''val'':85C ''valu'':36C,65C,80C ''value-min'':64C ''way'':5A,96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6293, 2, '2015-07-01 06:16:19.477', 0, NULL, NULL, '&lt;p&gt;Do you run your analysis algos in batch or live? Which programing language under which environment do you use?&lt;/p&gt;

&lt;p&gt;At a first naive look, i would recommend to parallelize your code as each indicator calculation seems to be independent of the other''s.&lt;/p&gt;
', NULL, NULL, '''/p'':21C,47C ''algo'':7C ''analysi'':6C ''batch'':9C ''calcul'':38C ''code'':34C ''environ'':17C ''first'':25C ''independ'':42C ''indic'':37C ''languag'':14C ''live'':11C ''look'':27C ''naiv'':26C ''p'':1C,22C ''parallel'':32C ''program'':13C ''recommend'':30C ''run'':4C ''seem'':39C ''use'':20C ''would'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5578, 2, '2015-04-21 22:30:04.51', 1, NULL, NULL, '&lt;p&gt;I''ll offer a slightly different perspective. &lt;/p&gt;

&lt;p&gt;While you can help many needy &lt;em&gt;humans&lt;/em&gt; -- e.g. individual refugees, poor people, sick people, war-torn people -- to help &lt;strong&gt;&lt;em&gt;humanity&lt;/em&gt;&lt;/strong&gt; it''s necessary to address &lt;strong&gt;&lt;em&gt;root causes&lt;/em&gt;&lt;/strong&gt;: i.e. &lt;em&gt;why&lt;/em&gt; are there so many refugees, persistent poverty, persistent (preventable) illness, and chronic violence. The root causes of so many of these are in the domain of the social sciences.&lt;/p&gt;

&lt;p&gt;So far, data science has only contributed to root cause analysis when it operates within a solid research program, integrating with other types of research and findings. (This research may or may not take place in an academic environment, or maybe in a mixture of environments.) It also places premium value on domain knowledge on the part of the data scientists involved.&lt;/p&gt;

&lt;p&gt;A related theme is to empower "change agents" (a.k.a. social entrepreneurs) with data science and related services.  Nearly all of them are under-resourced and often face severe or menacing opposition.  To do this, you will need to make &lt;em&gt;partnerships&lt;/em&gt; with some of them to understand their needs and their world.&lt;/p&gt;
', NULL, NULL, '''/em'':19C,36C,47C,52C,190C ''/p'':9C,82C,146C,202C ''/strong'':37C,48C ''a.k.a'':156C ''academ'':121C ''address'':42C ''agent'':155C ''also'':131C ''analysi'':94C ''caus'':46C,68C,93C ''chang'':154C ''chronic'':64C ''contribut'':90C ''data'':86C,143C,160C ''differ'':7C ''domain'':77C,136C ''e.g'':20C ''em'':17C,34C,44C,50C,188C ''empow'':153C ''entrepreneur'':158C ''environ'':122C,129C ''face'':175C ''far'':85C ''find'':110C ''help'':14C,32C ''human'':18C,35C ''i.e'':49C ''ill'':62C ''individu'':21C ''integr'':103C ''involv'':145C ''knowledg'':137C ''ll'':3C ''make'':187C ''mani'':15C,56C,71C ''may'':113C,115C ''mayb'':124C ''menac'':178C ''mixtur'':127C ''near'':165C ''necessari'':40C ''need'':185C,198C ''needi'':16C ''offer'':4C ''often'':174C ''oper'':97C ''opposit'':179C ''p'':1C,10C,83C,147C ''part'':140C ''partnership'':189C ''peopl'':24C,26C,30C ''persist'':58C,60C ''perspect'':8C ''place'':118C,132C ''poor'':23C ''poverti'':59C ''premium'':133C ''prevent'':61C ''program'':102C ''refuge'':22C,57C ''relat'':149C,163C ''research'':101C,108C,112C ''resourc'':172C ''root'':45C,67C,92C ''scienc'':81C,87C,161C ''scientist'':144C ''servic'':164C ''sever'':176C ''sick'':25C ''slight'':6C ''social'':80C,157C ''solid'':100C ''strong'':33C,43C ''take'':117C ''theme'':150C ''torn'':29C ''type'':106C ''under-resourc'':170C ''understand'':196C ''valu'':134C ''violenc'':65C ''war'':28C ''war-torn'':27C ''within'':98C ''world'':201C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5636, 2, '2015-04-28 15:29:24.093', 3, NULL, NULL, '&lt;p&gt;Cluster analysis is not &lt;em&gt;supposed&lt;/em&gt; to produce paritions of equal size. It is meant to discover structure in the data.&lt;/p&gt;

&lt;p&gt;If the majority of objects is highly similar, then this majority is supposed to be in the majority cluster.&lt;/p&gt;

&lt;p&gt;Consider all your data is identical. Any clustering algorithm producing more than one cluster has failed, in my opinion...&lt;/p&gt;

&lt;p&gt;So you may be using the wrong class of algorithms for your problem.&lt;/p&gt;
', NULL, NULL, '''/em'':8C ''/p'':24C,45C,66C,81C ''algorithm'':55C,77C ''analysi'':3C ''class'':75C ''cluster'':2C,44C,54C,60C ''consid'':47C ''data'':23C,50C ''discov'':19C ''em'':6C ''equal'':13C ''fail'':62C ''high'':32C ''ident'':52C ''major'':28C,36C,43C ''may'':70C ''meant'':17C ''object'':30C ''one'':59C ''opinion'':65C ''p'':1C,25C,46C,67C ''parit'':11C ''problem'':80C ''produc'':10C,56C ''similar'':33C ''size'':14C ''structur'':20C ''suppos'':7C,38C ''use'':72C ''wrong'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5579, 2, '2015-04-21 22:56:21.44', 2, NULL, NULL, '&lt;p&gt;Since I have already answered a similar question on &lt;em&gt;Data Science StackExchange&lt;/em&gt; site, plus some related ones, I will mention all of them here and let you decide, if you find them helpful:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/a/843/2452"&gt;on data science project ideas, including those with social impact&lt;/a&gt;;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/a/5081/2452"&gt;on working as a data scientist for a non-profit company&lt;/a&gt;;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/a/5446/2452"&gt;on using data science in political campaigns&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':56C,78C,95C ''/a/5081/2452'':65C ''/a/5446/2452'':87C ''/a/843/2452'':45C ''/em'':15C ''/li'':58C,80C,97C ''/p'':37C,57C,79C,96C ''/ul'':98C ''alreadi'':5C ''answer'':6C ''campaign'':94C ''compani'':77C ''data'':12C,47C,70C,90C ''datascience.stackexchange.com'':44C,64C,86C ''datascience.stackexchange.com/a/5081/2452'':63C ''datascience.stackexchange.com/a/5446/2452'':85C ''datascience.stackexchange.com/a/843/2452'':43C ''decid'':31C ''em'':11C ''find'':34C ''help'':36C ''href'':42C,62C,84C ''idea'':50C ''impact'':55C ''includ'':51C ''let'':29C ''li'':39C,59C,81C ''mention'':23C ''non'':75C ''non-profit'':74C ''one'':20C ''p'':1C,40C,60C,82C ''plus'':17C ''polit'':93C ''profit'':76C ''project'':49C ''question'':9C ''relat'':19C ''scienc'':13C,48C,91C ''scientist'':71C ''similar'':8C ''sinc'':2C ''site'':16C ''social'':54C ''stackexchang'':14C ''ul'':38C ''use'':89C ''work'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5580, 1, '2015-04-21 23:06:28.997', 0, 35, 'How do we mine associations from sequences?', '&lt;p&gt;My data mining problem is a next web page prediction using the existing web data. For that I have a set of frequent sequences which are obtained using cspade algorithm in R. Now I am not sure how to mine set of associations so that I can predict the next page? can someone help?&lt;/p&gt;

&lt;p&gt;Frequent sequencies look like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; items     support 
   1 [{1}]  0.1640261 
   2 [{10}] 0.05112657 
   3 [{11}] 0.05818949 
   4 [{12}] 0.11333700 
   5 [{13}] 0.07773954 
   6 [{14}] 0.12036354 
   7 [{15}] 0.02950037 
   8 [{17}] 0.01111922 
   9 [{2}]  0.17708912 
  10 [{3}]  0.12320245 
  11 [{4}]  0.12297109 
  12 [{5}]  0.02524403 
  13 [{6}]  0.21933426 
  14 [{7}]  0.08134223 
  15 [{8}]  0.09659857 
  16 [{9}]  0.09111978 
  17 [{6}, 
     {9}]  0.01086563 
  18 [{9},   
     {9}]  0.04410508 
  19 [{9},   
     {9},   
     {9}]  0.02321639 
  20 [{9},   
     {9},   
     {9},   
      {9}]  0.01316606 
  21 [{8},   
      {8}]  0.06783368 
  22 [{8},   
      {8},   
      {8}]  0.05253996 
  23 [{8},   
      {8},   
      {8},   
      {8}]  0.04431926 
&lt;/code&gt;&lt;/pre&gt;
', 'data-mining  bigdata  sequence', NULL, '''/code'':162C ''/p'':68C,75C ''/pre'':163C ''0.01086563'':131C ''0.01111922'':103C ''0.01316606'':146C ''0.02321639'':140C ''0.02524403'':115C ''0.02950037'':100C ''0.04410508'':135C ''0.04431926'':161C ''0.05112657'':85C ''0.05253996'':155C ''0.05818949'':88C ''0.06783368'':150C ''0.07773954'':94C ''0.08134223'':121C ''0.09111978'':127C ''0.09659857'':124C ''0.11333700'':91C ''0.12036354'':97C ''0.12297109'':112C ''0.12320245'':109C ''0.1640261'':82C ''0.17708912'':106C ''0.21933426'':118C ''1'':80C,81C ''10'':84C,107C ''11'':87C,110C ''12'':90C,113C ''13'':93C,116C ''14'':96C,119C ''15'':99C,122C ''16'':125C ''17'':102C,128C ''18'':132C ''19'':136C ''2'':83C,105C ''20'':141C ''21'':147C ''22'':151C ''23'':156C ''3'':86C,108C ''4'':89C,111C ''5'':92C,114C ''6'':95C,117C,129C ''7'':98C,120C ''8'':101C,123C,148C,149C,152C,153C,154C,157C,158C,159C,160C ''9'':104C,126C,130C,133C,134C,137C,138C,139C,142C,143C,144C,145C ''algorithm'':43C ''associ'':5A,56C ''bigdata'':11B ''code'':77C ''cspade'':42C ''data'':9B,15C,28C ''data-min'':8B ''exist'':26C ''frequent'':36C,70C ''help'':67C ''item'':78C ''like'':73C ''look'':72C ''mine'':4A,10B,16C,53C ''next'':20C,63C ''obtain'':40C ''p'':13C,69C ''page'':22C,64C ''pre'':76C ''predict'':23C,61C ''problem'':17C ''r'':45C ''sequenc'':7A,12B,37C,71C ''set'':34C,54C ''someon'':66C ''support'':79C ''sure'':50C ''use'':24C,41C ''web'':21C,27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5585, 1, '2015-04-22 12:32:10.243', 1, 195, 'Optimizing Weka for large data sets', '&lt;p&gt;First of all, I hope I''m in the right StackExchange here. If not, apologies!&lt;/p&gt;

&lt;p&gt;I''m currently working with huge amounts of feature-value vectors. There are millions of these vectors (up to 20 million possibly). They contain some linguistic/syntactic features and their values are all strings.&lt;/p&gt;

&lt;p&gt;Because most classifiers do not handle string data as values, I convert them to binary frequency values, so an attribute looks like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;@attribute ''feature#value'' numeric
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And per row, the value is either 1 or it is absent (so note it''s a &lt;em&gt;sparse&lt;/em&gt; ARFF file). &lt;/p&gt;

&lt;p&gt;The thing is, with 250K rows, there are over 500K attributes and so, most algorithms have a hard time with this.&lt;/p&gt;

&lt;p&gt;There are a lot of algorithms. I''m really curious as to what you would consider a suitable one (preferably unsupervised, but anything works), and if you even have some ideas how I could improve performance. I can train on small subsets of data, but the results only get better when using large amounts of data (at least 7 million events). &lt;/p&gt;

&lt;p&gt;For now, I''ve been using NaiveBayes variations (Multinomial and also DMNBText) and those are really the only ones that are able to chew up data with acceptable speed. &lt;/p&gt;

&lt;p&gt;Thanks a lot. If you need more information, please let me know.&lt;/p&gt;

&lt;p&gt;Cheers.&lt;/p&gt;
', 'machine-learning  classification  bigdata  clustering  algorithms', NULL, '''/code'':98C ''/em'':120C ''/p'':30C,66C,91C,123C,146C,209C,240C,254C,257C ''/pre'':99C ''1'':108C ''20'':52C ''250k'':129C ''500k'':134C ''7'':206C ''abl'':232C ''absent'':112C ''accept'':238C ''algorithm'':13B,139C,153C ''also'':221C ''amount'':38C,201C ''anyth'':170C ''apolog'':29C ''arff'':121C ''attribut'':87C,94C,135C ''better'':197C ''bigdata'':11B ''binari'':82C ''cheer'':256C ''chew'':234C ''classif'':10B ''classifi'':70C ''cluster'':12B ''code'':93C ''consid'':163C ''contain'':56C ''convert'':79C ''could'':181C ''curious'':157C ''current'':34C ''data'':5A,75C,191C,203C,236C ''dmnbtext'':222C ''either'':107C ''em'':118C ''even'':175C ''event'':208C ''featur'':41C,59C,95C ''feature-valu'':40C ''file'':122C ''first'':15C ''frequenc'':83C ''get'':196C ''handl'':73C ''hard'':142C ''hope'':19C ''huge'':37C ''idea'':178C ''improv'':182C ''inform'':249C ''know'':253C ''larg'':4A,200C ''learn'':9B ''least'':205C ''let'':251C ''like'':89C ''linguistic/syntactic'':58C ''look'':88C ''lot'':151C,244C ''m'':21C,33C,155C ''machin'':8B ''machine-learn'':7B ''million'':46C,53C,207C ''multinomi'':219C ''naivebay'':217C ''need'':247C ''note'':114C ''numer'':97C ''one'':166C,229C ''optim'':1A ''p'':14C,31C,67C,100C,124C,147C,210C,241C,255C ''per'':102C ''perform'':183C ''pleas'':250C ''possibl'':54C ''pre'':92C ''prefer'':167C ''realli'':156C,226C ''result'':194C ''right'':24C ''row'':103C,130C ''set'':6A ''small'':188C ''spars'':119C ''speed'':239C ''stackexchang'':25C ''string'':65C,74C ''subset'':189C ''suitabl'':165C ''thank'':242C ''thing'':126C ''time'':143C ''train'':186C ''unsupervis'':168C ''use'':199C,216C ''valu'':42C,62C,77C,84C,96C,105C ''variat'':218C ''ve'':214C ''vector'':43C,49C ''weka'':2A ''work'':35C,171C ''would'':162C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5586, 2, '2015-04-22 13:35:06.473', 1, NULL, NULL, '&lt;p&gt;I think you''re looking at it the wrong way. First, you need to have a question and then you may find out whether the data could potentially hold the answer to that question. Right now, you''re asking us to what questions this data may hold the answer. And that is a very difficult question!&lt;/p&gt;

&lt;p&gt;But to provide you with at least some ideas:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Find out what people deem important (the correlation between overall rating and the other ratings in (linear) regression analyses)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Find out the relation between certain characteristics of people (age/gender/nationality, if available) and their overall rating (or whatever rating available)&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Basically, the relation between certain variables and their outcomes. But make sure you have a clear question before you start looking for answers.&lt;/p&gt;
', NULL, NULL, '''/li'':93C,117C ''/p'':58C,69C,92C,116C,143C ''/ul'':118C ''age/gender/nationality'':105C ''analys'':91C ''answer'':32C,50C,142C ''ask'':40C ''avail'':107C,115C ''basic'':120C ''certain'':101C,124C ''characterist'':102C ''clear'':135C ''correl'':80C ''could'':28C ''data'':27C,46C ''deem'':77C ''difficult'':56C ''find'':23C,73C,96C ''first'':12C ''hold'':30C,48C ''idea'':68C ''import'':78C ''least'':66C ''li'':71C,94C ''linear'':89C ''look'':6C,140C ''make'':130C ''may'':22C,47C ''need'':14C ''outcom'':128C ''overal'':82C,110C ''p'':1C,59C,72C,95C,119C ''peopl'':76C,104C ''potenti'':29C ''provid'':62C ''question'':18C,35C,44C,57C,136C ''rate'':83C,87C,111C,114C ''re'':5C,39C ''regress'':90C ''relat'':99C,122C ''right'':36C ''start'':139C ''sure'':131C ''think'':3C ''ul'':70C ''us'':41C ''variabl'':125C ''way'':11C ''whatev'':113C ''whether'':25C ''wrong'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5610, 1, '2015-04-25 18:29:33', 0, 55, 'What are Hybrid Classifiers used in Sentiment Analysis?', '&lt;p&gt;What are Hybrid classifiers used for sentiment analysis? How are they built? Please suggest good tutorial/book/link for reference. Also how are they different from other classifiers like SVM and Naive Bayes? &lt;/p&gt;
', 'machine-learning  classification', NULL, '''/p'':45C ''also'':32C ''analysi'':8A,21C ''bay'':44C ''built'':25C ''classif'':12B ''classifi'':4A,17C,39C ''differ'':36C ''good'':28C ''hybrid'':3A,16C ''learn'':11B ''like'':40C ''machin'':10B ''machine-learn'':9B ''naiv'':43C ''p'':13C ''pleas'':26C ''refer'':31C ''sentiment'':7A,20C ''suggest'':27C ''svm'':41C ''tutorial/book/link'':29C ''use'':5A,18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5596, 2, '2015-04-23 15:10:58.97', 3, NULL, NULL, '&lt;p&gt;The new Amazon Machine Learning Service may work for you. It works directly with Redshift and might be a good way to start. &lt;a href="http://aws.amazon.com/machine-learning/" rel="nofollow"&gt;http://aws.amazon.com/machine-learning/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you''re looking to process using EMR, then you can use Redshift''s UNLOAD command to land data on S3. Spark on EMR can then access it directly without you having to pull it into HDFS.&lt;/p&gt;

&lt;p&gt;Spark on EMR: &lt;a href="https://aws.amazon.com/articles/Elastic-MapReduce/4926593393724923" rel="nofollow"&gt;https://aws.amazon.com/articles/Elastic-MapReduce/4926593393724923&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/articles/elastic-mapreduce/4926593393724923'':82C ''/articles/elastic-mapreduce/4926593393724923&lt;/a&gt;&lt;/p&gt;'':87C ''/machine-learning/'':29C ''/machine-learning/&lt;/a&gt;&lt;/p&gt;'':34C ''/p'':73C ''access'':62C ''amazon'':4C ''aws.amazon.com'':28C,33C,81C,86C ''aws.amazon.com/articles/elastic-mapreduce/4926593393724923'':80C ''aws.amazon.com/articles/elastic-mapreduce/4926593393724923&lt;/a&gt;&lt;/p&gt;'':85C ''aws.amazon.com/machine-learning/'':27C ''aws.amazon.com/machine-learning/&lt;/a&gt;&lt;/p&gt;'':32C ''command'':51C ''data'':54C ''direct'':14C,64C ''emr'':43C,59C,77C ''good'':21C ''hdfs'':72C ''href'':26C,79C ''land'':53C ''learn'':6C ''look'':39C ''machin'':5C ''may'':8C ''might'':18C ''new'':3C ''nofollow'':31C,84C ''p'':1C,35C,74C ''process'':41C ''pull'':69C ''re'':38C ''redshift'':16C,48C ''rel'':30C,83C ''s3'':56C ''servic'':7C ''spark'':57C,75C ''start'':24C ''unload'':50C ''use'':42C,47C ''way'':22C ''without'':65C ''work'':9C,13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5591, 2, '2015-04-23 07:34:34.667', 2, NULL, NULL, '&lt;p&gt;There is pretty much mess in terminology in your question :).
&lt;strong&gt;&lt;a href="https://en.wikipedia.org/wiki/Regularization_(mathematics)" rel="nofollow"&gt;Data Regularization&lt;/a&gt;&lt;/strong&gt; is used for model selection, it is not about data processing. &lt;a href="http://stats.stackexchange.com/questions/4961/what-is-regularization-in-plain-english"&gt;Here&lt;/a&gt; it is described in more friendly manner.&lt;/p&gt;

&lt;p&gt;What you mean is &lt;strong&gt;&lt;a href="http://en.wikipedia.org/wiki/Feature_scaling" rel="nofollow"&gt;Feature Scaling&lt;/a&gt;&lt;/strong&gt;. It can be done in &lt;strong&gt;several&lt;/strong&gt; ways including &lt;strong&gt;&lt;a href="http://en.wikipedia.org/wiki/Feature_scaling#Rescaling" rel="nofollow"&gt;Rescaling&lt;/a&gt;&lt;/strong&gt;, the method you described. You may also use Standardization (normalization) and Scaling to unit length.&lt;/p&gt;

&lt;p&gt;These answers may be helpful:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/questions/35591/normalization-vs-scaling"&gt;Normalization vs Scaling&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/questions/10289/whats-the-difference-between-normalization-and-standardization"&gt;Normalization vs Standardization&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':22C,41C,65C,86C,121C,132C ''/li'':122C,133C ''/p'':49C,103C,110C ''/questions/10289/whats-the-difference-between-normalization-and-standardization'':128C ''/questions/35591/normalization-vs-scaling'':117C ''/questions/4961/what-is-regularization-in-plain-english'':39C ''/strong'':23C,66C,74C,87C ''/ul'':134C ''/wiki/feature_scaling'':60C ''/wiki/feature_scaling#rescaling'':82C ''/wiki/regularization_(mathematics)'':17C ''also'':94C ''answer'':106C ''data'':20C,33C ''describ'':44C,91C ''done'':70C ''en.wikipedia.org'':16C,59C,81C ''en.wikipedia.org/wiki/feature_scaling'':58C ''en.wikipedia.org/wiki/feature_scaling#rescaling'':80C ''en.wikipedia.org/wiki/regularization_(mathematics)'':15C ''featur'':63C ''friend'':47C ''help'':109C ''href'':14C,36C,57C,79C,114C,125C ''includ'':76C ''length'':102C ''li'':112C,123C ''manner'':48C ''may'':93C,107C ''mean'':53C ''mess'':6C ''method'':89C ''model'':27C ''much'':5C ''nofollow'':19C,62C,84C ''normal'':97C,118C,129C ''p'':1C,50C,104C ''pretti'':4C ''process'':34C ''question'':11C ''regular'':21C ''rel'':18C,61C,83C ''rescal'':85C ''scale'':64C,99C,120C ''select'':28C ''sever'':73C ''standard'':96C,131C ''stats.stackexchange.com'':38C,116C,127C ''stats.stackexchange.com/questions/10289/whats-the-difference-between-normalization-and-standardization'':126C ''stats.stackexchange.com/questions/35591/normalization-vs-scaling'':115C ''stats.stackexchange.com/questions/4961/what-is-regularization-in-plain-english'':37C ''strong'':12C,55C,72C,77C ''terminolog'':8C ''ul'':111C ''unit'':101C ''use'':25C,95C ''vs'':119C,130C ''way'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5592, 2, '2015-04-23 08:10:27.503', 1, NULL, NULL, '&lt;p&gt;Complete-linkage clustering tends to find compact clusters with equal diameters. It is define the inter-group distances as the largest distance of objects between two clusters. Beside, it is may produce clusters that has the same number of objects and the distance between objects is contiguous.
&lt;img src="http://i.stack.imgur.com/7vyjS.png" alt="maximum distance of objects in two clusters"&gt; &lt;/p&gt;

&lt;p&gt;So, I can say that your conjecture is correct. I cannot wait for any experts to explain this in detail.&lt;/p&gt;
', NULL, NULL, '''/7vyjs.png'':55C ''/p'':64C,86C ''alt'':56C ''besid'':31C ''cannot'':76C ''cluster'':5C,10C,30C,36C,63C ''compact'':9C ''complet'':3C ''complete-linkag'':2C ''conjectur'':72C ''contigu'':50C ''correct'':74C ''defin'':16C ''detail'':85C ''diamet'':13C ''distanc'':21C,25C,46C,58C ''equal'':12C ''expert'':80C ''explain'':82C ''find'':8C ''group'':20C ''i.stack.imgur.com'':54C ''i.stack.imgur.com/7vyjs.png'':53C ''img'':51C ''inter'':19C ''inter-group'':18C ''largest'':24C ''linkag'':4C ''maximum'':57C ''may'':34C ''number'':41C ''object'':27C,43C,48C,60C ''p'':1C,65C ''produc'':35C ''say'':69C ''src'':52C ''tend'':6C ''two'':29C,62C ''wait'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5593, 2, '2015-04-23 09:12:02.23', 1, NULL, NULL, '&lt;p&gt;I may have missed something in the definition in Wikipedia, but wouldn''t the set {1,2,4,5,7,8} with $\Delta(x,y) = |x-y|$ be a counterexample? After the 4th step there are two clusters, {{1,2}, {4,5}}, {7,8} and x=5, y=1, z=7 seems to violate your claim.&lt;/p&gt;
', NULL, NULL, '''/p'':59C ''1'':17C,41C,51C ''2'':18C,42C ''4'':19C,43C ''4th'':35C ''5'':20C,44C,49C ''7'':21C,45C,53C ''8'':22C,46C ''claim'':58C ''cluster'':40C ''counterexampl'':32C ''definit'':9C ''delta'':24C ''may'':3C ''miss'':5C ''p'':1C ''seem'':54C ''set'':16C ''someth'':6C ''step'':36C ''two'':39C ''violat'':56C ''wikipedia'':11C ''wouldn'':13C ''x'':25C,28C,48C ''x-i'':27C ''y'':26C,29C,50C ''z'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5594, 1, '2015-04-23 12:50:52.173', 1, 60, 'non-linear optimization for a linear classifier? (scikit-learn)', '&lt;p&gt;Using scikit-learn, why would you use bfgs optimization which is non-linear for a linear classifier as logistic regression? I am confused. Does the optimization method finds the optimum of the chosen score function? if so, which one? I can''t choose it when defining the estimator. does the linearity or non-linearity of the score function depend on the model (whether it is linear or non-linear)?&lt;/p&gt;
', 'machine-learning  logistic-regression  scikit  optimization  kaggle', NULL, '''/p'':95C ''bfgs'':30C ''choos'':66C ''chosen'':56C ''classifi'':8A,40C ''confus'':46C ''defin'':69C ''depend'':83C ''estim'':71C ''find'':51C ''function'':58C,82C ''kaggl'':20B ''learn'':11A,14B,25C ''linear'':3A,7A,36C,39C,74C,78C,90C,94C ''logist'':16B,42C ''logistic-regress'':15B ''machin'':13B ''machine-learn'':12B ''method'':50C ''model'':86C ''non'':2A,35C,77C,93C ''non-linear'':1A,34C,76C,92C ''one'':62C ''optim'':4A,19B,31C,49C ''optimum'':53C ''p'':21C ''regress'':17B,43C ''scikit'':10A,18B,24C ''scikit-learn'':9A,23C ''score'':57C,81C ''use'':22C,29C ''whether'':87C ''would'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5595, 2, '2015-04-23 13:02:21.58', 1, NULL, NULL, '&lt;p&gt;Refer Aws documentation : &lt;a href="http://aws.amazon.com/code" rel="nofollow"&gt;http://aws.amazon.com/code&lt;/a&gt; there are libraries available for most of the programing languages.
So you can create a bucket and configure in your code to fetch data from url and write to this bucket in s3 &lt;/p&gt;

&lt;p&gt;for eg in python :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from boto.s3.key import Key
k = Key(bucket)
k.key = ''foobar''
k.set_contents_from_string(url_data)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Ref : &lt;a href="https://boto.readthedocs.org/en/latest/s3_tut.html" rel="nofollow"&gt;https://boto.readthedocs.org/en/latest/s3_tut.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/code'':9C,72C ''/code&lt;/a&gt;'':14C ''/en/latest/s3_tut.html'':80C ''/en/latest/s3_tut.html&lt;/a&gt;&lt;/p&gt;'':85C ''/p'':48C,54C ''/pre'':73C ''avail'':18C ''aw'':3C ''aws.amazon.com'':8C,13C ''aws.amazon.com/code'':7C ''aws.amazon.com/code&lt;/a&gt;'':12C ''boto.readthedocs.org'':79C,84C ''boto.readthedocs.org/en/latest/s3_tut.html'':78C ''boto.readthedocs.org/en/latest/s3_tut.html&lt;/a&gt;&lt;/p&gt;'':83C ''boto.s3.key'':58C ''bucket'':30C,45C,63C ''code'':35C,56C ''configur'':32C ''content'':67C ''creat'':28C ''data'':38C,71C ''document'':4C ''eg'':51C ''fetch'':37C ''foobar'':65C ''href'':6C,77C ''import'':59C ''k'':61C ''k.key'':64C ''k.set'':66C ''key'':60C,62C ''languag'':24C ''librari'':17C ''nofollow'':11C,82C ''p'':1C,49C,74C ''pre'':55C ''program'':23C ''python'':53C ''ref'':75C ''refer'':2C ''rel'':10C,81C ''s3'':47C ''string'':69C ''url'':40C,70C ''write'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6052, 1, '2015-06-09 13:49:57.683', 3, 100, 'How to recognize a two part term when the space is removed? ("bigdata" and "big data")', '&lt;p&gt;I''m not a NLP guy and I have this question. &lt;/p&gt;

&lt;p&gt;I have a text dataset containing terms which go like, "big data" and "bigdata". &lt;/p&gt;

&lt;p&gt;For my purpose both of them are the same. &lt;/p&gt;

&lt;p&gt;How can I detect them in NLTK (Python)?&lt;/p&gt;

&lt;p&gt;Or any other NLP module in Python?&lt;/p&gt;
', 'python  nlp  text-mining', NULL, '''/p'':34C,50C,61C,71C,80C ''big'':15A,46C ''bigdata'':13A,49C ''contain'':41C ''data'':16A,47C ''dataset'':40C ''detect'':66C ''go'':44C ''guy'':28C ''like'':45C ''m'':24C ''mine'':21B ''modul'':77C ''nlp'':18B,27C,76C ''nltk'':69C ''p'':22C,35C,51C,62C,72C ''part'':6A ''purpos'':54C ''python'':17B,70C,79C ''question'':33C ''recogn'':3A ''remov'':12A ''space'':10A ''term'':7A,42C ''text'':20B,39C ''text-min'':19B ''two'':5A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5603, 2, '2015-04-24 15:15:06.43', 2, NULL, NULL, '&lt;p&gt;Both message and answer are your input, so your feature vector should contain information about both.&lt;/p&gt;

&lt;p&gt;Here''s a simple structure of a possible solution using scikit-learn:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
from sklearn.svm import SVC
from sklearn.feature_extraction import DictVectorizer

dataset = (("Hey @foobar, have you been hacked?",
            "@barfoo it seems so, thx for suggesting",
            True), # True for relevant, False for not relevant
           ("Hey @foobar, have you been hacked?",
            "Lose 20 pounds quickly! http://blabla.com",
            False))

def extractMessageFeatures(message):
    # here comes your real feature extraction algorithm
    return { ''message_predicted_spam'': False,
             ''message_contains_valid_username'': True }

def extractAnswerFeatures(answer):
    # here comes your real feature extraction algorithm
    return { ''answer_predicted_spam'': False,
             ''answer_contains_valid_username'': True }

def extractFeatures(data):
    features = []
    for instance in data:
        instanceFeatures = extractMessageFeatures(data[0])
        instanceFeatures.update(extractAnswerFeatures(data[1]))
        features.append(instanceFeatures)
    return features

def trainClassifier(data):
    features = extractFeatures(data)
    vec = DictVectorizer()
    featureVector = vec.fit_transform(features)

    print vec.get_feature_names()
    print featureVector.toarray()

    svc = SVC()
    svc.fit(featureVector, np.array([i[2] for i in data]))
    return svc

clf = trainClassifier(dataset)

# now, you can clf.predict(...)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now, the hardest part is to decide which features to extract from both messages and answers. It''s up to you.&lt;/p&gt;

&lt;p&gt;One of the simplest solutions would be to use n-gram features.&lt;/p&gt;

&lt;p&gt;Other approach would be to use some spam detection to decide whether answer is spam or not and treat this information as a feature.&lt;/p&gt;

&lt;p&gt;You can also use Twitter-specific information (for example, whether users are mentioning each other in their tweets, using the same hashtags etc.).&lt;/p&gt;

&lt;p&gt;You can combine these features in whatever fashion you like, of course.&lt;/p&gt;

&lt;p&gt;Except of creating feature extraction functionality you need a labeled dataset of messages, answers and relevant/non-relevant labels.&lt;/p&gt;

&lt;p&gt;But once you have both (feature extraction functionality and a proper dataset), you''re good to go with a task which clearly matches standard machine learning approaches.&lt;/p&gt;
', NULL, NULL, '''/code'':181C ''/p'':18C,33C,205C,220C,246C,272C,286C,305C,334C ''/pre'':182C ''0'':134C ''1'':138C ''2'':167C ''20'':78C ''algorithm'':92C,112C ''also'':250C ''answer'':5C,105C,114C,118C,199C,234C,301C ''approach'':223C,333C ''barfoo'':56C ''blabla.com'':81C ''clear'':328C ''clf'':174C ''clf.predict'':180C ''code'':35C ''combin'':276C ''come'':87C,107C ''contain'':14C,99C,119C ''cours'':285C ''creat'':290C ''data'':125C,130C,133C,137C,145C,148C,171C ''dataset'':49C,176C,298C,318C ''decid'':190C,232C ''def'':83C,103C,123C,143C ''detect'':230C ''dictvector'':48C,150C ''etc'':271C ''exampl'':257C ''except'':288C ''extract'':46C,91C,111C,194C,292C,313C ''extractanswerfeatur'':104C,136C ''extractfeatur'':124C,147C ''extractmessagefeatur'':84C,132C ''fals'':67C,82C,97C,117C ''fashion'':281C ''featur'':11C,90C,110C,126C,142C,146C,154C,157C,192C,219C,245C,278C,291C,312C ''features.append'':139C ''featurevector'':151C,164C ''featurevector.toarray'':160C ''foobar'':51C,72C ''function'':293C,314C ''go'':323C ''good'':321C ''gram'':218C ''hack'':55C,76C ''hardest'':186C ''hashtag'':270C ''hey'':50C,71C ''import'':36C,42C,47C ''inform'':15C,242C,255C ''input'':8C ''instanc'':128C ''instancefeatur'':131C,140C ''instancefeatures.update'':135C ''label'':297C,304C ''learn'':32C,332C ''like'':283C ''lose'':77C ''machin'':331C ''match'':329C ''mention'':261C ''messag'':3C,85C,94C,98C,197C,300C ''n'':217C ''n-gram'':216C ''name'':158C ''need'':295C ''np'':39C ''np.array'':165C ''numpi'':37C ''one'':207C ''p'':1C,19C,183C,206C,221C,247C,273C,287C,306C ''part'':187C ''possibl'':27C ''pound'':79C ''pre'':34C ''predict'':95C,115C ''print'':155C,159C ''proper'':317C ''quick'':80C ''re'':320C ''real'':89C,109C ''relev'':66C,70C ''relevant/non-relevant'':303C ''return'':93C,113C,141C,172C ''scikit'':31C ''scikit-learn'':30C ''seem'':58C ''simpl'':23C ''simplest'':210C ''sklearn.feature'':45C ''sklearn.svm'':41C ''solut'':28C,211C ''spam'':96C,116C,229C,236C ''specif'':254C ''standard'':330C ''structur'':24C ''suggest'':62C ''svc'':43C,161C,162C,173C ''svc.fit'':163C ''task'':326C ''thx'':60C ''trainclassifi'':144C,175C ''transform'':153C ''treat'':240C ''true'':63C,64C,102C,122C ''tweet'':266C ''twitter'':253C ''twitter-specif'':252C ''use'':29C,215C,227C,251C,267C ''user'':259C ''usernam'':101C,121C ''valid'':100C,120C ''vec'':149C ''vec.fit'':152C ''vec.get'':156C ''vector'':12C ''whatev'':280C ''whether'':233C,258C ''would'':212C,224C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5605, 2, '2015-04-24 21:17:21.963', 3, NULL, NULL, '&lt;p&gt;You have at least three options:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Use some of many available datasets (for example: &lt;a href="http://mlg.ucd.ie/datasets/bbc.html" rel="nofollow"&gt;BBC documents&lt;/a&gt;); if you need more, simply go to Google Scholar or any similar service and search for &lt;em&gt;classification news politics sports&lt;/em&gt;. In the articles you may find many references to available datasets;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Crawl any news service, and use clustering techniques to group articles into clusters (this often separates the articles along their domain, such as politics, sports etc.), and label the articles by their affiliation to a cluster;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Crawl politics-specific, sports-specific news services and use them as labeled datasets.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':29C ''/datasets/bbc.html'':24C ''/em'':51C ''/li'':64C,104C,123C ''/p'':8C,63C,103C,122C ''/ul'':124C ''affili'':99C ''along'':85C ''articl'':54C,77C,84C,96C ''avail'':16C,61C ''bbc'':27C ''classif'':47C ''cluster'':73C,79C,102C ''crawl'':67C,107C ''dataset'':17C,62C,121C ''document'':28C ''domain'':87C ''em'':46C ''etc'':92C ''exampl'':19C ''find'':57C ''go'':35C ''googl'':37C ''group'':76C ''href'':21C ''label'':94C,120C ''least'':5C ''li'':10C,65C,105C ''mani'':15C,58C ''may'':56C ''mlg.ucd.ie'':23C ''mlg.ucd.ie/datasets/bbc.html'':22C ''need'':32C ''news'':48C,69C,114C ''nofollow'':26C ''often'':81C ''option'':7C ''p'':1C,11C,66C,106C ''polit'':49C,90C,109C ''politics-specif'':108C ''refer'':59C ''rel'':25C ''scholar'':38C ''search'':44C ''separ'':82C ''servic'':42C,70C,115C ''similar'':41C ''simpli'':34C ''specif'':110C,113C ''sport'':50C,91C,112C ''sports-specif'':111C ''techniqu'':74C ''three'':6C ''ul'':9C ''use'':12C,72C,117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5606, 1, '2015-04-25 01:48:19.27', 1, 90, 'Question on decision tree in the book Programming Collective Intelligence', '&lt;p&gt;I''m currently studying Chapter 7 of the Book Programming Collective intelligence. &lt;/p&gt;

&lt;p&gt;I find the output of the function mdclassify() p.157 confusing. The function deals with missing data. The explanation provided is : "In the basic decision tree, everything has an implied weight of 1, meaning that the observations count fully for the probability that an item fits into a certain category. If you are following multiple branches instead, you can give each branch a weight equal to the fraction of all the other rows that are on that side." From what I understand, a instance is then split between branches.&lt;/p&gt;

&lt;p&gt;Hence, I simply don''t understand how we can obtain : {''None'': 0.125, ''Premium'': 2.25, ''Basic'': 0.125} as 0.125+0.125+2.25 does not sum to one nor even an integer. How was the new observation split?&lt;/p&gt;

&lt;p&gt;The code is here : &lt;a href="https://github.com/arthur-e/Programming-Collective-Intelligence/blob/master/chapter7/treepredict.py" rel="nofollow"&gt;https://github.com/arthur-e/Programming-Collective-Intelligence/blob/master/chapter7/treepredict.py&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Using the original dataset, I obtain the tree shown here: &lt;a href="https://mattscodecave.com/system/uploads/assets/000/000/021/original/tree3.jpg?1398786714" rel="nofollow"&gt;https://mattscodecave.com/system/uploads/assets/000/000/021/original/tree3.jpg?1398786714&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Can anyone please explain me precisely what the numbers precisely mean and how they were exactly obtained ?&lt;/p&gt;

&lt;p&gt;Many thanks for your help !&lt;/p&gt;

&lt;p&gt;PS : The 1st example of the book is wrong as described on their errata page but just explaining the second example (mentioned above) would be nice.&lt;/p&gt;
', 'python', NULL, '''+0.125'':135C ''+2.25'':136C ''/arthur-e/programming-collective-intelligence/blob/master/chapter7/treepredict.py'':162C ''/arthur-e/programming-collective-intelligence/blob/master/chapter7/treepredict.py&lt;/a&gt;&lt;/p&gt;'':167C ''/p'':25C,115C,152C,207C,214C,242C ''/system/uploads/assets/000/000/021/original/tree3.jpg?1398786714'':183C ''/system/uploads/assets/000/000/021/original/tree3.jpg?1398786714&lt;/a&gt;&lt;/p&gt;'':188C ''0.125'':128C,132C,134C ''1'':58C ''1st'':218C ''2.25'':130C ''7'':18C ''anyon'':191C ''basic'':49C,131C ''book'':7A,21C,222C ''branch'':81C,87C,114C ''categori'':75C ''certain'':74C ''chapter'':17C ''code'':155C ''collect'':9A,23C ''confus'':36C ''count'':63C ''current'':15C ''data'':42C ''dataset'':172C ''deal'':39C ''decis'':3A,50C ''describ'':226C ''equal'':90C ''errata'':229C ''even'':143C ''everyth'':52C ''exact'':205C ''exampl'':219C,236C ''explain'':193C,233C ''explan'':44C ''find'':28C ''fit'':71C ''follow'':79C ''fraction'':93C ''fulli'':64C ''function'':33C,38C ''github.com'':161C,166C ''github.com/arthur-e/programming-collective-intelligence/blob/master/chapter7/treepredict.py'':160C ''github.com/arthur-e/programming-collective-intelligence/blob/master/chapter7/treepredict.py&lt;/a&gt;&lt;/p&gt;'':165C ''give'':85C ''help'':213C ''henc'':117C ''href'':159C,180C ''impli'':55C ''instanc'':109C ''instead'':82C ''integ'':145C ''intellig'':10A,24C ''item'':70C ''m'':14C ''mani'':209C ''mattscodecave.com'':182C,187C ''mattscodecave.com/system/uploads/assets/000/000/021/original/tree3.jpg?1398786714'':181C ''mattscodecave.com/system/uploads/assets/000/000/021/original/tree3.jpg?1398786714&lt;/a&gt;&lt;/p&gt;'':186C ''mdclassifi'':34C ''mean'':59C,200C ''mention'':237C ''miss'':41C ''multipl'':80C ''new'':149C ''nice'':241C ''nofollow'':164C,185C ''none'':127C ''number'':198C ''observ'':62C,150C ''obtain'':126C,174C,206C ''one'':141C ''origin'':171C ''output'':30C ''p'':12C,26C,116C,153C,168C,189C,208C,215C ''p.157'':35C ''page'':230C ''pleas'':192C ''precis'':195C,199C ''premium'':129C ''probabl'':67C ''program'':8A,22C ''provid'':45C ''ps'':216C ''python'':11B ''question'':1A ''rel'':163C,184C ''row'':98C ''second'':235C ''shown'':177C ''side'':103C ''simpli'':119C ''split'':112C,151C ''studi'':16C ''sum'':139C ''thank'':210C ''tree'':4A,51C,176C ''understand'':107C,122C ''use'':169C ''weight'':56C,89C ''would'':239C ''wrong'':224C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5615, 1, '2015-04-26 01:38:52.18', 2, 921, 'Gradient Descent Step for word2vec negative sampling', '&lt;p&gt;For word2vec with negative sampling, the cost function for a single word is the following according to &lt;a href="http://www-personal.umich.edu/~ronxin/pdf/w2vexp.pdf" rel="nofollow"&gt;word2vec&lt;/a&gt;:
$$
E = - log(\sigma(v_{w_{O}}^{''}.u_{w_{I}})) - \sum_{k=1}^K log(\sigma(-v_{w_{k}}^{''}.u_{w_{I}}))
$$&lt;/p&gt;

&lt;p&gt;$v_{w_{O}}^{''}$ = hidden-&gt;output word vector of the output word&lt;/p&gt;

&lt;p&gt;$u_{w_{I}}$ = input-&gt;hidden word vector of the output word&lt;/p&gt;

&lt;p&gt;$v_{w_{k}}^{''}$ = hidden-&gt;output word vector of the negative sampled word&lt;/p&gt;

&lt;p&gt;$\sigma$ is the sigmoid function&lt;/p&gt;

&lt;p&gt;And taking the derivative with respect to $v_{w_{O}}^{''}.u_{w_{j}}$ is:&lt;/p&gt;

&lt;p&gt;$
\frac{\partial E}{\partial v_{w_{j}}^{''}.u_{w_{I}}} = \sigma(v_{w_{j}}^{''}.u_{w_{I}}) * (\sigma(v_{w_{j}}^{''}.u_{w_{I}}) - 1)
$ $ if w_j = w_O $&lt;/p&gt;

&lt;p&gt;$
\frac{\partial E}{\partial v_{w_{j}}^{''}.u_{w_{I}}} = \sigma(v_{w_{j}}^{''}.u_{w_{I}}) * \sigma(-v_{w_{j}}^{''}.u_{w_{I}})
$ $ if w_j = w_k \ for \ k = 1...K$&lt;/p&gt;

&lt;p&gt;Then we can use chain rule to get &lt;/p&gt;

&lt;p&gt;$ \frac{\partial E}{\partial v_{w_{j}}^{''}} = \frac{\partial E}{\partial v_{w_{j}}^{''}.u_{w_{I}}} * \frac{\partial v_{w_{j}}^{''}.u_{w_{I}}}{\partial v_{w_{j}}^{''}} $&lt;/p&gt;

&lt;p&gt;Is my reasoning and derivative correct? I am still new to ML so any help would be great! &lt;/p&gt;
', 'gradient-descent  word-embeddings', NULL, '''/a'':40C ''/p'':62C,75C,88C,102C,109C,125C,157C,192C,202C,233C,253C ''/~ronxin/pdf/w2vexp.pdf'':36C ''1'':52C,151C,190C ''accord'':30C ''chain'':198C ''correct'':240C ''cost'':21C ''deriv'':114C,239C ''descent'':2A,10B ''e'':41C,129C,161C,206C,213C ''embed'':13B ''follow'':29C ''frac'':127C,159C,204C,211C,221C ''function'':22C,108C ''get'':201C ''gradient'':1A,9B ''gradient-desc'':8B ''great'':252C ''help'':249C ''hidden'':67C,81C,93C ''href'':33C ''input'':80C ''j'':123C,133C,140C,147C,154C,165C,172C,179C,185C,210C,217C,225C,232C ''k'':51C,53C,58C,92C,187C,189C,191C ''log'':42C,54C ''ml'':246C ''negat'':6A,18C,99C ''new'':244C ''nofollow'':38C ''o'':46C,66C,120C,156C ''output'':68C,73C,86C,94C ''p'':14C,63C,76C,89C,103C,110C,126C,158C,193C,203C,234C ''partial'':128C,130C,160C,162C,205C,207C,212C,214C,222C,229C ''reason'':237C ''rel'':37C ''respect'':116C ''rule'':199C ''sampl'':7A,19C,100C ''sigma'':43C,55C,104C,137C,144C,169C,176C ''sigmoid'':107C ''singl'':25C ''step'':3A ''still'':243C ''sum'':50C ''take'':112C ''u'':47C,59C,77C,121C,134C,141C,148C,166C,173C,180C,218C,226C ''use'':197C ''v'':44C,56C,64C,90C,118C,131C,138C,145C,163C,170C,177C,208C,215C,223C,230C ''vector'':70C,83C,96C ''w'':45C,48C,57C,60C,65C,78C,91C,119C,122C,132C,135C,139C,142C,146C,149C,153C,155C,164C,167C,171C,174C,178C,181C,184C,186C,209C,216C,219C,224C,227C,231C ''word'':12B,26C,69C,74C,82C,87C,95C,101C ''word-embed'':11B ''word2vec'':5A,16C,39C ''would'':250C ''www-personal.umich.edu'':35C ''www-personal.umich.edu/~ronxin/pdf/w2vexp.pdf'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6350, 1, '2015-07-06 10:10:34.6', 3, 107, 'Definition of "inside" in K-means?', '&lt;p&gt;After conducting a cluster analysis using K-means, I have new data coming online that I need to detect anomalies with.  Anomalies are assumed to not be within the clusters.&lt;/p&gt;

&lt;p&gt;So, how is one to define "inside a cluster" in K-means?&lt;/p&gt;
', 'machine-learning  clustering  k-means  anomaly-detection', NULL, '''/p'':50C,65C ''analysi'':23C ''anomali'':16B,39C,41C ''anomaly-detect'':15B ''assum'':43C ''cluster'':11B,22C,49C,60C ''come'':32C ''conduct'':20C ''data'':31C ''defin'':57C ''definit'':1A ''detect'':17B,38C ''insid'':3A,58C ''k'':6A,13B,26C,63C ''k-mean'':5A,12B,25C,62C ''learn'':10B ''machin'':9B ''machine-learn'':8B ''mean'':7A,14B,27C,64C ''need'':36C ''new'':30C ''one'':55C ''onlin'':33C ''p'':18C,51C ''use'':24C ''within'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6718, 2, '2015-08-07 00:22:14.48', 8, NULL, NULL, '&lt;p&gt;Normalization is not always required, but it rarely hurts. &lt;/p&gt;

&lt;p&gt;Some examples:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stats.stackexchange.com/a/21226/12359"&gt;K-means&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;K-means clustering is "isotropic" in all directions of space and
  therefore tends to produce more or less round (rather than elongated)
  clusters. In this situation leaving variances unequal is equivalent to
  putting more weight on variables with smaller variance.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Example in Matlab:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;X = [randn(100,2)+ones(100,2);...
     randn(100,2)-ones(100,2)];

% Introduce denormalization
% X(:, 2) = X(:, 2) * 1000 + 500;

opts = statset(''Display'',''final'');

[idx,ctrs] = kmeans(X,2,...
                    ''Distance'',''city'',...
                    ''Replicates'',5,...
                    ''Options'',opts);

plot(X(idx==1,1),X(idx==1,2),''r.'',''MarkerSize'',12)
hold on
plot(X(idx==2,1),X(idx==2,2),''b.'',''MarkerSize'',12)
plot(ctrs(:,1),ctrs(:,2),''kx'',...
     ''MarkerSize'',12,''LineWidth'',2)
plot(ctrs(:,1),ctrs(:,2),''ko'',...
     ''MarkerSize'',12,''LineWidth'',2)
legend(''Cluster 1'',''Cluster 2'',''Centroids'',...
       ''Location'',''NW'')
title(''K-means with normalization'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/N2unM.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/e5G2M.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;(FYI: &lt;a href="https://www.quora.com/Machine-Learning/How-can-I-detect-if-my-dataset-is-clustered-or-unclustered-i-e-forming-one-single-cluster/answer/Franck-Dernoncourt"&gt;How can I detect if my dataset is clustered or unclustered (i.e. forming one single cluster&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.medwelljournals.com/fulltext/?doi=ijscomp.2009.168.172"&gt;Distributed clustering&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The comparative analysis shows that the distributed clustering results
  depend on the type of normalization procedure.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://stackoverflow.com/a/4674770/395857"&gt;Artificial neural network (inputs)&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;If the input variables are combined linearly, as in an MLP, then it is
  rarely strictly necessary to standardize the inputs, at least in
  theory. The reason is that any rescaling of an input vector can be
  effectively undone by changing the corresponding weights and biases,
  leaving you with the exact same outputs as you had before. However,
  there are a variety of practical reasons why standardizing the inputs
  can make training faster and reduce the chances of getting stuck in
  local optima. Also, weight decay and Bayesian estimation can be done
  more conveniently with standardized inputs.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://www.faqs.org/faqs/ai-faq/neural-nets/part2/section-16.html"&gt;Artificial neural network (inputs/outputs)&lt;/a&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Should you do any of these things to your data? The answer is, it
  depends.&lt;/p&gt;
  
  &lt;p&gt;Standardizing either input or target variables tends to make the training
  process better behaved by improving the numerical condition (see 
  &lt;a href="ftp://ftp.sas.com/pub/neural/illcond/illcond.html"&gt;ftp://ftp.sas.com/pub/neural/illcond/illcond.html&lt;/a&gt;) of the optimization
  problem and ensuring that various default values involved in
  initialization and termination are appropriate. Standardizing targets
  can also affect the objective function. &lt;/p&gt;
  
  &lt;p&gt;Standardization of cases should be approached with caution because it
  discards information. If that information is irrelevant, then
  standardizing cases can be quite helpful. If that information is
  important, then standardizing cases can be disastrous.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;hr&gt;

&lt;p&gt;Interestingly, changing the measurement units may even lead one to see a very different clustering structure: &lt;a href="http://rads.stackoverflow.com/amzn/click/0471735787"&gt;Kaufman, Leonard, and Peter J. Rousseeuw.. "Finding groups in data: An introduction to cluster analysis." (2005).&lt;/a&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;In some applications, changing the measurement units may even lead one
  to see a very different clustering structure. For example, the age (in
  years) and height (in centimeters) of four imaginary people are given
  in Table 3 and plotted in Figure 3. It appears that {A, B ) and { C,
  0) are two well-separated clusters. On the other hand, when height is
  expressed in feet one obtains Table 4 and Figure 4, where the obvious
  clusters are now {A, C} and { B, D}. This partition is completely
  different from the first because each subject has received another
  companion. (Figure 4 would have been flattened even more if age had
  been measured in days.)&lt;/p&gt;
  
  &lt;p&gt;To avoid this dependence on the choice of measurement units, one has
  the option of  standardizing the data. This converts the original
  measurements to unitless variables.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Ppu5Y.png" alt="enter image description here"&gt;
&lt;img src="http://i.stack.imgur.com/zW6rI.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0471735787"&gt;Kaufman et al.&lt;/a&gt; continues with some interesting considerations (page 11):&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;From a philosophical point of view, standardization does not really
  solve the problem. Indeed, the choice of measurement units gives rise
  to relative weights of the variables. Expressing a variable in smaller
  units will lead to a larger range for that variable, which will then
  have a large effect on the resulting structure. On the other hand, by
  standardizing one attempts to give all variables an equal weight, in
  the hope of achieving objectivity. As such, it may be used by a
  practitioner who possesses no prior knowledge. However, it may well be
  that some variables are intrinsically more important than others in a
  particular application, and then the assignment of weights should be
  based on subject-matter knowledge (see, e.g., Abrahamowicz, 1985). On
  the other hand, there have been attempts to devise clustering
  techniques that are independent of the scale of the variables
  (Friedman and Rubin, 1967). The proposal of Hardy and Rasson (1982) is
  to search for a partition that minimizes the total volume of the
  convex hulls of the clusters. In principle such a method is invariant
  with respect to linear transformations of the data, but unfortunately
  no algorithm exists for its implementation (except for an
  approximation that is restricted to two dimensions). Therefore, the
  dilemma of standardization appears unavoidable at present and the
  programs described in this book leave the choice up to the user.&lt;/p&gt;
&lt;/blockquote&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5607, 2, '2015-04-25 05:50:05.257', 1, NULL, NULL, '&lt;p&gt;I would go for dimensionality reduction. You can start with SVD (should be available in Weka). If SVD is too slow / too memory consuming, then there are still some options:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;CUR-decomposition: a variant of singular-value decomposition that keeps the matrices of the decomposition sparse if the original matrix is sparse (see: this &lt;a href="http://infolab.stanford.edu/~ullman/mmds/ch11.pdf" rel="nofollow"&gt;chapter&lt;/a&gt; of Mining Massive Datasets book)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Random projections: projecting the data onto a random lower-dimensional subspace (see: the &lt;a href="http://people.inf.elte.hu/fekete/algoritmusok_msc/dimenzio_csokkentes/randon_projection_kdd.pdf" rel="nofollow"&gt;Random projection in dimensionality reduction: Applications to image and text data&lt;/a&gt; paper)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Coresets: Given a matrix A, a coreset C is defined as a weighted subset of rows of A such that the sum of squared distances from any given k-dimensional subspace to the rows of A is approximately the same as the sum of squared weighted distances to the rows in C &lt;a href="http://arxiv.org/pdf/1503.01663v1.pdf" rel="nofollow"&gt;see the Dimensionality Reduction of Massive Sparse Datasets Using Coresets&lt;/a&gt; paper)&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;That''s the tip of an iceberg. More approaches are there in the wild. The problem is that I doubt any of these solutions come with Weka (please, correct me if I am wrong on this). I would search for a usable Java implementation of any of these algorithms and try to port it to work with Weka''s arff files.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5609, 1, '2015-04-25 17:35:30.843', 4, 101, 'Algorithm to find common sequence', '&lt;p&gt;Assume that "1,2,3" are the ids of users, active means that person visited the stackoverflow in last one month (0=passive, 1=active), and there are positive and negative votes.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;id  question       votes                 active
 1     1        -1, +1, -1, -1, -1         0
 1     2        -1, +1, -1, -1, +1         0
 2     1        +1, +1, -1, -1             0
 3     1        +1, +1, +1, -1, +1         1
 3     2        +1, +1, -1, +1, +1, +1     1
 3     3        -1, +1                     1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to know what makes the users stop using stackoverflow. Think that, I have already calculate the how many times did they get negative votes, total vote, average vote for each question...&lt;/p&gt;

&lt;p&gt;I wonder what kind of information could I get from these sequences. I want to find something like this: these users who are passive have two negative votes sequentially. For example, one positive vote after two negative votes in the second question of user 1, doesn''t prevent the user churn. User 3 doesn''t have any 2 negative votes sequentially in any of his questions. Hence he is still active.&lt;/p&gt;

&lt;p&gt;I''m looking for something like PrefixSpan Algorithm but order is important for me. I mean, I can''t write the sequences like &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;(-1 +1 -1 -1 -1) (-1 +1 -1 -1 +1 )&amp;gt; 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;or &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;(-1) (+1) (-1) (-1) (-1) (-1) (+1) (-1) (-1) (+1 )&amp;gt;. 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Because the first one loses the order, and the second one jumbled the questions together. Is there any algorithm to find these sequences which is common for churners?&lt;/p&gt;
', 'algorithms', NULL, '''+1'':50C,58C,61C,65C,66C,72C,73C,74C,76C,80C,81C,83C,84C,85C,90C,231C,236C,239C,250C,255C,258C ''-1'':49C,51C,52C,53C,57C,59C,60C,67C,68C,75C,82C,89C,230C,232C,233C,234C,235C,237C,238C,249C,251C,252C,253C,254C,256C,257C ''/code'':92C,241C,260C ''/p'':40C,128C,201C,226C,245C,291C ''/pre'':93C,242C,261C ''0'':29C,54C,62C,69C ''1'':10C,31C,47C,48C,55C,64C,71C,77C,86C,91C,174C ''2'':11C,56C,63C,79C,187C ''3'':12C,70C,78C,87C,88C,182C ''activ'':18C,32C,46C,200C ''algorithm'':1A,6B,210C,281C ''alreadi'':110C ''assum'':8C ''averag'':123C ''calcul'':111C ''churn'':180C ''churner'':290C ''code'':42C,228C,247C ''common'':4A,288C ''could'':136C ''doesn'':175C,183C ''exampl'':160C ''find'':3A,145C,283C ''first'':265C ''get'':118C,138C ''gt'':240C,259C ''henc'':196C ''id'':15C,43C ''import'':214C ''inform'':135C ''jumbl'':274C ''kind'':133C ''know'':98C ''last'':26C ''like'':147C,208C,225C ''look'':205C ''lose'':267C ''lt'':229C,248C ''m'':204C ''make'':100C ''mani'':114C ''mean'':19C,218C ''month'':28C ''negat'':38C,119C,156C,166C,188C ''one'':27C,161C,266C,273C ''order'':212C,269C ''p'':7C,94C,129C,202C,243C,262C ''passiv'':30C,153C ''person'':21C ''posit'':36C,162C ''pre'':41C,227C,246C ''prefixspan'':209C ''prevent'':177C ''question'':44C,127C,171C,195C,276C ''second'':170C,272C ''sequenc'':5A,141C,224C,285C ''sequenti'':158C,190C ''someth'':146C,207C ''stackoverflow'':24C,105C ''still'':199C ''stop'':103C ''think'':106C ''time'':115C ''togeth'':277C ''total'':121C ''two'':155C,165C ''use'':104C ''user'':17C,102C,150C,173C,179C,181C ''visit'':22C ''vote'':39C,45C,120C,122C,124C,157C,163C,167C,189C ''want'':96C,143C ''wonder'':131C ''write'':222C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5611, 1, '2015-04-25 19:43:00.023', 0, 62, 'Tidying Time Intervals for Plotting a Histogram in R', '&lt;p&gt;I''m doing some cluster analysis on the &lt;code&gt;MLTobs&lt;/code&gt; from the &lt;code&gt;LifeTables&lt;/code&gt; package and have come across a tricky problem plotting frequency of the &lt;code&gt;Year&lt;/code&gt; variable in the &lt;code&gt;mlt.mx.info&lt;/code&gt; dataframe. &lt;code&gt;Year&lt;/code&gt; contains the period that the life table was measured over, in intervals. Here''s a table of the data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    1751-1754 1755-1759 1760-1764 1765-1769 1770-1774 1775-1779 1780-1784 1785-1789 1790-1794 
        1         1         1         1         1         1         1         1         1 
1795-1799 1800-1804 1805-1809 1810-1814 1815-1819 1816-1819 1820-1824 1825-1829 1830-1834 
        1         1         1         1         1         2         3         3         3 
1835-1839 1838-1839 1840-1844 1841-1844 1845-1849 1846-1849 1850-1854 1855-1859 1860-1864 
        4         1         5         3         8         1        10        11        11 
1865-1869 1870-1874 1872-1874 1875-1879 1876-1879 1878-1879 1880-1884 1885-1889 1890-1894 
       11        11         1        12         2         1        15        15        15 
1895-1899 1900-1904 1905-1909 1908-1909 1910-1914 1915-1919 1920-1924 1921-1924 1922-1924 
       15        15        15         1        16        16        16         2         1 
1925-1929 1930-1934 1933-1934 1935-1939 1937-1939 1940-1944 1945-1949 1947-1949 1948-1949 
       19        19         1        20         1        22        22         3         1 
1950-1954 1955-1959 1956-1959 1958-1959 1960-1964 1965-1969 1970-1974 1975-1979 1980-1984 
       30        30         2         1        40        40        41        41        41 
1983-1984 1985-1989 1990-1994 1991-1994 1992-1994 1995-1999 2000-2003 2000-2004 2005-2006 
        1        42        42         1         1        44         3        41        22 
2005-2007 
       14 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As you can see, some of the intervals sit within other intervals. Thankfully none of them overlap. I want to simplify the intervals so intervals such as &lt;code&gt;1992-1994&lt;/code&gt; and &lt;code&gt;1991-1994&lt;/code&gt; all go into &lt;code&gt;1990-1994&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;An idea might be to get the modulo of each interval and sort them into their new intervals that way but I''m unsure how to do this with the interval data type. If anyone has any ideas I''d really appreciate the help. Ultimately I want to create a histogram or barplot to illustrate the nicely.&lt;/p&gt;
', 'data-mining  r  clustering  data-cleaning  visualization', NULL, '''-1754'':84C ''-1759'':86C ''-1764'':88C ''-1769'':90C ''-1774'':92C ''-1779'':94C ''-1784'':96C ''-1789'':98C ''-1794'':100C ''-1799'':111C ''-1804'':113C ''-1809'':115C ''-1814'':117C ''-1819'':119C,121C ''-1824'':123C ''-1829'':125C ''-1834'':127C ''-1839'':138C,140C ''-1844'':142C,144C ''-1849'':146C,148C ''-1854'':150C ''-1859'':152C ''-1864'':154C ''-1869'':165C ''-1874'':167C,169C ''-1879'':171C,173C,175C ''-1884'':177C ''-1889'':179C ''-1894'':181C ''-1899'':192C ''-1904'':194C ''-1909'':196C,198C ''-1914'':200C ''-1919'':202C ''-1924'':204C,206C,208C ''-1929'':219C ''-1934'':221C,223C ''-1939'':225C,227C ''-1944'':229C ''-1949'':231C,233C,235C ''-1954'':246C ''-1959'':248C,250C,252C ''-1964'':254C ''-1969'':256C ''-1974'':258C ''-1979'':260C ''-1984'':262C,273C ''-1989'':275C ''-1994'':277C,279C,281C,334C,339C,346C ''-1999'':283C ''-2003'':285C ''-2004'':287C ''-2006'':289C ''-2007'':300C ''/code'':30C,35C,50C,56C,60C,302C,335C,340C,347C ''/p'':80C,348C,407C ''/pre'':303C ''1'':101C,102C,103C,104C,105C,106C,107C,108C,109C,128C,129C,130C,131C,132C,156C,160C,184C,187C,212C,217C,238C,240C,244C,266C,290C,293C,294C ''10'':161C ''11'':162C,163C,182C,183C ''12'':185C ''14'':301C ''15'':188C,189C,190C,209C,210C,211C ''16'':213C,214C,215C ''1751'':83C ''1755'':85C ''1760'':87C ''1765'':89C ''1770'':91C ''1775'':93C ''1780'':95C ''1785'':97C ''1790'':99C ''1795'':110C ''1800'':112C ''1805'':114C ''1810'':116C ''1815'':118C ''1816'':120C ''1820'':122C ''1825'':124C ''1830'':126C ''1835'':137C ''1838'':139C ''1840'':141C ''1841'':143C ''1845'':145C ''1846'':147C ''1850'':149C ''1855'':151C ''1860'':153C ''1865'':164C ''1870'':166C ''1872'':168C ''1875'':170C ''1876'':172C ''1878'':174C ''1880'':176C ''1885'':178C ''1890'':180C ''1895'':191C ''19'':236C,237C ''1900'':193C ''1905'':195C ''1908'':197C ''1910'':199C ''1915'':201C ''1920'':203C ''1921'':205C ''1922'':207C ''1925'':218C ''1930'':220C ''1933'':222C ''1935'':224C ''1937'':226C ''1940'':228C ''1945'':230C ''1947'':232C ''1948'':234C ''1950'':245C ''1955'':247C ''1956'':249C ''1958'':251C ''1960'':253C ''1965'':255C ''1970'':257C ''1975'':259C ''1980'':261C ''1983'':272C ''1985'':274C ''1990'':276C,345C ''1991'':278C,338C ''1992'':280C,333C ''1995'':282C ''2'':133C,186C,216C,265C ''20'':239C ''2000'':284C,286C ''2005'':288C,299C ''22'':241C,242C,298C ''3'':134C,135C,136C,158C,243C,296C ''30'':263C,264C ''4'':155C ''40'':267C,268C ''41'':269C,270C,271C,297C ''42'':291C,292C ''44'':295C ''5'':157C ''8'':159C ''across'':40C ''analysi'':25C ''anyon'':384C ''appreci'':391C ''barplot'':402C ''clean'':17B ''cluster'':14B,24C ''code'':28C,33C,48C,54C,58C,82C,332C,337C,344C ''come'':39C ''contain'':61C ''creat'':398C ''d'':389C ''data'':11B,16B,79C,381C ''data-clean'':15B ''data-min'':10B ''datafram'':57C ''frequenc'':45C ''get'':355C ''go'':342C ''help'':393C ''histogram'':7A,400C ''idea'':351C,387C ''illustr'':404C ''interv'':3A,72C,312C,316C,327C,329C,360C,367C,380C ''life'':66C ''lifet'':34C ''m'':21C,372C ''measur'':69C ''might'':352C ''mine'':12B ''mlt.mx.info'':55C ''mltob'':29C ''modulo'':357C ''new'':366C ''nice'':406C ''none'':318C ''overlap'':321C ''p'':19C,304C,349C ''packag'':36C ''period'':63C ''plot'':5A,44C ''pre'':81C ''problem'':43C ''r'':9A,13B ''realli'':390C ''see'':308C ''simplifi'':325C ''sit'':313C ''sort'':362C ''tabl'':67C,76C ''thank'':317C ''tidi'':1A ''time'':2A ''tricki'':42C ''type'':382C ''ultim'':394C ''unsur'':373C ''variabl'':51C ''visual'':18B ''want'':323C,396C ''way'':369C ''within'':314C ''year'':49C,59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5612, 2, '2015-04-25 19:48:05.637', 0, NULL, NULL, '&lt;p&gt;The &lt;a href="https://archive.ics.uci.edu/ml/datasets.html" rel="nofollow"&gt;UCI Machine Learning repo&lt;/a&gt; is my go to place to find data sets to work on across a really broad range of topics. The data sets are already cleaned and tagged with the task they were originally purposed for. If you google the data set you''ll often find papers citing them or forums with questions that you may have yourself. There''s a number of text based classification tasked data sets in there.&lt;/p&gt;
', NULL, NULL, '''/a'':14C ''/ml/datasets.html'':7C ''/p'':85C ''across'':27C ''alreadi'':38C ''archive.ics.uci.edu'':6C ''archive.ics.uci.edu/ml/datasets.html'':5C ''base'':78C ''broad'':30C ''cite'':61C ''classif'':79C ''clean'':39C ''data'':22C,35C,54C,81C ''find'':21C,59C ''forum'':64C ''go'':17C ''googl'':52C ''href'':4C ''learn'':12C ''ll'':57C ''machin'':11C ''may'':69C ''nofollow'':9C ''number'':75C ''often'':58C ''origin'':47C ''p'':1C ''paper'':60C ''place'':19C ''purpos'':48C ''question'':66C ''rang'':31C ''realli'':29C ''rel'':8C ''repo'':13C ''set'':23C,36C,55C,82C ''tag'':41C ''task'':44C,80C ''text'':77C ''topic'':33C ''uci'':10C ''work'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5614, 2, '2015-04-25 23:46:19.727', 1, NULL, NULL, '&lt;p&gt;This was a x-post. There is a great solution over &lt;a href="http://stackoverflow.com/questions/29869565/tidying-time-intervals-for-plotting-histogram-in-r/29870642#29870642"&gt;here&lt;/a&gt;, where I originally asked the question.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;If I understand your problem, you''ll want something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;bottom &amp;lt;- seq(1750, 2010, 5)
library(dplyr)
new_df &amp;lt;- mlt.mx.info %&amp;gt;%
  arrange(Year) %&amp;gt;%
  mutate(year2 = as.numeric(substr(Year, 6, 9))) %&amp;gt;%
  mutate(new_year = paste0(bottom[findInterval(year2, bottom)], "-",(bottom[findInterval(year2, bottom) + 1] - 1)))
View(new_df)
&lt;/code&gt;&lt;/pre&gt;
  
  &lt;p&gt;So what this does, it creates bins, and outputs a new column
  (new_year) that is the bottom of the bin. So everything from 1750-1754
  will correspond to a new value of 1750-1754 (in string form; the
  original is an integer type, not sure how to fix that). Does this do
  what you want? Double check the results, but it looks right to me.
  @goodtimeslim&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''-1754'':113C,122C ''/a'':20C ''/blockquote'':156C ''/code'':85C ''/p'':27C,41C,155C ''/pre'':86C ''/questions/29869565/tidying-time-intervals-for-plotting-histogram-in-r/29870642#29870642'':18C ''1'':80C,81C ''1750'':47C,112C,121C ''2010'':48C ''5'':49C ''6'':65C ''9'':66C ''arrang'':57C ''as.numeric'':62C ''ask'':24C ''bin'':94C,108C ''blockquot'':28C ''bottom'':44C,72C,75C,76C,79C,105C ''check'':145C ''code'':43C ''column'':99C ''correspond'':115C ''creat'':93C ''df'':53C,84C ''doubl'':144C ''dplyr'':51C ''everyth'':110C ''findinterv'':73C,77C ''fix'':136C ''form'':125C ''goodtimeslim'':154C ''great'':11C ''gt'':56C,59C,67C ''href'':15C ''integ'':130C ''librari'':50C ''like'':39C ''ll'':36C ''look'':150C ''lt'':45C,54C ''mlt.mx.info'':55C ''mutat'':60C,68C ''new'':52C,69C,83C,98C,100C,118C ''origin'':23C,127C ''output'':96C ''p'':1C,29C,87C ''paste0'':71C ''post'':7C ''pre'':42C ''problem'':34C ''question'':26C ''result'':147C ''right'':151C ''seq'':46C ''solut'':12C ''someth'':38C ''stackoverflow.com'':17C ''stackoverflow.com/questions/29869565/tidying-time-intervals-for-plotting-histogram-in-r/29870642#29870642'':16C ''string'':124C ''substr'':63C ''sure'':133C ''type'':131C ''understand'':32C ''valu'':119C ''view'':82C ''want'':37C,143C ''x'':6C ''x-post'':5C ''year'':58C,64C,70C,101C ''year2'':61C,74C,78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5616, 2, '2015-04-26 06:04:19.777', 1, NULL, NULL, '&lt;p&gt;There are four features:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;referer,&lt;/li&gt;
&lt;li&gt;location,&lt;/li&gt;
&lt;li&gt;FAQ,&lt;/li&gt;
&lt;li&gt;pages.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In your case, you''re trying to classify an instance where &lt;code&gt;FAQ&lt;/code&gt; and &lt;code&gt;pages&lt;/code&gt; are unknown: &lt;code&gt;mdclassify([''google'',''France'',None,None], tree)&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Since the first known attribute is &lt;code&gt;google&lt;/code&gt;, in your decision tree you''re only interested in the edge that comes out of &lt;code&gt;google&lt;/code&gt; node on the right-hand side.&lt;/p&gt;

&lt;p&gt;There are five instances: three labeled &lt;code&gt;Premium&lt;/code&gt;, one labeled &lt;code&gt;Basic&lt;/code&gt; and one labeled &lt;code&gt;None&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Instances with labels &lt;code&gt;Basic&lt;/code&gt; and &lt;code&gt;None&lt;/code&gt; split on the &lt;code&gt;FAQ&lt;/code&gt; attribute. There''s two of them, so the weight for both of them is &lt;code&gt;0.5&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Now, we split on the &lt;code&gt;pages&lt;/code&gt; attribute. There are 3 instances with &lt;code&gt;pages&lt;/code&gt; value larger than 20, and two with &lt;code&gt;pages&lt;/code&gt; value no larger than 20.&lt;/p&gt;

&lt;p&gt;Here''s the trick: we already know that the weights for two of these were altered from &lt;code&gt;1&lt;/code&gt; to &lt;code&gt;0.5&lt;/code&gt; each. So, now we have three instances weighted &lt;code&gt;1&lt;/code&gt; each, and 2 instances weighted &lt;code&gt;0.5&lt;/code&gt; each. So the total value is &lt;code&gt;4&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Now, we can count the weights for &lt;code&gt;pages&lt;/code&gt; attribute:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;pages_larger_than_20 = 3/4&lt;/li&gt;
&lt;li&gt;pages_not_larger_than_20 = 1/4 # the 1 is: 0.5 + 0.5&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;All weights are ascribed. Now we can multiply the weights by the "frequencies" of instances (remembering that for &lt;code&gt;Basic&lt;/code&gt; and &lt;code&gt;None&lt;/code&gt; the "frequency" is now &lt;code&gt;0.5&lt;/code&gt;):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;Premium&lt;/code&gt;: &lt;code&gt;3 * 3/4 = 2.25&lt;/code&gt; # because there are three &lt;code&gt;Premium&lt;/code&gt; instances, each weighting &lt;code&gt;0.75&lt;/code&gt;;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;Basic&lt;/code&gt;: &lt;code&gt;0.5 * 1/4 = 0.125&lt;/code&gt; # because &lt;code&gt;Basic&lt;/code&gt; is now &lt;code&gt;0.5&lt;/code&gt;, and the split on
&lt;code&gt;pages_not_larger_than_20&lt;/code&gt; is &lt;code&gt;1/4&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;None&lt;/code&gt;: &lt;code&gt;0.5 * 1/4 = 0.125&lt;/code&gt; # analogously&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;That''s at least where the numbers come from. I share your doubts about the maximum value of this metric, and whether it should sum to 1, but now that you know where these numbers come from you can think how to normalize them.&lt;/p&gt;
', NULL, NULL, '''/code'':35C,39C,49C,60C,78C,96C,101C,107C,115C,119C,125C,142C,152C,161C,171C,198C,202C,213C,221C,230C,242C,288C,292C,299C,305C,310C,317C,323C,328C,333C,337C,342C,353C,357C,362C,367C ''/li'':10C,13C,16C,19C,252C,265C,324C,358C,369C ''/p'':6C,50C,86C,108C,143C,177C,231C,244C,300C,416C ''/ul'':20C,266C,370C ''0.125'':332C,366C ''0.5'':141C,201C,220C,263C,264C,298C,330C,341C,364C ''0.75'':322C ''1'':197C,212C,261C,398C ''1/4'':259C,331C,356C,365C ''2'':216C ''2.25'':309C ''20'':165C,176C,250C,258C,352C ''3'':156C,307C ''3/4'':251C,308C ''4'':229C ''alreadi'':184C ''alter'':194C ''analog'':368C ''ascrib'':271C ''attribut'':56C,126C,153C,243C ''basic'':100C,114C,287C,327C,336C ''case'':24C ''classifi'':29C ''code'':33C,37C,42C,58C,76C,94C,99C,105C,113C,117C,123C,140C,150C,159C,169C,196C,200C,211C,219C,228C,240C,286C,290C,297C,303C,306C,315C,321C,326C,329C,335C,340C,347C,355C,360C,363C ''come'':73C,379C,407C ''count'':236C ''decis'':63C ''doubt'':384C ''edg'':71C ''faq'':15C,34C,124C ''featur'':5C ''first'':54C ''five'':90C ''four'':4C ''franc'':45C ''frequenc'':280C,294C ''googl'':44C,59C,77C ''hand'':84C ''instanc'':31C,91C,110C,157C,209C,217C,282C,318C ''interest'':68C ''know'':185C,403C ''known'':55C ''label'':93C,98C,104C,112C ''larger'':163C,174C,248C,256C,350C ''least'':375C ''li'':8C,11C,14C,17C,246C,253C,302C,325C,359C ''locat'':12C ''maximum'':387C ''mdclassifi'':43C ''metric'':391C ''multipli'':275C ''node'':79C ''none'':46C,47C,106C,118C,291C,361C ''normal'':414C ''number'':378C,406C ''one'':97C,103C ''p'':1C,21C,51C,87C,109C,144C,178C,232C,267C,371C ''page'':18C,38C,151C,160C,170C,241C,247C,254C,348C ''premium'':95C,304C,316C ''re'':26C,66C ''refer'':9C ''rememb'':283C ''right'':83C ''right-hand'':82C ''share'':382C ''side'':85C ''sinc'':52C ''split'':120C,147C,345C ''sum'':396C ''think'':411C ''three'':92C,208C,314C ''total'':225C ''tree'':48C,64C ''tri'':27C ''trick'':182C ''two'':129C,167C,190C ''ul'':7C,245C,301C ''unknown'':41C ''valu'':162C,172C,226C,388C ''weight'':134C,188C,210C,218C,238C,269C,277C,320C ''whether'':393C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5619, 1, '2015-04-27 06:43:09.423', 0, 45, 'Multiple classes DBN network with theano', '&lt;p&gt;I am planning to do scene recognition with point cloud data sets. These are generally (x,y,z) pairs points and a set of points represents an object. I have all the points labelled according to their class. I have previously implemented a network that takes a whole point cloud and a single label for it for training. How can i modify my network to use it with multiple labels i.e. a point cloud example which has 2 or more labels for it.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;self.logLayer = LogisticRegression(
        input=self.sigmoid_layers[-1].output,
        n_in=hidden_layers_sizes[-1],
        n_out=n_outs)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;previously n_outs = 10 and a cloud would belong to only 1 of the 10 labels. Now i still have 10 labels but a cloud will belong to multiple classes.&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''-1'':103C,110C ''/code'':115C ''/p'':95C,148C ''/pre'':116C ''1'':129C ''10'':121C,132C,138C ''2'':89C ''accord'':46C ''belong'':126C,144C ''class'':2A,49C,147C ''cloud'':21C,61C,85C,124C,142C ''code'':97C ''data'':22C ''dbn'':3A ''exampl'':86C ''general'':26C ''hidden'':107C ''i.e'':82C ''implement'':53C ''input'':100C ''label'':45C,65C,81C,92C,133C,139C ''layer'':102C,108C ''learn'':9B ''logisticregress'':99C ''machin'':8B ''machine-learn'':7B ''modifi'':73C ''multipl'':1A,80C,146C ''n'':105C,111C,113C,119C ''network'':4A,55C,75C ''neuralnetwork'':10B ''object'':39C ''out'':114C,120C ''output'':104C ''p'':11C,117C ''pair'':30C ''plan'':14C ''point'':20C,31C,36C,44C,60C,84C ''pre'':96C ''previous'':52C,118C ''recognit'':18C ''repres'':37C ''scene'':17C ''self.loglayer'':98C ''self.sigmoid'':101C ''set'':23C,34C ''singl'':64C ''size'':109C ''still'':136C ''take'':57C ''theano'':6A ''train'':69C ''use'':77C ''whole'':59C ''would'':125C ''x'':27C ''y'':28C ''z'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5621, 2, '2015-04-27 07:26:25.893', 1, NULL, NULL, '&lt;p&gt;Use Event as Row Label, Time as Column label and Activity as Value in a Pivot.
If you''re doing this in R, use library(reshape). Using melt and cast functions, you can get a solution to your problem i guess. Let me know if you need any further clarifications.&lt;/p&gt;
', NULL, NULL, '''/p'':52C ''activ'':12C ''cast'':31C ''clarif'':51C ''column'':9C ''event'':3C ''function'':32C ''get'':35C ''guess'':42C ''know'':45C ''label'':6C,10C ''let'':43C ''librari'':26C ''melt'':29C ''need'':48C ''p'':1C ''pivot'':17C ''problem'':40C ''r'':24C ''re'':20C ''reshap'':27C ''row'':5C ''solut'':37C ''time'':7C ''use'':2C,25C,28C ''valu'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5627, 2, '2015-04-27 13:42:48.057', 2, NULL, NULL, '&lt;p&gt;You have trained a model to recognize or discriminate between several particular classes. So when having a new test sample (that according to your knowledge) does not belong to any of this class, the model will fit it to the most similar class. &lt;/p&gt;

&lt;p&gt;This is, off course, a very general way of putting it. The behavior and things to consider wether you are using a probabilistic graphical model (like Naive Bayes), linear or non linear classifiers, etc, will vary. However, the principle is the same: the model has learnt the relation among the features of the training data you have used to match particular classes.&lt;/p&gt;

&lt;p&gt;More specifically, if you are using a probabilistic approach you can  use the probability of belonging to a class and define a threshold. So if a probability higher than certain value (let''s say 65%) is not given, then your confidence on the result will be low, and you might say "I cannot say wether it belongs to any of this 5 classes with certainty".&lt;/p&gt;

&lt;p&gt;Other non probabilistic methods have interesting approaches for scoring a new example in terms of probabilities. Check this link for SVM (&lt;a href="http://stats.stackexchange.com/questions/55072/svm-confidence-according-to-distance-from-hyperline"&gt;http://stats.stackexchange.com/questions/55072/svm-confidence-according-to-distance-from-hyperline&lt;/a&gt;), which is actually the approach used in Python scikit-learn: &lt;a href="http://scikit-learn.org/stable/modules/svm.html#scores-and-probabilities" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/svm.html#scores-and-probabilities&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you use Random Forests you could use the voting of each tree to define a rate of confidence in a similar way (&lt;a href="http://stats.stackexchange.com/questions/94845/how-to-estimate-confidence-level-for-svm-or-random-forest"&gt;http://stats.stackexchange.com/questions/94845/how-to-estimate-confidence-level-for-svm-or-random-forest&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;A similar thing can be done with K-Nearest-Neighbors by analyzing the distance of the K or J (where J &amp;lt; K) nearest neighbors and just trust the result if they are close to your test sample in some ratio (very generally said, this can be done in many ways).&lt;/p&gt;

&lt;p&gt;Now, methods based on deep learning are trying to learn features in an unsupervised way, so your problem could solved in a more interesting way. However, it would not be strict classification anymore, nor you would have the amount of data and servers required to even try it (but just saying :) )&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5628, 2, '2015-04-27 17:46:32.68', 3, NULL, NULL, '&lt;p&gt;Not a typical problem formulation indeed. I suggest two approaches:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Use a simple bayesian model where the states or nodes are each of your features. Connect the nodes directly when they haven a direct time dependency (for example vote1 -&gt; vote2 -&gt; vote3, but not vote3 -&gt; vote1) and calculate the probability of a final (output) node to be something (for example user type 1, user type 2, etc). You could also use a hidden markov model that naturally models transitions between states. In your case it would output just 1 state (the prediction). I am not very fan of this model for your problem, but it could be nice to try. You will need a lot of data though.&lt;/li&gt;
&lt;li&gt;You could use Fuzzy Inductive Reasoning (FIR). It is definitely not a simple algorithm, but works quite well for classification having time series or time-related features. In FIR you want to find an optimal mask of a given depth. 
If you have, say, 5 features per sample, and for instance you want to know which features to select you will learn an optimal mask for your data that address this (and creates fuzzy rules, etc...). Also if you want to relate two or more examples you will define a depth higher than 1 for the mask. This will allow you to find a mask that for instance uses features 1,2 and 4 of sample t, features 2,3 ,4 and 5 of sample t+1 and features 1 and 4 of sample t+2. Moving t from 1 to N number of samples. So, interesting combinations of features from samples that have a time-relation (or some sort of sequence) are created.&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''+1'':251C ''+2'':260C ''/li'':122C,290C ''/ol'':291C ''/p'':12C ''1'':66C,92C,218C,235C,254C,264C ''2'':69C,236C,243C ''3'':244C ''4'':238C,245C,256C ''5'':169C,247C ''address'':194C ''algorithm'':137C ''allow'':224C ''also'':73C,201C ''approach'':11C ''bayesian'':18C ''calcul'':51C ''case'':87C ''classif'':143C ''combin'':272C ''connect'':30C ''could'':72C,109C,125C ''creat'':197C,289C ''data'':120C,192C ''defin'':213C ''definit'':133C ''depend'':40C ''depth'':164C,215C ''direct'':33C,38C ''etc'':70C,200C ''exampl'':42C,63C,210C ''fan'':100C ''featur'':29C,151C,170C,181C,234C,242C,253C,274C ''final'':56C ''find'':157C,227C ''fir'':130C,153C ''formul'':6C ''fuzzi'':127C,198C ''given'':163C ''haven'':36C ''hidden'':76C ''higher'':216C ''inde'':7C ''induct'':128C ''instanc'':175C,232C ''interest'':271C ''know'':179C ''learn'':186C ''li'':14C,123C ''lot'':118C ''markov'':77C ''mask'':160C,189C,221C,229C ''model'':19C,78C,81C,103C ''move'':261C ''n'':266C ''natur'':80C ''need'':116C ''nice'':111C ''node'':24C,32C,58C ''number'':267C ''ol'':13C ''optim'':159C,188C ''output'':57C,90C ''p'':1C ''per'':171C ''predict'':95C ''probabl'':53C ''problem'':5C,106C ''quit'':140C ''reason'':129C ''relat'':150C,206C,282C ''rule'':199C ''sampl'':172C,240C,249C,258C,269C,276C ''say'':168C ''select'':183C ''sequenc'':287C ''seri'':146C ''simpl'':17C,136C ''someth'':61C ''sort'':285C ''state'':22C,84C,93C ''suggest'':9C ''though'':121C ''time'':39C,145C,149C,281C ''time-rel'':148C,280C ''transit'':82C ''tri'':113C ''two'':10C,207C ''type'':65C,68C ''typic'':4C ''use'':15C,74C,126C,233C ''user'':64C,67C ''vote1'':43C,49C ''vote2'':44C ''vote3'':45C,48C ''want'':155C,177C,204C ''well'':141C ''work'':139C ''would'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5629, 1, '2015-04-27 18:29:45.513', 1, 147, 'How do I access data on my EBS Volume from R-Studio Server on Ubuntu EC2 Instance', '&lt;p&gt;I have setup R-Studio Server on an Ubuntu EC2 instance for the first time and successfully started r-studio server in my browser. I also have putty ssh client. How do I set path in r-studio server to my mounted EBS volume and why do I not see the contents of my EBS volume in the r-studio files area (bottom right side? ) . Also, I had a file in an s3 bucket. I passed this command to bring it from s3 to my ebs volume: &lt;code&gt;s3cmd get s3://data-analysis/input-data/filename.csv&lt;/code&gt; . I assume this command downloads the file from s3 into the ebs volume. But I can''t find it in RStudio Server! I have scoured the internet looking for help on this but not able to solve my problem.&lt;/p&gt;
', 'aws  rstudio', NULL, '''/code'':118C ''/data-analysis/input-data/filename.csv'':117C ''/p'':158C ''abl'':153C ''access'':4A ''also'':49C,91C ''area'':87C ''assum'':120C ''aw'':19B ''bottom'':88C ''bring'':105C ''browser'':47C ''bucket'':99C ''client'':53C ''code'':113C ''command'':103C,122C ''content'':76C ''data'':5A ''download'':123C ''eb'':8A,67C,79C,111C,130C ''ec2'':17A,32C ''file'':86C,95C,125C ''find'':136C ''first'':36C ''get'':115C ''help'':148C ''instanc'':18A,33C ''internet'':145C ''look'':146C ''mount'':66C ''p'':21C ''pass'':101C ''path'':58C ''problem'':157C ''putti'':51C ''r'':12A,26C,42C,61C,84C ''r-studio'':11A,25C,41C,60C,83C ''right'':89C ''rstudio'':20B,139C ''s3'':98C,108C,116C,127C ''s3cmd'':114C ''scour'':143C ''see'':74C ''server'':14A,28C,44C,63C,140C ''set'':57C ''setup'':24C ''side'':90C ''solv'':155C ''ssh'':52C ''start'':40C ''studio'':13A,27C,43C,62C,85C ''success'':39C ''time'':37C ''ubuntu'':16A,31C ''volum'':9A,68C,80C,112C,131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5631, 2, '2015-04-27 21:46:21.373', 5, NULL, NULL, '&lt;p&gt;Spark is intended to be pointed at large distributed data sets, so as you suggest, the most typical use cases will involve connecting to some sort of Cloud system like AWS. &lt;/p&gt;

&lt;p&gt;In fact, if the data set you aim to analyze can fit on your local system, you''ll usually find that you can analyze it just as simply using pure python.  If you''re trying to leverage a series of local VMs, you''re going to run out of memory pretty quickly and jobs will either fail or grind to a halt.&lt;/p&gt;

&lt;p&gt;With that said, a local instance of spark is very useful for the purpose of development. &lt;/p&gt;

&lt;p&gt;One way that I''ve found that works is if I have a directory in HDFS with many files, I''ll pull over a single file, develop locally, then port my spark script to my cloud system for execution.  If you''re using AWS, this is really helpful for avoiding big fees while you''re developing.&lt;/p&gt;
', NULL, NULL, '''/p'':33C,97C,115C,173C ''aim'':42C ''analyz'':44C,58C ''avoid'':166C ''aw'':32C,160C ''big'':167C ''case'':21C ''cloud'':29C,152C ''connect'':24C ''data'':11C,39C ''develop'':114C,143C,172C ''directori'':130C ''distribut'':10C ''either'':90C ''execut'':155C ''fact'':36C ''fail'':91C ''fee'':168C ''file'':135C,142C ''find'':54C ''fit'':46C ''found'':122C ''go'':79C ''grind'':93C ''halt'':96C ''hdfs'':132C ''help'':164C ''instanc'':104C ''intend'':4C ''involv'':23C ''job'':88C ''larg'':9C ''leverag'':71C ''like'':31C ''ll'':52C,137C ''local'':49C,75C,103C,144C ''mani'':134C ''memori'':84C ''one'':117C ''p'':1C,34C,98C,116C ''point'':7C ''port'':146C ''pretti'':85C ''pull'':138C ''pure'':64C ''purpos'':112C ''python'':65C ''quick'':86C ''re'':68C,78C,158C,171C ''realli'':163C ''run'':81C ''said'':101C ''script'':149C ''seri'':73C ''set'':12C,40C ''simpli'':62C ''singl'':141C ''sort'':27C ''spark'':2C,106C,148C ''suggest'':16C ''system'':30C,50C,153C ''tri'':69C ''typic'':19C ''use'':20C,63C,109C,159C ''usual'':53C ''ve'':121C ''vms'':76C ''way'':118C ''work'':124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5632, 1, '2015-04-28 06:48:25.867', 3, 51, 'Predicting earthquakes using disturbances in DTH TV transmission', '&lt;p&gt;It is said that before an earthquake happens, a viewer experiences disturbances in DTH TV transmission in the form of distorted images on the screen which automatically correct after a few seconds.&lt;/p&gt;

&lt;p&gt;Is it possible to identify patterns of such disturbances by continuously monitoring TV images so that earthquakes can potentially be predicted at least few minutes in advance and many lives could be saved?&lt;/p&gt;
', 'predictive-modeling  image-classification', NULL, '''/p'':48C,83C ''advanc'':76C ''automat'':42C ''classif'':14B ''continu'':60C ''correct'':43C ''could'':80C ''distort'':36C ''disturb'':4A,27C,58C ''dth'':6A,29C ''earthquak'':2A,22C,66C ''experi'':26C ''form'':34C ''happen'':23C ''identifi'':54C ''imag'':13B,37C,63C ''image-classif'':12B ''least'':72C ''live'':79C ''mani'':78C ''minut'':74C ''model'':11B ''monitor'':61C ''p'':15C,49C ''pattern'':55C ''possibl'':52C ''potenti'':68C ''predict'':1A,10B,70C ''predictive-model'':9B ''said'':18C ''save'':82C ''screen'':40C ''second'':47C ''transmiss'':8A,31C ''tv'':7A,30C,62C ''use'':3A ''viewer'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5887, 2, '2015-05-24 03:48:42.427', 1, NULL, NULL, '&lt;p&gt;say you have a vector/array of values &lt;code&gt;v = [1, -2, 3]&lt;/code&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;minV = Math.min.apply(Math, v);;
for(var i=0; i&amp;lt;v.length; i++) {v[i] -= minV;}
maxV = Math.max.apply(Math, v);;
for(var i=0; i&amp;lt;v.length; i++) {v[i] /= maxV;}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Output at the end will be &lt;code&gt;v = [0.6, 0, 1]&lt;/code&gt;. Explanation:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Pushing the entire range of values to start from 0, so that we have no negatives&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Dividing the values by max of range, so that max will be 1&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''-2'':12C ''/code'':14C,48C,62C ''/li'':85C,102C ''/ol'':103C ''/p'':15C,64C,84C,101C ''/pre'':49C ''0'':25C,40C,60C,77C ''0.6'':59C ''1'':11C,61C,100C ''3'':13C ''code'':9C,17C,57C ''divid'':88C ''end'':54C ''entir'':70C ''explan'':63C ''li'':66C,86C ''lt'':27C,42C ''math'':20C,35C ''math.max.apply'':34C ''math.min.apply'':19C ''max'':92C,97C ''maxv'':33C,47C ''minv'':18C,32C ''negat'':83C ''ol'':65C ''output'':51C ''p'':1C,50C,67C,87C ''pre'':16C ''push'':68C ''rang'':71C,94C ''say'':2C ''start'':75C ''v'':10C,21C,30C,36C,45C,58C ''v.length'':28C,43C ''valu'':8C,73C,90C ''var'':23C,38C ''vector/array'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5699, 2, '2015-05-06 13:33:46.497', 1, NULL, NULL, '&lt;p&gt;You can easily take an existing &lt;a href="http://en.wikipedia.org/wiki/Multilayer_perceptron" rel="nofollow"&gt;Multilayer Perceptron&lt;/a&gt; implementation and modify the &lt;a href="http://en.wikipedia.org/wiki/Backpropagation" rel="nofollow"&gt;backpropagation&lt;/a&gt; algorithm to prevent weights from becoming negative. Of course, you would also need to make sure you initialize weights with only non-negative values.&lt;/p&gt;

&lt;p&gt;But you need to be aware that you are limiting the decision surfaces that the neural network can learn. So you should consider whether a neural network with only non-negative weights can provide an acceptable solution for your given data set.&lt;/p&gt;
', NULL, NULL, '''/a'':17C,30C ''/p'':56C,101C ''/wiki/backpropagation'':26C ''/wiki/multilayer_perceptron'':12C ''accept'':94C ''algorithm'':31C ''also'':42C ''awar'':63C ''backpropag'':29C ''becom'':36C ''consid'':80C ''cours'':39C ''data'':99C ''decis'':69C ''easili'':4C ''en.wikipedia.org'':11C,25C ''en.wikipedia.org/wiki/backpropagation'':24C ''en.wikipedia.org/wiki/multilayer_perceptron'':10C ''exist'':7C ''given'':98C ''href'':9C,23C ''implement'':18C ''initi'':48C ''learn'':76C ''limit'':67C ''make'':45C ''modifi'':20C ''multilay'':15C ''need'':43C,60C ''negat'':37C,54C,89C ''network'':74C,84C ''neural'':73C,83C ''nofollow'':14C,28C ''non'':53C,88C ''non-neg'':52C,87C ''p'':1C,57C ''perceptron'':16C ''prevent'':33C ''provid'':92C ''rel'':13C,27C ''set'':100C ''solut'':95C ''sure'':46C ''surfac'':70C ''take'':5C ''valu'':55C ''weight'':34C,49C,90C ''whether'':81C ''would'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5634, 2, '2015-04-28 08:44:09.613', 4, NULL, NULL, '&lt;p&gt;I think that if there are disturbances caused by something preceding earthquake, they might be used.&lt;/p&gt;

&lt;p&gt;The problem is that you need to find which cause produces that effect and measure it. For example you can team up with geologists, geophysicists or someone similar and try to build as complete hypothesis as possible and then design experiment to gather data. If your experiment would bring some non noise data then you can start to thinking about machine learning algorithms which will work in this situation.&lt;/p&gt;

&lt;p&gt;But you might also try to do it on "brute force" way by actually recording DTH TV images in seismically unstable regions and try to correlate those videos with seismic data. Then after some manual examination and categorization you could define (or not if this cause and effect hypothesis is wrong) some possible glitches that are observed and try to develop software that tries to detect them (&lt;a href="http://opencv.org/" rel="nofollow"&gt;OpenCV&lt;/a&gt; might be useful for example).&lt;/p&gt;

&lt;p&gt;IMHO in either way, some domain knowledge related to earthquakes would be more useful on beginning stage, and experience related to machine learning would be more useful on latest stages of such ambitious project.&lt;/p&gt;
', NULL, NULL, '''/a'':164C ''/p'':18C,89C,170C,206C ''actual'':104C ''algorithm'':82C ''also'':94C ''ambiti'':204C ''begin'':187C ''bring'':68C ''brute'':100C ''build'':51C ''categor'':128C ''caus'':9C,29C,136C ''complet'':53C ''correl'':116C ''could'':130C ''data'':63C,72C,121C ''defin'':131C ''design'':59C ''detect'':156C ''develop'':151C ''disturb'':8C ''domain'':177C ''dth'':106C ''earthquak'':13C,181C ''effect'':32C,138C ''either'':174C ''examin'':126C ''exampl'':37C,169C ''experi'':60C,66C,190C ''find'':27C ''forc'':101C ''gather'':62C ''geologist'':43C ''geophysicist'':44C ''glitch'':144C ''href'':159C ''hypothesi'':54C,139C ''imag'':108C ''imho'':172C ''knowledg'':178C ''latest'':200C ''learn'':81C,194C ''machin'':80C,193C ''manual'':125C ''measur'':34C ''might'':15C,93C,165C ''need'':25C ''nofollow'':162C ''nois'':71C ''non'':70C ''observ'':147C ''opencv'':163C ''opencv.org'':160C ''p'':1C,19C,90C,171C ''possibl'':56C,143C ''preced'':12C ''problem'':21C ''produc'':30C ''project'':205C ''record'':105C ''region'':112C ''rel'':161C ''relat'':179C,191C ''seismic'':110C,120C ''similar'':47C ''situat'':88C ''softwar'':152C ''someon'':46C ''someth'':11C ''stage'':188C,201C ''start'':76C ''team'':40C ''think'':3C,78C ''tri'':49C,95C,114C,149C,154C ''tv'':107C ''unstabl'':111C ''use'':17C,167C,185C,198C ''video'':118C ''way'':102C,175C ''work'':85C ''would'':67C,182C,195C ''wrong'':141C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5660, 2, '2015-05-01 08:47:31.613', 1, NULL, NULL, '&lt;p&gt;Ok, after a lot of looking I found the "optim" routine which is in "stats", one of the packages that is always loaded. It has quite a few methods including conjugate gradients and BGGS and a few others and worked well on the first few examples I tried. It doesn''t seem to get a lot of attention strangely. I guess optimization people tend to use Matlab.&lt;/p&gt;

&lt;p&gt;I knew there had to be something.&lt;/p&gt;
', NULL, NULL, '''/p'':69C,78C ''alway'':23C ''attent'':59C ''bggs'':35C ''conjug'':32C ''doesn'':51C ''exampl'':47C ''first'':45C ''found'':9C ''get'':55C ''gradient'':33C ''guess'':62C ''includ'':31C ''knew'':72C ''load'':24C ''look'':7C ''lot'':5C,57C ''matlab'':68C ''method'':30C ''ok'':2C ''one'':17C ''optim'':11C,63C ''other'':39C ''p'':1C,70C ''packag'':20C ''peopl'':64C ''quit'':27C ''routin'':12C ''seem'':53C ''someth'':77C ''stat'':16C ''strang'':60C ''tend'':65C ''tri'':49C ''use'':67C ''well'':42C ''work'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5637, 2, '2015-04-28 19:06:07.887', 0, NULL, NULL, '&lt;p&gt;Looks good to me. This derivative is also presented in the paper (equations 56-58).&lt;/p&gt;

&lt;p&gt;The paper you''re linking to is the most advanced attempt - at least to best of my knowledge - to explain how word2vec works, but there is also a lot of other resources on the topic (just search for word2vec on &lt;a href="http://arxiv.org/find/all/1/all:+word2vec/0/1/0/all/0/1" rel="nofollow"&gt;arxiv.org&lt;/a&gt;). If you''re interested in word2vec, you may find GloVe interesting too (see: &lt;a href="http://arxiv.org/pdf/1411.5595v2.pdf" rel="nofollow"&gt;Linking GloVe with word2vec&lt;/a&gt;).&lt;/p&gt;
', NULL, NULL, '''-58'':16C ''/a'':67C,92C ''/find/all/1/all:+word2vec/0/1/0/all/0/1'':63C ''/p'':17C,93C ''/pdf/1411.5595v2.pdf'':85C ''56'':15C ''advanc'':28C ''also'':9C,45C ''arxiv.org'':62C,66C,84C ''arxiv.org/find/all/1/all:+word2vec/0/1/0/all/0/1'':61C ''arxiv.org/pdf/1411.5595v2.pdf'':83C ''attempt'':29C ''best'':33C ''deriv'':7C ''equat'':14C ''explain'':38C ''find'':76C ''glove'':77C,89C ''good'':3C ''href'':60C,82C ''interest'':71C,78C ''knowledg'':36C ''least'':31C ''link'':23C,88C ''look'':2C ''lot'':47C ''may'':75C ''nofollow'':65C,87C ''p'':1C,18C ''paper'':13C,20C ''present'':10C ''re'':22C,70C ''rel'':64C,86C ''resourc'':50C ''search'':55C ''see'':80C ''topic'':53C ''word2vec'':40C,57C,73C,91C ''work'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5638, 2, '2015-04-28 19:34:42.863', 0, NULL, NULL, '&lt;p&gt;In sentiment analysis you may want to combine a number of classifiers. Let''s say: a separate classifier for emoticons, another one for emotionally loaded terms, another one for some special linguistic patterns and - let''s say - yet another one to detect and filter out spam messages. It''s all up to you.&lt;/p&gt;

&lt;p&gt;You can use either SVM, Naive Bayes, or anything else that best suits your problem. You may use majority voting, weights (for example based on cross validation results), or any other more advanced technique to decide which class is the most appropriate one.&lt;/p&gt;

&lt;p&gt;Also, googling for &lt;em&gt;hybrid sentiment&lt;/em&gt; returns tons of papers containing answers to the questions that you have stated. Please, don''t ask us to rewrite this papers here.&lt;/p&gt;
', NULL, NULL, '''/em'':108C ''/p'':55C,100C,132C ''advanc'':89C ''also'':102C ''analysi'':4C ''anoth'':22C,28C,40C ''answer'':114C ''anyth'':65C ''appropri'':98C ''ask'':125C ''base'':80C ''bay'':63C ''best'':68C ''class'':94C ''classifi'':13C,19C ''combin'':9C ''contain'':113C ''cross'':82C ''decid'':92C ''detect'':43C ''either'':60C ''els'':66C ''em'':105C ''emot'':25C ''emoticon'':21C ''exampl'':79C ''filter'':45C ''googl'':103C ''hybrid'':106C ''let'':14C,36C ''linguist'':33C ''load'':26C ''major'':75C ''may'':6C,73C ''messag'':48C ''naiv'':62C ''number'':11C ''one'':23C,29C,41C,99C ''p'':1C,56C,101C ''paper'':112C,130C ''pattern'':34C ''pleas'':122C ''problem'':71C ''question'':117C ''result'':84C ''return'':109C ''rewrit'':128C ''say'':16C,38C ''sentiment'':3C,107C ''separ'':18C ''spam'':47C ''special'':32C ''state'':121C ''suit'':69C ''svm'':61C ''techniqu'':90C ''term'':27C ''ton'':110C ''us'':126C ''use'':59C,74C ''valid'':83C ''vote'':76C ''want'':7C ''weight'':77C ''yet'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5639, 1, '2015-04-29 07:27:33.967', 3, 446, 'Fraud detection use text mining', '&lt;p&gt;I would like to find different patterns recognition algorithm to detect different type of fraud. I have 1 million unstructured text documents about the clients'' information with metadata about the client name, viewers, location in the cloud. &lt;/p&gt;

&lt;p&gt;Here are the patterns I was thinking of and the related fraud that i would like to detect:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Numeric Patterns - fictitious invoice numbers, fictitiously-generated transaction amounts. &lt;/p&gt;
  
  &lt;p&gt;Time Patterns - transactions occurring too regularly, activity at unusual times or dates. &lt;/p&gt;
  
  &lt;p&gt;Name Patterns - similar and alerted name and addresses. &lt;/p&gt;
  
  &lt;p&gt;Geographic Patterns - Proximity relationships between apparently unrelated entities. &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;What technique can I use ? any keywords? thx.&lt;/p&gt;
', 'text-mining', NULL, '''/blockquote'':115C ''/p'':47C,67C,80C,94C,104C,114C,125C ''1'':27C ''activ'':88C ''address'':103C ''alert'':100C ''algorithm'':18C ''amount'':79C ''appar'':111C ''blockquot'':68C ''client'':34C,40C ''cloud'':46C ''date'':93C ''detect'':2A,20C,66C ''differ'':15C,21C ''document'':31C ''entiti'':113C ''fictiti'':72C,76C ''fictitiously-gener'':75C ''find'':14C ''fraud'':1A,24C,60C ''generat'':77C ''geograph'':106C ''inform'':35C ''invoic'':73C ''keyword'':123C ''like'':12C,64C ''locat'':43C ''metadata'':37C ''million'':28C ''mine'':5A,8B ''name'':41C,96C,101C ''number'':74C ''numer'':70C ''occur'':85C ''p'':9C,48C,69C,81C,95C,105C,116C ''pattern'':16C,52C,71C,83C,97C,107C ''proxim'':108C ''recognit'':17C ''regular'':87C ''relat'':59C ''relationship'':109C ''similar'':98C ''techniqu'':118C ''text'':4A,7B,30C ''text-min'':6B ''think'':55C ''thx'':124C ''time'':82C,91C ''transact'':78C,84C ''type'':22C ''unrel'':112C ''unstructur'':29C ''unusu'':90C ''use'':3A,121C ''viewer'':42C ''would'':11C,63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5641, 2, '2015-04-29 12:07:56.71', 1, NULL, NULL, '&lt;p&gt;Since you obviously posses an AWS account I''d recommend the following:  &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Create an EC2 instance (any size)  &lt;/li&gt;
&lt;li&gt;Use &lt;a href="http://www.gnu.org/software/wget/" rel="nofollow"&gt;wget&lt;/a&gt;(or curl) to fetch the file(s) to that EC2 instance. For example: &lt;code&gt;wget http://example.com/my_large_file.csv&lt;/code&gt;.  &lt;/li&gt;
&lt;li&gt;Install &lt;a href="http://s3tools.org/" rel="nofollow"&gt;s3cmd&lt;/a&gt;  &lt;/li&gt;
&lt;li&gt;Use &lt;code&gt;s3cmd&lt;/code&gt; to upload the file to S3. For example: &lt;code&gt;s3cmd cp my_large_file.csv s3://my.bucket/my_large_file.csv&lt;/code&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Since connections made between various AWS services leverage AWS''s internal network, uploading from an EC2 instance to S3 is pretty fast. Much faster than uploading it from your own computer. This way allows you to avoid downloading the file to your computer and saving potentially significant time uploading it through the web interface.&lt;/p&gt;
', NULL, NULL, '''/a'':34C,62C ''/code'':68C,83C ''/li'':23C,53C,63C,84C ''/my.bucket/my_large_file.csv'':82C ''/my_large_file.csv&lt;/code&gt;.'':52C ''/p'':14C,141C ''/software/wget/'':30C ''/ul'':85C ''account'':8C ''allow'':120C ''avoid'':123C ''aw'':7C,92C,95C ''code'':48C,66C,77C ''comput'':117C,129C ''connect'':88C ''cp'':79C ''creat'':17C ''curl'':36C ''d'':10C ''download'':124C ''ec2'':19C,44C,102C ''exampl'':47C,76C ''example.com'':51C ''example.com/my_large_file.csv&lt;/code&gt;.'':50C ''fast'':108C ''faster'':110C ''fetch'':38C ''file'':40C,72C,126C ''follow'':13C ''href'':27C,57C ''instal'':55C ''instanc'':20C,45C,103C ''interfac'':140C ''intern'':97C ''leverag'':94C ''li'':16C,24C,54C,64C ''made'':89C ''much'':109C ''my_large_file.csv'':80C ''network'':98C ''nofollow'':32C,60C ''obvious'':4C ''p'':1C,86C ''poss'':5C ''potenti'':132C ''pretti'':107C ''recommend'':11C ''rel'':31C,59C ''s3'':74C,81C,105C ''s3cmd'':61C,67C,78C ''s3tools.org'':58C ''save'':131C ''servic'':93C ''signific'':133C ''sinc'':2C,87C ''size'':22C ''time'':134C ''ul'':15C ''upload'':70C,99C,112C,135C ''use'':25C,65C ''various'':91C ''way'':119C ''web'':139C ''wget'':33C,49C ''www.gnu.org'':29C ''www.gnu.org/software/wget/'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5642, 2, '2015-04-29 13:53:09.18', 9, NULL, NULL, '&lt;p&gt;The UK Government provide an excellent source of non-personal data collected throughout government departments: &lt;a href="http://data.gov.uk"&gt;http://data.gov.uk&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':22C ''/p'':23C ''collect'':14C ''data'':13C ''data.gov.uk'':20C,21C ''depart'':17C ''excel'':7C ''govern'':4C,16C ''href'':19C ''non'':11C ''non-person'':10C ''p'':1C ''person'':12C ''provid'':5C ''sourc'':8C ''throughout'':15C ''uk'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5888, 2, '2015-05-24 05:50:50.53', 14, NULL, NULL, '&lt;p&gt;This is called unity-based normalization. If you have a vector $X$, you can obtain a normalized version of it, say $Z$, by doing:&lt;/p&gt;

&lt;p&gt;$$Z = \frac{X - \min(X)}{\max(X) - \min(X)}$$ &lt;/p&gt;
', NULL, NULL, '''/p'':27C,38C ''base'':7C ''call'':4C ''frac'':30C ''max'':34C ''min'':32C,36C ''normal'':8C,19C ''obtain'':17C ''p'':1C,28C ''say'':23C ''uniti'':6C ''unity-bas'':5C ''vector'':13C ''version'':20C ''x'':14C,31C,33C,35C,37C ''z'':24C,29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5643, 1, '2015-04-29 14:51:59.177', 0, 108, 'Automomatic typographical error correction', '&lt;p&gt;Does anyone know where I might be able to find a list of the most common typing errors and their corrections? This is separate from more complicated considerations concerning general spelling checking (which can have very many candidates in relation to the correct spelling of the word in question); rather I am looking for a similar list as used by Microsoft Word (for instance correcting &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;teh&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;with &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;the&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;or&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;becaise&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;with &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;because&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Not only can the manner in which these sort of errors are fixed be hard-coded, their frequent occurrence in text provides significant dividends in textual mining (provided that such a list of errors and corrections can be obtained, of course).&lt;/p&gt;
', 'text-mining  data-cleaning', NULL, '''/blockquote'':82C,90C,98C,106C ''/p'':77C,81C,85C,89C,93C,97C,101C,105C,150C ''abl'':19C ''anyon'':13C ''automomat'':1A ''becais'':96C ''blockquot'':78C,86C,94C,102C ''candid'':49C ''check'':43C ''clean'':10B ''code'':124C ''common'':27C ''complic'':38C ''concern'':40C ''consider'':39C ''correct'':4A,32C,54C,76C,144C ''cours'':149C ''data'':9B ''data-clean'':8B ''dividend'':132C ''error'':3A,29C,118C,142C ''find'':21C ''fix'':120C ''frequent'':126C ''general'':41C ''hard'':123C ''hard-cod'':122C ''instanc'':75C ''know'':14C ''list'':23C,68C,140C ''look'':64C ''mani'':48C ''manner'':112C ''microsoft'':72C ''might'':17C ''mine'':7B,135C ''obtain'':147C ''occurr'':127C ''p'':11C,79C,83C,87C,91C,95C,99C,103C,107C ''provid'':130C,136C ''question'':60C ''rather'':61C ''relat'':51C ''separ'':35C ''signific'':131C ''similar'':67C ''sort'':116C ''spell'':42C,55C ''teh'':80C ''text'':6B,129C ''text-min'':5B ''textual'':134C ''type'':28C ''typograph'':2A ''use'':70C ''word'':58C,73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5644, 1, '2015-04-29 16:43:34.397', -1, 57, 'How is frequent itemsets compared with item-based collaborative filtering in recommender systems?', '&lt;p&gt;What is the difference between data mining approaches: frequent itemsets and item-based collaborative filtering in the area of recommender systems?&lt;/p&gt;
', 'data-mining', NULL, '''/p'':41C ''approach'':26C ''area'':37C ''base'':9A,32C ''collabor'':10A,33C ''compar'':5A ''data'':16B,24C ''data-min'':15B ''differ'':22C ''filter'':11A,34C ''frequent'':3A,27C ''item'':8A,31C ''item-bas'':7A,30C ''itemset'':4A,28C ''mine'':17B,25C ''p'':18C ''recommend'':13A,39C ''system'':14A,40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9084, 2, '2015-11-27 16:21:19.57', 1, NULL, NULL, '&lt;p&gt;&lt;strong&gt;No&lt;/strong&gt;, it is not a confusion matrix. &lt;/p&gt;

&lt;p&gt;Please have a look at the &lt;a href="https://stat.ethz.ch/R-manual/R-devel/library/stats/html/cor.html" rel="nofollow"&gt;docs&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.inside-r.org/node/86995" rel="nofollow"&gt;This&lt;/a&gt; is how the confusion matrix is created in R.&lt;/p&gt;
', NULL, NULL, '''/a'':27C,38C ''/node/86995'':34C ''/p'':11C,28C,48C ''/r-manual/r-devel/library/stats/html/cor.html'':23C ''/strong'':4C ''confus'':9C,42C ''creat'':45C ''doc'':26C ''href'':20C,31C ''look'':16C ''matrix'':10C,43C ''nofollow'':25C,36C ''p'':1C,12C,29C ''pleas'':13C ''r'':47C ''rel'':24C,35C ''stat.ethz.ch'':22C ''stat.ethz.ch/r-manual/r-devel/library/stats/html/cor.html'':21C ''strong'':2C ''www.inside-r.org'':33C ''www.inside-r.org/node/86995'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5645, 2, '2015-04-29 17:05:22.277', 1, NULL, NULL, '&lt;p&gt;I see this question got up-voted so someone else is looking for the answer. Here it is: &lt;/p&gt;

&lt;p&gt;I was logged in as the default user on Ubuntu instance when i did the &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;$s3cmd get s3://data-analysis/input-data/filename.csv
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The data got saved from the s3 bucket to the home directory of the user named ubuntu. this can be verified with : &lt;/p&gt;

&lt;p&gt;&lt;code&gt;$ df -h /home/ubuntu/&lt;/code&gt; &lt;/p&gt;

&lt;p&gt;Since I had followed &lt;a href="http://randyzwitch.com/r-amazon-ec2/" rel="nofollow" title="randy zwitch&amp;#39;s tutorial"&gt;randy zwitch''s tutorial&lt;/a&gt; on installing R on aws - i had created a user named rstudio and that was the user name I was using to log-in to rstudio server. Hence I had to move the file from the default ''ubuntu'' user''s home directory to the user ''rstudio''. this can be done with: &lt;/p&gt;

&lt;p&gt;&lt;code&gt;$ sudo mv /home/ubuntu/filename.csv /home/rstudio&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Happy to answer follow up questions. &lt;/p&gt;
', NULL, NULL, '''/a'':100C ''/code'':45C,76C,160C ''/data-analysis/input-data/filename.csv'':44C ''/home/rstudio'':159C ''/home/ubuntu'':75C ''/home/ubuntu/filename.csv'':158C ''/p'':21C,38C,70C,77C,153C,161C,169C ''/pre'':46C ''/r-amazon-ec2/'':87C ''39'':93C ''answer'':17C,165C ''aw'':105C ''bucket'':55C ''code'':40C,72C,155C ''creat'':108C ''data'':49C ''default'':29C,138C ''df'':73C ''directori'':59C,143C ''done'':151C ''els'':12C ''file'':135C ''follow'':82C,166C ''get'':42C ''got'':6C,50C ''h'':74C ''happi'':163C ''henc'':129C ''home'':58C,142C ''href'':84C ''instal'':102C ''instanc'':33C ''log'':25C,124C ''log-in'':123C ''look'':14C ''move'':133C ''mv'':157C ''name'':63C,111C,118C ''nofollow'':89C ''p'':1C,22C,47C,71C,78C,154C,162C ''pre'':39C ''question'':5C,168C ''r'':103C ''randi'':91C,96C ''randyzwitch.com'':86C ''randyzwitch.com/r-amazon-ec2/'':85C ''rel'':88C ''rstudio'':112C,127C,147C ''s3'':43C,54C ''s3cmd'':41C ''save'':51C ''see'':3C ''server'':128C ''sinc'':79C ''someon'':11C ''sudo'':156C ''titl'':90C ''tutori'':95C,99C ''ubuntu'':32C,64C,139C ''up-vot'':7C ''use'':121C ''user'':30C,62C,110C,117C,140C,146C ''verifi'':68C ''vote'':9C ''zwitch'':92C,97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5646, 2, '2015-04-29 17:07:14.157', 1, NULL, NULL, '&lt;p&gt;I will reference two sources:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Wikipedia has its own lists: &lt;a href="http://en.wikipedia.org/wiki/Wikipedia:Lists_of_common_misspellings" rel="nofollow"&gt;Lists of common misspellings&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://aspell.net/" rel="nofollow"&gt;Aspell&lt;/a&gt; is certainly a place to visit. You can find a list of misspellings and corrections &lt;a href="http://aspell.net/test/common-all/batch0.tab" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Still, it is a good idea to think of using a spell checker, such as Aspell, since it gives you the ability to deal with no-so-common misspellings (of course, at the cost of false positives). A good starting point is Peter Norvig''s &lt;a href="http://norvig.com/spell-correct.html" rel="nofollow"&gt;essay&lt;/a&gt; on creating a spelling corrector.&lt;/p&gt;
', NULL, NULL, '''/a'':27C,38C,62C,121C ''/li'':29C,64C ''/p'':7C,28C,63C,127C ''/spell-correct.html'':117C ''/test/common-all/batch0.tab'':58C ''/ul'':65C ''/wiki/wikipedia:lists_of_common_misspellings'':20C ''abil'':88C ''aspel'':37C,82C ''aspell.net'':34C,57C ''aspell.net/test/common-all/batch0.tab'':56C ''certain'':40C ''checker'':79C ''common'':25C,95C ''correct'':53C ''corrector'':126C ''cost'':101C ''cours'':98C ''creat'':123C ''deal'':90C ''en.wikipedia.org'':19C ''en.wikipedia.org/wiki/wikipedia:lists_of_common_misspellings'':18C ''essay'':120C ''fals'':103C ''find'':47C ''give'':85C ''good'':71C,106C ''href'':17C,33C,55C,114C ''idea'':72C ''li'':9C,30C ''list'':15C,23C,49C ''misspel'':26C,51C,96C ''no-so-common'':92C ''nofollow'':22C,36C,60C,119C ''norvig'':111C ''norvig.com'':116C ''norvig.com/spell-correct.html'':115C ''p'':1C,10C,31C,66C ''peter'':110C ''place'':42C ''point'':108C ''posit'':104C ''refer'':4C ''rel'':21C,35C,59C,118C ''sinc'':83C ''sourc'':6C ''spell'':78C,125C ''start'':107C ''still'':67C ''think'':74C ''two'':5C ''ul'':8C ''use'':76C ''visit'':44C ''wikipedia'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5717, 1, '2015-05-07 21:41:25.3', 2, 181, 'Where is the cost parameter C in the RBF kernel in SVM?', '&lt;p&gt;RBF kernel using SVM depends on two parameters C and gamma. If the equation of the kernel RBF as the following:&lt;/p&gt;

&lt;p&gt;$K(X,X'')= \exp(\gamma||X-X''||^2)$&lt;/p&gt;

&lt;p&gt;In the equation I can see where can I use gamma, but I can''t find the C parameter.
So, can enybody tell me please?&lt;/p&gt;

&lt;p&gt;Thanks in advance,&lt;/p&gt;
', 'machine-learning  classification  svm  search', NULL, '''/p'':41C,52C,79C,84C ''2'':51C ''advanc'':83C ''c'':6A,28C,71C ''classif'':16B ''cost'':4A ''depend'':24C ''enybodi'':75C ''equat'':33C,56C ''exp'':46C ''find'':69C ''follow'':40C ''gamma'':30C,47C,64C ''k'':43C ''kernel'':10A,21C,36C ''learn'':15B ''machin'':14B ''machine-learn'':13B ''p'':19C,42C,53C,80C ''paramet'':5A,27C,72C ''pleas'':78C ''rbf'':9A,20C,37C ''search'':18B ''see'':59C ''svm'':12A,17B,23C ''tell'':76C ''thank'':81C ''two'':26C ''use'':22C,63C ''x'':44C,45C,49C,50C ''x-x'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5517, 2, '2015-04-13 10:52:02.36', 0, NULL, NULL, '&lt;p&gt;PROC means data=d mean; var a; run;&lt;/p&gt;

&lt;p&gt;PROC means data=d mean; class b; var a; run;&lt;/p&gt;

&lt;p&gt;I knew that can be write so separately!
But I thought, it is can be write somehow together!&lt;/p&gt;
', NULL, NULL, '''/p'':10C,22C,42C ''b'':18C ''class'':17C ''d'':5C,15C ''data'':4C,14C ''knew'':25C ''mean'':3C,6C,13C,16C ''p'':1C,11C,23C ''proc'':2C,12C ''run'':9C,21C ''separ'':31C ''somehow'':40C ''thought'':34C ''togeth'':41C ''var'':7C,19C ''write'':29C,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5647, 2, '2015-04-29 18:44:29.593', 1, NULL, NULL, '&lt;p&gt;I think by "frequent itemsets" you mean the association rules that you derive from counting frequent itemsets. They tell us:&lt;/p&gt;

&lt;p&gt;&lt;em&gt;people who do (like, buy, etc.) X tend to do (...) Y.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;For this you only need "positive" information, i.e. people who liked/bought/did.
Say this is about movies, you only know who liked the movie, among the others you don''t know whether they disliked it or didn''t see it.&lt;/p&gt;

&lt;p&gt;item-based CF, and other CF techniques are typically used when you have both positive and negative feedback, such as ratings 0 to 5. You then use this information to &lt;em&gt;predict unknown ratings&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;For item-based CF, you''re trying to predict a person''s rating on item Y, and you look how this person has rated other &lt;em&gt;similar&lt;/em&gt; items, where "X is similar to Y" means that other people who have rated both X and Y have given them very similar ratings. The information you get is then:&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Your have previously liked similar items to Y, therefore we predict you''ll like Y.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;User-based CF is perhaps closer to association rules, and you get:&lt;/p&gt;

&lt;p&gt;&lt;em&gt;People similar to you have liked Y, therefore we predict you will like Y.&lt;/em&gt;&lt;/p&gt;
', NULL, NULL, '''/em'':36C,115C,146C,195C,228C ''/p'':22C,37C,78C,116C,177C,196C,211C,229C ''0'':102C ''5'':104C ''among'':62C ''associ'':10C,206C ''base'':82C,121C,200C ''buy'':29C ''cf'':83C,86C,122C,201C ''closer'':204C ''count'':16C ''deriv'':14C ''didn'':74C ''dislik'':71C ''em'':24C,111C,144C,179C,213C ''etc'':30C ''feedback'':98C ''frequent'':5C,17C ''get'':174C,210C ''given'':166C ''i.e'':46C ''inform'':45C,109C,172C ''item'':81C,120C,133C,147C,185C ''item-bas'':80C,119C ''itemset'':6C,18C ''know'':57C,68C ''like'':28C,59C,183C,193C,219C,226C ''liked/bought/did'':49C ''ll'':192C ''look'':137C ''mean'':8C,154C ''movi'':54C,61C ''need'':43C ''negat'':97C ''other'':64C ''p'':1C,23C,38C,79C,117C,178C,197C,212C ''peopl'':25C,47C,157C,214C ''perhap'':203C ''person'':129C,140C ''posit'':44C,95C ''predict'':112C,127C,190C,223C ''previous'':182C ''rate'':101C,114C,131C,142C,160C,170C ''re'':124C ''rule'':11C,207C ''say'':50C ''see'':76C ''similar'':145C,151C,169C,184C,215C ''techniqu'':87C ''tell'':20C ''tend'':32C ''therefor'':188C,221C ''think'':3C ''tri'':125C ''typic'':89C ''unknown'':113C ''us'':21C ''use'':90C,107C ''user'':199C ''user-bas'':198C ''whether'':69C ''x'':31C,149C,162C ''y'':35C,134C,153C,164C,187C,194C,220C,227C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5649, 2, '2015-04-30 06:19:06.69', 3, NULL, NULL, '&lt;p&gt;There is nothing wrong with using Pearson''s correlation coefficient (frequently called THE correlation coefficient) with these variables. I would also have a look at Spearman''s rank correlation coefficient. It is less sensitive to extreme values. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;However, both coefficients have the drawback that they only measure linear relationships. Hence it is worthwile to have a look at the scatterplot of the variables. Adding a smoother to the graph can be worthwile too, especially  if the plot is clutterd and you have trouble to spot a relationship with the naked eye.&lt;/p&gt;
', NULL, NULL, '''/p'':39C,106C ''/wiki/spearman%27s_rank_correlation_coefficient'':45C ''/wiki/spearman%27s_rank_correlation_coefficient&lt;/a&gt;&lt;/p&gt;'':50C ''ad'':78C ''also'':22C ''call'':13C ''clutterd'':93C ''coeffici'':11C,16C,31C,54C ''correl'':10C,15C,30C ''drawback'':57C ''en.wikipedia.org'':44C,49C ''en.wikipedia.org/wiki/spearman%27s_rank_correlation_coefficient'':43C ''en.wikipedia.org/wiki/spearman%27s_rank_correlation_coefficient&lt;/a&gt;&lt;/p&gt;'':48C ''especi'':88C ''extrem'':37C ''eye'':105C ''frequent'':12C ''graph'':83C ''henc'':64C ''howev'':52C ''href'':42C ''less'':34C ''linear'':62C ''look'':25C,71C ''measur'':61C ''nake'':104C ''nofollow'':47C ''noth'':4C ''p'':1C,40C,51C ''pearson'':8C ''plot'':91C ''rank'':29C ''rel'':46C ''relationship'':63C,101C ''scatterplot'':74C ''sensit'':35C ''smoother'':80C ''spearman'':27C ''spot'':99C ''troubl'':97C ''use'':7C ''valu'':38C ''variabl'':19C,77C ''worthwil'':67C,86C ''would'':21C ''wrong'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5650, 1, '2015-04-30 08:01:39.763', 2, 37, 'Pound notation in Summation', '&lt;p&gt;I was going through a paper comparing glove and word2vec. I came across the pound notation shown below. What does it mean when used like this?
&lt;img src="http://i.imgur.com/WQoNTdG.png" alt="equation"&gt;
The link for paper is &lt;a href="http://arxiv.org/pdf/1411.5595v2.pdf" rel="nofollow"&gt;here&lt;/a&gt;&lt;/p&gt;
', 'machine-learning  notation', NULL, '''/a'':56C ''/p'':57C ''/pdf/1411.5595v2.pdf'':52C ''/wqontdg.png'':40C ''across'':22C ''alt'':41C ''arxiv.org'':51C ''arxiv.org/pdf/1411.5595v2.pdf'':50C ''came'':21C ''compar'':16C ''equat'':42C ''glove'':17C ''go'':12C ''href'':49C ''i.imgur.com'':39C ''i.imgur.com/wqontdg.png'':38C ''img'':36C ''learn'':7B ''like'':34C ''link'':44C ''machin'':6B ''machine-learn'':5B ''mean'':31C ''nofollow'':54C ''notat'':2A,8B,25C ''p'':9C ''paper'':15C,46C ''pound'':1A,24C ''rel'':53C ''shown'':26C ''src'':37C ''summat'':4A ''use'':33C ''word2vec'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5654, 1, '2015-04-30 12:26:47.637', 4, 247, 'What types of features are used in a large-scale click-through rate prediction problem?', '&lt;p&gt;Something that I often see in papers (&lt;a href="http://papers.nips.cc/paper/5333-large-scale-l-bfgs-using-mapreduce.pdf" rel="nofollow"&gt;example&lt;/a&gt;) about large-scale learning is that click-through rate (CTR) problems can have up to a billion of features for each example. In &lt;a href="http://research.google.com/pubs/pub41159.html" rel="nofollow"&gt;this Google paper&lt;/a&gt; the authors mention:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The features used in our system are drawn from a variety of sources,
  including the query, the text of the ad creative, and various
  ad-related metadata.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I can imagine a few thousands of features coming from this type of source, I guess through some form of feature hashing.&lt;/p&gt;

&lt;p&gt;My question is: how does one get to a billion features? How do companies translate user behavior into features in order to reach that scale of features?&lt;/p&gt;
', 'machine-learning  classification  bigdata  dataset', NULL, '''/a'':40C,76C ''/blockquote'':112C ''/p'':80C,111C,136C,165C ''/paper/5333-large-scale-l-bfgs-using-mapreduce.pdf'':36C ''/pubs/pub41159.html'':70C ''ad'':103C,108C ''ad-rel'':107C ''author'':78C ''behavior'':154C ''bigdata'':22B ''billion'':59C,147C ''blockquot'':81C ''classif'':21B ''click'':13A,49C ''click-through'':12A,48C ''come'':122C ''compani'':151C ''creativ'':104C ''ctr'':52C ''dataset'':23B ''drawn'':90C ''exampl'':39C,64C ''featur'':4A,61C,84C,121C,134C,148C,156C,164C ''form'':132C ''get'':144C ''googl'':74C ''guess'':129C ''hash'':135C ''href'':33C,67C ''imagin'':116C ''includ'':96C ''larg'':10A,43C ''large-scal'':9A,42C ''learn'':20B,45C ''machin'':19B ''machine-learn'':18B ''mention'':79C ''metadata'':110C ''nofollow'':38C,72C ''often'':28C ''one'':143C ''order'':158C ''p'':24C,82C,113C,137C ''paper'':31C,75C ''papers.nips.cc'':35C ''papers.nips.cc/paper/5333-large-scale-l-bfgs-using-mapreduce.pdf'':34C ''predict'':16A ''problem'':17A,53C ''queri'':98C ''question'':139C ''rate'':15A,51C ''reach'':160C ''rel'':37C,71C ''relat'':109C ''research.google.com'':69C ''research.google.com/pubs/pub41159.html'':68C ''scale'':11A,44C,162C ''see'':29C ''someth'':25C ''sourc'':95C,127C ''system'':88C ''text'':100C ''thousand'':119C ''translat'':152C ''type'':2A,125C ''use'':6A,85C ''user'':153C ''varieti'':93C ''various'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9178, 1, '2015-12-03 00:31:32.943', 2, 61, 'Clustering and confusion matrix', '&lt;p&gt;Here is the problem
The initial four cluster partition {c1, c2, c3, c4} for the text collection is provided by this link &lt;a href="http://userpages.umbc.edu/~kogan/teaching/m710/15fall/project/list.txt" rel="nofollow"&gt;text vs cluster&lt;/a&gt;. Assuming that
the ground-truth partition is given by&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;cacm texts belong to cluster1
cisi texts belong to cluster2
cran texts belong to cluster3
med texts belong to cluster4 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;build the confusion matrix (CM) for the partition provided. That is build a 4X4 matrix whose row i shows distribution of ci elements
among cluster1, cluster2, cluster3, and cluster4. For example the first row is computed as follows:
CM11=|c1â©cluster1|,  CM12=|c1â©cluster2|,  CM13=|c1â©cluster3|, and CM14=|c1â©cluster4| 
I understand the problem just not sure how to code it up.
I was thinking along the lines of this&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;//open text file
fileID = fopen(''list.txt'');
C = textscan(fileID,''%s %s'');
fclose(fileID);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What this does is it orders the names as $C\{1\}\{j\}$is the file name and $C\{2\}\{j\}$ is the cluster name.
By the way I am using Matlab&lt;/p&gt;
', 'data-mining  bigdata', NULL, '''/a'':42C ''/code'':76C,166C ''/open'':153C ''/p'':53C,150C,200C ''/pre'':77C,167C ''/~kogan/teaching/m710/15fall/project/list.txt'':36C ''1'':179C ''2'':187C ''4x4'':92C ''along'':145C ''among'':102C ''assum'':43C ''belong'':58C,63C,68C,73C ''bigdata'':8B ''build'':79C,90C ''c'':159C,178C,186C ''c1'':19C ''c1â'':118C,121C,124C,128C ''c2'':20C ''c3'':21C ''c4'':22C ''cacm'':56C ''ci'':100C ''cisi'':61C ''cluster'':1A,17C,41C,191C ''cluster1'':60C,103C,119C ''cluster2'':65C,104C,122C ''cluster3'':70C,105C,125C ''cluster4'':75C,107C,129C ''cm'':83C ''cm11'':117C ''cm12'':120C ''cm13'':123C ''cm14'':127C ''code'':55C,139C,152C ''collect'':26C ''comput'':114C ''confus'':3A,81C ''cran'':66C ''data'':6B ''data-min'':5B ''distribut'':98C ''element'':101C ''exampl'':109C ''fclose'':164C ''file'':155C,183C ''fileid'':156C,161C,165C ''first'':111C ''follow'':116C ''fopen'':157C ''four'':16C ''given'':51C ''ground'':47C ''ground-truth'':46C ''href'':33C ''initi'':15C ''j'':180C,188C ''line'':147C ''link'':31C ''list.txt'':158C ''matlab'':199C ''matrix'':4A,82C,93C ''med'':71C ''mine'':7B ''name'':176C,184C,192C ''nofollow'':38C ''order'':174C ''p'':9C,78C,168C ''partit'':18C,49C,86C ''pre'':54C,151C ''problem'':13C,133C ''provid'':28C,87C ''rel'':37C ''row'':95C,112C ''show'':97C ''sure'':136C ''text'':25C,39C,57C,62C,67C,72C,154C ''textscan'':160C ''think'':144C ''truth'':48C ''understand'':131C ''use'':198C ''userpages.umbc.edu'':35C ''userpages.umbc.edu/~kogan/teaching/m710/15fall/project/list.txt'':34C ''vs'':40C ''way'':195C ''whose'':94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5782, 2, '2015-05-13 12:51:59.373', 3, NULL, NULL, '&lt;p&gt;Since your fixed the seed, with default option &lt;code&gt;bootstrap=True&lt;/code&gt; each tree is build on a fixed subsample of your data. With the default option &lt;code&gt;max_features=''auto''&lt;/code&gt; each tree uses 2 features. Since your fixed the seed, it is always the same, let us say, "1st and 4th" features for the first tree. And they are different in each of your shuffles. So, the tree is different. The same applies to each tree.&lt;/p&gt;

&lt;p&gt;By the way, with 4 features in total, only 6 (4*3/2=6) various trees are possible. With n_estimators=10 some of these trees are necessarily repeated.
And in each shuffle those are different trees.&lt;/p&gt;
', NULL, NULL, '''/code'':13C,33C ''/p'':80C,118C ''10'':102C ''1st'':52C ''2'':37C ''3/2'':93C ''4'':86C,92C ''4th'':54C ''6'':91C,94C ''alway'':46C ''appli'':76C ''auto'':32C ''bootstrap'':11C ''build'':17C ''code'':10C,29C ''data'':24C ''default'':8C,27C ''differ'':63C,73C,116C ''estim'':101C ''featur'':31C,38C,55C,87C ''first'':58C ''fix'':4C,20C,41C ''let'':49C ''max'':30C ''n'':100C ''necessarili'':108C ''option'':9C,28C ''p'':1C,81C ''possibl'':98C ''repeat'':109C ''say'':51C ''seed'':6C,43C ''shuffl'':68C,113C ''sinc'':2C,39C ''subsampl'':21C ''total'':89C ''tree'':15C,35C,59C,71C,79C,96C,106C,117C ''true'':12C ''us'':50C ''use'':36C ''various'':95C ''way'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5655, 2, '2015-04-30 13:38:55.75', 2, NULL, NULL, '&lt;p&gt;First of all, since you only seem to want to know about the Python error, your question is probably better suited for Stack Overflow.&lt;/p&gt;

&lt;p&gt;Secondly, this is not fun to answer with the information you''ve provided.  This is certainly not a minimal working example (MWE) - I can''t copy your code as is, and run it myself.  I''m left to guess many things:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Are you importing &lt;code&gt;matplotlib&lt;/code&gt;?&lt;/li&gt;
&lt;li&gt;Did you rename &lt;code&gt;matplotlib.pyplot&lt;/code&gt; as &lt;code&gt;plt&lt;/code&gt;?&lt;/li&gt;
&lt;li&gt;Are you importing &lt;code&gt;numpy&lt;/code&gt; and did you rename it &lt;code&gt;np&lt;/code&gt;?&lt;/li&gt;
&lt;li&gt;Does your traceback literally end in &lt;code&gt;KeyError is 0&lt;/code&gt; or is it &lt;code&gt;KeyError: 0&lt;/code&gt; or &lt;code&gt;KeyError: False&lt;/code&gt;?  Can you show the full traceback?  The traceback for a MWE is typically not long.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In the positional argument (&lt;code&gt;movieDF[movieDF[''%s''%genre]==1].year&lt;/code&gt;) on your problematic line, you ask for a key (&lt;code&gt;movieDF[''%s''%genre]==1&lt;/code&gt;) that looks like it should evaluate to a Boolean.  Is this never 0?  I''m forced to grasp at straws because you haven''t provided a sample dataset; I shouldn''t have to download from IMDb myself.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Just to troubleshoot, can you place &lt;code&gt;x = movieDF[movieDF[''%s''%genre]==1].year&lt;/code&gt; on a line by itself and tell me what &lt;code&gt;x[0]&lt;/code&gt; is?  What type would that &lt;code&gt;x&lt;/code&gt; be?  Is it a dictionary, where the &lt;code&gt;.hist()&lt;/code&gt; method is expecting a list or other array?  &lt;a href="http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist" rel="nofollow"&gt;This documentation&lt;/a&gt; makes it sound like the method will not accept a dictionary...what type is &lt;code&gt;movieDF.year&lt;/code&gt;?&lt;/p&gt;
', NULL, NULL, '''/a'':283C ''/api/pyplot_api.html#matplotlib.pyplot.hist'':278C ''/code'':77C,85C,89C,97C,105C,118C,125C,130C,160C,175C,234C,247C,255C,265C,300C ''/li'':78C,90C,106C,146C ''/p'':26C,69C,213C,218C,301C ''/strong'':217C ''/ul'':147C ''0'':117C,124C,188C,246C ''1'':158C,174C,232C ''accept'':292C ''answer'':34C ''argument'':152C ''array'':273C ''ask'':166C ''better'':21C ''boolean'':184C ''certain'':43C ''code'':55C,75C,83C,87C,95C,103C,114C,122C,127C,153C,170C,226C,244C,253C,263C,298C ''copi'':53C ''dataset'':203C ''dictionari'':260C,294C ''document'':282C ''download'':209C ''edit'':216C ''end'':112C ''error'':16C ''evalu'':181C ''exampl'':48C ''expect'':268C ''fals'':129C ''first'':2C ''forc'':191C ''full'':135C ''fun'':32C ''genr'':157C,173C,231C ''grasp'':193C ''guess'':66C ''haven'':198C ''hist'':264C ''href'':275C ''imdb'':211C ''import'':74C,94C ''inform'':37C ''key'':169C ''keyerror'':115C,123C,128C ''know'':12C ''left'':64C ''li'':71C,79C,91C,107C ''like'':178C,287C ''line'':164C,237C ''list'':270C ''liter'':111C ''long'':145C ''look'':177C ''m'':63C,190C ''make'':284C ''mani'':67C ''matplotlib'':76C ''matplotlib.org'':277C ''matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist'':276C ''matplotlib.pyplot'':84C ''method'':266C,289C ''minim'':46C ''moviedf'':154C,155C,171C,228C,229C ''moviedf.year'':299C ''mwe'':49C,141C ''never'':187C ''nofollow'':280C ''np'':104C ''numpi'':96C ''overflow'':25C ''p'':1C,27C,148C,214C,219C ''place'':225C ''plt'':88C ''posit'':151C ''probabl'':20C ''problemat'':163C ''provid'':40C,200C ''python'':15C ''question'':18C ''rel'':279C ''renam'':82C,101C ''run'':59C ''sampl'':202C ''second'':28C ''seem'':8C ''shouldn'':205C ''show'':133C ''sinc'':5C ''sound'':286C ''stack'':24C ''straw'':195C ''strong'':215C ''suit'':22C ''tell'':241C ''thing'':68C ''traceback'':110C,136C,138C ''troubleshoot'':222C ''type'':250C,296C ''typic'':143C ''ul'':70C ''ve'':39C ''want'':10C ''work'':47C ''would'':251C ''x'':227C,245C,254C ''year'':159C,233C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5656, 1, '2015-04-30 13:42:05.343', 8, 549, 'K-means: What are some good ways to choose an efficient set of initial centroids?', '&lt;p&gt;When a random initialization of centroids is used, different runs of K-means produce different total SSEs. And it is crucial in the performance of the algorithm. 
What are some effective approaches toward solving this problem? Recent approaches are appreciated.&lt;/p&gt;
', 'data-mining  clustering  k-means', NULL, '''/p'':66C ''algorithm'':52C ''appreci'':65C ''approach'':57C,63C ''centroid'':16A,30C ''choos'':10A ''cluster'':20B ''crucial'':46C ''data'':18B ''data-min'':17B ''differ'':33C,40C ''effect'':56C ''effici'':12A ''good'':7A ''initi'':15A,28C ''k'':2A,22B,37C ''k-mean'':1A,21B,36C ''mean'':3A,23B,38C ''mine'':19B ''p'':24C ''perform'':49C ''problem'':61C ''produc'':39C ''random'':27C ''recent'':62C ''run'':34C ''set'':13A ''solv'':59C ''ss'':42C ''total'':41C ''toward'':58C ''use'':32C ''way'':8A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5657, 2, '2015-04-30 18:26:46.633', 4, NULL, NULL, '&lt;p&gt;I may be misunderstanding your question, but usually k-means chooses your centroids randomly for you depending on the number of clusters you set (i.e. k). Choosing the number for k tends to be a subjective exercise. A good place to start is an Elbow/Scree plot which can be found here:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set#The_Elbow_Method" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Determining_the_number_of_clusters_in_a_data_set#The_Elbow_Method&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':54C ''/wiki/determining_the_number_of_clusters_in_a_data_set#the_elbow_method'':60C ''/wiki/determining_the_number_of_clusters_in_a_data_set#the_elbow_method&lt;/a&gt;&lt;/p&gt;'':65C ''centroid'':15C ''choos'':13C,29C ''cluster'':24C ''depend'':19C ''elbow/scree'':47C ''en.wikipedia.org'':59C,64C ''en.wikipedia.org/wiki/determining_the_number_of_clusters_in_a_data_set#the_elbow_method'':58C ''en.wikipedia.org/wiki/determining_the_number_of_clusters_in_a_data_set#the_elbow_method&lt;/a&gt;&lt;/p&gt;'':63C ''exercis'':39C ''found'':52C ''good'':41C ''href'':57C ''i.e'':27C ''k'':11C,28C,33C ''k-mean'':10C ''may'':3C ''mean'':12C ''misunderstand'':5C ''nofollow'':62C ''number'':22C,31C ''p'':1C,55C ''place'':42C ''plot'':48C ''question'':7C ''random'':16C ''rel'':61C ''set'':26C ''start'':44C ''subject'':38C ''tend'':34C ''usual'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5658, 2, '2015-04-30 19:17:04.32', 1, NULL, NULL, '&lt;p&gt;You can check it here. At least that one is in the list:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.scimagojr.com/journalrank.php?category=1712&amp;amp;area=0&amp;amp;year=2013&amp;amp;country=US&amp;amp;order=sjr&amp;amp;min=0&amp;amp;min_type=cd&amp;amp;page=0" rel="nofollow"&gt;http://www.scimagojr.com/journalrank.php?category=1712&amp;amp;area=0&amp;amp;year=2013&amp;amp;country=US&amp;amp;order=sjr&amp;amp;min=0&amp;amp;min_type=cd&amp;amp;page=0&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/journalrank.php?category=1712&amp;amp;area=0&amp;amp;year=2013&amp;amp;country=us&amp;amp;order=sjr&amp;amp;min=0&amp;amp;min_type=cd&amp;amp;page=0'':21C ''/journalrank.php?category=1712&amp;amp;area=0&amp;amp;year=2013&amp;amp;country=us&amp;amp;order=sjr&amp;amp;min=0&amp;amp;min_type=cd&amp;amp;page=0&lt;/a&gt;&lt;/p&gt;'':26C ''/p'':15C ''check'':4C ''href'':18C ''least'':8C ''list'':14C ''nofollow'':23C ''one'':10C ''p'':1C,16C ''rel'':22C ''www.scimagojr.com'':20C,25C ''www.scimagojr.com/journalrank.php?category=1712&amp;amp;area=0&amp;amp;year=2013&amp;amp;country=us&amp;amp;order=sjr&amp;amp;min=0&amp;amp;min_type=cd&amp;amp;page=0'':19C ''www.scimagojr.com/journalrank.php?category=1712&amp;amp;area=0&amp;amp;year=2013&amp;amp;country=us&amp;amp;order=sjr&amp;amp;min=0&amp;amp;min_type=cd&amp;amp;page=0&lt;/a&gt;&lt;/p&gt;'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5659, 2, '2015-04-30 19:23:00.97', 7, NULL, NULL, '&lt;p&gt;Instead of scraping,  you might try to get the data directly here: &lt;a href="http://www.imdb.com/interfaces"&gt;http://www.imdb.com/interfaces&lt;/a&gt;.  It looks like they have data available via ftp for movies, actors, etc.&lt;/p&gt;
', NULL, NULL, '''/interfaces'':18C ''/interfaces&lt;/a&gt;.'':21C ''/p'':35C ''actor'':33C ''avail'':28C ''data'':11C,27C ''direct'':12C ''etc'':34C ''ftp'':30C ''get'':9C ''href'':15C ''instead'':2C ''like'':24C ''look'':23C ''might'':6C ''movi'':32C ''p'':1C ''scrape'':4C ''tri'':7C ''via'':29C ''www.imdb.com'':17C,20C ''www.imdb.com/interfaces'':16C ''www.imdb.com/interfaces&lt;/a&gt;.'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5732, 1, '2015-05-09 10:52:56.29', 0, 17, 'Creating Strings corresponding to Location Co-ordinates', '&lt;p&gt;What are some Python libraries which can convert a (X,Y) tuple to strings? &lt;/p&gt;

&lt;p&gt;(1.23,4.56) yields strings â1_4â, â12_45â, â123_456â.&lt;/p&gt;
', 'data-mining', NULL, '''/p'':27C,42C ''1'':34C ''1.23'':29C ''12'':37C ''123'':40C ''4.56'':30C ''456â'':41C ''45â'':38C ''4â'':35C ''co'':7A ''co-ordin'':6A ''convert'':20C ''correspond'':3A ''creat'':1A ''data'':10B ''data-min'':9B ''librari'':17C ''locat'':5A ''mine'':11B ''ordin'':8A ''p'':12C,28C ''python'':16C ''string'':2A,26C,32C ''tupl'':24C ''x'':22C ''y'':23C ''yield'':31C ''â'':33C,36C,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5661, 2, '2015-05-01 12:54:39.857', 2, NULL, NULL, '&lt;p&gt;The usual approach to this problem is to re-run your K-means algorithm several times, with different random initializations of the centroids, and to keep the best solution. You can do that by evaluating the results on your training data or by means of cross validation. &lt;/p&gt;

&lt;p&gt;There are many other ways to initialize the centroids, but none of them is going to perform the best for every single problem. You could evaluate these approaches together with random initialization for your particular problem. &lt;/p&gt;
', NULL, NULL, '''/p'':51C,89C ''algorithm'':17C ''approach'':4C,80C ''best'':31C,71C ''centroid'':26C,61C ''could'':77C ''cross'':49C ''data'':44C ''differ'':21C ''evalu'':38C,78C ''everi'':73C ''go'':67C ''initi'':23C,59C,84C ''k'':15C ''k-mean'':14C ''keep'':29C ''mani'':55C ''mean'':16C,47C ''none'':63C ''p'':1C,52C ''particular'':87C ''perform'':69C ''problem'':7C,75C,88C ''random'':22C,83C ''re'':11C ''re-run'':10C ''result'':40C ''run'':12C ''sever'':18C ''singl'':74C ''solut'':32C ''time'':19C ''togeth'':81C ''train'':43C ''usual'':3C ''valid'':50C ''way'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5663, 1, '2015-05-01 15:19:17.943', 4, 248, 'Reference about social network data-mining', '&lt;p&gt;I am not in the data science field, but I would like to examine in depth this field and, particularly, I would like to start from the analysis of the social networks data.&lt;/p&gt;

&lt;p&gt;I am trying to find some good references, both paper, websites and books, in order to start learning about the topic. Browsing on the internet, one can find a lot of sites, forum, papers about the topic, but I''m not able to discriminate among good and bad readings.&lt;/p&gt;

&lt;p&gt;I am an R, Matlab, SAS user and I know a little bit of python language.&lt;/p&gt;

&lt;p&gt;Could you suggest any references from which I could start studying and deepen the industry?&lt;/p&gt;
', 'bigdata  beginner  social-network-analysis', NULL, '''/p'':48C,99C,117C,134C ''abl'':91C ''among'':94C ''analysi'':13B,42C ''bad'':97C ''beginn'':9B ''bigdata'':8B ''bit'':113C ''book'':62C ''brows'':71C ''could'':119C,127C ''data'':6A,20C,47C ''data-min'':5A ''deepen'':131C ''depth'':30C ''discrimin'':93C ''examin'':28C ''field'':22C,32C ''find'':54C,77C ''forum'':82C ''good'':56C,95C ''industri'':133C ''internet'':74C ''know'':110C ''languag'':116C ''learn'':67C ''like'':26C,37C ''littl'':112C ''lot'':79C ''m'':89C ''matlab'':105C ''mine'':7A ''network'':4A,12B,46C ''one'':75C ''order'':64C ''p'':14C,49C,100C,118C ''paper'':59C,83C ''particular'':34C ''python'':115C ''r'':104C ''read'':98C ''refer'':1A,57C,123C ''sas'':106C ''scienc'':21C ''site'':81C ''social'':3A,11B,45C ''social-network-analysi'':10B ''start'':39C,66C,128C ''studi'':129C ''suggest'':121C ''topic'':70C,86C ''tri'':52C ''user'':107C ''websit'':60C ''would'':25C,36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5664, 2, '2015-05-01 15:29:48.2', 2, NULL, NULL, '&lt;p&gt;Without more detail, it''s difficult to give detailed advice, but at first glance, this seems like a textbook case for machine learning. See if you can get a similar data set with known fraudulent activity and, depending on the data, pick a machine learning approach. &lt;/p&gt;
', NULL, NULL, '''/p'':48C ''activ'':37C ''advic'':11C ''approach'':47C ''case'':21C ''data'':32C,42C ''depend'':39C ''detail'':4C,10C ''difficult'':7C ''first'':14C ''fraudul'':36C ''get'':29C ''give'':9C ''glanc'':15C ''known'':35C ''learn'':24C,46C ''like'':18C ''machin'':23C,45C ''p'':1C ''pick'':43C ''see'':25C ''seem'':17C ''set'':33C ''similar'':31C ''textbook'':20C ''without'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5665, 2, '2015-05-01 16:52:15.717', 2, NULL, NULL, '&lt;p&gt;As a bit of general feedback, I think you would do well to improve your output format.  The problem with the format as it stands is there is not a transparent way to programmatically get the data. Consider instead trying:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;print "\t".join([title, genres,runtime, rating, year])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The nice thing about a tab delimited file is that if you end up scaling up, it can easily be read into something like impala (or at smaller scales, simple mySql tables).  Additionally, you can then programatically read in the data in python using:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; line.split("\t")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The second bit of advice, is I would suggest getting more information than you think you need on your initial scrape.  Disk space is cheaper than processing time, so rerunning the scraper every time you expand your analytic will not be fun.&lt;/p&gt;
', NULL, NULL, '''/code'':53C,105C ''/p'':42C,100C,150C ''/pre'':54C,106C ''addit'':88C ''advic'':112C ''analyt'':145C ''bit'':4C,110C ''cheaper'':132C ''code'':44C,102C ''consid'':39C ''data'':38C,96C ''delimit'':62C ''disk'':129C ''easili'':74C ''end'':68C ''everi'':140C ''expand'':143C ''feedback'':7C ''file'':63C ''format'':18C,23C ''fun'':149C ''general'':6C ''genr'':49C ''get'':36C,117C ''impala'':80C ''improv'':15C ''inform'':119C ''initi'':127C ''instead'':40C ''join'':47C ''like'':79C ''line.split'':103C ''mysql'':86C ''need'':124C ''nice'':57C ''output'':17C ''p'':1C,55C,107C ''pre'':43C,101C ''print'':45C ''problem'':20C ''process'':134C ''programat'':92C ''programmat'':35C ''python'':98C ''rate'':51C ''read'':76C,93C ''rerun'':137C ''runtim'':50C ''scale'':70C,84C ''scrape'':128C ''scraper'':139C ''second'':109C ''simpl'':85C ''smaller'':83C ''someth'':78C ''space'':130C ''stand'':26C ''suggest'':116C ''tab'':61C ''tabl'':87C ''thing'':58C ''think'':9C,122C ''time'':135C,141C ''titl'':48C ''transpar'':32C ''tri'':41C ''use'':99C ''way'':33C ''well'':13C ''would'':11C,115C ''year'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5690, 1, '2015-05-05 10:10:19.463', 1, 46, 'Classify the coordinate data', '&lt;p&gt;I''d like to classify the data on coordinate.&lt;/p&gt;

&lt;p&gt;Here are 2 example data.&lt;/p&gt;

&lt;p&gt;data1 = [(1,1), (2,2), &lt;strong&gt;(3, 3), (4, 2), (5, 3)&lt;/strong&gt;, (6, 0)]&lt;/p&gt;

&lt;p&gt;data2 = [(1,1), (2,2), &lt;strong&gt;(3, 10), (4, 9), (5, 10)&lt;/strong&gt;, (6, 0)]&lt;/p&gt;

&lt;p&gt;The bold part have the same wave in above data.&lt;/p&gt;

&lt;p&gt;The length are all different in my data set.&lt;/p&gt;

&lt;p&gt;Is there any way that I can find the similar wave in many data like this?&lt;/p&gt;

&lt;p&gt;Thank you.&lt;/p&gt;
', 'data-mining  algorithms', NULL, '''/p'':19C,26C,43C,60C,72C,83C,101C,105C ''/strong'':40C,57C ''0'':42C,59C ''1'':29C,30C,46C,47C ''10'':52C,56C ''2'':23C,31C,32C,37C,48C,49C ''3'':34C,35C,39C,51C ''4'':36C,53C ''5'':38C,55C ''6'':41C,58C ''9'':54C ''algorithm'':8B ''bold'':63C ''classifi'':1A,14C ''coordin'':3A,18C ''d'':11C ''data'':4A,6B,16C,25C,71C,81C,98C ''data-min'':5B ''data1'':28C ''data2'':45C ''differ'':78C ''exampl'':24C ''find'':92C ''length'':75C ''like'':12C,99C ''mani'':97C ''mine'':7B ''p'':9C,20C,27C,44C,61C,73C,84C,102C ''part'':64C ''set'':82C ''similar'':94C ''strong'':33C,50C ''thank'':103C ''wave'':68C,95C ''way'':88C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9179, 1, '2015-12-03 04:10:28.387', 1, 88, 'Library to count number of objects in image?', '&lt;p&gt;I''m reading a &lt;a href="https://www.robots.ox.ac.uk/~vgg/publications/2010/Lempitsky10b/lempitsky10b.pdf" rel="nofollow"&gt;paper&lt;/a&gt; on counting cells, humans, etc. Is there an off-the-shelf library for Python/Theano or TensorFlow I could use?&lt;/p&gt;
', 'python  theano  tensorflow', NULL, '''/a'':25C ''/p'':46C ''/~vgg/publications/2010/lempitsky10b/lempitsky10b.pdf'':21C ''cell'':28C ''could'':44C ''count'':3A,27C ''etc'':30C ''href'':18C ''human'':29C ''imag'':8A ''librari'':1A,38C ''m'':14C ''nofollow'':23C ''number'':4A ''object'':6A ''off-the-shelf'':34C ''p'':12C ''paper'':24C ''python'':9B ''python/theano'':40C ''read'':15C ''rel'':22C ''shelf'':37C ''tensorflow'':11B,42C ''theano'':10B ''use'':45C ''www.robots.ox.ac.uk'':20C ''www.robots.ox.ac.uk/~vgg/publications/2010/lempitsky10b/lempitsky10b.pdf'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5666, 1, '2015-05-01 19:31:34.57', 2, 102, 'Analyzing survey data for predictions', '&lt;p&gt;I''ve got survey data that resembles:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;|-------------| Q1a | Q1b | Q1c | Q2a | Q2b | Q2c | Classification
| Respondent  | 1   | 0   | 0   | 1   | 0   | 0   | Red
| Respondent  | 0   | 0   | 1   | 1   | 0   | 0   | Green
| Respondent  | 0   | 1   | 0   | 0   | 0   | 1   | Yellow
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am trying to predict the classification for new respondents. Currently I''m using a Naive Bayes, and getting pretty bad accuracy (~20%). I don''t have much training data, and the training data is hand scraped from non-standard sources (internal company procedures are a mess here).&lt;/p&gt;

&lt;p&gt;I''m looking for other ways to predict the classification.&lt;/p&gt;

&lt;p&gt;I''m thinking about assigning weights to each question, and magically predicting the result based on those, somehow. Although I don''t really know where to start learning about how to do that, and whether it''s appropriate for this data. I have very little background in this :(&lt;/p&gt;

&lt;p&gt;Any ideas or tips on predicting the classification column with no training data?&lt;/p&gt;
', 'predictive-modeling', NULL, '''/code'':51C ''/p'':17C,103C,115C,165C,180C ''/pre'':52C ''0'':29C,30C,32C,33C,36C,37C,40C,41C,44C,46C,47C,48C ''1'':28C,31C,38C,39C,45C,49C ''20'':76C ''accuraci'':75C ''although'':135C ''analyz'':1A ''appropri'':154C ''assign'':121C ''background'':162C ''bad'':74C ''base'':131C ''bay'':70C ''classif'':26C,60C,114C,174C ''code'':19C ''column'':175C ''compani'':97C ''current'':64C ''data'':3A,14C,83C,87C,157C,179C ''get'':72C ''got'':12C ''green'':42C ''hand'':89C ''idea'':168C ''intern'':96C ''know'':140C ''learn'':144C ''littl'':161C ''look'':107C ''m'':66C,106C,118C ''magic'':127C ''mess'':101C ''model'':8B ''much'':81C ''naiv'':69C ''new'':62C ''non'':93C ''non-standard'':92C ''p'':9C,53C,104C,116C,166C ''pre'':18C ''predict'':5A,7B,58C,112C,128C,172C ''predictive-model'':6B ''pretti'':73C ''procedur'':98C ''q1a'':20C ''q1b'':21C ''q1c'':22C ''q2a'':23C ''q2b'':24C ''q2c'':25C ''question'':125C ''realli'':139C ''red'':34C ''resembl'':16C ''respond'':27C,35C,43C,63C ''result'':130C ''scrape'':90C ''somehow'':134C ''sourc'':95C ''standard'':94C ''start'':143C ''survey'':2A,13C ''think'':119C ''tip'':170C ''train'':82C,86C,178C ''tri'':56C ''use'':67C ''ve'':11C ''way'':110C ''weight'':122C ''whether'':151C ''yellow'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5667, 1, '2015-05-01 20:32:51.9', 3, 2080, 'Spark, optimally splitting a single RDD into two', '&lt;p&gt;I have a large dataset that I need to split into groups according to specific parameters.  I want the job to process as efficiently as possible.  I can envision two ways of doing so&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Option 1&lt;/strong&gt; - Create map from original RDD and filter&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def customMapper(record):
    if passesSomeTest(record):
        return (1,record)
    else:
        return (0,record)

mappedRdd = rddIn.map(lambda x: customMapper(x))
rdd0 = mappedRdd.filter(lambda x: x[0]==0).cache()
rdd1 = mappedRdd.filter(lambda x: x[1]==1).cache()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Option 2&lt;/strong&gt; - Filter original RDD directly&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def customFilter(record):
    return passesSomeTest(record)

rdd0 = rddIn.filter(lambda x: customFilter(x)==False).cache()
rdd1 = rddIn.filter(customFilter).cache()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The fist method has to itterate over all the records of the original data set 3 times, where the second only has to do so twice, under normal circumstances, however, spark does some behind the scenes graph building, so I could imagine that they are effectively done in the same way.  My questions are:
a.) Is one method more efficient than the other, or does the spark graph building make them equivalent
b.) Is it possible to do this split in a single pass&lt;/p&gt;
', 'apache-spark', NULL, '''/code'':98C,130C ''/p'':47C,60C,109C,217C ''/pre'':99C,131C ''/strong'':52C,104C ''0'':74C,87C,88C ''1'':51C,70C,95C,96C ''2'':103C ''3'':148C ''accord'':25C ''apach'':10B ''apache-spark'':9B ''b'':205C ''behind'':166C ''build'':170C,201C ''cach'':89C,97C,125C,129C ''circumst'':161C ''code'':62C,111C ''could'':173C ''creat'':53C ''customfilt'':113C,122C,128C ''custommapp'':64C,80C ''data'':146C ''dataset'':17C ''def'':63C,112C ''direct'':108C ''done'':179C ''effect'':178C ''effici'':36C,192C ''els'':72C ''envis'':41C ''equival'':204C ''fals'':124C ''filter'':59C,105C ''fist'':134C ''graph'':169C,200C ''group'':24C ''howev'':162C ''imagin'':174C ''itter'':138C ''job'':32C ''lambda'':78C,84C,92C,120C ''larg'':16C ''make'':202C ''map'':54C ''mappedrdd'':76C ''mappedrdd.filter'':83C,91C ''method'':135C,190C ''need'':20C ''normal'':160C ''one'':189C ''optim'':2A ''option'':50C,102C ''origin'':56C,106C,145C ''p'':12C,48C,100C,132C ''paramet'':28C ''pass'':216C ''passessometest'':67C,116C ''possibl'':38C,208C ''pre'':61C,110C ''process'':34C ''question'':185C ''rdd'':6A,57C,107C ''rdd0'':82C,118C ''rdd1'':90C,126C ''rddin.filter'':119C,127C ''rddin.map'':77C ''record'':65C,68C,71C,75C,114C,117C,142C ''return'':69C,73C,115C ''scene'':168C ''second'':152C ''set'':147C ''singl'':5A,215C ''spark'':1A,11B,163C,199C ''specif'':27C ''split'':3A,22C,212C ''strong'':49C,101C ''time'':149C ''twice'':158C ''two'':8A,42C ''want'':30C ''way'':43C,183C ''x'':79C,81C,85C,86C,93C,94C,121C,123C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5668, 2, '2015-05-01 20:57:34.133', 2, NULL, NULL, '&lt;p&gt;Can you give a bit more information on the size of the data you''re training on (and if it''s really 6 parameters you''re basing the predictions off of)?  If it''s really 6 questions with binary answers (1, 0 as you suggest), then there are 2^6 (i.e. 64) unique answer combinations, and to determine a probability for them you''ll want a multiple entries per combination.  Standard error scales like 1/sqrt(n) so for 10% accuracy you''ll need roughly 6,400 inputs which given your description, sounds like more data than you may have.  You may want to invest time into automating data collection.&lt;/p&gt;

&lt;p&gt;If on the other hand, you have a reasonably large data set and are hoping for some alternative models, both boosted decision trees and random forest models sound like good candidates for this problem.  &lt;/p&gt;
', NULL, NULL, '''/p'':111C,147C ''0'':43C ''1'':42C ''1/sqrt'':76C ''10'':80C ''2'':50C ''400'':87C ''6'':24C,37C,51C,86C ''64'':53C ''accuraci'':81C ''altern'':130C ''answer'':41C,55C ''autom'':108C ''base'':28C ''binari'':40C ''bit'':6C ''boost'':133C ''candid'':143C ''collect'':110C ''combin'':56C,71C ''data'':14C,96C,109C,123C ''decis'':134C ''descript'':92C ''determin'':59C ''entri'':69C ''error'':73C ''forest'':138C ''give'':4C ''given'':90C ''good'':142C ''hand'':117C ''hope'':127C ''i.e'':52C ''inform'':8C ''input'':88C ''invest'':105C ''larg'':122C ''like'':75C,94C,141C ''ll'':65C,83C ''may'':99C,102C ''model'':131C,139C ''multipl'':68C ''n'':77C ''need'':84C ''p'':1C,112C ''paramet'':25C ''per'':70C ''predict'':30C ''probabl'':61C ''problem'':146C ''question'':38C ''random'':137C ''re'':16C,27C ''realli'':23C,36C ''reason'':121C ''rough'':85C ''scale'':74C ''set'':124C ''size'':11C ''sound'':93C,140C ''standard'':72C ''suggest'':46C ''time'':106C ''train'':17C ''tree'':135C ''uniqu'':54C ''want'':66C,103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5707, 1, '2015-05-07 06:22:51.47', 2, 32, 'Time Complexity notation in Big Data platforms', '&lt;p&gt;I am redesigning some of the classical algorithms for Hadoop/MapReduce framework. I was wondering if there any established approach for denoting Big(O) kind of expressions to measure time complexity?&lt;/p&gt;

&lt;p&gt;For example, hypothetically, a simple average calculation of n (=1 billion) numbers is O(n) + C operation using simple for loop, or O(log) I am assuming division to be a constant time operation for the sake for simplicity. If i break this massively parallelizable algorithm for MapReduce, by dividing data over k nodes, my time complexity would simply become O(n/k) + C + C''. Here, C'' can be assumed as the job planning time overhead. Note that there was no shuffling involved, and reducer''s job was nearly trivial.&lt;/p&gt;

&lt;p&gt;I am interested in a more complete analysis of algorithm with iterative loops over data and involve heavy shuffling and reducer operations. I want to incorporate, if possible, the I/O operations and network transfers of data.&lt;/p&gt;
', 'bigdata  algorithms  map-reduce', NULL, '''/p'':44C,135C,173C ''1'':55C ''algorithm'':9B,21C,91C,146C ''analysi'':144C ''approach'':32C ''assum'':72C,114C ''averag'':51C ''becom'':105C ''big'':5A,35C ''bigdata'':8B ''billion'':56C ''break'':87C ''c'':61C,108C,109C,111C ''calcul'':52C ''classic'':20C ''complet'':143C ''complex'':2A,43C,102C ''constant'':77C ''data'':6A,96C,151C,172C ''denot'':34C ''divid'':95C ''divis'':73C ''establish'':31C ''exampl'':47C ''express'':39C ''framework'':24C ''hadoop/mapreduce'':23C ''heavi'':154C ''hypothet'':48C ''i/o'':166C ''incorpor'':162C ''interest'':139C ''involv'':127C,153C ''iter'':148C ''job'':117C,131C ''k'':98C ''kind'':37C ''log'':69C ''loop'':66C,149C ''map'':11B ''map-reduc'':10B ''mapreduc'':93C ''massiv'':89C ''measur'':41C ''n'':54C,60C ''n/k'':107C ''near'':133C ''network'':169C ''node'':99C ''notat'':3A ''note'':121C ''number'':57C ''o'':36C,59C,68C,106C ''oper'':62C,79C,158C,167C ''overhead'':120C ''p'':13C,45C,136C ''paralleliz'':90C ''plan'':118C ''platform'':7A ''possibl'':164C ''redesign'':16C ''reduc'':12B,129C,157C ''sake'':82C ''shuffl'':126C,155C ''simpl'':50C,64C ''simpli'':104C ''simplic'':84C ''time'':1A,42C,78C,101C,119C ''transfer'':170C ''trivial'':134C ''use'':63C ''want'':160C ''wonder'':27C ''would'':103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5669, 2, '2015-05-01 21:42:55.35', 2, NULL, NULL, '&lt;p&gt;My favorite place to find information about social network analysis is from SNAP, the Stanford Network Analysis Project. Led by Jure Leskovec, this team of students and professors has built software tools, gathered data sets, and published papers on social network analysis.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://snap.stanford.edu/" rel="nofollow"&gt;http://snap.stanford.edu/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The collection of research papers there is outstanding. &lt;/p&gt;

&lt;p&gt;They also have a Python tool you could try. &lt;a href="http://snap.stanford.edu/snappy/index.html" rel="nofollow"&gt;http://snap.stanford.edu/snappy/index.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The focus is on graph analysis, because social networks fit this model well. If you are new to graph analysis, I suggest you take a undergraduate level discrete mathematics course, or check out my favorite book on the topic "Graph Theory with Algorithms and its Applications" by Santanu Ray.&lt;/p&gt;

&lt;p&gt;For a hands-on approach to social network analysis, check out "Mining the Social Web" by Matthew A Russell. It has examples which cover how to collect and analyze data from the major social networks like Twitter, Facebook, and LinkedIn.&lt;/p&gt;

&lt;p&gt;It was Jure Leskovec who initially excited me about this field. He has many great talks on YouTube, for example: &lt;a href="https://www.youtube.com/watch?v=LmQ_3nijMCs" rel="nofollow"&gt;https://www.youtube.com/watch?v=LmQ_3nijMCs&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':53C ''/p'':44C,63C,134C,177C ''/snappy/index.html'':78C ''/snappy/index.html&lt;/a&gt;&lt;/p&gt;'':83C ''/watch?v=lmq_3nijmcs'':203C ''/watch?v=lmq_3nijmcs&lt;/a&gt;&lt;/p&gt;'':208C ''algorithm'':127C ''also'':66C ''analysi'':11C,18C,43C,90C,104C,145C ''analyz'':165C ''applic'':130C ''approach'':141C ''book'':120C ''built'':31C ''check'':116C,146C ''collect'':56C,163C ''could'':72C ''cours'':114C ''cover'':160C ''data'':35C,166C ''discret'':112C ''exampl'':158C,198C ''excit'':185C ''facebook'':174C ''favorit'':3C,119C ''field'':189C ''find'':6C ''fit'':94C ''focus'':86C ''gather'':34C ''graph'':89C,103C,124C ''great'':193C ''hand'':139C ''hands-on'':138C ''href'':47C,75C,200C ''inform'':7C ''initi'':184C ''jure'':22C,181C ''led'':20C ''leskovec'':23C,182C ''level'':111C ''like'':172C ''linkedin'':176C ''major'':169C ''mani'':192C ''mathemat'':113C ''matthew'':153C ''mine'':148C ''model'':96C ''network'':10C,17C,42C,93C,144C,171C ''new'':101C ''nofollow'':50C,80C,205C ''outstand'':62C ''p'':1C,45C,54C,64C,84C,135C,178C ''paper'':39C,59C ''place'':4C ''professor'':29C ''project'':19C ''publish'':38C ''python'':69C ''ray'':133C ''rel'':49C,79C,204C ''research'':58C ''russel'':155C ''santanu'':132C ''set'':36C ''snap'':14C ''snap.stanford.edu'':48C,52C,77C,82C ''snap.stanford.edu/&lt;/a&gt;&lt;/p&gt;'':51C ''snap.stanford.edu/snappy/index.html'':76C ''snap.stanford.edu/snappy/index.html&lt;/a&gt;&lt;/p&gt;'':81C ''social'':9C,41C,92C,143C,150C,170C ''softwar'':32C ''stanford'':16C ''student'':27C ''suggest'':106C ''take'':108C ''talk'':194C ''team'':25C ''theori'':125C ''tool'':33C,70C ''topic'':123C ''tri'':73C ''twitter'':173C ''undergradu'':110C ''web'':151C ''well'':97C ''www.youtube.com'':202C,207C ''www.youtube.com/watch?v=lmq_3nijmcs'':201C ''www.youtube.com/watch?v=lmq_3nijmcs&lt;/a&gt;&lt;/p&gt;'':206C ''youtub'':196C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9180, 2, '2015-12-03 05:49:55.02', 0, NULL, NULL, '&lt;p&gt;Not sure I understand the question...  TensorFlow is OpenSourced, &lt;a href="https://www.tensorflow.org/tutorials/mnist/beginners/index.html" rel="nofollow"&gt;here is a tutorial&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;To import the TensorFlow library, all you need to do is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import tensorflow as tf
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':22C ''/code'':43C ''/p'':23C,36C ''/pre'':44C ''/tutorials/mnist/beginners/index.html'':15C ''code'':38C ''href'':12C ''import'':26C,39C ''librari'':29C ''need'':32C ''nofollow'':17C ''opensourc'':10C ''p'':1C,24C ''pre'':37C ''question'':7C ''rel'':16C ''sure'':3C ''tensorflow'':8C,28C,40C ''tf'':42C ''tutori'':21C ''understand'':5C ''www.tensorflow.org'':14C ''www.tensorflow.org/tutorials/mnist/beginners/index.html'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5670, 2, '2015-05-02 01:16:38.717', 1, NULL, NULL, '&lt;p&gt;Neural Networks are not a great introductory model, simply because of the complexity that you describe.  If you''re trying to get your feet wet, boosted decision trees tend to perform well by comparison, and are a bit more intuitive.  If you want a description on this method, and are already familiar with Coursera, The University of Washington has an introductory course on data science which explains it quite well.&lt;/p&gt;
', NULL, NULL, '''/p'':72C ''alreadi'':52C ''bit'':39C ''boost'':27C ''comparison'':35C ''complex'':14C ''cours'':63C ''coursera'':55C ''data'':65C ''decis'':28C ''describ'':17C ''descript'':46C ''explain'':68C ''familiar'':53C ''feet'':25C ''get'':23C ''great'':7C ''introductori'':8C,62C ''intuit'':41C ''method'':49C ''model'':9C ''network'':3C ''neural'':2C ''p'':1C ''perform'':32C ''quit'':70C ''re'':20C ''scienc'':66C ''simpli'':10C ''tend'':30C ''tree'':29C ''tri'':21C ''univers'':57C ''want'':44C ''washington'':59C ''well'':33C,71C ''wet'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5671, 2, '2015-05-02 01:26:46.707', 2, NULL, NULL, '&lt;p&gt;A lot of the features you mentioned are categorical, and with so many levels of each, the dimensions of your problem will expand.  Rather than initially focusing on Lasso and Ridge regression, why don''t you first look for clusters among the samples (records) to learn about the data set?  Under you current approach, you''re throwing everything into a model and expecting a high AUC(?).  You may find several clusters where frequencies of the category levels predominate in one or more potential clusters.  If you don''t know what the cluster structure is of the samples (records), try k-means clustering based on centroids of feature values to see if there are unique clusters.  &lt;/p&gt;

&lt;p&gt;Once you get a handle on the cluster structure, then address regression issues.  Your regression models may be breaking down, in part, because of large inhomogeneities in your data, along with the previously suggested issues.  &lt;/p&gt;

&lt;p&gt;Machine learning is all about performing unsupervised class discovery followed by class prediction (your output binary variable).   &lt;/p&gt;

&lt;p&gt;At this point, it''s not clear that you studied the data to learn about its cluster structure, and rather threw it into a supervised model expecting to attain high AUC values.   &lt;/p&gt;
', NULL, NULL, '''/p'':118C,155C,174C,208C ''address'':130C ''along'':149C ''among'':42C ''approach'':55C ''attain'':204C ''auc'':67C,206C ''base'':105C ''binari'':172C ''break'':138C ''categor'':10C ''categori'':77C ''centroid'':107C ''class'':164C,168C ''clear'':182C ''cluster'':41C,72C,85C,93C,104C,117C,127C,192C ''current'':54C ''data'':50C,148C,187C ''dimens'':19C ''discoveri'':165C ''everyth'':59C ''expand'':24C ''expect'':64C,202C ''featur'':6C,109C ''find'':70C ''first'':38C ''focus'':28C ''follow'':166C ''frequenc'':74C ''get'':122C ''handl'':124C ''high'':66C,205C ''inhomogen'':145C ''initi'':27C ''issu'':132C,154C ''k'':102C ''k-mean'':101C ''know'':90C ''larg'':144C ''lasso'':30C ''learn'':47C,158C,189C ''level'':15C,78C ''look'':39C ''lot'':3C ''machin'':157C ''mani'':14C ''may'':69C,136C ''mean'':103C ''mention'':8C ''model'':62C,135C,201C ''one'':81C ''output'':171C ''p'':1C,119C,156C,175C ''part'':141C ''perform'':162C ''point'':178C ''potenti'':84C ''predict'':169C ''predomin'':79C ''previous'':152C ''problem'':22C ''rather'':25C,195C ''re'':57C ''record'':45C,99C ''regress'':33C,131C,134C ''ridg'':32C ''sampl'':44C,98C ''see'':112C ''set'':51C ''sever'':71C ''structur'':94C,128C,193C ''studi'':185C ''suggest'':153C ''supervis'':200C ''threw'':196C ''throw'':58C ''tri'':100C ''uniqu'':116C ''unsupervis'':163C ''valu'':110C,207C ''variabl'':173C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6389, 2, '2015-07-08 07:51:17.783', 1, NULL, NULL, '&lt;p&gt;ConLL is a format optimised for processing efficiency (both speed and memory usage).&lt;/p&gt;

&lt;p&gt;XML formats for NLP often have the annotation layers one after the other; therefore you have to hold the whole file in memory for processing it.&lt;/p&gt;

&lt;p&gt;A drawback of ConLL is that you have to put in fillers (e.g., &lt;code&gt;0&lt;/code&gt;) for sparse annotations.&lt;/p&gt;
', NULL, NULL, '''/code'':60C ''/p'':15C,43C,64C ''0'':59C ''annot'':24C,63C ''code'':58C ''conll'':2C,48C ''drawback'':46C ''e.g'':57C ''effici'':9C ''file'':37C ''filler'':56C ''format'':5C,18C ''hold'':34C ''layer'':25C ''memori'':13C,39C ''nlp'':20C ''often'':21C ''one'':26C ''optimis'':6C ''p'':1C,16C,44C ''process'':8C,41C ''put'':54C ''spars'':62C ''speed'':11C ''therefor'':30C ''usag'':14C ''whole'':36C ''xml'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5682, 2, '2015-05-04 00:22:47.427', 2, NULL, NULL, '&lt;p&gt;Text can be converted to data via the use of concept clusters (after stemming and stopping), or to count (frequencies) via use of n-grams.  N-grams are basically tabulations of the 1-gram count (frequency) of alphabet characters (a though z) in each document, and counts of 2-grams (aa to zz), 3-grams (aaa through zzz), up to about 5-grams (aaaaa through zzzzz).  Beyond 5-grams, the data will be sparse and less informative.  Thus, a dataset can be constructed for which rows represent documents, and columns represent n-grams. The data values themselves are the total number of occurrences of each gram found in each document.  &lt;/p&gt;

&lt;p&gt;FYI - n-grams have proven to be the best technique for identifying different languages based on characters.    &lt;/p&gt;

&lt;p&gt;Regarding SVMs, focus on the SVM literature.           &lt;/p&gt;
', NULL, NULL, '''/p'':115C,135C,144C ''1'':36C ''2'':52C ''3'':57C ''5'':65C,71C ''aa'':54C ''aaa'':59C ''aaaaa'':67C ''alphabet'':41C ''base'':132C ''basic'':32C ''best'':126C ''beyond'':70C ''charact'':42C,134C ''cluster'':13C ''column'':93C ''concept'':12C ''construct'':86C ''convert'':5C ''count'':20C,38C,50C ''data'':7C,74C,99C ''dataset'':83C ''differ'':130C ''document'':48C,91C,114C ''focus'':139C ''found'':111C ''frequenc'':21C,39C ''fyi'':117C ''gram'':27C,30C,37C,53C,58C,66C,72C,97C,110C,120C ''identifi'':129C ''inform'':80C ''languag'':131C ''less'':79C ''literatur'':143C ''n'':26C,29C,96C,119C ''n-gram'':25C,28C,95C,118C ''number'':105C ''occurr'':107C ''p'':1C,116C,136C ''proven'':122C ''regard'':137C ''repres'':90C,94C ''row'':89C ''spars'':77C ''stem'':15C ''stop'':17C ''svm'':142C ''svms'':138C ''tabul'':33C ''techniqu'':127C ''text'':2C ''though'':44C ''thus'':81C ''total'':104C ''use'':10C,23C ''valu'':100C ''via'':8C,22C ''z'':45C ''zz'':56C ''zzz'':61C ''zzzzz'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5672, 2, '2015-05-02 02:55:09.197', 3, NULL, NULL, '&lt;p&gt;I''m going to pursue the following series of online courses on the Coursera: 
&lt;a href="https://www.coursera.org/specialization/socialscience/25?utm_medium=listingPage" rel="nofollow"&gt;&lt;strong&gt;Become a Social Scientist: Methods and Statistics&lt;/strong&gt;&lt;/a&gt; by &lt;em&gt;University of Amsterdam&lt;/em&gt;. The good news - it is free, or you can get a nice-looking certificate for $49 or so. The bad news - the nearest enrollment is Aug 31st 2015. You will have opportunity to get a lot of information in condensed way during a short time frame and you will be enforced to actually apply the knowledge in exercises, quizzes and project assignments. You will also have opportunity to discuss lessons/projects on the forum with many other students and lecturers. &lt;/p&gt;

&lt;p&gt;[update] I apologize, I just remembered there is another course &lt;a href="https://www.coursera.org/course/statistics101" rel="nofollow"&gt;&lt;strong&gt;Introduction to Statistics for the Social Sciences&lt;/strong&gt;&lt;/a&gt; by &lt;em&gt;University of Zurich&lt;/em&gt; - just started April 28th, 2015. If you want to pursue it - do not forget about deadlines for quizzes and homeworks. Good luck!&lt;/p&gt;
', NULL, NULL, '''/a'':32C,147C ''/course/statistics101'':135C ''/em'':38C,153C ''/p'':119C,176C ''/specialization/socialscience/25?utm_medium=listingpage'':20C ''/strong'':31C,146C ''2015'':67C,158C ''28th'':157C ''31st'':66C ''49'':55C ''actual'':92C ''also'':104C ''amsterdam'':37C ''anoth'':129C ''apolog'':123C ''appli'':93C ''april'':156C ''assign'':101C ''aug'':65C ''bad'':59C ''becom'':24C ''certif'':53C ''condens'':79C ''cours'':12C,130C ''coursera'':15C ''deadlin'':169C ''discuss'':108C ''em'':34C,149C ''enforc'':90C ''enrol'':63C ''exercis'':97C ''follow'':8C ''forget'':167C ''forum'':112C ''frame'':85C ''free'':44C ''get'':48C,73C ''go'':4C ''good'':40C,174C ''homework'':173C ''href'':17C,132C ''inform'':77C ''introduct'':139C ''knowledg'':95C ''lectur'':118C ''lessons/projects'':109C ''look'':52C ''lot'':75C ''luck'':175C ''m'':3C ''mani'':114C ''method'':28C ''nearest'':62C ''news'':41C,60C ''nice'':51C ''nice-look'':50C ''nofollow'':22C,137C ''onlin'':11C ''opportun'':71C,106C ''p'':1C,120C ''project'':100C ''pursu'':6C,163C ''quizz'':98C,171C ''rel'':21C,136C ''rememb'':126C ''scienc'':145C ''scientist'':27C ''seri'':9C ''short'':83C ''social'':26C,144C ''start'':155C ''statist'':30C,141C ''strong'':23C,138C ''student'':116C ''time'':84C ''univers'':35C,150C ''updat'':121C ''want'':161C ''way'':80C ''www.coursera.org'':19C,134C ''www.coursera.org/course/statistics101'':133C ''www.coursera.org/specialization/socialscience/25?utm_medium=listingpage'':18C ''zurich'':152C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5674, 2, '2015-05-03 00:30:18.57', 5, NULL, NULL, '&lt;p&gt;Learning curves or bias-variance decomposition are the gold standard for detecting high variance, aka: overfitting.  Separate your data (in your case the "back data") into 60% training data and 40% testing data.  Fit the model on the training data as you usually would and see how well it is working with the test data.  &lt;/p&gt;

&lt;p&gt;Finally, when you think you have the model that you want, split each of the training and test sets into 10-100 subsets and retrain and test with incrementally larger sets.  Apply your favorite performance metric and plot the results of performance vs. the number of cases used for testing and training.  &lt;/p&gt;

&lt;p&gt;The curves will never come together if the model is overfit (high variance).  The curves will come together but the performance will be lower than desired if the model is underfit (high bias) and the lines will come together at an acceptable performance for a well performing model that is not overfit.&lt;/p&gt;

&lt;p&gt;Here is an example of overfitting and underfitting with root mean square error as the performance metric:
&lt;img src="http://i.stack.imgur.com/m3a90.png" alt="Bias-Variance decomposition via learning curves"&gt;  &lt;/p&gt;

&lt;p&gt;&lt;a href="https://followthedata.wordpress.com/2012/06/02/practical-advice-for-machine-learning-bias-variance/"&gt;Here is a pretty good link&lt;/a&gt; on the process and &lt;a href="http://www.astroml.org/sklearn_tutorial/practical.html"&gt;here is another one&lt;/a&gt;.  Hope this helps!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9193, 2, '2015-12-03 14:41:58.103', 1, NULL, NULL, '&lt;p&gt;Assume you clustering results are store in R. You have the partition id &lt;em&gt;part_id&lt;/em&gt; and the real &lt;em&gt;clust_id&lt;/em&gt;, here in a small example &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;df &amp;lt;- read.table(text = "obj,  part_id, clust_id 
X, 1, 1
Y, 2, 2
Z, 3, 3 
U, 1, 3
V, 2, 3
W, 2, 3"
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Than the &lt;strong&gt;table&lt;/strong&gt; will do the work for you&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; table(df[,c("part_id","clust_id")])

       clust_id
part_id 1 2 3
      1 1 0 1
      2 0 1 2
      3 0 0 1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;UPDATE&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;For completeness you may also use SQL (as proposed in a comment), a sample query is shown below on a table identical to the data-frame above.&lt;/p&gt;

&lt;p&gt;You will see imediately, that there is a limitation because you must define the exact column list in the query - so the query must be adjusted based on the cluster / partition list.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;with clust_agg as (
select part_id, clust_id, count(*) cnt from clust
group by part_id, clust_id
)
select * from clust_agg
pivot(sum(cnt) clust  for (clust_id) in 
     (1 as "1",
      2 as "2",
      3 as "3"))
order by 1;
, header = TRUE, sep=",")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;   PART_ID    1_CLUST    2_CLUST    3_CLUST
---------- ---------- ---------- ----------
         1          1                     1 
         2                     1          2 
         3                                1
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5883, 2, '2015-05-24 00:42:07.003', 1, NULL, NULL, '&lt;p&gt;If you are looking specifically for books, the leaders in the industry have a variety of recommendations as posted on reddit AMA''s of machine learning. One specific book I recommend, which is in preparation at the time, is Deep Learning by Yoshua Bengio et al &lt;/p&gt;

&lt;p&gt;There are plenty of resources outside books to learn from as posted &lt;a href="http://zhengrui.github.io/zerryland/ML-CV-Resource.html" rel="nofollow"&gt;here&lt;/a&gt; and &lt;a href="http://digitalmind.io/post/deep-learning" rel="nofollow"&gt;here&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Depends on how much time you have. If you are in it for the long haul, start by brushing up your knowledge on math, specifically Linear Algebra, Calculus, Probability and Statistics&lt;/p&gt;
', NULL, NULL, '''/a'':70C,80C ''/p'':48C,81C,114C ''/post/deep-learning'':76C ''/zerryland/ml-cv-resource.html'':66C ''al'':47C ''algebra'':109C ''ama'':23C ''bengio'':45C ''book'':8C,30C,56C ''brush'':101C ''calculus'':110C ''deep'':41C ''depend'':83C ''digitalmind.io'':75C ''digitalmind.io/post/deep-learning'':74C ''et'':46C ''haul'':98C ''href'':63C,73C ''industri'':13C ''knowledg'':104C ''leader'':10C ''learn'':27C,42C,58C ''linear'':108C ''long'':97C ''look'':5C ''machin'':26C ''math'':106C ''much'':86C ''nofollow'':68C,78C ''one'':28C ''outsid'':55C ''p'':1C,49C,82C ''plenti'':52C ''post'':20C,61C ''prepar'':36C ''probabl'':111C ''recommend'':18C,32C ''reddit'':22C ''rel'':67C,77C ''resourc'':54C ''specif'':6C,29C,107C ''start'':99C ''statist'':113C ''time'':39C,87C ''varieti'':16C ''yoshua'':44C ''zhengrui.github.io'':65C ''zhengrui.github.io/zerryland/ml-cv-resource.html'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5675, 2, '2015-05-03 00:58:54.41', 1, NULL, NULL, '&lt;p&gt;I would suggest playing around with this in either &lt;a href="http://www.r-project.org/" rel="nofollow"&gt;R&lt;/a&gt; or &lt;a href="https://www.python.org/" rel="nofollow"&gt;Python&lt;/a&gt; using &lt;a href="http://scikit-learn.org/stable/" rel="nofollow"&gt;Scikit Learn&lt;/a&gt;.  The text will take a bit of playing with, but is pretty straightforward to normalize and turn into a feature set.  I suggest applying &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html" rel="nofollow"&gt;TFIDF&lt;/a&gt; to normalize each text document against the entire corpus.  You will then have lots of text features that you can join with your metadata features to do novelty detection.  Scikit-Learn and it''s excellent &lt;a href="http://scikit-learn.org/stable/user_guide.html#" rel="nofollow"&gt;User Guide&lt;/a&gt; are a great resource for getting started in this arena as are &lt;a href="http://mahout.apache.org/" rel="nofollow"&gt;Mahout&lt;/a&gt; (Java based) and the book &lt;a href="http://www.manning.com/owen/" rel="nofollow"&gt;Mahout In Action&lt;/a&gt;.  There are also countless packages in R to accomplish this.&lt;/p&gt;

&lt;p&gt;One thing to note is that you seem to suggest specific ways that you want to detect fraud.  This type of hunch based machine learning is known as applying heuristics and tends to perform worse than pure machine learning methods.  You should just focus on using novelty detection algorithms or possibly anomaly detection algorithms and let the statistics find the fraud rather than trying to apply your own intuition.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5701, 1, '2015-05-06 21:52:55.81', 3, 90, 'Lightweight data provenance tool', '&lt;p&gt;One of the problems I often encounter is that of poor data provenance.&lt;/p&gt;

&lt;p&gt;When I do research I continuously make modifications to my code and rerun experiments. Each time I''m faced with a number of questions, such as: do I save the old results somewhere, just in case? Should I include the parameter settings in the output filenames or perhaps save them in a different file? How do I know which version of the script was used to produce the results?&lt;/p&gt;

&lt;p&gt;I''ve recently stumbled upon &lt;a href="https://pythonhosted.org/Sumatra" rel="nofollow"&gt;Sumatra&lt;/a&gt;, a pretty lightweight Python package that can capture Code, Data, Environment (CDE) information that can be used to track data provenance. I like the fact that it can be used both from the command line and from within my Python scripts and requiring no GUI. The downside is that the project seems inactive and perhaps there''s something better out there.&lt;/p&gt;

&lt;p&gt;My question is: what is a good lightweight data provenance solution for my research? I''m coding small projects mostly in Python in the terminal on a remote server over SSH, so a command line solution would be perfect for me.&lt;/p&gt;

&lt;p&gt;EDIT: I have stuck with Sumatra. When I posted this question I didn''t look into the web interface yet, but that turns out to be a unique selling point. It displays a very detailed overview of the experiments, capturing not only the state of the data and code, but also the Python environment (package dependencies and versions!) and platform information (architecture and kernel version!).&lt;/p&gt;

&lt;p&gt;EDIT: I''ve updated the subject of my question to emphasize that I''m mostly concerned about  provenance.&lt;/p&gt;
', 'tools', NULL, '''/a'':106C ''/p'':20C,91C,168C,211C,278C,298C ''/sumatra'':102C ''also'':263C ''architectur'':274C ''better'':165C ''captur'':114C,252C ''case'':57C ''cde'':118C ''code'':32C,115C,186C,261C ''command'':140C,203C ''concern'':295C ''continu'':27C ''data'':2A,18C,116C,126C,178C,259C ''depend'':268C ''detail'':247C ''didn'':225C ''differ'':74C ''display'':244C ''downsid'':153C ''edit'':213C,280C ''emphas'':290C ''encount'':13C ''environ'':117C,266C ''experi'':35C,251C ''face'':40C ''fact'':131C ''file'':75C ''filenam'':67C ''good'':176C ''gui'':151C ''href'':99C ''inact'':159C ''includ'':60C ''inform'':119C,273C ''interfac'':231C ''kernel'':276C ''know'':79C ''lightweight'':1A,109C,177C ''like'':129C ''line'':141C,204C ''look'':227C ''m'':39C,185C,293C ''make'':28C ''modif'':29C ''most'':189C,294C ''nofollow'':104C ''number'':43C ''often'':12C ''old'':52C ''one'':7C ''output'':66C ''overview'':248C ''p'':6C,21C,92C,169C,212C,279C ''packag'':111C,267C ''paramet'':62C ''perfect'':208C ''perhap'':69C,161C ''platform'':272C ''point'':242C ''poor'':17C ''post'':221C ''pretti'':108C ''problem'':10C ''produc'':88C ''project'':157C,188C ''proven'':3A,19C,127C,179C,297C ''python'':110C,146C,191C,265C ''pythonhosted.org'':101C ''pythonhosted.org/sumatra'':100C ''question'':45C,171C,223C,288C ''recent'':95C ''rel'':103C ''remot'':197C ''requir'':149C ''rerun'':34C ''research'':25C,183C ''result'':53C,90C ''save'':50C,70C ''script'':84C,147C ''seem'':158C ''sell'':241C ''server'':198C ''set'':63C ''small'':187C ''solut'':180C,205C ''someth'':164C ''somewher'':54C ''ssh'':200C ''state'':256C ''stuck'':216C ''stumbl'':96C ''subject'':285C ''sumatra'':105C,218C ''termin'':194C ''time'':37C ''tool'':4A,5B ''track'':125C ''turn'':235C ''uniqu'':240C ''updat'':283C ''upon'':97C ''use'':86C,123C,136C ''ve'':94C,282C ''version'':81C,270C,277C ''web'':230C ''within'':144C ''would'':206C ''yet'':232C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5677, 1, '2015-05-03 07:11:50.737', 2, 78, 'How should I create a single score with two values as input?', '&lt;p&gt;I have two series of values, a and b as inputs and I want to create a score, c, which reflects both of them equally.  The distribution of a and b are below&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/TSKfA.png" alt="Distribution of a"&gt;
&lt;img src="http://i.stack.imgur.com/WivxT.png" alt="Distribution of b"&gt;&lt;/p&gt;

&lt;p&gt;In both cases, the x-axis is just an index.&lt;/p&gt;

&lt;p&gt;How should I go about creating an equation c = f(a,b) such that a and b are (on average) represented equally in c?&lt;/p&gt;

&lt;p&gt;Edit: c = (a+b)/2 or c = ab will not work because c will be too heavily weighted by a or b.&lt;/p&gt;

&lt;p&gt;I need a function, f, where c = f(a,b) and c'' = f(a + stdev(a),b) = f(a, b + stdev(b))&lt;/p&gt;
', 'statistics', NULL, '''/2'':113C ''/p'':48C,68C,81C,107C,131C,155C ''/tskfa.png'':54C ''/wivxt.png'':63C ''ab'':116C ''alt'':55C,64C ''averag'':102C ''axi'':76C ''b'':23C,45C,67C,94C,99C,112C,130C,142C,149C,152C,154C ''c'':33C,91C,106C,110C,115C,121C,139C,144C ''case'':72C ''creat'':4A,30C,88C ''distribut'':41C,56C,65C ''edit'':109C ''equal'':39C,104C ''equat'':90C ''f'':92C,137C,140C,145C,150C ''function'':136C ''go'':86C ''heavili'':125C ''i.stack.imgur.com'':53C,62C ''i.stack.imgur.com/tskfa.png'':52C ''i.stack.imgur.com/wivxt.png'':61C ''img'':50C,59C ''index'':80C ''input'':12A,25C ''need'':134C ''p'':14C,49C,69C,82C,108C,132C ''reflect'':35C ''repres'':103C ''score'':7A,32C ''seri'':18C ''singl'':6A ''src'':51C,60C ''statist'':13B ''stdev'':147C,153C ''two'':9A,17C ''valu'':10A,20C ''want'':28C ''weight'':126C ''work'':119C ''x'':75C ''x-axi'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5691, 2, '2015-05-05 10:32:10.873', 1, NULL, NULL, '&lt;p&gt;Calcualte distance?&lt;/p&gt;

&lt;p&gt;Distance = data2 - data1 = [(1,&lt;strong&gt;0&lt;/strong&gt;), (2,&lt;strong&gt;0&lt;/strong&gt;), &lt;em&gt;(3, &lt;strong&gt;7&lt;/strong&gt;), (4, &lt;strong&gt;7&lt;/strong&gt;), (5, &lt;strong&gt;7&lt;/strong&gt;)&lt;/em&gt;, (6, &lt;strong&gt;0&lt;/strong&gt;)]&lt;/p&gt;

&lt;p&gt;And what will you treat as &lt;em&gt;the same wave&lt;/em&gt;?&lt;/p&gt;

&lt;p&gt;Any three (or more) nearby points that have the same distance... &lt;/p&gt;
', NULL, NULL, '''/em'':30C,47C ''/p'':4C,35C,48C,61C ''/strong'':12C,16C,21C,25C,29C,34C ''0'':11C,15C,33C ''1'':9C ''2'':13C ''3'':18C ''4'':22C ''5'':26C ''6'':31C ''7'':20C,24C,28C ''calcualt'':2C ''data1'':8C ''data2'':7C ''distanc'':3C,6C,60C ''em'':17C,43C ''nearbi'':54C ''p'':1C,5C,36C,49C ''point'':55C ''strong'':10C,14C,19C,23C,27C,32C ''three'':51C ''treat'':41C ''wave'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9197, 1, '2015-12-04 06:40:57.113', 2, 70, 'Predicting future value with regression Model', '&lt;p&gt;I have a set of predictor variables and another target variable .&lt;/p&gt;

&lt;p&gt;Now I am really confused on what method to use to forecast the target variable .&lt;/p&gt;

&lt;p&gt;For e.g my data set have customer profit(which is my target variable) and a set of predictor variable(balances of different account) for one year for each customer .&lt;/p&gt;

&lt;p&gt;Now I need to predict profit of next 5 years .I am confused in the part that I dont have the data(predictor variables) for future .&lt;/p&gt;

&lt;p&gt;What are my possible choices of modelling .Please assist .&lt;/p&gt;
', 'r  predictive-modeling', NULL, '''/p'':23C,40C,71C,99C,110C ''5'':81C ''account'':64C ''anoth'':20C ''assist'':109C ''balanc'':61C ''choic'':105C ''confus'':29C,85C ''custom'':48C,70C ''data'':45C,94C ''differ'':63C ''dont'':91C ''e.g'':43C ''forecast'':36C ''futur'':2A,98C ''method'':32C ''model'':6A,10B,107C ''need'':75C ''next'':80C ''one'':66C ''p'':11C,24C,41C,72C,100C ''part'':88C ''pleas'':108C ''possibl'':104C ''predict'':1A,9B,77C ''predictive-model'':8B ''predictor'':17C,59C,95C ''profit'':49C,78C ''r'':7B ''realli'':28C ''regress'':5A ''set'':15C,46C,57C ''target'':21C,38C,53C ''use'':34C ''valu'':3A ''variabl'':18C,22C,39C,54C,60C,96C ''year'':67C,82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5678, 2, '2015-05-03 09:54:34.857', 2, NULL, NULL, '&lt;p&gt;Different domains might require different approaches, the news domain being one of the more elusive ones. News items are extremely short-lived (one, maybe two days): the very items the user viewed two days ago are just not interesting anymore. Also, CF works well if a large amount of items have "enough" events (that is, lots of users have viewed/rated them), and depending on the size of your data, most news items just don''t collect enough views during their lifetime for classical CF methods to be effective. &lt;/p&gt;

&lt;p&gt;For this reason, news recommender systems tend to use some combination of content-based filtering (the tags and categories you mention), popularity-based recommendation (this often discarded approach might work well for news), and recency scoring. There is some literature around it, try googling for "news recommender system".&lt;/p&gt;
', NULL, NULL, '''/p'':91C,143C ''ago'':37C ''also'':43C ''amount'':50C ''anymor'':42C ''approach'':7C,122C ''around'':135C ''base'':107C,117C ''categori'':112C ''cf'':44C,86C ''classic'':85C ''collect'':78C ''combin'':103C ''content'':106C ''content-bas'':105C ''data'':71C ''day'':28C,36C ''depend'':65C ''differ'':2C,6C ''discard'':121C ''domain'':3C,10C ''effect'':90C ''elus'':16C ''enough'':54C,79C ''event'':55C ''extrem'':21C ''filter'':108C ''googl'':138C ''interest'':41C ''item'':19C,31C,52C,74C ''larg'':49C ''lifetim'':83C ''literatur'':134C ''live'':24C ''lot'':58C ''mayb'':26C ''mention'':114C ''method'':87C ''might'':4C,123C ''news'':9C,18C,73C,96C,127C,140C ''often'':120C ''one'':12C,17C,25C ''p'':1C,92C ''popular'':116C ''popularity-bas'':115C ''reason'':95C ''recenc'':129C ''recommend'':97C,118C,141C ''requir'':5C ''score'':130C ''short'':23C ''short-liv'':22C ''size'':68C ''system'':98C,142C ''tag'':110C ''tend'':99C ''tri'':137C ''two'':27C,35C ''use'':101C ''user'':33C,60C ''view'':34C,80C ''viewed/rated'':62C ''well'':46C,125C ''work'':45C,124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5679, 1, '2015-05-03 14:12:09', 0, 120, 'Understanding text conversion into SVM input', '&lt;p&gt;In Support Vector Machines, when used for sentiment analysis, text gets converted into a set of data points. How does this happen, usually?&lt;/p&gt;
', 'machine-learning  nlp  svm  libsvm', NULL, '''/p'':37C ''analysi'':22C ''convers'':3A ''convert'':25C ''data'':30C ''get'':24C ''happen'':35C ''input'':6A ''learn'':9B ''libsvm'':12B ''machin'':8B,17C ''machine-learn'':7B ''nlp'':10B ''p'':13C ''point'':31C ''sentiment'':21C ''set'':28C ''support'':15C ''svm'':5A,11B ''text'':2A,23C ''understand'':1A ''use'':19C ''usual'':36C ''vector'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5680, 2, '2015-05-03 18:35:31.813', 1, NULL, NULL, '&lt;p&gt;using R syntax:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; c &amp;lt;- a/2+b/2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;or you can create a function:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;c &amp;lt;- function(a,b) { return(a/2+b/2) }
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':12C,32C ''/p'':5C,21C ''/pre'':13C,33C ''a/2'':10C,30C ''b'':28C ''b/2'':11C,31C ''c'':8C,24C ''code'':7C,23C ''creat'':18C ''function'':20C,26C ''lt'':9C,25C ''p'':1C,14C ''pre'':6C,22C ''r'':3C ''return'':29C ''syntax'':4C ''use'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5681, 2, '2015-05-03 21:36:27.67', 1, NULL, NULL, '&lt;p&gt;The problem is that you''re converting a large sparse dataset into a dense array via &lt;code&gt;toarray()&lt;/code&gt;. Don''t do that :) Why do you need it in dense array format? Try posting more of your code to see if we can figure out better ways to solve the problem. With any large dataset, you want to stay sparse as long as possible and only work with smaller sets of dense data (if dense is needed at all).&lt;/p&gt;
', NULL, NULL, '''/code'':20C ''/p'':81C ''array'':16C,32C ''better'':47C ''code'':18C,39C ''convert'':8C ''data'':74C ''dataset'':12C,56C ''dens'':15C,31C,73C,76C ''figur'':45C ''format'':33C ''larg'':10C,55C ''long'':63C ''need'':28C,78C ''p'':1C ''possibl'':65C ''post'':35C ''problem'':3C,52C ''re'':7C ''see'':41C ''set'':71C ''smaller'':70C ''solv'':50C ''spars'':11C,61C ''stay'':60C ''toarray'':19C ''tri'':34C ''via'':17C ''want'':58C ''way'':48C ''work'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5800, 2, '2015-05-15 10:00:06.307', -1, NULL, NULL, '&lt;p&gt;A good reference can be found there &lt;a href="http://scikit-learn.org/stable/modules/" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/p'':19C ''/stable/modules/'':13C ''/stable/modules/&lt;/a&gt;'':18C ''found'':7C ''good'':3C ''href'':10C ''nofollow'':15C ''p'':1C ''refer'':4C ''rel'':14C ''scikit-learn.org'':12C,17C ''scikit-learn.org/stable/modules/'':11C ''scikit-learn.org/stable/modules/&lt;/a&gt;'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5723, 1, '2015-05-08 03:47:00.857', 3, 386, 'In Weka, how to draw learning curve evaluated on both test and training set?', '&lt;p&gt;This is just for finding overfitting gap.&lt;br&gt;
After initial research, I can only find method to draw learning curve using evaluation of test set.  However, I could not evaluate on training set and over the two learning curves.&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':58C ''br'':26C ''could'':46C ''curv'':7A,38C,57C ''draw'':5A,36C ''evalu'':8A,40C,48C ''find'':23C,33C ''gap'':25C ''howev'':44C ''initi'':28C ''learn'':6A,17B,37C,56C ''machin'':16B ''machine-learn'':15B ''method'':34C ''overfit'':24C ''p'':18C ''research'':29C ''set'':14A,43C,51C ''test'':11A,42C ''train'':13A,50C ''two'':55C ''use'':39C ''weka'':2A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5684, 1, '2015-05-04 13:26:04.767', 3, 499, 'How Mllib in Spark select variables in logistic regression', '&lt;p&gt;I have a question about MLlib in Spark.(with Scala)&lt;/p&gt;

&lt;p&gt;I''m trying to understand how LogisticRegressionWithLBFGS and LogisticRegressionWithSGD work. I usually use SAS or R to do logistic regressions but I now have to do it on Spark to be able to analyze Big Data.&lt;/p&gt;

&lt;p&gt;How is the variable selection done? Is there any try of different variable combinations in LogisticRegressionWithLBFGS or LogisticRegressionWithSGD? Something like a test of significance of variable one by one? Or a correlation calculation with the variable of interest? Is there any calculation of BIC, AIC to choose the best model?&lt;/p&gt;

&lt;p&gt;Because the model only returns weights and intercept...&lt;/p&gt;

&lt;p&gt;How can I understand those Spark functions and compare to what I''m used to with SAS or R ?&lt;/p&gt;
', 'machine-learning  bigdata  logistic-regression  scala  apache-spark', NULL, '''/p'':32C,70C,122C,132C,153C ''abl'':65C ''aic'':116C ''analyz'':67C ''apach'':19B ''apache-spark'':18B ''best'':120C ''bic'':115C ''big'':68C ''bigdata'':13B ''calcul'':104C,113C ''choos'':118C ''combin'':85C ''compar'':142C ''correl'':103C ''data'':69C ''differ'':83C ''done'':77C ''function'':140C ''intercept'':131C ''interest'':109C ''learn'':12B ''like'':91C ''logist'':8A,15B,52C ''logistic-regress'':14B ''logisticregressionwithlbfg'':40C,87C ''logisticregressionwithsgd'':42C,89C ''m'':35C,146C ''machin'':11B ''machine-learn'':10B ''mllib'':2A,27C ''model'':121C,126C ''one'':98C,100C ''p'':21C,33C,71C,123C,133C ''question'':25C ''r'':49C,152C ''regress'':9A,16B,53C ''return'':128C ''sas'':47C,150C ''scala'':17B,31C ''select'':5A,76C ''signific'':95C ''someth'':90C ''spark'':4A,20B,29C,62C,139C ''test'':93C ''tri'':36C,81C ''understand'':38C,137C ''use'':46C,147C ''usual'':45C ''variabl'':6A,75C,84C,97C,107C ''weight'':129C ''work'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5708, 1, '2015-05-07 08:52:40.293', 4, 231, 'Which classification algorithms to try for classifying text data into 300 categories', '&lt;p&gt;I have 40000 rows of text data of health care domain. Data has one column for text (2-5 sentences) and one column for its category.
I want to classify that into 300 categories. Some categories are independent while some are somewhat related. Distribution of data among categories is not uniform either i.e some of the categories(around 40 of them) have less data about 2-3 rows.&lt;/p&gt;

&lt;p&gt;I am attaching log probablity of each class/categories. (OR distribution of classes) here.
&lt;img src="http://i.stack.imgur.com/nko61.png" alt="Class prior logarithm of probabilities (log class distribution of data)"&gt;&lt;/p&gt;
', 'machine-learning  classification  nlp  text-mining', NULL, '''-3'':88C ''-5'':40C ''/nko61.png'':109C ''/p'':90C,121C ''2'':39C,87C ''300'':11A,54C ''40'':80C ''40000'':24C ''algorithm'':3A ''alt'':110C ''among'':68C ''around'':79C ''attach'':94C ''care'':31C ''categori'':12A,47C,55C,57C,69C,78C ''class'':103C,111C,117C ''class/categories'':99C ''classif'':2A,16B ''classifi'':7A,51C ''column'':36C,44C ''data'':9A,28C,33C,67C,85C,120C ''distribut'':65C,101C,118C ''domain'':32C ''either'':73C ''health'':30C ''i.e'':74C ''i.stack.imgur.com'':108C ''i.stack.imgur.com/nko61.png'':107C ''img'':105C ''independ'':59C ''learn'':15B ''less'':84C ''log'':95C,116C ''logarithm'':113C ''machin'':14B ''machine-learn'':13B ''mine'':20B ''nlp'':17B ''one'':35C,43C ''p'':21C,91C ''prior'':112C ''probabl'':96C,115C ''relat'':64C ''row'':25C,89C ''sentenc'':41C ''somewhat'':63C ''src'':106C ''text'':8A,19B,27C,38C ''text-min'':18B ''tri'':5A ''uniform'':72C ''want'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5884, 2, '2015-05-24 00:51:22.027', 2, NULL, NULL, '&lt;p&gt;please refer &lt;a href="http://datascience.stackexchange.com/questions/5860/where-to-start-which-books"&gt;this&lt;/a&gt; question &lt;/p&gt;

&lt;p&gt;Books recommended by Yann Lecun can be found &lt;a href="http://www.reddit.com/r/MachineLearning/comments/25lnbt/ama_yann_lecun" rel="nofollow"&gt;here&lt;/a&gt; and by G. Hinton &lt;a href="http://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_geoffrey_hinton" rel="nofollow"&gt;here&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':10C,30C,43C ''/p'':12C,44C ''/questions/5860/where-to-start-which-books'':8C ''/r/machinelearning/comments/25lnbt/ama_yann_lecun'':26C ''/r/machinelearning/comments/2lmo0l/ama_geoffrey_hinton'':39C ''book'':14C ''datascience.stackexchange.com'':7C ''datascience.stackexchange.com/questions/5860/where-to-start-which-books'':6C ''found'':21C ''g'':33C ''hinton'':34C ''href'':5C,23C,36C ''lecun'':18C ''nofollow'':28C,41C ''p'':1C,13C ''pleas'':2C ''question'':11C ''recommend'':15C ''refer'':3C ''rel'':27C,40C ''www.reddit.com'':25C,38C ''www.reddit.com/r/machinelearning/comments/25lnbt/ama_yann_lecun'':24C ''www.reddit.com/r/machinelearning/comments/2lmo0l/ama_geoffrey_hinton'':37C ''yann'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5698, 2, '2015-05-06 12:56:09.333', 11, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;What is a dimension?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;To put it simply, if you have a tabular data set with m rows and n columns, then the dimensionality of your data is n&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;What is a manifold? &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The simplest example is our planet Earth. For us it looks flat, but it reality it''s a sphere. So it''s sort of a 2d manifold embedded in the 3d space. &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;What is the difference? &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;To answer this question, consider another example of a manifold: &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/FrBXu.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;This is so-called "swiss roll". The data points are in 3d, but they all lie on 2d manifold, so the dimensionality of the manifold is 2, while the dimensionality of the input space is 3.&lt;/p&gt;

&lt;p&gt;There are many techniques to "unwrap" these manifolds. One of them is called &lt;a href="https://www.google.com/search?q=locally+linear+embedding" rel="nofollow"&gt;Locally Linear Embedding&lt;/a&gt;, and this is how it would do that:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/pagFb.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Here''s a scikit-learn snippet for doing that:&lt;/p&gt;



&lt;pre&gt;&lt;code&gt;from sklearn.manifold import LocallyLinearEmbedding

lle = LocallyLinearEmbedding(n_neighbors=k, n_components=2)
X_lle = lle.fit_transform(data)
plt.scatter(X_lle[:, 0], X_lle[:, 1], c=color)
plt.show()
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5685, 2, '2015-05-04 14:59:36.43', 3, NULL, NULL, '&lt;p&gt;First, the spark programming guide for &lt;a href="http://spark.apache.org/docs/1.3.1/api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD" rel="nofollow"&gt;LogisticRegressionWithSGD&lt;/a&gt; recommends using &lt;a href="http://spark.apache.org/docs/1.3.1/api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS" rel="nofollow"&gt;L-BFGS&lt;/a&gt; instead, so perhaps focus on the one.  As for variable selection, the model description on the &lt;a href="https://spark.apache.org/docs/latest/mllib-linear-methods.html#logistic-regression" rel="nofollow"&gt;MLLib page for regressions&lt;/a&gt; has a nice explanation of how models are constructed and selected, but it does not address variable selection.  This leads me to believe that it considers all variables, and simply chooses the model with the best fit.&lt;/p&gt;
', NULL, NULL, '''/a'':16C,29C,57C ''/docs/1.3.1/api/scala/index.html#org.apache.spark.mllib.classification.logisticregressionwithlbfgs'':23C ''/docs/1.3.1/api/scala/index.html#org.apache.spark.mllib.classification.logisticregressionwithsgd'':12C ''/docs/latest/mllib-linear-methods.html#logistic-regression'':50C ''/p'':95C ''address'':73C ''believ'':80C ''best'':93C ''bfgs'':28C ''choos'':88C ''consid'':83C ''construct'':66C ''descript'':43C ''explan'':61C ''first'':2C ''fit'':94C ''focus'':33C ''guid'':6C ''href'':9C,20C,47C ''instead'':30C ''l'':27C ''l-bfgs'':26C ''lead'':77C ''logisticregressionwithsgd'':15C ''mllib'':53C ''model'':42C,64C,90C ''nice'':60C ''nofollow'':14C,25C,52C ''one'':36C ''p'':1C ''page'':54C ''perhap'':32C ''program'':5C ''recommend'':17C ''regress'':56C ''rel'':13C,24C,51C ''select'':40C,68C,75C ''simpli'':87C ''spark'':4C ''spark.apache.org'':11C,22C,49C ''spark.apache.org/docs/1.3.1/api/scala/index.html#org.apache.spark.mllib.classification.logisticregressionwithlbfgs'':21C ''spark.apache.org/docs/1.3.1/api/scala/index.html#org.apache.spark.mllib.classification.logisticregressionwithsgd'':10C ''spark.apache.org/docs/latest/mllib-linear-methods.html#logistic-regression'':48C ''use'':18C ''variabl'':39C,74C,85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5686, 2, '2015-05-04 19:55:24.797', 2, NULL, NULL, '&lt;p&gt;That really is a nice question, although once you''re Facebook or Google etc., you have the opposite problem: how to reduce the number of features from many billions, to let''s say, a billion or so.&lt;/p&gt;

&lt;p&gt;There really are billions of features out there.&lt;/p&gt;

&lt;p&gt;Imagine, that in your feature vector you have billions of possible phrases that the user could type in into search engine. Or, that you have billions of web sites a user could visit. Or millions of locations from which a user could log in to the system. Or billions of mail accounts a user could send mails to or receive mails from.&lt;/p&gt;

&lt;p&gt;Or, to swich a bit to social networking site-like problem. Imagine that in your feature vector you have billions of users which a particular user could either know or be in some degree of separation from. You can add billions of links that user could post in his SNS feed, or millions of pages a user could ''like'' (or do whatever the SNS allows him to do).&lt;/p&gt;

&lt;p&gt;Similar problems may be found in many domains from voice and image recognition, to various branches of biology, chemistry etc. I like your question, because it''s a good starting point to dive into the problems of dealing with the abundance of features. Good luck in exploring this area!&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE due to your comment:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Using features other than binary is just one step further in imagining things. You could somehow cluster the searches, and count frequencies of searches for a particular cluster.&lt;/p&gt;

&lt;p&gt;In a SNS setting you could build a vector of relations between users defined as degree of separation instead of a mere binary feature of being or not being friends.&lt;/p&gt;

&lt;p&gt;Imagine logs that global corporations are holding on millions of their users. There''s a whole lot of stuff that can be measured in a more detailed way than binary.&lt;/p&gt;

&lt;p&gt;Things become even more complicated once we''re considering an online setting. In such a case you do not have time for complicated computations and you''re often left with binary features since they are cheaper.&lt;/p&gt;

&lt;p&gt;And no, I am not saying, that the problem becomes tractable once it''s reduced to a magical number of billion features. I am only saying that a billion of features is something you may end up after a lot of effort in reducing the number of dimensions.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5687, 1, '2015-05-05 07:12:46.797', 3, 66, 'How to approach automated text writing?', '&lt;p&gt;What are the tools, practices and algorithms used in automated text writing?&lt;/p&gt;

&lt;p&gt;For example, lets assume that I have access to wikipedia/wikinews and similar websites API and I would like to produce article about "Data Science with Python".&lt;/p&gt;

&lt;p&gt;I believe that this task should be divided into two segments. First would be &lt;strong&gt;text mining&lt;/strong&gt; and second would be &lt;strong&gt;text building&lt;/strong&gt;. I''m more or less aware how text mining is performed and there are lots of materials about it in Internet. However, amount of materials related to automated text building seems to be lower. There are plenty of articles which says that some companies are using it, but there is lack of details. Are there any common ideas about such text building?&lt;/p&gt;
', 'text-mining', NULL, '''/p'':23C,51C,141C ''/strong'':70C,78C ''access'':32C ''algorithm'':17C ''amount'':101C ''api'':38C ''approach'':3A ''articl'':45C,117C ''assum'':28C ''autom'':4A,20C,106C ''awar'':84C ''believ'':54C ''build'':77C,108C,140C ''common'':135C ''compani'':122C ''data'':47C ''detail'':131C ''divid'':60C ''exampl'':26C ''first'':64C ''howev'':100C ''idea'':136C ''internet'':99C ''lack'':129C ''less'':83C ''let'':27C ''like'':42C ''lot'':93C ''lower'':112C ''m'':80C ''materi'':95C,103C ''mine'':9B,69C,87C ''p'':10C,24C,52C ''perform'':89C ''plenti'':115C ''practic'':15C ''produc'':44C ''python'':50C ''relat'':104C ''say'':119C ''scienc'':48C ''second'':72C ''seem'':109C ''segment'':63C ''similar'':36C ''strong'':67C,75C ''task'':57C ''text'':5A,8B,21C,68C,76C,86C,107C,139C ''text-min'':7B ''tool'':14C ''two'':62C ''use'':18C,124C ''websit'':37C ''wikipedia/wikinews'':34C ''would'':41C,65C,73C ''write'':6A,22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5688, 2, '2015-05-05 07:47:45.473', 6, NULL, NULL, '&lt;p&gt;You should probably do some reading in the field of "&lt;a href="http://en.wikipedia.org/wiki/Natural_language_generation"&gt;Natural Language Generation&lt;/a&gt;", since this seems to relate most directly to your question. &lt;/p&gt;

&lt;p&gt;But the way you have described the process -- "text mining...text building" -- leads me to wonder if you are aiming for something much more ambitious.  It seems as though you aim to automate the process of 1) reading natural language texts, 2) &lt;strong&gt;&lt;em&gt;understanding the meaning&lt;/em&gt;&lt;/strong&gt;, and then 3) generate new texts based on that semantic knowledge.  I''m not aware of any general-purpose end-to-end systems that can do that, not even specialized systems by the likes of &lt;a href="http://www.palantir.com/"&gt;Palantir&lt;/a&gt;.  What you are aiming for would probably pass the Turing Test for fully capable Artificial Intelligence.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5689, 1, '2015-05-05 08:14:29.573', 0, 105, 'Why is the Naive Bayes classifier of sklearn faster than sklearns SVM?', '&lt;p&gt;I''ve used &lt;code&gt;scikit-learn&lt;/code&gt; in Python to compare results of naive Bayes and SVM. I''ve found that naive Bayes is quicker than SVM. Could anyone shed some light on reasons for such finding?&lt;/p&gt;
', 'machine-learning  svm  sklearn  performance  naive-bayes-classifier', NULL, '''/code'':31C ''/p'':62C ''anyon'':53C ''bay'':5A,21B,39C,47C ''classifi'':6A,22B ''code'':27C ''compar'':35C ''could'':52C ''faster'':9A ''find'':61C ''found'':44C ''learn'':15B,30C ''light'':56C ''machin'':14B ''machine-learn'':13B ''naiv'':4A,20B,38C,46C ''naive-bayes-classifi'':19B ''p'':23C ''perform'':18B ''python'':33C ''quicker'':49C ''reason'':58C ''result'':36C ''scikit'':29C ''scikit-learn'':28C ''shed'':54C ''sklearn'':8A,11A,17B ''svm'':12A,16B,41C,51C ''use'':26C ''ve'':25C,43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5806, 2, '2015-05-16 02:36:52.97', 1, NULL, NULL, '&lt;p&gt;I would suggest you to consider either direct &lt;em&gt;dimensionality reduction&lt;/em&gt; approach. Check &lt;a href="http://datascience.stackexchange.com/a/4946/2452"&gt;my relevant answer&lt;/a&gt; on this site. Another valid option is to use &lt;em&gt;latent variable modeling&lt;/em&gt;, for example, &lt;em&gt;structural equation modeling&lt;/em&gt;. You can start with relevant articles on Wikipedia (&lt;a href="http://en.wikipedia.org/wiki/Latent_variable_model" rel="nofollow"&gt;this&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/Structural_equation_modeling" rel="nofollow"&gt;this&lt;/a&gt;, correspondingly) and then, as needed, read more specialized or more practical articles, papers and books.&lt;/p&gt;
', NULL, NULL, '''/a'':24C,62C,72C ''/a/4946/2452'':20C ''/em'':13C,38C,45C ''/p'':88C ''/wiki/latent_variable_model'':58C ''/wiki/structural_equation_modeling'':68C ''anoth'':28C ''answer'':23C ''approach'':14C ''articl'':51C,84C ''book'':87C ''check'':15C ''consid'':7C ''correspond'':73C ''datascience.stackexchange.com'':19C ''datascience.stackexchange.com/a/4946/2452'':18C ''dimension'':11C ''direct'':9C ''either'':8C ''em'':10C,34C,41C ''en.wikipedia.org'':57C,67C ''en.wikipedia.org/wiki/latent_variable_model'':56C ''en.wikipedia.org/wiki/structural_equation_modeling'':66C ''equat'':43C ''exampl'':40C ''href'':17C,55C,65C ''latent'':35C ''model'':37C,44C ''need'':77C ''nofollow'':60C,70C ''option'':30C ''p'':1C ''paper'':85C ''practic'':83C ''read'':78C ''reduct'':12C ''rel'':59C,69C ''relev'':22C,50C ''site'':27C ''special'':80C ''start'':48C ''structur'':42C ''suggest'':4C ''use'':33C ''valid'':29C ''variabl'':36C ''wikipedia'':53C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5693, 1, '2015-05-05 14:37:25.377', 3, 238, 'NoSQL engine/service recommendation for geolocation data', '&lt;p&gt;First of all, I am new in this field we call &lt;em&gt;big data&lt;/em&gt;, so my questions may be naive. &lt;/p&gt;

&lt;p&gt;In order to build an application, which deals with geolocation data, which could be : &lt;em&gt;latitude and longitude coordinates&lt;/em&gt; and &lt;a href="https://msdn.microsoft.com/en-us/library/cc280766.aspx" rel="nofollow"&gt;&lt;em&gt;Geography SQL Server&lt;/em&gt;&lt;/a&gt; column types.&lt;/p&gt;

&lt;p&gt;I need to have the following elements made easy:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Scalability : be prepared to receive huge amount of data, adding servers to the system have to be easy&lt;/li&gt;
&lt;li&gt;Proximity requests : in example, how much points are in a circle (at meter scale). &lt;/li&gt;
&lt;li&gt;Data must be accessible rapidly after being written. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I''ve been looking around for existing solutions, which are "Hadoop friendly" (Hortonworks, Cloudera) and available DBMS, like Cassandra. 
I have found some interesting information, but I still think it''s hard to decide, which one to choose. 
It also need drivers for &lt;em&gt;NodeJS&lt;/em&gt; &amp;amp; &lt;em&gt;.NET&lt;/em&gt; (Hadoop with Cassandra seem to be OK with that). 
I''ve also looked around the MongoDB ecosystem, but, again, I feel that it is hard to know where to look at. By (little) experience with Mongoose, MongoDB can be disqualified by the third point because data writes are slow. But my model could certainly be improved.&lt;/p&gt;

&lt;p&gt;Do any of you have some recent experiences, manipulating massive amount of &lt;em&gt;geolocation data&lt;/em&gt;? I would appreciate sharing them here as well as any quality and recent literature on the subject.&lt;/p&gt;
', 'bigdata  clustering', NULL, '''/a'':66C ''/em'':24C,52C,65C,175C,179C,253C ''/en-us/library/cc280766.aspx'':58C ''/li'':101C,117C,127C ''/p'':31C,69C,80C,236C,271C ''/ul'':128C ''access'':122C ''ad'':92C ''also'':169C,191C ''amount'':89C,248C ''amp'':176C ''applic'':38C ''appreci'':256C ''around'':134C,193C ''avail'':145C ''big'':22C ''bigdata'':7B ''build'':36C ''call'':20C ''cassandra'':148C,182C ''certain'':233C ''choos'':167C ''circl'':113C ''cloudera'':143C ''cluster'':8B ''column'':67C ''coordin'':51C ''could'':45C,232C ''data'':6A,23C,43C,91C,119C,225C,252C ''dbms'':146C ''deal'':40C ''decid'':163C ''disqualifi'':219C ''driver'':171C ''easi'':79C,100C ''ecosystem'':196C ''element'':77C ''em'':21C,47C,61C,173C,177C,250C ''engine/service'':2A ''exampl'':106C ''exist'':136C ''experi'':213C,245C ''feel'':200C ''field'':18C ''first'':10C ''follow'':76C ''found'':151C ''friend'':141C ''geographi'':62C ''geoloc'':5A,42C,251C ''hadoop'':140C,180C ''hard'':161C,204C ''hortonwork'':142C ''href'':55C ''huge'':88C ''improv'':235C ''inform'':154C ''interest'':153C ''know'':206C ''latitud'':48C ''li'':82C,102C,118C ''like'':147C ''literatur'':267C ''littl'':212C ''longitud'':50C ''look'':133C,192C,209C ''made'':78C ''manipul'':246C ''massiv'':247C ''may'':28C ''meter'':115C ''model'':231C ''mongodb'':195C,216C ''mongoos'':215C ''msdn.microsoft.com'':57C ''msdn.microsoft.com/en-us/library/cc280766.aspx'':56C ''much'':108C ''must'':120C ''naiv'':30C ''need'':72C,170C ''net'':178C ''new'':15C ''nodej'':174C ''nofollow'':60C ''nosql'':1A ''ok'':186C ''one'':165C ''order'':34C ''p'':9C,32C,70C,129C,237C ''point'':109C,223C ''prepar'':85C ''proxim'':103C ''qualiti'':264C ''question'':27C ''rapid'':123C ''receiv'':87C ''recent'':244C,266C ''recommend'':3A ''rel'':59C ''request'':104C ''scalabl'':83C ''scale'':116C ''seem'':183C ''server'':64C,93C ''share'':257C ''slow'':228C ''solut'':137C ''sql'':63C ''still'':157C ''subject'':270C ''system'':96C ''think'':158C ''third'':222C ''type'':68C ''ul'':81C ''ve'':131C,190C ''well'':261C ''would'':255C ''write'':226C ''written'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5694, 1, '2015-05-05 17:48:35.433', 3, 196, 'Dimensionality and Manifold', '&lt;p&gt;A commonly heard sentence in unsupervised Machine learning is&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;High dimensional inputs typically live on or near a low dimensional
  manifold&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;What is a dimension? What is a manifold? What is the difference?&lt;/strong&gt; &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Can you give an example to describe both?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Manifold&lt;/strong&gt; from Google/Wikipedia:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;In mathematics, a manifold is a topological space that resembles
  Euclidean space near each point. More precisely, each point of an
  n-dimensional manifold has a neighbourhood that is homeomorphic to the
  Euclidean space of dimension n.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Dimesion&lt;/strong&gt; from Google/Wikipedia:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;In physics and mathematics, the dimension of a mathematical space (or
  object) is informally defined as the minimum number of coordinates
  needed to specify any point within it.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;What does the Google/Wikipedia even mean in layman terms? It sounds like some bizarre definition like most machine learning definition?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;They are both spaces, so what''s the difference between a Euclidean space (i.e. Manifold) and a dimension space (i.e. feature-based)?&lt;/strong&gt; &lt;/p&gt;
', 'machine-learning  dimensionality-reduction', NULL, '''/blockquote'':36C,113C,152C ''/p'':20C,35C,52C,64C,71C,112C,120C,151C,174C,201C ''/strong'':51C,63C,68C,117C,200C ''base'':199C ''bizarr'':167C ''blockquot'':21C,72C,121C ''common'':12C ''coordin'':143C ''defin'':137C ''definit'':168C,173C ''describ'':61C ''differ'':50C,185C ''dimens'':42C,110C,128C,194C ''dimension'':1A,8B,24C,33C,97C ''dimensionality-reduct'':7B ''dimes'':116C ''euclidean'':84C,107C,188C ''even'':158C ''exampl'':59C ''featur'':198C ''feature-bas'':197C ''give'':57C ''google/wikipedia'':70C,119C,157C ''heard'':13C ''high'':23C ''homeomorph'':104C ''i.e'':190C,196C ''inform'':136C ''input'':25C ''layman'':161C ''learn'':6B,18C,172C ''like'':165C,169C ''live'':27C ''low'':32C ''machin'':5B,17C,171C ''machine-learn'':4B ''manifold'':3A,34C,46C,67C,77C,98C,191C ''mathemat'':75C,126C,131C ''mean'':159C ''minimum'':140C ''n'':96C,111C ''n-dimension'':95C ''near'':30C,86C ''need'':144C ''neighbourhood'':101C ''number'':141C ''object'':134C ''p'':10C,22C,37C,53C,65C,73C,114C,122C,153C,175C ''physic'':124C ''point'':88C,92C,148C ''precis'':90C ''reduct'':9B ''resembl'':83C ''sentenc'':14C ''sound'':164C ''space'':81C,85C,108C,132C,180C,189C,195C ''specifi'':146C ''strong'':38C,54C,66C,115C,176C ''term'':162C ''topolog'':80C ''typic'':26C ''unsupervis'':16C ''within'':149C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5696, 2, '2015-05-06 11:48:06.327', 6, NULL, NULL, '&lt;p&gt;The differences in speed between Naive Bayes and SVM simply boils down to the formulation and the assumptions of each model, and has little to do with the particular library or implementation. &lt;/p&gt;

&lt;p&gt;Not only is naive bayes a simple probabilistic classifier, it also makes an additional assumption of independence between its features, so that parameter estimates can be calculated independently and thus possibly very quickly.&lt;/p&gt;

&lt;p&gt;In comparison, with SVM, every single record in the data base will require a computation of the distance function to determine the optimal decision boundary. &lt;/p&gt;

&lt;p&gt;We can observe that for SVM, we have a complexity of $O(\text{#features} \times \text{#observations}^2)$ or more depending on the choice of SVM and kernel. On the other hand, for naive bayes we would have a complexity of $O(\text{#features} \times \text{#observations})$. &lt;/p&gt;

&lt;p&gt;Hence in general, we can say that SVM will take longer than Naive Bayes. &lt;/p&gt;
', NULL, NULL, '''/p'':34C,69C,96C,145C,161C ''2'':115C ''addit'':49C ''also'':46C ''assumpt'':19C,50C ''base'':81C ''bay'':8C,40C,132C,160C ''boil'':12C ''boundari'':95C ''calcul'':62C ''choic'':121C ''classifi'':44C ''comparison'':72C ''complex'':107C,137C ''comput'':85C ''data'':80C ''decis'':94C ''depend'':118C ''determin'':91C ''differ'':3C ''distanc'':88C ''estim'':59C ''everi'':75C ''featur'':55C,111C,141C ''formul'':16C ''function'':89C ''general'':149C ''hand'':129C ''henc'':147C ''implement'':33C ''independ'':52C,63C ''kernel'':125C ''librari'':31C ''littl'':25C ''longer'':157C ''make'':47C ''model'':22C ''naiv'':7C,39C,131C,159C ''o'':109C,139C ''observ'':100C,114C,144C ''optim'':93C ''p'':1C,35C,70C,97C,146C ''paramet'':58C ''particular'':30C ''possibl'':66C ''probabilist'':43C ''quick'':68C ''record'':77C ''requir'':83C ''say'':152C ''simpl'':42C ''simpli'':11C ''singl'':76C ''speed'':5C ''svm'':10C,74C,103C,123C,154C ''take'':156C ''text'':110C,113C,140C,143C ''thus'':65C ''time'':112C,142C ''would'':134C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5697, 2, '2015-05-06 12:25:24.843', 1, NULL, NULL, '&lt;p&gt;One possible method to approach neural networks with non-negative weights is using &lt;a href="http://en.wikipedia.org/wiki/Feedforward_neural_network" rel="nofollow"&gt;Feedforward neural network&lt;/a&gt;. We can construct the neural network using optimization techniques with the constraint of non-negative weights, as opposed to the normal back propagation method.&lt;/p&gt;

&lt;p&gt;A modified &lt;a href="https://github.com/pushkar/ml/tree/master/nn/hill" rel="nofollow"&gt;Matlab example taken for here is as follows&lt;/a&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;load iris_dataset

% Number of neurons
n = 4;

% Number of attributes and number of classifications
[n_attr, ~]  = size(irisInputs);
[n_class, ~] = size(irisTargets);

% Initialize neural network
net = feedforwardnet(n);

% Configure the neural network for this dataset
net = configure(net, irisInputs, irisTargets); %view(net);

fun = @(w) mse_test(w, net, irisInputs, irisTargets);

% Add ''Display'' option to display result of iterations
ps_opts = psoptimset ( ''CompletePoll'', ''off'', ''Display'', ''iter'', ''MaxIter'', 100); %, ''TimeLimit'', 120 );

% There is n_attr attributes in dataset, and there are n neurons so there 
% are total of n_attr*n input weights (uniform weight)
initial_il_weights = ones(1, n_attr*n)/(n_attr*n);
% There are n bias values, one for each neuron (random)
initial_il_bias    = rand(1, n);
% There is n_class output, so there are total of n_class*n output weights 
% (uniform weight)
initial_ol_weights = ones(1, n_class*n)/(n_class*n);
% There are n_class bias values, one for each output neuron (random)
initial_ol_bias    = rand(1, n_class);
% starting values
starting_values = [initial_il_weights, initial_il_bias, ...
                   initial_ol_weights, initial_ol_bias];

% alter the patternsearch function with the appropriate constraints, in your case we would change it so that the lower bounds of the weights are zero 
[x, fval, flag, output] = patternsearch(fun, starting_values, [], [],[],[], zeros(size(starting_values)), 1e10, ps_opts); 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where the &lt;code&gt;mse_test.m&lt;/code&gt; function is as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;function mse_calc = mse_test(x, net, inputs, targets)
net = setwb(net, x'');
y = net(inputs);
[row col] = size(y);
mse_calc = sum(sum((y - targets).^2))/(row * col);
end
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':26C,71C ''/code'':299C,307C,345C ''/p'':52C,72C,312C ''/pre'':300C,346C ''/pushkar/ml/tree/master/nn/hill'':60C ''/wiki/feedforward_neural_network'':20C ''1'':173C,194C,217C,240C ''100'':142C ''120'':144C ''1e10'':296C ''2'':341C ''4'':82C ''add'':126C ''alter'':259C ''approach'':6C ''appropri'':265C ''attr'':91C,148C,163C,175C,178C ''attribut'':85C,149C ''back'':49C ''bias'':183C,192C,228C,238C,252C,258C ''bound'':278C ''calc'':317C,336C ''case'':269C ''chang'':272C ''class'':95C,199C,207C,219C,222C,227C,242C ''classif'':89C ''code'':74C,304C,314C ''col'':332C,343C ''completepol'':137C ''configur'':104C,112C ''constraint'':38C,266C ''construct'':29C ''dataset'':77C,110C,151C ''display'':127C,130C,139C ''en.wikipedia.org'':19C ''en.wikipedia.org/wiki/feedforward_neural_network'':18C ''end'':344C ''exampl'':64C ''feedforward'':23C ''feedforwardnet'':102C ''flag'':286C ''follow'':70C,311C ''fun'':118C,289C ''function'':262C,308C,315C ''fval'':285C ''github.com'':59C ''github.com/pushkar/ml/tree/master/nn/hill'':58C ''href'':17C,57C ''il'':170C,191C,248C,251C ''initi'':98C,169C,190C,213C,236C,247C,250C,253C,256C ''input'':165C,322C,330C ''iri'':76C ''irisinput'':93C,114C,124C ''iristarget'':97C,115C,125C ''iter'':133C,140C ''load'':75C ''lower'':277C ''matlab'':63C ''maxit'':141C ''method'':4C,51C ''modifi'':55C ''mse'':120C,305C,316C,318C,335C ''n'':81C,90C,94C,103C,147C,155C,162C,164C,174C,176C,177C,179C,182C,195C,198C,206C,208C,218C,220C,221C,223C,226C,241C ''negat'':12C,42C ''net'':101C,111C,113C,117C,123C,321C,324C,326C,329C ''network'':8C,25C,32C,100C,107C ''neural'':7C,24C,31C,99C,106C ''neuron'':80C,156C,188C,234C ''nofollow'':22C,62C ''non'':11C,41C ''non-neg'':10C,40C ''normal'':48C ''number'':78C,83C,87C ''ol'':214C,237C,254C,257C ''one'':2C,172C,185C,216C,230C ''oppos'':45C ''opt'':135C,298C ''optim'':34C ''option'':128C ''output'':200C,209C,233C,287C ''p'':1C,53C,301C ''patternsearch'':261C,288C ''possibl'':3C ''pre'':73C,313C ''propag'':50C ''ps'':134C,297C ''psoptimset'':136C ''rand'':193C,239C ''random'':189C,235C ''rel'':21C,61C ''result'':131C ''row'':331C,342C ''setwb'':325C ''size'':92C,96C,293C,333C ''start'':243C,245C,290C,294C ''sum'':337C,338C ''taken'':65C ''target'':323C,340C ''techniqu'':35C ''test'':121C,319C ''test.m'':306C ''timelimit'':143C ''total'':160C,204C ''uniform'':167C,211C ''use'':15C,33C ''valu'':184C,229C,244C,246C,291C,295C ''view'':116C ''w'':119C,122C ''weight'':13C,43C,166C,168C,171C,210C,212C,215C,249C,255C,281C ''would'':271C ''x'':284C,320C,327C ''y'':328C,334C,339C ''zero'':283C,292C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5700, 2, '2015-05-06 18:27:39.693', 3, NULL, NULL, '&lt;p&gt;The dimensionality of a dataset is the number of variables used to represent it. For example, if we were interested in describing people in terms of their height and weight, our "people" dataset would have 2 dimensions. If instead we had a dataset of images, and each image is a million pixels, then the dimensionality of the dataset would be a million. In fact, in many modern machine learning applications, the dimensionality of a dataset could be massive. &lt;/p&gt;

&lt;p&gt;When dimensionality is very large (larger than the number of the samples in the dataset), we could run into some serious problems. Consider a simple classification algorithm that seeks to find a set of weights w such that when dotted with a sample x, gives a negative number for one class and a positive number for another. w will have a length equal to the dimensionality of the data, so it will have more parameters than there are samples in the entire dataset. This means that a learner will be able to overfit the data, and consequently won''t generalize well to other samples unseen during training. &lt;/p&gt;

&lt;p&gt;A manifold is an object of dimensionality d that is embedded in some higher dimensional space. Imagine a set of points on a sheet of paper. If we crinkle up the paper, the points are now in 3 dimensions. Many manifold learning algorithms seek to "uncrinkle" the sheet of paper to put the data back into 2 dimensions. Even if we aren''t concerned with overfitting our model, a non-linear manifold learner can produce a space that makes classification and regression problems easier.&lt;/p&gt;
', NULL, NULL, '''/p'':80C,189C,276C ''2'':37C,247C ''3'':228C ''abl'':172C ''algorithm'':108C,233C ''anoth'':138C ''applic'':71C ''aren'':252C ''back'':245C ''class'':132C ''classif'':107C,271C ''concern'':254C ''consequ'':178C ''consid'':104C ''could'':77C,98C ''crinkl'':219C ''d'':198C ''data'':150C,176C,244C ''dataset'':6C,34C,44C,59C,76C,96C,164C ''describ'':23C ''dimens'':38C,229C,248C ''dimension'':3C,56C,73C,83C,147C,197C,205C ''dot'':121C ''easier'':275C ''embed'':201C ''entir'':163C ''equal'':144C ''even'':249C ''exampl'':17C ''fact'':65C ''find'':112C ''general'':181C ''give'':126C ''height'':29C ''higher'':204C ''imag'':46C,49C ''imagin'':207C ''instead'':40C ''interest'':21C ''larg'':86C ''larger'':87C ''learn'':70C,232C ''learner'':169C,264C ''length'':143C ''linear'':262C ''machin'':69C ''make'':270C ''mani'':67C,230C ''manifold'':192C,231C,263C ''massiv'':79C ''mean'':166C ''million'':52C,63C ''model'':258C ''modern'':68C ''negat'':128C ''non'':261C ''non-linear'':260C ''number'':9C,90C,129C,136C ''object'':195C ''one'':131C ''overfit'':174C,256C ''p'':1C,81C,190C ''paper'':216C,222C,240C ''paramet'':156C ''peopl'':24C,33C ''pixel'':53C ''point'':211C,224C ''posit'':135C ''problem'':103C,274C ''produc'':266C ''put'':242C ''regress'':273C ''repres'':14C ''run'':99C ''sampl'':93C,124C,160C,185C ''seek'':110C,234C ''serious'':102C ''set'':114C,209C ''sheet'':214C,238C ''simpl'':106C ''space'':206C,268C ''term'':26C ''train'':188C ''uncrinkl'':236C ''unseen'':186C ''use'':12C ''variabl'':11C ''w'':117C,139C ''weight'':31C,116C ''well'':182C ''won'':179C ''would'':35C,60C ''x'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5702, 2, '2015-05-06 23:01:53.847', 2, NULL, NULL, '&lt;p&gt;Yes, you should save result files before you make major mods to the code.  Disk space is cheap, so you''re unlikely to run into issues unless your results sets are prolific.  I would suggest storing old results sets with folder names that include a time stamp of when they were generated.&lt;/p&gt;

&lt;p&gt;As far as time shots of your code, using github (or some other code repository tool) is as easy as can be, will save version information, allows for collaboration, and is an all around great way to backup and version your code. &lt;/p&gt;

&lt;p&gt;The combination of these two things, you''ll effectively have an easy way of mapping a result set to a specific version of the code.&lt;/p&gt;
', NULL, NULL, '''/p'':54C,98C,125C ''allow'':82C ''around'':89C ''backup'':93C ''cheap'':19C ''code'':15C,63C,69C,97C,124C ''collabor'':84C ''combin'':101C ''disk'':16C ''easi'':74C,111C ''effect'':108C ''far'':57C ''file'':7C ''folder'':42C ''generat'':53C ''github'':65C ''great'':90C ''includ'':45C ''inform'':81C ''issu'':27C ''ll'':107C ''major'':11C ''make'':10C ''map'':114C ''mod'':12C ''name'':43C ''old'':38C ''p'':1C,55C,99C ''prolif'':33C ''re'':22C ''repositori'':70C ''result'':6C,30C,39C,116C ''run'':25C ''save'':5C,79C ''set'':31C,40C,117C ''shot'':60C ''space'':17C ''specif'':120C ''stamp'':48C ''store'':37C ''suggest'':36C ''thing'':105C ''time'':47C,59C ''tool'':71C ''two'':104C ''unless'':28C ''unlik'':23C ''use'':64C ''version'':80C,95C,121C ''way'':91C,112C ''would'':35C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9189, 2, '2015-12-03 11:38:53.413', 3, NULL, NULL, '&lt;p&gt;If you really care about the number of dimensions, you still can try to apply a dimensionality reduction algorithm, such as PCA (Principal Component Analysis) or LDA (Linear Discriminant Analysis), after your one hot encoding. &lt;/p&gt;

&lt;p&gt;But know that "56 features" isn''t really large and it''s highly common in the industry to have thousands, millions or even billions of features. &lt;/p&gt;
', NULL, NULL, '''/p'':37C,65C ''56'':42C ''algorithm'':20C ''analysi'':26C,31C ''appli'':16C ''billion'':62C ''care'':5C ''common'':52C ''compon'':25C ''dimens'':10C ''dimension'':18C ''discrimin'':30C ''encod'':36C ''even'':61C ''featur'':43C,64C ''high'':51C ''hot'':35C ''industri'':55C ''isn'':44C ''know'':40C ''larg'':47C ''lda'':28C ''linear'':29C ''million'':59C ''number'':8C ''one'':34C ''p'':1C,38C ''pca'':23C ''princip'':24C ''realli'':4C,46C ''reduct'':19C ''still'':12C ''thousand'':58C ''tri'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8012, 2, '2015-09-05 01:54:04.087', 2, NULL, NULL, '&lt;p&gt;Check out the CART algorithm. This is essentially a bootstrapping method with subsampling that I am sure you can generalize for batch processing.&lt;/p&gt;
', NULL, NULL, '''/p'':25C ''algorithm'':6C ''batch'':23C ''bootstrap'':11C ''cart'':5C ''check'':2C ''essenti'':9C ''general'':21C ''method'':12C ''p'':1C ''process'':24C ''subsampl'':14C ''sure'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5703, 1, '2015-05-07 00:16:08.68', -1, 136, 'Does the ontology in the Semantic Web is dead?', '&lt;p&gt;Is the Semantic Web dead? Are ontologies dead?&lt;/p&gt;

&lt;p&gt;I am developing a work plan for my thesis about &lt;em&gt;"A knowledge base through a set ontology for interest groups around wetlands"&lt;/em&gt;. I have been researching and developing ontologies for it but I am still unclear about many things. What is the modeling language for ontologies?&lt;/p&gt;

&lt;p&gt;Which methodology for ontologies is better? &lt;a href="http://semanticweb.org/wiki/OTK_methodology" rel="nofollow"&gt;OTK&lt;/a&gt; or &lt;a href="http://semanticweb.org/wiki/METHONTOLOGY" rel="nofollow"&gt;METHONTOLOGY&lt;/a&gt;?&lt;/p&gt;

&lt;p&gt;Is there any program that does  as does &lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.hd.uib.no/AcoHum/abs/Mejia.htm" rel="nofollow"&gt;Cratilo&lt;/a&gt; is a software for analyzing of textual corpora and for extraction of specific terms of the domain of study (it is developed by professors Jorge Antonio Mejia, Francisco Javier Alvarez and John Albeiro SÃ¡nchez, Institute of Philosophy the University of Antioquia). It enables lexical analysis of texts, identifying the words that appear their frequency and location in the text. Through a process of recognition, Cratylus identifies all the words in the text and builds a database becomes the draft analysis of the work. Are there other similar tools?&lt;/p&gt;

&lt;p&gt;Can the terms found by Cratilo be used to create a knowledge base?&lt;/p&gt;

&lt;p&gt;What are the existing open semantic frameworks that can be used for such things? &lt;/p&gt;

&lt;p&gt;Is there software that automatically creates RDF, OWL, and XML? How does Tails work? Jena? Sesame? &lt;/p&gt;
', 'knowledge-base', NULL, '''/a'':88C,98C,119C ''/acohum/abs/mejia.htm'':115C ''/em'':47C ''/p'':22C,72C,99C,109C,209C,224C,240C,258C ''/wiki/methontology'':94C ''/wiki/otk_methodology'':84C ''albeiro'':152C ''alvarez'':149C ''analysi'':165C,200C ''analyz'':124C ''antioquia'':161C ''antonio'':145C ''appear'':172C ''around'':45C ''automat'':246C ''base'':12B,37C,223C ''becom'':197C ''better'':79C ''build'':194C ''corpora'':127C ''cratilo'':118C,216C ''cratylus'':185C ''creat'':220C,247C ''databas'':196C ''dead'':9A,18C,21C ''develop'':26C,53C,141C ''domain'':136C ''draft'':199C ''em'':34C ''enabl'':163C ''exist'':229C ''extract'':130C ''found'':214C ''framework'':232C ''francisco'':147C ''frequenc'':174C ''group'':44C ''href'':81C,91C,112C ''identifi'':168C,186C ''institut'':155C ''interest'':43C ''javier'':148C ''jena'':256C ''john'':151C ''jorg'':144C ''knowledg'':11B,36C,222C ''knowledge-bas'':10B ''languag'':69C ''lexic'':164C ''locat'':176C ''mani'':63C ''mejia'':146C ''methodolog'':75C ''methontolog'':97C ''model'':68C ''nchez'':154C ''nofollow'':86C,96C,117C ''ontolog'':3A,20C,41C,54C,71C,77C ''open'':230C ''otk'':87C ''owl'':249C ''p'':13C,23C,73C,100C,110C,210C,225C,241C ''philosophi'':157C ''plan'':29C ''process'':182C ''professor'':143C ''program'':104C ''rdf'':248C ''recognit'':184C ''rel'':85C,95C,116C ''research'':51C ''semant'':6A,16C,231C ''semanticweb.org'':83C,93C ''semanticweb.org/wiki/methontology'':92C ''semanticweb.org/wiki/otk_methodology'':82C ''sesam'':257C ''set'':40C ''similar'':207C ''softwar'':122C,244C ''specif'':132C ''still'':60C ''studi'':138C ''sã'':153C ''tail'':254C ''term'':133C,213C ''text'':167C,179C,192C ''textual'':126C ''thesi'':32C ''thing'':64C,239C ''tool'':208C ''unclear'':61C ''univers'':159C ''use'':218C,236C ''web'':7A,17C ''wetland'':46C ''word'':170C,189C ''work'':28C,203C,255C ''www.hd.uib.no'':114C ''www.hd.uib.no/acohum/abs/mejia.htm'':113C ''xml'':251C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5704, 2, '2015-05-07 03:52:08.877', 1, NULL, NULL, '&lt;p&gt;One way of doing dimensional reduction is to do feature hashing.  This was known about in the 1960''s.  So for example if your data is a sparse set of points in 3 dimensions (x,y,z) you create a (good) hash function h(x,y,z).  You can use that of course for a hash table or a Bloom filter lookup. This is a good form of data compression.  I don''t know why the AI community doesn''t use it.  It is much more to the point than a neural net. &lt;/p&gt;
', NULL, NULL, '''/p'':95C ''1960'':19C ''3'':34C ''ai'':78C ''bloom'':61C ''communiti'':79C ''compress'':71C ''cours'':54C ''creat'':40C ''data'':26C,70C ''dimens'':35C ''dimension'':6C ''doesn'':80C ''exampl'':23C ''featur'':11C ''filter'':62C ''form'':68C ''function'':44C ''good'':42C,67C ''h'':45C ''hash'':12C,43C,57C ''know'':75C ''known'':15C ''lookup'':63C ''much'':86C ''net'':94C ''neural'':93C ''one'':2C ''p'':1C ''point'':32C,90C ''reduct'':7C ''set'':30C ''spars'':29C ''tabl'':58C ''use'':51C,82C ''way'':3C ''x'':36C,46C ''y'':37C,47C ''z'':38C,48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5705, 2, '2015-05-07 04:05:13.373', 0, NULL, NULL, '&lt;p&gt;For sure you need to learn some maths.  However you should also make an effort to gain some broader engineering and science skills.  There are far too many people going into computer science and all they know is a few programming languages and math. The end result is a very boring person with little in the way of creativity to do anything new. Take a year out when you are 18 or 19 to travel the world.   &lt;/p&gt;
', NULL, NULL, '''/p'':79C ''18'':72C ''19'':74C ''also'':13C ''anyth'':63C ''bore'':52C ''broader'':20C ''comput'':33C ''creativ'':60C ''effort'':16C ''end'':47C ''engin'':21C ''far'':27C ''gain'':18C ''go'':31C ''howev'':10C ''know'':38C ''languag'':43C ''learn'':7C ''littl'':55C ''make'':14C ''mani'':29C ''math'':9C,45C ''need'':5C ''new'':64C ''p'':1C ''peopl'':30C ''person'':53C ''program'':42C ''result'':48C ''scienc'':23C,34C ''skill'':24C ''sure'':3C ''take'':65C ''travel'':76C ''way'':58C ''world'':78C ''year'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5759, 2, '2015-05-11 20:57:50.87', 4, NULL, NULL, '&lt;p&gt;What you are looking for can be found in &lt;a href="http://konect.uni-koblenz.de/networks" rel="nofollow"&gt;KONECT&lt;/a&gt; (the website is down as I''m writing this but it should be fixed soon!). It''s almost the most comprehensive data collection for network analysis. But the question is which one is more &lt;strong&gt;standard&lt;/strong&gt; to use?&lt;/p&gt;

&lt;p&gt;Well, there is no clear answer except of Zachary''s Karate Club!&lt;/p&gt;

&lt;p&gt;If you do a literature review in Community Detection algorithms you''ll see that almost all shining papers use different networks. My suggestion is going through what Andrea Lancichinetti and Santo Fortunato did for benchmarking graphs. They proposed some benchmark graph generation algorithms e.g. &lt;a href="http://arxiv.org/abs/0904.3940" rel="nofollow"&gt;this one&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;Hope it helps :) &lt;/p&gt;
', NULL, NULL, '''/a'':19C,128C ''/abs/0904.3940'':123C ''/networks'':15C ''/p'':59C,73C,129C,134C ''/strong'':56C ''algorithm'':84C,117C ''almost'':37C,89C ''analysi'':45C ''andrea'':102C ''answer'':66C ''arxiv.org'':122C ''arxiv.org/abs/0904.3940'':121C ''benchmark'':109C,114C ''clear'':65C ''club'':72C ''collect'':42C ''communiti'':82C ''comprehens'':40C ''data'':41C ''detect'':83C ''differ'':94C ''e.g'':118C ''except'':67C ''fix'':33C ''fortunato'':106C ''found'':9C ''generat'':116C ''go'':99C ''graph'':110C,115C ''help'':133C ''hope'':131C ''href'':12C,120C ''karat'':71C ''konect'':18C ''konect.uni-koblenz.de'':14C ''konect.uni-koblenz.de/networks'':13C ''lancichinetti'':103C ''literatur'':79C ''ll'':86C ''look'':5C ''m'':26C ''network'':44C,95C ''nofollow'':17C,125C ''one'':51C,127C ''p'':1C,60C,74C,130C ''paper'':92C ''propos'':112C ''question'':48C ''rel'':16C,124C ''review'':80C ''santo'':105C ''see'':87C ''shine'':91C ''soon'':34C ''standard'':55C ''strong'':54C ''suggest'':97C ''use'':58C,93C ''websit'':21C ''well'':61C ''write'':27C ''zachari'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5706, 1, '2015-05-07 04:11:56.6', 13, 906, 'What is the "dying ReLU" problem in neural networks?', '&lt;p&gt;Referring to the Stanford course notes on &lt;code&gt;[Convolutional Neural Networks for Visual Recognition][1]&lt;/code&gt;, a paragraph says:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;"Unfortunately, ReLU units can be fragile during training and can
  "die". For example, a large gradient flowing through a ReLU neuron
  could cause the weights to update in such a way that the neuron will
  never activate on any datapoint again. If this happens, then the
  gradient flowing through the unit will forever be zero from that point
  on. That is, the ReLU units can irreversibly die during training since
  they can get knocked off the data manifold. For example, you may find
  that as much as 40% of your network can be "dead" (i.e. neurons that
  never activate across the entire training dataset) if the learning
  rate is set too high. With a proper setting of the learning rate this
  is less frequently an issue."&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;What does dying of neurons here mean? &lt;/p&gt;

&lt;p&gt;Could you please provide an intuitive explanation in simpler terms.&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/blockquote'':164C ''/code'':30C ''/p'':34C,163C,173C,185C ''1'':29C ''40'':124C ''across'':136C ''activ'':73C,135C ''blockquot'':35C ''caus'':59C ''code'':22C ''convolut'':23C ''could'':58C,175C ''cours'':19C ''data'':113C ''datapoint'':76C ''dataset'':140C ''dead'':130C ''die'':4A,47C,103C,168C ''entir'':138C ''exampl'':49C,116C ''explan'':181C ''find'':119C ''flow'':53C,84C ''forev'':89C ''fragil'':42C ''frequent'':160C ''get'':109C ''gradient'':52C,83C ''happen'':80C ''high'':148C ''i.e'':131C ''intuit'':180C ''irrevers'':102C ''issu'':162C ''knock'':110C ''larg'':51C ''learn'':12B,143C,155C ''less'':159C ''machin'':11B ''machine-learn'':10B ''manifold'':114C ''may'':118C ''mean'':172C ''much'':122C ''network'':9A,25C,127C ''neural'':8A,24C ''neuralnetwork'':13B ''neuron'':57C,70C,132C,170C ''never'':72C,134C ''note'':20C ''p'':14C,36C,165C,174C ''paragraph'':32C ''pleas'':177C ''point'':94C ''problem'':6A ''proper'':151C ''provid'':178C ''rate'':144C,156C ''recognit'':28C ''refer'':15C ''relu'':5A,38C,56C,99C ''say'':33C ''set'':146C,152C ''simpler'':183C ''sinc'':106C ''stanford'':18C ''term'':184C ''train'':44C,105C,139C ''unfortun'':37C ''unit'':39C,87C,100C ''updat'':63C ''visual'':27C ''way'':67C ''weight'':61C ''zero'':91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5709, 2, '2015-05-07 09:09:39.333', 2, NULL, NULL, '&lt;p&gt;I think Social Media Mining: An Introduction by Zafarani et. al. is an excellent starting point. You can find more about it &lt;a href="http://dmml.asu.edu/smm/" rel="nofollow"&gt;here&lt;/a&gt;. Also a free PDF version is available.&lt;/p&gt;

&lt;p&gt;It first goes through the essentials in graph theory and data mining. It covers some more advanced topics in graph mining, social network analysis, recommendation systems, etc.&lt;/p&gt;

&lt;p&gt;Besides, I have seen some online courses in coursera (&lt;a href="https://www.coursera.org/course/sna" rel="nofollow"&gt;example&lt;/a&gt;). I am not sure about their quality though.&lt;/p&gt;

&lt;p&gt;Finally, note that social network analysis is data mining for the social media data like Facebook. It is not social science at all; it is computer science. While you may end up borrowing some ideas from them, what you will end up doing is far from what social science guys are doing. So, going through social science courses and books is likely not a good idea at this point.&lt;/p&gt;

&lt;p&gt;P.S. The book is the text book for the social media mining course offered in my university.&lt;/p&gt;
', NULL, NULL, '''/a'':32C,88C ''/course/sna'':84C ''/p'':40C,69C,97C,168C,187C ''/smm/'':28C ''advanc'':58C ''al'':12C ''also'':33C ''analysi'':65C,104C ''avail'':39C ''besid'':71C ''book'':158C,172C,176C ''borrow'':131C ''comput'':124C ''cours'':77C,156C,182C ''coursera'':79C ''cover'':55C ''data'':52C,106C,112C ''dmml.asu.edu'':27C ''dmml.asu.edu/smm/'':26C ''end'':129C,139C ''essenti'':47C ''et'':11C ''etc'':68C ''exampl'':87C ''excel'':15C ''facebook'':114C ''far'':143C ''final'':99C ''find'':20C ''first'':43C ''free'':35C ''go'':152C ''goe'':44C ''good'':163C ''graph'':49C,61C ''guy'':148C ''href'':25C,81C ''idea'':133C,164C ''introduct'':8C ''like'':113C,160C ''may'':128C ''media'':5C,111C,180C ''mine'':6C,53C,62C,107C,181C ''network'':64C,103C ''nofollow'':30C,86C ''note'':100C ''offer'':183C ''onlin'':76C ''p'':1C,41C,70C,98C,169C ''p.s'':170C ''pdf'':36C ''point'':17C,167C ''qualiti'':95C ''recommend'':66C ''rel'':29C,85C ''scienc'':119C,125C,147C,155C ''seen'':74C ''social'':4C,63C,102C,110C,118C,146C,154C,179C ''start'':16C ''sure'':92C ''system'':67C ''text'':175C ''theori'':50C ''think'':3C ''though'':96C ''topic'':59C ''univers'':186C ''version'':37C ''www.coursera.org'':83C ''www.coursera.org/course/sna'':82C ''zafarani'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5710, 1, '2015-05-07 13:23:47.44', 5, 1016, 'Why does logistic regression in Spark and R return different models for the same data?', '&lt;p&gt;I''ve compared the logistic regression models on R (&lt;code&gt;glm&lt;/code&gt;) and on Spark (&lt;code&gt;LogisticRegressionWithLBFGS&lt;/code&gt;) on a dataset of 390 obs. of 14 variables.&lt;/p&gt;

&lt;p&gt;The results are completely different in the intercept and the weights.
How to explain this?&lt;/p&gt;

&lt;p&gt;Here is the results of Spark (LogisticRegressionWithLBFGS) :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;model.intercept  : 
 1.119830027739959
model.weights :
 GEST    0.30798496002530473
 DILATE  0.28121771009716895
 EFFACE  0.01780105068588628
 CONSIS -0.22782058111362183
 CONTR  -0.8094592237248102
 MEMBRAN-1.788173534959893
 AGE    -0.05285751197750732
 STRAT  -1.6650305527536942
 GRAVID  0.38324952943210994
 PARIT  -0.9463956993328745
 DIAB   0.18151162744507293
 TRANSF -0.7413500749909346
 GEMEL  1.5953124037323745
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here is the result of R :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;             Estimate Std. Error z value Pr(&amp;gt;|z|)   
(Intercept)  3.0682091  3.3944407   0.904 0.366052    
GEST         0.0086545  0.1494487   0.058 0.953821    
DILATE       0.4898586  0.2049361   2.390 0.016835 *  
EFFACE       0.0131834  0.0059331   2.222 0.026283 *  
CONSIS       0.1598426  0.2332670   0.685 0.493196    
CONTR        0.0008504  0.5788959   0.001 0.998828    
MEMBRAN     -1.5497870  0.4215416  -3.676 0.000236 ***   
AGE         -0.0420145  0.0326184  -1.288 0.197725    
STRAT       -0.3781365  0.5860476  -0.645 0.518777    
GRAVID       0.1866430  0.1522925   1.226 0.220366    
PARIT       -0.6493312  0.2357530  -2.754 0.005882 **  
DIAB         0.0335458  0.2163165   0.155 0.876760    
TRANSF      -0.6239330  0.3396592  -1.837 0.066219 .  
GEMEL        2.2767331  1.0995245   2.071 0.038391 *  
Signif. codes:  0 â***â 0.001 â**â 0.01 â*â 0.05 â.â 0.1 â â 1
&lt;/code&gt;&lt;/pre&gt;
', 'machine-learning  r  logistic-regression  apache-spark', NULL, '''-0.0420145'':168C ''-0.05285751197750732'':99C ''-0.22782058111362183'':93C ''-0.3781365'':173C ''-0.6239330'':193C ''-0.645'':175C ''-0.6493312'':183C ''-0.7413500749909346'':109C ''-0.8094592237248102'':95C ''-0.9463956993328745'':105C ''-1.288'':170C ''-1.5497870'':163C ''-1.6650305527536942'':101C ''-1.788173534959893'':97C ''-1.837'':195C ''-2.754'':185C ''-3.676'':165C ''/code'':38C,44C,112C,220C ''/p'':54C,71C,80C,121C ''/pre'':113C,221C ''0'':204C ''0.000236'':166C ''0.0008504'':158C ''0.001'':160C,207C ''0.005882'':186C ''0.0059331'':149C ''0.0086545'':138C ''0.01'':210C ''0.0131834'':148C ''0.016835'':146C ''0.01780105068588628'':91C ''0.026283'':151C ''0.0326184'':169C ''0.0335458'':188C ''0.038391'':201C ''0.05'':213C ''0.058'':140C ''0.066219'':196C ''0.1'':216C ''0.1494487'':139C ''0.1522925'':179C ''0.155'':190C ''0.1598426'':153C ''0.18151162744507293'':107C ''0.1866430'':178C ''0.197725'':171C ''0.2049361'':144C ''0.2163165'':189C ''0.220366'':181C ''0.2332670'':154C ''0.2357530'':184C ''0.28121771009716895'':89C ''0.30798496002530473'':87C ''0.3396592'':194C ''0.366052'':136C ''0.38324952943210994'':103C ''0.4215416'':164C ''0.4898586'':143C ''0.493196'':156C ''0.518777'':176C ''0.5788959'':159C ''0.5860476'':174C ''0.685'':155C ''0.876760'':191C ''0.904'':135C ''0.953821'':141C ''0.998828'':161C ''1'':219C ''1.0995245'':199C ''1.119830027739959'':84C ''1.226'':180C ''1.5953124037323745'':111C ''14'':52C ''2.071'':200C ''2.222'':150C ''2.2767331'':198C ''2.390'':145C ''3.0682091'':133C ''3.3944407'':134C ''390'':49C ''age'':98C,167C ''apach'':24B ''apache-spark'':23B ''code'':36C,42C,82C,123C,203C ''compar'':29C ''complet'':59C ''consi'':92C,152C ''contr'':94C,157C ''data'':15A ''dataset'':47C ''diab'':106C,187C ''differ'':10A,60C ''dilat'':88C,142C ''effac'':90C,147C ''error'':126C ''estim'':124C ''explain'':69C ''gemel'':110C,197C ''gest'':86C,137C ''glm'':37C ''gravid'':102C,177C ''gt'':130C ''intercept'':63C,132C ''learn'':18B ''logist'':3A,21B,31C ''logistic-regress'':20B ''logisticregressionwithlbfg'':43C,79C ''machin'':17B ''machine-learn'':16B ''membran'':96C,162C ''model'':11A,33C ''model.intercept'':83C ''model.weights'':85C ''ob'':50C ''p'':26C,55C,72C,114C ''parit'':104C,182C ''pr'':129C ''pre'':81C,122C ''r'':8A,19B,35C,120C ''regress'':4A,22B,32C ''result'':57C,76C,118C ''return'':9A ''signif'':202C ''spark'':6A,25B,41C,78C ''std'':125C ''strat'':100C,172C ''transf'':108C,192C ''valu'':128C ''variabl'':53C ''ve'':28C ''weight'':66C ''z'':127C,131C ''â'':205C,206C,208C,209C,211C,212C,214C,215C,217C,218C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5712, 2, '2015-05-07 15:34:57.47', 4, NULL, NULL, '&lt;p&gt;A quick glance at the &lt;a href="https://spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS" rel="nofollow"&gt;docs for LogisticRegressionWithLBFGS&lt;/a&gt; indicates that it uses feature scaling and L2-Regularization by default. I suspect that R''s &lt;code&gt;glm&lt;/code&gt; is returning a maximum likelihood estimate of the model while Spark''s &lt;code&gt;LogisticRegressionWithLBFGS&lt;/code&gt; is returning a regularized model estimate. Note how the estimated model weights of the Spark model are all smaller in magnitude than those in the R model.&lt;/p&gt;

&lt;p&gt;I''m not sure whether or not &lt;code&gt;glm&lt;/code&gt; in R is implementing feature scaling, but this would also contribute to different model values.&lt;/p&gt;
', NULL, NULL, '''/a'':17C ''/code'':37C,52C,91C ''/docs/1.2.1/api/scala/index.html#org.apache.spark.mllib.classification.logisticregressionwithlbfgs'':11C ''/p'':80C,107C ''also'':101C ''code'':35C,50C,89C ''contribut'':102C ''default'':29C ''differ'':104C ''doc'':14C ''estim'':43C,58C,62C ''featur'':22C,96C ''glanc'':4C ''glm'':36C,90C ''href'':8C ''implement'':95C ''indic'':18C ''l2'':26C ''l2-regularization'':25C ''likelihood'':42C ''logisticregressionwithlbfg'':16C,51C ''m'':83C ''magnitud'':73C ''maximum'':41C ''model'':46C,57C,63C,68C,79C,105C ''nofollow'':13C ''note'':59C ''p'':1C,81C ''quick'':3C ''r'':33C,78C,93C ''regular'':27C,56C ''rel'':12C ''return'':39C,54C ''scale'':23C,97C ''smaller'':71C ''spark'':48C,67C ''spark.apache.org'':10C ''spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.mllib.classification.logisticregressionwithlbfgs'':9C ''sure'':85C ''suspect'':31C ''use'':21C ''valu'':106C ''weight'':64C ''whether'':86C ''would'':100C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5742, 2, '2015-05-10 16:14:00.56', 3, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Kaggle&lt;/strong&gt; has a short summary of applications:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.kaggle.com/wiki/DataScienceUseCases" rel="nofollow"&gt;http://www.kaggle.com/wiki/DataScienceUseCases&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Revolution Analytics&lt;/strong&gt; published many general case studies, datasheets, and white papers:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.revolutionanalytics.com/resources/case-studies?type[0]=case_study&amp;amp;keys=" rel="nofollow"&gt;http://www.revolutionanalytics.com/resources/case-studies?type[0]=case_study&amp;amp;keys=&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;For applications in sciences and engineering, you can consult &lt;strong&gt;Nutonian&lt;/strong&gt; case studies:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.nutonian.com/industries/" rel="nofollow"&gt;http://www.nutonian.com/industries/&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Analyx&lt;/strong&gt; told potential clients about applications in commerce:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.analyx.com/en/company/references/cases.aspx" rel="nofollow"&gt;http://www.analyx.com/en/company/references/cases.aspx&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The &lt;strong&gt;Financial Times&lt;/strong&gt; published a collection of stories about business applications of big data:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://im.ft-static.com/content/images/e91a32d0-2bac-11e3-bfe2-00144feab7de.pdf" rel="nofollow"&gt;http://im.ft-static.com/content/images/e91a32d0-2bac-11e3-bfe2-00144feab7de.pdf&lt;/a&gt;.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;McKinsey&lt;/strong&gt; outlined applications back in 2011:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation" rel="nofollow"&gt;http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Other consulting firms made similar reports.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Gartner&lt;/strong&gt; created Hype Cycle for Big data:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/YvQuk.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Not to mention the case studies and white papers by other companies that want to promote their products.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5713, 2, '2015-05-07 17:31:42.18', 2, NULL, NULL, '&lt;p&gt;There are too many too general problems in your post.&lt;/p&gt;

&lt;p&gt;We''re definitely in an AI summer era right now (as opposed to &lt;a href="http://en.wikipedia.org/wiki/AI_winter" rel="nofollow"&gt;AI winter&lt;/a&gt;), and the research on Semantic Web receives less attention.&lt;/p&gt;

&lt;p&gt;Still, there are many projects related to building ontologies. Google has &lt;a href="http://en.wikipedia.org/wiki/Knowledge_Graph" rel="nofollow"&gt;Knowledge Graph&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/Knowledge_Vault" rel="nofollow"&gt;Knowledge Vault&lt;/a&gt;. Both of these are using &lt;a href="https://www.freebase.com/" rel="nofollow"&gt;Freebase&lt;/a&gt; (among other sources).&lt;/p&gt;

&lt;p&gt;There are dozens of links I can give you to answer some of your questions, but the best thing you can do is to browse &lt;a href="https://www.w3.org/2001/sw/wiki/Main_Page" rel="nofollow"&gt;W3C Semantic Web&lt;/a&gt; pages.&lt;/p&gt;

&lt;p&gt;Take a look at RDF, SPARQL, OWL, Virtuoso, Protege - these are de facto standards.&lt;/p&gt;

&lt;p&gt;In terms of extracting ontologies from textual corpora - there are various tools out there. Neither of them is perfect, so you really have to do some research and find something that suits your needs. For example, there''s the &lt;a href="http://wwwusers.di.uniroma1.it/~navigli/pubs/CL_2013_Velardi_Faralli_Navigli.pdf" rel="nofollow"&gt;OntoLearn Reloaded&lt;/a&gt; (this paper is relatively new, so you can check out the bibliography to seek out for other approaches).&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5718, 1, '2015-05-07 21:53:32.54', 2, 258, 'Add Custom Labels to NLTK Information Extractor', '&lt;p&gt;I am working on an information extractor specifically purposed with parsing relationships between entities such as movies, directors, and actors. NLTK appears to provide the necessary tools to construct such a system. However, it is not clear how one would go about adding custom labels (e.g. actor, director, movie title).&lt;/p&gt;

&lt;p&gt;Similarly, &lt;a href="http://www.nltk.org/book/ch07.html" rel="nofollow"&gt;Chapter 7 of the NLTK Book&lt;/a&gt; discusses information extraction using a named entity recognizer, but it glosses over labeling details.&lt;/p&gt;

&lt;p&gt;So, I have two questions:&lt;/p&gt;

&lt;p&gt;How would I add custom labels?
If I have bare lists of relevant named entities (e.g. movies, actors, etc.), how can I include them as features? It appears that I would need to use IOB format, but I am unsure about how to do this when I only have lists of named entities.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5720, 2, '2015-05-08 00:32:02.25', 2, NULL, NULL, '&lt;p&gt;The C parameter in SVMs doesn''t have to do anything with the kernel function. C is the penalty associated to the instances which are either misclassified or violates the maximal margin. As you may know already, SVM returns the maximum margin for the linearly separable datasets (in the kernel space).&lt;/p&gt;

&lt;p&gt;It might be the case that the dataset is not linearly separable. In this case the corresponding SVM quadratic program is unsolvable. To make it solvable, we redefine the quadratic program to return the maximum margin with respect to some error cost C for the points violating the maximum margin (misclassified samples also violate the maximum margin). Below you can see a simple example I found on the Internet. This uses linear kernel but the story is the same for other non-linear kernels as well.
&lt;img src="http://i.stack.imgur.com/SIath.jpg" alt="Non-Linear dataset"&gt;&lt;/p&gt;

&lt;p&gt;The effect C has on the classification result is shown in the picture below:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/8va0T.png" alt="C effect"&gt;&lt;/p&gt;

&lt;p&gt;As you can see, if you set the error penalty to be very high, you will end up with less misclassifications and fewer support vectors (the highlighted points are SVs). However, if you set it too high when you are using non-linear kernels (specially Gaussian) you might overfit.&lt;/p&gt;

&lt;p&gt;If you are interested in the theory behind it, here is what we are trying to optimize:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/DmCSV.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;If we write its dual, we''ll get:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/e9yqg.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;See how convenient it is! No C in the kernel function!&lt;/p&gt;

&lt;p&gt;In other words, you are mapping the samples to a higher dimensional space using the kernel function, then solving it using some penalty term C.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5722, 2, '2015-05-08 03:41:54.23', 3, NULL, NULL, '&lt;p&gt;This question isn''t terribly clear.  Data analysis and strategic modeling (game theory) are different tasks.  Nash equilibrium is a way of understanding the incentives they have by assuming a set of players with assumed utility function and making &lt;strong&gt;deductive&lt;/strong&gt; inferences about what they ought to do to maximize those utility functions given their interaction.  Data analysis is an &lt;strong&gt;inductive&lt;/strong&gt; process.  &lt;/p&gt;

&lt;p&gt;There are a number of ways game theory and data analysis might interact, here are the easy top two: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Someone might use data to infer players'' utility functions (I''m sure this exists in econometrics-land somewhere; also, political scientists have a technique called "ideal point estimation," to infer political preferences from voting behavior---which you can easily google to learn more);&lt;/li&gt;
&lt;li&gt;Someone might use game theory to generate behavioral predictions which are testable by data.  &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Thinking about the specific kinds of cases you mention, the obvious application would be in the stock market one.  Suppose you have a ML model that can reliably predict the market behavior of other people at time T from a given feature set.  Then the consumer of the ML model might have an optimal purchase at T-1, and finding that optimal purchase is going to be strategic.  &lt;/p&gt;

&lt;p&gt;But combining the two approaches might just break the ML. This is really interesting to think about... musing out loud...&lt;/p&gt;

&lt;p&gt;Consider the simple case of a two-player market in one stock. Player 1 wants to buy at T-1 if player 2 will be buying at T (because the price will go up); player 1 wants to sell at T-1 if player 2 will sell at T (because the price will go down).  The naive approach for player 1 is "use my ML model to predict what player 2 will do, then do it first at T-1." But, of course, P1''s behavior at T-1 is itself observable by P2, and changes P2''s behavior (the price has gone up); moreover, by definition P1''s behavior at T-1 can''t be a feature of the ML model used to predict P2''s behavior at T, because it''s behavior that is chosen on the basis of the ML prediction.  All sorts of fun puzzles begin here, but none of them look real good...&lt;/p&gt;
', NULL, NULL, '''-1'':211C,266C,288C,326C,335C,359C ''/li'':135C,151C ''/ol'':152C ''/p'':67C,88C,222C,244C,405C ''/strong'':43C,65C ''1'':260C,282C,307C ''2'':269C,291C,317C ''also'':110C ''analysi'':9C,60C,79C ''applic'':165C ''approach'':228C,304C ''assum'':30C,36C ''basi'':386C ''begin'':396C ''behavior'':126C,144C,185C,332C,345C,356C,374C,380C ''break'':231C ''buy'':263C,272C ''call'':116C ''case'':160C,249C ''chang'':342C ''chosen'':383C ''clear'':7C ''combin'':225C ''consid'':246C ''consum'':199C ''cours'':329C ''data'':8C,59C,78C,94C,150C ''deduct'':42C ''definit'':353C ''differ'':16C ''easi'':85C ''easili'':130C ''econometr'':107C ''econometrics-land'':106C ''equilibrium'':19C ''estim'':119C ''exist'':104C ''featur'':195C,364C ''find'':213C ''first'':323C ''fun'':394C ''function'':38C,55C,99C ''game'':13C,75C,140C ''generat'':143C ''given'':56C,194C ''go'':218C,279C,300C ''gone'':349C ''good'':404C ''googl'':131C ''ideal'':117C ''incent'':26C ''induct'':64C ''infer'':44C,96C,121C ''interact'':58C,81C ''interest'':237C ''isn'':4C ''kind'':158C ''land'':108C ''learn'':133C ''li'':90C,136C ''look'':402C ''loud'':243C ''m'':101C ''make'':40C ''market'':171C,184C,255C ''maxim'':52C ''mention'':162C ''might'':80C,92C,138C,204C,229C ''ml'':177C,202C,233C,311C,367C,389C ''model'':12C,178C,203C,312C,368C ''moreov'':351C ''muse'':241C ''naiv'':303C ''nash'':18C ''none'':399C ''number'':72C ''observ'':338C ''obvious'':164C ''ol'':89C ''one'':172C,257C ''optim'':207C,215C ''ought'':48C ''p'':1C,68C,153C,223C,245C ''p1'':330C,354C ''p2'':340C,343C,372C ''peopl'':188C ''player'':34C,97C,254C,259C,268C,281C,290C,306C,316C ''point'':118C ''polit'':111C,122C ''predict'':145C,182C,314C,371C,390C ''prefer'':123C ''price'':277C,298C,347C ''process'':66C ''purchas'':208C,216C ''puzzl'':395C ''question'':3C ''real'':403C ''realli'':236C ''reliabl'':181C ''scientist'':112C ''sell'':285C,293C ''set'':32C,196C ''simpl'':248C ''someon'':91C,137C ''somewher'':109C ''sort'':392C ''specif'':157C ''stock'':170C,258C ''strateg'':11C,221C ''strong'':41C,63C ''suppos'':173C ''sure'':102C ''task'':17C ''techniqu'':115C ''terribl'':6C ''testabl'':148C ''theori'':14C,76C,141C ''think'':154C,239C ''time'':190C ''top'':86C ''two'':87C,227C,253C ''two-play'':252C ''understand'':24C ''use'':93C,139C,309C,369C ''util'':37C,54C,98C ''vote'':125C ''want'':261C,283C ''way'':22C,74C ''would'':166C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5724, 2, '2015-05-08 04:27:17.68', 1, NULL, NULL, '&lt;p&gt;When class-conditional distributions are gaussian with equal covariance matrices, the optimal decision boundary is a hyperplane.  This is the core concept behind Linear Discriminant Analysis (LDA).&lt;/p&gt;

&lt;p&gt;For any data point $x$, the probability that $x$ comes from class $\omega_1$ is: &lt;/p&gt;

&lt;p&gt;$P(x|\omega_1) \sim N(\mu_1,\Sigma) = (2\pi)^{-1}|\Sigma|^{-1/2}\exp\left\{ (-1/2) (x-\mu_1)''\Sigma^{-1}(x-\mu_1)
\right\}$&lt;/p&gt;

&lt;p&gt;Similarly, $P(x|\omega_2) \sim N(\mu_2,\Sigma)$.&lt;/p&gt;

&lt;p&gt;Let''s denote the prior probability of class 1 and 2 as $P(\omega_1)$ and $P(\omega_2)$, respectively.&lt;/p&gt;

&lt;h2&gt;Equal Misclassification Cost:&lt;/h2&gt;

&lt;p&gt;If the cost of misclassification is equal, we want to assign new data points such that the probability of misclassification is minimized. This decision rule assigns points to class 1 when $x$ satisfies:&lt;/p&gt;

&lt;p&gt;$P(x|\omega_1)P(\omega_1) &amp;gt; P(\omega_2)(P(x|\omega_2)$&lt;/p&gt;

&lt;p&gt;We can establish a similar rule for assigning points to class 2. &lt;/p&gt;

&lt;p&gt;o find the decision boundary, we need to find the values of $x$ that satisfy&lt;/p&gt;

&lt;p&gt;$P(x|\omega_1)P(\omega_1) = P(x|\omega_2)P(\omega_2)$&lt;/p&gt;

&lt;p&gt;The values of $x$ that satisfy this equality here lie on a line (or hyperplane for higher dimensions).&lt;/p&gt;

&lt;h2&gt;Unequal Misclassification Cost:&lt;/h2&gt;

&lt;p&gt;For the second question, you will need an additional term.  Let''s call $C(\omega_1)$ the cost of making an error when the data point was actually from class 1 and $C(\omega_2)$ the cost of making an error when the data point was from class 2.&lt;/p&gt;

&lt;p&gt;To minimize the cost of an error, your new decision rule would be to assign $x$ to class 1 when $x$ satisfies:&lt;/p&gt;

&lt;p&gt;$P(x|\omega_1)P(\omega_1)C(\omega_1) &amp;gt; P(x|\omega_2)P(\omega_2)C(\omega_2)$&lt;/p&gt;

&lt;p&gt;As before, to find the decision boundary, you need to solve for $x$ when:&lt;/p&gt;

&lt;p&gt;$P(x|\omega_1)P(\omega_1)C(\omega_1) = P(x|\omega_2)P(\omega_2)C(\omega_2)$&lt;/p&gt;

&lt;p&gt;This decision boundary will still be a line / hyperplane, however it may have a different offset or orientation from the solution in the case of equal misclassification cost.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5807, 2, '2015-05-16 05:36:33.213', 3, NULL, NULL, '&lt;p&gt;I would like to add one more dimension to the answers given above. Usually we use cosine similarity with large text, because using distance matrix on paragraphs of data is not recommended. And also if you intend your cluster to be broad you tend to go with cosine similarity as it captures similarity overall. &lt;/p&gt;

&lt;p&gt;For example if you have texts which are two or three words long at max I feel using cosine similarity does not achieve the precision as achieved by distance metric. &lt;/p&gt;
', NULL, NULL, '''/p'':56C,88C ''achiev'':80C,84C ''add'':6C ''also'':35C ''answer'':12C ''broad'':43C ''captur'':53C ''cluster'':40C ''cosin'':18C,49C,76C ''data'':30C ''dimens'':9C ''distanc'':25C,86C ''exampl'':59C ''feel'':74C ''given'':13C ''go'':47C ''intend'':38C ''larg'':21C ''like'':4C ''long'':70C ''matrix'':26C ''max'':72C ''metric'':87C ''one'':7C ''overal'':55C ''p'':1C,57C ''paragraph'':28C ''precis'':82C ''recommend'':33C ''similar'':19C,50C,54C,77C ''tend'':45C ''text'':22C,63C ''three'':68C ''two'':66C ''use'':17C,24C,75C ''usual'':15C ''word'':69C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5725, 1, '2015-05-08 05:27:14.263', 0, 65, 'Datasource for regression model prediction : Machine Learning', '&lt;p&gt;I am trying to work using Amazon machine learning, but the data set that I have is small. The model I want to build is for regression based predictions and the domain I am aiming for the data set to belong is financial, say product price prediction, price and demand prediction based on macro/micro economic factor.&lt;/p&gt;

&lt;p&gt;I am looking for a data set that contains factors that lead to variations in value of a product or commodity. For example, I would like to predict the value of 1 unit of polyester yarn after 1 year.  The factors which influence the yarn price are say - prices of crude oil, GDP of country,figures of IIP, inflation etc. So I would like a data set that contains the quotes of these factors on which the final price depends.&lt;/p&gt;

&lt;p&gt;I find difficulty assembling this data myself because I don''t know all the factors that contribute to a certain predictive price. Does anyone know of a dataset I can start with that sounds like it might contain these factors?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':68C,150C,192C ''1'':101C,107C ''aim'':46C ''amazon'':18C ''anyon'':175C ''assembl'':155C ''base'':39C,63C ''belong'':52C ''build'':35C ''certain'':171C ''commod'':90C ''contain'':78C,138C,189C ''contribut'':168C ''countri'':124C ''crude'':120C ''data'':23C,49C,75C,135C,157C ''dataset'':179C ''datasourc'':1A ''demand'':61C ''depend'':149C ''difficulti'':154C ''domain'':43C ''econom'':66C ''etc'':129C ''exampl'':92C ''factor'':67C,79C,110C,143C,166C,191C ''figur'':125C ''final'':147C ''financi'':54C ''find'':153C ''gdp'':122C ''iip'':127C ''inflat'':128C ''influenc'':112C ''know'':163C,176C ''lead'':81C ''learn'':7A,10B,20C ''like'':95C,133C,186C ''look'':72C ''machin'':6A,9B,19C ''machine-learn'':8B ''macro/micro'':65C ''might'':188C ''model'':4A,31C ''oil'':121C ''p'':11C,69C,151C ''polyest'':104C ''predict'':5A,40C,58C,62C,97C,172C ''price'':57C,59C,115C,118C,148C,173C ''product'':56C,88C ''quot'':140C ''regress'':3A,38C ''say'':55C,117C ''set'':24C,50C,76C,136C ''small'':29C ''sound'':185C ''start'':182C ''tri'':14C ''unit'':102C ''use'':17C ''valu'':85C,99C ''variat'':83C ''want'':33C ''work'':16C ''would'':94C,132C ''yarn'':105C,114C ''year'':108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5858, 2, '2015-05-21 19:29:24.063', 2, NULL, NULL, '&lt;p&gt;Data science is much more broad. It''s sort of a catch-all term that right now doesn''t honestly have a very clear definition. But data-science includes all of the skills and techniques required to make sense of data which has high velocity (it''s coming at you quickly), volume (there''s a lot of it), or variability (it''s messy, like natural language processing). This means that it certainly includes machine learning and AI, but that it''s also about the tools one might use in a real-world situation such as SQL, Hadoop or Spark (and related information such as a knowledge of parallel programming). Additionally, data science may or may not include the communication aspect such as making good graphs and using Excel.&lt;/p&gt;

&lt;p&gt;Basically, Data Science is ML+.&lt;/p&gt;
', NULL, NULL, '''/p'':133C,140C ''addit'':114C ''ai'':80C ''also'':85C ''aspect'':124C ''basic'':135C ''broad'':7C ''catch'':14C ''catch-al'':13C ''certain'':75C ''clear'':26C ''come'':51C ''communic'':123C ''data'':2C,30C,44C,115C,136C ''data-sci'':29C ''definit'':27C ''doesn'':20C ''excel'':132C ''good'':128C ''graph'':129C ''hadoop'':101C ''high'':47C ''honest'':22C ''includ'':32C,76C,121C ''inform'':106C ''knowledg'':110C ''languag'':69C ''learn'':78C ''like'':67C ''lot'':59C ''machin'':77C ''make'':41C,127C ''may'':117C,119C ''mean'':72C ''messi'':66C ''might'':90C ''ml'':139C ''much'':5C ''natur'':68C ''one'':89C ''p'':1C,134C ''parallel'':112C ''process'':70C ''program'':113C ''quick'':54C ''real'':95C ''real-world'':94C ''relat'':105C ''requir'':39C ''right'':18C ''scienc'':3C,31C,116C,137C ''sens'':42C ''situat'':97C ''skill'':36C ''sort'':10C ''spark'':103C ''sql'':100C ''techniqu'':38C ''term'':16C ''tool'':88C ''use'':91C,131C ''variabl'':63C ''veloc'':48C ''volum'':55C ''world'':96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5726, 2, '2015-05-08 06:57:00.863', 1, NULL, NULL, '&lt;p&gt;I have a very limited knowledge of game theory, but hope to learn more. However, I think that potential applications of Nash equilibrium in the context of big data environments, implies the need of analyzing a large number of &lt;strong&gt;features&lt;/strong&gt; (representing various strategic pathways or traits) as well as large number of &lt;strong&gt;cases&lt;/strong&gt; (representing significant number of actors). Considering these points, I would think that &lt;em&gt;complexity&lt;/em&gt; and, consequently, &lt;em&gt;performance requirements&lt;/em&gt; for Nash equilibrium in big data applications grow &lt;strong&gt;exponentially&lt;/strong&gt;. For some examples from the &lt;em&gt;Internet load-balancing&lt;/em&gt; domain, see paper by Even-Dar, Kesselman and Mansour (n.d.).&lt;/p&gt;

&lt;p&gt;The above-mentioned points touch only the &lt;strong&gt;volume&lt;/strong&gt; aspect of &lt;a href="http://www.ibmbigdatahub.com/infographic/four-vs-big-data" rel="nofollow"&gt;4V big data model&lt;/a&gt; (an update of Gartner''s original &lt;em&gt;3V model&lt;/em&gt;). If you add to that other aspects (&lt;em&gt;variety&lt;/em&gt;, &lt;em&gt;velocity&lt;/em&gt; and &lt;em&gt;veracity&lt;/em&gt;), the situation seems to become even more complex. Perhaps, people with &lt;em&gt;econometrics&lt;/em&gt; background and experience will have some of the most comprehensive opinions on this interesting question. A lot of such people are active on &lt;em&gt;Cross Validated&lt;/em&gt;, so I will let them know about this question - hopefully, some of them will be interested to share their view by answering this question.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Even-Dar, E., Kesselman, A., &amp;amp; Mansour, Y. (n.d.). &lt;em&gt;Convergence time to Nash equilibria.&lt;/em&gt; Retrieved from &lt;a href="http://www.tau.ac.il/~mansour/course_games/nash-load.pdf" rel="nofollow"&gt;http://www.tau.ac.il/~mansour/course_games/nash-load.pdf&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8023, 2, '2015-09-07 08:02:34.44', 0, NULL, NULL, '&lt;p&gt;In R I would strongly suggest the reshape2 package, in particular the cast / melt combo of functions.&lt;/p&gt;
', NULL, NULL, '''/p'':19C ''cast'':14C ''combo'':16C ''function'':18C ''melt'':15C ''p'':1C ''packag'':10C ''particular'':12C ''r'':3C ''reshape2'':9C ''strong'':6C ''suggest'':7C ''would'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8061, 2, '2015-09-10 17:06:43.22', 2, NULL, NULL, '&lt;p&gt;There are lots of function optimising routines that could be applied, based on the description so far. Random search, grid search, hill-climbing, gradient descent, genetic algorithms, simulated annealing, particle swarm optimisation are all possible contenders that I have heard of, and I am probably missing a few.&lt;/p&gt;

&lt;p&gt;The trouble is, starting with next to zero knowledge of the black box, it is almost impossible to guess a good candidate from these search options. All of them have strengths and weaknesses. To start with, you seem to have no indication of &lt;em&gt;scale&lt;/em&gt; - should you be trying input parameters in any particular ranges? So you might want to try very crude searches through a range of magnitudes (positive and negative values) to find the area worth searching. Such a grid search is expensive - if you have $k$ dimensions and want to search $n$ different magnitudes, then you need to call your black box $n^k$ times. &lt;/p&gt;

&lt;p&gt;This can be done in parallel though, and given you are confident that the function is roughly unimodal, you can start with a relatively low number of n (maybe check -10, -1, 0, +1, +10 for 15625 calls to your function taking roughly 8 hours 40 mins using 5 boxes). You may need to repeat with other params once you know whether you have found a bounding box for the mode or need to try yet more values, so this process could take a while longer - potentially days if the optimal value for param 6 is more like 20,000. You could also refine more closely, once you have a potential mode you might want to define another grid of values to search based around. This basic grid search might be my first point of attack on a black box system where I had no clue about parameter meaning, but some confidence that the black box output had a rough unimodal form. &lt;/p&gt;

&lt;p&gt;Given the speed of response you should be storing all input and output values in a database for faster lookup and better model building later. No point repeating a call taking 10 seconds when a cache could look it up in 1 millisecond.&lt;/p&gt;

&lt;p&gt;Once you have some range of values you think that a mode might be in, then it is time to pick a suitable optimiser.&lt;/p&gt;

&lt;p&gt;Given the information so far, I would be tempted to run either more grid search (with a separate linear scaling between values of each param) and/or a random search, constrained roughly to the boxes defined by the set of $2^6$ corner points &lt;em&gt;around&lt;/em&gt; the best result found in initial order-of-magnitude search.&lt;/p&gt;

&lt;p&gt;At that point you could also consider graphing the data, to see if there is any intuition about which other algorithms could perform well. &lt;/p&gt;

&lt;p&gt;With the possibility of parallel calls, then gradient descent might be a reasonable guess, because you can get approximate gradients by adding a small offset to each param and dividing difference that causes in the output by it. In addition, gradient descent (or simple hill climbing) has some chance of optimising with less calls to evaluate the function than approaches that rely on many iterations (simulated annealing) or lots of work in parallel (particle swarm or genetic algorithms). Gradient descent optimisers as used in neural networks, with additions like Nesterov momentum or RMSProp, can cope with changes in function output "feature scale" such as different sizes and heights of peaks, ridges, saddle points.&lt;/p&gt;

&lt;p&gt;However, gradient descent and hill climbing algorithms are not robust against all function shapes. A graph or several of what your explorations are seeing may help you to decide on a different approach. So keep all the data and graph it in case you can get clues.&lt;/p&gt;

&lt;p&gt;Finally, don''t rule out random brute-force search, and being able to just accept "best so far" under time constraints. With low knowledge of the internals of the black box, it is a reasonable strategy.&lt;/p&gt;
', NULL, NULL, '''+1'':197C ''+10'':198C ''-1'':195C ''-10'':194C ''/em'':98C,444C ''/p'':51C,162C,326C,371C,397C,456C,482C,597C,646C,685C ''0'':196C ''000'':263C ''1'':369C ''10'':359C ''15625'':200C ''2'':438C ''20'':262C ''40'':209C ''5'':212C ''6'':258C,439C ''8'':207C ''abl'':660C ''accept'':663C ''ad'':505C ''addit'':523C,571C ''algorithm'':29C,478C,561C,605C ''almost'':68C ''also'':266C,463C ''and/or'':424C ''anneal'':31C,550C ''anoth'':281C ''appli'':12C ''approach'':543C,631C ''approxim'':502C ''area'':130C ''around'':288C,443C ''attack'':299C ''base'':13C,287C ''basic'':290C ''best'':446C,664C ''better'':349C ''black'':64C,157C,302C,318C,678C ''bound'':230C ''box'':65C,158C,213C,231C,303C,319C,432C,679C ''brute'':655C ''brute-forc'':654C ''build'':351C ''cach'':363C ''call'':155C,201C,357C,489C,537C ''candid'':74C ''case'':641C ''caus'':516C ''chanc'':532C ''chang'':580C ''check'':193C ''climb'':25C,529C,604C ''close'':269C ''clue'':309C,645C ''confid'':175C,315C ''consid'':464C ''constrain'':428C ''constraint'':669C ''contend'':38C ''cope'':578C ''corner'':440C ''could'':10C,245C,265C,364C,462C,479C ''crude'':116C ''data'':467C,636C ''databas'':344C ''day'':251C ''decid'':627C ''defin'':280C,433C ''descent'':27C,492C,525C,563C,601C ''descript'':16C ''differ'':149C,514C,588C,630C ''dimens'':143C ''divid'':513C ''done'':167C ''either'':410C ''em'':96C,442C ''evalu'':539C ''expens'':138C ''explor'':620C ''far'':18C,403C,666C ''faster'':346C ''featur'':584C ''final'':648C ''find'':128C ''first'':296C ''forc'':656C ''form'':325C ''found'':228C,448C ''function'':6C,178C,204C,541C,582C,611C ''genet'':28C,560C ''get'':501C,644C ''given'':172C,328C,399C ''good'':73C ''gradient'':26C,491C,503C,524C,562C,600C ''graph'':465C,614C,638C ''grid'':21C,135C,282C,291C,412C ''guess'':71C,497C ''heard'':42C ''height'':591C ''help'':624C ''hill'':24C,528C,603C ''hill-climb'':23C ''hour'':208C ''howev'':599C ''imposs'':69C ''indic'':94C ''inform'':401C ''initi'':450C ''input'':103C,338C ''intern'':675C ''intuit'':474C ''iter'':548C ''k'':142C,160C ''keep'':633C ''know'':224C ''knowledg'':61C,672C ''later'':352C ''less'':536C ''like'':261C,572C ''linear'':417C ''longer'':249C ''look'':365C ''lookup'':347C ''lot'':4C,552C ''low'':188C,671C ''magnitud'':122C,150C,454C ''mani'':547C ''may'':215C,623C ''mayb'':192C ''mean'':312C ''might'':111C,277C,293C,385C,493C ''millisecond'':370C ''min'':210C ''miss'':48C ''mode'':234C,275C,384C ''model'':350C ''momentum'':574C ''n'':148C,159C,191C ''need'':153C,216C,236C ''negat'':125C ''nesterov'':573C ''network'':569C ''neural'':568C ''next'':58C ''number'':189C ''offset'':508C ''optim'':254C ''optimis'':7C,34C,396C,534C,564C ''option'':78C ''order'':452C ''order-of-magnitud'':451C ''output'':320C,340C,519C,583C ''p'':1C,52C,163C,327C,372C,398C,457C,483C,598C,647C ''parallel'':169C,488C,556C ''param'':221C,257C,423C,511C ''paramet'':104C,311C ''particl'':32C,557C ''particular'':107C ''peak'':593C ''perform'':480C ''pick'':393C ''point'':297C,354C,441C,460C,596C ''posit'':123C ''possibl'':37C,486C ''potenti'':250C,274C ''probabl'':47C ''process'':244C ''random'':19C,426C,653C ''rang'':108C,120C,377C ''reason'':496C,683C ''refin'':267C ''relat'':187C ''reli'':545C ''repeat'':218C,355C ''respons'':332C ''result'':447C ''ridg'':594C ''rmsprop'':576C ''robust'':608C ''rough'':180C,206C,323C,429C ''routin'':8C ''rule'':651C ''run'':409C ''saddl'':595C ''scale'':97C,418C,585C ''search'':20C,22C,77C,117C,132C,136C,147C,286C,292C,413C,427C,455C,657C ''second'':360C ''see'':469C,622C ''seem'':90C ''separ'':416C ''set'':436C ''sever'':616C ''shape'':612C ''simpl'':527C ''simul'':30C,549C ''size'':589C ''small'':507C ''speed'':330C ''start'':56C,87C,184C ''store'':336C ''strategi'':684C ''strength'':83C ''suitabl'':395C ''swarm'':33C,558C ''system'':304C ''take'':205C,246C,358C ''tempt'':407C ''think'':381C ''though'':170C ''time'':161C,391C,668C ''tri'':102C,114C,238C ''troubl'':54C ''unimod'':181C,324C ''use'':211C,566C ''valu'':126C,241C,255C,284C,341C,379C,420C ''want'':112C,145C,278C ''weak'':85C ''well'':481C ''whether'':225C ''work'':554C ''worth'':131C ''would'':405C ''yet'':239C ''zero'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5729, 2, '2015-05-08 17:32:38.147', 3, NULL, NULL, '&lt;p&gt;In general, a decent starting point for problems like these is Naive Bayes (NB) classification using a simple bag of words model. &lt;a href="https://web.stanford.edu/class/cs124/lec/naivebayes.pdf" rel="nofollow"&gt;Here are some slides describing NB as applied to natural language processing&lt;/a&gt;. There''s nothing especially fancy about this approach, but it''s pretty easy to implement and will give you a starting point to expand from.&lt;/p&gt;

&lt;p&gt;Once you''ve found some initial results assuming independence among your features and your output labels, you''ll probably have a better sense of where the model is weak.  From that point forward you can apply some feature engineering (maybe &lt;a href="http://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;TF-IDF&lt;/a&gt;) as well as some post processing to deal with samples that get assigned to related categories.&lt;/p&gt;
', NULL, NULL, '''/a'':43C,121C ''/class/cs124/lec/naivebayes.pdf'':28C ''/p'':69C,138C ''/wiki/tf%e2%80%93idf'':115C ''among'':80C ''appli'':38C,106C ''approach'':51C ''assign'':134C ''assum'':78C ''bag'':20C ''bay'':14C ''better'':92C ''categori'':137C ''classif'':16C ''deal'':129C ''decent'':5C ''describ'':35C ''easi'':56C ''en.wikipedia.org'':114C ''en.wikipedia.org/wiki/tf%e2%80%93idf'':113C ''engin'':109C ''especi'':47C ''expand'':67C ''fanci'':48C ''featur'':82C,108C ''forward'':103C ''found'':74C ''general'':3C ''get'':133C ''give'':61C ''href'':25C,112C ''idf'':120C ''implement'':58C ''independ'':79C ''initi'':76C ''label'':86C ''languag'':41C ''like'':10C ''ll'':88C ''mayb'':110C ''model'':23C,97C ''naiv'':13C ''natur'':40C ''nb'':15C,36C ''nofollow'':30C,117C ''noth'':46C ''output'':85C ''p'':1C,70C ''point'':7C,65C,102C ''post'':126C ''pretti'':55C ''probabl'':89C ''problem'':9C ''process'':42C,127C ''rel'':29C,116C ''relat'':136C ''result'':77C ''sampl'':131C ''sens'':93C ''simpl'':19C ''slide'':34C ''start'':6C,64C ''tf'':119C ''tf-idf'':118C ''use'':17C ''ve'':73C ''weak'':99C ''web.stanford.edu'':27C ''web.stanford.edu/class/cs124/lec/naivebayes.pdf'':26C ''well'':123C ''word'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5730, 1, '2015-05-09 05:44:50.633', 3, 100, 'Finding user similarities within informal data sets', '&lt;p&gt;I''m new to all this and am putting together a learning project. I''ve decided on finding similarities between users in a data set such as &lt;a href="http://en.wikipedia.org/wiki/Enron_Corpus" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Enron_Corpus&lt;/a&gt;. After doing a bit of research, I also came across &lt;a href="http://datascience.stackexchange.com/questions/641/dataset-for-named-entity-recognition-on-informal-text/5397#5397"&gt;Dataset for Named Entity Recognition on Informal Text&lt;/a&gt;. So I''m not short of data or a goal, I need to understand high-level techniques to get there.&lt;/p&gt;

&lt;p&gt;One valuable comment noted that this question appears too broad. What I was hoping to find with this question was the breadth of techniques I should focus research on, not answers that are immediately implementable. Please consider vague answers as entirely appropriate!!&lt;/p&gt;

&lt;p&gt;Expanding on the goal, I am hoping to discover which authors might have affinity toward each other, or conversely do not care much for each other. So I will definitely need to start with Named Entity Recognition and build a means to organize the documents against those entities. Beyond that, I am not so sure.&lt;/p&gt;

&lt;p&gt;What high level concepts should I be looking at? Thanks!&lt;/p&gt;
', 'machine-learning  classification  clustering', NULL, '''/a'':74C ''/p'':96C,140C,197C,209C ''/questions/641/dataset-for-named-entity-recognition-on-informal-text/5397#5397'':65C ''/wiki/enron_corpus'':45C ''/wiki/enron_corpus&lt;/a&gt;.'':50C ''across'':60C ''affin'':155C ''also'':58C ''answer'':128C,136C ''appear'':105C ''appropri'':139C ''author'':152C ''beyond'':190C ''bit'':54C ''breadth'':119C ''broad'':107C ''build'':180C ''came'':59C ''care'':163C ''classif'':11B ''cluster'':12B ''comment'':100C ''concept'':202C ''consid'':134C ''convers'':160C ''data'':6A,37C,81C ''datascience.stackexchange.com'':64C ''datascience.stackexchange.com/questions/641/dataset-for-named-entity-recognition-on-informal-text/5397#5397'':63C ''dataset'':66C ''decid'':29C ''definit'':171C ''discov'':150C ''document'':186C ''en.wikipedia.org'':44C,49C ''en.wikipedia.org/wiki/enron_corpus'':43C ''en.wikipedia.org/wiki/enron_corpus&lt;/a&gt;.'':48C ''entir'':138C ''entiti'':69C,177C,189C ''expand'':142C ''find'':1A,31C,113C ''focus'':124C ''get'':94C ''goal'':84C,145C ''high'':90C,200C ''high-level'':89C ''hope'':111C,148C ''href'':42C,62C ''immedi'':131C ''implement'':132C ''inform'':5A,72C ''learn'':10B,25C ''level'':91C,201C ''look'':206C ''m'':15C,77C ''machin'':9B ''machine-learn'':8B ''mean'':182C ''might'':153C ''much'':164C ''name'':68C,176C ''need'':86C,172C ''new'':16C ''nofollow'':47C ''note'':101C ''one'':98C ''organ'':184C ''p'':13C,97C,141C,198C ''pleas'':133C ''project'':26C ''put'':22C ''question'':104C,116C ''recognit'':70C,178C ''rel'':46C ''research'':56C,125C ''set'':7A,38C ''short'':79C ''similar'':3A,32C ''start'':174C ''sure'':196C ''techniqu'':92C,121C ''text'':73C ''thank'':208C ''togeth'':23C ''toward'':156C ''understand'':88C ''user'':2A,34C ''vagu'':135C ''valuabl'':99C ''ve'':28C ''within'':4A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5731, 2, '2015-05-09 08:01:05.72', 4, NULL, NULL, '&lt;p&gt;There are &lt;a href="http://quant.caltech.edu/historical-stock-data.html" rel="nofollow"&gt;multiple datasets&lt;/a&gt; available in the internet but you need to pay for most of them.
One of the best datasets for starting (and it is free) is the &lt;em&gt;Quantquote Free dataset&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;You can download it from &lt;a href="http://quantquote.com/files/quantquote_daily_sp500_83986.zip" rel="nofollow"&gt;here&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;This is the description (borrowed from their &lt;a href="https://quantquote.com/historical-stock-data" rel="nofollow"&gt;website&lt;/a&gt;):&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;This collection of daily resolution data goes back to 1998 for all symbols currently active in the S&amp;amp;P500. It is updated quarterly, the last update was 07/31/2013.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/a'':13C,60C,78C ''/blockquote'':112C ''/em'':44C ''/files/quantquote_daily_sp500_83986.zip'':56C ''/historical-stock-data'':74C ''/historical-stock-data.html'':8C ''/p'':45C,61C,79C,111C ''07/31/2013'':110C ''1998'':91C ''activ'':96C ''amp'':100C ''avail'':14C ''back'':89C ''best'':30C ''blockquot'':80C ''borrow'':67C ''collect'':83C ''current'':95C ''daili'':85C ''data'':87C ''dataset'':12C,31C,43C ''descript'':66C ''download'':49C ''em'':40C ''free'':37C,42C ''goe'':88C ''href'':5C,53C,71C ''internet'':17C ''last'':107C ''multipl'':11C ''need'':20C ''nofollow'':10C,58C,76C ''one'':27C ''p'':1C,46C,62C,81C ''p500'':101C ''pay'':22C ''quant.caltech.edu'':7C ''quant.caltech.edu/historical-stock-data.html'':6C ''quantquot'':41C ''quantquote.com'':55C,73C ''quantquote.com/files/quantquote_daily_sp500_83986.zip'':54C ''quantquote.com/historical-stock-data'':72C ''quarter'':105C ''rel'':9C,57C,75C ''resolut'':86C ''start'':33C ''symbol'':94C ''updat'':104C,108C ''websit'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5733, 2, '2015-05-09 11:21:34.607', 1, NULL, NULL, '&lt;p&gt;Your question has nothing to do with NLP or text-mining (as you claim in the attached tags), or data science in general. It''s a pure programming question best suitable for &lt;a href="http://stackoverflow.com"&gt;StackOverflow&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Moreover, you don''t really need any libraries to do, what you want to do. A simple function will do. NOTE: I am using &lt;code&gt;map&lt;/code&gt; and &lt;code&gt;reduce&lt;/code&gt; functions on purpose to include at least a little bit of data science-related stuff (WINK):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;coord = (1.23, 4.56)

def get_coord(coord):
   coord_str = map(lambda x: str(x).replace(''.'', ''''), coord)
   for level in range(1, len(coord_str[0])+1):
      yield reduce(lambda x, y: (x[:level]+''_''+y[:level]), coord_str)

print list(get_coord(coord))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Running this code will result in printing:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[''1_4'', ''12_45'', ''123_456'']
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You''re not discussing any corner cases in your question, so I assume there are none.&lt;/p&gt;
', NULL, NULL, '''+1'':118C ''/a'':39C ''/code'':68C,72C,135C,154C ''/p'':40C,90C,145C,173C ''/pre'':136C,155C ''0'':117C ''1'':113C,148C ''1.23'':94C ''12'':150C ''123'':152C ''4'':149C ''4.56'':95C ''45'':151C ''456'':153C ''assum'':169C ''attach'':19C ''best'':32C ''bit'':82C ''case'':163C ''claim'':16C ''code'':66C,70C,92C,140C,147C ''coord'':93C,98C,99C,100C,108C,115C,128C,133C,134C ''corner'':162C ''data'':22C,84C ''def'':96C ''discuss'':160C ''function'':59C,73C ''general'':25C ''get'':97C,132C ''href'':36C ''includ'':77C ''lambda'':103C,121C ''least'':79C ''len'':114C ''level'':110C,125C,127C ''librari'':49C ''list'':131C ''littl'':81C ''map'':67C,102C ''mine'':13C ''moreov'':42C ''need'':47C ''nlp'':9C ''none'':172C ''note'':62C ''noth'':5C ''p'':1C,41C,137C,156C ''pre'':91C,146C ''print'':130C,144C ''program'':30C ''pure'':29C ''purpos'':75C ''question'':3C,31C,166C ''rang'':112C ''re'':158C ''realli'':46C ''reduc'':71C,120C ''relat'':87C ''replac'':107C ''result'':142C ''run'':138C ''scienc'':23C,86C ''science-rel'':85C ''simpl'':58C ''stackoverflow'':38C ''stackoverflow.com'':37C ''str'':101C,105C,116C,129C ''stuff'':88C ''suitabl'':33C ''tag'':20C ''text'':12C ''text-min'':11C ''use'':65C ''want'':54C ''wink'':89C ''x'':104C,106C,122C,124C ''y'':123C,126C ''yield'':119C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5734, 2, '2015-05-09 14:25:59.33', 15, NULL, NULL, '&lt;p&gt;A "dead" ReLU always outputs the same value (zero as it happens, but that is not important) for any input. Probably this is arrived at by learning a large negative bias term for its weights.&lt;/p&gt;

&lt;p&gt;In turn, that means that it takes no role in discriminating between inputs. For classification, you could visualise this as a decision plane &lt;em&gt;outside&lt;/em&gt; of all possible input data.&lt;/p&gt;

&lt;p&gt;Once a ReLU ends up in this state, it is unlikely to recover, because the function gradient at 0 is also 0, so gradient descent learning will not alter the weights. "Leaky" ReLUs with a small positive gradient for negative inputs (&lt;code&gt;y=0.01x&lt;/code&gt; when x &amp;lt; 0 say) are one attempt to address this issue and give a chance to recover.&lt;/p&gt;

&lt;p&gt;The sigmoid and tanh neurons can suffer from similar problems as their values saturate, but there is always at least a small gradient allowing them to recover in the long term.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5735, 1, '2015-05-09 21:57:21.073', 2, 47, 'Library/package/tool for geographical data visualizations', '&lt;p&gt;I am looking for a good free existing tool which visualizes geographical data (let''s say in the form of coordinates) by plotting them on a map. &lt;/p&gt;

&lt;p&gt;It can be a library (see &lt;a href="http://stackoverflow.com/questions/9018607/library-for-map-visualization"&gt;this question on StackOverflow&lt;/a&gt;, which suggests a Python library called basemap, which is interesting but not dynamic enough, namely it does not allow for interactivity) or a complete toolkit. &lt;/p&gt;

&lt;p&gt;Existing things I found are oriented towards realizing web pages, which are not my ultimate goal (see &lt;a href="http://www.simile-widgets.org/exhibit/" rel="nofollow"&gt;Exhibit&lt;/a&gt; or &lt;a href="http://modestmaps.com/" rel="nofollow"&gt;Modest Maps&lt;/a&gt;). I''d like something to feed with data which spits out an interactive map where you can click on places and it displays the related data. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5736, 2, '2015-05-10 01:42:25.457', 4, NULL, NULL, '&lt;p&gt;Assuming you want your visualizations to be available on the internet, there are many options for this:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://developers.google.com/maps/" rel="nofollow"&gt;Google Maps&lt;/a&gt; (and &lt;a href="http://tables.googlelabs.com/" rel="nofollow"&gt;Google Fusion Tables&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.mapbox.com/" rel="nofollow"&gt;Mapbox&lt;/a&gt; (and the related &lt;a href="http://leafletjs.com/" rel="nofollow"&gt;LeafletJS&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;a href="http://cartodb.com/" rel="nofollow"&gt;CartoDB&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://bost.ocks.org/mike/map/" rel="nofollow"&gt;D3&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It really comes down to exactly what you want to do and how much control you want to have over the map.  Additionally you should consider what sort of interactivity you need.  &lt;/p&gt;

&lt;p&gt;Google Maps is probably the most user friendly for very basic map functions but is somewhat limited in what you can do stylistically.  &lt;/p&gt;

&lt;p&gt;Mapbox and CartoDB are both user friendly and offer good options for styling and displaying different varieties of data.  However, they also both tend to require subscription fees for anything other than small maps. Also, the last time I checked, CartoDB explicitly handles animation and time-series data where Mapbox does not.&lt;/p&gt;

&lt;p&gt;D3 will probably give you the most control over display, animation, and interactivity but also has a long learning curve. Even if you don''t want the map to be available on the internet, this is still a very good tool for making interactive visualizations that run in the browser.&lt;/p&gt;

&lt;p&gt;If you don''t care as much about the visualizations being online, you can get a lot of work done in open GIS software like &lt;a href="http://www.qgis.org/en/site/" rel="nofollow"&gt;QGIS&lt;/a&gt; or &lt;a href="http://grass.osgeo.org/" rel="nofollow"&gt;GrassGIS&lt;/a&gt;, though I don''t know if user interactivity is really an option there.&lt;/p&gt;

&lt;p&gt;As I said though, it really comes down to the specifics of exactly what you''re trying to do and how comfortable you are with various aspects of mapping and coding.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5737, 1, '2015-05-10 09:58:56.67', 4, 196, 'What kind of research can be done with an email data set?', '&lt;p&gt;I found a data set called &lt;a href="http://www.cs.cmu.edu/~./enron/" rel="nofollow"&gt;Enron Email Dataset&lt;/a&gt;. It is possibly the only substantial collection of "real" email that is public. I found some prior analysis of this work:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;A paper describing the Enron data was presented at the 2004 CEAS conference.&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Some experiments associated with this data are described on Ron Bekkerman''s home page&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.parakweet.com/" rel="nofollow"&gt;Parakweet&lt;/a&gt; has released an open source set of Enron sentence data, labeled for speech acts.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Work at the University of Pennsylvania includes a query dataset for email search as well as a tool for generating spelling errors based on the Enron corpus. &lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I''m looking for some interesting current trend topics to work with.please give me some suggestions.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5738, 2, '2015-05-10 10:03:20.36', 1, NULL, NULL, '&lt;p&gt;Once you have your own lists of named entities, and you''re only interested in extracting the relations, I believe there are simpler solutions (although I never tried relation extraction in NLTK, so I might be wrong):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://reverb.cs.washington.edu/" rel="nofollow"&gt;ReVerb&lt;/a&gt; - a tool written in Java. Once it produces the results, you can simply keep the rows, where your labels are present as objects of the relation.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://knowitall.github.io/openie/" rel="nofollow"&gt;OpenIE&lt;/a&gt; - the successor of ReVerb (also written in Java). The authors claim better perfomance, and the output might be more informative.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://iepy.readthedocs.org/en/latest/index.html" rel="nofollow"&gt;IEPY&lt;/a&gt; - a relation extraction tool in Python. You should be able to provide your own labels/named entities using &lt;a href="http://iepy.readthedocs.org/en/latest/gazettes.html" rel="nofollow"&gt;gazetees&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://github.com/mit-nlp/MITIE" rel="nofollow"&gt;MITIE&lt;/a&gt; - this library has bindings in Python, and it offers &lt;a href="https://github.com/mit-nlp/MITIE/blob/master/examples/python/ner.py" rel="nofollow"&gt;relation extraction&lt;/a&gt; functionality.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5741, 1, '2015-05-10 11:49:07.233', 3, 52, 'When to Perform non-linear dimension reduction', '&lt;p&gt;When do we feel need to go through non-linear transformation like kernel PCA ? Please share an example&lt;/p&gt;
', 'machine-learning  r', NULL, '''/p'':33C ''dimens'':7A ''exampl'':32C ''feel'':17C ''go'':20C ''kernel'':27C ''learn'':11B ''like'':26C ''linear'':6A,24C ''machin'':10B ''machine-learn'':9B ''need'':18C ''non'':5A,23C ''non-linear'':4A,22C ''p'':13C ''pca'':28C ''perform'':3A ''pleas'':29C ''r'':12B ''reduct'':8A ''share'':30C ''transform'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5743, 2, '2015-05-10 18:49:29.037', 0, NULL, NULL, '&lt;p&gt;I haven''t seen this mentioned, but it''s important to keep in mind that you may see a decrease in salary. I say this without knowing how much you make, but moving from (I assume) an experienced IT professional to an entry level data scientist level may not earn you as much.&lt;/p&gt;

&lt;p&gt;Here''s a link to the a portion of the 2015 Burtch Works study on Data Science salaries:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.burtchworks.com/files/2015/05/DS-2015_Changes-in-Base-Salaries.pdf" rel="nofollow"&gt;http://www.burtchworks.com/files/2015/05/DS-2015_Changes-in-Base-Salaries.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;As you can see, the median salary for level 1 individual contributors is 90k (across the nation). The full report has the breakdown based on region but again, assuming you''re an experienced IT professional, you''re probably making more than that.&lt;/p&gt;

&lt;p&gt;Anecdotal story with n=1: One of my classmates in my DS masters program was an experienced Java developer with a house, family, etc. Although he was very interested in data analytics (paid for the program out of pocket) his potential salary doing data analytics wouldn''t be able to support the lifestyle he currently had as a Java developer. As a result he essentially "wasted" his degree and went back to development. I would really hate to see that happen to more people.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5744, 2, '2015-05-10 21:12:49.86', 3, NULL, NULL, '&lt;p&gt;There are good/fast ways to model graphs in RDBMS, and dumb/slow ways. &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Some use clever indexing and Stored Procs, trading CPU load and tuned temp tables on RAM disks for faster graph retrieval speed.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Some use precomputed graph paths (this may be less feasible in social network scenario, but in a tree with majority of nodes being leaf nodes, it''s a pretty good tradeoff space-for-time&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Some simply compute in a loop, using un-tuned in-indexed temp table. From the #s thrown in the article, that smells like what they did (30 second- performance on fairly smallish data-set)&lt;/p&gt;

&lt;p&gt;For example, I have my own tree computation.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;It is encapsulated in a highly-tuned stored proc&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;While it''s running in an enterprise-sized-hardware Sybase ASE15 dataserver, that server is shared with a couple terabytes of data from &lt;strong&gt;all other&lt;/strong&gt; enterprise apps, some much more data hungry than mine; and isn''t dedicated solely to executing my queries.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;I did &lt;strong&gt;not&lt;/strong&gt; have access to the main speedup tool, a temp table on a RAM disk.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;A representative set of data I was retrieving that seems to somewhat match theirs was getting a 150,000 node subtree out of 2.5M node full forest dataset (unlimited depth of tree, which varies between 5 and 15, but smaller &lt;em&gt;average&lt;/em&gt; arity of a given node than the 50 friends listed in the experiment) &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;I tuned it to the point that this query ~30-45 seconds. It most certainly does NOT exhibit the exponential slowdown that the figures in the question seem to indicate on their RDBMS performance, which is extra double strange given there is no exponential growth in the result set (which to me reeks of un-tuned index on a temp table from personal experience).&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So, this comparison is &lt;strong&gt;most likely&lt;/strong&gt; incorrect and based on poor RDBMS side design, although as the previous answer noted, it is impossible to ascertain without them open sourcing 100% of their code and table definitions.&lt;/p&gt;
', NULL, NULL, '''-45'':292C ''/em'':264C ''/li'':41C,80C,145C,195C,218C,279C,348C,350C ''/p'':14C,40C,79C,120C,130C,144C,194C,217C,278C,347C,391C ''/strong'':175C,202C,360C ''/ul'':349C,351C ''000'':239C ''100'':384C ''15'':259C ''150'':238C ''2.5'':244C ''30'':111C,291C ''5'':257C ''50'':272C ''access'':204C ''although'':369C ''answer'':373C ''app'':177C ''ariti'':265C ''articl'':104C ''ascertain'':379C ''ase15'':159C ''averag'':263C ''base'':363C ''certain'':296C ''clever'':20C ''code'':387C ''comparison'':355C ''comput'':85C,129C ''coupl'':167C ''cpu'':26C ''data'':118C,170C,181C,225C ''data-set'':117C ''dataserv'':160C ''dataset'':249C ''dedic'':188C ''definit'':390C ''depth'':251C ''design'':368C ''disk'':34C,216C ''doubl'':319C ''dumb/slow'':12C ''em'':262C ''encapsul'':136C ''enterpris'':155C,176C ''enterprise-sized-hardwar'':154C ''exampl'':123C ''execut'':191C ''exhibit'':299C ''experi'':277C,346C ''exponenti'':301C,325C ''extra'':318C ''fair'':115C ''faster'':36C ''feasibl'':53C ''figur'':305C ''forest'':248C ''friend'':273C ''full'':247C ''get'':236C ''given'':268C,321C ''good'':73C ''good/fast'':4C ''graph'':8C,37C,47C ''growth'':326C ''hardwar'':157C ''high'':140C ''highly-tun'':139C ''hungri'':182C ''imposs'':377C ''in-index'':93C ''incorrect'':361C ''index'':21C,95C,339C ''indic'':311C ''isn'':186C ''leaf'':67C ''less'':52C ''li'':16C,42C,81C,132C,146C,196C,219C,280C ''like'':107C,359C ''list'':274C ''load'':27C ''loop'':88C ''m'':245C ''main'':207C ''major'':63C ''match'':233C ''may'':50C ''mine'':184C ''model'':7C ''much'':179C ''network'':56C ''node'':65C,68C,240C,246C,269C ''note'':374C ''open'':382C ''p'':1C,17C,43C,82C,121C,133C,147C,197C,220C,281C,352C ''path'':48C ''perform'':113C,315C ''person'':345C ''point'':287C ''poor'':365C ''precomput'':46C ''pretti'':72C ''previous'':372C ''proc'':24C,143C ''queri'':193C,290C ''question'':308C ''ram'':33C,215C ''rdbms'':10C,314C,366C ''reek'':334C ''repres'':222C ''result'':329C ''retriev'':38C,228C ''run'':151C ''scenario'':57C ''second'':112C,293C ''seem'':230C,309C ''server'':162C ''set'':119C,223C,330C ''share'':164C ''side'':367C ''simpli'':84C ''size'':156C ''slowdown'':302C ''smaller'':261C ''smallish'':116C ''smell'':106C ''social'':55C ''sole'':189C ''somewhat'':232C ''sourc'':383C ''space'':76C ''space-for-tim'':75C ''speed'':39C ''speedup'':208C ''store'':23C,142C ''strang'':320C ''strong'':172C,200C,357C ''subtre'':241C ''sybas'':158C ''tabl'':31C,97C,212C,343C,389C ''temp'':30C,96C,211C,342C ''terabyt'':168C ''thrown'':101C ''time'':78C ''tool'':209C ''trade'':25C ''tradeoff'':74C ''tree'':61C,128C,253C ''tune'':29C,92C,141C,283C,338C ''ul'':15C,131C ''un'':91C,337C ''un-tun'':90C,336C ''unlimit'':250C ''use'':19C,45C,89C ''vari'':255C ''way'':5C,13C ''without'':380C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5745, 2, '2015-05-11 04:21:20.767', 4, NULL, NULL, '&lt;p&gt;The following are some research that can done on e-mail dataset:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;linguistic analysis to abbreviate an email message&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Categorize e-mail as spam/ham using machine learning techniques.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;identifying concepts expressed in a collection of email messages, and
organizing them into an ontology or taxonomy for browsing&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':27C,41C,64C ''/p'':15C,26C,40C,63C ''/ul'':65C ''abbrevi'':22C ''analysi'':20C ''brows'':62C ''categor'':30C ''collect'':49C ''concept'':45C ''dataset'':14C ''done'':9C ''e'':12C,32C ''e-mail'':11C,31C ''email'':24C,51C ''express'':46C ''follow'':3C ''identifi'':44C ''learn'':38C ''li'':17C,28C,42C ''linguist'':19C ''machin'':37C ''mail'':13C,33C ''messag'':25C,52C ''ontolog'':58C ''organ'':54C ''p'':1C,18C,29C,43C ''research'':6C ''spam/ham'':35C ''taxonomi'':60C ''techniqu'':39C ''ul'':16C ''use'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6175, 1, '2015-06-22 14:41:12.013', 1, 58, 'Which open-source sgdb for kind of large data', '&lt;p&gt;I have a 7 giga confidential dataset which I want to use for a machine learning application.&lt;/p&gt;

&lt;p&gt;I tried :&lt;/p&gt;

&lt;p&gt;Every package recommanded for efficient dataset management in R like :&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;data.table, &lt;/li&gt;
&lt;li&gt;ff &lt;/li&gt;
&lt;li&gt;and sqldf with no success. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Data.table needs to load all the data in the memory from what I read, so it''s obvious that it will not work since my computer has only 4g RAM. Ff leads to a memory error too.&lt;/p&gt;

&lt;p&gt;So I decided to turn to sgdb and I tried :&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Mysql which managed to load my dataset in 2 hours and 21''. Then I began my requests (I have a few requests to do to prepare my data before I export a smaller set in R for machine learning application), and then I had to wait for hours before I got the following message "The total number of locks exceeds the lock table size" (my request was just an update to extract the month from a date for each tuple). &lt;/li&gt;
&lt;li&gt;I read that postgre was similar to mysql in performance so I didn''t try&lt;/li&gt;
&lt;li&gt;I read that redis was really performant but not at all adapted to massive importation like I want to do here so I didn''t try&lt;/li&gt;
&lt;li&gt;I tried mongoDb, the nosql upraising solution that I heard everywhere about. Not only I find rather disturbing that mongoimport is so limited in options (I had to change all semi-colon in commas using sed before I can import the data), but It seems to be less performant that mysql since I launched the loading yesterday and it is still running. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;What I can''t try : data are confidential so I don''t really want to rent some space on Azure or Amazon clouding solution. I am not sure that it is that big that I have to turn to Hadoop solution but maybe I am wrong about that. &lt;/p&gt;

&lt;p&gt;Is there an open-source performant solution that I didn''t try that you would recommend to perform some sql-like requests on a biggish dataset ?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Edit : 
Some more details about what I want to do with these data for you to visualize. These are events with a timestamp and a geolocalisation. I have 8 billions of lines. One example of what I want to do : &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;standardize series identified by geolocalisation (I need to compute mean grouping by geolocalisation for example), &lt;/li&gt;
&lt;li&gt;compute average count of events by type of season, day... (usual group by sql request)... &lt;/li&gt;
&lt;/ul&gt;

&lt;hr&gt;

&lt;p&gt;Edit &lt;/p&gt;

&lt;p&gt;As a beginning of answer for those who have limited hardware like me, rSQLite seems to be a possibility. I am still interested in other people experiences. &lt;/p&gt;
', 'r  performance  mongodb', NULL, '''/li'':52C,55C,62C,197C,214C,242C,307C,449C,466C ''/p'':32C,36C,48C,102C,114C,358C,388C,431C,471C,500C ''/ul'':63C,308C,467C ''2'':125C ''21'':128C ''4g'':93C ''7'':18C ''8'':419C ''adapt'':227C ''amazon'':331C ''answer'':477C ''applic'':31C,156C ''averag'':452C ''azur'':329C ''began'':131C ''begin'':475C ''big'':342C ''biggish'':386C ''billion'':420C ''chang'':272C ''cloud'':332C ''colon'':276C ''comma'':278C ''comput'':90C,442C,451C ''confidenti'':20C,317C ''count'':453C ''data'':10A,71C,144C,286C,315C,403C ''data.table'':51C,65C ''dataset'':21C,43C,123C,387C ''date'':193C ''day'':460C ''decid'':106C ''detail'':394C ''didn'':211C,239C,370C ''disturb'':261C ''edit'':391C,470C ''effici'':42C ''error'':100C ''event'':410C,455C ''everi'':38C ''everywher'':254C ''exampl'':424C,448C ''exceed'':176C ''experi'':499C ''export'':147C ''extract'':188C ''ff'':54C,95C ''find'':259C ''follow'':169C ''geolocalis'':416C,438C,446C ''giga'':19C ''got'':167C ''group'':444C,462C ''hadoop'':349C ''hardwar'':483C ''heard'':253C ''hour'':126C,164C ''hr'':389C,468C ''identifi'':436C ''import'':230C,284C ''interest'':495C ''kind'':7A ''larg'':9A ''launch'':298C ''lead'':96C ''learn'':30C,155C ''less'':292C ''li'':50C,53C,56C,116C,198C,215C,243C,433C,450C ''like'':47C,231C,382C,484C ''limit'':266C,482C ''line'':422C ''load'':68C,121C,300C ''lock'':175C,178C ''machin'':29C,154C ''manag'':44C,119C ''massiv'':229C ''mayb'':352C ''mean'':443C ''memori'':74C,99C ''messag'':170C ''mongodb'':13B,246C ''mongoimport'':263C ''month'':190C ''mysql'':117C,206C,295C ''need'':66C,440C ''nosql'':248C ''number'':173C ''obvious'':82C ''one'':423C ''open'':3A,364C ''open-sourc'':2A,363C ''option'':268C ''p'':14C,33C,37C,64C,103C,309C,359C,390C,469C,472C ''packag'':39C ''peopl'':498C ''perform'':12B,208C,222C,293C,366C,378C ''possibl'':491C ''postgr'':202C ''prepar'':142C ''r'':11B,46C,152C ''ram'':94C ''rather'':260C ''read'':78C,200C,217C ''realli'':221C,322C ''recommand'':40C ''recommend'':376C ''redi'':219C ''rent'':325C ''request'':133C,138C,182C,383C,465C ''rsqlite'':486C ''run'':306C ''season'':459C ''sed'':280C ''seem'':289C,487C ''semi'':275C ''semi-colon'':274C ''seri'':435C ''set'':150C ''sgdb'':5A,110C ''similar'':204C ''sinc'':88C,296C ''size'':180C ''smaller'':149C ''solut'':250C,333C,350C,367C ''sourc'':4A,365C ''space'':327C ''sql'':381C,464C ''sql-like'':380C ''sqldf'':58C ''standard'':434C ''still'':305C,494C ''success'':61C ''sure'':337C ''tabl'':179C ''timestamp'':413C ''total'':172C ''tri'':35C,113C,213C,241C,245C,314C,372C ''tupl'':196C ''turn'':108C,347C ''type'':457C ''ul'':49C,115C,432C ''updat'':186C ''uprais'':249C ''use'':26C,279C ''usual'':461C ''visual'':407C ''wait'':162C ''want'':24C,233C,323C,398C,428C ''work'':87C ''would'':375C ''wrong'':355C ''yesterday'':301C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5746, 1, '2015-05-11 06:53:52.973', 12, 277, 'Fast k-means like algorithm for 10^10 points?', '&lt;p&gt;I am looking to do k-means clustering on a set of 10-dimensional points.  The catch: &lt;strong&gt;there are 10^10 points&lt;/strong&gt;.  &lt;/p&gt;

&lt;p&gt;I am looking for just the center and size of the largest clusters (let''s say 10 to 100 clusters); I don''t care about what cluster each point ends up in.  Using k-means specifically is not important; I am just looking for a similar effect, any approximate k-means or related algorithm would be great (minibatch-SGD means, ...).  Since GMM is in a sense the same problem as k-means, doing GMM on the same size data is also interesting.&lt;/p&gt;

&lt;p&gt;At this scale, subsampling the data probably doesn''t change the result significantly: the odds of finding the same top 10 clusters using a 1/10000th sample of the data are very good.  But even then, that is a 10^6 point problem which is on/beyond the edge of tractable.  &lt;/p&gt;
', 'clustering  k-means', NULL, '''/p'':41C,129C,180C ''/strong'':40C ''1/10000th'':155C ''10'':8A,9A,29C,37C,38C,59C,151C,169C ''100'':61C ''6'':170C ''algorithm'':6A,98C ''also'':127C ''approxim'':92C ''care'':66C ''catch'':33C ''center'':49C ''chang'':140C ''cluster'':11B,24C,55C,62C,69C,152C ''data'':125C,136C,159C ''dimension'':30C ''doesn'':138C ''edg'':177C ''effect'':90C ''end'':72C ''even'':164C ''fast'':1A ''find'':147C ''gmm'':107C,120C ''good'':162C ''great'':101C ''import'':82C ''interest'':128C ''k'':3A,13B,22C,77C,94C,117C ''k-mean'':2A,12B,21C,76C,93C,116C ''largest'':54C ''let'':56C ''like'':5A ''look'':18C,45C,86C ''mean'':4A,14B,23C,78C,95C,105C,118C ''minibatch'':103C ''minibatch-sgd'':102C ''odd'':145C ''on/beyond'':175C ''p'':15C,42C,130C ''point'':10A,31C,39C,71C,171C ''probabl'':137C ''problem'':114C,172C ''relat'':97C ''result'':142C ''sampl'':156C ''say'':58C ''scale'':133C ''sens'':111C ''set'':27C ''sgd'':104C ''signific'':143C ''similar'':89C ''sinc'':106C ''size'':51C,124C ''specif'':79C ''strong'':34C ''subsampl'':134C ''top'':150C ''tractabl'':179C ''use'':75C,153C ''would'':99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5747, 1, '2015-05-11 11:14:21.043', 1, 19, 'Different "end of study" times for different cohorts - Cox PH model in survival analysis', '&lt;p&gt;I have a dataset with 4 cohorts of about the same size (~700 people each). I''m trying to apply a Cox PH model using the time needed to pass a very difficult exam as my "time" variable. The cohorts differ because they are different classes (class of 2009, class of 2010, 2011 and 2012). These are clearly also the time they enter in the study.&lt;/p&gt;

&lt;p&gt;All times are censored after 2013. Is there any way of accounting for the fact that the "time of study" is different for each cohort? I was thinking of stratifying for cohorts, but the coefficients would clearly be negative and would decrease faster and faster. This however would be due not to a real decrease in the hazard, but to the fact that the latter cohorts are monitored for less time. &lt;/p&gt;
', 'statistics  survival-analysis', NULL, '''/p'':86C,159C ''2009'':68C ''2010'':71C ''2011'':72C ''2012'':74C ''2013'':93C ''4'':25C ''700'':32C ''account'':99C ''also'':78C ''analysi'':14A,18B ''appli'':39C ''censor'':91C ''class'':65C,66C,69C ''clear'':77C,124C ''coeffici'':122C ''cohort'':8A,26C,59C,112C,119C,153C ''cox'':9A,41C ''dataset'':23C ''decreas'':129C,142C ''differ'':1A,7A,60C,64C,109C ''difficult'':52C ''due'':137C ''end'':2A ''enter'':82C ''exam'':53C ''fact'':102C,149C ''faster'':130C,132C ''hazard'':145C ''howev'':134C ''latter'':152C ''less'':157C ''m'':36C ''model'':11A,43C ''monitor'':155C ''need'':47C ''negat'':126C ''p'':19C,87C ''pass'':49C ''peopl'':33C ''ph'':10A,42C ''real'':141C ''size'':31C ''statist'':15B ''stratifi'':117C ''studi'':4A,85C,107C ''surviv'':13A,17B ''survival-analysi'':16B ''think'':115C ''time'':5A,46C,56C,80C,89C,105C,158C ''tri'':37C ''use'':44C ''variabl'':57C ''way'':97C ''would'':123C,128C,135C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5749, 2, '2015-05-11 12:49:42.757', 4, NULL, NULL, '&lt;p&gt;This is only possible with KnowledgeFlow. In WekaManual.pdf (which is included in Weka package) for version 3.7.12 there is an example in Chapter 7.4.2 "Plotting multiple ROC curves" with picture and step-by-step instructions. For other Weka versions it is the same, just find the appropriate chapter.&lt;/p&gt;

&lt;p&gt;To give an impression on how it goes, I extracted the picture from the manual. It will draw two curves for two classifiers. For your question it is very similar. You use one classifier and then connect trainingSet to one ClassifierPerformanceEvaluator and testSet to another.
&lt;img src="http://i.stack.imgur.com/ZzEKV.png" alt="enter image description here"&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':51C,108C ''/zzekv.png'':102C ''3.7.12'':18C ''7.4.2'':25C ''alt'':103C ''anoth'':97C ''appropri'':49C ''chapter'':24C,50C ''classifi'':75C,86C ''classifierperformanceevalu'':93C ''connect'':89C ''curv'':29C,72C ''descript'':106C ''draw'':70C ''enter'':104C ''exampl'':22C ''extract'':62C ''find'':47C ''give'':54C ''goe'':60C ''i.stack.imgur.com'':101C ''i.stack.imgur.com/zzekv.png'':100C ''imag'':105C ''img'':98C ''impress'':56C ''includ'':12C ''instruct'':37C ''knowledgeflow'':7C ''manual'':67C ''multipl'':27C ''one'':85C,92C ''p'':1C,52C ''packag'':15C ''pictur'':31C,64C ''plot'':26C ''possibl'':5C ''question'':78C ''roc'':28C ''similar'':82C ''src'':99C ''step'':34C,36C ''step-by-step'':33C ''testset'':95C ''trainingset'':90C ''two'':71C,74C ''use'':84C ''version'':17C,41C ''weka'':14C,40C ''wekamanual.pdf'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5750, 1, '2015-05-11 13:07:53.56', 1, 54, 'Determine what data, does certain column have in common', '&lt;p&gt;I have an Excel sheet with &lt;code&gt;n&lt;/code&gt; columns, these columns contain info about the students. For admissions we have the score of test scores in multiple subject areas, scores from an interview, and scores of a written test and comprehension test. There is a column which contains student''s academic level (High, M.High, Middle, M.Low, Low). I want to compare the last column with the others variables and see whether there are common features that passing students have in common.&lt;/p&gt;

&lt;p&gt;Is there software for this? If this can be done with excel, how can I do it? Does SPSS provide this kind of analysis?&lt;/p&gt;
', 'data-mining', NULL, '''/code'':22C ''/p'':96C,122C ''academ'':65C ''admiss'':32C ''analysi'':121C ''area'':43C ''certain'':5A ''code'':20C ''column'':6A,23C,25C,60C,78C ''common'':9A,88C,95C ''compar'':75C ''comprehens'':55C ''contain'':26C,62C ''data'':3A,11B ''data-min'':10B ''determin'':1A ''done'':107C ''excel'':17C,109C ''featur'':89C ''high'':67C ''info'':27C ''interview'':47C ''kind'':119C ''last'':77C ''level'':66C ''low'':71C ''m.high'':68C ''m.low'':70C ''middl'':69C ''mine'':12B ''multipl'':41C ''n'':21C ''other'':81C ''p'':13C,97C ''pass'':91C ''provid'':117C ''score'':36C,39C,44C,49C ''see'':84C ''sheet'':18C ''softwar'':100C ''spss'':116C ''student'':30C,63C,92C ''subject'':42C ''test'':38C,53C,56C ''variabl'':82C ''want'':73C ''whether'':85C ''written'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6177, 2, '2015-06-22 19:21:53.897', 3, NULL, NULL, '&lt;ul&gt;
&lt;li&gt;Determine the function of genes and the elements that regulate genes
throughout the genome.&lt;/li&gt;
&lt;li&gt;Find variations in the DNA sequence among people and determine their
significance. The most common type of genetic variation is known as a
single nucleotide polymorphism or SNP (pronounced âsnipâ).  These
small differences may help predict a personâs risk of particular
diseases and response to certain medications.&lt;/li&gt;
&lt;li&gt;Discover the 3-dimensional structures of proteins and identify their
functions.&lt;/li&gt;
&lt;li&gt;Explore how DNA and proteins interact with one another and with the
environment to create complex living systems.&lt;/li&gt;
&lt;li&gt;Develop and apply genome-based strategies for the early detection,
diagnosis, and treatment of disease.&lt;/li&gt;
&lt;li&gt;Sequence the genomes of other organisms, such as the rat, cow, and
chimpanzee, in order to compare similar genes between species.&lt;/li&gt;
&lt;li&gt;Develop new technologies to study genes and DNA on a large scale and
store genomic data efficiently.&lt;/li&gt;
&lt;li&gt;Continue to explore the ethical, legal, and social issues raised by
genomic research.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://ghr.nlm.nih.gov/handbook/genomicresearch?show=all" rel="nofollow"&gt;Source&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':186C ''/handbook/genomicresearch?show=all'':182C ''/li'':17C,68C,81C,101C,119C,142C,161C,176C,187C ''/ul'':188C ''3'':72C ''among'':25C ''anoth'':91C ''appli'':105C ''base'':108C ''certain'':66C ''chimpanze'':133C ''common'':33C ''compar'':137C ''complex'':98C ''continu'':163C ''cow'':131C ''creat'':97C ''data'':159C ''detect'':113C ''determin'':3C,28C ''develop'':103C,144C ''diagnosi'':114C ''differ'':52C ''dimension'':73C ''discov'':70C ''diseas'':62C,118C ''dna'':23C,85C,151C ''earli'':112C ''effici'':160C ''element'':10C ''environ'':95C ''ethic'':167C ''explor'':83C,165C ''find'':19C ''function'':5C,80C ''gene'':7C,13C,139C,149C ''genet'':36C ''genom'':16C,107C,123C,158C,174C ''genome-bas'':106C ''ghr.nlm.nih.gov'':181C ''ghr.nlm.nih.gov/handbook/genomicresearch?show=all'':180C ''help'':54C ''href'':179C ''identifi'':78C ''interact'':88C ''issu'':171C ''known'':39C ''larg'':154C ''legal'':168C ''li'':2C,18C,69C,82C,102C,120C,143C,162C,177C ''live'':99C ''may'':53C ''medic'':67C ''new'':145C ''nofollow'':184C ''nucleotid'':43C ''one'':90C ''order'':135C ''organ'':126C ''particular'':61C ''peopl'':26C ''personâ'':57C ''polymorph'':44C ''predict'':55C ''pronounc'':47C ''protein'':76C,87C ''rais'':172C ''rat'':130C ''regul'':12C ''rel'':183C ''research'':175C ''respons'':64C ''risk'':59C ''scale'':155C ''sequenc'':24C,121C ''signific'':30C ''similar'':138C ''singl'':42C ''small'':51C ''snipâ'':49C ''snp'':46C ''social'':170C ''sourc'':185C ''speci'':141C ''store'':157C ''strategi'':109C ''structur'':74C ''studi'':148C ''system'':100C ''technolog'':146C ''throughout'':14C ''treatment'':116C ''type'':34C ''ul'':1C ''variat'':20C,37C ''â'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5760, 2, '2015-05-11 21:06:39.957', 3, NULL, NULL, '&lt;p&gt;Using Community detection you can build a recommendation system. The most commonly used algorithm in this field is &lt;a href="http://arxiv.org/pdf/0803.0476.pdf" rel="nofollow"&gt;Blondel Algorithm&lt;/a&gt; which u have probably seen in SNAP. Blondel is almost the fastest Community Detection algorithm among widely accepted ones and its result is pretty acceptable (at least according to modularity score). As a side comment, you may also want to have a look at NetworkX and igraph libraries for more graph algorithms and visualizations.&lt;/p&gt;

&lt;p&gt;Hope it helps, good luck.&lt;/p&gt;
', NULL, NULL, '''/a'':29C ''/p'':84C,91C ''/pdf/0803.0476.pdf'':24C ''accept'':47C,54C ''accord'':57C ''algorithm'':15C,28C,44C,81C ''almost'':39C ''also'':67C ''among'':45C ''arxiv.org'':23C ''arxiv.org/pdf/0803.0476.pdf'':22C ''blondel'':27C,37C ''build'':7C ''comment'':64C ''common'':13C ''communiti'':3C,42C ''detect'':4C,43C ''fastest'':41C ''field'':18C ''good'':89C ''graph'':80C ''help'':88C ''hope'':86C ''href'':21C ''igraph'':76C ''least'':56C ''librari'':77C ''look'':72C ''luck'':90C ''may'':66C ''modular'':59C ''networkx'':74C ''nofollow'':26C ''one'':48C ''p'':1C,85C ''pretti'':53C ''probabl'':33C ''recommend'':9C ''rel'':25C ''result'':51C ''score'':60C ''seen'':34C ''side'':63C ''snap'':36C ''system'':10C ''u'':31C ''use'':2C,14C ''visual'':83C ''want'':68C ''wide'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5751, 1, '2015-05-11 15:07:47.143', 4, 175, 'How do you compare term counts between two different periods, with different underlying corpus sizes, without bias?', '&lt;p&gt;I''ll set the question up with an example. You are analysing news coverage text data from 2014, and find that a term appears less often in the third quarter of 2014 than the final quarter (let''s imagine it''s the term "Christmas"). Unfortunately, there are also far less news articles in the third quarter than in the second (due to the lack of news in the summer). So how do we accurately compare the counts in each quarter? We assume that there will be a greater number of occurrences in the fourth quarter, but how much does the magnitude of this difference depend on the change in size of the underlying text?&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Heaps%27_law" rel="nofollow"&gt;Heap''s law&lt;/a&gt; shows the relationship between text size and number of unique terms. It''s non-linearity implies that the rate of new, unique words introduced by the text decreases as you increase the size of the text, and the proportion of the text taken up by each existing word therefore increases. This applies given documents taken from the same ''distribution'' of text, in other words the underlying zipfian distribution of word ranks is identical (see &lt;a href="http://en.wikipedia.org/wiki/Zipf%27s_law" rel="nofollow"&gt;wiki&lt;/a&gt;). &lt;/p&gt;

&lt;p&gt;In my example above this is obviously not the case, since the underlying topics, and resultant term distribution, will change between summer and winter, especially with regards to the term "Christmas". But take the same term count but over the whole of 2013 and 2014; you would reasonably expect the general underlying term distribution to be the same in each period, so Heap''s law applies, but what if the volume of text has changed? Simply normalising by the size of the text, or the number of documents, does not, as far as I can tell, account for the relative change in expected value of the term count.&lt;/p&gt;

&lt;p&gt;I have a hunch that this might be a simple application of Heap''s or Zipf''s laws, but I can''t see how to apply them to this particular question. Appreciate any insight.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5752, 2, '2015-05-11 15:30:10.893', 0, NULL, NULL, '&lt;p&gt;Okay, I eventually found what I was looking for in the Data Mining Community. There seem to be two candidates, CRISP-DM which comes from SPSS originally but is "Cross-Industry", and SEMMA which comes from SAS. They are both pretty much what I was looking for.&lt;/p&gt;

&lt;p&gt;CRISP-DM &lt;a href="http://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;And &lt;/p&gt;

&lt;p&gt;SEMMA &lt;a href="http://en.wikipedia.org/wiki/SEMMA" rel="nofollow"&gt;http://en.wikipedia.org/wiki/SEMMA&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':51C,68C ''/wiki/cross_industry_standard_process_for_data_mining'':60C ''/wiki/cross_industry_standard_process_for_data_mining&lt;/a&gt;&lt;/p&gt;'':65C ''/wiki/semma'':75C ''/wiki/semma&lt;/a&gt;&lt;/p&gt;'':80C ''candid'':21C ''come'':26C,38C ''communiti'':15C ''crisp'':23C,54C ''crisp-dm'':22C,53C ''cross'':33C ''cross-industri'':32C ''data'':13C ''dm'':24C,55C ''en.wikipedia.org'':59C,64C,74C,79C ''en.wikipedia.org/wiki/cross_industry_standard_process_for_data_mining'':58C ''en.wikipedia.org/wiki/cross_industry_standard_process_for_data_mining&lt;/a&gt;&lt;/p&gt;'':63C ''en.wikipedia.org/wiki/semma'':73C ''en.wikipedia.org/wiki/semma&lt;/a&gt;&lt;/p&gt;'':78C ''eventu'':4C ''found'':5C ''href'':57C,72C ''industri'':34C ''look'':9C,49C ''mine'':14C ''much'':45C ''nofollow'':62C,77C ''okay'':2C ''origin'':29C ''p'':1C,52C,66C,69C ''pretti'':44C ''rel'':61C,76C ''sas'':40C ''seem'':17C ''semma'':36C,70C ''spss'':28C ''two'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5753, 2, '2015-05-11 16:14:02.463', 2, NULL, NULL, '&lt;p&gt;Wonderful dataset with many opportunities to brush up on text analysis skills!&lt;/p&gt;

&lt;p&gt;My first thought would be to try some Topic Modelling on the dataset. If you are using Python there is a library I''ve used called &lt;a href="https://radimrehurek.com/gensim/" rel="nofollow"&gt;gensim&lt;/a&gt; which has some fairly thorough tutorials to get you started. A friend of mine &lt;a href="https://github.com/rpetchler/Enron_Topic_Modeling" rel="nofollow"&gt;did something similar&lt;/a&gt; with the Enron dataset, using parallelized preprocessing and distributed latent Dirichlet allocation to infer topics over the email corpus. &lt;/p&gt;
', NULL, NULL, '''/a'':50C,75C ''/gensim/'':46C ''/p'':14C,95C ''/rpetchler/enron_topic_modeling'':69C ''alloc'':87C ''analysi'':12C ''brush'':8C ''call'':41C ''corpus'':94C ''dataset'':3C,28C,79C ''dirichlet'':86C ''distribut'':84C ''email'':93C ''enron'':78C ''fair'':54C ''first'':17C ''friend'':62C ''gensim'':49C ''get'':58C ''github.com'':68C ''github.com/rpetchler/enron_topic_modeling'':67C ''href'':43C,66C ''infer'':89C ''latent'':85C ''librari'':37C ''mani'':5C ''mine'':64C ''model'':25C ''nofollow'':48C,71C ''opportun'':6C ''p'':1C,15C ''parallel'':81C ''preprocess'':82C ''python'':33C ''radimrehurek.com'':45C ''radimrehurek.com/gensim/'':44C ''rel'':47C,70C ''similar'':74C ''skill'':13C ''someth'':73C ''start'':60C ''text'':11C ''thorough'':55C ''thought'':18C ''topic'':24C,90C ''tri'':22C ''tutori'':56C ''use'':32C,40C,80C ''ve'':39C ''wonder'':2C ''would'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8087, 1, '2015-09-14 08:01:06.213', 6, 190, 'Why does applying PCA on targets causes underfitting?', '&lt;p&gt;&lt;strong&gt;The goal:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I am new to machine learning and experimenting with neural networks.
I would like to build a network that takes as an input a series of 5 images and predicts the next image. My data set is completely artificial, just for my experimentation. As an illustration, here are a couple of examples of input and expected output:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/nVEjq.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/nVEjq.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The images of the data points and of the targets are from the same source: the target image of a data point appears in other data points, and vice-versa.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;What I have done:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;For now I have built a perceptron with one hidden layer and the output layer gives the pixels of the prediction. The two layers are dense and made of sigmoid neurons and I used the mean squared error as the objective. As the images are fairly simple and do not vary much, this does the job well: with 200-300 examples and 50 hidden units, I get a good error value (0.06) and good predictions on test data. The network is trained with gradient descent (with learning rate scaling). Here are the kinds of learning curves I get, and the error evolution with number of epochs:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/0KQbzm.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/0KQbzm.png" alt="enter image description here"&gt;&lt;/a&gt; &lt;a href="http://i.stack.imgur.com/63Qtpm.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/63Qtpm.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;What I am trying to do:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This is all good, but now I would like to reduce the dimensionality of the data set so that it would scale to bigger images and more examples. So I applied PCA. However I did not apply it on the list of data points but on the list of &lt;em&gt;images&lt;/em&gt;, for two reasons:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;On the data set as a whole, the convariance matrix would be 24000x24000, which does not fit in the memory of my laptop;&lt;/li&gt;
&lt;li&gt;By doing on the images, I can also compress the targets, since they are made of the same images.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;As the images look all similar, I managed to reduce their size from 4800 (40x40x3) to 36 while loosing only 1e-6 of the variance. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;What does not work:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;When I feed my reduced data set and its reduced targets to the network, the gradient descent converges very fast to a high error (around 50 !). You can see the equivalent plots as the ones above:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/Fvg7dm.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Fvg7dm.png" alt="enter image description here"&gt;&lt;/a&gt; &lt;a href="http://i.stack.imgur.com/ybYxbm.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/ybYxbm.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I had not imagined that a learning curve could start at a high value, and then go down and back up... And what are the usual causes of gradient descent stopping so fast? Could it be linked to parameter initialization (I use GlorotUniform, the default of the lasagne library).&lt;/p&gt;

&lt;p&gt;Then I noticed that if I feed the reduced data but the original (uncompressed) targets, I get back the initial performance. So it seems that applying PCA on the target images was not a good idea. Why is that? After all, I just multiplied the inputs and the targets by the same matrix, so the training input and target are still linked in a manner that the neural network should be able to figure out, no? What am I missing?&lt;/p&gt;

&lt;p&gt;Even if I introduce and extra layer of 4800 units so that there is the same total number of sigmoid neurons, I get the same results. To sum up, I have tried:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;24000 pixels =&gt; 50 sigmoids =&gt; 4800 sigmoids (= 4800 pixels)&lt;/li&gt;
&lt;li&gt;180 "pixels" =&gt; 50 sigmoids =&gt; 36 sigmoids (= 36 "pixels")&lt;/li&gt;
&lt;li&gt;180 "pixels" =&gt; 50 sigmoids =&gt; 4800 sigmoids (= 4800 pixels)&lt;/li&gt;
&lt;li&gt;180 "pixels" =&gt; 50 sigmoids =&gt; 4800 sigmoids =&gt; 36 sigmoids (= 36 "pixels")&lt;/li&gt;
&lt;li&gt;180 "pixels" =&gt; 50 sigmoids =&gt; 4800 sigmoids =&gt; 36 linear (= 36 "pixels")&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;(1) and (3) work fine; but not (2), (4) and (5), and I don''t understand why. In particular, since (3) works, (5) should be able to find the same parameters as (3) and the eigen vectors in the last linear layer. Is that not possible for a neural network?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5754, 2, '2015-05-11 17:01:05.93', 6, NULL, NULL, '&lt;p&gt;As a side comment note that using K-means for 10D data &lt;strong&gt;might&lt;/strong&gt; end up in nowhere according to the curse of dimensionality. Of course it varies a bit according to the nature of the data but once I tried to determine the threshold in which K-Means starts behaving strange regarding the dimensionalty, I got something like 7D. After 7 dimensions it started to miss correct clusters (my data was manually generated according to 4 well-separated Gaussian distributions and I used MATLAB &lt;em&gt;kmeans&lt;/em&gt; function for my little experiment).&lt;/p&gt;
', NULL, NULL, '''/em'':93C ''/p'':99C ''/strong'':17C ''10d'':13C ''4'':81C ''7'':66C ''7d'':64C ''accord'':22C,34C,79C ''behav'':55C ''bit'':33C ''cluster'':73C ''comment'':5C ''correct'':72C ''cours'':29C ''curs'':25C ''data'':14C,40C,75C ''determin'':46C ''dimens'':67C ''dimension'':27C ''dimensionalti'':59C ''distribut'':86C ''em'':91C ''end'':18C ''experi'':98C ''function'':94C ''gaussian'':85C ''generat'':78C ''got'':61C ''k'':10C,52C ''k-mean'':9C,51C ''kmean'':92C ''like'':63C ''littl'':97C ''manual'':77C ''matlab'':90C ''mean'':11C,53C ''might'':16C ''miss'':71C ''natur'':37C ''note'':6C ''nowher'':21C ''p'':1C ''regard'':57C ''separ'':84C ''side'':4C ''someth'':62C ''start'':54C,69C ''strang'':56C ''strong'':15C ''threshold'':48C ''tri'':44C ''use'':8C,89C ''vari'':31C ''well'':83C ''well-separ'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5755, 2, '2015-05-11 18:14:43.463', 2, NULL, NULL, '&lt;p&gt;A good place to start would be to look at the &lt;a href="http://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient" rel="nofollow"&gt;Spearman''s Rank Correlation&lt;/a&gt; between your dependent variable (academic level) and your individual independent variables (other columns). This should give you a basic indication of whether any of your columns are correlated with academic level.  This should be straightforward to implement in both Excel and SPSS.&lt;/p&gt;

&lt;p&gt;If you wanted to go a step further you could set the problem up as one of &lt;a href="http://en.wikipedia.org/wiki/Multinomial_logistic_regression" rel="nofollow"&gt;Multinomial Logistic Regression&lt;/a&gt;. This would allow you to build a model to directly attempt to predict academic level from your other variables. I''m certain there''s probably &lt;em&gt;some way&lt;/em&gt; to do this in Excel, but SPSS can definitely handle it.&lt;/p&gt;
', NULL, NULL, '''/a'':24C,96C ''/em'':125C ''/p'':67C,137C ''/wiki/multinomial_logistic_regression'':90C ''/wiki/spearman%27s_rank_correlation_coefficient'':17C ''academ'':29C,54C,110C ''allow'':99C ''attempt'':107C ''basic'':43C ''build'':102C ''certain'':118C ''column'':37C,50C ''correl'':23C,52C ''could'':78C ''definit'':134C ''depend'':27C ''direct'':106C ''em'':122C ''en.wikipedia.org'':16C,89C ''en.wikipedia.org/wiki/multinomial_logistic_regression'':88C ''en.wikipedia.org/wiki/spearman%27s_rank_correlation_coefficient'':15C ''excel'':64C,130C ''give'':40C ''go'':73C ''good'':3C ''handl'':135C ''href'':14C,87C ''implement'':61C ''independ'':34C ''indic'':44C ''individu'':33C ''level'':30C,55C,111C ''logist'':94C ''look'':10C ''m'':117C ''model'':104C ''multinomi'':93C ''nofollow'':19C,92C ''one'':84C ''p'':1C,68C ''place'':4C ''predict'':109C ''probabl'':121C ''problem'':81C ''rank'':22C ''regress'':95C ''rel'':18C,91C ''set'':79C ''spearman'':20C ''spss'':66C,132C ''start'':6C ''step'':75C ''straightforward'':59C ''variabl'':28C,35C,115C ''want'':71C ''way'':124C ''whether'':46C ''would'':7C,98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5756, 2, '2015-05-11 20:06:17.68', 2, NULL, NULL, '&lt;p&gt;&lt;strong&gt;So how can I couple my clusters to a feature label from my feature matrix?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Principal Components are not intuitive features. What seems common here is to cluster users based on PCs and investigate clusters based on original features afterward i.e. extract different clusters and plot data based on different subsets of features and use different colors for different clusters. It might give you some intution.&lt;/p&gt;

&lt;p&gt;These two paths can be seen in many PCA results where the information varies with e.g time. &lt;/p&gt;

&lt;p&gt;For instance in your case a beginner user visits less than an old one and the number of new users are most probably higher than old ones so data will be more dense around origin and far it gets from the origin less dense it becomes. Such a phenomenon affects your PCs as well so you''ll see some paths along different lines in PC space.&lt;/p&gt;

&lt;p&gt;Hope it helps :)&lt;/p&gt;
', NULL, NULL, '''/p'':19C,72C,91C,159C,164C ''/strong'':18C ''affect'':142C ''afterward'':45C ''along'':153C ''around'':126C ''base'':35C,41C,53C ''becom'':138C ''beginn'':99C ''case'':97C ''cluster'':9C,33C,40C,49C,65C ''color'':62C ''common'':29C ''compon'':22C ''coupl'':7C ''data'':52C,121C ''dens'':125C,136C ''differ'':48C,55C,61C,64C,154C ''e.g'':89C ''extract'':47C ''far'':129C ''featur'':12C,16C,26C,44C,58C ''get'':131C ''give'':68C ''help'':163C ''higher'':116C ''hope'':161C ''i.e'':46C ''inform'':86C ''instanc'':94C ''intuit'':25C ''intut'':71C ''investig'':39C ''label'':13C ''less'':102C,135C ''line'':155C ''ll'':149C ''mani'':81C ''matrix'':17C ''might'':67C ''new'':111C ''number'':109C ''old'':105C,118C ''one'':106C,119C ''origin'':43C,127C,134C ''p'':1C,20C,73C,92C,160C ''path'':76C,152C ''pc'':157C ''pca'':82C ''pcs'':37C,144C ''phenomenon'':141C ''plot'':51C ''princip'':21C ''probabl'':115C ''result'':83C ''see'':150C ''seem'':28C ''seen'':79C ''space'':158C ''strong'':2C ''subset'':56C ''time'':90C ''two'':75C ''use'':60C ''user'':34C,100C,112C ''vari'':87C ''visit'':101C ''well'':146C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5757, 2, '2015-05-11 20:22:42.187', 1, NULL, NULL, '&lt;p&gt;I personally use &lt;a href="https://networkx.github.io/" rel="nofollow"&gt;&lt;strong&gt;NetworkX&lt;/strong&gt;&lt;/a&gt; and &lt;a href="http://igraph.org/" rel="nofollow"&gt;&lt;strong&gt;igraph&lt;/strong&gt;&lt;/a&gt; where the first one is for programming in Python and the second one has interfaces for Python, R and C.&lt;/p&gt;

&lt;p&gt;If you are not into programming much, &lt;a href="http://gephi.github.io/" rel="nofollow"&gt;Gephi&lt;/a&gt; is what you are looking for!&lt;/p&gt;
', NULL, NULL, '''/a'':13C,23C,59C ''/p'':44C,66C ''/strong'':12C,22C ''c'':43C ''first'':26C ''gephi'':58C ''gephi.github.io'':55C ''href'':6C,16C,54C ''igraph'':21C ''igraph.org'':17C ''interfac'':38C ''look'':64C ''much'':52C ''networkx'':11C ''networkx.github.io'':7C ''nofollow'':9C,19C,57C ''one'':27C,36C ''p'':1C,45C ''person'':3C ''program'':30C,51C ''python'':32C,40C ''r'':41C ''rel'':8C,18C,56C ''second'':35C ''strong'':10C,20C ''use'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5758, 2, '2015-05-11 20:37:43.223', 1, NULL, NULL, '&lt;p&gt;So what you need is &lt;a href="http://supernet.isenberg.umass.edu/fulbright-catania/workshop-talks/nicosia-nagurney-daniele-workshop.pdf" rel="nofollow"&gt;Modularity score&lt;/a&gt;. Speaking as a Graph Clustering guy (my master thesis topic, PhD research and my main research direction during last 2.5 years) I recommend you to go through what physicists did in &lt;strong&gt;Complex Networks&lt;/strong&gt; field under the name of &lt;strong&gt;Community Detection&lt;/strong&gt;. If you search &lt;em&gt;Prof. Mark Newman&lt;/em&gt; who first proposed Modularity score you''ll find a bunch of interesting papers in this field. &lt;em&gt;Infomap&lt;/em&gt; algorithm by &lt;em&gt;Martin Rosvall&lt;/em&gt;, &lt;em&gt;Louvain&lt;/em&gt; algorithm by &lt;em&gt;Vincent Blondel&lt;/em&gt; and &lt;em&gt;CNM&lt;/em&gt; algorithm by &lt;em&gt;Aaron Clauset&lt;/em&gt; are some of the most known algorithms. &lt;/p&gt;

&lt;p&gt;The most commonly used algorithm for graph clustering nowadays is the one by &lt;a href="http://perso.crans.org/aynaud/communities/" rel="nofollow"&gt;Vincent Blondel&lt;/a&gt; which has implementations for both NetworkX and igraph (if you are a python guy!). This algorithm is originally for weighted graphs and probably answers your question.&lt;/p&gt;

&lt;p&gt;Hope it helps, Good luck!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5762, 2, '2015-05-11 22:48:36.383', 4, NULL, NULL, '&lt;p&gt;You can simply convert a &lt;strong&gt;M&lt;/strong&gt;x&lt;strong&gt;N&lt;/strong&gt; matrix (your image) into a &lt;strong&gt;MN&lt;/strong&gt; dimensional vector. For instance if you have 10 images each of which 10x10 pixels, you can convert them to 10 vectors each of them containing 100 element (you can do it row-wise or column-wise, does not matter). Then putting all these vectors on each other constructs a &lt;strong&gt;n&lt;/strong&gt;x&lt;strong&gt;d&lt;/strong&gt; matrix where &lt;strong&gt;n&lt;/strong&gt; is the number of images and &lt;strong&gt;d&lt;/strong&gt; is the number of pixels in each image. &lt;/p&gt;

&lt;p&gt;So far we just prepared data for analysis. Now let''s go to the question:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;em&gt;I''m looking for a supervised learning algorithm that can take 2d data
for input and output.&lt;/em&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;What does that mean? &lt;/p&gt;

&lt;p&gt;First- The dimension of input-output is not a property of a machine learning algorithm. &lt;/p&gt;

&lt;p&gt;Second- By &lt;strong&gt;2d&lt;/strong&gt;, you mean a matrix? I''m afraid your data is not really 2d! If you mean a matrix, a &lt;strong&gt;M&lt;/strong&gt;x&lt;strong&gt;N&lt;/strong&gt; matrix is a data of M samples in &lt;strong&gt;N&lt;/strong&gt; dimensional space. also those vectors you may produce from your images are in &lt;strong&gt;MN&lt;/strong&gt; dimensional space.&lt;/p&gt;

&lt;p&gt;Third- A supervised algorithm takes some teaching samples (called training) to learn a task. What are those &lt;em&gt;teachers&lt;/em&gt; here? If you have a training data which contains some blurred and corresponding original images then you can try to learn the place of white pixels by exploring blurred area. My suggestion:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;If a smoothing filter has been applied to your image, the blurred pixels most probably look like a gaussian. So first extract the gaussians.&lt;/li&gt;
&lt;li&gt;As you already have the place of sharp whites correspond to those gaussians you can use the mean, location of the mean and also variance as features (inputs) and the place of sharp white pixels as the target (&lt;em&gt;output&lt;/em&gt;).&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The classifier could be a simple neural network (a MLP should work here) or a simple SVM.&lt;/p&gt;

&lt;p&gt;The answer above can not be evaluated without more information about your data and a more specific description of the question (e.g. What are your images exactly? if you can post one of them here I can probably help more)&lt;/p&gt;
', NULL, NULL, '''/em'':140C,249C,351C ''/li'':141C,309C,352C ''/ol'':353C ''/p'':102C,119C,148C,166C,228C,282C,372C,414C ''/strong'':9C,13C,21C,75C,79C,84C,93C,172C,194C,198C,209C,225C ''/ul'':142C ''10'':29C,41C ''100'':47C ''10x10'':34C ''2d'':134C,171C,185C ''afraid'':179C ''algorithm'':130C,165C,233C ''alreadi'':313C ''also'':212C,334C ''analysi'':111C ''answer'':375C ''appli'':291C ''area'':279C ''blur'':260C,278C,296C ''call'':238C ''classifi'':356C ''column'':58C ''column-wis'':57C ''construct'':71C ''contain'':46C,258C ''convert'':5C,38C ''correspond'':262C,320C ''could'':357C ''d'':78C,92C ''data'':109C,135C,181C,202C,256C,386C ''descript'':391C ''dimens'':152C ''dimension'':22C,210C,226C ''e.g'':395C ''element'':48C ''em'':122C,247C,349C ''evalu'':380C ''exact'':400C ''explor'':277C ''extract'':306C ''far'':105C ''featur'':337C ''filter'':288C ''first'':150C,305C ''gaussian'':303C,308C,323C ''go'':115C ''help'':412C ''imag'':16C,30C,89C,101C,220C,264C,294C,399C ''inform'':383C ''input'':137C,155C,338C ''input-output'':154C ''instanc'':25C ''learn'':129C,164C,241C,270C ''let'':113C ''li'':121C,284C,310C ''like'':301C ''locat'':329C ''look'':125C,300C ''m'':8C,124C,178C,193C,204C ''machin'':163C ''matrix'':14C,80C,176C,190C,199C ''matter'':62C ''may'':216C ''mean'':147C,174C,188C,328C,332C ''mlp'':364C ''mn'':20C,224C ''n'':12C,74C,83C,197C,208C ''network'':362C ''neural'':361C ''number'':87C,96C ''ol'':283C ''one'':405C ''origin'':263C ''output'':139C,156C,350C ''p'':1C,103C,143C,149C,167C,229C,354C,373C ''pixel'':35C,98C,275C,297C,345C ''place'':272C,316C,341C ''post'':404C ''prepar'':108C ''probabl'':299C,411C ''produc'':217C ''properti'':160C ''put'':64C ''question'':118C,394C ''realli'':184C ''row'':54C ''row-wis'':53C ''sampl'':205C,237C ''second'':168C ''sharp'':318C,343C ''simpl'':360C,370C ''simpli'':4C ''smooth'':287C ''space'':211C,227C ''specif'':390C ''strong'':7C,11C,19C,73C,77C,82C,91C,170C,192C,196C,207C,223C ''suggest'':281C ''supervis'':128C,232C ''svm'':371C ''take'':133C,234C ''target'':348C ''task'':243C ''teach'':236C ''teacher'':248C ''third'':230C ''train'':239C,255C ''tri'':268C ''ul'':120C ''use'':326C ''varianc'':335C ''vector'':23C,42C,67C,214C ''white'':274C,319C,344C ''wise'':55C,59C ''without'':381C ''work'':366C ''x'':10C,76C,195C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10190, 2, '2016-02-10 23:04:51.403', 2, NULL, NULL, '&lt;p&gt;I don''t know about that specific implementation, but we use the &lt;a href="http://spark.apache.org/docs/latest/mllib-clustering.html" rel="nofollow"&gt;mllib k-means&lt;/a&gt; here at my work, to some degree of success. It is distributed and runs on Spark!&lt;/p&gt;
', NULL, NULL, '''/a'':25C ''/docs/latest/mllib-clustering.html'':18C ''/p'':42C ''degre'':32C ''distribut'':37C ''href'':15C ''implement'':9C ''k'':23C ''k-mean'':22C ''know'':5C ''mean'':24C ''mllib'':21C ''nofollow'':20C ''p'':1C ''rel'':19C ''run'':39C ''spark'':41C ''spark.apache.org'':17C ''spark.apache.org/docs/latest/mllib-clustering.html'':16C ''specif'':8C ''success'':34C ''use'':12C ''work'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5764, 2, '2015-05-12 10:10:58.977', 2, NULL, NULL, '&lt;p&gt;You''re learning, are you? Try to find something easy and interesting to start. Why don''t you start off something easy like building a Bayesian model to predict which email will get deleted. You should glance over those deleted emails, are they spams? are they just garbage?&lt;/p&gt;

&lt;p&gt;Here, you have a simply supervised model where the data-set already labels the emails for you (deleted or not). Think of something easy like words, titles, length of the email etc, see if you can build a model that predicts email deletion.&lt;/p&gt;
', NULL, NULL, '''/p'':50C,96C ''alreadi'':64C ''bayesian'':27C ''build'':25C,89C ''data'':62C ''data-set'':61C ''delet'':35C,41C,70C,95C ''easi'':11C,23C,76C ''email'':32C,42C,67C,83C,94C ''etc'':84C ''find'':9C ''garbag'':49C ''get'':34C ''glanc'':38C ''interest'':13C ''label'':65C ''learn'':4C ''length'':80C ''like'':24C,77C ''model'':28C,58C,91C ''p'':1C,51C ''predict'':30C,93C ''re'':3C ''see'':85C ''set'':63C ''simpli'':56C ''someth'':10C,22C,75C ''spam'':45C ''start'':15C,20C ''supervis'':57C ''think'':73C ''titl'':79C ''tri'':7C ''word'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5870, 2, '2015-05-22 22:39:53.793', 2, NULL, NULL, '&lt;p&gt;Well the text doesn''t get converted into data points ...
Let''s say we are doing sentence level opinion mining..
Features are extracted from a sentence .
Now it depends on case to case as to what features to use...
A common one is bag of words models in which features become distinct words in sentence and the value of features are the frequency it is repeated in a sentence. Those frequencies are your data points. &lt;/p&gt;
', NULL, NULL, '''..'':22C ''/p'':78C ''bag'':46C ''becom'':53C ''case'':33C,35C ''common'':43C ''convert'':8C ''data'':10C,76C ''depend'':31C ''distinct'':54C ''doesn'':5C ''extract'':25C ''featur'':23C,39C,52C,62C ''frequenc'':65C,73C ''get'':7C ''let'':12C ''level'':19C ''mine'':21C ''model'':49C ''one'':44C ''opinion'':20C ''p'':1C ''point'':11C,77C ''repeat'':68C ''say'':14C ''sentenc'':18C,28C,57C,71C ''text'':4C ''use'':41C ''valu'':60C ''well'':2C ''word'':48C,55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5765, 2, '2015-05-12 10:59:50.547', 1, NULL, NULL, '&lt;p&gt;The InterQuartileRangeFilter from weka library uses an IQR formula to designate some values as outliers/extreme values. Any value outside this range $[Q_1 - k(Q_3-Q_1), Q_3 + k(Q_3-Q_1)]$ is considered some sort of an outlier, where $k$ is some constant, and $ IQR = Q_3 - Q_1$. &lt;/p&gt;

&lt;p&gt;By default weka uses $k=3$ to define something as outlier, and $k=3*2$ to define something as extreme value (extreme outlier). &lt;/p&gt;

&lt;p&gt;The formula guarantees that at least 50% value are considered non-outliers. Having a single variable (univariate sample of values), it''s practically impossible to reproduce your result.&lt;/p&gt;

&lt;p&gt;Note however that this filter can be applied to a data frame. When applied like this, it will consider as an outlier any instance of the data frame which has at least one value of the instance considered as outlier for that variable. &lt;/p&gt;

&lt;p&gt;Now, supposing that you have a data frame with 2 variables, totally uncorrelated (independent). Considering again that only 10% of the values from each variable are considered outliers, due to independence, one can expect that $(1-0.9)^2$ values will not be outliers. If you have $p$ variables like that in your data frame, you might expect to have only $(1-0.9)^k$ normal values, and is not very hard to arrive in that situation.&lt;/p&gt;

&lt;p&gt;There are two things which you will have to consider. One is to increase the factors for outliers if in general too many values are considered outliers (ideally you would like to take a look at each variable graphically and if possible to get some idea about the distribution beneath). The second one is to check if you have many values which are totally independent. The second hint does not solve your problem but might give you a reason why it happens. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5766, 1, '2015-05-12 14:28:14.39', 0, 40, 'new and fresh ideas with stack exchange dataset', '&lt;p&gt;I''m a new bee for data analysis . i need to work on a research project in big data analysis. first of all i search for a dataset and i found interest in s&lt;a href="https://archive.org/details/stackexchange" rel="nofollow"&gt;tack exchange  data dump&lt;/a&gt;. however i browse for researches i found a lot .And whatever i thought about a idea based on this dataset , its already done by someone else . 
please help me out with a new and useful idea for my research .&lt;/p&gt;
', 'data-mining  bigdata  dataset', NULL, '''/a'':60C ''/details/stackexchange'':53C ''/p'':100C ''alreadi'':82C ''analysi'':22C,34C ''archive.org'':52C ''archive.org/details/stackexchange'':51C ''base'':77C ''bee'':19C ''big'':32C ''bigdata'':12B ''brows'':63C ''data'':10B,21C,33C,58C ''data-min'':9B ''dataset'':8A,13B,42C,80C ''done'':83C ''dump'':59C ''els'':86C ''exchang'':7A,57C ''first'':35C ''found'':45C,67C ''fresh'':3A ''help'':88C ''howev'':61C ''href'':50C ''idea'':4A,76C,96C ''interest'':46C ''lot'':69C ''m'':16C ''mine'':11B ''need'':24C ''new'':1A,18C,93C ''nofollow'':55C ''p'':14C ''pleas'':87C ''project'':30C ''rel'':54C ''research'':29C,65C,99C ''search'':39C ''someon'':85C ''stack'':6A ''tack'':56C ''thought'':73C ''use'':95C ''whatev'':71C ''work'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5799, 1, '2015-05-15 08:48:02.19', 3, 63, 'Why are HMMs called linear-chain?', '&lt;p&gt;I found in many sources that Hidden Markov Models are linear-chain networks(e.g. in Predicting Structured Data book by MIT). However, as I understand it, HMMs can have any edges in its graph. Even simple example of HMM in wikipedia has non-linear graph: &lt;img src="http://i.stack.imgur.com/JU1YV.png" alt="enter image description here"&gt; .&lt;/p&gt;

&lt;p&gt;So, the question is: what is the formal definition linear-chain structure and in which case forward-backward and Viterbi algorithms can give precise results.&lt;/p&gt;

&lt;p&gt;I have also taken into consideration this picture, taken from CRF tutorial, which says, that linear-chain CRF is "generative-discriminative pair" to HMM.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/9CA8H.png" alt="enter image description here"&gt;&lt;/p&gt;
', 'machine-learning  classification  algorithms  graphs', NULL, '''/9ca8h.png'':135C ''/ju1yv.png'':66C ''/p'':72C,101C,129C,141C ''algorithm'':12B,96C ''also'':105C ''alt'':67C,136C ''backward'':93C ''book'':34C ''call'':4A ''case'':90C ''chain'':7A,27C,85C,120C ''classif'':11B ''consider'':108C ''crf'':113C,121C ''data'':33C ''definit'':82C ''descript'':70C,139C ''discrimin'':125C ''e.g'':29C ''edg'':46C ''enter'':68C,137C ''even'':50C ''exampl'':52C ''formal'':81C ''forward'':92C ''forward-backward'':91C ''found'':16C ''generat'':124C ''generative-discrimin'':123C ''give'':98C ''graph'':13B,49C,61C ''hidden'':21C ''hmm'':54C,128C ''hmms'':3A,42C ''howev'':37C ''i.stack.imgur.com'':65C,134C ''i.stack.imgur.com/9ca8h.png'':133C ''i.stack.imgur.com/ju1yv.png'':64C ''imag'':69C,138C ''img'':62C,131C ''learn'':10B ''linear'':6A,26C,60C,84C,119C ''linear-chain'':5A,25C,83C,118C ''machin'':9B ''machine-learn'':8B ''mani'':18C ''markov'':22C ''mit'':36C ''model'':23C ''network'':28C ''non'':59C ''non-linear'':58C ''p'':14C,73C,102C,130C ''pair'':126C ''pictur'':110C ''precis'':99C ''predict'':31C ''question'':76C ''result'':100C ''say'':116C ''simpl'':51C ''sourc'':19C ''src'':63C,132C ''structur'':32C,86C ''taken'':106C,111C ''tutori'':114C ''understand'':40C ''viterbi'':95C ''wikipedia'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5767, 2, '2015-05-12 15:17:37.06', 2, NULL, NULL, '&lt;p&gt;It might help to have a bit more information on what you want to do, but have a look at &lt;a href="https://www.kaggle.com" rel="nofollow"&gt;Kaggle&lt;/a&gt; - it''s a regular big data science competition, all of the data sets are huge and &lt;a href="https://www.kaggle.com" rel="nofollow"&gt;available for download&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;There''s also some &lt;a href="https://public.tableau.com/s/resources" rel="nofollow"&gt;smaller datasets made available by Tableau&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Hope that give you some inspiration!&lt;/p&gt;
', NULL, NULL, '''/a'':28C,53C,73C ''/p'':54C,74C,82C ''/s/resources'':64C ''also'':58C ''avail'':50C,70C ''big'':33C ''bit'':8C ''competit'':36C ''data'':34C,40C ''dataset'':68C ''download'':52C ''give'':78C ''help'':4C ''hope'':76C ''href'':23C,46C,61C ''huge'':43C ''inform'':10C ''inspir'':81C ''kaggl'':27C ''look'':20C ''made'':69C ''might'':3C ''nofollow'':26C,49C,66C ''p'':1C,55C,75C ''public.tableau.com'':63C ''public.tableau.com/s/resources'':62C ''regular'':32C ''rel'':25C,48C,65C ''scienc'':35C ''set'':41C ''smaller'':67C ''tableau'':72C ''want'':14C ''www.kaggle.com'':24C,47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5769, 2, '2015-05-12 15:51:41.727', 3, NULL, NULL, '&lt;p&gt;Not much to add to the provided comments. Only thing is maybe this infographic comparing R vs Python for data science purposes &lt;a href="http://blog.datacamp.com/r-or-python-for-data-analysis/" rel="nofollow"&gt;http://blog.datacamp.com/r-or-python-for-data-analysis/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/r-or-python-for-data-analysis/'':28C ''/r-or-python-for-data-analysis/&lt;/a&gt;&lt;/p&gt;'':33C ''add'':5C ''blog.datacamp.com'':27C,32C ''blog.datacamp.com/r-or-python-for-data-analysis/'':26C ''blog.datacamp.com/r-or-python-for-data-analysis/&lt;/a&gt;&lt;/p&gt;'':31C ''comment'':9C ''compar'':16C ''data'':21C ''href'':25C ''infograph'':15C ''mayb'':13C ''much'':3C ''nofollow'':30C ''p'':1C ''provid'':8C ''purpos'':23C ''python'':19C ''r'':17C ''rel'':29C ''scienc'':22C ''thing'':11C ''vs'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5773, 2, '2015-05-12 20:08:09.497', 3, NULL, NULL, '&lt;p&gt;The question is too general to have a specific recipe as an answer. Depends on the data and the relation between features but to the best of my knowledge:&lt;/p&gt;

&lt;p&gt;When the relation between your features are non-linear i.e. when a non-linear interpretation of correlation between features makes more sense than the linear interpretation.&lt;/p&gt;

&lt;p&gt;A detailed explanation of what I mean can be found in &lt;a href="http://stats.stackexchange.com/questions/101344/is-kernel-pca-with-linear-kernel-equivalent-to-standard-pca"&gt;this&lt;/a&gt; nice answer. Please note that the $X^TX$ in the 4th line of the first paragraph should be $XX^T$, because in data mining, we conventionally use $N\times D$ format for data matrices. So in this example, we are looking for the gram matrix of the rows and not columns.&lt;/p&gt;

&lt;p&gt;Hope it helps :)&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5780, 2, '2015-05-13 07:59:15.927', 11, NULL, NULL, '&lt;p&gt;k-means is based on &lt;strong&gt;averages&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;It models clusters using means, and thus &lt;strong&gt;the improvement by adding more data is marginal.&lt;/strong&gt; The error of the average estimation reduces with 1/sqrt(n); so adding more data pays off less and less...&lt;/p&gt;

&lt;p&gt;Strategies for such large data always revolve around sampling:&lt;/p&gt;

&lt;p&gt;&lt;em&gt;If you want sublinear runtime, you have to do sampling!&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;In fact, Mini-Batch-Kmeans etc. do exactly this: repeatedly sample from the data set.&lt;/p&gt;

&lt;p&gt;However, sampling (in particular unbiased sampling) isn''t exactly free either... usually, you will have to read your data linearly to sample, because you don''t get random access to individual records.&lt;/p&gt;

&lt;p&gt;I''d go with MacQueen''s algorithm. It''s online; by default it does a single pass over your data (although it is popular to iterate this). It''s not easy to distribute, but I guess you can afford to linearly read your data say 10 times from a SSD?&lt;/p&gt;
', NULL, NULL, '''/em'':73C ''/p'':11C,49C,60C,74C,92C,126C,178C ''/strong'':10C,29C ''1/sqrt'':38C ''10'':173C ''access'':122C ''ad'':24C,41C ''afford'':166C ''algorithm'':134C ''although'':148C ''alway'':56C ''around'':58C ''averag'':9C,34C ''base'':6C ''batch'':80C ''cluster'':15C ''d'':129C ''data'':26C,43C,55C,90C,112C,147C,171C ''default'':139C ''distribut'':160C ''easi'':158C ''either'':104C ''em'':62C ''error'':31C ''estim'':35C ''etc'':82C ''exact'':84C,102C ''fact'':77C ''free'':103C ''get'':120C ''go'':130C ''guess'':163C ''howev'':94C ''improv'':22C ''individu'':124C ''isn'':100C ''iter'':153C ''k'':3C ''k-mean'':2C ''kmean'':81C ''larg'':54C ''less'':46C,48C ''linear'':113C,168C ''macqueen'':132C ''margin'':28C ''mean'':4C,17C ''mini'':79C ''mini-batch-kmean'':78C ''model'':14C ''n'':39C ''onlin'':137C ''p'':1C,12C,50C,61C,75C,93C,127C ''particular'':97C ''pass'':144C ''pay'':44C ''popular'':151C ''random'':121C ''read'':110C,169C ''record'':125C ''reduc'':36C ''repeat'':86C ''revolv'':57C ''runtim'':67C ''sampl'':59C,72C,87C,95C,99C,115C ''say'':172C ''set'':91C ''singl'':143C ''ssd'':177C ''strategi'':51C ''strong'':8C,20C ''sublinear'':66C ''thus'':19C ''time'':174C ''unbias'':98C ''use'':16C ''usual'':105C ''want'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5775, 1, '2015-05-12 22:33:38.013', 2, 391, 'Multi-label text classification with minimum confidence threshold', '&lt;p&gt;Building out a system that tries to apply zero or more predefined labels to text.&lt;/p&gt;

&lt;p&gt;For each label, we''ve:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;built out a reasonably good vocabulary of high-value words/features&lt;/li&gt;
&lt;li&gt;developed a corpus containing thousands of labeled entries&lt;/li&gt;
&lt;li&gt;trained a NaiveBayesClassifier for each topic that does a good job of classifying valid vs noisy content&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The problem seems to be that the individual classifier is great at differentiating between valid &amp;amp; noisy content &lt;strong&gt;WITHIN&lt;/strong&gt; a topic:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;"the green energy bill will revolutionize..." (green = "green energy")&lt;/li&gt;
&lt;li&gt;"the green bay packers went on to lose their..." (green != "green energy") &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;...but when classifying content that shouldn''t match ANY topic it has a very high rate of false positives. There''s no "everything else" label!&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;tl;dr it''s good at subtle, in-topic differentiation, but terrible at broad topic labeling&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Are there any algorithms that help you classify into N categories, but allow for "everything else" which might not fit into ANY of the categories?&lt;/p&gt;
', 'classification  python  nlp  text-mining', NULL, '''/li'':53C,63C,82C,120C,134C ''/p'':32C,39C,108C,162C,183C,210C ''/strong'':105C,182C ''/ul'':83C,135C ''algorithm'':188C ''allow'':197C ''amp'':100C ''appli'':24C ''bay'':124C ''bill'':114C ''broad'':179C ''build'':17C ''built'':42C ''categori'':195C,209C ''classif'':5A,10B ''classifi'':77C,93C,139C,192C ''confid'':8A ''contain'':58C ''content'':81C,102C,140C ''corpus'':57C ''develop'':55C ''differenti'':97C,175C ''dr'':166C ''els'':160C,200C ''energi'':113C,119C,133C ''entri'':62C ''everyth'':159C,199C ''fals'':154C ''fit'':204C ''good'':46C,74C,169C ''great'':95C ''green'':112C,117C,118C,123C,131C,132C ''help'':190C ''high'':50C,151C ''high-valu'':49C ''in-top'':172C ''individu'':92C ''job'':75C ''label'':3A,29C,36C,61C,161C,181C ''li'':41C,54C,64C,110C,121C ''lose'':129C ''match'':144C ''might'':202C ''mine'':15B ''minimum'':7A ''multi'':2A ''multi-label'':1A ''n'':194C ''naivebayesclassifi'':67C ''nlp'':12B ''noisi'':80C,101C ''p'':16C,33C,84C,136C,163C,184C ''packer'':125C ''posit'':155C ''predefin'':28C ''problem'':86C ''python'':11B ''rate'':152C ''reason'':45C ''revolution'':116C ''seem'':87C ''shouldn'':142C ''strong'':103C,164C ''subtl'':171C ''system'':20C ''terribl'':177C ''text'':4A,14B,31C ''text-min'':13B ''thousand'':59C ''threshold'':9A ''tl'':165C ''topic'':70C,107C,146C,174C,180C ''train'':65C ''tri'':22C ''ul'':40C,109C ''valid'':78C,99C ''valu'':51C ''ve'':38C ''vocabulari'':47C ''vs'':79C ''went'':126C ''within'':104C ''words/features'':52C ''zero'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5776, 1, '2015-05-13 01:27:32.653', 3, 270, 'Best or recommended R package for logit and probit regression', '&lt;p&gt;Could somebody please recommend a good R package for doing logit and probit regression? I have tried to find an answer by searching on Google but all the links I find go into lengthy explanations about what logit regression is, which I already know, but nobody seems to recommend an R package.&lt;/p&gt;

&lt;p&gt;Thanks in advance.&lt;/p&gt;

&lt;p&gt;Jerome Smith&lt;/p&gt;
', 'r  logistic-regression', NULL, '''/p'':68C,73C,77C ''advanc'':72C ''alreadi'':58C ''answer'':36C ''best'':1A ''could'':16C ''explan'':50C ''find'':34C,46C ''go'':47C ''good'':21C ''googl'':40C ''jerom'':75C ''know'':59C ''lengthi'':49C ''link'':44C ''logist'':13B ''logistic-regress'':12B ''logit'':7A,26C,53C ''nobodi'':61C ''p'':15C,69C,74C ''packag'':5A,23C,67C ''pleas'':18C ''probit'':9A,28C ''r'':4A,11B,22C,66C ''recommend'':3A,19C,64C ''regress'':10A,14B,29C,54C ''search'':38C ''seem'':62C ''smith'':76C ''somebodi'':17C ''thank'':70C ''tri'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8183, 2, '2015-09-24 06:17:23.487', 1, NULL, NULL, '&lt;p&gt;&lt;a href="https://www.cs.cmu.edu/~schneide/tut5/node42.html" rel="nofollow"&gt;&lt;code&gt;Leave-one-out&lt;/code&gt;&lt;/a&gt; cross validation is probably the most straight-forward way to address this.  If you happen to be using a model that requires a lot of time to train, then leave n% out might be more appropriate.&lt;/p&gt;

&lt;p&gt;The method involves eliminating one known rating and trying to predict it.  If you want to remove n percent of the ratings, just choose them randomly rather than choosing a specific number of every user''s ratings.  And keep n pretty small - on the order of 10% or less. &lt;/p&gt;

&lt;p&gt;Here is a &lt;a href="http://www.contentwise.tv/files/An-evaluation-Methodology-for-Collaborative-Recommender-Systems.pdf" rel="nofollow"&gt;good treatment of cross validation methods for recommender systems&lt;/a&gt;.  Generally:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Holdout is a method that splits a dataset into two parts: a training
  set and a test set. These sets could have different proportions. In
  the setting of recommender systems the partitioning is performed by
  randomly selecting some ratings from all (or some of) the users. The
  selected ratings constitute the test set, while the remaining ones are
  the training set. This method is also called leave-k-out. In [17],
  Sarwar et al. split the dataset into 80% training and 20% test data.
  In [18] several ratios among training and test (from 0.2 to 0.95 with
  an increment of
  0.05) are chosen and for each one the experiment is repeated ten times with different training and test sets and finally the results are
  averaged. In [13] the test set is made by 10% of users: 5 ratings for
  each user in the test set are withheld. &lt;/p&gt;
  
  &lt;p&gt;Leave-one-out is a method
  obtained by setting k = 1 in the leave-k-out method. Given an active
  user, we withhold in turn one rated item. The learning algorithm is
  trained on the remaining data. The withheld element is used to
  evaluate the correctness of the prediction and the results of all
  evaluations are averaged in order to compute the final quality
  estimate. This method has some disadvantages, such as the overfitting
  and the high computational complexity. This technique is suitable to
  evaluate the recommending quality of the model for users who are
  already registered as members of the system. Karypis et al. [10]
  adopted a trivial version of the leave-one-out creating the test set
  by randomly selecting one of the non-zero entries for each user and
  the remaining entries for training. In [7], Breese et al. split the
  URM in training and test set and then, in the test set, withhold a
  single randomly selected rating for each user. &lt;/p&gt;
  
  &lt;p&gt;A simple variant of the
  holdout method is the m-fold cross-validation. It consists in dividing
  the dataset into m independent folds (so that folds do not overlap).
  In turn, each fold is used exactly once as test set and the remaining
  folds are used for training the model. According to [20] and [11], the
  suggested number of folds is 10. This technique is suitable to
  evaluate the recommending capability of the model when new users
  (i.e., users do not already belong to the model) join the system. By
  choosing a reasonable number of folds we can compute mean, variance
  and confidence interval.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/a'':15C,125C ''/blockquote'':558C ''/code'':14C ''/files/an-evaluation-methodology-for-collaborative-recommender-systems.pdf'':113C ''/p'':53C,104C,127C,278C,449C,557C,563C ''/~schneide/tut5/node42.html'':6C ''0.05'':231C ''0.2'':224C ''0.95'':226C ''1'':291C ''10'':101C,264C,387C,514C ''11'':507C ''13'':257C ''17'':201C ''18'':216C ''20'':212C,505C ''5'':267C ''7'':422C ''80'':209C ''accord'':503C ''activ'':301C ''address'':27C ''adopt'':388C ''al'':204C,386C,425C ''algorithm'':312C ''alreadi'':377C,534C ''also'':194C ''among'':219C ''appropri'':52C ''averag'':255C,338C ''belong'':535C ''blockquot'':128C ''brees'':423C ''call'':195C ''capabl'':523C ''choos'':78C,83C,543C ''chosen'':233C ''code'':9C ''complex'':360C ''comput'':342C,359C,551C ''confid'':555C ''consist'':467C ''constitut'':179C ''correct'':327C ''could'':150C ''creat'':398C ''cross'':16C,119C,464C ''cross-valid'':463C ''data'':214C,318C ''dataset'':137C,207C,471C ''differ'':152C,245C ''disadvantag'':351C ''divid'':469C ''element'':321C ''elimin'':58C ''entri'':411C,418C ''estim'':346C ''et'':203C,385C,424C ''evalu'':325C,336C,366C,520C ''everi'':88C ''exact'':488C ''experi'':239C ''final'':251C,344C ''fold'':462C,475C,478C,485C,496C,512C,548C ''forward'':24C ''general'':126C ''given'':299C ''good'':116C ''happen'':31C ''help'':562C ''high'':358C ''holdout'':130C,456C ''hope'':560C ''href'':3C,110C ''i.e'':530C ''increment'':229C ''independ'':474C ''interv'':556C ''involv'':57C ''item'':309C ''join'':539C ''k'':198C,290C,296C ''karypi'':384C ''keep'':93C ''known'':60C ''learn'':311C ''leav'':11C,46C,197C,281C,295C,395C ''leave-k-out'':196C,294C ''leave-one-out'':10C,280C,394C ''less'':103C ''lot'':40C ''m'':461C,473C ''m-fold'':460C ''made'':262C ''mean'':552C ''member'':380C ''method'':56C,121C,133C,192C,286C,298C,348C,457C ''might'':49C ''model'':36C,372C,502C,526C,538C ''n'':47C,72C,94C ''new'':528C ''nofollow'':8C,115C ''non'':409C ''non-zero'':408C ''number'':86C,510C,546C ''obtain'':287C ''one'':12C,59C,186C,237C,282C,307C,396C,405C ''order'':99C,340C ''overfit'':355C ''overlap'':481C ''p'':1C,54C,105C,129C,279C,450C,559C ''part'':140C ''partit'':161C ''percent'':73C ''perform'':163C ''predict'':65C,330C ''pretti'':95C ''probabl'':19C ''proport'':153C ''qualiti'':345C,369C ''random'':80C,165C,403C,443C ''rate'':61C,76C,91C,168C,178C,268C,308C,445C ''rather'':81C ''ratio'':218C ''reason'':545C ''recommend'':123C,158C,368C,522C ''regist'':378C ''rel'':7C,114C ''remain'':185C,317C,417C,495C ''remov'':71C ''repeat'':241C ''requir'':38C ''result'':253C,333C ''sarwar'':202C ''select'':166C,177C,404C,444C ''set'':143C,147C,149C,156C,182C,190C,249C,260C,275C,289C,401C,433C,439C,492C ''sever'':217C ''simpl'':452C ''singl'':442C ''small'':96C ''specif'':85C ''split'':135C,205C,426C ''straight'':23C ''straight-forward'':22C ''suggest'':509C ''suitabl'':364C,518C ''system'':124C,159C,383C,541C ''techniqu'':362C,516C ''ten'':242C ''test'':146C,181C,213C,222C,248C,259C,274C,400C,432C,438C,491C ''time'':42C,243C ''train'':44C,142C,189C,210C,220C,246C,314C,420C,430C,500C ''treatment'':117C ''tri'':63C ''trivial'':390C ''turn'':306C,483C ''two'':139C ''urm'':428C ''use'':34C,323C,487C,498C ''user'':89C,175C,266C,271C,302C,374C,414C,448C,529C,531C ''valid'':17C,120C,465C ''varianc'':553C ''variant'':453C ''version'':391C ''want'':69C ''way'':25C ''withheld'':277C,320C ''withhold'':304C,440C ''www.contentwise.tv'':112C ''www.contentwise.tv/files/an-evaluation-methodology-for-collaborative-recommender-systems.pdf'':111C ''www.cs.cmu.edu'':5C ''www.cs.cmu.edu/~schneide/tut5/node42.html'':4C ''zero'':410C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5797, 1, '2015-05-15 08:02:41.523', 1, 46, 'Data cleaning: Relationships between columns', '&lt;p&gt;I have a training data set distributed in two files.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;File 1&lt;/strong&gt;: This contains actual classification for each X1. X1 is unique in this file. X1 has one-to-one relationship with X2, i.e. X2 is also unique. Y is binary.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;| X1 | X2 | Y  | 
| 1  | 4  | 0  | 
| 3  | 5  | 1  | 
...
| 8  | 9  | 1  | 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;File 2&lt;/strong&gt;: This contains the real ''observations'' of the experiment. X1 can appear multiple times. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;| X1 | X3 | X4 | 
| 3  | 4  | 5  | 
| 3  | 1  | 2  | 
...
| 1  | 4  | 8  | 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here I can combine the two tables to have a structure like below and use them as observations:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;| X1 | X2 | X3 | X4 | Y |
| 3  | 5  | 4  | 5  | 1 |
| 3  | 5  | 1  | 2  | 1 |
...
| 1  | 4  | 4  | 8  | 0 |
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For test data I have similar structure, just the Y column is missing in File 1.&lt;/p&gt;

&lt;p&gt;I have multiple concerns here:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;X1 and X2 has one-to-one dependency in the data, i.e. X1 = f(X2) and X2 = f(X1)&lt;/li&gt;
&lt;li&gt;Y = f''(X1) or f''(X2)&lt;/li&gt;
&lt;li&gt;Frequency distribution of X1,X2 and Y changes dramatically in the new joined data set.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Questions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Does this kind of transformation of data leads to any insights?&lt;/li&gt;
&lt;li&gt;Does regression and ensemble learning techniques are capable of capturing these internal relationships?&lt;/li&gt;
&lt;/ol&gt;
', 'classification  dataset  data-cleaning', NULL, '''/code'':73C,108C,152C ''/li'':201C,209C,226C,244C,259C ''/ol'':227C,260C ''/p'':22C,58C,93C,129C,171C,178C,230C ''/pre'':74C,109C,153C ''/strong'':27C,79C ''0'':66C,151C ''1'':26C,64C,69C,72C,103C,105C,141C,144C,146C,147C,170C ''2'':78C,104C,145C ''3'':67C,99C,102C,137C,142C ''4'':65C,100C,106C,139C,148C,149C ''5'':68C,101C,138C,140C,143C ''8'':70C,107C,150C ''9'':71C ''actual'':30C ''also'':53C ''appear'':90C ''binari'':57C ''capabl'':253C ''captur'':255C ''chang'':218C ''classif'':6B,31C ''clean'':2A,10B ''code'':60C,95C,131C ''column'':5A,165C ''combin'':114C ''concern'':176C ''contain'':29C,81C ''data'':1A,9B,16C,157C,192C,224C,239C ''data-clean'':8B ''dataset'':7B ''depend'':189C ''distribut'':18C,212C ''dramat'':219C ''ensembl'':249C ''experi'':87C ''f'':195C,199C,204C,207C ''file'':21C,25C,40C,77C,169C ''frequenc'':211C ''i.e'':50C,193C ''insight'':243C ''intern'':257C ''join'':223C ''kind'':235C ''lead'':240C ''learn'':250C ''li'':180C,202C,210C,232C,245C ''like'':122C ''miss'':167C ''multipl'':91C,175C ''new'':222C ''observ'':84C,128C ''ol'':179C,231C ''one'':44C,46C,186C,188C ''one-to-on'':43C,185C ''p'':11C,23C,75C,110C,154C,172C,228C ''pre'':59C,94C,130C ''question'':229C ''real'':83C ''regress'':247C ''relationship'':3A,47C,258C ''set'':17C,225C ''similar'':160C ''strong'':24C,76C ''structur'':121C,161C ''tabl'':117C ''techniqu'':251C ''test'':156C ''time'':92C ''train'':15C ''transform'':237C ''two'':20C,116C ''uniqu'':37C,54C ''use'':125C ''x1'':34C,35C,41C,61C,88C,96C,132C,181C,194C,200C,205C,214C ''x2'':49C,51C,62C,133C,183C,196C,198C,208C,215C ''x3'':97C,134C ''x4'':98C,135C ''y'':55C,63C,136C,164C,203C,217C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5816, 2, '2015-05-17 18:32:15.197', 1, NULL, NULL, '&lt;p&gt;I''d go for this:&lt;/p&gt;

&lt;p&gt;$S(object_i,object_j) = 1 - \frac{\sum_{k=1}^{N_c} |n_{object_i,cluster_k}-n_{object_j,cluster_k}|}{n_{object_i}+n_{object_j}}$&lt;/p&gt;

&lt;p&gt;where $N_c$ is the number of clusters (here 3), $n_{object_i,cluster_k}$ is the number of elements of object $i$ that belong to cluster $k$ (here $n_{object_2,cluster_2}=200$) and $n_{object_i}$ is the total number of elements in object $i$ (sum of $i^{th}$ row).&lt;/p&gt;

&lt;p&gt;I hope it helps :)&lt;/p&gt;
', NULL, NULL, '''/p'':7C,37C,92C,98C ''1'':14C,18C ''2'':70C,72C ''200'':73C ''3'':48C ''belong'':63C ''c'':20C,41C ''cluster'':24C,29C,46C,52C,65C,71C ''d'':3C ''element'':58C,83C ''frac'':15C ''go'':4C ''help'':97C ''hope'':95C ''j'':13C,28C,36C ''k'':17C,25C,30C,53C,66C ''n'':19C,21C,26C,31C,34C,40C,49C,68C,75C ''number'':44C,56C,81C ''object'':10C,12C,22C,27C,32C,35C,50C,60C,69C,76C,85C ''p'':1C,8C,38C,93C ''row'':91C ''sum'':16C,87C ''th'':90C ''total'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5777, 1, '2015-05-13 02:24:54.52', 3, 60, 'Why does the listed order of features specified in the data set matter to the random forest classifier', '&lt;pre&gt;&lt;code&gt;&amp;gt;&amp;gt;&amp;gt; from sklearn.ensemble import RandomForestClassifier

&amp;gt;&amp;gt;&amp;gt; clf = RandomForestClassifier(n_estimators=10, random_state=1)
&amp;gt;&amp;gt;&amp;gt; Y=[0,1]

&amp;gt;&amp;gt;&amp;gt; X = [[3,2,1,0], [7,6,5,4]]
&amp;gt;&amp;gt;&amp;gt; clf = clf.fit(X, Y)
&amp;gt;&amp;gt;&amp;gt; print clf.feature_importances_
[ 0.2  0.1  0.1  0. ]

&amp;gt;&amp;gt;&amp;gt; X = [[0, 1, 2,3], [4,5,6,7]]
&amp;gt;&amp;gt;&amp;gt; clf = clf.fit(X, Y)
&amp;gt;&amp;gt;&amp;gt; print clf.feature_importances_
[ 0.2  0.1  0.1  0. ]

&amp;gt;&amp;gt;&amp;gt; X = [[3,2,1,0], [7,6,5,4]]
&amp;gt;&amp;gt;&amp;gt; clf = clf.fit(X, Y)
&amp;gt;&amp;gt;&amp;gt; print clf.feature_importances_
[ 0.2  0.1  0.1  0. ]

&amp;gt;&amp;gt;&amp;gt; X = [[3,1,2,0], [7,5,6,4]]
&amp;gt;&amp;gt;&amp;gt; clf = clf.fit(X, Y)
&amp;gt;&amp;gt;&amp;gt; print clf.feature_importances_
[ 0.2  0.1  0.1  0. ]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Assume the features have names.
When I shuffle/change the listed order of features specified in the training data set, the importance for each feature changes.&lt;br&gt;
That means the resulted random forest classifier also changes.
Note that I have rule out the effect of randomness, by fixing the random seed.&lt;/p&gt;

&lt;p&gt;Why does the listed order of features specified in the data set matter to the random forest classifier, given that the random seed is fixed? Thanks.&lt;/p&gt;
', 'random-forest  scikit', NULL, '''/code'':165C ''/p'':218C,246C ''/pre'':166C ''0'':47C,56C,77C,82C,106C,114C,135C,143C,164C ''0.1'':75C,76C,104C,105C,133C,134C,162C,163C ''0.2'':74C,103C,132C,161C ''1'':42C,48C,55C,83C,113C,141C ''10'':39C ''2'':54C,84C,112C,142C ''3'':53C,85C,111C,140C ''4'':60C,86C,118C,147C ''5'':59C,87C,117C,145C ''6'':58C,88C,116C,146C ''7'':57C,89C,115C,144C ''also'':201C ''assum'':168C ''br'':193C ''chang'':192C,202C ''classifi'':18A,200C,237C ''clf'':35C,64C,93C,122C,151C ''clf.feature'':72C,101C,130C,159C ''clf.fit'':65C,94C,123C,152C ''code'':24C ''data'':11A,185C,230C ''effect'':210C ''estim'':38C ''featur'':7A,170C,180C,191C,226C ''fix'':214C,244C ''forest'':17A,21B,199C,236C ''given'':238C ''gt'':25C,26C,27C,32C,33C,34C,43C,44C,45C,49C,50C,51C,61C,62C,63C,68C,69C,70C,78C,79C,80C,90C,91C,92C,97C,98C,99C,107C,108C,109C,119C,120C,121C,126C,127C,128C,136C,137C,138C,148C,149C,150C,155C,156C,157C ''import'':30C,73C,102C,131C,160C,188C ''list'':4A,177C,223C ''matter'':13A,232C ''mean'':195C ''n'':37C ''name'':172C ''note'':203C ''order'':5A,178C,224C ''p'':167C,219C ''pre'':23C ''print'':71C,100C,129C,158C ''random'':16A,20B,40C,198C,212C,216C,235C,241C ''random-forest'':19B ''randomforestclassifi'':31C,36C ''result'':197C ''rule'':207C ''scikit'':22B ''seed'':217C,242C ''set'':12A,186C,231C ''shuffle/change'':175C ''sklearn.ensemble'':29C ''specifi'':8A,181C,227C ''state'':41C ''thank'':245C ''train'':184C ''x'':52C,66C,81C,95C,110C,124C,139C,153C ''y'':46C,67C,96C,125C,154C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5778, 2, '2015-05-13 02:47:46.463', 6, NULL, NULL, '&lt;p&gt;Unless you have some very specific or exotic requirements, in order to perform &lt;em&gt;logistic&lt;/em&gt; (&lt;em&gt;logit&lt;/em&gt; and &lt;em&gt;probit&lt;/em&gt;) regression analysis in &lt;code&gt;R&lt;/code&gt;, you can use standard (built-in and loaded by default) &lt;code&gt;stats&lt;/code&gt; package. In particular, you can use &lt;code&gt;glm()&lt;/code&gt; function, as shown in the following nice &lt;em&gt;tutorials&lt;/em&gt; from UCLA: &lt;a href="http://www.ats.ucla.edu/stat/r/dae/logit.htm"&gt;logit in R tutorial&lt;/a&gt; and &lt;a href="http://www.ats.ucla.edu/stat/r/dae/probit.htm"&gt;probit in R tutorial&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;If you are interested in &lt;em&gt;multinomial logistic regression&lt;/em&gt;, &lt;a href="http://www.ats.ucla.edu/stat/r/dae/mlogit.htm"&gt;this UCLA tutorial&lt;/a&gt; might be helpful (you can use &lt;code&gt;glm()&lt;/code&gt; or packages, such as &lt;code&gt;glmnet&lt;/code&gt; or &lt;code&gt;mlogit&lt;/code&gt;). For the above-mentioned very specific or exotic requirements, many other R packages are available, for example &lt;code&gt;logistf&lt;/code&gt; (&lt;a href="http://cran.r-project.org/web/packages/logistf"&gt;http://cran.r-project.org/web/packages/logistf&lt;/a&gt;) or &lt;code&gt;elrm&lt;/code&gt; (&lt;a href="http://cran.r-project.org/web/packages/elrm"&gt;http://cran.r-project.org/web/packages/elrm&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;I also recommend another nice &lt;a href="http://data.princeton.edu/R/glms.html"&gt;tutorial on GLMs&lt;/a&gt; from Princeton University (by GermÃ¡n RodrÃ­guez), which discusses some modeling aspects, not addressed in the UCLA materials, in particular &lt;em&gt;updating models&lt;/em&gt; and &lt;em&gt;model selection&lt;/em&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':75C,86C,107C,183C ''/code'':30C,44C,53C,116C,123C,127C,148C,160C ''/em'':17C,20C,24C,63C,98C,208C,213C ''/p'':87C,214C ''/r/glms.html'':179C ''/stat/r/dae/logit.htm'':70C ''/stat/r/dae/mlogit.htm'':103C ''/stat/r/dae/probit.htm'':81C ''/web/packages/elrm'':165C ''/web/packages/elrm&lt;/a&gt;).&lt;/p&gt;'':168C ''/web/packages/logistf'':153C ''/web/packages/logistf&lt;/a&gt;)'':156C ''above-ment'':130C ''address'':198C ''also'':171C ''analysi'':26C ''anoth'':173C ''aspect'':196C ''avail'':143C ''built'':36C ''built-in'':35C ''code'':28C,42C,51C,114C,121C,125C,146C,158C ''cran.r-project.org'':152C,155C,164C,167C ''cran.r-project.org/web/packages/elrm'':163C ''cran.r-project.org/web/packages/elrm&lt;/a&gt;).&lt;/p&gt;'':166C ''cran.r-project.org/web/packages/logistf'':151C ''cran.r-project.org/web/packages/logistf&lt;/a&gt;)'':154C ''data.princeton.edu'':178C ''data.princeton.edu/r/glms.html'':177C ''default'':41C ''discuss'':193C ''elrm'':159C ''em'':15C,18C,22C,61C,94C,205C,210C ''exampl'':145C ''exot'':9C,136C ''follow'':59C ''function'':54C ''germã'':188C ''glm'':52C,115C ''glmnet'':122C ''glms'':182C ''guez'':191C ''help'':110C ''href'':67C,78C,100C,150C,162C,176C ''interest'':92C ''load'':39C ''logist'':16C,96C ''logistf'':147C ''logit'':19C,71C ''mani'':138C ''materi'':202C ''mention'':132C ''might'':108C ''mlogit'':126C ''model'':195C,207C,211C ''multinomi'':95C ''n'':189C ''nice'':60C,174C ''order'':12C ''p'':1C,88C,169C ''packag'':45C,118C,141C ''particular'':47C,204C ''perform'':14C ''princeton'':185C ''probit'':23C,82C ''r'':29C,73C,84C,140C ''recommend'':172C ''regress'':25C,97C ''requir'':10C,137C ''rodrã'':190C ''select'':212C ''shown'':56C ''specif'':7C,134C ''standard'':34C ''stat'':43C ''tutori'':62C,74C,85C,106C,180C ''ucla'':65C,105C,201C ''univers'':186C ''unless'':2C ''updat'':206C ''use'':33C,50C,113C ''www.ats.ucla.edu'':69C,80C,102C ''www.ats.ucla.edu/stat/r/dae/logit.htm'':68C ''www.ats.ucla.edu/stat/r/dae/mlogit.htm'':101C ''www.ats.ucla.edu/stat/r/dae/probit.htm'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5824, 2, '2015-05-18 17:36:21.813', 2, NULL, NULL, '&lt;p&gt;Depending on your technical ability and what you''re trying to do, &lt;a href="http://www.qlik.com/uk/explore/products/sense" rel="nofollow"&gt;Qlik Sense&lt;/a&gt; might be a good fit as it is fairly easy to use and yet powerful, and &lt;a href="http://www.qlik.com/uk/videos/how-to/sense/working-with-maps" rel="nofollow"&gt;comes with mapping functionality&lt;/a&gt;. If it''s a commercial project though you will need to pay for (expensive) licenses, otherwise it''s a free download for personal non-commercial use.&lt;/p&gt;
', NULL, NULL, '''/a'':23C,51C ''/p'':79C ''/uk/explore/products/sense'':18C ''/uk/videos/how-to/sense/working-with-maps'':44C ''abil'':6C ''come'':47C ''commerci'':56C,77C ''depend'':2C ''download'':72C ''easi'':33C ''expens'':65C ''fair'':32C ''fit'':28C ''free'':71C ''function'':50C ''good'':27C ''href'':15C,41C ''licens'':66C ''map'':49C ''might'':24C ''need'':61C ''nofollow'':20C,46C ''non'':76C ''non-commerci'':75C ''otherwis'':67C ''p'':1C ''pay'':63C ''person'':74C ''power'':38C ''project'':57C ''qlik'':21C ''re'':10C ''rel'':19C,45C ''sens'':22C ''technic'':5C ''though'':58C ''tri'':11C ''use'':35C,78C ''www.qlik.com'':17C,43C ''www.qlik.com/uk/explore/products/sense'':16C ''www.qlik.com/uk/videos/how-to/sense/working-with-maps'':42C ''yet'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5783, 1, '2015-05-13 15:06:49.2', 3, 137, 'Preparation for Career in Data Analysis Without College', '&lt;p&gt;I dropped out of college but am interested in a career in data analysis. Now I am self-studying approximately 10 hours per day.  Browsing through job postings on Linkedin has allowed me to compose a rough curriculum.  It would be of great help to me if you would either add a subject I have omitted or eliminate a subject that is not necessary for success in the market place.  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Curriculum (in 3-subject groupings):&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Group 1&lt;/em&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Single-variable calculus&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Intro to python&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;SQL&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;em&gt;Group 2&lt;/em&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Multi-variable calculus/linear algebra&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Discrete math&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Data structures and algorithms&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;em&gt;Group 3&lt;/em&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Calculus-based statistics and probability&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Hadoop stack&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Differential equations&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;em&gt;Group 4&lt;/em&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Statistical learning/predictive modelling&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Python data analysis techniques/Statistical programming in R&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Fundamentals of machine learning&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;hr&gt;

&lt;p&gt;All the while I plan to practice using any data sets I can find online.  Will this be sufficient to land a job in data analysis?  Of course I plan to learn far more than just this, but is this foundation solid enough to land an entry level data engineering/science position?&lt;/p&gt;
', 'career', NULL, '''/em'':96C,124C,155C,185C ''/li'':106C,113C,118C,135C,141C,149C,167C,173C,179C,194C,205C,213C ''/ol'':119C,150C,180C,214C ''/p'':82C,91C,97C,105C,112C,117C,125C,134C,140C,148C,156C,166C,172C,178C,186C,193C,204C,212C,268C ''/strong'':90C ''1'':95C ''10'':32C ''2'':123C ''3'':87C,154C ''4'':184C ''add'':62C ''algebra'':133C ''algorithm'':147C ''allow'':43C ''analysi'':6A,24C,199C,242C ''approxim'':31C ''base'':162C ''brows'':36C ''calculus'':104C,161C ''calculus-bas'':160C ''calculus/linear'':132C ''career'':3A,9B,21C ''colleg'':8A,15C ''compos'':46C ''cours'':244C ''curriculum'':49C,85C ''data'':5A,23C,144C,198C,226C,241C,265C ''day'':35C ''differenti'':176C ''discret'':138C ''drop'':12C ''either'':61C ''elimin'':69C ''em'':93C,121C,152C,182C ''engineering/science'':266C ''enough'':259C ''entri'':263C ''equat'':177C ''far'':249C ''find'':230C ''foundat'':257C ''fundament'':208C ''great'':54C ''group'':89C,94C,122C,153C,183C ''hadoop'':170C ''help'':55C ''hour'':33C ''hr'':215C ''interest'':18C ''intro'':109C ''job'':38C,239C ''land'':237C,261C ''learn'':211C,248C ''learning/predictive'':191C ''level'':264C ''li'':99C,107C,114C,127C,136C,142C,158C,168C,174C,188C,195C,206C ''linkedin'':41C ''machin'':210C ''market'':80C ''math'':139C ''model'':192C ''multi'':130C ''multi-vari'':129C ''necessari'':75C ''ol'':98C,126C,157C,187C ''omit'':67C ''onlin'':231C ''p'':10C,83C,92C,100C,108C,115C,120C,128C,137C,143C,151C,159C,169C,175C,181C,189C,196C,207C,216C ''per'':34C ''place'':81C ''plan'':221C,246C ''posit'':267C ''post'':39C ''practic'':223C ''prepar'':1A ''probabl'':165C ''program'':201C ''python'':111C,197C ''r'':203C ''rough'':48C ''self'':29C ''self-studi'':28C ''set'':227C ''singl'':102C ''single-vari'':101C ''solid'':258C ''sql'':116C ''stack'':171C ''statist'':163C,190C ''strong'':84C ''structur'':145C ''studi'':30C ''subject'':64C,71C,88C ''success'':77C ''suffici'':235C ''techniques/statistical'':200C ''use'':224C ''variabl'':103C,131C ''without'':7A ''would'':51C,60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8275, 5, '2015-10-02 06:35:51.567', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8287, 2, '2015-10-03 08:05:06.717', 7, NULL, NULL, '&lt;p&gt;Very interesting question (+1). While I am not aware of any software tools that currently offer comprehensive functionality for &lt;em&gt;feature engineering&lt;/em&gt;, there is definitely a wide range of options in that regard. Currently, as far as I know, feature engineering is still largely a &lt;strong&gt;laborious&lt;/strong&gt; and &lt;strong&gt;manual&lt;/strong&gt; process (i.e., see &lt;a href="http://appliedpredictivemodeling.com/blog/2015/7/28/feature-engineering-versus-feature-extraction"&gt;this blog post&lt;/a&gt;). Speaking about the feature engineering subject domain, &lt;a href="http://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it"&gt;this excellent article&lt;/a&gt; by Jason Brownlee provides a rather comprehensive overview of the topic.&lt;/p&gt;

&lt;p&gt;Ben Lorica, Chief Data Scientist and Director of Content Strategy for Data at O''Reilly Media Inc., has written a &lt;a href="http://radar.oreilly.com/2014/06/streamlining-feature-engineering.html"&gt;very nice article&lt;/a&gt;, describing the state-of-art (as of June 2014) approaches, methods, tools and startups in the area of &lt;em&gt;automating&lt;/em&gt; (or, as he put it, &lt;em&gt;streamlining&lt;/em&gt;) feature engineering.&lt;/p&gt;

&lt;p&gt;I took a brief look at some &lt;em&gt;startups&lt;/em&gt; that Ben has referenced and a product by &lt;a href="http://www.skytree.net"&gt;Skytree&lt;/a&gt; indeed looks quite impressive, especially in regard to the subject of this question. Having said that, some of their claims sound really suspicious to me (i.e., &lt;em&gt;"Skytree speeds up machine learning methods by up to 150x compared to open source options"&lt;/em&gt;). Continuing talking about commercial data science and machine learning offerings, I have to mention solutions by Microsoft, in particular their &lt;em&gt;Azure Machine Learning Studio&lt;/em&gt;. This Web-based product is quite powerful and elegant and offers some feature engineering functionality (FEF). For an example of some simple FEF, see &lt;a href="http://datasciencedojo.com/tutorial-videos/feature-engineering-r-script-with-azure-ml-studio"&gt;this nice video&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Returning to the question, I think that the simplest approach one can apply for automating feature engineering is to use corresponding &lt;strong&gt;IDEs&lt;/strong&gt;. Since you (me, too) are interested in R language as a data science backend, I would suggest to check, in addition to RStudio, another similar open source IDE, called &lt;a href="https://rkward.kde.org"&gt;RKWard&lt;/a&gt;. One of the advantages of RKWard vs RStudio is that it supports &lt;a href="http://api.kde.org/doc/rkwardplugins"&gt;writing plugins&lt;/a&gt; for the IDE, thus, enabling data scientists to automate feature engineering and streamline their R-based data analysis.&lt;/p&gt;

&lt;p&gt;Finally, on the other side of the spectrum of feature engineering solutions we can find some &lt;strong&gt;research projects&lt;/strong&gt;. The two most notable seem to be Stanford University''s &lt;a href="http://i.stanford.edu/hazy/victor/columbus"&gt;Columbus project&lt;/a&gt;, described in detail in the &lt;a href="http://cs.stanford.edu/people/chrismre/papers/mod539-zhang.pdf"&gt;corresponding research paper&lt;/a&gt;, and &lt;em&gt;Brainwash&lt;/em&gt;, described in &lt;a href="http://www.cidrdb.org/cidr2013/Papers/CIDR13_Paper82.pdf"&gt;this paper&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8370, 1, '2015-10-09 16:31:44.32', 6, 223, 'Vector space model cosine tf-idf for finding similar documents', '&lt;p&gt;Have corpus of over million documents     &lt;/p&gt;

&lt;p&gt;For a given document want to find similar documents using cosine as in &lt;a href="https://en.wikipedia.org/wiki/Vector_space_model" rel="nofollow"&gt;vector space model&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;$d_1 \cdot d_2 / ( ||d_1|| ||d_2|| )$&lt;/p&gt;

&lt;p&gt;All tf have been normalized using augmented frequency, to prevent a bias towards longer documents as in this &lt;a href="https://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;tf-idf&lt;/a&gt;:&lt;/p&gt;

&lt;p&gt;$tf(t,d)=0.5+0.5\frac{f(t,d)}{\mathrm{max}\{f(t,d): t\in d\}}$&lt;/p&gt;

&lt;p&gt;Have pre-calculated all $||d||$&lt;br&gt;
Have the values for the denominator pre-calculated&lt;br&gt;
So for a given $d1$ need to score over 1 million $d2$&lt;br&gt;
Have a threshold of 0.6 cosine for similarity  &lt;/p&gt;

&lt;p&gt;I can observe that for a given $||d_1||$ there is a fairly narrow range of $||d_2||$ for cosine $\ge$ 0.6&lt;br&gt;
For example in one search for similar for a cosine of $\ge$ 0.6 and a $||d_1||$ of 7.7631 then $||d_2||$ range from 7.0867 to 8.8339&lt;br&gt;
Where outside the threshold of cosine 0.6 $||d_2||$ range from to 0.7223 to 89.3395&lt;br&gt;
This was with standard tf document normalization&lt;br&gt;
It is looking at a LOT of $||d_2||$ that don''t have a chance of being a cosine 0.6 match     &lt;/p&gt;

&lt;p&gt;Finally the question:&lt;br&gt;
For a give $||d_1||$ and cosine of &gt;= 0.6 how can determine the range of $||d_2||$ that have a chance?&lt;br&gt;
Which $||d_2||$ can I safely eliminate?     &lt;/p&gt;

&lt;p&gt;I also know the number of terms in $d_1$ and $d_2$ if there is term count range.&lt;/p&gt;

&lt;p&gt;Via experimentation&lt;br&gt;
$||d2|| &amp;gt; .8 ||d1||$ and $||d2|| &amp;lt; ||d1|| / .8 $
seems to be safe but hopefully there is range that is proven to be safe  &lt;/p&gt;

&lt;p&gt;Created some test cases with a very some unique terms, some not so unique, and some common.  Sure enough you can take the most unique term and increase that frequency in the compare.  The numerator will (dot product) go up and so will ||compare|| and will get a cosine very close to 1. &lt;/p&gt;

&lt;p&gt;Kind of related and NOT the question.&lt;br&gt;
I am also using the tf-idf to group documents into groups. 
The customer base I am selling into are used to near near dup groups. 
There I am taking a related approach in I look as the smallest term count and evaluate it against term count up to 3x.  So a term count of 10 looks at 10  thru 30 (4-9 already had their shot at 10).  Here I can afford to miss one have it picked up in another.  I am 10% done and the biggest ratio is 1.8.  &lt;/p&gt;

&lt;p&gt;Please identify the flaws in this analysis&lt;br&gt;
&lt;strong&gt;As pointed out by AN6U5 there is a flaw in this analysis&lt;/strong&gt;&lt;br&gt;
It is no longer a cosine if the document is normalized on weighted&lt;br&gt;
&lt;strong&gt;And as pointed out by Mathew also cannot conclude  d1âd2â¤d1âd1&lt;/strong&gt;&lt;br&gt;
I am still hoping for something to give me a hard bound but people that seems to know this stuff are telling me no&lt;br&gt;
I don''t want to change the question so just ignore this&lt;br&gt;
I will do some analysis and maybe post a separate question on document normalization&lt;br&gt;
For the purpose of this question assume the document is normalized on raw tf&lt;br&gt;
Sorry but I am just not good with what ever markup is used to make the   equations&lt;br&gt;
So in my notation&lt;br&gt;
||d1|| = sqrt(sum(w1 x w1))&lt;br&gt;
d1 dot d2 = sum(w1 X w2)&lt;br&gt;
Assume d1 is the shorter document&lt;br&gt;
The very best d1 dot d2 that can be achieved is d1 dot d1&lt;br&gt;
If d1 is marry 100 paul 20&lt;br&gt;
And d2 is marry 100 paul 20 peter 1&lt;br&gt;
Normalized&lt;br&gt;
d1 is marry 1 paul 1/5&lt;br&gt;
d2 is marry 1 paul 1/5  peter 1/100&lt;br&gt;
Clearly marry and paul have the same idf in both documents&lt;br&gt;
The best possible d1 dot d2 is d1 dot d1&lt;br&gt;
The maximum possible match to d1 is d1&lt;br&gt;
cos = d1 dot d1 / ||d1|| ||d2||&lt;br&gt;
square both sides&lt;br&gt;
cos X cos = (d1 dot d1) X (d1 dot d1) / ( (d1 dot d1) X (d2 dot d2) ) 
cos X cos = (d1 dot d1) / (d2 dot d2)&lt;br&gt;
take the square root of both side&lt;br&gt;
cos = ||d1|| / ||d2||&lt;br&gt;
is ||d2|| not bounded by the cos?&lt;br&gt;
If I just use ||d2|| &gt;= cos ||d1|| and ||d2|| &amp;lt;= ||d1|| / cos I get the computational speed I need  &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8415, 2, '2015-10-12 23:55:44.34', 2, NULL, NULL, '&lt;p&gt;First, let''s try to get some intuition why this would work. $||d_i||$ seems to serve as a word rarity measure, which seems plausible as something to filter on. If documents use dissimilar numbers of rare words, it''ll be difficult for them to line up on the cosine similarity measure. But it seems unlikely to me that this cutoff will &lt;em&gt;only&lt;/em&gt; depend on $||d_i||$, rather than also on the structure in the tf or idf weights that go into $||d_i||$.&lt;/p&gt;

&lt;p&gt;To work through some algebra, let me introduce a few more terms (and rename some to shorter ones):&lt;/p&gt;

&lt;p&gt;Let $d_1$ be a vector of tf weights $[t_1, t_2, ...]$ element-wise multiplied by a vector of idf weights $[w_1, w_2, ...]$ to get the final weights $[d_1, d_2, ...]$. We know that $0.5\le t_i\le 1$ and $0\le w_i\le 6$ (because of the corpus size and assuming we''re using base 10, it doesn''t matter if we''re not). Let $D_1=||d_1||$.&lt;/p&gt;

&lt;p&gt;Knowing $d_1$, we want to construct a delta vector $x$, such that $d_1+x$ has the minimal (or maximal) $X$ subject to the constraints that:&lt;/p&gt;

&lt;p&gt;$X=\sqrt{\sum_i w_i^2 (t_i+x_i)^2}$&lt;/p&gt;

&lt;p&gt;$0.6D_1X\le \sum_i w_i^2t_i(t_i+x_i)$ (1)&lt;/p&gt;

&lt;p&gt;$0.5\le t_i+x_i \le 1$ &lt;/p&gt;

&lt;p&gt;Because we didn''t use the raw tf weight for $x$, $x_i=0\ \forall i$ is in the solution space. I''m also ignoring the more complicated constraint that at least one $d_i+x_i=1$, because we can''t express that linearly. We''ll leave it as is and hope that the optimizer ends up setting one of them to 1.&lt;/p&gt;

&lt;p&gt;Intuitively, it seems like the set of possible $x$ should be convex, but even if so we''re already in the realm of &lt;a href="https://en.wikipedia.org/wiki/Quadratically_constrained_quadratic_program" rel="nofollow"&gt;quadratically constrained programming&lt;/a&gt;. Note that we can solve for minimal $X^2$ instead of minimal $X$, because $X&amp;gt;0$, but we probably can''t use this methodology to maximize $X$ (i.e. minimize $-X$). But thankfully this''ll be easily solvable if $P$ is &lt;a href="https://en.wikipedia.org/wiki/Positive-definite_matrix" rel="nofollow"&gt;positive semidefinite&lt;/a&gt;. So what is $P$? We need to rewrite (1) in the correct form, which starts by squaring both sides:&lt;/p&gt;

&lt;p&gt;$0\ge 0.36D_1^2\sum_i w_i^2 (t_i+x_i)^2-\sum_{i,j}w_i^4t_it_j(t_i+x_i)(t_j+x_j)$&lt;/p&gt;

&lt;p&gt;We can rewrite this as $0\ge x^TPx+q^Tx+r$ where $P_{i,j}=0.36D_1^2-w_i^2t_it_j$ if $i=j$ and $-w_i^2t_it_j$ otherwise. &lt;/p&gt;

&lt;p&gt;It''s non-obvious to me that $P$ has to be positive semidefinite, but that''ll be easy to check for any individual $d_1$. If so, pop this into a QP solver and you''ll get a lower bound on $X$. If not, we''re in trouble.&lt;/p&gt;

&lt;p&gt;Can we also get a practical upper bound? I''m not sure. Obviously there''s some finite upper bound, since we can calculate the maximum possible $X$ from the idf vector $w$ easily. But the fact that the minimum tf weight is 0.5 instead of 0 is throwing off my intuitions about how to create an adversarial $x$ with maximum $X$, and so the best approach that I''m coming up with is gradient descent, which may or may not find the actual global maximum but will probably be close.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5784, 2, '2015-05-13 15:33:22.677', 2, NULL, NULL, '&lt;p&gt;At least based on what I and other data analysts/scientists do in my company, your technical topics list seems sufficient. But I would also add:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Visualization (ggplot2 in R, matplotlib in Python, d3.js for really cool stuff)&lt;/li&gt;
&lt;li&gt;Design of experiments&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Communication skills are also quite important.&lt;/p&gt;

&lt;p&gt;For more inspiration, here''s a good "curriculum" represented as a metro map: &lt;a href="http://nirvacana.com/thoughts/becoming-a-data-scientist/" rel="nofollow"&gt;http://nirvacana.com/thoughts/becoming-a-data-scientist/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Let me also recommend building up a portfolio of data science projects. This could consist of your analyses of data sets you find online (e.g. on &lt;a href="https://archive.ics.uci.edu/ml/" rel="nofollow"&gt;UCI&lt;/a&gt;), Kaggle competitions, or class projects (e.g. via Udacity or Coursera). That way, you can give direct proof of your technical skills, your communication in the form of reports or graphics, and your ability to extract insight.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9257, 2, '2015-12-08 02:36:55.85', 1, NULL, NULL, '&lt;p&gt;No you cannot. I had a discussion with someone in their development/support team on the MSDN forums and currently they don''t support ''drag and drop'' type of functionality. However you CAN serialize the model output and then de-serialize them in Azure. &lt;a href="http://i.stack.imgur.com/QIILN.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/QIILN.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Note that the answer in the image is a bit outdated&lt;/strong&gt; and there is the ''Create R Script'' module to replace the serialization-deserialization steps &lt;em&gt;within&lt;/em&gt; Azure. However I believe you can still serialize outside Asure (in your Desktop version of R) and deserialize them in Azure.&lt;/p&gt;

&lt;p&gt;Link to the conversation in Image:
&lt;a href="https://social.msdn.microsoft.com/Forums/azure/en-US/5944c342-79ac-4ada-8006-8edf40f36ee1/r-script-as-a-trained-model?forum=MachineLearning" rel="nofollow"&gt;https://social.msdn.microsoft.com/Forums/azure/en-US/5944c342-79ac-4ada-8006-8edf40f36ee1/r-script-as-a-trained-model?forum=MachineLearning&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5785, 2, '2015-05-13 16:20:31.883', 1, NULL, NULL, '&lt;p&gt;Looks like the common approaches to multi-class classification actually solve this challenge.&lt;/p&gt;

&lt;p&gt;Building individual Naive Bayes Classifiers with only the training data for a single label is insufficient - we must also the include data from other labels as "everything else".&lt;/p&gt;

&lt;p&gt;See &lt;a href="http://datascience.stackexchange.com/questions/2384/text-classification-problem-what-is-the-right-approach"&gt;Text-Classification-Problem, what is the right approach?&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':63C ''/p'':16C,46C,64C ''/questions/2384/text-classification-problem-what-is-the-right-approach'':53C ''actual'':12C ''also'':36C ''approach'':6C,62C ''bay'':21C ''build'':18C ''challeng'':15C ''class'':10C ''classif'':11C,56C ''classifi'':22C ''common'':5C ''data'':27C,39C ''datascience.stackexchange.com'':52C ''datascience.stackexchange.com/questions/2384/text-classification-problem-what-is-the-right-approach'':51C ''els'':45C ''everyth'':44C ''href'':50C ''includ'':38C ''individu'':19C ''insuffici'':33C ''label'':31C,42C ''like'':3C ''look'':2C ''multi'':9C ''multi-class'':8C ''must'':35C ''naiv'':20C ''p'':1C,17C,47C ''problem'':57C ''right'':61C ''see'':48C ''singl'':30C ''solv'':13C ''text'':55C ''text-classification-problem'':54C ''train'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5788, 2, '2015-05-13 18:40:46.997', 2, NULL, NULL, '&lt;p&gt;If you are looking to calculate the relative importance of a word in this scenario, you might consider doing an inverse document frequency score on a quarterly basis:
 &lt;a href="http://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;tf-idf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Once you have inverse frequency scores for all the words in the corpus from quarter to quarter, you can normalize by the range of all scores, and then do a quarter to quarter comparison.&lt;/p&gt;

&lt;p&gt;If you''re trying to do a prediction, historical data from previous years would be very useful.  You can find trends over the course of a year by doing a regression on historical data and normalize to the predicted document output for that year.&lt;/p&gt;
', NULL, NULL, '''/a'':40C ''/p'':41C,77C,123C ''/wiki/tf%e2%80%93idf'':34C ''basi'':29C ''calcul'':7C ''comparison'':76C ''consid'':19C ''corpus'':55C ''cours'':102C ''data'':88C,112C ''document'':23C,118C ''en.wikipedia.org'':33C ''en.wikipedia.org/wiki/tf%e2%80%93idf'':32C ''find'':98C ''frequenc'':24C,47C ''histor'':87C,111C ''href'':31C ''idf'':39C ''import'':10C ''invers'':22C,46C ''look'':5C ''might'':18C ''nofollow'':36C ''normal'':62C,114C ''output'':119C ''p'':1C,42C,78C ''predict'':86C,117C ''previous'':90C ''quarter'':28C,57C,59C,73C,75C ''rang'':65C ''re'':81C ''regress'':109C ''rel'':35C ''relat'':9C ''scenario'':16C ''score'':25C,48C,68C ''tf'':38C ''tf-idf'':37C ''trend'':99C ''tri'':82C ''use'':95C ''word'':13C,52C ''would'':92C ''year'':91C,105C,122C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5789, 1, '2015-05-13 21:01:03.07', 6, 3587, 'LinkedIn web scraping', '&lt;p&gt;I recently discovered a &lt;a href="http://www.r-bloggers.com/analyze-linkedin-with-r/"&gt;new R package&lt;/a&gt; for connecting to the LinkedIn API. Unfortunately the LinkedIn API seems pretty limited to begin with; for example, you can only get basic data on companies, and this is detached from data on individuals. I''d like to get data on all employees of a given company, which you can do &lt;a href="https://www.linkedin.com/vsearch/p?keywords=stack%20exchange&amp;amp;f_CC=974353&amp;amp;sb=People%20who%20work%20at%20Stack%20Exchange&amp;amp;trk=tyah&amp;amp;trkInfo=clickedVertical%3Asuggestion%2Cidx%3A1-1-1%2CtarId%3A1431584515143%2Ctas%3Astack%20exchange"&gt;manually on the site&lt;/a&gt; but is not possible through the API.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://import.io/"&gt;import.io&lt;/a&gt; would be perfect if it &lt;a href="http://blog.import.io/post/tips-tricks"&gt;recognised the LinkedIn pagination&lt;/a&gt; (see end of page).&lt;/p&gt;

&lt;p&gt;Does anyone know any web scraping tools or techniques applicable to the current format of the LinkedIn site, or ways of bending the API to carry out more flexible analysis? Preferably in R or web based, but certainly open to other approaches.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5790, 2, '2015-05-13 23:52:57.347', 6, NULL, NULL, '&lt;p&gt;Beautiful Soup is specifically designed for web crawling and scraping, but is written for python and not R:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.crummy.com/software/BeautifulSoup/bs4/doc/"&gt;http://www.crummy.com/software/BeautifulSoup/bs4/doc/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':20C ''/software/beautifulsoup/bs4/doc/'':26C ''/software/beautifulsoup/bs4/doc/&lt;/a&gt;&lt;/p&gt;'':29C ''beauti'':2C ''crawl'':9C ''design'':6C ''href'':23C ''p'':1C,21C ''python'':16C ''r'':19C ''scrape'':11C ''soup'':3C ''specif'':5C ''web'':8C ''written'':14C ''www.crummy.com'':25C,28C ''www.crummy.com/software/beautifulsoup/bs4/doc/'':24C ''www.crummy.com/software/beautifulsoup/bs4/doc/&lt;/a&gt;&lt;/p&gt;'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8467, 2, '2015-10-16 17:10:38.963', 2, NULL, NULL, '&lt;p&gt;First, thanks for the edits to your original question since we now know that you are applying the same transformation to all of your data.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Q:&lt;/strong&gt; Why do &lt;code&gt;perceptrons&lt;/code&gt; perform so much better than &lt;code&gt;generalized linear models&lt;/code&gt; for some problems? &lt;strong&gt;A:&lt;/strong&gt; Because they are inherently nonlinear models, with a great deal of flexibility.  The drawback is that the additional knobs require more data to correctly tune.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Big picture:&lt;/strong&gt; &lt;/p&gt;

&lt;p&gt;Less data can cause &lt;code&gt;high-bias&lt;/code&gt;.  &lt;code&gt;High-bias&lt;/code&gt; can be overcome by more data.  You have reduced your data from a 4800 feature dataset to a 38 feature dataset, so should expect to see increased bias.  &lt;code&gt;Neural networks&lt;/code&gt; require more data than models without hidden layers.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Linearity vs. Nonlinearity&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Your &lt;code&gt;artificial neural network&lt;/code&gt; (&lt;code&gt;perceptron&lt;/code&gt;) is an inherently nonlinear model, yet you are deciding to remove features from your dataset with a linear model (&lt;code&gt;PCA&lt;/code&gt;).  The presence of a single hidden layer explicitly creates second order terms in your data, and there are two additional nonlinear transformations (input==&gt;hidden and hidden==&gt;prediction), which add sigmoidal nonlinearity at each step.&lt;/p&gt;

&lt;p&gt;The fact that the training data and the target data are both multiplied by the same matrix to achieve the reduced dimensionality really just means that your &lt;code&gt;perceptron&lt;/code&gt; needs to learn the matrix if it wants to reconstruct nonlinear aspects of the original data.  This requires more data or for you to reduce less of the dimensionality.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;An Experiment:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I suggest trying an experiment where you perform second order &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html" rel="nofollow"&gt;&lt;code&gt;polynomial feature extraction&lt;/code&gt;&lt;/a&gt; on the full dataset and then performing &lt;code&gt;PCA&lt;/code&gt; on that enhanced dataset.  See how many features you end up with while retaining 99% of the variance of the enhanced dataset.  If it is larger than the dimensionality of your initial dataset then stick with the un-enhanced and un-reduced dataset.  If it is betweeen the dimensionality of the original data and 38 then try training the &lt;code&gt;perceptron&lt;/code&gt; with that data.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;A better idea:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Rather than using the (linear) variance to determine the feature reduction of your PCA projection, try training and cross validating your model with different amounts of PCA dimensionality reduction.  You will likely find that there is a sweet spot for a given set of images.  For instance, an SVM on the MNIST digit data performs best when the 784 pixel features are reduced down to 50 linearly independent features using PCA.  Though this is not obvious from analyzing the variance of the principal components. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Other options:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;There are nonlinear dimensionality reduction techniques, such as &lt;a href="http://wearables.cc.gatech.edu/paper_of_week/isomap.pdf" rel="nofollow"&gt;isomap&lt;/a&gt;.  You could investigate the use of nonlinear feature reduction since you are clearly loosing information with the linear PCA that you have applied.&lt;/p&gt;

&lt;p&gt;You could also look into &lt;a href="https://en.wikipedia.org/wiki/Feature_extraction#Image_processing" rel="nofollow"&gt;image specific &lt;code&gt;feature extraction&lt;/code&gt; techniques&lt;/a&gt; to add some nonlinearity before reducing the dimensionality.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8475, 1, '2015-10-17 13:05:41.787', 1, 115, 'Neural network, Support Vector Machine or something else to classify into 7 groups', '&lt;p&gt;I''m an experienced developer but I''m only starting to discover data science.
I have a data set consisting of 62 parameters for each row and each row in that data set belongs to one of 7 groups (numbered from 0 to 6).&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;x0  x1  x2  x3  x4  x5  x6  x7  x8  x9  x10 x11 x12 x13 x14 x15 x16 x17 x18 x19 x20 x21 x22 x23 x24 x25 x26 x27 x28 x29 x30 x31 x32 x33 x34 x35 x36 x37 x38 x39 x40 x41 x42 x43 x44 x45 x46 x47 x48 x49 x50 x51 x52 x53 x54 x55 x56 x57 x58 x59 x60 x61 y
b4d8a653ea  16a14a2d17  06330986ed  ca63304de0  a62168d626  1746600cb0  1   1   -0.6887062641683063 7e5c97705a  e5df3eff9b  91bb549494  e33c63cf35  3694.0  6e40247e69  617a4ad3f9  718c61545b  c26d08129a  634e3cf3ac  dd9c9e0da2  17c99905b6  513a3e3f36  9aba4d7f51  40.57961189718329   -0.11269265451935975    -0.17219069579806134    1.1666666666666663  1.6745384722167482  0.6308894281294708  37.0    1.294921875 55.0    0.16666666666666666 10.0    0.0 0.0 1.0 9.0 0.0 1.0 23.0    ÐÐµÑ.67  0.12    1.935   02.ÐÑÑ  0.625   0.25    0.125   0.0 0.813   0.07400000000000001 0.634   0.5479999999999999  0.2353332208066929  0.2649521447821752  0.0 0.3333333333333333  0.3333333333333333  0.3333333333333333  0.0 0.0 9.0 2
467f9617a3  16a14a2d17  06330986ed  ca63304de0  b7584c2d52  1746600cb0  1   1   0.8708708626728477  5624b8f759  fa0b797a92  669ea3d319  f178803074  18156.0 01ede04b4b  617a4ad3f9  718c61545b  d342e2765f  bb20e1ca06  8a6c8cef83  1b02793146  992153ed65  9aba4d7f51  28.76550293196428   2.6122849082704658  2.1590908057403015  4.0 1.7107137612171608  1.7135384162978815  0.16666666666666666 0.027669270833333325    109.0   0.0 31.0    0.0 0.0 1.0 244.0   1.0 1.0 68.0    17.25   0.57    3.452   4.0 0.409   0.619   0.579   0.248   0.34600000000000003 0.541   0.522   0.0 1.782346041542782   1.3224094711633876  0.011647254575707157    0.39767054908485855 0.2396006655574044  0.2495840266222961  0.06821963394342763 0.033277870216306155    601.0   4
190436e528  16a14a2d17  06330986ed  ca63304de0  b7584c2d52  1746600cb0  1   1   0.4376549941058605  5624b8f759  152af2cb2f  91bb549494  e33c63cf35  1178.0  cc69cbe29a  617a4ad3f9  e8a040423a  c82c3dbd33  ee3501282b  199ce7c484  5f17dedd5c  5c5025bd0a  9aba4d7f51  24.94393348850157   -0.8146595838365664 -0.7083080633874904 01.Ð¢ÑÐ°  -0.5124221809900756 -0.7339666422629345 0.3333333333333333  14.837727864583336  11.0    0.0 24.0    0.0 0.0 1.0 29.0    0.0 3.0 11.0    ÐÐ²Ñ.42  0.15    0.161   0.2 1.0 1.0 1.0 1.0 1.0 0.52    0.5329999999999999  0.835   -0.5865396521883026 0.6724356815192951  0.0 0.6060606060606061  0.12121212121212124 0.21212121212121213 0.060606060606060615    0.0 33.0    3
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I don''t know the relationship between parameters or what influence they have on the group number (y). I need to create and train a model that will predict a group based on 62 variables, with a high success rate (&gt; 80%). Where should I start?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8508, 2, '2015-10-19 14:05:49.553', 2, NULL, NULL, '&lt;p&gt;Well, you can use this approach for stacking (AFAR this is how the process you are trying to perform is called):&lt;/p&gt;

&lt;p&gt;1) Prepare a dataset in the format:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Your original features which you used to fit the 4 estimators (remove the target variable from it)&lt;/li&gt;
&lt;li&gt;Add new target variable in it - the categorical output which shows which model should be picked up for that specific input. To obtain it - train your 4 estimators on your input and than label each input with the number of the estimator which has the closest output to the target&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;2) Train your stacker - on that dataset from #1 - for example the decision tree. Or any other classifier which will be able to accurately predict the number of the estimator which should be used for that specific input.&lt;/p&gt;

&lt;p&gt;3) When using in combat mode - first run the stacker trained in #2 on new input and obtain an id of an estimator which best suites this example. Than run that estimator against new input record and get your response.&lt;/p&gt;

&lt;p&gt;Hope that makes sense.
I tried it and it worked nicely for one of my problems.&lt;/p&gt;

&lt;p&gt;Also you could try to employ some kind a ''blending'' of these 4 predictors. Here is an approach you could take (my usage of terminology here becomes a bit frivolous since I am an engineer and not a scientist, so if someone provides a more elaborate answer on blending - please do).&lt;/p&gt;

&lt;p&gt;First of all, recall how ANNs for classification work - and would work for you example, if you will use ANN as a stacker. You have input layer which reads the input values, some hidden layers and an output layer with a set of neurons, each neuron is associated with a specific class. For our case it would be 4 neurons. After you feed input each of 4 output neurons will output some real value, for example:
Neuron 1 - 5.4
Neuron 2 - 0.3
Neuron 3 - 15.3
Neuron 4 - 3.3&lt;/p&gt;

&lt;p&gt;That output means that ANN thinks that the input belongs to ''class 3'' and that you should use 3rd predictor for that example to get best results.&lt;/p&gt;

&lt;p&gt;But! Let''s think about probabilities. What is the probability of input to belong to ANY of 4 classes? It is 1.0 (since you have only 4 classes). And by default ANN doesn''t have the knowledge that this the case. So for us it would be good if the ANN will output &lt;em&gt;probability of data belong to X class&lt;/em&gt; instead of just some real value. For example we would like to obtain such output:&lt;/p&gt;

&lt;p&gt;Neuron 1 - 0.6
Neuron 2 - 0.2
Neuron 3 - 0.15
Neuron 4 - 0.05&lt;/p&gt;

&lt;p&gt;Which will read as ''I am 60% sure that you should use estimator 1 for that input, 20% sure  - that estimator 2'' etc. Such objective is called ''cross-entropy''. Modern libraries for building ANNs support it and you could tell network to train to output these probabilities.&lt;/p&gt;

&lt;p&gt;By now I believe you already got what I suggest to do. Prepare the dataset as I described at the beginning of the answer, than train an ANN with cross-entropy objective to output the probabilities. When you run it in combat mode:&lt;/p&gt;

&lt;p&gt;1) For new input run it through ANN and obtain probabilities, like these:
Neuron 1 - 0.6
Neuron 2 - 0.2
Neuron 3 - 0.15
Neuron 4 - 0.05&lt;/p&gt;

&lt;p&gt;2) Run each predictor against input and obtain their predictions
3) Multiply each prediction by the associated probability and sum the results:&lt;/p&gt;

&lt;p&gt;predictor1_result*0.6 + predictor2_result*0.2 + predictor3_result*0.15 + predictor4_result*0.05&lt;/p&gt;

&lt;p&gt;And get you final result.&lt;/p&gt;

&lt;p&gt;Why do that? First of all, we can''t be sure that each time we realy pick up the best estimator, since our stacker (ANN in our case) could make mistakes. By using such approach we give the stacker a chance to compensate for possible mistakes - which minimizes the average prediction error.&lt;/p&gt;

&lt;p&gt;Also, consider that you get such output from ANN:
Neuron 1 - 0.45
Neuron 2 - 0.45
Neuron 3 - 0.05
Neuron 4 - 0.05&lt;/p&gt;

&lt;p&gt;In such case the best option for us is to almost average the predictions from predictor 1 and 2 - since ANN is not really sure which of predictors to use.&lt;/p&gt;
', NULL, NULL, '''/em'':440C ''/li'':52C,104C ''/p'':23C,32C,145C,187C,205C,258C,348C,377C,456C,470C,520C,566C,593C,617C,631C,638C,692C,715C,747C ''/ul'':105C ''0.05'':469C,592C,630C,711C,714C ''0.15'':466C,589C,627C ''0.2'':463C,586C,624C ''0.3'':341C ''0.45'':705C,708C ''0.6'':460C,583C,621C ''1'':25C,115C,337C,459C,485C,568C,582C,704C,733C ''1.0'':400C ''15.3'':344C ''2'':107C,159C,340C,462C,493C,585C,595C,707C,735C ''20'':489C ''3'':147C,343C,362C,465C,588C,605C,710C ''3.3'':347C ''3rd'':368C ''4'':44C,80C,219C,318C,326C,346C,396C,405C,468C,591C,713C ''5.4'':338C ''60'':478C ''abl'':128C ''accur'':130C ''add'':54C ''afar'':10C ''almost'':727C ''alreadi'':527C ''also'':207C,694C ''ann'':265C,279C,354C,410C,429C,506C,549C,575C,664C,702C,737C ''answer'':253C,545C ''approach'':7C,224C,674C ''associ'':307C,611C ''averag'':689C,728C ''becom'':233C ''begin'':542C ''believ'':525C ''belong'':359C,392C,436C ''best'':171C,375C,659C,721C ''bit'':235C ''blend'':216C,255C ''build'':505C ''call'':22C,498C ''case'':314C,419C,667C,719C ''categor'':61C ''chanc'':680C ''class'':311C,361C,397C,406C,439C ''classif'':267C ''classifi'':124C ''closest'':99C ''combat'':151C,564C ''compens'':682C ''consid'':695C ''could'':209C,226C,511C,668C ''cross'':500C,552C ''cross-entropi'':499C,551C ''data'':435C ''dataset'':28C,113C,536C ''decis'':119C ''default'':409C ''describ'':539C ''doesn'':411C ''elabor'':252C ''em'':432C ''employ'':212C ''engin'':241C ''entropi'':501C,553C ''error'':691C ''estim'':45C,81C,95C,136C,169C,178C,484C,492C,660C ''etc'':494C ''exampl'':117C,174C,274C,335C,372C,448C ''featur'':37C ''feed'':322C ''final'':636C ''first'':153C,260C,643C ''fit'':42C ''format'':31C ''frivol'':236C ''get'':184C,374C,634C,698C ''give'':676C ''good'':426C ''got'':528C ''hidden'':293C ''hope'':189C ''id'':166C ''input'':74C,84C,89C,144C,162C,181C,285C,290C,323C,358C,390C,488C,571C,600C ''instead'':441C ''kind'':214C ''knowledg'':415C ''label'':87C ''layer'':286C,294C,298C ''let'':380C ''li'':34C,53C ''librari'':503C ''like'':451C,579C ''make'':191C,669C ''mean'':352C ''minim'':687C ''mistak'':670C,685C ''mode'':152C,565C ''model'':66C ''modern'':502C ''multipli'':606C ''network'':513C ''neuron'':303C,305C,319C,328C,336C,339C,342C,345C,458C,461C,464C,467C,581C,584C,587C,590C,703C,706C,709C,712C ''new'':55C,161C,180C,570C ''nice'':199C ''number'':92C,133C ''object'':496C,554C ''obtain'':76C,164C,453C,577C,602C ''one'':201C ''option'':722C ''origin'':36C ''output'':62C,100C,297C,327C,330C,351C,431C,455C,517C,556C,700C ''p'':1C,24C,106C,146C,188C,206C,259C,349C,378C,457C,471C,521C,567C,594C,618C,632C,639C,693C,716C ''perform'':20C ''pick'':69C,656C ''pleas'':256C ''possibl'':684C ''predict'':131C,604C,608C,690C,730C ''predictor'':220C,369C,598C,732C,744C ''predictor1'':619C ''predictor2'':622C ''predictor3'':625C ''predictor4'':628C ''prepar'':26C,534C ''probabl'':384C,388C,433C,519C,558C,578C,612C ''problem'':204C ''process'':15C ''provid'':249C ''read'':288C,474C ''real'':332C,445C ''reali'':655C ''realli'':740C ''recal'':263C ''record'':182C ''remov'':46C ''respons'':186C ''result'':376C,616C,620C,623C,626C,629C,637C ''run'':154C,176C,561C,572C,596C ''scientist'':245C ''sens'':192C ''set'':301C ''show'':64C ''sinc'':237C,401C,661C,736C ''someon'':248C ''specif'':73C,143C,310C ''stack'':9C ''stacker'':110C,156C,282C,663C,678C ''suggest'':531C ''suit'':172C ''sum'':614C ''support'':507C ''sure'':479C,490C,650C,741C ''take'':227C ''target'':48C,56C,103C ''tell'':512C ''terminolog'':231C ''think'':355C,382C ''time'':653C ''train'':78C,108C,157C,515C,547C ''tree'':120C ''tri'':18C,194C,210C ''ul'':33C ''us'':422C,724C ''usag'':229C ''use'':5C,40C,140C,149C,278C,367C,483C,672C,746C ''valu'':291C,333C,446C ''variabl'':49C,57C ''well'':2C ''work'':198C,268C,271C ''would'':270C,316C,424C,450C ''x'':438C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5791, 1, '2015-05-14 01:57:48.797', 1, 79, 'What kind of machine learning algorithm can I use?', '&lt;p&gt;I have a data set of tweets regarding vaccines.  They have been collected from an API because they have keywords like "flu, measles, MMR, vaccine" etc.&lt;/p&gt;

&lt;p&gt;I need to find tweets specifically about measles and the outbreak that occurred in California this past February.  It isn''t enough to search the data set for words like "California" and "Measles" because tweets like "MMR vaccination rates in Palo Alto on the rise" are about measles and California, but wont be captured by a naive search.&lt;/p&gt;

&lt;p&gt;Are there any unsupervised algorithms that could help me out?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':40C,100C,112C ''algorithm'':6A,106C ''alto'':83C ''api'':29C ''california'':56C,72C,91C ''captur'':95C ''collect'':26C ''could'':108C ''data'':17C,67C ''enough'':63C ''etc'':39C ''februari'':59C ''find'':45C ''flu'':35C ''help'':109C ''isn'':61C ''keyword'':33C ''kind'':2A ''learn'':5A,12B ''like'':34C,71C,77C ''machin'':4A,11B ''machine-learn'':10B ''measl'':36C,49C,74C,89C ''mmr'':37C,78C ''naiv'':98C ''need'':43C ''occur'':54C ''outbreak'':52C ''p'':13C,41C,101C ''palo'':82C ''past'':58C ''rate'':80C ''regard'':21C ''rise'':86C ''search'':65C,99C ''set'':18C,68C ''specif'':47C ''tweet'':20C,46C,76C ''unsupervis'':105C ''use'':9A ''vaccin'':22C,38C,79C ''wont'':93C ''word'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5872, 2, '2015-05-22 22:48:36.107', 0, NULL, NULL, '&lt;p&gt;Model is as a stochastic process . 
Markov chains is the way to go. 
Create a stochastic matrix where states could be
  A team T  ...get all the combinations possible and use past data to get initial probabilities of winning ...and then use the beautiful property of Xn= XiP^n
Where Xn is probability vector of nth stage from now and Xi is initial vector and P is probability transition matrix .&lt;/p&gt;
', NULL, NULL, '''/p'':72C ''beauti'':45C ''chain'':9C ''combin'':29C ''could'':21C ''creat'':15C ''data'':34C ''get'':26C,36C ''go'':14C ''initi'':37C,64C ''markov'':8C ''matrix'':18C,71C ''model'':2C ''n'':50C ''nth'':57C ''p'':1C,67C ''past'':33C ''possibl'':30C ''probabl'':38C,54C,69C ''process'':7C ''properti'':46C ''stage'':58C ''state'':20C ''stochast'':6C,17C ''team'':24C ''transit'':70C ''use'':32C,43C ''vector'':55C,65C ''way'':12C ''win'':40C ''xi'':62C ''xip'':49C ''xn'':48C,52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5798, 2, '2015-05-15 08:42:14.577', 1, NULL, NULL, '&lt;p&gt;I see several issues in your data.&lt;/p&gt;

&lt;p&gt;First of all, if there is a one-to-one relationship between X1 and X2, you should remove one of the two columns, because they are redundant. Redundant data may have a negative impact on your classification performance.  &lt;/p&gt;

&lt;p&gt;Secondly, the fields X3 and X4 also seem to be totally redundant, since the value of the class label Y only depends on X1/X2. So unless the columns X3 and X4 may be interesting on their own, I don''t see the point of including them into the data. &lt;/p&gt;

&lt;p&gt;Having dealt with these issues, and in order to obtain Y from X1/X2, there are two possibilities. If file1 contains the value of Y for any possible value of X1 in your domain, you don''t need any machine learning technique. You have a perfect mapping. Otherwise, you will need to apply machine learning to find a function that "fills the gaps". Depending on the nature of the Y variable, you will need to use a regression (if Y is a real number) or classification (if Y is a discrete variable). &lt;/p&gt;
', NULL, NULL, '''/p'':9C,50C,101C,194C ''also'':58C ''appli'':154C ''class'':69C ''classif'':48C,187C ''column'':34C,79C ''contain'':122C ''data'':8C,40C,100C ''dealt'':104C ''depend'':73C,165C ''discret'':192C ''domain'':135C ''field'':54C ''file1'':121C ''fill'':162C ''find'':158C ''first'':11C ''function'':160C ''gap'':164C ''impact'':45C ''includ'':96C ''interest'':85C ''issu'':5C,107C ''label'':70C ''learn'':142C,156C ''machin'':141C,155C ''map'':148C ''may'':41C,83C ''natur'':168C ''need'':139C,152C,175C ''negat'':44C ''number'':185C ''obtain'':112C ''one'':19C,21C,30C ''one-to-on'':18C ''order'':110C ''otherwis'':149C ''p'':1C,10C,51C,102C ''perfect'':147C ''perform'':49C ''point'':94C ''possibl'':119C,129C ''real'':184C ''redund'':38C,39C,63C ''regress'':179C ''relationship'':22C ''remov'':29C ''second'':52C ''see'':3C,92C ''seem'':59C ''sever'':4C ''sinc'':64C ''techniqu'':143C ''total'':62C ''two'':33C,118C ''unless'':77C ''use'':177C ''valu'':66C,124C,130C ''variabl'':172C,193C ''x1'':24C,132C ''x1/x2'':75C,115C ''x2'':26C ''x3'':55C,80C ''x4'':57C,82C ''y'':71C,113C,126C,171C,181C,189C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5792, 2, '2015-05-14 05:33:12.03', 4, NULL, NULL, '&lt;p&gt;The approach suggested by &lt;a href="http://datascience.stackexchange.com/users/8041/j-a-gartner"&gt;j.a.gartner&lt;/a&gt; is good if you want to analyze the quarter-to-quarter change in frequency for a given term, considering &lt;strong&gt;&lt;em&gt;only that term in isolation&lt;/em&gt;&lt;/strong&gt;.  What it doesn''t do is evaluate the relative frequency compared to all other terms in the corpus.  For this you could compare frequency rank for all terms quarter-to-quarter. This is the analysis used in Zipf''s Law.  Another advantage of this analysis is that you can test whether the generating process is or is not stationary (e.g. governed by same distribution quarter to quarter).&lt;/p&gt;

&lt;p&gt;The advantage of comparing frequency rank is that it doesn''t depend on the relative size of the corpus for each quarter (as long as they are all "large").  In simple language, the frequency rank of the word "Christmas" in the 4th quarter will probably be the same, regardless of whether the (US English news) corpus is 10,000 articles or 1,000,000 articles.&lt;/p&gt;
', NULL, NULL, '''/a'':12C ''/em'':40C ''/p'':110C,177C ''/strong'':41C ''/users/8041/j-a-gartner'':10C ''000'':170C,174C,175C ''1'':173C ''10'':169C ''4th'':153C ''advantag'':84C,113C ''analysi'':77C,87C ''analyz'':19C ''anoth'':83C ''approach'':3C ''articl'':171C,176C ''chang'':25C ''christma'':150C ''compar'':52C,64C,115C ''consid'':32C ''corpus'':59C,130C,167C ''could'':63C ''datascience.stackexchange.com'':9C ''datascience.stackexchange.com/users/8041/j-a-gartner'':8C ''depend'':123C ''distribut'':106C ''doesn'':44C,121C ''e.g'':102C ''em'':34C ''english'':165C ''evalu'':48C ''frequenc'':27C,51C,65C,116C,145C ''generat'':95C ''given'':30C ''good'':14C ''govern'':103C ''href'':7C ''isol'':39C ''j.a.gartner'':11C ''languag'':143C ''larg'':140C ''law'':82C ''long'':135C ''news'':166C ''p'':1C,111C ''probabl'':156C ''process'':96C ''quarter'':22C,24C,71C,73C,107C,109C,133C,154C ''quarter-to-quart'':21C,70C ''rank'':66C,117C,146C ''regardless'':160C ''relat'':50C,126C ''simpl'':142C ''size'':127C ''stationari'':101C ''strong'':33C ''suggest'':4C ''term'':31C,37C,56C,69C ''test'':92C ''us'':164C ''use'':78C ''want'':17C ''whether'':93C,162C ''word'':149C ''zipf'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5814, 2, '2015-05-16 20:07:57.017', 5, NULL, NULL, '&lt;p&gt;You want&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data_frame[data_frame$X1 &amp;gt; 30, c("X1","X2","X4")]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;that will just print it, you probably want to update &lt;code&gt;data_frame&lt;/code&gt; or store it in something else:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data_frame = data_frame[data_frame$X1 &amp;gt; 30, c("X1","X2","X4")]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;also you probably want to try asking this on StackOverflow, or reading a bit more basic R documentation because it should be well covered. Its a bit simple to be "data science".&lt;/p&gt;
', NULL, NULL, '''/code'':18C,34C,57C ''/p'':4C,41C,92C ''/pre'':19C,58C ''30'':13C,52C ''also'':60C ''ask'':66C ''basic'':75C ''bit'':73C,86C ''c'':14C,53C ''code'':6C,31C,43C ''cover'':83C ''data'':7C,9C,32C,44C,46C,48C,90C ''document'':77C ''els'':40C ''frame'':8C,10C,33C,45C,47C,49C ''gt'':12C,51C ''p'':1C,20C,59C ''pre'':5C,42C ''print'':24C ''probabl'':27C,62C ''r'':76C ''read'':71C ''scienc'':91C ''simpl'':87C ''someth'':39C ''stackoverflow'':69C ''store'':36C ''tri'':65C ''updat'':30C ''want'':3C,28C,63C ''well'':82C ''x1'':11C,15C,50C,54C ''x2'':16C,55C ''x4'':17C,56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8006, 2, '2015-09-04 15:46:28.79', 1, NULL, NULL, '&lt;p&gt;So I wrote the script. It gave me an excuse to learn Tkinter. It''s pasted below. Note this is a one-off, not a model of good programming practice! If anyone uses this and has bugs or suggestions, let me know. Here''s the &lt;a href="https://github.com/sunnysideprodcorp/CascadeImagesorter/" rel="nofollow"&gt;git link&lt;/a&gt; and model code is pasted below:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import Tkinter
import Image, ImageTk
from Tkinter import Tk, BOTH
from ttk import Frame, Button, Style
import cv2
import os
import time
import itertools

IMAGE_DIRECTORY =            # Directory of existing files
POSITIVE_DIRECTORY =         # Where to store ''positive'' cropped images
NEGATIVE_DIRECTORY =         # Where to store ''negative'' cropped images (generated automatically based on ''positive'' image cropping
IMAGE_RESIZE_FACTOR =        # How much to scale images for display purposes. Images are not scaled when saved.

# Everything stuffed into one class, not exactly model programming but it works for now
class Example(Frame):

    def __init__(self, parent, list_of_files, write_file):
        Frame.__init__(self, parent)           
        self.parent = parent
        self.list_of_files = list_of_files
        self.write_file = write_file
        self.image = None
        self.canvas = None
        self.corners = []
        self.index = -1
        self.loadImage()
        self.initUI()
        self.resetCanvas()


    def loadImage(self):
        self.index += 1
        img = cv2.imread(self.list_of_files[self.index])            
        print(self.list_of_files[self.index])
        while not img.shape[0]:
            self.index += 1
            img = cv2.imread(self.list_of_files[self.index])            
        self.cv_img = img
        img_small = cv2.resize(img, (0,0), fx = IMAGE_RESIZE_FACTOR, fy = IMAGE_RESIZE_FACTOR)
        b, g, r = cv2.split(img_small)
        img_small = cv2.merge((r,g,b))
        im = Image.fromarray(img_small)
        self.image = ImageTk.PhotoImage(image=im)       

    def resetCanvas(self):        
        self.canvas.create_image(0, 0, image=self.image, anchor="nw")
        self.canvas.configure(height = self.image.height(), width = self.image.width())
        self.canvas.place(x = 0, y = 0, height = self.image.height(), width = self.image.width())

    def initUI(self):
        self.style = Style()
        self.style.theme_use("default")
        self.pack(fill=BOTH, expand=1)

        print "width and height of image should be ", self.image.width(), self.image.height()
        self.canvas = Tkinter.Canvas(self, width = self.image.width(), height = self.image.height())       
        self.canvas.bind("&amp;lt;Button-1&amp;gt;", self.OnMouseDown)
        self.canvas.pack()

        nextButton = Button(self, text="Next", command=self.nextButton)
        nextButton.place(x=0, y=0)

        restartButton = Button(self, text="Restart", command=self.restart)
        restartButton.place(x=0, y=22)       

    def nextButton(self):
        new_img = self.cv_img[self.corners[0][1]/IMAGE_RESIZE_FACTOR:self.corners[1][1]/IMAGE_RESIZE_FACTOR, self.corners[0][0]/IMAGE_RESIZE_FACTOR:self.corners[1][0]/IMAGE_RESIZE_FACTOR]
        files = self.list_of_files[self.index].split("/")
        try:
            os.stat(POSITIVE_DIRECTORY+files[-2])
        except:
            os.mkdir(POSITIVE_DIRECTORY+files[-2])
        print("saving to ", "{}{}/{}".format(POSITIVE_DIRECTORY, files[-2], files[-1]))
        cv2.imwrite("{}{}/{}".format(POSITIVE_DIRECTORY, files[-2], files[-1]), new_img)
        self.saveNegatives(files)
        self.restart()
        self.loadImage()
        self.resetCanvas()

    def saveNegatives(self, files):
        low_x = min(self.corners[0][0], self.corners[1][0])/IMAGE_RESIZE_FACTOR
        high_x = max(self.corners[0][0], self.corners[1][0])/IMAGE_RESIZE_FACTOR
        low_y = min(self.corners[0][1], self.corners[1][1])/IMAGE_RESIZE_FACTOR
        high_y = max(self.corners[0][1], self.corners[1][1])/IMAGE_RESIZE_FACTOR

        try:
            os.stat(NEGATIVE_DIRECTORY+files[-2])
        except:
            os.mkdir(NEGATIVE_DIRECTORY+files[-2])

        new_img = self.cv_img[ :low_y, :]
        cv2.imwrite("{}{}/{}{}".format(NEGATIVE_DIRECTORY, files[-2], "LY", files[-1]), new_img)
        new_img = self.cv_img[ high_y: , :]
        cv2.imwrite("{}{}/{}{}".format(NEGATIVE_DIRECTORY, files[-2], "HY", files[-1]), new_img)

        new_img = self.cv_img[ :, :low_x ]
        cv2.imwrite("{}{}/{}{}".format(NEGATIVE_DIRECTORY, files[-2], "LX", files[-1]), new_img)
        new_img = self.cv_img[:,  high_x: ]
        cv2.imwrite("{}{}/{}{}".format(NEGATIVE_DIRECTORY, files[-2], "HX", files[-1]), new_img)


    def restart(self):
        self.corners = []
        self.index -=1
        self.canvas.delete("all")
        self.loadImage()
        self.resetCanvas()


    def OnMouseDown(self, event):
        print(event.x, event.y)
        self.corners.append([event.x, event.y])
        if len(self.corners) == 2:
            self.canvas.create_rectangle(self.corners[0][0], self.corners[0][1], self.corners[1][0], self.corners[1][1], outline =''cyan'', width = 2)


def main():

    root = Tk()
    root.geometry("250x150+300+300")

    list_of_files = []
    file_names = []
    walker = iter(os.walk(IMAGE_DIRECTORY))
    next(walker)
    for dir, _, _ in walker:
        files = [dir + "/" +  file for file in os.listdir(dir)]            
        list_of_files.extend(files)
        file_names.extend(os.listdir(dir))

    list_of_processed_files = []
    processed_file_names = []
    walker = iter(os.walk(POSITIVE_DIRECTORY))
    next(walker)
    for dir, _, _ in walker:
        files = [dir + "/" +  file for file in os.listdir(dir)]            
        list_of_processed_files.extend(files)
        processed_file_names.extend(os.listdir(dir))

    good_names = set(file_names) - set(processed_file_names)
    list_of_files = [f for i, f in enumerate(list_of_files) if file_names[i] in good_names] 

    app = Example(root, list_of_files, IMAGE_DIRECTORY+"positives")
    root.mainloop()  


if __name__ == ''__main__'':
    main()  
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5793, 1, '2015-05-14 06:46:54.377', 2, 90, 'What is the difference between Latent and Explicit Semantic Analysis', '&lt;p&gt;I''m not quite sure what "latent" refers to in this context. &lt;/p&gt;

&lt;p&gt;In &lt;a href="http://ijcai.org/papers07/Papers/IJCAI07-259.pdf" rel="nofollow"&gt;Computing Semantic Relatedness using
Wikipedia-based Explicit Semantic Analysis&lt;/a&gt; they say &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;''''Our semantic analysis is explicit in the sense that we manipulate 
 manifest concepts grounded in human cognition, rather than 
 ''latent concepts'' used by Latent Semantic Analysis''''.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What does that mean in simple terms?&lt;/p&gt;

&lt;p&gt;I had a look at the related Wikipedia articles, ( &lt;code&gt;Latent&lt;/code&gt; (wikipedia.org/wiki/Latent_semantic_analysis), &lt;code&gt;Explicit&lt;/code&gt; wikipedia.org/wiki/Explicit_semantic_analysis) ), and I wasn''t able to make heads or tails of it.&lt;/p&gt;

&lt;p&gt;Perhaps someone with a better appreciate of the nuance involved here might be able to provide me with a clear indication of the similarities and differences, the pros and cons between these two methods for accessing document/text fragment relatedness to a particular concept.  &lt;/p&gt;
', 'similarity', NULL, '''/a'':45C ''/code'':77C,100C,106C ''/p'':25C,48C,87C,122C,167C ''/papers07/papers/ijcai07-259.pdf'':32C ''/pre'':78C ''/wiki/explicit_semantic_analysis)'':109C ''/wiki/latent_semantic_analysis),'':103C ''abl'':114C,137C ''access'':159C ''analysi'':10A,44C,53C,76C ''appreci'':129C ''articl'':97C ''base'':41C ''better'':128C ''clear'':143C ''code'':50C,98C,104C ''cognit'':67C ''comput'':35C ''con'':153C ''concept'':63C,71C,166C ''context'':24C ''differ'':4A,149C ''document/text'':160C ''explicit'':8A,42C,55C,105C ''fragment'':161C ''ground'':64C ''head'':117C ''href'':29C ''human'':66C ''ijcai.org'':31C ''ijcai.org/papers07/papers/ijcai07-259.pdf'':30C ''indic'':144C ''involv'':133C ''latent'':6A,19C,70C,74C,99C ''look'':92C ''m'':14C ''make'':116C ''manifest'':62C ''manipul'':61C ''mean'':83C ''method'':157C ''might'':135C ''nofollow'':34C ''nuanc'':132C ''p'':12C,26C,79C,88C,123C ''particular'':165C ''perhap'':124C ''pre'':49C ''pros'':151C ''provid'':139C ''quit'':16C ''rather'':68C ''refer'':20C ''rel'':33C ''relat'':95C ''related'':37C,162C ''say'':47C ''semant'':9A,36C,43C,52C,75C ''sens'':58C ''similar'':11B,147C ''simpl'':85C ''someon'':125C ''sure'':17C ''tail'':119C ''term'':86C ''two'':156C ''use'':38C,72C ''wasn'':112C ''wikipedia'':40C,96C ''wikipedia-bas'':39C ''wikipedia.org'':102C,108C ''wikipedia.org/wiki/explicit_semantic_analysis)'':107C ''wikipedia.org/wiki/latent_semantic_analysis),'':101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5794, 2, '2015-05-14 13:41:48.987', 3, NULL, NULL, '&lt;p&gt;The difference is that with ESA, the concepts are already known and labeled (hence, "manifest concepts"), whereas in LSA the concepts are latent (they are undefined and need to be discovered).&lt;/p&gt;

&lt;p&gt;Note the following statement from the &lt;a href="http://en.wikipedia.org/wiki/Explicit_semantic_analysis" rel="nofollow"&gt;ESA Wikipedia page&lt;/a&gt;:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;
The name "explicit semantic analysis" contrasts with latent semantic analysis (LSA), because the use of a knowledge base makes it possible to assign human-readable labels to the concepts that make up the vector space.[3][1]
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;In short, ESA uses prior knowledge of relationships between words and concepts (as well as labels for those concepts), whereas LSA does not.&lt;/p&gt;

&lt;p&gt;References cited by Wikipedia for the quote above:
&lt;code&gt;
[1] Ofer Egozi, Shaul Markovitch and Evgeniy Gabrilovich (2011). "Concept-Based Information Retrieval using Explicit Semantic Analysis" (PDF). ACM Transactions on Information Systems 29 (2). Retrieved January 3, 2015.
&lt;/code&gt;
&lt;code&gt;
[3] Gabrilovich, Evgeniy; Markovitch, Shaul (2007). Computing semantic relatedness using Wikipedia-based Explicit Semantic Analysis (PDF). Proc. 20th Int''l Joint Conf. on Artificial Intelligence (IJCAI). pp. 1606â1611.
&lt;/code&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5795, 2, '2015-05-14 17:30:10.477', 3, NULL, NULL, '&lt;p&gt;Divide the data into windows and find features for those windows like autocorrelation coefficients, wavelets, etc. and use those features for learning.&lt;/p&gt;

&lt;p&gt;For example, if you have temperature and pressure data, break it down to individual parameters and calculate features like number of local minima in that window and others, and use these features for your model. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5818, 2, '2015-05-18 02:32:19.643', 4, NULL, NULL, '&lt;p&gt;In addition to exploratory data analysis (EDA), both descriptive and visual, I would try to use &lt;a href="http://en.wikipedia.org/wiki/Time_series" rel="nofollow"&gt;time series analysis&lt;/a&gt; as a more &lt;em&gt;comprehensive&lt;/em&gt; and &lt;em&gt;sophisticated&lt;/em&gt; analysis. Specifically, I would perform &lt;em&gt;time series regression analysis&lt;/em&gt;. Time series analysis is a huge research and practice domain, so, if you''re not familiar with the fundamentals, I suggest starting with the above-linked Wikipedia article, gradually searching for more specific topics and reading corresponding articles, papers and books.&lt;/p&gt;

&lt;p&gt;Since time series analysis is a &lt;em&gt;very popular&lt;/em&gt; approach, it is supported by most open source and closed source commercial &lt;em&gt;data science&lt;/em&gt; and &lt;em&gt;statistical&lt;/em&gt; &lt;strong&gt;environments (software)&lt;/strong&gt;, such as &lt;em&gt;R&lt;/em&gt;, &lt;em&gt;Python&lt;/em&gt;, &lt;em&gt;SAS&lt;/em&gt;, &lt;em&gt;SPSS&lt;/em&gt; and many others. If you want to use &lt;em&gt;R&lt;/em&gt; for this, check my answers &lt;a href="http://stats.stackexchange.com/a/131133/31372"&gt;on general time series analysis&lt;/a&gt; and &lt;a href="http://stats.stackexchange.com/a/131284/31372"&gt;on time series classification and clustering&lt;/a&gt;. I hope that this is helpful.&lt;/p&gt;
', NULL, NULL, '''/a'':28C,168C,181C ''/a/131133/31372'':162C ''/a/131284/31372'':174C ''/em'':34C,38C,49C,103C,119C,123C,132C,135C,138C,141C,152C ''/p'':92C,188C ''/strong'':127C ''/wiki/time_series'':22C ''above-link'':74C ''addit'':3C ''analysi'':7C,27C,39C,48C,52C,97C,167C ''answer'':157C ''approach'':104C ''articl'':78C,88C ''book'':91C ''check'':155C ''classif'':178C ''close'':113C ''cluster'':180C ''commerci'':115C ''comprehens'':33C ''correspond'':87C ''data'':6C,117C ''descript'':10C ''domain'':59C ''eda'':8C ''em'':32C,36C,44C,100C,116C,121C,130C,133C,136C,139C,150C ''en.wikipedia.org'':21C ''en.wikipedia.org/wiki/time_series'':20C ''environ'':125C ''exploratori'':5C ''familiar'':65C ''fundament'':68C ''general'':164C ''gradual'':79C ''help'':187C ''hope'':183C ''href'':19C,159C,171C ''huge'':55C ''link'':76C ''mani'':143C ''nofollow'':24C ''open'':110C ''other'':144C ''p'':1C,93C ''paper'':89C ''perform'':43C ''popular'':102C ''practic'':58C ''python'':134C ''r'':131C,151C ''re'':63C ''read'':86C ''regress'':47C ''rel'':23C ''research'':56C ''sas'':137C ''scienc'':118C ''search'':80C ''seri'':26C,46C,51C,96C,166C,177C ''sinc'':94C ''softwar'':126C ''sophist'':37C ''sourc'':111C,114C ''specif'':40C,83C ''spss'':140C ''start'':71C ''statist'':122C ''stats.stackexchange.com'':161C,173C ''stats.stackexchange.com/a/131133/31372'':160C ''stats.stackexchange.com/a/131284/31372'':172C ''strong'':124C ''suggest'':70C ''support'':107C ''time'':25C,45C,50C,95C,165C,176C ''topic'':84C ''tri'':15C ''use'':17C,149C ''visual'':12C ''want'':147C ''wikipedia'':77C ''would'':14C,42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5796, 2, '2015-05-15 00:32:05.85', 2, NULL, NULL, '&lt;p&gt;A couple ideas:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;If you have a large data set of tweets, you could try using &lt;a href="https://en.wikipedia.org/wiki/Latent_semantic_indexing" rel="nofollow"&gt;latent semantic indexing&lt;/a&gt; to find out which terms are semantically related based on their usage and co-occurrence. Then after transformation, you can apply some document similarity metric, e.g. cosine similarity, to find tweets most relevant to your queries, e.g. "California measles".&lt;/li&gt;
&lt;li&gt;Use knowledge or lexical databases like &lt;a href="http://wiki.dbpedia.org/" rel="nofollow"&gt;DBPedia&lt;/a&gt; and &lt;a href="https://wordnet.princeton.edu/" rel="nofollow"&gt;WordNet&lt;/a&gt; to calculate semantic relatedness between your query and the text of your tweets, or to identify tweets with related concepts. &lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5848, 2, '2015-05-21 05:33:57.283', 3, NULL, NULL, '&lt;p&gt;I would suggest you to check &lt;a href="http://research.microsoft.com/en-US/people/deng/keynote-deeplearning-interspeech2014-sept18-removebackups.pdf" rel="nofollow"&gt;this excellent presentation&lt;/a&gt; by Li Deng (Microsoft Research). Many of the slides contain references to relevant &lt;em&gt;research papers&lt;/em&gt; and even several &lt;strong&gt;interesting books&lt;/strong&gt; on the topics of interest (it should be pretty easy to find). It might be also helpful to check references, listed in &lt;a href="http://arxiv.org/abs/1412.5567" rel="nofollow"&gt;this research paper&lt;/a&gt; by Prof. Andrew Ng and his colleagues at Baidu Research. Finally, a focused Internet search will provide you with &lt;em&gt;comprehensive&lt;/em&gt; list of resources for further research.&lt;/p&gt;
', NULL, NULL, '''/a'':18C,75C ''/abs/1412.5567'':69C ''/em'':35C,97C ''/en-us/people/deng/keynote-deeplearning-interspeech2014-sept18-removebackups.pdf'':12C ''/p'':104C ''/strong'':42C ''also'':58C ''andrew'':78C ''arxiv.org'':68C ''arxiv.org/abs/1412.5567'':67C ''baidu'':84C ''book'':41C ''check'':7C,61C ''colleagu'':82C ''comprehens'':96C ''contain'':28C ''deng'':21C ''easi'':52C ''em'':32C,95C ''even'':37C ''excel'':16C ''final'':86C ''find'':54C ''focus'':88C ''help'':59C ''href'':9C,66C ''interest'':40C,47C ''internet'':89C ''li'':20C ''list'':63C,98C ''mani'':24C ''microsoft'':22C ''might'':56C ''ng'':79C ''nofollow'':14C,71C ''p'':1C ''paper'':34C,74C ''present'':17C ''pretti'':51C ''prof'':77C ''provid'':92C ''refer'':29C,62C ''rel'':13C,70C ''relev'':31C ''research'':23C,33C,73C,85C,103C ''research.microsoft.com'':11C ''research.microsoft.com/en-us/people/deng/keynote-deeplearning-interspeech2014-sept18-removebackups.pdf'':10C ''resourc'':100C ''search'':90C ''sever'':38C ''slide'':27C ''strong'':39C ''suggest'':4C ''topic'':45C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8017, 2, '2015-09-05 21:11:42.217', 10, NULL, NULL, '&lt;p&gt;Howard Dresner, in 1989, is believed to have coined the term "business intelligence", to describe "concepts and methods to improve business decision making by using fact-based support systems.". When he was at Gartner Group. This is a common mantra, spread over the Web. I have not been able to trace the exact source for this origin yet. Many insist on he was not at Gartner group in 1989, which is confirmed in the &lt;a href="http://www.informationbuilders.com/new/magazine/v11-2/pdf/DRESNERI.PDF"&gt;following interview&lt;/a&gt;. In his 2008 book, Performance Management Revolution: Improving Results Through Visibility and Actionable Insight, the termed is defined as:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;BI is knowledge gained through the access and analysis of business
  information.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;He says, at the beginning, that&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;In 1989, for example, I started-some might say incited-the BI
  revolution with the premise that all users have a fundamental right to
  access information without the help of IT.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;No apparent claim of the invention of the term on his side. Indeed, one can find older roots in H. P. Luhn, &lt;a href="http://dx.doi.org/10.1147/rd.24.0314"&gt;A Business Intelligence System&lt;/a&gt;, IBM Journal of Research and Development, 1958, Vol. 2, Issue 4, p. 314--319. &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Abstract: An automatic system is being developed to disseminate information to the various sections of any industrial, scientific or government organization. This intelligence system will utilize data-processing machines for auto-abstracting and auto-encoding of documents and for creating interest profiles for each of the "action points" in an organization. Both incoming and internally generated documents are automatically abstracted, characterized by a word pattern, and sent automatically to appropriate action points. This paper shows the flexibility of such a system in identifying known information, in finding who needs to know it and in disseminating it efficiently either in abstract form or as a complete document.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The author claims that:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The techniques proposed here to make these things possible are:&lt;/p&gt;
  
  &lt;ol&gt;
  &lt;li&gt;Auto-abstracting of documents;&lt;/li&gt;
  &lt;li&gt;Auto-encoding of documents;&lt;/li&gt;
  &lt;li&gt;Automatic creation and updating of action-point profiles.&lt;/li&gt;
  &lt;/ol&gt;
  
  &lt;p&gt;All of these techniques are based on statistical procedures which can
  be performed on present-day data processing machines. Together with
  proper communication facilities and input-output equipment a
  comprehensive system may be assembled to accommodate all information
  problems of an organization. We call this a Business Intelligence
  System.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;He also gives the explanation of the terms "&lt;strong&gt;business&lt;/strong&gt;" and "&lt;strong&gt;intelligence&lt;/strong&gt;": &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;In this paper, business is a collection of activities carried on for
  whatever purpose, be it science, technology, commerce, industry, law,
  government, defense, et cetera. The communication facility serving the
  conduct of a business (in the broad sense) may be referred to as an
  intelligence system. The notion of intelligence is also defined here,
  in a more general sense, as "the ability to apprehend the
  interrelationships of presented facts in such a way as to guide action
  towards a desired goal."&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;So the idea of "linking the facts" is already present in H. P. Luhn paper. To many sources, Howard Dresner has re-invented "Business Intelligence" to re-brand decision support system (DSS) and executive information system (EIS) when at DEC, and the term became famous throught the influence of the Gartner group. &lt;/p&gt;

&lt;p&gt;Apparently, the term has already been used way before, as in the book &lt;a href="http://www.worldcat.org/title/wholesale-business-intelligence-and-southern-and-western-merchants-pocket-directory-to-the-principal-mercantile-houses-in-the-city-of-philadelphia-for-the-year-1839/oclc/35144726"&gt;Wholesale Business Intelligence and Southern and Western Merchants'' Pocket Directory to the Principal Mercantile Houses in the City of Philadelphia, for the Year 1839&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;As I could not fetch this source, I will stick to the Luhn/Dresner acception. It relates to the &lt;a href="http://www.etymonline.com/index.php?term=intelligence"&gt;etymology of intelligence&lt;/a&gt;: &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;late 14c., "faculty of understanding," from Old French intelligence
  (12c.), from Latin intelligentia, intellegentia "understanding, power
  of discerning; art, skill, taste," from intelligentem (nominative
  intelligens) "discerning," present participle of intelligere "to
  understand, comprehend," from inter- "between" (see inter-) + legere
  "choose, pick out&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;In Business Intelligence for Dummies (Scheps, 2008), the definition chapter plays on Military Intelligence:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Business Intelligence Defined: No CIA Experience Required So what the
  heck is business intelligence, anyway? In essence, BI is any activity,
  tool, or process used to obtain the best information to support the
  process of making decisions.&lt;/p&gt;
  
  &lt;p&gt;For our purposes, BI revolves around putting computing power (highly
  pecialized software in concert with other more common technology
  assets) to work, to help make the best choices for your organization.&lt;/p&gt;
  
  &lt;p&gt;Business intelligence is essentially timely, accurate, high-value, and
  actionable business insights, and the work processes and technologies
  used to obtain them.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I would thus bend toward "Gathering data and information about the business", maybe more "to better conduct business". Additional historical comments can be found in &lt;a href="http://monashbi.blogspot.fr/2006/11/father-of-bi-is-he-having-laugh.html"&gt;Father of BI? Is he having a laugh?!&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5801, 1, '2015-05-15 13:51:50.087', 2, 30, 'Various algorithms performance in a problem and what can be deduced about data and problem?', '&lt;p&gt;HI I am currently trying to apply various algorithms to a classification problem to assess which could be better and then try to fine tune the bests of the first approach. I am a beginner so I use Weka for now. I have basic ML concept understanding but am not in the details of algorithms yet.&lt;/p&gt;

&lt;p&gt;I observed that on my problem, RBF networks performed vastly worse than IBK and other K methods.&lt;/p&gt;

&lt;p&gt;From what I read about RBF networks, "it implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class.It standardizes all numeric attributes to zero mean and unit variance."&lt;/p&gt;

&lt;p&gt;So basically, it also use k means to classify at first. But for some reason, I get the worst results with it using my metrics (ROC), while K methods are among the bests. Can I deduce from that fact something important about my data, like the fact that it has not a gaussian distribution, or is not fitted for logistic regression, or whatever I can''t figure out?&lt;/p&gt;

&lt;p&gt;I also observed that random forests get similar results to K methods, and that adding a filter to reduce dimensionality improved these RF, random projection being better than PCA?&lt;/p&gt;

&lt;p&gt;Can this last point means that there is much randomness in my data so random dimension reduction is better than "ruled" dimension reduction like PCA? What can I deduce from the fact that RF perform equally to K methods?&lt;/p&gt;

&lt;p&gt;I feel there is some signification here, but I am not skilled enough to understand what, and I would be very glad for any insights. Thanks by advance.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5802, 1, '2015-05-15 14:14:20.28', 2, 184, 'Learning time of arrival (ETA) from historical location data of vehicle', '&lt;p&gt;I have location data of taxis moving around the city sourced from: &lt;a href="http://research.microsoft.com/apps/pubs/?id=152883" rel="nofollow"&gt;Microsoft Research&lt;/a&gt;&lt;br&gt;
Overall it has around 17million data points.&lt;/p&gt;

&lt;p&gt;I have converted the data to JSON and filled up mongo. A sample looks like this:&lt;br&gt;
&lt;code&gt;{''lat'': 39.84349, ''timestamp'': ''2008-02-08 17:38:10'', ''lon'': 116.33986, ''ID'': 1131}&lt;/code&gt;&lt;br&gt;
&lt;code&gt;{''lat'': 39.84441, ''timestamp'': ''2008-02-08 17:38:15'', ''lon'': 116.33995, ''ID'': 1131}&lt;/code&gt;&lt;br&gt;
&lt;code&gt;{''lat'': 39.8453, ''timestamp'': ''2008-02-08 17:38:20'', ''lon'': 116.34004, ''ID'': 1131}&lt;/code&gt;&lt;br&gt;
&lt;code&gt;{''lat'': 39.84615, ''timestamp'': ''2008-02-08 17:38:25'', ''lon'': 116.34012, ''ID'': 1131}&lt;/code&gt;&lt;br&gt;
&lt;code&gt;{''lat'': 39.84705, ''timestamp'': ''2008-02-08 17:38:30'', ''lon'': 116.34022, ''ID'': 1131}&lt;/code&gt;&lt;br&gt;
&lt;code&gt;{''lat'': 39.84891, ''timestamp'': ''2008-02-08 17:38:40'', ''lon'': 116.34039, ''ID'': 1131}&lt;/code&gt;&lt;br&gt;
&lt;code&gt;{''lat'': 39.85083, ''timestamp'': ''2008-02-08 17:38:50'', ''lon'': 116.3406, ''ID'': 1131}&lt;/code&gt;&lt;br&gt;
&lt;br&gt;
It consists of a taxiID - ID field, timestamp of its latitude and longitude combination.&lt;/p&gt;

&lt;p&gt;My question is: I want to use this data to calculate estimated time of arrival(ETA)  &lt;/p&gt;

&lt;p&gt;So far, I am doing it a crude way by querying mongoDB with aggregation. It is totally inefficient.&lt;/p&gt;

&lt;p&gt;I am looking at some sort of learning algorithm where the historical data can be used to train it. In the end, given two points, the algorithm should traverse the possible route by referring historical data and give an estimate of time.
Calculating time estimate is not a problem at all if I get the array of JSON documents between the points. But, getting those right arrays is.&lt;/p&gt;

&lt;p&gt;Any pointers in this direction will be very helpful. &lt;/p&gt;
', 'machine-learning  data-mining  graphs', NULL, '''-02'':74C,90C,106C,122C,138C,154C,170C ''-08'':75C,91C,107C,123C,139C,155C,171C ''/a'':41C ''/apps/pubs/?id=152883'':36C ''/code'':83C,99C,115C,131C,147C,163C,179C ''/p'':50C,196C,214C,234C,304C,315C ''10'':78C ''1131'':82C,98C,114C,130C,146C,162C,178C ''116.33986'':80C ''116.33995'':96C ''116.34004'':112C ''116.34012'':128C ''116.34022'':144C ''116.34039'':160C ''116.3406'':176C ''15'':94C ''17'':76C,92C,108C,124C,140C,156C,172C ''17million'':47C ''20'':110C ''2008'':73C,89C,105C,121C,137C,153C,169C ''25'':126C ''30'':142C ''38'':77C,93C,109C,125C,141C,157C,173C ''39.84349'':71C ''39.84441'':87C ''39.8453'':103C ''39.84615'':119C ''39.84705'':135C ''39.84891'':151C ''39.85083'':167C ''40'':158C ''50'':174C ''aggreg'':229C ''algorithm'':244C,262C ''around'':27C,46C ''array'':291C,302C ''arriv'':4A,212C ''br'':42C,68C,84C,100C,116C,132C,148C,164C,180C,181C ''calcul'':208C,278C ''citi'':29C ''code'':69C,85C,101C,117C,133C,149C,165C ''combin'':195C ''consist'':183C ''convert'':54C ''crude'':223C ''data'':9A,16B,23C,48C,56C,206C,248C,271C ''data-min'':15B ''direct'':310C ''document'':294C ''end'':257C ''estim'':209C,275C,280C ''eta'':5A,213C ''far'':217C ''field'':188C ''fill'':60C ''get'':289C,299C ''give'':273C ''given'':258C ''graph'':18B ''help'':314C ''histor'':7A,247C,270C ''href'':33C ''id'':81C,97C,113C,129C,145C,161C,177C,187C ''ineffici'':233C ''json'':58C,293C ''lat'':70C,86C,102C,118C,134C,150C,166C ''latitud'':192C ''learn'':1A,14B,243C ''like'':66C ''locat'':8A,22C ''lon'':79C,95C,111C,127C,143C,159C,175C ''longitud'':194C ''look'':65C,238C ''machin'':13B ''machine-learn'':12B ''microsoft'':39C ''mine'':17B ''mongo'':62C ''mongodb'':227C ''move'':26C ''nofollow'':38C ''overal'':43C ''p'':19C,51C,197C,215C,235C,305C ''point'':49C,260C,297C ''pointer'':307C ''possibl'':266C ''problem'':284C ''queri'':226C ''question'':199C ''refer'':269C ''rel'':37C ''research'':40C ''research.microsoft.com'':35C ''research.microsoft.com/apps/pubs/?id=152883'':34C ''right'':301C ''rout'':267C ''sampl'':64C ''sort'':241C ''sourc'':30C ''taxi'':25C ''taxiid'':186C ''time'':2A,210C,277C,279C ''timestamp'':72C,88C,104C,120C,136C,152C,168C,189C ''total'':232C ''train'':253C ''travers'':264C ''two'':259C ''use'':204C,251C ''vehicl'':11A ''want'':202C ''way'':224C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5803, 1, '2015-05-15 22:18:30.28', 2, 41, 'Attributing causality to single quasi-independent variable', '&lt;p&gt;Apologies if this isn''t the correct place to ask - I''m not sure if this fits best with Stats or Data Science.&lt;/p&gt;

&lt;p&gt;I''m using analytics to help marketers identify attributes of their users correspond to successful conversions (such as someone buying a product, signing up for a newsletter, or subscribing to a service). Attributes could be things like which site they came from (referrer), their location, time/day of week, device type, browser, etc.&lt;/p&gt;

&lt;p&gt;What I''d like to say (although I''m not certain it''s possible) is to isolate differences in conversion rate to an individual attribute, something like, ''11% of your users from Facebook converted whereas only 3% of non-Facebook users converted'', which would mean that the attribute ''referrer'' and the level of the attribute ''Facebook'' are responsible for driving conversions.&lt;/p&gt;

&lt;p&gt;Given that I may have 100s of quasi-independent variables, is it even possible to isolate the effect to one variable and one level of that variable? As opposed to a combination of them that is more likely to be driving the difference? If so, what technique or conceptual paradigm do I use to identify which variable-level is responsible for the greatest lift in my dependent variable, conversion rate?&lt;/p&gt;
', 'experiments  marketing', NULL, '''/p'':35C,89C,153C,227C ''100s'':160C ''11'':118C ''3'':127C ''although'':97C ''analyt'':40C ''apolog'':12C ''ask'':21C ''attribut'':1A,45C,69C,115C,139C,146C ''best'':29C ''browser'':87C ''buy'':56C ''came'':77C ''causal'':2A ''certain'':101C ''combin'':187C ''conceptu'':204C ''convers'':52C,110C,152C,225C ''convert'':124C,133C ''correct'':18C ''correspond'':49C ''could'':70C ''d'':93C ''data'':33C ''depend'':223C ''devic'':85C ''differ'':108C,198C ''drive'':151C,196C ''effect'':173C ''etc'':88C ''even'':168C ''experi'':9B ''facebook'':123C,131C,147C ''fit'':28C ''given'':155C ''greatest'':219C ''help'':42C ''identifi'':44C,210C ''independ'':7A,164C ''individu'':114C ''isn'':15C ''isol'':107C,171C ''level'':143C,179C,214C ''lift'':220C ''like'':73C,94C,117C,193C ''locat'':81C ''m'':23C,38C,99C ''market'':10B,43C ''may'':158C ''mean'':136C ''newslett'':63C ''non'':130C ''non-facebook'':129C ''one'':175C,178C ''oppos'':184C ''p'':11C,36C,90C,154C ''paradigm'':205C ''place'':19C ''possibl'':104C,169C ''product'':58C ''quasi'':6A,163C ''quasi-independ'':5A,162C ''rate'':111C,226C ''referr'':79C,140C ''respons'':149C,216C ''say'':96C ''scienc'':34C ''servic'':68C ''sign'':59C ''singl'':4A ''site'':75C ''someon'':55C ''someth'':116C ''stat'':31C ''subscrib'':65C ''success'':51C ''sure'':25C ''techniqu'':202C ''thing'':72C ''time/day'':82C ''type'':86C ''use'':39C,208C ''user'':48C,121C,132C ''variabl'':8A,165C,176C,182C,213C,224C ''variable-level'':212C ''week'':84C ''wherea'':125C ''would'':135C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9251, 4, '2015-12-07 16:43:44.823', 0, NULL, NULL, 'Keras is a minimalist, highly modular neural network library written in Python.', NULL, NULL, '''high'':5C ''kera'':1C ''librari'':9C ''minimalist'':4C ''modular'':6C ''network'':8C ''neural'':7C ''python'':12C ''written'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9252, 5, '2015-12-07 18:36:03.537', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5804, 1, '2015-05-16 00:15:37.807', 4, 98, 'Complex Chunking with NLTK', '&lt;p&gt;I am trying to figure out how to use NLTK''s cascading chunker as per &lt;a href="http://www.nltk.org/book/ch07.html" rel="nofollow"&gt;Chapter 7 of the NLTK book&lt;/a&gt;. Unfortunately, I''m running into a few issues when performing non-trivial chunking measures.&lt;/p&gt;

&lt;p&gt;Let''s start with this phrase:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;"adventure movies between 2000 and 2015 featuring performances by daniel craig"&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;I am able to find all the relevant NPs when I use the following grammar:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;grammar = "NP: {&amp;lt;DT&amp;gt;?&amp;lt;JJ&amp;gt;*&amp;lt;NN.*&amp;gt;+}"&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;However, I am not sure how to build nested structures with NLTK. The book gives the following format, but there are clearly a few things missing (e.g. How does one actually specify multiple rules?):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;grammar = r"""
  NP: {&amp;lt;DT|JJ|NN.*&amp;gt;+}          # Chunk sequences of DT, JJ, NN
  PP: {&amp;lt;IN&amp;gt;&amp;lt;NP&amp;gt;}               # Chunk prepositions followed by NP
  VP: {&amp;lt;VB.*&amp;gt;&amp;lt;NP|PP|CLAUSE&amp;gt;+$} # Chunk verbs and their arguments
  CLAUSE: {&amp;lt;NP&amp;gt;&amp;lt;VP&amp;gt;}           # Chunk NP, VP
  """
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In my case, I''d like to do something like the following:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;grammar = "MEDIA: {&amp;lt;DT&amp;gt;?&amp;lt;JJ&amp;gt;*&amp;lt;NN.*&amp;gt;+}
           RELATION: {&amp;lt;V.*&amp;gt;}{&amp;lt;DT&amp;gt;?&amp;lt;JJ&amp;gt;*&amp;lt;NN.*&amp;gt;+}
           ENTITY: {&amp;lt;NN.*&amp;gt;}"
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;It occurs to me that a CFG might be a better fit for this, but I only became aware of NLTK''s support for this function about 5 minutes ago (from &lt;a href="http://stackoverflow.com/questions/14692489/chunking-with-nltk"&gt;this question&lt;/a&gt;), and it does not appear that much documentation for the feature exists.&lt;/p&gt;

&lt;p&gt;So, assuming that I''d like to use a cascaded chunker for my task, what syntax would I need to use? Additionally, is it possible for me to specify specific words (e.g. "directed" or "acted") when using a chunker?&lt;/p&gt;
', 'python  nlp  nltk', NULL, '''/a'':37C,284C ''/book/ch07.html'':28C ''/code'':75C,107C,197C,243C ''/p'':53C,61C,76C,93C,108C,144C,212C,297C,338C ''/pre'':198C,244C ''/questions/14692489/chunking-with-nltk'':281C ''2000'':67C ''2015'':69C ''5'':273C ''7'':32C ''abl'':80C ''act'':333C ''actual'':140C ''addit'':320C ''adventur'':64C ''ago'':275C ''appear'':289C ''argument'':186C ''assum'':300C ''awar'':264C ''becam'':263C ''better'':256C ''book'':36C,123C ''build'':117C ''cascad'':20C,308C ''case'':202C ''cfg'':252C ''chapter'':31C ''chunk'':2A,51C,155C,168C,182C,194C ''chunker'':21C,309C,337C ''claus'':180C,187C ''clear'':131C ''code'':63C,95C,146C,214C ''complex'':1A ''craig'':74C ''d'':204C,303C ''daniel'':73C ''direct'':331C ''document'':292C ''dt'':99C,151C,158C,218C,231C ''e.g'':136C,330C ''entiti'':239C ''exist'':296C ''featur'':70C,295C ''figur'':13C ''find'':82C ''fit'':257C ''follow'':91C,126C,170C,211C ''format'':127C ''function'':271C ''give'':124C ''grammar'':92C,96C,147C,215C ''gt'':100C,103C,106C,154C,164C,167C,176C,181C,190C,193C,219C,222C,225C,229C,232C,235C,238C,242C ''howev'':110C ''href'':25C,278C ''issu'':45C ''jj'':102C,152C,159C,221C,234C ''let'':55C ''like'':205C,209C,304C ''lt'':98C,101C,104C,150C,162C,165C,174C,177C,188C,191C,217C,220C,223C,227C,230C,233C,236C,240C ''m'':40C ''measur'':52C ''media'':216C ''might'':253C ''minut'':274C ''miss'':135C ''movi'':65C ''much'':291C ''multipl'':142C ''need'':317C ''nest'':118C ''nlp'':6B ''nltk'':4A,7B,18C,35C,121C,266C ''nn'':105C,153C,160C,224C,237C,241C ''nofollow'':30C ''non'':49C ''non-trivi'':48C ''np'':97C,149C,166C,172C,178C,189C,195C ''nps'':86C ''occur'':247C ''one'':139C ''p'':8C,54C,62C,77C,94C,109C,199C,245C,298C ''per'':23C ''perform'':47C,71C ''phrase'':60C ''possibl'':323C ''pp'':161C,179C ''pre'':145C,213C ''preposit'':169C ''python'':5B ''question'':283C ''r'':148C ''rel'':29C ''relat'':226C ''relev'':85C ''rule'':143C ''run'':41C ''sequenc'':156C ''someth'':208C ''specif'':328C ''specifi'':141C,327C ''stackoverflow.com'':280C ''stackoverflow.com/questions/14692489/chunking-with-nltk'':279C ''start'':57C ''structur'':119C ''support'':268C ''sure'':114C ''syntax'':314C ''task'':312C ''thing'':134C ''tri'':11C ''trivial'':50C ''unfortun'':38C ''use'':17C,89C,306C,319C,335C ''v'':228C ''vb'':175C ''verb'':183C ''vp'':173C,192C,196C ''word'':329C ''would'':315C ''www.nltk.org'':27C ''www.nltk.org/book/ch07.html'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5805, 2, '2015-05-16 00:28:32.713', 2, NULL, NULL, '&lt;p&gt;Based on what I figured out from your problem:&lt;/p&gt;

&lt;h2&gt;1&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;You can easily convert your data to a graph using &lt;a href="https://networkx.github.io/" rel="nofollow"&gt;Networkx&lt;/a&gt;, &lt;a href="http://igraph.org/" rel="nofollow"&gt;igraph&lt;/a&gt; or any other tool/library/software. Then what you need is a &lt;a href="http://en.wikipedia.org/wiki/Shortest_path_problem" rel="nofollow"&gt;Shortest Path Algorithm&lt;/a&gt; (&lt;a href="http://en.wikipedia.org/wiki/Dijkstra%27s_algorithm" rel="nofollow"&gt;Dijkstra&lt;/a&gt; is widely used and implemented in all graph/network analysis softwares). Once you created the graph you can simply calculate the average estimated time.&lt;/li&gt;
&lt;li&gt;For turning the problem into a &lt;em&gt;Learning Problem&lt;/em&gt;, you can use historical time estimations for different paths and assign a weight to an edge proportional to the property of that edge (e.g. traffic jam probability, time conditions) and try to predict the ETA for a new query. &lt;/li&gt;
&lt;/ul&gt;

&lt;h2&gt;2&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;You can also turn it into a &lt;em&gt;Network Science Problem&lt;/em&gt; and use Graph Theoretc approaches to approach the question. You can start with statistical analysis of nodes and edges attributes e.g. passing time distribution, shortest path length distribution, probabilistic modeling of traffic jam and so on to see if some meaningful insight leads you the next step. &lt;/li&gt;
&lt;li&gt;Another idea is to use graph clustering algorithms to extract most connected parts of the town and go through the analysis of them i.e. calculate the ETA for different parts instead of whole the data and assign the estimated time to the members of corresponding cluster and reduce the computational complexty if your algorithm.&lt;/li&gt;
&lt;/ul&gt;

&lt;h2&gt;3&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;The last but not least is having a look at &lt;a href="https://www.arangodb.com/" rel="nofollow"&gt;ArangoDB&lt;/a&gt;. It''s a new database model which is based on graphs and you can run queries on millions of edges in an amazing speed! all what you need is a bit of javascript knowledge and even if you don''t have it you can use AQL language designed for arangoDB. The interesting point is that it uses JSON files as the standard data format so you are already half way through ;)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Hope i could help :)
Good Luck!&lt;/p&gt;
', NULL, NULL, '''/a'':33C,40C,61C,70C,290C ''/em'':105C,163C ''/h2'':14C,149C,271C ''/li'':94C,145C,211C,267C,362C ''/p'':11C,371C ''/ul'':146C,268C,363C ''/wiki/dijkstra%27s_algorithm'':66C ''/wiki/shortest_path_problem'':55C ''1'':13C ''2'':148C ''3'':270C ''algorithm'':60C,220C,266C ''alreadi'':358C ''also'':154C ''amaz'':313C ''analysi'':79C,178C,233C ''anoth'':213C ''approach'':168C,170C ''aql'':336C ''arangodb'':289C,340C ''assign'':116C,249C ''attribut'':183C ''averag'':91C ''base'':2C,299C ''bit'':321C ''calcul'':89C,237C ''cluster'':219C,258C ''complexti'':263C ''comput'':262C ''condit'':134C ''connect'':224C ''convert'':20C ''correspond'':257C ''could'':367C ''creat'':83C ''data'':22C,247C,353C ''databas'':295C ''design'':338C ''differ'':113C,241C ''dijkstra'':69C ''distribut'':187C,191C ''e.g'':129C,184C ''easili'':19C ''edg'':121C,128C,182C,310C ''em'':102C,159C ''en.wikipedia.org'':54C,65C ''en.wikipedia.org/wiki/dijkstra%27s_algorithm'':64C ''en.wikipedia.org/wiki/shortest_path_problem'':53C ''estim'':92C,111C,251C ''eta'':140C,239C ''even'':326C ''extract'':222C ''figur'':6C ''file'':349C ''format'':354C ''go'':230C ''good'':369C ''graph'':25C,85C,166C,218C,301C ''graph/network'':78C ''h2'':12C,147C,269C ''half'':359C ''help'':368C ''histor'':109C ''hope'':365C ''href'':28C,35C,52C,63C,285C ''i.e'':236C ''idea'':214C ''igraph'':39C ''igraph.org'':36C ''implement'':75C ''insight'':205C ''instead'':243C ''interest'':342C ''jam'':131C,196C ''javascript'':323C ''json'':348C ''knowledg'':324C ''languag'':337C ''last'':275C ''lead'':206C ''learn'':103C ''least'':278C ''length'':190C ''li'':16C,95C,151C,212C,273C ''look'':282C ''luck'':370C ''meaning'':204C ''member'':255C ''million'':308C ''model'':193C,296C ''need'':48C,318C ''network'':160C ''networkx'':32C ''networkx.github.io'':29C ''new'':143C,294C ''next'':209C ''node'':180C ''nofollow'':31C,38C,57C,68C,288C ''p'':1C,364C ''part'':225C,242C ''pass'':185C ''path'':59C,114C,189C ''point'':343C ''predict'':138C ''probabilist'':192C ''probabl'':132C ''problem'':10C,99C,104C,162C ''properti'':125C ''proport'':122C ''queri'':144C,306C ''question'':172C ''reduc'':260C ''rel'':30C,37C,56C,67C,287C ''run'':305C ''scienc'':161C ''see'':201C ''shortest'':58C,188C ''simpli'':88C ''softwar'':80C ''speed'':314C ''standard'':352C ''start'':175C ''statist'':177C ''step'':210C ''theoretc'':167C ''time'':93C,110C,133C,186C,252C ''tool/library/software'':44C ''town'':228C ''traffic'':130C,195C ''tri'':136C ''turn'':97C,155C ''ul'':15C,150C,272C ''use'':26C,73C,108C,165C,217C,335C,347C ''way'':360C ''weight'':118C ''whole'':245C ''wide'':72C ''www.arangodb.com'':286C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5845, 2, '2015-05-20 14:11:38.367', 2, NULL, NULL, '&lt;p&gt;The query is called a ''top-k'' query, and you can answer it quickly using the ranking cube approach. &lt;a href="http://www1.se.cuhk.edu.hk/~hcheng/paper/vldb06_rankcube.pdf" rel="nofollow"&gt;http://www1.se.cuhk.edu.hk/~hcheng/paper/vldb06_rankcube.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The paper does not derive the time complexity.&lt;/p&gt;
', NULL, NULL, '''/p'':41C ''/~hcheng/paper/vldb06_rankcube.pdf'':26C ''/~hcheng/paper/vldb06_rankcube.pdf&lt;/a&gt;&lt;/p&gt;'':31C ''answer'':14C ''approach'':21C ''call'':5C ''complex'':40C ''cube'':20C ''deriv'':37C ''href'':23C ''k'':9C ''nofollow'':28C ''p'':1C,32C ''paper'':34C ''queri'':3C,10C ''quick'':16C ''rank'':19C ''rel'':27C ''time'':39C ''top'':8C ''top-k'':7C ''use'':17C ''www1.se.cuhk.edu.hk'':25C,30C ''www1.se.cuhk.edu.hk/~hcheng/paper/vldb06_rankcube.pdf'':24C ''www1.se.cuhk.edu.hk/~hcheng/paper/vldb06_rankcube.pdf&lt;/a&gt;&lt;/p&gt;'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5808, 1, '2015-05-16 16:58:08.09', 2, 56, 'Is it my Sales growth is depend on Commisions/Discounts?', '&lt;p&gt;I prefer this model in R&lt;/p&gt;

&lt;p&gt;We are capturing sales data by time series (Month by month). Some of items have commissions and some have Discounts and others have both commissions and discounts. Is it Commissions or Discounts or commissions + Discounts have impact on my sales growth? Or is it my sales are growing because of those commissions or discounts or discounts +commissions Can you suggest me best model to solve my use case? I am thinking multiple regression. But I want to double check with experts like you.&lt;/p&gt;

&lt;p&gt;Thanks for your all your help&lt;/p&gt;

&lt;p&gt;Sample Data set: (5 variables)&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Year-Month -Product -Sales  -Commission -Discounts
2013-01 Milk    300 No  Yes
2013-02 Milk    400 No  Yes
2013-03 Milk    200 No  Yes
2013-04 Milk    150 No  Yes
2013-05 Milk    500 No  Yes
2013-01 Bread   800 Yes No
2013-02 Bread   879 Yes No
2013-03 Bread   790 Yes No
2013-04 Bread   459 Yes No
2013-05 Bread   600 Yes No
2013-01 Cheese  400 Yes Yes
2013-02 Cheese  350 Yes Yes
2013-03 Cheese  600 Yes Yes
2013-04 Cheese  590 Yes Yes
2013-05 Cheese  720 Yes Yes
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5809, 1, '2015-05-16 17:37:53.163', 3, 115, 'How to subset rows from a data frame with comparison operators in R', '&lt;p&gt;I have a data frame (a csv file) with dimensions 100x6 and I need only the columns c("X1", "X2", "X4") and the rows in which the value of "X1" is greater than 30. So I did:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  data_frame &amp;lt;- read.csv ("data_frame")
  data_frame [c("X1", "X2", "X4")]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The column subset problem is solved but now I need to subset rows from data_frame [c("X1", "X2", "X4")] where the values of "X1" is greater than 30. I tried:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  data_frame [c("X1" &amp;gt; 30), c("X1", "X2", "X4")] 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But it returned the same data frame as data_frame [c("X1", "X2", "X4")].
Also tried using the function subset() with the same approach but got the same results.&lt;/p&gt;
', 'r', NULL, '''/code'':68C,115C ''/p'':53C,102C,147C ''/pre'':69C,116C ''100x6'':26C ''30'':49C,99C,110C ''also'':132C ''approach'':141C ''c'':33C,64C,87C,107C,111C,128C ''code'':55C,104C ''column'':32C,72C ''comparison'':10A ''csv'':22C ''data'':7A,19C,56C,60C,62C,85C,105C,123C,126C ''dimens'':25C ''file'':23C ''frame'':8A,20C,57C,61C,63C,86C,106C,124C,127C ''function'':136C ''got'':143C ''greater'':47C,97C ''gt'':109C ''lt'':58C ''need'':29C,80C ''oper'':11A ''p'':15C,70C,117C ''pre'':54C,103C ''problem'':74C ''r'':13A,14B ''read.csv'':59C ''result'':146C ''return'':120C ''row'':4A,39C,83C ''solv'':76C ''subset'':3A,73C,82C,137C ''tri'':101C,133C ''use'':134C ''valu'':43C,93C ''x1'':34C,45C,65C,88C,95C,108C,112C,129C ''x2'':35C,66C,89C,113C,130C ''x4'':36C,67C,90C,114C,131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5810, 1, '2015-05-16 18:09:15.63', 0, 200, 'Application of Control Theory in Data Science', '&lt;p&gt;I have recently completed an MSc in Control Systems from a top university. It seems to me that control theory must have an application within data science. I would like to apply my degree within this domain, but I want to be sure that it is relevant to the role of a data scientist.&lt;/p&gt;

&lt;p&gt;The topics which I have particular interest and experience in are State Space Control, Systems Identification, Model Predictive Control and Optimal Control. I imagine that effective management of any large dataset must involve modelling of the system in terms of transfer functions/state space models (based on large sets of historical input/output data). These models could then be used to predict the evolution of a market/variable over time, and therefore optimise a given cost function such as profit, risk etc.&lt;/p&gt;

&lt;p&gt;If this kind of role exists within data science/ other areas, can you please give me more information/ ideas of job roles/ industries to research.&lt;/p&gt;
', 'bigdata  optimization  finance', NULL, '''/p'':66C,147C,174C ''appli'':43C ''applic'':1A,35C ''area'':159C ''base'':112C ''bigdata'':8B ''complet'':15C ''control'':3A,19C,30C,81C,86C,89C ''cost'':140C ''could'':122C ''data'':6A,37C,64C,119C,156C ''dataset'':98C ''degre'':45C ''domain'':48C ''effect'':93C ''etc'':146C ''evolut'':129C ''exist'':154C ''experi'':76C ''financ'':10B ''function'':141C ''functions/state'':109C ''give'':163C ''given'':139C ''histor'':117C ''idea'':167C ''identif'':83C ''imagin'':91C ''industri'':171C ''inform'':166C ''input/output'':118C ''interest'':74C ''involv'':100C ''job'':169C ''kind'':151C ''larg'':97C,114C ''like'':41C ''manag'':94C ''market/variable'':132C ''model'':84C,101C,111C,121C ''msc'':17C ''must'':32C,99C ''optim'':9B,88C ''optimis'':137C ''p'':11C,67C,148C ''particular'':73C ''pleas'':162C ''predict'':85C,127C ''profit'':144C ''recent'':14C ''relev'':58C ''research'':173C ''risk'':145C ''role'':61C,153C,170C ''scienc'':7A,38C,157C ''scientist'':65C ''seem'':26C ''set'':115C ''space'':80C,110C ''state'':79C ''sure'':54C ''system'':20C,82C,104C ''term'':106C ''theori'':4A,31C ''therefor'':136C ''time'':134C ''top'':23C ''topic'':69C ''transfer'':108C ''univers'':24C ''use'':125C ''want'':51C ''within'':36C,46C,155C ''would'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5811, 2, '2015-05-16 18:52:10.65', 2, NULL, NULL, '&lt;p&gt;You might be interested in this paper that explores a few of the questions you are asking: &lt;a href="http://arxiv.org/pdf/1312.6184.pdf" rel="nofollow"&gt;http://arxiv.org/pdf/1312.6184.pdf&lt;/a&gt;. It is aptly titled: "Do Deep Networks Really Need to Be Deep?"&lt;/p&gt;

&lt;p&gt;The crux of the matter is that deep networks allow for a LOT of non-linearity in the data that is being described. For CIFAR-10, I suspect that something similar to what works well for MNIST will work well. Yan LeCunn''s page about MNIST results gives a lot of clues about models with few parameters: &lt;a href="http://yann.lecun.com/exdb/mnist/" rel="nofollow"&gt;http://yann.lecun.com/exdb/mnist/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The double-problem of image classification is that in addition to the classification you need to extract features somehow. Often the parameters in deep networks go towards feature extraction rather than the classification itself, which is usually a softmax multi-class logistic regression classifier. &lt;/p&gt;
', NULL, NULL, '''-10'':69C ''/exdb/mnist/'':105C ''/exdb/mnist/&lt;/a&gt;&lt;/p&gt;'':110C ''/p'':41C,158C ''/pdf/1312.6184.pdf'':23C ''/pdf/1312.6184.pdf&lt;/a&gt;.'':28C ''addit'':122C ''allow'':52C ''apt'':31C ''arxiv.org'':22C,27C ''arxiv.org/pdf/1312.6184.pdf'':21C ''arxiv.org/pdf/1312.6184.pdf&lt;/a&gt;.'':26C ''ask'':18C ''cifar'':68C ''class'':154C ''classif'':118C,125C,145C ''classifi'':157C ''clue'':95C ''crux'':44C ''data'':62C ''deep'':34C,40C,50C,136C ''describ'':66C ''doubl'':114C ''double-problem'':113C ''explor'':10C ''extract'':129C,141C ''featur'':130C,140C ''give'':91C ''go'':138C ''href'':20C,102C ''imag'':117C ''interest'':5C ''lecunn'':85C ''linear'':59C ''logist'':155C ''lot'':55C,93C ''matter'':47C ''might'':3C ''mnist'':80C,89C ''model'':97C ''multi'':153C ''multi-class'':152C ''need'':37C,127C ''network'':35C,51C,137C ''nofollow'':25C,107C ''non'':58C ''non-linear'':57C ''often'':132C ''p'':1C,42C,111C ''page'':87C ''paper'':8C ''paramet'':100C,134C ''problem'':115C ''question'':15C ''rather'':142C ''realli'':36C ''regress'':156C ''rel'':24C,106C ''result'':90C ''similar'':74C ''softmax'':151C ''somehow'':131C ''someth'':73C ''suspect'':71C ''titl'':32C ''toward'':139C ''usual'':149C ''well'':78C,83C ''work'':77C,82C ''yan'':84C ''yann.lecun.com'':104C,109C ''yann.lecun.com/exdb/mnist/'':103C ''yann.lecun.com/exdb/mnist/&lt;/a&gt;&lt;/p&gt;'':108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5812, 2, '2015-05-16 19:01:43.953', 3, NULL, NULL, '&lt;p&gt;Multiple regression sounds appropriate in this case. Real question is &lt;strong&gt;what variables to use&lt;/strong&gt;. Definitely, you should include indicator variables &lt;code&gt;commisions&lt;/code&gt; and &lt;code&gt;discounts&lt;/code&gt; (and possibly their combination - &lt;code&gt;commisions_and_discounts&lt;/code&gt; - as a separate variable). Date and time information may play important role as well, though treating them is a little bit harder. At the very least, it''s worth to include &lt;code&gt;year&lt;/code&gt; (because there may be a global trend), &lt;code&gt;month&lt;/code&gt; and &lt;code&gt;day_of_week&lt;/code&gt; (many patterns are repeated periodically). &lt;/p&gt;

&lt;p&gt;Product type is a little bit more complicated. Obviously, some products will always have higher sales than others, so first idea is to include a set of &lt;code&gt;product_type_x&lt;/code&gt; dummy variables. But product type may affect sales not additively, but instead multiplicatively, i.e. not as &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;sales ~ beta_0 + beta_1*product_type_x + ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;but instead as &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;sales ~ beta_1 * prodcut_type_x * (beta_0 + ...)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In this case better solution would be to create &lt;strong&gt;separate regression models&lt;/strong&gt; for each product type. &lt;/p&gt;
', NULL, NULL, '''/code'':26C,30C,39C,73C,83C,89C,128C,156C,173C ''/p'':95C,145C,162C,194C ''/pre'':157C,174C ''/strong'':17C,189C ''0'':150C,172C ''1'':152C,167C ''addit'':138C ''affect'':135C ''alway'':109C ''appropri'':5C ''beta'':149C,151C,166C,171C ''better'':179C ''bit'':60C,102C ''case'':8C,178C ''code'':24C,28C,35C,71C,81C,85C,124C,147C,164C ''combin'':34C ''commis'':25C,36C ''complic'':104C ''creat'':184C ''date'':44C ''day'':86C ''definit'':18C ''discount'':29C,38C ''dummi'':129C ''first'':116C ''global'':79C ''harder'':61C ''higher'':111C ''i.e'':142C ''idea'':117C ''import'':50C ''includ'':21C,70C,120C ''indic'':22C ''inform'':47C ''instead'':140C,160C ''least'':65C ''littl'':59C,101C ''mani'':90C ''may'':48C,76C,134C ''model'':188C ''month'':82C ''multipl'':2C,141C ''obvious'':105C ''other'':114C ''p'':1C,96C,158C,175C ''pattern'':91C ''period'':94C ''play'':49C ''possibl'':32C ''pre'':146C,163C ''prodcut'':168C ''product'':97C,107C,125C,132C,153C,192C ''question'':10C ''real'':9C ''regress'':3C,187C ''repeat'':93C ''role'':51C ''sale'':112C,136C,148C,165C ''separ'':42C,186C ''set'':122C ''solut'':180C ''sound'':4C ''strong'':12C,185C ''though'':54C ''time'':46C ''treat'':55C ''trend'':80C ''type'':98C,126C,133C,154C,169C,193C ''use'':16C ''variabl'':14C,23C,43C,130C ''week'':88C ''well'':53C ''worth'':68C ''would'':181C ''x'':127C,155C,170C ''year'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5813, 2, '2015-05-16 19:16:26.623', 2, NULL, NULL, '&lt;p&gt;The offset parameter is sometimes called "bias" in classification tasks, and its intuitive understanding doesn''t have to do with what kind of kernel is used. It is basically used to compensate for feature vectors that are not centered around 0.&lt;/p&gt;

&lt;p&gt;I will try to intuitively explain what the bias does with a toy example. Let''s say you have some feature vector &lt;code&gt;x&lt;/code&gt; who''s parameters are always negative. The set of weights &lt;code&gt;w&lt;/code&gt; you use in your SVM (let''s say linear, just for clarity) will perhaps transform the features into the range [0, 1] - so they will always be negative. But, those elements that belong to class 1 fall in the range [0, 0.5], and the ones that are from class 2 fall into the range [0.5, 1]. &lt;/p&gt;

&lt;p&gt;To classify into a class, the SVM uses 0 as the threshold breakpoint - if greater than 0, it is an element of class 1, if less than 0, it is an element of class -1. But in this case, all the elements will be classified into class 2. However, with a bias of 0.5 (in this linear case), they will be classified correctly. &lt;/p&gt;

&lt;p&gt;This geometric interpretation doesn''t quite work for more complex kernels, but the idea is the same: the bias term attempts to compensate for features that are not centered around zero. In practice, if the features are centered around zero the bias term isn''t always needed. To get around the bias issue, you can either: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;center your features and forget about the bias term (doesn''t always work)&lt;/li&gt;
&lt;li&gt;&lt;p&gt;augment your feature data to include a 1 at the beginning. i.e.: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[feat1 feat2 feat3] --&amp;gt; [feat1 feat2 feat3 1]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;for all of your feature vectors. Then the bias term will just be learned as another SVM weight parameter. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;Learn your weights, then calculate the bias based on the optimal prediction rule:&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/0rOre.png" alt="&amp;quot;prediction rule&amp;quot; for SVMs"&gt;
That is, given all the support vectors $S$, the learned weights $\alpha_i$, the training class $y^{(i)}$, training features $x^{(i)}$, and features-to-use-for-prediction $x$, the predicted class will be $y(x)$. The optimal $w_0$ or bias will then be calculated as the average distance between the correct and calculated predicted class: 
&lt;img src="http://i.stack.imgur.com/kyukg.png" alt="optimal bias"&gt;&lt;/p&gt;

&lt;p&gt;Hope that helps. I think the easiest thing is to center your features ;)&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9249, 1, '2015-12-07 16:42:04.107', 9, 450, 'Choosing between TensorFlow or Theano as backend for Keras', '&lt;p&gt;&lt;a href="http://keras.io/"&gt;Keras&lt;/a&gt; supports both &lt;strong&gt;TensorFlow&lt;/strong&gt; and &lt;strong&gt;Theano&lt;/strong&gt; as backend: what are the pros/cons of choosing one versus the other, besides the fact that currently not all operations are implemented with the TensorFlow backend?&lt;/p&gt;
', 'neuralnetwork  deep-learning  theano  tensorflow  keras', NULL, '''/a'':22C ''/p'':58C ''/strong'':27C,31C ''backend'':7A,33C,57C ''besid'':44C ''choos'':1A,39C ''current'':48C ''deep'':12B ''deep-learn'':11B ''fact'':46C ''href'':19C ''implement'':53C ''kera'':9A,16B,21C ''keras.io'':20C ''learn'':13B ''neuralnetwork'':10B ''one'':40C ''oper'':51C ''p'':17C ''pros/cons'':37C ''strong'':25C,29C ''support'':23C ''tensorflow'':3A,15B,26C,56C ''theano'':5A,14B,30C ''versus'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5815, 2, '2015-05-17 08:30:38.917', 2, NULL, NULL, '&lt;p&gt;Have you tried the &lt;strong&gt;Internet search&lt;/strong&gt;? The results should be able to answer most, if not all, of your questions. The topics of your interest sound like rather general or high-level. I''m sure that they can, in one form or another, be applied in the &lt;em&gt;data science context&lt;/em&gt;. In my opinion, those topics are more related to &lt;em&gt;operations research (OR)&lt;/em&gt;, therefore, I would recommend you to perform some research on the Internet on the &lt;em&gt;intersections&lt;/em&gt; between control systems (theory) and data science.&lt;/p&gt;

&lt;p&gt;Having said that, first thing that comes to my mind is that the most likely candidate for use of control theory concepts and methods in data science context would be &lt;strong&gt;distributed systems&lt;/strong&gt; and &lt;strong&gt;algorithms&lt;/strong&gt; for &lt;em&gt;data analysis&lt;/em&gt;, such as &lt;em&gt;MapReduce&lt;/em&gt; (&lt;em&gt;Hadoop&lt;/em&gt;, etc.), as well as other &lt;strong&gt;parallel processing systems&lt;/strong&gt;. If there exist an intersection between OR''s area of optimization and control theory, then it very well could be used for &lt;strong&gt;big data algorithms optimization&lt;/strong&gt;, among other tasks.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5826, 1, '2015-05-18 18:55:59.763', 1, 167, 'Master degree in Data Science', '&lt;p&gt;I am looking to change careers and would appreciate some advice. &lt;/p&gt;

&lt;p&gt;I have an undergraduate degree in English Literature and a JD. Needless to say, these were not the best decisions and I would like to change my career. I have always enjoyed math and science, and after months of research and self study, I have decided that I would like to pursue statistics. &lt;/p&gt;

&lt;p&gt;My question is basically this : would it be better to get another Bach. Degree in statistics or should I take calculus 1-3, linear algebra, probably and statistics and some computer science courses at a community college then try to get into a grad program instead? &lt;/p&gt;

&lt;p&gt;I am afraid that without the bachelors degree I will not have the required knowledge for a masters even after taking the courses at my local c.c. However, another bachelors degree may be a worthless waste of time. &lt;/p&gt;

&lt;p&gt;Please share your thoughts. Also, sorry if this is not the right place for this question. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5819, 1, '2015-05-18 05:59:41.207', 3, 85, 'Looking for smallest set of rows that form a natural key in a data set', '&lt;p&gt;I have several sets of text files on hdfs that are exports from relations.  Unfortunately I do not know the structure of the table is, but I do know that each has a multi-part key that defines a row uniquely.  I know through domain knowledge that the key is multi-part (e.g. reporting-date, and item number) and I can identify some columns that are clearly not in the key (e.g. revenue from sale).  What is an effective way to identify potential sets of columns that are natural keys, because they are not duplicated in the observed data?  I can get several days of logs in a few Gig, so python or sql could work.  This seems like an great application for a dictionary, but I am not sure how to approach this.     &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8055, 2, '2015-09-10 00:41:26.4', 2, NULL, NULL, '&lt;p&gt;I, like you, took it upon myself to change career paths into the growing field of data science. As a bit of background, I was working in a neuroscience research lab studying the effects of allelic variation on protein transport in rodent models of alzheimer''s disease and post-traumatic stress disorder. I was a competent R, bash, and Matlab hacker and knew a bit of C and Java. I had a change of heart when I started applying for neuroscience PhD programs.&lt;/p&gt;

&lt;p&gt;Over the next year, i took a few grad-level stats courses and got some of my programming skill back in order. Last year, I started an MS program for CS with the intent of 1) getting an job as a data scientist in industry 2) learning how to think algorithmically / mathematically about data 3) really sharpen my fundamental CS skills 4) have some fun in the process. &lt;/p&gt;

&lt;p&gt;I won''t list specific courses that helped, but the major winning topics for me were:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Algorithms &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;both fundamental algos

&lt;ul&gt;
&lt;li&gt;complexity analysis, sorting and searching, graph algorithms, dynamic programming, randomized algos, etc. &lt;a href="https://www.coursera.org/course/algo" rel="nofollow"&gt;Tim Roughgarden&lt;/a&gt; has a great course for this.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;data mining/massive dataset/stream algorithms

&lt;ul&gt;
&lt;li&gt;locality sensitive hashing, sketch algorithms, kd/ball trees, reservoir sampling, sliding window methods, etc. &lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Databases/Data streams:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;noSQL, SQL, hadoop, etc. Working with them forces you to start appreciating that most of the data you will work with absolutely doesn''t fit in memory and requires unique methods to not only extract information from it, but just to work with it and examine it. Learn how to use one by building a web scraper or data harvester to populate a database (or however else you want...)&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Machine learning&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Learning the fundamental methods in the field, e.g., tree-based methods, optimization, neural networks, svms, regression, markov chains, graphical methods,  ensemble methods, overfitting, regularization, clustering, k-means, knn, etc. As mentioned, &lt;a href="https://www.coursera.org/learn/machine-learning" rel="nofollow"&gt;Andrew Ng''s coursera class&lt;/a&gt; is probably the best open-source solution for this one.&lt;/li&gt;
&lt;li&gt;&lt;em&gt;EDIT&lt;/em&gt;: The Coursera course is good iff you supplement it with stuff from the real version (&lt;a href="https://www.youtube.com/watch?v=UzxYlbK2c7E&amp;amp;list=PLA89DCFA6ADACE599" rel="nofollow"&gt;lectures&lt;/a&gt;, &lt;a href="http://cs229.stanford.edu/" rel="nofollow"&gt;notes&lt;/a&gt;). I think the coursera class is a bit light and a nice overview of topics in ML, but doesn''t go nearly deep enough into the mechanics of those topics. Tom Mitchell''s &lt;a href="http://www.cs.cmu.edu/~tom/mlbook.html" rel="nofollow"&gt;book&lt;/a&gt; is also a good resource. &lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;data visualization&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;nothing terribly fancy is totally necessary, but knowing how to visualize multidimensional data is quite useful. Learn a good plotting package and perhaps experiment with other tech, like D3.js or mapping visualizations. If you get &lt;a href="http://www.nytimes.com/interactive/2014/12/29/us/year-in-interactive-storytelling.html#data-visualization" rel="nofollow"&gt;really good at this&lt;/a&gt;, you''ll probably have a great job forever.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;"Real-world" experience&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;I learned about as much in three months at a big web company doing data science than i did in the previous 1.5~ years  of grad school and self-study. This can be approximated by doing Kaggle competitions or the like, but honestly, data in the wild is &lt;em&gt;considerably&lt;/em&gt; harder to work with than most of what is on Kaggle (note that the microsoft malware detection project or some of the computer-vision projects are much more useful for learning to work with messy data that is also rather "big"). &lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Notice that i didn''t mention anything like "learn R it''s the best" or "learn python it''s way betterz than R". I use Python, C, MySQL, MongoDB, and R for most of my work and current research (though I really prefer the python ecosystem these days). I''m sure that this will change in the future. &lt;/p&gt;

&lt;p&gt;This falls a bit outside of your question, but perhaps the most critical thing about being an industry data scientist is the ability to work in a mostly unsupervised manner and communicate results/methodology clearly to a team of non-experts. Having a background in scientific research helps with this, as the questions you are trying to answer are difficult, often unstructured, and are often at the precipice of the knowledge ledge for your domain. My friends, acquaintances, and past coworkers working as data scientists in industry were nearly all ex-academic-path folks with MS or PhDs and at least a few publications under their belts. I absolutely do not believe this is a strict requirement and if i were in a hiring position, I''d never elect to filter out someone just because they didn''t have an advanced degree, but the industry job postings do seem to be trending towards requiring an MS/PhD or equivalent experience.&lt;/p&gt;

&lt;p&gt;Bear in mind, all of this comes from just some dude from nowhere who hasn''t been directly in the field all that long but whose transition seems to be going well. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5821, 1, '2015-05-18 11:50:35.383', 3, 236, 'Convolutional neural network for sparse one-hot representation', '&lt;p&gt;I have some basic features which I encoded in a &lt;strong&gt;one-hot vector&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Length of the feature vector equals to 400.
It is &lt;strong&gt;sparse&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;I saw that conv nets is applied to a dense feature vectors.&lt;/p&gt;

&lt;p&gt;Is there any problems to apply conv nets to a sparse feature vectors?&lt;/p&gt;
', 'neuralnetwork  deep-learning  convnet', NULL, '''/p'':32C,47C,61C,76C ''/strong'':31C,46C ''400'':41C ''appli'':55C,68C ''basic'':19C ''conv'':52C,69C ''convnet'':14B ''convolut'':1A ''deep'':12B ''deep-learn'':11B ''dens'':58C ''encod'':23C ''equal'':39C ''featur'':20C,37C,59C,74C ''hot'':8A,29C ''learn'':13B ''length'':34C ''net'':53C,70C ''network'':3A ''neural'':2A ''neuralnetwork'':10B ''one'':7A,28C ''one-hot'':6A,27C ''p'':15C,33C,48C,62C ''problem'':66C ''represent'':9A ''saw'':50C ''spars'':5A,45C,73C ''strong'':26C,44C ''vector'':30C,38C,60C,75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5833, 1, '2015-05-19 13:10:37.463', 3, 70, 'What is the best technique/algorithm to compare trees changes?', '&lt;p&gt;I have a problem I would like to solve using machine learning. I would like to use some sort of classification to know if a just added change in a tree data structure is "good" or is "bad".
Let''s say I have this tree:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;        (A) 
        / \
       /   \
     (B)   (C)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And I apply a change to it (a "good" change, so the algorithm should associate this change with the "good" changes). The updated tree would be like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;       (A)
       / \
      /   \
    (D)   (C)
    /
   /
 (B)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Added a certain node (D) above another node (B) would be classified as a "good" change.
So when I have the learner with the correct data, the algorithm should be able to know that if I add a node of type D above a node of type B, it is a "good" change.&lt;/p&gt;

&lt;p&gt;I would like to work with XML files that keeps the tree structure, a simple classifier like a naive bayes would not work, because it wouldn''t be able to recognise if a node is added above another one, it only would be able to know that a node has been added.&lt;/p&gt;

&lt;p&gt;I don''t know how which algorithm/technique should I use and I don''t know how should I pass the data to the learner, because the context in this scenario is important.&lt;/p&gt;

&lt;p&gt;I am new to machine learning, so sorry if this is a stupid question.&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/code'':66C,104C ''/p'':60C,97C,160C,214C,248C,264C,267C ''/pre'':67C,105C ''abl'':137C,190C,205C ''ad'':41C,107C,197C,213C ''add'':143C ''algorithm'':81C,134C ''algorithm/technique'':222C ''anoth'':113C,199C ''appli'':71C ''associ'':83C ''b'':64C,103C,115C,154C ''bad'':52C ''bay'':181C ''best'':4A ''c'':65C,102C ''certain'':109C ''chang'':9A,42C,73C,78C,85C,89C,122C,159C ''classif'':13B,35C ''classifi'':118C,177C ''code'':62C,99C ''compar'':7A ''context'':242C ''correct'':131C ''d'':101C,111C,148C ''data'':46C,132C,236C ''file'':169C ''good'':49C,77C,88C,121C,158C ''import'':247C ''keep'':171C ''know'':37C,139C,207C,219C,230C ''learn'':12B,26C,255C ''learner'':128C,239C ''let'':53C ''like'':21C,29C,95C,164C,178C ''machin'':11B,25C,254C ''machine-learn'':10B ''naiv'':180C ''new'':252C ''node'':110C,114C,145C,151C,195C,210C ''one'':200C ''p'':14C,68C,106C,161C,215C,249C,265C ''pass'':234C ''pre'':61C,98C ''problem'':18C ''question'':263C ''recognis'':192C ''say'':55C ''scenario'':245C ''simpl'':176C ''solv'':23C ''sorri'':257C ''sort'':33C ''structur'':47C,174C ''stupid'':262C ''technique/algorithm'':5A ''thank'':266C ''tree'':8A,45C,59C,92C,173C ''type'':147C,153C ''updat'':91C ''use'':24C,31C,225C ''work'':166C,184C ''would'':20C,28C,93C,116C,163C,182C,203C ''wouldn'':187C ''xml'':168C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5822, 1, '2015-05-18 13:07:17.983', 2, 30, 'Identifying sequences of behavioral interactions between multiple individuals', '&lt;p&gt;I''m wondering if anyone might have some novel insights as to the best way to analyze the following data.  It''s a problem I''ve been thinking about in the back of my mind for a while, so I thought that I''d ask here. I have data that look like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;     day event actor recipient
995    8   128     G         J
996    8   129     G         K
997    8   130     G         B
998    8   131     B         G
999    8   132     H         G
1000   8   133     G         H
1001   8   134     E         G
1002   8   135     G         J
1003   8   136     B         H
1004   8   137     G         H
1005   8   138     G         H
1006   8   139     B         J
1007   9     1     D         J
1008   9     2     A         J
1009   9     3     A         J
1010   9     4     H         J
1011   9     5     A         J
1012   9     6     D         H
1013   9     7     A         F
1014   9     8     D         J
1015   9     9     A         H
1016   9    10     D         J
1017   9    11     A         J
1018   9    12     F         J
1019   9    13     F         J
1020   9    14     F         H
1021   9    15     F         G
1022   9    16     F         H
1023   9    17     C         F
1024   9    18     C         G
1025   9    19     D         H
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What you see here is an extract of a R dataframe.  The first column being the rownumber of the df, then the four variables.    These data start at day1 and end at day 22.  There are between 13 and 215 ''events'' on each day - each event is a separate behavioral event. Higher number events occur later in time than earlier numbered events.  Individuals are in the ''actor'' and ''recipient'' variables.   The data are available in csv format &lt;a href="https://github.com/jalapic/exampledata/blob/master/demodf.csv" rel="nofollow"&gt;here&lt;/a&gt;: &lt;/p&gt;

&lt;p&gt;There are 11 individuals (A - K). One thing you''ll notice is that recipients tend to be lower down the alphabet, and actors tend to be higher up the alphabet.  &lt;/p&gt;

&lt;p&gt;A key question I''m interested in working out a methodology to address is to see if the likelihood of becoming an actor increases if an individual has recently been a recipient. You can see this on line 997 that G-B and then B-G occur followed by H-G and G-H.    An individual recipient doesn''t have to appear on the next line to count as having an increased likelihood of appearing as an actor - I''m interested in the decay in the probability of this occurring over events (but not continuing to the next day).&lt;/p&gt;

&lt;p&gt;Further, I don''t think this will be true for all individuals, so I am keen to test which individuals it is true for.     &lt;/p&gt;

&lt;p&gt;Lastly, I am interested to know if an individual who has recently been a recipient but is now an actor is paired with more often an individual of a higher or lower letter than themselves.&lt;/p&gt;

&lt;p&gt;I hope that these questions are clear and make sense.   I obviously don''t expect a full analysis.  But I would be keen to hear of ideas for this type of analysis.  I believe that examining some Markov processes may be useful but I am interested in hearing about other ideas.&lt;/p&gt;
', 'r  markov-process', NULL, '''/a'':317C ''/code'':229C ''/jalapic/exampledata/blob/master/demodf.csv'':313C ''/p'':67C,318C,350C,455C,481C,518C,571C ''/pre'':230C ''1'':136C ''10'':181C ''1000'':99C ''1001'':104C ''1002'':109C ''1003'':114C ''1004'':119C ''1005'':124C ''1006'':129C ''1007'':134C ''1008'':139C ''1009'':144C ''1010'':149C ''1011'':154C ''1012'':159C ''1013'':164C ''1014'':169C ''1015'':174C ''1016'':179C ''1017'':184C ''1018'':189C ''1019'':194C ''1020'':199C ''1021'':204C ''1022'':209C ''1023'':214C ''1024'':219C ''1025'':224C ''11'':186C,322C ''12'':191C ''128'':76C ''129'':81C ''13'':196C,269C ''130'':86C ''131'':91C ''132'':96C ''133'':101C ''134'':106C ''135'':111C ''136'':116C ''137'':121C ''138'':126C ''139'':131C ''14'':201C ''15'':206C ''16'':211C ''17'':216C ''18'':221C ''19'':226C ''2'':141C ''215'':271C ''22'':265C ''3'':146C ''4'':151C ''5'':156C ''6'':161C ''7'':166C ''8'':75C,80C,85C,90C,95C,100C,105C,110C,115C,120C,125C,130C,171C ''9'':135C,140C,145C,150C,155C,160C,165C,170C,175C,176C,180C,185C,190C,195C,200C,205C,210C,215C,220C,225C ''995'':74C ''996'':79C ''997'':84C,390C ''998'':89C ''999'':94C ''actor'':72C,298C,342C,374C,433C,502C ''address'':364C ''alphabet'':340C,349C ''analysi'':537C,551C ''analyz'':30C ''anyon'':18C ''appear'':417C,430C ''ask'':58C ''avail'':305C ''b'':88C,92C,117C,132C,394C,398C ''b-g'':397C ''back'':45C ''becom'':372C ''behavior'':4A,281C ''believ'':553C ''best'':27C ''c'':217C,222C ''clear'':526C ''code'':69C ''column'':245C ''continu'':450C ''count'':423C ''csv'':307C ''d'':57C,137C,162C,172C,182C,227C ''data'':33C,62C,257C,303C ''datafram'':242C ''day'':70C,264C,275C,454C ''day1'':260C ''decay'':439C ''df'':251C ''doesn'':413C ''e'':107C ''earlier'':291C ''end'':262C ''event'':71C,272C,277C,282C,285C,293C,447C ''examin'':555C ''expect'':534C ''extract'':238C ''f'':168C,192C,197C,202C,207C,212C,218C ''first'':244C ''follow'':32C,401C ''format'':308C ''four'':254C ''full'':536C ''g'':77C,82C,87C,93C,98C,102C,108C,112C,122C,127C,208C,223C,393C,399C,405C,408C ''g-b'':392C ''g-h'':407C ''github.com'':312C ''github.com/jalapic/exampledata/blob/master/demodf.csv'':311C ''h'':97C,103C,118C,123C,128C,152C,163C,178C,203C,213C,228C,404C,409C ''h-g'':403C ''hear'':544C,567C ''higher'':283C,346C,512C ''hope'':521C ''href'':310C ''idea'':546C,570C ''identifi'':1A ''increas'':375C,427C ''individu'':8A,294C,323C,378C,411C,468C,476C,491C,509C ''insight'':23C ''interact'':5A ''interest'':357C,436C,486C,565C ''j'':78C,113C,133C,138C,143C,148C,153C,158C,173C,183C,188C,193C,198C ''k'':83C,325C ''keen'':472C,542C ''key'':353C ''know'':488C ''last'':483C ''later'':287C ''letter'':515C ''like'':65C ''likelihood'':370C,428C ''line'':389C,421C ''ll'':329C ''look'':64C ''lower'':337C,514C ''m'':15C,356C,435C ''make'':528C ''markov'':11B,557C ''markov-process'':10B ''may'':559C ''methodolog'':362C ''might'':19C ''mind'':48C ''multipl'':7A ''next'':420C,453C ''nofollow'':315C ''notic'':330C ''novel'':22C ''number'':284C,292C ''obvious'':531C ''occur'':286C,400C,445C ''often'':507C ''one'':326C ''p'':13C,231C,319C,351C,456C,482C,519C ''pair'':504C ''pre'':68C ''probabl'':442C ''problem'':37C ''process'':12B,558C ''question'':354C,524C ''r'':9B,241C ''recent'':380C,494C ''recipi'':73C,300C,333C,383C,412C,497C ''rel'':314C ''rownumb'':248C ''see'':234C,367C,386C ''sens'':529C ''separ'':280C ''sequenc'':2A ''start'':258C ''tend'':334C,343C ''test'':474C ''thing'':327C ''think'':41C,461C ''thought'':54C ''time'':289C ''true'':465C,479C ''type'':549C ''use'':561C ''variabl'':255C,301C ''ve'':39C ''way'':28C ''wonder'':16C ''work'':359C ''would'':540C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5823, 1, '2015-05-18 15:10:05.623', 2, 83, 'How do I cluster data that is a mix of text &amp; categorical data?', '&lt;p&gt;I have a set of strings, each also has soem categorical information associated with it. The categorical information isn''t always great though, so I need to cluster the messages based on the text content &amp;amp; the categories. What is the best way to do this? &lt;/p&gt;
', 'clustering  text-mining  k-means  categorical-data', NULL, '''/p'':71C ''also'':32C ''alway'':45C ''amp'':60C ''associ'':37C ''base'':55C ''best'':66C ''categor'':12A,22B,35C,41C ''categori'':62C ''categorical-data'':21B ''cluster'':4A,14B,52C ''content'':59C ''data'':5A,13A,23B ''great'':46C ''inform'':36C,42C ''isn'':43C ''k'':19B ''k-mean'':18B ''mean'':20B ''messag'':54C ''mine'':17B ''mix'':9A ''need'':50C ''p'':24C ''set'':28C ''soem'':34C ''string'':30C ''text'':11A,16B,58C ''text-min'':15B ''though'':47C ''way'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5825, 2, '2015-05-18 17:50:19.717', 5, NULL, NULL, '&lt;p&gt;I would not apply convolutional neural networks to your problem (at least from what I can gather from the description).&lt;/p&gt;

&lt;p&gt;Convolutional nets'' strengths and weaknesses are related to a core assumption in the model class: Translating patterns of features in a regular way either has a minor impact on the outcome, or has a specific useful meaning. So a pattern &lt;code&gt;1 0 1&lt;/code&gt; seen in features 9,10,11 is similar in some way to the same pattern seen in features 15,16,17. Having this assumption built in to the model allows you to train a network with far fewer free parameters when dealing with e.g. image data, where this is a key property of data captured by scanners and cameras. &lt;/p&gt;

&lt;p&gt;With one-hot encoding of features, you assign a feature vector index from a value or category essentially at random (via some hashing function). There is no meaning to translations between indices of the feature vectors. The patterns &lt;code&gt;0 0 1 0 1 0 0&lt;/code&gt;  and &lt;code&gt;0 0 0 1 0 1 0&lt;/code&gt; can represent entirely different things, and any associations between them are purely by chance. You can treat a sparse one-hot encoding as an image if you wish, but there is no good reason to do so, and models that assume translations can be made whilst preserving meaning will not do well.&lt;/p&gt;

&lt;p&gt;For such a small sparse feature vector, assuming you want to try a neural network model, use a simple fully-connected network.&lt;/p&gt;
', NULL, NULL, '''/code'':68C,177C,187C ''/p'':22C,128C,241C,266C ''0'':66C,170C,171C,173C,175C,176C,180C,181C,182C,184C,186C ''1'':65C,67C,172C,174C,183C,185C ''10'':73C ''11'':74C ''15'':87C ''16'':88C ''17'':89C ''9'':72C ''allow'':98C ''appli'':5C ''assign'':138C ''associ'':195C ''assum'':229C,250C ''assumpt'':34C,92C ''built'':93C ''camera'':127C ''captur'':123C ''categori'':147C ''chanc'':201C ''class'':38C ''code'':64C,169C,179C ''connect'':264C ''convolut'':6C,24C ''core'':33C ''data'':114C,122C ''deal'':110C ''descript'':21C ''differ'':191C ''e.g'':112C ''either'':47C ''encod'':134C,210C ''entir'':190C ''essenti'':148C ''far'':105C ''featur'':42C,71C,86C,136C,140C,165C,248C ''fewer'':106C ''free'':107C ''fulli'':263C ''fully-connect'':262C ''function'':154C ''gather'':18C ''good'':221C ''hash'':153C ''hot'':133C,209C ''imag'':113C,213C ''impact'':51C ''index'':142C ''indic'':162C ''key'':119C ''least'':13C ''made'':233C ''mean'':60C,158C,236C ''minor'':50C ''model'':37C,97C,227C,258C ''net'':25C ''network'':8C,103C,257C,265C ''neural'':7C,256C ''one'':132C,208C ''one-hot'':131C,207C ''outcom'':54C ''p'':1C,23C,129C,242C ''paramet'':108C ''pattern'':40C,63C,83C,168C ''preserv'':235C ''problem'':11C ''properti'':120C ''pure'':199C ''random'':150C ''reason'':222C ''regular'':45C ''relat'':30C ''repres'':189C ''scanner'':125C ''seen'':69C,84C ''similar'':76C ''simpl'':261C ''small'':246C ''spars'':206C,247C ''specif'':58C ''strength'':26C ''thing'':192C ''train'':101C ''translat'':39C,160C,230C ''treat'':204C ''tri'':254C ''use'':59C,259C ''valu'':145C ''vector'':141C,166C,249C ''via'':151C ''want'':252C ''way'':46C,79C ''weak'':28C ''well'':240C ''whilst'':234C ''wish'':216C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5827, 2, '2015-05-18 19:01:52.377', 3, NULL, NULL, '&lt;p&gt;First and foremost, there is no &lt;em&gt;best&lt;/em&gt; way to do this task in general. It will require some creativity and problem-solving&lt;/p&gt;

&lt;p&gt;Here is one potential idea. Clustering involves some kind of similarity metric. You can compute a similarity between text documents (after some term weighting like TF-IDF you could use the cosine similarity for example). You can also compute a similarity between objects with categorical data. Matching coefficients are good option for this. For example, &lt;a href="http://en.wikipedia.org/wiki/Simple_matching_coefficient" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Simple_matching_coefficient&lt;/a&gt;  is about as basic as it gets, but there are many other measures of similarity for categorical data that you can find with a simple google search.&lt;/p&gt;

&lt;p&gt;So if I have one similarity matrix &lt;strong&gt;A&lt;/strong&gt; that gives the similarity based on the text, and one similarity matrix &lt;strong&gt;B&lt;/strong&gt; that gives the similarity based on the categorical attributes, then I could take some weighted combination of &lt;strong&gt;A&lt;/strong&gt; and &lt;strong&gt;B&lt;/strong&gt; and use it as an overall similarity matrix for clustering. Perhaps a convex combination would work well and give you some flexibility to choose which measure of similarity you want to weight more heavily (since it sounds like the categorical information might not be as good):&lt;/p&gt;

&lt;p&gt;choose some parameter $0\leq \alpha\leq 1$ and compute an overall similarity matrix &lt;strong&gt;S&lt;/strong&gt; as&lt;/p&gt;

&lt;p&gt;$$\textbf{S} = \alpha \textbf{A} + (1-\alpha)\textbf{B}$$&lt;/p&gt;

&lt;p&gt;Then you could use any number of clustering algorithms using the similarity information. For example, $k$-means should work just fine on a similarity matrix as input. Any type of graph/spectral clustering algorithm is also well suited for this type of input.&lt;/p&gt;
', NULL, NULL, '''/em'':10C ''/p'':27C,122C,219C,239C,250C,294C ''/strong'':133C,148C,168C,172C,237C ''/wiki/simple_matching_coefficient'':89C ''/wiki/simple_matching_coefficient&lt;/a&gt;'':94C ''0'':224C ''1'':228C,246C ''algorithm'':260C,284C ''alpha'':226C,243C,247C ''also'':67C,286C ''attribut'':157C ''b'':147C,171C,249C ''base'':138C,153C ''basic'':98C ''best'':9C ''categor'':74C,111C,156C,212C ''choos'':196C,221C ''cluster'':34C,182C,259C,283C ''coeffici'':77C ''combin'':164C,186C ''comput'':43C,68C,230C ''convex'':185C ''cosin'':61C ''could'':58C,160C,254C ''creativ'':22C ''data'':75C,112C ''document'':48C ''em'':8C ''en.wikipedia.org'':88C,93C ''en.wikipedia.org/wiki/simple_matching_coefficient'':87C ''en.wikipedia.org/wiki/simple_matching_coefficient&lt;/a&gt;'':92C ''exampl'':64C,84C,266C ''find'':116C ''fine'':272C ''first'':2C ''flexibl'':194C ''foremost'':4C ''general'':17C ''get'':101C ''give'':135C,150C,191C ''good'':79C,218C ''googl'':120C ''graph/spectral'':282C ''heavili'':206C ''href'':86C ''idea'':33C ''idf'':56C ''inform'':213C,264C ''input'':278C,293C ''involv'':35C ''k'':267C ''kind'':37C ''leq'':225C,227C ''like'':53C,210C ''mani'':105C ''match'':76C ''matrix'':130C,145C,180C,234C,276C ''mean'':268C ''measur'':107C,198C ''metric'':40C ''might'':214C ''nofollow'':91C ''number'':257C ''object'':72C ''one'':31C,128C,143C ''option'':80C ''overal'':178C,232C ''p'':1C,28C,123C,220C,240C,251C ''paramet'':223C ''perhap'':183C ''potenti'':32C ''problem'':25C ''problem-solv'':24C ''rel'':90C ''requir'':20C ''search'':121C ''similar'':39C,45C,62C,70C,109C,129C,137C,144C,152C,179C,200C,233C,263C,275C ''simpl'':119C ''sinc'':207C ''solv'':26C ''sound'':209C ''strong'':131C,146C,166C,170C,235C ''suit'':288C ''take'':161C ''task'':15C ''term'':51C ''text'':47C,141C ''textbf'':241C,244C,248C ''tf'':55C ''tf-idf'':54C ''type'':280C,291C ''use'':59C,174C,255C,261C ''want'':202C ''way'':11C ''weight'':52C,163C,204C ''well'':189C,287C ''work'':188C,270C ''would'':187C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5828, 2, '2015-05-18 19:07:45.15', 1, NULL, NULL, '&lt;p&gt;Graduate degrees in data science or business analytics provide a fast-track toward a job these days, even more so than a traditional statistics degree. Check out the graduate programs and what they require in terms of prerequisites. Some may not even require Calculus sequence, only a good foundation of stat and linear algebra. &lt;/p&gt;

&lt;p&gt;A bachelor''s degree is necessarily going to waste your days with core courses like British Literature (no offense to english majors, but given your goals that time would be wasted).&lt;/p&gt;
', NULL, NULL, '''/p'':57C,90C ''algebra'':56C ''analyt'':9C ''bachelor'':60C ''british'':74C ''busi'':8C ''calculus'':46C ''check'':28C ''core'':71C ''cours'':72C ''data'':5C ''day'':19C,69C ''degre'':3C,27C,62C ''english'':79C ''even'':20C,44C ''fast'':13C ''fast-track'':12C ''foundat'':51C ''given'':82C ''go'':65C ''goal'':84C ''good'':50C ''graduat'':2C,31C ''job'':17C ''like'':73C ''linear'':55C ''literatur'':75C ''major'':80C ''may'':42C ''necessarili'':64C ''offens'':77C ''p'':1C,58C ''prerequisit'':40C ''program'':32C ''provid'':10C ''requir'':36C,45C ''scienc'':6C ''sequenc'':47C ''stat'':53C ''statist'':26C ''term'':38C ''time'':86C ''toward'':15C ''track'':14C ''tradit'':25C ''wast'':67C,89C ''would'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5832, 2, '2015-05-19 09:29:02.81', 3, NULL, NULL, '&lt;p&gt;Data science is a multi discipline subject.
In addition to math you need some programming skills and ideally - understanding of the domain where you are going to solve problems.&lt;/p&gt;

&lt;p&gt;You can start from online-courses and some introduction literature.
Look at the answers at current site:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/334/what-do-you-think-of-data-science-certifications"&gt;Data Science Sertifications&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/313/books-about-the-science-in-data-science"&gt;Books about Data Science&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/14/is-data-science-the-same-as-data-mining"&gt;Data Science vs Data Mining&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/159/is-data-science-just-a-trend-or-is-a-long-term-concept"&gt;Data Science is a trend or a long term concept&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/234/data-science-ph-d-program-what-do-you-think"&gt;Ph.D. in Data Science?&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/808/statistics-computer-science-data-science"&gt;Statistics + Computer Science = Data Science?&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/739/starting-my-career-as-data-scientist-is-software-engineering-experience-require"&gt;Starting career as Data Scientist, is Software Engineering experience required?&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/1216/career-switch-to-big-data-analytics"&gt;Career switch to Big Data Analytics&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/tagged/career"&gt;And more about career...&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I can''t resist from placing this illustration for the millionth time:
&lt;img src="http://i.stack.imgur.com/wpkyD.png" alt="enter image description here"&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':62C,74C,87C,105C,117C,130C,148C,162C,174C ''/li'':63C,75C,88C,106C,118C,131C,149C,163C,175C ''/p'':31C,51C,200C ''/questions/1216/career-switch-to-big-data-analytics'':155C ''/questions/14/is-data-science-the-same-as-data-mining'':81C ''/questions/159/is-data-science-just-a-trend-or-is-a-long-term-concept'':94C ''/questions/234/data-science-ph-d-program-what-do-you-think'':112C ''/questions/313/books-about-the-science-in-data-science'':69C ''/questions/334/what-do-you-think-of-data-science-certifications'':58C ''/questions/739/starting-my-career-as-data-scientist-is-software-engineering-experience-require'':137C ''/questions/808/statistics-computer-science-data-science'':124C ''/questions/tagged/career'':169C ''/ul'':176C ''/wpkyd.png'':194C ''addit'':10C ''alt'':195C ''analyt'':161C ''answer'':47C ''big'':159C ''book'':70C ''career'':139C,156C,173C ''comput'':126C ''concept'':104C ''cours'':39C ''current'':49C ''data'':2C,59C,72C,82C,85C,95C,115C,128C,141C,160C ''datascience.stackexchange.com'':57C,68C,80C,93C,111C,123C,136C,154C,168C ''datascience.stackexchange.com/questions/1216/career-switch-to-big-data-analytics'':153C ''datascience.stackexchange.com/questions/14/is-data-science-the-same-as-data-mining'':79C ''datascience.stackexchange.com/questions/159/is-data-science-just-a-trend-or-is-a-long-term-concept'':92C ''datascience.stackexchange.com/questions/234/data-science-ph-d-program-what-do-you-think'':110C ''datascience.stackexchange.com/questions/313/books-about-the-science-in-data-science'':67C ''datascience.stackexchange.com/questions/334/what-do-you-think-of-data-science-certifications'':56C ''datascience.stackexchange.com/questions/739/starting-my-career-as-data-scientist-is-software-engineering-experience-require'':135C ''datascience.stackexchange.com/questions/808/statistics-computer-science-data-science'':122C ''datascience.stackexchange.com/questions/tagged/career'':167C ''descript'':198C ''disciplin'':7C ''domain'':23C ''engin'':145C ''enter'':196C ''experi'':146C ''go'':27C ''href'':55C,66C,78C,91C,109C,121C,134C,152C,166C ''i.stack.imgur.com'':193C ''i.stack.imgur.com/wpkyd.png'':192C ''ideal'':19C ''illustr'':185C ''imag'':197C ''img'':190C ''introduct'':42C ''li'':53C,64C,76C,89C,107C,119C,132C,150C,164C ''literatur'':43C ''long'':102C ''look'':44C ''math'':12C ''millionth'':188C ''mine'':86C ''multi'':6C ''need'':14C ''onlin'':38C ''online-cours'':37C ''p'':1C,32C,177C ''ph.d'':113C ''place'':183C ''problem'':30C ''program'':16C ''requir'':147C ''resist'':181C ''scienc'':3C,60C,73C,83C,96C,116C,127C,129C ''scientist'':142C ''sertif'':61C ''site'':50C ''skill'':17C ''softwar'':144C ''solv'':29C ''src'':191C ''start'':35C,138C ''statist'':125C ''subject'':8C ''switch'':157C ''term'':103C ''time'':189C ''trend'':99C ''ul'':52C ''understand'':20C ''vs'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6450, 2, '2015-07-14 04:24:44.473', 0, NULL, NULL, '&lt;p&gt;In situations where a particular class is really a minority, I suggest using rare category detection. In this particular case of fraud/non-fraud, fraud is a rare category. Its an active field of research - Refere to &lt;a href="https://scholar.google.com/scholar?q=Rare%20category%20detection&amp;amp;hl=en&amp;amp;as_sdt=0&amp;amp;as_vis=1&amp;amp;oi=scholart&amp;amp;sa=X&amp;amp;ved=0CBsQgQMwAGoVChMI3P2tg-LZxgIV0DCICh0jVAtS" rel="nofollow" title="Rare Category Detection"&gt;Rare Category Detection&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':51C ''/p'':52C ''/scholar?q=rare%20category%20detection&amp;amp;hl=en&amp;amp;as_sdt=0&amp;amp;as_vis=1&amp;amp;oi=scholart&amp;amp;sa=x&amp;amp;ved=0cbsqgqmwagovchmi3p2tg-lzxgiv0dcich0jvats'':41C ''activ'':31C ''case'':21C ''categori'':16C,28C,46C,49C ''class'':7C ''detect'':17C,47C,50C ''field'':32C ''fraud'':24C ''fraud/non-fraud'':23C ''href'':38C ''minor'':11C ''nofollow'':43C ''p'':1C ''particular'':6C,20C ''rare'':15C,27C,45C,48C ''realli'':9C ''refer'':35C ''rel'':42C ''research'':34C ''scholar.google.com'':40C ''scholar.google.com/scholar?q=rare%20category%20detection&amp;amp;hl=en&amp;amp;as_sdt=0&amp;amp;as_vis=1&amp;amp;oi=scholart&amp;amp;sa=x&amp;amp;ved=0cbsqgqmwagovchmi3p2tg-lzxgiv0dcich0jvats'':39C ''situat'':3C ''suggest'':13C ''titl'':44C ''use'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5834, 2, '2015-05-19 15:46:55.28', 3, NULL, NULL, '&lt;p&gt;Here is an example of how to get pandas and sklearn to play nice &lt;/p&gt;

&lt;p&gt;say you have 2 columns that are both strings and you wish to vectorize - but you have no idea which vectorization params will result in the best downstream performance.&lt;/p&gt;

&lt;p&gt;create the vectorizer &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;to_vect = Pipeline([(''vect'',CountVectorizer(min_df =1,max_df=.9,ngram_range=(1,2),max_features=1000)),
                    (''tfidf'', TfidfTransformer())])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;create the DataFrameMapper obj.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;full_mapper = DataFrameMapper([
        (''col_name1'', to_vect),
        (''col_name2'',to_vect)
    ])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;this is the full pipeline&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;full_pipeline  = Pipeline([(''mapper'',full_mapper),(''clf'', SGDClassifier(n_iter=15, warm_start=True))])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;define the params you want the scan to consider&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;full_params = {''clf__alpha'': [1e-2,1e-3,1e-4],
                   ''clf__loss'':[''modified_huber'',''hinge''],
                   ''clf__penalty'':[''l2'',''l1''],
                   ''mapper__features'':[[(''col_name1'',deepcopy(to_vect)),
                                        (''col_name2'',deepcopy(to_vect))],
                                       [(''col_name1'',deepcopy(to_vect).set_params(vect__analyzer= ''char_wb'')),
                                        (''col_name2'',deepcopy(to_vect))]]}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Thats it! - note however that mapper_features are a single item in this dictionary - so use a for loop or itertools.product to generate a FLAT list of all to_vect options you wish to consider - but that is a separate task outside the scope of the question.&lt;/p&gt;

&lt;p&gt;Go on to create the optimal classifier or whatever else your pipeline ends with&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;gs_clf = GridSearchCV(full_pipe, full_params, n_jobs=-1)
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5835, 2, '2015-05-19 16:59:34.567', 1, NULL, NULL, '&lt;p&gt;You could do this a few ways...&lt;/p&gt;

&lt;p&gt;1) write a script that does the following.&lt;/p&gt;

&lt;p&gt;-pick a table,&lt;/p&gt;

&lt;p&gt;-get number of rows in the table, can do this as you go. (+=1 in loop)&lt;/p&gt;

&lt;p&gt;-select several fields(columns) in table that you think may form the key,&lt;/p&gt;

&lt;p&gt;-create empty Set()&lt;/p&gt;

&lt;p&gt;-burn through the file row by row, for each row grab the target fields and construct a string key str(field1)+''_''+str(field2)..etc.  Add this key to your set.&lt;/p&gt;

&lt;p&gt;-when you''re done going through all rows of a table, check len(set).  If the key you picked is good then len(set) will equal # rows.&lt;/p&gt;

&lt;p&gt;2) Another way, and this will depend on what you want to do with your data and how often you will access it...is to import it into a sql database.  I usually use mysql. Once your data is in:&lt;/p&gt;

&lt;p&gt;select count(*) from table_name;  will give you rows in table&lt;/p&gt;

&lt;p&gt;select count( distinct field1, field2, field3) from table;  gives number of distinct combos of field1+field2+field3&lt;/p&gt;

&lt;p&gt;if the two selects give the same number then you have a key that works. It may not guarantee a join-key but it will be unique to that table and will help the linking process. &lt;/p&gt;

&lt;p&gt;3) many people highly advocate the use of pandas. If you get the data imported into arrays you can create a dataframe and do some queries similar to SQL.  I''m not super familiar with pandas so I can''t give an example.&lt;/p&gt;

&lt;p&gt;If it''s a one-off, I''d make a python script.  But if you think you''ll be exploring the data further, spending the time to put the data in a database could be useful.&lt;/p&gt;
', NULL, NULL, '''/p'':9C,19C,24C,42C,57C,62C,94C,123C,165C,178C,197C,236C,281C,320C ''1'':11C,39C ''2'':125C ''3'':238C ''access'':146C ''add'':88C ''advoc'':242C ''anoth'':126C ''array'':254C ''burn'':64C ''check'':107C ''column'':47C ''combo'':192C ''construct'':79C ''could'':3C,317C ''count'':168C,181C ''creat'':59C,257C ''d'':291C ''data'':140C,162C,251C,305C,313C ''databas'':155C,316C ''datafram'':259C ''depend'':131C ''distinct'':182C,191C ''done'':99C ''empti'':60C ''equal'':121C ''etc'':87C ''exampl'':280C ''explor'':303C ''familiar'':271C ''field'':46C,77C ''field1'':84C,183C,194C ''field2'':86C,184C,195C ''field3'':185C,196C ''file'':67C ''follow'':18C ''form'':54C ''get'':26C,249C ''give'':173C,188C,203C,278C ''go'':38C,100C ''good'':116C ''grab'':74C ''guarante'':217C ''help'':232C ''high'':241C ''import'':150C,252C ''join'':220C ''join-key'':219C ''key'':56C,82C,90C,112C,211C,221C ''len'':108C,118C ''link'':234C ''ll'':301C ''loop'':41C ''m'':268C ''make'':292C ''mani'':239C ''may'':53C,215C ''mysql'':159C ''name'':171C ''number'':27C,189C,206C ''often'':143C ''one'':288C ''one-off'':287C ''p'':1C,10C,20C,25C,43C,58C,63C,95C,124C,166C,179C,198C,237C,282C ''panda'':246C,273C ''peopl'':240C ''pick'':21C,114C ''process'':235C ''put'':311C ''python'':294C ''queri'':263C ''re'':98C ''row'':29C,68C,70C,73C,103C,122C,175C ''script'':14C,295C ''select'':44C,167C,180C,202C ''set'':61C,93C,109C,119C ''sever'':45C ''similar'':264C ''spend'':307C ''sql'':154C,266C ''str'':83C,85C ''string'':81C ''super'':270C ''tabl'':23C,32C,49C,106C,170C,177C,187C,229C ''target'':76C ''think'':52C,299C ''time'':309C ''two'':201C ''uniqu'':226C ''use'':158C,244C,319C ''usual'':157C ''want'':135C ''way'':8C,127C ''work'':213C ''write'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5836, 2, '2015-05-19 17:13:18.667', 3, NULL, NULL, '&lt;p&gt;Although I agree with Neil Slater''s response, you should keep a couple of things in mind.&lt;/p&gt;

&lt;p&gt;1) "you never know!" In data exploration, you never know what you may find.  If you have a ton of data, perhaps playing around with a 20x20 conv net will give you some decent results.  Of course, it would be helpful if there are more than just a few features for it to learn...if your 400 length vector is the result of one-hotting 4 different features then it''s probably safe to say that a conv net won''t give you much.&lt;/p&gt;

&lt;p&gt;2)  If you''re looking for a reason to implement a conv net, then go for it. Even if your accuracy metrics are terrible you at least get to learn how to create your net, train, and predict using your own data...one cannot underestimate this learning experience!  So much more valuable that running yet another mnist example out of the box.&lt;/p&gt;

&lt;p&gt;3)  Comparison. Make a regular net and a conv net...then you get to compare the two. Not only that, compare it to a Random Forest, logistic regression, etc. etc.. Do this enough times and you start to develop intuition. &lt;/p&gt;

&lt;p&gt;I say do it! (unless somebody is paying you...in which case try the regular NN first)&lt;/p&gt;
', NULL, NULL, '''..'':202C ''/p'':19C,106C,170C,213C,232C ''1'':21C ''2'':108C ''20x20'':47C ''3'':172C ''4'':87C ''400'':77C ''accuraci'':128C ''agre'':4C ''although'':2C ''anoth'':163C ''around'':44C ''box'':169C ''cannot'':151C ''case'':226C ''compar'':186C,192C ''comparison'':173C ''conv'':48C,99C,119C,180C ''coupl'':14C ''cours'':57C ''creat'':140C ''data'':26C,41C,149C ''decent'':54C ''develop'':211C ''differ'':88C ''enough'':205C ''etc'':200C,201C ''even'':125C ''exampl'':165C ''experi'':155C ''explor'':27C ''featur'':70C,89C ''find'':34C ''first'':231C ''forest'':197C ''get'':135C,184C ''give'':51C,103C ''go'':122C ''help'':61C ''hot'':86C ''implement'':117C ''intuit'':212C ''keep'':12C ''know'':24C,30C ''learn'':74C,137C,154C ''least'':134C ''length'':78C ''logist'':198C ''look'':112C ''make'':174C ''may'':33C ''metric'':129C ''mind'':18C ''mnist'':164C ''much'':105C,157C ''neil'':6C ''net'':49C,100C,120C,142C,177C,181C ''never'':23C,29C ''nn'':230C ''one'':85C,150C ''one-hot'':84C ''p'':1C,20C,107C,171C,214C ''pay'':222C ''perhap'':42C ''play'':43C ''predict'':145C ''probabl'':93C ''random'':196C ''re'':111C ''reason'':115C ''regress'':199C ''regular'':176C,229C ''respons'':9C ''result'':55C,82C ''run'':161C ''safe'':94C ''say'':96C,216C ''slater'':7C ''somebodi'':220C ''start'':209C ''terribl'':131C ''thing'':16C ''time'':206C ''ton'':39C ''train'':143C ''tri'':227C ''two'':188C ''underestim'':152C ''unless'':219C ''use'':146C ''valuabl'':159C ''vector'':79C ''won'':101C ''would'':59C ''yet'':162C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5837, 2, '2015-05-19 20:32:50.033', 1, NULL, NULL, '&lt;p&gt;I like &lt;a href="http://blog.rstudio.org/2014/11/24/rvest-easy-web-scraping-with-r/" rel="nofollow"&gt;rvest&lt;/a&gt; in combination with the SelectorGadget chrome plug-in for selecting relevant sections.&lt;/p&gt;

&lt;p&gt;I''ve used rvest and built small scripts to paginate through forums by:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Look for the "Page n Of m" object &lt;/li&gt;
&lt;li&gt;Extract m &lt;/li&gt;
&lt;li&gt;Based on the page structure,  build a list of links from 1 to m (e.g.
    www.sample.com/page1)  &lt;/li&gt;
&lt;li&gt;Iterate the scraper through the full list of
    links&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/2014/11/24/rvest-easy-web-scraping-with-r/'':8C ''/a'':12C ''/li'':52C,56C,76C,87C ''/ol'':88C ''/p'':26C,41C ''/page1)'':75C ''1'':69C ''base'':58C ''blog.rstudio.org'':7C ''blog.rstudio.org/2014/11/24/rvest-easy-web-scraping-with-r/'':6C ''build'':63C ''built'':33C ''chrome'':18C ''combin'':14C ''e.g'':72C ''extract'':54C ''forum'':39C ''full'':83C ''href'':5C ''iter'':78C ''li'':43C,53C,57C,77C ''like'':3C ''link'':67C,86C ''list'':65C,84C ''look'':44C ''m'':50C,55C,71C ''n'':48C ''nofollow'':10C ''object'':51C ''ol'':42C ''p'':1C,27C ''page'':47C,61C ''pagin'':37C ''plug'':20C ''plug-in'':19C ''rel'':9C ''relev'':24C ''rvest'':11C,31C ''scraper'':80C ''script'':35C ''section'':25C ''select'':23C ''selectorgadget'':17C ''small'':34C ''structur'':62C ''use'':30C ''ve'':29C ''www.sample.com'':74C ''www.sample.com/page1)'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5838, 1, '2015-05-19 23:34:25.213', 3, 426, 'I am trying to classify/cluster users profile but don''t know how with my attributes', '&lt;p&gt;I have a dataset about users purchasing product from website. The attributes I have are  user id, region(state) of the user, the categories id of product, keywords id of product, keywords id of website, sales amount spent of the product. The goal is to use the information of product and website to identity who the users are, such as "male young gamer";"stay at home mom". I attached a sample picture as below.&lt;img src="http://i.stack.imgur.com/S7OSa.png" alt="enter image description here"&gt;
There are totally 1940 unique categories and 13845 unique keywords for products. For the website, there are 13063 unique keywords. The whole dataset is huge as that''s the daily logging data.&lt;/p&gt;

&lt;p&gt;I am thinking of clustering, as those are unsupervised, but those id are ordered number having no numeric meaning, then I don''t know how to apply the algorithm. I also think of classification if I add a column of class based on the sales amount of product purchased. I think clustering is more preferred. I don''t know what algorithm I should use for this case as the dimensions of the keywords id could be more than 10000 (each product could have many keywords, so does website). I need to use Spark for this project. Can anyone help me out with some ideas,suggestions? Thank you so much!&lt;/p&gt;
', 'machine-learning  data-mining  classification  clustering', NULL, '''/p'':141C,252C ''/s7osa.png'':103C ''10000'':221C ''13063'':126C ''13845'':116C ''1940'':112C ''add'':179C ''algorithm'':171C,203C ''also'':173C ''alt'':104C ''amount'':61C,188C ''anyon'':240C ''appli'':169C ''attach'':93C ''attribut'':15A,36C ''base'':184C ''case'':209C ''categori'':48C,114C ''class'':183C ''classif'':22B,176C ''classify/cluster'':5A ''cluster'':23B,147C,194C ''column'':181C ''could'':217C,224C ''daili'':138C ''data'':20B,140C ''data-min'':19B ''dataset'':28C,131C ''descript'':107C ''dimens'':212C ''enter'':105C ''gamer'':87C ''goal'':67C ''help'':241C ''home'':90C ''huge'':133C ''i.stack.imgur.com'':102C ''i.stack.imgur.com/s7osa.png'':101C ''id'':41C,49C,53C,57C,154C,216C ''idea'':246C ''ident'':78C ''imag'':106C ''img'':99C ''inform'':72C ''keyword'':52C,56C,118C,128C,215C,227C ''know'':11A,166C,201C ''learn'':18B ''log'':139C ''machin'':17B ''machine-learn'':16B ''male'':85C ''mani'':226C ''mean'':161C ''mine'':21B ''mom'':91C ''much'':251C ''need'':232C ''number'':157C ''numer'':160C ''order'':156C ''p'':24C,142C ''pictur'':96C ''prefer'':197C ''product'':32C,51C,55C,65C,74C,120C,190C,223C ''profil'':7A ''project'':238C ''purchas'':31C,191C ''region'':42C ''sale'':60C,187C ''sampl'':95C ''spark'':235C ''spent'':62C ''src'':100C ''state'':43C ''stay'':88C ''suggest'':247C ''thank'':248C ''think'':145C,174C,193C ''total'':111C ''tri'':3A ''uniqu'':113C,117C,127C ''unsupervis'':151C ''use'':70C,206C,234C ''user'':6A,30C,40C,46C,81C ''websit'':34C,59C,76C,123C,230C ''whole'':130C ''young'':86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5846, 1, '2015-05-20 17:41:15.18', 0, 73, 'Ideas for next step of Machine Learning', '&lt;p&gt;I have completely followed the machine learning course on &lt;a href="https://www.coursera.org/learn/machine-learning" rel="nofollow"&gt;coursera Machine Learning by professor Andrew Ng&lt;/a&gt; &lt;br&gt;&lt;/p&gt;

&lt;p&gt;Now I want to put my knowledge to action. Some ideas that I have include : &lt;/p&gt;

&lt;p&gt;-Voice synthesis
 -Voice recognition&lt;/p&gt;

&lt;p&gt;But since the course did not focus specifically on application of machine learning in these domains, could some one point me to some other course or books that can get me started.&lt;/p&gt;
', 'machine-learning  algorithms  beginner', NULL, '''/a'':37C ''/learn/machine-learning'':27C ''/p'':39C,56C,62C,96C ''action'':49C ''algorithm'':11B ''andrew'':35C ''applic'':73C ''beginn'':12B ''book'':90C ''br'':38C ''complet'':16C ''could'':80C ''cours'':21C,67C,88C ''coursera'':30C ''domain'':79C ''focus'':70C ''follow'':17C ''get'':93C ''href'':24C ''idea'':1A,51C ''includ'':55C ''knowledg'':47C ''learn'':7A,10B,20C,32C,76C ''machin'':6A,9B,19C,31C,75C ''machine-learn'':8B ''next'':3A ''ng'':36C ''nofollow'':29C ''one'':82C ''p'':13C,40C,57C,63C ''point'':83C ''professor'':34C ''put'':45C ''recognit'':61C ''rel'':28C ''sinc'':65C ''specif'':71C ''start'':95C ''step'':4A ''synthesi'':59C ''voic'':58C,60C ''want'':43C ''www.coursera.org'':26C ''www.coursera.org/learn/machine-learning'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5842, 1, '2015-05-20 11:04:18.223', 1, 19, 'Index for efficient argmax(w.x) query ~ 20d', '&lt;p&gt;I''m looking for a spatial index that can efficiently find the most extreme n points in a certain direction, i.e. for a given w, find x[0:n] in the dataset where x0 gives the largest value of w.x and x1 the second largest value of w.x, etc... . Is there a name for this type of query? What would be an efficient data structure to use? x might have around 20 dimensions.&lt;/p&gt;

&lt;p&gt;Thankyou!&lt;/p&gt;
', 'data-indexing-techniques  indexing', NULL, '''/p'':87C,90C ''0'':41C ''20'':85C ''20d'':7A ''argmax'':4A ''around'':84C ''certain'':32C ''data'':9B,77C ''data-indexing-techniqu'':8B ''dataset'':45C ''dimens'':86C ''direct'':33C ''effici'':3A,23C,76C ''etc'':62C ''extrem'':27C ''find'':24C,39C ''give'':48C ''given'':37C ''i.e'':34C ''index'':1A,10B,12B,20C ''largest'':50C,58C ''look'':16C ''m'':15C ''might'':82C ''n'':28C,42C ''name'':66C ''p'':13C,88C ''point'':29C ''queri'':6A,71C ''second'':57C ''spatial'':19C ''structur'':78C ''techniqu'':11B ''thankyou'':89C ''type'':69C ''use'':80C ''valu'':51C,59C ''w'':38C ''w.x'':5A,53C,61C ''would'':73C ''x'':40C,81C ''x0'':47C ''x1'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5856, 1, '2015-05-21 17:57:35.003', 6, 311, 'How is Data Science related to Machine learning?', '&lt;p&gt;I went through &lt;a href="http://www.datasciencecentral.com/profiles/blogs/17-analytic-disciplines-compared" rel="nofollow"&gt;this comparison of analytic disciplines&lt;/a&gt; and &lt;a href="http://www.win-vector.com/blog/2010/10/a-personal-perspective-on-machine-learning/" rel="nofollow"&gt;this perspective of machine learning&lt;/a&gt;, but I am not finding any answers on the following:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;How is Data Science related to Machine learning? &lt;/li&gt;
&lt;li&gt;How is it &lt;strong&gt;not&lt;/strong&gt; related to Machine Learning? &lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  definitions  knowledge-base', NULL, '''/a'':32C,46C ''/blog/2010/10/a-personal-perspective-on-machine-learning/'':38C ''/li'':68C,80C ''/ol'':81C ''/p'':57C ''/profiles/blogs/17-analytic-disciplines-compared'':24C ''/strong'':75C ''analyt'':30C ''answer'':53C ''base'':15B ''comparison'':28C ''data'':3A,62C ''definit'':12B ''disciplin'':31C ''find'':51C ''follow'':56C ''href'':21C,35C ''knowledg'':14B ''knowledge-bas'':13B ''learn'':8A,11B,45C,67C,79C ''li'':59C,69C ''machin'':7A,10B,44C,66C,78C ''machine-learn'':9B ''nofollow'':26C,40C ''ol'':58C ''p'':16C ''perspect'':42C ''rel'':25C,39C ''relat'':5A,64C,76C ''scienc'':4A,63C ''strong'':73C ''went'':18C ''www.datasciencecentral.com'':23C ''www.datasciencecentral.com/profiles/blogs/17-analytic-disciplines-compared'':22C ''www.win-vector.com'':37C ''www.win-vector.com/blog/2010/10/a-personal-perspective-on-machine-learning/'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5844, 1, '2015-05-20 13:24:33.733', 4, 101, 'What are available methods for modeling startup survival rates?', '&lt;p&gt;I am interested in modeling startup companies failure and success rates to describe what is the representative startup. &lt;/p&gt;

&lt;p&gt;I have 40 companies in a dataset. Each company is represented as a list of all the investment financing rounds it has gone through. For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Company 1: Seed Round, Series A Round
Company 2: Seed Round, Series A Round, Series B Round
Company 3: Seed Round, Series A Round, Series B Round
Company 4: Seed Round, Series A Round, Series B Round, Series C Round
Company 5: Seed Round
Company 6: Series A Round, Series B Round
Company 6: Series A Round
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And so on.&lt;/p&gt;

&lt;p&gt;I can see that each round can be represented as a state in a Markov Chain, and transitions are only allowed from earlier stages to later stages. I can go from Seed to Series A, and from Series A to Series B, but not from Series B to Series A.&lt;/p&gt;

&lt;p&gt;So we have N-order markov chains (production data has N &amp;lt;= 4).&lt;/p&gt;

&lt;p&gt;The output I''m looking for is a binary tree chart showing each stage as a node and each node can either transition to the next node or to a final state meaning the company has failed.&lt;/p&gt;

&lt;p&gt;This problem can also be seen as a real options model...&lt;/p&gt;

&lt;p&gt;Any ideas on how to implement this model?&lt;/p&gt;

&lt;p&gt;I can code in Python or Ruby (but I am no expert).&lt;/p&gt;
', 'python  survival-analysis', NULL, '''/code'':121C ''/p'':33C,61C,127C,179C,195C,234C,247C,257C,271C ''/pre'':122C ''1'':65C ''2'':72C ''3'':82C ''4'':92C,194C ''40'':37C ''5'':105C ''6'':109C,117C ''allow'':149C ''also'':239C ''analysi'':13B ''avail'':3A ''b'':79C,89C,99C,114C,170C,175C ''binari'':205C ''c'':102C ''chain'':144C,188C ''chart'':207C ''code'':63C,261C ''compani'':21C,38C,43C,64C,71C,81C,91C,104C,108C,116C,231C ''data'':190C ''dataset'':41C ''describ'':27C ''earlier'':151C ''either'':218C ''exampl'':60C ''expert'':270C ''fail'':233C ''failur'':22C ''final'':227C ''financ'':53C ''go'':158C ''gone'':57C ''idea'':250C ''implement'':254C ''interest'':17C ''invest'':52C ''later'':154C ''list'':48C ''look'':201C ''lt'':193C ''m'':200C ''markov'':143C,187C ''mean'':229C ''method'':4A ''model'':6A,19C,246C,256C ''n'':185C,192C ''n-order'':184C ''next'':222C ''node'':213C,216C,223C ''option'':245C ''order'':186C ''output'':198C ''p'':14C,34C,123C,128C,180C,196C,235C,248C,258C ''pre'':62C ''problem'':237C ''product'':189C ''python'':10B,263C ''rate'':9A,25C ''real'':244C ''repres'':31C,45C,137C ''round'':54C,67C,70C,74C,77C,80C,84C,87C,90C,94C,97C,100C,103C,107C,112C,115C,120C,134C ''rubi'':265C ''see'':131C ''seed'':66C,73C,83C,93C,106C,160C ''seen'':241C ''seri'':68C,75C,78C,85C,88C,95C,98C,101C,110C,113C,118C,162C,166C,169C,174C,177C ''show'':208C ''stage'':152C,155C,210C ''startup'':7A,20C,32C ''state'':140C,228C ''success'':24C ''surviv'':8A,12B ''survival-analysi'':11B ''transit'':146C,219C ''tree'':206C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5957, 2, '2015-05-29 20:40:51.467', 2, NULL, NULL, '&lt;p&gt;If the data are suitable, you can use Gaussian mixture modeling, fit via an EM algorithm to estimate various separate Gaussian clusters. When determining the number of clusters, you can use something like BIC (or other penalized likelihood criterion) to penalize based on the number of parameters that you are estimating. Then simply search over different numbers of clusters and choose the number with the lowest BIC. This is a form of model-based clustering. &lt;/p&gt;

&lt;p&gt;You should be able to use the &lt;code&gt;mclust&lt;/code&gt; package in R to do this: &lt;a href="http://cran.r-project.org/web/packages/mclust/index.html" rel="nofollow"&gt;http://cran.r-project.org/web/packages/mclust/index.html&lt;/a&gt;.  &lt;/p&gt;
', NULL, NULL, '''/code'':89C ''/p'':78C,106C ''/web/packages/mclust/index.html'':100C ''/web/packages/mclust/index.html&lt;/a&gt;.'':105C ''abl'':83C ''algorithm'':17C ''base'':43C,76C ''bic'':35C,68C ''choos'':62C ''cluster'':23C,29C,60C,77C ''code'':87C ''cran.r-project.org'':99C,104C ''cran.r-project.org/web/packages/mclust/index.html'':98C ''cran.r-project.org/web/packages/mclust/index.html&lt;/a&gt;.'':103C ''criterion'':40C ''data'':4C ''determin'':25C ''differ'':57C ''em'':16C ''estim'':19C,52C ''fit'':13C ''form'':72C ''gaussian'':10C,22C ''href'':97C ''like'':34C ''likelihood'':39C ''lowest'':67C ''mclust'':88C ''mixtur'':11C ''model'':12C,75C ''model-bas'':74C ''nofollow'':102C ''number'':27C,46C,58C,64C ''p'':1C,79C ''packag'':90C ''paramet'':48C ''penal'':38C,42C ''r'':92C ''rel'':101C ''search'':55C ''separ'':21C ''simpli'':54C ''someth'':33C ''suitabl'':6C ''use'':9C,32C,85C ''various'':20C ''via'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5839, 5, '2015-05-20 06:53:23.057', 0, NULL, NULL, '&lt;p&gt;The &lt;a href="https://hadoop.apache.org/" rel="nofollow"&gt;Apacheâ¢ Hadoopâ¢&lt;/a&gt; project develops open-source software for reliable, scalable, distributed computing.&lt;/p&gt;

&lt;p&gt;"&lt;a href="https://en.wikipedia.org/wiki/Apache_Hadoop" rel="nofollow"&gt;Hadoop&lt;/a&gt;" typically refers to the software in the project that implements the &lt;a href="https://en.wikipedia.org/wiki/MapReduce" rel="nofollow"&gt;map-reduce&lt;/a&gt; data analysis framework, plus the distributed file system (HDFS) that underlies it.  &lt;/p&gt;

&lt;p&gt;Since version 0.23, Hadoop disposes of a standalone resource manager : &lt;a href="https://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html" rel="nofollow"&gt;yarn&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;This resource manager makes it easier to use other modules alongside with the MapReduce engine, such as :&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://ambari.apache.org/" rel="nofollow"&gt;Ambari&lt;/a&gt;,  A web-based tool for provisioning, managing, and&lt;br&gt;
monitoring Apache Hadoop clusters which includes support for Hadoop&lt;br&gt;
HDFS, Hadoop MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig 
and Sqoop. Ambari also provides a dashboard for viewing cluster&lt;br&gt;
health such as heatmaps and ability to view MapReduce, Pig and Hive&lt;br&gt;
applications visually along with features to diagnose their&lt;br&gt;
performance characteristics in a user-friendly manner.     &lt;/li&gt;
&lt;li&gt;&lt;a href="https://avro.apache.org/" rel="nofollow"&gt;Avro&lt;/a&gt;, a data serialization system based on JSON schemas.   &lt;/li&gt;
&lt;li&gt;&lt;a href="https://cassandra.apache.org/" rel="nofollow"&gt;Cassandra&lt;/a&gt;, a replicated, fault-tolerant, decentralized and
scalable database system.&lt;/li&gt;
&lt;li&gt;&lt;a href="https://chukwa.apache.org/" rel="nofollow"&gt;Chukwa&lt;/a&gt;:  A data collection system for managing large distributed
systems.&lt;/li&gt;
&lt;li&gt;&lt;a href="https://hbase.apache.org/" rel="nofollow"&gt;HBase&lt;/a&gt;, A scalable, distributed database that supports structured
data storage for large tables. &lt;/li&gt;
&lt;li&gt;&lt;a href="https://hive.apache.org/" rel="nofollow"&gt;Hive&lt;/a&gt;, A data warehouse infrastructure that provides data
summarization and ad hoc querying.&lt;/li&gt;
&lt;li&gt;&lt;a href="https://mahout.apache.org/" rel="nofollow"&gt;Mahout&lt;/a&gt;, a library of machine learning algorithms compatible
with M/R paradigm.   &lt;/li&gt;
&lt;li&gt;&lt;a href="https://pig.apache.org/" rel="nofollow"&gt;Pig&lt;/a&gt;, a platform/programming language for authoring parallelizable
jobs&lt;/li&gt;
&lt;li&gt;&lt;a href="https://storm.apache.org/" rel="nofollow"&gt;Storm&lt;/a&gt;, a system for real-time and stream processing&lt;/li&gt;
&lt;li&gt;&lt;a href="https://zookeeper.apache.org/" rel="nofollow"&gt;ZooKeeper&lt;/a&gt;, a system for coordinating distributed nodes, similar to Google''s Chubby&lt;/li&gt;
&lt;li&gt;&lt;a href="https://oozie.apache.org/" rel="nofollow"&gt;Oozie&lt;/a&gt;, a workflow scheduler system to manage Apache Hadoop jobs.&lt;/li&gt;
&lt;li&gt;&lt;a href="https://spark.apache.org/" rel="nofollow"&gt;Spark&lt;/a&gt;, a fast and general engine for large-scale data processing.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://flink.apache.org/" rel="nofollow"&gt;Flink&lt;/a&gt;, a fast and reliable large-scale data processing engine. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Recommended reference sources:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://wiki.apache.org/hadoop/Hive/LanguageManual/DDL" rel="nofollow"&gt;Hive Language Reference&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Commercial support is available from &lt;a href="https://wiki.apache.org/hadoop/Distributions%20and%20Commercial%20Support" rel="nofollow"&gt;a variety of companies&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':10C,32C,54C,87C,116C,195C,212C,231C,249C,270C,291C,310C,326C,344C,364C,382C,402C,432C,452C ''/docs/current/hadoop-yarn/hadoop-yarn-site/yarn.html'':83C ''/hadoop/distributions%20and%20commercial%20support'':445C ''/hadoop/hive/languagemanual/ddl'':426C ''/li'':187C,204C,223C,241C,262C,283C,302C,318C,336C,356C,374C,394C,413C,433C ''/p'':22C,67C,88C,107C,419C,453C ''/ul'':414C,434C ''/wiki/apache_hadoop'':28C ''/wiki/mapreduce'':48C ''0.23'':71C ''abil'':162C ''ad'':280C ''algorithm'':297C ''along'':172C ''alongsid'':100C ''also'':149C ''ambari'':115C,148C ''ambari.apache.org'':112C ''analysi'':56C ''apach'':128C,371C ''apacheâ'':8C ''applic'':170C ''author'':315C ''avail'':439C ''avro'':194C ''avro.apache.org'':191C ''base'':120C,200C ''br'':126C,136C,156C,169C,178C ''cassandra'':211C ''cassandra.apache.org'':208C ''characterist'':180C ''chubbi'':355C ''chukwa'':230C ''chukwa.apache.org'':227C ''cluster'':130C,155C ''collect'':234C ''commerci'':436C ''compani'':451C ''compat'':298C ''comput'':21C ''coordin'':348C ''dashboard'':152C ''data'':55C,197C,233C,257C,272C,277C,392C,410C ''databas'':221C,253C ''decentr'':218C ''develop'':12C ''diagnos'':176C ''dispos'':73C ''distribut'':20C,60C,239C,252C,349C ''easier'':95C ''en.wikipedia.org'':27C,47C ''en.wikipedia.org/wiki/apache_hadoop'':26C ''en.wikipedia.org/wiki/mapreduce'':46C ''engin'':104C,387C,412C ''fast'':384C,404C ''fault'':216C ''fault-toler'':215C ''featur'':174C ''file'':61C ''flink'':401C ''flink.apache.org'':398C ''framework'':57C ''friend'':185C ''general'':386C ''googl'':353C ''hadoop'':31C,72C,129C,135C,138C,372C ''hadoop.apache.org'':5C,82C ''hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/yarn.html'':81C ''hadoopâ'':9C ''hbase'':142C,248C ''hbase.apache.org'':245C ''hcatalog'':141C ''hdfs'':63C,137C ''health'':157C ''heatmap'':160C ''hive'':140C,168C,269C,429C ''hive.apache.org'':266C ''hoc'':281C ''href'':4C,25C,45C,80C,111C,190C,207C,226C,244C,265C,286C,305C,321C,339C,359C,377C,397C,423C,442C ''implement'':42C ''includ'':132C ''infrastructur'':274C ''job'':317C,373C ''json'':202C ''languag'':313C,430C ''larg'':238C,260C,390C,408C ''large-scal'':389C,407C ''learn'':296C ''li'':109C,188C,205C,224C,242C,263C,284C,303C,319C,337C,357C,375C,395C,421C ''librari'':293C ''m/r'':300C ''machin'':295C ''mahout'':290C ''mahout.apache.org'':287C ''make'':93C ''manag'':78C,92C,124C,237C,370C ''manner'':186C ''map'':52C ''map-reduc'':51C ''mapreduc'':103C,139C,165C ''modul'':99C ''monitor'':127C ''node'':350C ''nofollow'':7C,30C,50C,85C,114C,193C,210C,229C,247C,268C,289C,308C,324C,342C,362C,380C,400C,428C,447C ''oozi'':144C,363C ''oozie.apache.org'':360C ''open'':14C ''open-sourc'':13C ''p'':1C,23C,68C,89C,415C,435C ''paradigm'':301C ''paralleliz'':316C ''perform'':179C ''pig'':145C,166C,309C ''pig.apache.org'':306C ''platform/programming'':312C ''plus'':58C ''process'':335C,393C,411C ''project'':11C,40C ''provid'':150C,276C ''provis'':123C ''queri'':282C ''real'':331C ''real-tim'':330C ''recommend'':416C ''reduc'':53C ''refer'':34C,417C,431C ''rel'':6C,29C,49C,84C,113C,192C,209C,228C,246C,267C,288C,307C,323C,341C,361C,379C,399C,427C,446C ''reliabl'':18C,406C ''replic'':214C ''resourc'':77C,91C ''scalabl'':19C,220C,251C ''scale'':391C,409C ''schedul'':367C ''schema'':203C ''serial'':198C ''similar'':351C ''sinc'':69C ''softwar'':16C,37C ''sourc'':15C,418C ''spark'':381C ''spark.apache.org'':378C ''sqoop'':147C ''standalon'':76C ''storag'':258C ''storm'':325C ''storm.apache.org'':322C ''stream'':334C ''structur'':256C ''summar'':278C ''support'':133C,255C,437C ''system'':62C,199C,222C,235C,240C,328C,346C,368C ''tabl'':261C ''time'':332C ''toler'':217C ''tool'':121C ''typic'':33C ''ul'':108C,420C ''under'':65C ''use'':97C ''user'':184C ''user-friend'':183C ''varieti'':449C ''version'':70C ''view'':154C,164C ''visual'':171C ''warehous'':273C ''web'':119C ''web-bas'':118C ''wiki.apache.org'':425C,444C ''wiki.apache.org/hadoop/distributions%20and%20commercial%20support'':443C ''wiki.apache.org/hadoop/hive/languagemanual/ddl'':424C ''workflow'':366C ''yarn'':86C ''zookeep'':143C,343C ''zookeeper.apache.org'':340C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5840, 4, '2015-05-20 06:53:23.057', 0, NULL, NULL, 'Hadoop is an Apache open-source project that provides software for reliable and scalable distributed computing. The project itself includes a variety of other complementary additions.', NULL, NULL, '''addit'':27C ''apach'':4C ''complementari'':26C ''comput'':17C ''distribut'':16C ''hadoop'':1C ''includ'':21C ''open'':6C ''open-sourc'':5C ''project'':8C,19C ''provid'':10C ''reliabl'':13C ''scalabl'':15C ''softwar'':11C ''sourc'':7C ''varieti'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5841, 2, '2015-05-20 09:59:47.257', 3, NULL, NULL, '&lt;p&gt;Most of the machine learning algorithms are designed to work with data in a tabular format. That mean, each data instance is contained in a single row, and the values from each column are the observed values for a specific instance for a given variable. There are few reasons why the most ML algorithms are designed to work on this kind of data. An important factor is that the structure is very simple and various operations can be done with ease. A second reason is that even if looks like a inflexible structure, some sort of additional structure in your data still can be represented on tabular format (using redundancy). Another reason would be that an algorithm designed to work for a specific structure of the data will be constraint to work on a much smaller set of problems. &lt;/p&gt;

&lt;p&gt;So, the main point is that &lt;em&gt;"If the mountain won''t come to Muhammad then Muhammad must go to the mountain"&lt;/em&gt; (note there''s nothing religion related here). So what you have to do would be to fabricate the features yourself in a tabular format. &lt;/p&gt;

&lt;p&gt;I will give you an example on how I see a starting point. Consider an instance a row in a table. Each row will be a &lt;strong&gt;change&lt;/strong&gt;. A &lt;strong&gt;change&lt;/strong&gt; has a label, it is &lt;em&gt;good&lt;/em&gt; or &lt;em&gt;bad&lt;/em&gt;. So, you can add a feature used as target feature called &lt;strong&gt;class&lt;/strong&gt;. We go further by noting that a change is an insertion of a node. If your changes are of multiple type you can add a feature called &lt;strong&gt;operation-type&lt;/strong&gt; having values: &lt;strong&gt;insert&lt;/strong&gt;, &lt;strong&gt;delete&lt;/strong&gt;, &lt;strong&gt;change&lt;/strong&gt;, etc. Now, a node has a type also. You can add a new feature called &lt;strong&gt;node-type&lt;/strong&gt;, which could be &lt;strong&gt;A&lt;/strong&gt;, &lt;strong&gt;B&lt;/strong&gt;, etc. What you have to do would be to invent those features by noting what is important for you or your business and eventually select only those features which would be relevant enough. I really hope it was clear enough.&lt;/p&gt;
', NULL, NULL, '''/em'':165C,232C,236C ''/p'':141C,190C,363C ''/strong'':220C,224C,250C,282C,287C,290C,293C,313C,319C,322C ''add'':240C,274C,304C ''addit'':98C ''algorithm'':7C,55C,118C ''also'':301C ''anoth'':112C ''b'':321C ''bad'':235C ''busi'':344C ''call'':247C,277C,308C ''chang'':219C,223C,258C,267C,292C ''class'':249C ''clear'':361C ''column'':34C ''come'':155C ''consid'':205C ''constraint'':131C ''contain'':24C ''could'':315C ''data'':13C,21C,64C,102C,128C ''delet'':289C ''design'':9C,57C,119C ''done'':80C ''eas'':82C ''em'':149C,230C,234C ''enough'':355C,362C ''etc'':294C,323C ''even'':88C ''eventu'':346C ''exampl'':197C ''fabric'':182C ''factor'':67C ''featur'':184C,242C,246C,276C,307C,334C,350C ''format'':17C,109C,189C ''give'':194C ''given'':45C ''go'':161C,252C ''good'':231C ''hope'':358C ''import'':66C,339C ''inflex'':93C ''insert'':261C,286C ''instanc'':22C,42C,207C ''invent'':332C ''kind'':62C ''label'':227C ''learn'':6C ''like'':91C ''look'':90C ''machin'':5C ''main'':145C ''mean'':19C ''ml'':54C ''mountain'':152C,164C ''much'':136C ''muhammad'':157C,159C ''multipl'':270C ''must'':160C ''new'':306C ''node'':264C,297C,311C ''node-typ'':310C ''note'':166C,255C,336C ''noth'':169C ''observ'':37C ''oper'':77C,280C ''operation-typ'':279C ''p'':1C,142C,191C ''point'':146C,204C ''problem'':140C ''realli'':357C ''reason'':50C,85C,113C ''redund'':111C ''relat'':171C ''relev'':354C ''religion'':170C ''repres'':106C ''row'':28C,209C,214C ''second'':84C ''see'':201C ''select'':347C ''set'':138C ''simpl'':74C ''singl'':27C ''smaller'':137C ''sort'':96C ''specif'':41C,124C ''start'':203C ''still'':103C ''strong'':218C,222C,248C,278C,285C,288C,291C,309C,317C,320C ''structur'':71C,94C,99C,125C ''tabl'':212C ''tabular'':16C,108C,188C ''target'':245C ''type'':271C,281C,300C,312C ''use'':110C,243C ''valu'':31C,38C,284C ''variabl'':46C ''various'':76C ''won'':153C ''work'':11C,59C,121C,133C ''would'':114C,179C,329C,352C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5843, 2, '2015-05-20 13:13:05.653', 2, NULL, NULL, '&lt;p&gt;&lt;a href="http://scrapy.org/" rel="nofollow"&gt;Scrapy&lt;/a&gt; is a great Python library which can help you scrape different sites faster and make your code structure better. Not all sites can be parsed with classic tools, because they can use dynamic JS content building. For this task it is better to use &lt;a href="http://www.seleniumhq.org/" rel="nofollow"&gt;Selenium&lt;/a&gt; (This is a test framework for web sites, but it also a great web scraping tool). There''s also a &lt;a href="http://selenium-python.readthedocs.org/" rel="nofollow"&gt;Python wrapper&lt;/a&gt; available for this library. In Google you can find a few tricks which can help you use Selenium inside &lt;a href="http://scrapy.org/" rel="nofollow"&gt;Scrapy&lt;/a&gt; and make your code clear, organized, and you can use some great tools for &lt;a href="http://scrapy.org/" rel="nofollow"&gt;Scrapy&lt;/a&gt; library.&lt;/p&gt;

&lt;p&gt;I think that Selenium would be a better scraper for Linkedin than classic tools. There is a lot of javascript and dynamic content. Also, if you want to make authentication in your account and scrape all available content, you will get a lot of problems with classic authentication using simple libraries like &lt;a href="http://docs.python-requests.org/en/latest/" rel="nofollow"&gt;requests&lt;/a&gt; or &lt;a href="https://docs.python.org/2/library/urllib.html" rel="nofollow"&gt;urllib&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/2/library/urllib.html'':204C ''/a'':8C,59C,87C,113C,134C,198C,208C ''/en/latest/'':194C ''/p'':136C,209C ''account'':170C ''also'':70C,78C,161C ''authent'':167C,185C ''avail'':88C,174C ''better'':27C,50C,145C ''build'':44C ''classic'':35C,150C,184C ''clear'':118C ''code'':25C,117C ''content'':43C,160C,175C ''differ'':19C ''docs.python-requests.org'':193C ''docs.python-requests.org/en/latest/'':192C ''docs.python.org'':203C ''docs.python.org/2/library/urllib.html'':202C ''dynam'':41C,159C ''faster'':21C ''find'':96C ''framework'':64C ''get'':178C ''googl'':93C ''great'':11C,72C,125C ''help'':16C,102C ''href'':3C,54C,81C,108C,129C,191C,201C ''insid'':106C ''javascript'':157C ''js'':42C ''librari'':13C,91C,135C,188C ''like'':189C ''linkedin'':148C ''lot'':155C,180C ''make'':23C,115C,166C ''nofollow'':6C,57C,84C,111C,132C,196C,206C ''organ'':119C ''p'':1C,137C ''pars'':33C ''problem'':182C ''python'':12C,85C ''rel'':5C,56C,83C,110C,131C,195C,205C ''request'':197C ''scrape'':18C,74C,172C ''scraper'':146C ''scrapi'':7C,112C,133C ''scrapy.org'':4C,109C,130C ''selenium'':58C,105C,141C ''selenium-python.readthedocs.org'':82C ''simpl'':187C ''site'':20C,30C,67C ''structur'':26C ''task'':47C ''test'':63C ''think'':139C ''tool'':36C,75C,126C,151C ''trick'':99C ''urllib'':207C ''use'':40C,52C,104C,123C,186C ''want'':164C ''web'':66C,73C ''would'':142C ''wrapper'':86C ''www.seleniumhq.org'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5849, 2, '2015-05-21 08:17:15.573', 1, NULL, NULL, '&lt;p&gt;Both previous answers take an interesting approach, but neither really tackles exactly what I was asking; how to compare a given terms &lt;em&gt;frequency&lt;/em&gt; over time, and compare with &lt;em&gt;other terms&lt;/em&gt; over the same period, in a statistically rigorous way that accounts for the power law distribution of term frequencies with collection size.&lt;/p&gt;

&lt;p&gt;The answer below is my work so far on the problem. &lt;strong&gt;It is not, by any means, a complete answer&lt;/strong&gt;, however I post it here to elicit feedback and suggestions for improvement.&lt;/p&gt;

&lt;p&gt;Take the definition of Zipf''s law,&lt;/p&gt;

&lt;p&gt;$$
f(r) \sim z_{max} r^{-\alpha}
$$&lt;/p&gt;

&lt;p&gt;here, $r$ is the rank of a given term, $z_{max}$ is the frequency of the most frequent term, $\alpha$ is Zipf''s exponent, and $f(r)$ is the frequency of the &lt;em&gt;r-th&lt;/em&gt; ranked term.&lt;/p&gt;

&lt;p&gt;Taking the log of both sides, you get a linear relationship between $log(f)$ and $log(r)$, with gradient $-\alpha$ and intercept $z_{max}$&lt;/p&gt;

&lt;p&gt;$log(f) \sim log(z_{max}) - \alpha log(r)$&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/xzlwB.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;We want to find the expected frequency of a single term over a given period. First, split the period into equal sized windows. For the first window, $i$, we know the frequency of our term $f_i$, and the frequency of the largest term $z_{max,i}$ and $\alpha$. We can use these to find the rank of our term,&lt;/p&gt;

&lt;p&gt;$r \sim \left(\frac{f_i(r)}{z_{max,i}}\right)^{-\alpha}$&lt;/p&gt;

&lt;p&gt;This rank can then be used to calculate the ''expected'' frequency for all subsequent time windows, which I''ll indicate with $\hat{f}$,&lt;/p&gt;

&lt;p&gt;$\hat{f}_{i+1}(r) \sim z_{max,i+1} r^{\alpha(i+1)} $&lt;/p&gt;

&lt;p&gt;The ratio $\frac{\hat{f}}{f}$ represents the normalised frequency.&lt;/p&gt;

&lt;p&gt;Some observations:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;For a stationary underlying distribution, this approach identifies minor deviations from the expected frequency of a term. A moving window could be used if the underlying text distribution changes dramatically, or exhibits seasonal behaviour.&lt;/li&gt;
&lt;li&gt;The initial choice of $r$ could be calculated for the entire period, rather than the first window.&lt;/li&gt;
&lt;li&gt;$\alpha$ could be calculated from the raw term and total frequency counts using heap''s law (see &lt;a href="http://arxiv.org/abs/1002.3861" rel="nofollow"&gt;Lu et al. 2010&lt;/a&gt; for a derivation of Heap''s law from Zipf''s).&lt;/li&gt;
&lt;li&gt;This approach assumes a linear relationship between $f$ and $r$ in log space.&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''+1'':308C,314C,318C ''/a'':421C ''/abs/1002.3861'':414C ''/em'':26C,35C,150C ''/li'':372C,391C,432C,447C ''/ol'':448C ''/p'':58C,94C,103C,112C,153C,179C,190C,202C,264C,278C,303C,319C,331C,335C ''/strong'':81C ''/xzlwb.png'':196C ''2010'':420C ''account'':46C ''al'':419C ''alpha'':111C,133C,174C,187C,252C,277C,316C,393C ''alt'':197C ''answer'':4C,61C,80C ''approach'':8C,344C,435C ''arxiv.org'':413C ''arxiv.org/abs/1002.3861'':412C ''ask'':17C ''assum'':436C ''behaviour'':371C ''calcul'':287C,381C,396C ''chang'':366C ''choic'':376C ''collect'':56C ''compar'':20C,30C ''complet'':79C ''could'':358C,379C,394C ''count'':404C ''definit'':98C ''deriv'':424C ''descript'':200C ''deviat'':347C ''distribut'':51C,342C,365C ''dramat'':367C ''elicit'':88C ''em'':24C,32C,146C ''enter'':198C ''entir'':384C ''equal'':224C ''et'':418C ''exact'':13C ''exhibit'':369C ''expect'':209C,289C,350C ''expon'':137C ''f'':105C,139C,168C,182C,239C,270C,302C,306C,325C,326C,441C ''far'':67C ''feedback'':89C ''find'':207C,258C ''first'':219C,229C,389C ''frac'':269C,323C ''frequenc'':25C,54C,127C,143C,210C,235C,243C,290C,330C,351C,403C ''frequent'':131C ''get'':162C ''given'':22C,121C,217C ''gradient'':173C ''hat'':301C,305C,324C ''heap'':406C,426C ''howev'':82C ''href'':411C ''i.stack.imgur.com'':195C ''i.stack.imgur.com/xzlwb.png'':194C ''identifi'':345C ''imag'':199C ''img'':192C ''improv'':93C ''indic'':299C ''initi'':375C ''intercept'':176C ''interest'':7C ''know'':233C ''largest'':246C ''law'':50C,102C,408C,428C ''left'':268C ''li'':337C,373C,392C,433C ''linear'':164C,438C ''ll'':298C ''log'':157C,167C,170C,181C,184C,188C,445C ''lu'':417C ''max'':109C,124C,178C,186C,249C,274C,312C ''mean'':77C ''minor'':346C ''move'':356C ''neither'':10C ''nofollow'':416C ''normalis'':329C ''observ'':334C ''ol'':336C ''p'':1C,59C,95C,104C,113C,154C,180C,191C,203C,265C,279C,304C,320C,332C ''period'':39C,218C,222C,385C ''post'':84C ''power'':49C ''previous'':3C ''problem'':70C ''r'':106C,110C,115C,140C,148C,171C,189C,266C,272C,309C,315C,378C,443C ''r-th'':147C ''rank'':118C,151C,260C,281C ''rather'':386C ''ratio'':322C ''raw'':399C ''realli'':11C ''rel'':415C ''relationship'':165C,439C ''repres'':327C ''right'':276C ''rigor'':43C ''season'':370C ''see'':409C ''side'':160C ''sim'':107C,183C,267C,310C ''singl'':213C ''size'':57C,225C ''space'':446C ''split'':220C ''src'':193C ''stationari'':340C ''statist'':42C ''strong'':71C ''subsequ'':293C ''suggest'':91C ''tackl'':12C ''take'':5C,96C,155C ''term'':23C,34C,53C,122C,132C,152C,214C,238C,247C,263C,354C,400C ''text'':364C ''th'':149C ''time'':28C,294C ''total'':402C ''under'':341C,363C ''use'':255C,285C,360C,405C ''want'':205C ''way'':44C ''window'':226C,230C,295C,357C,390C ''work'':65C ''z'':108C,123C,177C,185C,248C,273C,311C ''zipf'':100C,135C,430C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6178, 2, '2015-06-22 20:44:59.48', 1, NULL, NULL, '&lt;p&gt;You may or may not already know this, but here are a few basics about how R and Rstudio work and use resources.&lt;/p&gt;

&lt;p&gt;Rstudio is a &lt;em&gt;graphical user interface&lt;/em&gt; to R, not the interpeter/runtime environment.  There is a separate "R session" that actually executes your R programs and returns results for Rstudio to display.  Therefore, having &lt;em&gt;Rstudio&lt;/em&gt; use more memory won''t make any difference in the execution speed of your program.&lt;/p&gt;

&lt;p&gt;Second, memory allocation and cleanup (a.k.a. "garbage collection") is handled automatically by the R runtime environment.  "&lt;a href="http://stat.ethz.ch/R-manual/R-patched/library/base/html/Memory-limits.html" rel="nofollow"&gt;R holds objects it is using in virtual memory&lt;/a&gt;". Virtual memory is a combination of physical main memory and secondary storage, the mixture determined by the server OS configuration.  You can use command line to change the amount of virtual memory allocated to processes. (Consult with your favorite Linux expert on this.)&lt;/p&gt;

&lt;p&gt;Third, your speed of your program may or may not be limited by memory speed. You may be compute-bound.  Do some testing to find out what is constraining performance.&lt;/p&gt;

&lt;p&gt;Fourth, you should first ask yourself whether your program implements an efficient algorithm.  Even if you aren''t programming with loops and branches, the functions you call may use them, and maybe not efficiently for your application.  For example, there are dramatic performance gains to be had by switching to &lt;code&gt;data.table&lt;/code&gt; from &lt;code&gt;data.frame&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Fifth, once you have chosen an efficient algorithm, you can put effort into parallel execution.  The simplest way to do this is by using functions that automatically vectorize operations.  A bit more complicated is to recode your program use the packages &lt;code&gt;doParallel&lt;/code&gt; and &lt;code&gt;foreach&lt;/code&gt;.  With &lt;code&gt;doParallel&lt;/code&gt;, you can specify the number of CPU cores to use, which on a server may range from 32 to 64 or more.  Finally, if your server has a Graphics Processing Unit (GPU), it''s possible for some algorithms to reprogram using the GPU commands and get massive parallelism.  This option takes the most effort and has the most constraints.&lt;/p&gt;
', NULL, NULL, '''/a'':114C ''/code'':245C,249C,295C,299C,303C ''/em'':34C,64C ''/p'':25C,80C,158C,191C,250C,363C ''/r-manual/r-patched/library/base/html/memory-limits.html'':102C ''32'':321C ''64'':323C ''a.k.a'':87C ''actual'':48C ''algorithm'':205C,259C,341C ''alloc'':84C,147C ''alreadi'':7C ''amount'':143C ''applic'':229C ''aren'':209C ''ask'':197C ''automat'':92C,278C ''basic'':15C ''bit'':282C ''bound'':180C ''branch'':215C ''call'':219C ''chang'':141C ''chosen'':256C ''cleanup'':86C ''code'':243C,247C,293C,297C,301C ''collect'':89C ''combin'':119C ''command'':138C,347C ''complic'':284C ''comput'':179C ''compute-bound'':178C ''configur'':134C ''constrain'':189C ''constraint'':362C ''consult'':150C ''core'':311C ''cpu'':310C ''data.frame'':248C ''data.table'':244C ''determin'':129C ''differ'':72C ''display'':59C ''doparallel'':294C,302C ''dramat'':234C ''effici'':204C,226C,258C ''effort'':263C,357C ''em'':30C,62C ''environ'':40C,97C ''even'':206C ''exampl'':231C ''execut'':49C,75C,266C ''expert'':155C ''favorit'':153C ''fifth'':252C ''final'':326C ''find'':185C ''first'':196C ''foreach'':298C ''fourth'':193C ''function'':217C,276C ''gain'':236C ''garbag'':88C ''get'':349C ''gpu'':335C,346C ''graphic'':31C,332C ''handl'':91C ''hold'':106C ''href'':99C ''implement'':202C ''interfac'':33C ''interpeter/runtime'':39C ''know'':8C ''limit'':171C ''line'':139C ''linux'':154C ''loop'':213C ''main'':122C ''make'':70C ''massiv'':350C ''may'':3C,5C,166C,168C,176C,220C,318C ''mayb'':224C ''memori'':67C,83C,113C,116C,123C,146C,173C ''mixtur'':128C ''nofollow'':104C ''number'':308C ''object'':107C ''oper'':280C ''option'':353C ''os'':133C ''p'':1C,26C,81C,159C,192C,251C ''packag'':292C ''parallel'':265C,351C ''perform'':190C,235C ''physic'':121C ''possibl'':338C ''process'':149C,333C ''program'':52C,79C,165C,201C,211C,289C ''put'':262C ''r'':18C,36C,45C,51C,95C,105C ''rang'':319C ''recod'':287C ''rel'':103C ''reprogram'':343C ''resourc'':24C ''result'':55C ''return'':54C ''rstudio'':20C,27C,57C,63C ''runtim'':96C ''second'':82C ''secondari'':125C ''separ'':44C ''server'':132C,317C,329C ''session'':46C ''simplest'':268C ''specifi'':306C ''speed'':76C,162C,174C ''stat.ethz.ch'':101C ''stat.ethz.ch/r-manual/r-patched/library/base/html/memory-limits.html'':100C ''storag'':126C ''switch'':241C ''take'':354C ''test'':183C ''therefor'':60C ''third'':160C ''unit'':334C ''use'':23C,65C,110C,137C,221C,275C,290C,313C,344C ''user'':32C ''vector'':279C ''virtual'':112C,115C,145C ''way'':269C ''whether'':199C ''won'':68C ''work'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5851, 2, '2015-05-21 08:26:42.463', 1, NULL, NULL, '&lt;p&gt;Sounds like you could use a regression model to estimate the probabilities that a team wins/draws/loses vs. another team. Basically, for any outcome (win, draw and lose), you want this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;P(A|B) = ...
P(B|A) = ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Which means: the probability of &lt;strong&gt;outcome&lt;/strong&gt; for team A given that it is matched against team B (and vice versa).&lt;/p&gt;

&lt;p&gt;Estimations could be represented like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;P(A &amp;gt; B) = 0.75 % A wins
P(A = B) = 0.10 % A draws
P(A &amp;lt; B) = 0.15 % A loses

P(B &amp;gt; A) = 0.20 % B wins
P(B = A) = 0.10 % B draws
P(B &amp;gt; A) = 0.70 % B loses
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I think a logical step is to measure the bias towards a certain outcome. That would represent the &lt;em&gt;confidence ratio&lt;/em&gt; of your algorithm. The more the probabilities of any outcome looks alike (i.e. P(B &gt;/=/&amp;lt; A) = 0.33), the less confidence it has.&lt;/p&gt;
', NULL, NULL, '''/code'':41C,117C ''/em'':141C ''/p'':32C,66C,74C,165C ''/pre'':42C,118C ''/strong'':51C ''0.10'':87C,107C ''0.15'':94C ''0.20'':101C ''0.33'':159C ''0.70'':114C ''0.75'':81C ''algorithm'':144C ''alik'':153C ''anoth'':19C ''b'':37C,39C,62C,80C,86C,93C,98C,102C,105C,108C,111C,115C,156C ''basic'':21C ''bias'':129C ''certain'':132C ''code'':34C,76C ''confid'':139C,162C ''could'':5C,69C ''draw'':26C,89C,109C ''em'':138C ''estim'':11C,68C ''given'':55C ''gt'':79C,99C,112C ''i.e'':154C ''less'':161C ''like'':3C,72C ''logic'':123C ''look'':152C ''lose'':28C,96C,116C ''lt'':92C,157C ''match'':59C ''mean'':45C ''measur'':127C ''model'':9C ''outcom'':24C,50C,133C,151C ''p'':1C,35C,38C,43C,67C,77C,84C,90C,97C,104C,110C,119C,155C ''pre'':33C,75C ''probabl'':13C,47C,148C ''ratio'':140C ''regress'':8C ''repres'':71C,136C ''sound'':2C ''step'':124C ''strong'':49C ''team'':16C,20C,53C,61C ''think'':121C ''toward'':130C ''use'':6C ''versa'':65C ''vice'':64C ''vs'':18C ''want'':30C ''win'':25C,83C,103C ''wins/draws/loses'':17C ''would'':135C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5852, 2, '2015-05-21 08:46:49.783', -1, NULL, NULL, '&lt;p&gt;For orientation and exploration, I can recommend &lt;a href="http://www.cs.waikato.ac.nz/ml/weka/" rel="nofollow"&gt;WeKa&lt;/a&gt;, which is a very nice toolkit for machine learning. It does take a certain input format (.ARFF) so you might need to look into that as well.&lt;/p&gt;

&lt;p&gt;As for the keyword dilemma, I recommend performing some feature selection in order to eliminate redundant or non-indicative keywords.&lt;/p&gt;
', NULL, NULL, '''/a'':17C ''/ml/weka/'':13C ''/p'':45C,68C ''arff'':34C ''certain'':31C ''dilemma'':51C ''elimin'':61C ''explor'':5C ''featur'':56C ''format'':33C ''href'':10C ''indic'':66C ''input'':32C ''keyword'':50C,67C ''learn'':26C ''look'':40C ''machin'':25C ''might'':37C ''need'':38C ''nice'':22C ''nofollow'':15C ''non'':65C ''non-ind'':64C ''order'':59C ''orient'':3C ''p'':1C,46C ''perform'':54C ''recommend'':8C,53C ''redund'':62C ''rel'':14C ''select'':57C ''take'':29C ''toolkit'':23C ''weka'':16C ''well'':44C ''www.cs.waikato.ac.nz'':12C ''www.cs.waikato.ac.nz/ml/weka/'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5865, 2, '2015-05-22 14:01:28.91', 1, NULL, NULL, '&lt;p&gt;Your reference of Bishop is not entirely accurate. What he states in the paper you linked is&lt;/p&gt;

&lt;p&gt;&lt;code&gt;
It should be noted that the standard sum-of-squares error, introduced here from a heuristic viewpoint, can be derived from the principle of maximum likelihood on the assumption that the noise on the target data has a Gaussian distribution [references cited]. Even when this assumption is not satisfied, however, the sum-of-squares error function remains of great practical importance.
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;The important point with regard to your question is that there is no inherent assumption that there is Gaussian noise when training a Multilayer Perceptron (MLP). Therefore, for an MLP, the sum-of-squares error function is not derived from the principle of maximum likelihood.&lt;/p&gt;

&lt;p&gt;For example, consider training an MLP to learn the &lt;code&gt;XOR&lt;/code&gt; function. There are four pairs of inputs with corresponding outputs but there is no noise in the data. Yet the sum-of-squares error is still applicable.&lt;/p&gt;

&lt;p&gt;The relevance of using sum-of-squares for neural networks (and many other situations) is that the error function is differentiable and since the errors are squared, it can be used to reduce or minimize the magnitudes of both positive and negative errors.&lt;/p&gt;
', NULL, NULL, '''/code'':85C,147C ''/p'':19C,86C,134C,176C,222C ''accur'':9C ''applic'':175C ''assumpt'':51C,68C,102C ''bishop'':5C ''cite'':64C ''code'':21C,145C ''consid'':138C ''correspond'':156C ''data'':58C,165C ''deriv'':42C,127C ''differenti'':199C ''distribut'':62C ''entir'':8C ''error'':33C,78C,123C,172C,196C,203C,221C ''even'':65C ''exampl'':137C ''four'':151C ''function'':79C,124C,148C,197C ''gaussian'':61C,106C ''great'':82C ''heurist'':38C ''howev'':72C ''import'':84C,89C ''inher'':101C ''input'':154C ''introduc'':34C ''learn'':143C ''likelihood'':48C,133C ''link'':17C ''magnitud'':215C ''mani'':190C ''maximum'':47C,132C ''minim'':213C ''mlp'':113C,117C,141C ''multilay'':111C ''negat'':220C ''network'':188C ''neural'':187C ''nois'':54C,107C,162C ''note'':25C ''output'':157C ''p'':1C,20C,87C,135C,177C ''pair'':152C ''paper'':15C ''perceptron'':112C ''point'':90C ''posit'':218C ''practic'':83C ''principl'':45C,130C ''question'':95C ''reduc'':211C ''refer'':3C,63C ''regard'':92C ''relev'':179C ''remain'':80C ''satisfi'':71C ''sinc'':201C ''situat'':192C ''squar'':32C,77C,122C,171C,185C,205C ''standard'':28C ''state'':12C ''still'':174C ''sum'':30C,75C,120C,169C,183C ''sum-of-squar'':29C,74C,119C,168C,182C ''target'':57C ''therefor'':114C ''train'':109C,139C ''use'':181C,209C ''viewpoint'':39C ''xor'':146C ''yet'':166C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5868, 2, '2015-05-22 22:22:34.72', 2, NULL, NULL, '&lt;p&gt;Data Science is, as others have noted, a much broader term than machine learning. Applying Machine learning techniques is one aspect of data science. Data Science,  more generally, is the science of deriving knowledge from data. The term was coined back in 1960 and kept evolving to describe the flow and interplay of problem definition, data collection, data transformation, data modeling/ analysis,  and decision making. So to answer your question specifically: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Machine learning aids data science by providing a suit of algorithms for data modeling/ analysis (through training of machine learning algorithms), decision making (through streaming, online learning, real-time testing that are all topics that come under machine learning), and even data preparation (machine learning algorithms automatically detect anomalies in the data). &lt;/li&gt;
&lt;li&gt;Data Science stitches together a bunch of ideas/ algorithms drawn from machine learning to create a solution and in doing so borrows a lot of ideas from traditional statistics, domain expertise and basic mathematics. In this way, data science is the process of solving a use case, providing a solution as opposed to machine learning that is an important cog in that solution. &lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':129C,194C ''/ol'':195C ''/p'':73C ''1960'':44C ''aid'':78C ''algorithm'':86C,96C,122C,139C ''analysi'':63C,90C ''anomali'':125C ''answer'':69C ''appli'':16C ''aspect'':22C ''automat'':123C ''back'':42C ''basic'':163C ''borrow'':152C ''broader'':11C ''bunch'':136C ''case'':177C ''cog'':190C ''coin'':41C ''collect'':58C ''come'':112C ''creat'':145C ''data'':2C,24C,26C,37C,57C,59C,61C,79C,88C,118C,128C,131C,168C ''decis'':65C,97C ''definit'':56C ''deriv'':34C ''describ'':49C ''detect'':124C ''domain'':160C ''drawn'':140C ''even'':117C ''evolv'':47C ''expertis'':161C ''flow'':51C ''general'':29C ''idea'':138C,156C ''import'':189C ''interplay'':53C ''kept'':46C ''knowledg'':35C ''learn'':15C,18C,77C,95C,102C,115C,121C,143C,185C ''li'':75C,130C ''lot'':154C ''machin'':14C,17C,76C,94C,114C,120C,142C,184C ''make'':66C,98C ''mathemat'':164C ''model'':62C,89C ''much'':10C ''note'':8C ''ol'':74C ''one'':21C ''onlin'':101C ''oppos'':182C ''other'':6C ''p'':1C ''prepar'':119C ''problem'':55C ''process'':172C ''provid'':82C,178C ''question'':71C ''real'':104C ''real-tim'':103C ''scienc'':3C,25C,27C,32C,80C,132C,169C ''solut'':147C,180C,193C ''solv'':174C ''specif'':72C ''statist'':159C ''stitch'':133C ''stream'':100C ''suit'':84C ''techniqu'':19C ''term'':12C,39C ''test'':106C ''time'':105C ''togeth'':134C ''topic'':110C ''tradit'':158C ''train'':92C ''transform'':60C ''use'':176C ''way'':167C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5854, 2, '2015-05-21 15:23:44.27', 4, NULL, NULL, '&lt;p&gt;If the data you show are the only data that you have, then the Markov Chain is really boring: it is a linear chain, going from Round A to Round B to Round C, and all those states are connected to a base state (which is Death, or something).&lt;/p&gt;

&lt;p&gt;You can directly calculate the transition probabilities from the data you have, since the number of companies that reached round N are &lt;strong&gt;all companies that could have reached round N&lt;/strong&gt; (there is no alternative path).  The death probability at the previous stage is (1 - $P_{reaching N}$)&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;In [1]: raw_data = """
   ...: Company 1: Seed Round, Series A Round
   ...: Company 2: Seed Round, Series A Round, Series B Round
   ...: Company 3: Seed Round, Series A Round, Series B Round
   ...: Company 4: Seed Round, Series A Round, Series B Round, Series C Round
   ...: Company 5: Seed Round
   ...: Company 6: Series A Round, Series B Round
   ...: Company 6: Series A Round
   ...: """
In [2]: data_lines = raw_data.splitlines()[1:]
In [6]: key_vals = {}
In [12]: for line in data_lines:
             key, val = line.split('':'')
             key = key.strip()
             vals = [v.strip() for v in val.split('','')]
             key_vals[key] = vals

In [13]: key_vals
Out[13]:
{''Company 1'': [''Seed Round'', ''Series A Round''],
 ''Company 2'': [''Seed Round'', ''Series A Round'', ''Series B Round''],
 ''Company 3'': [''Seed Round'', ''Series A Round'', ''Series B Round''],
 ''Company 4'': [''Seed Round'',
  ''Series A Round'',
  ''Series B Round'',
  ''Series C Round''],
 ''Company 5'': [''Seed Round''],
 ''Company 6'': [''Series A Round'']}

In [14]: transitions = [''Seed Round'', ''Series A Round'', ''Series B Round'', ''Series C Round'']

In [19]: for transition in transitions:
    summed = 0
    for company, rounds in key_vals.iteritems():
        if transition in rounds:
            summed += 1
    prob = float(summed) / float(len(key_vals.keys()))
    death_prob = 1 - prob
    print "From previous to %s: probability %s" % (transition, prob)
    print "Death rate at %s: probability %s" % (transition, death_prob)

From previous to Seed Round: probability 0.833333333333
Death rate at Seed Round: probability 0.166666666667
From previous to Series A Round: probability 0.833333333333
Death rate at Series A Round: probability 0.166666666667
From previous to Series B Round: probability 0.5
Death rate at Series B Round: probability 0.5
From previous to Series C Round: probability 0.166666666667
Death rate at Series C Round: probability 0.833333333333
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However, if you had some more features of each company, like the amount of money they received at each stage, or the profit they were making, then you could train a &lt;a href="http://en.wikipedia.org/wiki/Decision_tree" rel="nofollow"&gt;decision tree&lt;/a&gt;, e.g. with &lt;a href="http://scikit-learn.org/stable/modules/tree.html" rel="nofollow"&gt;this implementation in sklearn&lt;/a&gt;, that told you, in simple words, "if a company arrived at round X with at least Y dollars raised and at least Z dollars of profit, then they are passing to the next round with 0.XX probability".  Which is, I think, what you are aiming at.&lt;/p&gt;
', NULL, NULL, '''/a'':420C,434C ''/code'':377C ''/p'':51C,102C,481C ''/pre'':378C ''/stable/modules/tree.html'':427C ''/strong'':84C ''/wiki/decision_tree'':415C ''0'':274C ''0.166666666667'':328C,344C,368C ''0.5'':352C,360C ''0.833333333333'':321C,336C,376C ''0.xx'':470C ''1'':98C,106C,110C,171C,205C,285C,294C ''12'':177C ''13'':199C,203C ''14'':254C ''19'':268C ''2'':117C,167C,212C ''3'':127C,222C ''4'':137C,232C ''5'':150C,245C ''6'':154C,162C,173C,249C ''aim'':479C ''altern'':88C ''amount'':392C ''arriv'':444C ''b'':32C,124C,134C,144C,159C,219C,229C,239C,262C,349C,357C ''base'':44C ''bore'':20C ''c'':35C,147C,242C,265C,365C,373C ''calcul'':56C ''chain'':17C,25C ''code'':104C ''compani'':69C,77C,109C,116C,126C,136C,149C,153C,161C,204C,211C,221C,231C,244C,248C,276C,389C,443C ''connect'':41C ''could'':79C,408C ''data'':4C,10C,62C,108C,168C,181C ''death'':48C,91C,292C,306C,313C,322C,337C,353C,369C ''decis'':418C ''direct'':55C ''dollar'':452C,458C ''e.g'':421C ''en.wikipedia.org'':414C ''en.wikipedia.org/wiki/decision_tree'':413C ''featur'':386C ''float'':287C,289C ''go'':26C ''howev'':380C ''href'':412C,424C ''implement'':431C ''key'':174C,183C,186C,194C,196C,200C ''key.strip'':187C ''key_vals.iteritems'':279C ''key_vals.keys'':291C ''least'':450C,456C ''len'':290C ''like'':390C ''line'':169C,179C,182C ''line.split'':185C ''linear'':24C ''make'':405C ''markov'':16C ''money'':394C ''n'':73C,83C,101C ''next'':467C ''nofollow'':417C,429C ''number'':67C ''p'':1C,52C,99C,379C ''pass'':464C ''path'':89C ''pre'':103C ''previous'':95C,298C,316C,330C,346C,362C ''print'':296C,305C ''prob'':286C,293C,295C,304C,314C ''probabl'':59C,92C,301C,310C,320C,327C,335C,343C,351C,359C,367C,375C,471C ''profit'':402C,460C ''rais'':453C ''rate'':307C,323C,338C,354C,370C ''raw'':107C ''raw_data.splitlines'':170C ''reach'':71C,81C,100C ''realli'':19C ''receiv'':396C ''rel'':416C,428C ''round'':28C,31C,34C,72C,82C,112C,115C,119C,122C,125C,129C,132C,135C,139C,142C,145C,148C,152C,157C,160C,165C,207C,210C,214C,217C,220C,224C,227C,230C,234C,237C,240C,243C,247C,252C,257C,260C,263C,266C,277C,283C,319C,326C,334C,342C,350C,358C,366C,374C,446C,468C ''scikit-learn.org'':426C ''scikit-learn.org/stable/modules/tree.html'':425C ''seed'':111C,118C,128C,138C,151C,206C,213C,223C,233C,246C,256C,318C,325C ''seri'':113C,120C,123C,130C,133C,140C,143C,146C,155C,158C,163C,208C,215C,218C,225C,228C,235C,238C,241C,250C,258C,261C,264C,332C,340C,348C,356C,364C,372C ''show'':6C ''simpl'':439C ''sinc'':65C ''sklearn'':433C ''someth'':50C ''stage'':96C,399C ''state'':39C,45C ''strong'':75C ''sum'':273C,284C,288C ''think'':475C ''told'':436C ''train'':409C ''transit'':58C,255C,270C,272C,281C,303C,312C ''tree'':419C ''v'':191C ''v.strip'':189C ''val'':175C,184C,188C,195C,197C,201C ''val.split'':193C ''word'':440C ''x'':447C ''y'':451C ''z'':457C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8852, 2, '2015-11-15 21:02:55.333', 1, NULL, NULL, '&lt;p&gt;This shouldn''t be terribly complicated.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Big picture&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Assuming you have Linux on your server: SSH to your server from your work laptop, train the network, receive results (the trained network).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Details&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Train the network&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;If you want to stick with Python, there are two basic options that I am familiar with. &lt;/p&gt;

&lt;p&gt;The first is &lt;em&gt;PyBrain&lt;/em&gt;, a library built specifically for training neural nets. The syntax is fairly straightforward. The dataset structure is a bit unusual (especially if you are used to just using numpy arrays in &lt;em&gt;scikit-learn&lt;/em&gt;), but otherwise, it works rather well. However, it doesn''t support GPUs AFAIK, and I don''t know how optimized/fast it is.&lt;/p&gt;

&lt;p&gt;The second is Google''s &lt;em&gt;Tensor Flow&lt;/em&gt;. A bit of a heavy-weight if you just want to run vanilla neural nets, but the syntax is also pretty easy to get around (although very different from &lt;em&gt;PyBrain&lt;/em&gt;). It''s also probably faster than anything else you could find in Python. It also supports GPU training.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Get the results back&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;This depends on the library you choose (or it could be definitely up to you write your own implementation). But both libraries mentioned above will let you return the trained weights, which you can save as csv files and download upon training. Or you could pickle them and get back that way. Also, Tensor Flow has this Tensor Board functionality which should let you visualize the training and the network structure, but I haven''t tried it yet, so can''t help on that front.&lt;/p&gt;
', NULL, NULL, '''/em'':50C,77C,113C,146C,179C,205C ''/p'':8C,14C,39C,44C,51C,70C,136C,198C,206C,293C ''/strong'':13C,43C ''afaik'':126C ''also'':167C,182C,194C,260C ''although'':173C ''anyth'':186C ''around'':172C ''array'':107C ''assum'':16C ''back'':204C,257C ''basic'':63C ''big'':11C ''bit'':96C,148C ''board'':266C ''built'':80C ''choos'':214C ''complic'':7C ''could'':189C,217C,252C ''csv'':244C ''dataset'':92C ''definit'':219C ''depend'':209C ''detail'':42C ''differ'':175C ''doesn'':122C ''download'':247C ''easi'':169C ''els'':187C ''em'':46C,75C,109C,143C,177C,200C ''especi'':98C ''fair'':89C ''familiar'':68C ''faster'':184C ''file'':245C ''find'':190C ''first'':73C ''flow'':145C,262C ''front'':292C ''function'':267C ''get'':171C,201C,256C ''googl'':141C ''gpu'':196C ''gpus'':125C ''haven'':281C ''heavi'':152C ''heavy-weight'':151C ''help'':289C ''howev'':120C ''implement'':226C ''know'':131C ''laptop'':30C ''learn'':112C ''let'':233C,270C ''librari'':79C,212C,229C ''linux'':19C ''mention'':230C ''net'':85C,162C ''network'':33C,38C,49C,277C ''neural'':84C,161C ''numpi'':106C ''optimized/fast'':133C ''option'':64C ''otherwis'':115C ''p'':1C,9C,15C,40C,45C,52C,71C,137C,199C,207C ''pickl'':253C ''pictur'':12C ''pretti'':168C ''probabl'':183C ''pybrain'':76C,178C ''python'':59C,192C ''rather'':118C ''receiv'':34C ''result'':35C,203C ''return'':235C ''run'':159C ''save'':242C ''scikit'':111C ''scikit-learn'':110C ''second'':139C ''server'':22C,26C ''shouldn'':3C ''specif'':81C ''ssh'':23C ''stick'':57C ''straightforward'':90C ''strong'':10C,41C ''structur'':93C,278C ''support'':124C,195C ''syntax'':87C,165C ''tensor'':144C,261C,265C ''terribl'':6C ''train'':31C,37C,47C,83C,197C,237C,249C,274C ''tri'':283C ''two'':62C ''unusu'':97C ''upon'':248C ''use'':102C,105C ''vanilla'':160C ''visual'':272C ''want'':55C,157C ''way'':259C ''weight'':153C,238C ''well'':119C ''work'':29C,117C ''write'':223C ''yet'':285C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5855, 1, '2015-05-21 15:25:35.377', 4, 60, 'Gerrymandering - Geospatial optimization to maximize votes in R', '&lt;p&gt;I am looking for a practical guide/tutorial preferably in R to show how to do gerrymandering. (I was looking for it also in CRAN but didn''t find such package) Gerrymandering is the manipulation of the boundaries of electorial districts in order to gain political advantage for one party. If there is an analogy/similar process in another area it would be also interesting.&lt;/p&gt;

&lt;p&gt;I have detailed historical  election results. I can see the results from each "voting office" and also the geographical area they cover. These results are aggregated to electorial district level. I''d like to see how moving voting offices to different electorial districts can influence the results of the election and how this process can be optimized for one party. Certainly there should be some constraints not to create really weird shaped districts at the end.&lt;/p&gt;
', 'r  optimization', NULL, '''/p'':75C,153C ''advantag'':57C ''aggreg'':102C ''also'':33C,73C,93C ''analogy/similar'':65C ''anoth'':68C ''area'':69C,96C ''boundari'':48C ''certain'':137C ''constraint'':142C ''cover'':98C ''cran'':35C ''creat'':145C ''d'':108C ''detail'':79C ''didn'':37C ''differ'':117C ''district'':51C,105C,119C,149C ''elect'':81C,126C ''electori'':50C,104C,118C ''end'':152C ''find'':39C ''gain'':55C ''geograph'':95C ''geospati'':2A ''gerrymand'':1A,27C,42C ''guide/tutorial'':18C ''histor'':80C ''influenc'':121C ''interest'':74C ''level'':106C ''like'':109C ''look'':14C,30C ''manipul'':45C ''maxim'':5A ''move'':113C ''offic'':91C,115C ''one'':59C,135C ''optim'':3A,10B,133C ''order'':53C ''p'':11C,76C ''packag'':41C ''parti'':60C,136C ''polit'':56C ''practic'':17C ''prefer'':19C ''process'':66C,130C ''r'':8A,9B,21C ''realli'':146C ''result'':82C,87C,100C,123C ''see'':85C,111C ''shape'':148C ''show'':23C ''vote'':6A,90C,114C ''weird'':147C ''would'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9253, 4, '2015-12-07 18:36:03.537', 0, NULL, NULL, 'Azure Machine Learning offers a streamlined experience for all data scientist skill levels, from setting up with only a web browser, to using drag and drop gestures and simple data flow graphs to set up experiments.', NULL, NULL, '''azur'':1C ''browser'':21C ''data'':10C,30C ''drag'':24C ''drop'':26C ''experi'':7C,36C ''flow'':31C ''gestur'':27C ''graph'':32C ''learn'':3C ''level'':13C ''machin'':2C ''offer'':4C ''scientist'':11C ''set'':15C,34C ''simpl'':29C ''skill'':12C ''streamlin'':6C ''use'':23C ''web'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5859, 2, '2015-05-21 19:46:02.663', 4, NULL, NULL, '&lt;p&gt;Machine Learning tries to create systems that can learn from data. As such it can be used in a wide variety of settings, for example to &lt;a href="https://www.youtube.com/watch?v=iNL5-0_T1D0" rel="nofollow"&gt;make robots learn to walk&lt;/a&gt; or &lt;a href="https://www.youtube.com/watch?v=m0tJLTXNT0A" rel="nofollow"&gt;train virtual agents to play video games&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Data science concerns itself with the extraction of knowledge from data. In order to do so it uses a bunch of different techniques from different disciplines. Machine learning includes some techniques that can be very useful for a data scientist such as deep learning, decision trees and different clustering algorithms. However, machine learning has more to offer than Data Science uses and Data Science does not solely rely on Machine Learning.&lt;/p&gt;
', NULL, NULL, '''/a'':40C,56C ''/p'':57C,130C ''/watch?v=inl5-0_t1d0'':32C ''/watch?v=m0tjltxnt0a'':46C ''agent'':51C ''algorithm'':108C ''bunch'':78C ''cluster'':107C ''concern'':61C ''creat'':6C ''data'':12C,59C,69C,97C,117C,121C ''decis'':103C ''deep'':101C ''differ'':80C,83C,106C ''disciplin'':84C ''exampl'':26C ''extract'':65C ''game'':55C ''howev'':109C ''href'':29C,43C ''includ'':87C ''knowledg'':67C ''learn'':3C,10C,37C,86C,102C,111C,129C ''machin'':2C,85C,110C,128C ''make'':35C ''nofollow'':34C,48C ''offer'':115C ''order'':71C ''p'':1C,58C ''play'':53C ''rel'':33C,47C ''reli'':126C ''robot'':36C ''scienc'':60C,118C,122C ''scientist'':98C ''set'':24C ''sole'':125C ''system'':7C ''techniqu'':81C,89C ''train'':49C ''tree'':104C ''tri'':4C ''use'':18C,76C,94C,119C ''varieti'':22C ''video'':54C ''virtual'':50C ''walk'':39C ''wide'':21C ''www.youtube.com'':31C,45C ''www.youtube.com/watch?v=inl5-0_t1d0'':30C ''www.youtube.com/watch?v=m0tjltxnt0a'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5860, 1, '2015-05-21 22:26:59.077', 1, 251, 'Where to start, which books', '&lt;p&gt;What''s a good book to start learning Artificial Intelligence ? 
What field to learn first ? What are the prerequisites ? 
Thanks in advance.&lt;/p&gt;
', 'beginner  books', NULL, '''/p'':31C ''advanc'':30C ''artifici'':17C ''beginn'':6B ''book'':5A,7B,13C ''field'':20C ''first'':23C ''good'':12C ''intellig'':18C ''learn'':16C,22C ''p'':8C ''prerequisit'':27C ''start'':3A,15C ''thank'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5869, 2, '2015-05-22 22:29:22.153', 1, NULL, NULL, '&lt;p&gt;Lots of techniques out there. If your information system has a decision attribute or labels attached, the best way I found is to use rough set based attribute reduction. Check out the quick reduction algorithm by Qiang Shen and Richard Jensen.&lt;/p&gt;

&lt;p&gt;If you have unlabeled data, check &lt;a href="http://en.wikipedia.org/wiki/Principal_component_analysis" rel="nofollow"&gt;Principal Component Analysis (PCA)&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':62C ''/p'':43C,63C ''/wiki/principal_component_analysis'':55C ''algorithm'':36C ''analysi'':60C ''attach'':17C ''attribut'':14C,29C ''base'':28C ''best'':19C ''check'':31C,50C ''compon'':59C ''data'':49C ''decis'':13C ''en.wikipedia.org'':54C ''en.wikipedia.org/wiki/principal_component_analysis'':53C ''found'':22C ''href'':52C ''inform'':9C ''jensen'':42C ''label'':16C ''lot'':2C ''nofollow'':57C ''p'':1C,44C ''pca'':61C ''princip'':58C ''qiang'':38C ''quick'':34C ''reduct'':30C,35C ''rel'':56C ''richard'':41C ''rough'':26C ''set'':27C ''shen'':39C ''system'':10C ''techniqu'':4C ''unlabel'':48C ''use'':25C ''way'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6054, 1, '2015-06-09 14:39:04.21', 2, 246, 'In SVM Algorithm, why vector w is orthogonal to the separating hyperplane?', '&lt;p&gt;I am a beginner on Machine Learning.
In SVM, the separating hyperplane is defined as $y = w^T x + b$.
Why we say vector $w$ orthogonal to the separating hyperplane?&lt;/p&gt;
', 'machine-learning  svm', NULL, '''/p'':48C ''algorithm'':3A ''b'':37C ''beginn'':21C ''defin'':31C ''hyperplan'':12A,29C,47C ''learn'':15B,24C ''machin'':14B,23C ''machine-learn'':13B ''orthogon'':8A,43C ''p'':17C ''say'':40C ''separ'':11A,28C,46C ''svm'':2A,16B,26C ''vector'':5A,41C ''w'':6A,34C,42C ''x'':36C ''y'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8853, 1, '2015-11-16 01:53:50.673', 3, 15, 'Completing MDS manually in R', '&lt;p&gt;Given a matrix &lt;code&gt;A&lt;/code&gt;, I want to complete Multidimensional Scaling by hand, instead of using any given R functions.&lt;/p&gt;

&lt;p&gt;As such, I have calculated the centered matrix &lt;code&gt;B&lt;/code&gt; with the following code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; n&amp;lt;-nrow(A)
 id&amp;lt;-diag(n)
 e&amp;lt;-diag(id)
 H &amp;lt;- id - (1/n)*e %*% etranspose
 B &amp;lt;- (-1/2)* H %*% A %*% H
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;My question is: how can I use my B matrix to complete multidimensional scaling on my A matrix, without the &lt;code&gt;cmdscale&lt;/code&gt; function or anything along those lines?&lt;/p&gt;
', 'machine-learning  statistics', NULL, '''-1'':72C ''/2'':73C ''/code'':16C,44C,77C,102C ''/p'':32C,49C,109C ''/pre'':78C ''1/n'':67C ''along'':106C ''anyth'':105C ''b'':43C,70C,88C ''calcul'':38C ''center'':40C ''cmdscale'':101C ''code'':14C,42C,48C,51C,100C ''complet'':1A,20C,91C ''diag'':58C,62C ''e'':60C,68C ''etranspos'':69C ''follow'':47C ''function'':31C,103C ''given'':11C,29C ''h'':64C,74C,76C ''hand'':24C ''id'':56C,63C,66C ''instead'':25C ''learn'':8B ''line'':108C ''lt'':53C,57C,61C,65C,71C ''machin'':7B ''machine-learn'':6B ''manual'':3A ''matrix'':13C,41C,89C,97C ''mds'':2A ''multidimension'':21C,92C ''n'':52C,59C ''nrow'':54C ''p'':10C,33C,79C ''pre'':50C ''question'':81C ''r'':5A,30C ''scale'':22C,93C ''statist'':9B ''use'':27C,86C ''want'':18C ''without'':98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5861, 2, '2015-05-22 09:22:12.163', 5, NULL, NULL, '&lt;p&gt;Data science is much broader concept than machine learning.
It starts from simple data visualization and descriptive statistics to get insights, manipulations like cleansing to prepare data. Before you can use some ML algorithms. &lt;/p&gt;

&lt;p&gt;Basically such huge stacks as bigdata, visualization and data preprocessing are out of machine learning scope. And they are all integral parts of "Data Science".&lt;/p&gt;

&lt;p&gt;&lt;img src="https://pbs.twimg.com/media/B6sZGVfIQAAMZvH.png:large" alt=""&gt;&lt;/p&gt;

&lt;p&gt;Large resolution image:
&lt;a href="https://whatsthebigdata.files.wordpress.com/2013/07/datascientistmap.png" rel="nofollow"&gt;https://whatsthebigdata.files.wordpress.com/2013/07/datascientistmap.png&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/2013/07/datascientistmap.png'':80C ''/2013/07/datascientistmap.png&lt;/a&gt;&lt;/p&gt;'':85C ''/media/b6szgvfiqaamzvh.png:large'':69C ''/p'':36C,63C,71C ''algorithm'':35C ''alt'':70C ''basic'':38C ''bigdata'':43C ''broader'':6C ''cleans'':25C ''concept'':7C ''data'':2C,15C,28C,46C,61C ''descript'':18C ''get'':21C ''href'':77C ''huge'':40C ''imag'':75C ''img'':65C ''insight'':22C ''integr'':58C ''larg'':73C ''learn'':10C,52C ''like'':24C ''machin'':9C,51C ''manipul'':23C ''ml'':34C ''much'':5C ''nofollow'':82C ''p'':1C,37C,64C,72C ''part'':59C ''pbs.twimg.com'':68C ''pbs.twimg.com/media/b6szgvfiqaamzvh.png:large'':67C ''prepar'':27C ''preprocess'':47C ''rel'':81C ''resolut'':74C ''scienc'':3C,62C ''scope'':53C ''simpl'':14C ''src'':66C ''stack'':41C ''start'':12C ''statist'':19C ''use'':32C ''visual'':16C,44C ''whatsthebigdata.files.wordpress.com'':79C,84C ''whatsthebigdata.files.wordpress.com/2013/07/datascientistmap.png'':78C ''whatsthebigdata.files.wordpress.com/2013/07/datascientistmap.png&lt;/a&gt;&lt;/p&gt;'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5862, 2, '2015-05-22 11:28:01.467', 0, NULL, NULL, '&lt;p&gt;I would also go with beautifulsoup, if you know python. In case you rather code javascript/JQuery (and you are familiar with node.js), you may want to checkout &lt;a href="http://coffeescript.org/" rel="nofollow"&gt;CoffeeScript&lt;/a&gt; (Check out the &lt;a href="http://blog.teamtreehouse.com/the-absolute-beginners-guide-to-coffeescript" rel="nofollow"&gt;Tutorial&lt;/a&gt;) I already used it successfully on several occasions for scraping web pages.&lt;/p&gt;
', NULL, NULL, '''/a'':35C,47C ''/p'':60C ''/the-absolute-beginners-guide-to-coffeescript'':43C ''alreadi'':49C ''also'':4C ''beautifulsoup'':7C ''blog.teamtreehouse.com'':42C ''blog.teamtreehouse.com/the-absolute-beginners-guide-to-coffeescript'':41C ''case'':13C ''check'':36C ''checkout'':28C ''code'':16C ''coffeescript'':34C ''coffeescript.org'':31C ''familiar'':21C ''go'':5C ''href'':30C,40C ''javascript/jquery'':17C ''know'':10C ''may'':25C ''node.js'':23C ''nofollow'':33C,45C ''occas'':55C ''p'':1C ''page'':59C ''python'':11C ''rather'':15C ''rel'':32C,44C ''scrape'':57C ''sever'':54C ''success'':52C ''tutori'':46C ''use'':50C ''want'':26C ''web'':58C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5863, 1, '2015-05-22 13:21:44.143', 3, 397, 'Where does the sum of squared errors function in neural networks come from?', '&lt;p&gt;Training a basic multilayer perceptron neural network boils down to minimizing some kind of error function. Often the sum of squared errors is chosen as a this error function, but where does this function come from?&lt;/p&gt;

&lt;p&gt;I always thought this function was chosen because it makes sense intuitively. However, recently I learned that this is only partly true and there is more behind it.&lt;/p&gt;

&lt;p&gt;Bishop wrote in one of his &lt;a href="http://research.microsoft.com/en-us/um/people/cmbishop/downloads/bishop%20-%20nna%20-%20rsi94.pdf" rel="nofollow"&gt;papers&lt;/a&gt; that the sum of squared errors function can be derived from the principle of &lt;a href="http://en.wikipedia.org/wiki/Maximum_likelihood" rel="nofollow"&gt;maximum likelihood&lt;/a&gt;. Furthermore he wrote that the squared error therefore makes the assumption that the noise on the target value has a Gaussian distribution.&lt;/p&gt;

&lt;p&gt;I am not sure what he means with that. How does the sum of squared errors relate to the maximum likelihood principle in the context of neural networks?&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/a'':100C,124C ''/en-us/um/people/cmbishop/downloads/bishop%20-%20nna%20-%20rsi94.pdf'':96C ''/p'':54C,84C,147C,177C ''/wiki/maximum_likelihood'':119C ''alway'':57C ''assumpt'':135C ''basic'':20C ''behind'':82C ''bishop'':86C ''boil'':25C ''chosen'':41C,62C ''come'':12A,52C ''context'':173C ''deriv'':110C ''distribut'':146C ''en.wikipedia.org'':118C ''en.wikipedia.org/wiki/maximum_likelihood'':117C ''error'':7A,32C,39C,45C,106C,131C,164C ''function'':8A,33C,46C,51C,60C,107C ''furthermor'':125C ''gaussian'':145C ''howev'':68C ''href'':93C,116C ''intuit'':67C ''kind'':30C ''learn'':15B,71C ''likelihood'':123C,169C ''machin'':14B ''machine-learn'':13B ''make'':65C,133C ''maximum'':122C,168C ''mean'':155C ''minim'':28C ''multilay'':21C ''network'':11A,24C,176C ''neural'':10A,23C,175C ''neuralnetwork'':16B ''nofollow'':98C,121C ''nois'':138C ''often'':34C ''one'':89C ''p'':17C,55C,85C,148C ''paper'':99C ''part'':76C ''perceptron'':22C ''principl'':113C,170C ''recent'':69C ''rel'':97C,120C ''relat'':165C ''research.microsoft.com'':95C ''research.microsoft.com/en-us/um/people/cmbishop/downloads/bishop%20-%20nna%20-%20rsi94.pdf'':94C ''sens'':66C ''squar'':6A,38C,105C,130C,163C ''sum'':4A,36C,103C,161C ''sure'':152C ''target'':141C ''therefor'':132C ''thought'':58C ''train'':18C ''true'':77C ''valu'':142C ''wrote'':87C,127C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5864, 2, '2015-05-22 13:38:27.207', 3, NULL, NULL, '&lt;p&gt;The standard textbooks that covers AI is "Artificial Intelligence: A Modern Approach" by Russel &amp;amp; Norvig. The book''s website can be found &lt;a href="http://aima.cs.berkeley.edu/" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I also recommend "Artificial Intelligence: Foundations of Computational Agents" by Poole and Mackworth. The book can be read &lt;a href="http://artint.info/" rel="nofollow"&gt;online&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':31C,58C ''/p'':32C,59C ''agent'':42C ''ai'':7C ''aima.cs.berkeley.edu'':27C ''also'':35C ''amp'':16C ''approach'':13C ''artifici'':9C,37C ''artint.info'':54C ''book'':19C,48C ''comput'':41C ''cover'':6C ''found'':24C ''foundat'':39C ''href'':26C,53C ''intellig'':10C,38C ''mackworth'':46C ''modern'':12C ''nofollow'':29C,56C ''norvig'':17C ''onlin'':57C ''p'':1C,33C ''pool'':44C ''read'':51C ''recommend'':36C ''rel'':28C,55C ''russel'':15C ''standard'':3C ''textbook'':4C ''websit'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5867, 1, '2015-05-22 21:22:15.133', 3, 159, 'What loss function does the ''multinomial'' distribution with the gbm package in R use?', '&lt;p&gt;All distributions in the &lt;code&gt;gbm&lt;/code&gt; package in R are associated with a loss function. For example, when we set &lt;code&gt;distribution = ''binomial''&lt;/code&gt;, the loss function chosen internally is the logistic loss function. Can anyone explain how multi-class classification works with &lt;code&gt;gbm&lt;/code&gt; and the loss function that is being used for it i.e. when we set &lt;code&gt;distribution=''multinomial''&lt;/code&gt;? Is it using one-vs-all or all-vs-all under the hood for doing its multi-class classification? &lt;/p&gt;
', 'r  gbm  multiclass-classification', NULL, '''/code'':27C,45C,68C,86C ''/p'':109C ''all-vs-al'':95C ''anyon'':57C ''associ'':32C ''binomi'':44C ''chosen'':49C ''class'':62C,107C ''classif'':19B,63C,108C ''code'':25C,42C,66C,83C ''distribut'':7A,22C,43C,84C ''exampl'':38C ''explain'':58C ''function'':3A,36C,48C,55C,72C ''gbm'':10A,16B,26C,67C ''hood'':101C ''i.e'':79C ''intern'':50C ''logist'':53C ''loss'':2A,35C,47C,54C,71C ''multi'':61C,106C ''multi-class'':60C,105C ''multiclass'':18B ''multiclass-classif'':17B ''multinomi'':6A,85C ''one'':91C ''one-vs-al'':90C ''p'':20C ''packag'':11A,28C ''r'':13A,15B,30C ''set'':41C,82C ''use'':14A,76C,89C ''vs'':92C,97C ''work'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6355, 1, '2015-07-06 13:42:12.23', 4, 102, 'Theoretical bound - regression error', '&lt;p&gt;The Bayes error rate is a theoretical bound that determines the lowest possible error rate for a classification problem, given some data. I was wondering whether an equivalent concept exists for the case of regression algorithms. My aim is to determine how far my regression algorithm''s error is from that theoretical bound, as a way to assess how far am I from the best possible solution. Is there any way to obtain a bound of the lowest regression error for a given dataset?&lt;/p&gt;
', 'regression  linear-regression', NULL, '''/p'':94C ''aim'':47C ''algorithm'':45C,55C ''assess'':67C ''bay'':11C ''best'':74C ''bound'':2A,17C,62C,84C ''case'':42C ''classif'':27C ''concept'':38C ''data'':31C ''dataset'':93C ''determin'':19C,50C ''equival'':37C ''error'':4A,12C,23C,57C,89C ''exist'':39C ''far'':52C,69C ''given'':29C,92C ''linear'':7B ''linear-regress'':6B ''lowest'':21C,87C ''obtain'':82C ''p'':9C ''possibl'':22C,75C ''problem'':28C ''rate'':13C,24C ''regress'':3A,5B,8B,44C,54C,88C ''solut'':76C ''theoret'':1A,16C,61C ''way'':65C,80C ''whether'':35C ''wonder'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5874, 1, '2015-05-23 02:20:24.87', 1, 33, 'What techniques are used to understand call patterns?', '&lt;p&gt;I have customer data since 2013 and there is a file which has the customer unique id, a timestamp, and the reason for the call (a drop down from the person who handled the call).  &lt;/p&gt;

&lt;p&gt;I did some cumulative counts based on customer ID and the timestamp and I saw that one customer called in over 1000 times alone. What''s the best way to make sense of the call driver data when I''m looking at millions of rows and around 200 categories of call types?  &lt;/p&gt;

&lt;p&gt;Is there a broader topic which looks into ''downstream''  issues or predicting the probability of future calls or events?  The end goal would be to visualize these calling patterns and focus on reducing the call backs. This is a specific problem but it seems like it should be common and I can learn about addressing it in a bigger picture manner. &lt;/p&gt;
', 'predictive-modeling', NULL, '''/p'':48C,102C,165C ''1000'':71C ''200'':97C ''2013'':18C ''address'':158C ''alon'':73C ''around'':96C ''back'':139C ''base'':55C ''best'':77C ''bigger'':162C ''broader'':107C ''call'':7A,37C,47C,68C,84C,100C,120C,131C,138C ''categori'':98C ''common'':152C ''count'':54C ''cumul'':53C ''custom'':15C,27C,57C,67C ''data'':16C,86C ''downstream'':112C ''driver'':85C ''drop'':39C ''end'':124C ''event'':122C ''file'':23C ''focus'':134C ''futur'':119C ''goal'':125C ''handl'':45C ''id'':29C,58C ''issu'':113C ''learn'':156C ''like'':148C ''look'':90C,110C ''m'':89C ''make'':80C ''manner'':164C ''million'':92C ''model'':11B ''one'':66C ''p'':12C,49C,103C ''pattern'':8A,132C ''person'':43C ''pictur'':163C ''predict'':10B,115C ''predictive-model'':9B ''probabl'':117C ''problem'':144C ''reason'':34C ''reduc'':136C ''row'':94C ''saw'':64C ''seem'':147C ''sens'':81C ''sinc'':17C ''specif'':143C ''techniqu'':2A ''time'':72C ''timestamp'':31C,61C ''topic'':108C ''type'':101C ''understand'':6A ''uniqu'':28C ''use'':4A ''visual'':129C ''way'':78C ''would'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5875, 1, '2015-05-23 11:45:23.043', 3, 193, 'How to find the input variables for a classification problem?', '&lt;p&gt;I am working on a  classification problem. I have 1000+ features in this dataset. I don''t know how to select the right variables/ features that can actually contribute to predicting the output. What are the different methods through which I can identify the important variables that can be used out of these 1000+ variables.&lt;/p&gt;
', 'classification  algorithms  feature-selection', NULL, '''/p'':72C ''1000'':26C,70C ''actual'':44C ''algorithm'':12B ''classif'':9A,11B,22C ''contribut'':45C ''dataset'':30C ''differ'':53C ''featur'':14B,27C,41C ''feature-select'':13B ''find'':3A ''identifi'':59C ''import'':61C ''input'':5A ''know'':34C ''method'':54C ''output'':49C ''p'':16C ''predict'':47C ''problem'':10A,23C ''right'':39C ''select'':15B,37C ''use'':66C ''variabl'':6A,40C,62C,71C ''work'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5877, 2, '2015-05-23 13:09:29.803', 5, NULL, NULL, '&lt;p&gt;Two approaches come to mind:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Use all of them and perform feature selection to identify indicative features. Algorithms include Information Gain and Mutual Information.&lt;/li&gt;
&lt;li&gt;Hand-pick some features you deem indicative intuitively, or delete the ones you deem non-indicative.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Generally, you have some sort of hypothesis that you are testing where you attest the capability of (a subset of) your features (and the algorithm you''ll use) to uniquely identify the label/class they belong to. The features you use are one of the most important things in the classification task. So it is good to spend time to intelligently find out which features contribute. &lt;/p&gt;

&lt;p&gt;For this task, you do have a lot of possible features so the automatic feature selection approach might be better.&lt;/p&gt;
', NULL, NULL, '''/li'':29C,49C ''/ol'':50C ''/p'':7C,117C,139C ''algorithm'':22C,76C ''approach'':3C,135C ''attest'':65C ''automat'':132C ''belong'':86C ''better'':138C ''capabl'':67C ''classif'':101C ''come'':4C ''contribut'':116C ''deem'':37C,45C ''delet'':41C ''featur'':16C,21C,35C,73C,89C,115C,129C,133C ''find'':112C ''gain'':25C ''general'':52C ''good'':106C ''hand'':32C ''hand-pick'':31C ''hypothesi'':58C ''identifi'':19C,82C ''import'':97C ''includ'':23C ''indic'':20C,38C,48C ''inform'':24C,28C ''intellig'':111C ''intuit'':39C ''label/class'':84C ''li'':9C,30C ''ll'':78C ''lot'':126C ''might'':136C ''mind'':6C ''mutual'':27C ''non'':47C ''non-ind'':46C ''ol'':8C ''one'':43C,93C ''p'':1C,51C,118C ''perform'':15C ''pick'':33C ''possibl'':128C ''select'':17C,134C ''sort'':56C ''spend'':108C ''subset'':70C ''task'':102C,121C ''test'':62C ''thing'':98C ''time'':109C ''two'':2C ''uniqu'':81C ''use'':10C,79C,91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5878, 2, '2015-05-23 20:45:02.473', 3, NULL, NULL, '&lt;p&gt;A somewhat popular introduction is Andrew Ng''s Stanford &lt;a href="https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599" rel="nofollow"&gt; machine learning lectures&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':21C ''/p'':22C ''/playlist?list=pla89dcfa6adace599'':15C ''andrew'':7C ''href'':12C ''introduct'':5C ''learn'':19C ''lectur'':20C ''machin'':18C ''ng'':8C ''nofollow'':17C ''p'':1C ''popular'':4C ''rel'':16C ''somewhat'':3C ''stanford'':10C ''www.youtube.com'':14C ''www.youtube.com/playlist?list=pla89dcfa6adace599'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5879, 2, '2015-05-23 20:56:03.84', 3, NULL, NULL, '&lt;p&gt;Start with neural nets. They are the basic building blocks of Deep Learning.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.youtube.com/watch?v=g-BJSl4zV_g" rel="nofollow"&gt;Neural Nets for Newbies video&lt;/a&gt; is a well-explained introduction. &lt;a href="http://www.willamette.edu/~gorr/classes/cs449/intro.html" rel="nofollow"&gt;CS-449&lt;/a&gt; from Willamette University is an free and approachable class in neural networks.&lt;/p&gt;

&lt;p&gt;That should lay the foundation for more advanced Deep Learning material. &lt;a href="http://www-labs.iro.umontreal.ca/~bengioy/dlbook/" rel="nofollow"&gt;Deep Learning&lt;/a&gt; by Bengio, Goodfellow, and Courville is a quality book on the subject.&lt;/p&gt;
', NULL, NULL, '''-449'':44C ''/a'':29C,45C,80C ''/p'':15C,58C,93C ''/watch?v=g-bjsl4zv_g'':21C ''/~bengioy/dlbook/'':75C ''/~gorr/classes/cs449/intro.html'':40C ''advanc'':67C ''approach'':53C ''basic'':9C ''bengio'':82C ''block'':11C ''book'':89C ''build'':10C ''class'':54C ''courvill'':85C ''cs'':43C ''deep'':13C,68C,78C ''explain'':34C ''foundat'':64C ''free'':51C ''goodfellow'':83C ''href'':18C,37C,72C ''introduct'':35C ''lay'':62C ''learn'':14C,69C,79C ''materi'':70C ''net'':5C,25C ''network'':57C ''neural'':4C,24C,56C ''newbi'':27C ''nofollow'':23C,42C,77C ''p'':1C,16C,59C ''qualiti'':88C ''rel'':22C,41C,76C ''start'':2C ''subject'':92C ''univers'':48C ''video'':28C ''well'':33C ''well-explain'':32C ''willamett'':47C ''www-labs.iro.umontreal.ca'':74C ''www-labs.iro.umontreal.ca/~bengioy/dlbook/'':73C ''www.willamette.edu'':39C ''www.willamette.edu/~gorr/classes/cs449/intro.html'':38C ''www.youtube.com'':20C ''www.youtube.com/watch?v=g-bjsl4zv_g'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5885, 1, '2015-05-24 02:11:36.64', 5, 2288, 'How do I normalize an array of positive and negative numbers so they are between 0 and 1?', '&lt;p&gt;Using &lt;a href="https://www.npmjs.com/package/brain"&gt;Brain&lt;/a&gt; to feed in an array of data with both positive and negative numbers; the output array will be in 0''s and 1''s and I believe Brain only allows inputs from 0 to 1. So how do I normalize an array of negative and positive numbers so it fits these requirements?&lt;/p&gt;
', 'machine-learning  javascript', NULL, '''/a'':31C ''/p'':84C ''/package/brain'':29C ''0'':16A,51C,64C ''1'':18A,54C,66C ''allow'':61C ''array'':6A,36C,47C,73C ''believ'':58C ''brain'':30C,59C ''data'':38C ''feed'':33C ''fit'':81C ''href'':26C ''input'':62C ''javascript'':22B ''learn'':21B ''machin'':20B ''machine-learn'':19B ''negat'':10A,43C,75C ''normal'':4A,71C ''number'':11A,44C,78C ''output'':46C ''p'':23C ''posit'':8A,41C,77C ''requir'':83C ''use'':24C ''www.npmjs.com'':28C ''www.npmjs.com/package/brain'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5913, 1, '2015-05-26 22:11:54.65', 2, 82, 'How do you ascertain the quality of your data?', '&lt;p&gt;How do you assess the quality of your data? In data scientists'' world, we come across several data. We often crunch numbers without formally assessing its quality due to various reasons. One such reason is we need to meet deadlines for reports &amp;amp; publications. I am wondering if anyone has adopted or come across a method/guidelines that help to find issues within the data (time-saving tips), so we can analyze the data efficiently. Please share your experience, tips, etc. &lt;/p&gt;
', 'dataset  recommendation  data-cleaning', NULL, '''/p'':97C ''across'':31C,69C ''adopt'':66C ''amp'':58C ''analyz'':87C ''anyon'':64C ''ascertain'':4A ''assess'':19C,40C ''clean'':14B ''come'':30C,68C ''crunch'':36C ''data'':9A,13B,24C,26C,33C,79C,89C ''data-clean'':12B ''dataset'':10B ''deadlin'':55C ''due'':43C ''effici'':90C ''etc'':96C ''experi'':94C ''find'':75C ''formal'':39C ''help'':73C ''issu'':76C ''meet'':54C ''method/guidelines'':71C ''need'':52C ''number'':37C ''often'':35C ''one'':47C ''p'':15C ''pleas'':91C ''public'':59C ''qualiti'':6A,21C,42C ''reason'':46C,49C ''recommend'':11B ''report'':57C ''save'':82C ''scientist'':27C ''sever'':32C ''share'':92C ''time'':81C ''time-sav'':80C ''tip'':83C,95C ''various'':45C ''within'':77C ''without'':38C ''wonder'':62C ''world'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5881, 1, '2015-05-23 21:49:17.603', 0, 75, 'Data prediction book', '&lt;p&gt;While I was studying, few years ago , one of the most interesting topic was evolution, genetic algorithms and neural networks. Many of the problems I faced could be solved by using that knowledge.&lt;/p&gt;

&lt;p&gt;I assume that since that time world has found more interesting algorithms, do you recommend some books worthy of reading in that domain ? &lt;/p&gt;

&lt;p&gt;Mainly I am looking a way to find patterns in huge amount of data. &lt;/p&gt;

&lt;p&gt;Ex. Having energy consumption for few years for one building.&lt;/p&gt;

&lt;p&gt;Lets have an algorithm that is trying to find all possible repeatings in many variations.&lt;/p&gt;

&lt;p&gt;Obviously at the beginning it should find that at the weekend energy consumption is less then average between Mon and Fri, but is that possible that an algorithm would tell me sth like this ? :&lt;/p&gt;

&lt;p&gt;Every Friday at third week in even months user sleeps for 3 hours and in uneven months 5 hours ?&lt;/p&gt;

&lt;p&gt;Or the algorithm finds it self that user like to save energy so if previous month he sees that he spent more then usual , next month he is trying to spent less, &lt;/p&gt;

&lt;p&gt;or lets assume that user eats breakfast at work but if he eats it at home then he will stay whole day at home, then lets assume that he has off day, then check energy usage after breakfast if is high, which means user is preparing to leave or is small which means that he wants to sleep and basically stays home. &lt;/p&gt;

&lt;p&gt;So I was wondering if this possible to auto detect this kind of patterns ? &lt;/p&gt;

&lt;p&gt;I am enthusiast of c# and interested in R.&lt;/p&gt;
', 'algorithms  neuralnetwork  genetic-algorithms', NULL, '''/p'':43C,68C,84C,96C,113C,149C,170C,204C,268C,284C,295C ''3'':162C ''5'':168C ''ago'':16C ''algorithm'':4B,8B,26C,56C,101C,142C,174C ''amount'':81C ''assum'':46C,208C,232C ''auto'':278C ''averag'':131C ''basic'':265C ''begin'':118C ''book'':3A,61C ''breakfast'':212C,243C ''build'':95C ''c'':290C ''check'':239C ''consumpt'':89C,127C ''could'':36C ''data'':1A,83C ''day'':227C,237C ''detect'':279C ''domain'':67C ''eat'':211C,218C ''energi'':88C,126C,183C,240C ''enthusiast'':288C ''even'':157C ''everi'':151C ''evolut'':24C ''ex'':86C ''face'':35C ''find'':77C,106C,121C,175C ''found'':53C ''fri'':135C ''friday'':152C ''genet'':7B,25C ''genetic-algorithm'':6B ''high'':246C ''home'':221C,229C,267C ''hour'':163C,169C ''huge'':80C ''interest'':21C,55C,292C ''kind'':281C ''knowledg'':42C ''leav'':253C ''less'':129C,203C ''let'':98C,207C,231C ''like'':147C,180C ''look'':73C ''main'':70C ''mani'':30C,111C ''mean'':248C,258C ''mon'':133C ''month'':158C,167C,187C,197C ''network'':29C ''neural'':28C ''neuralnetwork'':5B ''next'':196C ''obvious'':115C ''one'':17C,94C ''p'':9C,44C,69C,85C,97C,114C,150C,171C,205C,269C,285C ''pattern'':78C,283C ''possibl'':108C,139C,276C ''predict'':2A ''prepar'':251C ''previous'':186C ''problem'':33C ''r'':294C ''read'':64C ''recommend'':59C ''repeat'':109C ''save'':182C ''see'':189C ''self'':177C ''sinc'':48C ''sleep'':160C,263C ''small'':256C ''solv'':38C ''spent'':192C,202C ''stay'':225C,266C ''sth'':146C ''studi'':13C ''tell'':144C ''third'':154C ''time'':50C ''topic'':22C ''tri'':104C,200C ''uneven'':166C ''usag'':241C ''use'':40C ''user'':159C,179C,210C,249C ''usual'':195C ''variat'':112C ''want'':261C ''way'':75C ''week'':155C ''weekend'':125C ''whole'':226C ''wonder'':273C ''work'':214C ''world'':51C ''worthi'':62C ''would'':143C ''year'':15C,92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5882, 2, '2015-05-24 00:07:19.097', 3, NULL, NULL, '&lt;p&gt;you can trace the squared error in statistics through multivariate calculus all the way to Pythagorus. You are basically calculating the ''effective length'' of the error, the hypotenuse, among errors from multiple variables $(X_1 - X_2)^2 + (Y_1 - Y_2)^2 + ...$ like in a triangle.&lt;/p&gt;

&lt;p&gt;But where did the square root go?&lt;/p&gt;

&lt;p&gt;Somebody realized that calculating roots of multiple variables over multiple iterations is computationally very expensive. So they decided to drop it. Checkout the squared Euclidean distance &lt;a href="http://en.wikipedia.org/wiki/Euclidean_distance#Squared_Euclidean_distance" rel="nofollow"&gt;here&lt;/a&gt; for more details&lt;/p&gt;

&lt;p&gt;How would a cubic error or a logarithmic error affect the outcome?&lt;/p&gt;

&lt;p&gt;It just takes more time to converge because they are not as accurate. But we do see logarithmic errors over squares such as logistic regression where it is more optimal&lt;/p&gt;

&lt;p&gt;All in all it is a simple case of optimization&lt;/p&gt;
', NULL, NULL, '''/a'':94C ''/p'':49C,58C,98C,112C,144C,156C ''/wiki/euclidean_distance#squared_euclidean_distance'':90C ''1'':36C,41C ''2'':38C,39C,43C,44C ''accur'':126C ''affect'':109C ''among'':30C ''basic'':20C ''calcul'':21C,63C ''calculus'':12C ''case'':153C ''checkout'':81C ''comput'':72C ''converg'':120C ''cubic'':103C ''decid'':77C ''detail'':97C ''distanc'':85C ''drop'':79C ''effect'':23C ''en.wikipedia.org'':89C ''en.wikipedia.org/wiki/euclidean_distance#squared_euclidean_distance'':88C ''error'':7C,27C,31C,104C,108C,132C ''euclidean'':84C ''expens'':74C ''go'':57C ''href'':87C ''hypotenus'':29C ''iter'':70C ''length'':24C ''like'':45C ''logarithm'':107C,131C ''logist'':137C ''multipl'':33C,66C,69C ''multivari'':11C ''nofollow'':92C ''optim'':143C,155C ''outcom'':111C ''p'':1C,50C,59C,99C,113C,145C ''pythagorus'':17C ''realiz'':61C ''regress'':138C ''rel'':91C ''root'':56C,64C ''see'':130C ''simpl'':152C ''somebodi'':60C ''squar'':6C,55C,83C,134C ''statist'':9C ''take'':116C ''time'':118C ''trace'':4C ''triangl'':48C ''variabl'':34C,67C ''way'':15C ''would'':101C ''x'':35C,37C ''y'':40C,42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5889, 2, '2015-05-24 10:04:25.767', 2, NULL, NULL, '&lt;p&gt;In the third paragraph of the first section (page 1), they define $\#(w)$, $\#(w; c)$, and $\#(c)$. Quoting such paragraph:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Word-context pairs are denoted as $(w; c)$, and $\#(w; c)$ counts all observations of $(w; c)$ from the corpus. We use $\#( w ) =  \sum_c \#(w; c)$ and $\#(c) = \sum_w \#(w; c)$ to refer to the count of occurrences of a word (context) in all word-context pairs. Either $\sum_c \#(c)$ or  $\sum_w \#(w)$ may represent the count of all word-context pairs.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;So, considering the equation you cited:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;$\#(w_i, c_j)$ counts the occurrences of a specific word $w_i$ over the context $c_j$;&lt;/li&gt;
&lt;li&gt;and $\frac{\#(c_)}{\sum_w \#(w)}$ accounts for the the probability of having context $c_j$ in the dataset.&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/blockquote'':95C ''/li'':124C,145C ''/ol'':146C ''/p'':22C,94C,103C ''1'':11C ''account'':132C ''blockquot'':23C ''c'':16C,18C,33C,36C,42C,50C,52C,54C,58C,78C,79C,108C,122C,128C,140C ''cite'':102C ''consid'':98C ''context'':27C,69C,74C,92C,121C,139C ''corpus'':45C ''count'':37C,63C,87C,110C ''dataset'':144C ''defin'':13C ''denot'':30C ''either'':76C ''equat'':100C ''first'':8C ''frac'':127C ''j'':109C,123C,141C ''li'':105C,125C ''may'':84C ''observ'':39C ''occurr'':65C,112C ''ol'':104C ''p'':1C,24C,96C ''page'':10C ''pair'':28C,75C,93C ''paragraph'':5C,21C ''probabl'':136C ''quot'':19C ''refer'':60C ''repres'':85C ''section'':9C ''specif'':115C ''sum'':49C,55C,77C,81C,129C ''third'':4C ''use'':47C ''w'':14C,15C,32C,35C,41C,48C,51C,56C,57C,82C,83C,106C,117C,130C,131C ''word'':26C,68C,73C,91C,116C ''word-context'':25C,72C,90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5915, 2, '2015-05-27 03:38:45.067', 2, NULL, NULL, '&lt;p&gt;You can use &lt;a href="https://github.com/tinkerpop/rexster/wiki/Basic-REST-API" rel="nofollow"&gt;Rexster&lt;/a&gt; to expose a REST api and then you can use this api via &lt;a href="http://docs.python-requests.org/en/latest/" rel="nofollow"&gt;Requests&lt;/a&gt; module (there are many other modules to do this task - though Requests is what I prefer)&lt;/p&gt;
', NULL, NULL, '''/a'':13C,35C ''/en/latest/'':31C ''/p'':52C ''/tinkerpop/rexster/wiki/basic-rest-api'':9C ''api'':18C,25C ''docs.python-requests.org'':30C ''docs.python-requests.org/en/latest/'':29C ''expos'':15C ''github.com'':8C ''github.com/tinkerpop/rexster/wiki/basic-rest-api'':7C ''href'':6C,28C ''mani'':39C ''modul'':36C,41C ''nofollow'':11C,33C ''p'':1C ''prefer'':51C ''rel'':10C,32C ''request'':34C,47C ''rest'':17C ''rexster'':12C ''task'':45C ''though'':46C ''use'':4C,23C ''via'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5890, 1, '2015-05-24 11:54:43.347', 4, 102, 'Different methods for clustering skills in text', '&lt;p&gt;Consider a talent pool in which each member has some set of skills. Some of these talent are submitted to orders as potential candidates of which one is selected. It is reasonable to assume that the submitted talent have some dominant thing in common in their skill sets (let''s call it a segment) that qualifies them for the order. Example segments are "front end web-designer" or "brochure / sprint designer".&lt;/p&gt;

&lt;p&gt;Given the total set of skills over all of the talent submitted to an order (like 2-5 with say 10 skills each, so 20 - 50 skills total), I am looking for the dominant segment. Then, I am looking for the dominant segment for each individual talent.&lt;/p&gt;

&lt;p&gt;My plan is to use latent Dirichlet allocation (LDA) such that the skills of all the talent submitted for an order are a "document" that contains some segments or "topics" with some probability. Likely, there will be one or two dominant topics depending on the total topic number. I will then use this model to predict the dominant segment for each talent where the individual talent skill set is a "document" with some segments or "topics" within.&lt;/p&gt;

&lt;p&gt;I am curious if anyone has feedback about my use of LDA or other ideas about how I might go about discovering these segments?&lt;/p&gt;
', 'machine-learning  data-mining  nlp  text-mining', NULL, '''-5'':110C ''/p'':91C,140C,219C,245C ''10'':113C ''2'':109C ''20'':117C ''50'':118C ''alloc'':149C ''anyon'':225C ''assum'':52C ''brochur'':88C ''call'':69C ''candid'':42C ''cluster'':4A ''common'':62C ''consid'':19C ''contain'':167C ''curious'':223C ''data'':12B ''data-min'':11B ''depend'':184C ''design'':86C,90C ''differ'':1A ''dirichlet'':148C ''discov'':242C ''document'':165C,212C ''domin'':59C,126C,134C,182C,199C ''end'':83C ''exampl'':79C ''feedback'':227C ''front'':82C ''given'':93C ''go'':240C ''idea'':235C ''individu'':138C,206C ''latent'':147C ''lda'':150C,232C ''learn'':10B ''let'':67C ''like'':108C,175C ''look'':123C,131C ''machin'':9B ''machine-learn'':8B ''member'':26C ''method'':2A ''might'':239C ''mine'':13B,17B ''model'':195C ''nlp'':14B ''number'':189C ''one'':45C,179C ''order'':39C,78C,107C,162C ''p'':18C,92C,141C,220C ''plan'':143C ''pool'':22C ''potenti'':41C ''predict'':197C ''probabl'':174C ''qualifi'':74C ''reason'':50C ''say'':112C ''segment'':72C,80C,127C,135C,169C,200C,215C,244C ''select'':47C ''set'':29C,66C,96C,209C ''skill'':5A,31C,65C,98C,114C,119C,154C,208C ''sprint'':89C ''submit'':37C,55C,104C,159C ''talent'':21C,35C,56C,103C,139C,158C,203C,207C ''text'':7A,16B ''text-min'':15B ''thing'':60C ''topic'':171C,183C,188C,217C ''total'':95C,120C,187C ''two'':181C ''use'':146C,193C,230C ''web'':85C ''web-design'':84C ''within'':218C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5892, 2, '2015-05-24 16:11:35.86', 0, NULL, NULL, '&lt;p&gt;When you said "a broader topic", did you mean what algorithms to use to examine the event log with the goal of reducing future calls from the same customer on the same topic? In other words, a customer may call for help for a different topic. If a customer calls in for the same topic repetitively, something can be improved.&lt;/p&gt;

&lt;p&gt;You may get ideas from a Coursera class &lt;a href="https://www.coursera.org/course/procmin" rel="nofollow"&gt;Processing Mining&lt;/a&gt; since the issue you''re solving is similar to the example of a spaghetti process in lecture 6.7:&lt;/p&gt;

&lt;p&gt;"Spaghetti process describing the diagnosis and treatment of 2765 patients in a Dutch hospital. The process model was constructed based on an event log containing 114,592 events. There are 619 different activities (taking event types into account) executed by 266 different individuals (doctors, nurses, etc.)."&lt;/p&gt;

&lt;p&gt;By the way, you can click the drop down menu under "Sessions", choose "April 1, 2015 to May 19, 2015" then you can register and view the lectures. On the right of each lecture, there are some icons. The first icon is to download slides for the lecture. You may find reading the slides is faster than listening to the lecture.&lt;/p&gt;

&lt;p&gt;Suppose a customer called for installation of software release 1.5 then called a day later for running the new features of software release 1.5. Are these two issues logged as two of the 200 call categories? If so, we can use a time period (say one week) to judge whether it is about the same topic. Within a short time period, a customer is likely to work on the same topic, especially with the same key words such as "software release 1.5". We can coach the call center employees on solving possible follow up questions by saying something like "now that we finished installation, let me show you a couple of new features. It''ll save you time." This will reduce the number of calls on the same topic from the same customer.&lt;/p&gt;
', NULL, NULL, '''/a'':81C ''/course/procmin'':76C ''/p'':62C,100C,148C,211C,346C ''1'':164C ''1.5'':222C,236C,294C ''114'':127C ''19'':168C ''200'':246C ''2015'':165C,169C ''266'':142C ''2765'':110C ''592'':128C ''6.7'':99C ''619'':132C ''account'':139C ''activ'':134C ''algorithm'':12C ''april'':163C ''base'':121C ''broader'':6C ''call'':26C,41C,51C,216C,224C,247C,299C,337C ''categori'':248C ''center'':300C ''choos'':162C ''class'':71C ''click'':155C ''coach'':297C ''construct'':120C ''contain'':126C ''coupl'':322C ''coursera'':70C ''custom'':30C,39C,50C,215C,275C,345C ''day'':226C ''describ'':104C ''diagnosi'':106C ''differ'':46C,133C,143C ''doctor'':145C ''download'':193C ''drop'':157C ''dutch'':114C ''employe'':301C ''especi'':284C ''etc'':147C ''event'':18C,124C,129C,136C ''examin'':16C ''exampl'':92C ''execut'':140C ''faster'':205C ''featur'':232C,325C ''find'':200C ''finish'':315C ''first'':189C ''follow'':305C ''futur'':25C ''get'':66C ''goal'':22C ''help'':43C ''hospit'':115C ''href'':73C ''icon'':187C,190C ''idea'':67C ''improv'':61C ''individu'':144C ''instal'':218C,316C ''issu'':84C,240C ''judg'':261C ''key'':288C ''later'':227C ''lectur'':98C,177C,183C,197C,210C ''let'':317C ''like'':277C,311C ''listen'':207C ''ll'':327C ''log'':19C,125C,241C ''may'':40C,65C,167C,199C ''mean'':10C ''menu'':159C ''mine'':80C ''model'':118C ''new'':231C,324C ''nofollow'':78C ''number'':335C ''nurs'':146C ''one'':258C ''p'':1C,63C,101C,149C,212C ''patient'':111C ''period'':256C,273C ''possibl'':304C ''process'':79C,96C,103C,117C ''question'':307C ''re'':86C ''read'':201C ''reduc'':24C,333C ''regist'':173C ''rel'':77C ''releas'':221C,235C,293C ''repetit'':57C ''right'':180C ''run'':229C ''said'':4C ''save'':328C ''say'':257C,309C ''session'':161C ''short'':271C ''show'':319C ''similar'':89C ''sinc'':82C ''slide'':194C,203C ''softwar'':220C,234C,292C ''solv'':87C,303C ''someth'':58C,310C ''spaghetti'':95C,102C ''suppos'':213C ''take'':135C ''time'':255C,272C,330C ''topic'':7C,34C,47C,56C,268C,283C,341C ''treatment'':108C ''two'':239C,243C ''type'':137C ''use'':14C,253C ''view'':175C ''way'':152C ''week'':259C ''whether'':262C ''within'':269C ''word'':37C,289C ''work'':279C ''www.coursera.org'':75C ''www.coursera.org/course/procmin'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5893, 1, '2015-05-24 21:45:02.207', 3, 206, 'How to create a good list of stopwords', '&lt;p&gt;I am looking for some hints on how to curate a list of stopwords. Does someone know / can someone recommend a good method to extract stopword lists from the dataset itself for preprocessing and filtering?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;The Data:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;a huge amount of human text input of variable length (searchterms and whole sentences (up to 200 characters) ) over several years. The text contains a lot of spam (like machine input from bots, single words, stupid searches, product searches ... ) and only a few % of seems to be useful. I realised that sometimes (only very rarely) people search my side by asking really cool questions. These questions are so cool, that i think it is worth to have a deeper look into them to see how people search over time and what topics people have been interested in using my website.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;My problem:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;is that i am really struggling with the preprocessing (i.e. dropping the spam). I already tried some stopword list from the web (NLTK etc.), but these don''t really help my needs regarding this dataset. &lt;/p&gt;

&lt;p&gt;Thanks for your ideas and discussion folks!&lt;/p&gt;
', 'data-mining  nlp  information-retrieval  language-model', NULL, '''/p'':55C,61C,163C,169C,206C,215C ''/strong'':60C,168C ''200'':79C ''alreadi'':185C ''amount'':65C ''ask'':123C ''bot'':95C ''charact'':80C ''contain'':86C ''cool'':125C,131C ''creat'':3A ''curat'':29C ''data'':10B,59C ''data-min'':9B ''dataset'':49C,205C ''deeper'':141C ''discuss'':213C ''drop'':181C ''etc'':194C ''extract'':44C ''filter'':54C ''folk'':214C ''good'':5A,41C ''help'':200C ''hint'':25C ''huge'':64C ''human'':67C ''i.e'':180C ''idea'':211C ''inform'':14B ''information-retriev'':13B ''input'':69C,93C ''interest'':158C ''know'':36C ''languag'':17B ''language-model'':16B ''length'':72C ''like'':91C ''list'':6A,31C,46C,189C ''look'':22C,142C ''lot'':88C ''machin'':92C ''method'':42C ''mine'':11B ''model'':18B ''need'':202C ''nlp'':12B ''nltk'':193C ''p'':19C,56C,62C,164C,170C,207C ''peopl'':118C,148C,155C ''preprocess'':52C,179C ''problem'':167C ''product'':100C ''question'':126C,128C ''rare'':117C ''realis'':112C ''realli'':124C,175C,199C ''recommend'':39C ''regard'':203C ''retriev'':15B ''search'':99C,101C,119C,149C ''searchterm'':73C ''see'':146C ''seem'':107C ''sentenc'':76C ''sever'':82C ''side'':121C ''singl'':96C ''someon'':35C,38C ''sometim'':114C ''spam'':90C,183C ''stopword'':8A,33C,45C,188C ''strong'':57C,165C ''struggl'':176C ''stupid'':98C ''text'':68C,85C ''thank'':208C ''think'':134C ''time'':151C ''topic'':154C ''tri'':186C ''use'':110C,160C ''variabl'':71C ''web'':192C ''websit'':162C ''whole'':75C ''word'':97C ''worth'':137C ''year'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5894, 2, '2015-05-25 06:32:29.107', 6, NULL, NULL, '&lt;p&gt;I came across this collection on Github. The collection is categorised as well.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/caesar0301/awesome-public-datasets"&gt;https://github.com/caesar0301/awesome-public-datasets&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;And for the part regarding &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Can not a open-source model for sharing data sets devised for data scientists?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;you can refer &lt;a href="https://github.com/jtleek/datasharing"&gt;The Leek group guide to data sharing&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':67C ''/blockquote'':50C ''/caesar0301/awesome-public-datasets'':21C ''/caesar0301/awesome-public-datasets&lt;/a&gt;&lt;/p&gt;'':24C ''/jtleek/datasharing'':59C ''/p'':15C,31C,49C,68C ''across'':4C ''blockquot'':32C ''came'':3C ''categoris'':12C ''collect'':6C,10C ''data'':43C,47C,65C ''devis'':45C ''github'':8C ''github.com'':20C,23C,58C ''github.com/caesar0301/awesome-public-datasets'':19C ''github.com/caesar0301/awesome-public-datasets&lt;/a&gt;&lt;/p&gt;'':22C ''github.com/jtleek/datasharing'':57C ''group'':62C ''guid'':63C ''href'':18C,56C ''leek'':61C ''model'':40C ''open'':38C ''open-sourc'':37C ''p'':1C,16C,25C,33C,51C ''part'':29C ''refer'':54C ''regard'':30C ''scientist'':48C ''set'':44C ''share'':42C,66C ''sourc'':39C ''well'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6028, 1, '2015-06-06 03:30:10.683', 0, 103, 'Python - Increasing performance speed of permutation calculation of Itertools; repeat=15', '&lt;p&gt;What methods of increasing calculation when repeat is greater then 10-25.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;permutation.py&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;inputRepeat = input(''&amp;gt;&amp;gt;&amp;gt; Enter repeat value. (int): '')
L = ''ABCDEFGHIJKLMNOPQRSTUVWYZ''

i = list(''''.join(x) for x in itertools.product(L'', repeat=inputRepeat))
print i
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m running this on a Mac and working on data science project. It calculates when repeat value is &gt;= 9, but needs increased amount of time before the script begins to print after that value; it could be that this machine needs more CPU/memory, or can the current code be optimized for performance? As this becomes a problem when I need to generate the entire list of repeat=26.&lt;/p&gt;

&lt;p&gt;I''m thinking there are alternative solutions that could employ the use of a matrix list and combinations/permutations to arrive at the same results, and result in decreases of runtime/increased performance.&lt;/p&gt;
', 'python  optimization  performance', NULL, '''-25'':27C ''/code'':60C ''/p'':28C,33C,132C,165C ''/pre'':61C ''/strong'':32C ''10'':26C ''15'':11A ''26'':131C ''9'':82C ''abcdefghijklmnopqrstuvwyz'':46C ''altern'':139C ''amount'':86C ''arriv'':153C ''becom'':118C ''begin'':92C ''calcul'':7A,20C,77C ''code'':35C,111C ''combinations/permutations'':151C ''could'':99C,142C ''cpu/memory'':106C ''current'':110C ''data'':73C ''decreas'':161C ''employ'':143C ''enter'':41C ''entir'':127C ''generat'':125C ''greater'':24C ''gt'':38C,39C,40C ''increas'':2A,19C,85C ''input'':37C ''inputrepeat'':36C,57C ''int'':44C ''itertool'':9A ''itertools.product'':54C ''join'':49C ''l'':45C,55C ''list'':48C,128C,149C ''m'':64C,135C ''mac'':69C ''machin'':103C ''matrix'':148C ''method'':17C ''need'':84C,104C,123C ''optim'':13B,113C ''p'':15C,29C,62C,133C ''perform'':3A,14B,115C,164C ''permut'':6A ''permutation.py'':31C ''pre'':34C ''print'':58C,94C ''problem'':120C ''project'':75C ''python'':1A,12B ''repeat'':10A,22C,42C,56C,79C,130C ''result'':157C,159C ''run'':65C ''runtime/increased'':163C ''scienc'':74C ''script'':91C ''solut'':140C ''speed'':4A ''strong'':30C ''think'':136C ''time'':88C ''use'':145C ''valu'':43C,80C,97C ''work'':71C ''x'':50C,52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5901, 2, '2015-05-25 15:13:50.583', 1, NULL, NULL, '&lt;p&gt;The first idea in my mind is : view it forming a social graph where nodes are the email-ids (people) and 2 nodes are connected if they communicate with each other. You can also view it as a weighted graph where weight comes from the frequency of conversation and you can add the sense of direction as well using the sender-receiver information. Now you can apply all sort of social network analysis on this.&lt;/p&gt;
', NULL, NULL, '''/p'':79C ''2'':24C ''add'':54C ''also'':36C ''analysi'':76C ''appli'':70C ''come'':45C ''communic'':30C ''connect'':27C ''convers'':50C ''direct'':58C ''email'':20C ''email-id'':19C ''first'':3C ''form'':11C ''frequenc'':48C ''graph'':14C,42C ''id'':21C ''idea'':4C ''inform'':66C ''mind'':7C ''network'':75C ''node'':16C,25C ''p'':1C ''peopl'':22C ''receiv'':65C ''sender'':64C ''sender-receiv'':63C ''sens'':56C ''social'':13C,74C ''sort'':72C ''use'':61C ''view'':9C,37C ''weight'':41C,44C ''well'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8714, 2, '2015-11-04 12:48:06.27', 3, NULL, NULL, '&lt;p&gt;I can only speak about Graphs:&lt;/p&gt;

&lt;p&gt;Advantages:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Using graphs, you can easily find products bought/rated by users that bought or liked an item, or users that have similar "taste" to another user. From my experience the traversal process is fast enough.&lt;/li&gt;
&lt;li&gt;Closely matched products are easy to find, depending on how you model your graph: e.g. &lt;code&gt;(userA)-[]-&amp;gt;(basketA)-[with]-&amp;gt;(productA)&amp;lt;-(basketB)-[]-(userB)&lt;/code&gt;. Finding similar products by basket is computationally cheap here&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Disadvantages:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Depending on how you rank your results, you can easily run into the trap of constantly recommending the same products. Users that bought A, bought B. So you suggest B. People buy/like B. Next time you run the query, B will keep coming on top. &lt;/li&gt;
&lt;li&gt;Your results will rarely uncover new products. You''d have to find smart approaches around this, e.g. find other products, within this price range, with this category&lt;/li&gt;
&lt;li&gt;If you have very large data sets, you''ll have to use a distributed graph database that performs well (this is not as easy as it sounds, unless you''re willing to pay large sums of money)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;My little experience with ML for collaborative filtering, is that when your data grows large (50GB+), building a model takes a considerable amount of time (hours, days), and you''re not likely to get good recommendations on new products. Having to update your model becomes a huge problem too. From my experience, I lean towards graphs for small use cases.&lt;/p&gt;

&lt;p&gt;Note that for both cases, you never recommend newly added products that have not been bought or liked by someone. I say this because the goal of recommendations is to help users uncover new products. There is a blog post I wrote, where I talk about modelling graphs, and I discuss recommendations in some examples &lt;a href="https://thinkingthread.com/modelling-graphs-with-neo4j/" rel="nofollow"&gt;here&lt;/a&gt;, so you can skip to that section to get an idea on how to model this kind of problem.&lt;/p&gt;

&lt;p&gt;I advise you to read up on Amazon''s paper on their approach to collaborative filtering, which is pretty simple, in theory, and yields good results for them. I think you''d always want to implement a few different approaches combined, to tackle different parts of the problem.&lt;/p&gt;

&lt;p&gt;Good luck.&lt;/p&gt;
', NULL, NULL, '''/a'':333C ''/code'':75C ''/li'':48C,85C,137C,166C,205C ''/modelling-graphs-with-neo4j/'':329C ''/p'':8C,11C,89C,268C,353C,403C,407C ''/ul'':86C,206C ''50gb'':223C ''ad'':279C ''advantag'':10C ''advis'':356C ''alway'':387C ''amazon'':362C ''amount'':230C ''anoth'':37C ''approach'':152C,367C,394C ''around'':153C ''b'':117C,121C,124C,131C ''basket'':80C ''basketa'':68C ''basketb'':73C ''becom'':252C ''blog'':308C ''bought'':25C,114C,116C,285C ''bought/rated'':21C ''build'':224C ''buy/like'':123C ''case'':267C,274C ''categori'':165C ''cheap'':83C ''close'':50C ''code'':65C ''collabor'':214C,369C ''combin'':395C ''come'':134C ''comput'':82C ''consider'':229C ''constant'':107C ''d'':147C,386C ''data'':173C,220C ''databas'':183C ''day'':234C ''depend'':57C,92C ''differ'':393C,398C ''disadvantag'':88C ''discuss'':320C ''distribut'':181C ''e.g'':64C,155C ''easi'':54C,191C ''easili'':18C,101C ''enough'':47C ''exampl'':324C ''experi'':41C,210C,259C ''fast'':46C ''filter'':215C,370C ''find'':19C,56C,76C,150C,156C ''get'':241C,342C ''goal'':295C ''good'':242C,379C,405C ''graph'':7C,15C,63C,182C,263C,317C ''grow'':221C ''gt'':67C,70C ''help'':300C ''hour'':233C ''href'':326C ''huge'':254C ''idea'':344C ''implement'':390C ''item'':29C ''keep'':133C ''kind'':350C ''larg'':172C,201C,222C ''lean'':261C ''li'':13C,49C,91C,138C,167C ''like'':27C,239C,287C ''littl'':209C ''ll'':176C ''lt'':72C ''luck'':406C ''match'':51C ''ml'':212C ''model'':61C,226C,251C,316C,348C ''money'':204C ''never'':276C ''new'':144C,245C,303C ''newli'':278C ''next'':125C ''nofollow'':331C ''note'':270C ''p'':1C,9C,87C,207C,269C,354C,404C ''paper'':364C ''part'':399C ''pay'':200C ''peopl'':122C ''perform'':185C ''post'':309C ''pretti'':373C ''price'':161C ''problem'':255C,352C,402C ''process'':44C ''product'':20C,52C,78C,111C,145C,158C,246C,280C,304C ''producta'':71C ''queri'':130C ''rang'':162C ''rank'':96C ''rare'':142C ''re'':197C,237C ''read'':359C ''recommend'':108C,243C,277C,297C,321C ''rel'':330C ''result'':98C,140C,380C ''run'':102C,128C ''say'':291C ''section'':340C ''set'':174C ''similar'':34C,77C ''simpl'':374C ''skip'':337C ''small'':265C ''smart'':151C ''someon'':289C ''sound'':194C ''speak'':5C ''suggest'':120C ''sum'':202C ''tackl'':397C ''take'':227C ''talk'':314C ''tast'':35C ''theori'':376C ''think'':384C ''thinkingthread.com'':328C ''thinkingthread.com/modelling-graphs-with-neo4j/'':327C ''time'':126C,232C ''top'':136C ''toward'':262C ''trap'':105C ''travers'':43C ''ul'':12C,90C ''uncov'':143C,302C ''unless'':195C ''updat'':249C ''use'':14C,179C,266C ''user'':23C,31C,38C,112C,301C ''usera'':66C ''userb'':74C ''want'':388C ''well'':186C ''will'':198C ''within'':159C ''wrote'':311C ''yield'':378C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5895, 5, '2015-05-25 07:22:43.65', 0, NULL, NULL, '&lt;p&gt;&lt;a href="http://scikit-learn.org" rel="nofollow"&gt;Scikit-learn&lt;/a&gt; is a Python module comprising of simple and efficient tool for machine learning, data mining and data analysis. It is built on &lt;a href="http://numpy.org" rel="nofollow"&gt;NumPy&lt;/a&gt;, &lt;a href="http://www.scipy.org" rel="nofollow"&gt;SciPy&lt;/a&gt;, and &lt;a href="http://matplotlib.org/" rel="nofollow"&gt;matplotlib&lt;/a&gt;. It is distributed under the 3-Clause BSD license.&lt;/p&gt;

&lt;p&gt;Important links&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Official source code repo: &lt;a href="https://github.com/scikit-learn/scikit-learn" rel="nofollow"&gt;https://github.com/scikit-learn/scikit-learn&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;HTML documentation (stable release): &lt;a href="http://scikit-learn.org" rel="nofollow"&gt;http://scikit-learn.org&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;HTML documentation (development version): &lt;a href="http://scikit-learn.org/dev/" rel="nofollow"&gt;http://scikit-learn.org/dev/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Download releases: &lt;a href="http://sourceforge.net/projects/scikit-learn/files/" rel="nofollow"&gt;http://sourceforge.net/projects/scikit-learn/files/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Issue tracker: &lt;a href="https://github.com/scikit-learn/scikit-learn/issues" rel="nofollow"&gt;https://github.com/scikit-learn/scikit-learn/issues&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Mailing list: &lt;a href="https://lists.sourceforge.net/lists/listinfo/scikit-learn-general" rel="nofollow"&gt;https://lists.sourceforge.net/lists/listinfo/scikit-learn-general&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':10C,39C,46C,54C,96C ''/dev/'':107C ''/dev/&lt;/a&gt;&lt;/li&gt;'':112C ''/li'':97C ''/lists/listinfo/scikit-learn-general'':146C ''/lists/listinfo/scikit-learn-general&lt;/a&gt;&lt;/li&gt;'':151C ''/p'':64C,68C ''/projects/scikit-learn/files/'':120C ''/projects/scikit-learn/files/&lt;/a&gt;&lt;/li&gt;'':125C ''/scikit-learn/scikit-learn'':79C ''/scikit-learn/scikit-learn&lt;/a&gt;&lt;/li&gt;'':84C ''/scikit-learn/scikit-learn/issues'':133C ''/scikit-learn/scikit-learn/issues&lt;/a&gt;&lt;/li&gt;'':138C ''/ul'':152C ''3'':60C ''analysi'':28C ''bsd'':62C ''built'':31C ''claus'':61C ''code'':73C ''compris'':15C ''data'':24C,27C ''develop'':101C ''distribut'':57C ''document'':87C,100C ''download'':114C ''effici'':19C ''github.com'':78C,83C,132C,137C ''github.com/scikit-learn/scikit-learn'':77C ''github.com/scikit-learn/scikit-learn&lt;/a&gt;&lt;/li&gt;'':82C ''github.com/scikit-learn/scikit-learn/issues'':131C ''github.com/scikit-learn/scikit-learn/issues&lt;/a&gt;&lt;/li&gt;'':136C ''href'':3C,34C,41C,49C,76C,91C,104C,117C,130C,143C ''html'':86C,99C ''import'':66C ''issu'':127C ''learn'':9C,23C ''li'':70C,85C,98C,113C,126C,139C ''licens'':63C ''link'':67C ''list'':141C ''lists.sourceforge.net'':145C,150C ''lists.sourceforge.net/lists/listinfo/scikit-learn-general'':144C ''lists.sourceforge.net/lists/listinfo/scikit-learn-general&lt;/a&gt;&lt;/li&gt;'':149C ''machin'':22C ''mail'':140C ''matplotlib'':53C ''matplotlib.org'':50C ''mine'':25C ''modul'':14C ''nofollow'':6C,37C,44C,52C,81C,94C,109C,122C,135C,148C ''numpi'':38C ''numpy.org'':35C ''offici'':71C ''p'':1C,65C ''python'':13C ''rel'':5C,36C,43C,51C,80C,93C,108C,121C,134C,147C ''releas'':89C,115C ''repo'':74C ''scikit'':8C ''scikit-learn'':7C ''scikit-learn.org'':4C,92C,95C,106C,111C ''scikit-learn.org/dev/'':105C ''scikit-learn.org/dev/&lt;/a&gt;&lt;/li&gt;'':110C ''scipi'':45C ''simpl'':17C ''sourc'':72C ''sourceforge.net'':119C,124C ''sourceforge.net/projects/scikit-learn/files/'':118C ''sourceforge.net/projects/scikit-learn/files/&lt;/a&gt;&lt;/li&gt;'':123C ''stabl'':88C ''tool'':20C ''tracker'':128C ''ul'':69C ''version'':102C ''www.scipy.org'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5896, 4, '2015-05-25 07:22:43.65', 0, NULL, NULL, 'Scikit-learn is a Python module comprising of simple and efficient tool for machine learning, data mining and data analysis. It is built on NumPy, SciPy, and matplotlib. It is distributed under the 3-Clause BSD license.', NULL, NULL, '''3'':35C ''analysi'':21C ''bsd'':37C ''built'':24C ''claus'':36C ''compris'':8C ''data'':17C,20C ''distribut'':32C ''effici'':12C ''learn'':3C,16C ''licens'':38C ''machin'':15C ''matplotlib'':29C ''mine'':18C ''modul'':7C ''numpi'':26C ''python'':6C ''scikit'':2C ''scikit-learn'':1C ''scipi'':27C ''simpl'':10C ''tool'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5897, 1, '2015-05-25 07:54:09.283', 2, 69, 'How deep should ones linear algebra knowledge be before starting data science?', '&lt;p&gt;How important is linear algebra to being a data scientist? Are we talking college postgraduate level?&lt;/p&gt;
', 'education', NULL, '''/p'':31C ''algebra'':6A,19C ''colleg'':28C ''data'':11A,23C ''deep'':2A ''educ'':13B ''import'':16C ''knowledg'':7A ''level'':30C ''linear'':5A,18C ''one'':4A ''p'':14C ''postgradu'':29C ''scienc'':12A ''scientist'':24C ''start'':10A ''talk'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5925, 2, '2015-05-27 11:07:38.02', 2, NULL, NULL, '&lt;p&gt;Probably you can use multiple decision trees - each for slice of dataset. Then combine the results from each decision tree. You can also weigh the results from each decision tree. Eg the dataset for 2015 can be given more weight than dataset for 2014. I do not see much harm in spliting dataset and training different trees as it helps to account for the predictor values in a more accurate manner. If you could more details about the data or the "variable" you mentioned, people would be able to help more.&lt;/p&gt;
', NULL, NULL, '''/p'':93C ''2014'':45C ''2015'':36C ''abl'':89C ''account'':63C ''accur'':71C ''also'':24C ''combin'':15C ''could'':75C ''data'':80C ''dataset'':13C,34C,43C,54C ''decis'':7C,20C,30C ''detail'':77C ''differ'':57C ''eg'':32C ''given'':39C ''harm'':51C ''help'':61C,91C ''manner'':72C ''mention'':85C ''much'':50C ''multipl'':6C ''p'':1C ''peopl'':86C ''predictor'':66C ''probabl'':2C ''result'':17C,27C ''see'':49C ''slice'':11C ''splite'':53C ''train'':56C ''tree'':8C,21C,31C,58C ''use'':5C ''valu'':67C ''variabl'':83C ''weigh'':25C ''weight'':41C ''would'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5918, 1, '2015-05-27 07:53:53.17', 0, 56, 'How to install rattle in centos', '&lt;p&gt;While running rattle in my system I am getting this error&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;rattle()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Error: attempt to apply non-function
In addition: Warning message:
In method(obj, ...) : Unknown internal child: selection&lt;/p&gt;

&lt;p&gt;I am using R version 3.1.0 (2014-04-10)&lt;/p&gt;
', 'r', NULL, '''-04'':54C ''-10'':55C ''/code'':24C ''/p'':20C,45C,56C ''/pre'':25C ''2014'':53C ''3.1.0'':52C ''addit'':35C ''appli'':30C ''attempt'':28C ''cento'':6A ''child'':43C ''code'':22C ''error'':19C,27C ''function'':33C ''get'':17C ''instal'':3A ''intern'':42C ''messag'':37C ''method'':39C ''non'':32C ''non-funct'':31C ''obj'':40C ''p'':8C,26C,46C ''pre'':21C ''r'':7B,50C ''rattl'':4A,11C,23C ''run'':10C ''select'':44C ''system'':14C ''unknown'':41C ''use'':49C ''version'':51C ''warn'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5898, 2, '2015-05-25 08:57:19.877', 2, NULL, NULL, '&lt;p&gt;I think it truly depends on what you decide to specialize in. Data science is a very broad field, and you can actually work with data without knowing what eigenvalues and eigenvectors are. However, if you want to acquire a intermediate/advanced understanding of statistics or machine learning, you need at least an intermediate/advanced knowledge of linear algebra.&lt;/p&gt;

&lt;p&gt;I suggest to take an introductory class on linear algebra on MOOC - just to have a more precise idea of what linear algebra is - and then study some other topics that you are interested in. Linear algebra is a useful tool, but it can be very boring, especially if you are an "applied" kind of guy. Moreover, I think that some concepts like eigenvalues or eigenvectors are easier to understand when seen in an applied context, e.g. principal component analysis.&lt;/p&gt;
', NULL, NULL, '''/p'':59C,141C ''acquir'':40C ''actual'':24C ''algebra'':58C,70C,83C,97C ''analysi'':140C ''appli'':113C,135C ''bore'':107C ''broad'':19C ''class'':67C ''compon'':139C ''concept'':122C ''context'':136C ''data'':14C,27C ''decid'':10C ''depend'':6C ''e.g'':137C ''easier'':128C ''eigenvalu'':31C,124C ''eigenvector'':33C,126C ''especi'':108C ''field'':20C ''guy'':116C ''howev'':35C ''idea'':79C ''interest'':94C ''intermediate/advanced'':42C,54C ''introductori'':66C ''kind'':114C ''know'':29C ''knowledg'':55C ''learn'':48C ''least'':52C ''like'':123C ''linear'':57C,69C,82C,96C ''machin'':47C ''mooc'':72C ''moreov'':117C ''need'':50C ''p'':1C,60C ''precis'':78C ''princip'':138C ''scienc'':15C ''seen'':132C ''special'':12C ''statist'':45C ''studi'':87C ''suggest'':62C ''take'':64C ''think'':3C,119C ''tool'':101C ''topic'':90C ''truli'':5C ''understand'':43C,130C ''use'':100C ''want'':38C ''without'':28C ''work'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5899, 1, '2015-05-25 12:37:20.03', 1, 45, 'Do I need to apply a Ranking Algorithm for this?', '&lt;p&gt;I have data of the form :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Id1      A_Id2      B_Id2       C_Id2       D_Id2      E_Id2      F_Id2
1         6           3           9           23         20         5
1         4           7           8           9          11         56                                                   
1         2           36          98          73         2          4     
1         9           5           2           7          32         24           
1         14          7           5           9          12         5                                                   
2         34          4           7           10          7         12                                                       
2         5           57          23          91          4         6                                                    
2         7           .           .           .           .         .
2         3           .           .           .           .         .
2         .           .           .           .           .         .
.
.
100      .            .           .           .           .         .            
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Basically, I want to build a model such that I can get a best match of Id1, given top 5 Id2 matches of each attribute(A_Id2, B_Id2,...,F_Id2). Now every match should be computed keeping in mind A has highest priority, followed by B and C, followed by D and least priority to E and F. &lt;/p&gt;

&lt;p&gt;So the output will look like this :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Id1    Match_Id2
1        9
2        7
3        .
4        .
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I hope the problem is clear, if not please ask.
How should I go about building a Machine Learning model for this? I was wondering if ranking algorithm will help ?&lt;/p&gt;
', 'machine-learning  algorithms  predictive-modeling  ranking', NULL, '''/code'':95C,178C ''/p'':24C,157C,166C,211C ''/pre'':96C,179C ''1'':40C,47C,54C,61C,68C,172C ''10'':79C ''100'':94C ''11'':52C ''12'':73C,81C ''14'':69C ''2'':55C,59C,64C,75C,82C,89C,91C,93C,174C ''20'':45C ''23'':44C,85C ''24'':67C ''3'':42C,92C,176C ''32'':66C ''34'':76C ''36'':56C ''4'':48C,60C,77C,87C,177C ''5'':46C,63C,71C,74C,83C,117C ''56'':53C ''57'':84C ''6'':41C,88C ''7'':49C,65C,70C,78C,80C,90C,175C ''73'':58C ''8'':50C ''9'':43C,51C,62C,72C,173C ''91'':86C ''98'':57C ''algorithm'':8A,12B,208C ''appli'':5A ''ask'':190C ''attribut'':122C ''b'':30C,125C,144C ''basic'':98C ''best'':111C ''build'':102C,196C ''c'':32C,146C ''clear'':186C ''code'':26C,168C ''comput'':134C ''d'':34C,149C ''data'':20C ''e'':36C,154C ''everi'':130C ''f'':38C,127C,156C ''follow'':142C,147C ''form'':23C ''get'':109C ''given'':115C ''go'':194C ''help'':210C ''highest'':140C ''hope'':182C ''id1'':27C,114C,169C ''id2'':29C,31C,33C,35C,37C,39C,118C,124C,126C,128C,171C ''keep'':135C ''learn'':11B,199C ''least'':151C ''like'':164C ''look'':163C ''machin'':10B,198C ''machine-learn'':9B ''match'':112C,119C,131C,170C ''mind'':137C ''model'':15B,104C,200C ''need'':3A ''output'':161C ''p'':17C,97C,158C,180C ''pleas'':189C ''pre'':25C,167C ''predict'':14B ''predictive-model'':13B ''prioriti'':141C,152C ''problem'':184C ''rank'':7A,16B,207C ''top'':116C ''want'':100C ''wonder'':205C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5902, 2, '2015-05-25 15:19:21.227', 3, NULL, NULL, '&lt;p&gt;One approach would be to use &lt;a href="http://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;tf-idf&lt;/a&gt; score. The words which occur in most of the queries will be of little help in differentiating the good search queries from bad ones. But ones which occur very frequently (high tf or term-frequency) in only few queries (high idf or inverse document frequency) as likely to be more important in distinguishing the good queries from the bad ones.&lt;/p&gt;
', NULL, NULL, '''/a'':18C ''/p'':80C ''/wiki/tf%e2%80%93idf'':12C ''approach'':3C ''bad'':41C,78C ''differenti'':35C ''distinguish'':72C ''document'':63C ''en.wikipedia.org'':11C ''en.wikipedia.org/wiki/tf%e2%80%93idf'':10C ''frequenc'':54C,64C ''frequent'':48C ''good'':37C,74C ''help'':33C ''high'':49C,59C ''href'':9C ''idf'':17C,60C ''import'':70C ''invers'':62C ''like'':66C ''littl'':32C ''nofollow'':14C ''occur'':23C,46C ''one'':2C,42C,44C,79C ''p'':1C ''queri'':28C,39C,58C,75C ''rel'':13C ''score'':19C ''search'':38C ''term'':53C ''term-frequ'':52C ''tf'':16C,50C ''tf-idf'':15C ''use'':7C ''word'':21C ''would'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5903, 2, '2015-05-25 15:19:34.493', 0, NULL, NULL, '&lt;p&gt;Using TFIDF (term frequency inverse document frequency) will solve your purpose. Get the TFIDF score for each word in your document and sort the words by their scores by which you can select the important words in your data.&lt;/p&gt;
', NULL, NULL, '''/p'':41C ''data'':40C ''document'':7C,22C ''frequenc'':5C,8C ''get'':13C ''import'':36C ''invers'':6C ''p'':1C ''purpos'':12C ''score'':16C,29C ''select'':34C ''solv'':10C ''sort'':24C ''term'':4C ''tfidf'':3C,15C ''use'':2C ''word'':19C,26C,37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5904, 2, '2015-05-25 15:27:09.517', 2, NULL, NULL, '&lt;p&gt;A good introductory course for social network analysis : &lt;a href="https://www.coursera.org/course/sna" rel="nofollow"&gt;https://www.coursera.org/course/sna&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/course/sna'':14C ''/course/sna&lt;/a&gt;&lt;/p&gt;'':19C ''analysi'':9C ''cours'':5C ''good'':3C ''href'':11C ''introductori'':4C ''network'':8C ''nofollow'':16C ''p'':1C ''rel'':15C ''social'':7C ''www.coursera.org'':13C,18C ''www.coursera.org/course/sna'':12C ''www.coursera.org/course/sna&lt;/a&gt;&lt;/p&gt;'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5908, 2, '2015-05-26 05:11:36.16', 2, NULL, NULL, '&lt;p&gt;The general approach in feature selection is to get a score of each feature in the data set and select top features. We can run algorithms like GBM or Random Forest on all the variables simply to get a ranking of variable importance. We can also use ÏÂ² (chi-squared) statistic with cross validation to select a user-specified percentile of features with the highest scoring. But, the disadvantage with these approaches is not detecting correlations between features. &lt;/p&gt;

&lt;p&gt;We can also use backward elimination: features are tested one by one and the ones that are not statically significant are deleted from data set. In forward selection that starts with out any variable in dataset and then adds variables that are statically significant. Hope this helps. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5909, 2, '2015-05-26 11:26:14.063', 2, NULL, NULL, '&lt;p&gt;One approach can be to use a no-SQL database running on top of a distributed store (like Cassandra or Hbase). Add an external index which support spatial indexing (say elastic) for fast search. This makes your solution scalable (due to distributed store) and fast enough for spatial search queries.&lt;/p&gt;
', NULL, NULL, '''/p'':53C ''add'':24C ''approach'':3C ''cassandra'':21C ''databas'':12C ''distribut'':18C,44C ''due'':42C ''elast'':33C ''enough'':48C ''extern'':26C ''fast'':35C,47C ''hbase'':23C ''index'':27C,31C ''like'':20C ''make'':38C ''no-sql'':9C ''one'':2C ''p'':1C ''queri'':52C ''run'':13C ''say'':32C ''scalabl'':41C ''search'':36C,51C ''solut'':40C ''spatial'':30C,50C ''sql'':11C ''store'':19C,45C ''support'':29C ''top'':15C ''use'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5910, 1, '2015-05-26 12:36:54.197', 4, 115, 'Minimize correlation between input and output of black box system', '&lt;p&gt;I am not sure if "minimize correlation" is the right title for this issue but I could not find a better sentence to describe what I would like to achieve.&lt;/p&gt;

&lt;p&gt;Let''s say that I have a black box with multiple inputs and a single output. I know one of the inputs and the output and I have multiple example recordings of both. This known input modifies the output in a way that it is not desired, therefore, I would like to get rid of this "noise" caused by the known input. The transfer function for this input can be safely assumed as linear.&lt;/p&gt;

&lt;p&gt;What I am doing right now, it is to loop through the example recordings, creating a linear regression model to predict the unwanted outcome and subtracting it from the real measured output signal, for each example. Afterwards, I compute the average of all the fixed output signals to reveal meaningful data beyond noise.&lt;/p&gt;

&lt;p&gt;This strategy seems to work according to the following plot:
&lt;img src="http://i.stack.imgur.com/oAdyb.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;X axis is the known input signal, Y axis is the output signal, blue and green dots represent the averaged data before and after applying the linear regression algorithm, respectively. Lines are the best fit for each data set. &lt;/p&gt;

&lt;p&gt;You can see that the green line ("cleaned" dataset) has the smallest slope, meaning that the output variable is considerably less linearly correlated with the input than it was previously. Therefore, I assume that the regression technique explained before is working as expected.&lt;/p&gt;

&lt;p&gt;My question, looking at the plot, is there any mathematical procedure to directly "project" the original dataset in a way that the correlation between the input and output variables is minimized? Is there any math trick to avoid the use of the regression technique on all the example datasets to obtain a similar result? &lt;/p&gt;

&lt;p&gt;My written expression is not the best so please feel free to comment the question if you need further explanations.&lt;/p&gt;

&lt;p&gt;Any code is welcomed but python (pandas, numpy, etc.) and Matlab are preferred. Theoretical explanations are also very welcomed. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5922, 2, '2015-05-27 10:08:56.653', 2, NULL, NULL, '&lt;p&gt;What you are describing makes sense and relates to the naive Bayesian classifier. A useful tool in that respect is &lt;a href="http://en.wikipedia.org/wiki/Mutual_information" rel="nofollow"&gt;mutual information (MI)&lt;/a&gt; (and all its derivatives). MI was first proposed for feature selection by &lt;a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.74.7629&amp;amp;rep=rep1&amp;amp;type=pdf" rel="nofollow"&gt;Battiti&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;MI is based on marginal and joint density estimations so you will need some pdf estimators for your continuous variables, like histograms, or kernel-based density estimators.&lt;/p&gt;
', NULL, NULL, '''/a'':32C,53C ''/p'':54C,84C ''/viewdoc/download?doi=10.1.1.74.7629&amp;amp;rep=rep1&amp;amp;type=pdf'':49C ''/wiki/mutual_information'':26C ''base'':58C,81C ''battiti'':52C ''bayesian'':13C ''citeseerx.ist.psu.edu'':48C ''citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.74.7629&amp;amp;rep=rep1&amp;amp;type=pdf'':47C ''classifi'':14C ''continu'':74C ''densiti'':63C,82C ''deriv'':36C ''describ'':5C ''en.wikipedia.org'':25C ''en.wikipedia.org/wiki/mutual_information'':24C ''estim'':64C,71C,83C ''featur'':42C ''first'':39C ''histogram'':77C ''href'':23C,46C ''inform'':30C ''joint'':62C ''kernel'':80C ''kernel-bas'':79C ''like'':76C ''make'':6C ''margin'':60C ''mi'':31C,37C,56C ''mutual'':29C ''naiv'':12C ''need'':68C ''nofollow'':28C,51C ''p'':1C,55C ''pdf'':70C ''propos'':40C ''rel'':27C,50C ''relat'':9C ''respect'':20C ''select'':43C ''sens'':7C ''tool'':17C ''use'':16C ''variabl'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5916, 1, '2015-05-27 05:41:21.753', 5, 106, 'How to cluster a link traversal dataset', '&lt;p&gt;I''m using Google Analytics on my mobile app to see how different users use the app. I draw a path based on the pages they move to. Given a list of paths for say a 100 users, how do I go about clustering the users. Which algorithm to use? By the way, I''m thinking of using sckit learn package for the implementation.&lt;/p&gt;

&lt;p&gt;My dataset (in csv) would look like this :  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;DeviceID,Pageid,Time_spent_on_Page,Transition.&amp;lt;br&amp;gt; 
ABC,Page1, 3s, 1-&amp;gt;2.&amp;lt;br&amp;gt;
ABC,Page2, 2s, 2-&amp;gt;4.&amp;lt;br&amp;gt;
ABC,Page4,1s,4-&amp;gt;1.&amp;lt;br&amp;gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So the path, here is 1-&gt;2-&gt;4-&gt;1, where 1,2,4 are Pageids.&lt;/p&gt;
', 'machine-learning  clustering  scikit', NULL, '''/code'':128C ''/p'':78C,88C,146C ''/pre'':129C ''1'':104C,124C,136C,139C,141C ''100'':50C ''1s'':121C ''2'':106C,113C,137C,142C ''2s'':112C ''3s'':103C ''4'':115C,122C,138C,143C ''abc'':101C,110C,119C ''algorithm'':61C ''analyt'':18C ''app'':22C,30C ''base'':35C ''br'':99C,108C,117C,126C ''cluster'':3A,11B,57C ''code'':90C ''csv'':83C ''dataset'':7A,81C ''deviceid'':91C ''differ'':26C ''draw'':32C ''given'':42C ''go'':55C ''googl'':17C ''gt'':100C,105C,109C,114C,118C,123C,127C ''implement'':77C ''learn'':10B,73C ''like'':86C ''link'':5A ''list'':44C ''look'':85C ''lt'':98C,107C,116C,125C ''m'':15C,68C ''machin'':9B ''machine-learn'':8B ''mobil'':21C ''move'':40C ''p'':13C,79C,130C ''packag'':74C ''page'':38C,96C ''page1'':102C ''page2'':111C ''page4'':120C ''pageid'':92C,145C ''path'':34C,46C,133C ''pre'':89C ''say'':48C ''scikit'':12B ''sckit'':72C ''see'':24C ''spent'':94C ''think'':69C ''time'':93C ''transit'':97C ''travers'':6A ''use'':16C,28C,63C,71C ''user'':27C,51C,59C ''way'':66C ''would'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5917, 2, '2015-05-27 06:12:44.633', 1, NULL, NULL, '&lt;p&gt;I have not worked with such a dataset myself but I think you can model this problem as a graph where the pages form the node and then you have directed edges based on transition. Add weights to nodes based on time spent on them and then use graph clustering algorithms. If you choose to use this, you can use the &lt;a href="https://networkx.github.io/" rel="nofollow"&gt;networkx&lt;/a&gt; library in python for graph based analysis.&lt;/p&gt;

&lt;p&gt;Edit : We can use the information about different possible paths and how frequently they are use to classify the users. Let''s take example of Google search app. Suppose I want to search for images. One option is that I use the image search option, make the query and reach the results page. Other is that I make the query first and then switch to image option after getting the results. In both the cases I did up at the same page. I can use this information to classify my users. Now there can be quite a lot of paths possible so which all do I consider? The graph can be used to leveraged here along with the information about how you want to classify your users. Modeling it as a graph looks very intuitive to me as it lends itself to the concept of path.&lt;/p&gt;
', NULL, NULL, '''/a'':69C ''/p'':77C,225C ''add'':37C ''algorithm'':52C ''along'':194C ''analysi'':76C ''app'':106C ''base'':34C,41C,75C ''case'':153C ''choos'':55C ''classifi'':96C,167C,203C ''cluster'':51C ''concept'':222C ''consid'':185C ''dataset'':9C ''differ'':86C ''direct'':32C ''edg'':33C ''edit'':79C ''exampl'':102C ''first'':139C ''form'':25C ''frequent'':91C ''get'':147C ''googl'':104C ''graph'':21C,50C,74C,187C,210C ''href'':64C ''imag'':113C,121C,144C ''inform'':84C,165C,197C ''intuit'':213C ''lend'':218C ''let'':99C ''leverag'':192C ''librari'':70C ''look'':211C ''lot'':176C ''make'':124C,136C ''model'':16C,206C ''networkx'':68C ''networkx.github.io'':65C ''node'':27C,40C ''nofollow'':67C ''one'':114C ''option'':115C,123C,145C ''p'':1C,78C ''page'':24C,131C,160C ''path'':88C,178C,224C ''possibl'':87C,179C ''problem'':18C ''python'':72C ''queri'':126C,138C ''quit'':174C ''reach'':128C ''rel'':66C ''result'':130C,149C ''search'':105C,111C,122C ''spent'':44C ''suppos'':107C ''switch'':142C ''take'':101C ''think'':13C ''time'':43C ''transit'':36C ''use'':49C,57C,61C,82C,94C,119C,163C,190C ''user'':98C,169C,205C ''want'':109C,201C ''weight'':38C ''work'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5919, 2, '2015-05-27 08:02:10.797', 4, NULL, NULL, '&lt;p&gt;@Shagun''s answer is right actually. I just expand it!&lt;/p&gt;

&lt;p&gt;There are 2 different approaches to your problem:&lt;/p&gt;

&lt;h2&gt;Graph Approach&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;As stated in @Shagun''s answer you have a weighted directed graph and you want to cluster the paths. I mention again because it''s important to know that your problem is not a &lt;em&gt;Graph Clustering&lt;/em&gt; or &lt;em&gt;Community Detection&lt;/em&gt; problem where vertices are clustered!&lt;/li&gt;
&lt;li&gt;Cunstructing a Graph in networkx using the last two column of the data, you can add time spent as weight and users who passed that link as an edge attribute. After all you''ll have different features for clustering: the set of all vertices an individual ever met in the graph, total, mean and std of time spent, shortest path distribution parameters, ... which can be used for clustering the user behaviors.&lt;/li&gt;
&lt;/ul&gt;

&lt;h2&gt;Standard Data&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;All above can be done by reading data efficiently in a matrix. If you consider each edge for a specified user as a single row (i.e. you''ll have &lt;strong&gt;M&lt;/strong&gt;x&lt;strong&gt;N&lt;/strong&gt; rows where &lt;strong&gt;M&lt;/strong&gt; is the number of users and &lt;strong&gt;N&lt;/strong&gt; the number of edges in case you stick with 100 case!) and add properties as columns you''ll probably able to cluster behaviors. if a user passed an edge &lt;em&gt;n&lt;/em&gt; times, in the row corresponding to that user and that edge add a count column with value &lt;em&gt;n&lt;/em&gt; and same for time spend, etc. Starting and ending edges are also informative. Be careful that node names are categorical variables.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Regarding clustering algorithms you can find enough if you have a quick look at SKlearn.&lt;/p&gt;

&lt;p&gt;Hope it helped.
Good Luck :)&lt;/p&gt;
', NULL, NULL, '''/em'':66C,71C,239C,259C ''/h2'':26C,155C ''/li'':77C,150C,281C ''/p'':12C,22C,299C,306C ''/strong'':189C,193C,198C,207C ''/ul'':151C,282C ''100'':217C ''2'':16C ''abl'':227C ''actual'':7C ''add'':94C,220C,251C ''algorithm'':286C ''also'':271C ''answer'':4C,34C ''approach'':18C,25C ''attribut'':108C ''behavior'':149C,230C ''care'':274C ''case'':213C,218C ''categor'':279C ''cluster'':45C,65C,76C,117C,146C,229C,285C ''column'':88C,223C,254C ''communiti'':69C ''consid'':172C ''correspond'':244C ''count'':253C ''cunstruct'':79C ''data'':91C,154C,165C ''detect'':70C ''differ'':17C,114C ''direct'':39C ''distribut'':139C ''done'':162C ''edg'':107C,174C,211C,236C,250C,269C ''effici'':166C ''em'':63C,68C,237C,257C ''end'':268C ''enough'':290C ''etc'':265C ''ever'':125C ''expand'':10C ''featur'':115C ''find'':289C ''good'':304C ''graph'':24C,40C,64C,81C,129C ''h2'':23C,152C ''help'':303C ''hope'':301C ''i.e'':183C ''import'':54C ''individu'':124C ''inform'':272C ''know'':56C ''last'':86C ''li'':28C,78C,157C ''link'':104C ''ll'':112C,185C,225C ''look'':296C ''luck'':305C ''m'':188C,197C ''matrix'':169C ''mean'':131C ''mention'':49C ''met'':126C ''n'':192C,206C,238C,258C ''name'':277C ''networkx'':83C ''node'':276C ''number'':201C,209C ''p'':1C,13C,283C,300C ''paramet'':140C ''pass'':102C,234C ''path'':47C,138C ''probabl'':226C ''problem'':21C,59C,72C ''properti'':221C ''quick'':295C ''read'':164C ''regard'':284C ''right'':6C ''row'':182C,194C,243C ''set'':119C ''shagun'':2C,32C ''shortest'':137C ''singl'':181C ''sklearn'':298C ''specifi'':177C ''spend'':264C ''spent'':96C,136C ''standard'':153C ''start'':266C ''state'':30C ''std'':133C ''stick'':215C ''strong'':187C,191C,196C,205C ''time'':95C,135C,240C,263C ''total'':130C ''two'':87C ''ul'':27C,156C ''use'':84C,144C ''user'':100C,148C,178C,203C,233C,247C ''valu'':256C ''variabl'':280C ''vertic'':74C,122C ''want'':43C ''weight'':38C,98C ''x'':190C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5920, 2, '2015-05-27 09:41:38.917', 2, NULL, NULL, '&lt;p&gt;I got the answer.
I have to install some of the packages in terminal . I installed it and it works.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;sudo yum install gtk+-devel gtk2-devel
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':33C ''/p'':22C ''/pre'':34C ''answer'':5C ''code'':24C ''devel'':29C,32C ''got'':3C ''gtk'':28C ''gtk2'':31C ''gtk2-devel'':30C ''instal'':9C,17C,27C ''p'':1C ''packag'':13C ''pre'':23C ''sudo'':25C ''termin'':15C ''work'':21C ''yum'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5921, 1, '2015-05-27 10:02:03.657', 3, 150, 'Is automatic feature detection feasible?', '&lt;p&gt;I am searching for pointers to algorithms for feature detection. &lt;/p&gt;

&lt;p&gt;EDIT: all the answers helped me a lot, I cannot decide which one I should accept. THX guys!&lt;/p&gt;

&lt;h1&gt;What I did:&lt;/h1&gt;

&lt;p&gt;For discrete variables (i.e. $D_i, E$ are finite sets) $X_i : \Omega \to D_i$ and a given data table 
$$ 
\begin{pmatrix}{}
  X_1 &amp;amp; ... &amp;amp; X_n &amp;amp; X_{n+1} \\
  x_1^{(1)} &amp;amp; ... &amp;amp; x_n^{(1)} &amp;amp; x_{n+1}^{(1)} \\
  ... \\
  x_1^{(m)} &amp;amp; ... &amp;amp; x_n^{(m)} &amp;amp; x_{n+1}^{(m)} \\
\end{pmatrix}
$$
(the last variable will be the ''outcome'', thats why I stress it with a special index) and $X, Y$ being some of the $X_1, ..., X_{n+1}$ (so if $X=X_a, Y=X_b$ then $D=D_a, E=D_b$) compute&lt;/p&gt;

&lt;p&gt;$$H(X) = - \sum_{d \in D} P[X=d] * log(P[X=d])$$&lt;/p&gt;

&lt;p&gt;$$H(Y|X) = - \sum_{d \in D} {
             P[X=d] * \sum_{e \in E} {
               P[Y=e|X=d] * log(P[Y=e|X=d])
             }
           }$$&lt;/p&gt;

&lt;p&gt;where we estimate
  $$P[X_a=d] = |\{j \in \{1, ..., m\} : x_a^{(j)} = d\}|$$
and analogously
  $$P[X_a=d \cap X_b=e] = |\{j \in \{1, ..., m\} : x_a^{(j)} = d ~\text{and}~ x_b^{(j)}=e\}|$$
and then
  $$I(Y;X) = \frac{H(Y) - H(Y|X)}{\text{log}(\text{min}(|D|, |E|))}$$
 which is to be interpreted as the influence of $Y$ on $X$ (or vice versa, its symmetric).&lt;/p&gt;

&lt;p&gt;EDIT: A little late now but still:&lt;/p&gt;

&lt;p&gt;This is wrong:
&lt;s&gt;Exercise for you: show that if $X=Y$ then $I(X,Y)=1$.&lt;/s&gt;&lt;/p&gt;

&lt;p&gt;This is correct:
Exercise for you: show that if $X=Y$ then $I(X,X)=H(X)/log(|D|)$ and if $X$ is additionally equally distributed then $I(X,X)=1$.&lt;/p&gt;

&lt;p&gt;For selecting features start with the available set $\{X_1, ..., X_n\}$ and a set ''already selected''$ = ()$ [this is an ordered list!]. We select them step by step, always taking the one that maximizes   $$\text{goodness}(X) = I(X, X_{n+1}) - \beta \sum_{X_i ~\text{already selected}} I(X, X_i)$$ for a value $\beta$ to be determined (authors suggest $\beta = 0.5$). I.e. goodness = influence on outcome - redundancy introduced by selecting this variable. After doing this procedure, take the first ''few'' of them and throw away the ones with lower rank (whatever that means, I have to play with it a little bit). This is what is described in &lt;a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.74.7629&amp;amp;rep=rep1&amp;amp;type=pdf" rel="nofollow"&gt;this paper&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;For computing the $I$ for continuous variables one needs to bin them in some way. More concretely, the inventors of ''I'' suggest to take the maximal value over binning $X$ into $n_x$ bins, $Y$ into $n_y$ bins and $n_x \cdot n_y &amp;lt;= m^{0.6}$, i.e.  compute
  $$ \text{MIC}(X;Y) = \text{max}_{n_X \cdot n_Y \leq m^{0.6}} \left( \frac{I_{n_X, n_Y}(X;Y)}{log(\text{min}(n_X, n_Y)} \right)$$&lt;/p&gt;

&lt;p&gt;where $I_{n_X, n_Y}(X;Y)$ means: compute the $I$ precisely as you did for discrete variables by treating $X$ as a discrete random variable after binning it into $n_X$ bins and analogously with $Y$.&lt;/p&gt;

&lt;p&gt;===&lt;/p&gt;

&lt;h2&gt;ORIGINAL QUESTION&lt;/h2&gt;

&lt;p&gt;More precisely: I have a classification problem for one boolean variable, let''s call this variable &lt;code&gt;outcome&lt;/code&gt;. &lt;/p&gt;

&lt;p&gt;I have lots of data and lots of features (~150 or so) but these features are not totally ''meaningless'' as in image prediction (where every x and y coordinate is a feature) but they are of the form gender, age, etc. &lt;/p&gt;

&lt;p&gt;What I did until now: from these 150 features, I guessed the ones that ''seem'' to have some importance for the outcome. Still, I am unsure which features to select and also how to measure their importance before starting the actual learning algorithm (that involves yet more selection like PCA and stuff). &lt;/p&gt;

&lt;p&gt;For example, for a feature &lt;code&gt;f&lt;/code&gt; taking only finitely many values &lt;code&gt;x_1, ..., x_n&lt;/code&gt; my very naive approach would be to compute some relation between&lt;/p&gt;

&lt;p&gt;&lt;code&gt;P(outcome==TRUE | f==x_1)&lt;/code&gt;, ..., &lt;code&gt;P(outcome==TRUE | f==x_n)&lt;/code&gt; and &lt;code&gt;P(outcome==TRUE)&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;(i.e. the feature is important when I can deduce more information about the coutcome from it than without any knowledge about the feature).&lt;/p&gt;

&lt;h2&gt;&lt;em&gt;Concrete question(s): Is that a good idea? Which relation to take? What to do with continuous variables?&lt;/em&gt;&lt;/h2&gt;

&lt;p&gt;I''m sure that I''m not the first one ever wondering about this. I''ve read about (parts of) algorithms that do this selection in a sort-of automated way. Can somebody point me into the right direction (references, names of algorithms to look for, ...)?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6709, 1, '2015-08-06 06:03:20.613', 0, 28, 'Confusion in Policy Iteration and Value iteration in Reinforcement learning in Dynamic Programming', '&lt;p&gt;What I understood for value iteration while coding is that we need to have a policy fixed. According to that policy the value function of each state will be calculated. Right?&lt;/p&gt;

&lt;p&gt;But in policy iteration the policy will change from time to time. Am I right?&lt;/p&gt;
', 'machine-learning  reinforcement-learning', NULL, '''/p'':52C,69C ''accord'':38C ''calcul'':50C ''chang'':61C ''code'':28C ''confus'':1A ''dynam'':12A ''fix'':37C ''function'':44C ''iter'':4A,7A,26C,57C ''learn'':10A,16B,19B ''machin'':15B ''machine-learn'':14B ''need'':32C ''p'':20C,53C ''polici'':3A,36C,41C,56C,59C ''program'':13A ''reinforc'':9A,18B ''reinforcement-learn'':17B ''right'':51C,68C ''state'':47C ''time'':63C,65C ''understood'':23C ''valu'':6A,25C,43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5923, 2, '2015-05-27 10:17:19.21', 2, NULL, NULL, '&lt;p&gt;This entirely depends on what you wish or aim to do with your data, and what you mean by your tag &lt;code&gt;data-cleaning&lt;/code&gt;. This can involve the technical process of sanitising data i.e. fixing broken &lt;code&gt;XML&lt;/code&gt;, but also the manual deletion of outliers etc.&lt;/p&gt;

&lt;p&gt;Are you going to perform statistics to measure the correlation between certain variables? &lt;/p&gt;

&lt;p&gt;Or:
Are you going to include the data into a machine-learning solution?&lt;/p&gt;

&lt;p&gt;In any case, you can plot descriptive statistics to get an impression of the quality of your data, such as:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Box-plots to detect outliers&lt;/li&gt;
&lt;li&gt;Standard deviation, mean, mode, median to assess if it''s normally distributed or not&lt;/li&gt;
&lt;li&gt;More, don''t know from the top of my head&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Remember that those measures give you an &lt;em&gt;impression&lt;/em&gt; of the quality of your data, but even good measurements can come from bad data, and possibly vice versa.&lt;/p&gt;

&lt;p&gt;In short, the best way of assessing the quality of your data is to assess the quality of your results, which are acquired generally &lt;strong&gt;after&lt;/strong&gt; several experiments. Only then will you see how well your data is suited for the problem you are trying to solve. That - or you need to get your hands dirty and examine the data qualitatively before you use it.&lt;/p&gt;

&lt;p&gt;Data (in assumingly large quantities) are generally used to identify certain patterns within the phenomena described by the data. It is very important to describe your task in terms of a &lt;strong&gt;(research) question&lt;/strong&gt; and a &lt;strong&gt;proposed solution&lt;/strong&gt; or &lt;strong&gt;hypothesis&lt;/strong&gt;. Then, you can assess whether the data is suitable for your particular (research) project. Keep in mind that, theoretically, you can try to answer numerous questions on the same data, or answer the same question on different data sets. The goal is the right balance.&lt;/p&gt;
', NULL, NULL, '''/code'':27C,42C ''/em'':153C ''/li'':113C,129C,141C ''/ol'':142C ''/p'':51C,66C,82C,104C,173C,241C,333C ''/strong'':201C,277C,283C,287C ''acquir'':197C ''aim'':10C ''also'':44C ''answer'':311C,319C ''assess'':121C,181C,189C,291C ''assum'':245C ''bad'':167C ''balanc'':332C ''best'':178C ''box'':108C ''box-plot'':107C ''broken'':39C ''case'':86C ''certain'':64C,253C ''clean'':26C ''code'':23C,40C ''come'':165C ''correl'':62C ''data'':15C,25C,36C,75C,101C,159C,168C,186C,212C,235C,243C,261C,294C,317C,325C ''data-clean'':24C ''delet'':47C ''depend'':4C ''describ'':258C,267C ''descript'':90C ''detect'':111C ''deviat'':116C ''differ'':324C ''dirti'':231C ''distribut'':126C ''em'':151C ''entir'':3C ''etc'':50C ''even'':161C ''examin'':233C ''experi'':203C ''fix'':38C ''general'':198C,249C ''get'':93C,228C ''give'':148C ''go'':55C,71C ''goal'':328C ''good'':162C ''hand'':230C ''head'':140C ''hypothesi'':286C ''i.e'':37C ''identifi'':252C ''import'':265C ''impress'':95C,152C ''includ'':73C ''involv'':30C ''keep'':302C ''know'':134C ''larg'':246C ''learn'':80C ''li'':106C,114C,130C ''machin'':79C ''machine-learn'':78C ''manual'':46C ''mean'':19C,117C ''measur'':60C,147C,163C ''median'':119C ''mind'':304C ''mode'':118C ''need'':226C ''normal'':125C ''numer'':312C ''ol'':105C ''outlier'':49C,112C ''p'':1C,52C,67C,83C,143C,174C,242C ''particular'':299C ''pattern'':254C ''perform'':57C ''phenomena'':257C ''plot'':89C,109C ''possibl'':170C ''problem'':217C ''process'':33C ''project'':301C ''propos'':281C ''qualit'':236C ''qualiti'':98C,156C,183C,191C ''quantiti'':247C ''question'':276C,313C,322C ''rememb'':144C ''research'':275C,300C ''result'':194C ''right'':331C ''sanitis'':35C ''see'':208C ''set'':326C ''sever'':202C ''short'':176C ''solut'':81C,282C ''solv'':222C ''standard'':115C ''statist'':58C,91C ''strong'':199C,274C,280C,285C ''suit'':214C ''suitabl'':296C ''tag'':22C ''task'':269C ''technic'':32C ''term'':271C ''theoret'':306C ''top'':137C ''tri'':220C,309C ''use'':239C,250C ''variabl'':65C ''versa'':172C ''vice'':171C ''way'':179C ''well'':210C ''whether'':292C ''wish'':8C ''within'':255C ''xml'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10496, 1, '2016-03-03 01:30:38.14', -2, 17, 'Best Transaction Dataset to Practice Modeling on', '&lt;p&gt;Does any of you know of a good public transaction dataset for me to experiment some of my models on? Thanks in advance!&lt;/p&gt;
', 'data-mining  data', NULL, '''/p'':35C ''advanc'':34C ''best'':1A ''data'':8B,10B ''data-min'':7B ''dataset'':3A,22C ''experi'':26C ''good'':19C ''know'':16C ''mine'':9B ''model'':6A,30C ''p'':11C ''practic'':5A ''public'':20C ''thank'':32C ''transact'':2A,21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5924, 1, '2015-05-27 10:26:11.687', 0, 35, 'change in variable importance', '&lt;p&gt;I have a multi year dataset. Each time frame  of the data has different predictor importance. &lt;/p&gt;

&lt;p&gt;Say for example, I am slicing the data into two partions as follows:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;a dataset for the year 2014 (whole year)&lt;/li&gt;
&lt;li&gt;a 2015 Jan. &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;When i look for the predictor importance, the predictor variables are different for both the partions. (1) Hence i am not able to arrive at one  unique decision tree which can explain the model better. (2). I am not able to train a model which can predict the new data correctly.&lt;/p&gt;

&lt;p&gt;Is there anything I am going wrong here.&lt;/p&gt;
', 'classification', NULL, '''/li'':49C,54C ''/ol'':55C ''/p'':23C,38C,108C,118C ''1'':73C ''2'':92C ''2014'':46C ''2015'':52C ''abl'':78C,96C ''anyth'':112C ''arriv'':80C ''better'':91C ''chang'':1A ''classif'':5B ''correct'':107C ''data'':18C,32C,106C ''dataset'':12C,42C ''decis'':84C ''differ'':20C,68C ''exampl'':27C ''explain'':88C ''follow'':37C ''frame'':15C ''go'':115C ''henc'':74C ''import'':4A,22C,63C ''jan'':53C ''li'':40C,50C ''look'':59C ''model'':90C,100C ''multi'':10C ''new'':105C ''ol'':39C ''one'':82C ''p'':6C,24C,56C,109C ''partion'':35C,72C ''predict'':103C ''predictor'':21C,62C,65C ''say'':25C ''slice'':30C ''time'':14C ''train'':98C ''tree'':85C ''two'':34C ''uniqu'':83C ''variabl'':3A,66C ''whole'':47C ''wrong'':116C ''year'':11C,45C,48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5928, 2, '2015-05-27 15:43:31.75', 2, NULL, NULL, '&lt;p&gt;The exception list files are used to help the processor find base forms from ''irregular inflections'' according to the &lt;a href="http://wordnet.princeton.edu/wordnet/man/wndb.5WN.html" rel="nofollow"&gt;man page&lt;/a&gt;.  They mean that some words, when plural or a different tense, can''t be algorithmically processed to find the base/root word.  More details can be found in the &lt;a href="https://wordnet.princeton.edu/man/morphy.7WN.html#sect3" rel="nofollow"&gt;morphy man&lt;/a&gt;.  I''m not a language processing expert, but this is likely a result of English words that ''break the rules''.  If you think about the code like a human trying to learn English: the student learns rules to use (algorithm) and then has to memorize exceptions for the rules (exception lists).  An over-simplified analogy that does not involve endings/conjugation would a spell checking program.  An algorithm might check for ''i before e, except after c'' but would first have to check the word against an exception list to make sure it isn''t ''weird'' or ''caffeine'' - please don''t start a linguistics fight about this rule, I am not commenting on the validity of it/that''s not the point I''d like to make.&lt;/p&gt;
', NULL, NULL, '''/a'':30C,68C ''/man/morphy.7wn.html#sect3'':63C ''/p'':195C ''/wordnet/man/wndb.5wn.html'':25C ''accord'':18C ''algorithm'':45C,108C,136C ''analog'':124C ''base'':13C ''base/root'':50C ''break'':86C ''c'':145C ''caffein'':166C ''check'':133C,138C,151C ''code'':94C ''comment'':180C ''d'':191C ''detail'':53C ''differ'':40C ''e'':142C ''endings/conjugation'':129C ''english'':83C,101C ''except'':3C,114C,118C,143C,156C ''expert'':75C ''fight'':173C ''file'':5C ''find'':12C,48C ''first'':148C ''form'':14C ''found'':56C ''help'':9C ''href'':22C,60C ''human'':97C ''inflect'':17C ''involv'':128C ''irregular'':16C ''isn'':162C ''it/that'':185C ''languag'':73C ''learn'':100C,104C ''like'':79C,95C,192C ''linguist'':172C ''list'':4C,119C,157C ''m'':70C ''make'':159C,194C ''man'':28C,67C ''mean'':32C ''memor'':113C ''might'':137C ''morphi'':66C ''nofollow'':27C,65C ''over-simplifi'':121C ''p'':1C ''page'':29C ''pleas'':167C ''plural'':37C ''point'':189C ''process'':46C,74C ''processor'':11C ''program'':134C ''rel'':26C,64C ''result'':81C ''rule'':88C,105C,117C,176C ''simplifi'':123C ''spell'':132C ''start'':170C ''student'':103C ''sure'':160C ''tens'':41C ''think'':91C ''tri'':98C ''use'':7C,107C ''valid'':183C ''weird'':164C ''word'':35C,51C,84C,153C ''wordnet.princeton.edu'':24C,62C ''wordnet.princeton.edu/man/morphy.7wn.html#sect3'':61C ''wordnet.princeton.edu/wordnet/man/wndb.5wn.html'':23C ''would'':130C,147C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5929, 2, '2015-05-27 22:39:35.3', 0, NULL, NULL, '&lt;p&gt;Your question is very broad, and therefore I won''t offer very specific answers.  You asked for "time-saving tips" but there are many and they depend on context. &lt;/p&gt;

&lt;p&gt;Instead, I''ll offer a &lt;em&gt;general&lt;/em&gt; set of heuristics that are useful most of the time.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;em&gt;Start&lt;/em&gt; with a specification or definition of what "quality" means for your analysis, and therefore for your data.&lt;/li&gt;
&lt;li&gt;Use definition/specification in 1) to enumerate the types of errors, omissions, missteps, modifications, etc. that &lt;em&gt;might&lt;/em&gt; arise in the course of data collection and recording.  &lt;em&gt;This enumeration will always be provisional&lt;/em&gt; because there could be ways that quality could be degraded that you didn''t think of originally.&lt;/li&gt;
&lt;li&gt;Use 2) to define &lt;em&gt;inspection and test methods&lt;/em&gt; that might reveal the existence of any of these types of errors, omissions, etc.  The default method is &lt;em&gt;"eyeballing"&lt;/em&gt; -- having a knowledgeable, experienced person looking at the data to see if it looks right. &lt;/li&gt;
&lt;li&gt;When you uncover errors, omissions, etc. try to determine the &lt;em&gt;root causes and generating processes&lt;/em&gt;. E.g. erroneous data in dozens of columns resulted from an "off-by-one" bug in the ETL job that generated/translated the data from source files.&lt;/li&gt;
&lt;li&gt;Using 2), 3) and 4), define processes that might either &lt;em&gt;correct erroneous data&lt;/em&gt; or &lt;em&gt;mitigate&lt;/em&gt; the effects of data quality problems.  E.g. &lt;a href="http://en.wikipedia.org/wiki/Soundex" rel="nofollow"&gt;Soundex&lt;/a&gt; transformation of names can mitigate effects of misspelling, but can''t mitigate people misusing the "name" field to enter notes: e.g. "Nelson - DO NOT CALL".  Be aware that any process you might use to correct or mitigate errors or problems might &lt;em&gt;create&lt;/em&gt; new errors or problems (e.g. truncation of numerical data).&lt;/li&gt;
&lt;li&gt;After you perform your analysis, look back at the data and ask "Could these results be the result of data quality problems rather than true/accurate/appropriate data?"  In other words, &lt;em&gt;double check&lt;/em&gt;.&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':258C ''/em'':41C,57C,94C,111C,140C,161C,195C,238C,242C,302C,346C ''/li'':75C,129C,177C,222C,312C,347C ''/ol'':348C ''/p'':32C,52C ''/wiki/soundex'':254C ''1'':80C ''2'':132C,225C ''3'':226C ''4'':228C ''alway'':108C ''analysi'':69C,318C ''answer'':15C ''aris'':95C ''ask'':17C,325C ''awar'':285C ''back'':320C ''broad'':6C ''bug'':210C ''call'':283C ''caus'':191C ''check'':345C ''collect'':101C ''column'':202C ''context'':31C ''correct'':235C,293C ''could'':114C,119C,326C ''cours'':98C ''creat'':301C ''data'':74C,100C,170C,198C,218C,237C,246C,311C,323C,333C,339C ''default'':156C ''defin'':134C,229C ''definit'':62C ''definition/specification'':78C ''degrad'':121C ''depend'':29C ''determin'':187C ''didn'':124C ''doubl'':344C ''dozen'':200C ''e.g'':196C,249C,279C,307C ''effect'':244C,264C ''either'':233C ''em'':39C,55C,92C,104C,135C,159C,189C,234C,240C,300C,343C ''en.wikipedia.org'':253C ''en.wikipedia.org/wiki/soundex'':252C ''enter'':277C ''enumer'':82C,106C ''erron'':197C,236C ''error'':86C,152C,182C,296C,304C ''etc'':90C,154C,184C ''etl'':213C ''exist'':145C ''experienc'':165C ''eyebal'':160C ''field'':275C ''file'':221C ''general'':40C ''generat'':193C ''generated/translated'':216C ''heurist'':44C ''href'':251C ''inspect'':136C ''instead'':34C ''job'':214C ''knowledg'':164C ''li'':54C,76C,130C,178C,223C,313C ''ll'':36C ''look'':167C,175C,319C ''mani'':26C ''mean'':66C ''method'':139C,157C ''might'':93C,142C,232C,290C,299C ''misspel'':266C ''misstep'':88C ''misus'':272C ''mitig'':241C,263C,270C,295C ''modif'':89C ''name'':261C,274C ''nelson'':280C ''new'':303C ''nofollow'':256C ''note'':278C ''numer'':310C ''off-by-on'':206C ''offer'':12C,37C ''ol'':53C ''omiss'':87C,153C,183C ''one'':209C ''origin'':128C ''p'':1C,33C ''peopl'':271C ''perform'':316C ''person'':166C ''problem'':248C,298C,306C,335C ''process'':194C,230C,288C ''provision'':110C ''qualiti'':65C,118C,247C,334C ''question'':3C ''rather'':336C ''record'':103C ''rel'':255C ''result'':203C,328C,331C ''reveal'':143C ''right'':176C ''root'':190C ''save'':21C ''see'':172C ''set'':42C ''soundex'':257C ''sourc'':220C ''specif'':14C,60C ''start'':56C ''test'':138C ''therefor'':8C,71C ''think'':126C ''time'':20C,51C ''time-sav'':19C ''tip'':22C ''transform'':259C ''tri'':185C ''true/accurate/appropriate'':338C ''truncat'':308C ''type'':84C,150C ''uncov'':181C ''use'':47C,77C,131C,224C,291C ''way'':116C ''won'':10C ''word'':342C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5930, 2, '2015-05-27 23:25:19.413', 2, NULL, NULL, '&lt;p&gt;Please allow me to paraphrase your question to make sure I get your question right.&lt;/p&gt;

&lt;p&gt;Suppose you have 1 million data entries. Each entry consists of three inputs X1, X2, X3 and one output Y. One of the three inputs, X1, is a noise. You want to find the relation between X1 and Y. So you can remove the impact of X1 on Y.&lt;/p&gt;

&lt;p&gt;Let''s use a simplified example, Y = 2X1 + 3X2 + 4X3
If you update each of the 1 million entries with:
Y_new = Y - 2X1&lt;/p&gt;

&lt;p&gt;Now you can examine the updated 1 million entries to find the relation between input X2, X3 and Y_new:
Y_new = 3X2 + 4X3&lt;/p&gt;

&lt;p&gt;The relation between X1 and Y can be computed with different algorithms, such as regression, decision tree. How to measure which algorithm generates the best result, i.e. truly reflects the relation between X1 and Y hence removes the impact of X1 on Y as much as possible?&lt;/p&gt;

&lt;p&gt;The problem you''re solving is an interesting one. I would use a free tool such as Knime to find out which algorithm generates the best result in modeling the relation between X1 and Y. It''s faster than coding in Python or Matlab.&lt;/p&gt;

&lt;p&gt;Details: to put the problem intuitively, suppose the price of a house is determined by location, size of the house, condition of the house. Assume these three factors are independent from each other. You want to remove the impact of "condition of the house" on "price" as much as possible. In essence, you want to find the relation between "condition of the house" and "price" which may not be linear.&lt;/p&gt;

&lt;p&gt;You can let Knime read the 1 million entries of "condition of the house" and "price", try Decision Tree, SVM, or even Ensemble Learning, and see which algorithm generates the best model of X1 and Y. In other words, try and error :)&lt;/p&gt;
', NULL, NULL, '''/p'':17C,68C,94C,120C,169C,215C,287C,331C ''1'':22C,86C,102C,295C ''2x1'':77C,93C ''3x2'':78C,118C ''4x3'':79C,119C ''algorithm'':133C,143C,193C,316C ''allow'':3C ''assum'':241C ''best'':146C,196C,319C ''code'':210C ''comput'':130C ''condit'':237C,257C,276C,299C ''consist'':28C ''data'':24C ''decis'':137C,306C ''detail'':217C ''determin'':230C ''differ'':132C ''ensembl'':311C ''entri'':25C,27C,88C,104C,297C ''error'':330C ''essenc'':268C ''even'':310C ''examin'':99C ''exampl'':75C ''factor'':244C ''faster'':208C ''find'':51C,106C,190C,272C ''free'':184C ''generat'':144C,194C,317C ''get'':13C ''henc'':157C ''hous'':228C,236C,240C,260C,279C,302C ''i.e'':148C ''impact'':63C,160C,255C ''independ'':246C ''input'':31C,43C,110C ''interest'':178C ''intuit'':222C ''knime'':188C,292C ''learn'':312C ''let'':70C,291C ''linear'':286C ''locat'':232C ''make'':10C ''matlab'':214C ''may'':283C ''measur'':141C ''million'':23C,87C,103C,296C ''model'':199C,320C ''much'':166C,264C ''new'':91C,115C,117C ''nois'':47C ''one'':36C,39C,179C ''output'':37C ''p'':1C,18C,69C,95C,121C,170C,216C,288C ''paraphras'':6C ''pleas'':2C ''possibl'':168C,266C ''price'':225C,262C,281C,304C ''problem'':172C,221C ''put'':219C ''python'':212C ''question'':8C,15C ''re'':174C ''read'':293C ''reflect'':150C ''regress'':136C ''relat'':53C,108C,123C,152C,201C,274C ''remov'':61C,158C,253C ''result'':147C,197C ''right'':16C ''see'':314C ''simplifi'':74C ''size'':233C ''solv'':175C ''suppos'':19C,223C ''sure'':11C ''svm'':308C ''three'':30C,42C,243C ''tool'':185C ''tree'':138C,307C ''tri'':305C,328C ''truli'':149C ''updat'':82C,101C ''use'':72C,182C ''want'':49C,251C,270C ''word'':327C ''would'':181C ''x1'':32C,44C,55C,65C,125C,154C,162C,203C,322C ''x2'':33C,111C ''x3'':34C,112C ''y'':38C,57C,67C,76C,90C,92C,114C,116C,127C,156C,164C,205C,324C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5933, 1, '2015-05-28 08:44:13.343', 2, 107, 'What possible data products can be built using this dataset', '&lt;p&gt;I have a dataset which contains information about when do people enter and leave a premise. I have the following information in the dataset : &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Person Id&lt;/li&gt;
&lt;li&gt;Time of Entry&lt;/li&gt;
&lt;li&gt;Time of Leaving&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The dataset has around 50 unique persons. Each person will have multiple entries corresponding to multiple visits. The data spans over a year so I have quite a lot of entries (around 1 million).&lt;/p&gt;

&lt;p&gt;These people can be classified on the basis of the department they work under (2 departments - mutually exclusive) or on basis of  their role (4 possible roles - all mutually exclusive)&lt;/p&gt;

&lt;p&gt;I was wondering what kind of data analysis can be done with this kind of dataset. I am not looking for straight-forward things like "who spent the most time in building". However things like finding correlation between visits of 2 people would be interesting. So if person A visits the premise, what is the probability that person B would also visit. Since I have only around 50 unique visitors, I think such an analysis is feasible. &lt;/p&gt;

&lt;p&gt;Another line of thought was to apply some interval-pattern mining techniques but I am not much familiar with them.&lt;/p&gt;

&lt;p&gt;Can someone give me some pointers/ideas about what kind of data products can be build using this or what kind of techniques can be used with such data.  &lt;/p&gt;

&lt;p&gt;Edit : As discussed in comments, I call it a product in the sense that I do not want some simple or trivial analysis. And I am not looking for any commercially viable idea - just some cool fun idea :)&lt;/p&gt;
', 'machine-learning  data-mining  dataset', NULL, '''/li'':48C,53C,58C ''/ol'':59C ''/p'':43C,95C,127C,207C,230C,260C,300C ''1'':93C ''2'':111C,170C ''4'':121C ''50'':65C,197C ''also'':190C ''analysi'':136C,204C,284C ''anoth'':209C ''appli'':215C ''around'':64C,92C,196C ''b'':188C ''basi'':104C,117C ''build'':161C,246C ''built'':7A ''call'':268C ''classifi'':101C ''comment'':266C ''commerci'':292C ''contain'':24C ''cool'':297C ''correl'':166C ''correspond'':74C ''data'':3A,15B,79C,135C,242C,259C ''data-min'':14B ''dataset'':10A,17B,22C,42C,62C,144C ''depart'':107C,112C ''discuss'':264C ''done'':139C ''edit'':262C ''enter'':30C ''entri'':52C,73C,91C ''exclus'':114C,126C ''familiar'':227C ''feasibl'':206C ''find'':165C ''follow'':38C ''forward'':152C ''fun'':298C ''give'':234C ''howev'':162C ''id'':47C ''idea'':294C,299C ''inform'':25C,39C ''interest'':174C ''interv'':218C ''interval-pattern'':217C ''kind'':133C,142C,240C,251C ''learn'':13B ''leav'':32C,57C ''li'':45C,49C,54C ''like'':154C,164C ''line'':210C ''look'':148C,289C ''lot'':89C ''machin'':12B ''machine-learn'':11B ''million'':94C ''mine'':16B,220C ''much'':226C ''multipl'':72C,76C ''mutual'':113C,125C ''ol'':44C ''p'':18C,60C,96C,128C,208C,231C,261C ''pattern'':219C ''peopl'':29C,98C,171C ''person'':46C,67C,69C,177C,187C ''pointers/ideas'':237C ''possibl'':2A,122C ''premis'':34C,181C ''probabl'':185C ''product'':4A,243C,271C ''quit'':87C ''role'':120C,123C ''sens'':274C ''simpl'':281C ''sinc'':192C ''someon'':233C ''span'':80C ''spent'':156C ''straight'':151C ''straight-forward'':150C ''techniqu'':221C,253C ''thing'':153C,163C ''think'':201C ''thought'':212C ''time'':50C,55C,159C ''trivial'':283C ''uniqu'':66C,198C ''use'':8A,247C,256C ''viabl'':293C ''visit'':77C,168C,179C,191C ''visitor'':199C ''want'':279C ''wonder'':131C ''work'':109C ''would'':172C,189C ''year'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5934, 2, '2015-05-28 12:00:55.07', 1, NULL, NULL, '&lt;p&gt;I got the answer. One jar file was missing now it is solved. 
This file was missing.
hadoop-common-2.1.0-beta.jar&lt;/p&gt;
', NULL, NULL, '''/p'':20C ''answer'':5C ''file'':8C,16C ''got'':3C ''hadoop-common-2.1.0-beta.jar'':19C ''jar'':7C ''miss'':10C,18C ''one'':6C ''p'':1C ''solv'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5935, 2, '2015-05-28 12:49:25.243', 1, NULL, NULL, '&lt;p&gt;Artificial Intelligence: A Modern Approach by Stuart Russel and Peter Norvig.&lt;/p&gt;
', NULL, NULL, '''/p'':13C ''approach'':6C ''artifici'':2C ''intellig'':3C ''modern'':5C ''norvig'':12C ''p'':1C ''peter'':11C ''russel'':9C ''stuart'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5936, 1, '2015-05-28 15:23:27.847', 0, 47, 'Support vector regression and paremeters', '&lt;p&gt;I am doing load forecasting using SVR(kernel=''rbf'').How can I understand which is the best value for parameters C, epsilon and gamma?Thanks.&lt;/p&gt;
', 'python  svm  forecast', NULL, '''/p'':35C ''best'':26C ''c'':30C ''epsilon'':31C ''forecast'':8B,14C ''gamma'':33C ''kernel'':17C ''load'':13C ''p'':9C ''paramet'':29C ''paremet'':5A ''python'':6B ''rbf'':18C ''regress'':3A ''support'':1A ''svm'':7B ''svr'':16C ''thank'':34C ''understand'':22C ''use'':15C ''valu'':27C ''vector'':2A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6356, 2, '2015-07-06 17:11:49.963', 2, NULL, NULL, '&lt;p&gt;If there is no value column - introduce it yourself!&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;df["value"]=1
pd.pivot_table(df, values="value", index=["city"], columns="cuisine", fill_value=0) 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For your example I got (after fixing the misprint in ''Japanse'' to ''Japanese'')&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;cuisine  Chinese  French  German  Japanese
city                                      
NY             1       0       0         1
SF             0       1       1         1
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':29C,64C ''/p'':11C,45C ''/pre'':30C,65C ''0'':28C,56C,57C,60C ''1'':16C,55C,58C,61C,62C,63C ''chines'':49C ''citi'':23C,53C ''code'':13C,47C ''column'':7C,24C ''cuisin'':25C,48C ''df'':14C,19C ''exampl'':34C ''fill'':26C ''fix'':38C ''french'':50C ''german'':51C ''got'':36C ''index'':22C ''introduc'':8C ''japanes'':44C,52C ''japans'':42C ''misprint'':40C ''ny'':54C ''p'':1C,31C ''pd.pivot'':17C ''pre'':12C,46C ''sf'':59C ''tabl'':18C ''valu'':6C,15C,20C,21C,27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5937, 2, '2015-05-28 15:51:27.623', 4, NULL, NULL, '&lt;p&gt;It looks like that you are using scikit-learn. In this case use &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html" rel="nofollow"&gt;Grid Search Cross Validation&lt;/a&gt;  or &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.RandomizedSearchCV.html#sklearn.grid_search.RandomizedSearchCV" rel="nofollow"&gt;Randomized Search Cross Validation&lt;/a&gt; to find the best parameters. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;sklearn.grid_search.GridSearchCV(estimator, param_grid, scoring=None, cv=None, ...)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In these approaches you basically loop over possible sets of your parameters, specified via &lt;code&gt;param_grid&lt;/code&gt;. For each set you perform a cross-validation (default is 3-fold, but you can specify it via &lt;code&gt;cv&lt;/code&gt; parameter). Cross-validation gives you the mean value and deviation of your ''scoring'' parameter (e.g. ''accuracy'' for classification, ''r2'' for regression). The set of parameters with the best ''scoring'' is the winner. &lt;/p&gt;

&lt;p&gt;See example &lt;a href="http://scikit-learn.org/stable/auto_examples/model_selection/randomized_search.html#example-model-selection-randomized-search-py" rel="nofollow"&gt;here&lt;/a&gt;. It also show how to output not only &lt;em&gt;the&lt;/em&gt; best set of parameters, but also "top-N". I find it very useful in building intuition about my model. &lt;/p&gt;
', NULL, NULL, '''/a'':27C,40C,145C ''/code'':57C,77C,99C ''/em'':156C ''/p'':46C,133C,177C ''/pre'':58C ''/stable/auto_examples/model_selection/randomized_search.html#example-model-selection-randomized-search-py'':141C ''/stable/modules/generated/sklearn.grid_search.gridsearchcv.html'':20C ''/stable/modules/generated/sklearn.grid_search.randomizedsearchcv.html#sklearn.grid_search.randomizedsearchcv'':33C ''3'':89C ''accuraci'':116C ''also'':147C,162C ''approach'':62C ''basic'':64C ''best'':44C,128C,157C ''build'':172C ''case'':14C ''classif'':118C ''code'':48C,74C,97C ''cross'':25C,38C,85C,102C ''cross-valid'':84C,101C ''cv'':55C,98C ''default'':87C ''deviat'':110C ''e.g'':115C ''em'':154C ''estim'':50C ''exampl'':136C ''find'':42C,167C ''fold'':90C ''give'':104C ''grid'':23C,52C,76C ''href'':17C,30C,138C ''intuit'':173C ''learn'':11C ''like'':4C ''look'':3C ''loop'':65C ''mean'':107C ''model'':176C ''n'':165C ''nofollow'':22C,35C,143C ''none'':54C,56C ''output'':151C ''p'':1C,59C,134C ''param'':51C,75C ''paramet'':45C,71C,100C,114C,125C,160C ''perform'':82C ''possibl'':67C ''pre'':47C ''r2'':119C ''random'':36C ''regress'':121C ''rel'':21C,34C,142C ''scikit'':10C ''scikit-learn'':9C ''scikit-learn.org'':19C,32C,140C ''scikit-learn.org/stable/auto_examples/model_selection/randomized_search.html#example-model-selection-randomized-search-py'':139C ''scikit-learn.org/stable/modules/generated/sklearn.grid_search.gridsearchcv.html'':18C ''scikit-learn.org/stable/modules/generated/sklearn.grid_search.randomizedsearchcv.html#sklearn.grid_search.randomizedsearchcv'':31C ''score'':53C,113C,129C ''search'':24C,37C ''see'':135C ''set'':68C,80C,123C,158C ''show'':148C ''sklearn.grid_search.gridsearchcv'':49C ''specifi'':72C,94C ''top'':164C ''top-n'':163C ''use'':8C,15C,170C ''valid'':26C,39C,86C,103C ''valu'':108C ''via'':73C,96C ''winner'':132C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5938, 2, '2015-05-28 15:59:26.593', 2, NULL, NULL, '&lt;p&gt;I don''t know if this is the answer you are looking for, but generally start by asking "what would I do differently"? &lt;/p&gt;

&lt;p&gt;So, if I am running a business (to apply some context), at how often do I not have coverage for certain positions. How can I schedule shifts (for example) to ensure that all critical positions are covered but I have as few people in the building as possible (minimising cost)? &lt;/p&gt;

&lt;p&gt;As a simple time series, you could apply any sort of forecasting to this data set - predicting who and how long they will be in the building, when they will arrive next, for example.  Look for seasonality in the arrival patterns. &lt;/p&gt;

&lt;p&gt;You could also look to predict when a given person will next be in the building. And can you detect outliers? Who has been leaving early or breaking from their usual pattern of behaviour?&lt;/p&gt;
', NULL, NULL, '''/p'':25C,77C,120C,156C ''also'':124C ''answer'':10C ''appli'':35C,86C ''arriv'':109C,118C ''ask'':19C ''behaviour'':155C ''break'':149C ''build'':72C,105C,137C ''busi'':33C ''certain'':47C ''context'':37C ''cost'':76C ''could'':85C,123C ''cover'':63C ''coverag'':45C ''critic'':60C ''data'':93C ''detect'':141C ''differ'':24C ''earli'':147C ''ensur'':57C ''exampl'':55C,112C ''forecast'':90C ''general'':16C ''given'':130C ''know'':5C ''leav'':146C ''long'':99C ''look'':13C,113C,125C ''minimis'':75C ''next'':110C,133C ''often'':40C ''outlier'':142C ''p'':1C,26C,78C,121C ''pattern'':119C,153C ''peopl'':69C ''person'':131C ''posit'':48C,61C ''possibl'':74C ''predict'':95C,127C ''run'':31C ''schedul'':52C ''season'':115C ''seri'':83C ''set'':94C ''shift'':53C ''simpl'':81C ''sort'':88C ''start'':17C ''time'':82C ''usual'':152C ''would'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5939, 2, '2015-05-28 16:49:51.293', 4, NULL, NULL, '&lt;p&gt;It is a good idea to do automatic feature vector selection for classification, and is widely done for decision trees.&lt;/p&gt;

&lt;p&gt;If you are trying to find the relative importance of variables in classification schemes, a popular way of selecting variables is by performing entropy minimization.  This a technique that is often used for constructing decision trees.  Entropy is simply a way of numerically quantifying disorder of a system:&lt;/p&gt;

&lt;p&gt;&lt;img src="https://chart.googleapis.com/chart?cht=tx&amp;amp;chl=H=%5CSigma_x-p_x*log_2%28p_x%29" alt="Entropy"&gt;&lt;/p&gt;

&lt;p&gt;For binary classification, it''s simple to calculate entropy for every variable, moving the variables which minimize entropy to the top, &lt;/p&gt;

&lt;p&gt;As an example, consider a simple case where a game exists when one must choose between a right and left door, one of which is red, one of which is blue.    The color of the door changes from instance to instance (sometime left is red, sometimes right is red), but the prize is always behind the left door.  Suppose also that over many trials, both left-right, and red-blue have equal number of people choosing those options.  The entropy of the original system (P(prize)=.5, P(!prize)=.5) is 1.  Supposing that we select a door based on color, we have:&lt;/p&gt;

&lt;p&gt;&lt;img src="https://chart.googleapis.com/chart?cht=tx&amp;amp;chl=H=p_%7Bred%7D*(H_%7Bred%7D)%2Bp_%7Bblue%7D*(H_%7Bblue%7D)=0.5*(-.5*log_2(.5)-.5*log_2(.5))%2B.5*(-.5*log_2(.5)-.5*log_2(.5))" alt="RBEntropy"&gt;&lt;/p&gt;

&lt;p&gt;Here the probability of red and blue are both .5, and the entropy of each system is 1, so the Entropy of the new system is still 1.  This means that no information was gained from choosing red or blue.  Suppose instead we select on left right:&lt;/p&gt;

&lt;p&gt;&lt;img src="https://chart.googleapis.com/chart?cht=tx&amp;amp;chl=H=p_%7Bleft%7D*%28H_%7Bleft%7D%29%2Bp_%7Bright%7D*%28H_%7Bright%7D%29=0.5*%28-1*log_2%281%29%29%2B.5*%28-1*log_2%281%29%29" alt="LREntropy"&gt;&lt;/p&gt;

&lt;p&gt;And since log_2(1)=0, there is no disorder (i.e. a perfect predictor).&lt;/p&gt;

&lt;p&gt;For continuous variables, it becomes a bit more complicated because you then have to select based on a floating threshold (or a few for a single variable).  While the basic entropy calculations hold fast, tuning many thresholds on many variables becomes resource intensive, so at that point you''ll want to look at something like spark''s mllib. &lt;/p&gt;
', NULL, NULL, '''/chart?cht=tx&amp;amp;chl=h=%5csigma_x-p_x*log_2%28p_x%29'':78C ''/chart?cht=tx&amp;amp;chl=h=p_%7bleft%7d*%28h_%7bleft%7d%29%2bp_%7bright%7d*%28h_%7bright%7d%29=0.5*%28-1*log_2%281%29%29%2b.5*%28-1*log_2%281%29%29'':275C ''/chart?cht=tx&amp;amp;chl=h=p_%7bred%7d*(h_%7bred%7d)%2bp_%7bblue%7d*(h_%7bblue%7d)=0.5*(-.5*log_2(.5)-.5*log_2(.5))%2b.5*(-.5*log_2(.5)-.5*log_2(.5))'':217C ''/p'':22C,72C,81C,104C,211C,220C,269C,278C,294C,354C ''0'':285C ''1'':199C,239C,249C,284C ''2'':283C ''5'':194C,197C,231C ''also'':165C ''alt'':79C,218C,276C ''alway'':159C ''automat'':9C ''base'':206C,311C ''basic'':325C ''becom'':300C,336C ''behind'':160C ''binari'':84C ''bit'':302C ''blue'':136C,177C,228C,261C ''calcul'':90C,327C ''case'':112C ''chang'':142C ''chart.googleapis.com'':77C,216C,274C ''chart.googleapis.com/chart?cht=tx&amp;amp;chl=h=%5csigma_x-p_x*log_2%28p_x%29'':76C ''chart.googleapis.com/chart?cht=tx&amp;amp;chl=h=p_%7bleft%7d*%28h_%7bleft%7d%29%2bp_%7bright%7d*%28h_%7bright%7d%29=0.5*%28-1*log_2%281%29%29%2b.5*%28-1*log_2%281%29%29'':273C ''chart.googleapis.com/chart?cht=tx&amp;amp;chl=h=p_%7bred%7d*(h_%7bred%7d)%2bp_%7bblue%7d*(h_%7bblue%7d)=0.5*(-.5*log_2(.5)-.5*log_2(.5))%2b.5*(-.5*log_2(.5)-.5*log_2(.5))'':215C ''choos'':120C,183C,258C ''classif'':14C,36C,85C ''color'':138C,208C ''complic'':304C ''consid'':109C ''construct'':57C ''continu'':297C ''decis'':20C,58C ''disord'':68C,289C ''done'':18C ''door'':126C,141C,163C,205C ''entropi'':47C,60C,80C,91C,100C,187C,234C,242C,326C ''equal'':179C ''everi'':93C ''exampl'':108C ''exist'':116C ''fast'':329C ''featur'':10C ''find'':29C ''float'':314C ''gain'':256C ''game'':115C ''good'':5C ''hold'':328C ''i.e'':290C ''idea'':6C ''img'':74C,213C,271C ''import'':32C ''inform'':254C ''instanc'':144C,146C ''instead'':263C ''intens'':338C ''left'':125C,148C,162C,172C,267C ''left-right'':171C ''like'':350C ''ll'':344C ''log'':282C ''look'':347C ''lrentropi'':277C ''mani'':168C,331C,334C ''mean'':251C ''minim'':48C,99C ''mllib'':353C ''move'':95C ''must'':119C ''new'':245C ''number'':180C ''numer'':66C ''often'':54C ''one'':118C,127C,132C ''option'':185C ''origin'':190C ''p'':1C,23C,73C,82C,105C,192C,195C,212C,221C,270C,279C,295C ''peopl'':182C ''perfect'':292C ''perform'':46C ''point'':342C ''popular'':39C ''predictor'':293C ''prize'':157C,193C,196C ''probabl'':224C ''quantifi'':67C ''rbentropi'':219C ''red'':131C,150C,154C,176C,226C,259C ''red-blu'':175C ''relat'':31C ''resourc'':337C ''right'':123C,152C,173C,268C ''scheme'':37C ''select'':12C,42C,203C,265C,310C ''simpl'':88C,111C ''simpli'':62C ''sinc'':281C ''singl'':321C ''someth'':349C ''sometim'':147C,151C ''spark'':351C ''src'':75C,214C,272C ''still'':248C ''suppos'':164C,200C,262C ''system'':71C,191C,237C,246C ''techniqu'':51C ''threshold'':315C,332C ''top'':103C ''tree'':21C,59C ''tri'':27C ''trial'':169C ''tune'':330C ''use'':55C ''variabl'':34C,43C,94C,97C,298C,322C,335C ''vector'':11C ''want'':345C ''way'':40C,64C ''wide'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5940, 2, '2015-05-28 18:30:23.893', 3, NULL, NULL, '&lt;p&gt;You might be able to find individuals that act as anchors.  That if Abe is here, then everybody else is more likely to stay longer. Interesting to know if it was always the same people who stayed or not.&lt;/p&gt;

&lt;p&gt;There might also be some interesting patterns with groups that arrive, are present, or leave together.&lt;/p&gt;

&lt;p&gt;As a business owner you could look for individuals who not just work shorter hours, but arrive at their own time, and leave with a group.&lt;/p&gt;
', NULL, NULL, '''/p'':41C,59C,87C ''abe'':15C ''abl'':5C ''act'':10C ''also'':45C ''alway'':33C ''anchor'':12C ''arriv'':53C,77C ''busi'':63C ''could'':66C ''els'':20C ''everybodi'':19C ''find'':7C ''group'':51C,86C ''hour'':75C ''individu'':8C,69C ''interest'':27C,48C ''know'':29C ''leav'':57C,83C ''like'':23C ''longer'':26C ''look'':67C ''might'':3C,44C ''owner'':64C ''p'':1C,42C,60C ''pattern'':49C ''peopl'':36C ''present'':55C ''shorter'':74C ''stay'':25C,38C ''time'':81C ''togeth'':58C ''work'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5941, 1, '2015-05-28 22:07:35.367', 4, 221, 'Need help with LDA for selecting features', '&lt;p&gt;I am currently selecting features of products by using LDA to group 6000 keywords of product into topics. 
Here is the sample of my dataset after being organized into list of keywords for each product id.
&lt;img src="http://i.stack.imgur.com/H6Fhh.png" alt="enter image description here"&gt;
I consider each id as a "document" and each keywords as the "word" in a "document" for the case of LDA.
It didn''t work out as I expected as each topic have many identical keywords with different weight. I removed 100 most common keywords but there are still some identical keywords in the topics. Here is the sample output:
&lt;img src="http://i.stack.imgur.com/kOR8W.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;How can I deal with the identical keywords in my topics? and also how to deal with the 100 most common keywords I removed?&lt;/p&gt;

&lt;p&gt;Here is the graph showing the frequency of the words in all "documents"
&lt;img src="http://i.stack.imgur.com/gbMUC.png" alt="enter image description here"&gt;
Each word only presents once in each document, but may present in different documents. I updated a new graph of the frequency of the words&lt;/p&gt;

&lt;p&gt;Thank you so much. Any suggestion is appreciated.&lt;/p&gt;
', 'machine-learning  bigdata  topic-model  lda', NULL, '''/gbmuc.png'':178C ''/h6fhh.png'':57C ''/kor8w.png'':127C ''/p'':133C,159C,209C,219C ''100'':104C,153C ''6000'':29C ''also'':147C ''alt'':58C,128C,179C ''appreci'':218C ''bigdata'':11B ''case'':81C ''common'':106C,155C ''consid'':64C ''current'':19C ''dataset'':41C ''deal'':138C,150C ''descript'':61C,131C,182C ''didn'':85C ''differ'':100C,196C ''document'':69C,78C,173C,191C,197C ''enter'':59C,129C,180C ''expect'':91C ''featur'':7A,21C ''frequenc'':167C,205C ''graph'':164C,202C ''group'':28C ''help'':2A ''i.stack.imgur.com'':56C,126C,177C ''i.stack.imgur.com/gbmuc.png'':176C ''i.stack.imgur.com/h6fhh.png'':55C ''i.stack.imgur.com/kor8w.png'':125C ''id'':52C,66C ''ident'':97C,113C,141C ''imag'':60C,130C,181C ''img'':53C,123C,174C ''keyword'':30C,48C,72C,98C,107C,114C,142C,156C ''lda'':4A,15B,26C,83C ''learn'':10B ''list'':46C ''machin'':9B ''machine-learn'':8B ''mani'':96C ''may'':193C ''model'':14B ''much'':214C ''need'':1A ''new'':201C ''organ'':44C ''output'':122C ''p'':16C,134C,160C,210C ''present'':187C,194C ''product'':23C,32C,51C ''remov'':103C,158C ''sampl'':38C,121C ''select'':6A,20C ''show'':165C ''src'':54C,124C,175C ''still'':111C ''suggest'':216C ''thank'':211C ''topic'':13B,34C,94C,117C,145C ''topic-model'':12B ''updat'':199C ''use'':25C ''weight'':101C ''word'':75C,170C,185C,208C ''work'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5942, 2, '2015-05-29 00:41:17.407', 2, NULL, NULL, '&lt;p&gt;You can perform logistic regression (if your dependent variable has two classes) that penalizes based on the L1 norm. You can choose the correct sparsity parameter (typically $\lambda$) that chooses how strongly to seek sparsity based on cross-validation. The model will force many non-informative features to be 0. This is a form of feature selection. See here: &lt;a href="http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/p'':73C ''/stable/modules/linear_model.html#logistic-regression'':67C ''/stable/modules/linear_model.html#logistic-regression&lt;/a&gt;'':72C ''0'':53C ''base'':16C,37C ''choos'':23C,31C ''class'':13C ''correct'':25C ''cross'':40C ''cross-valid'':39C ''depend'':9C ''featur'':50C,59C ''forc'':45C ''form'':57C ''href'':64C ''inform'':49C ''l1'':19C ''lambda'':29C ''logist'':5C ''mani'':46C ''model'':43C ''nofollow'':69C ''non'':48C ''non-inform'':47C ''norm'':20C ''p'':1C ''paramet'':27C ''penal'':15C ''perform'':4C ''regress'':6C ''rel'':68C ''scikit-learn.org'':66C,71C ''scikit-learn.org/stable/modules/linear_model.html#logistic-regression'':65C ''scikit-learn.org/stable/modules/linear_model.html#logistic-regression&lt;/a&gt;'':70C ''see'':61C ''seek'':35C ''select'':60C ''sparsiti'':26C,36C ''strong'':33C ''two'':12C ''typic'':28C ''valid'':41C ''variabl'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5958, 1, '2015-05-29 23:04:54.58', 5, 115, 'Are there any unsupervised learning algorithms for time sequenced data?', '&lt;p&gt;Each observation in my data was collected with a difference of 0.1 seconds. I don''t call it a time series because it don''t have a date and time stamp. In the examples of clustering algorithms (I found online) and PCA the sample data have 1 observation per case and are not timed. But my data have hundreds of observations collected every 0.1 seconds per vehicle and there are many vehicles.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Note: I have asked this question on quora as well.&lt;/em&gt;&lt;/p&gt;
', 'algorithms', NULL, '''/em'':98C ''/p'':85C,99C ''0.1'':24C,76C ''1'':59C ''algorithm'':6A,11B,49C ''ask'':91C ''call'':29C ''case'':62C ''cluster'':48C ''collect'':19C,74C ''data'':10A,17C,57C,69C ''date'':40C ''differ'':22C ''em'':87C ''everi'':75C ''exampl'':46C ''found'':51C ''hundr'':71C ''learn'':5A ''mani'':83C ''note'':88C ''observ'':14C,60C,73C ''onlin'':52C ''p'':12C,86C ''pca'':54C ''per'':61C,78C ''question'':93C ''quora'':95C ''sampl'':56C ''second'':25C,77C ''sequenc'':9A ''seri'':33C ''stamp'':43C ''time'':8A,32C,42C,66C ''unsupervis'':4A ''vehicl'':79C,84C ''well'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6294, 1, '2015-07-01 06:18:06.55', 0, 37, 'Algorithm/Analysis that utilises incremental information?', '&lt;p&gt;I am looking for techniques which utilise information in an incremental manner. &lt;/p&gt;

&lt;p&gt;Example: A day with inclement climate is likely to be followed by another day with inclement climate. Or when an entire dataset sorted by date is available, an algorithm able to identify that a person is likely to call in sick when the previous day had bad weather.&lt;/p&gt;

&lt;p&gt;Is there any effective analysis which utilises this prior information where not all events are completely independent?&lt;/p&gt;
', 'algorithms  knowledge-base', NULL, '''/p'':23C,73C,92C ''abl'':54C ''algorithm'':6B,53C ''algorithm/analysis'':1A ''analysi'':79C ''anoth'':37C ''avail'':51C ''bad'':71C ''base'':9B ''call'':63C ''climat'':30C,41C ''complet'':90C ''dataset'':46C ''date'':49C ''day'':27C,38C,69C ''effect'':78C ''entir'':45C ''event'':88C ''exampl'':25C ''follow'':35C ''identifi'':56C ''inclement'':29C,40C ''increment'':4A,21C ''independ'':91C ''inform'':5A,18C,84C ''knowledg'':8B ''knowledge-bas'':7B ''like'':32C,61C ''look'':13C ''manner'':22C ''p'':10C,24C,74C ''person'':59C ''previous'':68C ''prior'':83C ''sick'':65C ''sort'':47C ''techniqu'':15C ''utilis'':3A,17C,81C ''weather'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5943, 2, '2015-05-29 01:09:27.367', 1, NULL, NULL, '&lt;p&gt;Here is the relevant source code. I''m pretty sure it''s using cross-entropy for multiclass: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    //  GBM by Greg Ridgeway  Copyright (C) 2003

#include "multinomial.h"

CMultinomial::CMultinomial(int cNumClasses, int cRows)
{
   mcNumClasses = cNumClasses;
   mcRows = cRows;

   madProb = new double[cNumClasses * cRows];
}

CMultinomial::~CMultinomial()
{
   if(madProb != NULL)
   {
      delete [] madProb;
   }
}


GBMRESULT CMultinomial::UpdateParams
(
   double *adF,
   double *adOffset,
   double *adWeight,
   unsigned long cLength
)
{
   // Local variables
   unsigned long ii=0;
   unsigned long kk=0;

   // Set the probabilities for each observation in each class
   for (ii = 0; ii &amp;lt; mcRows; ii++)
   {
      double dClassSum = 0.0;
      for (kk = 0; kk &amp;lt; mcNumClasses; kk++)
      {
         int iIdx = ii + kk * mcRows;
         double dF = (adOffset == NULL) ? adF[iIdx] : adF[iIdx] + adOffset[iIdx];
         madProb[iIdx] = adWeight[iIdx] * exp(dF);
         dClassSum += adWeight[iIdx] * exp(dF);
      }

      dClassSum = (dClassSum &amp;gt; 0) ? dClassSum : 1e-8;

      for (kk = 0; kk &amp;lt; mcNumClasses; kk++)
      {
         madProb[ii + kk * mcRows] /= dClassSum;
      }
   }

   return GBM_OK; 
}


GBMRESULT CMultinomial::ComputeWorkingResponse
(
    double *adY,
    double *adMisc,
    double *adOffset,
    double *adF, 
    double *adZ, 
    double *adWeight,
    bool *afInBag,
    unsigned long nTrain,
    int cIdxOff
)
{
    unsigned long i = 0;

    for(i=cIdxOff; i&amp;lt;nTrain+cIdxOff; i++)
    {
       adZ[i] = adY[i] - madProb[i];
    }

    return GBM_OK;
}


GBMRESULT CMultinomial::InitF
(
    double *adY,
    double *adMisc,
    double *adOffset, 
    double *adWeight,
    double &amp;amp;dInitF, 
    unsigned long cLength
)
{
    dInitF = 0.0;
    return GBM_OK;
}

double CMultinomial::Deviance
(
    double *adY,
    double *adMisc,
    double *adOffset, 
    double *adWeight,
    double *adF,
    unsigned long cLength,
    int cIdxOff
)
{
    unsigned long ii=0;
    double dL = 0.0;
    double dW = 0.0;

    for(ii=cIdxOff; ii&amp;lt;cLength+cIdxOff; ii++)
    {
        dL += -adWeight[ii] * adY[ii] * log(madProb[ii]);
        dW += adWeight[ii];
    }

    return dL/dW;
}


GBMRESULT CMultinomial::FitBestConstant
(
    double *adY,
    double *adMisc,
    double *adOffset,
    double *adW,
    double *adF,
    double *adZ,
    unsigned long *aiNodeAssign,
    unsigned long nTrain,
    VEC_P_NODETERMINAL vecpTermNodes,
    unsigned long cTermNodes,
    unsigned long cMinObsInNode,
    bool *afInBag,
    double *adFadj,
   int cIdxOff
)
{
      // Local variables
    GBMRESULT hr = GBM_OK;
    unsigned long iNode = 0;
    unsigned long iObs = 0;

   // Call LocM for the array of values on each node
    for(iNode=0; iNode&amp;lt;cTermNodes; iNode++)
    {
        if(vecpTermNodes[iNode]-&amp;gt;cN &amp;gt;= cMinObsInNode)
        {
         // Get the number of nodes here
         double dNum = 0.0;
         double dDenom = 0.0;
         for (iObs = 0; iObs &amp;lt; nTrain; iObs++)
         {
            if(afInBag[iObs] &amp;amp;&amp;amp; (aiNodeAssign[iObs] == iNode))
                {
               int iIdx = iObs + cIdxOff;
                    dNum += adW[iIdx] * adZ[iIdx];
               dDenom += adW[iIdx] * fabs(adZ[iIdx]) * (1 - fabs(adZ[iIdx]));
                }
         }

         dDenom = (dDenom &amp;gt; 0) ? dDenom : 1e-8;

         vecpTermNodes[iNode]-&amp;gt;dPrediction = dNum / dDenom;
        }
    }

    return hr;
}

double CMultinomial::BagImprovement
(
    double *adY,
    double *adMisc,
    double *adOffset,
    double *adWeight,
    double *adF,
    double *adFadj,
    bool *afInBag,
    double dStepSize,
    unsigned long nTrain
)
{
    double dReturnValue = 0.0;
    double dW = 0.0;

   unsigned long ii;
   unsigned long kk;

   // Calculate the probabilities after the step
   double *adStepProb = new double[mcNumClasses * mcRows];

   // Assume that this is last class - calculate new prob as in updateParams but
   // using (F_ik + ss*Fadj_ik) instead of F_ik. Then calculate OOB improve
   for (ii = 0; ii &amp;lt; mcRows; ii++)
   {
      double dClassSum = 0.0;
      for (kk = 0; kk &amp;lt; mcNumClasses; kk++)
      {
         int iIdx = ii + kk * mcRows;
         double dF = (adOffset == NULL) ? adF[iIdx] : adF[iIdx] + adOffset[iIdx];
         dF += dStepSize * adFadj[iIdx];
         adStepProb[iIdx] = adWeight[iIdx] * exp(dF);
         dClassSum += adWeight[iIdx] * exp(dF);
      }

      dClassSum = (dClassSum &amp;gt; 0) ? dClassSum : 1e-8;

      for (kk = 0; kk &amp;lt; mcNumClasses; kk++)
      {
         adStepProb[ii + kk * mcRows] /= dClassSum;
      }
   }

   // Calculate the improvement
    for(ii=0; ii&amp;lt;nTrain; ii++)
    {
        if(!afInBag[ii])
      {
         for (kk = 0; kk &amp;lt; mcNumClasses; kk++)
         {
            int iIdx = ii + kk * mcRows;
                dReturnValue += adWeight[iIdx] * adY[iIdx] * 
                               (log(adStepProb[iIdx]) - log(madProb[iIdx]));

            dW += adWeight[iIdx] * adY[iIdx];
         }
      }
    }

    return dReturnValue/dW;
}
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':579C ''/p'':20C ''/pre'':580C ''0'':71C,75C,87C,97C,131C,136C,174C,235C,309C,313C,326C,352C,387C,473C,483C,521C,526C,541C,551C ''0.0'':94C,210C,238C,241C,346C,349C,422C,425C,480C ''1'':380C ''1e-8'':133C,389C,523C ''2003'':29C ''adf'':58C,111C,113C,159C,226C,275C,410C,497C,499C ''adfadj'':297C,412C,505C ''adi'':153C,185C,196C,218C,253C,267C,402C,564C,575C ''admisc'':155C,198C,220C,269C,404C ''adoffset'':60C,109C,115C,157C,200C,222C,271C,406C,495C,501C ''adstepprob'':439C,507C,531C,567C ''adw'':273C,370C,375C ''adweight'':62C,119C,124C,163C,202C,224C,251C,259C,408C,509C,514C,562C,573C ''adz'':161C,183C,277C,372C,378C,382C ''afinbag'':165C,295C,358C,414C,547C ''ainodeassign'':280C,362C ''amp'':204C,360C,361C ''array'':318C ''assum'':444C ''bagimprov'':400C ''bool'':164C,294C,413C ''c'':28C ''calcul'':432C,450C,468C,536C ''call'':314C ''cidxoff'':170C,177C,181C,231C,244C,248C,299C,368C ''class'':84C,449C ''clength'':65C,208C,229C,247C ''cminobsinnod'':293C,337C ''cmultinomi'':32C,33C,47C,48C,55C,150C,193C,215C,264C,399C ''cn'':335C ''cnumclass'':35C,39C,45C ''code'':7C,22C ''computeworkingrespons'':151C ''copyright'':27C ''cross'':16C ''cross-entropi'':15C ''crow'':37C,41C,46C ''ctermnod'':290C,329C ''dclasssum'':93C,123C,128C,129C,132C,145C,479C,513C,518C,519C,522C,535C ''ddenom'':348C,374C,384C,385C,388C,395C ''delet'':52C ''devianc'':216C ''df'':108C,122C,127C,494C,503C,512C,517C ''dinitf'':205C,209C ''dl'':237C,250C ''dl/dw'':262C ''dnum'':345C,369C,394C ''doubl'':44C,57C,59C,61C,92C,107C,152C,154C,156C,158C,160C,162C,195C,197C,199C,201C,203C,214C,217C,219C,221C,223C,225C,236C,239C,266C,268C,270C,272C,274C,276C,296C,344C,347C,398C,401C,403C,405C,407C,409C,411C,415C,420C,423C,438C,441C,478C,493C ''dpredict'':393C ''dreturnvalu'':421C,561C ''dreturnvalue/dw'':578C ''dstepsiz'':416C,504C ''dw'':240C,258C,424C,572C ''entropi'':17C ''exp'':121C,126C,511C,516C ''f'':458C,465C ''fab'':377C,381C ''fadj'':461C ''fitbestconst'':265C ''gbm'':23C,147C,190C,212C,304C ''gbmresult'':54C,149C,192C,263C,302C ''get'':338C ''greg'':25C ''gt'':130C,334C,336C,386C,392C,520C ''hr'':303C,397C ''ii'':70C,86C,88C,91C,104C,142C,234C,243C,245C,249C,252C,254C,257C,260C,428C,472C,474C,477C,490C,532C,540C,542C,545C,548C,558C ''iidx'':103C,112C,114C,116C,118C,120C,125C,366C,371C,373C,376C,379C,383C,489C,498C,500C,502C,506C,508C,510C,515C,557C,563C,565C,568C,571C,574C,576C ''ik'':459C,462C,466C ''improv'':470C,538C ''includ'':30C ''initf'':194C ''inod'':308C,325C,327C,330C,333C,364C,391C ''instead'':463C ''int'':34C,36C,102C,169C,230C,298C,365C,488C,556C ''iob'':312C,351C,353C,356C,359C,363C,367C ''kk'':74C,96C,98C,101C,105C,135C,137C,140C,143C,431C,482C,484C,487C,491C,525C,527C,530C,533C,550C,552C,555C,559C ''last'':448C ''local'':66C,300C ''locm'':315C ''log'':255C,566C,569C ''long'':64C,69C,73C,167C,172C,207C,228C,233C,279C,282C,289C,292C,307C,311C,418C,427C,430C ''lt'':89C,99C,138C,179C,246C,328C,354C,475C,485C,528C,543C,553C ''m'':9C ''madprob'':42C,50C,53C,117C,141C,187C,256C,570C ''mcnumclass'':38C,100C,139C,442C,486C,529C,554C ''mcrow'':40C,90C,106C,144C,443C,476C,492C,534C,560C ''multiclass'':19C ''multinomial.h'':31C ''new'':43C,440C,451C ''node'':323C,342C ''nodetermin'':286C ''ntrain'':168C,180C,283C,355C,419C,544C ''null'':51C,110C,496C ''number'':340C ''observ'':81C ''ok'':148C,191C,213C,305C ''oob'':469C ''p'':1C,285C ''pre'':21C ''pretti'':10C ''prob'':452C ''probabl'':78C,434C ''relev'':5C ''return'':146C,189C,211C,261C,396C,577C ''ridgeway'':26C ''set'':76C ''sourc'':6C ''ss'':460C ''step'':437C ''sure'':11C ''unsign'':63C,68C,72C,166C,171C,206C,227C,232C,278C,281C,288C,291C,306C,310C,417C,426C,429C ''updateparam'':56C,455C ''use'':14C,457C ''valu'':320C ''variabl'':67C,301C ''vec'':284C ''vecptermnod'':287C,332C,390C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8715, 1, '2015-11-04 13:54:14.683', 0, 55, 'Naive Bayes where Feature Space is LDA Output', '&lt;p&gt;I understand that the output for Latent Dirichlet Allocation is a distribution over K topics.&lt;/p&gt;

&lt;p&gt;Suppose I have a Dx(K+1) matrix, where rows are documents and columns are the topic distribution + one column for class. For example, each row represents one movie review. The first K columns represent the topic distribution of the document, and the last column is the classification of this document. For example, if K=5, one row may read as:&lt;/p&gt;

&lt;p&gt;2 | 0.25 | 0.4 | 0.1 | 0.15 | 0.1 | 1&lt;/p&gt;

&lt;p&gt;where moview review 2 had 25% of the text about topic 1 (pleasure), 40% topic 2 (discontent), 10% topic 3 (personal feelings) ... and the classification of this document was to class #1.&lt;/p&gt;

&lt;p&gt;How would I go about creating a Naive Bayes Classifier using this data?&lt;/p&gt;

&lt;p&gt;Typically, I have used a Gaussian Naive Bayes where the feature space is iid over normally distributed variables, but this assumption I do not believe makes sense for LDA output. Would I need to assume the features are the individual columns distributed in a particular way (say a Dirichlet probability)?&lt;/p&gt;

&lt;p&gt;This exercise is more for proof of concept to use Naive Bayes. I want to use, say 60%, of the data to construct a Naive Bayes Classifier and test the accuracy of the remaining 40%. My main concern is to how to define the PDF for the Naive Bayes Classifier.&lt;/p&gt;
', 'machine-learning  classification  lda', NULL, '''+1'':38C ''/p'':30C,93C,102C,136C,151C,203C,255C ''0.1'':98C,100C ''0.15'':99C ''0.25'':96C ''0.4'':97C ''1'':101C,115C,135C ''10'':121C ''2'':95C,107C,119C ''25'':109C ''3'':123C ''40'':117C,239C ''5'':87C ''60'':222C ''accuraci'':235C ''alloc'':23C ''assum'':187C ''assumpt'':173C ''bay'':2A,146C,160C,216C,230C,253C ''believ'':177C ''class'':53C,134C ''classif'':12B,79C,128C ''classifi'':147C,231C,254C ''column'':45C,51C,65C,76C,193C ''concept'':212C ''concern'':242C ''construct'':227C ''creat'':143C ''data'':150C,225C ''defin'':247C ''dirichlet'':22C,201C ''discont'':120C ''distribut'':26C,49C,69C,169C,194C ''document'':43C,72C,82C,131C ''dx'':36C ''exampl'':55C,84C ''exercis'':206C ''featur'':4A,163C,189C ''feel'':125C ''first'':63C ''gaussian'':158C ''go'':141C ''iid'':166C ''individu'':192C ''k'':28C,37C,64C,86C ''last'':75C ''latent'':21C ''lda'':7A,13B,181C ''learn'':11B ''machin'':10B ''machine-learn'':9B ''main'':241C ''make'':178C ''matrix'':39C ''may'':90C ''movi'':60C ''moview'':105C ''naiv'':1A,145C,159C,215C,229C,252C ''need'':185C ''normal'':168C ''one'':50C,59C,88C ''output'':8A,19C,182C ''p'':14C,31C,94C,103C,137C,152C,204C ''particular'':197C ''pdf'':249C ''person'':124C ''pleasur'':116C ''probabl'':202C ''proof'':210C ''read'':91C ''remain'':238C ''repres'':58C,66C ''review'':61C,106C ''row'':41C,57C,89C ''say'':199C,221C ''sens'':179C ''space'':5A,164C ''suppos'':32C ''test'':233C ''text'':112C ''topic'':29C,48C,68C,114C,118C,122C ''typic'':153C ''understand'':16C ''use'':148C,156C,214C,220C ''variabl'':170C ''want'':218C ''way'':198C ''would'':139C,183C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6179, 2, '2015-06-23 02:37:33.75', 1, NULL, NULL, '&lt;p&gt;analyzing 8 billion lines on a 4gb computer is pretty silly, but you can try&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.asdfree.com/2013/03/column-store-r-or-how-i-learned-to-stop.html" rel="nofollow"&gt;http://www.asdfree.com/2013/03/column-store-r-or-how-i-learned-to-stop.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/2013/03/column-store-r-or-how-i-learned-to-stop.html'':23C ''/2013/03/column-store-r-or-how-i-learned-to-stop.html&lt;/a&gt;&lt;/p&gt;'':28C ''/p'':17C ''4gb'':8C ''8'':3C ''analyz'':2C ''billion'':4C ''comput'':9C ''href'':20C ''line'':5C ''nofollow'':25C ''p'':1C,18C ''pretti'':11C ''rel'':24C ''silli'':12C ''tri'':16C ''www.asdfree.com'':22C,27C ''www.asdfree.com/2013/03/column-store-r-or-how-i-learned-to-stop.html'':21C ''www.asdfree.com/2013/03/column-store-r-or-how-i-learned-to-stop.html&lt;/a&gt;&lt;/p&gt;'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5944, 1, '2015-05-29 02:55:18.203', 1, 37, 'How far from a specific state in a Markov chain', '&lt;p&gt;Problem:
Lets say we have an irreducible Markov chain.
Given a failure state or non desirable state F and a current state S
Is it possible to find how far we are from the failure state F.&lt;/p&gt;

&lt;p&gt;Example: if we try to model an engine failure using Markov process and using historic data we have a state transition matrix P, one of which is a state where the engine was failed .
Now , in a dynamic system, if the current state S and a state transition matrix are given, can we calculate how far we are from failure state F.&lt;/p&gt;

&lt;p&gt;Mathematically:&lt;/p&gt;

&lt;p&gt;x(n+1) = (x)P^n&lt;/p&gt;

&lt;p&gt;Right hand side of the equation gives us a vector v which consists of probabilities of transitioning to different states at stage n.
With different value of n, the values in this vector v will change
one of the values in v will be the probability of going to state F.
I want to find for what n , transitioning probability to F is maximum.&lt;/p&gt;
', 'statistics', NULL, '''+1'':121C ''/p'':50C,114C,117C,125C,191C ''calcul'':105C ''chain'':10A,21C ''chang'':162C ''consist'':139C ''current'':33C,93C ''data'':67C ''desir'':28C ''differ'':145C,151C ''dynam'':89C ''engin'':59C,83C ''equat'':132C ''exampl'':52C ''f'':30C,49C,113C,177C,188C ''fail'':85C ''failur'':24C,47C,60C,111C ''far'':2A,42C,107C ''find'':40C,181C ''give'':133C ''given'':22C,102C ''go'':174C ''hand'':128C ''histor'':66C ''irreduc'':19C ''let'':14C ''markov'':9A,20C,62C ''mathemat'':116C ''matrix'':73C,100C ''maximum'':190C ''model'':57C ''n'':120C,124C,149C,154C,184C ''non'':27C ''one'':75C,163C ''p'':12C,51C,74C,115C,118C,123C,126C ''possibl'':38C ''probabl'':141C,172C,186C ''problem'':13C ''process'':63C ''right'':127C ''say'':15C ''side'':129C ''specif'':5A ''stage'':148C ''state'':6A,25C,29C,34C,48C,71C,80C,94C,98C,112C,146C,176C ''statist'':11B ''system'':90C ''transit'':72C,99C,143C,185C ''tri'':55C ''us'':134C ''use'':61C,65C ''v'':137C,160C,168C ''valu'':152C,156C,166C ''vector'':136C,159C ''want'':179C ''x'':119C,122C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5946, 1, '2015-05-29 07:39:35.713', 3, 88, 'Algorithm for deriving mutiple clusters', '&lt;p&gt;Suppose I have a set of data(with  2 diemensional feature space), and I want to obtain clusters from them. But I do not know how many clusters will be formed.&lt;br&gt;&lt;br&gt; Yet I want separate clusters(The number of clusters is more than 2). &lt;br&gt;&lt;br&gt;I figured that k means of k medoid cannot be used in this case. Nor can I use hierarchical clustering. Also since there is no training set hence cannot use Knn classifier to any others(supervised learning cannot be used as no training set). I cannot use OPTICS algorithm as I do not want to specify the radius(I dont know the radius)&lt;br&gt;&lt;br&gt; Is there any machine learning technique that would give me multiple clusters(distance based clustering) that deals well with outlier points too? &lt;img src="http://i.stack.imgur.com/dAimG.png" alt="enter image description here"&gt;
This should be the output&lt;/p&gt;
', 'machine-learning  clustering', NULL, '''/daimg.png'':150C ''/p'':161C ''2'':19C,56C ''algorithm'':1A,107C ''also'':79C ''alt'':151C ''base'':137C ''br'':42C,43C,57C,58C,122C,123C ''cannot'':67C,87C,96C,104C ''case'':72C ''classifi'':90C ''cluster'':5A,9B,28C,38C,48C,52C,78C,135C,138C ''data'':17C ''deal'':140C ''deriv'':3A ''descript'':154C ''diemension'':20C ''distanc'':136C ''dont'':118C ''enter'':152C ''featur'':21C ''figur'':60C ''form'':41C ''give'':132C ''henc'':86C ''hierarch'':77C ''i.stack.imgur.com'':149C ''i.stack.imgur.com/daimg.png'':148C ''imag'':153C ''img'':146C ''k'':62C,65C ''knn'':89C ''know'':35C,119C ''learn'':8B,95C,128C ''machin'':7B,127C ''machine-learn'':6B ''mani'':37C ''mean'':63C ''medoid'':66C ''multipl'':134C ''mutipl'':4A ''number'':50C ''obtain'':27C ''optic'':106C ''other'':93C ''outlier'':143C ''output'':160C ''p'':10C ''point'':144C ''radius'':116C,121C ''separ'':47C ''set'':15C,85C,102C ''sinc'':80C ''space'':22C ''specifi'':114C ''src'':147C ''supervis'':94C ''suppos'':11C ''techniqu'':129C ''train'':84C,101C ''use'':69C,76C,88C,98C,105C ''want'':25C,46C,112C ''well'':141C ''would'':131C ''yet'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5947, 2, '2015-05-29 08:32:16.483', 3, NULL, NULL, '&lt;p&gt;I think, even before doing LDA, you should remove words which appear in more than "x" percent of your documents. Try different "x" starting from 80% and then going down. The logic is that if the word is common for many documents, it  does not distinguished those and should be neglected.&lt;/p&gt;
', NULL, NULL, '''/p'':53C ''80'':27C ''appear'':13C ''common'':40C ''differ'':23C ''distinguish'':47C ''document'':21C,43C ''even'':4C ''go'':30C ''lda'':7C ''logic'':33C ''mani'':42C ''neglect'':52C ''p'':1C ''percent'':18C ''remov'':10C ''start'':25C ''think'':3C ''tri'':22C ''word'':11C,38C ''x'':17C,24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5949, 2, '2015-05-29 09:12:01.65', 2, NULL, NULL, '&lt;p&gt;Feature selection is a very well established field in Machine Learning. The objective of feature selection algorithms is to select a subset of your feature set in order to maximize your system''s prediction performance. There are two kind of feature selection approaches:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Filter methods&lt;/strong&gt;: filter methods select features without taking into consideration any specific prediction algorithm. They are based in applying different kinds of measures (like mutual information or correlation) in order to evaluate the relative information that a feature or a set of features can provide about the output variable. Filter methods are faster than wrapping methods, but do not perform as well as wrapping methods.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Wrapper methods&lt;/strong&gt;: these methods evaluate features or sets of features by using a specific classification/regression algorithm. &lt;/p&gt;

&lt;p&gt;I''d suggest to read one of the seminal papers in the feature selection field as a starting point in order to get to know the basics:&lt;/p&gt;

&lt;p&gt;Guyon, Isabelle; Elisseeff, AndrÃ© (2003). "An Introduction to Variable and Feature Selection". JMLR&lt;/p&gt;
', NULL, NULL, '''/p'':45C,114C,134C,163C,178C ''/strong'':50C,119C ''2003'':169C ''algorithm'':18C,62C,133C ''andrã'':168C ''appli'':67C ''approach'':44C ''base'':65C ''basic'':162C ''classification/regression'':132C ''consider'':58C ''correl'':76C ''d'':137C ''differ'':68C ''elisseeff'':167C ''establish'':8C ''evalu'':80C,122C ''faster'':101C ''featur'':2C,16C,26C,42C,54C,86C,91C,123C,127C,148C,175C ''field'':9C,150C ''filter'':48C,51C,98C ''get'':158C ''guyon'':165C ''inform'':74C,83C ''introduct'':171C ''isabell'':166C ''jmlr'':177C ''kind'':40C,69C ''know'':160C ''learn'':12C ''like'':72C ''machin'':11C ''maxim'':31C ''measur'':71C ''method'':49C,52C,99C,104C,113C,118C,121C ''mutual'':73C ''object'':14C ''one'':141C ''order'':29C,78C,156C ''output'':96C ''p'':1C,46C,115C,135C,164C ''paper'':145C ''perform'':36C,108C ''point'':154C ''predict'':35C,61C ''provid'':93C ''read'':140C ''relat'':82C ''select'':3C,17C,21C,43C,53C,149C,176C ''semin'':144C ''set'':27C,89C,125C ''specif'':60C,131C ''start'':153C ''strong'':47C,116C ''subset'':23C ''suggest'':138C ''system'':33C ''take'':56C ''two'':39C ''use'':129C ''variabl'':97C,173C ''well'':7C,110C ''without'':55C ''wrap'':103C,112C ''wrapper'':117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5950, 2, '2015-05-29 09:22:53.75', 4, NULL, NULL, '&lt;p&gt;The fact is that you could use any of the algorithms you mentioned, and in general any algorithm that requires to set the number of clusters as a parameter (or any other parameter that indirectly sets the final number of clusters, like the threshold in a hierarchical clustering algorithm.)&lt;/p&gt;

&lt;p&gt;The solution to your problem is &lt;strong&gt;model selection&lt;/strong&gt;. Model selection methods evaluate different clustering solutions, and select the one that optimizes a given criterion. &lt;/p&gt;

&lt;p&gt;For instance, in the case of K-means, you could find a solution for a range of k values, and keep the one that maximizes any cluster validation measure (see the Wikipedia entry for cluster analysis to read about some examples of cluster validation measures). &lt;/p&gt;

&lt;p&gt;There are automatic and more complex approaches (one specific example is "Automatic Cluster Number Selection Using a Split and Merge K-Means Approach" by Muhr, M. and Granitzer, M., but this is just an example). These methods use cluster validation measures to automatically split or merge clusters, but the idea is basically the same. &lt;/p&gt;
', NULL, NULL, '''/p'':51C,79C,127C,184C ''/strong'':62C ''algorithm'':12C,19C,50C ''analysi'':117C ''approach'':135C,152C ''automat'':131C,140C,172C ''basic'':181C ''case'':85C ''cluster'':27C,42C,49C,68C,108C,116C,124C,141C,168C,176C ''complex'':134C ''could'':7C,91C ''criterion'':78C ''differ'':67C ''entri'':114C ''evalu'':66C ''exampl'':122C,138C,164C ''fact'':3C ''final'':39C ''find'':92C ''general'':17C ''given'':77C ''granitz'':157C ''hierarch'':48C ''idea'':179C ''indirect'':36C ''instanc'':82C ''k'':88C,99C,150C ''k-mean'':87C,149C ''keep'':102C ''like'':43C ''m'':155C,158C ''maxim'':106C ''mean'':89C,151C ''measur'':110C,126C,170C ''mention'':14C ''merg'':148C,175C ''method'':65C,166C ''model'':60C,63C ''muhr'':154C ''number'':25C,40C,142C ''one'':73C,104C,136C ''optim'':75C ''p'':1C,52C,80C,128C ''paramet'':30C,34C ''problem'':57C ''rang'':97C ''read'':119C ''requir'':21C ''see'':111C ''select'':61C,64C,71C,143C ''set'':23C,37C ''solut'':54C,69C,94C ''specif'':137C ''split'':146C,173C ''strong'':59C ''threshold'':45C ''use'':8C,144C,167C ''valid'':109C,125C,169C ''valu'':100C ''wikipedia'':113C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5951, 1, '2015-05-29 09:43:47.847', 0, 108, 'Kibana: How to add horizontal line which represents average value for selected time window?', '&lt;p&gt;I''m doing some analysis in ELK Stack. I have datetime histogram with average auto aggregation. Plot looks like that:&lt;img src="http://i.stack.imgur.com/FWdWs.png" alt="original plot"&gt;.&lt;/p&gt;

&lt;p&gt;I would like to add automatic horizontal line which will represent average value of plotted points in selected time window. It should looks like that:&lt;img src="http://i.stack.imgur.com/7lvHN.png" alt="plot with average"&gt;&lt;/p&gt;

&lt;p&gt;How to do that?&lt;/p&gt;
', 'elk', NULL, '''/7lvhn.png'':76C ''/fwdws.png'':41C ''/p'':45C,81C,87C ''add'':4A,51C ''aggreg'':32C ''alt'':42C,77C ''analysi'':21C ''auto'':31C ''automat'':52C ''averag'':9A,30C,58C,80C ''datetim'':27C ''elk'':15B,23C ''histogram'':28C ''horizont'':5A,53C ''i.stack.imgur.com'':40C,75C ''i.stack.imgur.com/7lvhn.png'':74C ''i.stack.imgur.com/fwdws.png'':39C ''img'':37C,72C ''kibana'':1A ''like'':35C,49C,70C ''line'':6A,54C ''look'':34C,69C ''m'':18C ''origin'':43C ''p'':16C,46C,82C ''plot'':33C,44C,61C,78C ''point'':62C ''repres'':8A,57C ''select'':12A,64C ''src'':38C,73C ''stack'':24C ''time'':13A,65C ''valu'':10A,59C ''window'':14A,66C ''would'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5981, 1, '2015-06-01 18:54:14.077', 1, 32, 'Iteratively construct Linear Classifier', '&lt;p&gt;sorry if this question is out of place. I''m a begginer to machine learning, and I have use for a technique, and I don''t even know where to look. The problem is:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;I have 5 features which are real valued (Parameters in a deterministic simulation). &lt;/li&gt;
&lt;li&gt;This features determine two aspects of the instance (model solution). Its feasibility (binary) and some measure of likelihood given certain experimental data (only for the instances that achieved feasibility). &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Since I want to avoid generating "infeasible" combination of features, what I devised was an algorithm that iteratively does the following:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Generate Nc candidate feature vectors&lt;/li&gt;
&lt;li&gt;Evaluate Feasibility and Likelihood for each&lt;/li&gt;
&lt;li&gt;Find linear combination of features that involves a compromise between least amount of features / holds largest cluster of feasibility. Add this as constraints to the feature vector generation. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In short, it detects and iteratively refines "simple" constraints that once added to the feature vector generation "guarantee" its feasibility to save computational time evaluating combination of parameters that lead to infeasible models. Afterwards, they could be tested by inverting them and looking for other "regions" (if any) of the feature vector where the model is feasible.  &lt;/p&gt;

&lt;p&gt;Any name of techniques and references I might look for ?&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'classification', NULL, '''/li'':57C,88C,120C,128C,158C ''/p'':41C,112C,218C,230C,233C ''/ul'':89C,159C ''5'':46C ''achiev'':86C ''ad'':172C ''add'':149C ''afterward'':194C ''algorithm'':106C ''amount'':141C ''aspect'':63C ''avoid'':95C ''beggin'':18C ''binari'':71C ''candid'':117C ''certain'':78C ''classif'':5B ''classifi'':4A ''cluster'':146C ''combin'':98C,132C,186C ''compromis'':138C ''comput'':183C ''constraint'':152C,169C ''construct'':2A ''could'':196C ''data'':80C ''detect'':164C ''determin'':61C ''determinist'':55C ''devis'':103C ''evalu'':122C,185C ''even'':33C ''experiment'':79C ''feasibl'':70C,87C,123C,148C,180C,217C ''featur'':47C,60C,100C,118C,134C,143C,155C,175C,211C ''find'':130C ''follow'':111C ''generat'':96C,115C,157C,177C ''given'':77C ''guarante'':178C ''hold'':144C ''infeas'':97C,192C ''instanc'':66C,84C ''invert'':200C ''involv'':136C ''iter'':1A,108C,166C ''know'':34C ''largest'':145C ''lead'':190C ''learn'':21C ''least'':140C ''li'':43C,58C,114C,121C,129C ''likelihood'':76C,125C ''linear'':3A,131C ''look'':37C,203C,228C ''m'':16C ''machin'':20C ''measur'':74C ''might'':227C ''model'':67C,193C,215C ''name'':221C ''nc'':116C ''p'':6C,90C,160C,219C,231C ''paramet'':52C,188C ''place'':14C ''problem'':39C ''question'':10C ''real'':50C ''refer'':225C ''refin'':167C ''region'':206C ''save'':182C ''short'':162C ''simpl'':168C ''simul'':56C ''sinc'':91C ''solut'':68C ''sorri'':7C ''techniqu'':28C,223C ''test'':198C ''thank'':232C ''time'':184C ''two'':62C ''ul'':42C,113C ''use'':25C ''valu'':51C ''vector'':119C,156C,176C,212C ''want'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5959, 2, '2015-05-30 13:50:32.703', 6, NULL, NULL, '&lt;p&gt;What you have is a sequence of events according to time so do not hesitate to call it Time Series!&lt;/p&gt;

&lt;p&gt;Clustering in time series has 2 different meanings:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;strong&gt;Segmentation of time series&lt;/strong&gt; i.e. you want to segment an individual time series into different time intervals according to internal similarities.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Time series clustering&lt;/strong&gt; i.e. you have several time series and you want to find different clusters according to similarities between them.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I assume you mean the second one and here is my suggestion:&lt;/p&gt;

&lt;p&gt;You have many vehicles and many observations per vehicle i.e you have many vehicles. So you have several matrices (each vehicle is a matrix) and each matrix contains &lt;strong&gt;N&lt;/strong&gt; rows (Nr of observations) and &lt;strong&gt;T&lt;/strong&gt; columns (time points). One suggestion could be applying PCA to each matrix to reduce the dimenssionality and observing data in PC space and see if there is meaningful relations between &lt;em&gt;different observations within a matrix (vehicle)&lt;/em&gt;. Then you can put each observation for all vehicles on each other and make a matrix and apply PCA to that to see relations of a single observation between different vehicles. &lt;/p&gt;

&lt;p&gt;If you do not have negative values &lt;strong&gt;Matrix Factorization&lt;/strong&gt; is strongly recommended for dimension reduction of matrix form data.&lt;/p&gt;

&lt;p&gt;Another suggestion could be putin all matrices on top of each other and build a &lt;strong&gt;N&lt;/strong&gt;x&lt;strong&gt;M&lt;/strong&gt;x&lt;strong&gt;T&lt;/strong&gt; tensor where &lt;em&gt;N&lt;/em&gt; is the number of vehicles, &lt;em&gt;M&lt;/em&gt; is the number of observations and &lt;em&gt;T&lt;/em&gt; is the time sequence and apply &lt;strong&gt;Tensor Decomposition&lt;/strong&gt; to see relations globally.&lt;/p&gt;

&lt;p&gt;A very nice approach to Time Series Clustering is shown in &lt;a href="https://www.researchgate.net/profile/Christian_Bauckhage/publication/262898400_Kernel_Archetypal_Analysis_for_Clustering_Web_Search_Frequency_Time_Series/links/5416e0770cf2bb7347db7a99.pdf"&gt;this&lt;/a&gt; paper where the implementation is quiet straight forward.&lt;/p&gt;

&lt;p&gt;I hope it helped!&lt;/p&gt;

&lt;p&gt;Good Luck :)&lt;/p&gt;

&lt;hr&gt;

&lt;h2&gt;EDIT&lt;/h2&gt;

&lt;p&gt;As you mentioned you mean Time Series Segmentation I add this to the answer.&lt;/p&gt;

&lt;p&gt;Time series segmentation is the only clustering problem that has a ground truth for evaluation. Indeed you consider the generating distribution behind the time series and analyze it I strongly recommend &lt;a href="http://machinelearning.wustl.edu/mlpapers/papers/icml2014c1_khaleghi14"&gt;this&lt;/a&gt;, &lt;a href="https://tel.archives-ouvertes.fr/tel-00920184/document"&gt;this&lt;/a&gt;, &lt;a href="http://128.84.21.199/pdf/1203.1515.pdf"&gt;this&lt;/a&gt;, &lt;a href="http://arxiv.org/pdf/1203.1515.pdf"&gt;this&lt;/a&gt;, &lt;a href="http://papers.nips.cc/paper/4623-locating-changes-in-highly-dependent-data-with-unknown-number-of-change-points"&gt;this&lt;/a&gt; and &lt;a href="http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2012_KhaleghiRMP12.pdf"&gt;this&lt;/a&gt; where your problem is comprehensively studied. Specially the last one and the PhD thesis.&lt;/p&gt;

&lt;p&gt;Good Luck!&lt;/p&gt;
', NULL, NULL, '''/a'':314C,392C,399C,405C,412C,419C,427C ''/em'':176C,263C,271C,280C ''/h2'':337C ''/li'':58C,83C ''/mlpapers/paper_files/aistats2012_khaleghirmp12.pdf'':425C ''/mlpapers/papers/icml2014c1_khaleghi14'':390C ''/ol'':84C ''/p'':22C,32C,98C,208C,231C,295C,323C,329C,333C,353C,442C,446C ''/paper/4623-locating-changes-in-highly-dependent-data-with-unknown-number-of-change-points'':417C ''/pdf/1203.1515.pdf'':403C,410C ''/profile/christian_bauckhage/publication/262898400_kernel_archetypal_analysis_for_clustering_web_search_frequency_time_series/links/5416e0770cf2bb7347db7a99.pdf'':312C ''/strong'':40C,64C,130C,138C,220C,250C,254C,258C,290C ''/tel-00920184/document'':397C ''128.84.21.199'':402C ''2'':29C ''accord'':10C,54C,78C ''add'':348C ''analyz'':381C ''anoth'':233C ''answer'':352C ''appli'':146C,194C,286C ''approach'':300C ''arxiv.org'':409C ''arxiv.org/pdf/1203.1515.pdf'':408C ''assum'':87C ''behind'':376C ''build'':246C ''call'':18C ''cluster'':24C,63C,77C,304C,361C ''column'':139C ''comprehens'':432C ''consid'':372C ''contain'':127C ''could'':144C,235C ''data'':157C,230C ''decomposit'':289C ''differ'':30C,51C,76C,170C,206C ''dimens'':225C ''dimenssion'':154C ''distribut'':375C ''edit'':336C ''em'':169C,261C,269C,278C ''evalu'':369C ''event'':9C ''factor'':219C ''find'':75C ''form'':229C ''forward'':322C ''generat'':374C ''global'':294C ''good'':331C,444C ''ground'':366C ''h2'':335C ''help'':328C ''hesit'':16C ''hope'':326C ''hr'':334C ''href'':309C,387C,394C,401C,407C,414C,422C ''i.e'':41C,65C,109C ''implement'':318C ''inde'':370C ''individu'':47C ''intern'':56C ''interv'':53C ''last'':436C ''li'':34C,59C ''luck'':332C,445C ''m'':253C,270C ''machinelearning.wustl.edu'':389C,424C ''machinelearning.wustl.edu/mlpapers/paper_files/aistats2012_khaleghirmp12.pdf'':423C ''machinelearning.wustl.edu/mlpapers/papers/icml2014c1_khaleghi14'':388C ''make'':190C ''mani'':102C,105C,112C ''matric'':118C,239C ''matrix'':123C,126C,150C,174C,192C,218C,228C ''mean'':31C,89C,343C ''meaning'':166C ''mention'':341C ''n'':129C,249C,262C ''negat'':215C ''nice'':299C ''nr'':132C ''number'':266C,274C ''observ'':106C,134C,156C,171C,182C,204C,276C ''ol'':33C ''one'':92C,142C,437C ''p'':1C,23C,85C,99C,209C,232C,296C,324C,330C,338C,354C,443C ''paper'':315C ''papers.nips.cc'':416C ''papers.nips.cc/paper/4623-locating-changes-in-highly-dependent-data-with-unknown-number-of-change-points'':415C ''pc'':159C ''pca'':147C,195C ''per'':107C ''phd'':440C ''point'':141C ''problem'':362C,430C ''put'':180C ''putin'':237C ''quiet'':320C ''recommend'':223C,385C ''reduc'':152C ''reduct'':226C ''relat'':167C,200C,293C ''row'':131C ''second'':91C ''see'':162C,199C,292C ''segment'':36C,45C,346C,357C ''sequenc'':7C,284C ''seri'':21C,27C,39C,49C,62C,70C,303C,345C,356C,379C ''sever'':68C,117C ''shown'':306C ''similar'':57C,80C ''singl'':203C ''space'':160C ''special'':434C ''straight'':321C ''strong'':35C,60C,128C,136C,217C,222C,248C,252C,256C,287C,384C ''studi'':433C ''suggest'':97C,143C,234C ''tel.archives-ouvertes.fr'':396C ''tel.archives-ouvertes.fr/tel-00920184/document'':395C ''tensor'':259C,288C ''thesi'':441C ''time'':12C,20C,26C,38C,48C,52C,61C,69C,140C,283C,302C,344C,355C,378C ''top'':241C ''truth'':367C ''valu'':216C ''vehicl'':103C,108C,113C,120C,175C,185C,207C,268C ''want'':43C,73C ''within'':172C ''www.researchgate.net'':311C ''www.researchgate.net/profile/christian_bauckhage/publication/262898400_kernel_archetypal_analysis_for_clustering_web_search_frequency_time_series/links/5416e0770cf2bb7347db7a99.pdf'':310C ''x'':251C,255C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6007, 2, '2015-06-04 19:38:40.603', 1, NULL, NULL, '&lt;p&gt;The latitude and longitude are stored in a &lt;a href="http://www.rforscience.com/wpmain/wp-content/uploads/2014/06/Koeppen-Geiger-ASCII.txt" rel="nofollow"&gt;text file&lt;/a&gt; that is read into a table, &lt;code&gt;Dat&lt;/code&gt;.  It has the form:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;lat long climate-group
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For your particular case, you would calculate &lt;code&gt;Dat[, 3]&lt;/code&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':19C ''/code'':28C,41C,54C ''/p'':33C,55C ''/pre'':42C ''/wpmain/wp-content/uploads/2014/06/koeppen-geiger-ascii.txt'':14C ''3'':53C ''calcul'':50C ''case'':47C ''climat'':39C ''climate-group'':38C ''code'':26C,35C,51C ''dat'':27C,52C ''file'':18C ''form'':32C ''group'':40C ''href'':11C ''lat'':36C ''latitud'':3C ''long'':37C ''longitud'':5C ''nofollow'':16C ''p'':1C,43C ''particular'':46C ''pre'':34C ''read'':22C ''rel'':15C ''store'':7C ''tabl'':25C ''text'':17C ''would'':49C ''www.rforscience.com'':13C ''www.rforscience.com/wpmain/wp-content/uploads/2014/06/koeppen-geiger-ascii.txt'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5960, 1, '2015-05-30 08:33:06.713', 7, 263, 'Theano in deep learning research', '&lt;p&gt;How widely is Theano used in deep learning research? &lt;/p&gt;

&lt;p&gt;Is Theano a good start to learn the implementation of machine learning algorithms?&lt;/p&gt;

&lt;p&gt;Will learning the implementation of something like a feed forward network really help? Do graduate students implement neural networks or other algorithms at least once during their college days?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Background:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I have a reasonable idea about feed forward and recurrent networks, backpropagation, the general pipeline for a machine learning problem and the necessary mathematics. &lt;/p&gt;
', 'machine-learning  python  deep-learning  library', NULL, '''/p'':24C,39C,70C,75C,101C ''/strong'':74C ''algorithm'':38C,62C ''background'':73C ''backpropag'':88C ''colleg'':68C ''day'':69C ''deep'':3A,11B,21C ''deep-learn'':10B ''feed'':49C,83C ''forward'':50C,84C ''general'':90C ''good'':29C ''graduat'':55C ''help'':53C ''idea'':81C ''implement'':34C,44C,57C ''learn'':4A,8B,12B,22C,32C,37C,42C,95C ''least'':64C ''librari'':13B ''like'':47C ''machin'':7B,36C,94C ''machine-learn'':6B ''mathemat'':100C ''necessari'':99C ''network'':51C,59C,87C ''neural'':58C ''p'':14C,25C,40C,71C,76C ''pipelin'':91C ''problem'':96C ''python'':9B ''realli'':52C ''reason'':80C ''recurr'':86C ''research'':5A,23C ''someth'':46C ''start'':30C ''strong'':72C ''student'':56C ''theano'':1A,18C,27C ''use'':19C ''wide'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5961, 2, '2015-05-30 12:48:52.713', 10, NULL, NULL, '&lt;p&gt;Theano used to be very popular in the last few years. However, from seeing what the top research labs in the world currently use instead of theano (Facebook AI and Google Deepmind), I would either go for caffe or Torch7, with a stronger preference on the latter.&lt;/p&gt;

&lt;p&gt;The reason is that you can implement from Recurrent Neural Nets to Convolutional Neural Networks very easily and there is a wide range of examples on github (&lt;a href="https://github.com/torch/demos"&gt;https://github.com/torch/demos&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;Graduate students in NYU, Stanford, Oxford definitely implement advanced models in their studies; your background will help you a lot, a great place to start and familiarise with such models, and implement them in Torch7, is the lessons of Prof. Nando de Freitas &lt;a href="https://www.youtube.com/user/ProfNandoDF"&gt;https://www.youtube.com/user/ProfNandoDF&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/p'':49C ''/torch/demos'':82C ''/torch/demos&lt;/a&gt;).&lt;/p&gt;'':85C ''/user/profnandodf'':134C ''/user/profnandodf&lt;/a&gt;.&lt;/p&gt;'':137C ''advanc'':95C ''ai'':30C ''background'':101C ''caff'':39C ''convolut'':63C ''current'':24C ''de'':128C ''deepmind'':33C ''definit'':93C ''easili'':67C ''either'':36C ''exampl'':75C ''facebook'':29C ''familiaris'':113C ''freita'':129C ''github'':77C ''github.com'':81C,84C ''github.com/torch/demos'':80C ''github.com/torch/demos&lt;/a&gt;).&lt;/p&gt;'':83C ''go'':37C ''googl'':32C ''graduat'':87C ''great'':108C ''help'':103C ''howev'':13C ''href'':79C,131C ''implement'':57C,94C,118C ''instead'':26C ''lab'':20C ''last'':10C ''latter'':48C ''lesson'':124C ''lot'':106C ''model'':96C,116C ''nando'':127C ''net'':61C ''network'':65C ''neural'':60C,64C ''nyu'':90C ''oxford'':92C ''p'':1C,50C,86C ''place'':109C ''popular'':7C ''prefer'':45C ''prof'':126C ''rang'':73C ''reason'':52C ''recurr'':59C ''research'':19C ''see'':15C ''stanford'':91C ''start'':111C ''stronger'':44C ''student'':88C ''studi'':99C ''theano'':2C,28C ''top'':18C ''torch7'':41C,121C ''use'':3C,25C ''wide'':72C ''world'':23C ''would'':35C ''www.youtube.com'':133C,136C ''www.youtube.com/user/profnandodf'':132C ''www.youtube.com/user/profnandodf&lt;/a&gt;.&lt;/p&gt;'':135C ''year'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5962, 2, '2015-05-31 09:47:23.37', 2, NULL, NULL, '&lt;p&gt;The radius in OPTICS is a &lt;strong&gt;maximum&lt;/strong&gt; value, and it can be set to infinity!
So you don''t need to know it, and you should give OPTICS as well as DBSCAN a try. There are heuristics to choose their parameters, if you know your data.&lt;/p&gt;

&lt;p&gt;Similarly, try hierarchical clustering. There are good heuristics on how to extract flat partitions out of it.&lt;/p&gt;

&lt;p&gt;You want something that handles noise well - this calls for DBSCAN, OPTICS and HAC.&lt;/p&gt;
', NULL, NULL, '''/p'':50C,69C,85C ''/strong'':10C ''call'':79C ''choos'':42C ''cluster'':55C ''data'':49C ''dbscan'':35C,81C ''extract'':63C ''flat'':64C ''give'':30C ''good'':58C ''hac'':84C ''handl'':75C ''heurist'':40C,59C ''hierarch'':54C ''infin'':18C ''know'':25C,47C ''maximum'':9C ''need'':23C ''nois'':76C ''optic'':5C,31C,82C ''p'':1C,51C,70C ''paramet'':44C ''partit'':65C ''radius'':3C ''set'':16C ''similar'':52C ''someth'':73C ''strong'':8C ''tri'':37C,53C ''valu'':11C ''want'':72C ''well'':33C,77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5982, 2, '2015-06-01 19:06:51.007', 2, NULL, NULL, '&lt;p&gt;This is actually a really in-depth problem that many people and companies have worked on. Here are some basics:&lt;/p&gt;

&lt;p&gt;First, we need to represent the data well. This involves somehow representing each document as a vector in $d$-dimensional space. Ideally, in this space, we want samples that have the same label to be nearby in euclidean distance, and samples that are different labels to be far away in euclidean distance. This step can be really hard, but one tried-and-true representation is called Term Frequency-Inverse Document Frequency (tf-idf). Here, each dimension in the space represents a particular word, and the value in that dimension for a particular sample basically represents the normalized number of times that word occurs in the document. You could read more about that &lt;a href="http://www.tfidf.com/" rel="nofollow"&gt;here&lt;/a&gt;. There''s a pretty good scikit-learn implementation of this representation if you want to try it out.&lt;/p&gt;

&lt;p&gt;Now the data is in a useful space, but a really high-dimensional space. I''d recommend reducing this dimensionality somehow, but that''s a whole subject for another thread.&lt;/p&gt;

&lt;p&gt;Finally you could train some algorithm to classify the samples (which is what the other answers are about). There are lots of great choices - neural networks, adaboost, SVMs, Naive Bayes, and graphical classification models will all give you good results. Many of these also have implementations in scikit-learn. &lt;/p&gt;

&lt;p&gt;But the best algorithms leverage the fact that this problem is actually a transfer learning one. That is, the distributions from which the training and testing data come might not be exactly the same - because the sorts of things one person thinks are spam might be different than the sorts of things another person thinks are spam.&lt;/p&gt;
', NULL, NULL, '''/a'':146C ''/p'':23C,166C,199C,251C,310C ''actual'':4C,264C ''adaboost'':227C ''algorithm'':206C,256C ''also'':244C ''anoth'':197C,305C ''answer'':216C ''away'':73C ''basic'':22C,121C ''bay'':230C ''best'':255C ''call'':91C ''choic'':224C ''classif'':233C ''classifi'':208C ''come'':280C ''compani'':15C ''could'':135C,203C ''d'':43C,184C ''data'':31C,170C,279C ''depth'':9C ''differ'':68C,299C ''dimens'':103C,116C ''dimension'':44C,181C,188C ''distanc'':63C,76C ''distribut'':272C ''document'':38C,96C,133C ''euclidean'':62C,75C ''exact'':284C ''fact'':259C ''far'':72C ''final'':201C ''first'':25C ''frequenc'':94C,97C ''frequency-invers'':93C ''give'':237C ''good'':151C,239C ''graphic'':232C ''great'':223C ''hard'':82C ''high'':180C ''high-dimension'':179C ''href'':141C ''ideal'':46C ''idf'':100C ''implement'':155C,246C ''in-depth'':7C ''invers'':95C ''involv'':34C ''label'':57C,69C ''learn'':154C,250C,267C ''leverag'':257C ''lot'':221C ''mani'':12C,241C ''might'':281C,297C ''model'':234C ''naiv'':229C ''nearbi'':60C ''need'':27C ''network'':226C ''neural'':225C ''nofollow'':144C ''normal'':124C ''number'':125C ''occur'':130C ''one'':84C,268C,292C ''p'':1C,24C,167C,200C,252C ''particular'':109C,119C ''peopl'':13C ''person'':293C,306C ''pretti'':150C ''problem'':10C,262C ''read'':136C ''realli'':6C,81C,178C ''recommend'':185C ''reduc'':186C ''rel'':143C ''repres'':29C,36C,107C,122C ''represent'':89C,158C ''result'':240C ''sampl'':52C,65C,120C,210C ''scikit'':153C,249C ''scikit-learn'':152C,248C ''somehow'':35C,189C ''sort'':289C,302C ''space'':45C,49C,106C,175C,182C ''spam'':296C,309C ''step'':78C ''subject'':195C ''svms'':228C ''term'':92C ''test'':278C ''tf'':99C ''tf-idf'':98C ''thing'':291C,304C ''think'':294C,307C ''thread'':198C ''time'':127C ''train'':204C,276C ''transfer'':266C ''tri'':86C,163C ''tried-and-tru'':85C ''true'':88C ''use'':174C ''valu'':113C ''vector'':41C ''want'':51C,161C ''well'':32C ''whole'':194C ''word'':110C,129C ''work'':17C ''www.tfidf.com'':142C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5963, 2, '2015-05-31 11:22:46.94', 1, NULL, NULL, '&lt;p&gt;I am assuming the context is office setting.&lt;/p&gt;

&lt;p&gt;Study of an Impact of a systemic event. e.g. how an organizational event impacted the attendance, or reverse of it (what happened which caused a shift in pattern). &lt;/p&gt;

&lt;p&gt;Build employee profile based on localized events e.g. school vacations, time-off patterns, response to extra workload. May not necessarily be "fun" (your question), but profiling can also be done on demographics (age, gender, ethnicity, etc.)&lt;/p&gt;

&lt;p&gt;Department or team analysis. e.g. generate networking index of a person, team or department (how correlated are their activities).&lt;/p&gt;

&lt;p&gt;Possible impact on internal org design (e.g. scale/reduce support staff  services, resource waste reduction, etc. ), use occupancy rate of property to decide on hotel office model, or relocation. &lt;/p&gt;
', NULL, NULL, '''/p'':10C,40C,79C,100C,130C ''activ'':99C ''age'':75C ''also'':70C ''analysi'':84C ''assum'':4C ''attend'':27C ''base'':45C ''build'':42C ''caus'':35C ''context'':6C ''correl'':96C ''decid'':123C ''demograph'':74C ''depart'':81C,94C ''design'':107C ''done'':72C ''e.g'':20C,49C,85C,108C ''employe'':43C ''etc'':78C,116C ''ethnic'':77C ''event'':19C,24C,48C ''extra'':58C ''fun'':64C ''gender'':76C ''generat'':86C ''happen'':33C ''hotel'':125C ''impact'':15C,25C,103C ''index'':88C ''intern'':105C ''local'':47C ''may'':60C ''model'':127C ''necessarili'':62C ''network'':87C ''occup'':118C ''offic'':8C,126C ''org'':106C ''organiz'':23C ''p'':1C,11C,41C,80C,101C ''pattern'':39C,55C ''person'':91C ''possibl'':102C ''profil'':44C,68C ''properti'':121C ''question'':66C ''rate'':119C ''reduct'':115C ''reloc'':129C ''resourc'':113C ''respons'':56C ''revers'':29C ''scale/reduce'':109C ''school'':50C ''servic'':112C ''set'':9C ''shift'':37C ''staff'':111C ''studi'':12C ''support'':110C ''system'':18C ''team'':83C,92C ''time'':53C ''time-off'':52C ''use'':117C ''vacat'':51C ''wast'':114C ''workload'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5964, 1, '2015-05-31 20:35:54.15', 3, 71, 'Brown clustering, graph partitioning, agglomerative clustering - libraries/software', '&lt;p&gt;I need to do some experimenting with Brown clustering, graph partitioning, agglomerative clustering.&lt;/p&gt;

&lt;p&gt;1) Are there Python/Matlab libraries for that? I know sklearn.cluster but it doesn''t have algorithms I need.&lt;/p&gt;

&lt;p&gt;2) Is it possible to install graphical interface for Cluto on Mac OS? &lt;/p&gt;

&lt;p&gt;3) Overall, are there useful tutorials on using Cluto?&lt;/p&gt;

&lt;p&gt;4) Other software for clustering that I could learn within a couple of hours?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'clustering', NULL, '''/p'':23C,43C,58C,69C,85C,88C ''1'':25C ''2'':45C ''3'':60C ''4'':71C ''agglom'':5A,21C ''algorithm'':40C ''brown'':1A,17C ''cluster'':2A,6A,8B,18C,22C,75C ''cluto'':54C,68C ''could'':78C ''coupl'':82C ''doesn'':37C ''experi'':15C ''graph'':3A,19C ''graphic'':51C ''hour'':84C ''instal'':50C ''interfac'':52C ''know'':33C ''learn'':79C ''librari'':29C ''libraries/software'':7A ''mac'':56C ''need'':11C,42C ''os'':57C ''overal'':61C ''p'':9C,24C,44C,59C,70C,86C ''partit'':4A,20C ''possibl'':48C ''python/matlab'':28C ''sklearn.cluster'':34C ''softwar'':73C ''thank'':87C ''tutori'':65C ''use'':64C,67C ''within'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5965, 2, '2015-05-31 21:52:47.957', 3, NULL, NULL, '&lt;p&gt;For graphs I definitely suggest &lt;a href="https://networkx.github.io/" rel="nofollow"&gt;Networkx&lt;/a&gt; and &lt;a href="http://igraph.org/" rel="nofollow"&gt;igraph&lt;/a&gt; libraries. They support many graph partitioning algorithms.&lt;/p&gt;
', NULL, NULL, '''/a'':13C,21C ''/p'':29C ''algorithm'':28C ''definit'':5C ''graph'':3C,26C ''href'':8C,16C ''igraph'':20C ''igraph.org'':17C ''librari'':22C ''mani'':25C ''networkx'':12C ''networkx.github.io'':9C ''nofollow'':11C,19C ''p'':1C ''partit'':27C ''rel'':10C,18C ''suggest'':6C ''support'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5966, 2, '2015-06-01 06:03:05.51', 2, NULL, NULL, '&lt;p&gt;I don''t think that EM clustering algorithms like k-means and Gaussian mixture models are quite what you''re looking for. There are definitely other algorithms that don''t require one to pick a number of clusters. My personal favorite (most of the time) is called mean-shift-clustering. You could find a great little blog post about it &lt;a href="http://spin.atomicobject.com/2015/05/26/mean-shift-clustering/" rel="nofollow"&gt;here&lt;/a&gt;, and it has a good implementation in python''s scikit-learn library.&lt;/p&gt;
', NULL, NULL, '''/2015/05/26/mean-shift-clustering/'':68C ''/a'':72C ''/p'':86C ''algorithm'':9C,29C ''blog'':60C ''call'':49C ''cluster'':8C,40C,53C ''could'':55C ''definit'':27C ''em'':7C ''favorit'':43C ''find'':56C ''gaussian'':15C ''good'':77C ''great'':58C ''href'':65C ''implement'':78C ''k'':12C ''k-mean'':11C ''learn'':84C ''librari'':85C ''like'':10C ''littl'':59C ''look'':23C ''mean'':13C,51C ''mean-shift-clust'':50C ''mixtur'':16C ''model'':17C ''nofollow'':70C ''number'':38C ''one'':34C ''p'':1C ''person'':42C ''pick'':36C ''post'':61C ''python'':80C ''quit'':19C ''re'':22C ''rel'':69C ''requir'':33C ''scikit'':83C ''scikit-learn'':82C ''shift'':52C ''spin.atomicobject.com'':67C ''spin.atomicobject.com/2015/05/26/mean-shift-clustering/'':66C ''think'':5C ''time'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5967, 2, '2015-06-01 06:37:06.68', 0, NULL, NULL, '&lt;p&gt;You should create /user folder at first.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;sudo -u hdfs hadoop fs -mkdir -p /user&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/blockquote'':21C ''/p'':9C,20C ''/user'':5C,19C ''blockquot'':10C ''creat'':4C ''first'':8C ''folder'':6C ''fs'':16C ''hadoop'':15C ''hdfs'':14C ''mkdir'':17C ''p'':1C,11C,18C ''sudo'':12C ''u'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5968, 1, '2015-06-01 09:58:29.58', 1, 28, 'How to paste string and int from map to an array in hive?', '&lt;p&gt;I am trying to paste a &lt;code&gt;string&lt;/code&gt; and &lt;code&gt;int&lt;/code&gt; from &lt;code&gt;map&lt;/code&gt; in &lt;code&gt;Hive&lt;/code&gt; to an &lt;code&gt;array&lt;/code&gt;.
For now, record looks like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;{"string1":1,"string2":1,"string3":15}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is there a way to convert it to an array like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;["string1:1","string2:1","string3:15"]
&lt;/code&gt;&lt;/pre&gt;
', 'hive', NULL, '''/code'':24C,28C,32C,36C,41C,57C,81C ''/p'':48C,72C ''/pre'':58C,82C ''1'':52C,54C,76C,78C ''15'':56C,80C ''array'':11A,40C,69C ''code'':22C,26C,30C,34C,39C,50C,74C ''convert'':65C ''hive'':13A,14B,35C ''int'':6A,27C ''like'':46C,70C ''look'':45C ''map'':8A,31C ''p'':15C,59C ''past'':3A,20C ''pre'':49C,73C ''record'':44C ''string'':4A,23C ''string1'':51C,75C ''string2'':53C,77C ''string3'':55C,79C ''tri'':18C ''way'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5997, 2, '2015-06-03 20:59:43.4', 7, NULL, NULL, '&lt;p&gt;For data load, Postgre outperforms MongoDB.
MongoDB is almost always faster when returning query counts.
PostgreSQL is almost always faster for queries using indexes.&lt;/p&gt;

&lt;p&gt;Check out this &lt;a href="http://tiborsimko.org/postgresql-mongodb-json-select-speed.html"&gt;website&lt;/a&gt;  and &lt;a href="http://www.enterprisedb.com/postgres-plus-edb-blog/marc-linster/postgres-outperforms-mongodb-and-ushers-new-developer-reality"&gt;this&lt;/a&gt; one too for more info. They have very detailed explanations. &lt;/p&gt;
', NULL, NULL, '''/a'':37C,45C ''/p'':26C,56C ''/postgres-plus-edb-blog/marc-linster/postgres-outperforms-mongodb-and-ushers-new-developer-reality'':43C ''/postgresql-mongodb-json-select-speed.html'':35C ''almost'':10C,19C ''alway'':11C,20C ''check'':28C ''count'':16C ''data'':3C ''detail'':54C ''explan'':55C ''faster'':12C,21C ''href'':32C,40C ''index'':25C ''info'':50C ''load'':4C ''mongodb'':7C,8C ''one'':46C ''outperform'':6C ''p'':1C,27C ''postgr'':5C ''postgresql'':17C ''queri'':15C,23C ''return'':14C ''tiborsimko.org'':34C ''tiborsimko.org/postgresql-mongodb-json-select-speed.html'':33C ''use'':24C ''websit'':36C ''www.enterprisedb.com'':42C ''www.enterprisedb.com/postgres-plus-edb-blog/marc-linster/postgres-outperforms-mongodb-and-ushers-new-developer-reality'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5969, 1, '2015-06-01 11:25:59.933', 3, 114, 'Making a labelled training data set', '&lt;p&gt;We are developing a classification system, where the categories are fixed, but many of them are inter-related. &lt;/p&gt;

&lt;p&gt;For example, we have a category called, "&lt;code&gt;roads&lt;/code&gt;" and another one called "&lt;code&gt;traffic&lt;/code&gt;". We believe that the model will be confused by the text samples, which could be in &lt;code&gt;roads&lt;/code&gt; category and also in &lt;code&gt;traffic&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Some of our text samples are suitable for multi class labelling too.  For example, "There is a garbage dump near the footpath. The footpath is broken completely". This text could be categorized into &lt;code&gt;garbage&lt;/code&gt; bucket or &lt;code&gt;footpath&lt;/code&gt; bucket. &lt;/p&gt;

&lt;p&gt;We are going to build a training set for this classifier, by manually annotating the text. So, can we put multiple labels for one issue? How should we deal with  text with multiple labels for it? Should they be added into all categories to which it is tagged to, as training sample ?&lt;/p&gt;

&lt;p&gt;For example, "There is a garbage dump near the footpath. The footpath is broken completely". This text could be categorized into &lt;code&gt;garbage&lt;/code&gt; bucket or &lt;code&gt;footpath&lt;/code&gt; bucket. So, should this text be added as a training sample for &lt;code&gt;garbage&lt;/code&gt; and &lt;code&gt;footpath&lt;/code&gt;? How should we consider the labels?&lt;/p&gt;

&lt;p&gt;Can you please give your insights?&lt;/p&gt;
', 'text-mining  multiclass-classification', NULL, '''/code'':44C,51C,70C,77C,115C,120C,201C,206C,221C,225C ''/p'':33C,78C,122C,176C,232C,240C ''ad'':163C,213C ''also'':73C ''annot'':137C ''anoth'':46C ''believ'':53C ''broken'':105C,191C ''bucket'':116C,121C,202C,207C ''build'':128C ''call'':41C,48C ''categor'':111C,197C ''categori'':22C,40C,71C,166C ''class'':89C ''classif'':12B,18C ''classifi'':134C ''code'':42C,49C,68C,75C,113C,118C,199C,204C,219C,223C ''complet'':106C,192C ''confus'':59C ''consid'':229C ''could'':65C,109C,195C ''data'':5A ''deal'':152C ''develop'':16C ''dump'':98C,184C ''exampl'':36C,93C,179C ''fix'':24C ''footpath'':101C,103C,119C,187C,189C,205C,224C ''garbag'':97C,114C,183C,200C,220C ''give'':237C ''go'':126C ''insight'':239C ''inter'':31C ''inter-rel'':30C ''issu'':148C ''label'':3A,90C,145C,157C,231C ''make'':1A ''mani'':26C ''manual'':136C ''mine'':9B ''model'':56C ''multi'':88C ''multiclass'':11B ''multiclass-classif'':10B ''multipl'':144C,156C ''near'':99C,185C ''one'':47C,147C ''p'':13C,34C,79C,123C,177C,233C ''pleas'':236C ''put'':143C ''relat'':32C ''road'':43C,69C ''sampl'':63C,84C,175C,217C ''set'':6A,131C ''suitabl'':86C ''system'':19C ''tag'':171C ''text'':8B,62C,83C,108C,139C,154C,194C,211C ''text-min'':7B ''traffic'':50C,76C ''train'':4A,130C,174C,216C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5970, 1, '2015-06-01 11:51:45.187', 1, 284, 'Package for SMOTEBoost in R', '&lt;p&gt;I am solving a rare event cum classification problem. I have come across that there is a package called SMOTEBoost which oversamples the rare event and boost the results. But i am not sure is that supported in R. Could you please help me how can i use SMOTEBoost in R? Any examples?&lt;/p&gt;
', 'machine-learning  classification  r  sampling', NULL, '''/p'':66C ''across'':25C ''boost'':39C ''call'':31C ''classif'':9B,20C ''come'':24C ''could'':52C ''cum'':19C ''event'':18C,37C ''exampl'':65C ''help'':55C ''learn'':8B ''machin'':7B ''machine-learn'':6B ''oversampl'':34C ''p'':12C ''packag'':1A,30C ''pleas'':54C ''problem'':21C ''r'':5A,10B,51C,63C ''rare'':17C,36C ''result'':41C ''sampl'':11B ''smoteboost'':3A,32C,61C ''solv'':15C ''support'':49C ''sure'':46C ''use'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5971, 2, '2015-06-01 11:55:49.633', 1, NULL, NULL, '&lt;p&gt;Good ideas there. But start with simple things first and then ramp up to a more complex system once you have something already working and some human feedback on the solution.&lt;/p&gt;

&lt;p&gt;Simpler solution is just a velocity check - if there is X number of failures per Y number of seconds, then send the notification. From your post it seems like 3 failures over 20 second time window should be a good start. It takes a fair bit of tinkering to get this kind of system running - work out the windowing functions, etc.&lt;/p&gt;

&lt;p&gt;Good luck :)&lt;/p&gt;
', NULL, NULL, '''/p'':33C,96C,100C ''20'':67C ''3'':64C ''alreadi'':24C ''bit'':80C ''check'':41C ''complex'':18C ''etc'':95C ''failur'':48C,65C ''fair'':79C ''feedback'':29C ''first'':10C ''function'':94C ''get'':84C ''good'':2C,74C,98C ''human'':28C ''idea'':3C ''kind'':86C ''like'':63C ''luck'':99C ''notif'':57C ''number'':46C,51C ''p'':1C,34C,97C ''per'':49C ''post'':60C ''ramp'':13C ''run'':89C ''second'':53C,68C ''seem'':62C ''send'':55C ''simpl'':8C ''simpler'':35C ''solut'':32C,36C ''someth'':23C ''start'':6C,75C ''system'':19C,88C ''take'':77C ''thing'':9C ''time'':69C ''tinker'':82C ''veloc'':40C ''window'':70C,93C ''work'':25C,90C ''x'':45C ''y'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6018, 1, '2015-06-05 11:02:43.003', 0, 89, 'Create a prediction formula from data input', '&lt;p&gt;I have an algorithm which have as an input about 20-25 numbers. Then in every step it uses some of these numbers with a random function to calculate the local result which will lead to the final output of A, B or C.&lt;/p&gt;

&lt;p&gt;Since every step has a random function, the formula is not deterministic. This means that with the same input, I could have either A, B or C.&lt;/p&gt;

&lt;p&gt;My first thought was to take step by step the algorithm and calculating mathematically the probability of each output. However, it is really difficult due to the size of the core.&lt;/p&gt;

&lt;p&gt;My next thought was to use machine learning with supervised algorithm. I can have as many labeled entries as I want.&lt;/p&gt;

&lt;p&gt;So, I have the following questions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;How many labeled inputs should I need for a decent approach of the probabilities? Yes, I can have as many as I want, but it needs time to run the algorithm and I want to estimate the cost of the simulations to gather the labeled data.&lt;/li&gt;
&lt;li&gt;Which technique do you suggest that works with so many inputs that can give the probability of the three possible outputs?&lt;/li&gt;
&lt;li&gt;As an extra question, the algorithm run in 10 steps and there is a possibility that some of the inputs will change in one of the steps. My simple approach is to not include this option on the prediction formula, since I have to set different inputs for some of the steps. If I try the advanced methods, is there any other technique I could use?&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  predictive-modeling', NULL, '''-25'':26C ''/li'':201C,224C,292C ''/ol'':293C ''/p'':59C,88C,121C,144C,152C ''10'':234C ''20'':25C ''advanc'':282C ''algorithm'':18C,100C,133C,185C,231C ''approach'':165C,255C ''b'':56C,85C ''c'':58C,87C ''calcul'':43C,102C ''chang'':247C ''core'':120C ''cost'':192C ''could'':81C,290C ''creat'':1A ''data'':6A,200C ''decent'':164C ''determinist'':72C ''differ'':271C ''difficult'':113C ''due'':114C ''either'':83C ''entri'':140C ''estim'':190C ''everi'':30C,62C ''extra'':228C ''final'':52C ''first'':91C ''follow'':150C ''formula'':4A,69C,265C ''function'':41C,67C ''gather'':197C ''give'':216C ''howev'':109C ''includ'':259C ''input'':7A,23C,79C,158C,213C,245C,272C ''label'':139C,157C,199C ''lead'':49C ''learn'':10B,130C ''li'':154C,202C,225C ''local'':45C ''machin'':9B,129C ''machine-learn'':8B ''mani'':138C,156C,174C,212C ''mathemat'':103C ''mean'':74C ''method'':283C ''model'':13B ''need'':161C,180C ''next'':124C ''number'':27C,37C ''ol'':153C ''one'':249C ''option'':261C ''output'':53C,108C,223C ''p'':14C,60C,89C,122C,145C ''possibl'':222C,240C ''predict'':3A,12B,264C ''predictive-model'':11B ''probabl'':105C,168C,218C ''question'':151C,229C ''random'':40C,66C ''realli'':112C ''result'':46C ''run'':183C,232C ''set'':270C ''simpl'':254C ''simul'':195C ''sinc'':61C,266C ''size'':117C ''step'':31C,63C,96C,98C,235C,252C,277C ''suggest'':207C ''supervis'':132C ''take'':95C ''techniqu'':204C,288C ''thought'':92C,125C ''three'':221C ''time'':181C ''tri'':280C ''use'':33C,128C,291C ''want'':143C,177C,188C ''work'':209C ''yes'':169C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5973, 1, '2015-06-01 13:10:41.363', 5, 156, 'Choosing best methods for estimating the unknown parameters in a linear regression model', '&lt;p&gt;Given some dataset for prediction, &lt;/p&gt;

&lt;p&gt;for eg say I have different housing price prediction dataset:&lt;/p&gt;

&lt;p&gt;dataset  1 : 100 training and 100 testing sample, 50 feature&lt;/p&gt;

&lt;p&gt;dataset  2 : 100 training and 100 testing sample, 120 feature &lt;/p&gt;

&lt;p&gt;dataset  3 : 1000 training and 1000 testing sample, 50 feature&lt;/p&gt;

&lt;p&gt;dataset  4 : 1000 training and 1000 testing sample, 5000 feature&lt;/p&gt;

&lt;p&gt;how should I choose the best methods for estimating the unknown parameters ( predict price) in a linear regression model from the following for each of these dataset?&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Ordinary least squares&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Stepwise regression&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Principal component regression &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Partial least squares regression&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Should I experiment with each of these one by one and compare the results or is there any rule of thump on when to use each of them based on the dataset ? &lt;/p&gt;

&lt;p&gt;Please help&lt;/p&gt;
', 'machine-learning  regression', NULL, '''/li'':121C,127C,134C,142C ''/p'':24C,36C,48C,60C,72C,84C,113C,120C,126C,133C,141C,177C,181C ''/ul'':143C ''1'':39C ''100'':40C,43C,52C,55C ''1000'':64C,67C,76C,79C ''120'':58C ''2'':51C ''3'':63C ''4'':75C ''50'':46C,70C ''5000'':82C ''base'':173C ''best'':2A,91C ''choos'':1A,89C ''compar'':156C ''compon'':131C ''dataset'':21C,35C,38C,50C,62C,74C,112C,176C ''differ'':31C ''eg'':27C ''estim'':5A,94C ''experi'':147C ''featur'':47C,59C,71C,83C ''follow'':107C ''given'':19C ''help'':180C ''hous'':32C ''learn'':16B ''least'':118C,138C ''li'':115C,122C,128C,135C ''linear'':11A,102C ''machin'':15B ''machine-learn'':14B ''method'':3A,92C ''model'':13A,104C ''one'':152C,154C ''ordinari'':117C ''p'':18C,25C,37C,49C,61C,73C,85C,116C,123C,129C,136C,144C,178C ''paramet'':8A,97C ''partial'':137C ''pleas'':179C ''predict'':23C,34C,98C ''price'':33C,99C ''princip'':130C ''regress'':12A,17B,103C,125C,132C,140C ''result'':158C ''rule'':163C ''sampl'':45C,57C,69C,81C ''say'':28C ''squar'':119C,139C ''stepwis'':124C ''test'':44C,56C,68C,80C ''thump'':165C ''train'':41C,53C,65C,77C ''ul'':114C ''unknown'':7A,96C ''use'':169C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5974, 2, '2015-06-01 14:37:08.643', 2, NULL, NULL, '&lt;p&gt;Assuming your map is called "M" and you want your array field to be called "A"&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;SELECT 
...
array(concat_ws(":","string1",M[1]),
      concat_ws(":","string2",M[2]),
      concat_ws(":","string3",M[3]) as A
....
FROM table;
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':42C ''/p'':18C ''/pre'':43C ''1'':27C ''2'':32C ''3'':37C ''array'':12C,22C ''assum'':2C ''call'':6C,16C ''code'':20C ''concat'':23C,28C,33C ''field'':13C ''m'':7C,26C,31C,36C ''map'':4C ''p'':1C ''pre'':19C ''select'':21C ''string1'':25C ''string2'':30C ''string3'':35C ''tabl'':41C ''want'':10C ''ws'':24C,29C,34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5976, 2, '2015-06-01 15:14:53.047', 6, NULL, NULL, '&lt;p&gt;First of all check &lt;a href="https://archive.ics.uci.edu/ml/datasets/Spambase"&gt;this&lt;/a&gt; carefully. You''ll find a simple dataset and some papers to review.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;BUT&lt;/strong&gt; as you want to start a simple learning project I recommend to not going through papers (which are obviously not &lt;em&gt;basic&lt;/em&gt;) but try to build your own bayesian learner which is not so difficult.&lt;/p&gt;

&lt;p&gt;I personally suggest &lt;a href="http://www.cs.cmu.edu/~guestrin/Class/10708-F08/schedule.html"&gt;Andrew Moore&lt;/a&gt;''s lecture slides on Probabilistic Graphical Models which are freely available and you can learn from them simply and step by step.&lt;/p&gt;

&lt;p&gt;If you need more detailed help just comment on this answer and I''ll be glad to help :)&lt;/p&gt;

&lt;p&gt;Enjoy baysian learning!&lt;/p&gt;
', NULL, NULL, '''/a'':12C,78C ''/em'':52C ''/ml/datasets/spambase'':10C ''/p'':25C,66C,101C,121C,126C ''/strong'':29C ''/~guestrin/class/10708-f08/schedule.html'':75C ''andrew'':76C ''answer'':113C ''archive.ics.uci.edu'':9C ''archive.ics.uci.edu/ml/datasets/spambase'':8C ''avail'':89C ''basic'':51C ''bayesian'':59C ''baysian'':124C ''build'':56C ''care'':13C ''check'':5C ''comment'':110C ''dataset'':19C ''detail'':107C ''difficult'':65C ''em'':50C ''enjoy'':123C ''find'':16C ''first'':2C ''freeli'':88C ''glad'':118C ''go'':43C ''graphic'':84C ''help'':108C,120C ''href'':7C,72C ''learn'':37C,93C,125C ''learner'':60C ''lectur'':80C ''ll'':15C,116C ''model'':85C ''moor'':77C ''need'':105C ''obvious'':48C ''p'':1C,26C,67C,102C,122C ''paper'':22C,45C ''person'':69C ''probabilist'':83C ''project'':38C ''recommend'':40C ''review'':24C ''simpl'':18C,36C ''simpli'':96C ''slide'':81C ''start'':34C ''step'':98C,100C ''strong'':27C ''suggest'':70C ''tri'':54C ''want'':32C ''www.cs.cmu.edu'':74C ''www.cs.cmu.edu/~guestrin/class/10708-f08/schedule.html'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5977, 2, '2015-06-01 16:03:47.11', 6, NULL, NULL, '&lt;p&gt;In Andrew Ng''s Machine Learning Course on Coursera (in someways the flagship course for Coursera) the programmers exercise for Support Vector Machines was an example doing a spam classifier. The lectures are great, famous even, and well worth watching.&lt;/p&gt;

&lt;p&gt;There is also this posted course from him:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=MachineLearning&amp;amp;doc=exercises/ex6/ex6.html"&gt;http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=MachineLearning&amp;amp;doc=exercises/ex6/ex6.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/mainfolder/documentpage.php?course=machinelearning&amp;amp;doc=exercises/ex6/ex6.html'':58C ''/mainfolder/documentpage.php?course=machinelearning&amp;amp;doc=exercises/ex6/ex6.html&lt;/a&gt;&lt;/p&gt;'':61C ''/p'':42C,52C ''also'':46C ''andrew'':3C ''classifi'':31C ''cours'':8C,15C,49C ''coursera'':10C,17C ''even'':37C ''exampl'':27C ''exercis'':20C ''famous'':36C ''flagship'':14C ''great'':35C ''href'':55C ''learn'':7C ''lectur'':33C ''machin'':6C,24C ''ng'':4C ''openclassroom.stanford.edu'':57C,60C ''openclassroom.stanford.edu/mainfolder/documentpage.php?course=machinelearning&amp;amp;doc=exercises/ex6/ex6.html'':56C ''openclassroom.stanford.edu/mainfolder/documentpage.php?course=machinelearning&amp;amp;doc=exercises/ex6/ex6.html&lt;/a&gt;&lt;/p&gt;'':59C ''p'':1C,43C,53C ''post'':48C ''programm'':19C ''someway'':12C ''spam'':30C ''support'':22C ''vector'':23C ''watch'':41C ''well'':39C ''worth'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6001, 2, '2015-06-04 08:27:43.573', 1, NULL, NULL, '&lt;p&gt;If I understood your question correctly, you want to be able to extract keywords from texts in different languages. One thing for sure, you will need a list of stopwords for each language. As someone else mentioned, that can be also obtained with TF-IDF. Algorithm for extracting keywords that I came across and seems promising is ToPMine - &lt;a href="http://web.engr.illinois.edu/~elkishk2/papers/ToPMine.pdf" rel="nofollow"&gt;http://web.engr.illinois.edu/~elkishk2/papers/ToPMine.pdf&lt;/a&gt; It is fairly well explained in this text.&lt;/p&gt;

&lt;p&gt;Hope this helps.&lt;/p&gt;
', NULL, NULL, '''/p'':79C,84C ''/~elkishk2/papers/topmine.pdf'':65C ''/~elkishk2/papers/topmine.pdf&lt;/a&gt;'':70C ''abl'':12C ''across'':55C ''algorithm'':48C ''also'':42C ''came'':54C ''correct'':7C ''differ'':19C ''els'':37C ''explain'':75C ''extract'':14C,50C ''fair'':73C ''help'':83C ''hope'':81C ''href'':62C ''idf'':47C ''keyword'':15C,51C ''languag'':20C,34C ''list'':29C ''mention'':38C ''need'':27C ''nofollow'':67C ''obtain'':43C ''one'':21C ''p'':1C,80C ''promis'':58C ''question'':6C ''rel'':66C ''seem'':57C ''someon'':36C ''stopword'':31C ''sure'':24C ''text'':17C,78C ''tf'':46C ''tf-idf'':45C ''thing'':22C ''topmin'':60C ''understood'':4C ''want'':9C ''web.engr.illinois.edu'':64C,69C ''web.engr.illinois.edu/~elkishk2/papers/topmine.pdf'':63C ''web.engr.illinois.edu/~elkishk2/papers/topmine.pdf&lt;/a&gt;'':68C ''well'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9100, 4, '2015-11-28 15:02:56', 0, NULL, NULL, 'Excel is a commercial spreadsheet program created by Microsoft.', NULL, NULL, '''commerci'':4C ''creat'':7C ''excel'':1C ''microsoft'':9C ''program'':6C ''spreadsheet'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5979, 2, '2015-06-01 17:08:46.35', 3, NULL, NULL, '&lt;p&gt;Generally with multiple classes you have to make a distinction between exclusive and inclusive groups. The simplest cases are "all classes are exclusive" (predict only one class), and "all classes are compatible" (predict list of classes that apply).&lt;/p&gt;

&lt;p&gt;Either way, label the classes as you would want your trained model to predict them. If you expect your classifier to predict an example is in both &lt;code&gt;garbage&lt;/code&gt; and &lt;code&gt;footpath&lt;/code&gt;, then you should label such an example with both. If you want it to disambiguate between them, then label with a single correct class.&lt;/p&gt;

&lt;p&gt;To train a classifier to predict multiple target classes at once, it is usually just a matter of picking the correct objective function and a classifier with architecture that can support it. &lt;/p&gt;

&lt;p&gt;For example, with a neural network, you would avoid using a "softmax" output which is geared towards predicting a single class - instead you might use a regular "sigmoid" function and predict class membership on a simple threshold on each output.&lt;/p&gt;

&lt;p&gt;You can get also more sophisticated perhaps with a pipeline model if your data can be split into several exclusive groups - predict the group in the first stage, and have multiple group-specific models predicting the combination of classes in each group in a second stage. This may be overkill for your problem, although it may still be handy if it keeps your individual models simple (e.g. they could all be logistic regression, and the first stage may gain some accuracy if the groups are easier to separate).&lt;/p&gt;
', NULL, NULL, '''/code'':71C,75C ''/p'':40C,100C,134C,176C,267C ''accuraci'':259C ''also'':181C ''although'':232C ''appli'':39C ''architectur'':129C ''avoid'':144C ''case'':19C ''class'':5C,22C,28C,31C,37C,46C,99C,110C,156C,167C,217C ''classifi'':61C,105C,127C ''code'':69C,73C ''combin'':215C ''compat'':33C ''correct'':98C,122C ''could'':247C ''data'':191C ''disambigu'':90C ''distinct'':11C ''e.g'':245C ''easier'':264C ''either'':42C ''exampl'':65C,82C,137C ''exclus'':13C,24C,197C ''expect'':59C ''first'':204C,254C ''footpath'':74C ''function'':124C,164C ''gain'':257C ''garbag'':70C ''gear'':151C ''general'':2C ''get'':180C ''group'':16C,198C,201C,210C,220C,262C ''group-specif'':209C ''handi'':237C ''inclus'':15C ''individu'':242C ''instead'':157C ''keep'':240C ''label'':44C,79C,94C ''list'':35C ''logist'':250C ''make'':9C ''matter'':118C ''may'':226C,234C,256C ''membership'':168C ''might'':159C ''model'':53C,188C,212C,243C ''multipl'':4C,108C,208C ''network'':141C ''neural'':140C ''object'':123C ''one'':27C ''output'':148C,175C ''overkil'':228C ''p'':1C,41C,101C,135C,177C ''perhap'':184C ''pick'':120C ''pipelin'':187C ''predict'':25C,34C,55C,63C,107C,153C,166C,199C,213C ''problem'':231C ''regress'':251C ''regular'':162C ''second'':223C ''separ'':266C ''sever'':196C ''sigmoid'':163C ''simpl'':171C,244C ''simplest'':18C ''singl'':97C,155C ''softmax'':147C ''sophist'':183C ''specif'':211C ''split'':194C ''stage'':205C,224C,255C ''still'':235C ''support'':132C ''target'':109C ''threshold'':172C ''toward'':152C ''train'':52C,103C ''use'':145C,160C ''usual'':115C ''want'':50C,87C ''way'':43C ''would'':49C,143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5980, 2, '2015-06-01 18:27:41.96', 2, NULL, NULL, '&lt;p&gt;Use &lt;a href="http://arxiv.org/pdf/1306.0040.pdf" rel="nofollow"&gt;Algorithm 1&lt;/a&gt; from this paper. The rest of paper is more about the analysis of algorithm and theoretical background which is very nice but not necessary for your purpose. Implementation of the algorithm in Python or R is pretty straightforward.&lt;/p&gt;

&lt;p&gt;Hope it helps :)&lt;/p&gt;
', NULL, NULL, '''/a'':12C ''/p'':51C,56C ''/pdf/1306.0040.pdf'':7C ''1'':11C ''algorithm'':10C,26C,43C ''analysi'':24C ''arxiv.org'':6C ''arxiv.org/pdf/1306.0040.pdf'':5C ''background'':29C ''help'':55C ''hope'':53C ''href'':4C ''implement'':40C ''necessari'':36C ''nice'':33C ''nofollow'':9C ''p'':1C,52C ''paper'':15C,19C ''pretti'':49C ''purpos'':39C ''python'':45C ''r'':47C ''rel'':8C ''rest'':17C ''straightforward'':50C ''theoret'':28C ''use'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6641, 2, '2015-07-31 06:44:49.997', 0, NULL, NULL, '&lt;p&gt;First, &lt;strong&gt;understand and solve the problem&lt;/strong&gt;. On manageable data. Gather experience on how to organize the data, and &lt;em&gt;where&lt;/em&gt; the difficulties are. Try to identify points where parallelism is possible.&lt;/p&gt;

&lt;p&gt;Second, &lt;strong&gt;parrallelize&lt;/strong&gt; and scale up as necessary.&lt;/p&gt;

&lt;p&gt;Don''t do it backwards, popular mistake. Solving the wrong problem with the wrong tools will fail big, with big data.&lt;/p&gt;
', NULL, NULL, '''/em'':24C ''/p'':36C,47C,70C ''/strong'':9C,41C ''backward'':53C ''big'':66C,68C ''data'':12C,20C,69C ''difficulti'':26C ''em'':22C ''experi'':14C ''fail'':65C ''first'':2C ''gather'':13C ''identifi'':30C ''manag'':11C ''mistak'':55C ''necessari'':46C ''organ'':18C ''p'':1C,37C,48C ''parallel'':33C ''parrallel'':40C ''point'':31C ''popular'':54C ''possibl'':35C ''problem'':8C,59C ''scale'':43C ''second'':38C ''solv'':6C,56C ''strong'':3C,39C ''tool'':63C ''tri'':28C ''understand'':4C ''wrong'':58C,62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5983, 1, '2015-06-02 08:39:41.997', 2, 91, 'Random Forest, Type - Regression, Calculation of Importance Example', '&lt;p&gt;I am trying to use a Random Forest Model (Regression Type) as a substitute of logistic regression model. I am using R - randomForest Package. I want to understand the meaning of Importance of Variables (%IncMSE and IncNodePurity) by example. &lt;/p&gt;

&lt;p&gt;Suppose I have a population of 100 employees out of which 30 left the company. 
Suppose in a particular decision tree, population is split by an attribute (say location) into two nodes. One node contains 50 employees out of which 10 left the company and other contains 50 employees from which 20 left the company. Can someone demonstrate me a calculation of %IncMSE and IncNodePurity. (if Required for averages etc., please consider another decision tree)&lt;/p&gt;

&lt;p&gt;This may look like a repeated question but I could not find a worked out example.&lt;/p&gt;
', 'machine-learning  r  logistic-regression  random-forest', NULL, '''/p'':59C,136C,154C ''10'':101C ''100'':67C ''20'':112C ''30'':72C ''50'':96C,108C ''anoth'':133C ''attribut'':87C ''averag'':129C ''calcul'':5A,121C ''compani'':75C,104C,115C ''consid'':132C ''contain'':95C,107C ''could'':147C ''decis'':80C,134C ''demonstr'':118C ''employe'':68C,97C,109C ''etc'':130C ''exampl'':8A,58C,153C ''find'':149C ''forest'':2A,18B,27C ''import'':7A,51C ''incms'':54C,123C ''incnodepur'':56C,125C ''learn'':11B ''left'':73C,102C,113C ''like'':141C ''locat'':89C ''logist'':14B,35C ''logistic-regress'':13B ''look'':140C ''machin'':10B ''machine-learn'':9B ''may'':139C ''mean'':49C ''model'':28C,37C ''node'':92C,94C ''one'':93C ''p'':19C,60C,137C ''packag'':43C ''particular'':79C ''pleas'':131C ''popul'':65C,82C ''question'':144C ''r'':12B,41C ''random'':1A,17B,26C ''random-forest'':16B ''randomforest'':42C ''regress'':4A,15B,29C,36C ''repeat'':143C ''requir'':127C ''say'':88C ''someon'':117C ''split'':84C ''substitut'':33C ''suppos'':61C,76C ''tree'':81C,135C ''tri'':22C ''two'':91C ''type'':3A,30C ''understand'':47C ''use'':24C,40C ''variabl'':53C ''want'':45C ''work'':151C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5984, 2, '2015-06-02 13:13:55.013', 3, NULL, NULL, '&lt;p&gt;MSE is measure of error of the overall regression model, $\frac{1}{n}\sum\|y_i-\hat y_i\|^2$.&lt;/p&gt;

&lt;p&gt;For an important variable, if it is replaced with random noise, you would imagine MSE with the faulty data to increase. &lt;strong&gt;IncMSE&lt;/strong&gt; (Incremental MSE) for a particular variable is how much the MSE will increase if the variable is completely randomized.&lt;/p&gt;

&lt;p&gt;This is usually computed on the out-of-bag data.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Node purity is a measure of how homogeneous a node is. An example of node purity is information entropy, i.e. $-p_1\log p_1-p_0\log p_0$ if there are two classes.
For regression models, node &lt;em&gt;im&lt;/em&gt;purity is usually taken as the variance in a node.&lt;/p&gt;

&lt;p&gt;Everytime you split a node, you do it to make the new nodes homogeneous, hence the purity increases.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;IncPurity&lt;/strong&gt; of a variable is weighted average of incremental purity because of each split by this variable was used to split, with the node population as the weight.&lt;/p&gt;
', NULL, NULL, '''/em'':124C ''/p'':22C,67C,80C,135C,155C,187C ''/strong'':47C,159C ''0'':109C,112C ''1'':13C,104C,107C ''2'':21C ''averag'':165C ''bag'':78C ''class'':117C ''complet'':65C ''comput'':72C ''data'':42C,79C ''em'':122C ''entropi'':101C ''error'':6C ''everytim'':137C ''exampl'':95C ''faulti'':41C ''frac'':12C ''hat'':18C ''henc'':151C ''homogen'':90C,150C ''hr'':81C ''i.e'':102C ''im'':123C ''imagin'':37C ''import'':26C ''incms'':46C ''incpur'':158C ''increas'':44C,60C,154C ''increment'':48C,167C ''inform'':100C ''log'':105C,110C ''make'':146C ''measur'':4C,87C ''model'':11C,120C ''mse'':2C,38C,49C,58C ''much'':56C ''n'':14C ''new'':148C ''node'':83C,92C,97C,121C,134C,141C,149C,182C ''nois'':34C ''out-of-bag'':75C ''overal'':9C ''p'':1C,23C,68C,82C,103C,106C,108C,111C,136C,156C ''particular'':52C ''popul'':183C ''puriti'':84C,98C,125C,153C,168C ''random'':33C,66C ''regress'':10C,119C ''replac'':31C ''split'':139C,172C,179C ''strong'':45C,157C ''sum'':15C ''taken'':128C ''two'':116C ''use'':177C ''usual'':71C,127C ''variabl'':27C,53C,63C,162C,175C ''varianc'':131C ''weight'':164C,186C ''would'':36C ''y'':16C,19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5985, 1, '2015-06-02 13:45:36.703', 1, 33, 'How to analyze which site has most numbers', '&lt;p&gt;I am trying to determine which site in our organization is in greater need of upgrades to SEP 12, so when I run a query to count, I get these type of numbers&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Group       Windows_SEP_11  Mac_SEP_11  Windows_SEP_12  Mac_SEP_12
Arizona\A   417                  29              219         6
Arizona\B   380                  20              282        15
Arizona\C   340                  30              383        507
Arizona\D   310                  104             186        857
Arizona\E   307                  74              403        243
Arizona\F   285                  171             522        14
Arizona\G   269                  1               559        41
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However, when I find percentages, I get these numbers&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Group          Win_Sep_11_%   Mac_SEP_11_%  Windows_SEP_12_%    Mac_SEP_12_%
Boston/Site 1   100               0                0               0
Boston/Site 2   100               0                0               0
Boston/Site 3   94                0                0               5
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And obviously, percentage isn''t good indicator because Boston/Site 1 has only 3 computers, Boston/Site 2 only has 4 computers, etc.&lt;/p&gt;

&lt;p&gt;What is the best way to analyze data? I ultimately need a visual of sites that have&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;many computers, and&lt;/li&gt;
&lt;li&gt;a great need for upgrades to SEP 12, i.e. if there are more computers with SEP 11 than SEP 12.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Please point me in the right direction.&lt;/p&gt;
', 'visualization', NULL, '''/code'':102C,148C ''/li'':197C,219C ''/ol'':220C ''/p'':44C,114C,172C,191C,229C ''/pre'':103C,149C ''0'':133C,134C,135C,139C,140C,141C,145C,146C ''1'':99C,131C,160C ''100'':132C,138C ''104'':81C ''11'':50C,53C,120C,123C,215C ''12'':29C,56C,59C,126C,129C,206C,218C ''14'':95C ''15'':71C ''171'':93C ''186'':82C ''2'':137C,166C ''20'':69C ''219'':64C ''243'':89C ''269'':98C ''282'':70C ''285'':92C ''29'':63C ''3'':143C,163C ''30'':75C ''307'':86C ''310'':80C ''340'':74C ''380'':68C ''383'':76C ''4'':169C ''403'':88C ''41'':101C ''417'':62C ''5'':147C ''507'':77C ''522'':94C ''559'':100C ''6'':65C ''74'':87C ''857'':83C ''94'':144C ''analyz'':3A,180C ''arizona'':60C,66C,72C,78C,84C,90C,96C ''b'':67C ''best'':177C ''boston/site'':130C,136C,142C,159C,165C ''c'':73C ''code'':46C,116C ''comput'':164C,170C,195C,212C ''count'':37C ''d'':79C ''data'':181C ''determin'':15C ''direct'':228C ''e'':85C ''etc'':171C ''f'':91C ''find'':108C ''g'':97C ''get'':39C,111C ''good'':156C ''great'':200C ''greater'':23C ''group'':47C,117C ''howev'':105C ''i.e'':207C ''indic'':157C ''isn'':154C ''li'':193C,198C ''mac'':51C,57C,121C,127C ''mani'':194C ''need'':24C,184C,201C ''number'':8A,43C,113C ''obvious'':152C ''ol'':192C ''organ'':20C ''p'':10C,104C,150C,173C,221C ''percentag'':109C,153C ''pleas'':222C ''point'':223C ''pre'':45C,115C ''queri'':35C ''right'':227C ''run'':33C ''sep'':28C,49C,52C,55C,58C,119C,122C,125C,128C,205C,214C,217C ''site'':5A,17C,188C ''tri'':13C ''type'':41C ''ultim'':183C ''upgrad'':26C,203C ''visual'':9B,186C ''way'':178C ''win'':118C ''window'':48C,54C,124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5987, 1, '2015-06-02 20:16:43.627', 6, 111, 'How do I calculate the delta term of a Convolutional Layer, given the delta terms and weights of the previous Convolutional Layer?', '&lt;p&gt;I am trying to train an artificial neural network with two convolutional layers (c1, c2) and two hidden layers (c1, c2). I am using the standard backpropagation approach. In the backward pass I calculate the error term of a layer (delta) based on the error of the previous layer, the weights of the previous layer and the gradient of the activation in respect to the activation function of the current layer. More specifically the delta of layer l looks like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;delta(l) = (w(l+1)'' * delta(l+1)) * grad_f_a(l)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am able to compute the gradient of c2, which connects into a regular layer. I just multiply the weights of h1 with it''s delta. Then I reshape that matrix into the form of the output of c2, multiply it with the gradient of the activation function and am done.&lt;/p&gt;

&lt;p&gt;Now I have a the delta term of c2 - Which is a 4D matrix of size (featureMapSize, featureMapSize, filterNum, patternNum). Furthermore I have the weights of c2, which are a 3D matrix of size (filterSize, filterSize, filterNum). &lt;/p&gt;

&lt;p&gt;With these two terms and the gradient of the activation of c1 I want to calculate the delta of c1.&lt;/p&gt;

&lt;p&gt;Long story short:&lt;/p&gt;

&lt;p&gt;Given the delta term of a previous convolutional layer and the weights of that layer, how do I compute the delta term of a convolutional layer?&lt;/p&gt;
', 'machine-learning  data-mining  neuralnetwork  deep-learning', NULL, '''+1'':122C,125C ''/code'':130C ''/p'':115C,184C,223C,245C,250C,278C ''/pre'':131C ''3d'':216C ''4d'':198C ''abl'':135C ''activ'':94C,99C,179C,234C ''approach'':61C ''artifici'':40C ''backpropag'':60C ''backward'':64C ''base'':75C ''c1'':47C,53C,236C,244C ''c2'':48C,54C,141C,171C,194C,212C ''calcul'':4A,67C,240C ''code'':117C ''comput'':137C,270C ''connect'':143C ''convolut'':10A,21A,45C,259C,276C ''current'':103C ''data'':27B ''data-min'':26B ''deep'':31B ''deep-learn'':30B ''delta'':6A,14A,74C,108C,118C,123C,158C,191C,242C,254C,272C ''done'':183C ''error'':69C,78C ''f'':127C ''featuremaps'':202C,203C ''filternum'':204C,222C ''filters'':220C,221C ''form'':166C ''function'':100C,180C ''furthermor'':206C ''given'':12A,252C ''grad'':126C ''gradient'':91C,139C,176C,231C ''h1'':154C ''hidden'':51C ''l'':111C,119C,121C,124C,129C ''layer'':11A,22A,46C,52C,73C,82C,88C,104C,110C,147C,260C,266C,277C ''learn'':25B,32B ''like'':113C ''long'':247C ''look'':112C ''machin'':24B ''machine-learn'':23B ''matrix'':163C,199C,217C ''mine'':28B ''multipli'':150C,172C ''network'':42C ''neural'':41C ''neuralnetwork'':29B ''output'':169C ''p'':33C,132C,185C,224C,246C,251C ''pass'':65C ''patternnum'':205C ''pre'':116C ''previous'':20A,81C,87C,258C ''regular'':146C ''reshap'':161C ''respect'':96C ''short'':249C ''size'':201C,219C ''specif'':106C ''standard'':59C ''stori'':248C ''term'':7A,15A,70C,192C,228C,255C,273C ''train'':38C ''tri'':36C ''two'':44C,50C,227C ''use'':57C ''w'':120C ''want'':238C ''weight'':17A,84C,152C,210C,263C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5988, 2, '2015-06-02 22:14:07.25', 1, NULL, NULL, '&lt;p&gt;The most obvious way of visualizing this is to have the number of computers on the Y-axis and the size of the dots representing the percentages. The categories (or sites in your case) can be represented on the X-axis. The image below shows an example where the Y-axis represents a continuous value (can be mapped to number of computers in your case), the X-axis represents a discrete value (can be mapped to sites in your case), and the size of the dots represents another attribute (like percentage in your case). I have used the &lt;code&gt;R&lt;/code&gt; package &lt;code&gt;ggplot2&lt;/code&gt; for this. &lt;img src="http://i.stack.imgur.com/bXH9m.png" alt="enter image description here"&gt;&lt;/p&gt;
', NULL, NULL, '''/bxh9m.png'':117C ''/code'':106C,110C ''/p'':123C ''alt'':118C ''anoth'':93C ''attribut'':94C ''axi'':20C,44C,55C,73C ''case'':36C,69C,85C,99C ''categori'':31C ''code'':104C,108C ''comput'':15C,66C ''continu'':58C ''descript'':121C ''discret'':76C ''dot'':26C,91C ''enter'':119C ''exampl'':50C ''ggplot2'':109C ''i.stack.imgur.com'':116C ''i.stack.imgur.com/bxh9m.png'':115C ''imag'':46C,120C ''img'':113C ''like'':95C ''map'':62C,80C ''number'':13C,64C ''obvious'':4C ''p'':1C ''packag'':107C ''percentag'':29C,96C ''r'':105C ''repres'':27C,39C,56C,74C,92C ''show'':48C ''site'':33C,82C ''size'':23C,88C ''src'':114C ''use'':102C ''valu'':59C,77C ''visual'':7C ''way'':5C ''x'':43C,72C ''x-axi'':42C,71C ''y'':19C,54C ''y-axi'':18C,53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5989, 2, '2015-06-03 00:58:36.53', 1, NULL, NULL, '&lt;p&gt;There are two packages in &lt;code&gt;R&lt;/code&gt; that should be able to use &lt;code&gt;SMOTE&lt;/code&gt; to up sample the minority class: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;code&gt;unbalanced&lt;/code&gt; &lt;a href="http://cran.r-project.org/web/packages/unbalanced/unbalanced.pdf" rel="nofollow"&gt;package&lt;/a&gt;, and &lt;/li&gt;
&lt;li&gt;&lt;code&gt;DMwR&lt;/code&gt; &lt;a href="http://cran.r-project.org/web/packages/DMwR/DMwR.pdf" rel="nofollow"&gt;package&lt;/a&gt; &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;It doesn''t seem like they provide the boosting that is described in the &lt;a href="http://www-users.cs.umn.edu/~aleks/papers/pkdd03.pdf" rel="nofollow"&gt;paper&lt;/a&gt; that you might be referring to. But it should be straightforward to sew in &lt;code&gt;SMOTE&lt;/code&gt; with the idea of boosting using some base tree packages in R like &lt;code&gt;rpart&lt;/code&gt; (for example). &lt;/p&gt;
', NULL, NULL, '''/a'':39C,54C,80C ''/code'':9C,18C,30C,45C,97C,113C ''/li'':41C,55C ''/ol'':56C ''/p'':25C,116C ''/web/packages/dmwr/dmwr.pdf'':50C ''/web/packages/unbalanced/unbalanced.pdf'':35C ''/~aleks/papers/pkdd03.pdf'':76C ''abl'':13C ''base'':105C ''boost'':66C,102C ''class'':24C ''code'':7C,16C,28C,43C,95C,111C ''cran.r-project.org'':34C,49C ''cran.r-project.org/web/packages/dmwr/dmwr.pdf'':48C ''cran.r-project.org/web/packages/unbalanced/unbalanced.pdf'':33C ''describ'':69C ''dmwr'':44C ''doesn'':59C ''exampl'':115C ''href'':32C,47C,73C ''idea'':100C ''li'':27C,42C ''like'':62C,110C ''might'':83C ''minor'':23C ''nofollow'':37C,52C,78C ''ol'':26C ''p'':1C,57C ''packag'':5C,38C,53C,107C ''paper'':79C ''provid'':64C ''r'':8C,109C ''refer'':85C ''rel'':36C,51C,77C ''rpart'':112C ''sampl'':21C ''seem'':61C ''sew'':93C ''smote'':17C,96C ''straightforward'':91C ''tree'':106C ''two'':4C ''unbalanc'':29C ''use'':15C,103C ''www-users.cs.umn.edu'':75C ''www-users.cs.umn.edu/~aleks/papers/pkdd03.pdf'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8483, 2, '2015-10-17 21:53:25.16', 2, NULL, NULL, '&lt;p&gt;That seems pretty straight forward to me. I''m pretty new too but in general I''m not sure if you can get better than one command. Though you could have combined all that:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; newValueVector &amp;lt;- c("+"="5", "?"="2", "K"="3", "k"="3")
&amp;gt; data$Multiplier &amp;lt;-  revalue(data$Multiplier, newValueVector)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':57C ''/p'':36C ''/pre'':58C ''2'':44C ''3'':46C,48C ''5'':43C ''better'':25C ''c'':42C ''code'':38C ''combin'':33C ''command'':28C ''could'':31C ''data'':50C,54C ''forward'':6C ''general'':16C ''get'':24C ''gt'':39C,49C ''k'':45C,47C ''lt'':41C,52C ''m'':10C,18C ''multipli'':51C,55C ''new'':12C ''newvaluevector'':40C,56C ''one'':27C ''p'':1C ''pre'':37C ''pretti'':4C,11C ''revalu'':53C ''seem'':3C ''straight'':5C ''sure'':20C ''though'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8716, 1, '2015-11-04 13:57:45.897', 0, 21, 'Methods for cleaning a complex list of medical providers within a healthcare network?', '&lt;p&gt;I am working on a project with a large network of approximately 4,000 healthcare providers, all of whom have complex relations to organizations, functions, practice locations, provider groups, payers, etc. This leaves me with dozens of lists with sometimes conflicting but, more often, slightly different information. For example, one provider might have 5 different ID numbers across systems, work with 3 provider groups, and provide services at multiple hospitals.&lt;/p&gt;

&lt;p&gt;My goal is to create a clean version of a master list so that I can begin other kinds of analysis (network analysis, etc.) and also provide a "source of truth" for our team about providers and their various attributes and affiliations.&lt;/p&gt;

&lt;p&gt;I am primarily an R user, but am open to other tools. OpenRefine, for example, is useful for its fuzzy matching on strings, but doesn''t seem to integrate with R. I would love to be able to do something similar in R, especially if it could identify potential matches across multiple variables.&lt;/p&gt;

&lt;p&gt;My question is general, I know, but what I am missing that I could be using here to solve the problem and derive some insight into the structure of the network?&lt;/p&gt;

&lt;p&gt;Thanks in advance.&lt;/p&gt;
', 'r  data-cleaning', NULL, '''/p'':89C,133C,188C,221C,226C ''000'':32C ''3'':80C ''4'':31C ''5'':72C ''abl'':171C ''across'':76C,185C ''advanc'':225C ''affili'':132C ''also'':116C ''analysi'':111C,113C ''approxim'':30C ''attribut'':130C ''begin'':107C ''clean'':3A,17B,97C ''complex'':5A,39C ''conflict'':59C ''could'':181C,203C ''creat'':95C ''data'':16B ''data-clean'':15B ''deriv'':212C ''differ'':64C,73C ''doesn'':159C ''dozen'':54C ''especi'':178C ''etc'':49C,114C ''exampl'':67C,149C ''function'':43C ''fuzzi'':154C ''general'':193C ''goal'':92C ''group'':47C,82C ''healthcar'':12A,33C ''hospit'':88C ''id'':74C ''identifi'':182C ''inform'':65C ''insight'':214C ''integr'':163C ''kind'':109C ''know'':195C ''larg'':27C ''leav'':51C ''list'':6A,56C,102C ''locat'':45C ''love'':168C ''master'':101C ''match'':155C,184C ''medic'':8A ''method'':1A ''might'':70C ''miss'':200C ''multipl'':87C,186C ''network'':13A,28C,112C,220C ''number'':75C ''often'':62C ''one'':68C ''open'':143C ''openrefin'':147C ''organ'':42C ''p'':18C,90C,134C,189C,222C ''payer'':48C ''potenti'':183C ''practic'':44C ''primarili'':137C ''problem'':210C ''project'':24C ''provid'':9A,34C,46C,69C,81C,84C,117C,126C ''question'':191C ''r'':14B,139C,165C,177C ''relat'':40C ''seem'':161C ''servic'':85C ''similar'':175C ''slight'':63C ''solv'':208C ''someth'':174C ''sometim'':58C ''sourc'':119C ''string'':157C ''structur'':217C ''system'':77C ''team'':124C ''thank'':223C ''tool'':146C ''truth'':121C ''use'':151C,205C ''user'':140C ''variabl'':187C ''various'':129C ''version'':98C ''within'':10A ''work'':21C,78C ''would'':167C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6252, 2, '2015-06-29 04:27:24.677', 1, NULL, NULL, '&lt;p&gt;One method I''ve used successfully is the &lt;a href="http://www.mif.vu.lt/atpazinimas/dip/FIP/fip-Segmenta.html" rel="nofollow"&gt;Triangle Method&lt;/a&gt; used for thresholding in image processing. Basically is looks for the "elbow" in the data by drawing a line between the data peak and the minimum at one end and defining the "elbow" point as being the farthest from that line. Checkout the link and the picture there will clearly explain what the algorithm does.&lt;/p&gt;
', NULL, NULL, '''/a'':19C ''/atpazinimas/dip/fip/fip-segmenta.html'':14C ''/p'':75C ''algorithm'':73C ''basic'':26C ''checkout'':61C ''clear'':69C ''data'':34C,41C ''defin'':50C ''draw'':36C ''elbow'':31C,52C ''end'':48C ''explain'':70C ''farthest'':57C ''href'':11C ''imag'':24C ''line'':38C,60C ''link'':63C ''look'':28C ''method'':3C,18C ''minimum'':45C ''nofollow'':16C ''one'':2C,47C ''p'':1C ''peak'':42C ''pictur'':66C ''point'':53C ''process'':25C ''rel'':15C ''success'':7C ''threshold'':22C ''triangl'':17C ''use'':6C,20C ''ve'':5C ''www.mif.vu.lt'':13C ''www.mif.vu.lt/atpazinimas/dip/fip/fip-segmenta.html'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5990, 1, '2015-06-03 05:56:50.243', 3, 96, 'What is a good way to transform Cyclic Ordinal attributes?', '&lt;p&gt;I am having ''hour'' field as my attribute, but it takes a cyclic values. How could I transform the feature to preserve the information like ''23'' and ''0'' hour are close not far. &lt;/p&gt;

&lt;p&gt;One way I could think is to do transformation: &lt;code&gt;min(h, 23-h)&lt;/code&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Input: [0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]

Output: [0 1 2 3 4 5 6 7 8 9 10 11 11 10 9 8 7 6 5 4 3 2 1]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is there any standard to handle such attributes?&lt;/p&gt;

&lt;p&gt;Update: I will be using superviseed learning, to train random forest classifier!&lt;/p&gt;
', 'feature-extraction  feature-scaling  featurization', NULL, '''/code'':68C,121C ''/p'':52C,69C,132C,146C ''/pre'':122C ''0'':46C,73C,98C ''1'':74C,99C,120C ''10'':83C,108C,111C ''11'':84C,109C,110C ''12'':85C ''13'':86C ''14'':87C ''15'':88C ''16'':89C ''17'':90C ''18'':91C ''19'':92C ''2'':75C,100C,119C ''20'':93C ''21'':94C ''22'':95C ''23'':44C,66C,96C ''3'':76C,101C,118C ''4'':77C,102C,117C ''5'':78C,103C,116C ''6'':79C,104C,115C ''7'':80C,105C,114C ''8'':81C,106C,113C ''9'':82C,107C,112C ''attribut'':10A,26C,131C ''classifi'':145C ''close'':49C ''code'':63C,71C ''could'':34C,57C ''cyclic'':8A,31C ''extract'':13B ''far'':51C ''featur'':12B,15B,17B,38C ''feature-extract'':11B ''feature-sc'':14B ''field'':23C ''forest'':144C ''good'':4A ''h'':65C,67C ''handl'':129C ''hour'':22C,47C ''inform'':42C ''input'':72C ''learn'':140C ''like'':43C ''min'':64C ''one'':54C ''ordin'':9A ''output'':97C ''p'':18C,53C,123C,133C ''pre'':70C ''preserv'':40C ''random'':143C ''scale'':16B ''standard'':127C ''supervise'':139C ''take'':29C ''think'':58C ''train'':142C ''transform'':7A,36C,62C ''updat'':134C ''use'':138C ''valu'':32C ''way'':5A,55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5991, 1, '2015-06-03 07:24:32.63', 2, 82, 'Where can I download a tagged dataset of text related to finance, programming, analytics etc.?', '&lt;p&gt;I want to create and train a model which classifies a new text content into finance, programming, analytics, design etc. Where can I get a relevant dataset to train my models? &lt;/p&gt;

&lt;p&gt;TIA. &lt;/p&gt;
', 'machine-learning  dataset  multiclass-classification', NULL, '''/p'':55C,58C ''analyt'':14A,41C ''classif'':22B ''classifi'':33C ''content'':37C ''creat'':27C ''dataset'':7A,19B,50C ''design'':42C ''download'':4A ''etc'':15A,43C ''financ'':12A,39C ''get'':47C ''learn'':18B ''machin'':17B ''machine-learn'':16B ''model'':31C,54C ''multiclass'':21B ''multiclass-classif'':20B ''new'':35C ''p'':23C,56C ''program'':13A,40C ''relat'':10A ''relev'':49C ''tag'':6A ''text'':9A,36C ''tia'':57C ''train'':29C,52C ''want'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5992, 1, '2015-06-03 12:19:59.147', 4, 79, 'Could one algorithm fetch keywords from texts of different natural languages?', '&lt;p&gt;I am new to practicing NLP and most topics related, but I want to make a program that can gather and extract data for me on its own.&lt;/p&gt;

&lt;p&gt;To be more specific, I want to tell the program "I want more information on this topic(i.e heart attacks)", and then the program shall find, gather and extract meaningful texts on the topic from around the www.&lt;/p&gt;

&lt;p&gt;I happen to live in Norway, which means that most interesting data will be in English, but I also want to fetch interesting data found in Norwegian.&lt;/p&gt;

&lt;p&gt;One challenge is the differences in stop words. For instance, "are" and "and" are both stop words in English and subjects in Norwegian.
Other challenges are also likely to occur.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;So my question is: Would I need to create separate algorithms for every natural language to be interpreted?&lt;/strong&gt;&lt;/p&gt;
', 'data-mining  nlp  algorithms', NULL, '''/p'':46C,86C,115C,147C,169C ''/strong'':168C ''algorithm'':3A,16B,160C ''also'':106C,143C ''around'':83C ''attack'':67C ''challeng'':118C,141C ''could'':1A ''creat'':158C ''data'':13B,40C,99C,111C ''data-min'':12B ''differ'':9A,121C ''english'':103C,135C ''everi'':162C ''extract'':39C,76C ''fetch'':4A,109C ''find'':73C ''found'':112C ''gather'':37C,74C ''happen'':89C ''heart'':66C ''i.e'':65C ''inform'':61C ''instanc'':126C ''interest'':98C,110C ''interpret'':167C ''keyword'':5A ''languag'':11A,164C ''like'':144C ''live'':91C ''make'':32C ''mean'':95C ''meaning'':77C ''mine'':14B ''natur'':10A,163C ''need'':156C ''new'':20C ''nlp'':15B,23C ''norway'':93C ''norwegian'':114C,139C ''occur'':146C ''one'':2A,117C ''p'':17C,47C,87C,116C,148C ''practic'':22C ''program'':34C,57C,71C ''question'':152C ''relat'':27C ''separ'':159C ''shall'':72C ''specif'':51C ''stop'':123C,132C ''strong'':149C ''subject'':137C ''tell'':55C ''text'':7A,78C ''topic'':26C,64C,81C ''want'':30C,53C,59C,107C ''word'':124C,133C ''would'':154C ''www'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5993, 2, '2015-06-03 16:55:37.517', 2, NULL, NULL, '&lt;p&gt;The question is very interesting and I do not remember to read about interesting answers. Because of that I dare to give you one possible solution even if it looks crazy enough. &lt;/p&gt;

&lt;p&gt;Usually one avoids having the same information in multiple features, since many algorithms can''t handle that. But this is not the case of random forest. Contrasting linear regression (and all models based on similar ideas), random forests test all the features, by taking into consideration each feature one at a time. This way it is possible to code the same information in multiple ways without affecting learning performance, onyly space and running time.&lt;/p&gt;

&lt;p&gt;So my suggestion would be to create 24 features, each of the form $(h+offset)%24$. It''s like when you encode the time in local time zones. Thus you give the occasion to rf to detect using the same units some interesting agglomerations around some hours, because each possible hour has the chance to be encoded properly in at least 1 of 24 features. &lt;/p&gt;

&lt;p&gt;It waste some space and time, but I would giv it a try to see how that works.&lt;/p&gt;
', NULL, NULL, '''/p'':34C,110C,177C,197C ''1'':173C ''24'':119C,127C,175C ''affect'':102C ''agglomer'':155C ''algorithm'':48C ''answer'':16C ''around'':156C ''avoid'':38C ''base'':68C ''case'':58C ''chanc'':165C ''code'':94C ''consider'':81C ''contrast'':62C ''crazi'':32C ''creat'':118C ''dare'':21C ''detect'':148C ''encod'':133C,168C ''enough'':33C ''even'':28C ''featur'':45C,77C,83C,120C,176C ''forest'':61C,73C ''form'':124C ''giv'':188C ''give'':23C,142C ''h'':125C ''handl'':51C ''hour'':158C,162C ''idea'':71C ''inform'':42C,97C ''interest'':6C,15C,154C ''learn'':103C ''least'':172C ''like'':130C ''linear'':63C ''local'':137C ''look'':31C ''mani'':47C ''model'':67C ''multipl'':44C,99C ''occas'':144C ''offset'':126C ''one'':25C,37C,84C ''onyli'':105C ''p'':1C,35C,111C,178C ''perform'':104C ''possibl'':26C,92C,161C ''proper'':169C ''question'':3C ''random'':60C,72C ''read'':13C ''regress'':64C ''rememb'':11C ''rf'':146C ''run'':108C ''see'':193C ''similar'':70C ''sinc'':46C ''solut'':27C ''space'':106C,182C ''suggest'':114C ''take'':79C ''test'':74C ''thus'':140C ''time'':87C,109C,135C,138C,184C ''tri'':191C ''unit'':152C ''use'':149C ''usual'':36C ''wast'':180C ''way'':89C,100C ''without'':101C ''work'':196C ''would'':115C,187C ''zone'':139C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6019, 1, '2015-06-05 11:37:57.63', 1, 54, 'What is the definition of knowledge within data science?', '&lt;p&gt;"Knowledge" is crucial within several fields like Knowledge Discovery, Knowledge Distraction, Natural Language Processing, Data Mining, Big Data, etc etc etc.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;What is the definition of knowledge within these fields?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is there 1 common definition, or does it depend on the exact context?&lt;/strong&gt;&lt;/p&gt;
', 'definitions  knowledge-base', NULL, '''/p'':36C,49C,66C ''/strong'':48C,65C ''1'':54C ''base'':13B ''big'':31C ''common'':55C ''context'':64C ''crucial'':17C ''data'':8A,29C,32C ''definit'':4A,10B,42C,56C ''depend'':60C ''discoveri'':23C ''distract'':25C ''etc'':33C,34C,35C ''exact'':63C ''field'':20C,47C ''knowledg'':6A,12B,15C,22C,24C,44C ''knowledge-bas'':11B ''languag'':27C ''like'':21C ''mine'':30C ''natur'':26C ''p'':14C,37C,50C ''process'':28C ''scienc'':9A ''sever'':19C ''strong'':38C,51C ''within'':7A,18C,45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5995, 2, '2015-06-03 20:05:43.563', 5, NULL, NULL, '&lt;p&gt;There are a few ways to deal with this issue.  Python has a package called NLTK which contains stop word lists for several languages (including English and Norwegian).  You can simply use this package, it''s usage is as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;&amp;gt;&amp;gt; from nltk.corpus import stopwords
&amp;gt;&amp;gt;&amp;gt; stop = stopwords.words(''english'')
&amp;gt;&amp;gt;&amp;gt; sentence = "this is a foo bar sentence"
&amp;gt;&amp;gt;&amp;gt; print [i for i in sentence.split() if i not in stop]
  [''foo'', ''bar'', ''sentence'']
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Alternatively, a method for automatically suppressing stop words is called tf-idf;  tf-idf is commonly used in search engines so that the most important words are promoted to the forefront.  In your case, I would suspect you''d want to have IDF scores for both English and Norwegian and apply only the appropriate one on a language to language basis.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Tf%E2%80%93idf"&gt;http://en.wikipedia.org/wiki/Tf%E2%80%93idf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/code'':85C ''/p'':42C,151C ''/pre'':86C ''/wiki/tf%e2%80%93idf'':157C ''/wiki/tf%e2%80%93idf&lt;/a&gt;&lt;/p&gt;'':160C ''altern'':88C ''appli'':140C ''appropri'':143C ''automat'':92C ''bar'':66C,83C ''basi'':150C ''call'':16C,97C ''case'':123C ''code'':44C ''common'':105C ''contain'':19C ''d'':128C ''deal'':8C ''en.wikipedia.org'':156C,159C ''en.wikipedia.org/wiki/tf%e2%80%93idf'':155C ''en.wikipedia.org/wiki/tf%e2%80%93idf&lt;/a&gt;&lt;/p&gt;'':158C ''engin'':109C ''english'':27C,57C,136C ''follow'':41C ''foo'':65C,82C ''forefront'':120C ''gt'':45C,46C,47C,52C,53C,54C,58C,59C,60C,68C,69C,70C ''href'':154C ''idf'':100C,103C,132C ''import'':50C,114C ''includ'':26C ''issu'':11C ''languag'':25C,147C,149C ''list'':22C ''method'':90C ''nltk'':17C ''nltk.corpus'':49C ''norwegian'':29C,138C ''one'':144C ''p'':1C,87C,152C ''packag'':15C,35C ''pre'':43C ''print'':71C ''promot'':117C ''python'':12C ''score'':133C ''search'':108C ''sentenc'':61C,67C,84C ''sentence.split'':76C ''sever'':24C ''simpli'':32C ''stop'':20C,55C,81C,94C ''stopword'':51C ''stopwords.words'':56C ''suppress'':93C ''suspect'':126C ''tf'':99C,102C ''tf-idf'':98C,101C ''usag'':38C ''use'':33C,106C ''want'':129C ''way'':6C ''word'':21C,95C,115C ''would'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8481, 1, '2015-10-17 21:10:40.957', 1, 26, 'Why does ada (adaboost) in R return different training error graphs and variable importance plots when running the same function multiple times?', '&lt;p&gt;Question says most of it. I created a matrix of descriptors, set the vectors of responses, and input a set number of iterations. Each time I run the function with the same exact inputs, I get the same confusion table, but I get a different training error graph and different variable importance plots. &lt;/p&gt;
', 'r', NULL, '''/p'':78C ''ada'':3A ''adaboost'':4A ''confus'':63C ''creat'':31C ''descriptor'':35C ''differ'':8A,69C,74C ''error'':10A,71C ''exact'':57C ''function'':20A,53C ''get'':60C,67C ''graph'':11A,72C ''import'':14A,76C ''input'':42C,58C ''iter'':47C ''matrix'':33C ''multipl'':21A ''number'':45C ''p'':24C ''plot'':15A,77C ''question'':25C ''r'':6A,23B ''respons'':40C ''return'':7A ''run'':17A,51C ''say'':26C ''set'':36C,44C ''tabl'':64C ''time'':22A,49C ''train'':9A,70C ''variabl'':13A,75C ''vector'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5996, 1, '2015-06-03 20:29:40.49', 6, 1848, 'Which is faster: PostgreSQL vs MongoDB on large JSON datasets?', '&lt;p&gt;I have a large dataset with 9m JSON objects at ~300 bytes each. They are posts from a link aggregator: basically links (a URL, title and author id) and comments (text and author ID) + metadata.&lt;/p&gt;

&lt;p&gt;They could very well be relational records in a table, except for the fact that they have one array field with IDs pointing to child records.&lt;/p&gt;

&lt;p&gt;What implementation looks more solid?&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;JSON objects on a PostgreSQL database (just one large table with one column, namely the JSON object)&lt;/li&gt;
&lt;li&gt;JSON objects on a MongoDB&lt;/li&gt;
&lt;li&gt;Explode the JSON objects into columns and use arrays on PostgreSQL&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I want to maximize performance in joins, so I can massage the data and explore it until I find interesting analyses, at which point I think it will be better to transform the data into a form specific to each analysis.&lt;/p&gt;
', 'data-mining  bigdata  databases  sql  mongodb', NULL, '''/li'':109C,116C,129C ''/ol'':130C ''/p'':54C,82C,89C,173C ''300'':29C ''9m'':25C ''aggreg'':38C ''analys'':152C ''analysi'':172C ''array'':74C,126C ''author'':45C,51C ''basic'':39C ''better'':161C ''bigdata'':14B ''byte'':30C ''child'':80C ''column'':104C,123C ''comment'':48C ''could'':57C ''data'':12B,144C,165C ''data-min'':11B ''databas'':15B,97C ''dataset'':10A,23C ''except'':66C ''explod'':118C ''explor'':146C ''fact'':69C ''faster'':3A ''field'':75C ''find'':150C ''form'':168C ''id'':46C,52C,77C ''implement'':85C ''interest'':151C ''join'':138C ''json'':9A,26C,92C,107C,111C,120C ''larg'':8A,22C,100C ''li'':91C,110C,117C ''link'':37C,40C ''look'':86C ''massag'':142C ''maxim'':135C ''metadata'':53C ''mine'':13B ''mongodb'':6A,17B,115C ''name'':105C ''object'':27C,93C,108C,112C,121C ''ol'':90C ''one'':73C,99C,103C ''p'':18C,55C,83C,131C ''perform'':136C ''point'':78C,155C ''post'':34C ''postgresql'':4A,96C,128C ''record'':62C,81C ''relat'':61C ''solid'':88C ''specif'':169C ''sql'':16B ''tabl'':65C,101C ''text'':49C ''think'':157C ''titl'':43C ''transform'':163C ''url'':42C ''use'':125C ''vs'':5A ''want'':133C ''well'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5998, 2, '2015-06-03 23:19:42.263', 2, NULL, NULL, '&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Named-entity_recognition#Formal_evaluation" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Named-entity_recognition#Formal_evaluation&lt;/a&gt; :&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;To evaluate the quality of a NER system''s output, several measures
  have been defined. While accuracy on the token level is one
  possibility, it suffers from two problems: the vast majority of tokens
  in real-world text are not part of entity names as usually defined, so
  the baseline accuracy (always predict "not an entity") is
  extravagantly high, typically &gt;90%; and mispredicting the full span of
  an entity name is not properly penalized (finding only a person''s
  first name when their last name follows is scored as Â½ accuracy).&lt;/p&gt;
  
  &lt;p&gt;In academic conferences such as CoNLL, a variant of the F1 score has
  been defined as follows:&lt;/p&gt;
  
  &lt;ul&gt;
  &lt;li&gt;Precision is the number of predicted entity name spans that line up exactly with spans in the gold standard evaluation data. I.e. when
  [Person Hans] [Person Blick] is predicted but [Person Hans Blick] was
  required, precision for the predicted name is zero. Precision is then
  averaged over all predicted entity names.&lt;/li&gt;
  &lt;li&gt;Recall is similarly the number of names in the gold standard that appear at exactly the same location in the predictions.&lt;/li&gt;
  &lt;li&gt;F1 score is the harmonic mean of these two.&lt;/li&gt;
  &lt;/ul&gt;
  
  &lt;p&gt;It follows from the above definition that any prediction that misses a
  single token, includes a spurious token, or has the wrong class,
  "scores no points", i.e. does not contribute to either precision or
  recall.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/blockquote'':253C ''/li'':180C,203C,214C ''/p'':12C,107C,126C,252C ''/ul'':215C ''/wiki/named-entity_recognition#formal_evaluation'':6C ''/wiki/named-entity_recognition#formal_evaluation&lt;/a&gt;'':11C ''90'':76C ''academ'':110C ''accuraci'':31C,66C,106C ''alway'':67C ''appear'':194C ''averag'':174C ''baselin'':65C ''blick'':155C,161C ''blockquot'':13C ''class'':239C ''confer'':111C ''conll'':114C ''contribut'':246C ''data'':149C ''defin'':29C,62C,123C ''definit'':222C ''either'':248C ''en.wikipedia.org'':5C,10C ''en.wikipedia.org/wiki/named-entity_recognition#formal_evaluation'':4C ''en.wikipedia.org/wiki/named-entity_recognition#formal_evaluation&lt;/a&gt;'':9C ''entiti'':58C,71C,84C,135C,178C ''evalu'':16C,148C ''exact'':141C,196C ''extravag'':73C ''f1'':119C,205C ''find'':90C ''first'':95C ''follow'':101C,125C,218C ''full'':80C ''gold'':146C,191C ''han'':153C,160C ''harmon'':209C ''high'':74C ''href'':3C ''i.e'':150C,243C ''includ'':231C ''last'':99C ''level'':35C ''li'':128C,181C,204C ''line'':139C ''locat'':199C ''major'':46C ''mean'':210C ''measur'':26C ''mispredict'':78C ''miss'':227C ''name'':59C,85C,96C,100C,136C,168C,179C,188C ''ner'':21C ''nofollow'':8C ''number'':132C,186C ''one'':37C ''output'':24C ''p'':1C,14C,108C,216C ''part'':56C ''penal'':89C ''person'':93C,152C,154C,159C ''point'':242C ''possibl'':38C ''precis'':129C,164C,171C,249C ''predict'':68C,134C,157C,167C,177C,202C,225C ''problem'':43C ''proper'':88C ''qualiti'':18C ''real'':51C ''real-world'':50C ''recal'':182C,251C ''rel'':7C ''requir'':163C ''score'':103C,120C,206C,240C ''sever'':25C ''similar'':184C ''singl'':229C ''span'':81C,137C,143C ''spurious'':233C ''standard'':147C,192C ''suffer'':40C ''system'':22C ''text'':53C ''token'':34C,48C,230C,234C ''two'':42C,213C ''typic'':75C ''ul'':127C ''usual'':61C ''variant'':116C ''vast'':45C ''world'':52C ''wrong'':238C ''zero'':170C ''â'':105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6017, 2, '2015-06-05 09:47:34.947', 2, NULL, NULL, '&lt;p&gt;Your classes express a certain order. You can classify apples to, say, "green", "red" or "yellow", and then every disagreement with a reference set is equal. After all, colours express no order. So as you already suggested, I would certainly use regression. Assume that the classes could be distributed as something like this:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Very bad = 0 - 0.25&lt;/li&gt;
&lt;li&gt;Bad = 0.25 - 0.50&lt;/li&gt;
&lt;li&gt;Good = 0.50 - 0.75&lt;/li&gt;
&lt;li&gt;Very good = 0.75 - 1.00&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Now, the mismatch of &lt;em&gt;Very good&lt;/em&gt; vs. &lt;em&gt;Bad&lt;/em&gt; is at least 0.25, where is must be at least 0.50 with &lt;em&gt;Very good&lt;/em&gt; vs. &lt;em&gt;Very bad&lt;/em&gt;, which gives a better and more honest impression of the performance of your model.&lt;/p&gt;
', NULL, NULL, '''/em'':88C,92C,108C,113C ''/li'':62C,67C,72C,78C ''/ol'':79C ''/p'':55C,128C ''0'':60C ''0.25'':61C,65C,96C ''0.50'':66C,70C,103C ''0.75'':71C,76C ''1.00'':77C ''alreadi'':37C ''appl'':11C ''assum'':44C ''bad'':59C,64C,91C,112C ''better'':117C ''certain'':6C,41C ''class'':3C,47C ''classifi'':10C ''colour'':30C ''could'':48C ''disagr'':21C ''distribut'':50C ''em'':85C,90C,105C,110C ''equal'':27C ''everi'':20C ''express'':4C,31C ''give'':115C ''good'':69C,75C,87C,107C ''green'':14C ''honest'':120C ''impress'':121C ''least'':95C,102C ''li'':57C,63C,68C,73C ''like'':53C ''mismatch'':83C ''model'':127C ''must'':99C ''ol'':56C ''order'':7C,33C ''p'':1C,80C ''perform'':124C ''red'':15C ''refer'':24C ''regress'':43C ''say'':13C ''set'':25C ''someth'':52C ''suggest'':38C ''use'':42C ''vs'':89C,109C ''would'':40C ''yellow'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5999, 2, '2015-06-04 03:31:21.937', 0, NULL, NULL, '&lt;p&gt;Have you tried Random Forest to do feature selection for categorical features. 
Random Forest uses proximity calculation(information gain) on deciding to split on a particular feature.
Random Forest:
"After each tree is built, all of the data are run down the tree, and proximities are computed for each pair of cases. If two cases occupy the same terminal node, their proximity is increased by one. At the end of the run, the proximities are normalized by dividing by the number of trees. Proximities are used in replacing missing data, locating outliers, and producing illuminating low-dimensional views of the data"
For more check this link out:
&lt;a href="http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#varimp" rel="nofollow"&gt;http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#varimp&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/~breiman/randomforests/cc_home.htm#varimp'':114C ''/~breiman/randomforests/cc_home.htm#varimp&lt;/a&gt;&lt;/p&gt;'':119C ''built'':35C ''calcul'':18C ''case'':53C,56C ''categor'':12C ''check'':106C ''comput'':48C ''data'':39C,91C,103C ''decid'':22C ''dimension'':99C ''divid'':79C ''end'':70C ''featur'':9C,13C,28C ''forest'':6C,15C,30C ''gain'':20C ''href'':111C ''illumin'':96C ''increas'':65C ''inform'':19C ''link'':108C ''locat'':92C ''low'':98C ''low-dimension'':97C ''miss'':90C ''node'':61C ''nofollow'':116C ''normal'':77C ''number'':82C ''occupi'':57C ''one'':67C ''outlier'':93C ''p'':1C ''pair'':51C ''particular'':27C ''produc'':95C ''proxim'':17C,46C,63C,75C,85C ''random'':5C,14C,29C ''rel'':115C ''replac'':89C ''run'':41C,73C ''select'':10C ''split'':24C ''termin'':60C ''tree'':33C,44C,84C ''tri'':4C ''two'':55C ''use'':16C,87C ''view'':100C ''www.stat.berkeley.edu'':113C,118C ''www.stat.berkeley.edu/~breiman/randomforests/cc_home.htm#varimp'':112C ''www.stat.berkeley.edu/~breiman/randomforests/cc_home.htm#varimp&lt;/a&gt;&lt;/p&gt;'':117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6000, 2, '2015-06-04 07:32:02.597', 2, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;So my question is: Would I need to create separate algorithms for every natural language to be interpreted?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Yes, I believe so. &lt;/p&gt;

&lt;p&gt;But building a model for detecting the used language is not hard: usually taking n-grams (n-shingles) and then doing classification on them works quite well in practice. By the way, for the start you can use stop words to detect a language, e.g. like it''s described &lt;a href="http://blog.alejandronolla.com/2013/05/15/detecting-text-language-with-python-and-nltk/" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Then once the language is detected, I''d do the NLP stuff for each language separately. &lt;/p&gt;
', NULL, NULL, '''/2013/05/15/detecting-text-language-with-python-and-nltk/'':85C ''/a'':89C ''/blockquote'':22C ''/p'':21C,28C,90C,108C ''algorithm'':13C ''believ'':26C ''blockquot'':1C ''blog.alejandronolla.com'':84C ''blog.alejandronolla.com/2013/05/15/detecting-text-language-with-python-and-nltk/'':83C ''build'':31C ''classif'':53C ''creat'':11C ''d'':99C ''describ'':80C ''detect'':35C,73C,97C ''e.g'':76C ''everi'':15C ''gram'':46C ''hard'':41C ''href'':82C ''interpret'':20C ''languag'':17C,38C,75C,95C,106C ''like'':77C ''model'':33C ''n'':45C,48C ''n-gram'':44C ''n-shingl'':47C ''natur'':16C ''need'':9C ''nlp'':102C ''nofollow'':87C ''p'':2C,23C,29C,91C ''practic'':60C ''question'':5C ''quit'':57C ''rel'':86C ''separ'':12C,107C ''shingl'':49C ''start'':66C ''stop'':70C ''stuff'':103C ''take'':43C ''use'':37C,69C ''usual'':42C ''way'':63C ''well'':58C ''word'':71C ''work'':56C ''would'':7C ''yes'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6004, 1, '2015-06-04 15:30:18.877', 2, 124, 'How to select features from text data?', '&lt;p&gt;I have a data set of questions belonging to ten different categories namely (definitions, factoids, abbreviations, fill in the blanks, verbs, numerals, dates, puzzle, etymology and category relation).&lt;/p&gt;

&lt;p&gt;The categories are briefly described as follows: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Definition â A question that contains a definition of the answer. &lt;/li&gt;
&lt;li&gt;Category Relation â The answer has a semantic relation to the question where the relation is specified in the category.&lt;/li&gt;
&lt;li&gt;FITB â These are generic fill in the blank questions â some of them ask for the completion of a phrase. &lt;/li&gt;
&lt;li&gt;Abbreviation â The answer is an expansion of an abbreviation in the question. &lt;/li&gt;
&lt;li&gt;Puzzle â These require derivation or synthesis for the answer. &lt;/li&gt;
&lt;li&gt;Etymology â The answer is an English word derived from a foreign word. &lt;/li&gt;
&lt;li&gt;Verb â The answer is a verb. &lt;/li&gt;
&lt;li&gt;Number â The answer is a numeral. &lt;/li&gt;
&lt;li&gt;Date â The question asks for a date or a year. &lt;/li&gt;
&lt;li&gt;Factoid â A question is a factoid if its answer can be found on Wikipedia. &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I used the Stanford core NLP package called shiftreducer to find out the Part-Of-Speech (POS) values for each question in a category. I thought of using this POS pattern as a discriminant among the classes but it turned out to be generalized since: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;All the classes follow a similar pattern&lt;/li&gt;
&lt;li&gt;Nouns top the POS count followed by Determinants, Prepositions, Adjectives, Plural nouns and finally verbs.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;What could be the other ways in which I could differentiate among the question categories? Or as my question was in its first place, "What kind of features do I select for efficient categorization?"&lt;/p&gt;
', 'data-mining  text-mining  feature-selection  feature-extraction', NULL, '''/li'':72C,94C,117C,132C,144C,159C,168C,177C,190C,207C,266C,283C ''/ol'':208C,284C ''/p'':49C,58C,256C,320C ''abbrevi'':36C,119C,128C ''adject'':277C ''among'':245C,297C ''answer'':71C,78C,122C,143C,149C,164C,173C,201C ''ask'':110C,183C ''belong'':28C ''blank'':40C,104C ''briefli'':54C ''call'':217C ''categor'':319C ''categori'':32C,47C,52C,74C,93C,234C,300C ''class'':247C,261C ''complet'':113C ''contain'':66C ''core'':214C ''could'':287C,295C ''count'':272C ''data'':7A,9B,24C ''data-min'':8B ''date'':43C,179C,186C ''definit'':34C,61C,68C ''deriv'':138C,154C ''describ'':55C ''determin'':275C ''differ'':31C ''differenti'':296C ''discrimin'':244C ''effici'':318C ''english'':152C ''etymolog'':45C,146C ''expans'':125C ''extract'':19B ''factoid'':35C,192C,198C ''featur'':4A,15B,18B,313C ''feature-extract'':17B ''feature-select'':14B ''fill'':37C,101C ''final'':281C ''find'':220C ''first'':308C ''fitb'':96C ''follow'':57C,262C,273C ''foreign'':157C ''found'':204C ''general'':254C ''generic'':100C ''kind'':311C ''li'':60C,73C,95C,118C,133C,145C,160C,169C,178C,191C,258C,267C ''mine'':10B,13B ''name'':33C ''nlp'':215C ''noun'':268C,279C ''number'':170C ''numer'':42C,176C ''ol'':59C,257C ''p'':20C,50C,209C,285C ''packag'':216C ''part'':224C ''part-of-speech'':223C ''pattern'':241C,265C ''phrase'':116C ''place'':309C ''plural'':278C ''pos'':227C,240C,271C ''preposit'':276C ''puzzl'':44C,134C ''question'':27C,64C,85C,105C,131C,182C,195C,231C,299C,304C ''relat'':48C,75C,82C,88C ''requir'':137C ''select'':3A,16B,316C ''semant'':81C ''set'':25C ''shiftreduc'':218C ''similar'':264C ''sinc'':255C ''specifi'':90C ''speech'':226C ''stanford'':213C ''synthesi'':140C ''ten'':30C ''text'':6A,12B ''text-min'':11B ''thought'':236C ''top'':269C ''turn'':250C ''use'':211C,238C ''valu'':228C ''verb'':41C,161C,167C,282C ''way'':291C ''wikipedia'':206C ''word'':153C,158C ''year'':189C ''â'':62C,76C,97C,106C,120C,135C,147C,162C,171C,180C,193C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6005, 1, '2015-06-04 18:37:59', 1, 175, 'Plotting multiple datasets on a 3D plot', '&lt;p&gt;I want to make a 3D scatter plot of multiple data selections on a single plot (i.e same axes). &lt;/p&gt;

&lt;p&gt;I know that in 2D this is possible by using par() function like so: &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;plot(6:25,rnorm(20),type="b",xlim=c(1,30),ylim=c(-2.5,2.5),col=2)&lt;/p&gt;
  
  &lt;p&gt;par(new=T)&lt;/p&gt;
  
  &lt;p&gt;plot(rnorm(30),type="b",axes=F,col=3)&lt;/p&gt;
  
  &lt;p&gt;par(new=F)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;(source: &lt;a href="http://cran.r-project.org/doc/contrib/Lemon-kickstart/kr_addat.html" rel="nofollow"&gt;http://cran.r-project.org/doc/contrib/Lemon-kickstart/kr_addat.html&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;Can I do something like that on a 3D plot, preferably an interactive 3D plot, like the ones created using plot3D from ''rgl'' package? &lt;/p&gt;
', 'r  visualization', NULL, '''-2.5'':62C ''/blockquote'':88C ''/doc/contrib/lemon-kickstart/kr_addat.html'':95C ''/doc/contrib/lemon-kickstart/kr_addat.html&lt;/a&gt;)&lt;/p&gt;'':100C ''/p'':30C,46C,66C,71C,82C,87C,126C ''1'':58C ''2'':65C ''2.5'':63C ''20'':53C ''25'':51C ''2d'':36C ''3'':81C ''30'':59C,75C ''3d'':6A,16C,110C,115C ''6'':50C ''axe'':29C,78C ''b'':55C,77C ''blockquot'':47C ''c'':57C,61C ''col'':64C,80C ''cran.r-project.org'':94C,99C ''cran.r-project.org/doc/contrib/lemon-kickstart/kr_addat.html'':93C ''cran.r-project.org/doc/contrib/lemon-kickstart/kr_addat.html&lt;/a&gt;)&lt;/p&gt;'':98C ''creat'':120C ''data'':21C ''dataset'':3A ''f'':79C,86C ''function'':43C ''href'':92C ''i.e'':27C ''interact'':114C ''know'':33C ''like'':44C,106C,117C ''make'':14C ''multipl'':2A,20C ''new'':69C,85C ''nofollow'':97C ''one'':119C ''p'':10C,31C,48C,67C,72C,83C,89C,101C ''packag'':125C ''par'':42C,68C,84C ''plot'':1A,7A,18C,26C,49C,73C,111C,116C ''plot3d'':122C ''possibl'':39C ''prefer'':112C ''r'':8B ''rel'':96C ''rgl'':124C ''rnorm'':52C,74C ''scatter'':17C ''select'':22C ''singl'':25C ''someth'':105C ''sourc'':90C ''type'':54C,76C ''use'':41C,121C ''visual'':9B ''want'':12C ''xlim'':56C ''ylim'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6006, 1, '2015-06-04 19:22:50.99', 0, 35, 'R Programming, how to replicate for districts in a city', '&lt;p&gt;I am new to R Programming and just learned basics through codeschool.com&lt;/p&gt;

&lt;p&gt;Our network spans the city, and it is divided into districts.&lt;/p&gt;

&lt;p&gt;I would like to create a map that assigns a value (based on ratio of outdated software and new software) to each district.&lt;/p&gt;

&lt;p&gt;This website has sample of 3-D maps that were created by R Programming, and I see one I am very interested in replicating, but for our city only. &lt;img src="http://i.stack.imgur.com/Q1HTK.jpg" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.rforscience.com/portfolio/koppen-geiger/" rel="nofollow"&gt;But when I see the source code&lt;/a&gt;, I don''t see any mention of latitude or longitude. &lt;/p&gt;

&lt;p&gt;My head is spinning, trying to figure out how I will input this, i.e latitude and longitude of a district in our city, versus an assigned ratio, which I believe will be read from a spreadsheet.&lt;/p&gt;

&lt;p&gt;Thanks for any guidance.&lt;/p&gt;
', 'visualization', NULL, '''/a'':120C ''/p'':25C,38C,63C,104C,131C,169C,175C ''/portfolio/koppen-geiger/'':110C ''/q1htk.jpg'':98C ''3'':70C ''alt'':99C ''assign'':48C,158C ''base'':51C ''basic'':22C ''believ'':162C ''citi'':10A,31C,92C,155C ''code'':119C ''codeschool.com'':24C ''creat'':44C,75C ''d'':71C ''descript'':102C ''district'':7A,37C,62C,152C ''divid'':35C ''enter'':100C ''figur'':139C ''guidanc'':174C ''head'':134C ''href'':107C ''i.e'':146C ''i.stack.imgur.com'':97C ''i.stack.imgur.com/q1htk.jpg'':96C ''imag'':101C ''img'':94C ''input'':144C ''interest'':86C ''latitud'':128C,147C ''learn'':21C ''like'':42C ''longitud'':130C,149C ''map'':46C,72C ''mention'':126C ''network'':28C ''new'':15C,58C ''nofollow'':112C ''one'':82C ''outdat'':55C ''p'':12C,26C,39C,64C,105C,132C,170C ''program'':2A,18C,78C ''r'':1A,17C,77C ''ratio'':53C,159C ''read'':165C ''rel'':111C ''replic'':5A,88C ''sampl'':68C ''see'':81C,116C,124C ''softwar'':56C,59C ''sourc'':118C ''span'':29C ''spin'':136C ''spreadsheet'':168C ''src'':95C ''thank'':171C ''tri'':137C ''valu'':50C ''versus'':156C ''visual'':11B ''websit'':66C ''would'':41C ''www.rforscience.com'':109C ''www.rforscience.com/portfolio/koppen-geiger/'':108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6008, 2, '2015-06-04 21:53:54.96', 1, NULL, NULL, '&lt;p&gt;If I understand you correctly, you''re looking to take the text of these questions and train classifiers to identify which of 10 categories they belong to. And you''d like to come up with a decent feature representation in order to do this. &lt;/p&gt;

&lt;p&gt;I think your finding about part-of-speech is intuitive. It makes sense that in grammatical English (assuming your question data is written in English), most questions would follow similar part-of-speech sequences since grammatically correct questions follow a particular syntactic form (at least when posed interrogatively as in the case of "When was George Washington born?")&lt;/p&gt;

&lt;p&gt;So, you''ve ruled something out - which you should actually view as progress. If you haven''t tried it already, one simple thing you might do is use the actual words within the questions as features. You could use any order n-gram you like, but unigrams stick out as an immediate linguistic feature to try. It seems likely to me that while the POS-tags are similar across classes, making them difficult to distinguish between, the actual &lt;i&gt;words&lt;/i&gt; being used in the questions may vary from class to class, giving your model a better shot of differentiating between classes.&lt;/p&gt;

&lt;p&gt;That is, maybe words like "time", "year", and "when" co-occur more highly with the &lt;code&gt;Date&lt;/code&gt; class while words like "numerical" and "quantity" co-occur more with the &lt;code&gt;Number&lt;/code&gt; class (obviously, this is speculation - I haven''t seen your data). You might also look at bigrams, trigrams, or any other number n-gram for this feature set as well.&lt;/p&gt;

&lt;p&gt;Finally, there may be other features you could generate using NLP methods that may be useful. I''m not familiar with the Shiftreducer software, but &lt;a href="http://en.wikipedia.org/wiki/Named-entity_recognition" rel="nofollow"&gt;Named-Entity Recognition&lt;/a&gt; could be helpful in generating features for the &lt;code&gt;Factoid&lt;/code&gt; class if there are many questions about proper nouns. Other really simple features such as length of the question (counted in number of tokens). A final thought would be to use only the tagged &lt;i&gt;verbs&lt;/i&gt; from your POS-tagger, tab these up and to see whether they differ between classes. This may be a useful feature for identifying questions present in your &lt;code&gt;Verb&lt;/code&gt; class. Hopefully, those are some ideas to get you started.&lt;/p&gt;
', NULL, NULL, '''/a'':320C ''/code'':234C,250C,331C,399C ''/i'':192C,368C ''/p'':46C,108C,214C,282C,410C ''/wiki/named-entity_recognition'':313C ''10'':24C ''across'':180C ''actual'':119C,139C,189C ''alreadi'':129C ''also'':264C ''assum'':66C ''belong'':27C ''better'':208C ''bigram'':267C ''born'':107C ''case'':101C ''categori'':25C ''class'':181C,201C,203C,213C,235C,251C,332C,384C,400C ''classifi'':19C ''co'':226C,243C ''co-occur'':225C,242C ''code'':232C,248C,329C,397C ''come'':34C ''correct'':6C,86C ''could'':147C,291C,321C ''count'':351C ''d'':31C ''data'':69C,261C ''date'':233C ''decent'':38C ''differ'':382C ''differenti'':211C ''difficult'':184C ''distinguish'':186C ''en.wikipedia.org'':312C ''en.wikipedia.org/wiki/named-entity_recognition'':311C ''english'':65C,73C ''entiti'':318C ''factoid'':330C ''familiar'':303C ''featur'':39C,145C,164C,278C,289C,326C,344C,390C ''final'':284C,357C ''find'':51C ''follow'':77C,88C ''form'':92C ''generat'':292C,325C ''georg'':105C ''get'':407C ''give'':204C ''gram'':153C,275C ''grammat'':64C,85C ''haven'':125C,257C ''help'':323C ''high'':229C ''hope'':401C ''href'':310C ''idea'':405C ''identifi'':21C,392C ''immedi'':162C ''interrog'':97C ''intuit'':58C ''least'':94C ''length'':347C ''like'':32C,155C,169C,220C,238C ''linguist'':163C ''look'':9C,265C ''m'':301C ''make'':60C,182C ''mani'':336C ''may'':198C,286C,297C,386C ''mayb'':218C ''method'':295C ''might'':134C,263C ''model'':206C ''n'':152C,274C ''n-gram'':151C,273C ''name'':317C ''named-ent'':316C ''nlp'':294C ''nofollow'':315C ''noun'':340C ''number'':249C,272C,353C ''numer'':239C ''obvious'':252C ''occur'':227C,244C ''one'':130C ''order'':42C,150C ''p'':1C,47C,109C,215C,283C ''part'':54C,80C ''part-of-speech'':53C,79C ''particular'':90C ''pos'':176C,372C ''pos-tag'':175C ''pos-tagg'':371C ''pose'':96C ''present'':394C ''progress'':122C ''proper'':339C ''quantiti'':241C ''question'':16C,68C,75C,87C,143C,197C,337C,350C,393C ''re'':8C ''realli'':342C ''recognit'':319C ''rel'':314C ''represent'':40C ''rule'':113C ''see'':379C ''seem'':168C ''seen'':259C ''sens'':61C ''sequenc'':83C ''set'':279C ''shiftreduc'':306C ''shot'':209C ''similar'':78C,179C ''simpl'':131C,343C ''sinc'':84C ''softwar'':307C ''someth'':114C ''specul'':255C ''speech'':56C,82C ''start'':409C ''stick'':158C ''syntact'':91C ''tab'':374C ''tag'':177C,365C ''tagger'':373C ''take'':11C ''text'':13C ''thing'':132C ''think'':49C ''thought'':358C ''time'':221C ''token'':355C ''train'':18C ''tri'':127C,166C ''trigram'':268C ''understand'':4C ''unigram'':157C ''use'':137C,148C,194C,293C,299C,362C,389C ''vari'':199C ''ve'':112C ''verb'':367C,398C ''view'':120C ''washington'':106C ''well'':281C ''whether'':380C ''within'':141C ''word'':140C,191C,219C,237C ''would'':76C,359C ''written'':71C ''year'':222C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6009, 2, '2015-06-05 02:49:48.877', 1, NULL, NULL, '&lt;p&gt;Your algorithm is very similar to a &lt;a href="http://en.wikipedia.org/wiki/Genetic_algorithm" rel="nofollow"&gt;Genetic Algorithm&lt;/a&gt;, which has almost the same 3 iterative steps.&lt;/p&gt;

&lt;p&gt;You would be missing a Termination condition to end the search for an answer, once your reach an acceptable one.&lt;/p&gt;

&lt;p&gt;You can probably adapt your algorithm to fit under a formal GA search heuristic.&lt;/p&gt;
', NULL, NULL, '''/a'':18C ''/p'':27C,49C,65C ''/wiki/genetic_algorithm'':13C ''3'':24C ''accept'':47C ''adapt'':54C ''algorithm'':3C,17C,56C ''almost'':21C ''answer'':42C ''condit'':35C ''en.wikipedia.org'':12C ''en.wikipedia.org/wiki/genetic_algorithm'':11C ''end'':37C ''fit'':58C ''formal'':61C ''ga'':62C ''genet'':16C ''heurist'':64C ''href'':10C ''iter'':25C ''miss'':32C ''nofollow'':15C ''one'':48C ''p'':1C,28C,50C ''probabl'':53C ''reach'':45C ''rel'':14C ''search'':39C,63C ''similar'':6C ''step'':26C ''termin'':34C ''would'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6011, 1, '2015-06-05 04:21:09.853', 3, 68, 'Comparing accuracy of models in ordinal regression / classification', '&lt;p&gt;I am looking into creating a model to predict whether an item is "Very Good", "Good", "Bad" or "Very Bad".&lt;/p&gt;

&lt;p&gt;After I fit the training data to the models, comparing the accuracy of the models during test stump me: should it matter if a model misclassified a G to VG while the other G to VB? What about a model that has two misclassifications of one level away versus another model with only one misclassification but three levels away (eg VG to VB)?&lt;/p&gt;

&lt;p&gt;Any guideline on what is the common approach? Also, my thinking at the moment is that this should be a regression problem, but I''m happy to be corrected if I should approach this labeling of datasets more as a classification problem.&lt;/p&gt;
', 'machine-learning  classification  regression  scoring', NULL, '''/p'':36C,101C,145C ''accuraci'':2A,49C ''also'':111C ''anoth'':87C ''approach'':110C,135C ''away'':85C,96C ''bad'':32C,35C ''classif'':8A,12B,143C ''common'':109C ''compar'':1A,47C ''correct'':131C ''creat'':20C ''data'':43C ''dataset'':139C ''eg'':97C ''fit'':40C ''g'':65C,71C ''good'':30C,31C ''guidelin'':104C ''happi'':128C ''item'':27C ''label'':137C ''learn'':11B ''level'':84C,95C ''look'':18C ''m'':127C ''machin'':10B ''machine-learn'':9B ''matter'':59C ''misclassif'':81C,92C ''misclassifi'':63C ''model'':4A,22C,46C,52C,62C,77C,88C ''moment'':116C ''one'':83C,91C ''ordin'':6A ''p'':15C,37C,102C ''predict'':24C ''problem'':124C,144C ''regress'':7A,13B,123C ''score'':14B ''stump'':55C ''test'':54C ''think'':113C ''three'':94C ''train'':42C ''two'':80C ''vb'':73C,100C ''versus'':86C ''vg'':67C,98C ''whether'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6013, 1, '2015-06-05 04:34:42.44', 0, 48, 'Automating lists on Facebook', '&lt;p&gt;How could one automate and improve the efficiency of suggesting &lt;strong&gt;lists&lt;/strong&gt; &lt;em&gt;(Close friends, Family and Acquaintances)&lt;/em&gt; on &lt;strong&gt;Facebook&lt;/strong&gt;? Are there some community detection or specific machine learning algorithms that I could use directly for accomplishing the same? &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;1. Currently this option is manual on your Facebook profile&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;2. There are suggestions but they are not close enough.&lt;/strong&gt;&lt;/p&gt;
', 'machine-learning', NULL, '''/em'':28C ''/p'':52C,66C,80C ''/strong'':21C,32C,65C,79C ''1'':55C ''2'':69C ''accomplish'':49C ''acquaint'':27C ''algorithm'':42C ''autom'':1A,12C ''close'':23C,77C ''communiti'':36C ''could'':10C,45C ''current'':56C ''detect'':37C ''direct'':47C ''effici'':16C ''em'':22C ''enough'':78C ''facebook'':4A,31C,63C ''famili'':25C ''friend'':24C ''improv'':14C ''learn'':7B,41C ''list'':2A,20C ''machin'':6B,40C ''machine-learn'':5B ''manual'':60C ''one'':11C ''option'':58C ''p'':8C,53C,67C ''profil'':64C ''specif'':39C ''strong'':19C,30C,54C,68C ''suggest'':18C,72C ''use'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6015, 1, '2015-06-05 05:18:26.46', 5, 125, 'Assumptions/Limitations of Random Forest Models', '&lt;p&gt;What are the general assumptions of a Random Forest Model? I could not find by searching online. For example, in a linear regression model, limitations/assumptions are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;It may not work well when there are non-linear relationship between dependent and independent variables. &lt;/li&gt;
&lt;li&gt;It may not work if the dependent variables considered in the model are linearly related. Therefore one has to remove correlated variable by some other technique. &lt;/li&gt;
&lt;li&gt;It assumes that model errors are uncorrelated and uniform (No hetroscedasticity). &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Are there any assumptions/limitations on similar lines.&lt;/p&gt;
', 'random-forest  ensemble-modeling', NULL, '''/li'':59C,87C,100C ''/ol'':101C ''/p'':39C,110C ''assum'':90C ''assumpt'':17C ''assumptions/limitations'':1A,106C ''consid'':69C ''correl'':81C ''could'':24C ''depend'':55C,67C ''ensembl'':10B ''ensemble-model'':9B ''error'':93C ''exampl'':31C ''find'':26C ''forest'':4A,8B,21C ''general'':16C ''hetroscedast'':99C ''independ'':57C ''li'':41C,60C,88C ''limitations/assumptions'':37C ''line'':109C ''linear'':34C,52C,74C ''may'':43C,62C ''model'':5A,11B,22C,36C,72C,92C ''non'':51C ''non-linear'':50C ''ol'':40C ''one'':77C ''onlin'':29C ''p'':12C,102C ''random'':3A,7B,20C ''random-forest'':6B ''regress'':35C ''relat'':75C ''relationship'':53C ''remov'':80C ''search'':28C ''similar'':108C ''techniqu'':86C ''therefor'':76C ''uncorrel'':95C ''uniform'':97C ''variabl'':58C,68C,82C ''well'':46C ''work'':45C,64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6016, 1, '2015-06-05 05:19:23.083', 0, 427, 'Machine Learning Startup Ideas', '&lt;p&gt;I just started using machine learning and was wondering if anybody have cool ideas for a Startup project, I''ve seen this website &lt;a href="http://treato.com" rel="nofollow"&gt;Treato&lt;/a&gt;  and was amazed by it. 
Thanks.&lt;/p&gt;
', 'machine-learning', NULL, '''/a'':38C ''/p'':45C ''amaz'':41C ''anybodi'':19C ''cool'':21C ''href'':33C ''idea'':4A,22C ''learn'':2A,7B,14C ''machin'':1A,6B,13C ''machine-learn'':5B ''nofollow'':36C ''p'':8C ''project'':26C ''rel'':35C ''seen'':29C ''start'':11C ''startup'':3A,25C ''thank'':44C ''treato'':37C ''treato.com'':34C ''use'':12C ''ve'':28C ''websit'':31C ''wonder'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8307, 2, '2015-10-04 19:43:27.58', 1, NULL, NULL, '&lt;p&gt;This will depend on the type of task you want to perform:
Object recognition is a wide task than can be approached in different ways such as:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;[Multi-part OR][1]&lt;/li&gt;
&lt;li&gt;[Multi-scale OR][2]&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.jmlr.org/papers/volume11/escalera10a/escalera10a.pdf" rel="nofollow"&gt;Multi-class OR&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;[Multi-label OR][4]&lt;/li&gt;
&lt;li&gt;[Multi-modal OR]&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Each approach (and its combinations) uses different representations for feature vectors extracted from the images. Most typical representations try to be invariant to certain conditions on the images depending on it purpose, such as scale, rotation and illumination changes.
Some successful representations are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Bag of Visual Words [5]&lt;/li&gt;
&lt;li&gt;Histogram of visual words [8]&lt;/li&gt;
&lt;li&gt;Haar-like features [2]&lt;/li&gt;
&lt;li&gt;Statistical-based structures (Active shape models, active appearance models) [9][10]&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;With the rise of deep networks, surprising results are being obtained feeding the images just as the raw pixels that represent them [6]. However, you will likely need hundreds of thousands of images to proper train a network that can abstract features to further recognize objects. You can actually take a look to the highly successful VGG Convolutional Network implementation at [Find actual links here][2]&lt;/p&gt;

&lt;p&gt;The previous approaches will typically required that you have each example labeled with target value (cat, dog, etc). In case of deep learning you are being able to learn first representation of images with unsupervised data (not labeled) and the being able to train a classifier on top of the network in a supervised way (with labeled data)&lt;/p&gt;

&lt;p&gt;You may find most of the references below at: [Google Scholar][3]&lt;/p&gt;

&lt;p&gt;[1]: [Xin Chen and Shen(2009)] Xiaohua Hu Xin Chen and Xiajiong Shen.
Spatial weighting for bag-of-visual-words and its application in
content-based image retrieval. Advances in Knowledge Discovery
and Data Mining, 2009&lt;/p&gt;

&lt;p&gt;[2]: [Viola and Jones(2004)] Paul Viola and Michael J. Jones. Robust realtime
face detection. Int. J. Comput. Vision, 57(2):137â154, May 2004.
ISSN 0920-5691.&lt;/p&gt;

&lt;p&gt;[4]: [Jia Deng and Fei-Fei(2014)] Jonathan Krause Michael Bernstein
Alex Berg Jia Deng, Olga Russakovsky and Li Fei-Fei. Scalable
multi-label annotation. ACM Conference on Human Factors in
Computing Systems, 2014.&lt;/p&gt;

&lt;p&gt;[5]: [Xin Chen and Shen(2009)] Xiaohua Hu Xin Chen and Xiajiong Shen.
Spatial weighting for bag-of-visual-words and its application in
content-based image retrieval. Advances in Knowledge Discovery
and Data Mining, 2009.&lt;/p&gt;

&lt;p&gt;[7]: [Krizhevsky et al.(2012)Krizhevsky, Sutskever, and Hinton] Alex
Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet
classification with deep convolutional neural networks. In
P. Bartlett, F.c.n. Pereira, C.j.c. Burges, L. Bottou, and K.q.
Weinberger, editors, Advances in Neural Information Processing
Systems 25, pages 1106â1114. 2012.&lt;/p&gt;

&lt;p&gt;[8]: [Lowe(2004)] David G. Lowe. Distinctive image features from scaleinvariant
keypoints. Int. J. Comput. Vision, 60(2):91â110, November
2004.&lt;/p&gt;

&lt;p&gt;[9]: [van Ginneken(2002)] A.F. Staal J.J. ter Haar Romeny B.M. van Ginneken,
B.Frangi. Active shape model segmentation with optimal
features. Medical Imaging, IEEE Transactions on. Volume 21, 2002.&lt;/p&gt;

&lt;p&gt;[10]: [Matthews and Baker(2004)] Iain Matthews and Simon Baker. Active
appearance models revisited. International Journal of Computer
Vision. Volume 60., 2004.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6079, 2, '2015-06-11 07:42:17.48', 1, NULL, NULL, '&lt;p&gt;From a geometric point of view, if all your data are unitary, $\forall x, ||x||^2 = \langle x,x \rangle = 1$, then the scalar product of two vectors defines an angle $\phi$, $\langle x,y \rangle = \cos \phi$, and you have a distance $\phi = \arccos \langle x,y \rangle$. &lt;/p&gt;

&lt;p&gt;Visually, all your data live on a unit sphere. Using a dot product as a distance will give you a chordal distance, but if you use this cosine distance, it corresponds to the length of the path between the two points on the sphere. That means, if you want an average of the two points, you should take the point in-between on this path (geodesic) rather than the mid-point obtained from the ''arithmetic average/dot product/euclidean geometry'' since this point does not live on the sphere (hence essentially not the same object)!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6022, 1, '2015-06-05 15:23:25.213', 0, 9, '+''s and -''s of transformation/normalization for tree-based decision systems', '&lt;p&gt;I am having a confusion about the possible benefits of data transformation for tree-based decision systems. &lt;/p&gt;

&lt;p&gt;Specifically, I am doing stock market analysis. In this particular problem, assume I use as inputs the 1-week (1w), 2w, 3w and 4w historic returns of stocks. Lets say the output to target is the 1-month (1m) forward return. One possibility I am considering is to for each stock "normalize" the returns so that they have the same standard deviation of 1m returns...essentially adjust for volatility. To get the output of the "transformed" system, i''ll just re-adjust the output by volatility. The inputs would also be adjusted in the same way as the output (they are all stock returns). My sense is that I am in a way INCREASING the amount of training data I have in doing so, since the inputs and outputs will look much more like each other than if I don''t do the transformation. &lt;/p&gt;

&lt;p&gt;Let me see if a numerical example helps, even though it is contrived. Suppose a volatile stock has inputs of -5% -7% -10% -12%. A low-volatility stock may never have inputs like this, but it might have something like -2.5% -3.5% -5% -6% (these are half of the other returns). With the transformation, it is as if the system gets to see the data twice. &lt;/p&gt;

&lt;p&gt;My sense is there may be a trade-off here. If the underlying nature of the data is such that the pattern followed by different stocks is consistent after the transformation, then I likely will get a gain in results with the transformation (i will have more data effectively). If on the other hand it is important to treat more volatile-looking inputs (like the first numbers) separately from less volatile, then I may reduce my variance with the transformation, but increase bias. &lt;/p&gt;

&lt;p&gt;The prior paragraph brings up yet another question: if there are advantages of both approaches, is there a way to combine them somehow? I honestly can''t think of how to, because the output is different in the two cases. Maybe I could feed the output from the transformed system to the non-transformed one? Or, maybe I could feed volatility as an input to a non-transformed system?&lt;/p&gt;

&lt;p&gt;Any help on how to address this issue would be very much appreciated. I''m really just trying to understand the trade-off here, not looking for any advice given my specific analysis (my analysis is actually a lot more complex!). &lt;/p&gt;

&lt;p&gt;Thanks so much.&lt;/p&gt;

&lt;p&gt;Mike&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6023, 2, '2015-06-05 15:23:58.38', 2, NULL, NULL, '&lt;p&gt;I''m not sure if I understood your question! Probably better to plot a scheme at least. But according to what I guess from your question:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Q2-&lt;/strong&gt; You probably need a simple &lt;a href="http://en.wikipedia.org/wiki/Multilayer_perceptron" rel="nofollow"&gt;MLP&lt;/a&gt; (Multilayer Perceptron)! it''s a traditional architecture for Neural Networks where you have $n$ input neurons (here 20-25), one or more hidden layers with several neurons and 3 neurons as output layer. If you use a &lt;a href="http://en.wikipedia.org/wiki/Sigmoid_function" rel="nofollow"&gt;sigmoid&lt;/a&gt; activation function ranged from 0 to 1, the output for each class will be $P(Y=1|X=x)$.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Q1-&lt;/strong&gt; So your question probably is: how many training data you need for learning a model? and to the best of my knowledge the answer is &lt;em&gt;as many as possible&lt;/em&gt;!&lt;/p&gt;

&lt;p&gt;and about the last question, I really could not figure out what you mean. You apparently have a very specific task so I suggest to share more insight for sake of clarification.&lt;/p&gt;

&lt;p&gt;I hope I could help a little!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6024, 2, '2015-06-05 15:32:19.307', 27, NULL, NULL, '&lt;p&gt;This &lt;a href="https://github.com/ChristosChristofidis/awesome-deep-learning"&gt;link&lt;/a&gt; contains an amazing amount of deep learning literature.
Summarizing it here(going in the order a beginner ideally should)-
NOTE: All these resources mainly use python.&lt;/p&gt;

&lt;p&gt;1) First of all, a basic knowledge of machine learning is required. I found Caltech''s Learning from data to be ideal of all the machine learning courses available on the net.&lt;/p&gt;

&lt;p&gt;Andrew Ng''s Coursera course is pretty good too.&lt;/p&gt;

&lt;p&gt;2) For Neural networks, nobody explains it better than &lt;a href="http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/lecture-12-learning-neural-nets-back-propagation/"&gt;Dr.Patrick Winston&lt;/a&gt;.
The assignments should be tried out for better understanding. They are in python.&lt;/p&gt;

&lt;p&gt;3) For a better understanding of Neural Networks, &lt;a href="http://neuralnetworksanddeeplearning.com/"&gt;Michael Nielsen&lt;/a&gt;''s course should be done(as suggested by Alexey). It is pretty basic but it works.&lt;/p&gt;

&lt;p&gt;4) For deep neural networks, and implementing them faster on GPUs, there are multiple frameworks available, such as &lt;a href="http://deeplearning.net/software/theano/"&gt;Theano&lt;/a&gt;, &lt;a href="http://caffe.berkeleyvision.org/"&gt;Caffe&lt;/a&gt;, &lt;a href="http://pybrain.org/"&gt;Pybrain&lt;/a&gt;, &lt;a href="http://torch.ch/"&gt;Torch&lt;/a&gt;,etc.
Out of these Theano provides a better low level functionality that allows its user to create custom NNs. It is a python library, so being able to use numpy,scikit-learn, matplotlib, scipy along with it is a big plus.
The &lt;a href="http://deeplearning.net/tutorial/"&gt;deep learning tutorial&lt;/a&gt; written by Lisa Lab should be tried out for a better understanding of theano.&lt;/p&gt;

&lt;p&gt;5) For Convolutional Neural Networks, follow &lt;a href="https://cs231n.github.io/"&gt;andrej karpathy''s tutorial&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;6) For unsupervised learning, follow &lt;a href="http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial"&gt;here&lt;/a&gt; and &lt;a href="http://ufldl.stanford.edu/tutorial/supervised/LinearRegression/"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;7) For an intersection of deep learning and NLP, follow &lt;a href="http://cs224d.stanford.edu/"&gt;Richard Socher''s class&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;8) For LSTMs, read &lt;a href="http://Hochreiter,%20S.,%20&amp;amp;%20Schmidhuber,%20J.%20(1997).%20Long%20short-term%20memory.%20Neural%20computation,%209(8),%201735-1780"&gt;Hochreiter, S., &amp;amp; Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780&lt;/a&gt; and &lt;a href="http://Graves,%20Alex.%20Supervised%20sequence%20labelling%20with%20recurrent%20neural%20networks.%20Vol.%20385.%20Springer,%202012."&gt;Graves, Alex. Supervised sequence labelling with recurrent neural networks. Vol. 385. Springer, 2012&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Here is LSTM''s &lt;a href="http://deeplearning.net/tutorial/code/lstm.py"&gt;Theano code&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''-1780'':337C,354C ''/a'':9C,99C,128C,171C,176C,181C,186C,238C,268C,282C,290C,310C,355C,385C,399C ''/christoschristofidis/awesome-deep-learning'':7C ''/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/lecture-12-learning-neural-nets-back-propagation/'':96C ''/p'':36C,70C,81C,113C,145C,253C,269C,291C,311C,386C,400C ''/software/theano/'':169C ''/tutorial/'':234C ''/tutorial/code/lstm.py'':396C ''/tutorial/supervised/linearregression/'':288C ''/wiki/index.php/ufldl_tutorial'':280C ''1'':38C ''1735'':353C ''1997'':326C,343C ''2'':83C ''20'':321C,325C ''2012'':384C ''201735'':336C ''202012'':371C ''20385'':369C ''209'':334C ''20alex'':360C ''20computation'':333C ''20j'':324C ''20labelling'':363C ''20long'':327C ''20memory'':331C ''20networks'':367C ''20neural'':332C,366C ''20recurrent'':365C ''20s'':320C ''20schmidhuber'':323C ''20sequence'':362C ''20short'':329C ''20short-term'':328C ''20springer'':370C ''20supervised'':361C ''20vol'':368C ''20with'':364C ''3'':115C ''385'':382C ''4'':147C ''5'':255C ''6'':271C ''7'':293C ''8'':313C,335C,352C ''9'':351C ''abl'':213C ''alex'':373C ''alexey'':137C ''allow'':199C ''along'':222C ''amaz'':12C ''amount'':13C ''amp'':322C,340C ''andrej'':264C ''andrew'':72C ''assign'':101C ''avail'':66C,162C ''basic'':43C,141C ''beginn'':26C ''better'':90C,107C,118C,194C,249C ''big'':227C ''caff'':175C ''caffe.berkeleyvision.org'':174C ''caltech'':52C ''class'':309C ''code'':398C ''comput'':350C ''contain'':10C ''convolut'':257C ''cours'':65C,76C,130C ''coursera'':75C ''creat'':203C ''cs224d.stanford.edu'':305C ''cs231n.github.io'':263C ''custom'':204C ''data'':56C ''deep'':15C,149C,235C,298C ''deeplearning.net'':168C,233C,395C ''deeplearning.net/software/theano/'':167C ''deeplearning.net/tutorial/'':232C ''deeplearning.net/tutorial/code/lstm.py'':394C ''deeplearning.stanford.edu'':279C ''deeplearning.stanford.edu/wiki/index.php/ufldl_tutorial'':278C ''done'':133C ''dr.patrick'':97C ''etc'':187C ''explain'':88C ''faster'':155C ''first'':39C ''follow'':260C,275C,302C ''found'':51C ''framework'':161C ''function'':197C ''github.com'':6C ''github.com/christoschristofidis/awesome-deep-learning'':5C ''go'':21C ''good'':79C ''gpus'':157C ''grave'':359C,372C ''hochreit'':319C,338C ''href'':4C,93C,124C,166C,173C,178C,183C,231C,262C,277C,285C,304C,318C,358C,393C ''ideal'':27C,59C ''implement'':153C ''intersect'':296C ''j'':342C ''karpathi'':265C ''knowledg'':44C ''lab'':242C ''label'':376C ''learn'':16C,47C,54C,64C,219C,236C,274C,299C ''level'':196C ''librari'':210C ''link'':8C ''lisa'':241C ''literatur'':17C ''long'':344C ''low'':195C ''lstm'':390C ''lstms'':315C ''machin'':46C,63C ''main'':33C ''matplotlib'':220C ''memori'':348C ''michael'':126C ''multipl'':160C ''net'':69C ''network'':86C,122C,151C,259C,380C ''neural'':85C,121C,150C,258C,349C,379C ''neuralnetworksanddeeplearning.com'':125C ''ng'':73C ''nielsen'':127C ''nlp'':301C ''nns'':205C ''nobodi'':87C ''note'':29C ''numpi'':216C ''ocw.mit.edu'':95C ''ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/lecture-12-learning-neural-nets-back-propagation/'':94C ''order'':24C ''p'':1C,37C,71C,82C,114C,146C,254C,270C,292C,312C,387C ''plus'':228C ''pretti'':78C,140C ''provid'':192C ''pybrain'':180C ''pybrain.org'':179C ''python'':35C,112C,209C ''read'':316C ''recurr'':378C ''requir'':49C ''resourc'':32C ''richard'':306C ''schmidhub'':341C ''scikit'':218C ''scikit-learn'':217C ''scipi'':221C ''sequenc'':375C ''short'':346C ''short-term'':345C ''socher'':307C ''springer'':383C ''suggest'':135C ''summar'':18C ''supervis'':374C ''term'':330C,347C ''theano'':170C,191C,252C,397C ''torch'':185C ''torch.ch'':184C ''tri'':104C,245C ''tutori'':237C,267C ''ufldl.stanford.edu'':287C ''ufldl.stanford.edu/tutorial/supervised/linearregression/'':286C ''understand'':108C,119C,250C ''unsupervis'':273C ''use'':34C,215C ''user'':201C ''vol'':381C ''winston'':98C ''work'':144C ''written'':239C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6025, 2, '2015-06-05 19:59:43.367', 2, NULL, NULL, '&lt;p&gt;First of all, I would get acquainted with some core concepts of Machine Learning before building websites:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Common algorithms, differences between them (Naive Bayes, KMeans, Regression, Support Vector Machines, Decision Trees, you name it...)&lt;/li&gt;
&lt;li&gt;Supervised vs. unsupervised&lt;/li&gt;
&lt;li&gt;Features and feature selection &lt;/li&gt;
&lt;li&gt;Classification evaluation (precision, recall, accuracy)&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Machine learning is a huge field which is applied in many different areas. I don''t know how proficient you are at this stage, but I would certainly play around with these things first in order to get a solid understanding of what you''re dealing with. After that, the options are virtually limitless in a world of Big Data (forgive me for using the annoying &lt;em&gt;buzz-word&lt;/em&gt;). What is truly valuable is someone knows &lt;em&gt;how&lt;/em&gt; to do machine-learning rather than &lt;em&gt;what&lt;/em&gt; (because there''s enough to go about).&lt;/p&gt;
', NULL, NULL, '''/em'':130C,140C,150C ''/li'':39C,44C,50C,57C ''/ol'':58C ''/p'':19C,158C ''accuraci'':56C ''acquaint'':8C ''algorithm'':23C ''annoy'':125C ''appli'':68C ''area'':72C ''around'':89C ''bay'':28C ''big'':118C ''build'':17C ''buzz'':128C ''buzz-word'':127C ''certain'':87C ''classif'':52C ''common'':22C ''concept'':12C ''core'':11C ''data'':119C ''deal'':105C ''decis'':34C ''differ'':24C,71C ''em'':126C,138C,148C ''enough'':154C ''evalu'':53C ''featur'':46C,48C ''field'':65C ''first'':2C,93C ''forgiv'':120C ''get'':7C,97C ''go'':156C ''huge'':64C ''kmean'':29C ''know'':76C,137C ''learn'':15C,61C,145C ''li'':21C,40C,45C,51C ''limitless'':113C ''machin'':14C,33C,60C,144C ''machine-learn'':143C ''mani'':70C ''naiv'':27C ''name'':37C ''ol'':20C ''option'':110C ''order'':95C ''p'':1C,59C ''play'':88C ''precis'':54C ''profici'':78C ''rather'':146C ''re'':104C ''recal'':55C ''regress'':30C ''select'':49C ''solid'':99C ''someon'':136C ''stage'':83C ''supervis'':41C ''support'':31C ''thing'':92C ''tree'':35C ''truli'':133C ''understand'':100C ''unsupervis'':43C ''use'':123C ''valuabl'':134C ''vector'':32C ''virtual'':112C ''vs'':42C ''websit'':18C ''word'':129C ''world'':116C ''would'':6C,86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6027, 1, '2015-06-05 21:52:05.92', 7, 101, 'project data from S^n to S^2', '&lt;p&gt;I have few points in $S^n$, i.e., the $n$-dimensional unit sphere embedded in $\mathbb{R}^{n+1}$, and I would like to project them down to $S^2$, i.e., the 2-dimensional unit sphere (embedded in $\mathbb{R}^3$) to visualize it with the constraint that neighboring points should be close by. I spent some time playing with t-sne but of course, the points no longer lie on $S^2$. I normalized the projections but that introduces weird distortions, for instance, if the variance of one dataset is very small in $S^n$ as compared to other, I expect the same to hold in their $S^2$ projections; that is not the case upon normalizing t-sne. Any ideas? I would really like something that makes the previous statement hold.&lt;/p&gt;
', 'machine-learning  dimensionality-reduction  visualization', NULL, '''+1'':35C ''/p'':152C ''2'':8A,46C,49C,90C,127C ''3'':57C ''case'':133C ''close'':69C ''compar'':115C ''constraint'':63C ''cours'':82C ''data'':2A ''dataset'':107C ''dimension'':13B,27C,50C ''dimensionality-reduct'':12B ''distort'':99C ''embed'':30C,53C ''expect'':119C ''hold'':123C,151C ''i.e'':24C,47C ''idea'':140C ''instanc'':101C ''introduc'':97C ''learn'':11B ''lie'':87C ''like'':39C,144C ''longer'':86C ''machin'':10B ''machine-learn'':9B ''make'':147C ''mathbb'':32C,55C ''n'':5A,23C,26C,34C,113C ''neighbor'':65C ''normal'':92C,135C ''one'':106C ''p'':16C ''play'':75C ''point'':20C,66C,84C ''previous'':149C ''project'':1A,41C,94C,128C ''r'':33C,56C ''realli'':143C ''reduct'':14B ''small'':110C ''sne'':79C,138C ''someth'':145C ''spent'':72C ''sphere'':29C,52C ''statement'':150C ''t-sne'':77C,136C ''time'':74C ''unit'':28C,51C ''upon'':134C ''varianc'':104C ''visual'':15B,59C ''weird'':98C ''would'':38C,142C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6029, 2, '2015-06-06 06:51:08.527', 1, NULL, NULL, '&lt;p&gt;You may benefit more from the schemaless design of Mongodb. This means its very easy to modify data structures on the fly.&lt;/p&gt;

&lt;p&gt;There is no such thing as a join in Mongodb. So how one thinks about data and how to use it needs to be modified to account for document based and schemaless db environments.&lt;/p&gt;

&lt;p&gt;Maybe speed becomes less important as perspective and priorities change.&lt;/p&gt;

&lt;p&gt;I hope that helps.&lt;/p&gt;

&lt;p&gt;-Todd&lt;/p&gt;
', NULL, NULL, '''/p'':24C,60C,72C,78C,81C ''account'':52C ''base'':55C ''becom'':64C ''benefit'':4C ''chang'':71C ''data'':19C,41C ''db'':58C ''design'':9C ''document'':54C ''easi'':16C ''environ'':59C ''fli'':23C ''help'':77C ''hope'':75C ''import'':66C ''join'':33C ''less'':65C ''may'':3C ''mayb'':62C ''mean'':13C ''modifi'':18C,50C ''mongodb'':11C,35C ''need'':47C ''one'':38C ''p'':1C,25C,61C,73C,79C ''perspect'':68C ''prioriti'':70C ''schemaless'':8C,57C ''speed'':63C ''structur'':20C ''thing'':30C ''think'':39C ''todd'':80C ''use'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6030, 1, '2015-06-06 07:00:13.363', 1, 89, 'Using Vowpal Wabbit for NER', '&lt;p&gt;The Vowpal Wabbit apparently supports sequence tagging functionality via SEARN. The problem is that I cannot find anywhere detailed parameter list with explanations and with some examples. The best I could find is &lt;a href="http://zinkov.com/posts/2013-08-13-vowpal-tutorial/" rel="nofollow"&gt;Zinkov''s blog entry&lt;/a&gt; with a very short example. The main wiki page barely mentions SEARN.&lt;/p&gt;

&lt;p&gt;In the checked out source code I found demo folder with some NER sample data. Unfortunately, the script running all the tests does not show how to run on this data. At least it was informative enough to see what is the expected format: almost the same as standard VW data format, except that entries are separated by blank lines (this is important).&lt;/p&gt;

&lt;p&gt;My current understanding is to run the following command:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;cat train.txt | vw -c --passes 10 --searn 25 --searn_task sequence \
--searn_passes_per_policy 2 -b 30 -f twpos.vw
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where&lt;/p&gt;

&lt;p&gt;&lt;code&gt;--searn 25&lt;/code&gt; - the total number of NER labels (?)&lt;/p&gt;

&lt;p&gt;&lt;code&gt;--searn_task sequence&lt;/code&gt; - sequence tagging task (?)&lt;/p&gt;

&lt;p&gt;&lt;code&gt;--searn_passes_per_policy 2&lt;/code&gt; - not clear what it does&lt;/p&gt;

&lt;p&gt;Other parameters are standard to VW and need no additional explanation. Perhaps there are more parameters specific to SEARN? What is their importance and impact? How to tune them? Any rules of thumb?&lt;/p&gt;

&lt;p&gt;Any pointers to examples will be appreciated.&lt;/p&gt;
', 'machine-learning  nlp', NULL, '''/a'':55C ''/code'':167C,176C,189C,201C ''/p'':68C,133C,144C,171C,183C,193C,207C,242C,251C ''/posts/2013-08-13-vowpal-tutorial/'':48C ''/pre'':168C ''10'':152C ''2'':162C,200C ''25'':154C,175C ''30'':164C ''addit'':218C ''almost'':114C ''anywher'':28C ''appar'':14C ''appreci'':250C ''b'':163C ''bare'':65C ''best'':39C ''blank'':128C ''blog'':53C ''c'':150C ''cannot'':26C ''cat'':147C ''check'':72C ''clear'':203C ''code'':75C,146C,173C,185C,195C ''command'':143C ''could'':41C ''current'':136C ''data'':84C,100C,120C ''demo'':78C ''detail'':29C ''enough'':106C ''entri'':54C,124C ''exampl'':37C,60C,247C ''except'':122C ''expect'':112C ''explan'':33C,219C ''f'':165C ''find'':27C,42C ''folder'':79C ''follow'':142C ''format'':113C,121C ''found'':77C ''function'':18C ''href'':45C ''impact'':233C ''import'':132C,231C ''inform'':105C ''label'':182C ''learn'':8B ''least'':102C ''line'':129C ''list'':31C ''machin'':7B ''machine-learn'':6B ''main'':62C ''mention'':66C ''need'':216C ''ner'':5A,82C,181C ''nlp'':9B ''nofollow'':50C ''number'':179C ''p'':10C,69C,134C,169C,172C,184C,194C,208C,243C ''page'':64C ''paramet'':30C,210C,224C ''pass'':151C,159C,197C ''per'':160C,198C ''perhap'':220C ''pointer'':245C ''polici'':161C,199C ''pre'':145C ''problem'':22C ''rel'':49C ''rule'':239C ''run'':88C,97C,140C ''sampl'':83C ''script'':87C ''searn'':20C,67C,153C,155C,158C,174C,186C,196C,227C ''see'':108C ''separ'':126C ''sequenc'':16C,157C,188C,190C ''short'':59C ''show'':94C ''sourc'':74C ''specif'':225C ''standard'':118C,212C ''support'':15C ''tag'':17C,191C ''task'':156C,187C,192C ''test'':91C ''thumb'':241C ''total'':178C ''train.txt'':148C ''tune'':236C ''twpos.vw'':166C ''understand'':137C ''unfortun'':85C ''use'':1A ''via'':19C ''vowpal'':2A,12C ''vw'':119C,149C,214C ''wabbit'':3A,13C ''wiki'':63C ''zinkov'':51C ''zinkov.com'':47C ''zinkov.com/posts/2013-08-13-vowpal-tutorial/'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6095, 2, '2015-06-12 12:44:06.463', 0, NULL, NULL, '&lt;p&gt;Training set must represent the dataset your application/algorithm is actually going to face. I suggest you to take a representative sample instead of dividing the training and test set with exactly half fraud an half non-fraud. But please make sure that the training set contains both positive and negative example for fraud for your classifier to perform better.&lt;/p&gt;
', NULL, NULL, '''/p'':62C ''actual'':11C ''application/algorithm'':9C ''better'':61C ''classifi'':58C ''contain'':48C ''dataset'':7C ''divid'':25C ''exact'':32C ''exampl'':53C ''face'':14C ''fraud'':34C,39C,55C ''go'':12C ''half'':33C,36C ''instead'':23C ''make'':42C ''must'':4C ''negat'':52C ''non'':38C ''non-fraud'':37C ''p'':1C ''perform'':60C ''pleas'':41C ''posit'':50C ''repres'':5C,21C ''sampl'':22C ''set'':3C,30C,47C ''suggest'':16C ''sure'':43C ''take'':19C ''test'':29C ''train'':2C,27C,46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6452, 2, '2015-07-14 08:18:37.257', 1, NULL, NULL, '&lt;p&gt;You may try an &lt;a href="https://en.wikipedia.org/wiki/Apriori_algorithm" rel="nofollow"&gt;Apriori algorithm&lt;/a&gt; or more advanced &lt;a href="https://en.wikipedia.org/wiki/Association_rule_learning#FP-growth_algorithm" rel="nofollow"&gt;FPGrowth algorithm&lt;/a&gt;. It will give you a set of association rules, which, I think, you required:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;e.g. if feature 10 is on, feature 14, 15, and 16 are usually on&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/a'':15C,28C ''/blockquote'':61C ''/p'':43C,60C ''/wiki/apriori_algorithm'':10C ''/wiki/association_rule_learning#fp-growth_algorithm'':23C ''10'':49C ''14'':53C ''15'':54C ''16'':56C ''advanc'':18C ''algorithm'':14C,27C ''apriori'':13C ''associ'':36C ''blockquot'':44C ''e.g'':46C ''en.wikipedia.org'':9C,22C ''en.wikipedia.org/wiki/apriori_algorithm'':8C ''en.wikipedia.org/wiki/association_rule_learning#fp-growth_algorithm'':21C ''featur'':48C,52C ''fpgrowth'':26C ''give'':31C ''href'':7C,20C ''may'':3C ''nofollow'':12C,25C ''p'':1C,45C ''rel'':11C,24C ''requir'':42C ''rule'':37C ''set'':34C ''think'':40C ''tri'':4C ''usual'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6031, 1, '2015-06-06 11:59:55.387', 3, 29, 'Geometric weighting of temperature', '&lt;p&gt;I have a number of weather stations, and I know their positions. I would like to interpolate measurements from them for various other positions as a weighted average of these stations, but of course I need weights for this. I am thinking the most logical choice here from a physics point of view will be weighting by the inverse of the distance to the station, but I haven''t quite convinced myself that that is right, and am not sure if I should maybe be using the distance squared, or maybe something in between.&lt;/p&gt;

&lt;p&gt;Any comments? Are there any other reasonable alternatives I should be considering?&lt;/p&gt;
', 'interpolation', NULL, '''/p'':101C,115C ''altern'':110C ''averag'':34C ''choic'':52C ''comment'':104C ''consid'':114C ''convinc'':77C ''cours'':40C ''distanc'':68C,94C ''geometr'':1A ''haven'':74C ''interpol'':5B,23C ''invers'':65C ''know'':16C ''like'':21C ''logic'':51C ''mayb'':90C,97C ''measur'':24C ''need'':42C ''number'':10C ''p'':6C,102C ''physic'':56C ''point'':57C ''posit'':18C,30C ''quit'':76C ''reason'':109C ''right'':82C ''someth'':98C ''squar'':95C ''station'':13C,37C,71C ''sure'':86C ''temperatur'':4A ''think'':48C ''use'':92C ''various'':28C ''view'':59C ''weather'':12C ''weight'':2A,33C,43C,62C ''would'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6032, 1, '2015-06-06 15:50:34.333', 0, 40, 'Iterative normalization of data', '&lt;p&gt;I''m working on a data processing framework (in Java) that implements various machine learning and meta-heuristic optimization algorithms to data streams iteratively in real-time.&lt;/p&gt;

&lt;p&gt;I''m working with massive data sets that transition from database to live feeds. I want the same algorithms to be running on both data sets without interruption when the switch from database to live data happens.&lt;/p&gt;

&lt;p&gt;Are there normalization methods that work well for noisy timeseries data and that can be implemented iteratively (i.e. without using the average and stdev of the entire data set)? Perhaps using rolling/tumbling windows?&lt;/p&gt;
', 'bigdata  time-series  data-cleaning', NULL, '''/p'':42C,81C,116C ''algorithm'':33C,62C ''averag'':104C ''bigdata'':5B ''clean'':11B ''data'':4A,10B,18C,35C,49C,68C,79C,93C,110C ''data-clean'':9B ''databas'':54C,76C ''entir'':109C ''feed'':57C ''framework'':20C ''happen'':80C ''heurist'':31C ''i.e'':100C ''implement'':24C,98C ''interrupt'':71C ''iter'':1A,37C,99C ''java'':22C ''learn'':27C ''live'':56C,78C ''m'':14C,45C ''machin'':26C ''massiv'':48C ''meta'':30C ''meta-heurist'':29C ''method'':86C ''noisi'':91C ''normal'':2A,85C ''optim'':32C ''p'':12C,43C,82C ''perhap'':112C ''process'':19C ''real'':40C ''real-tim'':39C ''rolling/tumbling'':114C ''run'':65C ''seri'':8B ''set'':50C,69C,111C ''stdev'':106C ''stream'':36C ''switch'':74C ''time'':7B,41C ''time-seri'':6B ''timeseri'':92C ''transit'':52C ''use'':102C,113C ''various'':25C ''want'':59C ''well'':89C ''window'':115C ''without'':70C,101C ''work'':15C,46C,88C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6033, 2, '2015-06-06 21:49:54.407', 3, NULL, NULL, '&lt;p&gt;The definition of &lt;em&gt;knowledge&lt;/em&gt; varies based on the context, but can be broadly defined as &lt;strong&gt;actionable&lt;/strong&gt; &lt;em&gt;information&lt;/em&gt; based on further analysis (statistical, heuristic, or otherwise) of those patterns, associations, or relationships identified in the &lt;em&gt;raw data&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Put another way, &lt;em&gt;knowledge&lt;/em&gt; is defined by what &lt;em&gt;information&lt;/em&gt; will most prove &lt;strong&gt;actionable&lt;/strong&gt; based on the needs of the client or user (sometimes called the semantics). &lt;/p&gt;
', NULL, NULL, '''/em'':7C,24C,45C,53C,60C ''/p'':46C,80C ''/strong'':21C,66C ''action'':20C,65C ''analysi'':28C ''anoth'':49C ''associ'':36C ''base'':9C,25C,67C ''broad'':16C ''call'':77C ''client'':73C ''context'':12C ''data'':44C ''defin'':17C,55C ''definit'':3C ''em'':5C,22C,42C,51C,58C ''heurist'':30C ''identifi'':39C ''inform'':23C,59C ''knowledg'':6C,52C ''need'':70C ''otherwis'':32C ''p'':1C,47C ''pattern'':35C ''prove'':63C ''put'':48C ''raw'':43C ''relationship'':38C ''semant'':79C ''sometim'':76C ''statist'':29C ''strong'':19C,64C ''user'':75C ''vari'':8C ''way'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6034, 2, '2015-06-07 05:38:32.923', 3, NULL, NULL, '&lt;p&gt;Knowledge is a &lt;em&gt;general&lt;/em&gt; term and I don''t think that there exist definitions of knowledge for specific disciplines, domains and areas of study. Therefore, in my opinion, knowledge, for a particular subject domain, can be defined just as a &lt;em&gt;domain-specific&lt;/em&gt; (or &lt;em&gt;context-specific&lt;/em&gt;, as mentioned by @JGreenwell +1) &lt;strong&gt;perspective&lt;/strong&gt; (projection) of a general concept of knowledge.&lt;/p&gt;
', NULL, NULL, '''+1'':59C ''/em'':7C,48C,54C ''/p'':70C ''/strong'':62C ''area'':25C ''concept'':67C ''context'':52C ''context-specif'':51C ''defin'':40C ''definit'':17C ''disciplin'':22C ''domain'':23C,37C,46C ''domain-specif'':45C ''em'':5C,44C,50C ''exist'':16C ''general'':6C,66C ''jgreenwel'':58C ''knowledg'':2C,19C,32C,69C ''mention'':56C ''opinion'':31C ''p'':1C ''particular'':35C ''perspect'':61C ''project'':63C ''specif'':21C,47C,53C ''strong'':60C ''studi'':27C ''subject'':36C ''term'':8C ''therefor'':28C ''think'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6037, 1, '2015-06-07 23:26:19.183', 0, 288, 'rpart and rpart2', '&lt;p&gt;I''m not sure if this is more appropriate for SO or DS in Stack Exchange since technically it''s not about coding: in &lt;code&gt;caret&lt;/code&gt; package for training in R, it''s possible to train the model using &lt;code&gt;rpart&lt;/code&gt; or &lt;code&gt;rpart2&lt;/code&gt; as the method.&lt;/p&gt;

&lt;p&gt;I understand that &lt;code&gt;rpart&lt;/code&gt; is an implementation of CART. What is &lt;code&gt;rpart2&lt;/code&gt; and how is it different from &lt;code&gt;rpart&lt;/code&gt;?&lt;/p&gt;

&lt;p&gt;My eventual aim is actually to compare the difference between the tree generated by &lt;code&gt;rpart&lt;/code&gt; and &lt;code&gt;rpart2&lt;/code&gt;, because my result seems to imply &lt;code&gt;rpart2&lt;/code&gt; has better accuracy for my dataset, but I have no clue how to view the &lt;code&gt;rpart2&lt;/code&gt; tree.&lt;/p&gt;
', 'machine-learning  r', NULL, '''/code'':35C,51C,55C,66C,76C,85C,104C,108C,117C,135C ''/p'':59C,86C,137C ''accuraci'':120C ''actual'':92C ''aim'':90C ''appropri'':17C ''better'':119C ''caret'':34C ''cart'':71C ''clue'':128C ''code'':31C,33C,49C,53C,64C,74C,83C,102C,106C,115C,133C ''compar'':94C ''dataset'':123C ''differ'':81C,96C ''ds'':21C ''eventu'':89C ''exchang'':24C ''generat'':100C ''implement'':69C ''impli'':114C ''learn'':6B ''m'':10C ''machin'':5B ''machine-learn'':4B ''method'':58C ''model'':47C ''p'':8C,60C,87C ''packag'':36C ''possibl'':43C ''r'':7B,40C ''result'':111C ''rpart'':1A,50C,65C,84C,103C ''rpart2'':3A,54C,75C,107C,116C,134C ''seem'':112C ''sinc'':25C ''stack'':23C ''sure'':12C ''technic'':26C ''train'':38C,45C ''tree'':99C,136C ''understand'':62C ''use'':48C ''view'':131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6040, 1, '2015-06-08 08:16:38.667', 2, 74, 'What kind of analysis can I do in this dataset?', '&lt;p&gt;I have a dataset of clients (their city, name, age, gender, number of children) and another dataset about the products that they have bought. i have been asked to do:&lt;/p&gt;

&lt;p&gt;extract knowledge about client profiles.&lt;/p&gt;

&lt;p&gt;I don''t know what knowledge should I extract.&lt;/p&gt;

&lt;p&gt;we are studying clustering and classification so they should connect to the question. what i thought about is to make clusters of clients. but i don''t know what criterisas should I depend on.&lt;/p&gt;

&lt;p&gt;should I just use a clustering algorithm like k means and let it give me the clusters ?&lt;/p&gt;

&lt;p&gt;any suggestion would be appreciated&lt;/p&gt;

&lt;h3&gt;Update&lt;/h3&gt;

&lt;p&gt;These are the dataset that I have:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Card: CardID, City, Region, PostalCode, CardStartDate, Gender, DateOfBirth, MaritalStatus, HasChildren, NumChildren, YoungestChild&lt;/li&gt;
&lt;li&gt;Item: ItemCode, ItemDescription, CategoryCode, SubCategoryCode, BrandCode, UpmarketFlag&lt;/li&gt;
&lt;li&gt;Transaction: Store, Date, Time, TransactionID, CardID, PaymentMethod&lt;/li&gt;
&lt;li&gt;Category: CategoryCode, CategDescription&lt;/li&gt;
&lt;li&gt;Transaction_Item: Store, Date, Time, TransactionID, ItemNumber, ItemCode, Amount&lt;/li&gt;
&lt;li&gt;SubCategory: SubCategoryCode, SubCategDescrip&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The teacher said that we &lt;strong&gt;should&lt;/strong&gt; categories the clients and then indicate which products best suit for each customer.&lt;/p&gt;
', 'data-mining  classification  clustering  clusters', NULL, '''/h3'':131C ''/li'':155C,164C,173C,178C,189C,194C ''/ol'':195C ''/p'':48C,55C,66C,102C,121C,128C,140C,218C ''/strong'':204C ''age'':27C ''algorithm'':110C ''amount'':188C ''analysi'':4A ''anoth'':33C ''appreci'':127C ''ask'':45C ''best'':213C ''bought'':41C ''brandcod'':162C ''card'':143C ''cardid'':144C,171C ''cardstartd'':148C ''categdescript'':177C ''categori'':175C,205C ''categorycod'':160C,176C ''children'':31C ''citi'':25C,145C ''classif'':14B,73C ''client'':23C,53C,90C,207C ''cluster'':15B,16B,71C,88C,109C,120C ''connect'':77C ''criterisa'':97C ''custom'':217C ''data'':12B ''data-min'':11B ''dataset'':10A,21C,34C,136C ''date'':168C,183C ''dateofbirth'':150C ''depend'':100C ''extract'':50C,65C ''gender'':28C,149C ''give'':117C ''h3'':129C ''haschildren'':152C ''indic'':210C ''item'':157C,181C ''itemcod'':158C,187C ''itemdescript'':159C ''itemnumb'':186C ''k'':112C ''kind'':2A ''know'':60C,95C ''knowledg'':51C,62C ''let'':115C ''li'':142C,156C,165C,174C,179C,190C ''like'':111C ''make'':87C ''maritalstatus'':151C ''mean'':113C ''mine'':13B ''name'':26C ''number'':29C ''numchildren'':153C ''ol'':141C ''p'':17C,49C,56C,67C,103C,122C,132C,196C ''paymentmethod'':172C ''postalcod'':147C ''product'':37C,212C ''profil'':54C ''question'':80C ''region'':146C ''said'':199C ''store'':167C,182C ''strong'':202C ''studi'':70C ''subcategdescrip'':193C ''subcategori'':191C ''subcategorycod'':161C,192C ''suggest'':124C ''suit'':214C ''teacher'':198C ''thought'':83C ''time'':169C,184C ''transact'':166C,180C ''transactionid'':170C,185C ''updat'':130C ''upmarketflag'':163C ''use'':107C ''would'':125C ''youngestchild'':154C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8868, 2, '2015-11-17 03:01:21.017', 5, NULL, NULL, '&lt;p&gt;Welcome to the real world of data science. Here, the data sets are not as clean as you thought while doing those courses/tutorials online.  Those are super polished and refined.  But, the real world data is not so.&lt;/p&gt;

&lt;p&gt;The step where one does the cleaning and scrubbing is called the data pre-processing step.&lt;/p&gt;

&lt;p&gt;So, some nice data cleaning techniques, in addition to @jknappen''s excellent answer are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;strong&gt;Elimination of zero variance columns/predictors:&lt;/strong&gt; These columns are not important, and they cause the model and the fit to crash and leak errors. So, eliminating them would make complete sense.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Correlated Predictors:&lt;/strong&gt; Reducing the level of correlation between the predictors would be a very nice step in the pre-processing process.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Scaling:&lt;/strong&gt; You must be knowing why scaling is important during pre-process. &lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Predictor Transformations&lt;/strong&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;A &lt;a href="https://www.kaggle.com/c/MerckActivity/forums/t/2900/what-do-you-use-to-preprocess-the-data/26554" rel="nofollow"&gt;nice reference from Kaggle forums&lt;/a&gt; where the pre-processing and cleaning of data sets is discussed.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6042, 1, '2015-06-08 10:52:39.433', 1, 147, 'Poor performance shown on Rare event modeling', '&lt;p&gt;I am working on a Rare event classification problem. I Have 95% of the data as a majority class and 5% of the data as the minority class. I use classification trees algorithm. I am measuring the goodness of the model using confusion matrix.&lt;/p&gt;

&lt;p&gt;As the i have the minority class just 5% of the total data, even though my prediction performance of minority class is close to 70%, the total number of errors are high.&lt;/p&gt;

&lt;p&gt;For example, here is my confusion matrix.
             0           1
     0     213812      7008
     1     29083       16877&lt;/p&gt;

&lt;p&gt;Though the Minority class(class 1) has predicted 16877 times correctly(70% and the misclassifcation is just 30%, but the absolute value of the misclassifcation is very high(29083) comparing to the correctly predicted minotriy class (16877). Which makes the solution less usable for the business. &lt;/p&gt;

&lt;p&gt;Is there any idea on handling these kind of  issues in such rare event modelling.&lt;/p&gt;

&lt;p&gt;Kind note: I have  balanced the target variable using the SMOTE algorithm before applying Classification tree.&lt;/p&gt;
', 'classification  sampling', NULL, '''/p'':55C,89C,106C,154C,171C,189C ''0'':98C,100C ''1'':99C,103C,113C ''16877'':105C,116C,144C ''213812'':101C ''29083'':104C,136C ''30'':125C ''5'':31C,65C ''70'':81C,119C ''7008'':102C ''95'':22C ''absolut'':128C ''algorithm'':43C,184C ''appli'':186C ''balanc'':177C ''busi'':153C ''class'':29C,38C,63C,77C,111C,112C,143C ''classif'':8B,18C,41C,187C ''close'':79C ''compar'':137C ''confus'':53C,96C ''correct'':118C,140C ''data'':25C,34C,69C ''error'':86C ''even'':70C ''event'':6A,17C,169C ''exampl'':92C ''good'':48C ''handl'':161C ''high'':88C,135C ''idea'':159C ''issu'':165C ''kind'':163C,173C ''less'':149C ''major'':28C ''make'':146C ''matrix'':54C,97C ''measur'':46C ''minor'':37C,62C,76C,110C ''minotriy'':142C ''misclassifc'':122C,132C ''model'':7A,51C,170C ''note'':174C ''number'':84C ''p'':10C,56C,90C,107C,155C,172C ''perform'':2A,74C ''poor'':1A ''predict'':73C,115C,141C ''problem'':19C ''rare'':5A,16C,168C ''sampl'':9B ''shown'':3A ''smote'':183C ''solut'':148C ''target'':179C ''though'':71C,108C ''time'':117C ''total'':68C,83C ''tree'':42C,188C ''usabl'':150C ''use'':40C,52C,181C ''valu'':129C ''variabl'':180C ''work'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6073, 2, '2015-06-10 19:06:56.943', 1, NULL, NULL, '&lt;p&gt;Both &lt;code&gt;rpart&lt;/code&gt; and &lt;code&gt;rpart2&lt;/code&gt; implement a CART and wrap the &lt;code&gt;rpart&lt;/code&gt; function from the &lt;strong&gt;rpart&lt;/strong&gt; library.  The difference is the constraints on the model each enforces.  &lt;code&gt;rpart&lt;/code&gt; uses the complexity parameter, &lt;code&gt;cp&lt;/code&gt;, while &lt;code&gt;rpart2&lt;/code&gt; uses the max tree depth, &lt;code&gt;maxdepth&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;See: &lt;code&gt;train_model_list&lt;/code&gt; section of &lt;a href="http://cran.r-project.org/web/packages/caret/caret.pdf" rel="nofollow"&gt;http://cran.r-project.org/web/packages/caret/caret.pdf&lt;/a&gt;  and &lt;code&gt;rpart.control&lt;/code&gt; section of &lt;a href="http://cran.r-project.org/web/packages/rpart/rpart.pdf" rel="nofollow"&gt;http://cran.r-project.org/web/packages/rpart/rpart.pdf&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/code'':5C,9C,18C,38C,45C,49C,57C,65C,81C ''/p'':58C ''/strong'':24C ''/web/packages/caret/caret.pdf'':72C ''/web/packages/caret/caret.pdf&lt;/a&gt;'':77C ''/web/packages/rpart/rpart.pdf'':88C ''/web/packages/rpart/rpart.pdf&lt;/a&gt;.&lt;/p&gt;'':93C ''cart'':12C ''code'':3C,7C,16C,36C,43C,47C,55C,61C,79C ''complex'':41C ''constraint'':30C ''cp'':44C ''cran.r-project.org'':71C,76C,87C,92C ''cran.r-project.org/web/packages/caret/caret.pdf'':70C ''cran.r-project.org/web/packages/caret/caret.pdf&lt;/a&gt;'':75C ''cran.r-project.org/web/packages/rpart/rpart.pdf'':86C ''cran.r-project.org/web/packages/rpart/rpart.pdf&lt;/a&gt;.&lt;/p&gt;'':91C ''depth'':54C ''differ'':27C ''enforc'':35C ''function'':19C ''href'':69C,85C ''implement'':10C ''librari'':25C ''list'':64C ''max'':52C ''maxdepth'':56C ''model'':33C,63C ''nofollow'':74C,90C ''p'':1C,59C ''paramet'':42C ''rel'':73C,89C ''rpart'':4C,17C,23C,37C ''rpart.control'':80C ''rpart2'':8C,48C ''section'':66C,82C ''see'':60C ''strong'':22C ''train'':62C ''tree'':53C ''use'':39C,50C ''wrap'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6074, 2, '2015-06-10 19:43:17.277', -1, NULL, NULL, '&lt;p&gt;If you are using tree based methods, you can replace the qualitative variables with random numbers or with the counts of each class.&lt;/p&gt;
', NULL, NULL, '''/p'':25C ''base'':7C ''class'':24C ''count'':21C ''method'':8C ''number'':17C ''p'':1C ''qualit'':13C ''random'':16C ''replac'':11C ''tree'':6C ''use'':5C ''variabl'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6043, 2, '2015-06-08 11:10:44.397', 1, NULL, NULL, '&lt;p&gt;You may go through two different approaches:&lt;/p&gt;

&lt;h2&gt;Unsupervised Learning (Clustering)&lt;/h2&gt;

&lt;p&gt;You can choose attributes who make someone''s profile and try to cluster it (e.g. using k-means). If you look at clusters according to different attributes it may give you some insight about the data. Do not forget to exclude names as they are meaningless for analysis.&lt;/p&gt;

&lt;h2&gt;Supervised Learning (Classification)&lt;/h2&gt;

&lt;p&gt;You can use relevant attributes which may affect an output e.g. age and city may affect gender or Nr of children so if u put these as targets you can classify different users according to their age and city.&lt;/p&gt;

&lt;p&gt;The point is that your data is not suitable for more sophisticated analysis as your features are not many and too complicated (i.e. answering the questions like age distribution among a certain gender or the relation between cities, age and number of children or something like them are the only questions can be answered)&lt;/p&gt;

&lt;p&gt;I hope it helped and if there are more questions please comment here.&lt;/p&gt;

&lt;h2&gt;UPDATE 1&lt;/h2&gt;

&lt;p&gt;You can look at your data from different point of view so first try to choose one. For example clients can be clustered via their transactions (when they bought, what they bought, how expensive they bought, etc) or by their personal info (gender, age, payment method, etc). It gives you a first impression of your client data. Then go through clustering using all those information i.e. put personal and transactional features together. It gives you an overview of customers in general and might result in some significant clusters (categories).&lt;/p&gt;

&lt;p&gt;After that you can look at the problem from product point of view meaning you look at the transactional features and try to see different distributions and histograms to get an overview of what''s happening to the product e.g. you can set time as the x axis and try to extract different time series like how many product are sold at this point of time?, what kind of people (age, gender, etc) bought at this point of time? and so on. &lt;/p&gt;

&lt;p&gt;For general analysis a dimensionality reduction (e.g. PCA) might reveal some information and give you insight. Please note that for any kind of analysis ONLY use relevant features e.g. IDs are not informative but categories.&lt;/p&gt;

&lt;p&gt;The most important point for recommending product to a customer (or predicting what they buy) is to use your input/output pair properly so the customer info (age, city, when does he/she usually buy something?, etc) are inputs and products (their categories, their type, whatever u know about them) are outputs.&lt;/p&gt;

&lt;p&gt;Good Luck!&lt;/p&gt;

&lt;p&gt;PS1: From experience of doing an industrial version of exactly the same project I would say payment method is not much informative.&lt;/p&gt;

&lt;p&gt;PS2: StackExchange provides an upvote and an accept button for good answers!&lt;/p&gt;
', NULL, NULL, '''/h2'':14C,70C,186C ''/p'':9C,65C,111C,167C,182C,277C,361C,398C,450C,454C,478C,492C ''1'':185C ''accept'':487C ''accord'':40C,105C ''affect'':79C,87C ''age'':83C,108C,140C,151C,231C,349C,426C ''among'':142C ''analysi'':64C,125C,365C,386C ''answer'':136C,166C,491C ''approach'':8C ''attribut'':19C,43C,76C ''axi'':326C ''bought'':216C,219C,223C,352C ''button'':488C ''buy'':414C,432C ''categori'':276C,397C,440C ''certain'':144C ''children'':92C,155C ''choos'':18C,203C ''citi'':85C,110C,150C,427C ''classif'':69C ''classifi'':102C ''client'':207C,243C ''cluster'':13C,28C,39C,210C,248C,275C ''comment'':180C ''complic'':134C ''custom'':266C,409C,424C ''data'':52C,118C,193C,244C ''differ'':7C,42C,103C,195C,303C,331C ''dimension'':367C ''distribut'':141C,304C ''e.g'':30C,82C,318C,369C,391C ''etc'':224C,234C,351C,434C ''exact'':465C ''exampl'':206C ''exclud'':57C ''expens'':221C ''experi'':458C ''extract'':330C ''featur'':128C,258C,298C,390C ''first'':200C,239C ''forget'':55C ''gender'':88C,145C,230C,350C ''general'':268C,364C ''get'':308C ''give'':46C,236C,261C,376C ''go'':4C,246C ''good'':452C,490C ''h2'':10C,66C,183C ''happen'':314C ''he/she'':430C ''help'':172C ''histogram'':306C ''hope'':170C ''i.e'':135C,253C ''id'':392C ''import'':402C ''impress'':240C ''industri'':462C ''info'':229C,425C ''inform'':252C,374C,395C,477C ''input'':436C ''input/output'':419C ''insight'':49C,378C ''k'':33C ''k-mean'':32C ''kind'':346C,384C ''know'':445C ''learn'':12C,68C ''like'':139C,158C,334C ''look'':37C,190C,283C,294C ''luck'':453C ''make'':21C ''mani'':131C,336C ''may'':3C,45C,78C,86C ''mean'':34C,292C ''meaningless'':62C ''method'':233C,473C ''might'':270C,371C ''much'':476C ''name'':58C ''note'':380C ''nr'':90C ''number'':153C ''one'':204C ''output'':81C,449C ''overview'':264C,310C ''p'':1C,15C,71C,112C,168C,187C,278C,362C,399C,451C,455C,479C ''pair'':420C ''payment'':232C,472C ''pca'':370C ''peopl'':348C ''person'':228C,255C ''pleas'':179C,379C ''point'':114C,196C,289C,342C,355C,403C ''predict'':411C ''problem'':286C ''product'':288C,317C,337C,406C,438C ''profil'':24C ''project'':468C ''proper'':421C ''provid'':482C ''ps1'':456C ''ps2'':480C ''put'':96C,254C ''question'':138C,163C,178C ''recommend'':405C ''reduct'':368C ''relat'':148C ''relev'':75C,389C ''result'':271C ''reveal'':372C ''say'':471C ''see'':302C ''seri'':333C ''set'':321C ''signific'':274C ''sold'':339C ''someon'':22C ''someth'':157C,433C ''sophist'':124C ''stackexchang'':481C ''suitabl'':121C ''supervis'':67C ''target'':99C ''time'':322C,332C,344C,357C ''togeth'':259C ''transact'':213C,257C,297C ''tri'':26C,201C,300C,328C ''two'':6C ''type'':442C ''u'':95C,444C ''unsupervis'':11C ''updat'':184C ''upvot'':484C ''use'':31C,74C,249C,388C,417C ''user'':104C ''usual'':431C ''version'':463C ''via'':211C ''view'':198C,291C ''whatev'':443C ''would'':470C ''x'':325C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6044, 1, '2015-06-08 19:50:16.51', 1, 62, 'What is a XML dataset?', '&lt;p&gt;What are xml datasets? Is it possible to convert them to csv files?
I''m working on a Java program and I sometimes download datasets wich are in a binary format, are those xml? &lt;/p&gt;

&lt;p&gt;Thank you.&lt;/p&gt;
', 'data-mining', NULL, '''/p'':44C,48C ''binari'':39C ''convert'':18C ''csv'':21C ''data'':7B ''data-min'':6B ''dataset'':5A,13C,34C ''download'':33C ''file'':22C ''format'':40C ''java'':28C ''m'':24C ''mine'':8B ''p'':9C,45C ''possibl'':16C ''program'':29C ''sometim'':32C ''thank'':46C ''wich'':35C ''work'':25C ''xml'':4A,12C,43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6045, 2, '2015-06-08 21:40:20.45', 3, NULL, NULL, '&lt;p&gt;XML is a markup language similar to html. One uses tags with attributes to build data structures. For example,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;sampleXML&amp;gt;
  &amp;lt;Menu&amp;gt;
    &amp;lt;Food&amp;gt;
      &amp;lt;item1&amp;gt;Spaghetti Bolognese&amp;lt;/item1&amp;gt;
      &amp;lt;item2&amp;gt;Spaghetti Carbonara&amp;lt;/item2&amp;gt;
    &amp;lt;/Food&amp;gt;
    &amp;lt;Drinks&amp;gt;
      &amp;lt;item1 class = ''drinks''&amp;gt;Sprite&amp;lt;/item1&amp;gt;
    &amp;lt;/Drinks&amp;gt;
  &amp;lt;/Menu&amp;gt;
&amp;lt;/sampleXML&amp;gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As you can see XML employs tags such as &lt;code&gt;&amp;lt;Food&amp;gt;&amp;lt;/Food&amp;gt;&lt;/code&gt; and attributes such as &lt;code&gt;class = ''drinks''&lt;/code&gt; which is exactly what HTML has. To access the XML data in java you got couple choices. &lt;a href="http://www.mkyong.com/java/how-to-read-xml-file-in-java-dom-parser/" rel="nofollow"&gt;You can read it in as a string and parse it using built in DOM parser&lt;/a&gt;. Or you can use JAXB to &lt;a href="https://docs.oracle.com/javase/tutorial/jaxb/intro/" rel="nofollow"&gt;map XML directly to Java objects&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;Surely you can convert XML to a csv file. There are free websites online for that. Just google "XML to csv converter."&lt;/p&gt;

&lt;p&gt;Binary files are not XML, but can be. This needs little explanation. The letters and words you see here are ASCII characters. This is human readable text. Each ASCII character has a binary representation. For example, j in binary is 1101010. A binary file is any file in computer language (0 an 1). A binary file can also be a combination of text and binary. You can convert binary to ASCII and those files you download may indeed be XML I described above. To convert binary to ASCII just google it.&lt;/p&gt;
', NULL, NULL, '''/a'':141C,161C ''/code'':73C,92C,100C ''/drinks'':65C ''/food'':50C,90C ''/item1'':39C,62C ''/item2'':47C ''/java/how-to-read-xml-file-in-java-dom-parser/'':122C ''/javase/tutorial/jaxb/intro/'':152C ''/menu'':68C ''/p'':21C,162C,186C,279C ''/pre'':74C ''/samplexml'':71C ''0'':238C ''1'':240C ''1101010'':228C ''access'':108C ''also'':245C ''ascii'':208C,216C,258C,275C ''attribut'':14C,94C ''binari'':188C,220C,226C,230C,242C,252C,256C,273C ''bolognes'':37C ''build'':16C ''built'':137C ''carbonara'':45C ''charact'':209C,217C ''choic'':117C ''class'':57C,98C ''code'':23C,85C,97C ''combin'':248C ''comput'':236C ''convert'':167C,185C,255C,272C ''coupl'':116C ''csv'':171C,184C ''data'':17C,111C ''describ'':269C ''direct'':157C ''docs.oracle.com'':151C ''docs.oracle.com/javase/tutorial/jaxb/intro/'':150C ''dom'':139C ''download'':263C ''drink'':53C,58C,99C ''employ'':81C ''exact'':103C ''exampl'':20C,223C ''explan'':199C ''file'':172C,189C,231C,234C,243C,261C ''food'':31C,87C ''free'':175C ''googl'':181C,277C ''got'':115C ''gt'':26C,29C,32C,35C,40C,43C,48C,51C,54C,59C,63C,66C,69C,72C,88C,91C ''href'':119C,149C ''html'':9C,105C ''human'':212C ''inde'':265C ''item1'':34C,56C ''item2'':42C ''j'':224C ''java'':113C,159C ''jaxb'':146C ''languag'':6C,237C ''letter'':201C ''littl'':198C ''lt'':24C,27C,30C,33C,38C,41C,46C,49C,52C,55C,61C,64C,67C,70C,86C,89C ''map'':155C ''markup'':5C ''may'':264C ''menu'':28C ''need'':197C ''nofollow'':124C,154C ''object'':160C ''one'':10C ''onlin'':177C ''p'':1C,75C,163C,187C ''pars'':134C ''parser'':140C ''pre'':22C ''read'':127C ''readabl'':213C ''rel'':123C,153C ''represent'':221C ''samplexml'':25C ''see'':79C,205C ''similar'':7C ''spaghetti'':36C,44C ''sprite'':60C ''string'':132C ''structur'':18C ''sure'':164C ''tag'':12C,82C ''text'':214C,250C ''use'':11C,136C,145C ''websit'':176C ''word'':203C ''www.mkyong.com'':121C ''www.mkyong.com/java/how-to-read-xml-file-in-java-dom-parser/'':120C ''xml'':2C,80C,110C,156C,168C,182C,192C,267C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6048, 1, '2015-06-09 09:37:11.537', 5, 773, 'Decision tree or logistic regression?', '&lt;p&gt;I am working on a classification problem. I have a dataset containing equal number of categorical variables and continuous variables. How will i know what technique to use? between a decision tree and a logistic regression?&lt;/p&gt;

&lt;p&gt;Is it right to assume that logistic regression will be more suitable for continuous variable and decision tree will be more suitable for continuous + categorical variable?&lt;/p&gt;
', 'classification  logistic-regression  decision-trees', NULL, '''/p'':50C,78C ''assum'':56C ''categor'':29C,76C ''classif'':6B,19C ''contain'':25C ''continu'':32C,65C,75C ''dataset'':24C ''decis'':1A,11B,44C,68C ''decision-tre'':10B ''equal'':26C ''know'':37C ''logist'':4A,8B,48C,58C ''logistic-regress'':7B ''number'':27C ''p'':13C,51C ''problem'':20C ''regress'':5A,9B,49C,59C ''right'':54C ''suitabl'':63C,73C ''techniqu'':39C ''tree'':2A,12B,45C,69C ''use'':41C ''variabl'':30C,33C,66C,77C ''work'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6050, 1, '2015-06-09 10:34:14.97', 0, 46, 'Encoding for k-level qualitatative variable', '&lt;p&gt;I have a qualitative variable, e.g. &lt;code&gt;userId&lt;/code&gt;, which could take around 30,000 different coded values ($k$). I would like to represent this variable as a dummy variable. Coding this into a vector of size $k$ doesn''t seem to be a good approach. Is there a more compact method for coding for this variable?&lt;/p&gt;
', 'feature-extraction', NULL, '''/code'':20C ''/p'':69C ''000'':26C ''30'':25C ''approach'':57C ''around'':24C ''code'':18C,28C,42C,65C ''compact'':62C ''could'':22C ''differ'':27C ''doesn'':50C ''dummi'':40C ''e.g'':17C ''encod'':1A ''extract'':10B ''featur'':9B ''feature-extract'':8B ''good'':56C ''k'':4A,30C,49C ''k-level'':3A ''level'':5A ''like'':33C ''method'':63C ''p'':11C ''qualit'':15C ''qualitat'':6A ''repres'':35C ''seem'':52C ''size'':48C ''take'':23C ''userid'':19C ''valu'':29C ''variabl'':7A,16C,37C,41C,68C ''vector'':46C ''would'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6051, 1, '2015-06-09 11:27:38.94', 0, 442, 'Do I need an Artificial Intelligence API?', '&lt;p&gt;I''m wondering if programmers tend to use AI apis. And if so, what are they like? And where can I find a nice one for JAVA?&lt;/p&gt;

&lt;p&gt;Sorry if my question is too vague or out of place, I''m just a beginner. 
Thank you.&lt;/p&gt;
', 'machine-learning  java', NULL, '''/p'':40C,60C ''ai'':21C ''api'':7A,22C ''artifici'':5A ''beginn'':57C ''find'':34C ''intellig'':6A ''java'':11B,39C ''learn'':10B ''like'':29C ''m'':14C,54C ''machin'':9B ''machine-learn'':8B ''need'':3A ''nice'':36C ''one'':37C ''p'':12C,41C ''place'':52C ''programm'':17C ''question'':45C ''sorri'':42C ''tend'':18C ''thank'':58C ''use'':20C ''vagu'':48C ''wonder'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6055, 2, '2015-06-09 15:12:19.093', 5, NULL, NULL, '&lt;p&gt;Geometrically, the vector w is directed orthogonal to the line defined by $w^{T} x = b$. This can be understood as follows: &lt;/p&gt;

&lt;p&gt;First take $b = 0$. Now it is clear that all vectors, $x$, with vanishing inner product with $w$ satisfy this equation, i.e. all vectors orthogonal to w satisfy this equation. &lt;/p&gt;

&lt;p&gt;Now translate the hyperplane away from the origin over a vector a. The equation for the plane now becomes: $(x â a)^{T} w = 0$, i.e. we find that for the offset $b = a^{T} w$, which is the projection of a onto to the vector $w$. &lt;/p&gt;

&lt;p&gt;Without loss of generality we may thus choose a perpendicular to the plane, in which case the length $\vert\vert a \vert\vert = \vert b \vert /\vert\vert w\vert\vert$ which represents the shortest, orthogonal distance between the origin and the hyperplane.&lt;/p&gt;

&lt;p&gt;Hence the vector $w$ is said to be orthogonal to the separating hyperplane. &lt;/p&gt;
', NULL, NULL, '''/p'':24C,56C,105C,150C,165C ''0'':29C,82C ''away'':62C ''b'':17C,28C,90C,131C ''becom'':76C ''case'':122C ''choos'':114C ''clear'':33C ''defin'':12C ''direct'':7C ''distanc'':143C ''equat'':46C,55C,71C ''find'':85C ''first'':26C ''follow'':23C ''general'':110C ''geometr'':2C ''henc'':152C ''hyperplan'':61C,149C,164C ''i.e'':47C,83C ''inner'':40C ''length'':124C ''line'':11C ''loss'':108C ''may'':112C ''offset'':89C ''onto'':100C ''origin'':65C,146C ''orthogon'':8C,50C,142C,160C ''p'':1C,25C,57C,106C,151C ''perpendicular'':116C ''plane'':74C,119C ''product'':41C ''project'':97C ''repres'':139C ''said'':157C ''satisfi'':44C,53C ''separ'':163C ''shortest'':141C ''take'':27C ''thus'':113C ''translat'':59C ''understood'':21C ''vanish'':39C ''vector'':4C,36C,49C,68C,103C,154C ''vert'':125C,126C,128C,129C,130C,132C,133C,134C,136C,137C ''w'':5C,14C,43C,52C,81C,93C,104C,135C,155C ''without'':107C ''x'':16C,37C,77C ''â'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6059, 2, '2015-06-09 16:58:44.353', 8, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Long story short&lt;/strong&gt;: &lt;em&gt;do what @untitledprogrammer said, try both models and cross-validate to help pick one.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;Both decision trees (depending on the implementation, e.g. C4.5) and logistic regression should be able to handle continuous and categorical data just fine. For logistic regression, you''ll want to &lt;a href="https://stats.stackexchange.com/questions/115049/why-do-we-need-to-dummy-code-categorical-variables"&gt;dummy code your categorical variables&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;As @untitledprogrammer mentioned, it''s difficult to know a priori which technique will be better based simply based on the types of features you have, continuous or otherwise. It really depends on your specific problem and the data you have. (See &lt;a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=585893" rel="nofollow"&gt;No Free Lunch Theorem&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;You''ll want to keep in mind though that a logistic regression model is searching for a single linear decision boundary in your feature space, whereas a decision tree is essentially partitioning your feature space into half-spaces using &lt;em&gt;axis-aligned&lt;/em&gt; linear decision boundaries. The net effect is that you have a non-linear decision boundary, possibly more than one. &lt;/p&gt;

&lt;p&gt;This is nice when your data points aren''t easily separated by a single hyperplane, but on the other hand, decisions trees are so flexible that they can be prone to overfitting. To combat this, you can try pruning. Logistic regression tends to be less susceptible (but not immune!) to overfitting.&lt;/p&gt;

&lt;p&gt;Lastly, another thing to consider is that decision trees can automatically take into account interactions between variables, e.g. $xy$ if you have two independent features $x$ and $y$. With logistic regression, you''ll have to manually add those interaction terms yourself.&lt;/p&gt;

&lt;p&gt;So you have to ask yourself: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;what kind of decision boundary makes more sense in your particular problem?&lt;/li&gt;
&lt;li&gt;how do you want to balance bias and variance?&lt;/li&gt;
&lt;li&gt;are there interactions between my features?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Of course, it''s always a good idea to just try both models and do cross-validation. This will help you find out which one is more like to have better generalization error.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6060, 2, '2015-06-09 17:46:48.943', 1, NULL, NULL, '&lt;p&gt;There is always clustering techniques you could do that would help you determine how to group userId into a dummy variable.&lt;/p&gt;
', NULL, NULL, '''/p'':23C ''alway'':4C ''cluster'':5C ''could'':8C ''determin'':14C ''dummi'':21C ''group'':17C ''help'':12C ''p'':1C ''techniqu'':6C ''userid'':18C ''variabl'':22C ''would'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8860, 1, '2015-11-16 14:41:08.553', 8, 217, 'Bagging vs Dropout in Deep Neural Networks', '&lt;p&gt;Bagging is the generation of multiple predictors that works as ensamble as a single predictor. Dropout is a technique that teach to a neural networks to average all possible subnetworks. Looking at the most important Kaggle''s competitions seem that this two techniques are used together very often. I can''t see any theoretical difference besides the actual implementation. Who can explain me why we should use both of them in any real application? and why performance improve when we use both of them?&lt;/p&gt;
', 'machine-learning  neuralnetwork  deep-learning', NULL, '''/p'':100C ''actual'':73C ''applic'':89C ''averag'':42C ''bag'':1A,16C ''besid'':71C ''competit'':53C ''deep'':5A,13B ''deep-learn'':12B ''differ'':70C ''dropout'':3A,31C ''ensambl'':26C ''explain'':77C ''generat'':19C ''implement'':74C ''import'':50C ''improv'':93C ''kaggl'':51C ''learn'':10B,14B ''look'':46C ''machin'':9B ''machine-learn'':8B ''multipl'':21C ''network'':7A,40C ''neural'':6A,39C ''neuralnetwork'':11B ''often'':63C ''p'':15C ''perform'':92C ''possibl'':44C ''predictor'':22C,30C ''real'':88C ''see'':67C ''seem'':54C ''singl'':29C ''subnetwork'':45C ''teach'':36C ''techniqu'':34C,58C ''theoret'':69C ''togeth'':61C ''two'':57C ''use'':60C,82C,96C ''vs'':2A ''work'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6061, 2, '2015-06-10 02:10:50.28', 4, NULL, NULL, '&lt;p&gt;There is a nice implementation of this in gensim: &lt;a href="http://radimrehurek.com/gensim/models/phrases.html" rel="nofollow"&gt;http://radimrehurek.com/gensim/models/phrases.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Basically, it uses a data-driven approach to detect phrases, ie. common collocations. So if you feed the Phrase class a bunch of sentences, and the phrase "big data" comes up a lot, then the class will learn to combine "big data" into a single token "big_data". There is a more complete tutorial-style blog post about it here: &lt;a href="http://www.markhneedham.com/blog/2015/02/12/pythongensim-creating-bigrams-over-how-i-met-your-mother-transcripts/" rel="nofollow"&gt;http://www.markhneedham.com/blog/2015/02/12/pythongensim-creating-bigrams-over-how-i-met-your-mother-transcripts/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/blog/2015/02/12/pythongensim-creating-bigrams-over-how-i-met-your-mother-transcripts/'':88C ''/blog/2015/02/12/pythongensim-creating-bigrams-over-how-i-met-your-mother-transcripts/&lt;/a&gt;&lt;/p&gt;'':93C ''/gensim/models/phrases.html'':15C ''/gensim/models/phrases.html&lt;/a&gt;&lt;/p&gt;'':20C ''approach'':29C ''basic'':22C ''big'':50C,63C,69C ''blog'':79C ''bunch'':44C ''class'':42C,58C ''colloc'':35C ''combin'':62C ''come'':52C ''common'':34C ''complet'':75C ''data'':27C,51C,64C,70C ''data-driven'':26C ''detect'':31C ''driven'':28C ''feed'':39C ''gensim'':10C ''href'':12C,85C ''ie'':33C ''implement'':6C ''learn'':60C ''lot'':55C ''nice'':5C ''nofollow'':17C,90C ''p'':1C,21C ''phrase'':32C,41C,49C ''post'':80C ''radimrehurek.com'':14C,19C ''radimrehurek.com/gensim/models/phrases.html'':13C ''radimrehurek.com/gensim/models/phrases.html&lt;/a&gt;&lt;/p&gt;'':18C ''rel'':16C,89C ''sentenc'':46C ''singl'':67C ''style'':78C ''token'':68C ''tutori'':77C ''tutorial-styl'':76C ''use'':24C ''www.markhneedham.com'':87C,92C ''www.markhneedham.com/blog/2015/02/12/pythongensim-creating-bigrams-over-how-i-met-your-mother-transcripts/'':86C ''www.markhneedham.com/blog/2015/02/12/pythongensim-creating-bigrams-over-how-i-met-your-mother-transcripts/&lt;/a&gt;&lt;/p&gt;'':91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6062, 2, '2015-06-10 02:19:16.067', 3, NULL, NULL, '&lt;p&gt;If you are willing to use the caret package in R and use random forests, you can use the method in the following blog post for downsampling with unbalanced datasets: &lt;a href="http://appliedpredictivemodeling.com/blog/2013/12/8/28rmc2lv96h8fw8700zm4nl50busep" rel="nofollow"&gt;http://appliedpredictivemodeling.com/blog/2013/12/8/28rmc2lv96h8fw8700zm4nl50busep&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Basically, you just add a single line to your &lt;em&gt;train&lt;/em&gt; call. Here is the relevant part: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; rfDownsampled &amp;lt;- train(Class ~ ., data = training,
+                        method = "rf",
+                        ntree = 1500,
+                        tuneLength = 5,
+                        metric = "ROC",
+                        trControl = ctrl,
+                        ## Tell randomForest to sample by strata. Here, 
+                        ## that means within each class
+                        strata = training$Class,
+                        ## Now specify that the number of samples selected
+                        ## within each class should be the same
+                        sampsize = rep(nmin, 2))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have had some success with this approach in your type of situation.&lt;/p&gt;

&lt;p&gt;For some more context, here is an in-depth post about experiments with unbalanced datasets: &lt;a href="http://www.win-vector.com/blog/2015/02/does-balancing-classes-improve-classifier-performance/" rel="nofollow"&gt;http://www.win-vector.com/blog/2015/02/does-balancing-classes-improve-classifier-performance/&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6063, 2, '2015-06-10 03:53:36.337', 2, NULL, NULL, '&lt;p&gt;One needs to use an &lt;em&gt;artificial intelligence (AI)&lt;/em&gt; API, if there is a need to add &lt;em&gt;AI functionality&lt;/em&gt; to a software application - this is pretty obvious. Traditionally, my advice on &lt;em&gt;machine learning (ML)&lt;/em&gt; software includes the following two excellent &lt;strong&gt;curated lists&lt;/strong&gt; of resources: &lt;a href="https://github.com/onurakpolat/awesome-bigdata" rel="nofollow"&gt;this one&lt;/a&gt; and &lt;a href="https://github.com/josephmisiti/awesome-machine-learning" rel="nofollow"&gt;this one&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;However, keep in mind that ML is just a &lt;strong&gt;subset&lt;/strong&gt; of AI domain, so if your tasks involve AI areas &lt;em&gt;beyond&lt;/em&gt; ML, you need more &lt;em&gt;AI-focused&lt;/em&gt; tools or platforms. For example, you can take a look at &lt;a href="http://www.ai-one.com" rel="nofollow"&gt;ai-one&lt;/a&gt;''s AI platforms and APIs as well as interesting &lt;em&gt;general AI&lt;/em&gt; open source project &lt;a href="http://opencog.org" rel="nofollow"&gt;OpenCog&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In addition to the above-mentioned AI-focused platforms, IBM''s &lt;a href="http://www.ibm.com/smarterplanet/us/en/ibmwatson" rel="nofollow"&gt;Watson&lt;/a&gt; AI system deserves a separate mention, as quite cool and promising. It offers its own &lt;em&gt;ecosystem&lt;/em&gt; for developers, called &lt;a href="http://www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud" rel="nofollow"&gt;IBM Watson Developer Cloud&lt;/a&gt;, based on IBM''s &lt;a href="https://ace.ng.bluemix.net" rel="nofollow"&gt;BlueMix&lt;/a&gt; cloud computing &lt;em&gt;platform-as-a-service (PaaS)&lt;/em&gt;. However, at the present time, I find this offering to be quite expensive as well as limiting, especially for individual developers, small startups and other small businesses, due to its tight integration with and reliance only on a single PaaS (Blue Mix). It will be interesting to watch this space as competition in AI domain and marketplace IMHO will surely intensify in the future.&lt;/p&gt;
', NULL, NULL, '''/a'':62C,73C,129C,152C,176C,209C,220C ''/em'':11C,23C,40C,100C,109C,142C,194C,230C ''/josephmisiti/awesome-machine-learning'':68C ''/onurakpolat/awesome-bigdata'':57C ''/p'':74C,153C,295C ''/smarterplanet/us/en/ibmwatson'':172C ''/smarterplanet/us/en/ibmwatson/developercloud'':202C ''/strong'':50C,87C ''above-ment'':159C ''ace.ng.bluemix.net'':216C ''add'':19C ''addit'':156C ''advic'':34C ''ai'':10C,21C,89C,96C,107C,127C,131C,141C,163C,177C,284C ''ai-focus'':106C,162C ''ai-on'':126C ''api'':12C,134C ''applic'':27C ''area'':97C ''artifici'':8C ''base'':210C ''beyond'':99C ''blue'':271C ''bluemix'':219C ''busi'':257C ''call'':197C ''cloud'':208C,221C ''competit'':282C ''comput'':222C ''cool'':185C ''curat'':48C ''deserv'':179C ''develop'':196C,207C,251C ''domain'':90C,285C ''due'':258C ''ecosystem'':193C ''em'':7C,20C,36C,98C,105C,139C,192C,223C ''especi'':248C ''exampl'':114C ''excel'':46C ''expens'':243C ''find'':237C ''focus'':108C,164C ''follow'':44C ''function'':22C ''futur'':294C ''general'':140C ''github.com'':56C,67C ''github.com/josephmisiti/awesome-machine-learning'':66C ''github.com/onurakpolat/awesome-bigdata'':55C ''howev'':76C,231C ''href'':54C,65C,122C,147C,169C,199C,215C ''ibm'':166C,205C,212C ''imho'':288C ''includ'':42C ''individu'':250C ''integr'':262C ''intellig'':9C ''intensifi'':291C ''interest'':138C,276C ''involv'':95C ''keep'':77C ''learn'':38C ''limit'':247C ''list'':49C ''look'':119C ''machin'':37C ''marketplac'':287C ''mention'':161C,182C ''mind'':79C ''mix'':272C ''ml'':39C,81C,101C ''need'':3C,17C,103C ''nofollow'':59C,70C,125C,150C,174C,204C,218C ''obvious'':31C ''offer'':189C,239C ''one'':2C,61C,72C,128C ''open'':143C ''opencog'':151C ''opencog.org'':148C ''p'':1C,75C,154C ''paa'':229C,270C ''platform'':112C,132C,165C,225C ''platform-as-a-servic'':224C ''present'':234C ''pretti'':30C ''project'':145C ''promis'':187C ''quit'':184C,242C ''rel'':58C,69C,124C,149C,173C,203C,217C ''relianc'':265C ''resourc'':52C ''separ'':181C ''servic'':228C ''singl'':269C ''small'':252C,256C ''softwar'':26C,41C ''sourc'':144C ''space'':280C ''startup'':253C ''strong'':47C,85C ''subset'':86C ''sure'':290C ''system'':178C ''take'':117C ''task'':94C ''tight'':261C ''time'':235C ''tool'':110C ''tradit'':32C ''two'':45C ''use'':5C ''watch'':278C ''watson'':175C,206C ''well'':136C,245C ''www.ai-one.com'':123C ''www.ibm.com'':171C,201C ''www.ibm.com/smarterplanet/us/en/ibmwatson'':170C ''www.ibm.com/smarterplanet/us/en/ibmwatson/developercloud'':200C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6082, 1, '2015-06-11 15:40:45.99', 5, 159, 'visualize a horizontal box plot in R', '&lt;p&gt;I have a dataset like this. The data has been collected through a questionnaire and I am going to do some exploratory data analysis.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;windows &amp;lt;- c("yes", "no","yes","yes","no")
sql     &amp;lt;- c("no","yes","no","no","no")
excel  &amp;lt;- c("yes","yes","yes","no","yes")
salary &amp;lt;- c(100,200,300,400,500 )

test&amp;lt;- as.data.frame (cbind(windows,sql,excel,salary),stringsAsFactors=TRUE)
test[,"salary"] &amp;lt;- as.numeric(as.character(test[,"salary"] ))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have an outcome variable (salary) in my dataset and a couple of input variables (tools). How can I visualize a horizontal box plot like this:
&lt;img src="http://i.stack.imgur.com/CNLC1.jpg" alt="enter image description here"&gt;&lt;/p&gt;
', 'r  visualization', NULL, '''/cnlc1.jpg'':120C ''/code'':87C ''/p'':35C,126C ''/pre'':88C ''100'':65C ''200'':66C ''300'':67C ''400'':68C ''500'':69C ''alt'':121C ''analysi'':34C ''as.character'':84C ''as.data.frame'':72C ''as.numeric'':83C ''box'':4A,112C ''c'':40C,48C,56C,64C ''cbind'':73C ''code'':37C ''collect'':21C ''coupl'':101C ''data'':18C,33C ''dataset'':14C,98C ''descript'':124C ''enter'':122C ''excel'':54C,76C ''exploratori'':32C ''go'':28C ''horizont'':3A,111C ''i.stack.imgur.com'':119C ''i.stack.imgur.com/cnlc1.jpg'':118C ''imag'':123C ''img'':116C ''input'':103C ''like'':15C,114C ''lt'':39C,47C,55C,63C,71C,82C ''outcom'':93C ''p'':10C,89C ''plot'':5A,113C ''pre'':36C ''questionnair'':24C ''r'':7A,8B ''salari'':62C,77C,81C,86C,95C ''sql'':46C,75C ''src'':117C ''stringsasfactor'':78C ''test'':70C,80C,85C ''tool'':105C ''true'':79C ''variabl'':94C,104C ''visual'':1A,9B,109C ''window'':38C,74C ''yes'':41C,43C,44C,50C,57C,58C,59C,61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6064, 1, '2015-06-10 04:57:02.477', 4, 116, 'Multivariate - Time series data pattern changes', '&lt;p&gt;New to R. In my example, my customers have restricted allocation of budget for Milk. I have more than 5 brands of milk in my store. Here my objective is how I know my customer is shifting from one brand to other? (Example: Customer is replacing Brand 1 with Brand 2 in my time series data). I would like to compute that shifting pattern every quarter and observe the trend quarter by quarter.&lt;/p&gt;

&lt;p&gt;Sample Quarter Data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Date    Milk-Brand1 Milk-Brand2 Milk-Brand3 Milk-Brand4 Milk-Brand5

1/1/2015    200 140 190 220 150
1/2/2015    204 138 195 226 144
1/3/2015    208 136 200 232 126
1/4/2015    212 134 205 238 108
2/2/2015    216 132 210 244 90
1/6/2015    220 130 215 250 72
1/7/2015    224 128 220 256 54
1/8/2015    228 126 225 262 36
1/9/2015    232 124 230 268 18
3/1/2015    236 122 235 274 0
3/2/2015    240 120 240 280 13
3/3/2015    244 118 245 286 33
3/4/2015    248 116 250 292 15
20/3/2015   252 114 255 298 33
20/3/2015   256 112 260 304 15
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Do you suggest compute correlation between each ''brand of milk'' and compare those correlations from one quarter to other quarter? Or Cross-correlation? Or others? I am open.&lt;/p&gt;

&lt;p&gt;Thanks for your advice.&lt;/p&gt;

&lt;p&gt;Here is the data via &lt;code&gt;dput&lt;/code&gt; for anyone wanting it:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;structure(list(Date = structure(c(1420070400, 1422748800, 1425168000, 
1427846400, 1422835200, 1433116800, 1435708800, 1438387200, 1441065600, 
1420243200, 1422921600, 1425340800, 1428019200, 1426809600, 1426809600
), tzone = "UTC", class = c("POSIXct", "POSIXt")), Milk.Brand1 = c(200L, 
204L, 208L, 212L, 216L, 220L, 224L, 228L, 232L, 236L, 240L, 244L, 
248L, 252L, 256L), Milk.Brand2 = c(140L, 138L, 136L, 134L, 132L, 
130L, 128L, 126L, 124L, 122L, 120L, 118L, 116L, 114L, 112L), 
    Milk.Brand3 = c(190L, 195L, 200L, 205L, 210L, 215L, 220L, 
    225L, 230L, 235L, 240L, 245L, 250L, 255L, 260L), Milk.Brand4 = c(220L, 
    226L, 232L, 238L, 244L, 250L, 256L, 262L, 268L, 274L, 280L, 
    286L, 292L, 298L, 304L), Milk.Brand5 = c(150L, 144L, 126L, 
    108L, 90L, 72L, 54L, 36L, 18L, 0L, 13L, 33L, 15L, 33L, 15L
    )), .Names = c("Date", "Milk.Brand1", "Milk.Brand2", "Milk.Brand3", 
"Milk.Brand4", "Milk.Brand5"), row.names = c(NA, -15L), class = "data.frame")
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6065, 2, '2015-06-10 11:13:37.083', 1, NULL, NULL, '&lt;p&gt;By using a parse tree, you divide your sentence into parts. Suppose, in the example of sentiment analysis, you can use those parts to assign a positive/negative sentiment to each part and then take the cumulative effect of those parts.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://cs224d.stanford.edu/images/treeFrontSentiment.png" alt="sentiment analysis"&gt;&lt;/p&gt;

&lt;p&gt;This image will help you understand more. The first half has a negative sentiment(mainly because of the word "dry") but because of the word "but" and the usage of the word "enjoyed", the negative sentiment is turned into a positive sentiment.&lt;/p&gt;

&lt;p&gt;As for using them, you can simply generate a &lt;a href="http://nlp.stanford.edu/projects/glove/" rel="nofollow"&gt;word vector representation&lt;/a&gt; of the individual words in the sentence and use neurons in place of the parent nodes. Each neuron should be connected to another neuron through weights. All the leaf nodes will be the word vector representations of words of the sentence. The top parent neuron(in this case the top blue + symbol) should generate a positive/negative sentiment according to the sentence. This tree structure can be trained in a supervised manner.&lt;/p&gt;

&lt;p&gt;Read this &lt;a href="http://arxiv.org/abs/1503.00075" rel="nofollow"&gt;paper&lt;/a&gt; for a more through understanding.&lt;/p&gt;

&lt;p&gt;Image credits: cs224.stanford.edu&lt;/p&gt;
', NULL, NULL, '''/a'':117C,201C ''/abs/1503.00075'':197C ''/images/treefrontsentiment.png'':48C ''/p'':42C,52C,96C,189C,207C,212C ''/projects/glove/'':111C ''accord'':175C ''alt'':49C ''analysi'':19C,51C ''anoth'':140C ''arxiv.org'':196C ''arxiv.org/abs/1503.00075'':195C ''assign'':26C ''blue'':168C ''case'':165C ''connect'':138C ''credit'':210C ''cs224.stanford.edu'':211C ''cs224d.stanford.edu'':47C ''cs224d.stanford.edu/images/treefrontsentiment.png'':46C ''cumul'':37C ''divid'':8C ''dri'':73C ''effect'':38C ''enjoy'':86C ''exampl'':16C ''first'':62C ''generat'':105C,171C ''half'':63C ''help'':57C ''href'':108C,194C ''imag'':55C,209C ''img'':44C ''individu'':120C ''leaf'':146C ''main'':68C ''manner'':188C ''negat'':66C,88C ''neuron'':127C,135C,141C,162C ''nlp.stanford.edu'':110C ''nlp.stanford.edu/projects/glove/'':109C ''node'':133C,147C ''nofollow'':113C,199C ''p'':1C,43C,53C,97C,190C,208C ''paper'':200C ''parent'':132C,161C ''pars'':5C ''part'':12C,24C,32C,41C ''place'':129C ''posit'':94C ''positive/negative'':28C,173C ''read'':191C ''rel'':112C,198C ''represent'':116C,153C ''sentenc'':10C,124C,158C,178C ''sentiment'':18C,29C,50C,67C,89C,95C,174C ''simpli'':104C ''src'':45C ''structur'':181C ''supervis'':187C ''suppos'':13C ''symbol'':169C ''take'':35C ''top'':160C,167C ''train'':184C ''tree'':6C,180C ''turn'':91C ''understand'':59C,206C ''usag'':82C ''use'':3C,22C,100C,126C ''vector'':115C,152C ''weight'':143C ''word'':72C,78C,85C,114C,121C,151C,155C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6066, 1, '2015-06-10 11:33:17.25', 2, 136, 'Text classification: Choosing training and testing set for each category', '&lt;p&gt;I am very new to machine learning.
I have a text classification problem in hand. I have a tagged dataset of around 750 documents( short texts), categorized manually into 16 buckets. I want to train a classifier on this data. I know that there should be a training set and a test set (an option could be 80-20 ). In my understanding, this should be for the complete set( 80% of my 750 documents- training, 20% of 750 documents - testing ). 
1. They should be randomly generated or is there some condition for category? ie. if category A constitutes 60%,category B 5%, C 7% etc. how to choose the training set?&lt;/p&gt;
', 'machine-learning  classification  text-mining', NULL, '''-20'':77C ''/p'':130C ''1'':99C ''16'':48C ''20'':94C ''5'':120C ''60'':117C ''7'':122C ''750'':41C,91C,96C ''80'':76C,88C ''around'':40C ''b'':119C ''bucket'':49C ''c'':121C ''categor'':45C ''categori'':10A,111C,114C,118C ''choos'':3A,126C ''classif'':2A,14B,30C ''classifi'':55C ''complet'':86C ''condit'':109C ''constitut'':116C ''could'':74C ''data'':58C ''dataset'':38C ''document'':42C,92C,97C ''etc'':123C ''generat'':104C ''hand'':33C ''ie'':112C ''know'':60C ''learn'':13B,25C ''machin'':12B,24C ''machine-learn'':11B ''manual'':46C ''mine'':17B ''new'':22C ''option'':73C ''p'':18C ''problem'':31C ''random'':103C ''set'':7A,67C,71C,87C,129C ''short'':43C ''tag'':37C ''test'':6A,70C,98C ''text'':1A,16B,29C,44C ''text-min'':15B ''train'':4A,53C,66C,93C,128C ''understand'':80C ''want'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6067, 2, '2015-06-10 13:00:47.707', 2, NULL, NULL, '&lt;p&gt;The most commonly used option is 2/3rd of the data as training and 1/3rd as testing sets respectively. What kind of software or tool are you using for this categorization process? For your classifier to work efficiently at the end of all the hard work, have you considered cross validating it using a multiple fold cross validation before using and assigning the training and testing sets? As in for the skewed data categories, try finding instances for the classes/ categories which have fewer samples (~10%).&lt;/p&gt;
', NULL, NULL, '''/p'':87C ''1/3rd'':15C ''10'':86C ''2/3rd'':8C ''assign'':62C ''categor'':31C ''categori'':74C,81C ''class'':80C ''classifi'':35C ''common'':4C ''consid'':49C ''cross'':50C,57C ''data'':11C,73C ''effici'':38C ''end'':41C ''fewer'':84C ''find'':76C ''fold'':56C ''hard'':45C ''instanc'':77C ''kind'':21C ''multipl'':55C ''option'':6C ''p'':1C ''process'':32C ''respect'':19C ''sampl'':85C ''set'':18C,67C ''skew'':72C ''softwar'':23C ''test'':17C,66C ''tool'':25C ''train'':13C,64C ''tri'':75C ''use'':5C,28C,53C,60C ''valid'':51C,58C ''work'':37C,46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6068, 1, '2015-06-10 13:16:17.53', -1, 278, 'How can an undergraduate learn more about deep learning?', '&lt;p&gt;I am an undergraduate who needs to submit a thesis for graduation. I am fairly interested in deep learning, and am working on a project that uses deep learning methods extensively (rCNNs to be precise). The caveat here is that I am working alone, with little help from my advisor. There are hardly any experts on deep learning in my university. How do I go about finishing my project. It is specially intimidating to see that most papers in deep learning are published by multiple accomplished scientists with very little involvement of undergraduate level students. &lt;/p&gt;

&lt;p&gt;PS: I am really interested in this topic, so please try not to advise me to switch to an easier topic.&lt;/p&gt;
', 'machine-learning  deep-learning  research', NULL, '''/p'':113C,136C ''accomplish'':103C ''advis'':128C ''advisor'':67C ''alon'':61C ''caveat'':54C ''deep'':8A,14B,35C,45C,74C,97C ''deep-learn'':13B ''easier'':134C ''expert'':72C ''extens'':48C ''fair'':32C ''finish'':84C ''go'':82C ''graduat'':29C ''hard'':70C ''help'':64C ''interest'':33C,119C ''intimid'':90C ''involv'':108C ''learn'':5A,9A,12B,15B,36C,46C,75C,98C ''level'':111C ''littl'':63C,107C ''machin'':11B ''machine-learn'':10B ''method'':47C ''multipl'':102C ''need'':23C ''p'':17C,114C ''paper'':95C ''pleas'':124C ''precis'':52C ''project'':42C,86C ''ps'':115C ''publish'':100C ''rcnns'':49C ''realli'':118C ''research'':16B ''scientist'':104C ''see'':92C ''special'':89C ''student'':112C ''submit'':25C ''switch'':131C ''thesi'':27C ''topic'':122C,135C ''tri'':125C ''undergradu'':4A,21C,110C ''univers'':78C ''use'':44C ''work'':39C,60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6069, 1, '2015-06-10 14:25:05.84', 1, 15, 'Rearange input data - unknown size of input', '&lt;p&gt;I have some data of form showed below to squezze through some neural network.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Customer Step Var0  ... VarN
0        0    some  ... stuff
0        1    again ... stuff
0        2    foo   ... bar
0        3    bla   ... blub
1        0    other ... stuff
1        1    and   ... ongoing
2        0    and   ... so on
.        .    .     ... .
.        .    .     ... .
.        .    .     ... .
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As the neural network has a fixed input layer size, but the customers step count can differ at each customer, I have following question:&lt;/p&gt;

&lt;p&gt;What is the best practise (if exists) to rearange the given data for using as input of a NN? &lt;/p&gt;
', 'neuralnetwork', NULL, '''/code'':60C ''/p'':24C,87C,108C ''/pre'':61C ''0'':31C,32C,35C,39C,43C,48C,56C ''1'':36C,47C,51C,52C ''2'':40C,55C ''3'':44C ''bar'':42C ''best'':92C ''bla'':45C ''blub'':46C ''code'':26C ''count'':77C ''custom'':27C,75C,82C ''data'':3A,13C,100C ''differ'':79C ''exist'':95C ''fix'':69C ''follow'':85C ''foo'':41C ''form'':15C ''given'':99C ''input'':2A,7A,70C,104C ''layer'':71C ''network'':23C,66C ''neural'':22C,65C ''neuralnetwork'':8B ''nn'':107C ''ongo'':54C ''p'':9C,62C,88C ''practis'':93C ''pre'':25C ''question'':86C ''rearang'':1A,97C ''show'':16C ''size'':5A,72C ''squezz'':19C ''step'':28C,76C ''stuff'':34C,38C,50C ''unknown'':4A ''use'':102C ''var0'':29C ''varn'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6080, 1, '2015-06-11 07:53:26.12', 4, 46, 'metric learning and information retrieval', '&lt;p&gt;I am interested in parsing semi-structured text.
Assume that I have a text with labels of the kind: year_field, year_value, identity_field, identity_value, ..., address_field, address_value, and so on.&lt;/p&gt;

&lt;p&gt;These fields and their associated values can be everywhere in the text, but usually they are near to each other, and more generally the text in organized in a (very) rough matrix, but rather often the value is just after the associated field with eventually some non-interesting information in between. &lt;/p&gt;

&lt;p&gt;The number of different format can be up to several dozens, and is not that rigid (do not count on spacing, moreover some information can be added and removed).&lt;/p&gt;

&lt;p&gt;I am looking toward machine learning techniques to extract all those (field,value) of interest.&lt;/p&gt;

&lt;p&gt;I think metric learning and/or conditional random fields (CRF) could be of a great help, but I have not practical experience with them.&lt;/p&gt;

&lt;p&gt;Does anyone have already encounter a similar problem?&lt;/p&gt;

&lt;p&gt;Any suggestion or literature on this topic?&lt;/p&gt;
', 'text-mining  information-retrieval', NULL, '''/p'':48C,102C,133C,150C,175C,185C,194C ''ad'':130C ''address'':41C,43C ''alreadi'':180C ''and/or'':156C ''anyon'':178C ''associ'':54C,91C ''assum'':22C ''condit'':157C ''could'':161C ''count'':122C ''crf'':160C ''differ'':107C ''dozen'':114C ''encount'':181C ''eventu'':94C ''everywher'':58C ''experi'':172C ''extract'':143C ''field'':34C,38C,42C,51C,92C,146C,159C ''format'':108C ''general'':72C ''great'':165C ''help'':166C ''ident'':37C,39C ''inform'':4A,10B,99C,127C ''information-retriev'':9B ''interest'':15C,98C,149C ''kind'':32C ''label'':29C ''learn'':2A,140C,155C ''literatur'':190C ''look'':137C ''machin'':139C ''matrix'':81C ''metric'':1A,154C ''mine'':8B ''moreov'':125C ''near'':66C ''non'':97C ''non-interest'':96C ''number'':105C ''often'':84C ''organ'':76C ''p'':12C,49C,103C,134C,151C,176C,186C ''pars'':17C ''practic'':171C ''problem'':184C ''random'':158C ''rather'':83C ''remov'':132C ''retriev'':5A,11B ''rigid'':119C ''rough'':80C ''semi'':19C ''semi-structur'':18C ''sever'':113C ''similar'':183C ''space'':124C ''structur'':20C ''suggest'':188C ''techniqu'':141C ''text'':7B,21C,27C,61C,74C ''text-min'':6B ''think'':153C ''topic'':193C ''toward'':138C ''usual'':63C ''valu'':36C,40C,44C,55C,86C,147C ''year'':33C,35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6070, 1, '2015-06-10 17:27:05.703', 5, 183, 'Statistical distances for time series of distributions', '&lt;p&gt;I am interested in clustering $N$ time series of $T$ ''values'' each. These values are distributions (which can be represented by their cumulative distribution functions (cdf), or their probability density functions (pdf), or more convenient forms &lt;a href="http://projects.csail.mit.edu/atemuri/wiki/images/f/fe/SrivastavaJermynJoshiCVPR2007.pdf" rel="nofollow"&gt;such as square-root pdfs yielding a simple spheric geometry&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;For comparing given distributions, there is an extensive literature on statistical distances (KL, Hellinger, Wasserstein, and so on), but for comparing given time series of distributions, I am not sure whether there is any literature at all?&lt;/p&gt;

&lt;p&gt;Such distances should somehow take into account dynamics information besides the distribution proximity at time t. Ideally, I wish I could have a kind of &lt;a href="http://arxiv.org/pdf/1506.00976v1.pdf" rel="nofollow"&gt;information factorization similar to this result&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I am wondering if such distances already exist and whether this kind of problem has already been formulated in the literature?&lt;/p&gt;

&lt;p&gt;--
edit for further precisions and answer to comments:&lt;/p&gt;

&lt;p&gt;Thanks for your answer, but dynamic time warping does not suit to my need. This dp technique only captures a rough similarity of shapes by allowing non-linear time distortion. But, it does not amount for the whole information in these time series, e.g. what about the distribution of distortions? Do the distributions of a given time series vary smoothly through time or violently? DTW is not always the solution, for instance, when working with random walks, it does not make sense to use a DTW since there are no time patterns! In this case, the only information is "correlation" and "distribution" (cf. Sklar''s theorem in Copula Theory), and the paper cited above.&lt;/p&gt;

&lt;p&gt;-- edit 2 Here are the papers that are somehow related to my question:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://pub.ist.ac.at/~chl/erc/papers/lampert-cvpr2015.pdf" rel="nofollow"&gt;Predicting the Future Behavior of a Time-Varying Probability Distribution&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://machinelearning.wustl.edu/mlpapers/paper_files/BanerjeeDGS05.pdf" rel="nofollow"&gt;Clustering on the unit hypersphere using von Mises-Fisher distributions&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://users.cis.fiu.edu/~lzhen001/activities/KDD2011Program/docs/p636.pdf" rel="nofollow"&gt;Unsupervised clustering of multidimensional distributions using earth mover distance&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.machinelearning.org/archive/icml2009/papers/538.pdf" rel="nofollow"&gt;Hilbert space embeddings of conditional distributions with applications to dynamical systems&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', 'clustering  time-series  distance', NULL, '''/a'':68C,148C,335C,356C,375C,396C ''/archive/icml2009/papers/538.pdf'':382C ''/atemuri/wiki/images/f/fe/srivastavajermynjoshicvpr2007.pdf'':54C ''/li'':336C,357C,376C,397C ''/mlpapers/paper_files/banerjeedgs05.pdf'':342C ''/p'':69C,108C,149C,172C,182C,299C,314C ''/pdf/1506.00976v1.pdf'':139C ''/ul'':398C ''/~chl/erc/papers/lampert-cvpr2015.pdf'':321C ''/~lzhen001/activities/kdd2011program/docs/p636.pdf'':363C ''2'':302C ''account'':116C ''allow'':209C ''alreadi'':157C,166C ''alway'':252C ''amount'':219C ''answer'':179C,187C ''applic'':392C ''arxiv.org'':138C ''arxiv.org/pdf/1506.00976v1.pdf'':137C ''behavior'':327C ''besid'':119C ''captur'':202C ''case'':279C ''cdf'':39C ''cf'':287C ''cite'':297C ''cluster'':8B,18C,345C,367C ''comment'':181C ''compar'':72C,91C ''condit'':389C ''conveni'':48C ''copula'':292C ''correl'':284C ''could'':130C ''cumul'':36C ''densiti'':43C ''distanc'':2A,12B,82C,111C,156C,374C ''distort'':214C,234C ''distribut'':7A,29C,37C,74C,96C,121C,232C,237C,286C,334C,355C,370C,390C ''dp'':199C ''dtw'':249C,270C ''dynam'':117C,189C,394C ''e.g'':228C ''earth'':372C ''edit'':174C,301C ''embed'':387C ''exist'':158C ''extens'':78C ''factor'':143C ''fisher'':354C ''form'':49C ''formul'':168C ''function'':38C,44C ''futur'':326C ''geometri'':67C ''given'':73C,92C,240C ''helling'':84C ''hilbert'':385C ''href'':51C,136C,318C,339C,360C,379C ''hyperspher'':349C ''ideal'':126C ''inform'':118C,142C,223C,282C ''instanc'':256C ''interest'':16C ''kind'':133C,162C ''kl'':83C ''li'':316C,337C,358C,377C ''linear'':212C ''literatur'':79C,105C,171C ''machinelearning.wustl.edu'':341C ''machinelearning.wustl.edu/mlpapers/paper_files/banerjeedgs05.pdf'':340C ''make'':265C ''mise'':353C ''mises-fish'':352C ''mover'':373C ''multidimension'':369C ''n'':19C ''need'':197C ''nofollow'':56C,141C,323C,344C,365C,384C ''non'':211C ''non-linear'':210C ''p'':13C,70C,109C,150C,173C,183C,300C ''paper'':296C,306C ''pattern'':276C ''pdf'':45C ''pdfs'':62C ''precis'':177C ''predict'':324C ''probabl'':42C,333C ''problem'':164C ''projects.csail.mit.edu'':53C ''projects.csail.mit.edu/atemuri/wiki/images/f/fe/srivastavajermynjoshicvpr2007.pdf'':52C ''proxim'':122C ''pub.ist.ac.at'':320C ''pub.ist.ac.at/~chl/erc/papers/lampert-cvpr2015.pdf'':319C ''question'':313C ''random'':260C ''rel'':55C,140C,322C,343C,364C,383C ''relat'':310C ''repres'':33C ''result'':147C ''root'':61C ''rough'':204C ''sens'':266C ''seri'':5A,11B,21C,94C,227C,242C ''shape'':207C ''similar'':144C,205C ''simpl'':65C ''sinc'':271C ''sklar'':288C ''smooth'':244C ''solut'':254C ''somehow'':113C,309C ''space'':386C ''spheric'':66C ''squar'':60C ''square-root'':59C ''statist'':1A,81C ''suit'':194C ''sure'':100C ''system'':395C ''take'':114C ''techniqu'':200C ''thank'':184C ''theorem'':290C ''theori'':293C ''time'':4A,10B,20C,93C,124C,190C,213C,226C,241C,246C,275C,331C ''time-seri'':9B ''time-vari'':330C ''ul'':315C ''unit'':348C ''unsupervis'':366C ''use'':268C,350C,371C ''users.cis.fiu.edu'':362C ''users.cis.fiu.edu/~lzhen001/activities/kdd2011program/docs/p636.pdf'':361C ''valu'':24C,27C ''vari'':243C,332C ''violent'':248C ''von'':351C ''walk'':261C ''warp'':191C ''wasserstein'':85C ''whether'':101C,160C ''whole'':222C ''wish'':128C ''wonder'':153C ''work'':258C ''www.machinelearning.org'':381C ''www.machinelearning.org/archive/icml2009/papers/538.pdf'':380C ''yield'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6071, 2, '2015-06-10 18:47:11.447', 3, NULL, NULL, '&lt;p&gt;&lt;a href="https://www.coursera.org" rel="nofollow"&gt;Coursera&lt;/a&gt; and &lt;a href="https://www.edx.org/" rel="nofollow"&gt;EdX&lt;/a&gt; have a courses on Neural Networks (which are the basis of Deep Learning).  Stanford also has a &lt;a href="http://deeplearning.stanford.edu/tutorial/" rel="nofollow"&gt;good series&lt;/a&gt; on Deep Learning.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/questions/3751/route-to-picking-up-deep-learning"&gt;This question&lt;/a&gt; also has some good tips. &lt;/p&gt;

&lt;p&gt;For me, what is most helpful is to program the algorithms to help me understand them step by step.  Python is good for that purpose.&lt;/p&gt;
', NULL, NULL, '''/a'':8C,16C,43C,56C ''/p'':47C,62C,89C ''/questions/3751/route-to-picking-up-deep-learning'':53C ''/tutorial/'':38C ''algorithm'':74C ''also'':31C,57C ''basi'':26C ''cours'':19C ''coursera'':7C ''datascience.stackexchange.com'':52C ''datascience.stackexchange.com/questions/3751/route-to-picking-up-deep-learning'':51C ''deep'':28C,45C ''deeplearning.stanford.edu'':37C ''deeplearning.stanford.edu/tutorial/'':36C ''edx'':15C ''good'':41C,60C,85C ''help'':69C,76C ''href'':3C,11C,35C,50C ''learn'':29C,46C ''network'':22C ''neural'':21C ''nofollow'':6C,14C,40C ''p'':1C,48C,63C ''program'':72C ''purpos'':88C ''python'':83C ''question'':55C ''rel'':5C,13C,39C ''seri'':42C ''stanford'':30C ''step'':80C,82C ''tip'':61C ''understand'':78C ''www.coursera.org'':4C ''www.edx.org'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9745, 1, '2016-01-11 12:19:27.103', 1, 68, 'A Simple Explanation of ZooKeeper in Hadoop', '&lt;p&gt;Can Anybody Explain me the role of ZooKeeper and ZooKeeper Quorum in a BigData structure?&lt;/p&gt;

&lt;p&gt;I''d appreciate if you explain me with a simple practical example instead of abstract theoretical definitions.&lt;/p&gt;

&lt;h2&gt;Please&lt;/h2&gt;

&lt;p&gt;As you would explain for a non-technical person&lt;/p&gt;
', 'bigdata  hadoop', NULL, '''/h2'':48C ''/p'':26C,45C,60C ''abstract'':42C ''anybodi'':12C ''appreci'':30C ''bigdata'':8B,24C ''d'':29C ''definit'':44C ''exampl'':39C ''explain'':13C,33C,53C ''explan'':3A ''h2'':46C ''hadoop'':7A,9B ''instead'':40C ''non'':57C ''non-techn'':56C ''p'':10C,27C,49C ''person'':59C ''pleas'':47C ''practic'':38C ''quorum'':21C ''role'':16C ''simpl'':2A,37C ''structur'':25C ''technic'':58C ''theoret'':43C ''would'':52C ''zookeep'':5A,18C,20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6101, 1, '2015-06-12 18:33:57.383', 4, 82, 'Price optimization for tiered and seasonal products', '&lt;p&gt;Assuming I can collect the demand of the purchase of a certain product that are of different market tiers. Example: Product A is low end goods. Product B is another low end goods. Product C and D are middle-tier goods and product E and F are high-tier goods.&lt;/p&gt;

&lt;p&gt;We have collected data the last year on the following
1. Which time period (season - festive? non-festive?) does the different tier product reacts based on the price set? Reacts refer to how many % of the product is sold at certain price range
2. How fast the reaction from the market after marketing is done? Marketing is done on 10 June and the products are all sold by 18 June for festive season that slated to happened in July (took 8 days at that price to finish selling)&lt;/p&gt;

&lt;p&gt;How can data science benefit in terms of recommending
1. If we should push the marketing earlier or later?
2. If we can higher or lower the price? (Based on demand and sealing rate?)&lt;/p&gt;

&lt;p&gt;Am I understanding it right that data science can help a marketer in this aspect? Which direction should I be looking into if I am interested to learn about it.&lt;/p&gt;
', 'recommendation', NULL, '''/p'':62C,153C,189C,221C ''1'':74C,164C ''10'':124C ''18'':133C ''2'':108C,174C ''8'':145C ''anoth'':39C ''aspect'':205C ''assum'':10C ''b'':37C ''base'':89C,183C ''benefit'':159C ''c'':44C ''certain'':21C,105C ''collect'':13C,66C ''d'':46C ''data'':67C,157C,197C ''day'':146C ''demand'':15C,185C ''differ'':26C,85C ''direct'':207C ''done'':119C,122C ''e'':54C ''earlier'':171C ''end'':34C,41C ''exampl'':29C ''f'':56C ''fast'':110C ''festiv'':79C,82C,136C ''finish'':151C ''follow'':73C ''good'':35C,42C,51C,61C ''happen'':141C ''help'':200C ''high'':59C ''high-tier'':58C ''higher'':178C ''interest'':216C ''juli'':143C ''june'':125C,134C ''last'':69C ''later'':173C ''learn'':218C ''look'':211C ''low'':33C,40C ''lower'':180C ''mani'':98C ''market'':27C,115C,117C,120C,170C,202C ''middl'':49C ''middle-ti'':48C ''non'':81C ''non-fest'':80C ''optim'':2A ''p'':9C,63C,154C,190C ''period'':77C ''price'':1A,92C,106C,149C,182C ''product'':7A,22C,30C,36C,43C,53C,87C,101C,128C ''purchas'':18C ''push'':168C ''rang'':107C ''rate'':188C ''react'':88C,94C ''reaction'':112C ''recommend'':8B,163C ''refer'':95C ''right'':195C ''scienc'':158C,198C ''seal'':187C ''season'':6A,78C,137C ''sell'':152C ''set'':93C ''slate'':139C ''sold'':103C,131C ''term'':161C ''tier'':4A,28C,50C,60C,86C ''time'':76C ''took'':144C ''understand'':193C ''year'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6075, 1, '2015-06-10 20:38:49.41', 0, 47, 'Cross selling + Classifier modeling strategy', '&lt;p&gt;Product A or B can be offered to a customer in a cross-selling setting. 
Through A/B testing, data was collected about the customers that buy A and those that buy B.&lt;/p&gt;

&lt;p&gt;Now there are different ways to model this. 
We could have two models that model P(buysA; X) and P(buysB; X) and offer the product that we believe has the highest probability of being bought. 
The problem with this is that it requires well calibrated probabilities. &lt;/p&gt;

&lt;p&gt;Another possibility is to use a model that directly chooses between showing A or B. The problem with this case is that there is no direct data where the customer HAD to choose between both, either they were presented with one OR the other. &lt;/p&gt;

&lt;p&gt;What would be the sane approach to model this?&lt;/p&gt;
', 'classification  predictive-modeling  unbalanced-classes', NULL, '''/p'':47C,96C,142C,153C ''a/b'':31C ''anoth'':98C ''approach'':149C ''b'':17C,46C,112C ''believ'':77C ''bought'':84C ''buy'':40C,45C ''buysa'':65C ''buysb'':69C ''calibr'':94C ''case'':117C ''choos'':107C,130C ''class'':12B ''classif'':6B ''classifi'':3A ''collect'':35C ''could'':58C ''cross'':1A,27C ''cross-sel'':26C ''custom'':23C,38C,127C ''data'':33C,124C ''differ'':52C ''direct'':106C,123C ''either'':133C ''highest'':80C ''model'':4A,9B,55C,61C,63C,104C,151C ''offer'':20C,72C ''one'':138C ''p'':13C,48C,64C,68C,97C,143C ''possibl'':99C ''predict'':8B ''predictive-model'':7B ''present'':136C ''probabl'':81C,95C ''problem'':86C,114C ''product'':14C,74C ''requir'':92C ''sane'':148C ''sell'':2A,28C ''set'':29C ''show'':109C ''strategi'':5A ''test'':32C ''two'':60C ''unbalanc'':11B ''unbalanced-class'':10B ''use'':102C ''way'':53C ''well'':93C ''would'':145C ''x'':66C,70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6076, 1, '2015-06-10 20:39:15.903', 3, 74, 'Most important part of feature standardization and how is standardization affected by sparsity?', '&lt;p&gt;I am thinking of preprocessing techniques for the input data to a convolutional neural network (CNN) using sparse datasets and trained with SGD. In Andrew Ng''s coursera course, &lt;a href="https://www.coursera.org/course/ml" rel="nofollow"&gt;Machine Learning&lt;/a&gt;, he states that it is important to preprocess the data so it fits into the interval $ \left[ 3, 3 \right] $ when using SGD. However, the most common preprocessing technique is to standardize each feature so $ \mu = 0 $ and $ \sigma = 1 $. When standardizing a highly sparse dataset many of the values will not end up in the interval.&lt;/p&gt;

&lt;p&gt;I am therefore curious - would it be better to aim for e.g. $ \mu = 0 $ and $ \sigma = 0.5 $ in order for the values be closer to the interval $ \left[ 3, 3 \right] $? Could anyone argue based on a knowledge of SGD on whether it is most important to aim for $ \mu = 0 $ and $ \sigma = 1 $ or $ \left[ 3, 3 \right] $?&lt;/p&gt;
', 'machine-learning  feature-scaling', NULL, '''/a'':59C ''/course/ml'':54C ''/p'':117C,178C ''0'':96C,132C,169C ''0.5'':135C ''1'':99C,172C ''3'':77C,78C,147C,148C,175C,176C ''affect'':11A ''aim'':128C,166C ''andrew'':45C ''anyon'':151C ''argu'':152C ''base'':153C ''better'':126C ''closer'':142C ''cnn'':36C ''common'':86C ''convolut'':33C ''could'':150C ''cours'':49C ''coursera'':48C ''curious'':122C ''data'':30C,69C ''dataset'':39C,105C ''e.g'':130C ''end'':112C ''featur'':5A,18B,93C ''feature-sc'':17B ''fit'':72C ''high'':103C ''howev'':83C ''href'':51C ''import'':2A,65C,164C ''input'':29C ''interv'':75C,116C,145C ''knowledg'':156C ''learn'':16B,58C ''left'':76C,146C,174C ''machin'':15B,57C ''machine-learn'':14B ''mani'':106C ''mu'':95C,131C,168C ''network'':35C ''neural'':34C ''ng'':46C ''nofollow'':56C ''order'':137C ''p'':20C,118C ''part'':3A ''preprocess'':25C,67C,87C ''rel'':55C ''right'':79C,149C,177C ''scale'':19B ''sgd'':43C,82C,158C ''sigma'':98C,134C,171C ''spars'':38C,104C ''sparsiti'':13A ''standard'':6A,10A,91C,101C ''state'':61C ''techniqu'':26C,88C ''therefor'':121C ''think'':23C ''train'':41C ''use'':37C,81C ''valu'':109C,140C ''whether'':160C ''would'':123C ''www.coursera.org'':53C ''www.coursera.org/course/ml'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6078, 1, '2015-06-11 06:20:43.703', 4, 102, 'Topic models for Relevance Prediction', '&lt;p&gt;Suppose I have data in the form of  Query/Document Pairs, along with corresponding relevance scores (or class labels). Is there a way to use topic modeling to devise a model so that later given a query and a document, we can predict its relevance score?&lt;/p&gt;
', 'text-mining  topic-model', NULL, '''/p'':58C ''along'':23C ''class'':29C ''correspond'':25C ''data'':16C ''devis'':40C ''document'':51C ''form'':19C ''given'':46C ''label'':30C ''later'':45C ''mine'':8B ''model'':2A,11B,38C,42C ''p'':12C ''pair'':22C ''predict'':5A,54C ''queri'':48C ''query/document'':21C ''relev'':4A,26C,56C ''score'':27C,57C ''suppos'':13C ''text'':7B ''text-min'':6B ''topic'':1A,10B,37C ''topic-model'':9B ''use'':36C ''way'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6083, 2, '2015-06-11 16:48:26.687', 2, NULL, NULL, '&lt;p&gt;You are going to need to make a column that contains software info-- for example name it software and the salary column has the corresponding salary so something like&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; Software   Salary
 Microsoft  100
 Microsoft  300
 Microsoft  400
 SQL        200
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and so on...then you can plot with the code below&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;p &amp;lt;- ggplot(test, aes(factor(software), salary))
p + geom_boxplot() + coord_flip()
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':44C,74C ''/p'':31C,58C ''/pre'':45C,75C ''100'':37C ''200'':43C ''300'':39C ''400'':41C ''ae'':65C ''boxplot'':71C ''code'':33C,56C,60C ''column'':10C,23C ''contain'':12C ''coord'':72C ''correspond'':26C ''exampl'':16C ''factor'':66C ''flip'':73C ''geom'':70C ''ggplot'':63C ''go'':4C ''info'':14C ''like'':30C ''lt'':62C ''make'':8C ''microsoft'':36C,38C,40C ''name'':17C ''need'':6C ''p'':1C,46C,61C,69C ''plot'':53C ''pre'':32C,59C ''salari'':22C,27C,35C,68C ''softwar'':13C,19C,34C,67C ''someth'':29C ''sql'':42C ''test'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6084, 1, '2015-06-11 16:58:53.38', 9, 599, 'How do I create a complex Radar Chart?', '&lt;p&gt;So, I want to create a Player Profile &lt;strong&gt;Radar Chart&lt;/strong&gt; something like this &lt;a href="https://twitter.com/mixedknuts/status/575984119739498496"&gt;https://twitter.com/mixedknuts/status/575984119739498496&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Not only the scale of each variable different, but also I want a reversed scale for some statistics like the ''dispossessed'' stat, where less actually means good.&lt;/p&gt;

&lt;p&gt;One solution for the variable scale for each statistic maybe is setting a benchmark and then calculating a score on a scale of 100? &lt;/p&gt;

&lt;p&gt;But, How do I display the actual numbers on the chart then? Also, how do I get the reversed scale for some of the statistics.&lt;/p&gt;

&lt;p&gt;Currently working in Excel. What is the most powerful tool to create a complex chart like this?&lt;/p&gt;
', 'visualization', NULL, '''/mixedknuts/status/575984119739498496'':30C ''/mixedknuts/status/575984119739498496&lt;/a&gt;&lt;/p&gt;'':33C ''/p'':62C,88C,115C,134C ''/strong'':22C ''100'':87C ''actual'':59C,96C ''also'':44C,102C ''benchmark'':77C ''calcul'':80C ''chart'':8A,21C,100C,131C ''complex'':6A,130C ''creat'':4A,15C,128C ''current'':117C ''differ'':42C ''display'':94C ''dispossess'':55C ''excel'':120C ''get'':106C ''good'':61C ''href'':27C ''less'':58C ''like'':24C,53C,132C ''mayb'':73C ''mean'':60C ''number'':97C ''one'':64C ''p'':10C,34C,63C,89C,116C ''player'':17C ''power'':125C ''profil'':18C ''radar'':7A,20C ''revers'':48C,108C ''scale'':38C,49C,69C,85C,109C ''score'':82C ''set'':75C ''solut'':65C ''someth'':23C ''stat'':56C ''statist'':52C,72C,114C ''strong'':19C ''tool'':126C ''twitter.com'':29C,32C ''twitter.com/mixedknuts/status/575984119739498496'':28C ''twitter.com/mixedknuts/status/575984119739498496&lt;/a&gt;&lt;/p&gt;'':31C ''variabl'':41C,68C ''visual'':9B ''want'':13C,46C ''work'':118C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9921, 4, '2016-01-23 17:39:45.1', 0, NULL, NULL, 'Orange is a free and open-source component-based data mining and machine learning software suite, featuring friendly yet powerful and flexible visual programming front-end for explorative data analysis and visualization. It includes a comprehensive set of components for data preprocessing, feature scoring and filtering, modeling, model evaluation, and various exploration techniques.', NULL, NULL, '''analysi'':33C ''base'':11C ''compon'':10C,42C ''component-bas'':9C ''comprehens'':39C ''data'':12C,32C,44C ''end'':29C ''evalu'':52C ''explor'':31C,55C ''featur'':19C,46C ''filter'':49C ''flexibl'':24C ''free'':4C ''friend'':20C ''front'':28C ''front-end'':27C ''includ'':37C ''learn'':16C ''machin'':15C ''mine'':13C ''model'':50C,51C ''open'':7C ''open-sourc'':6C ''orang'':1C ''power'':22C ''preprocess'':45C ''program'':26C ''score'':47C ''set'':40C ''softwar'':17C ''sourc'':8C ''suit'':18C ''techniqu'':56C ''various'':54C ''visual'':25C,35C ''yet'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6086, 1, '2015-06-11 21:21:55.473', 9, 151, 'MinHashing vs SimHashing', '&lt;p&gt;Suppose I have five sets I''d like to cluster. I understand that the SimHashing technique described here:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://moultano.wordpress.com/2010/01/21/simple-simhashing-3kbzhsxyg4467-6/"&gt;https://moultano.wordpress.com/2010/01/21/simple-simhashing-3kbzhsxyg4467-6/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;could yield three clusters (&lt;code&gt;{A}&lt;/code&gt;, &lt;code&gt;{B,C,D}&lt;/code&gt; and &lt;code&gt;{E}&lt;/code&gt;), for instance, if its results were:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;A -&amp;gt; h01
B -&amp;gt; h02
C -&amp;gt; h02
D -&amp;gt; h02
E -&amp;gt; h03
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Similarly, the MinHashing technique described in the Chapter 3 of the MMDS book:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://infolab.stanford.edu/~ullman/mmds/ch3.pdf"&gt;http://infolab.stanford.edu/~ullman/mmds/ch3.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;could also yield the same three clusters if its results were:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;A -&amp;gt; h01 - h02 - h03

B -&amp;gt; h04 - h05 - h06
      |
C -&amp;gt; h04 - h07 - h08
                  |
D -&amp;gt; h09 - h10 - h08

E -&amp;gt; h11 - h12 - h13
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;(Each set corresponds to a MH signature composed of three "bands", and two sets are grouped if at least one of their signature bands is matching. More bands would mean more matching chances.)&lt;/p&gt;

&lt;p&gt;However I have several questions related to these:&lt;/p&gt;

&lt;p&gt;(1) Can SH be understood as a &lt;em&gt;single band&lt;/em&gt; version of MH?&lt;/p&gt;

&lt;p&gt;(2) Does MH necessarily imply the use of a data structure like Union-Find to build the clusters?&lt;/p&gt;

&lt;p&gt;(3) Am I right in thinking that the clusters, in both techniques, are actually "pre-clusters", in the sense that they are just sets of "candidate pairs"?&lt;/p&gt;

&lt;p&gt;(4) If (3) is true, does it imply that I still have to do an $O(n^2)$ search inside each "pre-cluster", to partition them further into "real" clusters? (which might be reasonable if I have a lot of small and fairly balanced pre-clusters, not so much otherwise)&lt;/p&gt;
', 'clustering  similarity', NULL, '''/2010/01/21/simple-simhashing-3kbzhsxyg4467-6/'':31C ''/2010/01/21/simple-simhashing-3kbzhsxyg4467-6/&lt;/a&gt;&lt;/p&gt;'':34C ''/code'':42C,47C,51C,76C,142C ''/em'':200C ''/p'':25C,58C,92C,114C,178C,188C,204C,225C,255C,309C ''/pre'':77C,143C ''/~ullman/mmds/ch3.pdf'':98C ''/~ullman/mmds/ch3.pdf&lt;/a&gt;&lt;/p&gt;'':101C ''1'':190C ''2'':206C,274C ''3'':87C,227C,259C ''4'':257C ''actual'':240C ''also'':104C ''b'':44C,64C,122C ''balanc'':301C ''band'':155C,168C,172C,199C ''book'':91C ''build'':222C ''c'':45C,67C,127C ''candid'':253C ''chanc'':177C ''chapter'':86C ''cluster'':4B,16C,39C,109C,224C,235C,243C,280C,287C,304C ''code'':40C,43C,49C,60C,116C ''compos'':152C ''correspond'':147C ''could'':36C,103C ''d'':13C,46C,70C,132C ''data'':215C ''describ'':23C,83C ''e'':50C,73C,137C ''em'':197C ''fair'':300C ''find'':220C ''five'':10C ''group'':160C ''gt'':62C,65C,68C,71C,74C,118C,123C,128C,133C,138C ''h01'':63C,119C ''h02'':66C,69C,72C,120C ''h03'':75C,121C ''h04'':124C,129C ''h05'':125C ''h06'':126C ''h07'':130C ''h08'':131C,136C ''h09'':134C ''h10'':135C ''h11'':139C ''h12'':140C ''h13'':141C ''howev'':180C ''href'':28C,95C ''impli'':210C,264C ''infolab.stanford.edu'':97C,100C ''infolab.stanford.edu/~ullman/mmds/ch3.pdf'':96C ''infolab.stanford.edu/~ullman/mmds/ch3.pdf&lt;/a&gt;&lt;/p&gt;'':99C ''insid'':276C ''instanc'':53C ''least'':163C ''like'':14C,217C ''lot'':296C ''match'':170C,176C ''mean'':174C ''mh'':150C,203C,208C ''might'':289C ''minhash'':1A,81C ''mmds'':90C ''moultano.wordpress.com'':30C,33C ''moultano.wordpress.com/2010/01/21/simple-simhashing-3kbzhsxyg4467-6/'':29C ''moultano.wordpress.com/2010/01/21/simple-simhashing-3kbzhsxyg4467-6/&lt;/a&gt;&lt;/p&gt;'':32C ''much'':307C ''n'':273C ''necessarili'':209C ''o'':272C ''one'':164C ''otherwis'':308C ''p'':6C,26C,35C,78C,93C,102C,144C,179C,189C,205C,226C,256C ''pair'':254C ''partit'':282C ''pre'':59C,115C,242C,279C,303C ''pre-clust'':241C,278C,302C ''question'':184C ''real'':286C ''reason'':291C ''relat'':185C ''result'':56C,112C ''right'':230C ''search'':275C ''sens'':246C ''set'':11C,146C,158C,251C ''sever'':183C ''sh'':192C ''signatur'':151C,167C ''simhash'':3A,21C ''similar'':5B,79C ''singl'':198C ''small'':298C ''still'':267C ''structur'':216C ''suppos'':7C ''techniqu'':22C,82C,238C ''think'':232C ''three'':38C,108C,154C ''true'':261C ''two'':157C ''understand'':18C ''understood'':194C ''union'':219C ''union-find'':218C ''use'':212C ''version'':201C ''vs'':2A ''would'':173C ''yield'':37C,105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6354, 2, '2015-07-06 13:13:24.633', 1, NULL, NULL, '&lt;p&gt;It''s inside a cluster if it is part of the partition of the respective Voronoi diagram. This is a visual explanation that translates to "a point is inside cluster A if it is closest to the centroid of A (compared to all other centroids)."&lt;/p&gt;

&lt;p&gt;If your clusters don''t have infinite boundaries and outliers shouldn''t be in any cluster at all, you might need to refine your approach to something else than k-means that detects outliers.&lt;/p&gt;
', NULL, NULL, '''/p'':47C,84C ''approach'':73C ''boundari'':56C ''centroid'':39C,46C ''closest'':36C ''cluster'':6C,31C,51C,64C ''compar'':42C ''detect'':82C ''diagram'':18C ''els'':76C ''explan'':23C ''infinit'':55C ''insid'':4C,30C ''k'':79C ''k-mean'':78C ''mean'':80C ''might'':68C ''need'':69C ''outlier'':58C,83C ''p'':1C,48C ''part'':10C ''partit'':13C ''point'':28C ''refin'':71C ''respect'':16C ''shouldn'':59C ''someth'':75C ''translat'':25C ''visual'':22C ''voronoi'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6087, 2, '2015-06-11 22:34:19.643', 3, NULL, NULL, '&lt;p&gt;Let''s start by creating some fake dataset. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;software = sample(c("Windows","Linux","Mac"), n=100, replace=T) 
salary = runif(n=100,min=1,max=100) 
test = data.frame(software, salary)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This should create a dataframe &lt;code&gt;test&lt;/code&gt; that will look like somewhat like: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    software    salary
1    Windows 96.697217
2      Linux 29.770905
3    Windows 94.249612
4        Mac 71.188701
5      Linux 94.028326
6      Linux  7.482632
7        Mac 98.841689
8        Mac 81.152623
9    Windows 54.073761
10   Windows  1.707829
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;EDIT based on comment&lt;/strong&gt; Note that if the data does not already exist in the above format, it can be changed to this format. Let''s take a data frame provided in the original question and lets assume the dataframe is called &lt;code&gt;raw_test&lt;/code&gt;.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    windows sql excel salary
1     yes  no   yes    100
2      no  yes  yes    200
3     yes  no   yes    300
4     yes  no    no    400
5      no  no   yes    500
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now, using the &lt;code&gt;melt&lt;/code&gt; function/ method from the &lt;code&gt;reshape&lt;/code&gt; package in &lt;code&gt;R&lt;/code&gt;, first create the dataframe &lt;code&gt;test&lt;/code&gt; (that will be used for final plotting) as follows: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# use melt to convert from wide to long format 
test = melt(raw_test,id.vars=c("salary"))
# subset to only select where value is "yes"
test = subset(test, value == ''yes'')
# replace column name from "variable" to "software" 
names(test)[2] = "software"   
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now, you will get a datframe &lt;code&gt;test&lt;/code&gt; that looks like: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  salary software value
1     100  windows   yes
3     300  windows   yes
4     400  windows   yes
7     200      sql   yes
11    100    excel   yes
12    200    excel   yes
13    300    excel   yes
15    500    excel   yes
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Having created the dataset. We will now generate the plot. &lt;/p&gt;

&lt;p&gt;First, create the bar plot on the left based on the counts of software that represents usage rate. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;p1 &amp;lt;- ggplot(test, aes(factor(software))) + geom_bar() + coord_flip()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Next, create the boxplot on the right. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;p2 &amp;lt;- ggplot(test, aes(factor(software), salary)) + geom_boxplot() + coord_flip()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Finally, place both these plots next to each other. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;require(''gridExtra'')
grid.arrange(p1,p2,nrow=1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This should create a plot like: &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/AbyXC.png" alt="On the left pane shows the counts of how different software is being used represented through bar plots and on the right pane shows the distribution of salaries grouped by software used represented through box plots."&gt;&lt;/p&gt;
', NULL, NULL, '''/abyxc.png'':412C ''/code'':35C,45C,87C,137C,170C,178C,185C,190C,197C,250C,261C,303C,350C,375C,397C ''/p'':10C,52C,138C,207C,265C,316C,336C,360C,387C,406C,450C ''/pre'':36C,88C,171C,251C,304C,351C,376C,398C ''/strong'':95C ''1'':28C,57C,145C,271C,396C ''1.707829'':86C ''10'':84C ''100'':20C,26C,30C,149C,272C,288C ''11'':287C ''12'':291C ''13'':295C ''15'':299C ''2'':60C,150C,248C ''200'':154C,284C,292C ''29.770905'':62C ''3'':63C,155C,275C ''300'':159C,276C,296C ''4'':66C,160C,279C ''400'':164C,280C ''5'':69C,165C ''500'':169C,300C ''54.073761'':83C ''6'':72C ''7'':75C,283C ''7.482632'':74C ''71.188701'':68C ''8'':78C ''81.152623'':80C ''9'':81C ''94.028326'':71C ''94.249612'':65C ''96.697217'':59C ''98.841689'':77C ''ae'':343C,367C ''alreadi'':103C ''alt'':413C ''assum'':129C ''bar'':321C,347C,430C ''base'':92C,326C ''box'':448C ''boxplot'':356C,372C ''c'':15C,224C ''call'':133C ''chang'':112C ''code'':12C,43C,54C,134C,140C,176C,183C,188C,195C,209C,259C,267C,338C,362C,389C ''column'':240C ''comment'':94C ''convert'':213C ''coord'':348C,373C ''count'':329C,420C ''creat'':6C,40C,192C,307C,319C,354C,402C ''data'':100C,120C ''data.frame'':32C ''datafram'':42C,131C,194C ''dataset'':9C,309C ''datfram'':258C ''differ'':423C ''distribut'':439C ''edit'':91C ''excel'':143C,289C,293C,297C,301C ''exist'':104C ''factor'':344C,368C ''fake'':8C ''final'':203C,378C ''first'':191C,318C ''flip'':349C,374C ''follow'':206C ''format'':108C,115C,218C ''frame'':121C ''function'':179C ''generat'':313C ''geom'':346C,371C ''get'':256C ''ggplot'':341C,365C ''grid.arrange'':392C ''gridextra'':391C ''group'':442C ''i.stack.imgur.com'':411C ''i.stack.imgur.com/abyxc.png'':410C ''id.vars'':223C ''img'':408C ''left'':325C,416C ''let'':2C,116C,128C ''like'':49C,51C,264C,405C ''linux'':17C,61C,70C,73C ''long'':217C ''look'':48C,263C ''lt'':340C,364C ''mac'':18C,67C,76C,79C ''max'':29C ''melt'':177C,211C,220C ''method'':180C ''min'':27C ''n'':19C,25C ''name'':241C,246C ''next'':353C,383C ''note'':96C ''nrow'':395C ''origin'':125C ''p'':1C,37C,89C,172C,252C,305C,317C,352C,377C,399C,407C ''p1'':339C,393C ''p2'':363C,394C ''packag'':186C ''pane'':417C,436C ''place'':379C ''plot'':204C,315C,322C,382C,404C,431C,449C ''pre'':11C,53C,139C,208C,266C,337C,361C,388C ''provid'':122C ''question'':126C ''r'':189C ''rate'':335C ''raw'':135C,221C ''replac'':21C,239C ''repres'':333C,428C,446C ''requir'':390C ''reshap'':184C ''right'':359C,435C ''runif'':24C ''salari'':23C,34C,56C,144C,225C,268C,370C,441C ''sampl'':14C ''select'':229C ''show'':418C,437C ''softwar'':13C,33C,55C,245C,249C,269C,331C,345C,369C,424C,444C ''somewhat'':50C ''sql'':142C,285C ''src'':409C ''start'':4C ''strong'':90C ''subset'':226C,235C ''take'':118C ''test'':31C,44C,136C,196C,219C,222C,234C,236C,247C,260C,342C,366C ''usag'':334C ''use'':174C,201C,210C,427C,445C ''valu'':231C,237C,270C ''variabl'':243C ''wide'':215C ''window'':16C,58C,64C,82C,85C,141C,273C,277C,281C ''yes'':146C,148C,152C,153C,156C,158C,161C,168C,233C,238C,274C,278C,282C,286C,290C,294C,298C,302C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6088, 1, '2015-06-12 00:35:25.937', 6, 75, 'Training and testing AdaBoost for low probability classification', '&lt;p&gt;I have a dataset that I want to classify as fraud/not fraud and I have many weak learners. My concern is that there is much more fraud than not fraud, so my weak learners perform better than average, but none perform better than 50% accuracy in the complete set.&lt;/p&gt;

&lt;p&gt;My question is whether I should set up testing and training sets that are half fraud and half not fraud or if I should just use a representative sample.&lt;/p&gt;
', 'classification  unbalanced-classes', NULL, '''/p'':63C,94C ''50'':57C ''accuraci'':58C ''adaboost'':4A ''averag'':51C ''better'':49C,55C ''class'':12B ''classif'':8A,9B ''classifi'':22C ''complet'':61C ''concern'':33C ''dataset'':17C ''fraud'':25C,40C,43C,80C,84C ''fraud/not'':24C ''half'':79C,82C ''learner'':31C,47C ''low'':6A ''mani'':29C ''much'':38C ''none'':53C ''p'':13C,64C ''perform'':48C,54C ''probabl'':7A ''question'':66C ''repres'':92C ''sampl'':93C ''set'':62C,71C,76C ''test'':3A,73C ''train'':1A,75C ''unbalanc'':11B ''unbalanced-class'':10B ''use'':90C ''want'':20C ''weak'':30C,46C ''whether'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6089, 1, '2015-06-12 02:11:51.483', 3, 32, 'How to fit an odd relationship with a function?', '&lt;p&gt;Let''s say there is a function $f$ such that $y = f(x)$. However, if $f$ is a piecewise function such that:&lt;/p&gt;

&lt;p&gt;$$y = \begin{cases} 0 \quad x \leq 0 \\ 1 \quad x &amp;gt;0\end{cases} $$&lt;/p&gt;

&lt;p&gt;How do I fit $f$ in that case?&lt;/p&gt;

&lt;p&gt;Many thanks, guys.&lt;/p&gt;
', 'statistics  predictive-modeling  regression', NULL, '''/p'':38C,55C,65C,70C ''0'':43C,47C,52C ''1'':48C ''begin'':41C ''case'':42C,54C,64C ''end'':53C ''f'':23C,27C,31C,61C ''fit'':3A,60C ''function'':9A,22C,35C ''gt'':51C ''guy'':69C ''howev'':29C ''leq'':46C ''let'':16C ''mani'':67C ''model'':13B ''odd'':5A ''p'':15C,39C,56C,66C ''piecewis'':34C ''predict'':12B ''predictive-model'':11B ''quad'':44C,49C ''regress'':14B ''relationship'':6A ''say'':18C ''statist'':10B ''thank'':68C ''x'':28C,45C,50C ''y'':26C,40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6100, 1, '2015-06-12 15:11:33.597', 2, 57, 'Complete a Hungarian stem to a real word', '&lt;p&gt;I''m quite new to the NLTK package of Python and to NLP too (I usually work in R but for NLP purposes and scraping maybe Python is more able). &lt;/p&gt;

&lt;p&gt;I scrap articles from Hungarian newsportals and want to make a wordcloud out of it to show what are the current trending news topics. First I filter out stopwords and then stem the remaining words. (nltk has Hungarian stemmer) So I''m able to make a frequency table which can be the base of the wordcloud. My problem comes afterwards because stems are usually meaningless chunks (and not lemmas) of real words. I want to somehow complete the stem to a real word.&lt;/p&gt;

&lt;p&gt;My first idea was to assign the most common word or the shortest one (or some combination of this 2 rules) to the stem and represent that in the wordcloud.&lt;/p&gt;

&lt;p&gt;Is there a better solution for stem completion or should I follow a different workflow?&lt;/p&gt;
', 'python  nlp', NULL, '''/p'':42C,127C,159C,176C ''2'':148C ''abl'':41C,86C ''afterward'':103C ''articl'':46C ''assign'':134C ''base'':96C ''better'':164C ''chunk'':109C ''combin'':145C ''come'':102C ''common'':137C ''complet'':1A,120C,168C ''current'':64C ''differ'':174C ''filter'':70C ''first'':68C,130C ''follow'':172C ''frequenc'':90C ''hungarian'':3A,48C,81C ''idea'':131C ''lemma'':112C ''m'':13C,85C ''make'':53C,88C ''mayb'':37C ''meaningless'':108C ''new'':15C ''news'':66C ''newsport'':49C ''nlp'':10B,24C,33C ''nltk'':18C,79C ''one'':142C ''p'':11C,43C,128C,160C ''packag'':19C ''problem'':101C ''purpos'':34C ''python'':9B,21C,38C ''quit'':14C ''r'':30C ''real'':7A,114C,125C ''remain'':77C ''repres'':154C ''rule'':149C ''scrap'':45C ''scrape'':36C ''shortest'':141C ''show'':60C ''solut'':165C ''somehow'':119C ''stem'':4A,75C,105C,122C,152C,167C ''stemmer'':82C ''stopword'':72C ''tabl'':91C ''topic'':67C ''trend'':65C ''usual'':27C,107C ''want'':51C,117C ''word'':8A,78C,115C,126C,138C ''wordcloud'':55C,99C,158C ''work'':28C ''workflow'':175C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6090, 2, '2015-06-12 02:35:04.07', 2, NULL, NULL, '&lt;p&gt;The definition you gave &lt;em&gt;is&lt;/em&gt; the definition of the function. This is called the &lt;a href="http://en.wikipedia.org/wiki/Heaviside_step_function" rel="nofollow"&gt;Heaviside Step Function&lt;/a&gt;. There is not a simple &lt;em&gt;analytic&lt;/em&gt; way to express it (like as a ratio, product, or composition  of trigonometric functions, exponentials, or polynomials). Note that it is neither continuous nor differentiable at x = 0. &lt;/p&gt;

&lt;p&gt;There are a couple of cool ways to represent it. The coolest and most intuitive way is as an integral of a &lt;a href="http://en.wikipedia.org/wiki/Dirac_delta_function" rel="nofollow"&gt;Dirac Delta Function&lt;/a&gt;:&lt;/p&gt;

&lt;p&gt;$$ 
H(x) = \int_{-\infty}^x { \delta(s)} \, \mathrm{d}s 
$$&lt;/p&gt;

&lt;p&gt;Note, though, that a Dirac Delta Function is itself not an "official" function, since it is not well-defined at x = 0. Check out &lt;a href="http://en.wikipedia.org/wiki/Distribution_(mathematics)" rel="nofollow"&gt;Distribution Theory&lt;/a&gt; for some cool info on weird "functions" like this.&lt;/p&gt;

&lt;p&gt;Now, I think you may be trying to approximate this function, because you asked how to "fit" it. Taken straight from Wikipedia:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;For a smooth approximation to the step function, one can use the logistic   function
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;$$
H(x) \approx \frac{1}{2} + \frac{1}{2}\tanh(kx) = \frac{1}{1+\mathrm{e}^{-2kx}},
$$&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;where a larger k corresponds to a sharper transition at x = 0.
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''-2'':218C ''/a'':28C,99C,148C ''/code'':199C,235C ''/em'':8C,36C ''/p'':65C,100C,112C,158C,182C,220C ''/pre'':200C,236C ''/wiki/dirac_delta_function'':93C ''/wiki/distribution_(mathematics)'':143C ''/wiki/heaviside_step_function'':22C ''0'':64C,136C,234C ''1'':206C,209C,214C,215C ''2'':207C,210C ''analyt'':35C ''approx'':204C ''approxim'':168C,188C ''ask'':173C ''call'':16C ''check'':137C ''code'':184C,222C ''composit'':47C ''continu'':59C ''cool'':72C,151C ''coolest'':78C ''correspond'':227C ''coupl'':70C ''d'':110C ''defin'':133C ''definit'':3C,10C ''delta'':97C,107C,119C ''differenti'':61C ''dirac'':96C,118C ''distribut'':146C ''e'':217C ''em'':6C,34C ''en.wikipedia.org'':21C,92C,142C ''en.wikipedia.org/wiki/dirac_delta_function'':91C ''en.wikipedia.org/wiki/distribution_(mathematics)'':141C ''en.wikipedia.org/wiki/heaviside_step_function'':20C ''exponenti'':51C ''express'':39C ''fit'':176C ''frac'':205C,208C,213C ''function'':13C,27C,50C,98C,120C,126C,155C,170C,192C,198C ''gave'':5C ''h'':102C,202C ''heavisid'':25C ''href'':19C,90C,140C ''info'':152C ''infti'':105C ''int'':104C ''integr'':86C ''intuit'':81C ''k'':226C ''kx'':212C,219C ''larger'':225C ''like'':41C,156C ''logist'':197C ''mathrm'':109C,216C ''may'':164C ''neither'':58C ''nofollow'':24C,95C,145C ''note'':54C,114C ''offici'':125C ''one'':193C ''p'':1C,66C,101C,113C,159C,201C ''polynomi'':53C ''pre'':183C,221C ''product'':45C ''ratio'':44C ''rel'':23C,94C,144C ''repres'':75C ''sharper'':230C ''simpl'':33C ''sinc'':127C ''smooth'':187C ''step'':26C,191C ''straight'':179C ''taken'':178C ''tanh'':211C ''theori'':147C ''think'':162C ''though'':115C ''transit'':231C ''tri'':166C ''trigonometr'':49C ''use'':195C ''way'':37C,73C,82C ''weird'':154C ''well'':132C ''well-defin'':131C ''wikipedia'':181C ''x'':63C,103C,106C,135C,203C,233C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6092, 1, '2015-06-12 10:25:12.457', 0, 167, 'How to generate ratings without training data?', '&lt;p&gt;I am working on generating restaurant ratings automatically and I &lt;strong&gt;have various feature values&lt;/strong&gt; like delivery time, cost estimate, etc. I want to generate a rating for each restaurant between 0 to 5. But &lt;strong&gt;I don''t have any training data or ground truth to validate&lt;/strong&gt;. This rating might vary with user. Most of the related work, mostly related to the Yelp data challenge, have some relevance score as training data. I though of using &lt;em&gt;reinforcement learning&lt;/em&gt; to learn the rating with user feedback, but not sure how to do that. Can anyone please suggest a relevant technique or algorithm for this problem?&lt;/p&gt;
', 'machine-learning  dataset  cross-validation  reinforcement-learning', NULL, '''/em'':101C ''/p'':128C ''/strong'':34C,68C ''0'':51C ''5'':53C ''algorithm'':124C ''anyon'':117C ''automat'':26C ''challeng'':86C ''cost'':38C ''cross'':13B ''cross-valid'':12B ''data'':7A,62C,85C,93C ''dataset'':11B ''deliveri'':36C ''em'':98C ''estim'':39C ''etc'':40C ''featur'':32C ''feedback'':108C ''generat'':3A,23C,44C ''ground'':64C ''learn'':10B,17B,100C,103C ''like'':35C ''machin'':9B ''machine-learn'':8B ''might'':71C ''most'':80C ''p'':18C ''pleas'':118C ''problem'':127C ''rate'':4A,25C,46C,70C,105C ''reinforc'':16B,99C ''reinforcement-learn'':15B ''relat'':78C,81C ''relev'':89C,121C ''restaur'':24C,49C ''score'':90C ''strong'':29C,55C ''suggest'':119C ''sure'':111C ''techniqu'':122C ''though'':95C ''time'':37C ''train'':6A,61C,92C ''truth'':65C ''use'':97C ''user'':74C,107C ''valid'':14B,67C ''valu'':33C ''vari'':72C ''various'':31C ''want'':42C ''without'':5A ''work'':21C,79C ''yelp'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6093, 2, '2015-06-12 10:57:34.29', 0, NULL, NULL, '&lt;p&gt;There is another interesting technique in &lt;a href="https://www.cs.purdue.edu/homes/lsi/sigir04-cf-norm.pdf" rel="nofollow"&gt;this&lt;/a&gt; paper called the decoupling normalization method. I''ve used it and found that the results are good. This finds the affinity of user to a particular item and then you can scale it to a scale of 5 or 10, whichever you want. Hope it helps.&lt;/p&gt;
', NULL, NULL, '''/a'':16C ''/homes/lsi/sigir04-cf-norm.pdf'':12C ''/p'':63C ''10'':56C ''5'':54C ''affin'':37C ''anoth'':4C ''call'':18C ''decoupl'':20C ''find'':35C ''found'':28C ''good'':33C ''help'':62C ''hope'':60C ''href'':9C ''interest'':5C ''item'':43C ''method'':22C ''nofollow'':14C ''normal'':21C ''p'':1C ''paper'':17C ''particular'':42C ''rel'':13C ''result'':31C ''scale'':48C,52C ''techniqu'':6C ''use'':25C ''user'':39C ''ve'':24C ''want'':59C ''whichev'':57C ''www.cs.purdue.edu'':11C ''www.cs.purdue.edu/homes/lsi/sigir04-cf-norm.pdf'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6094, 2, '2015-06-12 11:25:02.01', 1, NULL, NULL, '&lt;p&gt;If you model your system by means of reinforcement learning, you will make your system learn from the user''s feedback. The system will provide a rating based on the input features (it may be just a random rating during the first stages, since you will not have any prior information), and then the user will tell the system how well it predicted such rating. Based on the difference between the suggested and the actual user''s ratings, the reinforcement learning algorithm will refine the recommendation system''s model in order to provide more accurate ratings in the future.  &lt;/p&gt;

&lt;p&gt;Sutton''s book on reinforcement learning (&lt;a href="http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html" rel="nofollow"&gt;http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html&lt;/a&gt;) is a good introduction to the reinforcement learning field. &lt;/p&gt;
', NULL, NULL, '''/p'':101C,128C ''/~sutton/book/the-book.html'':113C ''/~sutton/book/the-book.html&lt;/a&gt;)'':118C ''accur'':96C ''actual'':76C ''algorithm'':83C ''base'':29C,67C ''book'':105C ''differ'':70C ''featur'':33C ''feedback'':22C ''field'':127C ''first'':43C ''futur'':100C ''good'':121C ''href'':110C ''inform'':52C ''input'':32C ''introduct'':122C ''learn'':11C,17C,82C,108C,126C ''make'':14C ''may'':35C ''mean'':8C ''model'':4C,90C ''nofollow'':115C ''order'':92C ''p'':1C,102C ''predict'':64C ''prior'':51C ''provid'':26C,94C ''random'':39C ''rate'':28C,40C,66C,79C,97C ''recommend'':87C ''refin'':85C ''reinforc'':10C,81C,107C,125C ''rel'':114C ''sinc'':45C ''stage'':44C ''suggest'':73C ''sutton'':103C ''system'':6C,16C,24C,60C,88C ''tell'':58C ''user'':20C,56C,77C ''webdocs.cs.ualberta.ca'':112C,117C ''webdocs.cs.ualberta.ca/~sutton/book/the-book.html'':111C ''webdocs.cs.ualberta.ca/~sutton/book/the-book.html&lt;/a&gt;)'':116C ''well'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6097, 1, '2015-06-12 13:20:15.063', 1, 27, 'How to extract the values used in columns by a Classification model in R?', '&lt;p&gt;I want to find the variables (and its values) used to build a  classification model?&lt;/p&gt;

&lt;p&gt;I came to know that the following code does the same&lt;/p&gt;

&lt;p&gt;get_all_vars(model_built, dataset_used_for_building_a_model)&lt;/p&gt;

&lt;p&gt;However I would not be able to use the above code as I am going to use the "model_built" alone in the R code and i will not be having the "dataset_used_for_building_a_mode.&lt;/p&gt;

&lt;p&gt;Simply put i want to use this "Model_built" alone and fetch the variables used along with the values. &lt;/p&gt;
', 'machine-learning  classification  r', NULL, '''/p'':36C,49C,62C,102C,123C ''abl'':69C ''alon'':84C,113C ''along'':119C ''build'':32C,59C,99C ''built'':55C,83C,112C ''came'':39C ''classif'':11A,18B,34C ''code'':45C,74C,88C ''column'':8A ''dataset'':56C,96C ''extract'':3A ''fetch'':115C ''find'':24C ''follow'':44C ''get'':51C ''go'':78C ''howev'':64C ''know'':41C ''learn'':17B ''machin'':16B ''machine-learn'':15B ''mode'':101C ''model'':12A,35C,54C,61C,82C,111C ''p'':20C,37C,50C,63C,103C ''put'':105C ''r'':14A,19B,87C ''simpli'':104C ''use'':6A,30C,57C,71C,80C,97C,109C,118C ''valu'':5A,29C,122C ''var'':53C ''variabl'':26C,117C ''want'':22C,107C ''would'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6098, 1, '2015-06-12 13:39:15.88', 5, 85, 'how can I generate a Bernoulli block mixture model in matlab?', '&lt;p&gt;I am trying to write the code of a Bernoulli block mixture model in matlab, but am facing an error every time I run the function. In particular, I''m having a problem with how to relate the distribution parameter $\alpha$ to the latent variables $Z$ and $W$.&lt;/p&gt;

&lt;p&gt;In other words, \mathbf{Z} follows a multinomial distribution of parameter $\pi$, such that $\dim(\pi)=(1, g)$, where $g$ is the number of clusters of rows and $\sum \pi=1$. &lt;/p&gt;

&lt;p&gt;$W$ follows a multinomial distribution of parameter $\rho$ such that $\dim(\rho)=(1,m)$, where $m$ is the number of column clusters and $\sum \rho=1$.&lt;/p&gt;

&lt;p&gt;$\mathbf{Z}$ is a matrix of $\dim(N,g)$, and $\mathbf{W}$ is a matrix of $\dim(d,m)$, where $N$ is the number of observations, $p$ is the number of variables, $g$ is the number of row clusters, and $m$ is the number of column clusters.&lt;/p&gt;

&lt;p&gt;$X$ follows a bernoulli distribution of parameter $\alpha_{ZW}$, where the notation $\alpha_{ZW}$ denotes the values of $\alpha$ depends on $\mathbf{Z}$ and $\mathbf{W}$, and $\alpha$ is a $\dim(g,m)$ matrix. Please find attached a graphical representation of the model.&lt;/p&gt;

&lt;p&gt;How do I write the code of $\alpha_{ZW}$ in order to get generate the model?&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/btXXp.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/btXXp.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'machine-learning  clustering  matlab  binary', NULL, '''/a'':259C ''/btxxp.jpg'':246C,253C ''/p'':67C,100C,128C,177C,222C,240C,260C ''1'':85C,99C,114C,127C ''alpha'':59C,186C,191C,197C,206C,231C ''alt'':254C ''attach'':215C ''bernoulli'':6A,28C,182C ''binari'':17B ''block'':7A,29C ''cluster'':15B,93C,123C,168C,176C ''code'':25C,229C ''column'':122C,175C ''d'':147C ''denot'':193C ''depend'':198C ''descript'':257C ''dim'':83C,112C,136C,146C,209C ''distribut'':57C,77C,106C,183C ''enter'':255C ''error'':38C ''everi'':39C ''face'':36C ''find'':214C ''follow'':74C,103C,180C ''function'':44C ''g'':86C,88C,138C,162C,210C ''generat'':4A,237C ''get'':236C ''graphic'':217C ''href'':243C ''i.stack.imgur.com'':245C,252C ''i.stack.imgur.com/btxxp.jpg'':244C,251C ''imag'':256C ''img'':249C ''latent'':62C ''learn'':14B ''m'':48C,115C,117C,148C,170C,211C ''machin'':13B ''machine-learn'':12B ''mathbf'':72C,130C,140C,200C,203C ''matlab'':11A,16B,33C ''matrix'':134C,144C,212C ''mixtur'':8A,30C ''model'':9A,31C,221C,239C ''multinomi'':76C,105C ''n'':137C,150C ''nofollow'':248C ''notat'':190C ''number'':91C,120C,153C,159C,165C,173C ''observ'':155C ''order'':234C ''p'':18C,68C,101C,129C,156C,178C,223C,241C ''paramet'':58C,79C,108C,185C ''particular'':46C ''pi'':80C,84C,98C ''pleas'':213C ''problem'':51C ''rel'':247C ''relat'':55C ''represent'':218C ''rho'':109C,113C,126C ''row'':95C,167C ''run'':42C ''src'':250C ''sum'':97C,125C ''time'':40C ''tri'':21C ''valu'':195C ''variabl'':63C,161C ''w'':66C,102C,141C,204C ''word'':71C ''write'':23C,227C ''x'':179C ''z'':64C,73C,131C,201C ''zw'':187C,192C,232C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6099, 1, '2015-06-12 14:31:49.337', 0, 116, 'Sampling from a multivariate von Mises-Fisher distribution in Python', '&lt;p&gt;I am looking for a simple way to sample from a &lt;a href="https://en.wikipedia.org/wiki/Von_Mises%E2%80%93Fisher_distribution" rel="nofollow"&gt;multivariate von Mises-Fisher&lt;/a&gt; distribution in Python. I have looked in &lt;a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.vonmises.html" rel="nofollow"&gt;the stats module in scipy&lt;/a&gt; and the &lt;a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.vonmises.html#numpy.random.vonmises" rel="nofollow"&gt;numpy module&lt;/a&gt; but only found the univariate von Mises distribution. Is there any code available? I have not found yet.  &lt;/p&gt;

&lt;p&gt;-- edit. Apparently, Wood (1994) has designed an algorithm for sampling from the vMF distribution according to &lt;a href="http://idg.pl/mirrors/CRAN/web/packages/movMF/movMF.pdf" rel="nofollow"&gt;this link&lt;/a&gt;, but I can''t find the paper.&lt;/p&gt;
', 'python  distributed  sampling', NULL, '''/a'':39C,59C,71C,117C ''/doc/numpy/reference/generated/numpy.random.vonmises.html#numpy.random.vonmises'':66C ''/doc/scipy/reference/generated/scipy.stats.vonmises.html'':51C ''/mirrors/cran/web/packages/movmf/movmf.pdf'':112C ''/p'':90C,125C ''/wiki/von_mises%e2%80%93fisher_distribution'':31C ''1994'':95C ''accord'':106C ''algorithm'':99C ''appar'':93C ''avail'':84C ''code'':83C ''design'':97C ''distribut'':9A,13B,40C,79C,105C ''docs.scipy.org'':50C,65C ''docs.scipy.org/doc/numpy/reference/generated/numpy.random.vonmises.html#numpy.random.vonmises'':64C ''docs.scipy.org/doc/scipy/reference/generated/scipy.stats.vonmises.html'':49C ''edit'':92C ''en.wikipedia.org'':30C ''en.wikipedia.org/wiki/von_mises%e2%80%93fisher_distribution'':29C ''find'':122C ''fisher'':8A,38C ''found'':74C,88C ''href'':28C,48C,63C,109C ''idg.pl'':111C ''idg.pl/mirrors/cran/web/packages/movmf/movmf.pdf'':110C ''link'':116C ''look'':18C,45C ''mise'':7A,37C,78C ''mises-fish'':6A,36C ''modul'':56C,70C ''multivari'':4A,34C ''nofollow'':33C,53C,68C,114C ''numpi'':69C ''p'':15C,91C ''paper'':124C ''python'':11A,12B,42C ''rel'':32C,52C,67C,113C ''sampl'':1A,14B,24C,101C ''scipi'':58C ''simpl'':21C ''stat'':55C ''univari'':76C ''vmf'':104C ''von'':5A,35C,77C ''way'':22C ''wood'':94C ''yet'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6126, 2, '2015-06-15 19:02:12.293', 4, NULL, NULL, '&lt;p&gt;Both MCMC methods and network analysis play an important role in data science. I think you should go for the project you like more. However, in my experience, community detection in networks is a niche (applied math/graph theory) of network analysis, while MCMC methods involve lots of statistical and computational concepts. Personally, being a statistician, I would go for the MCMC methods.&lt;/p&gt;
', NULL, NULL, '''/p'':64C ''analysi'':7C,42C ''appli'':37C ''communiti'':30C ''comput'':51C ''concept'':52C ''data'':13C ''detect'':31C ''experi'':29C ''go'':19C,59C ''howev'':26C ''import'':10C ''involv'':46C ''like'':24C ''lot'':47C ''math/graph'':38C ''mcmc'':3C,44C,62C ''method'':4C,45C,63C ''network'':6C,33C,41C ''nich'':36C ''p'':1C ''person'':53C ''play'':8C ''project'':22C ''role'':11C ''scienc'':14C ''statist'':49C ''statistician'':56C ''theori'':39C ''think'':16C ''would'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6276, 2, '2015-06-30 08:05:31.83', 1, NULL, NULL, '&lt;p&gt;Check out PySemantic - &lt;a href="http://github.com/motherbox/pysemantic" rel="nofollow"&gt;http://github.com/motherbox/pysemantic&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;There''s a video too - &lt;a href="https://www.youtube.com/watch?v=6z-18zP4hOA" rel="nofollow"&gt;https://www.youtube.com/watch?v=6z-18zP4hOA&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/motherbox/pysemantic'':9C ''/motherbox/pysemantic&lt;/a&gt;&lt;/p&gt;'':14C ''/watch?v=6z-18zp4hoa'':25C ''/watch?v=6z-18zp4hoa&lt;/a&gt;&lt;/p&gt;'':30C ''check'':2C ''github.com'':8C,13C ''github.com/motherbox/pysemantic'':7C ''github.com/motherbox/pysemantic&lt;/a&gt;&lt;/p&gt;'':12C ''href'':6C,22C ''nofollow'':11C,27C ''p'':1C,15C ''pysemant'':4C ''rel'':10C,26C ''video'':19C ''www.youtube.com'':24C,29C ''www.youtube.com/watch?v=6z-18zp4hoa'':23C ''www.youtube.com/watch?v=6z-18zp4hoa&lt;/a&gt;&lt;/p&gt;'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6102, 2, '2015-06-12 20:01:55.923', 4, NULL, NULL, '&lt;p&gt;You should be able to use &lt;a href="https://en.wikipedia.org/wiki/Linear_regression" rel="nofollow"&gt;linear regression&lt;/a&gt; to find correlation between the factors which cause your products to sell better (or worse).&lt;/p&gt;

&lt;p&gt;There are many correlations you can test against in this data set. Some examples are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;If a product has been marketed aggressively, does it sell more quickly?&lt;/li&gt;
&lt;li&gt;If a low tier item is available, do fewer high-tier items sell?&lt;/li&gt;
&lt;li&gt;If multiple high-tier items are available, are fewer sold of each item?&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Keep in mind that correlation does not necessarily imply causation. Always think about other factors which may cause sales to go up and down. For example, you may sell more high tier items in a season one year than another year. But, this could be due to changes in the overall economy, rather than changes in your pricing.&lt;/p&gt;

&lt;p&gt;The second thing you can do is perform &lt;a href="https://en.wikipedia.org/wiki/A/B_testing" rel="nofollow"&gt;A/B tests&lt;/a&gt; on your product sales pages. This gives you clear feedback right away. Some example tests could be:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Show the user one high-tier product and one low-tier product (A). Show the user two high-tier products and no low-tier products(B). Which page generates more revenue?&lt;/li&gt;
&lt;li&gt;Send out marketing emails for a seasonal sale 5 days in advance to one group of users (A). Send the same email to a different set of users 1 day in advance (B).&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;There are many possibilities. Use your intuition and think about previous knowledge you have about your products.&lt;/p&gt;
', NULL, NULL, '''/a'':17C,177C ''/li'':65C,81C,97C,233C,268C ''/ol'':98C,269C ''/p'':33C,50C,158C,195C,288C ''/wiki/a/b_testing'':172C ''/wiki/linear_regression'':12C ''1'':263C ''5'':243C ''a/b'':175C ''abl'':5C ''advanc'':246C,266C ''aggress'':59C ''alway'':110C ''anoth'':139C ''avail'':73C,90C ''away'':189C ''b'':227C,267C ''better'':30C ''caus'':25C,117C ''causat'':109C ''chang'':147C,154C ''clear'':186C ''correl'':20C,38C,104C ''could'':143C,193C ''data'':45C ''day'':244C,264C ''differ'':259C ''due'':145C ''economi'':151C ''email'':238C,256C ''en.wikipedia.org'':11C,171C ''en.wikipedia.org/wiki/a/b_testing'':170C ''en.wikipedia.org/wiki/linear_regression'':10C ''exampl'':48C,125C,191C ''factor'':23C,114C ''feedback'':187C ''fewer'':75C,92C ''find'':19C ''generat'':230C ''give'':184C ''go'':120C ''group'':249C ''high'':77C,86C,130C,203C,218C ''high-tier'':76C,85C,202C,217C ''href'':9C,169C ''impli'':108C ''intuit'':277C ''item'':71C,79C,88C,96C,132C ''keep'':100C ''knowledg'':282C ''li'':52C,66C,82C,197C,234C ''linear'':15C ''low'':69C,209C,224C ''low-tier'':208C,223C ''mani'':37C,273C ''market'':58C,237C ''may'':116C,127C ''mind'':102C ''multipl'':84C ''necessarili'':107C ''nofollow'':14C,174C ''ol'':51C,196C ''one'':136C,201C,207C,248C ''overal'':150C ''p'':1C,34C,99C,159C,270C ''page'':182C,229C ''perform'':167C ''possibl'':274C ''previous'':281C ''price'':157C ''product'':27C,55C,180C,205C,211C,220C,226C,287C ''quick'':64C ''rather'':152C ''regress'':16C ''rel'':13C,173C ''revenu'':232C ''right'':188C ''sale'':118C,181C,242C ''season'':135C,241C ''second'':161C ''sell'':29C,62C,80C,128C ''send'':235C,253C ''set'':46C,260C ''show'':198C,213C ''sold'':93C ''test'':41C,176C,192C ''thing'':162C ''think'':111C,279C ''tier'':70C,78C,87C,131C,204C,210C,219C,225C ''two'':216C ''use'':7C,275C ''user'':200C,215C,251C,262C ''wors'':32C ''year'':137C,140C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6103, 1, '2015-06-12 20:55:33.353', 2, 114, 'AWS machine learning prediction schema problems', '&lt;p&gt;I''ve trained an AWS Machine Learning model with the training data from here : &lt;a href="https://www.kaggle.com/c/titanic/data" rel="nofollow"&gt;https://www.kaggle.com/c/titanic/data&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I''m now trying to run a batch prediction with the test data from the same source but I get the following error when I try to load the data : "  The schema in this data file must match the datasource used to create the ML model ml-xxxxxxxxx. Ensure that the data file you are using matches the schema structure."&lt;/p&gt;

&lt;p&gt;The schema, as far as I can see, is identical. I have tried it with and without the ''survived'' column which is the value I''m trying to predict. I even tried it with the same training set which obviously has an identical schema and got the same error.&lt;/p&gt;

&lt;p&gt;What am I doing wrong?&lt;/p&gt;
', 'machine-learning  aws', NULL, '''/c/titanic/data'':30C ''/c/titanic/data&lt;/a&gt;&lt;/p&gt;'':35C ''/p'':98C,149C,156C ''aw'':1A,10B,16C ''batch'':44C ''column'':119C ''creat'':79C ''data'':23C,49C,66C,71C,89C ''datasourc'':76C ''ensur'':86C ''error'':59C,148C ''even'':130C ''far'':103C ''file'':72C,90C ''follow'':58C ''get'':56C ''got'':145C ''href'':27C ''ident'':109C,142C ''learn'':3A,9B,18C ''load'':64C ''m'':38C,125C ''machin'':2A,8B,17C ''machine-learn'':7B ''match'':74C,94C ''ml'':81C,84C ''ml-xxxxxxxxx'':83C ''model'':19C,82C ''must'':73C ''nofollow'':32C ''obvious'':139C ''p'':11C,36C,99C,150C ''predict'':4A,45C,128C ''problem'':6A ''rel'':31C ''run'':42C ''schema'':5A,68C,96C,101C,143C ''see'':107C ''set'':137C ''sourc'':53C ''structur'':97C ''surviv'':118C ''test'':48C ''train'':14C,22C,136C ''tri'':40C,62C,112C,126C,131C ''use'':77C,93C ''valu'':123C ''ve'':13C ''without'':116C ''wrong'':155C ''www.kaggle.com'':29C,34C ''www.kaggle.com/c/titanic/data'':28C ''www.kaggle.com/c/titanic/data&lt;/a&gt;&lt;/p&gt;'':33C ''xxxxxxxxx'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6104, 1, '2015-06-12 22:03:46.95', 1, 22, 'Working with docker image on an oracle virtual box', '&lt;p&gt;I downloaded the docker application and installed it on my Windows machine. After the installation, I tried to access the &lt;strong&gt;boot2docker&lt;/strong&gt; application to work with the docker. But, it wasn''t work in the way it was supposed to work. So, I opened the oracle virtual box. I found the &lt;strong&gt;boot2docker vm&lt;/strong&gt; image there and accessed it. I have a spark application installed using the docker image.&lt;/p&gt;

&lt;p&gt;Now, I am trying to execute a simple spark application that I wrote usings scala. During the execution, I am getting the error &lt;strong&gt;" There is an insufficient memory for the Java Runtime Environment to continue"&lt;/strong&gt;. I have been trying different options to resolve this issue and could not resolve it. Can someone please help me with this issue. I am fairly new to the big data concepts and I haven''t gotten a complete sense over spark, docker and virtual box. &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/aE0rO.jpg" alt="Screenshot"&gt;&lt;/p&gt;
', 'bigdata  apache-spark  scala', NULL, '''/ae0ro.jpg'':178C ''/p'':87C,172C,181C ''/strong'':38C,71C,125C ''access'':34C,75C ''alt'':179C ''apach'':12B ''apache-spark'':11B ''applic'':20C,39C,81C,98C ''big'':155C ''bigdata'':10B ''boot2docker'':37C,69C ''box'':9A,64C,171C ''complet'':164C ''concept'':157C ''continu'':124C ''could'':137C ''data'':156C ''differ'':130C ''docker'':3A,19C,44C,85C,168C ''download'':17C ''environ'':122C ''error'':111C ''execut'':94C,106C ''fair'':151C ''found'':66C ''get'':109C ''gotten'':162C ''haven'':160C ''help'':144C ''i.stack.imgur.com'':177C ''i.stack.imgur.com/ae0ro.jpg'':176C ''imag'':4A,72C,86C ''img'':174C ''instal'':22C,30C,82C ''insuffici'':116C ''issu'':135C,148C ''java'':120C ''machin'':27C ''memori'':117C ''new'':152C ''open'':60C ''option'':131C ''oracl'':7A,62C ''p'':15C,88C,173C ''pleas'':143C ''resolv'':133C,139C ''runtim'':121C ''scala'':14B,103C ''screenshot'':180C ''sens'':165C ''simpl'':96C ''someon'':142C ''spark'':13B,80C,97C,167C ''src'':175C ''strong'':36C,68C,112C ''suppos'':55C ''tri'':32C,92C,129C ''use'':83C,102C ''virtual'':8A,63C,170C ''vm'':70C ''wasn'':47C ''way'':52C ''window'':26C ''work'':1A,41C,49C,57C ''wrote'':101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6105, 2, '2015-06-13 02:06:13.92', 4, NULL, NULL, '&lt;p&gt;It looks like you can sample the von Mises-Fisher distribution with that R package. Have you thought about calling R from within Python using the &lt;a href="https://sites.google.com/site/aslugsguidetopython/data-analysis/pandas/calling-r-from-python" rel="nofollow"&gt;rpy2&lt;/a&gt; package? I haven''t tried this for myself, but could you try the following?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from numpy import *
import scipy as sp
from pandas import *
from rpy2.robjects.packages import importr
import rpy2.robjects as ro
import pandas.rpy.common as com
from rpy2.robjects.packages import importr

# import the movMF R package
movMF = importr(''movMF'')
# call the rmovMF sampling function from the R package
print(movMF.rmovMF(10, 3 * c(1, -1) / sqrt(2)))
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''-1'':104C ''/a'':37C ''/code'':107C ''/p'':52C ''/pre'':108C ''/site/aslugsguidetopython/data-analysis/pandas/calling-r-from-python'':33C ''1'':103C ''10'':100C ''2'':106C ''3'':101C ''c'':102C ''call'':22C,89C ''code'':54C ''com'':76C ''could'':47C ''distribut'':13C ''fisher'':12C ''follow'':51C ''function'':93C ''haven'':40C ''href'':30C ''import'':57C,58C,64C,67C,69C,73C,79C,81C ''importr'':68C,80C,87C ''like'':4C ''look'':3C ''mise'':11C ''mises-fish'':10C ''movmf'':83C,86C,88C ''movmf.rmovmf'':99C ''nofollow'':35C ''numpi'':56C ''p'':1C ''packag'':17C,38C,85C,97C ''panda'':63C ''pandas.rpy.common'':74C ''pre'':53C ''print'':98C ''python'':26C ''r'':16C,23C,84C,96C ''rel'':34C ''rmovmf'':91C ''ro'':72C ''rpy2'':36C ''rpy2.robjects'':70C ''rpy2.robjects.packages'':66C,78C ''sampl'':7C,92C ''scipi'':59C ''sites.google.com'':32C ''sites.google.com/site/aslugsguidetopython/data-analysis/pandas/calling-r-from-python'':31C ''sp'':61C ''sqrt'':105C ''thought'':20C ''tri'':42C,49C ''use'':27C ''von'':9C ''within'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6139, 1, '2015-06-18 07:09:08.14', 1, 69, 'Can we use HDFS and big data Analytics for processing huge log files being processed through some application on some central server?', '&lt;p&gt;Detailed Question Explanation:&lt;/p&gt;

&lt;p&gt;Suppose say our application X is processing huge logs (size varying from MBs to GBs) and giving insight results in these logs(NOT A Social Data logs or Security Logs)&lt;/p&gt;

&lt;p&gt;now this logs are in format say log.y with different variety, using C++ as Engine to process these huge logs.(It generates imp. insights about data but need to be processed using our application X only and we don''t want to change core way processing of application X)&lt;/p&gt;

&lt;p&gt;If this processing happens on some server it under or over utilizes resources (That I already know).&lt;/p&gt;

&lt;p&gt;If we use cloud computing for this processing we get that processing power with optimum usage. &lt;/p&gt;

&lt;p&gt;How do we see help of BIG data analytics in this particular sort of usage?
Any help or suggestion is very deeply appreciated  &lt;/p&gt;
', 'bigdata  hadoop', NULL, '''/p'':29C,61C,112C,131C,149C,174C ''alreadi'':129C ''analyt'':8A,159C ''applic'':18A,34C,96C,110C ''appreci'':173C ''big'':6A,157C ''bigdata'':23B ''c'':75C ''central'':21A ''chang'':105C ''cloud'':136C ''comput'':137C ''core'':106C ''data'':7A,56C,88C,158C ''deepli'':172C ''detail'':26C ''differ'':72C ''engin'':77C ''explan'':28C ''file'':13A ''format'':68C ''gbs'':45C ''generat'':84C ''get'':142C ''give'':47C ''hadoop'':24B ''happen'':117C ''hdfs'':4A ''help'':155C,167C ''huge'':11A,38C,81C ''imp'':85C ''insight'':48C,86C ''know'':130C ''log'':12A,39C,52C,57C,60C,65C,82C ''log.y'':70C ''mbs'':43C ''need'':90C ''optimum'':147C ''p'':25C,30C,62C,113C,132C,150C ''particular'':162C ''power'':145C ''process'':10A,15A,37C,79C,93C,108C,116C,140C,144C ''question'':27C ''resourc'':126C ''result'':49C ''say'':32C,69C ''secur'':59C ''see'':154C ''server'':22A,120C ''size'':40C ''social'':55C ''sort'':163C ''suggest'':169C ''suppos'':31C ''usag'':148C,165C ''use'':3A,74C,94C,135C ''util'':125C ''vari'':41C ''varieti'':73C ''want'':103C ''way'':107C ''x'':35C,97C,111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6107, 1, '2015-06-13 09:56:45.397', 11, 2385, 'What are deconvolutional layers?', '&lt;p&gt;I recently read &lt;a href="http://arxiv.org/abs/1411.4038"&gt;Fully Convolutional Networks for Semantic Segmentation&lt;/a&gt; by Jonathan Long, Evan Shelhamer, Trevor Darrell. I don''t understand what "deconvolutional layers" do / how they work.&lt;/p&gt;

&lt;p&gt;The relevant part is&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;3.3. Upsampling is backwards strided convolution&lt;/p&gt;
  
  &lt;p&gt;Another way to connect coarse outputs to dense pixels
  is interpolation. For instance, simple bilinear interpolation
  computes each output $y_{ij}$ from the nearest four inputs by a
  linear map that depends only on the relative positions of the
  input and output cells.&lt;br/&gt;
  In a sense, upsampling with factor $f$ is convolution with
  a fractional input stride of 1/f. So long as $f$ is integral, a
  natural way to upsample is therefore backwards convolution
  (sometimes called deconvolution) with an output stride of
  $f$. Such an operation is trivial to implement, since it simply
  reverses the forward and backward passes of convolution.&lt;br/&gt;
  Thus upsampling is performed in-network for end-to-end
  learning by backpropagation from the pixelwise loss.&lt;br/&gt;
  Note that the deconvolution filter in such a layer need not
  be fixed (e.g., to bilinear upsampling), but can be learned.
  A stack of deconvolution layers and activation functions can
  even learn a nonlinear upsampling.&lt;br/&gt;
  In our experiments, we find that in-network upsampling
  is fast and effective for learning dense prediction. Our best
  segmentation architecture uses these layers to learn to upsample
  for refined prediction in Section 4.2.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I don''t think I really understood how convolutional layers are trained. &lt;/p&gt;

&lt;p&gt;What I think I''ve understood is that convolutional layers with a kernel size $k$ learn filters of size $k \times k$. The output of a convolutional layer with kernel size $k$, stride $s \in \mathbb{N}$ and $n$ filters is of dimension $\frac{\text{Input dim}}{s^2} \cdot n$. However, I don''t know how the learning of convolutional layers works. (I understand how simple MLPs learn with gradient descent, if that helps).&lt;/p&gt;

&lt;p&gt;So if my understanding of convolutional layers is correct, I have no clue how this can be reversed.&lt;/p&gt;

&lt;p&gt;Could anybody please help me to understand deconvolutional layers?&lt;/p&gt;
', 'neuralnetwork  convnet', NULL, '''/a'':22C ''/abs/1411.4038'':15C ''/blockquote'':253C ''/p'':41C,47C,56C,252C,267C,344C,364C,375C ''1/f'':117C ''2'':317C ''3.3'':50C ''4.2'':251C ''activ'':208C ''anoth'':58C ''anybodi'':367C ''architectur'':238C ''arxiv.org'':14C ''arxiv.org/abs/1411.4038'':13C ''backpropag'':175C ''backward'':53C,131C,156C ''best'':236C ''bilinear'':72C,196C ''blockquot'':48C ''br'':101C,160C,180C,216C ''call'':134C ''cdot'':318C ''cell'':100C ''clue'':358C ''coars'':62C ''comput'':74C ''connect'':61C ''convnet'':6B ''convolut'':17C,55C,110C,132C,159C,263C,277C,295C,329C,351C ''correct'':354C ''could'':366C ''darrel'':29C ''deconvolut'':3A,35C,135C,184C,205C,373C ''dens'':65C,233C ''depend'':89C ''descent'':340C ''dim'':315C ''dimens'':311C ''e.g'':194C ''effect'':230C ''end'':170C,172C ''end-to-end'':169C ''evan'':26C ''even'':211C ''experi'':219C ''f'':108C,121C,141C ''factor'':107C ''fast'':228C ''filter'':185C,285C,308C ''find'':221C ''fix'':193C ''forward'':154C ''four'':82C ''frac'':312C ''fraction'':113C ''fulli'':16C ''function'':209C ''gradient'':339C ''help'':343C,369C ''howev'':320C ''href'':12C ''ij'':78C ''implement'':148C ''in-network'':165C,223C ''input'':83C,97C,114C,314C ''instanc'':70C ''integr'':123C ''interpol'':68C,73C ''jonathan'':24C ''k'':283C,288C,290C,300C ''kernel'':281C,298C ''know'':324C ''layer'':4A,36C,189C,206C,241C,264C,278C,296C,330C,352C,374C ''learn'':173C,201C,212C,232C,243C,284C,327C,337C ''linear'':86C ''long'':25C,119C ''loss'':179C ''map'':87C ''mathbb'':304C ''mlps'':336C ''n'':305C,307C,319C ''natur'':125C ''nearest'':81C ''need'':190C ''network'':18C,167C,225C ''neuralnetwork'':5B ''nonlinear'':214C ''note'':181C ''oper'':144C ''output'':63C,76C,99C,138C,292C ''p'':7C,42C,49C,57C,254C,268C,345C,365C ''part'':45C ''pass'':157C ''perform'':164C ''pixel'':66C ''pixelwis'':178C ''pleas'':368C ''posit'':94C ''predict'':234C,248C ''read'':10C ''realli'':260C ''recent'':9C ''refin'':247C ''relat'':93C ''relev'':44C ''revers'':152C,363C ''section'':250C ''segment'':21C,237C ''semant'':20C ''sens'':104C ''shelham'':27C ''simpl'':71C,335C ''simpli'':151C ''sinc'':149C ''size'':282C,287C,299C ''sometim'':133C ''stack'':203C ''stride'':54C,115C,139C,301C ''text'':313C ''therefor'':130C ''think'':258C,271C ''thus'':161C ''time'':289C ''train'':266C ''trevor'':28C ''trivial'':146C ''understand'':33C,333C,349C,372C ''understood'':261C,274C ''upsampl'':51C,105C,128C,162C,197C,215C,226C,245C ''use'':239C ''ve'':273C ''way'':59C,126C ''work'':40C,331C ''y'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6108, 1, '2015-06-13 10:19:56.42', 1, 378, 'Caffe net.predict() outputs random results (GoogleNet)', '&lt;p&gt;I used pretrained GoogleNet from &lt;a href="https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet" rel="nofollow"&gt;https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet&lt;/a&gt; and finetuned it with my own data (~ 100k images, 101 classes). After one day training I achieved 62% in top-1 and 85% in top-5 classification and try to use this network to predict several images.&lt;/p&gt;

&lt;p&gt;I just followed example from &lt;a href="https://github.com/BVLC/caffe/blob/master/examples/classification.ipynb" rel="nofollow"&gt;https://github.com/BVLC/caffe/blob/master/examples/classification.ipynb&lt;/a&gt;,&lt;/p&gt;

&lt;p&gt;Here is my Python code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import caffe
import numpy as np


caffe_root = ''./caffe''


MODEL_FILE = ''caffe/models/bvlc_googlenet/deploy.prototxt''
PRETRAINED = ''caffe/models/bvlc_googlenet/bvlc_googlenet_iter_200000.caffemodel''

caffe.set_mode_gpu()

net = caffe.Classifier(MODEL_FILE, PRETRAINED,
               mean=np.load(''ilsvrc_2012_mean.npy'').mean(1).mean(1),
               channel_swap=(2,1,0),
               raw_scale=255,
               image_dims=(224, 224))

def caffe_predict(path):
        input_image = caffe.io.load_image(path)
        print path
        print input_image
        prediction = net.predict([input_image])


        print prediction
        print "----------"

        print ''prediction shape:'', prediction[0].shape
        print ''predicted class:'', prediction[0].argmax()


        proba = prediction[0][prediction[0].argmax()]
        ind = prediction[0].argsort()[-5:][::-1] # top-5 predictions


        return prediction[0].argmax(), proba, ind
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In my deploy.prototxt I changed the last layer only to predict my 101 classes.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;layer {
  name: "loss3/classifier"
  type: "InnerProduct"
  bottom: "pool5/7x7_s1"
  top: "loss3/classifier"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 101
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "loss3/classifier"
  top: "prob"
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here is the distribution of softmax output:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[[ 0.01106235  0.00343131  0.00807581  0.01530041  0.01077161  0.0081002
   0.00989228  0.00972753  0.00429183  0.01377776  0.02028225  0.01209726
   0.01318955  0.00669979  0.00720005  0.00838189  0.00335461  0.01461464
   0.01485041  0.00543212  0.00400191  0.0084842   0.02134697  0.02500303
   0.00561895  0.00776423  0.02176422  0.00752334  0.0116104   0.01328687
   0.00517187  0.02234021  0.00727272  0.02380056  0.01210031  0.00582192
   0.00729601  0.00832637  0.00819836  0.00520551  0.00625274  0.00426603
   0.01210176  0.00571806  0.00646495  0.01589645  0.00642173  0.00805364
   0.00364388  0.01553882  0.01549598  0.01824486  0.00483241  0.01231962
   0.00545738  0.0101487   0.0040346   0.01066607  0.01328133  0.01027429
   0.01581303  0.01199994  0.00371804  0.01241552  0.00831448  0.00789811
   0.00456275  0.00504562  0.00424598  0.01309276  0.0079432   0.0140427
   0.00487625  0.02614347  0.00603372  0.00892296  0.00924052  0.00712763
   0.01101298  0.00716757  0.01019373  0.01234141  0.00905332  0.0040798
   0.00846442  0.00924353  0.00709366  0.01535406  0.00653238  0.01083806
   0.01168014  0.02076091  0.00542234  0.01246306  0.00704035  0.00529556
   0.00751443  0.00797437  0.00408798  0.00891858  0.00444583]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;It seems just like random distribution with no sense.&lt;/p&gt;

&lt;p&gt;Thank you for any help or hint and best regards, Alex&lt;/p&gt;
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''softmax'':254C,267C ''swap'':126C ''thank'':387C ''top'':52C,57C,182C,218C,257C ''train'':47C ''tri'':61C ''type'':214C,242C,246C,253C ''use'':19C,63C ''valu'':248C ''weight'':240C ''xavier'':243C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6127, 2, '2015-06-15 20:36:40.303', 3, NULL, NULL, '&lt;p&gt;It is so called because it classifies the linear sequence, and not because the structure of the graph.&lt;/p&gt;
', NULL, NULL, '''/p'':20C ''call'':5C ''classifi'':8C ''graph'':19C ''linear'':10C ''p'':1C ''sequenc'':11C ''structur'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6109, 2, '2015-06-13 18:09:22.033', 1, NULL, NULL, '&lt;p&gt;It really depends on the structure of the underlying distribution of your data. If you have strong reason to believe that the data approximate a Bernoulli distribution, multinomial logistic regression will perform well and give you interpretable results. However if there exist nonlinear structures in the underlying distribution, you should seriously consider a nonparametric method. &lt;/p&gt;

&lt;p&gt;While you could use a decision tree as your nonparametric method, you might also consider looking into generating a random forest- this essentially generates a large number of individual decision trees from subsets of the data and the end classification is the agglomerated vote of all the trees. A random forest helps give you an idea of the share each predictor variable contributes to the response.&lt;/p&gt;

&lt;p&gt;Another factor to keep in mind is interpretability. If you are just trying to classify data, then you probably don''t care about the underlying relationships between explanatory and response variables. However, if you are interested at all in interpretability a multinomial logistic regression is much easier to interpret, parametric methods in general, because they make assumptions about the underlying distribution, tell you more intuitively interpretable relationships.&lt;/p&gt;
', NULL, NULL, '''/p'':57C,125C,194C ''agglomer'':101C ''also'':72C ''anoth'':127C ''approxim'':25C ''assumpt'':183C ''believ'':21C ''bernoulli'':27C ''care'':148C ''classif'':98C ''classifi'':141C ''consid'':53C,73C ''contribut'':121C ''could'':61C ''data'':14C,24C,94C,142C ''decis'':64C,88C ''depend'':4C ''distribut'':11C,28C,49C,187C ''easier'':173C ''end'':97C ''essenti'':81C ''exist'':43C ''explanatori'':154C ''factor'':128C ''forest'':79C,109C ''general'':179C ''generat'':76C,82C ''give'':36C,111C ''help'':110C ''howev'':40C,158C ''idea'':114C ''individu'':87C ''interest'':162C ''interpret'':38C,134C,166C,175C,192C ''intuit'':191C ''keep'':130C ''larg'':84C ''logist'':30C,169C ''look'':74C ''make'':182C ''method'':56C,69C,177C ''might'':71C ''mind'':132C ''much'':172C ''multinomi'':29C,168C ''nonlinear'':44C ''nonparametr'':55C,68C ''number'':85C ''p'':1C,58C,126C ''parametr'':176C ''perform'':33C ''predictor'':119C ''probabl'':145C ''random'':78C,108C ''realli'':3C ''reason'':19C ''regress'':31C,170C ''relationship'':152C,193C ''respons'':124C,156C ''result'':39C ''serious'':52C ''share'':117C ''strong'':18C ''structur'':7C,45C ''subset'':91C ''tell'':188C ''tree'':65C,89C,106C ''tri'':139C ''under'':10C,48C,151C,186C ''use'':62C ''variabl'':120C,157C ''vote'':102C ''well'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6110, 1, '2015-06-13 18:36:49.023', 7, 94, 'Relationship between VC dimension and degrees of freedom', '&lt;p&gt;I''m studying machine learning and I feel there is a strong relationship between the concept of VC dimension and the more classical (statistical) concept of degrees of freedom.&lt;/p&gt;

&lt;p&gt;Can anyone explain such a connection?&lt;/p&gt;
', 'machine-learning  self-study', NULL, '''/p'':45C,53C ''anyon'':48C ''classic'':38C ''concept'':31C,40C ''connect'':52C ''degre'':6A,42C ''dimens'':4A,34C ''explain'':49C ''feel'':23C ''freedom'':8A,44C ''learn'':11B,20C ''m'':17C ''machin'':10B,19C ''machine-learn'':9B ''p'':15C,46C ''relationship'':1A,28C ''self'':13B ''self-studi'':12B ''statist'':39C ''strong'':27C ''studi'':14B,18C ''vc'':3A,33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6111, 1, '2015-06-13 18:45:53.48', 1, 54, 'Probability tree in SPSS/ R/ others?', '&lt;p&gt;Similar questions have been asked in this context but this one seems a bit different.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;[Aim]&lt;/strong&gt;&lt;br&gt;
We would like to find out what the probability is of a person purchasing a 4USD column D (see below) price given that he has purchased C-2, B-2, A&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/mLlaR.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;[Context]&lt;/strong&gt;&lt;br&gt;
We have a sizeable dataset of about 500,000 observations of this pattern. E.g. customer decides to buy A), bought B-1, then C-2, D-3.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;[Issue]&lt;/strong&gt;&lt;br&gt;
We don''t know how deep the events reach. E.g. it can be to column E, F, G. That''s why we would like to auto-generate a probability tree in preferably SPSS but are open to other solutions as well.
How does this look like mathmatically? Can we just extend bayes conditional probability? If so, how does it look? Any potential issues?
Any other comments/ ideas are always welcome :)&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'bigdata  r  python', NULL, '''-1'':102C ''-2'':59C,61C,105C ''-3'':107C ''/mllar.png'':69C ''/p'':26C,63C,75C,108C,185C,188C ''/strong'':30C,79C,112C ''000'':89C ''4usd'':47C ''500'':88C ''aim'':29C ''alt'':70C ''alway'':183C ''ask'':15C ''auto'':140C ''auto-gener'':139C ''b'':60C,101C ''bay'':166C ''bigdata'':7B ''bit'':24C ''bought'':100C ''br'':31C,80C,113C ''buy'':98C ''c'':58C,104C ''column'':48C,128C ''comment'':180C ''condit'':167C ''context'':18C,78C ''custom'':95C ''d'':49C,106C ''dataset'':85C ''decid'':96C ''deep'':119C ''descript'':73C ''differ'':25C ''e'':129C ''e.g'':94C,123C ''enter'':71C ''event'':121C ''extend'':165C ''f'':130C ''find'':36C ''g'':131C ''generat'':141C ''given'':53C ''i.stack.imgur.com'':68C ''i.stack.imgur.com/mllar.png'':67C ''idea'':181C ''imag'':72C ''img'':65C ''issu'':111C,177C ''know'':117C ''like'':34C,137C,160C ''look'':159C,174C ''mathmat'':161C ''observ'':90C ''one'':21C ''open'':150C ''other'':6A ''p'':10C,27C,64C,76C,109C,186C ''pattern'':93C ''person'':44C ''potenti'':176C ''prefer'':146C ''price'':52C ''probabl'':1A,40C,143C,168C ''purchas'':45C,57C ''python'':9B ''question'':12C ''r'':5A,8B ''reach'':122C ''see'':50C ''seem'':22C ''similar'':11C ''sizeabl'':84C ''solut'':153C ''spss'':4A,147C ''src'':66C ''strong'':28C,77C,110C ''thank'':187C ''tree'':2A,144C ''welcom'':184C ''well'':155C ''would'':33C,136C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6113, 1, '2015-06-14 10:19:31.923', 5, 126, 'Algorithm for segmentation of sequence data', '&lt;p&gt;I have a large sequence of vectors of length N. I need some unsupervised learning algorithm to divide these vectors into M segments. E.g.:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/O1hyC.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;k-means is not suitable, because it puts similar elements from different locations into a single cluster.&lt;/p&gt;

&lt;p&gt;UPD:&lt;/p&gt;

&lt;p&gt;real data looks like this:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/VyVCU.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;I see here 3 clusters: [0..50], [50..200], [200..250]&lt;/p&gt;

&lt;p&gt;UPD 2:&lt;/p&gt;

&lt;p&gt;I use modified k-means and get this acceptable result:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/WBlZn.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;borders of clusters: [0, 38, 195, 246]&lt;/p&gt;
', 'machine-learning  clustering  sequence', NULL, '''/o1hyc.png'':43C ''/p'':37C,49C,69C,72C,79C,91C,104C,108C,121C,133C,142C ''/vyvcu.png'':85C ''/wblzn.png'':127C ''0'':98C,138C ''195'':140C ''2'':107C ''200'':101C,102C ''246'':141C ''250'':103C ''3'':96C ''38'':139C ''50'':99C,100C ''accept'':119C ''algorithm'':1A,28C ''alt'':44C,86C,128C ''border'':135C ''cluster'':10B,68C,97C,137C ''data'':6A,75C ''descript'':47C,89C,131C ''differ'':63C ''divid'':30C ''e.g'':36C ''element'':61C ''enter'':45C,87C,129C ''get'':117C ''i.stack.imgur.com'':42C,84C,126C ''i.stack.imgur.com/o1hyc.png'':41C ''i.stack.imgur.com/vyvcu.png'':83C ''i.stack.imgur.com/wblzn.png'':125C ''imag'':46C,88C,130C ''img'':39C,81C,123C ''k'':52C,114C ''k-mean'':51C,113C ''larg'':16C ''learn'':9B,27C ''length'':21C ''like'':77C ''locat'':64C ''look'':76C ''m'':34C ''machin'':8B ''machine-learn'':7B ''mean'':53C,115C ''modifi'':112C ''n'':22C ''need'':24C ''p'':12C,38C,50C,70C,73C,80C,92C,105C,109C,122C,134C ''put'':59C ''real'':74C ''result'':120C ''see'':94C ''segment'':3A,35C ''sequenc'':5A,11B,17C ''similar'':60C ''singl'':67C ''src'':40C,82C,124C ''suitabl'':56C ''unsupervis'':26C ''upd'':71C,106C ''use'':111C ''vector'':19C,32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6114, 2, '2015-06-14 10:32:51.277', 7, NULL, NULL, '&lt;p&gt;As stated by Prof Yaser Abu-Mostafa-&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Degrees of freedom are an abstraction of the effective number of parameters. The effective number is based on how many &lt;a href="https://en.wikipedia.org/wiki/Dichotomy"&gt;dichotomies&lt;/a&gt; one can get, rather than how many real-valued parameters are used. In the case of 2-dimensional perceptron, one can think of slope and intercept (plus a binary degree of freedom for which region goes to +1), or one can think of 3 parameters w_0,w_1,w_2 (though the weights can be simultaneously scaled up or down without affecting the resulting hypothesis). The degrees of freedom, however, are 3 because we have the flexibility to shatter 3 points, not because of one way or another of counting the number of parameters.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;img src="https://qph.is.quoracdn.net/main-qimg-c943643a81e326633b68e3c40387e793?convert_to_webp=true" alt="2-d perceptron"&gt;&lt;/p&gt;
', NULL, NULL, '''+1'':78C ''/a'':39C ''/blockquote'':137C ''/main-qimg-c943643a81e326633b68e3c40387e793?convert_to_webp=true'':143C ''/p'':10C,136C,148C ''/wiki/dichotomy'':37C ''0'':87C ''1'':89C ''2'':57C,91C,145C ''3'':84C,113C,121C ''abstract'':18C ''abu'':8C ''abu-mostafa'':7C ''affect'':103C ''alt'':144C ''anoth'':129C ''base'':29C ''binari'':69C ''blockquot'':11C ''case'':55C ''count'':131C ''d'':146C ''degre'':13C,70C,108C ''dichotomi'':38C ''dimension'':58C ''effect'':21C,26C ''en.wikipedia.org'':36C ''en.wikipedia.org/wiki/dichotomy'':35C ''flexibl'':118C ''freedom'':15C,72C,110C ''get'':42C ''goe'':76C ''howev'':111C ''href'':34C ''hypothesi'':106C ''img'':139C ''intercept'':66C ''mani'':32C,46C ''mostafa'':9C ''number'':22C,27C,133C ''one'':40C,60C,80C,126C ''p'':1C,12C,138C ''paramet'':24C,50C,85C,135C ''perceptron'':59C,147C ''plus'':67C ''point'':122C ''prof'':5C ''qph.is.quoracdn.net'':142C ''qph.is.quoracdn.net/main-qimg-c943643a81e326633b68e3c40387e793?convert_to_webp=true'':141C ''rather'':43C ''real'':48C ''real-valu'':47C ''region'':75C ''result'':105C ''scale'':98C ''shatter'':120C ''simultan'':97C ''slope'':64C ''src'':140C ''state'':3C ''think'':62C,82C ''though'':92C ''use'':52C ''valu'':49C ''w'':86C,88C,90C ''way'':127C ''weight'':94C ''without'':102C ''yaser'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6115, 1, '2015-06-14 11:28:46.043', 1, 240, 'How to convert a text to lower case using tm package?', '&lt;p&gt;I am using the below R code to convert text to lower case:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;movie_Clean &amp;lt;- tm_map(movie_Clean, content_transformer(tolower))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However I end up getting the below error:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Error in FUN(content(x), ...) :    invalid input ''I just wanna watch
  Jurassic World Ã­ Â½Ã­Â¸Â«'' in ''utf8towcs''.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Please help how to overcome this error.&lt;/p&gt;
', 'machine-learning  r  text-mining', NULL, '''/blockquote'':81C ''/code'':46C ''/p'':33C,57C,80C,90C ''/pre'':47C ''blockquot'':58C ''case'':8A,32C ''clean'':37C,42C ''code'':26C,35C ''content'':43C,63C ''convert'':3A,28C ''end'':51C ''error'':56C,60C,89C ''fun'':62C ''get'':53C ''help'':84C ''howev'':49C ''input'':66C ''invalid'':65C ''jurass'':71C ''learn'':14B ''lower'':7A,31C ''lt'':38C ''machin'':13B ''machine-learn'':12B ''map'':40C ''mine'':18B ''movi'':36C,41C ''overcom'':87C ''p'':19C,48C,59C,82C ''packag'':11A ''pleas'':83C ''pre'':34C ''r'':15B,25C ''text'':5A,17B,29C ''text-min'':16B ''tm'':10A,39C ''tolow'':45C ''transform'':44C ''use'':9A,22C ''utf8towcs'':79C ''wanna'':69C ''watch'':70C ''world'':72C ''x'':64C ''â'':74C,76C,77C ''ã'':73C,75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6116, 1, '2015-06-14 14:27:36.443', 18, 256, 'How do you manage expectations at work?', '&lt;p&gt;With all the hoopla around Data Science, Machine Learning, and all the success stories around, there are a lot of both justified, as well as overinflated, expectations from Data Scientists and their predictive models.&lt;/p&gt;

&lt;p&gt;My question to practicing Statisticians, Machine Learning experts, and Data Scientists is - how do you manage expectations from the businesspeople in you company, particularly with regards to predictive accuracy of models? To put it trivially, if your best model can only achieve 90% accuracy, and upper management expects nothing less than 99%, how do you handle situations like these?&lt;/p&gt;
', 'performance  accuracy', NULL, '''/p'':45C,106C ''90'':89C ''99'':98C ''accuraci'':9B,75C,90C ''achiev'':88C ''around'':15C,25C ''best'':84C ''businesspeopl'':66C ''compani'':69C ''data'':16C,39C,56C ''expect'':5A,37C,63C,94C ''expert'':54C ''handl'':102C ''hoopla'':14C ''justifi'':32C ''learn'':19C,53C ''less'':96C ''like'':104C ''lot'':29C ''machin'':18C,52C ''manag'':4A,62C,93C ''model'':44C,77C,85C ''noth'':95C ''overinfl'':36C ''p'':10C,46C ''particular'':70C ''perform'':8B ''practic'':50C ''predict'':43C,74C ''put'':79C ''question'':48C ''regard'':72C ''scienc'':17C ''scientist'':40C,57C ''situat'':103C ''statistician'':51C ''stori'':24C ''success'':23C ''trivial'':81C ''upper'':92C ''well'':34C ''work'':7A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6118, 2, '2015-06-14 20:18:40.73', 2, NULL, NULL, '&lt;p&gt;This seems like an encoding error. Try adding the line &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Encoding(movie_Clean)  &amp;lt;- "UTF-8"
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;before you lowercase the data. Check out this answer for a little context: &lt;a href="http://stackoverflow.com/a/28340080/4539807"&gt;http://stackoverflow.com/a/28340080/4539807&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''-8'':20C ''/a/28340080/4539807'':41C ''/a/28340080/4539807&lt;/a&gt;&lt;/p&gt;'':44C ''/code'':21C ''/p'':12C ''/pre'':22C ''ad'':9C ''answer'':32C ''check'':29C ''clean'':17C ''code'':14C ''context'':36C ''data'':28C ''encod'':6C,15C ''error'':7C ''href'':38C ''like'':4C ''line'':11C ''littl'':35C ''lowercas'':26C ''lt'':18C ''movi'':16C ''p'':1C,23C ''pre'':13C ''seem'':3C ''stackoverflow.com'':40C,43C ''stackoverflow.com/a/28340080/4539807'':39C ''stackoverflow.com/a/28340080/4539807&lt;/a&gt;&lt;/p&gt;'':42C ''tri'':8C ''utf'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6119, 2, '2015-06-15 06:23:32.277', 0, NULL, NULL, '&lt;p&gt;The VC dimension is very well explained in &lt;a href="http://research.microsoft.com/pubs/67119/svmtutorial.pdf" rel="nofollow"&gt;this paper&lt;/a&gt; in Section 2.1 and further, with the basic lemmas and proofs given. You can go through this.&lt;/p&gt;
', NULL, NULL, '''/a'':19C ''/p'':37C ''/pubs/67119/svmtutorial.pdf'':14C ''2.1'':22C ''basic'':27C ''dimens'':4C ''explain'':8C ''given'':31C ''go'':34C ''href'':11C ''lemma'':28C ''nofollow'':16C ''p'':1C ''paper'':18C ''proof'':30C ''rel'':15C ''research.microsoft.com'':13C ''research.microsoft.com/pubs/67119/svmtutorial.pdf'':12C ''section'':21C ''vc'':3C ''well'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6134, 2, '2015-06-17 11:25:19.117', 0, NULL, NULL, '&lt;p&gt;Why would you need a distance when you have the pdf? &lt;/p&gt;

&lt;p&gt;Time series can be assigned to clusters based on their fit to the cluster''s pdf. This also means the method works for ragged timeseries, for which distance methods would break down.&lt;/p&gt;
', NULL, NULL, '''/p'':13C,47C ''also'':32C ''assign'':19C ''base'':22C ''break'':45C ''cluster'':21C,28C ''distanc'':7C,42C ''fit'':25C ''mean'':33C ''method'':35C,43C ''need'':5C ''p'':1C,14C ''pdf'':12C,30C ''rag'':38C ''seri'':16C ''time'':15C ''timeseri'':39C ''work'':36C ''would'':3C,44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6120, 1, '2015-06-15 12:24:03.44', 1, 84, 'MCMC vs Network Theory for Big Data and Data Science applications', '&lt;p&gt;[Apologies if this post sounds naive, I''m fairly new to the world of data science/big data and very unsure where I''m heading career-wise]&lt;/p&gt;

&lt;p&gt;I''m currently an undergraduate MMath [integrated master''s] Mathematics student in the UK who has finished the third year of the course [out of four years].&lt;/p&gt;

&lt;p&gt;As I have been considering the possibility of doing further research in Mathematics/Statistics/Operational Research/Data Science, I have decided to stay on and complete the Master''s component of the course [as it is the only Master''s course I can get funding for at this stage]. After the Master''s I may continue on and do a PhD.&lt;/p&gt;

&lt;p&gt;There are currently two projects that appeal to me that seem to have relevant applications. The first one is on improved MCMC [Markov Chain Monte Carlo] methods, in particular MCMC using Hamiltonian Dynamics. There is scope for some big data applications here.&lt;/p&gt;

&lt;p&gt;The other project that I could take part in is one on the centrality/communities detection of networks within network science. This could possibly be useful with applications in Operational Research.&lt;/p&gt;

&lt;p&gt;Does anyone have an idea as to which project will be more relevant to data science/analytics?&lt;/p&gt;
', 'career', NULL, '''/p'':41C,70C,130C,174C,206C,224C ''anyon'':209C ''apolog'':14C ''appeal'':138C ''applic'':11A,146C,172C,202C ''big'':6A,170C ''career'':12B,39C ''career-wis'':38C ''carlo'':157C ''centrality/communities'':189C ''chain'':155C ''complet'':94C ''compon'':98C ''consid'':76C ''continu'':124C ''could'':181C,197C ''cours'':65C,101C,109C ''current'':45C,134C ''data'':7A,9A,28C,30C,171C,222C ''decid'':89C ''detect'':190C ''dynam'':164C ''fair'':22C ''finish'':59C ''first'':148C ''four'':68C ''fund'':113C ''get'':112C ''hamiltonian'':163C ''head'':37C ''idea'':212C ''improv'':152C ''integr'':49C ''m'':21C,36C,44C ''markov'':154C ''master'':50C,96C,107C,120C ''mathemat'':52C ''mathematics/statistics/operational'':84C ''may'':123C ''mcmc'':1A,153C,161C ''method'':158C ''mmath'':48C ''mont'':156C ''naiv'':19C ''network'':3A,192C,194C ''new'':23C ''one'':149C,186C ''oper'':204C ''p'':13C,42C,71C,131C,175C,207C ''part'':183C ''particular'':160C ''phd'':129C ''possibl'':78C,198C ''post'':17C ''project'':136C,178C,216C ''relev'':145C,220C ''research'':82C,205C ''research/data'':85C ''scienc'':10A,86C,195C ''science/analytics'':223C ''science/big'':29C ''scope'':167C ''seem'':142C ''sound'':18C ''stage'':117C ''stay'':91C ''student'':53C ''take'':182C ''theori'':4A ''third'':61C ''two'':135C ''uk'':56C ''undergradu'':47C ''unsur'':33C ''use'':162C,200C ''vs'':2A ''wise'':40C ''within'':193C ''world'':26C ''year'':62C,69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6122, 1, '2015-06-15 13:15:30.957', 0, 31, 'How to draw attention to sites with high levels of outdated software', '&lt;p&gt;I''m totally new to Data Science and even enrolled for R programming online course.&lt;/p&gt;

&lt;p&gt;I have some data that I would eventually plot on a map of Texas using an R package, similar to &lt;img src="http://i.stack.imgur.com/setlt.jpg" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;But the first step is determining how to portray these figures.&lt;/p&gt;

&lt;p&gt;Should I create percentage of outdated Windows SEP 11 vs up-to-date Windows SEP 12? Or should I create ratios? Or is there some other metric?&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/T8dxV.jpg" alt="enter image description here"&gt;&lt;/p&gt;
', 'r  visualization', NULL, '''/p'':31C,63C,76C,106C,118C ''/setlt.jpg'':57C ''/t8dxv.jpg'':112C ''11'':86C ''12'':94C ''alt'':58C,113C ''attent'':4A ''cours'':30C ''creat'':80C,98C ''data'':21C,36C ''date'':91C ''descript'':61C,116C ''determin'':70C ''draw'':3A ''enrol'':25C ''enter'':59C,114C ''even'':24C ''eventu'':40C ''figur'':75C ''first'':67C ''high'':8A ''i.stack.imgur.com'':56C,111C ''i.stack.imgur.com/setlt.jpg'':55C ''i.stack.imgur.com/t8dxv.jpg'':110C ''imag'':60C,115C ''img'':53C,108C ''level'':9A ''m'':17C ''map'':44C ''metric'':105C ''new'':19C ''onlin'':29C ''outdat'':11A,83C ''p'':15C,32C,64C,77C,107C ''packag'':50C ''percentag'':81C ''plot'':41C ''portray'':73C ''program'':28C ''r'':13B,27C,49C ''ratio'':99C ''scienc'':22C ''sep'':85C,93C ''similar'':51C ''site'':6A ''softwar'':12A ''src'':54C,109C ''step'':68C ''texa'':46C ''total'':18C ''up-to-d'':88C ''use'':47C ''visual'':14B ''vs'':87C ''window'':84C,92C ''would'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6123, 1, '2015-06-15 15:07:43.607', 1, 148, 'Implementing sklearn.linear_model.SGDClassifier using python', '&lt;p&gt;I have an excel file that contains details related to determining the quality of a wine and I want to implement the linear model concept using the function &lt;strong&gt;sklearn.linear_model.SGDClassifier(SVM =&gt; Hinge loss) and (Logarithmic regression =&gt;log loss)&lt;/strong&gt; using python. I learned the basics about these function through the &lt;em&gt;scikit learn&lt;/em&gt; website and I am not able to implement the model using excel file. I am very new to python and machine learning and I finding it hard to implement the model. I opened the excel file in python and tried to take two columns [randomly] from the file and use that as an input to call the &lt;strong&gt;fit&lt;/strong&gt; function available in the model. But, I got an error stating &lt;strong&gt;Unknown label type: array&lt;/strong&gt;. I tried a couple of other methods too, but, nothing worked. Can someone guide me with the implementation process? &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from xlrd import open_workbook
from sklearn import linear_model
i = 0
fa = []
ph = []

book = open_workbook(''F:/BIG DATA/winequality.xlsx'')
sheet = book.sheet_by_name(''Sheet1'')
num_rows = sheet.nrows - 1
num_cols = sheet.ncols - 1
curr_row = 0
while curr_row &amp;lt;num_rows:
    curr_row += 1
    cell_val = sheet.cell_value(curr_row,0)
    cell_val1 = sheet.cell_value(curr_row,10)

    fa.append([float(cell_val),float(cell_val1)])
    cell_val2 = sheet.cell_value(curr_row,8)
    ph.append(float(cell_val2))

model = linear_model.SGDClassifier()
print(model.fit(fa,ph))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/I9J8u.png" alt="Screenshot"&gt;&lt;/p&gt;

&lt;p&gt;The error message screenshot:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/lCJTu.png" alt="ERROR"&gt;&lt;/p&gt;
', 'machine-learning  classification  python  svm  regression', NULL, '''/big'':184C ''/code'':249C ''/em'':66C ''/i9j8u.png'':256C ''/lcjtu.png'':271C ''/p'':163C,259C,265C,274C ''/pre'':250C ''/strong'':51C,126C,143C ''0'':177C,201C,217C ''1'':194C,198C,210C ''10'':224C ''8'':238C ''abl'':72C ''alt'':257C,272C ''array'':142C ''avail'':128C ''basic'':57C ''book'':180C ''book.sheet'':187C ''call'':122C ''cell'':211C,218C,227C,230C,232C,241C ''classif'':8B ''code'':165C ''col'':196C ''column'':110C ''concept'':37C ''contain'':19C ''coupl'':147C ''curr'':199C,203C,208C,215C,222C,236C ''data/winequality.xlsx'':185C ''detail'':20C ''determin'':23C ''em'':63C ''error'':136C,262C,273C ''excel'':16C,78C,101C ''f'':183C ''fa'':178C,247C ''fa.append'':225C ''file'':17C,79C,102C,114C ''find'':91C ''fit'':125C ''float'':226C,229C,240C ''function'':40C,60C,127C ''got'':134C ''guid'':157C ''hard'':93C ''hing'':44C ''i.stack.imgur.com'':255C,270C ''i.stack.imgur.com/i9j8u.png'':254C ''i.stack.imgur.com/lcjtu.png'':269C ''img'':252C,267C ''implement'':1A,33C,74C,95C,161C ''import'':168C,173C ''input'':120C ''label'':140C ''learn'':7B,55C,65C,88C ''linear'':35C,174C ''linear_model.sgdclassifier'':244C ''log'':49C ''logarithm'':47C ''loss'':45C,50C ''lt'':205C ''machin'':6B,87C ''machine-learn'':5B ''messag'':263C ''method'':150C ''model'':36C,76C,97C,131C,175C,243C ''model.fit'':246C ''name'':189C ''new'':83C ''noth'':153C ''num'':191C,195C,206C ''open'':99C,169C,181C ''p'':12C,251C,260C,266C ''ph'':179C,248C ''ph.append'':239C ''pre'':164C ''print'':245C ''process'':162C ''python'':4A,9B,53C,85C,104C ''qualiti'':25C ''random'':111C ''regress'':11B,48C ''relat'':21C ''row'':192C,200C,204C,207C,209C,216C,223C,237C ''scikit'':64C ''screenshot'':258C,264C ''sheet'':186C ''sheet.cell'':213C,220C,234C ''sheet.ncols'':197C ''sheet.nrows'':193C ''sheet1'':190C ''sklearn'':172C ''sklearn.linear_model.sgdclassifier'':2A,42C ''someon'':156C ''src'':253C,268C ''state'':137C ''strong'':41C,124C,138C ''svm'':10B,43C ''take'':108C ''tri'':106C,145C ''two'':109C ''type'':141C ''unknown'':139C ''use'':3A,38C,52C,77C,116C ''val'':212C,228C ''val1'':219C,231C ''val2'':233C,242C ''valu'':214C,221C,235C ''want'':31C ''websit'':67C ''wine'':28C ''work'':154C ''workbook'':170C,182C ''xlrd'':167C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6124, 2, '2015-06-15 15:43:22.097', 1, NULL, NULL, '&lt;p&gt;I think that this is the same issue as in this question: &lt;a href="http://stackoverflow.com/questions/24923143/x-and-y-have-incompatible-shapes"&gt;http://stackoverflow.com/questions/24923143/x-and-y-have-incompatible-shapes&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;The shape of X must be (n_samples, n_features) as explained in the SVC.fit
docstring. A 1-d array is interpreted as a single sample (for convenience when      
doing predictions on single samples). Reshape your X to (n_samples, 1).
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;That means you should use numpy.reshape to reshape the X column. If the data frame has n rows, you should use&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;X_new = X.reshape(n, 1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then use the fit method with X_new. Note: you probably don''t need to do this if you use two or more X columns for your model fitting.&lt;/p&gt;
', NULL, NULL, '''/code'':65C,97C ''/p'':89C,129C ''/pre'':66C,98C ''/questions/24923143/x-and-y-have-incompatible-shapes'':18C ''/questions/24923143/x-and-y-have-incompatible-shapes&lt;/a&gt;&lt;/p&gt;'':21C ''1'':41C,64C,96C ''array'':43C ''code'':23C,91C ''column'':78C,124C ''conveni'':51C ''d'':42C ''data'':81C ''docstr'':39C ''explain'':35C ''featur'':33C ''fit'':103C,128C ''frame'':82C ''href'':15C ''interpret'':45C ''issu'':9C ''mean'':69C ''method'':104C ''model'':127C ''must'':28C ''n'':30C,32C,62C,84C,95C ''need'':113C ''new'':93C,107C ''note'':108C ''numpy.reshape'':73C ''p'':1C,67C,99C ''pre'':22C,90C ''predict'':54C ''probabl'':110C ''question'':13C ''reshap'':58C,75C ''row'':85C ''sampl'':31C,49C,57C,63C ''shape'':25C ''singl'':48C,56C ''stackoverflow.com'':17C,20C ''stackoverflow.com/questions/24923143/x-and-y-have-incompatible-shapes'':16C ''stackoverflow.com/questions/24923143/x-and-y-have-incompatible-shapes&lt;/a&gt;&lt;/p&gt;'':19C ''svc.fit'':38C ''think'':3C ''two'':120C ''use'':72C,88C,101C,119C ''x'':27C,60C,77C,92C,106C,123C ''x.reshape'':94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6125, 2, '2015-06-15 16:12:59.267', 9, NULL, NULL, '&lt;p&gt;Gather competitive counterparts. Try and determine a &lt;em&gt;state-of-the-art&lt;/em&gt; and see how your models compare with that. It also heavily depends on how long your team has been working on it. Science-driven models are not created statically, they develop dynamically because a good scientist will always try to find ways to improve it.&lt;/p&gt;

&lt;p&gt;Upper management personnel should know that a data &lt;strong&gt;scientist&lt;/strong&gt; explores &lt;strong&gt;new&lt;/strong&gt; methods, sometimes/often without knowing their quality. They should know that machine learning techniques do not produce perfect models right away. If they did, it wouldn''t be challenging anyway.&lt;/p&gt;

&lt;p&gt;A data scientist ought to be evaluated by how he justifies and discusses his results and how he plans the future. A way for the management personnel to handle with their expectations is to not have unrealistically high ones.&lt;/p&gt;

&lt;p&gt;Still, if reasonable results are expected in the field of context, think about these questions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Did/will results get better over time?&lt;/li&gt;
&lt;li&gt;Are future expectations positive?&lt;/li&gt;
&lt;li&gt;How well are results compared to similar systems (from competitors)?&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/em'':15C ''/li'':175C,181C,193C ''/ol'':194C ''/p'':62C,108C,149C,166C ''/strong'':74C,78C ''also'':25C ''alway'':54C ''anyway'':107C ''art'':14C ''away'':98C ''better'':172C ''challeng'':106C ''compar'':21C,187C ''competit'':3C ''competitor'':192C ''context'':161C ''counterpart'':4C ''creat'':44C ''data'':71C,111C ''depend'':27C ''determin'':7C ''develop'':47C ''did/will'':169C ''discuss'':122C ''driven'':40C ''dynam'':48C ''em'':9C ''evalu'':116C ''expect'':141C,156C,179C ''explor'':75C ''field'':159C ''find'':57C ''futur'':130C,178C ''gather'':2C ''get'':171C ''good'':51C ''handl'':138C ''heavili'':26C ''high'':147C ''improv'':60C ''justifi'':120C ''know'':68C,82C,87C ''learn'':90C ''li'':168C,176C,182C ''long'':30C ''machin'':89C ''manag'':65C,135C ''method'':79C ''model'':20C,41C,96C ''new'':77C ''ol'':167C ''one'':148C ''ought'':113C ''p'':1C,63C,109C,150C ''perfect'':95C ''personnel'':66C,136C ''plan'':128C ''posit'':180C ''produc'':94C ''qualiti'':84C ''question'':165C ''reason'':153C ''result'':124C,154C,170C,186C ''right'':97C ''scienc'':39C ''science-driven'':38C ''scientist'':52C,73C,112C ''see'':17C ''similar'':189C ''sometimes/often'':80C ''state'':11C ''state-of-the-art'':10C ''static'':45C ''still'':151C ''strong'':72C,76C ''system'':190C ''team'':32C ''techniqu'':91C ''think'':162C ''time'':174C ''tri'':5C,55C ''unrealist'':146C ''upper'':64C ''way'':58C,132C ''well'':184C ''without'':81C ''work'':35C ''wouldn'':103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6129, 2, '2015-06-16 03:48:30.78', 4, NULL, NULL, '&lt;p&gt;Main references:&lt;/p&gt;

&lt;p&gt;Courses on deep learning:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://www.coursera.org/course/ml" rel="nofollow"&gt;Andrew Ng''s course on machine learning&lt;/a&gt; has a nice introductory section on neural networks.&lt;/li&gt;
&lt;li&gt;Geoffrey Hinton''s course: &lt;a href="https://www.coursera.org/course/neuralnets" rel="nofollow"&gt;Coursera Neural Networks for Machine Learning (fall 2012)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://neuralnetworksanddeeplearning.com" rel="nofollow"&gt;Michael Nielsen''s free book &lt;em&gt;Neural Networks and Deep Learning&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Yoshua Bengio, Ian Goodfellow and Aaron Courville are writing a &lt;a href="http://www.iro.umontreal.ca/~bengioy/dlbook/" rel="nofollow"&gt;book on deep learning&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html" rel="nofollow"&gt;Hugo Larochelle''s course (videos + slides) at UniversitÃ© de Sherbrooke&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://ufldl.stanford.edu/wiki/index.php/Main_Page" rel="nofollow"&gt;Stanford''s tutorial (Andrew Ng et al.) on Unsupervised Feature Learning and Deep Learning&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/" rel="nofollow"&gt;Oxford''s ML 2014-2015 course&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://developer.nvidia.com/deep-learning-courses" rel="nofollow"&gt;NVIDIA Deep learning course (summer 2015)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.udacity.com/course/deep-learning--ud730" rel="nofollow"&gt;Google''s Deep Learning course on Udacity (January 2016)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;NLP-oriented:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://cs224d.stanford.edu/syllabus.html" rel="nofollow"&gt;Stanford CS224d: Deep Learning for Natural Language Processing (spring 2015) by Richard Socher&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://nlp.stanford.edu/courses/NAACL2013/" rel="nofollow"&gt;Tutorial given at NAACL HLT 2013: Deep Learning for Natural Language Processing (without Magic) (videos + slides)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Vision-oriented:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://cs231n.github.io/" rel="nofollow"&gt;CS231n Convolutional Neural Networks for Visual Recognition&lt;/a&gt; by Andrej Karpathy (a previous version, shorter and less polished: &lt;a href="http://karpathy.github.io/neuralnets/" rel="nofollow"&gt;Hacker''s guide to Neural Networks&lt;/a&gt;).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Toolkit-specific tutorials:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;DL4J (Java): &lt;a href="http://deeplearning4j.org/documentation.html" rel="nofollow"&gt;http://deeplearning4j.org/documentation.html&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Theano  (Python, Y. Bengio): &lt;a href="http://deeplearning.net/" rel="nofollow"&gt;http://deeplearning.net/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Machine Learning with Torch7 (Lua, LeCun): &lt;a href="http://code.madbits.com/wiki/doku.php" rel="nofollow"&gt;http://code.madbits.com/wiki/doku.php&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;H2O Deep Learning (Java): &lt;a href="http://0xdata.com/product/deep-learning/" rel="nofollow"&gt;http://0xdata.com/product/deep-learning/&lt;/a&gt; &lt;/li&gt;
&lt;li&gt;Caffee (C++, UCB): &lt;a href="http://caffe.berkeleyvision.org/" rel="nofollow"&gt;http://caffe.berkeleyvision.org/&lt;/a&gt; &lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6721, 1, '2015-08-07 10:43:50.747', 2, 189, 'How to preprocess different kinds of data (continuous, discrete, categorical) before Decision Tree learning', '&lt;p&gt;I want to use some Decision Tree learning, such as the Random Forest classifier.&lt;/p&gt;

&lt;p&gt;I have data of different types: continuous, discrete and categorical. How do I have to preprocess data in order to have consistent results?&lt;/p&gt;
', 'data-mining  random-forest  data  decision-trees  preprocessing', NULL, '''/p'':41C,66C ''categor'':10A,52C ''classifi'':40C ''consist'':64C ''continu'':8A,49C ''data'':7A,16B,21B,45C,59C ''data-min'':15B ''decis'':12A,23B,32C ''decision-tre'':22B ''differ'':4A,47C ''discret'':9A,50C ''forest'':20B,39C ''kind'':5A ''learn'':14A,34C ''mine'':17B ''order'':61C ''p'':26C,42C ''preprocess'':3A,25B,58C ''random'':19B,38C ''random-forest'':18B ''result'':65C ''tree'':13A,24B,33C ''type'':48C ''use'':30C ''want'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9348, 2, '2015-12-12 13:29:13.163', 1, NULL, NULL, '&lt;p&gt;In cases where the number of discovered clusters differs from the number of real classes, I like to use the &lt;a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.164.6189&amp;amp;rep=rep1&amp;amp;type=pdf" rel="nofollow"&gt;&lt;em&gt;Maximum-match measure&lt;/em&gt;&lt;/a&gt;. Basically it tries to find an optimal matching between your results and the true classes.&lt;/p&gt;

&lt;p&gt;Say you have a 3-class database and you found 4 clusters. Then you can get the following sample results:&lt;/p&gt;

&lt;p&gt;$\left[\matrix{2 &amp;amp; 7 &amp;amp; 3 \\ 6 &amp;amp; 2 &amp;amp; 3 \\ 3 &amp;amp; 3 &amp;amp; 1 \\ 2 &amp;amp; 1 &amp;amp; 8}\right]$&lt;/p&gt;

&lt;p&gt;Which can be read like this: cluster 1 has 2 elements from class 1, 7 from class 2 and 3 from class 3, and so on.&lt;/p&gt;

&lt;p&gt;Maximum-match tries to find the 3x3 matrix with maximum trace (diagonal sum). In this case, it''d be&lt;/p&gt;

&lt;p&gt;$\left[\matrix{ 6 &amp;amp; 2 &amp;amp; 3 \\ 2 &amp;amp; 7 &amp;amp; 3 \\ 2 &amp;amp; 1 &amp;amp; 8}\right]$&lt;/p&gt;

&lt;p&gt;The final measure would be:&lt;/p&gt;

&lt;p&gt;$mm = \frac{6+7+8}{41} = 0.51$&lt;/p&gt;

&lt;p&gt;where 41 is the sum of all the elements of the 4x3 matrix.&lt;/p&gt;

&lt;p&gt;This measure penalizes clustering methods that find extra clusters.&lt;/p&gt;
', NULL, NULL, '''+7'':180C ''+8'':181C ''/a'':35C ''/em'':34C ''/p'':51C,73C,98C,126C,148C,168C,175C,184C,199C,210C ''/viewdoc/download?doi=10.1.1.164.6189&amp;amp;rep=rep1&amp;amp;type=pdf'':26C ''0.51'':183C ''1'':91C,94C,107C,113C,164C ''2'':77C,84C,92C,109C,117C,154C,157C,162C ''3'':57C,81C,86C,87C,89C,119C,122C,156C,161C ''3x3'':135C ''4'':63C ''41'':182C,187C ''4x3'':197C ''6'':82C,152C,179C ''7'':79C,114C,159C ''8'':96C,166C ''amp'':78C,80C,83C,85C,88C,90C,93C,95C,153C,155C,158C,160C,163C,165C ''basic'':36C ''case'':3C,144C ''citeseerx.ist.psu.edu'':25C ''citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.164.6189&amp;amp;rep=rep1&amp;amp;type=pdf'':24C ''class'':16C,50C,58C,112C,116C,121C ''cluster'':9C,64C,106C,204C,209C ''d'':146C ''databas'':59C ''diagon'':140C ''differ'':10C ''discov'':8C ''element'':110C,194C ''em'':29C ''extra'':208C ''final'':171C ''find'':40C,133C,207C ''follow'':70C ''found'':62C ''frac'':178C ''get'':68C ''href'':23C ''left'':75C,150C ''like'':18C,104C ''match'':32C,43C,130C ''matrix'':76C,136C,151C,198C ''maximum'':31C,129C,138C ''maximum-match'':30C,128C ''measur'':33C,172C,202C ''method'':205C ''mm'':177C ''nofollow'':28C ''number'':6C,13C ''optim'':42C ''p'':1C,52C,74C,99C,127C,149C,169C,176C,185C,200C ''penal'':203C ''read'':103C ''real'':15C ''rel'':27C ''result'':46C,72C ''right'':97C,167C ''sampl'':71C ''say'':53C ''sum'':141C,190C ''trace'':139C ''tri'':38C,131C ''true'':49C ''use'':20C ''would'':173C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6563, 2, '2015-07-23 21:28:10.853', 0, NULL, NULL, '&lt;p&gt;I did some tests on a data set of 50k rows, using &lt;code&gt;sklearn.RandomForestRegressor&lt;/code&gt;. &lt;/p&gt;

&lt;p&gt;I get significantly different scores - I''m using a normalized gini for the metric - depending on whether I use &lt;code&gt;rf.oob_prediction_&lt;/code&gt; (0.2927) or a KFold CV (0.3258 for 7 folds and 0.3236 for 3 folds).&lt;/p&gt;

&lt;p&gt;With that, it appears your point about "especially if comparing RFs with other algorithms on the same dataset" is a strong consideration towards using manual CV rather than relying on the OOB prediction.&lt;/p&gt;
', NULL, NULL, '''/code'':16C,41C ''/p'':17C,56C,91C ''0.2927'':42C ''0.3236'':52C ''0.3258'':47C ''3'':54C ''50k'':11C ''7'':49C ''algorithm'':71C ''appear'':61C ''code'':14C,38C ''compar'':67C ''consider'':79C ''cv'':46C,83C ''data'':8C ''dataset'':75C ''depend'':33C ''differ'':22C ''especi'':65C ''fold'':50C,55C ''get'':20C ''gini'':29C ''kfold'':45C ''m'':25C ''manual'':82C ''metric'':32C ''normal'':28C ''oob'':89C ''p'':1C,18C,57C ''point'':63C ''predict'':40C,90C ''rather'':84C ''reli'':86C ''rf.oob'':39C ''rfs'':68C ''row'':12C ''score'':23C ''set'':9C ''signific'':21C ''sklearn.randomforestregressor'':15C ''strong'':78C ''test'':5C ''toward'':80C ''use'':13C,26C,37C,81C ''whether'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6130, 2, '2015-06-16 09:28:02.837', 1, NULL, NULL, '&lt;p&gt;Thanks to your help. I finally got my code working, plus some bibliography.&lt;/p&gt;

&lt;p&gt;I put my hands on &lt;em&gt;Directional Statistics&lt;/em&gt; (Mardia and Jupp, 1999) and on the Ulrich-Wood''s algorithm for sampling. I post here what I understood from it, i.e. my code (in Python), with a ''movMF'' flavour.&lt;/p&gt;

&lt;p&gt;The rejection sampling scheme:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def rW(n,kappa,m):
dim = m-1
b = dim / (np.sqrt(4*kappa*kappa + dim*dim) + 2*kappa)
x = (1-b) / (1+b)
c = kappa*x + dim*np.log(1-x*x)

y = []
for i in range(0,n):
    done = False
    while not done:
        z = sc.stats.beta.rvs(dim/2,dim/2)
        w = (1 - (1+b)*z) / (1 - (1-b)*z)
        u = sc.stats.uniform.rvs()
        if kappa*w + dim*np.log(1-x*w) - c &amp;gt;= np.log(u):
            done = True
    y.append(w)
return y
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then, the desired sampling is $v \sqrt{1-w^2} + w  \mu$, where $w$ is the result from the rejection sampling scheme, and $v$ is uniformly sampled over the hypersphere.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def rvMF(n,theta):
dim = len(theta)
kappa = np.linalg.norm(theta)
mu = theta / kappa

result = []
for sample in range(0,n):
    w = rW(kappa,dim)
    v = np.random.randn(dim)
    v = v / np.linalg.norm(v)

    result.append(np.sqrt(1-w**2)*v + w*mu)

return result
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And, for effectively sampling with this code, here is an example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
import scipy as sc
import scipy.stats

n = 10
kappa = 100000
direction = np.array([1,-1,1])
direction = direction / np.linalg.norm(direction)

res_sampling = rvMF(n, kappa * direction)
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6132, 2, '2015-06-16 13:42:47.06', 9, NULL, NULL, '&lt;p&gt;Rational business people don''t pay for accuracy, they pay to either &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;save money on a profitable process (thereby making it more profitable), or by&lt;/li&gt;
&lt;li&gt;creating new money (creating new profitable processes). &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So any project that is undertaken has to be couched in terms that
   reflect this. The first step is always understanding which of the two
   processes you are working on, and you should have a clear idea of how
   that might be achieved, while keeping in mind that as you make
   progress the details of how you do that might change.&lt;/p&gt;

&lt;p&gt;If you can improve the accuracy of a process, you can probably make money for the firm and the business people will invest in your progress. The only rational reason a business person could have for insisting on 99 percent accuracy and reject 90 percent is if they already had a way of doing it that was better than the 90 percent.  If that is the case they are of course justified in their position.  &lt;/p&gt;

&lt;p&gt;Understanding and presenting the business case of the projects you are working on in terms that the Business People understand is part of the maturing process of any Engineer. It is not unique to Data Science at all, though Data Science has some unique aspects (like lower maturity but higher probability of fortuous serendipity discovery - at least in today''s environment.&lt;/p&gt;

&lt;p&gt;A relevant process that is close to Data Science that makes this step explicit can be found here:&lt;a href="https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining"&gt;https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;But most &lt;em&gt;Enterprise Architecture Frameworks&lt;/em&gt; are similarly applicable. &lt;/p&gt;
', NULL, NULL, '''/em'':279C ''/li'':30C,39C ''/p'':14C,103C,180C,243C,271C,283C ''/ul'':40C ''/wiki/cross_industry_standard_process_for_data_mining'':267C ''/wiki/cross_industry_standard_process_for_data_mining&lt;/a&gt;'':270C ''90'':148C,165C ''99'':143C ''accuraci'':9C,110C,145C ''achiev'':84C ''alreadi'':153C ''alway'':61C ''applic'':282C ''architectur'':277C ''aspect'':226C ''better'':162C ''busi'':3C,124C,136C,186C,199C ''case'':171C,187C ''chang'':102C ''clear'':77C ''close'':250C ''couch'':51C ''could'':138C ''cours'':175C ''creat'':32C,35C ''data'':216C,221C,252C ''detail'':95C ''discoveri'':236C ''either'':13C ''em'':275C ''en.wikipedia.org'':266C,269C ''en.wikipedia.org/wiki/cross_industry_standard_process_for_data_mining'':265C ''en.wikipedia.org/wiki/cross_industry_standard_process_for_data_mining&lt;/a&gt;'':268C ''engin'':210C ''enterpris'':276C ''environ'':242C ''explicit'':258C ''firm'':121C ''first'':58C ''fortuous'':234C ''found'':261C ''framework'':278C ''higher'':231C ''href'':264C ''idea'':78C ''improv'':108C ''insist'':141C ''invest'':127C ''justifi'':176C ''keep'':86C ''least'':238C ''li'':16C,31C ''like'':227C ''lower'':228C ''make'':24C,92C,117C,255C ''matur'':206C,229C ''might'':82C,101C ''mind'':88C ''money'':18C,34C,118C ''new'':33C,36C ''p'':1C,41C,104C,181C,244C,272C ''part'':203C ''pay'':7C,11C ''peopl'':4C,125C,200C ''percent'':144C,149C,166C ''person'':137C ''posit'':179C ''present'':184C ''probabl'':116C,232C ''process'':22C,38C,67C,113C,207C,247C ''profit'':21C,27C,37C ''progress'':93C,130C ''project'':44C,190C ''ration'':2C,133C ''reason'':134C ''reflect'':55C ''reject'':147C ''relev'':246C ''save'':17C ''scienc'':217C,222C,253C ''serendip'':235C ''similar'':281C ''step'':59C,257C ''term'':53C,196C ''therebi'':23C ''though'':220C ''today'':240C ''two'':66C ''ul'':15C ''understand'':62C,182C,201C ''undertaken'':47C ''uniqu'':214C,225C ''way'':156C ''work'':70C,193C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6133, 1, '2015-06-17 09:45:49.593', 2, 172, 'Over-fitting issue in a classification problem (unbalanced data)', '&lt;p&gt;I am working on a rare event (unbalanced target variable) classification problem using decision trees. My dataset comprises of 95% non-event and 5% minority (events) class. &lt;/p&gt;

&lt;p&gt;I used decision tree over logistic regression because I had many categorical variables comparing to continuous variables. I get a  good performance for training data with the decision tree C5.0. However I get poor results for the new data. I use the confusion matrix as a measure of performance. Training model is over-fitting. &lt;/p&gt;

&lt;p&gt;I did pruning to reduce the over-fitting caused by the decision tree.  I used the following code to build the model&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Classifi_C5.0 &amp;lt;- C5.0(TARGET ~., , data = training_data_SMOTED, trails = 500,
                      control = C5.0Control(minCases = mincases_count,
                                            noGlobalPruning = FALSE))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I balanced the minority and majority class using the following code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;training_data_SMOTED &amp;lt;- SMOTE(TARGET ~ ., training_data,
                              perc.over = 100, k = 5, perc.under = 200)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Any sort of advice will be helpful. &lt;/p&gt;
', 'machine-learning  data-mining  classification  r  decision-trees', NULL, '''/code'':154C,185C ''/p'':51C,108C,133C,168C,195C ''/pre'':155C,186C ''100'':180C ''200'':184C ''5'':47C,182C ''500'':146C ''95'':42C ''advic'':191C ''balanc'':158C ''build'':130C ''c5.0'':82C,137C,139C ''c5.0control'':148C ''categor'':64C ''caus'':119C ''class'':50C,163C ''classif'':7A,17B,33C ''classifi'':136C ''code'':128C,135C,167C,170C ''compar'':66C ''compris'':40C ''confus'':95C ''continu'':68C ''control'':147C ''count'':151C ''data'':10A,15B,77C,91C,141C,143C,172C,178C ''data-min'':14B ''dataset'':39C ''decis'':20B,36C,55C,80C,122C ''decision-tre'':19B ''event'':29C,45C,49C ''fals'':153C ''fit'':3A,107C,118C ''follow'':127C,166C ''get'':71C,85C ''good'':73C ''help'':194C ''howev'':83C ''issu'':4A ''k'':181C ''learn'':13B ''logist'':58C ''lt'':138C,174C ''machin'':12B ''machine-learn'':11B ''major'':162C ''mani'':63C ''matrix'':96C ''measur'':99C ''mincas'':149C,150C ''mine'':16B ''minor'':48C,160C ''model'':103C,132C ''new'':90C ''noglobalprun'':152C ''non'':44C ''non-ev'':43C ''over-fit'':1A,105C,116C ''p'':22C,52C,109C,156C,187C ''perc.over'':179C ''perc.under'':183C ''perform'':74C,101C ''poor'':86C ''pre'':134C,169C ''problem'':8A,34C ''prune'':112C ''r'':18B ''rare'':28C ''reduc'':114C ''regress'':59C ''result'':87C ''smote'':144C,173C,175C ''sort'':189C ''target'':31C,140C,176C ''trail'':145C ''train'':76C,102C,142C,171C,177C ''tree'':21B,37C,56C,81C,123C ''unbalanc'':9A,30C ''use'':35C,54C,93C,125C,164C ''variabl'':32C,65C,69C ''work'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6137, 1, '2015-06-17 19:36:26.493', 4, 148, 'Is it ok to interprete PCA plot this way?', '&lt;p&gt;I have several samples (C2, C4, C5) and want to check if they are at a certain stage. I included some known samples (D0 - D77) which were generated at different stages by another lab. In the PCA plot, my samples cluster together on the left and the known samples are dispersed on the right. I think the major difference among all the samples is different experimental protocols (PC1) and the second is different stages (PC2). So my samples are at the same stage. Is that right? And can we say my samples are at a stage between D12 and D19 (when projected to known samples, my samples are located between D12 and D19)? I have no strong mathematical background. Hope someone with math background can give some explanation. Thanks!&lt;/p&gt;

&lt;p&gt;[ &lt;strong&gt;UPDATE1&lt;/strong&gt; ]
I did this analysis using &lt;a href="http://stat.ethz.ch/R-manual/R-patched/library/stats/html/prcomp.html" rel="nofollow"&gt;prcomp&lt;/a&gt; in R. The input is a 2D numeric matrix with 17436 rows and 42 columns. Each row represents a gene and each column represents a sample. The number is the gene expression level for a gene in a sample. The gene expression level is normalized using DESeq2 and thus the numbers are comparable across genes (rows) and samples (columns). For the 42 columns, 18 are from my experiments and the rest from published datasets. Besides different protocols, it is possible there are other differences. Generally, the table is a combination of two sources of data. In my data , C2, C4 and C5 are three cell lines which we processed in parallel in experiments. In published datasets, they sampled the cells at different time points (Day 0 to Day 77).&lt;/p&gt;

&lt;p&gt;[ &lt;strong&gt;UPDATE2&lt;/strong&gt; ] R Code for PCA and plotting&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# normCounts: normalized count from DESeq2, with 17436 rows and 42 columns
normCounts0 &amp;lt;- normCounts[ rowSums(normCounts) &amp;gt; 0, ]
tab &amp;lt;- t(normCounts0)
pca &amp;lt;- prcomp(tab, scale = TRUE)
tmp.x &amp;lt;- as.data.frame(pca$x)
tmp.x$sample&amp;lt;-c(rep("C2_Con",3),rep("C2_KD",3),rep("C5_Con",3),rep("C5_KD",3),rep("C4_Con",3),rep("C4_KD",3), rep("D0",4),rep("D7",4),rep("D12",2),rep("D19",4),rep("D26",2),rep("D33",2),rep("D49",2),rep("D63",2),rep("D77",2))

require("ggplot2")
p &amp;lt;- ggplot(tmp.x, aes(x=PC1, y=PC2, color=sample))
p + geom_point() + scale_color_discrete(breaks=c("C2_Con","C2_KD","C5_Con","C5_KD","C4_Con","C4_KD", "D0","D7", "D12","D19","D26","D33","D49","D63","D77"))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/LuU0y.jpg" alt="PCA plot"&gt;&lt;/p&gt;
', 'machine-learning  dimensionality-reduction', NULL, '''/a'':164C ''/code'':438C ''/luu0y.jpg'':445C ''/p'':146C,294C,305C,449C ''/pre'':439C ''/r-manual/r-patched/library/stats/html/prcomp.html'':160C ''/strong'':150C,298C ''0'':290C,325C ''17436'':175C,314C ''18'':228C ''2'':377C,383C,386C,389C,392C,395C ''2d'':171C ''3'':348C,352C,356C,360C,364C,368C ''4'':371C,374C,380C ''42'':178C,226C,317C ''77'':293C ''across'':218C ''ae'':402C ''alt'':446C ''among'':76C ''analysi'':154C ''anoth'':49C ''as.data.frame'':338C ''background'':135C,140C ''besid'':239C ''break'':415C ''c'':344C,416C ''c2'':21C,263C,346C,350C,417C,419C ''c4'':22C,264C,362C,366C,425C,427C ''c5'':23C,266C,354C,358C,421C,423C ''cell'':269C,284C ''certain'':33C ''check'':27C ''cluster'':57C ''code'':300C,307C ''color'':407C,413C ''column'':179C,187C,223C,227C,318C ''combin'':254C ''compar'':217C ''con'':347C,355C,363C,418C,422C,426C ''count'':310C ''d0'':40C,370C,429C ''d12'':114C,127C,376C,431C ''d19'':116C,129C,379C,432C ''d26'':382C,433C ''d33'':385C,434C ''d49'':388C,435C ''d63'':391C,436C ''d7'':373C,430C ''d77'':41C,394C,437C ''data'':259C,262C ''dataset'':238C,280C ''day'':289C,292C ''deseq2'':211C,312C ''differ'':46C,75C,81C,89C,240C,248C,286C ''dimension'':14B ''dimensionality-reduct'':13B ''discret'':414C ''dispers'':67C ''experi'':232C,277C ''experiment'':82C ''explan'':144C ''express'':196C,206C ''gene'':184C,195C,200C,205C,219C ''general'':249C ''generat'':44C ''geom'':410C ''ggplot'':400C ''ggplot2'':397C ''give'':142C ''gt'':324C ''hope'':136C ''href'':157C ''i.stack.imgur.com'':444C ''i.stack.imgur.com/luu0y.jpg'':443C ''img'':441C ''includ'':36C ''input'':168C ''interpret'':5A ''kd'':351C,359C,367C,420C,424C,428C ''known'':38C,64C,120C ''lab'':50C ''learn'':12B ''left'':61C ''level'':197C,207C ''line'':270C ''locat'':125C ''lt'':320C,327C,331C,337C,343C,399C ''machin'':11B ''machine-learn'':10B ''major'':74C ''math'':139C ''mathemat'':134C ''matrix'':173C ''nofollow'':162C ''normal'':209C,309C ''normcount'':308C,321C,323C ''normcounts0'':319C,329C ''number'':192C,215C ''numer'':172C ''ok'':3A ''p'':16C,147C,295C,398C,409C,440C ''parallel'':275C ''pc1'':84C,404C ''pc2'':91C,406C ''pca'':6A,53C,302C,330C,339C,447C ''plot'':7A,54C,304C,448C ''point'':288C,411C ''possibl'':244C ''prcomp'':163C,332C ''pre'':306C ''process'':273C ''project'':118C ''protocol'':83C,241C ''publish'':237C,279C ''r'':166C,299C ''reduct'':15B ''rel'':161C ''rep'':345C,349C,353C,357C,361C,365C,369C,372C,375C,378C,381C,384C,387C,390C,393C ''repres'':182C,188C ''requir'':396C ''rest'':235C ''right'':70C,102C ''row'':176C,181C,220C,315C ''rowsum'':322C ''sampl'':20C,39C,56C,65C,79C,94C,108C,121C,123C,190C,203C,222C,282C,342C,408C ''say'':106C ''scale'':334C,412C ''second'':87C ''sever'':19C ''someon'':137C ''sourc'':257C ''src'':442C ''stage'':34C,47C,90C,99C,112C ''stat.ethz.ch'':159C ''stat.ethz.ch/r-manual/r-patched/library/stats/html/prcomp.html'':158C ''strong'':133C,148C,296C ''tab'':326C,333C ''tabl'':251C ''thank'':145C ''think'':72C ''three'':268C ''thus'':213C ''time'':287C ''tmp.x'':336C,341C,401C ''togeth'':58C ''true'':335C ''two'':256C ''update1'':149C ''update2'':297C ''use'':155C,210C ''want'':25C ''way'':9A ''x'':340C,403C ''y'':405C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6140, 1, '2015-06-18 09:04:22.453', 1, 115, 'Reading Persian Characters in R', '&lt;p&gt;I have a csv file with characters in Persian and I cannot view them in R correctly. Also, I cannot subset based on Persian characters values. Here is a sample code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;list1 &amp;lt;- c("x","y")
list2 &amp;lt;- c("Ø¨","Ø§ÙÙ")

list1 
list2 ##OK-readable

writedf &amp;lt;- as.data.frame(cbind(list1,list2),encoding="UTF-8")
write.csv(writedf,"test.csv")
testdf &amp;lt;- read.csv("test.csv",encoding="UTF-8")

testdf  
## not readable

testdf[,testdf$list2=="Ø¨"]
## data frame with 0 columns and 2 rows  ???
&lt;/code&gt;&lt;/pre&gt;
', 'r  dataset', NULL, '''-8'':68C,78C ''/code'':94C ''/p'':40C ''/pre'':95C ''0'':89C ''2'':92C ''also'':26C ''as.data.frame'':62C ''base'':30C ''c'':45C,50C ''cannot'':20C,28C ''cbind'':63C ''charact'':3A,15C,33C ''code'':39C,42C ''column'':90C ''correct'':25C ''csv'':12C ''data'':86C ''dataset'':7B ''encod'':66C,76C ''file'':13C ''frame'':87C ''list1'':43C,55C,64C ''list2'':48C,56C,65C,84C ''lt'':44C,49C,61C,73C ''ok'':58C ''ok-read'':57C ''p'':8C ''persian'':2A,17C,32C ''pre'':41C ''r'':5A,6B,24C ''read'':1A ''read.csv'':74C ''readabl'':59C,81C ''row'':93C ''sampl'':38C ''subset'':29C ''test.csv'':71C,75C ''testdf'':72C,79C,82C,83C ''utf'':67C,77C ''valu'':34C ''view'':21C ''write.csv'':69C ''writedf'':60C,70C ''x'':46C ''y'':47C ''ø'':51C,52C,85C ''ù'':53C,54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6141, 1, '2015-06-18 10:43:22.037', 2, 71, 'Working with text files in Excel', '&lt;p&gt;I have an excel file containing a long text in column A. I am looking for the words starting by "popul" such as popular and populate . I can find these cells by the formula:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    =SEARCH("popul",A1,1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want a function that returns the whole words starting by popul such as popular and populate.&lt;/p&gt;
', 'dataset  excel', NULL, '''/code'':51C ''/p'':44C,71C ''/pre'':52C ''1'':50C ''a1'':49C ''cell'':40C ''code'':46C ''column'':20C ''contain'':15C ''dataset'':7B ''excel'':6A,8B,13C ''file'':4A,14C ''find'':38C ''formula'':43C ''function'':57C ''long'':17C ''look'':24C ''p'':9C,53C ''popul'':30C,35C,48C,65C,70C ''popular'':33C,68C ''pre'':45C ''return'':59C ''search'':47C ''start'':28C,63C ''text'':3A,18C ''want'':55C ''whole'':61C ''word'':27C,62C ''work'':1A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6142, 2, '2015-06-18 12:41:01.35', 2, NULL, NULL, '&lt;p&gt;All what I''d say is that you are &lt;strong&gt;most probably&lt;/strong&gt; right! &lt;/p&gt;

&lt;p&gt;Your plot is compatible to the explanation you have for your data so most probably that is the case. I assume you know what features to use (see example) but still better to post some info about input data -e.g. the dimensionality of the data- to be sure everything is right there. The only reason which my ruin the explanation is presence of a &lt;a href="https://en.wikipedia.org/wiki/Confounding" rel="nofollow"&gt;confounder&lt;/a&gt;.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;em&gt;Example&lt;/em&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I''m not an expert of your field but just imagine there is a difference between materials you and the other lab used to get the data. Then I''ll ask you how would you know that the difference on PC1 is not  according to the difference of materials but difference of protocoles? you can &lt;strong&gt;confidently&lt;/strong&gt; say that PC1 is capturing the difference of protocols if you are sure there is nothing else affecting it.&lt;/p&gt;

&lt;p&gt;Hope it helped!&lt;/p&gt;
', NULL, NULL, '''/a'':90C ''/em'':96C ''/li'':97C ''/p'':16C,91C,177C,182C ''/strong'':14C,157C ''/ul'':98C ''/wiki/confounding'':86C ''accord'':143C ''affect'':175C ''ask'':130C ''assum'':38C ''better'':49C ''captur'':162C ''case'':36C ''compat'':21C ''confid'':156C ''confound'':89C ''d'':5C ''data'':29C,56C,62C,126C ''differ'':114C,138C,146C,150C,164C ''dimension'':59C ''e.g'':57C ''els'':174C ''em'':94C ''en.wikipedia.org'':85C ''en.wikipedia.org/wiki/confounding'':84C ''everyth'':66C ''exampl'':46C,95C ''expert'':104C ''explan'':24C,77C ''featur'':42C ''field'':107C ''get'':124C ''help'':181C ''hope'':179C ''href'':83C ''imagin'':110C ''info'':53C ''input'':55C ''know'':40C,135C ''lab'':121C ''li'':93C ''ll'':129C ''m'':101C ''materi'':116C,148C ''nofollow'':88C ''noth'':173C ''p'':1C,17C,99C,178C ''pc1'':140C,160C ''plot'':19C ''post'':51C ''presenc'':79C ''probabl'':13C,32C ''protocol'':152C,166C ''reason'':72C ''rel'':87C ''right'':15C,68C ''ruin'':75C ''say'':6C,158C ''see'':45C ''still'':48C ''strong'':11C,155C ''sure'':65C,170C ''ul'':92C ''use'':44C,122C ''would'':133C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6143, 2, '2015-06-18 15:47:18.92', 0, NULL, NULL, '&lt;p&gt;First, $x_{ij}$ is not a bernoulli random variable. In the text, $x_{ij}$ is described as real valued. Also, the dimensions of $z$ and $w$ are incorrect.&lt;/p&gt;

&lt;p&gt;The text indicates that we''re drawing a matrix of data call it $X (n\times d)$ such that $X_{i,j}=x_{ij}$.&lt;/p&gt;

&lt;p&gt;A particular entry has distribution $x_{ij} \sim f(\alpha_{z_i, w_j})$. Where $\alpha$ is some given matrix of parameters. The maximum row index of $\alpha$ is $g$. The maximum column index of $\alpha$ is $m$. So $\alpha$ is $g \times m$.&lt;/p&gt;

&lt;p&gt;All that remains is to pin down the indices $z$ and $w$. These are vectors rather than matrices since they have a categorical distribution, for example $z_i$ should be an integer between $1$ and $g$.&lt;/p&gt;

&lt;p&gt;Putting this together we need to:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Draw $z$, a vector of length $n$, by $z_i \sim \text{Cat}(\pi), i=1,...,n$&lt;/li&gt;
&lt;li&gt;Draw $w$, a vector of length $d$, by $w_j \sim \text{Cat}(\rho), i=1,...,d$&lt;/li&gt;
&lt;li&gt;Draw $X$, an $(n\times d)$ matrix, by $x_{ij} \sim f(\alpha_{z_i, w_j})$ &lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':168C,187C,206C ''/ol'':207C ''/p'':30C,56C,102C,140C,148C ''1'':137C,166C,185C ''alpha'':67C,73C,85C,93C,97C,201C ''also'':21C ''bernoulli'':8C ''call'':43C ''cat'':163C,182C ''categor'':126C ''column'':90C ''d'':48C,176C,186C,194C ''data'':42C ''describ'':17C ''dimens'':23C ''distribut'':62C,127C ''draw'':38C,151C,170C,189C ''entri'':60C ''exampl'':129C ''f'':66C,200C ''first'':2C ''g'':87C,99C,139C ''given'':76C ''ij'':4C,15C,55C,64C,198C ''incorrect'':29C ''index'':83C,91C ''indic'':34C,112C ''integ'':135C ''j'':53C,71C,179C,205C ''length'':156C,175C ''li'':150C,169C,188C ''m'':95C,101C ''matric'':121C ''matrix'':40C,77C,195C ''maximum'':81C,89C ''n'':46C,157C,167C,192C ''need'':146C ''ol'':149C ''p'':1C,31C,57C,103C,141C ''paramet'':79C ''particular'':59C ''pi'':164C ''pin'':109C ''put'':142C ''random'':9C ''rather'':119C ''re'':37C ''real'':19C ''remain'':106C ''rho'':183C ''row'':82C ''sim'':65C,161C,180C,199C ''sinc'':122C ''text'':13C,33C,162C,181C ''time'':47C,100C,193C ''togeth'':144C ''valu'':20C ''variabl'':10C ''vector'':118C,154C,173C ''w'':27C,70C,115C,171C,178C,204C ''x'':3C,14C,45C,51C,54C,63C,190C,197C ''z'':25C,68C,113C,130C,152C,159C,202C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6144, 1, '2015-06-18 16:41:43.32', 5, 72, 'Sensitivity to scaling of features in a multivariate gaussians', '&lt;p&gt;I''m using the HMMLearn python package for hidden markov models.  That implementation is build on multivariate gaussian distributions.&lt;/p&gt;

&lt;p&gt;So I have a string of features.  How sensitive are gaussians to vastly different feature scales?  Will it be really skewed if one feature is scaled between 0 and 1, and another is scaled between 0 and 1e8?&lt;/p&gt;
', 'python  statistics  feature-scaling', NULL, '''/p'':35C,75C ''0'':64C,72C ''1'':66C ''1e8'':74C ''anoth'':68C ''build'':30C ''differ'':50C ''distribut'':34C ''featur'':5A,13B,43C,51C,60C ''feature-sc'':12B ''gaussian'':9A,33C,47C ''hidden'':24C ''hmmlearn'':20C ''implement'':28C ''m'':17C ''markov'':25C ''model'':26C ''multivari'':8A,32C ''one'':59C ''p'':15C,36C ''packag'':22C ''python'':10B,21C ''realli'':56C ''scale'':3A,14B,52C,62C,70C ''sensit'':1A,45C ''skew'':57C ''statist'':11B ''string'':41C ''use'':18C ''vast'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6182, 1, '2015-06-23 09:20:27.74', 2, 28, 'Is there an interface for bootstrap sampling in rattle in R?', '&lt;p&gt;I am using rattle in R for predictive models and am trying to see whether there is a difference in different sampling methods. The split function at the start of rattle for splitting into training and testing (optional validation) I take it is split validation. Is there a way to do bootstrap validation and cross validation directly in rattle?&lt;/p&gt;
', 'machine-learning  r', NULL, '''/p'':76C ''bootstrap'':6A,68C ''cross'':71C ''differ'':35C,37C ''direct'':73C ''function'':42C ''interfac'':4A ''learn'':14B ''machin'':13B ''machine-learn'':12B ''method'':39C ''model'':25C ''option'':54C ''p'':16C ''predict'':24C ''r'':11A,15B,22C ''rattl'':9A,20C,47C,75C ''sampl'':7A,38C ''see'':30C ''split'':41C,49C,60C ''start'':45C ''take'':57C ''test'':53C ''train'':51C ''tri'':28C ''use'':19C ''valid'':55C,61C,69C,72C ''way'':65C ''whether'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6145, 2, '2015-06-18 17:38:19.883', 5, NULL, NULL, '&lt;p&gt;I''m no Excel expert, as I generally use Python or R instead, but this might get you started until an Excel expert comes along.  In the meantime, it would help if you clarified your question.  And you should be aware that &lt;code&gt;search&lt;/code&gt; will only find you the index of the &lt;strong&gt;first&lt;/strong&gt; match, not all matches in the string.  If you only need the first hit, you can use&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;=MID(A1,SEARCH("popul",A1,1),IFERROR(FIND(" ",A1,SEARCH("popul",A1,1)),LEN(A1)+1)-SEARCH("popul",A1,1))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;although I cannot claim this is the &lt;em&gt;best&lt;/em&gt; way to do this.  You really didn''t specify where you want the results to appear, how they should look, or if you only have one cell you need to search in.  It would also help to know the version of Excel you have.  I''ll also present a crude way to return all the hits in the string:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/MrYrb.png" alt="cells"&gt;&lt;/p&gt;

&lt;p&gt;Cell A1 contains the string, B1 has no formula, and if you run out of "n/a"s you can extend columns B, C, and D by filling down.  The formulas are as follows:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;B3 and below use&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;=IF(C2+1&amp;lt;LEN($A$1),C2+1,"n/a")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;C2 and below use&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;=IFERROR(FIND(" ",$A$1,SEARCH("popul",$A$1,B2)),LEN($A$1)+1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;D2 and below use&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;=IFERROR(MID($A$1,SEARCH("popul",$A$1,B2),C2-SEARCH("popul",$A$1,B2)),"")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As you can see, there''s little to no error checking except to deal with the match at the end of the string.  In the end though, if you''re going to use Excel for this you should probably create a user defined function or utilize VBA instead of in-cell formulas.&lt;/p&gt;
', NULL, NULL, '''+1'':93C,227C,233C,259C ''/code'':46C,98C,235C,260C,288C ''/em'':110C ''/mryrb.png'':176C ''/p'':75C,170C,179C,214C,222C,244C,269C,344C ''/pre'':99C,236C,261C,289C ''/strong'':57C,221C,243C,268C ''1'':83C,90C,97C,231C,250C,254C,258C,275C,279C,286C ''a1'':79C,82C,86C,89C,92C,96C,182C ''along'':26C ''also'':145C,157C ''alt'':177C ''although'':101C ''appear'':126C ''awar'':42C ''b'':202C ''b1'':186C ''b2'':255C,280C,287C ''b3'':217C ''best'':109C ''c'':203C ''c2'':226C,232C,239C,282C ''c2-search'':281C ''cannot'':103C ''cell'':137C,178C,181C,342C ''check'':301C ''claim'':104C ''clarifi'':35C ''code'':44C,77C,224C,246C,271C ''column'':201C ''come'':25C ''contain'':183C ''creat'':330C ''crude'':160C ''d'':205C ''d2'':264C ''deal'':304C ''defin'':333C ''didn'':117C ''em'':108C ''end'':310C,316C ''error'':300C ''excel'':5C,23C,152C,324C ''except'':302C ''expert'':6C,24C ''extend'':200C ''fill'':207C ''find'':49C,85C,248C ''first'':56C,70C ''follow'':213C ''formula'':189C,210C,343C ''function'':334C ''general'':9C ''get'':18C ''go'':321C ''help'':32C,146C ''hit'':71C,166C ''i.stack.imgur.com'':175C ''i.stack.imgur.com/mryrb.png'':174C ''iferror'':84C,247C,272C ''img'':172C ''in-cel'':340C ''index'':52C ''instead'':14C,338C ''know'':148C ''len'':91C,229C,256C ''littl'':297C ''ll'':156C ''look'':130C ''lt'':228C ''m'':3C ''match'':58C,61C,307C ''meantim'':29C ''mid'':78C,273C ''might'':17C ''n/a'':196C,234C ''need'':68C,139C ''one'':136C ''p'':1C,100C,171C,180C,215C,237C,262C,290C ''popul'':81C,88C,95C,252C,277C,284C ''pre'':76C,223C,245C,270C ''present'':158C ''probabl'':329C ''python'':11C ''question'':37C ''r'':13C ''re'':320C ''realli'':116C ''result'':124C ''return'':163C ''run'':193C ''search'':45C,80C,87C,94C,141C,251C,276C,283C ''see'':294C ''specifi'':119C ''src'':173C ''start'':20C ''string'':64C,169C,185C,313C ''strong'':55C,216C,238C,263C ''though'':317C ''use'':10C,74C,220C,242C,267C,323C ''user'':332C ''util'':336C ''vba'':337C ''version'':150C ''want'':122C ''way'':111C,161C ''would'':31C,144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6146, 1, '2015-06-18 18:23:07.763', 6, 239, 'Best way to store large data set using R from Twitter?', '&lt;p&gt;I am working on a project that aims to retrieve a large data-set (i.e., tweet data which is a couple of days old) from Twitter using the twitteR library on R.  have difficulty storing tweets because my machine has only 8 GB of memory. It ran out of memory even before I set it to retrieve for one day. Is there a way where I can store the tweets straight to my disk without storing into RAM? I am not using the streaming API as I need to get old tweets. &lt;/p&gt;
', 'r  dataset', NULL, '''/p'':108C ''8'':57C ''aim'':22C ''api'':100C ''best'':1A ''coupl'':36C ''data'':6A,28C,32C ''data-set'':27C ''dataset'':13B ''day'':38C,75C ''difficulti'':49C ''disk'':89C ''even'':66C ''gb'':58C ''get'':105C ''i.e'':30C ''larg'':5A,26C ''librari'':45C ''machin'':54C ''memori'':60C,65C ''need'':103C ''old'':39C,106C ''one'':74C ''p'':14C ''project'':20C ''r'':9A,12B,47C ''ram'':93C ''ran'':62C ''retriev'':24C,72C ''set'':7A,29C,69C ''store'':4A,50C,83C,91C ''straight'':86C ''stream'':99C ''tweet'':31C,51C,85C,107C ''twitter'':11A,41C,44C ''use'':8A,42C,97C ''way'':2A,79C ''without'':90C ''work'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6147, 2, '2015-06-18 18:57:08.203', 2, NULL, NULL, '&lt;p&gt;I worked on a Twitter data project last Fall wherein we used Java libraries to pull in tweet data from the streaming and the rest API''s. We used &lt;a href="http://twitter4j.org/en/index.html" rel="nofollow"&gt;Twitter4J&lt;/a&gt; (an unofficial Java library) for the  &lt;a href="https://dev.twitter.com/overview/documentation" rel="nofollow"&gt;Twitter API&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;The tweet data was fetched and directly written onto text files on our hard drives. Yes, we did increase the memory and heap. I believe R studio will have a similar option. An alternative would be to pull in lesser amounts of tweet data with more number of repetitions. &lt;/p&gt;
', NULL, NULL, '''/a'':39C,55C ''/en/index.html'':35C ''/overview/documentation'':50C ''/p'':56C,107C ''altern'':91C ''amount'':98C ''api'':27C,54C ''believ'':82C ''data'':7C,20C,60C,101C ''dev.twitter.com'':49C ''dev.twitter.com/overview/documentation'':48C ''direct'':64C ''drive'':72C ''fall'':10C ''fetch'':62C ''file'':68C ''hard'':71C ''heap'':80C ''href'':32C,47C ''increas'':76C ''java'':14C,42C ''last'':9C ''lesser'':97C ''librari'':15C,43C ''memori'':78C ''nofollow'':37C,52C ''number'':104C ''onto'':66C ''option'':89C ''p'':1C,57C ''project'':8C ''pull'':17C,95C ''r'':83C ''rel'':36C,51C ''repetit'':106C ''rest'':26C ''similar'':88C ''stream'':23C ''studio'':84C ''text'':67C ''tweet'':19C,59C,100C ''twitter'':6C,53C ''twitter4j'':38C ''twitter4j.org'':34C ''twitter4j.org/en/index.html'':33C ''unoffici'':41C ''use'':13C,30C ''wherein'':11C ''work'':3C ''would'':92C ''written'':65C ''yes'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6150, 1, '2015-06-19 05:41:26.783', 1, 208, 'How to import a tsv file to Matlab', '&lt;p&gt;I am really new to data science. Please don''t mark me down as this website is my only hope of progress.
I have set of data I obtained from NASA website. When I saved it, it saved as "tsv'' file. (Tab separated values). I want to open it on Matlab as a Matrix as I have a code to run on that matrix. 
Basically I want to import that file to matlab and start running the code on it. 
Can someone please help me or guide me in the right direction.&lt;/p&gt;

&lt;p&gt;I tried various things such as import data, tdfread but so far nothing has worked for me. I was first trying to export the tsv file to MS Excel and then go from Excel to Matlab. That too I don''t know how to do. I will give you the link of my data which I want to import on to Matlab. &lt;/p&gt;

&lt;p&gt;The link for my data is the following.&lt;/p&gt;

&lt;p&gt;Please take a look at &lt;a href="http://vizier.u-strasbg.fr/viz-bin/VizieR?-source=J%2FApJS%2F209%2F31" rel="nofollow"&gt;http://vizier.u-strasbg.fr/viz-bin/VizieR?-source=J%2FApJS%2F209%2F31&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you just click on submit at the lower right corner, one will see the data.&lt;/p&gt;

&lt;p&gt;a = importdata(''J_ApJS_209_31_table3-150618.tsv'') [This the command I used].
Error message on matlab is &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    ??? Error using ==&amp;gt; importdata at 136
    Unable to open file.
    Error in ==&amp;gt; data at 1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then I wrote a script.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    tdfread(J_ApJS_209_31_table3-150618,''\t'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Error message I get is 
        ??? Undefined function or variable ''J_ApJS_209_31_table3''.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    Error in ==&amp;gt; data at 1
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6151, 1, '2015-06-19 07:12:07.153', 2, 157, 'NER on Twitter data', '&lt;p&gt;What are the best method/library/data available to extract named entities [Names and Location] from Twitter data ? [Other than dictionary lookup]&lt;/p&gt;

&lt;p&gt;I tried with Python-Stanford NER, But it seems to fail when named entities is not capitalized. &lt;/p&gt;

&lt;p&gt;I also tried to predict NER after converting text to upper case 
eg : &lt;/p&gt;

&lt;pre&gt;&lt;code&gt; text = "david beckham played for england"

 stanford.NERTagger.tag(text)
 [(u''david'', u''PERSON''), (u''beckham'', u''PERSON''), (u''played'', u''O''), (u''for'', u''O''), (u''england'', u''O'')]

 stanford.NERTagger.tag(text.upper())
 output : [(u''DAVID'', u''PERSON''), (u''BECKHAM'', u''PERSON''), (u''PLAYED'', u''O''), (u''FOR'', u''O''), (u''ENGLAND'', u''LOCATION'')]
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6216, 1, '2015-06-25 19:12:04.583', 2, 56, 'learning rate in reinforcement learning', '&lt;p&gt;Does anyone know how to get the learning rate from participant data?&lt;/p&gt;

&lt;p&gt;I''m computing all the expected values for all trials (=200)&lt;/p&gt;

&lt;p&gt;$$V_t(S) = V_{t-1}(S)+ \alpha \cdot \text{error }_t $$&lt;/p&gt;

&lt;p&gt;$$(\text{error }_t = R_t â V_{t-1}(S))$$&lt;/p&gt;

&lt;p&gt;and then computing alpha at each trial using&lt;/p&gt;

&lt;p&gt;$$\alpha= (V_t(S)  - V_{t-1}(S) )/ \text{error }_t$$&lt;/p&gt;

&lt;p&gt;but it seems not the right way!&lt;/p&gt;

&lt;p&gt;Suggestions?&lt;/p&gt;
', 'machine-learning  reinforcement-learning', NULL, '''-1'':45C,62C,82C ''/p'':25C,38C,52C,64C,74C,87C,96C,99C ''200'':37C ''alpha'':47C,69C,76C ''anyon'':14C ''cdot'':48C ''comput'':29C,68C ''data'':24C ''error'':50C,55C,85C ''expect'':32C ''get'':18C ''know'':15C ''learn'':1A,5A,8B,11B,20C ''m'':28C ''machin'':7B ''machine-learn'':6B ''p'':12C,26C,39C,53C,65C,75C,88C,97C ''particip'':23C ''r'':57C ''rate'':2A,21C ''reinforc'':4A,10B ''reinforcement-learn'':9B ''right'':94C ''seem'':91C ''suggest'':98C ''text'':49C,54C,84C ''trial'':36C,72C ''use'':73C ''v'':40C,43C,60C,77C,80C ''valu'':33C ''way'':95C ''â'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8861, 1, '2015-11-16 15:14:37.75', 3, 41, 'Why are there currently no content-based evaluation metrics for information retrieval?', '&lt;p&gt;Consider the problem of learning to rank for Google-like searching - i.e., learning to return a good ordering of URL''s when given a query. Most (if not all) current evaluation metrics for this problem are URL-based.  Current evaluation metrics like Mean Reciprocal Rank (MRR) and Discounted Cumulative Gain (DCG) sum up weighted relevance scores for the pages in a ranked list.  If a ranked list contains n pages, for instance, these metrics give a sum of n terms.&lt;/p&gt;

&lt;p&gt;From my own literature review, it seems no metrics incorporate words.  Although some theoretically incorporate "information nuggets", none incorporate "information nuggets=words" in practice.  I have not found recent or past literature that concretely utilizes words, phrases, or anything similar in scoring ranked lists. Since I haven''t found past literature, it does not seem that word-based metrics were attempted once and then replaced with coarser URL-based metrics.  Documents contain words, as do the snippets returned in a Google-like ranked list, so it seems natural that word-based metrics should have been attempted.  Thoughts?&lt;/p&gt;

&lt;p&gt;(Note: One obvious objection is that TREC-based assessment of "word relevance" is a vague and even expensive endeavor (asking assessors to mark for words for relevance).  But there''s other ways to get relevance feedback that ignore this problem, such as using the dwell time on pages as a proxy.)&lt;/p&gt;
', 'information-retrieval  evaluation', NULL, '''/p'':101C,204C,258C ''although'':114C ''anyth'':141C ''ask'':226C ''assess'':215C ''assessor'':227C ''attempt'':164C,202C ''base'':8A,58C,161C,173C,197C,214C ''coarser'':170C ''concret'':136C ''consid'':19C ''contain'':88C,176C ''content'':7A ''content-bas'':6A ''cumul'':69C ''current'':4A,49C,59C ''dcg'':71C ''discount'':68C ''document'':175C ''dwell'':251C ''endeavor'':225C ''evalu'':9A,17B,50C,60C ''even'':223C ''expens'':224C ''feedback'':242C ''found'':130C,151C ''gain'':70C ''get'':240C ''give'':95C ''given'':42C ''good'':36C ''googl'':28C,186C ''google-lik'':27C,185C ''haven'':149C ''i.e'':31C ''ignor'':244C ''incorpor'':112C,117C,121C ''inform'':12A,15B,118C,122C ''information-retriev'':14B ''instanc'':92C ''learn'':23C,32C ''like'':29C,62C,187C ''list'':83C,87C,146C,189C ''literatur'':106C,134C,153C ''mark'':229C ''mean'':63C ''metric'':10A,51C,61C,94C,111C,162C,174C,198C ''mrr'':66C ''n'':89C,99C ''natur'':193C ''none'':120C ''note'':206C ''nugget'':119C,123C ''object'':209C ''obvious'':208C ''one'':207C ''order'':37C ''p'':18C,102C,205C ''page'':79C,90C,254C ''past'':133C,152C ''phrase'':139C ''practic'':126C ''problem'':21C,54C,246C ''proxi'':257C ''queri'':44C ''rank'':25C,65C,82C,86C,145C,188C ''recent'':131C ''reciproc'':64C ''relev'':75C,218C,233C,241C ''replac'':168C ''retriev'':13A,16B ''return'':34C,182C ''review'':107C ''score'':76C,144C ''search'':30C ''seem'':109C,157C,192C ''similar'':142C ''sinc'':147C ''snippet'':181C ''sum'':72C,97C ''term'':100C ''theoret'':116C ''thought'':203C ''time'':252C ''trec'':213C ''trec-bas'':212C ''url'':39C,57C,172C ''url-bas'':56C,171C ''use'':249C ''util'':137C ''vagu'':221C ''way'':238C ''weight'':74C ''word'':113C,124C,138C,160C,177C,196C,217C,231C ''word-bas'':159C,195C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6152, 2, '2015-06-19 07:30:04.703', 1, NULL, NULL, '&lt;p&gt;Conditional Random Fields (CRFs) can be used for segmenting/labeling sequential problems. Try CRF++: Yet Another CRF toolkit,  a simple, customizable, and open source implementation of Conditional Random Fields (CRFs) &lt;/p&gt;

&lt;p&gt;You can label and create a tagged training corpus and use CRF++ &lt;/p&gt;

&lt;p&gt;You also need to create a feature template&lt;/p&gt;

&lt;p&gt;Refer : &lt;a href="http://taku910.github.io/crfpp/" rel="nofollow"&gt;http://taku910.github.io/crfpp/&lt;/a&gt; for more details. &lt;/p&gt;

&lt;p&gt;Check the example from data for CoNLL shared task (PoS tagging).&lt;/p&gt;
', NULL, NULL, '''/crfpp/'':62C ''/crfpp/&lt;/a&gt;'':67C ''/p'':31C,45C,55C,71C,84C ''also'':48C ''anoth'':16C ''check'':73C ''condit'':2C,27C ''conll'':79C ''corpus'':41C ''creat'':37C,51C ''crf'':14C,17C,44C ''crfs'':5C,30C ''customiz'':21C ''data'':77C ''detail'':70C ''exampl'':75C ''featur'':53C ''field'':4C,29C ''href'':59C ''implement'':25C ''label'':35C ''need'':49C ''nofollow'':64C ''open'':23C ''p'':1C,32C,46C,56C,72C ''pos'':82C ''problem'':12C ''random'':3C,28C ''refer'':57C ''rel'':63C ''segmenting/labeling'':10C ''sequenti'':11C ''share'':80C ''simpl'':20C ''sourc'':24C ''tag'':39C,83C ''taku910.github.io'':61C,66C ''taku910.github.io/crfpp/'':60C ''taku910.github.io/crfpp/&lt;/a&gt;'':65C ''task'':81C ''templat'':54C ''toolkit'':18C ''train'':40C ''tri'':13C ''use'':8C,43C ''yet'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6153, 1, '2015-06-19 07:37:44.407', 1, 19, 'Importance of Random initialisation VS number of hidden units', '&lt;p&gt;A question crossed my mind not so long ago: I am doing experiments on Language Model with RNN (always with the same network topology: 50 hidden units, and 10M "directs connections" that are emulating N_grams models) and different fraction of corpus (10,25,50,75,100%) (9M words).&lt;/p&gt;

&lt;p&gt;I noticed that while perplexity seems to decrease when the training data become more abundant, certain times it does not.&lt;/p&gt;

&lt;p&gt;Last example : 143 118 109 106 112&lt;/p&gt;

&lt;p&gt;My first thought was network initialization, so I began testing with a smaller corpus and 20 hidden units (for technical reasons. Even with 10% corpus, learning can take up to 30h, which is problematic for me), and I found after 50 tries that all nets converged on values within 3% of each other.&lt;/p&gt;

&lt;p&gt;But I thought that maybe the importance of this initialization is a function of the number of hidden units? I mean the more hidden units the more parameters to tune.&lt;/p&gt;

&lt;p&gt;Also maybe my stop criterion is too sensitive (It stops if evolution of perplexity between two iterations is inferior to a certain number).
Do you think it would make an impact to allow it to run one of two iterations after the criterion was met to see if it was just a local thing ?&lt;/p&gt;

&lt;p&gt;Thanks,
Marc&lt;/p&gt;
', 'neuralnetwork', NULL, '''/p'':61C,83C,92C,147C,179C,235C,239C ''10'':54C,117C ''100'':58C ''106'':90C ''109'':89C ''10m'':40C ''112'':91C ''118'':88C ''143'':87C ''20'':109C ''25'':55C ''3'':143C ''30h'':124C ''50'':36C,56C,134C ''75'':57C ''9m'':59C ''abund'':77C ''ago'':20C ''allow'':213C ''also'':181C ''alway'':30C ''becom'':75C ''began'':102C ''certain'':78C,202C ''connect'':42C ''converg'':139C ''corpus'':53C,107C,118C ''criterion'':185C,223C ''cross'':14C ''data'':74C ''decreas'':70C ''differ'':50C ''direct'':41C ''emul'':45C ''even'':115C ''evolut'':192C ''exampl'':86C ''experi'':24C ''first'':95C ''found'':132C ''fraction'':51C ''function'':161C ''gram'':47C ''hidden'':8A,37C,110C,166C,172C ''impact'':211C ''import'':1A,155C ''inferior'':199C ''initi'':99C,158C ''initialis'':4A ''iter'':197C,220C ''languag'':26C ''last'':85C ''learn'':119C ''local'':233C ''long'':19C ''make'':209C ''marc'':238C ''mayb'':153C,182C ''mean'':169C ''met'':225C ''mind'':16C ''model'':27C,48C ''n'':46C ''net'':138C ''network'':34C,98C ''neuralnetwork'':10B ''notic'':64C ''number'':6A,164C,203C ''one'':217C ''p'':11C,62C,84C,93C,148C,180C,236C ''paramet'':176C ''perplex'':67C,194C ''problemat'':127C ''question'':13C ''random'':3A ''reason'':114C ''rnn'':29C ''run'':216C ''see'':227C ''seem'':68C ''sensit'':188C ''smaller'':106C ''stop'':184C,190C ''take'':121C ''technic'':113C ''test'':103C ''thank'':237C ''thing'':234C ''think'':206C ''thought'':96C,151C ''time'':79C ''topolog'':35C ''train'':73C ''tri'':135C ''tune'':178C ''two'':196C,219C ''unit'':9A,38C,111C,167C,173C ''valu'':141C ''vs'':5A ''within'':142C ''word'':60C ''would'':208C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6154, 2, '2015-06-19 10:17:17.027', 6, NULL, NULL, '&lt;p&gt;This &lt;a href="https://cs231n.github.io/"&gt;tutorial&lt;/a&gt; by Andrej Karpathy does an excellent job of explaining convolutional neural networks.&lt;/p&gt;

&lt;p&gt;Reading this paper should give you a rough idea about &lt;a href="http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf"&gt;deconv networks&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;These &lt;a href="http://cs.nyu.edu/~fergus/drafts/utexas2.pdf"&gt;slides&lt;/a&gt; are great for deconv networks.&lt;/p&gt;
', NULL, NULL, '''/a'':7C,39C,49C ''/p'':20C,40C,55C ''/pubs/cvpr2010/cvpr2010.pdf'':36C ''/~fergus/drafts/utexas2.pdf'':47C ''andrej'':9C ''convolut'':17C ''cs.nyu.edu'':46C ''cs.nyu.edu/~fergus/drafts/utexas2.pdf'':45C ''cs231n.github.io'':5C ''deconv'':37C,53C ''excel'':13C ''explain'':16C ''give'':26C ''great'':51C ''href'':4C,33C,44C ''idea'':30C ''job'':14C ''karpathi'':10C ''network'':19C,38C,54C ''neural'':18C ''p'':1C,21C,41C ''paper'':24C ''read'':22C ''rough'':29C ''slide'':48C ''tutori'':6C ''www.matthewzeiler.com'':35C ''www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6156, 2, '2015-06-19 15:14:58.097', 1, NULL, NULL, '&lt;ul&gt;
&lt;li&gt;make sure the files are saved in UTF&lt;/li&gt;
&lt;li&gt;try &lt;code&gt;Sys.setlocale("LC_ALL", locale_code)&lt;/code&gt; and have a look at the &lt;a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/locales.html" rel="nofollow"&gt;documentation&lt;/a&gt; of this function &lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':35C ''/code'':20C ''/li'':11C,39C ''/r-manual/r-devel/library/base/html/locales.html'':31C ''/ul'':40C ''code'':14C,19C ''document'':34C ''file'':6C ''function'':38C ''href'':28C ''lc'':16C ''li'':2C,12C ''local'':18C ''look'':24C ''make'':3C ''nofollow'':33C ''rel'':32C ''save'':8C ''stat.ethz.ch'':30C ''stat.ethz.ch/r-manual/r-devel/library/base/html/locales.html'':29C ''sure'':4C ''sys.setlocale'':15C ''tri'':13C ''ul'':1C ''utf'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6158, 2, '2015-06-19 16:39:48.01', 4, NULL, NULL, '&lt;p&gt;Given the information provided, I do not think it''s enough to say that your samples are between stages D12 and D19. I admit knowing nothing about your domain, but with what you''re showing it is very conceivable that other factors are causing the spread of your samples from the others on PC1 &lt;em&gt;and&lt;/em&gt; it''s too much of a stretch to say that your samples "project" into the others in between D12 and D19. Why does it have to be a projection orthogonal to the axis described by PC1? Remeber that PC1 is a linear combination of all your inputs. That is, it describes an arbitrary line through your input space that is probably not aligned with any one thing in particular. For example, all three of the following projections could be envisioned for your problem:
&lt;img src="http://i.stack.imgur.com/se2FV.png" alt="PCA plot showing possible projections"&gt;&lt;/p&gt;

&lt;p&gt;Another red flag for me is that you say PC1 is related to differences in experimental protocols. You haven''t posted a scree plot or listed the eigenvalues for the different PCs but obviously PC1 soaks up the most variance so it''s a pretty big deal. If you''re saying that PC1 is correlated with experimental protocol, then that would seem good because hopefully the impact of experimental protocol is minimized in other PCs. However, that''s never completely the case, really.&lt;/p&gt;

&lt;p&gt;Again, not knowing your domain it''s hard to tell what the impact of experimental protocol is on the distribution of your data in the PC plot, but I''d be VERY wary of comparing these data in this way if your data was produced using a different experimental design and, as you noted, "there may be other differences." &lt;/p&gt;

&lt;p&gt;My hunch is that you would need to continue minimizing differences in the protocols between your experiments and the published experiments before you could really use this type of analysis. Others might disagree but if you posted the sources of your data, the eigenvectors, and possibly a subset of the data, experts in your field may be able to chime in on this methodology. &lt;/p&gt;

&lt;p&gt;The short answer is: you &lt;em&gt;could&lt;/em&gt; be right in your interpretation but at this point you don''t have enough evidence to suggest your samples are "in between" D12 and D19 in terms of the stage. And I worry about differences in methodology completely swamping your ability to interpret things this way. &lt;/p&gt;
', NULL, NULL, '''/em'':58C,374C ''/p'':153C,238C,299C,365C,421C ''/se2fv.png'':146C ''abil'':415C ''abl'':358C ''admit'':25C ''align'':121C ''alt'':147C ''analysi'':330C ''anoth'':155C ''answer'':369C ''arbitrari'':111C ''axi'':91C ''big'':200C ''case'':236C ''caus'':45C ''chime'':360C ''combin'':101C ''compar'':274C ''complet'':234C,412C ''conceiv'':40C ''continu'':309C ''correl'':209C ''could'':136C,324C,373C ''d'':269C ''d12'':21C,77C,397C ''d19'':23C,79C,399C ''data'':262C,276C,282C,342C,351C ''deal'':201C ''describ'':92C,109C ''design'':289C ''differ'':168C,185C,287C,298C,311C,409C ''disagre'':333C ''distribut'':259C ''domain'':30C,244C ''eigenvalu'':182C ''eigenvector'':344C ''em'':56C,372C ''enough'':12C,388C ''envis'':138C ''evid'':389C ''exampl'':129C ''experi'':317C,321C ''experiment'':170C,211C,223C,254C,288C ''expert'':352C ''factor'':43C ''field'':355C ''flag'':157C ''follow'':134C ''given'':2C ''good'':217C ''hard'':247C ''haven'':173C ''hope'':219C ''howev'':230C ''hunch'':302C ''i.stack.imgur.com'':145C ''i.stack.imgur.com/se2fv.png'':144C ''img'':142C ''impact'':221C,252C ''inform'':4C ''input'':105C,115C ''interpret'':379C,417C ''know'':26C,242C ''line'':112C ''linear'':100C ''list'':180C ''may'':295C,356C ''methodolog'':364C,411C ''might'':332C ''minim'':226C,310C ''much'':62C ''need'':307C ''never'':233C ''note'':293C ''noth'':27C ''obvious'':188C ''one'':124C ''orthogon'':88C ''other'':53C,74C,331C ''p'':1C,154C,239C,300C,366C ''particular'':127C ''pc'':265C ''pc1'':55C,94C,97C,164C,189C,207C ''pca'':148C ''pcs'':186C,229C ''plot'':149C,178C,266C ''point'':383C ''possibl'':151C,346C ''post'':175C,337C ''pretti'':199C ''probabl'':119C ''problem'':141C ''produc'':284C ''project'':71C,87C,135C,152C ''protocol'':171C,212C,224C,255C,314C ''provid'':5C ''publish'':320C ''re'':35C,204C ''realli'':237C,325C ''red'':156C ''relat'':166C ''remeb'':95C ''right'':376C ''sampl'':17C,50C,70C,393C ''say'':14C,67C,163C,205C ''scree'':177C ''seem'':216C ''short'':368C ''show'':36C,150C ''soak'':190C ''sourc'':339C ''space'':116C ''spread'':47C ''src'':143C ''stage'':20C,404C ''stretch'':65C ''subset'':348C ''suggest'':391C ''swamp'':413C ''tell'':249C ''term'':401C ''thing'':125C,418C ''think'':9C ''three'':131C ''type'':328C ''use'':285C,326C ''varianc'':194C ''wari'':272C ''way'':279C,420C ''worri'':407C ''would'':215C,306C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6722, 2, '2015-08-07 11:02:11.387', 0, NULL, NULL, '&lt;p&gt;I found in some cases useful to define a "business evaluation" function, defining the "importance" of the dimensions used for clustering. E.g. for greengrocer clustering the customers, if apples are twice as expensive as oranges, the number of apples will be doubled.&lt;/p&gt;
', NULL, NULL, '''/p'':44C ''appl'':30C,40C ''busi'':11C ''case'':6C ''cluster'':22C,26C ''custom'':28C ''defin'':9C,14C ''dimens'':19C ''doubl'':43C ''e.g'':23C ''evalu'':12C ''expens'':34C ''found'':3C ''function'':13C ''greengroc'':25C ''import'':16C ''number'':38C ''orang'':36C ''p'':1C ''twice'':32C ''use'':7C,20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6598, 2, '2015-07-27 12:15:37.55', 0, NULL, NULL, '&lt;p&gt;Possible, yes, but why do you want to do this?&lt;/p&gt;

&lt;p&gt;Topic modelling is a method of &lt;em&gt;unsupervised learning&lt;/em&gt; with all it weaknesses.&lt;/p&gt;

&lt;p&gt;With the type of data you sketch you can used methods of &lt;em&gt;supervised learning&lt;/em&gt; that will be much more stable, less prone to overfitting, and more reliable in the long run.&lt;/p&gt;
', NULL, NULL, '''/em'':23C,45C ''/p'':12C,28C,63C ''data'':34C ''em'':20C,42C ''learn'':22C,44C ''less'':52C ''long'':61C ''method'':18C,40C ''model'':15C ''much'':49C ''overfit'':55C ''p'':1C,13C,29C ''possibl'':2C ''prone'':53C ''reliabl'':58C ''run'':62C ''sketch'':36C ''stabl'':51C ''supervis'':43C ''topic'':14C ''type'':32C ''unsupervis'':21C ''use'':39C ''want'':8C ''weak'':27C ''yes'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6159, 2, '2015-06-19 16:46:00.387', 1, NULL, NULL, '&lt;p&gt;&lt;code&gt;tdfread&lt;/code&gt; displays the File Open dialog box for interactive selection of a data file, then reads data from the file. The file should have variable names separated by tabs in the first row, and data values separated by tabs in the remaining rows. &lt;code&gt;tdfread&lt;/code&gt; creates variables in the workspace, one for each column of the file. The variable names are taken from the first row of the file. If a column of the file contains only numeric data in the second and following rows, tdfread creates a double variable. Otherwise, tdfread creates a char variable. After all values are imported, &lt;code&gt;tdfread&lt;/code&gt; displays information about the imported values using the format of the &lt;code&gt;tdfread&lt;/code&gt; command.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;tdfread(filename)&lt;/code&gt; allows command line specification of the name of a file in the current folder, or the complete path name of any file, using the string filename.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;tdfread(filename,delimiter)&lt;/code&gt; indicates that the character specified by delimiter separates columns in the file. Accepted values for delimiter are:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;'' '' or ''space''
''\t'' or ''tab''
'','' or ''comma''
'';'' or ''semi''
''|'' or ''bar''
The default delimiter is ''tab''.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;code&gt;s = tdfread(filename,...)&lt;/code&gt; returns a scalar structure s whose fields each contain a variable.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6165, 1, '2015-06-21 08:22:39.63', 5, 125, 'Implementing Complementary Naive Bayes in python?', '&lt;h3&gt;Problem&lt;/h3&gt;

&lt;p&gt;I have tried using Naive bayes on a labeled data set of crime data but got really poor results (7% accuracy). Naive Bayes runs much faster than other alogorithms I''ve been using so I wanted to try finding out why the score was so low. &lt;/p&gt;

&lt;h3&gt;Research&lt;/h3&gt;

&lt;p&gt;After reading I found that Naive bayes should be used with balanced datasets because it has a bias for classes with higher frequency. Since my data is unbalanced I wanted to try using the Complementary Naive Bayes since it is specifically made for dealing with data skews. In the paper that describes the process, the application is for text classification but I don''t see why the technique wouldn''t work in other situations. You can find the paper I''m referring to &lt;a href="http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf" rel="nofollow"&gt;here&lt;/a&gt;. In short the idea is to use weights based on the occurences where a class doesn''t show up.&lt;/p&gt;

&lt;p&gt;After doing some research I was able to find an implementation in Java but unfortunately I don''t know any Java and I just don''t understand the algorithm well enough to implement myself.&lt;/p&gt;

&lt;h3&gt;Question&lt;/h3&gt;

&lt;p&gt;where I can find an implementation in python? If that doesn''t exist how should I go about implementing it myself?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6166, 2, '2015-06-21 09:03:26.883', 3, NULL, NULL, '&lt;p&gt;Naive Bayes should be able to handle imbalanced datasets. Recall that the Bayes formula is&lt;/p&gt;

&lt;p&gt;$$P(y \mid x) = \cfrac{P(x \mid y) \, P(y)}{P(x)} \propto P(x \mid y) \, P(y)$$&lt;/p&gt;

&lt;p&gt;So $P(x \mid y) \, P(y)$ takes the prior $P(y)$ into account.&lt;/p&gt;

&lt;p&gt;In your case maybe you overfit and need some smoothing? You can start with +1 smoothing and see if it gives any improvements. In python, when using numpy, I''d implement the smoothing this way:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;table = # counts for each feature 
PT = (table + 1) / (table + 1).sum(axis=1, keepdims=1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Note that this is gives you Multinomial Naive Bayes - which applies only to categorical data. &lt;/p&gt;

&lt;p&gt;I can also suggest the following link: &lt;a href="http://www.itshared.org/2015/03/naive-bayes-on-apache-flink.html" rel="nofollow"&gt;http://www.itshared.org/2015/03/naive-bayes-on-apache-flink.html&lt;/a&gt;. It''s about implementing Naive Bayes on Apache Flink. While it''s Java, maybe it''ll give you some theory you need to understand the algorithm better.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6167, 2, '2015-06-21 10:57:07.127', 3, NULL, NULL, '&lt;h3&gt;Don''t just mix the data of two different sources.&lt;/h3&gt;

&lt;p&gt;It looks as if the two sources may simply be using different scales. But that makes the whole PCA analysis meaningless.&lt;/p&gt;

&lt;p&gt;All your plot may be visualizing is that the other samples use a different way of measuring things. It seems &lt;strong&gt;all the variance is in the other samples&lt;/strong&gt;. In particular this means that &lt;strong&gt;you cannot draw any conclusions about how your samples relate to the reference samples&lt;/strong&gt;.&lt;/p&gt;
', NULL, NULL, '''/h3'':12C ''/p'':35C,88C ''/strong'':66C,87C ''analysi'':33C ''cannot'':74C ''conclus'':77C ''data'':7C ''differ'':10C,25C,50C ''draw'':75C ''h3'':1C ''look'':15C ''make'':29C ''may'':21C,40C ''mean'':70C ''meaningless'':34C ''measur'':53C ''mix'':5C ''p'':13C,36C ''particular'':68C ''pca'':32C ''plot'':39C ''refer'':85C ''relat'':82C ''sampl'':47C,65C,81C,86C ''scale'':26C ''seem'':56C ''simpli'':22C ''sourc'':11C,20C ''strong'':57C,72C ''thing'':54C ''two'':9C,19C ''use'':24C,48C ''varianc'':60C ''visual'':42C ''way'':51C ''whole'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6168, 2, '2015-06-21 17:11:39.947', 1, NULL, NULL, '&lt;p&gt;It depends on the usage of various packages (Numpy/Scipy etc), which are written in C and be incredibly fast also Python can be complied using JIT.  Here is an excellent comparison between R and Python : &lt;a href="https://learnanalyticshere.wordpress.com/2015/05/14/clash-of-the-titans-r-vs-python/" rel="nofollow"&gt;https://learnanalyticshere.wordpress.com/2015/05/14/clash-of-the-titans-r-vs-python/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/2015/05/14/clash-of-the-titans-r-vs-python/'':41C ''/2015/05/14/clash-of-the-titans-r-vs-python/&lt;/a&gt;&lt;/p&gt;'':46C ''also'':21C ''c'':16C ''comparison'':32C ''compli'':25C ''depend'':3C ''etc'':11C ''excel'':31C ''fast'':20C ''href'':38C ''incred'':19C ''jit'':27C ''learnanalyticshere.wordpress.com'':40C,45C ''learnanalyticshere.wordpress.com/2015/05/14/clash-of-the-titans-r-vs-python/'':39C ''learnanalyticshere.wordpress.com/2015/05/14/clash-of-the-titans-r-vs-python/&lt;/a&gt;&lt;/p&gt;'':44C ''nofollow'':43C ''numpy/scipy'':10C ''p'':1C ''packag'':9C ''python'':22C,36C ''r'':34C ''rel'':42C ''usag'':6C ''use'':26C ''various'':8C ''written'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6217, 1, '2015-06-25 21:24:37.237', 7, 202, 'Is there a method that is opposite of dimensionality reduction?', '&lt;p&gt;I am new to the field of machine learning, but have done my share of signal processing. Please let me know if this question has been mislabeled.&lt;/p&gt;

&lt;p&gt;I have two dimensional data which is defined by at least three variables, with a highly non-linear model way too complicated to simulate.&lt;/p&gt;

&lt;p&gt;I have had varying level of success at extracting the two main components from the data using methods like PCA and ICA (from the python library Scikit-Learn), but it seems these method (or at least, these implementation of the methods) are limited to extracting as many components as there are dimensions in the data, for example, 2 components from a 2D point cloud.&lt;/p&gt;

&lt;p&gt;When plotting the data, it is clear to the &lt;em&gt;trained&lt;/em&gt; eye that there are three different linear trends, the three color lines show the directions.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/rigJH.png" alt="data plot"&gt;&lt;/p&gt;

&lt;p&gt;When using PCA, the main component is aligned to one of the color lines, and the other is at 90Â°, as expected. When using ICA, the first component is aligned with the blue line, and the second is somewhere in between the red and green ones. I am looking for a tool which could reproduce all three components in my signal.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT, Additional info:&lt;/strong&gt; I am here working in a small subset of a bigger phase plane. In this small subset, each input variables produce a linear change on the plane, but the direction and amplitude of this change is non-linear and depends on where exactly on the bigger plane I am working. At some places, two of the variables can be degenerate: they produce change in the same direction. for example, say the model depends on X, Y, and Z. A change in the variable X will produce a variation along the blue line; Y causes a variation along the green line; Z, along the red one.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6169, 1, '2015-06-21 17:31:05.457', 4, 1246, 'How to run a pyspark application in windows 8 command prompt', '&lt;p&gt;I have a python script written with Spark Context and I want to run it. I tried to integrate IPython with Spark, but I could not do that. So, I tried to set the spark path [ Installation folder/bin ] as an environment variable and called &lt;strong&gt;spark-submit&lt;/strong&gt; command in the cmd prompt. I believe that it is finding the spark context, but it produces a really big error. Can someone please help me with this issue? &lt;/p&gt;

&lt;p&gt;Environment variable path: C:/Users/Name/Spark-1.4;C:/Users/Name/Spark-1.4/bin&lt;/p&gt;

&lt;p&gt;After that, in cmd prompt: spark-submit script.py&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Run3y.png" alt="enter image description here"&gt;&lt;/p&gt;
', 'python  apache-spark', NULL, '''/p'':95C,104C,115C,127C ''/run3y.png'':121C ''/strong'':65C ''/users/name/spark-1.4'':101C ''/users/name/spark-1.4/bin'':103C ''8'':9A ''alt'':122C ''apach'':14B ''apache-spark'':13B ''applic'':6A ''believ'':72C ''big'':85C ''c'':100C,102C ''call'':60C ''cmd'':69C,109C ''command'':10A,66C ''context'':25C,79C ''could'':41C ''descript'':125C ''enter'':123C ''environ'':57C,97C ''error'':86C ''find'':76C ''folder/bin'':54C ''help'':90C ''i.stack.imgur.com'':120C ''i.stack.imgur.com/run3y.png'':119C ''imag'':124C ''img'':117C ''instal'':53C ''integr'':35C ''ipython'':36C ''issu'':94C ''p'':16C,96C,105C,116C ''path'':52C,99C ''pleas'':89C ''produc'':82C ''prompt'':11A,70C,110C ''pyspark'':5A ''python'':12B,20C ''realli'':84C ''run'':3A,30C ''script'':21C ''script.py'':114C ''set'':49C ''someon'':88C ''spark'':15B,24C,38C,51C,63C,78C,112C ''spark-submit'':62C,111C ''src'':118C ''strong'':61C ''submit'':64C,113C ''tri'':33C,47C ''variabl'':58C,98C ''want'':28C ''window'':8A ''written'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6170, 2, '2015-06-21 18:52:37.563', 2, NULL, NULL, '&lt;p&gt;&lt;strong&gt;IntelliJ&lt;/strong&gt; supports R via this plugin:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://plugins.jetbrains.com/plugin/6632" rel="nofollow"&gt;https://plugins.jetbrains.com/plugin/6632&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It''s a recent project, so RStudio is still more powerful, including its focus on data-friendly environment (plots and data are always in sight).&lt;/p&gt;
', NULL, NULL, '''/p'':10C,51C ''/plugin/6632'':17C ''/plugin/6632&lt;/a&gt;&lt;/li&gt;'':22C ''/strong'':4C ''/ul'':23C ''alway'':48C ''data'':41C,46C ''data-friend'':40C ''environ'':43C ''focus'':38C ''friend'':42C ''href'':14C ''includ'':36C ''intellij'':3C ''li'':12C ''nofollow'':19C ''p'':1C,24C ''plot'':44C ''plugin'':9C ''plugins.jetbrains.com'':16C,21C ''plugins.jetbrains.com/plugin/6632'':15C ''plugins.jetbrains.com/plugin/6632&lt;/a&gt;&lt;/li&gt;'':20C ''power'':35C ''project'':29C ''r'':6C ''recent'':28C ''rel'':18C ''rstudio'':31C ''sight'':50C ''still'':33C ''strong'':2C ''support'':5C ''ul'':11C ''via'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6172, 1, '2015-06-22 02:13:40.85', 3, 71, 'What kind of research can be done with genomic data?', '&lt;p&gt;It''s well known that science has given us large amount of free accessible data, such as &lt;a href="http://www.1000genomes.org" rel="nofollow"&gt;http://www.1000genomes.org&lt;/a&gt; and &lt;a href="http://www.ncbi.nlm.nih.gov/genbank" rel="nofollow"&gt;http://www.ncbi.nlm.nih.gov/genbank&lt;/a&gt;. How can we play around with the data and apply data science/machine learning to it? What could be some ideas?&lt;/p&gt;

&lt;p&gt;My own ideas:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Biological data visualisation&lt;/li&gt;
&lt;li&gt;Gene prediction using hidden-markov-model&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Any more?&lt;/p&gt;
', 'machine-learning  classification  bioinformatics  data', NULL, '''/a'':41C ''/genbank'':47C ''/genbank&lt;/a&gt;.'':52C ''/li'':84C,93C ''/p'':73C,78C,98C ''/ul'':94C ''access'':31C ''amount'':28C ''appli'':62C ''around'':57C ''bioinformat'':15B ''biolog'':81C ''classif'':14B ''could'':69C ''data'':10A,16B,32C,60C,63C,82C ''done'':7A ''free'':30C ''gene'':86C ''genom'':9A ''given'':25C ''hidden'':90C ''hidden-markov-model'':89C ''href'':36C,44C ''idea'':72C,77C ''kind'':2A ''known'':21C ''larg'':27C ''learn'':13B,65C ''li'':80C,85C ''machin'':12B ''machine-learn'':11B ''markov'':91C ''model'':92C ''nofollow'':39C,49C ''p'':17C,74C,95C ''play'':56C ''predict'':87C ''rel'':38C,48C ''research'':4A ''scienc'':23C ''science/machine'':64C ''ul'':79C ''us'':26C ''use'':88C ''visualis'':83C ''well'':20C ''www.1000genomes.org'':37C,40C ''www.ncbi.nlm.nih.gov'':46C,51C ''www.ncbi.nlm.nih.gov/genbank'':45C ''www.ncbi.nlm.nih.gov/genbank&lt;/a&gt;.'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6173, 2, '2015-06-22 12:00:29.197', 1, NULL, NULL, '&lt;p&gt;First of all I should mention that this is an OS specific question. You will see this problem in Windows but not in Linux (I''m not sure about OS X, but maybe no problem there too.)&lt;/p&gt;

&lt;p&gt;The problem with your code is not with the reading part.
If you open your &lt;code&gt;test.csv&lt;/code&gt; in Notepad++ you will see something like this:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/IhqXP.png" alt="file written from R"&gt;&lt;/p&gt;

&lt;p&gt;If you edit it in Notepad++ so that it looks like &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/909pp.png" alt="file edited in Notepad++"&gt;&lt;/p&gt;

&lt;p&gt;and then you run this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;testdf &amp;lt;- read.csv("test.csv",encoding="UTF-8")
testdf[1,3]
testdf$list2
plot(1:10,main=testdf2[2,3])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You will see that there is no problem.&lt;/p&gt;

&lt;p&gt;So in fact there are two problems:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;When writing Unicode text, R changes it back to system locale and then again to unicode, that''s why writing data to csv file does not work correctly.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;R has problems showing Unicode data in &lt;code&gt;data.frame&lt;/code&gt; so even after reading it correctly, &lt;code&gt;testdf&lt;/code&gt; would print those codes there not the Unicode characters.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;see this question on &lt;a href="http://r.789695.n4.nabble.com/Writing-Unicode-Text-into-Text-File-from-R-in-Windows-td4684693.html" rel="nofollow"&gt;Writing Unicode Text into Text File from R (in Windows)&lt;/a&gt; in R mailing list archives,
and also this question on &lt;a href="http://stackoverflow.com/questions/10675360/utf-8-file-output-in-r"&gt;UTF-8 file output in R&lt;/a&gt; on stackoverflow.&lt;/p&gt;
', NULL, NULL, '''-8'':120C,259C ''/909pp.png'':98C ''/a'':242C,264C ''/code'':58C,133C,198C,207C ''/ihqxp.png'':73C ''/li'':186C,218C ''/ol'':219C ''/p'':39C,67C,79C,92C,104C,111C,144C,153C,185C,217C,267C ''/pre'':134C ''/questions/10675360/utf-8-file-output-in-r'':257C ''/writing-unicode-text-into-text-file-from-r-in-windows-td4684693.html'':229C ''1'':122C,127C ''10'':128C ''2'':131C ''3'':123C,132C ''also'':249C ''alt'':74C,99C ''archiv'':247C ''back'':164C ''chang'':162C ''charact'':216C ''code'':45C,56C,113C,196C,205C,211C ''correct'':184C,204C ''csv'':179C ''data'':177C,194C ''data.frame'':197C ''edit'':83C,101C ''encod'':118C ''even'':200C ''fact'':148C ''file'':75C,100C,180C,237C,260C ''first'':2C ''href'':226C,254C ''i.stack.imgur.com'':72C,97C ''i.stack.imgur.com/909pp.png'':96C ''i.stack.imgur.com/ihqxp.png'':71C ''img'':69C,94C ''li'':155C,187C ''like'':65C,91C ''linux'':25C ''list'':246C ''list2'':125C ''local'':167C ''look'':90C ''lt'':115C ''m'':27C ''mail'':245C ''main'':129C ''mayb'':34C ''mention'':7C ''nofollow'':231C ''notepad'':60C,86C,103C ''ol'':154C ''open'':54C ''os'':12C,31C ''output'':261C ''p'':1C,40C,68C,80C,93C,105C,135C,145C,156C,188C,220C ''part'':51C ''plot'':126C ''pre'':112C ''print'':209C ''problem'':19C,36C,42C,143C,152C,191C ''question'':14C,223C,251C ''r'':78C,161C,189C,239C,244C,263C ''r.789695.n4.nabble.com'':228C ''r.789695.n4.nabble.com/writing-unicode-text-into-text-file-from-r-in-windows-td4684693.html'':227C ''read'':50C,202C ''read.csv'':116C ''rel'':230C ''run'':109C ''see'':17C,63C,138C,221C ''show'':192C ''someth'':64C ''specif'':13C ''src'':70C,95C ''stackoverflow'':266C ''stackoverflow.com'':256C ''stackoverflow.com/questions/10675360/utf-8-file-output-in-r'':255C ''sure'':29C ''system'':166C ''test.csv'':57C,117C ''testdf'':114C,121C,124C,206C ''testdf2'':130C ''text'':160C,234C,236C ''two'':151C ''unicod'':159C,172C,193C,215C,233C ''utf'':119C,258C ''window'':21C,241C ''work'':183C ''would'':208C ''write'':158C,176C,232C ''written'':76C ''x'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6174, 1, '2015-06-22 13:22:57.95', -2, 425, 'Rstudio using 2.5% of 250GB RAM. how to Increase it', '&lt;p&gt;I am working on Rstudio on a server which has 250GB ram. But its taking too much time to handle a 2GB data file. how should i speed up my work?&lt;/p&gt;
', 'r  rstudio', NULL, '''/p'':44C ''2.5'':3A ''250gb'':5A,23C ''2gb'':34C ''data'':35C ''file'':36C ''handl'':32C ''increas'':9A ''much'':29C ''p'':12C ''r'':10B ''ram'':6A,24C ''rstudio'':1A,11B,17C ''server'':20C ''speed'':40C ''take'':27C ''time'':30C ''use'':2A ''work'':15C,43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6183, 1, '2015-06-23 12:59:13.607', 6, 66, 'What is an alternative name for "Unstructured Data"?', '&lt;p&gt;I''m writing my thesis at the moment, and for some time - due to a lack of a proper alternative - I''ve stuck with "unstructured data" for referring to natural, free flowing text, e.g. Wikipedia articles.&lt;/p&gt;

&lt;p&gt;This nomenclature has bothered me from the very beginning, since it opens a debate that I don''t want to get into. Namely, that "unstructured" implies that natural language lacks structure, which it does not - the most obvious being syntax. It also gives a negative impression, since it is the opposite of "structured", which is accepted as being positive. This is not the focus of my thesis, though the "unstructured" part itself plays an important role.&lt;/p&gt;

&lt;p&gt;I completely agree with the writer of &lt;a href="https://gigaom.com/2013/03/17/can-we-please-stop-saying-unstructured-data/"&gt;this article&lt;/a&gt;, but he proposes no alternative except for "rich data", which doesn''t cover my point. The point I''m trying to make that the text lacks a traditional database-like (e.g. tabular) structure of the data, with every piece of data having a clear data type and semantics that is easy to interpret using computer programs. Of course I''d like to condense this definition into a term, but so far I''ve been unsuccessful coming up with, or discovering an acceptable taxonomy in literature.&lt;/p&gt;
', 'text-mining  definitions  terminology', NULL, '''/2013/03/17/can-we-please-stop-saying-unstructured-data/'':142C ''/a'':145C ''/p'':51C,129C,232C ''accept'':108C,228C ''agre'':133C ''also'':94C ''altern'':4A,34C,150C ''articl'':50C,144C ''begin'':61C ''bother'':56C ''clear'':190C ''come'':222C ''complet'':132C ''comput'':201C ''condens'':209C ''cours'':204C ''cover'':158C ''d'':206C ''data'':8A,40C,154C,182C,187C,191C ''databas'':175C ''database-lik'':174C ''debat'':66C ''definit'':12B,211C ''discov'':226C ''doesn'':156C ''due'':27C ''e.g'':48C,177C ''easi'':197C ''everi'':184C ''except'':151C ''far'':217C ''flow'':46C ''focus'':116C ''free'':45C ''get'':73C ''gigaom.com'':141C ''gigaom.com/2013/03/17/can-we-please-stop-saying-unstructured-data/'':140C ''give'':95C ''href'':139C ''impli'':78C ''import'':127C ''impress'':98C ''interpret'':199C ''lack'':30C,82C,171C ''languag'':81C ''like'':176C,207C ''literatur'':231C ''m'':16C,164C ''make'':167C ''mine'':11B ''moment'':22C ''name'':5A,75C ''natur'':44C,80C ''negat'':97C ''nomenclatur'':54C ''obvious'':90C ''open'':64C ''opposit'':103C ''p'':14C,52C,130C ''part'':123C ''piec'':185C ''play'':125C ''point'':160C,162C ''posit'':111C ''program'':202C ''proper'':33C ''propos'':148C ''refer'':42C ''rich'':153C ''role'':128C ''semant'':194C ''sinc'':62C,99C ''structur'':83C,105C,179C ''stuck'':37C ''syntax'':92C ''tabular'':178C ''taxonomi'':229C ''term'':214C ''terminolog'':13B ''text'':10B,47C,170C ''text-min'':9B ''thesi'':19C,119C ''though'':120C ''time'':26C ''tradit'':173C ''tri'':165C ''type'':192C ''unstructur'':7A,39C,77C,122C ''unsuccess'':221C ''use'':200C ''ve'':36C,219C ''want'':71C ''wikipedia'':49C ''write'':17C ''writer'':136C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6181, 1, '2015-06-23 08:36:13.723', 5, 72, 'What kinds of data other than geographical are topologically spherical?', '&lt;p&gt;I''m trying to think of a data set that is essentially topologically spherical. It''s easier to think of cylindrical datasets (two dimensions, one periodic) or toroidal datasets (two dimensions, both periodic). &lt;/p&gt;

&lt;p&gt;Obvious candidates are geographical and astronomical, ground and sky; but I think the only thing spherical about the sky is its projection onto the ground, so it really just comes back to Earth.&lt;/p&gt;

&lt;p&gt;I find it helpful to think about in terms of a circle''s &lt;a href="https://en.wikipedia.org/wiki/Fundamental_polygon#Examples"&gt;fundamental polygon&lt;/a&gt;:
&lt;img src="http://i.stack.imgur.com/vxubU.png" alt="Fundamental polygons"&gt;&lt;/p&gt;
', 'dataset', NULL, '''/a'':103C ''/p'':46C,81C,112C ''/vxubu.png'':108C ''/wiki/fundamental_polygon#examples'':100C ''alt'':109C ''astronom'':53C ''back'':78C ''candid'':49C ''circl'':94C ''come'':77C ''cylindr'':33C ''data'':4A,20C ''dataset'':11B,34C,41C ''dimens'':36C,43C ''earth'':80C ''easier'':29C ''en.wikipedia.org'':99C ''en.wikipedia.org/wiki/fundamental_polygon#examples'':98C ''essenti'':24C ''find'':84C ''fundament'':101C,110C ''geograph'':7A,51C ''ground'':54C,72C ''help'':86C ''href'':97C ''i.stack.imgur.com'':107C ''i.stack.imgur.com/vxubu.png'':106C ''img'':104C ''kind'':2A ''m'':14C ''obvious'':48C ''one'':37C ''onto'':70C ''p'':12C,47C,82C ''period'':38C,45C ''polygon'':102C,111C ''project'':69C ''realli'':75C ''set'':21C ''sky'':56C,66C ''spheric'':10A,26C,63C ''src'':105C ''term'':91C ''thing'':62C ''think'':17C,31C,59C,88C ''topolog'':9A,25C ''toroid'':40C ''tri'':15C ''two'':35C,42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6184, 1, '2015-06-23 13:54:03.99', 3, 52, 'Finding aggregated information of data', '&lt;p&gt;I am new to data science. I have a dataset of around 200,000 records, having 5 columns. There is a field called, &lt;code&gt;class&lt;/code&gt;. For each &lt;code&gt;class&lt;/code&gt;, there are one or many &lt;code&gt;divisions&lt;/code&gt;. I have to do this:
1. Filter the dataset, such that only those &lt;code&gt;classes&lt;/code&gt; with at least 5 divisions turn up.&lt;/p&gt;

&lt;ol start="2"&gt;
&lt;li&gt;&lt;p&gt;For each division, I have to calculate &lt;code&gt;attendance&lt;/code&gt; from another column.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;There is a minimum &lt;code&gt;attendance&lt;/code&gt; value for each &lt;code&gt;class&lt;/code&gt;. I have to find the &lt;code&gt;percentage of divisions in each class with the minimum attendance&lt;/code&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I started with importing the data in python using Pandas and started writing loops for processing this. But I am sure this is not the right way to do. Can you please give some idea.Can I do this in Excel pivot table?&lt;/p&gt;
', 'r  python  data', NULL, '''/code'':35C,40C,48C,64C,87C,101C,107C,124C ''/li'':92C,126C ''/ol'':127C ''/p'':72C,91C,125C,171C ''000'':23C ''1'':54C ''2'':75C ''200'':22C ''5'':26C,68C ''aggreg'':2A ''anoth'':89C ''around'':21C ''attend'':86C,100C,123C ''calcul'':84C ''call'':32C ''class'':34C,39C,63C,106C,119C ''code'':33C,38C,46C,62C,85C,99C,105C,113C ''column'':27C,90C ''data'':5A,8B,14C,134C ''dataset'':19C,57C ''divis'':47C,69C,80C,116C ''excel'':168C ''field'':31C ''filter'':55C ''find'':1A,111C ''give'':161C ''idea.can'':163C ''import'':132C ''inform'':3A ''least'':67C ''li'':76C,93C ''loop'':142C ''mani'':45C ''minimum'':98C,122C ''new'':12C ''ol'':73C ''one'':43C ''p'':9C,77C,94C,128C ''panda'':138C ''percentag'':114C ''pivot'':169C ''pleas'':160C ''process'':144C ''python'':7B,136C ''r'':6B ''record'':24C ''right'':154C ''scienc'':15C ''start'':74C,130C,140C ''sure'':149C ''tabl'':170C ''turn'':70C ''use'':137C ''valu'':102C ''way'':155C ''write'':141C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6274, 1, '2015-06-30 05:28:31.537', 2, 98, 'Kmeans on mixed dataset with high level for categ', '&lt;p&gt;My retail dataset contains 3 numeric attributes and two categorical attributes  Time and ID with 50,000 records. Both categorical attributes have more than 20 thousand levels and their format is 1/11/2011 11:54 and 1TD10051 respectively.&lt;/p&gt;

&lt;p&gt;How do I do kmeans on these dataset? Converting categorical to binary will give very sparse dataset?&lt;/p&gt;

&lt;p&gt;How to proceed?&lt;/p&gt;
', 'clustering  k-means  categorical-data', NULL, '''/p'':55C,74C,79C ''000'':34C ''1/11/2011'':49C ''11'':50C ''1td10051'':53C ''20'':42C ''3'':22C ''50'':33C ''54'':51C ''attribut'':24C,28C,38C ''binari'':68C ''categ'':9A ''categor'':15B,27C,37C,66C ''categorical-data'':14B ''cluster'':10B ''contain'':21C ''convert'':65C ''data'':16B ''dataset'':4A,20C,64C,73C ''format'':47C ''give'':70C ''high'':6A ''id'':31C ''k'':12B ''k-mean'':11B ''kmean'':1A,61C ''level'':7A,44C ''mean'':13B ''mix'':3A ''numer'':23C ''p'':17C,56C,75C ''proceed'':78C ''record'':35C ''respect'':54C ''retail'':19C ''spars'':72C ''thousand'':43C ''time'':29C ''two'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6186, 1, '2015-06-23 14:40:47.173', 2, 95, 'Is there a difference between on-line learning, incremental learning and sequential learning?', '&lt;p&gt;What I mean is the following: Instead of processing all the training data at once and calculating a model, we process one data point at a time and update the model directly afterwards. &lt;/p&gt;

&lt;p&gt;I have seen the terms "on-line (or online) learning" and "incremental learning" for this. Is there a subtle difference? Is one term used more frequently? Or does it depend on the research community?&lt;/p&gt;

&lt;p&gt;Edit: The Bishop book (Pattern Recognition and Machine Learning) uses the terms on-line learning and sequential learning as synonyms but does not mention incremental learning. &lt;/p&gt;
', 'machine-learning  online-learning', NULL, '''/p'':55C,92C,121C ''afterward'':54C ''bishop'':96C ''book'':97C ''calcul'':38C ''communiti'':91C ''data'':34C,44C ''depend'':87C ''differ'':4A,77C ''direct'':53C ''edit'':94C ''follow'':27C ''frequent'':83C ''increment'':10A,69C,119C ''instead'':28C ''learn'':9A,11A,14A,17B,20B,67C,70C,102C,109C,112C,120C ''line'':8A,64C,108C ''machin'':16B,101C ''machine-learn'':15B ''mean'':24C ''mention'':118C ''model'':40C,52C ''on-lin'':6A,62C,106C ''one'':43C,79C ''onlin'':19B,66C ''online-learn'':18B ''p'':21C,56C,93C ''pattern'':98C ''point'':45C ''process'':30C,42C ''recognit'':99C ''research'':90C ''seen'':59C ''sequenti'':13A,111C ''subtl'':76C ''synonym'':114C ''term'':61C,80C,105C ''time'':48C ''train'':33C ''updat'':50C ''use'':81C,103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6192, 1, '2015-06-23 19:26:12.69', 0, 95, 'algorithm and validation ideas of customers profiling (kmeans clustering)', '&lt;p&gt;The intention of my project is to create customer profiles and it is related to advertisements. There are two types of activities: one is the advertisements that customers solely clicked on, and the other one is the advertisements that customers clicked on and purchase the product afterwards. I have lists of keywords describing the advertisements and those are the information for me to cluster customers in groups for creating customer profiles. &lt;/p&gt;

&lt;p&gt;I had a post about the second type of the activities before:&lt;a href="http://datascience.stackexchange.com/questions/5838/i-am-trying-to-classify-cluster-users-profile-but-dont-know-how-with-my-attribu/5857?noredirect=1#comment6272_5857"&gt;I am trying to classify/cluster users profile but don&amp;#39;t know how with my attributes&lt;/a&gt;  By logc ''s suggestion, I used the sales amount of the product customer purchased to create the customers profile, such as customer #1234 purchased a bracelet for $20 and the bracelet has keywords: fashion, gold and accessories, here is the way to represent this customer as followed:
&lt;img src="http://i.stack.imgur.com/MaWoJ.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;I used the dataset like this to do the kmeans clustering after applying PCA and it worked well, but now I was told to add the information about the first activity about solely clicking, then I am lack of ideas of the algorithm. &lt;/p&gt;

&lt;p&gt;As there is no numeric meaning of those advertisements solely clicked on, I tried LDA by considering each customer as a document and combining all the keywords of advertisements customer clicked on or purchased about into this document. The result is more about topics other than customers. I also tried taking the keywords in activity one as the dummy variables and then combining this with the numeric variables of activity two to do the clustering. I can''t tell whether it''s good and I am thinking that whether the dummy variables part made the data very sparse,so that it wouldn''t work well for Kmeans.The WSSSE of this upadated combination case is very large.   &lt;/p&gt;

&lt;p&gt;Any suggestions for my case?   Thanks a lot!   &lt;/p&gt;
', 'clustering  algorithms  k-means', NULL, '''/a'':122C ''/mawoj.png'':174C ''/p'':87C,180C,225C,343C,353C ''/questions/5838/i-am-trying-to-classify-cluster-users-profile-but-dont-know-how-with-my-attribu/5857?noredirect=1#comment6272_5857'':105C ''1234'':145C ''20'':150C ''39'':115C ''accessori'':159C ''activ'':37C,99C,212C,281C,296C ''add'':206C ''advertis'':31C,41C,53C,70C,235C,255C ''afterward'':62C ''algorithm'':1A,11B,224C ''also'':275C ''alt'':175C ''amount'':131C ''appli'':194C ''attribut'':121C ''bracelet'':148C,153C ''case'':339C,349C ''classify/cluster'':110C ''click'':45C,56C,215C,237C,257C ''cluster'':9A,10B,79C,192C,301C ''combin'':250C,289C,338C ''consid'':243C ''creat'':23C,84C,138C ''custom'':6A,24C,43C,55C,80C,85C,135C,140C,144C,167C,245C,256C,273C ''data'':322C ''datascience.stackexchange.com'':104C ''datascience.stackexchange.com/questions/5838/i-am-trying-to-classify-cluster-users-profile-but-dont-know-how-with-my-attribu/5857?noredirect=1#comment6272_5857'':103C ''dataset'':185C ''describ'':68C ''descript'':178C ''document'':248C,264C ''dummi'':285C,317C ''enter'':176C ''fashion'':156C ''first'':211C ''follow'':169C ''gold'':157C ''good'':309C ''group'':82C ''href'':102C ''i.stack.imgur.com'':173C ''i.stack.imgur.com/mawoj.png'':172C ''idea'':4A,221C ''imag'':177C ''img'':170C ''inform'':75C,208C ''intent'':17C ''k'':13B ''k-mean'':12B ''keyword'':67C,155C,253C,279C ''kmean'':8A,191C ''kmeans.the'':333C ''know'':117C ''lack'':219C ''larg'':342C ''lda'':241C ''like'':186C ''list'':65C ''logc'':124C ''lot'':352C ''made'':320C ''mean'':14B,232C ''numer'':231C,293C ''one'':38C,50C,282C ''p'':15C,88C,181C,226C,344C ''part'':319C ''pca'':195C ''post'':92C ''product'':61C,134C ''profil'':7A,25C,86C,112C,141C ''project'':20C ''purchas'':59C,136C,146C,260C ''relat'':29C ''repres'':165C ''result'':266C ''sale'':130C ''second'':95C ''sole'':44C,214C,236C ''spars'':324C ''src'':171C ''suggest'':126C,346C ''take'':277C ''tell'':305C ''thank'':350C ''think'':313C ''told'':204C ''topic'':270C ''tri'':108C,240C,276C ''two'':34C,297C ''type'':35C,96C ''upad'':337C ''use'':128C,183C ''user'':111C ''valid'':3A ''variabl'':286C,294C,318C ''way'':163C ''well'':199C,331C ''whether'':306C,315C ''work'':198C,330C ''wouldn'':328C ''wssse'':334C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6187, 2, '2015-06-23 15:38:09.92', 1, NULL, NULL, '&lt;p&gt;I''m not seeing where the relationship between hypotheses and research paper is. I know what the relationship is in reality, but your question is a bit unorganized. Perhaps we could think about it in a different way... &lt;/p&gt;

&lt;p&gt;I would first create a definition of possible hypotheses (i.e., $H_{0}$ and $H_{1}$ are my two possible hypotheses, and every hypothesis will have both a null and an alternative), and then associate a $H_{0}$ with a $H_{1}$ such that a combination of both null and alternative hypotheses equate to one hypothesis. &lt;/p&gt;

&lt;p&gt;With this, it''s a matter of iteratively counting.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;With $p \in P$, where $p$ is a paper in the set of papers $P$,&lt;/li&gt;
&lt;li&gt;With $w \in W$, where $w$ is a word in a set of words $W$ that indicate whether $p$ is a research paper or not,&lt;/li&gt;
&lt;li&gt;With $i$ as the number of times a $w$ occurred in a $p$, &lt;/li&gt;
&lt;li&gt;count the number of times a $w$ occurs in each $p$ and iterate $i$ accordingly. &lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':126C,153C,168C,185C ''/p'':40C,97C,108C ''/ul'':186C ''0'':53C,78C ''1'':56C,82C ''accord'':184C ''altern'':72C,91C ''associ'':75C ''bit'':28C ''combin'':86C ''could'':32C ''count'':107C,170C ''creat'':45C ''definit'':47C ''differ'':38C ''equat'':93C ''everi'':63C ''first'':44C ''h'':52C,55C,77C,81C ''hypothes'':10C,50C,61C,92C ''hypothesi'':64C,96C ''i.e'':51C ''indic'':144C ''iter'':106C,182C ''know'':16C ''li'':110C,127C,154C,169C ''m'':3C ''matter'':104C ''null'':69C,89C ''number'':159C,172C ''occur'':164C,177C ''one'':95C ''p'':1C,41C,98C,112C,114C,116C,125C,146C,167C,180C ''paper'':13C,119C,124C,150C ''perhap'':30C ''possibl'':49C,60C ''question'':25C ''realiti'':22C ''relationship'':8C,19C ''research'':12C,149C ''see'':5C ''set'':122C,139C ''think'':33C ''time'':161C,174C ''two'':59C ''ul'':109C ''unorgan'':29C ''w'':129C,131C,133C,142C,163C,176C ''way'':39C ''whether'':145C ''word'':136C,141C ''would'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6188, 1, '2015-06-23 16:29:23.32', 3, 115, 'What is the best way to propose an item from a set based on previous choices?', '&lt;p&gt;The goal of this question is to be able to propose a user further choices based on his past experiences: like Amazon''s book advices.&lt;/p&gt;

&lt;p&gt;From a set of mp3 files, I assume that a set of mp3 tags data is already filled, based on the music he/she has alredy listened to  : what is the easiest way to  implement a machine learning that is able to propose a list of music choices  based on the user''s set ?&lt;/p&gt;

&lt;p&gt;&lt;em&gt;NB : I''m a Machine learning novice I''d appreciate if the answer could be based on Orange, Weka or these kind of tools.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;Update: removed the &lt;em&gt;classification&lt;/em&gt; tag as recommanded.&lt;/p&gt;

&lt;p&gt;For new comers as me:&lt;br&gt;
- the book &lt;a href="http://amzn.com/1118728963" rel="nofollow"&gt;Predictive Analytics For Dummies&lt;/a&gt; is a nice general introduction about this subject&lt;br&gt;
- a next step would be the the paper &lt;a href="http://www.csc.kth.se/utbildning/kth/kurser/DD143X/dkand12/Group89Michael/report/Nordstrom+Hakansson.pdf" rel="nofollow"&gt;Finding Clusters of Similar Artists&lt;/a&gt; which is really interesting especially for the K-means approach&lt;br&gt;
- &lt;a href="http://labrosa.ee.columbia.edu/millionsong/pages/tutorial" rel="nofollow"&gt;The millions songs dataset&lt;/a&gt; which is a gold mine with its huge dataset as well as tutorials with Python codes to use with it&lt;br&gt;
- special thanks sheldonkreger for his answer with neo4J graph usage idea wich is really interesting &lt;/p&gt;
', 'machine-learning  data-mining  python', NULL, '''/1118728963'':158C ''/a'':165C,195C,219C ''/em'':132C,140C ''/millionsong/pages/tutorial'':212C ''/p'':50C,105C,133C,144C,256C ''/utbildning/kth/kurser/dd143x/dkand12/group89michael/report/nordstrom+hakansson.pdf'':187C ''abl'':33C,91C ''advic'':49C ''alreadi'':68C ''alredi'':76C ''amazon'':46C ''amzn.com'':157C ''amzn.com/1118728963'':156C ''analyt'':162C ''answer'':120C,246C ''appreci'':117C ''approach'':206C ''artist'':194C ''assum'':59C ''base'':13A,40C,70C,99C,123C ''best'':4A ''book'':48C,153C ''br'':151C,174C,207C,240C ''choic'':16A,39C,98C ''classif'':139C ''cluster'':191C ''code'':235C ''comer'':148C ''could'':121C ''d'':116C ''data'':21B,66C ''data-min'':20B ''dataset'':218C,228C ''dummi'':164C ''easiest'':82C ''em'':107C,138C ''especi'':200C ''experi'':44C ''file'':57C ''fill'':69C ''find'':190C ''general'':169C ''goal'':26C ''gold'':223C ''graph'':249C ''he/she'':74C ''href'':155C,184C,209C ''huge'':227C ''idea'':251C ''implement'':85C ''interest'':199C,255C ''introduct'':170C ''item'':9A ''k'':204C ''k-mean'':203C ''kind'':129C ''labrosa.ee.columbia.edu'':211C ''labrosa.ee.columbia.edu/millionsong/pages/tutorial'':210C ''learn'':19B,88C,113C ''like'':45C ''list'':95C ''listen'':77C ''m'':110C ''machin'':18B,87C,112C ''machine-learn'':17B ''mean'':205C ''million'':216C ''mine'':22B,224C ''mp3'':56C,64C ''music'':73C,97C ''nb'':108C ''neo4j'':248C ''new'':147C ''next'':176C ''nice'':168C ''nofollow'':160C,189C,214C ''novic'':114C ''orang'':125C ''p'':24C,51C,106C,134C,145C ''paper'':182C ''past'':43C ''predict'':161C ''previous'':15A ''propos'':7A,35C,93C ''python'':23B,234C ''question'':29C ''realli'':198C,254C ''recommand'':143C ''rel'':159C,188C,213C ''remov'':136C ''set'':12A,54C,62C,104C ''sheldonkreg'':243C ''similar'':193C ''song'':217C ''special'':241C ''step'':177C ''subject'':173C ''tag'':65C,141C ''thank'':242C ''tool'':131C ''tutori'':232C ''updat'':135C ''usag'':250C ''use'':237C ''user'':37C,102C ''way'':5A,83C ''weka'':126C ''well'':230C ''wich'':252C ''would'':178C ''www.csc.kth.se'':186C ''www.csc.kth.se/utbildning/kth/kurser/dd143x/dkand12/group89michael/report/nordstrom+hakansson.pdf'':185C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6212, 1, '2015-06-25 12:37:48.03', 2, 46, 'How to optimising caret for sensitivity', '&lt;p&gt;I am currently using caret which optimises for accuracy. Is it possible to optimise for sensitivity. I see documentation mentioning that metric = ''roc'' has been used, but sensitivity does not appear to be one of the options.&lt;/p&gt;
', 'machine-learning  r', NULL, '''/p'':49C ''accuraci'':20C ''appear'':42C ''caret'':4A,16C ''current'':14C ''document'':30C ''learn'':9B ''machin'':8B ''machine-learn'':7B ''mention'':31C ''metric'':33C ''one'':45C ''optimis'':3A,18C,25C ''option'':48C ''p'':11C ''possibl'':23C ''r'':10B ''roc'':34C ''see'':29C ''sensit'':6A,27C,39C ''use'':15C,37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8756, 2, '2015-11-08 14:05:24.28', 0, NULL, NULL, '&lt;p&gt;If you did a simple linear regression for each of the 5 outputs, your r-squared would be a good measure of the "aggregate correlation." If you wanted to compare to the other results, it would be helpful to do the same for each of your single features as well.&lt;/p&gt;
', NULL, NULL, '''/p'':53C ''5'':13C ''aggreg'':26C ''compar'':32C ''correl'':27C ''featur'':50C ''good'':22C ''help'':40C ''linear'':7C ''measur'':23C ''output'':14C ''p'':1C ''r'':17C ''r-squar'':16C ''regress'':8C ''result'':36C ''simpl'':6C ''singl'':49C ''squar'':18C ''want'':30C ''well'':52C ''would'':19C,38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6190, 1, '2015-06-23 19:15:08.96', 4, 67, 'What is the right algorithm to detect segmentations of a line chart?', '&lt;p&gt;To be concrete, given 2D numerical data as is shown as line plots below. There are peaks on a background average movement (with small vibrations). We want to find the values of pairs (x1, x2) if those peaks drops down to average; or (x1) only if the line doesn''t back to the average.
&lt;img src="http://i.stack.imgur.com/2TeLq.png" alt="how to find x1 and x2 from data without plotting?"&gt;
&lt;img src="http://i.stack.imgur.com/eVnwb.png" alt="how to find x1 from data without plotting?"&gt;&lt;/p&gt;

&lt;p&gt;There are thousands of such 2D data.&lt;/p&gt;

&lt;p&gt;What is the right statistic or machine learning algorithm to find x1 and x2 above without plotting?&lt;/p&gt;
', 'machine-learning  logistic-regression', NULL, '''/2telq.png'':78C ''/evnwb.png'':94C ''/p'':104C,113C,132C ''2d'':24C,111C ''algorithm'':5A,123C ''alt'':79C,95C ''averag'':40C,61C,73C ''back'':70C ''background'':39C ''chart'':12A ''concret'':22C ''data'':26C,87C,101C,112C ''detect'':7A ''doesn'':68C ''drop'':58C ''find'':48C,82C,98C,125C ''given'':23C ''i.stack.imgur.com'':77C,93C ''i.stack.imgur.com/2telq.png'':76C ''i.stack.imgur.com/evnwb.png'':92C ''img'':74C,90C ''learn'':15B,122C ''line'':11A,31C,67C ''logist'':17B ''logistic-regress'':16B ''machin'':14B,121C ''machine-learn'':13B ''movement'':41C ''numer'':25C ''p'':19C,105C,114C ''pair'':52C ''peak'':36C,57C ''plot'':32C,89C,103C,131C ''regress'':18B ''right'':4A,118C ''segment'':8A ''shown'':29C ''small'':43C ''src'':75C,91C ''statist'':119C ''thousand'':108C ''valu'':50C ''vibrat'':44C ''want'':46C ''without'':88C,102C,130C ''x1'':53C,63C,83C,99C,126C ''x2'':54C,85C,128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6191, 2, '2015-06-23 19:22:12.353', 1, NULL, NULL, '&lt;p&gt;I''m not sure what you are saying in the second paragraph, but &lt;em&gt;if&lt;/em&gt; your assumptions are that all the mp3s are already tagged, then the &lt;em&gt;easiest&lt;/em&gt; way to suggest further is to suggest mp3s with the highest overlap in tags.&lt;/p&gt;
', NULL, NULL, '''/em'':17C,32C ''/p'':47C ''alreadi'':26C ''assumpt'':19C ''easiest'':31C ''em'':15C,30C ''highest'':43C ''m'':3C ''mp3s'':24C,40C ''overlap'':44C ''p'':1C ''paragraph'':13C ''say'':9C ''second'':12C ''suggest'':35C,39C ''sure'':5C ''tag'':27C,46C ''way'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6194, 2, '2015-06-23 21:07:20.51', 5, NULL, NULL, '&lt;p&gt;One way to do what you''re talking about is called "change point analysis." There is an R package for this called &lt;a href="http://www.jstatsoft.org/v58/i03/paper"&gt;changepoint&lt;/a&gt; that you might want to check out. &lt;/p&gt;

&lt;p&gt;In Python, you could try &lt;a href="https://pypi.python.org/pypi/changefinder/0.03"&gt;changefinder&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':30C,51C ''/p'':38C,52C ''/pypi/changefinder/0.03'':49C ''/v58/i03/paper'':28C ''analysi'':15C ''call'':12C,23C ''chang'':13C ''changefind'':50C ''changepoint'':29C ''check'':36C ''could'':43C ''href'':25C,46C ''might'':33C ''one'':2C ''p'':1C,39C ''packag'':20C ''point'':14C ''pypi.python.org'':48C ''pypi.python.org/pypi/changefinder/0.03'':47C ''python'':41C ''r'':19C ''re'':8C ''talk'':9C ''tri'':44C ''want'':34C ''way'':3C ''www.jstatsoft.org'':27C ''www.jstatsoft.org/v58/i03/paper'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6198, 2, '2015-06-24 02:17:05.16', 3, NULL, NULL, '&lt;p&gt;Any data consisting of a three element vector of numbers  $v =\{x,y,z\}$ could be viewed as topologically spherical if, after converting to polar coordinates, there is only one datum for every value of the 3D angle $\theta$. Thus, every point in the data represents an exterior of a topological sphere. This is taking the term "topological" literally and formally.&lt;/p&gt;

&lt;p&gt;There are also graph structures (acyclic) that have neighborhood structures that allows the graph to be mapped topologically to a sphere. For example, all graphs that can be mapped to Platonic Solids (i.e. vertexes to vertexes, edges to edges, 1:1) are topological spheres. This can include irregular neighborhood structures, too.&lt;/p&gt;

&lt;p&gt;As far as practical examples of these, none come to mind immediately.&lt;/p&gt;
', NULL, NULL, '''/p'':63C,116C,130C ''1'':104C,105C ''3d'':38C ''acycl'':70C ''allow'':76C ''also'':67C ''angl'':39C ''come'':126C ''consist'':4C ''convert'':24C ''coordin'':27C ''could'':16C ''data'':3C,46C ''datum'':32C ''edg'':101C,103C ''element'':8C ''everi'':34C,42C ''exampl'':87C,122C ''exterior'':49C ''far'':119C ''formal'':62C ''graph'':68C,78C,89C ''i.e'':97C ''immedi'':129C ''includ'':111C ''irregular'':112C ''liter'':60C ''map'':81C,93C ''mind'':128C ''neighborhood'':73C,113C ''none'':125C ''number'':11C ''one'':31C ''p'':1C,64C,117C ''platon'':95C ''point'':43C ''polar'':26C ''practic'':121C ''repres'':47C ''solid'':96C ''sphere'':53C,85C,108C ''spheric'':21C ''structur'':69C,74C,114C ''take'':56C ''term'':58C ''theta'':40C ''three'':7C ''thus'':41C ''topolog'':20C,52C,59C,82C,107C ''v'':12C ''valu'':35C ''vector'':9C ''vertex'':98C,100C ''view'':18C ''x'':13C ''y'':14C ''z'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6200, 1, '2015-06-24 11:23:03.077', 2, 120, 'What are the basic approaches for balancing a dataset for machine learning?', '&lt;p&gt;Some times we come across datasets in which classes are imbalanced. For eg. class A may have 2000 instances but class B has only 200. How can we train a classifier for such datasets?&lt;/p&gt;
', 'machine-learning  dataset', NULL, '''/p'':52C ''200'':42C ''2000'':35C ''across'':22C ''approach'':5A ''b'':39C ''balanc'':7A ''basic'':4A ''class'':26C,31C,38C ''classifi'':48C ''come'':21C ''dataset'':9A,16B,23C,51C ''eg'':30C ''imbalanc'':28C ''instanc'':36C ''learn'':12A,15B ''machin'':11A,14B ''machine-learn'':13B ''may'':33C ''p'':17C ''time'':19C ''train'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6201, 1, '2015-06-24 12:22:46.497', 2, 257, 'Resources on Data Science for Football / Soccer?', '&lt;p&gt;I am searching for resources on data science projects which used football/soccer data.&lt;/p&gt;
', 'data-mining  reference-request  sports', NULL, '''/p'':29C ''data'':3A,9B,22C,28C ''data-min'':8B ''footbal'':6A ''football/soccer'':27C ''mine'':10B ''p'':15C ''project'':24C ''refer'':12B ''reference-request'':11B ''request'':13B ''resourc'':1A,20C ''scienc'':4A,23C ''search'':18C ''soccer'':7A ''sport'':14B ''use'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6202, 1, '2015-06-24 12:24:03.54', 1, 28, 'SPSS style custom tables in Orange', '&lt;p&gt;Is there a way to draw custom tables like in SPSS where you put some attributes to rows and some to column to visualize clustering results?&lt;/p&gt;
', 'clustering  orange', NULL, '''/p'':36C ''attribut'':25C ''cluster'':7B,34C ''column'':31C ''custom'':3A,16C ''draw'':15C ''like'':18C ''orang'':6A,8B ''p'':9C ''put'':23C ''result'':35C ''row'':27C ''spss'':1A,20C ''style'':2A ''tabl'':4A,17C ''visual'':33C ''way'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6203, 2, '2015-06-24 15:12:37.963', 2, NULL, NULL, '&lt;p&gt;What you are describing is not a classification task. You need to use a recommender engine to do this - that''s what they are actually developed for. I suggest you to google ''Mahout in action'' book which is brilliantly covers the development of recommender engines. After reading the theory you will be able to find a tool suitable for your task.&lt;/p&gt;
', NULL, NULL, '''/p'':63C ''abl'':54C ''action'':36C ''actual'':26C ''book'':37C ''brilliant'':40C ''classif'':9C ''cover'':41C ''describ'':5C ''develop'':27C,43C ''engin'':17C,46C ''find'':56C ''googl'':33C ''mahout'':34C ''need'':12C ''p'':1C ''read'':48C ''recommend'':16C,45C ''suggest'':30C ''suitabl'':59C ''task'':10C,62C ''theori'':50C ''tool'':58C ''use'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6204, 2, '2015-06-24 15:17:38.227', 5, NULL, NULL, '&lt;p&gt;Well, you just train it on unbalanced dataset, it is not a problem. I don''t think you need to apply some special techniques.&lt;/p&gt;

&lt;p&gt;The only case where you may want to do something special is if your classes are skewed by their nature (if the skewness is the property of class itself, not of just your specific dataset). For example, if you build a classifier which will tell if someone has a malignant tumor in X-ray image or not. By nature of the task absolute majority of patients will not have a malignant tumor. In such case you may want to tune your classifier algorithm a bit, for example introduce a weighting into your SVM or smth like it.&lt;/p&gt;

&lt;p&gt;A special case is if you have EXTREMELY unbalanced classes. For example 100000 of positive examples and 20 negatives. In such case you will want to go away from classification task to the approach called ''anomaly detection''. &lt;/p&gt;
', NULL, NULL, '''/p'':26C,126C,165C ''100000'':140C ''20'':145C ''absolut'':91C ''algorithm'':111C ''anomali'':163C ''appli'':22C ''approach'':161C ''away'':155C ''bit'':113C ''build'':67C ''call'':162C ''case'':30C,103C,130C,149C ''class'':42C,55C,137C ''classif'':157C ''classifi'':69C,110C ''dataset'':9C,62C ''detect'':164C ''exampl'':64C,115C,139C,143C ''extrem'':135C ''go'':154C ''imag'':83C ''introduc'':116C ''like'':124C ''major'':92C ''malign'':77C,99C ''may'':33C,105C ''natur'':47C,87C ''need'':20C ''negat'':146C ''p'':1C,27C,127C ''patient'':94C ''posit'':142C ''problem'':14C ''properti'':53C ''ray'':82C ''skew'':44C,50C ''smth'':123C ''someon'':74C ''someth'':37C ''special'':24C,38C,129C ''specif'':61C ''svm'':121C ''task'':90C,158C ''techniqu'':25C ''tell'':72C ''think'':18C ''train'':5C ''tumor'':78C,100C ''tune'':108C ''unbalanc'':8C,136C ''want'':34C,106C,152C ''weight'':118C ''well'':2C ''x'':81C ''x-ray'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6205, 1, '2015-06-24 15:21:29.59', 3, 50, 'Word taxonomies for Facebook likes categories', '&lt;p&gt;I query the &lt;a href="https://developers.facebook.com/docs/graph-api" rel="nofollow"&gt;Facebook graph API&lt;/a&gt; to get some users'' likes, which come with a "category" field, which can be, for instance, &lt;em&gt;Italian Restaurant&lt;/em&gt; or &lt;em&gt;Health &amp;amp; Wellness website&lt;/em&gt; and so on.&lt;/p&gt;

&lt;p&gt;I need to draw a profile of the user so I was thinking of retrieving the top level root in a taxonomy graph of the aforementioned categories in such a way that the first example falls into something like &lt;em&gt;Food&lt;/em&gt; and the second one into something like &lt;em&gt;Health&lt;/em&gt;. This way I''d know, by a majority rule, who is a sporty person, who likes fashion etc.&lt;/p&gt;

&lt;p&gt;Is there a free public API I could use to get word taxonomies? I found &lt;a href="http://www.alchemyapi.com" rel="nofollow"&gt;Alchemy&lt;/a&gt;, an IBM project which is pretty cool but unfortunately limited to 1000 API requests per day and I''d need way more than that given the big amount of users and categories I have.&lt;/p&gt;

&lt;p&gt;I know I''m probably asking for too much (a free and unlimited tool!) so the question might become "do you have better ideas about how to solve this"?&lt;/p&gt;
', 'nlp', NULL, '''/a'':22C,148C ''/docs/graph-api'':16C ''/em'':42C,49C,96C,106C ''/p'':53C,125C,183C,214C ''1000'':160C ''aforement'':80C ''alchemi'':147C ''amount'':176C ''amp'':46C ''api'':21C,132C,161C ''ask'':190C ''becom'':203C ''better'':207C ''big'':175C ''categori'':6A,32C,81C,180C ''come'':29C ''cool'':155C ''could'':134C ''d'':110C,167C ''day'':164C ''developers.facebook.com'':15C ''developers.facebook.com/docs/graph-api'':14C ''draw'':58C ''em'':39C,44C,94C,104C ''etc'':124C ''exampl'':89C ''facebook'':4A,19C ''fall'':90C ''fashion'':123C ''field'':33C ''first'':88C ''food'':95C ''found'':141C ''free'':130C,195C ''get'':24C,137C ''given'':173C ''graph'':20C,77C ''health'':45C,105C ''href'':13C,143C ''ibm'':150C ''idea'':208C ''instanc'':38C ''italian'':40C ''know'':111C,186C ''level'':72C ''like'':5A,27C,93C,103C,122C ''limit'':158C ''m'':188C ''major'':114C ''might'':202C ''much'':193C ''need'':56C,168C ''nlp'':7B ''nofollow'':18C,146C ''one'':100C ''p'':8C,54C,126C,184C ''per'':163C ''person'':120C ''pretti'':154C ''probabl'':189C ''profil'':60C ''project'':151C ''public'':131C ''queri'':10C ''question'':201C ''rel'':17C,145C ''request'':162C ''restaur'':41C ''retriev'':69C ''root'':73C ''rule'':115C ''second'':99C ''solv'':212C ''someth'':92C,102C ''sporti'':119C ''taxonomi'':2A,76C,139C ''think'':67C ''tool'':198C ''top'':71C ''unfortun'':157C ''unlimit'':197C ''use'':135C ''user'':26C,63C,178C ''way'':85C,108C,169C ''websit'':48C ''well'':47C ''word'':1A,138C ''www.alchemyapi.com'':144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6214, 2, '2015-06-25 15:54:24.02', 1, NULL, NULL, '&lt;p&gt;Check if &lt;a href="https://spark.apache.org/docs/0.8.1/python-programming-guide.html" rel="nofollow"&gt;this&lt;/a&gt; link could help you out. &lt;/p&gt;
', NULL, NULL, '''/a'':12C ''/docs/0.8.1/python-programming-guide.html'':8C ''/p'':18C ''check'':2C ''could'':14C ''help'':15C ''href'':5C ''link'':13C ''nofollow'':10C ''p'':1C ''rel'':9C ''spark.apache.org'':7C ''spark.apache.org/docs/0.8.1/python-programming-guide.html'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6206, 1, '2015-06-24 15:46:52.68', 2, 64, 'How many observations in a neural networks dataset?', '&lt;p&gt;I started to study and programming in neural networks for a little while now, but I never read about the minimum number of observations one must collect in a dataset to get robust results. Of course, more observations better results, but, Does exist an empirical or theoretical relationship between variables and observations number?&lt;/p&gt;

&lt;p&gt;I mean, neither in econometrics you can compute the minimum number of observations, but it does exist some rule of thumbs that relies the number of exogenous variables to the target variable.&lt;/p&gt;

&lt;p&gt;I wonder if there is something similar to that in neural networks too, but, till now, browsing on the internet, I did not find anything of useful.&lt;/p&gt;

&lt;p&gt;Any ideas, advises or hint will be appreciated.&lt;/p&gt;
', 'dataset  neuralnetwork', NULL, '''/p'':65C,99C,128C,138C ''advis'':132C ''anyth'':125C ''appreci'':137C ''better'':50C ''brows'':117C ''collect'':38C ''comput'':74C ''cours'':47C ''dataset'':8A,9B,41C ''econometr'':71C ''empir'':56C ''exist'':54C,83C ''exogen'':93C ''find'':124C ''get'':43C ''hint'':134C ''idea'':131C ''internet'':120C ''littl'':23C ''mani'':2A ''mean'':68C ''minimum'':32C,76C ''must'':37C ''neither'':69C ''network'':7A,20C,112C ''neural'':6A,19C,111C ''neuralnetwork'':10B ''never'':28C ''number'':33C,64C,77C,91C ''observ'':3A,35C,49C,63C,79C ''one'':36C ''p'':11C,66C,100C,129C ''program'':17C ''read'':29C ''relationship'':59C ''reli'':89C ''result'':45C,51C ''robust'':44C ''rule'':85C ''similar'':107C ''someth'':106C ''start'':13C ''studi'':15C ''target'':97C ''theoret'':58C ''thumb'':87C ''till'':115C ''use'':127C ''variabl'':61C,94C,98C ''wonder'':102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6207, 2, '2015-06-24 16:04:32.073', 4, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;&lt;p&gt;There''s this website called &lt;a href="http://sportsdatamart.com/category/football/" rel="nofollow"&gt;Sportsdatamart&lt;/a&gt; which allows you to
download CSV and XLS file formats for a number of Football series
like the England Premier League, the Italian Serie A, the German
Bundesliga and the Spanish La Liga. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;On &lt;a href="http://www.football-data.co.uk/downloadm.php" rel="nofollow"&gt;Football Data UK&lt;/a&gt;, you can find EXCEL and CSV data files to
use for quantitative testing of betting systems in spreadsheet
applications. League tables, head2head statistics and information on
goalscrores, first scorers and top scorers could be accessed through
the &lt;a href="http://livescore.football-data.co.uk/" rel="nofollow"&gt;Livescore&lt;/a&gt; service.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://en.soccerwiki.org/wiki.php" rel="nofollow"&gt;Soccer Wiki&lt;/a&gt; is a collaborative database and anyone can create
and edit data. This community driven database contains information
on players, clubs, stadiums, managers, referees, leagues and other
data related to the world of soccer.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;If you are looking for data sets centered on European Football,
check &lt;a href="http://www.quora.com/Where-can-I-download-a-database-with-european-football-matches-data" rel="nofollow"&gt;this&lt;/a&gt; out.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':17C,66C,110C,125C,183C ''/category/football/'':13C ''/downloadm.php'':60C ''/li'':52C,113C,160C,186C ''/ol'':187C ''/p'':51C,112C,159C,185C ''/where-can-i-download-a-database-with-european-football-matches-data'':179C ''/wiki.php'':120C ''access'':101C ''allow'':19C ''anyon'':131C ''applic'':85C ''bet'':81C ''bundesliga'':45C ''call'':8C ''center'':170C ''check'':174C ''club'':145C ''collabor'':128C ''communiti'':138C ''contain'':141C ''could'':99C ''creat'':133C ''csv'':23C,72C ''data'':64C,73C,136C,152C,168C ''databas'':129C,140C ''download'':22C ''driven'':139C ''edit'':135C ''en.soccerwiki.org'':119C ''en.soccerwiki.org/wiki.php'':118C ''england'':36C ''european'':172C ''excel'':70C ''file'':26C,74C ''find'':69C ''first'':94C ''footbal'':32C,63C,173C ''format'':27C ''german'':44C ''goalscror'':93C ''head2head'':88C ''href'':10C,57C,105C,117C,176C ''inform'':91C,142C ''italian'':40C ''la'':49C ''leagu'':38C,86C,149C ''li'':2C,53C,114C,161C ''liga'':50C ''like'':34C ''livescor'':109C ''livescore.football-data.co.uk'':106C ''look'':166C ''manag'':147C ''nofollow'':15C,62C,108C,122C,181C ''number'':30C ''ol'':1C ''p'':3C,54C,115C,162C ''player'':144C ''premier'':37C ''quantit'':78C ''refere'':148C ''rel'':14C,61C,107C,121C,180C ''relat'':153C ''scorer'':95C,98C ''seri'':33C,41C ''servic'':111C ''set'':169C ''soccer'':123C,158C ''spanish'':48C ''sportsdatamart'':16C ''sportsdatamart.com'':12C ''sportsdatamart.com/category/football/'':11C ''spreadsheet'':84C ''stadium'':146C ''statist'':89C ''system'':82C ''tabl'':87C ''test'':79C ''top'':97C ''uk'':65C ''use'':76C ''websit'':7C ''wiki'':124C ''world'':156C ''www.football-data.co.uk'':59C ''www.football-data.co.uk/downloadm.php'':58C ''www.quora.com'':178C ''www.quora.com/where-can-i-download-a-database-with-european-football-matches-data'':177C ''xls'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6275, 2, '2015-06-30 06:44:20.367', 2, NULL, NULL, '&lt;p&gt;There are plenty of methods, variations of k-means for the case of mixed dataset: k-modes, k-protoypes etc.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;a href="http://datascience.stackexchange.com/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data"&gt;It has been discussed already.&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':38C ''/p'':25C,40C ''/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data'':32C ''/strong'':39C ''alreadi'':37C ''case'':14C ''datascience.stackexchange.com'':31C ''datascience.stackexchange.com/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data'':30C ''dataset'':17C ''discuss'':36C ''etc'':24C ''href'':29C ''k'':10C,19C,22C ''k-mean'':9C ''k-mode'':18C ''k-protoyp'':21C ''mean'':11C ''method'':6C ''mix'':16C ''mode'':20C ''p'':1C,26C ''plenti'':4C ''protoyp'':23C ''strong'':27C ''variat'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6208, 1, '2015-06-24 16:36:32.73', 6, 129, 'Working with inaccurate (incorrect) dataset', '&lt;p&gt;This is my problem description:&lt;/p&gt;

&lt;p&gt;"According to the Survey on Household Income and Wealth, we need to find out the top 10% households with the most income and expenditures. However, we know that these collected data is not reliable due to many misstatements. Despite these misstatements, we have some features in the dataset which are certainly reliable. But these certain features are just a little part of information for each household wealth."&lt;/p&gt;

&lt;p&gt;Unreliable data means that households tell lies to government. These households misstate their income and wealth in order to unfairly get more governmental services. Therefore, these fraudulent statements in original data will lead to incorrect results and patterns.&lt;/p&gt;

&lt;p&gt;Now, I have below questions:&lt;/p&gt;

&lt;p&gt;How should we deal with unreliable data in data science?
Is there any way to figure out these misstatements and then report the top 10% rich people with better accuracy using Machine Learning algorithms? -How can we evaluate our errors in this study? Since we have unlabeled dataset, should I look for labeling techniques? Or, should I use unsupervised methods? Or, should I work with semi-supervised learning methods?
Is there any idea or application in Machine Learning which tries to improve the quality of collected data?
Please introduce me any ideas or references which can help me in this issue.&lt;/p&gt;

&lt;p&gt;Thanks in advance.&lt;/p&gt;
', 'machine-learning  data-mining  dataset', NULL, '''/p'':19C,88C,128C,135C,239C,244C ''10'':37C,161C ''accord'':21C ''accuraci'':166C ''advanc'':243C ''algorithm'':170C ''applic'':212C ''better'':165C ''certain'':71C,75C ''collect'':50C,223C ''data'':10B,51C,91C,120C,143C,145C,224C ''data-min'':9B ''dataset'':5A,12B,68C,184C ''deal'':140C ''descript'':18C ''despit'':59C ''due'':55C ''error'':176C ''evalu'':174C ''expenditur'':44C ''featur'':65C,76C ''figur'':152C ''find'':33C ''fraudul'':116C ''get'':110C ''govern'':98C ''government'':112C ''help'':234C ''household'':26C,38C,86C,94C,100C ''howev'':45C ''idea'':210C,229C ''improv'':219C ''inaccur'':3A ''incom'':27C,42C,103C ''incorrect'':4A,124C ''inform'':83C ''introduc'':226C ''issu'':238C ''know'':47C ''label'':189C ''lead'':122C ''learn'':8B,169C,205C,215C ''lie'':96C ''littl'':80C ''look'':187C ''machin'':7B,168C,214C ''machine-learn'':6B ''mani'':57C ''mean'':92C ''method'':196C,206C ''mine'':11B ''misstat'':58C,61C,101C,155C ''need'':31C ''order'':107C ''origin'':119C ''p'':13C,20C,89C,129C,136C,240C ''part'':81C ''pattern'':127C ''peopl'':163C ''pleas'':225C ''problem'':17C ''qualiti'':221C ''question'':134C ''refer'':231C ''reliabl'':54C,72C ''report'':158C ''result'':125C ''rich'':162C ''scienc'':146C ''semi'':203C ''semi-supervis'':202C ''servic'':113C ''sinc'':180C ''statement'':117C ''studi'':179C ''supervis'':204C ''survey'':24C ''techniqu'':190C ''tell'':95C ''thank'':241C ''therefor'':114C ''top'':36C,160C ''tri'':217C ''unfair'':109C ''unlabel'':183C ''unreli'':90C,142C ''unsupervis'':195C ''use'':167C,194C ''way'':150C ''wealth'':29C,87C,105C ''work'':1A,200C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6209, 1, '2015-06-24 20:05:17.32', 3, 67, 'Regular Expressions in Word', '&lt;p&gt;I''ve used optical character recognition (OCR) on a historic directory, and am trying to clean up the text with Microsoft Word. Specifically, I need some help writing a Regular Expression to combine two lines together. For example something that is&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;John Smith, 87 Bank&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Bldg&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;should actually be&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;John Smith, 87 Bank Bldg&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I''ve tried several approaches, but haven''t been successful at all. Can anyone help me with this?&lt;/p&gt;
', 'text-mining  regular-expressions', NULL, '''/p'':53C,61C,66C,71C,80C,100C ''/strong'':60C,65C,79C ''87'':58C,76C ''actual'':69C ''anyon'':95C ''approach'':86C ''bank'':59C,77C ''bldg'':64C,78C ''charact'':16C ''clean'':27C ''combin'':44C ''directori'':22C ''exampl'':49C ''express'':2A,10B,42C ''haven'':88C ''help'':38C,96C ''histor'':21C ''john'':56C,74C ''line'':46C ''microsoft'':32C ''mine'':7B ''need'':36C ''ocr'':18C ''optic'':15C ''p'':11C,54C,62C,67C,72C,81C ''recognit'':17C ''regular'':1A,9B,41C ''regular-express'':8B ''sever'':85C ''smith'':57C,75C ''someth'':50C ''specif'':34C ''strong'':55C,63C,73C ''success'':91C ''text'':6B,30C ''text-min'':5B ''togeth'':47C ''tri'':25C,84C ''two'':45C ''use'':14C ''ve'':13C,83C ''word'':4A,33C ''write'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6210, 2, '2015-06-24 20:26:24.657', 1, NULL, NULL, '&lt;p&gt;I have a solution that might not be very standardized but will suffice your need. Copy all of your data into any advanced text editor like, Notepad++ or Sublime Text. Next, Use CTRL+H to toggle the find and replace feature. Find : ''\n'' and Replace with ''''. &lt;/p&gt;
', NULL, NULL, '''/p'':48C ''advanc'':24C ''copi'':17C ''ctrl'':34C ''data'':21C ''editor'':26C ''featur'':42C ''find'':39C,43C ''h'':35C ''like'':27C ''might'':7C ''n'':44C ''need'':16C ''next'':32C ''notepad'':28C ''p'':1C ''replac'':41C,46C ''solut'':5C ''standard'':11C ''sublim'':30C ''suffic'':14C ''text'':25C,31C ''toggl'':37C ''use'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6224, 1, '2015-06-26 08:07:59.093', 3, 127, 'What does the relative sum of squares error signify in neural network analysis in SPSS?', '&lt;p&gt;What range of values of the relative sum of sqaures error is acceptable for a good neural network? I am getting around 0.9 of the relative error for 1 model and around 0.4 for another. Are both the networks significant?&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/p'':61C ''0.4'':53C ''0.9'':43C ''1'':49C ''accept'':33C ''analysi'':13A ''anoth'':55C ''around'':42C,52C ''error'':8A,31C,47C ''get'':41C ''good'':36C ''learn'':18B ''machin'':17B ''machine-learn'':16B ''model'':50C ''network'':12A,38C,59C ''neural'':11A,37C ''neuralnetwork'':19B ''p'':20C ''rang'':22C ''relat'':4A,27C,46C ''signifi'':9A ''signific'':60C ''spss'':15A ''sqaur'':30C ''squar'':7A ''sum'':5A,28C ''valu'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6211, 2, '2015-06-24 20:52:38.97', 1, NULL, NULL, '&lt;p&gt;A neural network is nothing but a set of equations. And the basic rule of any set of equations is that you must have as many data points as the number of parameters.&lt;/p&gt;

&lt;p&gt;The parameters of any neural network are its weights and biases.&lt;/p&gt;

&lt;p&gt;So that means that as the neural network gets deeper and wider, the number of parameters increase a lot, and so must the data points.&lt;/p&gt;

&lt;p&gt;This being said, the more proper and detailed way to know whether the model is &lt;a href="https://en.wikipedia.org/wiki/Overfitting" rel="nofollow"&gt;overfitting&lt;/a&gt; is to check if the validation error is close to the training error. If yes, then the model is working fine. If no, then the model is most likely overfitting and that means that you need to reduce the size of your model or introduce regularization techniques.&lt;/p&gt;
', NULL, NULL, '''/a'':100C ''/p'':35C,48C,75C,148C ''/wiki/overfitting'':96C ''basic'':14C ''bias'':47C ''check'':103C ''close'':109C ''data'':28C,73C ''deeper'':59C ''detail'':84C ''en.wikipedia.org'':95C ''en.wikipedia.org/wiki/overfitting'':94C ''equat'':11C,20C ''error'':107C,113C ''fine'':121C ''get'':58C ''href'':93C ''increas'':66C ''introduc'':145C ''know'':87C ''like'':129C ''lot'':68C ''mani'':27C ''mean'':52C,133C ''model'':90C,118C,126C,143C ''must'':24C,71C ''need'':136C ''network'':4C,42C,57C ''neural'':3C,41C,56C ''nofollow'':98C ''noth'':6C ''number'':32C,63C ''overfit'':99C,130C ''p'':1C,36C,49C,76C ''paramet'':34C,38C,65C ''point'':29C,74C ''proper'':82C ''reduc'':138C ''regular'':146C ''rel'':97C ''rule'':15C ''said'':79C ''set'':9C,18C ''size'':140C ''techniqu'':147C ''train'':112C ''valid'':106C ''way'':85C ''weight'':45C ''whether'':88C ''wider'':61C ''work'':120C ''yes'':115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6218, 2, '2015-06-26 03:10:04.37', 3, NULL, NULL, '&lt;p&gt;Check out the Github repositories for Christopher Long (octonion):&lt;/p&gt;

&lt;p&gt;Football: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://github.com/octonion/football-public" rel="nofollow"&gt;https://github.com/octonion/football-public&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://github.com/octonion/football" rel="nofollow"&gt;https://github.com/octonion/football&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Soccer: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://github.com/octonion/soccer-m" rel="nofollow"&gt;https://github.com/octonion/soccer-m&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://github.com/octonion/soccer" rel="nofollow"&gt;https://github.com/octonion/soccer&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Many others:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://github.com/octonion?tab=repositories" rel="nofollow"&gt;https://github.com/octonion?tab=repositories&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/octonion/football'':32C ''/octonion/football&lt;/a&gt;&lt;/li&gt;'':37C ''/octonion/football-public'':21C ''/octonion/football-public&lt;/a&gt;&lt;/li&gt;'':26C ''/octonion/soccer'':59C ''/octonion/soccer&lt;/a&gt;&lt;/li&gt;'':64C ''/octonion/soccer-m'':48C ''/octonion/soccer-m&lt;/a&gt;&lt;/li&gt;'':53C ''/octonion?tab=repositories'':76C ''/octonion?tab=repositories&lt;/a&gt;&lt;/li&gt;'':81C ''/p'':11C,14C,41C,69C ''/ul'':38C,65C,82C ''check'':2C ''christoph'':8C ''footbal'':13C ''github'':5C ''github.com'':20C,25C,31C,36C,47C,52C,58C,63C,75C,80C ''github.com/octonion/football'':30C ''github.com/octonion/football&lt;/a&gt;&lt;/li&gt;'':35C ''github.com/octonion/football-public'':19C ''github.com/octonion/football-public&lt;/a&gt;&lt;/li&gt;'':24C ''github.com/octonion/soccer'':57C ''github.com/octonion/soccer&lt;/a&gt;&lt;/li&gt;'':62C ''github.com/octonion/soccer-m'':46C ''github.com/octonion/soccer-m&lt;/a&gt;&lt;/li&gt;'':51C ''github.com/octonion?tab=repositories'':74C ''github.com/octonion?tab=repositories&lt;/a&gt;&lt;/li&gt;'':79C ''href'':18C,29C,45C,56C,73C ''li'':16C,27C,43C,54C,71C ''long'':9C ''mani'':67C ''nofollow'':23C,34C,50C,61C,78C ''octonion'':10C ''other'':68C ''p'':1C,12C,39C,66C ''rel'':22C,33C,49C,60C,77C ''repositori'':6C ''soccer'':40C ''ul'':15C,42C,70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6219, 2, '2015-06-26 03:49:58.613', 0, NULL, NULL, '&lt;p&gt;You can try Graph databases (Neo4j/Orient DB etc). Store location and connection between the location as nodes and edges. Then do analysis over graph data. Based on your need, you can use additional attributes (like count) and assign weights for edges etc. Neo4j supports collaborative filtering also.&lt;/p&gt;
', NULL, NULL, '''/p'':49C ''addit'':34C ''also'':48C ''analysi'':23C ''assign'':39C ''attribut'':35C ''base'':27C ''collabor'':46C ''connect'':13C ''count'':37C ''data'':26C ''databas'':6C ''db'':8C ''edg'':20C,42C ''etc'':9C,43C ''filter'':47C ''graph'':5C,25C ''like'':36C ''locat'':11C,16C ''need'':30C ''neo4j'':44C ''neo4j/orient'':7C ''node'':18C ''p'':1C ''store'':10C ''support'':45C ''tri'':4C ''use'':33C ''weight'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6220, 2, '2015-06-26 07:32:27.147', 1, NULL, NULL, '&lt;p&gt;I think you might have to keep overall Data pipelines and Machine learning pipelines in mind. For which you need a robust framework to move data between table like and graph like storage apart from powerful distributed processing. From my understanding Spark GraphX is promising to build these pipelines. Joseph Gonzalez''s (one of the creator of GraphLab from CMU) talk on GraphX on youtube is worth watching.&lt;/p&gt;
', NULL, NULL, '''/p'':70C ''apart'':35C ''build'':48C ''cmu'':61C ''creator'':57C ''data'':10C,27C ''distribut'':38C ''framework'':24C ''gonzalez'':52C ''graph'':32C ''graphlab'':59C ''graphx'':44C,64C ''joseph'':51C ''keep'':8C ''learn'':14C ''like'':30C,33C ''machin'':13C ''might'':5C ''mind'':17C ''move'':26C ''need'':21C ''one'':54C ''overal'':9C ''p'':1C ''pipelin'':11C,15C,50C ''power'':37C ''process'':39C ''promis'':46C ''robust'':23C ''spark'':43C ''storag'':34C ''tabl'':29C ''talk'':62C ''think'':3C ''understand'':42C ''watch'':69C ''worth'':68C ''youtub'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6221, 2, '2015-06-26 07:41:55.48', 0, NULL, NULL, '&lt;p&gt;Good info @Sean Owen. Would like to add one additional. Spark may help to build Unified data pipelines in Lambda architecture addressing both Batch and Streaming layers with an ability to write to common serving layer. It is huge advantage to reuse the logic between batch and Streaming. Also Streaming K-Means algorithms in Spark1.3 is an added plus to ML apart from excellent job monitoring and process visualizations in 1.4.&lt;/p&gt;
', NULL, NULL, '''/p'':74C ''1.4'':73C ''abil'':31C ''ad'':60C ''add'':9C ''addit'':11C ''address'':23C ''advantag'':41C ''algorithm'':55C ''also'':50C ''apart'':64C ''architectur'':22C ''batch'':25C,47C ''build'':16C ''common'':35C ''data'':18C ''excel'':66C ''good'':2C ''help'':14C ''huge'':40C ''info'':3C ''job'':67C ''k'':53C ''k-mean'':52C ''lambda'':21C ''layer'':28C,37C ''like'':7C ''logic'':45C ''may'':13C ''mean'':54C ''ml'':63C ''monitor'':68C ''one'':10C ''owen'':5C ''p'':1C ''pipelin'':19C ''plus'':61C ''process'':70C ''reus'':43C ''sean'':4C ''serv'':36C ''spark'':12C ''spark1.3'':57C ''stream'':27C,49C,51C ''unifi'':17C ''visual'':71C ''would'':6C ''write'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6222, 2, '2015-06-26 07:54:24.64', 1, NULL, NULL, '&lt;p&gt;For comprehending the derivation of Back propagation algorithm, I suggest &lt;a href="https://www.youtube.com/watch?v=aVId8KMsdUU" rel="nofollow" title="Ryan Harris"&gt;Ryan Harris&lt;/a&gt; youtube video which is less daunting. You may find second video as well.&lt;/p&gt;
', NULL, NULL, '''/a'':24C ''/p'':38C ''/watch?v=avid8kmsduu'':16C ''algorithm'':9C ''back'':7C ''comprehend'':3C ''daunt'':30C ''deriv'':5C ''find'':33C ''harri'':21C,23C ''href'':13C ''less'':29C ''may'':32C ''nofollow'':18C ''p'':1C ''propag'':8C ''rel'':17C ''ryan'':20C,22C ''second'':34C ''suggest'':11C ''titl'':19C ''video'':26C,35C ''well'':37C ''www.youtube.com'':15C ''www.youtube.com/watch?v=avid8kmsduu'':14C ''youtub'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6223, 2, '2015-06-26 08:07:36.27', 1, NULL, NULL, '&lt;p&gt;You may build models to classify genomes by population.
Run unsupervised learning (clustering) to see if populations are reconstructed in the model.
Build models to infer missing genotypes&lt;/p&gt;

&lt;p&gt;To do a Scalable DNA analysis you may check &lt;a href="http://bdgenomics.org/" rel="nofollow"&gt;Adam&lt;/a&gt; software based on Apache Spark&lt;/p&gt;
', NULL, NULL, '''/a'':47C ''/p'':30C,53C ''adam'':46C ''analysi'':37C ''apach'':51C ''base'':49C ''bdgenomics.org'':43C ''build'':4C,24C ''check'':40C ''classifi'':7C ''cluster'':14C ''dna'':36C ''genom'':8C ''genotyp'':29C ''href'':42C ''infer'':27C ''learn'':13C ''may'':3C,39C ''miss'':28C ''model'':5C,23C,25C ''nofollow'':45C ''p'':1C,31C ''popul'':10C,18C ''reconstruct'':20C ''rel'':44C ''run'':11C ''scalabl'':35C ''see'':16C ''softwar'':48C ''spark'':52C ''unsupervis'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6225, 2, '2015-06-26 10:17:42.997', 4, NULL, NULL, '&lt;p&gt;I think total number of default categories in Facebook is somewhat static. There is around 140-160 categories. &lt;/p&gt;

&lt;p&gt;ref: &lt;a href="http://stackoverflow.com/questions/4216648/facebook-pages-authoritative-list-of-categories/8576572#8576572"&gt;http://stackoverflow.com/questions/4216648/facebook-pages-authoritative-list-of-categories/8576572#8576572&lt;/a&gt; and &lt;a href="http://www.marketinggum.com/types-of-facebook-pages-for-business/" rel="nofollow"&gt;http://www.marketinggum.com/types-of-facebook-pages-for-business/&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;You can cluster (manually) them in to 10-12 generic groups. &lt;/p&gt;

&lt;p&gt;eg: Sports Event, 
    Sports League, 
    Amateur sports team,
    Sports Venue,
    Athlete,
    Sports/Recreation/Activities etc
all belongs to Sports category. &lt;/p&gt;

&lt;p&gt;Similarly cluster and form generic groups.&lt;/p&gt;

&lt;p&gt;And then you can assign a user to any of these generic group based on the like category.&lt;/p&gt;
', NULL, NULL, '''-12'':52C ''-160'':18C ''/p'':20C,42C,55C,75C,83C,103C ''/questions/4216648/facebook-pages-authoritative-list-of-categories/8576572#8576572'':27C ''/questions/4216648/facebook-pages-authoritative-list-of-categories/8576572#8576572&lt;/a&gt;'':30C ''/types-of-facebook-pages-for-business/'':36C ''/types-of-facebook-pages-for-business/&lt;/a&gt;'':41C ''10'':51C ''140'':17C ''amateur'':62C ''around'':16C ''assign'':89C ''athlet'':67C ''base'':98C ''belong'':71C ''categori'':8C,19C,74C,102C ''cluster'':46C,78C ''default'':7C ''eg'':57C ''etc'':69C ''event'':59C ''facebook'':10C ''form'':80C ''generic'':53C,81C,96C ''group'':54C,82C,97C ''href'':24C,33C ''leagu'':61C ''like'':101C ''manual'':47C ''nofollow'':38C ''number'':5C ''p'':1C,21C,43C,56C,76C,84C ''ref'':22C ''rel'':37C ''similar'':77C ''somewhat'':12C ''sport'':58C,60C,63C,65C,73C ''sports/recreation/activities'':68C ''stackoverflow.com'':26C,29C ''stackoverflow.com/questions/4216648/facebook-pages-authoritative-list-of-categories/8576572#8576572'':25C ''stackoverflow.com/questions/4216648/facebook-pages-authoritative-list-of-categories/8576572#8576572&lt;/a&gt;'':28C ''static'':13C ''team'':64C ''think'':3C ''total'':4C ''user'':91C ''venu'':66C ''www.marketinggum.com'':35C,40C ''www.marketinggum.com/types-of-facebook-pages-for-business/'':34C ''www.marketinggum.com/types-of-facebook-pages-for-business/&lt;/a&gt;'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6230, 2, '2015-06-26 13:58:42.547', 6, NULL, NULL, '&lt;p&gt;The short answer is yes. &lt;/p&gt;

&lt;p&gt;Essentially you will be performing some sort of feature engineering. This means constructing a series of functions of your data, often:&lt;/p&gt;

&lt;p&gt;$\phi_j(x): \mathbb R^p \rightarrow \mathbb R \ ,\ \  j=1,...,K$&lt;/p&gt;

&lt;p&gt;Which, strung together, define a transformed data vector $\boldsymbol \phi(x)$ of length $K$.&lt;/p&gt;

&lt;p&gt;There are a number of ways, better and worse, of doing this. You may want to look up terms like:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Splines and generalised additive models.&lt;/li&gt;
&lt;li&gt;The kernel trick (how to make a model where $K\rightarrow \infty$).&lt;/li&gt;
&lt;li&gt;Feature engineering (of the manual variety, e.g. adding an $x^2$ column to your data).&lt;/li&gt;
&lt;li&gt;Deep learning, representation learning&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;As you might guess from such a varied bag of techniques, this is a large area. It goes without saying really but care has to be taken to avoid overfitting.&lt;/p&gt;

&lt;p&gt;This paper &lt;a href="http://arxiv.org/pdf/1206.5538.pdf" rel="nofollow"&gt;Representation Learning: A Review and New Perspectives&lt;/a&gt; deals with some of the issues around what makes a particular set of features ''good'', from a deep learning perspective.&lt;/p&gt;
', NULL, NULL, '''/a'':178C ''/li'':90C,104C,121C,127C ''/ol'':128C ''/p'':7C,30C,44C,60C,82C,160C,199C ''/pdf/1206.5538.pdf'':168C ''1'':42C ''2'':116C ''ad'':113C ''addit'':88C ''answer'':4C ''area'':145C ''around'':185C ''arxiv.org'':167C ''arxiv.org/pdf/1206.5538.pdf'':166C ''avoid'':158C ''bag'':138C ''better'':68C ''boldsymbol'':54C ''care'':152C ''column'':117C ''construct'':21C ''data'':28C,52C,120C ''deal'':179C ''deep'':123C,196C ''defin'':49C ''e.g'':112C ''engin'':18C,107C ''essenti'':9C ''featur'':17C,106C,192C ''function'':25C ''generalis'':87C ''goe'':147C ''good'':193C ''guess'':133C ''href'':165C ''infti'':103C ''issu'':184C ''j'':33C,41C ''k'':43C,59C,101C ''kernel'':93C ''larg'':144C ''learn'':124C,126C,172C,197C ''length'':58C ''li'':84C,91C,105C,122C ''like'':81C ''look'':78C ''make'':97C,187C ''manual'':110C ''mathbb'':35C,39C ''may'':75C ''mean'':20C ''might'':132C ''model'':89C,99C ''new'':176C ''nofollow'':170C ''number'':65C ''often'':29C ''ol'':83C ''overfit'':159C ''p'':1C,8C,31C,37C,45C,61C,129C,161C ''paper'':163C ''particular'':189C ''perform'':13C ''perspect'':177C,198C ''phi'':32C,55C ''r'':36C,40C ''realli'':150C ''rel'':169C ''represent'':125C,171C ''review'':174C ''rightarrow'':38C,102C ''say'':149C ''seri'':23C ''set'':190C ''short'':3C ''sort'':15C ''spline'':85C ''strung'':47C ''taken'':156C ''techniqu'':140C ''term'':80C ''togeth'':48C ''transform'':51C ''trick'':94C ''vari'':137C ''varieti'':111C ''vector'':53C ''want'':76C ''way'':67C ''without'':148C ''wors'':70C ''x'':34C,56C,115C ''yes'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6232, 1, '2015-06-26 20:48:12.417', 6, 174, 'How to find similarity between different factors in a dataset', '&lt;h1&gt;Introduction&lt;/h1&gt;

&lt;p&gt;Let''s say I have a dataset of different observation of different people and I want to group people together to know which person is closest to the other one. I also want to have a measure to know how close they are to each others and know the statistical significance.&lt;/p&gt;

&lt;h1&gt;Data&lt;/h1&gt;

&lt;pre&gt;&lt;code&gt;       eat_rate drink_rate   sleep_rate    play_rate  name   game
1  0.0542192259 0.13041721 5.013682e-03 1.023533e-06  Paul Rayman
4  0.0688171511 0.01050611 6.178833e-03 3.238838e-07  Paul  Mario
6  0.0928997660 0.01828468 9.321211e-03 3.525951e-07  Jenn  Mario
7  0.0001631273 0.02212345 7.061524e-05 1.531270e-07  Jean   FIFA
8  0.0028735509 0.05414688 1.341689e-03 4.533366e-07  Mark   FIFA
10 0.0034844717 0.09152440 4.589990e-04 5.802708e-07  Mark Rayman
11 0.0340738956 0.03384180 1.636508e-02 1.354973e-07  Mark   FIFA
12 0.0266112679 0.20002020 3.380704e-02 4.533366e-07  Mark  Sonic
14 0.0046597056 0.01848672 5.472681e-04 4.034696e-07  Paul   FIFA
15 0.0202715299 0.16365289 2.994086e-02 4.044770e-07 Lucas   SSBM
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Reproduce it:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;structure(list(eat_rate = c(0.0542192259374624, 0.0688171511010916, 
0.0928997659570807, 0.000163127341146237, 0.00287355085557602, 
0.00348447171120939, 0.0340738956099744, 0.0266112679045701, 
0.00465970561072008, 0.0202715299408583), drink_rate = c(0.130417213859986, 
0.0105061117284574, 0.0182846752197192, 0.0221234468128094, 0.0541468835235882, 
0.0915243964036772, 0.0338418022022427, 0.200020204061016, 0.0184867158298818, 
0.163652894231741), sleep_rate = c(0.00501368170182717, 0.00617883308323771, 
0.00932121105128431, 7.06152352370024e-05, 0.00134168946950305, 
0.000458999029040516, 0.0163650807661753, 0.0338070438697149, 
0.000547268073086768, 0.029940859740489), play_rate = c(1.02353325645595e-06, 
3.23883801132467e-07, 3.52595117873603e-07, 1.53127022619393e-07, 
4.53336580123204e-07, 5.80270822557701e-07, 1.35497266725713e-07, 
4.53336580123204e-07, 4.03469556309652e-07, 4.04476970932148e-07
), name = structure(c(5L, 5L, 2L, 1L, 4L, 4L, 4L, 4L, 5L, 3L), .Label = c("Jean", 
"Jenn", "Lucas", "Mark", "Paul"), class = "factor"), game = structure(c(3L, 
2L, 2L, 1L, 1L, 3L, 1L, 4L, 1L, 5L), .Label = c("FIFA", "Mario", 
"Rayman", "Sonic", "SSBM"), class = "factor")), .Names = c("eat_rate", 
"drink_rate", "sleep_rate", "play_rate", "name", "game"), row.names = c(1L, 
4L, 6L, 7L, 8L, 10L, 11L, 12L, 14L, 15L), class = "data.frame")
&lt;/code&gt;&lt;/pre&gt;

&lt;h1&gt;Question&lt;/h1&gt;

&lt;p&gt;Given a dataset as fellow (with continuous and categorical feature), how can I know if a person (a categorical answer) identified by a name is more correlated to another person?&lt;/p&gt;
', 'machine-learning  r  similarity  correlation', NULL, '''/code'':158C,290C ''/h1'':19C,75C,294C ''/p'':72C,163C,326C ''/pre'':159C,291C ''0.0001631273'':110C ''0.000163127341146237'':174C ''0.000458999029040516'':202C ''0.000547268073086768'':205C ''0.00134168946950305'':201C ''0.00287355085557602'':175C ''0.0028735509'':117C ''0.0034844717'':124C ''0.00348447171120939'':176C ''0.0046597056'':145C ''0.00465970561072008'':179C ''0.00501368170182717'':197C ''0.00617883308323771'':198C ''0.00932121105128431'':199C ''0.01050611'':97C ''0.0105061117284574'':185C ''0.0163650807661753'':203C ''0.0182846752197192'':186C ''0.01828468'':104C ''0.0184867158298818'':192C ''0.01848672'':146C ''0.0202715299'':152C ''0.0202715299408583'':180C ''0.0221234468128094'':187C ''0.02212345'':111C ''0.0266112679'':138C ''0.0266112679045701'':178C ''0.029940859740489'':206C ''0.0338070438697149'':204C ''0.03384180'':132C ''0.0338418022022427'':190C ''0.0340738956'':131C ''0.0340738956099744'':177C ''0.05414688'':118C ''0.0541468835235882'':188C ''0.0542192259'':89C ''0.0542192259374624'':171C ''0.0688171511'':96C ''0.0688171511010916'':172C ''0.0915243964036772'':189C ''0.09152440'':125C ''0.0928997659570807'':173C ''0.0928997660'':103C ''0.13041721'':90C ''0.130417213859986'':184C ''0.16365289'':153C ''0.163652894231741'':193C ''0.20002020'':139C ''0.200020204061016'':191C ''1'':88C ''1.02353325645595e-06'':210C ''1.023533e-06'':92C ''1.341689e-03'':119C ''1.35497266725713e-07'':216C ''1.354973e-07'':134C ''1.53127022619393e-07'':213C ''1.531270e-07'':113C ''1.636508e-02'':133C ''10'':123C ''10l'':283C ''11'':130C ''11l'':284C ''12'':137C ''12l'':285C ''14'':144C ''14l'':286C ''15'':151C ''15l'':287C ''1l'':226C,248C,249C,251C,253C,278C ''2.994086e-02'':154C ''2l'':225C,246C,247C ''3.23883801132467e-07'':211C ''3.238838e-07'':99C ''3.380704e-02'':140C ''3.52595117873603e-07'':212C ''3.525951e-07'':106C ''3l'':232C,245C,250C ''4'':95C ''4.03469556309652e-07'':218C ''4.034696e-07'':148C ''4.04476970932148e-07'':219C ''4.044770e-07'':155C ''4.53336580123204e-07'':214C,217C ''4.533366e-07'':120C,141C ''4.589990e-04'':126C ''4l'':227C,228C,229C,230C,252C,279C ''5.013682e-03'':91C ''5.472681e-04'':147C ''5.80270822557701e-07'':215C ''5.802708e-07'':127C ''5l'':223C,224C,231C,254C ''6'':102C ''6.178833e-03'':98C ''6l'':280C ''7'':109C ''7.06152352370024e-05'':200C ''7.061524e-05'':112C ''7l'':281C ''8'':116C ''8l'':282C ''9.321211e-03'':105C ''also'':52C ''anoth'':324C ''answer'':315C ''c'':170C,183C,196C,209C,222C,234C,244C,256C,265C,277C ''categor'':304C,314C ''class'':240C,262C,288C ''close'':61C ''closest'':46C ''code'':77C,165C ''continu'':302C ''correl'':16B,322C ''data'':74C ''data.frame'':289C ''dataset'':10A,27C,298C ''differ'':6A,29C,32C ''drink'':80C,181C,268C ''eat'':78C,168C,266C ''factor'':7A,241C,263C ''featur'':305C ''fellow'':300C ''fifa'':115C,122C,136C,150C,257C ''find'':3A ''game'':87C,242C,275C ''given'':296C ''group'':38C ''h1'':17C,73C,292C ''identifi'':316C ''introduct'':18C ''jean'':114C,235C ''jenn'':107C,236C ''know'':42C,59C,68C,309C ''label'':233C,255C ''learn'':13B ''let'':21C ''list'':167C ''luca'':156C,237C ''machin'':12B ''machine-learn'':11B ''mario'':101C,108C,258C ''mark'':121C,128C,135C,142C,238C ''measur'':57C ''name'':86C,220C,264C,274C,319C ''observ'':30C ''one'':50C ''other'':66C ''p'':20C,160C,295C ''paul'':93C,100C,149C,239C ''peopl'':33C,39C ''person'':44C,312C,325C ''play'':84C,207C,272C ''pre'':76C,164C ''question'':293C ''r'':14B ''rate'':79C,81C,83C,85C,169C,182C,195C,208C,267C,269C,271C,273C ''rayman'':94C,129C,259C ''reproduc'':161C ''row.names'':276C ''say'':23C ''signific'':71C ''similar'':4A,15B ''sleep'':82C,194C,270C ''sonic'':143C,260C ''ssbm'':157C,261C ''statist'':70C ''structur'':166C,221C,243C ''togeth'':40C ''want'':36C,53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6234, 2, '2015-06-27 02:28:24.02', 6, NULL, NULL, '&lt;p&gt;One way is to normalize your quantitative  values (play, eat, drink, sleep rates) so they all have the same range (say, 0 -&gt; 1), then assign each game to its own "dimension", that takes value 0 or 1. Turn each row into a vector and normalize the length to 1. Now, you can compare the inner product of any two people''s normalized vectors as a measure of similarity. Something like this is used in text mining quite often&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;R Code for Similarity Matrix&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Assumes you''ve saved your dataframe to the variable "D"&lt;/em&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;#Get normalization factors for quantitative measures
maxvect&amp;lt;-apply(D[,1:4],MARGIN=2,FUN=max)
minvect&amp;lt;-apply(D[,1:4],MARGIN=2,FUN=min)
rangevect&amp;lt;-maxvect-minvect
#Normalize quantative factors
D_matrix &amp;lt;- as.matrix(D[,1:4])
NormDMatrix&amp;lt;-matrix(nrow=10,ncol=4)
colnames(NormDMatrix)&amp;lt;-colnames(D_matrix)
for (i in 1:4) NormDMatrix[,i]&amp;lt;-(D_matrix[,i]-minvect[i]*rep(1,10))/rangevect[i]
gamenames&amp;lt;-unique(D[,"game"])
#Create dimension matrix for games
Ngames&amp;lt;-length(gamenames)
GameMatrix&amp;lt;-matrix(nrow=10,ncol=Ngames)
for (i in 1:Ngames) GameMatrix[,i]&amp;lt;-as.numeric(D[,"game"]==gamenames[i])
colnames(GameMatrix)&amp;lt;-gamenames
#combine game matrix with normalized quantative matrix
People&amp;lt;-D[,"name"]
RowVectors&amp;lt;-cbind(GameMatrix,NormDMatrix)
#normalize each row vector to length of 1 and then store as a data frame with person names
NormRowVectors&amp;lt;-t(apply(RowVectors,MARGIN=1,FUN=function(x) x/sqrt(sum(x*x))))
dfNorm&amp;lt;-data.frame(People,NormRowVectors)

#create person vectors via addition of appropriate row vectors
PersonMatrix&amp;lt;-array(dim=c(length(unique(People)),ncol(RowVectors)))
rownames(PersonMatrix)&amp;lt;-unique(People)
for (p in unique(People)){
  print(p)
  MatchIndex&amp;lt;-(dfNorm[,1]==p)*seq(1,nrow(NormRowVectors))
  MatchIndex&amp;lt;-MatchIndex[MatchIndex&amp;gt;0]
  nclm&amp;lt;-length(MatchIndex)
  SubMatrix&amp;lt;-matrix(NormRowVectors[MatchIndex,],nrow=length(MatchIndex),ncol=dim(NormRowVectors)[2])
  CSUMS&amp;lt;-colSums(SubMatrix)
  NormSum&amp;lt;-sqrt(sum(CSUMS*CSUMS))
  PersonMatrix[p,]&amp;lt;-CSUMS/NormSum
}
colnames(PersonMatrix)&amp;lt;-colnames(NormRowVectors)
#Calculate matrix of dot products
Similarity&amp;lt;-(PersonMatrix)%*%t(PersonMatrix)
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9372, 2, '2015-12-14 14:09:46.347', 1, NULL, NULL, '&lt;p&gt;First point is that convolutional neural networks would be incredibly expensive to train on images that large. The minimum size you could allow would be &lt;code&gt;num_pixels_per_letter x num_letters_per_row_or_col&lt;/code&gt;.  &lt;/p&gt;

&lt;p&gt;It sounds like you want to achieve two tasks: OCR then document classification. Given that you have predominantly text in these documents, you would almost certainly be better served by using a more a traditional method for OCR. If you want or need to use NNs for the OCR piece, you will almost certainly need to use a sliding window. For inspiration on how you might do that, you could look to &lt;a href="https://en.wikipedia.org/wiki/Recurrent_neural_network" rel="nofollow"&gt;Recurrent Neural Networks&lt;/a&gt; that &lt;a href="http://karpathy.github.io/2015/05/21/rnn-effectiveness/" rel="nofollow"&gt;process images in sequences&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Once you have the text there are again much simpler methods to deal with text than NNs, but they can achieve great results. To deal with different document lengths, you can use an RNN architecture like &lt;a href="http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf" rel="nofollow"&gt;LSTM&lt;/a&gt;. Or you can use paragraph vectors, which do most of the heavy lifting for you and give you an N-dimensional representation of your text.&lt;/p&gt;
', NULL, NULL, '''/2015/05/21/rnn-effectiveness/'':130C ''/a'':124C,137C,184C ''/code'':39C ''/p'':40C,138C,211C ''/pdfs/hochreiter97_lstm.pdf'':180C ''/wiki/recurrent_neural_network'':118C ''achiev'':48C,160C ''allow'':24C ''almost'':66C,94C ''architectur'':174C ''better'':69C ''certain'':67C,95C ''classif'':54C ''code'':27C ''col'':38C ''convolut'':6C ''could'':23C,111C ''deal'':152C,164C ''deeplearning.cs.cmu.edu'':179C ''deeplearning.cs.cmu.edu/pdfs/hochreiter97_lstm.pdf'':178C ''differ'':166C ''dimension'':206C ''document'':53C,63C,167C ''en.wikipedia.org'':117C ''en.wikipedia.org/wiki/recurrent_neural_network'':116C ''expens'':12C ''first'':2C ''give'':201C ''given'':55C ''great'':161C ''heavi'':196C ''href'':115C,127C,177C ''imag'':16C,134C ''incred'':11C ''inspir'':103C ''karpathy.github.io'':129C ''karpathy.github.io/2015/05/21/rnn-effectiveness/'':128C ''larg'':18C ''length'':168C ''letter'':31C,34C ''lift'':197C ''like'':44C,175C ''look'':112C ''lstm'':183C ''method'':77C,150C ''might'':107C ''minimum'':20C ''much'':148C ''n'':205C ''n-dimension'':204C ''need'':84C,96C ''network'':8C,123C ''neural'':7C,122C ''nns'':87C,156C ''nofollow'':120C,132C,182C ''num'':28C,33C ''ocr'':51C,79C,90C ''p'':1C,41C,139C ''paragraph'':189C ''per'':30C,35C ''piec'':91C ''pixel'':29C ''point'':3C ''predomin'':59C ''process'':133C ''recurr'':121C ''rel'':119C,131C,181C ''represent'':207C ''result'':162C ''rnn'':173C ''row'':36C ''sequenc'':136C ''serv'':70C ''simpler'':149C ''size'':21C ''slide'':100C ''sound'':43C ''task'':50C ''text'':60C,144C,154C,210C ''tradit'':76C ''train'':14C ''two'':49C ''use'':72C,86C,98C,171C,188C ''vector'':190C ''want'':46C,82C ''window'':101C ''would'':9C,25C,65C ''x'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6236, 1, '2015-06-27 06:34:46.957', 2, 27, 'Data produced as an output to Dumbo API of Python not getting distributed to all the nodes of cluster', '&lt;p&gt;On the node from which I run Dumbo commands, all the files produced as output are produced on the same node. For example, suppose there is a node having name hvs on which I ran the script:&lt;/p&gt;

&lt;p&gt;dumbo start matrix2seqfile.py -input hdfs://hm1/user/trainf1.csv -output hdfs://hm1/user/train_hdfs5.mseq -numreducetasks 25 -hadoop $HADOOP_INSTALL &lt;/p&gt;

&lt;p&gt;When I inspect my file system, I find that all the files produced are accumulated only in the hvs node.&lt;/p&gt;

&lt;p&gt;Ideally, I''d like the files to get distributed throughout the cluster--my data is not getting balanced throughout the cluster. Can anyone advise me on how to fix this?&lt;/p&gt;
', 'bigdata  python  hadoop  map-reduce', NULL, '''/p'':64C,78C,100C,132C ''25'':74C ''accumul'':94C ''advis'':125C ''anyon'':124C ''api'':8A ''balanc'':119C ''bigdata'':20B ''cluster'':19A,113C,122C ''command'':35C ''d'':104C ''data'':1A,115C ''distribut'':13A,110C ''dumbo'':7A,34C,66C ''exampl'':49C ''file'':38C,84C,91C,107C ''find'':87C ''fix'':130C ''get'':12A,109C,118C ''hadoop'':22B,75C,76C ''hm1/user/train_hdfs5.mseq'':72C ''hm1/user/trainf1.csv'':70C ''hvs'':57C,98C ''ideal'':102C ''input'':69C ''inspect'':82C ''instal'':77C ''like'':105C ''map'':24B ''map-reduc'':23B ''matrix2seqfile.py'':68C ''name'':56C ''node'':17A,29C,47C,54C,99C ''numreducetask'':73C ''output'':5A,41C,71C ''p'':26C,65C,79C,101C ''produc'':2A,39C,43C,92C ''python'':10A,21B ''ran'':61C ''reduc'':25B ''run'':33C ''script'':63C ''start'':67C ''suppos'':50C ''system'':85C ''throughout'':111C,120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6239, 2, '2015-06-27 15:40:36.653', 2, NULL, NULL, '&lt;p&gt;There''re lots and tons of data sets for biological data.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.ncbi.nlm.nih.gov/genbank/" rel="nofollow"&gt;http://www.ncbi.nlm.nih.gov/genbank/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.1000genomes.org/" rel="nofollow"&gt;http://www.1000genomes.org/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.ddbj.nig.ac.jp/" rel="nofollow"&gt;http://www.ddbj.nig.ac.jp/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/List_of_biological_databases" rel="nofollow"&gt;https://en.wikipedia.org/wiki/List_of_biological_databases&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/li&gt;'':34C,43C ''/genbank/'':20C ''/genbank/&lt;/a&gt;&lt;/li&gt;'':25C ''/p'':13C ''/ul'':55C ''/wiki/list_of_biological_databases'':49C ''/wiki/list_of_biological_databases&lt;/a&gt;&lt;/li&gt;'':54C ''biolog'':11C ''data'':8C,12C ''en.wikipedia.org'':48C,53C ''en.wikipedia.org/wiki/list_of_biological_databases'':47C ''en.wikipedia.org/wiki/list_of_biological_databases&lt;/a&gt;&lt;/li&gt;'':52C ''href'':17C,28C,37C,46C ''li'':15C,26C,35C,44C ''lot'':4C ''nofollow'':22C,31C,40C,51C ''p'':1C ''re'':3C ''rel'':21C,30C,39C,50C ''set'':9C ''ton'':6C ''ul'':14C ''www.1000genomes.org'':29C,33C ''www.1000genomes.org/&lt;/a&gt;&lt;/li&gt;'':32C ''www.ddbj.nig.ac.jp'':38C,42C ''www.ddbj.nig.ac.jp/&lt;/a&gt;&lt;/li&gt;'':41C ''www.ncbi.nlm.nih.gov'':19C,24C ''www.ncbi.nlm.nih.gov/genbank/'':18C ''www.ncbi.nlm.nih.gov/genbank/&lt;/a&gt;&lt;/li&gt;'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6240, 2, '2015-06-27 17:14:44.76', 2, NULL, NULL, '&lt;p&gt;Despite normalized euclidean distance you can also have a look at the pearson distance as a similarity measure. Here is a neat description : &lt;a href="http://mines.humanoriented.com/classes/2010/fall/csci568/portfolio_exports/sphilip/pear.html" rel="nofollow"&gt;http://mines.humanoriented.com/classes/2010/fall/csci568/portfolio_exports/sphilip/pear.html&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/classes/2010/fall/csci568/portfolio_exports/sphilip/pear.html'':29C ''/classes/2010/fall/csci568/portfolio_exports/sphilip/pear.html&lt;/a&gt;'':34C ''/p'':35C ''also'':8C ''descript'':24C ''despit'':2C ''distanc'':5C,15C ''euclidean'':4C ''href'':26C ''look'':11C ''measur'':19C ''mines.humanoriented.com'':28C,33C ''mines.humanoriented.com/classes/2010/fall/csci568/portfolio_exports/sphilip/pear.html'':27C ''mines.humanoriented.com/classes/2010/fall/csci568/portfolio_exports/sphilip/pear.html&lt;/a&gt;'':32C ''neat'':23C ''nofollow'':31C ''normal'':3C ''p'':1C ''pearson'':14C ''rel'':30C ''similar'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6242, 1, '2015-06-28 06:44:01.293', 1, 194, 'GATE with Python', '&lt;p&gt;I am working on named entity recognition project and find &lt;strong&gt;&lt;a href="https://gate.ac.uk/" rel="nofollow"&gt;GATE&lt;/a&gt;&lt;/strong&gt; very useful. But I couldn''t find a Python connector for GATE yet. Is it possible or is there a way to integrate GATE work flows into Python ? Or how can I write &lt;strong&gt;JAPE&lt;/strong&gt; rules for extracting entities in python ?&lt;/p&gt;
', 'python  nlp  java', NULL, '''/a'':25C ''/p'':69C ''/strong'':26C,62C ''connector'':36C ''couldn'':31C ''entiti'':13C,66C ''extract'':65C ''find'':17C,33C ''flow'':52C ''gate'':1A,24C,38C,50C ''gate.ac.uk'':21C ''href'':20C ''integr'':49C ''jape'':61C ''java'':6B ''name'':12C ''nlp'':5B ''nofollow'':23C ''p'':7C ''possibl'':42C ''project'':15C ''python'':3A,4B,35C,54C,68C ''recognit'':14C ''rel'':22C ''rule'':63C ''strong'':18C,60C ''use'':28C ''way'':47C ''work'':10C,51C ''write'':59C ''yet'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6243, 1, '2015-06-28 14:26:12.743', 1, 49, 'Self-designed objective for linear regression learning', '&lt;p&gt;A multiple linear regression is to use several predictor variables to predict the outcome of a response variable, like the following relationship:&lt;/p&gt;

&lt;p&gt;$y_{i}=\beta_{1}x_{i1}+...+\beta_{p}x_{ip}+\epsilon_{i},   i=1,...,n$&lt;/p&gt;

&lt;p&gt;I understand the typical objective to learn the $\beta$ paramters is least-squares, which means to minimize the sum of the sqaure of $\epsilon_{i}$. Now I want other kinds of objective, for example &lt;strong&gt;to maximize the Shannon entropy of the sequences of $\epsilon$&lt;/strong&gt; (or other self-specified objective). I googled towards this direction but no luck. I am wondering if there is any problem (and tool to solve it if possible) I can look into to do that?&lt;/p&gt;

&lt;p&gt;Thank you for your help.&lt;/p&gt;
', 'regression  linear-regression', NULL, '''/p'':36C,53C,138C,145C ''/strong'':101C ''1'':41C,51C ''beta'':40C,44C,63C ''design'':3A ''direct'':112C ''entropi'':95C ''epsilon'':48C,79C,100C ''exampl'':89C ''follow'':34C ''googl'':109C ''help'':144C ''i1'':43C ''ip'':47C ''kind'':85C ''learn'':8A,61C ''least'':67C ''least-squar'':66C ''like'':32C ''linear'':6A,11B,16C ''linear-regress'':10B ''look'':133C ''luck'':115C ''maxim'':92C ''mean'':70C ''minim'':72C ''multipl'':15C ''n'':52C ''object'':4A,59C,87C,107C ''outcom'':27C ''p'':13C,37C,45C,54C,139C ''paramt'':64C ''possibl'':130C ''predict'':25C ''predictor'':22C ''problem'':123C ''regress'':7A,9B,12B,17C ''relationship'':35C ''respons'':30C ''self'':2A,105C ''self-design'':1A ''self-specifi'':104C ''sequenc'':98C ''sever'':21C ''shannon'':94C ''solv'':127C ''specifi'':106C ''sqaur'':77C ''squar'':68C ''strong'':90C ''sum'':74C ''thank'':140C ''tool'':125C ''toward'':110C ''typic'':58C ''understand'':56C ''use'':20C ''variabl'':23C,31C ''want'':83C ''wonder'':118C ''x'':42C,46C ''y'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9373, 1, '2015-12-14 14:44:54.697', 2, 68, 'Can PyLearn do everything that Theano can?', '&lt;p&gt;Since PyLearn2 is build upon Theano, is it possible to do anything I can do in Theano in Pylearn2? For example, if I have some snippets of Theano code, can I run them as-is in Pylearn2, or would this not work? If it wouldn''t, why not?&lt;/p&gt;
', 'machine-learning  deep-learning  theano', NULL, '''/p'':64C ''anyth'':26C ''as-i'':48C ''build'':18C ''code'':43C ''deep'':11B ''deep-learn'':10B ''everyth'':4A ''exampl'':35C ''learn'':9B,12B ''machin'':8B ''machine-learn'':7B ''p'':14C ''possibl'':23C ''pylearn'':2A ''pylearn2'':16C,33C,52C ''run'':46C ''sinc'':15C ''snippet'':40C ''theano'':6A,13B,20C,31C,42C ''upon'':19C ''work'':57C ''would'':54C ''wouldn'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6244, 1, '2015-06-28 15:55:34.763', 0, 22, 'How does the test data gets collected?', '&lt;p&gt;I''m learning the linear regression now. I used R to build linear model upon a set of train model and try to predict() data based on the test data.&lt;/p&gt;

&lt;p&gt;My question: I understand how train data gets collected. But what''s with the test data? How did it get collected? Is the test data built, or is it collected, or is it predicted or what?&lt;/p&gt;

&lt;p&gt;PS: I''m learning data science by method of self-learning, so I lack the structural in my knowledge. I might know something on one place while lack the knowledge at another place. Please forgive and guide. Thanks. :)&lt;/p&gt;
', 'beginner  linear-regression', NULL, '''/p'':43C,81C,122C ''anoth'':115C ''base'':38C ''beginn'':8B ''build'':24C ''built'':70C ''collect'':7A,53C,65C,74C ''data'':5A,37C,42C,51C,60C,69C,87C ''forgiv'':118C ''get'':6A,52C,64C ''guid'':120C ''know'':105C ''knowledg'':102C,113C ''lack'':97C,111C ''learn'':15C,86C,94C ''linear'':10B,17C,25C ''linear-regress'':9B ''m'':14C,85C ''method'':90C ''might'':104C ''model'':26C,32C ''one'':108C ''p'':12C,44C,82C ''place'':109C,116C ''pleas'':117C ''predict'':36C,78C ''ps'':83C ''question'':46C ''r'':22C ''regress'':11B,18C ''scienc'':88C ''self'':93C ''self-learn'':92C ''set'':29C ''someth'':106C ''structur'':99C ''test'':4A,41C,59C,68C ''thank'':121C ''train'':31C,50C ''tri'':34C ''understand'':48C ''upon'':27C ''use'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6245, 2, '2015-06-28 16:14:24.527', 1, NULL, NULL, '&lt;p&gt;In general, you randomly splits the available data into following 3 sets. Train, validation and test. [Different ratios could be used depending on total amount of data at hand and the difficulty of the problem.] You can start with a simple 80%/10%/10% split.
You use the first two sets to build your model. So you would use the 80% split of your data to lets say build a logistic regression model. Now you use the 10% validation set to see how good your model is. You can iterate on this process until you are satisfied with the validation data set performance of the model.&lt;/p&gt;

&lt;p&gt;Now, you use the last set (the "test" split) to see how your model would generalize on an "unseen" data set. [Here unseen means, your model never had access to "see" this data while in the learning phase]. You never use use this 3rd set to glean knowledge about how your model could be improved. Think of this set as a set your customer has, and you don''t have access to, and to whom you are going to deliver your model.&lt;/p&gt;

&lt;p&gt;Once you get a hang of this concept also learn more about how you can do &lt;a href="https://en.wikipedia.org/wiki/Cross-validation_(statistics)" rel="nofollow"&gt;cross-validation&lt;/a&gt; if you have limited data and to improve confidence on your model.&lt;/p&gt;
', NULL, NULL, '''/10'':44C,45C ''/a'':220C ''/p'':108C,192C,233C ''/wiki/cross-validation_(statistics)'':214C ''10'':79C ''3'':12C ''3rd'':153C ''80'':43C,62C ''access'':138C,180C ''also'':202C ''amount'':26C ''avail'':8C ''build'':54C,70C ''concept'':201C ''confid'':229C ''could'':20C,162C ''cross'':218C ''cross-valid'':217C ''custom'':173C ''data'':9C,28C,66C,102C,129C,142C,225C ''deliv'':189C ''depend'':23C ''differ'':18C ''difficulti'':33C ''en.wikipedia.org'':213C ''en.wikipedia.org/wiki/cross-validation_(statistics)'':212C ''first'':50C ''follow'':11C ''general'':3C,125C ''get'':196C ''glean'':156C ''go'':187C ''good'':85C ''hand'':30C ''hang'':198C ''href'':211C ''improv'':164C,228C ''iter'':91C ''knowledg'':157C ''last'':114C ''learn'':146C,203C ''let'':68C ''limit'':224C ''logist'':72C ''mean'':133C ''model'':56C,74C,87C,107C,123C,135C,161C,191C,232C ''never'':136C,149C ''nofollow'':216C ''p'':1C,109C,193C ''perform'':104C ''phase'':147C ''problem'':36C ''process'':94C ''random'':5C ''ratio'':19C ''regress'':73C ''rel'':215C ''satisfi'':98C ''say'':69C ''see'':83C,120C,140C ''set'':13C,52C,81C,103C,115C,130C,154C,168C,171C ''simpl'':42C ''split'':6C,46C,63C,118C ''start'':39C ''test'':17C,117C ''think'':165C ''total'':25C ''train'':14C ''two'':51C ''unseen'':128C,132C ''use'':22C,48C,60C,77C,112C,150C,151C ''valid'':15C,80C,101C,219C ''would'':59C,124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6247, 1, '2015-06-28 16:56:58.467', 3, 55, 'Can you use clustering to pick out signals in noisy data?', '&lt;p&gt;As my first project into data science, I would like to pick out the main clusters in noisy data. I think a good example would be trying to pick out certain links on a given StackExchange question that has a number of answers. The most common type of link is a link to a question on the SE network. The next common is either tag links, or links to user profiles. The remaining links might be random links included in posts, which is considered noise.  Ideally, I''m looking for a solution where I don''t know how many clusters of links there will be ahead of time.&lt;/p&gt;

&lt;p&gt;I''ve implemented my first attempt using &lt;code&gt;scikit-learn&lt;/code&gt; and KMeans. However, it''s not ideal because I appear to have to specify the number of clusters ahead of time, and I think the random, noisy links get grouped improperly. I also think it''s more effective on a larger corpus compared to the relatively small one of URL tokens (though that''s just a guess).&lt;/p&gt;

&lt;p&gt;Is there a way to do this type of clustering, where the number of clusters is unknown or where one of the clusters is a sort of miscellaneous cluster containing objects that don''t closely match the other clusters?&lt;/p&gt;
', 'python  clustering  scikit', NULL, '''/code'':137C ''/p'':124C,195C,236C ''ahead'':121C,156C ''also'':170C ''answer'':58C ''appear'':147C ''attempt'':131C ''certain'':46C ''close'':231C ''cluster'':4A,13B,31C,115C,155C,206C,211C,219C,225C,235C ''code'':133C ''common'':61C,77C ''compar'':180C ''consid'':99C ''contain'':226C ''corpus'':179C ''data'':11A,21C,34C ''effect'':175C ''either'':79C ''exampl'':39C ''first'':18C,130C ''get'':166C ''given'':50C ''good'':38C ''group'':167C ''guess'':194C ''howev'':140C ''ideal'':101C,144C ''implement'':128C ''improp'':168C ''includ'':94C ''kmean'':139C ''know'':112C ''larger'':178C ''learn'':136C ''like'':25C ''link'':47C,64C,67C,81C,83C,89C,93C,117C,165C ''look'':104C ''m'':103C ''main'':30C ''mani'':114C ''match'':232C ''might'':90C ''miscellan'':224C ''network'':74C ''next'':76C ''nois'':100C ''noisi'':10A,33C,164C ''number'':56C,153C,209C ''object'':227C ''one'':185C,216C ''p'':15C,125C,196C ''pick'':6A,27C,44C ''post'':96C ''profil'':86C ''project'':19C ''python'':12B ''question'':52C,70C ''random'':92C,163C ''relat'':183C ''remain'':88C ''scienc'':22C ''scikit'':14B,135C ''scikit-learn'':134C ''se'':73C ''signal'':8A ''small'':184C ''solut'':107C ''sort'':222C ''specifi'':151C ''stackexchang'':51C ''tag'':80C ''think'':36C,161C,171C ''though'':189C ''time'':123C,158C ''token'':188C ''tri'':42C ''type'':62C,204C ''unknown'':213C ''url'':187C ''use'':3A,132C ''user'':85C ''ve'':127C ''way'':200C ''would'':24C,40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6277, 2, '2015-06-30 10:52:18.617', 0, NULL, NULL, '&lt;p&gt;The square in RMSE is used because it always gives a positive value for error, so avoiding errors cancelling each other out, and affords greater weight to values further from the target function, so emphasising points for which the estimator is poor.&lt;/p&gt;

&lt;p&gt;The square root is used to remove the effects of the squaring.&lt;/p&gt;

&lt;p&gt;You could look at using the Mean Absolute Error ( MAE ) which does not have the distance weighting effect of the RMSE and just takes the average of the absolute value of the errors. &lt;/p&gt;
', NULL, NULL, '''/p'':44C,58C,93C ''absolut'':67C,88C ''afford'':25C ''alway'':10C ''averag'':85C ''avoid'':18C ''cancel'':20C ''could'':61C ''distanc'':75C ''effect'':54C,77C ''emphasis'':36C ''error'':16C,19C,68C,92C ''estim'':41C ''function'':34C ''give'':11C ''greater'':26C ''look'':62C ''mae'':69C ''mean'':66C ''p'':1C,45C,59C ''point'':37C ''poor'':43C ''posit'':13C ''remov'':52C ''rmse'':5C,80C ''root'':48C ''squar'':3C,47C,57C ''take'':83C ''target'':33C ''use'':7C,50C,64C ''valu'':14C,29C,89C ''weight'':27C,76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6249, 2, '2015-06-28 22:47:23.027', 2, NULL, NULL, '&lt;p&gt;There are a few of other approaches you can take to try to balance your class distribution.&lt;/p&gt;

&lt;p&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Subsample Majority Class&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;You can balance the class distributions by subsampling the majority class.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Oversample Minority Class&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Sampling with replacement can be used to increase your minority class proportion.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Adding Noise&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;A more sophisticated scheme is to add Gaussian, or other suitable, noise to the existing instances of the minority class in an effort to create a greater number of representative, but diverse,  instances.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;SMOTE&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;A popular method to synthesise
minority class instances with greater complexity than pure noise addition is &lt;a href="https://www.jair.org/media/953/live-953-2037-jair.pdf" rel="nofollow"&gt;SMOTE&lt;/a&gt; ( Synthetic Minority Oversampling TEchnique ). This uses a K-member neighbourhood in feature space to impute new instances.&lt;/p&gt;

&lt;p&gt;WEKA has a filter for this. &lt;/p&gt;

&lt;p&gt;Though there is some evidence that this technique is not overly beneficial with high dimensional data &lt;a href="http://www.biomedcentral.com/1471-2105/14/106" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/1471-2105/14/106'':185C ''/a'':136C,189C ''/em'':108C ''/media/953/live-953-2037-jair.pdf'':132C ''/p'':19C,21C,28C,41C,48C,62C,68C,103C,110C,155C,163C,190C ''/strong'':27C,47C,67C,109C ''ad'':65C ''add'':76C ''addit'':126C ''approach'':8C ''balanc'':15C,32C ''benefici'':176C ''class'':17C,26C,34C,40C,46C,60C,89C,118C ''complex'':122C ''creat'':94C ''data'':180C ''dimension'':179C ''distribut'':18C,35C ''divers'':101C ''effort'':92C ''em'':106C ''evid'':169C ''exist'':84C ''featur'':149C ''filter'':160C ''gaussian'':77C ''greater'':96C,121C ''high'':178C ''href'':129C,182C ''imput'':152C ''increas'':57C ''instanc'':85C,102C,119C,154C ''k'':145C ''k-member'':144C ''major'':25C,39C ''member'':146C ''method'':114C ''minor'':45C,59C,88C,117C,138C ''neighbourhood'':147C ''new'':153C ''nofollow'':134C,187C ''nois'':66C,81C,125C ''number'':97C ''over'':175C ''oversampl'':44C,139C ''p'':1C,20C,22C,29C,42C,49C,63C,69C,104C,111C,156C,164C ''popular'':113C ''proport'':61C ''pure'':124C ''rel'':133C,186C ''replac'':52C ''repres'':99C ''sampl'':50C ''scheme'':73C ''smote'':107C,135C ''sophist'':72C ''space'':150C ''strong'':23C,43C,64C,105C ''subsampl'':24C,37C ''suitabl'':80C ''synthesis'':116C ''synthet'':137C ''take'':11C ''techniqu'':140C,172C ''though'':165C ''tri'':13C ''use'':55C,142C ''weka'':157C ''www.biomedcentral.com'':184C ''www.biomedcentral.com/1471-2105/14/106'':183C ''www.jair.org'':131C ''www.jair.org/media/953/live-953-2037-jair.pdf'':130C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6251, 2, '2015-06-29 04:11:05.683', 3, NULL, NULL, '&lt;p&gt;Have you looked at &lt;a href="https://en.wikipedia.org/wiki/DBSCAN" rel="nofollow"&gt;DBSCAN&lt;/a&gt;? It is a density-based spatial clustering of data with noise that can define non-linear clusters (unlike k-means).&lt;/p&gt;

&lt;p&gt;It doesn''t require knowing the number of clusters. However, it does require two parameters (minimum cluster size and neighborhood size) that measure density. But you may be able to estimate them in your particular domain.&lt;/p&gt;
', NULL, NULL, '''/a'':14C ''/p'':38C,76C ''/wiki/dbscan'':10C ''abl'':68C ''base'':20C ''cluster'':22C,33C,48C,56C ''data'':24C ''dbscan'':13C ''defin'':29C ''densiti'':19C,63C ''density-bas'':18C ''doesn'':41C ''domain'':75C ''en.wikipedia.org'':9C ''en.wikipedia.org/wiki/dbscan'':8C ''estim'':70C ''howev'':49C ''href'':7C ''k'':36C ''k-mean'':35C ''know'':44C ''linear'':32C ''look'':4C ''may'':66C ''mean'':37C ''measur'':62C ''minimum'':55C ''neighborhood'':59C ''nofollow'':12C ''nois'':26C ''non'':31C ''non-linear'':30C ''number'':46C ''p'':1C,39C ''paramet'':54C ''particular'':74C ''rel'':11C ''requir'':43C,52C ''size'':57C,60C ''spatial'':21C ''two'':53C ''unlik'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6261, 2, '2015-06-29 16:32:44.14', 0, NULL, NULL, '&lt;p&gt;It depends on what you are using the RMSE for. If you are merely trying to compare two models/estimators, then there is no significance to the square root. However, if you are trying to plot the error in terms of the same units as you made the measurements/estimates, then you need to take the square root to transform the squared units to the original units (much like variance vs standard deviation)&lt;/p&gt;
', NULL, NULL, '''/p'':73C ''compar'':18C ''depend'':3C ''deviat'':72C ''error'':38C ''howev'':30C ''like'':68C ''made'':47C ''measurements/estimates'':49C ''mere'':15C ''models/estimators'':20C ''much'':67C ''need'':52C ''origin'':65C ''p'':1C ''plot'':36C ''rmse'':10C ''root'':29C,57C ''signific'':25C ''squar'':28C,56C,61C ''standard'':71C ''take'':54C ''term'':40C ''transform'':59C ''tri'':16C,34C ''two'':19C ''unit'':44C,62C,66C ''use'':8C ''varianc'':69C ''vs'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6253, 1, '2015-06-29 04:30:30.007', 1, 36, 'OCR Spell Check based upon previously found words', '&lt;p&gt;I have a bunch of OCR produced text files with numerous spelling errors. I want to automatically correct these spelling based upon the prevalence of the closest correctly spelled words. For example, if the text reads "junp" and the options are "jump" or "june", then it would pick between the two based upon both context and the frequency of the two words. Is there any software like this?&lt;/p&gt;
', 'data-mining  text-mining', NULL, '''/p'':84C ''automat'':32C ''base'':4A,36C,67C ''bunch'':19C ''check'':3A ''closest'':42C ''context'':70C ''correct'':33C,43C ''data'':10B ''data-min'':9B ''error'':28C ''exampl'':47C ''file'':24C ''found'':7A ''frequenc'':73C ''jump'':57C ''june'':59C ''junp'':52C ''like'':82C ''mine'':11B,14B ''numer'':26C ''ocr'':1A,21C ''option'':55C ''p'':15C ''pick'':63C ''preval'':39C ''previous'':6A ''produc'':22C ''read'':51C ''softwar'':81C ''spell'':2A,27C,35C,44C ''text'':13B,23C,50C ''text-min'':12B ''two'':66C,76C ''upon'':5A,37C,68C ''want'':30C ''word'':8A,45C,77C ''would'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6254, 1, '2015-06-29 09:36:45.27', 1, 16, 'Parameters for OnlineLogisticRegression function in Mahout', '&lt;p&gt;Can anyone tell me where do I find any documentation for parameters like:&lt;/p&gt;

&lt;p&gt;-stepOffset&lt;/p&gt;

&lt;p&gt;-alpha&lt;/p&gt;

&lt;p&gt;-decayExponent&lt;/p&gt;

&lt;p&gt;in an &lt;code&gt;OnlineLogisticRegression&lt;/code&gt; function in &lt;code&gt;Mahout&lt;/code&gt;?&lt;/p&gt;

&lt;p&gt;I am interested in what do they change in calls like this one:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;int FEATURES = 10000;       
OnlineLogisticRegression learningAlgorithm = new OnlineLogisticRegression(20, FEATURES, new L1())
                        .alpha(1).stepOffset(1000).decayExponent(0.9).lambda(3.0e-5).learningRate(20);
&lt;/code&gt;&lt;/pre&gt;
', 'logistic-regression  online-learning  apache-mahout', NULL, '''/code'':45C,50C,90C ''/p'':30C,33C,36C,39C,51C,66C ''/pre'':91C ''0.9'':85C ''1'':81C ''1000'':83C ''10000'':71C ''20'':76C,89C ''3.0e-5'':87C ''alpha'':35C,80C ''anyon'':18C ''apach'':14B ''apache-mahout'':13B ''call'':62C ''chang'':60C ''code'':43C,48C,68C ''decayexpon'':38C,84C ''document'':26C ''featur'':70C,77C ''find'':24C ''function'':4A,46C ''int'':69C ''interest'':55C ''l1'':79C ''lambda'':86C ''learn'':12B ''learningalgorithm'':73C ''learningr'':88C ''like'':29C,63C ''logist'':8B ''logistic-regress'':7B ''mahout'':6A,15B,49C ''new'':74C,78C ''one'':65C ''onlin'':11B ''online-learn'':10B ''onlinelogisticregress'':3A,44C,72C,75C ''p'':16C,31C,34C,37C,40C,52C ''paramet'':1A,28C ''pre'':67C ''regress'':9B ''stepoffset'':32C,82C ''tell'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6255, 1, '2015-06-29 13:09:52.317', 1, 162, 'root mean square error - significance of square root', '&lt;p&gt;what is the significance of the square root in root-mean-square-error? In effect, my question is what is the difference between (rms error) and (rms error)^2 ?&lt;/p&gt;
', 'machine-learning  statistics', NULL, '''/p'':44C ''2'':43C ''differ'':36C ''effect'':29C ''error'':4A,27C,39C,42C ''learn'':11B ''machin'':10B ''machine-learn'':9B ''mean'':2A,25C ''p'':13C ''question'':31C ''rms'':38C,41C ''root'':1A,8A,21C,24C ''root-mean-square-error'':23C ''signific'':5A,17C ''squar'':3A,7A,20C,26C ''statist'':12B');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6257, 1, '2015-06-29 14:33:18.37', 1, 41, 'Predict set elements based on other elements', '&lt;p&gt;Sorry if this has been answered before but could someone help me with solving the following problem:&lt;/p&gt;

&lt;p&gt;Each symbol in a dataset has a set of labels. Given a set of labels how can we predict more labels for that set?&lt;/p&gt;

&lt;p&gt;Or to attempt a more formal wording: let a set of sets $S = \{ s_1, s_2, s_3,  ... \} $ where $ s_i \subseteq L $ and $ L = \{ l_1, l_2, l_3,...,l_m\} $ where $L$ is the finite set of labels. Given a set of labels $Y = \{ y_1 ,..., y_n \} $ where $Y \subseteq X $ what is the probability $ P(r = l_i) \forall l_i \in L$ so that $Y \cup \{r\} \in S $.&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/p'':30C,56C,134C ''1'':72C,85C,107C ''2'':74C,87C ''3'':76C,89C ''answer'':18C ''attempt'':60C ''base'':4A ''classif'':11B ''could'':21C ''cup'':130C ''dataset'':36C ''element'':3A,7A ''finit'':96C ''follow'':28C ''foral'':122C ''formal'':63C ''given'':42C,100C ''help'':23C ''l'':81C,83C,84C,86C,88C,90C,93C,120C,123C,126C ''label'':41C,46C,52C,99C,104C ''learn'':10B ''let'':65C ''m'':91C ''machin'':9B ''machine-learn'':8B ''n'':109C ''p'':12C,31C,57C,118C ''predict'':1A,50C ''probabl'':117C ''problem'':29C ''r'':119C,131C ''set'':2A,39C,44C,55C,67C,69C,97C,102C ''solv'':26C ''someon'':22C ''sorri'':13C ''subseteq'':80C,112C ''symbol'':33C ''word'':64C ''x'':113C ''y'':105C,106C,108C,111C,129C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6258, 2, '2015-06-29 16:02:18.727', 1, NULL, NULL, '&lt;p&gt;Maybe you could use some algorithm that solves "Market Basket Analysis". The problem is explained here: &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Market Basket Analysis is a modelling technique based upon the theory
  that if you buy a certain group of items, you are more (or less)
  likely to buy another group of items. For example, if you are in an
  English pub and you buy a pint of beer and don''t buy a bar meal, you
  are more likely to buy crisps (US. chips) at the same time than
  somebody who didn''t buy beer.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://albionresearch.com/data_mining/market_basket.php" rel="nofollow"&gt;http://albionresearch.com/data_mining/market_basket.php&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;One example of such an algorithm is: &lt;a href="https://en.wikipedia.org/wiki/Association_rule_learning" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Association_rule_learning&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/blockquote'':97C ''/data_mining/market_basket.php'':103C ''/data_mining/market_basket.php&lt;/a&gt;&lt;/p&gt;'':108C ''/p'':18C,96C ''/wiki/association_rule_learning'':121C ''/wiki/association_rule_learning&lt;/a&gt;&lt;/p&gt;'':126C ''albionresearch.com'':102C,107C ''albionresearch.com/data_mining/market_basket.php'':101C ''albionresearch.com/data_mining/market_basket.php&lt;/a&gt;&lt;/p&gt;'':106C ''algorithm'':7C,115C ''analysi'':12C,23C ''anoth'':49C ''bar'':74C ''base'':28C ''basket'':11C,22C ''beer'':68C,95C ''blockquot'':19C ''buy'':35C,48C,64C,72C,81C,94C ''certain'':37C ''chip'':84C ''could'':4C ''crisp'':82C ''didn'':92C ''en.wikipedia.org'':120C,125C ''en.wikipedia.org/wiki/association_rule_learning'':119C ''en.wikipedia.org/wiki/association_rule_learning&lt;/a&gt;&lt;/p&gt;'':124C ''english'':60C ''exampl'':54C,111C ''explain'':16C ''group'':38C,50C ''href'':100C,118C ''item'':40C,52C ''less'':45C ''like'':46C,79C ''market'':10C,21C ''mayb'':2C ''meal'':75C ''model'':26C ''nofollow'':105C,123C ''one'':110C ''p'':1C,20C,98C,109C ''pint'':66C ''problem'':14C ''pub'':61C ''rel'':104C,122C ''solv'':9C ''somebodi'':90C ''techniqu'':27C ''theori'':31C ''time'':88C ''upon'':29C ''us'':83C ''use'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6259, 1, '2015-06-29 16:05:19.52', 4, 230, 'Web Scraping - a scientific database', '&lt;p&gt;I am searching a scientific database for abstracts of papers containing the words project management. Here is the &lt;a href="http://en.journals.sid.ir/SearchPaper.aspx?str=project%20management" rel="nofollow"&gt;link&lt;/a&gt;:&lt;/p&gt;

&lt;p&gt;For getting abstracts, I need to click on any paper and open a new page. How can I do that for 68 papers? I program in R and bash.&lt;/p&gt;
', 'r  crawling  scraping', NULL, '''/a'':36C ''/p'':37C,68C ''/searchpaper.aspx?str=project%20management'':32C ''68'':60C ''abstract'':17C,41C ''bash'':67C ''click'':45C ''contain'':20C ''crawl'':7B ''databas'':5A,15C ''en.journals.sid.ir'':31C ''en.journals.sid.ir/searchpaper.aspx?str=project%20management'':30C ''get'':40C ''href'':29C ''link'':35C ''manag'':24C ''need'':43C ''new'':52C ''nofollow'':34C ''open'':50C ''p'':9C,38C ''page'':53C ''paper'':19C,48C,61C ''program'':63C ''project'':23C ''r'':6B,65C ''rel'':33C ''scientif'':4A,14C ''scrape'':2A,8B ''search'':12C ''web'':1A ''word'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6288, 2, '2015-06-30 20:15:50.307', 1, NULL, NULL, '&lt;p&gt;Probably.  It depends on what you are doing exactly, and in particular which summaryFunction you are using.  If it''s twoClassSummary for common binary classification then you should be able to specify &lt;code&gt;metric=''Sensitivity''&lt;/code&gt;.  If that''s not the case you also have the option of overriding the default performance summaryFunction with your own implementation that calculates the metrics you desire.&lt;/p&gt;

&lt;p&gt;I highly recommend reading through the documentation at &lt;a href="http://topepo.github.io/caret/training.html#control" rel="nofollow"&gt;http://topepo.github.io/caret/training.html#control&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/caret/training.html#control'':79C ''/caret/training.html#control&lt;/a&gt;'':84C ''/code'':37C ''/p'':65C,85C ''abl'':31C ''also'':45C ''binari'':25C ''calcul'':60C ''case'':43C ''classif'':26C ''code'':34C ''common'':24C ''default'':52C ''depend'':4C ''desir'':64C ''document'':73C ''exact'':10C ''high'':68C ''href'':76C ''implement'':58C ''metric'':35C,62C ''nofollow'':81C ''option'':48C ''overrid'':50C ''p'':1C,66C ''particular'':13C ''perform'':53C ''probabl'':2C ''read'':70C ''recommend'':69C ''rel'':80C ''sensit'':36C ''specifi'':33C ''summaryfunct'':15C,54C ''topepo.github.io'':78C,83C ''topepo.github.io/caret/training.html#control'':77C ''topepo.github.io/caret/training.html#control&lt;/a&gt;'':82C ''twoclasssummari'':22C ''use'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6264, 1, '2015-06-29 17:32:31.397', 3, 120, 'User profiling with Mahout from categorized user behavior', '&lt;p&gt;I''m trying to cluster and classify users with Mahout. At the moment I am at the planning phase, my mind is completely mixed with ideas, and since I''m relatively new to the area I''m stuck at the data formatting.&lt;/p&gt;

&lt;p&gt;Let''s say we have two data table (big enough). In the first table there are users and their actions. Every user has at least one action and they can have too many actions, too. About 10000 different user_actions and millions of records are in the table.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;user        - user_action
u1          - a
u2          - b
u3          - a
u1          - c
u2          - c
u2          - c
u1          - b
u4          - f
u4          - e
u1          - e
u1          - d
u5          - d
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In the other table, there''re action categories.  Every action may have none or multiple categories. There are 60 categories.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;user_action - category
a           - cat1
b           - cat2
c           - cat1
d           - NULL
e           - cat1, cat3
f           - cat4
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m going to try to build a user classification model with &lt;strong&gt;Mahout&lt;/strong&gt; but I''ve no idea what I should do. &lt;strong&gt;What type of user vectors should I create? Or do I really need user vectors?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I think I need to create something like;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;u1 (a, c, b, e, d)
u2 (b, c, c)
u3 (a)
u4 (f, e)
u5 ()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Problem in here, some users performed more than 100000 actions (some of them are same actions)&lt;/p&gt;

&lt;p&gt;So; this is more useful, I think;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;u1 (cat1, cat1, cat2, cat1, cat3)
u2 (cat2, cat1, cat1)
u3 (cat1)
u4 (cat4, cat1, cat3)
u5 ()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The things I also worry about are&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;How should I weight categories for users? For example u1 has at least three action that related with cat1, while u3 has only 1. These one should be different?&lt;/li&gt;
&lt;li&gt;How can I decrease the difference between active users and passive ones? Like u1 has too many actions and so categories, u3 has only 1.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Any guidance are welcome.&lt;/p&gt;
', 'classification  clustering  apache-mahout', NULL, '''/code'':137C,179C,252C,300C ''/li'':342C,369C ''/p'':57C,107C,160C,223C,233C,271C,280C,310C,376C ''/pre'':138C,180C,253C,301C ''/strong'':196C,222C ''/ul'':370C ''1'':336C,368C ''10000'':95C ''100000'':263C ''60'':158C ''action'':78C,85C,92C,98C,112C,146C,149C,164C,264C,270C,327C,361C ''activ'':351C ''also'':306C ''apach'':12B ''apache-mahout'':11B ''area'':49C ''b'':116C,126C,168C,239C,243C ''behavior'':8A ''big'':67C ''build'':188C ''c'':120C,122C,124C,170C,238C,244C,245C ''cat1'':167C,171C,175C,284C,285C,287C,291C,292C,294C,297C,331C ''cat2'':169C,286C,290C ''cat3'':176C,288C,298C ''cat4'':178C,296C ''categor'':6A ''categori'':147C,155C,159C,165C,317C,364C ''classif'':9B,191C ''classifi'':21C ''cluster'':10B,19C ''code'':109C,162C,235C,282C ''complet'':37C ''creat'':214C,230C ''d'':134C,136C,172C,241C ''data'':55C,65C ''decreas'':347C ''differ'':96C,341C,349C ''e'':130C,132C,174C,240C,250C ''enough'':68C ''everi'':79C,148C ''exampl'':321C ''f'':128C,177C,249C ''first'':71C ''format'':56C ''go'':184C ''guidanc'':373C ''idea'':40C,201C ''least'':83C,325C ''let'':59C ''li'':312C,343C ''like'':232C,356C ''m'':16C,44C,51C,183C ''mahout'':4A,13B,24C,195C ''mani'':91C,360C ''may'':150C ''million'':100C ''mind'':35C ''mix'':38C ''model'':192C ''moment'':27C ''multipl'':154C ''need'':219C,228C ''new'':46C ''none'':152C ''null'':173C ''one'':84C,338C,355C ''p'':14C,58C,139C,181C,224C,254C,272C,302C,371C ''passiv'':354C ''perform'':260C ''phase'':33C ''plan'':32C ''pre'':108C,161C,234C,281C ''problem'':255C ''profil'':2A ''re'':145C ''realli'':218C ''record'':102C ''relat'':45C,329C ''say'':61C ''sinc'':42C ''someth'':231C ''strong'':194C,206C ''stuck'':52C ''tabl'':66C,72C,106C,143C ''thing'':304C ''think'':226C,279C ''three'':326C ''tri'':17C,186C ''two'':64C ''type'':208C ''u1'':113C,119C,125C,131C,133C,236C,283C,322C,357C ''u2'':115C,121C,123C,242C,289C ''u3'':117C,246C,293C,333C,365C ''u4'':127C,129C,248C,295C ''u5'':135C,251C,299C ''ul'':311C ''use'':277C ''user'':1A,7A,22C,75C,80C,97C,110C,111C,163C,190C,210C,220C,259C,319C,352C ''ve'':199C ''vector'':211C,221C ''weight'':316C ''welcom'':375C ''worri'':307C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6267, 1, '2015-06-29 20:26:54.153', 1, 66, 'How to speed up optimization using Differential Evolution?', '&lt;p&gt;My application is high frequency trading.  My data are time series of the bid and ask prices of a stock recorded on every tick (change in price). For each data point I also have a certain indicators that predict the future movement of the price. The indicators have different horizons of the predictions, some being optimal at few second intervals and others few minutes. I need to assign these predictors weights and based on whether the linear combination crosses a threshold, the decision will be taken to buy of sell the stock. So far I have tried the Differential Evolution (DE) method to figure out the weights. I use a black box model with the weights vector $w_i$ and threshold as inputs. For each data point I have a vector of indicators $\alpha _i$. 
$$
total\_alpha = \sum\alpha _i*w_i
$$
If
$$
total\_alpha &amp;gt; threshold, BUY
$$
Else If
$$
total\_alpha &amp;lt; -threshold, SELL
$$
The output of the model is the sum of difference between each between the price of each consecutive buy and sell. This output is being optimised by the DE algorithm. &lt;/p&gt;

&lt;p&gt;I am having trouble with the computational aspects. My data is very large (~$7 \times 10^8$ rows by 20 columns), and thus the execution time for the DE algorithm is unacceptable. &lt;/p&gt;

&lt;p&gt;My question: Is there a better and a faster way to solve this problem? &lt;/p&gt;
', 'machine-learning  optimization', NULL, '''/p'':199C,233C,249C ''10'':216C ''20'':220C ''7'':214C ''8'':217C ''algorithm'':198C,230C ''alpha'':147C,150C,152C,158C,165C ''also'':46C ''applic'':15C ''ask'':29C ''aspect'':208C ''assign'':81C ''base'':86C ''better'':240C ''bid'':27C ''black'':124C ''box'':125C ''buy'':101C,161C,187C ''certain'':49C ''chang'':38C ''column'':221C ''combin'':91C ''comput'':207C ''consecut'':186C ''cross'':92C ''data'':21C,43C,139C,210C ''de'':114C,197C,229C ''decis'':96C ''differ'':62C,178C ''differenti'':7A,112C ''els'':162C ''everi'':36C ''evolut'':8A,113C ''execut'':225C ''far'':107C ''faster'':243C ''figur'':117C ''frequenc'':18C ''futur'':54C ''gt'':159C ''high'':17C ''horizon'':63C ''indic'':50C,60C,146C ''input'':136C ''interv'':73C ''larg'':213C ''learn'':11B ''linear'':90C ''lt'':166C ''machin'':10B ''machine-learn'':9B ''method'':115C ''minut'':77C ''model'':126C,173C ''movement'':55C ''need'':79C ''optim'':5A,12B,69C ''optimis'':194C ''other'':75C ''output'':170C,191C ''p'':13C,200C,234C ''point'':44C,140C ''predict'':52C,66C ''predictor'':83C ''price'':30C,40C,58C,183C ''problem'':248C ''question'':236C ''record'':34C ''row'':218C ''second'':72C ''sell'':103C,168C,189C ''seri'':24C ''solv'':246C ''speed'':3A ''stock'':33C,105C ''sum'':151C,176C ''taken'':99C ''threshold'':94C,134C,160C,167C ''thus'':223C ''tick'':37C ''time'':23C,215C,226C ''total'':149C,157C,164C ''trade'':19C ''tri'':110C ''troubl'':204C ''unaccept'':232C ''use'':6A,122C ''vector'':130C,144C ''w'':131C,154C ''way'':244C ''weight'':84C,120C,129C ''whether'':88C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6295, 1, '2015-07-01 08:44:01.663', 1, 515, 'Finding both count and average of a column in R data.table, after group by', '&lt;p&gt;I have a data table of 5 columns. I want to do a group by on one column  and want to get the count of another column, &lt;code&gt;id&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;I do this in this way:  &lt;code&gt;dt[,length(id),by=dt$cid]&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;If I also want to get &lt;code&gt;sum of id&lt;/code&gt;, how do I proceed?
I tried this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dt[,(sum(id) length(id)),by=dt$cid]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;but it did not work. Could you please help out.&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'data-mining  r', NULL, '''/code'':48C,64C,77C,96C ''/p'':49C,65C,85C,109C,112C ''/pre'':97C ''5'':25C ''also'':69C ''anoth'':44C ''averag'':5A ''cid'':63C,95C ''code'':46C,57C,73C,87C ''column'':8A,26C,36C,45C ''could'':104C ''count'':3A,42C ''data'':15B,22C ''data-min'':14B ''data.table'':11A ''dt'':58C,62C,88C,94C ''find'':1A ''get'':40C,72C ''group'':13A,32C ''help'':107C ''id'':47C,60C,76C,90C,92C ''length'':59C,91C ''mine'':16B ''one'':35C ''p'':18C,50C,66C,98C,110C ''pleas'':106C ''pre'':86C ''proceed'':81C ''r'':10A,17B ''sum'':74C,89C ''tabl'':23C ''thank'':111C ''tri'':83C ''want'':28C,38C,70C ''way'':56C ''work'':103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10467, 2, '2016-03-01 18:29:57.163', 1, NULL, NULL, '&lt;p&gt;It &lt;a href="http://techport.nasa.gov/view/9418" rel="nofollow"&gt;seems that there is no downloadable data&lt;/a&gt; (I clicked &lt;em&gt;Show the complete Project library&lt;/em&gt;), but the project was completed in 2012, so you could ask the listed contact, Gary Jahns, explaining your motivation, to be sure: &lt;code&gt;gary.c.jahns@nasa.gov&lt;/code&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':17C ''/code'':51C ''/em'':26C ''/p'':52C ''/view/9418'':7C ''2012'':33C ''ask'':37C ''click'':19C ''code'':49C ''complet'':23C,31C ''contact'':40C ''could'':36C ''data'':16C ''download'':15C ''em'':20C ''explain'':43C ''gari'':41C ''gary.c.jahns@nasa.gov'':50C ''href'':4C ''jahn'':42C ''librari'':25C ''list'':39C ''motiv'':45C ''nofollow'':9C ''p'':1C ''project'':24C,29C ''rel'':8C ''seem'':10C ''show'':21C ''sure'':48C ''techport.nasa.gov'':6C ''techport.nasa.gov/view/9418'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6268, 1, '2015-06-29 22:55:28.1', 1, 768, 'How to convert categorical data to numerical data in Pyspark', '&lt;p&gt;I am using Ipython notebook to work with pyspark applications. I have a CSV file with lots of categorical columns to determine whether the income falls under or over the 50k range. I would like to perform a classification algorithm taking all the inputs to determine the income range. I need to build a dictionary of variables to mapped variables and use a map function to map the variables to numbers for processing. Essentially, I would my dataset to be in a numerical format so that I can work on implementing the models. &lt;/p&gt;

&lt;p&gt;In the data set, there are categorical columns like education, marital status, working class etc. Can someone tell me how to convert them into numerical columns in pyspark?  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;workclass = {''?'':0,''Federal-gov'':1,''Local-gov'':2,''Never-  worked'':3,''Private'':4,''Self-emp-inc'':5,''Self-emp-not-inc'':6,''State-gov'':7,''Without-pay'':8}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I created a sample dictionary with key value pairs for work class. But, I don''t know how to use this in a map function and replace the categorical data in the CSV file with the corresponding value. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;wc = pd.read_csv(''PATH'', usecols = [''Workclass''])

df = pd.DataFrame(wc)
wcdict = {'' ?'':0,'' Federal-gov'':1,'' Local-gov'':2,'' Never-worked'':3,'' Private'':4,'' Self-emp-inc'':5,'' Self-emp-n-inc'':6,'' State-gov'':7,'' Without-pay'':8}
df_new = df.applymap(lambda s: wcdict.get(s) if s in wcdict else s)
print(df_new)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This is the code I have written in normal python to convert the categorical data into numerical data. It works fine. I want to do the conversion in spark context. And, there are 9 categorical columns in the data source. Is there a way to automate the dictionary update process to have a KV pair for all 9 columns?&lt;/p&gt;
', 'python  categorical-data  apache-spark', NULL, '''/code'':179C,283C ''/p'':112C,142C,220C,345C ''/pre'':180C,284C ''0'':146C,233C ''1'':150C,237C ''2'':154C,241C ''3'':157C,245C ''4'':159C,247C ''5'':164C,252C ''50k'':49C ''6'':170C,258C ''7'':174C,262C ''8'':178C,266C ''9'':319C,343C ''algorithm'':58C ''apach'':16B ''apache-spark'':15B ''applic'':28C ''autom'':331C ''build'':71C ''categor'':4A,13B,37C,120C,210C,299C,320C ''categorical-data'':12B ''class'':127C,193C ''classif'':57C ''code'':144C,222C,289C ''column'':38C,121C,139C,321C,344C ''context'':315C ''convers'':312C ''convert'':3A,135C,297C ''correspond'':218C ''creat'':183C ''csv'':32C,214C,225C ''data'':5A,8A,14B,116C,211C,300C,303C,324C ''dataset'':96C ''determin'':40C,64C ''df'':229C,267C,281C ''df.applymap'':269C ''dictionari'':73C,186C,333C ''educ'':123C ''els'':278C ''emp'':162C,167C,250C,255C ''essenti'':92C ''etc'':128C ''fall'':44C ''feder'':148C,235C ''federal-gov'':147C,234C ''file'':33C,215C ''fine'':306C ''format'':102C ''function'':83C,206C ''gov'':149C,153C,173C,236C,240C,261C ''implement'':109C ''inc'':163C,169C,251C,257C ''incom'':43C,66C ''input'':62C ''ipython'':22C ''key'':188C ''know'':198C ''kv'':339C ''lambda'':270C ''like'':53C,122C ''local'':152C,239C ''local-gov'':151C,238C ''lot'':35C ''map'':77C,82C,85C,205C ''marit'':124C ''model'':111C ''n'':256C ''need'':69C ''never'':155C,243C ''never-work'':242C ''new'':268C,282C ''normal'':294C ''notebook'':23C ''number'':89C ''numer'':7A,101C,138C,302C ''p'':18C,113C,181C,285C ''pair'':190C,340C ''path'':226C ''pay'':177C,265C ''pd.dataframe'':230C ''pd.read'':224C ''perform'':55C ''pre'':143C,221C ''print'':280C ''privat'':158C,246C ''process'':91C,335C ''pyspark'':10A,27C,141C ''python'':11B,295C ''rang'':50C,67C ''replac'':208C ''sampl'':185C ''self'':161C,166C,249C,254C ''self-emp-inc'':160C,248C ''self-emp-n-inc'':253C ''self-emp-not-inc'':165C ''set'':117C ''someon'':130C ''sourc'':325C ''spark'':17B,314C ''state'':172C,260C ''state-gov'':171C,259C ''status'':125C ''take'':59C ''tell'':131C ''updat'':334C ''use'':21C,80C,201C ''usecol'':227C ''valu'':189C,219C ''variabl'':75C,78C,87C ''want'':308C ''way'':329C ''wc'':223C,231C ''wcdict'':232C,277C ''wcdict.get'':272C ''whether'':41C ''without'':176C,264C ''without-pay'':175C,263C ''work'':25C,107C,126C,156C,192C,244C,305C ''workclass'':145C,228C ''would'':52C,94C ''written'':292C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6271, 2, '2015-06-30 04:34:50.527', 1, NULL, NULL, '&lt;p&gt;Since CV data is mostly structured you can use &lt;a href="http://nlp.stanford.edu/software/CRF-NER.shtml" rel="nofollow"&gt;Stanford NER&lt;/a&gt; for named entity recognition or use &lt;a href="https://en.wikipedia.org/wiki/Conditional_random_field" rel="nofollow"&gt;CRF&lt;/a&gt; to train and model your own named entity recognizer.&lt;/p&gt;
', NULL, NULL, '''/a'':20C,35C ''/p'':45C ''/software/crf-ner.shtml'':15C ''/wiki/conditional_random_field'':31C ''crf'':34C ''cv'':3C ''data'':4C ''en.wikipedia.org'':30C ''en.wikipedia.org/wiki/conditional_random_field'':29C ''entiti'':23C,43C ''href'':12C,28C ''model'':39C ''most'':6C ''name'':22C,42C ''ner'':19C ''nlp.stanford.edu'':14C ''nlp.stanford.edu/software/crf-ner.shtml'':13C ''nofollow'':17C,33C ''p'':1C ''recogn'':44C ''recognit'':24C ''rel'':16C,32C ''sinc'':2C ''stanford'':18C ''structur'':7C ''train'':37C ''use'':10C,26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6272, 2, '2015-06-30 04:46:02.77', 2, NULL, NULL, '&lt;pre&gt;&lt;code&gt;workclass = {''?'':0,''Federal-gov'':1,''Local-gov'':2,''Never-  worked'':3,''Private'':4,''Self-emp-inc'':5,''Self-emp-not-inc'':6,''State-gov'':7,''Without-pay'':8}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;try defining a mapper fuction which return key :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def mapr(dict_key):
    return workclass[dict_key]

print list(map(mapr,workclass))
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':37C,64C ''/p'':48C ''/pre'':38C,65C ''0'':4C ''1'':8C ''2'':12C ''3'':15C ''4'':17C ''5'':22C ''6'':28C ''7'':32C ''8'':36C ''code'':2C,50C ''def'':51C ''defin'':41C ''dict'':53C,57C ''emp'':20C,25C ''feder'':6C ''federal-gov'':5C ''fuction'':44C ''gov'':7C,11C,31C ''inc'':21C,27C ''key'':47C,54C,58C ''list'':60C ''local'':10C ''local-gov'':9C ''map'':61C ''mapper'':43C ''mapr'':52C,62C ''never'':13C ''p'':39C ''pay'':35C ''pre'':1C,49C ''print'':59C ''privat'':16C ''return'':46C,55C ''self'':19C,24C ''self-emp-inc'':18C ''self-emp-not-inc'':23C ''state'':30C ''state-gov'':29C ''tri'':40C ''without'':34C ''without-pay'':33C ''work'':14C ''workclass'':3C,56C,63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6273, 2, '2015-06-30 04:53:38.793', 1, NULL, NULL, '&lt;p&gt;Check out Clearbit.com -- &lt;a href="https://dashboard.clearbit.com/docs#company-api" rel="nofollow"&gt;https://dashboard.clearbit.com/docs#company-api&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Super comprehensive company lookup. Not free, but 50 free calls a month. &lt;/p&gt;
', NULL, NULL, '''/docs#company-api'':9C ''/docs#company-api&lt;/a&gt;&lt;/p&gt;'':14C ''/p'':28C ''50'':23C ''call'':25C ''check'':2C ''clearbit.com'':4C ''compani'':18C ''comprehens'':17C ''dashboard.clearbit.com'':8C,13C ''dashboard.clearbit.com/docs#company-api'':7C ''dashboard.clearbit.com/docs#company-api&lt;/a&gt;&lt;/p&gt;'':12C ''free'':21C,24C ''href'':6C ''lookup'':19C ''month'':27C ''nofollow'':11C ''p'':1C,15C ''rel'':10C ''super'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6279, 1, '2015-06-30 13:39:03.97', 1, 51, 'How to include class as a feature', '&lt;p&gt;I am currently experimenting with the idea of including the class of a feature vector as a separate feature. My work is about preposition selection in learner language use. I want to train a classifier that, given a certain position of a sentence, uses relevant context features around that position to build vectors. The classifier detects if there should be a preposition or if it should remain empty. Some features include:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Neighbouring N-grams, where N={2,3}, at Part-Of-Speech and token-level&lt;/li&gt;
&lt;li&gt;Neighbouring nouns and verbs&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The class expresses a binary distribution: 1 for preposition &lt;strong&gt;presence&lt;/strong&gt; and 0 for preposition &lt;strong&gt;absence&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Consider that if a language learner opted to select a preposition in a given sentence, but the classifier thinks there should &lt;strong&gt;NOT&lt;/strong&gt; be one, it can be interpreted as the classifier marking a language error: The learner inserted a preposition in a prohibited context. Conversely, if a learner did &lt;strong&gt;NOT&lt;/strong&gt; use a preposition at a given position in a sentence, but the classifier does expect one, it is also an error.&lt;/p&gt;

&lt;p&gt;The model is trained on correct negative (no preposition) and positive ("yes" preposition) &lt;em&gt;events&lt;/em&gt; (context features around a certain position: a whitespace or a preposition). This means that the assigned labels are the gold standard, similarly for the test data, because they are trained on *native *language use which is assumed to be error-free. The same assumption cannot be made when running the classifier on learner''s language - after all, learners make mistakes, which is what I am trying to detect. &lt;/p&gt;

&lt;p&gt;I want to include the decision (yes/no) of the learner into the classifier rather than just as the assigned label. Because in practice, the chance on either error is very small, so the classifier should be very confident before marking it as such. But when I do this, the classifier always selects the value of that &lt;strong&gt;decision feature&lt;/strong&gt; as the class. After all, it is trained so that there is a 100% correlation between said feature and the assigned class. &lt;/p&gt;

&lt;p&gt;How do I turn this learner''s decision - which is NOT an absolute indication of the actual answer - into an &lt;strong&gt;indication&lt;/strong&gt; instead?&lt;/p&gt;

&lt;p&gt;I''m experimenting with Linear Support Vector Classifier and Logistic/Linear Regression.&lt;/p&gt;

&lt;p&gt;Thanks a bunch, if more details are needed, please ask.&lt;/p&gt;
', 'machine-learning  feature-selection', NULL, '''/em'':228C ''/li'':106C,112C ''/ol'':113C ''/p'':86C,134C,211C,298C,382C,408C,421C,433C ''/strong'':126C,133C,159C,189C,359C,406C ''0'':128C ''1'':121C ''100'':373C ''2'':95C ''3'':96C ''absenc'':132C ''absolut'':396C ''actual'':400C ''also'':208C ''alway'':350C ''answer'':401C ''around'':62C,231C ''ask'':432C ''assign'':244C,318C,380C ''assum'':265C ''assumpt'':273C ''binari'':119C ''build'':66C ''bunch'':425C ''cannot'':274C ''certain'':53C,233C ''chanc'':324C ''class'':4A,25C,116C,362C,381C ''classifi'':49C,69C,153C,168C,202C,280C,312C,333C,349C,417C ''confid'':337C ''consid'':136C ''context'':60C,181C,229C ''convers'':182C ''correct'':218C ''correl'':374C ''current'':17C ''data'':254C ''decis'':305C,357C,391C ''detail'':428C ''detect'':70C,297C ''distribut'':120C ''either'':326C ''em'':226C ''empti'':82C ''error'':172C,210C,269C,327C ''error-fre'':268C ''event'':227C ''expect'':204C ''experi'':18C,412C ''express'':117C ''featur'':7A,12B,28C,33C,61C,84C,230C,358C,377C ''feature-select'':11B ''free'':270C ''given'':51C,149C,195C ''gold'':248C ''gram'':92C ''idea'':21C ''includ'':3A,23C,85C,303C ''indic'':397C,405C ''insert'':175C ''instead'':407C ''interpret'':165C ''label'':245C,319C ''languag'':42C,140C,171C,261C,284C ''learn'':10B ''learner'':41C,141C,174C,185C,282C,287C,309C,389C ''level'':105C ''li'':88C,107C ''linear'':414C ''logistic/linear'':419C ''m'':411C ''machin'':9B ''machine-learn'':8B ''made'':276C ''make'':288C ''mark'':169C,339C ''mean'':241C ''mistak'':289C ''model'':214C ''n'':91C,94C ''n-gram'':90C ''nativ'':260C ''need'':430C ''negat'':219C ''neighbour'':89C,108C ''noun'':109C ''ol'':87C ''one'':161C,205C ''opt'':142C ''p'':14C,114C,135C,212C,299C,383C,409C,422C ''part'':99C ''part-of-speech'':98C ''pleas'':431C ''posit'':54C,64C,196C,223C,234C ''practic'':322C ''preposit'':38C,76C,123C,130C,146C,177C,192C,221C,225C,239C ''presenc'':125C ''prohibit'':180C ''rather'':313C ''regress'':420C ''relev'':59C ''remain'':81C ''run'':278C ''said'':376C ''select'':13B,39C,144C,351C ''sentenc'':57C,150C,199C ''separ'':32C ''similar'':250C ''small'':330C ''speech'':101C ''standard'':249C ''strong'':124C,131C,157C,187C,356C,404C ''support'':415C ''test'':253C ''thank'':423C ''think'':154C ''token'':104C ''token-level'':103C ''train'':47C,216C,258C,367C ''tri'':295C ''turn'':387C ''use'':43C,58C,190C,262C ''valu'':353C ''vector'':29C,67C,416C ''verb'':111C ''want'':45C,301C ''whitespac'':236C ''work'':35C ''yes'':224C ''yes/no'':306C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6297, 1, '2015-07-01 10:52:34.003', 0, 82, 'NA/NaN/Inf in foreign function call (arg 1) while running RFE', '&lt;p&gt;I am trying to find the important variables using RFE package using the below code:&lt;/p&gt;

&lt;p&gt;results &amp;lt;- rfe(training_main_part1[,1:313], training_main_part1[,314], sizes=c(1:50), rfeControl=control)  &lt;/p&gt;

&lt;p&gt;I am getting the below error.&lt;/p&gt;

&lt;p&gt;Error in { : 
  task 1 failed - "NA/NaN/Inf in foreign function call (arg 1)"&lt;/p&gt;

&lt;p&gt;I made sure that I dont have NA/NaN/Inf values using is.na/is.nan/is.infinite. &lt;/p&gt;

&lt;p&gt;Any suggestion on this please? Did i miss out something?&lt;/p&gt;
', 'machine-learning  data-mining', NULL, '''/is.nan/is.infinite.'':89C ''/p'':33C,53C,61C,75C,90C,102C ''1'':7A,41C,49C,66C,74C ''313'':42C ''314'':46C ''50'':50C ''arg'':6A,73C ''c'':48C ''call'':5A,72C ''code'':32C ''control'':52C ''data'':15B ''data-min'':14B ''dont'':82C ''error'':60C,63C ''fail'':67C ''find'':22C ''foreign'':3A,70C ''function'':4A,71C ''get'':57C ''import'':24C ''is.na'':88C ''is.na/is.nan/is.infinite.'':87C ''learn'':13B ''lt'':36C ''machin'':12B ''machine-learn'':11B ''made'':78C ''main'':39C,44C ''mine'':16B ''miss'':99C ''na/nan/inf'':1A,68C,84C ''p'':17C,34C,54C,62C,76C,91C ''packag'':28C ''part1'':40C,45C ''pleas'':96C ''result'':35C ''rfe'':10A,27C,37C ''rfecontrol'':51C ''run'':9A ''size'':47C ''someth'':101C ''suggest'':93C ''sure'':79C ''task'':65C ''train'':38C,43C ''tri'':20C ''use'':26C,29C,86C ''valu'':85C ''variabl'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6280, 1, '2015-06-30 14:40:03.72', -3, 901, 'Error in R:: Error: Unexpected ''}'' in " }"', '&lt;p&gt;I am getting a error as &lt;strong&gt;"Error: Unexpected ''}'' in "   }"&lt;/strong&gt; when i run the following code. I have put closing brackets for every open bracket. But i am not sure why i get this error. &lt;/p&gt;

&lt;h1&gt;find number of Columns &amp;amp; Rows&lt;/h1&gt;

&lt;pre&gt;&lt;code&gt;numcol &amp;lt;- ncol(training_main)  
numrow &amp;lt;- nrow(training_main)


attach(training_main)  
NA_count &amp;lt;- as.data.frame(sapply(training_main, function(x) sum(is.na(x))))  
for(i in 1:numcol)    
{  
     if (NA_count &amp;gt; 0)  
  {  
  for(j in 1:numrow)  
  {    
    if(is.na(training_main[j,i])    
      {   
         training_main[j,i] &amp;lt;- as.character(training_main[j,i])    
         training_main[j,i] &amp;lt;- "Empty"    
         training_main[j,i] &amp;lt;- as.factor(training_main[j,i])     
         print("empty printed")    

       }     
  }     
}    
}   
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is the nested for -if - for - if as mentioned below  is allowed in R? That is the logic i have to use. Is there any other way i can run this code?&lt;/p&gt;
', 'r', NULL, '''/code'':130C ''/h1'':52C ''/p'':44C,165C ''/pre'':131C ''/strong'':18C ''0'':89C ''1'':83C,93C ''allow'':144C ''amp'':50C ''as.character'':106C ''as.data.frame'':71C ''as.factor'':122C ''attach'':65C ''bracket'':29C,33C ''close'':28C ''code'':24C,54C,164C ''column'':49C ''count'':69C,87C ''empti'':116C,128C ''error'':1A,4A,12C,15C,43C ''everi'':31C ''find'':46C ''follow'':23C ''function'':75C ''get'':10C,41C ''gt'':88C ''h1'':45C ''is.na'':78C,96C ''j'':91C,99C,103C,109C,113C,119C,125C ''logic'':150C ''lt'':56C,61C,70C,105C,115C,121C ''main'':59C,64C,67C,74C,98C,102C,108C,112C,118C,124C ''mention'':141C ''na'':68C,86C ''ncol'':57C ''nest'':135C ''nrow'':62C ''number'':47C ''numcol'':55C,84C ''numrow'':60C,94C ''open'':32C ''p'':7C,132C ''pre'':53C ''print'':127C,129C ''put'':27C ''r'':3A,6B,146C ''row'':51C ''run'':21C,162C ''sappli'':72C ''strong'':14C ''sum'':77C ''sure'':38C ''train'':58C,63C,66C,73C,97C,101C,107C,111C,117C,123C ''unexpect'':5A,16C ''use'':154C ''way'':159C ''x'':76C,79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8649, 1, '2015-10-29 16:18:45.837', 0, 74, 'crow probability problem', '&lt;p&gt;I am trying to solve the following problem, but am having some difficulty. Can anyone give me some guidance?&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;There are lots of tourists in Grandeville. The streets in Grandeville run
  east to west and go from&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;..., S. 2nd St., S. 1st St., Broadway St., N. 1st St., N. 2nd St., ...
&lt;/code&gt;&lt;/pre&gt;
  
  &lt;p&gt;The avenues run north to south and go from&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;..., E. 2nd Ave., E. 1st Ave., Broadway Ave., W. 1st Ave., W. 2nd Ave., ...
&lt;/code&gt;&lt;/pre&gt;
  
  &lt;p&gt;These streets form a square block grid. For each of the questions
  below, the tourist starts at the intersection of Broadway St. and
  Broadway Avenue and moves one block in each of the four cardinal
  directions with equal probability.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Q1)&lt;/strong&gt; What is the probability that the tourist is at least 3 city blocks (as
the crow flies) from Broadway and Broadway after 10 moves?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Q2)&lt;/strong&gt; What is the probability that the tourist is at least 10 city blocks
(as the crow flies) from Broadway and Broadway after 60 moves?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Q3)&lt;/strong&gt; What is the probability that the tourist is ever at least 5 city
blocks (as the crow flies) from Broadway and Broadway within 10 moves?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Q4)&lt;/strong&gt; What is the probability that the tourist is ever at least 10 city
blocks (as the crow flies) from Broadway and Broadway within 60 moves?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Q5)&lt;/strong&gt; What is the probability that the tourist is ever east of East 1st
Avenue but ends up west of West 1st Avenue in 10 moves?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Q6)&lt;/strong&gt; What is the probability that the tourist is ever east of East 1st
Avenue but ends up west of West 1st Avenue in 30 moves?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Q7)&lt;/strong&gt; What is the average number of moves until the first time the tourist
is at least 10 city blocks (as the crow flies) from Broadway and
Broadway.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Q8)&lt;/strong&gt; What is the average number of moves until the first time the tourist
is at least 60 city blocks (as the crow flies) from Broadway and
Broadway.&lt;/p&gt;

&lt;p&gt;I am running this matlab code. But i am not sure how to find probabilities even to the first question?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;function random_walk_2d_simulation ( step_num, walk_num )

%*****************************************************************************80
%
%% RANDOM_WALK_2D_SIMULATION simulates a random walk in 2D.
%
%  Discussion:
%
%    The expectation should be that, the average distance squared D^2 
%    is equal to the time, or number of steps N.
%
%    Or, equivalently
%
%      average ( D ) = sqrt ( N )
%
%    The program makes a plot of both the average and the maximum values
%    of D^2 versus time.  The maximum value grows much more quickly,
%    and that curve is much more jagged.
%
%  Licensing:
%
%    This code is distributed under the GNU LGPL license.
%
%  Modified:
%
%    03 November 2009
%
%  Author:
%
%    John Burkardt
%
%  Parameters:
%
%    Input, integer STEP_NUM, the number of steps to take in one test.
%
%    Input, integer WALK_NUM, the number of walks to take.
%

%
%  Set up arrays for plotting.
%
  time = 0 : step_num;
  d2_ave = zeros(step_num+1,1);
  d2_max = zeros(step_num+1,1);
%
%  Take the walk WALK_NUM times.
%
  for walk = 1 : walk_num

    x = zeros(step_num+1,1);
    y = zeros(step_num+1,1);

   for step = 2 : step_num + 1
%
%  We are currently at ( X(STEP-1), Y(STEP-1) ).
%  Consider the four possible points to step to.
%
      destination = [ x(step-1) + 1.0, y(step-1); ...
                      x(step-1) - 1.0, y(step-1); ...
                      x(step-1),       y(step-1) + 1.0; ...
                      x(step-1),       y(step-1) - 1.0 ];
%
%  Choose destination 1, 2, 3 or 4.
%
      k = ceil ( 4.0 * rand );
%
%  Move there.
%
      x(step) = destination(k,1);
      y(step) = destination(k,2);
%
%  Update the sum of every particle''s distance at step J.
%
      d2 = x(step)^2 + y(step)^2;
      d2_ave(step) = d2_ave(step) + d2;
      d2_max(step) = max ( d2_max(step), d2 );

    end

  end
%
%  Average the squared distance at each step over all walks.
%
  d2_ave(:,1) = d2_ave(:,1) / walk_num;
%
%  Make a plot.
%
  clf
  plot ( time, d2_ave, time, d2_max, ''LineWidth'', 2 );

  xlabel ( ''Time'' )
  ylabel ( ''Distance squared'' )
  title_string = sprintf ( ''2D Random Walk Ave and Max - %d walks, %d steps'', walk_num, step_num );
  title ( title_string );

  return
end
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here is the plot of running the following command
random_walk_2d_simulation(60,10000)&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/nLARF.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/nLARF.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'probability', NULL, '''+1'':537C,544C,561C,567C ''-1'':581C,584C,596C,600C,603C,607C,610C,613C,617C,620C ''/a'':775C ''/blockquote'':135C ''/code'':63C,92C,738C ''/nlarf.jpg'':762C,769C ''/p'':25C,46C,75C,134C,164C,193C,223C,253C,283C,313C,345C,377C,399C,756C,776C ''/pre'':64C,93C,739C ''/strong'':139C,168C,197C,227C,257C,287C,317C,349C ''0'':529C ''03'':493C ''1'':538C,545C,554C,562C,568C,574C,624C,639C,692C,695C ''1.0'':597C,604C,614C,621C ''10'':162C,179C,221C,239C,281C,334C ''10000'':755C ''1st'':53C,58C,82C,87C,270C,278C,300C,308C ''2'':433C,465C,571C,625C,644C,659C,662C,710C ''2009'':495C ''2d'':405C,414C,421C,719C,752C ''2nd'':50C,61C,79C,90C ''3'':150C,626C ''30'':311C ''4'':628C ''4.0'':631C ''5'':209C ''60'':191C,251C,366C,754C ''80'':411C ''alt'':770C ''anyon'':20C ''array'':525C ''author'':496C ''ave'':80C,83C,85C,88C,91C,533C,664C,667C,691C,694C,705C,722C ''avenu'':67C,119C,271C,279C,301C,309C ''averag'':321C,353C,429C,446C,458C,680C ''block'':100C,123C,152C,181C,211C,241C,336C,368C ''blockquot'':26C ''broadway'':55C,84C,115C,118C,158C,160C,187C,189C,217C,219C,247C,249C,342C,344C,374C,376C ''burkardt'':498C ''cardin'':129C ''ceil'':630C ''choos'':622C ''citi'':151C,180C,210C,240C,335C,367C ''clf'':701C ''code'':48C,77C,384C,401C,484C ''command'':749C ''consid'':585C ''crow'':1A,155C,184C,214C,244C,339C,371C ''current'':577C ''curv'':477C ''d'':432C,447C,464C,725C,727C ''d2'':532C,539C,656C,663C,666C,669C,670C,674C,677C,690C,693C,704C,707C ''descript'':773C ''destin'':593C,623C,637C,642C ''difficulti'':18C ''direct'':130C ''discuss'':422C ''distanc'':430C,652C,683C,714C ''distribut'':486C ''e'':78C,81C ''east'':40C,267C,269C,297C,299C ''end'':273C,303C,678C,679C,737C ''enter'':771C ''equal'':132C,435C ''equival'':445C ''even'':394C ''ever'':206C,236C,266C,296C ''everi'':649C ''expect'':424C ''find'':392C ''first'':327C,359C,397C ''fli'':156C,185C,215C,245C,340C,372C ''follow'':12C,748C ''form'':97C ''four'':128C,587C ''function'':402C ''give'':21C ''gnu'':489C ''go'':44C,73C ''grandevill'':34C,38C ''grid'':101C ''grow'':471C ''guidanc'':24C ''href'':759C ''i.stack.imgur.com'':761C,768C ''i.stack.imgur.com/nlarf.jpg'':760C,767C ''imag'':772C ''img'':765C ''input'':500C,513C ''integ'':501C,514C ''intersect'':113C ''j'':655C ''jag'':481C ''john'':497C ''k'':629C,638C,643C ''least'':149C,178C,208C,238C,333C,365C ''lgpl'':490C ''licens'':482C,491C ''linewidth'':709C ''lot'':30C ''make'':452C,698C ''matlab'':383C ''max'':540C,671C,673C,675C,708C,724C ''maximum'':461C,469C ''modifi'':492C ''move'':121C,163C,192C,222C,252C,282C,312C,324C,356C,633C ''much'':472C,479C ''n'':57C,60C,443C,449C ''nofollow'':764C ''north'':69C ''novemb'':494C ''num'':408C,410C,503C,516C,531C,536C,543C,550C,556C,560C,566C,573C,697C,730C,732C ''number'':322C,354C,440C,505C,518C ''one'':122C,511C ''p'':5C,27C,65C,94C,136C,165C,194C,224C,254C,284C,314C,346C,378C,740C,757C ''paramet'':499C ''particl'':650C ''plot'':454C,527C,700C,702C,744C ''point'':589C ''possibl'':588C ''pre'':47C,76C,400C ''probabl'':2A,4B,133C,143C,172C,201C,231C,261C,291C,393C ''problem'':3A,13C ''program'':451C ''q1'':138C ''q2'':167C ''q3'':196C ''q4'':226C ''q5'':256C ''q6'':286C ''q7'':316C ''q8'':348C ''question'':106C,398C ''quick'':474C ''rand'':632C ''random'':403C,412C,418C,720C,750C ''rel'':763C ''return'':736C ''run'':39C,68C,381C,746C ''set'':523C ''simul'':406C,415C,416C,753C ''solv'':10C ''south'':71C ''sprintf'':718C ''sqrt'':448C ''squar'':99C,431C,682C,715C ''src'':766C ''st'':51C,54C,56C,59C,62C,116C ''start'':110C ''step'':407C,442C,502C,507C,530C,535C,542C,559C,565C,570C,572C,580C,583C,591C,595C,599C,602C,606C,609C,612C,616C,619C,636C,641C,654C,658C,661C,665C,668C,672C,676C,686C,728C,731C ''street'':36C,96C ''string'':717C,735C ''strong'':137C,166C,195C,225C,255C,285C,315C,347C ''sum'':647C ''sure'':389C ''take'':509C,522C,546C ''test'':512C ''time'':328C,360C,438C,467C,528C,551C,703C,706C,712C ''titl'':716C,733C,734C ''tourist'':32C,109C,146C,175C,204C,234C,264C,294C,330C,362C ''tri'':8C ''updat'':645C ''valu'':462C,470C ''versus'':466C ''w'':86C,89C ''walk'':404C,409C,413C,419C,515C,520C,548C,549C,553C,555C,689C,696C,721C,726C,729C,751C ''west'':42C,275C,277C,305C,307C ''within'':220C,250C ''x'':557C,579C,594C,601C,608C,615C,635C,657C ''xlabel'':711C ''y'':563C,582C,598C,605C,611C,618C,640C,660C ''ylabel'':713C ''zero'':534C,541C,558C,564C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6282, 2, '2015-06-30 15:04:10.57', 3, NULL, NULL, '&lt;p&gt;try RSelenium. with phantomjs since the date is requested and filled in by ajax calls. so any static web scraping tools wont work.&lt;/p&gt;

&lt;p&gt;I managed to get the list on the first page. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://cran.r-project.org/web/packages/RSelenium/vignettes/RSelenium-headless.html" rel="nofollow"&gt;http://cran.r-project.org/web/packages/RSelenium/vignettes/RSelenium-headless.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;sample of what i managed to pull.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;remove( mopub, m, run , rx, x , first1)
library(RSelenium)
pjs&amp;lt;- phantom( pjs_cmd="C:/Users/bhavin.patel/Downloads/phantomjs-2.0.0-    windows/bin/phantomjs.exe")
Sys.sleep(5)
remDr &amp;lt;- remoteDriver( browserName = ''PhantomJS'')
dsurl &amp;lt;- "http://en.journals.sid.ir/SearchPaper.aspxstr=project%20management"
remDr$open()
remDr$navigate(dsurl)
allt3 &amp;lt;-remDr$findElements(''id'', ''Table3'')
lapply( allt3 , FUN=function(dst){ dst$getElementText(); })

[[1]]
[[1]][[1]]
[1] " 1 :   EFFECTIVE FACTORS ON RURAL PEOPLEâS NON-PARTICIPATION OF     MAHABADâS DAM CATCHMENT IN WATERSHED MANAGEMENT PROJECTS\nAuthor(s): RASOULIAZAR SOLEIMAN*,FEALY SAEID\nJournal: INTERNATIONAL JOURNAL OF AGRICULTURAL MANAGEMENT AND DEVELOPMENT (IJAMAD)\nNumber: MARCH 2015 , Volume  5 , Number  1 ; Page(s) 19 To 26.\nKeyword(s): NON-PARTICIPATION, CATCHMENT, WATERSHED MANAGEMENT, MAHABAD TOWNSHIP, IRAN\nReference(s):  (0)      Citation(s):  (0) FullText:"
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':175C ''/p'':25C,37C,57C ''/pre'':176C ''/searchpaper.aspxstr=project%20management'':88C ''/users/bhavin.patel/downloads/phantomjs-2.0.0-'':75C ''/web/packages/rselenium/vignettes/rselenium-headless.html'':43C ''/web/packages/rselenium/vignettes/rselenium-headless.html&lt;/a&gt;&lt;/p&gt;'':48C ''0'':170C,173C ''1'':107C,108C,109C,110C,111C,151C ''19'':154C ''2015'':147C ''26'':156C ''5'':78C,149C ''agricultur'':140C ''ajax'':15C ''allt3'':94C,101C ''browsernam'':82C ''c'':74C ''call'':16C ''catchment'':125C,162C ''citat'':171C ''cmd'':73C ''code'':59C ''cran.r-project.org'':42C,47C ''cran.r-project.org/web/packages/rselenium/vignettes/rselenium-headless.html'':41C ''cran.r-project.org/web/packages/rselenium/vignettes/rselenium-headless.html&lt;/a&gt;&lt;/p&gt;'':46C ''dam'':124C ''date'':8C ''develop'':143C ''dst'':104C,105C ''dsurl'':84C,93C ''effect'':112C ''en.journals.sid.ir'':87C ''en.journals.sid.ir/searchpaper.aspxstr=project%20management'':86C ''factor'':113C ''feali'':134C ''fill'':12C ''findel'':97C ''first'':35C ''first1'':66C ''fulltext'':174C ''fun'':102C ''function'':103C ''get'':30C ''getelementtext'':106C ''href'':40C ''id'':98C ''ijamad'':144C ''intern'':137C ''iran'':167C ''journal'':138C ''lappli'':100C ''librari'':67C ''list'':32C ''lt'':70C,80C,85C,95C ''m'':62C ''mahabad'':165C ''mahabadâ'':122C ''manag'':28C,54C,128C,141C,164C ''march'':146C ''mopub'':61C ''nauthor'':130C ''navig'':92C ''njournal'':136C ''nkeyword'':157C ''nnumber'':145C ''nofollow'':45C ''non'':119C,160C ''non-particip'':118C,159C ''nrefer'':168C ''number'':150C ''open'':90C ''p'':1C,26C,38C,49C ''page'':36C,152C ''particip'':120C,161C ''peopleâ'':116C ''phantom'':71C ''phantomj'':5C,83C ''pjs'':69C,72C ''pre'':58C ''project'':129C ''pull'':56C ''rasouliazar'':132C ''rel'':44C ''remdr'':79C,89C,91C,96C ''remotedriv'':81C ''remov'':60C ''request'':10C ''rselenium'':3C,68C ''run'':63C ''rural'':115C ''rx'':64C ''saeid'':135C ''sampl'':50C ''scrape'':21C ''sinc'':6C ''soleiman'':133C ''static'':19C ''sys.sleep'':77C ''table3'':99C ''tool'':22C ''township'':166C ''tri'':2C ''volum'':148C ''watersh'':127C,163C ''web'':20C ''windows/bin/phantomjs.exe'':76C ''wont'':23C ''work'':24C ''x'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6283, 2, '2015-06-30 17:45:52.297', 3, NULL, NULL, '&lt;p&gt;There are many great ways to handle this problem. It is a recommendation problem, not a classification problem, as pointed out by others. There are many ways to do recommendation with a data set like this. I''ll point out a few methods and you can choose one or try them all.&lt;/p&gt;

&lt;p&gt;The first method is called user-based collaborative filtering. The basic idea is to give users recommendations based on the tastes of like-minded users. So, you''d be trying to recommend music based on the listening history of users who have listened to the same songs. Such data can be modeled as a graph or sparse matrix. Then, you choose the exact algorithm depending on how you want to model your data. &lt;/p&gt;

&lt;p&gt;The second method is called item-based collaborative filtering. Rather than associating users together, this strategy looks at the set of items a user has ''rated'' (the songs a user has listened to) and calculates how similar they are to a specific target item (song), or even to all the songs in your data set. It grabs the set of most-similar items and uses various methods to predict how much a user will like the song. &lt;/p&gt;

&lt;p&gt;In this case, you only have binary data (user listened to it or they did not). These calculations tend to work best with actual rating scores (like a 5 star system) because this gives more detailed variation amongst items in the data set. &lt;/p&gt;

&lt;p&gt;The third option is to model your data in a graph database like Neo4J and write graph traversal queries in order to find similar items. If you like graph theory, this can be a lot of fun. The sky is the limit in regards to what kinds of traversals will return good results. To get started, think of the users and songs as nodes in the graph, and ''listened'' as the edge. $user-&amp;gt;listened-&amp;gt;$song  &lt;/p&gt;

&lt;p&gt;Because of ratings and item-based filtering, and because there are probably many songs in your data set, and each user only listens to a very small portion of them, I''d first try a user-based collaborative filtering method which uses sparse matrix operations to calculate recommendations. If your data set is large, these computations scale horizontally so you can leverage parallel processing if you run into performance issues.&lt;/p&gt;

&lt;p&gt;You can find more detail about collaborative filtering in this paper: &lt;a href="http://files.grouplens.org/papers/www10_sarwar.pdf" rel="nofollow"&gt;http://files.grouplens.org/papers/www10_sarwar.pdf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':54C,131C,212C,257C,336C,410C ''/papers/www10_sarwar.pdf'':427C ''/papers/www10_sarwar.pdf&lt;/a&gt;&lt;/p&gt;'':432C ''5'':242C ''actual'':237C ''algorithm'':121C ''amongst'':251C ''associ'':145C ''base'':63C,74C,91C,140C,344C,376C ''basic'':67C ''best'':235C ''binari'':220C ''calcul'':168C,231C,386C ''call'':60C,137C ''case'':216C ''choos'':48C,118C ''classif'':18C ''collabor'':64C,141C,377C,418C ''comput'':395C ''d'':85C,370C ''data'':34C,106C,130C,187C,221C,255C,266C,355C,390C ''databas'':270C ''depend'':122C ''detail'':249C,416C ''edg'':330C ''even'':180C ''exact'':120C ''files.grouplens.org'':426C,431C ''files.grouplens.org/papers/www10_sarwar.pdf'':425C ''files.grouplens.org/papers/www10_sarwar.pdf&lt;/a&gt;&lt;/p&gt;'':430C ''filter'':65C,142C,345C,378C,419C ''find'':281C,414C ''first'':57C,371C ''fun'':295C ''get'':313C ''give'':71C,247C ''good'':310C ''grab'':190C ''graph'':112C,269C,275C,287C,325C ''great'':5C ''gt'':332C,334C ''handl'':8C ''histori'':95C ''horizont'':397C ''href'':424C ''idea'':68C ''issu'':409C ''item'':139C,155C,177C,197C,252C,283C,343C ''item-bas'':138C,342C ''kind'':305C ''larg'':393C ''leverag'':401C ''like'':36C,80C,209C,240C,271C,286C ''like-mind'':79C ''limit'':300C ''listen'':94C,100C,165C,223C,327C,333C,361C ''ll'':39C ''look'':150C ''lot'':293C ''mani'':4C,27C,351C ''matrix'':115C,383C ''method'':44C,58C,135C,201C,379C ''mind'':81C ''model'':109C,128C,264C ''most-similar'':194C ''much'':205C ''music'':90C ''neo4j'':272C ''node'':322C ''nofollow'':429C ''one'':49C ''oper'':384C ''option'':261C ''order'':279C ''other'':24C ''p'':1C,55C,132C,213C,258C,337C,411C ''paper'':422C ''parallel'':402C ''perform'':408C ''point'':21C,40C ''portion'':366C ''predict'':203C ''probabl'':350C ''problem'':10C,15C,19C ''process'':403C ''queri'':277C ''rate'':159C,238C,340C ''rather'':143C ''recommend'':14C,31C,73C,89C,387C ''regard'':302C ''rel'':428C ''result'':311C ''return'':309C ''run'':406C ''scale'':396C ''score'':239C ''second'':134C ''set'':35C,153C,188C,192C,256C,356C,391C ''similar'':170C,196C,282C ''sky'':297C ''small'':365C ''song'':104C,161C,178C,184C,211C,320C,335C,352C ''spars'':114C,382C ''specif'':175C ''star'':243C ''start'':314C ''strategi'':149C ''system'':244C ''target'':176C ''tast'':77C ''tend'':232C ''theori'':288C ''think'':315C ''third'':260C ''togeth'':147C ''travers'':276C,307C ''tri'':51C,87C,372C ''use'':199C,381C ''user'':62C,72C,82C,97C,146C,157C,163C,207C,222C,318C,331C,359C,375C ''user-bas'':61C,374C ''variat'':250C ''various'':200C ''want'':126C ''way'':6C,28C ''work'':234C ''write'':274C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6287, 2, '2015-06-30 20:09:09.82', 1, NULL, NULL, '&lt;p&gt;Error is due to missing &lt;code&gt;'')''&lt;/code&gt; in the second &lt;code&gt;if&lt;/code&gt; statement in your code.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;if(is.na(training_main[j,i]) 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;change to &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;if(is.na(training_main[j,i]))
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':8C,14C,28C,42C ''/p'':19C,33C ''/pre'':29C,43C ''chang'':31C ''code'':7C,12C,18C,21C,35C ''due'':4C ''error'':2C ''is.na'':23C,37C ''j'':26C,40C ''main'':25C,39C ''miss'':6C ''p'':1C,30C ''pre'':20C,34C ''second'':11C ''statement'':15C ''train'':24C,38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6289, 2, '2015-06-30 21:52:02.733', 0, NULL, NULL, '&lt;p&gt;Another workaround is to get the listing by &lt;code&gt;POST&lt;/code&gt; requests using curl in bash.&lt;br&gt;&lt;/p&gt;

&lt;p&gt;You can get the curl post statement from Firebug ( Firefox F12 ) under Network , filter for XHR requests and copy the last statement which requests &lt;code&gt;SearchPaper.aspx?str=project+management&lt;/code&gt; (right-click -&gt; copy curl-adress).&lt;/p&gt;

&lt;p&gt;In this post request statement you have to increase the parameter &lt;code&gt;ctl00$ContentPlaceHolder1$txtPageNo&lt;/code&gt;  to a desired pagination number (1-6 in this case).&lt;/p&gt;

&lt;p&gt;Then parse the output to a static xml parsing tool to get your data.&lt;/p&gt;
', NULL, NULL, '''-6'':82C ''/code'':12C,50C,75C ''/p'':19C,58C,86C,102C ''1'':81C ''adress'':57C ''anoth'':2C ''bash'':17C ''br'':18C ''case'':85C ''click'':53C ''code'':10C,45C,71C ''contentplaceholder1'':73C ''copi'':39C,54C ''ctl00'':72C ''curl'':15C,25C,56C ''curl-adress'':55C ''data'':101C ''desir'':78C ''f12'':31C ''filter'':34C ''firebug'':29C ''firefox'':30C ''get'':6C,23C,99C ''increas'':68C ''last'':41C ''list'':8C ''manag'':49C ''network'':33C ''number'':80C ''output'':91C ''p'':1C,20C,59C,87C ''pagin'':79C ''paramet'':70C ''pars'':89C,96C ''post'':11C,26C,62C ''project'':48C ''request'':13C,37C,44C,63C ''right'':52C ''right-click'':51C ''searchpaper.aspx'':46C ''statement'':27C,42C,64C ''static'':94C ''str'':47C ''tool'':97C ''txtpageno'':74C ''use'':14C ''workaround'':3C ''xhr'':36C ''xml'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6290, 1, '2015-06-30 22:01:36.153', 2, 79, 'Denormalise data in Neural Networks', '&lt;p&gt;I wrote a Neural Networks prediction model in Python.&lt;/p&gt;

&lt;p&gt;My data has a few inputs and two outputs. In order to make it work, I have to normalise every column on data for good prediction results.&lt;/p&gt;

&lt;p&gt;However, I had an issue. I run several times the prediction model with the same inputs to get the average and standard deviation of it. But obviously, those are also normalised to 0-1. On the test data I knew the min and max, so I could denormalised them. On the prediction values, I cannot the min and max real value.&lt;/p&gt;

&lt;p&gt;How do you solve this kind of problem and if you cannot, are there any other decent prediction techniques without the need of normalisation?&lt;/p&gt;
', 'python  neuralnetwork  predictive-modeling', NULL, '''-1'':85C ''/p'':21C,50C,113C,139C ''0'':84C ''also'':81C ''averag'':71C ''cannot'':106C,126C ''column'':43C ''could'':98C ''data'':2A,24C,45C,89C ''decent'':131C ''denormalis'':1A,99C ''deviat'':74C ''everi'':42C ''get'':69C ''good'':47C ''howev'':52C ''input'':28C,67C ''issu'':56C ''kind'':120C ''knew'':91C ''make'':35C ''max'':95C,110C ''min'':93C,108C ''model'':10B,18C,63C ''need'':136C ''network'':5A,16C ''neural'':4A,15C ''neuralnetwork'':7B ''normalis'':41C,82C,138C ''obvious'':78C ''order'':33C ''output'':31C ''p'':11C,22C,51C,114C ''predict'':9B,17C,48C,62C,103C,132C ''predictive-model'':8B ''problem'':122C ''python'':6B,20C ''real'':111C ''result'':49C ''run'':58C ''sever'':59C ''solv'':118C ''standard'':73C ''techniqu'':133C ''test'':88C ''time'':60C ''two'':30C ''valu'':104C,112C ''without'':134C ''work'':37C ''wrote'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6298, 2, '2015-07-01 10:56:58.213', 0, NULL, NULL, '&lt;p&gt;Go for NodeXL &lt;a href="https://www.youtube.com/watch?v=GyvwQSTO8Gc" rel="nofollow"&gt;SocialNetImporter&lt;/a&gt; pluggin&lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/p'':15C ''/watch?v=gyvwqsto8gc'':9C ''go'':2C ''href'':6C ''nodexl'':4C ''nofollow'':11C ''p'':1C ''pluggin'':14C ''rel'':10C ''socialnetimport'':12C ''www.youtube.com'':8C ''www.youtube.com/watch?v=gyvwqsto8gc'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6300, 2, '2015-07-01 14:44:13.52', 1, NULL, NULL, '&lt;p&gt;Use tapply,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;with(dt,tapply(id, cid, FUN=sum))
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':14C ''/p'':4C ''/pre'':15C ''cid'':11C ''code'':6C ''dt'':8C ''fun'':12C ''id'':10C ''p'':1C ''pre'':5C ''sum'':13C ''tappli'':3C,9C ''use'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6301, 1, '2015-07-01 15:52:15.467', 2, 95, 'python - Will this data mining approach work? Is it a good idea?', '&lt;p&gt;I need to extract fields like the document number, date, and invoice amount from a bunch of .csv files, which I believe are referred to as "unstructured text." I have some labeled input files and will use the NLTK and Python to design a data extraction algorithm.&lt;/p&gt;

&lt;p&gt;For the first round of classification, I plan to use tf-idf weighting with a classifier to identify the document type - there are multiple files that use the same format.&lt;/p&gt;

&lt;p&gt;At this point, I need I way to extract the field from the document, given that it is X type of document. I thought about using features like the "most common numbers" or "largest number with a comma" to find the invoice amount, for example, but since the invoice amount can any numerical value I believe the sample size would be smaller than the number of possible features? (I have no training here, bear with me.)&lt;/p&gt;

&lt;p&gt;Is there a better way to do the second part? I think the first part should be okay, but I''m not sure that second part will work or if I even really understand the problem. How is my approach in general? I''m new to this kind of thing and this was the best I could come up with.&lt;/p&gt;
', 'python  data', NULL, '''/p'':63C,96C,174C,236C ''algorithm'':62C ''amount'':28C,140C,147C ''approach'':6A,215C ''bear'':171C ''believ'':37C,153C ''best'':230C ''better'':179C ''bunch'':31C ''classif'':70C ''classifi'':81C ''come'':233C ''comma'':135C ''common'':128C ''could'':232C ''csv'':33C ''data'':4A,14B,60C ''date'':25C ''design'':58C ''document'':23C,85C,111C,119C ''even'':207C ''exampl'':142C ''extract'':19C,61C,106C ''featur'':124C,165C ''field'':20C,108C ''file'':34C,49C,90C ''find'':137C ''first'':67C,189C ''format'':95C ''general'':217C ''given'':112C ''good'':11A ''idea'':12A ''identifi'':83C ''idf'':77C ''input'':48C ''invoic'':27C,139C,146C ''kind'':223C ''label'':47C ''largest'':131C ''like'':21C,125C ''m'':196C,219C ''mine'':5A ''multipl'':89C ''need'':17C,102C ''new'':220C ''nltk'':54C ''number'':24C,129C,132C,162C ''numer'':150C ''okay'':193C ''p'':15C,64C,97C,175C ''part'':185C,190C,201C ''plan'':72C ''point'':100C ''possibl'':164C ''problem'':211C ''python'':1A,13B,56C ''realli'':208C ''refer'':39C ''round'':68C ''sampl'':155C ''second'':184C,200C ''sinc'':144C ''size'':156C ''smaller'':159C ''sure'':198C ''text'':43C ''tf'':76C ''tf-idf'':75C ''thing'':225C ''think'':187C ''thought'':121C ''train'':169C ''type'':86C,117C ''understand'':209C ''unstructur'':42C ''use'':52C,74C,92C,123C ''valu'':151C ''way'':104C,180C ''weight'':78C ''work'':7A,203C ''would'':157C ''x'':116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6303, 2, '2015-07-01 20:15:53.563', 1, NULL, NULL, '&lt;p&gt;You can use tapply or aggregate along with a custom function.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;aggregate(Id~cid, data=dt, FUN=function(x) c(mn=mean(x),ln=length(x)))&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;You may like to see my blog where I discuss few other scenario.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://r4sqlminded.blogspot.com/2015/06/selecting-data-from-data-frame.html" rel="nofollow"&gt;http://r4sqlminded.blogspot.com/2015/06/selecting-data-from-data-frame.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/2015/06/selecting-data-from-data-frame.html'':53C ''/2015/06/selecting-data-from-data-frame.html&lt;/a&gt;&lt;/p&gt;'':58C ''/blockquote'':32C ''/p'':13C,31C,47C ''aggreg'':7C,16C ''along'':8C ''blockquot'':14C ''blog'':40C ''c'':24C ''custom'':11C ''data'':19C ''discuss'':43C ''dt'':20C ''fun'':21C ''function'':12C,22C ''href'':50C ''id'':17C ''length'':29C ''like'':36C ''ln'':28C ''may'':35C ''mean'':26C ''mn'':25C ''nofollow'':55C ''p'':1C,15C,33C,48C ''r4sqlminded.blogspot.com'':52C,57C ''r4sqlminded.blogspot.com/2015/06/selecting-data-from-data-frame.html'':51C ''r4sqlminded.blogspot.com/2015/06/selecting-data-from-data-frame.html&lt;/a&gt;&lt;/p&gt;'':56C ''rel'':54C ''scenario'':46C ''see'':38C ''tappli'':5C ''use'':4C ''x'':23C,27C,30C ''~cid'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6304, 1, '2015-07-01 20:18:20.783', 0, 55, 'Cross validation for C5.0 algorithm', '&lt;ol&gt;
&lt;li&gt;I want to try K-fold cross validation in R for C5.0 algorithm,&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The following is the code i use. Can someone suggest me how can i include k-fold as well?&lt;/p&gt;

&lt;p&gt;Classifi_C5.0 &amp;lt;- C5.0(TARGET ~., , data = training_data_SMOTED, trails = 500,
                      control = C5.0Control(minCases = mincases_count,
                                            noGlobalPruning = FALSE))&lt;/p&gt;

&lt;ol start="2"&gt;
&lt;li&gt;Is it required to do k-fold cross validation for Random forest? &lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  data-mining  classification  random-forest  decision-trees', NULL, '''/li'':35C,96C ''/ol'':36C,97C ''/p'':58C,78C ''2'':81C ''500'':70C ''algorithm'':5A,34C ''c5.0'':4A,33C,61C,63C ''c5.0control'':72C ''classif'':12B ''classifi'':60C ''code'':42C ''control'':71C ''count'':75C ''cross'':1A,28C,91C ''data'':10B,65C,67C ''data-min'':9B ''decis'':17B ''decision-tre'':16B ''fals'':77C ''fold'':27C,55C,90C ''follow'':39C ''forest'':15B,95C ''includ'':52C ''k'':26C,54C,89C ''k-fold'':25C,53C,88C ''learn'':8B ''li'':20C,82C ''lt'':62C ''machin'':7B ''machine-learn'':6B ''mincas'':73C,74C ''mine'':11B ''noglobalprun'':76C ''ol'':19C,79C ''p'':37C,59C ''r'':31C ''random'':14B,94C ''random-forest'':13B ''requir'':85C ''smote'':68C ''someon'':46C ''start'':80C ''suggest'':47C ''target'':64C ''trail'':69C ''train'':66C ''tree'':18B ''tri'':24C ''use'':44C ''valid'':2A,29C,92C ''want'':22C ''well'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6305, 2, '2015-07-01 20:34:48.35', 1, NULL, NULL, '&lt;p&gt;I am not sure if using a classifier is the best way to approach this problem.  If it is something which can be easily extracted using regex, then that is the best way to do it. If however, you want to use classifiers, here are two questions you need to ask yourself.&lt;/p&gt;

&lt;p&gt;One, what does the unlabelled data look like and can you design good features from it? Depending on the kind of feature vector you design, the complexity of the classification task may range from very easy, to impossible. (A perceptron cannot solve XOR usually, except when you provide it with specific linear combinations of the input variable).&lt;/p&gt;

&lt;p&gt;Two, what does the labelled data look like? Is it representative of the entire dataset or does it only contain very specific types of format? If it is the former, then your classifier will not work well on files which are not represented in the labelled data. &lt;/p&gt;

&lt;p&gt;If you just want to test run a classifier first, you can solve the problem of having more features than training samples by using Regularization. Regularization forces the training algorithm of the classifier to accept the simplest possible solution (think occam''s razor). &lt;/p&gt;

&lt;p&gt;Almost all Machine Learning related packages in Python will have regularization options you can use, so enjoy. &lt;/p&gt;
', NULL, NULL, '''/p'':54C,113C,162C,207C,226C ''accept'':198C ''algorithm'':193C ''almost'':209C ''approach'':15C ''ask'':52C ''best'':12C,33C ''cannot'':96C ''classif'':85C ''classifi'':9C,44C,147C,172C,196C ''combin'':108C ''complex'':82C ''contain'':134C ''data'':61C,120C,161C ''dataset'':129C ''depend'':72C ''design'':67C,80C ''easi'':91C ''easili'':25C ''enjoy'':225C ''entir'':128C ''except'':100C ''extract'':26C ''featur'':69C,77C,182C ''file'':153C ''first'':173C ''forc'':190C ''format'':139C ''former'':144C ''good'':68C ''howev'':39C ''imposs'':93C ''input'':111C ''kind'':75C ''label'':119C,160C ''learn'':212C ''like'':63C,122C ''linear'':107C ''look'':62C,121C ''machin'':211C ''may'':87C ''need'':50C ''occam'':204C ''one'':56C ''option'':220C ''p'':1C,55C,114C,163C,208C ''packag'':214C ''perceptron'':95C ''possibl'':201C ''problem'':17C,178C ''provid'':103C ''python'':216C ''question'':48C ''rang'':88C ''razor'':206C ''regex'':28C ''regular'':188C,189C,219C ''relat'':213C ''repres'':125C,157C ''run'':170C ''sampl'':185C ''simplest'':200C ''solut'':202C ''solv'':97C,176C ''someth'':21C ''specif'':106C,136C ''sure'':5C ''task'':86C ''test'':169C ''think'':203C ''train'':184C,192C ''two'':47C,115C ''type'':137C ''unlabel'':60C ''use'':7C,27C,43C,187C,223C ''usual'':99C ''variabl'':112C ''vector'':78C ''want'':41C,167C ''way'':13C,34C ''well'':151C ''work'':150C ''xor'':98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6306, 2, '2015-07-01 20:50:14.187', 1, NULL, NULL, '&lt;p&gt;It looks like you are looking for time series based Machine Learning (or Sequential Machine Learning as Emre pointed out). &lt;/p&gt;

&lt;p&gt;This means that you have an entire dataset available to you beforehand. You train on this dataset and then use it to predict next day''s weather given the weather of the last n days. Please note that you do not learn incrementally here, i.e. you do not learn from the new data that is rolling in every day. The learning is limited to the dataset that you had beforehand. &lt;/p&gt;

&lt;p&gt;Widely used algorithms for this kind of learning are Conditional Random Fields(CRF), Hidden Markov Models etc. Statistical methods like Autoregressive Models can also be used sometimes. A good starting point would be CRFs. &lt;/p&gt;

&lt;p&gt;If on the other hand you want to learn incrementally, read about online learning.  &lt;/p&gt;
', NULL, NULL, '''/p'':22C,94C,130C,146C ''algorithm'':98C ''also'':119C ''autoregress'':116C ''avail'':32C ''base'':11C ''beforehand'':35C,93C ''condit'':105C ''crf'':108C ''crfs'':129C ''data'':76C ''dataset'':31C,40C,89C ''day'':48C,58C,82C ''emr'':19C ''entir'':30C ''etc'':112C ''everi'':81C ''field'':107C ''given'':51C ''good'':124C ''hand'':136C ''hidden'':109C ''i.e'':68C ''increment'':66C,141C ''kind'':101C ''last'':56C ''learn'':13C,17C,65C,72C,84C,103C,140C,145C ''like'':4C,115C ''limit'':86C ''look'':3C,7C ''machin'':12C,16C ''markov'':110C ''mean'':25C ''method'':114C ''model'':111C,117C ''n'':57C ''new'':75C ''next'':47C ''note'':60C ''onlin'':144C ''p'':1C,23C,95C,131C ''pleas'':59C ''point'':20C,126C ''predict'':46C ''random'':106C ''read'':142C ''roll'':79C ''sequenti'':15C ''seri'':10C ''sometim'':122C ''start'':125C ''statist'':113C ''time'':9C ''train'':37C ''use'':43C,97C,121C ''want'':138C ''weather'':50C,53C ''wide'':96C ''would'':127C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6323, 2, '2015-07-02 16:58:29.833', 1, NULL, NULL, '&lt;p&gt;I would say cross validation is unnecessary here since the multiple partitioning of the data and variables is already implicit in Random Forests.   But it''s still a good practice to hold out a testing set that is distinct from the training set.  This is mostly because you may introduce changes in your random forest to improve the performance on the test sets overall, thereby introducing the bias that the random forests are trying to overcome.   So if you withheld a portion of your data and judged the final performance of the RF on that withheld set only in the predict step, then it''s fine.&lt;/p&gt;
', NULL, NULL, '''/p'':108C ''alreadi'':20C ''bias'':69C ''chang'':52C ''cross'':5C ''data'':16C,86C ''distinct'':40C ''final'':90C ''fine'':107C ''forest'':24C,56C,73C ''good'':30C ''hold'':33C ''implicit'':21C ''improv'':58C ''introduc'':51C,67C ''judg'':88C ''may'':50C ''most'':47C ''multipl'':12C ''overal'':65C ''overcom'':77C ''p'':1C ''partit'':13C ''perform'':60C,91C ''portion'':83C ''practic'':31C ''predict'':102C ''random'':23C,55C,72C ''rf'':94C ''say'':4C ''set'':37C,44C,64C,98C ''sinc'':10C ''step'':103C ''still'':28C ''test'':36C,63C ''therebi'':66C ''train'':43C ''tri'':75C ''unnecessari'':8C ''valid'':6C ''variabl'':18C ''withheld'':81C,97C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6340, 1, '2015-07-04 12:18:51.56', 0, 211, 'What are the performance measures in the neural networks field?', '&lt;p&gt;I constructed a neural networks in R using &lt;code&gt;neuralnet&lt;/code&gt; package.&lt;/p&gt;

&lt;p&gt;I want to test that using cross-validation, that is a technique based on using 4/5 of the dataset to train the network and the fifth one as the test set.&lt;/p&gt;

&lt;p&gt;I wonder about what measures I should use to measure the neural networks performance in terms of predictability.&lt;/p&gt;

&lt;p&gt;Could you suggest what measures are commonly used in the field and explain me why?&lt;/p&gt;

&lt;p&gt;Any hint and ideas about that will be appreciated.&lt;/p&gt;
', 'machine-learning  r  neuralnetwork  predictive-modeling  performance', NULL, '''/code'':31C ''/p'':33C,67C,87C,104C,115C ''4/5'':51C ''appreci'':114C ''base'':48C ''code'':29C ''common'':95C ''construct'':22C ''could'':89C ''cross'':42C ''cross-valid'':41C ''dataset'':54C ''explain'':101C ''field'':10A,99C ''fifth'':61C ''hint'':107C ''idea'':109C ''learn'':13B ''machin'':12B ''machine-learn'':11B ''measur'':5A,73C,78C,93C ''model'':18B ''network'':9A,25C,58C,81C ''neural'':8A,24C,80C ''neuralnet'':30C ''neuralnetwork'':15B ''one'':62C ''p'':20C,34C,68C,88C,105C ''packag'':32C ''perform'':4A,19B,82C ''predict'':17B,86C ''predictive-model'':16B ''r'':14B,27C ''set'':66C ''suggest'':91C ''techniqu'':47C ''term'':84C ''test'':38C,65C ''train'':56C ''use'':28C,40C,50C,76C,96C ''valid'':43C ''want'':36C ''wonder'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6308, 1, '2015-07-01 21:12:52.027', 0, 1415, 'How to convert a SQLContext Dataframe to RDD of vectors in Python?', '&lt;p&gt;I have a SQLContext data frame derived from pandas data frame consisting of several numerical columns. I want to perform &lt;strong&gt;multivariate statistical analysis&lt;/strong&gt; using the &lt;strong&gt;pyspark.mllib.stats&lt;/strong&gt; package. The statistics function expects a RDD of vectors. I could not convert this data frame into RDD of vectors. Is there a way to convert the data frame? &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Code:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; rdd = sqlCtx.createDataFrame(df_new)
 summary = Statistics.colStats(rdd)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am getting df_new from &lt;/p&gt;

&lt;pre&gt;&lt;code&gt; df_new = df.applymap(lambda s: dic.get(s) if s in dic else s) #df is a pandas dataframe
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am getting a PY4JJava error at the summary line. The issue is with the format of rdd. &lt;/p&gt;
', 'statistics  apache-spark', NULL, '''/code'':92C,122C ''/p'':77C,82C,101C,143C ''/pre'':93C,123C ''/strong'':42C,47C,81C ''analysi'':41C ''apach'':15B ''apache-spark'':14B ''code'':80C,84C,103C ''column'':33C ''consist'':29C ''convert'':3A,60C,73C ''could'':58C ''data'':22C,27C,62C,75C ''datafram'':6A,121C ''deriv'':24C ''df'':87C,98C,104C,117C ''df.applymap'':106C ''dic'':114C ''dic.get'':109C ''els'':115C ''error'':130C ''expect'':52C ''format'':140C ''frame'':23C,28C,63C,76C ''function'':51C ''get'':97C,127C ''issu'':136C ''lambda'':107C ''line'':134C ''multivari'':39C ''new'':88C,99C,105C ''numer'':32C ''p'':17C,78C,94C,124C ''packag'':48C ''panda'':26C,120C ''perform'':37C ''pre'':83C,102C ''py4jjava'':129C ''pyspark.mllib.stats'':46C ''python'':12A ''rdd'':8A,54C,65C,85C,91C,142C ''sever'':31C ''spark'':16B ''sqlcontext'':5A,21C ''sqlctx.createdataframe'':86C ''statist'':13B,40C,50C ''statistics.colstats'':90C ''strong'':38C,45C,79C ''summari'':89C,133C ''use'':43C ''vector'':10A,56C,67C ''want'':35C ''way'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6309, 2, '2015-07-01 21:38:16.94', 2, NULL, NULL, '&lt;p&gt;I think one solution for this might be using Matrix factorization, to perform some kind of collaborative Filtering. This way you will have not have to deal with the two concern points that you have, explicitly.&lt;/p&gt;

&lt;p&gt;To do this, create a [User X Action] Matrix W, where rows are users and columns are actions. W(i,j) i.e. the Matrix entry on i row and j column would be the number of times the user i has performed action j. The matrix entries where no data is given are treated as missing data values by most collaborative filtering algorithms. &lt;/p&gt;

&lt;p&gt;When you factorize this Matrix, you factorize it into two parts W= A*B. The A matrix will have rows are users and columns as latent dimensions. From this A, you get a feature vector for each User. You can then run clustering on these feature vectors to cluster the users. For more intuition about the factorization approach, look up "Matrix Factorization for collaborative Filtering" on google. Mahout has matrix factorization implementations that you can use.&lt;/p&gt;

&lt;p&gt;Using the action categories is another matter altogether. It depends on what a category means. Does it mean that every action inside a category is very similar to each other or that they just have one or two aspect of similarity. A very easy approach would be to factorize a [User X (Action + Category)] Matrix. This can be solved using the same implementation mentioned earlier. You can also have more fancy schemes, like fixing part of the Matrix B to be very similar to the [Action X Category] matrix. But these schemes will require you to write optimization code yourself.&lt;/p&gt;
', NULL, NULL, '''/p'':38C,103C,181C,283C ''action'':47C,57C,82C,185C,203C,235C,268C ''algorithm'':102C ''also'':250C ''altogeth'':190C ''anoth'':188C ''approach'':162C,227C ''aspect'':221C ''b'':118C,261C ''categori'':186C,196C,206C,236C,270C ''cluster'':147C,153C ''code'':281C ''collabor'':18C,100C,168C ''column'':55C,70C,128C ''concern'':32C ''creat'':43C ''data'':89C,96C ''deal'':28C ''depend'':192C ''dimens'':131C ''earlier'':247C ''easi'':226C ''entri'':64C,86C ''everi'':202C ''explicit'':37C ''factor'':12C,107C,111C,161C,166C,175C,231C ''fanci'':253C ''featur'':138C,150C ''filter'':19C,101C,169C ''fix'':256C ''get'':136C ''given'':91C ''googl'':171C ''i.e'':61C ''implement'':176C,245C ''insid'':204C ''intuit'':158C ''j'':60C,69C,83C ''kind'':16C ''latent'':130C ''like'':255C ''look'':163C ''mahout'':172C ''matrix'':11C,48C,63C,85C,109C,121C,165C,174C,237C,260C,271C ''matter'':189C ''mean'':197C,200C ''mention'':246C ''might'':8C ''miss'':95C ''number'':74C ''one'':4C,218C ''optim'':280C ''p'':1C,39C,104C,182C ''part'':115C,257C ''perform'':14C,81C ''point'':33C ''requir'':276C ''row'':51C,67C,124C ''run'':146C ''scheme'':254C,274C ''similar'':209C,223C,265C ''solut'':5C ''solv'':241C ''think'':3C ''time'':76C ''treat'':93C ''two'':31C,114C,220C ''use'':10C,180C,183C,242C ''user'':45C,53C,78C,126C,142C,155C,233C ''valu'':97C ''vector'':139C,151C ''w'':49C,58C,116C ''way'':21C ''would'':71C,228C ''write'':279C ''x'':46C,234C,269C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6310, 2, '2015-07-02 01:07:56.313', 0, NULL, NULL, '&lt;p&gt;I cannot comment directly on how to accelerate the DE algorithm you are using, because I have not personally used it. I would however, like to suggest breaking up your problem into two different parts. &lt;/p&gt;

&lt;p&gt;The first part is to calculate the weights, by trying to predict the price on the next data-point given prices of "n" previous data-points. The second part of the problem is given the weights that you have determined, get the threshold which will allow you to get the best output ( i.e. sum of earnings). &lt;/p&gt;

&lt;p&gt;These two problems are somewhat decoupled (in my opinion), i.e. solving each optimally or suboptimally should provide you with an good overall solution. &lt;/p&gt;

&lt;p&gt;The first part is just standard least squares, you can find many references to accelerate it or parallelize different versions of it. In the second part, you can just run a grid search, to find upper-threshold (to sell) and lower-threshold(to buy). Each run in the grid search with a different set of parameters should have O(n) complexity. Grid search itself, can be parallelized very easily.&lt;/p&gt;
', NULL, NULL, '''/p'':37C,97C,122C,195C ''acceler'':9C,138C ''algorithm'':12C ''allow'':86C ''best'':91C ''break'':29C ''buy'':169C ''calcul'':44C ''cannot'':3C ''comment'':4C ''complex'':186C ''data'':57C,65C ''data-point'':56C,64C ''de'':11C ''decoupl'':104C ''determin'':80C ''differ'':35C,142C,178C ''direct'':5C ''earn'':96C ''easili'':194C ''find'':134C,158C ''first'':40C,125C ''get'':81C,89C ''given'':59C,74C ''good'':119C ''grid'':155C,174C,187C ''howev'':25C ''i.e'':93C,108C ''least'':130C ''like'':26C ''lower'':166C ''lower-threshold'':165C ''mani'':135C ''n'':62C,185C ''next'':55C ''o'':184C ''opinion'':107C ''optim'':111C ''output'':92C ''overal'':120C ''p'':1C,38C,98C,123C ''parallel'':141C,192C ''paramet'':181C ''part'':36C,41C,69C,126C,149C ''person'':20C ''point'':58C,66C ''predict'':50C ''previous'':63C ''price'':52C,60C ''problem'':32C,72C,101C ''provid'':115C ''refer'':136C ''run'':153C,171C ''search'':156C,175C,188C ''second'':68C,148C ''sell'':163C ''set'':179C ''solut'':121C ''solv'':109C ''somewhat'':103C ''squar'':131C ''standard'':129C ''suboptim'':113C ''suggest'':28C ''sum'':94C ''threshold'':83C,161C,167C ''tri'':48C ''two'':34C,100C ''upper'':160C ''upper-threshold'':159C ''use'':15C,21C ''version'':143C ''weight'':46C,76C ''would'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6322, 1, '2015-07-02 16:45:35.833', 1, 70, 'Clustering uncertain data with independent uncertainty per dimension', '&lt;p&gt;I have $n$ objects located in a $d$ dimensional space, however I do not know their exact coordinates. For each object and each dimension, I have a set of noisy measurements of the coordinate.&lt;/p&gt;

&lt;p&gt;I would like to cluster this data in the &lt;/p&gt;

&lt;p&gt;For example, with $n = 3$ and $d = 2$, I could have access to the following data:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;object $a$, dim 1: 0.8 0.7 0.6&lt;/li&gt;
&lt;li&gt;object $a$, dim 2: 1.0 1.0 1.0 0.9&lt;/li&gt;
&lt;li&gt;object $b$, dim 1: 0.4 0.3&lt;/li&gt;
&lt;li&gt;object $b$, dim 2: 0.2 0.1&lt;/li&gt;
&lt;li&gt;object $c$, dim 1: 0.9 0.6&lt;/li&gt;
&lt;li&gt;object $c$, dim 2: $\emptyset$&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In my current approach, I take the for each (object, dimension) pair that has data the average value, and I imput the average value of the dimension for the missing data.&lt;/p&gt;

&lt;p&gt;So I would get&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;$a$ [0.7 0.975]&lt;/li&gt;
&lt;li&gt;$b$ [0.35 0.15]&lt;/li&gt;
&lt;li&gt;$c$ [0.75 0.6] (where $0.6 = \frac{1+1+1+0.9+0.2+0.1}{6}$)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Then I use the scikit-learn python library to run the mean-shift algorithm and obtain clusters.&lt;/p&gt;

&lt;p&gt;I am not completely satisfied with this methods for two main reasons (maybe they''re the same):&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;an (object, dimension) pair with a single observation is treated as as reliable as one with numerous observations&lt;/li&gt;
&lt;li&gt;there is a discontinuity between how a pair with zero observations and a pair with some observations are treated. In the second case, the value of the other objects does not influence the attribute at all.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;My questions are: What would be a more principled approach to this problem? If I need to use another algorithm, are there open source decent quality libraries available that would implement it?&lt;/p&gt;

&lt;p&gt;I currently use $d = 256$ and $n = 10$ for my testing, but I aim to use $d = 16000$ and $n = 1000$ (but maybe a smaller $n$ if it''s unrealistic) in my target application.&lt;/p&gt;
', 'clustering  data-cleaning', NULL, '''+0.1'':192C ''+0.2'':191C ''+0.9'':190C ''+1'':188C,189C ''/li'':87C,97C,105C,113C,121C,128C,174C,179C,194C,256C,294C ''/ol'':295C ''/p'':48C,59C,77C,162C,168C,216C,235C,329C,365C ''/ul'':129C,195C ''0.1'':112C ''0.15'':178C ''0.2'':111C ''0.3'':104C ''0.35'':177C ''0.4'':103C ''0.6'':86C,120C,183C,185C ''0.7'':85C,172C ''0.75'':182C ''0.8'':84C ''0.9'':96C,119C ''0.975'':173C ''1'':83C,102C,118C,187C ''1.0'':93C,94C,95C ''10'':338C ''1000'':351C ''16000'':348C ''2'':68C,92C,110C,126C ''256'':335C ''3'':65C ''6'':193C ''access'':72C ''aim'':344C ''algorithm'':212C,316C ''anoth'':315C ''applic'':364C ''approach'':134C,306C ''attribut'':291C ''avail'':324C ''averag'':147C,153C ''b'':100C,108C,176C ''c'':116C,124C,181C ''case'':280C ''clean'':12B ''cluster'':1A,9B,54C,215C ''complet'':221C ''coordin'':31C,47C ''could'':70C ''current'':133C,332C ''d'':21C,67C,334C,347C ''data'':3A,11B,56C,76C,145C,161C ''data-clean'':10B ''decent'':321C ''dim'':82C,91C,101C,109C,117C,125C ''dimens'':8A,37C,141C,157C,240C ''dimension'':22C ''discontinu'':261C ''emptyset'':127C ''exact'':30C ''exampl'':62C ''follow'':75C ''frac'':186C ''get'':167C ''howev'':24C ''implement'':327C ''imput'':151C ''independ'':5A ''influenc'':289C ''know'':28C ''learn'':203C ''li'':79C,88C,98C,106C,114C,122C,170C,175C,180C,237C,257C ''librari'':205C,323C ''like'':52C ''locat'':18C ''main'':228C ''mayb'':230C,353C ''mean'':210C ''mean-shift'':209C ''measur'':44C ''method'':225C ''miss'':160C ''n'':16C,64C,337C,350C,356C ''need'':312C ''noisi'':43C ''numer'':254C ''object'':17C,34C,80C,89C,99C,107C,115C,123C,140C,239C,286C ''observ'':245C,255C,268C,274C ''obtain'':214C ''ol'':236C ''one'':252C ''open'':319C ''p'':13C,49C,60C,130C,163C,196C,217C,296C,330C ''pair'':142C,241C,265C,271C ''per'':7A ''principl'':305C ''problem'':309C ''python'':204C ''qualiti'':322C ''question'':298C ''re'':232C ''reason'':229C ''reliabl'':250C ''run'':207C ''satisfi'':222C ''scikit'':202C ''scikit-learn'':201C ''second'':279C ''set'':41C ''shift'':211C ''singl'':244C ''smaller'':355C ''sourc'':320C ''space'':23C ''take'':136C ''target'':363C ''test'':341C ''treat'':247C,276C ''two'':227C ''ul'':78C,169C ''uncertain'':2A ''uncertainti'':6A ''unrealist'':360C ''use'':199C,314C,333C,346C ''valu'':148C,154C,282C ''would'':51C,166C,301C,326C ''zero'':267C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6313, 2, '2015-07-02 09:55:32.537', 4, NULL, NULL, '&lt;p&gt;So, the following answer is just based on different opinions of collegues and professors from the field. I want to try to summarize it briefly:&lt;/p&gt;

&lt;p&gt;Sequential and online learning is mostly associated with Bayesian updating. The sequential learning is used widely for an order in time of the data, meaning that $x_1$ is coming always first, then $x_2$, then $x_3$ and so on. The Dataset each has a certain order in that sense. 
In contrast to that incremental may be a whole block of data at time x and another block of data at time y. While the block internally may be randomly ordered. &lt;/p&gt;

&lt;p&gt;Concerning online learning, the people mostly referred to a data stream, hence a online learning is always incremental learning but incremental learning does not have to be online. &lt;/p&gt;

&lt;p&gt;These are quite fuzzy definitions, and in my opinion there is not clear definition though. I still hope that helps.&lt;/p&gt;
', NULL, NULL, '''/p'':27C,111C,141C,163C ''1'':56C ''2'':63C ''3'':66C ''alway'':59C,129C ''anoth'':96C ''answer'':5C ''associ'':35C ''base'':8C ''bayesian'':37C ''block'':89C,97C,105C ''briefli'':26C ''certain'':75C ''clear'':155C ''collegu'':13C ''come'':58C ''concern'':113C ''contrast'':81C ''data'':52C,91C,99C,122C ''dataset'':71C ''definit'':147C,156C ''differ'':10C ''field'':18C ''first'':60C ''follow'':4C ''fuzzi'':146C ''help'':162C ''henc'':124C ''hope'':160C ''increment'':84C,130C,133C ''intern'':106C ''learn'':32C,41C,115C,127C,131C,134C ''may'':85C,107C ''mean'':53C ''most'':34C,118C ''onlin'':31C,114C,126C,140C ''opinion'':11C,151C ''order'':47C,76C,110C ''p'':1C,28C,112C,142C ''peopl'':117C ''professor'':15C ''quit'':145C ''random'':109C ''refer'':119C ''sens'':79C ''sequenti'':29C,40C ''still'':159C ''stream'':123C ''summar'':24C ''though'':157C ''time'':49C,93C,101C ''tri'':22C ''updat'':38C ''use'':43C ''want'':20C ''whole'':88C ''wide'':44C ''x'':55C,62C,65C,94C ''y'':102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6314, 1, '2015-07-02 11:12:44.083', 0, 36, 'Improving data point accuracy using multiple observation at single point', '&lt;p&gt;Say at each point Pi, I am taking m GPS observations. Even though all readings are supposed to be the same, it is varying due to errors. I need to verify the integrity of these values at each point and improve the accuracy. Assume O11, O12 and O13 are observations at point P1 using devices D1, D2 and D3 resp. As an example &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/aF8YO.png" alt="Data Points"&gt;&lt;/p&gt;

&lt;p&gt;My aim is to improve the accuracy of the observation or IOW how to arrive at the conclusion which observation is the nearest correct?
What is the best algorithm or method to achieve the same.&lt;/p&gt;
', 'statistics  accuracy  data', NULL, '''/af8yo.png'':84C ''/p'':78C,88C,125C ''accuraci'':4A,12B,57C,96C ''achiev'':122C ''aim'':91C ''algorithm'':118C ''alt'':85C ''arriv'':104C ''assum'':58C ''best'':117C ''conclus'':107C ''correct'':113C ''d1'':70C ''d2'':71C ''d3'':73C ''data'':2A,13B,86C ''devic'':69C ''due'':39C ''error'':41C ''even'':26C ''exampl'':77C ''gps'':24C ''i.stack.imgur.com'':83C ''i.stack.imgur.com/af8yo.png'':82C ''img'':80C ''improv'':1A,55C,94C ''integr'':47C ''iow'':101C ''m'':23C ''method'':120C ''multipl'':6A ''nearest'':112C ''need'':43C ''o11'':59C ''o12'':60C ''o13'':62C ''observ'':7A,25C,64C,99C,109C ''p'':14C,79C,89C ''p1'':67C ''pi'':19C ''point'':3A,10A,18C,53C,66C,87C ''read'':29C ''resp'':74C ''say'':15C ''singl'':9A ''src'':81C ''statist'':11B ''suppos'':31C ''take'':22C ''though'':27C ''use'':5A,68C ''valu'':50C ''vari'':38C ''verifi'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6315, 2, '2015-07-02 13:14:48.217', 1, NULL, NULL, '&lt;p&gt;In hadoop etc folder,hdfs-site.xml was having number of replication units as 1.That is why,all the files were getting saved on one single node.I changed it and the problem resolved.&lt;/p&gt;
', NULL, NULL, '''/p'':33C ''1.that'':14C ''chang'':27C ''etc'':4C ''file'':19C ''folder'':5C ''get'':21C ''hadoop'':3C ''hdfs-site.xml'':6C ''node.i'':26C ''number'':9C ''one'':24C ''p'':1C ''problem'':31C ''replic'':11C ''resolv'':32C ''save'':22C ''singl'':25C ''unit'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6316, 1, '2015-07-02 13:16:43.333', 1, 23, 'Weka class attribute suggestion', '&lt;p&gt;We are trying to run J48 on a classified data set. Our class attribute has two possible values ( 0,1) when running J48 the tree terminates at the very first node and doesnt process any further.&lt;/p&gt;

&lt;p&gt;Instead of considering (0- false) as the starting point of J48. How can we consider running J48 by selecting (1-true) as the starting point of the tree?&lt;/p&gt;

&lt;p&gt;Any suggestion will be greatly appreciated.&lt;/p&gt;
', 'decision-trees', NULL, '''/p'':45C,75C,83C ''0'':27C,50C ''1'':28C,66C ''appreci'':82C ''attribut'':3A,22C ''class'':2A,21C ''classifi'':17C ''consid'':49C,61C ''data'':18C ''decis'':6B ''decision-tre'':5B ''doesnt'':41C ''fals'':51C ''first'':38C ''great'':81C ''instead'':47C ''j48'':14C,31C,57C,63C ''node'':39C ''p'':8C,46C,76C ''point'':55C,71C ''possibl'':25C ''process'':42C ''run'':13C,30C,62C ''select'':65C ''set'':19C ''start'':54C,70C ''suggest'':4A,78C ''termin'':34C ''tree'':7B,33C,74C ''tri'':11C ''true'':67C ''two'':24C ''valu'':26C ''weka'':1A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6318, 1, '2015-07-02 14:36:59.107', 5, 475, 'Data science projects explained step by step?', '&lt;p&gt;I am looking for a website or book where several practical examples are given step by step, explaining how they choose the relevant features, the model selection procedure, etc...&lt;/p&gt;
', 'machine-learning  feature-selection', NULL, '''/p'':44C ''book'':22C ''choos'':35C ''data'':1A ''etc'':43C ''exampl'':26C ''explain'':4A,32C ''featur'':12B,38C ''feature-select'':11B ''given'':28C ''learn'':10B ''look'':17C ''machin'':9B ''machine-learn'':8B ''model'':40C ''p'':14C ''practic'':25C ''procedur'':42C ''project'':3A ''relev'':37C ''scienc'':2A ''select'':13B,41C ''sever'':24C ''step'':5A,7A,29C,31C ''websit'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6321, 2, '2015-07-02 15:46:37.28', 1, NULL, NULL, '&lt;p&gt;I can recommend this collection of Ipython Notebooks which includes Data Science, Statistics and Machine Learning commented notebooks.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks" rel="nofollow"&gt;https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Cheers,
Thomas&lt;/p&gt;
', NULL, NULL, '''/ipython/ipython/wiki/a-gallery-of-interesting-ipython-notebooks'':26C ''/ipython/ipython/wiki/a-gallery-of-interesting-ipython-notebooks&lt;/a&gt;&lt;/p&gt;'':31C ''/p'':20C,35C ''cheer'':33C ''collect'':6C ''comment'':18C ''data'':12C ''github.com'':25C,30C ''github.com/ipython/ipython/wiki/a-gallery-of-interesting-ipython-notebooks'':24C ''github.com/ipython/ipython/wiki/a-gallery-of-interesting-ipython-notebooks&lt;/a&gt;&lt;/p&gt;'':29C ''href'':23C ''includ'':11C ''ipython'':8C ''learn'':17C ''machin'':16C ''nofollow'':28C ''notebook'':9C,19C ''p'':1C,21C,32C ''recommend'':4C ''rel'':27C ''scienc'':13C ''statist'':14C ''thoma'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9983, 5, '2016-01-27 07:56:42.26', 0, NULL, NULL, '&lt;p&gt;Naive Bayes classifiers make use of Bayes theorem:&lt;/p&gt;

&lt;p&gt;$$\overbrace{P(c | X)}^{\text{A posteriori}} = \frac{\overbrace{P(X | c)}^{\text{Likelihood}} \cdot \overbrace{P(c)}^{\text{A priori} } }{\underbrace{P(X)}_{\text{evidence}}}$$&lt;/p&gt;

&lt;h2&gt;See also&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Naive_Bayes_classifier" rel="nofollow"&gt;Wikipedia&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':53C ''/h2'':42C ''/li'':54C ''/p'':10C,38C ''/ul'':55C ''/wiki/naive_bayes_classifier'':49C ''also'':41C ''bay'':3C,8C ''c'':14C,23C,29C ''cdot'':26C ''classifi'':4C ''en.wikipedia.org'':48C ''en.wikipedia.org/wiki/naive_bayes_classifier'':47C ''evid'':37C ''frac'':19C ''h2'':39C ''href'':46C ''li'':44C ''likelihood'':25C ''make'':5C ''naiv'':2C ''nofollow'':51C ''overbrac'':12C,20C,27C ''p'':1C,11C,13C,21C,28C,34C ''posteriori'':18C ''priori'':32C ''rel'':50C ''see'':40C ''text'':16C,24C,30C,36C ''theorem'':9C ''ul'':43C ''underbrac'':33C ''use'':6C ''wikipedia'':52C ''x'':15C,22C,35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6325, 1, '2015-07-02 20:41:18.9', 2, 58, 'Joint spatial clustering: How to force clusters to minimally contain datapoints from all datasets', '&lt;p&gt;I want to jointly cluster datapoints coming from different datasets (50 datasets with around 2000 points each). I would like to then extract information associated to the datapoints belonging to the different clusters to compare aspects of the datasets.&lt;/p&gt;

&lt;p&gt;Now my problem is that if I just place the datapoints originating from the different datasets into the same feature space, too few clusters comprise datapoints coming from all datasets simultaneously, so that after filtering I''m left with too few clusters (I tried with kmeans and similar methods).&lt;/p&gt;

&lt;p&gt;My question is: Which is the best way to jointly cluster my points while imposing the condition that a given cluster should contain points from all datasets? The ideal solution would also allow some outliers to not fulfill this condition. The first thing I could think about is to define distances between points and clusters which are updated depending on whether a point belonging to the same dataset is already present in the cluster? Seems too far fetched though.&lt;/p&gt;

&lt;p&gt;I would appreciate any ideas, thanks a lot in advance!&lt;/p&gt;

&lt;p&gt;Edit: Three of my six features are spatial coordinates and ideally I would also want the clusters to be connected within a given dataset.&lt;/p&gt;
', 'clustering  clusters', NULL, '''/p'':57C,108C,189C,201C,227C ''2000'':32C ''50'':28C ''advanc'':200C ''allow'':142C ''alreadi'':179C ''also'':141C,216C ''appreci'':193C ''around'':31C ''aspect'':53C ''associ'':42C ''belong'':46C,173C ''best'':116C ''cluster'':3A,7A,15B,16B,22C,50C,82C,100C,120C,130C,164C,183C,219C ''come'':24C,85C ''compar'':52C ''compris'':83C ''condit'':126C,149C ''connect'':222C ''contain'':10A,132C ''coordin'':211C ''could'':154C ''datapoint'':11A,23C,45C,69C,84C ''dataset'':14A,27C,29C,56C,74C,88C,136C,177C,226C ''defin'':159C ''depend'':168C ''differ'':26C,49C,73C ''distanc'':160C ''edit'':203C ''extract'':40C ''far'':186C ''featur'':78C,208C ''fetch'':187C ''filter'':93C ''first'':151C ''forc'':6A ''fulfil'':147C ''given'':129C,225C ''idea'':195C ''ideal'':138C,213C ''impos'':124C ''inform'':41C ''joint'':1A,21C,119C ''kmean'':104C ''left'':96C ''like'':37C ''lot'':198C ''m'':95C ''method'':107C ''minim'':9A ''origin'':70C ''outlier'':144C ''p'':17C,58C,109C,190C,202C ''place'':67C ''point'':33C,122C,133C,162C,172C ''present'':180C ''problem'':61C ''question'':111C ''seem'':184C ''similar'':106C ''simultan'':89C ''six'':207C ''solut'':139C ''space'':79C ''spatial'':2A,210C ''thank'':196C ''thing'':152C ''think'':155C ''though'':188C ''three'':204C ''tri'':102C ''updat'':167C ''want'':19C,217C ''way'':117C ''whether'':170C ''within'':223C ''would'':36C,140C,192C,215C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6326, 2, '2015-07-02 22:13:46.977', 0, NULL, NULL, '&lt;p&gt;There are two separate issues and you should be able to combine the two following ideas to solve your problem.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Multiple measurements&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;What if you treat all the measurements as separate data points during the clustering and just cluster all the data together. Run your favorite clustering algorithm. Then, if one of your objects has measurements that get clustered in different clusters that will provide you with a measure of the &lt;a href="https://en.wikipedia.org/wiki/Fuzzy_clustering" rel="nofollow"&gt;fuzziness&lt;/a&gt; of the object. Assign the object to the cluster with the most measurements assigned to it.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Incomplete measurements&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This one is trickier. You don''t just have missing data points, but you have missing dimensions. &lt;a href="http://web.eecs.umich.edu/~girasole/pubs/balzanophdthesis.pdf" rel="nofollow"&gt;Here&lt;/a&gt; is a dissertation that starts to address some of these issues. The literature review may point you in the right direction.&lt;/p&gt;

&lt;p&gt;My initial idea would be to cluster the data sequentially in order of subspace rank. That is, first cluster all data points with similar rank &lt;em&gt;d&lt;/em&gt; together, then use those cluster labels as the initial clustering to rank those with rank &lt;em&gt;d-1&lt;/em&gt;, then rank &lt;em&gt;d-2&lt;/em&gt;, etc.&lt;/p&gt;
', NULL, NULL, '''-1'':206C ''-2'':212C ''/a'':87C,137C ''/em'':188C,207C,213C ''/p'':22C,28C,104C,110C,159C,215C ''/strong'':27C,109C ''/wiki/fuzzy_clustering'':83C ''/~girasole/pubs/balzanophdthesis.pdf'':133C ''abl'':11C ''address'':144C ''algorithm'':55C ''assign'':91C,101C ''cluster'':43C,46C,54C,66C,69C,96C,167C,179C,193C,198C ''combin'':13C ''d'':187C,205C,211C ''data'':39C,49C,122C,169C,181C ''differ'':68C ''dimens'':128C ''direct'':158C ''dissert'':140C ''em'':186C,204C,210C ''en.wikipedia.org'':82C ''en.wikipedia.org/wiki/fuzzy_clustering'':81C ''etc'':214C ''favorit'':53C ''first'':178C ''follow'':16C ''fuzzi'':86C ''get'':65C ''href'':80C,130C ''idea'':17C,163C ''incomplet'':107C ''initi'':162C,197C ''issu'':6C,148C ''label'':194C ''literatur'':150C ''may'':152C ''measur'':26C,36C,63C,76C,100C,108C ''miss'':121C,127C ''multipl'':25C ''nofollow'':85C,135C ''object'':61C,90C,93C ''one'':58C,113C ''order'':172C ''p'':1C,23C,29C,105C,111C,160C ''point'':40C,123C,153C,182C ''problem'':21C ''provid'':72C ''rank'':175C,185C,200C,203C,209C ''rel'':84C,134C ''review'':151C ''right'':157C ''run'':51C ''separ'':5C,38C ''sequenti'':170C ''similar'':184C ''solv'':19C ''start'':142C ''strong'':24C,106C ''subspac'':174C ''togeth'':50C,189C ''treat'':33C ''trickier'':115C ''two'':4C,15C ''use'':191C ''web.eecs.umich.edu'':132C ''web.eecs.umich.edu/~girasole/pubs/balzanophdthesis.pdf'':131C ''would'':164C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6327, 2, '2015-07-03 01:57:51.177', 1, NULL, NULL, '&lt;p&gt;I had the same question a few weeks ago.&lt;/p&gt;

&lt;p&gt;I personally found O''Reilly''s &lt;em&gt;Python for Data Analysis&lt;/em&gt; very useful in learning the basics. The book assumes you have some python programming experience, but it also has an appendix in the back to go through the basics.&lt;/p&gt;

&lt;p&gt;The author gives you a wide variety of real world (not Monty Python) examples in the beginning you can create within the first few chapters, then goes into detail about each thing as the book goes on, building your knowledge.&lt;/p&gt;

&lt;p&gt;I found the instructions very easy and step by step. My professor who is my guide in all this was impressed how quickly I learned. &lt;/p&gt;

&lt;p&gt;I also have heard good things about Kaggle.&lt;/p&gt;
', NULL, NULL, '''/em'':24C ''/p'':11C,54C,96C,123C,133C ''ago'':10C ''also'':42C,126C ''analysi'':23C ''appendix'':45C ''assum'':33C ''author'':57C ''back'':48C ''basic'':30C,53C ''begin'':72C ''book'':32C,90C ''build'':93C ''chapter'':80C ''creat'':75C ''data'':22C ''detail'':84C ''easi'':103C ''em'':19C ''exampl'':69C ''experi'':39C ''first'':78C ''found'':15C,99C ''give'':58C ''go'':50C ''goe'':82C,91C ''good'':129C ''guid'':113C ''heard'':128C ''impress'':118C ''instruct'':101C ''kaggl'':132C ''knowledg'':95C ''learn'':28C,122C ''monti'':67C ''o'':16C ''p'':1C,12C,55C,97C,124C ''person'':14C ''professor'':109C ''program'':38C ''python'':20C,37C,68C ''question'':6C ''quick'':120C ''real'':64C ''reilli'':17C ''step'':105C,107C ''thing'':87C,130C ''use'':26C ''varieti'':62C ''week'':9C ''wide'':61C ''within'':76C ''world'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6341, 1, '2015-07-04 21:55:51.21', 4, 83, 'How are clusters from DBSCAN sometimes non-convex?', '&lt;p&gt;I''ve been using clustering in my bag of ML techniques for quite some time now, and  I''ve never found a satisfying answer to this question.  &lt;/p&gt;

&lt;p&gt;In DBSCAN, we define a maximum radius with which to form clusters.  The algorithm will scan the space and group together points that are ALL reachable from one another.  However, we can sometimes end up with a non-convex cluster.  &lt;/p&gt;

&lt;p&gt;My confusion is around how the notion of a "radius", which describes a convex object, can be an input to an algorithm which results in a non-convex object?&lt;/p&gt;
', 'machine-learning  clustering  dbscan', NULL, '''/p'':43C,86C,118C ''algorithm'':58C,109C ''anoth'':73C ''answer'':39C ''around'':91C ''bag'':23C ''cluster'':3A,13B,20C,56C,85C ''confus'':89C ''convex'':9A,84C,101C,116C ''dbscan'':5A,14B,46C ''defin'':48C ''describ'':99C ''end'':78C ''form'':55C ''found'':36C ''group'':64C ''howev'':74C ''input'':106C ''learn'':12B ''machin'':11B ''machine-learn'':10B ''maximum'':50C ''ml'':25C ''never'':35C ''non'':8A,83C,115C ''non-convex'':7A,82C,114C ''notion'':94C ''object'':102C,117C ''one'':72C ''p'':15C,44C,87C ''point'':66C ''question'':42C ''quit'':28C ''radius'':51C,97C ''reachabl'':70C ''result'':111C ''satisfi'':38C ''scan'':60C ''sometim'':6A,77C ''space'':62C ''techniqu'':26C ''time'':30C ''togeth'':65C ''use'':19C ''ve'':17C,34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9985, 5, '2016-01-27 08:06:17.083', 0, NULL, NULL, '&lt;p&gt;The hinge loss function $\mathcal{l}$ is defined as&lt;/p&gt;

&lt;p&gt;$$\mathcal{l}(y, t) = \max(0, 1 - t \cdot y)$$&lt;/p&gt;

&lt;p&gt;where $t$ is the target value and $y$ is the prediction.&lt;/p&gt;

&lt;p&gt;It  has values in $[0,  \infty)$&lt;/p&gt;

&lt;h2&gt;See also&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Hinge_loss" rel="nofollow"&gt;Wikipedia&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':59C ''/h2'':48C ''/li'':60C ''/p'':11C,23C,36C,44C ''/ul'':61C ''/wiki/hinge_loss'':55C ''0'':18C,42C ''1'':19C ''also'':47C ''cdot'':21C ''defin'':9C ''en.wikipedia.org'':54C ''en.wikipedia.org/wiki/hinge_loss'':53C ''function'':5C ''h2'':45C ''hing'':3C ''href'':52C ''infti'':43C ''l'':7C,14C ''li'':50C ''loss'':4C ''mathcal'':6C,13C ''max'':17C ''nofollow'':57C ''p'':1C,12C,24C,37C ''predict'':35C ''rel'':56C ''see'':46C ''target'':29C ''ul'':49C ''valu'':30C,40C ''wikipedia'':58C ''y'':15C,22C,32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6328, 1, '2015-07-03 02:27:59.847', 0, 104, 'matplotlib geostrophic wind plot', '&lt;p&gt;I am working on an assignment that is teaching how to plot and label using matplotlib using Python. Science or math is not my background. I have been given the formula for calculating the geostrophic wind and we are to plot it (on the y-axis) versus the latitude on the x-axis.&lt;/p&gt;

&lt;p&gt;I know how to plot give an x and a y. Beyond that, the formula is not making sense to me given my lack of background in the area.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/mgjFC.jpg" alt="Formula for geostrophic wind"&gt;&lt;/p&gt;

&lt;p&gt;Furthermore, there is a key to translate the symbols to the correct python code, but I don''t know the English names to the symbols, making it very difficult.&lt;/p&gt;

&lt;p&gt;For example, I am given &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;r''$x^{10}$''
r''$R_^{final}$''
r''$alpha^{\eta}$'' 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The first two are superscript and subscript. That I understand. But how this helps with the formula calculations I do not know. &lt;/p&gt;

&lt;p&gt;I am given the values to put into the formula as well. An explanation of the order of operations would help.
If,
g0=9.81 ms-Â­â2;
ÎZ=60m;
În=2x10^5m;
and
f=2Î©sin(Ï) &lt;/p&gt;

&lt;p&gt;My question is how do I put the values into the formula and then plot them in matplotlib? is it as easy as x and y?&lt;/p&gt;
', 'python', NULL, '''/code'':155C ''/mgjfc.jpg'':99C ''/p'':62C,93C,105C,136C,143C,180C,223C,251C ''/pre'':156C ''10'':148C ''2'':210C ''2x10'':216C ''2î'':220C ''5m'':217C ''60m'':213C ''9.81'':205C ''alpha'':153C ''alt'':100C ''area'':92C ''assign'':12C ''axi'':53C,61C ''background'':31C,89C ''beyond'':75C ''calcul'':39C,175C ''code'':120C,145C ''correct'':118C ''difficult'':135C ''easi'':246C ''english'':127C ''eta'':154C ''exampl'':139C ''explan'':195C ''f'':219C ''final'':151C ''first'':159C ''formula'':37C,78C,101C,174C,191C,236C ''furthermor'':107C ''g0'':204C ''geostroph'':2A,41C,103C ''give'':69C ''given'':35C,85C,142C,184C ''help'':171C,202C ''i.stack.imgur.com'':98C ''i.stack.imgur.com/mgjfc.jpg'':97C ''img'':95C ''key'':111C ''know'':65C,125C,179C ''label'':20C ''lack'':87C ''latitud'':56C ''make'':81C,132C ''math'':27C ''matplotlib'':1A,22C,242C ''ms'':207C ''ms-â'':206C ''n'':215C ''name'':128C ''oper'':200C ''order'':198C ''p'':6C,63C,94C,106C,137C,157C,181C,224C ''plot'':4A,18C,47C,68C,239C ''pre'':144C ''put'':188C,231C ''python'':5B,24C,119C ''question'':226C ''r'':146C,149C,150C,152C ''scienc'':25C ''sens'':82C ''sin'':221C ''src'':96C ''subscript'':164C ''superscript'':162C ''symbol'':115C,131C ''teach'':15C ''translat'':113C ''two'':160C ''understand'':167C ''use'':21C,23C ''valu'':186C,233C ''versus'':54C ''well'':193C ''wind'':3A,42C,104C ''work'':9C ''would'':201C ''x'':60C,71C,147C,248C ''x-axi'':59C ''y'':52C,74C,250C ''y-axi'':51C ''z'':212C ''â'':208C,209C ''î'':211C,214C ''ï'':222C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6329, 2, '2015-07-03 05:38:17.023', 0, NULL, NULL, '&lt;p&gt;If the set that you are using the RMSE on is a linear space, a good reason to use the square root is that you turn the set into a metric space. The square root ensures the right scaling property. Essentially, the RMSE is equivalent to the Euclidean norm. As a benefit, it is possible to use results of the general theory of metric spaces.&lt;/p&gt;
', NULL, NULL, '''/p'':67C ''benefit'':53C ''ensur'':37C ''equival'':46C ''essenti'':42C ''euclidean'':49C ''general'':62C ''good'':17C ''linear'':14C ''metric'':32C,65C ''norm'':50C ''p'':1C ''possibl'':56C ''properti'':41C ''reason'':18C ''result'':59C ''right'':39C ''rmse'':10C,44C ''root'':23C,36C ''scale'':40C ''set'':4C,29C ''space'':15C,33C,66C ''squar'':22C,35C ''theori'':63C ''turn'':27C ''use'':8C,20C,58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6330, 2, '2015-07-03 06:47:43.017', 1, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Model the measurement process&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;For each dimension you could attempt to model the distribution of the measurement process variance. Perhaps every dimension has the same measurement process or perhaps it is distinct. Combine all measurements from all dimensions if the process is the same.&lt;/p&gt;

&lt;p&gt;If you have enough data then you can build an empirical model of the distribution of errors using perhaps a kernel based estimator.&lt;/p&gt;

&lt;p&gt;Otherwise you might choose to use a Gaussian distribution formed from the mean value of each measurement and an overall measure of the variance of the measurement process. Then you replace observations you have with a sample drawn from a  distribution with the observation mean and overall measurement process variance.&lt;/p&gt;
', NULL, NULL, '''/p'':8C,50C,75C,127C ''/strong'':7C ''attempt'':15C ''base'':73C ''build'':60C ''choos'':80C ''combin'':38C ''could'':14C ''data'':56C ''dimens'':12C,27C,43C ''distinct'':37C ''distribut'':19C,66C,85C,117C ''drawn'':114C ''empir'':62C ''enough'':55C ''error'':68C ''estim'':74C ''everi'':26C ''form'':86C ''gaussian'':84C ''kernel'':72C ''mean'':89C,121C ''measur'':5C,22C,31C,40C,93C,97C,103C,124C ''might'':79C ''model'':3C,17C,63C ''observ'':108C,120C ''otherwis'':77C ''overal'':96C,123C ''p'':1C,9C,51C,76C ''perhap'':25C,34C,70C ''process'':6C,23C,32C,46C,104C,125C ''replac'':107C ''sampl'':113C ''strong'':2C ''use'':69C,82C ''valu'':90C ''varianc'':24C,100C,126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6331, 1, '2015-07-03 08:52:36.247', 3, 68, 'Storing Sensor Data for Analysis of the Office', '&lt;p&gt;I have currently been tasked with designing an application that tracks several different measurements around the office, eg. the temperature, light, presence of people, etc. Having never really worked on data analysis before, I would like some guidance on how to store this data (which database design to use).&lt;/p&gt;

&lt;p&gt;What we''re looking at currently are around 50 sensors that only send data when an event of interest occurs: if the temperature changes by 0.5 degrees or if the light turns on/off or if a room becomes occupied/vacant. So, the data will only be updated every few seconds. Also, in the future, I''d like to analyse some of the data. Hence, the data must be persistent in the database. What kind of technologies would you suggest to carry out this task?&lt;/p&gt;
', 'hadoop  databases  sql  mongodb', NULL, '''/p'':63C,148C ''0.5'':90C ''50'':73C ''also'':114C ''analys'':122C ''analysi'':5A,45C ''applic'':22C ''around'':28C,72C ''becom'':102C ''carri'':144C ''chang'':88C ''current'':16C,70C ''d'':119C ''data'':3A,44C,57C,78C,106C,126C,129C ''databas'':10B,59C,135C ''degre'':91C ''design'':20C,60C ''differ'':26C ''eg'':31C ''etc'':38C ''event'':81C ''everi'':111C ''futur'':117C ''guidanc'':51C ''hadoop'':9B ''henc'':127C ''interest'':83C ''kind'':137C ''light'':34C,95C ''like'':49C,120C ''look'':68C ''measur'':27C ''mongodb'':12B ''must'':130C ''never'':40C ''occupied/vacant'':103C ''occur'':84C ''offic'':8A,30C ''on/off'':97C ''p'':13C,64C ''peopl'':37C ''persist'':132C ''presenc'':35C ''re'':67C ''realli'':41C ''room'':101C ''second'':113C ''send'':77C ''sensor'':2A,74C ''sever'':25C ''sql'':11B ''store'':1A,55C ''suggest'':142C ''task'':18C,147C ''technolog'':139C ''temperatur'':33C,87C ''track'':24C ''turn'':96C ''updat'':110C ''use'':62C ''work'':42C ''would'':48C,140C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6336, 2, '2015-07-04 05:44:08.447', 1, NULL, NULL, '&lt;p&gt;I have been doing similar project in my college. I have classroom and I''m supposed to collect data like temp, humidity, light, occupancy, etc. Assuming that you have worked with sensors and motes to use, I''m going to explain rest of the structure.&lt;/p&gt;

&lt;p&gt;You need sensor network setup and like you said you have done it. These sensor networks generally do not send data directly over internet so you need a Gateway that can collect data from sensors and send it over internet to local server.&lt;/p&gt;

&lt;p&gt;On server side you need REST API and you could use any language to develop it and I use PHP. I find it very easy to use and develop using PHP. This REST API shall receive data from Gateway and store it into database. I use mysql database because amount of data is not so big for us. But if your data is big enough you can use big data Nosql tool like mongoDB or so. Whatever type of database you use structure remains same. &lt;/p&gt;

&lt;p&gt;For sending data from Gateway to server you can use protocols like HTTP or MQTT whichever you feel comfortable. What I do is I have WSN controller that sends data over USB to Gateway then Gateway sends data to server over Ethernet. So I had to develop USB to Ethernet Gateway. If you can just take two UART terminals out of your controller you can build UART to Ethernet Gateway using any microcontroller or even Arduino Ethernet shield would work in that case.&lt;/p&gt;

&lt;p&gt;In my case data is sensed periodically but as you said you are sensing data when event of interest occurs then you can use poisson distribution method over periodically collected data to predict what is average number of events per day and then you can decide if your data is big or not.&lt;/p&gt;
', NULL, NULL, '''/p'':47C,92C,179C,264C,319C ''amount'':143C ''api'':100C,127C ''arduino'':256C ''assum'':27C ''averag'':301C ''big'':149C,157C,162C,316C ''build'':246C ''case'':263C,268C ''classroom'':13C ''collect'':19C,80C,295C ''colleg'':10C ''comfort'':199C ''control'':207C,243C ''could'':103C ''data'':20C,69C,81C,130C,145C,155C,163C,183C,210C,218C,269C,280C,296C,314C ''databas'':137C,141C,173C ''day'':306C ''decid'':311C ''develop'':108C,122C,227C ''direct'':70C ''distribut'':291C ''done'':60C ''easi'':118C ''enough'':158C ''etc'':26C ''ethernet'':222C,230C,249C,257C ''even'':255C ''event'':282C,304C ''explain'':42C ''feel'':198C ''find'':115C ''gateway'':77C,132C,185C,214C,216C,231C,250C ''general'':65C ''go'':40C ''http'':193C ''humid'':23C ''interest'':284C ''internet'':72C,88C ''languag'':106C ''light'':24C ''like'':21C,55C,166C,192C ''local'':90C ''m'':16C,39C ''method'':292C ''microcontrol'':253C ''mongodb'':167C ''mote'':35C ''mqtt'':195C ''mysql'':140C ''need'':50C,75C,98C ''network'':52C,64C ''nosql'':164C ''number'':302C ''occup'':25C ''occur'':285C ''p'':1C,48C,93C,180C,265C ''per'':305C ''period'':272C,294C ''php'':113C,124C ''poisson'':290C ''predict'':298C ''project'':7C ''protocol'':191C ''receiv'':129C ''remain'':177C ''rest'':43C,99C,126C ''said'':57C,276C ''send'':68C,85C,182C,209C,217C ''sens'':271C,279C ''sensor'':33C,51C,63C,83C ''server'':91C,95C,187C,220C ''setup'':53C ''shall'':128C ''shield'':258C ''side'':96C ''similar'':6C ''store'':134C ''structur'':46C,176C ''suppos'':17C ''take'':236C ''temp'':22C ''termin'':239C ''tool'':165C ''two'':237C ''type'':171C ''uart'':238C,247C ''us'':151C ''usb'':212C,228C ''use'':37C,104C,112C,120C,123C,139C,161C,175C,190C,251C,289C ''whatev'':170C ''whichev'':196C ''work'':31C,260C ''would'':259C ''wsn'':206C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6338, 2, '2015-07-04 06:26:36.84', 1, NULL, NULL, '&lt;p&gt;Your answer was incorrect because the axes were flipped and it didn''t plot the entire line.&lt;/p&gt;

&lt;p&gt;This was the correct answer I was looking for.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import matplotlib.pyplot as plt 
import numpy as np

# define the parameters
g = -9.81 # m/s^2
dZ = 60 # m
dx = 2e5 # m
omega = 7.2921e-5 # rad/s
phi = np.linspace(10,90) # deg
f = 2 * omega * np.sin(np.radians(phi)) # coriolis frequency, s^-1

# compute geostrophic wind, x-component
u_g = -1. * g/f * dZ/dx

# plot phi vs V_g
fig, ax = plt.subplots()
ax.plot(phi, u_g, color=''#FF33CC'')
ax.set_xlabel(''$\phi$'')
ax.set_ylabel(''$V_{g}$ (m $s^-1$)'')
plt.title(''Geostrophic Wind vs. Latitude'')
plt.show()
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''-1'':71C,80C,106C ''-9.81'':45C ''/code'':113C ''/p'':19C,30C ''/pre'':114C ''10'':59C ''2'':47C,63C ''2e5'':52C ''60'':49C ''7.2921e-5'':55C ''90'':60C ''answer'':3C,25C ''ax'':89C ''ax.plot'':91C ''ax.set'':97C,100C ''axe'':8C ''code'':32C ''color'':95C ''compon'':77C ''comput'':72C ''corioli'':68C ''correct'':24C ''defin'':41C ''deg'':61C ''didn'':13C ''dx'':51C ''dz'':48C ''dz/dx'':82C ''entir'':17C ''f'':62C ''ff33cc'':96C ''fig'':88C ''flip'':10C ''frequenc'':69C ''g'':44C,79C,87C,94C,103C ''g/f'':81C ''geostroph'':73C,108C ''import'':33C,37C ''incorrect'':5C ''latitud'':111C ''line'':18C ''look'':28C ''m'':50C,53C,104C ''m/s'':46C ''matplotlib.pyplot'':34C ''np'':40C ''np.linspace'':58C ''np.radians'':66C ''np.sin'':65C ''numpi'':38C ''omega'':54C,64C ''p'':1C,20C ''paramet'':43C ''phi'':57C,67C,84C,92C,99C ''plot'':15C,83C ''plt'':36C ''plt.show'':112C ''plt.subplots'':90C ''plt.title'':107C ''pre'':31C ''rad/s'':56C ''u'':78C,93C ''v'':86C,102C ''vs'':85C,110C ''wind'':74C,109C ''x'':76C ''x-compon'':75C ''xlabel'':98C ''ylabel'':101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6339, 1, '2015-07-04 11:08:18.03', 0, 37, 'Questions for data analysis of a class room', '&lt;p&gt;I have Wireless Sensor Network setup in a classroom. I''m sensing data like temp, humidity, light, occupancy, etc. Classroom can accomodate 150 students and has 6 AC with light. From one side of classroom has big windows and that side is towards west and other 3 sides have thick walls and doors always closed. I have been looking for some questions that can be answered by analysing data collected. Some of the questions I can think of are,&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Predicting optimum time to turn on AC before class starts so that class is attains required temp with least energy consumed.&lt;/li&gt;
&lt;li&gt;As of now I have 9 sensors on all walls and ceiling too. So to decide best position for sensor such that I can co-relate and predict other sensor values from only one or two sensor values. &lt;/li&gt;
&lt;li&gt;Decide optimum amount of light needed by occupants to be comfortable in classroom.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Please suggest me more questions that can asked about this situation. Also suggest if I can use somehow machine learning in WSN I have deployed.&lt;/p&gt;
', 'machine-learning  data-mining', NULL, '''/li'':119C,160C,175C ''/p'':95C,203C ''/ul'':176C ''150'':38C ''3'':62C ''6'':42C ''9'':126C ''ac'':43C,104C ''accomod'':37C ''also'':189C ''alway'':69C ''amount'':164C ''analys'':83C ''analysi'':4A ''answer'':81C ''ask'':185C ''attain'':112C ''best'':137C ''big'':52C ''ceil'':132C ''class'':7A,106C,110C ''classroom'':24C,35C,50C,174C ''close'':70C ''co'':146C ''co-rel'':145C ''collect'':85C ''comfort'':172C ''consum'':118C ''data'':3A,13B,28C,84C ''data-min'':12B ''decid'':136C,162C ''deploy'':202C ''door'':68C ''energi'':117C ''etc'':34C ''humid'':31C ''learn'':11B,197C ''least'':116C ''li'':97C,120C,161C ''light'':32C,45C,166C ''like'':29C ''look'':74C ''m'':26C ''machin'':10B,196C ''machine-learn'':9B ''mine'':14B ''need'':167C ''network'':20C ''occup'':33C,169C ''one'':47C,155C ''optimum'':99C,163C ''p'':15C,177C ''pleas'':178C ''posit'':138C ''predict'':98C,149C ''question'':1A,77C,89C,182C ''relat'':147C ''requir'':113C ''room'':8A ''sens'':27C ''sensor'':19C,127C,140C,151C,158C ''setup'':21C ''side'':48C,56C,63C ''situat'':188C ''somehow'':195C ''start'':107C ''student'':39C ''suggest'':179C,190C ''temp'':30C,114C ''thick'':65C ''think'':92C ''time'':100C ''toward'':58C ''turn'':102C ''two'':157C ''ul'':96C ''use'':194C ''valu'':152C,159C ''wall'':66C,130C ''west'':59C ''window'':53C ''wireless'':18C ''wsn'':199C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6342, 1, '2015-07-05 01:07:42.47', 1, 59, 'Spatial clustering based on response to inputs and building a reduced model', '&lt;p&gt;Stats version: I have a few measurements of a function that takes three inputs and produces a few 2D fields of outputs: $f_i(a,b,c;x,y)$, with $f$ being a vector of several quantities. I would like to cluster the points in $(x,y)$-space into regions that have a similar response to the input variables $(a,b,c)$. If I treat the points independently, applying a clustering algorithm is fairly easy. (I can elaborate on which one I''m using if you think it''s relevant, but I''m open to suggestions on your favorites.) But there''s no guarantee that the clusters are near each other spatially. Having contiguous clusters of roughly equal size is definitely a plus. What are some ways to modify the approach to add in that constraint?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Science version: I''m running an atmospheric chemistry model and varying emissions over the US. In that setup, $(a,b,c)$ are the national-total emissions of NOx, SO2, and NH3; $(x,y)$ are grid points over the US; and $f_i$ is a vector of the various chemical species I''m tracking. I''ve varied $(a,b,c)$ in a number of simulations. Now I want to divide the country into regions that have broadly similar responses to the emissions changes, and create simplified models for a manageable number of regions. I''m particularly interested in nonlinearities/mixed-effects/second-order derivatives in the chemistry response. It''s possible that different regions of the country do respond similarly to emissions even if they''re not near each other, but the different regions should have some sort of spatial agglomeration. Thoughts?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Side question: I originally played around with this a few years ago with Matlab code but have since migrated to Python. Suggestions on how to efficiently use Python stats packages for this sort of problem?&lt;/p&gt;
', 'python  clustering  data', NULL, '''/p'':153C,294C,332C ''2d'':35C ''add'':149C ''agglomer'':292C ''ago'':308C ''algorithm'':88C ''appli'':85C ''approach'':147C ''around'':302C ''atmospher'':162C ''b'':42C,77C,175C,214C ''base'':3A ''broad'':232C ''build'':9A ''c'':43C,78C,176C,215C ''chang'':238C ''chemic'':205C ''chemistri'':163C,258C ''cluster'':2A,14B,58C,87C,123C,131C ''code'':311C ''constraint'':152C ''contigu'':130C ''countri'':227C,268C ''creat'':240C ''data'':15B ''definit'':137C ''deriv'':255C ''differ'':264C,284C ''divid'':225C ''easi'':91C ''effici'':322C ''elabor'':94C ''emiss'':167C,182C,237C,273C ''equal'':134C ''even'':274C ''f'':39C,47C,197C ''fair'':90C ''favorit'':115C ''field'':36C ''function'':26C ''grid'':191C ''guarante'':120C ''hr'':154C,295C ''independ'':84C ''input'':7A,30C,74C ''interest'':252C ''like'':56C ''m'':99C,109C,159C,208C,250C ''manag'':245C ''matlab'':310C ''measur'':23C ''migrat'':315C ''model'':12A,164C,242C ''modifi'':145C ''nation'':180C ''national-tot'':179C ''near'':125C,279C ''nh3'':187C ''nonlinearities/mixed-effects/second-order'':254C ''nox'':184C ''number'':218C,246C ''one'':97C ''open'':110C ''origin'':300C ''output'':38C ''p'':16C,155C,296C ''packag'':326C ''particular'':251C ''play'':301C ''plus'':139C ''point'':60C,83C,192C ''possibl'':262C ''problem'':331C ''produc'':32C ''python'':13B,317C,324C ''quantiti'':53C ''question'':298C ''re'':277C ''reduc'':11A ''region'':66C,229C,248C,265C,285C ''relev'':106C ''respond'':270C ''respons'':5A,71C,234C,259C ''rough'':133C ''run'':160C ''scienc'':156C ''setup'':173C ''sever'':52C ''side'':297C ''similar'':70C,233C,271C ''simplifi'':241C ''simul'':220C ''sinc'':314C ''size'':135C ''so2'':185C ''sort'':289C,329C ''space'':64C ''spatial'':1A,128C,291C ''speci'':206C ''stat'':17C,325C ''suggest'':112C,318C ''take'':28C ''think'':103C ''thought'':293C ''three'':29C ''total'':181C ''track'':209C ''treat'':81C ''us'':170C,195C ''use'':100C,323C ''vari'':166C,212C ''variabl'':75C ''various'':204C ''ve'':211C ''vector'':50C,201C ''version'':18C,157C ''want'':223C ''way'':143C ''would'':55C ''x'':44C,62C,188C ''y'':45C,63C,189C ''year'':307C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10303, 2, '2016-02-18 10:31:17.507', 2, NULL, NULL, '&lt;p&gt;Have a look at the example on &lt;a href="http://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter4_TheGreatestTheoremNeverTold/Chapter4.ipynb#Example:-How-to-order-Reddit-comments" rel="nofollow"&gt;"How to order Reddit comments"&lt;/a&gt; using their up- &amp;amp; down-votes in Cam Davidson Pilon''s &lt;a href="https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers" rel="nofollow"&gt;book&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;$$
\frac{a}{a+b} - 1.65\sqrt{\frac{a b}{(a+b)^2(a+b+1)}}
$$
where
$$
a = 1 + u
$$
$$
b = 1 + d
$$&lt;/p&gt;

&lt;p&gt;$u$ is the number of yes votes and $d$ is the number of no votes.&lt;/p&gt;

&lt;p&gt;Sorting your data using the score obtained from that formula results in the following table (highest score first):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;id  total  yes  no     score
13      2    2   0  0.430479
27      4    3   1  0.372679
17     10    6   4  0.357720
18      1    1   0  0.277758
26      1    1   0  0.277758
21      2    0   2 -0.069521
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''+1'':59C ''-0.069521'':141C ''/a'':21C,42C ''/camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers'':38C ''/code'':142C ''/github/camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers/blob/master/chapter4_thegreatesttheoremnevertold/chapter4.ipynb#example:-how-to-order-reddit-comments'':13C ''/p'':43C,67C,84C,104C ''/pre'':143C ''0'':115C,130C,135C,139C ''0.277758'':131C,136C ''0.357720'':126C ''0.372679'':121C ''0.430479'':116C ''1'':62C,65C,120C,128C,129C,133C,134C ''1.65'':49C ''10'':123C ''13'':112C ''17'':122C ''18'':127C ''2'':56C,113C,114C,138C,140C ''21'':137C ''26'':132C ''27'':117C ''3'':119C ''4'':118C,125C ''6'':124C ''amp'':25C ''b'':48C,53C,55C,58C,64C ''book'':41C ''cam'':30C ''code'':106C ''comment'':20C ''d'':66C,77C ''data'':88C ''davidson'':31C ''down-vot'':26C ''exampl'':7C ''first'':103C ''follow'':99C ''formula'':95C ''frac'':45C,51C ''github.com'':37C ''github.com/camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers'':36C ''highest'':101C ''href'':10C,35C ''id'':107C ''look'':4C ''nbviewer.jupyter.org'':12C ''nbviewer.jupyter.org/github/camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers/blob/master/chapter4_thegreatesttheoremnevertold/chapter4.ipynb#example:-how-to-order-reddit-comments'':11C ''nofollow'':15C,40C ''number'':72C,80C ''obtain'':92C ''order'':18C ''p'':1C,44C,68C,85C ''pilon'':32C ''pre'':105C ''reddit'':19C ''rel'':14C,39C ''result'':96C ''score'':91C,102C,111C ''sort'':86C ''sqrt'':50C ''tabl'':100C ''total'':108C ''u'':63C,69C ''use'':22C,89C ''vote'':28C,75C,83C ''yes'':74C,109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6343, 2, '2015-07-05 08:44:07.217', 0, NULL, NULL, '&lt;p&gt;I think it''s non-convex because the particular cluster assignment you get when applying DBSCAN depends on the order you traverse the data.&lt;/p&gt;

&lt;p&gt;Let''s try to illustrate it with an example. Consider this dataset:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ScA8N.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;You want to run DBSCAN with radius $r = 3$ and $\text{min_pts} = 4$, so you get this: &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/OK6rC.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;The point in the center is not a core point because it has only 3 points, not 4, and we have only two core points. And depending on how you traverse the data points, you may get different cluster assignments: &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/YdMK9.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;The top picture shows the result we''d get by traversing left-to-right, and the bottom picture - by traversing right-to-left.&lt;/p&gt;

&lt;p&gt;Apparently both these results would correspond to the same value of the cost function, thus the cost function has several minima and it is not convex. &lt;/p&gt;
', NULL, NULL, '''/ok6rc.png'':79C ''/p'':27C,41C,53C,73C,85C,127C,139C,166C,194C ''/sca8n.png'':47C ''/ydmk9.png'':133C ''3'':63C,101C ''4'':68C,104C ''alt'':48C,80C,134C ''appar'':168C ''appli'':17C ''assign'':13C,126C ''bottom'':158C ''center'':91C ''cluster'':12C,125C ''consid'':38C ''convex'':8C,193C ''core'':95C,110C ''correspond'':173C ''cost'':180C,184C ''d'':148C ''data'':26C,119C ''dataset'':40C ''dbscan'':18C,59C ''depend'':19C,113C ''descript'':51C,83C,137C ''differ'':124C ''enter'':49C,81C,135C ''exampl'':37C ''function'':181C,185C ''get'':15C,71C,123C,149C ''i.stack.imgur.com'':46C,78C,132C ''i.stack.imgur.com/ok6rc.png'':77C ''i.stack.imgur.com/sca8n.png'':45C ''i.stack.imgur.com/ydmk9.png'':131C ''illustr'':33C ''imag'':50C,82C,136C ''img'':43C,75C,129C ''left'':153C,165C ''left-to-right'':152C ''let'':29C ''may'':122C ''min'':66C ''minima'':188C ''non'':7C ''non-convex'':6C ''order'':22C ''p'':1C,28C,42C,54C,74C,86C,128C,140C,167C ''particular'':11C ''pictur'':143C,159C ''point'':88C,96C,102C,111C,120C ''pts'':67C ''r'':62C ''radius'':61C ''result'':146C,171C ''right'':155C,163C ''right-to-left'':162C ''run'':58C ''sever'':187C ''show'':144C ''src'':44C,76C,130C ''text'':65C ''think'':3C ''thus'':182C ''top'':142C ''travers'':24C,117C,151C,161C ''tri'':31C ''two'':109C ''valu'':177C ''want'':56C ''would'':172C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6344, 2, '2015-07-05 13:22:46.627', 1, NULL, NULL, '&lt;p&gt;A cluster in DBSCAN consists of &lt;em&gt;multiple&lt;/em&gt; core points.&lt;/p&gt;

&lt;p&gt;The radius is the area covered by a single core point, but together with neighbor core points the shape will be much more complex. In particular, they can be much larger than epsilon, so you should choose a small value, and rely on this "cover" functionality.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.m.wikipedia.org/wiki/DBSCAN#/media/File:DBSCAN-density-data.svg" rel="nofollow"&gt;Wikipedia has an example of a non-convex cluster&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':80C ''/em'':10C ''/p'':13C,61C,81C ''/wiki/dbscan#/media/file:dbscan-density-data.svg'':67C ''area'':19C ''choos'':51C ''cluster'':3C,79C ''complex'':38C ''consist'':6C ''convex'':78C ''core'':11C,24C,30C ''cover'':20C,59C ''dbscan'':5C ''em'':8C ''en.m.wikipedia.org'':66C ''en.m.wikipedia.org/wiki/dbscan#/media/file:dbscan-density-data.svg'':65C ''epsilon'':47C ''exampl'':73C ''function'':60C ''href'':64C ''larger'':45C ''much'':36C,44C ''multipl'':9C ''neighbor'':29C ''nofollow'':69C ''non'':77C ''non-convex'':76C ''p'':1C,14C,62C ''particular'':40C ''point'':12C,25C,31C ''radius'':16C ''rel'':68C ''reli'':56C ''shape'':33C ''singl'':23C ''small'':53C ''togeth'':27C ''valu'':54C ''wikipedia'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6345, 1, '2015-07-05 15:10:56.79', 2, 99, 'Pivoting a two-column feature table in Pandas', '&lt;p&gt;How can I transform the following DataFrame into one with cities as rows and each cuisine as a column, and 1 or 0 as values (1 if the city has that kind of cuisine)? &lt;/p&gt;

&lt;p&gt;I think this turns out to be a very common problem in transforming data into features for machine learning.&lt;/p&gt;

&lt;p&gt;I am aware of the Pandas pivot_table functionality, but it asks for a value column, and in this case we don''t have any.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas as pd

data = {
    ''city'': [''NY'',''NY'', ''SF'',''SF'',''SF''],
    ''cuisine'': [''Japanese'', ''Chinese'', ''French'', ''Japanse'', ''German'']
}

df = pd.DataFrame(data)
&lt;/code&gt;&lt;/pre&gt;
', 'data-mining  feature-extraction  pandas', NULL, '''/code'':123C ''/p'':52C,73C,100C ''/pre'':124C ''0'':40C ''1'':38C,43C ''ask'':86C ''awar'':77C ''case'':94C ''chines'':116C ''citi'':28C,46C,108C ''code'':102C ''column'':5A,36C,90C ''common'':63C ''cuisin'':33C,51C,114C ''data'':11B,67C,107C,122C ''data-min'':10B ''datafram'':24C ''df'':120C ''extract'':15B ''featur'':6A,14B,69C ''feature-extract'':13B ''follow'':23C ''french'':117C ''function'':83C ''german'':119C ''import'':103C ''japanes'':115C ''japans'':118C ''kind'':49C ''learn'':72C ''machin'':71C ''mine'':12B ''ny'':109C,110C ''one'':26C ''p'':17C,53C,74C ''panda'':9A,16B,80C,104C ''pd'':106C ''pd.dataframe'':121C ''pivot'':1A,81C ''pre'':101C ''problem'':64C ''row'':30C ''sf'':111C,112C,113C ''tabl'':7A,82C ''think'':55C ''transform'':21C,66C ''turn'':57C ''two'':4A ''two-column'':3A ''valu'':42C,89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6776, 1, '2015-08-12 16:17:14.967', 5, 279, 'Any idea about application of deep dream?', '&lt;p&gt;Recently Google publicized interesting deep dream. Besides art generation such as &lt;a href="http://deepdreamgenerator.com/"&gt;http://deepdreamgenerator.com/&lt;/a&gt;, do you see any potential applications of deep dream in computer vision or machine learning?&lt;/p&gt;
', 'machine-learning  deep-learning', NULL, '''/&lt;/a&gt;,'':31C ''/p'':47C ''applic'':4A,37C ''art'':22C ''besid'':21C ''comput'':42C ''deep'':6A,12B,19C,39C ''deep-learn'':11B ''deepdreamgenerator.com'':28C,30C ''deepdreamgenerator.com/&lt;/a&gt;,'':29C ''dream'':7A,20C,40C ''generat'':23C ''googl'':16C ''href'':27C ''idea'':2A ''interest'':18C ''learn'':10B,13B,46C ''machin'':9B,45C ''machine-learn'':8B ''p'':14C ''potenti'':36C ''public'':17C ''recent'':15C ''see'':34C ''vision'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6346, 2, '2015-07-05 15:50:02.373', 2, NULL, NULL, '&lt;ul&gt;
&lt;li&gt;&lt;p&gt;If you want an application-oriented book, consider Christopher Bishop''s &lt;a href="http://www.mbmlbook.com/" rel="nofollow"&gt;Model-Based Machine Learning&lt;/a&gt;. He has more technical books that are well regarded.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;If you are looking for lots of code, &lt;a href="http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/" rel="nofollow"&gt;Probabilistic Programming &amp;amp; Bayesian Methods for Hackers&lt;/a&gt; is an option.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Another introductory book with a more statistical bent is &lt;a href="http://www-bcf.usc.edu/~gareth/ISL/" rel="nofollow"&gt;An Introduction to Statistical Learning with Applications in R&lt;/a&gt;. Again, the authors have a well-regarded technical version of the book.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':26C,62C,95C ''/li'':37C,67C,110C ''/p'':36C,66C,109C ''/probabilistic-programming-and-bayesian-methods-for-hackers/'':52C ''/ul'':111C ''/~gareth/isl/'':83C ''amp'':57C ''anoth'':70C ''applic'':9C,92C ''application-ori'':8C ''author'':98C ''base'':23C ''bayesian'':58C ''bent'':77C ''bishop'':14C ''book'':11C,31C,72C,108C ''camdavidsonpilon.github.io'':51C ''camdavidsonpilon.github.io/probabilistic-programming-and-bayesian-methods-for-hackers/'':50C ''christoph'':13C ''code'':47C ''consid'':12C ''hacker'':61C ''href'':17C,49C,80C ''introduct'':87C ''introductori'':71C ''learn'':25C,90C ''li'':2C,38C,68C ''look'':43C ''lot'':45C ''machin'':24C ''method'':59C ''model'':22C ''model-bas'':21C ''nofollow'':20C,54C,85C ''option'':65C ''orient'':10C ''p'':3C,39C,69C ''probabilist'':55C ''program'':56C ''r'':94C ''regard'':35C,103C ''rel'':19C,53C,84C ''statist'':76C,89C ''technic'':30C,104C ''ul'':1C ''version'':105C ''want'':6C ''well'':34C,102C ''well-regard'':101C ''www-bcf.usc.edu'':82C ''www-bcf.usc.edu/~gareth/isl/'':81C ''www.mbmlbook.com'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6404, 1, '2015-07-09 02:29:10.683', 2, 24, 'What Framework To Use for Asynchronous Algorithms?', '&lt;p&gt;I have a problem with an extremely large dataset (who doesn''t?) which is stored in chunks such that there is low variance across chunks (i.e., the chunks are &lt;em&gt;sort of&lt;/em&gt; representative). I wanted to play around with algorithms to do some classification in an asynchronous fashion but I wanted to code it up myself.&lt;/p&gt;

&lt;p&gt;A sample code would look like &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;start a master
distribute 10 chunks on 10 slaves
while some criterion is not met 
 for each s in slave:
  classify the data inexactly using some kind of iterative algorithm and return to master
 master waits for any 2 slaves to report the classifier, averages the classifier and sends it back for the slaves to continue 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What framework do I use? Hadoop, Spark, Other?&lt;/p&gt;

&lt;p&gt;If I was doing this in pure-C, I would use pthreads and have a very fine control over threads, locks and mutexes. Is there any analogous framework in this distributed data-science environment?&lt;/p&gt;
', 'algorithms', NULL, '''/code'':134C ''/em'':42C ''/p'':67C,75C,145C,183C ''/pre'':135C ''10'':82C,85C ''2'':116C ''across'':33C ''algorithm'':7A,8B,50C,107C ''analog'':174C ''around'':48C ''asynchron'':6A,57C ''averag'':122C ''back'':128C ''c'':155C ''chunk'':26C,34C,37C,83C ''classif'':54C ''classifi'':98C,121C,124C ''code'':63C,71C,77C ''continu'':133C ''control'':165C ''criterion'':89C ''data'':100C,180C ''data-sci'':179C ''dataset'':18C ''distribut'':81C,178C ''doesn'':20C ''em'':39C ''environ'':182C ''extrem'':16C ''fashion'':58C ''fine'':164C ''framework'':2A,138C,175C ''hadoop'':142C ''i.e'':35C ''inexact'':101C ''iter'':106C ''kind'':104C ''larg'':17C ''like'':74C ''lock'':168C ''look'':73C ''low'':31C ''master'':80C,111C,112C ''met'':92C ''mutex'':170C ''p'':9C,68C,136C,146C ''play'':47C ''pre'':76C ''problem'':13C ''pthread'':159C ''pure'':154C ''pure-c'':153C ''report'':119C ''repres'':43C ''return'':109C ''sampl'':70C ''scienc'':181C ''send'':126C ''slave'':86C,97C,117C,131C ''sort'':40C ''spark'':143C ''start'':78C ''store'':24C ''thread'':167C ''use'':4A,102C,141C,158C ''varianc'':32C ''wait'':113C ''want'':45C,61C ''would'':72C,157C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8895, 1, '2015-11-18 20:14:30.133', 3, 62, 'With unbalanced class, do I have to use under sampling on my validation/testing datasets?', '&lt;p&gt;Iâm a beginner in machine learning and Iâm facing a situation. Iâm working on a Real Time Bidding problem, with the IPinYou dataset and Iâm trying to do a click prediction.&lt;/p&gt;

&lt;p&gt;The thing is that, as you may know, the dataset is very unbalanced : Around 1300 negative examples (non click) for 1 positive example (click).&lt;/p&gt;

&lt;p&gt;This is what I do:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Load the data&lt;/li&gt;
&lt;li&gt;Split the dataset into 3 datasets : 
    A = Training (60%)
    B = Validating (20%)
    C = Testing (20%)&lt;/li&gt;
&lt;li&gt;For each dataset (A, B, C), do an under-sampling on each negative class in order to have a ratio of 5 (5 negative example for 1 positive example). This give me 3 new datasets which are more balanced:
    Aâ
    Bâ
    Câ&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Then I train my model with the dataset Aâ and logistic regression. &lt;/p&gt;

&lt;p&gt;My question are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Which dataset do I have to use for validation ? B or Bâ ?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Which dataset do I have to use for testing ? C or Câ&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Which metrics are the most relevant to evaluate my model? F1Score seems to be a well used metric. But here due to the unbalanced class (if I use the datasets B and C), the precision is low (under 0.20) and the F1Score is very influenced by low recall/precision. Would that be more accurate to use aucPR or aucROC ?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;If I want to plot the learning curve, which metrics should I use ? (knowing that the %error isnât relevant if I use the Bâ dataset for validating)&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Thanks in advance for your time !&lt;/p&gt;

&lt;p&gt;Regards.&lt;/p&gt;
', 'machine-learning  dataset  sampling', NULL, '''/li'':95C,112C,157C,194C,210C,272C,304C ''/ol'':158C,305C ''/p'':56C,82C,89C,172C,177C,193C,209C,271C,303C,313C,316C ''0.20'':251C ''1'':78C,141C ''1300'':72C ''20'':108C,111C ''3'':101C,147C ''5'':136C,137C ''60'':105C ''accur'':265C ''advanc'':309C ''around'':71C ''aucpr'':268C ''aucroc'':270C ''aâ'':154C,168C ''b'':106C,118C,190C,243C ''balanc'':153C ''beginn'':24C ''bid'':41C ''bâ'':155C,192C,299C ''c'':109C,119C,206C,245C ''class'':3A,128C,237C ''click'':54C,76C,81C ''curv'':282C ''câ'':156C,208C ''data'':94C ''dataset'':14A,18B,46C,67C,99C,102C,116C,149C,167C,182C,198C,242C,300C ''due'':233C ''error'':291C ''evalu'':220C ''exampl'':74C,80C,139C,143C ''f1score'':223C,254C ''face'':31C ''give'':145C ''influenc'':257C ''ipinyou'':45C ''isnâ'':292C ''iâ'':21C,29C,34C,48C ''know'':65C,288C ''learn'':17B,27C,281C ''li'':91C,96C,113C,179C,195C,211C,273C ''load'':92C ''logist'':170C ''low'':249C,259C ''m'':22C,30C,35C,49C ''machin'':16B,26C ''machine-learn'':15B ''may'':64C ''metric'':214C,230C,284C ''model'':164C,222C ''negat'':73C,127C,138C ''new'':148C ''non'':75C ''ol'':90C,178C ''order'':130C ''p'':20C,57C,83C,159C,173C,180C,196C,212C,274C,306C,314C ''plot'':279C ''posit'':79C,142C ''precis'':247C ''predict'':55C ''problem'':42C ''question'':175C ''ratio'':134C ''real'':39C ''recall/precision'':260C ''regard'':315C ''regress'':171C ''relev'':218C,294C ''sampl'':10A,19B,124C ''seem'':224C ''situat'':33C ''split'':97C ''test'':110C,205C ''thank'':307C ''thing'':59C ''time'':40C,312C ''train'':104C,162C ''tri'':50C ''unbalanc'':2A,70C,236C ''under-sampl'':122C ''use'':8A,187C,203C,229C,240C,267C,287C,297C ''valid'':107C,189C,302C ''validation/testing'':13A ''want'':277C ''well'':228C ''work'':36C ''would'':261C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6347, 2, '2015-07-05 17:10:14.417', 2, NULL, NULL, '&lt;p&gt;The best choice for storage technologies will depend largely on how much data (in terms of bytes) you expect to accumulate over the lifetime of your project, so the first thing i would do is try to get some sample data, or make some educated guesses (e.g. how many bytes does 1 temperature recording take up X how many change events am I expecting per day X how many temperature sensors X how many days worth of data you want to store and analyse over time).&lt;/p&gt;

&lt;p&gt;Once you have a rough idea of how much data you need to store and analyse, you can use that to start narrowing down your choices. There''s no right answer, and others may disagree, but I would suggest that if you''re dealing with anything less than terabytes of data, you don''t need hadoop (I noticed that''s a tag in your question) - hadoop is not really a data storage solution (although it does have it''s own file system called HDFS or just DFS), it''s more of a framework for processing and transforming huge quantities of data. Also if you don''t have thousands of events per second to record, you probably don''t need NoSQL solutions either.&lt;/p&gt;

&lt;p&gt;For storage of structured data, given that you''ve never really done data analysis before, SQL databases are probably the way to go if you have gigabytes or less, and SQL will be easier and more useful to learn - it''s mature, been around for ages and is still the go-to standard in most industries, so there are &lt;a href="http://www.w3schools.com/sql/" rel="nofollow"&gt;plenty of learning resources&lt;/a&gt;. Maybe try out MySQL Community Edition server (free, open source) as a start, I would also recommend the MySQL Workbench to help you get started (a bunch of GUI tools you can use to mess around with SQL when learning)&lt;/p&gt;

&lt;p&gt;PS I don''t know anything about capturing signals from sensors, so maybe there are more appropriate technologies which I''m not aware of!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6348, 2, '2015-07-06 06:49:46.487', 1, NULL, NULL, '&lt;p&gt;Typical predictive performance measures used to compare accuracy of ANN models are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;RMSE - (root mean squared error) measures the distance between estimated and actual outcomes. Other metrics that measure the same concept are MSE, MAE or MPE.&lt;/li&gt;
&lt;li&gt;R square - (R^2 or coefficient of determination) measures the reduction of variance when using the model.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;When comparing two different ANN models for performance, metrics that take into account the complexity of the model may be used, such as AIC or BIC.&lt;/p&gt;
', NULL, NULL, '''/li'':42C,61C ''/p'':14C,90C ''/ul'':62C ''2'':47C ''account'':76C ''accuraci'':9C ''actual'':28C ''aic'':87C ''ann'':11C,68C ''bic'':89C ''coeffici'':49C ''compar'':8C,65C ''complex'':78C ''concept'':36C ''determin'':51C ''differ'':67C ''distanc'':24C ''error'':21C ''estim'':26C ''li'':16C,43C ''mae'':39C ''may'':82C ''mean'':19C ''measur'':5C,22C,33C,52C ''metric'':31C,72C ''model'':12C,60C,69C,81C ''mpe'':41C ''mse'':38C ''outcom'':29C ''p'':1C,63C ''perform'':4C,71C ''predict'':3C ''r'':44C,46C ''reduct'':54C ''rmse'':17C ''root'':18C ''squar'':20C,45C ''take'':74C ''two'':66C ''typic'':2C ''ul'':15C ''use'':6C,58C,84C ''varianc'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6349, 1, '2015-07-06 09:40:59.947', 2, 127, 'What should I care about while stacking as an ensemble method?', '&lt;p&gt;I''m using SMO, Logistic Regression, Bayesian Network and Simple CART algorithms for classification. Results form WEKA: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Algorithm               Sensitivity (%)       Specificity (%)         Overall accuracy (%)
Bayesian Network            57.49                 76.09                    65.24
Logistic Regression         64.73                 69.86                    66.87
SMO                         54.32                 79.20                    64.69
Simple CART                 71.88                 61.51                    67.56
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;SMO gives the best result for my classification problem, since it correctly classify the 79.20% of the class which is important for me. I want to increase this accuracy by stacking. I tried to combine some of them. In most of the cases I couldn''t increase the accuracy but stacking SMO with Logistic Regression made a little increment in accuracy. &lt;/p&gt;

&lt;p&gt;How can I explain why stacking SMO with Logistic Regression is better than others? &lt;/p&gt;

&lt;p&gt;Is there any generalization such as combining tree classifiers gives good result in stacking? What should I care about while stacking? &lt;/p&gt;

&lt;h2&gt;EDIT:&lt;/h2&gt;

&lt;pre&gt;&lt;code&gt;                                Bayesian Network    Logistic Reg.   SMO         CART 
Kappa statistic                   0.3196             0.3367         0.3158      0.3335 
Mean absolute error               0.3517             0.4164         0.3531      0.4107 
Root mean squared error           0.5488             0.4548         0.5942      0.4547 
Relative absolute error (%)      72.3389              85.65        72.6299      84.477 
Root relative squared error (%) 111.3076            92.2452       120.5239     92.2318 
Weighted Avg. of F-Measure        0.653               0.671          0.676     92.2318 
ROC Area                          0.725               0.727          0.668       0.721
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Total number of instance is 25106. 14641 of them is class a, and 10465 of them belong to class b.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;=== Confusion Matrix of Simple CART ===
     a     b   &amp;lt;-- classified as
 10524  4117 |     a = 0
  4028  6437 |     b = 1

=== Confusion Matrix of SMO ===

    a    b   &amp;lt;-- classified as
 7953 6688 |    a = 0
 2177 8288 |    b = 1

=== Confusion Matrix of Logistic Regression ===

    a    b   &amp;lt;-- classified as
 9477 5164 |    a = 0
 3154 7311 |    b = 1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Since SMO is successful at class b and CART is successful at class a, I tried to ensemble these two algorithms. But I couldn''t increase the accuracy. Then I tried to combine SMO with Logistic Regression, the accuracy is increased a little bit. Why ensembling SMO with Logistic Regression is better than ensebling SMO with CART, is there any explanation?&lt;/p&gt;
', 'machine-learning  data-mining  classification', NULL, '''/code'':64C,231C,310C ''/h2'':170C ''/p'':37C,128C,144C,167C,254C,374C ''/pre'':65C,232C,311C ''0'':270C,287C,305C ''0.3158'':183C ''0.3196'':181C ''0.3335'':184C ''0.3367'':182C ''0.3517'':188C ''0.3531'':190C ''0.4107'':191C ''0.4164'':189C ''0.4547'':199C ''0.4548'':197C ''0.5488'':196C ''0.5942'':198C ''0.653'':221C ''0.668'':229C ''0.671'':222C ''0.676'':223C ''0.721'':230C ''0.725'':227C ''0.727'':228C ''1'':274C,291C,309C ''10465'':247C ''10524'':267C ''111.3076'':211C ''120.5239'':213C ''14641'':240C ''2177'':288C ''25106'':239C ''3154'':306C ''4028'':271C ''4117'':268C ''5164'':303C ''54.32'':56C ''57.49'':47C ''61.51'':62C ''64.69'':58C ''64.73'':52C ''6437'':272C ''65.24'':49C ''66.87'':54C ''6688'':285C ''67.56'':63C ''69.86'':53C ''71.88'':61C ''72.3389'':203C ''72.6299'':205C ''7311'':307C ''76.09'':48C ''79.20'':57C,81C ''7953'':284C ''8288'':289C ''84.477'':206C ''85.65'':204C ''92.2318'':214C,224C ''92.2452'':212C ''9477'':302C ''absolut'':186C,201C ''accuraci'':44C,95C,115C,127C,340C,351C ''algorithm'':31C,40C,333C ''area'':226C ''avg'':216C ''b'':253C,263C,273C,280C,290C,298C,308C,319C ''bayesian'':26C,45C,173C ''belong'':250C ''best'':70C ''better'':141C,364C ''bit'':356C ''care'':4A,163C ''cart'':30C,60C,178C,261C,321C,369C ''case'':109C ''class'':84C,244C,252C,318C,325C ''classif'':18B,33C,74C ''classifi'':79C,154C,265C,282C,300C ''code'':39C,172C,256C ''combin'':101C,152C,345C ''confus'':257C,275C,292C ''correct'':78C ''couldn'':111C,336C ''data'':16B ''data-min'':15B ''edit'':169C ''ensebl'':366C ''ensembl'':10A,330C,358C ''error'':187C,195C,202C,210C ''explain'':133C ''explan'':373C ''f'':219C ''f-measur'':218C ''form'':35C ''general'':149C ''give'':68C,155C ''good'':156C ''h2'':168C ''import'':87C ''increas'':93C,113C,338C,353C ''increment'':125C ''instanc'':237C ''kappa'':179C ''learn'':14B ''littl'':124C,355C ''logist'':24C,50C,120C,138C,175C,295C,348C,361C ''lt'':264C,281C,299C ''m'':21C ''machin'':13B ''machine-learn'':12B ''made'':122C ''matrix'':258C,276C,293C ''mean'':185C,193C ''measur'':220C ''method'':11A ''mine'':17B ''network'':27C,46C,174C ''number'':235C ''other'':143C ''overal'':43C ''p'':19C,66C,129C,145C,233C,312C ''pre'':38C,171C,255C ''problem'':75C ''reg'':176C ''regress'':25C,51C,121C,139C,296C,349C,362C ''relat'':200C,208C ''result'':34C,71C,157C ''roc'':225C ''root'':192C,207C ''sensit'':41C ''simpl'':29C,59C,260C ''sinc'':76C,313C ''smo'':23C,55C,67C,118C,136C,177C,278C,314C,346C,359C,367C ''specif'':42C ''squar'':194C,209C ''stack'':7A,97C,117C,135C,159C,166C ''statist'':180C ''success'':316C,323C ''total'':234C ''tree'':153C ''tri'':99C,328C,343C ''two'':332C ''use'':22C ''want'':91C ''weight'':215C ''weka'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6357, 2, '2015-07-06 20:12:07.84', 0, NULL, NULL, '&lt;p&gt;With the Voronoi cells that runDosrun brings, it is true that they are likely unbounded, so your new data that is "outside of the cluster" isn''t actually outside the cluster.  You should probably look at a different clustering algorithm if that is indeed what you are looking to do.  &lt;/p&gt;

&lt;p&gt;If you are intent on using k-means, you could attempt to classify anomalous data into its own cluster.&lt;/p&gt;

&lt;p&gt;There are libraries out there that you can use to constrain the boundaries of the k-means during cluster analysis to the initial domain.  Without knowing what environment you are trying to do this in or what the context of the data is, it would be really difficult to define "inside a cluster" with out you specifically choosing certain boundaries.&lt;/p&gt;
', NULL, NULL, '''/p'':52C,74C,137C ''actual'':29C ''algorithm'':41C ''analysi'':96C ''anomal'':68C ''attempt'':65C ''boundari'':88C,136C ''bring'':8C ''cell'':5C ''certain'':135C ''choos'':134C ''classifi'':67C ''cluster'':26C,32C,40C,73C,95C,129C ''constrain'':86C ''context'':115C ''could'':64C ''data'':20C,69C,118C ''defin'':126C ''differ'':39C ''difficult'':124C ''domain'':100C ''environ'':104C ''inde'':45C ''initi'':99C ''insid'':127C ''intent'':57C ''isn'':27C ''k'':61C,92C ''k-mean'':60C,91C ''know'':102C ''librari'':78C ''like'':15C ''look'':36C,49C ''mean'':62C,93C ''new'':19C ''outsid'':23C,30C ''p'':1C,53C,75C ''probabl'':35C ''realli'':123C ''rundosrun'':7C ''specif'':133C ''tri'':107C ''true'':11C ''unbound'':16C ''use'':59C,84C ''voronoi'':4C ''without'':101C ''would'':121C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6358, 2, '2015-07-06 20:24:27.203', 1, NULL, NULL, '&lt;p&gt;Very difficult question for someone to answer as ultimately your examples and many questions contain certain constraints you won''t get from your sensors.
For example:
Your first question is predicting "optimum" time to turn on AC.  who defines optimum?  Optimum for the classroom at full capcity, half capacity?
Your second question looks like it one, requires a map of the classroom and where the sensors are, two you won''t be able to predict 7 sensor values from 2 sensor values, I don''t know why you would want to do this.
Your third questions is a social sciences problem.  Define "light needed to be comfortable".  I imagine you would get 150 answers if you had 150 students.&lt;/p&gt;

&lt;p&gt;Your scenario lacks context/comparison.  Let''s say you had sensors outside the classroom for your various data points.  Then you could do things like, can I predict the temp inside the room based on my other room and external data points.&lt;/p&gt;

&lt;p&gt;Finally, yes machine learning can be used with your wireless sensors.&lt;/p&gt;
', NULL, NULL, '''/p'':121C,164C,177C ''150'':114C,119C ''2'':81C ''7'':77C ''abl'':74C ''ac'':38C ''answer'':8C,115C ''base'':155C ''capac'':50C ''capciti'':48C ''certain'':17C ''classroom'':45C,63C,135C ''comfort'':108C ''constraint'':18C ''contain'':16C ''context/comparison'':126C ''could'':143C ''data'':139C,162C ''defin'':40C,103C ''difficult'':3C ''exampl'':12C,27C ''extern'':161C ''final'':166C ''first'':29C ''full'':47C ''get'':22C,113C ''half'':49C ''imagin'':110C ''insid'':152C ''know'':87C ''lack'':125C ''learn'':169C ''let'':127C ''light'':104C ''like'':55C,146C ''look'':54C ''machin'':168C ''mani'':14C ''map'':60C ''need'':105C ''one'':57C ''optimum'':33C,41C,42C ''outsid'':133C ''p'':1C,122C,165C ''point'':140C,163C ''predict'':32C,76C,149C ''problem'':102C ''question'':4C,15C,30C,53C,97C ''requir'':58C ''room'':154C,159C ''say'':129C ''scenario'':124C ''scienc'':101C ''second'':52C ''sensor'':25C,67C,78C,82C,132C,176C ''social'':100C ''someon'':6C ''student'':120C ''temp'':151C ''thing'':145C ''third'':96C ''time'':34C ''turn'':36C ''two'':69C ''ultim'':10C ''use'':172C ''valu'':79C,83C ''various'':138C ''want'':91C ''wireless'':175C ''won'':20C,71C ''would'':90C,112C ''yes'':167C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6419, 2, '2015-07-09 21:23:51.79', 0, NULL, NULL, '&lt;p&gt;Actually deep learning can be run in spark using h2o sparkling water feature.Also you can use h2o.deeplearning to run deeplearning on your data in cluster or single node.Spark is good for munging the data in cluster as it does so distributedly but in memory,otherwise h2o has limited functions for data munging and that too it can''t distribute data munging.&lt;/p&gt;
', NULL, NULL, '''/p'':63C ''actual'':2C ''cluster'':26C,37C ''data'':24C,35C,52C,61C ''deep'':3C ''deeplearn'':21C ''distribut'':42C,60C ''feature.also'':14C ''function'':50C ''good'':31C ''h2o'':11C,47C ''h2o.deeplearning'':18C ''learn'':4C ''limit'':49C ''memori'':45C ''mung'':33C,53C,62C ''node.spark'':29C ''otherwis'':46C ''p'':1C ''run'':7C,20C ''singl'':28C ''spark'':9C ''sparkl'':12C ''use'':10C,17C ''water'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6359, 2, '2015-07-06 20:42:57.077', 1, NULL, NULL, '&lt;p&gt;I don''t understand the purpose of imposing a condition that requires any cluster to contain at least some (let''s say even 1) point from every dataset but at the same time find a solution that also allows that condition to be broken by outliers (I assume these are specific cluster outliers or data set outliers?).  &lt;/p&gt;

&lt;p&gt;Have you considered implementing an overlapping cluster algorithm?  You don''t get the full distinctness of a traditional single membership but your might better fit your data to an algorithm that achieves the desired condition.  It will probably require some exploration of the data and testing for cluster membership after the fact.&lt;/p&gt;
', NULL, NULL, '''/p'':59C,113C ''1'':25C ''achiev'':92C ''algorithm'':68C,90C ''allow'':40C ''also'':39C ''assum'':49C ''better'':84C ''broken'':45C ''cluster'':15C,53C,67C,108C ''condit'':11C,42C,95C ''consid'':63C ''contain'':17C ''data'':56C,87C,104C ''dataset'':29C ''desir'':94C ''distinct'':75C ''even'':24C ''everi'':28C ''explor'':101C ''fact'':112C ''find'':35C ''fit'':85C ''full'':74C ''get'':72C ''implement'':64C ''impos'':9C ''least'':19C ''let'':21C ''membership'':80C,109C ''might'':83C ''outlier'':47C,54C,58C ''overlap'':66C ''p'':1C,60C ''point'':26C ''probabl'':98C ''purpos'':7C ''requir'':13C,99C ''say'':23C ''set'':57C ''singl'':79C ''solut'':37C ''specif'':52C ''test'':106C ''time'':34C ''tradit'':78C ''understand'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6832, 2, '2015-08-19 15:56:45.85', 0, NULL, NULL, '&lt;p&gt;A two dimensional array is a list of vectors, so &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;{{userid1,1a,1b,1c,1d,2a,2b,2c,2d,...,na,nb,nc,nd}
{userid2,1a,1b,1c,1d,2a,2b,2c,2d,...,na,nb,nc,nd},
...,
{useridk,1a,1b,1c,1d,2a,2b,2c,2d,...,na,nb,nc,nd}}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;would be a suitable input for a test with &lt;code&gt;n&lt;/code&gt; questions and &lt;code&gt;k&lt;/code&gt; contestants, where &lt;code&gt;1a&lt;/code&gt; represents the response &lt;code&gt;a&lt;/code&gt; for question one.&lt;/p&gt;
', NULL, NULL, '''/code'':54C,68C,73C,78C,84C ''/p'':12C,88C ''/pre'':55C ''1a'':16C,29C,42C,77C ''1b'':17C,30C,43C ''1c'':18C,31C,44C ''1d'':19C,32C,45C ''2a'':20C,33C,46C ''2b'':21C,34C,47C ''2c'':22C,35C,48C ''2d'':23C,36C,49C ''array'':5C ''code'':14C,66C,71C,76C,82C ''contest'':74C ''dimension'':4C ''input'':61C ''k'':72C ''list'':8C ''n'':67C ''na'':24C,37C,50C ''nb'':25C,38C,51C ''nc'':26C,39C,52C ''nd'':27C,40C,53C ''one'':87C ''p'':1C,56C ''pre'':13C ''question'':69C,86C ''repres'':79C ''respons'':81C ''suitabl'':60C ''test'':64C ''two'':3C ''userid1'':15C ''userid2'':28C ''useridk'':41C ''vector'':10C ''would'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6360, 2, '2015-07-06 21:14:53.163', 5, NULL, NULL, '&lt;p&gt;&lt;a href="https://www.getdatajoy.com" rel="nofollow"&gt;Data Joy&lt;/a&gt; - Online Python &amp;amp; R Editor&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Collaborate with other people in real time&lt;/li&gt;
&lt;li&gt;Private VM''s for each project&lt;/li&gt;
&lt;li&gt;Work via  your browser&lt;/li&gt;
&lt;li&gt;In built track changes/history feature&lt;/li&gt;
&lt;li&gt;Dropbox &amp;amp; Github Sync&lt;/li&gt;
&lt;li&gt;Chat to other users&lt;/li&gt;
&lt;li&gt;Publish your project to its own page you can share, with comments rendered using markdown&lt;/li&gt;
&lt;li&gt;Open source &lt;a href="https://github.com/sharelatex/web-sharelatex/tree/datajoy" rel="nofollow"&gt;https://github.com/sharelatex/web-sharelatex/tree/datajoy&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/4LJ67.png" alt="Datajoy editor screen shot"&gt;&lt;/p&gt;

&lt;p&gt;Source - I helped build this&lt;/p&gt;
', NULL, NULL, '''/4lj67.png'':95C ''/a'':9C ''/li'':25C,33C,39C,46C,52C,58C,75C ''/p'':15C,101C,108C ''/sharelatex/web-sharelatex/tree/datajoy'':83C ''/sharelatex/web-sharelatex/tree/datajoy&lt;/a&gt;&lt;/li&gt;'':88C ''/ul'':89C ''alt'':96C ''amp'':12C,49C ''browser'':38C ''build'':106C ''built'':42C ''changes/history'':44C ''chat'':54C ''collabor'':18C ''comment'':71C ''data'':7C ''datajoy'':97C ''dropbox'':48C ''editor'':14C,98C ''featur'':45C ''github'':50C ''github.com'':82C,87C ''github.com/sharelatex/web-sharelatex/tree/datajoy'':81C ''github.com/sharelatex/web-sharelatex/tree/datajoy&lt;/a&gt;&lt;/li&gt;'':86C ''help'':105C ''href'':3C,80C ''i.stack.imgur.com'':94C ''i.stack.imgur.com/4lj67.png'':93C ''img'':91C ''joy'':8C ''li'':17C,26C,34C,40C,47C,53C,59C,76C ''markdown'':74C ''nofollow'':6C,85C ''onlin'':10C ''open'':77C ''p'':1C,90C,102C ''page'':66C ''peopl'':21C ''privat'':27C ''project'':32C,62C ''publish'':60C ''python'':11C ''r'':13C ''real'':23C ''rel'':5C,84C ''render'':72C ''screen'':99C ''share'':69C ''shot'':100C ''sourc'':78C,103C ''src'':92C ''sync'':51C ''time'':24C ''track'':43C ''ul'':16C ''use'':73C ''user'':57C ''via'':36C ''vm'':28C ''work'':35C ''www.getdatajoy.com'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6361, 1, '2015-07-06 21:16:52.867', 1, 26, 'How to simulate customer walk-ins for a given period in a fast food chain in R', '&lt;p&gt;I would really appreciate it if someone could tell me where I would start in tackling the following tasks in R.&lt;/p&gt;

&lt;p&gt;I''m not a data-science expert and am trying to teach myself data-analysis from a background in physics. I have introductory statistics and have just finished Udacity''s course in R programming and exploratory data analysis.&lt;/p&gt;

&lt;p&gt;Tasks:&lt;/p&gt;

&lt;p&gt;1 )Simulate customer walk-ins for a given period. The simulations should take into account peak volumes at different times of the days and differences in week days and weekends.&lt;/p&gt;

&lt;p&gt;2) Simulate customer purchases based on the customer walk-ins. Make assumptions on the average dollar price and variations for different times of the day.&lt;/p&gt;

&lt;p&gt;Thank you&lt;/p&gt;
', 'r  statistics  simulation', NULL, '''/p'':44C,86C,89C,122C,150C,154C ''1'':91C ''2'':124C ''account'':106C ''analysi'':62C,85C ''appreci'':26C ''assumpt'':136C ''averag'':139C ''background'':65C ''base'':128C ''chain'':16A ''could'':30C ''cours'':78C ''custom'':4A,93C,126C,131C ''data'':51C,61C,84C ''data-analysi'':60C ''data-sci'':50C ''day'':114C,119C,149C ''differ'':110C,116C,145C ''dollar'':140C ''expert'':53C ''exploratori'':83C ''fast'':14A ''finish'':75C ''follow'':40C ''food'':15A ''given'':10A,99C ''in'':7A,96C,134C ''introductori'':70C ''m'':47C ''make'':135C ''p'':22C,45C,87C,90C,123C,151C ''peak'':107C ''period'':11A,100C ''physic'':67C ''price'':141C ''program'':81C ''purchas'':127C ''r'':18A,19B,43C,80C ''realli'':25C ''scienc'':52C ''simul'':3A,21B,92C,102C,125C ''someon'':29C ''start'':36C ''statist'':20B,71C ''tackl'':38C ''take'':104C ''task'':41C,88C ''teach'':58C ''tell'':31C ''thank'':152C ''time'':111C,146C ''tri'':56C ''udac'':76C ''variat'':143C ''volum'':108C ''walk'':6A,95C,133C ''walk-in'':5A,94C,132C ''week'':118C ''weekend'':121C ''would'':24C,35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8795, 2, '2015-11-11 07:10:12.17', 0, NULL, NULL, '&lt;p&gt;In the given example from the e-book, the number 4 comes from (12-5+1)/2, where 12 is the input image size (12*12) of the second constitutional layer; 5 is the filter size (5*5) used in that layer; and 2 is the poolsize.&lt;br&gt;
This is similar to how you get the number 12 from the first constitutional layer: 12=(28-5+1)/2. It''s well explained in your linked chapter.&lt;/p&gt;

&lt;p&gt;Regarding your "For instance" code, your 6th line is not correct:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 1),   &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The number 12 should be replaced by (81-5+1)/2 which unfortunately is not an integer. You may want to change the filter_shape in the first layer to (6,1) to make it work. In that case, your 6th line should be: &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;ConvPoolLayer(image_shape=(mini_batch_size, 20, 38, 1),   &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;and your 11th line should be: &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;n_in=40*17*1, n_out=1000, activation_fn=ReLU, p_dropout=0.5),&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''+1'':18C,69C,116C ''-5'':17C,68C,115C ''/2'':19C,70C,117C ''/blockquote'':105C,164C,190C ''/p'':79C,92C,104C,151C,163C,172C,189C ''0.5'':188C ''1'':103C,138C,162C,179C ''1000'':182C ''11th'':168C ''12'':16C,21C,27C,28C,60C,66C,102C,109C ''17'':178C ''2'':46C ''20'':101C,160C ''28'':67C ''38'':161C ''4'':13C ''40'':177C ''5'':34C,39C,40C ''6'':137C ''6th'':87C,147C ''81'':114C ''activ'':183C ''batch'':99C,158C ''blockquot'':93C,152C,173C ''book'':10C ''br'':50C ''case'':145C ''chang'':128C ''chapter'':78C ''code'':85C ''come'':14C ''constitut'':32C,64C ''convpoollay'':95C,154C ''correct'':91C ''dropout'':187C ''e'':9C ''e-book'':8C ''exampl'':5C ''explain'':74C ''filter'':37C,130C ''first'':63C,134C ''fn'':184C ''get'':57C ''given'':4C ''imag'':25C,96C,155C ''input'':24C ''instanc'':84C ''integ'':123C ''layer'':33C,44C,65C,135C ''line'':88C,148C,169C ''link'':77C ''make'':140C ''may'':125C ''mini'':98C,157C ''n'':175C,180C ''number'':12C,59C,108C ''p'':1C,80C,94C,106C,153C,165C,174C,186C ''poolsiz'':49C ''regard'':81C ''relu'':185C ''replac'':112C ''second'':31C ''shape'':97C,131C,156C ''similar'':53C ''size'':26C,38C,100C,159C ''unfortun'':119C ''use'':41C ''want'':126C ''well'':73C ''work'':142C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6363, 2, '2015-07-06 21:26:01.297', 0, NULL, NULL, '&lt;p&gt;You need either an initial data set or a large set of constraints to "fake" a data set.  if you have an initial data set you could make somewhat valid assumptions regarding traffic flow, purchasing behaviors and the likes.&lt;/p&gt;

&lt;p&gt;If not, you are going to need to research these different metrics from existing companies that are like what you want data for and then I assume put together a little program with those metrics as constraints and simulate your data.  Once you have those constraints you could use simple averaging or weighted options for developing a constraint per variable.&lt;/p&gt;

&lt;p&gt;Make sure they are semi accurate.  For example, if you were modeling company growth for a "really good idea" research growth trends for like companies and for companies you want to emulate and then scale BACK from there.&lt;/p&gt;

&lt;p&gt;You can also always purchase a dataset:
&lt;a href="http://datamarket.azure.com/browse/data" rel="nofollow"&gt;http://datamarket.azure.com/browse/data&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/browse/data'':156C ''/browse/data&lt;/a&gt;&lt;/p&gt;'':161C ''/p'':41C,103C,143C ''accur'':110C ''also'':147C ''alway'':148C ''assum'':69C ''assumpt'':32C ''averag'':93C ''back'':140C ''behavior'':37C ''compani'':57C,117C,129C,132C ''constraint'':14C,79C,88C,100C ''could'':28C,90C ''data'':7C,18C,25C,64C,83C ''datamarket.azure.com'':155C,160C ''datamarket.azure.com/browse/data'':154C ''datamarket.azure.com/browse/data&lt;/a&gt;&lt;/p&gt;'':159C ''dataset'':151C ''develop'':98C ''differ'':53C ''either'':4C ''emul'':136C ''exampl'':112C ''exist'':56C ''fake'':16C ''flow'':35C ''go'':47C ''good'':122C ''growth'':118C,125C ''href'':153C ''idea'':123C ''initi'':6C,24C ''larg'':11C ''like'':40C,60C,128C ''littl'':73C ''make'':29C,105C ''metric'':54C,77C ''model'':116C ''need'':3C,49C ''nofollow'':158C ''option'':96C ''p'':1C,42C,104C,144C ''per'':101C ''program'':74C ''purchas'':36C,149C ''put'':70C ''realli'':121C ''regard'':33C ''rel'':157C ''research'':51C,124C ''scale'':139C ''semi'':109C ''set'':8C,12C,19C,26C ''simpl'':92C ''simul'':81C ''somewhat'':30C ''sure'':106C ''togeth'':71C ''traffic'':34C ''trend'':126C ''use'':91C ''valid'':31C ''variabl'':102C ''want'':63C,134C ''weight'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6432, 2, '2015-07-10 22:42:22.63', 1, NULL, NULL, '&lt;p&gt;First of all, you cannot always consider what a machine learning algorithm outputs as a "probability". Logistic regression outputs a sigmoid activation on a &lt;code&gt;(0, 1)&lt;/code&gt; scale, but that doesn''t magically make it so! &lt;/p&gt;

&lt;p&gt;We simply often scale things to a &lt;code&gt;(0, 1)&lt;/code&gt; scale in ML as a measure of &lt;strong&gt;confidence&lt;/strong&gt;. &lt;/p&gt;

&lt;p&gt;Also in your example, if the events are mutually exclusive (like classification), just think of them as "event 1" and "NOT event 1". Something like &lt;code&gt;p(e1) + p(~e1) = 1&lt;/code&gt;. &lt;/p&gt;

&lt;p&gt;So when your book tells you to lower the threshold, it is simply saying that you require a smaller level of &lt;strong&gt;confidence&lt;/strong&gt; to choose e1 over e2. This doesn''t mean you are choosing one with smaller likelihood, you are simply making a conscious choice to adjust your &lt;a href="https://en.wikipedia.org/wiki/Precision_and_recall" rel="nofollow"&gt;precision-recall curve&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;There are other ways to combat class imbalance, but changing the threshold to be more sensitive to any indication of &lt;strong&gt;confidence&lt;/strong&gt; of one class over another is certainly a way to do that. &lt;/p&gt;
', NULL, NULL, '''/a'':159C ''/code'':29C,51C,95C ''/p'':39C,62C,96C,160C,197C ''/strong'':61C,121C,184C ''/wiki/precision_and_recall'':152C ''0'':27C,49C ''1'':28C,50C,82C,86C,94C ''activ'':23C ''adjust'':146C ''algorithm'':13C ''also'':64C ''alway'':7C ''anoth'':189C ''book'':101C ''cannot'':6C ''certain'':191C ''chang'':171C ''choic'':144C ''choos'':123C,133C ''class'':168C,187C ''classif'':75C ''code'':26C,48C,89C ''combat'':167C ''confid'':60C,120C,183C ''conscious'':143C ''consid'':8C ''curv'':158C ''doesn'':33C,128C ''e1'':91C,93C,124C ''e2'':126C ''en.wikipedia.org'':151C ''en.wikipedia.org/wiki/precision_and_recall'':150C ''event'':70C,81C,85C ''exampl'':67C ''exclus'':73C ''first'':2C ''href'':149C ''imbal'':169C ''indic'':180C ''learn'':12C ''level'':117C ''like'':74C,88C ''likelihood'':137C ''logist'':18C ''lower'':105C ''machin'':11C ''magic'':35C ''make'':36C,141C ''mean'':130C ''measur'':57C ''ml'':54C ''mutual'':72C ''nofollow'':154C ''often'':43C ''one'':134C,186C ''output'':14C,20C ''p'':1C,40C,63C,90C,92C,97C,161C ''precis'':156C ''precision-recal'':155C ''probabl'':17C ''recal'':157C ''regress'':19C ''rel'':153C ''requir'':114C ''say'':111C ''scale'':30C,44C,52C ''sensit'':177C ''sigmoid'':22C ''simpli'':42C,110C,140C ''smaller'':116C,136C ''someth'':87C ''strong'':59C,119C,182C ''tell'':102C ''thing'':45C ''think'':77C ''threshold'':107C,173C ''way'':165C,193C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6364, 2, '2015-07-06 22:37:50.287', 3, NULL, NULL, '&lt;p&gt;Strictly speaking, the k-means algorithm does not have a definition for "inside the cluster" and is therefore not a great candidate for anomaly detection.  In k-means, every point is assigned to one of k clusters and then a new cluster centroid is calculated.  &lt;/p&gt;

&lt;p&gt;But as previous uses have pointed out, you could construct some sort of ad-hoc system where you process a set of data and then define new data as anomalous when its extends beyond 2 standard deviations of the centroid location. DON''T DO THIS! &lt;/p&gt;

&lt;p&gt;K-Means will not work well for this ad-hoc method.  If k is poorly chosen, then the distribution within a cluster will not be normally distributed.  You very, very frequently see natural distributions of points which are split between two clusters.  For instance, take a look at the ad-hoc segmentation of points in this location data for a cell phone user''s location over a month:
&lt;a href="http://i.stack.imgur.com/2vLSn.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/2vLSn.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I suggest you use another clustering method.  The first option that comes to mind is DBSCAN.  This allows one to set a threshold for noise and the cluster numbers are not set a-priori.  DBSCAN is therefor much more likely to return normal distributions within a cluster.  Here is a single DBSCAN cluster of the same data:
&lt;a href="http://i.stack.imgur.com/w6kJm.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/w6kJm.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Finally, I''ll point out that the method you are proposing is not as good as other novelty and anomaly detection methods.  You should consider doing novelty detection using a single (or possibly even multiple) class support vector machine (SVM) with a nonlinear kernel. The nonlinear kernel will allow you to recover multiple "clusters" while the SVM will do much better at predicting which points are inside the class.&lt;/p&gt;
', NULL, NULL, '''/2vlsn.png'':174C,181C ''/a'':187C,265C ''/p'':49C,97C,188C,266C,337C ''/w6kjm.png'':252C,259C ''2'':86C ''a-priori'':222C ''ad'':65C,109C,151C ''ad-hoc'':64C,108C,150C ''algorithm'':8C ''allow'':207C,316C ''alt'':182C,260C ''anomal'':81C ''anomali'':26C,287C ''anoth'':194C ''assign'':35C ''better'':328C ''beyond'':85C ''calcul'':48C ''candid'':24C ''cell'':162C ''centroid'':46C,91C ''chosen'':116C ''class'':303C,336C ''cluster'':17C,40C,45C,122C,142C,195C,217C,237C,243C,321C ''come'':201C ''consid'':292C ''construct'':60C ''could'':59C ''data'':74C,79C,159C,247C ''dbscan'':205C,225C,242C ''defin'':77C ''definit'':13C ''descript'':185C,263C ''detect'':27C,288C,295C ''deviat'':88C ''distribut'':119C,127C,134C,234C ''enter'':183C,261C ''even'':301C ''everi'':32C ''extend'':84C ''final'':268C ''first'':198C ''frequent'':131C ''good'':282C ''great'':23C ''hoc'':66C,110C,152C ''href'':171C,249C ''i.stack.imgur.com'':173C,180C,251C,258C ''i.stack.imgur.com/2vlsn.png'':172C,179C ''i.stack.imgur.com/w6kjm.png'':250C,257C ''imag'':184C,262C ''img'':177C,255C ''insid'':15C,334C ''instanc'':144C ''k'':6C,30C,39C,100C,113C ''k-mean'':5C,29C,99C ''kernel'':311C,314C ''like'':230C ''ll'':270C ''locat'':92C,158C,166C ''look'':147C ''machin'':306C ''mean'':7C,31C,101C ''method'':111C,196C,275C,289C ''mind'':203C ''month'':169C ''much'':228C,327C ''multipl'':302C,320C ''natur'':133C ''new'':44C,78C ''nofollow'':176C,254C ''nois'':214C ''nonlinear'':310C,313C ''normal'':126C,233C ''novelti'':285C,294C ''number'':218C ''one'':37C,208C ''option'':199C ''p'':1C,50C,98C,189C,267C ''phone'':163C ''point'':33C,56C,136C,155C,271C,332C ''poor'':115C ''possibl'':300C ''predict'':330C ''previous'':53C ''priori'':224C ''process'':70C ''propos'':278C ''recov'':319C ''rel'':175C,253C ''return'':232C ''see'':132C ''segment'':153C ''set'':72C,210C,221C ''singl'':241C,298C ''sort'':62C ''speak'':3C ''split'':139C ''src'':178C,256C ''standard'':87C ''strict'':2C ''suggest'':191C ''support'':304C ''svm'':307C,324C ''system'':67C ''take'':145C ''therefor'':20C,227C ''threshold'':212C ''two'':141C ''use'':54C,193C,296C ''user'':164C ''vector'':305C ''well'':105C ''within'':120C,235C ''work'':104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6410, 1, '2015-07-09 16:12:00.94', 2, 35, 'Does the network learn based on previous training or does it restart? Matlab, neuralnetworks', '&lt;p&gt;In Matlab, if you build a simple network and train it: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;OP = feedforwardnet(5, ''traingdm'');
inputsVals = [0,1,2,3,4];
targetVals = [3,2,5,1,9];
OP = train(OP,inputsVals,targetVals);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;then you train it again so another &lt;code&gt;OP = train(OP,inputsVals,targetVals);&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;What is happens to the network? Does it train again based on what it learned the first time you did &lt;code&gt;OP = train(OP,inputsVals,targetVals);&lt;/code&gt; or does it train as if it were the first time training the network. &lt;/p&gt;
', 'neuralnetwork  matlab', NULL, '''/code'':53C,69C,98C ''/p'':29C,70C,113C ''/pre'':54C ''0'':37C ''1'':38C,46C ''2'':39C,44C ''3'':40C,43C ''4'':41C ''5'':34C,45C ''9'':47C ''anoth'':62C ''base'':5A,82C ''build'':22C ''code'':31C,63C,92C ''feedforwardnet'':33C ''first'':88C,108C ''happen'':74C ''inputsv'':36C,51C,67C,96C ''learn'':4A,86C ''matlab'':13A,16B,19C ''network'':3A,25C,77C,112C ''neuralnetwork'':14A,15B ''op'':32C,48C,50C,64C,66C,93C,95C ''p'':17C,55C,71C ''pre'':30C ''previous'':7A ''restart'':12A ''simpl'':24C ''targetv'':42C,52C,68C,97C ''time'':89C,109C ''train'':8A,27C,49C,58C,65C,80C,94C,102C,110C ''traingdm'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6365, 1, '2015-07-07 00:21:53.52', 1, 198, 'Question about (Python/Orange) Apriori associative algorithm', '&lt;p&gt;I''m trying to wrap my head around &lt;a href="http://orange.biolab.si/docs/latest/reference/rst/Orange.associate.html" rel="nofollow"&gt;Association rules and frequent itemsets&lt;/a&gt;.
So I threw my data in, instead of the samples one and sometime it works, sometimes it doesn''t.  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;rules = Orange.associate.AssociationRulesSparseInducer(data, support = 0.3)

print "%5s   %5s" % ("supp", "conf")
for r in rules:
    print "%5.3f   %5.3f   %s" % (r.support, r.confidence, r)

inducer = Orange.associate.AssociationRulesSparseInducer(support = 0.2, store_examples = True)
itemsets = inducer.get_itemsets(data)
print itemsets
print data.domain
print [data.domain[i].name for i in itemsets[4][0]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;More often than not, itemsets[4][0] shows an &lt;code&gt;IndexError: list index out of range&lt;/code&gt; error.
So I start playing around with support = 0.3, support = 0.5, support = 0.2 and itemsets[2][0] or itemsets[3][0].&lt;/p&gt;

&lt;p&gt;From the docs:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;support&lt;br&gt;
  Minimal support for the rule. Depending on the data set it should be set to sufficiently high value to avoid running out of working memory (default: 0.3).&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;True - I tried 0.2 and it quickly blasted my memory on a 800 rows data file.&lt;/p&gt;

&lt;p&gt;Any idea what I should do best there or which are viable values for a shopping cart analysis?&lt;/p&gt;

&lt;p&gt;800 rows of data (800 orders)&lt;br&gt;
1 to x item(-categories) per order&lt;br&gt;
15 different item-categories in the file, so my data looks like:  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ItemCat1
ItemCat2, ItemCat2, ItemCat2, ItemCat2, ItemCat7, ItemCat7, ItemCat7,     ItemCat7, ItemCat7
ItemCat1, ItemCat1, ItemCat1, ItemCat1, ItemCat1, ItemCat1, ItemCat1,     ItemCat2
ItemCat4, ItemCat4
ItemCat1, ItemCat1, ItemCat1, ItemCat1, ItemCat1, ItemCat1, ItemCat1,     ItemCat1, ItemCat2
ItemCat5
&lt;/code&gt;&lt;/pre&gt;
', 'data-mining  python  orange', NULL, '''/a'':33C ''/blockquote'':183C ''/code'':104C,123C,284C ''/docs/latest/reference/rst/orange.associate.html'':25C ''/p'':53C,145C,150C,182C,201C,221C,251C ''/pre'':105C,285C ''0'':103C,113C,140C,144C ''0.2'':82C,136C,188C ''0.3'':60C,132C,181C ''0.5'':134C ''1'':230C ''15'':238C ''2'':139C ''3'':143C ''4'':102C,112C ''5.3'':71C,73C ''5s'':62C,63C ''800'':197C,223C,227C ''algorithm'':6A ''analysi'':220C ''apriori'':4A ''around'':20C,129C ''associ'':5A,28C ''avoid'':174C ''best'':209C ''blast'':192C ''blockquot'':151C ''br'':154C,229C,237C ''cart'':219C ''categori'':234C,242C ''code'':55C,116C,253C ''conf'':65C ''data'':8B,38C,58C,89C,163C,199C,226C,248C ''data-min'':7B ''data.domain'':93C,95C ''default'':180C ''depend'':160C ''differ'':239C ''doc'':149C ''doesn'':51C ''error'':124C ''exampl'':84C ''f'':72C,74C ''file'':200C,245C ''frequent'':31C ''head'':19C ''high'':171C ''href'':22C ''idea'':204C ''index'':119C ''indexerror'':117C ''induc'':79C ''inducer.get'':87C ''instead'':40C ''item'':233C,241C ''item-categori'':240C ''itemcat1'':254C,264C,265C,266C,267C,268C,269C,270C,274C,275C,276C,277C,278C,279C,280C,281C ''itemcat2'':255C,256C,257C,258C,271C,282C ''itemcat4'':272C,273C ''itemcat5'':283C ''itemcat7'':259C,260C,261C,262C,263C ''itemset'':32C,86C,88C,91C,101C,111C,138C,142C ''like'':250C ''list'':118C ''look'':249C ''m'':14C ''memori'':179C,194C ''mine'':9B ''minim'':155C ''name'':97C ''nofollow'':27C ''often'':108C ''one'':44C ''orang'':11B ''orange.associate.associationrulessparseinducer'':57C,80C ''orange.biolab.si'':24C ''orange.biolab.si/docs/latest/reference/rst/orange.associate.html'':23C ''order'':228C,236C ''p'':12C,106C,146C,152C,184C,202C,222C ''per'':235C ''play'':128C ''pre'':54C,252C ''print'':61C,70C,90C,92C,94C ''python'':10B ''python/orange'':3A ''question'':1A ''quick'':191C ''r'':67C,78C ''r.confidence'':77C ''r.support'':76C ''rang'':122C ''rel'':26C ''row'':198C,224C ''rule'':29C,56C,69C,159C ''run'':175C ''sampl'':43C ''set'':164C,168C ''shop'':218C ''show'':114C ''sometim'':46C,49C ''start'':127C ''store'':83C ''suffici'':170C ''supp'':64C ''support'':59C,81C,131C,133C,135C,153C,156C ''threw'':36C ''tri'':15C,187C ''true'':85C,185C ''valu'':172C,215C ''viabl'':214C ''work'':48C,178C ''wrap'':17C ''x'':232C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10241, 1, '2016-02-14 23:22:19.023', 0, 12, 'Unable to diagnose invalid syntax error (python)', '&lt;p&gt;&lt;a href="http://i.stack.imgur.com/AnzpW.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/AnzpW.png" alt=""&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I have an invalid syntax error popping up on line 55. I think the problem is being caused earlier in the code, but I am unable to diagnose it. Please help.&lt;/p&gt;
', 'python  error-handling', NULL, '''/a'':26C ''/anzpw.png'':17C,24C ''/p'':27C,60C ''55'':39C ''alt'':25C ''caus'':46C ''code'':50C ''diagnos'':3A,56C ''earlier'':47C ''error'':6A,10B,34C ''error-handl'':9B ''handl'':11B ''help'':59C ''href'':14C ''i.stack.imgur.com'':16C,23C ''i.stack.imgur.com/anzpw.png'':15C,22C ''img'':20C ''invalid'':4A,32C ''line'':38C ''nofollow'':19C ''p'':12C,28C ''pleas'':58C ''pop'':35C ''problem'':43C ''python'':7A,8B ''rel'':18C ''src'':21C ''syntax'':5A,33C ''think'':41C ''unabl'':1A,54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6366, 2, '2015-07-07 01:15:04.84', 0, NULL, NULL, '&lt;p&gt;If the size of your logs are still growing then a distributed data system is definitely the right way to go. I have been using &lt;a href="http://mesos.apache.org/" rel="nofollow"&gt;Mesos&lt;/a&gt; in production for almost a year now and it solved the problem of &lt;code&gt;if this processing happens on some server it under or over utilizes resources&lt;/code&gt;. &lt;/p&gt;

&lt;p&gt;I probably would look into some stacks like this:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Mesos as your fault-tolerant and elastic distributed systems &lt;/li&gt;
&lt;li&gt;&lt;a href="https://spark.apache.org/" rel="nofollow"&gt;Spark&lt;/a&gt; or some Hadoop-based solutions for log processing and store the output in a DFS like HDFS&lt;/li&gt;
&lt;li&gt;Have your applications consume data stored in HDFS as final steps&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':33C,94C ''/code'':61C ''/li'':86C,113C,126C ''/p'':62C,73C ''/ul'':127C ''almost'':37C ''applic'':117C ''base'':99C ''code'':47C ''consum'':118C ''data'':14C,119C ''definit'':17C ''dfs'':110C ''distribut'':13C,84C ''elast'':83C ''fault'':80C ''fault-toler'':79C ''final'':124C ''go'':22C ''grow'':10C ''hadoop'':98C ''hadoop-bas'':97C ''happen'':51C ''hdfs'':112C,122C ''href'':28C,89C ''li'':75C,87C,114C ''like'':71C,111C ''log'':7C,102C ''look'':67C ''meso'':32C,76C ''mesos.apache.org'':29C ''nofollow'':31C,92C ''output'':107C ''p'':1C,63C ''probabl'':65C ''problem'':45C ''process'':50C,103C ''product'':35C ''rel'':30C,91C ''resourc'':60C ''right'':19C ''server'':54C ''size'':4C ''solut'':100C ''solv'':43C ''spark'':93C ''spark.apache.org'':90C ''stack'':70C ''step'':125C ''still'':9C ''store'':105C,120C ''system'':15C,85C ''toler'':81C ''ul'':74C ''use'':26C ''util'':59C ''way'':20C ''would'':66C ''year'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6367, 1, '2015-07-07 03:23:12.333', 0, 22, 'Similar Candidate Recommendations', '&lt;p&gt;I''ve just embarked on a project to recommend similar candidates to employers for a website that offers employers the ability to search its CV database. &lt;/p&gt;

&lt;p&gt;My background is in engineering research on biomedical images - from simple predefined feature extraction to CNNs.&lt;/p&gt;

&lt;p&gt;I''m about to start with a simple bag of words approach standardised by the frequency of appearance and see where that gets me.&lt;/p&gt;

&lt;p&gt;Has anyone done anything similar before and has suggestions for which algorithms might give good results?&lt;/p&gt;

&lt;p&gt;The data is very well structured, separated into seperate sections for each previous employment etc.&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':34C,52C,78C,96C,113C ''abil'':28C ''algorithm'':91C ''anyon'':81C ''anyth'':83C ''appear'':71C ''approach'':65C ''background'':37C ''bag'':62C ''biomed'':43C ''candid'':2A,18C ''cnns'':51C ''cv'':32C ''data'':99C ''databas'':33C ''done'':82C ''embark'':11C ''employ'':20C,26C,111C ''engin'':40C ''etc'':112C ''extract'':49C ''featur'':48C ''frequenc'':69C ''get'':76C ''give'':93C ''good'':94C ''imag'':44C ''learn'':6B ''m'':55C ''machin'':5B ''machine-learn'':4B ''might'':92C ''offer'':25C ''p'':7C,35C,53C,79C,97C ''predefin'':47C ''previous'':110C ''project'':14C ''recommend'':3A,16C ''research'':41C ''result'':95C ''search'':30C ''section'':107C ''see'':73C ''separ'':104C ''seper'':106C ''similar'':1A,17C,84C ''simpl'':46C,61C ''standardis'':66C ''start'':58C ''structur'':103C ''suggest'':88C ''ve'':9C ''websit'':23C ''well'':102C ''word'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6368, 2, '2015-07-07 03:46:55.903', 1, NULL, NULL, '&lt;p&gt;&lt;em&gt;&lt;a href="http://www.oreilly.com/data/free/data-science-in-the-cloud.csp" rel="nofollow"&gt;Data Science in the Cloud with Microsoft Azure Machine Learning and R&lt;/a&gt;&lt;/em&gt; is a free textbook which works through an example in great detail. Don''t be put off by the particular tools used as you don''t need them to get some benefit out of the book. &lt;/p&gt;

&lt;p&gt;Another one which I enjoyed is &lt;a href="http://shop.oreilly.com/product/9780596529321.do" rel="nofollow"&gt;&lt;em&gt;Programming Collective Intelligence&lt;/em&gt;&lt;/a&gt; which also goes through a number of projects in detail, including the web scraping part which most books gloss over.&lt;/p&gt;
', NULL, NULL, '''/a'':22C,80C ''/data/free/data-science-in-the-cloud.csp'':7C ''/em'':23C,79C ''/p'':60C,101C ''/product/9780596529321.do'':72C ''also'':82C ''anoth'':62C ''azur'':17C ''benefit'':55C ''book'':59C,98C ''cloud'':14C ''collect'':77C ''data'':10C ''detail'':35C,90C ''em'':2C,75C ''enjoy'':66C ''exampl'':32C ''free'':26C ''get'':53C ''gloss'':99C ''goe'':83C ''great'':34C ''href'':4C,69C ''includ'':91C ''intellig'':78C ''learn'':19C ''machin'':18C ''microsoft'':16C ''need'':50C ''nofollow'':9C,74C ''number'':86C ''one'':63C ''p'':1C,61C ''part'':95C ''particular'':43C ''program'':76C ''project'':88C ''put'':39C ''r'':21C ''rel'':8C,73C ''scienc'':11C ''scrape'':94C ''shop.oreilly.com'':71C ''shop.oreilly.com/product/9780596529321.do'':70C ''textbook'':27C ''tool'':44C ''use'':45C ''web'':93C ''work'':29C ''www.oreilly.com'':6C ''www.oreilly.com/data/free/data-science-in-the-cloud.csp'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6369, 2, '2015-07-07 06:50:32.313', 0, NULL, NULL, '&lt;p&gt;Read the following by MLWave:
&lt;a href="http://mlwave.com/kaggle-ensembling-guide/" rel="nofollow"&gt;http://mlwave.com/kaggle-ensembling-guide/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This is very good starting point to stacking / ensembles.&lt;/p&gt;
', NULL, NULL, '''/kaggle-ensembling-guide/'':11C ''/kaggle-ensembling-guide/&lt;/a&gt;&lt;/p&gt;'':16C ''/p'':27C ''ensembl'':26C ''follow'':4C ''good'':21C ''href'':8C ''mlwave'':6C ''mlwave.com'':10C,15C ''mlwave.com/kaggle-ensembling-guide/'':9C ''mlwave.com/kaggle-ensembling-guide/&lt;/a&gt;&lt;/p&gt;'':14C ''nofollow'':13C ''p'':1C,17C ''point'':23C ''read'':2C ''rel'':12C ''stack'':25C ''start'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6599, 2, '2015-07-27 12:24:39.65', 1, NULL, NULL, '&lt;p&gt;It is an old question, but I want to suggest a different approach than the other answers.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Using a sentiment analysis on reviews&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Let''s say that the data we have are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;5-stars rating (zero to five)&lt;/li&gt;
&lt;li&gt;N number of reviews for each entry&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;We use a sentiment analysis model to calculate the "mood" of each review. The result would be "negative", "neutral" and "positive", based on the content of the review.&lt;/p&gt;

&lt;p&gt;You can read more about sentiment analysis and find some APIs in this &lt;a href="http://www.quora.com/Is-there-an-API-for-analyzing-the-emotion-in-text" rel="nofollow"&gt;Quora''s question&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Then, you will use a multiplier for each mood.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;0: negative mood&lt;/li&gt;
&lt;li&gt;0.5: neutral mood&lt;/li&gt;
&lt;li&gt;1: positive mood&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;As a result, you can add the review score like &lt;code&gt;multiplier*5&lt;/code&gt; for each review in your dataset. For example in pseudocode,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;var current_5_star;
for each review in this_entry:
    multiplier = calc_sentiment(review)
    current_5_star += multiplier * 5
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If you want to improve further the model, you can use the total number of reviews on your dataset to normalize the multiplier.&lt;/p&gt;

&lt;p&gt;To sum up, you do not only use the number of reviews per se, but also the content of them to evaluate the exact impact of it in the rating score.&lt;/p&gt;
', NULL, NULL, '''/a'':114C ''/code'':158C,191C ''/is-there-an-api-for-analyzing-the-emotion-in-text'':108C ''/li'':49C,58C,132C,137C,142C ''/p'':19C,29C,40C,89C,115C,126C,169C,217C,250C ''/pre'':192C ''/strong'':28C ''/ul'':59C,143C ''0'':129C ''0.5'':134C ''1'':139C ''5'':43C,157C,174C,187C,190C ''add'':150C ''also'':234C ''analysi'':25C,65C,97C ''answer'':18C ''api'':101C ''approach'':14C ''base'':82C ''calc'':183C ''calcul'':68C ''code'':155C,171C ''content'':85C,236C ''current'':173C,186C ''data'':36C ''dataset'':164C,212C ''differ'':13C ''entri'':57C,181C ''evalu'':240C ''exact'':242C ''exampl'':166C ''find'':99C ''five'':48C ''href'':105C ''impact'':243C ''improv'':198C ''let'':31C ''li'':42C,50C,128C,133C,138C ''like'':154C ''model'':66C,201C ''mood'':70C,125C,131C,136C,141C ''multipli'':122C,156C,182C,189C,216C ''n'':51C ''negat'':78C,130C ''neutral'':79C,135C ''nofollow'':110C ''normal'':214C ''number'':52C,207C,228C ''old'':5C ''p'':1C,20C,30C,60C,90C,116C,144C,193C,218C ''per'':231C ''posit'':81C,140C ''pre'':170C ''pseudocod'':168C ''question'':6C,113C ''quora'':111C ''rate'':45C,248C ''read'':93C ''rel'':109C ''result'':75C,147C ''review'':27C,54C,73C,88C,152C,161C,178C,185C,209C,230C ''say'':33C ''score'':153C,249C ''se'':232C ''sentiment'':24C,64C,96C,184C ''star'':44C,175C,188C ''strong'':21C ''suggest'':11C ''sum'':220C ''total'':206C ''ul'':41C,127C ''use'':22C,62C,120C,204C,226C ''var'':172C ''want'':9C,196C ''would'':76C ''www.quora.com'':107C ''www.quora.com/is-there-an-api-for-analyzing-the-emotion-in-text'':106C ''zero'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8766, 2, '2015-11-09 12:19:02.747', 0, NULL, NULL, '&lt;p&gt;&lt;strong&gt;2nd Update&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The below can be improved as it does not consider the hierarchical structure of an IP address.  To account for this the elements of the IP vectors can be non-linearly scaled before computing the distance vector and its norm. This gives more weight to the elements higher in the hierarchy.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Mathematica code&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Once we have the 4D vectors from the 1st update each element is scaled based on its position $[x^{2}_{1},x^{\frac{3}{2}}_{2},x^{1}_{3},x^{\frac{1}{2}}_{4}]$.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Subtract @@ (MapIndexed[
       Function[{value, index}, 
        value^((5 - First@index)/2)], #] &amp;amp; /@ {ip1, ip2}) // Norm // N
(* 2209.17 *)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;There is information lost in collapsing from 4D down to 1D but this can''t be helped if you are looking for a 1D distance metric.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;1st Update&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;An IP address is made up of 4 numbers. Takes these as vectors in 4D and calculate the distance between them ( &lt;a href="https://en.wikipedia.org/wiki/Distance" rel="nofollow"&gt;Distance in Euclidean space&lt;/a&gt; ).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Mathematica code&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(* Make some IP addresses *)
{ip1, ip2} = 
 StringRiffle[#, "."] &amp;amp; /@ 
  Map[ToString, RandomInteger[{1, 255}, {2, 4}], {2}]
(* {"50.229.29.146", "27.167.216.58"} *)

(* Extract 4D vector *)
{ip1, ip2} = Map[FromDigits, StringSplit[#, "."] &amp;amp; /@ {ip1, ip2}, {2}]
(* {{50, 229, 29, 146}, {27, 167, 216, 58}} *)

(* Calculate distance *)
Norm[ip1 - ip2] // N
(* 216.993 *)
&lt;/code&gt;&lt;/pre&gt;

&lt;hr&gt;

&lt;p&gt;Consider an IP address as a 4D vector. Subtract and calculate the norm.&lt;/p&gt;
', NULL, NULL, '''/2'':114C ''/a'':191C ''/code'':121C,246C ''/p'':6C,60C,66C,102C,150C,157C,192C,198C,263C ''/pre'':122C,247C ''/strong'':5C,65C,156C,197C ''/wiki/distance'':184C ''1'':88C,95C,99C,212C ''146'':234C ''167'':236C ''1d'':134C,147C ''1st'':76C,154C ''2'':87C,92C,93C,100C,214C,216C,230C ''216'':237C ''216.993'':245C ''2209.17'':120C ''229'':232C ''255'':213C ''27'':235C ''27.167.216.58'':218C ''29'':233C ''2nd'':3C ''3'':91C,96C ''4'':101C,166C,215C ''4d'':72C,131C,173C,220C,256C ''5'':111C ''50'':231C ''50.229.29.146'':217C ''58'':238C ''account'':26C ''address'':24C,161C,204C,253C ''amp'':115C,208C,227C ''base'':82C ''calcul'':175C,239C,260C ''code'':64C,104C,196C,200C ''collaps'':129C ''comput'':42C ''consid'':17C,250C ''distanc'':44C,148C,177C,187C,240C ''element'':30C,55C,79C ''en.wikipedia.org'':183C ''en.wikipedia.org/wiki/distance'':182C ''euclidean'':189C ''extract'':219C ''first'':112C ''frac'':90C,98C ''fromdigit'':225C ''function'':107C ''give'':50C ''help'':140C ''hierarch'':19C ''hierarchi'':59C ''higher'':56C ''hr'':151C,248C ''href'':181C ''improv'':12C ''index'':109C,113C ''inform'':126C ''ip'':23C,33C,160C,203C,252C ''ip1'':116C,205C,222C,228C,242C ''ip2'':117C,206C,223C,229C,243C ''linear'':39C ''look'':144C ''lost'':127C ''made'':163C ''make'':201C ''map'':209C,224C ''mapindex'':106C ''mathematica'':63C,195C ''metric'':149C ''n'':119C,244C ''nofollow'':186C ''non'':38C ''non-linear'':37C ''norm'':48C,118C,241C,262C ''number'':167C ''p'':1C,7C,61C,67C,123C,152C,158C,193C,249C ''posit'':85C ''pre'':103C,199C ''randominteg'':211C ''rel'':185C ''scale'':40C,81C ''space'':190C ''stringriffl'':207C ''stringsplit'':226C ''strong'':2C,62C,153C,194C ''structur'':20C ''subtract'':105C,258C ''take'':168C ''tostr'':210C ''updat'':4C,77C,155C ''valu'':108C,110C ''vector'':34C,45C,73C,171C,221C,257C ''weight'':52C ''x'':86C,89C,94C,97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6667, 2, '2015-08-03 13:31:35.363', 4, NULL, NULL, '&lt;p&gt;It could be partially due to the number of topics you selected but the fact that two words rank high for a given topic doesn''t necessarily mean that the two words will frequently occur in the same document. Consider a topic that generally corresponds to "medicine". You could have a number of medical documents associated with heart disease and a number of medical documents associated with injuries. They may all be linked by common terms such as "diagnose", "treatment", "patient", etc. But you wouldn''t necessarily expect that words like "heart" and "aorta" would appear in documents containing words such as "sprain" and "fracture", even though they are all associated with the general topic of medicine. If one were to choose a larger number of topics, those words might be separated into more specific topics (e.g., "heart disease" and "sports injuries").&lt;/p&gt;
', NULL, NULL, '''/p'':144C ''aorta'':95C ''appear'':97C ''associ'':57C,67C,112C ''choos'':123C ''common'':76C ''consid'':41C ''contain'':100C ''correspond'':46C ''could'':3C,50C ''diagnos'':80C ''diseas'':60C,140C ''document'':40C,56C,66C,99C ''doesn'':26C ''due'':6C ''e.g'':138C ''etc'':83C ''even'':107C ''expect'':89C ''fact'':16C ''fractur'':106C ''frequent'':35C ''general'':45C,115C ''given'':24C ''heart'':59C,93C,139C ''high'':21C ''injuri'':69C,143C ''larger'':125C ''like'':92C ''link'':74C ''may'':71C ''mean'':29C ''medic'':55C,65C ''medicin'':48C,118C ''might'':131C ''necessarili'':28C,88C ''number'':9C,53C,63C,126C ''occur'':36C ''one'':120C ''p'':1C ''partial'':5C ''patient'':82C ''rank'':20C ''select'':13C ''separ'':133C ''specif'':136C ''sport'':142C ''sprain'':104C ''term'':77C ''though'':108C ''topic'':11C,25C,43C,116C,128C,137C ''treatment'':81C ''two'':18C,32C ''word'':19C,33C,91C,101C,130C ''would'':96C ''wouldn'':86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6370, 1, '2015-07-07 09:23:45.25', 1, 82, 'How to use REST API to execute Map-Reduce Task?', '&lt;p&gt;Let''s say I have one Text File (Size 1 GB). I want to search particular word from file and if it is found the Line number should be returned.&lt;/p&gt;

&lt;p&gt;I can Execute my java program using command line in linux. But what I want is some Interface using REST API. &lt;/p&gt;

&lt;p&gt;When I submit that word in GUI(My Interface) and submit request it should be able to call HDFS , commands should be provided by itself and it should return me the result. &lt;/p&gt;

&lt;p&gt;How Can you approach this problem ? 
(I have very primitive knowledge about REST API)&lt;/p&gt;

&lt;p&gt;Referred &lt;/p&gt;

&lt;p&gt;&lt;a href="https://hadoop.apache.org/docs/r1.0.4/webhdfs.html#ParameterDictionary[]" rel="nofollow"&gt;https://hadoop.apache.org/docs/r1.0.4/webhdfs.html#ParameterDictionary[]&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.youtube.com/watch?v=7YcW25PHnAA" rel="nofollow"&gt;https://www.youtube.com/watch?v=7YcW25PHnAA&lt;/a&gt;&lt;/p&gt;
', 'hadoop', NULL, '''/docs/r1.0.4/webhdfs.html#parameterdictionary[]'':126C ''/docs/r1.0.4/webhdfs.html#parameterdictionary[]&lt;/a&gt;&lt;/p&gt;'':131C ''/p'':44C,67C,101C,117C,120C ''/watch?v=7ycw25phnaa'':137C ''/watch?v=7ycw25phnaa&lt;/a&gt;&lt;/p&gt;'':142C ''1'':23C ''abl'':84C ''api'':5A,66C,116C ''approach'':106C ''call'':86C ''command'':53C,88C ''execut'':7A,48C ''file'':21C,32C ''found'':37C ''gb'':24C ''gui'':75C ''hadoop'':12B ''hadoop.apache.org'':125C,130C ''hadoop.apache.org/docs/r1.0.4/webhdfs.html#parameterdictionary[]'':124C ''hadoop.apache.org/docs/r1.0.4/webhdfs.html#parameterdictionary[]&lt;/a&gt;&lt;/p&gt;'':129C ''hdfs'':87C ''href'':123C,134C ''interfac'':63C,77C ''java'':50C ''knowledg'':113C ''let'':14C ''line'':39C,54C ''linux'':56C ''map'':9A ''map-reduc'':8A ''nofollow'':128C,139C ''number'':40C ''one'':19C ''p'':13C,45C,68C,102C,118C,121C,132C ''particular'':29C ''primit'':112C ''problem'':108C ''program'':51C ''provid'':91C ''reduc'':10A ''refer'':119C ''rel'':127C,138C ''request'':80C ''rest'':4A,65C,115C ''result'':100C ''return'':43C,97C ''say'':16C ''search'':28C ''size'':22C ''submit'':71C,79C ''task'':11A ''text'':20C ''use'':3A,52C,64C ''want'':26C,60C ''word'':30C,73C ''www.youtube.com'':136C,141C ''www.youtube.com/watch?v=7ycw25phnaa'':135C ''www.youtube.com/watch?v=7ycw25phnaa&lt;/a&gt;&lt;/p&gt;'':140C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6372, 2, '2015-07-07 12:37:14.897', 1, NULL, NULL, '&lt;p&gt;Try content based filtering, you can cluster candidates based on similarity of keywords and by assigning similarity scores. Check tf-idf /cosine similarity measures. Additionally you can customize your algorithm by giving extra weightage for section wise similarities since your data is very well structured&lt;/p&gt;

&lt;p&gt;Also check collaborative filtering on &lt;a href="http://Ref%20:%20https://mahout.apache.org/users/recommender/recommender-documentation.html" rel="nofollow"&gt;Mahout&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/a'':64C ''/cosine'':24C ''/mahout.apache.org/users/recommender/recommender-documentation.html'':60C ''/p'':48C,65C ''20'':58C ''20https'':59C ''addit'':27C ''algorithm'':32C ''also'':50C ''assign'':17C ''base'':4C,10C ''candid'':9C ''check'':20C,51C ''cluster'':8C ''collabor'':52C ''content'':3C ''custom'':30C ''data'':43C ''extra'':35C ''filter'':5C,53C ''give'':34C ''href'':56C ''idf'':23C ''keyword'':14C ''mahout'':63C ''measur'':26C ''nofollow'':62C ''p'':1C,49C ''ref'':57C ''rel'':61C ''score'':19C ''section'':38C ''similar'':12C,18C,25C,40C ''sinc'':41C ''structur'':47C ''tf'':22C ''tf-idf'':21C ''tri'':2C ''weightag'':36C ''well'':46C ''wise'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6374, 2, '2015-07-07 14:45:11.84', -1, NULL, NULL, '&lt;p&gt;It is not available in Orange, but i would try FP-Growth. Its way faster in combination with low support values than apriori!&lt;/p&gt;
', NULL, NULL, '''/p'':26C ''apriori'':25C ''avail'':5C ''combin'':19C ''faster'':17C ''fp'':13C ''fp-growth'':12C ''growth'':14C ''low'':21C ''orang'':7C ''p'':1C ''support'':22C ''tri'':11C ''valu'':23C ''way'':16C ''would'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6375, 1, '2015-07-07 15:33:41.323', 1, 21, 'Trying to come up with a feature to improve emotion classifier based on facial movement using facial landmarks', '&lt;p&gt;I managed to create an emotion recognition system that uses dense optical flow on each entire frame. While the accuracy range is within 80-90% with cross-validation, I am aiming to improve the accuracy of the program. &lt;/p&gt;

&lt;p&gt;There are four emotions: Neutral, happy, surprised, and angry. So far my classifier works pretty well, though it tends to over guess ''neutral'' when  the answer is ''happy'' or ''surprised''. This tends to happen when the mouth is only slightly opened, but the subject is visible smiling or have mouth opened in shock while the classifier still thinks the mouth is closed.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Confusion Matrix for Dense Optical Flow:   
[[27 22  0  0]  
 [ 0 57  1  0]  
 [ 0 12 60  0]  
 [ 0  9  3 68]]  
Accuracy: 80-90% range
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;There is something I want to try in order to solve this though.&lt;/p&gt;

&lt;p&gt;I have the ability to get the position of facial landmarks, though I don''t know what I can do to turn this information into an effective additional feature I can use that would increase the accuracy. I was thinking of just simply getting the face landmark coordinates at the end of each video, but I feel like that it would not be the solution to differentiate between a closed mouth and slightly opened one (the difference in coordinate values will be small I think and am guessing that machine learning won''t notice the difference).&lt;/p&gt;

&lt;p&gt;I considered the possibility of just simply taking a still image of the subject''s mouth and just analyzing that, but rejected it as it vulnerable to factors like lighting and people''s appearance, and inconsistent matrix sizes. Plus I want my additional feature to take advantage of facial movement tracking.&lt;/p&gt;

&lt;p&gt;I was wondering if there is a smart way to implement facial landmark tracking into a feature that would increase the accuracy of my classifier by dealing with my classifier''s tendency to over predict the emotion ''neutral''. Any ways I can accomplish that?&lt;/p&gt;
', 'machine-learning  classification  feature-selection  confusion-matrix  computer-vision', NULL, '''-90'':57C,163C ''/code'':165C ''/p'':72C,136C,181C,279C,332C,378C ''/pre'':166C ''0'':147C,148C,149C,152C,153C,156C,157C ''1'':151C ''12'':154C ''22'':146C ''27'':145C ''3'':159C ''57'':150C ''60'':155C ''68'':160C ''80'':56C,162C ''9'':158C ''abil'':186C ''accomplish'':376C ''accuraci'':52C,68C,161C,219C,355C ''addit'':210C,323C ''advantag'':327C ''aim'':64C ''analyz'':299C ''angri'':82C ''answer'':99C ''appear'':314C ''base'':12A ''classif'':22B ''classifi'':11A,86C,129C,358C,363C ''close'':135C,252C ''code'':138C ''come'':3A ''comput'':30B ''computer-vis'':29B ''confus'':27B,139C ''confusion-matrix'':26B ''consid'':282C ''coordin'':230C,261C ''creat'':36C ''cross'':60C ''cross-valid'':59C ''deal'':360C ''dens'':43C,142C ''differ'':259C,278C ''differenti'':249C ''effect'':209C ''emot'':10A,38C,77C,370C ''end'':233C ''entir'':48C ''face'':228C ''facial'':14A,17A,192C,329C,345C ''factor'':308C ''far'':84C ''featur'':7A,24B,211C,324C,350C ''feature-select'':23B ''feel'':239C ''flow'':45C,144C ''four'':76C ''frame'':49C ''get'':188C,226C ''guess'':95C,270C ''happen'':107C ''happi'':79C,101C ''imag'':291C ''implement'':344C ''improv'':9A,66C ''inconsist'':316C ''increas'':217C,353C ''inform'':206C ''know'':198C ''landmark'':18A,193C,229C,346C ''learn'':21B,273C ''light'':310C ''like'':240C,309C ''machin'':20B,272C ''machine-learn'':19B ''manag'':34C ''matrix'':28B,140C,317C ''mouth'':110C,123C,133C,253C,296C ''movement'':15A,330C ''neutral'':78C,96C,371C ''notic'':276C ''one'':257C ''open'':114C,124C,256C ''optic'':44C,143C ''order'':176C ''p'':32C,73C,167C,182C,280C,333C ''peopl'':312C ''plus'':319C ''posit'':190C ''possibl'':284C ''pre'':137C ''predict'':368C ''pretti'':88C ''program'':71C ''rang'':53C,164C ''recognit'':39C ''reject'':302C ''select'':25B ''shock'':126C ''simpli'':225C,287C ''size'':318C ''slight'':113C,255C ''small'':265C ''smart'':341C ''smile'':120C ''solut'':247C ''solv'':178C ''someth'':170C ''still'':130C,290C ''subject'':117C,294C ''surpris'':80C,103C ''system'':40C ''take'':288C,326C ''tend'':92C,105C ''tendenc'':365C ''think'':131C,222C,267C ''though'':90C,180C,194C ''track'':331C,347C ''tri'':1A,174C ''turn'':204C ''use'':16A,42C,214C ''valid'':61C ''valu'':262C ''video'':236C ''visibl'':119C ''vision'':31B ''vulner'':306C ''want'':172C,321C ''way'':342C,373C ''well'':89C ''within'':55C ''won'':274C ''wonder'':336C ''work'':87C ''would'':216C,243C,352C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6376, 2, '2015-07-07 16:06:07.573', 0, NULL, NULL, '&lt;p&gt;I''m going to agree with AN6U5 in general. However, in practice what I''ve done in the past is used k-means with cross validation and and Silhouetting to find a ''good'' clustering of historical data. &lt;/p&gt;

&lt;p&gt;What I then did was take the clusters and calculated their covariance matrix so I could use the Mahalanobis distance. This allows me to take into account the actual shape of the distribution along each dimension in determining whether a point lies to far outside my previous examples. Since the Mahalanobis distance is in standard deviations, if its say 3 sigma outside each cluster its usually flagged for further study.  &lt;/p&gt;

&lt;p&gt;I''ve been able to get away with this since I know the general process for whats generating my data from testing and I have a rather large amount of data for known operating conditions, so I have pretty good faith in my historical data clusters.&lt;/p&gt;
', NULL, NULL, '''/p'':40C,112C,160C ''3'':101C ''abl'':117C ''account'':68C ''actual'':70C ''agre'':6C ''allow'':63C ''along'':75C ''amount'':142C ''an6u5'':8C ''away'':120C ''calcul'':51C ''cluster'':36C,49C,105C,159C ''condit'':148C ''could'':57C ''covari'':53C ''cross'':27C ''data'':39C,133C,144C,158C ''determin'':79C ''deviat'':97C ''dimens'':77C ''distanc'':61C,93C ''distribut'':74C ''done'':17C ''exampl'':89C ''faith'':154C ''far'':85C ''find'':33C ''flag'':108C ''general'':10C,127C ''generat'':131C ''get'':119C ''go'':4C ''good'':35C,153C ''histor'':38C,157C ''howev'':11C ''k'':24C ''k-mean'':23C ''know'':125C ''known'':146C ''larg'':141C ''lie'':83C ''m'':3C ''mahalanobi'':60C,92C ''matrix'':54C ''mean'':25C ''oper'':147C ''outsid'':86C,103C ''p'':1C,41C,113C ''past'':20C ''point'':82C ''practic'':13C ''pretti'':152C ''previous'':88C ''process'':128C ''rather'':140C ''say'':100C ''shape'':71C ''sigma'':102C ''silhouet'':31C ''sinc'':90C,123C ''standard'':96C ''studi'':111C ''take'':47C,66C ''test'':135C ''use'':22C,58C ''usual'':107C ''valid'':28C ''ve'':16C,115C ''what'':130C ''whether'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6377, 2, '2015-07-07 16:10:09.023', 1, NULL, NULL, '&lt;p&gt;I''m not sure I''m following your question completely, but for spatial type clustering maybe a Self-Organizing Map would meet your needs. Basically it maps out the space based on nearby activations. &lt;/p&gt;

&lt;p&gt;Side answer: I haven''t used python&lt;/p&gt;
', NULL, NULL, '''/p'':37C,46C ''activ'':36C ''answer'':40C ''base'':33C ''basic'':27C ''cluster'':16C ''complet'':11C ''follow'':8C ''haven'':42C ''m'':3C,7C ''map'':22C,29C ''mayb'':17C ''meet'':24C ''nearbi'':35C ''need'':26C ''organ'':21C ''p'':1C,38C ''python'':45C ''question'':10C ''self'':20C ''self-organ'':19C ''side'':39C ''space'':32C ''spatial'':14C ''sure'':5C ''type'':15C ''use'':44C ''would'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6378, 1, '2015-07-07 16:26:18.23', 2, 29, 'Interpreting the evaluation result of multiple linear regression', '&lt;p&gt;I am learning the multiple linear regression model.
I''ve built a &lt;code&gt;model&lt;/code&gt; and using R command:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;summary(model)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I got this result:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Signif. codes:  0 â***â 0.001 â**â 0.01 â*â 0.05 â.â 0.1 â â 1

Residual standard error: 253.2 on 44 degrees of freedom Multiple
R-squared:  0.3336, Adjusted R-squared:  0.2579  F-statistic: 4.405 on
5 and 44 DF,  p-value: 0.002444
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;How can I interpret this result in order to have a decision regarding the goodness of the model? In specific, what is the &lt;code&gt;44 degrees of freedom&lt;/code&gt; means for this case?&lt;/p&gt;

&lt;p&gt;Also, why do we have adjusted and multiple r squared parameters?&lt;/p&gt;
', 'linear-regression', NULL, '''/code'':27C,37C,97C,128C ''/p'':32C,44C,133C,146C ''/pre'':38C,98C ''0'':49C ''0.001'':52C ''0.002444'':96C ''0.01'':55C ''0.05'':58C ''0.1'':61C ''0.2579'':83C ''0.3336'':78C ''1'':64C ''253.2'':68C ''4.405'':87C ''44'':70C,91C,124C ''5'':89C ''adjust'':79C,140C ''also'':135C ''built'':23C ''case'':132C ''code'':25C,34C,46C,48C,123C ''command'':31C ''decis'':111C ''degre'':71C,125C ''df'':92C ''error'':67C ''evalu'':3A ''f'':85C ''f-statist'':84C ''freedom'':73C,127C ''good'':114C ''got'':41C ''interpret'':1A,103C ''learn'':15C ''linear'':7A,10B,18C ''linear-regress'':9B ''mean'':129C ''model'':20C,26C,36C,117C ''multipl'':6A,17C,74C,142C ''order'':107C ''p'':12C,39C,94C,99C,134C ''p-valu'':93C ''paramet'':145C ''pre'':33C,45C ''r'':30C,76C,81C,143C ''r-squar'':75C,80C ''regard'':112C ''regress'':8A,11B,19C ''residu'':65C ''result'':4A,43C,105C ''signif'':47C ''specif'':119C ''squar'':77C,82C,144C ''standard'':66C ''statist'':86C ''summari'':35C ''use'':29C ''valu'':95C ''ve'':22C ''â'':50C,51C,53C,54C,56C,57C,59C,60C,62C,63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6379, 1, '2015-07-07 16:48:10.223', 0, 51, 'What is the proper way way to -log10 transform ggplot2 axis?', '&lt;p&gt;What is the ''proper'' way to -log10 transform an axis in ggplo2 using the scale functions?  I can write my own transform function using the scales library, but something cleaner would be just combining scale_x_log10 + scale_x_reverse.&lt;/p&gt;

&lt;p&gt;I''d like to figure out something a bit more concise than the following:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; scale_x_continuous(trans=trans_new(''nlog10_trans'', transform=function(x) { -log10(x) }, inverse=function(x) { 10**x }) )
&lt;/code&gt;&lt;/pre&gt;
', 'r', NULL, '''/code'':91C ''/p'':54C,70C ''/pre'':92C ''10'':89C ''axi'':11A,23C ''bit'':64C ''cleaner'':43C ''code'':72C ''combin'':47C ''concis'':66C ''continu'':75C ''d'':57C ''figur'':60C ''follow'':69C ''function'':29C,36C,82C,87C ''ggplo2'':25C ''ggplot2'':10A ''invers'':86C ''librari'':40C ''like'':58C ''log10'':8A,20C,50C,84C ''new'':78C ''nlog10'':79C ''p'':13C,55C ''pre'':71C ''proper'':4A,17C ''r'':12B ''revers'':53C ''scale'':28C,39C,48C,51C,73C ''someth'':42C,62C ''tran'':76C,77C,80C ''transform'':9A,21C,35C,81C ''use'':26C,37C ''way'':5A,6A,18C ''would'':44C ''write'':32C ''x'':49C,52C,74C,83C,85C,88C,90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6380, 1, '2015-07-07 18:05:23.903', 4, 238, 'How to avoid overfitting in random forest?', '&lt;ol&gt;
&lt;li&gt;&lt;p&gt;I want to avoid overfitting in random forest. In this regard, I intend to use mtry, nodesize and maxnodes etc. Could you please help me how to choose values for these parameters. I use R.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Also if possible please tell me how can i use k-fold cross validation for random forest. in R.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  data-mining  r  predictive-modeling  random-forest', NULL, '''/li'':60C,84C ''/ol'':85C ''/p'':59C,83C ''also'':63C ''avoid'':3A,27C ''choos'':51C ''could'':44C ''cross'':76C ''data'':12B ''data-min'':11B ''etc'':43C ''fold'':75C ''forest'':7A,20B,31C,80C ''help'':47C ''intend'':36C ''k'':74C ''k-fold'':73C ''learn'':10B ''li'':22C,61C ''machin'':9B ''machine-learn'':8B ''maxnod'':42C ''mine'':13B ''model'':17B ''mtri'':39C ''nodes'':40C ''ol'':21C ''overfit'':4A,28C ''p'':23C,62C ''paramet'':55C ''pleas'':46C,66C ''possibl'':65C ''predict'':16B ''predictive-model'':15B ''r'':14B,58C,82C ''random'':6A,19B,30C,79C ''random-forest'':18B ''regard'':34C ''tell'':67C ''use'':38C,57C,72C ''valid'':77C ''valu'':52C ''want'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6433, 1, '2015-07-11 01:55:26.303', 2, 61, 'Is it possible to connect three neural networks in Matlab?', '&lt;p&gt;If I have 3 separate feedforward neural networks in Matlab, is it possible to connect them so that, given input data and target data the 3 work in parallel to produce output? If so, how do I do this? &lt;/p&gt;
', 'neuralnetwork  matlab', NULL, '''/p'':53C ''3'':17C,39C ''connect'':5A,28C ''data'':34C,37C ''feedforward'':19C ''given'':32C ''input'':33C ''matlab'':10A,12B,23C ''network'':8A,21C ''neural'':7A,20C ''neuralnetwork'':11B ''output'':45C ''p'':13C ''parallel'':42C ''possibl'':3A,26C ''produc'':44C ''separ'':18C ''target'':36C ''three'':6A ''work'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6381, 2, '2015-07-07 18:07:39.507', 1, NULL, NULL, '&lt;p&gt;It seems like you are overthinking this and getting caught up in your own head a little.&lt;/p&gt;

&lt;p&gt;We can define a restricted set of time series as those which are well behaved (non-asymptotic) and single valued.  These time series then form a time series algebra which have additive, multiplicative, commutative, and associative properties.  There is an identity element (straight line with value 1) and a zero element (straight line with value 0).  We even have a well defined inverse member for all time series not crossing zero.  I believe this even forms an Abelian group.&lt;/p&gt;

&lt;p&gt;Further, previous posters are correct that this group can be perfectly represented by various orthogonal vector spaces such as polynomials and Fourier space.  This is true whether we are talking about Lagrange polynomials, Fourier series, or Gauss-Lobatto-Legendre polynomials.&lt;/p&gt;

&lt;p&gt;The point then becomes... where does this get you? You got caught up in the suggestion to employ Fourier series in that you didn''t want to remain in frequency space, but this is entirely unnecessary.  You can, for instance, Fourier transform into frequency space, apply a high frequency filter, and then transform back into the time domain.  This is incredibly effective in removing noise (although band pass filters tend to work better).&lt;/p&gt;

&lt;p&gt;Stepping back from the question a little, it seems like you might really be asking:  Given typical time series and typical time series operations (smoothing, de-seasoning, de-trending, etc) do the time series and their typical operations constitute a group with a well defined algebra?  The answer is, yes.  All of these operations just involve finding other time series which can be added, subtracted, multiplied, or divided by the original time series in order to yield a more physically tractable (interpretable) representation.&lt;/p&gt;
', NULL, NULL, '''/p'':19C,101C,144C,219C,306C ''0'':77C ''1'':68C ''abelian'':99C ''ad'':286C ''addit'':53C ''algebra'':50C,268C ''although'':211C ''answer'':270C ''appli'':191C ''ask'':235C ''associ'':57C ''asymptot'':38C ''back'':199C,222C ''band'':212C ''becom'':149C ''behav'':35C ''believ'':94C ''better'':218C ''caught'':11C,157C ''commut'':55C ''constitut'':261C ''correct'':107C ''cross'':91C ''de'':247C,250C ''de-season'':246C ''de-trend'':249C ''defin'':23C,83C,267C ''didn'':169C ''divid'':290C ''domain'':203C ''effect'':207C ''element'':63C,72C ''employ'':163C ''entir'':180C ''etc'':252C ''even'':79C,96C ''filter'':195C,214C ''find'':279C ''form'':46C,97C ''fourier'':124C,136C,164C,186C ''frequenc'':175C,189C,194C ''gauss'':140C ''gauss-lobatto-legendr'':139C ''get'':10C,153C ''given'':236C ''got'':156C ''group'':100C,110C,263C ''head'':16C ''high'':193C ''ident'':62C ''incred'':206C ''instanc'':185C ''interpret'':304C ''invers'':84C ''involv'':278C ''lagrang'':134C ''legendr'':142C ''like'':4C,230C ''line'':65C,74C ''littl'':18C,227C ''lobatto'':141C ''member'':85C ''might'':232C ''multipl'':54C ''multipli'':288C ''nois'':210C ''non'':37C ''non-asymptot'':36C ''oper'':244C,260C,276C ''order'':297C ''origin'':293C ''orthogon'':117C ''overthink'':7C ''p'':1C,20C,102C,145C,220C ''pass'':213C ''perfect'':113C ''physic'':302C ''point'':147C ''polynomi'':122C,135C,143C ''poster'':105C ''previous'':104C ''properti'':58C ''question'':225C ''realli'':233C ''remain'':173C ''remov'':209C ''repres'':114C ''represent'':305C ''restrict'':25C ''season'':248C ''seem'':3C,229C ''seri'':29C,44C,49C,89C,137C,165C,239C,243C,256C,282C,295C ''set'':26C ''singl'':40C ''smooth'':245C ''space'':119C,125C,176C,190C ''step'':221C ''straight'':64C,73C ''subtract'':287C ''suggest'':161C ''talk'':132C ''tend'':215C ''time'':28C,43C,48C,88C,202C,238C,242C,255C,281C,294C ''tractabl'':303C ''transform'':187C,198C ''trend'':251C ''true'':128C ''typic'':237C,241C,259C ''unnecessari'':181C ''valu'':41C,67C,76C ''various'':116C ''vector'':118C ''want'':171C ''well'':34C,82C,266C ''whether'':129C ''work'':217C ''yes'':272C ''yield'':299C ''zero'':71C,92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6384, 1, '2015-07-07 23:15:34.657', 2, 143, 'What is the actual output of Principal Component Analysis?', '&lt;p&gt;I''m trying to understand PCA, but I don''t have a machine learning background.  I come from software engineering, but the literature I''ve tried to read so far is hard for me to digest.&lt;/p&gt;

&lt;p&gt;As far as I understand PCA, it will take a set of datapoints from an N dimensional space and translate them to an M dimensional space, where N &gt; M.  &lt;strong&gt;I don''t yet understand what the actual output of PCA is.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;For example, take this 5 dimensional input data with values in the range [0,10):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;// dimensions:
// a  b  c  d  e

[[ 4, 1, 2, 8, 8],      // component 1
 [ 3, 0, 2, 9, 8],
 [ 4, 0, 0, 9, 1],
 ...
 [ 7, 9, 1, 2, 3],      // component 2
 [ 9, 9, 0, 2, 7],
 [ 7, 8, 1, 0, 0]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;My assumption is that PCA could be used to reduce the data from 5 dimensions to, say, 1 dimension.&lt;/p&gt;

&lt;h3&gt;Data details:&lt;/h3&gt;

&lt;p&gt;There are two "components" in the data.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;One component has mid &lt;code&gt;a&lt;/code&gt; levels, low &lt;code&gt;b&lt;/code&gt; and &lt;code&gt;c&lt;/code&gt; levels, high &lt;code&gt;d&lt;/code&gt;, and nondeterministic &lt;code&gt;e&lt;/code&gt; levels.&lt;/li&gt;
&lt;li&gt;The other component has high &lt;code&gt;a&lt;/code&gt; and &lt;code&gt;b&lt;/code&gt; levels, low &lt;code&gt;c&lt;/code&gt; and &lt;code&gt;d&lt;/code&gt; levels, and nondeterministic &lt;code&gt;e&lt;/code&gt; levels.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;This means that the two components are most differentiated by &lt;code&gt;b&lt;/code&gt; and &lt;code&gt;d&lt;/code&gt;, somewhat differentiated by &lt;code&gt;a&lt;/code&gt;, and negligibly differentiated by &lt;code&gt;c&lt;/code&gt; and &lt;code&gt;e&lt;/code&gt;.  &lt;/p&gt;

&lt;h3&gt;Outputs?&lt;/h3&gt;

&lt;p&gt;&lt;em&gt;I''m making this up&lt;/em&gt;, but &lt;em&gt;say&lt;/em&gt; the (non-normalized) linear combination with the highest differentiating power is something like&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;5*a + 10*b + 0*c + 10*d + 0*e
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The above input data translated along that single axis is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[[110],
 [105],
 [110],
 ...etc
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Is that linear combination (or a vector describing it) the output of PCA?  Or is the output the actual reduced dataset?  Or something else entirely?&lt;/strong&gt;&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/code'':156C,200C,205C,209C,214C,219C,230C,234C,239C,243C,249C,266C,270C,276C,283C,287C,331C,351C ''/em'':299C,303C ''/h3'':182C,291C ''/li'':221C,251C ''/ol'':252C ''/p'':51C,96C,113C,178C,191C,288C,318C,344C,381C ''/pre'':157C,332C,352C ''/strong'':95C,380C ''0'':111C,130C,135C,136C,148C,154C,155C,325C,329C ''1'':123C,128C,138C,141C,153C,176C ''10'':112C,323C,327C ''105'':348C ''110'':347C,349C ''2'':124C,131C,142C,145C,149C ''3'':129C,143C ''4'':122C,134C ''5'':102C,172C,321C ''7'':139C,150C,151C ''8'':125C,126C,133C,152C ''9'':132C,137C,140C,146C,147C ''actual'':4A,90C,373C ''along'':339C ''analysi'':9A ''assumpt'':160C ''axi'':342C ''b'':118C,204C,233C,265C,324C ''background'':29C ''c'':119C,208C,238C,282C,326C ''classif'':13B ''code'':115C,198C,203C,207C,212C,217C,228C,232C,237C,241C,247C,264C,268C,274C,281C,285C,320C,346C ''combin'':309C,358C ''come'':31C ''compon'':8A,127C,144C,187C,195C,225C,259C ''could'':164C ''d'':120C,213C,242C,269C,328C ''data'':105C,170C,180C,190C,337C ''datapoint'':65C ''dataset'':375C ''describ'':362C ''detail'':181C ''differenti'':262C,272C,279C,313C ''digest'':50C ''dimens'':116C,173C,177C ''dimension'':69C,77C,103C ''e'':121C,218C,248C,286C,330C ''els'':378C ''em'':293C,301C ''engin'':34C ''entir'':379C ''etc'':350C ''exampl'':99C ''far'':44C,54C ''h3'':179C,289C ''hard'':46C ''high'':211C,227C ''highest'':312C ''input'':104C,336C ''learn'':12B,28C ''level'':201C,210C,220C,235C,244C,250C ''li'':193C,222C ''like'':317C ''linear'':308C,357C ''literatur'':37C ''low'':202C,236C ''m'':16C,76C,81C,295C ''machin'':11B,27C ''machine-learn'':10B ''make'':296C ''mean'':255C ''mid'':197C ''n'':68C,80C ''neglig'':278C ''non'':306C ''non-norm'':305C ''nondeterminist'':216C,246C ''normal'':307C ''ol'':192C ''one'':194C ''output'':5A,91C,290C,365C,371C ''p'':14C,52C,97C,158C,183C,253C,292C,333C,353C ''pca'':20C,58C,93C,163C,367C ''power'':314C ''pre'':114C,319C,345C ''princip'':7A ''rang'':110C ''read'':42C ''reduc'':168C,374C ''say'':175C,302C ''set'':63C ''singl'':341C ''softwar'':33C ''someth'':316C,377C ''somewhat'':271C ''space'':70C,78C ''strong'':82C,354C ''take'':61C,100C ''translat'':72C,338C ''tri'':17C,40C ''two'':186C,258C ''understand'':19C,57C,87C ''use'':166C ''valu'':107C ''ve'':39C ''vector'':361C ''yet'':86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6422, 2, '2015-07-10 00:23:22.263', 3, NULL, NULL, '&lt;p&gt;@xof6 is correct in the sense that the more depth the model has the more it tends to overfit, but I wanted to add some more parameters that might be useful to you. I do not know which package you are using with R and I am not familiar with R at all, but I think there must be counterparts of these parameters implemented there.&lt;/p&gt;

&lt;p&gt;Number of trees - The bigger this number, the less likely the forest is to overfit. This means that as each decision tree is learning some aspect of the training data, you are getting more options to choose from, so to speak.
Number of features - This number constitutes how many features each individual tree learns. As this number grows, the trees get more and more complicated, hence they are learning patterns that might not be there in the test data. It will take some experimenting to find the right value, but such is machine learning.
Experiment with the general depth as well, as we mentioned!&lt;/p&gt;
', NULL, NULL, '''/p'':67C,173C ''add'':25C ''aspect'':94C ''bigger'':73C ''choos'':105C ''complic'':133C ''constitut'':115C ''correct'':4C ''counterpart'':61C ''data'':98C,147C ''decis'':89C ''depth'':11C,167C ''experi'':152C,163C ''familiar'':50C ''featur'':112C,118C ''find'':154C ''forest'':80C ''general'':166C ''get'':101C,129C ''grow'':126C ''henc'':134C ''implement'':65C ''individu'':120C ''know'':38C ''learn'':92C,122C,137C,162C ''less'':77C ''like'':78C ''machin'':161C ''mani'':117C ''mean'':85C ''mention'':172C ''might'':30C,140C ''model'':13C ''must'':59C ''number'':69C,75C,110C,114C,125C ''option'':103C ''overfit'':20C,83C ''p'':1C,68C ''packag'':40C ''paramet'':28C,64C ''pattern'':138C ''r'':45C,52C ''right'':156C ''sens'':7C ''speak'':109C ''take'':150C ''tend'':18C ''test'':146C ''think'':57C ''train'':97C ''tree'':71C,90C,121C,128C ''use'':32C,43C ''valu'':157C ''want'':23C ''well'':169C ''xof6'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6566, 2, '2015-07-24 05:02:23.25', 2, NULL, NULL, '&lt;p&gt;I assume the feature you use to detect abnormality is one row of data in a logfile. If so, Sklearn is your good friend and you can use it as a blackbox. Check the tutorial of &lt;a href="http://scikit-learn.org/stable/auto_examples/svm/plot_oneclass.html" rel="nofollow"&gt;one-class SVM&lt;/a&gt; and &lt;a href="http://scikit-learn.org/stable/modules/outlier_detection.html" rel="nofollow"&gt;Novelty detection&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;However, in case that your feature is an entire logfile, you need to first summarize it to some feature of same dimension, and then apply Novealty detection. &lt;/p&gt;
', NULL, NULL, '''/a'':49C,60C ''/p'':61C,90C ''/stable/auto_examples/svm/plot_oneclass.html'':42C ''/stable/modules/outlier_detection.html'':55C ''abnorm'':10C ''appli'':87C ''assum'':3C ''blackbox'':33C ''case'':65C ''check'':34C ''class'':47C ''data'':15C ''detect'':9C,59C,89C ''dimens'':84C ''entir'':71C ''featur'':5C,68C,81C ''first'':76C ''friend'':25C ''good'':24C ''howev'':63C ''href'':39C,52C ''logfil'':18C,72C ''need'':74C ''nofollow'':44C,57C ''novealti'':88C ''novelti'':58C ''one'':12C,46C ''one-class'':45C ''p'':1C,62C ''rel'':43C,56C ''row'':13C ''scikit-learn.org'':41C,54C ''scikit-learn.org/stable/auto_examples/svm/plot_oneclass.html'':40C ''scikit-learn.org/stable/modules/outlier_detection.html'':53C ''sklearn'':21C ''summar'':77C ''svm'':48C ''tutori'':36C ''use'':7C,29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6385, 2, '2015-07-08 00:50:49.643', 4, NULL, NULL, '&lt;p&gt;Have you tried reading the &lt;a href="https://en.wikipedia.org/wiki/Principal_component_analysis#Intuition" rel="nofollow"&gt;Intuition&lt;/a&gt; section of the PCA page on wiki? Also, the &lt;a href="https://en.wikipedia.org/wiki/Principal_component_analysis#Further_considerations" rel="nofollow"&gt;Further Considerations&lt;/a&gt; section, I think, explains what the various components represent and addresses your questions.&lt;/p&gt;

&lt;p&gt;A short summary of what those sections describe is that the original components (i.e., axes) of the data do not properly represent the relationship inherit in the data. Instead, the components can be combined to yield a new set of components, or axes, that better describe how the data is distributed.&lt;/p&gt;

&lt;p&gt;The wiki link states that "PCA can be thought of as fitting an n-dimensional ellipsoid to the data". In that line of thinking, the output of PCA is the eigenvectors and eigenvalues that define the orientation and length of the ellipsoid that best fits the data. &lt;/p&gt;

&lt;p&gt;What you do with that ellipsoid is up to you and your application of PCA.&lt;/p&gt;
', NULL, NULL, '''/a'':15C,34C ''/p'':48C,101C,151C,168C ''/wiki/principal_component_analysis#further_considerations'':29C ''/wiki/principal_component_analysis#intuition'':11C ''address'':45C ''also'':23C ''applic'':165C ''axe'':64C,92C ''best'':147C ''better'':94C ''combin'':83C ''compon'':42C,62C,80C,90C ''consider'':33C ''data'':67C,77C,98C,122C,150C ''defin'':138C ''describ'':57C,95C ''dimension'':118C ''distribut'':100C ''eigenvalu'':136C ''eigenvector'':134C ''ellipsoid'':119C,145C,158C ''en.wikipedia.org'':10C,28C ''en.wikipedia.org/wiki/principal_component_analysis#further_considerations'':27C ''en.wikipedia.org/wiki/principal_component_analysis#intuition'':9C ''explain'':38C ''fit'':114C,148C ''href'':8C,26C ''i.e'':63C ''inherit'':74C ''instead'':78C ''intuit'':14C ''length'':142C ''line'':125C ''link'':105C ''n'':117C ''n-dimension'':116C ''new'':87C ''nofollow'':13C,31C ''orient'':140C ''origin'':61C ''output'':129C ''p'':1C,49C,102C,152C ''page'':20C ''pca'':19C,108C,131C,167C ''proper'':70C ''question'':47C ''read'':5C ''rel'':12C,30C ''relationship'':73C ''repres'':43C,71C ''section'':16C,35C,56C ''set'':88C ''short'':51C ''state'':106C ''summari'':52C ''think'':37C,127C ''thought'':111C ''tri'':4C ''various'':41C ''wiki'':22C,104C ''yield'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6386, 2, '2015-07-08 05:29:27.883', 1, NULL, NULL, '&lt;p&gt;Data Science is a field demanding a variety of skills. Having knowledge of Hadoop is one of them. The main tasks of a Data Scientist include:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Gathering data from different resources.&lt;/li&gt;
&lt;li&gt;Cleaning and pre-processing the data.&lt;/li&gt;
&lt;li&gt;Studying statistical properties of the data.&lt;/li&gt;
&lt;li&gt;Using Machine Learning techniques to do forecasting and derive insights from the data.&lt;/li&gt;
&lt;li&gt;Communicating the results to decision makers in an easy to understand way.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Out of the above points knowledge of Hadoop is useful for points 1,2 and 3, but you also need to have strong mathematical/statistical background and strong knowledge of Computational techniques to work in data science field. Also Hadoop is not the only framework that is being used in Data Science. Big Data ecosystem has a range of frameworks, each specific to a particular use case. This article gives introductory material regarding major Big Data frameworks that could be used in Data Science:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.codophile.com/big-data-frameworks-every-programmer-should-know/" rel="nofollow"&gt;http://www.codophile.com/big-data-frameworks-every-programmer-should-know/&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6387, 1, '2015-07-08 07:35:31.75', 2, 58, 'Why ConLL is not in XML format', '&lt;p&gt;I am new to NLP, I found a format named ConLL which seems a tab-separated file,
like &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ID FORM LEMMA PLEMMA POS PPOS FEAT PFEAT HEAD PHEAD DEPREL PDEPREL 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I found a dependency Treebank in this format for Persian. I would like to do some experiment on it. I am familiar with C# and C# has great libraries for XML document files. &lt;/p&gt;

&lt;p&gt;I just wonder why such a corpus is not in XML format! I don''t say a sophisticated one but they could put each line in a node of XML. Then at least, I would know what is the tag for each element.&lt;/p&gt;

&lt;p&gt;However, I can convert it to XML.&lt;/p&gt;
', 'nlp  data-formats', NULL, '''/code'':47C ''/p'':32C,83C,128C,137C ''/pre'':48C ''c'':73C,75C ''code'':34C ''conll'':2A,23C ''convert'':133C ''corpus'':91C ''could'':106C ''data'':10B ''data-format'':9B ''depend'':53C ''deprel'':45C ''document'':81C ''element'':127C ''experi'':66C ''familiar'':71C ''feat'':41C ''file'':30C,82C ''form'':36C ''format'':7A,11B,21C,57C,96C ''found'':19C,51C ''great'':77C ''head'':43C ''howev'':130C ''id'':35C ''know'':120C ''least'':117C ''lemma'':37C ''librari'':78C ''like'':31C,62C ''line'':109C ''name'':22C ''new'':15C ''nlp'':8B,17C ''node'':112C ''one'':103C ''p'':12C,49C,84C,129C ''pdeprel'':46C ''persian'':59C ''pfeat'':42C ''phead'':44C ''plemma'':38C ''pos'':39C ''ppos'':40C ''pre'':33C ''put'':107C ''say'':100C ''seem'':25C ''separ'':29C ''sophist'':102C ''tab'':28C ''tab-separ'':27C ''tag'':124C ''treebank'':54C ''wonder'':87C ''would'':61C,119C ''xml'':6A,80C,95C,114C,136C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6390, 2, '2015-07-08 08:47:16.22', 3, NULL, NULL, '&lt;p&gt;I agree with dpmcmlxxvi''s answer that the common "output" of PCA is computing and finding the eigenvectors for the principal components and the eigenvalues for the variances, but I can''t add comments yet and would still like to contribute. &lt;/p&gt;

&lt;p&gt;Once you hit this step of calculating the eigenvectors and eigenvalues of the principal components, you can do many types of analyses depending on your needs.&lt;/p&gt;

&lt;p&gt;I believe the "output" you are specifically asking about in your question is the resultant data set of applying a transformation or projection of the original data set into the desired linear subspace (of n-dimensions). This is taking the output of PCA and applying it on your original data set. &lt;/p&gt;

&lt;p&gt;This &lt;a href="http://sebastianraschka.com/Articles/2014_pca_step_by_step.html" rel="nofollow" title="PCA step by step example"&gt;PCA step by step example&lt;/a&gt; may help. The ultimate output of this 6 step analysis was the projection of a 3 dimensional data set into 2 dimensions. Here are the high level steps:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;ol&gt;
  &lt;li&gt;Taking the whole dataset ignoring the class labels&lt;/li&gt;
  &lt;li&gt;Compute the d-dimensional mean vector&lt;/li&gt;
  &lt;li&gt;Computing the scatter matrix (alternatively, the covariance matrix)&lt;/li&gt;
  &lt;li&gt;Computing eigenvectors and corresponding eigenvalues&lt;/li&gt;
  &lt;li&gt;Ranking and choosing k eigenvectors&lt;/li&gt;
  &lt;li&gt;Transforming the samples onto the new subspace&lt;/li&gt;
  &lt;/ol&gt;
&lt;/blockquote&gt;

&lt;p&gt;Ultimately, step 4 is the "output" since that is where the common requirements for performing PCA are fulfilled. We can make different decisions at steps 5 and 6 and produce alternative output there.  &lt;/p&gt;

&lt;p&gt;A few more possibilities:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;You could decide to project the observations &lt;em&gt;with outliers removed&lt;/em&gt;&lt;/li&gt;
&lt;li&gt;Another possible outcome here would be to calculate the proportion of
variance explained by one or any combination of principal components. For example, the proportion of variance explained by the first two principal components of K components is &lt;code&gt;(Î»1+Î»2)/(Î»1+Î»2+. . .+Î»K)&lt;/code&gt;.&lt;/li&gt;
&lt;li&gt;After plotting the projected observations into the first two principal components (as in the given example), you can impose a plot of the loadings of each of the original dimensions into the subspace (scaled by the standard deviation of the principal components). This way, we can see the contribution of the original dimensions (in your case a - e) to principal component 1 and 2. The biplot is another common product of PCA.&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6391, 1, '2015-07-08 12:05:49.663', 4, 132, 'R - Interpreting neural networks plot', '&lt;p&gt;I know there are similar question on stats.SE, but I didn''t find one that fulfills my request; please, before mark the question as a duplicate, ping me in the comment.&lt;/p&gt;

&lt;p&gt;I run a neural network based on &lt;code&gt;neuralnet&lt;/code&gt; to forecast SP500 index time series and I want to understand how I can interpret the plot posted below:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/EFQwW.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Particularly, I''m interested to understand what is the interpretation of the hidden layer weight and the input weight; could someone explain me how to interpret that number, please?&lt;/p&gt;

&lt;p&gt;Any hint will be appreciated.&lt;/p&gt;
', 'machine-learning  r  neuralnetwork  predictive-modeling  forecast', NULL, '''/code'':58C ''/efqww.png'':84C ''/p'':47C,78C,90C,121C,128C ''alt'':85C ''appreci'':127C ''base'':54C ''code'':56C ''comment'':46C ''could'':111C ''descript'':88C ''didn'':26C ''duplic'':41C ''enter'':86C ''explain'':113C ''find'':28C ''forecast'':14B,60C ''fulfil'':31C ''hidden'':104C ''hint'':124C ''i.stack.imgur.com'':83C ''i.stack.imgur.com/efqww.png'':82C ''imag'':87C ''img'':80C ''index'':62C ''input'':109C ''interest'':95C ''interpret'':2A,73C,101C,117C ''know'':17C ''layer'':105C ''learn'':8B ''m'':94C ''machin'':7B ''machine-learn'':6B ''mark'':36C ''model'':13B ''network'':4A,53C ''neural'':3A,52C ''neuralnet'':57C ''neuralnetwork'':10B ''number'':119C ''one'':29C ''p'':15C,48C,79C,91C,122C ''particular'':92C ''ping'':42C ''pleas'':34C,120C ''plot'':5A,75C ''post'':76C ''predict'':12B ''predictive-model'':11B ''question'':21C,38C ''r'':1A,9B ''request'':33C ''run'':50C ''seri'':64C ''similar'':20C ''someon'':112C ''sp500'':61C ''src'':81C ''stats.se'':23C ''time'':63C ''understand'':69C,97C ''want'':67C ''weight'':106C,110C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6393, 1, '2015-07-08 15:22:09.047', 1, 93, 'Modules on Python which are useful for missing Word/Letter prediction in text paragraphs from a coprpus', '&lt;p&gt;Can someone please recommend me some Python packages which I can use for missing word prediction in a paragraph of text. The missing words are a part of existing semantic list of words.( List of missing words are a common set of words like was ,were etc). If there are better packages in R which can help me with this  it would be very useful if you post it here.&lt;/p&gt;

&lt;p&gt;Thank you.&lt;/p&gt;
', 'data-mining  python  text-mining  predictive-modeling', NULL, '''/p'':98C,102C ''better'':78C ''common'':67C ''coprpus'':16A ''data'':18B ''data-min'':17B ''etc'':74C ''exist'':56C ''help'':84C ''like'':71C ''list'':58C,61C ''mine'':19B,23B ''miss'':8A,41C,50C,63C ''model'':26B ''modul'':1A ''p'':27C,99C ''packag'':35C,79C ''paragraph'':13A,46C ''part'':54C ''pleas'':30C ''post'':95C ''predict'':10A,25B,43C ''predictive-model'':24B ''python'':3A,20B,34C ''r'':81C ''recommend'':31C ''semant'':57C ''set'':68C ''someon'':29C ''text'':12A,22B,48C ''text-min'':21B ''thank'':100C ''use'':6A,39C,92C ''word'':42C,51C,60C,64C,70C ''word/letter'':9A ''would'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6448, 2, '2015-07-13 20:19:39.093', 1, NULL, NULL, '&lt;p&gt;I can''t see any way to magically identify unreliable data in this context. I''m no expert but 2 ideas spring to mind:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Domain knowledge: an expert on household income may be able to describe some general rules for identifying suspect data based on experience, and you could try to convert these into rules&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Benford%27s_law" rel="nofollow"&gt;Benford''s Law&lt;/a&gt; - I don''t know if it would be suitable for this scenario but Benfords Law has applications in detecting accounting fraud, based on the expected frequency of numbers appearing in numeric values. Probably of no use especially if incomes are reported in rounded figures (e.g. nearest 1000) but might be worth a look for inspiration.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':75C ''/li'':62C,132C ''/p'':26C,61C,131C ''/ul'':133C ''/wiki/benford%27s_law'':69C ''1000'':122C ''2'':21C ''abl'':39C ''account'':95C ''appear'':104C ''applic'':92C ''base'':49C,97C ''benford'':72C,89C ''context'':15C ''convert'':57C ''could'':54C ''data'':12C,48C ''describ'':41C ''detect'':94C ''domain'':30C ''e.g'':120C ''en.wikipedia.org'':68C ''en.wikipedia.org/wiki/benford%27s_law'':67C ''especi'':112C ''expect'':100C ''experi'':51C ''expert'':19C,33C ''figur'':119C ''fraud'':96C ''frequenc'':101C ''general'':43C ''household'':35C ''href'':66C ''idea'':22C ''identifi'':10C,46C ''incom'':36C,114C ''inspir'':130C ''know'':79C ''knowledg'':31C ''law'':74C,90C ''li'':28C,63C ''look'':128C ''m'':17C ''magic'':9C ''may'':37C ''might'':124C ''mind'':25C ''nearest'':121C ''nofollow'':71C ''number'':103C ''numer'':106C ''p'':1C,29C,64C ''probabl'':108C ''rel'':70C ''report'':116C ''round'':118C ''rule'':44C,60C ''scenario'':87C ''see'':5C ''spring'':23C ''suitabl'':84C ''suspect'':47C ''tri'':55C ''ul'':27C ''unreli'':11C ''use'':111C ''valu'':107C ''way'':7C ''worth'':126C ''would'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6399, 2, '2015-07-09 00:15:00.987', 5, NULL, NULL, '&lt;p&gt;I like &lt;a href="http://blocks.readthedocs.org/" rel="nofollow"&gt;Blocks&lt;/a&gt;, which is also built on top of Theano. Way more approachable than PyLearn2, and more feature rich than Lasagne. Neatly written, too.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Updated Jan 2016:&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;At the time of writing, &lt;a href="http://keras.io/" rel="nofollow"&gt;Keras&lt;/a&gt; has by far the most momentum. It is highly modular and can run on both Theano and Tensorflow, giving it great opportunities.&lt;/p&gt;
', NULL, NULL, '''/a'':10C,53C ''/em'':39C ''/p'':33C,40C,76C ''2016'':38C ''also'':13C ''approach'':21C ''block'':9C ''blocks.readthedocs.org'':6C ''built'':14C ''em'':35C ''far'':56C ''featur'':26C ''give'':72C ''great'':74C ''high'':62C ''href'':5C,48C ''jan'':37C ''kera'':52C ''keras.io'':49C ''lasagn'':29C ''like'':3C ''modular'':63C ''momentum'':59C ''neat'':30C ''nofollow'':8C,51C ''opportun'':75C ''p'':1C,34C,41C ''pylearn2'':23C ''rel'':7C,50C ''rich'':27C ''run'':66C ''tensorflow'':71C ''theano'':18C,69C ''time'':44C ''top'':16C ''updat'':36C ''way'':19C ''write'':46C ''written'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6394, 1, '2015-07-08 18:58:25.897', 1, 172, 'any reason for this project to use hadoop/spark?', '&lt;p&gt;I am setting up for a couple self study projects to explore machine learning techniques.&lt;/p&gt;

&lt;p&gt;1st project has 10,000 time series with 24 float data points each day for 10 years (876 million points). I will be creating a bunch of calendar and weather features for the data, then trying to forecast using a variety of machine learning techniques.&lt;/p&gt;

&lt;p&gt;2nd is some 13 million rows of text data (several paragraphs for each row)  for classification. (currently in solr database)&lt;/p&gt;

&lt;p&gt;My compute rig is 6-core, 32g ram, gforce GPU. I plan to install Ubuntu 14.2.&lt;/p&gt;

&lt;p&gt;I expect to be using python for file processing, scilearn, pylearn2 and word2vec for general exploration and training. R for getting a taste of the language.&lt;/p&gt;

&lt;p&gt;Clearly data set 1 will require joining weather and calendar data to date/time and aggregation across time and location. I know how to stuff it all into a MySQL database and do the aggregations and joins there, but I have been reading about spark and wondering.&lt;/p&gt;

&lt;p&gt;......&lt;/p&gt;

&lt;p&gt;If I take the time to simulate a cluster using virtual box/hadoop/spark (for my learning experience, not performance), can/should I do the aggregations there and write the results to the distributed data store? &lt;/p&gt;

&lt;p&gt;Since deep learning can not be run on spark, does that mean I would need to copy the aggregated data back out to the local file system to use some of those techniques?&lt;/p&gt;

&lt;p&gt;For data set 2, I want to run the word2vec algorithm as found in the kaggle tutorial &lt;a href="https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-2-word-vectors" rel="nofollow"&gt;https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-2-word-vectors&lt;/a&gt;. In that example, that is a deeplearning method, so I should just leave the data in the solr.. right?&lt;/p&gt;

&lt;p&gt;In general I am looking for appropriate applications and insight into data flow from app to app to help me get to the part where I start trying various ML techniques.&lt;/p&gt;

&lt;p&gt;Thanks for helping me along&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6395, 1, '2015-07-08 20:04:16.703', 3, 41, 'How flexible is the link between objective function and output layer activation function?', '&lt;p&gt;It seems standard in many neural network packages to pair up the objective  function to be minimised with the activation function in the output layer.&lt;/p&gt;

&lt;p&gt;For instance, for a linear output layer used for regression it is standard (and often only choice) to have a squared error objective function. Another usual pairing is logistic output and log loss (or cross-entropy). And yet another is softmax and multi log loss.&lt;/p&gt;

&lt;p&gt;Using notation, $z$ for pre-activation value (sum of weights times activations from previous layer), $a$ for activation, $y$ for ground truth used for training, $i$ for index of output neuron.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Linear activation $a_i=z_i$ goes with squared error $\frac{1}{2} \sum\limits_{\forall i} (y_i-a_i)^2$&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Sigmoid activation $a_i = \frac{1}{1+e^{-z_i}}$ goes with logloss/cross-entropy objective $-\sum\limits_{\forall i} (y_i*log(a_i) + (1-y_i)*log(1-a_i))$&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Softmax activation $a_i = \frac{e^{z_i}}{\sum_{\forall j} e^{z_j}}$ goes with multiclass logloss objective $-\sum\limits_{\forall i} (y_i*log(a_i))$&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Those are the ones I know, and I expect there are many that I still haven''t heard of.&lt;/p&gt;

&lt;p&gt;It seems that log loss would only work and be numerically stable when the output and targets are in range [0,1]. So it may not make sense to try linear output layer with a logloss objective function. Unless there is a more general logloss function that can cope with values of $y$ that are outside of the range?&lt;/p&gt;

&lt;p&gt;However, it doesn''t seem quite so bad to try sigmoid output with a squared error objective. It should be stable and converge at least.&lt;/p&gt;

&lt;p&gt;I understand that some of the design behind these pairings is that it makes the formula for $\frac{\delta E}{\delta z}$ - where $E$ is the value of the objective function - easy for back propagation. But it should still be possible to find that derivative using other pairings. Also, there are many other activation functions that are not commonly seen in output layers, but feasibly could be, such as &lt;code&gt;tanh&lt;/code&gt;, and where it is not clear what objective function could be applied.&lt;/p&gt;

&lt;p&gt;Are there any situations when designing the architecture of a neural network, that you would or should use "non-standard" pairings of output activation and objective functions?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6396, 2, '2015-07-08 20:18:27.417', 3, NULL, NULL, '&lt;p&gt;It sounds like you''re describing optimal interpolation (AKA Gauss-Markov analysis, objective analysis, probably others too). &lt;a href="http://modb.oce.ulg.ac.be/wiki/upload/diva_intro.pdf" rel="nofollow"&gt;This&lt;/a&gt; is a solid intro, and &lt;a href="http://www.atmos.berkeley.edu/~inez/MSRI-NCAR_CarbonDA/lectures/Kalnay2_CH5OptimalInterpolation.pdf" rel="nofollow"&gt;this&lt;/a&gt; is a powerpoint on the subject. It''s a hard process to summarize quickly, but roughly speaking, you''re on the right track.&lt;/p&gt;

&lt;p&gt;Optimal interpolation is fairly common in meteorology and other environmental sciences, but I''m not convinced it''s really the best tool for the job. Any statisticians want to take a crack at it?&lt;/p&gt;
', NULL, NULL, '''/a'':28C,42C ''/p'':66C,102C ''/wiki/upload/diva_intro.pdf'':24C ''/~inez/msri-ncar_carbonda/lectures/kalnay2_ch5optimalinterpolation.pdf'':38C ''aka'':10C ''analysi'':14C,16C ''best'':88C ''common'':72C ''convinc'':83C ''crack'':99C ''describ'':7C ''environment'':77C ''fair'':71C ''gauss'':12C ''gauss-markov'':11C ''hard'':52C ''href'':21C,35C ''interpol'':9C,69C ''intro'':32C ''job'':92C ''like'':4C ''m'':81C ''markov'':13C ''meteorolog'':74C ''modb.oce.ulg.ac.be'':23C ''modb.oce.ulg.ac.be/wiki/upload/diva_intro.pdf'':22C ''nofollow'':26C,40C ''object'':15C ''optim'':8C,68C ''other'':18C ''p'':1C,67C ''powerpoint'':45C ''probabl'':17C ''process'':53C ''quick'':56C ''re'':6C,61C ''realli'':86C ''rel'':25C,39C ''right'':64C ''rough'':58C ''scienc'':78C ''solid'':31C ''sound'':3C ''speak'':59C ''statistician'':94C ''subject'':48C ''summar'':55C ''take'':97C ''tool'':89C ''track'':65C ''want'':95C ''www.atmos.berkeley.edu'':37C ''www.atmos.berkeley.edu/~inez/msri-ncar_carbonda/lectures/kalnay2_ch5optimalinterpolation.pdf'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6694, 1, '2015-08-05 05:58:44.543', 6, 152, 'Books on Reinforcement Learning', '&lt;p&gt;I have been trying to understand reinforcement learning for quite sometime, but somehow I am not able to visualize how to write a program for reinforcement learning to solve a grid world problem. Can you suggest me some text books which would help me build a clear conception of Reinforcement Learning? &lt;/p&gt;
', 'machine-learning  books  reinforcement-learning', NULL, '''/p'':64C ''abl'':29C ''book'':1A,8B,52C ''build'':57C ''clear'':59C ''concept'':60C ''grid'':43C ''help'':55C ''learn'':4A,7B,11B,20C,39C,63C ''machin'':6B ''machine-learn'':5B ''p'':12C ''problem'':45C ''program'':36C ''quit'':22C ''reinforc'':3A,10B,19C,38C,62C ''reinforcement-learn'':9B ''solv'':41C ''somehow'':25C ''sometim'':23C ''suggest'':48C ''text'':51C ''tri'':16C ''understand'':18C ''visual'':31C ''world'':44C ''would'':54C ''write'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6398, 1, '2015-07-08 20:57:09.627', 2, 33, 'Can I conclude my finding with just one linear regression result?', '&lt;p&gt;I''m trying to learn Linear Regression and I''d like someone to look at my notebook to see if I miss out anything at all. How how else I can tune my model better. &lt;/p&gt;

&lt;p&gt;Here''s my notebook.&lt;/p&gt;

&lt;p&gt;I get the dataset from here which is really simple. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://college.cengage.com/mathematics/brase/understandable_statistics/7e/students/datasets/slr/frames/frame.html" rel="nofollow"&gt;http://college.cengage.com/mathematics/brase/understandable_statistics/7e/students/datasets/slr/frames/frame.html&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;%matplotlib inline
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt

df = pd.read_csv(''slr12.csv'', names=[''annual'', ''cost''], header=0)
df.describe()

df.plot(kind=''scatter'', x=''X'', y=''Y'');
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/jApbl.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;slope, intercept, r_value, p_value, std_err = stats.linregress(df[''X''], df[''Y''])

plt.plot(df[''X''], df[''Y''], ''o'', label=''Original data'', markersize=2)
plt.plot(df[''X''], slope*df[''X''] + intercept, ''r'', label=''Fitted line'')
plt.legend()
plt.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/0sBy7.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;So from this trend we can predict that if you annual fee is high then you need your startup cost will be high as well.&lt;/p&gt;
', 'python  pandas  linear-regression', NULL, '''/0sby7.png'':180C ''/code'':120C,173C ''/japbl.png'':127C ''/mathematics/brase/understandable_statistics/7e/students/datasets/slr/frames/frame.html'':77C ''/mathematics/brase/understandable_statistics/7e/students/datasets/slr/frames/frame.html&lt;/a&gt;&lt;/p&gt;'':82C ''/p'':53C,59C,71C,133C,186C,213C ''/pre'':121C,174C ''0'':111C ''2'':159C ''alt'':128C,181C ''annual'':108C,198C ''anyth'':41C ''better'':52C ''code'':84C,135C ''college.cengage.com'':76C,81C ''college.cengage.com/mathematics/brase/understandable_statistics/7e/students/datasets/slr/frames/frame.html'':75C ''college.cengage.com/mathematics/brase/understandable_statistics/7e/students/datasets/slr/frames/frame.html&lt;/a&gt;&lt;/p&gt;'':80C ''conclud'':3A ''cost'':109C,207C ''csv'':105C ''d'':27C ''data'':157C ''dataset'':64C ''descript'':131C,184C ''df'':103C,145C,147C,150C,152C,161C,164C ''df.describe'':112C ''df.plot'':113C ''els'':46C ''enter'':129C,182C ''err'':143C ''fee'':199C ''find'':5A ''fit'':169C ''get'':62C ''header'':110C ''high'':201C,210C ''href'':74C ''i.stack.imgur.com'':126C,179C ''i.stack.imgur.com/0sby7.png'':178C ''i.stack.imgur.com/japbl.png'':125C ''imag'':130C,183C ''img'':123C,176C ''import'':87C,91C,97C,99C ''inlin'':86C ''intercept'':137C,166C ''kind'':114C ''label'':155C,168C ''learn'':22C ''like'':28C ''line'':170C ''linear'':9A,15B,23C ''linear-regress'':14B ''look'':31C ''m'':19C ''markers'':158C ''matplotlib'':85C ''matplotlib.pyplot'':100C ''miss'':39C ''model'':51C ''name'':107C ''need'':204C ''nofollow'':79C ''notebook'':34C,58C ''np'':94C ''numpi'':92C ''o'':154C ''one'':8A ''origin'':156C ''p'':17C,54C,60C,72C,122C,140C,175C,187C ''panda'':13B,88C ''pd'':90C ''pd.read'':104C ''plt'':102C ''plt.legend'':171C ''plt.plot'':149C,160C ''plt.show'':172C ''pre'':83C,134C ''predict'':194C ''python'':12B ''r'':138C,167C ''realli'':69C ''regress'':10A,16B,24C ''rel'':78C ''result'':11A ''scatter'':115C ''scipi'':96C ''see'':36C ''simpl'':70C ''slope'':136C,163C ''slr12.csv'':106C ''someon'':29C ''src'':124C,177C ''startup'':206C ''stat'':98C ''stats.linregress'':144C ''std'':142C ''trend'':191C ''tri'':20C ''tune'':49C ''valu'':139C,141C ''well'':212C ''x'':116C,117C,146C,151C,162C,165C ''y'':118C,119C,148C,153C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8817, 2, '2015-11-12 14:38:57.153', 4, NULL, NULL, '&lt;p&gt;The equation that you show &lt;em&gt;calculates&lt;/em&gt; (the negative of) a gradient to the objective function. The value $\delta_j$ is the rate of change of the objective function for an isolated change of output of a neuron &lt;em&gt;before&lt;/em&gt; applying the activation function for the neuron. The factor of $fâ²(net_i)$ is required because it is &lt;em&gt;correct&lt;/em&gt; - it is due to &lt;a href="https://en.wikipedia.org/wiki/Chain_rule" rel="nofollow"&gt;the chain rule&lt;/a&gt; applied twice - once due to the linear relationship linking weights between layers, and again to get the gradient pre-activation function in the next layer down. &lt;/p&gt;

&lt;p&gt;An interpretation of $\delta_i$ is "the (negative) gradient of objective function with respect to a pre-activation value of a neuron the network". If, somehow, the pre-activation value could be changed - ignoring &lt;em&gt;how&lt;/em&gt; it could be changed - then $\delta_i$ tells you the linear scale of the impact on the objective function, at least for very small changes. &lt;/p&gt;

&lt;p&gt;This value is not normally used directly in weight updates, but is fed deeper backwards into the networks layers if there are yet lower layers to update, because it can be used for weight updates in those layers using the same rule.&lt;/p&gt;

&lt;p&gt;The property of low gradients associated with strong activations is because you previously chose &lt;code&gt;tanh&lt;/code&gt; as an activation function, and is not because of the way you calculate the gradient. Once you chose &lt;code&gt;tanh&lt;/code&gt;, you were not really free to choose a different backpropagation gradient calculation. Or rather, there is not much to gain - and potentially a lot to lose - by doing so.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;What is the purpose of this for weight updating? &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The value shown is not used directly, but used to derive $\frac{\partial J}{\partial W_{ij}}$ gradients for weights in deeper layers (where $J$ is value of your objective function).&lt;/p&gt;

&lt;p&gt;In terms of your equations&lt;/p&gt;

&lt;p&gt;$$\frac{\partial J}{\partial W_{ij}} = -\delta_j y_i$$&lt;/p&gt;

&lt;p&gt;This is very simple, so it has been folded in to your delta update rule. However, doing that is hiding something important. Out of all the intermediate values you calculate during back propagation, $\frac{\partial J}{\partial W_{ij}}$ is the key one. It is the gradient of the objective function &lt;em&gt;with respect to parameters that you can change&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;Once you have a set of gradients for  $\frac{\partial J}{\partial W_{ij}}$, this can be used in various ways.  Using this gradient &lt;em&gt;directly&lt;/em&gt; for weight updates using the delta rule (as in your equation $\Delta W_{ij} = \eta \delta_j y_i$) is a basic approach. The purpose of this rule is to reduce the value of the objective function by taking a small step in the direction that you have just calculated &lt;em&gt;will&lt;/em&gt; reduce the value of objective function. The step size needs to be small (multiplied by a factor $\eta$) because you have only measured the gradient at one point, and don''t know for what step size the relationship still holds - complicated by the fact that you are making a similar step in multiple dimensions at once.&lt;/p&gt;

&lt;p&gt;The delta update rule is a relatively weak update method compared to e.g. Nesterov momentum, Adagrad, Rmsprop optimisers which will use the calculated gradient as input, but adjust the update weights based on history of previous gradients. These optimisers can deal with low but consistent gradient values and make large updates.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;How would the networks behave differently 
  if one would remove or change this factor?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This is not advisable as effectively you would be optimising against a &lt;em&gt;different&lt;/em&gt; network architecture than the feed-forward network you had constructed. The most likely way they would behave is to either diverge or settle on a larger value of your objective function than you would otherwise see. Making small changes (such as a minimum absolute gradient) can work, but usually such a tweak is best done consistently, by changing the activation function as well.&lt;/p&gt;

&lt;p&gt;In a few edge cases, you might consider "lying" in the gradient calculation. A trivial case could be considered the ReLU activation function ($y = 0 (if x \lt 0), y = x (otherwise)$) where the gradient is technically undefined at $x=0$, but can safely be treated as either 0 or 1.&lt;/p&gt;

&lt;p&gt;An alteration to updates that is more usually done (by e.g. momentum variants, Adagrad, RMSProp et al) is to use the gradient as-is, but then multiply update amounts by various factors based on history or estimates of higher-order derivatives, because you can guess that the gradient will not switch sign if you update a weight by a smaller or larger amount. These approaches replace the delta update rule with update mechanisms that are more robust when gradients become small.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6400, 2, '2015-07-09 00:59:20.503', 1, NULL, NULL, '&lt;p&gt;This problem seems to be exactly (or at least very similar to) that in the Billion Word Imputation (&lt;a href="https://www.kaggle.com/c/billion-word-imputation" rel="nofollow"&gt;https://www.kaggle.com/c/billion-word-imputation&lt;/a&gt;) challenge at Kaggle. Some of the approaches discussed in the forums might help you out. My initial thoughts for useful tools/methods would be word2vec, the stanford NLP tools and sklearn.&lt;/p&gt;
', NULL, NULL, '''/c/billion-word-imputation'':24C ''/c/billion-word-imputation&lt;/a&gt;)'':29C ''/p'':60C ''approach'':36C ''billion'':17C ''challeng'':30C ''discuss'':37C ''exact'':7C ''forum'':40C ''help'':42C ''href'':21C ''imput'':19C ''initi'':46C ''kaggl'':32C ''least'':10C ''might'':41C ''nlp'':56C ''nofollow'':26C ''p'':1C ''problem'':3C ''rel'':25C ''seem'':4C ''similar'':12C ''sklearn'':59C ''stanford'':55C ''thought'':47C ''tool'':57C ''tools/methods'':50C ''use'':49C ''word'':18C ''word2vec'':53C ''would'':51C ''www.kaggle.com'':23C,28C ''www.kaggle.com/c/billion-word-imputation'':22C ''www.kaggle.com/c/billion-word-imputation&lt;/a&gt;)'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6401, 2, '2015-07-09 01:42:27.827', 1, NULL, NULL, '&lt;p&gt;Spark does have a pretty good Python API, check out this &lt;a href="https://districtdatalabs.silvrback.com/getting-started-with-spark-in-python" rel="nofollow"&gt;tutorial&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;For traditional Hadoop stack, take a look at &lt;a href="https://github.com/Yelp/mrjob" rel="nofollow"&gt;mrjob&lt;/a&gt;, it lets you write MapReduce jobs in Python and run them on several platforms.&lt;/p&gt;
', NULL, NULL, '''/a'':21C,40C ''/getting-started-with-spark-in-python'':17C ''/p'':22C,55C ''/yelp/mrjob'':36C ''api'':9C ''check'':10C ''districtdatalabs.silvrback.com'':16C ''districtdatalabs.silvrback.com/getting-started-with-spark-in-python'':15C ''github.com'':35C ''github.com/yelp/mrjob'':34C ''good'':7C ''hadoop'':26C ''href'':14C,33C ''job'':46C ''let'':42C ''look'':30C ''mapreduc'':45C ''mrjob'':39C ''nofollow'':19C,38C ''p'':1C,23C ''platform'':54C ''pretti'':6C ''python'':8C,48C ''rel'':18C,37C ''run'':50C ''sever'':53C ''spark'':2C ''stack'':27C ''take'':28C ''tradit'':25C ''tutori'':20C ''write'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6403, 2, '2015-07-09 02:21:55.417', 1, NULL, NULL, '&lt;p&gt;Hey is Python or other tools an option here? Since you mentioned it is a large dataset, you might want to iterate over it instead of loading all of them at once. &lt;/p&gt;

&lt;p&gt;Here is a solution in Python:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas as pd
from collections import defaultdict


inputs = [
''cat1/: Topic1_Variable1'',
''cat2/: Topic1_Variable2'',
''cat3/: Topic1_Variable3'',
''cat4/: Topic1_Variable4'',
''cat1/: Topic2_Variable1'',
''cat2/: Topic2_Variable2'',
''cat3/: Topic2_Variable3'',
''cat4/: Topic2_Variable4'',
''cat1/: Topic3_Variable1'',
''cat2/: Topic3_Variable2'',
''cat3/: Topic3_Variable3'',
''cat4/: Topic3_Variable4'',]


outputs = defaultdict(list)

for item in inputs:
    cat, topic = item.split(''/: '')
    outputs[cat].append(topic)

print pd.DataFrame(outputs)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Output:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;               cat1              cat2              cat3              cat4
0  Topic1_Variable1  Topic1_Variable2  Topic1_Variable3  Topic1_Variable4
1  Topic2_Variable1  Topic2_Variable2  Topic2_Variable3  Topic2_Variable4
2  Topic3_Variable1  Topic3_Variable2  Topic3_Variable3  Topic3_Variable4
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':107C,145C ''/p'':34C,42C,111C ''/pre'':108C,146C ''0'':118C ''1'':127C ''2'':136C ''append'':102C ''cat'':97C,101C ''cat1'':54C,66C,78C,114C ''cat2'':57C,69C,81C,115C ''cat3'':60C,72C,84C,116C ''cat4'':63C,75C,87C,117C ''code'':44C,113C ''collect'':50C ''dataset'':18C ''defaultdict'':52C,91C ''hey'':2C ''import'':45C,51C ''input'':53C,96C ''instead'':26C ''item'':94C ''item.split'':99C ''iter'':23C ''larg'':17C ''list'':92C ''load'':28C ''mention'':13C ''might'':20C ''option'':9C ''output'':90C,100C,106C,110C ''p'':1C,35C,109C ''panda'':46C ''pd'':48C ''pd.dataframe'':105C ''pre'':43C,112C ''print'':104C ''python'':4C,41C ''sinc'':11C ''solut'':39C ''tool'':7C ''topic'':98C,103C ''topic1'':55C,58C,61C,64C,119C,121C,123C,125C ''topic2'':67C,70C,73C,76C,128C,130C,132C,134C ''topic3'':79C,82C,85C,88C,137C,139C,141C,143C ''variable1'':56C,68C,80C,120C,129C,138C ''variable2'':59C,71C,83C,122C,131C,140C ''variable3'':62C,74C,86C,124C,133C,142C ''variable4'':65C,77C,89C,126C,135C,144C ''want'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6405, 1, '2015-07-09 03:10:08.48', 2, 36, 'Temporal Difference Learning Getting Stuck', '&lt;p&gt;I''m trying to implement a temporal difference algorithm that learns the maximum revenue over a period of time using prices as the actions, inventory as the state, and revenues realized as the reward. The problem I''m having is that I can''t seem to get it to converge on an optimal policy. It seems like it gets stuck at a revenue somewhere around 60% of optimization and then won''t budge anymore. Are there some common pitfalls that might be causing this? I''ve tried playing with the rate of exploration a little bit, but that hasn''t seemed to help it much.&lt;/p&gt;

&lt;p&gt;EDIT:
Ok, so I went back through everything, and it seems like the problem is that, when it stops exploring, it just keeps increasing the Q-values of states that it''s already deemed the best in the past. So for example, it visits the price 5 at a certain state and time and gets a reward. Then, in the next several episodes, it continues to visit 5, and continues getting a reward, adding that reward to the Q-value until it''s pretty high. At that point, even if it were to explore at the same state and time, the reward it gets isn''t enough to overcome the inflated Q-value, so it just goes right back to 5 in the next episode. &lt;a href="http://imgur.com/tQ6qXLV" rel="nofollow"&gt;Here&lt;/a&gt; are the steps I''m trying to follow.&lt;/p&gt;
', 'machine-learning', NULL, '''/a'':253C ''/p'':115C,262C ''/tq6qxlv'':249C ''5'':164C,185C,240C ''60'':75C ''action'':33C ''ad'':191C ''algorithm'':18C ''alreadi'':150C ''anymor'':83C ''around'':74C ''back'':122C,238C ''best'':153C ''bit'':105C ''budg'':82C ''caus'':92C ''certain'':167C ''common'':87C ''continu'':182C,187C ''converg'':59C ''deem'':151C ''differ'':2A,17C ''edit'':117C ''enough'':225C ''episod'':180C,244C ''even'':207C ''everyth'':124C ''exampl'':159C ''explor'':102C,136C,212C ''follow'':261C ''get'':4A,56C,68C,172C,188C,222C ''goe'':236C ''hasn'':108C ''help'':112C ''high'':203C ''href'':246C ''imgur.com'':248C ''imgur.com/tq6qxlv'':247C ''implement'':14C ''increas'':140C ''inflat'':229C ''inventori'':34C ''isn'':223C ''keep'':139C ''learn'':3A,8B,20C ''like'':66C,128C ''littl'':104C ''m'':11C,47C,258C ''machin'':7B ''machine-learn'':6B ''maximum'':22C ''might'':90C ''much'':114C ''next'':178C,243C ''nofollow'':251C ''ok'':118C ''optim'':62C,77C ''overcom'':227C ''p'':9C,116C ''past'':156C ''period'':26C ''pitfal'':88C ''play'':97C ''point'':206C ''polici'':63C ''pretti'':202C ''price'':30C,163C ''problem'':45C,130C ''q'':143C,197C,231C ''q-valu'':142C,196C,230C ''rate'':100C ''realiz'':40C ''rel'':250C ''revenu'':23C,39C,72C ''reward'':43C,174C,190C,193C,220C ''right'':237C ''seem'':54C,65C,110C,127C ''sever'':179C ''somewher'':73C ''state'':37C,146C,168C,216C ''step'':256C ''stop'':135C ''stuck'':5A,69C ''tempor'':1A,16C ''time'':28C,170C,218C ''tri'':12C,96C,259C ''use'':29C ''valu'':144C,198C,232C ''ve'':95C ''visit'':161C,184C ''went'':121C ''won'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6406, 1, '2015-07-09 04:44:42.5', 5, 111, 'How to evaluate clustering success in a completely unsupervised system?', '&lt;p&gt;The algorithm in question is Kohonen''s SOM. But the question could also apply to PCA and some others.&lt;/p&gt;

&lt;p&gt;When the umatrix (or the codebook?) is examined, is there a way to tell how successful clustering was?&lt;/p&gt;

&lt;p&gt;And would it be a good idea to apply GA''s to optimize size, lattice structure, learning rate, and the learning degradation functions as well as the epoch count for clustering, or is there a danger of overfitting in this instance?&lt;/p&gt;

&lt;p&gt;Assume that the SOM data is coming from a demonstrably weak PRNG and that the very first attempt shows a distinct structure. Is there some statistical property or algorithm that can evaluate the presence and degree of structure to be used for a GA fitness function?&lt;/p&gt;
', 'clustering', NULL, '''/p'':32C,52C,95C,143C ''algorithm'':14C,125C ''also'':25C ''appli'':26C,62C ''assum'':97C ''attempt'':114C ''cluster'':4A,11B,50C,84C ''codebook'':39C ''come'':103C ''complet'':8A ''could'':24C ''count'':82C ''danger'':89C ''data'':101C ''degrad'':75C ''degre'':132C ''demonstr'':106C ''distinct'':117C ''epoch'':81C ''evalu'':3A,128C ''examin'':41C ''first'':113C ''fit'':141C ''function'':76C,142C ''ga'':63C,140C ''good'':59C ''idea'':60C ''instanc'':94C ''kohonen'':18C ''lattic'':68C ''learn'':70C,74C ''optim'':66C ''other'':31C ''overfit'':91C ''p'':12C,33C,53C,96C ''pca'':28C ''presenc'':130C ''prng'':108C ''properti'':123C ''question'':16C,23C ''rate'':71C ''show'':115C ''size'':67C ''som'':20C,100C ''statist'':122C ''structur'':69C,118C,134C ''success'':5A,49C ''system'':10A ''tell'':47C ''umatrix'':36C ''unsupervis'':9A ''use'':137C ''way'':45C ''weak'':107C ''well'':78C ''would'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6431, 2, '2015-07-10 18:33:46.307', 1, NULL, NULL, '&lt;p&gt;I have read @MaximHaytovich''s answer and it is a good one. I would just like to suggest some further options, which were generalized in that answer as feature engineering. I would suggest trying to do the obvious first and analyze the data before transforming it to become ready for any machine learning algorithm. First look at the data and learn yourself and hypothesize about what patterns could be indicators of fraud. What is the nature of the data that you are 100% sure is reliable (it is mentioned in the question you posted that there is available data that is reliable) and how informative are they for the goal? The more familiar you are with the data, the better interpretation you can provide to any output you get from the methods you use. Try doing some outlier analysis on the incomes or any features that may be fraudulent that may make sense to do outlier detection on. Are there any suspicious missing values? Try doing clustering (one of the unsupervised algorithms you were talking about) to see if there are any in-betweens or irregularities. &lt;/p&gt;
', NULL, NULL, '''/p'':189C ''100'':84C ''algorithm'':55C,173C ''analysi'':140C ''analyz'':42C ''answer'':7C,28C ''avail'':99C ''becom'':49C ''better'':121C ''between'':186C ''cluster'':168C ''could'':69C ''data'':44C,60C,80C,100C,119C ''detect'':158C ''engin'':31C ''familiar'':114C ''featur'':30C,146C ''first'':40C,56C ''fraud'':73C ''fraudul'':150C ''general'':25C ''get'':130C ''goal'':111C ''good'':12C ''hypothes'':65C ''in-between'':184C ''incom'':143C ''indic'':71C ''inform'':106C ''interpret'':122C ''irregular'':188C ''learn'':54C,62C ''like'':17C ''look'':57C ''machin'':53C ''make'':153C ''maximhaytovich'':5C ''may'':148C,152C ''mention'':90C ''method'':133C ''miss'':164C ''natur'':77C ''obvious'':39C ''one'':13C,169C ''option'':22C ''outlier'':139C,157C ''output'':128C ''p'':1C ''pattern'':68C ''post'':95C ''provid'':125C ''question'':93C ''read'':4C ''readi'':50C ''reliabl'':87C,103C ''see'':179C ''sens'':154C ''suggest'':19C,34C ''sure'':85C ''suspici'':163C ''talk'':176C ''transform'':46C ''tri'':35C,136C,166C ''unsupervis'':172C ''use'':135C ''valu'':165C ''would'':15C,33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6408, 2, '2015-07-09 10:50:15.607', 1, NULL, NULL, '&lt;p&gt;Sounds like an interesting application. To debug RL applications, I like to perform rollouts. First identify a state where the current policy appears to be clearly wrong; (maybe sample some states and look at them individually). Then switch off the learning and run a sample from that state for both the current RL policy and the action you think is correct. This should give you the true action values. Hopefully, that will give you a hint as to what''s going wrong. If it''s an exploration problem, then the current policy won''t be trying any actions similar to the action you think is correct. If the form of the value function is wrong, then it won''t fit the values coming from your samples.&lt;/p&gt;
', NULL, NULL, '''/p'':128C ''action'':58C,69C,99C,103C ''appear'':24C ''applic'':6C,10C ''clear'':27C ''come'':124C ''correct'':62C,107C ''current'':22C,53C,92C ''debug'':8C ''explor'':88C ''first'':16C ''fit'':121C ''form'':110C ''function'':114C ''give'':65C,74C ''go'':82C ''hint'':77C ''hope'':71C ''identifi'':17C ''individu'':37C ''interest'':5C ''learn'':42C ''like'':3C,12C ''look'':34C ''mayb'':29C ''p'':1C ''perform'':14C ''polici'':23C,55C,93C ''problem'':89C ''rl'':9C,54C ''rollout'':15C ''run'':44C ''sampl'':30C,46C,127C ''similar'':100C ''sound'':2C ''state'':19C,32C,49C ''switch'':39C ''think'':60C,105C ''tri'':97C ''true'':68C ''valu'':70C,113C,123C ''won'':94C,119C ''wrong'':28C,83C,116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6409, 2, '2015-07-09 13:25:24.823', 3, NULL, NULL, '&lt;p&gt;First, PCA is not a clustering method. It is a dimensionality reduction scheme.  You can assess the performance of PCA through analysis of the percent of variance in the dataset that is retained as you decrease the number of dimensions.  Retaining 99%,95%, or 90% is usually ideal, depending on your problem. &lt;/p&gt;

&lt;p&gt;With regards to clustering, you probably want to start with the &lt;a href="https://en.wikipedia.org/wiki/Silhouette_(clustering)" rel="nofollow"&gt;Silhouette Coefficient&lt;/a&gt;.  This combines assessments of both the cohesion (how tight a cluster is) and separation (how well separated each cluster is from other clusters).&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Calculate a = average distance of i to the points in its cluster&lt;/li&gt;
&lt;li&gt;Calculate b = min (average distance of i to points in another cluster) &lt;/li&gt;
&lt;li&gt;The silhouette coefficient for a point is then given by:
&lt;code&gt;s =1 â a/b, if a &amp;lt; b&lt;/code&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It is typically between 0 and 1 with larger numbers being "better".  You can average the coefficients over a cluster or the entire region to get an assessment of the cluster or the entire clustering procedure for your data.&lt;/p&gt;

&lt;p&gt;More generally, try googling "assessing clustering" or "cluster validity" to read about all of the other ways that you can score your clustering algorithm.  &lt;a href="http://www.cs.kent.edu/~jin/DM08/ClusterValidation.pdf" rel="nofollow"&gt;Here is a very complete treatment of the topic&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/a'':76C,235C ''/code'':150C ''/li'':115C,129C,151C ''/p'':54C,100C,193C,236C,241C ''/ul'':152C ''/wiki/silhouette_(clustering)'':71C ''/~jin/dm08/clustervalidation.pdf'':223C ''0'':158C ''1'':143C,160C ''90'':46C ''95'':44C ''99'':43C ''a/b'':145C ''algorithm'':218C ''analysi'':23C ''anoth'':127C ''assess'':17C,79C,181C,199C ''averag'':105C,120C,168C ''b'':118C,149C ''better'':165C ''calcul'':103C,117C ''cluster'':7C,59C,87C,95C,99C,114C,128C,173C,184C,188C,200C,202C,217C ''code'':141C ''coeffici'':75C,133C,170C ''cohes'':83C ''combin'':78C ''complet'':230C ''data'':192C ''dataset'':31C ''decreas'':37C ''depend'':50C ''dimens'':41C ''dimension'':12C ''distanc'':106C,121C ''en.wikipedia.org'':70C ''en.wikipedia.org/wiki/silhouette_(clustering)'':69C ''entir'':176C,187C ''first'':2C ''general'':196C ''get'':179C ''given'':139C ''googl'':198C ''help'':240C ''hope'':238C ''href'':68C,220C ''ideal'':49C ''larger'':162C ''li'':102C,116C,130C ''lt'':148C ''method'':8C ''min'':119C ''nofollow'':73C,225C ''number'':39C,163C ''p'':1C,55C,153C,194C,237C ''pca'':3C,21C ''percent'':26C ''perform'':19C ''point'':111C,125C,136C ''probabl'':61C ''problem'':53C ''procedur'':189C ''read'':205C ''reduct'':13C ''regard'':57C ''region'':177C ''rel'':72C,224C ''retain'':34C,42C ''scheme'':14C ''score'':215C ''separ'':90C,93C ''silhouett'':74C,132C ''start'':64C ''tight'':85C ''topic'':234C ''treatment'':231C ''tri'':197C ''typic'':156C ''ul'':101C ''usual'':48C ''valid'':203C ''varianc'':28C ''want'':62C ''way'':211C ''well'':92C ''www.cs.kent.edu'':222C ''www.cs.kent.edu/~jin/dm08/clustervalidation.pdf'':221C ''â'':144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6691, 1, '2015-08-04 22:15:52.833', 1, 172, 'Sentiment Analysis model for Spanish', '&lt;p&gt;I barely know about Data Analysis, tools and techniques, so bare with me if I''m asking something too trivial.&lt;/p&gt;

&lt;p&gt;I''m looking for a Sentiment Analysis tool to process comments in Spanish. I do know some options for Sentiment analysis but those all work for English. &lt;/p&gt;

&lt;p&gt;Is there a model/tool that already works with Spanish? &lt;/p&gt;

&lt;p&gt;I''m language agnostic so it does not matter if it''s a Java, Python or even Go code. &lt;/p&gt;

&lt;p&gt;Thanks.&lt;/p&gt;
', 'machine-learning  nlp  social-network-analysis', NULL, '''/p'':35C,64C,75C,96C,99C ''agnost'':80C ''alreadi'':71C ''analysi'':2A,13B,20C,43C,57C ''ask'':31C ''bare'':16C,25C ''code'':95C ''comment'':47C ''data'':19C ''english'':63C ''even'':93C ''go'':94C ''java'':90C ''know'':17C,52C ''languag'':79C ''learn'':8B ''look'':39C ''m'':30C,38C,78C ''machin'':7B ''machine-learn'':6B ''matter'':85C ''model'':3A ''model/tool'':69C ''network'':12B ''nlp'':9B ''option'':54C ''p'':14C,36C,65C,76C,97C ''process'':46C ''python'':91C ''sentiment'':1A,42C,56C ''social'':11B ''social-network-analysi'':10B ''someth'':32C ''spanish'':5A,49C,74C ''techniqu'':23C ''thank'':98C ''tool'':21C,44C ''trivial'':34C ''work'':61C,72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6696, 2, '2015-08-05 06:48:49.087', 5, NULL, NULL, '&lt;p&gt;There is a free online course on Reinforcement Learning by Udacity. Check : &lt;a href="https://www.udacity.com/course/machine-learning-reinforcement-learning--ud820"&gt;Machine Learning: Reinforcement Learning&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/a'':23C ''/course/machine-learning-reinforcement-learning--ud820'':18C ''/p'':24C ''check'':13C ''cours'':7C ''free'':5C ''href'':15C ''learn'':10C,20C,22C ''machin'':19C ''onlin'':6C ''p'':1C ''reinforc'':9C,21C ''udac'':12C ''www.udacity.com'':17C ''www.udacity.com/course/machine-learning-reinforcement-learning--ud820'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6476, 1, '2015-07-16 08:08:45.27', 0, 113, 'Finding Episodes in event sequence', '&lt;p&gt;In the &lt;a href="http://www.win.tue.nl/~aserebre/ks/dmkd97episodes.pdf" rel="nofollow"&gt;paper&lt;/a&gt; "Discovery of frequent episodes in event sequences" by Mannila et al., a method for finding frequent episodes in an event sequence if a class of episodes and a sequence of events is given. &lt;/p&gt;

&lt;p&gt;It is not clear to me how to find episodes in the first place. I would like to know if there are any tools, algorithms, or published work that can be used to this end, or which describes a method to find episodes in a given stream.&lt;/p&gt;
', 'machine-learning  data-mining', NULL, '''/a'':23C ''/p'':57C,106C ''/~aserebre/ks/dmkd97episodes.pdf'':19C ''al'':34C ''algorithm'':83C ''class'':47C ''clear'':62C ''data'':10B ''data-min'':9B ''describ'':96C ''discoveri'':24C ''end'':93C ''episod'':2A,27C,40C,49C,68C,101C ''et'':33C ''event'':4A,29C,43C,54C ''find'':1A,38C,67C,100C ''first'':71C ''frequent'':26C,39C ''given'':56C,104C ''href'':16C ''know'':77C ''learn'':8B ''like'':75C ''machin'':7B ''machine-learn'':6B ''mannila'':32C ''method'':36C,98C ''mine'':11B ''nofollow'':21C ''p'':12C,58C ''paper'':22C ''place'':72C ''publish'':85C ''rel'':20C ''sequenc'':5A,30C,44C,52C ''stream'':105C ''tool'':82C ''use'':90C ''work'':86C ''would'':74C ''www.win.tue.nl'':18C ''www.win.tue.nl/~aserebre/ks/dmkd97episodes.pdf'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9714, 2, '2016-01-09 15:17:31.107', 3, NULL, NULL, '&lt;p&gt;&lt;strong&gt;SVC Parameters:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;What prompted you to use a polynomial kernel?  There are cases where this might make sense, but it certainly wouldn''t be my first choice.  A radial basis function is likely a better fit, which is why it is the default in SKLearn. Step back a minute and take in the big picture perspective. Does your human mind want to create a decision surface using an 11th order polynomial given the points that you showed? It looks like there are a couple islands of white points in your second image that could easily be wrapped with two &lt;code&gt;Gaussian functions&lt;/code&gt; aka &lt;code&gt;radial basis functions&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;What does the SVC with only default parameters do? I''ve seen plenty of cases where a regularization parameter as high as 10 or 100 is needed, but you haven''t spoken to the cross validation procedure you used to arrive at $C=10$, so I wonder how you got there.&lt;/p&gt;

&lt;p&gt;I suggest running SVC with default parameters and seeing what this returns.  Then maybe try tuning gamma (this is effectively the buffer zone between the decision surface and points around it) and C (this is the regularization term that controls overfitting aka high variance).&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/code'':108C,114C ''/p'':6C,115C,168C,214C,219C ''/strong'':5C ''10'':139C,160C ''100'':141C ''11th'':74C ''aka'':109C,211C ''around'':199C ''arriv'':157C ''back'':52C ''basi'':35C,112C ''better'':40C ''big'':59C ''buffer'':191C ''c'':159C,202C ''case'':18C,131C ''certain'':26C ''choic'':32C ''code'':105C,110C ''control'':209C ''could'':99C ''coupl'':89C ''creat'':68C ''cross'':151C ''decis'':70C,195C ''default'':48C,123C,175C ''easili'':100C ''effect'':189C ''first'':31C ''fit'':41C ''function'':36C,107C,113C ''gamma'':186C ''gaussian'':106C ''given'':77C ''got'':166C ''haven'':146C ''help'':218C ''high'':137C,212C ''hope'':216C ''human'':64C ''imag'':97C ''island'':90C ''kernel'':15C ''like'':38C,85C ''look'':84C ''make'':22C ''mayb'':183C ''might'':21C ''mind'':65C ''minut'':54C ''need'':143C ''order'':75C ''overfit'':210C ''p'':1C,7C,116C,169C,215C ''paramet'':4C,124C,135C,176C ''perspect'':61C ''pictur'':60C ''plenti'':129C ''point'':79C,93C,198C ''polynomi'':14C,76C ''procedur'':153C ''prompt'':9C ''radial'':34C,111C ''regular'':134C,206C ''return'':181C ''run'':172C ''second'':96C ''see'':178C ''seen'':128C ''sens'':23C ''show'':82C ''sklearn'':50C ''spoken'':148C ''step'':51C ''strong'':2C ''suggest'':171C ''surfac'':71C,196C ''svc'':3C,120C,173C ''take'':56C ''term'':207C ''tri'':184C ''tune'':185C ''two'':104C ''use'':12C,72C,155C ''valid'':152C ''varianc'':213C ''ve'':127C ''want'':66C ''white'':92C ''wonder'':163C ''wouldn'':27C ''wrap'':102C ''zone'':192C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6412, 2, '2015-07-09 17:47:34.793', 1, NULL, NULL, '&lt;p&gt;To directly answer your question about stacking: you should care about minimizing 1) bias, and 2) variance. This is obvious, but in practice this often comes down to simply having models which are &lt;a href="http://dl.acm.org/citation.cfm?id=640232" rel="nofollow"&gt;"diverse"&lt;/a&gt;. (I apologize that link is behind a paywall, but there are a few others like it and you may well find it other ways)&lt;/p&gt;

&lt;p&gt;You don''t want ensembles of like-minded models - they will make the same mistakes and reinforce each other. &lt;/p&gt;

&lt;p&gt;In the case of stacking, what is happening? You are letting the outputs of the probabilistic classifiers on the actual feature input become the new features. A diverse set of classifiers which can in any way give signals about edge cases is desirable. If classifier 1 is terrible at classes A, B, and C but fantastic at class D, or a certain edge case, it is still a good contribution to the ensemble. &lt;/p&gt;

&lt;p&gt;This is why neural nets are so good at what they do in image recognition - deep nets are in fact recursive logistic regression stacking ensembles! Nowadays people don''t always use the sigmoid activation and there are many layer architectures, but it''s the same general idea. &lt;/p&gt;

&lt;p&gt;What I would recommend is trying to maximize the diversity of your ensemble by using some of the similarity metrics on the classifiers'' prediction output vectors (ie, Diettrich''s Kappa statistic) in training. &lt;a href="http://www.sandia.gov/~wpk/pubs/publications/infoFusion.pdf" rel="nofollow"&gt;Here is another good reference&lt;/a&gt;.  &lt;/p&gt;

&lt;p&gt;Hope that helps.&lt;/p&gt;
', NULL, NULL, '''/a'':43C,261C ''/citation.cfm?id=640232'':39C ''/p'':68C,90C,165C,214C,262C,267C ''/~wpk/pubs/publications/infofusion.pdf'':253C ''1'':14C,137C ''2'':17C ''activ'':200C ''actual'':111C ''alway'':196C ''anoth'':258C ''answer'':4C ''apolog'':45C ''architectur'':206C ''b'':143C ''becom'':114C ''behind'':49C ''bias'':15C ''c'':145C ''care'':11C ''case'':94C,132C,155C ''certain'':153C ''class'':141C,149C ''classifi'':108C,122C,136C,238C ''come'':27C ''contribut'':161C ''d'':150C ''deep'':182C ''desir'':134C ''diettrich'':243C ''direct'':3C ''divers'':42C,119C,225C ''dl.acm.org'':38C ''dl.acm.org/citation.cfm?id=640232'':37C ''edg'':131C,154C ''ensembl'':74C,164C,191C,228C ''fact'':186C ''fantast'':147C ''featur'':112C,117C ''find'':64C ''general'':212C ''give'':128C ''good'':160C,174C,259C ''happen'':99C ''help'':266C ''hope'':264C ''href'':36C,250C ''idea'':213C ''ie'':242C ''imag'':180C ''input'':113C ''kappa'':245C ''layer'':205C ''let'':102C ''like'':58C,77C ''like-mind'':76C ''link'':47C ''logist'':188C ''make'':82C ''mani'':204C ''maxim'':223C ''may'':62C ''metric'':235C ''mind'':78C ''minim'':13C ''mistak'':85C ''model'':32C,79C ''net'':171C,183C ''neural'':170C ''new'':116C ''nofollow'':41C,255C ''nowaday'':192C ''obvious'':21C ''often'':26C ''other'':57C ''output'':104C,240C ''p'':1C,69C,91C,166C,215C,263C ''paywal'':51C ''peopl'':193C ''practic'':24C ''predict'':239C ''probabilist'':107C ''question'':6C ''recognit'':181C ''recommend'':219C ''recurs'':187C ''refer'':260C ''regress'':189C ''reinforc'':87C ''rel'':40C,254C ''set'':120C ''sigmoid'':199C ''signal'':129C ''similar'':234C ''simpli'':30C ''stack'':8C,96C,190C ''statist'':246C ''still'':158C ''terribl'':139C ''train'':248C ''tri'':221C ''use'':197C,230C ''varianc'':18C ''vector'':241C ''want'':73C ''way'':67C,127C ''well'':63C ''would'':218C ''www.sandia.gov'':252C ''www.sandia.gov/~wpk/pubs/publications/infofusion.pdf'':251C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8362, 2, '2015-10-09 06:31:39.117', 0, NULL, NULL, '&lt;p&gt;A Gaussian mixture model is a special case of a mixture distribution, which is a simple way of combining probability distributions.&lt;/p&gt;

&lt;p&gt;However, the SVM does not make any assumptions. It is just a function, which depends on the distance of a data point from another point/plane. Som there is absolutely no probabilistic assumptions.&lt;/p&gt;
', NULL, NULL, '''/p'':23C,57C ''absolut'':53C ''anoth'':48C ''assumpt'':32C,56C ''case'':9C ''combin'':20C ''data'':45C ''depend'':39C ''distanc'':42C ''distribut'':13C,22C ''function'':37C ''gaussian'':3C ''howev'':25C ''make'':30C ''mixtur'':4C,12C ''model'':5C ''p'':1C,24C ''point'':46C ''point/plane'':49C ''probabilist'':55C ''probabl'':21C ''simpl'':17C ''som'':50C ''special'':8C ''svm'':27C ''way'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6413, 2, '2015-07-09 18:15:42.02', 0, NULL, NULL, '&lt;p&gt;It trains again based on what it learned the first time you did &lt;code&gt;OP = train(OP,inputsVals,targetVals)&lt;/code&gt;. More generally, &lt;code&gt;train&lt;/code&gt; uses your network''s weights, i.e. it does not initialize the weights. The weight initialization happens in &lt;code&gt;feedforwardnet&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;% To generate reproducible results 
% http://stackoverflow.com/a/7797635/395857
rng(1234,''twister'')

% Prepare input and target vectors
[x,t] = simplefit_dataset;

% Create ANN
net = feedforwardnet(10);

% Loop to see where train() initializes the weights
for i = 1:10

    % Learn
    net.trainParam.epochs = 1;
    net = train(net,x,t);

    % Score
    y = net(x);
    perf = perform(net,y,t)
end
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;yields&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;perf =

    0.4825


perf =

    0.0093


perf =

    0.0034


perf =

    0.0034


perf =

    0.0034


perf =

    0.0034


perf =

    0.0034


perf =

    0.0034


perf =

    0.0034


perf =

    0.0028
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a/7797635/395857'':59C ''/code'':21C,26C,46C,107C,134C ''/p'':47C,50C,111C ''/pre'':108C,135C ''0.0028'':133C ''0.0034'':119C,121C,123C,125C,127C,129C,131C ''0.0093'':117C ''0.4825'':115C ''1'':87C,91C ''10'':76C,88C ''1234'':61C ''ann'':73C ''base'':5C ''code'':15C,24C,44C,52C,113C ''creat'':72C ''dataset'':71C ''end'':106C ''exampl'':49C ''feedforwardnet'':45C,75C ''first'':11C ''general'':23C ''generat'':54C ''happen'':42C ''i.e'':32C ''initi'':36C,41C,82C ''input'':64C ''inputsv'':19C ''learn'':9C,89C ''loop'':77C ''net'':74C,92C,94C,99C,103C ''net.trainparam.epochs'':90C ''network'':29C ''op'':16C,18C ''p'':1C,48C,109C ''perf'':101C,114C,116C,118C,120C,122C,124C,126C,128C,130C,132C ''perform'':102C ''pre'':51C,112C ''prepar'':63C ''reproduc'':55C ''result'':56C ''rng'':60C ''score'':97C ''see'':79C ''simplefit'':70C ''stackoverflow.com'':58C ''stackoverflow.com/a/7797635/395857'':57C ''target'':66C ''targetv'':20C ''time'':12C ''train'':3C,17C,25C,81C,93C ''twister'':62C ''use'':27C ''vector'':67C ''weight'':31C,38C,40C,84C ''x'':68C,95C,100C ''y'':98C,104C ''yield'':110C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6414, 1, '2015-07-09 20:38:22.933', 2, 116, 'Scalable open source machine learning library written in python', '&lt;p&gt;I believe sci kit learn is written in python,however that not scalable.Spark mlib or ml is scalabale but written in scala.I am looking for an ongoing effort where a machine learning library is being built in python (available in github or so) so that I can contribute to that.Is anyone aware of such effort.&lt;/p&gt;
', 'machine-learning  scalability  sklearn  apache-spark', NULL, '''/p'':74C ''anyon'':69C ''apach'':16B ''apache-spark'':15B ''avail'':57C ''awar'':70C ''believ'':20C ''built'':54C ''contribut'':66C ''effort'':46C,73C ''github'':59C ''howev'':28C ''kit'':22C ''learn'':5A,12B,23C,50C ''librari'':6A,51C ''look'':42C ''machin'':4A,11B,49C ''machine-learn'':10B ''ml'':34C ''mlib'':32C ''ongo'':45C ''open'':2A ''p'':18C ''python'':9A,27C,56C ''scala.i'':40C ''scalabal'':36C ''scalabl'':1A,13B ''scalable.spark'':31C ''sci'':21C ''sklearn'':14B ''sourc'':3A ''spark'':17B ''that.is'':68C ''written'':7A,25C,38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6415, 2, '2015-07-09 20:44:47.437', 2, NULL, NULL, '&lt;p&gt;I am not sure I understood the problem,however if you are trying to predict sales amount my guess is ARIMA might not be the right choice as it will not consider external variables.My suggestion is to gather related features such as how did the other movie from same genre did at that time of the year in that region,weather,star presence,presence of other popular matches or tournament at that time and etc.Let me know your thoughts. &lt;/p&gt;
', NULL, NULL, '''/p'':81C ''amount'':18C ''arima'':22C ''choic'':28C ''consid'':33C ''etc.let'':76C ''extern'':34C ''featur'':41C ''gather'':39C ''genr'':51C ''guess'':20C ''howev'':10C ''know'':78C ''match'':69C ''might'':23C ''movi'':48C ''p'':1C ''popular'':68C ''predict'':16C ''presenc'':64C,65C ''problem'':9C ''region'':61C ''relat'':40C ''right'':27C ''sale'':17C ''star'':63C ''suggest'':36C ''sure'':5C ''thought'':80C ''time'':55C,74C ''tournament'':71C ''tri'':14C ''understood'':7C ''variables.my'':35C ''weather'':62C ''year'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6416, 2, '2015-07-09 21:00:32.84', 4, NULL, NULL, '&lt;p&gt;Here is a nice link on that on stackexchange  &lt;a href="http://stats.stackexchange.com/questions/111968/random-forest-how-to-handle-overfitting"&gt;http://stats.stackexchange.com/questions/111968/random-forest-how-to-handle-overfitting&lt;/a&gt; ,however my general experience is the more depth the model has the more it tends to overfit.&lt;/p&gt;
', NULL, NULL, '''/p'':36C ''/questions/111968/random-forest-how-to-handle-overfitting'':15C ''/questions/111968/random-forest-how-to-handle-overfitting&lt;/a&gt;'':18C ''depth'':26C ''experi'':22C ''general'':21C ''howev'':19C ''href'':12C ''link'':6C ''model'':28C ''nice'':5C ''overfit'':35C ''p'':1C ''stackexchang'':10C ''stats.stackexchange.com'':14C,17C ''stats.stackexchange.com/questions/111968/random-forest-how-to-handle-overfitting'':13C ''stats.stackexchange.com/questions/111968/random-forest-how-to-handle-overfitting&lt;/a&gt;'':16C ''tend'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9374, 1, '2015-12-14 15:09:13.993', 2, 23, '"Trending" feature to predict number of views', '&lt;p&gt;I am working on a problem where I have access to a database with news articles, their publication date and the number of views they got 24hrs they got published.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;The objective is to be able to predict the number of views for any new article.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I want to add a feature that, for a given article, gives an indication of the number of views / publications of all articles with a similar theme that happened a short time before the publication of this article. This would capture the "buzz" phenomenon. (If there has been a lot of articles on elections, then it is quite likely that a new article on elections will get lots of views.)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Here is my strategy :&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;We classify all articles among X themes.&lt;/p&gt;

&lt;p&gt;We segment the timeline into Y timesteps.&lt;/p&gt;

&lt;p&gt;For each timestep:&lt;/p&gt;

&lt;p&gt;For each theme:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;we count the number of views of articles of this theme published in this timestep&lt;/li&gt;
&lt;li&gt;ditto for the number of publications&lt;/li&gt;
&lt;li&gt;we add these two values as features for all articles published in the next timestep&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So, I was wondering if this violates the i.i.d. assumption? (Because, if I randomly separate the data into train/test, this injects information from the target values of the test set as a feature of the training set.) Is this "allowed"?&lt;/p&gt;

&lt;p&gt;Am I missing something? Do you see another strategy?&lt;/p&gt;
', 'machine-learning  nlp', NULL, '''/li'':189C,197C,214C ''/p'':43C,63C,135C,143C,152C,161C,166C,171C,257C,268C ''/strong'':62C,142C ''/ul'':215C ''24hrs'':39C ''abl'':51C ''access'':22C ''add'':68C,200C ''allow'':256C ''among'':149C ''anoth'':266C ''articl'':28C,61C,75C,87C,102C,116C,127C,148C,181C,208C ''assumpt'':226C ''buzz'':107C ''captur'':105C ''classifi'':146C ''count'':175C ''data'':233C ''databas'':25C ''date'':31C ''ditto'':191C ''elect'':118C,129C ''featur'':2A,70C,205C,249C ''get'':131C ''give'':76C ''given'':74C ''got'':38C,41C ''happen'':93C ''i.i.d'':225C ''indic'':78C ''inform'':238C ''inject'':237C ''learn'':10B ''li'':173C,190C,198C ''like'':123C ''lot'':114C,132C ''machin'':9B ''machine-learn'':8B ''miss'':261C ''new'':60C,126C ''news'':27C ''next'':212C ''nlp'':11B ''number'':5A,34C,55C,81C,177C,194C ''object'':47C ''p'':12C,44C,64C,136C,144C,153C,162C,167C,216C,258C ''phenomenon'':108C ''predict'':4A,53C ''problem'':18C ''public'':30C,84C,99C,196C ''publish'':42C,185C,209C ''quit'':122C ''random'':230C ''see'':265C ''segment'':155C ''separ'':231C ''set'':246C,253C ''short'':95C ''similar'':90C ''someth'':262C ''strategi'':141C,267C ''strong'':45C,137C ''target'':241C ''test'':245C ''theme'':91C,151C,170C,184C ''time'':96C ''timelin'':157C ''timestep'':160C,165C,188C,213C ''train'':252C ''train/test'':235C ''trend'':1A ''two'':202C ''ul'':172C ''valu'':203C,242C ''view'':7A,36C,57C,83C,134C,179C ''violat'':223C ''want'':66C ''wonder'':220C ''work'':15C ''would'':104C ''x'':150C ''y'':159C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6417, 1, '2015-07-09 21:09:15.107', 3, 79, 'Adjusting Probabilities When Using Alternative Cutoffs For Classification', '&lt;p&gt;I am reading &lt;em&gt;Applied Predictive Modeling&lt;/em&gt; by Max Khun. I chapter 16 he discusses using alternate cutoffs as a remedy for class imbalance.&lt;/p&gt;

&lt;p&gt;Suppose our model predicts the most likely outcome of 2 events, e1 and e2. We have e1 occurring with a predicted probability 0.52 and e2 with a predicted probability 0.48. Using the standard 0.5 for e1 cutoff we would predict e1, but using an alternative cutoff of 0.56 for e1 we would predict e2 because we only predict e1 when p(e1) &gt; 0.56.&lt;/p&gt;

&lt;p&gt;My question is, does it make sense to also readjust the probabilities when using alternate cutoffs. For example, in my previous example using 0.56 cutoff of e1.&lt;/p&gt;

&lt;p&gt;p(e1) = 0.52;
p(e2) = 0.48&lt;/p&gt;

&lt;p&gt;Then we apply an adjustment of 0.56 - 0.5 = 0.06.&lt;/p&gt;

&lt;p&gt;So &lt;/p&gt;

&lt;p&gt;p_adj(e1) =  0.52 - 0.06 = 0.46;
p_adj(e2) = 0.48 + 0.06 = 0.54&lt;/p&gt;

&lt;p&gt;Basically we shift the probabilities so that they predict e1 when p_adj(e1) &gt; 0.5.&lt;/p&gt;

&lt;p&gt;I apologize if there is something obviously flawed with my logic but it feels intuitively wrong to me to predict e2 when p(e1) &gt; p(e2). Which probabilities would be more in line with the real-world probabilities?&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/em'':21C ''/p'':39C,104C,133C,141C,152C,155C,169C,186C,227C ''0.06'':151C,161C,167C ''0.46'':162C ''0.48'':70C,140C,166C ''0.5'':74C,150C,185C ''0.52'':63C,137C,160C ''0.54'':168C ''0.56'':88C,103C,129C,149C ''16'':27C ''2'':50C ''adj'':158C,164C,183C ''adjust'':1A,147C ''also'':114C ''altern'':5A,31C,85C,120C ''apolog'':189C ''appli'':18C,145C ''basic'':171C ''chapter'':26C ''class'':37C ''classif'':8A,12B ''cutoff'':6A,32C,77C,86C,121C,130C ''discuss'':29C ''e1'':52C,57C,76C,81C,90C,99C,102C,132C,136C,159C,180C,184C,211C ''e2'':54C,65C,94C,139C,165C,208C,213C ''em'':17C ''event'':51C ''exampl'':123C,127C ''feel'':201C ''flaw'':195C ''imbal'':38C ''intuit'':202C ''khun'':24C ''learn'':11B ''like'':47C ''line'':220C ''logic'':198C ''machin'':10B ''machine-learn'':9B ''make'':111C ''max'':23C ''model'':20C,43C ''obvious'':194C ''occur'':58C ''outcom'':48C ''p'':13C,40C,101C,105C,134C,135C,138C,142C,153C,156C,157C,163C,170C,182C,187C,210C,212C ''predict'':19C,44C,61C,68C,80C,93C,98C,179C,207C ''previous'':126C ''probabl'':2A,62C,69C,117C,175C,215C,226C ''question'':107C ''read'':16C ''readjust'':115C ''real'':224C ''real-world'':223C ''remedi'':35C ''sens'':112C ''shift'':173C ''someth'':193C ''standard'':73C ''suppos'':41C ''use'':4A,30C,71C,83C,119C,128C ''world'':225C ''would'':79C,92C,216C ''wrong'':203C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6420, 1, '2015-07-09 21:29:17.05', 2, 302, 'Can theano work on mapreduce or on spark', '&lt;p&gt;I''m not sure whether the Theano library can be used to write parallelized code in map reduce or in spark.Any expert opinion is welcome.A discussion was on at
&lt;a href="https://groups.google.com/forum/#!searchin/theano-dev/theano$20spark/theano-dev/QDN94MW3LkE/k817aa75xZoJ" rel="nofollow"&gt;Theano-dev&lt;/a&gt;&lt;/p&gt;
', 'machine-learning  deep-learning  apache-spark', NULL, '''/a'':58C ''/forum/#!searchin/theano-dev/theano$20spark/theano-dev/qdn94mw3lke/k817aa75xzoj'':52C ''/p'':59C ''apach'':16B ''apache-spark'':15B ''code'':33C ''deep'':13B ''deep-learn'':12B ''dev'':57C ''discuss'':44C ''expert'':40C ''groups.google.com'':51C ''groups.google.com/forum/#!searchin/theano-dev/theano$20spark/theano-dev/qdn94mw3lke/k817aa75xzoj'':50C ''href'':49C ''learn'':11B,14B ''librari'':26C ''m'':20C ''machin'':10B ''machine-learn'':9B ''map'':35C ''mapreduc'':5A ''nofollow'':54C ''opinion'':41C ''p'':18C ''parallel'':32C ''reduc'':36C ''rel'':53C ''spark'':8A,17B ''spark.any'':39C ''sure'':22C ''theano'':2A,25C,56C ''theano-dev'':55C ''use'':29C ''welcome.a'':43C ''whether'':23C ''work'':3A ''write'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6421, 2, '2015-07-09 23:46:29.367', 3, NULL, NULL, '&lt;p&gt;Is there a specific reason beside the fact that you would like to contribute? I am asking because there is always &lt;a href="https://github.com/apache/spark/tree/master/python/pyspark/mllib" rel="nofollow"&gt;pyspark&lt;/a&gt; that you can use, the Spark python API that exposes the Spark programming model to Python.&lt;/p&gt;

&lt;p&gt;For deep learning specifically, there are a lot of frameworks built on top of &lt;a href="https://github.com/Theano/Theano" rel="nofollow"&gt;Theano&lt;/a&gt; -which is a python library for mathematical expressions involving multi-dimensional arrays-, like Lasagne, so they are able to use GPU for intense training. Getting an EC2 instance with GPU on AWS is always an option.&lt;/p&gt;
', NULL, NULL, '''/a'':31C,72C ''/apache/spark/tree/master/python/pyspark/mllib'':27C ''/p'':48C,110C ''/theano/theano'':68C ''abl'':91C ''alway'':22C,107C ''api'':39C ''array'':85C ''ask'':18C ''aw'':105C ''besid'':7C ''built'':60C ''contribut'':15C ''deep'':51C ''dimension'':84C ''ec2'':100C ''expos'':41C ''express'':80C ''fact'':9C ''framework'':59C ''get'':98C ''github.com'':26C,67C ''github.com/apache/spark/tree/master/python/pyspark/mllib'':25C ''github.com/theano/theano'':66C ''gpu'':94C,103C ''href'':24C,65C ''instanc'':101C ''intens'':96C ''involv'':81C ''lasagn'':87C ''learn'':52C ''librari'':77C ''like'':13C,86C ''lot'':57C ''mathemat'':79C ''model'':45C ''multi'':83C ''multi-dimension'':82C ''nofollow'':29C,70C ''option'':109C ''p'':1C,49C ''program'':44C ''pyspark'':30C ''python'':38C,47C,76C ''reason'':6C ''rel'':28C,69C ''spark'':37C,43C ''specif'':5C,53C ''theano'':71C ''top'':62C ''train'':97C ''use'':35C,93C ''would'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6496, 2, '2015-07-18 06:29:29.183', 1, NULL, NULL, '&lt;p&gt;I expect you have, or can make, a matrix of transition counts. Consider the data in each row to be draws from a multinomial distribution. Then you should be able to use &lt;a href="http://stats.stackexchange.com/questions/19120/sample-size-for-a-variable-number-of-answers"&gt;sample size calculations for the multinomial&lt;/a&gt; to get off the ground. &lt;/p&gt;

&lt;p&gt;It is also possible that your data is not well described by a simple Markov chain. There are some available techniques for this, e.g. &lt;a href="https://cran.r-project.org/web/packages/msm/vignettes/msm-manual.pdf" rel="nofollow"&gt;multistate modelling&lt;/a&gt;, but which may or may not fit your particular problem. &lt;/p&gt;
', NULL, NULL, '''/a'':45C,86C ''/p'':51C,97C ''/questions/19120/sample-size-for-a-variable-number-of-answers'':38C ''/web/packages/msm/vignettes/msm-manual.pdf'':81C ''abl'':31C ''also'':55C ''avail'':72C ''calcul'':41C ''chain'':68C ''consid'':14C ''count'':13C ''cran.r-project.org'':80C ''cran.r-project.org/web/packages/msm/vignettes/msm-manual.pdf'':79C ''data'':16C,59C ''describ'':63C ''distribut'':26C ''draw'':22C ''e.g'':76C ''expect'':3C ''fit'':93C ''get'':47C ''ground'':50C ''href'':35C,78C ''make'':8C ''markov'':67C ''matrix'':10C ''may'':89C,91C ''model'':85C ''multinomi'':25C,44C ''multist'':84C ''nofollow'':83C ''p'':1C,52C ''particular'':95C ''possibl'':56C ''problem'':96C ''rel'':82C ''row'':19C ''sampl'':39C ''simpl'':66C ''size'':40C ''stats.stackexchange.com'':37C ''stats.stackexchange.com/questions/19120/sample-size-for-a-variable-number-of-answers'':36C ''techniqu'':73C ''transit'':12C ''use'':33C ''well'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6423, 1, '2015-07-10 03:47:16.397', 1, 33, 'Identifying repeating sequences of data in byte array', '&lt;p&gt;Given a sample of hexadecimal data, I would like to identify UNKNOWN sequences of bytes that are repeated throughout the sample. (Not searching for a known string or value) I am attempting to reverse engineer a network protocol, and I am working on determining data structures within the packet. As an example of what I''m trying to do (albeit on a smaller scale):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(af:b6:ea:3d:83:02:00:00):{21:03:00:00}:[b3:49:96:23:01]

{21:03:00:00}:(af:b6:ea:3d:83:02:00:00):01:42:00:00:00:00:01:57
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(38:64:88:6e:83:02:00:00):{26:03:00:00}:[b3:49:96:23:01]

{26:03:00:00}:(38:64:88:6e:83:02:00:00):01:42:00:00:00:00:00:01
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Obviously, these are easy to spot by eye, but patterns that are hundreds of chars into the data are not. I''m not expecting a magic bullet for the solution, just a nudge in the right direction, or even better, a premade tool.&lt;/p&gt;

&lt;p&gt;I''m currently needing this for a C# project, but I am open to any and all tools.&lt;/p&gt;
', 'data-mining', NULL, '''/code'':117C,161C ''/p'':77C,121C,207C,227C ''/pre'':118C,162C ''00'':86C,87C,90C,91C,99C,100C,107C,108C,111C,112C,113C,114C,130C,131C,134C,135C,143C,144C,151C,152C,155C,156C,157C,158C,159C ''01'':96C,109C,115C,140C,153C,160C ''02'':85C,106C,129C,150C ''03'':89C,98C,133C,142C ''21'':88C,97C ''23'':95C,139C ''26'':132C,141C ''38'':124C,145C ''3d'':83C,104C ''42'':110C,154C ''49'':93C,137C ''57'':116C ''64'':125C,146C ''6e'':127C,148C ''83'':84C,105C,128C,149C ''88'':126C,147C ''96'':94C,138C ''af'':80C,101C ''albeit'':72C ''array'':8A ''attempt'':44C ''b3'':92C,136C ''b6'':81C,102C ''better'':203C ''bullet'':190C ''byte'':7A,27C ''c'':216C ''char'':178C ''code'':79C,123C ''current'':211C ''data'':5A,10B,18C,57C,181C ''data-min'':9B ''determin'':56C ''direct'':200C ''ea'':82C,103C ''easi'':167C ''engin'':47C ''even'':202C ''exampl'':64C ''expect'':187C ''eye'':171C ''given'':13C ''hexadecim'':17C ''hundr'':176C ''identifi'':1A,23C ''known'':38C ''like'':21C ''m'':68C,185C,210C ''magic'':189C ''mine'':11B ''need'':212C ''network'':49C ''nudg'':196C ''obvious'':164C ''open'':221C ''p'':12C,119C,163C,208C ''packet'':61C ''pattern'':173C ''pre'':78C,122C ''premad'':205C ''project'':217C ''protocol'':50C ''repeat'':2A,30C ''revers'':46C ''right'':199C ''sampl'':15C,33C ''scale'':76C ''search'':35C ''sequenc'':3A,25C ''smaller'':75C ''solut'':193C ''spot'':169C ''string'':39C ''structur'':58C ''throughout'':31C ''tool'':206C,226C ''tri'':69C ''unknown'':24C ''valu'':41C ''within'':59C ''work'':54C ''would'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6424, 2, '2015-07-10 05:34:10.93', 3, NULL, NULL, '&lt;p&gt;I believe the problem that you are referring to, is that of "Motif Discovery in Time Series Data". An appreciable amount of research literature already exists in this domain, so you can look through those. If the data that you handle is not very large, you can find some relatively easy to implement algorithms. &lt;/p&gt;

&lt;p&gt;If the data is large , you can look at more recent publications in this domain. As a starting point I would recommend taking a look at how Motif Discovery is done in SAX. SAX takes continuous signals as inputs and discretizes them. These discrete levels are then stored as alphabets. This resulting data looks very much like yours in my opinion. Take a look at what they do in "Mining Motifs in Massive Time Series Databases".&lt;/p&gt;
', NULL, NULL, '''/p'':56C,134C ''algorithm'':55C ''alphabet'':107C ''alreadi'':26C ''amount'':22C ''appreci'':21C ''believ'':3C ''continu'':93C ''data'':19C,39C,60C,110C ''databas'':133C ''discoveri'':15C,86C ''discret'':98C,101C ''domain'':30C,72C ''done'':88C ''easi'':52C ''exist'':27C ''find'':49C ''handl'':42C ''implement'':54C ''input'':96C ''larg'':46C,62C ''level'':102C ''like'':114C ''literatur'':25C ''look'':34C,65C,82C,111C,121C ''massiv'':130C ''mine'':127C ''motif'':14C,85C,128C ''much'':113C ''opinion'':118C ''p'':1C,57C ''point'':76C ''problem'':5C ''public'':69C ''recent'':68C ''recommend'':79C ''refer'':9C ''relat'':51C ''research'':24C ''result'':109C ''sax'':90C,91C ''seri'':18C,132C ''signal'':94C ''start'':75C ''store'':105C ''take'':80C,92C,119C ''time'':17C,131C ''would'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6425, 2, '2015-07-10 10:47:50.59', 1, NULL, NULL, '&lt;p&gt;It is very hard to evaluate the correctness of the results produced by an unsupervised algorithm. In many cases, such evaluation will be totally subjective, and it will require some knowledge about the domain of the problem.&lt;/p&gt;

&lt;p&gt;If we focus on clustering algorithms (as stated in a previous answer, PCA is not a clustering algorithm), many cluster validation measures may be applied, like the ones enumerated in the "Evaluation and assessment" section in the "Cluster analysis" Wikipedia page. These measures return a number that you can use to compare different clustering solutions, in terms of cluster compactness (how close to each other are the elements within each cluster) and separation (how far to each other are the elements from different clusters). You can, of course, use these measures in order to perform hyperparameter selection (lattice size, structure, learning rate, etc.) by means of cross validation.&lt;/p&gt;

&lt;p&gt;However, it must be noted that different cluster validation measures may produce different validation results, and as a consequence, your best clustering solution may vary depending on the chosen measure. Therefore, even the choice of a validation measure is subjective. Once again, knowledge about your data is very important to take this decision.&lt;/p&gt;
', NULL, NULL, '''/p'':39C,149C,204C ''algorithm'':17C,46C,58C ''analysi'':79C ''answer'':52C ''appli'':65C ''assess'':74C ''best'':171C ''case'':20C ''choic'':184C ''chosen'':179C ''close'':102C ''cluster'':45C,57C,60C,78C,94C,99C,111C,124C,158C,172C ''compact'':100C ''compar'':92C ''consequ'':169C ''correct'':9C ''cours'':128C ''cross'':147C ''data'':196C ''decis'':203C ''depend'':176C ''differ'':93C,123C,157C,163C ''domain'':35C ''element'':108C,121C ''enumer'':69C ''etc'':143C ''evalu'':7C,22C,72C ''even'':182C ''far'':115C ''focus'':43C ''hard'':5C ''howev'':151C ''hyperparamet'':136C ''import'':199C ''knowledg'':32C,193C ''lattic'':138C ''learn'':141C ''like'':66C ''mani'':19C,59C ''may'':63C,161C,174C ''mean'':145C ''measur'':62C,83C,131C,160C,180C,188C ''must'':153C ''note'':155C ''number'':86C ''one'':68C ''order'':133C ''p'':1C,40C,150C ''page'':81C ''pca'':53C ''perform'':135C ''previous'':51C ''problem'':38C ''produc'':13C,162C ''rate'':142C ''requir'':30C ''result'':12C,165C ''return'':84C ''section'':75C ''select'':137C ''separ'':113C ''size'':139C ''solut'':95C,173C ''state'':48C ''structur'':140C ''subject'':26C,190C ''take'':201C ''term'':97C ''therefor'':181C ''total'':25C ''unsupervis'':16C ''use'':90C,129C ''valid'':61C,148C,159C,164C,187C ''vari'':175C ''wikipedia'':80C ''within'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6438, 1, '2015-07-11 23:08:14.3', 2, 70, 'Machine learning for state-based transforms?', '&lt;p&gt;If I provide:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;A list of possible transforms, and,&lt;/li&gt;
&lt;li&gt;A list of input states, and,&lt;/li&gt;
&lt;li&gt;A corresponding list of output states for each input state, and,&lt;/li&gt;
&lt;li&gt;A fitness function to score each output state&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Which subset of machine learning can direct me towards an optimization algorithm that can map each input state to a dictionary of input states, and, failing to find a match, apply the necessary transforms to get me to the closest-related output state?&lt;/p&gt;

&lt;p&gt;An example involving polygon legalization:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Any given "window" can contain N different polygons, where each polygon has lower-left and upper-right co-ordinates, as well as a polygon "type".&lt;/li&gt;
&lt;li&gt;The input state of the polygons may or may not be "illegal".&lt;/li&gt;
&lt;li&gt;A list of transforms includes: move, copy, rotate, resize&lt;/li&gt;
&lt;li&gt;If the input state maps directly to any output state, the input state is decided to be legal. Nothing more to be done; move on the next window.&lt;/li&gt;
&lt;li&gt;If the input state matches any previously seen input state, transform to the matching (known-legal) output state. Nothing more to be done; move on the next window.&lt;/li&gt;
&lt;li&gt;Attempt transforms in different sequences until a state is reached that satisfies a fitness function. Store this input:output state combination. Move on to the next window.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Would this imply some combination of neural networking (for classification) and genetic/evolutionary algorithms? Or, does the presence of a fitness function negate the need to store combinations of input:output states?&lt;/p&gt;
', 'machine-learning  optimization', NULL, '''/li'':25C,33C,46C,56C,141C,155C,166C,196C,227C,256C ''/ol'':57C ''/p'':16C,103C,110C,290C ''/ul'':257C ''algorithm'':70C,271C ''appli'':89C ''attempt'':229C ''base'':6A ''classif'':268C ''closest'':99C ''closest-rel'':98C ''co'':133C ''co-ordin'':132C ''combin'':249C,263C,285C ''contain'':117C ''copi'':163C ''correspond'':36C ''decid'':182C ''dictionari'':79C ''differ'':119C,232C ''direct'':65C,173C ''done'':190C,221C ''exampl'':106C ''fail'':84C ''find'':86C ''fit'':49C,242C,278C ''function'':50C,243C,279C ''genetic/evolutionary'':270C ''get'':94C ''given'':114C ''illeg'':154C ''impli'':261C ''includ'':161C ''input'':30C,43C,75C,81C,144C,170C,179C,200C,206C,246C,287C ''involv'':107C ''known'':213C ''known-leg'':212C ''learn'':2A,10B,63C ''left'':127C ''legal'':109C,185C,214C ''li'':18C,26C,34C,47C,112C,142C,156C,167C,197C,228C ''list'':20C,28C,37C,158C ''lower'':126C ''lower-left'':125C ''machin'':1A,9B,62C ''machine-learn'':8B ''map'':73C,172C ''match'':88C,202C,211C ''may'':149C,151C ''move'':162C,191C,222C,250C ''n'':118C ''necessari'':91C ''need'':282C ''negat'':280C ''network'':266C ''neural'':265C ''next'':194C,225C,254C ''noth'':186C,217C ''ol'':17C ''optim'':11B,69C ''ordin'':134C ''output'':39C,54C,101C,176C,215C,247C,288C ''p'':12C,58C,104C,258C ''polygon'':108C,120C,123C,139C,148C ''possibl'':22C ''presenc'':275C ''previous'':204C ''provid'':15C ''reach'':238C ''relat'':100C ''resiz'':165C ''right'':131C ''rotat'':164C ''satisfi'':240C ''score'':52C ''seen'':205C ''sequenc'':233C ''state'':5A,31C,40C,44C,55C,76C,82C,102C,145C,171C,177C,180C,201C,207C,216C,236C,248C,289C ''state-bas'':4A ''store'':244C,284C ''subset'':60C ''toward'':67C ''transform'':7A,23C,92C,160C,208C,230C ''type'':140C ''ul'':111C ''upper'':130C ''upper-right'':129C ''well'':136C ''window'':115C,195C,226C,255C ''would'':259C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6426, 1, '2015-07-10 13:09:57.497', 0, 38, 'How to pass data around an oozie workflow', '&lt;p&gt;I''m setting up a data processing stack, and figuring out how to co-ordinate jobs.&lt;/p&gt;

&lt;p&gt;My team has settled on Oozie for workflow management. There are a lot of examples of control flow, but I can''t see anything about how to orchestrate the passage of data between tasks in the workflow.&lt;/p&gt;

&lt;p&gt;We''re also planning on using spark as our main platform for writing the jobs.&lt;/p&gt;

&lt;p&gt;How do people do it? Do the tasks themselves need to know what files they should be waiting for? Are there abstractions for piping data between the tasks?&lt;/p&gt;
', 'bigdata  dataset  hadoop  apache-spark  data', NULL, '''/p'':34C,73C,90C,120C ''abstract'':113C ''also'':77C ''anyth'':59C ''apach'':13B ''apache-spark'':12B ''around'':5A ''bigdata'':9B ''co'':31C ''co-ordin'':30C ''control'':52C ''data'':4A,15B,22C,67C,116C ''dataset'':10B ''exampl'':50C ''figur'':26C ''file'':105C ''flow'':53C ''hadoop'':11B ''job'':33C,89C ''know'':103C ''lot'':48C ''m'':18C ''main'':84C ''manag'':44C ''need'':101C ''oozi'':7A,41C ''orchestr'':63C ''ordin'':32C ''p'':16C,35C,74C,91C ''pass'':3A ''passag'':65C ''peopl'':94C ''pipe'':115C ''plan'':78C ''platform'':85C ''process'':23C ''re'':76C ''see'':58C ''set'':19C ''settl'':39C ''spark'':14B,81C ''stack'':24C ''task'':69C,99C,119C ''team'':37C ''use'':80C ''wait'':109C ''workflow'':8A,43C,72C ''write'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6427, 1, '2015-07-10 14:05:44.183', 1, 62, 'How to replace levels with certain value?', '&lt;p&gt;This is a doubt on R. Sorry if I had posted under wrong tag. &lt;/p&gt;

&lt;p&gt;I have a categorical variable in my dataset. I want to replace the levels which are present in test dataset and not present in training set with a value called "others"&lt;/p&gt;

&lt;p&gt;Here is how it looks:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;levels(training$var1)&lt;/code&gt; has levels as "1"  "2"  "3"  "Others"       &lt;/p&gt;

&lt;p&gt;&lt;code&gt;levels(testing$var1)&lt;/code&gt; has levels as "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10"  &lt;/p&gt;

&lt;p&gt;I want to replace all the levels in testing data which are not training data. To achieve this I take the difference between the levels first.   &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;a &amp;lt;- setdiff(levels(levels(testing$var1),training$var1)).  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and I get output as  &lt;/p&gt;

&lt;p&gt;&lt;code&gt;levels(a)&lt;/code&gt; as "4"  "5"  "6"  "7"  "8"  "9"  "10"  &lt;/p&gt;

&lt;p&gt;Now I need to replace all the above difference values with "Others". Kindly note that I do not want to drop out these levels but I want to replace with "Others".  &lt;/p&gt;

&lt;p&gt;For this I tried  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;testing$var1[testing$var1 == "4" &amp;lt;- ''Others"  
testing$var1[testing$var1 == "5" &amp;lt;- ''Others" ==&amp;gt; these works.  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However I want to make it in run time, something like this:  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;testing$var1[testing$var1 == a[1,] &amp;lt;- ''Others"
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But this is not working.&lt;/p&gt;
', 'r', NULL, '''/code'':70C,84C,138C,151C,219C,245C ''/p'':24C,57C,64C,78C,98C,126C,146C,160C,193C,199C,234C,253C ''/pre'':139C,220C,246C ''1'':74C,88C,242C ''10'':97C,159C ''2'':75C,89C ''3'':76C,90C ''4'':91C,153C,206C ''5'':92C,154C,213C ''6'':93C,155C ''7'':94C,156C ''8'':95C,157C ''9'':96C,158C ''achiev'':116C ''call'':55C ''categor'':29C ''certain'':6A ''code'':66C,80C,128C,148C,201C,236C ''data'':109C,114C ''dataset'':33C,45C ''differ'':121C,170C ''doubt'':13C ''drop'':182C ''first'':125C ''get'':143C ''gt'':216C ''howev'':222C ''kind'':174C ''level'':4A,39C,67C,72C,81C,86C,106C,124C,132C,133C,149C,185C ''like'':232C ''look'':63C ''lt'':130C,207C,214C,243C ''make'':226C ''need'':164C ''note'':175C ''other'':56C,77C,173C,192C,208C,215C,244C ''output'':144C ''p'':9C,25C,58C,65C,79C,99C,140C,147C,161C,194C,221C,247C ''post'':20C ''pre'':127C,200C,235C ''present'':42C,48C ''r'':8B,15C ''replac'':3A,37C,103C,166C,190C ''run'':229C ''set'':51C ''setdiff'':131C ''someth'':231C ''sorri'':16C ''tag'':23C ''take'':119C ''test'':44C,82C,108C,134C,202C,204C,209C,211C,237C,239C ''time'':230C ''train'':50C,68C,113C,136C ''tri'':198C ''valu'':7A,54C,171C ''var1'':69C,83C,135C,137C,203C,205C,210C,212C,238C,240C ''variabl'':30C ''want'':35C,101C,180C,188C,224C ''work'':218C,252C ''wrong'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6428, 2, '2015-07-10 14:32:53.177', 3, NULL, NULL, '&lt;p&gt;I could be able to achieve this with the following code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;testing$var1 &amp;lt;- as.character(testing$var1)
a &amp;lt;- data.frame(a)
testing$var1 [testing$var1 %in% a[1,] &amp;lt;- "Others"
testing$var1 &amp;lt;- as.factor(testing$var1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In case if there is any other better/effective solution/function to achieve this, please let me know. &lt;/p&gt;

&lt;p&gt;Thanks all.&lt;/p&gt;
', NULL, NULL, '''/code'':41C ''/p'':13C,60C,64C ''/pre'':42C ''1'':32C ''abl'':5C ''achiev'':7C,54C ''as.character'':19C ''as.factor'':38C ''better/effective'':51C ''case'':45C ''code'':12C,15C ''could'':3C ''data.frame'':24C ''follow'':11C ''know'':59C ''let'':57C ''lt'':18C,23C,33C,37C ''other'':34C ''p'':1C,43C,61C ''pleas'':56C ''pre'':14C ''solution/function'':52C ''test'':16C,20C,26C,28C,35C,39C ''thank'':62C ''var1'':17C,21C,27C,29C,36C,40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6429, 2, '2015-07-10 15:16:06.27', 0, NULL, NULL, '&lt;p&gt;I believe you can try with range of cut offs to see which one gives highest accuracy or F-score in hold out set.I''ve seen winners in kaggle stating to do experiment with cut-offs to get best result.&lt;/p&gt;
', NULL, NULL, '''/p'':44C ''accuraci'':18C ''believ'':3C ''best'':42C ''cut'':10C,38C ''cut-off'':37C ''experi'':35C ''f'':21C ''f-score'':20C ''get'':41C ''give'':16C ''highest'':17C ''hold'':24C ''kaggl'':31C ''off'':11C,39C ''one'':15C ''p'':1C ''rang'':8C ''result'':43C ''score'':22C ''see'':13C ''seen'':28C ''set.i'':26C ''state'':32C ''tri'':6C ''ve'':27C ''winner'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6449, 1, '2015-07-13 20:37:28.867', 4, 55, 'Learn and sample from a generative model with 2000 boolean features', '&lt;p&gt;My data is a set of about 1 million training examples, where each example is represented by about 2000 boolean features. Examples aren''t labelled - it wouldn''t really have any meaning in this domain. The features fully define the example. My goal is to learn a generative model that I can use to generate more examples. I would like to capture some of the dependencies between the features (e.g. if feature 10 is on, feature 14, 15, and 16 are usually on). &lt;/p&gt;

&lt;p&gt;I don''t have much background in ML, so I''m not sure what a simple way to do this would be (both algorithms and software packages). My initial thought was to find a structure learning package to learn a Bayesian network and then sample from it, but I figured I''d ask here to see if anyone had any recommendations. &lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':99C,162C,165C ''1'':23C ''10'':88C ''14'':92C ''15'':93C ''16'':95C ''2000'':9A,34C ''algorithm'':124C ''anyon'':158C ''aren'':38C ''ask'':153C ''background'':106C ''bayesian'':141C ''boolean'':10A,35C ''captur'':77C ''d'':152C ''data'':17C ''defin'':54C ''depend'':81C ''domain'':50C ''e.g'':85C ''exampl'':26C,29C,37C,56C,72C ''featur'':11A,36C,52C,84C,87C,91C ''figur'':150C ''find'':133C ''fulli'':53C ''generat'':6A,63C,70C ''goal'':58C ''initi'':129C ''label'':40C ''learn'':1A,14B,61C,136C,139C ''like'':75C ''m'':111C ''machin'':13B ''machine-learn'':12B ''mean'':47C ''million'':24C ''ml'':108C ''model'':7A,64C ''much'':105C ''network'':142C ''p'':15C,100C,163C ''packag'':127C,137C ''realli'':44C ''recommend'':161C ''repres'':31C ''sampl'':3A,145C ''see'':156C ''set'':20C ''simpl'':116C ''softwar'':126C ''structur'':135C ''sure'':113C ''thank'':164C ''thought'':130C ''train'':25C ''use'':68C ''usual'':97C ''way'':117C ''would'':74C,121C ''wouldn'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6430, 2, '2015-07-10 15:18:36.517', 3, NULL, NULL, '&lt;p&gt;Relative to other models, Random Forests are less likely to overfit but it is still something that you want to make an explicit effort to avoid. Tuning model parameters is definitely one element of avoiding overfitting but it isn''t the only one. In fact I would say that your training features are more likely to lead to overfitting than model parameters, especially with a Random Forests. So I think the key is really having a reliable method to evaluate your model to check for overfitting more than anything else, which brings us to your second question.&lt;/p&gt;

&lt;p&gt;As alluded to above, running cross validation will allow to you avoid overfitting. Choosing your best model based on CV results will lead to a model that hasn''t overfit, which isn''t necessarily the case for something like out of the bag error. The easiest way to run CV in R is with the &lt;code&gt;caret&lt;/code&gt; package. A simple example is below:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; library(caret)
&amp;gt; 
&amp;gt; data(iris)
&amp;gt; 
&amp;gt; tr &amp;lt;- trainControl(method = "cv", number = 5)
&amp;gt; 
&amp;gt; train(Species ~ .,data=iris,method="rf",trControl= tr)
Random Forest 

150 samples
  4 predictor
  3 classes: ''setosa'', ''versicolor'', ''virginica'' 

No pre-processing
Resampling: Cross-Validated (5 fold) 

Summary of sample sizes: 120, 120, 120, 120, 120 

Resampling results across tuning parameters:

  mtry  Accuracy  Kappa  Accuracy SD  Kappa SD  
  2     0.96      0.94   0.04346135   0.06519202
  3     0.96      0.94   0.04346135   0.06519202
  4     0.96      0.94   0.04346135   0.06519202

Accuracy was used to select the optimal model using  the largest value.
The final value used for the model was mtry = 2. 
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':158C,273C ''/p'':99C,165C ''/pre'':274C ''0.04346135'':239C,244C,249C ''0.06519202'':240C,245C,250C ''0.94'':238C,243C,248C ''0.96'':237C,242C,247C ''120'':219C,220C,221C,222C,223C ''150'':196C ''2'':236C,272C ''3'':200C,241C ''4'':198C,246C ''5'':183C,213C ''accuraci'':230C,232C,251C ''across'':226C ''allow'':109C ''allud'':102C ''anyth'':90C ''avoid'':27C,36C,112C ''bag'':143C ''base'':118C ''best'':116C ''bring'':93C ''caret'':157C,170C ''case'':136C ''check'':85C ''choos'':114C ''class'':201C ''code'':156C,167C ''cross'':106C,211C ''cross-valid'':210C ''cv'':120C,150C,181C ''data'':173C,188C ''definit'':32C ''easiest'':146C ''effort'':25C ''element'':34C ''els'':91C ''error'':144C ''especi'':64C ''evalu'':81C ''exampl'':162C ''explicit'':24C ''fact'':46C ''featur'':53C ''final'':264C ''fold'':214C ''forest'':7C,68C,195C ''gt'':168C,171C,172C,175C,176C,184C,185C ''hasn'':128C ''iri'':174C,189C ''isn'':40C,132C ''kappa'':231C,234C ''key'':73C ''largest'':261C ''lead'':58C,123C ''less'':9C ''librari'':169C ''like'':10C,56C,139C ''lt'':178C ''make'':22C ''method'':79C,180C,190C ''model'':5C,29C,62C,83C,117C,126C,258C,269C ''mtri'':229C,271C ''necessarili'':134C ''number'':182C ''one'':33C,44C ''optim'':257C ''overfit'':12C,37C,60C,87C,113C,130C ''p'':1C,100C ''packag'':159C ''paramet'':30C,63C,228C ''pre'':166C,207C ''pre-process'':206C ''predictor'':199C ''process'':208C ''question'':98C ''r'':152C ''random'':6C,67C,194C ''realli'':75C ''relat'':2C ''reliabl'':78C ''resampl'':209C,224C ''result'':121C,225C ''rf'':191C ''run'':105C,149C ''sampl'':197C,217C ''say'':49C ''sd'':233C,235C ''second'':97C ''select'':255C ''setosa'':202C ''simpl'':161C ''size'':218C ''someth'':17C,138C ''speci'':187C ''still'':16C ''summari'':215C ''think'':71C ''tr'':177C,193C ''train'':52C,186C ''traincontrol'':179C ''trcontrol'':192C ''tune'':28C,227C ''us'':94C ''use'':253C,259C,266C ''valid'':107C,212C ''valu'':262C,265C ''versicolor'':203C ''virginica'':204C ''want'':20C ''way'':147C ''would'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6434, 2, '2015-07-11 17:12:17.687', 3, NULL, NULL, '&lt;p&gt;No, you are misinterpreting his comments.  If you have data that has some outliers in it then the outliers will extend beyond 3 standard deviations.  Then if you standardize the data some will extend beyond the [-3,3] region.  &lt;/p&gt;

&lt;p&gt;He is simply saying that you need to remove your outliers so the outliers don''t reap havoc on your stochastic gradient descent algorithm. He is NOT saying that you need to use some weird scaling algorithm.&lt;/p&gt;

&lt;p&gt;You should standardize your data by subtracting the mean and dividing by the standard deviation, and then remove any points that extend beyond [-3,3], which are the outliers.&lt;/p&gt;

&lt;p&gt;In stochastic gradient descent, the presence of outliers could increase the instability of the minimization and make it thrash around excessively, so its best to remove them.  &lt;/p&gt;

&lt;p&gt;If the sparseness of the data prevents removal then... Do you need to use stochastic gradient descent, or can you just use gradient descent?  Gradient descent (GD) might help to alleviate some of the problems relating to convergence.  Finally, if GD is having trouble converging, you could always do an direct solve (e.g. direct matrix inversion) rather than an iterative solve.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''-3'':38C,105C ''/p'':41C,80C,111C,140C,203C,208C ''3'':24C,39C,106C ''algorithm'':66C,79C ''allevi'':172C ''alway'':189C ''around'':132C ''best'':136C ''beyond'':23C,36C,104C ''comment'':7C ''converg'':179C,186C ''could'':121C,188C ''data'':11C,32C,86C,147C ''descent'':65C,116C,158C,165C,167C ''deviat'':26C,96C ''direct'':192C,195C ''divid'':92C ''e.g'':194C ''excess'':133C ''extend'':22C,35C,103C ''final'':180C ''gd'':168C,182C ''gradient'':64C,115C,157C,164C,166C ''havoc'':60C ''help'':170C,207C ''hope'':205C ''increas'':122C ''instabl'':124C ''invers'':197C ''iter'':201C ''make'':129C ''matrix'':196C ''mean'':90C ''might'':169C ''minim'':127C ''misinterpret'':5C ''need'':49C,73C,153C ''outlier'':15C,20C,53C,56C,110C,120C ''p'':1C,42C,81C,112C,141C,204C ''point'':101C ''presenc'':118C ''prevent'':148C ''problem'':176C ''rather'':198C ''reap'':59C ''region'':40C ''relat'':177C ''remov'':51C,99C,138C,149C ''say'':46C,70C ''scale'':78C ''simpli'':45C ''solv'':193C,202C ''spars'':144C ''standard'':25C,30C,84C,95C ''stochast'':63C,114C,156C ''subtract'':88C ''thrash'':131C ''troubl'':185C ''use'':75C,155C,163C ''weird'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6435, 1, '2015-07-11 18:04:05.71', 3, 42, 'Cooperative Reinforcement Learning', '&lt;p&gt;I already have a functioning $Q(\lambda)$ implementation for a single agent working on a dynamic pricing problem with the goal of maximizing revenue. The problem that I''m working with, however, involves several different products that are replacements for each other, so dynamically pricing them all with independent learners seems incorrect, because the price of one influences the reward of the other. The goal would be to dynamically price them all so as to maximize the sum of each individual revenue.&lt;/p&gt;

&lt;p&gt;I''ve been doing some research to try to find something that applies reinforcement learning in this way, but many multi-agent implementations I have found focus more on competitive games than cooperative, or they assume incomplete knowledge of other agents (I would have complete knowledge of each agent in this scenario). Are there any well-researched/documented applications of cooperative learning in this way?&lt;/p&gt;
', 'machine-learning  reinforcement-learning', NULL, '''/documented'':154C ''/p'':93C,162C ''agent'':22C,117C,136C,144C ''alreadi'':12C ''appli'':107C ''applic'':155C ''assum'':131C ''competit'':125C ''complet'':140C ''cooper'':1A,128C,157C ''differ'':45C ''dynam'':26C,54C,79C ''find'':104C ''focus'':122C ''found'':121C ''function'':15C ''game'':126C ''goal'':31C,75C ''howev'':42C ''implement'':18C,118C ''incomplet'':132C ''incorrect'':62C ''independ'':59C ''individu'':91C ''influenc'':68C ''involv'':43C ''knowledg'':133C,141C ''lambda'':17C ''learn'':3A,6B,9B,109C,158C ''learner'':60C ''m'':39C ''machin'':5B ''machine-learn'':4B ''mani'':114C ''maxim'':33C,86C ''multi'':116C ''multi-ag'':115C ''one'':67C ''p'':10C,94C ''price'':27C,55C,65C,80C ''problem'':28C,36C ''product'':46C ''q'':16C ''reinforc'':2A,8B,108C ''reinforcement-learn'':7B ''replac'':49C ''research'':100C,153C ''revenu'':34C,92C ''reward'':70C ''scenario'':147C ''seem'':61C ''sever'':44C ''singl'':21C ''someth'':105C ''sum'':88C ''tri'':102C ''ve'':96C ''way'':112C,161C ''well'':152C ''well-research'':151C ''work'':23C,40C ''would'':76C,138C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8889, 4, '2015-11-18 11:23:07.643', 0, NULL, NULL, 'Use this tag for questions concerning creating, processing, or maintaining datasets.', NULL, NULL, '''concern'':6C ''creat'':7C ''dataset'':11C ''maintain'':10C ''process'':8C ''question'':5C ''tag'':3C ''use'':1C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6437, 2, '2015-07-11 21:31:45.743', 3, NULL, NULL, '&lt;p&gt;If you want to combine the results from three different Neural Networks to "boost" the performance :) , you might want to look at the different Ensemble Learning Methods as I mentioned earlier. &lt;/p&gt;

&lt;p&gt;Which method you should use, depends on how you share or divide the training data between the three NNs. For example if the NNs are trained on same data but have different parameters, you can look at simple voting ( if you are doing a classification task) or averaging ( if you are using them for regression).&lt;/p&gt;

&lt;p&gt;The more advanced methods like AdaBoost divide the training data between the classifiers. You can read about it in &lt;a href="http://www.iro.umontreal.ca/~lisa/pointeurs/ada-nc.pdf" rel="nofollow"&gt;Boosting Neural Networks&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':121C ''/p'':33C,90C,122C ''/~lisa/pointeurs/ada-nc.pdf'':115C ''adaboost'':97C ''advanc'':94C ''averag'':82C ''boost'':15C,118C ''classif'':79C ''classifi'':104C ''combin'':6C ''data'':49C,63C,101C ''depend'':40C ''differ'':11C,25C,66C ''divid'':46C,98C ''earlier'':32C ''ensembl'':26C ''exampl'':55C ''href'':112C ''learn'':27C ''like'':96C ''look'':22C,70C ''mention'':31C ''method'':28C,36C,95C ''might'':19C ''network'':13C,120C ''neural'':12C,119C ''nns'':53C,58C ''nofollow'':117C ''p'':1C,34C,91C ''paramet'':67C ''perform'':17C ''read'':107C ''regress'':89C ''rel'':116C ''result'':8C ''share'':44C ''simpl'':72C ''task'':80C ''three'':10C,52C ''train'':48C,60C,100C ''use'':39C,86C ''vote'':73C ''want'':4C,20C ''www.iro.umontreal.ca'':114C ''www.iro.umontreal.ca/~lisa/pointeurs/ada-nc.pdf'':113C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6453, 1, '2015-07-14 08:45:36.813', 1, 57, 'Find correlation in observed data', '&lt;p&gt;I have a method that calculates a certain variable. This variable is biased by an error which should be related to the observables that were used to calculated the variable. I don''t have an exact formula of the calculation and also no errors of the observables, therefore I can''t just do error propagation.&lt;/p&gt;

&lt;p&gt;I do have training data (the true error). Now I want to use machine learning to find the correlations between the input observables and the error of the calculated output. For now I tried to gather as many observables and used their statistics (mean, variance etc.) as "feature vectors" for machine learning. So far I tried approaches like SVR, linear Regression, SGD Regression etc. but all showed the same bad result.&lt;/p&gt;

&lt;p&gt;I''m quite new in this field. So some things are still a little bit unclear to me. Do you have any ideas how to find the relations? Or do you think there is no relation at all? What if there are observables that are totally random; do they destroy the learning?&lt;/p&gt;

&lt;p&gt;Thanks in advance!&lt;/p&gt;
', 'machine-learning  regression', NULL, '''/p'':66C,139C,193C,198C ''advanc'':197C ''also'':52C ''approach'':124C ''bad'':137C ''bias'':23C ''bit'':155C ''calcul'':16C,38C,50C,96C ''certain'':18C ''correl'':2A,86C ''data'':5A,72C ''destroy'':190C ''error'':26C,54C,64C,75C,93C ''etc'':113C,131C ''exact'':46C ''far'':121C ''featur'':115C ''field'':147C ''find'':1A,84C,166C ''formula'':47C ''gather'':103C ''idea'':163C ''input'':89C ''learn'':8B,82C,119C,192C ''like'':125C ''linear'':127C ''littl'':154C ''m'':142C ''machin'':7B,81C,118C ''machine-learn'':6B ''mani'':105C ''mean'':111C ''method'':14C ''new'':144C ''observ'':4A,33C,57C,90C,106C,183C ''output'':97C ''p'':10C,67C,140C,194C ''propag'':65C ''quit'':143C ''random'':187C ''regress'':9B,128C,130C ''relat'':30C,168C,176C ''result'':138C ''sgd'':129C ''show'':134C ''statist'':110C ''still'':152C ''svr'':126C ''thank'':195C ''therefor'':58C ''thing'':150C ''think'':172C ''total'':186C ''train'':71C ''tri'':101C,123C ''true'':74C ''unclear'':156C ''use'':36C,80C,108C ''variabl'':19C,21C,40C ''varianc'':112C ''vector'':116C ''want'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6498, 1, '2015-07-18 19:39:18.78', 1, 19, 'Advantage of a treebank in XML format', '&lt;p&gt;Which treebanks are based on an XML format? 
What is the advantage of XML format for a treebank? I think it may have effects on annotation and querying the treebank. &lt;/p&gt;

&lt;p&gt;for example &lt;a href="http://www.let.rug.nl/vannoord/Lassy/" rel="nofollow"&gt;LASSY&lt;/a&gt; and Alpino or TIGER are in xml format.&lt;/p&gt;
', 'nlp  data-formats', NULL, '''/a'':55C ''/p'':43C,64C ''/vannoord/lassy/'':51C ''advantag'':1A,24C ''alpino'':57C ''annot'':38C ''base'':16C ''data'':10B ''data-format'':9B ''effect'':36C ''exampl'':46C ''format'':7A,11B,20C,27C,63C ''href'':48C ''lassi'':54C ''may'':34C ''nlp'':8B ''nofollow'':53C ''p'':12C,44C ''queri'':40C ''rel'':52C ''think'':32C ''tiger'':59C ''treebank'':4A,14C,30C,42C ''www.let.rug.nl'':50C ''www.let.rug.nl/vannoord/lassy/'':49C ''xml'':6A,19C,26C,62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6439, 2, '2015-07-12 15:57:38.307', 1, NULL, NULL, '&lt;p&gt;I think you are trying to see which original (not PCA) features contribute to which cluster users fall into, yes?&lt;/p&gt;

&lt;p&gt;First, be aware that you just reduced the dimensionality of a 5d dataset down to 2d. You need to be careful of how much variance of the data you just threw away by projecting it into 2d. You can &lt;a href="http://stats.stackexchange.com/a/31911/7362"&gt;easily calculate this&lt;/a&gt;. If your 2d features account for 95% of the variance of the data, then great! You''ve got some valid insights about which features are important. If lower, like say 40%, then not so much. &lt;/p&gt;

&lt;p&gt;(From a qualitative standpoint, just plot the clusters with different colors. That will give you some idea of how much variance you threw away - if the clusters look contiguous and not a lot of mixing, then qualitatively, you didn''t throw away too much variance.)&lt;/p&gt;

&lt;p&gt;Second, realize that in order to intuit on which original feature values contribute to which clusters, you''ll need to use the original features. &lt;/p&gt;

&lt;p&gt;The real answer to your question is that after that you should 1) cluster in 5d, 2) use a classifier, and 3) see the important features using 5d feature vectors. This is critical because in 2d, just because you might have kept most of the variance doesn''t mean that last bit might have contained very discriminatory (read: important) information that might improve your underlying clustering error and give you a much more accurate answer. Most importantly, the feature importance will be computed on the actual features, not the PCA linear-combination features which don''t translate directly to single site visit histories.&lt;/p&gt;

&lt;p&gt;Hope that helps.&lt;/p&gt;
', NULL, NULL, '''/a'':71C ''/a/31911/7362'':67C ''/p'':22C,107C,154C,180C,286C,291C ''1'':194C ''2'':198C ''2d'':39C,60C,74C,217C ''3'':203C ''40'':102C ''5d'':35C,197C,209C ''95'':78C ''account'':76C ''accur'':255C ''actual'':267C ''answer'':184C,256C ''awar'':26C ''away'':55C,132C,150C ''bit'':233C ''calcul'':69C ''care'':44C ''classifi'':201C ''cluster'':17C,116C,135C,171C,195C,247C ''color'':119C ''combin'':274C ''comput'':264C ''contain'':236C ''contigu'':137C ''contribut'':14C,168C ''critic'':214C ''data'':51C,84C ''dataset'':36C ''didn'':147C ''differ'':118C ''dimension'':32C ''direct'':280C ''discriminatori'':238C ''doesn'':228C ''easili'':68C ''error'':248C ''fall'':19C ''featur'':13C,75C,95C,166C,179C,207C,210C,260C,268C,275C ''first'':24C ''give'':122C,250C ''got'':89C ''great'':86C ''help'':290C ''histori'':285C ''hope'':288C ''href'':64C ''idea'':125C ''import'':97C,206C,240C,258C,261C ''improv'':244C ''inform'':241C ''insight'':92C ''intuit'':162C ''kept'':223C ''last'':232C ''like'':100C ''linear'':273C ''linear-combin'':272C ''ll'':173C ''look'':136C ''lot'':141C ''lower'':99C ''mean'':230C ''might'':221C,234C,243C ''mix'':143C ''much'':47C,106C,128C,152C,253C ''need'':41C,174C ''order'':160C ''origin'':10C,165C,178C ''p'':1C,23C,108C,155C,181C,287C ''pca'':12C,271C ''plot'':114C ''project'':57C ''qualit'':111C,145C ''question'':187C ''read'':239C ''real'':183C ''realiz'':157C ''reduc'':30C ''say'':101C ''second'':156C ''see'':8C,204C ''singl'':282C ''site'':283C ''standpoint'':112C ''stats.stackexchange.com'':66C ''stats.stackexchange.com/a/31911/7362'':65C ''think'':3C ''threw'':54C,131C ''throw'':149C ''translat'':279C ''tri'':6C ''under'':246C ''use'':176C,199C,208C ''user'':18C ''valid'':91C ''valu'':167C ''varianc'':48C,81C,129C,153C,227C ''ve'':88C ''vector'':211C ''visit'':284C ''yes'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6440, 1, '2015-07-12 19:08:38.467', 5, 307, 'How to do multitask learning using Caffe?', '&lt;p&gt;I wonder how to do multitask learning using &lt;a href="https://github.com/BVLC/caffe"&gt;Caffe&lt;/a&gt;. Should I simply use the output layer &lt;a href="https://github.com/BVLC/caffe/blob/master/docs/tutorial/layers.md#sigmoid-cross-entropy"&gt;SigmoidCrossEntropyLoss&lt;/a&gt; or &lt;a href="https://github.com/BVLC/caffe/blob/master/docs/tutorial/layers.md#sum-of-squares--euclidean"&gt;EuclideanLoss&lt;/a&gt;, and define more than one outputs?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;E.g. is the following architecture valid (3 outputs, i.e. 3 tasks concurrently learnt)?&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Yc4Sf.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Corresponding prototxt file:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;name: "IrisNet"
layer {
  name: "iris"
  type: "HDF5Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  hdf5_data_param {
    source: "iris_train_data.txt"
    batch_size: 1

  }
}

layer {
  name: "iris"
  type: "HDF5Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  hdf5_data_param {
    source: "iris_test_data.txt"
    batch_size: 1

  }
}

layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "data"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 50
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}


layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}




layer {
  name: "loss"
  type: "SigmoidCrossEntropyLoss" 
  # type: "EuclideanLoss" 
  # type: "HingeLoss"  
  bottom: "ip2"
  bottom: "label"
  top: "loss"
}
&lt;/code&gt;&lt;/pre&gt;
', 'deep-learning  multitask-learning', NULL, '''/a'':29C,43C,51C ''/bvlc/caffe'':27C ''/bvlc/caffe/blob/master/docs/tutorial/layers.md#sigmoid-cross-entropy'':41C ''/bvlc/caffe/blob/master/docs/tutorial/layers.md#sum-of-squares--euclidean'':49C ''/code'':213C ''/p'':58C,74C,86C,91C ''/pre'':214C ''/yc4sf.png'':80C ''1'':115C,135C,148C,179C ''2'':152C,183C ''3'':67C,70C,189C ''50'':158C ''alt'':81C ''architectur'':65C ''batch'':113C,133C ''bias'':163C,194C ''bottom'':141C,172C,207C,209C ''caff'':7A,28C ''code'':93C ''concurr'':72C ''constant'':166C,197C ''correspond'':88C ''data'':102C,109C,122C,129C,142C ''deep'':9B ''deep-learn'':8B ''defin'':53C ''descript'':84C ''e.g'':61C ''enter'':82C ''euclideanloss'':50C,204C ''file'':90C ''filler'':160C,164C,191C,195C ''follow'':64C ''github.com'':26C,40C,48C ''github.com/bvlc/caffe'':25C ''github.com/bvlc/caffe/blob/master/docs/tutorial/layers.md#sigmoid-cross-entropy'':39C ''github.com/bvlc/caffe/blob/master/docs/tutorial/layers.md#sum-of-squares--euclidean'':47C ''hdf5'':108C,128C ''hdf5data'':100C,120C ''hingeloss'':206C ''hr'':59C ''href'':24C,38C,46C ''i.e'':69C ''i.stack.imgur.com'':79C ''i.stack.imgur.com/yc4sf.png'':78C ''imag'':83C ''img'':76C ''includ'':105C,125C ''inner'':153C,184C ''innerproduct'':140C,171C ''ip1'':138C,144C,173C ''ip2'':169C,175C,208C ''iri'':98C,118C ''iris_test_data.txt'':132C ''iris_train_data.txt'':112C ''irisnet'':95C ''label'':104C,124C,210C ''layer'':36C,96C,116C,136C,167C,198C ''learn'':5A,10B,13B,21C ''learnt'':73C ''loss'':200C,212C ''lr'':146C,150C,177C,181C ''mult'':147C,151C,178C,182C ''multitask'':4A,12B,20C ''multitask-learn'':11B ''name'':94C,97C,117C,137C,168C,199C ''num'':156C,187C ''one'':56C ''output'':35C,57C,68C,157C,188C ''p'':14C,60C,75C,87C ''param'':110C,130C,145C,149C,155C,176C,180C,186C ''phase'':106C,126C ''pre'':92C ''product'':154C,185C ''prototxt'':89C ''sigmoidcrossentropyloss'':42C,202C ''simpli'':32C ''size'':114C,134C ''sourc'':111C,131C ''src'':77C ''task'':71C ''test'':127C ''top'':101C,103C,121C,123C,143C,174C,211C ''train'':107C ''type'':99C,119C,139C,161C,165C,170C,192C,196C,201C,203C,205C ''use'':6A,22C,33C ''valid'':66C ''weight'':159C,190C ''wonder'':16C ''xavier'':162C,193C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6444, 2, '2015-07-13 03:02:48.13', -2, NULL, NULL, '&lt;p&gt;I guess u are looking for feature which extract out new feature. A feature which best representthe dataset. If that is the case then we call such method "feature extraction".&lt;/p&gt;
', NULL, NULL, '''/p'':32C ''best'':17C ''call'':27C ''case'':24C ''dataset'':19C ''extract'':10C,31C ''featur'':8C,13C,15C,30C ''guess'':3C ''look'':6C ''method'':29C ''new'':12C ''p'':1C ''representth'':18C ''u'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6446, 1, '2015-07-13 13:34:51.983', 1, 88, 'Errror in confusionMatrix in R', '&lt;p&gt;While i am using the &lt;code&gt;confusionMatrix&lt;/code&gt; in R, I am getting the following error:&lt;/p&gt;

&lt;p&gt;Error in &lt;code&gt;binom.test(sum(diag(x)), sum(x))&lt;/code&gt; : 
  ''n'' must be a positive integer &gt;= ''x''&lt;/p&gt;

&lt;p&gt;The code i use as follows:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;rf.pred &amp;lt;- predict(rf, testing, type = "class")&lt;br&gt;
confusionMatrix(rf.pred, testing$target)&lt;/code&gt;&lt;/p&gt;
', 'machine-learning  data-mining  r  classification  predictive-modeling', NULL, '''/code'':25C,45C,76C ''/p'':34C,53C,61C,77C ''binom.test'':39C ''br'':71C ''class'':70C ''classif'':13B ''code'':23C,38C,56C,63C ''confusionmatrix'':3A,24C,72C ''data'':10B ''data-min'':9B ''diag'':41C ''error'':33C,36C ''errror'':1A ''follow'':32C,60C ''get'':30C ''integ'':51C ''learn'':8B ''lt'':65C ''machin'':7B ''machine-learn'':6B ''mine'':11B ''model'':16B ''must'':47C ''n'':46C ''p'':17C,35C,54C,62C ''posit'':50C ''predict'':15B,66C ''predictive-model'':14B ''r'':5A,12B,27C ''rf'':67C ''rf.pred'':64C,73C ''sum'':40C,43C ''target'':75C ''test'':68C,74C ''type'':69C ''use'':21C,58C ''x'':42C,44C,52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6447, 1, '2015-07-13 15:39:13.29', 1, 50, 'Problem while reusing model for new observations', '&lt;p&gt;I want to  build a model using R, save it and reloading it when required. I am able to save the model and reload it without any problem, however when i use the model along with predict statement, I am getting errors.&lt;/p&gt;

&lt;p&gt;This is what i do:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;set.seed(345)
  df &amp;lt;- data.frame(x = rnorm(20))
  df &amp;lt;- transform(df, y = 5 + (2.3 * x) + rnorm(20))&lt;/p&gt;
  
  &lt;h2&gt;model&lt;/h2&gt;
  
  &lt;p&gt;m1 &amp;lt;- lm(y ~ x, data = df)&lt;/p&gt;
  
  &lt;h2&gt;save this model&lt;/h2&gt;
  
  &lt;p&gt;save(m1, file = "my_model1.rda")&lt;/p&gt;
&lt;/blockquote&gt;

&lt;h2&gt;a month later, new observations are available:&lt;/h2&gt;

&lt;blockquote&gt;
  &lt;p&gt;newdf &amp;lt;- data.frame(x = rnorm(20))&lt;/p&gt;
  
  &lt;h2&gt;load the model&lt;/h2&gt;
  
  &lt;p&gt;load("my_model1.rda")&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;When i use the above model to predict for new values, I am getting output values as "NA"  &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;predict(m1, newdata = newdf)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;"NA"  "NA"  "NA"  "NA"  "NA"  "NA" "NA"  "NA"  "NA"   &lt;/p&gt;

&lt;p&gt;I could able to see that the model has been loaded correctly by giving a print command&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;print(m1)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Call:
lm(formula = y ~ x, data = df)&lt;/p&gt;

&lt;p&gt;Coefficients:
(Intercept)            x&lt;br&gt;
      4.792        2.119  &lt;/p&gt;

&lt;p&gt;Is there anything that I miss here?&lt;/p&gt;
', 'machine-learning  data-mining  r  linear-regression', NULL, '''/blockquote'':113C,141C,169C,205C ''/h2'':92C,106C,122C,136C ''/p'':61C,68C,89C,101C,112C,131C,140C,161C,168C,180C,199C,204C,214C,222C,231C ''2.119'':221C ''2.3'':85C ''20'':78C,88C,130C ''345'':72C ''4.792'':220C ''5'':84C ''abl'':36C,184C ''along'':53C ''anyth'':226C ''avail'':121C ''blockquot'':69C,123C,162C,200C ''br'':219C ''build'':22C ''call'':207C ''coeffici'':216C ''command'':198C ''correct'':193C ''could'':183C ''data'':12B,99C,212C ''data-min'':11B ''data.frame'':75C,127C ''df'':73C,79C,82C,100C,213C ''error'':60C ''file'':110C ''formula'':209C ''get'':59C,156C ''give'':195C ''h2'':90C,102C,114C,132C ''howev'':47C ''intercept'':217C ''later'':117C ''learn'':10B ''linear'':16B ''linear-regress'':15B ''lm'':96C,208C ''load'':133C,138C,192C ''lt'':74C,80C,95C,126C ''m1'':94C,109C,165C,203C ''machin'':9B ''machine-learn'':8B ''mine'':13B ''miss'':229C ''model'':4A,24C,40C,52C,91C,105C,135C,148C,189C ''month'':116C ''my_model1.rda'':111C,139C ''na'':160C,171C,172C,173C,174C,175C,176C,177C,178C,179C ''new'':6A,118C,152C ''newdata'':166C ''newdf'':125C,167C ''observ'':7A,119C ''output'':157C ''p'':18C,62C,70C,93C,107C,124C,137C,142C,163C,170C,181C,201C,206C,215C,223C ''predict'':55C,150C,164C ''print'':197C,202C ''problem'':1A,46C ''r'':14B,26C ''regress'':17B ''reload'':30C,42C ''requir'':33C ''reus'':3A ''rnorm'':77C,87C,129C ''save'':27C,38C,103C,108C ''see'':186C ''set.seed'':71C ''statement'':56C ''transform'':81C ''use'':25C,50C,145C ''valu'':153C,158C ''want'':20C ''without'':44C ''x'':76C,86C,98C,128C,211C,218C ''y'':83C,97C,210C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6454, 2, '2015-07-14 09:02:54.843', 1, NULL, NULL, '&lt;p&gt;I''m not sure it is possible to generalise as to how people may mis-state financial information they report to governments. &lt;/p&gt;

&lt;p&gt;This probably varies by country and hence culture and if the information is used directly to affect the welfare benefits received by that individual, it may not be. &lt;/p&gt;

&lt;p&gt;If there is data on household income from a more trusted source, tax receipts for instance, that might be used to normalise the self reported data.&lt;/p&gt;
', NULL, NULL, '''/p'':25C,55C,83C ''affect'':43C ''benefit'':46C ''countri'':31C ''cultur'':34C ''data'':60C,82C ''direct'':41C ''financi'':19C ''generalis'':10C ''govern'':24C ''henc'':33C ''household'':62C ''incom'':63C ''individu'':50C ''inform'':20C,38C ''instanc'':72C ''m'':3C ''may'':15C,52C ''might'':74C ''mis'':17C ''mis-stat'':16C ''normalis'':78C ''p'':1C,26C,56C ''peopl'':14C ''possibl'':8C ''probabl'':28C ''receipt'':70C ''receiv'':47C ''report'':22C,81C ''self'':80C ''sourc'':68C ''state'':18C ''sure'':5C ''tax'':69C ''trust'':67C ''use'':40C,76C ''vari'':29C ''welfar'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6456, 1, '2015-07-14 15:05:21.473', 0, 15, 'Is there a way to set the number of times a neural network encounters certain input in Matlab?', '&lt;p&gt;If I have input data and it''s corresponding target data, and for each input there is a number associated with it, where this number represents the probability of the input occurring, is there a way to use this number to determine how many times a feedforward neural network in Matlab encounters the input. &lt;/p&gt;

&lt;p&gt;Currently what I''m doing to work around this is manually have the input present x amount of times where x corresponds to the probability associated with the input. &lt;/p&gt;

&lt;p&gt;So, for example say I have input: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1,2,1,0   
1,4,1,0
0,1,1,0
1,4,2,1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where each row corresponds to a specific item in the input data and for each row a number is associated with it which represents the probability of that item occurring: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;.2394
.4958
.0123
.8721
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is there a way to use these numbers to determine the amount of times a feedforward neural network encounters an item? &lt;/p&gt;
', 'neuralnetwork  matlab', NULL, '''/code'':135C,175C ''/p'':76C,107C,116C,168C,199C ''/pre'':136C,176C ''0'':122C,126C,127C,130C ''0123'':173C ''1'':119C,121C,123C,125C,128C,129C,131C,134C ''2'':120C,133C ''2394'':171C ''4'':124C,132C ''4958'':172C ''8721'':174C ''amount'':94C,189C ''around'':85C ''associ'':41C,103C,157C ''certain'':15A ''code'':118C,170C ''correspond'':30C,99C,141C ''current'':78C ''data'':26C,32C,149C ''determin'':63C,187C ''encount'':14A,73C,196C ''exampl'':111C ''feedforward'':68C,193C ''input'':16A,25C,36C,52C,75C,91C,106C,115C,148C ''item'':145C,166C,198C ''m'':81C ''mani'':65C ''manual'':88C ''matlab'':18A,20B,72C ''network'':13A,70C,195C ''neural'':12A,69C,194C ''neuralnetwork'':19B ''number'':8A,40C,46C,61C,155C,185C ''occur'':53C,167C ''p'':21C,77C,108C,137C,177C ''pre'':117C,169C ''present'':92C ''probabl'':49C,102C,163C ''repres'':47C,161C ''row'':140C,153C ''say'':112C ''set'':6A ''specif'':144C ''target'':31C ''time'':10A,66C,96C,191C ''use'':59C,183C ''way'':4A,57C,181C ''work'':84C ''x'':93C,98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6458, 2, '2015-07-14 21:30:00.377', 1, NULL, NULL, '&lt;p&gt;If i get it correctly:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;You have an input polygon&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;As a first step you want to "match" that against a list of previously seen templates. If this is successful, you pick it''s corresponding output and move on.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;If not, you wish to find some optimal transformation, in order for it to satisfy some constraints that you have (your "objective function"). Then add the original+transformed shape to the templates list and move on.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Is this correct? I''ll risk an answer anyways:&lt;/p&gt;

&lt;p&gt;For the first part, I believe that there is a &lt;a href="https://www.google.com.tr/search?rls=en&amp;amp;q=shape%20matching%20algorithm&amp;amp;ie=UTF-8&amp;amp;oe=UTF-8" rel="nofollow"&gt;slew of literature out there&lt;/a&gt;. It''s not my expertise, but first thing that comes to mind is measuring the distance in feature space between your shape and each template, and picking the closest one, it the distance is below a threshold that you set. "Feature" here would be either some low-level polygon property, e.g. x and y coordinates of vertices, or an abstraction, e.g. perimeter, area, no. vertices, mean side length/side length variance, etc.&lt;/p&gt;

&lt;p&gt;For the second part, it really depends on the nature of your constraints/objective functions. Are they convex? Uni- or multi-modal? Single or multi-objective? Do you want to incorporate some domain knowledge (i.e. knowledge about what "good" transformations would be?)? One can really not tell without further details. Evolutionary algorithms are quite versatile but expensive methods (although some argue on that). If you can spare the possibly large amount of function evaluations, you could try EAs as a first step, and then refine your approach.&lt;/p&gt;

&lt;p&gt;Finally, while not exactly related to what you describe in your process, I believe you may benefit by taking a look into auto-associative networks (and models in general); these are models that are able to perform constraint-satisfaction on their input, effectively enforcing learned relationships on input values. I could see this being used in your case by inputing a shape, and having a transformed shape as an output, which would be "legal", i.e. satisfying the constraints learned by the auto associative model. Thus, you would eliminate the need for a template matching + optimization altogether.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8908, 2, '2015-11-19 04:16:41.117', 2, NULL, NULL, '&lt;p&gt;&lt;a href="https://mxnet.readthedocs.org/en/latest/" rel="nofollow"&gt;MXNet&lt;/a&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;written in C++ but has an API in Python (and a few other programming languages such as R, Julia, and Go)&lt;/li&gt;
&lt;li&gt;Scales up to multi GPUs and distributed setting with auto parallelism.&lt;/li&gt;
&lt;li&gt;Automatic differentiation&lt;/li&gt;
&lt;li&gt;Decent performances:&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/lImAG.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/lImAG.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':10C,76C ''/en/latest/'':6C ''/li'':35C,48C,52C,56C ''/limag.png'':63C,70C ''/p'':11C,77C ''/ul'':57C ''alt'':71C ''api'':20C ''auto'':46C ''automat'':50C ''c'':16C ''decent'':54C ''descript'':74C ''differenti'':51C ''distribut'':43C ''enter'':72C ''go'':34C ''gpus'':41C ''href'':3C,60C ''i.stack.imgur.com'':62C,69C ''i.stack.imgur.com/limag.png'':61C,68C ''imag'':73C ''img'':66C ''julia'':32C ''languag'':28C ''li'':13C,36C,49C,53C ''multi'':40C ''mxnet'':9C ''mxnet.readthedocs.org'':5C ''mxnet.readthedocs.org/en/latest/'':4C ''nofollow'':8C,65C ''p'':1C,58C ''parallel'':47C ''perform'':55C ''program'':27C ''python'':22C ''r'':31C ''rel'':7C,64C ''scale'':37C ''set'':44C ''src'':67C ''ul'':12C ''written'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6459, 1, '2015-07-14 22:39:37.943', 3, 199, 'What features from sound waves to use for an AI song composer?', '&lt;p&gt;I am planning on making an AI song composer that would take in a bunch of songs of one instrument, extract musical notes (like ABCDEFG) and certain features from the sound wave, preform machine learning (most likely through recurrent neural networks), and output a sequence of ABCDEFG notes (aka generate its own songs / music). &lt;/p&gt;

&lt;p&gt;I think that this would be an unsupervised learning problem, but I am not really sure.&lt;/p&gt;

&lt;p&gt;I figured that I would use recurrent neural networks, but I have a few questions on how to approach this:&lt;br&gt;
- What features from the sound wave I should extract so that the output music is melodious?&lt;br&gt;
- Is it possible, with recurrent neural networks, to output a vector of sequenced musical notes (ABCDEF)?&lt;br&gt;
- Any smart way I can feed in the features of the soundwaves as well as sequence of musical notes? &lt;/p&gt;
', 'machine-learning  neuralnetwork  feature-selection  feature-extraction', NULL, '''/p'':78C,96C,172C ''abcdef'':151C ''abcdefg'':48C,70C ''ai'':10A,30C ''aka'':72C ''approach'':116C ''br'':118C,135C,152C ''bunch'':38C ''certain'':50C ''compos'':12A,32C ''extract'':22B,44C,127C ''featur'':2A,18B,21B,51C,120C,161C ''feature-extract'':20B ''feature-select'':17B ''feed'':158C ''figur'':99C ''generat'':73C ''instrument'':43C ''learn'':15B,58C,88C ''like'':47C,60C ''machin'':14B,57C ''machine-learn'':13B ''make'':28C ''melodi'':134C ''music'':45C,77C,132C,149C,170C ''network'':64C,106C,142C ''neural'':63C,105C,141C ''neuralnetwork'':16B ''note'':46C,71C,150C,171C ''one'':42C ''output'':66C,131C,144C ''p'':23C,79C,97C ''plan'':26C ''possibl'':138C ''preform'':56C ''problem'':89C ''question'':112C ''realli'':94C ''recurr'':62C,104C,140C ''select'':19B ''sequenc'':68C,148C,168C ''smart'':154C ''song'':11A,31C,40C,76C ''sound'':4A,54C,123C ''soundwav'':164C ''sure'':95C ''take'':35C ''think'':81C ''unsupervis'':87C ''use'':7A,103C ''vector'':146C ''wave'':5A,55C,124C ''way'':155C ''well'':166C ''would'':34C,84C,102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6460, 1, '2015-07-14 23:37:01.94', 2, 157, 'Fisher''s Iris data set with Caffe', '&lt;p&gt;I am trying to use &lt;a href="https://github.com/BVLC/caffe" rel="nofollow"&gt;Caffe&lt;/a&gt; on the usual &lt;a href="https://en.wikipedia.org/wiki/Iris_flower_data_set" rel="nofollow"&gt;Fisher''s Iris data set&lt;/a&gt; (150 flowers, each having 4 features, and split into 3 classes):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;if a flower belong to class 1 (setosa), the network output should be [1, 0, 0]&lt;/li&gt;
&lt;li&gt;if a flower belong to class 2 (versicolor), the network output should be [0, 1, 0]&lt;/li&gt;
&lt;li&gt;if a flower belong to class 3 (virginica), the network output should be [0, 0, 1]&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I use the &lt;a href="https://github.com/BVLC/caffe/blob/master/docs/tutorial/layers.md#sigmoid-cross-entropy" rel="nofollow"&gt;SigmoidCrossEntropyLoss&lt;/a&gt; as it &lt;a href="https://en.wikipedia.org/wiki/Convolutional_neural_network#Loss_layer" rel="nofollow"&gt;used for predicting K independent probability values in [0,1]&lt;/a&gt;. Let ip3 be the layer connected to SigmoidCrossEntropyLoss (in addition to the label layer): looking at ip3''s output, I interpret negative values as class absent (0), and positive values as class present (1). E.g. if ip3''s output is [-0.3; 0.9; -0.4], then I interpret it as [0; 1; 0], i.e. class 2 is present but classes 1 and 3 are absent.&lt;/p&gt;

&lt;p&gt;The network does a good job classifying classes 1 and 3 (accuracy over 90%), but consistently fails to predict class 2: the network always predicts that class 2 is absent, i.e. the network''s second output is always 0, i.e. that ip3''s second output is always negative, no matter what the input is.&lt;/p&gt;

&lt;p&gt;Here is the architecture I use. Am I doing something wrong? Is the architecture not suited for the task?&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/UL0cr.png" alt="enter image description here"&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6693, 1, '2015-08-05 05:18:55.957', 4, 62, 'Limitations on the number of items to use in apriori algorithm?', '&lt;p&gt;Are there any limitations on the number of items to use in a transaction for applying apriori algorithm.&lt;/p&gt;

&lt;p&gt;I have a dataset with just 20 records, but the number of items extend upto 900. I am getting memory issue when I apply the apriori algorithm on the dataset.&lt;/p&gt;

&lt;p&gt;Thank you&lt;/p&gt;
', 'machine-learning  data-mining', NULL, '''/p'':37C,69C,73C ''20'':45C ''900'':54C ''algorithm'':11A,36C,65C ''appli'':34C,62C ''apriori'':10A,35C,64C ''data'':16B ''data-min'':15B ''dataset'':42C,68C ''extend'':52C ''get'':57C ''issu'':59C ''item'':6A,27C,51C ''learn'':14B ''limit'':1A,22C ''machin'':13B ''machine-learn'':12B ''memori'':58C ''mine'':17B ''number'':4A,25C,49C ''p'':18C,38C,70C ''record'':46C ''thank'':71C ''transact'':32C ''upto'':53C ''use'':8A,29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6461, 2, '2015-07-15 00:15:11.733', 2, NULL, NULL, '&lt;p&gt;I believe the question is, you want to learn from musical pieces and try to generate a tune from the trained instance. Lets see if I can set up a simple model to do this, and then you can extrapolate from there. &lt;/p&gt;

&lt;p&gt;So, &lt;a href="https://en.wikipedia.org/wiki/Mel-frequency_cepstrum" rel="nofollow"&gt;MFCC&lt;/a&gt; is a good feature when working with sound. You can use that to extract the features from lets say 1-2 second windows of your song. You now have a fingerprint for the audio file. Take a look at &lt;a href="http://www.uoguelph.ca/~gwtaylor/thesis/4/" rel="nofollow"&gt;Conditional Restricted Boltzmann Machines&lt;/a&gt;. They are Neural Networks which use multiple binary states to encode time series information. As you can see in the webpage, they trained on human-gait data and can now generate their own human gait. This is essentially what you want but for music files. So you can train CRBMs on the Audio MFCC vectors that you have. &lt;/p&gt;

&lt;p&gt;After the training is done, to generate an audio file you can either "seed" the CRBM with a few seconds of some melody or just randomly initialize it. Then just allow the CRBM to go nuts and record whatever it produces. This is your new audio file. To produce another sample use a different seed.&lt;/p&gt;

&lt;p&gt;This solves the question of how you can implement a "melody" generation scheme. There are of course variations. You can add other features to you vector apart from MFCC. You can also use other time series predictors like &lt;a href="http://deeplearning.net/tutorial/lstm.html" rel="nofollow"&gt;LSTM&lt;/a&gt; or Markov models. &lt;/p&gt;

&lt;p&gt;All of this being said, the problem of generating music might be much more nuanced than it looks at first glance. Machine Learning algorithms just apply previously learned patterns in the data. How does that correspond to "creating" new music , is a philosophical question. If we analyze the aforementioned algorithm, essentially the CRBM will generate a next output based on the probability distribution that it has learnt. It would be very interesting to see what kind of output it generates when the said distribution is that of musical notes.  &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6462, 1, '2015-07-15 01:17:20.583', 0, 32, 'Obtain Zip-Code Level Estimates from State Level Observed Data and Correlated Zip-Code Data', '&lt;p&gt;I''d like to use actual state estimates for X (financial variable in percentage format) and obtain zip-code estimates for X (same financial variable estimate in percentage format). &lt;/p&gt;

&lt;p&gt;I have zip-code level data for variables (let''s call them A,B,C, etc) that most likely would help describe X at the zip-code level, but again I have no real observations of X at the zip code level. &lt;/p&gt;

&lt;p&gt;My first attempt was to aggregated zip code level data for (A, B, C, etc) to the state level, then regress against X at the state level. I then took the regression result and used it to predict X at the zip code level. &lt;/p&gt;

&lt;p&gt;To check the results, I then aggregated and averaged the zip code estimates for X. They aren''t ideally close to the real observed state level X variable. &lt;/p&gt;

&lt;p&gt;I''m wondering if there''s a better way to do this?&lt;/p&gt;
', 'statistics  regression', NULL, '''/p'':51C,97C,143C,173C,187C ''actual'':26C ''aggreg'':104C,151C ''aren'':161C ''attempt'':101C ''averag'':153C ''b'':67C,111C ''better'':182C ''c'':68C,112C ''call'':64C ''check'':146C ''close'':164C ''code'':4A,16A,40C,57C,81C,95C,106C,141C,156C ''correl'':13A ''d'':22C ''data'':11A,17A,59C,108C ''describ'':75C ''estim'':6A,28C,41C,47C,157C ''etc'':69C,113C ''financi'':31C,45C ''first'':100C ''format'':35C,50C ''help'':74C ''ideal'':163C ''let'':62C ''level'':5A,9A,58C,82C,96C,107C,117C,125C,142C,170C ''like'':23C,72C ''m'':176C ''observ'':10A,89C,168C ''obtain'':1A,37C ''p'':20C,52C,98C,144C,174C ''percentag'':34C,49C ''predict'':136C ''real'':88C,167C ''regress'':19B,119C,130C ''result'':131C,148C ''state'':8A,27C,116C,124C,169C ''statist'':18B ''took'':128C ''use'':25C,133C ''variabl'':32C,46C,61C,172C ''way'':183C ''wonder'':177C ''would'':73C ''x'':30C,43C,76C,91C,121C,137C,159C,171C ''zip'':3A,15A,39C,56C,80C,94C,105C,140C,155C ''zip-cod'':2A,14A,38C,55C,79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6464, 1, '2015-07-15 14:15:17.773', 2, 76, 'Spam detection in social media', '&lt;p&gt;I want to train a binary classification algorithm for spam detection using labeled data set of mentions from social media. These mentions have the following features:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;URL, text message, posting date, is deleted, &lt;em&gt;author&lt;/em&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;where the &lt;em&gt;author&lt;/em&gt; has its own fields:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;URL, nick, type (&lt;em&gt;person or community&lt;/em&gt;), number of subscribers, registration date, last activity date&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I have $\approx 13.5k$ observations with $\approx 450$ spam messages among them. &lt;/p&gt;

&lt;p&gt;My suggestions: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;transform data features in real numbers&lt;/li&gt;
&lt;li&gt;binarize URLs by media sources (e.g. facebook, instagram, etc.)&lt;/li&gt;
&lt;li&gt;use SVM with Gaussian kernel for all data&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Questions: &lt;strong&gt;Am I on the right way? What else can I do? How can I process text data?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Thanks in advance for any suggestions!&lt;/p&gt;
', 'machine-learning  classification  social-network-analysis', NULL, '''/blockquote'':55C,86C ''/em'':53C,61C,76C ''/li'':114C,125C,135C ''/p'':41C,54C,66C,85C,101C,105C,158C,166C ''/strong'':157C ''/ul'':136C ''13.5'':91C ''450'':96C ''activ'':83C ''advanc'':162C ''algorithm'':22C ''among'':99C ''analysi'':13B ''approx'':90C,95C ''author'':52C,60C ''binar'':116C ''binari'':20C ''blockquot'':42C,67C ''classif'':9B,21C ''communiti'':75C ''data'':28C,109C,134C,156C ''date'':48C,81C,84C ''delet'':50C ''detect'':2A,25C ''e.g'':121C ''els'':147C ''em'':51C,59C,72C ''etc'':124C ''facebook'':122C ''featur'':40C,110C ''field'':65C ''follow'':39C ''gaussian'':130C ''instagram'':123C ''k'':92C ''kernel'':131C ''label'':27C ''last'':82C ''learn'':8B ''li'':107C,115C,126C ''machin'':7B ''machine-learn'':6B ''media'':5A,34C,119C ''mention'':31C,36C ''messag'':46C,98C ''network'':12B ''nick'':70C ''number'':77C,113C ''observ'':93C ''p'':14C,43C,56C,68C,87C,102C,137C,159C ''person'':73C ''post'':47C ''process'':154C ''question'':138C ''real'':112C ''registr'':80C ''right'':144C ''set'':29C ''social'':4A,11B,33C ''social-network-analysi'':10B ''sourc'':120C ''spam'':1A,24C,97C ''strong'':139C ''subscrib'':79C ''suggest'':104C,165C ''svm'':128C ''text'':45C,155C ''thank'':160C ''train'':18C ''transform'':108C ''type'':71C ''ul'':106C ''url'':44C,69C,117C ''use'':26C,127C ''want'':16C ''way'':145C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6466, 2, '2015-07-15 17:56:33.763', 1, NULL, NULL, '&lt;p&gt;The approach seems correct, don''t worry. But I''d suggest to use Naive Bayes Classifier instead of SVM - it perfectly works for this task (and actually popular email services use this algorithm for spam detection).&lt;/p&gt;
', NULL, NULL, '''/p'':38C ''actual'':28C ''algorithm'':34C ''approach'':3C ''bay'':16C ''classifi'':17C ''correct'':5C ''d'':11C ''detect'':37C ''email'':30C ''instead'':18C ''naiv'':15C ''p'':1C ''perfect'':22C ''popular'':29C ''seem'':4C ''servic'':31C ''spam'':36C ''suggest'':12C ''svm'':20C ''task'':26C ''use'':14C,32C ''work'':23C ''worri'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6467, 1, '2015-07-15 17:59:31.063', 4, 58, 'Deriving Confidences from Distribution of Class Probabilities for a Prediction', '&lt;p&gt;I run into this problem from time to time and have always felt like there should be an obvious answer.&lt;/p&gt;

&lt;p&gt;I have probabilities for potential classes (from some classifier).  I will offer the prediction of the class with the highest probability, however, I would also like to attach a confidence for that prediction.&lt;/p&gt;

&lt;p&gt;Example: If I have Classes &lt;code&gt;[C1, C2, C3, C4, C5]&lt;/code&gt; and my Probabilities are &lt;code&gt;{C1: 50, C2: 12, C3: 13, C4: 12, C5:13}&lt;/code&gt; my confidence in predicting C1 should be higher than if I had Probabilities &lt;code&gt;{C1: 50, C2: 45, C3: 2, C4: 1, C5: 2}&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Reporting that I predict class C1 with 60% probability isn''t the whole story.  I should be able to derive a confidence from the distribution of probabilities as well.  I am certain there is a known method for solving this but I do not know what it is.&lt;/p&gt;

&lt;p&gt;EDIT: Taking this to the extreme for clarification: 
If I had a class C1 with 100% probability (and assuming the classifier had an accurate representation of each class) then I would be extremely confident that C1 was the correct classification.  On the other hand if all 5 classes had almost equal probability (Say they are all roughly 20%) than I would be very uncertain claiming that any one was the correct classification. These two extreme cases are more obvious, the challenge is derive a confidence for intermediate examples like the one above.&lt;/p&gt;

&lt;p&gt;Any suggestions or references would be of great help.&lt;/p&gt;

&lt;p&gt;Thanks in advance.&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/code'':84C,100C,125C ''/p'':36C,71C,126C,176C,270C,281C,286C ''1'':122C ''100'':193C ''12'':93C,97C ''13'':95C,99C ''2'':120C,124C ''20'':235C ''45'':118C ''5'':224C ''50'':91C,116C ''60'':135C ''abl'':145C ''accur'':201C ''advanc'':285C ''almost'':227C ''also'':62C ''alway'':27C ''answer'':35C ''assum'':196C ''attach'':65C ''c1'':79C,90C,105C,115C,133C,191C,213C ''c2'':80C,92C,117C ''c3'':81C,94C,119C ''c4'':82C,96C,121C ''c5'':83C,98C,123C ''case'':253C ''certain'':159C ''challeng'':258C ''claim'':242C ''clarif'':185C ''class'':6A,43C,54C,77C,132C,190C,205C,225C ''classif'':14B,217C,249C ''classifi'':46C,198C ''code'':78C,89C,114C ''confid'':2A,67C,102C,149C,211C,262C ''correct'':216C,248C ''deriv'':1A,147C,260C ''distribut'':4A,152C ''edit'':178C ''equal'':228C ''exampl'':73C,265C ''extrem'':183C,210C,252C ''felt'':28C ''great'':279C ''hand'':221C ''help'':280C ''higher'':108C ''highest'':57C ''howev'':59C ''intermedi'':264C ''isn'':137C ''know'':172C ''known'':163C ''learn'':13B ''like'':29C,63C,266C ''machin'':12B ''machine-learn'':11B ''method'':164C ''obvious'':34C,256C ''offer'':49C ''one'':245C,268C ''p'':15C,37C,72C,127C,177C,271C,282C ''potenti'':42C ''predict'':10A,51C,70C,104C,131C ''probabl'':7A,40C,58C,87C,113C,136C,154C,194C,229C ''problem'':20C ''refer'':275C ''report'':128C ''represent'':202C ''rough'':234C ''run'':17C ''say'':230C ''solv'':166C ''stori'':141C ''suggest'':273C ''take'':179C ''thank'':283C ''time'':22C,24C ''two'':251C ''uncertain'':241C ''well'':156C ''whole'':140C ''would'':61C,208C,238C,276C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6468, 2, '2015-07-15 18:11:33.163', 2, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;If I have Classes [C1, C2, C3, C4, C5] and my Probabilities are {C1:
  50, C2: 12, C3: 13, C4: 12, C5:13} my confidence in predicting C1
  should be higher than if I had Probabilities {C1: 50, C2: 45, C3: 2,
  C4: 1, C5: 2}.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Assuming that those probabilities are accurate, this isn''t true. In your second case you can be a lot more confident that the ground truth is one of C1 or C2, but in terms of absolute confidence about C1 the probability is the same across both examples. To illustrate this with a more clear example, if you had a 100 sided die that had 50 sides labeled with "C1" then the labels on the the other 50 sides are irrelevant to the likelihood that you would roll a "C1".&lt;/p&gt;

&lt;p&gt;Now with that said, your probabilities from your model are most certainly not perfect so there may be a way to use the intra-class correlations to improve them. Can you provide some more details about your specific problem and modeling workflow that you have used to get your probabilities?&lt;/p&gt;
', NULL, NULL, '''/blockquote'':50C ''/p'':49C,141C,194C ''1'':46C ''100'':111C ''12'':19C,23C ''13'':21C,25C ''2'':44C,48C ''45'':42C ''50'':17C,40C,116C,128C ''absolut'':87C ''accur'':57C ''across'':96C ''assum'':52C ''blockquot'':1C ''c1'':7C,16C,30C,39C,80C,90C,120C,140C ''c2'':8C,18C,41C,82C ''c3'':9C,20C,43C ''c4'':10C,22C,45C ''c5'':11C,24C,47C ''case'':65C ''certain'':154C ''class'':6C,168C ''clear'':105C ''confid'':27C,72C,88C ''correl'':169C ''detail'':178C ''die'':113C ''exampl'':98C,106C ''get'':191C ''ground'':75C ''higher'':33C ''illustr'':100C ''improv'':171C ''intra'':167C ''intra-class'':166C ''irrelev'':131C ''isn'':59C ''label'':118C,123C ''likelihood'':134C ''lot'':70C ''may'':159C ''model'':151C,184C ''one'':78C ''p'':2C,51C,142C ''perfect'':156C ''predict'':29C ''probabl'':14C,38C,55C,92C,148C,193C ''problem'':182C ''provid'':175C ''roll'':138C ''said'':146C ''second'':64C ''side'':112C,117C,129C ''specif'':181C ''term'':85C ''true'':61C ''truth'':76C ''use'':164C,189C ''way'':162C ''workflow'':185C ''would'':137C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6470, 2, '2015-07-15 18:47:02.09', 1, NULL, NULL, '&lt;p&gt;Yes, you are essentially on the right track. You can normalize the data , if your text samples have varied length. Also, you can use categorical data but not in the same feature. For example, having one feature which is labelled "1" for facebook and "2" for instagram is not good. Have separate features for these kind of categorical data with {0,1} labels. There are two additional inputs that I would like to give here. &lt;/p&gt;

&lt;p&gt;First, use a systematic grid search instead of just guessing the parameters. This is a very good practice which I picked up very late. If you are using a soft margin SVM ( which you should) with the gaussian kernel, you will have just two parameters to search over, C and gamma. This will make sure that you are extracting the best parameters from your classifier. &lt;/p&gt;

&lt;p&gt;Second, you have 13.5K samples and only 450 are spam. Which means you have 13 K desired mails. So you have 450 positive samples and 13K negative samples. If this is indeed the data distribution, you have an unbalanced data. You will have to weight the data according to the proportion otherwise SVM will end up classifying most of the samples as negative. To read about this in more detail look at Section 7 of this &lt;a href="http://www.cs.colostate.edu/~asa/pdfs/howto.pdf" rel="nofollow"&gt;document&lt;/a&gt; . Weighted SVMs are implemented in LibSVM, so you can directly use that. &lt;/p&gt;
', NULL, NULL, '''/a'':231C ''/p'':77C,144C,244C ''/~asa/pdfs/howto.pdf'':227C ''0'':62C ''1'':42C,63C ''13'':161C ''13.5'':149C ''13k'':172C ''2'':46C ''450'':154C,168C ''7'':220C ''accord'':194C ''addit'':68C ''also'':22C ''best'':139C ''c'':127C ''categor'':26C,59C ''classifi'':143C,203C ''data'':14C,27C,60C,180C,186C,193C ''desir'':163C ''detail'':216C ''direct'':241C ''distribut'':181C ''document'':230C ''end'':201C ''essenti'':5C ''exampl'':35C ''extract'':137C ''facebook'':44C ''featur'':33C,38C,54C ''first'':79C ''gamma'':129C ''gaussian'':116C ''give'':75C ''good'':51C,95C ''grid'':83C ''guess'':88C ''href'':224C ''implement'':235C ''inde'':178C ''input'':69C ''instagram'':48C ''instead'':85C ''k'':150C,162C ''kernel'':117C ''kind'':57C ''label'':41C,64C ''late'':102C ''length'':21C ''libsvm'':237C ''like'':73C ''look'':217C ''mail'':164C ''make'':132C ''margin'':109C ''mean'':158C ''negat'':173C,209C ''nofollow'':229C ''normal'':12C ''one'':37C ''otherwis'':198C ''p'':1C,78C,145C ''paramet'':90C,123C,140C ''pick'':99C ''posit'':169C ''practic'':96C ''proport'':197C ''read'':211C ''rel'':228C ''right'':8C ''sampl'':18C,151C,170C,174C,207C ''search'':84C,125C ''second'':146C ''section'':219C ''separ'':53C ''soft'':108C ''spam'':156C ''sure'':133C ''svm'':110C,199C ''svms'':233C ''systemat'':82C ''text'':17C ''track'':9C ''two'':67C,122C ''unbalanc'':185C ''use'':25C,80C,106C,242C ''vari'':20C ''weight'':191C,232C ''would'':72C ''www.cs.colostate.edu'':226C ''www.cs.colostate.edu/~asa/pdfs/howto.pdf'':225C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6471, 2, '2015-07-15 19:58:35.51', 1, NULL, NULL, '&lt;p&gt;As @David says, in your initial example, your confidence about C1 is the same in both cases. In your second example, you most certainly are less confident about the most-probable class in the second case, since the most-probable class is far less probable!&lt;/p&gt;

&lt;p&gt;You may have to unpack what you''re getting at when you say ''confidence'' then, since here you''re not using it as a term of art but an English word.&lt;/p&gt;

&lt;p&gt;I suspect you may be looking for the idea of &lt;a href="https://en.wikipedia.org/wiki/Entropy_(information_theory)" rel="nofollow"&gt;entropy&lt;/a&gt;, or uncertainty present in the distribution of all class probabilities. In your first example, it is indeed lower in the second case than the first. I don''t think what you''re getting at is just a function of the most-probable class, that is.&lt;/p&gt;
', NULL, NULL, '''/a'':102C ''/p'':49C,82C,149C ''/wiki/entropy_(information_theory)'':98C ''art'':77C ''c1'':12C ''case'':18C,38C,124C ''certain'':25C ''class'':34C,44C,111C,146C ''confid'':10C,28C,64C ''david'':3C ''distribut'':108C ''en.wikipedia.org'':97C ''en.wikipedia.org/wiki/entropy_(information_theory)'':96C ''english'':80C ''entropi'':101C ''exampl'':8C,22C,116C ''far'':46C ''first'':115C,127C ''function'':140C ''get'':59C,135C ''href'':95C ''idea'':92C ''inde'':119C ''initi'':7C ''less'':27C,47C ''look'':89C ''lower'':120C ''may'':52C,87C ''most-prob'':31C,41C,143C ''nofollow'':100C ''p'':1C,50C,83C ''present'':105C ''probabl'':33C,43C,48C,112C,145C ''re'':58C,69C,134C ''rel'':99C ''say'':4C,63C ''second'':21C,37C,123C ''sinc'':39C,66C ''suspect'':85C ''term'':75C ''think'':131C ''uncertainti'':104C ''unpack'':55C ''use'':71C ''word'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6477, 1, '2015-07-16 10:03:38.97', 0, 14, 'What data format is required for RegressionDataset in lib shark?', '&lt;p&gt;The format required for ClassificationDataset is straightforwardly a sequence of variable followed by a lable,but this form can''t be applied for RegressionDataset and it throws an exception like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; "[importCSVReaderSingleValues] problems parsing file (2)"
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The following is the code to parse a string in the data file&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;inline std::vector&amp;lt;std::vector&amp;lt;double&amp;gt; &amp;gt; importCSVReaderSingleValues(
   std::string const&amp;amp; contents,
    char separator,
    char comment = ''#''
) {
    std::string::const_iterator first = contents.begin();
    std::string::const_iterator last = contents.end();

    using namespace boost::spirit::qi;
    std::vector&amp;lt;std::vector&amp;lt;double&amp;gt; &amp;gt;  fileContents;

    double qnan = std::numeric_limits&amp;lt;double&amp;gt;::quiet_NaN();

    if(std::isspace(separator)){
        separator = 0;
    }

    bool r;
    if( separator == 0){
        r = phrase_parse(
            first, last,
            (
                +(double_ | (''?'' &amp;gt;&amp;gt;  attr(qnan) ))
            ) % eol &amp;gt;&amp;gt; -eol,
            (space-eol) | (comment &amp;gt;&amp;gt; *(char_ - eol) &amp;gt;&amp;gt; (eol| eoi)), fileContents
        );
    }
    else{
        r = phrase_parse(
            first, last,
            (
                (double_ | ((lit(''?'')| &amp;amp;lit(separator)) &amp;gt;&amp;gt;  attr(qnan))) % separator
           ) % eol &amp;gt;&amp;gt; -eol,
           (space-eol)| (comment &amp;gt;&amp;gt; *(char_ - eol) &amp;gt;&amp;gt; (eol| eoi)) , fileContents
       );
   }

   if(!r || first != last){
       throw SHARKEXCEPTION("[importCSVReaderSingleValues] problems parsing file (2)");
   }
   return fileContents;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;}&lt;/p&gt;
', 'machine-learning  regression', NULL, '''/code'':55C,215C ''/p'':47C,71C,218C ''/pre'':56C,216C ''0'':136C,141C ''2'':54C,212C ''amp'':88C,177C ''appli'':37C ''attr'':150C,182C ''bool'':137C ''boost'':108C ''char'':90C,92C,162C,195C ''classificationdataset'':20C ''code'':49C,62C,73C ''comment'':93C,159C,192C ''const'':87C,96C,102C ''content'':89C ''contents.begin'':99C ''contents.end'':105C ''data'':2A,69C ''doubl'':81C,117C,121C,127C,147C,175C ''els'':169C ''eoi'':167C,200C ''eol'':152C,155C,158C,163C,166C,185C,188C,191C,196C,199C ''except'':44C ''file'':53C,70C,211C ''filecont'':120C,168C,201C,214C ''first'':98C,145C,173C,204C ''follow'':27C,59C ''form'':33C ''format'':3A,17C ''gt'':82C,83C,118C,119C,128C,148C,149C,153C,154C,160C,161C,164C,165C,180C,181C,186C,187C,193C,194C,197C,198C ''importcsvreadersinglevalu'':50C,84C,208C ''inlin'':74C ''isspac'':133C ''iter'':97C,103C ''labl'':30C ''last'':104C,146C,174C,205C ''learn'':13B ''lib'':9A ''like'':45C ''limit'':125C ''lit'':176C,178C ''lt'':77C,80C,113C,116C,126C ''machin'':12B ''machine-learn'':11B ''namespac'':107C ''nan'':130C ''numer'':124C ''p'':15C,57C,217C ''pars'':52C,64C,144C,172C,210C ''phrase'':143C,171C ''pre'':48C,72C ''problem'':51C,209C ''qi'':110C ''qnan'':122C,151C,183C ''quiet'':129C ''r'':138C,142C,170C,203C ''regress'':14B ''regressiondataset'':7A,39C ''requir'':5A,18C ''return'':213C ''separ'':91C,134C,135C,140C,179C,184C ''sequenc'':24C ''shark'':10A ''sharkexcept'':207C ''space'':157C,190C ''space-eol'':156C,189C ''spirit'':109C ''std'':75C,78C,85C,94C,100C,111C,114C,123C,132C ''straightforward'':22C ''string'':66C,86C,95C,101C ''throw'':42C,206C ''use'':106C ''variabl'':26C ''vector'':76C,79C,112C,115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6487, 2, '2015-07-17 10:13:29.843', 3, NULL, NULL, '&lt;p&gt;You have a nice free course about it on DataCamp&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;a href="https://www.datacamp.com/courses/data-table-data-manipulation-r-tutorial" rel="nofollow"&gt;Data Analysis in R, the data.table Way&lt;/a&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The author of the course is Matt Dowle, the guy that actually wrote the data.table package.&lt;/p&gt;

&lt;p&gt;Hope that helps.&lt;/p&gt;
', NULL, NULL, '''/a'':29C ''/blockquote'':31C ''/courses/data-table-data-manipulation-r-tutorial'':19C ''/p'':12C,30C,49C,54C ''actual'':44C ''analysi'':23C ''author'':34C ''blockquot'':13C ''cours'':7C,37C ''data'':22C ''data.table'':27C,47C ''datacamp'':11C ''dowl'':40C ''free'':6C ''guy'':42C ''help'':53C ''hope'':51C ''href'':16C ''matt'':39C ''nice'':5C ''nofollow'':21C ''p'':1C,14C,32C,50C ''packag'':48C ''r'':25C ''rel'':20C ''way'':28C ''wrote'':45C ''www.datacamp.com'':18C ''www.datacamp.com/courses/data-table-data-manipulation-r-tutorial'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6478, 1, '2015-07-16 11:06:49.757', 4, 93, 'How to extract information from plot images?', '&lt;p&gt;Are there any free tools or libraries that can understand a plot image file automatically? Things like type detection (line, bar, scatter), as well as label and axis scaling detection.&lt;/p&gt;

&lt;p&gt;In the tools I have found so far, one has to do manual configuration. I''m asking if it is possible in our AI driven world to let a machine do this.&lt;/p&gt;

&lt;p&gt;If there are none, I will start a project for myself as it seems like a challenging but instructive AI task. Is this effort doomed from start? Are there any helpful libraries in C++ or Java and theoretical algorithms one could consider?&lt;/p&gt;

&lt;p&gt;Thanks,&lt;br&gt;
Tomas&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':42C,76C,120C,125C ''ai'':67C,97C ''algorithm'':116C ''ask'':60C ''automat'':26C ''axi'':39C ''bar'':32C ''br'':123C ''c'':111C ''challeng'':94C ''configur'':57C ''consid'':119C ''could'':118C ''detect'':30C,41C ''doom'':102C ''driven'':68C ''effort'':101C ''extract'':3A ''far'':51C ''file'':25C ''found'':49C ''free'':15C ''help'':108C ''imag'':7A,24C ''inform'':4A ''instruct'':96C ''java'':113C ''label'':37C ''learn'':10B ''let'':71C ''librari'':18C,109C ''like'':28C,92C ''line'':31C ''m'':59C ''machin'':9B,73C ''machine-learn'':8B ''manual'':56C ''none'':81C ''one'':52C,117C ''p'':11C,43C,77C,121C ''plot'':6A,23C ''possibl'':64C ''project'':86C ''scale'':40C ''scatter'':33C ''seem'':91C ''start'':84C,104C ''task'':98C ''thank'':122C ''theoret'':115C ''thing'':27C ''toma'':124C ''tool'':16C,46C ''type'':29C ''understand'':21C ''well'':35C ''world'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6479, 1, '2015-07-16 12:32:00.63', 1, 46, 'Where can I get a list of ATM identifiers that I can map to Geographic Location?', '&lt;p&gt;Most bank statements (at least mine here in the UK) contain coded information for cash withdrawls such as:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;CASH RB SCOT JAN11 TESCO STREAT@08:56
CASH SAINSBY JUN23 STREATHAM C @10:46
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m fairly sure this can be broken down into the following tuple (tran_type, operator, date, location, @time)&lt;/p&gt;

&lt;p&gt;What I''d like to do is translate from (operator,location) to GPS coordinates, a postcode, or some other geographic identifier.&lt;/p&gt;

&lt;p&gt;Is anyone aware of where I might find such an information source? Is it commercial, or open-source?&lt;/p&gt;
', 'dataset  data', NULL, '''/code'':56C ''/p'':38C,78C,101C,122C ''/pre'':57C ''08'':47C ''10'':54C ''46'':55C ''56'':48C ''anyon'':104C ''atm'':8A ''awar'':105C ''bank'':21C ''broken'':66C ''c'':53C ''cash'':34C,41C,49C ''code'':31C,40C ''commerci'':117C ''contain'':30C ''coordin'':93C ''d'':82C ''data'':18B ''dataset'':17B ''date'':75C ''fair'':61C ''find'':110C ''follow'':70C ''geograph'':15A,99C ''get'':4A ''gps'':92C ''identifi'':9A,100C ''inform'':32C,113C ''jan11'':44C ''jun23'':51C ''least'':24C ''like'':83C ''list'':6A ''locat'':16A,76C,90C ''m'':60C ''map'':13A ''might'':109C ''mine'':25C ''open'':120C ''open-sourc'':119C ''oper'':74C,89C ''p'':19C,58C,79C,102C ''postcod'':95C ''pre'':39C ''rb'':42C ''sainsbi'':50C ''scot'':43C ''sourc'':114C,121C ''statement'':22C ''streat'':46C ''streatham'':52C ''sure'':62C ''tesco'':45C ''time'':77C ''tran'':72C ''translat'':87C ''tupl'':71C ''type'':73C ''uk'':29C ''withdrawl'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9959, 1, '2016-01-26 01:51:21.14', 1, 46, 'Autoencoders for feature creation', '&lt;p&gt;When using an auto encoder to create non-linear dimensional reduced featires, is it more common to use the output of the network (the prediction of the input features) or to use the weights from the (or 1 of the if there are multiple) hidden layers? If the hidden layer is used, do you use the hidden layer activation as features or weights from the hidden layer to the output?&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/p'':81C ''1'':48C ''activ'':69C ''auto'':13C ''autoencod'':1A ''common'':26C ''creat'':16C ''creation'':4A ''dimension'':20C ''encod'':14C ''featir'':22C ''featur'':3A,39C,71C ''hidden'':55C,59C,67C,76C ''input'':38C ''layer'':56C,60C,68C,77C ''learn'':7B ''linear'':19C ''machin'':6B ''machine-learn'':5B ''multipl'':54C ''network'':33C ''neuralnetwork'':8B ''non'':18C ''non-linear'':17C ''output'':30C,80C ''p'':9C ''predict'':35C ''reduc'':21C ''use'':11C,28C,42C,62C,65C ''weight'':44C,73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6481, 2, '2015-07-16 13:52:17.24', 3, NULL, NULL, '&lt;p&gt;In the general sense this is most definitely a non-trivial problem and any full featured solution will require a considerable degree of work.&lt;/p&gt;

&lt;p&gt;Success will probably be dependent on how much you constrain your problem.
Monochrome will be easier than color.
Limited font, font size, line and marker styles will help.&lt;/p&gt;

&lt;p&gt;The more constrained, the more chance of success.&lt;/p&gt;

&lt;p&gt;For line detection, such as axes, you might want to consider the &lt;a href="https://en.wikipedia.org/wiki/Hough_transform" rel="nofollow"&gt;Hough&lt;/a&gt; and &lt;a href="https://en.wikipedia.org/wiki/Radon_transform" rel="nofollow"&gt;Radon&lt;/a&gt; transforms.&lt;/p&gt;

&lt;p&gt;Here are some stackexchange questions covering some of the topics you might want to address, sadly no bargraphs, some have a manual component which you might think how to automate. They are a good starting point to review some of the difficulties involved in the problem.&lt;/p&gt;

&lt;p&gt;Selecting a curve:
&lt;a href="http://mathematica.stackexchange.com/questions/44355/how-to-make-a-curve-selectable-from-a-scaned-image-and-convert-it-to-a-list-of-c"&gt;http://mathematica.stackexchange.com/questions/44355/how-to-make-a-curve-selectable-from-a-scaned-image-and-convert-it-to-a-list-of-c&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Datapoints and Axes:
&lt;a href="http://mathematica.stackexchange.com/questions/1524/recovering-data-points-from-an-image"&gt;http://mathematica.stackexchange.com/questions/1524/recovering-data-points-from-an-image&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Scatter:
&lt;a href="http://mathematica.stackexchange.com/questions/26356/how-do-i-find-the-coordinates-of-points-in-this-image"&gt;http://mathematica.stackexchange.com/questions/26356/how-do-i-find-the-coordinates-of-points-in-this-image&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Datapoints:
&lt;a href="http://mathematica.stackexchange.com/questions/3831/how-can-i-extract-data-points-from-a-black-and-white-image"&gt;http://mathematica.stackexchange.com/questions/3831/how-can-i-extract-data-points-from-a-black-and-white-image&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://mathematica.stackexchange.com/questions/14444/obtain-data-points-from-a-graph-on-an-image-without-axes"&gt;http://mathematica.stackexchange.com/questions/14444/obtain-data-points-from-a-graph-on-an-image-without-axes&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':89C,99C ''/p'':27C,57C,67C,101C,149C ''/questions/14444/obtain-data-points-from-a-graph-on-an-image-without-axes'':199C ''/questions/14444/obtain-data-points-from-a-graph-on-an-image-without-axes&lt;/a&gt;&lt;/p&gt;'':202C ''/questions/1524/recovering-data-points-from-an-image'':170C ''/questions/1524/recovering-data-points-from-an-image&lt;/a&gt;&lt;/p&gt;'':173C ''/questions/26356/how-do-i-find-the-coordinates-of-points-in-this-image'':180C ''/questions/26356/how-do-i-find-the-coordinates-of-points-in-this-image&lt;/a&gt;&lt;/p&gt;'':183C ''/questions/3831/how-can-i-extract-data-points-from-a-black-and-white-image'':190C ''/questions/3831/how-can-i-extract-data-points-from-a-black-and-white-image&lt;/a&gt;&lt;/p&gt;'':193C ''/questions/44355/how-to-make-a-curve-selectable-from-a-scaned-image-and-convert-it-to-a-list-of-c'':158C ''/questions/44355/how-to-make-a-curve-selectable-from-a-scaned-image-and-convert-it-to-a-list-of-c&lt;/a&gt;&lt;/p&gt;'':161C ''/wiki/hough_transform'':85C ''/wiki/radon_transform'':95C ''address'':117C ''autom'':132C ''axe'':74C,165C ''bargraph'':120C ''chanc'':64C ''color'':46C ''compon'':125C ''consid'':79C ''consider'':23C ''constrain'':38C,61C ''cover'':108C ''curv'':153C ''datapoint'':163C,185C ''definit'':9C ''degre'':24C ''depend'':33C ''detect'':71C ''difficulti'':144C ''easier'':44C ''en.wikipedia.org'':84C,94C ''en.wikipedia.org/wiki/hough_transform'':83C ''en.wikipedia.org/wiki/radon_transform'':93C ''featur'':18C ''font'':48C,49C ''full'':17C ''general'':4C ''good'':136C ''help'':56C ''hough'':88C ''href'':82C,92C,155C,167C,177C,187C,196C ''involv'':145C ''limit'':47C ''line'':51C,70C ''manual'':124C ''marker'':53C ''mathematica.stackexchange.com'':157C,160C,169C,172C,179C,182C,189C,192C,198C,201C ''mathematica.stackexchange.com/questions/14444/obtain-data-points-from-a-graph-on-an-image-without-axes'':197C ''mathematica.stackexchange.com/questions/14444/obtain-data-points-from-a-graph-on-an-image-without-axes&lt;/a&gt;&lt;/p&gt;'':200C ''mathematica.stackexchange.com/questions/1524/recovering-data-points-from-an-image'':168C ''mathematica.stackexchange.com/questions/1524/recovering-data-points-from-an-image&lt;/a&gt;&lt;/p&gt;'':171C ''mathematica.stackexchange.com/questions/26356/how-do-i-find-the-coordinates-of-points-in-this-image'':178C ''mathematica.stackexchange.com/questions/26356/how-do-i-find-the-coordinates-of-points-in-this-image&lt;/a&gt;&lt;/p&gt;'':181C ''mathematica.stackexchange.com/questions/3831/how-can-i-extract-data-points-from-a-black-and-white-image'':188C ''mathematica.stackexchange.com/questions/3831/how-can-i-extract-data-points-from-a-black-and-white-image&lt;/a&gt;&lt;/p&gt;'':191C ''mathematica.stackexchange.com/questions/44355/how-to-make-a-curve-selectable-from-a-scaned-image-and-convert-it-to-a-list-of-c'':156C ''mathematica.stackexchange.com/questions/44355/how-to-make-a-curve-selectable-from-a-scaned-image-and-convert-it-to-a-list-of-c&lt;/a&gt;&lt;/p&gt;'':159C ''might'':76C,114C,128C ''monochrom'':41C ''much'':36C ''nofollow'':87C,97C ''non'':12C ''non-trivi'':11C ''p'':1C,28C,58C,68C,102C,150C,162C,174C,184C,194C ''point'':138C ''probabl'':31C ''problem'':14C,40C,148C ''question'':107C ''radon'':98C ''rel'':86C,96C ''requir'':21C ''review'':140C ''sad'':118C ''scatter'':175C ''select'':151C ''sens'':5C ''size'':50C ''solut'':19C ''stackexchang'':106C ''start'':137C ''style'':54C ''success'':29C,66C ''think'':129C ''topic'':112C ''transform'':100C ''trivial'':13C ''want'':77C,115C ''work'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6482, 2, '2015-07-16 14:50:34.69', 1, NULL, NULL, '&lt;p&gt;&lt;a href="http://ipython.org/ipython-doc/1/interactive/notebook.html#keyboard-shortcuts" rel="nofollow"&gt;IPython notebook Keyboard shortcuts&lt;/a&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;Shift-Enter&lt;/code&gt;: run cell&lt;/li&gt;
&lt;li&gt;&lt;code&gt;Ctrl-Enter&lt;/code&gt;: run cell in-place&lt;/li&gt;
&lt;li&gt;&lt;code&gt;Alt-Enter&lt;/code&gt;: run cell, insert below&lt;/li&gt;
&lt;li&gt;&lt;code&gt;Ctrl-m&lt;/code&gt;: This is the prefix for all other shortcuts, which consist of Ctrl-m followed by a single letter or character. For example, if you type Ctrl-m h (that is, the sole letter h after Ctrl-m), IPython will show you all the available keyboard shortcuts.
&lt;img src="http://i.stack.imgur.com/WlnUP.png" alt="enter image description here"&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':13C ''/code'':21C,30C,42C,53C ''/ipython-doc/1/interactive/notebook.html#keyboard-shortcuts'':6C ''/li'':24C,36C,47C,113C ''/p'':14C ''/ul'':114C ''/wlnup.png'':107C ''alt'':40C,108C ''alt-ent'':39C ''avail'':100C ''cell'':23C,32C,44C ''charact'':74C ''code'':17C,26C,38C,49C ''consist'':63C ''ctrl'':28C,51C,66C,81C,92C ''ctrl-enter'':27C ''ctrl-m'':50C,65C,80C,91C ''descript'':111C ''enter'':20C,29C,41C,109C ''exampl'':76C ''follow'':68C ''h'':83C,89C ''href'':3C ''i.stack.imgur.com'':106C ''i.stack.imgur.com/wlnup.png'':105C ''imag'':110C ''img'':103C ''in-plac'':33C ''insert'':45C ''ipython'':9C,94C ''ipython.org'':5C ''ipython.org/ipython-doc/1/interactive/notebook.html#keyboard-shortcuts'':4C ''keyboard'':11C,101C ''letter'':72C,88C ''li'':16C,25C,37C,48C ''m'':52C,67C,82C,93C ''nofollow'':8C ''notebook'':10C ''p'':1C ''place'':35C ''prefix'':57C ''rel'':7C ''run'':22C,31C,43C ''shift'':19C ''shift-ent'':18C ''shortcut'':12C,61C,102C ''show'':96C ''singl'':71C ''sole'':87C ''src'':104C ''type'':79C ''ul'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6484, 2, '2015-07-16 23:06:47.217', 1, NULL, NULL, '&lt;p&gt;The &lt;a href="https://www.cs.helsinki.fi/u/htoivone/pubs/dmkd97episodes.pdf" rel="nofollow"&gt;paper that you cited&lt;/a&gt; references two other papers that explain the method you are looking for &lt;a href="https://www.aaai.org/Papers/KDD/1996/KDD96-024.pdf" rel="nofollow"&gt;here&lt;/a&gt; and &lt;a href="http://link.springer.com/article/10.1023%2FA%3A1009748302351" rel="nofollow"&gt;here&lt;/a&gt;.  The second one is behind a pay wall.&lt;/p&gt;

&lt;p&gt;But... these look like deterministic methods to me, and I suggest you use a machine learning method instead.  You basically need to separate events from each other using some sort of clustering algorithm.  I suggest you use &lt;code&gt;DBSCAN&lt;/code&gt; and adjust the parameters based on whether you want every event to be assigned to an episode or not.&lt;/p&gt;

&lt;p&gt;Another algorithm that might work well is simple &lt;code&gt;k-means&lt;/code&gt; clustering with the addition of an &lt;code&gt;elbow method&lt;/code&gt; or &lt;code&gt;silhouette score&lt;/code&gt; to determine the optimal number of clusters. &lt;/p&gt;
', NULL, NULL, '''/a'':14C,35C,45C ''/article/10.1023%2fa%3a1009748302351'':41C ''/code'':95C,129C,139C,144C ''/p'':54C,115C,152C ''/papers/kdd/1996/kdd96-024.pdf'':31C ''/u/htoivone/pubs/dmkd97episodes.pdf'':7C ''addit'':133C ''adjust'':97C ''algorithm'':88C,118C ''anoth'':117C ''assign'':109C ''base'':100C ''basic'':75C ''behind'':50C ''cite'':13C ''cluster'':87C,130C,151C ''code'':93C,125C,136C,141C ''dbscan'':94C ''determin'':146C ''determinist'':60C ''elbow'':137C ''episod'':112C ''event'':79C,106C ''everi'':105C ''explain'':20C ''href'':4C,28C,38C ''instead'':73C ''k'':127C ''k-mean'':126C ''learn'':71C ''like'':59C ''link.springer.com'':40C ''link.springer.com/article/10.1023%2fa%3a1009748302351'':39C ''look'':25C,58C ''machin'':70C ''mean'':128C ''method'':22C,61C,72C,138C ''might'':120C ''need'':76C ''nofollow'':9C,33C,43C ''number'':149C ''one'':48C ''optim'':148C ''p'':1C,55C,116C ''paper'':10C,18C ''paramet'':99C ''pay'':52C ''refer'':15C ''rel'':8C,32C,42C ''score'':143C ''second'':47C ''separ'':78C ''silhouett'':142C ''simpl'':124C ''sort'':85C ''suggest'':66C,90C ''two'':16C ''use'':68C,83C,92C ''wall'':53C ''want'':104C ''well'':122C ''whether'':102C ''work'':121C ''www.aaai.org'':30C ''www.aaai.org/papers/kdd/1996/kdd96-024.pdf'':29C ''www.cs.helsinki.fi'':6C ''www.cs.helsinki.fi/u/htoivone/pubs/dmkd97episodes.pdf'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6507, 1, '2015-07-20 06:15:34.527', 1, 52, 'how to calculate recency(RFM) from a given date attribute in R', '&lt;pre&gt;&lt;code&gt;custid  tot_prod       tot_rev          Last_tran   total_trans
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;1   1002          13       1465.25         11/23/2011       4
2   1003          2         353.25          1/1/2011        1
3   1004          12        2466.5          11/25/2011      12&lt;/p&gt;

&lt;p&gt;Above is the Retail dataset of customer. I need to calculate recency(a new attribute when the customer is last seen) from last transaction(last_tran)  in terms of month.i.e. he purchased in last 3 months or last 6 months or may be one year in R. please suggest.    &lt;/p&gt;
', 'r', NULL, '''/code'':25C ''/p'':46C,97C ''/pre'':26C ''1'':28C,39C ''1/1/2011'':38C ''1002'':29C ''1003'':35C ''1004'':41C ''11/23/2011'':32C ''11/25/2011'':44C ''12'':42C,45C ''13'':30C ''1465.25'':31C ''2'':34C,36C ''2466.5'':43C ''3'':40C,82C ''353.25'':37C ''4'':33C ''6'':86C ''attribut'':10A,62C ''calcul'':3A,58C ''code'':15C ''custid'':16C ''custom'':54C,65C ''dataset'':52C ''date'':9A ''given'':8A ''last'':21C,67C,70C,72C,81C,85C ''may'':89C ''month'':83C,87C ''month.i.e'':77C ''need'':56C ''new'':61C ''one'':91C ''p'':27C,47C ''pleas'':95C ''pre'':14C ''prod'':18C ''purchas'':79C ''r'':12A,13B,94C ''recenc'':4A,59C ''retail'':51C ''rev'':20C ''rfm'':5A ''seen'':68C ''suggest'':96C ''term'':75C ''tot'':17C,19C ''total'':23C ''tran'':22C,24C,73C ''transact'':71C ''year'':92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6508, 1, '2015-07-20 08:03:21.013', 3, 569, 'K-means incoherent behaviour choosing K with Elbow method, BIC, variance explained and silhouette', '&lt;p&gt;I''m trying to cluster some vectors with 90 features with K-means. Since this algorithm asks me the number of clusters, I want to validate my choice with some nice math.
I expect to have from 8 to 10 clusters. The features are Z-score scaled.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Elbow method and variance explained&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from scipy.spatial.distance import cdist, pdist
from sklearn.cluster import KMeans

K = range(1,50)
KM = [KMeans(n_clusters=k).fit(dt_trans) for k in K]
centroids = [k.cluster_centers_ for k in KM]

D_k = [cdist(dt_trans, cent, ''euclidean'') for cent in centroids]
cIdx = [np.argmin(D,axis=1) for D in D_k]
dist = [np.min(D,axis=1) for D in D_k]
avgWithinSS = [sum(d)/dt_trans.shape[0] for d in dist]

# Total with-in sum of square
wcss = [sum(d**2) for d in dist]
tss = sum(pdist(dt_trans)**2)/dt_trans.shape[0]
bss = tss-wcss

kIdx = 10-1

# elbow curve
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(K, avgWithinSS, ''b*-'')
ax.plot(K[kIdx], avgWithinSS[kIdx], marker=''o'', markersize=12, 
markeredgewidth=2, markeredgecolor=''r'', markerfacecolor=''None'')
plt.grid(True)
plt.xlabel(''Number of clusters'')
plt.ylabel(''Average within-cluster sum of squares'')
plt.title(''Elbow for KMeans clustering'')

fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(K, bss/tss*100, ''b*-'')
plt.grid(True)
plt.xlabel(''Number of clusters'')
plt.ylabel(''Percentage of variance explained'')
plt.title(''Elbow for KMeans clustering'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/VZZgt.png" alt="Elbow method"&gt;
&lt;img src="http://i.stack.imgur.com/NIyI2.png" alt="Variance"&gt;&lt;/p&gt;

&lt;p&gt;From these two pictures, it seems that the number of clusters never stops :D. Strange! Where is the elbow? How can I choose K?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Bayesian information criterion&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This methods comes directly from X-means and uses the &lt;a href="http://Bayesian%20information%20criterion" rel="nofollow"&gt;BIC&lt;/a&gt; to choose the number of clusters. &lt;a href="http://stackoverflow.com/questions/1793532/how-do-i-determine-k-when-using-k-means-clustering"&gt;another ref&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    from sklearn.metrics import euclidean_distances
from sklearn.cluster import KMeans

def bic(clusters, centroids):
    num_points = sum(len(cluster) for cluster in clusters)
    num_dims = clusters[0][0].shape[0]
    log_likelihood = _loglikelihood(num_points, num_dims, clusters, centroids)
    num_params = _free_params(len(clusters), num_dims)
    return log_likelihood - num_params / 2.0 * np.log(num_points)


def _free_params(num_clusters, num_dims):
    return num_clusters * (num_dims + 1)


def _loglikelihood(num_points, num_dims, clusters, centroids):
    ll = 0
    for cluster in clusters:
        fRn = len(cluster)
        t1 = fRn * np.log(fRn)
        t2 = fRn * np.log(num_points)
        variance = _cluster_variance(num_points, clusters, centroids) or np.nextafter(0, 1)
        t3 = ((fRn * num_dims) / 2.0) * np.log((2.0 * np.pi) * variance)
        t4 = (fRn - 1.0) / 2.0
        ll += t1 - t2 - t3 - t4
    return ll

def _cluster_variance(num_points, clusters, centroids):
    s = 0
    denom = float(num_points - len(centroids))
    for cluster, centroid in zip(clusters, centroids):
        distances = euclidean_distances(cluster, centroid)
        s += (distances*distances).sum()
    return s / denom

from scipy.spatial import distance
def compute_bic(kmeans,X):
    """
    Computes the BIC metric for a given clusters

    Parameters:
    -----------------------------------------
    kmeans:  List of clustering object from scikit learn

    X     :  multidimension np array of data points

    Returns:
    -----------------------------------------
    BIC value
    """
    # assign centers and labels
    centers = [kmeans.cluster_centers_]
    labels  = kmeans.labels_
    #number of clusters
    m = kmeans.n_clusters
    # size of the clusters
    n = np.bincount(labels)
    #size of data set
    N, d = X.shape

    #compute variance for all clusters beforehand
    cl_var = (1.0 / (N - m) / d) * sum([sum(distance.cdist(X[np.where(labels == i)], [centers[0][i]], ''euclidean'')**2) for i in range(m)])

    const_term = 0.5 * m * np.log(N) * (d+1)

    BIC = np.sum([n[i] * np.log(n[i]) -
               n[i] * np.log(N) -
             ((n[i] * d) / 2) * np.log(2*np.pi*cl_var) -
             ((n[i] - 1) * d/ 2) for i in range(m)]) - const_term

    return(BIC)



sns.set_style("ticks")
sns.set_palette(sns.color_palette("Blues_r"))
bics = []
for n_clusters in range(2,50):
    kmeans = KMeans(n_clusters=n_clusters)
    kmeans.fit(dt_trans)

    labels = kmeans.labels_
    centroids = kmeans.cluster_centers_

    clusters = {}
    for i,d in enumerate(kmeans.labels_):
        if d not in clusters:
            clusters[d] = []
        clusters[d].append(dt_trans[i])

    bics.append(compute_bic(kmeans,dt_trans))#-bic(clusters.values(), centroids))

plt.plot(bics)
plt.ylabel("BIC score")
plt.xlabel("k")
plt.title("BIC scoring for K-means cell''s behaviour")
sns.despine()
#plt.savefig(''figures/K-means-BIC.pdf'', format=''pdf'', dpi=330,bbox_inches=''tight'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/lon6L.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Same problem here... What is K?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Silhouette&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    from sklearn.metrics import silhouette_score

s = []
for n_clusters in range(2,30):
    kmeans = KMeans(n_clusters=n_clusters)
    kmeans.fit(dt_trans)

    labels = kmeans.labels_
    centroids = kmeans.cluster_centers_

    s.append(silhouette_score(dt_trans, labels, metric=''euclidean''))

plt.plot(s)
plt.ylabel("Silouette")
plt.xlabel("k")
plt.title("Silouette for K-means cell''s behaviour")
sns.despine()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/5Znt5.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Alleluja! Here it seems to make sense and this is what I expect. But why is this different from the others?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6490, 1, '2015-07-17 14:17:23.207', 1, 26, 'Parameter estimation: reduce time', '&lt;p&gt;Sorry if this is a duplicate.&lt;/p&gt;

&lt;p&gt;I have a two-class prediction model; it has &lt;code&gt;n&lt;/code&gt; configurable (numeric) parameters. The model can work pretty well if you tune those parameters properly, but the specific values for those parameters are hard to find. I used grid search for that (providing, say, &lt;code&gt;m&lt;/code&gt; values for each parameter). This yields &lt;code&gt;m ^ n&lt;/code&gt; times to learn, and it is &lt;em&gt;very&lt;/em&gt; time-consuming even when run in parallel on a machine with 24 cores.&lt;/p&gt;

&lt;p&gt;I tried fixing all parameters but one and changing this only one parameter (which yields &lt;code&gt;m Ã n&lt;/code&gt; times), but it''s not obvious for me what to do with the results I got. This is a sample plot of precision (triangles) and recall (dots) for negative (red) and positive (blue) samples:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/NTMtd.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Simply taking the "winner" values for each parameter obtained this way and combining them doesn''t lead to best (or even good) prediction results. I thought about building regression on parameter sets with precision/recall as dependent variable, but I don''t think that regression with more than 5 independent variables will be much faster than grid search scenario. &lt;/p&gt;

&lt;p&gt;What would you propose to find good parameter values, but with reasonable estimation time?  &lt;/p&gt;
', 'parameter', NULL, '''/code'':27C,64C,74C,119C ''/em'':83C ''/ntmtd.png'':160C ''/p'':13C,98C,154C,166C,226C,242C ''24'':96C ''5'':215C ''alt'':161C ''best'':186C ''blue'':152C ''build'':195C ''chang'':108C ''class'':20C ''code'':25C,62C,71C,115C ''combin'':180C ''configur'':28C ''consum'':86C ''core'':97C ''depend'':203C ''descript'':164C ''doesn'':182C ''dot'':146C ''duplic'':12C ''em'':81C ''enter'':162C ''estim'':2A,240C ''even'':87C,188C ''faster'':221C ''find'':53C,233C ''fix'':102C ''good'':189C,234C ''got'':135C ''grid'':56C,223C ''hard'':51C ''i.stack.imgur.com'':159C ''i.stack.imgur.com/ntmtd.png'':158C ''imag'':163C ''img'':156C ''independ'':216C ''lead'':184C ''learn'':77C ''m'':63C,72C,116C ''machin'':94C ''model'':22C,32C ''much'':220C ''n'':26C,73C,118C ''negat'':148C ''numer'':29C ''obtain'':176C ''obvious'':125C ''one'':106C,111C ''p'':6C,14C,99C,155C,167C,227C ''parallel'':91C ''paramet'':1A,5B,30C,41C,49C,68C,104C,112C,175C,198C,235C ''plot'':140C ''posit'':151C ''precis'':142C ''precision/recall'':201C ''predict'':21C,190C ''pretti'':35C ''proper'':42C ''propos'':231C ''provid'':60C ''reason'':239C ''recal'':145C ''red'':149C ''reduc'':3A ''regress'':196C,211C ''result'':133C,191C ''run'':89C ''sampl'':139C,153C ''say'':61C ''scenario'':225C ''search'':57C,224C ''set'':199C ''simpli'':168C ''sorri'':7C ''specif'':45C ''src'':157C ''take'':169C ''think'':209C ''thought'':193C ''time'':4A,75C,85C,120C,241C ''time-consum'':84C ''tri'':101C ''triangl'':143C ''tune'':39C ''two'':19C ''two-class'':18C ''use'':55C ''valu'':46C,65C,172C,236C ''variabl'':204C,217C ''way'':178C ''well'':36C ''winner'':171C ''work'':34C ''would'':229C ''yield'':70C,114C ''ã'':117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6491, 2, '2015-07-17 17:39:28.3', 1, NULL, NULL, '&lt;p&gt;If an exhaustive nonlinear scan is too expensive and a linear scan doesn''t yield the best results then I suggest you try a stochastic nonlinear search i.e. a random search for hyperparameter optimization.  &lt;/p&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/" rel="nofollow"&gt;Scikit learn&lt;/a&gt; has a user friendly description in its user guide.&lt;/p&gt;

&lt;p&gt;Here is a &lt;a href="http://jmlr.csail.mit.edu/papers/volume13/bergstra12a/bergstra12a.pdf" rel="nofollow"&gt;paper on random search for hyperparameter optimization&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':45C,74C ''/p'':36C,55C,75C ''/papers/volume13/bergstra12a/bergstra12a.pdf'':64C ''best'':18C ''descript'':50C ''doesn'':14C ''exhaust'':4C ''expens'':9C ''friend'':49C ''guid'':54C ''href'':39C,61C ''hyperparamet'':34C,72C ''i.e'':29C ''jmlr.csail.mit.edu'':63C ''jmlr.csail.mit.edu/papers/volume13/bergstra12a/bergstra12a.pdf'':62C ''learn'':44C ''linear'':12C ''nofollow'':42C,66C ''nonlinear'':5C,27C ''optim'':35C,73C ''p'':1C,37C,56C ''paper'':67C ''random'':31C,69C ''rel'':41C,65C ''result'':19C ''scan'':6C,13C ''scikit'':43C ''scikit-learn.org'':40C ''search'':28C,32C,70C ''stochast'':26C ''suggest'':22C ''tri'':24C ''user'':48C,53C ''yield'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8909, 1, '2015-11-19 06:04:53.053', 2, 538, 'Best Julia library for neural networks', '&lt;p&gt;I have been using this library for basic neural network construction and analysis.&lt;/p&gt;

&lt;p&gt;However, it does not have support for building multi-layered neural networks, etc.&lt;/p&gt;

&lt;p&gt;So, I would like to know of any nice libraries for doing advanced neural networks and Deep Learning in Julia.&lt;/p&gt;
', 'machine-learning  neuralnetwork  julia', NULL, '''/p'':26C,42C,64C ''advanc'':56C ''analysi'':25C ''basic'':20C ''best'':1A ''build'':35C ''construct'':23C ''deep'':60C ''etc'':41C ''howev'':28C ''julia'':2A,11B,63C ''know'':49C ''layer'':38C ''learn'':9B,61C ''librari'':3A,18C,53C ''like'':47C ''machin'':8B ''machine-learn'':7B ''multi'':37C ''multi-lay'':36C ''network'':6A,22C,40C,58C ''neural'':5A,21C,39C,57C ''neuralnetwork'':10B ''nice'':52C ''p'':12C,27C,43C ''support'':33C ''use'':16C ''would'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6492, 1, '2015-07-17 18:50:39.33', 3, 105, 'How to calculate most frequent value combinations', '&lt;p&gt;I have the following CSV data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;shot_id,round_id,hole,shotType,clubType,desiredShape,lineDirection,shotQuality,note
48,2,1,tee,driver,straight,straight,good,
49,2,1,approach,iron,straight,right,bad,
50,2,1,approach,wedge,straight,straight,bad,
51,2,1,approach,wedge,straight,straight,bad,
52,2,1,putt,putter,straight,straight,good,
53,2,1,putt,putter,straight,straight,good,
54,2,2,tee,driver,draw,straight,good,
55,2,2,approach,iron,draw,straight,good,
56,2,2,putt,putter,straight,straight,good,
57,2,2,putt,putter,straight,straight,good,
58,2,3,tee,driver,draw,straight,good,
59,2,3,approach,iron,straight,right,good,
60,2,3,chip,wedge,straight,straight,good,
61,2,3,putt,putter,straight,straight,good,
62,2,4,tee,iron,straight,straight,good,
63,2,4,putt,putter,straight,straight,good,
64,2,4,putt,putter,straight,straight,good,
65,2,5,tee,driver,straight,left,good,
66,2,5,approach,wedge,straight,straight,good,
67,2,5,putt,putter,straight,straight,bad,
68,2,5,putt,putter,straight,straight,good,
69,2,6,tee,driver,draw,straight,bad,
70,2,6,approach,hybrid,draw,straight,good,
71,2,6,putt,putter,straight,straight,good,
72,2,6,putt,putter,straight,straight,good,
73,2,7,tee,driver,straight,straight,good,
74,2,7,approach,wood,fade,straight,good,
75,2,7,approach,wedge,straight,straight,bad,long
76,2,7,putt,putter,straight,straight,good,
77,2,7,putt,putter,straight,straight,good,
78,2,8,tee,iron,straight,right,bad,
79,2,8,approach,wedge,straight,straight,good,
80,2,8,putt,putter,straight,straight,bad,
81,2,9,tee,driver,straight,straight,good,
82,2,9,approach,iron,straight,straight,good,
83,2,9,approach,wedge,straight,straight,bad,
84,2,9,putt,putter,straight,straight,good,
85,2,9,putt,putter,straight,straight,good,
86,2,10,tee,driver,straight,left,good,
87,2,10,approach,iron,straight,left,good,
88,2,10,chip,wedge,straight,straight,good,
89,2,10,putt,putter,straight,straight,good,
90,2,10,putt,putter,straight,straight,good,
91,2,11,tee,driver,draw,straight,good,
92,2,11,approach,iron,draw,straight,good,
93,2,11,putt,putter,straight,straight,good,
94,2,11,putt,putter,straight,straight,good,
95,2,12,tee,iron,draw,straight,good,
96,2,12,putt,putter,straight,straight,good,
97,2,12,putt,putter,straight,straight,good,
98,2,13,tee,driver,draw,straight,good,
99,2,13,approach,wood,straight,straight,bad,topped
100,2,13,putt,putter,straight,straight,good,
101,2,13,putt,putter,straight,straight,good,
102,2,14,tee,driver,draw,straight,good,
103,2,14,approach,wood,straight,straight,bad,
104,2,14,approach,iron,draw,straight,good,
105,2,14,approach,wedge,straight,straight,bad,
106,2,14,putt,putter,straight,straight,bad,
107,2,14,putt,putter,straight,straight,good,
108,2,15,tee,iron,draw,right,bad,
109,2,15,approach,wedge,straight,straight,good,
110,2,15,putt,putter,straight,straight,good,
111,2,15,putt,putter,straight,straight,good,
112,2,16,tee,driver,draw,right,good,
113,2,16,approach,iron,straight,left,bad,
114,2,16,approach,wedge,straight,left,bad,
115,2,16,putt,putter,straight,straight,good,
116,2,17,tee,driver,straight,straight,good,
117,2,17,approach,wood,straight,right,bad,
118,2,17,approach,wedge,straight,straight,good,
119,2,17,putt,putter,straight,straight,good,
120,2,17,putt,putter,straight,straight,good,
121,2,18,tee,driver,fade,right,bad,
122,2,18,approach,wedge,straight,straight,good,
123,2,18,approach,wedge,straight,straight,good,
124,2,18,putt,putter,straight,straight,good,
125,2,18,putt,putter,straight,straight,good,
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And I would like to be able to identify which combinations of values are the most frequently occurring.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;club types:&lt;/strong&gt; driver, wood, iron, wedge, putter&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Shot types:&lt;/strong&gt; tee, approach, chip, putt&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;line directions:&lt;/strong&gt; left, center, right&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;shot qualities:&lt;/strong&gt; good, bad, neutral&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Where ideally I''d be able to identify a sweet spot (no pun intended) combination: "driver" + "tee" + "straight" + "good"&lt;/p&gt;

&lt;p&gt;I intend only to measure this for a static dataset, not for any future values or prediction. So, my thought is that this is probably a clustering / k-means problem. Is that correct?&lt;/p&gt;

&lt;p&gt;If so, how would I begin doing a K-Mean analysis with these types of values in R?&lt;/p&gt;

&lt;p&gt;If it isn''t a kmeans problem, then what is it?&lt;/p&gt;
', 'r  clustering  k-means', NULL, '''/code'':660C ''/li'':693C,703C,712C,721C ''/p'':20C,681C,743C,779C,800C,813C ''/pre'':661C ''/strong'':687C,698C,708C,717C ''/ul'':722C ''1'':36C,44C,52C,60C,68C,76C ''10'':341C,349C,357C,365C,373C ''100'':452C ''101'':460C ''102'':468C ''103'':476C ''104'':484C ''105'':492C ''106'':500C ''107'':508C ''108'':516C ''109'':524C ''11'':381C,389C,397C,405C ''110'':532C ''111'':540C ''112'':548C ''113'':556C ''114'':564C ''115'':572C ''116'':580C ''117'':588C ''118'':596C ''119'':604C ''12'':413C,421C,429C ''120'':612C ''121'':620C ''122'':628C ''123'':636C ''124'':644C ''125'':652C ''13'':437C,445C,454C,462C ''14'':470C,478C,486C,494C,502C,510C ''15'':518C,526C,534C,542C ''16'':550C,558C,566C,574C ''17'':582C,590C,598C,606C,614C ''18'':622C,630C,638C,646C,654C ''2'':35C,43C,51C,59C,67C,75C,83C,84C,91C,92C,99C,100C,107C,108C,115C,123C,131C,139C,147C,155C,163C,171C,179C,187C,195C,203C,211C,219C,227C,235C,243C,251C,260C,268C,276C,284C,292C,300C,308C,316C,324C,332C,340C,348C,356C,364C,372C,380C,388C,396C,404C,412C,420C,428C,436C,444C,453C,461C,469C,477C,485C,493C,501C,509C,517C,525C,533C,541C,549C,557C,565C,573C,581C,589C,597C,605C,613C,621C,629C,637C,645C,653C ''3'':116C,124C,132C,140C ''4'':148C,156C,164C ''48'':34C ''49'':42C ''5'':172C,180C,188C,196C ''50'':50C ''51'':58C ''52'':66C ''53'':74C ''54'':82C ''55'':90C ''56'':98C ''57'':106C ''58'':114C ''59'':122C ''6'':204C,212C,220C,228C ''60'':130C ''61'':138C ''62'':146C ''63'':154C ''64'':162C ''65'':170C ''66'':178C ''67'':186C ''68'':194C ''69'':202C ''7'':236C,244C,252C,261C,269C ''70'':210C ''71'':218C ''72'':226C ''73'':234C ''74'':242C ''75'':250C ''76'':259C ''77'':267C ''78'':275C ''79'':283C ''8'':277C,285C,293C ''80'':291C ''81'':299C ''82'':307C ''83'':315C ''84'':323C ''85'':331C ''86'':339C ''87'':347C ''88'':355C ''89'':363C ''9'':301C,309C,317C,325C,333C ''90'':371C ''91'':379C ''92'':387C ''93'':395C ''94'':403C ''95'':411C ''96'':419C ''97'':427C ''98'':435C ''99'':443C ''abl'':669C,729C ''analysi'':792C ''approach'':45C,53C,61C,93C,125C,181C,213C,245C,253C,286C,310C,318C,350C,390C,446C,479C,487C,495C,527C,559C,567C,591C,599C,631C,639C,700C ''bad'':49C,57C,65C,193C,209C,257C,282C,298C,322C,450C,483C,499C,507C,523C,563C,571C,595C,627C,719C ''begin'':786C ''calcul'':3A ''center'':710C ''chip'':133C,358C,701C ''club'':685C ''clubtyp'':29C ''cluster'':9B,771C ''code'':22C ''combin'':7A,673C,738C ''correct'':778C ''csv'':18C ''d'':727C ''data'':19C ''dataset'':754C ''desiredshap'':30C ''direct'':707C ''draw'':87C,95C,119C,207C,215C,384C,392C,416C,440C,473C,489C,521C,553C ''driver'':38C,86C,118C,174C,206C,238C,303C,343C,383C,439C,472C,552C,584C,624C,688C,739C ''fade'':247C,625C ''follow'':17C ''frequent'':5A,679C ''futur'':758C ''good'':41C,73C,81C,89C,97C,105C,113C,121C,129C,137C,145C,153C,161C,169C,177C,185C,201C,217C,225C,233C,241C,249C,266C,274C,290C,306C,314C,330C,338C,346C,354C,362C,370C,378C,386C,394C,402C,410C,418C,426C,434C,442C,459C,467C,475C,491C,515C,531C,539C,547C,555C,579C,587C,603C,611C,619C,635C,643C,651C,659C,718C,742C ''hole'':27C ''hybrid'':214C ''id'':24C,26C ''ideal'':725C ''identifi'':671C,731C ''intend'':737C,746C ''iron'':46C,94C,126C,150C,279C,311C,351C,391C,415C,488C,520C,560C,690C ''isn'':804C ''k'':11B,773C,790C ''k-mean'':10B,772C,789C ''kmean'':807C ''left'':176C,345C,353C,562C,570C,709C ''li'':683C,694C,704C,713C ''like'':666C ''line'':706C ''linedirect'':31C ''long'':258C ''mean'':12B,774C,791C ''measur'':749C ''neutral'':720C ''note'':33C ''occur'':680C ''p'':13C,662C,723C,744C,780C,801C ''pre'':21C ''predict'':761C ''probabl'':769C ''problem'':775C,808C ''pun'':736C ''putt'':69C,77C,101C,109C,141C,157C,165C,189C,197C,221C,229C,262C,270C,294C,326C,334C,366C,374C,398C,406C,422C,430C,455C,463C,503C,511C,535C,543C,575C,607C,615C,647C,655C,702C ''putter'':70C,78C,102C,110C,142C,158C,166C,190C,198C,222C,230C,263C,271C,295C,327C,335C,367C,375C,399C,407C,423C,431C,456C,464C,504C,512C,536C,544C,576C,608C,616C,648C,656C,692C ''qualiti'':716C ''r'':8B,799C ''right'':48C,128C,281C,522C,554C,594C,626C,711C ''round'':25C ''shot'':23C,696C,715C ''shotqual'':32C ''shottyp'':28C ''spot'':734C ''static'':753C ''straight'':39C,40C,47C,55C,56C,63C,64C,71C,72C,79C,80C,88C,96C,103C,104C,111C,112C,120C,127C,135C,136C,143C,144C,151C,152C,159C,160C,167C,168C,175C,183C,184C,191C,192C,199C,200C,208C,216C,223C,224C,231C,232C,239C,240C,248C,255C,256C,264C,265C,272C,273C,280C,288C,289C,296C,297C,304C,305C,312C,313C,320C,321C,328C,329C,336C,337C,344C,352C,360C,361C,368C,369C,376C,377C,385C,393C,400C,401C,408C,409C,417C,424C,425C,432C,433C,441C,448C,449C,457C,458C,465C,466C,474C,481C,482C,490C,497C,498C,505C,506C,513C,514C,529C,530C,537C,538C,545C,546C,561C,569C,577C,578C,585C,586C,593C,601C,602C,609C,610C,617C,618C,633C,634C,641C,642C,649C,650C,657C,658C,741C ''strong'':684C,695C,705C,714C ''sweet'':733C ''tee'':37C,85C,117C,149C,173C,205C,237C,278C,302C,342C,382C,414C,438C,471C,519C,551C,583C,623C,699C,740C ''thought'':764C ''top'':451C ''type'':686C,697C,795C ''ul'':682C ''valu'':6A,675C,759C,797C ''wedg'':54C,62C,134C,182C,254C,287C,319C,359C,496C,528C,568C,600C,632C,640C,691C ''wood'':246C,447C,480C,592C,689C ''would'':665C,784C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6503, 2, '2015-07-19 14:54:16.683', 1, NULL, NULL, '&lt;p&gt;I haven''t tried R (well, a bit, but not enough to make a good comparison). However, here are some of Pythons strengths:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Very intuitive syntax&lt;/strong&gt;: tuple unpacking, &lt;code&gt;element in a_list&lt;/code&gt;, &lt;code&gt;for element in sequence&lt;/code&gt;, &lt;code&gt;matrix_a * matrix_b&lt;/code&gt; (for matrix multiplication), ...&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Many libraries&lt;/strong&gt;:

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.scipy.org/" rel="nofollow"&gt;scipy&lt;/a&gt;: Scientific computations; many parts of it are only wrappers for pretty fast Fortran code&lt;/li&gt;
&lt;li&gt;&lt;a href="http://deeplearning.net/software/theano/" rel="nofollow"&gt;theano&lt;/a&gt; &gt; &lt;a href="http://lasagne.readthedocs.org/en/latest/" rel="nofollow"&gt;Lasagne&lt;/a&gt; &gt; &lt;a href="https://github.com/dnouri/nolearn" rel="nofollow"&gt;nolearn&lt;/a&gt;: Libraries for neural networks - they can be trained on GPU (nvidia, CUDA is required) without any adjustment&lt;/li&gt;
&lt;li&gt;&lt;a href="http://scikit-learn.org/" rel="nofollow"&gt;sklearn&lt;/a&gt;: General learning algorithms&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Good community&lt;/strong&gt;:

&lt;ul&gt;
&lt;li&gt;Python has &lt;a href="http://stackoverflow.com/questions/tagged/python"&gt;448,000+ questions on SO&lt;/a&gt; (R has 99,000+)&lt;/li&gt;
&lt;li&gt;Python has &lt;a href="http://datascience.stackexchange.com/questions/tagged/python"&gt;103 questions on datascience.SE&lt;/a&gt; (R has 140)&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.python.org/dev/peps/" rel="nofollow"&gt;PEPs&lt;/a&gt;, 63,000+ packages on &lt;a href="https://pypi.python.org/pypi" rel="nofollow"&gt;PyPI&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://ipython.org/" rel="nofollow"&gt;IPython notebooks&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Misc&lt;/strong&gt;:

&lt;ul&gt;
&lt;li&gt;0-indexed arrays ... I made that error all the time with R.&lt;/li&gt;
&lt;li&gt;Established package structures&lt;/li&gt;
&lt;li&gt;Good support for testing your code&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':70C,95C,104C,113C,139C,165C,183C,197C,210C,222C ''/code'':40C,46C,52C ''/dev/peps/'':193C ''/dnouri/nolearn'':109C ''/en/latest/'':100C ''/li'':56C,85C,131C,143C,145C,170C,187C,211C,213C,223C,242C,247C,255C,257C ''/p'':25C ''/pypi'':206C ''/questions/tagged/python'':159C,178C ''/software/theano/'':91C ''/strong'':32C,61C,150C,227C ''/ul'':144C,212C,256C,258C ''0'':230C ''000'':161C,169C,199C ''103'':179C ''140'':186C ''448'':160C ''63'':198C ''99'':168C ''adjust'':130C ''algorithm'':142C ''array'':232C ''b'':51C ''bit'':9C ''code'':35C,41C,47C,84C,254C ''communiti'':149C ''comparison'':17C ''comput'':72C ''cuda'':125C ''datascience.se'':182C ''datascience.stackexchange.com'':177C ''datascience.stackexchange.com/questions/tagged/python'':176C ''deeplearning.net'':90C ''deeplearning.net/software/theano/'':89C ''element'':36C,43C ''enough'':12C ''error'':236C ''establish'':244C ''fast'':82C ''fortran'':83C ''general'':140C ''github.com'':108C ''github.com/dnouri/nolearn'':107C ''good'':16C,148C,249C ''gpu'':123C ''haven'':3C ''howev'':18C ''href'':65C,88C,97C,106C,134C,156C,175C,190C,203C,216C ''index'':231C ''intuit'':30C ''ipython'':220C ''ipython.org'':217C ''lasagn'':103C ''lasagne.readthedocs.org'':99C ''lasagne.readthedocs.org/en/latest/'':98C ''learn'':141C ''li'':27C,57C,63C,86C,132C,146C,152C,171C,188C,214C,224C,229C,243C,248C ''librari'':60C,114C ''list'':39C ''made'':234C ''make'':14C ''mani'':59C,73C ''matrix'':48C,50C,54C ''misc'':226C ''multipl'':55C ''network'':117C ''neural'':116C ''nofollow'':68C,93C,102C,111C,137C,195C,208C,219C ''nolearn'':112C ''notebook'':221C ''nvidia'':124C ''p'':1C ''packag'':200C,245C ''part'':74C ''pep'':196C ''pretti'':81C ''pypi'':209C ''pypi.python.org'':205C ''pypi.python.org/pypi'':204C ''python'':23C,153C,172C ''question'':162C,180C ''r'':6C,166C,184C,241C ''rel'':67C,92C,101C,110C,136C,194C,207C,218C ''requir'':127C ''scientif'':71C ''scikit-learn.org'':135C ''scipi'':69C ''sequenc'':45C ''sklearn'':138C ''stackoverflow.com'':158C ''stackoverflow.com/questions/tagged/python'':157C ''strength'':24C ''strong'':28C,58C,147C,225C ''structur'':246C ''support'':250C ''syntax'':31C ''test'':252C ''theano'':94C ''time'':239C ''train'':121C ''tri'':5C ''tupl'':33C ''ul'':26C,62C,151C,228C ''unpack'':34C ''well'':7C ''without'':128C ''wrapper'':79C ''www.python.org'':192C ''www.python.org/dev/peps/'':191C ''www.scipy.org'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6493, 2, '2015-07-17 19:30:02.82', 2, NULL, NULL, '&lt;p&gt;If I understand your question you want to know which combination is most frequent or how frequent a combination is relative to others. This is a static method that will determine the unique combinations in total (i.e., combinations of all five columns). &lt;/p&gt;

&lt;p&gt;The &lt;code&gt;plyr&lt;/code&gt; package has a nifty utility for grouping unique combinations of columns in a &lt;code&gt;data.frame&lt;/code&gt;. We can specify the names of the columns we want to group by, and then specify a function to perform for each of those combinations. In this case, we specify the columns associated with your golf shot qualities and use the function &lt;code&gt;nrow&lt;/code&gt; which will count the number of rows in every subset of the large data.frame for which the columns are the identical.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# You need this library for the ddply() function
require(plyr)

# These are the columns that determine a unique situation (change this if you need)
qualities &amp;lt;- c("shotType","clubType","desiredShape","lineDirection","shotQuality")

# The call to ddply() actually gives us what we want, which is the number 
# of times that combination is present in the dataset
countedCombos &amp;lt;- ddply(golf,qualities,nrow)

# To be nice, let''s give that newly added column a meaningful name
names(countedCombos) &amp;lt;- c(qualities,"count")

# Finally, you probably want to order it (decreasing, in this case)
countedCombos &amp;lt;- countedCombos[with(countedCombos, order(-count)),]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now check out your product. The final column has the count associated with each unique combination of columns you provided to &lt;code&gt;ddply&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;head(countedCombos)
   shotType clubType desiredShape lineDirection shotQuality count
16     putt   putter     straight      straight        good    30
10 approach    wedge     straight      straight        good     6
9  approach    wedge     straight      straight         bad     5
19      tee   driver         draw      straight        good     5
22      tee   driver     straight      straight        good     4
2  approach     iron         draw      straight        good     3
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;To see the results for a particular cross-section (say, for example, the driver &lt;code&gt;clubType&lt;/code&gt;):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;countedCombos[which(countedCombos$clubType=="driver"),]
   shotType clubType desiredShape lineDirection shotQuality count
19      tee   driver         draw      straight        good     5
22      tee   driver     straight      straight        good     4
21      tee   driver     straight          left        good     2
17      tee   driver         draw         right        good     1
18      tee   driver         draw      straight         bad     1
20      tee   driver         fade         right         bad     1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As a bonus, you can dig into these results with &lt;code&gt;ddply&lt;/code&gt; again. For example, if you wanted to look at the ratio of "good" to "bad" shotQuality based on &lt;code&gt;shotType&lt;/code&gt; and &lt;code&gt;clubType&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;shotPerformance &amp;lt;- ddply(countedCombos,c("shotType","clubType"),
    function(x){
        total&amp;lt;- length(x$shotQuality)
            good &amp;lt;- length(which(x$shotQuality=="good"))
        bad &amp;lt;- length(which(x$shotQuality=="bad"))
        c(total,good,bad,good/(good+bad))
    }
 )
names(shotPerformance)&amp;lt;-c("count","shotType","clubType","good","bad","goodPct")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This gives you a new breakdown of some math performed on the counts of a character field (&lt;code&gt;shotQuality&lt;/code&gt;) and shows you how you can build custom functions for &lt;code&gt;ddply&lt;/code&gt;. Of course, you can still order these whichever way you want, too.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;head(shotPerformance)
  shotType clubType total good bad   goodPct
1 approach   hybrid    1  1   0 1.0000000
2 approach     iron    6  4   2 0.6666667
3 approach    wedge    3  1   2 0.3333333
4 approach     wood    3  1   2 0.3333333
5     chip    wedge    1  1   0 1.0000000
6     putt   putter    2  1   1 0.5000000
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':49C,65C,110C,233C,259C,313C,333C,390C,405C,426C,430C,479C,501C,514C,580C ''/p'':44C,132C,260C,334C,431C,527C ''/pre'':234C,314C,391C,480C,581C ''0'':543C,571C ''0.3333333'':558C,565C ''0.5000000'':579C ''0.6666667'':551C ''1'':375C,382C,389C,538C,541C,542C,556C,563C,569C,570C,577C,578C ''1.0000000'':544C,572C ''10'':278C ''16'':271C ''17'':369C ''18'':376C ''19'':292C,348C ''2'':306C,368C,545C,550C,557C,564C,576C ''20'':383C ''21'':362C ''22'':299C,355C ''3'':312C,552C,555C,562C ''30'':277C ''4'':305C,361C,549C,559C ''5'':291C,298C,354C,566C ''6'':284C,548C,573C ''9'':285C ''actual'':171C ''ad'':204C ''approach'':279C,286C,307C,539C,546C,553C,560C ''associ'':98C,247C ''bad'':290C,381C,388C,420C,455C,461C,465C,468C,477C,536C ''base'':422C ''bonus'':395C ''breakdown'':487C ''build'':508C ''c'':161C,212C,438C,462C,472C ''call'':168C ''case'':93C,225C ''chang'':154C ''charact'':497C ''check'':237C ''chip'':567C ''clubtyp'':163C,266C,332C,340C,343C,429C,440C,475C,533C ''code'':47C,63C,108C,134C,257C,262C,331C,336C,403C,424C,428C,433C,499C,512C,529C ''column'':43C,60C,73C,97C,128C,148C,205C,243C,253C ''combin'':12C,20C,35C,39C,58C,90C,184C,251C ''count'':113C,214C,232C,246C,270C,347C,473C,494C ''countedcombo'':190C,210C,226C,228C,230C,264C,337C,339C,437C ''cours'':516C ''cross'':324C ''cross-sect'':323C ''custom'':509C ''data.frame'':64C,124C ''dataset'':189C ''ddpli'':141C,170C,192C,258C,404C,436C,513C ''decreas'':222C ''desiredshap'':164C,267C,344C ''determin'':32C,150C ''dig'':398C ''draw'':295C,309C,351C,372C,379C ''driver'':294C,301C,330C,341C,350C,357C,364C,371C,378C,385C ''everi'':119C ''exampl'':328C,408C ''fade'':386C ''field'':498C ''final'':215C,242C ''five'':42C ''frequent'':15C,18C ''function'':83C,107C,142C,441C,510C ''give'':172C,201C,483C ''golf'':101C,193C ''good'':276C,283C,297C,304C,311C,353C,360C,367C,374C,418C,448C,454C,464C,466C,467C,476C,535C ''goodpct'':478C,537C ''group'':56C,77C ''head'':263C,530C ''hybrid'':540C ''i.e'':38C ''ident'':131C ''iron'':308C,547C ''know'':10C ''larg'':123C ''left'':366C ''length'':445C,450C,457C ''let'':199C ''librari'':138C ''linedirect'':165C,268C,345C ''look'':413C ''lt'':160C,191C,211C,227C,435C,444C,449C,456C,471C ''math'':490C ''meaning'':207C ''method'':29C ''name'':70C,208C,209C,469C ''need'':136C,158C ''new'':486C ''newli'':203C ''nice'':198C ''nifti'':53C ''nrow'':109C,195C ''number'':115C,180C ''order'':220C,231C,520C ''other'':24C ''p'':1C,45C,235C,315C,392C,481C ''packag'':50C ''particular'':322C ''perform'':85C,491C ''plyr'':48C,144C ''pre'':133C,261C,335C,432C,528C ''present'':186C ''probabl'':217C ''product'':240C ''provid'':255C ''putt'':272C,574C ''putter'':273C,575C ''qualiti'':103C,159C,194C,213C ''question'':6C ''ratio'':416C ''relat'':22C ''requir'':143C ''result'':319C,401C ''right'':373C,387C ''row'':117C ''say'':326C ''section'':325C ''see'':317C ''shot'':102C ''shotperform'':434C,470C,531C ''shotqual'':166C,269C,346C,421C,447C,453C,460C,500C ''shottyp'':162C,265C,342C,425C,439C,474C,532C ''show'':503C ''situat'':153C ''specifi'':68C,81C,95C ''static'':28C ''still'':519C ''straight'':274C,275C,281C,282C,288C,289C,296C,302C,303C,310C,352C,358C,359C,365C,380C ''subset'':120C ''tee'':293C,300C,349C,356C,363C,370C,377C,384C ''time'':182C ''total'':37C,443C,463C,534C ''understand'':4C ''uniqu'':34C,57C,152C,250C ''us'':173C ''use'':105C ''util'':54C ''want'':8C,75C,176C,218C,411C,525C ''way'':523C ''wedg'':280C,287C,554C,568C ''whichev'':522C ''wood'':561C ''x'':442C,446C,452C,459C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6494, 1, '2015-07-17 21:04:15.433', 2, 86, 'Markov Chains: How much steps to conclude a Transition Matrix', '&lt;p&gt;I have just learned Markov Chains which I am using to model a real world problem. The model comprises 3 states &lt;code&gt;[a b c]&lt;/code&gt;. For now I am collection data and calculating transitional probabilities:-&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;T[a][b] = #transitions from a to b / #total transitions to a
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However I am stuck at determining the correct Transition Matrix. As I am getting more data, the matrix is changing drastically. So when do I finalize Transition Matrix? Does that mean that my data is too random and cannot be modelled or I am doing some mistake here?&lt;/p&gt;
', 'machine-learning  markov-process', NULL, '''/code'':43C,69C ''/p'':54C,120C ''/pre'':70C ''3'':37C ''b'':41C,59C,64C ''c'':42C ''calcul'':51C ''cannot'':110C ''chain'':2A,23C ''chang'':91C ''code'':39C,56C ''collect'':48C ''compris'':36C ''conclud'':7A ''correct'':79C ''data'':49C,87C,105C ''determin'':77C ''drastic'':92C ''final'':97C ''get'':85C ''howev'':72C ''learn'':13B,21C ''machin'':12B ''machine-learn'':11B ''markov'':1A,15B,22C ''markov-process'':14B ''matrix'':10A,81C,89C,99C ''mean'':102C ''mistak'':118C ''model'':29C,35C,112C ''much'':4A ''p'':17C,71C ''pre'':55C ''probabl'':53C ''problem'':33C ''process'':16B ''random'':108C ''real'':31C ''state'':38C ''step'':5A ''stuck'':75C ''total'':65C ''transit'':9A,52C,60C,66C,80C,98C ''use'':27C ''world'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6497, 2, '2015-07-18 16:58:37.283', 2, NULL, NULL, '&lt;p&gt;In gereral, there are four ways one can "connect" neural networks (depending on you application at hand) as described in &lt;a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.28.2383&amp;amp;rep=rep1&amp;amp;type=pdf" rel="nofollow"&gt;Combining Artificial Neural Networks, Sharkey et al.&lt;/a&gt;:
&lt;img src="http://i.stack.imgur.com/d2RrP.png" alt="Four ways to connect "&gt;&lt;/p&gt;

&lt;p&gt;In the cooperative mode, there are various ways in which one can combine the decisions made by different models. One common way is to take the average of the predictions. Other ways are taking the median, or some weighed average of them.&lt;/p&gt;

&lt;p&gt;In the competitive mode, it is winner-take-all, where the prediction from the best model for that test point is selected.&lt;/p&gt;

&lt;p&gt;In sequential mode, you classify on coarse classes (i.e. natural objects vs. man made objects), the prediction from this stage would be used to decide what to do next..(i.e. if the model predicts, man-made objects, one can have a classifier to do fine grained classification).&lt;/p&gt;

&lt;p&gt;Neural networks have lot of &lt;a href="http://colinraffel.com/wiki/neural_network_hyperparameters" rel="nofollow"&gt;hyper-parameters&lt;/a&gt; to tune. In the supervisory mode, one can train an neuralnet just to select these hyper-parameters of a "real" neuralnet that is actually trained on your problem at hand.&lt;/p&gt;

&lt;p&gt;Please dig deeper into the above mentioned article for more details.&lt;/p&gt;
', NULL, NULL, '''/a'':36C,183C ''/d2rrp.png'':41C ''/p'':47C,91C,116C,166C,215C,228C ''/viewdoc/download?doi=10.1.1.28.2383&amp;amp;rep=rep1&amp;amp;type=pdf'':26C ''/wiki/neural_network_hyperparameters'':177C ''actual'':208C ''al'':35C ''alt'':42C ''applic'':16C ''articl'':224C ''artifici'':30C ''averag'':75C,88C ''best'':108C ''citeseerx.ist.psu.edu'':25C ''citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.28.2383&amp;amp;rep=rep1&amp;amp;type=pdf'':24C ''class'':125C ''classif'':165C ''classifi'':122C,160C ''coars'':124C ''colinraffel.com'':176C ''colinraffel.com/wiki/neural_network_hyperparameters'':175C ''combin'':29C,61C ''common'':69C ''competit'':95C ''connect'':10C,46C ''cooper'':51C ''decid'':142C ''decis'':63C ''deeper'':219C ''depend'':13C ''describ'':20C ''detail'':227C ''differ'':66C ''dig'':218C ''et'':34C ''fine'':163C ''four'':6C,43C ''gerer'':3C ''grain'':164C ''hand'':18C,214C ''href'':23C,174C ''hyper'':181C,200C ''hyper-paramet'':180C,199C ''i.e'':126C,147C ''i.stack.imgur.com'':40C ''i.stack.imgur.com/d2rrp.png'':39C ''img'':37C ''lot'':171C ''made'':64C,131C,154C ''man'':130C,153C ''man-mad'':152C ''median'':84C ''mention'':223C ''mode'':52C,96C,120C,189C ''model'':67C,109C,150C ''natur'':127C ''network'':12C,32C,169C ''neural'':11C,31C,168C ''neuralnet'':194C,205C ''next'':146C ''nofollow'':28C,179C ''object'':128C,132C,155C ''one'':8C,59C,68C,156C,190C ''p'':1C,48C,92C,117C,167C,216C ''paramet'':182C,201C ''pleas'':217C ''point'':113C ''predict'':78C,105C,134C,151C ''problem'':212C ''real'':204C ''rel'':27C,178C ''select'':115C,197C ''sequenti'':119C ''sharkey'':33C ''src'':38C ''stage'':137C ''supervisori'':188C ''take'':73C,82C,101C ''test'':112C ''train'':192C,209C ''tune'':185C ''use'':140C ''various'':55C ''vs'':129C ''way'':7C,44C,56C,70C,80C ''weigh'':87C ''winner'':100C ''winner-take-al'':99C ''would'':138C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6499, 2, '2015-07-18 22:10:05.05', 0, NULL, NULL, '&lt;p&gt;If you are using sklearn, there is a good function available called model.feature_importances_.
Give it a try with your model/new feature and see if it helps. Also look &lt;a href="http://machinelearningmastery.com/feature-selection-in-python-with-scikit-learn/" rel="nofollow"&gt;here&lt;/a&gt; and &lt;a href="http://blog.datadive.net/selecting-good-features-part-iii-random-forests/" rel="nofollow"&gt;here&lt;/a&gt; for examples.&lt;/p&gt;
', NULL, NULL, '''/a'':39C,49C ''/feature-selection-in-python-with-scikit-learn/'':35C ''/p'':52C ''/selecting-good-features-part-iii-random-forests/'':45C ''also'':29C ''avail'':12C ''blog.datadive.net'':44C ''blog.datadive.net/selecting-good-features-part-iii-random-forests/'':43C ''call'':13C ''exampl'':51C ''featur'':23C ''function'':11C ''give'':16C ''good'':10C ''help'':28C ''href'':32C,42C ''import'':15C ''look'':30C ''machinelearningmastery.com'':34C ''machinelearningmastery.com/feature-selection-in-python-with-scikit-learn/'':33C ''model.feature'':14C ''model/new'':22C ''nofollow'':37C,47C ''p'':1C ''rel'':36C,46C ''see'':25C ''sklearn'':6C ''tri'':19C ''use'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6500, 1, '2015-07-19 02:40:32.413', 1, 130, 'Sort a data frame when column name is assigned in a variable', '&lt;p&gt;I am new to R and hence my question is likely to be basic. I tried researching the answer before posting here however didn''t get the answer I was looking for.&lt;/p&gt;

&lt;p&gt;I am trying to order a data frame on the basis of certain columns. The column is dynamic and depends on a certain "outcome" which would be from a vector of "validoutcomes".&lt;/p&gt;

&lt;p&gt;&lt;code&gt;validoutcomes &amp;lt;- c("A","B","C")&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;if outcome is "A" then I am supposed to read data from column 11. For "B" its columns 17 and for "C" it is 23.&lt;/p&gt;

&lt;p&gt;I renamed the columns so that it is easier for my readability and alignment with  validoutcomes. "input" is my source data from the CSV.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;colnames(input)[11] &amp;lt;- validoutcomes[1]
 colnames(input)[17] &amp;lt;- validoutcomes[2]
 colnames(input)[23] &amp;lt;- validoutcomes[3]&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;I then tried to sort using&lt;/p&gt;

&lt;p&gt;&lt;code&gt;statelist &amp;lt;- statelist[order(statelist$outcome)]&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Any ideas?&lt;/p&gt;
', 'r', NULL, '''/code'':90C,165C,183C ''/p'':47C,81C,91C,118C,144C,166C,174C,184C,188C ''1'':152C ''11'':106C,149C ''17'':111C,155C ''2'':158C ''23'':117C,161C ''3'':164C ''align'':133C ''answer'':33C,42C ''assign'':9A ''b'':88C,108C ''basi'':59C ''basic'':28C ''c'':86C,89C,114C ''certain'':61C,71C ''code'':83C,146C,176C ''colnam'':147C,153C,159C ''column'':6A,62C,64C,105C,110C,123C ''csv'':143C ''data'':3A,55C,103C,140C ''depend'':68C ''didn'':38C ''dynam'':66C ''easier'':128C ''frame'':4A,56C ''get'':40C ''henc'':21C ''howev'':37C ''idea'':187C ''input'':136C,148C,154C,160C ''like'':25C ''look'':45C ''lt'':85C,150C,156C,162C,178C ''name'':7A ''new'':17C ''order'':53C,180C ''outcom'':72C,94C,182C ''p'':14C,48C,82C,92C,119C,145C,167C,175C,185C ''post'':35C ''question'':23C ''r'':13B,19C ''read'':102C ''readabl'':131C ''renam'':121C ''research'':31C ''sort'':1A,172C ''sourc'':139C ''statelist'':177C,179C,181C ''suppos'':100C ''tri'':30C,51C,170C ''use'':173C ''validoutcom'':80C,84C,135C,151C,157C,163C ''variabl'':12A ''vector'':78C ''would'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6711, 2, '2015-08-06 09:25:34.667', 0, NULL, NULL, '&lt;p&gt;In value iteration, you implicitly solve for the state values under an ideal policy. There is no need to define an actual policy during the iterations, you can derive it at the end from the utilities that you calculate. You could if you wish, after any iteration, use the state values to determine what "current" policy is predicted.&lt;/p&gt;

&lt;p&gt;In policy iteration, you define a starting policy and iterate towards the best one. So the policy is more explicitly tracked and re-calculated on each step.&lt;/p&gt;
', NULL, NULL, '''/p'':60C,90C ''actual'':23C ''best'':74C ''calcul'':40C,86C ''could'':42C ''current'':56C ''defin'':21C,66C ''deriv'':30C ''determin'':54C ''end'':34C ''explicit'':81C ''ideal'':14C ''implicit'':6C ''iter'':4C,27C,48C,64C,71C ''need'':19C ''one'':75C ''p'':1C,61C ''polici'':15C,24C,57C,63C,69C,78C ''predict'':59C ''re'':85C ''re-calcul'':84C ''solv'':7C ''start'':68C ''state'':10C,51C ''step'':89C ''toward'':72C ''track'':82C ''use'':49C ''util'':37C ''valu'':3C,11C,52C ''wish'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6504, 1, '2015-07-19 19:08:05.297', 0, 26, 'How to efficiently categorise transactions', '&lt;p&gt;I''m doing analysis of our family spending over the past couple of years. I have a dump of our credit card and bank account statements, which consists of: date, description, amount. What I want to do is assign a category to each transaction, and from there do analysis of how much we''re spending on each category. I already have a good idea of the categories (Food, Travel, Petrol, Socialising, Bills, Kids, etc.). When I look through the data, I can manually assign a category to each transaction, by looking at the description.&lt;/p&gt;

&lt;p&gt;So I could do this manually, but there''s 2000+ transactions to go through. I''ve found that certain keywords in the description always result in a particular category (e.g. Sainsburys -&gt; food; Shell -&gt; petrol, etc.) I''ve done a quick and dirty python script that helps with this a bit. But as I''ve been doing this, I have a feeling that I''m missing a trick. There''s probably some tool that can do this much better than my dodgy python script.&lt;/p&gt;

&lt;p&gt;So please, if you have any suggestions to improve my analysis workflow, let me know. I''m open to tool recommendations, or just general ideas for improving this. Thanks in advance for anything you can offer.&lt;/p&gt;
', 'categorical-data  finance', NULL, '''/p'':105C,190C,228C ''2000'':116C ''account'':35C ''advanc'':222C ''alreadi'':70C ''alway'':130C ''amount'':42C ''analysi'':14C,59C,202C ''anyth'':224C ''assign'':49C,94C ''bank'':34C ''better'':184C ''bill'':82C ''bit'':156C ''card'':32C ''categor'':7B ''categori'':51C,68C,77C,96C,135C ''categorical-data'':6B ''categoris'':4A ''certain'':125C ''consist'':38C ''could'':109C ''coupl'':22C ''credit'':31C ''data'':8B,90C ''date'':40C ''descript'':41C,104C,129C ''dirti'':148C ''dodgi'':187C ''done'':144C ''dump'':28C ''e.g'':136C ''effici'':3A ''etc'':84C,141C ''famili'':17C ''feel'':167C ''financ'':9B ''food'':78C,138C ''found'':123C ''general'':215C ''go'':119C ''good'':73C ''help'':152C ''idea'':74C,216C ''improv'':200C,218C ''keyword'':126C ''kid'':83C ''know'':206C ''let'':204C ''look'':87C,101C ''m'':12C,170C,208C ''manual'':93C,112C ''miss'':171C ''much'':62C,183C ''offer'':227C ''open'':209C ''p'':10C,106C,191C ''particular'':134C ''past'':21C ''petrol'':80C,140C ''pleas'':193C ''probabl'':176C ''python'':149C,188C ''quick'':146C ''re'':64C ''recommend'':212C ''result'':131C ''sainsburi'':137C ''script'':150C,189C ''shell'':139C ''socialis'':81C ''spend'':18C,65C ''statement'':36C ''suggest'':198C ''thank'':220C ''tool'':178C,211C ''transact'':5A,54C,99C,117C ''travel'':79C ''trick'':173C ''ve'':122C,143C,160C ''want'':45C ''workflow'':203C ''year'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6505, 2, '2015-07-19 21:10:09.263', 0, NULL, NULL, '&lt;p&gt;Ok, I have found the likely answer. I am not sure if this is the best answer but it worked for me. &lt;/p&gt;

&lt;p&gt;Since &lt;code&gt;$&lt;/code&gt; is used to call the colnames, R was looking for a column name as &lt;code&gt;outcome&lt;/code&gt;. However all it needed was square brackets&lt;code&gt;[]&lt;/code&gt;. I replaced it and so far seems to be working for me.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;statelist &amp;lt;- statelist[order(statelist[outcome])]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am still very interested in knowing if there is a better solution or if someone sees issue with my observation.&lt;/p&gt;
', NULL, NULL, '''/code'':28C,45C,54C,76C ''/p'':24C,67C,100C ''/pre'':77C ''answer'':8C,18C ''best'':17C ''better'':90C ''bracket'':52C ''call'':32C ''code'':27C,43C,53C,69C ''colnam'':34C ''column'':40C ''far'':60C ''found'':5C ''howev'':46C ''interest'':83C ''issu'':96C ''know'':85C ''like'':7C ''look'':37C ''lt'':71C ''name'':41C ''need'':49C ''observ'':99C ''ok'':2C ''order'':73C ''outcom'':44C,75C ''p'':1C,25C,78C ''pre'':68C ''r'':35C ''replac'':56C ''see'':95C ''seem'':61C ''sinc'':26C ''solut'':91C ''someon'':94C ''squar'':51C ''statelist'':70C,72C,74C ''still'':81C ''sure'':12C ''use'':30C ''work'':21C,64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8768, 1, '2015-11-09 22:55:49.043', 1, 63, 'Is there any difference between feature extraction and feature learning?', '&lt;p&gt;It appears to me that "feature extraction" and "feature learning" are equivalent  concepts, however there are 2 separate wikipedia articles dedicated to them that are notably different. In particular, only in the Feature Learning article Neural Networks/Deep Learning are mentioned. However, it seems like they would be equally appropriate for either because autoencoders extract features from the raw (typically image) data and those now extracted features feed into the next layers.&lt;/p&gt;

&lt;p&gt;So, what''s the real difference between these terms?&lt;/p&gt;
', 'machine-learning  feature-selection  feature-construction', NULL, '''/p'':92C,103C ''2'':37C ''appear'':22C ''appropri'':69C ''articl'':40C,55C ''autoencod'':73C ''concept'':33C ''construct'':19B ''data'':81C ''dedic'':41C ''differ'':4A,47C,99C ''either'':71C ''equal'':68C ''equival'':32C ''extract'':7A,27C,74C,85C ''featur'':6A,9A,15B,18B,26C,29C,53C,75C,86C ''feature-construct'':17B ''feature-select'':14B ''feed'':87C ''howev'':34C,61C ''imag'':80C ''layer'':91C ''learn'':10A,13B,30C,54C,58C ''like'':64C ''machin'':12B ''machine-learn'':11B ''mention'':60C ''networks/deep'':57C ''neural'':56C ''next'':90C ''notabl'':46C ''p'':20C,93C ''particular'':49C ''raw'':78C ''real'':98C ''seem'':63C ''select'':16B ''separ'':38C ''term'':102C ''typic'':79C ''wikipedia'':39C ''would'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6506, 1, '2015-07-20 04:48:17.547', 2, 178, 'Shall I use the Euclidean Distance or the Cosine Similarity to compute the semantic similarity of two words?', '&lt;p&gt;I want to compute the semantic similarity of two words using their vector representations (obtained using e.g. word2vec, GloVe, etc.). Shall I use the Euclidean Distance or the Cosine Similarity?&lt;/p&gt;

&lt;p&gt;The &lt;a href="http://nlp.stanford.edu/projects/glove/" rel="nofollow"&gt;GloVe website&lt;/a&gt; mentions both measures without telling the pros and cons of each:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The Euclidean distance (or cosine similarity) between two word vectors provides an effective method for measuring the linguistic or semantic similarity of the corresponding words.&lt;/p&gt;
&lt;/blockquote&gt;
', 'nlp  word-embeddings', NULL, '''/a'':66C ''/blockquote'':107C ''/p'':54C,78C,106C ''/projects/glove/'':61C ''blockquot'':79C ''comput'':12A,27C ''con'':75C ''correspond'':104C ''cosin'':9A,52C,85C ''distanc'':6A,49C,83C ''e.g'':40C ''effect'':93C ''embed'':22B ''etc'':43C ''euclidean'':5A,48C,82C ''glove'':42C,64C ''href'':58C ''linguist'':98C ''measur'':69C,96C ''mention'':67C ''method'':94C ''nlp'':19B ''nlp.stanford.edu'':60C ''nlp.stanford.edu/projects/glove/'':59C ''nofollow'':63C ''obtain'':38C ''p'':23C,55C,80C ''pros'':73C ''provid'':91C ''rel'':62C ''represent'':37C ''semant'':14A,29C,100C ''shall'':1A,44C ''similar'':10A,15A,30C,53C,86C,101C ''tell'':71C ''two'':17A,32C,88C ''use'':3A,34C,39C,46C ''vector'':36C,90C ''want'':25C ''websit'':65C ''without'':70C ''word'':18A,21B,33C,89C,105C ''word-embed'':20B ''word2vec'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6509, 1, '2015-07-20 10:52:59.633', 1, 29, 'Techniques for dealing with unevenly spaced time series data that have missing time-stamps?', '&lt;p&gt;What are the techniques to infer missing time-stamps in the unevenly spaced time series data that has missing time-stamps?&lt;/p&gt;

&lt;p&gt;The frequency of data recording is about 1-3 times a day without any fixed time of measurement. In some cases the time stamp of the measurement is unavailable.&lt;/p&gt;

&lt;p&gt;It is possible to predict at which time $t_0$ the new sample $x_0$ was recorded knowing the history of previous measurements $(t_n,x_n)$?&lt;/p&gt;

&lt;p&gt;$$
(t_{-n},x_{-n}), ... ,(t_{-3},x_{-3}), (t_{-2},x_{-2}), (t_{-1},x_{-1}), (t_0,x_0)
$$&lt;/p&gt;

&lt;p&gt;Or predict multiple missing time-stamps e.g. $t_{-2},t_{-6},t_{-7}$ from recorded sequence $t_n,x_n$?&lt;/p&gt;
', 'predictive-modeling  time-series', NULL, '''-1'':121C,123C ''-2'':117C,119C,139C ''-3'':56C,113C,115C ''-6'':141C ''-7'':143C ''/p'':46C,77C,106C,128C,151C ''0'':88C,93C,125C,127C ''1'':55C ''case'':68C ''data'':9A,39C,51C ''day'':59C ''deal'':3A ''e.g'':137C ''fix'':62C ''frequenc'':49C ''histori'':98C ''infer'':28C ''know'':96C ''measur'':65C,74C,101C ''miss'':12A,29C,42C,133C ''model'':18B ''multipl'':132C ''n'':103C,105C,109C,111C,148C,150C ''new'':90C ''p'':22C,47C,78C,107C,129C ''possibl'':81C ''predict'':17B,83C,131C ''predictive-model'':16B ''previous'':100C ''record'':52C,95C,145C ''sampl'':91C ''sequenc'':146C ''seri'':8A,21B,38C ''space'':6A,36C ''stamp'':15A,32C,45C,71C,136C ''techniqu'':1A,26C ''time'':7A,14A,20B,31C,37C,44C,57C,63C,70C,86C,135C ''time-seri'':19B ''time-stamp'':13A,30C,43C,134C ''unavail'':76C ''uneven'':5A,35C ''without'':60C ''x'':92C,104C,110C,114C,118C,122C,126C,149C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6510, 1, '2015-07-20 13:42:21.283', 3, 201, 'Does modeling with Random Forests requre cross-validation?', '&lt;p&gt;As far as I''ve seen, opinions tend to differ about this. Best practice would certainly dictate using cross-validation (especially if comparing RFs with other algorithms on the same dataset). On the other hand, the original source states that the fact OOB error is calculated during model training is enough of an indicator of test set performance. Even Trevor Hastie, in a relatively recent talks says that "Random Forests provide free cross-validation". Intuitively, this makes sense to me, if training and trying to improve a RF-based model on one dataset.&lt;/p&gt;

&lt;p&gt;Can someone please lay out the arguments for and against the need for cross-validation with random forests?&lt;/p&gt;
', 'random-forest  cross-validation', NULL, '''/p'':113C,134C ''algorithm'':44C ''argument'':121C ''base'':108C ''best'':29C ''calcul'':63C ''certain'':32C ''compar'':40C ''cross'':8A,14B,36C,91C,129C ''cross-valid'':7A,13B,35C,90C,128C ''dataset'':48C,112C ''dictat'':33C ''differ'':26C ''enough'':68C ''error'':61C ''especi'':38C ''even'':76C ''fact'':59C ''far'':18C ''forest'':5A,12B,87C,133C ''free'':89C ''hand'':52C ''hasti'':78C ''improv'':104C ''indic'':71C ''intuit'':93C ''lay'':118C ''make'':95C ''model'':2A,65C,109C ''need'':126C ''one'':111C ''oob'':60C ''opinion'':23C ''origin'':54C ''p'':16C,114C ''perform'':75C ''pleas'':117C ''practic'':30C ''provid'':88C ''random'':4A,11B,86C,132C ''random-forest'':10B ''recent'':82C ''relat'':81C ''requr'':6A ''rf'':107C ''rf-base'':106C ''rfs'':41C ''say'':84C ''seen'':22C ''sens'':96C ''set'':74C ''someon'':116C ''sourc'':55C ''state'':56C ''talk'':83C ''tend'':24C ''test'':73C ''train'':66C,100C ''trevor'':77C ''tri'':102C ''use'':34C ''valid'':9A,15B,37C,92C,130C ''ve'':21C ''would'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6511, 1, '2015-07-20 15:54:31.44', 0, 155, 'Are there any interesting application of linear regression', '&lt;p&gt;Linear regression is a widely used ML algorithm. So far I have only encountered ''boring'' applications of it. (e.g predict sales for next quarter, predict housing prices for next year , predict population of a country by 2020 etc.)&lt;/p&gt;

&lt;p&gt;What are some interesting/cool application of linear regression? Finding cool applications greatly helps in motivating myself to learn, hence the quest.&lt;/p&gt;

&lt;p&gt;Cool as in: Application to stock trading, application to video games, application to astronomy, application to sports betting, application to airfare prediction&lt;/p&gt;
', 'linear-regression', NULL, '''/p'':51C,74C,98C ''2020'':49C ''airfar'':96C ''algorithm'':20C ''applic'':5A,28C,57C,63C,79C,83C,87C,90C,94C ''astronomi'':89C ''bet'':93C ''bore'':27C ''cool'':62C,76C ''countri'':47C ''e.g'':31C ''encount'':26C ''etc'':50C ''far'':22C ''find'':61C ''game'':86C ''great'':64C ''help'':65C ''henc'':71C ''hous'':38C ''interest'':4A ''interesting/cool'':56C ''learn'':70C ''linear'':7A,10B,13C,59C ''linear-regress'':9B ''ml'':19C ''motiv'':67C ''next'':35C,41C ''p'':12C,52C,75C ''popul'':44C ''predict'':32C,37C,43C,97C ''price'':39C ''quarter'':36C ''quest'':73C ''regress'':8A,11B,14C,60C ''sale'':33C ''sport'':92C ''stock'':81C ''trade'':82C ''use'':18C ''video'':85C ''wide'':17C ''year'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6512, 2, '2015-07-20 15:59:25.22', 0, NULL, NULL, '&lt;p&gt;I think those were the coolest.
I recommend you that video also
&lt;a href="https://www.khanacademy.org/math/probability/regression/regression-correlation/v/regression-line-example" rel="nofollow"&gt;https://www.khanacademy.org/math/probability/regression/regression-correlation/v/regression-line-example&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/math/probability/regression/regression-correlation/v/regression-line-example'':18C ''/math/probability/regression/regression-correlation/v/regression-line-example&lt;/a&gt;&lt;/p&gt;'':23C ''also'':13C ''coolest'':7C ''href'':15C ''nofollow'':20C ''p'':1C ''recommend'':9C ''rel'':19C ''think'':3C ''video'':12C ''www.khanacademy.org'':17C,22C ''www.khanacademy.org/math/probability/regression/regression-correlation/v/regression-line-example'':16C ''www.khanacademy.org/math/probability/regression/regression-correlation/v/regression-line-example&lt;/a&gt;&lt;/p&gt;'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9739, 2, '2016-01-11 10:26:43.33', 2, NULL, NULL, '&lt;p&gt;Word2Vec isn''t a good choice for a dataset of such size. From researches I have seen, it will unleash its power if you feed at least couple of million of words, 3k tweets wouldn''t be enough for a concise word similarity.&lt;/p&gt;
', NULL, NULL, '''/p'':45C ''3k'':34C ''choic'':7C ''concis'':42C ''coupl'':29C ''dataset'':10C ''enough'':39C ''feed'':26C ''good'':6C ''isn'':3C ''least'':28C ''million'':31C ''p'':1C ''power'':23C ''research'':15C ''seen'':18C ''similar'':44C ''size'':13C ''tweet'':35C ''unleash'':21C ''word'':33C,43C ''word2vec'':2C ''wouldn'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8982, 1, '2015-11-23 05:47:01.023', 1, 100, 'What kind of research I can do with my data set?', '&lt;p&gt;I have a data containing 200K ads (sale and rent property in the United Arab Emirates, or UAE). Here is a sample:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;190459 obs. of  29 variables:
 $ id                  : chr  "tnydu.biz/DafdVO" "tnydu.biz/DafloP" "tnydu.biz/Dafmvy" "tnydu.biz/Dafuy5" ...
     $ AdType              : chr  "sale" "sale" "sale" "sale" ...
 $ AgencyFees          : num  NA NA NA NA NA NA NA NA NA NA ...
     $ Amenities           : chr  "CentralA/C&amp;amp;Heating,SharedPool,SharedGym,Security,BuiltinWardrobes,ViewofWater,ViewofLandmark" "Study,CentralA/C&amp;amp;Heating,Balcony,Security,MaidService,CoveredParking,BuiltinWardrobes,Walk-inCloset,BuiltinKitchenAppliances,Vi"| __truncated__ "CentralA/C&amp;amp;Heating,SharedSpa,Security,ConciergeService,MaidService,CoveredParking,BuiltinWardrobes,Walk-inCloset,BuiltinKitchen"| __truncated__ "CentralA/C&amp;amp;Heating,Balcony,Security,CoveredParking,BuiltinWardrobes,BuiltinKitchenAppliances,ViewofLandmark" ...
 $ AnnualCommunityFee  : chr  "9000" NA NA NA ...
     $ AreaDescription     : chr  "\r\n            \r\n                \r\n                    1.9 km from Najmat Reem Marina\r\n                \r\n            \"| __truncated__ "\r\n            \r\n                \r\n                    0.3 km from The Dubai Mall\r\n                \r\n            \r\n "| __truncated__ "\r\n            \r\n                \r\n            \r\n        " "\r\n            \r\n                \r\n                    0.7 km from The Dubai Mall\r\n                \r\n            \r\n "| __truncated__ ...
 $ Bathrooms           : num  1 NA NA NA 5 3 NA NA 3 3 ...
     $ Bedrooms            : num  0 3 0 1 3 2 2 2 3 3 ...
 $ Building            : chr  "HydraAvenueTowers" "BurjVista1" "TheAddressDubaiMall" "TheAddressDowntown" ...
     $ City                : chr  "AbuDhabi" "Dubai" "Dubai" "Dubai" ...
 $ PublishDate         : POSIXct, format: "2015-10-30" "2015-11-11" "2015-11-13" "2015-11-09" ...
     $ DealerCode          : chr  "599942" "604296" "604296" "604296" ...
 $ DealerName          : chr  "STARWOOD PROPERTIES BROKER" "BLUE PALACE REAL ESTATE BROKERS" "BLUE PALACE REAL ESTATE BROKERS" "BLUE PALACE REAL ESTATE BROKERS" ...
     $ Developer           : chr  "HydraProperties" "EMAAR" "EMAAR" NA ...
 $ Furnished           : chr  NA NA NA NA ...
     $ ListedBy            : chr  "Agent" "Agent" "Agent" "Agent" ...
 $ Location            : chr  "City of Lights, Tamouh Marina Square" "Downtown Dubai, Dubai" "Downtown, Cairo" "Downtown Dubai, Dubai" ...
     $ LocationGPSLatitude : num  24.5 25.2 30 25.2 25.1 ...
 $ LocationGPSLongitude: num  54.4 55.3 31.3 55.3 55.1 ...
     $ Price               : num  900000 3822888 2150000 3200000 5500000 ...
 $ PriceSqFt           : num  1129 2185 3909 3422 2431 ...
     $ PropertyReference   : chr  NA "BP9801" "DT-K20" " BP8958" ...
 $ PropertyType        : chr  "apartment" "apartment" "apartment" "apartment" ...
     $ ReadyBy             : POSIXct, format: "2015-04-30" NA NA NA ...
 $ RentIsPaid          : chr  NA NA NA NA ...
     $ ShortLink           : chr  "tnydu.biz/DafdVO" "tnydu.biz/DafloP" "tnydu.biz/Dafmvy" "tnydu.biz/Dafuy5" ...
 $ Size                : num  797 1749 550 935 2262 ...
     $ TotalClosingFee     : chr  NA NA NA NA ...
 $ VirtualView         : chr  NA NA NA NA ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m looking for help figuring out what kind of questions I can answer using these data.
For example,
1. What the mean price per year for a 1-bedroom apartment to rent in different areas of Dubai?
2. How are supplies for apartments and villas distributed on the city map?
3. Does the number of bathrooms influence on sale/rent price?
4. Where is the best place for investing to get more profit from apartment cost and rent revenue? &lt;/p&gt;

&lt;p&gt;I have already done some of this (plotted the map with mean price for studio in Dubai in thousands AED/year, 1USD = 3.66AED):&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/HDm3t.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/HDm3t.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;From your experience, what other questions can I ask? What I have to be aware in this data set? I can share my data set if anyone want to examine it (it is a 12mb zip).&lt;/p&gt;
', 'visualization  data', NULL, '''-04'':354C ''-09'':247C ''-10'':237C ''-11'':240C,241C,243C,246C ''-13'':244C ''-30'':238C,355C ''/a'':524C ''/code'':398C ''/dafdvo'':49C,369C ''/daflop'':52C,372C ''/dafmvy'':55C,375C ''/dafuy5'':58C,378C ''/hdm3t.jpg'':511C,518C ''/p'':37C,480C,505C,525C,563C ''/pre'':399C ''0'':211C,213C ''0.3'':157C ''0.7'':184C ''1'':199C,214C,420C,429C ''1.9'':140C ''1129'':331C ''12mb'':561C ''1749'':382C ''190459'':40C ''1usd'':502C ''2'':216C,217C,218C,439C ''200k'':20C ''2015'':236C,239C,242C,245C,353C ''2150000'':326C ''2185'':332C ''2262'':385C ''24.5'':310C ''2431'':335C ''25.1'':314C ''25.2'':311C,313C ''29'':43C ''3'':204C,207C,208C,212C,215C,219C,220C,452C ''3.66'':503C ''30'':312C ''31.3'':319C ''3200000'':327C ''3422'':334C ''3822888'':325C ''3909'':333C ''4'':462C ''5'':203C ''54.4'':317C ''55.1'':321C ''55.3'':318C,320C ''550'':383C ''5500000'':328C ''599942'':250C ''604296'':251C,252C,253C ''797'':381C ''9000'':128C ''900000'':324C ''935'':384C ''a'':504C ''abudhabi'':229C ''ad'':21C ''adtyp'':59C ''aed/year'':501C ''agencyfe'':65C ''agent'':288C,289C,290C,291C ''alreadi'':484C ''alt'':519C ''amen'':77C ''amp'':80C,90C,104C,118C ''annualcommunityfe'':126C ''answer'':414C ''anyon'':553C ''apart'':346C,347C,348C,349C,431C,444C,475C ''arab'':29C ''area'':436C ''areadescript'':132C ''ask'':535C ''awar'':541C ''balconi'':92C,120C ''bathroom'':197C,457C ''bedroom'':209C,430C ''best'':466C ''blue'':259C,264C,269C ''bp8958'':343C ''bp9801'':339C ''broker'':258C,263C,268C,273C ''build'':221C ''builtinkitchen'':115C ''builtinkitchenappli'':100C,124C ''builtinwardrob'':85C,96C,111C,123C ''burjvista1'':224C ''cairo'':304C ''centrala/c'':79C,89C,103C,117C ''chr'':46C,60C,78C,127C,133C,222C,228C,249C,255C,275C,281C,287C,293C,337C,345C,360C,366C,387C,393C ''citi'':227C,294C,450C ''code'':39C ''conciergeservic'':108C ''contain'':19C ''cost'':476C ''coveredpark'':95C,110C,122C ''data'':10A,13B,18C,417C,544C,550C ''dealercod'':248C ''dealernam'':254C ''descript'':522C ''develop'':274C ''differ'':435C ''distribut'':447C ''done'':485C ''downtown'':300C,303C,305C ''dt'':341C ''dt-k20'':340C ''dubai'':161C,188C,230C,231C,232C,301C,302C,306C,307C,438C,498C ''emaar'':277C,278C ''emir'':30C ''enter'':520C ''estat'':262C,267C,272C ''examin'':556C ''exampl'':419C ''experi'':529C ''figur'':406C ''format'':235C,352C ''furnish'':280C ''get'':471C ''heat'':81C,91C,105C,119C ''help'':405C ''href'':508C ''hydraavenuetow'':223C ''hydraproperti'':276C ''i.stack.imgur.com'':510C,517C ''i.stack.imgur.com/hdm3t.jpg'':509C,516C ''id'':45C ''imag'':521C ''img'':514C ''incloset'':99C,114C ''influenc'':458C ''invest'':469C ''k20'':342C ''kind'':2A,409C ''km'':141C,158C,185C ''light'':296C ''listedbi'':286C ''locat'':292C ''locationgpslatitud'':308C ''locationgpslongitud'':315C ''look'':403C ''m'':402C ''maidservic'':94C,109C ''mall'':162C,189C ''map'':451C,491C ''marina'':145C,298C ''mean'':423C,493C ''n'':135C,137C,139C,147C,149C,152C,154C,156C,164C,166C,168C,171C,173C,175C,177C,179C,181C,183C,191C,193C,195C ''na'':67C,68C,69C,70C,71C,72C,73C,74C,75C,76C,129C,130C,131C,200C,201C,202C,205C,206C,279C,282C,283C,284C,285C,338C,356C,357C,358C,361C,362C,363C,364C,388C,389C,390C,391C,394C,395C,396C,397C ''najmat'':143C ''nofollow'':513C ''num'':66C,198C,210C,309C,316C,323C,330C,380C ''number'':455C ''ob'':41C ''p'':14C,400C,481C,506C,526C ''palac'':260C,265C,270C ''per'':425C ''place'':467C ''plot'':489C ''posixct'':234C,351C ''pre'':38C ''price'':322C,424C,461C,494C ''pricesqft'':329C ''profit'':473C ''properti'':25C,257C ''propertyrefer'':336C ''propertytyp'':344C ''publishd'':233C ''question'':411C,532C ''r'':134C,136C,138C,146C,148C,151C,153C,155C,163C,165C,167C,170C,172C,174C,176C,178C,180C,182C,190C,192C,194C ''readybi'':350C ''real'':261C,266C,271C ''reem'':144C ''rel'':512C ''rent'':24C,433C,478C ''rentispaid'':359C ''research'':4A ''revenu'':479C ''sale'':22C,61C,62C,63C,64C ''sale/rent'':460C ''sampl'':36C ''secur'':84C,93C,107C,121C ''set'':11A,545C,551C ''share'':548C ''sharedgym'':83C ''sharedpool'':82C ''sharedspa'':106C ''shortlink'':365C ''size'':379C ''squar'':299C ''src'':515C ''starwood'':256C ''studi'':88C ''studio'':496C ''suppli'':442C ''tamouh'':297C ''theaddressdowntown'':226C ''theaddressdubaimal'':225C ''thousand'':500C ''tnydu.biz'':48C,51C,54C,57C,368C,371C,374C,377C ''tnydu.biz/dafdvo'':47C,367C ''tnydu.biz/daflop'':50C,370C ''tnydu.biz/dafmvy'':53C,373C ''tnydu.biz/dafuy5'':56C,376C ''totalclosingfe'':386C ''truncat'':102C,116C,150C,169C,196C ''uae'':32C ''unit'':28C ''use'':415C ''variabl'':44C ''vi'':101C ''viewoflandmark'':87C,125C ''viewofwat'':86C ''villa'':446C ''virtualview'':392C ''visual'':12B ''walk'':98C,113C ''walk-incloset'':97C,112C ''want'':554C ''year'':426C ''zip'':562C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6554, 2, '2015-07-22 23:27:40.203', 0, NULL, NULL, '&lt;p&gt;R''s performance depends incredibly on how you write it.  For example, you mostly should never use for loops in R - they''re horribly slow because they execute a function call with every iteration.  (One should vectorize and use the apply family of functions instead.  Weird, I know..)  Vectorization is king in R if you want fast code.  Assuming you vectorize both your R and Python code (and other factors), you should probably get the same order of magnitude in speed.  For data larger than memory (you can specify the limit), R starts to become a bad choice.  I don''t know much about python''s internals, so I can''t speak on that.&lt;/p&gt;
', NULL, NULL, '''/p'':116C ''appli'':42C ''assum'':60C ''bad'':98C ''becom'':96C ''call'':32C ''choic'':99C ''code'':59C,68C ''data'':84C ''depend'':5C ''everi'':34C ''exampl'':13C ''execut'':29C ''factor'':71C ''famili'':43C ''fast'':58C ''function'':31C,45C ''get'':75C ''horribl'':25C ''incred'':6C ''instead'':46C ''intern'':108C ''iter'':35C ''king'':52C ''know'':49C,103C ''larger'':85C ''limit'':92C ''loop'':20C ''magnitud'':80C ''memori'':87C ''most'':15C ''much'':104C ''never'':17C ''one'':36C ''order'':78C ''p'':1C ''perform'':4C ''probabl'':74C ''python'':67C,106C ''r'':2C,22C,54C,65C,93C ''re'':24C ''slow'':26C ''speak'':113C ''specifi'':90C ''speed'':82C ''start'':94C ''use'':18C,40C ''vector'':38C,50C,62C ''want'':57C ''weird'':47C ''write'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6528, 1, '2015-07-21 09:39:32.693', 1, 60, 'Newton-Raphson or EM Algorithm in Python', '&lt;p&gt;Is there any implementation of Newton-Raphson or EM Algorithm? Can I get the source code of it?&lt;/p&gt;

&lt;p&gt;I tried googling, but didn''t come across any. So asking here.&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'python  algorithms', NULL, '''/p'':31C,45C,48C ''across'':40C ''algorithm'':6A,10B,22C ''ask'':43C ''code'':28C ''come'':39C ''didn'':37C ''em'':5A,21C ''get'':25C ''googl'':35C ''implement'':15C ''newton'':2A,18C ''newton-raphson'':1A,17C ''p'':11C,32C,46C ''python'':8A,9B ''raphson'':3A,19C ''sourc'':27C ''thank'':47C ''tri'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6513, 1, '2015-07-20 16:00:53.09', 1, 45, 'How to control false positives in sequential A/B testing while keeping a low sample size?', '&lt;p&gt;I am working on a planned sequence of n independent A/B tests(=run a maximum of n tests or stop earlier if a good improvement is found) and in order to keep the significance level within an acceptable level(=0.05) I''m considering ways of controlling the false positives rate while keeping the sample size as low as I can(it''s for a low traffic website)&lt;/p&gt;

&lt;p&gt;I''m aware of the Bonferroni, Benjiamimi-Hochberg and related methods designed to control the proportion of false positives in the multiple comparisons situation. However the problem of computing the sample size still remains, the simplest approach seems the one of computing the required sample size starting from alpha = 0.05/n as dictated by the Bonferroni correction and then perhaps use Benjiamimi-Hochberg for the actual testing. Is there a better way of computing the sample size when Benjiamimi-Hochberg is used?&lt;/p&gt;

&lt;p&gt;Further as the A/B tests are designed to test possible improvements I was thinking that if the sequential tests are based on independent samples then then probability of two consecutive false positives would be alpha^2 = 0.025. My understanding is that the sequential tests would be independent if the respective samples do not refer to the same users. These could for example be users that joined the website after the previous test.&lt;/p&gt;

&lt;p&gt;If the above thinking makes some sense, I could use a "weak" Bonferroni (i.e. alpha'' = alpha/(n*0.2) )correction in order to run tests with a reasonable power and lower sample size and once a positive is found(the null hypothesis is rejected), I could repeat the test and if it''s positive again then the result is positive with p &amp;lt; 0.025 which means that I would accept the result. This approach would allow for some control over the sample size but... is it sound?&lt;/p&gt;

&lt;p&gt;Any opinion would be greatly appreciated. Thanks in advance. AMAR&lt;/p&gt;
', 'data-mining  ab-test', NULL, '''/n'':143C ''/p'':90C,177C,251C,339C,351C ''0.025'':215C,315C ''0.05'':62C,142C ''0.2'':270C ''2'':214C ''a/b'':8A,33C,182C ''ab'':20B ''ab-test'':19B ''accept'':60C,321C ''actual'':159C ''advanc'':349C ''allow'':327C ''alpha'':141C,213C,267C,268C ''amar'':350C ''appreci'':346C ''approach'':129C,325C ''awar'':94C ''base'':199C ''benjiamimi'':99C,155C,173C ''benjiamimi-hochberg'':98C,154C,172C ''better'':164C ''bonferroni'':97C,148C,265C ''comparison'':115C ''comput'':121C,134C,167C ''consecut'':208C ''consid'':65C ''control'':3A,68C,106C,330C ''correct'':149C,271C ''could'':238C,261C,297C ''data'':17B ''data-min'':16B ''design'':104C,185C ''dictat'':145C ''earlier'':43C ''exampl'':240C ''fals'':4A,70C,110C,209C ''found'':49C,290C ''good'':46C ''great'':345C ''hochberg'':100C,156C,174C ''howev'':117C ''hypothesi'':293C ''i.e'':266C ''improv'':47C,189C ''independ'':32C,201C,225C ''join'':244C ''keep'':11A,54C,74C ''level'':57C,61C ''low'':13A,79C,87C ''lower'':282C ''lt'':314C ''m'':64C,93C ''make'':257C ''maximum'':37C ''mean'':317C ''method'':103C ''mine'':18B ''multipl'':114C ''n'':31C,39C,269C ''null'':292C ''one'':132C ''opinion'':342C ''order'':52C,273C ''p'':22C,91C,178C,252C,313C,340C ''perhap'':152C ''plan'':28C ''posit'':5A,71C,111C,210C,288C,305C,311C ''possibl'':188C ''power'':280C ''previous'':249C ''probabl'':205C ''problem'':119C ''proport'':108C ''rate'':72C ''reason'':279C ''refer'':232C ''reject'':295C ''relat'':102C ''remain'':126C ''repeat'':298C ''requir'':136C ''respect'':228C ''result'':309C,323C ''run'':35C,275C ''sampl'':14A,76C,123C,137C,169C,202C,229C,283C,333C ''seem'':130C ''sens'':259C ''sequenc'':29C ''sequenti'':7A,196C,221C ''signific'':56C ''simplest'':128C ''situat'':116C ''size'':15A,77C,124C,138C,170C,284C,334C ''sound'':338C ''start'':139C ''still'':125C ''stop'':42C ''test'':9A,21B,34C,40C,160C,183C,187C,197C,222C,250C,276C,300C ''thank'':347C ''think'':192C,256C ''traffic'':88C ''two'':207C ''understand'':217C ''use'':153C,176C,262C ''user'':236C,242C ''way'':66C,165C ''weak'':264C ''websit'':89C,246C ''within'':58C ''work'':25C ''would'':211C,223C,320C,326C,343C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6514, 2, '2015-07-20 16:10:10.593', 1, NULL, NULL, '&lt;p&gt;Try this&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;as.POSIXct(Sys.Date(), format= "%Y-%m-%d", tz="UCT") - as.POSIXct("11/25/2011", format = "%m/%d/%Y",tz="UCT")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;More vectorized operation in your situation&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;as.POSIXct(Sys.Date(), format= "%Y-%m-%d", tz="UCT") - as.POSIXct(Last_tran, format = "%m/%d/%Y",tz="UCT")
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':23C,52C ''/p'':4C,32C ''/pre'':24C,53C ''11/25/2011'':16C ''as.posixct'':7C,15C,35C,43C ''code'':6C,34C ''d'':12C,19C,40C,48C ''format'':9C,17C,37C,46C ''last'':44C ''m'':11C,18C,39C,47C ''oper'':28C ''p'':1C,25C ''pre'':5C,33C ''situat'':31C ''sys.date'':8C,36C ''tran'':45C ''tri'':2C ''tz'':13C,21C,41C,50C ''uct'':14C,22C,42C,51C ''vector'':27C ''y'':10C,20C,38C,49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6515, 1, '2015-07-20 16:15:54.27', 2, 50, 'How to extract a column that has the highest value within row in Hive?', '&lt;p&gt;I have a table, more or less in the following format&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;col1 col2 col3 ... col100
val1 val2 val3 ... val100
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Where val* are doubles. Is there a way to extract for each row in which column is the highest value within row in Hive?&lt;/p&gt;

&lt;p&gt;For example, for table like&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;col1 col2 col3
2     4   5
8     1   2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I would get&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;col3
col1
&lt;/code&gt;&lt;/pre&gt;
', 'sql  hive', NULL, '''/code'':40C,86C,97C ''/p'':29C,67C,74C,92C ''/pre'':41C,87C,98C ''1'':84C ''2'':80C,85C ''4'':81C ''5'':82C ''8'':83C ''code'':31C,76C,94C ''col1'':32C,77C,96C ''col100'':35C ''col2'':33C,78C ''col3'':34C,79C,95C ''column'':5A,58C ''doubl'':46C ''exampl'':70C ''extract'':3A,52C ''follow'':27C ''format'':28C ''get'':91C ''highest'':9A,61C ''hive'':14A,16B,66C ''less'':24C ''like'':73C ''p'':17C,42C,68C,88C ''pre'':30C,75C,93C ''row'':12A,55C,64C ''sql'':15B ''tabl'':21C,72C ''val'':44C ''val1'':36C ''val100'':39C ''val2'':37C ''val3'':38C ''valu'':10A,62C ''way'':50C ''within'':11A,63C ''would'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6517, 2, '2015-07-20 17:19:25.467', 1, NULL, NULL, '&lt;p&gt;I can''t test in hive, but a possible SQL query is as follows (greatest returns the maximum value from the list):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;select 
  case 
   when col1 = greatest(col1,col2,col3) then ''col1'' 
   when col2 = greatest(col1,col2,col3) then ''col2''  
   when col3 = greatest(col1,col2,col3) then ''col3''    
  end as c1 
from test;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Additional note:
you should check, how ties are to be handled, in my solution I simple take the first column.&lt;/p&gt;
', NULL, NULL, '''/code'':58C ''/p'':24C,81C ''/pre'':59C ''addit'':61C ''c1'':55C ''case'':28C ''check'':65C ''code'':26C ''col1'':30C,32C,36C,40C,48C ''col2'':33C,38C,41C,44C,49C ''col3'':34C,42C,46C,50C,52C ''column'':80C ''end'':53C ''first'':79C ''follow'':15C ''greatest'':16C,31C,39C,47C ''handl'':71C ''hive'':7C ''list'':23C ''maximum'':19C ''note'':62C ''p'':1C,60C ''possibl'':10C ''pre'':25C ''queri'':12C ''return'':17C ''select'':27C ''simpl'':76C ''solut'':74C ''sql'':11C ''take'':77C ''test'':5C,57C ''tie'':67C ''valu'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8774, 2, '2015-11-10 04:55:46.59', 0, NULL, NULL, '&lt;p&gt;You want to count the number of ordered combinaisons from a list.&lt;/p&gt;

&lt;p&gt;Let (i) be the index of the list. (len) the length of the list.&lt;/p&gt;

&lt;p&gt;count = 0&lt;/p&gt;

&lt;p&gt;for each i in list:   #(1,2,3,4,5)
    count = count + len - i&lt;/p&gt;

&lt;p&gt;The count is the number of combinaisons.&lt;/p&gt;

&lt;p&gt;Example:
For the list = [2,3,4,10]&lt;/p&gt;

&lt;p&gt;count = 0 + 4 - 1 = 3&lt;/p&gt;

&lt;p&gt;count = 3 + 4 - 2 = 5&lt;/p&gt;

&lt;p&gt;count = 5 + 4 - 3 = 6&lt;/p&gt;

&lt;p&gt;count = 6 + 4 - 4 = 6&lt;/p&gt;

&lt;p&gt;With best wishes.&lt;/p&gt;
', NULL, NULL, '''/p'':14C,30C,34C,50C,59C,69C,76C,83C,90C,97C,102C ''0'':33C,72C ''1'':41C,74C ''10'':68C ''2'':42C,65C,81C ''3'':43C,66C,75C,79C,88C ''4'':44C,67C,73C,80C,87C,94C,95C ''5'':45C,82C,86C ''6'':89C,93C,96C ''best'':100C ''combinaison'':10C,58C ''count'':5C,32C,46C,47C,53C,71C,78C,85C,92C ''exampl'':61C ''index'':20C ''len'':24C,48C ''length'':26C ''let'':16C ''list'':13C,23C,29C,40C,64C ''number'':7C,56C ''order'':9C ''p'':1C,15C,31C,35C,51C,60C,70C,77C,84C,91C,98C ''want'':3C ''wish'':101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6557, 2, '2015-07-23 09:36:01.793', 2, NULL, NULL, '&lt;p&gt;By default random forest picks up 2/3rd data for training and rest for testing for regression and almost 70% data for training and rest for testing during classification.By principle since it randomizes the variable selection during each tree split it''s not prone to overfit unlike other models.However if you want to use CV using nfolds in sklearn you can still use the concept of hold out set such as oob_score(out of bag)=True which shows model performance with or without using CV.
So in a nutshell using oob_score=True with or without nfolds can itself tell whether using CV is good for your data.Generally if your target is following a certain distribution and you don''t have much observation data with you then using CV will not give much improvement.&lt;/p&gt;
', NULL, NULL, '''/p'':136C ''2/3rd'':8C ''70'':20C ''almost'':19C ''bag'':76C ''certain'':116C ''classification.by'':29C ''concept'':65C ''cv'':55C,86C,104C,130C ''data'':9C,21C,125C ''data.generally'':109C ''default'':3C ''distribut'':117C ''follow'':114C ''forest'':5C ''give'':133C ''good'':106C ''hold'':67C ''improv'':135C ''model'':80C ''models.however'':49C ''much'':123C,134C ''nfold'':57C,98C ''nutshel'':90C ''observ'':124C ''oob'':72C,92C ''overfit'':46C ''p'':1C ''perform'':81C ''pick'':6C ''principl'':30C ''prone'':44C ''random'':4C,33C ''regress'':17C ''rest'':13C,25C ''score'':73C,93C ''select'':36C ''set'':69C ''show'':79C ''sinc'':31C ''sklearn'':59C ''split'':40C ''still'':62C ''target'':112C ''tell'':101C ''test'':15C,27C ''train'':11C,23C ''tree'':39C ''true'':77C,94C ''unlik'':47C ''use'':54C,56C,63C,85C,91C,103C,129C ''variabl'':35C ''want'':52C ''whether'':102C ''without'':84C,97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6518, 2, '2015-07-20 17:22:41.453', 4, NULL, NULL, '&lt;p&gt;The way that you have phrased this question makes it tough for people to answer without first offering you some background on linear regression (LR).  Its great that you are interested in learning some ML and LR is a great place to start.&lt;/p&gt;

&lt;p&gt;Linear regression is really just finding a line (or plain or hyperplain) that maps a relationship between two or more variables or features.  The important requirement is that the target variable be a continuous numerical value.  So its less about finding a problem for which linear regression "works" and more about finding some data that interests you and playing with it using linear regression.&lt;/p&gt;

&lt;p&gt;I suggest you download some open data in the realms that you have mentioned.  There is plenty of open data in every topic that you mentioned that contain continuous numerical values that you can predict using other features of the data.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="https://www.quandl.com/data/WIKI" rel="nofollow"&gt;stock trading&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.giantbomb.com/api/" rel="nofollow"&gt;video games&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://sdo.gsfc.nasa.gov/data/dataaccess.php" rel="nofollow"&gt;astronomy&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.opensourcesports.com/" rel="nofollow"&gt;sports betting&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.firebase.com/docs/open-data/airports.html" rel="nofollow"&gt;flight time prediction&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Also think about taking some sort of online MOOC as this will help you gain some footing in the subject.  &lt;a href="https://www.coursera.org/learn/machine-learning/home/info" rel="nofollow"&gt;Andrew Ng''s Coursera on Machine Learning&lt;/a&gt; is highly recommended as a starting point and include some linear regression during the first portion and &lt;a href="http://scikit-learn.org/" rel="nofollow"&gt;scikit-learn&lt;/a&gt; is a great Python based library.&lt;/p&gt;
', NULL, NULL, '''/a'':166C,178C,189C,199C,212C,250C,276C ''/api/'':173C ''/data/dataaccess.php'':185C ''/data/wiki'':161C ''/docs/open-data/airports.html'':206C ''/learn/machine-learning/home/info'':240C ''/li'':167C,179C,190C,200C,213C ''/ol'':214C ''/p'':45C,111C,154C,283C ''also'':216C ''andrew'':243C ''answer'':16C ''astronomi'':188C ''background'':22C ''base'':281C ''bet'':198C ''contain'':140C ''continu'':80C,141C ''coursera'':246C ''data'':100C,119C,132C,153C ''download'':116C ''everi'':134C ''featur'':69C,150C ''find'':52C,87C,98C ''first'':18C,265C ''flight'':209C ''foot'':232C ''gain'':230C ''game'':177C ''great'':28C,41C,279C ''help'':228C ''high'':252C ''href'':158C,170C,182C,193C,203C,237C,269C ''hyperplain'':58C ''import'':71C ''includ'':259C ''interest'':32C,102C ''learn'':34C,249C,275C ''less'':85C ''li'':156C,168C,180C,191C,201C ''librari'':282C ''line'':54C ''linear'':24C,47C,92C,109C,261C ''lr'':26C,38C ''machin'':248C ''make'':10C ''map'':60C ''mention'':126C,138C ''ml'':36C ''mooc'':224C ''ng'':244C ''nofollow'':163C,175C,187C,196C,208C,242C,272C ''numer'':81C,142C ''offer'':19C ''ol'':155C ''onlin'':223C ''open'':118C,131C ''p'':1C,46C,112C,215C ''peopl'':14C ''phrase'':7C ''place'':42C ''plain'':56C ''play'':105C ''plenti'':129C ''point'':257C ''portion'':266C ''predict'':147C,211C ''problem'':89C ''python'':280C ''question'':9C ''realli'':50C ''realm'':122C ''recommend'':253C ''regress'':25C,48C,93C,110C,262C ''rel'':162C,174C,186C,195C,207C,241C,271C ''relationship'':62C ''requir'':72C ''scikit'':274C ''scikit-learn'':273C ''scikit-learn.org'':270C ''sdo.gsfc.nasa.gov'':184C ''sdo.gsfc.nasa.gov/data/dataaccess.php'':183C ''sort'':221C ''sport'':197C ''start'':44C,256C ''stock'':164C ''subject'':235C ''suggest'':114C ''take'':219C ''target'':76C ''think'':217C ''time'':210C ''topic'':135C ''tough'':12C ''trade'':165C ''two'':64C ''use'':108C,148C ''valu'':82C,143C ''variabl'':67C,77C ''video'':176C ''way'':3C ''without'':17C ''work'':94C ''www.coursera.org'':239C ''www.coursera.org/learn/machine-learning/home/info'':238C ''www.firebase.com'':205C ''www.firebase.com/docs/open-data/airports.html'':204C ''www.giantbomb.com'':172C ''www.giantbomb.com/api/'':171C ''www.opensourcesports.com'':194C ''www.quandl.com'':160C ''www.quandl.com/data/wiki'':159C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6519, 1, '2015-07-20 20:13:53.77', 1, 100, 'Combining Datasets with Different Features', '&lt;p&gt;I have multiple datasets, with slightly differing features. What tools can I use to make this a homogeneous dataset?&lt;/p&gt;

&lt;p&gt;Dataset1:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;featureA,featureB,featureC
1,7,3
4,8,4
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Dataset2:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;featureA,featureC,featureD,featureE
3,4,5,6
9,8,4,6
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Homogeneous Dataset&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;featureA,featureB,featureC,featureD,featureE
1,7,3,,
4,8,4,,
3,,4,5,6
9,,8,4,6
&lt;/code&gt;&lt;/pre&gt;
', 'machine-learning  dataset', NULL, '''/code'':45C,64C,93C ''/p'':30C,33C,49C,71C ''/pre'':46C,65C,94C ''/strong'':70C ''1'':39C,79C ''3'':41C,56C,81C,85C ''4'':42C,44C,57C,62C,82C,84C,86C,91C ''5'':58C,87C ''6'':59C,63C,88C,92C ''7'':40C,80C ''8'':43C,61C,83C,90C ''9'':60C,89C ''code'':35C,51C,73C ''combin'':1A ''dataset'':2A,9B,14C,29C,69C ''dataset1'':32C ''dataset2'':48C ''differ'':4A,17C ''featur'':5A,18C,54C,77C ''feature'':55C,78C ''featurea'':36C,52C,74C ''featureb'':37C,75C ''featurec'':38C,53C,76C ''homogen'':28C,68C ''learn'':8B ''machin'':7B ''machine-learn'':6B ''make'':25C ''multipl'':13C ''p'':10C,31C,47C,66C ''pre'':34C,50C,72C ''slight'':16C ''strong'':67C ''tool'':20C ''use'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6529, 2, '2015-07-21 09:59:49.177', 1, NULL, NULL, '&lt;p&gt;scikit learn has the EM algorithm &lt;a href="http://scikit-learn.org/stable/modules/mixture.html" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Source code is available.&lt;/p&gt;

&lt;p&gt;And if you are an R fan the &lt;code&gt;mclust&lt;/code&gt; package is available &lt;a href="http://www.stat.washington.edu/mclust/" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':16C,47C ''/code'':35C ''/mclust/'':43C ''/p'':17C,23C,48C ''/stable/modules/mixture.html'':12C ''algorithm'':7C ''avail'':22C,38C ''code'':20C,33C ''em'':6C ''fan'':31C ''href'':9C,40C ''learn'':3C ''mclust'':34C ''nofollow'':14C,45C ''p'':1C,18C,24C ''packag'':36C ''r'':30C ''rel'':13C,44C ''scikit'':2C ''scikit-learn.org'':11C ''scikit-learn.org/stable/modules/mixture.html'':10C ''sourc'':19C ''www.stat.washington.edu'':42C ''www.stat.washington.edu/mclust/'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6521, 2, '2015-07-20 20:41:25.427', 4, NULL, NULL, '&lt;p&gt;First of all, if GloVe gives you &lt;em&gt;normalized&lt;/em&gt; unit vectors, then the two calculations are equivalent. In general, I would use the cosine similarity since it removes the effect of document length. For example, a postcard and a full-length book may be about the same topic, but will likely be quite far apart in pure "term frequency" space using the Euclidean distance. They will be right on top of each other in cosine similarity.&lt;/p&gt;
', NULL, NULL, '''/em'':11C ''/p'':80C ''apart'':58C ''book'':45C ''calcul'':17C ''cosin'':26C,78C ''distanc'':67C ''document'':34C ''effect'':32C ''em'':9C ''equival'':19C ''euclidean'':66C ''exampl'':37C ''far'':57C ''first'':2C ''frequenc'':62C ''full'':43C ''full-length'':42C ''general'':21C ''give'':7C ''glove'':6C ''length'':35C,44C ''like'':54C ''may'':46C ''normal'':10C ''p'':1C ''postcard'':39C ''pure'':60C ''quit'':56C ''remov'':30C ''right'':71C ''similar'':27C,79C ''sinc'':28C ''space'':63C ''term'':61C ''top'':73C ''topic'':51C ''two'':16C ''unit'':12C ''use'':24C,64C ''vector'':13C ''would'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6522, 2, '2015-07-20 20:48:56.047', 2, NULL, NULL, '&lt;p&gt;You can use &lt;a href="http://www.r-project.org/" rel="nofollow"&gt;R&lt;/a&gt; to do that. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.inside-r.org/packages/cran/gtools/docs/smartbind" rel="nofollow"&gt;The smartbind function&lt;/a&gt; is the perfect way to combine datsets in the way you are asking for:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(gtools)

d1&amp;lt;-as.data.frame(rbind(c(1,7,3),c(4,8,4))))
names(d1)&amp;lt;-c("featureA","featureB","featureC")

d2&amp;lt;-as.data.frame(rbind(c(3,4,5,6),c(9,8,4,6)))
names(d2)&amp;lt;-c("featureA","featureC","featureD","featureE")

d3&amp;lt;-smartbind(d1,d2)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':11C,27C ''/code'':93C ''/p'':15C,42C ''/packages/cran/gtools/docs/smartbind'':21C ''/pre'':94C ''1'':52C ''3'':54C,71C ''4'':56C,58C,72C,78C ''5'':73C ''6'':74C,79C ''7'':53C ''8'':57C,77C ''9'':76C ''as.data.frame'':49C,68C ''ask'':40C ''c'':51C,55C,62C,70C,75C,83C ''code'':44C ''combin'':33C ''d1'':47C,60C,91C ''d2'':66C,81C,92C ''d3'':88C ''datset'':34C ''featur'':86C ''feature'':87C ''featurea'':63C,84C ''featureb'':64C ''featurec'':65C,85C ''function'':26C ''gtool'':46C ''href'':6C,18C ''librari'':45C ''lt'':48C,61C,67C,82C,89C ''name'':59C,80C ''nofollow'':9C,23C ''p'':1C,16C ''perfect'':30C ''pre'':43C ''r'':10C ''rbind'':50C,69C ''rel'':8C,22C ''smartbind'':25C,90C ''use'':4C ''way'':31C,37C ''www.inside-r.org'':20C ''www.inside-r.org/packages/cran/gtools/docs/smartbind'':19C ''www.r-project.org'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6523, 2, '2015-07-20 21:51:46.383', 1, NULL, NULL, '&lt;p&gt;In anomaly detection (one sort of STL application), it''s easier to see outliers if you can normalize the original time series to a residual series (called "remainder" in R''s STL package). That involves identifying components that you can subtract from the original series. In STL, the components are the seasonal series and the trend.&lt;/p&gt;

&lt;p&gt;If there is a regular, recurring pattern in the data (i.e., a seasonality), then you don''t want the expected ups/downs of that pattern to drive the anomaly detector. Instead you want to be able to say "are we higher or lower than expected, factoring out the regular ups/downs that happen over the course of the day (or week, or month, etc.)". Identifying and removing the seasonal series allows you to ask that question.&lt;/p&gt;

&lt;p&gt;The decomposition you generated does not look right: the seasonal pattern doesn''t seem to have captured much structure, the trend overfits the data, and the residuals have a regular structure that the seasonal series ought to have captured. I don''t know whether this is because the dataset itself doesn''t lend itself to an STL decomposition (the series tail appears qualitatively different than the initial part) or if it''s because the parameters are wrong. Figure 6.10 on &lt;a href="https://www.otexts.org/fpp/6/5" rel="nofollow"&gt;this page&lt;/a&gt; shows a more prototypical case.&lt;/p&gt;
', NULL, NULL, '''/a'':225C ''/fpp/6/5'':220C ''/p'':58C,134C,231C ''6.10'':214C ''abl'':94C ''allow'':128C ''anomali'':3C,87C ''appear'':197C ''applic'':9C ''ask'':131C ''call'':28C ''captur'':152C,174C ''case'':230C ''compon'':38C,50C ''cours'':113C ''data'':69C,159C ''dataset'':184C ''day'':116C ''decomposit'':137C,193C ''detect'':4C ''detector'':88C ''differ'':199C ''doesn'':147C,186C ''drive'':85C ''easier'':12C ''etc'':121C ''expect'':79C,103C ''factor'':104C ''figur'':213C ''generat'':139C ''happen'':110C ''higher'':99C ''href'':217C ''i.e'':70C ''identifi'':37C,122C ''initi'':202C ''instead'':89C ''involv'':36C ''know'':178C ''lend'':188C ''look'':142C ''lower'':101C ''month'':120C ''much'':153C ''nofollow'':222C ''normal'':19C ''one'':5C ''origin'':21C,45C ''ought'':171C ''outlier'':15C ''overfit'':157C ''p'':1C,59C,135C ''packag'':34C ''page'':224C ''paramet'':210C ''part'':203C ''pattern'':66C,83C,146C ''prototyp'':229C ''qualit'':198C ''question'':133C ''r'':31C ''recur'':65C ''regular'':64C,107C,165C ''rel'':221C ''remaind'':29C ''remov'':124C ''residu'':26C,162C ''right'':143C ''say'':96C ''season'':53C,72C,126C,145C,169C ''see'':14C ''seem'':149C ''seri'':23C,27C,46C,54C,127C,170C,195C ''show'':226C ''sort'':6C ''stl'':8C,33C,48C,192C ''structur'':154C,166C ''subtract'':42C ''tail'':196C ''time'':22C ''trend'':57C,156C ''ups/downs'':80C,108C ''want'':77C,91C ''week'':118C ''whether'':179C ''wrong'':212C ''www.otexts.org'':219C ''www.otexts.org/fpp/6/5'':218C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6524, 2, '2015-07-21 03:22:58.723', 1, NULL, NULL, '&lt;p&gt;There are many pitfalls to not scaling your data and it is generally very advisable to scale it. It is so easy to do, it is reversible, and it useful in other operations like removing outliers.&lt;/p&gt;

&lt;p&gt;Upon further analysis of the specifics of Hidden Markov Models using Multivariate Gaussians the theoretical accuracy should not suffer as a result of drastic differences in the scales of your features.  But, an important operation involving multivariate Gaussian distribution is matrix inversion.&lt;/p&gt;

&lt;p&gt;Though the theoretical invertibility won''t change, the practical numerical solution to your problem will likely suffer inaccuracy due to the difference in scales.  Iterative methods will have issues with convergence and direct solve methods will suffer from stiffness.  This is especially true when common complications like linear dependence are present.&lt;/p&gt;

&lt;p&gt;Here is a set of 3 lectures on HMM with some specifics on multivariate Gaussians (&lt;a href="http://www.cs.berkeley.edu/~jordan/courses/281A-fall04/lectures/lec-10-21.pdf" rel="nofollow"&gt;1&lt;/a&gt;-&lt;a href="http://www.cs.berkeley.edu/~jordan/courses/281A-fall04/lectures/lec-10-26.pdf" rel="nofollow"&gt;2&lt;/a&gt;-&lt;a href="http://www.cs.berkeley.edu/~jordan/courses/281A-fall04/lectures/lec-10-28.pdf" rel="nofollow"&gt;3&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;I know we''ve already discussed this in the comments, but I wanted to close out this question, so have added it as an answer.&lt;/p&gt;

&lt;p&gt;I hope this helps! &lt;/p&gt;
', NULL, NULL, '''/a'':160C,169C,178C ''/p'':38C,82C,135C,179C,206C,212C ''/~jordan/courses/281a-fall04/lectures/lec-10-21.pdf'':156C ''/~jordan/courses/281a-fall04/lectures/lec-10-26.pdf'':165C ''/~jordan/courses/281a-fall04/lectures/lec-10-28.pdf'':174C ''1'':159C ''2'':168C ''3'':142C,177C ''accuraci'':55C ''ad'':201C ''advis'':16C ''alreadi'':185C ''analysi'':42C ''answer'':205C ''chang'':90C ''close'':195C ''comment'':190C ''common'':128C ''complic'':129C ''converg'':114C ''data'':10C ''depend'':132C ''differ'':64C,105C ''direct'':116C ''discuss'':186C ''distribut'':78C ''drastic'':63C ''due'':102C ''easi'':23C ''especi'':125C ''featur'':70C ''gaussian'':52C,77C,151C ''general'':14C ''help'':211C ''hidden'':47C ''hmm'':145C ''hope'':209C ''href'':153C,162C,171C ''import'':73C ''inaccuraci'':101C ''invers'':81C ''invert'':87C ''involv'':75C ''issu'':112C ''iter'':108C ''know'':182C ''lectur'':143C ''like'':35C,99C,130C ''linear'':131C ''mani'':4C ''markov'':48C ''matrix'':80C ''method'':109C,118C ''model'':49C ''multivari'':51C,76C,150C ''nofollow'':158C,167C,176C ''numer'':93C ''oper'':34C,74C ''outlier'':37C ''p'':1C,39C,83C,136C,180C,207C ''pitfal'':5C ''practic'':92C ''present'':134C ''problem'':97C ''question'':198C ''rel'':157C,166C,175C ''remov'':36C ''result'':61C ''revers'':28C ''scale'':8C,18C,67C,107C ''set'':140C ''solut'':94C ''solv'':117C ''specif'':45C,148C ''stiff'':122C ''suffer'':58C,100C,120C ''theoret'':54C,86C ''though'':84C ''true'':126C ''upon'':40C ''use'':31C,50C ''ve'':184C ''want'':193C ''won'':88C ''www.cs.berkeley.edu'':155C,164C,173C ''www.cs.berkeley.edu/~jordan/courses/281a-fall04/lectures/lec-10-21.pdf'':154C ''www.cs.berkeley.edu/~jordan/courses/281a-fall04/lectures/lec-10-26.pdf'':163C ''www.cs.berkeley.edu/~jordan/courses/281a-fall04/lectures/lec-10-28.pdf'':172C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6601, 1, '2015-07-27 17:27:24.527', 4, 125, 'Time series classification', '&lt;p&gt;I am looking at time series security attack data where a given IP can either be labeled as (1) attack or (0) no attack. 
In total we will have thousands of IPs and roughly an equal number of attacks and non attacks. The data is rather noisy and every time series sequence can have a different length. &lt;/p&gt;

&lt;p&gt;I am looking for advise on state of the art approaches to time series classification. I am past the stage of simple things like moving averages and I am looking for ways to improve my current methods or new things to try. &lt;/p&gt;

&lt;p&gt;I have currently implemented a few different techniques:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;K-nearest neighbor with DTW. I am successfully using &lt;a href="http://www.cs.ucr.edu/~eamonn/UCRsuite.html" rel="nofollow"&gt;http://www.cs.ucr.edu/~eamonn/UCRsuite.html&lt;/a&gt; which provides state of the art performance. &lt;/li&gt;
&lt;li&gt;Logical shapelets (&lt;a href="http://www.cs.ucr.edu/~mueen/LogicalShapelet/" rel="nofollow"&gt;http://www.cs.ucr.edu/~mueen/LogicalShapelet/&lt;/a&gt;). This seems promising but have not been able to get any existing code base to work.  &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Can anyone suggest different technique to try? I have seen papers about discords and motifs but still need to investigate if they are relevant for my problem. &lt;/p&gt;
', 'classification  time-series', NULL, '''/li'':150C,180C ''/ol'':181C ''/p'':66C,110C,120C,210C ''/~eamonn/ucrsuite.html'':137C ''/~eamonn/ucrsuite.html&lt;/a&gt;'':142C ''/~mueen/logicalshapelet/'':158C ''/~mueen/logicalshapelet/&lt;/a&gt;).'':163C ''0'':30C ''1'':27C ''abl'':171C ''advis'':72C ''anyon'':184C ''approach'':78C ''art'':77C,148C ''attack'':16C,28C,32C,47C,50C ''averag'':93C ''base'':177C ''classif'':3A,4B,82C ''code'':176C ''current'':103C,114C ''data'':17C,52C ''differ'':64C,118C,186C ''discord'':195C ''dtw'':128C ''either'':23C ''equal'':44C ''everi'':57C ''exist'':175C ''get'':173C ''given'':20C ''href'':134C,155C ''implement'':115C ''improv'':101C ''investig'':202C ''ip'':21C,40C ''k'':124C ''k-nearest'':123C ''label'':25C ''length'':65C ''li'':122C,151C ''like'':91C ''logic'':152C ''look'':11C,70C,97C ''method'':104C ''motif'':197C ''move'':92C ''nearest'':125C ''need'':200C ''neighbor'':126C ''new'':106C ''nofollow'':139C,160C ''noisi'':55C ''non'':49C ''number'':45C ''ol'':121C ''p'':8C,67C,111C,182C ''paper'':193C ''past'':85C ''perform'':149C ''problem'':209C ''promis'':166C ''provid'':144C ''rather'':54C ''rel'':138C,159C ''relev'':206C ''rough'':42C ''secur'':15C ''seem'':165C ''seen'':192C ''sequenc'':60C ''seri'':2A,7B,14C,59C,81C ''shapelet'':153C ''simpl'':89C ''stage'':87C ''state'':74C,145C ''still'':199C ''success'':131C ''suggest'':185C ''techniqu'':119C,187C ''thing'':90C,107C ''thousand'':38C ''time'':1A,6B,13C,58C,80C ''time-seri'':5B ''total'':34C ''tri'':109C,189C ''use'':132C ''way'':99C ''work'':179C ''www.cs.ucr.edu'':136C,141C,157C,162C ''www.cs.ucr.edu/~eamonn/ucrsuite.html'':135C ''www.cs.ucr.edu/~eamonn/ucrsuite.html&lt;/a&gt;'':140C ''www.cs.ucr.edu/~mueen/logicalshapelet/'':156C ''www.cs.ucr.edu/~mueen/logicalshapelet/&lt;/a&gt;).'':161C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6525, 1, '2015-07-21 06:25:38.057', 0, 15, 'Frechet distance v/s correlation', '&lt;p&gt;I have a series of arrays that correspond to some data measured over a period of time. This data is supposed to stabilize with time. I want to find the time at which this happens. I am thinking of finding the similarity between the plots of these arrays over time (and the point at which the measure of similarity doesn''t change significantly would be the point where the data stabilizes). I wish to know if it would be better to find the correlation coefficient or the Frechet distance for the same. &lt;/p&gt;
', 'similarity  correlation', NULL, '''/p'':100C ''array'':13C,55C ''better'':87C ''chang'':69C ''coeffici'':92C ''correl'':4A,6B,91C ''correspond'':15C ''data'':18C,26C,77C ''distanc'':2A,96C ''doesn'':67C ''find'':36C,47C,89C ''frechet'':1A,95C ''happen'':42C ''know'':82C ''measur'':19C,64C ''p'':7C ''period'':22C ''plot'':52C ''point'':60C,74C ''seri'':11C ''signific'':70C ''similar'':5B,49C,66C ''stabil'':30C,78C ''suppos'':28C ''think'':45C ''time'':24C,32C,38C,57C ''v/s'':3A ''want'':34C ''wish'':80C ''would'':71C,85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9747, 1, '2016-01-11 12:46:06.03', 1, 68, 'Simple Explanation of Apache Flume', '&lt;p&gt;Can anybody explain Apache Flume for me in a plain language? I''d appreciate an explanation with a practical example instead of abstract theoretical definitions, then I can understand better.&lt;/p&gt;

&lt;p&gt;What is it used for? At which stage of a BigData analysis is it used?&lt;/p&gt;

&lt;p&gt;And what are prerequisites for learning it?&lt;/p&gt;

&lt;h2&gt;Please&lt;/h2&gt;

&lt;p&gt;As you would explain for a non-technical person&lt;/p&gt;
', 'bigdata', NULL, '''/h2'':67C ''/p'':38C,55C,64C,79C ''abstract'':30C ''analysi'':51C ''anybodi'':9C ''apach'':4A,11C ''appreci'':21C ''better'':37C ''bigdata'':6B,50C ''d'':20C ''definit'':32C ''exampl'':27C ''explain'':10C,72C ''explan'':2A,23C ''flume'':5A,12C ''h2'':65C ''instead'':28C ''languag'':18C ''learn'':62C ''non'':76C ''non-techn'':75C ''p'':7C,39C,56C,68C ''person'':78C ''plain'':17C ''pleas'':66C ''practic'':26C ''prerequisit'':60C ''simpl'':1A ''stage'':47C ''technic'':77C ''theoret'':31C ''understand'':36C ''use'':43C,54C ''would'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6527, 2, '2015-07-21 07:26:51.743', 0, NULL, NULL, '&lt;p&gt;One key difference is that cross validation ensures all samples will appear in the training and test sets, so 100% of your data gets used at some point for training and for testing. &lt;/p&gt;

&lt;p&gt;Depending on the size of your dataset the bootstrapping , sampling with replacement, occurring in the random forest will not guarantee the splits the trees see will contain all instances. If you have enough trees in your forest the OOB estimate should asymptotically converge towards the best OOB estimate value. &lt;/p&gt;

&lt;p&gt;The accuracy for both methods will to some degree be data dependent so it may be prudent to compare both methods on the particular data you have in front of you and see if CV and RF OOB estimates give similar values. &lt;/p&gt;

&lt;p&gt;If they do not, then it would be worth exploring further estimates of the true error rate, perhaps by much higher vales of K in CV.&lt;/p&gt;
', NULL, NULL, '''/p'':35C,86C,130C,158C ''100'':21C ''accuraci'':89C ''appear'':13C ''asymptot'':78C ''best'':82C ''bootstrap'':45C ''compar'':106C ''contain'':63C ''converg'':79C ''cross'':7C ''cv'':122C,157C ''data'':24C,98C,112C ''dataset'':43C ''degre'':96C ''depend'':37C,99C ''differ'':4C ''enough'':69C ''ensur'':9C ''error'':147C ''estim'':76C,84C,126C,143C ''explor'':141C ''forest'':53C,73C ''front'':116C ''get'':25C ''give'':127C ''guarante'':56C ''higher'':152C ''instanc'':65C ''k'':155C ''key'':3C ''may'':102C ''method'':92C,108C ''much'':151C ''occur'':49C ''one'':2C ''oob'':75C,83C,125C ''p'':1C,36C,87C,131C ''particular'':111C ''perhap'':149C ''point'':29C ''prudent'':104C ''random'':52C ''rate'':148C ''replac'':48C ''rf'':124C ''sampl'':11C,46C ''see'':61C,120C ''set'':19C ''similar'':128C ''size'':40C ''split'':58C ''test'':18C,34C ''toward'':80C ''train'':16C,31C ''tree'':60C,70C ''true'':146C ''use'':26C ''vale'':153C ''valid'':8C ''valu'':85C,129C ''worth'':140C ''would'':138C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6530, 1, '2015-07-21 12:24:18.423', 1, 359, 'How to convert unstructured texts to structured data?', '&lt;p&gt;I would like to right an algorithm to convert unstructured texts (with contests descriptions) to structured data with the following fields:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;contest start date (optional)&lt;/li&gt;
&lt;li&gt;contest end date&lt;/li&gt;
&lt;li&gt;main prize&lt;/li&gt;
&lt;li&gt;additional prizes (optional)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I have hundreds of text examples, which could be used for model learning. &lt;/p&gt;

&lt;p&gt;How to approach this task? Just in case this is important - the preferable language is Python. But I never worked on such tasks before. &lt;/p&gt;
', 'machine-learning  python', NULL, '''/li'':42C,47C,51C,56C ''/p'':35C,72C,98C ''/ul'':57C ''addit'':53C ''algorithm'':20C ''approach'':76C ''case'':81C ''contest'':26C,38C,44C ''convert'':3A,22C ''could'':66C ''data'':8A,30C ''date'':40C,46C ''descript'':27C ''end'':45C ''exampl'':64C ''field'':34C ''follow'':33C ''hundr'':61C ''import'':84C ''languag'':87C ''learn'':11B,71C ''li'':37C,43C,48C,52C ''like'':16C ''machin'':10B ''machine-learn'':9B ''main'':49C ''model'':70C ''never'':92C ''option'':41C,55C ''p'':13C,58C,73C ''prefer'':86C ''prize'':50C,54C ''python'':12B,89C ''right'':18C ''start'':39C ''structur'':7A,29C ''task'':78C,96C ''text'':5A,24C,63C ''ul'':36C ''unstructur'':4A,23C ''use'':68C ''work'':93C ''would'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6531, 1, '2015-07-21 12:44:33.683', -1, 54, 'K MEANS Big dataset', '&lt;p&gt;Can you please recommend me for a big dataset for k means?&lt;/p&gt;

&lt;p&gt;It would be cool if it will integrate easily with python, but any thing will be good.&lt;/p&gt;
', 'python  dataset  k-means', NULL, '''/p'':23C,42C ''big'':3A,18C ''cool'':28C ''dataset'':4A,6B,19C ''easili'':33C ''good'':41C ''integr'':32C ''k'':1A,8B,21C ''k-mean'':7B ''mean'':2A,9B,22C ''p'':10C,24C ''pleas'':13C ''python'':5B,35C ''recommend'':14C ''thing'':38C ''would'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6532, 2, '2015-07-21 13:52:56.563', 1, NULL, NULL, '&lt;p&gt;For the very first start I''d recoment synthetical data. Simple draw a K sets of random distributed numbers with different means and required dimensionality.
The big advantage is that you know the result clustering and you can easily verify the result. Also scaling the data is not problem.&lt;/p&gt;
', NULL, NULL, '''/p'':51C ''advantag'':29C ''also'':44C ''big'':28C ''cluster'':36C ''d'':8C ''data'':11C,47C ''differ'':22C ''dimension'':26C ''distribut'':19C ''draw'':13C ''easili'':40C ''first'':5C ''k'':15C ''know'':33C ''mean'':23C ''number'':20C ''p'':1C ''problem'':50C ''random'':18C ''recoment'':9C ''requir'':25C ''result'':35C,43C ''scale'':45C ''set'':16C ''simpl'':12C ''start'':6C ''synthet'':10C ''verifi'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6548, 2, '2015-07-22 18:14:36.907', 1, NULL, NULL, '&lt;p&gt;I am assuming that you have only 1 attribute (numeric).
What you can do is &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;modify the .arff file and set 0s to 1 and 1s to 0.&lt;/li&gt;
&lt;li&gt;Then you can again run j48 classifier and visualise the decision tree.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;It would give you inverted result.
Also, try watching this video.&lt;a href="https://www.youtube.com/watch?v=l7R9NHqvI0Y" rel="nofollow"&gt;WEKA on MOOC&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':71C ''/li'':33C,47C ''/ol'':48C ''/p'':17C,72C ''/watch?v=l7r9nhqvi0y'':65C ''0'':32C ''0s'':26C ''1'':9C,28C ''1s'':30C ''also'':56C ''arff'':22C ''assum'':4C ''attribut'':10C ''classifi'':41C ''decis'':45C ''file'':23C ''give'':52C ''href'':62C ''invert'':54C ''j48'':40C ''li'':19C,34C ''modifi'':20C ''mooc'':70C ''nofollow'':67C ''numer'':11C ''ol'':18C ''p'':1C,49C ''rel'':66C ''result'':55C ''run'':39C ''set'':25C ''tree'':46C ''tri'':57C ''video'':60C ''visualis'':43C ''watch'':58C ''weka'':68C ''would'':51C ''www.youtube.com'':64C ''www.youtube.com/watch?v=l7r9nhqvi0y'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6533, 1, '2015-07-21 15:52:30.01', 2, 67, 'How to store complex tables and structures?', '&lt;p&gt;I''m wondering about general approaches to storing complex tables and structures. For example, imagine I have a table like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;               A1 A2 A3 A4 A5 B1 B2 B3 B4 B5 C1 ....Z5
 individuals   
 1             .  .  .  .  . .  .  .  .  .
 2             .  .  .  .  . .  .  .  .  .
 3             .  .  .  .  . .  .  .  .  .
 4             .  .  .  .  . .  .  .  .  .
 5             .  .  .  .  . .  .  .  .  .
 ...           .  .  .  .  . .  .  .  .  .
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;(I''m just showing the row and column names (variables), but the table would be filled, or could be even much larger, with A1 to A999, B1 to B999 colums.)&lt;/p&gt;

&lt;p&gt;I can want to perform some calculation on all $Ax$ columns, or on all $x3$ ones. Since it can be difficult to deal with so many columns, it may be better to write this as:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; i  subcase  A B C ... Z
 1  1      .  .  .  .  
 1  2      .  .  .  .  
 1  3      .  .  .  .  
 1  4      .  .  .  .  
 1  5      .  .  .  .  
 1  6      .  .  .  .  
 1  7      .  .  .  .  
 1  8      .  .  .  .  
 1  9      .  .  .  .  
 2  1      .  .  .  .  
 2  2      .  .  .  .  
 ...       .  .  .  .  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I remember seeing that kind of structure several times, sometimes referred to as &lt;em&gt;long format&lt;/em&gt;. Later, one can perform arbitrary operations on all rows meeting some condition on "case". This has some advantages, but could be confusing.&lt;/p&gt;

&lt;p&gt;One could average or &lt;em&gt;crate&lt;/em&gt; the differences of all the $\text{subcase}==1$ rows, only from $\text{individual}==1$, or when $\text{individual}==subcase$,  for example.&lt;/p&gt;

&lt;p&gt;But what would be the best way to store the resulting numbers--columnwise or rowise? If I wanted to store the average of all $\text{subcase}==1$ rows, I would need to repeat the number on every row meeting that condition.&lt;/p&gt;

&lt;p&gt;Another option would be to keep the &lt;em&gt;wide&lt;/em&gt; format structure, but having 2 rows for the headersâone for the letters and a second one for the numbers. I think it could be a natural way, but I''ve never seen it before.&lt;/p&gt;

&lt;p&gt;Are there other structures people can recommend for this problem, or for more complex situations, such as trees?&lt;/p&gt;
', 'data', NULL, '''/code'':52C,153C ''/em'':172C,203C,279C ''/p'':31C,85C,122C,195C,225C,268C,315C,335C ''/pre'':53C,154C ''1'':47C,131C,132C,133C,135C,137C,139C,141C,143C,145C,147C,150C,211C,217C,253C ''2'':48C,134C,149C,151C,152C,284C ''3'':49C,136C ''4'':50C,138C ''5'':51C,140C ''6'':142C ''7'':144C ''8'':146C ''9'':148C ''a1'':34C,78C ''a2'':35C ''a3'':36C ''a4'':37C ''a5'':38C ''a999'':80C ''advantag'':190C ''anoth'':270C ''approach'':15C ''arbitrari'':177C ''averag'':199C,248C ''ax'':96C ''b'':128C ''b1'':39C,81C ''b2'':40C ''b3'':41C ''b4'':42C ''b5'':43C ''b999'':83C ''best'':232C ''better'':117C ''c'':129C ''c1'':44C ''calcul'':93C ''case'':186C ''code'':33C,124C ''colum'':84C ''column'':62C,97C,113C ''columnwis'':239C ''complex'':4A,18C,330C ''condit'':184C,267C ''confus'':194C ''could'':72C,192C,198C,303C ''crate'':202C ''data'':8B ''deal'':109C ''differ'':205C ''difficult'':107C ''em'':169C,201C,277C ''even'':74C ''everi'':263C ''exampl'':23C,224C ''fill'':70C ''format'':171C,280C ''general'':14C ''headersâ'':288C ''imagin'':24C ''individu'':46C,216C,221C ''keep'':275C ''kind'':160C ''larger'':76C ''later'':173C ''letter'':292C ''like'':29C ''long'':170C ''m'':11C,56C ''mani'':112C ''may'':115C ''meet'':182C,265C ''much'':75C ''name'':63C ''natur'':306C ''need'':257C ''never'':311C ''number'':238C,261C,299C ''one'':102C,174C,197C,289C,296C ''oper'':178C ''option'':271C ''p'':9C,54C,86C,155C,196C,226C,269C,316C ''peopl'':321C ''perform'':91C,176C ''pre'':32C,123C ''problem'':326C ''recommend'':323C ''refer'':166C ''rememb'':157C ''repeat'':259C ''result'':237C ''row'':60C,181C,212C,254C,264C,285C ''rowis'':241C ''second'':295C ''see'':158C ''seen'':312C ''sever'':163C ''show'':58C ''sinc'':103C ''situat'':331C ''sometim'':165C ''store'':3A,17C,235C,246C ''structur'':7A,21C,162C,281C,320C ''subcas'':126C,210C,222C,252C ''tabl'':5A,19C,28C,67C ''text'':209C,215C,220C,251C ''think'':301C ''time'':164C ''tree'':334C ''variabl'':64C ''ve'':310C ''want'':89C,244C ''way'':233C,307C ''wide'':278C ''wonder'':12C ''would'':68C,229C,256C,272C ''write'':119C ''x3'':101C ''z'':130C ''z5'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6562, 2, '2015-07-23 21:26:54.077', 2, NULL, NULL, '&lt;p&gt;I would recommend this one - &lt;a href="http://www.cheat-sheets.org/saved-copy/ubunturef.pdf" rel="nofollow"&gt;http://www.cheat-sheets.org/saved-copy/ubunturef.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Its comprehensive, but not data science specific. &lt;/p&gt;

&lt;p&gt;There are a number of great cheat sheets here that may be what you are looking for:&lt;a href="https://dzone.com/refcardz" rel="nofollow"&gt;https://dzone.com/refcardz&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you are wanting to create your own, this is a very nice tool: &lt;a href="http://www.cheatography.com/" rel="nofollow"&gt;http://www.cheatography.com/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':76C ''/p'':25C ''/refcardz'':48C ''/refcardz&lt;/a&gt;&lt;/p&gt;'':53C ''/saved-copy/ubunturef.pdf'':11C ''/saved-copy/ubunturef.pdf&lt;/a&gt;&lt;/p&gt;'':16C ''cheat'':33C ''comprehens'':19C ''creat'':60C ''data'':22C ''dzone.com'':47C,52C ''dzone.com/refcardz'':46C ''dzone.com/refcardz&lt;/a&gt;&lt;/p&gt;'':51C ''great'':32C ''href'':8C,45C,70C ''look'':42C ''may'':37C ''nice'':67C ''nofollow'':13C,50C,73C ''number'':30C ''one'':6C ''p'':1C,17C,26C,54C ''recommend'':4C ''rel'':12C,49C,72C ''scienc'':23C ''sheet'':34C ''specif'':24C ''tool'':68C ''want'':58C ''would'':3C ''www.cheat-sheets.org'':10C,15C ''www.cheat-sheets.org/saved-copy/ubunturef.pdf'':9C ''www.cheat-sheets.org/saved-copy/ubunturef.pdf&lt;/a&gt;&lt;/p&gt;'':14C ''www.cheatography.com'':71C,75C ''www.cheatography.com/&lt;/a&gt;&lt;/p&gt;'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8775, 1, '2015-11-10 08:06:57.003', 1, 39, 'Events prediction with time series of continuous variables as features', '&lt;p&gt;We have the feeling that behavior of a device in terms of continuous variables (fans speeds, temperatures, voltages, ...) has influence on rare events happening (components failures).&lt;/p&gt;

&lt;p&gt;I now have to build a predictive model for that, to proof the influence.&lt;/p&gt;

&lt;p&gt;Those continuous features are given as time series, and events are punctual.&lt;/p&gt;

&lt;p&gt;I''ve made a model based on descriptive statistics of those variable (see &lt;a href="http://datascience.stackexchange.com/q/8245/3024"&gt;this question&lt;/a&gt;) with decision tree, random forest, adaboost, and clustering but it doesn''t works. I will still improve by balancing classes, but I''m convinced it is not the best approach.&lt;/p&gt;

&lt;p&gt;I''m pretty sure that there are nicer algorithms for such predictions (this is quite common problem), but I don''t find anything.&lt;/p&gt;

&lt;p&gt;Do you have ideas?&lt;/p&gt;

&lt;p&gt;Thanks a lot&lt;/p&gt;

&lt;p&gt;PS: I''m working with Python and cython&lt;/p&gt;
', 'predictive-modeling  time-series', NULL, '''/a'':96C ''/p'':44C,60C,74C,127C,152C,158C,163C,173C ''/q/8245/3024'':93C ''adaboost'':102C ''algorithm'':137C ''anyth'':151C ''approach'':126C ''balanc'':115C ''base'':81C ''behavior'':23C ''best'':125C ''build'':50C ''class'':116C ''cluster'':104C ''common'':144C ''compon'':42C ''continu'':7A,30C,63C ''convinc'':120C ''cython'':172C ''datascience.stackexchange.com'':92C ''datascience.stackexchange.com/q/8245/3024'':91C ''decis'':98C ''descript'':83C ''devic'':26C ''doesn'':107C ''event'':1A,40C,71C ''failur'':43C ''fan'':32C ''featur'':10A,64C ''feel'':21C ''find'':150C ''forest'':101C ''given'':66C ''happen'':41C ''href'':90C ''idea'':157C ''improv'':113C ''influenc'':37C,59C ''lot'':162C ''m'':119C,130C,167C ''made'':78C ''model'':13B,53C,80C ''nicer'':136C ''p'':17C,45C,61C,75C,128C,153C,159C,164C ''predict'':2A,12B,52C,140C ''predictive-model'':11B ''pretti'':131C ''problem'':145C ''proof'':57C ''ps'':165C ''punctual'':73C ''python'':170C ''question'':95C ''quit'':143C ''random'':100C ''rare'':39C ''see'':88C ''seri'':5A,16B,69C ''speed'':33C ''statist'':84C ''still'':112C ''sure'':132C ''temperatur'':34C ''term'':28C ''thank'':160C ''time'':4A,15B,68C ''time-seri'':14B ''tree'':99C ''variabl'':8A,31C,87C ''ve'':77C ''voltag'':35C ''work'':109C,168C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9056, 5, '2015-11-26 10:00:43.78', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9058, 5, '2015-11-26 10:01:14.917', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6534, 2, '2015-07-21 17:45:54.623', 4, NULL, NULL, '&lt;p&gt;If I get you right, you would like to extract the mentioned entities from the unstructured text. This sounds as Named Entity Recognition (NER) problem for me. In your case I would define the following entities:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;START_DATE&lt;/li&gt;
&lt;li&gt;END_DATE&lt;/li&gt;
&lt;li&gt;MAIN_PRIZE&lt;/li&gt;
&lt;li&gt;EXTRA_PRIZE&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Your text now can be tagged or annotated with those entities. You would like to build a machine which will automatically tag the encountered entities in the unstructured text (free text).&lt;/p&gt;

&lt;p&gt;I would suggest to take a look at Conditional Random Fields (CRF), a classic approach to NER. There are also Recurrent Neural Networks (RNN) which are state of the art for sequence modeling but I am not an expert with those methods.&lt;/p&gt;

&lt;p&gt;Let''s begin the process of model building. You need to decide what will be a single training example. This can be a sentence and this can be also a paragraph.&lt;/p&gt;

&lt;p&gt;Since you did not provide any example, let me invent two:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;The contest started on &amp;lt;START_DATE&amp;gt;july 1st&amp;lt;/START_DATE&amp;gt; and ended on &amp;lt;END_DATE&amp;gt;july 25&amp;lt;/END_DATE&amp;gt;.

The winner got away with &amp;lt;MAIN_PRIZE&amp;gt;1 million dollars&amp;lt;/MAIN_PRIZE&amp;gt; and &amp;lt;EXTRA_PRIZE&amp;gt;bunch of flowers&amp;lt;/EXTRA_PRIZE&amp;gt;.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Notice the way how the text is structured. Every word now is either label (surrounded by tags) or not labeled at all (outside of any tag). This is one way how to structure the text. This would be also the desired output on some unstructured text.&lt;/p&gt;

&lt;p&gt;The machine does not understand the text and annotation. You need to come up with features that you can extract from each word. It can be the word itself, the previous word, the next word, left bigram, right bigram, suffix of length 1,2,3, prefixes of length 1,2,3 and so on. I suggest you to look at Stanford NER features.&lt;/p&gt;

&lt;p&gt;Once you got the features, you need to create a training set. The training set is your annotated text (see my example) converted to label + feature information. It may look like this&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;O   feature1 feature2 ...
O   feature1 feature2 ...
B-START_DATE   feature1 feature2 ...
I-START_DATE   feature1 feature3 ...
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This basically says that the first two words are not labeled, but the last two are part of an entity START_DATE.&lt;/p&gt;

&lt;p&gt;Each line is essentially a word followed by features for it. The label "O" is the way to say that the word is not labeled, the labels B- and I- for DATE mean the first and second words of a named entity (e.g. "july 1st").&lt;/p&gt;

&lt;p&gt;The sentences can be separated by empty lines.&lt;/p&gt;

&lt;p&gt;Once you''ve got the training set file ready, you can do the model training. With a model in hand, you can apply it on any unstructured data (extracted features) to get the desired entities extracted.&lt;/p&gt;

&lt;p&gt;As a tool I suggest you CRFSuite (also available with Python bindings). I can detail more each step if you would like.&lt;/p&gt;
', NULL, NULL, '''/code'':233C,402C ''/end_date'':205C ''/extra_prize'':231C ''/li'':43C,47C,51C,55C ''/main_prize'':220C ''/p'':38C,89C,133C,166C,179C,282C,347C,381C,427C,474C,484C,522C,546C ''/pre'':234C,403C ''/start_date'':193C ''/ul'':56C ''1'':216C,326C,332C ''1st'':191C,473C ''2'':327C,333C ''25'':203C ''3'':328C,334C ''also'':110C,163C,274C,531C ''annot'':65C,292C,366C ''appli'':508C ''approach'':105C ''art'':120C ''automat'':78C ''avail'':532C ''away'':210C ''b'':391C,456C ''b-start'':390C ''basic'':406C ''begin'':137C ''bigram'':320C,322C ''bind'':535C ''build'':73C,142C ''bunch'':227C ''case'':31C ''classic'':104C ''code'':181C,383C ''come'':296C ''condit'':99C ''contest'':183C ''convert'':371C ''creat'':357C ''crf'':102C ''crfsuit'':530C ''data'':513C ''date'':42C,46C,188C,200C,393C,399C,426C,460C ''decid'':146C ''defin'':34C ''desir'':276C,519C ''detail'':538C ''dollar'':218C ''e.g'':471C ''either'':248C ''empti'':482C ''encount'':81C ''end'':45C,196C,199C ''entiti'':14C,23C,37C,68C,82C,424C,470C,520C ''essenti'':432C ''everi'':244C ''exampl'':153C,174C,370C ''expert'':129C ''extra'':53C,224C ''extract'':11C,303C,514C,521C ''featur'':299C,346C,353C,374C,437C,515C ''feature1'':385C,388C,394C,400C ''feature2'':386C,389C,395C ''feature3'':401C ''field'':101C ''file'':493C ''first'':410C,463C ''flower'':229C ''follow'':36C,435C ''free'':87C ''get'':4C,517C ''got'':209C,351C,489C ''gt'':189C,194C,201C,206C,215C,221C,226C,232C ''hand'':505C ''i-start'':396C ''inform'':375C ''invent'':177C ''juli'':190C,202C,472C ''label'':249C,255C,373C,415C,441C,453C,455C ''last'':418C ''left'':319C ''length'':325C,331C ''let'':135C,175C ''li'':40C,44C,48C,52C ''like'':9C,71C,379C,545C ''line'':430C,483C ''look'':97C,342C,378C ''lt'':186C,192C,198C,204C,212C,219C,223C,230C ''machin'':75C,285C ''main'':49C,213C ''may'':377C ''mean'':461C ''mention'':13C ''method'':132C ''million'':217C ''model'':123C,141C,499C,503C ''name'':22C,469C ''need'':144C,294C,355C ''ner'':25C,107C,345C ''network'':113C ''neural'':112C ''next'':317C ''notic'':236C ''o'':384C,387C,442C ''one'':264C ''output'':277C ''outsid'':258C ''p'':1C,57C,90C,134C,167C,235C,283C,348C,404C,428C,475C,485C,523C ''paragraph'':165C ''part'':421C ''pre'':180C,382C ''prefix'':329C ''previous'':314C ''prize'':50C,54C,214C,225C ''problem'':26C ''process'':139C ''provid'':172C ''python'':534C ''random'':100C ''readi'':494C ''recognit'':24C ''recurr'':111C ''right'':6C,321C ''rnn'':114C ''say'':407C,447C ''second'':465C ''see'':368C ''sentenc'':158C,477C ''separ'':480C ''sequenc'':122C ''set'':360C,363C,492C ''sinc'':168C ''singl'':151C ''sound'':20C ''stanford'':344C ''start'':41C,184C,187C,392C,398C,425C ''state'':117C ''step'':541C ''structur'':243C,268C ''suffix'':323C ''suggest'':93C,339C,528C ''surround'':250C ''tag'':63C,79C,252C,261C ''take'':95C ''text'':18C,59C,86C,88C,241C,270C,281C,290C,367C ''tool'':526C ''train'':152C,359C,362C,491C,500C ''two'':178C,411C,419C ''ul'':39C ''understand'':288C ''unstructur'':17C,85C,280C,512C ''ve'':488C ''way'':238C,265C,445C ''winner'':208C ''word'':245C,306C,311C,315C,318C,412C,434C,450C,466C ''would'':8C,33C,70C,92C,272C,544C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6535, 2, '2015-07-21 19:03:19.13', 0, NULL, NULL, '&lt;p&gt;Look for NLTK, a Python package for analysing natural language data. With it, you can add some structure to text as written by humans. &lt;/p&gt;
', NULL, NULL, '''/p'':26C ''add'':17C ''analys'':9C ''data'':12C ''human'':25C ''languag'':11C ''look'':2C ''natur'':10C ''nltk'':4C ''p'':1C ''packag'':7C ''python'':6C ''structur'':19C ''text'':21C ''written'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6536, 2, '2015-07-21 19:20:19.363', 0, NULL, NULL, '&lt;p&gt;It all depends on what you''re trying to accomplish. For example, if you want to save space and you have sparse data, you want your table to grow "long" as you describe and let the absence of a row signify that you don''t have data (rather than structuring your sparse field as a column, which would typically require space to store your empty data points). &lt;/p&gt;

&lt;p&gt;If you need a data structure that makes it easiest to "process" the data, it depends on what your process is. Sometimes, a complex, nested data structure is best (e.g., JSON), other times, a flat table where some fields have repeating values is best. &lt;/p&gt;
', NULL, NULL, '''/p'':69C,115C ''absenc'':38C ''accomplish'':11C ''best'':99C,114C ''column'':57C ''complex'':94C ''data'':24C,48C,67C,75C,84C,96C ''depend'':4C,86C ''describ'':34C ''e.g'':100C ''easiest'':80C ''empti'':66C ''exampl'':13C ''field'':54C,109C ''flat'':105C ''grow'':30C ''json'':101C ''let'':36C ''long'':31C ''make'':78C ''need'':73C ''nest'':95C ''p'':1C,70C ''point'':68C ''process'':82C,90C ''rather'':49C ''re'':8C ''repeat'':111C ''requir'':61C ''row'':41C ''save'':18C ''signifi'':42C ''sometim'':92C ''space'':19C,62C ''spars'':23C,53C ''store'':64C ''structur'':51C,76C,97C ''tabl'':28C,106C ''time'':103C ''tri'':9C ''typic'':60C ''valu'':112C ''want'':16C,26C ''would'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6603, 2, '2015-07-27 21:32:58.897', 1, NULL, NULL, '&lt;p&gt;e1071 is a highly popular and therefore safe solution for this purpose. &lt;/p&gt;

&lt;p&gt;But if data starts to get bigger I would go with classyfire (&lt;a href="https://cran.r-project.org/web/packages/classyfire/classyfire.pdf" rel="nofollow"&gt;https://cran.r-project.org/web/packages/classyfire/classyfire.pdf&lt;/a&gt;). It''s a quite fresh package built on e1071. It is highly optimized and have some other nice feature too. In my experience it produced similar results to e1071 but with a much faster speed as it was able to utilize all 4 core of my processor.&lt;/p&gt;
', NULL, NULL, '''/p'':14C,85C ''/web/packages/classyfire/classyfire.pdf'':32C ''/web/packages/classyfire/classyfire.pdf&lt;/a&gt;).'':37C ''4'':80C ''abl'':76C ''bigger'':22C ''built'':44C ''classyfir'':27C ''core'':81C ''cran.r-project.org'':31C,36C ''cran.r-project.org/web/packages/classyfire/classyfire.pdf'':30C ''cran.r-project.org/web/packages/classyfire/classyfire.pdf&lt;/a&gt;).'':35C ''data'':18C ''e1071'':2C,46C,66C ''experi'':60C ''faster'':71C ''featur'':56C ''fresh'':42C ''get'':21C ''go'':25C ''high'':5C,49C ''href'':29C ''much'':70C ''nice'':55C ''nofollow'':34C ''optim'':50C ''p'':1C,15C ''packag'':43C ''popular'':6C ''processor'':84C ''produc'':62C ''purpos'':13C ''quit'':41C ''rel'':33C ''result'':64C ''safe'':9C ''similar'':63C ''solut'':10C ''speed'':72C ''start'':19C ''therefor'':8C ''util'':78C ''would'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6537, 2, '2015-07-21 21:45:39.853', 1, NULL, NULL, '&lt;p&gt;I am first deriving the error for a convolutional layer below for simplicity for a one dimensional array (input) which can easily be transferred to a multidimensional then:  &lt;/p&gt;

&lt;p&gt;We assume here that the $y^{l-1}$ of length $N$ are the inputs of the $l-1$-th conv. layer, $m$ is the kernel-size of weights $w$ denoting each weight by $w_i$ and the output is $x^l$.&lt;br&gt;
Hence we can write (note the summation from zero): $$x_i^l = \sum\limits_{a=0}^{m-1} w_a y_{a+i}^{l-1}$$ 
where $y_i^l = f(x_i^l)$ and $f$ the activation function (e.g. sigmoidal).
With this at hand we can now consider some error function $E$ and the error function at the convolutional layer (the one of your previous layer) given by $\partial E / \partial y_i^l $. We now want to find out the dependency of the error in one the weights in the previous layer(s): 
\begin{equation} \frac{\partial E}{\partial w_a} = \sum\limits_{a=0}^{N-m} \frac{\partial E}{\partial x_i^l} \frac{\partial x_i^l}{\partial w_a} = \sum\limits_{a=0}^{N-m}\frac{\partial E}{\partial w_a} y_{i+a}^{l-1} \end{equation}&lt;br&gt;
where we have the sum over all expression in which $w_a$ occurs, which are $N-m$. Note also that we know the last term arises from the fact that $\frac{\partial x_i^l}{\partial w_a}= y_{i+a}^{l-1}$ which you can see from the first equation.&lt;br&gt;
To compute the gradient we need to know the first term, which can be calculated by: 
$$ \frac{\partial E}{\partial x_i^l} = \frac{\partial E}{\partial y_i^l} \frac{\partial y_i^l}{\partial x_i^l} = \frac{\partial E}{\partial y_i^l} \frac{\partial}{\partial x_i^l} f(x_i^{l})$$ 
where again the first term is the error in the previous layer and $f$ the nonlinear activation function.&lt;/p&gt;

&lt;p&gt;Having all necessary entities we are now able to calculate the error and propagate it back efficiently to the precious layer:
$$ \delta^{l-1}_a = \frac{\partial E}{\partial y_i^{l-1} } = \sum\limits_{a=0}^{m-1} \frac{\partial E}{\partial x_{i-a}^l} \frac{\partial x_{i-a}^l}{\partial y_i^{l-1}} = \sum\limits_{a=0}^{m-1} \frac{\partial E}{\partial x^l_{i-a}} w_a^{flipped}$$
Note that the last step can be understood easy when writing down the $x_i^l$-s w.r.t. the $y_i^{l-1}$-s. The $flipped$ refers to a transposed weight maxtrix ($T$).&lt;/p&gt;

&lt;p&gt;Therefore you can just calculate the error in the next layer by (now in vector notation):  &lt;/p&gt;

&lt;p&gt;$$\delta^{l} = (w^{l})^{T} \delta^{l+1} f''(x^{l})$$&lt;/p&gt;

&lt;p&gt;which becomes for a convolutional and subsampling layer:
$$\delta^{l} = upsample((w^{l})^{T} \delta^{l+1}) f''(x^{l})$$
where the $upsample$ operation propagates the error through the max pooling layer.&lt;/p&gt;

&lt;p&gt;Please feel free to add or correct me!&lt;/p&gt;

&lt;p&gt;For references see:  &lt;/p&gt;

&lt;p&gt;&lt;a href="http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/" rel="nofollow"&gt;http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/&lt;/a&gt;
&lt;a href="http://andrew.gibiansky.com/blog/machine-learning/convolutional-neural-networks/" rel="nofollow"&gt;http://andrew.gibiansky.com/blog/machine-learning/convolutional-neural-networks/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;and for a C++ implementation (without requirement to install):
&lt;a href="https://github.com/nyanp/tiny-cnn#supported-networks" rel="nofollow"&gt;https://github.com/nyanp/tiny-cnn#supported-networks&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''+1'':487C,509C ''-1'':39C,49C,92C,99C,216C,263C,372C,381C,387C,408C,414C,449C ''/blog/machine-learning/convolutional-neural-networks/'':556C ''/blog/machine-learning/convolutional-neural-networks/&lt;/a&gt;&lt;/p&gt;'':561C ''/nyanp/tiny-cnn#supported-networks'':576C ''/nyanp/tiny-cnn#supported-networks&lt;/a&gt;&lt;/p&gt;'':581C ''/p'':30C,347C,460C,478C,491C,525C,535C,540C ''/tutorial/supervised/convolutionalneuralnetwork/'':546C ''/tutorial/supervised/convolutionalneuralnetwork/&lt;/a&gt;'':551C ''0'':90C,180C,202C,385C,412C ''abl'':356C ''activ'':111C,345C ''add'':531C ''also'':239C ''andrew.gibiansky.com'':555C,560C ''andrew.gibiansky.com/blog/machine-learning/convolutional-neural-networks/'':554C ''andrew.gibiansky.com/blog/machine-learning/convolutional-neural-networks/&lt;/a&gt;&lt;/p&gt;'':559C ''aris'':246C ''array'':19C ''assum'':33C ''back'':364C ''becom'':494C ''begin'':169C ''br'':74C,219C,272C ''c'':566C ''calcul'':287C,358C,466C ''comput'':274C ''consid'':122C ''conv'':51C ''convolut'':10C,133C,497C ''correct'':533C ''delta'':370C,480C,485C,501C,507C ''denot'':62C ''depend'':156C ''deriv'':5C ''dimension'':18C ''e'':126C,144C,173C,186C,208C,291C,298C,314C,376C,390C,417C ''e.g'':113C ''easi'':435C ''easili'':23C ''effici'':365C ''end'':217C ''entiti'':352C ''equat'':170C,218C,271C ''error'':7C,124C,129C,159C,336C,360C,468C,519C ''express'':227C ''f'':104C,109C,325C,342C,488C,510C ''fact'':249C ''feel'':528C ''find'':153C ''first'':4C,270C,282C,332C ''flip'':426C,452C ''frac'':171C,184C,191C,206C,251C,289C,296C,303C,312C,319C,374C,388C,397C,415C ''free'':529C ''function'':112C,125C,130C,346C ''github.com'':575C,580C ''github.com/nyanp/tiny-cnn#supported-networks'':574C ''github.com/nyanp/tiny-cnn#supported-networks&lt;/a&gt;&lt;/p&gt;'':579C ''given'':141C ''gradient'':276C ''hand'':118C ''henc'':75C ''href'':543C,553C,573C ''i-a'':393C,400C,421C ''implement'':567C ''input'':20C,45C ''instal'':571C ''kernel'':57C ''kernel-s'':56C ''know'':242C,280C ''l'':38C,48C,73C,86C,98C,103C,107C,148C,190C,195C,215C,255C,262C,295C,302C,307C,311C,318C,324C,328C,371C,380C,396C,403C,407C,420C,442C,448C,481C,483C,486C,490C,502C,505C,508C,512C ''last'':244C,430C ''layer'':11C,52C,134C,140C,167C,340C,369C,472C,500C,524C ''length'':41C ''limit'':88C,178C,200C,383C,410C ''m'':53C,91C,183C,205C,237C,386C,413C ''max'':522C ''maxtrix'':458C ''multidimension'':28C ''n'':42C,182C,204C,236C ''n-m'':181C,203C,235C ''necessari'':351C ''need'':278C ''next'':471C ''nofollow'':548C,558C,578C ''nonlinear'':344C ''notat'':477C ''note'':79C,238C,427C ''occur'':232C ''one'':17C,136C,161C ''oper'':516C ''output'':70C ''p'':1C,31C,348C,461C,479C,492C,526C,536C,541C,562C ''partial'':143C,145C,172C,174C,185C,187C,192C,196C,207C,209C,252C,256C,290C,292C,297C,299C,304C,308C,313C,315C,320C,321C,375C,377C,389C,391C,398C,404C,416C,418C ''pleas'':527C ''pool'':523C ''precious'':368C ''previous'':139C,166C,339C ''propag'':362C,517C ''refer'':453C,538C ''rel'':547C,557C,577C ''requir'':569C ''see'':267C,539C ''sigmoid'':114C ''simplic'':14C ''size'':58C ''step'':431C ''subsampl'':499C ''sum'':87C,177C,199C,224C,382C,409C ''summat'':81C ''term'':245C,283C,333C ''th'':50C ''therefor'':462C ''transfer'':25C ''transpos'':456C ''ufldl.stanford.edu'':545C,550C ''ufldl.stanford.edu/tutorial/supervised/convolutionalneuralnetwork/'':544C ''ufldl.stanford.edu/tutorial/supervised/convolutionalneuralnetwork/&lt;/a&gt;'':549C ''understood'':434C ''upsampl'':503C,515C ''vector'':476C ''w'':61C,66C,93C,175C,197C,210C,230C,257C,424C,482C,504C ''w.r.t'':444C ''want'':151C ''weight'':60C,64C,163C,457C ''without'':568C ''write'':78C,437C ''x'':72C,84C,105C,188C,193C,253C,293C,309C,322C,326C,392C,399C,419C,440C,489C,511C ''y'':37C,95C,101C,146C,212C,259C,300C,305C,316C,378C,405C,446C ''zero'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9740, 2, '2016-01-11 10:29:30.257', 0, NULL, NULL, '&lt;p&gt;If I understood you correctly, you are asking about this line of code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here you only specify which part of batch is used for features and which for your predicted class.&lt;/p&gt;
', NULL, NULL, '''/code'':30C ''/p'':15C,51C ''/pre'':31C ''0'':23C ''0.5'':29C ''1'':26C ''ask'':9C ''batch'':22C,25C,40C ''class'':50C ''code'':14C,17C ''correct'':6C ''dict'':20C ''featur'':44C ''feed'':19C ''keep'':27C ''line'':12C ''p'':1C,32C ''part'':38C ''pre'':16C ''predict'':49C ''prob'':28C ''specifi'':36C ''train_step.run'':18C ''understood'':4C ''use'':42C ''x'':21C ''y'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6538, 1, '2015-07-22 00:28:50.967', 2, 28, 'Different estimation methods of Time series model', '&lt;p&gt;This question might be a huge topic, but let''s talk about the simple case, say AR (auto-regressive) model. Usually people have various methods of estimating the AR model, like Burgs algorithm, Yule walker algorithm, or even OLS (ordinary least squares). Could some one tell me what is the motivation of using so many different skills to estimate the same model. Is there any literature of book discussing these topic? &lt;/p&gt;

&lt;p&gt;Thanks a lot, &lt;/p&gt;
', 'time-series', NULL, '''/p'':84C,89C ''algorithm'':45C,48C ''ar'':28C,41C ''auto'':30C ''auto-regress'':29C ''book'':80C ''burg'':44C ''case'':26C ''could'':55C ''differ'':1A,68C ''discuss'':81C ''estim'':2A,39C,71C ''even'':50C ''huge'':17C ''least'':53C ''let'':20C ''like'':43C ''literatur'':78C ''lot'':88C ''mani'':67C ''method'':3A,37C ''might'':14C ''model'':7A,32C,42C,74C ''motiv'':63C ''ol'':51C ''one'':57C ''ordinari'':52C ''p'':11C,85C ''peopl'':34C ''question'':13C ''regress'':31C ''say'':27C ''seri'':6A,10B ''simpl'':25C ''skill'':69C ''squar'':54C ''talk'':22C ''tell'':58C ''thank'':86C ''time'':5A,9B ''time-seri'':8B ''topic'':18C,83C ''use'':65C ''usual'':33C ''various'':36C ''walker'':47C ''yule'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6543, 2, '2015-07-22 06:22:54.263', 1, NULL, NULL, '&lt;p&gt;The simplest way to setup a user directory is to login to Hue as an admin and create a user account. This will create a directory in HDFS located at /user/yourusername with the correct permissions to work in.&lt;/p&gt;
', NULL, NULL, '''/p'':40C ''/user/yourusername'':32C ''account'':22C ''admin'':17C ''correct'':35C ''creat'':19C,25C ''directori'':9C,27C ''hdfs'':29C ''hue'':14C ''locat'':30C ''login'':12C ''p'':1C ''permiss'':36C ''setup'':6C ''simplest'':3C ''user'':8C,21C ''way'':4C ''work'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6544, 1, '2015-07-22 09:21:21.573', 2, 119, 'Normalized Euclidean Distance versus cross correlation?', '&lt;p&gt;Normalized Euclidean Distance and Normalized Cross - Correlation can both be used as a metric of distance between vectors.  &lt;/p&gt;

&lt;p&gt;What is the difference between these metrics?  It seems to me that they are the same, although I have not seen this explicitly stated in any textbook or literature.&lt;/p&gt;

&lt;p&gt;thank you.  &lt;/p&gt;
', 'time-series  correlation', NULL, '''/p'':30C,61C,65C ''although'':48C ''correl'':6A,10B,18C ''cross'':5A,17C ''differ'':35C ''distanc'':3A,14C,27C ''euclidean'':2A,13C ''explicit'':54C ''literatur'':60C ''metric'':25C,38C ''normal'':1A,12C,16C ''p'':11C,31C,62C ''seem'':40C ''seen'':52C ''seri'':9B ''state'':55C ''textbook'':58C ''thank'':63C ''time'':8B ''time-seri'':7B ''use'':22C ''vector'':29C ''versus'':4A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6545, 2, '2015-07-22 11:27:06.997', 1, NULL, NULL, '&lt;p&gt;These two metrics are &lt;strong&gt;not&lt;/strong&gt; the same.&lt;/p&gt;

&lt;p&gt;The normalized Euclidean distance is the distance between two normalized vectors that have been normalized to length one.  If the vectors are identical then the distance is &lt;code&gt;0&lt;/code&gt;, if the vectors point in opposite directions the distance is &lt;code&gt;2&lt;/code&gt;, and if the vectors are orthogonal (perpendicular) the distance is &lt;code&gt;sqrt(2)&lt;/code&gt;.  It is a positive definite scalar value between &lt;code&gt;0&lt;/code&gt; and &lt;code&gt;2&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/QSIBn.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/QSIBn.png" alt="NED equation"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The normalized cross-correlation is the dot product between the two normalized vectors.  If the vectors are identical, then the correlation is &lt;code&gt;1&lt;/code&gt;, if the vectors point in opposite directions the correlation is &lt;code&gt;-1&lt;/code&gt;, and if the vectors are orthogonal (perpendicular) the correlation is &lt;code&gt;0&lt;/code&gt;.  It is a scalar value between &lt;code&gt;-1&lt;/code&gt; and &lt;code&gt;1&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/XHjSu.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/XHjSu.png" alt="NCC equation"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This all comes with the understanding that in time-series analysis the cross-correlation is a measure of similarity of two series as a function of the lag of one relative to the other.&lt;/p&gt;
', NULL, NULL, '''-1'':142C,164C ''/a'':102C,187C ''/code'':42C,55C,69C,80C,84C,130C,143C,156C,165C,169C ''/p'':11C,85C,103C,170C,188C,226C ''/qsibn.png'':91C,98C ''/strong'':8C ''/xhjsu.png'':176C,183C ''0'':41C,79C,155C ''1'':129C,168C ''2'':54C,68C,83C ''alt'':99C,184C ''analysi'':201C ''code'':40C,53C,66C,78C,82C,128C,141C,154C,163C,167C ''come'':192C ''correl'':109C,126C,139C,152C,205C ''cross'':108C,204C ''cross-correl'':107C,203C ''definit'':74C ''direct'':49C,137C ''distanc'':16C,19C,38C,51C,64C ''dot'':112C ''equat'':101C,186C ''euclidean'':15C ''function'':216C ''href'':88C,173C ''i.stack.imgur.com'':90C,97C,175C,182C ''i.stack.imgur.com/qsibn.png'':89C,96C ''i.stack.imgur.com/xhjsu.png'':174C,181C ''ident'':35C,123C ''img'':94C,179C ''lag'':219C ''length'':29C ''measur'':208C ''metric'':4C ''ncc'':185C ''ned'':100C ''nofollow'':93C,178C ''normal'':14C,22C,27C,106C,117C ''one'':30C,221C ''opposit'':48C,136C ''orthogon'':61C,149C ''p'':1C,12C,86C,104C,171C,189C ''perpendicular'':62C,150C ''point'':46C,134C ''posit'':73C ''product'':113C ''rel'':92C,177C ''relat'':222C ''scalar'':75C,160C ''seri'':200C,213C ''similar'':210C ''sqrt'':67C ''src'':95C,180C ''strong'':6C ''time'':199C ''time-seri'':198C ''two'':3C,21C,116C,212C ''understand'':195C ''valu'':76C,161C ''vector'':23C,33C,45C,59C,118C,121C,133C,147C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6546, 1, '2015-07-22 14:16:22.823', 3, 790, 'How to calculate the mean of a dataframe column and find the top 10%', '&lt;p&gt;I am very new to Scala and Spark, and am working on some self-made exercises using baseball statistics. I am using a case class create a RDD and assign a schema to the data, and am then turning it into a DataFrame so I can use SparkSQL to select groups of players via their stats that meet certain criteria.&lt;/p&gt;

&lt;p&gt;Once I have the subset of players I am interested in looking at further, I would like to find the mean of a column; eg Batting Average or RBIs. From there I would like to break all the players into percentile groups based on their average performance compared to all players; the top 10%, bottom 10%, 40-50%&lt;/p&gt;

&lt;p&gt;I''ve been able to use the DataFrame.describe() function to return a summary of a desired column (mean, stddev, count, min, and max) all as strings though. Is there a better way to get just the mean and stddev as Doubles, and what is the best way of breaking the players into groups of 10-percentiles?&lt;/p&gt;

&lt;p&gt;So far my thoughts are to find the values that bookend the percentile ranges and writing a function that groups players via comparators, but that feels like it is bordering on reinventing the wheel.&lt;/p&gt;

&lt;p&gt;I have the following imports currently:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; import org.apache.spark.rdd.RDD 
 import org.apache.spark.sql.SQLContext 
 import org.apache.spark.{SparkConf, SparkContext} 
 import org.joda.time.format.DateTimeFormat  
&lt;/code&gt;&lt;/pre&gt;
', 'apache-spark  scala', NULL, '''-50'':140C ''/code'':256C ''/p'':81C,141C,199C,235C,243C ''/pre'':257C ''10'':14A,136C,138C,197C ''40'':139C ''abl'':146C ''apach'':16B ''apache-spark'':15B ''assign'':50C ''averag'':109C,128C ''base'':125C ''basebal'':38C ''bat'':108C ''best'':188C ''better'':173C ''bookend'':211C ''border'':230C ''bottom'':137C ''break'':118C,191C ''calcul'':3A ''case'':44C ''certain'':79C ''class'':45C ''code'':245C ''column'':9A,106C,159C ''compar'':130C,223C ''count'':162C ''creat'':46C ''criteria'':80C ''current'':242C ''data'':55C ''datafram'':8A,63C ''dataframe.describe'':150C ''desir'':158C ''doubl'':183C ''eg'':107C ''exercis'':36C ''far'':202C ''feel'':226C ''find'':11A,101C,207C ''follow'':240C ''function'':151C,218C ''get'':176C ''group'':71C,124C,195C,220C ''import'':241C,246C,248C,250C,254C ''interest'':92C ''like'':99C,116C,227C ''look'':94C ''made'':35C ''max'':165C ''mean'':5A,103C,160C,179C ''meet'':78C ''min'':163C ''new'':23C ''org.apache.spark'':251C ''org.apache.spark.rdd.rdd'':247C ''org.apache.spark.sql.sqlcontext'':249C ''org.joda.time.format.datetimeformat'':255C ''p'':19C,82C,142C,200C,236C ''percentil'':123C,198C,213C ''perform'':129C ''player'':73C,89C,121C,133C,193C,221C ''pre'':244C ''rang'':214C ''rbis'':111C ''rdd'':48C ''reinvent'':232C ''return'':153C ''scala'':18B,25C ''schema'':52C ''select'':70C ''self'':34C ''self-mad'':33C ''spark'':17B,27C ''sparkconf'':252C ''sparkcontext'':253C ''sparksql'':68C ''stat'':76C ''statist'':39C ''stddev'':161C,181C ''string'':168C ''subset'':87C ''summari'':155C ''though'':169C ''thought'':204C ''top'':13A,135C ''turn'':59C ''use'':37C,42C,67C,148C ''valu'':209C ''ve'':144C ''via'':74C,222C ''way'':174C,189C ''wheel'':234C ''work'':30C ''would'':98C,115C ''write'':216C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6642, 1, '2015-07-31 07:08:07.88', 2, 126, 'Custom trained model in Azure ML', '&lt;p&gt;I have a predictive model which I trained on a training set. I have written it in R. Now I want to deploy it as a web service so anyone can just input the data into it and get the output from the predictive model.&lt;/p&gt;

&lt;p&gt;I wanted to use Azure ML for deploying. I wanted to know whether I can drag and drop my already created/custom trained model to Azure ML studio instead of re-training it there ? I know we can train and save models in AML Studio but I am not sure about adding already created models and using them in AML solution. Help regarding this will be appreciated.&lt;/p&gt;
', 'machine-learning  r  predictive-modeling  azure-ml', NULL, '''/p'':63C,132C ''ad'':116C ''alreadi'':84C,117C ''aml'':108C,124C ''anyon'':47C ''appreci'':131C ''azur'':5A,15B,69C,89C ''azure-ml'':14B ''creat'':118C ''created/custom'':85C ''custom'':1A ''data'':52C ''deploy'':40C,72C ''drag'':80C ''drop'':82C ''get'':56C ''help'':126C ''input'':50C ''instead'':92C ''know'':76C,100C ''learn'':9B ''machin'':8B ''machine-learn'':7B ''ml'':6A,16B,70C,90C ''model'':3A,13B,22C,62C,87C,106C,119C ''output'':58C ''p'':17C,64C ''predict'':12B,21C,61C ''predictive-model'':11B ''r'':10B,35C ''re'':95C ''re-train'':94C ''regard'':127C ''save'':105C ''servic'':45C ''set'':29C ''solut'':125C ''studio'':91C,109C ''sure'':114C ''train'':2A,25C,28C,86C,96C,103C ''use'':68C,121C ''want'':38C,66C,74C ''web'':44C ''whether'':77C ''written'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6549, 2, '2015-07-22 18:55:05.25', 10, NULL, NULL, '&lt;p&gt;Anomaly Detection or Event Detection can be done in different ways:&lt;/p&gt;

&lt;h2&gt;Basic Way&lt;/h2&gt;

&lt;p&gt;Derivative! If the deviation of your signal from its past &amp;amp; future is high you most probably have an event. This can be extracted by finding large zero crossings in derivative of the signal.&lt;/p&gt;

&lt;h2&gt;Statistical Way&lt;/h2&gt;

&lt;p&gt;Mean of anything is its usual, basic behavior. if something deviates from mean it means that it''s an event. Please note that mean in time-series is not that trivial and is not a constant but changing according to changes in time-series so you need to see the &lt;em&gt;"moving average"&lt;/em&gt; instead of average. It looks like this:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/GCaNT.png"&gt;&lt;img src="http://i.stack.imgur.com/GCaNT.png" alt="Events are peaks larger than 1 standard deviation from moving average"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The Moving Average code can be found &lt;a href="http://stackoverflow.com/questions/11352047/finding-moving-average-from-data-points-in-python"&gt;here&lt;/a&gt;. In signal processing terminology you are applying a &lt;em&gt;"Low-Pass"&lt;/em&gt; filter by applying the moving average.&lt;/p&gt;

&lt;p&gt;You can follow the code bellow:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;MOV = movingaverage(TimeSEries,5).tolist()
STD = np.std(MOV)
events= []
ind = []
for ii in range(len(TimeSEries)):
    if TimeSEries[ii] &amp;gt; MOV[ii]+STD:
        events.append(TimeSEries[ii])
&lt;/code&gt;&lt;/pre&gt;

&lt;h2&gt;Probabilistic Way&lt;/h2&gt;

&lt;p&gt;They are more sophisticated specially for people new to Machine Learning. Kalman Filter is a great idea to &lt;a href="http://www.hamilton.ie/florian/infocom2008.pdf"&gt;find the anomalies&lt;/a&gt;. Simpler probabilistic approaches using &lt;em&gt;"Maximum-Likelihood Estimation"&lt;/em&gt; also work well but my suggestion is to stay with moving average idea. It works in practice very well.&lt;/p&gt;

&lt;p&gt;I hope I could help :)
Good Luck!&lt;/p&gt;
', NULL, NULL, '''/a'':146C,162C,252C ''/code'':219C ''/em'':114C,175C,262C ''/florian/infocom2008.pdf'':248C ''/gcant.png'':128C,133C ''/h2'':17C,57C,224C ''/p'':13C,53C,122C,147C,182C,190C,282C,291C ''/pre'':220C ''/questions/11352047/finding-moving-average-from-data-points-in-python'':160C ''1'':140C ''5'':196C ''accord'':98C ''also'':263C ''alt'':134C ''amp'':29C ''anomali'':2C,251C ''anyth'':61C ''appli'':169C,178C ''approach'':255C ''averag'':113C,117C,145C,151C,181C,274C ''basic'':15C,65C ''behavior'':66C ''bellow'':189C ''chang'':97C,100C ''code'':152C,188C,192C ''constant'':95C ''could'':287C ''cross'':47C ''deriv'':19C,49C ''detect'':3C,6C ''deviat'':22C,69C,142C ''differ'':11C ''done'':9C ''em'':111C,171C,257C ''estim'':261C ''event'':5C,38C,78C,135C,201C ''events.append'':216C ''extract'':42C ''filter'':176C,238C ''find'':44C,249C ''follow'':186C ''found'':155C ''futur'':30C ''good'':289C ''great'':241C ''gt'':212C ''h2'':14C,54C,221C ''help'':288C ''high'':32C ''hope'':285C ''href'':125C,157C,245C ''i.stack.imgur.com'':127C,132C ''i.stack.imgur.com/gcant.png'':126C,131C ''idea'':242C,275C ''ii'':204C,211C,214C,218C ''img'':129C ''ind'':202C ''instead'':115C ''kalman'':237C ''larg'':45C ''larger'':138C ''learn'':236C ''len'':207C ''like'':120C ''likelihood'':260C ''look'':119C ''low'':173C ''low-pass'':172C ''luck'':290C ''machin'':235C ''maximum'':259C ''maximum-likelihood'':258C ''mean'':59C,71C,73C,82C ''mov'':193C,200C,213C ''move'':112C,144C,150C,180C,273C ''movingaverag'':194C ''need'':107C ''new'':233C ''note'':80C ''np.std'':199C ''p'':1C,18C,58C,123C,148C,183C,225C,283C ''pass'':174C ''past'':28C ''peak'':137C ''peopl'':232C ''pleas'':79C ''practic'':279C ''pre'':191C ''probabilist'':222C,254C ''probabl'':35C ''process'':165C ''rang'':206C ''see'':109C ''seri'':86C,104C ''signal'':25C,52C,164C ''simpler'':253C ''someth'':68C ''sophist'':229C ''special'':230C ''src'':130C ''stackoverflow.com'':159C ''stackoverflow.com/questions/11352047/finding-moving-average-from-data-points-in-python'':158C ''standard'':141C ''statist'':55C ''stay'':271C ''std'':198C,215C ''suggest'':268C ''terminolog'':166C ''time'':85C,103C ''time-seri'':84C,102C ''timeseri'':195C,208C,210C,217C ''tolist'':197C ''trivial'':90C ''use'':256C ''usual'':64C ''way'':12C,16C,56C,223C ''well'':265C,281C ''work'':264C,277C ''www.hamilton.ie'':247C ''www.hamilton.ie/florian/infocom2008.pdf'':246C ''zero'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6550, 1, '2015-07-22 18:58:53.04', 1, 77, 'What is a good non cryptographic Hash for string feature translation?', '&lt;p&gt;What would be a good non cryptographic Hash function to use for converting string features to a numerical representation for feeding into machine learning algorithms?&lt;/p&gt;

&lt;p&gt;To explain the scenario my feature set has both categorical data (e.g.: &lt;code&gt;Country&lt;/code&gt;) and non categorical data (e.g.: &lt;code&gt;IP Address&lt;/code&gt;, &lt;code&gt;Email address&lt;/code&gt;). I have used MurMur3 Hash function so far, is there some better algorithm?&lt;/p&gt;
', 'machine-learning  data-mining', NULL, '''/code'':60C,69C,73C ''/p'':44C,87C ''address'':68C,72C ''algorithm'':43C,86C ''better'':85C ''categor'':55C,63C ''code'':58C,66C,70C ''convert'':31C ''countri'':59C ''cryptograph'':6A,25C ''data'':16B,56C,64C ''data-min'':15B ''e.g'':57C,65C ''email'':71C ''explain'':47C ''far'':81C ''featur'':10A,33C,51C ''feed'':39C ''function'':27C,79C ''good'':4A,23C ''hash'':7A,26C,78C ''ip'':67C ''learn'':14B,42C ''machin'':13B,41C ''machine-learn'':12B ''mine'':17B ''murmur3'':77C ''non'':5A,24C,62C ''numer'':36C ''p'':18C,45C ''represent'':37C ''scenario'':49C ''set'':52C ''string'':9A,32C ''translat'':11A ''use'':29C,76C ''would'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6551, 2, '2015-07-22 19:52:55.367', 6, NULL, NULL, '&lt;p&gt;Maybe  this helps  cause  you mentioned  about steady states:
&lt;a href="https://github.com/twitter/AnomalyDetection"&gt;https://github.com/twitter/AnomalyDetection&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://blog.twitter.com/2015/introducing-practical-and-robust-anomaly-detection-in-a-time-series"&gt;https://blog.twitter.com/2015/introducing-practical-and-robust-anomaly-detection-in-a-time-series&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/2015/introducing-practical-and-robust-anomaly-detection-in-a-time-series'':24C ''/2015/introducing-practical-and-robust-anomaly-detection-in-a-time-series&lt;/a&gt;&lt;/p&gt;'':27C ''/twitter/anomalydetection'':15C ''/twitter/anomalydetection&lt;/a&gt;&lt;/p&gt;'':18C ''blog.twitter.com'':23C,26C ''blog.twitter.com/2015/introducing-practical-and-robust-anomaly-detection-in-a-time-series'':22C ''blog.twitter.com/2015/introducing-practical-and-robust-anomaly-detection-in-a-time-series&lt;/a&gt;&lt;/p&gt;'':25C ''caus'':5C ''github.com'':14C,17C ''github.com/twitter/anomalydetection'':13C ''github.com/twitter/anomalydetection&lt;/a&gt;&lt;/p&gt;'':16C ''help'':4C ''href'':12C,21C ''mayb'':2C ''mention'':7C ''p'':1C,19C ''state'':10C ''steadi'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6552, 2, '2015-07-22 21:42:19.01', 4, NULL, NULL, '&lt;p&gt;h2o has an &lt;a href="http://learn.h2o.ai/content/hands-on_training/anomaly_detection.html" rel="nofollow"&gt;anomaly detection module&lt;/a&gt; and traditionally the code is available in R.However beyond version 3 it has similar module available in python as well,and since h2o is open source it might fit your bill.&lt;/p&gt;

&lt;p&gt;You can see an working example over &lt;a href="https://github.com/h2oai/h2o-3/blob/master/h2o-py/tests/testdir_algos/deeplearning/pyunit_anomaly_largeDeepLearning.py" rel="nofollow"&gt;here&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import sys
sys.path.insert(1,"../../../")
import h2o

def anomaly(ip, port):
    h2o.init(ip, port)

    print "Deep Learning Anomaly Detection MNIST"

    train = h2o.import_frame(h2o.locate("bigdata/laptop/mnist/train.csv.gz"))
    test = h2o.import_frame(h2o.locate("bigdata/laptop/mnist/test.csv.gz"))

    predictors = range(0,784)
    resp = 784

    # unsupervised -&amp;gt; drop the response column (digit: 0-9)
    train = train[predictors]
    test = test[predictors]

    # 1) LEARN WHAT''S NORMAL
    # train unsupervised Deep Learning autoencoder model on train_hex
    ae_model = h2o.deeplearning(x=train[predictors], training_frame=train, activation="Tanh", autoencoder=True,
                                hidden=[50], l1=1e-5, ignore_const_cols=False, epochs=1)

    # 2) DETECT OUTLIERS
    # anomaly app computes the per-row reconstruction error for the test data set
    # (passing it through the autoencoder model and computing mean square error (MSE) for each row)
    test_rec_error = ae_model.anomaly(test)

    # 3) VISUALIZE OUTLIERS
    # Let''s look at the test set points with low/median/high reconstruction errors.
    # We will now visualize the original test set points and their reconstructions obtained
    # by propagating them through the narrow neural net.

    # Convert the test data into its autoencoded representation (pass through narrow neural net)
    test_recon = ae_model.predict(test)

    # In python, the visualization could be done with tools like numpy/matplotlib or numpy/PIL

if __name__ == ''__main__'':
    h2o.run_test(sys.argv, anomaly)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''-9'':112C ''/../..'':72C ''/a'':15C,64C ''/code'':266C ''/content/hands-on_training/anomaly_detection.html'':9C ''/h2oai/h2o-3/blob/master/h2o-py/tests/testdir_algos/deeplearning/pyunit_anomaly_largedeeplearning.py'':60C ''/p'':47C,65C ''/pre'':267C ''0'':100C,111C ''1'':71C,119C,155C ''1e-5'':149C ''2'':156C ''3'':26C,193C ''50'':147C ''784'':101C,103C ''activ'':142C ''ae'':133C ''ae_model.anomaly'':191C ''ae_model.predict'':244C ''anomali'':12C,76C,85C,159C,265C ''app'':160C ''autoencod'':128C,144C,177C,235C ''avail'':21C,31C ''beyond'':24C ''bigdata/laptop/mnist/test.csv.gz'':97C ''bigdata/laptop/mnist/train.csv.gz'':92C ''bill'':46C ''code'':19C,67C ''col'':152C ''column'':109C ''comput'':161C,180C ''const'':151C ''convert'':229C ''could'':250C ''data'':171C,232C ''deep'':83C,126C ''def'':75C ''detect'':13C,86C,157C ''digit'':110C ''done'':252C ''drop'':106C ''epoch'':154C ''error'':167C,183C,190C,207C ''exampl'':54C ''fals'':153C ''fit'':44C ''frame'':90C,95C,140C ''github.com'':59C ''github.com/h2oai/h2o-3/blob/master/h2o-py/tests/testdir_algos/deeplearning/pyunit_anomaly_largedeeplearning.py'':58C ''gt'':105C ''h2o'':2C,38C,74C ''h2o.deeplearning'':135C ''h2o.import'':89C,94C ''h2o.init'':79C ''h2o.locate'':91C,96C ''h2o.run'':262C ''hex'':132C ''hidden'':146C ''href'':6C,57C ''ignor'':150C ''import'':68C,73C ''ip'':77C,80C ''l1'':148C ''learn'':84C,120C,127C ''learn.h2o.ai'':8C ''learn.h2o.ai/content/hands-on_training/anomaly_detection.html'':7C ''let'':196C ''like'':255C ''look'':198C ''low/median/high'':205C ''main'':261C ''mean'':181C ''might'':43C ''mnist'':87C ''model'':129C,134C,178C ''modul'':14C,30C ''mse'':184C ''name'':260C ''narrow'':226C,239C ''net'':228C,241C ''neural'':227C,240C ''nofollow'':11C,62C ''normal'':123C ''numpy/matplotlib'':256C ''numpy/pil'':258C ''obtain'':220C ''open'':40C ''origin'':213C ''outlier'':158C,195C ''p'':1C,48C ''pass'':173C,237C ''per'':164C ''per-row'':163C ''point'':203C,216C ''port'':78C,81C ''pre'':66C ''predictor'':98C,115C,118C,138C ''print'':82C ''propag'':222C ''python'':33C,247C ''r.however'':23C ''rang'':99C ''rec'':189C ''recon'':243C ''reconstruct'':166C,206C,219C ''rel'':10C,61C ''represent'':236C ''resp'':102C ''respons'':108C ''row'':165C,187C ''see'':51C ''set'':172C,202C,215C ''similar'':29C ''sinc'':37C ''sourc'':41C ''squar'':182C ''sys'':69C ''sys.argv'':264C ''sys.path.insert'':70C ''tanh'':143C ''test'':93C,116C,117C,170C,188C,192C,201C,214C,231C,242C,245C,263C ''tool'':254C ''tradit'':17C ''train'':88C,113C,114C,124C,131C,137C,139C,141C ''true'':145C ''unsupervis'':104C,125C ''version'':25C ''visual'':194C,211C,249C ''well'':35C ''work'':53C ''x'':136C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6553, 1, '2015-07-22 22:34:43.403', 3, 273, 'How much training data does word2vec need?', '&lt;p&gt;I''d like to compare the difference among the same word mentioned in different sources. That is, how authors differ in their usage of ill-defined words, such as "democracy".&lt;/p&gt;

&lt;p&gt;A brief plan was&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Take the books mentioning the term "democracy" as plain text&lt;/li&gt;
&lt;li&gt;In each book, replace &lt;code&gt;democracy&lt;/code&gt; with &lt;code&gt;democracy_%AuthorName%&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Train a &lt;code&gt;word2vec&lt;/code&gt; model on these books&lt;/li&gt;
&lt;li&gt;Calculate the distance between &lt;code&gt;democracy_AuthorA&lt;/code&gt;, &lt;code&gt;democracy_AuthorB&lt;/code&gt;, and other relabeled mentions of "democracy"&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;So each author''s "democracy" gets its own vector, which is used for comparison.&lt;/p&gt;

&lt;p&gt;But it seems that &lt;code&gt;word2vec&lt;/code&gt; requires much more than several books (each relabeled word occurs only in a subset of books) to train reliable vectors. The &lt;a href="https://code.google.com/p/word2vec/" rel="nofollow"&gt;official page&lt;/a&gt; recommends datasets including billions of words.&lt;/p&gt;

&lt;p&gt;I just wanted to ask how large should be the subset of one author''s books to make such inference with &lt;code&gt;word2vec&lt;/code&gt; or alternative tools, if available?&lt;/p&gt;
', 'text-mining  word-embeddings', NULL, '''/a'':166C ''/code'':73C,78C,85C,99C,103C,135C,198C ''/li'':65C,79C,90C,110C ''/ol'':111C ''/p'':46C,52C,127C,173C,204C ''/p/word2vec/'':161C ''altern'':200C ''among'':22C ''ask'':179C ''author'':33C,115C,188C ''authora'':98C ''authorb'':102C ''authornam'':77C ''avail'':203C ''billion'':170C ''book'':57C,69C,89C,141C,151C,190C ''brief'':49C ''calcul'':92C ''code'':71C,75C,83C,96C,100C,133C,196C ''code.google.com'':160C ''code.google.com/p/word2vec/'':159C ''compar'':19C ''comparison'':126C ''d'':16C ''data'':4A ''dataset'':168C ''defin'':41C ''democraci'':45C,61C,72C,76C,97C,101C,109C,117C ''differ'':21C,28C,34C ''distanc'':94C ''embed'':13B ''get'':118C ''href'':158C ''ill'':40C ''ill-defin'':39C ''includ'':169C ''infer'':194C ''larg'':181C ''li'':54C,66C,80C,91C ''like'':17C ''make'':192C ''mention'':26C,58C,107C ''mine'':10B ''model'':86C ''much'':2A,137C ''need'':7A ''nofollow'':163C ''occur'':145C ''offici'':164C ''ol'':53C ''one'':187C ''p'':14C,47C,112C,128C,174C ''page'':165C ''plain'':63C ''plan'':50C ''recommend'':167C ''rel'':162C ''relabel'':106C,143C ''reliabl'':154C ''replac'':70C ''requir'':136C ''seem'':131C ''sever'':140C ''sourc'':29C ''subset'':149C,185C ''take'':55C ''term'':60C ''text'':9B,64C ''text-min'':8B ''tool'':201C ''train'':3A,81C,153C ''usag'':37C ''use'':124C ''vector'':121C,155C ''want'':177C ''word'':12B,25C,42C,144C,172C ''word-embed'':11B ''word2vec'':6A,84C,134C,197C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6555, 1, '2015-07-23 03:45:36.867', 9, 2607, 'Issue with IPython/Jupyter on Spark (Unrecognized alias)', '&lt;p&gt;I am working on setting up a set of VMs to experiment with Spark before I spend go out and spend money on building up a cluster with some hardware. Quick note: I am an academic with a background in applied machine learning and work quit a bit in data science. I use the tools for computing, rarely would I need to set them up. &lt;/p&gt;

&lt;p&gt;I''ve created 3 VMs (1 master, 2 slaves) and installed Spark successfully. Everything appears to be working as it should. My problem lies in creating a Jupyter server that can be connected to from a browser not running on a machine on the cluster. &lt;/p&gt;

&lt;p&gt;I''ve installed &lt;a href="https://github.com/jupyter/notebook"&gt;Jupyter notebook&lt;/a&gt; successfully... and it runs. I''ve added a new IPython profile connecting to a &lt;a href="http://ipython.org/ipython-doc/1/interactive/public_server.html"&gt;remote server&lt;/a&gt; with Spark. &lt;/p&gt;

&lt;h2&gt;now the problem&lt;/h2&gt;

&lt;p&gt;The command&lt;/p&gt;

&lt;p&gt;&lt;code&gt;$ ipython --profile=pyspark
&lt;/code&gt; 
runs fine and it connects to the spark cluster. However, &lt;/p&gt;

&lt;p&gt;&lt;code&gt;$ ipython notebook --profile=pyspark
[&amp;lt;stuff is here&amp;gt;] Unrecognized alias: "profile=pyspark", it will probably have no effect.
&lt;/code&gt; 
defaults to the &lt;code&gt;default&lt;/code&gt; profile not the &lt;code&gt;pyspark&lt;/code&gt; profile. &lt;/p&gt;

&lt;p&gt;My notebook config for &lt;code&gt;pyspark&lt;/code&gt; has: &lt;/p&gt;

&lt;p&gt;&lt;code&gt;c = get_config()
c.NotebookApp.ip = ''*''
c.NotebookApp.open_browser = False
c.NotebookApp.port = 8880
c.NotebookApp.server_extensions.append(''ipyparallel.nbextension'')
c.NotebookApp.password = u''some password is here''
&lt;/code&gt;&lt;/p&gt;
', 'python  apache-spark', NULL, '''/a'':137C,159C ''/code'':177C,210C,216C,222C,232C,254C ''/h2'':167C ''/ipython-doc/1/interactive/public_server.html'':156C ''/jupyter/notebook'':134C ''/p'':78C,125C,162C,171C,188C,224C,234C,255C ''1'':85C ''2'':87C ''3'':83C ''8880'':245C ''academ'':48C ''ad'':144C ''alia'':7A,201C ''apach'':10B ''apache-spark'':9B ''appear'':94C ''appli'':53C ''background'':51C ''bit'':60C ''browser'':116C,242C ''build'':36C ''c'':237C ''c.notebookapp.ip'':240C ''c.notebookapp.open'':241C ''c.notebookapp.password'':248C ''c.notebookapp.port'':244C ''c.notebookapp.server_extensions.append'':246C ''cluster'':39C,124C,186C ''code'':173C,190C,214C,220C,230C,236C ''command'':170C ''comput'':69C ''config'':228C,239C ''connect'':112C,149C,182C ''creat'':82C,105C ''data'':62C ''default'':211C,215C ''effect'':209C ''everyth'':93C ''experi'':24C ''fals'':243C ''fine'':179C ''get'':238C ''github.com'':133C ''github.com/jupyter/notebook'':132C ''go'':30C ''gt'':199C ''h2'':163C ''hardwar'':42C ''howev'':187C ''href'':131C,153C ''instal'':90C,129C ''ipyparallel.nbextension'':247C ''ipython'':147C,174C,191C ''ipython.org'':155C ''ipython.org/ipython-doc/1/interactive/public_server.html'':154C ''ipython/jupyter'':3A ''issu'':1A ''jupyt'':107C,135C ''learn'':55C ''lie'':103C ''lt'':195C ''machin'':54C,121C ''master'':86C ''money'':34C ''need'':73C ''new'':146C ''note'':44C ''notebook'':136C,192C,227C ''p'':12C,79C,126C,168C,172C,189C,225C,235C ''password'':251C ''probabl'':206C ''problem'':102C,166C ''profil'':148C,175C,193C,202C,217C,223C ''pyspark'':176C,194C,203C,221C,231C ''python'':8B ''quick'':43C ''quit'':58C ''rare'':70C ''remot'':157C ''run'':118C,141C,178C ''scienc'':63C ''server'':108C,158C ''set'':17C,20C,75C ''slave'':88C ''spark'':5A,11B,26C,91C,161C,185C ''spend'':29C,33C ''stuff'':196C ''success'':92C,138C ''tool'':67C ''u'':249C ''unrecogn'':6A,200C ''use'':65C ''ve'':81C,128C,143C ''vms'':22C,84C ''work'':15C,57C,97C ''would'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9067, 2, '2015-11-26 16:40:11.53', 2, NULL, NULL, '&lt;p&gt;&lt;strong&gt;If no pair of your &lt;em&gt;classes&lt;/em&gt; are overlapping and they do classify each and every element of the data set, then they constitute a &lt;a href="https://en.wikipedia.org/wiki/Partition_of_a_set" rel="nofollow"&gt;partition&lt;/a&gt; of that set. So does your clustering&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;In such a case&lt;/strong&gt;, I would suggest to use a distance metric to compare two partitions, instead of any bona fide index, even if it''s popular -unless that index happens to be a metric as well.&lt;/p&gt;

&lt;p&gt;One such distances for comparing partitions is the $VI$ index, rediscovered by Maria Meila$\phantom{,}^\dagger$ a few years back, but its origin dating back to the 80''s in a physics paper by Zurek$\phantom{,}^{\ddagger}$.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How to calculate distance between two partitions P and Q&lt;/strong&gt;:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Determine $P\wedge Q, $the "intersection" (I always mix up the words join and meet here) of P and Q. This is simply the partition formed by taking the intersection of each cluster of P against each cluster of Q.&lt;/li&gt;
&lt;li&gt;For all three partitions $P$, $Q$, $P\wedge Q$ calculate their entropy $H$ defined as follows: $$H(P)=-\frac{1}{N}\sum_{i=1}^Np_i\log p_i\;,\;p_i\equiv\frac{n_i}{N}$$ where $N$ is the total number of elements you clustered/classified and $n_i$ is the number of elements within cluster/class $i$ of a given partition $P$. &lt;/li&gt;
&lt;li&gt;The distance $d(P,Q)$ between partitions P and Q is then given by $$d(P,Q)=2H(P\wedge Q)-H(P)-H(Q)$$&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The reason for using a real metric is simple: a metric satisfy the triangular inequality and our intuitions of "close" and "similar" heavily relies on that. Any ad-hoc measure of dis-similarity (distance) that doesn''t satisfy the triangular inequality will eventually give you results like this: A is "$d$ away" from B (d being concrete number), with $d$ smaller than a given bound, say $D$. Then you find A close to C by an amount $d''&amp;lt;D$. If this distance were not a metric, it could then happen that the distance $d''''$ between B and C is say $2D$. For a sound metric distance, it should be 
$d''''\leq d+d''&amp;lt;2D$. In other words A would be "similar" to B and C, but the last two would be "different"/"not similar" from each other. This is counterintuitive.&lt;/p&gt;

&lt;p&gt;The downside is that the VI distance may be computationally more expensive than calculating say a Rand index. The upside is that it''s mathematically sound and &lt;em&gt;makes sense&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;You can use R to calculate the VI distance. You can also use Partanalyzer a program I wrote some time ago (&lt;a href="https://github.com/MASantos/Partanalyzer" rel="nofollow"&gt;https://github.com/MASantos/Partanalyzer&lt;/a&gt;). It needs a rewrite to make it more efficient, but it''s open source, so you can modify it at will.&lt;/p&gt;

&lt;p&gt;$\dagger$: Meila, M. Journal of Multivariate Analysis, 98, 873 (2007). &lt;/p&gt;

&lt;p&gt;$\ddagger$: W.H. Zurek, Nature, vol.341, p.119 (1989).&lt;/p&gt;
', NULL, NULL, '''/a'':37C ''/em'':10C,453C ''/li'':183C,247C,274C ''/masantos/partanalyzer'':481C ''/masantos/partanalyzer&lt;/a&gt;).'':486C ''/ol'':275C ''/p'':46C,87C,126C,140C,421C,454C,508C,520C,529C ''/strong'':45C,53C,139C ''/wiki/partition_of_a_set'':33C ''1'':204C,208C ''1989'':528C ''2007'':519C ''2d'':380C,394C ''2h'':266C ''80'':116C ''873'':518C ''98'':517C ''ad'':305C ''ad-hoc'':304C ''ago'':476C ''also'':467C ''alway'':150C ''amount'':355C ''analysi'':516C ''away'':330C ''b'':332C,375C,403C ''back'':108C,113C ''bona'':69C ''bound'':343C ''c'':352C,377C,405C ''calcul'':131C,194C,436C,461C ''case'':52C ''class'':9C ''classifi'':16C ''close'':296C,350C ''cluster'':44C,175C,180C ''cluster/class'':240C ''clustered/classified'':230C ''compar'':63C,93C ''comput'':432C ''concret'':335C ''constitut'':27C ''could'':367C ''counterintuit'':420C ''d'':251C,263C,329C,333C,338C,345C,356C,358C,373C,389C,391C,392C ''dagger'':104C,510C ''data'':23C ''date'':112C ''ddagger'':125C,522C ''defin'':198C ''determin'':143C ''differ'':412C ''dis'':310C ''dis-similar'':309C ''distanc'':60C,91C,132C,250C,312C,361C,372C,385C,429C,464C ''doesn'':314C ''downsid'':424C ''effici'':495C ''element'':20C,228C,238C ''em'':8C,450C ''en.wikipedia.org'':32C ''en.wikipedia.org/wiki/partition_of_a_set'':31C ''entropi'':196C ''equiv'':216C ''even'':72C ''eventu'':321C ''everi'':19C ''expens'':434C ''fide'':70C ''find'':348C ''follow'':200C ''form'':168C ''frac'':203C,217C ''github.com'':480C,485C ''github.com/masantos/partanalyzer'':479C ''github.com/masantos/partanalyzer&lt;/a&gt;).'':484C ''give'':322C ''given'':244C,261C,342C ''h'':197C,201C,270C,272C ''happen'':80C,369C ''heavili'':299C ''hoc'':306C ''href'':30C,478C ''index'':71C,79C,98C,440C ''inequ'':291C,319C ''instead'':66C ''intersect'':148C,172C ''intuit'':294C ''join'':155C ''journal'':513C ''last'':408C ''leq'':390C ''li'':142C,184C,248C ''like'':325C ''log'':211C ''lt'':357C,393C ''m'':512C ''make'':451C,492C ''maria'':101C ''mathemat'':447C ''may'':430C ''measur'':307C ''meet'':157C ''meila'':102C,511C ''metric'':61C,84C,283C,287C,365C,384C ''mix'':151C ''modifi'':504C ''multivari'':515C ''n'':205C,218C,220C,222C,232C ''natur'':525C ''need'':488C ''nofollow'':35C,483C ''np'':209C ''number'':226C,236C,336C ''ol'':141C ''one'':89C ''open'':499C ''origin'':111C ''overlap'':12C ''p'':1C,47C,88C,127C,136C,144C,160C,177C,189C,191C,202C,212C,214C,246C,252C,256C,264C,267C,271C,276C,422C,455C,509C,521C ''p.119'':527C ''pair'':5C ''paper'':121C ''partanalyz'':469C ''partit'':36C,65C,94C,135C,167C,188C,245C,255C ''phantom'':103C,124C ''physic'':120C ''popular'':76C ''program'':471C ''q'':138C,146C,162C,182C,190C,193C,253C,258C,265C,269C,273C ''r'':459C ''rand'':439C ''real'':282C ''reason'':278C ''rediscov'':99C ''rel'':34C,482C ''reli'':300C ''result'':324C ''rewrit'':490C ''satisfi'':288C,316C ''say'':344C,379C,437C ''sens'':452C ''set'':24C,40C ''similar'':298C,311C,401C,414C ''simpl'':285C ''simpli'':165C ''smaller'':339C ''sound'':383C,448C ''sourc'':500C ''strong'':2C,48C,128C ''suggest'':56C ''sum'':206C ''take'':170C ''three'':187C ''time'':475C ''total'':225C ''triangular'':290C,318C ''two'':64C,134C,409C ''unless'':77C ''upsid'':442C ''use'':58C,280C,458C,468C ''vi'':97C,428C,463C ''vol.341'':526C ''w.h'':523C ''wedg'':145C,192C,268C ''well'':86C ''within'':239C ''word'':154C,397C ''would'':55C,399C,410C ''wrote'':473C ''year'':107C ''zurek'':123C,524C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6558, 1, '2015-07-23 17:15:10.287', 2, 49, 'semantic relation or semantic relatedness between terms or phrases', '&lt;p&gt;I am looking for a method that can realize if there is any semantic relation between two terms using ontologies. For example given two terms &lt;em&gt;"kitchen"&lt;/em&gt; and &lt;em&gt;"Chef"&lt;/em&gt; it can return s.th like &lt;em&gt;chef works in a kitchen&lt;/em&gt; and a correlation value (for example 0.7). What about proper nouns or noun phrases? The relatedness of two phrases such as &lt;code&gt;"the biggest defense against the US Navy''s Laser Weapon System"&lt;/code&gt; and &lt;code&gt;"China''s crippling pollution problem"&lt;/code&gt;.
I would be appreciated if you could give me some references.&lt;/p&gt;
', 'machine-learning  nlp  similarity  correlation', NULL, '''/code'':94C,102C ''/em'':44C,48C,60C ''/p'':114C ''0.7'':67C ''appreci'':106C ''biggest'':84C ''chef'':47C,55C ''china'':97C ''code'':82C,96C ''correl'':15B,63C ''could'':109C ''crippl'':99C ''defens'':85C ''em'':42C,46C,54C ''exampl'':38C,66C ''give'':110C ''given'':39C ''kitchen'':43C,59C ''laser'':91C ''learn'':12B ''like'':53C ''look'':19C ''machin'':11B ''machine-learn'':10B ''method'':22C ''navi'':89C ''nlp'':13B ''noun'':71C,73C ''ontolog'':36C ''p'':16C ''phrase'':9A,74C,79C ''pollut'':100C ''problem'':101C ''proper'':70C ''realiz'':25C ''refer'':113C ''relat'':2A,31C ''related'':5A,76C ''return'':51C ''s.th'':52C ''semant'':1A,4A,30C ''similar'':14B ''system'':93C ''term'':7A,34C,41C ''two'':33C,40C,78C ''us'':88C ''use'':35C ''valu'':64C ''weapon'':92C ''work'':56C ''would'':104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6559, 2, '2015-07-23 17:29:03.497', 1, NULL, NULL, '&lt;p&gt;My recommendation would be to explore some statistical approach to represent the reviews/rating pair as significance. For example, to translate the [# reviews, rating] into some test statistic type model, such as &lt;a href="https://en.wikipedia.org/wiki/Student%27s_t-test" rel="nofollow"&gt;Student''s t&lt;/a&gt; for starters. &lt;/p&gt;

&lt;p&gt;Considering your example numbers, some approaches can be:&lt;/p&gt;

&lt;p&gt;1) &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;&amp;gt;&amp;gt; 5/(1/sqrt(10))
15.811388300841896
&amp;gt;&amp;gt;&amp;gt; 3/(1/sqrt(10))
9.4868329805051381
&amp;gt;&amp;gt;&amp;gt; 
&amp;gt;&amp;gt;&amp;gt; 
&amp;gt;&amp;gt;&amp;gt; 4/(1/sqrt(60))
30.98386676965934
&amp;gt;&amp;gt;&amp;gt; 5/(1/sqrt(10))
15.811388300841896
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;2) Or, diving deeper into stats,:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;&amp;gt;&amp;gt; (5-3)/(sqrt(1/10+1/10))
4.4721359549995796
&amp;gt;&amp;gt;&amp;gt; (4-5)/(sqrt(1/60+1/10))
-2.9277002188455996

(you will need to do some work on the alpha level to get at the significance in ex 2 above)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You can see how these work out for you; if too basic/too many inaccurate assumptions/some other filtering that you need more resolution in your model/etc., you can explore ways to better represent:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;the sample standard deviation (assumed to be 1 in my examples above), or&lt;/li&gt;
&lt;li&gt;the distribution (assumed Gaussian in examples above), or &lt;/li&gt;
&lt;li&gt;the appropriate statistical test (plow through Wikipedia), or &lt;/li&gt;
&lt;li&gt;etc&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Point is you can continuously refine your model, depending how much resolution you need. Only you can make that call. Hope this helps!&lt;/p&gt;
', NULL, NULL, '''+1'':115C,125C ''-2.9277002188455996'':127C ''-3'':112C ''-5'':122C ''/10'':116C,126C ''/a'':43C ''/code'':96C,148C ''/li'':199C,209C,219C,222C ''/p'':46C,56C,59C,105C,183C,248C ''/pre'':97C,149C ''/ul'':223C ''/wiki/student%27s_t-test'':37C ''1'':58C,193C ''1/10'':114C ''1/60'':124C ''1/sqrt'':66C,73C,86C,93C ''10'':67C,74C,94C ''15.811388300841896'':68C,95C ''2'':99C,146C ''3'':72C ''30.98386676965934'':88C ''4'':85C,121C ''4.4721359549995796'':117C ''5'':65C,92C,111C ''60'':87C ''9.4868329805051381'':75C ''alpha'':137C ''approach'':10C,53C ''appropri'':212C ''assum'':190C,203C ''assumptions/some'':165C ''basic/too'':162C ''better'':181C ''call'':244C ''code'':61C,107C ''consid'':48C ''continu'':229C ''deeper'':102C ''depend'':233C ''deviat'':189C ''distribut'':202C ''dive'':101C ''en.wikipedia.org'':36C ''en.wikipedia.org/wiki/student%27s_t-test'':35C ''etc'':221C ''ex'':145C ''exampl'':19C,50C,196C,206C ''explor'':7C,178C ''filter'':167C ''gaussian'':204C ''get'':140C ''gt'':62C,63C,64C,69C,70C,71C,76C,77C,78C,79C,80C,81C,82C,83C,84C,89C,90C,91C,108C,109C,110C,118C,119C,120C ''help'':247C ''hope'':245C ''href'':34C ''inaccur'':164C ''level'':138C ''li'':185C,200C,210C,220C ''make'':242C ''mani'':163C ''model'':30C,232C ''model/etc'':175C ''much'':235C ''need'':130C,170C,238C ''nofollow'':39C ''number'':51C ''p'':1C,47C,57C,98C,150C,224C ''pair'':15C ''plow'':215C ''point'':225C ''pre'':60C,106C ''rate'':24C ''recommend'':3C ''refin'':230C ''rel'':38C ''repres'':12C,182C ''resolut'':172C,236C ''review'':23C ''reviews/rating'':14C ''sampl'':187C ''see'':153C ''signific'':17C,143C ''sqrt'':113C,123C ''standard'':188C ''starter'':45C ''stat'':104C ''statist'':9C,28C,213C ''student'':40C ''test'':27C,214C ''translat'':21C ''type'':29C ''ul'':184C ''way'':179C ''wikipedia'':217C ''work'':134C,156C ''would'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6561, 1, '2015-07-23 20:52:46.68', 3, 124, 'Cheat Sheet of UNIX commands for Data Science', '&lt;p&gt;I was looking for a cheat sheet of UNIX commands, which are specifically usable for data science. I mean an introduction to very basic commands (starting from &lt;code&gt;cd&lt;/code&gt;, &lt;code&gt;ls&lt;/code&gt;, &lt;code&gt;pwd&lt;/code&gt;, to some still simple but usable for data - e.g. &lt;code&gt;wc&lt;/code&gt;, a few simple things with pipes, &lt;code&gt;ssh&lt;/code&gt;, maybe &lt;code&gt;s3cmd&lt;/code&gt;, etc), with some minimalistic examples.&lt;/p&gt;

&lt;p&gt;I couldn''t find one; or at least, nothing close to:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://byte.kde.org/~zrusin/git/git-cheat-sheet-medium.png" rel="nofollow"&gt;Git Cheat Sheet&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.cheatography.com/davechild/cheat-sheets/regular-expressions/" rel="nofollow"&gt;Regular Expressions Cheat Sheet&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.rstudio.com/resources/cheatsheets/" rel="nofollow"&gt;series of R Cheat Sheets&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I am pretty much aware of &lt;a href="http://datascienceatthecommandline.com/" rel="nofollow"&gt;datascienceatthecommandline.com&lt;/a&gt;, but my goal would be to have a 1-2 A4 pages printout, with really simple stuff.&lt;/p&gt;

&lt;p&gt;If there is one (but I missed it), I would be grateful for posting it as an answer. If there isn''t, then I am asking to post pieces of such list.&lt;/p&gt;

&lt;p&gt;(If Moderator permits, we may create one list with Community Wiki.)&lt;/p&gt;

&lt;p&gt;EDIT: The closest thing I''ve found is here: &lt;a href="http://www.gregreda.com/2013/07/15/unix-commands-for-data-science/" rel="nofollow"&gt;Useful Unix commands for data science&lt;/a&gt; (very nice, but missing the very introductory part).&lt;/p&gt;
', 'tools  beginner  processing', NULL, '''-2'':160C ''/2013/07/15/unix-commands-for-data-science/'':230C ''/a'':105C,119C,134C,150C,239C ''/code'':42C,45C,48C,60C,69C,73C ''/davechild/cheat-sheets/regular-expressions/'':112C ''/li'':106C,120C,135C ''/p'':79C,92C,168C,202C,215C,248C ''/resources/cheatsheets/'':126C ''/ul'':136C ''/~zrusin/git/git-cheat-sheet-medium.png'':99C ''1'':159C ''a4'':161C ''answer'':187C ''ask'':195C ''awar'':142C ''basic'':36C ''beginn'':10B ''byte.kde.org'':98C ''byte.kde.org/~zrusin/git/git-cheat-sheet-medium.png'':97C ''cd'':41C ''cheat'':1A,18C,103C,117C,132C ''close'':90C ''closest'':219C ''code'':40C,43C,46C,58C,67C,71C ''command'':5A,22C,37C,235C ''communiti'':213C ''couldn'':82C ''creat'':209C ''data'':7A,28C,56C,237C ''datascienceatthecommandline.com'':146C,149C ''e.g'':57C ''edit'':217C ''etc'':74C ''exampl'':78C ''express'':116C ''find'':84C ''found'':223C ''git'':102C ''goal'':153C ''grate'':181C ''href'':96C,109C,123C,145C,227C ''introduct'':33C ''introductori'':246C ''isn'':190C ''least'':88C ''li'':94C,107C,121C ''list'':201C,211C ''look'':15C ''ls'':44C ''may'':208C ''mayb'':70C ''mean'':31C ''minimalist'':77C ''miss'':176C,243C ''moder'':205C ''much'':141C ''nice'':241C ''nofollow'':101C,114C,128C,148C,232C ''noth'':89C ''one'':85C,173C,210C ''p'':12C,80C,137C,169C,203C,216C ''page'':162C ''part'':247C ''permit'':206C ''piec'':198C ''pipe'':66C ''post'':183C,197C ''pretti'':140C ''printout'':163C ''process'':11B ''pwd'':47C ''r'':131C ''realli'':165C ''regular'':115C ''rel'':100C,113C,127C,147C,231C ''s3cmd'':72C ''scienc'':8A,29C,238C ''seri'':129C ''sheet'':2A,19C,104C,118C,133C ''simpl'':52C,63C,166C ''specif'':25C ''ssh'':68C ''start'':38C ''still'':51C ''stuff'':167C ''thing'':64C,220C ''tool'':9B ''ul'':93C ''unix'':4A,21C,234C ''usabl'':26C,54C ''use'':233C ''ve'':222C ''wc'':59C ''wiki'':214C ''would'':154C,179C ''www.cheatography.com'':111C ''www.cheatography.com/davechild/cheat-sheets/regular-expressions/'':110C ''www.gregreda.com'':229C ''www.gregreda.com/2013/07/15/unix-commands-for-data-science/'':228C ''www.rstudio.com'':125C ''www.rstudio.com/resources/cheatsheets/'':124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8398, 2, '2015-10-12 09:23:40.853', 1, NULL, NULL, '&lt;p&gt;It depends on your application.&lt;/p&gt;

&lt;p&gt;When you are doing topic modelling, try the default stopwords first. When there are some words occurring prominently in many topics (note my rather vague formulation) they are good candidates for additional stopwords.&lt;/p&gt;

&lt;p&gt;E.g., in a corpus with texts containing figures and tabular material, the words "fig", "figure", "tab", or "table" are good additional stopwords. In the result, your topics become more well-defined.&lt;/p&gt;
', NULL, NULL, '''/p'':7C,42C,76C ''addit'':40C,64C ''applic'':6C ''becom'':71C ''candid'':38C ''contain'':50C ''corpus'':47C ''default'':17C ''defin'':75C ''depend'':3C ''e.g'':44C ''fig'':57C ''figur'':51C,58C ''first'':19C ''formul'':34C ''good'':37C,63C ''mani'':28C ''materi'':54C ''model'':14C ''note'':30C ''occur'':25C ''p'':1C,8C,43C ''promin'':26C ''rather'':32C ''result'':68C ''stopword'':18C,41C,65C ''tab'':59C ''tabl'':61C ''tabular'':53C ''text'':49C ''topic'':13C,29C,70C ''tri'':15C ''vagu'':33C ''well'':74C ''well-defin'':73C ''word'':24C,56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6567, 2, '2015-07-24 05:53:55.76', -1, NULL, NULL, '&lt;p&gt;To be frank, I really donot understand why this is an issue for you. If you want to a generic solution, I will say save it to a csv file. A file with 30,000 rows and 1,000 cols is NOT that big. Even a shell pipe with simple one-liners can do tasks you mentioned. For example, the following one-liner compute the mean of cells located at Ax rows and x3 cols of data.csv (I generate this data matrix of size 30,000x1,000 by filling random integers )&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;cat data.csv | perl -F'','' -lane ''BEGIN{ $avg=0; $num=0; $row=0; } $row++; next if( $row%3!=0);foreach $i (0..$#F){ if($i%26 == 0){ $avg+=$F[$i]; $num++;} }END{ print "avg=" . ($avg/$num); print "num=$num"; }'' 
avg=0.1998
num=390000
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;It takes about 3 seconds on my laptop. You may put whatever interested conditions to filter data, collect filtered ones and compute stats. &lt;/p&gt;

&lt;p&gt;Since for whatever complicate condition you AT MOST scan each element once, the complexity is thus at most linear. Even for a big data file, this will not be a big issue. By the way, adding extra auxiliary is simply to ''pre-compute'' something in advance. &lt;/p&gt;
', NULL, NULL, '''/code'':140C ''/p'':95C,166C,215C ''/pre'':141C ''0'':105C,107C,109C,115C,118C,123C ''0.1998'':137C ''000'':36C,40C,90C ''000x1'':89C ''1'':39C ''26'':122C ''3'':114C,146C ''30'':35C,88C ''390000'':139C ''ad'':203C ''advanc'':214C ''auxiliari'':205C ''avg'':104C,124C,130C,131C,136C ''ax'':74C ''begin'':103C ''big'':45C,190C,198C ''cat'':98C ''cell'':71C ''code'':97C ''col'':41C,78C ''collect'':160C ''complex'':181C ''complic'':171C ''comput'':67C,164C,211C ''condit'':156C,172C ''csv'':30C ''data'':84C,159C,191C ''data.csv'':80C,99C ''donot'':7C ''element'':178C ''end'':128C ''even'':46C,187C ''exampl'':61C ''extra'':204C ''f'':101C,119C,125C ''file'':31C,33C,192C ''fill'':92C ''filter'':158C,161C ''follow'':63C ''foreach'':116C ''frank'':4C ''generat'':82C ''generic'':21C ''integ'':94C ''interest'':155C ''issu'':13C,199C ''lane'':102C ''laptop'':150C ''linear'':186C ''liner'':54C,66C ''locat'':72C ''matrix'':85C ''may'':152C ''mean'':69C ''mention'':59C ''next'':111C ''num'':106C,127C,132C,134C,135C,138C ''one'':53C,65C,162C ''one-lin'':52C,64C ''p'':1C,142C,167C ''perl'':100C ''pipe'':49C ''pre'':96C,210C ''pre-comput'':209C ''print'':129C,133C ''put'':153C ''random'':93C ''realli'':6C ''row'':37C,75C,108C,110C,113C ''save'':26C ''say'':25C ''scan'':176C ''second'':147C ''shell'':48C ''simpl'':51C ''simpli'':207C ''sinc'':168C ''size'':87C ''solut'':22C ''someth'':212C ''stat'':165C ''take'':144C ''task'':57C ''thus'':183C ''understand'':8C ''want'':18C ''way'':202C ''whatev'':154C,170C ''x3'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6570, 1, '2015-07-24 13:21:32.583', 2, 59, 'Non-parametric approach to healthcare dataset?', '&lt;p&gt;I have a Healthcare dataset. I have been told to look at non-parametric approach to solve certain questions related to the dataset. I am little bit confused about non-parametric approach. &lt;/p&gt;

&lt;p&gt;Do they mean density plot based approach (such as looking at the histogram)? 
I know this is a vague question to ask here. However, I don''t have access to anybody else whom I can ask and hence I am asking for some input from others in this forum.&lt;/p&gt;

&lt;p&gt;Any response/thought would be appreciated.&lt;/p&gt;

&lt;p&gt;Thanks and regards. &lt;/p&gt;
', 'data-mining', NULL, '''/p'':46C,97C,104C,109C ''access'':76C ''anybodi'':78C ''appreci'':103C ''approach'':4A,27C,45C,54C ''ask'':69C,83C,88C ''base'':53C ''bit'':39C ''certain'':30C ''confus'':40C ''data'':9B ''data-min'':8B ''dataset'':7A,16C,35C ''densiti'':51C ''els'':79C ''forum'':96C ''healthcar'':6A,15C ''henc'':85C ''histogram'':60C ''howev'':71C ''input'':91C ''know'':62C ''littl'':38C ''look'':22C,57C ''mean'':50C ''mine'':10B ''non'':2A,25C,43C ''non-parametr'':1A,24C,42C ''other'':93C ''p'':11C,47C,98C,105C ''parametr'':3A,26C,44C ''plot'':52C ''question'':31C,67C ''regard'':108C ''relat'':32C ''response/thought'':100C ''solv'':29C ''thank'':106C ''told'':20C ''vagu'':66C ''would'':101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8248, 2, '2015-09-30 15:14:08.353', 0, NULL, NULL, '&lt;p&gt;Alternatively you could consider the distribution of the blocks areas per section and use the most popular value (the top peak of the distribution).&lt;/p&gt;

&lt;p&gt;If it is possible to meanifully cluster the distribution curves, you may even define the feature as a factor - the nearest centroid of the cluster of distribution curves. &lt;/p&gt;
', NULL, NULL, '''/p'':26C,56C ''altern'':2C ''area'':11C ''block'':10C ''centroid'':49C ''cluster'':34C,52C ''consid'':5C ''could'':4C ''curv'':37C,55C ''defin'':41C ''distribut'':7C,25C,36C,54C ''even'':40C ''factor'':46C ''featur'':43C ''may'':39C ''meani'':33C ''nearest'':48C ''p'':1C,27C ''peak'':22C ''per'':12C ''popular'':18C ''possibl'':31C ''section'':13C ''top'':21C ''use'':15C ''valu'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6572, 2, '2015-07-24 14:56:44.693', 2, NULL, NULL, '&lt;p&gt;They are not specifically referring to a plot based approach.  They are referring to a class of methods that must be employed when the data is not normal enough or not well-powered enough to use regular statistics.&lt;/p&gt;

&lt;p&gt;Parametric and nonparametric are two broad classifications of statistical procedures with loose definitions separating them:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Parametric tests usually assume that the data are approximately normally distributed.&lt;/li&gt;
&lt;li&gt;Nonparametric tests do not rely on a normally distributed data assumption.&lt;/li&gt;
&lt;li&gt;Using parametric statistics on non-normal data could lead to incorrect results.&lt;/li&gt;
&lt;li&gt;If you are not sure that your data is normal enough or that your sample size is big enough (n &amp;lt; 30), use nonparametric procedures rather than parametric procedures.&lt;/li&gt;
&lt;li&gt;Nonparametric procedures generally have less power for the same sample
size than the corresponding parametric procedure if the data truly are normal.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Take a look at some examples of parametric and analogous nonparametric tests from &lt;a href="http://www.mayo.edu/mayo-edu-docs/center-for-translational-science-activities-documents/berd-5-6.pdf" rel="nofollow"&gt;Tanya Hoskin''s Demystifying Summary&lt;/a&gt;:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/Q5JE7.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Q5JE7.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Here are some summary references:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://changingminds.org/explanations/research/analysis/parametric_non-parametric.htm" rel="nofollow"&gt;Another general table with some different information&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.statoek.wiso.uni-goettingen.de/veranstaltungen/graduateseminar/Nonpar2006.pdf" rel="nofollow"&gt;Nonparametric Statistics&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://ksu.edu.sa/sites/py/ar/mpy/departments/math/learnResources/ResourceCenter/Documents/All%20of%20Nonparametric%20Statistics.pdf" rel="nofollow"&gt;All of Nonparametric Statistics, by Larry Wasserman&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.r-tutor.com/elementary-statistics/non-parametric-methods" rel="nofollow"&gt;R tutorial&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://statsmodels-np.blogspot.ca/" rel="nofollow"&gt;Nonparametric Econometrics with Python&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':183C,203C,228C,240C,257C,269C,281C ''/elementary-statistics/non-parametric-methods'':264C ''/explanations/research/analysis/parametric_non-parametric.htm'':218C ''/li'':72C,85C,100C,131C,155C,229C,241C,258C,270C,282C ''/mayo-edu-docs/center-for-translational-science-activities-documents/berd-5-6.pdf'':175C ''/p'':41C,58C,184C,204C,211C ''/q5je7.png'':190C,197C ''/sites/py/ar/mpy/departments/math/learnresources/resourcecenter/documents/all%20of%20nonparametric%20statistics.pdf'':247C ''/ul'':156C,283C ''/veranstaltungen/graduateseminar/nonpar2006.pdf'':235C ''30'':123C ''alt'':198C ''analog'':167C ''anoth'':221C ''approach'':11C ''approxim'':69C ''assum'':64C ''assumpt'':84C ''base'':10C ''big'':119C ''broad'':48C ''changingminds.org'':217C ''changingminds.org/explanations/research/analysis/parametric_non-parametric.htm'':216C ''class'':17C ''classif'':49C ''correspond'':146C ''could'':95C ''data'':26C,67C,83C,94C,109C,151C ''definit'':55C ''demystifi'':181C ''descript'':201C ''differ'':226C ''distribut'':71C,82C ''econometr'':278C ''employ'':23C ''enough'':30C,36C,112C,120C ''enter'':199C ''exampl'':163C ''general'':135C,222C ''hoskin'':179C ''href'':172C,187C,215C,232C,244C,261C,273C ''i.stack.imgur.com'':189C,196C ''i.stack.imgur.com/q5je7.png'':188C,195C ''imag'':200C ''img'':193C ''incorrect'':98C ''inform'':227C ''ksu.edu.sa'':246C ''ksu.edu.sa/sites/py/ar/mpy/departments/math/learnresources/resourcecenter/documents/all%20of%20nonparametric%20statistics.pdf'':245C ''larri'':255C ''lead'':96C ''less'':137C ''li'':60C,73C,86C,101C,132C,213C,230C,242C,259C,271C ''look'':160C ''loos'':54C ''lt'':122C ''method'':19C ''must'':21C ''n'':121C ''nofollow'':177C,192C,220C,237C,249C,266C,276C ''non'':92C ''non-norm'':91C ''nonparametr'':45C,74C,125C,133C,168C,238C,252C,277C ''normal'':29C,70C,81C,93C,111C,154C ''p'':1C,42C,157C,185C,205C ''parametr'':43C,61C,88C,129C,147C,165C ''plot'':9C ''power'':35C,138C ''procedur'':52C,126C,130C,134C,148C ''python'':280C ''r'':267C ''rather'':127C ''refer'':6C,14C,210C ''regular'':39C ''rel'':176C,191C,219C,236C,248C,265C,275C ''reli'':78C ''result'':99C ''sampl'':116C,142C ''separ'':56C ''size'':117C,143C ''specif'':5C ''src'':194C ''statist'':40C,51C,89C,239C,253C ''statsmodels-np.blogspot.ca'':274C ''summari'':182C,209C ''sure'':106C ''tabl'':223C ''take'':158C ''tanya'':178C ''test'':62C,75C,169C ''truli'':152C ''tutori'':268C ''two'':47C ''ul'':59C,212C ''use'':38C,87C,124C ''usual'':63C ''wasserman'':256C ''well'':34C ''well-pow'':33C ''www.mayo.edu'':174C ''www.mayo.edu/mayo-edu-docs/center-for-translational-science-activities-documents/berd-5-6.pdf'':173C ''www.r-tutor.com'':263C ''www.r-tutor.com/elementary-statistics/non-parametric-methods'':262C ''www.statoek.wiso.uni-goettingen.de'':234C ''www.statoek.wiso.uni-goettingen.de/veranstaltungen/graduateseminar/nonpar2006.pdf'':233C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6573, 2, '2015-07-24 15:48:54.19', 0, NULL, NULL, '&lt;p&gt;To be honest binary classification is the easiest type compared to multi-class classification as at times by error you can classify a wrong class to a right one.So if you have a dataset with multiclass you will need a good distribution among them,so the expectation is more sample will give better insight,i.e CV should be less.However in case of binary classification if your class distribution is balanced enough you can easily go fo CV=10 for 25k observations,however if the class distribution is skewed you better go with less CV.&lt;/p&gt;

&lt;p&gt;So in a nutshell in case of binary distribution CV value really depends on your class distribution and not much on number of observations.&lt;/p&gt;
', NULL, NULL, '''/p'':97C,123C ''10'':80C ''25k'':82C ''among'':45C ''balanc'':72C ''better'':55C,92C ''binari'':5C,65C,106C ''case'':63C,104C ''class'':15C,27C,69C,87C,114C ''classif'':6C,16C,66C ''classifi'':24C ''compar'':11C ''cv'':58C,79C,96C,108C ''dataset'':36C ''depend'':111C ''distribut'':44C,70C,88C,107C,115C ''easiest'':9C ''easili'':76C ''enough'':73C ''error'':21C ''expect'':49C ''fo'':78C ''give'':54C ''go'':77C,93C ''good'':43C ''honest'':4C ''howev'':84C ''i.e'':57C ''insight'':56C ''less'':95C ''less.however'':61C ''much'':118C ''multi'':14C ''multi-class'':13C ''multiclass'':38C ''need'':41C ''number'':120C ''nutshel'':102C ''observ'':83C,122C ''one.so'':31C ''p'':1C,98C ''realli'':110C ''right'':30C ''sampl'':52C ''skew'':90C ''time'':19C ''type'':10C ''valu'':109C ''wrong'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6574, 2, '2015-07-24 15:55:45.9', 2, NULL, NULL, '&lt;p&gt;You can also use Python to do this. If you''re familiar using &lt;a href="http://pandas.pydata.org/" rel="nofollow"&gt;Pandas&lt;/a&gt; data frames:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas as pd

d1 = pd.DataFrame({''A'':[1,4], ''B'':[7,8], ''C'':[3,4]})
d2 = pd.DataFrame({''A'':[3,4], ''C'':[4,8], ''D'':[5,4], ''E'':[6,6]})

d1.append(d2)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This will output what you''re looking for, except the empty cells would have NaNs (not a number) in them.&lt;/p&gt;
', NULL, NULL, '''/a'':21C ''/code'':58C ''/p'':24C,81C ''/pre'':59C ''1'':34C ''3'':40C,45C ''4'':35C,41C,46C,48C,52C ''5'':51C ''6'':54C,55C ''7'':37C ''8'':38C,49C ''also'':4C ''b'':36C ''c'':39C,47C ''cell'':72C ''code'':26C ''d'':50C ''d1'':31C ''d1.append'':56C ''d2'':42C,57C ''data'':22C ''e'':53C ''empti'':71C ''except'':69C ''familiar'':13C ''frame'':23C ''href'':16C ''import'':27C ''look'':67C ''nan'':75C ''nofollow'':19C ''number'':78C ''output'':63C ''p'':1C,60C ''panda'':20C,28C ''pandas.pydata.org'':17C ''pd'':30C ''pd.dataframe'':32C,43C ''pre'':25C ''python'':6C ''re'':12C,66C ''rel'':18C ''use'':5C,14C ''would'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6575, 1, '2015-07-24 16:09:38.027', 0, 10, 'Business Intelligence solution with robust backward browser compatibility', '&lt;p&gt;For a while I was wondering where this question would suit best and I think that the &lt;strong&gt;DS&lt;/strong&gt; is the best match but I presume that it may be migrated to &lt;code&gt;programmers&lt;/code&gt; or somewhere else.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;I''m looking at designing a business intelligence solution with a high degree of browser compatibility due to heterogeneous nature of IT systems across a wide user base. For example, the easy solution would be to program things in R and deploy some interactive gadgets via Shiny or do some front-end in Spotfire/Tableau but this will fall  when folk will try to access stuff with archaic browsers, like IE9. I would like to know how folk address such challenges, is the only solution $AWS + Python$ or are there some other less resource intense ways of developing BI products that would be accessible to across wide range of heterogeneous system configurations?&lt;/p&gt;
', 'programming  consumerweb', NULL, '''/code'':47C ''/p'':51C,166C ''/strong'':31C ''access'':118C,157C ''across'':77C,159C ''address'':132C ''archaic'':121C ''aw'':139C ''backward'':6A ''base'':81C ''best'':23C,34C ''bi'':152C ''browser'':7A,68C,122C ''busi'':1A,60C ''challeng'':134C ''code'':45C ''compat'':8A,69C ''configur'':165C ''consumerweb'':10B ''degre'':66C ''deploy'':95C ''design'':58C ''develop'':151C ''ds'':30C ''due'':70C ''easi'':85C ''els'':50C ''end'':106C ''exampl'':83C ''fall'':112C ''folk'':114C,131C ''front'':105C ''front-end'':104C ''gadget'':98C ''heterogen'':72C,163C ''high'':65C ''hr'':52C ''ie9'':124C ''intellig'':2A,61C ''intens'':148C ''interact'':97C ''know'':129C ''less'':146C ''like'':123C,127C ''look'':56C ''m'':55C ''match'':35C ''may'':41C ''migrat'':43C ''natur'':73C ''p'':11C,53C ''presum'':38C ''product'':153C ''program'':9B,90C ''programm'':46C ''python'':140C ''question'':20C ''r'':93C ''rang'':161C ''resourc'':147C ''robust'':5A ''shini'':100C ''solut'':3A,62C,86C,138C ''somewher'':49C ''spotfire/tableau'':108C ''strong'':29C ''stuff'':119C ''suit'':22C ''system'':76C,164C ''thing'':91C ''think'':26C ''tri'':116C ''user'':80C ''via'':99C ''way'':149C ''wide'':79C,160C ''wonder'':17C ''would'':21C,87C,126C,155C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6576, 1, '2015-07-24 20:10:07.617', 11, 640, 'I am a programmer, how do I get into field of Data Science?', '&lt;p&gt;First of all this term sounds so obscure.&lt;/p&gt;

&lt;p&gt;Anyways..I am a software programmer. One of the languages I can code is Python. Speaking of Data I can use SQL and can do Data Scraping. What I figured out so far after reading soo many articles that Data Science is all about good at:&lt;/p&gt;

&lt;p&gt;1- Stats&lt;/p&gt;

&lt;p&gt;2- Algebra&lt;/p&gt;

&lt;p&gt;3- Data Analysis&lt;/p&gt;

&lt;p&gt;4- Visualisation.&lt;/p&gt;

&lt;p&gt;5- Machine Learning.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;What I know so far:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;1- Python Programming
2- Data scrapping in Python&lt;/p&gt;

&lt;p&gt;Can you experts guide me or suggest a roadmap to brush up both theory and practical? I have given around 8 months of time frame to myself.&lt;/p&gt;
', 'beginner  career', NULL, '''/p'':25C,73C,77C,81C,86C,90C,95C,104C,114C,143C ''/strong'':103C ''1'':75C,106C ''2'':79C,109C ''3'':83C ''4'':88C ''5'':92C ''8'':136C ''algebra'':80C ''analysi'':85C ''anyway'':27C ''around'':135C ''articl'':64C ''beginn'':14B ''brush'':126C ''career'':15B ''code'':39C ''data'':12A,44C,52C,66C,84C,110C ''expert'':118C ''far'':59C,102C ''field'':10A ''figur'':56C ''first'':17C ''frame'':140C ''get'':8A ''given'':134C ''good'':71C ''guid'':119C ''know'':100C ''languag'':36C ''learn'':94C ''machin'':93C ''mani'':63C ''month'':137C ''obscur'':24C ''one'':33C ''p'':16C,26C,74C,78C,82C,87C,91C,96C,105C,115C ''practic'':131C ''program'':108C ''programm'':4A,32C ''python'':41C,107C,113C ''read'':61C ''roadmap'':124C ''scienc'':13A,67C ''scrap'':111C ''scrape'':53C ''softwar'':31C ''soo'':62C ''sound'':22C ''speak'':42C ''sql'':48C ''stat'':76C ''strong'':97C ''suggest'':122C ''term'':21C ''theori'':129C ''time'':139C ''use'':47C ''visualis'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6577, 1, '2015-07-24 20:39:06.317', 1, 84, 'how to perform Step calcuations in R', '&lt;p&gt;I have a rate-step plan for consumption with which I''m trying to total up costs. I have a rate plant that looks like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;First 20 Kwh is Free; 
Second 5 Kwh is $.1
    Next 100 Kwh is $.053 
Next 875 Kwh is $.042 
    Over 1000Kwh is $.039
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Can someone provide a worked example for me, e.g., with $\text{consumtpion}=625$? If possible, with R code for calculating the total cost.&lt;/p&gt;

&lt;p&gt;For my own approach, I was thinking of using a loop, but if it''s a lot of accounts the loop will take too long.  Can anyone think of a vector-based approach for calculating the result?&lt;/p&gt;
', 'r', NULL, '''/code'':64C ''/p'':37C,90C,130C ''/pre'':65C ''039'':63C ''042'':59C ''053'':54C ''1'':49C ''100'':51C ''1000kwh'':61C ''20'':41C ''5'':46C ''625'':79C ''875'':56C ''account'':110C ''anyon'':118C ''approach'':95C,125C ''base'':124C ''calcuat'':5A ''calcul'':86C,127C ''code'':39C,84C ''consumpt'':18C ''consumtpion'':78C ''cost'':27C,89C ''e.g'':75C ''exampl'':72C ''first'':40C ''free'':44C ''kwh'':42C,47C,52C,57C ''like'':35C ''long'':116C ''look'':34C ''loop'':102C,112C ''lot'':108C ''m'':22C ''next'':50C,55C ''p'':9C,66C,91C ''perform'':3A ''plan'':16C ''plant'':32C ''possibl'':81C ''pre'':38C ''provid'':69C ''r'':7A,8B,83C ''rate'':14C,31C ''rate-step'':13C ''result'':129C ''second'':45C ''someon'':68C ''step'':4A,15C ''take'':114C ''text'':77C ''think'':98C,119C ''total'':25C,88C ''tri'':23C ''use'':100C ''vector'':123C ''vector-bas'':122C ''work'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6578, 1, '2015-07-24 21:06:16.493', 1, 101, 'Identify document''s owner by content', '&lt;p&gt;Can anyone suggest a machine learning algorithm that would be useful for identifying the organization a document belongs to? I''m working with a relatively large number of organizations (on the order of 1000s). Naturally, the input data is unstructured, but most documents look almost, but not quite, the same. Also, there are some organizations that follow more then a one template, again, in that "almost but not quite" manner.&lt;/p&gt;

&lt;p&gt;Documents are in PDF format, and are reports of some kind and can contain several rectangular regions of text and tables. Most of them are not more than five pages long.&lt;/p&gt;

&lt;p&gt;New organizations can eventually appear - how does interfere with algorithm?&lt;/p&gt;
', 'classification  pdf', NULL, '''/p'':80C,113C,125C ''1000s'':43C ''algorithm'':16C,124C ''almost'':54C,75C ''also'':60C ''anyon'':11C ''appear'':119C ''belong'':27C ''classif'':7B ''contain'':95C ''content'':6A ''data'':47C ''document'':2A,26C,52C,82C ''eventu'':118C ''five'':110C ''follow'':66C ''format'':86C ''identifi'':1A,22C ''input'':46C ''interfer'':122C ''kind'':92C ''larg'':35C ''learn'':15C ''long'':112C ''look'':53C ''m'':30C ''machin'':14C ''manner'':79C ''natur'':44C ''new'':115C ''number'':36C ''one'':70C ''order'':41C ''organ'':24C,38C,64C,116C ''owner'':4A ''p'':9C,81C,114C ''page'':111C ''pdf'':8B,85C ''quit'':57C,78C ''rectangular'':97C ''region'':98C ''relat'':34C ''report'':89C ''sever'':96C ''suggest'':12C ''tabl'':102C ''templat'':71C ''text'':100C ''unstructur'':49C ''use'':20C ''work'':31C ''would'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6580, 2, '2015-07-24 23:00:01.503', 3, NULL, NULL, '&lt;p&gt;Try a simple equation like this:
\begin{eqnarray}
&amp;amp;&amp;amp;\max(x-20,0)*0.1 + \max(0,x-25)*(-.047) + \\
&amp;amp;&amp;amp;\max(0, x-125)*(-.011) + \max(0,x-1000)*(-.003)
\end{eqnarray}&lt;/p&gt;

&lt;p&gt;This should take the usage $(x)$ and walk it through steps, initially charging 0.1 for each Kwh over 20: $\max(x-20,0)*0.1$&lt;/p&gt;

&lt;p&gt;Then, if $x$ is above 20, it takes away 0.047 (0.1-0.053) for every Kwh between 20 and 25: $\max(0, x-25)*(-0.047)$&lt;/p&gt;

&lt;p&gt;Then, if $x$ is above 125, it takes away 0.011 (0.053-0.042) for every Kwh between 25 and 125: $\max(0, x-125)*(-0.011)$&lt;/p&gt;
', NULL, NULL, '''-0.011'':113C ''-0.042'':101C ''-0.047'':87C ''-0.053'':75C ''-1000'':32C ''-125'':27C,112C ''-20'':14C,59C ''-25'':20C,86C ''/p'':36C,62C,88C,114C ''0'':15C,18C,25C,30C,60C,84C,110C ''0.011'':99C ''0.047'':73C ''0.053'':100C ''0.1'':16C,51C,61C,74C ''003'':33C ''011'':28C ''047'':21C ''125'':95C,108C ''20'':56C,69C,80C ''25'':82C,106C ''amp'':10C,11C,22C,23C ''away'':72C,98C ''begin'':8C ''charg'':50C ''end'':34C ''eqnarray'':9C,35C ''equat'':5C ''everi'':77C,103C ''initi'':49C ''kwh'':54C,78C,104C ''like'':6C ''max'':12C,17C,24C,29C,57C,83C,109C ''p'':1C,37C,63C,89C ''simpl'':4C ''step'':48C ''take'':40C,71C,97C ''tri'':2C ''usag'':42C ''walk'':45C ''x'':13C,19C,26C,31C,43C,58C,66C,85C,92C,111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6604, 1, '2015-07-28 08:53:18.033', 0, 37, 'What does the Ip mean in the Bayesian Ridge Regression formula?', '&lt;p&gt;From &lt;a href="http://scikit-learn.org/stable/modules/linear_model.html#bayesian-ridge-regression" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/linear_model.html#bayesian-ridge-regression&lt;/a&gt;, they gave the bayesian ridge distribution as this:&lt;/p&gt;

&lt;p&gt;$p(w|\lambda) = \mathcal{N}(w|0,\lambda^{-1}{I_{p}})$&lt;/p&gt;

&lt;p&gt;And there is a variable $I_p$ but it''s unexplained what does the $I_p$ refer to?&lt;/p&gt;

&lt;p&gt;Also, the variable $\mathcal{N}$ is unexplained but I''m not sure whether I''ve guessed correctly but is that the Gaussian prior as described in the Bayesian regression section above the Bayesian Ridge?&lt;/p&gt;
', 'regression', NULL, '''-1'':43C ''/p'':33C,46C,66C,102C ''/stable/modules/linear_model.html#bayesian-ridge-regression'':19C ''/stable/modules/linear_model.html#bayesian-ridge-regression&lt;/a&gt;,'':24C ''0'':41C ''also'':68C ''bayesian'':8A,28C,95C,100C ''correct'':84C ''describ'':92C ''distribut'':30C ''formula'':11A ''gaussian'':89C ''gave'':26C ''guess'':83C ''href'':16C ''ip'':4A ''lambda'':37C,42C ''m'':77C ''mathcal'':38C,71C ''mean'':5A ''n'':39C,72C ''nofollow'':21C ''p'':13C,34C,35C,45C,47C,54C,63C,67C ''prior'':90C ''refer'':64C ''regress'':10A,12B,96C ''rel'':20C ''ridg'':9A,29C,101C ''scikit-learn.org'':18C,23C ''scikit-learn.org/stable/modules/linear_model.html#bayesian-ridge-regression'':17C ''scikit-learn.org/stable/modules/linear_model.html#bayesian-ridge-regression&lt;/a&gt;,'':22C ''section'':97C ''sure'':79C ''unexplain'':58C,74C ''variabl'':52C,70C ''ve'':82C ''w'':36C,40C ''whether'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6581, 2, '2015-07-25 04:27:41.21', 1, NULL, NULL, '&lt;p&gt;I do think Leaning Hadoop framework (hard way) is not a requirement of being a Data Scientist. 
General knowledge on all big data platforms is essential. 
I will suggest to know concept on it and only part need from Hadoop is the MapReduce
&lt;a href="http://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html" rel="nofollow"&gt;http://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;A Data Scientist does not build cluster, administer ... is just make "magic" with data and does not care where is coming from. 
The term "Hadoop" has come to refer not just to the base modules above, but also to the "ecosystem", or collection of additional software packages that can be installed on top of or alongside Hadoop, such as Apache Pig, Apache Hive, Apache HBase, Apache Spark, and others.&lt;/p&gt;

&lt;p&gt;Most important is the Programing language, math and statistics for working with data (you''ll need to find a way to connect with data and move forward).
I wish I had somebody to point me to the concept and do not spend weeks on learning framework and build from scratch nodes and clusters, because that part is Administrator role and not Data Engineer or Data Scientist.
Also one thing: all are changing and evolving but math, programing, statistics are still the requirements. &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;accessing data from hdfs is essential, for example PROC Hadoop, Hive,  SparkContext or any other driver or pipe (treat hadoop as a point of accesing data or storage :)&lt;/p&gt;
  
  &lt;p&gt;already are in place tools or frameworks what take care of resource allocation and management, performance. &lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/blockquote'':259C ''/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/mapreducetutorial.html'':49C ''/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/mapreducetutorial.html&lt;/a&gt;&lt;/p&gt;'':54C ''/p'':125C,209C,240C,258C ''acces'':236C ''access'':212C ''addit'':100C ''administ'':63C ''administr'':184C ''alloc'':254C ''alongsid'':111C ''alreadi'':242C ''also'':93C,193C ''apach'':115C,117C,119C,121C ''base'':89C ''big'':23C ''blockquot'':210C ''build'':61C,174C ''care'':73C,251C ''chang'':198C ''cluster'':62C,179C ''collect'':98C ''come'':76C,82C ''concept'':33C,164C ''connect'':148C ''data'':17C,24C,57C,69C,139C,150C,188C,191C,213C,237C ''driver'':227C ''ecosystem'':96C ''engin'':189C ''essenti'':27C,217C ''evolv'':200C ''exampl'':219C ''find'':144C ''forward'':153C ''framework'':7C,172C,248C ''general'':19C ''hadoop'':6C,41C,80C,112C,221C,231C ''hadoop.apache.org'':48C,53C ''hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/mapreducetutorial.html'':47C ''hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/mapreducetutorial.html&lt;/a&gt;&lt;/p&gt;'':52C ''hard'':8C ''hbase'':120C ''hdfs'':215C ''hive'':118C,222C ''href'':46C ''import'':128C ''instal'':106C ''know'':32C ''knowledg'':20C ''languag'':132C ''lean'':5C ''learn'':171C ''ll'':141C ''magic'':67C ''make'':66C ''manag'':256C ''mapreduc'':44C ''math'':133C,202C ''modul'':90C ''move'':152C ''need'':39C,142C ''node'':177C ''nofollow'':51C ''one'':194C ''other'':124C ''p'':1C,55C,126C,211C,241C ''packag'':102C ''part'':38C,182C ''perform'':257C ''pig'':116C ''pipe'':229C ''place'':245C ''platform'':25C ''point'':160C,234C ''proc'':220C ''program'':131C,203C ''refer'':84C ''rel'':50C ''requir'':13C,208C ''resourc'':253C ''role'':185C ''scientist'':18C,58C,192C ''scratch'':176C ''softwar'':101C ''somebodi'':158C ''spark'':122C ''sparkcontext'':223C ''spend'':168C ''statist'':135C,204C ''still'':206C ''storag'':239C ''suggest'':30C ''take'':250C ''term'':79C ''thing'':195C ''think'':4C ''tool'':246C ''top'':108C ''treat'':230C ''way'':9C,146C ''week'':169C ''wish'':155C ''work'':137C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9105, 5, '2015-11-28 15:12:29.987', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6582, 2, '2015-07-25 04:49:31.85', 6, NULL, NULL, '&lt;p&gt;I do like Berkeley course on Data Science, will give a good foundation and taste for Data Science, After moved to udacity and coursera and many more resources. So if you have Programming skills than will need math and stat and a lot of visualization. Also will be great to get used to IPython because is essential to see every step(visualize)how it perform instead writing a whole script and test after (anaconda is easy to install and work with). Course is listed bellow: 
bcourses.berkeley.edu/courses/1267848/wiki
also the stat i find good free course from SAS: Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression support.sas.com/edu/schedules.html?ctry=us&amp;amp;id=1979&lt;/p&gt;

&lt;p&gt;Starting with ML will recommend: www.kaggle.com/c/titanic/details/getting-started-with-python&lt;/p&gt;

&lt;p&gt;on left side is also for Excel using Pivot tables  and R. DataCamp has released the tutorial on how to use R. Once you complete this steps than more competitions in gaining experience are on kaggle (recently released one for San Francisco Crime Classification) and ultimately amazing video tutorials from www.dataschool.io&lt;/p&gt;

&lt;p&gt;hope it helps ...&lt;/p&gt;
', NULL, NULL, '''/c/titanic/details/getting-started-with-python&lt;/p&gt;'':120C ''/courses/1267848/wiki'':89C ''/edu/schedules.html?ctry=us&amp;amp;id=1979&lt;/p&gt;'':111C ''/p'':173C,178C ''1'':101C ''also'':47C,90C,126C ''amaz'':168C ''anaconda'':75C ''anova'':104C ''bcourses.berkeley.edu'':88C ''bcourses.berkeley.edu/courses/1267848/wiki'':87C ''bellow'':86C ''berkeley'':5C ''classif'':165C ''competit'':151C ''complet'':146C ''cours'':6C,83C,97C ''coursera'':25C ''crime'':164C ''data'':8C,18C ''datacamp'':134C ''easi'':77C ''essenti'':58C ''everi'':61C ''excel'':128C ''experi'':154C ''find'':94C ''foundat'':14C ''francisco'':163C ''free'':96C ''gain'':153C ''get'':52C ''give'':11C ''good'':13C,95C ''great'':50C ''help'':177C ''hope'':175C ''instal'':79C ''instead'':67C ''introduct'':102C ''ipython'':55C ''kaggl'':157C ''left'':123C ''like'':4C ''list'':85C ''logist'':107C ''lot'':44C ''mani'':27C ''math'':39C ''ml'':115C ''move'':21C ''need'':38C ''one'':160C ''p'':1C,112C,121C,174C ''perform'':66C ''pivot'':130C ''program'':34C ''r'':133C,143C ''recent'':158C ''recommend'':117C ''regress'':105C,108C ''releas'':136C,159C ''resourc'':29C ''san'':162C ''sas'':99C ''scienc'':9C,19C ''script'':71C ''see'':60C ''side'':124C ''skill'':35C ''start'':113C ''stat'':41C,92C ''statist'':100C ''step'':62C,148C ''support.sas.com'':110C ''support.sas.com/edu/schedules.html?ctry=us&amp;amp;id=1979&lt;/p&gt;'':109C ''tabl'':131C ''tast'':16C ''test'':73C ''tutori'':138C,170C ''udac'':23C ''ultim'':167C ''use'':53C,129C,142C ''video'':169C ''visual'':46C,63C ''whole'':70C ''work'':81C ''write'':68C ''www.dataschool.io'':172C ''www.kaggle.com'':119C ''www.kaggle.com/c/titanic/details/getting-started-with-python&lt;/p&gt;'':118C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6583, 2, '2015-07-25 07:36:25.153', 0, NULL, NULL, '&lt;p&gt;Try Decision Trees,Support Vector Machines or Naive-Bayes method with TF-IDF weighting for creating document vectors and check the Precision/Recall/F Measure scores. But this will not deal with unknown/new organizations, they will get classified to any other one. One way would be remodel/train again when there are so many unknown/new organization and the Precision/Recall/F Measure score too low.&lt;/p&gt;
', NULL, NULL, '''/p'':64C ''bay'':11C ''check'':23C ''classifi'':39C ''creat'':19C ''deal'':32C ''decis'':3C ''document'':20C ''get'':38C ''idf'':16C ''low'':63C ''machin'':7C ''mani'':54C ''measur'':26C,60C ''method'':12C ''naiv'':10C ''naive-bay'':9C ''one'':43C,44C ''organ'':35C,56C ''p'':1C ''precision/recall/f'':25C,59C ''remodel/train'':48C ''score'':27C,61C ''support'':5C ''tf'':15C ''tf-idf'':14C ''tree'':4C ''tri'':2C ''unknown/new'':34C,55C ''vector'':6C,21C ''way'':45C ''weight'':17C ''would'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6584, 2, '2015-07-25 13:27:49.453', -1, NULL, NULL, '&lt;p&gt;simple... use advanced Text Editor
like...
Sublime text ,Notepad  ,Vim or Atom..&lt;/p&gt;

&lt;p&gt;paste all Bunch of data on any Editior and just remove ''\n and replaced it by "" ..&lt;/p&gt;

&lt;p&gt;use FIND AND REPLACE function&lt;/p&gt;

&lt;p&gt;Notepad   ,sublime text,vim and atom&lt;/p&gt;

&lt;p&gt;use Ctrl   H&lt;/p&gt;

&lt;p&gt;find box type "\n" and in replace box "" &lt;/p&gt;

&lt;p&gt;done :) ;)&lt;/p&gt;

&lt;p&gt;tnx&lt;/p&gt;
', NULL, NULL, '''/p'':14C,32C,39C,47C,52C,62C,65C,68C ''advanc'':4C ''atom'':13C,46C ''box'':55C,61C ''bunch'':18C ''ctrl'':50C ''data'':20C ''done'':64C ''editior'':23C ''editor'':6C ''find'':35C,54C ''function'':38C ''h'':51C ''like'':7C ''n'':27C,57C ''notepad'':10C,41C ''p'':1C,15C,33C,40C,48C,53C,63C,66C ''past'':16C ''remov'':26C ''replac'':29C,37C,60C ''simpl'':2C ''sublim'':8C,42C ''text'':5C,9C,43C ''tnx'':67C ''type'':56C ''use'':3C,34C,49C ''vim'':11C,44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6585, 1, '2015-07-25 18:50:36.5', 1, 25, 'Ontologies with user interface', '&lt;p&gt;I am working on the development of an ontology for my dissertation project. I have read plenty of resources and tutorials on how to develop ontologies, why are they useful and how to use them. &lt;/p&gt;

&lt;p&gt;I''ve been trying to find examples of ontologies that include user interface such as textboxes, buttons etc. to study them for the development of my ontology. I checked databases such as ontoligua for similar projects but I didn''t find anything to relate to.&lt;/p&gt;

&lt;p&gt;I would be grateful anyone can help me with that. &lt;/p&gt;
', 'knowledge-base', NULL, '''/p'':44C,91C,103C ''anyon'':97C ''anyth'':87C ''base'':7B ''button'':62C ''check'':74C ''databas'':75C ''develop'':14C,33C,69C ''didn'':84C ''dissert'':20C ''etc'':63C ''exampl'':52C ''find'':51C,86C ''grate'':96C ''help'':99C ''includ'':56C ''interfac'':4A,58C ''knowledg'':6B ''knowledge-bas'':5B ''ontoligua'':78C ''ontolog'':1A,17C,34C,54C,72C ''p'':8C,45C,92C ''plenti'':25C ''project'':21C,81C ''read'':24C ''relat'':89C ''resourc'':27C ''similar'':80C ''studi'':65C ''textbox'':61C ''tri'':49C ''tutori'':29C ''use'':38C,42C ''user'':3A,57C ''ve'':47C ''work'':11C ''would'':94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6654, 2, '2015-08-01 10:56:11.233', 1, NULL, NULL, '&lt;p&gt;Other-than Ontologies do check &lt;a href="https://code.google.com/p/word2vec/" rel="nofollow"&gt;word2vec&lt;/a&gt;, this tool provides an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words and can be used to find the closest words for a user-specified word&lt;/p&gt;
', NULL, NULL, '''/a'':16C ''/p'':56C ''/p/word2vec/'':12C ''architectur'':34C ''bag'':27C ''bag-of-word'':26C ''check'':7C ''closest'':48C ''code.google.com'':11C ''code.google.com/p/word2vec/'':10C ''comput'':36C ''continu'':25C ''effici'':21C ''find'':46C ''gram'':33C ''href'':9C ''implement'':22C ''nofollow'':14C ''ontolog'':5C ''other-than'':2C ''p'':1C ''provid'':19C ''rel'':13C ''represent'':38C ''skip'':32C ''skip-gram'':31C ''specifi'':54C ''tool'':18C ''use'':44C ''user'':53C ''user-specifi'':52C ''vector'':37C ''word'':29C,40C,49C,55C ''word2vec'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6586, 2, '2015-07-25 21:48:43.1', 14, NULL, NULL, '&lt;p&gt;Focus less on gaining skills and more on gaining experience. Try to actually solve some problems and post your work on github. You''ll learn more in the process and be able to demonstrate knowledge and experience to employers, which is much more valuable than having a supposedly deep understanding of a topic or theory. &lt;/p&gt;

&lt;p&gt;Data Science is a pretty loaded field these days so I''m not sure what kind of work you specifically want to do, but assuming that machine learning is a component of it then kaggle.com is a good place to start. In terms of goals, if you''re able to work with the data in pandas/numpy/scipy, build models in sci-kit learn and make some pretty graphs in seaborn, ggplot or even matplotlib then you won''t have a problem getting a job from a skills perspective -- especially if you have code samples and examples to demonstrate your abilities. If you get stuck then stackexchange will either have the answer or you can post a questions and you''ll have an answer shortly. Once you''re doing the work for a living then you''ll learn even more, likely from a senior team member who mentors you.&lt;/p&gt;

&lt;p&gt;Best of luck.&lt;/p&gt;
', NULL, NULL, '''/p'':57C,207C,212C ''abil'':158C ''abl'':33C,107C ''actual'':14C ''answer'':169C,181C ''assum'':83C ''best'':209C ''build'':115C ''code'':151C ''compon'':89C ''data'':59C,112C ''day'':67C ''deep'':50C ''demonstr'':35C,156C ''either'':166C ''employ'':40C ''especi'':147C ''even'':131C,196C ''exampl'':154C ''experi'':11C,38C ''field'':65C ''focus'':2C ''gain'':5C,10C ''get'':140C,161C ''ggplot'':129C ''github'':23C ''goal'':103C ''good'':96C ''graph'':126C ''job'':142C ''kaggle.com'':93C ''kind'':74C ''kit'':120C ''knowledg'':36C ''learn'':26C,86C,121C,195C ''less'':3C ''like'':198C ''live'':191C ''ll'':25C,178C,194C ''load'':64C ''luck'':211C ''m'':70C ''machin'':85C ''make'':123C ''matplotlib'':132C ''member'':203C ''mentor'':205C ''model'':116C ''much'':43C ''p'':1C,58C,208C ''pandas/numpy/scipy'':114C ''perspect'':146C ''place'':97C ''post'':19C,173C ''pretti'':63C,125C ''problem'':17C,139C ''process'':30C ''question'':175C ''re'':106C,185C ''sampl'':152C ''sci'':119C ''sci-kit'':118C ''scienc'':60C ''seaborn'':128C ''senior'':201C ''short'':182C ''skill'':6C,145C ''solv'':15C ''specif'':78C ''stackexchang'':164C ''start'':99C ''stuck'':162C ''suppos'':49C ''sure'':72C ''team'':202C ''term'':101C ''theori'':56C ''topic'':54C ''tri'':12C ''understand'':51C ''valuabl'':45C ''want'':79C ''won'':135C ''work'':21C,76C,109C,188C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6587, 1, '2015-07-26 10:15:25.393', 1, 31, 'visualizing large number of points as a 3D density map', '&lt;p&gt;The  result of my computational simulation is a (time-dependent) system of large number (~100k) of moving points in a confined space. Each point has its own Cartesian coordinates as well as a weight (w) in the form of $(x_i,y_i,z_i;w_i)$. &lt;/p&gt;

&lt;p&gt;I''m looking for a software/app/package to create a snapshot of the 3D spatial density map of these points. (&lt;a href="http://www.nature.com/ncomms/2013/130906/ncomms3414/fig_tab/ncomms3414_F3.html" rel="nofollow"&gt;something like this&lt;/a&gt;). Like you see in this figure, the points are not going to be displayed individually, but only a transparent cloud will be drawn whose local intensity is proportional with the local number of points. &lt;/p&gt;

&lt;p&gt;The final goal is to create a movie of the changing 3D spatial density with time.&lt;br&gt;
So far, I have tried R, Matlab, Origin, and ImageJ. But no success!&lt;/p&gt;

&lt;p&gt;Thank you for your help!&lt;/p&gt;
', 'visualization', NULL, '''/a'':92C ''/ncomms/2013/130906/ncomms3414/fig_tab/ncomms3414_f3.html'':86C ''/p'':61C,127C,159C,166C ''100k'':28C ''3d'':8A,75C,140C ''br'':145C ''cartesian'':41C ''chang'':139C ''cloud'':112C ''comput'':17C ''confin'':34C ''coordin'':42C ''creat'':70C,134C ''densiti'':9A,77C,142C ''depend'':23C ''display'':106C ''drawn'':115C ''far'':147C ''figur'':98C ''final'':130C ''form'':51C ''go'':103C ''goal'':131C ''help'':165C ''href'':83C ''imagej'':155C ''individu'':107C ''intens'':118C ''larg'':2A,26C ''like'':90C,93C ''local'':117C,123C ''look'':65C ''m'':64C ''map'':10A,78C ''matlab'':152C ''move'':30C ''movi'':136C ''nofollow'':88C ''number'':3A,27C,124C ''origin'':153C ''p'':12C,62C,128C,160C ''point'':5A,31C,37C,81C,100C,126C ''proport'':120C ''r'':151C ''rel'':87C ''result'':14C ''see'':95C ''simul'':18C ''snapshot'':72C ''software/app/package'':68C ''someth'':89C ''space'':35C ''spatial'':76C,141C ''success'':158C ''system'':24C ''thank'':161C ''time'':22C,144C ''time-depend'':21C ''transpar'':111C ''tri'':150C ''visual'':1A,11B ''w'':48C,59C ''weight'':47C ''well'':44C ''whose'':116C ''www.nature.com'':85C ''www.nature.com/ncomms/2013/130906/ncomms3414/fig_tab/ncomms3414_f3.html'':84C ''x'':53C ''y'':55C ''z'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6590, 1, '2015-07-26 12:17:09.947', 3, 57, 'Feature selection for Support Vector Machines', '&lt;p&gt;My question is three-fold&lt;/p&gt;

&lt;p&gt;In the context of "Kernelized" support vector machines&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Is variable/feature selection desirable - especially since we regularize the parameter C to prevent overfitting and the main motive behind introducing kernels to a SVM is to increase the dimensionality of the problem, in such a case reducing the dimensions by parameter reduction seems counter-intuitive&lt;/li&gt;
&lt;li&gt;If the answer to the 1st question is "NO", then, On what conditions would the answer change that one should keep in mind ?&lt;/li&gt;
&lt;li&gt;Are there any good methods that have been tried to bring about feature reduction for SVMs in scikit-learn library of python - I have tried the SelectFpr method and am looking for people with experiences with different methods.&lt;/li&gt;
&lt;/ol&gt;
', 'svm  feature-selection  scikit', NULL, '''/li'':78C,103C,144C ''/ol'':145C ''/p'':19C,29C ''1st'':85C ''answer'':82C,95C ''behind'':50C ''bring'':115C ''c'':42C ''case'':67C ''chang'':96C ''condit'':92C ''context'':23C ''counter'':76C ''counter-intuit'':75C ''desir'':35C ''differ'':142C ''dimens'':70C ''dimension'':60C ''especi'':36C ''experi'':140C ''featur'':1A,9B,117C ''feature-select'':8B ''fold'':18C ''good'':108C ''increas'':58C ''introduc'':51C ''intuit'':77C ''keep'':100C ''kernel'':25C,52C ''learn'':124C ''li'':31C,79C,104C ''librari'':125C ''look'':136C ''machin'':6A,28C ''main'':48C ''method'':109C,133C,143C ''mind'':102C ''motiv'':49C ''ol'':30C ''one'':98C ''overfit'':45C ''p'':12C,20C ''paramet'':41C,72C ''peopl'':138C ''prevent'':44C ''problem'':63C ''python'':127C ''question'':14C,86C ''reduc'':68C ''reduct'':73C,118C ''regular'':39C ''scikit'':11B,123C ''scikit-learn'':122C ''seem'':74C ''select'':2A,10B,34C ''selectfpr'':132C ''sinc'':37C ''support'':4A,26C ''svm'':7B,55C ''svms'':120C ''three'':17C ''three-fold'':16C ''tri'':113C,130C ''variable/feature'':33C ''vector'':5A,27C ''would'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6591, 1, '2015-07-26 17:08:34.013', 3, 37, 'Train a classifier for a game with feedback on chosen move instead of true labels', '&lt;p&gt;I''m having some trouble describing in one line what I want, which is probably why I haven''t had much luck with Google.&lt;/p&gt;

&lt;p&gt;Say I have a game like 2048 where the possible actions each step are fixed (and more than two). I want to train a neural network that chooses a move, so I have 4 neurons in output layer and I make the move with the highest prediction. The output vector is normalized (softmax layer).&lt;/p&gt;

&lt;p&gt;However, the training data I have is just the state, the move that was made, and whether that had good or bad results. If the chosen move is bad, I don''t know which of the other ones was better (if any).&lt;/p&gt;

&lt;p&gt;How should I train this? My current thought is like this:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Good move? -&gt; chosen action gets positive error (so prediction goes up)&lt;/li&gt;
&lt;li&gt;Bad move? -&gt; chosen step gets negative error (so prediction goes down)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;But I haven''t found literature supporting this guess. There are alternatives:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Maybe I should also update the options that weren''t chosen (in the opposite direction)?&lt;/li&gt;
&lt;li&gt;Is it a good idea to set error directly instead of using goal predictions?&lt;/li&gt;
&lt;li&gt;The error for correct and incorrect could be different, maybe to preserve normalization?&lt;/li&gt;
&lt;li&gt;...&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;(I''m doing 2048 and using neural networks, but I think this is not limited to this game or this method.)&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/li'':172C,185C,218C,234C,249C,251C ''/ol'':252C ''/p'':45C,101C,145C,158C,200C,275C ''/ul'':186C ''2048'':53C,257C ''4'':80C ''action'':57C,164C ''also'':206C ''altern'':199C ''bad'':124C,131C,174C ''better'':142C ''choos'':74C ''chosen'':10A,128C,163C,176C,213C ''classifi'':3A ''correct'':239C ''could'':242C ''current'':153C ''data'':106C ''describ'':26C ''differ'':244C ''direct'':217C,228C ''error'':167C,180C,227C,237C ''feedback'':8A ''fix'':61C ''found'':192C ''game'':6A,51C,271C ''get'':165C,178C ''goal'':232C ''goe'':170C,183C ''good'':122C,161C,223C ''googl'':44C ''guess'':196C ''haven'':38C,190C ''highest'':92C ''howev'':103C ''idea'':224C ''incorrect'':241C ''instead'':12A,229C ''know'':135C ''label'':15A ''layer'':84C,100C ''learn'':18B ''li'':160C,173C,202C,219C,235C,250C ''like'':52C,156C ''limit'':268C ''line'':29C ''literatur'':193C ''luck'':42C ''m'':22C,255C ''machin'':17B ''machine-learn'':16B ''made'':117C ''make'':87C ''mayb'':203C,245C ''method'':274C ''move'':11A,76C,89C,114C,129C,162C,175C ''much'':41C ''negat'':179C ''network'':72C,261C ''neural'':71C,260C ''neuralnetwork'':19B ''neuron'':81C ''normal'':98C,248C ''ol'':201C ''one'':28C,140C ''opposit'':216C ''option'':209C ''output'':83C,95C ''p'':20C,46C,102C,146C,187C,253C ''posit'':166C ''possibl'':56C ''predict'':93C,169C,182C,233C ''preserv'':247C ''probabl'':35C ''result'':125C ''say'':47C ''set'':226C ''softmax'':99C ''state'':112C ''step'':59C,177C ''support'':194C ''think'':264C ''thought'':154C ''train'':1A,69C,105C,150C ''troubl'':25C ''true'':14A ''two'':65C ''ul'':159C ''updat'':207C ''use'':231C,259C ''vector'':96C ''want'':32C,67C ''weren'':211C ''whether'':119C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6592, 1, '2015-07-27 01:12:06.98', 0, 26, 'Integer optimization with R', '&lt;p&gt;I''m trying to find a optimal solution for my portfolio with DEoptim.
This is the objective function I''m trying to optimize:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;obj &amp;lt;- function(w) {
  r &amp;lt;- sum(return_of_stock * price * w)
  return(r)
}

return_of_stock is a vector of rates between 0 and infinite
price is a vector of the current price of each stock
w is a vector of integers between 0 and infinite
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have data regarding 100 stocks, and I want to find the optimal solution, regarding some constraints:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;sum(w*price) &amp;lt; value_today
price &amp;gt; price_threshold # i don''t want blue chips stocks
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I didn''t implement these constraints yet, but here is what I have done:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;min_stocks &amp;lt;- list(rep(0, nrow(retorno)))
max_stocks &amp;lt;- list(rep(99, nrow(retorno)))

DEoptim(obj, min_stocks,max_stocks)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But DEoptim returns:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Error in DEoptim(obj, min_stocks, max_stocks) : 
  (list) object cannot be coerced to type ''double''
&lt;/code&gt;&lt;/pre&gt;
', 'r  optimization', NULL, '''/code'':81C,121C,163C,188C ''/p'':31C,101C,138C,169C ''/pre'':82C,122C,164C,189C ''0'':57C,78C,146C ''100'':88C ''99'':154C ''blue'':118C ''cannot'':182C ''chip'':119C ''code'':33C,103C,140C,171C ''coerc'':184C ''constraint'':100C,129C ''current'':66C ''data'':86C ''deoptim'':20C,157C,167C,174C ''didn'':125C ''done'':137C ''doubl'':187C ''error'':172C ''find'':12C,94C ''function'':25C,36C ''gt'':111C ''implement'':127C ''infinit'':59C,80C ''integ'':1A,76C ''list'':144C,152C,180C ''lt'':35C,39C,107C,143C,151C ''m'':9C,27C ''max'':149C,161C,178C ''min'':141C,159C,176C ''nrow'':147C,155C ''obj'':34C,158C,175C ''object'':24C,181C ''optim'':2A,6B,14C,30C,96C ''p'':7C,83C,123C,165C ''portfolio'':18C ''pre'':32C,102C,139C,170C ''price'':44C,60C,67C,106C,110C,112C ''r'':4A,5B,38C,47C ''rate'':55C ''regard'':87C,98C ''rep'':145C,153C ''retorno'':148C,156C ''return'':41C,46C,48C,168C ''solut'':15C,97C ''stock'':43C,50C,70C,89C,120C,142C,150C,160C,162C,177C,179C ''sum'':40C,104C ''threshold'':113C ''today'':109C ''tri'':10C,28C ''type'':186C ''valu'':108C ''vector'':53C,63C,74C ''w'':37C,45C,71C,105C ''want'':92C,117C ''yet'':130C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6600, 2, '2015-07-27 12:30:12.22', 0, NULL, NULL, '&lt;p&gt;Just some random thoughts&lt;/p&gt;

&lt;p&gt;Do you have a mathematical model to base on? For example, you want to predict how pressure is changing against temperature. You won''t drop any predictor that is related to ''temperature'', no matter how remote it is. If so, that should govern your choice of predictors and you should start with that as that would lend more credibility to your final model.&lt;/p&gt;

&lt;p&gt;If not and you just want an algorithm to pick the best predictors, have you thought of running a regression model with L1 norm on? This will drive out insignificant factors and you can start with that set as a basis.&lt;/p&gt;
', NULL, NULL, '''/p'':6C,71C,114C ''algorithm'':80C ''base'':15C ''basi'':113C ''best'':84C ''chang'':26C ''choic'':52C ''credibl'':66C ''drive'':100C ''drop'':32C ''exampl'':18C ''factor'':103C ''final'':69C ''govern'':50C ''insignific'':102C ''l1'':95C ''lend'':64C ''mathemat'':12C ''matter'':41C ''model'':13C,70C,93C ''norm'':96C ''p'':1C,7C,72C ''pick'':82C ''predict'':22C ''predictor'':34C,54C,85C ''pressur'':24C ''random'':4C ''regress'':92C ''relat'':37C ''remot'':43C ''run'':90C ''set'':110C ''start'':58C,107C ''temperatur'':28C,39C ''thought'':5C,88C ''want'':20C,78C ''won'':30C ''would'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8717, 1, '2015-11-04 14:38:45.187', 0, 47, 'SPARK 1.5.1: Convert multi-labeled data into binary vector', '&lt;p&gt;I am using SPARK 1.5.1, and I have DataFrame that looks like follow:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;labelsCol, featureCol
(Label1, Label2, Label 32), FeatureVector
(Label1, Label10, Label16, Label30, Label48), FeatureVector
...
(Label1, label 95), FeatureVector
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The first column is the list of labels for that sample, and in total I have 100 label.&lt;/p&gt;

&lt;p&gt;I would like to build a binary classifier for each label, so I want to transform the labels list column into a binary vector.&lt;/p&gt;

&lt;p&gt;The binary vector will have a length of 100 and the value will be 0 or 1 depends on the existence of the label for sample.&lt;/p&gt;

&lt;p&gt;Is there any strait forward solution for this?&lt;/p&gt;
', 'classification  apache-spark  multilabel-classification', NULL, '''/code'':52C ''/p'':32C,73C,99C,127C,137C ''/pre'':53C ''0'':115C ''1'':117C ''1.5.1'':2A,23C ''100'':71C,109C ''32'':40C ''95'':50C ''apach'':13B ''apache-spark'':12B ''binari'':9A,81C,97C,102C ''build'':79C ''classif'':11B,17B ''classifi'':82C ''code'':34C ''column'':57C,94C ''convert'':3A ''data'':7A ''datafram'':27C ''depend'':118C ''exist'':121C ''featurecol'':36C ''featurevector'':41C,47C,51C ''first'':56C ''follow'':31C ''forward'':133C ''label'':6A,39C,49C,62C,72C,85C,92C,124C ''label1'':37C,42C,48C ''label10'':43C ''label16'':44C ''label2'':38C ''label30'':45C ''label48'':46C ''labelscol'':35C ''length'':107C ''like'':30C,77C ''list'':60C,93C ''look'':29C ''multi'':5A ''multi-label'':4A ''multilabel'':16B ''multilabel-classif'':15B ''p'':18C,54C,74C,100C,128C ''pre'':33C ''sampl'':65C,126C ''solut'':134C ''spark'':1A,14B,22C ''strait'':132C ''total'':68C ''transform'':90C ''use'':21C ''valu'':112C ''vector'':10A,98C,103C ''want'':88C ''would'':76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6594, 1, '2015-07-27 03:24:46.26', 1, 21, 'Algorithm selection - Improve initial assignments in combinatorial allocation', '&lt;p&gt;I hope this is the right place to ask for help.&lt;/p&gt;

&lt;p&gt;I have a problem that approximates the following example: 
Imagine a data set of articles of clothing (T-shirts, sweaters and dressed) which come in a variety of colors (red, green, blue), sizes (Small, Medium, Large) and price ranges (0-9.99,10-19.99,20-29.99). Example below:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Item Number Type    Colour  Size    Price Range
1234    T-Shirt blue    Medium  0-9.99
1235    Sweater green   Small   10-19.99
1236    Dress   red Large   20-29.99
â¦   â¦   â¦   â¦   â¦
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have an initial solution that allocates all articles to two boxes, which will be sent to different stores. E.g. Item 1234 - Box 1, Item 1235 - Box 2, Item 1236 - Box 1 etc. The aim is to send a well balanced box to each store, so that they both get a similar distribution of goods with regard to all variables.&lt;/p&gt;

&lt;p&gt;However my initial solution may not spread the items between boxes 1 and 2 in a way that equally distributes each characteristic. For instance the initial solution may have the following allocations with regard to item type:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Box Type    Count   Percent
1   T-Shirt 23  57.5%
1   Sweater 11  27.5%
1   Dress   6   15.0%
2   T-Shirt 27  67.5%
2   Sweater 8   20.0%
2   Dress   5   12.5%
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to use an algorithm that will improve the initial solution by moving items between Box 1 and Box 2 in such a way that the &lt;strong&gt;distribution of the each attribute of the variables type, colour, size and price range is as equally distributed between Box 1 and Box 2 as possible&lt;/strong&gt;. The second criteria is to &lt;strong&gt;minimize the amount of swaps&lt;/strong&gt; needed to get in balance, because time for this task requires labor and thus cost. It does not have to be a 1-1 swap, e.g. it may be allowed to swap 1 item for 2 items, as long as in the final solution the number of items per box is as balanced as possible.&lt;/p&gt;

&lt;p&gt;Some initial thoughts for me are that I may somehow be able to check which variables each box is "too high" and "too low" on and then try to identify candidate items to swap. I am wondering if some variation on a stable marriage algorithm could help me here? Maybe each box could somehow identify its least desired item. And then if Box 1 prefers Box 2''s least desired item over its own least desired item, and Box 2 is at least indifferent to the swap, then the swap will occur. And this algorithm would continue until no more swaps are accepted in either direction. &lt;/p&gt;

&lt;p&gt;Is there any algorithm like that that I could look into? I have a feeling that I would somehow need a way to measure the utility of each item to each box though and I am really unsure how to approach this.&lt;/p&gt;

&lt;p&gt;Thanks in advance for any help you can give!&lt;/p&gt;
', 'optimization', NULL, '''-1'':337C ''-19.99'':67C,95C ''-29.99'':69C,101C ''-9.99'':65C,89C ''/code'':107C,244C ''/p'':22C,72C,170C,209C,369C,477C,521C,532C ''/pre'':108C,245C ''/strong'':301C,313C ''0'':64C,88C ''1'':133C,141C,183C,216C,222C,226C,264C,295C,336C,346C,434C ''10'':66C,94C ''11'':224C ''12.5'':243C ''1234'':82C,131C ''1235'':90C,135C ''1236'':96C,139C ''15.0'':229C ''2'':137C,185C,230C,236C,240C,267C,298C,349C,437C,450C ''20'':68C,100C ''20.0'':239C ''23'':220C ''27'':234C ''27.5'':225C ''5'':242C ''57.5'':221C ''6'':228C ''67.5'':235C ''8'':238C ''abl'':382C ''accept'':473C ''advanc'':525C ''aim'':144C ''algorithm'':1A,252C,415C,465C,482C ''alloc'':8A,116C,203C ''allow'':343C ''amount'':310C ''approach'':519C ''approxim'':29C ''articl'':38C,118C ''ask'':19C ''assign'':5A ''attribut'':279C ''balanc'':150C,318C,366C ''blue'':56C,86C ''box'':121C,132C,136C,140C,151C,182C,212C,263C,266C,294C,297C,363C,388C,422C,433C,436C,449C,510C ''candid'':401C ''characterist'':193C ''check'':384C ''cloth'':40C ''code'':74C,211C ''color'':53C ''colour'':78C,284C ''combinatori'':7A ''come'':48C ''continu'':467C ''cost'':328C ''could'':416C,423C,487C ''count'':214C ''criteria'':304C ''data'':35C ''desir'':428C,440C,446C ''differ'':127C ''direct'':476C ''distribut'':162C,191C,275C,292C ''dress'':46C,97C,227C,241C ''e.g'':129C,339C ''either'':475C ''equal'':190C,291C ''etc'':142C ''exampl'':32C,70C ''feel'':493C ''final'':356C ''follow'':31C,202C ''get'':159C,316C ''give'':531C ''good'':164C ''green'':55C,92C ''help'':21C,417C,528C ''high'':391C ''hope'':12C ''howev'':172C ''identifi'':400C,425C ''imagin'':33C ''improv'':3A,255C ''indiffer'':454C ''initi'':4A,113C,174C,197C,257C,372C ''instanc'':195C ''item'':75C,130C,134C,138C,180C,207C,261C,347C,350C,361C,402C,429C,441C,447C,507C ''labor'':325C ''larg'':60C,99C ''least'':427C,439C,445C,453C ''like'':483C ''long'':352C ''look'':488C ''low'':394C ''marriag'':414C ''may'':176C,199C,341C,379C ''mayb'':420C ''measur'':502C ''medium'':59C,87C ''minim'':308C ''move'':260C ''need'':314C,498C ''number'':76C,359C ''occur'':462C ''optim'':9B ''p'':10C,23C,109C,171C,246C,370C,478C,522C ''per'':362C ''percent'':215C ''place'':17C ''possibl'':300C,368C ''pre'':73C,210C ''prefer'':435C ''price'':62C,80C,287C ''problem'':27C ''rang'':63C,81C,288C ''realli'':515C ''red'':54C,98C ''regard'':166C,205C ''requir'':324C ''right'':16C ''second'':303C ''select'':2A ''send'':147C ''sent'':125C ''set'':36C ''shirt'':43C,85C,219C,233C ''similar'':161C ''size'':57C,79C,285C ''small'':58C,93C ''solut'':114C,175C,198C,258C,357C ''somehow'':380C,424C,497C ''spread'':178C ''stabl'':413C ''store'':128C,154C ''strong'':274C,307C ''swap'':312C,338C,345C,404C,457C,460C,471C ''sweater'':44C,91C,223C,237C ''t-shirt'':41C,83C,217C,231C ''task'':323C ''thank'':523C ''though'':511C ''thought'':373C ''thus'':327C ''time'':320C ''tri'':398C ''two'':120C ''type'':77C,208C,213C,283C ''unsur'':516C ''use'':250C ''util'':504C ''variabl'':169C,282C,386C ''variat'':410C ''varieti'':51C ''want'':248C ''way'':188C,271C,500C ''well'':149C ''wonder'':407C ''would'':466C,496C ''â'':102C,103C,104C,105C,106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6775, 1, '2015-08-12 15:11:36.953', 2, 87, 'algorithmic difference between image analysis and video analysis', '&lt;p&gt;Is there algorithmic difference between analyzing video and an image, say for example,if I want object recognition? Or do I just have to analyze every frame of the the video just as an image?&lt;/p&gt;

&lt;p&gt;Example, detecting an object in a single image is easy compared to video becuase time dimension is added to video. In addition, on video, in every frame, the object is most probably moving, which makes the frames in motion... So how can u handle the time factor and the "in motion" part in the video. Those are the problem I imagine in video, it would be nice if u add ur own thoughts on it. Thanks&lt;/p&gt;
', 'machine-learning  object-recognition', NULL, '''/p'':51C,129C ''ad'':70C ''add'':122C ''addit'':74C ''algorithm'':1A,18C ''analysi'':5A,8A ''analyz'':21C,40C ''becuas'':66C ''compar'':63C ''detect'':54C ''differ'':2A,19C ''dimens'':68C ''easi'':62C ''everi'':41C,78C ''exampl'':28C,53C ''factor'':99C ''frame'':42C,79C,89C ''handl'':96C ''imag'':4A,25C,50C,60C ''imagin'':113C ''learn'':11B ''machin'':10B ''machine-learn'':9B ''make'':87C ''motion'':91C,103C ''move'':85C ''nice'':119C ''object'':13B,32C,56C,81C ''object-recognit'':12B ''p'':15C,52C ''part'':104C ''probabl'':84C ''problem'':111C ''recognit'':14B,33C ''say'':26C ''singl'':59C ''thank'':128C ''thought'':125C ''time'':67C,98C ''u'':95C,121C ''ur'':123C ''video'':7A,22C,46C,65C,72C,76C,107C,115C ''want'':31C ''would'':117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6602, 1, '2015-07-27 21:15:03.97', 0, 92, 'What kind of RAM to choose for data analysis?', '&lt;p&gt;I''m planning to upgrade my computer and also want to buy some new DDR3 RAM modules. Making some research I realized that there are 2 main characteristics of RAMs (beside capacity): frequency and latency.&lt;/p&gt;

&lt;p&gt;I want to use my computer for data analysis as well (like Kaggle competitions). Usually I work in R and sometimes in Python.&lt;/p&gt;

&lt;p&gt;I understand that there is a trade-off between latency and frequency (that is also reflected in the price sometimes). My question is which one is more important for data analysis purposes? How can it effect real world performance? How much difference should I expect between choosing a model with overall better and lower parameter values?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE&lt;/strong&gt;: I don''t ask which RAM is better in general. I need answer from users who know how memory management happens in R (or Python). How are the different ML algorithms managed in memory and which RAM characteristics are preferred to support it. I would also appreciate personal experiences on this field. &lt;/p&gt;
', 'r  python', NULL, '''/p'':48C,73C,132C,189C ''/strong'':136C ''2'':38C ''algorithm'':167C ''also'':21C,90C,182C ''analysi'':9A,58C,106C ''answer'':149C ''appreci'':183C ''ask'':140C ''besid'':43C ''better'':127C,144C ''buy'':24C ''capac'':44C ''characterist'':40C,174C ''choos'':6A,122C ''competit'':63C ''comput'':19C,55C ''data'':8A,57C,105C ''ddr3'':27C ''differ'':117C,165C ''effect'':111C ''expect'':120C ''experi'':185C ''field'':188C ''frequenc'':45C,87C ''general'':146C ''happen'':157C ''import'':103C ''kaggl'':62C ''kind'':2A ''know'':153C ''latenc'':47C,85C ''like'':61C ''lower'':129C ''m'':14C ''main'':39C ''make'':30C ''manag'':156C,168C ''memori'':155C,170C ''ml'':166C ''model'':124C ''modul'':29C ''much'':116C ''need'':148C ''new'':26C ''one'':100C ''overal'':126C ''p'':12C,49C,74C,133C ''paramet'':130C ''perform'':114C ''person'':184C ''plan'':15C ''prefer'':176C ''price'':94C ''purpos'':107C ''python'':11B,72C,161C ''question'':97C ''r'':10B,68C,159C ''ram'':4A,28C,42C,142C,173C ''real'':112C ''realiz'':34C ''reflect'':91C ''research'':32C ''sometim'':70C,95C ''strong'':134C ''support'':178C ''trade'':82C ''trade-off'':81C ''understand'':76C ''updat'':135C ''upgrad'':17C ''use'':53C ''user'':151C ''usual'':64C ''valu'':131C ''want'':22C,51C ''well'':60C ''work'':66C ''world'':113C ''would'':181C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6595, 1, '2015-07-27 05:55:57.13', 0, 141, 'Identifying top predictors from a mix of categorical and ordinal data', '&lt;p&gt;I have a dataset with 261 predictors scraped from a larger set of survey questions. 224 have values which are in a range of scale (some 1-10, some 1-4, some simply binary, all using 0 where no value is given), and the rest are unordered categories.&lt;/p&gt;

&lt;p&gt;I''m trying to perform classification using these predictors and identify the top n predictors. Am thinking of the following approach:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;convert the 224 ordered predictors into numeric, centered and scaled.&lt;/li&gt;
&lt;li&gt;Run separate modeling (I use &lt;code&gt;caret&lt;/code&gt; from R): one for using the numeric predictors, another using the  remaining 37 categorical predictors (both cross-validated within each modelling exercise)&lt;/li&gt;
&lt;li&gt;Choose the respective best-fitting models modelN and modelC for the numeric and categorical predictors.&lt;/li&gt;
&lt;li&gt;Choose top n (say 10) predictors from modelN and modelC&lt;/li&gt;
&lt;li&gt;Combine them in an ensemble model that can handle both numeric and categorical data (say, random forest)&lt;/li&gt;
&lt;li&gt;Choose top n predictors in the ensemble model.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I am going through this roundabout way rather than directly fitting all predictors into an ensemble model to try reduce the complexity of the problem first (and because in R, I''m having problem with too many levels from the predictors).&lt;/p&gt;

&lt;p&gt;Would this be a valid approach to identifying the n most salient predictors? Any possible issues to mitigate?&lt;/p&gt;
', 'r  classification  feature-selection  categorical-data', NULL, '''-10'':48C ''-4'':51C ''/code'':114C ''/li'':105C,138C,156C,168C,187C,197C ''/ol'':198C ''/p'':69C,92C,241C,261C ''0'':57C ''1'':47C,50C ''10'':162C ''224'':36C,97C ''261'':26C ''37'':127C ''anoth'':123C ''approach'':91C,248C ''best'':144C ''best-fit'':143C ''binari'':54C ''caret'':113C ''categor'':8A,18B,128C,154C,182C ''categori'':68C ''categorical-data'':17B ''center'':102C ''choos'':140C,158C,189C ''classif'':13B,76C ''code'':112C ''combin'':170C ''complex'':221C ''convert'':95C ''cross'':132C ''cross-valid'':131C ''data'':11A,19B,183C ''dataset'':24C ''direct'':209C ''ensembl'':174C,195C,215C ''exercis'':137C ''featur'':15B ''feature-select'':14B ''first'':225C ''fit'':145C,210C ''follow'':90C ''forest'':186C ''given'':62C ''go'':202C ''handl'':178C ''identifi'':1A,81C,250C ''issu'':258C ''larger'':31C ''level'':237C ''li'':94C,106C,139C,157C,169C,188C ''m'':72C,231C ''mani'':236C ''mitig'':260C ''mix'':6A ''model'':109C,136C,146C,175C,196C,216C ''modelc'':149C,167C ''modeln'':147C,165C ''n'':84C,160C,191C,252C ''numer'':101C,121C,152C,180C ''ol'':93C ''one'':117C ''order'':98C ''ordin'':10A ''p'':20C,70C,199C,242C ''perform'':75C ''possibl'':257C ''predictor'':3A,27C,79C,85C,99C,122C,129C,155C,163C,192C,212C,240C,255C ''problem'':224C,233C ''question'':35C ''r'':12B,116C,229C ''random'':185C ''rang'':43C ''rather'':207C ''reduc'':219C ''remain'':126C ''respect'':142C ''rest'':65C ''roundabout'':205C ''run'':107C ''salient'':254C ''say'':161C,184C ''scale'':45C,104C ''scrape'':28C ''select'':16B ''separ'':108C ''set'':32C ''simpli'':53C ''survey'':34C ''think'':87C ''top'':2A,83C,159C,190C ''tri'':73C,218C ''unord'':67C ''use'':56C,77C,111C,119C,124C ''valid'':133C,247C ''valu'':38C,60C ''way'':206C ''within'':134C ''would'':243C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6597, 2, '2015-07-27 11:46:01.08', 0, NULL, NULL, '&lt;p&gt;My team and I recently experienced a similar problem. We used a Random Forest to be able to predict between 10 authors with about an 82% accuracy, as the number of users went up the accuracy went down. We were then asked to try and identify a new author which we implemented by creating a dynamic threshold for votes from the individual trees in forest. With the new user identification and 10 authors we were at about 65% accurate. We were working with twitter data also, so our document sets were fairly small. I have seen documentation where other teams have simply used the count of stop words in documents and a random forest that was 80% accurate, they weren''t trying to identify a new user however. &lt;/p&gt;

&lt;p&gt;In your situation, breaking up your problem into two different problems might yield better results. First identify if the new document belongs to any of your known users and if it does then try and decide which one.&lt;/p&gt;
', NULL, NULL, '''/p'':130C,170C ''10'':22C,73C ''65'':79C ''80'':118C ''82'':27C ''abl'':18C ''accur'':80C,119C ''accuraci'':28C,37C ''also'':87C ''ask'':43C ''author'':23C,50C,74C ''belong'':153C ''better'':145C ''break'':135C ''count'':106C ''creat'':55C ''data'':86C ''decid'':167C ''differ'':141C ''document'':90C,98C,111C,152C ''dynam'':57C ''experienc'':7C ''fair'':93C ''first'':147C ''forest'':15C,66C,115C ''howev'':129C ''identif'':71C ''identifi'':47C,125C,148C ''implement'':53C ''individu'':63C ''known'':158C ''might'':143C ''new'':49C,69C,127C,151C ''number'':31C ''one'':169C ''p'':1C,131C ''predict'':20C ''problem'':10C,138C,142C ''random'':14C,114C ''recent'':6C ''result'':146C ''seen'':97C ''set'':91C ''similar'':9C ''simpli'':103C ''situat'':134C ''small'':94C ''stop'':108C ''team'':3C,101C ''threshold'':58C ''tree'':64C ''tri'':45C,123C,165C ''twitter'':85C ''two'':140C ''use'':12C,104C ''user'':33C,70C,128C,159C ''vote'':60C ''went'':34C,38C ''weren'':121C ''word'':109C ''work'':83C ''yield'':144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6777, 2, '2015-08-12 19:58:32.517', 1, NULL, NULL, '&lt;p&gt;OK if I understood correctly you can do something like: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;df$observations &amp;lt;- rep(1, nrow(df))
df &amp;lt;- df[ ,-file_name_column]
new_data &amp;lt;- data.frame(aggregate(df, by= ID, FUN=sum))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Caution: this might not work exactly since I am not sure what you data frame looks like. &lt;/p&gt;
', NULL, NULL, '''/code'':38C ''/p'':12C,58C ''/pre'':39C ''1'':19C ''aggreg'':32C ''caution'':41C ''code'':14C ''column'':27C ''correct'':6C ''data'':29C,54C ''data.frame'':31C ''df'':15C,21C,22C,24C,33C ''exact'':46C ''file'':25C ''frame'':55C ''fun'':36C ''id'':35C ''like'':11C,57C ''look'':56C ''lt'':17C,23C,30C ''might'':43C ''name'':26C ''new'':28C ''nrow'':20C ''observ'':16C ''ok'':2C ''p'':1C,40C ''pre'':13C ''rep'':18C ''sinc'':47C ''someth'':10C ''sum'':37C ''sure'':51C ''understood'':5C ''work'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6607, 2, '2015-07-28 12:39:20.25', 1, NULL, NULL, '&lt;p&gt;It''s not easy to compare two RAMs with different frequency and latency, since both of them affect your performance with not the same way.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;The short answer is:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;If you have two RAMs with same capacity and frequency, choose the one with lower latency.&lt;/li&gt;
&lt;li&gt;If you have two RAMs with same capacity and latency, choose the one with the higher frequency.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;From &lt;a href="http://lifehacker.com/when-ram-speed-matters-and-how-it-affects-your-games-1436679680" rel="nofollow"&gt;lifehacker.com&lt;/a&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Essentially, you have two things to worry about when it comes to RAM "speed": frequency, which deals with how much data can be transferred to the stick at one time, and latency, which is how quickly it responds to requests. In the current market, as you get to higher frequencies, latency tends to increase, so in many cases, they tend to balance each other out. Buying RAM with a higher speed doesn''t matter a ton.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;The long answer:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Don''t worry about RAM. Yes, if you have a crappy RAM, you will also have a problem with your performance. But, the most important thing in Data Analysis is the performance of your code. An example from my personal experience:&lt;/p&gt;

&lt;p&gt;I had a text dataset, about 30GB. I needed to create a corpus for a simple TF-IDF. The brute-force code, spent about 15 hours, whereas a map-reduce algorithm spent about 1.5 minute for the same dataset.&lt;/p&gt;

&lt;p&gt;I work on a MacBook Pro with 16GB RAM. I didn''t have any problem with performance. However, if you have huge datasets and even code improvements and RAM upgrades cannot help, you may reconsider to move to Hadoop or similar. &lt;/p&gt;
', NULL, NULL, '''/a'':85C ''/blockquote'':166C ''/li'':54C,73C ''/p'':27C,35C,86C,165C,173C,216C,260C,303C ''/strong'':34C,172C ''/ul'':74C ''/when-ram-speed-matters-and-how-it-affects-your-games-1436679680'':81C ''1.5'':254C ''15'':244C ''16gb'':269C ''30gb'':224C ''affect'':19C ''algorithm'':251C ''also'':189C ''analysi'':203C ''answer'':32C,171C ''balanc'':150C ''blockquot'':87C ''brute'':239C ''brute-forc'':238C ''buy'':154C ''cannot'':292C ''capac'':45C,63C ''case'':146C ''choos'':48C,66C ''code'':209C,241C,287C ''come'':99C ''compar'':7C ''corpus'':230C ''crappi'':185C ''creat'':228C ''current'':131C ''data'':109C,202C ''dataset'':222C,259C,284C ''deal'':105C ''didn'':272C ''differ'':11C ''doesn'':160C ''easi'':5C ''essenti'':89C ''even'':286C ''exampl'':211C ''experi'':215C ''forc'':240C ''frequenc'':12C,47C,72C,103C,138C ''get'':135C ''hadoop'':300C ''help'':293C ''higher'':71C,137C,158C ''hour'':245C ''howev'':279C ''href'':78C ''huge'':283C ''idf'':236C ''import'':199C ''improv'':288C ''increas'':142C ''latenc'':14C,53C,65C,120C,139C ''li'':37C,55C ''lifehacker.com'':80C,84C ''lifehacker.com/when-ram-speed-matters-and-how-it-affects-your-games-1436679680'':79C ''long'':170C ''lower'':52C ''macbook'':266C ''mani'':145C ''map'':249C ''map-reduc'':248C ''market'':132C ''matter'':162C ''may'':295C ''minut'':255C ''move'':298C ''much'':108C ''need'':226C ''nofollow'':83C ''one'':50C,68C,117C ''p'':1C,28C,75C,88C,167C,174C,217C,261C ''perform'':21C,195C,206C,278C ''person'':214C ''pro'':267C ''problem'':192C,276C ''quick'':124C ''ram'':9C,42C,60C,101C,155C,179C,186C,270C,290C ''reconsid'':296C ''reduc'':250C ''rel'':82C ''request'':128C ''respond'':126C ''short'':31C ''similar'':302C ''simpl'':233C ''sinc'':15C ''speed'':102C,159C ''spent'':242C,252C ''stick'':115C ''strong'':29C,168C ''tend'':140C,148C ''text'':221C ''tf'':235C ''tf-idf'':234C ''thing'':93C,200C ''time'':118C ''ton'':164C ''transfer'':112C ''two'':8C,41C,59C,92C ''ul'':36C ''upgrad'':291C ''way'':26C ''wherea'':246C ''work'':263C ''worri'':95C,177C ''yes'':180C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6608, 1, '2015-07-28 12:40:39.007', 0, 25, 'Accessing directory of small files as one file', '&lt;p&gt;I have a job that results in a directory of part- files. I''d like to read it as if it were one file. Specifically, I''d like to read it that way over a web interface.&lt;/p&gt;

&lt;p&gt;How can I do either one of these things? Is there a hadoop component which makes this easy?&lt;/p&gt;
', 'dataset  hadoop', NULL, '''/p'':49C,69C ''access'':1A ''compon'':64C ''d'':25C,38C ''dataset'':9B ''directori'':2A,20C ''easi'':68C ''either'':55C ''file'':5A,8A,23C,35C ''hadoop'':10B,63C ''interfac'':48C ''job'':15C ''like'':26C,39C ''make'':66C ''one'':7A,34C,56C ''p'':11C,50C ''part'':22C ''read'':28C,41C ''result'':17C ''small'':4A ''specif'':36C ''thing'':59C ''way'':44C ''web'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6609, 2, '2015-07-28 12:44:44.997', 1, NULL, NULL, '&lt;p&gt;Ricky,&lt;/p&gt;

&lt;p&gt;Loose thoughts: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Depending on the algorithm you intend to use, centering might not be a good idea (e.g. if you go for SVM, centering will destroy sparsity)&lt;/li&gt;
&lt;li&gt;I would suggest not to handle ordered / unordered separately, as &lt;strong&gt;you are likely to miss interactions that way&lt;/strong&gt;. If the categorical ones don''t have too many possible values, randomForest in R can handle factors.&lt;/li&gt;
&lt;li&gt;if that is an issue (as you seem to hint), I think you have two possibilities: binary indicators or response rates&lt;/li&gt;
&lt;li&gt;if it''s feasible in terms of computational cost, i would convert all factors to binaries (use sparse matrices if necessary) and then try a greedy feature selection. caret, if memory serves, has rfe or somesuch.&lt;/li&gt;
&lt;li&gt;if that''s too much trouble, try calculating response rates / average values per factor level (I don''t see any info whether your problem is classification or regression): you split your set into folds, and then for each fold fit a mixed effects model (e.g. via lme4) on the remainder, using the factor of interest as the main variable. It''s a bit of a pain to setup all the cv correctly, but it''s the only way to avoid leaking information.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Hope this helps,
K&lt;/p&gt;
', NULL, NULL, '''/li'':35C,74C,97C,135C,219C ''/p'':3C,7C,226C ''/strong'':56C ''/ul'':220C ''algorithm'':13C ''averag'':147C ''avoid'':216C ''binari'':92C,114C ''bit'':199C ''calcul'':144C ''caret'':127C ''categor'':59C ''center'':18C,31C ''classif'':162C ''comput'':106C ''convert'':110C ''correct'':208C ''cost'':107C ''cv'':207C ''depend'':10C ''destroy'':33C ''e.g'':25C,181C ''effect'':179C ''factor'':73C,112C,150C,189C ''feasibl'':102C ''featur'':125C ''fit'':176C ''fold'':170C,175C ''go'':28C ''good'':23C ''greedi'':124C ''handl'':42C,72C ''help'':224C ''hint'':85C ''hope'':222C ''idea'':24C ''indic'':93C ''info'':157C ''inform'':218C ''intend'':15C ''interact'':53C ''interest'':191C ''issu'':80C ''k'':225C ''leak'':217C ''level'':151C ''li'':9C,36C,75C,98C,136C ''like'':50C ''lme4'':183C ''loos'':5C ''main'':194C ''mani'':65C ''matric'':117C ''memori'':129C ''might'':19C ''miss'':52C ''mix'':178C ''model'':180C ''much'':141C ''necessari'':119C ''one'':60C ''order'':43C ''p'':1C,4C,221C ''pain'':202C ''per'':149C ''possibl'':66C,91C ''problem'':160C ''r'':70C ''randomforest'':68C ''rate'':96C,146C ''regress'':164C ''remaind'':186C ''respons'':95C,145C ''rfe'':132C ''ricki'':2C ''see'':155C ''seem'':83C ''select'':126C ''separ'':45C ''serv'':130C ''set'':168C ''setup'':204C ''somesuch'':134C ''spars'':116C ''sparsiti'':34C ''split'':166C ''strong'':47C ''suggest'':39C ''svm'':30C ''term'':104C ''think'':87C ''thought'':6C ''tri'':122C,143C ''troubl'':142C ''two'':90C ''ul'':8C ''unord'':44C ''use'':17C,115C,187C ''valu'':67C,148C ''variabl'':195C ''via'':182C ''way'':55C,214C ''whether'':158C ''would'':38C,109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6778, 1, '2015-08-12 23:14:17.48', 0, 25, 'Python library to compute some metrics for multioutput-multiclass classification task', '&lt;p&gt;Is there any Python library that provides ready-to-use metrics to analyze the performance of a classifier for a multioutput-multiclass classification task? scikit-learn doesn''t have this option yet (as stated in the &lt;a href="http://scikit-learn.org/stable/modules/multiclass.html" rel="nofollow"&gt;documentation&lt;/a&gt; and in the corresponding &lt;a href="https://github.com/scikit-learn/scikit-learn/issues/3453" rel="nofollow"&gt;feature request on GitHub&lt;/a&gt;).&lt;/p&gt;
', 'performance  multiclass-classification  software-recommendation  multilabel-classification', NULL, '''/a'':71C,87C ''/p'':88C ''/scikit-learn/scikit-learn/issues/3453'':80C ''/stable/modules/multiclass.html'':67C ''analyz'':37C ''classif'':11A,16B,22B,48C ''classifi'':42C ''comput'':4A ''correspond'':75C ''document'':70C ''doesn'':53C ''featur'':83C ''github'':86C ''github.com'':79C ''github.com/scikit-learn/scikit-learn/issues/3453'':78C ''href'':64C,77C ''learn'':52C ''librari'':2A,28C ''metric'':6A,35C ''multiclass'':10A,15B,47C ''multiclass-classif'':14B ''multilabel'':21B ''multilabel-classif'':20B ''multioutput'':9A,46C ''multioutput-multiclass'':8A,45C ''nofollow'':69C,82C ''option'':57C ''p'':23C ''perform'':13B,39C ''provid'':30C ''python'':1A,27C ''readi'':32C ''ready-to-us'':31C ''recommend'':19B ''rel'':68C,81C ''request'':84C ''scikit'':51C ''scikit-learn'':50C ''scikit-learn.org'':66C ''scikit-learn.org/stable/modules/multiclass.html'':65C ''softwar'':18B ''software-recommend'':17B ''state'':60C ''task'':12A,49C ''use'':34C ''yet'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6610, 1, '2015-07-28 14:55:13.34', 3, 238, 'Is our data "Big Data" (Startup)', '&lt;p&gt;I worked at a startup/medium sized company and I am concerned that we may be over-engineering one of our products.&lt;/p&gt;

&lt;p&gt;In essence, we will be consuming real-time coordinates from vehicles and users and performing analytics and machine learning on this incoming data. This processing can be rather intensive as we try predict the ETAs of this entities matched to historical data and static paths. &lt;/p&gt;

&lt;p&gt;The approach they want to take is using the latest and most powerful technology stack, that being Hadoop, Storm etc to process these coordinates. Problem is that no-one in the team has implemented such a system and only has had the last month or so to skill up on it.&lt;/p&gt;

&lt;p&gt;My belief is that a safer approach would be to use NoSQL storage such as "Azure Table Storage" in an event based system to achieve the same result in less time. To me it''s the agile approach, as this is a system that we are familiar with. Then if the demand warrants it, we can look at implementing Hadoop in the future.&lt;/p&gt;

&lt;p&gt;I haven''t done a significant amount of research in this field, so would appreciate your input.&lt;/p&gt;

&lt;p&gt;Questions:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;How many tracking entities (sending coordinates every 10 seconds) would warrant Hadoop?&lt;/li&gt;
&lt;li&gt;Would it be easy to initially start off with a simpler approach such as "Azure Table Storage" then onto Hadoop at a later point?&lt;/li&gt;
&lt;li&gt;If you had to estimate, how long would you say a team of 3 developers would take to implement a basic Hadoop/Storm system?&lt;/li&gt;
&lt;li&gt;Is Hadoop necessary to invest from the get go as we will quickly incur major costs?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I know these are vague questions, but I want to make sure we aren''t going to invest unnecessary resources with a deadline coming up. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9157, 2, '2015-12-01 22:05:41.837', 5, NULL, NULL, '&lt;p&gt;I take Natural Language Processing as an example because that''s the field that I have more experience in so I encourage others to share their insights in other fields like in Computer Vision, Biostatistics, time series, etc. I''m sure in those fields there are similar examples.&lt;/p&gt;

&lt;p&gt;I agree that sometimes model visualizations can be meaningless but I think the main purpose of visualizations of this kind are to help us check if the model actually relates to human intuition or some other (non-computational) model. Additionally, Exploratory Data Analysis can be performed on the data.&lt;/p&gt;

&lt;p&gt;Let''s assume we have a word embedding model built from Wikipedia''s corpus using &lt;a href="https://radimrehurek.com/gensim/models/word2vec.html" rel="nofollow"&gt;Gensim&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;model = gensim.models.Word2Vec(sentences, min_count=2)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;We would then have a 100 dimension vector for each word represented in that corpus that''s present at least twice. So if we wanted to visualize these words we would have to reduce them to 2 or 3 dimensions using the t-sne algorithm. Here is where very interesting characteristics arise.&lt;/p&gt;

&lt;p&gt;Take the example:&lt;/p&gt;

&lt;p&gt;vector("king") + vector("man") - vector("woman") = vector("queen")&lt;/p&gt;

&lt;p&gt;&lt;img src="http://multithreaded.stitchfix.com/assets/images/blog/vectors.gif" alt="http://multithreaded.stitchfix.com/blog/2015/03/11/word-is-worth-a-thousand-vectors/"&gt;&lt;/p&gt;

&lt;p&gt;Here each direction encode certain semantic features. The same can be done in 3d&lt;/p&gt;

&lt;p&gt;&lt;img src="http://www.tensorflow.org/versions/master/images/linear-relationships.png" alt="http://www.tensorflow.org/versions/master/images/linear-relationships.png"&gt;&lt;/p&gt;

&lt;p&gt;See how in this example past tense is located in a certain position respective to its participle. The same for gender. Same with countries and capitals.&lt;/p&gt;

&lt;p&gt;In the word embedding world, older and more naive models, didn''t have this property. &lt;/p&gt;

&lt;p&gt;See this Stanford lecture for more details.
&lt;a href="https://www.youtube.com/watch?v=T8tQZChniMk" rel="nofollow"&gt;Simple Word Vector representations: word2vec, GloVe&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;They only were limited to clustering similar words together without regard for semantics (gender or verb tense weren''t encoded as directions). Unsurprisingly models which have a semantic encoding as directions in lower dimensions are more accurate. And more importantly, they can be used to explore each data point in a more appropriate way. &lt;/p&gt;

&lt;p&gt;In this particular case, I don''t think t-SNE is used to aid classification per se, it''s more like a sanity check for your model and sometimes to find insight in the particular corpus you are using. As for the problem of the vectors not being in original feature space anymore. Richard Socher explains in the lecture (link above) that low dimensional vectors share statistical distributions with its own larger representation as well as other statistical properties which make plausible visually analyse in lower dimensions embedding vectors.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Additional resources &amp;amp; Image Sources:&lt;/strong&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://multithreaded.stitchfix.com/blog/2015/03/11/word-is-worth-a-thousand-vectors/" rel="nofollow"&gt;http://multithreaded.stitchfix.com/blog/2015/03/11/word-is-worth-a-thousand-vectors/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.tensorflow.org/tutorials/word2vec/index.html#motivation_why_learn_word_embeddings%3F" rel="nofollow"&gt;http://www.tensorflow.org/tutorials/word2vec/index.html#motivation_why_learn_word_embeddings%3F&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://deeplearning4j.org/word2vec.html" rel="nofollow"&gt;http://deeplearning4j.org/word2vec.html&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.tensorflow.org/tutorials/word2vec/index.html#motivation_why_learn_word_embeddings%3F" rel="nofollow"&gt;http://www.tensorflow.org/tutorials/word2vec/index.html#motivation_why_learn_word_embeddings%3F&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6611, 1, '2015-07-28 16:15:43.783', 5, 561, 'Item-Item similarity based on text', '&lt;p&gt;We''re build an item-item recommender based on the text descriptions of the items. Our initial approach was to calculate the TF-IDF vectors for each item. We used a hashing tf with 5000 possible hashes for the words. Then approximate all-pairs using a sampling technique (DIMSUM).&lt;/p&gt;

&lt;p&gt;We have an mxn matrix where m is the number of words, n is the number of items. The naive approach of calculating all column cosine similarities won''t work here since we have n=10^7 m=10^4. &lt;/p&gt;

&lt;p&gt;Our first attempt was using DIMSUM &lt;a href="http://stanford.edu/~rezab/papers/dimsum.pdf"&gt;http://stanford.edu/~rezab/papers/dimsum.pdf&lt;/a&gt; which is an all-pairs sampling technique. The problem with DIMSUM is it works for data where m &gt;&gt; n. Our matrix is short and wide n&gt;m.&lt;/p&gt;

&lt;p&gt;My Question:
What is a good approach for estimating all-pairs similarity of items based on words. Where number of items is 10^7, number of words is 10^4. We only want items pairs above a certain threshold of similarity.&lt;/p&gt;

&lt;p&gt;We don''t have to do it this way, our task is to recommend a small set of items given one item, we have ways of doing this using collaborative filtering, but we want to also handle new items that we have no user data for so we''re trying to find a way to use tf-idf vectors for that case.&lt;/p&gt;
', 'machine-learning  recommendation  apache-spark', NULL, '''/p'':68C,110C,154C,198C,263C ''/~rezab/papers/dimsum.pdf'':122C ''/~rezab/papers/dimsum.pdf&lt;/a&gt;'':125C ''10'':105C,108C,179C,185C ''4'':109C,186C ''5000'':52C ''7'':106C,180C ''all-pair'':60C,129C,165C ''also'':235C ''apach'':13B ''apache-spark'':12B ''approach'':34C,90C,162C ''approxim'':59C ''attempt'':114C ''base'':5A,24C,171C ''build'':18C ''calcul'':37C,92C ''case'':262C ''certain'':194C ''collabor'':229C ''column'':94C ''cosin'':95C ''data'':142C,244C ''descript'':28C ''dimsum'':67C,117C,137C ''estim'':164C ''filter'':230C ''find'':251C ''first'':113C ''given'':219C ''good'':161C ''handl'':236C ''hash'':49C,54C ''href'':119C ''idf'':41C,258C ''initi'':33C ''item'':2A,3A,21C,22C,31C,45C,87C,170C,177C,190C,218C,221C,238C ''item-item'':1A,20C ''learn'':10B ''m'':76C,107C,144C,153C ''machin'':9B ''machine-learn'':8B ''matrix'':74C,147C ''mxn'':73C ''n'':82C,104C,145C,152C ''naiv'':89C ''new'':237C ''number'':79C,85C,175C,181C ''one'':220C ''p'':15C,69C,111C,155C,199C ''pair'':62C,131C,167C,191C ''possibl'':53C ''problem'':135C ''question'':157C ''re'':17C,248C ''recommend'':11B,23C,213C ''sampl'':65C,132C ''set'':216C ''short'':149C ''similar'':4A,96C,168C,197C ''sinc'':101C ''small'':215C ''spark'':14B ''stanford.edu'':121C,124C ''stanford.edu/~rezab/papers/dimsum.pdf'':120C ''stanford.edu/~rezab/papers/dimsum.pdf&lt;/a&gt;'':123C ''task'':210C ''techniqu'':66C,133C ''text'':7A,27C ''tf'':40C,50C,257C ''tf-idf'':39C,256C ''threshold'':195C ''tri'':249C ''use'':47C,63C,116C,228C,255C ''user'':243C ''vector'':42C,259C ''want'':189C,233C ''way'':208C,224C,253C ''wide'':151C ''won'':97C ''word'':57C,81C,173C,183C ''work'':99C,140C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6615, 2, '2015-07-28 20:23:27.53', 5, NULL, NULL, '&lt;p&gt;Yes, this is a how-long-is-a-piece-of-string question. I think it''s good to beware of over-engineering, while also making sure you engineer for where you think you''ll be in a year.&lt;/p&gt;

&lt;p&gt;First I''d suggest you distinguish between processing and storage. Storm is a (stream) processing framework; NoSQL databases are a storage paradigm. These are not alternatives. The Hadoop ecosystem has HBase for NoSQL; I suspect Azure has some kind of stream processing story.&lt;/p&gt;

&lt;p&gt;The bigger difference in your two alternatives is consuming a cloud provider''s ecosystem vs Hadoop. The upside to Azure, or AWS, or GCE, is that these services optimize for integrating with each other, with billing, machine management, etc. The downside is being locked in to the cloud provider; you can''t run Azure stuff anywhere but Azure. Hadoop takes more work to integrate since it''s really a confederation of sometimes loosely-related projects. You''re investing in both a distribution, and a place to run that distribution. But, you get a lot less lock-in, and probably more easy access to talent, and a broader choice of tools.&lt;/p&gt;

&lt;p&gt;The Azure road is also a "big data" solution in that it has a lot of the scalability properties you want for big data, and the complexity as well. It does not strike me as an easier route. Do you need to invest in distributed/cloud anything at this scale? given your IoT-themed use case, I believe you will need to soon, if not now, so yes. You''re not talking about gigabytes, but many terabytes in just the first year.&lt;/p&gt;

&lt;p&gt;I''d give a fresh team 6-12 months to fully productionize something based on either of these platforms. That can certainly be staged as a POC, followed by more elaborate engineering.&lt;/p&gt;
', NULL, NULL, '''-12'':294C ''/p'':42C,87C,201C,285C,319C ''6'':293C ''access'':192C ''also'':27C,207C ''altern'':69C,95C ''anyth'':248C ''anywher'':144C ''aw'':110C ''azur'':79C,108C,142C,146C,204C ''base'':300C ''believ'':260C ''bewar'':21C ''big'':209C,225C ''bigger'':90C ''bill'':124C ''broader'':197C ''case'':258C ''certain'':308C ''choic'':198C ''cloud'':99C,136C ''complex'':229C ''confeder'':158C ''consum'':97C ''d'':46C,288C ''data'':210C,226C ''databas'':61C ''differ'':91C ''distinguish'':49C ''distribut'':171C,178C ''distributed/cloud'':247C ''downsid'':129C ''easi'':191C ''easier'':239C ''ecosystem'':72C,102C ''either'':302C ''elabor'':317C ''engin'':25C,31C,318C ''etc'':127C ''first'':44C,283C ''follow'':314C ''framework'':59C ''fresh'':291C ''fulli'':297C ''gce'':112C ''get'':181C ''gigabyt'':276C ''give'':289C ''given'':252C ''good'':19C ''hadoop'':71C,104C,147C ''hbase'':74C ''how-long-is-a-piece-of-str'':6C ''integr'':119C,152C ''invest'':167C,245C ''iot'':255C ''iot-them'':254C ''kind'':82C ''less'':184C ''ll'':37C ''lock'':132C,186C ''lock-in'':185C ''long'':8C ''loos'':162C ''loosely-rel'':161C ''lot'':183C,217C ''machin'':125C ''make'':28C ''manag'':126C ''mani'':278C ''month'':295C ''need'':243C,263C ''nosql'':60C,76C ''optim'':117C ''over-engin'':23C ''p'':1C,43C,88C,202C,286C ''paradigm'':65C ''piec'':11C ''place'':174C ''platform'':305C ''poc'':313C ''probabl'':189C ''process'':51C,58C,85C ''production'':298C ''project'':164C ''properti'':221C ''provid'':100C,137C ''question'':14C ''re'':166C,272C ''realli'':156C ''relat'':163C ''road'':205C ''rout'':240C ''run'':141C,176C ''scalabl'':220C ''scale'':251C ''servic'':116C ''sinc'':153C ''solut'':211C ''someth'':299C ''sometim'':160C ''soon'':265C ''stage'':310C ''storag'':53C,64C ''stori'':86C ''storm'':54C ''stream'':57C,84C ''strike'':235C ''string'':13C ''stuff'':143C ''suggest'':47C ''sure'':29C ''suspect'':78C ''take'':148C ''talent'':194C ''talk'':274C ''team'':292C ''terabyt'':279C ''theme'':256C ''think'':16C,35C ''tool'':200C ''two'':94C ''upsid'':106C ''use'':257C ''vs'':103C ''want'':223C ''well'':231C ''work'':150C ''year'':41C,284C ''yes'':2C,270C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6616, 1, '2015-07-28 22:14:31.177', 2, 25, 'What makes a graph algorithm a good candidate for concurrency?', '&lt;p&gt;GraphX is the Apache Spark library for handling graph data. I was able to find a list of ''graph-parallel'' algorithms on &lt;a href="http://stanford.edu/~rezab/sparkclass/slides/ankur_graphx.pdf" rel="nofollow"&gt;these slides&lt;/a&gt; (see slide 23). However, I am curious what characteristics of these algorithms make them parallelizable. &lt;/p&gt;
', 'algorithms  graphs  apache-spark  parallel', NULL, '''/a'':50C ''/p'':66C ''/~rezab/sparkclass/slides/ankur_graphx.pdf'':45C ''23'':53C ''abl'':30C ''algorithm'':5A,11B,39C,62C ''apach'':14B,21C ''apache-spark'':13B ''candid'':8A ''characterist'':59C ''concurr'':10A ''curious'':57C ''data'':27C ''find'':32C ''good'':7A ''graph'':4A,12B,26C,37C ''graph-parallel'':36C ''graphx'':18C ''handl'':25C ''howev'':54C ''href'':42C ''librari'':23C ''list'':34C ''make'':2A,63C ''nofollow'':47C ''p'':17C ''parallel'':16B,38C ''paralleliz'':65C ''rel'':46C ''see'':51C ''slide'':49C,52C ''spark'':15B,22C ''stanford.edu'':44C ''stanford.edu/~rezab/sparkclass/slides/ankur_graphx.pdf'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6617, 2, '2015-07-29 00:51:18.11', 4, NULL, NULL, '&lt;p&gt;Two words: associative and commutative&lt;/p&gt;

&lt;p&gt;In other words, the operations that the algorithm does need to be independent of how you order or group your data...this minimizes the need for cross-talk in the algorithm and leads to more efficiency.&lt;/p&gt;
', NULL, NULL, '''/p'':7C,46C ''algorithm'':16C,40C ''associ'':4C ''commut'':6C ''cross'':36C ''cross-talk'':35C ''data'':29C ''effici'':45C ''group'':27C ''independ'':21C ''lead'':42C ''minim'':31C ''need'':18C,33C ''oper'':13C ''order'':25C ''p'':1C,8C ''talk'':37C ''two'':2C ''word'':3C,11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6630, 1, '2015-07-30 13:39:40.077', 0, 10, 'Estimating mean time given currently active processes', '&lt;p&gt;Suppose that there are $N$ processes, $\{p_i\}$, and that we wish to find that each process has a unique total time which it is active, $\{t_i\}$, such that we can calculate statistics of average, $\mu_t$, and standard deviation, $\sigma_t$, for the population times. We can assume that some processes in the global group have already finished.&lt;/p&gt;

&lt;p&gt;If the information of total active time is lost after each process is finished, how can we estimate these parameters, $\mu_t$ and $\sigma_t$, while the processes are active?&lt;/p&gt;

&lt;p&gt;If the current time a process has been running is denoted $T_i$, then we can easily get a lower bound on the average time by $\mu_t &amp;gt;= \mu(\{T_i\})$, but how can we improve this to get an estimate what the average time of life will be for the population?&lt;/p&gt;

&lt;p&gt;As an example, suppose that we never kept information about human deaths. How might one speculate what the average life expectancy is of of mankind based on the total ages of everyone on earth, assuming that people on earth have already died at sometime in the past.&lt;/p&gt;
', 'statistics  parameter', NULL, '''/p'':71C,103C,158C,207C ''activ'':6A,36C,78C,102C ''age'':189C ''alreadi'':69C,200C ''assum'':60C,194C ''averag'':46C,128C,149C,178C ''base'':185C ''bound'':125C ''calcul'':43C ''current'':5A,107C ''death'':171C ''denot'':115C ''deviat'':51C ''die'':201C ''earth'':193C,198C ''easili'':121C ''estim'':1A,90C,146C ''everyon'':191C ''exampl'':162C ''expect'':180C ''find'':24C ''finish'':70C,86C ''get'':122C,144C ''given'':4A ''global'':66C ''group'':67C ''gt'':133C ''human'':170C ''improv'':141C ''inform'':75C,168C ''kept'':167C ''life'':152C,179C ''lost'':81C ''lower'':124C ''mankind'':184C ''mean'':2A ''might'':173C ''mu'':47C,93C,131C,134C ''n'':15C ''never'':166C ''one'':174C ''p'':10C,17C,72C,104C,159C ''paramet'':9B,92C ''past'':206C ''peopl'':196C ''popul'':56C,157C ''process'':7A,16C,27C,63C,84C,100C,110C ''run'':113C ''sigma'':52C,96C ''sometim'':203C ''specul'':175C ''standard'':50C ''statist'':8B,44C ''suppos'':11C,163C ''time'':3A,32C,57C,79C,108C,129C,150C ''total'':31C,77C,188C ''uniqu'':30C ''wish'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6618, 1, '2015-07-29 09:08:30.853', 0, 29, 'Which algorithm is good for genetics duplicated data?', '&lt;p&gt;My question is more related to find the best algorithm for my data set.&lt;/p&gt;

&lt;p&gt;I have data which has three columns namely, individuals, and disease and test score (I have 50 test scores features but only one test score feature is mentioned here). I have 3000 individuals and possible values for disease feature is disA, disB and disC where as test score is a discrete variable. Disease feature is my class attribute.&lt;/p&gt;

&lt;p&gt;One individual can have up to three different diseases but only one test score value. My objective is to classify test scores on the basis of disease (which test scores are associated with which disease) But here problem is if one individual has three diseases then all of test scores will be repeated three times. For example, for individual aa (with all disA, disB and disC) test score is 12. And then analysis file will look like that&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;individuals, Disease, Test Score
aa,disA,12,...
aa,disB,12,...
aa,disC,12,...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This will result into biased analysis. Is there any data mining algorithm or statistical test for such type of data? I cannot remove these patients because they are highest proportion of data set.&lt;/p&gt;

&lt;p&gt;One possible answser which I got is to arrange the file like that:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;individuals, DiseaseA, DiseaseB, DiseaseC, Test Score
aa,true,true,true,12
bb,true,false,false,10 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But, I am bit afraid about this binary data arrangement. For example, if I have many individuals like aa (true for all diseases) then there will not be a significant difference between disease variables.Â Â  &lt;/p&gt;
', 'machine-learning  data-mining  statistics  data-cleaning  data', NULL, '''/code'':191C,261C ''/p'':35C,95C,175C,227C,242C,300C ''/pre'':192C,262C ''10'':260C ''12'':166C,184C,187C,190C,255C ''3000'':68C ''50'':53C ''aa'':156C,182C,185C,188C,251C,282C ''afraid'':268C ''algorithm'':2A,30C,205C ''analysi'':169C,199C ''answser'':231C ''arrang'':237C,273C ''associ'':128C ''attribut'':94C ''basi'':121C ''bb'':256C ''best'':29C ''bias'':198C ''binari'':271C ''bit'':267C ''cannot'':215C ''class'':93C ''classifi'':116C ''clean'':18B ''code'':177C,244C ''column'':43C ''data'':8A,13B,17B,19B,33C,39C,203C,213C,225C,272C ''data-clean'':16B ''data-min'':12B ''differ'':104C,294C ''disa'':77C,159C,183C ''disb'':78C,160C,186C ''disc'':80C,162C,189C ''discret'':87C ''diseas'':47C,74C,89C,105C,123C,131C,141C,179C,286C,296C ''diseasea'':246C ''diseaseb'':247C ''diseasec'':248C ''duplic'':7A ''exampl'':153C,275C ''fals'':258C,259C ''featur'':56C,62C,75C,90C ''file'':170C,239C ''find'':27C ''genet'':6A ''good'':4A ''got'':234C ''highest'':222C ''individu'':45C,69C,98C,138C,155C,178C,245C,280C ''learn'':11B ''like'':173C,240C,281C ''look'':172C ''machin'':10B ''machine-learn'':9B ''mani'':279C ''mention'':64C ''mine'':14B,204C ''name'':44C ''object'':113C ''one'':59C,97C,108C,137C,229C ''p'':20C,36C,96C,193C,228C,263C ''patient'':218C ''possibl'':71C,230C ''pre'':176C,243C ''problem'':134C ''proport'':223C ''question'':22C ''relat'':25C ''remov'':216C ''repeat'':149C ''result'':196C ''score'':50C,55C,61C,84C,110C,118C,126C,146C,164C,181C,250C ''set'':34C,226C ''signific'':293C ''statist'':15B,207C ''test'':49C,54C,60C,83C,109C,117C,125C,145C,163C,180C,208C,249C ''three'':42C,103C,140C,150C ''time'':151C ''true'':252C,253C,254C,257C,283C ''type'':211C ''valu'':72C,111C ''variabl'':88C,297C ''â'':298C,299C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8382, 2, '2015-10-11 08:04:58.277', 4, NULL, NULL, '&lt;p&gt;If you are testing or building something that will eventually be run on a distributed cluster, yes it make sense. If you''re playing around to learn, yes. Otherwise no I don''t see much value.&lt;/p&gt;
', NULL, NULL, '''/p'':38C ''around'':26C ''build'':7C ''cluster'':17C ''distribut'':16C ''eventu'':11C ''learn'':28C ''make'':20C ''much'':36C ''otherwis'':30C ''p'':1C ''play'':25C ''re'':24C ''run'':13C ''see'':35C ''sens'':21C ''someth'':8C ''test'':5C ''valu'':37C ''yes'':18C,29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6619, 2, '2015-07-29 10:37:15.3', 0, NULL, NULL, '&lt;p&gt;Clarification: are we talking about multiclass&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Multiclass_classification" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Multiclass_classification&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;or multilabel?&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Multi-label_classification" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Multi-label_classification&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':8C,23C ''/wiki/multi-label_classification'':29C ''/wiki/multi-label_classification&lt;/a&gt;&lt;/p&gt;'':34C ''/wiki/multiclass_classification'':14C ''/wiki/multiclass_classification&lt;/a&gt;&lt;/p&gt;'':19C ''clarif'':2C ''en.wikipedia.org'':13C,18C,28C,33C ''en.wikipedia.org/wiki/multi-label_classification'':27C ''en.wikipedia.org/wiki/multi-label_classification&lt;/a&gt;&lt;/p&gt;'':32C ''en.wikipedia.org/wiki/multiclass_classification'':12C ''en.wikipedia.org/wiki/multiclass_classification&lt;/a&gt;&lt;/p&gt;'':17C ''href'':11C,26C ''multiclass'':7C ''multilabel'':22C ''nofollow'':16C,31C ''p'':1C,9C,20C,24C ''rel'':15C,30C ''talk'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6620, 1, '2015-07-28 12:15:31.23', 1, 134, 'Which language best to use for Machine Learning library?', '&lt;p&gt;We have a body of theoretical work on nearest neighbors that we would like to implement and make the code publicly available. Question: what''s the best language to use? We''re considering java, python, c, c++. We would like the code to be fast (big data and all) and easy to use -- so that others can readily download and run it. Any thoughts and suggestions are welcome.&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':82C ''avail'':35C ''best'':3A,40C ''big'':59C ''bodi'':17C ''c'':49C,50C ''code'':33C,55C ''consid'':46C ''data'':60C ''download'':72C ''easi'':64C ''fast'':58C ''implement'':29C ''java'':47C ''languag'':2A,41C ''learn'':8A,12B ''librari'':9A ''like'':27C,53C ''machin'':7A,11B ''machine-learn'':10B ''make'':31C ''nearest'':22C ''neighbor'':23C ''other'':69C ''p'':13C ''public'':34C ''python'':48C ''question'':36C ''re'':45C ''readili'':71C ''run'':74C ''suggest'':79C ''theoret'':19C ''thought'':77C ''use'':5A,43C,66C ''welcom'':81C ''work'':20C ''would'':26C,52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6622, 2, '2015-07-29 15:35:00.647', -1, NULL, NULL, '&lt;p&gt;Almost all machine learning library such as scikit learn,mlib,weka can either be used by a native or wrapper version of R and Python.However I prefer python as it''s a full scale language compared to R and at least 2-50x faster than R in different situations.&lt;/p&gt;
', NULL, NULL, '''-50'':44C ''/p'':52C ''2'':43C ''almost'':2C ''compar'':37C ''differ'':50C ''either'':14C ''faster'':46C ''full'':34C ''languag'':36C ''learn'':5C,10C ''least'':42C ''librari'':6C ''machin'':4C ''mlib'':11C ''nativ'':19C ''p'':1C ''prefer'':28C ''python'':29C ''python.however'':26C ''r'':24C,39C,48C ''scale'':35C ''scikit'':9C ''situat'':51C ''use'':16C ''version'':22C ''weka'':12C ''wrapper'':21C ''x'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6623, 1, '2015-07-29 17:16:15.64', 1, 23, 'Sharing industry data with academic class contracts?', '&lt;p&gt;Could someone point me to a standard data sharing agreement? I have an interesting data set that I want to share with my local university for  their Data Mining class. The data set includes time-series and email statistics. All domains and IPs are anonymized before release. &lt;/p&gt;

&lt;p&gt;Main points:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Use at your own risk&lt;/li&gt;
&lt;li&gt;Protect my company and client''s privacy&lt;/li&gt;
&lt;li&gt;Cite my company&lt;/li&gt;
&lt;li&gt;Do not re-share without permission&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Am I missing anything important here?&lt;/p&gt;
', 'data-mining  dataset', NULL, '''/li'':73C,82C,87C,96C ''/ol'':97C ''/p'':61C,65C,105C ''academ'':5A ''agreement'':22C ''anonym'':58C ''anyth'':102C ''cite'':84C ''class'':6A,42C ''client'':79C ''compani'':77C,86C ''contract'':7A ''could'':13C ''data'':3A,9B,20C,27C,40C,44C ''data-min'':8B ''dataset'':11B ''domain'':54C ''email'':51C ''import'':103C ''includ'':46C ''industri'':2A ''interest'':26C ''ip'':56C ''li'':67C,74C,83C,88C ''local'':36C ''main'':63C ''mine'':10B,41C ''miss'':101C ''ol'':66C ''p'':12C,62C,98C ''permiss'':95C ''point'':15C,64C ''privaci'':81C ''protect'':75C ''re'':92C ''re-shar'':91C ''releas'':60C ''risk'':72C ''seri'':49C ''set'':28C,45C ''share'':1A,21C,33C,93C ''someon'':14C ''standard'':19C ''statist'':52C ''time'':48C ''time-seri'':47C ''univers'':37C ''use'':68C ''want'':31C ''without'':94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6624, 1, '2015-07-29 17:40:21.78', 2, 53, 'Adaptive learning of user''s IoT setting preferences', '&lt;p&gt;We are working on a project to Predict the settings of the IoT devices(fan/light/AC) the user is using, based on his: location, outside temperature, humidity, time of the day etc...&lt;/p&gt;

&lt;p&gt;Training data is not available and the model should start building/adapting itself every time the user uses the IoT device, to predict his preferred settings in any new environment he goes. This model will be unique for each user. Possibly we can start off with a demo model but it should adapt itself as per each user''s preferences.&lt;/p&gt;

&lt;p&gt;Which (machine?)learning algorithm can be useful here? Any links to the same and some tips on implementation would be appreciated.&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':44C,105C,128C ''adapt'':1A,97C ''algorithm'':110C ''appreci'':127C ''avail'':50C ''base'':32C ''building/adapting'':56C ''data'':47C ''day'':42C ''demo'':92C ''devic'':26C,65C ''environ'':74C ''etc'':43C ''everi'':58C ''fan/light/ac'':27C ''goe'':76C ''humid'':38C ''implement'':124C ''iot'':6A,25C,64C ''learn'':2A,11B,109C ''link'':116C ''locat'':35C ''machin'':10B,108C ''machine-learn'':9B ''model'':53C,78C,93C ''new'':73C ''outsid'':36C ''p'':12C,45C,106C ''per'':100C ''possibl'':85C ''predict'':20C,67C ''prefer'':8A,69C,104C ''project'':18C ''set'':7A,22C,70C ''start'':55C,88C ''temperatur'':37C ''time'':39C,59C ''tip'':122C ''train'':46C ''uniqu'':81C ''use'':31C,62C,113C ''user'':4A,29C,61C,84C,102C ''work'':15C ''would'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6673, 2, '2015-08-04 01:29:21.623', 2, NULL, NULL, '&lt;p&gt;Disagree with David, a true data scientist is an applied statistician who codes and knows how to use machine learning algorithms for the right reasons. Statistics is the base of all data science. It is the "cake" per se. Everything else is just icing. &lt;/p&gt;

&lt;p&gt;The question is what kind of data scientist do you want to be? Do you want to be a master of the subject (knowledge of how, why, when and when not to apply an algorithm or technique) or a Kaggle Script Kiddie using Scipy and thinking that he is a Data Scientist? &lt;/p&gt;

&lt;p&gt;1 - Stats&lt;/p&gt;

&lt;p&gt;2- Everything else&lt;/p&gt;
', NULL, NULL, '''/p'':46C,100C,104C,109C ''1'':102C ''2'':106C ''algorithm'':22C,82C ''appli'':11C,80C ''base'':30C ''cake'':38C ''code'':14C ''data'':7C,33C,54C,98C ''david'':4C ''disagre'':2C ''els'':42C,108C ''everyth'':41C,107C ''ice'':45C ''kaggl'':87C ''kiddi'':89C ''kind'':52C ''know'':16C ''knowledg'':71C ''learn'':21C ''machin'':20C ''master'':67C ''p'':1C,47C,101C,105C ''per'':39C ''question'':49C ''reason'':26C ''right'':25C ''scienc'':34C ''scientist'':8C,55C,99C ''scipi'':91C ''script'':88C ''se'':40C ''stat'':103C ''statist'':27C ''statistician'':12C ''subject'':70C ''techniqu'':84C ''think'':93C ''true'':6C ''use'':19C,90C ''want'':58C,63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6626, 1, '2015-07-29 20:50:40.583', 1, 66, '"Recursive ConvNets for Dummies" Library', '&lt;p&gt;I''ve look at the questions on here regarding the different python libraries around for deep learning and neural nets. They include:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Keras&lt;/li&gt;
&lt;li&gt;Caffe&lt;/li&gt;
&lt;li&gt;Lasagne&lt;/li&gt;
&lt;li&gt;PyLearn2&lt;/li&gt;
&lt;li&gt;Deepy&lt;/li&gt;
&lt;li&gt;Theano&lt;/li&gt;
&lt;li&gt;Torch&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;My understanding is that Keras and Lasagne require the user to have varying degrees of interaction with Theano. &lt;/p&gt;

&lt;p&gt;I''m looking for the one that would be the easiest to start testing an algorithm I have already developed an apply deep learning on it in a packaged way. I am not looking to extend, modify or tweak anything. I certainly won''t be doing any research in the field with it. &lt;/p&gt;

&lt;p&gt;The only advanced feature I would need would be GPU support. &lt;/p&gt;

&lt;p&gt;In short, what would be the best "dummy" library to test recursive convolutional neural nets? &lt;/p&gt;
', 'neuralnetwork  deep-learning  library', NULL, '''/li'':38C,41C,44C,47C,50C,53C,56C ''/p'':34C,77C,132C,145C,162C ''/ul'':57C ''advanc'':136C ''algorithm'':94C ''alreadi'':97C ''anyth'':118C ''appli'':100C ''around'':25C ''best'':153C ''caff'':40C ''certain'':120C ''convnet'':2A ''convolut'':159C ''deep'':8B,27C,101C ''deep-learn'':7B ''deepi'':49C ''degre'':72C ''develop'':98C ''differ'':22C ''dummi'':4A,154C ''easiest'':89C ''extend'':114C ''featur'':137C ''field'':129C ''gpu'':143C ''includ'':33C ''interact'':74C ''kera'':37C,63C ''lasagn'':43C,65C ''learn'':9B,28C,102C ''li'':36C,39C,42C,45C,48C,51C,54C ''librari'':5A,10B,24C,155C ''look'':14C,81C,112C ''m'':80C ''modifi'':115C ''need'':140C ''net'':31C,161C ''neural'':30C,160C ''neuralnetwork'':6B ''one'':84C ''p'':11C,58C,78C,133C,146C ''packag'':107C ''pylearn2'':46C ''python'':23C ''question'':17C ''recurs'':1A,158C ''regard'':20C ''requir'':66C ''research'':126C ''short'':148C ''start'':91C ''support'':144C ''test'':92C,157C ''theano'':52C,76C ''torch'':55C ''tweak'':117C ''ul'':35C ''understand'':60C ''user'':68C ''vari'':71C ''ve'':13C ''way'':108C ''won'':121C ''would'':86C,139C,141C,150C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6628, 2, '2015-07-30 04:21:27.09', 1, NULL, NULL, '&lt;pre&gt;&lt;code&gt;hadoop fs -getmerge &amp;lt;hdfs-output-directory&amp;gt; &amp;lt;local-file&amp;gt; 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This command can be used to concatenate the HDFS files into a single local file.&lt;/p&gt;

&lt;p&gt;Ref : &lt;a href="http://hadoop.apache.org/docs/r2.7.0/hadoop-project-dist/hadoop-common/FileSystemShell.html#getmerge" rel="nofollow"&gt;http://hadoop.apache.org/docs/r2.7.0/hadoop-project-dist/hadoop-common/FileSystemShell.html#getmerge&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/code'':17C ''/docs/r2.7.0/hadoop-project-dist/hadoop-common/filesystemshell.html#getmerge'':42C ''/docs/r2.7.0/hadoop-project-dist/hadoop-common/filesystemshell.html#getmerge&lt;/a&gt;&lt;/p&gt;'':47C ''/p'':35C ''/pre'':18C ''code'':2C ''command'':21C ''concaten'':26C ''directori'':10C ''file'':15C,29C,34C ''fs'':4C ''getmerg'':5C ''gt'':11C,16C ''hadoop'':3C ''hadoop.apache.org'':41C,46C ''hadoop.apache.org/docs/r2.7.0/hadoop-project-dist/hadoop-common/filesystemshell.html#getmerge'':40C ''hadoop.apache.org/docs/r2.7.0/hadoop-project-dist/hadoop-common/filesystemshell.html#getmerge&lt;/a&gt;&lt;/p&gt;'':45C ''hdfs'':8C,28C ''hdfs-output-directori'':7C ''href'':39C ''local'':14C,33C ''local-fil'':13C ''lt'':6C,12C ''nofollow'':44C ''output'':9C ''p'':19C,36C ''pre'':1C ''ref'':37C ''rel'':43C ''singl'':32C ''use'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6629, 1, '2015-07-30 07:16:07.637', 5, 90, 'Anonymizing Datasets', '&lt;p&gt;I would like to know what are the best practices for anonymizing datasets? 
Ideally I should be able to get the original data back after performing analysis on the anonymized dataset. Should I be using some encryption functions? Hashing maybe? &lt;/p&gt;
', 'data-mining  dataset  data-cleaning', NULL, '''/p'':51C ''abl'':28C ''analysi'':37C ''anonym'':1A,22C,40C ''back'':34C ''best'':19C ''clean'':9B ''data'':4B,8B,33C ''data-clean'':7B ''data-min'':3B ''dataset'':2A,6B,23C,41C ''encrypt'':47C ''function'':48C ''get'':30C ''hash'':49C ''ideal'':24C ''know'':15C ''like'':13C ''mayb'':50C ''mine'':5B ''origin'':32C ''p'':10C ''perform'':36C ''practic'':20C ''use'':45C ''would'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6646, 2, '2015-07-31 11:59:35.597', 0, NULL, NULL, '&lt;p&gt;I don''t think you can currently upload a trained model. &lt;/p&gt;

&lt;p&gt;Your options would be to either re-train the model in AzureML or expose them as a web-service using an Azure Virtual Machine running something like:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://cran.r-project.org/web/packages/Rook/index.html" rel="nofollow"&gt;Rook&lt;/a&gt; &lt;/li&gt;
&lt;li&gt;&lt;a href="http://shiny.rstudio.com/" rel="nofollow"&gt;Shiny&lt;/a&gt; &lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.revolutionanalytics.com/whitepaper/using-deployr-to-solve-the-integration-problem" rel="nofollow"&gt;DeployR&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':55C,64C,75C ''/li'':56C,65C,76C ''/p'':13C,44C ''/ul'':77C ''/web/packages/rook/index.html'':51C ''/whitepaper/using-deployr-to-solve-the-integration-problem'':71C ''azur'':38C ''azureml'':27C ''cran.r-project.org'':50C ''cran.r-project.org/web/packages/rook/index.html'':49C ''current'':8C ''deployr'':74C ''either'':20C ''expos'':29C ''href'':48C,59C,68C ''li'':46C,57C,66C ''like'':43C ''machin'':40C ''model'':12C,25C ''nofollow'':53C,62C,73C ''option'':16C ''p'':1C,14C ''re'':22C ''re-train'':21C ''rel'':52C,61C,72C ''rook'':54C ''run'':41C ''servic'':35C ''shini'':63C ''shiny.rstudio.com'':60C ''someth'':42C ''think'':5C ''train'':11C,23C ''ul'':45C ''upload'':9C ''use'':36C ''virtual'':39C ''web'':34C ''web-servic'':33C ''would'':17C ''www.revolutionanalytics.com'':70C ''www.revolutionanalytics.com/whitepaper/using-deployr-to-solve-the-integration-problem'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6631, 1, '2015-07-30 14:07:31.163', 4, 30, 'Alignment of square nonorientable images/data', '&lt;p&gt;Another post where I don''t know enough terminology to describe things efficiently. For the comments, please suggest some tags and keywords I can add to this post to make it better.&lt;/p&gt;

&lt;p&gt;Say I have a 2D data structure where ''orientation'' doesn''t matter. The examples I ran into:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;The state of a &lt;a href="https://gabrielecirulli.github.io/2048/" rel="nofollow"&gt;2048&lt;/a&gt; game. In terms of symmetry groups this would be D&lt;sub&gt;4&lt;/sub&gt; / D&lt;sub&gt;8&lt;/sub&gt;, except that an operation doesn''t yield an identical state, it just yields another state that has the same solution.&lt;/li&gt;
&lt;li&gt;Images of &lt;a href="https://www.kaggle.com/c/datasciencebowl" rel="nofollow"&gt;plankton&lt;/a&gt; or &lt;a href="https://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge" rel="nofollow"&gt;galaxies&lt;/a&gt; (without background). Somewhat similar to above except that any rotation (not just 90&lt;sup&gt;o&lt;/sup&gt;) yields an equally valid image (and one might take scale into account, but let''s forget about that).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In both cases I''ve wanted to transform all these equivalent states/images to remove all but one of the equivalent images. To illustrate with two that worked:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;I can use &lt;a href="https://en.wikipedia.org/wiki/Image_moment" rel="nofollow"&gt;image moments&lt;/a&gt; M&lt;sub&gt;10&lt;/sub&gt; and M&lt;sub&gt;01&lt;/sub&gt; to transform horizontally and vertically mirrored equivalent data. E.g. apply horizontal mirroring iff it makes M&lt;sub&gt;10&lt;/sub&gt; bigger. This would transform a 2048 state and it''s horizontal mirror image to the same state.&lt;/li&gt;
&lt;li&gt;I can use the eigenvector of the covariance matrix which has the largest eigenvalue as the orientation. Then I can rotate the image to align this eigenvector with some predetermined axis (e.g. horizontally).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;That still leaves a lot of operations though (diagonal mirroring, rotations around the center, inversion). And these operations do not commute (D&lt;sub&gt;8&lt;/sub&gt; is non-Abelian). Is there any comprehensive approach?&lt;/p&gt;

&lt;p&gt;The reason I want to do this is to help machine learning methods by removing variance that isn''t actually meaningful. Hopefully that makes sure they don''t have to learn these equivalences, so possibly need less train data (and time).&lt;/p&gt;
', 'data-cleaning  processing', NULL, '''/2048/'':73C ''/a'':77C,127C,137C,217C ''/c/datasciencebowl'':123C ''/c/galaxy-zoo-the-galaxy-challenge'':133C ''/li'':115C,172C,263C,298C ''/p'':43C,62C,202C,335C,378C ''/sub'':90C,94C,221C,226C,245C,325C ''/sup'':153C ''/ul'':173C,299C ''/wiki/image_moment'':212C ''01'':225C ''10'':220C,244C ''2048'':76C,251C ''2d'':49C ''4'':89C ''8'':93C,324C ''90'':150C ''abelian'':329C ''account'':165C ''actual'':356C ''add'':35C ''align'':1A,289C ''anoth'':11C,108C ''appli'':236C ''approach'':334C ''around'':312C ''axi'':295C ''background'':139C ''better'':42C ''bigger'':246C ''case'':177C ''center'':314C ''clean'':8B ''comment'':26C ''commut'':321C ''comprehens'':333C ''covari'':272C ''d'':87C,91C,322C ''data'':7B,50C,234C,375C ''data-clean'':6B ''describ'':21C ''diagon'':309C ''doesn'':54C,99C ''e.g'':235C,296C ''effici'':23C ''eigenvalu'':278C ''eigenvector'':269C,291C ''en.wikipedia.org'':211C ''en.wikipedia.org/wiki/image_moment'':210C ''enough'':18C ''equal'':156C ''equival'':185C,194C,233C,369C ''exampl'':58C ''except'':95C,144C ''forget'':169C ''gabrielecirulli.github.io'':72C ''gabrielecirulli.github.io/2048/'':71C ''galaxi'':136C ''game'':78C ''group'':83C ''help'':346C ''hope'':358C ''horizont'':229C,237C,256C,297C ''href'':70C,120C,130C,209C ''ident'':103C ''iff'':239C ''illustr'':197C ''imag'':117C,158C,195C,215C,258C,287C ''images/data'':5A ''invers'':315C ''isn'':354C ''keyword'':32C ''know'':17C ''largest'':277C ''learn'':348C,367C ''leav'':303C ''less'':373C ''let'':167C ''li'':64C,116C,204C,264C ''lot'':305C ''m'':218C,223C,242C ''machin'':347C ''make'':40C,241C,360C ''matrix'':273C ''matter'':56C ''meaning'':357C ''method'':349C ''might'':161C ''mirror'':232C,238C,257C,310C ''moment'':216C ''need'':372C ''nofollow'':75C,125C,135C,214C ''non'':328C ''non-abelian'':327C ''nonorient'':4A ''o'':152C ''one'':160C,191C ''oper'':98C,307C,318C ''orient'':53C,281C ''p'':10C,44C,174C,300C,336C ''plankton'':126C ''pleas'':27C ''possibl'':371C ''post'':12C,38C ''predetermin'':294C ''process'':9B ''ran'':60C ''reason'':338C ''rel'':74C,124C,134C,213C ''remov'':188C,351C ''rotat'':147C,285C,311C ''say'':45C ''scale'':163C ''similar'':141C ''solut'':114C ''somewhat'':140C ''squar'':3A ''state'':66C,104C,109C,252C,262C ''states/images'':186C ''still'':302C ''structur'':51C ''sub'':88C,92C,219C,224C,243C,323C ''suggest'':28C ''sup'':151C ''sure'':361C ''symmetri'':82C ''tag'':30C ''take'':162C ''term'':80C ''terminolog'':19C ''thing'':22C ''though'':308C ''time'':377C ''train'':374C ''transform'':182C,228C,249C ''two'':199C ''ul'':63C,203C ''use'':207C,267C ''valid'':157C ''varianc'':352C ''ve'':179C ''vertic'':231C ''want'':180C,340C ''without'':138C ''work'':201C ''would'':85C,248C ''www.kaggle.com'':122C,132C ''www.kaggle.com/c/datasciencebowl'':121C ''www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge'':131C ''yield'':101C,107C,154C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6632, 1, '2015-07-30 14:15:58.803', 0, 21, 'Getting prob of class using naive bayes', '&lt;p&gt;I am trying to classify input with two classes, here is the simple code for the same. &lt;code&gt;dino&lt;/code&gt; and &lt;code&gt;crypto&lt;/code&gt; are two classes&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for w, cnt in list(counts.items()): #count is dict with word and it''s count value
    p_word = vocab[w] / sum(vocab.values()) 
    p_w_given_dino = (word_counts["dino"].get(w, 0.0) + 1) / (sum(word_counts["dino"].values()) + v) 
    p_w_given_crypto = (word_counts["crypto"].get(w, 0.0) + 1) / (sum(word_counts["crypto"].values()) + v)

    log_prob_dino += math.log(cnt * p_w_given_dino / p_word)
    log_prob_crypto += math.log(cnt * p_w_given_crypto / p_word)

print("Score(dino)  :", math.exp(log_prob_dino + math.log(prior_dino)))
print("Score(crypto):", math.exp(log_prob_crypto + math.log(prior_crypto)))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;another approach is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;prior_dino = (priors["dino"] / sum(priors.values()))
prior_crypto = (priors["crypto"] / sum(priors.values()))
for w, cnt in list(counts.items()):
    p_word = vocab[w] / sum(vocab.values())
    p_w_given_dino = (word_counts["dino"].get(w, 0.0) + 1) / (sum(word_counts["dino"].values()) + v) 
    p_w_given_crypto = (word_counts["crypto"].get(w, 0.0) + 1) / (sum(word_counts["crypto"].values()) + v)
    prob_dino *= p_w_given_dino
    prob_crypto *= p_w_given_crypto
t_prior_dino = prob_dino * prior_dino
t_prior_crypto = prob_crypto * prior_crypto
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Actually on 2nd approach I am getting very very small values so I got first approach. &lt;/p&gt;

&lt;p&gt;Which one is correct, or both of them?&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/code'':32C,36C,141C,234C ''/p'':40C,147C,253C,263C ''/pre'':142C,235C ''0.0'':74C,91C,183C,200C ''1'':75C,92C,184C,201C ''2nd'':239C ''actual'':237C ''anoth'':144C ''approach'':145C,240C,252C ''bay'':7A ''class'':4A,21C,39C ''classif'':11B ''classifi'':17C ''cnt'':45C,103C,114C,164C ''code'':26C,30C,34C,42C,149C ''correct'':258C ''count'':49C,57C,70C,78C,87C,95C,179C,187C,196C,204C ''counts.items'':48C,167C ''crypto'':35C,85C,88C,96C,112C,118C,133C,137C,140C,157C,159C,194C,197C,205C,215C,219C,229C,231C,233C ''dict'':51C ''dino'':31C,68C,71C,79C,101C,107C,123C,127C,130C,151C,153C,177C,180C,188C,209C,213C,222C,224C,226C ''first'':251C ''get'':1A,72C,89C,181C,198C,243C ''given'':67C,84C,106C,117C,176C,193C,212C,218C ''got'':250C ''input'':18C ''learn'':10B ''list'':47C,166C ''log'':99C,110C,125C,135C ''machin'':9B ''machine-learn'':8B ''math.exp'':124C,134C ''math.log'':102C,113C,128C,138C ''naiv'':6A ''one'':256C ''p'':12C,59C,65C,82C,104C,108C,115C,119C,143C,168C,174C,191C,210C,216C,236C,254C ''pre'':41C,148C ''print'':121C,131C ''prior'':129C,139C,150C,152C,156C,158C,221C,225C,228C,232C ''priors.values'':155C,161C ''prob'':2A,100C,111C,126C,136C,208C,214C,223C,230C ''score'':122C,132C ''simpl'':25C ''small'':246C ''sum'':63C,76C,93C,154C,160C,172C,185C,202C ''tri'':15C ''two'':20C,38C ''use'':5A ''v'':81C,98C,190C,207C ''valu'':58C,80C,97C,189C,206C,247C ''vocab'':61C,170C ''vocab.values'':64C,173C ''w'':44C,62C,66C,73C,83C,90C,105C,116C,163C,171C,175C,182C,192C,199C,211C,217C ''word'':53C,60C,69C,77C,86C,94C,109C,120C,169C,178C,186C,195C,203C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6633, 1, '2015-07-30 14:30:15.82', 0, 24, 'Issue with 1L format', '&lt;p&gt;When doing this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;np.array(data.ix[:,i])).shape
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I get &lt;code&gt;(number,)&lt;/code&gt; on a machine  with Python 3, but on a Python 2  doing the same thing I  get the long format &lt;code&gt;(numberL,)&lt;/code&gt;. How can I make python 2 use the standard numeric type instead of the long type?&lt;/p&gt;
', 'python', NULL, '''/code'':17C,24C,47C ''/p'':10C,64C ''/pre'':18C ''1l'':3A ''2'':35C,53C ''3'':30C ''code'':12C,22C,45C ''data.ix'':14C ''format'':4A,44C ''get'':21C,41C ''instead'':59C ''issu'':1A ''long'':43C,62C ''machin'':27C ''make'':51C ''np.array'':13C ''number'':23C ''numberl'':46C ''numer'':57C ''p'':6C,19C ''pre'':11C ''python'':5B,29C,34C,52C ''shape'':16C ''standard'':56C ''thing'':39C ''type'':58C,63C ''use'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6634, 1, '2015-07-30 14:47:48.963', 4, 130, 'Sales Predictions Over Time', '&lt;p&gt;I''d like to model the evolution of the sales of a store.&lt;/p&gt;

&lt;p&gt;Here are the data I have :&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/6FsZ8.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/6FsZ8.png" alt="data table"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Customers are aggregated into monthly cohort depending on the date of the first purchase
&lt;em&gt;eg&lt;/em&gt; : customers who did their 1st purchase in Jan 2015 are in the cohort 1, customers who did their 1st purchase in Fev 2015 are in the cohort 2.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Sales : aggregated for each &lt;em&gt;Month&lt;/em&gt; * &lt;em&gt;Cohort&lt;/em&gt; (cohort 4 is not visible because there is no line for only 1 record)&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/xfAsk.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/xfAsk.png" alt="sales over the time"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;if we put all the cohorts at the same origin:
&lt;a href="http://i.stack.imgur.com/kq0jQ.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/kq0jQ.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Question:&lt;/strong&gt; How to predict the spending of the next 2 months, ie :&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;the spending of the cohort 1 in its aging 5 and 6&lt;/li&gt;
&lt;li&gt;the spending of the cohort 2 in its aging 4 and 5&lt;/li&gt;
&lt;li&gt;the spending of the cohort 3 in its aging 3 and 4&lt;/li&gt;
&lt;li&gt;the spending of the cohort 4 in its aging 2 and 3&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;But also for the cohort who don''t exist yet :&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;the spending of the cohort 5 in its aging 1 and 2&lt;/li&gt;
&lt;li&gt;the spending of the cohort 6 in its aging 1 &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;I have 2 methodologies in mind, but don''t know if they''re good:&lt;/strong&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Polynomial regression by using 2 parameters:

&lt;ul&gt;
&lt;li&gt;aging: &lt;em&gt;because there is a clear evolution over the time, up then down.&lt;/em&gt;&lt;/li&gt;
&lt;li&gt;cohort number: &lt;em&gt;because more recent cohort seems to spend less.&lt;/em&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;Time series : I didn''t perform time series recently I''m a bit rust so I would prefer to use something else but I think it could fit with this problem.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Indeed it seems obvious that y&lt;em&gt;t-1&lt;/em&gt; t in y&lt;em&gt;t-2&lt;/em&gt;... are good predictors of y&lt;em&gt;t&lt;/em&gt; but how to predict spending of future cohort with no history ?&lt;/p&gt;
', 'time-series', NULL, '''-1'':395C ''-2'':402C ''/6fsz8.png'':36C,43C ''/a'':47C,146C,176C ''/em'':68C,107C,110C,334C,348C,396C,403C,411C ''/kq0jq.png'':163C,170C ''/li'':98C,126C,208C,222C,236C,250C,278C,290C,335C,349C,351C,384C ''/ol'':385C ''/p'':22C,30C,48C,97C,125C,147C,177C,193C,263C,309C,423C ''/strong'':181C,308C ''/ul'':127C,251C,291C,350C ''/xfask.png'':133C,140C ''1'':82C,123C,201C,275C,289C ''1st'':73C,87C ''2'':96C,190C,215C,247C,277C,296C,316C ''2015'':77C,91C ''3'':229C,233C,249C ''4'':112C,219C,235C,243C ''5'':205C,221C,271C ''6'':207C,285C ''age'':204C,218C,232C,246C,274C,288C,320C ''aggreg'':54C,102C ''also'':254C ''alt'':44C,141C,171C ''bit'':365C ''clear'':326C ''cohort'':57C,81C,95C,109C,111C,154C,200C,214C,228C,242C,257C,270C,284C,337C,343C,419C ''could'':379C ''custom'':52C,69C,83C ''d'':10C ''data'':27C,45C ''date'':61C ''depend'':58C ''descript'':174C ''didn'':356C ''eg'':67C ''els'':374C ''em'':66C,105C,108C,321C,339C,393C,400C,409C ''enter'':172C ''evolut'':15C,327C ''exist'':261C ''fev'':90C ''first'':64C ''fit'':380C ''futur'':418C ''good'':307C,405C ''histori'':422C ''href'':33C,130C,160C ''i.stack.imgur.com'':35C,42C,132C,139C,162C,169C ''i.stack.imgur.com/6fsz8.png'':34C,41C ''i.stack.imgur.com/kq0jq.png'':161C,168C ''i.stack.imgur.com/xfask.png'':131C,138C ''ie'':192C ''imag'':173C ''img'':39C,136C,166C ''inde'':387C ''jan'':76C ''know'':303C ''less'':347C ''li'':50C,99C,195C,209C,223C,237C,265C,279C,311C,319C,336C,352C ''like'':11C ''line'':120C ''m'':363C ''methodolog'':297C ''mind'':299C ''model'':13C ''month'':56C,106C,191C ''next'':189C ''nofollow'':38C,135C,165C ''number'':338C ''obvious'':390C ''ol'':310C ''origin'':158C ''p'':8C,23C,31C,51C,100C,128C,148C,178C,252C,292C,386C ''paramet'':317C ''perform'':358C ''polynomi'':312C ''predict'':2A,184C,415C ''predictor'':406C ''prefer'':370C ''problem'':383C ''purchas'':65C,74C,88C ''put'':151C ''question'':180C ''re'':306C ''recent'':342C,361C ''record'':124C ''regress'':313C ''rel'':37C,134C,164C ''rust'':366C ''sale'':1A,18C,101C,142C ''seem'':344C,389C ''seri'':7B,354C,360C ''someth'':373C ''spend'':186C,197C,211C,225C,239C,267C,281C,346C,416C ''src'':40C,137C,167C ''store'':21C ''strong'':179C,293C ''tabl'':46C ''think'':377C ''time'':4A,6B,145C,330C,353C,359C ''time-seri'':5B ''ul'':49C,194C,264C,318C ''use'':315C,372C ''visibl'':115C ''would'':369C ''y'':392C,399C,408C ''yet'':262C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6635, 1, '2015-07-30 17:48:12.383', 1, 73, 'Program to fine-tune pre-trained word embeddings on my data set', '&lt;p&gt;I am looking for a program that would allow me to fine-tune pre-trained word embeddings on my data set. Ideally, open source and working on Linux or Windows.&lt;/p&gt;
', 'nlp  word-embeddings  software-recommendation', NULL, '''/p'':55C ''allow'':31C ''data'':13A,44C ''embed'':10A,18B,41C ''fine'':4A,35C ''fine-tun'':3A,34C ''ideal'':46C ''linux'':52C ''look'':25C ''nlp'':15B ''open'':47C ''p'':22C ''pre'':7A,38C ''pre-train'':6A,37C ''program'':1A,28C ''recommend'':21B ''set'':14A,45C ''softwar'':20B ''software-recommend'':19B ''sourc'':48C ''train'':8A,39C ''tune'':5A,36C ''window'':54C ''word'':9A,17B,40C ''word-embed'':16B ''work'':50C ''would'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6665, 1, '2015-08-03 11:45:11.44', 3, 88, 'Why do my Latent Dirichlet Allocation Topics mix words that never co-occurred?', '&lt;p&gt;I have one corpus of documents on diabetes, another on Leonardo da Vinci, and another on animation and computer graphics.  I combined all of these documents into a LDA and got a topic like the one below.  I''m listing the top 30 terms, in descending order.  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;TOPIC 83: [''produced'', ''company'', ''john'', ''weight'', ''motion'', ''information'', ''painting'', ''original'', ''training'', ''people'', ''health'', ''life'', ''jesus'', ''feature'', ''body'', ''lucretia'', ''computer'', ''graphics'', ''time'', ''madonna'', ''story'', ''florence'', ''type'', ''animated'', ''paul'', ''diabetes'', ''animation'', ''exercise'', ''peter'', ''film'']&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Many of these words do not co-occur with each other in any documents.  For instance, ''lucretia'' does not co-occur with ''exercise''.  Yet how are these terms put together in the same topic, in the top 30 words? (30 out of 20K words or so)&lt;/p&gt;

&lt;p&gt;Perhaps it has to do with my particular implementation?  I''m using the &lt;a href="https://radimrehurek.com/gensim/" rel="nofollow"&gt;gensim&lt;/a&gt; library for Python.  Or is it a flaw of LDA generally?&lt;/p&gt;
', 'python  topic-model  lda', NULL, '''/a'':177C ''/gensim/'':173C ''/p'':68C,104C,154C,189C ''/strong'':103C ''20k'':150C ''30'':63C,145C,147C ''83'':72C ''alloc'':6A ''anim'':37C,96C,99C ''anoth'':29C,35C ''bodi'':87C ''co'':13A,113C,127C ''co-occur'':12A,112C,126C ''combin'':42C ''compani'':74C ''comput'':39C,89C ''corpus'':24C ''da'':32C ''descend'':66C ''diabet'':28C,98C ''dirichlet'':5A ''document'':26C,46C,120C ''exercis'':100C,130C ''featur'':86C ''film'':102C ''flaw'':185C ''florenc'':94C ''general'':188C ''gensim'':176C ''got'':51C ''graphic'':40C,90C ''health'':83C ''href'':170C ''implement'':164C ''inform'':78C ''instanc'':122C ''jesus'':85C ''john'':75C ''latent'':4A ''lda'':19B,49C,187C ''leonardo'':31C ''librari'':178C ''life'':84C ''like'':54C ''list'':60C ''lucretia'':88C,123C ''m'':59C,166C ''madonna'':92C ''mani'':106C ''mix'':8A ''model'':18B ''motion'':77C ''never'':11A ''nofollow'':175C ''occur'':14A,114C,128C ''one'':23C,56C ''order'':67C ''origin'':80C ''p'':20C,69C,105C,155C ''paint'':79C ''particular'':163C ''paul'':97C ''peopl'':82C ''perhap'':156C ''peter'':101C ''produc'':73C ''put'':136C ''python'':15B,180C ''radimrehurek.com'':172C ''radimrehurek.com/gensim/'':171C ''rel'':174C ''stori'':93C ''strong'':70C ''term'':64C,135C ''time'':91C ''togeth'':137C ''top'':62C,144C ''topic'':7A,17B,53C,71C,141C ''topic-model'':16B ''train'':81C ''type'':95C ''use'':167C ''vinci'':33C ''weight'':76C ''word'':9A,109C,146C,151C ''yet'':131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6638, 2, '2015-07-30 21:14:40.95', 1, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Fun with Group Theory!&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;There are only 8 unique rotation-inversion operations for a square matrix.&lt;/p&gt;

&lt;p&gt;The four rotation operators are &lt;code&gt;(0,90,180,270)&lt;/code&gt;.  Further rotation or rotation in the reverse direction is the same as these four.  Two successive rotations just yields one of the rotation operators, so we will only consider these four rotations applied one time.&lt;/p&gt;

&lt;p&gt;The five inversion operators are &lt;code&gt;(0,/,\,|,-)&lt;/code&gt;.  Two successive inversions just yields a rotation, so we will only allow for a single inversion.&lt;/p&gt;

&lt;p&gt;We can thus derive all operators by combining these two vectors, which yields &lt;code&gt;4*5=20&lt;/code&gt; possible states.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(0,90,180,270,0/,90/,180/,270/,0\,90\,180\,270\,0|,90|,180|,270|,0-,90-,180-,270-)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But there is still symmetry to be exploited in the inversion operators.  You can probably intuit that the 4x4 matrix only has 8 final states: inverted or not and rotated by (0,90,180,270).  It turns out that you can arrive at any possible state involving an inversion using any of the other inversion operators followed by one of the rotations.  So we only need to retain a single inversion operator!  So the final set of 8 orthogonal operations are:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(0,90,180,270,0|,90|,180|,270|)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If there is any symmetry in the matrix''s members then some of the resulting states may be degenerate.&lt;/p&gt;

&lt;p&gt;In terms of mapping possible states into a ground state, it makes sense to apply a set of successive deterministic rules to determine the ground state orientation.  I suggest finding the largest corner square and locating it in the lower right corner. If there are multiple candidates with equally large values in the corner square then use the next closest square as a tie breaker.  There are 16 squares, so you can eventually break all ties or declare degeneracy. There is one remaining &lt;code&gt;\&lt;/code&gt; operation that you can decide to apply in order to locate the larger of the two squares adjacent to the lower right corner at the bottom.  Again, you can use squares adjacent to these as tie breakers. &lt;/p&gt;
', NULL, NULL, '''/code'':35C,80C,116C,142C,237C,345C ''/p'':8C,23C,71C,97C,119C,226C,259C,383C ''/pre'':143C,238C ''/strong'':7C ''0'':31C,79C,122C,126C,130C,134C,138C,176C,229C,233C ''16'':328C ''180'':33C,124C,128C,132C,136C,140C,178C,231C,235C ''20'':115C ''270'':34C,125C,129C,133C,137C,141C,179C,232C,236C ''4'':113C ''4x4'':163C ''5'':114C ''8'':13C,167C,222C ''90'':32C,123C,127C,131C,135C,139C,177C,230C,234C ''adjac'':363C,377C ''allow'':92C ''appli'':68C,275C,352C ''arriv'':186C ''bottom'':371C ''break'':334C ''breaker'':325C,382C ''candid'':307C ''closest'':320C ''code'':30C,78C,112C,121C,228C,344C ''combin'':106C ''consid'':64C ''corner'':293C,302C,314C,368C ''decid'':350C ''declar'':338C ''degener'':258C ''degeneraci'':339C ''deriv'':102C ''determin'':283C ''determinist'':280C ''direct'':43C ''equal'':309C ''eventu'':333C ''exploit'':152C ''final'':168C,219C ''find'':290C ''five'':74C ''follow'':201C ''four'':26C,49C,66C ''fun'':3C ''ground'':269C,285C ''group'':5C ''intuit'':160C ''invers'':17C,75C,83C,96C,155C,193C,199C,215C ''invert'':170C ''involv'':191C ''larg'':310C ''larger'':358C ''largest'':292C ''locat'':296C,356C ''lower'':300C,366C ''make'':272C ''map'':264C ''matrix'':22C,164C,247C ''may'':256C ''member'':249C ''multipl'':306C ''need'':210C ''next'':319C ''one'':55C,69C,203C,342C ''oper'':18C,28C,59C,76C,104C,156C,200C,216C,224C,346C ''order'':354C ''orient'':287C ''orthogon'':223C ''p'':1C,9C,24C,72C,98C,144C,239C,260C ''possibl'':117C,189C,265C ''pre'':120C,227C ''probabl'':159C ''remain'':343C ''result'':254C ''retain'':212C ''revers'':42C ''right'':301C,367C ''rotat'':16C,27C,37C,39C,52C,58C,67C,87C,174C,206C ''rotation-invers'':15C ''rule'':281C ''sens'':273C ''set'':220C,277C ''singl'':95C,214C ''squar'':21C,294C,315C,321C,329C,362C,376C ''state'':118C,169C,190C,255C,266C,270C,286C ''still'':148C ''strong'':2C ''success'':51C,82C,279C ''suggest'':289C ''symmetri'':149C,244C ''term'':262C ''theori'':6C ''thus'':101C ''tie'':324C,336C,381C ''time'':70C ''turn'':181C ''two'':50C,81C,108C,361C ''uniqu'':14C ''use'':194C,317C,375C ''valu'':311C ''vector'':109C ''yield'':54C,85C,111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6648, 1, '2015-07-27 12:59:05.89', 4, 142, 'Contributions of each feature in classification?', '&lt;p&gt;I have some features and I am using Weka to classify my instances&lt;/p&gt;

&lt;p&gt;For example I have :&lt;/p&gt;

&lt;p&gt;Number of adj   number of adverb    number of punctuation in my feature set&lt;/p&gt;

&lt;p&gt;but I want to know the contribution of each feature in the feature set so what metrics or parameters are helpful to get the contribution of features?&lt;/p&gt;
', 'machine-learning  nlp', NULL, '''/p'':25C,31C,46C,75C ''adj'':35C ''adverb'':38C ''classif'':6A ''classifi'':22C ''contribut'':1A,54C,72C ''exampl'':28C ''featur'':4A,15C,44C,57C,60C,74C ''get'':70C ''help'':68C ''instanc'':24C ''know'':52C ''learn'':9B ''machin'':8B ''machine-learn'':7B ''metric'':64C ''nlp'':10B ''number'':33C,36C,39C ''p'':11C,26C,32C,47C ''paramet'':66C ''punctuat'':41C ''set'':45C,61C ''use'':19C ''want'':50C ''weka'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6639, 1, '2015-07-31 00:25:36.347', 3, 224, 'Extra output layer in a neural network', '&lt;p&gt;I''m working through a question from the online book: &lt;/p&gt;

&lt;p&gt;&lt;a href="http://neuralnetworksanddeeplearning.com/chap1.html" rel="nofollow"&gt;http://neuralnetworksanddeeplearning.com/chap1.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I can understand that if the additional output layer is of 5 output neurons, I could probably set bias at 0.5 and weight of 0.5 each for the previous layer. But the question now ask for a new layer of four output neurons - which is more than enough to represent 10 possible outputs at $2^{4}$. &lt;/p&gt;

&lt;p&gt;Can someone walk me through the steps involved in understanding and solving this problem?&lt;/p&gt;

&lt;p&gt;The exercise question:&lt;/p&gt;

&lt;p&gt;There is a way of determining the bitwise representation of a digit by adding an extra layer to the three-layer network above. The extra layer converts the output from the previous layer into a binary representation, as illustrated in the figure below. Find a set of weights and biases for the new output layer. Assume that the first 3 layers of neurons are such that the correct output in the third layer (i.e., the old output layer) has activation at least 0.99, and incorrect outputs have activation less than 0.01.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/OqQ6N.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/OqQ6N.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'neuralnetwork', NULL, '''/a'':223C ''/chap1.html'':26C ''/chap1.html&lt;/a&gt;&lt;/p&gt;'':31C ''/oqq6n.png'':210C,217C ''/p'':20C,89C,105C,110C,204C,224C ''0.01'':203C ''0.5'':53C,57C ''0.99'':195C ''10'':83C ''2'':87C ''3'':172C ''4'':88C ''5'':44C ''activ'':192C,200C ''ad'':125C ''addit'':39C ''alt'':218C ''ask'':67C ''assum'':168C ''bias'':51C,162C ''binari'':148C ''bitwis'':119C ''book'':19C ''convert'':139C ''correct'':180C ''could'':48C ''descript'':221C ''determin'':117C ''digit'':123C ''enough'':80C ''enter'':219C ''exercis'':108C ''extra'':1A,127C,137C ''figur'':154C ''find'':156C ''first'':171C ''four'':73C ''href'':23C,207C ''i.e'':186C ''i.stack.imgur.com'':209C,216C ''i.stack.imgur.com/oqq6n.png'':208C,215C ''illustr'':151C ''imag'':220C ''img'':213C ''incorrect'':197C ''involv'':98C ''layer'':3A,41C,62C,71C,128C,133C,138C,145C,167C,173C,185C,190C ''least'':194C ''less'':201C ''m'':11C ''network'':7A,134C ''neural'':6A ''neuralnetwork'':8B ''neuralnetworksanddeeplearning.com'':25C,30C ''neuralnetworksanddeeplearning.com/chap1.html'':24C ''neuralnetworksanddeeplearning.com/chap1.html&lt;/a&gt;&lt;/p&gt;'':29C ''neuron'':46C,75C,175C ''new'':70C,165C ''nofollow'':28C,212C ''old'':188C ''onlin'':18C ''output'':2A,40C,45C,74C,85C,141C,166C,181C,189C,198C ''p'':9C,21C,32C,90C,106C,111C,205C ''possibl'':84C ''previous'':61C,144C ''probabl'':49C ''problem'':104C ''question'':15C,65C,109C ''rel'':27C,211C ''repres'':82C ''represent'':120C,149C ''set'':50C,158C ''solv'':102C ''someon'':92C ''src'':214C ''step'':97C ''third'':184C ''three'':132C ''three-lay'':131C ''understand'':35C,100C ''walk'':93C ''way'':115C ''weight'':55C,160C ''work'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6640, 2, '2015-07-31 04:39:50.677', 1, NULL, NULL, '&lt;p&gt;While I am not aware of software &lt;em&gt;specifically&lt;/em&gt; for &lt;strong&gt;tuning&lt;/strong&gt; trained word embeddings, perhaps the following &lt;em&gt;open source software&lt;/em&gt; might be helpful, if you can figure out what parts can be modified for the fine-tuning part (just an idea off the top of my head - I''m not too familiar with the details):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://nlp.stanford.edu/projects/glove" rel="nofollow"&gt;GloVe: Global Vectors for Word Representation&lt;/a&gt; (part of &lt;em&gt;Stanford NLP Group&lt;/em&gt; software);&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://ml.nec-labs.com/senna" rel="nofollow"&gt;SENNA&lt;/a&gt; (its use for word embeddings is mentioned in &lt;a href="http://lebret.ch/words" rel="nofollow"&gt;this blog post&lt;/a&gt;);&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://github.com/vaswani/WORD_OBSERVATION_RBM_NCE" rel="nofollow"&gt;Code on GitHub&lt;/a&gt; for Deep Learning-based word embeddings training;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://nlg.isi.edu/software/nplm" rel="nofollow"&gt;Neural Probabilistic Language Model Toolkit&lt;/a&gt;, mentioned above (also Deep Learning-based).&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':80C,101C,120C,135C,160C ''/em'':11C,26C,87C ''/li'':90C,122C,145C,169C ''/p'':63C,89C,121C,144C,168C ''/projects/glove'':71C ''/senna'':97C ''/software/nplm'':152C ''/strong'':15C ''/ul'':170C ''/vaswani/word_observation_rbm_nce'':129C ''/words'':114C ''also'':163C ''awar'':6C ''base'':140C,167C ''blog'':118C ''code'':132C ''deep'':137C,164C ''detail'':62C ''em'':9C,22C,83C ''embed'':18C,106C,142C ''familiar'':59C ''figur'':33C ''fine'':43C ''fine-tun'':42C ''follow'':21C ''github'':134C ''github.com'':128C ''github.com/vaswani/word_observation_rbm_nce'':127C ''global'':75C ''glove'':74C ''group'':86C ''head'':54C ''help'':29C ''href'':68C,94C,111C,126C,149C ''idea'':48C ''languag'':157C ''learn'':139C,166C ''learning-bas'':138C,165C ''lebret.ch'':113C ''lebret.ch/words'':112C ''li'':65C,91C,123C,146C ''m'':56C ''mention'':108C,161C ''might'':27C ''ml.nec-labs.com'':96C ''ml.nec-labs.com/senna'':95C ''model'':158C ''modifi'':39C ''neural'':155C ''nlg.isi.edu'':151C ''nlg.isi.edu/software/nplm'':150C ''nlp'':85C ''nlp.stanford.edu'':70C ''nlp.stanford.edu/projects/glove'':69C ''nofollow'':73C,99C,116C,131C,154C ''open'':23C ''p'':1C,66C,92C,124C,147C ''part'':36C,45C,81C ''perhap'':19C ''post'':119C ''probabilist'':156C ''rel'':72C,98C,115C,130C,153C ''represent'':79C ''senna'':100C ''softwar'':8C,25C,88C ''sourc'':24C ''specif'':10C ''stanford'':84C ''strong'':13C ''toolkit'':159C ''top'':51C ''train'':16C,143C ''tune'':14C,44C ''ul'':64C ''use'':103C ''vector'':76C ''word'':17C,78C,105C,141C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6643, 1, '2015-07-31 08:21:49.083', 1, 840, 'Do I need to buy a NVIDIA graphic card to run deep learning algorithm?', '&lt;p&gt;I am new in Deep learning. I am running a MacBook Pro yosemite (upgraded from Snowleopard), I don''t have a CUDA-enabled card GPU, and running the code on the CPU is extremely slow. I heart that I can buy some instances on AWS, but it seems that they don''t support Mac OS.&lt;/p&gt;

&lt;p&gt;My question is, to continue with the Deep learning, do I need to purchase a graphic card? Or is there other solutions? I don''t want to spend too much on this... &lt;/p&gt;

&lt;p&gt;Thank you very much!&lt;/p&gt;
', 'deep-learning', NULL, '''/p'':75C,109C,115C ''algorithm'':14A ''aw'':64C ''buy'':5A,60C ''card'':9A,43C,93C ''code'':48C ''continu'':81C ''cpu'':51C ''cuda'':41C ''cuda-en'':40C ''deep'':12A,16B,23C,84C ''deep-learn'':15B ''enabl'':42C ''extrem'':53C ''gpu'':44C ''graphic'':8A,92C ''heart'':56C ''instanc'':62C ''learn'':13A,17B,24C,85C ''mac'':73C ''macbook'':29C ''much'':106C,114C ''need'':3A,88C ''new'':21C ''nvidia'':7A ''os'':74C ''p'':18C,76C,110C ''pro'':30C ''purchas'':90C ''question'':78C ''run'':11A,27C,46C ''seem'':67C ''slow'':54C ''snowleopard'':34C ''solut'':98C ''spend'':104C ''support'':72C ''thank'':111C ''upgrad'':32C ''want'':102C ''yosemit'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6644, 2, '2015-07-31 09:26:34.927', 5, NULL, NULL, '&lt;p&gt;The question is asking you to make the following mapping between old representation and new representation:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Represent    Old                     New
0            1 0 0 0 0 0 0 0 0 0     0 0 0 0 
1            0 1 0 0 0 0 0 0 0 0     0 0 0 1 
2            0 0 1 0 0 0 0 0 0 0     0 0 1 0 

3            0 0 0 1 0 0 0 0 0 0     0 0 1 1 
4            0 0 0 0 1 0 0 0 0 0     0 1 0 0 
5            0 0 0 0 0 1 0 0 0 0     0 1 0 1 

6            0 0 0 0 0 0 1 0 0 0     0 1 1 0 
7            0 0 0 0 0 0 0 1 0 0     0 1 1 1 
8            0 0 0 0 0 0 0 0 1 0     1 0 0 0 

9            0 0 0 0 0 0 0 0 0 1     1 0 0 1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Because the old output layer has a simple form, this is quite easy to achieve. Each output neuron should have a positive weight between itself and output neurons which should be on to represent it, and a negative weight between itself and output neurons that should be off. The values should combine to be large enough to cleanly switch on or off, so I would use largish weights, such as +10 and -10.&lt;/p&gt;

&lt;p&gt;If you have sigmoid activations here, the bias is not that relevant. You just want to simply saturate each neuron towards on or off. The question has allowed you to assume very clear signals in the old output layer.&lt;/p&gt;

&lt;p&gt;So taking example of representing a 3 and using zero-indexing for the neurons in the order I am showing them (these options are not set in the question), I might have weights going from activation of old output $i=3$, $A_3^{Old}$ to logit of new outputs $Z_j^{New}$, where $Z_j^{New} = \Sigma_{i=0}^{i=9} W_{ij} * A_i^{Old}$ as follows:&lt;/p&gt;

&lt;p&gt;$$W_{3,0} = -10$$
$$W_{3,1} = -10$$
$$W_{3,2} = +10$$
$$W_{3,3} = +10$$&lt;/p&gt;

&lt;p&gt;This should clearly produce close to &lt;code&gt;0 0 1 1&lt;/code&gt; output when only the old output layer''s neuron representing a "3" is active. In the question, you can assume 0.99 activation of one neuron and &amp;lt;0.01 for competing ones in the old layer. So, if you use the same magnitude of weights throughout, then relatively small values coming from +-0.1 (0.01 * 10) from the other old layer activation values will not seriously affect the +-9.9 value, and the outputs in the new layer will be saturated at very close to either 0 or 1.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6645, 1, '2015-07-31 10:54:37.76', 1, 236, 'Error::Type of predictors in new data do not match that of the training data', '&lt;p&gt;I am building a classification model using &lt;code&gt;randomForest&lt;/code&gt;. When trying to predict I get the below error&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Type of predictors in new data do not match that of the training data&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I made sure that the testing and training data has same levels.&lt;/p&gt;

&lt;p&gt;I also included &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;levels(test_var) &amp;lt;- levels(train_var)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;to make sure that the levels are matching.&lt;/p&gt;

&lt;p&gt;But still I end up getting this error, is there anything else that I should look for?&lt;/p&gt;

&lt;p&gt;&lt;code&gt;**EDITED ON 3rd Aug 2015**&lt;/code&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Here is the structure of the training dataset&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/xnCH6.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/xnCH6.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Structure of the test dataset&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/gJQcP.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/gJQcP.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Sapply training data&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/OhRLR.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/OhRLR.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Sapply testing data&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/evf0i.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/evf0i.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;In order to make sure that the levels are matching between the training and test datasets, I wrote this loop to see if there are any differences exist between the datasets&lt;/p&gt;
&lt;/blockquote&gt;

&lt;pre&gt;&lt;code&gt;for(i in 1:28) 

 {
   if(is.factor(testing[,i]))
    {

    print(names((testing[i])))
    difference_in_test = setdiff(levels(testing[,i]), levels(training_data[,i]))
      print(difference_in_test)

    }
 }
&lt;/code&gt;&lt;/pre&gt;

&lt;blockquote&gt;
  &lt;p&gt;Results of the above For loop which shows that there are no differences exist between the levels.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;pre&gt;&lt;code&gt;[1] "VAR1"
character(0)
[1] "VAR2"
character(0)
[1] "VAR3"
character(0)
[1] "VAR4"
character(0)
[1] "VAR5"
character(0)
[1] "VAR7"
&lt;/code&gt;&lt;/pre&gt;

&lt;blockquote&gt;
  &lt;p&gt;I still continue to get the error as mentioned below:&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/7Dyv0.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/7Dyv0.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Edit on 4th Aug&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I started getting the above error only after including a filter in my dataset like this:&lt;/p&gt;

&lt;p&gt;training_data &amp;lt;- subset(training_data, gender !="F")&lt;/p&gt;
', 'machine-learning  data-mining  r  classification  random-forest', NULL, '''/7dyv0.png'':382C,389C ''/a'':164C,193C,220C,247C,395C ''/blockquote'':65C,145C,174C,201C,228C,283C,336C,376C,404C ''/code'':37C,94C,132C,314C,361C ''/evf0i.png'':234C,241C ''/gjqcp.png'':180C,187C ''/ohrlr.png'':207C,214C ''/p'':47C,64C,79C,84C,105C,124C,133C,144C,165C,173C,194C,200C,221C,227C,248C,282C,335C,375C,396C,403C,422C,432C ''/pre'':95C,315C,362C ''/xnch6.png'':151C,158C ''0'':342C,346C,350C,354C,358C ''1'':289C,339C,343C,347C,351C,355C,359C ''2015'':131C ''28'':290C ''3rd'':129C ''4th'':401C ''also'':82C ''alt'':159C,188C,215C,242C,390C ''anyth'':117C ''aug'':130C,402C ''blockquot'':48C,134C,166C,195C,222C,249C,316C,363C,397C ''build'':30C ''charact'':341C,345C,349C,353C,357C ''classif'':23B,32C ''code'':35C,86C,126C,285C,338C ''continu'':367C ''data'':7A,15A,20B,55C,63C,75C,199C,226C,308C,425C,429C ''data-min'':19B ''dataset'':143C,172C,266C,281C,419C ''descript'':162C,191C,218C,245C,393C ''differ'':277C,299C,311C,330C ''edit'':127C,399C ''els'':118C ''end'':110C ''enter'':160C,189C,216C,243C,391C ''error'':1A,46C,114C,371C,411C ''exist'':278C,331C ''f'':431C ''filter'':416C ''forest'':26B ''gender'':430C ''get'':43C,112C,369C,408C ''href'':148C,177C,204C,231C,379C ''i.stack.imgur.com'':150C,157C,179C,186C,206C,213C,233C,240C,381C,388C ''i.stack.imgur.com/7dyv0.png'':380C,387C ''i.stack.imgur.com/evf0i.png'':232C,239C ''i.stack.imgur.com/gjqcp.png'':178C,185C ''i.stack.imgur.com/ohrlr.png'':205C,212C ''i.stack.imgur.com/xnch6.png'':149C,156C ''imag'':161C,190C,217C,244C,392C ''img'':154C,183C,210C,237C,385C ''includ'':83C,414C ''is.factor'':292C ''learn'':18B ''level'':78C,87C,91C,102C,258C,303C,306C,334C ''like'':420C ''look'':122C ''loop'':270C,323C ''lt'':90C,426C ''machin'':17B ''machine-learn'':16B ''made'':68C ''make'':98C,254C ''match'':10A,58C,104C,260C ''mention'':373C ''mine'':21B ''model'':33C ''name'':296C ''new'':6A,54C ''nofollow'':153C,182C,209C,236C,384C ''order'':252C ''p'':27C,49C,66C,80C,96C,106C,125C,135C,146C,167C,175C,196C,202C,223C,229C,250C,317C,364C,377C,398C,405C,423C ''pre'':85C,284C,337C ''predict'':41C ''predictor'':4A,52C ''print'':295C,310C ''r'':22B ''random'':25B ''random-forest'':24B ''randomforest'':36C ''rel'':152C,181C,208C,235C,383C ''result'':318C ''sappli'':197C,224C ''see'':272C ''setdiff'':302C ''show'':325C ''src'':155C,184C,211C,238C,386C ''start'':407C ''still'':108C,366C ''structur'':139C,168C ''subset'':427C ''sure'':69C,99C,255C ''test'':72C,88C,171C,225C,265C,293C,297C,301C,304C,313C ''train'':14A,62C,74C,92C,142C,198C,263C,307C,424C,428C ''tri'':39C ''type'':2A,50C ''use'':34C ''var'':89C,93C ''var1'':340C ''var2'':344C ''var3'':348C ''var4'':352C ''var5'':356C ''var7'':360C ''wrote'':268C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6649, 1, '2015-07-31 17:20:10.873', 2, 107, 'Testing bimodality of data', '&lt;p&gt;I am trying to see if my data is multimodal (in fact, I am more interested in bimodality of the data). I performed dip test and it does evidence against unmodal data. However, I want to see, in particular, if it is bimodal. I believe silver man''s test can be used. However, I couldn''t find the implementation of it in either r or in python. (The one in R is old and not working with the current version of R). 
Also, assuming that I have a bimodal data and that I am able to get the two components (using mixtools in R), how do I figure out how to find the point of intersection of the two components. For example, here is the histogram (overlaid with its density estimation) of the entire data. &lt;a href="http://i.stack.imgur.com/FO43X.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/FO43X.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Here are the two components:&lt;a href="http://i.stack.imgur.com/gFoDm.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/gFoDm.png" alt="enter image description here"&gt;&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;I want to get the the x value where the curves intersected. I could have uploaded the data, but the length of the vector is rather long. Any general thought and idea is welcome including the R and/or Python packages are welcome.&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'machine-learning  r  python  statistics', NULL, '''/a'':164C,189C ''/fo43x.png'':151C,158C ''/gfodm.png'':176C,183C ''/p'':165C,190C,234C,237C ''abl'':106C ''also'':94C ''alt'':159C,184C ''and/or'':229C ''assum'':95C ''believ'':56C ''bimod'':2A,29C,54C,100C ''compon'':111C,131C,171C ''could'':205C ''couldn'':66C ''current'':90C ''curv'':202C ''data'':4A,19C,32C,43C,101C,146C,209C ''densiti'':141C ''descript'':162C,187C ''dip'':35C ''either'':74C ''enter'':160C,185C ''entir'':145C ''estim'':142C ''evid'':40C ''exampl'':133C ''fact'':23C ''figur'':119C ''find'':68C,123C ''general'':220C ''get'':108C,195C ''histogram'':137C ''howev'':44C,64C ''href'':148C,173C ''i.stack.imgur.com'':150C,157C,175C,182C ''i.stack.imgur.com/fo43x.png'':149C,156C ''i.stack.imgur.com/gfodm.png'':174C,181C ''idea'':223C ''imag'':161C,186C ''img'':154C,179C ''implement'':70C ''includ'':226C ''interest'':27C ''intersect'':127C,203C ''learn'':7B ''length'':212C ''long'':218C ''machin'':6B ''machine-learn'':5B ''man'':58C ''mixtool'':113C ''multimod'':21C ''nofollow'':153C,178C ''old'':84C ''one'':80C ''overlaid'':138C ''p'':11C,166C,191C,235C ''packag'':231C ''particular'':50C ''perform'':34C ''point'':125C ''python'':9B,78C,230C ''r'':8B,75C,82C,93C,115C,228C ''rather'':217C ''rel'':152C,177C ''see'':16C,48C ''silver'':57C ''src'':155C,180C ''statist'':10B ''test'':1A,36C,60C ''thank'':236C ''thought'':221C ''tri'':14C ''two'':110C,130C,170C ''unmod'':42C ''upload'':207C ''use'':63C,112C ''valu'':199C ''vector'':215C ''version'':91C ''want'':46C,193C ''welcom'':225C,233C ''work'':87C ''x'':198C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6650, 2, '2015-07-31 18:58:25.117', 2, NULL, NULL, '&lt;p&gt;I would recommend familiarizing yourself with &lt;a href="https://www.youtube.com/watch?v=Py0VInjRSBE" rel="nofollow"&gt;AWS spot instances&lt;/a&gt;. It''s the most practical solution I can think of for your problem, and &lt;a href="https://www.youtube.com/watch?v=NdR03RpCpac" rel="nofollow"&gt;it works your computer too&lt;/a&gt;. So, no you don''t have to &lt;em&gt;buy&lt;/em&gt; an Nvidia card, but as of today you will want to use one since almost all the solutions rely on them.&lt;/p&gt;
', NULL, NULL, '''/a'':18C,45C ''/em'':55C ''/p'':77C ''/watch?v=ndr03rpcpac'':37C ''/watch?v=py0vinjrsbe'':12C ''almost'':70C ''aw'':15C ''buy'':54C ''card'':58C ''comput'':43C ''em'':53C ''familiar'':5C ''href'':9C,34C ''instanc'':17C ''nofollow'':14C,39C ''nvidia'':57C ''one'':68C ''p'':1C ''practic'':23C ''problem'':31C ''recommend'':4C ''rel'':13C,38C ''reli'':74C ''sinc'':69C ''solut'':24C,73C ''spot'':16C ''think'':27C ''today'':62C ''use'':67C ''want'':65C ''work'':41C ''would'':3C ''www.youtube.com'':11C,36C ''www.youtube.com/watch?v=ndr03rpcpac'':35C ''www.youtube.com/watch?v=py0vinjrsbe'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6672, 2, '2015-08-04 01:14:53.83', 1, NULL, NULL, '&lt;p&gt;It sounds like you are making this more complicated than it is with trying to predict spend at the cohort level. It is best to use cohorts for understanding "why" something occurred and time series analysis for understanding "what" will occur. &lt;/p&gt;

&lt;p&gt;The Forecast package in R seems to be solution to your problem. Just forecast Monthly spend and be done with it! &lt;/p&gt;
', NULL, NULL, '''/p'':43C,66C ''analysi'':37C ''best'':25C ''cohort'':21C,28C ''complic'':10C ''done'':63C ''forecast'':46C,58C ''level'':22C ''like'':4C ''make'':7C ''month'':59C ''occur'':33C,42C ''p'':1C,44C ''packag'':47C ''predict'':17C ''problem'':56C ''r'':49C ''seem'':50C ''seri'':36C ''solut'':53C ''someth'':32C ''sound'':3C ''spend'':18C,60C ''time'':35C ''tri'':15C ''understand'':30C,39C ''use'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6651, 2, '2015-07-31 19:49:03.447', 3, NULL, NULL, '&lt;p&gt;This is called feature ranking, which is closely related to &lt;a href="https://en.wikipedia.org/wiki/Feature_selection" rel="nofollow"&gt;feature selection&lt;/a&gt;.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;feature ranking = determining the importance of any individual feature&lt;/li&gt;
&lt;li&gt;feature selection = selecting a subset of relevant features for use in model construction. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So if you are able to ranked features, you can use it to select features, and if you can select a subset of useful features, you''ve done at least a partial ranking by removing the useless ones. &lt;/p&gt;

&lt;p&gt;This &lt;a href="https://en.wikipedia.org/wiki/Feature_selection" rel="nofollow"&gt;Wikipedia page&lt;/a&gt; and this &lt;a href="https://www.quora.com/How-do-I-perform-feature-selection?share=1" rel="nofollow"&gt;Quora post&lt;/a&gt; should give some ideas. The distinction filter methods vs. wrapper based methods vs. embedded methods is the most common one.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;One straightforward approximate way is to use &lt;a href="http://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html" rel="nofollow"&gt;feature importance with forests of trees&lt;/a&gt;:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/r7Io5.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/r7Io5.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Other common ways:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html" rel="nofollow"&gt;recursive feature elimination&lt;/a&gt;.&lt;/li&gt;
&lt;li&gt;stepwise regression (or &lt;a href="http://scikit-learn.org/stable/modules/linear_model.html#lars-lasso" rel="nofollow"&gt;LARS Lasso&lt;/a&gt;).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If you use scikit-learn, check out &lt;a href="http://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection" rel="nofollow"&gt;module-sklearn.feature_selection&lt;/a&gt;. I''d guess Weka has some similar functions.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6653, 2, '2015-07-31 21:03:23.593', 2, NULL, NULL, '&lt;p&gt;Normalise using your training data statistics. Save the values used (e.g. mean and sd per feature), treating them as part of your model. Once you have used these values to transform input, they become fixed translate/scale factors in the model. &lt;/p&gt;

&lt;p&gt;Use the same values to normalise test data or new inputs as required. You do not need to calculate new normalisation constants for new data. In fact doing so will most likely reduce the effectiveness of your model.&lt;/p&gt;

&lt;p&gt;The same principle applies to interpreting output values if you need to scale those into range that your model produces. Scale according to your training data.&lt;/p&gt;
', NULL, NULL, '''/p'':42C,82C,110C ''accord'':105C ''appli'':87C ''becom'':35C ''calcul'':62C ''constant'':65C ''data'':6C,51C,68C,109C ''e.g'':12C ''effect'':78C ''fact'':70C ''factor'':38C ''featur'':17C ''fix'':36C ''input'':33C,54C ''interpret'':89C ''like'':75C ''mean'':13C ''model'':24C,41C,81C,102C ''need'':60C,94C ''new'':53C,63C,67C ''normalis'':2C,49C,64C ''output'':90C ''p'':1C,43C,83C ''part'':21C ''per'':16C ''principl'':86C ''produc'':103C ''rang'':99C ''reduc'':76C ''requir'':56C ''save'':8C ''scale'':96C,104C ''sd'':15C ''statist'':7C ''test'':50C ''train'':5C,108C ''transform'':32C ''translate/scale'':37C ''treat'':18C ''use'':3C,11C,28C,44C ''valu'':10C,30C,47C,91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6680, 1, '2015-08-04 12:36:20.967', 1, 57, 'Sequence of numbers as single feature', '&lt;p&gt;Is it possible to use a sequence of numbers as one feature?&lt;/p&gt;

&lt;p&gt;For example, using libsvm data format:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;label&amp;gt; &amp;lt;index1&amp;gt;:&amp;lt;value1&amp;gt; &amp;lt;index2&amp;gt;:&amp;lt;value2&amp;gt;

+1 1:123.02 2:1.23 3:5.45,2.22,6.76
+1 1:120.12 2:2.23 3:4.98,2.55,4.45
-1 1:199.99 2:2.13 3:4.98,2.22,6.98
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is there any special machine learning algorithm for this kind of data? Thanks in advance:) &lt;/p&gt;
', 'machine-learning  dataset  algorithms', NULL, '''+1'':51C,60C ''-1'':69C ''/code'':78C ''/p'':25C,33C,96C ''/pre'':79C ''1'':52C,61C,70C ''1.23'':55C ''120.12'':62C ''123.02'':53C ''199.99'':71C ''2'':54C,63C,72C ''2.13'':73C ''2.22'':58C,76C ''2.23'':64C ''2.55'':67C ''3'':56C,65C,74C ''4.45'':68C ''4.98'':66C,75C ''5.45'':57C ''6.76'':59C ''6.98'':77C ''advanc'':95C ''algorithm'':11B,87C ''code'':35C ''data'':31C,92C ''dataset'':10B ''exampl'':28C ''featur'':6A,24C ''format'':32C ''gt'':38C,41C,44C,47C,50C ''index1'':40C ''index2'':46C ''kind'':90C ''label'':37C ''learn'':9B,86C ''libsvm'':30C ''lt'':36C,39C,42C,45C,48C ''machin'':8B,85C ''machine-learn'':7B ''number'':3A,21C ''one'':23C ''p'':12C,26C,80C ''possibl'':15C ''pre'':34C ''sequenc'':1A,19C ''singl'':5A ''special'':84C ''thank'':93C ''use'':17C,29C ''value1'':43C ''value2'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6655, 2, '2015-08-01 17:47:09.34', 3, NULL, NULL, '&lt;p&gt;You should make the difference between the following concepts:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Encryption" rel="nofollow"&gt;encryption&lt;/a&gt; = encoding information in such a way that only authorized parties can read it.&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Data_anonymization" rel="nofollow"&gt;data anonymization&lt;/a&gt; = removing personally identifiable information from data sets, so that the people whom the data describe remain anonymous.&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/De-identification" rel="nofollow"&gt;de-identification&lt;/a&gt; = preventing a personâs identity from being connected with information, while preserving identifying information which could only be re-linked by a trusted party in certain situations (unlike data anonymization which aims to be irreversible).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Your choice depends on your use case.&lt;/p&gt;
', NULL, NULL, '''/a'':22C,47C,77C ''/li'':36C,65C,114C ''/p'':11C,124C ''/ul'':115C ''/wiki/data_anonymization'':42C ''/wiki/de-identification'':71C ''/wiki/encryption'':18C ''aim'':110C ''anonym'':46C,64C,108C ''author'':31C ''case'':123C ''certain'':104C ''choic'':118C ''concept'':10C ''connect'':85C ''could'':93C ''data'':45C,53C,61C,107C ''de'':75C ''de-identif'':74C ''depend'':119C ''describ'':62C ''differ'':6C ''en.wikipedia.org'':17C,41C,70C ''en.wikipedia.org/wiki/data_anonymization'':40C ''en.wikipedia.org/wiki/de-identification'':69C ''en.wikipedia.org/wiki/encryption'':16C ''encod'':23C ''encrypt'':21C ''follow'':9C ''href'':15C,39C,68C ''ident'':82C ''identif'':76C ''identifi'':50C,90C ''inform'':24C,51C,87C,91C ''irrevers'':113C ''li'':13C,37C,66C ''link'':98C ''make'':4C ''nofollow'':20C,44C,73C ''p'':1C,116C ''parti'':32C,102C ''peopl'':58C ''person'':49C ''personâ'':80C ''preserv'':89C ''prevent'':78C ''re'':97C ''re-link'':96C ''read'':34C ''rel'':19C,43C,72C ''remain'':63C ''remov'':48C ''set'':54C ''situat'':105C ''trust'':101C ''ul'':12C ''unlik'':106C ''use'':122C ''way'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6659, 1, '2015-08-02 09:22:41.89', 2, 35, 'What proxies could be used to assess economic value of Stackoverflow for its users?', '&lt;p&gt;I''m intrigued by the open data provided by StackExchange, and have been running some really interesting queries on the &lt;em&gt;data.stackexchange.com&lt;/em&gt; page (using the Stackoverflow dataset).&lt;/p&gt;

&lt;p&gt;In particular, I would like to dive deep on this claim taken from the &lt;a href="http://stackoverflow.com/research/developer-survey-2015"&gt;Stackoverflow Developer Survey 2015 summary page&lt;/a&gt;: &lt;a href="http://i.stack.imgur.com/oqrtI.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/oqrtI.png" alt="Taken from the recap of the Stackoverflow developer survey page"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In other words: what is the "value" of Stackoverflow? Not in terms of its own economic success or funding amount, rather how it affects its users, for example accelerating a pay increase for active users.&lt;/p&gt;

&lt;p&gt;My ideal solution would be to correlate the activity of its users to some economic independent variable such as salary. Although the data on activity exists on Stackoverflow servers, this Salary data is not available (the downloadable StackOverflow Developer Survey dataset is anonymized).&lt;/p&gt;

&lt;p&gt;What other proxies/datasets could be used to conduct such a study? I would use github commits but it is more oriented towards productivity, and there already is &lt;a href="https://bvasiles.github.io/papers/socialcom13.pdf" rel="nofollow"&gt;a great paper on this topic&lt;/a&gt;.&lt;/p&gt;
', 'data', NULL, '''/a'':72C,96C,221C ''/em'':39C ''/oqrti.png'':77C,84C ''/p'':45C,97C,134C,179C,222C ''/papers/socialcom13.pdf'':212C ''/research/developer-survey-2015'':65C ''2015'':69C ''acceler'':127C ''activ'':132C,144C,160C ''affect'':122C ''alreadi'':206C ''alt'':85C ''although'':156C ''amount'':118C ''anonym'':178C ''assess'':7A ''avail'':170C ''bvasiles.github.io'':211C ''bvasiles.github.io/papers/socialcom13.pdf'':210C ''claim'':57C ''commit'':196C ''conduct'':188C ''correl'':142C ''could'':3A,184C ''data'':15B,23C,158C,167C ''data.stackexchange.com'':38C ''dataset'':44C,176C ''deep'':54C ''develop'':67C,93C,174C ''dive'':53C ''download'':172C ''econom'':8A,114C,150C ''em'':37C ''exampl'':126C ''exist'':161C ''fund'':117C ''github'':195C ''great'':216C ''href'':62C,74C,209C ''i.stack.imgur.com'':76C,83C ''i.stack.imgur.com/oqrti.png'':75C,82C ''ideal'':137C ''img'':80C ''increas'':130C ''independ'':151C ''interest'':33C ''intrigu'':19C ''like'':51C ''m'':18C ''nofollow'':79C,214C ''open'':22C ''orient'':201C ''p'':16C,46C,98C,135C,180C ''page'':40C,71C,95C ''paper'':217C ''particular'':48C ''pay'':129C ''product'':203C ''provid'':24C ''proxi'':2A ''proxies/datasets'':183C ''queri'':34C ''rather'':119C ''realli'':32C ''recap'':89C ''rel'':78C,213C ''run'':30C ''salari'':155C,166C ''server'':164C ''solut'':138C ''src'':81C ''stackexchang'':26C ''stackoverflow'':11A,43C,66C,92C,107C,163C,173C ''stackoverflow.com'':64C ''stackoverflow.com/research/developer-survey-2015'':63C ''studi'':191C ''success'':115C ''summari'':70C ''survey'':68C,94C,175C ''taken'':58C,86C ''term'':110C ''topic'':220C ''toward'':202C ''use'':5A,41C,186C,194C ''user'':14A,124C,133C,147C ''valu'':9A,105C ''variabl'':152C ''word'':101C ''would'':50C,139C,193C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6670, 2, '2015-08-03 22:08:42.203', 1, NULL, NULL, '&lt;p&gt;&lt;a href="http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using_cluster_computing.html" rel="nofollow"&gt;AWS GPU instances&lt;/a&gt; are an option, if you want to do CUDA development. If you don''t want to leverage the cloud, you can look into the &lt;a href="https://developer.nvidia.com/jetson-tk1" rel="nofollow"&gt;Nvidia Jetson TK1&lt;/a&gt; development kits.  They are about 200 dollars (in July 2015), have 192 CUDA cores, a quad-core ARM processor, and 2GB of RAM.  &lt;/p&gt;

&lt;p&gt;Conversely, the same amount of money could buy you a 640 CUDA core GeForce GTX 750 Ti, or maybe a 1024 CUDA core GTX 960.  A GT 720 with 1GB RAM and 192 cores could be had for 45 dollars (in July 2015).&lt;/p&gt;

&lt;p&gt;You don''t &lt;em&gt;have&lt;/em&gt; to use NVidia GPUs with deep learning.  GPUs will increase the speed dramatically, though.   There is very little support for non-NVidia GPUs with common deep learning toolkits.  &lt;/p&gt;
', NULL, NULL, '''/a'':12C,47C ''/awsec2/latest/userguide/using_cluster_computing.html'':6C ''/em'':124C ''/jetson-tk1'':41C ''/p'':72C,117C,154C ''1024'':94C ''192'':59C,106C ''1gb'':103C ''200'':53C ''2015'':57C,116C ''2gb'':69C ''45'':112C ''640'':84C ''720'':101C ''750'':89C ''960'':98C ''amount'':77C ''arm'':66C ''aw'':9C ''buy'':81C ''cloud'':31C ''common'':150C ''convers'':74C ''core'':61C,65C,86C,96C,107C ''could'':80C,108C ''cuda'':21C,60C,85C,95C ''deep'':130C,151C ''develop'':22C,48C ''developer.nvidia.com'':40C ''developer.nvidia.com/jetson-tk1'':39C ''docs.aws.amazon.com'':5C ''docs.aws.amazon.com/awsec2/latest/userguide/using_cluster_computing.html'':4C ''dollar'':54C,113C ''dramat'':137C ''em'':122C ''geforc'':87C ''gpu'':10C ''gpus'':128C,132C,148C ''gt'':100C ''gtx'':88C,97C ''href'':3C,38C ''increas'':134C ''instanc'':11C ''jetson'':45C ''juli'':56C,115C ''kit'':49C ''learn'':131C,152C ''leverag'':29C ''littl'':142C ''look'':34C ''mayb'':92C ''money'':79C ''nofollow'':8C,43C ''non'':146C ''non-nvidia'':145C ''nvidia'':44C,127C,147C ''option'':15C ''p'':1C,73C,118C ''processor'':67C ''quad'':64C ''quad-cor'':63C ''ram'':71C,104C ''rel'':7C,42C ''speed'':136C ''support'':143C ''though'':138C ''ti'':90C ''tk1'':46C ''toolkit'':153C ''use'':126C ''want'':18C,27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6675, 1, '2015-08-04 06:57:33.607', 0, 71, 'identifying input sequence using neural network', '&lt;p&gt;I fount simple neural scenario sayinh : network having 3 layers, 3 inputs and 2 outputs. It should be trained to recognize a simple pattern - if inputs are correspondingly 6.0,7.0 and 8.0 then outputs should be 3.0 and 4.0, otherwise outputs should be 23.0&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/84WUh.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/84WUh.png" alt="3 layered neural network"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;What I understand is, hidden layer should Check input pattern in sequence with simple if else condition. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;if in1 == 6 and in2 == 7 and in3 ==8:
    out1, out2 = 3, 4
else
    out = 5
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Do I understand the problem correctly or something I am missing?&lt;/p&gt;
', 'machine-learning  classification  neuralnetwork', NULL, '''/84wuh.png'':63C,70C ''/a'':76C ''/code'':115C ''/p'':57C,77C,96C,129C ''/pre'':116C ''2'':26C ''23.0'':56C ''3'':21C,23C,72C,110C ''3.0'':49C ''4'':111C ''4.0'':51C ''5'':114C ''6'':101C ''6.0'':41C ''7'':104C ''7.0'':42C ''8'':107C ''8.0'':44C ''alt'':71C ''check'':86C ''classif'':10B ''code'':98C ''condit'':95C ''correct'':123C ''correspond'':40C ''els'':94C,112C ''fount'':14C ''hidden'':83C ''href'':60C ''i.stack.imgur.com'':62C,69C ''i.stack.imgur.com/84wuh.png'':61C,68C ''identifi'':1A ''img'':66C ''in1'':100C ''in2'':103C ''in3'':106C ''input'':2A,24C,38C,87C ''layer'':22C,73C,84C ''learn'':9B ''machin'':8B ''machine-learn'':7B ''miss'':128C ''network'':6A,19C,75C ''neural'':5A,16C,74C ''neuralnetwork'':11B ''nofollow'':65C ''otherwis'':52C ''out1'':108C ''out2'':109C ''output'':27C,46C,53C ''p'':12C,58C,78C,117C ''pattern'':36C,88C ''pre'':97C ''problem'':122C ''recogn'':33C ''rel'':64C ''sayinh'':18C ''scenario'':17C ''sequenc'':3A,90C ''simpl'':15C,35C,92C ''someth'':125C ''src'':67C ''train'':31C ''understand'':81C,120C ''use'':4A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6660, 1, '2015-08-02 11:48:27.367', 1, 122, 'User-based nearest neighbour implementation in R?', '&lt;p&gt;I am just starting to learn to use R and am not sure how to find the best packages yet. I am looking for a package that will allow me to calculate user-based nearest neighbours as an input for a recommendation.&lt;/p&gt;

&lt;p&gt;My original data is approximately as below:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;User    Item    Rating
Anna    Apple   2
Bob Apple   4
Carlos  Apple   1
Ethan   Apple   1
Gene    Apple   2
Anna    Banana  4
Dana    Banana  3
Ethan   Banana  5
Frank   Banana  5
Bob Carrot  1
Ethan   Carrot  2
Gene    Carrot  4
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have used the package reshape2 and dcast method to generate a different datastructure where users are rows, items are columns and the rating is in the value section (similar to an Excel pivot).&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;User    Apple   Banana  Carrot
Anna    2   4   n/a
Bob 4   n/a 1
Carlos  1   n/a n/a
Dana    n/a 3   n/a
Ethan   1   5   2
Frank   n/a 5   n/a
Gene    2   n/a 4
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is there any R package that will allow me to compute the similarity between users based on the user vectors? Preferably I would like to use the Jaccard coefficient as my similarity metric. I tried to google for R packages with user-based nearest neighbour implementations but came up empty. &lt;/p&gt;

&lt;p&gt;Any help of alternative suggestions will be much appreciated.&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'r  recommendation  similarity', NULL, '''/code'':107C,179C ''/p'':56C,65C,144C,233C,244C,247C ''/pre'':108C,180C ''1'':79C,82C,100C,158C,160C,168C ''2'':73C,85C,103C,152C,170C,176C ''3'':91C,165C ''4'':76C,88C,106C,153C,156C,178C ''5'':94C,97C,169C,173C ''allow'':41C,189C ''altern'':238C ''anna'':71C,86C,151C ''appl'':72C,75C,78C,81C,84C,148C ''appreci'':243C ''approxim'':62C ''banana'':87C,90C,93C,96C,149C ''base'':3A,47C,197C,225C ''best'':30C ''bob'':74C,98C,155C ''calcul'':44C ''came'':230C ''carlo'':77C,159C ''carrot'':99C,102C,105C,150C ''code'':67C,146C ''coeffici'':210C ''column'':130C ''comput'':192C ''dana'':89C,163C ''data'':60C ''datastructur'':123C ''dcast'':117C ''differ'':122C ''empti'':232C ''ethan'':80C,92C,101C,167C ''excel'':142C ''find'':28C ''frank'':95C,171C ''gene'':83C,104C,175C ''generat'':120C ''googl'':218C ''help'':236C ''implement'':6A,228C ''input'':52C ''item'':69C,128C ''jaccard'':209C ''learn'':18C ''like'':205C ''look'':35C ''method'':118C ''metric'':214C ''much'':242C ''n/a'':154C,157C,161C,162C,164C,166C,172C,174C,177C ''nearest'':4A,48C,226C ''neighbour'':5A,49C,227C ''origin'':59C ''p'':12C,57C,109C,181C,234C,245C ''packag'':31C,38C,114C,186C,221C ''pivot'':143C ''pre'':66C,145C ''prefer'':202C ''r'':8A,9B,21C,185C,220C ''rate'':70C,133C ''recommend'':10B,55C ''reshape2'':115C ''row'':127C ''section'':138C ''similar'':11B,139C,194C,213C ''start'':16C ''suggest'':239C ''sure'':25C ''thank'':246C ''tri'':216C ''use'':20C,112C,207C ''user'':2A,46C,68C,125C,147C,196C,200C,224C ''user-bas'':1A,45C,223C ''valu'':137C ''vector'':201C ''would'':204C ''yet'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6664, 2, '2015-08-03 06:27:50.22', 1, NULL, NULL, '&lt;p&gt;If you want to get but the &lt;code&gt;sum&lt;/code&gt; and &lt;code&gt;length&lt;/code&gt; of &lt;code&gt;id&lt;/code&gt; with &lt;code&gt;data.table&lt;/code&gt;, you can do it like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dt[,.(total=sum(id), n=length(id)), by=cid]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You could even write this in shorter format with the &lt;code&gt;.N&lt;/code&gt; operator of &lt;code&gt;data.table&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dt[,.(total=sum(id), .N), by=cid]
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':11C,15C,19C,23C,42C,57C,62C,73C ''/p'':30C,63C ''/pre'':43C,74C ''cid'':41C,72C ''code'':9C,13C,17C,21C,32C,55C,60C,65C ''could'':46C ''data.table'':22C,61C ''dt'':33C,66C ''even'':47C ''format'':52C ''get'':6C ''id'':18C,36C,39C,69C ''length'':14C,38C ''like'':28C ''n'':37C,56C,70C ''oper'':58C ''p'':1C,44C ''pre'':31C,64C ''shorter'':51C ''sum'':10C,35C,68C ''total'':34C,67C ''want'':4C ''write'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6666, 2, '2015-08-03 12:19:06.493', 2, NULL, NULL, '&lt;p&gt;It is a feature (I won''t call it flaw) of LDA generally. Usually, you get some very clearly defined topics and some very muddy topics (best termed "Miscellaneous" or so).&lt;/p&gt;

&lt;p&gt;I also advice not to overinterprete the topics from topic modelling. Just changing a little (e.g. the random seed, taking a few texts out, applying some "small" modifications to the corpus) changes a significant amount of the topics.&lt;/p&gt;
', NULL, NULL, '''/p'':33C,73C ''advic'':37C ''also'':36C ''amount'':69C ''appli'':59C ''best'':28C ''call'':9C ''chang'':47C,66C ''clear'':20C ''corpus'':65C ''defin'':21C ''e.g'':50C ''featur'':5C ''flaw'':11C ''general'':14C ''get'':17C ''lda'':13C ''littl'':49C ''miscellan'':30C ''model'':45C ''modif'':62C ''muddi'':26C ''overinterpret'':40C ''p'':1C,34C ''random'':52C ''seed'':53C ''signific'':68C ''small'':61C ''take'':54C ''term'':29C ''text'':57C ''topic'':22C,27C,42C,44C,72C ''usual'':15C ''won'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6676, 1, '2015-08-04 08:11:30.99', 2, 1044, 'Scikit-learn: Getting SGDClassifier to predict as well as a Logistic Regression', '&lt;p&gt;A way to train a Logistic Regression is by using stochastic gradient descent, which scikit-learn offers an interface to.&lt;/p&gt;

&lt;p&gt;What I would like to do is take a scikit-learn''s &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html" rel="nofollow"&gt;SGDClassifier&lt;/a&gt; and have it score the same as a Logistic Regression &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html" rel="nofollow"&gt;here&lt;/a&gt;. However, I must be missing some machine learning enhancements, since my scores are not equivalent.&lt;/p&gt;

&lt;p&gt;This is my current code. What am I missing on the SGDClassifier which would have it produce the same results as a Logistic Regression?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import SGDClassifier
import numpy as np
import pandas as pd
from sklearn.cross_validation import KFold
from sklearn.metrics import accuracy_score

# Note that the iris dataset is available in sklearn by default.
# This data is also conveniently preprocessed.
iris = datasets.load_iris()
X = iris["data"]
Y = iris["target"]

numFolds = 10
kf = KFold(len(X), numFolds, shuffle=True)

# These are "Class objects". For each Class, find the AUC through
# 10 fold cross validation.
Models = [LogisticRegression, SGDClassifier]
params = [{}, {"loss": "log", "penalty": "l2"}]
for param, Model in zip(params, Models):
    total = 0
    for train_indices, test_indices in kf:

        train_X = X[train_indices, :]; train_Y = Y[train_indices]
        test_X = X[test_indices, :]; test_Y = Y[test_indices]

        reg = Model(**param)
        reg.fit(train_X, train_Y)
        predictions = reg.predict(test_X)
        total += accuracy_score(test_Y, predictions)
    accuracy = total / numFolds
    print "Accuracy score of {0}: {1}".format(Model.__name__, accuracy)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;My output:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Accuracy score of LogisticRegression: 0.946666666667
Accuracy score of SGDClassifier: 0.76
&lt;/code&gt;&lt;/pre&gt;
', 'python  logistic-regression  scikit  gradient-descent', NULL, '''/a'':67C,86C ''/code'':287C,305C ''/p'':44C,102C,128C,292C ''/pre'':288C,306C ''/stable/modules/generated/sklearn.linear_model.logisticregression.html'':82C ''/stable/modules/generated/sklearn.linear_model.sgdclassifier.html'':63C ''0'':229C,282C ''0.76'':304C ''0.946666666667'':299C ''1'':283C ''10'':190C,209C ''accuraci'':161C,270C,275C,279C,286C,295C,300C ''also'':177C ''auc'':207C ''avail'':169C ''class'':200C,204C ''code'':108C,130C,294C ''conveni'':178C ''cross'':211C ''current'':107C ''data'':175C,185C ''dataset'':134C,167C ''datasets.load'':181C ''default'':173C ''descent'':21B,35C ''enhanc'':95C ''equival'':101C ''find'':205C ''fold'':210C ''format'':284C ''get'':4A ''gradient'':20B,34C ''gradient-desc'':19B ''howev'':87C ''href'':60C,79C ''import'':133C,138C,143C,145C,149C,156C,160C ''indic'':232C,234C,241C,246C,251C,256C ''interfac'':42C ''iri'':166C,180C,182C,184C,187C ''kf'':191C,236C ''kfold'':157C,192C ''l2'':220C ''learn'':3A,39C,57C,94C ''len'':193C ''like'':49C ''log'':218C ''logist'':12A,16B,28C,76C,126C ''logistic-regress'':15B ''logisticregress'':139C,214C,298C ''loss'':217C ''machin'':93C ''miss'':91C,112C ''model'':137C,142C,213C,223C,227C,258C ''model.__name__'':285C ''must'':89C ''nofollow'':65C,84C ''note'':163C ''np'':148C ''numfold'':189C,195C,277C ''numpi'':146C ''object'':201C ''offer'':40C ''output'':291C ''p'':22C,45C,103C,289C ''panda'':150C ''param'':216C,222C,226C,259C ''pd'':152C ''penalti'':219C ''pre'':129C,293C ''predict'':7A,265C,274C ''preprocess'':179C ''print'':278C ''produc'':120C ''python'':14B ''reg'':257C ''reg.fit'':260C ''reg.predict'':266C ''regress'':13A,17B,29C,77C,127C ''rel'':64C,83C ''result'':123C ''scikit'':2A,18B,38C,56C ''scikit-learn'':1A,37C,55C ''scikit-learn.org'':62C,81C ''scikit-learn.org/stable/modules/generated/sklearn.linear_model.logisticregression.html'':80C ''scikit-learn.org/stable/modules/generated/sklearn.linear_model.sgdclassifier.html'':61C ''score'':71C,98C,162C,271C,280C,296C,301C ''sgdclassifi'':5A,66C,115C,144C,215C,303C ''shuffl'':196C ''sinc'':96C ''sklearn'':132C,171C ''sklearn.cross'':154C ''sklearn.linear'':136C,141C ''sklearn.metrics'':159C ''stochast'':33C ''take'':53C ''target'':188C ''test'':233C,247C,250C,252C,255C,267C,272C ''total'':228C,269C,276C ''train'':26C,231C,237C,240C,242C,245C,261C,263C ''true'':197C ''use'':32C ''valid'':155C,212C ''way'':24C ''well'':9A ''would'':48C,117C ''x'':183C,194C,238C,239C,248C,249C,262C,268C ''y'':186C,243C,244C,253C,254C,264C,273C ''zip'':225C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6677, 1, '2015-08-04 08:48:19.787', 0, 94, 'How do you make the x axis a log scale using metafor for a forest plot?', '&lt;p&gt;I am using the metafor library for plotting a forest plot of odds ratios.  I want the scale to be in log format:
The default code is as below:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data&amp;lt;-read.csv(''test.csv'')
# If OR''s and 95% CI''s, run these commented out lines
data$beta &amp;lt;- log(data$OR)
data$se   &amp;lt;- (log(data$UCL)-log(data$LCL))/(2*1.96)

# Assign values for plotting
labs &amp;lt;- data$group
    yi   &amp;lt;- data$beta
sei  &amp;lt;- data$se

# Combine data into summary estimate
res  &amp;lt;- rma(yi=yi, sei=sei, method="FE", measure=''OR'')
#summary(res)

# Plot combined data
forest(res, atransf=log, refline=1, xlab="Odds Ratio (95%CI)", slab=labs,      mlab="Summary Estimate")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The plot that comes out is very nice, but the scale is not correct despite changing the atransf or the method and measure options.
Anyone with metafor experience who has managed to make a log scale on the x axis, please let me know how they did it.&lt;/p&gt;
', 'r', NULL, '''/code'':137C ''/p'':48C,188C ''/pre'':138C ''1'':126C ''1.96'':83C ''2'':82C ''95'':59C,130C ''anyon'':164C ''assign'':84C ''atransf'':123C,157C ''axi'':7A,179C ''beta'':68C,95C ''chang'':155C ''ci'':60C,131C ''code'':44C,50C ''combin'':100C,119C ''come'':143C ''comment'':64C ''correct'':153C ''data'':51C,67C,71C,73C,77C,80C,90C,94C,98C,101C,120C ''default'':43C ''despit'':154C ''estim'':104C,136C ''experi'':167C ''fe'':113C ''forest'':15A,28C,121C ''format'':41C ''group'':91C ''know'':183C ''lab'':88C,133C ''lcl'':81C ''let'':181C ''librari'':24C ''line'':66C ''log'':9A,40C,70C,76C,79C,124C,174C ''lt'':52C,69C,75C,89C,93C,97C,106C ''make'':4A,172C ''manag'':170C ''measur'':114C,162C ''metafor'':12A,23C,166C ''method'':112C,160C ''mlab'':134C ''nice'':147C ''odd'':31C,128C ''option'':163C ''p'':18C,139C ''pleas'':180C ''plot'':16A,26C,29C,87C,118C,141C ''pre'':49C ''r'':17B ''ratio'':32C,129C ''read.csv'':53C ''reflin'':125C ''res'':105C,117C,122C ''rma'':107C ''run'':62C ''scale'':10A,36C,150C,175C ''se'':74C,99C ''sei'':96C,110C,111C ''slab'':132C ''summari'':103C,116C,135C ''test.csv'':54C ''ucl'':78C ''use'':11A,21C ''valu'':85C ''want'':34C ''x'':6A,178C ''xlab'':127C ''yi'':92C,108C,109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6679, 1, '2015-08-04 10:48:20.727', 1, 61, 'R: Comparing dissimilarity between metabolic models with discrete wavelet transformation', '&lt;p&gt;Iâm working on comparing bacteria metabolic models. Each model has a set of metabolites (around 2000) and their concentration for 200 time points. Iâm in the process of comparing the models to cluster them based on their similarity. One method I followed is I did a pair wise comparison for each of the metabolite pairs in two models using Euclidean distance. Below is how my data look like. This is a &lt;a href="http://1drv.ms/1JD8PHX" rel="nofollow"&gt;sample data file&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/yvoPj.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/yvoPj.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I computed pair wise Euclidean distance for Met1 from Model A and Met1 from Model B. Likewise computed the distances for all the common metabolites between the 2 models (Met4 in Model A and Met4 in Model B) and summed up the distances to get a distance (dissimilarity) between the two models. Similarly I computed the dissimilarity matrix for all the models and I used hierarchical clustering to cluster them.&lt;/p&gt;

&lt;p&gt;As mentioned above, now I want to compute the dissimilarity of the models using Discrete Wavelet Transformation as my distance measure. I would like to know how to use Discrete Wavelet Transformation to compute a dissimilarity distance between 2 time series and hence for my models.&lt;/p&gt;

&lt;p&gt;Previously I used DWT as a distance measure with DBSCAN for clustering metabolites in one model according to their behavior. It worked fine.&lt;/p&gt;
', 'r  clustering  time-series  bioinformatics', NULL, '''/1jd8phx'':95C ''/a'':101C,121C ''/p'':102C,122C,194C,242C,267C ''/yvopj.jpg'':108C,115C ''1drv.ms'':94C ''1drv.ms/1jd8phx'':93C ''2'':151C,234C ''200'':39C ''2000'':34C ''accord'':260C ''alt'':116C ''around'':33C ''b'':139C,161C ''bacteria'':23C ''base'':54C ''behavior'':263C ''bioinformat'':16B ''cluster'':12B,52C,190C,192C,255C ''common'':147C ''compar'':2A,22C,48C ''comparison'':68C ''comput'':125C,141C,178C,203C,229C ''concentr'':37C ''data'':85C,99C ''dbscan'':253C ''descript'':119C ''discret'':8A,210C,225C ''dissimilar'':3A,171C,180C,205C,231C ''distanc'':80C,129C,143C,166C,170C,215C,232C,250C ''dwt'':247C ''enter'':117C ''euclidean'':79C,128C ''file'':100C ''fine'':266C ''follow'':61C ''get'':168C ''henc'':238C ''hierarch'':189C ''href'':92C,105C ''i.stack.imgur.com'':107C,114C ''i.stack.imgur.com/yvopj.jpg'':106C,113C ''imag'':118C ''img'':111C ''iâ'':18C,42C ''know'':221C ''like'':87C,219C ''likewis'':140C ''look'':86C ''m'':19C,43C ''matrix'':181C ''measur'':216C,251C ''mention'':197C ''met1'':131C,136C ''met4'':153C,158C ''metabol'':5A,24C ''metabolit'':32C,73C,148C,256C ''method'':59C ''model'':6A,25C,27C,50C,77C,133C,138C,152C,155C,160C,175C,185C,208C,241C,259C ''nofollow'':97C,110C ''one'':58C,258C ''p'':17C,103C,123C,195C,243C ''pair'':66C,74C,126C ''point'':41C ''previous'':244C ''process'':46C ''r'':1A,11B ''rel'':96C,109C ''sampl'':98C ''seri'':15B,236C ''set'':30C ''similar'':57C,176C ''src'':112C ''sum'':163C ''time'':14B,40C,235C ''time-seri'':13B ''transform'':10A,212C,227C ''two'':76C,174C ''use'':78C,188C,209C,224C,246C ''want'':201C ''wavelet'':9A,211C,226C ''wise'':67C,127C ''work'':20C,265C ''would'':218C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8845, 1, '2015-11-14 16:10:59.64', 0, 15, 'What is a good use case for CALL EXECUTE', '&lt;p&gt;What is a good use case that demonstrates a proper use of CALL EXECITE in SAS? It seems that this is a way to call a ''proc'' from a DATA step so we don''t need to save the output of the PROC to some temporary dataset?&lt;/p&gt;

&lt;p&gt;Is there all there is to it? What is a good use case where CALL EXECUTE is a natural fit?&lt;/p&gt;
', 'sas', NULL, '''/p'':59C,81C ''call'':8A,24C,36C,75C ''case'':6A,17C,73C ''data'':41C ''dataset'':58C ''demonstr'':19C ''execit'':25C ''execut'':9A,76C ''fit'':80C ''good'':4A,15C,71C ''natur'':79C ''need'':47C ''output'':51C ''p'':11C,60C ''proc'':38C,54C ''proper'':21C ''sas'':10B,27C ''save'':49C ''seem'':29C ''step'':42C ''temporari'':57C ''use'':5A,16C,22C,72C ''way'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6681, 1, '2015-08-04 13:44:22.333', 3, 142, 'Data driven approach to define a churn user', '&lt;p&gt;I''m trying to define a churn prediction model for an online service (betting/gambling). A lot of papers talk about churn analysis/prediction for telco companies where defining a churn user is straightforward: a churn user is a user who cancels his or her contract. I''m wondering how to define a churn user within the context of an industry where users don''t cancel any contract/account?&lt;/p&gt;

&lt;p&gt;I can envision a general approach: if a user who plays every day for a month stops playing for 2 days, this is could be flagged, but a user who only plays once a week and then has no activity for 2 days would not. What''s the best approach to encoding this logic algorithmically? Is survival analysis and good approach?&lt;/p&gt;
', 'machine-learning  predictive-modeling  survival-analysis  churn', NULL, '''/p'':86C,149C ''2'':107C,129C ''activ'':127C ''algorithm'':142C ''analysi'':17B,145C ''analysis/prediction'':41C ''approach'':3A,93C,137C,148C ''best'':136C ''betting/gambling'':33C ''cancel'':59C,83C ''churn'':7A,18B,26C,40C,48C,53C,71C ''compani'':44C ''context'':75C ''contract'':63C ''contract/account'':85C ''could'':111C ''data'':1A ''day'':100C,108C,130C ''defin'':5A,24C,46C,69C ''driven'':2A ''encod'':139C ''envis'':90C ''everi'':99C ''flag'':113C ''general'':92C ''good'':147C ''industri'':78C ''learn'':11B ''logic'':141C ''lot'':35C ''m'':21C,65C ''machin'':10B ''machine-learn'':9B ''model'':14B,28C ''month'':103C ''onlin'':31C ''p'':19C,87C ''paper'':37C ''play'':98C,105C,119C ''predict'':13B,27C ''predictive-model'':12B ''servic'':32C ''stop'':104C ''straightforward'':51C ''surviv'':16B,144C ''survival-analysi'':15B ''talk'':38C ''telco'':43C ''tri'':22C ''user'':8A,49C,54C,57C,72C,80C,96C,116C ''week'':122C ''within'':73C ''wonder'':66C ''would'':131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6683, 1, '2015-08-04 17:44:35.277', 1, 425, 'Feature selection using feature importances in random forests with scikit-learn', '&lt;p&gt;I have &lt;a href="http://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html" rel="nofollow"&gt;plotted the feature importances in random forests with scikit-learn&lt;/a&gt;. In order to improve the prediction using random forests, how can I use the plot information to remove features? I.e. how to spot whether a feature is useless or even worse decrease of the random forests performance, based on the plot information? The plot is based on the attribute &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html" rel="nofollow"&gt;&lt;code&gt;feature_importances_&lt;/code&gt;&lt;/a&gt; and I use the classifier &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" rel="nofollow"&gt;&lt;code&gt;sklearn.ensemble.RandomForestClassifier&lt;/code&gt;&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I am aware that there exist &lt;a href="http://datascience.stackexchange.com/a/6651/843"&gt;other techniques for feature selection&lt;/a&gt;, but in this question I want to focus on how to use feature &lt;code&gt;feature_importances_&lt;/code&gt;.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Examples of such feature importance plots:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/ySztK.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/ySztK.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/xeXHY.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/xeXHY.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'feature-selection  random-forest  scikit', NULL, '''/a'':41C,102C,118C,137C,183C,203C ''/a/6651/843'':131C ''/code'':101C,117C,154C ''/p'':119C,155C,164C,184C,204C ''/stable/auto_examples/ensemble/plot_forest_importances.html'':27C ''/stable/modules/generated/sklearn.ensemble.randomforestclassifier.html'':95C ''/stable/modules/generated/sklearn.ensemble.randomforestclassifier.html#sklearn.ensemble.randomforestclassifier'':112C ''/xexhy.png'':190C,197C ''/ysztk.png'':170C,177C ''alt'':178C,198C ''attribut'':90C ''awar'':123C ''base'':79C,87C ''classifi'':107C ''code'':98C,115C,151C ''datascience.stackexchange.com'':130C ''datascience.stackexchange.com/a/6651/843'':129C ''decreas'':73C ''descript'':181C,201C ''enter'':179C,199C ''even'':71C ''exampl'':158C ''exist'':126C ''featur'':1A,4A,14B,32C,60C,67C,99C,135C,150C,152C,161C ''feature-select'':13B ''focus'':145C ''forest'':8A,18B,36C,50C,77C ''hr'':156C ''href'':24C,92C,109C,128C,167C,187C ''i.e'':61C ''i.stack.imgur.com'':169C,176C,189C,196C ''i.stack.imgur.com/xexhy.png'':188C,195C ''i.stack.imgur.com/ysztk.png'':168C,175C ''imag'':180C,200C ''img'':173C,193C ''import'':5A,33C,100C,153C,162C ''improv'':45C ''inform'':57C,83C ''learn'':12A,40C ''nofollow'':29C,97C,114C,172C,192C ''order'':43C ''p'':20C,120C,157C,165C,185C ''perform'':78C ''plot'':30C,56C,82C,85C,163C ''predict'':47C ''question'':141C ''random'':7A,17B,35C,49C,76C ''random-forest'':16B ''rel'':28C,96C,113C,171C,191C ''remov'':59C ''scikit'':11A,19B,39C ''scikit-learn'':10A,38C ''scikit-learn.org'':26C,94C,111C ''scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html'':25C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.randomforestclassifier.html'':93C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.randomforestclassifier.html#sklearn.ensemble.randomforestclassifier'':110C ''select'':2A,15B,136C ''sklearn.ensemble.randomforestclassifier'':116C ''spot'':64C ''src'':174C,194C ''techniqu'':133C ''use'':3A,48C,54C,105C,149C ''useless'':69C ''want'':143C ''whether'':65C ''wors'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6684, 2, '2015-08-04 17:55:20.787', 1, NULL, NULL, '&lt;p&gt;You can simply use the &lt;code&gt;feature_importances_&lt;/code&gt; attribute to select the features with the highest importance score. So for example you could use the following function to select the K best features according to importance.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def selectKImportance(model, X, k=5):
     return X[:,model.feature_importances_.argsort()[::-1][:k]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Or if you''re using a pipeline the following class&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;class ImportanceSelect(BaseEstimator, TransformerMixin):
    def __init__(self, model, n=1):
         self.model = model
         self.n = n
    def fit(self, *args, **kwargs):
         self.model.fit(*args, **kwargs)
         return self
    def transform(self, X):
         return X[:,self.model.feature_importances_.argsort()[::-1][:self.n]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So for example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;&amp;gt;&amp;gt; from sklearn.datasets import load_iris
&amp;gt;&amp;gt;&amp;gt; from sklearn.ensemble import RandomForestClassifier
&amp;gt;&amp;gt;&amp;gt; iris = load_iris()
&amp;gt;&amp;gt;&amp;gt; X = iris.data
&amp;gt;&amp;gt;&amp;gt; y = iris.target
&amp;gt;&amp;gt;&amp;gt; 
&amp;gt;&amp;gt;&amp;gt; model = RandomForestClassifier()
&amp;gt;&amp;gt;&amp;gt; model.fit(X,y)
RandomForestClassifier(bootstrap=True, class_weight=None, criterion=''gini'',
            max_depth=None, max_features=''auto'', max_leaf_nodes=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
            oob_score=False, random_state=None, verbose=0,
            warm_start=False)
&amp;gt;&amp;gt;&amp;gt; 
&amp;gt;&amp;gt;&amp;gt; newX = selectKImportance(model,X,2)
&amp;gt;&amp;gt;&amp;gt; newX.shape
(150, 2)
&amp;gt;&amp;gt;&amp;gt; X.shape
(150, 4)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And clearly if you wanted to selected based on some other criteria than "top k features" then you can just adjust the functions accordingly.&lt;/p&gt;
', NULL, NULL, '''-1'':53C,104C ''/code'':10C,55C,106C,231C ''/p'':39C,68C,112C,258C ''/pre'':56C,107C,232C ''0'':204C ''0.0'':190C ''1'':80C,181C,196C ''10'':193C ''150'':223C,229C ''2'':185C,218C,224C ''4'':230C ''5'':47C ''accord'':36C,257C ''adjust'':254C ''arg'':88C,91C ''argsort'':52C,103C ''attribut'':11C ''auto'':173C ''base'':241C ''baseestim'':73C ''best'':34C ''bootstrap'':161C ''class'':67C,71C,163C ''clear'':235C ''code'':7C,41C,70C,114C ''could'':25C ''criteria'':245C ''criterion'':166C ''def'':42C,75C,85C,95C ''depth'':169C ''estim'':192C ''exampl'':23C,111C ''fals'':199C,207C ''featur'':8C,15C,35C,172C,249C ''fit'':86C ''follow'':28C,66C ''fraction'':188C ''function'':29C,256C ''gini'':167C ''gt'':115C,116C,117C,123C,124C,125C,130C,131C,132C,136C,137C,138C,141C,142C,143C,146C,147C,148C,149C,150C,151C,154C,155C,156C,208C,209C,210C,211C,212C,213C,219C,220C,221C,225C,226C,227C ''highest'':18C ''import'':9C,19C,38C,51C,102C,120C,128C ''importanceselect'':72C ''init'':76C ''iri'':122C,133C,135C ''iris.data'':140C ''iris.target'':145C ''job'':195C ''k'':33C,46C,54C,248C ''kwarg'':89C,92C ''leaf'':175C,180C,189C ''load'':121C,134C ''max'':168C,171C,174C ''min'':178C,182C,186C ''model'':44C,78C,82C,152C,216C ''model.feature'':50C ''model.fit'':157C ''n'':79C,84C,191C,194C ''newx'':214C ''newx.shape'':222C ''node'':176C ''none'':165C,170C,177C,202C ''oob'':197C ''p'':1C,57C,108C,233C ''pipelin'':64C ''pre'':40C,69C,113C ''random'':200C ''randomforestclassifi'':129C,153C,160C ''re'':61C ''return'':48C,93C,99C ''sampl'':179C,183C ''score'':20C,198C ''select'':13C,31C,240C ''selectkimport'':43C,215C ''self'':77C,87C,94C,97C ''self.model'':81C ''self.model.feature'':101C ''self.model.fit'':90C ''self.n'':83C,105C ''simpli'':4C ''sklearn.datasets'':119C ''sklearn.ensemble'':127C ''split'':184C ''start'':206C ''state'':201C ''top'':247C ''transform'':96C ''transformermixin'':74C ''true'':162C ''use'':5C,26C,62C ''verbos'':203C ''want'':238C ''warm'':205C ''weight'':164C,187C ''x'':45C,49C,98C,100C,139C,158C,217C ''x.shape'':228C ''y'':144C,159C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6685, 2, '2015-08-04 17:59:06.823', 1, NULL, NULL, '&lt;p&gt;2 solutions:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;You aggregate each sequence of numbers into a single number, which use as a feature. There exist plenty of aggregation functions, such as some derived from descriptive statistics root-mean-square, kurtosis, skewness, max, min, duration, standard deviation, crest factor, mean, or more specific aggregation such as fourier transforms or wavelet transforms.&lt;/li&gt;
&lt;li&gt;You use some model that accepts sequences as input: all such models I have in mind would require your sequences of numbers to be of same length though. Example of such model: &lt;a href="https://en.wikipedia.org/wiki/Recurrent_neural_network" rel="nofollow"&gt;recurrent neural networks&lt;/a&gt;, &lt;a href="https://en.wikipedia.org/wiki/Dynamic_Bayesian_network" rel="nofollow"&gt;Dynamic Bayesian networks&lt;/a&gt;.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':104C,115C ''/li'':60C,116C ''/p'':4C ''/ul'':117C ''/wiki/dynamic_bayesian_network'':109C ''/wiki/recurrent_neural_network'':98C ''2'':2C ''accept'':67C ''aggreg'':8C,26C,52C ''bayesian'':113C ''crest'':46C ''deriv'':31C ''descript'':33C ''deviat'':45C ''durat'':43C ''dynam'':112C ''en.wikipedia.org'':97C,108C ''en.wikipedia.org/wiki/dynamic_bayesian_network'':107C ''en.wikipedia.org/wiki/recurrent_neural_network'':96C ''exampl'':90C ''exist'':23C ''factor'':47C ''featur'':21C ''fourier'':55C ''function'':27C ''href'':95C,106C ''input'':70C ''kurtosi'':39C ''length'':88C ''li'':6C,61C ''max'':41C ''mean'':37C,48C ''min'':42C ''mind'':77C ''model'':65C,73C,93C ''network'':103C,114C ''neural'':102C ''nofollow'':100C,111C ''number'':12C,16C,83C ''p'':1C ''plenti'':24C ''recurr'':101C ''rel'':99C,110C ''requir'':79C ''root'':36C ''root-mean-squar'':35C ''sequenc'':10C,68C,81C ''singl'':15C ''skew'':40C ''solut'':3C ''specif'':51C ''squar'':38C ''standard'':44C ''statist'':34C ''though'':89C ''transform'':56C,59C ''ul'':5C ''use'':18C,63C ''wavelet'':58C ''would'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6686, 1, '2015-08-04 18:13:03.29', 0, 21, 'Digit recognition with semi-supervised learning and few labelled data points', '&lt;p&gt;Several months ago I watched a talk about a very interesting ML approach that was able to achieve high accuracy in digit recognition with only a few dozen labelled data points, while using the rest of the dataset (probably MNIST) for unsupervised learning.
The algorithm used convolutional NNs but instead of MAX pooling between the convolutional layers it, it somehow passed the information about location of the features in the image.&lt;/p&gt;

&lt;p&gt;The problem is I did not save the link and I am unable to find the relevant talk/paper anymore. The closest match I found was an interesting paper &lt;a href="https://kaggle2.blob.core.windows.net/forum-message-attachments/8924/pseudo_label_final.pdf?sv=2012-02-12&amp;amp;se=2015-08-07T17%3A54%3A55Z&amp;amp;sr=b&amp;amp;sp=r&amp;amp;sig=Fn5Eu7HaOL8vdmHqkN7t7RQ1oQnV%2B3TZ5c%2B%2FUt6KMyc%3D" rel="nofollow"&gt;Pseudo-Label : The Simple and Efficient Semi-Supervised Learning
Method for Deep Neural Networks&lt;/a&gt; that achieved very good performance with 100 labelled data points. However traversing the citation graph from there didn''t reveal my desired paper.&lt;/p&gt;

&lt;p&gt;Does anyone here recall a similar talk/paper and could point me where to find it? A link to any other interesting work on digit/image recognition with severely limited ( &amp;lt; 100) amount of labelled datapoints is also very welcome.&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'machine-learning', NULL, '''/a'':141C ''/forum-message-attachments/8924/pseudo_label_final.pdf?sv=2012-02-12&amp;amp;se=2015-08-07t17%3a54%3a55z&amp;amp;sr=b&amp;amp;sp=r&amp;amp;sig=fn5eu7haol8vdmhqkn7t7rq1oqnv%2b3tz5c%2b%2fut6kmyc%3d'':122C ''/p'':88C,165C,205C,208C ''100'':148C,196C ''abl'':32C ''accuraci'':36C ''achiev'':34C,143C ''ago'':19C ''algorithm'':61C ''also'':202C ''amount'':197C ''anymor'':108C ''anyon'':168C ''approach'':29C ''citat'':155C ''closest'':110C ''convolut'':63C,72C ''could'':175C ''data'':11A,46C,150C ''datapoint'':200C ''dataset'':54C ''deep'':138C ''desir'':163C ''didn'':159C ''digit'':1A,38C ''digit/image'':190C ''dozen'':44C ''effici'':131C ''featur'':84C ''find'':104C,180C ''found'':113C ''good'':145C ''graph'':156C ''high'':35C ''howev'':152C ''href'':119C ''imag'':87C ''inform'':79C ''instead'':66C ''interest'':27C,116C,187C ''kaggle2.blob.core.windows.net'':121C ''kaggle2.blob.core.windows.net/forum-message-attachments/8924/pseudo_label_final.pdf?sv=2012-02-12&amp;amp;se=2015-08-07t17%3a54%3a55z&amp;amp;sr=b&amp;amp;sp=r&amp;amp;sig=fn5eu7haol8vdmhqkn7t7rq1oqnv%2b3tz5c%2b%2fut6kmyc%3d'':120C ''label'':10A,45C,127C,149C,199C ''layer'':73C ''learn'':7A,15B,59C,135C ''limit'':194C ''link'':98C,183C ''locat'':81C ''lt'':195C ''machin'':14B ''machine-learn'':13B ''match'':111C ''max'':68C ''method'':136C ''ml'':28C ''mnist'':56C ''month'':18C ''network'':140C ''neural'':139C ''nns'':64C ''nofollow'':124C ''p'':16C,89C,166C,206C ''paper'':117C,164C ''pass'':77C ''perform'':146C ''point'':12A,47C,151C,176C ''pool'':69C ''probabl'':55C ''problem'':91C ''pseudo'':126C ''pseudo-label'':125C ''recal'':170C ''recognit'':2A,39C,191C ''rel'':123C ''relev'':106C ''rest'':51C ''reveal'':161C ''save'':96C ''semi'':5A,133C ''semi-supervis'':4A,132C ''sever'':17C,193C ''similar'':172C ''simpl'':129C ''somehow'':76C ''supervis'':6A,134C ''talk'':23C ''talk/paper'':107C,173C ''thank'':207C ''travers'':153C ''unabl'':102C ''unsupervis'':58C ''use'':49C,62C ''watch'':21C ''welcom'':204C ''work'':188C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8859, 2, '2015-11-16 12:44:05.323', 4, NULL, NULL, '&lt;p&gt;You can use techniques of semi-supervised learning where you have a small clean training set and some dirty data. You than extend your data base by judging how good the other data are and incorporate the "best" data points into your training set.&lt;/p&gt;
', NULL, NULL, '''/p'':47C ''base'':28C ''best'':40C ''clean'':16C ''data'':22C,27C,35C,41C ''dirti'':21C ''extend'':25C ''good'':32C ''incorpor'':38C ''judg'':30C ''learn'':10C ''p'':1C ''point'':42C ''semi'':8C ''semi-supervis'':7C ''set'':18C,46C ''small'':15C ''supervis'':9C ''techniqu'':5C ''train'':17C,45C ''use'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6704, 2, '2015-08-05 14:35:57.897', 0, NULL, NULL, '&lt;p&gt;Take a look at the &lt;code&gt;TSclust&lt;/code&gt; package. Here how you would apply it to your sample data. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;require(TSclust)

#read in the data
model_a &amp;lt;- read.csv("~/Desktop/Model A.csv", header = TRUE, stringsAsFactors = FALSE)
model_b &amp;lt;- read.csv("~/Desktop/Model B.csv", header = TRUE, stringsAsFactors = FALSE)

#data must be in rows rather than columns
model_a &amp;lt;- as.data.frame(t(model_a))

model_b &amp;lt;- as.data.frame(t(model_b))

#calculate dissimlarities between metabolites in models 1 and 2
met1_DWT.diss &amp;lt;- as.numeric(diss.DWT(rbind(model_a[''Met1'', ], model_b[''Met1'', ])))
met1_DWT.diss
[1] 90.80332

met2_DWT.diss &amp;lt;- as.numeric(diss.DWT(rbind(model_a[''Met2'', ], model_b[''Met2'', ])))
met2_DWT.diss
[1] 1.499241
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':9C,109C ''/desktop/model'':34C,44C ''/p'':21C ''/pre'':110C ''1'':78C,93C,107C ''1.499241'':108C ''2'':80C ''90.80332'':94C ''a.csv'':35C ''appli'':15C ''as.data.frame'':61C,68C ''as.numeric'':83C,97C ''b'':41C,66C,71C,90C,104C ''b.csv'':45C ''calcul'':72C ''code'':7C,23C ''column'':57C ''data'':20C,29C,50C ''diss.dwt'':84C,98C ''dissimlar'':73C ''fals'':39C,49C ''header'':36C,46C ''look'':4C ''lt'':32C,42C,60C,67C,82C,96C ''met1'':88C,91C ''met1_dwt.diss'':81C,92C ''met2'':102C,105C ''met2_dwt.diss'':95C,106C ''metabolit'':75C ''model'':30C,40C,58C,63C,65C,70C,77C,86C,89C,100C,103C ''must'':51C ''p'':1C ''packag'':10C ''pre'':22C ''rather'':55C ''rbind'':85C,99C ''read'':26C ''read.csv'':33C,43C ''requir'':24C ''row'':54C ''sampl'':19C ''stringsasfactor'':38C,48C ''take'':2C ''true'':37C,47C ''tsclust'':8C,25C ''would'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6705, 2, '2015-08-05 16:22:23.003', 4, NULL, NULL, '&lt;p&gt;I really enjoyed &lt;a href="https://books.google.cz/books?id=CAFR6IBF4xYC" rel="nofollow"&gt;Reinforcement Leraning: An introduction&lt;/a&gt; by Richard Sutton. It provides a very nice unifying view on RL, although it does not mention the newest approaches (it''s from 1998).&lt;/p&gt;
', NULL, NULL, '''/a'':16C ''/books?id=cafr6ibf4xyc'':9C ''/p'':41C ''1998'':40C ''although'':29C ''approach'':36C ''books.google.cz'':8C ''books.google.cz/books?id=cafr6ibf4xyc'':7C ''enjoy'':4C ''href'':6C ''introduct'':15C ''leran'':13C ''mention'':33C ''newest'':35C ''nice'':24C ''nofollow'':11C ''p'':1C ''provid'':21C ''realli'':3C ''reinforc'':12C ''rel'':10C ''richard'':18C ''rl'':28C ''sutton'':19C ''unifi'':25C ''view'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6687, 1, '2015-08-04 19:25:34.437', 2, 32, 'Spatial clustering of data points on a grid to obtain variable resolution map with constant statistical confidence', '&lt;p&gt;I have a grey valued image which is calculated as the mean of a series of images. The value of each pixel is therefore associated to a standard error.&lt;/p&gt;

&lt;p&gt;The pixel values and the relative standard error (RSE) vary smoothly in pixel space, so that the pixels of a downscaled image resulting as the mean of the underlying downscaled images would exhibit a lower RSE. Meaning that a high resolution image is associated to high RSEs and a low resolution image to lower RSEs. However, the RSE vary in space.&lt;/p&gt;

&lt;p&gt;Effectively this behaves like the trade-off of an uncertainty relation: Resolution in space vs resolution in the mean value.&lt;/p&gt;

&lt;p&gt;For representing this I am looking for a way to divide the image into regions with constant RSEs, which would be larger in parts of the image with high RSEs and smaller in parts with low RSEs.&lt;/p&gt;

&lt;p&gt;The aim would be to obtain irregularly sized superpixels with regular RSEs as opposed to my current image which contains pixels on a regular grid but with irregular RSEs.&lt;/p&gt;

&lt;p&gt;This would be equivalent to downscaling the image but not on a regular grid. One approach I tried is agglomerative clustering of the pixels computing new distances in every step, but so far I couldn''t find the right distance and objective functions to obtain a more or less regular map of superpixels (generally one big cluster would eat up its neighbours).&lt;/p&gt;

&lt;p&gt;Do you know of any existing approach for this or if not which would be a good clustering method to apply? Would quadtrees help me?&lt;/p&gt;

&lt;p&gt;Thanks a lot in advance!&lt;/p&gt;
', 'clustering  clusters  hierarchical-data-format', NULL, '''/p'':54C,117C,140C,179C,210C,274C,301C,308C ''advanc'':307C ''agglom'':231C ''aim'':182C ''appli'':296C ''approach'':227C,282C ''associ'':49C,99C ''behav'':121C ''big'':267C ''calcul'':33C ''cluster'':2A,18B,19B,232C,268C,293C ''comput'':236C ''confid'':17A ''constant'':15A,158C ''contain'':200C ''couldn'':246C ''current'':197C ''data'':4A,22B ''distanc'':238C,251C ''divid'':152C ''downscal'':76C,85C,217C ''eat'':270C ''effect'':119C ''equival'':215C ''error'':53C,63C ''everi'':240C ''exhibit'':88C ''exist'':281C ''far'':244C ''find'':248C ''format'':23B ''function'':254C ''general'':265C ''good'':292C ''grey'':28C ''grid'':8A,205C,225C ''help'':299C ''hierarch'':21B ''hierarchical-data-format'':20B ''high'':95C,101C,170C ''howev'':111C ''imag'':30C,41C,77C,86C,97C,107C,154C,168C,198C,219C ''irregular'':187C,208C ''know'':278C ''larger'':163C ''less'':260C ''like'':122C ''look'':147C ''lot'':305C ''low'':105C,177C ''lower'':90C,109C ''map'':13A,262C ''mean'':36C,81C,92C,138C ''method'':294C ''neighbour'':273C ''new'':237C ''object'':253C ''obtain'':10A,186C,256C ''one'':226C,266C ''oppos'':194C ''p'':24C,55C,118C,141C,180C,211C,275C,302C ''part'':165C,175C ''pixel'':46C,57C,68C,73C,201C,235C ''point'':5A ''quadtre'':298C ''region'':156C ''regular'':191C,204C,224C,261C ''relat'':61C,130C ''repres'':143C ''resolut'':12A,96C,106C,131C,135C ''result'':78C ''right'':250C ''rse'':64C,91C,113C ''rses'':102C,110C,159C,171C,178C,192C,209C ''seri'':39C ''size'':188C ''smaller'':173C ''smooth'':66C ''space'':69C,116C,133C ''spatial'':1A ''standard'':52C,62C ''statist'':16A ''step'':241C ''superpixel'':189C,264C ''thank'':303C ''therefor'':48C ''trade'':125C ''trade-off'':124C ''tri'':229C ''uncertainti'':129C ''under'':84C ''valu'':29C,43C,58C,139C ''vari'':65C,114C ''variabl'':11A ''vs'':134C ''way'':150C ''would'':87C,161C,183C,213C,269C,289C,297C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6701, 1, '2015-08-05 10:53:17.293', 0, 28, 'How to keep a subsetted value for calculating mean', '&lt;p&gt;I am currently learning R and I have to solve an Issue were I have to extract values from a data set which are from a specific month and from this values I should calculate the mean Temp. &lt;/p&gt;

&lt;p&gt;I did it like that:&lt;/p&gt;

&lt;p&gt;data[data$X..Month.. == 6,]   &lt;/p&gt;

&lt;p&gt;mean(data$X..Temp.., na.rm=TRUE)&lt;/p&gt;

&lt;p&gt;It gave me the mean value but without taking my first statement into consideration. 
What do I need to do that both statements are taken into consideration?&lt;/p&gt;
', 'r', NULL, '''..'':63C ''/p'':50C,57C,65C,73C,102C ''6'':64C ''calcul'':8A,46C ''consider'':88C,101C ''current'':14C ''data'':32C,59C,60C,68C ''extract'':28C ''first'':85C ''gave'':76C ''issu'':23C ''keep'':3A ''learn'':15C ''like'':55C ''mean'':9A,48C,67C,79C ''month'':39C,62C ''na.rm'':71C ''need'':92C ''p'':11C,51C,58C,66C,74C ''r'':10B,16C ''set'':33C ''solv'':21C ''specif'':38C ''statement'':86C,97C ''subset'':5A ''take'':83C ''taken'':99C ''temp'':49C,70C ''true'':72C ''valu'':6A,29C,43C,80C ''without'':82C ''x'':61C,69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6690, 2, '2015-08-04 21:39:31.06', 1, NULL, NULL, '&lt;p&gt;Maybe the best answer for Your question is &lt;strong&gt;recommenderlab&lt;/strong&gt; package for R available from CRAN. Here''s the link to &lt;a href="https://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf" rel="nofollow"&gt;vignette&lt;/a&gt;. It has CF algorithms implemented and Jaccard coef. for 0/1 user feedback case.  &lt;/p&gt;

&lt;p&gt;Only similarity(distance) computation in base stats R package there is &lt;em&gt;dist&lt;/em&gt; function. But it''s limited to few distance measures. I know that when I search for distance measures in R package called &lt;em&gt;vegan&lt;/em&gt; claimed to have many methods implemented. But I have never used it. &lt;/p&gt;

&lt;p&gt;IMHO most basic recommender systems algorithms are really simple. It worth spending few minutes and implement it to get a better understanding and intuition how it works. 
Nevertheless, below my quick and dirty user-based kNN implementation for Your case. The code can be rewritten in better way (Adv.R is really good resource) - but this is only example for learning purpose and I do not take responsibilities for any damages :-)&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;require(reshape2)

ratings &amp;lt;- read.csv(con &amp;lt;- textConnection(''
User,Item,Rating
Anna,Apple,2
Bob,Apple,4
Carlos,Apple,1
Ethan,Apple,1
Gene,Apple,2
Anna,Banana,4
Dana,Banana,3
Ethan,Banana,5
Frank,Banana,5
Bob,Carrot,1
Ethan,Carrot,2
Gene,Carrot,4
''), header = T)

R &amp;lt;- ratings %&amp;gt;% dcast(Item ~ User)
ProductNames &amp;lt;- R[,1]
R &amp;lt;- R[,-1] # only ratings

# tanimoto similarity (~Jaccard coefficiet)
tanimoto &amp;lt;- function(v1,v2) length(intersect(v1,v2))/length(union(v1,v2))

# return top-k similar users
getKNN &amp;lt;- function(R, i, k, sim = tanimoto) {
  similarity &amp;lt;- array(0,length(R))
  for(j in 1:length(R)) {
    if(i!=j) similarity[j] = sim(which(!is.na(R[,i])),which(!is.na(R[,j])))
  }
  idx &amp;lt;- order(similarity, decreasing = T)[1:k]
  data.frame(idx = idx, similarity = similarity[idx])
}

kNNRecommender &amp;lt;- function(R, k) {
  reco &amp;lt;- data.frame()
  for(u in 1:length(R)) {
    userItems &amp;lt;- which(!is.na(R[,u])) #items rated by user
    nn &amp;lt;- getKNN(R,u, k) # get user neighbours

    # rating prediction - wiegted by similarity
    r &amp;lt;- array(0, c(dim(R)[1], k))
    for(i in 1:k) {
      r[,i] = R[,nn[i,]$idx] * nn[i,]$similarity
    }
    r[is.na(r)] &amp;lt;- 0
    userReco &amp;lt;- rowSums(r) / sum(nn$similarity)
    userRecoIdx &amp;lt;- order(userReco, decreasing = T)

    # remove items already rated by user
    userRecoIdx &amp;lt;- userRecoIdx[-userItems]

    # add to recommendations result
    reco &amp;lt;- rbind(reco, data.frame(
      User=u,
      Item=(if(length(userRecoIdx)==0) NA else userRecoIdx),
      Prediction=(if(length(userRecoIdx)==0) NA else userReco[userRecoIdx])
    ))
  }
  reco
}

kNNRecommender(R, 2)
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6698, 2, '2015-08-05 07:01:10.34', 1, NULL, NULL, '&lt;p&gt;As correctly pointed out above MinHash and SimHash both belong to Locality Sensitive Hashing. 
Reference: &lt;a href="https://en.wikipedia.org/wiki/Locality-sensitive_hashing" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Locality-sensitive_hashing&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The main difference between the two is the way collision is handled,&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;SimHash, uses cosine similarity&lt;/li&gt;
&lt;li&gt;MinHash, uses Jaccard Index.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Answers to your questions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;No. They uses different collision handling techniques to validate similarity. Also there is a variant on single Hash Function for Min Hash but it works differently. For more details, check the following reference out, &lt;a href="https://en.wikipedia.org/wiki/MinHash" rel="nofollow"&gt;https://en.wikipedia.org/wiki/MinHash&lt;/a&gt; (Variant with a single hash function) &lt;/li&gt;
&lt;li&gt;Yes, &lt;a href="https://github.com/chrisjmccormick/MinHash/blob/master/runMinHashExample.py" rel="nofollow"&gt;https://github.com/chrisjmccormick/MinHash/blob/master/runMinHashExample.py&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;I think the complexity can be reduced to $O(n \log n)$ with modified form of binary search while clustering.&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/chrisjmccormick/minhash/blob/master/runminhashexample.py'':120C ''/chrisjmccormick/minhash/blob/master/runminhashexample.py&lt;/a&gt;&lt;/li&gt;'':125C ''/li'':47C,53C,113C,147C ''/ol'':54C,148C ''/p'':40C,60C ''/wiki/locality-sensitive_hashing'':21C ''/wiki/locality-sensitive_hashing&lt;/a&gt;&lt;/p&gt;'':26C ''/wiki/minhash'':101C ''/wiki/minhash&lt;/a&gt;'':106C ''also'':73C ''answer'':56C ''belong'':11C ''binari'':143C ''check'':92C ''cluster'':146C ''collis'':37C,67C ''complex'':130C ''correct'':3C ''cosin'':45C ''detail'':91C ''differ'':30C,66C,88C ''en.wikipedia.org'':20C,25C,100C,105C ''en.wikipedia.org/wiki/locality-sensitive_hashing'':19C ''en.wikipedia.org/wiki/locality-sensitive_hashing&lt;/a&gt;&lt;/p&gt;'':24C ''en.wikipedia.org/wiki/minhash'':99C ''en.wikipedia.org/wiki/minhash&lt;/a&gt;'':104C ''follow'':94C ''form'':141C ''function'':81C,112C ''github.com'':119C,124C ''github.com/chrisjmccormick/minhash/blob/master/runminhashexample.py'':118C ''github.com/chrisjmccormick/minhash/blob/master/runminhashexample.py&lt;/a&gt;&lt;/li&gt;'':123C ''handl'':39C,68C ''hash'':15C,80C,84C,111C ''href'':18C,98C,117C ''index'':52C ''jaccard'':51C ''li'':42C,48C,62C,114C,126C ''local'':13C ''log'':137C ''main'':29C ''min'':83C ''minhash'':7C,49C ''modifi'':140C ''n'':136C,138C ''nofollow'':23C,103C,122C ''o'':135C ''ol'':41C,61C ''p'':1C,27C,55C ''point'':4C ''question'':59C ''reduc'':133C ''refer'':16C,95C ''rel'':22C,102C,121C ''search'':144C ''sensit'':14C ''simhash'':9C,43C ''similar'':46C,72C ''singl'':79C,110C ''techniqu'':69C ''think'':128C ''two'':33C ''use'':44C,50C,65C ''valid'':71C ''variant'':77C,107C ''way'':36C ''work'':87C ''yes'':115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6699, 2, '2015-08-05 07:17:04.6', 0, NULL, NULL, '&lt;p&gt;check the following links for Spanish sentiment analysis related links :&lt;/p&gt;

&lt;p&gt;Working Model :&lt;a href="http://dtminredis.housing.salle.url.edu:8080/EmoLib/es/" rel="nofollow"&gt;http://dtminredis.housing.salle.url.edu:8080/EmoLib/es/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Data : &lt;a href="http://www.daedalus.es/TASS2015/tass2015.php" rel="nofollow"&gt;http://www.daedalus.es/TASS2015/tass2015.php&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Api : &lt;a href="https://www.mashape.com/molinodeideas/sentiment-analysis-spanish" rel="nofollow"&gt;https://www.mashape.com/molinodeideas/sentiment-analysis-spanish&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/emolib/es/'':20C ''/emolib/es/&lt;/a&gt;&lt;/p&gt;'':25C ''/molinodeideas/sentiment-analysis-spanish'':44C ''/molinodeideas/sentiment-analysis-spanish&lt;/a&gt;&lt;/p&gt;'':49C ''/p'':12C ''/tass2015/tass2015.php'':32C ''/tass2015/tass2015.php&lt;/a&gt;&lt;/p&gt;'':37C ''analysi'':9C ''api'':39C ''check'':2C ''data'':27C ''dtminredis.housing.salle.url.edu:8080'':19C,24C ''dtminredis.housing.salle.url.edu:8080/emolib/es/'':18C ''dtminredis.housing.salle.url.edu:8080/emolib/es/&lt;/a&gt;&lt;/p&gt;'':23C ''follow'':4C ''href'':17C,29C,41C ''link'':5C,11C ''model'':15C ''nofollow'':22C,34C,46C ''p'':1C,13C,26C,38C ''rel'':21C,33C,45C ''relat'':10C ''sentiment'':8C ''spanish'':7C ''work'':14C ''www.daedalus.es'':31C,36C ''www.daedalus.es/tass2015/tass2015.php'':30C ''www.daedalus.es/tass2015/tass2015.php&lt;/a&gt;&lt;/p&gt;'':35C ''www.mashape.com'':43C,48C ''www.mashape.com/molinodeideas/sentiment-analysis-spanish'':42C ''www.mashape.com/molinodeideas/sentiment-analysis-spanish&lt;/a&gt;&lt;/p&gt;'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6700, 1, '2015-08-05 10:27:51.37', 3, 52, 'When to stop calculating values of each cell in the grid in Reinforcement Learning(dynamic programming) applied on gridworld', '&lt;p&gt;Considering application of Reinforcement learning(dynamic programming method performing value iteration) on grid world, in each of the iteration, I go through each of the cell of the grid and update its value depending on its present value and the present value of the taking action from that state. Now&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;How long do I keep updating value of each cell? Shall I keep updating unless the change in the previous and the present value function is the least? I am not able to understand how to implement the stopping mechanism in the grid-world scenario(discount not considered)&lt;br&gt;&lt;br&gt;&lt;/li&gt;
&lt;li&gt;Is the value function the values of all the grids in the grid world?&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  markov-process  reinforcement-learning', NULL, '''/li'':134C,150C ''/ol'':151C ''/p'':80C ''abl'':114C ''action'':75C ''appli'':17A ''applic'':31C ''br'':132C,133C ''calcul'':4A ''cell'':8A,55C,92C ''chang'':99C ''consid'':30C,131C ''depend'':63C ''discount'':129C ''dynam'':15A,35C ''function'':107C,139C ''go'':50C ''grid'':11A,42C,58C,126C,145C,148C ''grid-world'':125C ''gridworld'':19A ''implement'':119C ''iter'':40C,48C ''keep'':87C,95C ''learn'':14A,22B,28B,34C ''least'':110C ''li'':82C,135C ''long'':84C ''machin'':21B ''machine-learn'':20B ''markov'':24B ''markov-process'':23B ''mechan'':122C ''method'':37C ''ol'':81C ''p'':29C ''perform'':38C ''present'':66C,70C,105C ''previous'':102C ''process'':25B ''program'':16A,36C ''reinforc'':13A,27B,33C ''reinforcement-learn'':26B ''scenario'':128C ''shall'':93C ''state'':78C ''stop'':3A,121C ''take'':74C ''understand'':116C ''unless'':97C ''updat'':60C,88C,96C ''valu'':5A,39C,62C,67C,71C,89C,106C,138C,141C ''world'':43C,127C,149C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6710, 2, '2015-08-06 09:09:00.657', 2, NULL, NULL, '&lt;p&gt;If you need complexity of this calculation in big O notation - it is: $$O(n^3)$$
Why? Because matrix inverse needs $$O(n^3)$$ operations, and it is biggest complexity here.&lt;/p&gt;

&lt;p&gt;Multiplication matrix by its transpose is $$O(n^2p)$$ (Because for computing every value in the resulting matrix of size NxN you have to compute p multiplications).
Matrix transpose is $$O(np)$$ But you can ignore any complexities lesser than $$O(n^3)$$ (like matrix sum, transpose, or multiplying by constant) because their order of growth much lesser than order of growth of inverse operation.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Computational_complexity_of_mathematical_operations#Matrix_algebra" rel="nofollow"&gt;Computational_complexity_of_mathematical_operations&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':114C ''/p'':33C,100C,115C ''/wiki/computational_complexity_of_mathematical_operations#matrix_algebra'':106C ''2p'':43C ''3'':17C,25C,77C ''big'':10C ''biggest'':30C ''calcul'':8C ''complex'':5C,31C,72C,110C ''comput'':46C,59C,109C ''constant'':85C ''en.wikipedia.org'':105C ''en.wikipedia.org/wiki/computational_complexity_of_mathematical_operations#matrix_algebra'':104C ''everi'':47C ''growth'':90C,96C ''href'':103C ''ignor'':70C ''invers'':21C,98C ''lesser'':73C,92C ''like'':78C ''mathemat'':112C ''matrix'':20C,36C,52C,62C,79C ''much'':91C ''multipl'':35C,61C ''multipli'':83C ''n'':16C,24C,42C,76C ''need'':4C,22C ''nofollow'':108C ''notat'':12C ''np'':66C ''nxn'':55C ''o'':11C,15C,23C,41C,65C,75C ''oper'':26C,99C,113C ''order'':88C,94C ''p'':1C,34C,60C,101C ''rel'':107C ''result'':51C ''size'':54C ''sum'':80C ''transpos'':39C,63C,81C ''valu'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6761, 2, '2015-08-11 12:27:50.903', 2, NULL, NULL, '&lt;p&gt;David has a good point , I would suggest you focus on whatever it is that drives your interest more. It''s the only way to succeed in every kind of effort. If you want to build something cool start with it. If you want to read a book thats good too. The starting point doesn''t matter. A few days ahead you will have a better understanding on what you want and should do next.&lt;/p&gt;
', NULL, NULL, '''/p'':77C ''ahead'':62C ''better'':67C ''book'':49C ''build'':37C ''cool'':39C ''david'':2C ''day'':61C ''doesn'':56C ''drive'':17C ''effort'':32C ''everi'':29C ''focus'':11C ''good'':5C,51C ''interest'':19C ''kind'':30C ''matter'':58C ''next'':76C ''p'':1C ''point'':6C,55C ''read'':47C ''someth'':38C ''start'':40C,54C ''succeed'':27C ''suggest'':9C ''that'':50C ''understand'':68C ''want'':35C,45C,72C ''way'':25C ''whatev'':13C ''would'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6714, 2, '2015-08-06 16:32:40.243', 1, NULL, NULL, '&lt;p&gt;@Olologin is correct for very basic direct solves with no optimization, but there are optimizations that exist, so I wanted to add a few points...&lt;/p&gt;

&lt;p&gt;A great many mathematicians spend their entire careers on speeding up and optimizing this problem.  So, the solution depends a lot on the composition of the matrix (&lt;code&gt;sparsity&lt;/code&gt;), whether the problem is &lt;code&gt;convex&lt;/code&gt;,  whether it is being solved &lt;code&gt;iteratively&lt;/code&gt; or &lt;code&gt;directly&lt;/code&gt;, and what algorithm is being used.&lt;/p&gt;

&lt;p&gt;For lots of data science problems $O(n^2)$ and better solves are readily achieved.&lt;/p&gt;

&lt;p&gt;Some common libraries include:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Basic Linear Algebra Subroutines (&lt;a href="http://www.netlib.org/blas/" rel="nofollow"&gt;BLAS&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.netlib.org/linpack/" rel="nofollow"&gt;LINPACK&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.netlib.org/lapack/" rel="nofollow"&gt;LAPACK&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Here are some surveys of the subject:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.cs.nthu.edu.tw/~cchen/CS6331/ch2.pdf" rel="nofollow"&gt;General survey&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://web.stanford.edu/class/cme324/saad.pdf" rel="nofollow"&gt;Survey of Iterative Methods&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/LU_decomposition" rel="nofollow"&gt;LU decomposition&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Conjugate_gradient_method" rel="nofollow"&gt;Conjugate Gradient&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/ebooks/html/csa/node37.html" rel="nofollow"&gt;Direct Methods for Solving Linear Systems&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6909, 2, '2015-08-26 19:00:27.29', 2, NULL, NULL, '&lt;p&gt;The preprocessing will depend on what your data is like: textual? numerical? If in whatever analysis you want to do the data is not allowed to have duplicates, you''ll have to filter those out. If not, they''ll get to stay, etc. 
If you already know exactly what you want to do theoretically and just need to find some software, python pandas is very good. Load the data, clean it, group it, perform elementary analysis.&lt;/p&gt;
', NULL, NULL, '''/p'':78C ''allow'':26C ''alreadi'':47C ''analysi'':17C,77C ''clean'':71C ''data'':9C,23C,70C ''depend'':5C ''duplic'':29C ''elementari'':76C ''etc'':44C ''exact'':49C ''filter'':34C ''find'':60C ''get'':41C ''good'':67C ''group'':73C ''know'':48C ''like'':11C ''ll'':31C,40C ''load'':68C ''need'':58C ''numer'':13C ''p'':1C ''panda'':64C ''perform'':75C ''preprocess'':3C ''python'':63C ''softwar'':62C ''stay'':43C ''textual'':12C ''theoret'':55C ''want'':19C,52C ''whatev'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6715, 1, '2015-08-06 20:58:57.38', 8, 621, 'Is it necessary to standardize your data before clustering?', '&lt;p&gt;Is it necessary to standardize your data before cluster?  In the example from &lt;code&gt;scikit learn&lt;/code&gt; about DBSCAN, &lt;a href="http://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#example-cluster-plot-dbscan-py"&gt;here&lt;/a&gt; they do this in the line:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;X = StandardScaler().fit_transform(X)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But I do not understand why it is necessary.  After all, clustering does not assume any particular distribution of data - it is an unsupervised learning method so its objective is to explore the data.  &lt;/p&gt;

&lt;p&gt;Why would it be necessary to transform the data?  &lt;/p&gt;
', 'python  clustering  clusters  anomaly-detection', NULL, '''/a'':42C ''/code'':33C,57C ''/p'':49C,94C,105C ''/pre'':58C ''/stable/auto_examples/cluster/plot_dbscan.html#example-cluster-plot-dbscan-py'':40C ''anomali'':14B ''anomaly-detect'':13B ''assum'':74C ''cluster'':9A,11B,12B,25C,71C ''code'':30C,51C ''data'':7A,23C,79C,93C,104C ''dbscan'':35C ''detect'':15B ''distribut'':77C ''exampl'':28C ''explor'':91C ''fit'':54C ''href'':37C ''learn'':32C,84C ''line'':48C ''method'':85C ''necessari'':3A,19C,68C,100C ''object'':88C ''p'':16C,59C,95C ''particular'':76C ''pre'':50C ''python'':10B ''scikit'':31C ''scikit-learn.org'':39C ''scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#example-cluster-plot-dbscan-py'':38C ''standard'':5A,21C ''standardscal'':53C ''transform'':55C,102C ''understand'':64C ''unsupervis'':83C ''would'':97C ''x'':52C,56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6716, 2, '2015-08-06 21:38:01.087', 3, NULL, NULL, '&lt;p&gt;Standardizing data is recommended because otherwise the range of values in each feature will act as a weight when determining how to cluster data, which is typically undesired.&lt;/p&gt;

&lt;p&gt;For example consider the standard metric for most clustering algorithms (including DBSCAN in sci-kit learn) -- &lt;code&gt;euclidean&lt;/code&gt;, otherwise known as the L2 norm. If one of your features has a range of values much larger than the others, clustering will be completely dominated by that one feature. To illustrate this look at the simple example below:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;&amp;gt;&amp;gt; import numpy as np
&amp;gt;&amp;gt;&amp;gt; from sklearn.preprocessing import StandardScaler
&amp;gt;&amp;gt;&amp;gt; from sklearn.metrics.pairwise import euclidean_distances
&amp;gt;&amp;gt;&amp;gt; 
&amp;gt;&amp;gt;&amp;gt; X = np.array([[1,2,100],[4,3,50],[1,1,75]])
&amp;gt;&amp;gt;&amp;gt; 
&amp;gt;&amp;gt;&amp;gt; print X
[[  1   2 100]
 [  4   3  50]
 [  1   1  75]]
&amp;gt;&amp;gt;&amp;gt; 
&amp;gt;&amp;gt;&amp;gt; print euclidean_distances(X)
[[  0.          50.0999002   25.01999201]
 [ 50.0999002    0.          25.25866188]
 [ 25.01999201  25.25866188   0.        ]]
&amp;gt;&amp;gt;&amp;gt; print euclidean_distances(StandardScaler().fit_transform(X))
[[ 0.          3.46410162  1.73205081]
 [ 3.46410162  0.          3.46410162]
 [ 1.73205081  3.46410162  0.        ]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;From this you should see that the euclidean distances between the non-standardized versions are dominated by the third column because its range of values is much larger than the other two. However when the data is standardized this no longer becomes an issue and weights each feature as being equal when calculating the distance between each data point.&lt;/p&gt;
', NULL, NULL, '''/code'':51C,188C ''/p'':30C,91C,251C ''/pre'':189C ''0'':160C,164C,168C,179C,183C,187C ''1'':124C,130C,131C,141C,147C,148C ''1.73205081'':181C,185C ''100'':126C,143C ''2'':125C,142C ''25.01999201'':162C,166C ''25.25866188'':165C,167C ''3'':128C,145C ''3.46410162'':180C,182C,184C,186C ''4'':127C,144C ''50'':129C,146C ''50.0999002'':161C,163C ''75'':132C,149C ''act'':16C ''algorithm'':41C ''becom'':233C ''calcul'':244C ''cluster'':24C,40C,73C ''code'':49C,93C ''column'':211C ''complet'':76C ''consid'':34C ''data'':3C,25C,227C,249C ''dbscan'':43C ''determin'':21C ''distanc'':115C,158C,174C,199C,246C ''domin'':77C,207C ''equal'':242C ''euclidean'':50C,114C,157C,173C,198C ''exampl'':33C,89C ''featur'':14C,62C,81C,239C ''fit'':176C ''gt'':94C,95C,96C,101C,102C,103C,108C,109C,110C,116C,117C,118C,119C,120C,121C,133C,134C,135C,136C,137C,138C,150C,151C,152C,153C,154C,155C,169C,170C,171C ''howev'':224C ''illustr'':83C ''import'':97C,106C,113C ''includ'':42C ''issu'':235C ''kit'':47C ''known'':53C ''l2'':56C ''larger'':69C,219C ''learn'':48C ''longer'':232C ''look'':85C ''metric'':37C ''much'':68C,218C ''non'':203C ''non-standard'':202C ''norm'':57C ''np'':100C ''np.array'':123C ''numpi'':98C ''one'':59C,80C ''other'':72C ''otherwis'':7C,52C ''p'':1C,31C,190C ''point'':250C ''pre'':92C ''print'':139C,156C,172C ''rang'':9C,65C,214C ''recommend'':5C ''sci'':46C ''sci-kit'':45C ''see'':195C ''simpl'':88C ''sklearn.metrics.pairwise'':112C ''sklearn.preprocessing'':105C ''standard'':2C,36C,204C,229C ''standardscal'':107C,175C ''third'':210C ''transform'':177C ''two'':223C ''typic'':28C ''undesir'':29C ''valu'':11C,67C,216C ''version'':205C ''weight'':19C,237C ''x'':122C,140C,159C,178C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6717, 2, '2015-08-07 00:20:03.757', 1, NULL, NULL, '&lt;p&gt;It isn''t strictly necessary to standardise, whether it is required or not may depend on the distance metric you choose. &lt;/p&gt;

&lt;p&gt;For instance if you opt for the Mahalanobis distance then separation will be based upon the number of standard deviations separating points and not the absolute distance between them, as such it is a scale invariant metric.&lt;/p&gt;

&lt;p&gt;As with many things in machine learning there is no hard and fast answer and the only way to know for sure is to apply a variety of techniques see which gives the most appropriate results for your data.&lt;/p&gt;
', NULL, NULL, '''/p'':23C,62C,103C ''absolut'':50C ''answer'':77C ''appli'':88C ''appropri'':98C ''base'':38C ''choos'':22C ''data'':102C ''depend'':16C ''deviat'':44C ''distanc'':19C,33C,51C ''fast'':76C ''give'':95C ''hard'':74C ''instanc'':26C ''invari'':60C ''isn'':3C ''know'':83C ''learn'':70C ''machin'':69C ''mahalanobi'':32C ''mani'':66C ''may'':15C ''metric'':20C,61C ''necessari'':6C ''number'':41C ''opt'':29C ''p'':1C,24C,63C ''point'':46C ''requir'':12C ''result'':99C ''scale'':59C ''see'':93C ''separ'':35C,45C ''standard'':43C ''standardis'':8C ''strict'':5C ''sure'':85C ''techniqu'':92C ''thing'':67C ''upon'':39C ''varieti'':90C ''way'':81C ''whether'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6719, 1, '2015-08-07 04:31:06.123', 1, 37, 'Value Updation Dynamic Programming Reinforcement learning', '&lt;p&gt;Regarding Value Iteration of Dynamic Programming(reinforcement learning) in grid world, the value updation of each state is given by: &lt;br&gt;
&lt;a href="http://i.stack.imgur.com/Y3Sar.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Y3Sar.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;br&gt;
Now Suppose i am in say box (3,2). I can go to (4,2)(up) (3,3)(right) and (1,3)(left) and none of these are my final state so i get a reward of -0.1 for going in each of the states. The present value of all states are 0. The probability of going north is 0.8, and going left/right is 0.1 each. So since going left/right gives me more reward(as reward*probability will be negative) i go left or right. Is this the mechanism. Am I correct? But In the formula there is a summation term given. So I basically cannot understand this formula. Can anyone explain me with an example?&lt;/p&gt;
', 'machine-learning  reinforcement-learning', NULL, '''-0.1'':93C ''/a'':52C ''/p'':53C,172C ''/y3sar.png'':39C,46C ''0'':108C ''0.1'':120C ''0.8'':115C ''1'':76C ''2'':64C,70C ''3'':63C,72C,73C,77C ''4'':69C ''alt'':47C ''anyon'':166C ''basic'':160C ''box'':62C ''br'':34C,55C ''cannot'':161C ''correct'':147C ''descript'':50C ''dynam'':3A,18C ''enter'':48C ''exampl'':171C ''explain'':167C ''final'':85C ''formula'':151C,164C ''get'':89C ''give'':126C ''given'':32C,157C ''go'':67C,95C,112C,117C,124C,137C ''grid'':23C ''href'':36C ''i.stack.imgur.com'':38C,45C ''i.stack.imgur.com/y3sar.png'':37C,44C ''imag'':49C ''img'':42C ''iter'':16C ''learn'':6A,9B,12B,21C ''left'':78C,138C ''left/right'':118C,125C ''machin'':8B ''machine-learn'':7B ''mechan'':144C ''negat'':135C ''nofollow'':41C ''none'':80C ''north'':113C ''p'':13C,54C ''present'':102C ''probabl'':110C,132C ''program'':4A,19C ''regard'':14C ''reinforc'':5A,11B,20C ''reinforcement-learn'':10B ''rel'':40C ''reward'':91C,129C,131C ''right'':74C,140C ''say'':61C ''sinc'':123C ''src'':43C ''state'':30C,86C,100C,106C ''summat'':155C ''suppos'':57C ''term'':156C ''understand'':162C ''updat'':2A,27C ''valu'':1A,15C,26C,103C ''world'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9078, 2, '2015-11-27 09:50:28.027', 0, NULL, NULL, '&lt;p&gt;If you work on Linux, you may create a script that loads data and runs R inside and give the users the access to execute the main script. I do not know the details but this may be a possible solution.&lt;/p&gt;
', NULL, NULL, '''/p'':43C ''access'':24C ''creat'':9C ''data'':14C ''detail'':35C ''execut'':26C ''give'':20C ''insid'':18C ''know'':33C ''linux'':6C ''load'':13C ''main'':28C ''may'':8C,38C ''p'':1C ''possibl'':41C ''r'':17C ''run'':16C ''script'':11C,29C ''solut'':42C ''user'':22C ''work'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6744, 1, '2015-08-10 12:48:17.207', 0, 55, 'Prepare data for SVM, Is it valid to normalise the data before and after PCA dimension reduction', '&lt;p&gt;Is it valid to normalise a dataset, reduce dimensionality with PCA and then to normalise the reduced dimension data.    Assuming this is performed on training data, should the same PCA coefficients be used to reduce the dimension of the test data.  Should the same max and min normalisation values be used for the test and training data. I have included a simplified example of the code I am using which may describe I said better. Thanks in advance. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;%% Prepare Training Data


% Normalise training data
mindata=min(TRAINDATA); maxdata=max(TRAINDATA);
TRAINDATA = ((TRAINDATA-repmat(mindata,[size(TRAINDATA,1),1]))./(repmat(maxdata,[size(TRAINDATA,1),1])-repmat(mindata,[size(TRAINDATA,1),1])) - 0.5 ) *2;

% Perform PCA
mTRAINDATA = mean(mean(TRAINDATA));
TRAINDATA = TRAINDATA - mTRAINDATA;
[Cpca,~,~,~,~]=princomp(TRAINDATA,''econ'');
EigenRange = 1:2;
Cpca = Cpca(:,EigenRange);
TRAINDATA = TRAINDATA*Cpca;
TRAINDATA = TRAINDATA + mTRAINDATA;

% Normalise training data second time
mindata2=min(TRAINDATA); maxdata2=max(TRAINDATA);
TRAINDATA = ((TRAINDATA-repmat(mindata2,[size(TRAINDATA,1),1]))./(repmat(maxdata2,[size(TRAINDATA,1),1])-repmat(mindata2,[size(TRAINDATA,1),1])) - 0.5 ) *2;



%% Prepare Test Data

% Normalise using first normalisation values from training data
TESTDATA = ((TESTDATA-repmat(mindata,[size(TESTDATA,1),1]))./(repmat(maxdata,[size(TESTDATA,1),1])-repmat(mindata,[size(TESTDATA,1),1])) - 0.5 ) *2;

% Perform PCA
mTESTDATA = mean(mean(TESTDATA));
TESTDATA = TESTDATA - mTESTDATA;
TESTDATA = TESTDATA*Cpca;
TESTDATA = TESTDATA + mTRAINDATA;

% Normalise using second normalisation values from training data
TESTDATA = ((TESTDATA-repmat(mindata2,[size(TESTDATA,1),1]))./(repmat(maxdata2,[size(TESTDATA,1),1])-repmat(mindata2,[size(TESTDATA,1),1])) - 0.5 ) *2;
&lt;/code&gt;&lt;/pre&gt;
', 'machine-learning  svm  dimensionality-reduction  libsvm', NULL, '''/code'':282C ''/p'':105C ''/pre'':283C ''0.5'':141C,200C,234C,280C ''1'':127C,128C,133C,134C,139C,140C,157C,186C,187C,192C,193C,198C,199C,220C,221C,226C,227C,232C,233C,266C,267C,272C,273C,278C,279C ''2'':142C,158C,201C,235C,281C ''advanc'':104C ''assum'':46C ''better'':101C ''code'':92C,107C ''coeffici'':57C ''cpca'':152C,159C,160C,164C,247C ''data'':2A,11A,45C,52C,67C,83C,110C,113C,170C,204C,212C,258C ''dataset'':33C ''describ'':98C ''dimens'':16A,44C,63C ''dimension'':23B,35C ''dimensionality-reduct'':22B ''econ'':155C ''eigenrang'':156C,161C ''exampl'':89C ''first'':207C ''includ'':86C ''learn'':20B ''libsvm'':25B ''machin'':19B ''machine-learn'':18B ''max'':71C,118C,177C ''maxdata'':117C,130C,223C ''maxdata2'':176C,189C,269C ''may'':97C ''mean'':146C,147C,239C,240C ''min'':73C,115C,174C ''mindata'':114C,124C,136C,217C,229C ''mindata2'':173C,183C,195C,263C,275C ''mtestdata'':238C,244C ''mtraindata'':145C,151C,167C,250C ''normalis'':9A,31C,41C,74C,111C,168C,205C,208C,251C,254C ''p'':26C ''pca'':15A,37C,56C,144C,237C ''perform'':49C,143C,236C ''pre'':106C ''prepar'':1A,108C,202C ''princomp'':153C ''reduc'':34C,43C,61C ''reduct'':17A,24B ''repmat'':123C,129C,135C,182C,188C,194C,216C,222C,228C,262C,268C,274C ''said'':100C ''second'':171C,253C ''simplifi'':88C ''size'':125C,131C,137C,184C,190C,196C,218C,224C,230C,264C,270C,276C ''svm'':4A,21B ''test'':66C,80C,203C ''testdata'':213C,215C,219C,225C,231C,241C,242C,243C,245C,246C,248C,249C,259C,261C,265C,271C,277C ''testdata-repmat'':214C,260C ''thank'':102C ''time'':172C ''train'':51C,82C,109C,112C,169C,211C,257C ''traindata'':116C,119C,120C,122C,126C,132C,138C,148C,149C,150C,154C,162C,163C,165C,166C,175C,178C,179C,181C,185C,191C,197C ''traindata-repmat'':121C,180C ''use'':59C,77C,95C,206C,252C ''valid'':7A,29C ''valu'':75C,209C,255C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6720, 2, '2015-08-07 05:15:00.393', 2, NULL, NULL, '&lt;p&gt;The probabilities you describe refer only to the go-north action. It means that if you want to go north, you have 80% chance of actually going north and 20% of going left or right, making the problem more difficult (non-deterministic). This rule applies to every direction. Also, the formula does not tell which action to chose, just how to update the values. In order to select an action, assuming a greedy-policy, you''d select the one with the highest expected value ($V(s'')$).&lt;/p&gt;

&lt;p&gt;The formula says to sum the values for all possible outcomes from the best action. So, supposing go-north is indeed the best action, you have: &lt;/p&gt;

&lt;p&gt;$$.8 * (-.1 + 0) + .1 * (-.1 + 0) + .1 * (-.1 + 0) = -.1$$&lt;/p&gt;

&lt;p&gt;But let us suppose that you still don''t know which is the best action and want to select one greedily. Then you must compute the sum for each possible action (north, south, east, west). Your example has all values set to 0 and the same reward and so is not very interesting. Let''s say you have a +1 reward to east (-0.1 for the remaining directions) and that south already has V(s) = 0.5 (0 for the remaining states). Then you compute the value for each action (let $\gamma = 1$, since it is a user-adjusted parameter):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;North: $.8 * (-.1 + 0) + .1 * (-.1 + 0) + .1 * (1 + 0) = -.08 - .01 + .1 = .01$&lt;/li&gt;
&lt;li&gt;South: $.8 * (-.1 + .5) + .1 * (-.1 + 0) + .1 * (1 + 0) = 0.32 - .01 + .1 = .41$&lt;/li&gt;
&lt;li&gt;East: $.8 * (1 + 0) + .1 * (-.1 + 0) + .1 * (-.1 + .5) = .8 - .01 + .04 = .83$&lt;/li&gt;
&lt;li&gt;West: $.8 * (-.1 + 0) + .1 * (-.1 + 0) + .1 * (-.1 + .5) = -.08 - .01 + .04 = -.05$&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So you would update your policy to go &lt;strong&gt;East&lt;/strong&gt; from the current state, and update the current state value to &lt;strong&gt;0.83&lt;/strong&gt;.&lt;/p&gt;
', NULL, NULL, '''+1'':193C ''-0.1'':197C ''/li'':251C,267C,283C,299C ''/p'':91C,120C,132C,234C,327C ''/strong'':312C,326C ''/ul'':300C ''0'':124C,127C,130C,176C,210C,240C,243C,246C,259C,262C,272C,275C,288C,291C ''0.32'':263C ''0.5'':209C ''0.83'':325C ''01'':248C,250C,264C,280C,296C ''04'':281C,297C ''05'':298C ''08'':247C,295C ''1'':123C,125C,126C,128C,129C,131C,225C,239C,241C,242C,244C,245C,249C,255C,257C,258C,260C,261C,265C,271C,273C,274C,276C,277C,287C,289C,290C,292C,293C ''20'':32C ''41'':266C ''5'':256C,278C,294C ''8'':122C,238C,254C,270C,279C,286C ''80'':25C ''83'':282C ''action'':13C,59C,73C,107C,117C,148C,164C,222C ''actual'':28C ''adjust'':232C ''alreadi'':205C ''also'':52C ''appli'':48C ''assum'':74C ''best'':106C,116C,147C ''chanc'':26C ''chose'':61C ''comput'':158C,217C ''current'':315C,320C ''d'':80C ''describ'':5C ''determinist'':45C ''difficult'':42C ''direct'':51C,201C ''east'':167C,196C,269C,311C ''everi'':50C ''exampl'':170C ''expect'':87C ''formula'':54C,94C ''gamma'':224C ''go'':11C,21C,29C,34C,111C,309C ''go-north'':10C,110C ''greedi'':77C ''greedili'':154C ''greedy-polici'':76C ''highest'':86C ''inde'':114C ''interest'':186C ''know'':143C ''left'':35C ''let'':135C,187C,223C ''li'':236C,252C,268C,284C ''make'':38C ''mean'':15C ''must'':157C ''non'':44C ''non-determinist'':43C ''north'':12C,22C,30C,112C,165C,237C ''one'':83C,153C ''order'':69C ''outcom'':103C ''p'':1C,92C,121C,133C,301C ''paramet'':233C ''polici'':78C,307C ''possibl'':102C,163C ''probabl'':3C ''problem'':40C ''refer'':6C ''remain'':200C,213C ''reward'':180C,194C ''right'':37C ''rule'':47C ''say'':95C,189C ''select'':71C,81C,152C ''set'':174C ''sinc'':226C ''south'':166C,204C,253C ''state'':214C,316C,321C ''still'':140C ''strong'':310C,324C ''sum'':97C,160C ''suppos'':109C,137C ''tell'':57C ''ul'':235C ''updat'':65C,305C,318C ''us'':136C ''user'':231C ''user-adjust'':230C ''v'':89C,207C ''valu'':67C,88C,99C,173C,219C,322C ''want'':19C,150C ''west'':168C,285C ''would'':304C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6724, 2, '2015-08-07 21:37:39.207', 3, NULL, NULL, '&lt;p&gt;If you want to be a practical man with true knowledge, start with math(calculus, probability + stat, lelinear algebra). On every step try to implement everything with programing, python is nice for this. When u get good ground, play with real data and solve for problems&lt;/p&gt;

&lt;p&gt;Courses.
Linear algebra - edx Laff or coding the matrix
Stat - edx stat 2x Barkley
Calculus - read...its simple&lt;/p&gt;
', NULL, NULL, '''/p'':48C,68C ''2x'':62C ''algebra'':20C,52C ''barkley'':63C ''calculus'':16C,64C ''code'':56C ''cours'':50C ''data'':43C ''edx'':53C,60C ''everi'':22C ''everyth'':27C ''get'':37C ''good'':38C ''ground'':39C ''implement'':26C ''knowledg'':12C ''laff'':54C ''lelinear'':19C ''linear'':51C ''man'':9C ''math'':15C ''matrix'':58C ''nice'':32C ''p'':1C,49C ''play'':40C ''practic'':8C ''probabl'':17C ''problem'':47C ''program'':29C ''python'':30C ''read'':65C ''real'':42C ''simpl'':67C ''solv'':45C ''start'':13C ''stat'':18C,59C,61C ''step'':23C ''tri'':24C ''true'':11C ''u'':36C ''want'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6725, 2, '2015-08-07 22:13:30.82', 4, NULL, NULL, '&lt;p&gt;One of the benefits of decision trees is that ordinal (continuous or discrete) input data does not require any significant preprocessing. In fact, the results should be consistent regardless of any scaling or translational normalization, since the trees can choose equivalent splitting points. The best preprocessing for decision trees is typically whatever is easiest or whatever is best for visualization, as long as it doesn''t change the relative order of values within each data dimension.&lt;/p&gt;

&lt;p&gt;Categorical inputs, which have no sensible order, are a special case. If your random forest implementation doesn''t have a built-in way to deal with categorical input, you should probably use a 1-hot encoding:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;If a categorical value has $n$ categories, you encode the value using $n$ dimensions, one corresponding to each category. &lt;/li&gt;
&lt;li&gt;For each data point, if it is in category $k$, the corresponding $k$th dimension is set to 1, while the rest are set to 0.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;This 1-hot encoding allows decision trees to perform category equality tests in one split since inequality splits on non-ordinal data doesn''t make much sense.&lt;/p&gt;
', NULL, NULL, '''/li'':139C,167C ''/p'':78C,117C,198C ''/ul'':168C ''0'':166C ''1'':114C,159C,171C ''allow'':174C ''benefit'':5C ''best'':46C,59C ''built'':101C ''built-in'':100C ''case'':90C ''categor'':80C,107C,122C ''categori'':126C,138C,149C,179C ''chang'':68C ''choos'':41C ''consist'':29C ''continu'':12C ''correspond'':135C,152C ''data'':16C,76C,143C,192C ''deal'':105C ''decis'':7C,49C,175C ''dimens'':77C,133C,155C ''discret'':14C ''doesn'':66C,96C,193C ''easiest'':55C ''encod'':116C,128C,173C ''equal'':180C ''equival'':42C ''fact'':24C ''forest'':94C ''hot'':115C,172C ''implement'':95C ''inequ'':186C ''input'':15C,81C,108C ''k'':150C,153C ''li'':119C,140C ''long'':63C ''make'':195C ''much'':196C ''n'':125C,132C ''non'':190C ''non-ordin'':189C ''normal'':36C ''one'':2C,134C,183C ''order'':71C,86C ''ordin'':11C,191C ''p'':1C,79C,169C ''perform'':178C ''point'':44C,144C ''preprocess'':22C,47C ''probabl'':111C ''random'':93C ''regardless'':30C ''relat'':70C ''requir'':19C ''rest'':162C ''result'':26C ''scale'':33C ''sens'':197C ''sensibl'':85C ''set'':157C,164C ''signific'':21C ''sinc'':37C,185C ''special'':89C ''split'':43C,184C,187C ''test'':181C ''th'':154C ''translat'':35C ''tree'':8C,39C,50C,176C ''typic'':52C ''ul'':118C ''use'':112C,131C ''valu'':73C,123C,130C ''visual'':61C ''way'':103C ''whatev'':53C,57C ''within'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6726, 1, '2015-08-08 08:16:15.16', 1, 95, 'Random forest model gives same result for all test data, Next step?', '&lt;p&gt;I am teaching myself some data science and have started a Kaggle project. 
I have fitted a random forest classification model (using sci-kit learn) with a few millions rows of data. There are two possible outcomes for each row (0 or 1). When I run it against the test data, I get 0 for every row. This is a practical impossibility, but I am at a loss as to how to diagnose my model and how to move forward. 
Is this an example of extreme overfitting ? Is it more likely a problem with my training data (or test data) that i am not seeing ?&lt;/p&gt;

&lt;p&gt;Should I simply increase the # of estimators? Is it possible that I have misformatted the input files in a subtle way that doesn''t cause an error ? I am at a loss as to how to move forward.&lt;/p&gt;
', 'python  random-forest  scikit', NULL, '''/p'':125C,165C ''0'':60C,73C ''1'':62C ''caus'':151C ''classif'':38C ''data'':10A,24C,51C,70C,116C,119C ''diagnos'':92C ''doesn'':149C ''error'':153C ''estim'':133C ''everi'':75C ''exampl'':103C ''extrem'':105C ''file'':143C ''fit'':34C ''forest'':2A,16B,37C ''forward'':99C,164C ''get'':72C ''give'':4A ''imposs'':81C ''increas'':130C ''input'':142C ''kaggl'':30C ''kit'':43C ''learn'':44C ''like'':110C ''loss'':87C,158C ''million'':48C ''misformat'':140C ''model'':3A,39C,94C ''move'':98C,163C ''next'':11A ''outcom'':56C ''overfit'':106C ''p'':18C,126C ''possibl'':55C,136C ''practic'':80C ''problem'':112C ''project'':31C ''python'':13B ''random'':1A,15B,36C ''random-forest'':14B ''result'':6A ''row'':49C,59C,76C ''run'':65C ''sci'':42C ''sci-kit'':41C ''scienc'':25C ''scikit'':17B ''see'':124C ''simpli'':129C ''start'':28C ''step'':12A ''subtl'':146C ''teach'':21C ''test'':9A,69C,118C ''train'':115C ''two'':54C ''use'':40C ''way'':147C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6727, 1, '2015-08-08 11:14:35.63', 1, 59, 'Merging repeating data cells in csv', '&lt;p&gt;I have a CSV file with around 1 Million rows. 
Let say its have details like &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Name      |   Age   | Salary 
name 1      52       10000
name 2      55       10043 
name 3      50       100054
name 2      55       10023
name 1      52       100322...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and soon .&lt;/p&gt;

&lt;p&gt;but i need to merge the redundant details .
and need a output like &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Name      |   Age   | Salary 
name 1      52       110322*
name 2      55       20066 *
name 3      50       100054 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;you might notice that the repeating Name 1 and Name 2 details are merged and the Salary values are added .So i''m looking for a way to apply this change to my original data set. so i need a python script to fix my problem . &lt;/p&gt;
', 'python  csv', NULL, '''/code'':52C,90C ''/p'':26C,57C,72C,139C ''/pre'':53C,91C ''1'':17C,33C,49C,79C,100C ''10000'':35C ''100054'':43C,89C ''10023'':47C ''100322'':51C ''10043'':39C ''110322'':81C ''2'':37C,45C,83C,103C ''20066'':85C ''3'':41C,87C ''50'':42C,88C ''52'':34C,50C,80C ''55'':38C,46C,84C ''ad'':112C ''age'':30C,76C ''appli'':121C ''around'':16C ''cell'':4A ''chang'':123C ''code'':28C,74C ''csv'':6A,8B,13C ''data'':3A,127C ''detail'':24C,66C,104C ''file'':14C ''fix'':136C ''let'':20C ''like'':25C,71C ''look'':116C ''m'':115C ''merg'':1A,63C,106C ''might'':94C ''million'':18C ''name'':29C,32C,36C,40C,44C,48C,75C,78C,82C,86C,99C,102C ''need'':61C,68C,131C ''notic'':95C ''origin'':126C ''output'':70C ''p'':9C,54C,58C,92C ''pre'':27C,73C ''problem'':138C ''python'':7B,133C ''redund'':65C ''repeat'':2A,98C ''row'':19C ''salari'':31C,77C,109C ''say'':21C ''script'':134C ''set'':128C ''soon'':56C ''valu'':110C ''way'':119C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6728, 1, '2015-08-08 11:52:25.107', 0, 43, 'Validating hierarchical cluster solutioins in R', '&lt;p&gt;I have a data set which consists of dissimilarity distance among 55 bacteria models. I''m trying to cluster them into 4 groups. Here is &lt;a href="http://1drv.ms/1J2sqCH" rel="nofollow"&gt;the dissimilarity matrix&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I''m looking for a method to &lt;strong&gt;evaluate the goodness of clusters&lt;/strong&gt; produced by different agglomeration methods &lt;strong&gt;in hierarchical clustering&lt;/strong&gt;  (e.g: single, average etc). My goal is to find find the most suitable agglomeration method. According to my understand Silhouette can''t be used for hierarchical clustering. &lt;/p&gt;

&lt;p&gt;One method I tried was figuring out which models are clustered into which cluster segments for different agglomeration methods. Below is the matrix image. But it doesn''t give me much information. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/QGjHd.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/QGjHd.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Also I went thought an article which mentioned about using boxplots to plot cluster elevations. However it was not clear how to generate them and interpret them. Any help is appreciated.&lt;/p&gt;
', 'r  clustering  bioinformatics', NULL, '''/1j2sqch'':40C ''/a'':46C,153C ''/p'':47C,100C,134C,154C,187C ''/qgjhd.png'':140C,147C ''/strong'':62C,72C ''1drv.ms'':39C ''1drv.ms/1j2sqch'':38C ''4'':32C ''55'':22C ''accord'':88C ''agglomer'':66C,86C,119C ''also'':156C ''alt'':148C ''among'':21C ''appreci'':186C ''articl'':161C ''averag'':75C ''bacteria'':23C ''bioinformat'':9B ''boxplot'':166C ''clear'':175C ''cluster'':3A,8B,29C,61C,71C,99C,112C,115C,169C ''consist'':17C ''data'':14C ''descript'':151C ''differ'':65C,118C ''dissimilar'':19C,44C ''distanc'':20C ''doesn'':128C ''e.g'':73C ''elev'':170C ''enter'':149C ''etc'':76C ''evalu'':57C ''figur'':107C ''find'':81C,82C ''generat'':178C ''give'':130C ''goal'':78C ''good'':59C ''group'':33C ''help'':184C ''hierarch'':2A,70C,98C ''howev'':171C ''href'':37C,137C ''i.stack.imgur.com'':139C,146C ''i.stack.imgur.com/qgjhd.png'':138C,145C ''imag'':125C,150C ''img'':143C ''inform'':133C ''interpret'':181C ''look'':51C ''m'':26C,50C ''matrix'':45C,124C ''mention'':163C ''method'':54C,67C,87C,103C,120C ''model'':24C,110C ''much'':132C ''nofollow'':42C,142C ''one'':102C ''p'':10C,48C,101C,135C,155C ''plot'':168C ''produc'':63C ''r'':6A,7B ''rel'':41C,141C ''segment'':116C ''set'':15C ''silhouett'':92C ''singl'':74C ''solutioin'':4A ''src'':144C ''strong'':56C,68C ''suitabl'':85C ''thought'':159C ''tri'':27C,105C ''understand'':91C ''use'':96C,165C ''valid'':1A ''went'':158C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6729, 2, '2015-08-08 13:22:01.227', 0, NULL, NULL, '&lt;p&gt;My solution may not be the best, but I would opt for this one since it is the simplest.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Create an empty dictionary&lt;/li&gt;
&lt;li&gt;Iterate on the CSV. If the name is already in the dictionary, sum up the salaries. If not, then create a new key with the salary.&lt;/li&gt;
&lt;li&gt;After that, iterate again on the dictionary to write a new CSV with the new values.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If you are not familiar with Python code, just ask it and I will write it for you :)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit with a code sample:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import csv

## open CSV file and rea it
myfile  = open(''test.csv'', "rb")
reader = csv.reader(myfile)

## create an empty dictionary
mydictionary = {}

rownum = 0

for row in reader:
    ## check if it is the header
    if rownum == 0:
        pass
    else:
        ## split the line of CSV in elements..Use the name for the key in dictionary and the other two in a list
        line = row.split(",")
        key = line[0]
        age = line[1]
        salary = line[2]

        if key in mydictionary:
            mydictionary[key][1] += salary
        else:
            mydictionary[key] = [age,salary]

    rownum += 1

ifile.close()

## create a new list of lists with the data from the dictionary
newcsvfile = ["name","age","salary"]

for i in mydictionary:
    newcsvfile.append(i,mydictionary[i][0],mydictionary[i][1])

## write the new list of lists in a new CSV file
with open("output.csv", "wb") as f:
    writer = csv.writer(f)
    writer.writerows(newcsvfile)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':243C ''/li'':28C,56C,74C ''/p'':21C,95C,104C ''/pre'':244C ''/strong'':103C ''/ul'':75C ''0'':128C,141C,170C,217C ''1'':173C,183C,191C,220C ''2'':176C ''age'':171C,188C,207C ''alreadi'':38C ''ask'':86C ''best'':8C ''check'':133C ''code'':84C,101C,106C ''creat'':24C,49C,122C,193C ''csv'':33C,69C,108C,110C,148C,230C ''csv.reader'':120C ''csv.writer'':239C ''data'':201C ''dictionari'':27C,41C,64C,125C,158C,204C ''edit'':98C ''element'':150C ''els'':143C,185C ''empti'':26C,124C ''f'':237C,240C ''familiar'':81C ''file'':111C,231C ''header'':138C ''ifile.close'':192C ''import'':107C ''iter'':30C,60C ''key'':52C,156C,168C,178C,182C,187C ''li'':23C,29C,57C ''line'':146C,166C,169C,172C,175C ''list'':165C,196C,198C,224C,226C ''may'':4C ''mydictionari'':126C,180C,181C,186C,212C,215C,218C ''myfil'':115C,121C ''name'':36C,153C,206C ''new'':51C,68C,72C,195C,223C,229C ''newcsvfil'':205C,242C ''newcsvfile.append'':213C ''one'':15C ''open'':109C,116C,233C ''opt'':12C ''output.csv'':234C ''p'':1C,76C,96C ''pass'':142C ''pre'':105C ''python'':83C ''rb'':118C ''rea'':113C ''reader'':119C,132C ''row'':130C ''row.split'':167C ''rownum'':127C,140C,190C ''salari'':45C,55C,174C,184C,189C,208C ''sampl'':102C ''simplest'':20C ''sinc'':16C ''solut'':3C ''split'':144C ''strong'':97C ''sum'':42C ''test.csv'':117C ''two'':162C ''ul'':22C ''use'':151C ''valu'':73C ''wb'':235C ''would'':11C ''write'':66C,91C,221C ''writer'':238C ''writer.writerows'':241C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6730, 1, '2015-08-08 17:47:46.043', 0, 13, 'How to land job for Information retrieval and/or related to data?', '&lt;p&gt;I don''t know if this is right place to post
question. If not please ignore and/or ask me to delete and I''ll.&lt;/p&gt;

&lt;p&gt;I have done masters with focus on Info Retrieval,Machine Learning,Map Reduce. I''m currently working for 2 years in domain which is nothing
like that - (system level programming) I am trying hard to get job
on data science area, how should I convince others (believe me
2 years is worst - cant ask for New Grad position and cant go for
experience one)&lt;/p&gt;
', 'career', NULL, '''/p'':38C,104C ''2'':58C,88C ''and/or'':8A,30C ''area'':80C ''ask'':31C,93C ''believ'':86C ''cant'':92C,99C ''career'':12B ''convinc'':84C ''current'':55C ''data'':11A,78C ''delet'':34C ''domain'':61C ''done'':42C ''experi'':102C ''focus'':45C ''get'':75C ''go'':100C ''grad'':96C ''hard'':73C ''ignor'':29C ''info'':47C ''inform'':6A ''job'':4A,76C ''know'':17C ''land'':3A ''learn'':50C ''level'':68C ''like'':65C ''ll'':37C ''m'':54C ''machin'':49C ''map'':51C ''master'':43C ''new'':95C ''noth'':64C ''one'':103C ''other'':85C ''p'':13C,39C ''place'':22C ''pleas'':28C ''posit'':97C ''post'':24C ''program'':69C ''question'':25C ''reduc'':52C ''relat'':9A ''retriev'':7A,48C ''right'':21C ''scienc'':79C ''system'':67C ''tri'':72C ''work'':56C ''worst'':91C ''year'':59C,89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9741, 1, '2016-01-11 10:38:54.74', 1, 27, 'How to set weight in Weighted Kernel K-Means?', '&lt;p&gt;The objective function of kernel K-means is &lt;/p&gt;

&lt;p&gt;$$      \sum\limits_{c=1}^k \sum\nolimits_{a_i \in c}   w_i \Vert \phi(a_i)- m_c \Vert^2 \ $$&lt;/p&gt;

&lt;p&gt;where&lt;/p&gt;

&lt;p&gt;$$ m_c = \frac{\sum\nolimits_{a_i \in c }w_i\phi(a_i)}{\sum\nolimits_{a_i \in c  }w_i}  $$
I need to know how to determine wi &lt;/p&gt;
', 'machine-learning  data-mining  clustering  k-means  clusters', NULL, '''/p'':32C,55C,58C,90C ''1'':37C ''2'':54C ''c'':36C,44C,52C,61C,68C,79C ''cluster'':17B,21B ''data'':15B ''data-min'':14B ''determin'':88C ''frac'':62C ''function'':25C ''k'':9A,19B,29C,38C ''k-mean'':8A,18B,28C ''kernel'':7A,27C ''know'':85C ''learn'':13B ''limit'':35C ''m'':51C,60C ''machin'':12B ''machine-learn'':11B ''mean'':10A,20B,30C ''mine'':16B ''need'':83C ''nolimit'':40C,64C,75C ''object'':24C ''p'':22C,33C,56C,59C ''phi'':48C,71C ''set'':3A ''sum'':34C,39C,63C,74C ''vert'':47C,53C ''w'':45C,69C,80C ''weight'':4A,6A ''wi'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6732, 2, '2015-08-09 03:06:58.42', 2, NULL, NULL, '&lt;p&gt;Some of the &lt;strong&gt;possibilities&lt;/strong&gt; include the following:&lt;/p&gt;

&lt;p&gt;1) The training data has class imbalance.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Solution:&lt;/em&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Train the model using CV = 5 or 10;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Do a log transformation to make the target distribution more normal in nature;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Check if you can add a weight variable that can fix the distribution.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;2) Testing data is a small sub sample. Since it''s Kaggle, I believe the testing data will be standard in nature, so the problem might be in the process that is generating the submission script.&lt;/p&gt;

&lt;p&gt;As a beginner, you should use any benchmark script available in the forum to start with, which shouldn''t have such issues.&lt;/p&gt;
', NULL, NULL, '''/em'':24C ''/li'':38C,55C,72C ''/p'':11C,20C,25C,37C,54C,71C,111C,135C ''/strong'':7C ''/ul'':73C ''1'':13C ''10'':36C ''2'':75C ''5'':34C ''add'':62C ''avail'':122C ''beginn'':115C ''believ'':88C ''benchmark'':120C ''check'':58C ''class'':18C ''cv'':33C ''data'':16C,77C,91C ''distribut'':49C,70C ''em'':22C ''fix'':68C ''follow'':10C ''forum'':125C ''generat'':107C ''imbal'':19C ''includ'':8C ''issu'':134C ''kaggl'':86C ''li'':27C,39C,56C ''log'':43C ''make'':46C ''might'':100C ''model'':31C ''natur'':53C,96C ''normal'':51C ''p'':1C,12C,21C,28C,40C,57C,74C,112C ''possibl'':6C ''problem'':99C ''process'':104C ''sampl'':82C ''script'':110C,121C ''shouldn'':130C ''sinc'':83C ''small'':80C ''solut'':23C ''standard'':94C ''start'':127C ''strong'':5C ''sub'':81C ''submiss'':109C ''target'':48C ''test'':76C,90C ''train'':15C,29C ''transform'':44C ''ul'':26C ''use'':32C,118C ''variabl'':65C ''weight'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6735, 1, '2015-08-09 12:05:42.797', 2, 110, 'Can i use chi square test to remove a particular variable from the model?', '&lt;p&gt;I have 5 variables - $v_{1}$, $v_{2}$, $v_{3}$, $v_{4}$, and $v_{5}$. All are categorical variables.&lt;/p&gt;

&lt;p&gt;I conducted a $\chi^{2}$ (chi-square) test to understand the relationship between these variables. I could see that $v_{3}$ and $v_{4}$ have p-values of 0.  I understand that these two variables are dependent on each other. Should I remove one of the variable from further analysis?&lt;/p&gt;

&lt;p&gt;What if the same $v_{3}$ and $v_{4}$ are independent of other variables, like $v_{1}$, $v_{2}$, and $v_{5}$?&lt;/p&gt;
', 'statistics  correlation  linear-regression', NULL, '''/p'':40C,94C,118C ''0'':72C ''1'':26C,112C ''2'':28C,46C,114C ''3'':30C,63C,101C ''4'':32C,66C,104C ''5'':23C,35C,117C ''analysi'':93C ''categor'':38C ''chi'':4A,45C,48C ''chi-squar'':47C ''conduct'':43C ''correl'':16B ''could'':59C ''depend'':80C ''independ'':106C ''like'':110C ''linear'':18B ''linear-regress'':17B ''model'':14A ''one'':87C ''p'':20C,41C,69C,95C ''p-valu'':68C ''particular'':10A ''regress'':19B ''relationship'':54C ''remov'':8A,86C ''see'':60C ''squar'':5A,49C ''statist'':15B ''test'':6A,50C ''two'':77C ''understand'':52C,74C ''use'':3A ''v'':25C,27C,29C,31C,34C,62C,65C,100C,103C,111C,113C,116C ''valu'':70C ''variabl'':11A,24C,39C,57C,78C,90C,109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6746, 2, '2015-08-10 15:38:47.583', 0, NULL, NULL, '&lt;p&gt;TM7 of The Netherlands has an advanced NLP engine named CARP that could do this for you. Their examples are largely in Dutch and English language, but they have established ontologies for Spanish for a number of their functional modules, including sentiment analysis. &lt;/p&gt;

&lt;p&gt;Find the public ''playground'' at &lt;a href="http://www.tm7.nl" rel="nofollow"&gt;http://www.tm7.nl&lt;/a&gt; . Select ''Carp Language Technologie'' and follow the link in the blue box to the Playground.&lt;/p&gt;

&lt;p&gt;Information about available APIs using SOAP services is available as well under the link ''SOAP Webservices'' in English&lt;/p&gt;
', NULL, NULL, '''/a'':58C ''/p'':45C,74C,94C ''advanc'':8C ''analysi'':44C ''api'':79C ''avail'':78C,84C ''blue'':69C ''box'':70C ''carp'':12C,60C ''could'':14C ''dutch'':24C ''engin'':10C ''english'':26C,93C ''establish'':31C ''exampl'':20C ''find'':47C ''follow'':64C ''function'':40C ''href'':53C ''includ'':42C ''inform'':76C ''languag'':27C,61C ''larg'':22C ''link'':66C,89C ''modul'':41C ''name'':11C ''netherland'':5C ''nlp'':9C ''nofollow'':56C ''number'':37C ''ontolog'':32C ''p'':1C,46C,75C ''playground'':50C,73C ''public'':49C ''rel'':55C ''select'':59C ''sentiment'':43C ''servic'':82C ''soap'':81C,90C ''spanish'':34C ''technologi'':62C ''tm7'':2C ''use'':80C ''webservic'':91C ''well'':86C ''www.tm7.nl'':54C,57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6736, 1, '2015-08-09 13:56:21.477', 0, 62, 'Do you have any real example of Data Science reports?', '&lt;p&gt;I recently found this use cases on Kaggle for Data Science and Data Analytics.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.kaggle.com/wiki/DataScienceUseCases" rel="nofollow"&gt;Data Science Use Cases&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;However, I am curious to find examples and case studies of real reports from other professionals in some of those use cases on the link. Including hypothesis, testing, reports, conclusions (and maybe also the datasets they have used)&lt;/p&gt;

&lt;p&gt;Do you have any kind of those reports to share?&lt;/p&gt;

&lt;p&gt;I am aware of the difficulty to share this kind of details and this is why it is not a Google-search-and-find answer. &lt;/p&gt;
', 'usecase', NULL, '''/a'':40C ''/p'':27C,41C,81C,93C,121C ''/wiki/datascienceusecases'':33C ''also'':75C ''analyt'':26C ''answer'':120C ''awar'':97C ''case'':18C,39C,51C,64C ''conclus'':72C ''curious'':46C ''data'':8A,22C,25C,36C ''dataset'':77C ''detail'':106C ''difficulti'':100C ''exampl'':6A,49C ''find'':48C,119C ''found'':15C ''googl'':116C ''google-search-and-find'':115C ''howev'':43C ''href'':30C ''hypothesi'':69C ''includ'':68C ''kaggl'':20C ''kind'':87C,104C ''link'':67C ''mayb'':74C ''nofollow'':35C ''p'':12C,28C,42C,82C,94C ''profession'':58C ''real'':5A,54C ''recent'':14C ''rel'':34C ''report'':10A,55C,71C,90C ''scienc'':9A,23C,37C ''search'':117C ''share'':92C,102C ''studi'':52C ''test'':70C ''use'':17C,38C,63C,80C ''usecas'':11B ''www.kaggle.com'':32C ''www.kaggle.com/wiki/datascienceusecases'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6737, 1, '2015-08-09 14:27:33.883', 1, 174, 'Can natural language generation algorithms generate valid words too?', '&lt;blockquote&gt;
  &lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Natural_language_generation" rel="nofollow"&gt;Natural Language Generation (NLG)&lt;/a&gt; is the natural language
  processing task of generating natural language from a machine
  representation system such as a knowledge base or a logical form. â 
  Wikipedia&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Is NLG about building meaningful sentences, reports, etc.? Can NLG build valid dictionary words as well? For example, without consulting/reading from an English (or any language) dictionary, can an algorithm generate such words?&lt;/p&gt;
', 'nlp', NULL, '''/a'':23C ''/blockquote'':51C ''/p'':50C,86C ''/wiki/natural_language_generation'':16C ''algorithm'':5A,82C ''base'':43C ''blockquot'':10C ''build'':56C,63C ''consulting/reading'':72C ''dictionari'':65C,79C ''en.wikipedia.org'':15C ''en.wikipedia.org/wiki/natural_language_generation'':14C ''english'':75C ''etc'':60C ''exampl'':70C ''form'':47C ''generat'':4A,6A,21C,31C,83C ''href'':13C ''knowledg'':42C ''languag'':3A,20C,27C,33C,78C ''logic'':46C ''machin'':36C ''meaning'':57C ''natur'':2A,19C,26C,32C ''nlg'':22C,54C,62C ''nlp'':9B ''nofollow'':18C ''p'':11C,52C ''process'':28C ''rel'':17C ''report'':59C ''represent'':37C ''sentenc'':58C ''system'':38C ''task'':29C ''valid'':7A,64C ''well'':68C ''wikipedia'':49C ''without'':71C ''word'':8A,66C,85C ''â'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6739, 2, '2015-08-09 18:49:55.17', 3, NULL, NULL, '&lt;p&gt;Outside of context of NLG (thus not a direct answer to your whole question, but an answer to your question''s title): Generating words from a character-level model has been done using RNNs exposed to large corpora of text, such as Wikipedia content, and trained to predict text &lt;em&gt;character-by-character&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Used to generate content, the model is normally fed a few starting characters and asked to predict the next one. A choice is made from its most-likely predictions and fed back to it to continue the sequence. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://karpathy.github.io/2015/05/21/rnn-effectiveness/" rel="nofollow"&gt;Here is a blog showing some examples trained on some Shakespear and Wikipedia.&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Such a network can and does generate nonsense words, although they are often fitting and might read like e.g. a noun or verb as you could expect depending on context. The sentence structure and grammar can come out sort of right, but the semantic content is usually complete gibberish.&lt;/p&gt;
', NULL, NULL, '''/2015/05/21/rnn-effectiveness/'':105C ''/a'':121C ''/em'':57C ''/p'':58C,99C,122C,173C ''although'':133C ''answer'':11C,18C ''ask'':74C ''back'':92C ''blog'':111C ''charact'':29C,54C,56C,72C ''character-by-charact'':53C ''character-level'':28C ''choic'':81C ''come'':160C ''complet'':171C ''content'':46C,63C,168C ''context'':4C,153C ''continu'':96C ''corpora'':40C ''could'':149C ''depend'':151C ''direct'':10C ''done'':34C ''e.g'':142C ''em'':52C ''exampl'':114C ''expect'':150C ''expos'':37C ''fed'':68C,91C ''fit'':137C ''generat'':24C,62C,130C ''gibberish'':172C ''grammar'':158C ''href'':102C ''karpathy.github.io'':104C ''karpathy.github.io/2015/05/21/rnn-effectiveness/'':103C ''larg'':39C ''level'':30C ''like'':88C,141C ''made'':83C ''might'':139C ''model'':31C,65C ''most-lik'':86C ''network'':126C ''next'':78C ''nlg'':6C ''nofollow'':107C ''nonsens'':131C ''normal'':67C ''noun'':144C ''often'':136C ''one'':79C ''outsid'':2C ''p'':1C,59C,100C,123C ''predict'':50C,76C,89C ''question'':15C,21C ''read'':140C ''rel'':106C ''right'':164C ''rnns'':36C ''semant'':167C ''sentenc'':155C ''sequenc'':98C ''shakespear'':118C ''show'':112C ''sort'':162C ''start'':71C ''structur'':156C ''text'':42C,51C ''thus'':7C ''titl'':23C ''train'':48C,115C ''use'':35C,60C ''usual'':170C ''verb'':146C ''whole'':14C ''wikipedia'':45C,120C ''word'':25C,132C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6740, 1, '2015-08-09 19:20:37.687', 1, 20, 'fit model with sd square', '&lt;p&gt;If I want to fit a nonlinear regression model with some parameters like $\sigma^2$(where $\sigma$ is the standard deviation, which is positive), how can I guarantee that $\hat{\sigma}$ is positive?&lt;/p&gt;

&lt;p&gt;I mean, if I use maximum likelihood(and the model only have square term of $\sigma$), how does the optimization method know $\sigma$ is positive? (well, it seems OK that the result estimation of $\sigma$ is negative, but it looks weird, and this is why I ask this question.)&lt;/p&gt;
', 'optimization', NULL, '''/p'':41C,92C ''2'':22C ''ask'':89C ''deviat'':28C ''estim'':75C ''fit'':1A,12C ''guarante'':35C ''hat'':37C ''know'':64C ''like'':20C ''likelihood'':49C ''look'':82C ''maximum'':48C ''mean'':44C ''method'':63C ''model'':2A,16C,52C ''negat'':79C ''nonlinear'':14C ''ok'':71C ''optim'':6B,62C ''p'':7C,42C ''paramet'':19C ''posit'':31C,40C,67C ''question'':91C ''regress'':15C ''result'':74C ''sd'':4A ''seem'':70C ''sigma'':21C,24C,38C,58C,65C,77C ''squar'':5A,55C ''standard'':27C ''term'':56C ''use'':47C ''want'':10C ''weird'':83C ''well'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6743, 2, '2015-08-10 08:53:30.417', 2, NULL, NULL, '&lt;p&gt;I would suggest parametrizing with a logarithm of volatility so you don''t have to care about positivity, run the estimation and then invert back to original scale. Alternatively, you can consider constrained optimization routine. Without knowing more about the problem (at least the language you''re using), that''s about it.&lt;/p&gt;
', NULL, NULL, '''/p'':54C ''altern'':30C ''back'':26C ''care'':17C ''consid'':33C ''constrain'':34C ''estim'':22C ''invert'':25C ''know'':38C ''languag'':46C ''least'':44C ''logarithm'':8C ''optim'':35C ''origin'':28C ''p'':1C ''parametr'':5C ''posit'':19C ''problem'':42C ''re'':48C ''routin'':36C ''run'':20C ''scale'':29C ''suggest'':4C ''use'':49C ''volatil'':10C ''without'':37C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6745, 2, '2015-08-10 15:24:05.837', 0, NULL, NULL, '&lt;p&gt;This isn''t a python program so strictly it isn''t a solution to your problem, technically it isn''t even a program - just a command line, but it does illustrate that are many ways to skin a cat, some of them quite elegantly.&lt;/p&gt;

&lt;p&gt;Dictionaries are a lovely feature of the modern language python but much earlier languages such as AWK, or it''s linux equivalent GAWK, have had them for just short of 40 years, though in those days they were called associative arrays. &lt;/p&gt;

&lt;p&gt;So here is a one line command using &lt;code&gt;cat&lt;/code&gt; and &lt;code&gt;gawk&lt;/code&gt;, with its ability to accept code as an argument, to accomplish your task, demonstrating that the design principle of linux to be a framework for collaborating utilities is still alive and well.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; $&amp;gt; cat in.dat | gawk ''{if(NR==1)hdr=$0; else{emp[$1" "$2" "$3]=emp[$1" "$2" "$3]+$4}} END{print hdr; for(i in emp) print i,emp[i];}''  &amp;gt; out.dat
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''ve taken your file formats literally, if you do actually require true &lt;code&gt;CSVs&lt;/code&gt; then a small mod will accomplish that.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;cat in.csv | awk -F"," ''{if(NR==1)hdr=$0; else{emp[$1","$2]=emp[$1","$2]+$3}} END{OFS=",";print hdr; for(i in emp) print i,emp[i];}'' &amp;gt; out.csv
&lt;/code&gt;&lt;/pre&gt;

&lt;blockquote&gt;
  &lt;p&gt;Name,Age,Salary &lt;/p&gt;
  
  &lt;p&gt;name 2,55,20066&lt;/p&gt;
  
  &lt;p&gt;name 1,52,110322&lt;/p&gt;
  
  &lt;p&gt;name 3,50,100054&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Designed as a text manipulation language by some very clever computer scientists, including Brian Kernighan of &lt;code&gt;C&lt;/code&gt; fame, AWK achieves this compactness by automatically parsing any input it is given into fields ( \$1, \$2, ... ) and keeping track of the number of lines processed. This allows a lot of the I/O management you might need to do in another language to be skipped over as it already exists.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':259C ''/code'':101C,105C,173C,191C,233C,279C ''/p'':46C,89C,138C,199C,240C,246C,252C,258C,330C ''/pre'':174C,234C ''0'':149C,210C ''1'':147C,152C,156C,208C,213C,216C,249C,295C ''100054'':257C ''110322'':251C ''2'':153C,157C,214C,217C,243C,296C ''20066'':245C ''3'':154C,158C,218C,255C ''4'':159C ''40'':78C ''50'':256C ''52'':250C ''55'':244C ''abil'':108C ''accept'':110C ''accomplish'':116C,197C ''achiev'':282C ''actual'':186C ''age'':238C ''aliv'':135C ''allow'':307C ''alreadi'':328C ''anoth'':320C ''argument'':114C ''array'':88C ''associ'':87C ''automat'':286C ''awk'':64C,204C,281C ''blockquot'':235C ''brian'':274C ''c'':278C ''call'':86C ''cat'':40C,100C,142C,202C ''clever'':270C ''code'':99C,103C,111C,140C,189C,201C,277C ''collabor'':131C ''command'':27C,97C ''compact'':284C ''comput'':271C ''csvs'':190C ''day'':83C ''demonstr'':119C ''design'':122C,261C ''dictionari'':48C ''earlier'':60C ''eleg'':45C ''els'':150C,211C ''emp'':151C,155C,166C,169C,212C,215C,226C,229C ''end'':160C,219C ''equival'':69C ''even'':22C ''exist'':329C ''f'':205C ''fame'':280C ''featur'':52C ''field'':294C ''file'':180C ''format'':181C ''framework'':129C ''gawk'':70C,104C,144C ''given'':292C ''gt'':141C,171C,231C ''hdr'':148C,162C,209C,222C ''i/o'':312C ''illustr'':32C ''in.csv'':203C ''in.dat'':143C ''includ'':273C ''input'':289C ''isn'':3C,11C,20C ''keep'':298C ''kernighan'':275C ''languag'':56C,61C,266C,321C ''line'':28C,96C,304C ''linux'':68C,125C ''liter'':182C ''lot'':309C ''love'':51C ''manag'':313C ''mani'':35C ''manipul'':265C ''might'':315C ''mod'':195C ''modern'':55C ''much'':59C ''name'':237C,242C,248C,254C ''need'':316C ''nr'':146C,207C ''number'':302C ''of'':220C ''one'':95C ''out.csv'':232C ''out.dat'':172C ''p'':1C,47C,90C,175C,236C,241C,247C,253C,260C ''pars'':287C ''pre'':139C,200C ''principl'':123C ''print'':161C,167C,221C,227C ''problem'':17C ''process'':305C ''program'':7C,24C ''python'':6C,57C ''quit'':44C ''requir'':187C ''salari'':239C ''scientist'':272C ''short'':76C ''skin'':38C ''skip'':324C ''small'':194C ''solut'':14C ''still'':134C ''strict'':9C ''taken'':178C ''task'':118C ''technic'':18C ''text'':264C ''though'':80C ''track'':299C ''true'':188C ''use'':98C ''util'':132C ''ve'':177C ''way'':36C ''well'':137C ''year'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6747, 2, '2015-08-10 18:25:21.68', 1, NULL, NULL, '&lt;p&gt;&lt;a href="http://pandas.pydata.org/" rel="nofollow"&gt;Pandas&lt;/a&gt; is a python library that you will find very useful for these types of tasks.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stackoverflow.com/questions/29583312/pandas-sum-of-duplicate-attributes"&gt;Here is a stack overflow post&lt;/a&gt; that tells you how to do what you want to accomplish.&lt;/p&gt;

&lt;p&gt;It boils down to three very pythonic lines with a &lt;a href="http://pandas.pydata.org/pandas-docs/stable/groupby.html" rel="nofollow"&gt;groupby and transformation&lt;/a&gt; followed by a &lt;a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop_duplicates.html" rel="nofollow"&gt;drop_duplicates&lt;/a&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas
df = pandas.read_csv(''csvfile.csv'', header = 0)
df[''Total''] = df.groupby([''Name'', ''Age''])[''Salary''].transform(''sum'')
df.drop_duplicates(take_last=True)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':8C,37C,71C,84C ''/code'':109C ''/p'':24C,49C,85C ''/pandas-docs/stable/generated/pandas.dataframe.drop_duplicates.html'':79C ''/pandas-docs/stable/groupby.html'':65C ''/pre'':110C ''/questions/29583312/pandas-sum-of-duplicate-attributes'':30C ''0'':95C ''accomplish'':48C ''age'':100C ''boil'':52C ''code'':87C ''csv'':92C ''csvfile.csv'':93C ''df'':90C,96C ''df.drop'':104C ''df.groupby'':98C ''drop'':82C ''duplic'':83C,105C ''find'':16C ''follow'':72C ''groupbi'':68C ''header'':94C ''href'':3C,27C,62C,76C ''import'':88C ''last'':107C ''librari'':12C ''line'':58C ''name'':99C ''nofollow'':6C,67C,81C ''overflow'':35C ''p'':1C,25C,50C ''panda'':7C,89C ''pandas.pydata.org'':4C,64C,78C ''pandas.pydata.org/pandas-docs/stable/generated/pandas.dataframe.drop_duplicates.html'':77C ''pandas.pydata.org/pandas-docs/stable/groupby.html'':63C ''pandas.read'':91C ''post'':36C ''pre'':86C ''python'':11C,57C ''rel'':5C,66C,80C ''salari'':101C ''stack'':34C ''stackoverflow.com'':29C ''stackoverflow.com/questions/29583312/pandas-sum-of-duplicate-attributes'':28C ''sum'':103C ''take'':106C ''task'':23C ''tell'':39C ''three'':55C ''total'':97C ''transform'':70C,102C ''true'':108C ''type'':21C ''use'':18C ''want'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6748, 1, '2015-08-10 18:45:08.413', 1, 48, 'Time threshold maps plotting in R', '&lt;p&gt;I am trying to plot &lt;em&gt;time threshold maps&lt;/em&gt;, proposed by Fryzlewicz. The relevant paper is: &lt;a href="http://stats.lse.ac.uk/fryzlewicz/allth/allth.pdf" rel="nofollow"&gt;http://stats.lse.ac.uk/fryzlewicz/allth/allth.pdf&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Questions:&lt;/strong&gt; Is the top plot in &lt;code&gt;Figure 1&lt;/code&gt; a heat map? How can I plot this in &lt;code&gt;R&lt;/code&gt;?&lt;/p&gt;
', 'r  time-series', NULL, '''/code'':51C,63C ''/em'':21C ''/fryzlewicz/allth/allth.pdf'':33C ''/fryzlewicz/allth/allth.pdf&lt;/a&gt;.&lt;/p&gt;'':38C ''/p'':64C ''/strong'':42C ''1'':50C ''code'':48C,61C ''em'':17C ''figur'':49C ''fryzlewicz'':24C ''heat'':53C ''href'':30C ''map'':3A,20C,54C ''nofollow'':35C ''p'':11C,39C ''paper'':27C ''plot'':4A,16C,46C,58C ''propos'':22C ''question'':41C ''r'':6A,7B,62C ''rel'':34C ''relev'':26C ''seri'':10B ''stats.lse.ac.uk'':32C,37C ''stats.lse.ac.uk/fryzlewicz/allth/allth.pdf'':31C ''stats.lse.ac.uk/fryzlewicz/allth/allth.pdf&lt;/a&gt;.&lt;/p&gt;'':36C ''strong'':40C ''threshold'':2A,19C ''time'':1A,9B,18C ''time-seri'':8B ''top'':45C ''tri'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6749, 1, '2015-08-10 20:09:46.923', 0, 43, 'plotting graph in R: show 3 values in one graph', '&lt;p&gt;I have a function: m=a*n+2
the range of a is 1 to 100 and the range of a is 1 to 5
My question: is it possible to plot a figure with x-axis is n, and y-axis is a and also shows the value of m ?&lt;/p&gt;
', 'r', NULL, '''+2'':20C ''/p'':66C ''1'':26C,35C ''100'':28C ''3'':6A ''5'':37C ''also'':60C ''axi'':50C,56C ''figur'':46C ''function'':16C ''graph'':2A,10A ''m'':17C,65C ''n'':19C,52C ''one'':9A ''p'':12C ''plot'':1A,44C ''possibl'':42C ''question'':39C ''r'':4A,11B ''rang'':22C,31C ''show'':5A,61C ''valu'':7A,63C ''x'':49C ''x-axi'':48C ''y'':55C ''y-axi'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6750, 1, '2015-08-11 02:15:24.13', 3, 266, 'How to explain decision tree algortihm in layman''s terms?', '&lt;p&gt;I have a task at hand, where I have to explain &lt;em&gt;&lt;code&gt;decision tree&lt;/code&gt;&lt;/em&gt; algorithm to a person who has not much understanding of &lt;code&gt;machine learning&lt;/code&gt;. I have been looking around, but find it difficult to explain the algorithm in layman''s terms, so that a person will understand what is happening in the process.&lt;/p&gt;

&lt;p&gt;What is the best way to describe the algorithm, possibly using some very simple basic example, so that the whole process of the algorithm is broken down to simple steps for understanding?&lt;/p&gt;
', 'machine-learning  bigdata  algorithms  decision-trees', NULL, '''/code'':35C,50C ''/em'':36C ''/p'':80C,114C ''algorithm'':15B,37C,63C,90C,105C ''algortihm'':6A ''around'':55C ''basic'':96C ''best'':85C ''bigdata'':14B ''broken'':107C ''code'':32C,47C ''decis'':4A,17B,33C ''decision-tre'':16B ''describ'':88C ''difficult'':59C ''em'':31C ''exampl'':97C ''explain'':3A,30C,61C ''find'':57C ''hand'':25C ''happen'':76C ''layman'':8A,65C ''learn'':13B,49C ''look'':54C ''machin'':12B,48C ''machine-learn'':11B ''much'':44C ''p'':19C,81C ''person'':40C,71C ''possibl'':91C ''process'':79C,102C ''simpl'':95C,110C ''step'':111C ''task'':23C ''term'':10A,67C ''tree'':5A,18B,34C ''understand'':45C,73C,113C ''use'':92C ''way'':86C ''whole'':101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6751, 1, '2015-08-11 05:04:52.3', 1, 315, 'How word2vec can handle unseen / new words to bypass this for new classifications?', '&lt;p&gt;In simple terms, if my classification is based on word2vec as features, what I am supposed to do, if a new word comes, which doesn''t have a word2vec.&lt;/p&gt;

&lt;p&gt;I am trying to used word2vec or word vectors for classification based on entity.&lt;/p&gt;

&lt;p&gt;For example ,&lt;/p&gt;

&lt;p&gt;I have to classify the following words in a sentence as :&lt;/p&gt;

&lt;p&gt;" Google gives information about Nigeria " Here , I want to classify Nigeria as location.&lt;/p&gt;

&lt;p&gt;Suppose I am having good word2vec vectors for each of the word, based on some readings I came to know that, Recurrent Neural Network can be used for this. So, word2vec will capture most locations with a kind of similar word vectors.&lt;/p&gt;

&lt;p&gt;But my questions are:&lt;/p&gt;

&lt;p&gt;a.) Suppose a new location is there. lets say, Russia . So, do I need to assign a new word vector for this location ?&lt;/p&gt;

&lt;p&gt;b.) If my input for training does not have grammatical sense. For example,&lt;/p&gt;

&lt;p&gt;" Google information Nigeria " . Everything else Nigeria is associated with a non-location label. Will this condition work for find new location in non-grammatical sentences.&lt;/p&gt;

&lt;p&gt;Please help .&lt;/p&gt;
', 'machine-learning  nlp  deep-learning  word-embeddings', NULL, '''/p'':54C,70C,74C,87C,102C,146C,152C,177C,192C,221C,225C ''assign'':169C ''associ'':201C ''b'':179C ''base'':32C,67C,116C ''bypass'':9A ''came'':121C ''captur'':136C ''classif'':13A,30C,66C ''classifi'':79C,98C ''come'':47C ''condit'':210C ''deep'':19B ''deep-learn'':18B ''doesn'':49C ''els'':198C ''embed'':23B ''entiti'':69C ''everyth'':197C ''exampl'':73C,191C ''featur'':36C ''find'':213C ''follow'':81C ''give'':90C ''good'':108C ''googl'':89C,194C ''grammat'':188C,219C ''handl'':4A ''help'':224C ''inform'':91C,195C ''input'':182C ''kind'':141C ''know'':123C ''label'':207C ''learn'':16B,20B ''let'':161C ''locat'':101C,138C,158C,176C,206C,215C ''machin'':15B ''machine-learn'':14B ''need'':167C ''network'':127C ''neural'':126C ''new'':6A,12A,45C,157C,171C,214C ''nigeria'':93C,99C,196C,199C ''nlp'':17B ''non'':205C,218C ''non-grammat'':217C ''non-loc'':204C ''p'':24C,55C,71C,75C,88C,103C,147C,153C,178C,193C,222C ''pleas'':223C ''question'':150C ''read'':119C ''recurr'':125C ''russia'':163C ''say'':162C ''sens'':189C ''sentenc'':85C,220C ''similar'':143C ''simpl'':26C ''suppos'':40C,104C,155C ''term'':27C ''train'':184C ''tri'':58C ''unseen'':5A ''use'':60C,130C ''vector'':64C,110C,145C,173C ''want'':96C ''word'':7A,22B,46C,63C,82C,115C,144C,172C ''word-embed'':21B ''word2vec'':2A,34C,53C,61C,109C,134C ''work'':211C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6752, 2, '2015-08-11 06:40:58.583', 4, NULL, NULL, '&lt;p&gt;First of all let me tell you that I''m not a Spark expert; I''ve been using it quite a lot in the last few months, and I believe I now understand it, but I may be wrong.&lt;/p&gt;

&lt;p&gt;So, answering your questions:&lt;/p&gt;

&lt;p&gt;a.) they are equivalent, but not in the way you''re seeing it; Spark will not optimize the graph if you are wondering, but the &lt;code&gt;customMapper&lt;/code&gt; will still be executed twice in both cases; this is due to the fact that for spark, &lt;code&gt;rdd1&lt;/code&gt; and &lt;code&gt;rdd2&lt;/code&gt; are two completely different RDDs, and it will build the transformation graph bottom-up starting from the leafs; so Option 1 will translate to:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;rdd0 = rddIn.map(lambda x: customMapper(x)).filter(lambda x: x[0]==0).cache()
rdd1 = rddIn.map(lambda x: customMapper(x)).filter(lambda x: x[0]==1).cache()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As you said, &lt;code&gt;customMapper&lt;/code&gt; is executed twice (moreover, also &lt;code&gt;rddIn&lt;/code&gt; will be read twice, which means that if it comes from a database, it may be even slower).&lt;/p&gt;

&lt;p&gt;b.) there is a way, you just have to move &lt;code&gt;cache()&lt;/code&gt; in the right place:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;mappedRdd = rddIn.map(lambda x: customMapper(x)).cache()
rdd0 = mappedRdd.filter(lambda x: x[0]==0)
rdd1 = mappedRdd.filter(lambda x: x[0]==1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;By doing this, we are telling spark that it can store the partial results of &lt;code&gt;mappedRdd&lt;/code&gt;; it will then use these partial results both for &lt;code&gt;rdd1&lt;/code&gt; and &lt;code&gt;rdd2&lt;/code&gt;. From the spark point of view this is equivalent to:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;mappedRdd = rddIn.map(lambda x: customMapper(x)).saveAsObjectFile(''..'')
# forget about everything
rdd0 = sc.objectFile(''..'').filter(lambda x: x[0]==0)
rdd1 = sc.objectFile(''..'').filter(lambda x: x[0]==1)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':76C,96C,100C,155C,163C,171C,204C,233C,253C,265C,269C,309C ''/p'':41C,47C,126C,190C,209C,280C ''/pre'':156C,234C,310C ''0'':139C,140C,152C,224C,225C,231C,299C,300C,307C ''1'':122C,153C,232C,308C ''also'':168C ''answer'':44C ''b'':192C ''believ'':31C ''bottom'':114C ''bottom-up'':113C ''build'':109C ''cach'':141C,154C,203C,218C ''case'':84C ''code'':74C,94C,98C,128C,161C,169C,202C,211C,251C,263C,267C,282C ''come'':181C ''complet'':103C ''custommapp'':75C,133C,146C,162C,216C,287C ''databas'':184C ''differ'':104C ''due'':87C ''equival'':52C,278C ''even'':188C ''everyth'':292C ''execut'':80C,165C ''expert'':15C ''fact'':90C ''filter'':135C,148C,295C,303C ''first'':2C ''forget'':290C ''graph'':67C,112C ''lambda'':131C,136C,144C,149C,214C,221C,228C,285C,296C,304C ''last'':26C ''leaf'':119C ''let'':5C ''lot'':23C ''m'':11C ''mappedrdd'':212C,252C,283C ''mappedrdd.filter'':220C,227C ''may'':38C,186C ''mean'':177C ''month'':28C ''moreov'':167C ''move'':201C ''optim'':65C ''option'':121C ''p'':1C,42C,48C,157C,191C,235C ''partial'':248C,259C ''place'':208C ''point'':273C ''pre'':127C,210C,281C ''question'':46C ''quit'':21C ''rdd0'':129C,219C,293C ''rdd1'':95C,142C,226C,264C,301C ''rdd2'':99C,268C ''rddin'':170C ''rddin.map'':130C,143C,213C,284C ''rdds'':105C ''re'':59C ''read'':174C ''result'':249C,260C ''right'':207C ''said'':160C ''saveasobjectfil'':289C ''sc.objectfile'':294C,302C ''see'':60C ''slower'':189C ''spark'':14C,62C,93C,242C,272C ''start'':116C ''still'':78C ''store'':246C ''tell'':7C,241C ''transform'':111C ''translat'':124C ''twice'':81C,166C,175C ''two'':102C ''understand'':34C ''use'':19C,257C ''ve'':17C ''view'':275C ''way'':57C,196C ''wonder'':71C ''wrong'':40C ''x'':132C,134C,137C,138C,145C,147C,150C,151C,215C,217C,222C,223C,229C,230C,286C,288C,297C,298C,305C,306C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6758, 2, '2015-08-11 12:14:32.297', 0, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;Suppose a new location is there. lets say, Russia . So, do I need to
  assign a new word vector for this location ?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Define an Unknown Word Vector that is going to represent every word that is not in your list.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':26C ''/p'':25C,46C ''assign'':17C ''blockquot'':1C ''defin'':28C ''everi'':38C ''go'':35C ''let'':9C ''list'':45C ''locat'':6C,24C ''need'':15C ''new'':5C,19C ''p'':2C,27C ''repres'':37C ''russia'':11C ''say'':10C ''suppos'':3C ''unknown'':30C ''vector'':21C,32C ''word'':20C,31C,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6764, 2, '2015-08-11 17:10:07.043', 1, NULL, NULL, '&lt;p&gt;I had to do something similar for Regression Tree. I came up with as very simple complete example; I wrote out tables of dependent and independent variables on the board. The trickiest part is to explain how the first split happens, (that it minimizes MSE of dependent variable within the sub-samples), being able to point to the table helps. And after that we do the same thing just for a sub problem.&lt;/p&gt;
', NULL, NULL, '''/p'':76C ''abl'':56C ''board'':31C ''came'':12C ''complet'':18C ''depend'':25C,48C ''exampl'':19C ''explain'':37C ''first'':40C ''happen'':42C ''help'':62C ''independ'':27C ''minim'':45C ''mse'':46C ''p'':1C ''part'':34C ''point'':58C ''problem'':75C ''regress'':9C ''sampl'':54C ''similar'':7C ''simpl'':17C ''someth'':6C ''split'':41C ''sub'':53C,74C ''sub-sampl'':52C ''tabl'':23C,61C ''thing'':70C ''tree'':10C ''trickiest'':33C ''variabl'':28C,49C ''within'':50C ''wrote'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6760, 1, '2015-08-11 09:26:26.34', 1, 67, 'How to discard trash topics from topic models?', '&lt;p&gt;I am undertaking text analysis of some twitter data. In the end I want to have a data that is interpretable. And so in the end I would like to reduce the data to relevant unit of analysis. Topic models seem a nice fit since they reduce the noise significantly, but still at the end there are quite a lot of trash topic lists which do not mean anything.What would be the best way to remove these topics. Is there a working example of this? &lt;/p&gt;

&lt;p&gt;I am using LDA. Probably I will use Gensim. Its going to be unsupervised and its a simple twitter data. So the whole corpora will be 1500 tweets of 160 characters. &lt;/p&gt;
', 'nlp  topic-model', NULL, '''/p'':99C,132C ''1500'':127C ''160'':130C ''analysi'':18C,51C ''anything.what'':82C ''best'':86C ''charact'':131C ''corpora'':124C ''data'':22C,31C,46C,120C ''discard'':3A ''end'':25C,39C,68C ''exampl'':96C ''fit'':57C ''gensim'':109C ''go'':111C ''interpret'':34C ''lda'':104C ''like'':42C ''list'':77C ''lot'':73C ''mean'':81C ''model'':8A,12B,53C ''nice'':56C ''nlp'':9B ''nois'':62C ''p'':13C,100C ''probabl'':105C ''quit'':71C ''reduc'':44C,60C ''relev'':48C ''remov'':89C ''seem'':54C ''signific'':63C ''simpl'':118C ''sinc'':58C ''still'':65C ''text'':17C ''topic'':5A,7A,11B,52C,76C,91C ''topic-model'':10B ''trash'':4A,75C ''tweet'':128C ''twitter'':21C,119C ''undertak'':16C ''unit'':49C ''unsupervis'':114C ''use'':103C,108C ''want'':27C ''way'':87C ''whole'':123C ''work'':95C ''would'':41C,83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6754, 2, '2015-08-11 09:20:53.177', 1, NULL, NULL, '&lt;p&gt;A decision tree is a graph that uses a branching method to illustrate every possible outcome of a decision. Here each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represents classification rules. This can be represented as graph. Now you can parse through the possible nodes and edges as per the condition rules (if condition1 and condition2 and condition3 then outcome.)&lt;/p&gt;

&lt;p&gt;Wikipedia link itself has some great examples :&lt;a href="https://en.wikipedia.org/wiki/Decision_tree" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Decision_tree&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Do check these links also : &lt;/p&gt;

&lt;p&gt;&lt;a href="http://blog.echen.me/2011/03/14/laymans-introduction-to-random-forests/" rel="nofollow"&gt;http://blog.echen.me/2011/03/14/laymans-introduction-to-random-forests/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://whatis.techtarget.com/definition/decision-tree" rel="nofollow"&gt;http://whatis.techtarget.com/definition/decision-tree&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/2011/03/14/laymans-introduction-to-random-forests/'':132C ''/2011/03/14/laymans-introduction-to-random-forests/&lt;/a&gt;&lt;/p&gt;'':137C ''/definition/decision-tree'':143C ''/definition/decision-tree&lt;/a&gt;&lt;/p&gt;'':148C ''/p'':101C,126C ''/wiki/decision_tree'':114C ''/wiki/decision_tree&lt;/a&gt;&lt;/p&gt;'':119C ''also'':125C ''attribut'':30C,62C ''blog.echen.me'':131C,136C ''blog.echen.me/2011/03/14/laymans-introduction-to-random-forests/'':130C ''blog.echen.me/2011/03/14/laymans-introduction-to-random-forests/&lt;/a&gt;&lt;/p&gt;'':135C ''branch'':11C,42C ''check'':122C ''class'':55C ''classif'':70C ''coin'':34C ''come'':36C ''comput'':60C ''condit'':91C ''condition1'':94C ''condition2'':96C ''condition3'':98C ''decis'':3C,20C,57C ''e.g'':31C ''edg'':87C ''en.wikipedia.org'':113C,118C ''en.wikipedia.org/wiki/decision_tree'':112C ''en.wikipedia.org/wiki/decision_tree&lt;/a&gt;&lt;/p&gt;'':117C ''everi'':15C ''exampl'':109C ''flip'':35C ''graph'':7C,77C ''great'':108C ''head'':38C ''href'':111C,129C,140C ''illustr'':14C ''intern'':23C ''label'':56C ''leaf'':51C,68C ''link'':104C,124C ''method'':12C ''node'':24C,52C,85C ''nofollow'':116C,134C,145C ''outcom'':17C,45C,100C ''p'':1C,102C,120C,127C,138C ''pars'':81C ''path'':64C ''per'':89C ''possibl'':16C,84C ''rel'':115C,133C,144C ''repres'':25C,43C,53C,69C,75C ''root'':66C ''rule'':71C,92C ''tail'':40C ''taken'':58C ''test'':27C,48C ''tree'':4C ''use'':9C ''whatis.techtarget.com'':142C,147C ''whatis.techtarget.com/definition/decision-tree'':141C ''whatis.techtarget.com/definition/decision-tree&lt;/a&gt;&lt;/p&gt;'':146C ''whether'':32C ''wikipedia'':103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6755, 2, '2015-08-11 09:25:24.34', 1, NULL, NULL, '&lt;p&gt;I think you can better go through various university thesis reports and data science related journal papers to get more details on "Including hypothesis, testing, reports, conclusions" of the above mentioned Data science related problems.&lt;/p&gt;

&lt;p&gt;Check these links from Stanford university :&lt;/p&gt;

&lt;p&gt;&lt;a href="http://cs229.stanford.edu/projects2014.html" rel="nofollow"&gt;http://cs229.stanford.edu/projects2014.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://cs229.stanford.edu/projects2013.html" rel="nofollow"&gt;http://cs229.stanford.edu/projects2013.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://cs229.stanford.edu/projects2012.html" rel="nofollow"&gt;http://cs229.stanford.edu/projects2012.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':37C,45C ''/projects2012.html'':73C ''/projects2012.html&lt;/a&gt;&lt;/p&gt;'':78C ''/projects2013.html'':62C ''/projects2013.html&lt;/a&gt;&lt;/p&gt;'':67C ''/projects2014.html'':51C ''/projects2014.html&lt;/a&gt;&lt;/p&gt;'':56C ''better'':6C ''check'':39C ''conclus'':28C ''cs229.stanford.edu'':50C,55C,61C,66C,72C,77C ''cs229.stanford.edu/projects2012.html'':71C ''cs229.stanford.edu/projects2012.html&lt;/a&gt;&lt;/p&gt;'':76C ''cs229.stanford.edu/projects2013.html'':60C ''cs229.stanford.edu/projects2013.html&lt;/a&gt;&lt;/p&gt;'':65C ''cs229.stanford.edu/projects2014.html'':49C ''cs229.stanford.edu/projects2014.html&lt;/a&gt;&lt;/p&gt;'':54C ''data'':14C,33C ''detail'':22C ''get'':20C ''go'':7C ''href'':48C,59C,70C ''hypothesi'':25C ''includ'':24C ''journal'':17C ''link'':41C ''mention'':32C ''nofollow'':53C,64C,75C ''p'':1C,38C,46C,57C,68C ''paper'':18C ''problem'':36C ''rel'':52C,63C,74C ''relat'':16C,35C ''report'':12C,27C ''scienc'':15C,34C ''stanford'':43C ''test'':26C ''thesi'':11C ''think'':3C ''univers'':10C,44C ''various'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6756, 2, '2015-08-11 10:19:02.817', 1, NULL, NULL, '&lt;p&gt;Maybe there''s another way to go. The idea would be to generate the dataset you will be processing with your algorithms. You define the models of behaviour of events (those you''re looking for and those into which they are hiden). Then generate the data, then analyse.&lt;/p&gt;

&lt;p&gt;This approach has the benefit to let you control exactly what is inside the processed data. And make sure your algorithm identifies exactly what it is supposed to identify, no more, no less.&lt;/p&gt;

&lt;p&gt;With GEDIS Studio we model events behaviours with activity profiles and the generator produces those events. We have implemented generators for telecom CDR, credit card usages, smart metering, etc.&lt;/p&gt;

&lt;p&gt;They are freely available online from the evaluation account on &lt;a href="http://www.data-generator.com" rel="nofollow"&gt;http://www.data-generator.com&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Check the detailed CDR use case at &lt;a href="http://www.gedis-studio.com/online-call-detail-records-cdr-generator.html" rel="nofollow"&gt;http://www.gedis-studio.com/online-call-detail-records-cdr-generator.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Regards &lt;/p&gt;
', NULL, NULL, '''/a'':134C ''/online-call-detail-records-cdr-generator.html'':148C ''/online-call-detail-records-cdr-generator.html&lt;/a&gt;&lt;/p&gt;'':153C ''/p'':50C,85C,116C,135C,156C ''account'':126C ''activ'':95C ''algorithm'':23C,72C ''analys'':49C ''anoth'':5C ''approach'':53C ''avail'':121C ''behaviour'':29C,93C ''benefit'':56C ''card'':111C ''case'':142C ''cdr'':109C,140C ''check'':137C ''control'':60C ''credit'':110C ''data'':47C,67C ''dataset'':16C ''defin'':25C ''detail'':139C ''etc'':115C ''evalu'':125C ''event'':31C,92C,102C ''exact'':61C,74C ''freeli'':120C ''gedi'':88C ''generat'':14C,45C,99C,106C ''go'':8C ''hiden'':43C ''href'':129C,145C ''idea'':10C ''identifi'':73C,80C ''implement'':105C ''insid'':64C ''less'':84C ''let'':58C ''look'':35C ''make'':69C ''mayb'':2C ''meter'':114C ''model'':27C,91C ''nofollow'':132C,150C ''onlin'':122C ''p'':1C,51C,86C,117C,136C,154C ''process'':20C,66C ''produc'':100C ''profil'':96C ''re'':34C ''regard'':155C ''rel'':131C,149C ''smart'':113C ''studio'':89C ''suppos'':78C ''sure'':70C ''telecom'':108C ''usag'':112C ''use'':141C ''way'':6C ''would'':11C ''www.data-generator.com'':130C,133C ''www.gedis-studio.com'':147C,152C ''www.gedis-studio.com/online-call-detail-records-cdr-generator.html'':146C ''www.gedis-studio.com/online-call-detail-records-cdr-generator.html&lt;/a&gt;&lt;/p&gt;'':151C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6757, 2, '2015-08-11 11:01:44.517', 1, NULL, NULL, '&lt;p&gt;Is it possible that adding generated data to your data set will decrease the fraud/non fraud ration and make your dataset more representative / usable ?&lt;/p&gt;

&lt;p&gt;At GenieLog, we are producing test data for designing and testing fraud detection tools. We our generator GEDIS Studio we can define regular profiles and fraudster profiles, instantiating each category to a customizable ratio (for ex. 2 % of customer will have fraudulent usage of generated events.)&lt;/p&gt;

&lt;p&gt;We did it successfully for telecom CDR (&lt;a href="http://www.gedis-studio.com/online-call-detail-records-cdr-generator.html" rel="nofollow"&gt;http://www.gedis-studio.com/online-call-detail-records-cdr-generator.html&lt;/a&gt;) and Credit Card usages. There''s a freely available access to the online generator on &lt;a href="http://www.data-generator.com" rel="nofollow"&gt;http://www.data-generator.com&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;I''m pretty sure that even if the tool is not matching your needs at least the approach can be valuable. Otherwise I would be interested to read any objection :)&lt;/p&gt;

&lt;p&gt;Regards&lt;/p&gt;
', NULL, NULL, '''/a'':114C ''/online-call-detail-records-cdr-generator.html'':87C ''/online-call-detail-records-cdr-generator.html&lt;/a&gt;)'':92C ''/p'':26C,74C,115C,147C,150C ''2'':64C ''access'':102C ''ad'':6C ''approach'':134C ''avail'':101C ''card'':95C ''categori'':57C ''cdr'':82C ''credit'':94C ''custom'':66C ''customiz'':60C ''data'':8C,11C,34C ''dataset'':22C ''decreas'':14C ''defin'':49C ''design'':36C ''detect'':40C ''even'':122C ''event'':73C ''ex'':63C ''fraud'':17C,39C ''fraud/non'':16C ''fraudster'':53C ''fraudul'':69C ''freeli'':100C ''gedi'':45C ''generat'':7C,44C,72C,106C ''genielog'':29C ''href'':84C,109C ''instanti'':55C ''interest'':142C ''least'':132C ''m'':118C ''make'':20C ''match'':128C ''need'':130C ''nofollow'':89C,112C ''object'':146C ''onlin'':105C ''otherwis'':138C ''p'':1C,27C,75C,116C,148C ''possibl'':4C ''pretti'':119C ''produc'':32C ''profil'':51C,54C ''ratio'':61C ''ration'':18C ''read'':144C ''regard'':149C ''regular'':50C ''rel'':88C,111C ''repres'':24C ''set'':12C ''studio'':46C ''success'':79C ''sure'':120C ''telecom'':81C ''test'':33C,38C ''tool'':41C,125C ''usabl'':25C ''usag'':70C,96C ''valuabl'':137C ''would'':140C ''www.data-generator.com'':110C,113C ''www.gedis-studio.com'':86C,91C ''www.gedis-studio.com/online-call-detail-records-cdr-generator.html'':85C ''www.gedis-studio.com/online-call-detail-records-cdr-generator.html&lt;/a&gt;)'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6762, 5, '2015-08-11 12:59:07.74', 0, NULL, NULL, '&lt;p&gt;If observations are words collected into documents, it posits that each document is a mixture of a small number of &lt;a href="http://en.wikipedia.org/wiki/Topic_model" rel="nofollow"&gt;topics&lt;/a&gt; and that each word''s creation is attributable to one of the document''s topics. LDA represents documents as mixtures of topics that spit out words with certain probabilities.&lt;/p&gt;

&lt;p&gt;Popular software packages to perform LDA include&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://mallet.cs.umass.edu/" rel="nofollow"&gt;Mallet&lt;/a&gt; (Java) &lt;/li&gt;
&lt;li&gt;&lt;a href="http://nlp.stanford.edu/software/tmt/tmt-0.4/" rel="nofollow"&gt;Stanford Topic Modeling Toolbox&lt;/a&gt; (software)  &lt;/li&gt;
&lt;li&gt;&lt;a href="http://radimrehurek.com/gensim/" rel="nofollow"&gt;Gensim â Topic Modelling for Humans&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It should not be confused with Linear Discriminant Analysis, a supervised learning procedure for classifying observations into a set of categories.&lt;/p&gt;
', NULL, NULL, '''/a'':30C,78C,93C,110C ''/gensim/'':101C ''/li'':80C,95C,111C ''/p'':60C,69C,135C ''/software/tmt/tmt-0.4/'':86C ''/ul'':112C ''/wiki/topic_model'':26C ''analysi'':122C ''attribut'':38C ''categori'':134C ''certain'':58C ''classifi'':128C ''collect'':6C ''confus'':118C ''creation'':36C ''discrimin'':121C ''document'':8C,13C,43C,48C ''en.wikipedia.org'':25C ''en.wikipedia.org/wiki/topic_model'':24C ''gensim'':104C ''href'':23C,73C,83C,98C ''human'':109C ''includ'':68C ''java'':79C ''lda'':46C,67C ''learn'':125C ''li'':71C,81C,96C ''linear'':120C ''mallet'':77C ''mallet.cs.umass.edu'':74C ''mixtur'':16C,50C ''model'':91C,107C ''nlp.stanford.edu'':85C ''nlp.stanford.edu/software/tmt/tmt-0.4/'':84C ''nofollow'':28C,76C,88C,103C ''number'':20C ''observ'':3C,129C ''one'':40C ''p'':1C,61C,113C ''packag'':64C ''perform'':66C ''popular'':62C ''posit'':10C ''probabl'':59C ''procedur'':126C ''radimrehurek.com'':100C ''radimrehurek.com/gensim/'':99C ''rel'':27C,75C,87C,102C ''repres'':47C ''set'':132C ''small'':19C ''softwar'':63C,94C ''spit'':54C ''stanford'':89C ''supervis'':124C ''toolbox'':92C ''topic'':29C,45C,52C,90C,106C ''ul'':70C ''word'':5C,34C,56C ''â'':105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6765, 1, '2015-08-11 19:28:29.793', 3, 83, 'Advise on making predictions given collection of dimensions and corresponding probabilities', '&lt;p&gt;I am a CS graduate but am very new to data science. I could use some expert advise/insight on a problem I am trying to solve. I''ve been through the titanic tutorial on gaggle.com which I think was helpful but my problem is a bit different.&lt;/p&gt;

&lt;p&gt;I am trying to predict diabetes risk based upon Age, Sex...and other factors given this data: &lt;a href="http://www.healthindicators.gov/Indicators/Diabetes-new-cases-per-1000_555/Profile/ClassicData" rel="nofollow"&gt;http://www.healthindicators.gov/Indicators/Diabetes-new-cases-per-1000_555/Profile/ClassicData&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The data gives new cases people per 1,000 people for each dimension (Age, Sex...etc). What I would like to do is devise a way to predict, given a list of dimensions (Age, Sex...etc) a probability factor for a new diagnosis.&lt;/p&gt;

&lt;p&gt;So far my strategy is to load this data into R and use some package to create a decision tree, similar to what I saw in the titanic example on kaggle.com, then feed in a dimension list. However, I am a bit overwhelmed. Any direction on what I should be studying, packages/methods/examples would be helpful.&lt;/p&gt;
', 'machine-learning  data-mining  r  predictive-modeling  data', NULL, '''/indicators/diabetes-new-cases-per-1000_555/profile/classicdata'':94C ''/indicators/diabetes-new-cases-per-1000_555/profile/classicdata&lt;/a&gt;&lt;/p&gt;'':99C ''/p'':71C,144C,201C ''000'':109C ''1'':108C ''advis'':1A ''advise/insight'':41C ''age'':82C,114C,134C ''base'':80C ''bit'':69C,187C ''case'':105C ''collect'':6A ''correspond'':10A ''could'':37C ''creat'':162C ''cs'':27C ''data'':16B,22B,34C,89C,102C,154C ''data-min'':15B ''decis'':164C ''devis'':124C ''diabet'':78C ''diagnosi'':143C ''differ'':70C ''dimens'':8A,113C,133C,181C ''direct'':190C ''etc'':116C,136C ''exampl'':174C ''expert'':40C ''factor'':86C,139C ''far'':147C ''feed'':178C ''gaggle.com'':58C ''give'':103C ''given'':5A,87C,129C ''graduat'':28C ''help'':63C,200C ''howev'':183C ''href'':91C ''kaggle.com'':176C ''learn'':14B ''like'':120C ''list'':131C,182C ''load'':152C ''machin'':13B ''machine-learn'':12B ''make'':3A ''mine'':17B ''model'':21B ''new'':32C,104C,142C ''nofollow'':96C ''overwhelm'':188C ''p'':23C,72C,100C,145C ''packag'':160C ''packages/methods/examples'':197C ''peopl'':106C,110C ''per'':107C ''predict'':4A,20B,77C,128C ''predictive-model'':19B ''probabl'':11A,138C ''problem'':44C,66C ''r'':18B,156C ''rel'':95C ''risk'':79C ''saw'':170C ''scienc'':35C ''sex'':83C,115C,135C ''similar'':166C ''solv'':49C ''strategi'':149C ''studi'':196C ''think'':61C ''titan'':55C,173C ''tree'':165C ''tri'':47C,75C ''tutori'':56C ''upon'':81C ''use'':38C,158C ''ve'':51C ''way'':126C ''would'':119C,198C ''www.healthindicators.gov'':93C,98C ''www.healthindicators.gov/indicators/diabetes-new-cases-per-1000_555/profile/classicdata'':92C ''www.healthindicators.gov/indicators/diabetes-new-cases-per-1000_555/profile/classicdata&lt;/a&gt;&lt;/p&gt;'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6767, 2, '2015-08-11 21:37:41.603', 4, NULL, NULL, '&lt;h2&gt;Aggregate Data&lt;/h2&gt;

&lt;p&gt;Since you''re only given aggregate data, and not individual examples, machine learning techniques like decision trees won''t really help you much. Those algorithms gain a lot of traction by looking at correlations within a single example. For instance, the increase in risk from being both obese and over 40 might be much higher than the sum of the individual risks of being obese or over 40 (i.e. the effect is greater than the sum of its parts). Aggregate data loses this information.&lt;/p&gt;

&lt;h2&gt;The Bayesian Approach&lt;/h2&gt;

&lt;p&gt;On the bright side, though, using aggregate data like this is fairly straightforward, but requires some probability theory. If $D$ is whether the person has diabetes and $F_1,\ldots,F_n$ are the factors from that link you provided, and if I''m doing my math correctly, we can use the formula:
$$ \text{Prob}(D\ |\ F_1,\ldots,F_n) \propto \frac{\prod_{k=1}^n \text{Prob}(D\ |\ F_k)}{\text{Prob}(D)^{n-1}} $$
(The proof for this is an extension of the one found &lt;a href="http://stats.stackexchange.com/a/112361"&gt;here&lt;/a&gt;). This assumes that the factors $F_1,\ldots,F_n$ are conditionally independent given $D$, though that''s usually reasonable. To calculate the probabilities, compute the outputs for $D=\text{Diabetes}$ and $\neg D=\text{No diabetes}$ and divide them both by their sum so that they add to 1.&lt;/p&gt;

&lt;h2&gt;Example&lt;/h2&gt;

&lt;p&gt;Suppose we had a married, 48-year-old male. Looking at the 2010-2012 data, 0.73% of all people get diabetes ($\text{Prob}(D) = 0.73\%$), 0.77% of married people get diabetes ($\text{Prob}(D\ |\ F_1)$$= 0.77\%$), 1.02% of people age 45-54 get diabetes ($\text{Prob}(D\ |\ F_2) = 1.02\%$), and 0.70% of males get diabetes ($\text{Prob}(D\ |\ F_3) = 0.70\%$). This gives us the unnormalized probabilities:
$$ \begin{align*} P(D\ |\ F_1,F_2,F_3) &amp;amp;= \frac{(0.77\%)(1.02\%)(0.70\%)}{(0.73\%)^2} &amp;amp;= 0.0103 \\
P(\neg D\ |\ F_1,F_2,F_3) &amp;amp;= \frac{(99.23\%)(98.98\%)(99.30\%)}{(99.27\%)^2} &amp;amp;= 0.9897 \end{align*}$$
After normalizing these to add to one (which they already do in this case), we get a 1.03% chance of this person getting diabetes, and a 98.97% chance for them not getting diabetes.&lt;/p&gt;
', NULL, NULL, '''-1'':173C ''-2012'':261C ''-54'':290C ''/a'':191C ''/a/112361'':189C ''/h2'':4C,95C,245C ''/p'':90C,242C,389C ''0.0103'':335C ''0.70'':300C,310C,331C ''0.73'':263C,272C,332C ''0.77'':273C,284C,329C ''0.9897'':353C ''1'':125C,154C,162C,198C,241C,283C,322C,340C ''1.02'':285C,298C,330C ''1.03'':373C ''2'':297C,324C,333C,342C,351C ''2010'':260C ''3'':309C,326C,344C ''40'':56C,73C ''45'':289C ''48'':252C ''98.97'':382C ''98.98'':348C ''99.23'':347C ''99.27'':350C ''99.30'':349C ''add'':239C,360C ''age'':288C ''aggreg'':2C,11C,85C,103C ''algorithm'':30C ''align'':318C,355C ''alreadi'':365C ''amp'':327C,334C,345C,352C ''approach'':94C ''assum'':193C ''bayesian'':93C ''begin'':317C ''bright'':99C ''calcul'':213C ''case'':369C ''chanc'':374C,383C ''comput'':216C ''condit'':203C ''correct'':144C ''correl'':39C ''d'':116C,152C,166C,171C,206C,220C,225C,271C,281C,295C,307C,320C,338C ''data'':3C,12C,86C,104C,262C ''decis'':21C ''diabet'':122C,222C,228C,268C,278C,292C,304C,379C,388C ''divid'':230C ''effect'':76C ''end'':354C ''exampl'':16C,43C,244C ''extens'':180C ''f'':124C,127C,153C,156C,167C,197C,200C,282C,296C,308C,321C,323C,325C,339C,341C,343C ''factor'':131C,196C ''fair'':108C ''formula'':149C ''found'':184C ''frac'':159C,328C,346C ''gain'':31C ''get'':267C,277C,291C,303C,371C,378C,387C ''give'':312C ''given'':10C,205C ''greater'':78C ''h2'':1C,91C,243C ''help'':26C ''higher'':60C ''href'':186C ''i.e'':74C ''increas'':47C ''independ'':204C ''individu'':15C,66C ''inform'':89C ''instanc'':45C ''k'':161C,168C ''ldot'':126C,155C,199C ''learn'':18C ''like'':20C,105C ''link'':134C ''look'':37C,257C ''lose'':87C ''lot'':33C ''m'':140C ''machin'':17C ''male'':256C,302C ''marri'':251C,275C ''math'':143C ''might'':57C ''much'':28C,59C ''n'':128C,157C,163C,172C,201C ''neg'':224C,337C ''normal'':357C ''obes'':53C,70C ''old'':255C ''one'':183C,362C ''output'':218C ''p'':5C,96C,246C,319C,336C ''part'':84C ''peopl'':266C,276C,287C ''person'':120C,377C ''prob'':151C,165C,170C,270C,280C,294C,306C ''probabl'':113C,215C,316C ''prod'':160C ''proof'':175C ''propto'':158C ''provid'':136C ''re'':8C ''realli'':25C ''reason'':211C ''requir'':111C ''risk'':49C,67C ''side'':100C ''sinc'':6C ''singl'':42C ''stats.stackexchange.com'':188C ''stats.stackexchange.com/a/112361'':187C ''straightforward'':109C ''sum'':63C,81C,235C ''suppos'':247C ''techniqu'':19C ''text'':150C,164C,169C,221C,226C,269C,279C,293C,305C ''theori'':114C ''though'':101C,207C ''traction'':35C ''tree'':22C ''unnorm'':315C ''us'':313C ''use'':102C,147C ''usual'':210C ''whether'':118C ''within'':40C ''won'':23C ''year'':254C ''year-old'':253C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6769, 2, '2015-08-11 22:34:51.927', 0, NULL, NULL, '&lt;p&gt;R also has an htmlwidgets package that incorporated D3 javascript visualizations at: &lt;a href="http://www.htmlwidgets.org/" rel="nofollow"&gt;http://www.htmlwidgets.org/&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':21C ''/p'':22C ''also'':3C ''d3'':10C ''href'':15C ''htmlwidget'':6C ''incorpor'':9C ''javascript'':11C ''nofollow'':18C ''p'':1C ''packag'':7C ''r'':2C ''rel'':17C ''visual'':12C ''www.htmlwidgets.org'':16C,20C ''www.htmlwidgets.org/&lt;/a&gt;'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6770, 2, '2015-08-12 03:54:32.253', 2, NULL, NULL, '&lt;p&gt;I learn well by examples, so perhaps this will be helpful:&lt;/p&gt;

&lt;p&gt;Lets say you are a resort considering your next advertizing campaign. You will be sending out 1,000 vouchers that give two &lt;em&gt;free&lt;/em&gt; days at the resort, with the hope that the customer will decide to extend it to a full week (who want''s a two day vacation?!). &lt;/p&gt;

&lt;p&gt;You have a rather extensive database of customer data, including where they are from, income range, duration of stay(s) and money spent at each stay, number of children, marital status, and employment status (working, unemployed, retired). You would like to create a decision tree to help select the customers to send the vouchers to (specifically, those who will spend at least $200 per day and stay at least one week.&lt;/p&gt;

&lt;p&gt;To create the tree, a computer will perform the following steps:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;From the above list of customer data, find the property that best separates the customers into two groups.&lt;/li&gt;
&lt;li&gt;Repeat step one for each of the above groups using the remaining properties.&lt;/li&gt;
&lt;li&gt;In the end, you will have a tree where at each point, you can make one of two decisions. Following a path leads to a decision. The split points will be chosen to maximize the probability of a correct classification.&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/em'':39C ''/li'':173C,188C,230C ''/ol'':231C ''/p'':13C,66C,139C,152C ''000'':32C ''1'':31C ''200'':130C ''advert'':24C ''best'':166C ''campaign'':25C ''children'':96C ''chosen'':221C ''classif'':229C ''comput'':146C ''consid'':21C ''correct'':228C ''creat'':109C,142C ''custom'':49C,75C,117C,160C,169C ''data'':76C,161C ''databas'':73C ''day'':40C,64C,132C ''decid'':51C ''decis'':111C,208C,215C ''durat'':84C ''em'':37C ''employ'':100C ''end'':192C ''exampl'':6C ''extend'':53C ''extens'':72C ''find'':162C ''follow'':150C,209C ''free'':38C ''full'':57C ''give'':35C ''group'':172C,183C ''help'':12C,114C ''hope'':46C ''includ'':77C ''incom'':82C ''lead'':212C ''learn'':3C ''least'':129C,136C ''let'':15C ''li'':154C,174C,189C ''like'':107C ''list'':158C ''make'':204C ''marit'':97C ''maxim'':223C ''money'':89C ''next'':23C ''number'':94C ''ol'':153C ''one'':137C,177C,205C ''p'':1C,14C,67C,140C ''path'':211C ''per'':131C ''perform'':148C ''perhap'':8C ''point'':201C,218C ''probabl'':225C ''properti'':164C,187C ''rang'':83C ''rather'':71C ''remain'':186C ''repeat'':175C ''resort'':20C,43C ''retir'':104C ''say'':16C ''select'':115C ''send'':29C,119C ''separ'':167C ''specif'':123C ''spend'':127C ''spent'':90C ''split'':217C ''status'':98C,101C ''stay'':86C,93C,134C ''step'':151C,176C ''tree'':112C,144C,197C ''two'':36C,63C,171C,207C ''unemploy'':103C ''use'':184C ''vacat'':65C ''voucher'':33C,121C ''want'':60C ''week'':58C,138C ''well'':4C ''work'':102C ''would'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6771, 2, '2015-08-12 09:56:15.27', 3, NULL, NULL, '&lt;p&gt;What I am ending up using is a sort of hybrid solution:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;backup of the raw data&lt;/li&gt;
&lt;li&gt;git of the workflow&lt;/li&gt;
&lt;li&gt;manual snapshots of workflow + processed data, that are of relevance, e.g.:

&lt;ul&gt;
&lt;li&gt;standard preprocessing&lt;/li&gt;
&lt;li&gt;really time-consuming&lt;/li&gt;
&lt;li&gt;for publication&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I believe it is seldom sensible to have a full revision history of large amount of binary data, because the time required to review the changes will eventually be so overwhelming that it will not pay off in the long run.
Maybe a semi-automatic snapshot procedure (eventually to save some disk-space, by not replicating the unchanged data across different snapshots) would be of help.&lt;/p&gt;
', NULL, NULL, '''/li'':22C,28C,45C,51C,55C,57C ''/p'':14C,128C ''/ul'':56C,58C ''across'':121C ''amount'':74C ''automat'':105C ''backup'':17C ''believ'':61C ''binari'':76C ''chang'':85C ''consum'':50C ''data'':21C,35C,77C,120C ''differ'':122C ''disk'':113C ''disk-spac'':112C ''e.g'':40C ''end'':5C ''eventu'':87C,108C ''full'':69C ''git'':24C ''help'':127C ''histori'':71C ''hybrid'':12C ''larg'':73C ''li'':16C,23C,29C,42C,46C,52C ''long'':99C ''manual'':30C ''mayb'':101C ''overwhelm'':90C ''p'':1C,59C ''pay'':95C ''preprocess'':44C ''procedur'':107C ''process'':34C ''public'':54C ''raw'':20C ''realli'':47C ''relev'':39C ''replic'':117C ''requir'':81C ''review'':83C ''revis'':70C ''run'':100C ''save'':110C ''seldom'':64C ''semi'':104C ''semi-automat'':103C ''sensibl'':65C ''snapshot'':31C,106C,123C ''solut'':13C ''sort'':10C ''space'':114C ''standard'':43C ''time'':49C,80C ''time-consum'':48C ''ul'':15C,41C ''unchang'':119C ''use'':7C ''workflow'':27C,33C ''would'':124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6773, 1, '2015-08-12 14:01:23.257', 2, 455, 'How to count observations per ID in R?', '&lt;p&gt;I have a large amount of Data where I have to count meassurments per one ID. What I already did was creating a Data Frame over all Files and I omited the NAs. This part works properly. I was wondering if the nrow-function is the right function to solve this but I figured out that this will not lead me to the target as it returns a single number as output. &lt;/p&gt;

&lt;p&gt;What I am looking for is if you have entries like that:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1155 2010-05-02  2.7200    1
1156 2010-05-05  2.6000    3
1157 2010-05-08  2.6700    1
1158 2010-05-11  3.5700    2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;That I get a list:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ID          Number of observations
1           2
2           1
3           1
&lt;/code&gt;&lt;/pre&gt;
', 'r', NULL, '''-02'':104C ''-05'':103C,109C,110C,115C,121C ''-08'':116C ''-11'':122C ''/code'':125C,146C ''/p'':84C,98C,133C ''/pre'':126C,147C ''1'':106C,118C,140C,143C,145C ''1155'':101C ''1156'':107C ''1157'':113C ''1158'':119C ''2'':124C,141C,142C ''2.6000'':111C ''2.6700'':117C ''2.7200'':105C ''2010'':102C,108C,114C,120C ''3'':112C,144C ''3.5700'':123C ''alreadi'':29C ''amount'':15C ''code'':100C,135C ''count'':3A,22C ''creat'':32C ''data'':17C,34C ''entri'':95C ''figur'':65C ''file'':38C ''frame'':35C ''function'':55C,59C ''get'':130C ''id'':6A,26C,136C ''larg'':14C ''lead'':71C ''like'':96C ''list'':132C ''look'':89C ''meassur'':23C ''nas'':43C ''nrow'':54C ''nrow-funct'':53C ''number'':81C,137C ''observ'':4A,139C ''omit'':41C ''one'':25C ''output'':83C ''p'':10C,85C,127C ''part'':45C ''per'':5A,24C ''pre'':99C,134C ''proper'':47C ''r'':8A,9B ''return'':78C ''right'':58C ''singl'':80C ''solv'':61C ''target'':75C ''wonder'':50C ''work'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6774, 1, '2015-08-12 14:16:42.16', 1, 152, 'emission probability using hmmlearn package in python', '&lt;p&gt;I am learning hmm and try to implement it in  Python hmmlearn package(&lt;a href="http://hmmlearn.github.io/hmmlearn/hmm.html#building-hmm-and-generating-samples" rel="nofollow"&gt;http://hmmlearn.github.io/hmmlearn/hmm.html#building-hmm-and-generating-samples&lt;/a&gt;). &lt;/p&gt;

&lt;p&gt;However I am not quite understand what the documentation says:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Classes in this module include MultinomialHMM, GaussianHMM, and
  GMMHMM. They implement HMM with emission probabilities determined by
  multimomial distributions, Gaussian distributions and mixtures of
  Gaussian distributions.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Does this means when we try to estimate the model given an observation sequence, the  emission probability can only be these three kind? what if I want to type in the emission probability by meself?&lt;/p&gt;
', 'python  markov-process', NULL, '''/blockquote'':78C ''/hmmlearn/hmm.html#building-hmm-and-generating-samples'':30C ''/hmmlearn/hmm.html#building-hmm-and-generating-samples&lt;/a&gt;).'':35C ''/p'':36C,48C,77C,115C ''blockquot'':49C ''class'':51C ''determin'':66C ''distribut'':69C,71C,76C ''document'':46C ''emiss'':1A,64C,95C,111C ''estim'':87C ''gaussian'':70C,75C ''gaussianhmm'':57C ''given'':90C ''gmmhmm'':59C ''hmm'':16C,62C ''hmmlearn'':4A,24C ''hmmlearn.github.io'':29C,34C ''hmmlearn.github.io/hmmlearn/hmm.html#building-hmm-and-generating-samples'':28C ''hmmlearn.github.io/hmmlearn/hmm.html#building-hmm-and-generating-samples&lt;/a&gt;).'':33C ''howev'':38C ''href'':27C ''implement'':20C,61C ''includ'':55C ''kind'':102C ''learn'':15C ''markov'':10B ''markov-process'':9B ''mean'':82C ''meself'':114C ''mixtur'':73C ''model'':89C ''modul'':54C ''multimomi'':68C ''multinomialhmm'':56C ''nofollow'':32C ''observ'':92C ''p'':12C,37C,50C,79C ''packag'':5A,25C ''probabl'':2A,65C,96C,112C ''process'':11B ''python'':7A,8B,23C ''quit'':42C ''rel'':31C ''say'':47C ''sequenc'':93C ''three'':101C ''tri'':18C,85C ''type'':108C ''understand'':43C ''use'':3A ''want'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6782, 2, '2015-08-13 07:31:42.373', 1, NULL, NULL, '&lt;p&gt;To see if SVM can capture any signal at all, try to balance your data: create training and test sets that consist of exactly 50% positive and 50% negative samples (i.e., by subsampling randomly from whichever one is bigger). Also standardize the data (subtract the mean and divide by standard deviation).&lt;/p&gt;

&lt;p&gt;(For balancing, you might try changing the &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html" rel="nofollow"&gt;class_weight&lt;/a&gt; parameter in sklearn, but we found the manual method (subsampling) to work better.)&lt;/p&gt;
', NULL, NULL, '''/a'':71C ''/p'':53C,85C ''/stable/modules/generated/sklearn.svm.svc.html'':66C ''50'':26C,29C ''also'':41C ''balanc'':14C,56C ''better'':84C ''bigger'':40C ''captur'':7C ''chang'':60C ''class'':69C ''consist'':23C ''creat'':17C ''data'':16C,44C ''deviat'':52C ''divid'':49C ''exact'':25C ''found'':77C ''href'':63C ''i.e'':32C ''manual'':79C ''mean'':47C ''method'':80C ''might'':58C ''negat'':30C ''nofollow'':68C ''one'':38C ''p'':1C,54C ''paramet'':72C ''posit'':27C ''random'':35C ''rel'':67C ''sampl'':31C ''scikit-learn.org'':65C ''scikit-learn.org/stable/modules/generated/sklearn.svm.svc.html'':64C ''see'':3C ''set'':21C ''signal'':9C ''sklearn'':74C ''standard'':42C,51C ''subsampl'':34C,81C ''subtract'':45C ''svm'':5C ''test'':20C ''train'':18C ''tri'':12C,59C ''weight'':70C ''whichev'':37C ''work'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6783, 1, '2015-08-13 07:37:46.547', 2, 31, 'Multiclass Classification that includes a Geospatial Element', '&lt;p&gt;I am attempting to train a classifier to predict different prices for an item in different suburbs. I have several features, two of which are a latitude and longitude for the centroid of the suburb.&lt;/p&gt;

&lt;p&gt;I am attempting to train the model to classify the price of an item in a bin of $10 size. The geospatial element will definitely affect the price of the item, however the training data I have will have gaps in it (i.e. I don''t have prices for all suburbs).&lt;/p&gt;

&lt;p&gt;What is the best way to engineer a feature that will include this geospatial information and be able to fill in the gaps in the training/test data?&lt;/p&gt;

&lt;p&gt;So far I have tried creating new features for the bearing and distance from the capital city which seemed to work okay, as well as binning the latitude and longitude which performs worse than the bearing/distance. I did consider using a geohash, however I think that this will be too complex a feature for a classifier to understand.&lt;/p&gt;
', 'feature-extraction  multiclass-classification', NULL, '''/p'':50C,103C,132C,192C ''10'':70C ''abl'':122C ''affect'':77C ''attempt'':17C,54C ''bear'':144C ''bearing/distance'':169C ''best'':108C ''bin'':68C,159C ''capit'':149C ''centroid'':46C ''citi'':150C ''classif'':2A,13B ''classifi'':21C,60C,189C ''complex'':184C ''consid'':172C ''creat'':139C ''data'':86C,131C ''definit'':76C ''differ'':24C,30C ''distanc'':146C ''element'':7A,74C ''engin'':111C ''extract'':10B ''far'':135C ''featur'':9B,35C,113C,141C,186C ''feature-extract'':8B ''fill'':124C ''gap'':91C,127C ''geohash'':175C ''geospati'':6A,73C,118C ''howev'':83C,176C ''i.e'':94C ''includ'':4A,116C ''inform'':119C ''item'':28C,65C,82C ''latitud'':41C,161C ''longitud'':43C,163C ''model'':58C ''multiclass'':1A,12B ''multiclass-classif'':11B ''new'':140C ''okay'':155C ''p'':14C,51C,104C,133C ''perform'':165C ''predict'':23C ''price'':25C,62C,79C,99C ''seem'':152C ''sever'':34C ''size'':71C ''suburb'':31C,49C,102C ''think'':178C ''train'':19C,56C,85C ''training/test'':130C ''tri'':138C ''two'':36C ''understand'':191C ''use'':173C ''way'':109C ''well'':157C ''work'':154C ''wors'':166C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6791, 1, '2015-08-14 16:12:54.317', 0, 24, 'Amplifying the impact of outliers in a dataset', '&lt;p&gt;I have a conundrum.  I currently have 8 datasets that are forced ranked and then weighted to create an overall index.  The interesting thing about these datasets is that many of them have a high sigma and are highly right skewed when looking at their distribution.  So minimal change in the data results in a large rank change.&lt;/p&gt;

&lt;p&gt;Is there a method to use the distribution of the data, specifically the outliers, and amplify the ranking based on the distance the value is from the mean? &lt;/p&gt;
', 'dataset  statistics  beginner', NULL, '''/p'':71C,101C ''8'':20C ''amplifi'':1A,88C ''base'':91C ''beginn'':11B ''chang'':61C,70C ''conundrum'':16C ''creat'':30C ''current'':18C ''data'':64C,83C ''dataset'':8A,9B,21C,39C ''distanc'':94C ''distribut'':58C,80C ''forc'':24C ''high'':47C,51C ''impact'':3A ''index'':33C ''interest'':35C ''larg'':68C ''look'':55C ''mani'':42C ''mean'':100C ''method'':76C ''minim'':60C ''outlier'':5A,86C ''overal'':32C ''p'':12C,72C ''rank'':25C,69C,90C ''result'':65C ''right'':52C ''sigma'':48C ''skew'':53C ''specif'':84C ''statist'':10B ''thing'':36C ''use'':78C ''valu'':96C ''weight'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6784, 2, '2015-08-13 07:45:45.927', 10, NULL, NULL, '&lt;p&gt;IPython has now moved to &lt;strong&gt;&lt;a href="http://blog.jupyter.org/2015/08/12/first-release-of-jupyter/"&gt;version 4.0&lt;/a&gt;&lt;/strong&gt;, which means that if you are using it, it will be reading its configuration from &lt;code&gt;~/.jupyter&lt;/code&gt;, not &lt;code&gt;~/.ipython&lt;/code&gt;. You have to create a new configuration file with&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;jupyter notebook --generate-config
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and then edit the resulting &lt;code&gt;~/.jupyter/jupyter_notebook_config.py&lt;/code&gt; file according to your needs.&lt;/p&gt;

&lt;p&gt;More installation instructions &lt;a href="https://jupyter.readthedocs.org/en/latest/config.html"&gt;here&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/2015/08/12/first-release-of-jupyter/'':12C ''/a'':15C,83C ''/code'':34C,38C,56C,66C ''/en/latest/config.html'':81C ''/p'':48C,72C,84C ''/pre'':57C ''/strong'':16C ''4.0'':14C ''accord'':68C ''blog.jupyter.org'':11C ''blog.jupyter.org/2015/08/12/first-release-of-jupyter/'':10C ''code'':32C,36C,50C,64C ''config'':55C ''configur'':30C,45C ''creat'':42C ''edit'':61C ''file'':46C,67C ''generat'':54C ''generate-config'':53C ''href'':9C,78C ''instal'':75C ''instruct'':76C ''ipython'':2C ''jupyt'':51C ''jupyter.readthedocs.org'':80C ''jupyter.readthedocs.org/en/latest/config.html'':79C ''mean'':18C ''move'':5C ''need'':71C ''new'':44C ''notebook'':52C ''p'':1C,58C,73C ''pre'':49C ''read'':28C ''result'':63C ''strong'':7C ''use'':23C ''version'':13C ''~/.ipython'':37C ''~/.jupyter'':33C ''~/.jupyter/jupyter_notebook_config.py'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6786, 1, '2015-08-13 13:52:11.463', 2, 77, 'Weighted k nearest neighbor search', '&lt;p&gt;I''ve searched quite a bit and haven''t landed on any useful results.&lt;/p&gt;

&lt;p&gt;The problem statement is:
Given a set of vectors, I wish to find its approximate k-nearest neighbors.
The caveat here is that each of my dimensions resemble a different entity and hence we cannot use the same weight for each dimension while computing the distance.
Thus, solutions like kd-tree don''t work as is.&lt;/p&gt;

&lt;p&gt;Is there any data-structure or any alternate algorithm that I can use to find such approximate weighted k-nearest neighbors.&lt;/p&gt;

&lt;p&gt;Note: Multiplying the initial input data with their weights so as to get a uniform weight is not an option.&lt;/p&gt;
', 'machine-learning  data  search', NULL, '''/p'':26C,86C,111C,133C ''algorithm'':97C ''altern'':96C ''approxim'':42C,105C ''bit'':17C ''cannot'':63C ''caveat'':48C ''comput'':72C ''data'':9B,92C,118C ''data-structur'':91C ''differ'':58C ''dimens'':55C,70C ''distanc'':74C ''entiti'':59C ''find'':40C,103C ''get'':125C ''given'':32C ''haven'':19C ''henc'':61C ''initi'':116C ''input'':117C ''k'':2A,44C,108C ''k-nearest'':43C,107C ''kd'':79C ''kd-tree'':78C ''land'':21C ''learn'':8B ''like'':77C ''machin'':7B ''machine-learn'':6B ''multipli'':114C ''nearest'':3A,45C,109C ''neighbor'':4A,46C,110C ''note'':113C ''option'':132C ''p'':11C,27C,87C,112C ''problem'':29C ''quit'':15C ''resembl'':56C ''result'':25C ''search'':5A,10B,14C ''set'':34C ''solut'':76C ''statement'':30C ''structur'':93C ''thus'':75C ''tree'':80C ''uniform'':127C ''use'':24C,64C,101C ''ve'':13C ''vector'':36C ''weight'':1A,67C,106C,121C,128C ''wish'':38C ''work'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6787, 1, '2015-08-13 13:59:52.603', 2, 194, 'Is decision tree algorithm a linear or nonlinear algorithm?', '&lt;p&gt;Recently a friend of mine was asked whether  decision tree algorithm a linear or nonlinear algorithm in an interview. I tried to look for answers to this question but couldn''t find any satisfactory explanation. Can anyone answer and explain the solution to this question? Also what are some other examples of nonlinear machine learning algorithms?&lt;/p&gt;
', 'machine-learning  algorithms  decision-trees', NULL, '''/p'':74C ''algorithm'':4A,9A,13B,28C,33C,73C ''also'':63C ''answer'':42C,55C ''anyon'':54C ''ask'':24C ''couldn'':47C ''decis'':2A,15B,26C ''decision-tre'':14B ''exampl'':68C ''explain'':57C ''explan'':52C ''find'':49C ''friend'':20C ''interview'':36C ''learn'':12B,72C ''linear'':6A,30C ''look'':40C ''machin'':11B,71C ''machine-learn'':10B ''mine'':22C ''nonlinear'':8A,32C,70C ''p'':17C ''question'':45C,62C ''recent'':18C ''satisfactori'':51C ''solut'':59C ''tree'':3A,16B,27C ''tri'':38C ''whether'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6788, 2, '2015-08-13 15:41:05.987', 1, NULL, NULL, '&lt;p&gt;It is impossible to prove a negative, but other than using the same pattern detection system in general to detect shapes/images and replace them with other similar images, possibly for use in automatic image correction or similar, I don''t think it has real potential outside of modifying pictures.&lt;/p&gt;

&lt;p&gt;I may have to delete this answer if it is proven wrong.&lt;/p&gt;
', NULL, NULL, '''/p'':51C,65C ''answer'':59C ''automat'':34C ''correct'':36C ''delet'':57C ''detect'':16C,21C ''general'':19C ''imag'':29C,35C ''imposs'':4C ''may'':54C ''modifi'':49C ''negat'':8C ''outsid'':47C ''p'':1C,52C ''pattern'':15C ''pictur'':50C ''possibl'':30C ''potenti'':46C ''prove'':6C ''proven'':63C ''real'':45C ''replac'':24C ''shapes/images'':22C ''similar'':28C,38C ''system'':17C ''think'':42C ''use'':12C,32C ''wrong'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6789, 1, '2015-08-14 07:19:18.463', 1, 204, 'Neural network for MNIST: very low accuracy', '&lt;p&gt;I am working on solving the handwritten digit recognition problem by implementing a neural network. But the accuracy of the network is coming out to be very low, around 11% for the train dataset. I am not sure what is wrong with my program. I tried changing the learning rate and the number of hidden units, but no luck. Could anyone please take a look and help me out with what I am missing? I am pasting my Julia code below:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# install
Pkg.add("MNIST");
using MNIST

# training data
X,y = traindata(); 
m = size(X, 2);
inputLayerSize = size(X,1); 
hiddenLayerSize = 300;
outputLayerSize = 10;

# representing each output as an array of size of the output layer
eyeY = eye(outputLayerSize);
intY = [convert(Int64,i)+1 for i in y];
Y = zeros(outputLayerSize, m);
for i = 1:m
    Y[:,i] = eyeY[:,intY[i],];
end

# weights with bias
Theta1 = randn(inputLayerSize+1, hiddenLayerSize); 
Theta2 = randn(hiddenLayerSize+1, outputLayerSize); 

function sigmoid(z)
    g = 1.0 ./ (1.0 + exp(-z));
    return g;
end

function sigmoidGradient(z)
  return sigmoid(z).*(1-sigmoid(z));
end

# learning rate
alpha = 0.01;
# number of iterations
epoch = 20;
# cost per epoch
J = zeros(epoch,1);
# backpropagation algorithm
for i = 1:epoch
    for j = 1:m # for each input
        # Feedforward
        # input layer
        # add one bias element
        x1 = [1, X[:,j]];

        # hidden layer
        z2 = Theta1''*x1;
        x2 = sigmoid(z2);
        # add one bias element
        x2 = [1, x2];

        # output layer
        z3 = Theta2''*x2;
        x3 = sigmoid(z3);

        # Backpropagation process
        # delta for output layer
        delta3 = x3 - Y[:,j];
        delta2 = (Theta2[2:end,:]*delta3).*sigmoidGradient(z2) ;

        # update weights
        Theta1 = Theta1 - alpha* x1*delta2'';
        Theta2 = Theta2 - alpha* x2*delta3'';
    end
end

function predict(Theta1, Theta2, X)
    m = size(X, 2); 
    p = zeros(m, 1);
    h1 = sigmoid(Theta1''*[ones(1,size(X,2)), X]);
    h2 = sigmoid(Theta2''*[ones(1,size(h1,2)), h1]);
    # 1 index is for 0, 2 for 1 ...so forth
    for i=1:m
        p[i,:] = indmax(h2[:,i])-1;
    end
    return p;
end

function accuracy(truth, prediction)
    m = length(truth);
    sum =0;
    for i=1:m
        if truth[i,:] == pred[i,:]
            sum = sum +1;
        end
    end
  return (sum/m)*100;
end

pred = predict(Theta1, Theta2, X);
println("train accuracy: ", accuracy(y, pred));
&lt;/code&gt;&lt;/pre&gt;
', 'machine-learning  neuralnetwork  julia', NULL, '''+1'':139C,164C,169C,361C ''-1'':336C ''/code'':379C ''/p'':95C ''/pre'':380C ''0'':321C,349C ''0.01'':195C ''1'':115C,150C,188C,207C,212C,216C,229C,245C,298C,303C,312C,317C,324C,329C,352C ''1.0'':175C,176C ''10'':119C ''100'':366C ''11'':43C ''2'':111C,267C,294C,306C,315C,322C ''20'':200C ''300'':117C ''accuraci'':7A,31C,342C,375C,376C ''add'':224C,240C ''algorithm'':209C ''alpha'':194C,276C,281C ''anyon'':74C ''around'':42C ''array'':125C ''backpropag'':208C,255C ''bias'':160C,226C,242C ''chang'':60C ''code'':93C,97C ''come'':36C ''convert'':136C ''cost'':201C ''could'':73C ''data'':104C ''dataset'':47C ''delta'':257C ''delta2'':265C,278C ''delta3'':261C,269C,283C ''digit'':21C ''element'':227C,243C ''end'':157C,181C,191C,268C,284C,285C,337C,340C,362C,363C,367C ''epoch'':199C,203C,206C,213C ''exp'':177C ''eye'':133C ''eyey'':132C,154C ''feedforward'':221C ''forth'':326C ''function'':171C,182C,286C,341C ''g'':174C,180C ''h1'':299C,314C,316C ''h2'':308C,334C ''handwritten'':20C ''help'':80C ''hidden'':68C,232C ''hiddenlayers'':116C,165C,168C ''implement'':25C ''index'':318C ''indmax'':333C ''input'':220C,222C ''inputlayers'':112C,163C ''instal'':98C ''int64'':137C ''inti'':135C,155C ''iter'':198C ''j'':204C,215C,231C,264C ''julia'':12B,92C ''layer'':131C,223C,233C,248C,260C ''learn'':10B,62C,192C ''length'':346C ''look'':78C ''low'':6A,41C ''luck'':72C ''m'':108C,147C,151C,217C,291C,297C,330C,345C,353C ''machin'':9B ''machine-learn'':8B ''miss'':87C ''mnist'':4A,100C,102C ''network'':2A,28C,34C ''neural'':1A,27C ''neuralnetwork'':11B ''number'':66C,196C ''one'':225C,241C,302C,311C ''output'':122C,130C,247C,259C ''outputlayers'':118C,134C,146C,170C ''p'':13C,295C,331C,339C ''past'':90C ''per'':202C ''pkg.add'':99C ''pleas'':75C ''pre'':96C ''pred'':357C,368C,378C ''predict'':287C,344C,369C ''println'':373C ''problem'':23C ''process'':256C ''program'':57C ''randn'':162C,167C ''rate'':63C,193C ''recognit'':22C ''repres'':120C ''return'':179C,185C,338C,364C ''sigmoid'':172C,186C,189C,238C,253C,300C,309C ''sigmoidgradi'':183C,270C ''size'':109C,113C,127C,292C,304C,313C ''solv'':18C ''sum'':348C,359C,360C ''sum/m'':365C ''sure'':51C ''take'':76C ''theta1'':161C,235C,274C,275C,288C,301C,370C ''theta2'':166C,250C,266C,279C,280C,289C,310C,371C ''train'':46C,103C,374C ''traindata'':107C ''tri'':59C ''truth'':343C,347C,355C ''unit'':69C ''updat'':272C ''use'':101C ''weight'':158C,273C ''work'':16C ''wrong'':54C ''x'':105C,110C,114C,230C,290C,293C,305C,307C,372C ''x1'':228C,236C,277C ''x2'':237C,244C,246C,251C,282C ''x3'':252C,262C ''y'':106C,143C,144C,152C,263C,377C ''z'':173C,178C,184C,187C,190C ''z2'':234C,239C,271C ''z3'':249C,254C ''zero'':145C,205C,296C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6790, 2, '2015-08-14 12:13:05.467', 4, NULL, NULL, '&lt;p&gt;A decision tree is a non-linear mapping of &lt;code&gt;X&lt;/code&gt; to &lt;code&gt;y&lt;/code&gt;. This is easy to see if you take an arbitrary function and create a tree to its maximum depth.&lt;/p&gt;

&lt;p&gt;For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;if x = 1, y = 1
if x = 2, y = 15
if x = 3, y = 3
if x = 4, y = 27
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Of course, this is a completely over-fit tree and won''t generalize. But it demonstrates why a decision tree is a non-linear mapping.&lt;/p&gt;
', NULL, NULL, '''/code'':14C,18C,65C ''/p'':38C,42C,95C ''/pre'':66C ''1'':47C,49C ''15'':54C ''2'':52C ''27'':64C ''3'':57C,59C ''4'':62C ''arbitrari'':28C ''code'':12C,16C,44C ''complet'':73C ''cours'':69C ''creat'':31C ''decis'':3C,87C ''demonstr'':84C ''depth'':37C ''easi'':21C ''exampl'':41C ''fit'':76C ''function'':29C ''general'':81C ''linear'':9C,93C ''map'':10C,94C ''maximum'':36C ''non'':8C,92C ''non-linear'':7C,91C ''over-fit'':74C ''p'':1C,39C,67C ''pre'':43C ''see'':23C ''take'':26C ''tree'':4C,33C,77C,88C ''won'':79C ''x'':13C,46C,51C,56C,61C ''y'':17C,48C,53C,58C,63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6792, 1, '2015-08-14 16:43:19.007', 2, 104, 'Privacy through fake data?', '&lt;p&gt;With companies and governments hungry for all the data about people, I was wondering if it was possible to gain some privacy by drowning relevant information in a sea of random data. For example a browser extension which keeps searching for random words and expressions in the background. Maybe sending generated emails to generated addresses. Or producing made up location data on my phone.&lt;/p&gt;

&lt;p&gt;Would these measures help at all or the data miner AIs would just see right through them?&lt;/p&gt;
', 'bigdata', NULL, '''/p'':71C,90C ''address'':61C ''ai'':83C ''background'':54C ''bigdata'':5B ''browser'':42C ''compani'':8C ''data'':4A,15C,38C,67C,81C ''drown'':30C ''email'':58C ''exampl'':40C ''express'':51C ''extens'':43C ''fake'':3A ''gain'':26C ''generat'':57C,60C ''govern'':10C ''help'':76C ''hungri'':11C ''inform'':32C ''keep'':45C ''locat'':66C ''made'':64C ''mayb'':55C ''measur'':75C ''miner'':82C ''p'':6C,72C ''peopl'':17C ''phone'':70C ''possibl'':24C ''privaci'':1A,28C ''produc'':63C ''random'':37C,48C ''relev'':31C ''right'':87C ''sea'':35C ''search'':46C ''see'':86C ''send'':56C ''wonder'':20C ''word'':49C ''would'':73C,84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6794, 1, '2015-08-14 19:36:11.59', 2, 56, 'What is the standard for dealing with null values in plotting?', '&lt;p&gt;There''s really no "standard," I assume, but how do you deal with null values when plotting data? An example of what I''m talking about is listed below:
&lt;a href="http://i.stack.imgur.com/HSsHA.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/HSsHA.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Suppose I''m plotting data on number of toys by color. Nearly half of these toys in my database have &lt;code&gt;null&lt;/code&gt; values for color. Do you guys include the toys with &lt;code&gt;null&lt;/code&gt; values in your visualization? How do you address that issue? Saying "nearly half of the toys didn''t have colors listed" doesn''t really add much to the conversation.&lt;/p&gt;
', 'visualization', NULL, '''/a'':60C ''/code'':85C,98C ''/hssha.png'':47C,54C ''/p'':61C,128C ''add'':123C ''address'':106C ''alt'':55C ''assum'':20C ''code'':83C,96C ''color'':73C,88C,118C ''convers'':127C ''data'':31C,67C ''databas'':81C ''deal'':6A,25C ''descript'':58C ''didn'':115C ''doesn'':120C ''enter'':56C ''exampl'':33C ''guy'':91C ''half'':75C,111C ''href'':44C ''i.stack.imgur.com'':46C,53C ''i.stack.imgur.com/hssha.png'':45C,52C ''imag'':57C ''img'':50C ''includ'':92C ''issu'':108C ''list'':41C,119C ''m'':37C,65C ''much'':124C ''near'':74C,110C ''nofollow'':49C ''null'':8A,27C,84C,97C ''number'':69C ''p'':13C,62C ''plot'':11A,30C,66C ''realli'':16C,122C ''rel'':48C ''say'':109C ''src'':51C ''standard'':4A,18C ''suppos'':63C ''talk'':38C ''toy'':71C,78C,94C,114C ''valu'':9A,28C,86C,99C ''visual'':12B,102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6795, 2, '2015-08-14 21:12:39.333', 1, NULL, NULL, '&lt;p&gt;Imo, It depends on two major factors:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;What is the purpose of the plot&lt;/li&gt;
&lt;li&gt;Does the &lt;code&gt;null&lt;/code&gt; value indicate incompleteness (&lt;em&gt;one of the values, but unknown which one&lt;/em&gt;), or does it indicate a last final option, (&lt;em&gt;like: multiple colors&lt;/em&gt;)&lt;/li&gt;
&lt;/ul&gt;

&lt;hr&gt;

&lt;p&gt;Imagine if the purpose of the plot is to compare which toy colors are most popular to a particular person/group.&lt;/p&gt;

&lt;p&gt;If &lt;code&gt;null&lt;/code&gt; indicates multi-colored toys, it may be important to include the data but rename it to something more meaningful. (&lt;em&gt;Multi-colored&lt;/em&gt;)&lt;/p&gt;

&lt;p&gt;If &lt;code&gt;null&lt;/code&gt; indicates that we just don''t know the color data for those toys, it would probably be best to exclude that data from the plot. However, with such a large percentage, it would be good to note that "x%" were excluded from your plot because they had no color information associated with them. Perhaps purple is in such a majority because this person/group really likes purple, or maybe its because purple toys almost never made it into the &lt;code&gt;null&lt;/code&gt; category for whatever reason.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;The same line of thinking should be applied to whatever the purpose of the plot is. If the &lt;code&gt;null&lt;/code&gt; values are relevant to the purpose, include them. If not, don''t - and maybe take a note of how much data is excluded if it could influence the results substantially (such as this case)&lt;/p&gt;
', NULL, NULL, '''/code'':25C,81C,112C,193C,221C ''/em'':38C,51C,106C ''/li'':19C,52C ''/p'':9C,76C,107C,198C,256C ''/ul'':53C ''almost'':185C ''appli'':208C ''associ'':163C ''best'':130C ''case'':255C ''categori'':194C ''code'':23C,79C,110C,191C,219C ''color'':50C,68C,85C,105C,121C,161C ''compar'':65C ''could'':247C ''data'':94C,122C,134C,242C ''depend'':4C ''em'':29C,47C,102C ''exclud'':132C,153C,244C ''factor'':8C ''final'':45C ''good'':147C ''howev'':138C ''hr'':54C,199C ''imagin'':56C ''imo'':2C ''import'':90C ''includ'':92C,228C ''incomplet'':28C ''indic'':27C,42C,82C,113C ''influenc'':248C ''inform'':162C ''know'':119C ''larg'':142C ''last'':44C ''li'':11C,20C ''like'':48C,177C ''line'':203C ''made'':187C ''major'':7C,172C ''may'':88C ''mayb'':180C,235C ''meaning'':101C ''much'':241C ''multi'':84C,104C ''multi-color'':83C,103C ''multipl'':49C ''never'':186C ''note'':149C,238C ''null'':24C,80C,111C,192C,220C ''one'':30C,37C ''option'':46C ''p'':1C,55C,77C,108C,200C ''particular'':74C ''percentag'':143C ''perhap'':166C ''person/group'':75C,175C ''plot'':18C,62C,137C,156C,215C ''popular'':71C ''probabl'':128C ''purpl'':167C,178C,183C ''purpos'':15C,59C,212C,227C ''realli'':176C ''reason'':197C ''relev'':224C ''renam'':96C ''result'':250C ''someth'':99C ''substanti'':251C ''take'':236C ''think'':205C ''toy'':67C,86C,125C,184C ''two'':6C ''ul'':10C ''unknown'':35C ''valu'':26C,33C,222C ''whatev'':196C,210C ''would'':127C,145C ''x'':151C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6796, 2, '2015-08-14 22:44:27.743', 1, NULL, NULL, '&lt;p&gt;This was the idea behind paranoid linux which began as fiction and became a real project that did not reach fruition:&lt;/p&gt;

&lt;p&gt;"Paranoid Linux is an operating system that assumes that its operator is under assault from the government (it was intended for use by Chinese and Syrian dissidents), and it does everything it can to keep your communications and documents a secret. It even throws up a bunch of "chaff" communications that are supposed to disguise the fact that you''re doing anything covert. So while you''re receiving a political message one character at a time, ParanoidLinux is pretending to surf the Web and fill in questionnaires and flirt in chat-rooms. Meanwhile, one in every five hundred characters you receive is your real message, a needle buried in a huge haystack.
~Cory Doctorow (Little Brother, 2008)&lt;/p&gt;

&lt;p&gt;When those words were written, ParanoidLinux was just a fiction. It is our goal to make this a reality. The project officially started on May 14th, and has been growing ever since. We welcome your ideas, contributions, designs, or code. You can find us on freenode''s irc server in the #paranoidlinux channel. Hope to see you there!"&lt;/p&gt;

&lt;p&gt;I think the most modern equivalent is &lt;a href="https://piratelinux.org/" rel="nofollow"&gt;Pirate Linux&lt;/a&gt; along with the &lt;a href="https://www.torproject.org/" rel="nofollow"&gt;TOR Project&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;BTW, Cory Doctorow''s book "&lt;a href="http://rads.stackoverflow.com/amzn/click/0765323117" rel="nofollow"&gt;Little Brother&lt;/a&gt;" is kind of a fun read if you are a geek.  If you are reading this then you probably &lt;strong&gt;ARE&lt;/strong&gt; a geek :-)&lt;/p&gt;

&lt;p&gt;The reality is that it is very difficult to hide one''s true intent when high quality clustering, classification and anomaly/novelty detection is used.  I work with guys who have done this type of behavior detection for some very high profile three letter agencies and it is very very easy to detect nefarious behavior if you have enough data.  So much so that undercover good guys sometimes clearly show up in the data sets.&lt;/p&gt;
', NULL, NULL, '''/a'':219C,230C,247C ''/amzn/click/0765323117'':242C ''/p'':23C,143C,203C,231C,272C,348C ''/strong'':269C ''14th'':170C ''2008'':142C ''agenc'':317C ''along'':220C ''anomaly/novelty'':294C ''anyth'':86C ''assault'':38C ''assum'':32C ''becam'':14C ''began'':10C ''behavior'':308C,327C ''behind'':6C ''book'':237C ''brother'':141C,246C ''btw'':233C ''bunch'':71C ''buri'':133C ''chaff'':73C ''channel'':197C ''charact'':97C,124C ''chat'':116C ''chat-room'':115C ''chines'':48C ''classif'':292C ''clear'':341C ''cluster'':291C ''code'':184C ''communic'':61C,74C ''contribut'':181C ''cori'':138C,234C ''covert'':87C ''data'':332C,346C ''design'':182C ''detect'':295C,309C,325C ''difficult'':281C ''disguis'':79C ''dissid'':51C ''doctorow'':139C,235C ''document'':63C ''done'':304C ''easi'':323C ''enough'':331C ''equival'':210C ''even'':67C ''ever'':175C ''everi'':121C ''everyth'':55C ''fact'':81C ''fiction'':12C,154C ''fill'':109C ''find'':187C ''five'':122C ''flirt'':113C ''freenod'':190C ''fruition'':22C ''fun'':252C ''geek'':258C,271C ''goal'':158C ''good'':338C ''govern'':41C ''grow'':174C ''guy'':301C,339C ''haystack'':137C ''hide'':283C ''high'':289C,313C ''hope'':198C ''href'':213C,224C,239C ''huge'':136C ''hundr'':123C ''idea'':5C,180C ''intend'':44C ''intent'':287C ''irc'':192C ''keep'':59C ''kind'':249C ''letter'':316C ''linux'':8C,26C,218C ''littl'':140C,245C ''make'':160C ''may'':169C ''meanwhil'':118C ''messag'':95C,130C ''modern'':209C ''much'':334C ''needl'':132C ''nefari'':326C ''nofollow'':216C,227C,244C ''offici'':166C ''one'':96C,119C,284C ''oper'':29C,35C ''p'':1C,24C,144C,204C,232C,273C ''paranoid'':7C,25C ''paranoidlinux'':101C,150C,196C ''pirat'':217C ''piratelinux.org'':214C ''polit'':94C ''pretend'':103C ''probabl'':266C ''profil'':314C ''project'':17C,165C,229C ''qualiti'':290C ''questionnair'':111C ''rads.stackoverflow.com'':241C ''rads.stackoverflow.com/amzn/click/0765323117'':240C ''re'':84C,91C ''reach'':21C ''read'':253C,262C ''real'':16C,129C ''realiti'':163C,275C ''receiv'':92C,126C ''rel'':215C,226C,243C ''room'':117C ''secret'':65C ''see'':200C ''server'':193C ''set'':347C ''show'':342C ''sinc'':176C ''sometim'':340C ''start'':167C ''strong'':267C ''suppos'':77C ''surf'':105C ''syrian'':50C ''system'':30C ''think'':206C ''three'':315C ''throw'':68C ''time'':100C ''tor'':228C ''true'':286C ''type'':306C ''undercov'':337C ''us'':188C ''use'':46C,297C ''web'':107C ''welcom'':178C ''word'':147C ''work'':299C ''written'':149C ''www.torproject.org'':225C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6797, 1, '2015-08-14 23:12:44.48', 2, 78, 'Tune hyperparameters for cost-sensitive classification', '&lt;p&gt;I have an unbalanced &lt;a href="http://s000.tinyupload.com/?file_id=03859222027158375634" rel="nofollow"&gt;data set&lt;/a&gt; with about 8% of negative examples. The goal is to minimize false negatives given a cost matrix. It seems like SVM (with radial kernel) and random forest work best. &lt;strong&gt;How should I tune hyperparameters in this setting?&lt;/strong&gt; &lt;/p&gt;

&lt;p&gt;&lt;em&gt;My suggestion: separate data into train/validation set, use probabilistic output together with cost matrix to assign predicted classes, tune hyperparameters to maximize &lt;a href="https://en.wikipedia.org/wiki/Accuracy_and_precision" rel="nofollow"&gt;accuracy&lt;/a&gt;.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How can I increase performance?&lt;/strong&gt; Currently I use random forest with &lt;em&gt;nodesize=1&lt;/em&gt; and &lt;em&gt;mtry=5&lt;/em&gt; which gives about 97% accuracy.&lt;/p&gt;
', 'r  classification  unbalanced-classes  hyperparameter', NULL, '''/?file_id=03859222027158375634'':23C ''/a'':28C,101C ''/em'':102C,121C,126C ''/p'':68C,103C,132C ''/strong'':67C,111C ''/wiki/accuracy_and_precision'':97C ''1'':120C ''5'':125C ''8'':31C ''97'':130C ''accuraci'':100C,131C ''assign'':86C ''best'':57C ''class'':12B,88C ''classif'':7A,9B ''cost'':5A,44C,83C ''cost-sensit'':4A ''current'':112C ''data'':26C,74C ''em'':70C,118C,123C ''en.wikipedia.org'':96C ''en.wikipedia.org/wiki/accuracy_and_precision'':95C ''exampl'':34C ''fals'':40C ''forest'':55C,116C ''give'':128C ''given'':42C ''goal'':36C ''href'':20C,94C ''hyperparamet'':2A,13B,63C,90C ''increas'':109C ''kernel'':52C ''like'':48C ''matrix'':45C,84C ''maxim'':92C ''minim'':39C ''mtri'':124C ''negat'':33C,41C ''nodes'':119C ''nofollow'':25C,99C ''output'':80C ''p'':14C,69C,104C ''perform'':110C ''predict'':87C ''probabilist'':79C ''r'':8B ''radial'':51C ''random'':54C,115C ''rel'':24C,98C ''s000.tinyupload.com'':22C ''s000.tinyupload.com/?file_id=03859222027158375634'':21C ''seem'':47C ''sensit'':6A ''separ'':73C ''set'':27C,66C,77C ''strong'':58C,105C ''suggest'':72C ''svm'':49C ''togeth'':81C ''train/validation'':76C ''tune'':1A,62C,89C ''unbalanc'':11B,18C ''unbalanced-class'':10B ''use'':78C,114C ''work'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6802, 2, '2015-08-15 19:12:31.76', 0, NULL, NULL, '&lt;p&gt;Take the cluster centers and project them back to the original feature vector using the inverse transform of your PCA. That gives you an intuition of what the clusters represent. As a second step you can take all original feature vectors that were assigned to one cluster and calculate histograms on each feature dimension. This will give you a feeling of in-cluster variation and cluster boundaries along the original feature dimensions.&lt;/p&gt;
', NULL, NULL, '''/p'':75C ''along'':70C ''assign'':45C ''back'':9C ''boundari'':69C ''calcul'':50C ''center'':5C ''cluster'':4C,30C,48C,65C,68C ''dimens'':55C,74C ''featur'':13C,41C,54C,73C ''feel'':61C ''give'':23C,58C ''histogram'':51C ''in-clust'':63C ''intuit'':26C ''invers'':17C ''one'':47C ''origin'':12C,40C,72C ''p'':1C ''pca'':21C ''project'':7C ''repres'':31C ''second'':34C ''step'':35C ''take'':2C,38C ''transform'':18C ''use'':15C ''variat'':66C ''vector'':14C,42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6798, 1, '2015-08-15 01:08:48.49', 2, 87, 'List of NLP challenges', '&lt;p&gt;Is there any comprehensive list of past, current and future NLP challenges?&lt;/p&gt;

&lt;p&gt;E.g. for NLP conferences, &lt;a href="http://www.cs.rochester.edu/~tetreaul/conferences.html" rel="nofollow"&gt;Joel Tetreault''s unofficially official conference calendar&lt;/a&gt; and &lt;a href="http://www.wikicfp.com/cfp/call?conference=natural%20language%20processing" rel="nofollow"&gt;WikiCFP&lt;/a&gt; are pretty good.&lt;/p&gt;

&lt;p&gt;The &lt;a href="http://aclweb.org/aclwiki/index.php?title=Competitions_and_Challenges" rel="nofollow"&gt;"Competitions and Challenges" page on the ACL wiki&lt;/a&gt; quite incomplete.&lt;/p&gt;
', 'nlp  reference-request', NULL, '''/a'':42C,52C,74C ''/aclwiki/index.php?title=competitions_and_challenges'':63C ''/cfp/call?conference=natural%20language%20processing'':48C ''/p'':22C,56C,77C ''/~tetreaul/conferences.html'':32C ''acl'':72C ''aclweb.org'':62C ''aclweb.org/aclwiki/index.php?title=competitions_and_challenges'':61C ''calendar'':41C ''challeng'':4A,21C,68C ''competit'':66C ''comprehens'':13C ''confer'':27C,40C ''current'':17C ''e.g'':24C ''futur'':19C ''good'':55C ''href'':29C,45C,60C ''incomplet'':76C ''joel'':35C ''list'':1A,14C ''nlp'':3A,5B,20C,26C ''nofollow'':34C,50C,65C ''offici'':39C ''p'':9C,23C,57C ''page'':69C ''past'':16C ''pretti'':54C ''quit'':75C ''refer'':7B ''reference-request'':6B ''rel'':33C,49C,64C ''request'':8B ''tetreault'':36C ''unoffici'':38C ''wiki'':73C ''wikicfp'':51C ''www.cs.rochester.edu'':31C ''www.cs.rochester.edu/~tetreaul/conferences.html'':30C ''www.wikicfp.com'':47C ''www.wikicfp.com/cfp/call?conference=natural%20language%20processing'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6799, 2, '2015-08-15 10:14:33.27', 2, NULL, NULL, '&lt;p&gt;There are some C++ tools for statistics and data science like ROOT &lt;a href="https://root.cern.ch/drupal/" rel="nofollow"&gt;https://root.cern.ch/drupal/&lt;/a&gt; , BAT &lt;a href="https://www.mppmu.mpg.de/bat/" rel="nofollow"&gt;https://www.mppmu.mpg.de/bat/&lt;/a&gt; , boost  , or OpenCV &lt;/p&gt;
', NULL, NULL, '''/bat/'':29C ''/bat/&lt;/a&gt;'':34C ''/drupal/'':18C ''/drupal/&lt;/a&gt;'':23C ''/p'':38C ''bat'':24C ''boost'':35C ''c'':5C ''data'':10C ''href'':15C,26C ''like'':12C ''nofollow'':20C,31C ''opencv'':37C ''p'':1C ''rel'':19C,30C ''root'':13C ''root.cern.ch'':17C,22C ''root.cern.ch/drupal/'':16C ''root.cern.ch/drupal/&lt;/a&gt;'':21C ''scienc'':11C ''statist'':8C ''tool'':6C ''www.mppmu.mpg.de'':28C,33C ''www.mppmu.mpg.de/bat/'':27C ''www.mppmu.mpg.de/bat/&lt;/a&gt;'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6800, 2, '2015-08-15 18:52:20.147', 5, NULL, NULL, '&lt;p&gt;I agree that the current trend is to use Python/R and to bind it to some C/C++ extensions for computationally expensive tasks.&lt;/p&gt;

&lt;p&gt;However, if you want to stay in C/C++, you might want to have a look at 
&lt;a href="http://dlib.net/"&gt;Dlib&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Dlib is a general purpose cross-platform C++ library designed using contract programming and modern C++ techniques. It is open source software and licensed under the Boost Software License. &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/QmR2X.png"&gt;&lt;img src="http://i.stack.imgur.com/QmR2X.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':46C,98C ''/blockquote'':81C ''/p'':24C,47C,80C,99C ''/qmr2x.png'':87C,92C ''agre'':3C ''alt'':93C ''bind'':14C ''blockquot'':48C ''boost'':77C ''c'':58C,66C ''c/c'':18C,33C ''comput'':21C ''contract'':62C ''cross'':56C ''cross-platform'':55C ''current'':6C ''descript'':96C ''design'':60C ''dlib'':45C,50C ''dlib.net'':44C ''enter'':94C ''expens'':22C ''extens'':19C ''general'':53C ''howev'':26C ''href'':43C,84C ''i.stack.imgur.com'':86C,91C ''i.stack.imgur.com/qmr2x.png'':85C,90C ''imag'':95C ''img'':88C ''librari'':59C ''licens'':74C,79C ''look'':40C ''might'':35C ''modern'':65C ''open'':70C ''p'':1C,25C,49C,82C ''platform'':57C ''program'':63C ''purpos'':54C ''python/r'':11C ''softwar'':72C,78C ''sourc'':71C ''src'':89C ''stay'':31C ''task'':23C ''techniqu'':67C ''trend'':7C ''use'':10C,61C ''want'':29C,36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6823, 2, '2015-08-19 04:31:09.787', 3, NULL, NULL, '&lt;p&gt;Using the &lt;code&gt;data.table&lt;/code&gt; structure (see the &lt;a href="https://github.com/Rdatatable/data.table/wiki" rel="nofollow"&gt;wiki&lt;/a&gt;),&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(data.table)
D &amp;lt;- data.table(x = c(1155, 1156, 1157, 1158),
                date = as.Date(c("2010-05-02", "2010-05-05", "2010-05-08", "2010-05-11")),
                y = c(2.7200, 2.6000, 2.6700, 3.5700),
                id = c(1, 3, 1, 2))
counts &amp;lt;- D[, .(rowCount = .N), by = id]
counts
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This will return&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;counts
##    id rowCount
## 1:  1        2
## 2:  3        1
## 3:  2        1
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''-02'':38C ''-05'':37C,40C,41C,43C,46C ''-08'':44C ''-11'':47C ''/a'':18C ''/code'':6C,68C,89C ''/p'':19C,74C ''/pre'':69C,90C ''/rdatatable/data.table/wiki'':14C ''1'':56C,58C,80C,81C,85C,88C ''1155'':29C ''1156'':30C ''1157'':31C ''1158'':32C ''2'':59C,82C,83C,87C ''2.6000'':51C ''2.6700'':52C ''2.7200'':50C ''2010'':36C,39C,42C,45C ''3'':57C,84C,86C ''3.5700'':53C ''as.date'':34C ''c'':28C,35C,49C,55C ''code'':4C,21C,76C ''count'':60C,67C,77C ''d'':24C,62C ''data.table'':5C,23C,26C ''date'':33C ''github.com'':13C ''github.com/rdatatable/data.table/wiki'':12C ''href'':11C ''id'':54C,66C,78C ''librari'':22C ''lt'':25C,61C ''n'':64C ''nofollow'':16C ''p'':1C,70C ''pre'':20C,75C ''rel'':15C ''return'':73C ''rowcount'':63C,79C ''see'':8C ''structur'':7C ''use'':2C ''wiki'':17C ''x'':27C ''y'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6801, 2, '2015-08-15 18:52:22.243', 1, NULL, NULL, '&lt;p&gt;There are methods that approximate Jaccard similarity using hash functions, one of them &lt;a href="https://en.wikipedia.org/wiki/MinHash" rel="nofollow"&gt;MinHash&lt;/a&gt;. They scale well with the number of dimensions and  evaluate similarity of two items. If you want to do nearest neighbor queries (finding similar items given one item) you should look at locality sensitive hashing (LSH), which essentially maps similar items to the same hash value. There is a nice implementation in scikit-learn called &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LSHForest.html#sklearn.neighbors.LSHForest" rel="nofollow"&gt;LSHForest&lt;/a&gt;. It approximates cosine distance and scales well to large number of items and dimensions.&lt;/p&gt;
', NULL, NULL, '''/a'':23C,88C ''/p'':103C ''/stable/modules/generated/sklearn.neighbors.lshforest.html#sklearn.neighbors.lshforest'':84C ''/wiki/minhash'':19C ''approxim'':6C,90C ''call'':79C ''cosin'':91C ''dimens'':31C,102C ''distanc'':92C ''en.wikipedia.org'':18C ''en.wikipedia.org/wiki/minhash'':17C ''essenti'':61C ''evalu'':33C ''find'':46C ''function'':11C ''given'':49C ''hash'':10C,58C,68C ''href'':16C,81C ''implement'':74C ''item'':37C,48C,51C,64C,100C ''jaccard'':7C ''larg'':97C ''learn'':78C ''local'':56C ''look'':54C ''lsh'':59C ''lshforest'':87C ''map'':62C ''method'':4C ''minhash'':22C ''nearest'':43C ''neighbor'':44C ''nice'':73C ''nofollow'':21C,86C ''number'':29C,98C ''one'':12C,50C ''p'':1C ''queri'':45C ''rel'':20C,85C ''scale'':25C,94C ''scikit'':77C ''scikit-learn'':76C ''scikit-learn.org'':83C ''scikit-learn.org/stable/modules/generated/sklearn.neighbors.lshforest.html#sklearn.neighbors.lshforest'':82C ''sensit'':57C ''similar'':8C,34C,47C,63C ''two'':36C ''use'':9C ''valu'':69C ''want'':40C ''well'':26C,95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6803, 2, '2015-08-15 20:46:37.993', 2, NULL, NULL, '&lt;p&gt;Especially when your main goal is learning, I would break it into several phases:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Get familiar with pandas dataframes and visualization using matplotlib. Try loading subsets of your datasets using pandas and visualize them using matplotlib, for example plot the time-series, or word count histograms. This will come in handy lateron to be able to understand the predictions/clusters you will create using machine learning methods. Pandas also provides functions to clean, aggregate and resample the data, which will be useful to align multiple time-series (e.g. weather and time-series) to the same sampling rate.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Familiarize with scikit-learn using the articles and examples in the excellent online documentation. Play with the feature extraction, dimensionality reduction, classification and regression methods relevant to your data, apply them on smaller subsets and visualize the results. Learn about cross-validation and find a good algorithm and parameters that work well on a subset of your data.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;One you have a pipeline setup and you want to run it on the full dataset, Spark can come in handy for feature extraction and cross-validation, as those tasks can be often run very well independently and in parallel. Install the Spark libraries on your machine, and reproduce the Pyspark examples (e.g. calculating Pi) in local mode. No need to setup a Hadoop cluster for that, Spark can make full use of your machine in local mode, reading from local files on disk. Once you have that running, try to express the expensive steps of your code from step 2 as Spark RDD operations. As Spark can make use of all cores of your machine, you should already see a speedup - debugging becomes much harder though compared to working with pure Python scripts.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Play with advanced stuff like deep learning, using the word2vec feature extraction. Compare with traditional feature extraction (bag of words).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;(optional) If you want to experience the speedup you can get from Spark in a distributed setting, get an account in AWS, put your data on S3 and fire your Spark scripts against a multi-node cluster instantiated using Amazon EMR. &lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Regarding data storage, from my experience CSV files or similar work best to load the data into both Pandas and Spark. There are Spark connectors for databases (e.g. Cassandra), but I am not sure about MySQL and Solr. For processed data and intermediate results (e.g. models) I would always use Python''s serialization framework pickle, so you can directly get the object instances back into Python without the need to parse file formats and reinstantiate objects.&lt;/p&gt;
', NULL, NULL, '''/li'':106C,170C,309C,333C,379C ''/ol'':380C ''/p'':16C,105C,169C,308C,332C,378C,458C ''2'':274C ''abl'':61C ''account'':355C ''advanc'':314C ''aggreg'':79C ''algorithm'':157C ''align'':89C ''alreadi'':292C ''also'':74C ''alway'':430C ''amazon'':376C ''appli'':139C ''articl'':116C ''aw'':357C ''back'':445C ''bag'':329C ''becom'':297C ''best'':393C ''break'':11C ''calcul'':227C ''cassandra'':410C ''classif'':131C ''clean'':78C ''cluster'':238C,373C ''code'':271C ''come'':55C,191C ''compar'':301C,324C ''connector'':406C ''core'':286C ''count'':51C ''creat'':68C ''cross'':151C,199C ''cross-valid'':150C,198C ''csv'':388C ''data'':83C,138C,168C,360C,383C,397C,422C ''databas'':408C ''datafram'':24C ''dataset'':34C,188C ''debug'':296C ''deep'':317C ''dimension'':129C ''direct'':440C ''disk'':257C ''distribut'':351C ''document'':123C ''e.g'':94C,226C,409C,426C ''emr'':377C ''especi'':2C ''exampl'':43C,118C,225C ''excel'':121C ''expens'':267C ''experi'':341C,387C ''express'':265C ''extract'':128C,196C,323C,328C ''familiar'':21C,109C ''featur'':127C,195C,322C,327C ''file'':255C,389C,453C ''find'':154C ''fire'':364C ''format'':454C ''framework'':435C ''full'':187C,244C ''function'':76C ''get'':20C,346C,353C,441C ''goal'':6C ''good'':156C ''hadoop'':237C ''handi'':57C,193C ''harder'':299C ''histogram'':52C ''independ'':210C ''instal'':214C ''instanc'':444C ''instanti'':374C ''intermedi'':424C ''lateron'':58C ''learn'':8C,71C,113C,148C,318C ''li'':18C,107C,171C,310C,334C ''librari'':217C ''like'':316C ''load'':30C,395C ''local'':230C,250C,254C ''machin'':70C,220C,248C,289C ''main'':5C ''make'':243C,282C ''matplotlib'':28C,41C ''method'':72C,134C ''mode'':231C,251C ''model'':427C ''much'':298C ''multi'':371C ''multi-nod'':370C ''multipl'':90C ''mysql'':417C ''need'':233C,450C ''node'':372C ''object'':443C,457C ''often'':206C ''ol'':17C ''one'':173C ''onlin'':122C ''oper'':278C ''option'':336C ''p'':1C,19C,108C,172C,311C,335C,381C ''panda'':23C,36C,73C,400C ''parallel'':213C ''paramet'':159C ''pars'':452C ''phase'':15C ''pi'':228C ''pickl'':436C ''pipelin'':177C ''play'':124C,312C ''plot'':44C ''predictions/clusters'':65C ''process'':421C ''provid'':75C ''pure'':305C ''put'':358C ''pyspark'':224C ''python'':306C,432C,447C ''rate'':104C ''rdd'':277C ''read'':252C ''reduct'':130C ''regard'':382C ''regress'':133C ''reinstanti'':456C ''relev'':135C ''reproduc'':222C ''resampl'':81C ''result'':147C,425C ''run'':183C,207C,262C ''s3'':362C ''sampl'':103C ''scikit'':112C ''scikit-learn'':111C ''script'':307C,367C ''see'':293C ''seri'':48C,93C,99C ''serial'':434C ''set'':352C ''setup'':178C,235C ''sever'':14C ''similar'':391C ''smaller'':142C ''solr'':419C ''spark'':189C,216C,241C,276C,280C,348C,366C,402C,405C ''speedup'':295C,343C ''step'':268C,273C ''storag'':384C ''stuff'':315C ''subset'':31C,143C,165C ''sure'':415C ''task'':203C ''though'':300C ''time'':47C,92C,98C ''time-seri'':46C,91C,97C ''tradit'':326C ''tri'':29C,263C ''understand'':63C ''use'':27C,35C,40C,69C,87C,114C,245C,283C,319C,375C,431C ''valid'':152C,200C ''visual'':26C,38C,145C ''want'':181C,339C ''weather'':95C ''well'':162C,209C ''without'':448C ''word'':50C,331C ''word2vec'':321C ''work'':161C,303C,392C ''would'':10C,429C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10267, 1, '2016-02-16 16:19:22.91', 0, 8, 'Are Markov switching models and structural change the same?', '&lt;p&gt;What is the difference between Markov switching and structural change in time series &lt;/p&gt;
', 'bigdata  statistics  time-series  markov-process', NULL, '''/p'':30C ''bigdata'':8B ''chang'':7A,26C ''differ'':20C ''markov'':2A,14B,22C ''markov-process'':13B ''model'':4A ''p'':16C ''process'':15B ''seri'':12B,29C ''statist'':9B ''structur'':6A,25C ''switch'':3A,23C ''time'':11B,28C ''time-seri'':10B');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6804, 2, '2015-08-16 04:35:34.507', 1, NULL, NULL, '&lt;p&gt;&lt;a href="http://topsy.com/" rel="nofollow"&gt;Topsy&lt;/a&gt; - The site provides much more rate limit, and has archived all the tweets from 2006.
Its API provides a free trial.&lt;/p&gt;

&lt;p&gt;A solution that is paid will be &lt;a href="https://blog.twitter.com/2015/full-archive-search-api" rel="nofollow"&gt;Gnip''s Full history search API&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/2015/full-archive-search-api'':43C ''/a'':8C,52C ''/p'':30C,53C ''2006'':23C ''api'':25C,51C ''archiv'':18C ''blog.twitter.com'':42C ''blog.twitter.com/2015/full-archive-search-api'':41C ''free'':28C ''full'':48C ''gnip'':46C ''histori'':49C ''href'':3C,40C ''limit'':15C ''much'':12C ''nofollow'':6C,45C ''p'':1C,31C ''paid'':36C ''provid'':11C,26C ''rate'':14C ''rel'':5C,44C ''search'':50C ''site'':10C ''solut'':33C ''topsi'':7C ''topsy.com'':4C ''trial'':29C ''tweet'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6805, 1, '2015-08-16 19:14:01.43', 1, 53, 'Ensemble model overfitting?', '&lt;p&gt;I am attempting a classification project. I have split my data, 20000ish, into training and test sets. On the training sets I run a selection of classifieds including knn random forests and gbm . These give me about 20-30% accuracy at best. For each sample I generate probabities of each class and make a new model&lt;/p&gt;

&lt;p&gt;Knn proba 1, knn proba 2.... Random forests proba 1 etc&lt;/p&gt;

&lt;p&gt;On this I then run a random forest classifier which gives me a 90ish% accuracy against the test set.&lt;/p&gt;

&lt;p&gt;Fantastic!... But when I use the model against new data the accuracy is very poor.&lt;/p&gt;

&lt;p&gt;In part it feels like a case of overfitting but surely the test set should be poor as well&lt;/p&gt;

&lt;p&gt;Why might the test data so good but new data so poor... What have I done wrong?&lt;/p&gt;

&lt;p&gt;Thanks 
Chris&lt;/p&gt;
', 'classification', NULL, '''-30'':44C ''/p'':62C,75C,96C,113C,134C,153C,157C ''1'':66C,73C ''2'':69C ''20'':43C ''20000ish'':17C ''90ish'':90C ''accuraci'':45C,91C,109C ''attempt'':8C ''best'':47C ''case'':121C ''chris'':156C ''class'':56C ''classif'':4B,10C ''classifi'':32C,85C ''data'':16C,107C,140C,145C ''done'':151C ''ensembl'':1A ''etc'':74C ''fantast'':98C ''feel'':118C ''forest'':36C,71C,84C ''gbm'':38C ''generat'':52C ''give'':40C,87C ''good'':142C ''includ'':33C ''knn'':34C,64C,67C ''like'':119C ''make'':58C ''might'':137C ''model'':2A,61C,104C ''new'':60C,106C,144C ''overfit'':3A,123C ''p'':5C,63C,76C,97C,114C,135C,154C ''part'':116C ''poor'':112C,131C,147C ''proba'':65C,68C,72C ''probab'':53C ''project'':11C ''random'':35C,70C,83C ''run'':28C,81C ''sampl'':50C ''select'':30C ''set'':22C,26C,95C,128C ''split'':14C ''sure'':125C ''test'':21C,94C,127C,139C ''thank'':155C ''train'':19C,25C ''use'':102C ''well'':133C ''wrong'':152C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6806, 2, '2015-08-17 04:31:05.6', 0, NULL, NULL, '&lt;p&gt;try cross-validation on training data, cross-validation combines (averages) measures of fit (prediction error) to correct for the optimistic nature of training error and derive a more accurate estimate of model prediction performance (eg : k-fold), check accuracy (Precision/recall/f measure) and then use this classifier on test data.&lt;/p&gt;
', NULL, NULL, '''/p'':54C ''accur'':32C ''accuraci'':43C ''averag'':13C ''check'':42C ''classifi'':50C ''combin'':12C ''correct'':20C ''cross'':4C,10C ''cross-valid'':3C,9C ''data'':8C,53C ''deriv'':29C ''eg'':38C ''error'':18C,27C ''estim'':33C ''fit'':16C ''fold'':41C ''k'':40C ''k-fold'':39C ''measur'':14C,45C ''model'':35C ''natur'':24C ''optimist'':23C ''p'':1C ''perform'':37C ''precision/recall/f'':44C ''predict'':17C,36C ''test'':52C ''train'':7C,26C ''tri'':2C ''use'':48C ''valid'':5C,11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6808, 1, '2015-08-17 08:00:17.577', 0, 57, 'Which one for text mining is better IronPython or Fsharp?', '&lt;p&gt;I want best approach which has: 1. Best Performance 2. Best Libraries 3. Good Integration with .net&lt;/p&gt;

&lt;p&gt;F# is static type and has less overhead on run-time, while IronPython is dynamic type and it cause overhead on run-time. How much time does IronPython takes for type checking at run-time. Is it reasonable to sacrifice it because of this issue?&lt;/p&gt;

&lt;p&gt;I am not quite sure that Fsharp has good libraries in machine learning algorithms but I am sure that IronPython has vast and charge of free libs.&lt;/p&gt;

&lt;p&gt;Thank you in advanced for your help.&lt;/p&gt;
', 'data-mining  python  bigdata  text-mining  .net', NULL, '''/p'':38C,88C,117C,126C ''1'':27C ''2'':30C ''3'':33C ''advanc'':122C ''algorithm'':103C ''approach'':24C ''best'':23C,28C,31C ''better'':7A ''bigdata'':15B ''caus'':59C ''charg'':113C ''check'':73C ''data'':12B ''data-min'':11B ''dynam'':55C ''f'':40C ''free'':115C ''fsharp'':10A,96C ''good'':34C,98C ''help'':125C ''integr'':35C ''ironpython'':8A,53C,69C,109C ''issu'':87C ''learn'':102C ''less'':46C ''lib'':116C ''librari'':32C,99C ''machin'':101C ''mine'':5A,13B,18B ''much'':66C ''net'':19B,37C ''one'':2A ''overhead'':47C,60C ''p'':20C,39C,89C,118C ''perform'':29C ''python'':14B ''quit'':93C ''reason'':80C ''run'':50C,63C,76C ''run-tim'':49C,62C,75C ''sacrific'':82C ''static'':42C ''sure'':94C,107C ''take'':70C ''text'':4A,17B ''text-min'':16B ''thank'':119C ''time'':51C,64C,67C,77C ''type'':43C,56C,72C ''vast'':111C ''want'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6850, 2, '2015-08-20 19:43:11.243', 0, NULL, NULL, '&lt;p&gt;xgboost comes with an own cv method, see an example here:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/dmlc/xgboost/blob/master/demo/guide-python/cross_validation.py" rel="nofollow"&gt;https://github.com/dmlc/xgboost/blob/master/demo/guide-python/cross_validation.py&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/dmlc/xgboost/blob/master/demo/guide-python/cross_validation.py'':19C ''/dmlc/xgboost/blob/master/demo/guide-python/cross_validation.py&lt;/a&gt;&lt;/p&gt;'':24C ''/p'':13C ''come'':3C ''cv'':7C ''exampl'':11C ''github.com'':18C,23C ''github.com/dmlc/xgboost/blob/master/demo/guide-python/cross_validation.py'':17C ''github.com/dmlc/xgboost/blob/master/demo/guide-python/cross_validation.py&lt;/a&gt;&lt;/p&gt;'':22C ''href'':16C ''method'':8C ''nofollow'':21C ''p'':1C,14C ''rel'':20C ''see'':9C ''xgboost'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8662, 2, '2015-10-30 17:41:10.997', 4, NULL, NULL, '&lt;p&gt;Looking at the source (seaborn/seaborn/categorical.py, line 2166), we find&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def barplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None,
        estimator=np.mean, ci=95, n_boot=1000, units=None,
        orient=None, color=None, palette=None, saturation=.75,
        errcolor=".26", ax=None, **kwargs):
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;so the default value is, indeed, .95, as you guessed.&lt;/p&gt;

&lt;p&gt;EDIT: How CI is calculated: &lt;code&gt;barplot&lt;/code&gt; calls &lt;code&gt;utils.ci()&lt;/code&gt; which has&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;seaborn/seaborn/utils.py&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def ci(a, which=95, axis=None):
    """Return a percentile range from an array of values."""
    p = 50 - which / 2, 50 + which / 2
    return percentiles(a, p, axis)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and this call to &lt;code&gt;percentiles()&lt;/code&gt; is calling:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def percentiles(a, pcts, axis=None):
    """Like scoreatpercentile but can take and return array of percentiles.
    Parameters
    ----------
    a : array
        data
    pcts : sequence of percentile values
        percentile or percentiles to find score at
    axis : int or None
        if not None, computes scores over this axis
    Returns
    -------
    scores: array
        array of scores at requested percentiles
        first dimension is length of object passed to ``pcts``
    """
    scores = []
    try:
        n = len(pcts)
    except TypeError:
        pcts = [pcts]
        n = 0
    for i, p in enumerate(pcts):
        if axis is None:
            score = stats.scoreatpercentile(a.ravel(), p)
        else:
            score = np.apply_along_axis(stats.scoreatpercentile, axis, a, p)
        scores.append(score)
    scores = np.asarray(scores)
    if not n:
        scores = scores.squeeze()
    return scores
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;code&gt;axis=None&lt;/code&gt; so &lt;code&gt;score = stats.scoreatpercentile(a.ravel(), p)&lt;/code&gt; which is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;scipy.stats.scoreatpercentile(a, per, limit=(), interpolation_method=''fraction'', axis=None)[source]
Calculate the score at a given percentile of the input sequence.
&lt;/code&gt;&lt;/pre&gt;

&lt;blockquote&gt;
  &lt;p&gt;For example, the score at per=50 is the median. If the desired quantile lies between two data points, we interpolate between them, according to the value of interpolation. If the parameter limit is provided, it should be a tuple (lower, upper) of two values.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;pre&gt;&lt;code&gt;Parameters: 
a : array_like
A 1-D array of values from which to extract score.
per : array_like
Percentile(s) at which to extract score. Values should be in range [0,100].
limit : tuple, optional
Tuple of two scalars, the lower and upper limits within which to compute the percentile. Values of a outside this (closed) interval will be ignored.
interpolation_method : {âfractionâ, âlowerâ, âhigherâ}, optional
This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and j
fraction: i + (j - i) * fraction where fraction is the fractional part of the index surrounded by i and j.
lower: i.
higher: j.
axis : int, optional
Axis along which the percentiles are computed. Default is None. If None, compute over the whole array a.
Returns:    
score : float or ndarray
Score at percentile(s).
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and looking in the source for &lt;strong&gt;scipy.stats.stats.py&lt;/strong&gt; we see the signature&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def scoreatpercentile(a, per, limit=(), interpolation_method=''fraction'',
                      axis=None):
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;so since seaboard calls it with no param for &lt;code&gt;interpolation&lt;/code&gt; it is using &lt;code&gt;fraction&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;On a side note, there is a warning of future obsolescence in &lt;code&gt;stats.scoreatpercentile()&lt;/code&gt;, namely &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;This function will become obsolete in the future. For Numpy 1.9 and higher, numpy.percentile provides all the functionality that scoreatpercentile provides. And itâs significantly faster. Therefore itâs recommended to use numpy.percentile for users that have numpy &gt;= 1.9.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/blockquote'':329C,586C ''/code'':51C,73C,77C,116C,125C,239C,245C,252C,279C,475C,504C,518C,524C,541C ''/p'':11C,64C,80C,85C,128C,255C,328C,491C,525C,543C,585C ''/pre'':52C,117C,240C,280C,476C,505C ''/strong'':84C,486C ''0'':203C,362C ''1'':337C ''1.9'':556C,584C ''100'':363C ''1000'':35C ''2'':107C,110C ''2166'':8C ''26'':47C ''50'':105C,108C,289C ''75'':45C ''95'':32C,60C,92C ''a.ravel'':216C,250C ''accord'':306C ''along'':221C,449C ''array'':101C,144C,149C,177C,178C,334C,339C,348C,464C ''ax'':48C ''axi'':93C,115C,135C,163C,174C,211C,222C,224C,243C,265C,445C,448C,502C ''barplot'':15C,72C ''becom'':549C ''blockquot'':281C,544C ''boot'':34C ''calcul'':70C,268C ''call'':74C,121C,127C,510C ''ci'':31C,68C,89C ''close'':387C ''code'':13C,71C,75C,87C,123C,130C,242C,247C,257C,331C,493C,516C,522C,539C ''color'':40C ''comput'':170C,379C,454C,460C ''d'':338C ''data'':22C,150C,300C,417C ''def'':14C,88C,131C,494C ''default'':56C,455C ''desir'':295C,412C ''dimens'':185C ''edit'':66C ''els'':218C ''enumer'':208C ''errcolor'':46C ''estim'':29C ''exampl'':284C ''except'':198C ''extract'':345C,355C ''faster'':571C ''find'':10C,160C ''first'':184C ''float'':468C ''fraction'':264C,422C,426C,428C,431C,501C,523C ''fractionâ'':395C ''function'':547C,563C ''futur'':536C,553C ''given'':273C ''guess'':63C ''higher'':443C,558C ''higherâ'':399C ''hue'':20C,26C ''ignor'':391C ''inde'':59C ''index'':435C ''input'':277C ''int'':164C,446C ''interpol'':262C,303C,311C,392C,406C,499C,517C ''interv'':388C ''itâ'':568C,573C ''j'':421C,424C,440C,444C ''kwarg'':50C ''len'':196C ''length'':187C ''lie'':297C,414C ''like'':137C,335C,349C ''limit'':261C,315C,364C,375C,498C ''line'':7C ''look'':2C,479C ''lower'':323C,372C,441C ''lowerâ'':397C ''median'':292C ''method'':263C,393C,407C,500C ''n'':33C,195C,202C,234C ''name'':542C ''ndarray'':470C ''none'':17C,19C,21C,23C,25C,28C,37C,39C,41C,43C,49C,94C,136C,166C,169C,213C,244C,266C,457C,459C,503C ''note'':530C ''np.apply'':220C ''np.asarray'':230C ''np.mean'':30C ''numpi'':555C,583C ''numpy.percentile'':559C,578C ''object'':189C ''obsolesc'':537C ''obsolet'':550C ''option'':366C,400C,402C,447C ''order'':24C,27C ''orient'':38C ''outsid'':385C ''p'':1C,53C,65C,81C,104C,114C,118C,206C,217C,226C,241C,251C,282C,477C,506C,526C,545C ''palett'':42C ''param'':514C ''paramet'':147C,314C,332C,403C ''part'':432C ''pass'':190C ''pcts'':134C,151C,192C,197C,200C,201C,209C ''per'':260C,288C,347C,497C ''percentil'':97C,112C,124C,132C,146C,154C,156C,158C,183C,274C,350C,381C,452C,473C ''point'':301C,418C ''pre'':12C,86C,129C,256C,330C,492C ''provid'':317C,560C,566C ''quantil'':296C,413C ''rang'':98C,361C ''recommend'':575C ''request'':182C ''return'':95C,111C,143C,175C,237C,466C ''satur'':44C ''scalar'':370C ''scipy.stats.scoreatpercentile'':258C ''scipy.stats.stats.py'':485C ''score'':161C,171C,176C,180C,193C,214C,219C,228C,229C,231C,235C,238C,248C,270C,286C,346C,356C,467C,471C ''scoreatpercentil'':138C,495C,565C ''scores.append'':227C ''scores.squeeze'':236C ''seaboard'':509C ''seaborn/seaborn/categorical.py'':6C ''seaborn/seaborn/utils.py'':83C ''see'':488C ''sequenc'':152C,278C ''side'':529C ''signatur'':490C ''signific'':570C ''sinc'':508C ''sourc'':5C,267C,482C ''specifi'':404C ''stats.scoreatpercentile'':215C,223C,249C,540C ''strong'':82C,484C ''surround'':436C ''take'':141C ''therefor'':572C ''tri'':194C ''tupl'':322C,365C,367C ''two'':299C,326C,369C,416C ''typeerror'':199C ''unit'':36C ''upper'':324C,374C ''use'':409C,521C,577C ''user'':580C ''utils.ci'':76C ''valu'':57C,103C,155C,309C,327C,341C,357C,382C ''warn'':534C ''whole'':463C ''within'':376C ''x'':16C ''y'':18C ''â'':394C,396C,398C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6830, 1, '2015-08-19 13:06:42.75', 3, 91, 'How to convert vector values to fit k-means algorithm function?', '&lt;p&gt;I have a set of user objects that I want to group using a $k$-means function from their quiz answers. Each quiz question had predefined answers with letter values &lt;em&gt;"a", "b", "c", "d"&lt;/em&gt;. If a user answers the &lt;em&gt;question #1&lt;/em&gt; with letter &lt;em&gt;"b"&lt;/em&gt;, I put this answer into vector $(0, 1, 0, 0)$.  The $k$-means function I have to use takes a two-dimensional array of numbers as an input vector (in this case array[user][question]), and I can''t figure out how to use it, because, instead of a number value representing a user''s answer to question, I have a vector input.
How can I convert my vector values to numbers so that I can use my $k$-means function?&lt;/p&gt;
', 'clustering  k-means', NULL, '''/em'':53C,62C,67C ''/p'':150C ''0'':74C,76C,77C ''1'':61C,75C ''algorithm'':11A ''answer'':38C,44C,57C,71C,124C ''array'':91C,101C ''b'':50C,66C ''c'':51C ''case'':100C ''cluster'':13B ''convert'':3A,135C ''d'':52C ''dimension'':90C ''em'':48C,59C,65C ''figur'':108C ''fit'':7A ''function'':12A,34C,81C,149C ''group'':29C ''input'':96C,131C ''instead'':115C ''k'':9A,15B,32C,79C,147C ''k-mean'':8A,14B ''letter'':46C,64C ''mean'':10A,16B,33C,80C,148C ''number'':93C,118C,140C ''object'':24C ''p'':17C ''predefin'':43C ''put'':69C ''question'':41C,60C,103C,126C ''quiz'':37C,40C ''repres'':120C ''set'':21C ''take'':86C ''two'':89C ''two-dimension'':88C ''use'':30C,85C,112C,145C ''user'':23C,56C,102C,122C ''valu'':5A,47C,119C,138C ''vector'':4A,73C,97C,130C,137C ''want'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9065, 2, '2015-11-26 15:34:36.003', 2, NULL, NULL, '&lt;p&gt;Sounds like you want &lt;a href="https://en.m.wikipedia.org/wiki/Named-entity_recognition" rel="nofollow"&gt;named entity recognition&lt;/a&gt;.  There are a variety of approaches to NER, and plenty of implementations, like the &lt;a href="http://nlp.stanford.edu/software/CRF-NER.shtml" rel="nofollow"&gt;Stanford NER package&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;After you find named entities, determining what the named entity refers to is called &lt;a href="https://en.m.wikipedia.org/wiki/Entity_linking" rel="nofollow"&gt;concept normalization&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':16C,41C,67C ''/p'':42C,68C ''/software/crf-ner.shtml'':35C ''/wiki/entity_linking'':62C ''/wiki/named-entity_recognition'':10C ''approach'':22C ''call'':57C ''concept'':65C ''determin'':49C ''en.m.wikipedia.org'':9C,61C ''en.m.wikipedia.org/wiki/entity_linking'':60C ''en.m.wikipedia.org/wiki/named-entity_recognition'':8C ''entiti'':14C,48C,53C ''find'':46C ''href'':7C,32C,59C ''implement'':28C ''like'':3C,29C ''name'':13C,47C,52C ''ner'':24C,39C ''nlp.stanford.edu'':34C ''nlp.stanford.edu/software/crf-ner.shtml'':33C ''nofollow'':12C,37C,64C ''normal'':66C ''p'':1C,43C ''packag'':40C ''plenti'':26C ''recognit'':15C ''refer'':54C ''rel'':11C,36C,63C ''sound'':2C ''stanford'':38C ''varieti'':20C ''want'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6809, 1, '2015-08-17 08:07:58.28', 1, 548, 'How to plot/visualize clusters in scikit-learn (sklearn)?', '&lt;p&gt;I have done some clustering and I would like to visualize the results.  &lt;/p&gt;

&lt;p&gt;Here is the function I have written to plot my clusters:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import sklearn
from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.preprocessing import StandardScaler

from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs
from sklearn.preprocessing import StandardScaler


def plot_cluster(cluster, sample_matrix):
    ''''''Input:  "cluster", which is an object from DBSCAN, 
       e.g. dbscan_object = DBSCAN(3.0,4)
"sample_matrix" which is a data matrix:  
X = [
    [0,5,1,2],
    [0,4,1,3],
    [0,5,1,3],
    [0,5,0,2],
    [5,5,5,5],
    ]
        Output: Plots the clusters nicely.    
    ''''''
    import matplotlib.pyplot as plt
    import numpy as np

    f = lambda row: [float(x) for x in row]

    sample_matrix = map(f,sample_matrix)
    print sample_matrix
    sample_matrix = StandardScaler().fit_transform(sample_matrix)

    core_samples_mask = np.zeros_like(cluster.labels_, dtype=bool)
    core_samples_mask[cluster.core_sample_indices_] = True
    labels = cluster.labels_

    # Black removed and is used for noise instead.
    unique_labels = set(labels)
    colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
    for k, col in zip(unique_labels, colors):
        if k == -1:
            # Black used for noise.
            col = ''k''

        class_member_mask = (labels == k)  # generator comprehension 
        # X is your data matrix
        X = np.array(sample_matrix)

        xy = X[class_member_mask &amp;amp; core_samples_mask]

        plt.plot(xy[:, 0], xy[:, 1], ''o'', markerfacecolor=col,
                 markeredgecolor=''k'', markersize=14)

        xy = X[class_member_mask &amp;amp; ~core_samples_mask]
        plt.plot(xy[:, 0], xy[:, 1], ''o'', markerfacecolor=col,
                 markeredgecolor=''k'', markersize=6)

    plt.ylim([0,10]) 
    plt.xlim([0,10])    
#        plt.title(''Estimated number of clusters: %d'' % n_clusters_)
    plt.savefig(''cluster.png'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The function above is copied almost verbatim from the scikit-learn demo &lt;a href="http://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#example-cluster-plot-dbscan-py" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Yet, when I try it on the following:  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dbscan_object = DBSCAN(3.0,4)

X = [
    [0,5,1,2],
    [0,4,1,3],
    [0,5,1,3],
    [0,5,0,2],
    [5,5,5,5],
    ]

result = dbscan_object.fit(X)
print result.labels_

print ''plotting ''
plot_cluster(result, X)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;...It produces a single point.  What is the best way to plot clusters in python?&lt;/p&gt;
', 'python  clustering  scikit  clusters  dbscan', NULL, '''-1'':210C ''/a'':315C ''/code'':291C,366C ''/p'':29C,42C,316C,326C,384C ''/pre'':292C,367C ''/stable/auto_examples/cluster/plot_dbscan.html#example-cluster-plot-dbscan-py'':311C ''0'':105C,109C,113C,117C,119C,195C,244C,265C,276C,279C,335C,339C,343C,347C,349C ''1'':107C,111C,115C,196C,246C,267C,337C,341C,345C ''10'':277C,280C ''14'':253C ''2'':108C,120C,338C,350C ''3'':112C,116C,342C,346C ''3.0'':95C,332C ''4'':96C,110C,333C,340C ''5'':106C,114C,118C,121C,122C,123C,124C,336C,344C,348C,351C,352C,353C,354C ''6'':274C ''almost'':299C ''amp'':238C,259C ''best'':377C ''black'':180C,211C ''blob'':72C ''bool'':170C ''class'':217C,235C,256C ''cluster'':4A,11B,13B,20C,41C,79C,80C,84C,128C,285C,288C,363C,381C ''cluster.core'':174C ''cluster.labels'':168C,179C ''cluster.png'':290C ''code'':44C,328C ''col'':202C,215C,249C,270C ''color'':192C,207C ''comprehens'':223C ''copi'':298C ''core'':163C,171C,239C,260C ''d'':286C ''data'':102C,227C ''dbscan'':14B,50C,62C,90C,92C,94C,329C,331C ''dbscan_object.fit'':356C ''def'':77C ''demo'':306C ''done'':18C ''dtype'':169C ''e.g'':91C ''estim'':282C ''f'':138C,150C ''fit'':159C ''float'':141C ''follow'':325C ''function'':34C,295C ''generat'':69C,222C ''href'':308C ''import'':45C,49C,53C,57C,61C,65C,70C,75C,130C,134C ''indic'':176C ''input'':83C ''instead'':187C ''k'':201C,209C,216C,221C,251C,272C ''label'':178C,189C,191C,199C,206C,220C ''lambda'':139C ''learn'':8A,305C ''len'':197C ''like'':24C,167C ''make'':71C ''map'':149C ''markeredgecolor'':250C,271C ''markerfacecolor'':248C,269C ''markers'':252C,273C ''mask'':165C,173C,219C,237C,241C,258C,262C ''matplotlib.pyplot'':131C ''matrix'':82C,98C,103C,148C,152C,155C,157C,162C,228C,232C ''member'':218C,236C,257C ''metric'':54C,66C ''n'':287C ''nice'':129C ''nofollow'':313C ''nois'':186C,214C ''np'':137C ''np.array'':230C ''np.linspace'':194C ''np.zeros'':166C ''number'':283C ''numpi'':135C ''o'':247C,268C ''object'':88C,93C,330C ''output'':125C ''p'':15C,30C,293C,317C,368C ''plot'':39C,78C,126C,361C,362C,380C ''plot/visualize'':3A ''plt'':133C ''plt.cm.spectral'':193C ''plt.plot'':242C,263C ''plt.savefig'':289C ''plt.title'':281C ''plt.xlim'':278C ''plt.ylim'':275C ''point'':373C ''pre'':43C,327C ''print'':153C,358C,360C ''produc'':370C ''python'':10B,383C ''rel'':312C ''remov'':181C ''result'':28C,355C,364C ''result.labels'':359C ''row'':140C,146C ''sampl'':81C,97C,147C,151C,154C,156C,161C,164C,172C,175C,231C,240C,261C ''scikit'':7A,12B,304C ''scikit-learn'':6A,303C ''scikit-learn.org'':310C ''scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#example-cluster-plot-dbscan-py'':309C ''set'':190C ''singl'':372C ''sklearn'':9A,46C,52C,64C ''sklearn.cluster'':48C,60C ''sklearn.datasets.samples'':68C ''sklearn.preprocessing'':56C,74C ''standardscal'':58C,76C,158C ''transform'':160C ''tri'':321C ''true'':177C ''uniqu'':188C,198C,205C ''use'':184C,212C ''verbatim'':300C ''visual'':26C ''way'':378C ''would'':23C ''written'':37C ''x'':104C,142C,144C,224C,229C,234C,255C,334C,357C,365C ''xy'':233C,243C,245C,254C,264C,266C ''yet'':318C ''zip'':204C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6826, 2, '2015-08-19 09:11:55.007', 0, NULL, NULL, '&lt;p&gt;I solve this task as follows: cross-validate train data in order to maximize F1-score then use probabilistic output and cost matrix for predicting a class for a new item. 
Any suggestions are welcome.&lt;/p&gt;
', NULL, NULL, '''/p'':39C ''class'':30C ''cost'':25C ''cross'':9C ''cross-valid'':8C ''data'':12C ''f1'':18C ''f1-score'':17C ''follow'':7C ''item'':34C ''matrix'':26C ''maxim'':16C ''new'':33C ''order'':14C ''output'':23C ''p'':1C ''predict'':28C ''probabilist'':22C ''score'':19C ''solv'':3C ''suggest'':36C ''task'':5C ''train'':11C ''use'':21C ''valid'':10C ''welcom'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6810, 2, '2015-08-17 12:47:39.6', 1, NULL, NULL, '&lt;p&gt;Yes I suspect you are overfitting. When you build your first stage of models (nearest neighbors, random forest, gradient boosting, etc...) is the process like this?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;random_forest.fit(train_data,target)
random_forest_probabilities = random_forest.predict_proba(train_data)
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If so you are plugging in the same data you used to train the models into your models to get probabilities, which will lead to better probabilities than should be expected in real life and thus much better than should be expected results on your 2nd stage model (i.e.: overfitting). To remedy this you must do everything within a cross validation loop, that looks like this in pseudo code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for &amp;lt;fold&amp;gt; in &amp;lt;crossvalidation&amp;gt;:
     random_forest.fit(&amp;lt;train_data_fold&amp;gt;,&amp;lt;target_fold&amp;gt;)
     &amp;lt;fold predictions&amp;gt; = random_forest.fit(&amp;lt;test_data_fold&amp;gt;)
     ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Doing things this way will emulate real life since your predictions will be gathered using models that did not include that data when training them. You can build your final model using all of your prediction variables as input. If you struggle with actually executing on this either edit your question with code or post a new question on how to run everything with a CV loop.&lt;/p&gt;
', NULL, NULL, '''/code'':42C,145C ''/p'':28C,114C,215C ''/pre'':43C,146C ''2nd'':90C ''actual'':191C ''better'':70C,82C ''boost'':21C ''build'':10C,175C ''code'':30C,113C,116C,200C ''cross'':104C ''crossvalid'':123C ''cv'':213C ''data'':33C,41C,53C,128C,142C,169C ''edit'':196C ''either'':195C ''emul'':153C ''etc'':22C ''everyth'':101C,210C ''execut'':192C ''expect'':75C,86C ''final'':177C ''first'':12C ''fold'':119C,129C,133C,136C,143C ''forest'':19C,36C ''gather'':161C ''get'':64C ''gradient'':20C ''gt'':120C,124C,130C,134C,138C,144C ''i.e'':93C ''includ'':167C ''input'':186C ''lead'':68C ''life'':78C,155C ''like'':26C,109C ''look'':108C ''loop'':106C,214C ''lt'':118C,122C,126C,131C,135C,140C ''model'':15C,59C,62C,92C,163C,178C ''much'':81C ''must'':99C ''nearest'':16C ''neighbor'':17C ''new'':204C ''overfit'':7C,94C ''p'':1C,44C,147C ''plug'':49C ''post'':202C ''pre'':29C,115C ''predict'':137C,158C,183C ''proba'':39C ''probabl'':37C,65C,71C ''process'':25C ''pseudo'':112C ''question'':198C,205C ''random'':18C,35C ''random_forest.fit'':31C,125C,139C ''random_forest.predict'':38C ''real'':77C,154C ''remedi'':96C ''result'':87C ''run'':209C ''sinc'':156C ''stage'':13C,91C ''struggl'':189C ''suspect'':4C ''target'':34C,132C ''test'':141C ''thing'':149C ''thus'':80C ''train'':32C,40C,57C,127C,171C ''use'':55C,162C,179C ''valid'':105C ''variabl'':184C ''way'':151C ''within'':102C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6811, 2, '2015-08-17 13:56:53.857', 5, NULL, NULL, '&lt;p&gt;When I run the code you posted, I get three points on my plot:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/KpbTX.png"&gt;&lt;img src="http://i.stack.imgur.com/KpbTX.png" alt="clusters"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The "point" at (0, 4) corresponds to &lt;code&gt;X[1]&lt;/code&gt; and the "point" at (0, 5) is actually three points, corresponding to &lt;code&gt;X[0]&lt;/code&gt;, &lt;code&gt;X[2]&lt;/code&gt;, and &lt;code&gt;X[3]&lt;/code&gt;. The point at (5, 5) is the last point in your &lt;code&gt;X&lt;/code&gt; array. The data at (0, 4) and (0, 5) belong to one cluster, and the point at (5, 5) is considered noise (plotted in black).&lt;/p&gt;

&lt;p&gt;The issue here seems to be that you''re trying to run the &lt;code&gt;DBSCAN&lt;/code&gt; algorithm on a dataset containing 5 points, with at least 4 points required per cluster (the second argument to the &lt;code&gt;DBSCAN&lt;/code&gt; constructor). In the &lt;code&gt;sklearn&lt;/code&gt; example, the clustering algorithm is run on a dataset containing 750 points with three distinct centers. Try creating a larger &lt;code&gt;X&lt;/code&gt; dataset and running this code again.&lt;/p&gt;

&lt;p&gt;You might also want to remove the &lt;code&gt;plt.ylim([0,10])&lt;/code&gt; and &lt;code&gt;plt.xlim([0,10])&lt;/code&gt; lines from the code; they''re making it a bit difficult to see the points on the edge of the plot! If you omit the &lt;code&gt;ylim&lt;/code&gt; and &lt;code&gt;xlim&lt;/code&gt; then &lt;code&gt;matplotlib&lt;/code&gt; will automatically determine the plot limits.&lt;/p&gt;
', NULL, NULL, '''/a'':30C ''/code'':43C,59C,63C,68C,82C,125C,148C,154C,177C,197C,203C,231C,235C,239C ''/kpbtx.png'':22C,27C ''/p'':16C,31C,108C,184C,246C ''0'':36C,48C,58C,87C,90C,195C,201C ''1'':42C ''10'':196C,202C ''2'':62C ''3'':67C ''4'':37C,88C,136C ''5'':49C,72C,73C,91C,100C,101C,131C ''750'':165C ''actual'':51C ''algorithm'':126C,158C ''also'':188C ''alt'':28C ''argument'':143C ''array'':83C ''automat'':241C ''belong'':92C ''bit'':213C ''black'':107C ''center'':170C ''cluster'':29C,95C,140C,157C ''code'':6C,40C,56C,60C,65C,80C,123C,146C,152C,175C,182C,193C,199C,207C,229C,233C,237C ''consid'':103C ''constructor'':149C ''contain'':130C,164C ''correspond'':38C,54C ''creat'':172C ''data'':85C ''dataset'':129C,163C,178C ''dbscan'':124C,147C ''determin'':242C ''difficult'':214C ''distinct'':169C ''edg'':221C ''exampl'':155C ''get'':10C ''href'':19C ''i.stack.imgur.com'':21C,26C ''i.stack.imgur.com/kpbtx.png'':20C,25C ''img'':23C ''issu'':111C ''larger'':174C ''last'':76C ''least'':135C ''limit'':245C ''line'':204C ''make'':210C ''matplotlib'':238C ''might'':187C ''nois'':104C ''omit'':227C ''one'':94C ''p'':1C,17C,32C,109C,185C ''per'':139C ''plot'':15C,105C,224C,244C ''plt.xlim'':200C ''plt.ylim'':194C ''point'':12C,34C,46C,53C,70C,77C,98C,132C,137C,166C,218C ''post'':8C ''re'':118C,209C ''remov'':191C ''requir'':138C ''run'':4C,121C,160C,180C ''second'':142C ''see'':216C ''seem'':113C ''sklearn'':153C ''src'':24C ''three'':11C,52C,168C ''tri'':119C,171C ''want'':189C ''x'':41C,57C,61C,66C,81C,176C ''xlim'':234C ''ylim'':230C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9402, 2, '2015-12-15 17:24:34.147', 1, NULL, NULL, '&lt;p&gt;To robustify your estimator, you might model your ratings as a Gaussian mixture model (GMM) that is a mixture of two Gaussian rvs: 1) true ratings, 2) junk rating that are equal to one.  Scikit-learn already has a canned GMM classifier: &lt;a href="http://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_classifier.html#example-mixture-plot-gmm-classifier-py" rel="nofollow"&gt;http://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_classifier.html#example-mixture-plot-gmm-classifier-py&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Digging in a little more, a simple approach would be to let scikit-learn partition your ratings into two gaussians.  If one of the partitions ends up with a mean near one, then we can throw out those ratings.  Or, more elegantly, we can take the mean of the other, non-near-one Gaussian, as the true rating mean.&lt;/p&gt;

&lt;p&gt;Here is a bit of code for a ipython notebook that does this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from sklearn.mixture import GMM
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import collections

def make_ratings(mean,std,rating_cnt):
    rating_sample = np.random.randn(rating_cnt)*std + mean
    return np.clip(rating_sample,1,5).astype(int)

def make_collection(true_mean,true_std,true_cnt,junk_count):
    true_ratings = make_ratings(true_mean,true_std,true_cnt)
    junk_ratings = make_ratings(1,0,junk_count)
    return np.hstack([true_ratings,junk_ratings])[:,np.newaxis]

def robust_mean(X, th = 2.5, agg_th=2.5, default_agg=np.mean):
    classifier = GMM(n_components=2)
    classifier.fit(X)
    if np.min(classifier.means_) &amp;gt; th or default_agg(X)&amp;lt;agg_th:
        return default_agg(X)
    else:
        return np.max(classifier.means_)

r_mean = 4.2
X = make_collection(r_mean,2,40,10)
plt.hist(X,5)
classifier = GMM(n_components=2)
classifier.fit(X)
plt.show()
print "vars =",classifier.covars_.flatten()
print "means = ",classifier.means_.flatten()
print "mean = ",np.mean(X)
print "median = ",np.median(X)
print "robust mean = ", robust_mean(X)
print "true mean = ", r_mean
print "prob(rating=1|class) = ",classifier.predict_proba(1).flatten()
print "prob(rating=true_mean|class) = ",classifier.predict_proba(r_mean).flatten()
print "prediction: ", classifier.predict(X)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The output for one run looks like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;vars = [ 0.22386589  0.56931527]
means =  [ 1.32310978  4.00603523]
mean =  2.9
median =  3.0
robust mean =  4.00603523034
true mean =  4.2
prob(rating=1|class) =  [  9.99596493e-01   4.03507425e-04]
prob(rating=true_mean|class) =  [  1.08366762e-08   9.99999989e-01]
prediction:  [1 0 1 0 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1 1 0
 1 1 1 0 0 0 0 0 0 0 0 0 0]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;We can simulate how will this works with a few monte carlo trials:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;true_means = np.arange(1.5,4.5,.2)
true_ratings = 40
junk_ratings = 10
true_std = 1
m_out = []
m_in = []
m_reg = []
runs = 40
for m in true_means:
    Xs = [make_collection(m,true_std,true_ratings,junk_ratings) for x in range(runs)]
    m_in.append([[m]*runs])
    m_out.append([[robust_mean(X, th = 2.5, agg_th=2,default_agg=np.mean) for X in Xs]])
    m_reg.append([[np.mean(X) for X in Xs]])

m_in = np.array(m_in).T[:,0,:]
m_out = np.array(m_out).T[:,0,:]
m_reg = np.array(m_reg).T[:,0,:]

plt.plot(m_in,m_out,''b.'',alpha=.25)
plt.plot(m_in,m_reg,''r.'',alpha=.25)
plt.plot(np.arange(0,5,.1),np.arange(0,5,.1),''k.'')
plt.xlim([0,5])
plt.ylim([0,5])
plt.xlabel(''true mean'')
plt.ylabel(''predicted mean'')
plt.title("true_ratings=" + str(true_ratings)
          + "; junk_ratings=" + str(junk_ratings)
         + "; std="+str(true_std))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The output is pasted below.  The red is the mean rating and the blue is the proposed rating.  You can tweak the parameters to get slightly different behaviors.
&lt;a href="http://i.stack.imgur.com/BjmBj.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/BjmBj.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6900, 2, '2015-08-25 17:17:27.533', 1, NULL, NULL, '&lt;p&gt;As I understand it, you don''t want to use the &lt;code&gt;dot product&lt;/code&gt;, because it is unbounded.  Yet you also don''t want to use the &lt;a href="http://blog.christianperone.com/?p=2497" rel="nofollow"&gt;&lt;code&gt;cosine similarity metric&lt;/code&gt;&lt;/a&gt;:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/oFN5J.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/oFN5J.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;, which is just the &lt;a href="https://en.wikipedia.org/wiki/Cosine_similarity" rel="nofollow"&gt;dot product normalized by the length&lt;/a&gt; of the two vectors being dotted because it does not take magnitude into account e.g. $(1,1)\cdot(1,1)=1=(1,1)\cdot(2,2)$&lt;/p&gt;

&lt;p&gt;Perhaps you could therefor use the &lt;a href="https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient" rel="nofollow"&gt;sorensen dice coefficient&lt;/a&gt; or possibly 1-soresen:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/75xmf.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/75xmf.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This varies from 0 to 1 and accounts for magnitudes of the constituent vectors e.g.:&lt;/p&gt;

&lt;p&gt;$$s_{\nu}[(1,1),(1,1)]=1$$&lt;/p&gt;

&lt;p&gt;, where as &lt;/p&gt;

&lt;p&gt;$$s_{\nu}[(1,1),(2,2)]=\frac{4}{5}$$&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6812, 2, '2015-08-17 14:13:39.08', 1, NULL, NULL, '&lt;p&gt;Selecting a learning rate is an example of a "meta-problem" known as &lt;em&gt;hyperparameter optimization&lt;/em&gt;. The best learning rate depends on the problem at hand, as well as on the architecture of the model being optimized, and even on the state of the model in the current optimization process! There are even software packages devoted to hyperparameter optimization such as &lt;a href="https://github.com/JasperSnoek/spearmint" rel="nofollow"&gt;spearmint&lt;/a&gt; and &lt;a href="https://github.com/hyperopt/hyperopt" rel="nofollow"&gt;hyperopt&lt;/a&gt; (just a couple of examples, there are many others!).&lt;/p&gt;

&lt;p&gt;Apart from full-scale hyperparameter optimization, I wanted to mention one technique that''s quite common for selecting learning rates that hasn''t been mentioned so far. &lt;a href="https://en.wikipedia.org/wiki/Simulated_annealing" rel="nofollow"&gt;Simulated annealing&lt;/a&gt; is a technique for optimizing a model whereby one starts with a large learning rate and gradually reduces the learning rate as optimization progresses. Generally you optimize your model with a large learning rate (0.1 or so), and then progressively reduce this rate, often by an order of magnitude (so to 0.01, then 0.001, 0.0001, etc.).&lt;/p&gt;

&lt;p&gt;This can be combined with &lt;a href="https://en.wikipedia.org/wiki/Early_stopping" rel="nofollow"&gt;early stopping&lt;/a&gt; to optimize the model with one learning rate as long as progress is being made, then switch to a smaller learning rate once progress appears to slow. The larger learning rates appear to help the model locate regions of general, large-scale optima, while smaller rates help the model focus on one particular local optimum.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6831, 1, '2015-08-19 15:21:53.017', 0, 33, 'Developing an Empirical Model for Non-Linear Data', '&lt;p&gt;I have collected Temp vs. Time data for a controlled environment. I performed three tests, with the thermocouple in the same location for all three tests. I now have three Temp. vs. Time curves for this location. The graphs are shown here: &lt;a href="http://i.stack.imgur.com/3vCzj.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/3vCzj.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I am looking to generate an empirical model based on the Temp. vs. Time at this location that can predict the temperature at this location over time. How should I approach this? My initial approach was to identify the local minima and maxima, and average the three points together (for each maximum and minimum). And then correlate that average to a sequence of time, for example, 30C between 750 and 1250s, or something like that. I really don''t think this is an efficient approach...&lt;/p&gt;

&lt;p&gt;What do you all think would be an efficient approach to this?&lt;/p&gt;
', 'dataset', NULL, '''/3vczj.jpg'':58C,65C ''/a'':71C ''/p'':72C,159C,173C ''1250s'':144C ''30c'':140C ''750'':142C ''alt'':66C ''approach'':104C,108C,158C,170C ''averag'':118C,132C ''base'':82C ''collect'':14C ''control'':21C ''correl'':130C ''curv'':45C ''data'':9A,18C ''dataset'':10B ''descript'':69C ''develop'':1A ''effici'':157C,169C ''empir'':3A,80C ''enter'':67C ''environ'':22C ''exampl'':139C ''generat'':78C ''graph'':50C ''href'':55C ''i.stack.imgur.com'':57C,64C ''i.stack.imgur.com/3vczj.jpg'':56C,63C ''identifi'':111C ''imag'':68C ''img'':61C ''initi'':107C ''like'':147C ''linear'':8A ''local'':113C ''locat'':33C,48C,90C,98C ''look'':76C ''maxima'':116C ''maximum'':125C ''minima'':114C ''minimum'':127C ''model'':4A,81C ''nofollow'':60C ''non'':7A ''non-linear'':6A ''p'':11C,73C,160C ''perform'':24C ''point'':121C ''predict'':93C ''realli'':150C ''rel'':59C ''sequenc'':135C ''shown'':52C ''someth'':146C ''src'':62C ''temp'':15C,42C,85C ''temperatur'':95C ''test'':26C,37C ''thermocoupl'':29C ''think'':153C,165C ''three'':25C,36C,41C,120C ''time'':17C,44C,87C,100C,137C ''togeth'':122C ''vs'':16C,43C,86C ''would'':166C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6813, 2, '2015-08-17 15:09:24.39', 7, NULL, NULL, '&lt;p&gt;While ffriend''s answer gives some excellent pointers for learning more about how neural networks can be (extremely) difficult to tune properly, I thought it might be helpful to list a couple specific techniques that are currently used in top-performing classification architectures in the neural network literature.&lt;/p&gt;

&lt;h2&gt;Rectified linear activations&lt;/h2&gt;

&lt;p&gt;The first thing that might help in your case is to switch your model''s activation function from the &lt;a href="https://en.wikipedia.org/wiki/Logistic_function"&gt;logistic sigmoid&lt;/a&gt; -- $f(z) = \left(1 + e^{-z}\right)^{-1}$ -- to a &lt;a href="https://en.wikipedia.org/wiki/Rectifier_(neural_networks)"&gt;rectified linear (aka relu)&lt;/a&gt; -- $f(z) = \max(0, z)$.&lt;/p&gt;

&lt;p&gt;The relu activation has two big advantages:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;its output is a true zero (not just a small value close to zero) for $z \le 0$ and&lt;/li&gt;
&lt;li&gt;its derivative is constant, either 0 for $z \le 0$ or 1 for $z &amp;gt; 0$.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;A network of relu units basically acts like an ensemble of exponentially many linear networks, because units that receive input $z \le 0$ are essentially "off" (their output is 0), while units that receive input $z &amp;gt; 0$ collapse into a single linear model for that input. Also the constant derivatives are important because a deep network with relu activations  tends to avoid the &lt;a href="https://en.wikipedia.org/wiki/Vanishing_gradient_problem"&gt;vanishing gradient problem&lt;/a&gt; and can be trained without layerwise pretraining.&lt;/p&gt;

&lt;p&gt;See "Deep Sparse Rectifier Neural Networks" by Glorot, Bordes, &amp;amp; Bengio (&lt;a href="http://jmlr.csail.mit.edu/proceedings/papers/v15/glorot11a/glorot11a.pdf"&gt;http://jmlr.csail.mit.edu/proceedings/papers/v15/glorot11a/glorot11a.pdf&lt;/a&gt;) for a good paper about these topics.&lt;/p&gt;

&lt;h2&gt;Dropout&lt;/h2&gt;

&lt;p&gt;Many research groups in the past few years have been advocating for the use of "dropout" in classifier networks to avoid overtraining. (See for example "Dropout: A simple way to prevent neural networks from overfitting" by Srivastava, Hinton, Krizhevsky, Sutskever, &amp;amp; Salakhutdinov &lt;a href="http://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf"&gt;http://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf&lt;/a&gt;) In dropout, during training, some constant proportion of the units in a given layer are randomly set to 0 for each input that the network processes. This forces the units that aren''t set to 0 to "make up" for the "missing" units. Dropout seems to be an extremely effective regularizer for neural network models in classification tasks. See a blog article about this at &lt;a href="http://fastml.com/regularizing-neural-networks-with-dropout-and-with-dropconnect/"&gt;http://fastml.com/regularizing-neural-networks-with-dropout-and-with-dropconnect/&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6999, 2, '2015-09-03 17:43:29.93', 1, NULL, NULL, '&lt;p&gt;One more thing to consider, beyond everything that Ben Haley said, is to &lt;strong&gt;convert to user local time&lt;/strong&gt;. For example, if you are trying to predict something that occurs around 8pm for all users, if you look at UTC time, it will be harder to predict from.&lt;/p&gt;
', NULL, NULL, '''/p'':51C ''/strong'':21C ''8pm'':34C ''around'':33C ''ben'':10C ''beyond'':7C ''consid'':6C ''convert'':16C ''everyth'':8C ''exampl'':23C ''haley'':11C ''harder'':47C ''local'':19C ''look'':40C ''occur'':32C ''one'':2C ''p'':1C ''predict'':29C,49C ''said'':12C ''someth'':30C ''strong'':15C ''thing'':4C ''time'':20C,43C ''tri'':27C ''user'':18C,37C ''utc'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6814, 1, '2015-08-17 20:34:15.33', 4, 197, 'How to split train/test in recommender systems', '&lt;p&gt;I am working with the MovieLens10M dataset, predicting user ratings.  If I want to fairly evaluate my algorithm, how should I split my training v. test data?&lt;/p&gt;

&lt;p&gt;By default, I believe the data is split into train v. test sets where ''test'' contains movies previously unseen in the training set.  If my model requires each movie to have been seen at least once in the training set, how should I split my data?  Should I take all but N of each user''s ratings for all the data and evaluate my performance on the held out NxUser_num  ratings?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8663, 2, '2015-10-30 18:07:44.627', 1, NULL, NULL, '&lt;p&gt;There are several issues I see with the implementation. Some are just unnecessarily complicated ways of doing it, but some are genuine errors. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Primary takeaways&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;A: &lt;em&gt;Try to start from the math behind the model.&lt;/em&gt; The logistic regression is a relatively simple one. Find the two equations you need and stick to them, replicate them letter by letter.&lt;/p&gt;

&lt;p&gt;B: &lt;em&gt;Vectorize.&lt;/em&gt; It will save you a lot of unnecessary steps and computations, if you step back for a bit and think of the best vectorized implementation.&lt;/p&gt;

&lt;p&gt;C: &lt;em&gt;Write more comments in the code.&lt;/em&gt; It will help those trying to help you. It will also help you understand each part better and maybe uncover errors yourself.&lt;/p&gt;

&lt;p&gt;Now let''s go over the code step by step.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;1. The sigmoid function&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Is there a reason for such a complicate implementation in &lt;code&gt;phi(t)&lt;/code&gt;? Assuming that &lt;code&gt;t&lt;/code&gt; is a vector (a numpy array), then all you really need is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def phi(t):
   1. / (1. + np.exp(-t))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As &lt;code&gt;np.exp()&lt;/code&gt; operates element-wise over arrays. Ideally, I''d implement it as a function that can also return its derivative (not necessary here, but might be handy if you try to implement a basic neural net with sigmoid activations):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def phi(t, dt = False):
   if dt:
      phi(t) * (1. - phi(t))
   else:
      1. / (1. + np.exp(-t))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;2. Cost function&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Usually, the logistic cost function is defined as a log cost in the following way (vectorized): $ \frac{1}{m} (-(y^T \log{(\phi(X\theta))})-(1-y^T)(\log{(1 - \phi(X\theta))}) + \frac{\lambda}{2m} \theta^{1T}\theta $  where $\phi(z)$ is the logistic (sigmoid) function, $\theta$ is the full parameter vector (including bias weight), $\theta^1$ is parameter vector with $\theta_1=0$ (by convention, bias is not regularized) and $\lambda$ is the regularization parameter. &lt;/p&gt;

&lt;p&gt;What I really don''t understand is the part where you multiply &lt;code&gt;y * z&lt;/code&gt;. Assuming &lt;code&gt;y&lt;/code&gt; is your label vector $y$, why are you multiplying it with your &lt;code&gt;z&lt;/code&gt; before applying the sigmoid function? And why do you need to split the cost function into zeros and ones and calculate losses for either sample separately? 
I think the problem in your code really lies in this part: you must be erroneously multiplying $y$ with $X\theta$ before applying $\phi(.)$.&lt;/p&gt;

&lt;p&gt;Also, this bit here: &lt;code&gt;X.dot(w) + c&lt;/code&gt;. So &lt;code&gt;c&lt;/code&gt; is your bias parameter, right? Why are you adding it to every element of $X\theta$? It shouldn''t be added - it should be the first element of the vector $X\theta$. Yes, you don''t regularize it, but you need to use in the "prediction" part of the loss function.&lt;/p&gt;

&lt;p&gt;In your code, I also see the cost function as being overly complicated. Here''s what I would try:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def loss(X,y,w,lam):
   #calculate "prediction"
   Z = np.dot(X,w)
   #calculate cost without regularization
   #shape of X is (m,n), shape of w is (n,1)
   J = (1./len(X)) * (-np.dot(y.T, np.log(phi(Z))) * np.dot((1-y.T),np.log(1 - phi(Z))))
   #add regularization
   #temporary weight vector
   w1 = copy.copy(w) #import copy to create a true copy
   w1[0] = 0
   J += (lam/(2.*len(X))) * np.dot(w1.T, w)
   return J
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;3. Gradient&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Again, let''s first go over the formula for the gradient of the logistic loss, again, vectorized: $\frac{1}{m} ((\phi(X\theta) - y)^TX)^T + \frac{\lambda}{m}\theta^1$.
This will return a vector of derivatives (i.e. gradient) for all parameters, regularized properly (without the bias term regularized).&lt;/p&gt;

&lt;p&gt;Here again, you''ve multiplied by $y$ way too soon: &lt;code&gt;phi(y * z)&lt;/code&gt;. In fact, you shouldn''t have multiplied by $y$ in gradient at all. &lt;/p&gt;

&lt;p&gt;This is what I would do for the gradient:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def gradient(X, y, w, lam):
   #calculate the prediction
   Z = np.dot(X,w)
   #temporary weight vector
   w1 = copy.copy(w) #import copy to create a true copy
   w1[0] = 0
   #calc gradient
   grad = (1./len(X)) * (np.dot((phi(Z) - y).T,X).T) + (lam/len(X)) * w1
   return grad
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The actual gradient descent implementation seems ok to me, but because there are errors in the gradient and cost function implementations, it fails to deliver :/&lt;/p&gt;

&lt;p&gt;Hope this will help you get on track.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8673, 5, '2015-10-31 16:59:23.013', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6837, 1, '2015-08-19 20:58:44.523', 1, 43, 'Website navigation path using graph theory', '&lt;p&gt;In web analytics, one of the main issues is to find the &lt;em&gt;optimal path of navigation&lt;/em&gt;. &lt;strong&gt;Question:&lt;/strong&gt; Is there any literature in &lt;em&gt;graph theory&lt;/em&gt; area, which is focused on studying optimal website navigation path?&lt;/p&gt;

&lt;p&gt;My Comment to Tom was pretty large. So putting it here:&lt;/p&gt;

&lt;p&gt;But in network theory there''s a concept of main node (forgot the exact terminology). This node is most important because it has most connection to other pages. Home page is a very simple kind of main node. Also different set of visitors might have different main node. So, if we could capture what is the main node, then if we directly drop the visitor to this page or some other page, then the conversion might increase. Because the visitor who will come to this main page, will able to easily navigate to his required page. And then able to get what he is looking for. Another method people use for shortest path is MCMC methodology. But I''ll prefer network method. Figuring out the entire method is pretty cumbersome and I don''t have much time. So I was wondering if somebody wrote some journal paper on it, I can refer that. &lt;/p&gt;
', 'graphs  optimization', NULL, '''/em'':27C,39C ''/p'':50C,63C,218C ''/strong'':30C ''abl'':153C,163C ''also'':103C ''analyt'':12C ''anoth'':171C ''area'':40C ''captur'':117C ''come'':147C ''comment'':53C ''concept'':72C ''connect'':89C ''convers'':139C ''could'':116C ''cumbersom'':194C ''differ'':104C,110C ''direct'':126C ''drop'':127C ''easili'':155C ''em'':22C,36C ''entir'':190C ''exact'':78C ''figur'':187C ''find'':20C ''focus'':43C ''forgot'':76C ''get'':165C ''graph'':5A,7B,37C ''home'':93C ''import'':84C ''increas'':141C ''issu'':17C ''journal'':210C ''kind'':99C ''larg'':58C ''literatur'':34C ''ll'':183C ''look'':169C ''main'':16C,74C,101C,111C,121C,150C ''mcmc'':179C ''method'':172C,186C,191C ''methodolog'':180C ''might'':108C,140C ''much'':200C ''navig'':2A,26C,48C,156C ''network'':67C,185C ''node'':75C,81C,102C,112C,122C ''one'':13C ''optim'':8B,23C,46C ''p'':9C,51C,64C ''page'':92C,94C,132C,136C,151C,160C ''paper'':211C ''path'':3A,24C,49C,177C ''peopl'':173C ''prefer'':184C ''pretti'':57C,193C ''put'':60C ''question'':29C ''refer'':216C ''requir'':159C ''set'':105C ''shortest'':176C ''simpl'':98C ''somebodi'':207C ''strong'':28C ''studi'':45C ''terminolog'':79C ''theori'':6A,38C,68C ''time'':201C ''tom'':55C ''use'':4A,174C ''visitor'':107C,129C,144C ''web'':11C ''websit'':1A,47C ''wonder'':205C ''wrote'':208C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6816, 1, '2015-08-18 09:25:02.24', 2, 80, 'What are the features to distinguish two short term signals (time series)', '&lt;p&gt;Imagine that I have a set of short term time series data (signals) with different shapes and parameters. I would like to extract/select a set of features (parameters/functions/indeces) to identify each sequence and distinguish between them. Such features are simple features e.g. max and min value, mean, variance, start and end value, etc. or more complex ones e.g. entropy, no. peaks, power spectrum, etc. 
I am looking for more "representative" features to express the signals and distinguish them. What features do you know (in signal processing or data mining fields) that can represent a parameter in signals?&lt;br&gt;
For example, in the following image, there are two signals with the same parameters such as mean, min-max values. But what are the features that differ them (for instance no. peaks, but generally what else?)
&lt;a href="http://i.stack.imgur.com/Wb5a8.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Wb5a8.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'data-mining  time-series  feature-selection  feature-extraction', NULL, '''/a'':178C ''/p'':179C ''/wb5a8.png'':165C,172C ''alt'':173C ''br'':123C ''complex'':81C ''data'':14B,37C,113C ''data-min'':13B ''descript'':176C ''differ'':40C,151C ''distinguish'':6A,59C,102C ''e.g'':67C,83C ''els'':160C ''end'':76C ''enter'':174C ''entropi'':84C ''etc'':78C,89C ''exampl'':125C ''express'':98C ''extract'':24B ''extract/select'':48C ''featur'':4A,20B,23B,52C,63C,66C,96C,105C,149C ''feature-extract'':22B ''feature-select'':19B ''field'':115C ''follow'':128C ''general'':158C ''href'':162C ''i.stack.imgur.com'':164C,171C ''i.stack.imgur.com/wb5a8.png'':163C,170C ''identifi'':55C ''imag'':129C,175C ''imagin'':26C ''img'':168C ''instanc'':154C ''know'':108C ''like'':46C ''look'':92C ''max'':68C,143C ''mean'':72C,140C ''min'':70C,142C ''min-max'':141C ''mine'':15B,114C ''nofollow'':167C ''one'':82C ''p'':25C ''paramet'':43C,120C,137C ''parameters/functions/indeces'':53C ''peak'':86C,156C ''power'':87C ''process'':111C ''rel'':166C ''repres'':95C,118C ''select'':21B ''sequenc'':57C ''seri'':12A,18B,36C ''set'':31C,50C ''shape'':41C ''short'':8A,33C ''signal'':10A,38C,100C,110C,122C,133C ''simpl'':65C ''spectrum'':88C ''src'':169C ''start'':74C ''term'':9A,34C ''time'':11A,17B,35C ''time-seri'':16B ''two'':7A,132C ''valu'':71C,77C,144C ''varianc'':73C ''would'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6817, 1, '2015-08-18 09:34:51.143', 2, 67, 'Clasification - ROC Curve with very high number of false negatives', '&lt;p&gt;I have the following curve as the result of a azure machine learning classification experiment.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/XcyS6.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/XcyS6.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This obviously shows a lot of false negatives, but I''m struggling to interpret this, as my data has a high number of negatives and a low number of positives (I''m doing a binary classification). &lt;/p&gt;

&lt;p&gt;I am interpreting this as saying my data is almost entirely made up of positive values, with the majority being identified as negative. I believe that the false negatives on the chart are actually true negatives. Am I reading the chart correctly?&lt;/p&gt;
', 'classification', NULL, '''/a'':47C ''/p'':28C,48C,86C,130C ''/xcys6.png'':34C,41C ''actual'':121C ''almost'':97C ''alt'':42C ''azur'':23C ''believ'':112C ''binari'':84C ''chart'':119C,128C ''clasif'':1A ''classif'':11B,26C,85C ''correct'':129C ''curv'':3A,17C ''data'':67C,95C ''descript'':45C ''enter'':43C ''entir'':98C ''experi'':27C ''fals'':9A,56C,115C ''follow'':16C ''high'':6A,70C ''href'':31C ''i.stack.imgur.com'':33C,40C ''i.stack.imgur.com/xcys6.png'':32C,39C ''identifi'':108C ''imag'':44C ''img'':37C ''interpret'':63C,90C ''learn'':25C ''lot'':54C ''low'':76C ''m'':60C,81C ''machin'':24C ''made'':99C ''major'':106C ''negat'':10A,57C,73C,110C,116C,123C ''nofollow'':36C ''number'':7A,71C,77C ''obvious'':51C ''p'':12C,29C,49C,87C ''posit'':79C,102C ''read'':126C ''rel'':35C ''result'':20C ''roc'':2A ''say'':93C ''show'':52C ''src'':38C ''struggl'':61C ''true'':122C ''valu'':103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6818, 2, '2015-08-18 10:11:05.097', 2, NULL, NULL, '&lt;p&gt;Another way is simply with the "table" function. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ids&amp;lt;-c(1,3,1,2)
counts&amp;lt;-data.frame(table(ids))
counts
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':26C ''/p'':10C ''/pre'':27C ''1'':16C,18C ''2'':19C ''3'':17C ''anoth'':2C ''c'':15C ''code'':12C ''count'':20C,25C ''data.frame'':22C ''function'':9C ''id'':13C,24C ''lt'':14C,21C ''p'':1C ''pre'':11C ''simpli'':5C ''tabl'':8C,23C ''way'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6819, 2, '2015-08-18 18:39:45.197', 2, NULL, NULL, '&lt;p&gt;If this is a U.S. model you can try County (FIPS) codes, which are coarser-grained areas than ZIP codes (there can be many ZIP codes in a county).  If bearing and distance worked for you, this should also work.  Your classifier would need to handle categorical data.  In R, this is pretty easy to do if you already have a ZIP code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(noncensus)
data(zip_codes)
my_dataframe[''fips'']&amp;lt;-zip_codes$fips[match(my_dataframe$zip, zip_codes$zip)]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This assumes you already have a dataframe called &lt;code&gt;my_dataframe&lt;/code&gt; with ZIP codes as a column named ''zip''.&lt;/p&gt;

&lt;p&gt;As a bonus you can use the &lt;a href="https://cran.r-project.org/web/packages/choroplethr/index.html" rel="nofollow"&gt;choroplethr&lt;/a&gt; package to create a nice visualization of some scalar value, for example, your item price:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(choroplethr)
df_to_plot&amp;lt;-data.frame()
df_to_plot[''region'']&amp;lt;-my_dataframe$fips
    df_to_plot[''value'']&amp;lt;-my_dataframe$price

county_choropleth(df_to_plot, title=''price by location'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/rtpvH.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/rtpvH.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':128C,199C ''/code'':88C,102C,179C ''/p'':66C,111C,144C,200C ''/pre'':89C,180C ''/rtpvh.png'':186C,193C ''/web/packages/choroplethr/index.html'':124C ''alreadi'':61C,94C ''also'':41C ''alt'':194C ''area'':19C ''assum'':92C ''bear'':33C ''bonus'':115C ''call'':98C ''categor'':49C ''choropleth'':171C ''choroplethr'':127C,148C ''classifi'':44C ''coarser'':17C ''coarser-grain'':16C ''code'':13C,22C,28C,65C,68C,73C,79C,86C,99C,105C,146C ''column'':108C ''counti'':11C,31C,170C ''cran.r-project.org'':123C ''cran.r-project.org/web/packages/choroplethr/index.html'':122C ''creat'':131C ''data'':50C,71C ''data.frame'':153C ''datafram'':75C,83C,97C,101C,160C,168C ''descript'':197C ''df'':149C,154C,162C,172C ''distanc'':35C ''easi'':56C ''enter'':195C ''exampl'':140C ''fip'':12C,76C,80C,161C ''grain'':18C ''handl'':48C ''href'':121C,183C ''i.stack.imgur.com'':185C,192C ''i.stack.imgur.com/rtpvh.png'':184C,191C ''imag'':196C ''img'':189C ''item'':142C ''librari'':69C,147C ''locat'':178C ''lt'':77C,152C,158C,166C ''mani'':26C ''match'':81C ''model'':7C ''name'':109C ''need'':46C ''nice'':133C ''nofollow'':126C,188C ''noncensus'':70C ''p'':1C,90C,112C,181C ''packag'':129C ''plot'':151C,156C,164C,174C ''pre'':67C,145C ''pretti'':55C ''price'':143C,169C,176C ''r'':52C ''region'':157C ''rel'':125C,187C ''scalar'':137C ''src'':190C ''titl'':175C ''tri'':10C ''u.s'':6C ''use'':118C ''valu'':138C,165C ''visual'':134C ''work'':36C,42C ''would'':45C ''zip'':21C,27C,64C,72C,78C,84C,85C,87C,104C,110C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6820, 2, '2015-08-18 19:21:20.387', 0, NULL, NULL, '&lt;p&gt;This is similar to Jeremy''s but using &lt;code&gt;dplyr&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(dplyr)
mytable &amp;lt;-
"a    date        b         id
 1155 2010-05-02  2.7200    1
 1156 2010-05-05  2.6000    3
 1157 2010-05-08  2.6700    1
 1158 2010-05-11  3.5700    2"

mytable &amp;lt;- read.delim(textConnection(mytable), header=TRUE,  sep="")
mytable %&amp;gt;% count(id)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''-02'':27C ''-05'':26C,32C,33C,38C,44C ''-08'':39C ''-11'':45C ''/code'':12C,60C ''/p'':13C ''/pre'':61C ''1'':29C,41C ''1155'':24C ''1156'':30C ''1157'':36C ''1158'':42C ''2'':47C ''2.6000'':34C ''2.6700'':40C ''2.7200'':28C ''2010'':25C,31C,37C,43C ''3'':35C ''3.5700'':46C ''b'':22C ''code'':10C,15C ''count'':58C ''date'':21C ''dplyr'':11C,17C ''gt'':57C ''header'':53C ''id'':23C,59C ''jeremi'':6C ''librari'':16C ''lt'':19C,49C ''mytabl'':18C,48C,52C,56C ''p'':1C ''pre'':14C ''read.delim'':50C ''sep'':55C ''similar'':4C ''textconnect'':51C ''true'':54C ''use'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8788, 2, '2015-11-10 19:19:26.1', 1, NULL, NULL, '&lt;p&gt;Your problem is separable. You can 1) transform your vectors into a weighted combination of the "raw" vector dimensions and &lt;strong&gt;then&lt;/strong&gt; 2) compute similarity or distance with things like correlation or Euclidean distance.&lt;/p&gt;

&lt;p&gt;Problem 1: Weights are typically linear, just a matrix multiplied by your feature vector to rotate and scale it however you like. A matrix multiplication can even reduce your vectors dimensions (all the way down to a scalar score, if you like). The state of the art for dimension reduction and optimal feature vector transformation is SVD/PCA/LSI/LDA/NMF. FYI, the "eigenvectors" that some of these approaches produce are simply the columns or rows of your transformation matrix, the eigenvalues are the relative importance/weight of each of your new vectors. Some eigenvalues will be zero if you''re doing dimension reduction and there''s redundancy in your features.&lt;/p&gt;

&lt;p&gt;Problem 2: Some common distance metrics based on &lt;a href="https://en.wikipedia.org/wiki/Norm_(mathematics)" rel="nofollow"&gt;vector norms&lt;/a&gt; are&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;L_1 (Manhattan)&lt;/li&gt;
&lt;li&gt;L_2 (Euclidian)&lt;/li&gt;
&lt;li&gt;L_inf (Supremum)&lt;/li&gt;
&lt;li&gt;Cosine (vector product)&lt;/li&gt;
&lt;li&gt;Fractional (p-norm)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The state-of-the-art distance metric for high-dimensional problems is &lt;a href="https://en.wikipedia.org/wiki/Norm_(mathematics)" rel="nofollow"&gt;fractional distance or p-norm&lt;/a&gt; which is like Euclidean distance/norm, but the exponent is set to a value between 0 and 1 rather than 2.&lt;/p&gt;
', NULL, NULL, '''/a'':164C,223C ''/li'':172C,177C,182C,187C,193C ''/p'':37C,145C,166C,244C ''/strong'':24C ''/ul'':194C ''/wiki/norm_(mathematics)'':159C,214C ''0'':238C ''1'':8C,40C,170C,240C ''2'':25C,148C,175C,243C ''approach'':103C ''art'':85C,201C ''base'':153C ''column'':108C ''combin'':15C ''common'':150C ''comput'':26C ''correl'':33C ''cosin'':184C ''dimens'':20C,69C,87C,136C ''dimension'':207C ''distanc'':29C,36C,151C,202C,218C ''distance/norm'':228C ''eigenvalu'':116C,128C ''eigenvector'':98C ''en.wikipedia.org'':158C,213C ''en.wikipedia.org/wiki/norm_(mathematics)'':157C,212C ''euclidean'':35C,227C ''euclidian'':176C ''even'':65C ''expon'':231C ''featur'':51C,91C,144C ''fraction'':189C,217C ''fyi'':96C ''high'':206C ''high-dimension'':205C ''howev'':58C ''href'':156C,211C ''importance/weight'':120C ''inf'':180C ''l'':169C,174C,179C ''li'':168C,173C,178C,183C,188C ''like'':32C,60C,80C,226C ''linear'':44C ''manhattan'':171C ''matrix'':47C,62C,114C ''metric'':152C,203C ''multipl'':63C ''multipli'':48C ''new'':125C ''nofollow'':161C,216C ''norm'':163C,192C,222C ''optim'':90C ''p'':1C,38C,146C,191C,195C,221C ''p-norm'':190C,220C ''problem'':3C,39C,147C,208C ''produc'':104C ''product'':186C ''rather'':241C ''raw'':18C ''re'':134C ''reduc'':66C ''reduct'':88C,137C ''redund'':141C ''rel'':160C,215C ''relat'':119C ''rotat'':54C ''row'':110C ''scalar'':76C ''scale'':56C ''score'':77C ''separ'':5C ''set'':233C ''similar'':27C ''simpli'':106C ''state'':82C,198C ''state-of-the-art'':197C ''strong'':22C ''supremum'':181C ''svd/pca/lsi/lda/nmf'':95C ''thing'':31C ''transform'':9C,93C,113C ''typic'':43C ''ul'':167C ''valu'':236C ''vector'':11C,19C,52C,68C,92C,126C,162C,185C ''way'':72C ''weight'':14C,41C ''zero'':131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6947, 1, '2015-08-30 05:14:24.837', 1, 96, 'Looking for language and framework for data munging/wrangling', '&lt;p&gt;I have an extensive series of projects that require importing data from many different types of different sources (websites, web apis, sensors, legacy text files, etc). Is there a good framework (preferably open source) that has been built bottom up to tackle this type of problem? Preferably one leveraging an appropriate language (python, most likely) and already having an extensive plugin database.&lt;/p&gt;
', 'data-mining  data-cleaning', NULL, '''/p'':78C ''alreadi'':72C ''api'':36C ''appropri'':66C ''bottom'':54C ''built'':53C ''clean'':14B ''data'':7A,10B,13B,26C ''data-clean'':12B ''data-min'':9B ''databas'':77C ''differ'':29C,32C ''etc'':41C ''extens'':19C,75C ''file'':40C ''framework'':5A,46C ''good'':45C ''import'':25C ''languag'':3A,67C ''legaci'':38C ''leverag'':64C ''like'':70C ''look'':1A ''mani'':28C ''mine'':11B ''munging/wrangling'':8A ''one'':63C ''open'':48C ''p'':15C ''plugin'':76C ''prefer'':47C,62C ''problem'':61C ''project'':22C ''python'':68C ''requir'':24C ''sensor'':37C ''seri'':20C ''sourc'':33C,49C ''tackl'':57C ''text'':39C ''type'':30C,59C ''web'':35C ''websit'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6891, 2, '2015-08-25 04:51:10.447', 0, NULL, NULL, '&lt;p&gt;A good method to reduce overfitting in Decision Trees is Pruning, i.e. not to generate the tree downto the last attribute (pre-prune), or generate the "full" tree and then post-prune. 
Not sure if it pertains to Bayesian theory per se, but the leaf nodes of the "pruned" tree have probabilities about the various classifications that are constrained by the pruned branches.
There is an implementation in Weka (J48 classifier) and the accompanying book Data Mining - Practical Machine Learning Tools and Techniques:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0120884070" rel="nofollow"&gt;http://www.amazon.com/Data-Mining-Practical-Techniques-Management/dp/0120884070&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you would like to read in more detail on what I just wrote above, it is explained in detail in Chapter 6.1&lt;/p&gt;
', NULL, NULL, '''/amzn/click/0120884070'':93C ''/data-mining-practical-techniques-management/dp/0120884070&lt;/a&gt;&lt;/p&gt;'':98C ''/p'':87C,123C ''6.1'':122C ''accompani'':77C ''attribut'':22C ''bayesian'':42C ''book'':78C ''branch'':66C ''chapter'':121C ''classif'':59C ''classifi'':74C ''constrain'':62C ''data'':79C ''decis'':9C ''detail'':108C,119C ''downto'':19C ''explain'':117C ''full'':29C ''generat'':16C,27C ''good'':3C ''href'':90C ''i.e'':13C ''implement'':70C ''j48'':73C ''last'':21C ''leaf'':48C ''learn'':83C ''like'':103C ''machin'':82C ''method'':4C ''mine'':80C ''node'':49C ''nofollow'':95C ''overfit'':7C ''p'':1C,88C,99C ''per'':44C ''pertain'':40C ''post'':34C ''post-prun'':33C ''practic'':81C ''pre'':24C ''pre-prun'':23C ''probabl'':55C ''prune'':12C,25C,35C,52C,65C ''rads.stackoverflow.com'':92C ''rads.stackoverflow.com/amzn/click/0120884070'':91C ''read'':105C ''reduc'':6C ''rel'':94C ''se'':45C ''sure'':37C ''techniqu'':86C ''theori'':43C ''tool'':84C ''tree'':10C,18C,30C,53C ''various'':58C ''weka'':72C ''would'':102C ''wrote'':113C ''www.amazon.com'':97C ''www.amazon.com/data-mining-practical-techniques-management/dp/0120884070&lt;/a&gt;&lt;/p&gt;'':96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6821, 2, '2015-08-19 00:05:38.06', 1, NULL, NULL, '&lt;p&gt;The examples that you provided look similar to one another in duration and amplitude, so normalization might not be a problem.  But consider whether you will always be comparing the same duration and whether you want to compare the relative change in the time series or the absolute change.  The relative change can be measured by normalizing the series so that the first value (starting point) is set to 0.  Depending on your problem, this might be worth considering.&lt;/p&gt;

&lt;p&gt;Second, think about some sort of smoothing algorithm to get rid of some of the jitter in your data.  Examples include an &lt;a href="https://en.wikipedia.org/wiki/Moving_average" rel="nofollow"&gt;Exponentially Weighted Moving Average&lt;/a&gt; or a second order &lt;a href="http://connor-johnson.com/2014/11/23/time-series-forecasting-in-python-and-r/" rel="nofollow"&gt;Holt-Winters EWMA&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The suggestions in the comments are good ones... both Fourier Transform via FFT or moving to a wavelet formulation are interesting, but they are still a one-to-one in terms of the amount of data in (in temporal space) being equal to the amount of data out (in frequency space).&lt;/p&gt;

&lt;p&gt;I would suggest first including the raw data or a down-sampling of the raw data with some additional features like area under the curve, max, min.  I would stay away from procedures which amplify the jitter of your data (like derivatives) in favor of those which decrease the jitter (like integrals). &lt;/p&gt;

&lt;p&gt;A nice option, which implicitly smooths would be to take a running integration of your data and then plot the running integral rather than the values themselves.  This wouldn''t require apriori smoothing and is still deterministic. &lt;/p&gt;

&lt;p&gt;Following this, I suggest doing a whole bunch of feature extraction/feature engineering and then trying a &lt;a href="http://stats.stackexchange.com/questions/17251/what-is-the-lasso-in-regression-analysis"&gt;LASSO regression&lt;/a&gt; to pick out the most useful features.&lt;/p&gt;

&lt;p&gt;Some feature engineering ideas include:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Fourier Transform via FFT&lt;/li&gt;
&lt;li&gt;Possibly only keeping the n (~10) largest Fourier Modes from above rather than the full set.&lt;/li&gt;
&lt;li&gt;Integrals of your data.&lt;/li&gt;
&lt;li&gt;Min.&lt;/li&gt;
&lt;li&gt;Max.&lt;/li&gt;
&lt;li&gt;Number of zero crossings.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Suggestions to look at the number of changes in the second derivative are tough with time series data due to its stochastic nature.  The slopes (derivatives) are very wild and depend largely on how much smoothing has been applied and how the data is being sampled.&lt;/p&gt;
', NULL, NULL, '''/2014/11/23/time-series-forecasting-in-python-and-r/'':125C ''/a'':116C,132C,306C ''/li'':328C,346C,352C,355C,358C,364C ''/p'':81C,133C,187C,242C,281C,314C,321C,413C ''/questions/17251/what-is-the-lasso-in-regression-analysis'':303C ''/ul'':365C ''/wiki/moving_average'':109C ''0'':71C ''10'':335C ''absolut'':49C ''addit'':208C ''algorithm'':90C ''alway'':28C ''amount'':169C,180C ''amplifi'':224C ''amplitud'':15C ''anoth'':11C ''appli'':405C ''apriori'':275C ''area'':211C ''averag'':115C ''away'':220C ''bunch'':290C ''chang'':42C,50C,53C,374C ''comment'':139C ''compar'':30C,39C ''connor-johnson.com'':124C ''connor-johnson.com/2014/11/23/time-series-forecasting-in-python-and-r/'':123C ''consid'':24C,80C ''cross'':363C ''curv'':214C ''data'':101C,171C,182C,196C,205C,229C,259C,351C,384C,409C ''decreas'':237C ''depend'':72C,397C ''deriv'':231C,378C,392C ''determinist'':280C ''down-sampl'':199C ''due'':385C ''durat'':13C,33C ''en.wikipedia.org'':108C ''en.wikipedia.org/wiki/moving_average'':107C ''engin'':294C,318C ''equal'':177C ''ewma'':131C ''exampl'':3C,102C ''exponenti'':112C ''extraction/feature'':293C ''favor'':233C ''featur'':209C,292C,313C,317C ''fft'':147C,327C ''first'':64C,192C ''follow'':283C ''formul'':153C ''fourier'':144C,324C,337C ''frequenc'':185C ''full'':344C ''get'':92C ''good'':141C ''holt'':129C ''holt-wint'':128C ''href'':106C,122C,300C ''idea'':319C ''implicit'':248C ''includ'':103C,193C,320C ''integr'':241C,256C,265C,348C ''interest'':155C ''jitter'':98C,226C,239C ''keep'':332C ''larg'':398C ''largest'':336C ''lasso'':304C ''li'':323C,329C,347C,353C,356C,359C ''like'':210C,230C,240C ''look'':7C,369C ''max'':215C,357C ''measur'':56C ''might'':18C,77C ''min'':216C,354C ''mode'':338C ''move'':114C,149C ''much'':401C ''n'':334C ''natur'':389C ''nice'':245C ''nofollow'':111C,127C ''normal'':17C,58C ''number'':360C,372C ''one'':10C,142C,162C,164C ''one-to-on'':161C ''option'':246C ''order'':120C ''p'':1C,82C,134C,188C,243C,282C,315C,366C ''pick'':308C ''plot'':262C ''point'':67C ''possibl'':330C ''problem'':22C,75C ''procedur'':222C ''provid'':6C ''rather'':266C,341C ''raw'':195C,204C ''regress'':305C ''rel'':110C,126C ''relat'':41C,52C ''requir'':274C ''rid'':93C ''run'':255C,264C ''sampl'':201C,412C ''second'':83C,119C,377C ''seri'':46C,60C,383C ''set'':69C,345C ''similar'':8C ''slope'':391C ''smooth'':89C,249C,276C,402C ''sort'':87C ''space'':175C,186C ''start'':66C ''stats.stackexchange.com'':302C ''stats.stackexchange.com/questions/17251/what-is-the-lasso-in-regression-analysis'':301C ''stay'':219C ''still'':159C,279C ''stochast'':388C ''suggest'':136C,191C,286C,367C ''take'':253C ''tempor'':174C ''term'':166C ''think'':84C ''time'':45C,382C ''tough'':380C ''transform'':145C,325C ''tri'':297C ''ul'':322C ''use'':312C ''valu'':65C,269C ''via'':146C,326C ''want'':37C ''wavelet'':152C ''weight'':113C ''whether'':25C,35C ''whole'':289C ''wild'':395C ''winter'':130C ''worth'':79C ''would'':190C,218C,250C ''wouldn'':272C ''zero'':362C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6822, 2, '2015-08-19 02:56:06.593', 3, NULL, NULL, '&lt;p&gt;Notice how the &lt;code&gt;precision&lt;/code&gt; is very high and all of the other metrics are very low.  Now look at the class balance of your problem:&lt;/p&gt;

&lt;p&gt;$$TP+FN=Actual Positive=31,245$$
$$TN+FP=Actual Negative=508$$&lt;/p&gt;

&lt;p&gt;So your data is heavily skewed toward positives.  To have gotten a model that is producing this poorly, I think you may have provided the model with the &lt;code&gt;precision&lt;/code&gt; as the cross validation metric.  The &lt;code&gt;precision&lt;/code&gt; is a very bad cross validation metric in this case since it will result in poor &lt;code&gt;accuracy&lt;/code&gt; and poor &lt;code&gt;recall&lt;/code&gt;.  &lt;code&gt;accuracy&lt;/code&gt; is also not a good metric as your model could classify everything as positive and get an accuracy of:&lt;/p&gt;

&lt;p&gt;$$AC=\frac{31,245}{31,763}=.984$$&lt;/p&gt;

&lt;p&gt;For cases like this where the classes are grossly weighted toward one value, I suggest using the &lt;code&gt;F1-score&lt;/code&gt; as your cross validation metric.  The &lt;code&gt;F1-score&lt;/code&gt; is the &lt;code&gt;harmonic mean&lt;/code&gt; of &lt;code&gt;precision&lt;/code&gt; and &lt;code&gt;recall&lt;/code&gt; and hence balances these two factors nicely.  Wikipedia actually has a &lt;a href="https://en.wikipedia.org/wiki/Precision_and_recall#Definition_.28classification_context.29" rel="nofollow"&gt;very nice explanation of classification metrics here&lt;/a&gt; and &lt;a href="http://rali.iro.umontreal.ca/rali/sites/default/files/publis/SokolovaLapalme-JIPM09.pdf" rel="nofollow"&gt;this paper is top notch&lt;/a&gt; if you even need to understand multi-class metrics and confusion matrices.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/a'':213C,227C ''/code'':7C,74C,83C,102C,107C,110C,162C,173C,179C,183C,187C ''/p'':29C,42C,130C,139C,241C,246C ''/rali/sites/default/files/publis/sokolovalapalme-jipm09.pdf'':219C ''/wiki/precision_and_recall#definition_.28classification_context.29'':203C ''245'':36C,135C ''31'':35C,134C,136C ''508'':41C ''763'':137C ''984'':138C ''ac'':132C ''accuraci'':101C,109C,128C ''actual'':33C,39C,196C ''also'':112C ''bad'':87C ''balanc'':25C,190C ''case'':93C,142C ''class'':24C,147C,236C ''classif'':210C ''classifi'':121C ''code'':5C,72C,81C,100C,105C,108C,158C,169C,176C,181C,185C ''confus'':239C ''could'':120C ''cross'':77C,88C,165C ''data'':46C ''en.wikipedia.org'':202C ''en.wikipedia.org/wiki/precision_and_recall#definition_.28classification_context.29'':201C ''even'':230C ''everyth'':122C ''explan'':208C ''f1'':160C,171C ''f1-score'':159C,170C ''factor'':193C ''fn'':32C ''fp'':38C ''frac'':133C ''get'':126C ''good'':115C ''gotten'':54C ''grossli'':149C ''harmon'':177C ''heavili'':48C ''help'':245C ''henc'':189C ''high'':10C ''hope'':243C ''href'':200C,216C ''like'':143C ''look'':21C ''low'':19C ''matric'':240C ''may'':65C ''mean'':178C ''metric'':16C,79C,90C,116C,167C,211C,237C ''model'':56C,69C,119C ''multi'':235C ''multi-class'':234C ''need'':231C ''negat'':40C ''nice'':194C,207C ''nofollow'':205C,221C ''notch'':226C ''notic'':2C ''one'':152C ''p'':1C,30C,43C,131C,140C,242C ''paper'':223C ''poor'':61C,99C,104C ''posit'':34C,51C,124C ''precis'':6C,73C,82C,182C ''problem'':28C ''produc'':59C ''provid'':67C ''rali.iro.umontreal.ca'':218C ''rali.iro.umontreal.ca/rali/sites/default/files/publis/sokolovalapalme-jipm09.pdf'':217C ''recal'':106C,186C ''rel'':204C,220C ''result'':97C ''score'':161C,172C ''sinc'':94C ''skew'':49C ''suggest'':155C ''think'':63C ''tn'':37C ''top'':225C ''toward'':50C,151C ''tp'':31C ''two'':192C ''understand'':233C ''use'':156C ''valid'':78C,89C,166C ''valu'':153C ''weight'':150C ''wikipedia'':195C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6827, 1, '2015-08-19 11:09:40.287', 1, 122, 'Machine learning to predict apps (recomendation)', '&lt;p&gt;I am currently collecting mobile device data about user location and the time at which an app is being used for a cohort of users and apps.  I am trying to predict which apps are likely to be used at a given time by a given user.&lt;/p&gt;

&lt;p&gt;Which model should i use in order to predict the apps that are likely to be used by a particular user? For example, should I collect data for 5 days a week then use the 3 days for training and then the other 2 days for testing? Given location, time of day, and day, what model should I use to predict which app is likely to be used?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':58C,128C ''2'':103C ''3'':95C ''5'':88C ''app'':5A,27C,37C,44C,70C,122C ''cohort'':33C ''collect'':14C,85C ''current'':13C ''data'':17C,86C ''day'':89C,96C,104C,111C,113C ''devic'':16C ''exampl'':82C ''given'':52C,56C,107C ''learn'':2A,9B ''like'':46C,73C,124C ''locat'':20C,108C ''machin'':1A,8B ''machine-learn'':7B ''mobil'':15C ''model'':61C,115C ''order'':66C ''p'':10C,59C ''particular'':79C ''predict'':4A,42C,68C,120C ''recomend'':6A ''test'':106C ''time'':23C,53C,109C ''train'':98C ''tri'':40C ''use'':30C,49C,64C,76C,93C,118C,127C ''user'':19C,35C,57C,80C ''week'':91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8718, 2, '2015-11-04 14:46:15.06', 2, NULL, NULL, '&lt;p&gt;One advantage of many ML-based recommendation techniques is they allow you to work in a lower-dimensional space.  &lt;a href="https://en.wikipedia.org/wiki/Non-negative_matrix_factorization" rel="nofollow"&gt;Matrix factorization&lt;/a&gt; techniques for example, allow you to view a user or an item in terms of a learned latent-variable space.  This allows for easier computations after the model has been trained (which is often very expensive).&lt;/p&gt;

&lt;p&gt;Some of the best results on the MovieLens recommendation dataset have been &lt;a href="http://users.cecs.anu.edu.au/~akmenon/papers/autorec/paper.pdf" rel="nofollow"&gt;achieved by autoencoders&lt;/a&gt;.  These also have the benefit of reducing the dimensionality of the problem.&lt;/p&gt;

&lt;p&gt;Furthermore learned representations of the data likely represent (perhaps non-linear, in the case of auto-encoders) combinations of features.  Navigating the edges of a graph is likely to focus on one feature at a time.&lt;/p&gt;

&lt;p&gt;If your idea for using neo4j came from &lt;a href="https://linkurio.us/using-neo4j-to-build-a-recommendation-engine-based-on-collaborative-filtering/" rel="nofollow"&gt;here&lt;/a&gt;, one thing to remember is that the data you''re talking about is not just ratings/likes data (common in collaborative filtering), but also &lt;a href="https://en.wikipedia.org/wiki/Recommender_system#Content-based_filtering" rel="nofollow"&gt;content-based&lt;/a&gt; data.  You might want to read about hybrid recommender systems to leverage both content-based and collaborative filtering based recommendation.&lt;/p&gt;
', NULL, NULL, '''/a'':32C,94C,165C,199C ''/p'':70C,107C,147C,221C ''/using-neo4j-to-build-a-recommendation-engine-based-on-collaborative-filtering/'':161C ''/wiki/non-negative_matrix_factorization'':27C ''/wiki/recommender_system#content-based_filtering'':193C ''/~akmenon/papers/autorec/paper.pdf'':88C ''achiev'':91C ''advantag'':3C ''allow'':13C,36C,55C ''also'':96C,188C ''auto'':126C ''auto-encod'':125C ''autoencod'':93C ''base'':8C,198C,215C,219C ''benefit'':99C ''best'':75C ''came'':155C ''case'':123C ''collabor'':185C,217C ''combin'':128C ''common'':183C ''comput'':58C ''content'':197C,214C ''content-bas'':196C,213C ''data'':114C,173C,182C,200C ''dataset'':81C ''dimension'':21C,103C ''easier'':57C ''edg'':133C ''en.wikipedia.org'':26C,192C ''en.wikipedia.org/wiki/non-negative_matrix_factorization'':25C ''en.wikipedia.org/wiki/recommender_system#content-based_filtering'':191C ''encod'':127C ''exampl'':35C ''expens'':69C ''factor'':31C ''featur'':130C,143C ''filter'':186C,218C ''focus'':140C ''furthermor'':109C ''graph'':136C ''href'':24C,85C,158C,190C ''hybrid'':207C ''idea'':151C ''item'':44C ''latent'':51C ''latent-vari'':50C ''learn'':49C,110C ''leverag'':211C ''like'':115C,138C ''linear'':120C ''linkurio.us'':160C ''linkurio.us/using-neo4j-to-build-a-recommendation-engine-based-on-collaborative-filtering/'':159C ''lower'':20C ''lower-dimension'':19C ''mani'':5C ''matrix'':30C ''might'':202C ''ml'':7C ''ml-base'':6C ''model'':61C ''movielen'':79C ''navig'':131C ''neo4j'':154C ''nofollow'':29C,90C,163C,195C ''non'':119C ''non-linear'':118C ''often'':67C ''one'':2C,142C,166C ''p'':1C,71C,108C,148C ''perhap'':117C ''problem'':106C ''ratings/likes'':181C ''re'':175C ''read'':205C ''recommend'':9C,80C,208C,220C ''reduc'':101C ''rel'':28C,89C,162C,194C ''rememb'':169C ''repres'':116C ''represent'':111C ''result'':76C ''space'':22C,53C ''system'':209C ''talk'':176C ''techniqu'':10C,33C ''term'':46C ''thing'':167C ''time'':146C ''train'':64C ''use'':153C ''user'':41C ''users.cecs.anu.edu.au'':87C ''users.cecs.anu.edu.au/~akmenon/papers/autorec/paper.pdf'':86C ''variabl'':52C ''view'':39C ''want'':203C ''work'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8747, 2, '2015-11-07 14:01:29.81', 1, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Update&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Picking up from where we left off in the original post a quick interactive view to of the background areas can be made. I show this in a gif file here as you would need either the free Wolfram CDF Player or Mathematica installed to see the interactivity (there is also the Wolfram Cloud that does not need an install but I don''t want you all using up my cloud credits while you play with this).&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/ELX69.gif" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/ELX69.gif" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The controls look better in Mathematica but the gif export uses more plain representations of them. In any case your report could lead with this interactive view of the backgrounds and use the static table with all the employees as an appendix. You could write your report in Mathematica and sent it out as a CDF (they would have to install the reader). &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Mathematica code&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The &lt;code&gt;Manipulate&lt;/code&gt; call may look a big but it only has two parts. The first &lt;code&gt;Grid&lt;/code&gt; is for the table and it uses the sorting variables defined in the second &lt;code&gt;Grid&lt;/code&gt; call. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(* Pregroup the data by decade *)
backgroundByDecade = Function[{decade},
      decade -&amp;gt; 
       background[
        Select[IntervalMemberQ[Interval[{#["YearFrom"], #["YearTo"]}],
            decade] &amp;amp;]]
      ] /@ decades // Association // Dataset;

(* Create interactive information grid *)
Manipulate[
 Grid[Transpose[Normal[
    backgroundByDecade[All,
      Function[{bkgDecade},
         Column[
          Normal@bkgDecade[
             SortBy[{firstDesc #[firstCol], secondDesc #[secondCol], 
                thirdDesc #[thirdCol]} &amp;amp;]][All, #["BkgChart"] &amp;amp;], 
          Spacings -&amp;gt; 0]]][KeyValueMap[{#1, #2} &amp;amp;]]
    ]],
  Alignment -&amp;gt; {Center, Top},
  Dividers -&amp;gt; {Gray, {None, {2 -&amp;gt; Gray}}},
  BaseStyle -&amp;gt; {FontFamily -&amp;gt; "Calibri"}],
 Style["Sory By", 14, Bold],
 Grid[{
   {"First", 
    Control[{{firstCol, "BkgArea1", ""}, {"BkgArea1", "BkgArea2", 
       "BkgArea3"}}], 
    Control[{{firstDesc, -1, "Desc",}, {1, -1}, Checkbox}]},
   {"Second", 
    Control[{{secondCol, "BkgArea2", ""}, {"BkgArea1", "BkgArea2", 
       "BkgArea3"}}], 
    Control[{{secondDesc, -1, "Desc"}, {1, -1}, Checkbox}]},
   {"Third", 
    Control[{{thirdCol, "BkgArea3", ""}, {"BkgArea1", "BkgArea2", 
       "BkgArea3"}}], 
    Control[{{thirdDesc, -1, "Desc"}, {1, -1}, Checkbox}]}
   }, Alignment -&amp;gt; Left],
 ControlPlacement -&amp;gt; Left, Paneled -&amp;gt; False
 ]
&lt;/code&gt;&lt;/pre&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Original Post&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;From what I understand you would like to see a person''s background area at the start of each decade that they are present. &lt;/p&gt;

&lt;p&gt;Below a colour for each background area is used and each is lightened to show the strength/weakness of the background.  The background number is also given but these can be removed and placed in a tooltip of the boxes if you want to. Each box set contains the 3 dimensions of background area.&lt;/p&gt;

&lt;p&gt;These sets are arranged in time columns for the start of each decade. The background areas align within the columns so you can scan down a decade background area to see the distribution of its strength across persons.  A summary background area box set could be added for each decade column as well.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/TUtcO.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/TUtcO.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;h2&gt;&lt;strong&gt;Mathematica code&lt;/strong&gt;&lt;/h2&gt;

&lt;pre&gt;&lt;code&gt;data = Import[FileNameJoin[{NotebookDirectory[], "BackgroundArea.txt"}], "Table"];

(* Background area chart function *)
bkgAreaBarChart[bkgArea1_, bkgArea2_, bkgArea3_] :=
 BarChart[{{1, 1, 1}}, ChartLayout -&amp;gt; "Stacked",
  ChartStyle -&amp;gt; {EdgeForm[Gray], 
    Inner[#1[#2] &amp;amp;, 
     Function[{value}, 
       Nest[Lighter, #, 3 - value]] &amp;amp; /@ {Purple,Orange, Blue}, {bkgArea1, bkgArea2, bkgArea3}, List]}, 
  Axes -&amp;gt; False,
  PlotRangePadding -&amp;gt; None,
  BarOrigin -&amp;gt; Left,
  ChartLabels -&amp;gt; Placed[{bkgArea1, bkgArea2, bkgArea3}, Center],
  BaseStyle -&amp;gt; {Background -&amp;gt; None},
  ImagePadding -&amp;gt; None,
  ImageSize -&amp;gt; {Automatic, 20},
  AspectRatio -&amp;gt; 1/3]

(* Create Dataset from data and add chart column *)
background = Dataset[
  AssociationThread[
     Join[First@data, {"BkgChart"}],
     Join[#, {bkgAreaBarChart[Sequence @@ #[[{2, 3, 4}]]]}]
     ] &amp;amp; /@ Rest@data]

(* Calculate decades from dataset *)
decades = 
 Range[Sequence @@ Normal[background[MinMax, {"YearFrom", "YearTo"}][Round[#, 10] &amp;amp;]], 10]

(* Create information grid *)
Grid[
 Join[
  {Join[{""}, decades]},
  Transpose[
   Join[{background[All, "Person"] // Normal},
    Function[{decade},
      background[All, 
        If[IntervalMemberQ[Interval[{#["YearFrom"], #["YearTo"]}], decade],
          #["BkgChart"], 
          Graphics[ImageSize -&amp;gt; {Automatic, 20},
           AspectRatio -&amp;gt; 1/3]] &amp;amp;] // Normal] /@ decades
    ]],
  {Join[{""}, decades]}
  ],
 Alignment -&amp;gt; {{Left, {Center}}, Center},
 Spacings -&amp;gt; {0, 0},
 Dividers -&amp;gt; {{None, {Gray}}, {None, {2 -&amp;gt; Gray, -2 -&amp;gt; Gray}}},
 Background -&amp;gt; {None, {None, {LightGray, None}, None}},
 BaseStyle -&amp;gt; {FontFamily -&amp;gt; "Calibri"}]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This was interesting to consider.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8767, 2, '2015-11-09 21:04:51.97', 3, NULL, NULL, '&lt;p&gt;If I understood them correctly, both Jeremy and Edmund''s (first) solutions are the same, namely, plain euclidean distance in a 4-dimensional space of IP addresses.BTW, I think a very fast alternative to euclidean distance would be to calculate a hamming distance bit-wise.&lt;/p&gt;

&lt;p&gt;Edmund''s first update would be better than his second. The reason is simple to state: his 2nd update tries to define a distance measure by considering a &lt;strong&gt;non-linear&lt;/strong&gt; function of the coordinates of a 4D vector. That however will most likely destroy the key properties that it needs to satisfy in order to be a metric, namely &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Injectivity: $d(IP_1,IP_2)=0 \iff IP_1=IP_2$, &lt;/li&gt;
&lt;li&gt;Symmetry: $d(IP_1,IP_2)=d(IP_2,IP_1)$, and &lt;/li&gt;
&lt;li&gt;Triangular inequality: $d(IP_1,IP_2)\leq d(IP_1,IP_3)+d(IP_3,IP_2)\,\forall IP_3$. &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The latter is key for later interpreting small distances as close points in IP space. One would need a linear (in the coordinates) distance function. However, simple euclidean distance is not enough as you saw. &lt;/p&gt;

&lt;p&gt;Physics (well, differential geometry actually) could lead to a nice solution to this problem: define a metric tensor $g$. In plain english, give weights to each &lt;strong&gt;pair of coordinates&lt;/strong&gt;, take each pair difference, square it and multiply it by its weight, and then add those products. Take the square root of that sum and define it as your distance.&lt;/p&gt;

&lt;p&gt;For the sake of simplicity, one could start trying with a diagonal metric tensor.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Example: Say you take $g=\begin{pmatrix}1000 &amp;amp;0 &amp;amp;0 &amp;amp;0 \\0 &amp;amp;100&amp;amp;0&amp;amp;0\\0&amp;amp;0&amp;amp;10&amp;amp;0\\0&amp;amp;0&amp;amp;0&amp;amp;1\end{pmatrix}$ $IP_1=(x_1,x_2,x_3,x_4)$ and
  $IP_2=(y_1,y_2,y_3,y_4)$. Then the square of the distance is given by
  $$d(IP_1,IP_2)^2=1000*(x_1-y_1)^2+100*(x_2-y_2)^2+\\ \,+10*(x_3-y_3)^2+1*(x_4-y_4)^2$$
  For $IP_1=192.168.1.1,\,IP_2=192.168.1.2$ the distance is clearly 1.
  However, for $192.168.1.1$ and $191.168.1.1$ the distance is
  $\sqrt{1000}\approx 32$&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Eventually you could play around with different weights and set a kind of normalization where you could fix the value of the maximal distance $d(0.0.0.0,FF.FF.FF.FF)$. &lt;/p&gt;

&lt;p&gt;Furthermore, this set up allows for more complex descriptions of your data where the relevant distance would contain "cross-products" of coordinates like say $g_{13}*(x_1-y_1)*(x_3-y_3)$.&lt;/p&gt;

&lt;p&gt;EDIT: While this would be a better "weighting" method than using those other I addressed, I realize now it is actualy meaningless: As Anony-Mousse and Phillip mention, IP are indeed 32 dimensional. This means in particular that giving the same weight to all bits in say the 2nd group is in general not sound: One bit could be part of the netmask while the other not. See Anony-Mousse answer for additional objections. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6828, 1, '2015-08-19 11:22:29.927', 0, 34, 'How to migrate application which has backend running using DLL methods and front end in JAVA to Apache spark?', '&lt;p&gt;I have one Java Client which Reads 4 to 5 GBs of data and uses DLL file in order to process it and give O/P back to the JAVA client. 
Can anyone tell me if this can be implemented using Apache Spark or not ?
And if yes can you back you Answer by some examples or documentation Links ?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT&lt;/strong&gt; &lt;/p&gt;

&lt;p&gt;I have knowledge about &lt;strong&gt;JNI&lt;/strong&gt; and how to do this in windows environement. 
What I''m actually looking for is How do you implement such thing in Apache Spark. &lt;/p&gt;

&lt;p&gt;As all methods and processing is done via DLL files and JAVA Client is just having represntational logic so any documentation is helpful. &lt;/p&gt;

&lt;p&gt;I''m currently using windows development env. but gonna use Ubuntu 14.04 for Apache Spark implemetation. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT&lt;/strong&gt; after Hold&lt;/p&gt;

&lt;p&gt;This question is really intending to ask where to find such documentation or help with which we can migrate such application smoothly. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6829, 2, '2015-08-19 11:46:46.08', 1, NULL, NULL, '&lt;p&gt;You will need to use the Java Native Interface (JNI) for this. Refer to a Stack Overflow question for &lt;a href="http://stackoverflow.com/a/5963294/2074605"&gt;how to call DLL methods via Java over JNI&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':35C ''/a/5963294/2074605'':25C ''/p'':36C ''call'':28C ''dll'':29C ''href'':22C ''interfac'':10C ''java'':8C,32C ''jni'':11C,34C ''method'':30C ''nativ'':9C ''need'':4C ''overflow'':18C ''p'':1C ''question'':19C ''refer'':14C ''stack'':17C ''stackoverflow.com'':24C ''stackoverflow.com/a/5963294/2074605'':23C ''use'':6C ''via'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6879, 1, '2015-08-23 20:01:24.36', 0, 20, 'Universal cost function for process control?', '&lt;p&gt;I have a problem of process control which looks like this:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;The outputs of the process are a number of outcome variables 0-100
where 0 is worst and 100 is best. There is no specific rule (already
known, or related to the nature of the problem) for how to combine
them into a single cost function.  There is some intuition about what results are good, however.&lt;/li&gt;
&lt;li&gt;All the variables are equally important, and scaled the same way. 
The cost function should be symmetric with respect to permutation of
the values.&lt;/li&gt;
&lt;li&gt;If all the variables are 100 except one is 0, that is a bad
outcome.&lt;/li&gt;
&lt;li&gt;If all the variables are 90, that is a very good outcome.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The last two statements imply that the right way to combine variables into a cost function is something similar to min(v1, v2, ...).  But that has a problem, in that the direction of optimization would always be determined by the lowest variable alone.&lt;/p&gt;

&lt;p&gt;Intuitively, it is desirable to trade off a slight worsening in one variable for a large improvement in another, even if the variable that is getting worse is already pretty bad, for example: 30 and 50 is less preferred than 28 and 75 (or something like that).  This implies some relative elasticity of variables which in this case is close to 100:1 for variables near the opposite ends of the range, and 1:1 for variables which are equal valued.  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How can a good cost function be constructed for this kind of problem in a general-purpose way?&lt;/strong&gt;  (with some adjustable parameters for elasticity, etc)&lt;/p&gt;
', 'optimization  loss-function', NULL, '''-100'':38C ''/li'':82C,108C,125C,139C ''/p'':23C,185C,268C,298C ''/strong'':290C ''/ul'':140C ''0'':37C,40C,119C ''1'':249C,260C,261C ''100'':44C,115C,248C ''28'':227C ''30'':220C ''50'':222C ''75'':229C ''90'':132C ''adjust'':293C ''alon'':184C ''alreadi'':52C,215C ''alway'':177C ''anoth'':205C ''bad'':123C,217C ''best'':46C ''case'':244C ''close'':246C ''combin'':65C,152C ''construct'':278C ''control'':6A,18C ''cost'':2A,70C,96C,156C,275C ''desir'':190C ''determin'':179C ''direct'':173C ''elast'':238C,296C ''end'':255C ''equal'':88C,266C ''etc'':297C ''even'':206C ''exampl'':219C ''except'':116C ''function'':3A,10B,71C,97C,157C,276C ''general'':287C ''general-purpos'':286C ''get'':212C ''good'':80C,137C,274C ''howev'':81C ''impli'':146C,235C ''import'':89C ''improv'':203C ''intuit'':75C,187C ''kind'':281C ''known'':53C ''larg'':202C ''last'':143C ''less'':224C ''li'':25C,83C,109C,126C ''like'':21C,232C ''look'':20C ''loss'':9B ''loss-funct'':8B ''lowest'':182C ''min'':162C ''natur'':58C ''near'':252C ''number'':33C ''one'':117C,198C ''opposit'':254C ''optim'':7B,175C ''outcom'':35C,124C,138C ''output'':27C ''p'':11C,141C,186C,269C ''paramet'':294C ''permut'':104C ''prefer'':225C ''pretti'':216C ''problem'':15C,61C,169C,283C ''process'':5A,17C,30C ''purpos'':288C ''rang'':258C ''relat'':55C,237C ''respect'':102C ''result'':78C ''right'':149C ''rule'':51C ''scale'':91C ''similar'':160C ''singl'':69C ''slight'':195C ''someth'':159C,231C ''specif'':50C ''statement'':145C ''strong'':270C ''symmetr'':100C ''trade'':192C ''two'':144C ''ul'':24C ''univers'':1A ''v1'':163C ''v2'':164C ''valu'':107C,267C ''variabl'':36C,86C,113C,130C,153C,183C,199C,209C,240C,251C,263C ''way'':94C,150C,289C ''wors'':213C ''worsen'':196C ''worst'':42C ''would'':176C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6833, 2, '2015-08-19 16:14:21.407', 2, NULL, NULL, '&lt;p&gt;You are 95% there, you just have one hangup...&lt;/p&gt;

&lt;p&gt;The vectorization that you are doing is alternatively known as binarization or &lt;a href="https://en.wikipedia.org/wiki/One-hot" rel="nofollow"&gt;one-hot encoding&lt;/a&gt;.  The only thing you need to do now is break apart all of those vectors and think of them as individual features.  &lt;/p&gt;

&lt;p&gt;So instead of thinking of the question one vector as $(0,0,1,0)$ and the question two vector as $(0,1,0,0)$, you can now think of them as individual features.&lt;/p&gt;

&lt;p&gt;So this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;-      q1,        q2
-      (a,b,c,d), (a,b,c,d)
user1  (0,0,1,0), (0,1,0,0)
user2  (1,0,0,0), (0,0,0,1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Becomes this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;-      q1a,q1b,q1c,q1d,q2a,q2b,q2c,q2d
user1  0   0   1   0   0   1   0   0
user2  1   0   0   0   0   0   0   1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And you can think of each one of those binary features as an orthogonal  dimension in your data that lies in a 8-dimensional space. &lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/a'':36C ''/code'':129C,163C ''/p'':11C,59C,94C,98C,134C,191C,196C ''/pre'':130C,164C ''/wiki/one-hot'':29C ''0'':71C,72C,74C,81C,83C,84C,112C,113C,115C,116C,118C,119C,122C,123C,124C,125C,126C,127C,146C,147C,149C,150C,152C,153C,156C,157C,158C,159C,160C,161C ''1'':73C,82C,114C,117C,121C,128C,148C,151C,155C,162C ''8'':188C ''95'':4C ''altern'':20C ''apart'':47C ''b'':104C,108C ''becom'':132C ''binar'':23C ''binari'':175C ''break'':46C ''c'':105C,109C ''code'':100C,136C ''d'':106C,110C ''data'':183C ''dimens'':180C ''dimension'':189C ''en.wikipedia.org'':28C ''en.wikipedia.org/wiki/one-hot'':27C ''encod'':35C ''featur'':58C,93C,176C ''hangup'':10C ''help'':195C ''hope'':193C ''hot'':34C ''href'':26C ''individu'':57C,92C ''instead'':62C ''known'':21C ''lie'':185C ''need'':41C ''nofollow'':31C ''one'':9C,33C,68C,172C ''one-hot'':32C ''orthogon'':179C ''p'':1C,12C,60C,95C,131C,165C,192C ''pre'':99C,135C ''q1'':101C ''q1a'':137C ''q1b'':138C ''q1c'':139C ''q1d'':140C ''q2'':102C ''q2a'':141C ''q2b'':142C ''q2c'':143C ''q2d'':144C ''question'':67C,77C ''rel'':30C ''space'':190C ''thing'':39C ''think'':53C,64C,88C,169C ''two'':78C ''user1'':111C,145C ''user2'':120C,154C ''vector'':14C,51C,69C,79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6834, 1, '2015-08-19 16:25:57.53', 3, 56, 'Cluster directed graph into DAG', '&lt;p&gt;I am looking for an algorithm that clusters a directed graph into a set of clusters that form a directed acyclic graph.&lt;/p&gt;

&lt;p&gt;For example, given:
nodes: {A, B, C, D}
with edges: {(A,B), (B,A), (A,C), (C,D), (D,C)}&lt;/p&gt;

&lt;p&gt;A valid output would be: {{A,B},{C,D})
An invalid output would be: {{A,C},{B,D}} (since the node edges imply a cycle between the clusters)&lt;/p&gt;

&lt;p&gt;Thanks for any pointers or suggestions!&lt;/p&gt;
', 'clustering  graphs', NULL, '''/p'':31C,53C,83C,91C ''acycl'':29C ''algorithm'':14C ''b'':38C,44C,45C,61C,71C ''c'':39C,48C,49C,52C,62C,70C ''cluster'':1A,6B,16C,24C,82C ''cycl'':79C ''d'':40C,50C,51C,63C,72C ''dag'':5A ''direct'':2A,18C,28C ''edg'':42C,76C ''exampl'':34C ''form'':26C ''given'':35C ''graph'':3A,7B,19C,30C ''impli'':77C ''invalid'':65C ''look'':11C ''node'':36C,75C ''output'':57C,66C ''p'':8C,32C,54C,84C ''pointer'':88C ''set'':22C ''sinc'':73C ''suggest'':90C ''thank'':85C ''valid'':56C ''would'':58C,67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8725, 1, '2015-11-05 06:10:40.18', 2, 28, 'Why Markov Chain Monte Carlo allows sampling from a large class of distributions and scales well with the dimensionality of the sample space?', '&lt;p&gt;How can I understand "Markov chain Monte Carlo allows sampling from a large class of distributions and which scales well with the dimensionality of the sample space"&lt;/p&gt;
', 'sampling', NULL, '''/p'':53C ''allow'':6A,34C ''carlo'':5A,33C ''chain'':3A,31C ''class'':11A,39C ''dimension'':19A,48C ''distribut'':13A,41C ''larg'':10A,38C ''markov'':2A,30C ''mont'':4A,32C ''p'':25C ''sampl'':7A,22A,24B,35C,51C ''scale'':15A,44C ''space'':23A,52C ''understand'':29C ''well'':16A,45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8808, 2, '2015-11-11 22:08:33.383', 0, NULL, NULL, '&lt;p&gt;The accepted answer is correct in terms of explaining the interpretation of R^2 as being the amount of variation in the dependent variation caused by the dependent variation. It ranges from 0.0 (0%) to 1.00 (100%) of correlated variation (for a linear regression), so if it 100%, all of the changes in Y (dependent or predictor variable) can be attributed to changes in X (independent r response variable). Regression coefficients represent the mean change in the response variable for one unit of change in the predictor variable while holding other predictors in the model constant. This statistical control that regression provides is important because it isolates the role of one variable from all of the others in the model.&lt;/p&gt;

&lt;p&gt;The p-value interpretation is the likelihood one can reject the null hypothesis. The p-value for each term tests the null hypothesis that the coefficient is equal to zero (no effect). A low p-value (say &amp;lt; 0.05) indicates that you can reject the null hypothesis. In other words, a predictor that has a low p-value is likely to be a meaningful addition to your model because changes in the predictor''s value are related to changes in the response variable.&lt;/p&gt;

&lt;p&gt;Conversely, a larger (insignificant) p-value suggests that changes in the predictor are not associated with changes in the response.&lt;/p&gt;

&lt;p&gt;You will never get a p = 0 in a real scenario, because there is no way to eliminate the case that the null is correct, because there will be some error in the observations due to measurement alone, leaving out other factors. You need to establish how certain you need to be to feel comfortable. There is nothing magical in the p = 0.05 number, it is one that was established as a standard and now is taken as doctrinal by many not understanding it. If you can feel comfortable in your situation with an 80% certainty that the null can be rejected, then there is nothing wrong with that level. &lt;/p&gt;

&lt;p&gt;The real reason that I wanted to add an answer is that the other answer doesn''t deal with your using &lt;strong&gt;adjusted R^2&lt;/strong&gt;, sometimes called &lt;strong&gt;R bar squared&lt;/strong&gt;. &lt;strong&gt;Adjusted R^2&lt;/strong&gt; is &lt;strong&gt;not&lt;/strong&gt; R^2 and should not be confused for it. R^2 &gt;= adjusted R^2 and if you are only dealing with correlation between two variables, as hinted at in your question, you should use R^2. R^2 measures the correlation between two variables. Adjusted R^2 has additional factors that attempt to take account of the phenomenon of the R2 automatically and spuriously increasing when extra explanatory variables are added to the model. &lt;/p&gt;

&lt;p&gt;The adjusted R2 can be negative while R^2 can not be negative. The adjusted R^2 value will always be less than or equal to that of R^2. Unlike R^2, the adjusted R^2 increases only when the increase in R^2 (due to the inclusion of a new explanatory variable) is more than one would expect to see by chance. If a set of explanatory variables with a predetermined hierarchy of importance are introduced into a regression one at a time, with the adjusted R^2 computed each time, the level at which adjusted R^2 reaches a maximum, and decreases afterward, would be the regression with the ideal combination of having the best fit without excess/unnecessary terms.&lt;/p&gt;

&lt;p&gt;Adjusted R2 is particularly useful in the feature selection stage of model building.&lt;/p&gt;
', NULL, NULL, '''/p'':122C,211C,234C,346C,464C,588C,603C ''/strong'':373C,380C,385C,389C ''0'':35C,242C ''0.0'':34C ''0.05'':165C,298C ''1.00'':37C ''100'':38C,49C ''2'':15C,372C,384C,391C,400C,403C,425C,427C,436C,474C,482C,495C,498C,502C,510C,555C,565C ''80'':330C ''accept'':3C ''account'':444C ''ad'':460C ''add'':355C ''addit'':192C,438C ''adjust'':370C,382C,401C,434C,467C,480C,500C,553C,563C,590C ''afterward'':571C ''alon'':273C ''alway'':485C ''amount'':19C ''answer'':4C,357C,362C ''associ'':228C ''attempt'':441C ''attribut'':62C ''automat'':451C ''bar'':378C ''best'':583C ''build'':602C ''call'':375C ''case'':255C ''caus'':26C ''certain'':283C ''certainti'':331C ''chanc'':529C ''chang'':53C,64C,76C,85C,197C,206C,222C,230C ''coeffici'':72C,151C ''combin'':579C ''comfort'':290C,324C ''comput'':556C ''confus'':396C ''constant'':97C ''control'':100C ''convers'':213C ''correct'':6C,260C ''correl'':40C,411C,430C ''deal'':365C,409C ''decreas'':570C ''depend'':24C,29C,56C ''doctrin'':314C ''doesn'':363C ''due'':270C,511C ''effect'':157C ''elimin'':253C ''equal'':153C,490C ''error'':266C ''establish'':281C,305C ''excess/unnecessary'':586C ''expect'':525C ''explain'':10C ''explanatori'':457C,518C,534C ''extra'':456C ''factor'':277C,439C ''featur'':597C ''feel'':289C,323C ''fit'':584C ''get'':239C ''hierarchi'':539C ''hint'':416C ''hold'':91C ''hypothesi'':137C,148C,173C ''ideal'':578C ''import'':105C,541C ''inclus'':514C ''increas'':454C,503C,507C ''independ'':67C ''indic'':166C ''insignific'':216C ''interpret'':12C,128C ''introduc'':543C ''isol'':108C ''larger'':215C ''leav'':274C ''less'':487C ''level'':345C,560C ''like'':187C ''likelihood'':131C ''linear'':44C ''low'':159C,182C ''lt'':164C ''magic'':294C ''mani'':316C ''maximum'':568C ''mean'':75C ''meaning'':191C ''measur'':272C,428C ''model'':96C,121C,195C,463C,601C ''need'':279C,285C ''negat'':471C,478C ''never'':238C ''new'':517C ''noth'':293C,341C ''null'':136C,147C,172C,258C,334C ''number'':299C ''observ'':269C ''one'':82C,112C,132C,302C,523C,547C ''other'':118C ''p'':1C,123C,126C,140C,161C,184C,212C,218C,235C,241C,297C,347C,465C,589C ''p-valu'':125C,139C,160C,183C,217C ''particular'':593C ''phenomenon'':447C ''predetermin'':538C ''predictor'':58C,88C,93C,178C,200C,225C ''provid'':103C ''question'':420C ''r'':14C,68C,371C,377C,383C,390C,399C,402C,424C,426C,435C,473C,481C,494C,497C,501C,509C,554C,564C ''r2'':450C,468C,591C ''rang'':32C ''reach'':566C ''real'':245C,349C ''reason'':350C ''regress'':45C,71C,102C,546C,575C ''reject'':134C,170C,337C ''relat'':204C ''repres'':73C ''respons'':69C,79C,209C,233C ''role'':110C ''say'':163C ''scenario'':246C ''see'':527C ''select'':598C ''set'':532C ''situat'':327C ''sometim'':374C ''spurious'':453C ''squar'':379C ''stage'':599C ''standard'':308C ''statist'':99C ''strong'':369C,376C,381C,387C ''suggest'':220C ''take'':443C ''taken'':312C ''term'':8C,144C,587C ''test'':145C ''time'':550C,558C ''two'':413C,432C ''understand'':318C ''unit'':83C ''unlik'':496C ''use'':368C,423C,594C ''valu'':127C,141C,162C,185C,202C,219C,483C ''variabl'':59C,70C,80C,89C,113C,210C,414C,433C,458C,519C,535C ''variat'':21C,25C,30C,41C ''want'':353C ''way'':251C ''without'':585C ''word'':176C ''would'':524C,572C ''wrong'':342C ''x'':66C ''y'':55C ''zero'':155C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8931, 5, '2015-11-20 11:11:34.763', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8972, 5, '2015-11-22 05:41:54.86', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6835, 1, '2015-08-19 18:15:00.463', 1, 137, 'Bechmark for Movielens', '&lt;p&gt;I''m looking for a place to find benchmarks against which to evaluate performance on public datasets.&lt;/p&gt;

&lt;p&gt;In this instance, I''m interested in results on the MovieLens10M dataset.  It seems to be referenced fairly frequently in literature, often using RMSE, but I have had trouble determining what might be considered state-of-the-art.&lt;/p&gt;

&lt;p&gt;I did find this site, but it is only for the 100K dataset and is far from inclusive:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.recsyswiki.com/wiki/MovieLens_100k_benchmark_results" rel="nofollow"&gt;http://www.recsyswiki.com/wiki/MovieLens_100k_benchmark_results&lt;/a&gt;&lt;/p&gt;
', 'machine-learning  dataset  recommendation', NULL, '''/p'':27C,68C,88C ''/wiki/movielens_100k_benchmark_results'':94C ''/wiki/movielens_100k_benchmark_results&lt;/a&gt;&lt;/p&gt;'':99C ''100k'':81C ''art'':67C ''bechmark'':1A ''benchmark'':18C ''consid'':62C ''dataset'':7B,26C,40C,82C ''determin'':58C ''evalu'':22C ''fair'':46C ''far'':85C ''find'':17C,72C ''frequent'':47C ''href'':91C ''inclus'':87C ''instanc'':31C ''interest'':34C ''learn'':6B ''literatur'':49C ''look'':12C ''m'':11C,33C ''machin'':5B ''machine-learn'':4B ''might'':60C ''movielen'':3A ''movielens10m'':39C ''nofollow'':96C ''often'':50C ''p'':9C,28C,69C,89C ''perform'':23C ''place'':15C ''public'':25C ''recommend'':8B ''referenc'':45C ''rel'':95C ''result'':36C ''rmse'':52C ''seem'':42C ''site'':74C ''state'':64C ''state-of-the-art'':63C ''troubl'':57C ''use'':51C ''www.recsyswiki.com'':93C,98C ''www.recsyswiki.com/wiki/movielens_100k_benchmark_results'':92C ''www.recsyswiki.com/wiki/movielens_100k_benchmark_results&lt;/a&gt;&lt;/p&gt;'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10325, 1, '2016-02-19 20:44:06.953', 1, 23, 'How Orange makes decision about declaration of a meta attribute', '&lt;p&gt;I wonder how Orange decides which attribute is declared as a string? I have few discrete attributes in Excel, which I can''t use as Orange does not recognize them as discrete. Have tried export excel to csv format as well, no difference. Where in Orange I can enforce change from meta to discrete attribute?&lt;/p&gt;

&lt;p&gt;Many thanks in advance,
Marko &lt;/p&gt;
', 'orange', NULL, '''/p'':68C,75C ''advanc'':73C ''attribut'':10A,19C,29C,67C ''chang'':62C ''csv'':50C ''decid'':17C ''decis'':4A ''declar'':6A,21C ''differ'':55C ''discret'':28C,44C,66C ''enforc'':61C ''excel'':31C,48C ''export'':47C ''format'':51C ''make'':3A ''mani'':70C ''marko'':74C ''meta'':9A,64C ''orang'':2A,11B,16C,38C,58C ''p'':12C,69C ''recogn'':41C ''string'':24C ''thank'':71C ''tri'':46C ''use'':36C ''well'':53C ''wonder'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6836, 1, '2015-08-19 18:53:22.35', 0, 58, 'Optimizing regressors with weights for minimum RMSE with scipy', '&lt;p&gt;I am trying to find an example to get weights for 5 linear regressions so I could minimize the root mean squared error (RMSE) function. I found some examples, but for the logistic one.&lt;/p&gt;

&lt;p&gt;I am interested in what method should I use from scipy optimization for things to be well fitted. So far, I did the following in a few "pseudocode" lines:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;function weightrm (weights):
    prediction = 0
    for w, p in tuple(weights, predictions):
        prediction = prediction + w * p
    yield rmse(prediction, trueprediction)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The question is when I should call &lt;code&gt;minimize(weightrm, method="?")&lt;/code&gt;. Any suggestions will be highly appreciated.&lt;/p&gt;
', 'python  regression  optimization  parameter  programming', NULL, '''/code'':104C,118C ''/p'':50C,81C,125C ''/pre'':105C ''0'':88C ''5'':27C ''appreci'':124C ''call'':113C ''code'':83C,114C ''could'':32C ''error'':38C ''exampl'':22C,44C ''far'':71C ''find'':20C ''fit'':69C ''follow'':75C ''found'':42C ''function'':40C,84C ''get'':24C ''high'':123C ''interest'':54C ''line'':80C ''linear'':28C ''logist'':48C ''mean'':36C ''method'':57C,117C ''minim'':33C,115C ''minimum'':6A ''one'':49C ''optim'':1A,12B,63C ''p'':15C,51C,91C,99C,106C ''paramet'':13B ''pre'':82C ''predict'':87C,95C,96C,97C,102C ''program'':14B ''pseudocod'':79C ''python'':10B ''question'':108C ''regress'':11B,29C ''regressor'':2A ''rmse'':7A,39C,101C ''root'':35C ''scipi'':9A,62C ''squar'':37C ''suggest'':120C ''thing'':65C ''tri'':18C ''truepredict'':103C ''tupl'':93C ''use'':60C ''w'':90C,98C ''weight'':4A,25C,86C,94C ''weightrm'':85C,116C ''well'':68C ''yield'':100C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6838, 1, '2015-08-20 04:16:43.303', 4, 190, 'When to use Random Forest over SVM and vice versa?', '&lt;p&gt;&lt;strong&gt;When would one use &lt;code&gt;Random Forest&lt;/code&gt; over &lt;code&gt;SVM&lt;/code&gt; and vice versa?&lt;/strong&gt; &lt;/p&gt;

&lt;p&gt;I understand that &lt;code&gt;cross-validation&lt;/code&gt; and model comparison is an important aspect of choosing a model, but here I would like to learn more about rules of thumb and heuristics of the two methods.&lt;/p&gt;

&lt;p&gt;Can someone please explain the subtleties, strengths, and weaknesses of the classifiers as well as problems, which are best suited to each of them?&lt;/p&gt;

&lt;p&gt;Thanks for your help!  &lt;/p&gt;
', 'machine-learning  svm  random-forest  data', NULL, '''/code'':28C,32C,46C ''/p'':37C,76C,102C,108C ''/strong'':36C ''aspect'':53C ''best'':96C ''choos'':55C ''classifi'':89C ''code'':25C,30C,42C ''comparison'':49C ''cross'':44C ''cross-valid'':43C ''data'':18B ''explain'':81C ''forest'':5A,17B,27C ''help'':107C ''heurist'':71C ''import'':52C ''learn'':13B,64C ''like'':62C ''machin'':12B ''machine-learn'':11B ''method'':75C ''model'':48C,57C ''one'':23C ''p'':19C,38C,77C,103C ''pleas'':80C ''problem'':93C ''random'':4A,16B,26C ''random-forest'':15B ''rule'':67C ''someon'':79C ''strength'':84C ''strong'':20C ''subtleti'':83C ''suit'':97C ''svm'':7A,14B,31C ''thank'':104C ''thumb'':69C ''two'':74C ''understand'':40C ''use'':3A,24C ''valid'':45C ''versa'':10A,35C ''vice'':9A,34C ''weak'':86C ''well'':91C ''would'':22C,61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6881, 2, '2015-08-24 00:20:05.32', 1, NULL, NULL, '&lt;p&gt;The fastest way would be,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;require(data.table)
data &amp;lt;- data.table(data)
# Remove the percentage from your file and convert the field to numeric.
data[, Profit := as.numeric(gsub("%", "", Profit))]

data
##           Symbol      Date     Time Profit
##  1: BANKNIFTY  4/1/2010  9:55:00  -1.18
##  2: BANKNIFTY  4/1/2010 12:30:00  -2.84
##  3: BANKNIFTY  4/1/2010 12:45:00   7.17
##  4: BANKNIFTY  5/1/2010 11:40:00  -7.11
##  5:      ZEEL 26/6/2012 13:50:00  24.75
##  6:      ZEEL 27/6/2012 15:15:00  -1.90
##  7:      ZEEL 28/6/2012  9:45:00  37.58
##  8:      ZEEL 28/6/2012 14:55:00  23.95
##  9:      ZEEL 29/6/2012 14:20:00  -4.65
## 10:      ZEEL 29/6/2012 14:30:00  -6.01
## 11:      ZEEL 29/6/2012 14:55:00 -12.23
## 12:      ZEEL 29/6/2012 15:15:00  35.13

# Melt the data so that we can easily dcast afterwards.
molten_data &amp;lt;- melt(data[, list(Symbol, Date, Profit)]

# Create a summary by date and Symbol.
dcast(molten_data, id = c("Symbol", "Date")), Date ~ variable + Symbol, fun = sum)

 ##         Date Profit_BANKNIFTY Profit_ZEEL
 ## 1: 26/6/2012             0.00       24.75
 ## 2: 27/6/2012             0.00       -1.90
 ## 3: 28/6/2012             0.00       61.53
 ## 4: 29/6/2012             0.00       12.24
 ## 5:  4/1/2010             3.15        0.00
 ## 6:  5/1/2010            -7.11        0.00
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''-1.18'':44C ''-1.90'':79C,172C ''-12.23'':114C ''-2.84'':51C ''-4.65'':100C ''-6.01'':107C ''-7.11'':65C,187C ''/code'':189C ''/p'':7C ''/pre'':190C ''0.00'':167C,171C,175C,179C,184C,188C ''00'':43C,50C,57C,64C,71C,78C,85C,92C,99C,106C,113C,120C ''1'':38C,165C ''10'':101C ''11'':62C,108C ''12'':48C,55C,115C ''12.24'':180C ''13'':69C ''14'':90C,97C,104C,111C ''15'':76C,77C,118C,119C ''2'':45C,169C ''20'':98C ''23.95'':93C ''24.75'':72C,168C ''26/6/2012'':68C,166C ''27/6/2012'':75C,170C ''28/6/2012'':82C,89C,174C ''29/6/2012'':96C,103C,110C,117C,178C ''3'':52C,173C ''3.15'':183C ''30'':49C,105C ''35.13'':121C ''37.58'':86C ''4'':59C,177C ''4/1/2010'':40C,47C,54C,182C ''40'':63C ''45'':56C,84C ''5'':66C,181C ''5/1/2010'':61C,186C ''50'':70C ''55'':42C,91C,112C ''6'':73C,185C ''61.53'':176C ''7'':80C ''7.17'':58C ''8'':87C ''9'':41C,83C,94C ''afterward'':131C ''as.numeric'':30C ''banknifti'':39C,46C,53C,60C,162C ''c'':152C ''code'':9C ''convert'':23C ''creat'':141C ''data'':12C,15C,28C,33C,124C,133C,136C,150C ''data.table'':11C,14C ''date'':35C,139C,145C,154C,155C,160C ''dcast'':130C,148C ''easili'':129C ''fastest'':3C ''field'':25C ''file'':21C ''fun'':158C ''gsub'':31C ''id'':151C ''list'':137C ''lt'':13C,134C ''melt'':122C,135C ''molten'':132C,149C ''numer'':27C ''p'':1C ''percentag'':18C ''pre'':8C ''profit'':29C,32C,37C,140C,161C,163C ''remov'':16C ''requir'':10C ''sum'':159C ''summari'':143C ''symbol'':34C,138C,147C,153C,157C ''time'':36C ''variabl'':156C ''way'':4C ''would'':5C ''zeel'':67C,74C,81C,88C,95C,102C,109C,116C,164C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8987, 5, '2015-11-23 10:58:13.177', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9044, 5, '2015-11-26 09:54:27.533', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9047, 4, '2015-11-26 09:55:44.8', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6839, 2, '2015-08-20 06:08:22.557', 1, NULL, NULL, '&lt;p&gt;A couple of thoughts...&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;First as a (former) physicist:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;As a scientist/physicist I would likely never be satisfied to analyze the data that you have presented because there are some obvious issues with it.  I was initially thinking that it was way to schizophrenic, but then I noticed that you referred to the time units as seconds, in which case this is a pretty slowly varying time series.  Are the units really seconds?  If they happen to be milliseconds or microseconds then you might want to think about a probe that is less noisy (i.e. a larger probe with more of a temperature sink) and check weather you are seeing the effects of ground loop feedback in your system or just EMF interference from your electrical system.&lt;/p&gt;

&lt;p&gt;Secondly, there are clear patterns in the data and these do not line up very well, so I would rerun the experiment with things timed much more accurately.  For instance, the event that occurs midway through the experiment that drops the temperature (looks like an icicle or dagger) occurs 5-8 minutes apart in comparing the dark blue experiment to the purple experiment.  It would be great if these could be synced better.  The point being that if you can''t sync them better, then the noise of the time series renders most phenomenon apart from a flat line, almost useless.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Now as a data scientist:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This data is really noisy or at least fluctuates significantly!  You should stay away from averaging minima and maxima as you suggest in your post.  extrema are very sensitive to noise where as you are trying to reduce the noise in your system.&lt;/p&gt;

&lt;p&gt;You should perhaps think about applying some sort of smoothing method to reduce the jitter in your data.  Perhaps a second-order exponentially weighted moving average like &lt;a href="http://connor-johnson.com/2014/11/23/time-series-forecasting-in-python-and-r/" rel="nofollow"&gt;Holt-Winters&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Finally, you could probably just average the three signals to produce a mean signal.  Means are more suseptable to outliers than medians,so the median signal is also an option. &lt;/p&gt;

&lt;p&gt;Some other options include separating the trend from the general behavior using some type of &lt;a href="https://www.stat.auckland.ac.nz/~wild/ChanceEnc/Ch14.pdf" rel="nofollow"&gt;de-seasoning&lt;/a&gt; or &lt;a href="http://www.research.att.com/techdocs/TD_100381.pdf" rel="nofollow"&gt;double ARIMA&lt;/a&gt; procedure.  But, it seems like these are too extreme given that the dagger/icicle event looks significant and you don''t want to transform it away.  So I would only use these if there is some type of periodicity to your data.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''-8'':187C ''/2014/11/23/time-series-forecasting-in-python-and-r/'':326C ''/a'':332C,392C,403C ''/p'':6C,15C,135C,238C,247C,292C,333C,365C,445C,450C ''/strong'':14C,246C ''/techdocs/td_100381.pdf'':398C ''/~wild/chanceenc/ch14.pdf'':386C ''5'':186C ''accur'':164C ''almost'':236C ''also'':362C ''analyz'':27C ''apart'':189C,231C ''appli'':299C ''arima'':402C ''averag'':264C,320C,340C ''away'':262C,428C ''behavior'':377C ''better'':209C,220C ''blue'':194C ''case'':67C ''check'':113C ''clear'':140C ''compar'':191C ''connor-johnson.com'':325C ''connor-johnson.com/2014/11/23/time-series-forecasting-in-python-and-r/'':324C ''could'':206C,337C ''coupl'':3C ''dagger'':184C ''dagger/icicle'':416C ''dark'':193C ''data'':29C,144C,244C,250C,311C,444C ''de'':390C ''de-season'':389C ''doubl'':401C ''drop'':176C ''effect'':119C ''electr'':133C ''emf'':129C ''event'':168C,417C ''experi'':158C,174C,195C,199C ''exponenti'':317C ''extrem'':412C ''extrema'':274C ''feedback'':123C ''final'':335C ''first'':9C ''flat'':234C ''fluctuat'':257C ''former'':12C ''general'':376C ''given'':413C ''great'':203C ''ground'':121C ''happen'':83C ''help'':449C ''holt'':330C ''holt-wint'':329C ''hope'':447C ''href'':323C,383C,395C ''i.e'':102C ''icicl'':182C ''includ'':370C ''initi'':44C ''instanc'':166C ''interfer'':130C ''issu'':39C ''jitter'':308C ''larger'':104C ''least'':256C ''less'':100C ''like'':22C,180C,321C,408C ''line'':149C,235C ''look'':179C,418C ''loop'':122C ''maxima'':267C ''mean'':347C,349C ''median'':356C,359C ''method'':304C ''microsecond'':88C ''midway'':171C ''might'':91C ''millisecond'':86C ''minima'':265C ''minut'':188C ''move'':319C ''much'':162C ''never'':23C ''nofollow'':328C,388C,400C ''nois'':223C,279C,288C ''noisi'':101C,253C ''notic'':55C ''obvious'':38C ''occur'':170C,185C ''option'':364C,369C ''order'':316C ''outlier'':354C ''p'':1C,7C,16C,136C,239C,248C,293C,334C,366C,446C ''pattern'':141C ''perhap'':296C,312C ''period'':441C ''phenomenon'':230C ''physicist'':13C ''point'':211C ''post'':273C ''present'':33C ''pretti'':71C ''probabl'':338C ''probe'':97C,105C ''procedur'':404C ''produc'':345C ''purpl'':198C ''realli'':79C,252C ''reduc'':286C,306C ''refer'':58C ''rel'':327C,387C,399C ''render'':228C ''rerun'':156C ''satisfi'':25C ''schizophren'':51C ''scientist'':245C ''scientist/physicist'':19C ''season'':391C ''second'':64C,80C,137C,315C ''second-ord'':314C ''see'':117C ''seem'':407C ''sensit'':277C ''separ'':371C ''seri'':75C,227C ''signal'':343C,348C,360C ''signific'':258C,419C ''sink'':111C ''slowli'':72C ''smooth'':303C ''sort'':301C ''stay'':261C ''strong'':8C,240C ''suggest'':270C ''susept'':352C ''sync'':208C,218C ''system'':126C,134C,291C ''temperatur'':110C,178C ''thing'':160C ''think'':45C,94C,297C ''thought'':5C ''three'':342C ''time'':61C,74C,161C,226C ''transform'':426C ''trend'':373C ''tri'':284C ''type'':380C,439C ''unit'':62C,78C ''use'':378C,433C ''useless'':237C ''vari'':73C ''want'':92C,424C ''way'':49C ''weather'':114C ''weight'':318C ''well'':152C ''winter'':331C ''would'':21C,155C,201C,431C ''www.research.att.com'':397C ''www.research.att.com/techdocs/td_100381.pdf'':396C ''www.stat.auckland.ac.nz'':385C ''www.stat.auckland.ac.nz/~wild/chanceenc/ch14.pdf'':384C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6840, 1, '2015-08-20 09:12:13.357', 1, 92, 'How to estimate a user''s gender based on what apps the user download?', '&lt;p&gt;I have a dataset of what apps users downloaded and I try to estimate these users'' gender based on what apps they downloaded, using machine learning algorithm. However, what kind of features of the apps should I focus on? As far as I know, app category plays a big role. Do Google Play and Apple App Store estimate users'' gender based on what apps they download?&lt;/p&gt;
', 'machine-learning  data-mining  bigdata', NULL, '''/p'':89C ''algorithm'':49C ''app'':11A,29C,43C,57C,67C,78C,86C ''appl'':77C ''base'':8A,40C,83C ''big'':71C ''bigdata'':21B ''categori'':68C ''data'':19B ''data-min'':18B ''dataset'':26C ''download'':14A,31C,45C,88C ''estim'':3A,36C,80C ''far'':63C ''featur'':54C ''focus'':60C ''gender'':7A,39C,82C ''googl'':74C ''howev'':50C ''kind'':52C ''know'':66C ''learn'':17B,48C ''machin'':16B,47C ''machine-learn'':15B ''mine'':20B ''p'':22C ''play'':69C,75C ''role'':72C ''store'':79C ''tri'':34C ''use'':46C ''user'':5A,13A,30C,38C,81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6841, 1, '2015-08-20 09:49:51.23', 1, 84, 'Data frame mutation in R', '&lt;p&gt;I have a data frame of the following format:
&lt;br&gt;Symbol  Date    Time    Profit
&lt;br&gt;$BANKNIFTY 4/1/2010 9:55:00 -1.18%
&amp;lt;br&amp;gt;$BANKNIFTY   4/1/2010    12:30:00    -2.84%
&lt;br&gt;$BANKNIFTY 4/1/2010 12:45:00 7.17%
&amp;lt;br&amp;gt;$BANKNIFTY   5/1/2010    11:40:00    -7.11%
&lt;br&gt;ZEEL    26/6/2012   13:50:00    24.75%
&lt;br&gt;ZEEL    27/6/2012   15:15:00    -1.90%
&lt;br&gt;ZEEL    28/6/2012   9:45:00 37.58%
&lt;br&gt;ZEEL    28/6/2012   14:55:00    23.95%
&lt;br&gt;ZEEL    29/6/2012   14:20:00    -4.65%
&lt;br&gt;ZEEL    29/6/2012   14:30:00    -6.01%
&lt;br&gt;ZEEL    29/6/2012   14:55:00    -12.23%
&lt;br&gt;ZEEL    29/6/2012   15:15:00    35.13%&lt;/p&gt;

&lt;p&gt;What I''d like to achieve is convert that data frame into a data frame which has dates for row names, symbol names for columns and sum of percentage profit for each day. Like in the following:&lt;/p&gt;

&lt;p&gt;&lt;br&gt;Date    BankNifty   ZEEL
&lt;br&gt;4/1/2010    3.15%   0
&lt;br&gt;5/1/2010    -7.11%  0
&lt;br&gt;26/6/2012   0   24.75%
&lt;br&gt;27/6/2012   0   -1.90%
&lt;br&gt;28/6/2012   0   61.53%
&lt;br&gt;29/6/2012   0   12.24%&lt;/p&gt;

&lt;p&gt;How can I achieve that in R? &lt;code&gt;dplyr&lt;/code&gt; mutation or some apply function?&lt;/p&gt;

&lt;p&gt;I''m a beginner in R programming. 
Thanks in advance.&lt;/p&gt;

&lt;p&gt;The data in R is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;structure(list(Symbol = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L), .Label = c("BANKNIFTY", "ZEEL"), class = "factor"), 
    Date = structure(c(5L, 5L, 5L, 6L, 1L, 2L, 3L, 3L, 4L, 4L, 
    4L, 4L), .Label = c("26/6/2012", "27/6/2012", "28/6/2012", 
    "29/6/2012", "4/1/2010", "5/1/2010"), class = "factor"), 
    Time = structure(c(10L, 2L, 3L, 1L, 4L, 8L, 9L, 7L, 5L, 6L, 
    7L, 8L), .Label = c("11:40:00", "12:30:00", "12:45:00", "13:50:00", 
    "14:20:00", "14:30:00", "14:55:00", "15:15:00", "9:45:00", 
    "9:55:00"), class = "factor"), Profit = structure(c(1L, 4L, 
    12L, 7L, 9L, 2L, 11L, 8L, 5L, 6L, 3L, 10L), .Label = c("-1.18%", 
    "-1.90%", "-12.23%", "-2.84%", "-4.65%", "-6.01%", "-7.11%", 
    "23.95%", "24.75%", "35.13%", "37.58%", "7.17%"), class = "factor")), .Names = c("Symbol", 
"Date", "Time", "Profit"), class = "data.frame", row.names = c(NA, 
-12L))
&lt;/code&gt;&lt;/pre&gt;
', 'r  data-cleaning  data', NULL, '''-1.18'':32C,336C ''-1.90'':71C,174C,337C ''-12'':361C ''-12.23'':106C,338C ''-2.84'':41C,339C ''-4.65'':92C,340C ''-6.01'':99C,341C ''-7.11'':57C,165C,342C ''/code'':194C,363C ''/p'':114C,153C,183C,200C,212C,219C ''/pre'':364C ''0'':162C,166C,169C,173C,177C,181C ''00'':31C,40C,47C,56C,63C,70C,77C,84C,91C,98C,105C,112C,289C,292C,295C,298C,301C,304C,307C,310C,313C,316C ''10l'':273C,333C ''11'':54C,287C ''11l'':328C ''12'':38C,45C,290C,293C ''12.24'':182C ''12l'':324C ''13'':61C,296C ''14'':82C,89C,96C,103C,299C,302C,305C ''15'':68C,69C,110C,111C,308C,309C ''1l'':227C,228C,229C,230C,252C,276C,322C ''20'':90C,300C ''23.95'':85C,343C ''24.75'':64C,170C,344C ''26/6/2012'':60C,168C,262C ''27/6/2012'':67C,172C,263C ''28/6/2012'':74C,81C,176C,264C ''29/6/2012'':88C,95C,102C,109C,180C,265C ''2l'':231C,232C,233C,234C,235C,236C,237C,238C,253C,274C,327C ''3.15'':161C ''30'':39C,97C,291C,303C ''35.13'':113C,345C ''37.58'':78C,346C ''3l'':254C,255C,275C,332C ''4/1/2010'':28C,37C,44C,160C,266C ''40'':55C,288C ''45'':46C,76C,294C,312C ''4l'':256C,257C,258C,259C,277C,323C ''5/1/2010'':53C,164C,267C ''50'':62C,297C ''55'':30C,83C,104C,306C,315C ''5l'':248C,249C,250C,281C,330C ''61.53'':178C ''6l'':251C,282C,331C ''7.17'':48C,347C ''7l'':280C,283C,325C ''8l'':278C,284C,329C ''9'':29C,75C,311C,314C ''9l'':279C,326C ''achiev'':121C,188C ''advanc'':211C ''appli'':198C ''banknifti'':27C,36C,43C,52C,157C,241C ''beginn'':205C ''br'':21C,26C,34C,42C,50C,58C,65C,72C,79C,86C,93C,100C,107C,155C,159C,163C,167C,171C,175C,179C ''c'':226C,240C,247C,261C,272C,286C,321C,335C,351C,359C ''class'':243C,268C,317C,348C,356C ''clean'':9B ''code'':192C,221C ''column'':140C ''convert'':123C ''d'':118C ''data'':1A,8B,10B,15C,125C,129C,215C ''data-clean'':7B ''data.frame'':357C ''date'':23C,133C,156C,245C,353C ''day'':148C ''dplyr'':193C ''factor'':244C,269C,318C,349C ''follow'':19C,152C ''format'':20C ''frame'':2A,16C,126C,130C ''function'':199C ''gt'':35C,51C ''l'':362C ''label'':239C,260C,285C,334C ''like'':119C,149C ''list'':223C ''lt'':33C,49C ''m'':203C ''mutat'':3A,195C ''na'':360C ''name'':136C,138C,350C ''p'':11C,115C,154C,184C,201C,213C ''percentag'':144C ''pre'':220C ''profit'':25C,145C,319C,355C ''program'':208C ''r'':5A,6B,191C,207C,217C ''row'':135C ''row.names'':358C ''structur'':222C,225C,246C,271C,320C ''sum'':142C ''symbol'':22C,137C,224C,352C ''thank'':209C ''time'':24C,270C,354C ''zeel'':59C,66C,73C,80C,87C,94C,101C,108C,158C,242C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6842, 1, '2015-08-20 09:53:43.28', 5, 938, 'Is there a way of performing stratified cross validation using xgboost module in python?', '&lt;p&gt;I am training and predicting on the same data-set, but I want to perform 10-fold cross-validation and predict on the left out fold and thus predict on the whole data set. How can I do this? &lt;/p&gt;

&lt;p&gt;The libraries which I am using are: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from sklearn import cross_validation
import xgboost as xgb
&lt;/code&gt;&lt;/pre&gt;
', 'python  sklearn  cross-validation', NULL, '''/code'':83C ''/p'':62C,71C ''/pre'':84C ''10'':37C ''code'':73C ''cross'':8A,18B,40C,77C ''cross-valid'':17B,39C ''data'':30C,55C ''data-set'':29C ''fold'':38C,48C ''import'':76C,79C ''left'':46C ''librari'':65C ''modul'':12A ''p'':20C,63C ''perform'':6A,36C ''pre'':72C ''predict'':25C,43C,51C ''python'':14A,15B ''set'':31C,56C ''sklearn'':16B,75C ''stratifi'':7A ''thus'':50C ''train'':23C ''use'':10A,69C ''valid'':9A,19B,41C,78C ''want'':34C ''way'':4A ''whole'':54C ''xgb'':82C ''xgboost'':11A,80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8720, 2, '2015-11-04 15:57:38.84', 2, NULL, NULL, '&lt;p&gt;One major issue with this approach is that if you represent your image as a vector of pixel intensities, you have zero translation-invariance.  Two pictures that are exactly the same but shifted over 10 pixels will likely look like dissimilar vectors.  That is why people try to use other methods that handle translation, like &lt;a href="http://deeplearning.net/tutorial/lenet.html" rel="nofollow"&gt;convolutional neural networks&lt;/a&gt; or &lt;a href="http://courses.cs.washington.edu/courses/cse576/11sp/notes/SIFT_white2011.pdf" rel="nofollow"&gt;SIFT features&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':68C,79C ''/courses/cse576/11sp/notes/sift_white2011.pdf'':74C ''/p'':80C ''/tutorial/lenet.html'':62C ''10'':37C ''approach'':7C ''convolut'':65C ''courses.cs.washington.edu'':73C ''courses.cs.washington.edu/courses/cse576/11sp/notes/sift_white2011.pdf'':72C ''deeplearning.net'':61C ''deeplearning.net/tutorial/lenet.html'':60C ''dissimilar'':43C ''exact'':31C ''featur'':78C ''handl'':55C ''href'':59C,71C ''imag'':14C ''intens'':20C ''invari'':26C ''issu'':4C ''like'':40C,42C,57C ''look'':41C ''major'':3C ''method'':53C ''network'':67C ''neural'':66C ''nofollow'':64C,76C ''one'':2C ''p'':1C ''peopl'':48C ''pictur'':28C ''pixel'':19C,38C ''rel'':63C,75C ''repres'':12C ''shift'':35C ''sift'':77C ''translat'':25C,56C ''translation-invari'':24C ''tri'':49C ''two'':27C ''use'':51C ''vector'':17C,44C ''zero'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9052, 5, '2015-11-26 09:59:25.59', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9053, 4, '2015-11-26 09:59:25.59', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9073, 1, '2015-11-27 08:35:19.797', 1, 32, 'Are all images in ImageNet in the leaves?', '&lt;p&gt;If I understand it correctly, then the labels of &lt;a href="http://image-net.org/" rel="nofollow"&gt;ImageNet&lt;/a&gt; are based on WordNet:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;ImageNet is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). In ImageNet, we aim to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated. In its completion, we hope ImageNet will offer tens of millions of cleanly sorted images for most of the concepts in the WordNet hierarchy.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Source: &lt;a href="http://image-net.org/about-overview" rel="nofollow"&gt;http://image-net.org/about-overview&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The text above states already that WordNet defines a hierarchy. You can see that by using the Python &lt;a href="http://www.nltk.org/" rel="nofollow"&gt;&lt;code&gt;nltk&lt;/code&gt; package&lt;/a&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from nltk.corpus import wordnet
nltk.download()
def get_hyponyms(synset):
    hyponyms = set()
    for hyponym in synset.hyponyms():
        hyponyms |= set(get_hyponyms(hyponym))
    return hyponyms | set(synset.hyponyms())
dog = wordnet.synset(''dog.n.01'')
print(get_hyponyms(dog))
harrier = wordnet.synset(''harrier.n.02'')
print(get_hyponyms(harrier))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;(thanks to &lt;a href="http://stackoverflow.com/a/33662890/562769"&gt;Stephan Ds answer&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;gives&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;set([Synset(''harrier.n.02''), Synset(''water_spaniel.n.01''), Synset(''standard_poodle.n.01''), Synset(''dandie_dinmont.n.01''), Synset(''courser.n.03''), Synset(''wirehair.n.01''), Synset(''toy_manchester.n.01''), Synset(''puppy.n.01''), Synset(''briard.n.01''), Synset(''beagle.n.01''), Synset(''siberian_husky.n.01''), Synset(''manchester_terrier.n.01''), Synset(''bloodhound.n.01''), Synset(''gordon_setter.n.01''), Synset(''leonberg.n.01''), Synset(''king_charles_spaniel.n.01''), Synset(''yorkshire_terrier.n.01''), Synset(''sealyham_terrier.n.01''), Synset(''american_water_spaniel.n.01''), Synset(''skye_terrier.n.01''), Synset(''clumber.n.01''), Synset(''pembroke.n.01''), Synset(''wire-haired_fox_terrier.n.01''), Synset(''shih-tzu.n.01''), Synset(''newfoundland.n.01''), Synset(''retriever.n.01''), Synset(''cocker_spaniel.n.01''), Synset(''springer_spaniel.n.01''), Synset(''american_foxhound.n.01''), Synset(''large_poodle.n.01''), Synset(''lapdog.n.01''), Synset(''bull_mastiff.n.01''), Synset(''affenpinscher.n.01''), Synset(''irish_water_spaniel.n.01''), Synset(''terrier.n.01''), Synset(''keeshond.n.01''), Synset(''vizsla.n.01''), Synset(''dalmatian.n.02''), Synset(''bird_dog.n.01''), Synset(''irish_terrier.n.01''), Synset(''miniature_poodle.n.01''), Synset(''dachshund.n.01''), Synset(''australian_terrier.n.01''), Synset(''blenheim_spaniel.n.01''), Synset(''weimaraner.n.01''), Synset(''soft-coated_wheaten_terrier.n.01''), Synset(''doberman.n.01''), Synset(''kelpie.n.02''), Synset(''water_dog.n.02''), Synset(''feist.n.01''), Synset(''attack_dog.n.01''), Synset(''french_bulldog.n.01''), Synset(''papillon.n.01''), Synset(''bedlington_terrier.n.01''), Synset(''foxhound.n.01''), Synset(''labrador_retriever.n.01''), Synset(''great_dane.n.01''), Synset(''kerry_blue_terrier.n.01''), Synset(''miniature_schnauzer.n.01''), Synset(''pariah_dog.n.01''), Synset(''border_terrier.n.01''), Synset(''staghound.n.01''), Synset(''norwegian_elkhound.n.01''), Synset(''redbone.n.01''), Synset(''pooch.n.01''), Synset(''old_english_sheepdog.n.01''), Synset(''police_dog.n.01''), Synset(''welsh_terrier.n.01''), Synset(''spitz.n.01''), Synset(''boxer.n.04''), Synset(''tibetan_terrier.n.01''), Synset(''shetland_sheepdog.n.01''), Synset(''boarhound.n.01''), Synset(''border_collie.n.01''), Synset(''wolfhound.n.01''), Synset(''lhasa.n.02''), Synset(''scotch_terrier.n.01''), Synset(''coondog.n.01''), Synset(''giant_schnauzer.n.01''), Synset(''japanese_spaniel.n.01''), Synset(''german_short-haired_pointer.n.01''), Synset(''entlebucher.n.01''), Synset(''griffon.n.03''), Synset(''griffon.n.02''), Synset(''welsh_springer_spaniel.n.01''), Synset(''clydesdale_terrier.n.01''), Synset(''hound.n.01''), Synset(''brittany_spaniel.n.01''), Synset(''corgi.n.01''), Synset(''pekinese.n.01''), Synset(''mastiff.n.01''), Synset(''flat-coated_retriever.n.01''), Synset(''sennenhunde.n.01''), Synset(''schipperke.n.01''), Synset(''english_toy_spaniel.n.01''), Synset(''ibizan_hound.n.01''), Synset(''airedale.n.01''), Synset(''cardigan.n.02''), Synset(''miniature_pinscher.n.01''), Synset(''bluetick.n.01''), Synset(''west_highland_white_terrier.n.01''), Synset(''seizure-alert_dog.n.01''), Synset(''pomeranian.n.01''), Synset(''english_foxhound.n.01''), Synset(''bernese_mountain_dog.n.01''), Synset(''norfolk_terrier.n.01''), Synset(''greater_swiss_mountain_dog.n.01''), Synset(''collie.n.01''), Synset(''chow.n.03''), Synset(''pug.n.01''), Synset(''scottish_deerhound.n.01''), Synset(''groenendael.n.01''), Synset(''golden_retriever.n.01''), Synset(''schnauzer.n.01''), Synset(''irish_setter.n.01''), Synset(''german_shepherd.n.01''), Synset(''walker_hound.n.01''), Synset(''english_setter.n.01''), Synset(''english_springer.n.01''), Synset(''sporting_dog.n.01''), Synset(''afghan_hound.n.01''), Synset(''cairn.n.02''), Synset(''rhodesian_ridgeback.n.01''), Synset(''chesapeake_bay_retriever.n.01''), Synset(''irish_wolfhound.n.01''), Synset(''fox_terrier.n.01''), Synset(''sled_dog.n.01''), Synset(''toy_dog.n.01''), Synset(''staffordshire_bullterrier.n.01''), Synset(''bullterrier.n.01''), Synset(''seeing_eye_dog.n.01''), Synset(''samoyed.n.03''), Synset(''bouvier_des_flandres.n.01''), Synset(''otterhound.n.01''), Synset(''kuvasz.n.01''), Synset(''cur.n.01''), Synset(''guide_dog.n.01''), Synset(''malinois.n.01''), Synset(''malamute.n.01''), Synset(''poodle.n.01''), Synset(''curly-coated_retriever.n.01''), Synset(''toy_spaniel.n.01''), Synset(''basset.n.01''), Synset(''toy_terrier.n.01''), Synset(''tibetan_mastiff.n.01''), Synset(''basenji.n.01''), Synset(''field_spaniel.n.01''), Synset(''mexican_hairless.n.01''), Synset(''setter.n.02''), Synset(''great_pyrenees.n.01''), Synset(''american_staffordshire_terrier.n.01''), Synset(''rottweiler.n.01''), Synset(''standard_schnauzer.n.01''), Synset(''black-and-tan_coonhound.n.01''), Synset(''borzoi.n.01''), Synset(''bulldog.n.01''), Synset(''sausage_dog.n.01''), Synset(''sussex_spaniel.n.01''), Synset(''spaniel.n.01''), Synset(''working_dog.n.01''), Synset(''belgian_sheepdog.n.01''), Synset(''watchdog.n.02''), Synset(''silky_terrier.n.01''), Synset(''eskimo_dog.n.01''), Synset(''brabancon_griffon.n.01''), Synset(''whippet.n.01''), Synset(''plott_hound.n.01''), Synset(''liver-spotted_dalmatian.n.01''), Synset(''coonhound.n.01''), Synset(''saluki.n.01''), Synset(''toy_poodle.n.01''), Synset(''hearing_dog.n.01''), Synset(''chihuahua.n.03''), Synset(''lakeland_terrier.n.01''), Synset(''shepherd_dog.n.01''), Synset(''rat_terrier.n.01''), Synset(''italian_greyhound.n.01''), Synset(''komondor.n.01''), Synset(''saint_bernard.n.01''), Synset(''norwich_terrier.n.01''), Synset(''pointer.n.04''), Synset(''appenzeller.n.01''), Synset(''maltese_dog.n.01''), Synset(''hunting_dog.n.01''), Synset(''smooth-haired_fox_terrier.n.01''), Synset(''housedog.n.01''), Synset(''boston_bull.n.01''), Synset(''pinscher.n.01''), Synset(''greyhound.n.01'')])&lt;/p&gt;
  
  &lt;p&gt;set([])&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Now my question:&lt;/p&gt;

&lt;p&gt;Are all images of ImageNet in the leaves, e.g. &lt;code&gt;''harrier.n.02''&lt;/code&gt; or are some images only labeled as &lt;code&gt;''dog.n.01''&lt;/code&gt;?&lt;/p&gt;

&lt;p&gt;(Sub-question: ImageNet was labeled by ordinary people (not specialists in the topic, e.g. for dogs not biologists with specialization in dogs or something similar) via Amazon Mechanical Turk, if I remember it correctly. How did the people there know all these different kinds of dogs? I wouldn''t know what a borzoi is... so how did they check if the label was correct and specific enough?)&lt;/p&gt;

&lt;p&gt;Are the 21841 synsets (&lt;a href="http://image-net.org/about-stats" rel="nofollow"&gt;source&lt;/a&gt;) leaves or also inner nodes?&lt;/p&gt;

&lt;p&gt;Are all images in ImageNet in the leaves? How many leaves are there?&lt;/p&gt;
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''bluetick.n.01'':512C ''boarhound.n.01'':439C ''border'':407C,441C ''borzoi'':856C ''borzoi.n.01'':673C ''boston'':762C ''bouvier'':610C ''boxer.n.04'':431C ''brabancon'':699C ''briard.n.01'':260C ''brittani'':480C ''bull'':327C ''bull.n.01'':763C ''bulldog.n.01'':382C,675C ''bullterrier.n.01'':600C,602C ''cairn.n.02'':578C ''call'':62C ''cardigan.n.02'':507C ''charl'':278C ''check'':862C ''chesapeak'':583C ''chihuahua.n.03'':722C ''chow.n.03'':543C ''classif'':12B ''clean'':122C ''clumber.n.01'':294C ''clydesdal'':475C ''coat'':365C,491C,631C ''cocker'':313C ''code'':172C,179C,788C,798C ''collie.n.01'':442C,541C ''complet'':112C ''concept'':50C,101C,129C ''control'':105C ''coondog.n.01'':451C ''coonhound.n.01'':671C,712C ''corgi.n.01'':483C ''correct'':18C,837C,867C ''courser.n.03'':251C ''cur'':630C ''cur.n.01'':618C ''curly-co'':629C ''dachshund.n.01'':353C ''dalmatian.n.01'':710C ''dalmatian.n.02'':342C ''dandi'':248C ''dane.n.01'':395C ''dataset'':9B,41C ''deerhound.n.01'':548C ''def'':185C ''defin'':156C ''des'':611C ''describ'':54C ''differ'':846C ''dinmont.n.01'':249C ''doberman.n.01'':369C ''dog'':204C,210C,819C,825C,849C ''dog.n.01'':206C,345C,379C,405C,424C,522C,531C,539C,573C,594C,597C,606C,621C,678C,686C,697C,720C,728C,749C,752C,799C ''dog.n.02'':374C ''ds'':229C ''e.g'':787C,817C ''elkhound.n.01'':413C ''english'':420C,498C,526C,566C,569C ''enough'':870C ''entlebucher.n.01'':465C ''eskimo'':696C ''eye'':605C ''feist.n.01'':376C ''field'':647C ''flandres.n.01'':612C ''flat'':490C ''flat-coat'':489C ''fox'':301C,590C,757C ''foxhound.n.01'':320C,389C,527C ''french'':381C ''german'':459C,560C ''get'':186C,197C,208C,215C ''giant'':453C ''give'':234C ''golden'':552C ''gordon'':272C ''great'':394C,655C ''greater'':536C ''greyhound.n.01'':734C,767C ''griffon.n.01'':700C ''griffon.n.02'':469C ''griffon.n.03'':467C ''groenendael.n.01'':550C ''guid'':620C ''hair'':300C,462C,756C ''hairless.n.01'':651C ''harrier'':211C,217C ''harrier.n.02'':213C,240C,789C ''hear'':719C ''hierarchi'':47C,133C,158C ''highland'':515C ''hope'':114C ''hound.n.01'':478C,503C,564C,576C,705C ''housedog.n.01'':760C ''href'':24C,139C,168C,224C,878C ''human'':108C ''human-annot'':107C ''hunt'':751C ''husky.n.01'':265C ''hyponym'':187C,189C,192C,195C,198C,199C,201C,209C,216C ''ibizan'':502C ''illustr'':95C ''imag'':3A,11B,40C,93C,98C,124C,781C,794C,895C ''image-classif'':10B ''image-net.org'':25C,141C,146C,880C ''image-net.org/about-overview'':140C ''image-net.org/about-overview&lt;/a&gt;&lt;/p&gt;'':145C ''image-net.org/about-stats'':879C ''imagenet'':5A,28C,37C,85C,115C,783C,806C,897C ''import'':182C ''inner'':889C ''irish'':332C,347C,557C,587C ''italian'':733C ''japanes'':456C ''keeshond.n.01'':338C ''kelpie.n.02'':371C ''kerri'':397C ''kind'':847C ''king'':277C ''know'':843C,853C ''komondor.n.01'':736C ''kuvasz.n.01'':616C ''label'':21C,796C,808C,865C ''labrador'':391C ''lakeland'':724C ''lapdog.n.01'':325C ''larg'':322C ''leav'':8A,786C,886C,900C,903C 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''pomeranian.n.01'':524C ''pooch.n.01'':417C ''poodle.n.01'':246C,323C,351C,627C,717C ''possibl'':53C ''pre'':178C ''print'':207C,214C ''provid'':89C ''pug.n.01'':545C ''puppy.n.01'':258C ''pyrenees.n.01'':656C ''python'':166C ''qualiti'':104C ''quality-control'':103C ''question'':776C,805C ''rat'':730C ''redbone.n.01'':415C ''rel'':26C,143C,170C,882C ''rememb'':835C ''retriever.n.01'':311C,392C,492C,553C,585C,632C ''return'':200C ''rhodesian'':580C ''ridgeback.n.01'':581C ''rottweiler.n.01'':662C ''saint'':738C ''saluki.n.01'':714C ''samoyed.n.03'':608C ''sausag'':677C ''schipperke.n.01'':496C ''schnauzer.n.01'':402C,454C,555C,665C ''scotch'':448C ''scottish'':547C ''sealyham'':284C ''see'':161C,604C ''seizur'':520C ''seizure-alert'':519C ''sennenhunde.n.01'':494C ''set'':65C,190C,196C,202C,238C,770C ''setter.n.01'':273C,558C,567C ''setter.n.02'':653C ''sheepdog.n.01'':421C,437C,689C ''shepherd'':727C ''shepherd.n.01'':561C ''shetland'':436C ''shih'':305C ''shih-tzu'':304C ''short'':461C ''short-hair'':460C ''siberian'':264C ''silki'':693C ''similar'':828C ''skye'':291C ''sled'':593C ''smooth'':755C ''smooth-hair'':754C ''soft'':364C ''soft-coat'':363C ''someth'':827C ''sort'':123C ''sourc'':137C,884C ''spaniel.n.01'':243C,279C,289C,314C,317C,334C,359C,457C,473C,481C,500C,635C,648C,681C,683C ''special'':823C ''specialist'':813C ''specif'':869C ''spitz.n.01'':429C ''sport'':572C ''spot'':709C ''springer'':316C,472C ''springer.n.01'':570C ''stackoverflow.com'':226C ''stackoverflow.com/a/33662890/562769'':225C ''staffordshir'':599C,659C ''staghound.n.01'':410C ''standard'':245C,664C ''state'':152C ''stephan'':228C ''sub'':804C ''sub-quest'':803C ''sussex'':680C ''swiss'':537C ''synonym'':64C ''synset'':67C,74C,97C,188C,239C,241C,244C,247C,250C,252C,254C,257C,259C,261C,263C,266C,269C,271C,274C,276C,280C,283C,286C,290C,293C,295C,297C,303C,308C,310C,312C,315C,318C,321C,324C,326C,329C,331C,335C,337C,339C,341C,343C,346C,349C,352C,354C,357C,360C,362C,368C,370C,372C,375C,377C,380C,383C,385C,388C,390C,393C,396C,400C,403C,406C,409C,411C,414C,416C,418C,422C,425C,428C,430C,432C,435C,438C,440C,443C,445C,447C,450C,452C,455C,458C,464C,466C,468C,470C,474C,477C,479C,482C,484C,486C,488C,493C,495C,497C,501C,504C,506C,508C,511C,513C,518C,523C,525C,528C,532C,535C,540C,542C,544C,546C,549C,551C,554C,556C,559C,562C,565C,568C,571C,574C,577C,579C,582C,586C,589C,592C,595C,598C,601C,603C,607C,609C,613C,615C,617C,619C,622C,624C,626C,628C,633C,636C,638C,641C,644C,646C,649C,652C,654C,657C,661C,663C,666C,672C,674C,676C,679C,682C,684C,687C,690C,692C,695C,698C,701C,703C,706C,711C,713C,715C,718C,721C,723C,726C,729C,732C,735C,737C,740C,743C,745C,747C,750C,753C,759C,761C,764C,766C,876C ''synset.hyponyms'':194C,203C ''tan'':670C ''ten'':118C ''terrier.n.01'':268C,282C,285C,292C,302C,336C,348C,356C,367C,387C,399C,408C,427C,434C,449C,476C,517C,534C,591C,640C,660C,694C,725C,731C,742C,758C ''text'':150C ''thank'':221C ''tibetan'':433C,642C ''topic'':816C ''toy'':255C,499C,596C,634C,639C,716C ''turk'':832C ''tzu'':306C ''understand'':16C ''use'':164C ''via'':829C ''vizsla.n.01'':340C ''walker'':563C ''watchdog.n.02'':691C ''water'':242C,288C,333C,373C ''weimaraner.n.01'':361C ''welsh'':426C,471C ''west'':514C ''wheaten'':366C ''whippet.n.01'':702C ''white'':516C ''wire'':299C ''wire-hair'':298C ''wirehair.n.01'':253C ''wolfhound.n.01'':444C,588C ''word'':57C,59C ''wordnet'':33C,46C,52C,76C,132C,155C,183C ''wordnet.synset'':205C,212C ''work'':685C ''wouldn'':851C ''www.nltk.org'':169C ''yorkshir'':281C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9095, 5, '2015-11-28 14:59:51.69', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6882, 2, '2015-08-24 00:57:48.23', 2, NULL, NULL, '&lt;p&gt;There''s already at least one application out, if you interpret ''application'' broadly enough: &lt;a href="http://arxiv.org/abs/1506.04924" rel="nofollow"&gt;Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation by Hong, Noh and Han&lt;/a&gt;. They use it for &lt;a href="https://en.wikipedia.org/wiki/Image_segmentation" rel="nofollow"&gt;image segmentation&lt;/a&gt;. Standard image recognition networks can only give you a bounding box for each object recognized on an image. If you want to know which pixels constitute that object, you have to do image segmentation.&lt;/p&gt;

&lt;p&gt;Basically, after finding a dog on an image, Hong et al''s architecture back-propagates the dog-ness through the neural network down to the pixel level, to find the pixels that were the most responsible for the dog appearing. (They then use this heatmap as input for a supervised segmentation network, there''s no deep dreaming in that part.)&lt;/p&gt;

&lt;p&gt;This is already kind of an existence proof that the Deep Dream idea can be useful outside image manipulation. But I wouldn''t downplay image manipulation itself either. I mention two things that are not immediate applications of Deep Dreaming, and we don''t have them currently, but I can kinda see a plausible road from the original Deep Dream algorithm towards these:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Beautifying pictures and human faces and bodies. (Automating what a Photoshop retouch artist does.)&lt;/li&gt;
&lt;li&gt;CSI-style image upscaling with fake but believable interpolated detail.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':38C,52C ''/abs/1506.04924'':20C ''/li'':233C,246C ''/p'':87C,151C,216C ''/ul'':247C ''/wiki/image_segmentation'':47C ''al'':99C ''algorithm'':213C ''alreadi'':4C,155C ''appear'':130C ''applic'':8C,13C,189C ''architectur'':101C ''artist'':231C ''arxiv.org'':19C ''arxiv.org/abs/1506.04924'':18C ''autom'':226C ''back'':103C ''back-propag'':102C ''basic'':89C ''beautifi'':219C ''believ'':243C ''bodi'':225C ''bound'':62C ''box'':63C ''broad'':14C ''constitut'':78C ''csi'':236C ''csi-styl'':235C ''current'':199C ''decoupl'':23C ''deep'':24C,146C,163C,191C,211C ''detail'':245C ''dog'':93C,107C,129C ''dog-'':106C ''downplay'':176C ''dream'':147C,164C,192C,212C ''either'':180C ''en.wikipedia.org'':46C ''en.wikipedia.org/wiki/image_segmentation'':45C ''enough'':15C ''et'':98C ''exist'':159C ''face'':223C ''fake'':241C ''find'':91C,119C ''give'':59C ''han'':37C ''heatmap'':135C ''hong'':34C,97C ''href'':17C,44C ''human'':222C ''idea'':165C ''imag'':50C,54C,70C,85C,96C,170C,177C,238C ''immedi'':188C ''input'':137C ''interpol'':244C ''interpret'':12C ''kind'':156C ''kinda'':203C ''know'':75C ''least'':6C ''level'':117C ''li'':218C,234C ''manipul'':171C,178C ''mention'':182C ''ness'':108C ''network'':26C,56C,112C,142C ''neural'':25C,111C ''nofollow'':22C,49C ''noh'':35C ''object'':66C,80C ''one'':7C ''origin'':210C ''outsid'':169C ''p'':1C,88C,152C ''part'':150C ''photoshop'':229C ''pictur'':220C ''pixel'':77C,116C,121C ''plausibl'':206C ''proof'':160C ''propag'':104C ''recogn'':67C ''recognit'':55C ''rel'':21C,48C ''respons'':126C ''retouch'':230C ''road'':207C ''see'':204C ''segment'':32C,51C,86C,141C ''semant'':31C ''semi'':29C ''semi-supervis'':28C ''standard'':53C ''style'':237C ''supervis'':30C,140C ''thing'':184C ''toward'':214C ''two'':183C ''ul'':217C ''upscal'':239C ''use'':40C,133C,168C ''want'':73C ''wouldn'':174C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6843, 2, '2015-08-20 09:59:59.09', 2, NULL, NULL, '&lt;p&gt;If you have data on how many downloads have been done for each category, you can use that data to perform logistic regression, where your target variable would be 0/1 classification of male/female. This will form your benchmark result and then you can improve upon the results. Especially look for feature selection, because there might be categories of apps which have nothing to do with target. You should also try bi variate profiling of variables with target to see if there is good correlation with target. 
Try using some advanced techniques once you have done some data exploration. 
KNN, Random Forest and Neural network for classification might give good results. Also cart might work fantastically here. &lt;/p&gt;
', NULL, NULL, '''/p'':118C ''0/1'':31C ''advanc'':91C ''also'':70C,112C ''app'':60C ''benchmark'':39C ''bi'':72C ''cart'':113C ''categori'':15C,58C ''classif'':32C,107C ''correl'':85C ''data'':5C,20C,98C ''done'':12C,96C ''download'':9C ''especi'':49C ''explor'':99C ''fantast'':116C ''featur'':52C ''forest'':102C ''form'':37C ''give'':109C ''good'':84C,110C ''improv'':45C ''knn'':100C ''logist'':23C ''look'':50C ''male/female'':34C ''mani'':8C ''might'':56C,108C,114C ''network'':105C ''neural'':104C ''noth'':63C ''p'':1C ''perform'':22C ''profil'':74C ''random'':101C ''regress'':24C ''result'':40C,48C,111C ''see'':80C ''select'':53C ''target'':27C,67C,78C,87C ''techniqu'':92C ''tri'':71C,88C ''upon'':46C ''use'':18C,89C ''variabl'':28C,76C ''variat'':73C ''work'':115C ''would'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9079, 2, '2015-11-27 10:39:27.05', 2, NULL, NULL, '&lt;p&gt;&lt;a href="http://conferences.oreilly.com/strata" rel="nofollow"&gt;Strata + Hadoop World&lt;/a&gt; by O''Reilly&lt;/p&gt;

&lt;p&gt;&lt;a href="https://dataedge.ischool.berkeley.edu/" rel="nofollow"&gt;DataEDGE&lt;/a&gt; by Berkeley School of Information. &lt;a href="https://dataedge.ischool.berkeley.edu/2015/video" rel="nofollow"&gt;Link&lt;/a&gt; to the videos.&lt;/p&gt;
', NULL, NULL, '''/2015/video'':34C ''/a'':12C,24C,38C ''/p'':16C,42C ''/strata'':6C ''berkeley'':26C ''conferences.oreilly.com'':5C ''conferences.oreilly.com/strata'':4C ''dataedg'':23C ''dataedge.ischool.berkeley.edu'':20C,33C ''dataedge.ischool.berkeley.edu/2015/video'':32C ''hadoop'':10C ''href'':3C,19C,31C ''inform'':29C ''link'':37C ''nofollow'':8C,22C,36C ''o'':14C ''p'':1C,17C ''reilli'':15C ''rel'':7C,21C,35C ''school'':27C ''strata'':9C ''video'':41C ''world'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6844, 2, '2015-08-20 10:52:36.83', 3, NULL, NULL, '&lt;p&gt;I''m working on a similar classification problem using Quinlans &lt;code&gt;C5.0&lt;/code&gt; and in my experience there are couple of tweaks inside that might help you. &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;First, I believe using such big number (trials=500) for boosting is
affecting the overfitting, since the model will repeatedly try and
improve on the misclassified instances in every iteration. Try using
a smaller boosting size, or even turning it off - it might help with
the overfitting problem.&lt;/li&gt;
&lt;li&gt;There are a couple of other parameters you might find useful. The minimal leaf size (&lt;code&gt;minCases&lt;/code&gt; in &lt;code&gt;C5.0Control()&lt;/code&gt; is something to you want to keep as big as possible when avoiding overfitting issues. &lt;/li&gt;
&lt;li&gt;Another one is the confidence factor (&lt;code&gt;CF&lt;/code&gt;) which lets you control
the severity of pruning of the tree, meaning lower factor levels will likely prune away the leaves which overspecify the classification. &lt;/li&gt;
&lt;li&gt;Finally, you might try using the &lt;code&gt;fuzzyThreshold&lt;/code&gt; control option which lets you ''soften'' sharp thresholds with an inbetween grey area. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;These are all very well documented in the &lt;a href="https://cran.r-project.org/web/packages/C50/C50.pdf" rel="nofollow"&gt;&lt;code&gt;C5.0&lt;/code&gt;&lt;/a&gt; vignette as well as on the &lt;a href="https://www.rulequest.com/see5-unix.html" rel="nofollow"&gt;informal tutorial page&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;Hope this helps you a bit. &lt;/p&gt;
', NULL, NULL, '''/a'':200C,217C ''/code'':14C,99C,103C,129C,165C,199C ''/li'':80C,119C,155C,179C ''/p'':29C,218C,226C ''/see5-unix.html'':211C ''/ul'':180C ''/web/packages/c50/c50.pdf'':194C ''500'':40C ''affect'':44C ''anoth'':121C ''area'':178C ''avoid'':116C ''away'':148C ''believ'':34C ''big'':37C,112C ''bit'':225C ''boost'':42C,66C ''c5.0'':13C,198C ''c5.0control'':102C ''cf'':128C ''classif'':8C,154C ''code'':12C,97C,101C,127C,163C,197C ''confid'':125C ''control'':133C,166C ''coupl'':21C,85C ''cran.r-project.org'':193C ''cran.r-project.org/web/packages/c50/c50.pdf'':192C ''document'':187C ''even'':69C ''everi'':60C ''experi'':18C ''factor'':126C,143C ''final'':157C ''find'':91C ''first'':32C ''fuzzythreshold'':164C ''grey'':177C ''help'':27C,75C,222C ''hope'':220C ''href'':191C,208C ''improv'':54C ''inbetween'':176C ''inform'':214C ''insid'':24C ''instanc'':58C ''issu'':118C ''iter'':61C ''keep'':110C ''leaf'':95C ''leav'':150C ''let'':131C,169C ''level'':144C ''li'':31C,81C,120C,156C ''like'':146C ''lower'':142C ''m'':3C ''mean'':141C ''might'':26C,74C,90C,159C ''mincas'':98C ''minim'':94C ''misclassifi'':57C ''model'':49C ''nofollow'':196C,213C ''number'':38C ''one'':122C ''option'':167C ''overfit'':46C,78C,117C ''overspecifi'':152C ''p'':1C,181C,219C ''page'':216C ''paramet'':88C ''possibl'':114C ''problem'':9C,79C ''prune'':137C,147C ''quinlan'':11C ''rel'':195C,212C ''repeat'':51C ''sever'':135C ''sharp'':172C ''similar'':7C ''sinc'':47C ''size'':67C,96C ''smaller'':65C ''soften'':171C ''someth'':105C ''threshold'':173C ''tree'':140C ''tri'':52C,62C,160C ''trial'':39C ''turn'':70C ''tutori'':215C ''tweak'':23C ''ul'':30C ''use'':10C,35C,63C,92C,161C ''vignett'':201C ''want'':108C ''well'':186C,203C ''work'':4C ''www.rulequest.com'':210C ''www.rulequest.com/see5-unix.html'':209C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6846, 2, '2015-08-20 12:15:13.117', 1, NULL, NULL, '&lt;p&gt;SVM models perform better on sparse data than does trees in general.  For example in document classification you may have thousands, even tens of thousands of features and in any given document vector only a small fraction of these features may have a value greater than zero.  There are probably other differences between them, but this is what I found for my problems.  &lt;/p&gt;
', NULL, NULL, '''/p'':65C ''better'':5C ''classif'':18C ''data'':8C ''differ'':53C ''document'':17C,33C ''even'':23C ''exampl'':15C ''featur'':28C,41C ''found'':61C ''fraction'':38C ''general'':13C ''given'':32C ''greater'':46C ''may'':20C,42C ''model'':3C ''p'':1C ''perform'':4C ''probabl'':51C ''problem'':64C ''small'':37C ''spars'':7C ''svm'':2C ''ten'':24C ''thousand'':22C,26C ''tree'':11C ''valu'':45C ''vector'':34C ''zero'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6907, 1, '2015-08-26 15:35:29.083', 3, 165, 'How to identify outliers from a small list of numbers', '&lt;p&gt;I want to identify outliers from a very small group of numbers. How shall I do that? &lt;/p&gt;

&lt;p&gt;For example, for the group of numbers: -0.4, 0.4, 52.1, actually 52.1 is an outlier. &lt;/p&gt;

&lt;p&gt;I''ve tried using interquartile range to identify the outliers, but it won''t identify 52.1 as the outlier. &lt;/p&gt;
', 'machine-learning  statistics', NULL, '''-0.4'':42C ''/p'':33C,50C,71C ''0.4'':43C ''52.1'':44C,46C,67C ''actual'':45C ''exampl'':36C ''group'':25C,39C ''identifi'':3A,19C,59C,66C ''interquartil'':56C ''learn'':13B ''list'':8A ''machin'':12B ''machine-learn'':11B ''number'':10A,27C,41C ''outlier'':4A,20C,49C,61C,70C ''p'':15C,34C,51C ''rang'':57C ''shall'':29C ''small'':7A,24C ''statist'':14B ''tri'':54C ''use'':55C ''ve'':53C ''want'':17C ''won'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9096, 4, '2015-11-28 14:59:51.69', 0, NULL, NULL, 'Gradient Boosting Machine&#x0d;
', NULL, NULL, '''boost'':2C ''gradient'':1C ''machin'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10168, 1, '2016-02-09 19:43:01.207', 5, 110, 'Stuck on deconvolution in Theano and TensorFlow', '&lt;p&gt;I''m captivated by autoencoders and really like the idea of convolution.  It seems though that both Theano and TensorFlow only support conv2d to go from an array of 2D-RGB (n 3D arrays) to an array of higher-depth images.  That makes sense from the traditional tensor-product math, c_ijkl = sum{a_ijk*b_klm}, but means it''s hard to ''de-convolve'' an image.  &lt;/p&gt;

&lt;p&gt;In both cases, if I have an image (in #batch, depth, height, width form), I can do a conv to get (#batch, num_filters, height/k, width/k).  I''d really like to do the opposite, like going from (#batch, some_items, height/k, width/k) to (#batch, depth, height, width).&lt;/p&gt;

&lt;p&gt;TensorFlow had the hidden deconv2d function for a while (in 0.6, I think, undocumented), but I''d like to know if there''s a math trick I can use to get a bigger output in the last two dimensions after a convolution than the input.  I''d settle for a series of differentiable operations, like conv -&gt; resize, but I want to avoid just doing a dense matrix multiplication -&gt; resize like I''ve been doing so far.&lt;/p&gt;

&lt;p&gt;EDIT: As of today (2016/02/17) TensorFlow 0.7 has the tf.depth_to_space method, which helps greatly in this endeavor.  (&lt;a href="https://www.tensorflow.org/versions/r0.7/api_docs/python/array_ops.html#depth_to_space" rel="nofollow"&gt;https://www.tensorflow.org/versions/r0.7/api_docs/python/array_ops.html#depth_to_space&lt;/a&gt;)  I would still love a Theano based solution, too, to complete my understanding of the material.&lt;/p&gt;
', 'theano  convnet  tensorflow  autoencoder', NULL, '''/p'':84C,133C,211C,258C ''/versions/r0.7/api_docs/python/array_ops.html#depth_to_space'':236C ''/versions/r0.7/api_docs/python/array_ops.html#depth_to_space&lt;/a&gt;)'':241C ''0.6'':145C ''0.7'':219C ''2016/02/17'':217C ''2d'':43C ''2d-rgb'':42C ''3d'':46C ''array'':40C,47C,50C ''autoencod'':11B,17C ''avoid'':196C ''b'':71C ''base'':248C ''batch'':95C,107C,123C,129C ''bigger'':167C ''c'':66C ''captiv'':15C ''case'':88C ''complet'':252C ''conv'':104C,190C ''conv2d'':35C ''convnet'':9B ''convolut'':24C,176C ''convolv'':81C ''d'':113C,151C,181C ''de'':80C ''de-convolv'':79C ''deconv2d'':139C ''deconvolut'':3A ''dens'':200C ''depth'':54C,96C,130C ''differenti'':187C ''dimens'':173C ''edit'':213C ''endeavor'':231C ''far'':210C ''filter'':109C ''form'':99C ''function'':140C ''get'':106C,165C ''go'':37C,121C ''great'':228C ''hard'':77C ''height'':97C,131C ''height/k'':110C,126C ''help'':227C ''hidden'':138C ''higher'':53C ''higher-depth'':52C ''href'':233C ''idea'':22C ''ijk'':70C ''ijkl'':67C ''imag'':55C,83C,93C ''input'':179C ''item'':125C ''klm'':72C ''know'':154C ''last'':171C ''like'':20C,115C,120C,152C,189C,204C ''love'':245C ''m'':14C ''make'':57C ''materi'':257C ''math'':65C,159C ''matrix'':201C ''mean'':74C ''method'':225C ''multipl'':202C ''n'':45C ''nofollow'':238C ''num'':108C ''oper'':188C ''opposit'':119C ''output'':168C ''p'':12C,85C,134C,212C ''product'':64C ''realli'':19C,114C ''rel'':237C ''resiz'':191C,203C ''rgb'':44C ''seem'':26C ''sens'':58C ''seri'':185C ''settl'':182C ''solut'':249C ''space'':224C ''still'':244C ''stuck'':1A ''sum'':68C ''support'':34C ''tensor'':63C ''tensor-product'':62C ''tensorflow'':7A,10B,32C,135C,218C ''tf.depth'':222C ''theano'':5A,8B,30C,247C ''think'':147C ''though'':27C ''today'':216C ''tradit'':61C ''trick'':160C ''two'':172C ''understand'':254C ''undocu'':148C ''use'':163C ''ve'':206C ''want'':194C ''width'':98C,132C ''width/k'':111C,127C ''would'':243C ''www.tensorflow.org'':235C,240C ''www.tensorflow.org/versions/r0.7/api_docs/python/array_ops.html#depth_to_space'':234C ''www.tensorflow.org/versions/r0.7/api_docs/python/array_ops.html#depth_to_space&lt;/a&gt;)'':239C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6847, 1, '2015-08-20 14:56:40.847', 2, 30, 'Estimating destination according to previous data', '&lt;p&gt;I need an advice. I can resume my problem like that :&lt;/p&gt;

&lt;p&gt;I have some travels in a database, for example :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Person1 travelled from CityA to CityB on Date1
Person1 travelled from CityB to CityC on Date2
Person2 travelled from CityB to CityD on Date3
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;We can consider that these cities are in the complete graph.&lt;/p&gt;

&lt;p&gt;Now, according to all the travels in the database, I would like to know where a PersonX is likely to go. I can know when he come from (or not).&lt;/p&gt;

&lt;p&gt;I don''t know if I should use machine learning, data-mining or graph theory.&lt;/p&gt;
', 'machine-learning  data-mining  graphs', NULL, '''/code'':64C ''/p'':26C,37C,78C,110C,128C ''/pre'':65C ''accord'':3A,81C ''advic'':18C ''citi'':72C ''citya'':43C ''cityb'':45C,51C,59C ''cityc'':53C ''cityd'':61C ''code'':39C ''come'':106C ''complet'':76C ''consid'':69C ''data'':6A,11B,123C ''data-min'':10B,122C ''databas'':34C,88C ''date1'':47C ''date2'':55C ''date3'':63C ''destin'':2A ''estim'':1A ''exampl'':36C ''go'':100C ''graph'':13B,77C,126C ''know'':93C,103C,115C ''learn'':9B,121C ''like'':24C,91C,98C ''machin'':8B,120C ''machine-learn'':7B ''mine'':12B,124C ''need'':16C ''p'':14C,27C,66C,79C,111C ''person1'':40C,48C ''person2'':56C ''personx'':96C ''pre'':38C ''previous'':5A ''problem'':23C ''resum'':21C ''theori'':127C ''travel'':31C,41C,49C,57C,85C ''use'':119C ''would'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6848, 1, '2015-08-20 17:42:23.987', 2, 105, 'Research in high-dimensional statistics vs. machine learning?', '&lt;p&gt;&lt;strong&gt;(I''ve posted this question on CV, but I feel it would also be great to hear from experts in DS community.)&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;As a PhD student starting to think about dissertation topics, I am particularly interested in high-dimensional statistical learning. I wish to find some research review/survey/papers (or webpages, blogs, whatever...) about the state-of-the-art research in this research area, but there seems limited resources I can obtain. My first question is then,&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Could you describe some current interesting research topics in
high-dimensional statistics? If you can list any relevant resources
(papers, webpages, etc.), that would be really helpful.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;In addition, I''ve noticed that high-dimensional statistical learning is closed related with machine learning research. For example, the idea of penalized regularization in high-dimensional statistics was used in machine learning domain, like support vector machine, boosting tree, (sparse) additive models, etc. My question is,&lt;/p&gt;

&lt;ol start="2"&gt;
&lt;li&gt;what are good research papers about the interplay of
high-dimensional statistics and machine learning?&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Last, since high-dimensional statistics was really motivated by genetic research (like gene-expression analysis, or genome-wide association studies), most of applications in high-dimensional research are devoted in that area.&lt;/p&gt;

&lt;ol start="3"&gt;
&lt;li&gt;&lt;p&gt;Are there any successful applications of high-dimensional statistics
in areas other than genetics, particularly say, image/text mining,
recommendation, etc, areas where machine learning techniques have
long been used?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;A new question to machine learning researchers/practitioners&lt;/strong&gt;: I might be wrong, but as I understand, most machine learning algorithms are designed for low-dimensional problems (or at least the number of features is smaller that number of observations). Are there any successful applications of machine learning techniques for modeling high-dimensional data? &lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Any resources/comments are highly appreciated. Thanks.&lt;/p&gt;
', 'machine-learning  statistics', NULL, '''/li'':128C,201C,277C,337C ''/ol'':129C,202C,338C ''/p'':39C,97C,180C,240C,276C,336C,346C ''/strong'':38C,288C ''2'':183C ''3'':243C ''addit'':132C,174C ''algorithm'':300C ''also'':28C ''analysi'':220C ''applic'':229C,250C,325C ''appreci'':344C ''area'':83C,239C,257C,267C ''art'':78C ''associ'':225C ''blog'':70C ''boost'':171C ''close'':143C ''communiti'':37C ''could'':100C ''current'':104C ''cv'':22C ''data'':335C ''describ'':102C ''design'':302C ''devot'':236C ''dimension'':5A,58C,111C,139C,159C,196C,208C,233C,254C,306C,334C ''dissert'':49C ''domain'':166C ''ds'':36C ''etc'':122C,176C,266C ''exampl'':150C ''expert'':34C ''express'':219C ''featur'':314C ''feel'':25C ''find'':64C ''first'':93C ''gene'':218C ''gene-express'':217C ''genet'':214C,260C ''genom'':223C ''genome-wid'':222C ''good'':187C ''great'':30C ''hear'':32C ''help'':127C ''high'':4A,57C,110C,138C,158C,195C,207C,232C,253C,333C,343C ''high-dimension'':3A,56C,109C,137C,157C,194C,206C,231C,252C,332C ''idea'':152C ''image/text'':263C ''interest'':54C,105C ''interplay'':192C ''last'':204C ''learn'':9A,12B,60C,141C,147C,165C,200C,270C,286C,299C,328C ''least'':310C ''li'':99C,184C,244C,278C ''like'':167C,216C ''limit'':87C ''list'':116C ''long'':273C ''low'':305C ''low-dimension'':304C ''machin'':8A,11B,146C,164C,170C,199C,269C,285C,298C,327C ''machine-learn'':10B ''might'':290C ''mine'':264C ''model'':175C,331C ''motiv'':212C ''new'':282C ''notic'':135C ''number'':312C,318C ''observ'':320C ''obtain'':91C ''ol'':98C,181C,241C ''p'':14C,40C,130C,203C,245C,279C,339C ''paper'':120C,189C ''particular'':53C,261C ''penal'':154C ''phd'':43C ''post'':18C ''problem'':307C ''question'':20C,94C,178C,283C ''realli'':126C,211C ''recommend'':265C ''regular'':155C ''relat'':144C ''relev'':118C ''research'':1A,66C,79C,82C,106C,148C,188C,215C,234C ''researchers/practitioners'':287C ''resourc'':88C,119C ''resources/comments'':341C ''review/survey/papers'':67C ''say'':262C ''seem'':86C ''sinc'':205C ''smaller'':316C ''spars'':173C ''start'':45C,182C,242C ''state'':75C ''state-of-the-art'':74C ''statist'':6A,13B,59C,112C,140C,160C,197C,209C,255C ''strong'':15C,280C ''student'':44C ''studi'':226C ''success'':249C,324C ''support'':168C ''techniqu'':271C,329C ''thank'':345C ''think'':47C ''topic'':50C,107C ''tree'':172C ''understand'':296C ''use'':162C,275C ''ve'':17C,134C ''vector'':169C ''vs'':7A ''webpag'':69C,121C ''whatev'':71C ''wide'':224C ''wish'':62C ''would'':27C,124C ''wrong'':292C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6849, 2, '2015-08-20 17:52:15.133', 2, NULL, NULL, '&lt;p&gt;This is a spatio-temporal clustering problem that is likely best solved with a Markov model. You could reasonable group this into machine learning or data mining. Develop your model using machine learning and then (the data mining part) leverage those pattern recognition techniques (that have been developed in machine learning). I think there are at least one or two threads on this over at Cross-Validated that go into more detail.  &lt;/p&gt;

&lt;p&gt;Here are a couple of papers to look at if you are just getting started.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://link.springer.com/article/10.1007/s00779-003-0240-0" rel="nofollow"&gt;Using GPS to learn significant locations and predict movement across multiple users&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://lbsn2012.cmuchimps.org/papers/Paper16_Mathew.pdf" rel="nofollow"&gt;Predicting Future Locations with Hidden Markov Models&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':114C,131C ''/article/10.1007/s00779-003-0240-0'':99C ''/p'':76C,93C,115C,132C ''/papers/paper16_mathew.pdf'':121C ''across'':111C ''best'':13C ''cluster'':8C ''could'':20C ''coupl'':81C ''cross'':69C ''cross-valid'':68C ''data'':28C,39C ''detail'':75C ''develop'':30C,50C ''futur'':125C ''get'':91C ''go'':72C ''gps'':103C ''group'':22C ''hidden'':128C ''href'':96C,118C ''lbsn2012.cmuchimps.org'':120C ''lbsn2012.cmuchimps.org/papers/paper16_mathew.pdf'':119C ''learn'':26C,35C,53C,105C ''least'':59C ''leverag'':42C ''like'':12C ''link.springer.com'':98C ''link.springer.com/article/10.1007/s00779-003-0240-0'':97C ''locat'':107C,126C ''look'':85C ''machin'':25C,34C,52C ''markov'':17C,129C ''mine'':29C,40C ''model'':18C,32C,130C ''movement'':110C ''multipl'':112C ''nofollow'':101C,123C ''one'':60C ''p'':1C,77C,94C,116C ''paper'':83C ''part'':41C ''pattern'':44C ''predict'':109C,124C ''problem'':9C ''reason'':21C ''recognit'':45C ''rel'':100C,122C ''signific'':106C ''solv'':14C ''spatio'':6C ''spatio-tempor'':5C ''start'':92C ''techniqu'':46C ''tempor'':7C ''think'':55C ''thread'':63C ''two'':62C ''use'':33C,102C ''user'':113C ''valid'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6851, 1, '2015-08-20 20:46:07.74', 6, 111, 'What''s an efficient way to compare and group millions of store names?', '&lt;p&gt;I''m a total amateur as far as data science goes, and I''m trying to figure out a way to do some string comparison on a large dataset.  &lt;/p&gt;

&lt;p&gt;I''ve a Google BigQuery table storing merchant transactions, but the store names are all over the board.  For example, there can be ''Wal-Mart Super Center'' and ''Wal-Mart SC #1234'', or ''McDonalds F2222'' and ''McDonalds #321''.  &lt;/p&gt;

&lt;p&gt;What I need to do is group ALL ''Wal-mart'' and ''McDonalds'' and whatever else.  My first approach was doing a recursive reg-ex check, but that took forever and eventually timed-out. &lt;/p&gt;

&lt;p&gt;What''s the best approach for doing that with a table of 20 million+ rows?  I''m open to trying out any technology that would fit this job. &lt;/p&gt;
', 'bigdata', NULL, '''/p'':45C,87C,125C,155C ''1234'':80C ''20'':139C ''321'':86C ''amateur'':20C ''approach'':107C,131C ''best'':130C ''bigdata'':14B ''bigqueri'':51C ''board'':64C ''center'':74C ''check'':115C ''compar'':7A ''comparison'':40C ''data'':24C ''dataset'':44C ''effici'':4A ''els'':104C ''eventu'':121C ''ex'':114C ''exampl'':66C ''f2222'':83C ''far'':22C ''figur'':32C ''first'':106C ''fit'':152C ''forev'':119C ''goe'':26C ''googl'':50C ''group'':9A,95C ''job'':154C ''larg'':43C ''m'':17C,29C,143C ''mart'':72C,78C,99C ''mcdonald'':82C,85C,101C ''merchant'':54C ''million'':10A,140C ''name'':13A,59C ''need'':91C ''open'':144C ''p'':15C,46C,88C,126C ''recurs'':111C ''reg'':113C ''reg-ex'':112C ''row'':141C ''sc'':79C ''scienc'':25C ''store'':12A,53C,58C ''string'':39C ''super'':73C ''tabl'':52C,137C ''technolog'':149C ''time'':123C ''timed-out'':122C ''took'':118C ''total'':19C ''transact'':55C ''tri'':30C,146C ''ve'':48C ''wal'':71C,77C,98C ''wal-mart'':70C,76C,97C ''way'':5A,35C ''whatev'':103C ''would'':151C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10402, 2, '2016-02-26 06:00:07.287', -1, NULL, NULL, '&lt;p&gt;The corpus quality can be determined using the zipf law ,vocabulary growth curve and the pareto distribution. An interesting thesis which explains this can be found at   &lt;/p&gt;

&lt;p&gt;&lt;a href="http://ilk.uvt.nl/downloads/pub/papers/hait/camp2008.pdf" rel="nofollow"&gt;http://ilk.uvt.nl/downloads/pub/papers/hait/camp2008.pdf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/downloads/pub/papers/hait/camp2008.pdf'':35C ''/downloads/pub/papers/hait/camp2008.pdf&lt;/a&gt;&lt;/p&gt;'':40C ''/p'':29C ''corpus'':3C ''curv'':14C ''determin'':7C ''distribut'':18C ''explain'':23C ''found'':27C ''growth'':13C ''href'':32C ''ilk.uvt.nl'':34C,39C ''ilk.uvt.nl/downloads/pub/papers/hait/camp2008.pdf'':33C ''ilk.uvt.nl/downloads/pub/papers/hait/camp2008.pdf&lt;/a&gt;&lt;/p&gt;'':38C ''interest'':20C ''law'':11C ''nofollow'':37C ''p'':1C,30C ''pareto'':17C ''qualiti'':4C ''rel'':36C ''thesi'':21C ''use'':8C ''vocabulari'':12C ''zipf'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9097, 5, '2015-11-28 15:01:23.757', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6866, 1, '2015-08-21 14:26:10.297', 2, 47, 'Denoising Autoenoders with Variable Length Input', '&lt;p&gt;I''m working on a problem with data from a continuous real-valued signal. The goal is to use ML to smooth the signal based off of past data. To accomplish this, the signal is windowed into a period that''s meaningful within the domain. The problem is that this period is highly variable in length. &lt;/p&gt;

&lt;p&gt;I''ve reviewed &lt;a href="https://datascience.stackexchange.com/questions/2673/training-neural-networks-with-unknown-length-of-input?rq=1"&gt;this question&lt;/a&gt; and &lt;a href="https://datascience.stackexchange.com/questions/595/how-to-use-neural-networks-with-large-and-variable-number-of-inputs?rq=1"&gt;this question&lt;/a&gt; and neither solve the problem, they are more about how to deal with missing values. &lt;/p&gt;

&lt;p&gt;Seeing as denoising autoencoders are based off of matrix multiplication, this presents a serious problem. What is the standard approach in such a situation? Should I define an arbitrary (large) window size, and expand windows that are too small (and vice versa)? Or is there a better approach for variable length inputs? &lt;/p&gt;
', 'neuralnetwork  preprocessing', NULL, '''/a'':79C,88C ''/p'':67C,104C,158C ''/questions/2673/training-neural-networks-with-unknown-length-of-input?rq=1'':76C ''/questions/595/how-to-use-neural-networks-with-large-and-variable-number-of-inputs?rq=1'':85C ''accomplish'':41C ''approach'':125C,153C ''arbitrari'':134C ''autoencod'':109C ''autoenod'':2A ''base'':35C,111C ''better'':152C ''continu'':20C ''data'':17C,39C ''datascience.stackexchange.com'':75C,84C ''datascience.stackexchange.com/questions/2673/training-neural-networks-with-unknown-length-of-input?rq=1'':74C ''datascience.stackexchange.com/questions/595/how-to-use-neural-networks-with-large-and-variable-number-of-inputs?rq=1'':83C ''deal'':100C ''defin'':132C ''denois'':1A,108C ''domain'':55C ''expand'':139C ''goal'':26C ''high'':63C ''href'':73C,82C ''input'':6A,157C ''larg'':135C ''length'':5A,66C,156C ''m'':11C ''matrix'':114C ''meaning'':52C ''miss'':102C ''ml'':30C ''multipl'':115C ''neither'':90C ''neuralnetwork'':7B ''p'':9C,68C,105C ''past'':38C ''period'':49C,61C ''preprocess'':8B ''present'':117C ''problem'':15C,57C,93C,120C ''question'':78C,87C ''real'':22C ''real-valu'':21C ''review'':71C ''see'':106C ''serious'':119C ''signal'':24C,34C,44C ''situat'':129C ''size'':137C ''small'':144C ''smooth'':32C ''solv'':91C ''standard'':124C ''use'':29C ''valu'':23C,103C ''variabl'':4A,64C,155C ''ve'':70C ''versa'':147C ''vice'':146C ''window'':46C,136C,140C ''within'':53C ''work'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6852, 2, '2015-08-20 21:30:19.697', 2, NULL, NULL, '&lt;p&gt;This makes me think right off the bat of &lt;a href="https://en.wikipedia.org/wiki/Gaussian_process" rel="nofollow"&gt;Gaussian processes&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The GP was initially developed by geostatisticians in the 1970s as a model for the distribution of resources over a geographic area; this process is known as &lt;a href="https://en.wikipedia.org/wiki/Kriging" rel="nofollow"&gt;kriging&lt;/a&gt; (or also Gaussian process regression). But more recently GPs have been used pretty widely in the movement/control/robotics area to model movements and trajectories of robotic limbs etc. over time.&lt;/p&gt;

&lt;p&gt;Statistically, GPs are treated as priors over vectors of infinite dimension, such that the distribution of any subset of the values of a vector is Gaussian distributed. There''s an &lt;a href="http://www.gaussianprocess.org/gpml/chapters/" rel="nofollow"&gt;online book&lt;/a&gt; if you want to read more!&lt;/p&gt;
', NULL, NULL, '''/a'':20C,58C,129C ''/gpml/chapters/'':124C ''/p'':21C,88C,136C ''/wiki/gaussian_process'':15C ''/wiki/kriging'':54C ''1970s'':32C ''also'':60C ''area'':44C,76C ''bat'':9C ''book'':128C ''develop'':27C ''dimens'':100C ''distribut'':38C,104C,116C ''en.wikipedia.org'':14C,53C ''en.wikipedia.org/wiki/gaussian_process'':13C ''en.wikipedia.org/wiki/kriging'':52C ''etc'':85C ''gaussian'':18C,61C,115C ''geograph'':43C ''geostatistician'':29C ''gp'':24C ''gps'':67C,91C ''href'':12C,51C,121C ''infinit'':99C ''initi'':26C ''known'':48C ''krige'':57C ''limb'':84C ''make'':3C ''model'':35C,78C ''movement'':79C ''movement/control/robotics'':75C ''nofollow'':17C,56C,126C ''onlin'':127C ''p'':1C,22C,89C ''pretti'':71C ''prior'':95C ''process'':19C,46C,62C ''read'':134C ''recent'':66C ''regress'':63C ''rel'':16C,55C,125C ''resourc'':40C ''right'':6C ''robot'':83C ''statist'':90C ''subset'':107C ''think'':5C ''time'':87C ''trajectori'':81C ''treat'':93C ''use'':70C ''valu'':110C ''vector'':97C,113C ''want'':132C ''wide'':72C ''www.gaussianprocess.org'':123C ''www.gaussianprocess.org/gpml/chapters/'':122C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6853, 2, '2015-08-20 22:19:26.357', 7, NULL, NULL, '&lt;p&gt;This is an &lt;a href="http://www.umiacs.umd.edu/~getoor/Tutorials/ER_VLDB2012.pdf"&gt;entity resolution&lt;/a&gt; aka &lt;a href="https://en.wikipedia.org/wiki/Record_linkage"&gt;record linkage&lt;/a&gt; aka &lt;a href="http://www.springer.com/us/book/9783642311635"&gt;data matching&lt;/a&gt; problem.&lt;/p&gt;

&lt;p&gt;I would solve this by removing all of the non-alphabetical characters including numbers, casting into all uppercase and then employing a hierarchical match.  First match up the exact cases and then move to a Levenshtein scoring between the fields.  Make some sort of a decision about how large you will allow the Levenshtein or normalized Levenshtein score to get before you declare something a non-match.&lt;/p&gt;

&lt;p&gt;Assign every row an id and when you have a match, reassign the lower of the IDs to both members of the match.&lt;/p&gt;

&lt;p&gt;The &lt;a href="https://en.wikipedia.org/wiki/Levenshtein_distance"&gt;Levenshtein distance&lt;/a&gt; algorithm is simple but brilliant (&lt;a href="http://hetland.org/coding/python/levenshtein.py"&gt;taken from here&lt;/a&gt;):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def levenshtein(a,b):
    "Calculates the Levenshtein distance between a and b."
    n, m = len(a), len(b)
    if n &amp;gt; m:
        # Make sure n &amp;lt;= m, to use O(min(n,m)) space
        a,b = b,a
        n,m = m,n

    current = range(n+1)
    for i in range(1,m+1):
        previous, current = current, [i]+[0]*n
        for j in range(1,n+1):
            add, delete = previous[j]+1, current[j-1]+1
            change = previous[j-1]
            if a[j-1] != b[i-1]:
                change = change + 1
            current[j] = min(add, delete, change)

    return current[n]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This &lt;a href="http://rads.stackoverflow.com/amzn/click/3642311636"&gt;Data Matching&lt;/a&gt; book is a good resource and is free for seven days on Amazon.&lt;/p&gt;

&lt;p&gt;Nominally, this is an $n^2$ algorithm without exploiting some sorting efficiencies, so I would expect to have to use multiple cores on $2\times10^7$ rows.  But this should run just fine on an 8 core &lt;a href="https://aws.amazon.com/"&gt;AWS instance&lt;/a&gt;.  It will eventually finish on a single core, but might take several hours.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''+1'':201C,208C,221C,226C,230C ''-1'':229C,234C,238C,241C ''/a'':12C,21C,30C,138C,152C,265C,323C ''/amzn/click/3642311636'':262C ''/code'':254C ''/coding/python/levenshtein.py'':148C ''/p'':32C,103C,128C,153C,279C,337C,342C ''/pre'':255C ''/us/book/9783642311635'':27C ''/wiki/levenshtein_distance'':135C ''/wiki/record_linkage'':18C ''/~getoor/tutorials/er_vldb2012.pdf'':9C ''0'':213C ''1'':206C,219C,244C ''2'':286C,304C ''7'':306C ''8'':316C ''add'':222C,248C ''aka'':13C,22C ''algorithm'':139C,287C ''allow'':86C ''alphabet'':45C ''amazon'':278C ''assign'':105C ''aw'':321C ''aws.amazon.com'':320C ''b'':159C,167C,173C,191C,192C,239C ''book'':266C ''brilliant'':143C ''calcul'':160C ''case'':64C ''cast'':49C ''chang'':231C,242C,243C,250C ''charact'':46C ''code'':155C ''core'':302C,317C,331C ''current'':198C,210C,211C,227C,245C,252C ''data'':28C,263C ''day'':276C ''decis'':80C ''declar'':97C ''def'':156C ''delet'':223C,249C ''distanc'':137C,163C ''effici'':292C ''employ'':55C ''en.wikipedia.org'':17C,134C ''en.wikipedia.org/wiki/levenshtein_distance'':133C ''en.wikipedia.org/wiki/record_linkage'':16C ''entiti'':10C ''eventu'':326C ''everi'':106C ''exact'':63C ''expect'':296C ''exploit'':289C ''field'':74C ''fine'':313C ''finish'':327C ''first'':59C ''free'':273C ''get'':94C ''good'':269C ''gt'':176C ''help'':341C ''hetland.org'':147C ''hetland.org/coding/python/levenshtein.py'':146C ''hierarch'':57C ''hope'':339C ''hour'':336C ''href'':6C,15C,24C,132C,145C,259C,319C ''id'':109C,121C ''includ'':47C ''instanc'':322C ''j'':216C,225C,228C,233C,237C,246C ''larg'':83C ''len'':170C,172C ''levenshtein'':70C,88C,91C,136C,157C,162C ''linkag'':20C ''lower'':118C ''lt'':181C ''m'':169C,177C,182C,188C,195C,196C,207C ''make'':75C,178C ''match'':29C,58C,60C,102C,115C,127C,264C ''member'':124C ''might'':333C ''min'':186C,247C ''move'':67C ''multipl'':301C ''n'':168C,175C,180C,187C,194C,197C,200C,214C,220C,253C,285C ''nomin'':281C ''non'':44C,101C ''non-alphabet'':43C ''non-match'':100C ''normal'':90C ''number'':48C ''o'':185C ''p'':1C,33C,104C,129C,256C,280C,338C ''pre'':154C ''previous'':209C,224C,232C ''problem'':31C ''rads.stackoverflow.com'':261C ''rads.stackoverflow.com/amzn/click/3642311636'':260C ''rang'':199C,205C,218C ''reassign'':116C ''record'':19C ''remov'':39C ''resolut'':11C ''resourc'':270C ''return'':251C ''row'':107C,307C ''run'':311C ''score'':71C,92C ''seven'':275C ''sever'':335C ''simpl'':141C ''singl'':330C ''solv'':36C ''someth'':98C ''sort'':77C,291C ''space'':189C ''sure'':179C ''take'':334C ''taken'':149C ''times10'':305C ''uppercas'':52C ''use'':184C,300C ''without'':288C ''would'':35C,295C ''www.springer.com'':26C ''www.springer.com/us/book/9783642311635'':25C ''www.umiacs.umd.edu'':8C ''www.umiacs.umd.edu/~getoor/tutorials/er_vldb2012.pdf'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6854, 1, '2015-08-20 22:21:12.06', 3, 76, 'Recommendations for storing time series data', '&lt;p&gt;As part of my thesis I''ve done some experiments that have resulted in a reasonable amount of time-series data (motion-capture + eye movements). I have a way of storing and organizing all of this data, but it''s made me wonder whether there are best practices out there for this sort of task.&lt;/p&gt;

&lt;p&gt;I''ll describe what I''ve got, and maybe that will help provide some recommendations. So, I have an experiment that requires subjects to use their vision and move their body to complete a task. Each task is one trial, and each subject performs multiple trials to complete the experiment. During a trial I record the movement and the eye tracker (~200 channels) at regularly sampled time points (~100Hz). I store these in a CSV file (one file per trial), with one row per time point, and one column per variable (e.g., left-fingertip-x, left-fingertip-y, left-fingertip-z, etc. for the mocap, and left-eye-x, left-eye-y for the eyes).&lt;/p&gt;

&lt;p&gt;Associated with each trial is some metadata such as the experimental condition of the trial (e.g., how fast a target in the trial is moving, say). I store these values in the CSV filename itself, using a "key=value" sort of syntax.&lt;/p&gt;

&lt;p&gt;While this works well enough for my purposes, it''s really ad-hoc! I''d like to get a sense of whether other people have solved problems like this, and, if so, how?&lt;/p&gt;
', 'time-series  data  experiments  csv  metadata', NULL, '''/p'':72C,194C,238C,274C ''100hz'':142C ''200'':135C ''ad'':252C ''ad-hoc'':251C ''amount'':31C ''associ'':196C ''best'':63C ''bodi'':104C ''captur'':39C ''channel'':136C ''column'':162C ''complet'':106C,121C ''condit'':207C ''csv'':12B,148C,228C ''d'':255C ''data'':6A,10B,36C,53C ''describ'':76C ''done'':22C ''e.g'':165C,211C ''enough'':244C ''etc'':178C ''experi'':11B,24C,93C,123C ''experiment'':206C ''eye'':40C,133C,185C,189C,193C ''fast'':213C ''file'':149C,151C ''filenam'':229C ''fingertip'':168C,172C,176C ''get'':258C ''got'':80C ''help'':85C ''hoc'':253C ''key'':233C ''left'':167C,171C,175C,184C,188C ''left-eye-i'':187C ''left-eye-x'':183C ''left-fingertip-i'':170C ''left-fingertip-x'':166C ''left-fingertip-z'':174C ''like'':256C,268C ''ll'':75C ''made'':57C ''mayb'':82C ''metadata'':13B,202C ''mocap'':181C ''motion'':38C ''motion-captur'':37C ''move'':102C,220C ''movement'':41C,130C ''multipl'':118C ''one'':112C,150C,155C,161C ''organ'':49C ''p'':14C,73C,195C,239C ''part'':16C ''peopl'':264C ''per'':152C,157C,163C ''perform'':117C ''point'':141C,159C ''practic'':64C ''problem'':267C ''provid'':86C ''purpos'':247C ''realli'':250C ''reason'':30C ''recommend'':1A,88C ''record'':128C ''regular'':138C ''requir'':95C ''result'':27C ''row'':156C ''sampl'':139C ''say'':221C ''sens'':260C ''seri'':5A,9B,35C ''solv'':266C ''sort'':69C,235C ''store'':3A,47C,144C,223C ''subject'':96C,116C ''syntax'':237C ''target'':215C ''task'':71C,108C,110C ''thesi'':19C ''time'':4A,8B,34C,140C,158C ''time-seri'':7B,33C ''tracker'':134C ''trial'':113C,119C,126C,153C,199C,210C,218C ''use'':98C,231C ''valu'':225C,234C ''variabl'':164C ''ve'':21C,79C ''vision'':100C ''way'':45C ''well'':243C ''whether'':60C,262C ''wonder'':59C ''work'':242C ''x'':169C,186C ''y'':173C,190C ''z'':177C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6855, 2, '2015-08-21 04:36:06.367', 8, NULL, NULL, '&lt;p&gt;I would say, the choice depends very much on what data you have and what is your purpose. A few "rules of thumb".&lt;/p&gt;

&lt;p&gt;Random Forest is intrinsically suited for multiclass problems, while SVM is intrinsically two-class. For multiclass problem you will need to reduce it into multiple binary classification problems.&lt;/p&gt;

&lt;p&gt;Random Forest works well with a mixture of numerical and categorical features. When features are on the various scales, it is also fine. Roughly speaking, with Random Forest you can use data as they are. SVM maximizes the "margin" and thus relies on the concept of "distance" between different points. It is up to you to decide if "distance" is meaningful. As a consequence, one-hot encoding for categorical features is a must-do. Further, min-max or other scaling is highly recommended at preprocessing step. &lt;/p&gt;

&lt;p&gt;If you have data with $n$ points and $m$ features, an intermediate step in SVM is constructing an $n\times n$ matrix (think about memory requirements for storage) by calculating $n^2$ dot products (computational complexity).
Therefore, as a rule of thumb, SVM is hardly scalable beyond 10^5 points.
Large number of features (homogeneous features with meaningful distance, pixel of image would be a perfect example) is generally not a problem. &lt;/p&gt;

&lt;p&gt;For a classification problem Random Forest gives you probability of belonging to class. SVM gives you distance to the boundary, you still need to convert it to probability somehow if you need probability.&lt;/p&gt;

&lt;p&gt;For those problems, where SVM applies, it generally performs better than Random Forest.&lt;/p&gt;

&lt;p&gt;SVM gives you "support vectors", that is points in each class closest to the boundary between classes. They may be of interest by themselves for interpretation. &lt;/p&gt;
', NULL, NULL, '''/p'':25C,56C,147C,221C,256C,271C,299C ''10'':196C ''2'':180C ''5'':197C ''also'':79C ''appli'':263C ''belong'':233C ''better'':267C ''beyond'':195C ''binari'':53C ''boundari'':242C,287C ''calcul'':178C ''categor'':68C,127C ''choic'':6C ''class'':41C,235C,283C,289C ''classif'':54C,225C ''closest'':284C ''complex'':184C ''comput'':183C ''concept'':102C ''consequ'':121C ''construct'':165C ''convert'':247C ''data'':12C,89C,152C ''decid'':114C ''depend'':7C ''differ'':106C ''distanc'':104C,116C,207C,239C ''dot'':181C ''encod'':125C ''exampl'':215C ''featur'':69C,71C,128C,158C,202C,204C ''fine'':80C ''forest'':28C,59C,85C,228C,270C ''general'':217C,265C ''give'':229C,237C,274C ''hard'':193C ''high'':142C ''homogen'':203C ''hot'':124C ''imag'':210C ''interest'':294C ''intermedi'':160C ''interpret'':298C ''intrins'':30C,38C ''larg'':199C ''m'':157C ''margin'':96C ''matrix'':170C ''max'':137C ''maxim'':94C ''may'':291C ''meaning'':118C,206C ''memori'':173C ''min'':136C ''min-max'':135C ''mixtur'':64C ''much'':9C ''multiclass'':33C,43C ''multipl'':52C ''must'':132C ''must-do'':131C ''n'':154C,167C,169C,179C ''need'':47C,245C,254C ''number'':200C ''numer'':66C ''one'':123C ''one-hot'':122C ''p'':1C,26C,57C,148C,222C,257C,272C ''perfect'':214C ''perform'':266C ''pixel'':208C ''point'':107C,155C,198C,280C ''preprocess'':145C ''probabl'':231C,250C,255C ''problem'':34C,44C,55C,220C,226C,260C ''product'':182C ''purpos'':19C ''random'':27C,58C,84C,227C,269C ''recommend'':143C ''reduc'':49C ''reli'':99C ''requir'':174C ''rough'':81C ''rule'':22C,188C ''say'':4C ''scalabl'':194C ''scale'':76C,140C ''somehow'':251C ''speak'':82C ''step'':146C,161C ''still'':244C ''storag'':176C ''suit'':31C ''support'':276C ''svm'':36C,93C,163C,191C,236C,262C,273C ''therefor'':185C ''think'':171C ''thumb'':24C,190C ''thus'':98C ''time'':168C ''two'':40C ''two-class'':39C ''use'':88C ''various'':75C ''vector'':277C ''well'':61C ''work'':60C ''would'':3C,211C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9753, 1, '2016-01-11 22:50:16.85', 6, 56, 'How do I choose a discount factor in Markov Decision Problems?', '&lt;p&gt;I''m referring to the gamma in the Value function:
&lt;img src="http://image.slidesharecdn.com/ml-sep-09-091009141615-phpapp01/95/regretbased-reward-elicitation-for-markov-decision-processes-42-728.jpg?cb=1255098159" alt=""&gt;&lt;/p&gt;
', 'machine-learning  markov-process', NULL, '''/ml-sep-09-091009141615-phpapp01/95/regretbased-reward-elicitation-for-markov-decision-processes-42-728.jpg?cb=1255098159'':33C ''/p'':35C ''alt'':34C ''choos'':4A ''decis'':10A ''discount'':6A ''factor'':7A ''function'':28C ''gamma'':24C ''image.slidesharecdn.com'':32C ''image.slidesharecdn.com/ml-sep-09-091009141615-phpapp01/95/regretbased-reward-elicitation-for-markov-decision-processes-42-728.jpg?cb=1255098159'':31C ''img'':29C ''learn'':14B ''m'':20C ''machin'':13B ''machine-learn'':12B ''markov'':9A,16B ''markov-process'':15B ''p'':18C ''problem'':11A ''process'':17B ''refer'':21C ''src'':30C ''valu'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6856, 2, '2015-08-21 08:21:09.977', 2, NULL, NULL, '&lt;p&gt;Regarding the features, except for the app category, I would try:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;app size (in MBs) (i.e. avg of size of the downloaded apps, sum of different sizes, etc.) This would be important, since games tend to be huge, whereas productivity apps tend to have a smaller footprint. &lt;/li&gt;
&lt;li&gt;number of app downloads (again, avg and/or sum of the download counts for all the apps the user has downloaded).&lt;/li&gt;
&lt;li&gt;count of downloaded apps&lt;/li&gt;
&lt;li&gt;number of different categories (that gives an estimation of the variance of the categories the subject user inspects)&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':52C,74C,80C,100C ''/p'':13C ''/ul'':101C ''and/or'':60C ''app'':8C,16C,27C,45C,56C,69C,79C ''avg'':21C,59C ''categori'':9C,85C,95C ''count'':65C,76C ''differ'':30C,84C ''download'':26C,57C,64C,73C,78C ''estim'':89C ''etc'':32C ''except'':5C ''featur'':4C ''footprint'':51C ''game'':38C ''give'':87C ''huge'':42C ''i.e'':20C ''import'':36C ''inspect'':99C ''li'':15C,53C,75C,81C ''mbs'':19C ''number'':54C,82C ''p'':1C ''product'':44C ''regard'':2C ''sinc'':37C ''size'':17C,23C,31C ''smaller'':50C ''subject'':97C ''sum'':28C,61C ''tend'':39C,46C ''tri'':12C ''ul'':14C ''user'':71C,98C ''varianc'':92C ''wherea'':43C ''would'':11C,34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6867, 2, '2015-08-21 16:26:36.13', 1, NULL, NULL, '&lt;p&gt;I always learned to model for 1. See below for the impact of switching the encoding,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;model &amp;lt;- glm(ans ~ x, data = simulated_data, family = binomial)
summary(model)

# 
# Call:
# glm(formula = ans ~ x, family = binomial, data = simulated_data)
# 
# Deviance Residuals: 
#       1        2        3        4        5  
#  1.6388  -0.6249  -1.2146  -0.8083   0.7389  
# 
# Coefficients:
#             Estimate Std. Error z value Pr(&amp;gt;|z|)
# (Intercept)  -2.4753     2.5006   -0.99    0.322
# x             0.5957     0.6543    0.91    0.363
# 
# (Dispersion parameter for binomial family taken to be 1)
# 
#     Null deviance: 6.7301  on 4  degrees of freedom
# Residual deviance: 5.7505  on 3  degrees of freedom
# AIC: 9.7505
# 
# Number of Fisher Scoring iterations: 4
# 

simulated_data$ans &amp;lt;- !simulated_data$ans
model_opp &amp;lt;- glm(ans ~ x, data = simulated_data, family = binomial)
summary(model_opp)

# 
# Call:
# glm(formula = ans ~ x, family = binomial, data = simulated_data)
# 
# Deviance Residuals: 
#       1        2        3        4        5  
# -1.6388   0.6249   1.2146   0.8083  -0.7389  
# 
# Coefficients:
#             Estimate Std. Error z value Pr(&amp;gt;|z|)
# (Intercept)   2.4753     2.5006    0.99    0.322
# x            -0.5957     0.6543   -0.91    0.363
# 
# (Dispersion parameter for binomial family taken to be 1)
# 
#     Null deviance: 6.7301  on 4  degrees of freedom
# Residual deviance: 5.7505  on 3  degrees of freedom
# AIC: 9.7505
# 
# Number of Fisher Scoring iterations: 4
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Hope this helps.&lt;/p&gt;
', NULL, NULL, '''-0.5957'':165C ''-0.6249'':51C ''-0.7389'':149C ''-0.8083'':53C ''-0.91'':167C ''-0.99'':67C ''-1.2146'':52C ''-1.6388'':145C ''-2.4753'':65C ''/code'':202C ''/p'':18C,208C ''/pre'':203C ''0.322'':68C,163C ''0.363'':73C,168C ''0.5957'':70C ''0.6249'':146C ''0.6543'':71C,166C ''0.7389'':54C ''0.8083'':148C ''0.91'':72C ''0.99'':162C ''1'':8C,45C,82C,140C,177C ''1.2146'':147C ''1.6388'':50C ''2'':46C,141C ''2.4753'':160C ''2.5006'':66C,161C ''3'':47C,95C,142C,190C ''4'':48C,87C,106C,143C,182C,201C ''5'':49C,144C ''5.7505'':93C,188C ''6.7301'':85C,180C ''9.7505'':100C,195C ''aic'':99C,194C ''alway'':3C ''an'':24C,36C,109C,113C,118C,131C ''binomi'':30C,39C,77C,124C,134C,172C ''call'':33C,128C ''code'':20C ''coeffici'':55C,150C ''data'':26C,28C,40C,42C,108C,112C,120C,122C,135C,137C ''degre'':88C,96C,183C,191C ''devianc'':43C,84C,92C,138C,179C,187C ''dispers'':74C,169C ''encod'':17C ''error'':58C,153C ''estim'':56C,151C ''famili'':29C,38C,78C,123C,133C,173C ''fisher'':103C,198C ''formula'':35C,130C ''freedom'':90C,98C,185C,193C ''glm'':23C,34C,117C,129C ''gt'':62C,157C ''help'':207C ''hope'':205C ''impact'':13C ''intercept'':64C,159C ''iter'':105C,200C ''learn'':4C ''lt'':22C,110C,116C ''model'':6C,21C,32C,114C,126C ''null'':83C,178C ''number'':101C,196C ''opp'':115C,127C ''p'':1C,204C ''paramet'':75C,170C ''pr'':61C,156C ''pre'':19C ''residu'':44C,91C,139C,186C ''score'':104C,199C ''see'':9C ''simul'':27C,41C,107C,111C,121C,136C ''std'':57C,152C ''summari'':31C,125C ''switch'':15C ''taken'':79C,174C ''valu'':60C,155C ''x'':25C,37C,69C,119C,132C,164C ''z'':59C,63C,154C,158C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6857, 2, '2015-08-21 09:47:09.547', 5, NULL, NULL, '&lt;p&gt;There are two solutions that are worth looking at:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;a href="http://www.influxdb.com"&gt;InfluxDB&lt;/a&gt; is an open source database platform specifically designed for time series data.&lt;/strong&gt; The platform includes many optimized functions related to time and you can collect data on any interval and compute rollups/aggregations when reporting. The company recently launched a query app called &lt;a href="https://influxdb.com/chronograf/index.html"&gt;Chronograf&lt;/a&gt;. I have not used this - but if its no good, you can also check out Grafana (which is very widely used and stable).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;The alternative strategy you may want to pursue is an elasticsearch index.&lt;/strong&gt; Elasticsearch is great at running aggregations and other mathematical functions on data. Many use it to store server log data and then query said data using Kibana.&lt;/p&gt;
', NULL, NULL, '''/a'':18C,68C ''/chronograf/index.html'':66C ''/p'':11C,92C,135C ''/strong'':31C,107C ''aggreg'':113C ''also'':81C ''altern'':96C ''app'':60C ''call'':61C ''check'':82C ''chronograf'':67C ''collect'':44C ''compani'':55C ''comput'':50C ''data'':30C,45C,119C,127C,132C ''databas'':23C ''design'':26C ''elasticsearch'':105C,108C ''function'':37C,117C ''good'':78C ''grafana'':84C ''great'':110C ''href'':15C,63C ''includ'':34C ''index'':106C ''influxdb'':17C ''influxdb.com'':65C ''influxdb.com/chronograf/index.html'':64C ''interv'':48C ''kibana'':134C ''launch'':57C ''log'':126C ''look'':9C ''mani'':35C,120C ''mathemat'':116C ''may'':99C ''open'':21C ''optim'':36C ''p'':1C,12C,93C ''platform'':24C,33C ''pursu'':102C ''queri'':59C,130C ''recent'':56C ''relat'':38C ''report'':53C ''rollups/aggregations'':51C ''run'':112C ''said'':131C ''seri'':29C ''server'':125C ''solut'':5C ''sourc'':22C ''specif'':25C ''stabl'':91C ''store'':124C ''strategi'':97C ''strong'':13C,94C ''time'':28C,40C ''two'':4C ''use'':72C,89C,121C,133C ''want'':100C ''wide'':88C ''worth'':8C ''www.influxdb.com'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6858, 2, '2015-08-21 09:53:20.35', 1, NULL, NULL, '&lt;p&gt;I''ve done a lot of web analytics work and while we did do path analysis, we never focused so much on whether the user took the absolute shortest path to the page.&lt;/p&gt;

&lt;p&gt;Unlike building a road system, a website should be built to provide different paths to the same content depending on the intent of the user. For example, some users know exactly what they want and will do a search. Other users are exploratory and want to browse.&lt;/p&gt;

&lt;p&gt;Finally, sometimes you specifically want the user to step through a handful of pages to sell them on a conclusion. This is called a conversion funnel. If the user landed directly on the conversion point without seeing the info leading up to it, they''d bounce off the site and we''d lose the lead.&lt;/p&gt;
', NULL, NULL, '''/p'':35C,84C,141C ''absolut'':29C ''analysi'':17C ''analyt'':9C ''bounc'':131C ''brows'':83C ''build'':38C ''built'':46C ''call'':108C ''conclus'':105C ''content'':54C ''convers'':110C,119C ''d'':130C,137C ''depend'':55C ''differ'':49C ''direct'':116C ''done'':4C ''exact'':67C ''exampl'':63C ''exploratori'':79C ''final'':86C ''focus'':20C ''funnel'':111C ''hand'':97C ''info'':124C ''intent'':58C ''know'':66C ''land'':115C ''lead'':125C,140C ''lose'':138C ''lot'':6C ''much'':22C ''never'':19C ''p'':1C,36C,85C ''page'':34C,99C ''path'':16C,31C,50C ''point'':120C ''provid'':48C ''road'':40C ''search'':75C ''see'':122C ''sell'':101C ''shortest'':30C ''site'':134C ''sometim'':87C ''specif'':89C ''step'':94C ''system'':41C ''took'':27C ''unlik'':37C ''user'':26C,61C,65C,77C,92C,114C ''ve'':3C ''want'':70C,81C,90C ''web'':8C ''websit'':43C ''whether'':24C ''without'':121C ''work'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6859, 1, '2015-08-21 10:27:19.047', 1, 31, 'Best way to fuse clusters smaller than a certain size in a distributed way?', '&lt;p&gt;I have X clusters of size (n1,n2...,nX) and I want to fuse small clusters with less than T members with the nearest adjacent cluster i.e. the nearest neighboring cluster.&lt;/p&gt;

&lt;p&gt;The problem is when I''ve got two small neighbor clusters, A-&gt;B and B-&gt;A which create an infinite loop.&lt;/p&gt;

&lt;p&gt;I try to avoid that by removing one of those which have this property [(A,B),(B,A)].&lt;/p&gt;

&lt;p&gt;But is there a better way to accomplish this task in sequential way ?&lt;/p&gt;

&lt;p&gt;Is it possible to do the trick in distributed way ( Spark ) ?&lt;/p&gt;
', 'clustering', NULL, '''/p'':48C,71C,91C,106C,119C ''accomplish'':100C ''adjac'':41C ''avoid'':76C ''b'':62C,64C,88C,89C ''best'':1A ''better'':97C ''certain'':9A ''cluster'':5A,15B,20C,32C,42C,47C,60C ''creat'':67C ''distribut'':13A,116C ''fuse'':4A,30C ''got'':56C ''i.e'':43C ''infinit'':69C ''less'':34C ''loop'':70C ''member'':37C ''n1'':23C ''n2'':24C ''nearest'':40C,45C ''neighbor'':46C,59C ''nx'':25C ''one'':80C ''p'':16C,49C,72C,92C,107C ''possibl'':110C ''problem'':51C ''properti'':86C ''remov'':79C ''sequenti'':104C ''size'':10A,22C ''small'':31C,58C ''smaller'':6A ''spark'':118C ''task'':102C ''tri'':74C ''trick'':114C ''two'':57C ''ve'':55C ''want'':28C ''way'':2A,14A,98C,105C,117C ''x'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9125, 2, '2015-11-30 05:51:36.977', 1, NULL, NULL, '&lt;p&gt;Unfortunately, there isn''t any.  However, the &lt;code&gt;AbstractDifferenceRecommenderEvaluator&lt;/code&gt; can be extended for the purpose. The class would look something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;public abstract class AbstractKFoldRecommenderEvaluator extends AbstractDifferenceRecommenderEvaluator {

private final Random random;
public double noEstimateCounterAverage = 0.0;
public double totalEstimateCount = 0.0;
public double totalEstimateCountAverage = 0.0;

private static final Logger log = LoggerFactory
        .getLogger(AbstractKFoldRecommenderEvaluator.class);

public AbstractKFoldRecommenderEvaluator() {
    super();
    random = RandomUtils.getRandom();
}

public double getNoEstimateCounterAverage(){
    return noEstimateCounterAverage;
}

public double getTotalEstimateCount(){
    return totalEstimateCount;
}

public double getTotalEstimateCountAverage(){
    return totalEstimateCountAverage;
}

/**
 * We use the same evaluate function from the RecommenderEvaluator interface
 * the trainingPercentage is used as the number of folds, so it can have
 * values bigger than 0 to the number of folds.
 */
@Override
public double evaluate(RecommenderBuilder recommenderBuilder,
        DataModelBuilder dataModelBuilder, DataModel dataModel,
        double trainingPercentage, double evaluationPercentage)
        throws TasteException {
    Preconditions.checkNotNull(recommenderBuilder);
    Preconditions.checkNotNull(dataModel);
    Preconditions.checkArgument(trainingPercentage &amp;gt;= 0.0,
            "Invalid trainingPercentage: " + trainingPercentage);

    Preconditions.checkArgument(evaluationPercentage &amp;gt;= 0.0
            &amp;amp;&amp;amp; evaluationPercentage &amp;lt;= 1.0,
            "Invalid evaluationPercentage: " + evaluationPercentage);

    log.info("Beginning evaluation using {} of {}", trainingPercentage,
            dataModel);

    int numUsers = dataModel.getNumUsers();

    // Get the number of folds
    int noFolds = (int) trainingPercentage;

    // Initialize buckets for the number of folds
    List&amp;lt;FastByIDMap&amp;lt;PreferenceArray&amp;gt;&amp;gt; folds = new ArrayList&amp;lt;FastByIDMap&amp;lt;PreferenceArray&amp;gt;&amp;gt;();
    for (int i = 0; i &amp;lt; noFolds; i++) {
        folds.add(new FastByIDMap&amp;lt;PreferenceArray&amp;gt;(
                1 + (int) (i / noFolds * numUsers)));
    }

    // Split the dataModel into K folds per user
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
        long userID = it.nextLong();
        if (random.nextDouble() &amp;lt; evaluationPercentage) {
            splitOneUsersPrefs2(noFolds, folds, userID, dataModel);
        }
    }

    double result = Double.NaN;
    List&amp;lt;Double&amp;gt; intermediateResults = new ArrayList&amp;lt;&amp;gt;();
    List&amp;lt;Integer&amp;gt; unableToRecoomend = new ArrayList&amp;lt;&amp;gt;();
    List&amp;lt;Integer&amp;gt; averageEstimateCounterIntermediate = new ArrayList&amp;lt;&amp;gt;();

    noEstimateCounterAverage = 0.0;
    totalEstimateCount = 0.0;
    totalEstimateCountAverage = 0.0;
    int totalEstimateCounter = 0;

    // Rotate the folds. Each time only one is used for testing and the rest
    // k-1 folds are used for training
    for (int k = 0; k &amp;lt; noFolds; k++) {
        FastByIDMap&amp;lt;PreferenceArray&amp;gt; trainingPrefs = new FastByIDMap&amp;lt;PreferenceArray&amp;gt;(
                1 + (int) (evaluationPercentage * numUsers));
        FastByIDMap&amp;lt;PreferenceArray&amp;gt; testPrefs = new FastByIDMap&amp;lt;PreferenceArray&amp;gt;(
                1 + (int) (evaluationPercentage * numUsers));

        for (int i = 0; i &amp;lt; folds.size(); i++) {

            // The testing fold
            testPrefs = folds.get(k);

            // Build the training set from the remaining folds
            if (i != k) {
                for (Map.Entry&amp;lt;Long, PreferenceArray&amp;gt; entry : folds.get(i)
                        .entrySet()) {
                    if (!trainingPrefs.containsKey(entry.getKey())) {
                        trainingPrefs.put(entry.getKey(), entry.getValue());
                    } else {
                        List&amp;lt;Preference&amp;gt; userPreferences = new ArrayList&amp;lt;Preference&amp;gt;();
                        PreferenceArray existingPrefs = trainingPrefs
                                .get(entry.getKey());
                        for (int j = 0; j &amp;lt; existingPrefs.length(); j++) {
                            userPreferences.add(existingPrefs.get(j));
                        }

                        PreferenceArray newPrefs = entry.getValue();
                        for (int j = 0; j &amp;lt; newPrefs.length(); j++) {
                            userPreferences.add(newPrefs.get(j));
                        }
                        trainingPrefs.remove(entry.getKey());
                        trainingPrefs.put(entry.getKey(),
                                new GenericUserPreferenceArray(
                                        userPreferences));

                    }
                }
            }
        }

        DataModel trainingModel = dataModelBuilder == null ? new GenericDataModel(
                trainingPrefs) : dataModelBuilder
                .buildDataModel(trainingPrefs);

        Recommender recommender = recommenderBuilder
                .buildRecommender(trainingModel);

        Double[] retVal = getEvaluation(testPrefs, recommender);
        double intermediate = retVal[0];
        int noEstimateCounter = ((Double)retVal[1]).intValue();
        totalEstimateCounter += ((Double)retVal[2]).intValue();
        averageEstimateCounterIntermediate.add(((Double)retVal[2]).intValue());

        log.info("Evaluation result from fold {} : {}", k, intermediate);
        log.info("Average Unable to recommend  for fold {} in: {} cases out of {}", k, noEstimateCounter, ((Double)retVal[2]).intValue());
        intermediateResults.add(intermediate);
        unableToRecoomend.add(noEstimateCounter);

    }

    double sum = 0;
    double noEstimateSum = 0;
    double totalEstimateSum = 0;
    // Sum the results in each fold
    for (int i = 0; i &amp;lt; intermediateResults.size(); i++) {
        if (!Double.isNaN(intermediateResults.get(i))) {
            sum += intermediateResults.get(i);
            noEstimateSum+=unableToRecoomend.get(i);
            totalEstimateSum+=averageEstimateCounterIntermediate.get(i);
        }
    }

    if (sum &amp;gt; 0) {
        // Get an average for the folds
        result = sum / intermediateResults.size();
    }

    double noEstimateCount = 0;
    if(noEstimateSum&amp;gt;0){
        noEstimateCount = noEstimateSum / unableToRecoomend.size();
    }

    double avgEstimateCount = 0;
    if(totalEstimateSum&amp;gt;0){
        avgEstimateCount = totalEstimateSum / averageEstimateCounterIntermediate.size();
    }

    log.info("Average Evaluation result: {} ", result);
    log.info("Average Unable to recommend in: {} cases out of avg. {} cases or total {} ", noEstimateCount, avgEstimateCount, totalEstimateCounter);

    noEstimateCounterAverage = noEstimateCount;
    totalEstimateCount = totalEstimateCounter;
    totalEstimateCountAverage = avgEstimateCount;
    return result;
}

/**
 * Split the preference values for one user into K folds, randomly
 * Generate random number until is not the same as the previously generated on
 * in order to make sure that at least two buckets are populated.
 * 
 * @param k
 * @param folds
 * @param userID
 * @param dataModel
 * @throws TasteException
 */
private void splitOneUsersPrefs(int k,
        List&amp;lt;FastByIDMap&amp;lt;PreferenceArray&amp;gt;&amp;gt; folds, long userID,
        DataModel dataModel) throws TasteException {

    List&amp;lt;List&amp;lt;Preference&amp;gt;&amp;gt; oneUserPrefs = Lists
            .newArrayListWithCapacity(k + 1);
    for (int i = 0; i &amp;lt; k; i++) {
        oneUserPrefs.add(null);
    }

    PreferenceArray prefs = dataModel.getPreferencesFromUser(userID);

    int size = prefs.length();
    int previousBucket = -1;
    Double rand = -2.0;
    for (int i = 0; i &amp;lt; size; i++) {
        Preference newPref = new GenericPreference(userID,
                prefs.getItemID(i), prefs.getValue(i));
        do {
            rand = random.nextDouble() * k * 10;

            rand = (double) Math.floor(rand / 10);
            // System.out.println("inside Rand "+rand);

        } while (rand.intValue() == previousBucket);
        // System.out.println("outside rand "+rand);
        if (oneUserPrefs.get(rand.intValue()) == null) {
            oneUserPrefs.set(rand.intValue(), new ArrayList&amp;lt;Preference&amp;gt;());
        }
        oneUserPrefs.get(rand.intValue()).add(newPref);

        previousBucket = rand.intValue();

    }

    for (int i = 0; i &amp;lt; k; i++) {
        if (oneUserPrefs.get(i) != null) {
            folds.get(i).put(userID,
                    new GenericUserPreferenceArray(oneUserPrefs.get(i)));
        }
    }

}

/**
 * Split the preference values for one user into K folds, by shuffling.
 * First Shuffle the Preference array for the user. Then distribute the item-preference pairs
 * starting from the first buckets to the k-th bucket, and then start from the beggining.
 * 
 * @param k
 * @param folds
 * @param userID
 * @param dataModel
 * @throws TasteException
 */
private void splitOneUsersPrefs2(int k, List&amp;lt;FastByIDMap&amp;lt;PreferenceArray&amp;gt;&amp;gt; folds, long userID, DataModel dataModel) throws TasteException {

    List&amp;lt;List&amp;lt;Preference&amp;gt;&amp;gt; oneUserPrefs = Lists.newArrayListWithCapacity(k + 1);
    for (int i = 0; i &amp;lt; k; i++) {
        oneUserPrefs.add(null);
    }

    PreferenceArray prefs = dataModel.getPreferencesFromUser(userID);
    int size = prefs.length();


    List&amp;lt;Preference&amp;gt; userPrefs = new ArrayList&amp;lt;&amp;gt;();
    Iterator&amp;lt;Preference&amp;gt; it = prefs.iterator();
    while (it.hasNext()) {
        userPrefs.add(it.next());
    }

    // Shuffle the items
    Collections.shuffle(userPrefs);

    int currentBucket = 0;
    for (int i = 0; i &amp;lt; size; i++) {
        if (currentBucket == k) {
            currentBucket = 0;
        }

        Preference newPref = new GenericPreference(userID, userPrefs.get(i).getItemID(), userPrefs.get(i).getValue());

        if (oneUserPrefs.get(currentBucket) == null) {
            oneUserPrefs.set(currentBucket, new ArrayList&amp;lt;Preference&amp;gt;());
        }
        oneUserPrefs.get(currentBucket).add(newPref);
        currentBucket++;
    }

    for (int i = 0; i &amp;lt; k; i++) {
        if (oneUserPrefs.get(i) != null) {
            folds.get(i).put(userID, new GenericUserPreferenceArray(oneUserPrefs.get(i)));
        }
    }

}

private Double[] getEvaluation(FastByIDMap&amp;lt;PreferenceArray&amp;gt; testPrefs, Recommender recommender) throws TasteException {
    reset();
    Collection&amp;lt;Callable&amp;lt;Void&amp;gt;&amp;gt; estimateCallables = Lists.newArrayList();
    AtomicInteger noEstimateCounter = new AtomicInteger();
    AtomicInteger totalEstimateCounter = new AtomicInteger();
    for (Map.Entry&amp;lt;Long, PreferenceArray&amp;gt; entry : testPrefs.entrySet()) {
        estimateCallables.add(new PreferenceEstimateCallable(recommender, entry.getKey(), entry.getValue(), noEstimateCounter, totalEstimateCounter));
    }
    log.info("Beginning evaluation of {} users", estimateCallables.size());
    RunningAverageAndStdDev timing = new FullRunningAverageAndStdDev();
    execute(estimateCallables, noEstimateCounter, timing);


    Double[] retVal = new Double[3];
    retVal[0] = computeFinalEvaluation();
    retVal[1] = (double) noEstimateCounter.get();
    retVal[2] = (double) totalEstimateCounter.get();
    //retVal.put(computeFinalEvaluation(), noEstimateCounter.get());
    //return computeFinalEvaluation();
    return retVal;
}}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The implementation can be something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;public class RMSRecommenderEvaluatorModified extends AbstractKFoldRecommenderEvaluator {

private RunningAverage average;


@Override
protected void reset() {
    average = new FullRunningAverage();
}

@Override
protected void processOneEstimate(float estimatedPreference, Preference realPref) {
    double diff = realPref.getValue() - estimatedPreference;
    average.addDatum(diff * diff);
}

@Override
protected double computeFinalEvaluation() {
    return Math.sqrt(average.getAverage());
}

@Override
public String toString() {
    return "RMSRecommenderEvaluator";
}}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://stackoverflow.com/a/30207754/4993513"&gt;Source&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9130, 2, '2015-11-30 14:24:17.62', 7, NULL, NULL, '&lt;p&gt;Richard Hamming is attributed with the sentence: "The purpose of computing is insight, not numbers."  In this 1973 &lt;a href="http://ww.w.lithoguru.com/scientist/statistics/Anscombe_Graphs%20in%20Statistical%20Analysis_1973.pdf" rel="nofollow"&gt;academic paper&lt;/a&gt; (see discussion in &lt;a href="http://stats.stackexchange.com/questions/141614/what-is-the-famous-data-set-that-looks-totally-different-but-has-similar-summary/180805"&gt;What is the famous data set that looks totally different but has similar summary stats?&lt;/a&gt;), Francis Anscombe argues that "graphs are essential to good statistical analysis." Anscombe''s quartet is a long time favorite: same stats and regression, low dimension, yet very different behavior, regarding noise, outliers, dependancy. The projection of data in 11 dimensions onto two dimensions shown below is quite misleading: one has correlation and  dispersion, the second (bottom down) has  exact match, except one outlier. The third has clear relationship, but not linear. The fourth shows the variables are potentially not related, except for a threshold.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Anscombe&amp;#39;s_quartet_3.svg/500px-Anscombe&amp;#39;s_quartet_3.svg.png" rel="nofollow"&gt;&lt;img src="https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Anscombe&amp;#39;s_quartet_3.svg/500px-Anscombe&amp;#39;s_quartet_3.svg.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In the book &lt;a href="http://eu.wiley.com/WileyCDA/WileyTitle/productCd-EHEP002310.html" rel="nofollow"&gt;Multivariate Analysis for the Biobehavioral and Social Sciences&lt;/a&gt; by Bruce L. Brown &lt;em&gt;et al.&lt;/em&gt;, we can find:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;In his 1990 work "Drawing Things Together," Latour claims that the
  mindset of hard scientists is one of intense âobsessionâ with graphism&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Whether limited to 3D space, up to &lt;a href="https://www.youtube.com/watch?v=OdnhKE95AqM" rel="nofollow"&gt;six dimension plots&lt;/a&gt; (space, color, shape and time), or &lt;a href="http://www.tenthdimension.com/medialinks.php" rel="nofollow"&gt;even imagining the tenth dimension&lt;/a&gt;, humans have limited sights. Relationships between observable phenomena: not.&lt;/p&gt;

&lt;p&gt;Additionally, the curse of dimensions is assorted with even low dimension paradoxes, to give a few: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/questions/149936/why-is-the-curse-of-dimensionality-also-called-the-empty-space-phenomenon"&gt;Why is the curse of dimensionality also called the empty space phenomenon?&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.slideshare.net/NikhilSharma6/curse-of-dimensionality" rel="nofollow"&gt;Curse of dimensionality&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/questions/29627/euclidean-distance-is-usually-not-good-for-sparse-data"&gt;Why is Euclidean distance not a good metric in high dimensions?&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://sbjoshi.wordpress.com/2013/03/27/paradox-about-high-dimensional-spheres/" rel="nofollow"&gt;Paradox about high-dimensional spheres!&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://bit-player.org/2011/the-n-ball-game" rel="nofollow"&gt;The n-ball game&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Even if all norms are equivalent in finite dimensions, relationships between variables might be misleading. This is one reason for preserving distances from one space to another. Such concepts are at of the heart of lower dimension embeddings for signals (such as &lt;a href="https://en.wikipedia.org/wiki/Compressed_sensing" rel="nofollow"&gt;compressive sensing&lt;/a&gt; and the &lt;a href="https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma" rel="nofollow"&gt;Johnson-Lindenstauss lemma&lt;/a&gt; concerning low-distortion embeddings of points from high-dimensional into low-dimensional Euclidean space) or features (&lt;a href="http://www.di.ens.fr/data/scattering/" rel="nofollow"&gt;scattering transforms&lt;/a&gt; for classifications).&lt;/p&gt;

&lt;p&gt;So visualization is another help in getting insights in the data, and it goes hand in hand with calculations, including dimension reduction.&lt;/p&gt;

&lt;p&gt;Last example: put touching   $n$-spheres in an $n$-cube (the bubble inside the box, taken from &lt;a href="https://www.quora.com/Do-good-mathematicians-visualize-everything-even-algebra" rel="nofollow"&gt;Do good mathematicians visualize everything (even algebra)?&lt;/a&gt;):&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/t57Ls.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/t57Ls.png" alt="Pizza box paradox"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In two dimensions, the  center blue  ball is small. In 3D too. But very quickly, the center ball grows and its radius exceeds that of the cube. This insight is vital n clustering, for instance.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9163, 2, '2015-12-02 06:50:55.213', 1, NULL, NULL, '&lt;p&gt;Let me explain it using some examples for clear intuition:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;When do you use linear regression vs Decision Trees?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Linear regression is a linear model, which means it works really nicely when the data has a linear shape. But, when the data has a non-linear shape, then a linear model cannot capture the non-linear features. &lt;/p&gt;

&lt;p&gt;So in this case, you can use the decision trees, which do a better job at capturing the non-linearity in the data by dividing the space into smaller sub-spaces depending on the questions asked.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;When do you use linear regression vs Decision Trees?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I guess the &lt;a href="https://www.quora.com/Random-Forests/How-do-random-forests-work-in-laymans-terms/answer/Edwin-Chen-1" rel="nofollow"&gt;Quora answer&lt;/a&gt; here would do a better job than me, at explaining the difference between them and their applications. Let me quote that for you:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Suppose you''re very indecisive, so whenever you want to watch a movie,
  you ask your friend Willow if she thinks you''ll like it. In order to
  answer, Willow first needs to figure out what movies you like, so you
  give her a bunch of movies and tell her whether you liked each one or
  not (i.e., you give her a labeled training set). Then, when you ask
  her if she thinks you''ll like movie X or not, she plays a 20
  questions-like game with IMDB, asking questions like "Is X a romantic
  movie?", "Does Johnny Depp star in X?", and so on. She asks more
  informative questions first (i.e., she maximizes the information gain
  of each question), and gives you a yes/no answer at the end.&lt;/p&gt;
  
  &lt;p&gt;Thus, Willow is a decision tree for your movie preferences.&lt;/p&gt;
  
  &lt;p&gt;But Willow is only human, so she doesn''t always generalize your
  preferences very well (i.e., she overfits). In order to get more
  accurate recommendations, you''d like to ask a bunch of your friends,
  and watch movie X if most of them say they think you''ll like it. That
  is, instead of asking only Willow, you want to ask Woody, Apple, and
  Cartman as well, and they vote on whether you''ll like a movie (i.e.,
  you build an ensemble classifier, aka a forest in this case).&lt;/p&gt;
  
  &lt;p&gt;Now you don''t want each of your friends to do the same thing and give
  you the same answer, so you first give each of them slightly different
  data. After all, you''re not absolutely sure of your preferences
  yourself -- you told Willow you loved Titanic, but maybe you were just
  happy that day because it was your birthday, so maybe some of your
  friends shouldn''t use the fact that you liked Titanic in making their
  recommendations. Or maybe you told her you loved Cinderella, but
  actually you &lt;em&gt;really really&lt;/em&gt; loved it, so some of your friends should
  give Cinderella more weight. So instead of giving your friends the
  same data you gave Willow, you give them slightly perturbed versions.
  You don''t change your love/hate decisions, you just say you love/hate
  some movies a little more or less (you give each of your friends a
  bootstrapped version of your original training data). For example,
  whereas you told Willow that you liked Black Swan and Harry Potter and
  disliked Avatar, you tell Woody that you liked Black Swan so much you
  watched it twice, you disliked Avatar, and don''t mention Harry Potter
  at all.&lt;/p&gt;
  
  &lt;p&gt;By using this ensemble, you hope that while each of your friends gives
  somewhat idiosyncratic recommendations (Willow thinks you like vampire
  movies more than you do, Woody thinks you like Pixar movies, and
  Cartman thinks you just hate everything), the errors get canceled out
  in the majority. Thus, your friends now form a bagged (bootstrap
  aggregated) forest of your movie preferences.&lt;/p&gt;
  
  &lt;p&gt;There''s still one problem with your data, however. While you loved
  both Titanic and Inception, it wasn''t because you like movies that
  star Leonardio DiCaprio. Maybe you liked both movies for other
  reasons. Thus, you don''t want your friends to all base their
  recommendations on whether Leo is in a movie or not. So when each
  friend asks IMDB a question, only a random subset of the possible
  questions is allowed (i.e., when you''re building a decision tree, at
  each node you use some randomness in selecting the attribute to split
  on, say by randomly selecting an attribute or by selecting an
  attribute from a random subset). This means your friends aren''t
  allowed to ask whether Leonardo DiCaprio is in the movie whenever they
  want. So whereas previously you injected randomness at the data level,
  by perturbing your movie preferences slightly, now you''re injecting
  randomness at the model level, by making your friends ask different
  questions at different times.&lt;/p&gt;
  
  &lt;p&gt;And so your friends now form a random forest.&lt;/p&gt;
&lt;/blockquote&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6860, 2, '2015-08-21 10:27:51.137', 2, NULL, NULL, '&lt;p&gt;As You said, the most common situation for recommender system is to predict rating. Then RMSE/MAE is used. For results of a ranked item list different measures are used, e.g. Prec@K, Rec@K, AUC, NDCG, MRR, ERR. However, the are many algorithms for recommendation with its own hyper-parameters and specific use cases. Depending what You are trying to achieve You should benchmark Your solution with suitable method. The best source for this can be publication, where as You noticed, many times results for ML dataset are given. What''s more about benchmarking recommender systems, sometimes the best prediction results are not so important as other aspect e.g. novelty or diversity.  &lt;/p&gt;

&lt;p&gt;For most common methods try this benchmark results for librec project:
&lt;a href="http://www.librec.net/example.html" rel="nofollow"&gt;http://www.librec.net/example.html&lt;/a&gt;
There are no results for ML-10M, only ML-1M for rating and ML-100K for ranking. But You can always run chosen algorithm by yourself. There are other projects of this kind: &lt;a href="http://www.mymedialite.net/" rel="nofollow"&gt;http://www.mymedialite.net/&lt;/a&gt;, &lt;a href="http://lenskit.org/" rel="nofollow"&gt;http://lenskit.org/&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Saying about evaluation of recommender systems this project should be mention:
&lt;a href="https://github.com/recommenders/rival" rel="nofollow"&gt;https://github.com/recommenders/rival&lt;/a&gt;
It''s dedicated for data preparation (splitting) and evaluation for recommender systems. It was presented at last RecSys''2014 [1]. And also look at this [2]. There are some benchmark results too.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;[1] A. Said and A. BellogÃ­n, âRiVal â A Toolkit to Foster Reproducibility in Recommender System Evaluation,â pp. 371â372, 2014.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;[2] A. Said and A. BellogÃ­n, âComparative Recommender System Evaluation : Benchmarking Recommendation Frameworks,â pp. 129â136.&lt;/em&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6868, 1, '2015-08-21 18:10:54.847', 7, 162, 'Which of the NIPS 2014 papers are most significant, and why?', '&lt;p&gt;As a newcomer to the field, I find many of the &lt;a href="http://papers.nips.cc/book/advances-in-neural-information-processing-systems-27-2014"&gt;NIPS 2014 papers&lt;/a&gt; fascinating, but it is difficult for me to evaluate which ones represent real progress over current approaches.&lt;/p&gt;

&lt;p&gt;Which papers do you think are most significant and are likely to have a major impact?&lt;/p&gt;
', 'machine-learning  research  state-of-the-art', NULL, '''/a'':39C ''/book/advances-in-neural-information-processing-systems-27-2014'':35C ''/p'':57C,75C ''2014'':5A,37C ''approach'':56C ''art'':18B ''current'':55C ''difficult'':44C ''evalu'':48C ''fascin'':40C ''field'':25C ''find'':27C ''href'':32C ''impact'':74C ''learn'':12B ''like'':69C ''machin'':11B ''machine-learn'':10B ''major'':73C ''mani'':28C ''newcom'':22C ''nip'':4A,36C ''one'':50C ''p'':19C,58C ''paper'':6A,38C,60C ''papers.nips.cc'':34C ''papers.nips.cc/book/advances-in-neural-information-processing-systems-27-2014'':33C ''progress'':53C ''real'':52C ''repres'':51C ''research'':13B ''signific'':9A,66C ''state'':15B ''state-of-the-art'':14B ''think'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6861, 2, '2015-08-21 10:47:02.427', 2, NULL, NULL, '&lt;p&gt;I''d be really tempted to be lazy and apply some old technology for a quick and dirty solution, with no programming, using the linux &lt;code&gt;sort&lt;/code&gt; command. 
This will give you a lexicographically sorted list. &lt;/p&gt;

&lt;p&gt;If the store names are not the first field, if  just reorder them or tell &lt;code&gt;sort&lt;/code&gt; to use a different field via the &lt;code&gt;-k&lt;/code&gt; switch.&lt;/p&gt;

&lt;p&gt;Save the data to a plain CSV text file and then sort them:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;$sort myStores.csv &amp;gt; sortedByStore.csv
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You can give sort a hand by allocating it plenty of memory, 16GB in this case:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;$sort -S16G myStores.csv &amp;gt; sortedByStore.csv
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You could go further and produce a list of unique store names and counts of instances for them to help you get a handle on what the data looks like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;$sort -S16G myStores.csv  | cut -f1 -d, | uniq -c &amp;gt; storeIdsAndCounts.csv
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Or to avoid resorting and  have only the unique IDs:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;$cat sortedByStore.csv   | cut -f1 -d, | uniq  &amp;gt; storeIds.csv
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':29C,58C,68C,92C,119C,165C,189C ''/p'':39C,70C,85C,111C,152C,178C ''/pre'':93C,120C,166C,190C ''16gb'':107C ''alloc'':102C ''appli'':11C ''avoid'':170C ''c'':162C ''case'':110C ''cat'':181C ''code'':27C,56C,66C,87C,113C,154C,180C ''command'':30C ''could'':123C ''count'':135C ''csv'':78C ''cut'':158C,183C ''d'':3C,160C,185C ''data'':74C,149C ''differ'':62C ''dirti'':19C ''f1'':159C,184C ''field'':49C,63C ''file'':80C ''first'':48C ''get'':143C ''give'':33C,97C ''go'':124C ''gt'':90C,117C,163C,187C ''hand'':100C ''handl'':145C ''help'':141C ''id'':177C ''instanc'':137C ''k'':67C ''lazi'':9C ''lexicograph'':36C ''like'':151C ''linux'':26C ''list'':38C,129C ''look'':150C ''memori'':106C ''mystores.csv'':89C,116C,157C ''name'':44C,133C ''old'':13C ''p'':1C,40C,71C,94C,121C,167C ''plain'':77C ''plenti'':104C ''pre'':86C,112C,153C,179C ''produc'':127C ''program'':23C ''quick'':17C ''realli'':5C ''reorder'':52C ''resort'':171C ''s16g'':115C,156C ''save'':72C ''solut'':20C ''sort'':28C,37C,57C,83C,88C,98C,114C,155C ''sortedbystore.csv'':91C,118C,182C ''store'':43C,132C ''storeids.csv'':188C ''storeidsandcounts.csv'':164C ''switch'':69C ''technolog'':14C ''tell'':55C ''tempt'':6C ''text'':79C ''uniq'':161C,186C ''uniqu'':131C,176C ''use'':24C,60C ''via'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6862, 2, '2015-08-21 11:11:13.983', 1, NULL, NULL, '&lt;p&gt;My suggestion is just a greedy brute force approach. I am sure that more efficient solutions may exist, but it may be a first step towards your final solution.&lt;/p&gt;

&lt;p&gt;Store in a table, for each cluster which size is lesser than T, the distance to its closest neighbouring cluster and the ID of that closest cluster. &lt;/p&gt;

&lt;p&gt;Now, sort the table entries in ascending distance order. Merge the cluster in the first entry with its closest neighbouring one. If the closest cluster also had an entry in the table, and its size got larger than T, remove its corresponding entry. Finally, update all the entries of the table, and sort them in ascending distance order again.&lt;/p&gt;

&lt;p&gt;Repeat the steps above until there are not more entries in the table. &lt;/p&gt;
', NULL, NULL, '''/p'':31C,60C,121C,136C ''also'':87C ''approach'':10C ''ascend'':68C,117C ''brute'':8C ''closest'':50C,58C,80C,85C ''cluster'':39C,52C,59C,73C,86C ''correspond'':103C ''distanc'':47C,69C,118C ''effici'':16C ''entri'':66C,77C,90C,104C,109C,132C ''exist'':19C ''final'':29C,105C ''first'':25C,76C ''forc'':9C ''got'':97C ''greedi'':7C ''id'':55C ''larger'':98C ''lesser'':43C ''may'':18C,22C ''merg'':71C ''neighbour'':51C,81C ''one'':82C ''order'':70C,119C ''p'':1C,32C,61C,122C ''remov'':101C ''repeat'':123C ''size'':41C,96C ''solut'':17C,30C ''sort'':63C,114C ''step'':26C,125C ''store'':33C ''suggest'':3C ''sure'':13C ''tabl'':36C,65C,93C,112C,135C ''toward'':27C ''updat'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8726, 2, '2015-11-05 11:32:54.16', 1, NULL, NULL, '&lt;p&gt;Yes, you can do it (indeed in many ways).
I like to reduce the problem into a classification problem and derive the proper way to combine them.
For details see:
&lt;a href="http://stats.stackexchange.com/questions/61351/how-to-combine-multiple-similarity-measures/166419#166419"&gt;http://stats.stackexchange.com/questions/61351/how-to-combine-multiple-similarity-measures/166419#166419&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/questions/61351/how-to-combine-multiple-similarity-measures/166419#166419'':36C ''/questions/61351/how-to-combine-multiple-similarity-measures/166419#166419&lt;/a&gt;&lt;/p&gt;'':39C ''classif'':19C ''combin'':27C ''deriv'':22C ''detail'':30C ''href'':33C ''inde'':7C ''like'':12C ''mani'':9C ''p'':1C ''problem'':16C,20C ''proper'':24C ''reduc'':14C ''see'':31C ''stats.stackexchange.com'':35C,38C ''stats.stackexchange.com/questions/61351/how-to-combine-multiple-similarity-measures/166419#166419'':34C ''stats.stackexchange.com/questions/61351/how-to-combine-multiple-similarity-measures/166419#166419&lt;/a&gt;&lt;/p&gt;'':37C ''way'':10C,25C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9204, 5, '2015-12-04 12:17:25.853', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9226, 2, '2015-12-05 19:00:34.283', 2, NULL, NULL, '&lt;p&gt;So you have collected data that shows which app is being used at any time, binned into hours in the day. And you have several apps. You mention other dimensions, like user state when using the app (walking, not-walking), (active, not-active - it appears to me that you are not collecting much usage 2-6. is that because the usage is from a self-directed ping from an app while the user is truly away?), location (is this going to be all possible values, or are you going to use something like the fact that this location has been seen often before?). Another interesting relationship could be pairing apps, i.e., mining for a relationship between App A being used after using App B or before App B. &lt;/p&gt;

&lt;p&gt;Regardless, then you will definitely have many different dimensions upon which to measure the usage characteristics for any particular usage measurement, and so you are definitely going to have a multiple dimensional problem. You might try to visualize this as an N-space problem, with an axis of measurement for each of your characteristics. Each of your previous measurements represents vectors and you are producing a new vector with your next measurement.&lt;/p&gt;

&lt;p&gt;From this, you want to predict future behavior based on measuring the input characteristics from your usage space. You could go for something that classifies as nearest neighbor, and you probably want to do this for your first stab at the problem. You might end up wanting to make the predictive model more sophisticated by adding probabilities to the classifier and acting on that. This means getting estimates of class membership probability rather than just simple classifications. But I would build the whole thing incrementally. Start simple and add complexity as you require it. The increased complexity will also have effects on performance, so why not baseline with something.&lt;/p&gt;

&lt;p&gt;For the aging of data, are you wanting to reduce the predictive power of characteristics that are too long in the tooth? If so, be explicit with yourself about what that means, quantitively. Do I trust the usage data from last month less than yesterday''s data? Perhaps so, but then why? is my usage different because I am different or because last month was special compared to yesterday, or vice-versa? Again, you might benefit from ignoring this at first, but then trying to &lt;strong&gt;search&lt;/strong&gt; for "seasonal" or periodicity characteristics from the data. Once you determine if/how it changes, you can weight that contribution compared to your immediate usage in different ways. Perhaps you want to amplify the contribution of a similar period (same time of day &amp;amp;&amp;amp; same location &amp;amp;&amp;amp; same previous app usage). Perhaps you want to provide an exponential dampening on historical data because the usage is always adapting and changing, and recent usage seems to be a &lt;strong&gt;much&lt;/strong&gt; better predictor than 3xcurrent. &lt;/p&gt;

&lt;p&gt;For all of this, the proper data science perspective is to let the data lead you. &lt;/p&gt;
', NULL, NULL, '''-6'':59C ''/p'':133C,208C,319C,495C,513C ''/strong'':409C,490C ''2'':58C ''3xcurrent'':494C ''act'':271C ''activ'':43C,46C ''ad'':265C ''adapt'':478C ''add'':298C ''age'':323C ''also'':308C ''alway'':477C ''amp'':452C,453C,456C,457C ''amplifi'':441C ''anoth'':108C ''app'':10C,27C,38C,74C,114C,121C,127C,131C,460C ''appear'':48C ''away'':80C ''axi'':182C ''b'':128C,132C ''base'':218C ''baselin'':316C ''behavior'':217C ''benefit'':397C ''better'':491C ''bin'':17C ''build'':290C ''chang'':423C,480C ''characterist'':150C,189C,223C,335C,414C ''class'':279C ''classif'':286C ''classifi'':234C,269C ''collect'':5C,55C ''compar'':387C,429C ''complex'':299C,306C ''contribut'':428C,443C ''could'':111C,229C ''dampen'':469C ''data'':6C,325C,359C,367C,417C,472C,503C,510C ''day'':22C,451C ''definit'':139C,160C ''determin'':420C ''differ'':142C,376C,380C,435C ''dimens'':31C,143C ''dimension'':166C ''direct'':70C ''effect'':310C ''end'':254C ''estim'':277C ''explicit'':346C ''exponenti'':468C ''fact'':99C ''first'':247C,402C ''futur'':216C ''get'':276C ''go'':84C,93C,161C,230C ''histor'':471C ''hour'':19C ''i.e'':115C ''if/how'':421C ''ignor'':399C ''immedi'':432C ''increas'':305C ''increment'':294C ''input'':222C ''interest'':109C ''last'':361C,383C ''lead'':511C ''less'':363C ''let'':508C ''like'':32C,97C ''locat'':81C,102C,455C ''long'':339C ''make'':258C ''mani'':141C ''mean'':275C,352C ''measur'':147C,155C,184C,194C,207C,220C ''membership'':280C ''mention'':29C ''might'':169C,253C,396C ''mine'':116C ''model'':261C ''month'':362C,384C ''much'':56C,489C ''multipl'':165C ''n'':177C ''n-space'':176C ''nearest'':236C ''neighbor'':237C ''new'':202C ''next'':206C ''not-act'':44C ''not-walk'':40C ''often'':106C ''p'':1C,134C,209C,320C,496C ''pair'':113C ''particular'':153C ''perform'':312C ''perhap'':368C,437C,462C ''period'':413C,447C ''perspect'':505C ''ping'':71C ''possibl'':88C ''power'':333C ''predict'':215C,260C,332C ''predictor'':492C ''previous'':193C,459C ''probabl'':240C,266C,281C ''problem'':167C,179C,251C ''produc'':200C ''proper'':502C ''provid'':466C ''quantit'':353C ''rather'':282C ''recent'':482C ''reduc'':330C ''regardless'':135C ''relationship'':110C,119C ''repres'':195C ''requir'':302C ''scienc'':504C ''search'':408C ''season'':411C ''seem'':484C ''seen'':105C ''self'':69C ''self-direct'':68C ''sever'':26C ''show'':8C ''similar'':446C ''simpl'':285C,296C ''someth'':96C,232C,318C ''sophist'':263C ''space'':178C,227C ''special'':386C ''stab'':248C ''start'':295C ''state'':34C ''strong'':407C,488C ''thing'':293C ''time'':16C,449C ''tooth'':342C ''tri'':170C,405C ''truli'':79C ''trust'':356C ''upon'':144C ''usag'':57C,64C,149C,154C,226C,358C,375C,433C,461C,475C,483C ''use'':13C,36C,95C,124C,126C ''user'':33C,77C ''valu'':89C ''vector'':196C,203C ''versa'':393C ''vice'':392C ''vice-versa'':391C ''visual'':172C ''walk'':39C,42C ''want'':213C,241C,256C,328C,439C,464C ''way'':436C ''weight'':426C ''whole'':292C ''would'':289C ''yesterday'':365C,389C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6863, 1, '2015-08-21 11:18:27.883', 0, 56, 'normalize dependent variable with respect to one independent variable', '&lt;p&gt;I have a data set with 50-60 independent variables and 1 categorical (1-0) dependent variable. Out of all the independent variables say, A is one variable which has majority of the effect on the target variable. For ex&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;A        target variable
a              1 
a              1   
a              1    
a              1 
b              0  
b              1 
b              1 
b              1 
c              0 
c              0
c              0
c              1  
c              1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Clearly whatever analysis I do here, whatever probabilities I calculate for With respect to other variables, I would not be able to get the actual picture with respect to other variables.&lt;/p&gt;

&lt;p&gt;Also I do not want to build any model at this point, I just want to see the individual effects of other variables on the dependent variable after removing the effect of variable ''A'' i.e. normalizing dependent variable with respect to ''A''. How do I do? If the dependent variable was a continuous variable, I would have simply divided it by the respective averages and removed the effect. How do I do this in this case where the dependent variable is 1-0?&lt;/p&gt;
', 'dataset', NULL, '''-0'':26C,202C ''-60'':19C ''/code'':84C ''/p'':52C,118C,203C ''/pre'':85C ''0'':67C,75C,77C,79C ''1'':23C,25C,59C,61C,63C,65C,69C,71C,73C,81C,83C,201C ''50'':18C ''abl'':107C ''actual'':111C ''also'':120C ''analysi'':89C ''averag'':183C ''b'':66C,68C,70C,72C ''build'':126C ''c'':74C,76C,78C,80C,82C ''calcul'':96C ''case'':195C ''categor'':24C ''clear'':87C ''code'':54C ''continu'':172C ''data'':15C ''dataset'':10B ''depend'':2A,27C,145C,156C,168C,198C ''divid'':178C ''effect'':45C,139C,150C,187C ''ex'':51C ''get'':109C ''i.e'':154C ''independ'':8A,20C,33C ''individu'':138C ''major'':42C ''model'':128C ''normal'':1A,155C ''one'':7A,38C ''p'':11C,86C,119C ''pictur'':112C ''point'':131C ''pre'':53C ''probabl'':94C ''remov'':148C,185C ''respect'':5A,99C,114C,159C,182C ''say'':35C ''see'':136C ''set'':16C ''simpli'':177C ''target'':48C,56C ''variabl'':3A,9A,21C,28C,34C,39C,49C,57C,102C,117C,142C,146C,152C,157C,169C,173C,199C ''want'':124C,134C ''whatev'':88C,93C ''would'':104C,175C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6865, 1, '2015-08-21 13:49:10.677', 0, 58, 'Logistic Regression in R models for 1 or 0?', '&lt;p&gt;According to this &lt;a href="http://www.ats.ucla.edu/stat/sas/dae/logit.htm" rel="nofollow"&gt;link&lt;/a&gt;, it says SAS models for 0,&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;To model 1s rather than 0s, we use the descending option. We do this because by default, proc logistic models 0s rather than 1s  &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;What happens in case of R''s glm function? Does it model for 1 or 0? Is there a way to change it? Does it matter?&lt;/p&gt;
', 'r  logistic-regression  sas', NULL, '''/a'':27C ''/blockquote'':62C ''/p'':34C,61C,90C ''/stat/sas/dae/logit.htm'':23C ''0'':9A,33C,79C ''0s'':42C,57C ''1'':7A,77C ''1s'':39C,60C ''accord'':16C ''blockquot'':35C ''case'':67C ''chang'':85C ''default'':53C ''descend'':46C ''function'':72C ''glm'':71C ''happen'':65C ''href'':20C ''link'':26C ''logist'':1A,12B,55C ''logistic-regress'':11B ''matter'':89C ''model'':5A,31C,38C,56C,75C ''nofollow'':25C ''option'':47C ''p'':15C,36C,63C ''proc'':54C ''r'':4A,10B,69C ''rather'':40C,58C ''regress'':2A,13B ''rel'':24C ''sas'':14B,30C ''say'':29C ''use'':44C ''way'':83C ''www.ats.ucla.edu'':22C ''www.ats.ucla.edu/stat/sas/dae/logit.htm'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6994, 2, '2015-09-03 13:39:51.937', 2, NULL, NULL, '&lt;p&gt;If you used the default kernel in SVC(), the &lt;a href="http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=MachineLearning&amp;amp;doc=exercises/ex8/ex8.html" rel="nofollow"&gt;Radial Basis Function&lt;/a&gt; (rbf) kernel, then you probably learned a more nonlinear decision boundary.  In the case of the &lt;a href="http://scikit-learn.org/stable/auto_examples/datasets/plot_digits_last_image.html" rel="nofollow"&gt;digits dataset&lt;/a&gt;, this will vastly outperform a linear decision boundary &lt;a href="http://yann.lecun.com/exdb/publis/pdf/bottou-94.pdf" rel="nofollow"&gt;on this task&lt;/a&gt; (see 3.1 ''Baseline Linear Classifier'')&lt;/p&gt;
', NULL, NULL, '''/a'':21C,47C,66C ''/exdb/publis/pdf/bottou-94.pdf'':60C ''/mainfolder/documentpage.php?course=machinelearning&amp;amp;doc=exercises/ex8/ex8.html'':15C ''/p'':72C ''/stable/auto_examples/datasets/plot_digits_last_image.html'':42C ''3.1'':68C ''baselin'':69C ''basi'':19C ''boundari'':32C,55C ''case'':35C ''classifi'':71C ''dataset'':46C ''decis'':31C,54C ''default'':6C ''digit'':45C ''function'':20C ''href'':12C,39C,57C ''kernel'':7C,23C ''learn'':27C ''linear'':53C,70C ''nofollow'':17C,44C,62C ''nonlinear'':30C ''openclassroom.stanford.edu'':14C ''openclassroom.stanford.edu/mainfolder/documentpage.php?course=machinelearning&amp;amp;doc=exercises/ex8/ex8.html'':13C ''outperform'':51C ''p'':1C ''probabl'':26C ''radial'':18C ''rbf'':22C ''rel'':16C,43C,61C ''scikit-learn.org'':41C ''scikit-learn.org/stable/auto_examples/datasets/plot_digits_last_image.html'':40C ''see'':67C ''svc'':9C ''task'':65C ''use'':4C ''vast'':50C ''yann.lecun.com'':59C ''yann.lecun.com/exdb/publis/pdf/bottou-94.pdf'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6869, 2, '2015-08-22 14:10:12.447', 4, NULL, NULL, '&lt;p&gt;The &lt;a href="https://nips.cc/Conferences/2014/Awards" rel="nofollow"&gt;awards&lt;/a&gt; went to "&lt;a href="http://papers.nips.cc/paper/5329-asymmetric-lsh-alsh-for-sublinear-time-maximum-inner-product-search-mips" rel="nofollow"&gt;Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)&lt;/a&gt;" by Anshumali Shrivastava and Ping Li and "&lt;a href="http://papers.nips.cc/paper/5449-a-sampling" rel="nofollow"&gt;A* Sampling&lt;/a&gt;" by Chris J. Maddison, Daniel Tarlow, and Tom Minka.&lt;br&gt;
Further Quora discussion can be found &lt;a href="http://www.quora.com/What-were-the-scientific-highlights-of-the-NIPS-2014" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':11C,32C,49C,74C ''/conferences/2014/awards'':7C ''/p'':75C ''/paper/5329-asymmetric-lsh-alsh-for-sublinear-time-maximum-inner-product-search-mips'':18C ''/paper/5449-a-sampling'':44C ''/what-were-the-scientific-highlights-of-the-nips-2014'':70C ''alsh'':23C ''anshumali'':34C ''asymmetr'':21C ''award'':10C ''br'':59C ''chris'':51C ''daniel'':54C ''discuss'':62C ''found'':65C ''href'':4C,15C,41C,67C ''inner'':28C ''j'':52C ''li'':38C ''lsh'':22C ''maddison'':53C ''maximum'':27C ''minka'':58C ''mip'':31C ''nips.cc'':6C ''nips.cc/conferences/2014/awards'':5C ''nofollow'':9C,20C,46C,72C ''p'':1C ''papers.nips.cc'':17C,43C ''papers.nips.cc/paper/5329-asymmetric-lsh-alsh-for-sublinear-time-maximum-inner-product-search-mips'':16C ''papers.nips.cc/paper/5449-a-sampling'':42C ''ping'':37C ''product'':29C ''quora'':61C ''rel'':8C,19C,45C,71C ''sampl'':48C ''search'':30C ''shrivastava'':35C ''sublinear'':25C ''tarlow'':55C ''time'':26C ''tom'':57C ''went'':12C ''www.quora.com'':69C ''www.quora.com/what-were-the-scientific-highlights-of-the-nips-2014'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6870, 1, '2015-08-22 18:06:21.51', 1, 189, 'Entropy calculation for MNIST dataset to form classification decision tree', '&lt;p&gt;I am trying to get classification tree in R. I am using MNIST (handwritten digit dataset) to train my decision tree. The dataset has 28*28 pixel box. Pixels are organized in row-wise. Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black). I have to calculate entropy to get best attributes among 784 attributes (pixel0 to pixel783). Can anyone suggest, how to calculate entropy or information gain for these dense attributes?&lt;/p&gt;
', 'r  decision-trees', NULL, '''/p'':94C ''0'':54C,57C ''255'':56C,61C ''28'':40C,41C ''784'':75C ''among'':74C ''anyon'':81C ''attribut'':73C,76C,93C ''background'':59C ''best'':72C ''black'':64C ''box'':43C ''calcul'':2A,68C,85C ''classif'':8A,21C ''dataset'':5A,31C,38C ''decis'':9A,13B,35C ''decision-tre'':12B ''dens'':92C ''digit'':30C ''entropi'':1A,69C,86C ''foreground'':63C ''form'':7A ''gain'':89C ''get'':20C,71C ''handwritten'':29C ''inform'':88C ''mean'':58C,62C ''mnist'':4A,28C ''organ'':46C ''p'':15C ''pixel'':42C,44C,51C ''pixel0'':77C ''pixel783'':79C ''r'':11B,24C ''row'':49C ''row-wis'':48C ''suggest'':82C ''train'':33C ''tree'':10A,14B,22C,36C ''tri'':18C ''use'':27C ''valu'':52C ''white'':60C ''wise'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9241, 1, '2015-12-07 01:34:51.48', 5, 98, 'Troubleshooting Neural Network Implementation', '&lt;p&gt;I''ve been going through the &lt;a href="https://www.coursera.org/learn/machine-learning" rel="nofollow"&gt;Standford/Coursera Machine Learning course&lt;/a&gt;; and it''s been going pretty well. I''m really more interested in the understanding of the topic than getting the grade from the course and as such I''m trying to write all the code in a programming language I''m more fluent in (something I can easily dig down into the roots of).&lt;/p&gt;

&lt;p&gt;The way I learn best is via working through problems, so I''ve implemented a neural network and it doesn''t work. I seem to get the same probability of each class irrespective of the test example (for example 0.45 of class 0, 0.55 of class 1, irrespective of the input values). Strangely this isn''t the case if I remove all the hidden layers.&lt;/p&gt;

&lt;p&gt;Here''s a brief run through of what I do;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Set all Theta''s (weights) to a small random number

for each training example

set activation 0 on layer 0 as 1 (bias)
set layer 1 activations = inputs

forward propagate;
 Z(j+1) = Theta(j) x activation(j)  [matrix operations]
 activation(j+1) = Sigmoid function (Z(j+1)) [element wise sigmoid]

Set Hx = final layer activations

Set bias of each layer (activation 0,0) = 1

[back propagate]
calculate delta;
delta(last layer) = activation(last layer) - Y  [Y is the expected answer from training set]

delta(j) = transpose(Theta(j)) x delta(j+1) .* (activation(j) .*(Ones - activation(j))

[where ones is a matrix of 1''s in every cell; and .* is the element wise multiplication]
[Don''t calculate delta(0) since there ins''t one for input layer]


DeltaCap(j) = DeltaCap(j) + delta(j+1) x transpose(activation(j))

Next [End for]

Calculate D;

D(j) = 1/#Training * DeltaCap(j) (for j = 0)

D(j) = 1/#Training * DeltaCap(j) + Lambda/#Training * Theta(j) (for j = 0)


[calculate cost function]

J(theta) = -1/#training * Y*Log(Hx) + (1-Y)*log(1-Hx) + lambda/ (2 * #training) * theta^2

Recalculate Theta

Theta = Theta - alpha * D
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;That''s probably not a great deal to go on. If someone can tell me if there''s any major flaw in my code that would be fantastic, otherwise some general idea of where I might be going wrong/how to debug something like that that would also be great.&lt;/p&gt;

&lt;p&gt;EDIT:&lt;/p&gt;

&lt;p&gt;Here''s a quick image of the network (including a test case of inputs and responses) (this is after 1 million iterations of gradient descent);&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/xIcEi.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/xIcEi.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The data set I''ve used is two exam scores as the x''s and the success/failure of getting into a university as the y. Clearly two test scores of 0 would mean failure in getting into university however the network suggests 56% chance of getting it with 0''s as inputs.&lt;/p&gt;

&lt;p&gt;Edit #2;&lt;/p&gt;

&lt;p&gt;I''ve ran a gradient checking algorithm with the following sort of results;&lt;/p&gt;

&lt;p&gt;Numerical calculation: -0.0074962585205895493
Value from propagation: 0.62021047431540277&lt;/p&gt;

&lt;p&gt;Numerical calculation: 0.0032635827218463476
Value from propagation: -0.39564819922432665&lt;/p&gt;

&lt;p&gt;etc. Clearly there''s something wrong here; I''m going to work through it.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9368, 2, '2015-12-14 02:02:02.803', 5, NULL, NULL, '&lt;p&gt;Whenever I work with xgboost I often make my own homebrew parameter search but you can do it with the caret package as well like KrisP just mentioned.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;strong&gt;Caret&lt;/strong&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;See this answer on Cross Validated for a thorough explanation on how to use the caret package for hyperparameter search on xgboost.
&lt;a href="https://stats.stackexchange.com/questions/171043/how-to-tune-hyperparameters-of-xgboost-trees"&gt;How to tune hyperparameters of xgboost trees?&lt;/a&gt;&lt;/p&gt;

&lt;ol start="2"&gt;
&lt;li&gt;&lt;strong&gt;Custom Grid Search&lt;/strong&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I often begin with a few assumptions based on &lt;a href="https://www.kaggle.com/owenzhang1" rel="nofollow"&gt;Owen Zhang&lt;/a&gt;''s slides on &lt;a href="http://www.slideshare.net/OwenZhang2/tips-for-data-science-competitions" rel="nofollow"&gt;tips for data science&lt;/a&gt; P. 14&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/9GgQK.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/9GgQK.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Here you can see that you''ll mostly need to tune row sampling, column sampling and maybe maximum tree depth. This is how I do a custom row sampling and column sampling search for a problem I am working on at the moment:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;searchGridSubCol &amp;lt;- expand.grid(subsample = c(0.5, 0.75, 1), 
                                colsample_bytree = c(0.6, 0.8, 1))
ntrees &amp;lt;- 100

#Build a xgb.DMatrix object
DMMatrixTrain &amp;lt;- xgb.DMatrix(data = yourMatrix, label = yourTarget)

rmseErrorsHyperparameters &amp;lt;- apply(searchGridSubCol, 1, function(parameterList){

    #Extract Parameters to test
    currentSubsampleRate &amp;lt;- parameterList[["subsample"]]
    currentColsampleRate &amp;lt;- parameterList[["colsample_bytree"]]

    xgboostModelCV &amp;lt;- xgb.cv(data =  DMMatrixTrain, nrounds = ntrees, nfold = 5, showsd = TRUE, 
                           metrics = "rmse", verbose = TRUE, "eval_metric" = "rmse",
                           "objective" = "reg:linear", "max.depth" = 15, "eta" = 2/ntrees,                               
                           "subsample" = currentSubsampleRate, "colsample_bytree" = currentColsampleRate)

    xvalidationScores &amp;lt;- as.data.frame(xgboostModelCV)
    #Save rmse of the last iteration
    rmse &amp;lt;- tail(xvalidationScores$test.rmse.mean, 1)

    return(c(rmse, currentSubsampleRate, currentColsampleRate))

})
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And combined with some ggplot2 magic using the results of that apply function you can plot a graphical representation of the search.&lt;a href="http://i.stack.imgur.com/jaAuy.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/jaAuy.png" alt="My xgboost hyperparameter search"&gt;&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;In this plot lighter colors represent lower error and each block represents a unique combination of column sampling and row sampling. So if you want to perform an additional search of say eta (or tree depth) you will end up with one of these plots for each eta parameters tested.&lt;/p&gt;

&lt;p&gt;I see you have a different evaluation metric (RMPSE), just plug that in the cross validation function and you''ll get the desired result. Besides that I wouldn''t worry too much about fine tuning the other parameters because doing so won''t improve performance too much, at least not so much compared to spending more time engineering features or cleaning the data.&lt;/p&gt;

&lt;ol start="3"&gt;
&lt;li&gt;&lt;strong&gt;Others&lt;/strong&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Random search and Bayesian parameter selection are also possible but I haven''t made/found an implementation of them yet.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6874, 1, '2015-08-23 15:49:14.167', 1, 33, 'Merging two datasets with unclear identifier in Excel', '&lt;p&gt;I''m trying to merge two datasets on Mergers &amp;amp; Acquisitions. They both consist of c.10''000 observations with c.50-100 variables each. One contains information about the actual M&amp;amp;A deal whereas the other one contains info on how a deal was financed.&lt;/p&gt;

&lt;p&gt;The problem is that there is no clear and unique identifier. For example, I could use the date that the deal was announced but that wouldn''t be unique because on some days 10 deals were announced. Using company names is difficult since they mostly aren''t identical in both datasets. For example if in one dataset I find "Ebay", in the other the same company could be called "eBay", "Ebay Inc", or "Ebay, Inc."&lt;/p&gt;

&lt;p&gt;I''ve been working with the Fuzzy Lookup add-on for Excel, as well as concacenating various identifiers that are not unique but in their combination become useful (e.g. Date &amp;amp; Country &amp;amp; SIC Industry Classification Code, etc.). However I haven''t been able to generate as many matches as I would have hoped.&lt;/p&gt;

&lt;p&gt;I''d be grateful for any ideas or pointers towards resources that would help me merge the datasets more efficiently.&lt;/p&gt;
', 'databases', NULL, '''/p'':55C,132C,189C,211C ''000'':27C ''10'':90C ''abl'':178C ''acquisit'':21C ''actual'':38C ''add'':143C ''add-on'':142C ''amp'':20C,40C,165C,167C ''announc'':79C,93C ''aren'':102C ''becom'':161C ''c.10'':26C ''c.50-100'':30C ''call'':125C ''classif'':170C ''clear'':64C ''code'':171C ''combin'':160C ''compani'':95C,122C ''concacen'':150C ''consist'':24C ''contain'':34C,47C ''could'':71C,123C ''countri'':166C ''d'':192C ''databas'':9B ''dataset'':3A,17C,107C,113C,208C ''date'':74C,164C ''day'':89C ''deal'':42C,52C,77C,91C ''difficult'':98C ''e.g'':163C ''ebay'':116C,126C,127C,130C ''effici'':210C ''etc'':172C ''exampl'':69C,109C ''excel'':8A,146C ''financ'':54C ''find'':115C ''fuzzi'':140C ''generat'':180C ''grate'':194C ''haven'':175C ''help'':204C ''hope'':188C ''howev'':173C ''idea'':197C ''ident'':104C ''identifi'':6A,67C,152C ''inc'':128C,131C ''industri'':169C ''info'':48C ''inform'':35C ''lookup'':141C ''m'':12C,39C ''mani'':182C ''match'':183C ''merg'':1A,15C,206C ''merger'':19C ''most'':101C ''name'':96C ''observ'':28C ''one'':33C,46C,112C ''p'':10C,56C,133C,190C ''pointer'':199C ''problem'':58C ''resourc'':201C ''sic'':168C ''sinc'':99C ''toward'':200C ''tri'':13C ''two'':2A,16C ''unclear'':5A ''uniqu'':66C,85C,156C ''use'':72C,94C,162C ''variabl'':31C ''various'':151C ''ve'':135C ''well'':148C ''wherea'':43C ''work'':137C ''would'':186C,203C ''wouldn'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6875, 2, '2015-08-23 16:59:32.163', 1, NULL, NULL, '&lt;p&gt;I typically proceed the following way,&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;convert to the same case with &lt;code&gt;LOWER()&lt;/code&gt; or &lt;code&gt;UPPER()&lt;/code&gt;;&lt;/li&gt;
&lt;li&gt;remove all non-alphanumeric characters with several &lt;code&gt;SUBSTITUTE()&lt;/code&gt;, e.g. &lt;code&gt;SUBSTITUTE(A1, ".", "")&lt;/code&gt;;&lt;/li&gt;
&lt;li&gt;trim white spaces &lt;code&gt;TRIM()&lt;/code&gt;;&lt;/li&gt;
&lt;li&gt;extract the first word with &lt;code&gt;LEFT(A1, FIND(" ", A1) - 1)&lt;/code&gt;;&lt;/li&gt;
&lt;li&gt;trim white spaces again;&lt;/li&gt;
&lt;li&gt;perform a join on the key that you created.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Often this gives you a decent match. &lt;/p&gt;
', NULL, NULL, '''/code'':19C,23C,36C,41C,49C,63C ''/li'':24C,42C,50C,64C,70C,81C ''/ol'':82C ''/p'':8C,91C ''1'':62C ''a1'':40C,59C,61C ''alphanumer'':30C ''case'':15C ''charact'':31C ''code'':17C,21C,34C,38C,47C,57C ''convert'':11C ''creat'':80C ''decent'':89C ''e.g'':37C ''extract'':52C ''find'':60C ''first'':54C ''follow'':6C ''give'':86C ''join'':74C ''key'':77C ''left'':58C ''li'':10C,25C,43C,51C,65C,71C ''lower'':18C ''match'':90C ''non'':29C ''non-alphanumer'':28C ''often'':84C ''ol'':9C ''p'':1C,83C ''perform'':72C ''proceed'':4C ''remov'':26C ''sever'':33C ''space'':46C,68C ''substitut'':35C,39C ''trim'':44C,48C,66C ''typic'':3C ''upper'':22C ''way'':7C ''white'':45C,67C ''word'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6946, 2, '2015-08-30 03:20:29.35', 5, NULL, NULL, '&lt;p&gt;Probably the most useful (and fun) answer is provided by the excellent &lt;a href="http://nirvacana.com/thoughts/becoming-a-data-scientist/"&gt;Swami Chandrasekaran&lt;/a&gt; ... in the form of a subway map:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/bBuTr.png"&gt;&lt;img src="http://i.stack.imgur.com/bBuTr.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I would add just one comment to that:  school (or learning for the sake of learning) doesn''t provide the same kind of experience as solving a real problem does.  Therefore, to learn, find real problems you can tackle.  To stay motivated, make them problems you really care about.  Kaggle competitions are a great place to start.  Even reproducing a winning solution (perhaps with some variations, or on a new dataset) will be a huge learning experience.&lt;/p&gt;
', NULL, NULL, '''/a'':21C,46C ''/bbutr.png'':35C,40C ''/p'':29C,47C,126C ''/thoughts/becoming-a-data-scientist/'':18C ''add'':51C ''alt'':41C ''answer'':8C ''care'':96C ''chandrasekaran'':20C ''comment'':54C ''competit'':99C ''dataset'':119C ''descript'':44C ''doesn'':65C ''enter'':42C ''even'':106C ''excel'':13C ''experi'':72C,125C ''find'':82C ''form'':24C ''fun'':7C ''great'':102C ''href'':15C,32C ''huge'':123C ''i.stack.imgur.com'':34C,39C ''i.stack.imgur.com/bbutr.png'':33C,38C ''imag'':43C ''img'':36C ''kaggl'':98C ''kind'':70C ''learn'':59C,64C,81C,124C ''make'':91C ''map'':28C ''motiv'':90C ''new'':118C ''nirvacana.com'':17C ''nirvacana.com/thoughts/becoming-a-data-scientist/'':16C ''one'':53C ''p'':1C,30C,48C ''perhap'':111C ''place'':103C ''probabl'':2C ''problem'':77C,84C,93C ''provid'':10C,67C ''real'':76C,83C ''realli'':95C ''reproduc'':107C ''sake'':62C ''school'':57C ''solut'':110C ''solv'':74C ''src'':37C ''start'':105C ''stay'':89C ''subway'':27C ''swami'':19C ''tackl'':87C ''therefor'':79C ''use'':5C ''variat'':114C ''win'':109C ''would'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6876, 2, '2015-08-23 17:23:07.187', 1, NULL, NULL, '&lt;p&gt;It is a bit hard to solve your problem without data but I tried to give it a go
with a how I think the data would be encoded. I used R with &lt;a href="https://cran.r-project.org/web/packages/data.table/" rel="nofollow"&gt;data.tables&lt;/a&gt;. You can read data.tables with &lt;code&gt;fread()&lt;/code&gt;.&lt;/p&gt;

&lt;h1&gt;Step 1&lt;/h1&gt;

&lt;pre&gt;&lt;code&gt; require(data.table)

 # Assume sample_data has the following format:
 #   class: the class
 #   division: the division
 #   attendance: the attandance for a match
 #
 # I assume the table is in long format e.g. multiple rows exist per class with
 # per class one or different divisions.

 # Make the list of classes with at least 5 divisions.
 classes_of_interest &amp;lt;- 
   sample_data[, 
               .(num_divisions = length(unique(divisions))),
               by = class][num_divisions &amp;gt; 4, class]
&lt;/code&gt;&lt;/pre&gt;

&lt;h1&gt;Step 2&lt;/h1&gt;

&lt;pre&gt;&lt;code&gt; # Only consider the classes that were in at least 5 divisions.
 attandance_by_division &amp;lt;- 
   sample_data[class %in% classes_of_interest, 
               .(attendance = sum(num_people)),
               by = list(division, class)]
 setkey(attandance_by_division, "class")
&lt;/code&gt;&lt;/pre&gt;

&lt;h1&gt;Step 3&lt;/h1&gt;

&lt;pre&gt;&lt;code&gt; # Merge the data set with a datas set that contains the required number
 # of attendants per class.
 # The  format is as follows:
 #   class: the class
 #   mininum_attendance: the minimum attendance
 attendance_data &amp;lt;- 
   merge(attendance_requirements, 
         attandance_by_division, by = "class")

 # Here I exploit the fact that the true/false condition will be converted
 # to a 1 and 0. So I can sum and divide by the length of index subset created
 # by aggregating on ''class''.
 pct_of_division &amp;lt;- 
   sample_data[, 
               .(pct_with_min_attendance = (sum(attendance &amp;gt; minimum_attendance)
                                            / length(.I))),
                 by = class]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Hope this helps&lt;/p&gt;
', NULL, NULL, '''/a'':43C ''/code'':51C,129C,172C,274C ''/h1'':56C,134C,177C ''/p'':52C,280C ''/pre'':130C,173C,275C ''/web/packages/data.table/'':39C ''0'':237C ''1'':55C,235C ''2'':133C ''3'':176C ''4'':127C ''5'':109C,146C ''aggreg'':252C ''assum'':61C,81C ''attand'':76C,148C,168C,216C ''attend'':74C,159C,194C,206C,209C,210C,214C,264C,266C,269C ''bit'':5C ''class'':68C,70C,93C,96C,105C,111C,123C,128C,140C,154C,156C,166C,171C,196C,202C,204C,220C,254C,273C ''code'':49C,58C,136C,179C ''condit'':229C ''consid'':138C ''contain'':189C ''convert'':232C ''cran.r-project.org'':38C ''cran.r-project.org/web/packages/data.table/'':37C ''creat'':250C ''data'':12C,27C,63C,116C,153C,182C,186C,211C,260C ''data.table'':60C ''data.tables'':42C,47C ''differ'':99C ''divid'':243C ''divis'':71C,73C,100C,110C,118C,121C,125C,147C,150C,165C,170C,218C,257C ''e.g'':88C ''encod'':30C ''exist'':91C ''exploit'':223C ''fact'':225C ''follow'':66C,201C ''format'':67C,87C,198C ''fread'':50C ''give'':17C ''go'':20C ''gt'':126C,267C ''h1'':53C,131C,174C ''hard'':6C ''help'':279C ''hope'':277C ''href'':36C ''index'':248C ''interest'':113C,158C ''least'':108C,145C ''length'':119C,246C,270C ''list'':103C,164C ''long'':86C ''lt'':114C,151C,212C,258C ''make'':101C ''match'':79C ''merg'':180C,213C ''min'':263C ''minimum'':208C,268C ''mininum'':205C ''multipl'':89C ''nofollow'':41C ''num'':117C,124C,161C ''number'':192C ''one'':97C ''p'':1C,276C ''pct'':255C,261C ''peopl'':162C ''per'':92C,95C,195C ''pre'':57C,135C,178C ''problem'':10C ''r'':33C ''read'':46C ''rel'':40C ''requir'':59C,191C,215C ''row'':90C ''sampl'':62C,115C,152C,259C ''set'':183C,187C ''setkey'':167C ''solv'':8C ''step'':54C,132C,175C ''subset'':249C ''sum'':160C,241C,265C ''tabl'':83C ''think'':25C ''tri'':15C ''true/false'':228C ''uniqu'':120C ''use'':32C ''without'':11C ''would'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6877, 2, '2015-08-23 17:30:57.12', 1, NULL, NULL, '&lt;p&gt;Your first attempt was more accurate. Since I do not have your data I will just use the iris data set,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; iris_ordered_on_Sepal_Length &amp;lt;- iris[order(iris$Sepal.Length),]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;or if you use &lt;a href="https://cran.r-project.org/web/packages/data.table/" rel="nofollow"&gt;data.table&lt;/a&gt;,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;iris_dt &amp;lt;- data.table(iris)
iris_dt[order(Sepal.Length)]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;data.table will make renaming easier too,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;setnames(input, c("colname_11, "colname_17", "colname_23"), validoutcomes[1 : 3])
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':51C ''/code'':36C,64C,88C ''/p'':23C,52C,73C ''/pre'':37C,65C,89C ''/web/packages/data.table/'':47C ''1'':86C ''11'':80C ''17'':82C ''23'':84C ''3'':87C ''accur'':7C ''attempt'':4C ''c'':78C ''code'':25C,54C,75C ''colnam'':79C,81C,83C ''cran.r-project.org'':46C ''cran.r-project.org/web/packages/data.table/'':45C ''data'':14C,21C ''data.table'':50C,58C,67C ''dt'':56C,61C ''easier'':71C ''first'':3C ''href'':44C ''input'':77C ''iri'':20C,26C,32C,34C,55C,59C,60C ''length'':30C ''lt'':31C,57C ''make'':69C ''nofollow'':49C ''order'':27C,33C,62C ''p'':1C,38C,66C ''pre'':24C,53C,74C ''rel'':48C ''renam'':70C ''sepal'':29C ''sepal.length'':35C,63C ''set'':22C ''setnam'':76C ''sinc'':8C ''use'':18C,42C ''validoutcom'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6883, 2, '2015-08-24 01:53:27.273', 1, NULL, NULL, '&lt;p&gt;Though continuum regression methods appear to be significantly different from binary classification methods, the crux of the difference is usually tied up in a &lt;a href="https://en.wikipedia.org/wiki/Sigmoid_function" rel="nofollow"&gt;sigmoid function&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;$$S(t)=\frac{1}{1+e^-t}$$&lt;/p&gt;

&lt;p&gt;Sigmoid literally means sigma like or "S" like, as becomes obvious in looking at its graphical representation:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/2Zz9l.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/2Zz9l.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The sigmoid provides a tractable means to map from continuum analytics into discrete space while retaining differentiability and integrability needed for some theoretical derivations.  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;The important part&lt;/strong&gt; is that in classification methods, one has to choose a threshold value of the sigma function for mapping to a 0 or 1.  This is overwhelmingly chosen as 0.5, but there are cases of unbalanced classes where other values might work better.&lt;/p&gt;

&lt;p&gt;That said, you should be able to proceed exactly as you have already suggested: "I would have simply divided it by the respective averages and removed the effect."  Do this, but then you have to turn the results back into 0''s and 1''s by choosing where your cutoff threshold lies.&lt;/p&gt;

&lt;p&gt;Hope this helps! &lt;/p&gt;
', NULL, NULL, '''/2zz9l.png'':70C,77C ''/a'':35C,83C ''/p'':36C,45C,64C,84C,110C,159C,213C,218C ''/strong'':116C ''/wiki/sigmoid_function'':30C ''0'':137C,201C ''0.5'':145C ''1'':41C,42C,139C,204C ''abl'':166C ''alreadi'':173C ''alt'':78C ''analyt'':96C ''appear'':6C ''averag'':184C ''back'':199C ''becom'':56C ''better'':158C ''binari'':12C ''case'':149C ''choos'':125C,207C ''chosen'':143C ''class'':152C ''classif'':13C,120C ''continuum'':3C,95C ''crux'':16C ''cutoff'':210C ''deriv'':109C ''descript'':81C ''differ'':10C,19C ''differenti'':102C ''discret'':98C ''divid'':179C ''e'':43C ''effect'':188C ''en.wikipedia.org'':29C ''en.wikipedia.org/wiki/sigmoid_function'':28C ''enter'':79C ''exact'':169C ''frac'':40C ''function'':34C,132C ''graphic'':62C ''help'':217C ''hope'':215C ''href'':27C,67C ''i.stack.imgur.com'':69C,76C ''i.stack.imgur.com/2zz9l.png'':68C,75C ''imag'':80C ''img'':73C ''import'':114C ''integr'':104C ''lie'':212C ''like'':51C,54C ''liter'':48C ''look'':59C ''map'':93C,134C ''mean'':49C,91C ''method'':5C,14C,121C ''might'':156C ''need'':105C ''nofollow'':32C,72C ''obvious'':57C ''one'':122C ''overwhelm'':142C ''p'':1C,37C,46C,65C,85C,111C,160C,214C ''part'':115C ''proceed'':168C ''provid'':88C ''regress'':4C ''rel'':31C,71C ''remov'':186C ''represent'':63C ''respect'':183C ''result'':198C ''retain'':101C ''said'':162C ''sigma'':50C,131C ''sigmoid'':33C,47C,87C ''signific'':9C ''simpli'':178C ''space'':99C ''src'':74C ''strong'':112C ''suggest'':174C ''theoret'':108C ''though'':2C ''threshold'':127C,211C ''tie'':22C ''tractabl'':90C ''turn'':196C ''unbalanc'':151C ''usual'':21C ''valu'':128C,155C ''work'':157C ''would'':176C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6884, 1, '2015-08-24 07:38:03.147', 1, 271, 'How is H2o faster than R/SAS?', '&lt;p&gt;I am trying to understand the abstract details that explain how h2o is faster than R and SAS for data science computations.&lt;/p&gt;
', 'data-mining  r  bigdata  sas', NULL, '''/p'':36C ''abstract'':20C ''bigdata'':11B ''comput'':35C ''data'':8B,33C ''data-min'':7B ''detail'':21C ''explain'':23C ''faster'':4A,27C ''h2o'':3A,25C ''mine'':9B ''p'':13C ''r'':10B,29C ''r/sas'':6A ''sas'':12B,31C ''scienc'':34C ''tri'':16C ''understand'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6885, 1, '2015-08-24 15:42:19.56', 1, 197, 'Project ideas related to neural networks in medical field', '&lt;p&gt;I am bit new to neural networks and I am currently planning to do a project on this topic. I want to do something in medical field. But image processing is not something that I am fond of, so it narrows down my options. Can any one suggest me a nice project related to this. Well data analysis or language processing with neural networks is fine with me.&lt;/p&gt;

&lt;p&gt;Thanks everyone ---  (in advance)&lt;/p&gt;

&lt;p&gt;EDIT:
Project on Deep learning using Neural Networks in the field of medicine. I do not have any data so if the someone can give the links to corresponding data it will be better.&lt;/p&gt;
', 'machine-learning  nlp  neuralnetwork  deep-learning  data', NULL, '''/p'':88C,94C,130C ''advanc'':93C ''analysi'':77C ''better'':129C ''bit'':22C ''correspond'':124C ''current'':30C ''data'':18B,76C,114C,125C ''deep'':16B,99C ''deep-learn'':15B ''edit'':96C ''everyon'':91C ''field'':9A,46C,106C ''fine'':85C ''fond'':56C ''give'':120C ''idea'':2A ''imag'':48C ''languag'':79C ''learn'':12B,17B,100C ''link'':122C ''machin'':11B ''machine-learn'':10B ''medic'':8A,45C ''medicin'':108C ''narrow'':60C ''network'':6A,26C,83C,103C ''neural'':5A,25C,82C,102C ''neuralnetwork'':14B ''new'':23C ''nice'':70C ''nlp'':13B ''one'':66C ''option'':63C ''p'':19C,89C,95C ''plan'':31C ''process'':49C,80C ''project'':1A,35C,71C,97C ''relat'':3A,72C ''someon'':118C ''someth'':43C,52C ''suggest'':67C ''thank'':90C ''topic'':38C ''use'':101C ''want'':40C ''well'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6886, 2, '2015-08-24 16:36:51.427', 1, NULL, NULL, '&lt;p&gt;You could try &lt;a href="http://rstudio.github.io/shinydashboard/" rel="nofollow"&gt;ShinyDashboard&lt;/a&gt; and leave out any interactive aspects. In my opinion it is still nice to allow users to zoom in on plots or search for certain values in a column. Shiny will allow you to do this.&lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/p'':50C ''/shinydashboard/'':9C ''allow'':28C,45C ''aspect'':19C ''certain'':38C ''column'':42C ''could'':3C ''href'':6C ''interact'':18C ''leav'':15C ''nice'':26C ''nofollow'':11C ''opinion'':22C ''p'':1C ''plot'':34C ''rel'':10C ''rstudio.github.io'':8C ''rstudio.github.io/shinydashboard/'':7C ''search'':36C ''shini'':43C ''shinydashboard'':12C ''still'':25C ''tri'':4C ''user'':29C ''valu'':39C ''zoom'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6887, 1, '2015-08-24 16:51:45.647', 0, 36, 'Find the column(s) name where the value of the variable matches a regex', '&lt;p&gt;I am trying to filter a data frame where I need to search for a regular expression within the data frame. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dim(df)
[1] 10299 561
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I wanted to know if there is a way, I could find the name(s) of the column(s) where a particular regex may be available.&lt;/p&gt;

&lt;p&gt;As an example, how do I find the name of the column where ''rsi'' is present. I am referring to iris. &lt;/p&gt;
', 'r', NULL, '''/code'':46C ''/p'':38C,75C,98C ''/pre'':47C ''1'':43C ''10299'':44C ''561'':45C ''avail'':74C ''code'':40C ''column'':3A,66C,88C ''could'':59C ''data'':23C,36C ''df'':42C ''dim'':41C ''exampl'':79C ''express'':33C ''filter'':21C ''find'':1A,60C,83C ''frame'':24C,37C ''iri'':97C ''know'':52C ''match'':12A ''may'':72C ''name'':5A,62C,85C ''need'':27C ''p'':16C,48C,76C ''particular'':70C ''pre'':39C ''present'':92C ''r'':15B ''refer'':95C ''regex'':14A,71C ''regular'':32C ''rsi'':90C ''search'':29C ''tri'':19C ''valu'':8A ''variabl'':11A ''want'':50C ''way'':57C ''within'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6889, 2, '2015-08-24 18:51:56.09', 2, NULL, NULL, '&lt;p&gt;Would this work for you,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; names(iris)[apply(iris, 2, function(x) any(grepl("rsi", x)))]
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':21C ''/p'':7C ''/pre'':22C ''2'':14C ''appli'':12C ''code'':9C ''function'':15C ''grepl'':18C ''iri'':11C,13C ''name'':10C ''p'':1C ''pre'':8C ''rsi'':19C ''work'':4C ''would'':2C ''x'':16C,20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6890, 2, '2015-08-25 03:15:11.023', 1, NULL, NULL, '&lt;p&gt;I''m not really seeing the issue with an infinite loop.  It seems like you are looping through your clusters and finding all the clusters that need to be merged, then in a separate loop doing the merging and are getting tripped up when two clusters are flagged in such a way that they both need to be merged into one another.  Instead, you can think of this as more of a queue.&lt;/p&gt;

&lt;p&gt;Then you can loop through one time and merge the small clusters with their neighbors and then destroy them.&lt;/p&gt;

&lt;p&gt;Imagine you have a &lt;code&gt;cluster&lt;/code&gt; class with attributes: &lt;code&gt;membercount&lt;/code&gt;, &lt;code&gt;members&lt;/code&gt;, and nearestNeighbor, and methods &lt;code&gt;addMembers()&lt;/code&gt; and &lt;code&gt;destroy()&lt;/code&gt;.  &lt;/p&gt;

&lt;p&gt;Then you could do something like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for (cluster in clusterList):
    if cluster.membercount&amp;lt;T:
        cluster.nearestNeighbor.addMembers(cluster.members)
        cluster.destroy()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This will only cycle through clusterList one time and will instantly merge and destroy the clusters that are smaller than T.&lt;/p&gt;

&lt;p&gt;Assuming you have experience with object oriented programming, this would be pretty straightforward to implement.  Even if you do this within an array, you can just &lt;code&gt;pop&lt;/code&gt; clusters after you merge them with other clusters.&lt;/p&gt;

&lt;p&gt;Hope this helps! &lt;/p&gt;
', NULL, NULL, '''/code'':104C,110C,113C,120C,124C,147C,201C ''/p'':75C,96C,125C,133C,171C,210C,215C ''/pre'':148C ''addmemb'':119C ''anoth'':63C ''array'':195C ''assum'':173C ''attribut'':107C ''class'':105C ''cluster'':21C,26C,47C,88C,103C,137C,165C,202C,209C ''cluster.destroy'':146C ''cluster.membercount'':141C ''cluster.members'':145C ''cluster.nearestneighbor.addmembers'':144C ''clusterlist'':139C,155C ''code'':102C,108C,111C,118C,122C,135C,199C ''could'':129C ''cycl'':153C ''destroy'':94C,123C,163C ''even'':188C ''experi'':176C ''find'':23C ''flag'':49C ''get'':42C ''help'':214C ''hope'':212C ''imagin'':98C ''implement'':187C ''infinit'':11C ''instant'':160C ''instead'':64C ''issu'':8C ''like'':15C,132C ''loop'':12C,18C,36C,80C ''lt'':142C ''m'':3C ''member'':112C ''membercount'':109C ''merg'':31C,39C,60C,85C,161C,205C ''method'':117C ''nearestneighbor'':115C ''need'':28C,57C ''neighbor'':91C ''object'':178C ''one'':62C,82C,156C ''orient'':179C ''p'':1C,76C,97C,126C,149C,172C,211C ''pop'':200C ''pre'':134C ''pretti'':184C ''program'':180C ''queue'':74C ''realli'':5C ''see'':6C ''seem'':14C ''separ'':35C ''small'':87C ''smaller'':168C ''someth'':131C ''straightforward'':185C ''think'':67C ''time'':83C,157C ''trip'':43C ''two'':46C ''way'':53C ''within'':193C ''would'':182C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6895, 2, '2015-08-25 13:21:20.567', 2, NULL, NULL, '&lt;p&gt;It returns the log odds. You can see that with this basic example,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Create a perfect correlated data.
data &amp;lt;- data.frame(x = c(1,0,1,0), y = c(T, F, T, F))

# Estimate the model.
model &amp;lt;- glm(y ~ x, data)

# Show predictions.
predict(model)
##        1         2         3         4 
## 23.56607 -23.56607  23.56607 -23.56607

# Compute the inverse of the log odd to come to your prediction. 
boot::inv.logit(predict(model))
##           1            2            3            4 
##1.000000e+00 5.826215e-11 1.000000e+00 5.826215e-11 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If logit would return probabilities you could not take the inverse to come to your predicted values.&lt;/p&gt;
', NULL, NULL, '''-23.56607'':56C,58C ''/code'':83C ''/p'':15C,103C ''/pre'':84C ''0'':29C,31C ''1'':28C,30C,51C,75C ''1.000000e+00'':79C,81C ''2'':52C,76C ''23.56607'':55C,57C ''3'':53C,77C ''4'':54C,78C ''5.826215e-11'':80C,82C ''basic'':13C ''boot'':71C ''c'':27C,33C ''code'':17C ''come'':67C,98C ''comput'':59C ''correl'':21C ''could'':92C ''creat'':18C ''data'':22C,23C,46C ''data.frame'':25C ''estim'':38C ''exampl'':14C ''f'':35C,37C ''glm'':43C ''inv.logit'':72C ''invers'':61C,96C ''log'':5C,64C ''logit'':87C ''lt'':24C,42C ''model'':40C,41C,50C,74C ''odd'':6C,65C ''p'':1C,85C ''perfect'':20C ''pre'':16C ''predict'':48C,49C,70C,73C,101C ''probabl'':90C ''return'':3C,89C ''see'':9C ''show'':47C ''take'':94C ''valu'':102C ''would'':88C ''x'':26C,45C ''y'':32C,44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9408, 2, '2015-12-16 13:29:30.29', 4, NULL, NULL, '&lt;p&gt;One way to interpret cross-entropy is to see it as a (minus) log-likelihood for the data $y_i''$, under a model $y_i$.&lt;/p&gt;

&lt;p&gt;Namely, suppose that you have some fixed model (a.k.a. "hypothesis"), which predicts for $n$ classes $\{1,2,\dots, n\}$ their hypothetical occurrence probabilities $y_1, y_2,\dots, y_n$. Suppose that you now observe (in reality) $k_1$ instances of class $1$, $k_2$ instances of class $2$, $k_n$ instances of class $n$, etc. According to your model the likelihood of this happening is:
$$
P[data|model] := y_1^{k_1}y_2^{k_2}\dots y_n^{k_n}.
$$
Taking the logarithm and changing the sign:
$$
-\log P[data|model] = -k_1\log y_1 -k_2\log y_2 - \dots -k_n\log y_n = -\sum_i k_i \log y_i
$$
If you now divide the right-hand sum by the number of observations $N = k_1+k_2+\dots+k_n$, and denote the empirical probabilities as $y_i''=k_i/N$, you''ll get the cross-entropy:
$$
-\frac{1}{N} \log P[data|model] = -\frac{1}{N}\sum_i k_i \log y_i = -\sum_i y_i''\log y_i =: H(y'', y)
$$&lt;/p&gt;

&lt;p&gt;Furthermore, a log-likelihood of a dataset given a model can be interpreted as a measure of "encoding length" - the number of bits you expect to spend to encode this information if your encoding scheme would be based on your hypothesis. &lt;/p&gt;

&lt;p&gt;This follows from the observation that an independent event with probability $y_i$ requires at least $-\log_2 y_i$ bits to encode it (assuming efficient coding), and consequently the expression 
$$-\sum_i y_i''\log_2 y_i,$$
is literally the expected length of the encoding, where the encoding lengths for the events are computed using the "hypothesized" distribution, while the expectation is taken over the actual one.&lt;/p&gt;

&lt;p&gt;Finally, instead of saying "measure of expected encoding length" I really like to use the informal term "measure of surprise". If you need a lot of bits to encode an expected event from a distribution, the distribution is "really surprising" for you.&lt;/p&gt;

&lt;p&gt;With those intuitions in mind, the answers to your questions can be seen as follows:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Question 1&lt;/strong&gt;. Yes. It is a problem &lt;em&gt;whenever the corresponding $y_i''$ is nonzero at the same time&lt;/em&gt;. It corresponds to the situation where your model believes that some class has zero probablity of occurrence, and yet the class pops up in reality. As a result, the "surprise" of your model is infinitely great: your model did not account for that event and now needs infinitely many bits to encode it. That''s why you get inifinity as your cross-entropy. &lt;/p&gt;

&lt;p&gt;To avoid this problem you need to make sure that your model does not make rash assumptions about something being impossible while it can happen. In reality people tend to use sigmoid or "softmax" functions as their hypothesis models, which are conservative enough to leave at least some chance for every option.&lt;/p&gt;

&lt;p&gt;If you use some other hypothesis model, it is up to you to &lt;em&gt;regularize&lt;/em&gt; (aka "smooth") it so that it wouldn''t hypothesize zeros where it should not have.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Question 2&lt;/strong&gt;. In this formula one usually assumes $y_i''$ to be either $0$ or $1$, while $y_i$ is the model''s probability hypothesis for the corresponding input. If you look close enough, you will see that it is simply a $-\log P[data|model]$ for binary data, an equivalent of the second equation in this answer.&lt;/p&gt;

&lt;p&gt;Hence, strictly speaking, although it is still a log-likelihood, this is not syntactically equivalent to cross-entropy. What some people mean when referring to such an expression as &lt;em&gt;cross-entropy&lt;/em&gt; is that it is, in fact, a &lt;em&gt;sum&lt;/em&gt; over over binary cross-entropies for individual points in the dataset:
$$
\sum_i H(y_i'', y_i),
$$
where $y_i''$ and $y_i$ have to be interpreted as the corresponding binary distributions $(y_i'', 1-y_i'')$ and $(y_i, 1-y_i)$.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9439, 2, '2015-12-19 05:55:50.047', 1, NULL, NULL, '&lt;p&gt;I am a self-taught data scientist, and I''d try my best to explain you how to go about it.&lt;/p&gt;

&lt;hr&gt;

&lt;blockquote&gt;
  &lt;p&gt;What fields are there within data science? (e.g., Artificial
  Intelligence, machine learning, data analysis, etc.)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Data Science is a very wide domain. It is about the science of data. So, any field which uses data to take decisions come under this domain. Some of the fields include:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;AI&lt;/li&gt;
&lt;li&gt;Pattern Recognition and Analytics&lt;/li&gt;
&lt;li&gt;Bio-statistics&lt;/li&gt;
&lt;li&gt;Statistical Learning&lt;/li&gt;
&lt;li&gt;Machine Learning&lt;/li&gt;
&lt;li&gt;Data Aesthetics(or data visialization)&lt;/li&gt;
&lt;li&gt;Data Journalism&lt;/li&gt;
&lt;/ul&gt;

&lt;hr&gt;

&lt;blockquote&gt;
  &lt;p&gt;Are there online classes people can recommend?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I have answered a &lt;a href="http://datascience.stackexchange.com/a/8965/11097"&gt;similar question&lt;/a&gt;. So I''d quote it here:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Start with the &lt;a href="https://www.coursera.org/learn/machine-learning" rel="nofollow"&gt;Coursera''s Machine Learning&lt;/a&gt; course. It does a
  really good job in introducing the student to the domain of Machine
  Learning and helps you lay a solid foundation in the concepts.&lt;/p&gt;
  
  &lt;p&gt;In case, you feel that the math is a bit dumbed down in that course,
  you can take &lt;a href="https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599" rel="nofollow"&gt;this course&lt;/a&gt;, taught by the same professor and is
  math-intensive than the former.&lt;/p&gt;
  
  &lt;p&gt;Now, you would have a clear intuition about the basic concepts of
  Machine Learning. Now, take &lt;a href="https://www.youtube.com/playlist?list=PLD63A284B7615313A" rel="nofollow"&gt;this course&lt;/a&gt;, which can be said as a
  follow-up or a supplementary for the course of Andrew Ng.&lt;/p&gt;
  
  &lt;p&gt;&lt;a href="http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mltut.htm" rel="nofollow"&gt;This resource from IAPR&lt;/a&gt; has in-depth notes on a lot of ML
  concepts like cross-validation, regularization, etc.&lt;/p&gt;
  
  &lt;p&gt;You can also have a look at these &lt;a href="https://www.quora.com/profile/Eren-Golge/Machine-Learning/Some-of-useful-machine-learning-resources-from-beginner-to-intermediate" rel="nofollow"&gt;amazing list of resources&lt;/a&gt;
  compiled into a blog on Quora.&lt;/p&gt;
  
  &lt;p&gt;Now, for diving into advanced concepts of neural networks and deep
  learning, you can make use of &lt;a href="http://neuralnetworksanddeeplearning.com/" rel="nofollow"&gt;this free book&lt;/a&gt;.&lt;/p&gt;
  
  &lt;p&gt;Finally, the free e-book: &lt;a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/" rel="nofollow"&gt;Elements of Statistical Learning&lt;/a&gt; is a
  wonderful book for beginners in ML or Statistical Learning.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I addition to that, do check out this &lt;a href="https://www.quora.com/topic/Data-Science/faq" rel="nofollow"&gt;repository of data science references by Quora&lt;/a&gt;.&lt;/p&gt;

&lt;hr&gt;

&lt;blockquote&gt;
  &lt;p&gt;Are there projects available out there that I can practice on (e.g.,
  open datasets).&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I have started doing projects with open datasets of India. However, I would recommend you to check out this amazing &lt;a href="https://www.quora.com/Data-Science/What-are-some-good-toy-problems-in-data-science" rel="nofollow"&gt;discussion here&lt;/a&gt;, and after doing those projects, you can start with Kaggle.&lt;/p&gt;

&lt;hr&gt;

&lt;blockquote&gt;
  &lt;p&gt;Are there certifications I can apply for or complete?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;In my opinion, there aren''t any &lt;em&gt;data science&lt;/em&gt; certifications. Yeah, there are a lot of Big Data certifications out there, but I didn''t see them being really useful for a budding data scientist, so I recommend you not to chase them atleast till you are confident enough with your ML and data skills.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9485, 2, '2015-12-22 12:58:33.743', 9, NULL, NULL, '&lt;p&gt;It seems that XGBoost uses &lt;strong&gt;regression trees&lt;/strong&gt; as base learners by default. XGBoost (or Gradient boosting in general) work by combining multiple of these base learners. Regression trees can not extrapolate the patterns in the training data, so any input above 3 or below 1 will not be predicted correctly in your case. Your model is trained to predict outputs for inputs in the interval &lt;code&gt;[1,3]&lt;/code&gt;, an input higher than 3 will be given the same output as 3, and an input less than 1 will be given the same output as 1.&lt;/p&gt;

&lt;p&gt;Additionally, regression trees do not really see your data as a &lt;em&gt;straight line&lt;/em&gt; as they are non-parametric models, which means they can theoretically fit any shape that is more complicated than a straight line. Roughly, a regression tree works by assigning your new input data to some of the training data points it have seen during training, and produce the output based on that. &lt;/p&gt;

&lt;p&gt;This is in contrast to parametric regressors (like &lt;strong&gt;linear regression&lt;/strong&gt;) which actually look for the best parameters of a hyperplane (straight line in your case) to fit your data. Linear regression &lt;strong&gt;does&lt;/strong&gt; see your data as a straight line with a slope and and intercept.&lt;/p&gt;

&lt;p&gt;You can change the base learner of your XGB model to a GLM (generalised linear model) by adding &lt;code&gt;"booster":"gblinear"&lt;/code&gt; to your model &lt;code&gt;params&lt;/code&gt; :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas as pd
import xgboost as xgb

df = pd.DataFrame({''x'':[1,2,3], ''y'':[10,20,30]})
X_train = df.drop(''y'',axis=1)
Y_train = df[''y'']
T_train_xgb = xgb.DMatrix(X_train, Y_train)

params = {"objective": "reg:linear", "booster":"gblinear"}
gbm = xgb.train(dtrain=T_train_xgb,params=params)
Y_pred = gbm.predict(xgb.DMatrix(pd.DataFrame({''x'':[4,5]})))
print Y_pred
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In general, to debug why your XGBoost model is behaving in a particular way, see the model parameters :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;gbm.get_dump()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If your base learner is linear model, the get_dump output is :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[''bias:\n4.49469\nweight:\n7.85942\n'']
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In your code above, since you tree base learners, the output will be :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[''0:[x&amp;lt;3] yes=1,no=2,missing=1\n\t1:[x&amp;lt;2] yes=3,no=4,missing=3\n\t\t3:leaf=2.85\n\t\t4:leaf=5.85\n\t2:leaf=8.85\n'',
 ''0:[x&amp;lt;3] yes=1,no=2,missing=1\n\t1:[x&amp;lt;2] yes=3,no=4,missing=3\n\t\t3:leaf=1.995\n\t\t4:leaf=4.095\n\t2:leaf=6.195\n'',
 ''0:[x&amp;lt;3] yes=1,no=2,missing=1\n\t1:[x&amp;lt;2] yes=3,no=4,missing=3\n\t\t3:leaf=1.3965\n\t\t4:leaf=2.8665\n\t2:leaf=4.3365\n'',
 ''0:[x&amp;lt;3] yes=1,no=2,missing=1\n\t1:[x&amp;lt;2] yes=3,no=4,missing=3\n\t\t3:leaf=0.97755\n\t\t4:leaf=2.00655\n\t2:leaf=3.03555\n'',
 ''0:[x&amp;lt;3] yes=1,no=2,missing=1\n\t1:[x&amp;lt;2] yes=3,no=4,missing=3\n\t\t3:leaf=0.684285\n\t\t4:leaf=1.40458\n\t2:leaf=2.12489\n'',
 ''0:[x&amp;lt;3] yes=1,no=2,missing=1\n\t1:[x&amp;lt;2] yes=3,no=4,missing=3\n\t\t3:leaf=0.478999\n\t\t4:leaf=0.983209\n\t2:leaf=1.48742\n'',
 ''0:[x&amp;lt;3] yes=1,no=2,missing=1\n\t1:[x&amp;lt;2] yes=3,no=4,missing=3\n\t\t3:leaf=0.3353\n\t\t4:leaf=0.688247\n\t2:leaf=1.04119\n'',
 ''0:[x&amp;lt;3] yes=1,no=2,missing=1\n\t1:[x&amp;lt;2] yes=3,no=4,missing=3\n\t\t3:leaf=0.23471\n\t\t4:leaf=0.481773\n\t2:leaf=0.728836\n'',
 ''0:[x&amp;lt;3] yes=1,no=2,missing=1\n\t1:[x&amp;lt;2] yes=3,no=4,missing=3\n\t\t3:leaf=0.164297\n\t\t4:leaf=0.337241\n\t2:leaf=0.510185\n'',
 ''0:[x&amp;lt;2] yes=1,no=2,missing=1\n\t1:leaf=0.115008\n\t2:[x&amp;lt;3] yes=3,no=4,missing=3\n\t\t3:leaf=0.236069\n\t\t4:leaf=0.357129\n'']
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Tip : I actually prefer to use xgb.XGBRegressor or xgb.XGBClassifier classes, since they follow the &lt;a href="http://scikit-learn.org/" rel="nofollow"&gt;sci-kit learn&lt;/a&gt; API. And because sci-kit learn has so many machine learning algorithm implementations, using XGB as an additional library does not disturb my workflow only when I use the sci-kit interface of XGB.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6896, 2, '2015-08-25 13:32:01.33', 6, NULL, NULL, '&lt;p&gt;I have used R, SAS Base and H2O. First, I do not think that H2O seeks to be either R or SAS. &lt;/p&gt;

&lt;p&gt;H2O provides data mining algorithms that are highly efficient. You can interface with H2O with the several APIs such as their R API. The benefit of combining R and H2O is that it H2O is very good at exploiting multi cores or clusters with minimal effort of the user. It is much harder to achieve the same efficiency in R. &lt;/p&gt;

&lt;p&gt;The reason why H2O is much faster is that they have a very good indexing of their data and their algorithms are written such that they exploit parallelism to the fullest. See &lt;a href="http://h2o.ai/blog/2014/03/h2o-architecture/"&gt;http://h2o.ai/blog/2014/03/h2o-architecture/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;R with the default matrix dynamic libraries can only use one CPU core. Revolution R community edition ships with the Intel Math Kernel Library. This allows to do some matrix computations in parallel but definitely not as efficient as H2O. For SAS it is a bit harder to say anything considering its closed source but based on my CPU utilization I would assume that they have a similar approach as Revolution R. Their matrix algebra exploits parallelism but they algorithms are not as efficient as H2O. Their data storage is also not as efficient as H2O.&lt;/p&gt;

&lt;p&gt;Lastly, H2O with R comes at a very different price tag than SAS.&lt;/p&gt;

&lt;p&gt;Hopes this clarifies a bit.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6898, 1, '2015-08-25 15:39:24.467', 0, 75, 'users'' percentile similarity measure', '&lt;p&gt;Having n vectors of percentile ranks for a list of common users between group #1 and groups #2:n&lt;/p&gt;

&lt;p&gt;e.g. vec1 = {0.25, 0.1, 0.8, 0.75, 0.5, 0.6}
     vec2 = {0.35, 0.2, 0.6, 0.45, 0.2, 0.9}&lt;/p&gt;

&lt;p&gt;The percentile ranks represent activity frequency within the group for instance, opening times.
The goal is to find similarity between group #1 and groups #2..n in terms of these common users''s rank.&lt;/p&gt;

&lt;p&gt;The direction taken so far is to use dot product in order to take the magnitude into account (due to rank). The problem is that the scalar answer can take any value and so a threshold cannot be drawn.&lt;/p&gt;

&lt;p&gt;Do I need to draw a threshold with respect to the dot product of vec #1 (group 1) with itself? or is there another way to set a threshold (maybe dynamic) ?&lt;/p&gt;
', 'similarity', NULL, '''/p'':26C,43C,79C,120C,153C ''0.1'':31C ''0.2'':38C,41C ''0.25'':30C ''0.35'':37C ''0.45'':40C ''0.5'':34C ''0.6'':35C,39C ''0.75'':33C ''0.8'':32C ''0.9'':42C ''1'':21C,66C,137C,139C ''2'':24C,69C ''account'':98C ''activ'':49C ''anoth'':145C ''answer'':108C ''cannot'':117C ''common'':17C,75C ''direct'':82C ''dot'':89C,133C ''draw'':126C ''drawn'':119C ''due'':99C ''dynam'':152C ''e.g'':28C ''far'':85C ''find'':62C ''frequenc'':50C ''goal'':59C ''group'':20C,23C,53C,65C,68C,138C ''instanc'':55C ''list'':15C ''magnitud'':96C ''mayb'':151C ''measur'':4A ''n'':8C,25C,70C ''need'':124C ''open'':56C ''order'':92C ''p'':6C,27C,44C,80C,121C ''percentil'':2A,11C,46C ''problem'':103C ''product'':90C,134C ''rank'':12C,47C,78C,101C ''repres'':48C ''respect'':130C ''scalar'':107C ''set'':148C ''similar'':3A,5B,63C ''take'':94C,110C ''taken'':83C ''term'':72C ''threshold'':116C,128C,150C ''time'':57C ''use'':88C ''user'':1A,18C,76C ''valu'':112C ''vec'':136C ''vec1'':29C ''vec2'':36C ''vector'':9C ''way'':146C ''within'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6899, 1, '2015-08-25 16:04:08.067', 5, 83, 'Efficient algorithm to compute the ROC curve for a classifier consisting of an ensemble of disjoint classifiers', '&lt;p&gt;Suppose I have classifiers C_1 ... C_n that are disjoint in the sense that no two will return true on the same input (e.g. the nodes in a decision tree).  I want to build a new classifier that is the union of some subset of these (e.g. I want to decide on which leaves of a decision tree to give a positive classification).  Of course, in doing so there will be a trade off between sensitivity and positive predictive value.  So I would like to see a ROC curve.  In principle I could do this by enumerating all subsets of the classifiers and computing the resulting sensitivity and PPV.  However, this is prohibitively expensive if n is more than 30 or so.  On the other hand, there are almost certainly some combinations that are not Pareto optimal, so there might be some branch and bound strategy, or something, that avoids most of the computation in many cases.&lt;/p&gt;

&lt;p&gt;I would like advice about whether this approach is likely to be fruitful and whether there is any work or if you have any ideas about efficiently computing the ROC curve in the situation above.&lt;/p&gt;
', 'algorithms', NULL, '''/p'':178C,215C ''1'':25C ''30'':140C ''advic'':183C ''algorithm'':2A,18B ''almost'':149C ''approach'':187C ''avoid'':170C ''bound'':165C ''branch'':163C ''build'':54C ''c'':24C,26C ''case'':177C ''certain'':150C ''classif'':83C ''classifi'':10A,17A,23C,57C,122C ''combin'':152C ''comput'':4A,124C,174C,207C ''consist'':11A ''could'':113C ''cours'':85C ''curv'':7A,109C,210C ''decid'':71C ''decis'':49C,77C ''disjoint'':16A,30C ''e.g'':44C,67C ''effici'':1A,206C ''ensembl'':14A ''enumer'':117C ''expens'':134C ''fruit'':192C ''give'':80C ''hand'':146C ''howev'':130C ''idea'':204C ''input'':43C ''leav'':74C ''like'':104C,182C,189C ''mani'':176C ''might'':160C ''n'':27C,136C ''new'':56C ''node'':46C ''optim'':157C ''p'':19C,179C ''pareto'':156C ''posit'':82C,98C ''ppv'':129C ''predict'':99C ''principl'':111C ''prohibit'':133C ''result'':126C ''return'':38C ''roc'':6A,108C,209C ''see'':106C ''sens'':33C ''sensit'':96C,127C ''situat'':213C ''someth'':168C ''strategi'':166C ''subset'':64C,119C ''suppos'':20C ''trade'':93C ''tree'':50C,78C ''true'':39C ''two'':36C ''union'':61C ''valu'':100C ''want'':52C,69C ''whether'':185C,194C ''work'':198C ''would'':103C,181C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6908, 1, '2015-08-26 17:37:49.187', 1, 90, 'Preprocessing in Data mining?', '&lt;p&gt;I am still new to data mining but I really want (and need) to learn it so badly. I know that before I can actually process my data in softwares like WEKA, I need to do some filtering like cleaning the data, integrating, transforming, etc to actually get your data cleaned from any kind of duplicate, missing value, noise, etc. But I only know all of these theoretically. &lt;/p&gt;

&lt;p&gt;The problem I have now is I have a very big set of data that I need to filter first before going on to the processing part. But I don''t know where to start. Fyi, my dataset is very big that the usual spreadsheet programs like Ms Excel, Libre Office, WPS,etc can''t open it. I have to use Linux terminal and commands to count the number of rows, columns etc. &lt;/p&gt;

&lt;p&gt;What do I do in the preprocessing start? How do I ''clean'' my data? I have been thinking to use Linux commands to do all of these, but I am also wondering how real data scientists clean their data. Do they do these manually or they already have some sort of software to help them? Because seriously I don''t know where to start or to do. Every reference I found in the internet only explain things theoretically. ANd I need something more practical to help me understand. &lt;/p&gt;

&lt;p&gt;What do I do with my dataset? Help please?&lt;/p&gt;
', 'data-mining  dataset  data-cleaning  preprocessing', NULL, '''/p'':82C,157C,247C,258C ''actual'':38C,60C ''alreadi'':205C ''also'':189C ''bad'':31C ''big'':94C,124C ''clean'':11B,53C,64C,170C,195C ''column'':155C ''command'':148C,180C ''count'':150C ''data'':3A,6B,10B,19C,41C,55C,63C,97C,172C,193C,197C ''data-clean'':9B ''data-min'':5B ''dataset'':8B,121C,255C ''duplic'':69C ''etc'':58C,73C,136C,156C ''everi'':226C ''excel'':132C ''explain'':234C ''filter'':51C,102C ''first'':103C ''found'':229C ''fyi'':119C ''get'':61C ''go'':105C ''help'':212C,244C,256C ''integr'':56C ''internet'':232C ''kind'':67C ''know'':33C,77C,115C,219C ''learn'':28C ''libr'':133C ''like'':44C,52C,130C ''linux'':145C,179C ''manual'':202C ''mine'':4A,7B,20C ''miss'':70C ''ms'':131C ''need'':26C,47C,100C,239C ''new'':17C ''nois'':72C ''number'':152C ''offic'':134C ''open'':139C ''p'':13C,83C,158C,248C ''part'':110C ''pleas'':257C ''practic'':242C ''preprocess'':1A,12B,165C ''problem'':85C ''process'':39C,109C ''program'':129C ''real'':192C ''realli'':23C ''refer'':227C ''row'':154C ''scientist'':194C ''serious'':215C ''set'':95C ''softwar'':43C,210C ''someth'':240C ''sort'':208C ''spreadsheet'':128C ''start'':118C,166C,222C ''still'':16C ''termin'':146C ''theoret'':81C,236C ''thing'':235C ''think'':176C ''transform'':57C ''understand'':246C ''use'':144C,178C ''usual'':127C ''valu'':71C ''want'':24C ''weka'':45C ''wonder'':190C ''wps'':135C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6902, 1, '2015-08-26 07:45:14.15', 0, 18, 'Automating the rumour identification process', '&lt;p&gt;Currenlty what we do, check the user discussion based on some keywords on social media. As per the keywords detection we identify that this can be rumour. &lt;/p&gt;

&lt;p&gt;Approach to automate the process:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Keyword based : verifying the conversation for 1-2 gram based keywords. If keyword present, marking it as suspected conversation&lt;/li&gt;
&lt;li&gt;Classifier based approach : Training the classifier with some prelabeled suspected conversations. Which ever being classified with &gt;50% probability, marked as suspected.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;For 2nd approach I am thinking of naive bayes classifier, and identifying the result with precision, recall, F measure value using scikit learn.&lt;/p&gt;

&lt;p&gt;Is there any better approach to this? Or some model which can be combination of both approach?&lt;/p&gt;
', 'machine-learning  data-mining  classification  scikit', NULL, '''-2'':59C ''/li'':71C,94C ''/ol'':95C ''/p'':42C,49C,120C,139C ''1'':58C ''2nd'':98C ''50'':89C ''approach'':44C,75C,99C,126C,138C ''autom'':1A,46C ''base'':23C,53C,61C,74C ''bay'':105C ''better'':125C ''check'':19C ''classif'':12B ''classifi'':73C,78C,87C,106C ''combin'':135C ''convers'':56C,70C,83C ''currenlti'':15C ''data'':10B ''data-min'':9B ''detect'':34C ''discuss'':22C ''ever'':85C ''f'':114C ''gram'':60C ''identif'':4A ''identifi'':36C,108C ''keyword'':26C,33C,52C,62C,64C ''learn'':8B,119C ''li'':51C,72C ''machin'':7B ''machine-learn'':6B ''mark'':66C,91C ''measur'':115C ''media'':29C ''mine'':11B ''model'':131C ''naiv'':104C ''ol'':50C ''p'':14C,43C,96C,121C ''per'':31C ''precis'':112C ''prelabel'':81C ''present'':65C ''probabl'':90C ''process'':5A,48C ''recal'':113C ''result'':110C ''rumour'':3A,41C ''scikit'':13B,118C ''social'':28C ''suspect'':69C,82C,93C ''think'':102C ''train'':76C ''use'':117C ''user'':21C ''valu'':116C ''verifi'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9570, 1, '2015-12-31 22:26:11.447', 0, 49, 'Wierd Theano error', '&lt;p&gt;While I am studying Michael Nielsen''s Deep Learning tutorial, I copied CNN code from &lt;a href="http://neuralnetworksanddeeplearning.com/chap6.html" rel="nofollow"&gt;http://neuralnetworksanddeeplearning.com/chap6.html&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;net = Network([
    ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28), 
                  filter_shape=(20, 1, 5, 5), 
                  poolsize=(2, 2)),
    FullyConnectedLayer(n_in=20*12*12, n_out=100),
    SoftmaxLayer(n_in=100, n_out=10)], mini_batch_size)
net.SGD(training_data, 60, mini_batch_size, 0.1, 
        validation_data, test_data)   
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But it gives me wierd error that is shown below&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;mod.cu(294): warning: subscript out of range
mod.cu(303): error: too few arguments in function call

    1 error detected in the compilation of "/tmp/tmpxft_00005e58_00000000-9_mod.cpp1.ii".

    [''nvcc'', ''-shared'', ''-O3'', ''-use_fast_math'', ''-arch=sm_30'', ''-m64'', ''-Xcompiler'', ''-fno-math-errno,-Wno-unused-label,-Wno-unused-variable,-Wno-write-strings,-DCUDA_NDARRAY_CUH=c72d035fdf91890f3b36710688069b2e,-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION,-fPIC,-fvisibility=hidden'', ''-Xlinker'', ''-rpath,/home/user1/.theano/compiledir_Linux-4.2--generic-x86_64-with-Ubuntu-15.10-wily-x86_64-2.7.10-64/cuda_ndarray'', ''-I/home/user1/programming/src/theano/theano/sandbox/cuda'', ''-I/usr/local/lib/python2.7/dist-packages/numpy/core/include'', ''-I/usr/include/python2.7'', ''-I/home/user1/programming/src/theano/theano/gof'', ''-o'', ''/home/user1/.theano/compiledir_Linux-4.2--generic-x86_64-with-Ubuntu-15.10-wily-x86_64-2.7.10-64/tmpYxVaVI/b409a1a0bc2bda394e733f9b89a4c08f.so'', ''mod.cu'', ''-L/usr/lib'', ''-lpython2.7'', ''-lcudnn'', ''-lcudart'']
    Traceback (most recent call last):
      File "classifyCNN.py", line 14, in &amp;lt;module&amp;gt;
        validation_data, test_data)
      File "/home/user1/programming/PH2Dataset/trainingImages/network3.py", line 133, in SGD 
        training_y[i*self.mini_batch_size: (i+1)*self.mini_batch_size]
      File "/home/user1/programming/src/theano/theano/compile/function.py", line 316, in function
        output_keys=output_keys)
      File "/home/user1/programming/src/theano/theano/compile/pfunc.py", line 523, in pfunc
        output_keys=output_keys)
      File "/home/user1/programming/src/theano/theano/compile/function_module.py", line 1526, in orig_function
        defaults)
      File "/home/user1/programming/src/theano/theano/compile/function_module.py", line 1390, in create
        input_storage=input_storage_lists)
      File "/home/user1/programming/src/theano/theano/gof/link.py", line 607, in make_thunk
        output_storage=output_storage)[:3]
      File "/home/user1/programming/src/theano/theano/gof/vm.py", line 1025, in make_all
        no_recycling))
      File "/home/user1/programming/src/theano/theano/sandbox/cuda/__init__.py", line 279, in make_thunk
        compute_map, no_recycling)
      File "/home/user1/programming/src/theano/theano/gof/op.py", line 807, in make_thunk
        no_recycling)
      File "/home/user1/programming/src/theano/theano/gof/op.py", line 733, in make_c_thunk
        output_storage=node_output_storage)
      File "/home/user1/programming/src/theano/theano/gof/cc.py", line 1065, in make_thunk
        keep_lock=keep_lock)
      File "/home/user1/programming/src/theano/theano/gof/cc.py", line 1007, in __compile__
        keep_lock=keep_lock)
      File "/home/user1/programming/src/theano/theano/gof/cc.py", line 1435, in cthunk_factory
        key=key, lnk=self, keep_lock=keep_lock)
      File "/home/user1/programming/src/theano/theano/gof/cmodule.py", line 1094, in module_from_key
        module = lnk.compile_cmodule(location)
      File "/home/user1/programming/src/theano/theano/gof/cc.py", line 1347, in compile_cmodule
        preargs=preargs)
      File "/home/user1/programming/src/theano/theano/sandbox/cuda/nvcc_compiler.py", line 429, in compile_str
        ''for cmd'', '' ''.join(cmd))
    Exception: (''The following error happened while compiling the node'', GpuDnnConvDesc{border_mode=''valid'', subsample=(1, 1), conv_mode=''cross''}(TensorConstant{[10  1 28 28]}, TensorConstant{[20  1  5  5]}), ''\n'', ''nvcc return status'', 2, ''for cmd'', ''nvcc -shared -O3 -use_fast_math -arch=sm_30 -m64 -Xcompiler -fno-math-errno,-Wno-unused-label,-Wno-unused-variable,-Wno-write-strings,-DCUDA_NDARRAY_CUH=c72d035fdf91890f3b36710688069b2e,-DNPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION,-fPIC,-fvisibility=hidden -Xlinker -rpath,/home/user1/.theano/compiledir_Linux-4.2--generic-x86_64-with-Ubuntu-15.10-wily-x86_64-2.7.10-64/cuda_ndarray -I/home/user1/programming/src/theano/theano/sandbox/cuda -I/usr/local/lib/python2.7/dist-packages/numpy/core/include -I/usr/include/python2.7 -I/home/user1/programming/src/theano/theano/gof -o /home/user1/.theano/compiledir_Linux-4.2--generic-x86_64-with-Ubuntu-15.10-wily-x86_64-2.7.10-64/tmpYxVaVI/b409a1a0bc2bda394e733f9b89a4c08f.so mod.cu -L/usr/lib -lpython2.7 -lcudnn -lcudart'', "[GpuDnnConvDesc{border_mode=''valid'', subsample=(1, 1), conv_mode=''cross''}(TensorConstant{[10  1 28 28]}, TensorConstant{[20  1  5  5]})]")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Can anyone help me figure this out?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6903, 2, '2015-08-26 10:34:55.373', 0, NULL, NULL, '&lt;p&gt;As you are using percentile values, your vector components are effectively normalised on the interval $[0,1]$. As a result the element wise subtraction of components, normalised by the number of components in the vector, will give you a bounded similarity value between 0 and 1.&lt;/p&gt;

&lt;p&gt;So for two vectors $x$ and $y$ of length $n$, similarity can be computed thus:&lt;/p&gt;

&lt;p&gt;$$S(x,y)=1-\frac{1}{n}\sum_{i=1}^n{\lvert x_i-y_i\lvert}$$&lt;/p&gt;

&lt;p&gt;You can refine this approach by taking account of the variance for each component within the vector and standardising this around 0.5.&lt;/p&gt;
', NULL, NULL, '''/p'':48C,65C,85C,109C ''0'':17C,45C ''0.5'':108C ''1'':18C,47C,70C,72C,76C ''account'':94C ''approach'':91C ''around'':107C ''bound'':41C ''compon'':10C,27C,33C,100C ''comput'':63C ''effect'':12C ''element'':23C ''frac'':71C ''give'':38C ''i-i'':80C ''interv'':16C ''length'':58C ''lvert'':78C,84C ''n'':59C,73C,77C ''normalis'':13C,28C ''number'':31C ''p'':1C,49C,66C,86C ''percentil'':6C ''refin'':89C ''result'':21C ''similar'':42C,60C ''standardis'':105C ''subtract'':25C ''sum'':74C ''take'':93C ''thus'':64C ''two'':52C ''use'':5C ''valu'':7C,43C ''varianc'':97C ''vector'':9C,36C,53C,103C ''wise'':24C ''within'':101C ''x'':54C,68C,79C ''y'':56C,69C,82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6981, 2, '2015-09-02 00:22:32.53', 0, NULL, NULL, '&lt;p&gt;That is exactly what the &lt;a href="http://www.trifacta.com/" rel="nofollow"&gt;Trifecta&lt;/a&gt; product does (in addition to other features). It uses the Wrangle language which is a DSL (domain specific language) designed for data manipulation. There is a much earlier research project called &lt;a href="http://vis.stanford.edu/papers/wrangler" rel="nofollow"&gt;Wrangler&lt;/a&gt; from the same people. The Wrangler papers might give you ideas.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6904, 1, '2015-08-26 12:22:06.38', 2, 113, 'Which type of machine learning to use', '&lt;p&gt;We are working with a complex application i.e. a physical measurement in a lab, that has approximately 230 different input parameters, many of which are ranges or multiple-value.&lt;/p&gt;

&lt;p&gt;The application produces a single output, which is then verified in an external (physical) process. At the end of the process the individual tests are marked as "success" or "fail". That is, despite the many input parameters, the output is assessed in a boolean manner.&lt;/p&gt;

&lt;p&gt;When tests fail, the parameters are ''loosened'' slightly and re-tested.&lt;/p&gt;

&lt;p&gt;We have about 20,000 entries in our database, with both "success" and "fail", and we are considering a machine learning application to help in two areas:&lt;/p&gt;

&lt;p&gt;1) Initial selection of optimum parameters&lt;br&gt;
2) Suggestions for how to tune the parameters after a "fail"    &lt;/p&gt;

&lt;p&gt;Many of the input parameters are strongly related to each other. &lt;/p&gt;

&lt;p&gt;I studied computer science in the mid-90s, when the focus was mostly expert systems and neural networks. We also have access to some free CPU hours of Microsoft Azure Machine Learning. &lt;/p&gt;

&lt;p&gt;What type of machine learning would fit these use-cases?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6906, 2, '2015-08-26 14:53:36.273', 3, NULL, NULL, '&lt;p&gt;With using R, You could look at trees / randomforests. Since you have correlated variables, you could look into Projection pursuit classification trees (R package pptree). And there soon will be a ppforest package. But this is still under  development.  You could also combine randomforest with the package forestFloor to see the curvature of the randomforest and work from there.&lt;/p&gt;
', NULL, NULL, '''/p'':61C ''also'':43C ''classif'':22C ''combin'':44C ''correl'':14C ''could'':6C,17C,42C ''curvatur'':53C ''develop'':40C ''forestfloor'':49C ''look'':7C,18C ''p'':1C ''packag'':25C,34C,48C ''ppforest'':33C ''pptree'':26C ''project'':20C ''pursuit'':21C ''r'':4C,24C ''randomforest'':10C,45C,56C ''see'':51C ''sinc'':11C ''soon'':29C ''still'':38C ''tree'':9C,23C ''use'':3C ''variabl'':15C ''work'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6910, 1, '2015-08-26 20:44:06.54', 3, 176, 'Can all statistical algorithms be parallelized using a Map Reduce framework', '&lt;p&gt;Is it correct to say that any statistical learning algorithm (linear/logistic regression, SVM, neural network, random forest) can be implemented inside a Map Reduce framework?  Or are there restrictions?&lt;/p&gt;

&lt;p&gt;I guess there may be some algorithms that is not possible to parallelize?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6911, 1, '2015-08-26 20:49:40.957', 2, 86, 'Can Hadoop be beneficial when data is in database tables and not in a file system', '&lt;p&gt;I work for a bank. Most of our data is in the form of database tables. Would we benefit by implementing Hadoop? I am of the impression that Hadoop is more for a Distributed File System (unstructured data) as opposed to OLAP databases (Netezza)&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6913, 2, '2015-08-27 09:06:06.957', 2, NULL, NULL, '&lt;p&gt;I think different people probably have varying approaches to this depending upon their background. Often the linux toolset gets over looked, things like &lt;code&gt;awk&lt;/code&gt;, &lt;code&gt;sed&lt;/code&gt;, &lt;code&gt;grep&lt;/code&gt;, &lt;code&gt;cut&lt;/code&gt;, &lt;code&gt;paste&lt;/code&gt;, &lt;code&gt;sort&lt;/code&gt;, &lt;code&gt;uniq&lt;/code&gt; and so on, can be combined in many sophisticated ways and are very powerful and scalable, but they aren''t for everyone. &lt;/p&gt;

&lt;p&gt;I suspect many people use a programming language that they already have familiarity with or that they  use for their data science work, &lt;code&gt;R&lt;/code&gt;, &lt;code&gt;Python&lt;/code&gt;, &lt;code&gt;Matlab&lt;/code&gt;, &lt;code&gt;Mathematica&lt;/code&gt;, &lt;code&gt;C++&lt;/code&gt;, &lt;code&gt;Julia&lt;/code&gt;, the list goes on. Others may use commercial products like SAS. &lt;/p&gt;

&lt;p&gt;Certainly Python, and it''s relevant libraries, and R have large users bases and are popular choices. Ultimately you may have to look at a few options and decide which one suits you and your skill set best.&lt;/p&gt;

&lt;p&gt;As you''ve already mentioned, &lt;code&gt;WEKA&lt;/code&gt; provides an accessible user interface to get started quickly and is certainly a good  place to start for early experimentation and to produce results quickly. It has a range of filters which can be used to preprocess data which may be of use to you. It exposes much more of its functionality via its &lt;code&gt;API&lt;/code&gt; which is available via &lt;code&gt;Java&lt;/code&gt; and this gives you the ability to be much more creative as you build your own solutions to your problems, but you will need to learn to program in &lt;code&gt;Java&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Another, more eclectic, approach is that you draw from many toolboxes like &lt;code&gt;R&lt;/code&gt;, &lt;code&gt;Python&lt;/code&gt;, &lt;code&gt;WEKA&lt;/code&gt;, &lt;code&gt;C++&lt;/code&gt;, &lt;code&gt;Apache Mahout&lt;/code&gt; etc and fuse them using your own framework written in your favourite programming language.&lt;/p&gt;

&lt;p&gt;Exactly which data cleansing operations you need to apply, missing value replacement, addition of noise, resampling, etc.,  will depend more heavily on the specific nature of your data and it''s application. &lt;/p&gt;
', NULL, NULL, '''/code'':27C,30C,33C,36C,39C,42C,45C,95C,98C,101C,104C,107C,110C,171C,228C,235C,267C,284C,287C,290C,293C,297C ''/p'':68C,122C,162C,268C,312C,346C ''abil'':241C ''access'':174C ''addit'':326C ''alreadi'':80C,167C ''anoth'':270C ''apach'':295C ''api'':227C ''appli'':322C ''applic'':345C ''approach'':9C,273C ''aren'':64C ''avail'':231C ''awk'':26C ''background'':15C ''base'':136C ''best'':161C ''build'':249C ''c'':106C,292C ''certain'':124C,183C ''choic'':140C ''cleans'':317C ''code'':25C,28C,31C,34C,37C,40C,43C,93C,96C,99C,102C,105C,108C,169C,226C,233C,265C,282C,285C,288C,291C,294C ''combin'':51C ''commerci'':118C ''creativ'':246C ''cut'':35C ''data'':90C,209C,316C,341C ''decid'':152C ''depend'':12C,332C ''differ'':4C ''draw'':277C ''earli'':190C ''eclect'':272C ''etc'':298C,330C ''everyon'':67C ''exact'':314C ''experiment'':191C ''expos'':218C ''familiar'':82C ''favourit'':309C ''filter'':202C ''framework'':305C ''function'':223C ''fuse'':300C ''get'':20C,178C ''give'':238C ''goe'':113C ''good'':185C ''grep'':32C ''heavili'':334C ''interfac'':176C ''java'':234C,266C ''julia'':109C ''languag'':77C,311C ''larg'':134C ''learn'':261C ''librari'':130C ''like'':24C,120C,281C ''linux'':18C ''list'':112C ''look'':22C,146C ''mahout'':296C ''mani'':53C,72C,279C ''mathematica'':103C ''matlab'':100C ''may'':116C,143C,211C ''mention'':168C ''miss'':323C ''much'':219C,244C ''natur'':338C ''need'':259C,320C ''nois'':328C ''often'':16C ''one'':154C ''oper'':318C ''option'':150C ''other'':115C ''p'':1C,69C,123C,163C,269C,313C ''past'':38C ''peopl'':5C,73C ''place'':186C ''popular'':139C ''power'':59C ''preprocess'':208C ''probabl'':6C ''problem'':255C ''produc'':194C ''product'':119C ''program'':76C,263C,310C ''provid'':172C ''python'':97C,125C,286C ''quick'':180C,196C ''r'':94C,132C,283C ''rang'':200C ''relev'':129C ''replac'':325C ''resampl'':329C ''result'':195C ''sas'':121C ''scalabl'':61C ''scienc'':91C ''sed'':29C ''set'':160C ''skill'':159C ''solut'':252C ''sophist'':54C ''sort'':41C ''specif'':337C ''start'':179C,188C ''suit'':155C ''suspect'':71C ''thing'':23C ''think'':3C ''toolbox'':280C ''toolset'':19C ''ultim'':141C ''uniq'':44C ''upon'':13C ''use'':74C,87C,117C,206C,214C,302C ''user'':135C,175C ''valu'':324C ''vari'':8C ''ve'':166C ''via'':224C,232C ''way'':55C ''weka'':170C,289C ''work'':92C ''written'':306C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9640, 2, '2016-01-05 17:40:49.8', 4, NULL, NULL, '&lt;p&gt;&lt;a href="http://lethain.com/genetic-algorithms-cool-name-damn-simple/" rel="nofollow"&gt;Here is a trivial example&lt;/a&gt;, which captures the essence of genetic algorithms more meaningfully than the polynomial you provided.  The polynomial you provided is solvable via &lt;a href="https://en.wikipedia.org/wiki/Stochastic_gradient_descent" rel="nofollow"&gt;&lt;code&gt;stochastic gradient descent&lt;/code&gt;&lt;/a&gt;, which is a simpler minimimization technique.  For this reason, I am instead suggesting this excellent article and example by Will Larson.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://lethain.com/genetic-algorithms-cool-name-damn-simple/" rel="nofollow"&gt;Quoted from the original article&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;strong&gt;Defining a Problem to Optimize&lt;/strong&gt; Now we''re going to put together a simple example of using a genetic algorithm in Python. We''re going to
  optimize a very simple problem: trying to create a list of N numbers
  that equal X when summed together.&lt;/p&gt;
  
  &lt;p&gt;If we set N = 5 and X = 200, then these would all be appropriate
  solutions. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;lst = [40,40,40,40,40]
lst = [50,50,50,25,25]
lst = [200,0,0,0,0]
&lt;/code&gt;&lt;/pre&gt;
&lt;/blockquote&gt;

&lt;p&gt;Take a look at the &lt;a href="http://lethain.com/genetic-algorithms-cool-name-damn-simple/" rel="nofollow"&gt;entire article&lt;/a&gt;, but here is the &lt;strong&gt;complete code&lt;/strong&gt;:&lt;/p&gt;

&lt;blockquote&gt;
&lt;pre&gt;&lt;code&gt;# Example usage
from genetic import *
target = 371
p_count = 100
i_length = 6
i_min = 0
i_max = 100
p = population(p_count, i_length, i_min, i_max)
fitness_history = [grade(p, target),]
for i in xrange(100):
    p = evolve(p, target)
    fitness_history.append(grade(p, target))

for datum in fitness_history:
   print datum
"""
from random import randint, random
from operator import add

def individual(length, min, max):
    ''Create a member of the population.''
    return [ randint(min,max) for x in xrange(length) ]

def population(count, length, min, max):
    """
    Create a number of individuals (i.e. a population).

    count: the number of individuals in the population
    length: the number of values per individual
    min: the minimum possible value in an individual''s list of values
    max: the maximum possible value in an individual''s list of values

    """
    return [ individual(length, min, max) for x in xrange(count) ]

def fitness(individual, target):
    """
    Determine the fitness of an individual. Higher is better.

    individual: the individual to evaluate
    target: the target number individuals are aiming for
    """
    sum = reduce(add, individual, 0)
    return abs(target-sum)

def grade(pop, target):
    ''Find average fitness for a population.''
    summed = reduce(add, (fitness(x, target) for x in pop))
    return summed / (len(pop) * 1.0)

def evolve(pop, target, retain=0.2, random_select=0.05, mutate=0.01):
    graded = [ (fitness(x, target), x) for x in pop]
    graded = [ x[1] for x in sorted(graded)]
    retain_length = int(len(graded)*retain)
    parents = graded[:retain_length]
    # randomly add other individuals to
    # promote genetic diversity
    for individual in graded[retain_length:]:
        if random_select &amp;gt; random():
            parents.append(individual)
    # mutate some individuals
    for individual in parents:
        if mutate &amp;gt; random():
            pos_to_mutate = randint(0, len(individual)-1)
            # this mutation is not ideal, because it
            # restricts the range of possible values,
            # but the function is unaware of the min/max
            # values used to create the individuals,
            individual[pos_to_mutate] = randint(
                min(individual), max(individual))
    # crossover parents to create children
    parents_length = len(parents)
    desired_length = len(pop) - parents_length
    children = []
    while len(children) &amp;lt; desired_length:
        male = randint(0, parents_length-1)
        female = randint(0, parents_length-1)
        if male != female:
            male = parents[male]
            female = parents[female]
            half = len(male) / 2
            child = male[:half] + female[half:]
            children.append(child)
    parents.extend(children)
    return parents
&lt;/code&gt;&lt;/pre&gt;
&lt;/blockquote&gt;

&lt;p&gt;I think it could be quite pedagogically useful to also solve your original problem using this algorithm and then also construct a solution using &lt;code&gt;stochastic grid search&lt;/code&gt; or &lt;code&gt;stochastic gradient descent&lt;/code&gt; and you will gain a deep understanding of the juxtaposition of those three algorithms.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9673, 5, '2016-01-07 11:23:52.943', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9675, 5, '2016-01-07 11:24:42.38', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9712, 2, '2016-01-09 06:59:39.42', 2, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Some of the settings are likely too aggressive for your machine:&lt;/strong&gt;  &lt;/p&gt;

&lt;p&gt;As others have pointed out, &lt;code&gt;n_jobs&lt;/code&gt; might help by taking advantage of openMP threading that is likely available on a multicore processor.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;But lets also look at &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html" rel="nofollow"&gt;some other parameters from the documentation&lt;/a&gt;:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;RandomForestRegressor(n_estimators=10, criterion=''mse'', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=''auto'', max_leaf_nodes=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False)
&lt;/code&gt;&lt;/pre&gt;

&lt;ul&gt;
&lt;li&gt;Why don''t you set &lt;code&gt;n_estimators&lt;/code&gt; to 10 and then slowly ramp it up.&lt;/li&gt;
&lt;li&gt;max_depth should probably be set both so you trees aren''t too computationally intensive and so you don''t overfit your model (are you cross validating?)&lt;/li&gt;
&lt;li&gt;You have set &lt;code&gt;max_features&lt;/code&gt; to &lt;code&gt;auto&lt;/code&gt; which specifies that it use &lt;code&gt;n_features&lt;/code&gt;, but you have not set &lt;code&gt;n_features&lt;/code&gt;.  You could consider turning this down to some percentage of your full feature set.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In general you need to cross validate your model and tinker with the parameters in order to find something that runs on your box in finite time using the available memory and performs adequately.  You could also consider tuning and cross validating a simpler decision tree and then using that insight to tweak the parameters of the random forest. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Sample the data:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Big datasets often require lots of computing power.  I sometimes drastically reduce the size of my training data while I am zeroing in on the best model and then time the scaling as I increase the dataset.  This will give you a better idea of how much time you should expect for the full problem.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Other models:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Finally, maybe you have a problem for which ''bootstrap aggregation'' is a bad fit. Maybe another type of aggregation would work better like ''boosting'' in which case a ''random jungle'' or ''ada-boost'' algo might work better.  Or perhaps you might want to move to a computationally less intense method that doesn''t require aggregation like a ''support vector regressor''.  &lt;code&gt;Scikit-learn&lt;/code&gt; has a great benefit in that it is very simple to swap out different models and try different pipelines. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Cloud Computing:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I''m not sure what box or laptop you are running this on, but it might just be easier to spin up an AWS (Google/MS/IBM) instance and run this on a well-powered box in the cloud.  The AWS spot market is crazy affordable over the weekends or in the middle of the night, so you can work on some very powerful 8 core machines for $.50 per hour, so a 24 hour job is only $12.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9737, 2, '2016-01-11 09:15:44.103', 5, NULL, NULL, '&lt;p&gt;[&lt;strong&gt;Later edit - Rephrase everything&lt;/strong&gt;]&lt;/p&gt;

&lt;h3&gt;Types of trees&lt;/h3&gt;

&lt;p&gt;A &lt;em&gt;shallow tree&lt;/em&gt; is a small tree (most of the cases it has a small depth). A &lt;em&gt;full grown tree&lt;/em&gt; is a big tree (most of the cases it has a large depth). &lt;/p&gt;

&lt;p&gt;Suppose you have a training set of data which looks like a non-linear structure. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/7Y2k1.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/7Y2k1.png" alt="Synthetic sample with 2 regression trees. Red line is for tree with depth 3, blue line is for tree with depth 10"&gt;&lt;/a&gt;  &lt;/p&gt;

&lt;h3&gt;Bias variance decomposition as a way to see the learning error&lt;/h3&gt;

&lt;p&gt;Considering bias variance decomposition we know that the learning error has 3 components:&lt;/p&gt;

&lt;p&gt;$$Err = \text{Bias}^2 + \text{Var} + \epsilon$$&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Bias&lt;/strong&gt; is the error produced by the fit model when it is not capable to represent the true function; it is in general associated with &lt;em&gt;underfitting&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Var&lt;/strong&gt; is the error produced by the fit model due to sampled data, it describes how unstable a model is if the training data changes; it is in general associated with &lt;em&gt;overfitting&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;$\epsilon$&lt;/strong&gt; is the irreducible error which envelops the true function; this can''t be learned&lt;/p&gt;

&lt;p&gt;&lt;a href="https://onlinecourses.science.psu.edu/stat857/sites/onlinecourses.science.psu.edu.stat857/files/lesson04/model_complexity.png" rel="nofollow"&gt;&lt;img src="https://onlinecourses.science.psu.edu/stat857/sites/onlinecourses.science.psu.edu.stat857/files/lesson04/model_complexity.png" alt="Model compexity"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Considering our shallow tree we can say that the model has low variance since changing the sample does not change too much the model. It needs too many changed data points to be considered unstable. At the same time we can say that has a high bias, since it really can''t represent the sine function which is the true model. We can say also that it has a low complexity. It can be described by 3 constants and 3 regions. &lt;/p&gt;

&lt;p&gt;Consequently, the full grown tree has low bias. It is very complex since it can be described only using many regions and many constants on those regions. This is why it has low bias. The complexity of the model impacts also variance which is high. In some regions at least, a single point sampled differently can change the shape of the fitted tree. &lt;/p&gt;

&lt;p&gt;As a general rule of thumb when a model has low bias it has also high variance and when it has low variance it has high bias. This is not true always, but it happens very often. And intuitively is a correct idea. The reason is that when you are getting close to the points in the sample you learn the patterns but also learn the errors from sample, when you are far away from sample you are instead very stable since you do not incorporate errors from sample. &lt;/p&gt;

&lt;h3&gt;How can we build ensembles using those kind of trees?&lt;/h3&gt;

&lt;p&gt;&lt;strong&gt;Bagging&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The statistical model behind bagging is based on bootstrapping, which is a statistical procedure to evaluate the error of a statistic. Assuming you have a sample and you want to evaluate the error of a statistical estimation, bootstrapping procedure allows you to approximate the distribution of the estimator. But trees are only a simple function of sample "split the space into regions and predict with the average, a statistic". Thus if one builds multiple trees from bootstrap samples and averages the trees can be considered i.i.d. and the same principle works to reduce variance. Because of that bagging allows one to reduce the variance without affecting too much the bias. Why it needs full depth trees? Well it is a simple reason, perhaps not so obvious at first sight. It need classifiers with high variance in order to reduce it. This procedure does not affect the bias. If the underlying models have low variance high bias, the bagging will reduce slightly the already small variance and produce nothing for bias. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Bootstrapping&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;How bootstrapping works? Many compare bootstrapping with model averaging, but the comparison is flawed. The idea of bootstrapping is that the ensemble is an iterative procedure. It is true that the final rule for classification looks like a weighted average of some weak models, but the point is that those models were built iteratively. Has nothing to do with bagging and how it works. Any $k$ tree is build using information learned from all previous $k-1$ trees. So we have initially a weak classifier which we fit to data, where all the points have the same importance. This importance can be changed by weights like in adaboost or by residuals like in gradient boosting, it really does not matter. The next weak classifier will not treat in the same way all the points, but those previously classified correctly has smaller importance than those classifier incorrectly. The consequence is that the model enriches it''s complexity, it''s ability to reproduce more complex surfaces. This is translated in the fact that it reduces the bias, since it can go closer to data. A similar intuition is behind: if the classifier has already a low bias, what will happen when I boost it? Probably a much unbearable overfit, that''s all.&lt;/p&gt;

&lt;h3&gt;Which one is better?&lt;/h3&gt;

&lt;p&gt;There is no clear winner. It depends too much on data set and on other parameters. For example bagging can''t hurt. It is possible to be useless ut usually does not hurt performance. Boosting can lead to overfit. That is because you can go eventually too close to data. &lt;/p&gt;

&lt;p&gt;The is a lot of literature which says that when the irreducible error is high, the bagging is much better and boosting does not progress too much. &lt;/p&gt;

&lt;h3&gt;Can we decrease both variance and bias?&lt;/h3&gt;

&lt;p&gt;Pure bootstrap and bagging approaches serves a single purpose. Either reduce variance either reduce bias. However modern implementations changed various things in how those approaches works. Sampling can be used in boosting and it seems to work towards reducing also the variance. There are bagging procedures which takes some ideas prom boosting, for example iterative bagging (or adaptive bagging) published by Breiman. So, the answer is yes, is possible.&lt;/p&gt;

&lt;h3&gt;Can we use other learners other than trees?&lt;/h3&gt;

&lt;p&gt;Of course. Often you will see boosting to use this approach. I read some papers on bagging svm or another learners also. I tried myself to bag some svms but without much success. The trees are the preferred way, however, for a very simple reason, they are simple to build, simple to adapt and simple to control their effect. My personal opinion is that it is not everything said regarding ensembles of trees.&lt;/p&gt;

&lt;p&gt;PS: a last note on the number of weak classifiers: this depends entirely on the complexity of the data set mixed with the complexity of the learner. There is no recipe. Often 20 of them are enough to get most of the information and additional ones are only for tiny tuning. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9779, 2, '2016-01-13 16:05:14.54', 1, NULL, NULL, '&lt;h2&gt;Should I use Self Organising Map to reduce the dimensionality?&lt;/h2&gt;

&lt;p&gt;Not the first choice. For dimensionality reduction you have more handy and basic methods like &lt;a href="https://en.wikipedia.org/wiki/Principal_component_analysis" rel="nofollow"&gt;PCA&lt;/a&gt; and &lt;a href="https://en.wikipedia.org/wiki/Non-negative_matrix_factorization" rel="nofollow"&gt;NMF&lt;/a&gt; (if you don''t have negative values) or &lt;a href="https://www.researchgate.net/profile/Christian_Bauckhage/publication/262898400_Kernel_Archetypal_Analysis_for_Clustering_Web_Search_Frequency_Time_Series/links/5416e0770cf2bb7347db7a99.pdf" rel="nofollow"&gt;Archetypal Analysis&lt;/a&gt; which are recommended to be used first.&lt;/p&gt;

&lt;h2&gt;and then K-means to classify them into normal and abnormal?&lt;/h2&gt;

&lt;p&gt;Red alarm! Kmeans is not for classification but clustering. Be careful about the fundamental understanding of what you are about to do otherwise you get confused. If you have labels K-NN for instance is a supervised (thus proper) method.&lt;/p&gt;

&lt;h2&gt;Or I can use just K-NN to classify them into normal - abnormal without any dimensionality reduction?&lt;/h2&gt;

&lt;p&gt;Impossible. As you already know you have the problem called &lt;a href="https://en.wikipedia.org/wiki/Curse_of_dimensionality" rel="nofollow"&gt;Curse of Dimensionality&lt;/a&gt; where the euclidean distances are highly distorted. You need to reduce the dimenstionality before K-NN. The only class of methods I know which may not need dimentionality reduction are &lt;a href="https://en.wikipedia.org/wiki/Kernel_method" rel="nofollow"&gt;Kernel Methods&lt;/a&gt; e.g. &lt;a href="https://en.wikipedia.org/wiki/Support_vector_machine" rel="nofollow"&gt;SVM&lt;/a&gt;.&lt;/p&gt;

&lt;h2&gt;How many features can I use with K-NN?&lt;/h2&gt;

&lt;p&gt;Once I examined the Curse of Dimentionality with K-Means in MATLAB and I saw after 7 dimensions everything was distorted. It of course depends on the nature of the data (distribution,etc) but this is what I got from my experiment.&lt;/p&gt;

&lt;h2&gt;And if later I''d like to find why this person has included into the abnormal class how can I find that this happened because of these two features, his weight according to his height?&lt;/h2&gt;

&lt;p&gt;This question is so fuzzy and about the goal of Machine Learning. In Machine Learning you get a set of answers (called labels) and try to learn them. You are not supposed to say if they are right or not. So if you are said a specific guy is abnormal you should ask the one who provided labels (in the terminology we call him &lt;em&gt;expert&lt;/em&gt;) about the reason. If a guy with a certain weight and hight is called abnormal you only know this weight and hight indicate abnormal people. Why? Well, who knows?&lt;/p&gt;

&lt;p&gt;If I did not get the last question correctly please drop me a line in comments. Would be glad to help.&lt;/p&gt;

&lt;h2&gt;My Suggestion&lt;/h2&gt;

&lt;p&gt;I recommend you to apply PCA to your data and take the first 5 PCs and feed them to K-NN or &lt;a href="https://en.wikipedia.org/wiki/Linear_discriminant_analysis" rel="nofollow"&gt;LDA&lt;/a&gt;. You can plot some of these PCs against each other and see how they discriminate your classes. It gives you a good impression of what is happening. You can also plot the variance or entropy of all features beforehand and drop those features whose variance or entropy is lower than a threshold.&lt;/p&gt;

&lt;p&gt;Good Luck :)&lt;/p&gt;
', NULL, NULL, '''/a'':37C,47C,65C,172C,213C,223C,476C ''/em'':386C ''/h2'':12C,87C,150C,236C,318C,443C ''/p'':73C,130C,224C,281C,416C,439C,529C,533C ''/profile/christian_bauckhage/publication/262898400_kernel_archetypal_analysis_for_clustering_web_search_frequency_time_series/links/5416e0770cf2bb7347db7a99.pdf'':60C ''/wiki/curse_of_dimensionality'':166C ''/wiki/kernel_method'':208C ''/wiki/linear_discriminant_analysis'':472C ''/wiki/non-negative_matrix_factorization'':43C ''/wiki/principal_component_analysis'':33C ''/wiki/support_vector_machine'':219C ''5'':458C ''7'':255C ''abnorm'':86C,145C,298C,369C,401C,410C ''accord'':314C ''alarm'':90C ''alreadi'':155C ''also'':506C ''analysi'':64C ''answer'':340C ''appli'':449C ''archetyp'':63C ''ask'':372C ''basic'':26C ''beforehand'':515C ''call'':161C,341C,382C,400C ''care'':99C ''certain'':395C ''choic'':17C ''class'':192C,299C,493C ''classif'':95C ''classifi'':81C,141C ''cluster'':97C ''comment'':433C ''confus'':114C ''correct'':426C ''cours'':262C ''curs'':169C,242C ''d'':287C ''data'':269C,453C ''depend'':263C ''dimens'':256C ''dimension'':11C,19C,148C,171C ''dimenst'':185C ''diment'':201C,244C ''discrimin'':491C ''distanc'':176C ''distort'':179C,259C ''distribut'':270C ''drop'':428C,517C ''e.g'':214C ''em'':384C ''en.wikipedia.org'':32C,42C,165C,207C,218C,471C ''en.wikipedia.org/wiki/curse_of_dimensionality'':164C ''en.wikipedia.org/wiki/kernel_method'':206C ''en.wikipedia.org/wiki/linear_discriminant_analysis'':470C ''en.wikipedia.org/wiki/non-negative_matrix_factorization'':41C ''en.wikipedia.org/wiki/principal_component_analysis'':31C ''en.wikipedia.org/wiki/support_vector_machine'':217C ''entropi'':511C,523C ''etc'':271C ''euclidean'':175C ''everyth'':257C ''examin'':240C ''experi'':280C ''expert'':385C ''featur'':228C,311C,514C,519C ''feed'':461C ''find'':290C,303C ''first'':16C,72C,457C ''fundament'':102C ''fuzzi'':324C ''get'':113C,336C,422C ''give'':495C ''glad'':436C ''goal'':328C ''good'':498C,531C ''got'':277C ''guy'':367C,392C ''h2'':1C,74C,131C,225C,282C,440C ''handi'':24C ''happen'':306C,503C ''height'':317C ''help'':438C ''high'':178C ''hight'':398C,408C ''href'':30C,40C,57C,163C,205C,216C,469C ''imposs'':152C ''impress'':499C ''includ'':295C ''indic'':409C ''instanc'':123C ''k'':78C,120C,138C,188C,234C,247C,465C ''k-mean'':77C,246C ''k-nn'':119C,137C,187C,233C,464C ''kernel'':211C ''kmean'':91C ''know'':156C,196C,404C,415C ''label'':118C,342C,377C ''last'':424C ''later'':285C ''lda'':475C ''learn'':331C,334C,346C ''like'':28C,288C ''line'':431C ''lower'':525C ''luck'':532C ''machin'':330C,333C ''mani'':227C ''map'':7C ''matlab'':250C ''may'':198C ''mean'':79C,248C ''method'':27C,129C,194C,212C ''natur'':266C ''need'':181C,200C ''negat'':53C ''nmf'':46C ''nn'':121C,139C,189C,235C,466C ''nofollow'':35C,45C,62C,168C,210C,221C,474C ''normal'':84C,144C ''one'':374C ''organis'':6C ''otherwis'':111C ''p'':13C,88C,151C,237C,319C,417C,444C,530C ''pca'':36C,450C ''pcs'':459C,483C ''peopl'':411C ''person'':293C ''pleas'':427C ''plot'':479C,507C ''problem'':160C ''proper'':128C ''provid'':376C ''question'':321C,425C ''reason'':389C ''recommend'':68C,446C ''red'':89C ''reduc'':9C,183C ''reduct'':20C,149C,202C ''rel'':34C,44C,61C,167C,209C,220C,473C ''right'':357C ''said'':364C ''saw'':253C ''say'':353C ''see'':488C ''self'':5C ''set'':338C ''specif'':366C ''suggest'':442C ''supervis'':126C ''suppos'':351C ''svm'':222C ''take'':455C ''terminolog'':380C ''threshold'':528C ''thus'':127C ''tri'':344C ''two'':310C ''understand'':103C ''use'':4C,71C,135C,231C ''valu'':54C ''varianc'':509C,521C ''weight'':313C,396C,406C ''well'':413C ''whose'':520C ''without'':146C ''would'':434C ''www.researchgate.net'':59C ''www.researchgate.net/profile/christian_bauckhage/publication/262898400_kernel_archetypal_analysis_for_clustering_web_search_frequency_time_series/links/5416e0770cf2bb7347db7a99.pdf'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8032, 1, '2015-09-08 03:14:09.23', 0, 1217, 'How to predict probabilities in xgboost?', '&lt;p&gt;The below predict function is giving -ve values as well so it cannot be probabilities. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;param &amp;lt;- list(max.depth = 5, eta = 0.01,  objective="binary:logistic",subsample=0.9)
bst &amp;lt;- xgboost(param, data = x_mat, label = y_mat,nround = 3000)

pred_s &amp;lt;- predict(bst, x_mat_s2)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I google &amp;amp; tried &lt;code&gt;pred_s &amp;lt;- predict(bst, x_mat_s2,type="response")&lt;/code&gt;
but it didn''t work.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Question&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;How to predict probabilities instead?&lt;/p&gt;
', 'machine-learning  r  predictive-modeling  decision-trees', NULL, '''/code'':68C,86C ''/p'':33C,92C,97C,104C ''/pre'':69C ''/strong'':96C ''0.01'':42C ''0.9'':47C ''3000'':59C ''5'':40C ''amp'':73C ''binari'':44C ''bst'':48C,64C,80C ''cannot'':30C ''code'':35C,75C ''data'':52C ''decis'':15B ''decision-tre'':14B ''didn'':89C ''eta'':41C ''function'':21C ''give'':23C ''googl'':72C ''instead'':103C ''label'':55C ''learn'':9B ''list'':38C ''logist'':45C ''lt'':37C,49C,62C,78C ''machin'':8B ''machine-learn'':7B ''mat'':54C,57C,66C,82C ''max.depth'':39C ''model'':13B ''nround'':58C ''object'':43C ''p'':17C,70C,93C,98C ''param'':36C,51C ''pre'':34C ''pred'':60C,76C ''predict'':3A,12B,20C,63C,79C,101C ''predictive-model'':11B ''probabl'':4A,32C,102C ''question'':95C ''r'':10B ''respons'':85C ''s2'':67C,83C ''strong'':94C ''subsampl'':46C ''tree'':16B ''tri'':74C ''type'':84C ''valu'':25C ''ve'':24C ''well'':27C ''work'':91C ''x'':53C,65C,81C ''xgboost'':6A,50C ''y'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6914, 2, '2015-08-27 10:12:30.49', 1, NULL, NULL, '&lt;p&gt;I think you are referring to the HDFS part of Hadoop. If I''m correct, using Hadoop IMHO should not be considered as an alternative, but as a necessity. Hadoop answers the question : how can I take advantage advantage of the massive amount of data I have since I can''t use it now as it is. &lt;/p&gt;

&lt;p&gt;So yes, HDFS is a distributed file system (that''s why Hadoop DFS), and if you are not experiencing the limits of your database tables for now (or planning to in the future), then there is no reason to consider switching. In addition I think that depending on the data, you might not be able to do whatever you want with a bank database.&lt;/p&gt;

&lt;p&gt;I should add that in the big data ecosystem there are alternative to databases (NoSQL system for example), but my answer is entirely conditioned on the fact that you are not mentioning any specific task/goal so my answer is basically :&lt;/p&gt;

&lt;p&gt;_ I have a computer which is sufficient for my work today and in the future, do I need to change it ?&lt;/p&gt;

&lt;p&gt;_ If you believe it is sufficient for your future use, then no.&lt;/p&gt;
', NULL, NULL, '''/p'':59C,125C,167C,190C,204C ''abl'':115C ''add'':129C ''addit'':103C ''advantag'':39C,40C ''altern'':26C,138C ''amount'':44C ''answer'':32C,147C,164C ''bank'':123C ''basic'':166C ''believ'':194C ''big'':133C ''chang'':188C ''comput'':172C ''condit'':150C ''consid'':23C,100C ''correct'':16C ''data'':46C,110C,134C ''databas'':84C,124C,140C ''depend'':107C ''dfs'':73C ''distribut'':66C ''ecosystem'':135C ''entir'':149C ''exampl'':144C ''experienc'':79C ''fact'':153C ''file'':67C ''futur'':93C,183C,200C ''hadoop'':12C,18C,31C,72C ''hdfs'':9C,63C ''imho'':19C ''limit'':81C ''m'':15C ''massiv'':43C ''mention'':158C ''might'':112C ''necess'':30C ''need'':186C ''nosql'':141C ''p'':1C,60C,126C,168C,191C ''part'':10C ''plan'':89C ''question'':34C ''reason'':98C ''refer'':6C ''sinc'':49C ''specif'':160C ''suffici'':175C,197C ''switch'':101C ''system'':68C,142C ''tabl'':85C ''take'':38C ''task/goal'':161C ''think'':3C,105C ''today'':179C ''use'':17C,53C,201C ''want'':120C ''whatev'':118C ''work'':178C ''yes'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6915, 2, '2015-08-27 13:03:55.96', 4, NULL, NULL, '&lt;p&gt;''SQL'' on Hadoop is very much a thing, though I use quotes since it''s probably more accurate to say it''s SQL-like. Some options for bringing SQL-like capabilities to Hadoop include Hue, Hive/bee (Heading towards Stinger? So punny Apache), Impala, SparkSQL (probably not a great solution for a bank given the possibility of concurrency issues), among others (Seems like everyone has their own version of it these days)&lt;/p&gt;

&lt;p&gt;To be honest though, if you''re asking if it could be helpful, you probably don''t need Hadoop (sorry in advance of that comes off harshly, it''s not intended to). Lots an lots of places &lt;em&gt;think&lt;/em&gt; they need Hadoop, but very few actually &lt;em&gt;need&lt;/em&gt; Hadoop. There are of business that are down on the tech because they transitioned to it when the need didn''t really exist. If you truly do need Hadoop or another distributed system it''d be almost impossible to determine which setup would be beneficial to your org without an intimate understanding of your data, and your specific business means.  &lt;/p&gt;
', NULL, NULL, '''/em'':116C,126C ''/p'':75C,187C ''accur'':19C ''actual'':123C ''advanc'':98C ''almost'':163C ''among'':62C ''anoth'':157C ''apach'':45C ''ask'':84C ''bank'':55C ''benefici'':171C ''bring'':30C ''busi'':131C,185C ''capabl'':34C ''come'':101C ''concurr'':60C ''could'':87C ''d'':161C ''data'':181C ''day'':74C ''determin'':166C ''didn'':146C ''distribut'':158C ''em'':114C,124C ''everyon'':66C ''exist'':149C ''given'':56C ''great'':51C ''hadoop'':4C,36C,95C,119C,127C,155C ''harsh'':103C ''head'':40C ''help'':89C ''hive/bee'':39C ''honest'':79C ''hue'':38C ''impala'':46C ''imposs'':164C ''includ'':37C ''intend'':107C ''intim'':177C ''issu'':61C ''like'':26C,33C,65C ''lot'':109C,111C ''mean'':186C ''much'':7C ''need'':94C,118C,125C,145C,154C ''option'':28C ''org'':174C ''other'':63C ''p'':1C,76C ''place'':113C ''possibl'':58C ''probabl'':17C,48C,91C ''punni'':44C ''quot'':13C ''re'':83C ''realli'':148C ''say'':21C ''seem'':64C ''setup'':168C ''sinc'':14C ''solut'':52C ''sorri'':96C ''sparksql'':47C ''specif'':184C ''sql'':2C,25C,32C ''sql-like'':24C,31C ''stinger'':42C ''system'':159C ''tech'':137C ''thing'':9C ''think'':115C ''though'':10C,80C ''toward'':41C ''transit'':140C ''truli'':152C ''understand'':178C ''use'':12C ''version'':70C ''without'':175C ''would'':169C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6916, 2, '2015-08-27 13:54:30.117', 5, NULL, NULL, '&lt;p&gt;Indeed there are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Gradient Boosting is by construction sequential, so parallelization is not really possible

&lt;ul&gt;
&lt;li&gt;Generalized Linear Models need all data at the same time, although technically you can parallelize some of the inner linear algebra nuts and bolts&lt;/li&gt;
&lt;li&gt;Support Vector Machines&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':46C,51C,53C ''/p'':5C ''/ul'':52C,54C ''algebra'':42C ''although'':32C ''bolt'':45C ''boost'':9C ''construct'':12C ''data'':27C ''general'':22C ''gradient'':8C ''inde'':2C ''inner'':40C ''li'':7C,21C,47C ''linear'':23C,41C ''machin'':50C ''model'':24C ''need'':25C ''nut'':43C ''p'':1C ''parallel'':15C,36C ''possibl'':19C ''realli'':18C ''sequenti'':13C ''support'':48C ''technic'':33C ''time'':31C ''ul'':6C,20C ''vector'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6923, 2, '2015-08-27 20:43:32.347', 3, NULL, NULL, '&lt;p&gt;In dropout as described in &lt;a href="http://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf" rel="nofollow"&gt;http://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf&lt;/a&gt;, weights are &lt;em&gt;not&lt;/em&gt; masked. Instead, the neuron activations are masked, &lt;em&gt;per example as it is presented for training&lt;/em&gt; (i.e. the mask is randomised for each run forward and gradient backprop, not ever repeated).&lt;/p&gt;

&lt;p&gt;The activations are masked during forward pass, and gradient calculations use the same mask during back-propagation of that example. This can be implemented as a modifier within a layer description, or as a separate dropout layer.&lt;/p&gt;

&lt;p&gt;During weight update phase, typically applied on a mini-batch (where each example would have had different mask applied) there is no further use of dropout masks. The gradient values used for update have already been affected by masks applied during back propagation.&lt;/p&gt;

&lt;p&gt;I found a useful reference for learning how dropout works, for maybe implementing yourself, is the &lt;a href="https://github.com/rasmusbergpalm/DeepLearnToolbox" rel="nofollow"&gt;Deep Learn Toolbox&lt;/a&gt; for Matlab/Octave.&lt;/p&gt;
', NULL, NULL, '''/a'':168C ''/em'':21C,38C ''/p'':54C,94C,140C,171C ''/rasmusbergpalm/deeplearntoolbox'':162C ''/~hinton/absps/jmlrdropout.pdf'':11C ''/~hinton/absps/jmlrdropout.pdf&lt;/a&gt;,'':16C ''activ'':26C,57C ''affect'':133C ''alreadi'':131C ''appli'':101C,115C,136C ''back'':72C,138C ''back-propag'':71C ''backprop'':50C ''batch'':106C ''calcul'':65C ''deep'':165C ''describ'':5C ''descript'':87C ''differ'':113C ''dropout'':3C,92C,122C,150C ''em'':19C,29C ''ever'':52C ''exampl'':31C,76C,109C ''forward'':47C,61C ''found'':143C ''github.com'':161C ''github.com/rasmusbergpalm/deeplearntoolbox'':160C ''gradient'':49C,64C,125C ''href'':8C,159C ''i.e'':39C ''implement'':80C,154C ''instead'':23C ''layer'':86C,93C ''learn'':148C,166C ''mask'':22C,28C,41C,59C,69C,114C,123C,135C ''matlab/octave'':170C ''mayb'':153C ''mini'':105C ''mini-batch'':104C ''modifi'':83C ''neuron'':25C ''nofollow'':13C,164C ''p'':1C,55C,95C,141C ''pass'':62C ''per'':30C ''phase'':99C ''present'':35C ''propag'':73C,139C ''randomis'':43C ''refer'':146C ''rel'':12C,163C ''repeat'':53C ''run'':46C ''separ'':91C ''toolbox'':167C ''train'':37C ''typic'':100C ''updat'':98C,129C ''use'':66C,120C,127C,145C ''valu'':126C ''weight'':17C,97C ''within'':84C ''work'':151C ''would'':110C ''www.cs.toronto.edu'':10C,15C ''www.cs.toronto.edu/~hinton/absps/jmlrdropout.pdf'':9C ''www.cs.toronto.edu/~hinton/absps/jmlrdropout.pdf&lt;/a&gt;,'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (386, 2, '2014-06-15 14:23:19.793', 9, NULL, NULL, '&lt;p&gt;For low parameters, pretty limited sample size,  and a binary classifier logistic regression should be plenty powerful enough.  You can use a more advanced algorithm but it''s probably overkill. &lt;/p&gt;
', NULL, NULL, '''/p'':32C ''advanc'':25C ''algorithm'':26C ''binari'':11C ''classifi'':12C ''enough'':19C ''limit'':6C ''logist'':13C ''low'':3C ''overkil'':31C ''p'':1C ''paramet'':4C ''plenti'':17C ''power'':18C ''pretti'':5C ''probabl'':30C ''regress'':14C ''sampl'':7C ''size'':8C ''use'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6917, 2, '2015-08-27 14:08:36.973', 1, NULL, NULL, '&lt;p&gt;It depends on what your specific situation is - I think you need to add more detail for you to get a good answer on this. e.g. what problems are you experiencing with data storage currently? What are you trying to change or achieve in the future?&lt;/p&gt;

&lt;p&gt;Your data (you don''t mention what exactly the data is, but I assume this is bank transactions, customer info etc?) will be in a traditional relational database management system (RDBMS), probably SQL based, for a very good reason - traditional RDBMSs are extremely good at transactional processing, i.e. being able to add new records, update and delete existing records, extremely quickly and without ''messing up'' the data - e.g. if two updates on the same customer records occur almost simultaneously, the database system will order the updates correctly, ''lock'' the relevant records to prevent conflicts, etc. so that you are never left with inconsistent data. That would be EXTREMELY bad for a bank - imagine if someone''s bank account accidentally got overwritten with the wrong balance! This is generally referred to as OLTP (online transaction processing).&lt;/p&gt;

&lt;p&gt;For this reason I imagine most banks will continue to use these systems for many many years into the future, which are extremely mature, well tested, well understood, and there are tons of people to hire with the necessary skills. Also, to change the underlying storage system for a banking system would be extremely risky, hence why most banks run very old software - the risk of something going wrong by updating software is just too big.&lt;/p&gt;

&lt;p&gt;However, if you are talking about just wanting to produce analytical reports on this data, and keep the original source data as-is, then yes Hadoop may well be a useful system to investigate. The general idea will be to export your raw data from your banking system into HDFS for example, and use Hadoop to produce analytical reports. the benefit of this kind of system is that it may be much faster (and safer) to create a query such as ''what is the average bank balance of all my customers'', without locking-up your source database system while the query runs (again, that would be really, really bad for a bank!). Hadoop and similar systems excel at this kind of task, generally called OLAP (offline analytical processing), but are no good at all for OLTP.&lt;/p&gt;

&lt;p&gt;Sounds like you need to really think about what you''re trying to do here and do lots of research into the differences in OLAP and OLTP as a starter, and go from there. Hope that helps.&lt;/p&gt;
', NULL, NULL, '''/p'':48C,185C,263C,403C,442C ''abl'':99C ''accident'':168C ''account'':167C ''achiev'':44C ''add'':15C,101C ''almost'':127C ''also'':227C ''analyt'':275C,323C,393C ''answer'':24C ''as-i'':286C ''assum'':63C ''averag'':350C ''bad'':158C,375C ''balanc'':174C,352C ''bank'':66C,161C,166C,193C,236C,245C,312C,351C,378C ''base'':83C ''benefit'':326C ''big'':262C ''call'':390C ''chang'':42C,229C ''conflict'':143C ''continu'':195C ''correct'':136C ''creat'':342C ''current'':36C ''custom'':68C,124C,356C ''data'':34C,51C,59C,116C,153C,279C,285C,309C ''databas'':77C,130C,363C ''delet'':106C ''depend'':3C ''detail'':17C ''differ'':427C ''e.g'':27C,117C ''etc'':70C,144C ''exact'':57C ''exampl'':317C ''excel'':383C ''exist'':107C ''experienc'':32C ''export'':306C ''extrem'':92C,109C,157C,209C,240C ''faster'':338C ''futur'':47C,206C ''general'':177C,301C,389C ''get'':21C ''go'':254C,436C ''good'':23C,87C,93C,398C ''got'':169C ''hadoop'':291C,320C,379C ''hdfs'':315C ''help'':441C ''henc'':242C ''hire'':222C ''hope'':439C ''howev'':265C ''i.e'':97C ''idea'':302C ''imagin'':162C,191C ''inconsist'':152C ''info'':69C ''investig'':299C ''keep'':281C ''kind'':329C,386C ''left'':150C ''like'':406C ''lock'':137C,359C ''locking-up'':358C ''lot'':422C ''manag'':78C ''mani'':201C,202C ''matur'':210C ''may'':292C,335C ''mention'':55C ''mess'':113C ''much'':337C ''necessari'':225C ''need'':13C,408C ''never'':149C ''new'':102C ''occur'':126C ''offlin'':392C ''olap'':391C,429C ''old'':248C ''oltp'':181C,402C,431C ''onlin'':182C ''order'':133C ''origin'':283C ''overwritten'':170C ''p'':1C,49C,186C,264C,404C ''peopl'':220C ''prevent'':142C ''probabl'':81C ''problem'':29C ''process'':96C,184C,394C ''produc'':274C,322C ''queri'':344C,367C ''quick'':110C ''raw'':308C ''rdbms'':80C ''rdbmss'':90C ''re'':415C ''realli'':373C,374C,410C ''reason'':88C,189C ''record'':103C,108C,125C,140C ''refer'':178C ''relat'':76C ''relev'':139C ''report'':276C,324C ''research'':424C ''risk'':251C ''riski'':241C ''run'':246C,368C ''safer'':340C ''similar'':381C ''simultan'':128C ''situat'':8C ''skill'':226C ''softwar'':249C,258C ''someon'':164C ''someth'':253C ''sound'':405C ''sourc'':284C,362C ''specif'':7C ''sql'':82C ''starter'':434C ''storag'':35C,232C ''system'':79C,131C,199C,233C,237C,297C,313C,331C,364C,382C ''talk'':269C ''task'':388C ''test'':212C ''think'':11C,411C ''ton'':218C ''tradit'':75C,89C ''transact'':67C,95C,183C ''tri'':40C,416C ''two'':119C ''under'':231C ''understood'':214C ''updat'':104C,120C,135C,257C ''use'':197C,296C,319C ''want'':272C ''well'':211C,213C,293C ''without'':112C,357C ''would'':155C,238C,371C ''wrong'':173C,255C ''year'':203C ''yes'':290C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6918, 2, '2015-08-27 14:30:25.843', 4, NULL, NULL, '&lt;p&gt;I would suggest that you can implement pretty much any kind of data processing you want in Map Reduce given enough time to code it, but the degree of parallelisation you will get will vary depending on what your data is and what you''re doing to it.&lt;/p&gt;

&lt;p&gt;I can imagine a simple scenario where parallelisation would be reduced dramatically. For example, a simple JOIN task with 4 mapper and 4 reducer nodes will be highly parallelised if the join keys (1, 2, 3 and 4) are evenly distributed across your mapper nodes, and there are the same number of each (4) - 4 reducers would then get an equal share of work for each join key:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Mapper    1 2 3 4   |   1 2 3 4   |   1 2 3 4   |   1 2 3 4
             / \         / \            / \           / \
            v   v       v   v          v   v         v   v

Reducer   1 1 1 1   |   2 2 2 2   |  3 3 3 3    |   4 4 4 4
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However if you have a situation where most of your join keys are the same (e.g. nearly all 1s), the reducer dealing with 1s will get swamped:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Mapper    1 1 1 1   |   1 1 1 1   |   1 1 1 1   |   1 1 1 2
             /          /            /             /     /
            v          v            v             v     v

Reducer   1 1 1 1 1 1 1 1 1 1 1 1 1 1 1  |  2  |  nothing!  |  nothing!
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Because 1 reducer gets totally swamped while others do nothing, you will lose most of your parallelisation while Hadoop stalls and waits for the reducer to finish.&lt;/p&gt;

&lt;p&gt;Caveat: There may be more sophisticated Map Reduce implementations out there which deal with this kind of problem, I only know the basics of Hadoop.&lt;/p&gt;

&lt;p&gt;Also I know this is nothing to do with statistical learning algorithms (which i know almost nothing about) but I imagine the principle is still true - some algorithms or data just won''t divide into highly parallel sub-tasks.&lt;/p&gt;
', NULL, NULL, '''/code'':165C,239C ''/p'':50C,120C,195C,269C,296C,338C ''/pre'':166C,240C ''1'':85C,124C,128C,132C,136C,149C,150C,151C,152C,199C,200C,201C,202C,203C,204C,205C,206C,207C,208C,209C,210C,211C,212C,213C,221C,222C,223C,224C,225C,226C,227C,228C,229C,230C,231C,232C,233C,234C,235C,243C ''1s'':186C,191C ''2'':86C,125C,129C,133C,137C,153C,154C,155C,156C,214C,236C ''3'':87C,126C,130C,134C,138C,157C,158C,159C,160C ''4'':71C,74C,89C,105C,106C,127C,131C,135C,139C,161C,162C,163C,164C ''across'':93C ''algorithm'':309C,325C ''almost'':313C ''also'':298C ''basic'':293C ''caveat'':271C ''code'':25C,122C,197C ''data'':14C,41C,327C ''deal'':189C,283C ''degre'':29C ''depend'':37C ''distribut'':92C ''divid'':331C ''dramat'':63C ''e.g'':183C ''enough'':22C ''equal'':112C ''even'':91C ''exampl'':65C ''finish'':268C ''get'':34C,110C,193C,245C ''given'':21C ''hadoop'':260C,295C ''high'':79C,333C ''howev'':168C ''imagin'':54C,318C ''implement'':8C,279C ''join'':68C,83C,118C,178C ''key'':84C,119C,179C ''kind'':12C,286C ''know'':291C,300C,312C ''learn'':308C ''lose'':254C ''map'':19C,277C ''mapper'':72C,95C,123C,198C ''may'':273C ''much'':10C ''near'':184C ''node'':76C,96C ''noth'':237C,238C,251C,303C,314C ''number'':102C ''other'':249C ''p'':1C,51C,167C,241C,270C,297C ''parallel'':334C ''parallelis'':31C,59C,80C,258C ''pre'':121C,196C ''pretti'':9C ''principl'':320C ''problem'':288C ''process'':15C ''re'':46C ''reduc'':20C,62C,75C,107C,148C,188C,220C,244C,266C,278C ''scenario'':57C ''share'':113C ''simpl'':56C,67C ''situat'':173C ''sophist'':276C ''stall'':261C ''statist'':307C ''still'':322C ''sub'':336C ''sub-task'':335C ''suggest'':4C ''swamp'':194C,247C ''task'':69C,337C ''time'':23C ''total'':246C ''true'':323C ''v'':140C,141C,142C,143C,144C,145C,146C,147C,215C,216C,217C,218C,219C ''vari'':36C ''wait'':263C ''want'':17C ''won'':329C ''work'':115C ''would'':3C,60C,108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6943, 2, '2015-08-29 21:44:24.07', 3, NULL, NULL, '&lt;p&gt;We don''t version control the actual data files. We wouldn''t want to even if we stored it as CSV instead of in a binary form. As &lt;a href="http://datascience.stackexchange.com/a/6771/12328"&gt;Riccardo M.&lt;/a&gt; said, we''re not going to spend our time reviewing row-by-row changes on a 10M row data set.&lt;/p&gt;

&lt;p&gt;Instead, along with the processing code, I version control the metadata:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Modification date&lt;/li&gt;
&lt;li&gt;File size&lt;/li&gt;
&lt;li&gt;Row count&lt;/li&gt;
&lt;li&gt;Column names&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;This gives me enough information to know &lt;strong&gt;if&lt;/strong&gt; a data file has changed and an idea of &lt;strong&gt;what&lt;/strong&gt; has changed (e.g., rows added/deleted, new/renamed columns), without stressing the VCS.&lt;/p&gt;
', NULL, NULL, '''/a'':37C ''/a/6771/12328'':34C ''/li'':77C,81C,85C,89C ''/p'':59C,72C,125C ''/strong'':101C,113C ''/ul'':90C ''10m'':55C ''actual'':8C ''added/deleted'':118C ''along'':62C ''binari'':27C ''chang'':52C,106C,115C ''code'':66C ''column'':87C,120C ''control'':6C,69C ''count'':84C ''csv'':22C ''data'':9C,57C,103C ''datascience.stackexchange.com'':33C ''datascience.stackexchange.com/a/6771/12328'':32C ''date'':76C ''e.g'':116C ''enough'':95C ''even'':16C ''file'':10C,79C,104C ''form'':28C ''give'':93C ''go'':42C ''href'':31C ''idea'':109C ''inform'':96C ''instead'':23C,61C ''know'':98C ''li'':74C,78C,82C,86C ''m'':36C ''metadata'':71C ''modif'':75C ''name'':88C ''new/renamed'':119C ''p'':1C,60C,91C ''process'':65C ''re'':40C ''review'':47C ''riccardo'':35C ''row'':49C,51C,56C,83C,117C ''row-by-row'':48C ''said'':38C ''set'':58C ''size'':80C ''spend'':44C ''store'':19C ''stress'':122C ''strong'':99C,111C ''time'':46C ''ul'':73C ''vcs'':124C ''version'':5C,68C ''want'':14C ''without'':121C ''wouldn'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6920, 2, '2015-08-27 19:29:43.663', 4, NULL, NULL, '&lt;p&gt;An "outlier" is an observation that is so unexpected that we suspect it wasn''t valid -- corrupted by noise or something. But what is &lt;em&gt;unexpected&lt;/em&gt;? An observation that is highly &lt;em&gt;improbable&lt;/em&gt;? But then how do we know what''s probable?&lt;/p&gt;

&lt;p&gt;Unless you''re able or willing to make some assumptions about the distribution that generated these numbers, you can''t really declare things outliers. For example, quartiles don''t help, since it will only help you find the x% largest or smallest values. But every data set has these -- indeed every data set has a min and max, and being the min or max doesn''t mean being an outlier.&lt;/p&gt;

&lt;p&gt;In practice, I sense there''s an assumption lurking here, that the numbers are probably normally distributed about some mean with roughly some standard deviation. If you know what the mean and/or stdev is supposed to be, you can use it directly to decide how unlikely an observation is and discard it as an outlier if it exceeds a threshold you choose.&lt;/p&gt;

&lt;p&gt;You can take the mean and stdev of this &lt;em&gt;sample&lt;/em&gt; as a surrogate for that, and for a large enough sample, the sample mean and stdev could be close enough to the real population mean and stdev to work. Here, with such small sets, the outliers influence stats like the mean so much that they throw off attempts to evaluate them in terms of the stats they influence. It''s kind of circular.&lt;/p&gt;
', NULL, NULL, '''/em'':28C,36C,195C ''/p'':46C,118C,182C,259C ''abl'':51C ''and/or'':151C ''assumpt'':57C,127C ''attempt'':243C ''choos'':181C ''circular'':258C ''close'':214C ''corrupt'':18C ''could'':212C ''data'':93C,99C ''decid'':163C ''declar'':69C ''deviat'':144C ''direct'':161C ''discard'':170C ''distribut'':60C,136C ''doesn'':112C ''em'':26C,34C,193C ''enough'':205C,215C ''evalu'':245C ''everi'':92C,98C ''exampl'':73C ''exceed'':177C ''find'':84C ''generat'':62C ''help'':77C,82C ''high'':33C ''improb'':35C ''inde'':97C ''influenc'':232C,253C ''kind'':256C ''know'':42C,147C ''larg'':204C ''largest'':87C ''like'':234C ''lurk'':128C ''make'':55C ''max'':105C,111C ''mean'':114C,139C,150C,188C,209C,220C,236C ''min'':103C,109C ''much'':238C ''nois'':20C ''normal'':135C ''number'':64C,132C ''observ'':6C,30C,167C ''outlier'':3C,71C,117C,174C,231C ''p'':1C,47C,119C,183C ''popul'':219C ''practic'':121C ''probabl'':45C,134C ''quartil'':74C ''re'':50C ''real'':218C ''realli'':68C ''rough'':141C ''sampl'':194C,206C,208C ''sens'':123C ''set'':94C,100C,229C ''sinc'':78C ''small'':228C ''smallest'':89C ''someth'':22C ''standard'':143C ''stat'':233C,251C ''stdev'':152C,190C,211C,222C ''suppos'':154C ''surrog'':198C ''suspect'':13C ''take'':186C ''term'':248C ''thing'':70C ''threshold'':179C ''throw'':241C ''unexpect'':10C,27C ''unless'':48C ''unlik'':165C ''use'':159C ''valid'':17C ''valu'':90C ''wasn'':15C ''will'':53C ''work'':224C ''x'':86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6921, 1, '2015-08-27 19:36:53.297', 3, 126, 'Understanding dropout and gradient descent', '&lt;p&gt;I am looking at how to implement dropout on deep neural network, and I found something counter intuitive. In the forward phase dropout mask activations with a random tensor of 1s and 0s to force net to learn the average of the weights. This help the net to generalize better. But during the update phase of the gradient descent the activations are not masked. This to me seems counter intuitive. If I mask connections activations with dropout , why I should not mask the gradient descent phase?&lt;/p&gt;
', 'neuralnetwork  deep-learning  gradient-descent', NULL, '''/p'':100C ''0s'':46C ''1s'':44C ''activ'':38C,74C,88C ''averag'':53C ''better'':63C ''connect'':87C ''counter'':30C,82C ''deep'':8B,23C ''deep-learn'':7B ''descent'':5A,12B,72C,98C ''dropout'':2A,21C,36C,90C ''forc'':48C ''forward'':34C ''found'':28C ''general'':62C ''gradient'':4A,11B,71C,97C ''gradient-desc'':10B ''help'':58C ''implement'':20C ''intuit'':31C,83C ''learn'':9B,51C ''look'':16C ''mask'':37C,77C,86C,95C ''net'':49C,60C ''network'':25C ''neural'':24C ''neuralnetwork'':6B ''p'':13C ''phase'':35C,68C,99C ''random'':41C ''seem'':81C ''someth'':29C ''tensor'':42C ''understand'':1A ''updat'':67C ''weight'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6922, 1, '2015-08-27 20:04:03.597', 1, 45, 'TF-IDF not a strong measure in this senario?', '&lt;p&gt;I am dealing with a data where I have only two documents and there are some words which are present in both. Now Term Frequencies (tf) of these words are very high for respective single document than the other. For e.g. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Word1 is present in Documents D1 and D2, and
tf(Word1,D1) = 1000
tf(Word1,D2) = 3
But since Word1 is present in both the documents 
IDF(Word1) = 0
TF-IDF(Word1,d) = 0 for all d belonging to {D1,D2}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So inspite having a very powerful presence in a single document, TF-IDF score will always be 0. One solution I could think is to take word1 as absent if &lt;code&gt;tf(Word1) &amp;lt; threshold&lt;/code&gt;. However I still don''t feel this is good enough as the Word present in only one single document is given IDF score of only &lt;code&gt;0.5&lt;/code&gt;. I feel like TF-IDF is not a good measurement when number of documents are very low. Any suggestions here?&lt;/p&gt;
', 'text-mining', NULL, '''/code'':101C,140C,168C ''/p'':56C,190C ''/pre'':102C ''0'':87C,93C,122C ''0.5'':167C ''1000'':71C ''3'':75C ''absent'':133C ''alway'':120C ''belong'':97C ''code'':58C,135C,166C ''could'':126C ''d'':92C,96C ''d1'':64C,70C,99C ''d2'':66C,74C,100C ''data'':20C ''deal'':17C ''document'':26C,50C,63C,84C,114C,159C,183C ''e.g'':55C ''enough'':150C ''feel'':146C,170C ''frequenc'':39C ''given'':161C ''good'':149C,178C ''high'':46C ''howev'':141C ''idf'':3A,85C,90C,117C,162C,174C ''inspit'':105C ''like'':171C ''low'':186C ''lt'':138C ''measur'':7A,179C ''mine'':13B ''number'':181C ''one'':123C,157C ''p'':14C,103C ''power'':109C ''pre'':57C ''presenc'':110C ''present'':34C,61C,80C,154C ''respect'':48C ''score'':118C,163C ''senario'':10A ''sinc'':77C ''singl'':49C,113C,158C ''solut'':124C ''still'':143C ''strong'':6A ''suggest'':188C ''take'':130C ''term'':38C ''text'':12B ''text-min'':11B ''tf'':2A,40C,68C,72C,89C,116C,136C,173C ''tf-idf'':1A,88C,115C,172C ''think'':127C ''threshold'':139C ''two'':25C ''word'':31C,43C,153C ''word1'':59C,69C,73C,78C,86C,91C,131C,137C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6924, 1, '2015-08-27 22:06:09.47', 3, 81, 'Illustrating the dimensionality reduction done by a classification or regression model', '&lt;p&gt;&lt;strong&gt;Tl;DR:&lt;/strong&gt; You can predict something, but how do you explain the prediction?&lt;/p&gt;

&lt;hr&gt;

&lt;h3&gt;Your usual classification/regression setup&lt;/h3&gt;

&lt;p&gt;Lets say the data is a classic regression/classification problem: several numerical columns, several nominal columns, and an event which we are trying to predict:&lt;/p&gt;

&lt;pre&gt;
user1, age:18, wealth:20000, likes:tomatoes, isInBigCity:yes, hasClicked:yes
user2, age:25, wealth:24000, likes:carrots , isInBigCity:no , hasClicked:no
...
&lt;/pre&gt;

&lt;p&gt;With the help of Random Forests, SVM, Logistic Regression, Deep Neural Network, or some other method we export a model that can output a probability of the event &lt;code&gt;hasClicked:yes&lt;/code&gt; for a new user faced with the choice of clicking.&lt;/p&gt;

&lt;hr&gt;

&lt;h3&gt;Extracting the inner topology surfaced by a model&lt;/h3&gt;

&lt;p&gt;Now, those algorithm do some dimensionality reduction, reducing those inputs to a single probability. My question is: how would you extract what those models are doing and show the dimensionality reduction steps to a human? How would you illustrate the inner topology of the dataset with regards to the predicted class?&lt;/p&gt;

&lt;p&gt;I am looking for either:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Visualizations of what a model produced by Random Forests, SVM, Logistic Regression, Deep Neural Network is doing.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Clusterers being extracted from regression/classification models (Surely a single decision tree can be viewed as a hierarchical clusterer)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;A model-specific way to project the input data in a space where the Euclidian distance of T-SNE makes sense.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;A way to learn a T-SNE-compatible distance out of a regression/classification model.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Clustering methods that optimise the separation of one variable while not using it to cluster.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Clusterers built out of regression/classification models &lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;hr&gt;

&lt;p&gt;The goal is to extract some sort market segmentation based on the behaviour of users. And give a high level visualization of it. Something that would expose clearly the &lt;strong&gt;reasons&lt;/strong&gt; why some users transform better than others.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6926, 1, '2015-08-28 02:15:14.793', 2, 151, 'How to rank documents using Bag of words approach', '&lt;p&gt;I want to cluster the documents I get for Google scholar search using the Bag of words model. I thought of using Java as the language.&lt;/p&gt;

&lt;p&gt;Assume for the keyword &lt;strong&gt;&lt;em&gt;k&lt;/em&gt;&lt;/strong&gt;, Google scholar gives me 50 results. If I have a predefined set of words &lt;strong&gt;&lt;em&gt;w1, w2, w3... wn,&lt;/em&gt;&lt;/strong&gt; how can I rank the the documents which have the predefined set of words most? How can I apply Bag of words model for this? Do I need a clustering algorithm like k-means? And do I need to perform NLP techniques as well?&lt;/p&gt;

&lt;p&gt;Say that the word &lt;strong&gt;&lt;em&gt;w1&lt;/em&gt;&lt;/strong&gt; has several synonyms. How can I consider those synonyms as well for document ranking? Will I have to create a corpus containing all the abbreviations, synonym etc for that? &lt;/p&gt;

&lt;p&gt;Are there any good tutorials available for this? Will choosing Java over Python will be an disadvantage since most of the resources (Ex - Scikit) were in Python? &lt;/p&gt;
', 'clustering  algorithms  java  information-retrieval', NULL, '''/em'':52C,74C,130C ''/p'':43C,121C,161C,190C ''/strong'':53C,75C,131C ''50'':58C ''abbrevi'':156C ''algorithm'':11B,106C ''appli'':94C ''approach'':9A ''assum'':45C ''avail'':168C ''bag'':6A,31C,95C ''choos'':172C ''cluster'':10B,20C,105C ''consid'':138C ''contain'':153C ''corpus'':152C ''creat'':150C ''disadvantag'':179C ''document'':4A,22C,82C,144C ''em'':50C,69C,128C ''etc'':158C ''ex'':185C ''get'':24C ''give'':56C ''good'':166C ''googl'':26C,54C ''inform'':14B ''information-retriev'':13B ''java'':12B,39C,173C ''k'':51C,109C ''k-mean'':108C ''keyword'':48C ''languag'':42C ''like'':107C ''mean'':110C ''model'':34C,98C ''need'':103C,114C ''nlp'':117C ''p'':16C,44C,122C,162C ''perform'':116C ''predefin'':64C,86C ''python'':175C,189C ''rank'':3A,79C,145C ''resourc'':184C ''result'':59C ''retriev'':15B ''say'':123C ''scholar'':27C,55C ''scikit'':186C ''search'':28C ''set'':65C,87C ''sever'':133C ''sinc'':180C ''strong'':49C,68C,127C ''synonym'':134C,140C,157C ''techniqu'':118C ''thought'':36C ''tutori'':167C ''use'':5A,29C,38C ''w1'':70C,129C ''w2'':71C ''w3'':72C ''want'':18C ''well'':120C,142C ''wn'':73C ''word'':8A,33C,67C,89C,97C,126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8911, 2, '2015-11-19 07:05:58.493', 2, NULL, NULL, '&lt;p&gt;You should test your classifier on a dataset that represents the why it will be used. The best is usually unmodified distribution.&lt;/p&gt;

&lt;p&gt;During the learning, modify the dataset in anyway that helps you.&lt;/p&gt;

&lt;p&gt;For details, see &lt;a href="http://datascience.stackexchange.com/questions/810/should-i-go-for-a-balanced-dataset-or-a-representative-dataset/8628#8628"&gt;Should I go for a &amp;#39;balanced&amp;#39; dataset or a &amp;#39;representative&amp;#39; dataset?&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':62C ''/p'':24C,37C,63C ''/questions/810/should-i-go-for-a-balanced-dataset-or-a-representative-dataset/8628#8628'':46C ''39'':52C,54C,58C,60C ''anyway'':33C ''balanc'':53C ''best'':19C ''classifi'':6C ''datascience.stackexchange.com'':45C ''datascience.stackexchange.com/questions/810/should-i-go-for-a-balanced-dataset-or-a-representative-dataset/8628#8628'':44C ''dataset'':9C,31C,55C,61C ''detail'':40C ''distribut'':23C ''go'':49C ''help'':35C ''href'':43C ''learn'':28C ''modifi'':29C ''p'':1C,25C,38C ''repres'':11C,59C ''see'':41C ''test'':4C ''unmodifi'':22C ''use'':17C ''usual'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6942, 2, '2015-08-29 13:05:40.547', 0, NULL, NULL, '&lt;p&gt;I might suggest that you use a greedy methods. Give a classifier to start, you will include the classifier that make the ensemble get the best performance improvement. If no improvement could be get through include more classifiers, then stop. You will start with every classifiers. The complexity will be at most N*N. &lt;/p&gt;

&lt;p&gt;I have one more question, What do you mean by "Pareto optimal", especially in your context? I found from wiki this explanation, &lt;a href="https://en.wikipedia.org/wiki/Pareto_efficiency" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Pareto_efficiency&lt;/a&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;through reallocation, improvements can be made to at least one participant''s well-being without reducing any other participant''s well-being.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The improvement for the Pareto efficiency is for each participant, which might correspond to each classifier. How do you define the improvement over one classifier?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8036, 2, '2015-09-08 10:35:08.143', 0, NULL, NULL, '&lt;p&gt;Look at using Power Query.   For example: &lt;a href="http://www.poweredsolutions.co/2014/11/06/getting-api-results-from-azure-machine-learning-into-power-query/" rel="nofollow"&gt;http://www.poweredsolutions.co/2014/11/06/getting-api-results-from-azure-machine-learning-into-power-query/&lt;/a&gt;   As per the bottom of the url, power query can be a source into AML.&lt;/p&gt;
', NULL, NULL, '''/2014/11/06/getting-api-results-from-azure-machine-learning-into-power-query/'':13C ''/2014/11/06/getting-api-results-from-azure-machine-learning-into-power-query/&lt;/a&gt;'':18C ''/p'':34C ''aml'':33C ''bottom'':22C ''exampl'':8C ''href'':10C ''look'':2C ''nofollow'':15C ''p'':1C ''per'':20C ''power'':5C,26C ''queri'':6C,27C ''rel'':14C ''sourc'':31C ''url'':25C ''use'':4C ''www.poweredsolutions.co'':12C,17C ''www.poweredsolutions.co/2014/11/06/getting-api-results-from-azure-machine-learning-into-power-query/'':11C ''www.poweredsolutions.co/2014/11/06/getting-api-results-from-azure-machine-learning-into-power-query/&lt;/a&gt;'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6927, 2, '2015-08-28 03:17:25.713', 1, NULL, NULL, '&lt;p&gt;There has/is been a lot of hype around the word "Big Data". So, as someone who has been setting up an analytics infrastructure at a fast growing company, I would like to share my experiences.&lt;/p&gt;

&lt;p&gt;You would like to check &lt;a href="http://stats.stackexchange.com/questions/18276/what-data-and-statistics-skills-are-currently-in-high-demand-and-where-are-they/164861#164861"&gt;this&lt;/a&gt; out, which I have written for an almost similar question on CV.&lt;/p&gt;

&lt;p&gt;As the tools like H2O, R, Julia, Spark, etc; are being evolving for dealing and playing with Big Data, the size of the data is becoming a less of a problem these days.&lt;/p&gt;

&lt;p&gt;As there is a lots of data available, and a wonderful set of tools at disposal, the art of analytics and data science is becoming more of a pleasure for data scientists rather than pain.&lt;/p&gt;

&lt;p&gt;But, the real challenge is the availability of people who know what they are doing, who know how to use these tools for mining gold from the data.
Just anyone who has run a Linear Regression on a data set can''t call himself a data scientist. One has to explain what the data is, what can be pulled out of it, and why is the algorithm applied on it, and why not something else.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How do they differ from a small amount of data?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;In my experience, there isn''t any huge differences. The only differences which I have found are &lt;a href="http://people.seas.harvard.edu/~minilek/cs229r/fall15/index.html" rel="nofollow"&gt;algorithms&lt;/a&gt; which help find the needle in the hay stack, like MinHash, HyperLogLog, etc; which had to evolve for dealing with the size.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6928, 2, '2015-08-28 08:56:19.443', 2, NULL, NULL, '&lt;p&gt;If you''re just looking to rank documents according to how many appearances your words w1,..,wn contain, then there''s no need for clustering or machine learning in general: Clustering your 50 results will give you a partition of these results into clusters containing results that are similar to one another and different from the results in other clusters. &lt;/p&gt;

&lt;p&gt;If you just need to rank by word occ, just count the frequencies of your words in each document (including synonyms, which you can get e.g. from Wordnet automatically if you prefer) and sum them up. &lt;/p&gt;
', NULL, NULL, '''/p'':62C,100C ''50'':34C ''accord'':10C ''anoth'':53C ''appear'':14C ''automat'':92C ''cluster'':26C,32C,45C,61C ''contain'':19C,46C ''count'':74C ''differ'':55C ''document'':9C,82C ''e.g'':89C ''frequenc'':76C ''general'':31C ''get'':88C ''give'':37C ''includ'':83C ''learn'':29C ''look'':6C ''machin'':28C ''mani'':13C ''need'':24C,67C ''occ'':72C ''one'':52C ''p'':1C,63C ''partit'':40C ''prefer'':95C ''rank'':8C,69C ''re'':4C ''result'':35C,43C,47C,58C ''similar'':50C ''sum'':97C ''synonym'':84C ''w1'':17C ''wn'':18C ''word'':16C,71C,79C ''wordnet'':91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6934, 2, '2015-08-28 23:44:59.233', 0, NULL, NULL, '&lt;p&gt;It really depends on what your goal is.  If you''re looking for validation by a small number of users who will give you feedback, you should simply reach out and find what works for them.&lt;/p&gt;

&lt;p&gt;On the other hand, if you''re looking for the most wide spread use, you''re going to want to target python and R.  If you are well versed in calling C++ from python wrappers, that will lend itself to scalability (Numpy has libraries for this).  You''ll also need a pip installer.  If I can''t install through pip, it''s unlikely I''ll go through the effort of installing otherwise.  You may even look to contribute to either scikit-learn (&lt;a href="http://scikit-learn.org/stable/" rel="nofollow"&gt;http://scikit-learn.org/stable/&lt;/a&gt;) or ml-lib (&lt;a href="http://spark.apache.org/mllib/" rel="nofollow"&gt;http://spark.apache.org/mllib/&lt;/a&gt;), which will give you a much greater reach.&lt;/p&gt;

&lt;p&gt;I don''t regularly use R, so I don''t know if there are standardized toolkits like those I mention above, but you''d be well served finding someone familiar with creating R packages and putting one up there.  Despite that I don''t regularly use R, it is undeniable that it is widely used at this time, so for maximal exposure, it''s worth the effort.&lt;/p&gt;
', NULL, NULL, '''/mllib/'':141C ''/mllib/&lt;/a&gt;),'':146C ''/p'':38C,155C,224C ''/stable/'':127C ''/stable/&lt;/a&gt;)'':132C ''also'':88C ''c'':71C ''call'':70C ''contribut'':117C ''creat'':188C ''d'':180C ''depend'':4C ''despit'':196C ''effort'':108C,223C ''either'':119C ''even'':114C ''exposur'':218C ''familiar'':186C ''feedback'':26C ''find'':33C,184C ''give'':24C,149C ''go'':56C,105C ''goal'':8C ''greater'':153C ''hand'':43C ''href'':124C,138C ''instal'':92C,97C,110C ''know'':167C ''learn'':122C ''lend'':77C ''lib'':136C ''librari'':83C ''like'':173C ''ll'':87C,104C ''look'':13C,47C,115C ''maxim'':217C ''may'':113C ''mention'':176C ''ml'':135C ''ml-lib'':134C ''much'':152C ''need'':89C ''nofollow'':129C,143C ''number'':19C ''numpi'':81C ''one'':193C ''otherwis'':111C ''p'':1C,39C,156C ''packag'':190C ''pip'':91C,99C ''put'':192C ''python'':61C,73C ''r'':63C,162C,189C,203C ''re'':12C,46C,55C ''reach'':30C,154C ''realli'':3C ''regular'':160C,201C ''rel'':128C,142C ''scalabl'':80C ''scikit'':121C ''scikit-learn'':120C ''scikit-learn.org'':126C,131C ''scikit-learn.org/stable/'':125C ''scikit-learn.org/stable/&lt;/a&gt;)'':130C ''serv'':183C ''simpli'':29C ''small'':18C ''someon'':185C ''spark.apache.org'':140C,145C ''spark.apache.org/mllib/'':139C ''spark.apache.org/mllib/&lt;/a&gt;),'':144C ''spread'':52C ''standard'':171C ''target'':60C ''time'':214C ''toolkit'':172C ''undeni'':206C ''unlik'':102C ''use'':53C,161C,202C,211C ''user'':21C ''valid'':15C ''vers'':68C ''want'':58C ''well'':67C,182C ''wide'':51C,210C ''work'':35C ''worth'':221C ''wrapper'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6937, 1, '2015-08-29 00:46:37.01', 5, 363, 'What courses / subjects are most important to the field of Data Science?', '&lt;p&gt;I''ve taken it upon myself to begin a career change. I have a decent background in mathematics, but lack in programming or data science specific skills (such as data munging). I have been looking through data science curricula and already feel overwhelmed by the amount of subjects a proper data science curriculum contains. Just to name a few: natural language processing, machine learning, R, Python, SQL, NoSQL, probability, statistics, numerical methods, algorithms, and the list goes on.&lt;/p&gt;

&lt;p&gt;I can''t afford to go back to school, but if my goal is to get into data science, &lt;em&gt;what subjects should I focus my attention on&lt;/em&gt;?&lt;/p&gt;

&lt;p&gt;I fully understand "data science" is a very big field, but certainly there must be several subjects that are important to all, if not the vast majority, of these sub-fields. I suppose this is essentially what my question is asking.  I have not yet obtained the amount of exposure to determine what I want to specialize in, so at this point, my concern is obtaining a foundation that wouldn''t restrict me from entering any particular sub-field, or in other words, studying the subjects that benefit all sub-fields.&lt;/p&gt;

&lt;p&gt;Note: Math isn''t as much a concern for me compared to the other subjects.&lt;/p&gt;
', 'self-study', NULL, '''/em'':125C ''/p'':95C,126C,222C,239C ''afford'':100C ''algorithm'':89C ''alreadi'':57C ''amount'':62C,176C ''ask'':169C ''attent'':123C ''back'':103C ''background'':32C ''begin'':24C ''benefit'':217C ''big'':136C ''career'':26C ''certain'':139C ''chang'':27C ''compar'':234C ''concern'':192C,231C ''contain'':70C ''cours'':2A ''curricula'':55C ''curriculum'':69C ''data'':11A,40C,46C,53C,67C,114C,131C ''decent'':31C ''determin'':180C ''em'':116C ''enter'':203C ''essenti'':164C ''exposur'':178C ''feel'':58C ''field'':9A,137C,159C,208C,221C ''focus'':121C ''foundat'':196C ''fulli'':129C ''get'':112C ''go'':102C ''goal'':109C ''goe'':93C ''import'':6A,147C ''isn'':226C ''lack'':36C ''languag'':77C ''learn'':80C ''list'':92C ''look'':51C ''machin'':79C ''major'':154C ''math'':225C ''mathemat'':34C ''method'':88C ''much'':229C ''mung'':47C ''must'':141C ''name'':73C ''natur'':76C ''nosql'':84C ''note'':224C ''numer'':87C ''obtain'':174C,194C ''overwhelm'':59C ''p'':16C,96C,127C,223C ''particular'':205C ''point'':190C ''probabl'':85C ''process'':78C ''program'':38C ''proper'':66C ''python'':82C ''question'':167C ''r'':81C ''restrict'':200C ''school'':105C ''scienc'':12A,41C,54C,68C,115C,132C ''self'':14B ''self-studi'':13B ''sever'':143C ''skill'':43C ''special'':185C ''specif'':42C ''sql'':83C ''statist'':86C ''studi'':15B,213C ''sub'':158C,207C,220C ''sub-field'':157C,206C,219C ''subject'':3A,64C,118C,144C,215C,238C ''suppos'':161C ''taken'':19C ''understand'':130C ''upon'':21C ''vast'':153C ''ve'':18C ''want'':183C ''word'':212C ''wouldn'':198C ''yet'':173C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6984, 1, '2015-09-02 11:40:08.637', 2, 205, 'Which accuracy metric of a ML classifier can maximize map@K of a recommender system for an unbalanced dataset?', '&lt;p&gt;I have to build a recommender system &amp;amp; it will be evaluated using map@10 criteria.
I have rolled up the data/rows at user-item level &amp;amp; is using Gradient Boosting in scikit learn to build the model.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Challenge&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;But there is a big class imbalance where only 6%( out of 40 K use-item pairs) have actually purchased the item in the entire base. Given this challenge i want to know the accuracy metric I should use to tune the parameters of GBM model.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Question&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I thought about using recall, precision, f1 score etc. to tune the hyperparameters of GBM. But which one will maximize map@10 given the class imbalance?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'machine-learning  classification  recommendation  unbalanced-classes  loss-function', NULL, '''/p'':71C,76C,125C,130C,158C,161C ''/strong'':75C,129C ''10'':46C,153C ''40'':90C ''6'':87C ''accuraci'':2A,113C ''actual'':97C ''amp'':39C,59C ''base'':104C ''big'':82C ''boost'':63C ''build'':35C,68C ''challeng'':74C,107C ''class'':27B,83C,156C ''classif'':23B ''classifi'':7A ''criteria'':47C ''data/rows'':53C ''dataset'':19A ''entir'':103C ''etc'':140C ''evalu'':43C ''f1'':138C ''function'':30B ''gbm'':123C,146C ''given'':105C,154C ''gradient'':62C ''hyperparamet'':144C ''imbal'':84C,157C ''item'':57C,94C,100C ''k'':11A,91C ''know'':111C ''learn'':22B,66C ''level'':58C ''loss'':29B ''loss-funct'':28B ''machin'':21B ''machine-learn'':20B ''map'':10A,45C,152C ''maxim'':9A,151C ''metric'':3A,114C ''ml'':6A ''model'':70C,124C ''one'':149C ''p'':31C,72C,77C,126C,131C,159C ''pair'':95C ''paramet'':121C ''precis'':137C ''purchas'':98C ''question'':128C ''recal'':136C ''recommend'':14A,24B,37C ''roll'':50C ''scikit'':65C ''score'':139C ''strong'':73C,127C ''system'':15A,38C ''thank'':160C ''thought'':133C ''tune'':119C,142C ''unbalanc'':18A,26B ''unbalanced-class'':25B ''use'':44C,61C,93C,117C,135C ''use-item'':92C ''user'':56C ''user-item'':55C ''want'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6929, 1, '2015-08-28 12:56:52.233', 3, 82, 'Classification problem where one attribute is a vector', '&lt;p&gt;Hello I am a layman trying to analyze game data from League of Legends, specifically looking at predicting the win rate for a given champion given an item build.&lt;/p&gt;

&lt;h3&gt;Outline&lt;/h3&gt;

&lt;p&gt;A player can own up to 6 items at the end of a game. They could have purchased these items in different orders or adjusted their inventory position during the course of the game.&lt;/p&gt;

&lt;p&gt;In this fashion the dataset may contain the following rows with:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;   champion id   |                 items ids               | win(1)/loss(0)
----------------------------------------------------------------------------
       45        |   [3089, 3135, 3151, 3157, 3165, 3285]  |       1
       45        |   [3151, 3285, 3135, 3089, 3157, 3165]  |       1
       45        |   [3165, 3285, 3089, 3135, 3157, 3151]  |       0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;While the items are in a different order the build is the same, my initial thought would be to simply multiply the item ids as this would give me an integer value representing that combination of 6 items.&lt;/p&gt;

&lt;p&gt;While there are hundreds of items, in reality a champion draws off a small subset (~20) of those to form the core (3 items) of their build. A game may also finish before players have had time to purchase 6 items:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;                items ids               
------------------------------------------
   [3089, XXXX, 3151, 3285, 3165, 0000]
   [XXXX, 3285, XXXX, 3165, 3151, 0000]
   [3165, 3285, 3089, XXXX, 0000, 0000]

XXXX item from outside core subset
0000 empty inventory slot
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As item 3089 compliments champion 45 core builds that have item 3089 have a higher win rate than core builds which are missing item 3089.&lt;/p&gt;

&lt;p&gt;The size of the data set available for each champion varies between 10000 and 100000. The mean is probably around 35000.&lt;/p&gt;

&lt;h3&gt;Questions&lt;/h3&gt;

&lt;ol&gt;
&lt;li&gt;Is this a suitable problem for supervised classification?&lt;/li&gt;
&lt;li&gt;How should I approach finding groups of core items and their win rates?&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  classification  clustering', NULL, '''/code'':131C,248C ''/h3'':47C,302C ''/li'':313C,328C ''/loss'':105C ''/ol'':329C ''/p'':44C,83C,96C,172C,215C,276C,299C ''/pre'':132C,249C ''0'':106C,130C ''0000'':225C,231C,236C,237C,244C ''1'':104C,114C,122C ''10000'':290C ''100000'':292C ''20'':189C ''3'':196C ''3089'':108C,119C,126C,220C,234C,253C,262C,275C ''3135'':109C,118C,127C ''3151'':110C,116C,129C,222C,230C ''3157'':111C,120C,128C ''3165'':112C,121C,124C,224C,229C,232C ''3285'':113C,117C,125C,223C,227C,233C ''35000'':298C ''45'':107C,115C,123C,256C ''6'':55C,170C,213C ''adjust'':73C ''also'':204C ''analyz'':22C ''approach'':318C ''around'':297C ''attribut'':5A ''avail'':284C ''build'':43C,143C,200C,258C,270C ''champion'':39C,99C,183C,255C,287C ''classif'':1A,12B,312C ''cluster'':13B ''code'':98C,217C ''combin'':168C ''compliment'':254C ''contain'':91C ''core'':195C,242C,257C,269C,322C ''could'':64C ''cours'':79C ''data'':24C,282C ''dataset'':89C ''differ'':70C,140C ''draw'':184C ''empti'':245C ''end'':59C ''fashion'':87C ''find'':319C ''finish'':205C ''follow'':93C ''form'':193C ''game'':23C,62C,82C,202C ''give'':161C ''given'':38C,40C ''group'':320C ''h3'':45C,300C ''hello'':15C ''higher'':265C ''hundr'':177C ''id'':100C,102C,157C,219C ''initi'':148C ''integ'':164C ''inventori'':75C,246C ''item'':42C,56C,68C,101C,136C,156C,171C,179C,197C,214C,218C,239C,252C,261C,274C,323C ''layman'':19C ''leagu'':26C ''learn'':11B ''legend'':28C ''li'':304C,314C ''look'':30C ''machin'':10B ''machine-learn'':9B ''may'':90C,203C ''mean'':294C ''miss'':273C ''multipli'':154C ''ol'':303C ''one'':4A ''order'':71C,141C ''outlin'':46C ''outsid'':241C ''p'':14C,48C,84C,133C,173C,250C,277C ''player'':50C,207C ''posit'':76C ''pre'':97C,216C ''predict'':32C ''probabl'':296C ''problem'':2A,309C ''purchas'':66C,212C ''question'':301C ''rate'':35C,267C,327C ''realiti'':181C ''repres'':166C ''row'':94C ''set'':283C ''simpli'':153C ''size'':279C ''slot'':247C ''small'':187C ''specif'':29C ''subset'':188C,243C ''suitabl'':308C ''supervis'':311C ''thought'':149C ''time'':210C ''tri'':20C ''valu'':165C ''vari'':288C ''vector'':8A ''win'':34C,103C,266C,326C ''would'':150C,160C ''xxxx'':221C,226C,228C,235C,238C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6930, 2, '2015-08-28 14:17:43.353', 1, NULL, NULL, '&lt;p&gt;One way to do this is to use context information to represent each word along with the w2v vector. You can choose to represent this information in any way you like: add another 600 dimensions (100D w2v vectors for 3 left and 3 right context words), just another 100D as the sum of context vectors or any other fixed length representation for your context.&lt;/p&gt;

&lt;p&gt;When you''re training, you can use a version of ''word dropout'' that will utilize this information. 20% of the time, set your w2v vectors to zero, forcing your classifier to use the context dimensions to represent the word.&lt;/p&gt;

&lt;p&gt;When you encounter a new word, the hope is, the classifier learned to use the context information as well as it learned to use the w2v information.&lt;/p&gt;
', NULL, NULL, '''/p'':66C,107C,136C ''100d'':37C,50C ''20'':85C ''3'':41C,44C ''600'':35C ''add'':33C ''along'':16C ''anoth'':34C,49C ''choos'':23C ''classifi'':97C,119C ''context'':10C,46C,55C,65C,101C,124C ''dimens'':36C,102C ''dropout'':79C ''encount'':111C ''fix'':60C ''forc'':95C ''hope'':116C ''inform'':11C,27C,84C,125C,135C ''learn'':120C,130C ''left'':42C ''length'':61C ''like'':32C ''new'':113C ''one'':2C ''p'':1C,67C,108C ''re'':70C ''repres'':13C,25C,104C ''represent'':62C ''right'':45C ''set'':89C ''sum'':53C ''time'':88C ''train'':71C ''use'':9C,74C,99C,122C,132C ''util'':82C ''vector'':20C,39C,56C,92C ''version'':76C ''w2v'':19C,38C,91C,134C ''way'':3C,30C ''well'':127C ''word'':15C,47C,78C,106C,114C ''zero'':94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6931, 1, '2015-08-28 14:53:34.843', 0, 39, 'Any usable libs to build and visualise SOM in python?', '&lt;p&gt;I tried SOMpy, though it is very crude now and works only with oldest versions of matplotlib. 
Is there any fancy lib that can build SOM based on array and visualize it in Python? &lt;/p&gt;
', 'python  visualization', NULL, '''/p'':48C ''array'':42C ''base'':40C ''build'':5A,38C ''crude'':21C ''fanci'':34C ''lib'':3A,35C ''matplotlib'':30C ''oldest'':27C ''p'':13C ''python'':10A,11B,47C ''som'':8A,39C ''sompi'':16C ''though'':17C ''tri'':15C ''usabl'':2A ''version'':28C ''visual'':12B,44C ''visualis'':7A ''work'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6932, 2, '2015-08-28 15:06:02.47', 3, NULL, NULL, '&lt;p&gt;Please have a look at the weighting scheme 2 in table &lt;strong&gt;Recommended TF-IDF weighting schemes&lt;/strong&gt; in &lt;a href="https://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Tf%E2%80%93idf&lt;/a&gt;. That should solve your problem.&lt;/p&gt;
', NULL, NULL, '''/p'':37C ''/strong'':20C ''/wiki/tf%e2%80%93idf'':26C ''/wiki/tf%e2%80%93idf&lt;/a&gt;.'':31C ''2'':10C ''en.wikipedia.org'':25C,30C ''en.wikipedia.org/wiki/tf%e2%80%93idf'':24C ''en.wikipedia.org/wiki/tf%e2%80%93idf&lt;/a&gt;.'':29C ''href'':23C ''idf'':17C ''look'':5C ''nofollow'':28C ''p'':1C ''pleas'':2C ''problem'':36C ''recommend'':14C ''rel'':27C ''scheme'':9C,19C ''solv'':34C ''strong'':13C ''tabl'':12C ''tf'':16C ''tf-idf'':15C ''weight'':8C,18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6933, 2, '2015-08-28 17:38:30.597', 3, NULL, NULL, '&lt;p&gt;1) If you want to build a model with:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Input: Items bought
Output: Win/Loss
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;then you will probably want to learn a non-linear combination of the inputs to represent a build.  For example &lt;code&gt;item_X&lt;/code&gt; may have very different purpose when paired with &lt;code&gt;item_Y&lt;/code&gt; than with &lt;code&gt;item_Z&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;For the input format, you may consider creating a binary vector from the item list.  For example if there were only ten items, a game in which the champion purchased &lt;code&gt;items 1,4,5,9&lt;/code&gt; (in any order) would look like row 1; a game where he also purchased &lt;code&gt;item 2&lt;/code&gt; and &lt;code&gt;7&lt;/code&gt; would look like row 2:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;item_ID   | 0  1  2  3  4  5  6  7  8  9 
________________________________________    
champion_1| 0  1  0  0  1  1  0  0  0  1
champion_1| 0  1  1  0  1  1  0  1  0  1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;There are a variety of models that might suit this task.  You might use &lt;a href="https://en.wikipedia.org/wiki/Decision_tree_learning" rel="nofollow"&gt;decision trees&lt;/a&gt; for interpretability.  A simple &lt;a href="https://en.wikipedia.org/wiki/Artificial_neural_network" rel="nofollow"&gt;neural net&lt;/a&gt; or &lt;a href="https://en.wikipedia.org/wiki/Support_vector_machine" rel="nofollow"&gt;SVM&lt;/a&gt; would likely also do a good job.  These should all be found in most basic ML packages.&lt;/p&gt;

&lt;p&gt;2) The win rates of various items are directly computable.  Simply count the number of times a champion used the items in question and won and divide by the total number of times a champion used that item combination.  You can do this for any given group size (1 to 6)&lt;/p&gt;
', NULL, NULL, '''/a'':196C,210C,220C ''/code'':19C,46C,58C,64C,103C,121C,125C,170C ''/p'':11C,65C,131C,238C,291C ''/pre'':20C,171C ''/wiki/artificial_neural_network'':205C ''/wiki/decision_tree_learning'':191C ''/wiki/support_vector_machine'':216C ''0'':136C,148C,150C,151C,154C,155C,156C,160C,163C,166C,168C ''1'':2C,99C,111C,137C,147C,149C,152C,153C,157C,159C,161C,162C,164C,165C,167C,169C,288C ''2'':120C,130C,138C,240C ''3'':139C ''4'':100C,140C ''5'':101C,141C ''6'':142C,290C ''7'':124C,143C ''8'':144C ''9'':102C,145C ''also'':116C,223C ''basic'':235C ''binari'':76C ''bought'':16C ''build'':7C,40C ''champion'':95C,146C,158C,257C,274C ''code'':13C,43C,55C,61C,97C,118C,123C,133C ''combin'':33C,278C ''comput'':249C ''consid'':73C ''count'':251C ''creat'':74C ''decis'':194C ''differ'':50C ''direct'':248C ''divid'':266C ''en.wikipedia.org'':190C,204C,215C ''en.wikipedia.org/wiki/artificial_neural_network'':203C ''en.wikipedia.org/wiki/decision_tree_learning'':189C ''en.wikipedia.org/wiki/support_vector_machine'':214C ''exampl'':42C,83C ''format'':70C ''found'':232C ''game'':91C,113C ''given'':285C ''good'':226C ''group'':286C ''href'':188C,202C,213C ''id'':135C ''input'':14C,36C,69C ''interpret'':198C ''item'':15C,44C,56C,62C,80C,89C,98C,119C,134C,246C,260C,277C ''job'':227C ''learn'':28C ''like'':109C,128C,222C ''linear'':32C ''list'':81C ''look'':108C,127C ''may'':47C,72C ''might'':180C,185C ''ml'':236C ''model'':9C,178C ''net'':209C ''neural'':208C ''nofollow'':193C,207C,218C ''non'':31C ''non-linear'':30C ''number'':253C,270C ''order'':106C ''output'':17C ''p'':1C,21C,66C,172C,239C ''packag'':237C ''pair'':53C ''pre'':12C,132C ''probabl'':25C ''purchas'':96C,117C ''purpos'':51C ''question'':262C ''rate'':243C ''rel'':192C,206C,217C ''repres'':38C ''row'':110C,129C ''simpl'':200C ''simpli'':250C ''size'':287C ''suit'':181C ''svm'':219C ''task'':183C ''ten'':88C ''time'':255C,272C ''total'':269C ''tree'':195C ''use'':186C,258C,275C ''varieti'':176C ''various'':245C ''vector'':77C ''want'':5C,26C ''win'':242C ''win/loss'':18C ''won'':264C ''would'':107C,126C,221C ''x'':45C ''y'':57C ''z'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6985, 2, '2015-09-02 13:02:04.277', 2, NULL, NULL, '&lt;p&gt;If you want to stay in Python, I would suggest going for xgboost instead of GradientBoostingClassifier - among other advantages, it supports map / ndcg as metrics. If you are considering R, gbm supports both map and ndcg.&lt;/p&gt;
', NULL, NULL, '''/p'':38C ''advantag'':20C ''among'':18C ''consid'':30C ''gbm'':32C ''go'':12C ''gradientboostingclassifi'':17C ''instead'':15C ''map'':23C,35C ''metric'':26C ''ndcg'':24C,37C ''p'':1C ''python'':8C ''r'':31C ''stay'':6C ''suggest'':11C ''support'':22C,33C ''want'':4C ''would'':10C ''xgboost'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9597, 2, '2016-01-03 23:54:03.493', 1, NULL, NULL, '&lt;p&gt;For text data, &lt;strong&gt;linear SVMs&lt;/strong&gt; are still state of the art.&lt;/p&gt;

&lt;p&gt;For named entity recognition, look up some NER toolkits.&lt;/p&gt;
', NULL, NULL, '''/p'':15C,26C ''/strong'':8C ''art'':14C ''data'':4C ''entiti'':19C ''linear'':6C ''look'':21C ''name'':18C ''ner'':24C ''p'':1C,16C ''recognit'':20C ''state'':11C ''still'':10C ''strong'':5C ''svms'':7C ''text'':3C ''toolkit'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6938, 2, '2015-08-29 08:55:58.29', 1, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Lets break this down...&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;You have 3 numerical attributes.  Great... &lt;a href="http://www.dataminingblog.com/standardization-vs-normalization/" rel="nofollow"&gt;standardize them&lt;/a&gt; by subtracting their means and dividing by their standard deviation.  You always need to standardize when you cluster in multiple dimensions or your clustering won''t make much sense i.e. the distance vector only makes sense if it is agnostic to how it is oriented.&lt;/p&gt;

&lt;p&gt;You have 2 categorical attributes.  One, however, is time, which doesn''t seem very categorical.  Figure out how to turn time into a numerical value.  I suggest using a &lt;a href="https://en.wikipedia.org/wiki/Unix_time" rel="nofollow"&gt;unix time stamp&lt;/a&gt;.  Don''t just blindly call it a categorical feature.  Your csv reader didn''t know how to treat the colon the slash, so called it a factor, but you can convert it &lt;a href="http://stackoverflow.com/questions/23348992/convert-factor-to-date-time-in-r"&gt;pretty easily and quickly&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Now I would suggest first cluster the 3 numerical factors on their own.  Then I would do some &lt;a href="https://en.wikipedia.org/wiki/Feature_extraction" rel="nofollow"&gt;feature extraction&lt;/a&gt; from the time data.  You should be able to extract day of week, week of month, day of month, etc.  These could all be useful in seeing some sort of signal in your data.  I would also suggest plotting your numerical data as a function of these extracted features and this will give you insight.&lt;/p&gt;

&lt;p&gt;Also, think about the value that customer ID might or might not add to the problem.  You might be able to remove the letters and it could tell you when they first shopped at the store, or it could just contain useless information that will cloud your clustering.&lt;/p&gt;

&lt;p&gt;Finally, think about why you are clustering your data and what insights you want to recover.  This will guide you in how to proceed.  Do you want to separate predominantly weekend shoppers from predominantly weekday shoppers?  Do you want to broadly classify shoppers as one of ten different types and try to ascertain the behavior of each of those types?&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/a'':25C,112C,154C,184C ''/p'':8C,71C,155C,240C,290C,352C,357C ''/questions/23348992/convert-factor-to-date-time-in-r'':149C ''/standardization-vs-normalization/'':20C ''/strong'':7C ''/wiki/feature_extraction'':179C ''/wiki/unix_time'':106C ''2'':75C ''3'':12C,164C ''abl'':192C,261C ''add'':254C ''agnost'':65C ''also'':221C,242C ''alway'':37C ''ascertain'':344C ''attribut'':14C,77C ''behavior'':346C ''blind'':116C ''break'':4C ''broad'':332C ''call'':117C,136C ''categor'':76C,87C,120C ''classifi'':333C ''cloud'':287C ''cluster'':43C,49C,162C,289C,298C ''colon'':132C ''contain'':282C ''convert'':143C ''could'':206C,268C,280C ''csv'':123C ''custom'':248C ''data'':188C,218C,226C,300C ''day'':195C,201C ''deviat'':35C ''didn'':125C ''differ'':339C ''dimens'':46C ''distanc'':57C ''divid'':31C ''doesn'':83C ''easili'':151C ''en.wikipedia.org'':105C,178C ''en.wikipedia.org/wiki/feature_extraction'':177C ''en.wikipedia.org/wiki/unix_time'':104C ''etc'':204C ''extract'':183C,194C,232C ''factor'':139C,166C ''featur'':121C,182C,233C ''figur'':88C ''final'':292C ''first'':161C,273C ''function'':229C ''give'':237C ''great'':15C ''guid'':310C ''help'':356C ''hope'':354C ''howev'':79C ''href'':17C,103C,146C,176C ''i.e'':55C ''id'':249C ''inform'':284C ''insight'':239C,303C ''know'':127C ''let'':3C ''letter'':265C ''make'':52C,60C ''mean'':29C ''might'':250C,252C,259C ''month'':200C,203C ''much'':53C ''multipl'':45C ''need'':38C ''nofollow'':22C,108C,181C ''numer'':13C,96C,165C,225C ''one'':78C,336C ''orient'':70C ''p'':1C,9C,72C,156C,241C,291C,353C ''plot'':223C ''predomin'':321C,325C ''pretti'':150C ''problem'':257C ''proceed'':315C ''quick'':153C ''reader'':124C ''recov'':307C ''rel'':21C,107C,180C ''remov'':263C ''see'':211C ''seem'':85C ''sens'':54C,61C ''separ'':320C ''shop'':274C ''shopper'':323C,327C,334C ''signal'':215C ''slash'':134C ''sort'':213C ''stackoverflow.com'':148C ''stackoverflow.com/questions/23348992/convert-factor-to-date-time-in-r'':147C ''stamp'':111C ''standard'':23C,34C,40C ''store'':277C ''strong'':2C ''subtract'':27C ''suggest'':99C,160C,222C ''tell'':269C ''ten'':338C ''think'':243C,293C ''time'':81C,93C,110C,187C ''treat'':130C ''tri'':342C ''turn'':92C ''type'':340C,351C ''unix'':109C ''use'':100C,209C ''useless'':283C ''valu'':97C,246C ''vector'':58C ''want'':305C,318C,330C ''week'':197C,198C ''weekday'':326C ''weekend'':322C ''won'':50C ''would'':159C,172C,220C ''www.dataminingblog.com'':19C ''www.dataminingblog.com/standardization-vs-normalization/'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6939, 1, '2015-08-29 10:27:02.597', 2, 208, 'Ratio of positive to negative sample in data set for best classification', '&lt;p&gt;Suppose I have 100 positive samples. How many negative samples do I need to have in order to make the classifier work the best. In many papers, I have noticed that they take 4 times or 5 times the number of positive data sample to get the negative data sample. Will such a data set be useful?&lt;/p&gt;
', 'classification  dataset  unbalanced-classes', NULL, '''/p'':76C ''100'':22C ''4'':52C ''5'':55C ''best'':11A,42C ''class'':17B ''classif'':12A,13B ''classifi'':39C ''data'':8A,61C,67C,72C ''dataset'':14B ''get'':64C ''make'':37C ''mani'':26C,44C ''need'':31C ''negat'':5A,27C,66C ''notic'':48C ''number'':58C ''order'':35C ''p'':18C ''paper'':45C ''posit'':3A,23C,60C ''ratio'':1A ''sampl'':6A,24C,28C,62C,68C ''set'':9A,73C ''suppos'':19C ''take'':51C ''time'':53C,56C ''unbalanc'':16B ''unbalanced-class'':15B ''use'':75C ''work'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6940, 2, '2015-08-29 12:08:45.923', 0, NULL, NULL, '&lt;p&gt;It is recommended to have around equal number of instances in each class. If not then you should normalize the data. 4 or 5 times more positive class size could produce highly biased classifier. However if your negative class is not well defined then there are ways to train your classes with only one class as mentioned in the paper here - &lt;a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2248178/" rel="nofollow"&gt;http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2248178/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/pmc/articles/pmc2248178/'':67C ''/pmc/articles/pmc2248178/&lt;/a&gt;&lt;/p&gt;'':72C ''4'':23C ''5'':25C ''around'':7C ''bias'':34C ''class'':14C,29C,40C,52C,56C ''classifi'':35C ''could'':31C ''data'':22C ''defin'':44C ''equal'':8C ''high'':33C ''howev'':36C ''href'':64C ''instanc'':11C ''mention'':58C ''negat'':39C ''nofollow'':69C ''normal'':20C ''number'':9C ''one'':55C ''p'':1C ''paper'':61C ''posit'':28C ''produc'':32C ''recommend'':4C ''rel'':68C ''size'':30C ''time'':26C ''train'':50C ''way'':48C ''well'':43C ''www.ncbi.nlm.nih.gov'':66C,71C ''www.ncbi.nlm.nih.gov/pmc/articles/pmc2248178/'':65C ''www.ncbi.nlm.nih.gov/pmc/articles/pmc2248178/&lt;/a&gt;&lt;/p&gt;'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6941, 2, '2015-08-29 12:29:50.363', 1, NULL, NULL, '&lt;p&gt;You could have a try on this package, 
&lt;a href="http://www.pymvpa.org/examples/som.html" rel="nofollow"&gt;http://www.pymvpa.org/examples/som.html&lt;/a&gt;. 
There is a working example on this page. &lt;/p&gt;

&lt;p&gt;If what you are interested is the Manifold learning, 
you could also apply many package from sklearn, &lt;a href="http://scikit-learn.org/stable/modules/manifold.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/manifold.html&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/examples/som.html'':14C ''/examples/som.html&lt;/a&gt;.'':19C ''/p'':28C ''/stable/modules/manifold.html'':51C ''/stable/modules/manifold.html&lt;/a&gt;.&lt;/p&gt;'':56C ''also'':41C ''appli'':42C ''could'':3C,40C ''exampl'':24C ''href'':11C,48C ''interest'':34C ''learn'':38C ''mani'':43C ''manifold'':37C ''nofollow'':16C,53C ''p'':1C,29C ''packag'':9C,44C ''page'':27C ''rel'':15C,52C ''scikit-learn.org'':50C,55C ''scikit-learn.org/stable/modules/manifold.html'':49C ''scikit-learn.org/stable/modules/manifold.html&lt;/a&gt;.&lt;/p&gt;'':54C ''sklearn'':46C ''tri'':6C ''work'':23C ''www.pymvpa.org'':13C,18C ''www.pymvpa.org/examples/som.html'':12C ''www.pymvpa.org/examples/som.html&lt;/a&gt;.'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8222, 1, '2015-09-28 12:57:15.443', 0, 27, 'Herarchical class classifier with default parent class labelling', '&lt;p&gt;I have a hierarchy of classes for which I need to train a classifier which will assign the lowest level class in the hierarchy  and default to an upper level class , is this possible to do with scikit out of the box or should I collapse the class labels as the entire path in the hierarchy and use it as the label?&lt;/p&gt;
', 'bigdata  multiclass-classification', NULL, '''/p'':76C ''assign'':30C ''bigdata'':9B ''box'':55C ''class'':2A,7A,19C,34C,44C,61C ''classif'':12B ''classifi'':3A,27C ''collaps'':59C ''default'':5A,39C ''entir'':65C ''herarch'':1A ''hierarchi'':17C,37C,69C ''label'':8A,62C,75C ''level'':33C,43C ''lowest'':32C ''multiclass'':11B ''multiclass-classif'':10B ''need'':23C ''p'':13C ''parent'':6A ''path'':66C ''possibl'':47C ''scikit'':51C ''train'':25C ''upper'':42C ''use'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6955, 2, '2015-08-31 15:08:04.943', 0, NULL, NULL, '&lt;p&gt;As a general principle Adaboost builds and ensemble by sequentially adding members which have been trained on those instances of data which are proving most difficult to correctly classify/predict. &lt;/p&gt;

&lt;p&gt;Each new classifier/predictor is given a training set where the difficult examples are increasingly represented, this is achieved either through weighting or resampling.  &lt;/p&gt;

&lt;p&gt;There should be very little difference in the approach for classification or regression.&lt;/p&gt;
', NULL, NULL, '''/p'':31C,56C,71C ''achiev'':50C ''ad'':12C ''adaboost'':6C ''approach'':66C ''build'':7C ''classif'':68C ''classifier/predictor'':35C ''classify/predict'':30C ''correct'':29C ''data'':22C ''differ'':63C ''difficult'':27C,43C ''either'':51C ''ensembl'':9C ''exampl'':44C ''general'':4C ''given'':37C ''increas'':46C ''instanc'':20C ''littl'':62C ''member'':13C ''new'':34C ''p'':1C,32C,57C ''principl'':5C ''prove'':25C ''regress'':70C ''repres'':47C ''resampl'':55C ''sequenti'':11C ''set'':40C ''train'':17C,39C ''weight'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6964, 1, '2015-08-31 20:58:15.07', 3, 985, 'LSTM or other RNN package for R', '&lt;p&gt;I saw some impressive result from LSTM models producing Shakespeare like texts. I was wondering if an LSTM package exists for R. I googled for it but only found packages for Python and Julia. (maybe there are some performance issue which explains why these programs are more preferred over R) Do You know about an LSTM (or at least an RNN) package for R? If exists are there any tutorials for using them?&lt;/p&gt;
', 'r  neuralnetwork', NULL, '''/p'':84C ''exist'':30C,76C ''explain'':52C ''found'':39C ''googl'':34C ''impress'':14C ''issu'':50C ''julia'':44C ''know'':63C ''least'':69C ''like'':21C ''lstm'':1A,17C,28C,66C ''mayb'':45C ''model'':18C ''neuralnetwork'':9B ''p'':10C ''packag'':5A,29C,40C,72C ''perform'':49C ''prefer'':58C ''produc'':19C ''program'':55C ''python'':42C ''r'':7A,8B,32C,60C,74C ''result'':15C ''rnn'':4A,71C ''saw'':12C ''shakespear'':20C ''text'':22C ''tutori'':80C ''use'':82C ''wonder'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6944, 1, '2015-08-29 21:59:07.543', 1, 122, 'remove seasonality from weekly time series data', '&lt;p&gt;I need to decompose a series to remove seasonality. The series has 2 columns date and volume.&lt;/p&gt;

&lt;p&gt;This is what my time series object looks like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;salestsDec &amp;lt;- ts(salests, frequency=52, start=c(2010, 1), end=c(2014,12))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I ran the &lt;code&gt;decompose()&lt;/code&gt; function on a ''ts'' object.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;salests = sales[, c(1,6)]
View(salests)
salestsDec &amp;lt;- ts(salests, frequency=52, start=c(2010, 1), end=c(2014,12))
salestsDec &amp;lt;- decompose(salestsDec, type=c("additive"))
plot(salestsDec)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Upon, running the &lt;code&gt;decompose()&lt;/code&gt; function, I get a list of 6 components, observed, trend, seasonal, random for both date and volume. I should only be seeing, observed, trend, seasonal and random component for Volume in my plot.&lt;/p&gt;

&lt;p&gt;I''ve attached an image of what the plot looks like.&lt;/p&gt;

&lt;p&gt;Moreover, when I try to remove seasonal component from the series, I am getting an error. It appears that it''s the same underlying issue.&lt;/p&gt;

&lt;p&gt;Error:&lt;/p&gt;

&lt;p&gt;Error in salests - salestsDec$seasonal : non-numeric argument to binary operator In addition: Warning message: Incompatible methods ("Ops.data.frame", "Ops.ts") for "-"&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/pxzjf.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/pxzjf.png" alt=" Time series components plot after running decomposed() "&gt;&lt;/a&gt;&lt;/p&gt;
', 'r  time-series', NULL, '''/a'':235C ''/code'':58C,66C,105C,113C ''/p'':30C,41C,72C,147C,160C,187C,190C,213C,236C ''/pre'':59C,106C ''/pxzjf.png'':219C,226C ''1'':53C,78C,91C ''12'':57C,95C ''2'':25C ''2010'':52C,90C ''2014'':56C,94C ''52'':49C,87C ''6'':79C,120C ''addit'':102C,205C ''alt'':227C ''appear'':179C ''argument'':200C ''attach'':151C ''binari'':202C ''c'':51C,55C,77C,89C,93C,101C ''code'':43C,64C,74C,111C ''column'':26C ''compon'':121C,141C,169C,230C ''data'':7A ''date'':27C,128C ''decompos'':16C,65C,98C,112C,234C ''end'':54C,92C ''error'':177C,189C,192C ''frequenc'':48C,86C ''function'':67C,114C ''get'':116C,175C ''href'':216C ''i.stack.imgur.com'':218C,225C ''i.stack.imgur.com/pxzjf.png'':217C,224C ''imag'':153C ''img'':222C ''incompat'':208C ''issu'':186C ''like'':40C,159C ''list'':118C ''look'':39C,158C ''lt'':45C,83C,97C ''messag'':207C ''method'':209C ''moreov'':162C ''need'':14C ''nofollow'':221C ''non'':198C ''non-numer'':197C ''numer'':199C ''object'':38C,71C ''observ'':122C,136C ''oper'':203C ''ops.data.frame'':210C ''ops.ts'':211C ''p'':12C,31C,60C,107C,148C,161C,188C,191C,214C ''plot'':103C,146C,157C,231C ''pre'':42C,73C ''r'':8B ''ran'':62C ''random'':125C,140C ''rel'':220C ''remov'':1A,20C,167C ''run'':109C,233C ''sale'':76C ''salest'':47C,75C,81C,85C,194C ''salestsdec'':44C,82C,96C,99C,104C,195C ''season'':2A,21C,124C,138C,168C,196C ''see'':135C ''seri'':6A,11B,18C,23C,37C,172C,229C ''src'':223C ''start'':50C,88C ''time'':5A,10B,36C,228C ''time-seri'':9B ''trend'':123C,137C ''tri'':165C ''ts'':46C,70C,84C ''type'':100C ''under'':185C ''upon'':108C ''ve'':150C ''view'':80C ''volum'':29C,130C,143C ''warn'':206C ''week'':4A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6945, 2, '2015-08-30 02:27:31.543', 3, NULL, NULL, '&lt;p&gt;"Data Science" is not very well defined term and people mean different things by it. For somebody it might mean working with huge data sets on distributed systems, to somebody it is basically data analysis, to somebody it is writing predictive models using sparse matrices (that''s me). Therefore, you should approach what interests you. You don''t have to know everything.&lt;/p&gt;

&lt;p&gt;Generally, Data Science is combination of two fields: Computer Science and Statistics. Fundamental understanding of both is crucial. &lt;/p&gt;
', NULL, NULL, '''/p'':64C,84C ''analysi'':36C ''approach'':53C ''basic'':34C ''combin'':70C ''comput'':74C ''crucial'':83C ''data'':2C,25C,35C,67C ''defin'':8C ''differ'':13C ''distribut'':28C ''everyth'':63C ''field'':73C ''fundament'':78C ''general'':66C ''huge'':24C ''interest'':55C ''know'':62C ''matric'':46C ''mean'':12C,21C ''might'':20C ''model'':43C ''p'':1C,65C ''peopl'':11C ''predict'':42C ''scienc'':3C,68C,75C ''set'':26C ''somebodi'':18C,31C,38C ''spars'':45C ''statist'':77C ''system'':29C ''term'':9C ''therefor'':50C ''thing'':14C ''two'':72C ''understand'':79C ''use'':44C ''well'':7C ''work'':22C ''write'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6948, 2, '2015-08-30 11:24:44.31', 0, NULL, NULL, '&lt;p&gt;If you are just looking to rank documents, @Sharon answer is what you need (+1).&lt;/p&gt;

&lt;p&gt;After you have ranked them, indeed the next logical step would be to cluster the documents, to find out which of them are similar. In particular, if your list of words is heterogeneous, the rank by itself might not give you very clear information. This is quite a good &lt;code&gt;python notebook&lt;/code&gt;, everything is kept simple but at the same time it explains all the steps you should follow.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://nbviewer.ipython.org/github/brandomr/document_cluster/blob/master/cluster_analysis_web.ipynb" rel="nofollow"&gt;Document clustering in Python&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you just wanted to detect the topics of your document (i.e. without your list of words), this is a good &lt;a href="http://stats.stackexchange.com/questions/155880/text-mining-how-to-cluster-texts-e-g-news-articles-with-artificial-intellige"&gt;SO answer&lt;/a&gt;, which includes links to resources.&lt;/p&gt;

&lt;p&gt;As for your question about Java v. Python, I cannot tell you for sure that Java would be a disadvantage. Even though all the links I provide are in python, I''m certain there''s quite a few &lt;code&gt;nl&lt;/code&gt; resources in Java. However I don''t know them well and I cannot compare.&lt;/p&gt;
', NULL, NULL, '''+1'':16C ''/a'':102C,133C ''/code'':71C,181C ''/github/brandomr/document_cluster/blob/master/cluster_analysis_web.ipynb'':95C ''/p'':17C,89C,103C,139C,196C ''/questions/155880/text-mining-how-to-cluster-texts-e-g-news-articles-with-artificial-intellige'':130C ''answer'':11C,132C ''cannot'':150C,194C ''certain'':173C ''clear'':61C ''cluster'':32C,99C ''code'':68C,179C ''compar'':195C ''detect'':110C ''disadvantag'':160C ''document'':9C,34C,98C,115C ''even'':161C ''everyth'':72C ''explain'':82C ''find'':36C ''follow'':88C ''give'':58C ''good'':67C,125C ''heterogen'':51C ''howev'':185C ''href'':92C,127C ''i.e'':116C ''includ'':135C ''inde'':24C ''inform'':62C ''java'':146C,156C,184C ''kept'':74C ''know'':189C ''link'':136C,165C ''list'':47C,119C ''logic'':27C ''look'':6C ''m'':172C ''might'':56C ''nbviewer.ipython.org'':94C ''nbviewer.ipython.org/github/brandomr/document_cluster/blob/master/cluster_analysis_web.ipynb'':93C ''need'':15C ''next'':26C ''nl'':180C ''nofollow'':97C ''notebook'':70C ''p'':1C,18C,90C,104C,140C ''particular'':44C ''provid'':167C ''python'':69C,101C,148C,170C ''question'':144C ''quit'':65C,176C ''rank'':8C,22C,53C ''rel'':96C ''resourc'':138C,182C ''sharon'':10C ''similar'':42C ''simpl'':75C ''stats.stackexchange.com'':129C ''stats.stackexchange.com/questions/155880/text-mining-how-to-cluster-texts-e-g-news-articles-with-artificial-intellige'':128C ''step'':28C,85C ''sure'':154C ''tell'':151C ''though'':162C ''time'':80C ''topic'':112C ''v'':147C ''want'':108C ''well'':191C ''without'':117C ''word'':49C,121C ''would'':29C,157C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6949, 1, '2015-08-31 05:45:00.513', 1, 104, 'How will ADA Boost be used for solving regression problems?', '&lt;p&gt;I have an idea of how ADABOOST will be used for classification but I want to get the idea of how to re-weight and thus use ADABOOST in case of regression problems.&lt;/p&gt;
', 'machine-learning  data-mining  bigdata  regression', NULL, '''/p'':54C ''ada'':3A ''adaboost'':26C,48C ''bigdata'':17B ''boost'':4A ''case'':50C ''classif'':31C ''data'':15B ''data-min'':14B ''get'':36C ''idea'':23C,38C ''learn'':13B ''machin'':12B ''machine-learn'':11B ''mine'':16B ''p'':19C ''problem'':10A,53C ''re'':43C ''re-weight'':42C ''regress'':9A,18B,52C ''solv'':8A ''thus'':46C ''use'':6A,29C,47C ''want'':34C ''weight'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6950, 1, '2015-08-31 10:13:43.08', 0, 41, 'How does Performance function classify predictions as positive or negative? Package:ROCR', '&lt;p&gt;I''m performing a logistic regression on my training data. I used the &lt;em&gt;glm&lt;/em&gt; function to get the model &lt;em&gt;m&lt;/em&gt;. Now using the below codes from this &lt;a href="https://cran.r-project.org/doc/contrib/Sharma-CreditScoring.pdf" rel="nofollow"&gt;link&lt;/a&gt;, I calculated AUC&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;$test\$score&amp;lt;-predict(m,type = ''response'',test)$&lt;br&gt;
  $pred &amp;lt;- prediction(test\$score,test\$good_bad)$&lt;br&gt;
  $perf &amp;lt;- performance(pred,"tpr","fpr")$  &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;where &lt;em&gt;score&lt;/em&gt; is the dependent variable (&lt;em&gt;0&lt;/em&gt; or &lt;em&gt;1&lt;/em&gt;).&lt;br&gt;
To score the &lt;em&gt;tpr&lt;/em&gt; (True positive rate) and &lt;em&gt;fpr&lt;/em&gt; (False positive rate), you have to classify the predicted probabilities into 1 or 0.&lt;br&gt;
What is the cutoff used for that? how can we change it?&lt;/p&gt;

&lt;p&gt;Could not find anything useful in this &lt;a href="http://www.hpl.hp.com/techreports/2003/HPL-2003-4.pdf" rel="nofollow"&gt;main documentation&lt;/a&gt; as well.&lt;/p&gt;
', 'r  classification  logistic-regression', NULL, '''/a'':58C,166C ''/blockquote'':90C ''/doc/contrib/sharma-creditscoring.pdf'':54C ''/em'':34C,42C,95C,102C,106C,113C,120C ''/p'':62C,89C,148C,169C ''/techreports/2003/hpl-2003-4.pdf'':161C ''0'':101C,134C ''1'':105C,132C ''anyth'':153C ''auc'':61C ''bad'':81C ''blockquot'':63C ''br'':73C,82C,107C,135C ''calcul'':60C ''chang'':146C ''classif'':14B ''classifi'':5A,127C ''code'':47C ''could'':150C ''cran.r-project.org'':53C ''cran.r-project.org/doc/contrib/sharma-creditscoring.pdf'':52C ''cutoff'':139C ''data'':28C ''depend'':98C ''document'':165C ''em'':32C,40C,93C,100C,104C,111C,118C ''fals'':121C ''find'':152C ''fpr'':88C,119C ''function'':4A,35C ''get'':37C ''glm'':33C ''good'':80C ''href'':51C,158C ''link'':57C ''logist'':16B,23C ''logistic-regress'':15B ''lt'':67C,75C,84C ''m'':20C,41C,69C ''main'':164C ''model'':39C ''negat'':10A ''nofollow'':56C,163C ''p'':18C,64C,91C,149C ''packag'':11A ''perf'':83C ''perform'':3A,21C,85C ''posit'':8A,115C,122C ''pred'':74C,86C ''predict'':6A,68C,76C,129C ''probabl'':130C ''r'':13B ''rate'':116C,123C ''regress'':17B,24C ''rel'':55C,162C ''respons'':71C ''rocr'':12A ''score'':66C,78C,94C,109C ''test'':65C,72C,77C,79C ''tpr'':87C,112C ''train'':27C ''true'':114C ''type'':70C ''use'':30C,44C,140C,154C ''variabl'':99C ''well'':168C ''www.hpl.hp.com'':160C ''www.hpl.hp.com/techreports/2003/hpl-2003-4.pdf'':159C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6951, 2, '2015-08-31 10:33:28.057', 0, NULL, NULL, '&lt;p&gt;I cant access an R console at the moment to check, but I''m quite certain the cutoff is 0.5: if your glm model does prediction, it first produces real values and then applies the link function on top. To the best of my knowledge, you can''t change it inside the glm function, so your best bet is probably to check ROC, find what the optimal threshold is and use that as cutoff. &lt;/p&gt;
', NULL, NULL, '''/p'':76C ''0.5'':21C ''access'':4C ''appli'':35C ''best'':43C,58C ''bet'':59C ''cant'':3C ''certain'':17C ''chang'':50C ''check'':12C,63C ''consol'':7C ''cutoff'':19C,75C ''find'':65C ''first'':29C ''function'':38C,55C ''glm'':24C,54C ''insid'':52C ''knowledg'':46C ''link'':37C ''m'':15C ''model'':25C ''moment'':10C ''optim'':68C ''p'':1C ''predict'':27C ''probabl'':61C ''produc'':30C ''quit'':16C ''r'':6C ''real'':31C ''roc'':64C ''threshold'':69C ''top'':40C ''use'':72C ''valu'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8921, 2, '2015-11-19 12:32:59.207', 4, NULL, NULL, '&lt;p&gt;This is similar to the fundamental information theory problem that Shannon explored.  In that domain, it is framed this way: given two rvs, X and Y, what information does X convey about Y?&lt;/p&gt;

&lt;p&gt;An example would be that I create sequence of numbers/bit/letters from a known PDF (X) and you receive a distorted version of those values for which you also know the PDF (Y).  The mutual information is the number of bits that Y communicates about X which can be thought of as a type of correlation.&lt;/p&gt;

&lt;p&gt;In the case of time varying PDFs (ie stochastic processes), information theory would just treat the ensemble of rvs as one joint rv and then calculate the mutual information of the joint PDFs.  If the PDFs are iid, then significant simplifications are possible.  Joint Gaussian PDFs also make things easier.&lt;/p&gt;

&lt;p&gt;Another information theory concept that might be useful is the entropy rate of a process.  ER is a quantification of the amount of information contained in a process.  Depending on your problem, you might be able to compute the ER for each process and then use those values as realizations of a feature that can be grouped with a clustering algorithm.&lt;/p&gt;
', NULL, NULL, '''/p'':35C,92C,144C,207C ''abl'':181C ''algorithm'':206C ''also'':64C,140C ''amount'':167C ''anoth'':146C ''bit'':76C ''calcul'':119C ''case'':96C ''cluster'':205C ''communic'':79C ''comput'':183C ''concept'':149C ''contain'':170C ''convey'':32C ''correl'':91C ''creat'':43C ''depend'':174C ''distort'':56C ''domain'':16C ''easier'':143C ''ensembl'':110C ''entropi'':156C ''er'':161C,185C ''exampl'':38C ''explor'':13C ''featur'':198C ''frame'':19C ''fundament'':7C ''gaussian'':138C ''given'':22C ''group'':202C ''ie'':101C ''iid'':131C ''inform'':8C,29C,71C,104C,122C,147C,169C ''joint'':115C,125C,137C ''know'':65C ''known'':49C ''make'':141C ''might'':151C,179C ''mutual'':70C,121C ''number'':74C ''numbers/bit/letters'':46C ''one'':114C ''p'':1C,36C,93C,145C ''pdf'':50C,67C ''pdfs'':100C,126C,129C,139C ''possibl'':136C ''problem'':10C,177C ''process'':103C,160C,173C,188C ''quantif'':164C ''rate'':157C ''realiz'':195C ''receiv'':54C ''rv'':116C ''rvs'':24C,112C ''sequenc'':44C ''shannon'':12C ''signific'':133C ''similar'':4C ''simplif'':134C ''stochast'':102C ''theori'':9C,105C,148C ''thing'':142C ''thought'':85C ''time'':98C ''treat'':108C ''two'':23C ''type'':89C ''use'':153C,191C ''valu'':60C,193C ''vari'':99C ''version'':57C ''way'':21C ''would'':39C,106C ''x'':25C,31C,51C,81C ''y'':27C,34C,68C,78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6952, 2, '2015-08-31 10:39:00.73', 1, NULL, NULL, '&lt;p&gt;I can''t replicate (without having the data), but from what I see, you are trying to apply the &lt;em&gt;decompose&lt;/em&gt; function to the entire object - it''s probably expecting a single vector, but receives a data-frame-like object and attempts to apply the decomposition to both columns. either use a different time series package (like zoo) which does not store timestamps as a column, or apply decompose explicitly to salestsDec[,2].&lt;/p&gt;
', NULL, NULL, '''/em'':23C ''/p'':77C ''2'':76C ''appli'':19C,47C,71C ''attempt'':45C ''column'':52C,69C ''data'':9C,40C ''data-frame-lik'':39C ''decompos'':22C,72C ''decomposit'':49C ''differ'':56C ''either'':53C ''em'':21C ''entir'':27C ''expect'':32C ''explicit'':73C ''frame'':41C ''function'':24C ''like'':42C,60C ''object'':28C,43C ''p'':1C ''packag'':59C ''probabl'':31C ''receiv'':37C ''replic'':5C ''salestsdec'':75C ''see'':14C ''seri'':58C ''singl'':34C ''store'':65C ''time'':57C ''timestamp'':66C ''tri'':17C ''use'':54C ''vector'':35C ''without'':6C ''zoo'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6953, 2, '2015-08-31 10:42:41.17', 0, NULL, NULL, '&lt;p&gt;AdaBoost is a meta algorithm, so the underlying principle is the same: i would suggest going through &lt;/p&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/modules/ensemble.html#adaboost" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/ensemble.html#adaboost&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;for references.&lt;/p&gt;
', NULL, NULL, '''/p'':19C,34C ''/stable/modules/ensemble.html#adaboost'':25C ''/stable/modules/ensemble.html#adaboost&lt;/a&gt;&lt;/p&gt;'':30C ''adaboost'':2C ''algorithm'':6C ''go'':17C ''href'':22C ''meta'':5C ''nofollow'':27C ''p'':1C,20C,31C ''principl'':10C ''refer'':33C ''rel'':26C ''scikit-learn.org'':24C,29C ''scikit-learn.org/stable/modules/ensemble.html#adaboost'':23C ''scikit-learn.org/stable/modules/ensemble.html#adaboost&lt;/a&gt;&lt;/p&gt;'':28C ''suggest'':16C ''under'':9C ''would'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6954, 1, '2015-08-31 13:02:02.49', 0, 211, 'R - Error in KNN - Test and training differ', '&lt;p&gt;I am trying to classify set of text files whether it belong to category A or category B using KNN algorithm&lt;/p&gt;

&lt;p&gt;Here is the error that i get&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; knn.pred &amp;lt;- knn(tdm.stack.nl, tdm.stack.nl_test, tdm.cand)  
&lt;/code&gt;&lt;/pre&gt;

&lt;blockquote&gt;
  &lt;p&gt;Error in knn(tdm.stack.nl, tdm.stack.nl_test, tdm.cand) :      dims of
  ''test'' and ''train'' differ&lt;/p&gt;
&lt;/blockquote&gt;

&lt;pre&gt;&lt;code&gt; &amp;gt; dim(tdm.stack.nl)  
    [1]  184 1599  
    &amp;gt; dim(tdm.stack.nl_test)  
    [1]   1 992  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As obvious the number of words used by training dataset is different from train. How can I avoid this?&lt;/p&gt;

&lt;p&gt;I want to mention that this error i get when i divide the training and test dataset manually to (70:30) ratio. However i dont get this error if i divide the training and test dataset using samlpling method.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; # create hold-out   
&amp;gt; set.seed(500)  
&amp;gt; train.idx &amp;lt;- sample(nrow(tdm.stack), ceiling(nrow(tdm.stack) * 0.7))   
&amp;gt; test.idx &amp;lt;- (1:nrow(tdm.stack))[-train.idx]   
&amp;gt; 
&amp;gt; # create model - knn clustering
&amp;gt; tdm.cand &amp;lt;- tdm.stack[, "targetcandidate"]
&amp;gt; tdm.stack.nl &amp;lt;- tdm.stack[, !colnames(tdm.stack) %in% "targetcandidate"]
&amp;gt; 
&amp;gt; # set up model
&amp;gt; knn.pred &amp;lt;- knn(tdm.stack.nl[train.idx,], tdm.stack.nl[test.idx,],    tdm.cand[train.idx])  
&amp;gt; dim(tdm.stack.nl[train.idx,])  
[1]  129 1599  
&amp;gt; dim(tdm.stack.nl[test.idx,])  
[1]   55 1599   
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However this method will not work for me as I want to classify a document in a real time using the model that was built before.&lt;/p&gt;

&lt;p&gt;Please help. Kindly let me know if you need any other info. 
Arun&lt;/p&gt;
', 'machine-learning  data-mining  r  classification', NULL, '''/blockquote'':77C ''/code'':59C,93C,232C ''/p'':39C,48C,76C,115C,156C,261C,276C ''/pre'':60C,94C,233C ''0.7'':176C ''1'':83C,90C,91C,180C,222C,229C ''129'':223C ''1599'':85C,224C,231C ''184'':84C ''30'':137C ''500'':166C ''55'':230C ''70'':136C ''992'':92C ''algorithm'':38C ''arun'':275C ''avoid'':113C ''b'':35C ''belong'':29C ''blockquot'':61C ''built'':259C ''categori'':31C,34C ''ceil'':173C ''classif'':16B ''classifi'':22C,247C ''cluster'':189C ''code'':50C,79C,158C ''colnam'':199C ''creat'':160C,186C ''data'':13B ''data-min'':12B ''dataset'':105C,133C,152C ''differ'':8A,75C,107C ''dim'':70C,81C,87C,219C,226C ''divid'':128C,147C ''document'':249C ''dont'':141C ''error'':2A,44C,63C,123C,144C ''file'':26C ''get'':47C,125C,142C ''gt'':51C,80C,86C,159C,164C,167C,177C,184C,185C,190C,195C,203C,204C,208C,218C,225C ''help'':264C ''hold'':162C ''hold-out'':161C ''howev'':139C,235C ''info'':274C ''kind'':265C ''knn'':4A,37C,54C,65C,188C,211C ''knn.pred'':52C,209C ''know'':268C ''learn'':11B ''let'':266C ''lt'':53C,169C,179C,192C,197C,210C ''machin'':10B ''machine-learn'':9B ''manual'':134C ''mention'':120C ''method'':155C,237C ''mine'':14B ''model'':187C,207C,256C ''need'':271C ''nrow'':171C,174C,181C ''number'':99C ''obvious'':97C ''p'':17C,40C,62C,95C,116C,234C,262C ''pleas'':263C ''pre'':49C,78C,157C ''r'':1A,15B ''ratio'':138C ''real'':252C ''samlpl'':154C ''sampl'':170C ''set'':23C,205C ''set.seed'':165C ''targetcandid'':194C,202C ''tdm.cand'':58C,69C,191C,216C ''tdm.stack'':172C,175C,182C,193C,198C,200C ''tdm.stack.nl'':55C,56C,66C,67C,82C,88C,196C,212C,214C,220C,227C ''test'':5A,57C,68C,72C,89C,132C,151C ''test.idx'':178C,215C,228C ''text'':25C ''time'':253C ''train'':7A,74C,104C,109C,130C,149C ''train.idx'':168C,183C,213C,217C,221C ''tri'':20C ''use'':36C,102C,153C,254C ''want'':118C,245C ''whether'':27C ''word'':101C ''work'':240C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6957, 1, '2015-08-31 18:10:21.677', 0, 15, 'Minimizing a functional', '&lt;p&gt;I have two bounded functions $f(t)$, $g(t)$ of some parameter $t$. For theoretical reasons I expect there exists another parameter $k$ such that $f(k)$ and $g(k)$ are independently periodic. I am interested in finding this parameter.&lt;/p&gt;

&lt;p&gt;Periodicity in terms of a parameter $l$ can be measured by computing (e.g.) the standard deviation $\chi[l] = \chi[f(l)] + \chi[g(l)] $ of peak-to-peak distances. Up to machine error $\chi[k]=0$ should be a global minimum of this functional.&lt;/p&gt;

&lt;p&gt;I am considering a neural-network based solution to this optimization problem. The input vector would be a linearly increasing array representing $k$. The network would thus represent the reparametrization $\phi(k)$. I would train the network using backpropagation based on the energy functional $\chi$.&lt;/p&gt;

&lt;p&gt;Since I have never written a neural network before this could be a substantial effort. I was just wondering if my idea makes any sense before I invest too much time.&lt;/p&gt;

&lt;p&gt;Thank you!&lt;/p&gt;
', 'neuralnetwork  optimization', NULL, '''/p'':47C,94C,142C,175C,179C ''0'':85C ''anoth'':27C ''array'':117C ''backpropag'':135C ''base'':103C,136C ''bound'':10C ''chi'':65C,67C,70C,83C,141C ''comput'':60C ''consid'':98C ''could'':154C ''deviat'':64C ''distanc'':78C ''e.g'':61C ''effort'':158C ''energi'':139C ''error'':82C ''exist'':26C ''expect'':24C ''f'':12C,32C,68C ''find'':44C ''function'':3A,11C,93C,140C ''g'':14C,35C,71C ''global'':89C ''idea'':165C ''increas'':116C ''independ'':38C ''input'':110C ''interest'':42C ''invest'':171C ''k'':29C,33C,36C,84C,119C,128C ''l'':55C,66C,69C,72C ''linear'':115C ''machin'':81C ''make'':166C ''measur'':58C ''minim'':1A ''minimum'':90C ''much'':173C ''network'':102C,121C,133C,151C ''neural'':101C,150C ''neural-network'':100C ''neuralnetwork'':4B ''never'':147C ''optim'':5B,107C ''p'':6C,48C,95C,143C,176C ''paramet'':18C,28C,46C,54C ''peak'':75C,77C ''peak-to-peak'':74C ''period'':39C,49C ''phi'':127C ''problem'':108C ''reason'':22C ''reparametr'':126C ''repres'':118C,124C ''sens'':168C ''sinc'':144C ''solut'':104C ''standard'':63C ''substanti'':157C ''term'':51C ''thank'':177C ''theoret'':21C ''thus'':123C ''time'':174C ''train'':131C ''two'':9C ''use'':134C ''vector'':111C ''wonder'':162C ''would'':112C,122C,130C ''written'':148C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6958, 2, '2015-08-31 18:26:55.673', 1, NULL, NULL, '&lt;p&gt;Here are link to some famous boost of regressor.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Adaboost.R2: Improving Regressors using Boosting Techniques, &lt;a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.314&amp;amp;rep=rep1&amp;amp;type=pdf" rel="nofollow"&gt;http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.314&amp;amp;rep=rep1&amp;amp;type=pdf&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Adaboost.RT: Experiments with AdaBoost.RT, an Improved Boosting
Scheme for Regression, 
&lt;a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.100.4560&amp;amp;rep=rep1&amp;amp;type=pdf" rel="nofollow"&gt;http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.100.4560&amp;amp;rep=rep1&amp;amp;type=pdf&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Scikit-Learn have many implementations:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostRegressor.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostRegressor.html&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html" rel="nofollow"&gt;http://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/p'':11C,59C ''/stable/auto_examples/ensemble/plot_adaboost_regression.html'':77C ''/stable/auto_examples/ensemble/plot_adaboost_regression.html&lt;/a&gt;&lt;/li&gt;'':82C ''/stable/modules/generated/sklearn.ensemble.adaboostregressor.html'':66C ''/stable/modules/generated/sklearn.ensemble.adaboostregressor.html&lt;/a&gt;&lt;/li&gt;'':71C ''/ul'':51C,83C ''/viewdoc/download?doi=10.1.1.100.4560&amp;amp;rep=rep1&amp;amp;type=pdf'':45C ''/viewdoc/download?doi=10.1.1.100.4560&amp;amp;rep=rep1&amp;amp;type=pdf&lt;/a&gt;&lt;/li&gt;'':50C ''/viewdoc/download?doi=10.1.1.31.314&amp;amp;rep=rep1&amp;amp;type=pdf'':24C ''/viewdoc/download?doi=10.1.1.31.314&amp;amp;rep=rep1&amp;amp;type=pdf&lt;/a&gt;&lt;/li&gt;'':29C ''adaboost.r2'':14C ''adaboost.rt'':31C,34C ''boost'':8C,18C,37C ''citeseerx.ist.psu.edu'':23C,28C,44C,49C ''citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.100.4560&amp;amp;rep=rep1&amp;amp;type=pdf'':43C ''citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.100.4560&amp;amp;rep=rep1&amp;amp;type=pdf&lt;/a&gt;&lt;/li&gt;'':48C ''citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.314&amp;amp;rep=rep1&amp;amp;type=pdf'':22C ''citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.314&amp;amp;rep=rep1&amp;amp;type=pdf&lt;/a&gt;&lt;/li&gt;'':27C ''experi'':32C ''famous'':7C ''href'':21C,42C,63C,74C ''implement'':58C ''improv'':15C,36C ''learn'':55C ''li'':13C,30C,61C,72C ''link'':4C ''mani'':57C ''nofollow'':26C,47C,68C,79C ''p'':1C,52C ''regress'':40C ''regressor'':10C,16C ''rel'':25C,46C,67C,78C ''scheme'':38C ''scikit'':54C ''scikit-learn'':53C ''scikit-learn.org'':65C,70C,76C,81C ''scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html'':75C ''scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html&lt;/a&gt;&lt;/li&gt;'':80C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.adaboostregressor.html'':64C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.adaboostregressor.html&lt;/a&gt;&lt;/li&gt;'':69C ''techniqu'':19C ''ul'':12C,60C ''use'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6959, 1, '2015-08-31 18:31:59.68', 0, 63, 'r - rpart in text mining for classification of documents', '&lt;p&gt;I am using rpart to classify the documents whether it belongs to class a or class b. I want to prove or provide reasons  on what basis the document was classified correctly or wrongly. How can i do that?&lt;/p&gt;

&lt;p&gt;I displayed the summary of the model. But it was not so convincing. &lt;/p&gt;

&lt;p&gt;Please help&lt;/p&gt;
', 'machine-learning  r  classification', NULL, '''/p'':55C,70C,74C ''b'':32C ''basi'':42C ''belong'':26C ''class'':28C,31C ''classif'':7A,14B ''classifi'':21C,46C ''convinc'':69C ''correct'':47C ''display'':58C ''document'':9A,23C,44C ''help'':73C ''learn'':12B ''machin'':11B ''machine-learn'':10B ''mine'':5A ''model'':63C ''p'':15C,56C,71C ''pleas'':72C ''prove'':36C ''provid'':38C ''r'':1A,13B ''reason'':39C ''rpart'':2A,19C ''summari'':60C ''text'':4A ''use'':18C ''want'':34C ''whether'':24C ''wrong'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8922, 1, '2015-11-19 12:59:40.403', 1, 47, 'removing strings after a certain character in a given text', '&lt;p&gt;I have a dataset like the one below. I want to remove all characters after the character Â©. How can I do that in R?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data_clean_phrase &amp;lt;- c("Copyright Â© The Society of Geomagnetism and Earth", 
"Â© 2013 Chinese National Committee ")

data_clean_df &amp;lt;- as.data.frame(data_clean_phrase)
&lt;/code&gt;&lt;/pre&gt;
', 'r  data-cleaning', NULL, '''/code'':70C ''/p'':41C ''/pre'':71C ''2013'':58C ''as.data.frame'':66C ''c'':48C ''certain'':5A ''charact'':6A,29C,32C ''chines'':59C ''clean'':14B,45C,63C,68C ''code'':43C ''committe'':61C ''copyright'':49C ''data'':13B,44C,62C,67C ''data-clean'':12B ''dataset'':19C ''df'':64C ''earth'':56C ''geomagnet'':54C ''given'':9A ''like'':20C ''lt'':47C,65C ''nation'':60C ''one'':22C ''p'':15C ''phrase'':46C,69C ''pre'':42C ''r'':11B,40C ''remov'':1A,27C ''societi'':52C ''string'':2A ''text'':10A ''want'':25C ''â'':33C,50C,57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6960, 1, '2015-08-31 18:39:36.183', 2, 193, 'StackOverflow Tags Predictor...Suggest an Machine Learning Approach please?', '&lt;p&gt;I am trying to predict tags for stackoverflow questions and I am not able to decide which Machine Learning algorithm will be a correct approach for this.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Input:&lt;/strong&gt; As a dataset I have mined stackoverflow questions, I have tokenized the data set and removed stopwords and punctuation from this data.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Things i have tried:&lt;/strong&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;TF-IDF&lt;/li&gt;
&lt;li&gt;Trained Naive Bayes on the dataset and then gave user defined input to predict tags, but its not working correctly&lt;/li&gt;
&lt;li&gt;Linear SVM&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Which ML algorithm I should use Supervised or Unsupervised?
If possible please, suggest a correct ML approach from the scratch.
PS: I have the list of all tags present on StackOverflow so, will this help in anyway?
Thanks&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/li'':83C,105C,109C ''/ol'':110C ''/p'':42C,69C,77C,150C ''/strong'':46C,76C ''abl'':28C ''algorithm'':34C,114C ''anyway'':148C ''approach'':8A,39C,128C ''bay'':87C ''classif'':13B ''correct'':38C,104C,126C ''data'':59C,68C ''dataset'':49C,90C ''decid'':30C ''defin'':95C ''gave'':93C ''help'':146C ''idf'':82C ''input'':45C,96C ''learn'':7A,12B,33C ''li'':79C,84C,106C ''linear'':107C ''list'':136C ''machin'':6A,11B,32C ''machine-learn'':10B ''mine'':52C ''ml'':113C,127C ''naiv'':86C ''ol'':78C ''p'':14C,43C,70C,111C ''pleas'':9A,123C ''possibl'':122C ''predict'':19C,98C ''predictor'':3A ''present'':140C ''ps'':132C ''punctuat'':65C ''question'':23C,54C ''remov'':62C ''scratch'':131C ''set'':60C ''stackoverflow'':1A,22C,53C,142C ''stopword'':63C ''strong'':44C,71C ''suggest'':4A,124C ''supervis'':118C ''svm'':108C ''tag'':2A,20C,99C,139C ''tf'':81C ''tf-idf'':80C ''thank'':149C ''thing'':72C ''token'':57C ''train'':85C ''tri'':17C,75C ''unsupervis'':120C ''use'':117C ''user'':94C ''work'':103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6961, 2, '2015-08-31 19:09:54.973', 0, NULL, NULL, '&lt;p&gt;All of the scientific computing development going on around Python is a good option. &lt;a href="https://store.continuum.io/cshop/anaconda/" rel="nofollow"&gt;Continuum''s Anaconda&lt;/a&gt; is very easy to get up and running. Also well supported in distributed environments.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Numpy for fast matrix operations&lt;/li&gt;
&lt;li&gt;Pandas for dataframes, munging, analytics, visualization&lt;/li&gt;
&lt;li&gt;Statsmodels and Scikit for models, metrics, ML algs and so on&lt;/li&gt;
&lt;li&gt;...&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':26C ''/cshop/anaconda/'':20C ''/li'':49C,57C,70C,72C ''/p'':41C ''/ul'':73C ''alg'':66C ''also'':35C ''anaconda'':25C ''analyt'':55C ''around'':10C ''comput'':6C ''continuum'':23C ''datafram'':53C ''develop'':7C ''distribut'':39C ''easi'':29C ''environ'':40C ''fast'':46C ''get'':31C ''go'':8C ''good'':14C ''href'':17C ''li'':43C,50C,58C,71C ''matrix'':47C ''metric'':64C ''ml'':65C ''model'':63C ''mung'':54C ''nofollow'':22C ''numpi'':44C ''oper'':48C ''option'':15C ''p'':1C ''panda'':51C ''python'':11C ''rel'':21C ''run'':34C ''scientif'':5C ''scikit'':61C ''statsmodel'':59C ''store.continuum.io'':19C ''store.continuum.io/cshop/anaconda/'':18C ''support'':37C ''ul'':42C ''visual'':56C ''well'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6970, 1, '2015-09-01 09:57:10.733', 3, 190, 'Newbie: What is the difference between hypothesis class and models?', '&lt;p&gt;I am new to machine learning and I am confused with the terminology. Thus far, I used to view a hypothesis class as different instance of hypothesis function... Example: If we are talking about linear classification then different lines characterized by different weights would together form the hypothesis class. &lt;/p&gt;

&lt;p&gt;Is my understanding correct or can a hypothesis class represent anything which could approximate the target function?  For instance, can a linear or quadratic function that approximates the target function together form a single hypothesis class or both are from different hypothesis classes?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':64C,109C ''anyth'':76C ''approxim'':79C,92C ''character'':54C ''class'':8A,36C,63C,74C,101C,108C ''classif'':50C ''confus'':24C ''correct'':69C ''could'':78C ''differ'':5A,38C,52C,56C,106C ''exampl'':43C ''far'':29C ''form'':60C,97C ''function'':42C,82C,90C,95C ''hypothesi'':7A,35C,41C,62C,73C,100C,107C ''instanc'':39C,84C ''learn'':13B,20C ''line'':53C ''linear'':49C,87C ''machin'':12B,19C ''machine-learn'':11B ''model'':10A ''new'':17C ''newbi'':1A ''p'':14C,65C ''quadrat'':89C ''repres'':75C ''singl'':99C ''talk'':47C ''target'':81C,94C ''terminolog'':27C ''thus'':28C ''togeth'':59C,96C ''understand'':68C ''use'':31C ''view'':33C ''weight'':57C ''would'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6971, 1, '2015-09-01 13:12:35.833', 1, 51, 'How to build a unique decision tree for each subset of data based on a grouping variable?', '&lt;p&gt;Given: a data frame of 2.5 million records and 25 columns, which contains 1 grouping column with a variable number of groups (e.g. 2.5M records / 10 groups today; 2.5M records / 50 groups tomorrow).&lt;/p&gt;

&lt;p&gt;How can I build a decision tree for each group?&lt;/p&gt;

&lt;p&gt;I was going to do this using Python but thought there might be an easier way in R (tapply?). Here was my initial thought process in Python:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;instantiate an empty dictionary to hold the prediction results&lt;/li&gt;
&lt;li&gt;identify the unique ids in the grouping column and iterate over the rows of data in a &lt;code&gt;for&lt;/code&gt; loop&lt;/li&gt;
&lt;li&gt;in each loop, build the decision tree, predict and store the results in the dictionary as a &lt;code&gt;{grouping_var:prediction}&lt;/code&gt; pair&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Thought on how to do this faster or more efficiently in R?&lt;/p&gt;
', 'r  python  decision-trees', NULL, '''/code'':133C,158C ''/li'':112C,135C,160C ''/p'':59C,71C,100C,175C ''/ul'':161C ''1'':37C ''10'':50C ''2.5'':29C,47C,53C ''25'':33C ''50'':56C ''base'':13A ''build'':3A,64C,140C ''code'':131C,154C ''column'':34C,39C,121C ''contain'':36C ''data'':12A,26C,128C ''decis'':6A,21B,66C,142C ''decision-tre'':20B ''dictionari'':106C,151C ''e.g'':46C ''easier'':87C ''effici'':172C ''empti'':105C ''faster'':169C ''frame'':27C ''given'':24C ''go'':75C ''group'':16A,38C,45C,51C,57C,70C,120C,155C ''hold'':108C ''id'':117C ''identifi'':114C ''initi'':95C ''instanti'':103C ''iter'':123C ''li'':102C,113C,136C ''loop'':134C,139C ''m'':48C,54C ''might'':84C ''million'':30C ''number'':43C ''p'':23C,60C,72C,162C ''pair'':159C ''predict'':110C,144C,157C ''process'':97C ''python'':19B,80C,99C ''r'':18B,90C,174C ''record'':31C,49C,55C ''result'':111C,148C ''row'':126C ''store'':146C ''subset'':10A ''tappli'':91C ''thought'':82C,96C,163C ''today'':52C ''tomorrow'':58C ''tree'':7A,22B,67C,143C ''ul'':101C ''uniqu'':5A,116C ''use'':79C ''var'':156C ''variabl'':17A,42C ''way'':88C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6993, 2, '2015-09-03 11:41:03.39', 0, NULL, NULL, '&lt;p&gt;There''s potentially some useful ideas for you in the answers to &lt;a href="http://datascience.stackexchange.com/questions/412/how-can-i-transform-names-in-a-confidential-data-set-to-make-it-anonymous-but-p/446#446"&gt;this question about transforming names in a confidential data set but preserve some of the characteristics of the names&lt;/a&gt; I read a while ago and found very interesting. It''s not exactly the right topic but there is some discussion on encrypting strings while preserving edit distance, which may not answer your question but perhaps will give you some ideas to think about if you''re still stuck...&lt;/p&gt;
', NULL, NULL, '''/a'':38C ''/p'':88C ''/questions/412/how-can-i-transform-names-in-a-confidential-data-set-to-make-it-anonymous-but-p/446#446'':18C ''ago'':43C ''answer'':12C,70C ''characterist'':34C ''confidenti'':26C ''data'':27C ''datascience.stackexchange.com'':17C ''datascience.stackexchange.com/questions/412/how-can-i-transform-names-in-a-confidential-data-set-to-make-it-anonymous-but-p/446#446'':16C ''discuss'':59C ''distanc'':66C ''edit'':65C ''encrypt'':61C ''exact'':51C ''found'':45C ''give'':76C ''href'':15C ''idea'':7C,79C ''interest'':47C ''may'':68C ''name'':23C,37C ''p'':1C ''perhap'':74C ''potenti'':4C ''preserv'':30C,64C ''question'':20C,72C ''re'':85C ''read'':40C ''right'':53C ''set'':28C ''still'':86C ''string'':62C ''stuck'':87C ''think'':81C ''topic'':54C ''transform'':22C ''use'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6972, 1, '2015-09-01 13:56:11.747', 2, 104, 'Identify given patterns in unstructured data like text files', '&lt;p&gt;I wasn''t sure if I had to ask it here or in Stackoverflow, but since I am also seeking research papers/algorithms and not only code, I decided to do it here.&lt;/p&gt;

&lt;p&gt;When I have a text, I can manually write a regex to find all the possible outputs from what I want to extract from the file. What I want to do, is to find an algorithm or a research, which can let you highlight (set the input) different positions of the same (repeated) data you want to extract in the text file, train the algorithm and then identify all the others under the same contentions of those you set.&lt;/p&gt;

&lt;p&gt;For example, let''s say that I have a text with several titles which are following with &lt;code&gt;\n\n\n&lt;/code&gt; and starting with &lt;code&gt;\n\n&lt;/code&gt;. It is easy with regex, but I want to do it dynamically.&lt;/p&gt;

&lt;p&gt;An idea is to build an algorithm which will take examples and create regex automatically. But I am not aware of any research like this and maybe there are also other techniques that you can achieve it.&lt;/p&gt;

&lt;p&gt;Any ideas?&lt;/p&gt;
', 'text-mining  algorithms  research', NULL, '''/code'':152C,159C ''/p'':48C,129C,172C,211C,215C ''achiev'':209C ''algorithm'':13B,85C,114C,180C ''also'':34C,203C ''ask'':24C ''automat'':188C ''awar'':193C ''build'':178C ''code'':41C,148C,156C ''content'':124C ''creat'':186C ''data'':6A,103C ''decid'':43C ''differ'':97C ''dynam'':171C ''easi'':162C ''exampl'':132C,184C ''extract'':72C,107C ''file'':9A,75C,111C ''find'':62C,83C ''follow'':146C ''given'':2A ''highlight'':93C ''idea'':175C,214C ''identifi'':1A,117C ''input'':96C ''let'':91C,133C ''like'':7A,197C ''manual'':57C ''mayb'':200C ''mine'':12B ''n'':149C,150C,151C,157C,158C ''other'':120C ''output'':66C ''p'':15C,49C,130C,173C,212C ''papers/algorithms'':37C ''pattern'':3A ''posit'':98C ''possibl'':65C ''regex'':60C,164C,187C ''repeat'':102C ''research'':14B,36C,88C,196C ''say'':135C ''seek'':35C ''set'':94C,128C ''sever'':142C ''sinc'':31C ''stackoverflow'':29C ''start'':154C ''sure'':19C ''take'':183C ''techniqu'':205C ''text'':8A,11B,54C,110C,140C ''text-min'':10B ''titl'':143C ''train'':112C ''unstructur'':5A ''want'':70C,78C,105C,167C ''wasn'':17C ''write'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6974, 2, '2015-09-01 14:38:45.337', 0, NULL, NULL, '&lt;p&gt;If you have the tags of each of the questions that you mined, then supervised methods make sense.  &lt;/p&gt;

&lt;p&gt;You might use the tf-idf representation of a given question, feed it to an SVM or neural net, and use that to predict 0/1 for each tag in your target set.  If there are too many possible classes (tags), it may be tricky to balance your data.&lt;/p&gt;

&lt;p&gt;A simpler approach might be to use the tf-idf vectors to compute the &lt;a href="https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm" rel="nofollow"&gt;K-Nearest-Neighbors&lt;/a&gt; of a question; then you can use the tags of the most similar documents (by whatever &lt;a href="https://en.wikipedia.org/wiki/Statistical_distance" rel="nofollow"&gt;distance metric&lt;/a&gt; does best) to predict the likelihood that question has each tag.&lt;/p&gt;

&lt;p&gt;If you don''t have the tags for the questions you mined, you should consider unsupervised methods.  &lt;a href="https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation" rel="nofollow"&gt;LDA&lt;/a&gt;, for example, could identify &lt;a href="https://en.wikipedia.org/wiki/Topic_model" rel="nofollow"&gt;topics&lt;/a&gt; within the questions and important words within those topics &lt;/p&gt;
', NULL, NULL, '''/a'':99C,125C,164C,177C ''/p'':20C,71C,137C,187C ''/wiki/k-nearest_neighbors_algorithm'':92C ''/wiki/latent_dirichlet_allocation'':160C ''/wiki/statistical_distance'':120C ''/wiki/topic_model'':173C ''0/1'':47C ''approach'':75C ''balanc'':68C ''best'':127C ''class'':61C ''comput'':86C ''consid'':153C ''could'':167C ''data'':70C ''distanc'':123C ''document'':113C ''en.wikipedia.org'':91C,119C,159C,172C ''en.wikipedia.org/wiki/k-nearest_neighbors_algorithm'':90C ''en.wikipedia.org/wiki/latent_dirichlet_allocation'':158C ''en.wikipedia.org/wiki/statistical_distance'':118C ''en.wikipedia.org/wiki/topic_model'':171C ''exampl'':166C ''feed'':34C ''given'':32C ''href'':89C,117C,157C,170C ''identifi'':168C ''idf'':28C,83C ''import'':182C ''k'':96C ''k-nearest-neighbor'':95C ''lda'':163C ''likelihood'':131C ''make'':18C ''mani'':59C ''may'':64C ''method'':17C,155C ''metric'':124C ''might'':23C,76C ''mine'':14C,150C ''nearest'':97C ''neighbor'':98C ''net'':41C ''neural'':40C ''nofollow'':94C,122C,162C,175C ''p'':1C,21C,72C,138C ''possibl'':60C ''predict'':46C,129C ''question'':11C,33C,102C,133C,148C,180C ''rel'':93C,121C,161C,174C ''represent'':29C ''sens'':19C ''set'':54C ''similar'':112C ''simpler'':74C ''supervis'':16C ''svm'':38C ''tag'':6C,50C,62C,108C,136C,145C ''target'':53C ''tf'':27C,82C ''tf-idf'':26C,81C ''topic'':176C,186C ''tricki'':66C ''unsupervis'':154C ''use'':24C,43C,79C,106C ''vector'':84C ''whatev'':115C ''within'':178C,184C ''word'':183C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6975, 2, '2015-09-01 17:14:03.977', 1, NULL, NULL, '&lt;p&gt;Just out of curiousity: why use 3-channel images? I work in CV as well and from what I''ve seen, it''s standard to grayscale the images. Especially with MNIST, which is not colored to begin with, it doesn''t seem that using color has any benefit. &lt;/p&gt;

&lt;p&gt;I also believe that there''s an intuitive reason to use grayscale - often times color is a confounding variable. If you were to look at a red "1" and and blue "1", you''d say "Hey! Those are both ones! They''re just different colors". However, computers are much stupider than you or I. Even with intelligent algorithms, a computer may still end up in the situation where it''s looking at a blue "1" and say to itself, "Oh crap, what is that?!? Why is it blue? I''ve never seen anything like this before!" Keep in mind how large of a difference there is from the color (255, 0, 0) and (0, 255, 255) and how slight the differences in images can be (especially outside the comfortable playground of MNIST). &lt;/p&gt;

&lt;p&gt;At any rate, most of the NoLearn examples I''ve seen use data shapes like:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;X = X.reshape(-1, 1, size, size)
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;If you grayscale your images you can coerce it into this shape. Unfortunately, I''m not sure of how you''d cram your colored data into noLearn and get the results you''re looking for. &lt;/p&gt;
', NULL, NULL, '''-1'':206C ''/code'':210C ''/p'':50C,184C,201C,211C,248C ''0'':162C,163C,165C ''1'':79C,83C,126C,207C ''255'':161C,166C,167C ''3'':8C ''algorithm'':109C ''also'':53C ''anyth'':144C ''begin'':38C ''believ'':54C ''benefit'':49C ''blue'':82C,125C,139C ''channel'':9C ''code'':203C ''coerc'':220C ''color'':36C,46C,66C,96C,160C,236C ''comfort'':180C ''comput'':98C,111C ''confound'':69C ''cram'':234C ''crap'':132C ''curious'':5C ''cv'':14C ''d'':85C,233C ''data'':198C,237C ''differ'':95C,155C,172C ''doesn'':41C ''end'':114C ''especi'':30C,177C ''even'':106C ''exampl'':193C ''get'':241C ''grayscal'':27C,63C,215C ''hey'':87C ''howev'':97C ''imag'':10C,29C,174C,217C ''intellig'':108C ''intuit'':59C ''keep'':148C ''larg'':152C ''like'':145C,200C ''look'':75C,122C,246C ''m'':227C ''may'':112C ''mind'':150C ''mnist'':32C,183C ''much'':100C ''never'':142C ''nolearn'':192C,239C ''often'':64C ''oh'':131C ''one'':91C ''outsid'':178C ''p'':1C,51C,185C,202C,212C ''playground'':181C ''rate'':188C ''re'':93C,245C ''reason'':60C ''red'':78C ''result'':243C ''say'':86C,128C ''seem'':43C ''seen'':22C,143C,196C ''shape'':199C,224C ''situat'':118C ''size'':208C,209C ''slight'':170C ''standard'':25C ''still'':113C ''stupid'':101C ''sure'':229C ''time'':65C ''unfortun'':225C ''use'':7C,45C,62C,197C ''variabl'':70C ''ve'':21C,141C,195C ''well'':16C ''work'':12C ''x'':204C ''x.reshape'':205C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6978, 2, '2015-09-01 18:53:47.93', 1, NULL, NULL, '&lt;p&gt;I am always inclined to the &lt;strong&gt;"do while you learn"&lt;/strong&gt; principle, when learning new skills. I am writing this completely based on my experience, which helped me solve Kaggle problems and do what I am doing now.&lt;/p&gt;

&lt;p&gt;So, this is roughly how you do it, and the courses which you need to take:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;strong&gt;Introduction to Machine Learning&lt;/strong&gt; - And the best course for studying this is Andrew Ng''s coursera course. You don''t need to complete the HW''s etc. It is self-paced and archived. So, just watch the courses, and do the assignments for a hands-on knowledge. I even tried doing the same assignments in both Octave and Python. Completing this course would also help you solve Kaggle problems.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Exploratory Analysis&lt;/strong&gt; - Doing preliminary cleaning and exploratory analysis is very important in every data science problem. So, learning those methods would help in learning visualizations, exploratory techniques etc. This &lt;a href="https://www.coursera.org/course/introstats" rel="nofollow"&gt;Coursera course&lt;/a&gt; is a nice one.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Databases&lt;/strong&gt; - Data is not always available in neat tsv''s or csv''s like in online courses. One needs to learn to handle data stored in databases. They might be SQL based, or NoSQL, they might be row-based or columnar. So, this is a &lt;a href="https://www.coursera.org/course/db" rel="nofollow"&gt;nice course&lt;/a&gt; for learning about databases getting a know of how to deal with them.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Algorithms&lt;/strong&gt; - Knowledge of basic data structures and algorithms would be enough to solve most Kaggle and real-world data problems (along with all the mentioned above, though). So, get yourself a nice Intro. to algos book.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;So, all those mentioned above are essential for understanding and tackling a data science problem. All the others are just improvements above these concepts, including NLP, Deep Learning, etc.&lt;/p&gt;

&lt;p&gt;So, have these foundations nice and good, so that you can build upon these and get familiar and test the waters of much deeper concepts like neural nets, NLP, etc.&lt;/p&gt;

&lt;p&gt;Finally, it is always useful to get a knack of Big Data and distributed algorithms and concepts. This &lt;a href="https://www.coursera.org/course/mmds" rel="nofollow"&gt;coursera course&lt;/a&gt; is a perfect start for learning the concepts of parallel computing and handling huge data.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6979, 2, '2015-09-01 21:54:01.573', 1, NULL, NULL, '&lt;p&gt;Your hypothesis class consists of all possible hypotheses that you are searching over, regardless of their form. For convenience''s sake, the hypothesis class is usually constrained to be only one type of function or model at a time, since learning methods typically only work on one type at a time. This doesn''t have to be the case, though: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Hypothesis classes don''t have to consist of only one type of function. If you''re searching over all linear, quadratic, and exponential functions, then those are what your combined hypothesis class contains.&lt;/li&gt;
&lt;li&gt;Hypothesis classes also don''t have to consist of only simple functions. If you manage to search over all piecewise-$\tanh^2$ functions, then those functions are what your hypothesis class includes.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The big tradeoff is that the larger your hypothesis class, the better the best hypothesis models the underlying true function, but the harder it is to find that best hypothesis. This is related to the &lt;a href="https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff" rel="nofollow"&gt;biasâvariance tradeoff&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':180C ''/li'':98C,132C ''/ol'':133C ''/p'':62C,181C ''/wiki/bias%e2%80%93variance_tradeoff'':174C ''2'':121C ''also'':102C ''best'':148C,163C ''better'':146C ''biasâ'':177C ''big'':136C ''case'':60C ''class'':4C,25C,66C,96C,101C,130C,144C ''combin'':94C ''consist'':5C,71C,107C ''constrain'':28C ''contain'':97C ''conveni'':20C ''doesn'':54C ''en.wikipedia.org'':173C ''en.wikipedia.org/wiki/bias%e2%80%93variance_tradeoff'':172C ''exponenti'':87C ''find'':161C ''form'':18C ''function'':35C,77C,88C,111C,122C,125C,154C ''harder'':157C ''href'':171C ''hypothes'':9C ''hypothesi'':3C,24C,65C,95C,100C,129C,143C,149C,164C ''includ'':131C ''larger'':141C ''learn'':42C ''li'':64C,99C ''linear'':84C ''manag'':114C ''method'':43C ''model'':37C,150C ''nofollow'':176C ''ol'':63C ''one'':32C,48C,74C ''p'':1C,134C ''piecewis'':119C ''possibl'':8C ''quadrat'':85C ''re'':80C ''regardless'':15C ''rel'':175C ''relat'':167C ''sake'':22C ''search'':13C,81C,116C ''simpl'':110C ''sinc'':41C ''tanh'':120C ''though'':61C ''time'':40C,52C ''tradeoff'':137C,179C ''true'':153C ''type'':33C,49C,75C ''typic'':44C ''under'':152C ''usual'':27C ''varianc'':178C ''work'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6980, 2, '2015-09-02 00:07:15.973', 0, NULL, NULL, '&lt;p&gt;You might have to extend another package to implement LSTM and RNN in R. Here are some packages to get you started:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://cran.r-project.org/web/packages/deepnet/" rel="nofollow"&gt;deepnet&lt;/a&gt; Implements a variety of deep learning architectures&lt;/li&gt;
&lt;li&gt;&lt;a href="https://cran.r-project.org/web/packages/darch/index.htmlh" rel="nofollow"&gt;darch&lt;/a&gt; A deep architecture&lt;/li&gt;
&lt;li&gt;&lt;a href="https://cran.r-project.org/web/packages/h2o/index.html" rel="nofollow"&gt;H2O&lt;/a&gt; An open-source company with deep learning packages&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':35C,53C,67C ''/li'':43C,57C,77C ''/p'':24C ''/ul'':78C ''/web/packages/darch/index.htmlh'':49C ''/web/packages/deepnet/'':31C ''/web/packages/h2o/index.html'':63C ''anoth'':7C ''architectur'':42C,56C ''compani'':72C ''cran.r-project.org'':30C,48C,62C ''cran.r-project.org/web/packages/darch/index.htmlh'':47C ''cran.r-project.org/web/packages/deepnet/'':29C ''cran.r-project.org/web/packages/h2o/index.html'':61C ''darch'':52C ''deep'':40C,55C,74C ''deepnet'':34C ''extend'':6C ''get'':21C ''h2o'':66C ''href'':28C,46C,60C ''implement'':10C,36C ''learn'':41C,75C ''li'':26C,44C,58C ''lstm'':11C ''might'':3C ''nofollow'':33C,51C,65C ''open'':70C ''open-sourc'':69C ''p'':1C ''packag'':8C,19C,76C ''r'':15C ''rel'':32C,50C,64C ''rnn'':13C ''sourc'':71C ''start'':23C ''ul'':25C ''varieti'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6986, 1, '2015-09-02 13:34:53.967', 4, 34, 'Is Minimax Linkage a Lance-Williams hierarchical clustering?', '&lt;p&gt;I found the following article on &lt;a href="http://statweb.stanford.edu/~jbien/jasa2011minimax.pdf" rel="nofollow"&gt;"Hierarchical Clustering With Prototypes
via Minimax Linkage"&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;It is stated in Property 6 that &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Minimax linkage cannot be written using &lt;a href="https://en.wikipedia.org/wiki/Ward%27s_method" rel="nofollow"&gt;LanceâWilliams&lt;/a&gt; updates.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;A succint proof using a counter-example is given:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Proof. Figure 9 shows a simple one-dimensional example that could not
  arise if minimax linkage followed Lanceâ Williams updates. The upper
  and lower panels show two configurations of points for which the
  right side of (4) is identical but the left side differs; in
  particular, $d(G_1 \cup G_2,H) = 9$ for the upper panel, whereas
  $d(G_1 \cup G_2,H) = 8$ for the lower panel.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;But I do not understand their proof. For both cases (upper and lower panels), $d(G_1,H) = 16$, $d(G_2,H) = 7$, $d(G_1,G_2) = 5$.&lt;/p&gt;

&lt;p&gt;I cannot see any reasons that $\alpha(G_2)$ in the first case must equal to $\alpha(G_2)$ in the second case as, for instance, $G_2$ has not the same cardinal.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6987, 1, '2015-09-02 14:49:33.52', 2, 417, 'Can you explain the difference between SVC and LinearSVC in scikit-learn?', '&lt;p&gt;I''ve recently started learning to work with &lt;code&gt;sklearn&lt;/code&gt; and have just come across this peculiar result.&lt;/p&gt;

&lt;p&gt;I used the &lt;code&gt;digits&lt;/code&gt; dataset available in &lt;code&gt;sklearn&lt;/code&gt; to try different models and estimation methods. &lt;/p&gt;

&lt;p&gt;When I tested a Support Vector Machine model on the data, I found out there are two different classes in &lt;code&gt;sklearn&lt;/code&gt; for SVM classification: &lt;code&gt;SVC&lt;/code&gt; and &lt;code&gt;LinearSVC&lt;/code&gt;, where the former uses &lt;em&gt;one-against-one&lt;/em&gt; approach and the other uses &lt;em&gt;one-against-rest&lt;/em&gt; approach. &lt;/p&gt;

&lt;p&gt;I didn''t know what effect that could have on the results, so I tried both. I did a Monte Carlo-style estimation where I ran both models 500 times, each time splitting the sample randomly into 60% training and 40% test and calculating the error of the prediction on the test set.&lt;/p&gt;

&lt;p&gt;The regular SVC estimator produced the following histogram of errors:
&lt;a href="http://i.stack.imgur.com/NkKQM.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/NkKQM.png" alt="SVC Error Rate"&gt;&lt;/a&gt;
While the linear SVC estimator produced the following histogram:
&lt;a href="http://i.stack.imgur.com/ajHfx.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/ajHfx.png" alt="Linear SVC Error Rate"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;What could account for such a stark difference? Why does the linear model have such higher accuracy most of the time?&lt;/p&gt;

&lt;p&gt;And, relatedly, what could be causing the stark polarization in the results? Either an accuracy close to 1 or an accuracy close to 0, nothing in between.&lt;/p&gt;

&lt;p&gt;For comparison, a decision tree classification produced a much more normally distributed error rate with an accuracy of around .85.&lt;/p&gt;
', 'svm  scikit', NULL, '''/a'':198C,225C ''/ajhfx.png'':212C,219C ''/code'':27C,43C,49C,81C,87C,91C ''/em'':101C,112C ''/nkkqm.png'':186C,193C ''/p'':36C,57C,114C,170C,226C,249C,278C,300C ''0'':274C ''1'':268C ''40'':157C ''500'':145C ''60'':154C ''85'':299C ''account'':230C ''accuraci'':244C,265C,271C,296C ''across'':32C ''alt'':194C,220C ''approach'':102C,113C ''around'':298C ''avail'':45C ''calcul'':160C ''carlo'':137C ''carlo-styl'':136C ''caus'':256C ''class'':77C ''classif'':84C,285C ''close'':266C,272C ''code'':25C,41C,47C,79C,85C,89C ''come'':31C ''comparison'':281C ''could'':123C,229C,254C ''data'':69C ''dataset'':44C ''decis'':283C ''didn'':117C ''differ'':5A,52C,76C,235C ''digit'':42C ''distribut'':291C ''effect'':121C ''either'':263C ''em'':96C,107C ''error'':162C,181C,196C,223C,292C ''estim'':55C,139C,175C,203C ''explain'':3A ''follow'':178C,206C ''former'':94C ''found'':71C ''higher'':243C ''histogram'':179C,207C ''href'':183C,209C ''i.stack.imgur.com'':185C,192C,211C,218C ''i.stack.imgur.com/ajhfx.png'':210C,217C ''i.stack.imgur.com/nkkqm.png'':184C,191C ''img'':189C,215C ''know'':119C ''learn'':13A,21C ''linear'':201C,221C,239C ''linearsvc'':9A,90C ''machin'':65C ''method'':56C ''model'':53C,66C,144C,240C ''mont'':135C ''much'':288C ''nofollow'':188C,214C ''normal'':290C ''noth'':275C ''one'':98C,100C,109C ''one-against-on'':97C ''one-against-rest'':108C ''p'':16C,37C,58C,115C,171C,227C,250C,279C ''peculiar'':34C ''polar'':259C ''predict'':165C ''produc'':176C,204C,286C ''ran'':142C ''random'':152C ''rate'':197C,224C,293C ''recent'':19C ''regular'':173C ''rel'':187C,213C ''relat'':252C ''rest'':111C ''result'':35C,127C,262C ''sampl'':151C ''scikit'':12A,15B ''scikit-learn'':11A ''set'':169C ''sklearn'':26C,48C,80C ''split'':149C ''src'':190C,216C ''stark'':234C,258C ''start'':20C ''style'':138C ''support'':63C ''svc'':7A,86C,174C,195C,202C,222C ''svm'':14B,83C ''test'':61C,158C,168C ''time'':146C,148C,248C ''train'':155C ''tree'':284C ''tri'':51C,130C ''two'':75C ''use'':39C,95C,106C ''ve'':18C ''vector'':64C ''work'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6988, 1, '2015-09-02 15:21:23.047', 4, 78, 'Connection between Regularization and Gradient Descent', '&lt;p&gt;I would like to understand regularization/shrinkage in the light of MLE/Gradient Descent. I know both concepts but I do not know/understand whether both are used to determine coefficients of a linear model. If so, what are the steps followed? &lt;/p&gt;

&lt;p&gt;To further elaborate, regularization is used to reduce variance which is accomplished through penalizing coefficients of a linear model. The tuning parameter, lambda, is determined through cross-validation. Once, lambda is determined the coefficients are automatically determined, right? Hence, why do we need to minimize (RSS + regularization term) to find coefficients? Are the steps the following: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Find lambda through cross-validation &lt;/li&gt;
&lt;li&gt;Minimize (RSS + regularization) through MLE or GD&lt;/li&gt;
&lt;li&gt;Find coefficients&lt;/li&gt;
&lt;li&gt;Penalize coefficients to decrease variance&lt;/li&gt;
&lt;li&gt;We are left with a small subset of coefficients&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  data-mining', NULL, '''/li'':121C,130C,134C,141C,152C ''/ol'':153C ''/p'':53C,112C ''accomplish'':66C ''automat'':91C ''coeffici'':41C,69C,89C,106C,133C,137C,151C ''concept'':29C ''connect'':1A ''cross'':82C,119C ''cross-valid'':81C,118C ''data'':11B ''data-min'':10B ''decreas'':139C ''descent'':6A,25C ''determin'':40C,79C,87C,92C ''elabor'':57C ''find'':105C,115C,132C ''follow'':52C,111C ''gd'':129C ''gradient'':5A ''henc'':94C ''know'':27C ''know/understand'':34C ''lambda'':77C,85C,116C ''learn'':9B ''left'':145C ''li'':114C,122C,131C,135C,142C ''light'':22C ''like'':16C ''linear'':44C,72C ''machin'':8B ''machine-learn'':7B ''mine'':12B ''minim'':100C,123C ''mle'':127C ''mle/gradient'':24C ''model'':45C,73C ''need'':98C ''ol'':113C ''p'':13C,54C ''paramet'':76C ''penal'':68C,136C ''reduc'':62C ''regular'':3A,58C,102C,125C ''regularization/shrinkage'':19C ''right'':93C ''rss'':101C,124C ''small'':148C ''step'':51C,109C ''subset'':149C ''term'':103C ''tune'':75C ''understand'':18C ''use'':38C,60C ''valid'':83C,120C ''varianc'':63C,140C ''whether'':35C ''would'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6989, 1, '2015-09-02 18:20:20.493', 3, 72, 'Python script/GUI to generate positive/negative images for CascadeClassifier?', '&lt;p&gt;For the first time, I am playing around with a &lt;a href="http://docs.opencv.org/doc/user_guide/ug_traincascade.html" rel="nofollow"&gt;Cascade Classifier&lt;/a&gt; with the OpenCV package (also new to the latter). I realized that it would probably be faster to write my own GUI/script to generate the needed positive and negative images from the set of images I have than to open each file in Photoshop or Paint, but I also suspect this has been done many times before. &lt;/p&gt;

&lt;p&gt;In particular, I am looking for a GUI that lets users page through files in a directory and then use mouse clicks to draw rectangles on a particular image and have the coordinates of the rectangle recorded for later purposes. Any suggestions? If not, I''ll  be sure to post a link when/if I finish this. It seems like something of general enough utility I am surprised I can''t find it in the OpenCV package itself.  &lt;/p&gt;
', 'classification  image-classification', NULL, '''/a'':33C ''/doc/user_guide/ug_traincascade.html'':28C ''/p'':91C,170C ''also'':38C,82C ''around'':21C ''cascad'':31C ''cascadeclassifi'':8A ''classif'':9B,12B ''classifi'':32C ''click'':114C ''coordin'':125C ''directori'':109C ''docs.opencv.org'':27C ''docs.opencv.org/doc/user_guide/ug_traincascade.html'':26C ''done'':87C ''draw'':116C ''enough'':155C ''faster'':50C ''file'':75C,106C ''find'':163C ''finish'':147C ''first'':16C ''general'':154C ''generat'':4A,57C ''gui'':100C ''gui/script'':55C ''href'':25C ''imag'':6A,11B,63C,68C,121C ''image-classif'':10B ''later'':131C ''latter'':42C ''let'':102C ''like'':151C ''link'':144C ''ll'':138C ''look'':97C ''mani'':88C ''mous'':113C ''need'':59C ''negat'':62C ''new'':39C ''nofollow'':30C ''open'':73C ''opencv'':36C,167C ''p'':13C,92C ''packag'':37C,168C ''page'':104C ''paint'':79C ''particular'':94C,120C ''photoshop'':77C ''play'':20C ''posit'':60C ''positive/negative'':5A ''post'':142C ''probabl'':48C ''purpos'':132C ''python'':1A ''realiz'':44C ''record'':129C ''rectangl'':117C,128C ''rel'':29C ''script/gui'':2A ''seem'':150C ''set'':66C ''someth'':152C ''suggest'':134C ''sure'':140C ''surpris'':159C ''suspect'':83C ''time'':17C,89C ''use'':112C ''user'':103C ''util'':156C ''when/if'':145C ''would'':47C ''write'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6991, 2, '2015-09-03 07:22:39.27', 1, NULL, NULL, '&lt;p&gt;See also: &lt;a href="http://datascience.stackexchange.com/questions/869/neural-network-parse-string-data"&gt;Neural Network parse string data?&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I do not see a problem with using MurMur3 per se.&lt;/p&gt;

&lt;p&gt;For the categorical labels, you can use one-hot encoding / one-of-k encoding.&lt;/p&gt;

&lt;p&gt;For the strings, it''s an application-specific question.  Presumably if you use exactly those strings as features, it will be very sparse.  The effect of this will depend on the algorithm that you are using, and how the training data compare to the data you see in practice.  You are running the risk that you will effectively either only create a traditional IP/email whitelist/blacklist OR throw out the feature altogether.&lt;/p&gt;

&lt;p&gt;You must decide what you want (eg should a certain email address &lt;em&gt;always&lt;/em&gt; get a certain output label?) and have some intuition about the application so as to generate more features from &lt;em&gt;IP address&lt;/em&gt; and &lt;em&gt;email address&lt;/em&gt;.  For example, from &lt;em&gt;email address&lt;/em&gt; you can extract the &lt;em&gt;local part&lt;/em&gt; (eg "john1972") and &lt;em&gt;domain&lt;/em&gt;, and from each of those you can extract:&lt;br&gt;
- &lt;em&gt;length&lt;/em&gt;&lt;br&gt;
- &lt;em&gt;character tri-grams&lt;/em&gt;&lt;br&gt;
- &lt;em&gt;count/proportion of numbers to alphachars&lt;/em&gt;&lt;br&gt;
- &lt;em&gt;number of hyphens&lt;/em&gt;&lt;br&gt;
- &lt;em&gt;dictionary validity&lt;/em&gt;&lt;br&gt;
...&lt;br&gt;
(From &lt;em&gt;domain&lt;/em&gt; you can also extract TLD and possibly subdomains.)
You can try to tokenise .  You can even hit external services to get information like &lt;em&gt;number of Google hits&lt;/em&gt;, &lt;em&gt;detected language&lt;/em&gt;, &lt;em&gt;spam score&lt;/em&gt; etc.&lt;/p&gt;
', NULL, NULL, '''/a'':14C ''/em'':136C,159C,164C,171C,179C,185C,197C,204C,212C,218C,223C,229C,258C,262C,266C ''/p'':15C,28C,46C,120C,268C ''/questions/869/neural-network-parse-string-data'':8C ''address'':133C,158C,163C,170C ''algorithm'':80C ''alphachar'':211C ''also'':3C,232C ''altogeth'':119C ''alway'':135C ''applic'':55C,148C ''application-specif'':54C ''br'':194C,198C,205C,213C,219C,224C,225C ''categor'':32C ''certain'':131C,139C ''charact'':200C ''compar'':90C ''count/proportion'':207C ''creat'':109C ''data'':13C,89C,93C ''datascience.stackexchange.com'':7C ''datascience.stackexchange.com/questions/869/neural-network-parse-string-data'':6C ''decid'':124C ''depend'':77C ''detect'':260C ''dictionari'':221C ''domain'':184C,228C ''effect'':73C,106C ''eg'':128C,180C ''either'':107C ''em'':134C,156C,161C,168C,176C,183C,195C,199C,206C,214C,220C,227C,253C,259C,263C ''email'':132C,162C,169C ''encod'':40C,45C ''etc'':267C ''even'':245C ''exact'':62C ''exampl'':166C ''extern'':247C ''extract'':174C,193C,233C ''featur'':66C,118C,154C ''generat'':152C ''get'':137C,250C ''googl'':256C ''gram'':203C ''hit'':246C,257C ''hot'':39C ''href'':5C ''hyphen'':217C ''inform'':251C ''intuit'':145C ''ip'':157C ''ip/email'':112C ''john1972'':181C ''k'':44C ''label'':33C,141C ''languag'':261C ''length'':196C ''like'':252C ''local'':177C ''murmur3'':25C ''must'':123C ''network'':10C ''neural'':9C ''number'':209C,215C,254C ''one'':38C,42C ''one-hot'':37C ''one-of-k'':41C ''output'':140C ''p'':1C,16C,29C,47C,121C ''pars'':11C ''part'':178C ''per'':26C ''possibl'':236C ''practic'':97C ''presum'':58C ''problem'':22C ''question'':57C ''risk'':102C ''run'':100C ''score'':265C ''se'':27C ''see'':2C,20C,95C ''servic'':248C ''spam'':264C ''spars'':71C ''specif'':56C ''string'':12C,50C,64C ''subdomain'':237C ''throw'':115C ''tld'':234C ''tokenis'':242C ''tradit'':111C ''train'':88C ''tri'':202C,240C ''tri-gram'':201C ''use'':24C,36C,61C,84C ''valid'':222C ''want'':127C ''whitelist/blacklist'':113C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6992, 1, '2015-09-03 09:57:16.537', 2, 77, 'Any mini-batch implementation of Random Forest?', '&lt;p&gt;I''m using the &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html" rel="nofollow"&gt;scikit-learn''s Random Forest&lt;/a&gt; to perform some classification task but run out of memory because of the amount of data.&lt;/p&gt;

&lt;p&gt;Is there any mini-batch implementation of the Random Forest algorithm (or similar decision trees-based method)?&lt;/p&gt;
', 'random-forest', NULL, '''/a'':30C ''/p'':47C,68C ''/stable/modules/generated/sklearn.ensemble.randomforestclassifier.html'':21C ''algorithm'':60C ''amount'':44C ''base'':66C ''batch'':4A,54C ''classif'':34C ''data'':46C ''decis'':63C ''forest'':8A,11B,29C,59C ''href'':18C ''implement'':5A,55C ''learn'':26C ''m'':14C ''memori'':40C ''method'':67C ''mini'':3A,53C ''mini-batch'':2A,52C ''nofollow'':23C ''p'':12C,48C ''perform'':32C ''random'':7A,10B,28C,58C ''random-forest'':9B ''rel'':22C ''run'':37C ''scikit'':25C ''scikit-learn'':24C ''scikit-learn.org'':20C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.randomforestclassifier.html'':19C ''similar'':62C ''task'':35C ''tree'':65C ''trees-bas'':64C ''use'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6997, 2, '2015-09-03 14:25:53.153', 3, NULL, NULL, '&lt;p&gt;You might try looking into &lt;a href="https://en.wikipedia.org/wiki/Sentiment_analysis" rel="nofollow"&gt;sentiment analysis&lt;/a&gt;.  There was a &lt;a href="https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews" rel="nofollow"&gt;kaggle competition&lt;/a&gt; on it, and you might find insight there.&lt;/p&gt;

&lt;p&gt;Treating this as either a regression or a classification problem is fair.  Also, it''s important to judge your performance against the &lt;a href="https://en.wikipedia.org/wiki/Sentiment_analysis#Evaluation" rel="nofollow"&gt;proper baselines&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;Your feature space might not be rich enough for the classes to be linearly separable.  You might do better using an &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html" rel="nofollow"&gt;SVM with a non-linear kernel&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;It also appears you haven''t &lt;a href="https://en.wikipedia.org/wiki/Feature_scaling" rel="nofollow"&gt;scaled&lt;/a&gt; the counts of the bigrams, which is generally helpful for SVMs. &lt;/p&gt;

&lt;p&gt;Another thought for an approach would be to apply &lt;a href="https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation" rel="nofollow"&gt;LDA&lt;/a&gt; to the set of documents (reviews) and use the topics as your feature space (you''ll have a topic vector per document).&lt;/p&gt;

&lt;p&gt;Some places to get python LDA implementations:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://pypi.python.org/pypi/gensim" rel="nofollow"&gt;gensim&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.cs.princeton.edu/~blei/topicmodeling.html" rel="nofollow"&gt;Blei&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (7000, 2, '2015-09-03 22:30:58.17', 0, NULL, NULL, '&lt;p&gt;This exact problem was a kaggle competition sponsored by Facebook. The particular forum thread of interest for you is the one where many of the top competitors explained their methodology, this should provide you with more information than you were probably looking for: &lt;a href="https://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/forums/t/6650/share-your-approach" rel="nofollow"&gt;https://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/forums/t/6650/share-your-approach&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;In general, it appears that most people treated the problem as a supervised one. Their primary feature was a tf-idf, or unweighted BOW, representations of the text and they ensembled 1000s of single-tag models. Owen, the winner of the competition, noted that the title text was a more powerful feature than the content of the body of the post.&lt;/p&gt;
', NULL, NULL, '''/c/facebook-recruiting-iii-keyword-extraction/forums/t/6650/share-your-approach'':49C ''/c/facebook-recruiting-iii-keyword-extraction/forums/t/6650/share-your-approach&lt;/a&gt;'':54C ''/p'':55C,120C ''1000s'':89C ''appear'':60C ''bodi'':116C ''bow'':81C ''competit'':8C,100C ''competitor'':28C ''content'':113C ''ensembl'':88C ''exact'':3C ''explain'':29C ''facebook'':11C ''featur'':73C,110C ''forum'':14C ''general'':58C ''href'':46C ''idf'':78C ''inform'':38C ''interest'':17C ''kaggl'':7C ''look'':43C ''mani'':24C ''methodolog'':31C ''model'':94C ''nofollow'':51C ''note'':101C ''one'':22C,70C ''owen'':95C ''p'':1C,56C ''particular'':13C ''peopl'':63C ''post'':119C ''power'':109C ''primari'':72C ''probabl'':42C ''problem'':4C,66C ''provid'':34C ''rel'':50C ''represent'':82C ''singl'':92C ''single-tag'':91C ''sponsor'':9C ''supervis'':69C ''tag'':93C ''text'':85C,105C ''tf'':77C ''tf-idf'':76C ''thread'':15C ''titl'':104C ''top'':27C ''treat'':64C ''unweight'':80C ''winner'':97C ''www.kaggle.com'':48C,53C ''www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/forums/t/6650/share-your-approach'':47C ''www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction/forums/t/6650/share-your-approach&lt;/a&gt;'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (7001, 2, '2015-09-04 00:27:40.43', 1, NULL, NULL, '&lt;p&gt;A first count on items can prune some of them. If you have only 20 records you can simply count the occurrence of items in dataset and prune those items which did not happen enough because their supersets also do not happen frequently and them make item-sets based on the rest of items.&lt;/p&gt;
', NULL, NULL, '''/p'':57C ''20'':16C ''also'':40C ''base'':51C ''count'':4C,21C ''dataset'':27C ''enough'':36C ''first'':3C ''frequent'':44C ''happen'':35C,43C ''item'':6C,25C,31C,49C,56C ''item-set'':48C ''make'':47C ''occurr'':23C ''p'':1C ''prune'':8C,29C ''record'':17C ''rest'':54C ''set'':50C ''simpli'':20C ''superset'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (7002, 2, '2015-09-04 00:31:32.127', 1, NULL, NULL, '&lt;p&gt;What you are exactly looking for is a modification to the &lt;a href="http://kghost.de/cig_proc/full/paper_31.pdf" rel="nofollow"&gt;DEDICOM&lt;/a&gt; Algorithm (page 4). the DEDICOM itself gives you measure for relation between different components of a directed graph. You just need to be a bit creative to use it for converting a graph into DAG. Read the paper and if further help needed just drop me a comment.&lt;/p&gt;
', NULL, NULL, '''/a'':21C ''/cig_proc/full/paper_31.pdf'':17C ''/p'':70C ''4'':24C ''algorithm'':22C ''bit'':46C ''comment'':69C ''compon'':35C ''convert'':52C ''creativ'':47C ''dag'':56C ''dedicom'':20C,26C ''differ'':34C ''direct'':38C ''drop'':66C ''exact'':5C ''give'':28C ''graph'':39C,54C ''help'':63C ''href'':14C ''kghost.de'':16C ''kghost.de/cig_proc/full/paper_31.pdf'':15C ''look'':6C ''measur'':30C ''modif'':10C ''need'':42C,64C ''nofollow'':19C ''p'':1C ''page'':23C ''paper'':59C ''read'':57C ''rel'':18C ''relat'':32C ''use'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8001, 2, '2015-09-04 04:48:33.52', 3, NULL, NULL, '&lt;p&gt;The fitting procedure is the one that actually finds the coefficients of the model. The regularization term is used to indirectly find the coefficients by penalizing big coefficients &lt;em&gt;during the fitting procedure&lt;/em&gt;. A simple (albeit somewhat biased/naive) example might help illustrate this difference between regularization and gradient descent:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;X, y &amp;lt;- read input data
for different values of lambda L
    for each fold of cross-validation using X,y,L
        theta &amp;lt;- minimize (RSS + regularization using L) via MLE/GD
        score &amp;lt;- calculate performance of model using theta on the validation set 
    if average score across folds for L is better than the current best average score 
        L_best &amp;lt;- L
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As you can see, the fitting procedure (MLE or GD in our case) finds the best coefficients given the specific value of lambda.&lt;/p&gt;

&lt;p&gt;As a side note, I would look at this answer &lt;a href="http://stats.stackexchange.com/questions/137481/how-bad-is-hyperparameter-tuning-outside-cross-validation"&gt;here&lt;/a&gt; about tuning the regularization parameter, because it tends a little bit murky in terms of bias. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8003, 2, '2015-09-04 04:57:27.22', 1, NULL, NULL, '&lt;p&gt;First I want to say Dirk is basically correct that survival analysis doesn''t have to model death it''s used essentially the same way for looking at user groups in cohort analysis.  However, it is unreliable for specific individual behavior activities, for say fraud management. Revenue analysis and trend forecasting are great uses of survival models.  &lt;/p&gt;

&lt;p&gt;That having been said the situation is not hopeless for individual behavior, simply not so straight-forward.  Having run into a similar situation in pre-paid card accounts for a mobile company ... you can model an individual''s behavior in the way your trying to, by modeling it as a process control issue, and then following good practice. By randomly sampled data(important) of an individual''s behavior (usage) and comparing it to similar behaviors of other user''s (normalizing for environmental factors) and looking at the control points, more than 3 sequential out of control points in a period or more than 8 overall is base set.  This can then give you a set "suspicious" potentially "churn or fraud" activities which can then be analyzed and labeled to train better models.&lt;/p&gt;

&lt;p&gt;Hope that''s helpful :)&lt;/p&gt;
', NULL, NULL, '''/p'':59C,195C,201C ''3'':154C ''8'':166C ''account'':90C ''activ'':43C,183C ''analysi'':13C,34C,49C ''analyz'':188C ''base'':169C ''basic'':9C ''behavior'':42C,72C,101C,130C,137C ''better'':193C ''card'':89C ''churn'':180C ''cohort'':33C ''compani'':94C ''compar'':133C ''control'':114C,150C,158C ''correct'':10C ''data'':124C ''death'':19C ''dirk'':7C ''doesn'':14C ''environment'':144C ''essenti'':23C ''factor'':145C ''first'':2C ''follow'':118C ''forecast'':52C ''forward'':78C ''fraud'':46C,182C ''give'':174C ''good'':119C ''great'':54C ''group'':31C ''help'':200C ''hope'':197C ''hopeless'':69C ''howev'':35C ''import'':125C ''individu'':41C,71C,99C,128C ''issu'':115C ''label'':190C ''look'':28C,147C ''manag'':47C ''mobil'':93C ''model'':18C,58C,97C,109C,194C ''normal'':142C ''overal'':167C ''p'':1C,60C,196C ''paid'':88C ''period'':162C ''point'':151C,159C ''potenti'':179C ''practic'':120C ''pre'':87C ''pre-paid'':86C ''process'':113C ''random'':122C ''revenu'':48C ''run'':80C ''said'':64C ''sampl'':123C ''say'':6C,45C ''sequenti'':155C ''set'':170C,177C ''similar'':83C,136C ''simpli'':73C ''situat'':66C,84C ''specif'':40C ''straight'':77C ''straight-forward'':76C ''surviv'':12C,57C ''suspici'':178C ''train'':192C ''trend'':51C ''tri'':106C ''unreli'':38C ''usag'':131C ''use'':22C,55C ''user'':30C,140C ''want'':4C ''way'':26C,104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8004, 1, '2015-09-04 10:20:30.41', 3, 24, 'Meta-analysis of public 16S data', '&lt;p&gt;I am trying to start a meta-analysis for which I want to extract some 16S-based information from public databases. Moreover, I want to relate this information with any metadata found in the associated studies (everything from environmental variables to sequencing details).&lt;/p&gt;

&lt;p&gt;For this, I realized some databases are available, like NCBI-Nucletotide, NCBI-SRA and EMBL-EBI-ENA, but I am not sure about which one to use or whether I can use them all.&lt;/p&gt;

&lt;p&gt;How can I filter only whole 16S sequences? Or even the most used 16S region?&lt;/p&gt;

&lt;p&gt;One more thing would be how can I extract all the corresponding metadata?&lt;/p&gt;

&lt;p&gt;Has anyone here tried this or anything similar before?&lt;/p&gt;
', 'metadata  bioinformatics', NULL, '''/p'':56C,95C,112C,127C,138C ''16s'':6A,28C,103C,110C ''16s-based'':27C ''analysi'':3A,19C ''anyon'':130C ''anyth'':135C ''associ'':47C ''avail'':65C ''base'':29C ''bioinformat'':9B ''correspond'':125C ''data'':7A ''databas'':33C,63C ''detail'':55C ''ebi'':76C ''embl'':75C ''embl-ebi-ena'':74C ''ena'':77C ''environment'':51C ''even'':106C ''everyth'':49C ''extract'':25C,122C ''filter'':100C ''found'':44C ''inform'':30C,40C ''like'':66C ''meta'':2A,18C ''meta-analysi'':1A,17C ''metadata'':8B,43C,126C ''moreov'':34C ''ncbi'':68C,71C ''ncbi-nucletotid'':67C ''ncbi-sra'':70C ''nucletotid'':69C ''one'':85C,114C ''p'':10C,57C,96C,113C,128C ''public'':5A,32C ''realiz'':61C ''region'':111C ''relat'':38C ''sequenc'':54C,104C ''similar'':136C ''sra'':72C ''start'':15C ''studi'':48C ''sure'':82C ''thing'':116C ''tri'':13C,132C ''use'':87C,92C,109C ''variabl'':52C ''want'':23C,36C ''whether'':89C ''whole'':102C ''would'':117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8005, 1, '2015-09-04 14:57:20.02', 1, 121, 'Develop a âCredit Risk Scoring Systemâ with R', '&lt;p&gt;I want to achieve during my internship a credit risk scoring system based on "credit german dataset".&lt;/p&gt;

&lt;p&gt;I develop a model with R using logistic regression and i validate it. Now i must use this model to calculate a new applicant score and deduce whether good or bad.&lt;/p&gt;

&lt;p&gt;I don''t know how to proceed?&lt;/p&gt;
', 'data-mining  r  predictive-modeling', NULL, '''/p'':35C,68C,77C ''achiev'':21C ''applic'':60C ''bad'':67C ''base'':30C ''calcul'':57C ''credit'':4A,26C,32C ''data'':11B ''data-min'':10B ''dataset'':34C ''deduc'':63C ''develop'':1A,38C ''german'':33C ''good'':65C ''internship'':24C ''know'':73C ''logist'':44C ''mine'':12B ''model'':16B,40C,55C ''must'':52C ''new'':59C ''p'':17C,36C,69C ''predict'':15B ''predictive-model'':14B ''proceed'':76C ''r'':9A,13B,42C ''regress'':45C ''risk'':5A,27C ''score'':6A,28C,61C ''system'':29C ''systemâ'':7A ''use'':43C,53C ''valid'':48C ''want'':19C ''whether'':64C ''â'':3A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8010, 2, '2015-09-04 22:03:28.517', 3, NULL, NULL, '&lt;p&gt;Another approach would be to model "churn" (aka "diminished use of the service, including non-use") as a &lt;strong&gt;&lt;em&gt;process&lt;/em&gt;&lt;/strong&gt; and not an event.  Years ago in retention marketing this was called a "defection funnel", to mirror the "sales funnel" on the customer acquisition side (suspect -&gt; prospect -&gt; trial customer -&gt; repeat customer -&gt; loyal customer).  So a defection funnel might look like this:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;loyal/frequent customer -&gt; disaffected customer -&gt; infrequent customer -&gt; non-customer&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;It is proper to label this a "funnel" because a subset of each stage move on to the next stage.  Not all "disaffected customers" become "infrequent customers", and so on.&lt;/p&gt;

&lt;p&gt;There are two keys to defining a defection funnel.  First, define the behavioral characteristics for the funnel as a whole, and for each stage (category) within the funnel.  In your case, it might be a variation of the familiar RFM ("recency" "frequency" and "monetary") score.  Second, you need to identify &lt;em&gt;other&lt;/em&gt; behavioral characteristics or patterns that show a &lt;em&gt;propensity&lt;/em&gt; to move from one stage to another.  These might include: complaints, being victim of trolling, experiencing service interruptions, experiencing betting losses, how long they have been using the service, or maybe it is just demographics.&lt;/p&gt;

&lt;p&gt;With these definitions in place, you are in a position to build a predictive model and also track over time the population of customers in each category.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;By the way, this approach is not dependent on dividing the funnel into "stages", nor does it matter fundamentally how many stages you have.  However, defining discrete stages has many practical benefits, including communicating your model and results to other people who are not quantitative or statistically minded.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':80C ''/em'':24C,166C,176C ''/p'':67C,79C,112C,212C,241C,292C ''/strong'':25C ''acquisit'':49C ''ago'':31C ''aka'':9C ''also'':230C ''anoth'':2C,183C ''approach'':3C,248C ''becom'':106C ''behavior'':126C,167C ''benefit'':275C ''bet'':196C ''blockquot'':68C ''build'':225C ''call'':37C ''case'':144C ''categori'':138C,240C ''characterist'':127C,168C ''churn'':8C ''communic'':277C ''complaint'':187C ''custom'':48C,54C,56C,58C,71C,73C,75C,78C,105C,108C,237C ''defect'':39C,61C,121C ''defin'':119C,124C,269C ''definit'':216C ''demograph'':211C ''depend'':251C ''diminish'':10C ''disaffect'':72C,104C ''discret'':270C ''divid'':253C ''em'':22C,164C,174C ''event'':29C ''experienc'':192C,195C ''familiar'':152C ''first'':123C ''frequenc'':155C ''fundament'':262C ''funnel'':40C,45C,62C,89C,122C,130C,141C,255C ''howev'':268C ''hr'':242C ''identifi'':163C ''includ'':15C,186C,276C ''infrequ'':74C,107C ''interrupt'':194C ''key'':117C ''label'':86C ''like'':65C ''long'':199C ''look'':64C ''loss'':197C ''loyal'':57C ''loyal/frequent'':70C ''mani'':264C,273C ''market'':34C ''matter'':261C ''mayb'':207C ''might'':63C,146C,185C ''mind'':291C ''mirror'':42C ''model'':7C,228C,279C ''monetari'':157C ''move'':96C,178C ''need'':161C ''next'':100C ''non'':17C,77C ''non-custom'':76C ''non-us'':16C ''one'':180C ''p'':1C,69C,81C,113C,213C,243C ''pattern'':170C ''peopl'':284C ''place'':218C ''popul'':235C ''posit'':223C ''practic'':274C ''predict'':227C ''process'':23C ''propens'':175C ''proper'':84C ''prospect'':52C ''quantit'':288C ''recenc'':154C ''repeat'':55C ''result'':281C ''retent'':33C ''rfm'':153C ''sale'':44C ''score'':158C ''second'':159C ''servic'':14C,193C,205C ''show'':172C ''side'':50C ''stage'':95C,101C,137C,181C,257C,265C,271C ''statist'':290C ''strong'':21C ''subset'':92C ''suspect'':51C ''time'':233C ''track'':231C ''trial'':53C ''troll'':191C ''two'':116C ''use'':11C,18C,203C ''variat'':149C ''victim'':189C ''way'':246C ''whole'':133C ''within'':139C ''would'':4C ''year'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8014, 2, '2015-09-05 13:24:59.433', 0, NULL, NULL, '&lt;p&gt;I recommend doing a stacked model.&lt;/p&gt;

&lt;p&gt;If you are doing categorical prediction, you could use a Naive Bayes model to form a prediction on the string data using a bag of words model. That is now a continuous variable to be fed into the ANN.&lt;/p&gt;

&lt;p&gt;Another way to process text is to term frequency/inverse document freq stats and use a cosine distance from reference docs. Let''s say you have a document that speaks about topic A and one about topic B, you could convert unclassified docs to a numerical distance from A and B. (Typically you build a "reference" A vector from many A docs...)&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Tf" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Tf&lt;/a&gt;âidf&lt;/p&gt;

&lt;p&gt;Hope this helps.&lt;/p&gt;
', NULL, NULL, '''/p'':8C,49C,112C,126C,131C ''/wiki/tf'':118C ''/wiki/tf&lt;/a&gt;'':123C ''ann'':48C ''anoth'':51C ''b'':87C,100C ''bag'':33C ''bay'':21C ''build'':103C ''categor'':14C ''continu'':41C ''convert'':90C ''cosin'':66C ''could'':17C,89C ''data'':30C ''distanc'':67C,96C ''doc'':70C,92C,111C ''document'':60C,77C ''en.wikipedia.org'':117C,122C ''en.wikipedia.org/wiki/tf'':116C ''en.wikipedia.org/wiki/tf&lt;/a&gt;'':121C ''fed'':45C ''form'':24C ''freq'':61C ''frequency/inverse'':59C ''help'':130C ''hope'':128C ''href'':115C ''idf'':125C ''let'':71C ''mani'':109C ''model'':7C,22C,36C ''naiv'':20C ''nofollow'':120C ''numer'':95C ''one'':84C ''p'':1C,9C,50C,113C,127C ''predict'':15C,26C ''process'':54C ''recommend'':3C ''refer'':69C,105C ''rel'':119C ''say'':73C ''speak'':79C ''stack'':6C ''stat'':62C ''string'':29C ''term'':58C ''text'':55C ''topic'':81C,86C ''typic'':101C ''unclassifi'':91C ''use'':18C,31C,64C ''variabl'':42C ''vector'':107C ''way'':52C ''word'':35C ''â'':124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8015, 1, '2015-09-05 13:49:06.513', 2, 41, 'Naive about which Naive Bayes in article', '&lt;p&gt;Working with the Naive Bayes spam filtering article on Wikipedia 
(&lt;a href="https://en.wikipedia.org/wiki/Naive_Bayes_spam_filtering" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Naive_Bayes_spam_filtering&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/K9cbG.jpg" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/oJGqu.jpg" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Is it a binary multinomial equation or a Bernoulli form or some other?&lt;/p&gt;

&lt;p&gt;In this 2006 paper, it discusses that there are many Naive Bayes algorithms: Spam Filtering with Naive Bayes â Which Naive Bayes? 
&lt;a href="http://www.aueb.gr/users/ion/docs/ceas2006_paper.pdf" rel="nofollow"&gt;http://www.aueb.gr/users/ion/docs/ceas2006_paper.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The paper states that binary Multinomial NB performs best.&lt;/p&gt;

&lt;p&gt;If it is not multinomial, what changes are needed to make it so?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8964, 2, '2015-11-21 08:17:54.233', 2, NULL, NULL, '&lt;p&gt;Data science is still a domain in fusion, borrowing from neighboring fields.&lt;/p&gt;

&lt;p&gt;A recent interesting contender is DSAA, "IEEE International Conference on Data Science and Advanced Analytics". The first edition (&lt;a href="http://datamining.it.uts.edu.au/conferences/dsaa14/" rel="nofollow"&gt;DSAA''2014&lt;/a&gt;) held in Shanghai. The second &lt;a href="http://dsaa2015.lip6.fr/" rel="nofollow"&gt;DSAA''2015&lt;/a&gt; was in Paris, the &lt;a href="http://www.idawise.com/News/Dsaa2016.html" rel="nofollow"&gt;DSAA 2016&lt;/a&gt; edition is announced in Montreal, Canada, on October 17-19, 2016. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://nuit-blanche.blogspot.fr/2015/11/pcmi-summer-school-mathematics-of-data.html" rel="nofollow"&gt;Nuit Blanche&lt;/a&gt; recently anounced the PCMI Summer school "&lt;a href="https://pcmi.ias.edu/program-index/2016" rel="nofollow"&gt;The mathematics of data&lt;/a&gt;", June-July 2016, Midway, Utah, USA.&lt;/p&gt;

&lt;p&gt;Other related conferences and workshops are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;COLT: &lt;a href="http://www.learningtheory.org/" rel="nofollow"&gt;Annual Conference on Learning Theory&lt;/a&gt; (&lt;a href="http://www.learningtheory.org/colt2016/" rel="nofollow"&gt;COLT 2016&lt;/a&gt;, June, New-York, USA)&lt;/li&gt;
&lt;li&gt;MMDS: &lt;a href="http://mmds-data.org/" rel="nofollow"&gt;Workshop on Algorithms for Modern Massive Data Sets&lt;/a&gt; (2016 edition not announced yet)&lt;/li&gt;
&lt;li&gt;ICML: &lt;a href="http://www.machinelearning.org/icml.html" rel="nofollow"&gt;International Conference on Machine Learning&lt;/a&gt; (&lt;a href="http://icml.cc/2016/" rel="nofollow"&gt;ICML 2016&lt;/a&gt;, June, New York, USA)&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9667, 1, '2016-01-07 08:32:42.97', 4, 43, 'Classify Customers based on 2 features AND a Time series of events', '&lt;p&gt;I hope anyone can help me, I am a little bit stuck on what should be my next movement in an algorithm I am designing.&lt;/p&gt;

&lt;p&gt;Due to NDAs,I can''t disclosure much, but I''ll try to be generic and understandable.&lt;/p&gt;

&lt;p&gt;Basically, after several steps in the algorithms what I have is this:&lt;/p&gt;

&lt;p&gt;For each customer that I have, and events that they do during a month, during first steps I have clustered the events into several categories (each customer will have the events separated into categories that goes from 1 to x being x between 1 to 25, generally the first categories has more density of events than the others).&lt;/p&gt;

&lt;p&gt;For each category and customer I have created a time series aggregating the events of the month per hour (getting patterns of when this events are being done). Also I am using a couple of normalizing variables based on the number of days over a month (30 days) that the guy perform at least one event, and the number of days with at least one event over the total of days with at least one event (aggregating all clusters). The first one give me a ratio of how active is the customer during the month, and the second one weight the category against the others&lt;/p&gt;

&lt;p&gt;The final table look like this&lt;/p&gt;

&lt;p&gt;|&lt;em&gt;Identifier&lt;/em&gt;|  &lt;em&gt;firstCat&lt;/em&gt; | &lt;em&gt;feature1&lt;/em&gt; | &lt;em&gt;feature2&lt;/em&gt;  |   {      &lt;em&gt;(TIME SERIES)&lt;/em&gt;   }&lt;/p&gt;

&lt;p&gt;CustomerID  |  ClusterID |  DaysOver30 | DaysOverTotal | Events9AM Events10AM ... &lt;/p&gt;

&lt;p&gt;xx | 1 | 0,69 |  0,72 |  0,2   0,13   ...&lt;/p&gt;

&lt;p&gt;xx | 2 | 0,11 |  0,28 |  0,1   0,45   ...&lt;/p&gt;

&lt;p&gt;xy | 1 | 0,23 |  0,88 |  0,00  0,60   ...&lt;/p&gt;

&lt;p&gt;xy | 2 | 0,11 |  0,08 |  1,00  0,00   ...&lt;/p&gt;

&lt;p&gt;xy | 3 | 0,10 |  0,04 |  0,40  0,60   ...&lt;/p&gt;

&lt;p&gt;The time series variables are the percentage over the total of events per day on each specific category (this means that per each row adding up all variables should be 1) The reason of doing it like that is because for example a time series with events 0 0 0 1 0 and 1 1 1 2 1 are completely different, and standardizing to normal would give similar results. And due to high skew between different categories I check the values on the time series independently with the others.&lt;/p&gt;

&lt;p&gt;Now, sorry for the long text, and I hope I where able to explain what I have (English is not my native language).&lt;/p&gt;

&lt;p&gt;What I need to do now is identify this categories (remember, they can be from 1 to x being x any number from 1 to 25) into 3 tags: tag A, tag B and Neither of Them. Looking at those variables I can identify manually which tag they belong, and the idea is to identify manually as much as I can and use any classifier algorithm to learn from that and identify all of them&lt;/p&gt;

&lt;p&gt;My idea was to use multiple logistic regression on the table, but all the variables of the time series are correlated (since they are a linear combination of each other), so I though I better use a clustering algorithm only over the time series using euclidean distance to categorize the different patterns and use the result and the other two normalizing variables in the logistic regression.&lt;/p&gt;

&lt;p&gt;The other concern that I have is that this approach takes each row independently to the others, and in theory, for each customer there should be only 0 or 1 tag A, 0 or 1 tag B and the remaining of them should be Neither (another tip is that normally Tag A and B are between first categories, because is highly dependent on the normalizing features (if days over total is High, there is a high possibility that the row is either A or B, depending on the Time Series Pattern, ). &lt;/p&gt;

&lt;p&gt;Edit: This is no longer a concern, I will just perform two different logistic regressions, one for Tag A or Other and another for Tag B or another, with the result probabilities I can select only the best of each.&lt;/p&gt;

&lt;p&gt;The dataset is enormous and the final algorithms needs to be applied using SQL (on Teradata), but for getting the coefficients of the logistic regression, or the centers of the clustering I get a sample and use R for that.&lt;/p&gt;

&lt;p&gt;I hope you can help me.
Thanks.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8016, 1, '2015-09-05 16:42:25.473', 7, 142, 'Original Meaning of "Intelligence" in "Business Intelligence"', '&lt;p&gt;What does the term &lt;strong&gt;"Intelligence"&lt;/strong&gt; originally stand for in &lt;strong&gt;"Business Intelligence"&lt;/strong&gt; ? Does it mean as used in &lt;em&gt;"&lt;a href="https://en.wikipedia.org/wiki/Artificial_intelligence"&gt;Artificial Intelligence&lt;/a&gt;"&lt;/em&gt; or as used in &lt;em&gt;"&lt;a href="https://en.wikipedia.org/wiki/Intelligence_agency"&gt;Intelligence Agency&lt;/a&gt;"&lt;/em&gt; ?&lt;/p&gt;

&lt;p&gt;In other words, does "&lt;a href="https://en.wikipedia.org/wiki/Business_intelligence"&gt;Business Intelligence&lt;/a&gt;" mean: "Acting smart &amp;amp; intelligently in business" or "Gathering data and information about the business" ?&lt;/p&gt;

&lt;p&gt;This question was the topic of a debate among some fellows in our data-science team, so I thought to ask about it from other experts. One might say that both meanings are applicable, but I''m asking for the original intended meaning of the word as proposed in the 1980s.&lt;/p&gt;

&lt;p&gt;An acceptable answer should definitely cite original references, and personal opinions are not what I''m seeking.&lt;/p&gt;
', 'bigdata  definitions  terminology  history', NULL, '''/a'':42C,56C,71C ''/em'':43C,57C ''/p'':58C,87C,141C,160C ''/strong'':19C,27C ''/wiki/artificial_intelligence'':39C ''/wiki/business_intelligence'':68C ''/wiki/intelligence_agency'':53C ''1980s'':140C ''accept'':144C ''act'':73C ''agenc'':55C ''among'':97C ''amp'':75C ''answer'':145C ''applic'':123C ''artifici'':40C ''ask'':110C,127C ''bigdata'':8B ''busi'':6A,25C,69C,78C,86C ''cite'':148C ''data'':81C,103C ''data-sci'':102C ''debat'':96C ''definit'':9B,147C ''em'':34C,48C ''en.wikipedia.org'':38C,52C,67C ''en.wikipedia.org/wiki/artificial_intelligence'':37C ''en.wikipedia.org/wiki/business_intelligence'':66C ''en.wikipedia.org/wiki/intelligence_agency'':51C ''expert'':115C ''fellow'':99C ''gather'':80C ''histori'':11B ''href'':36C,50C,65C ''inform'':83C ''intellig'':4A,7A,18C,26C,41C,54C,70C,76C ''intend'':131C ''m'':126C,158C ''mean'':2A,30C,72C,121C,132C ''might'':117C ''one'':116C ''opinion'':153C ''origin'':1A,20C,130C,149C ''p'':12C,59C,88C,142C ''person'':152C ''propos'':137C ''question'':90C ''refer'':150C ''say'':118C ''scienc'':104C ''seek'':159C ''smart'':74C ''stand'':21C ''strong'':17C,24C ''team'':105C ''term'':16C ''terminolog'':10B ''thought'':108C ''topic'':93C ''use'':32C,46C ''word'':62C,135C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8018, 1, '2015-09-06 02:04:05.053', 7, 207, 'Are there any machine learning techniques to identify points on plots/ images?', '&lt;p&gt;I have data for each vehicle''s lateral position over time and lane number as shown in these 3 plots in the image and sample data below.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/RfJJQ.png"&gt;&lt;img src="http://i.stack.imgur.com/RfJJQ.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; a
   Frame.ID   xcoord Lane
1       452 27.39400    3
2       453 27.38331    3
3       454 27.42999    3
4       455 27.46512    3
5       456 27.49066    3
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The lateral position varies over time because a human driver does not have perfect control over vehicle''s position. The lane change maneuver starts when the lateral position changes drastically and ends when the variation becomes ''normal'' again. This can not be identified from the data directly. I have to manually look at each vehicle''s plot to determine the start and end points of lane change maneuver in order to estimate the duration of lane change. But I have thousands of vehicles in the data set. Could you please direct me to any relevant image analysis/ machine learning algorithm that could be trained to identify these points? I work in R. Thanks in advance.&lt;/p&gt;
', 'machine-learning  r', NULL, '''/a'':62C ''/code'':91C ''/p'':45C,63C,209C ''/pre'':92C ''/rfjjq.png'':51C,56C ''1'':71C ''2'':75C ''27.38331'':77C ''27.39400'':73C ''27.42999'':81C ''27.46512'':85C ''27.49066'':89C ''3'':36C,74C,78C,79C,82C,86C,90C ''4'':83C ''452'':72C ''453'':76C ''454'':80C ''455'':84C ''456'':88C ''5'':87C ''advanc'':208C ''algorithm'':193C ''alt'':57C ''analysi'':190C ''becom'':129C ''chang'':115C,122C,160C,170C ''code'':65C ''control'':108C ''could'':181C,195C ''data'':20C,43C,139C,179C ''descript'':60C ''determin'':152C ''direct'':140C,184C ''drastic'':123C ''driver'':103C ''durat'':167C ''end'':125C,156C ''enter'':58C ''estim'':165C ''frame.id'':68C ''gt'':66C ''href'':48C ''human'':102C ''i.stack.imgur.com'':50C,55C ''i.stack.imgur.com/rfjjq.png'':49C,54C ''identifi'':8A,136C,199C ''imag'':12A,40C,59C,189C ''img'':52C ''lane'':30C,70C,114C,159C,169C ''later'':25C,95C,120C ''learn'':5A,15B,192C ''look'':145C ''machin'':4A,14B,191C ''machine-learn'':13B ''maneuv'':116C,161C ''manual'':144C ''normal'':130C ''number'':31C ''order'':163C ''p'':17C,46C,93C ''perfect'':107C ''pleas'':183C ''plot'':11A,37C,150C ''point'':9A,157C,201C ''posit'':26C,96C,112C,121C ''pre'':64C ''r'':16B,205C ''relev'':188C ''sampl'':42C ''set'':180C ''shown'':33C ''src'':53C ''start'':117C,154C ''techniqu'':6A ''thank'':206C ''thousand'':174C ''time'':28C,99C ''train'':197C ''vari'':97C ''variat'':128C ''vehicl'':23C,110C,148C,176C ''work'':203C ''xcoord'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8238, 2, '2015-09-30 00:57:13.99', 2, NULL, NULL, '&lt;p&gt;You could use the median. That protects against the "Bill Gates walks into a bar, making everyone there a millionaire, on average" effect. It''s used with &lt;a href="http://www.ssa.gov/OACT/COLA/central.html" rel="nofollow"&gt;wage distributions&lt;/a&gt; for exactly the reason that they tend to be very skewed.&lt;/p&gt;
', NULL, NULL, '''/a'':38C ''/oact/cola/central.html'':33C ''/p'':50C ''averag'':23C ''bar'':16C ''bill'':11C ''could'':3C ''distribut'':37C ''effect'':24C ''everyon'':18C ''exact'':40C ''gate'':12C ''href'':30C ''make'':17C ''median'':6C ''millionair'':21C ''nofollow'':35C ''p'':1C ''protect'':8C ''reason'':42C ''rel'':34C ''skew'':49C ''tend'':45C ''use'':4C,27C ''wage'':36C ''walk'':13C ''www.ssa.gov'':32C ''www.ssa.gov/oact/cola/central.html'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8028, 1, '2015-09-07 15:41:26.427', 1, 71, 'Data Science education curriculum design and guidelines in Computer Science and other Disciplines', '&lt;p&gt;I am studying data science education curriculum design and guidelines. All resources I have up to now is about data science in statistics education and library and information science education.&lt;/p&gt;

&lt;p&gt;Is there any work on data science in computer science/software engineering education (adding data science courses to a traditional software engineering curriculum) ,management and other disciplines? &lt;/p&gt;

&lt;p&gt;An example of including the curriculum for statistics is this &lt;a href="https://www.researchgate.net/publication/266856309_Data_Science_in_Statistics_Curricula_Preparing_Students_to_Think_with_Data" rel="nofollow"&gt;paper&lt;/a&gt;&lt;/p&gt;
', 'data  education', NULL, '''/a'':95C ''/p'':47C,75C,96C ''/publication/266856309_data_science_in_statistics_curricula_preparing_students_to_think_with_data'':91C ''ad'':61C ''comput'':9A,57C ''cours'':64C ''curriculum'':4A,23C,70C,82C ''data'':1A,14B,20C,36C,54C,62C ''design'':5A,24C ''disciplin'':13A,74C ''educ'':3A,15B,22C,40C,46C,60C ''engin'':59C,69C ''exampl'':78C ''guidelin'':7A,26C ''href'':88C ''includ'':80C ''inform'':44C ''librari'':42C ''manag'':71C ''nofollow'':93C ''p'':16C,48C,76C ''paper'':94C ''rel'':92C ''resourc'':28C ''scienc'':2A,10A,21C,37C,45C,55C,63C ''science/software'':58C ''softwar'':68C ''statist'':39C,84C ''studi'':19C ''tradit'':67C ''work'':52C ''www.researchgate.net'':90C ''www.researchgate.net/publication/266856309_data_science_in_statistics_curricula_preparing_students_to_think_with_data'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8029, 1, '2015-09-07 16:14:16.143', 2, 120, 'Online/incremental unsupervised dimensionality reduction for use with classification for event prediction', '&lt;p&gt;Consider the application:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;We have a set of users and items.&lt;/li&gt;
&lt;li&gt;Users can perform different action types (think browsing, clicking, upvoting etc.) on different items.&lt;/li&gt;
&lt;li&gt;Users and items accumulate a "profile" for each action type.&lt;/li&gt;
&lt;li&gt;For users such profile is a list of items on which they had performed a given action type&lt;/li&gt;
&lt;li&gt;For items such profile is a list of users who performed a given action type on them&lt;/li&gt;
&lt;li&gt;We assume that accumulated profiles define future actions.&lt;/li&gt;
&lt;li&gt;We want to predict the action a user will take using supervised learning (classification with probability estimation)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Consider the following problem:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;These profiles can be very sparse (millions of items and 100 million users) and it is not feasible to use them directly as features&lt;/li&gt;
&lt;li&gt;We would like to compute "compressed" profiles (eigenprofiles?:)) with dimensionality &amp;lt; 300 that can then be efficiently stored and fed to different classification algorithms&lt;/li&gt;
&lt;li&gt;Before you say "Use TruncatedSVD/Collapsed Gibbs Sampling/Random Projections on historical data" bare with me for a second.&lt;/li&gt;
&lt;li&gt;Enters concept drift.&lt;/li&gt;
&lt;li&gt;New items and users are being introduced all the time to the system.&lt;/li&gt;
&lt;li&gt;Old users churn.&lt;/li&gt;
&lt;li&gt;Old items churn.&lt;/li&gt;
&lt;li&gt;At some point there are items with most of the users never seen in historical data and users with only fresh items.&lt;/li&gt;
&lt;li&gt;Before you say "retrain periodically", remember that we have a classifier in the pipeline that was taught on the "historic" decomposition and the new decomposition could assign entirely different "meaning" to cells of output vectors (abs(decompose_v1(sample)[0] - decompose_v2(sample)[0]) &gt;&gt; epsilon) rendering this classifier unusable.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Some requirements:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;The prediction service has to be available 24/7.&lt;/li&gt;
&lt;li&gt;The prediction cannot take more than 15ms and should use a maximum of 4 cpu cores (preferably only one)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Some ideas I had so far:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;ul&gt;
&lt;li&gt;&lt;p&gt;We could retrain the classifier on the new decomposition but this would mean that we have to re-run the decomposition on the whole training dataset (with snapshot of profiles at the time of the event we want to predict) and the whole database (all current profiles) plus store it. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;To make this work we would have to have a second database for storing the decomposed profiles that would be hot-swapped once the new retrained model is ready and all profiles have been decomposed.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;This approach is quite inefficient in both computational resources and storage resources (this is expensive storage because the retrieval has to be super-fast)&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;ul&gt;
&lt;li&gt;We could retrain the classifier as in solution 1. But do the decomposition ad_hoc.&lt;/li&gt;
&lt;li&gt;This puts a lot of constraints on the speed of the decomposition (has to have sub-millisecond computation times for a single sample).&lt;/li&gt;
&lt;li&gt;This does a lot of redundant computation (especially for item profiles) unless we add an extra caching layer.&lt;/li&gt;
&lt;li&gt;This avoids redundant storage and redundant computation of churned users/items at the cost of extra prediction latency and extra caching layer complexity. &lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&amp;lt;---- Please help me here&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;We could use one of online learning algorithms such as VFT or Mondrian Forests for the classifier - so no more retraining + nice handling of concept drift.&lt;/li&gt;
&lt;li&gt;We would need an online algorithm for decomposition that satisfies strict requirements:
a) at least a part of output vectors should be stable between increments (batches).
b) it can introduce new features to account for new variance in the data but should do so at a controllable rate
c) should not break if it encounters new users/items&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Questions/points of action:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Please evaluate my proposed solutions and propose alternatives&lt;/li&gt;
&lt;li&gt;Please provide algorithms suitable for online learning and online decomposition (if they exist) as described in alternative 3. Preferably with efficient python/scala/java implementations with a sufficient layer of abstraction to use them in a web service (python scripts that take in a text file as dataset would be much less valuable than scikit modules)&lt;/li&gt;
&lt;li&gt;Please provide links to relevant literature that dealt with similar problems/describes algorithms that could be suitable&lt;/li&gt;
&lt;li&gt;Please share experiences/caveats/tips that you learned while dealing with similar problems  &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Some background reading that you may find useful:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.quora.com/What-machine-learning-algorithms-are-used-for-internet-advertising" rel="nofollow"&gt;quora question on ad click prediction&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/pubs/archive/41159.pdf" rel="nofollow"&gt;google "view from the trenches"&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://people.csail.mit.edu/romer/papers/TISTRespPredAds.pdf" rel="nofollow"&gt;criteo paper on click prediction&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.quinonero.net/Publications/predicting-clicks-facebook.pdf" rel="nofollow"&gt;facebook on predicting ad clicks&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Disclaimer: Our application is not strictly ad conversion prediction and some problems such as rarity do not apply. The event we would like to predict has 8 classes and occurs c.a. 0.3%-3% of times a user browses an item. &lt;/p&gt;
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''varianc'':656C ''vector'':300C,639C ''vft'':602C ''view'':821C ''want'':115C,405C ''web'':732C ''whole'':391C,410C ''work'':425C ''would'':168C,378C,427C,440C,621C,744C,880C ''www.quinonero.net'':846C ''www.quinonero.net/publications/predicting-clicks-facebook.pdf'':845C ''www.quora.com'':800C ''www.quora.com/what-machine-learning-algorithms-are-used-for-internet-advertising'':799C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8019, 2, '2015-09-06 10:33:39.82', 0, NULL, NULL, '&lt;p&gt;There''s an inherent problem that anybody who chooses to use a system designed primarily for extreme privacy is interesting to the surveillance org.&lt;/p&gt;

&lt;p&gt;So the surveillance org will likely learn about any such system or class of systems if it becomes significant, and develop means of identifying its traffic (and possibly filtering out the fake traffic), and then give special attention to the users sending that traffic.&lt;/p&gt;

&lt;p&gt;You can also consider the opposite approach: instead of auto-generating traffic for existing users with false negatives, auto-generating new users with traffic full of false positives.  However it suffers from the same &lt;a href="https://en.wikipedia.org/wiki/Cat_and_mouse" rel="nofollow"&gt;cat-and-mouse dynamic&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The solution is for the tools used by most users (Android, iOS, telcos, Gmail, Facebook, Google Search, OEMs) to feature security capabilities client-side &lt;em&gt;by default&lt;/em&gt; AND to &lt;em&gt;not&lt;/em&gt; provide backdoors to surveillance orgs.  That would also make it more difficult to provide useful features like recommendations and well-targeted adverts.   For various reasons, in large part because most consumers do not demand it, they do not prioritise this, and that may not ever change.&lt;/p&gt;

&lt;p&gt;Further reading: &lt;a href="https://blog.kaspersky.com/chrome_ext_encrypt_data_leaving_browser/5063/" rel="nofollow"&gt;https://blog.kaspersky.com/chrome_ext_encrypt_data_leaving_browser/5063/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':122C ''/chrome_ext_encrypt_data_leaving_browser/5063/'':214C ''/chrome_ext_encrypt_data_leaving_browser/5063/&lt;/a&gt;&lt;/p&gt;'':219C ''/em'':153C,158C ''/p'':26C,72C,123C,206C ''/wiki/cat_and_mouse'':114C ''advert'':181C ''also'':76C,166C ''android'':135C ''anybodi'':8C ''approach'':80C ''attent'':65C ''auto'':84C,94C ''auto-gener'':83C,93C ''backdoor'':160C ''becom'':45C ''blog.kaspersky.com'':213C,218C ''blog.kaspersky.com/chrome_ext_encrypt_data_leaving_browser/5063/'':212C ''blog.kaspersky.com/chrome_ext_encrypt_data_leaving_browser/5063/&lt;/a&gt;&lt;/p&gt;'':217C ''capabl'':146C ''cat'':118C ''cat-and-mous'':117C ''chang'':205C ''choos'':10C ''class'':40C ''client'':148C ''client-sid'':147C ''consid'':77C ''consum'':190C ''default'':152C ''demand'':193C ''design'':15C ''develop'':48C ''difficult'':170C ''dynam'':121C ''em'':150C,156C ''en.wikipedia.org'':113C ''en.wikipedia.org/wiki/cat_and_mouse'':112C ''ever'':204C ''exist'':88C ''extrem'':18C ''facebook'':139C ''fake'':59C ''fals'':91C,102C ''featur'':144C,174C ''filter'':56C ''full'':100C ''generat'':85C,95C ''give'':63C ''gmail'':138C ''googl'':140C ''howev'':104C ''href'':111C,211C ''identifi'':51C ''inher'':5C ''instead'':81C ''interest'':21C ''io'':136C ''larg'':186C ''learn'':34C ''like'':33C,175C ''make'':167C ''may'':202C ''mean'':49C ''mous'':120C ''negat'':92C ''new'':96C ''nofollow'':116C,216C ''oem'':142C ''opposit'':79C ''org'':25C,31C,163C ''p'':1C,27C,73C,124C,207C ''part'':187C ''posit'':103C ''possibl'':55C ''primarili'':16C ''prioritis'':198C ''privaci'':19C ''problem'':6C ''provid'':159C,172C ''read'':209C ''reason'':184C ''recommend'':176C ''rel'':115C,215C ''search'':141C ''secur'':145C ''send'':69C ''side'':149C ''signific'':46C ''solut'':126C ''special'':64C ''suffer'':106C ''surveil'':24C,30C,162C ''system'':14C,38C,42C ''target'':180C ''telco'':137C ''tool'':130C ''traffic'':53C,60C,71C,86C,99C ''use'':12C,131C,173C ''user'':68C,89C,97C,134C ''various'':183C ''well'':179C ''well-target'':178C ''would'':165C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8020, 2, '2015-09-06 10:49:47.89', 1, NULL, NULL, '&lt;p&gt;Much depends on the data available to you.  Perhaps you can be more specific about the scale and scope.&lt;/p&gt;

&lt;p&gt;Modelling time is the straightforward bit.  To understand how to conceptualise time as useful features, see &lt;a href="http://datascience.stackexchange.com/a/2370/12363"&gt;this excellent answer&lt;/a&gt; on &lt;a href="http://datascience.stackexchange.com/questions/2368/machine-learning-features-engineering-from-date-time-data"&gt;Machine learning - features engineering from date/time data&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Modelling the user is more complicated.  You will likely not have enough data on each user, but you can build some user models.  (Too few, then the system will make similar predictions for all users, without nuance.  Too many and there will be sparsity, overfitting, and generally the same problems as having no profile models at all, ie one model per actual user.)&lt;/p&gt;

&lt;p&gt;This can be done supervised or unsupervised, finding representative clusters.&lt;br&gt;
(Search for &lt;em&gt;user profile categorisation&lt;/em&gt;, &lt;em&gt;user models&lt;/em&gt;, &lt;em&gt;user model clustering&lt;/em&gt;)&lt;/p&gt;
', NULL, NULL, '''/a'':47C,61C ''/a/2370/12363'':43C ''/em'':146C,150C,155C ''/p'':21C,62C,127C,156C ''/questions/2368/machine-learning-features-engineering-from-date-time-data'':53C ''actual'':125C ''answer'':46C ''avail'':7C ''bit'':28C ''br'':139C ''build'':83C ''categoris'':145C ''cluster'':138C,154C ''complic'':69C ''conceptualis'':33C ''data'':6C,60C,76C ''datascience.stackexchange.com'':42C,52C ''datascience.stackexchange.com/a/2370/12363'':41C ''datascience.stackexchange.com/questions/2368/machine-learning-features-engineering-from-date-time-data'':51C ''date/time'':59C ''depend'':3C ''done'':132C ''em'':142C,147C,151C ''engin'':57C ''enough'':75C ''excel'':45C ''featur'':37C,56C ''find'':136C ''general'':110C ''href'':40C,50C ''ie'':121C ''learn'':55C ''like'':72C ''machin'':54C ''make'':93C ''mani'':102C ''model'':23C,64C,86C,118C,123C,149C,153C ''much'':2C ''nuanc'':100C ''one'':122C ''overfit'':108C ''p'':1C,22C,63C,128C ''per'':124C ''perhap'':10C ''predict'':95C ''problem'':113C ''profil'':117C,144C ''repres'':137C ''scale'':18C ''scope'':20C ''search'':140C ''see'':38C ''similar'':94C ''sparsiti'':107C ''specif'':15C ''straightforward'':27C ''supervis'':133C ''system'':91C ''time'':24C,34C ''understand'':30C ''unsupervis'':135C ''use'':36C ''user'':66C,79C,85C,98C,126C,143C,148C,152C ''without'':99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8021, 2, '2015-09-06 14:01:21.123', 3, NULL, NULL, '&lt;p&gt;Assume your configure file is ~/.ipython/profile_pyspark/ipython_notebook_config.py, you can still use this configure file by:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ipython notebook --config=''~/.ipython/profile_pyspark/ipython_notebook_config.py''
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;or &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;jupyter-notebook --config=''~/.ipython/profile_pyspark/ipython_notebook_config.py''
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/.ipython/profile_pyspark/ipython_notebook_config.py'':7C,22C,34C ''/code'':23C,35C ''/p'':16C,27C ''/pre'':24C,36C ''assum'':2C ''code'':18C,29C ''config'':21C,33C ''configur'':4C,13C ''file'':5C,14C ''ipython'':19C ''jupyt'':31C ''jupyter-notebook'':30C ''notebook'':20C,32C ''p'':1C,25C ''pre'':17C,28C ''still'':10C ''use'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8024, 1, '2015-09-07 08:29:25.507', 5, 173, 'Does reinforcement learning require the help of other learning algorithms?', '&lt;p&gt;Can''t reinforcement learning be used without the help of other learning algorithms like SVM and MLP back propagation? I consulted two papers:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="https://www.cs.duke.edu/~parr/icml03.pdf" rel="nofollow"&gt;Paper 1&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5967372&amp;amp;tag=1" rel="nofollow"&gt;Paper 2&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;both have used other machine learning methods in the inner loop.&lt;/p&gt;
', 'machine-learning  reinforcement-learning', NULL, '''/a'':53C,65C ''/li'':54C,66C ''/ol'':67C ''/p'':41C,80C ''/xpls/abs_all.jsp?arnumber=5967372&amp;amp;tag=1'':60C ''/~parr/icml03.pdf'':48C ''1'':52C ''2'':64C ''algorithm'':10A,30C ''back'':35C ''consult'':38C ''help'':6A,26C ''href'':45C,57C ''ieeexplore.ieee.org'':59C ''ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5967372&amp;amp;tag=1'':58C ''inner'':78C ''learn'':3A,9A,13B,16B,21C,29C,74C ''li'':43C,55C ''like'':31C ''loop'':79C ''machin'':12B,73C ''machine-learn'':11B ''method'':75C ''mlp'':34C ''nofollow'':50C,62C ''ol'':42C ''p'':17C,68C ''paper'':40C,51C,63C ''propag'':36C ''reinforc'':2A,15B,20C ''reinforcement-learn'':14B ''rel'':49C,61C ''requir'':4A ''svm'':32C ''two'':39C ''use'':23C,71C ''without'':24C ''www.cs.duke.edu'':47C ''www.cs.duke.edu/~parr/icml03.pdf'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8025, 2, '2015-09-07 09:21:14.393', 2, NULL, NULL, '&lt;p&gt;First of all, you need to decide how you evaluate "good" vs "bad", which means picking a metric. From the top of my head you have three choices:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;accuracy: simplest, but potentially misleading (if you have few defaults and predict all 0s, you still get high accuracy)&lt;/li&gt;
&lt;li&gt;logloss: penalize assigning a high def probability to good quality credit and vice versa&lt;/li&gt;
&lt;li&gt;ROC: sort the credit from best to worst.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Once you''ve made your pick, make sure your model can generate predictions in the right format (i.e. output probabilities and not just 0/1) and the go through the training / validation / test paradigm. This should get you started.&lt;/p&gt;
', NULL, NULL, '''/li'':52C,68C,78C ''/p'':30C,119C ''/ul'':79C ''0/1'':104C ''0s'':46C ''accuraci'':33C,51C ''assign'':56C ''bad'':14C ''best'':75C ''choic'':29C ''credit'':64C,73C ''decid'':8C ''def'':59C ''default'':42C ''evalu'':11C ''first'':2C ''format'':97C ''generat'':92C ''get'':49C,116C ''go'':107C ''good'':12C,62C ''head'':25C ''high'':50C,58C ''i.e'':98C ''li'':32C,53C,69C ''logloss'':54C ''made'':84C ''make'':87C ''mean'':16C ''metric'':19C ''mislead'':37C ''model'':90C ''need'':6C ''output'':99C ''p'':1C,80C ''paradigm'':113C ''penal'':55C ''pick'':17C,86C ''potenti'':36C ''predict'':44C,93C ''probabl'':60C,100C ''qualiti'':63C ''right'':96C ''roc'':70C ''simplest'':34C ''sort'':71C ''start'':118C ''still'':48C ''sure'':88C ''test'':112C ''three'':28C ''top'':22C ''train'':110C ''ul'':31C ''valid'':111C ''ve'':83C ''versa'':67C ''vice'':66C ''vs'':13C ''worst'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8026, 1, '2015-09-07 14:42:14.13', 1, 119, 'Fittting histograms with combination of an unknown number of Gaussians', '&lt;p&gt;I have many data in the form of one dimensional histogram, to give an example consider the data at &lt;a href="http://pastebin.com/embed_js.php?i=1mNRuEHZ" rel="nofollow"&gt; http://pastebin.com/embed_js.php?i=1mNRuEHZ&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/fhps8.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/fhps8.jpg" alt="histogram test"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I expect that these data are obtained from a pdf composed of a sum of Gaussian distributions but with their actual number being unknown.&lt;/p&gt;

&lt;p&gt;I want then to fit these data with a model given by a sum of Gaussians&lt;/p&gt;

&lt;p&gt;$$
f_N(x) = \sum_{k=1}^N c_k \exp \left[ - \frac{(x-a_k)^2}{2 b_k^2} \right]
$$&lt;/p&gt;

&lt;p&gt;where $a_k$, $b_k$, $c_k$ &lt;strong&gt;and&lt;/strong&gt; $N$ are in principle parameters to be fitted.&lt;/p&gt;

&lt;p&gt;The problem can be viewed as a problem of model selection between different $f_N$ for all the possible values of $N$.&lt;/p&gt;

&lt;p&gt;My idea is then to make an usual fit by least squares at fixed $N$ and then compare the results for different $N$ via some statistical measure of the quality of the fit, as e.g. Akaike information criterion (AIC).
Is this an acceptable test?&lt;/p&gt;

&lt;p&gt;For the data of the previous example (that I generated as a test from 3 gaussians) I will obtain&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; N        AIC
 -------------
 1      +568.1
 2      +557.4
 3      -446.6
 4      -443.5
 5      -442.7
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As you see, the AIC decreases quickly till $N=3$, where it starts to increase again very slowly (due to overfitting). So it is clear that $N=3$ is the best choice here.&lt;/p&gt;

&lt;p&gt;I mention that in some cases I have more data histograms extracted from the same pdf, so I could do some cross validation. Although this always a good test, the availability of my data changes from sample to sample, so I''d prefer to have a criterion for the single histogram (if the quality of the test is not too much different).  &lt;/p&gt;
', 'statistics  beginner', NULL, '''+557.4'':247C ''+568.1'':245C ''-442.7'':253C ''-443.5'':251C ''-446.6'':249C ''/a'':59C ''/code'':254C ''/embed_js.php?i=1mnruehz'':37C ''/embed_js.php?i=1mnruehz&lt;/a&gt;&lt;/p&gt;'':42C ''/fhps8.jpg'':48C,55C ''/p'':60C,86C,104C,128C,148C,172C,218C,239C,290C,354C ''/pre'':255C ''/strong'':139C ''1'':111C,244C ''2'':122C,123C,126C,246C ''3'':234C,248C,266C,284C ''4'':250C ''5'':252C ''accept'':216C ''actual'':82C ''aic'':212C,243C,261C ''akaik'':209C ''alt'':56C ''although'':315C ''alway'':317C ''avail'':322C ''b'':124C,133C ''beginn'':12B ''best'':287C ''c'':113C,135C ''case'':297C ''chang'':326C ''choic'':288C ''clear'':281C ''code'':241C ''combin'':4A ''compar'':191C ''compos'':72C ''consid'':29C ''could'':310C ''criterion'':211C,338C ''cross'':313C ''d'':333C ''data'':17C,31C,66C,94C,222C,301C,325C ''decreas'':262C ''differ'':162C,195C,353C ''dimension'':23C ''distribut'':78C ''due'':275C ''e.g'':208C ''exampl'':28C,226C ''exp'':115C ''expect'':63C ''extract'':303C ''f'':106C,163C ''fit'':92C,147C,182C,206C ''fitt'':1A ''fix'':187C ''form'':20C ''frac'':117C ''gaussian'':10A,77C,103C,235C ''generat'':229C ''give'':26C ''given'':98C ''good'':319C ''histogram'':2A,24C,57C,302C,342C ''href'':34C,45C ''i.stack.imgur.com'':47C,54C ''i.stack.imgur.com/fhps8.jpg'':46C,53C ''idea'':175C ''img'':51C ''increas'':271C ''inform'':210C ''k'':110C,114C,121C,125C,132C,134C,136C ''least'':184C ''left'':116C ''make'':179C ''mani'':16C ''measur'':200C ''mention'':293C ''model'':97C,159C ''much'':352C ''n'':107C,112C,140C,164C,171C,188C,196C,242C,265C,283C ''nofollow'':39C,50C ''number'':8A,83C ''obtain'':68C,238C ''one'':22C ''overfit'':277C ''p'':13C,43C,61C,87C,105C,129C,149C,173C,219C,256C,291C ''paramet'':144C ''pastebin.com'':36C,41C ''pastebin.com/embed_js.php?i=1mnruehz'':35C ''pastebin.com/embed_js.php?i=1mnruehz&lt;/a&gt;&lt;/p&gt;'':40C ''pdf'':71C,307C ''possibl'':168C ''pre'':240C ''prefer'':334C ''previous'':225C ''principl'':143C ''problem'':151C,157C ''qualiti'':203C,345C ''quick'':263C ''rel'':38C,49C ''result'':193C ''right'':127C ''sampl'':328C,330C ''see'':259C ''select'':160C ''singl'':341C ''slowli'':274C ''squar'':185C ''src'':52C ''start'':269C ''statist'':11B,199C ''strong'':137C ''sum'':75C,101C,109C ''test'':58C,217C,232C,320C,348C ''till'':264C ''unknown'':7A,85C ''usual'':181C ''valid'':314C ''valu'':169C ''via'':197C ''view'':154C ''want'':89C ''x'':108C,119C ''x-a'':118C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8027, 1, '2015-09-07 15:16:28.267', 1, 34, 'Quantity Prediction Algorithm', '&lt;p&gt;I want to make prediction for quantity of stock that will be sufficient over a period of time i.e from one delivery to another.&lt;/p&gt;

&lt;p&gt;Assuming, i want a store to have minimum amount of item between deliveries e.g 30 items, delivery interval can be 7 , 14, 30, days. &lt;/p&gt;

&lt;p&gt;Also i have history data of past delivery interval, amount of item delivered and the amount of item available before next delivery.&lt;/p&gt;

&lt;p&gt;What will be the best way to relate these variable: delivery interval and on hand quantity to quantity delivered.&lt;/p&gt;

&lt;p&gt;also can i predict quantity to be delivered in other to get a given amount of on hand stock before next delivery.&lt;/p&gt;
', 'predictive-modeling', NULL, '''/p'':32C,58C,82C,103C,127C ''14'':55C ''30'':48C,56C ''7'':54C ''algorithm'':3A ''also'':60C,105C ''amount'':42C,69C,75C,119C ''anoth'':31C ''assum'':34C ''avail'':78C ''best'':88C ''data'':64C ''day'':57C ''deliv'':72C,102C,112C ''deliveri'':29C,46C,50C,67C,81C,94C,126C ''e.g'':47C ''get'':116C ''given'':118C ''hand'':98C,122C ''histori'':63C ''i.e'':26C ''interv'':51C,68C,95C ''item'':44C,49C,71C,77C ''make'':11C ''minimum'':41C ''model'':6B ''next'':80C,125C ''one'':28C ''p'':7C,33C,59C,83C,104C ''past'':66C ''period'':23C ''predict'':2A,5B,12C,108C ''predictive-model'':4B ''quantiti'':1A,14C,99C,101C,109C ''relat'':91C ''stock'':16C,123C ''store'':38C ''suffici'':20C ''time'':25C ''variabl'':93C ''want'':9C,36C ''way'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8066, 2, '2015-09-11 05:37:09.75', 0, NULL, NULL, '&lt;p&gt;Solr is a highly scalable, fault tolerant search server.&lt;/p&gt;

&lt;p&gt;So, you can store files in the form of JSON, XML, CSV or binary over HTTP.
And you can query it via &lt;strong&gt;GET&lt;/strong&gt; and receive JSON, XML, CSV and binary results. It also has a distributed architecture for the purpose, which is the primary and most important concept in Big Data and it''s handling.&lt;/p&gt;

&lt;p&gt;So, as it is highly scalable and fault tolerant for high traffic and huge documents, it is a choice for a reliable search engine for huge documents, or in short &lt;strong&gt;Big Data&lt;/strong&gt;.&lt;/p&gt;
', NULL, NULL, '''/p'':11C,70C,106C ''/strong'':37C,105C ''also'':47C ''architectur'':51C ''big'':64C,103C ''binari'':26C,44C ''choic'':90C ''concept'':62C ''csv'':24C,42C ''data'':65C,104C ''distribut'':50C ''document'':86C,98C ''engin'':95C ''fault'':7C,79C ''file'':17C ''form'':20C ''get'':36C ''handl'':69C ''high'':5C,76C,82C ''http'':28C ''huge'':85C,97C ''import'':61C ''json'':22C,40C ''p'':1C,12C,71C ''primari'':58C ''purpos'':54C ''queri'':32C ''receiv'':39C ''reliabl'':93C ''result'':45C ''scalabl'':6C,77C ''search'':9C,94C ''server'':10C ''short'':101C ''solr'':2C ''store'':16C ''strong'':35C,102C ''toler'':8C,80C ''traffic'':83C ''via'':34C ''xml'':23C,41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8038, 1, '2015-09-08 12:11:47.187', 1, 73, 'Passing TFIDF Feature Vector to a SGDClassifier from sklearn', '&lt;pre&gt;&lt;code&gt;import numpy as np
from sklearn import linear_model

X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
Y = np.array([''C++'', ''C#'', ''java'',''python''])
clf = linear_model.SGDClassifier()
clf.fit(X, Y)
print (clf.predict([[1.7, 0.7]]))
#python
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am trying to predict the values from arrays Y by giving a test case and training it on a training data which is &lt;strong&gt;X&lt;/strong&gt;, Now my problem is that, I want to change the training set &lt;strong&gt;X&lt;/strong&gt; to &lt;strong&gt;TF-IDF Feature Vectors&lt;/strong&gt;, so how can that be possible?
Vaguely, I want to do something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
from sklearn import linear_model

X = np.array_str([[''abcd'', ''efgh''], [''qwert'', ''yuiop''], [''xyz'',''abc''],  [''opi'', ''iop'']])
Y = np.array([''C++'', ''C#'', ''java'',''python''])
clf = linear_model.SGDClassifier()
clf.fit(X, Y)
&lt;/code&gt;&lt;/pre&gt;
', 'machine-learning  classification  python  sklearn', NULL, '''-1'':29C,30C,32C ''-2'':31C ''/code'':53C,154C ''/p'':120C ''/pre'':54C,155C ''/strong'':82C,97C,105C ''0.7'':51C ''1'':33C,34C,36C ''1.7'':50C ''2'':35C ''abc'':140C ''abcd'':135C ''array'':64C ''c'':39C,40C,145C,146C ''case'':70C ''chang'':91C ''classif'':13B ''clf'':43C,149C ''clf.fit'':45C,151C ''clf.predict'':49C ''code'':17C,122C ''data'':77C ''efgh'':136C ''featur'':3A,103C ''give'':67C ''idf'':102C ''import'':18C,24C,123C,129C ''iop'':142C ''java'':41C,147C ''learn'':12B ''like'':118C ''linear'':25C,130C ''linear_model.sgdclassifier'':44C,150C ''machin'':11B ''machine-learn'':10B ''model'':26C,131C ''np'':21C,126C ''np.array'':28C,38C,133C,144C ''numpi'':19C,124C ''opi'':141C ''p'':55C ''pass'':1A ''possibl'':111C ''pre'':16C,121C ''predict'':60C ''print'':48C ''problem'':85C ''python'':14B,42C,52C,148C ''qwert'':137C ''set'':94C ''sgdclassifi'':7A ''sklearn'':9A,15B,23C,128C ''someth'':117C ''str'':134C ''strong'':80C,95C,99C ''test'':69C ''tf'':101C ''tf-idf'':100C ''tfidf'':2A ''train'':72C,76C,93C ''tri'':58C ''vagu'':112C ''valu'':62C ''vector'':4A,104C ''want'':89C,114C ''x'':27C,46C,81C,96C,132C,152C ''xyz'':139C ''y'':37C,47C,65C,143C,153C ''yuiop'':138C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8048, 1, '2015-09-09 14:46:20.6', 3, 279, 'Simple implementation of Apriori algorithm in R', '&lt;p&gt;I am preparing a lecture on data mining algorithms in R and I want to demonstrate the famous &lt;a href="https://en.wikipedia.org/wiki/Apriori_algorithm" rel="nofollow"&gt;Apriori algorithm&lt;/a&gt; in it.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;My question&lt;/strong&gt;&lt;br&gt;
Could anybody point me to a simple implementation of this algorithm in R?&lt;/p&gt;

&lt;p&gt;(I am not looking for a package, e.g. arules, but for comprehensible source code of an implementation from scratch. It also does not help to look at the source code of arules: It just calls a C implementation of the algorithm!)&lt;/p&gt;
', 'r  algorithms', NULL, '''/a'':38C ''/p'':41C,61C,104C ''/strong'':46C ''/wiki/apriori_algorithm'':33C ''algorithm'':5A,9B,19C,37C,58C,103C ''also'':83C ''anybodi'':49C ''apriori'':4A,36C ''arul'':71C,94C ''br'':47C ''c'':99C ''call'':97C ''code'':76C,92C ''comprehens'':74C ''could'':48C ''data'':17C ''demonstr'':26C ''e.g'':70C ''en.wikipedia.org'':32C ''en.wikipedia.org/wiki/apriori_algorithm'':31C ''famous'':28C ''help'':86C ''href'':30C ''implement'':2A,55C,79C,100C ''lectur'':15C ''look'':66C,88C ''mine'':18C ''nofollow'':35C ''p'':10C,42C,62C ''packag'':69C ''point'':50C ''prepar'':13C ''question'':45C ''r'':7A,8B,21C,60C ''rel'':34C ''scratch'':81C ''simpl'':1A,54C ''sourc'':75C,91C ''strong'':43C ''want'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8039, 2, '2015-09-08 13:49:19.217', 0, NULL, NULL, '&lt;p&gt;You can find a nice tutorial how to achieve that on this blog: &lt;strong&gt;&lt;a href="http://blog.christianperone.com/2011/10/machine-learning-text-feature-extraction-tf-idf-part-ii/" rel="nofollow"&gt;http://blog.christianperone.com/2011/10/machine-learning-text-feature-extraction-tf-idf-part-ii/&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This is acctually a part II. In part I the author discusses how and what is "Term frequency".&lt;/p&gt;

&lt;p&gt;Link to part I.: &lt;strong&gt;&lt;a href="http://blog.christianperone.com/2011/09/machine-learning-text-feature-extraction-tf-idf-part-i/" rel="nofollow"&gt;http://blog.christianperone.com/2011/09/machine-learning-text-feature-extraction-tf-idf-part-i/&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;
', NULL, NULL, '''/2011/09/machine-learning-text-feature-extraction-tf-idf-part-i/'':56C ''/2011/09/machine-learning-text-feature-extraction-tf-idf-part-i/&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;'':61C ''/2011/10/machine-learning-text-feature-extraction-tf-idf-part-ii/'':20C ''/2011/10/machine-learning-text-feature-extraction-tf-idf-part-ii/&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;'':25C ''/p'':45C ''acctual'':29C ''achiev'':10C ''author'':37C ''blog'':14C ''blog.christianperone.com'':19C,24C,55C,60C ''blog.christianperone.com/2011/09/machine-learning-text-feature-extraction-tf-idf-part-i/'':54C ''blog.christianperone.com/2011/09/machine-learning-text-feature-extraction-tf-idf-part-i/&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;'':59C ''blog.christianperone.com/2011/10/machine-learning-text-feature-extraction-tf-idf-part-ii/'':18C ''blog.christianperone.com/2011/10/machine-learning-text-feature-extraction-tf-idf-part-ii/&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;'':23C ''discuss'':38C ''find'':4C ''frequenc'':44C ''href'':17C,53C ''ii'':32C ''link'':47C ''nice'':6C ''nofollow'':22C,58C ''p'':1C,26C,46C ''part'':31C,34C,49C ''rel'':21C,57C ''strong'':15C,51C ''term'':43C ''tutori'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8040, 1, '2015-09-08 14:59:49.16', 0, 32, 'How to get Top 3 or Top N predictions using sklearn''s SGDClassifier', '&lt;pre&gt;&lt;code&gt;from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
from sklearn import linear_model
arr=[''dogs cats lions'',''apple pineapple orange'',''water fire earth air'', ''sodium potassium calcium'']
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(arr)
feature_names = vectorizer.get_feature_names()
Y = [''animals'', ''fruits'', ''elements'',''chemicals'']
T=["eating apple roasted in fire and enjoying fresh air"]
test = vectorizer.transform(T)
clf = linear_model.SGDClassifier(loss=''log'')
clf.fit(X,Y)
x=clf.predict(test)
#prints: elements&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In the above code, &lt;code&gt;&lt;strong&gt;clf.predict()&lt;/strong&gt;&lt;/code&gt; prints only 1 best prediction for a sample from &lt;strong&gt;list X&lt;/strong&gt;.
I am interested in &lt;strong&gt;top 3 predictions&lt;/strong&gt; for a particular sample in the &lt;strong&gt;list X&lt;/strong&gt;, i know the function &lt;code&gt;&lt;strong&gt;predict_proba&lt;/strong&gt;&lt;/code&gt;/&lt;code&gt;&lt;strong&gt;predict_log_proba&lt;/strong&gt;&lt;/code&gt; returns a list of all probabilities for each feature in &lt;strong&gt;list Y&lt;/strong&gt;, but it has to sorted and then associated with the features in list Y before getting the top 3 results.
Is there any direct and efficient way? &lt;/p&gt;
', 'scikit  sklearn', NULL, '''/code'':86C,97C,139C,146C ''/p'':188C ''/pre'':87C ''/strong'':96C,110C,119C,129C,138C,145C,160C ''1'':100C ''3'':5A,117C,179C ''air'':41C,70C ''anim'':57C ''appl'':35C,63C ''arr'':31C,50C ''associ'':168C ''best'':101C ''calcium'':44C ''cat'':33C ''chemic'':60C ''clf'':74C ''clf.fit'':78C ''clf.predict'':82C,95C ''code'':17C,92C,93C,134C,140C ''direct'':184C ''dog'':32C ''earth'':40C ''eat'':62C ''effici'':186C ''element'':59C,85C ''enjoy'':68C ''featur'':51C,54C,155C,171C ''fire'':39C,66C ''fresh'':69C ''fruit'':58C ''function'':133C ''get'':3A,176C ''import'':20C,22C,28C ''interest'':113C ''know'':131C ''linear'':29C ''linear_model.sgdclassifier'':75C ''lion'':34C ''list'':108C,127C,149C,158C,173C ''log'':77C,143C ''loss'':76C ''model'':30C ''n'':8A ''name'':52C,55C ''np'':25C ''numpi'':23C ''orang'':37C ''p'':88C ''particular'':122C ''pineappl'':36C ''potassium'':43C ''pre'':16C ''predict'':9A,102C,118C,136C,142C ''print'':84C,98C ''proba'':137C,144C ''probabl'':152C ''result'':180C ''return'':147C ''roast'':64C ''sampl'':105C,123C ''scikit'':14B ''sgdclassifi'':13A ''sklearn'':11A,15B,27C ''sklearn.feature_extraction.text'':19C ''sodium'':42C ''sort'':165C ''strong'':94C,107C,115C,126C,135C,141C,157C ''test'':71C,83C ''tfidfvector'':21C,46C ''top'':4A,7A,116C,178C ''transform'':49C ''use'':10A ''vector'':45C ''vectorizer.fit'':48C ''vectorizer.get'':53C ''vectorizer.transform'':72C ''water'':38C ''way'':187C ''x'':47C,79C,81C,109C,128C ''y'':56C,80C,159C,174C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2269, 1, '2014-10-13 21:13:48.447', 15, 3450, 'Any Online R console?', '&lt;p&gt;I am looking for an online console for the language R. Like I write the code and the server should execute and provide me with the output.&lt;/p&gt;

&lt;p&gt;Similar to the website Datacamp.&lt;/p&gt;
', 'r  statistics', NULL, '''/p'':35C,42C ''code'':23C ''consol'':4A,14C ''datacamp'':41C ''execut'':28C ''languag'':17C ''like'':19C ''look'':10C ''onlin'':2A,13C ''output'':34C ''p'':7C,36C ''provid'':30C ''r'':3A,5B,18C ''server'':26C ''similar'':37C ''statist'':6B ''websit'':40C ''write'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8043, 1, '2015-09-08 17:51:50.85', 1, 46, 'Optimizing parameters for a closed (black-box) system', '&lt;p&gt;I am working on a problem that involves finding optimal parameter values for a black-box system. This system consists of 6 inputs and produces a single output. I can compare the returned values to observed values to determine whether the system is well calibrated based on the parameter values I specified.&lt;/p&gt;

&lt;p&gt;The main problem that I am having is that I do not know how these parameters are used in the black-box system to produce the output. The parameters may be correlated so changing the value of one parameter may affect the way the others behave in the system.&lt;/p&gt;

&lt;p&gt;Since I do not know the function I am not sure how to optimize this problem efficiently.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Question:&lt;/strong&gt; what are some methods for optimization when the function is unknown?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Edits:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The variable types are each a vector of real numbers but we can make some assumptions if it is is helpful.&lt;/p&gt;

&lt;p&gt;The cost to run the black-box system is only time. Let''s say that it takes 10 seconds to run the system but I happen to have five black boxes -- so if I can run my algorithm in parallel then I can cut down the run time. If the algorithm runs sequentially then I don''t gain anything by having the extra boxes (expect perhaps choosing different starting positions).&lt;/p&gt;

&lt;p&gt;For this problem I am interested in learning about how to solve this type of problem -- not trying to brute force my way to a solution. That is to say I am &lt;em&gt;less&lt;/em&gt; interested in obtaining an answer and &lt;em&gt;more&lt;/em&gt; finding out &lt;em&gt;how&lt;/em&gt; I would obtain the the answer. I think it is easy to say that given sufficient resources (time, processing power) that simply generating many many random numbers and choosing the combination that gives the best result is the simplest (and probably worst) approach.&lt;/p&gt;
', 'optimization', NULL, '''/em'':290C,299C,304C ''/p'':65C,117C,136C,152C,158C,181C,254C,348C ''/strong'':140C,157C ''10'':201C ''6'':34C ''affect'':108C ''algorithm'':221C,234C ''answer'':295C,310C ''anyth'':242C ''approach'':347C ''assumpt'':175C ''base'':58C ''behav'':113C ''best'':339C ''black'':7A,27C,88C,189C,213C ''black-box'':6A,26C,87C,188C ''box'':8A,28C,89C,190C,214C,247C ''brute'':275C ''calibr'':57C ''chang'':101C ''choos'':250C,333C ''close'':5A ''combin'':335C ''compar'':43C ''consist'':32C ''correl'':99C ''cost'':184C ''cut'':227C ''determin'':51C ''differ'':251C ''easi'':315C ''edit'':156C ''effici'':135C ''em'':288C,297C,302C ''expect'':248C ''extra'':246C ''find'':20C,300C ''five'':212C ''forc'':276C ''function'':125C,149C ''gain'':241C ''generat'':327C ''give'':337C ''given'':319C ''happen'':209C ''help'':180C ''hr'':153C ''input'':35C ''interest'':261C,291C ''involv'':19C ''know'':79C,123C ''learn'':263C ''less'':289C ''let'':195C ''main'':68C ''make'':173C ''mani'':328C,329C ''may'':97C,107C ''method'':144C ''number'':169C,331C ''observ'':48C ''obtain'':293C,307C ''one'':105C ''optim'':1A,10B,21C,132C,146C ''other'':112C ''output'':40C,94C ''p'':11C,66C,118C,137C,154C,159C,182C,255C ''parallel'':223C ''paramet'':2A,22C,61C,82C,96C,106C ''perhap'':249C ''posit'':253C ''power'':324C ''probabl'':345C ''problem'':17C,69C,134C,258C,271C ''process'':323C ''produc'':37C,92C ''question'':139C ''random'':330C ''real'':168C ''resourc'':321C ''result'':340C ''return'':45C ''run'':186C,204C,219C,230C,235C ''say'':197C,285C,317C ''second'':202C ''sequenti'':236C ''simplest'':343C ''simpli'':326C ''sinc'':119C ''singl'':39C ''solut'':281C ''solv'':267C ''specifi'':64C ''start'':252C ''strong'':138C,155C ''suffici'':320C ''sure'':129C ''system'':9A,29C,31C,54C,90C,116C,191C,206C ''take'':200C ''think'':312C ''time'':194C,231C,322C ''tri'':273C ''type'':162C,269C ''unknown'':151C ''use'':84C ''valu'':23C,46C,49C,62C,103C ''variabl'':161C ''vector'':166C ''way'':110C,278C ''well'':56C ''whether'':52C ''work'':14C ''worst'':346C ''would'':306C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8046, 1, '2015-09-09 13:11:46.47', 1, 25, 'Random Forest Class Weighting for Logistic Probabilities', '&lt;p&gt;I have a model at work that I am building and am running into some odd outputs from the random forest as it pertains to the probability of response. In my case, the class distributions are very unbalanced, around 45000 records in my training data, and only 300 are responders. So I took a smaller sample of the non-responders, 1000, so that my training data was 1300 total. This worked well, but it appears like the outputted predicted probability needs to be adjusted in some way given that I altered the distribution of the classes. &lt;/p&gt;

&lt;p&gt;When I create a gains table, the predicted response rate of the first decile comes out at 50%, but it should be around 2%. I dont have this problem with running an xgboost model with all training variables. &lt;/p&gt;

&lt;p&gt;I know that in traditional logistic regression, you can weight the classes if you take a smaller sample so that it can accordingly adjust the predicted probability. &lt;/p&gt;

&lt;p&gt;Is there a way using random forest to solve this problem?&lt;/p&gt;
', 'r  logistic-regression  random-forest', NULL, '''/p'':113C,153C,182C,195C ''1000'':77C ''1300'':84C ''2'':138C ''300'':63C ''45000'':55C ''50'':132C ''accord'':177C ''adjust'':100C,178C ''alter'':107C ''appear'':91C ''around'':54C,137C ''build'':25C ''case'':47C ''class'':3A,49C,112C,166C ''come'':129C ''creat'':117C ''data'':60C,82C ''decil'':128C ''distribut'':50C,109C ''dont'':140C ''first'':127C ''forest'':2A,14B,36C,190C ''gain'':119C ''given'':104C ''know'':156C ''like'':92C ''logist'':6A,10B,160C ''logistic-regress'':9B ''model'':19C,148C ''need'':97C ''non'':75C ''non-respond'':74C ''odd'':31C ''output'':32C,94C ''p'':15C,114C,154C,183C ''pertain'':39C ''predict'':95C,122C,180C ''probabl'':7A,42C,96C,181C ''problem'':143C,194C ''r'':8B ''random'':1A,13B,35C,189C ''random-forest'':12B ''rate'':124C ''record'':56C ''regress'':11B,161C ''respond'':65C,76C ''respons'':44C,123C ''run'':28C,145C ''sampl'':71C,172C ''smaller'':70C,171C ''solv'':192C ''tabl'':120C ''take'':169C ''took'':68C ''total'':85C ''tradit'':159C ''train'':59C,81C,151C ''unbalanc'':53C ''use'':188C ''variabl'':152C ''way'':103C,187C ''weight'':4A,164C ''well'':88C ''work'':21C,87C ''xgboost'':147C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9681, 1, '2016-01-07 15:31:37.57', 3, 42, 'Rules by which RStudio sets Headings', '&lt;p&gt;RStudio automatically recognizes headers in an R script that are set via comments:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;a href="http://i.stack.imgur.com/LnGac.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/LnGac.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I would like to exploit that feature, but I don''t quite what the rules are for RStudio to recognize them as headers. Can someone explain?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8050, 2, '2015-09-09 17:33:26.853', 2, NULL, NULL, '&lt;p&gt;Yes, data science work do exist in domains of management and computer science.&lt;/p&gt;

&lt;p&gt;I think data science is a huge domain in itself to include in any of the curricula. So, I would explain how data science is used in the domains:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Management:&lt;/strong&gt; 
Data Science used in a lot of ways. I use data science and analytics in every phase of the customer acquisition and retention funnel; which includes designing models for using the customer event tracking data and identify patterns and predict churn. Here is an &lt;a href="https://moz.com/devblog/deep-learning-for-customer-churn-prediction/" rel="nofollow"&gt;example article&lt;/a&gt; from the Moz blog, which explains about the use of deep for churn prediction and customer retention. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Computer Science:&lt;/strong&gt; 
Most of the data science has roots in computer science basics. However, including the concepts of distributed computing would help the students understand and appreciate the concept of Big Data, when they later enter the industry.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;So, I think data science is a huge domain in itself, and has it''s share of unique applications in a wide range of domains. However, bulk of data science concepts and techniques have taken roots in math, stats and computer science basics and concepts. So, I think the knowledge of those domains would be helpful and suffice. &lt;/p&gt;

&lt;p&gt;So, these are some links which can help in curriculum re-design for accommodating data science concepts in the management and CS curricula:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;MBA speciality.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.scu.edu/business/graduates/current-students/data-science.cfm" rel="nofollow"&gt;Curriculum of the Data Science and Business Analytics program&lt;/a&gt; at the Santa Clara University.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://dsi.virginia.edu/curriculum" rel="nofollow"&gt;Curriculum&lt;/a&gt; of the Masters of Data Science in Virginia University.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The third bullet is something very close to how data science can be included or blended with the Computer Science degree.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9767, 1, '2016-01-12 21:19:16.74', 0, 26, 'Datamodel for cluster analysis terms &amp; segmentation', '&lt;p&gt;Hey fellow data lovers!&lt;/p&gt;

&lt;p&gt;I have a data modelling problem for a clustering analysis that I can''t wrap my head around. Perhaps I''m thinking to advanced and I should simplify my analysis.&lt;/p&gt;

&lt;p&gt;I have 2 data sets:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Persons with their properties:

&lt;ul&gt;
&lt;li&gt;PersonId&lt;/li&gt;
&lt;li&gt;Gender&lt;/li&gt;
&lt;li&gt;Age&lt;/li&gt;
&lt;li&gt;Country&lt;/li&gt;
&lt;li&gt;Etc.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;Conversations and their properties:

&lt;ul&gt;
&lt;li&gt;ConversationId&lt;/li&gt;
&lt;li&gt;Sentences of the conversations&lt;/li&gt;
&lt;li&gt;Keyword of a sentence + it''s frequency&lt;/li&gt;
&lt;li&gt;Polarity of the sentence (pos, neg or neutral)&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I would like to cluster terms that have the same polarity for a group of people. Eg. male persons between 20 and 35 speak positively about economy and neg about privacy
Eg. male persons from BE, NL and DE speak neg about climat changes.&lt;/p&gt;

&lt;p&gt;Problem is that I need to cluster multiple terms for an undefined group of people. For one term (eg. privacy) it would be ''easy'' to determine the properties of the different segments as this is a classification problem. As I would like to cluster multiple terms together, I''m strugling to model my data as this means I have multiple records per person (they can talk about multiple terms). &lt;/p&gt;

&lt;p&gt;Assumption: you can assume I have one record per person per term.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8051, 2, '2015-09-09 17:58:20.117', 2, NULL, NULL, '&lt;p&gt;I found an implementation here: &lt;a href="https://books.google.de/books?id=ifpzBgAAQBAJ&amp;amp;lpg=PA50&amp;amp;ots=rkVf2uZvYo&amp;amp;pg=PA47#v=onepage&amp;amp;q&amp;amp;f=false" rel="nofollow"&gt;Learning Data Mining with R by Bater Makhabel&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The source code can be downloaded here: &lt;a href="https://www.packtpub.com/big-data-and-business-intelligence/learning-data-mining-r" rel="nofollow"&gt;https://www.packtpub.com/big-data-and-business-intelligence/learning-data-mining-r&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':22C ''/big-data-and-business-intelligence/learning-data-mining-r'':36C ''/big-data-and-business-intelligence/learning-data-mining-r&lt;/a&gt;&lt;/p&gt;'':41C ''/books?id=ifpzbgaaqbaj&amp;amp;lpg=pa50&amp;amp;ots=rkvf2uzvyo&amp;amp;pg=pa47#v=onepage&amp;amp;q&amp;amp;f=false'':11C ''/p'':23C ''bater'':20C ''books.google.de'':10C ''books.google.de/books?id=ifpzbgaaqbaj&amp;amp;lpg=pa50&amp;amp;ots=rkvf2uzvyo&amp;amp;pg=pa47#v=onepage&amp;amp;q&amp;amp;f=false'':9C ''code'':27C ''data'':15C ''download'':30C ''found'':3C ''href'':8C,33C ''implement'':5C ''learn'':14C ''makhabel'':21C ''mine'':16C ''nofollow'':13C,38C ''p'':1C,24C ''r'':18C ''rel'':12C,37C ''sourc'':26C ''www.packtpub.com'':35C,40C ''www.packtpub.com/big-data-and-business-intelligence/learning-data-mining-r'':34C ''www.packtpub.com/big-data-and-business-intelligence/learning-data-mining-r&lt;/a&gt;&lt;/p&gt;'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8052, 1, '2015-09-09 23:13:20.37', 0, 28, 'Can I have an emission probability &gt; 1.0 in an HMM?', '&lt;p&gt;What are the consequences of introducing ''weights'' in a hidden Markov model, decoded using the Viterbi algorithm? That is, setting the emission probabilities for some state such that they sum to greater than 1.0 appears to work perfectly fine in practice. Is there some theoretical reason I shouldn''t?&lt;/p&gt;

&lt;p&gt;I have developed software that uses a profile hidden Markov model to recognize user-defined patterns in genomic data. If the pattern was 5 symbols long, I would use 5 "match" states, each emitting 1 symbol, and connected via "insert" and "delete" states to allow for missing symbols or random insertions. This all works very well, but I noticed something in the process. For one particular pattern, there was one symbol that was more consistently present than the others, so I wanted to assign it higher probability. I could have set that emission probability to 1.0 and all others to 0.5, but instead I left the others at 1.0 and set this special symbol to a probability of 2.0. Nothing in my implementation relied on the probabilities summing to 1, so how bad is it to do this?&lt;/p&gt;

&lt;p&gt;A slightly different take on the same question is using weights. So my model searches for a defined pattern of symbols within a very long sequence of symbols. But this long sequence also contains confidence values for each symbol (think E-values or p-values). Sometimes I can be very confident that some symbol is what it appears, other times it''s less certain. So when searching for patterns using my HMM, I want to give extra weight to those symbols that are confident in their identity as well as their position in the pattern. My thinking is to multiply the emission probability of some state j to emit symbol s, by a new weight factor w that comes from the confidence score of that particular symbol s. My current definition of w has its values between 1.0 and 2.0. I see no reason why this will not work in practice, but is there some theoretical flaw with adding new values like this?&lt;/p&gt;

&lt;p&gt;As a note, this has been cross-posted to the Theoretical Computer Science forum as well.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8053, 2, '2015-09-09 23:13:43.42', 1, NULL, NULL, '&lt;p&gt;as @image_doctor mentioned, this is clearly a non-trivial problem, but if you want a bit of help extracting numerical data from existing graphs, &lt;a href="http://arohatgi.info/WebPlotDigitizer/" rel="nofollow"&gt;WebPlotDigitizer&lt;/a&gt; might be of assistance.&lt;/p&gt;

&lt;p&gt;Perhaps investigating their methods via &lt;a href="https://github.com/ankitrohatgi/WebPlotDigitizer?" rel="nofollow"&gt;their source&lt;/a&gt; may be fruitful for you.&lt;/p&gt;
', NULL, NULL, '''/a'':36C,57C ''/ankitrohatgi/webplotdigitizer?'':52C ''/p'':41C,63C ''/webplotdigitizer/'':32C ''arohatgi.info'':31C ''arohatgi.info/webplotdigitizer/'':30C ''assist'':40C ''bit'':19C ''clear'':8C ''data'':24C ''doctor'':4C ''exist'':26C ''extract'':22C ''fruit'':60C ''github.com'':51C ''github.com/ankitrohatgi/webplotdigitizer?'':50C ''graph'':27C ''help'':21C ''href'':29C,49C ''imag'':3C ''investig'':44C ''may'':58C ''mention'':5C ''method'':46C ''might'':37C ''nofollow'':34C,54C ''non'':11C ''non-trivi'':10C ''numer'':23C ''p'':1C,42C ''perhap'':43C ''problem'':13C ''rel'':33C,53C ''sourc'':56C ''trivial'':12C ''via'':47C ''want'':17C ''webplotdigit'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8067, 1, '2015-09-11 10:45:52.06', 0, 21, 'Check if a variable is important is behaving differently for differnet categories/ranges of other variables', '&lt;p&gt;I am fitting a GBM model on a dataset having a 0-1 target variable. There are say 3 variables ''var1'', ''var2'' and ''var3'' and 10-15 more variables. &lt;/p&gt;

&lt;p&gt;After fitting the model, I am seeing that ''var3'' is an important variable in predicting the target variable.&lt;/p&gt;

&lt;p&gt;What I want to know if the variable "var3" is behaving differently for different values/categories of other variables say, "var1", "var2"&lt;/p&gt;

&lt;p&gt;To make it more clear, let us say the dataset looks like following:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;var1    var2    var3   target
34.3     a       Q      1
34.4     a       Q      1
1        g       R      1 
.3       g       R      1
75       g       F      0
463      f       R      0
44       f       F      0
3        f       F      0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Generally model is saying that categories ''Q'' and ''R'' of variable var3 have higher probabilities and "F" has relatively low. This is the global trend of variable "var3". What I want to know if the trend is different for different categories/range of other variables like "var1" and "var2", that is for category "g" of variable "var2" the trend of ''var3'' is different form its global trend but for category "f" of ''var2" the trend is similar as the global trend. How do I do this check using the model output or in any other way without manually analysing the data.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8054, 1, '2015-09-09 23:32:26.267', 1, 30, 'How can I rank paths through an HMM?', '&lt;p&gt;I have a profile hidden Markov model that I use to identify all instances of a user-defined pattern of symbols in a long sequence of symbols. I use the Viterbi algorithm to find the most probable path that generates that long sequence of symbols, and it all works very well to identify the user''s pattern. But I''m now interested in extending this, and identifying the k-most probable paths through my model that would generate some long sequence. Is there an existing algorithm to do this? I''ve come across something called the "1-best" or "k-best" algorithm, but I can''t find much of anything describing it. &lt;/p&gt;

&lt;p&gt;I''ve also considered what I''m sure would be a heuristic, by finding the most probable path and then returning the neighbourhood around it. This would be found by iterating over each move between states in the most probable path, setting that particular step to 0, and resolving using Viterbi. This would obviously increase the run-time by a factor of (length of the path ~= length of pattern), which I suspect will be reasonable for my purposes.&lt;/p&gt;

&lt;p&gt;Has anybody come across something like this, or can anybody see anything obviously wrong in my neighbourhood idea? I''d appreciate any feedback. As a note, this has been cross-posted to the Theoretical Computer Science forum.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8090, 1, '2015-09-13 17:24:06.52', 3, 211, 'How do I setup a server in the cloud for machine learning?', '&lt;p&gt;As datasets and the number of parameters get larger, it becomes increasingly difficult to run validation locally because of limited disk size and computing power.  As a result, people might use an old laptop or a server in the cloud.  In particular, I''m interested in learning more about the second option.&lt;/p&gt;

&lt;p&gt;For the setup, it sounds like I will need a storage system and a computing system.  Is this S3 and EC2?  Redshift and Amazon ML?  Combinations from some other vendor?  At the moment, I''m asking as an amateur participant for Kaggle, but I''d also be curious to know what the pros use.&lt;/p&gt;

&lt;p&gt;On the machine itself, is there any way to interact with a GUI rather than a command line to set this up? Would I have to install Anaconda or some other Python distribution before getting started?  Is there a specific file structure to use? What are the main pitfalls to watch out for?&lt;/p&gt;

&lt;p&gt;Ultimately, I am looking for the practical advice to set everything up.  If you''re able to just offer links to documentation, that would be immensely helpful as well.  Thanks!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8057, 2, '2015-09-10 04:39:05.057', 2, NULL, NULL, '&lt;p&gt;No.&lt;/p&gt;

&lt;p&gt;Map reduce &lt;em&gt;by definition&lt;/em&gt; works at &lt;em&gt;one record at a time&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Thus, &lt;strong&gt;you cannot compute a distance/similarity matrix&lt;/strong&gt; without abusing the programming model. Methods such as SVM &lt;em&gt;do&lt;/em&gt; require pairwise similarities.&lt;/p&gt;

&lt;p&gt;Such abuses are really common though (which is why map reduce is dead). Most easily, you can map every object to the key 0, and do all the work in the "reducer". More clever approaches divide the data into k partitions, so they can run at least some work in parallel.&lt;/p&gt;

&lt;p&gt;But all of these approaches are ugly hacks that hack around a deliberate design decision made for map reduce: in map reduce, every record &lt;em&gt;must&lt;/em&gt; be processed independently, to allow the platform to automatically optimize parallelism and recovery. In above hack, the map phase is abused to split the data into the manually chosen parallelism, and then the reducers are simple parallel jobs. It''s no longer mapreduce, but the abstract model of parallel programming on partitioned data.&lt;/p&gt;

&lt;p&gt;After all the early hype, mapreduce is now dead. Tools such as Apache Mahout &lt;strong&gt;no longer accept mapreduce&lt;/strong&gt; but expect you to move on to more flexible pardigms that don''t need such abusive hacks.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8059, 2, '2015-09-10 15:07:14.92', 2, NULL, NULL, '&lt;p&gt;My take:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;I agree with the issues raised in 1., so not much to add here - retraining and storage is indeed inefficient&lt;/li&gt;
&lt;li&gt;Vowpal Wabbit &lt;a href="http://hunch.net/~vw/" rel="nofollow"&gt;http://hunch.net/~vw/&lt;/a&gt; would be my first choice&lt;/li&gt;
&lt;li&gt;stability of output between increments is really more of a data than algorithm feature - if you have plenty of variation on input, you won''t have that much stability on output (at least not by default)&lt;/li&gt;
&lt;li&gt;hashing can take care of the variation - you can control by a combination of three parameters: the size of the hashing table and the l1/l2 regularization&lt;/li&gt;
&lt;li&gt;same for the new features / users (I &lt;em&gt;think&lt;/em&gt; - most of the applications i used it had a ercord representing a user clicking or not, so new users / ads were sort of treated "the same")&lt;/li&gt;
&lt;li&gt;normally I use VW from the command line, but an example approach (not too elegant) for controlling from Python is given here: &lt;a href="http://fastml.com/how-to-run-external-programs-from-python-and-capture-their-output/" rel="nofollow"&gt;http://fastml.com/how-to-run-external-programs-from-python-and-capture-their-output/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;if you prefer sth purely Python, then a version (without decomposition) of an online learner in the Criteo spirit can be found here: &lt;a href="https://www.kaggle.com/c/tradeshift-text-classification/forums/t/10537/beat-the-benchmark-with-less-than-400mb-of-memory" rel="nofollow"&gt;https://www.kaggle.com/c/tradeshift-text-classification/forums/t/10537/beat-the-benchmark-with-less-than-400mb-of-memory&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;I am not sure how to handle the concept drift - haven''t paid that much attention to it so far beyond rolling statistics: for the relevant variables of interest, keep track of mean / count over recent N periods. It is a crude approach, but does seem to get the job done it terms of capturing lack of "stationarity" &lt;/li&gt;
&lt;li&gt;helpful trick 1: single pass over data before first run to create per feature dictionary and flag certain values as rare (lump them into a single value)&lt;/li&gt;
&lt;li&gt;helpful trick 2: ensembling predictions from more than one model (varying interaction order, learning rate)&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/c/tradeshift-text-classification/forums/t/10537/beat-the-benchmark-with-less-than-400mb-of-memory'':212C ''/c/tradeshift-text-classification/forums/t/10537/beat-the-benchmark-with-less-than-400mb-of-memory&lt;/a&gt;&lt;/li&gt;'':217C ''/em'':123C ''/how-to-run-external-programs-from-python-and-capture-their-output/'':178C ''/how-to-run-external-programs-from-python-and-capture-their-output/&lt;/a&gt;&lt;/li&gt;'':183C ''/li'':27C,46C,84C,112C,150C,277C,306C,323C ''/p'':4C ''/ul'':324C ''/~vw/'':35C ''/~vw/&lt;/a&gt;'':40C ''1'':14C,281C ''2'':310C ''ad'':143C ''add'':19C ''agre'':8C ''algorithm'':60C ''applic'':127C ''approach'':163C,261C ''attent'':234C ''beyond'':239C ''captur'':273C ''care'':89C ''certain'':296C ''choic'':45C ''click'':137C ''combin'':98C ''command'':158C ''concept'':227C ''control'':95C,168C ''count'':252C ''creat'':290C ''criteo'':202C ''crude'':260C ''data'':58C,285C ''decomposit'':195C ''default'':83C ''dictionari'':293C ''done'':269C ''drift'':228C ''eleg'':166C ''em'':121C ''ensembl'':311C ''ercord'':133C ''exampl'':162C ''far'':238C ''fastml.com'':177C,182C ''fastml.com/how-to-run-external-programs-from-python-and-capture-their-output/'':176C ''fastml.com/how-to-run-external-programs-from-python-and-capture-their-output/&lt;/a&gt;&lt;/li&gt;'':181C ''featur'':61C,118C,292C ''first'':44C,287C ''flag'':295C ''found'':206C ''get'':266C ''given'':172C ''handl'':225C ''hash'':86C,106C ''haven'':229C ''help'':279C,308C ''href'':32C,175C,209C ''hunch.net'':34C,39C ''hunch.net/~vw/'':33C ''hunch.net/~vw/&lt;/a&gt;'':38C ''increment'':52C ''inde'':25C ''ineffici'':26C ''input'':69C ''interact'':319C ''interest'':247C ''issu'':11C ''job'':268C ''keep'':248C ''l1/l2'':110C ''lack'':274C ''learn'':321C ''learner'':199C ''least'':80C ''li'':6C,28C,47C,85C,113C,151C,184C,218C,278C,307C ''line'':159C ''lump'':300C ''mean'':251C ''model'':317C ''much'':17C,75C,233C ''n'':255C ''new'':117C,141C ''nofollow'':37C,180C,214C ''normal'':152C ''one'':316C ''onlin'':198C ''order'':320C ''output'':50C,78C ''p'':1C ''paid'':231C ''paramet'':101C ''pass'':283C ''per'':291C ''period'':256C ''plenti'':65C ''predict'':312C ''prefer'':187C ''pure'':189C ''python'':170C,190C ''rais'':12C ''rare'':299C ''rate'':322C ''realli'':54C ''recent'':254C ''regular'':111C ''rel'':36C,179C,213C ''relev'':244C ''repres'':134C ''retrain'':21C ''roll'':240C ''run'':288C ''seem'':264C ''singl'':282C,304C ''size'':103C ''sort'':145C ''spirit'':203C ''stabil'':48C,76C ''stationar'':276C ''statist'':241C ''sth'':188C ''storag'':23C ''sure'':222C ''tabl'':107C ''take'':3C,88C ''term'':271C ''think'':122C ''three'':100C ''track'':249C ''treat'':147C ''trick'':280C,309C ''ul'':5C ''use'':129C,154C ''user'':119C,136C,142C ''valu'':297C,305C ''vari'':318C ''variabl'':245C ''variat'':67C,92C ''version'':193C ''vowpal'':29C ''vw'':155C ''wabbit'':30C ''without'':194C ''won'':71C ''would'':41C ''www.kaggle.com'':211C,216C ''www.kaggle.com/c/tradeshift-text-classification/forums/t/10537/beat-the-benchmark-with-less-than-400mb-of-memory'':210C ''www.kaggle.com/c/tradeshift-text-classification/forums/t/10537/beat-the-benchmark-with-less-than-400mb-of-memory&lt;/a&gt;&lt;/li&gt;'':215C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8060, 2, '2015-09-10 15:38:23.237', 0, NULL, NULL, '&lt;p&gt;It''s useful to do this with a &lt;code&gt;Pipeline&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
from sklearn import linear_model, pipeline, feature_extraction

X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
Y = np.array([''C++'', ''C#'', ''java'',''python''])
clf = pipeline.make_pipeline(
        feature_extraction.text.TfidfTransformer(use_idf=True),
        linear_model.SGDClassifier())
clf.fit(X, Y)
print(clf.predict([[1.7, 0.7]]))
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''-1'':30C,31C,33C ''-2'':32C ''/code'':12C,59C ''/p'':13C ''/pre'':60C ''0.7'':58C ''1'':34C,35C,37C ''1.7'':57C ''2'':36C ''c'':40C,41C ''clf'':44C ''clf.fit'':52C ''clf.predict'':56C ''code'':10C,15C ''extract'':27C ''featur'':26C ''feature_extraction.text.tfidftransformer'':47C ''idf'':49C ''import'':16C,22C ''java'':42C ''linear'':23C ''linear_model.sgdclassifier'':51C ''model'':24C ''np'':19C ''np.array'':29C,39C ''numpi'':17C ''p'':1C ''pipelin'':11C,25C,46C ''pipeline.make'':45C ''pre'':14C ''print'':55C ''python'':43C ''sklearn'':21C ''true'':50C ''use'':4C,48C ''x'':28C,53C ''y'':38C,54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8062, 1, '2015-09-10 19:45:25.617', 2, 125, 'When, if ever, is nearest neighbor classification the best choice?', '&lt;p&gt;I''ve been researching the history and use of k-nearest neighbor classification and regression, and various tweaks including k-d trees and LAESA. &lt;/p&gt;

&lt;p&gt;I understand that it is useful because it is simple and flexible, but can be computationally expensive and requires a lot of data storage.&lt;/p&gt;

&lt;p&gt;But here''s what I don''t know:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Is there any class of problems for which nearest neighbor classification is the best or one of the best algorithms to use?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;By ''class of problems'' I mean either a class based on data structure (for instance, maybe KNN is great for low-dimensional data with a mix of nominal and numerical data), or a class of real-life problems (maybe KNN is useful in predicting diseases for insurance holders). &lt;/p&gt;
', 'classification', NULL, '''/blockquote'':101C ''/p'':39C,65C,75C,100C,153C ''algorithm'':97C ''base'':112C ''best'':9A,91C,96C ''blockquot'':76C ''choic'':10A ''class'':81C,104C,111C,137C ''classif'':7A,11B,26C,88C ''comput'':56C ''d'':35C ''data'':63C,114C,126C,134C ''dimension'':125C ''diseas'':149C ''either'':109C ''ever'':3A ''expens'':57C ''flexibl'':52C ''great'':121C ''histori'':18C ''holder'':152C ''includ'':32C ''instanc'':117C ''insur'':151C ''k'':23C,34C ''k-d'':33C ''k-nearest'':22C ''knn'':119C,144C ''know'':74C ''laesa'':38C ''life'':141C ''lot'':61C ''low'':124C ''low-dimension'':123C ''mayb'':118C,143C ''mean'':108C ''mix'':129C ''nearest'':5A,24C,86C ''neighbor'':6A,25C,87C ''nomin'':131C ''numer'':133C ''one'':93C ''p'':12C,40C,66C,77C,102C ''predict'':148C ''problem'':83C,106C,142C ''real'':140C ''real-lif'':139C ''regress'':28C ''requir'':59C ''research'':16C ''simpl'':50C ''storag'':64C ''structur'':115C ''tree'':36C ''tweak'':31C ''understand'':42C ''use'':20C,46C,99C,146C ''various'':30C ''ve'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8063, 2, '2015-09-11 01:29:52.387', 1, NULL, NULL, '&lt;p&gt;You''re almost done with your results.
Try to substitute the AIM for the Likelihood of the sum of Gaussian with your data, and as soon as your Likelihood value stabilizes and doesn''t increase with the number of Gaussians you''ve found the right value for your N.&lt;/p&gt;

&lt;p&gt;You can even try to look for the EM algorithm for the estimation of the Mixture of gaussians that fit your data.&lt;/p&gt;

&lt;p&gt;The value of L = f(N) increases as a log function and the so called "knee" is where your optimal value of N lies on!&lt;/p&gt;
', NULL, NULL, '''/p'':51C,75C,102C ''aim'':13C ''algorithm'':62C ''almost'':4C ''call'':91C ''data'':24C,74C ''doesn'':34C ''done'':5C ''em'':61C ''estim'':65C ''even'':55C ''f'':81C ''fit'':72C ''found'':44C ''function'':87C ''gaussian'':21C,41C,70C ''increas'':36C,83C ''knee'':92C ''l'':80C ''lie'':100C ''likelihood'':16C,30C ''log'':86C ''look'':58C ''mixtur'':68C ''n'':50C,82C,99C ''number'':39C ''optim'':96C ''p'':1C,52C,76C ''re'':3C ''result'':8C ''right'':46C ''soon'':27C ''stabil'':32C ''substitut'':11C ''sum'':19C ''tri'':9C,56C ''valu'':31C,47C,78C,97C ''ve'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9380, 2, '2015-12-14 17:50:53.123', 0, NULL, NULL, '&lt;p&gt;Check this &lt;a href="http://cs231n.github.io/convolutional-networks/" rel="nofollow"&gt;lecture&lt;/a&gt; and this &lt;a href="http://cs.stanford.edu/people/karpathy/convnetjs/demo/cifar10.html" rel="nofollow"&gt;visualization&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Usually it is used type 2.1 convolution. In the input you have NxMx1 image, then after first convolution you will obtain N_1xM_1xk_1, so your image after first convolution will have k_1 channels. The new dimension N_1 and M_1 will depend on your stride S and padding P: N_1 = (N - 3 + 2P)/S + 1, you compute M_1 in analogy. For the first conv layer you will have 3x3xk_1 + k_1 weights. There is added k_1 for biases in nonlinear function.&lt;/p&gt;

&lt;p&gt;In the second layer you have as an input image with size N_1xM_1xk_1, where k_1 is new number of channels (you can think about this as new image ;)). And after second convolution you obtain N_2xM_2xk_2 image (array). You have 5x5xk_2+k_2 parameters in the second layer.&lt;/p&gt;

&lt;p&gt;For 1x1 convolution with k_3 filters and input NxMxC (C is number of input channels) you will obtain new image (array) NxMxk_3, so 1x1 make sense. They were introduced in this &lt;a href="http://arxiv.org/pdf/1312.4400v3.pdf" rel="nofollow"&gt;paper&lt;/a&gt;  &lt;/p&gt;

&lt;p&gt;Bonus 1: pooling is applied per feature map.&lt;/p&gt;

&lt;p&gt;For details please see &lt;a href="http://cs231n.github.io/convolutional-networks/" rel="nofollow"&gt;slides&lt;/a&gt; for CNN course on Stanford - you have there nice visualisation how convolution is summed from several input channels.  &lt;/p&gt;
', NULL, NULL, '''/a'':12C,23C,215C,240C ''/convolutional-networks/'':8C,236C ''/p'':24C,115C,172C,216C,226C,259C ''/pdf/1312.4400v3.pdf'':211C ''/people/karpathy/convnetjs/demo/cifar10.html'':19C ''/s'':84C ''1'':50C,60C,66C,69C,80C,85C,89C,101C,103C,109C,132C,135C,219C ''1x1'':175C,199C ''1xk'':49C,131C ''1xm'':48C,130C ''2'':158C,164C,166C ''2.1'':31C ''2p'':83C ''2xk'':157C ''2xm'':156C ''3'':82C,179C,197C ''3x3xk'':100C ''5x5xk'':163C ''ad'':107C ''analog'':91C ''appli'':222C ''array'':160C,195C ''arxiv.org'':210C ''arxiv.org/pdf/1312.4400v3.pdf'':209C ''bias'':111C ''bonus'':218C ''c'':184C ''channel'':61C,140C,189C,258C ''check'':2C ''cnn'':242C ''comput'':87C ''conv'':95C ''convolut'':32C,43C,56C,152C,176C,252C ''cours'':243C ''cs.stanford.edu'':18C ''cs.stanford.edu/people/karpathy/convnetjs/demo/cifar10.html'':17C ''cs231n.github.io'':7C,235C ''cs231n.github.io/convolutional-networks/'':6C,234C ''depend'':71C ''detail'':229C ''dimens'':64C ''featur'':224C ''filter'':180C ''first'':42C,55C,94C ''function'':114C ''href'':5C,16C,208C,233C ''imag'':39C,53C,126C,148C,159C,194C ''input'':35C,125C,182C,188C,257C ''introduc'':204C ''k'':59C,102C,108C,134C,165C,178C ''layer'':96C,120C,171C ''lectur'':11C ''m'':68C,88C ''make'':200C ''map'':225C ''n'':47C,65C,79C,81C,129C,155C ''new'':63C,137C,147C,193C ''nice'':249C ''nofollow'':10C,21C,213C,238C ''nonlinear'':113C ''number'':138C,186C ''nxmx1'':38C ''nxmxc'':183C ''nxmxk'':196C ''obtain'':46C,154C,192C ''p'':1C,25C,78C,116C,173C,217C,227C ''pad'':77C ''paper'':214C ''paramet'':167C ''per'':223C ''pleas'':230C ''pool'':220C ''rel'':9C,20C,212C,237C ''second'':119C,151C,170C ''see'':231C ''sens'':201C ''sever'':256C ''size'':128C ''slide'':239C ''stanford'':245C ''stride'':74C ''sum'':254C ''think'':143C ''type'':30C ''use'':29C ''usual'':26C ''visual'':22C ''visualis'':250C ''weight'':104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9882, 2, '2016-01-21 11:59:55.633', -1, NULL, NULL, '&lt;p&gt;I experimented with PyBrain and PyLearn2
I rather find Keras [&lt;a href="http://keras.io/" rel="nofollow"&gt;http://keras.io/&lt;/a&gt; ] as an excellent choice. It way ahead of PyBrain for sure. PyBrain development has been stopped&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':19C ''/p'':36C ''ahead'':26C ''choic'':23C ''develop'':32C ''excel'':22C ''experi'':3C ''find'':10C ''href'':13C ''kera'':11C ''keras.io'':14C,18C ''keras.io/&lt;/a&gt;'':17C ''nofollow'':16C ''p'':1C ''pybrain'':5C,28C,31C ''pylearn2'':7C ''rather'':9C ''rel'':15C ''stop'':35C ''sure'':30C ''way'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8064, 1, '2015-09-11 03:00:48.897', 1, 227, 'How to use Cohen''s Kappa as the evaluation metric in GridSearchCV in Scikit Learn?', '&lt;p&gt;I have class imbalance in the ratio 1:15 i.e. very low event rate. So to select tuning parameters of GBM in scikit learn I want to use Kappa instead of F1 score. My understanding is Kappa is a better metric than F1 score for class imbalance.&lt;/p&gt;

&lt;p&gt;But I couldn''t find kappa as an evaluation_metric in scikit learn here 
&lt;a href="http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics" rel="nofollow"&gt;sklearn.metrics&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Questions&lt;/strong&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Is there any workaround for including kappa in gridsearchcv?&lt;/li&gt;
&lt;li&gt;Is there any other better metric i can use instead of kappa in scikit learn?&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  classification  python  predictive-modeling  scikit', NULL, '''/a'':97C ''/li'':115C,132C ''/ol'':133C ''/p'':73C,98C,103C ''/stable/modules/classes.html#module-sklearn.metrics'':93C ''/strong'':102C ''1'':33C ''15'':34C ''better'':65C,121C ''class'':28C,71C ''classif'':19B ''cohen'':4A ''couldn'':77C ''evalu'':9A,83C ''event'':38C ''f1'':57C,68C ''find'':79C ''gbm'':46C ''gridsearchcv'':12A,114C ''href'':90C ''i.e'':35C ''imbal'':29C,72C ''includ'':111C ''instead'':55C,126C ''kappa'':6A,54C,62C,80C,112C,128C ''learn'':15A,18B,49C,87C,131C ''li'':105C,116C ''low'':37C ''machin'':17B ''machine-learn'':16B ''metric'':10A,66C,84C,122C ''model'':23B ''nofollow'':95C ''ol'':104C ''p'':25C,74C,99C ''paramet'':44C ''predict'':22B ''predictive-model'':21B ''python'':20B ''question'':101C ''rate'':39C ''ratio'':32C ''rel'':94C ''scikit'':14A,24B,48C,86C,130C ''scikit-learn.org'':92C ''scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics'':91C ''score'':58C,69C ''select'':42C ''sklearn.metrics'':96C ''strong'':100C ''tune'':43C ''understand'':60C ''use'':3A,53C,125C ''want'':51C ''workaround'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8065, 1, '2015-09-11 04:49:48.16', 0, 75, 'What are the applications of Solr in Big Data?', '&lt;p&gt;Why is solr used in Big Data? &lt;/p&gt;

&lt;p&gt;What is it''s purpose?&lt;/p&gt;
', 'bigdata', NULL, '''/p'':19C,26C ''applic'':4A ''big'':8A,17C ''bigdata'':10B ''data'':9A,18C ''p'':11C,20C ''purpos'':25C ''solr'':6A,14C ''use'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8069, 1, '2015-09-11 18:02:44.743', 3, 58, 'Question on reservoir sampling', '&lt;p&gt;I have a general question on &lt;a href="https://en.wikipedia.org/wiki/Reservoir_sampling" rel="nofollow"&gt;reservoir sampling&lt;/a&gt;. When I use this method to sample a very large dataset for training machine learning classification algorithms, I am curious as to how to make my pipeline robust to fluctuations in  class distribution across samples. &lt;/p&gt;

&lt;p&gt;For example, suppose I am working on a binary classification problem. I want to sample a relatively small subset of data on which to evaluate my algorithms, so I used reservoir sampling. However, it seems to me (thought I could be wrong) that by chance I may draw a sample wherein the class distribution is significantly different from that seen in the larger population of data. If this is a correct inference, can anybody tell me how to remedy the situation? If reservoir sampling is not the answer, what other procedure are out there that I can leverage in this case (apart from using distributed environment)? &lt;/p&gt;

&lt;p&gt;I am learning, so some explanation, hint, and direction would be greatly appreciated.&lt;/p&gt;
', 'machine-learning  data-mining  bigdata  sampling', NULL, '''/a'':29C ''/p'':65C,173C,188C ''/wiki/reservoir_sampling'':24C ''across'':63C ''algorithm'':46C,93C ''answer'':154C ''anybodi'':140C ''apart'':168C ''appreci'':187C ''bigdata'':11B ''binari'':75C ''case'':167C ''chanc'':111C ''class'':61C,119C ''classif'':45C,76C ''correct'':137C ''could'':106C ''curious'':49C ''data'':9B,87C,132C ''data-min'':8B ''dataset'':40C ''differ'':123C ''direct'':183C ''distribut'':62C,120C,171C ''draw'':114C ''en.wikipedia.org'':23C ''en.wikipedia.org/wiki/reservoir_sampling'':22C ''environ'':172C ''evalu'':91C ''exampl'':68C ''explan'':180C ''fluctuat'':59C ''general'':17C ''great'':186C ''hint'':181C ''howev'':99C ''href'':21C ''infer'':138C ''larg'':39C ''larger'':129C ''learn'':7B,44C,177C ''leverag'':164C ''machin'':6B,43C ''machine-learn'':5B ''make'':54C ''may'':113C ''method'':34C ''mine'':10B ''nofollow'':26C ''p'':13C,66C,174C ''pipelin'':56C ''popul'':130C ''problem'':77C ''procedur'':157C ''question'':1A,18C ''rel'':25C ''relat'':83C ''remedi'':145C ''reservoir'':3A,27C,97C,149C ''robust'':57C ''sampl'':4A,12B,28C,36C,64C,81C,98C,116C,150C ''seem'':101C ''seen'':126C ''signific'':122C ''situat'':147C ''small'':84C ''subset'':85C ''suppos'':69C ''tell'':141C ''thought'':104C ''train'':42C ''use'':32C,96C,170C ''want'':79C ''wherein'':117C ''work'':72C ''would'':184C ''wrong'':108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8070, 1, '2015-09-11 18:05:06.02', 3, 188, 'How to visualize categorical data over time?', '&lt;p&gt;I''m visualizing results from lab tests. Most of these results are numerical and therefore easily communicated using line graphs, where the x-axis is time and the y-axis is determined by the range of possible values, like &lt;code&gt;[0, 100]&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;EDIT: To be clear, I''m not measuring frequency. I''m displaying an individual''s lab test results for one specific test (like cholesterol) over time. Since most people don''t get more than 1 or 2 lab tests a year, most people''s graphs will have less than a dozen (x,y) values.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;I''m less sure how to visualize this information when the y-axis values are categorical:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Sometimes the possible values are themselves ranges. A test value will be something like &lt;code&gt;2-6&lt;/code&gt;, meaning that the value falls between 2 and 6 &amp;mdash; and that''s all we need to know. Would it make sense to graph these values with something like &lt;a href="http://www.excel-easy.com/examples/images/error-bars/error-bars.png" rel="nofollow"&gt;error bars&lt;/a&gt;? Or would it make more sense to graph the average of the range (in this case, 4) as a regular line chart?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;The other types involve categories which are non-numerical. Sometimes these are ordinal. In the ordinal case the categories might be something like &lt;code&gt;[none, few, moderate, many]&lt;/code&gt;. Does it make sense to just assign an index to each &lt;code&gt;[0, 1, 2, 3]&lt;/code&gt; and use a standard line graph? If not, what would make more sense?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Lastly, non-numerical, nominal types. These are categories with no clear rank hierarchy, like colors: &lt;code&gt;[yellow, orange, red]&lt;/code&gt;. In this case, I think it might be misleading to use a standard line graph, because it would imply that certain colors are "higher" or "better" than others. What would be appropriate?&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8074, 2, '2015-09-12 02:53:08.243', 1, NULL, NULL, '&lt;p&gt;One situation where NN would be ideal is if the data are sample points of a piecewise constant function. In this case, the true function is composed of a tesselation of its domain, with a constant value for all points within each division.&lt;/p&gt;
', NULL, NULL, '''/p'':45C ''case'':23C ''compos'':28C ''constant'':19C,37C ''data'':12C ''divis'':44C ''domain'':34C ''function'':20C,26C ''ideal'':8C ''nn'':5C ''one'':2C ''p'':1C ''piecewis'':18C ''point'':15C,41C ''sampl'':14C ''situat'':3C ''tessel'':31C ''true'':25C ''valu'':38C ''within'':42C ''would'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8077, 2, '2015-09-12 10:01:17.14', 1, NULL, NULL, '&lt;p&gt;I''ll give it a go, but a lot depends on how you want to communicate your results.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;try to keep the buckets, and add counts to them, so you end up with something like:&lt;/li&gt;
&lt;/ol&gt;

&lt;blockquote&gt;
&lt;pre&gt;&lt;code&gt;date bucket count
2015-09-11 2-6 6
2015-09-12 2-6 9
2015-09-13 2-6 2
2015-09-11 10-16 9
2015-09-12 10-16 15
2015-09-13 10-16 4
&lt;/code&gt;&lt;/pre&gt;
&lt;/blockquote&gt;

&lt;p&gt;This you can communicate either via a line chart, but I would go with side by side bar charts per period, maybe aggregated to week or month depending your timeline. If you have many ranges, splitting them in different graphs. &lt;/p&gt;

&lt;ol start="2"&gt;
&lt;li&gt;&lt;p&gt;Don''t index the ordinal values. The reader of the graph should not look somewhere else to figure out what the numbers mean. Just use counts with the ordinals and use line charts or bar charts, summary tables or a cross(table)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Count the occurences and use side-by-side or stacked bar charts and color the bars according to the colors you have. But human perception will always give a higher bar / line / area more weight than a smaller / lower one. Or just represent it as a table. That way you don''t give any opinion except that some colors might occur more frequently than other. &lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9870, 2, '2016-01-20 13:41:33.503', 4, NULL, NULL, '&lt;p&gt;This answer is on the &lt;em&gt;general&lt;/em&gt; side of cost functions, not related to TensorFlow, and will mosty adress the "some explanation about this topic" part of your question.&lt;/p&gt;

&lt;p&gt;In most examples/tutorial i followed, the cost function used was somewhat arbitrary. The point was more to introduce the reader to a specific method, not to the cost function specifically. It should not stop you to follow the tutorial to be familiar with the tools, but my answer should help you on how to choose the cost function for your own problems.&lt;/p&gt;

&lt;p&gt;If you want answers regarding Cross-Entropy, Logit, L2 norms, or anything specific, I advise you to post multiple, more specific questions. This will increase the probability that someone with the specific knowledge will see your question.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Choosing the right cost function for achieving the desired result is a critical point of machine learning problems. The basic approach, if you do not know exactly what you want out of your method, is to use &lt;a href="https://en.wikipedia.org/wiki/Mean_squared_error" rel="nofollow"&gt;Mean Square Error (Wikipedia)&lt;/a&gt; for regression problems and Percentage of error for classification problems. However, if you want &lt;em&gt;good&lt;/em&gt; results out of your method, you need to &lt;em&gt;define good&lt;/em&gt;, and thus define the adequate cost function. This comes from both domain knowledge (what is your data, what are you trying to achieve), and knowledge of the tools at your disposal. &lt;/p&gt;

&lt;p&gt;I do not believe I can guide you through the costs functions already implemented in TensorFlow, as I have very little knowledge of the tool, but I can give you an example on how to write and assess different cost functions.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;To illustrate the various differences between cost functions, let us use the exemple of the binary classification problem, where we want, for each sample $x_n$, the class $f(x_n) \in \{0,1\}$.&lt;/p&gt;

&lt;p&gt;Starting with &lt;strong&gt;computational properties&lt;/strong&gt;; how two functions measuring the "same thing" could lead to different results. Take the following, simple cost function; the percentage of error. If you have $N$ samples, $f(y_n)$ is the predicted class and $y_n$ the true class, you want to minimize&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;$\frac{1}{N} \sum_n \left\{
\begin{array}{ll}
1 &amp;amp; \text{ if } f(x_n) \not= y_n\\
0 &amp;amp; \text{ otherwise}\\
\end{array} \right. = \sum_n y_n[1-f(x_n)] + [1-y_n]f(x_n)$.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;This cost function has the benefit of being easily interpretable. However, it is not smooth; if you have only two samples, the function "jumps" from 0, to 0.5, to 1. This will lead to inconsistencies if you try to use gradient descent on this function. One way to avoid it is to change the cost function to use probabilities of assignment; $p(y_n = 1 | x_n)$. The function becomes&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;$\frac{1}{N} \sum_n y_n p(y_n = 0 | x_n) + (1 - y_n) p(y_n = 1 | x_n)$.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;This function is smoother, and will work better with a gradient descent approach. You will get a ''finer'' model. However, it has other problem; if you have a sample that is ambiguous, let say that you do not have enough information to say anything better than $p(y_n = 1 | x_n) = 0.5$. Then, using gradient descent on this cost function will lead to a model which increases this probability as much as possible, and thus, maybe, overfit.&lt;/p&gt;

&lt;p&gt;Another problem of this function is that if $p(y_n = 1 | x_n) = 1$ while $y_n = 0$, you are certain to be right, but you are wrong. In order to avoid this issue, you can take the log of the probability, $\log p(y_n | x_n)$. As $\log(0) = \infty$ and $\log(1) = 0$, the following function does not have the problem described in the previous paragraph:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;$\frac{1}{N} \sum_n y_n \log p(y_n = 0 | x_n) + (1 - y_n) \log p(y_n = 1 | x_n)$.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;This should illustrate that in order to optimize the &lt;em&gt;same thing&lt;/em&gt;, the percentage of error, different definitions might yield different results if they are easier to make sense of, computationaly.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;It is possible for cost functions $A$ and $B$ to measure the &lt;em&gt;same concept&lt;/em&gt;, but $A$ might lead your method to better results than $B$.&lt;/strong&gt;&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Now let see how different costs function can measure different concepts. In the context of information retrieval, as in google search (if we ignore ranking), we want the returned results to&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;have high &lt;em&gt;precision&lt;/em&gt;, not return irrelevant information&lt;/li&gt;
&lt;li&gt;have high &lt;em&gt;recall&lt;/em&gt;, return as much relevant results as possible&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Precision_and_recall" rel="nofollow"&gt;Precision and Recall (Wikipedia)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Note that if your algorithm returns &lt;em&gt;everything&lt;/em&gt;, it will return every relevant result possible, and thus have high recall, but have very poor precision. On the other hand, if it returns only &lt;em&gt;one&lt;/em&gt; element, the one that it is the most certain is relevant, it will have high precision but low recall.&lt;/p&gt;

&lt;p&gt;In order to judge such algorithms, the common cost function is the &lt;a href="https://en.wikipedia.org/wiki/F1_score" rel="nofollow"&gt;$F$-score (Wikipedia)&lt;/a&gt;. The common case is the $F_1$-score, which gives equal weight to precision and recall, but the general case it the $F_\beta$-score, and you can tweak $\beta$ to get&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Higher recall, if you use $\beta &amp;gt; 1$&lt;/li&gt;
&lt;li&gt;Higher precision, if you use $\beta &amp;lt; 1$.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In such scenario, &lt;strong&gt;choosing the cost function is choosing what tradeoff your algorithm should do&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Another example that is often brought up is the case of medical diagnosis, you can choose a cost function that punishes more false negatives or false positives depending on what is preferrable:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;More healthy people being classified as sick (But then, we might treat healthy people, which is costly and might hurt them if they are actually not sick)&lt;/li&gt;
&lt;li&gt;More sick people being classified as healthy (But then, they might die without treatment)&lt;/li&gt;
&lt;/ul&gt;

&lt;hr&gt;

&lt;p&gt;In conclusion, defining the cost function is defining the goal of your algorithm. The algorithm defines how to get there.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Sidenote: Some cost functions have nice algorithm ways to get to their goals. For example, a nice way to the minimum of the &lt;a href="https://en.wikipedia.org/wiki/Hinge_loss" rel="nofollow"&gt;Hinge loss (Wikipedia)&lt;/a&gt; exists, by solving the dual problem in &lt;a href="https://en.wikipedia.org/wiki/Support_vector_machine" rel="nofollow"&gt;SVM (Wikipedia)&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9919, 2, '2016-01-23 16:11:23.523', 4, NULL, NULL, '&lt;p&gt;Recurrent Neural Networks can learn very complex mappings between sequences (unlike Feed Forward Nets which are capable of transforming only fixed-sized vectors). You really only need a RNN which takes the sequence of the form $(a_t, b_t)$ as the input (assuming $t \in \{1...T\}$ you have seq. $a$ and $b$ both with $T$ samples and you have to classify these signals as one of $k$ classes). Then you can run the following procedure (pseudocode) for the sampling (given already trained model):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for t in (1..T)           //loop over an input sequence
    rnn.input[0] = a[t]   //assign t-th point of the blue seq.
    rnn.input[1] = b[t]   //assign t-th point of the green seq.
    rnn.forward()         //make forward pass
rnn.output.normalize()    //renormalize output probabilities
class_prob = rnn.output.max()
class      = rnn.output.argmax()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For example we have the following sequence with length 4:&lt;br&gt;
signal A: {1, 3, 4, 6}&lt;br&gt;
signal B: {4, 3, 0, 1}&lt;br&gt;
Then in the 1st timestep we assign vector $(1, 4)$ to the input layer, then $(3, 3)$ and so on. At each timestep we propagate the input forward the network updating its hidden state which is responsible for remembering past datapoints. After every forward pass our neural network generates a prediction at the output layer which is temporarily uninmportant. This fluctuating prediction reflects the rnn''s belief about the part of our dataset- it have seen only $t$ first input vectors. After we propagated entire signals we can do the classification.&lt;br&gt;
Your RNN''s output layer should be $k$-dimensional and its $i$-th component is the pseudo-probability of the signal beeing in class $i$. For example you can do argmax (take the index with greatest prediction) or sample with a softmax distribution. For example if you have class $a$ and class $b$ and your output vector is $(5, 15)$ then your signal is classified as $b$. Or if you do softmax then $softmax(5, 15) = (0.000045, 0.999955)$ so you assign the class $b$ with nearly 100% probability.&lt;br&gt;
Training RNN is much more triky. I would suggest you taking some existing toolkit which have RNN such as LSTM already implemented. To train Recurrent NN we can apply the above procedure but at the end (after iterating over the sequence) we calculate Gradient of the loss function and do a backward pass. However this technique is called Backpropagation through time (it is really similar to ordinary FFN backprop, but we must "unfold" out RNN in time to adjust recurrent connections). Also notice that if you choose to have softmax output units your loss function should be a Cross-Entropy loss function.&lt;br&gt;
But what is the expected output vector to compute gradient loss from? If your dataset contains $N$ (green-blue) sequences each of them is in $k_n$ class then their expected output vector is simply one-hot vector $(0...,1,...0)$ where $1$ is at $k_n$-th component only. For example if you have a sequence classified as $b$, then its output vector target is $(0,1)$. @EDIT: Suppose that as in a previous example your sequence have class $b$ so you expect to see the vector $(0,1)$. You take now the prediction of the net (say $(0.34, 0.57)$) and calculate the error function as normal f.e. squared error or cross-entropy.&lt;/p&gt;

&lt;p&gt;More information you can learn from &lt;a href="http://karpathy.github.io/2015/05/21/rnn-effectiveness/" rel="nofollow"&gt;karpathy&lt;/a&gt; and &lt;a href="https://www.youtube.com/watch?v=vShMxxqtDDs" rel="nofollow"&gt;hinton&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/2015/05/21/rnn-effectiveness/'':583C ''/a'':587C,597C ''/assign'':105C,118C ''/code'':140C ''/loop'':96C ''/make'':128C ''/p'':88C,571C,598C ''/pre'':141C ''/renormalize'':132C ''/watch?v=vshmxxqtdds'':593C ''0'':102C,165C,494C,496C,522C,544C ''0.000045'':337C ''0.34'':555C ''0.57'':556C ''0.999955'':338C ''1'':49C,94C,115C,156C,166C,176C,495C,498C,523C,545C ''100'':347C ''15'':320C,336C ''1st'':171C ''3'':157C,164C,183C,184C ''4'':152C,158C,163C,177C ''5'':319C,335C ''6'':159C ''adjust'':428C ''alreadi'':85C,370C ''also'':431C ''appli'':378C ''argmax'':291C ''assign'':174C,341C ''assum'':46C ''b'':41C,56C,116C,162C,313C,327C,344C,515C,536C ''backprop'':418C ''backpropag'':408C ''backward'':401C ''bee'':282C ''belief'':234C ''blue'':112C,473C ''br'':153C,160C,167C,259C,349C,453C ''calcul'':392C,558C ''call'':407C ''capabl'':18C ''choos'':436C ''class'':72C,135C,138C,284C,309C,312C,343C,482C,535C ''classif'':258C ''classifi'':65C,325C,513C ''code'':90C ''complex'':8C ''compon'':273C,504C ''comput'':462C ''connect'':430C ''contain'':469C ''cross'':449C,569C ''cross-entropi'':448C,568C ''datapoint'':208C ''dataset'':240C,468C ''dimension'':268C ''distribut'':303C ''edit'':524C ''end'':385C ''entir'':252C ''entropi'':450C,570C ''error'':560C,566C ''everi'':210C ''exampl'':144C,287C,305C,507C,531C ''exist'':362C ''expect'':458C,485C,539C ''f.e'':564C ''feed'':13C ''ffn'':417C ''first'':246C ''fix'':23C ''fixed-s'':22C ''fluctuat'':228C ''follow'':78C,148C ''form'':38C ''forward'':14C,129C,195C,211C ''function'':397C,444C,452C,561C ''generat'':216C ''given'':84C ''gradient'':393C,463C ''greatest'':296C ''green'':125C,472C ''green-blu'':471C ''hidden'':200C ''hinton'':596C ''hot'':492C ''howev'':403C ''href'':580C,590C ''implement'':371C ''index'':294C ''inform'':574C ''input'':45C,99C,180C,194C,247C ''iter'':387C ''k'':71C,267C,480C,501C ''karpathi'':586C ''karpathy.github.io'':582C ''karpathy.github.io/2015/05/21/rnn-effectiveness/'':581C ''layer'':181C,222C,264C ''learn'':6C,577C ''length'':151C ''loss'':396C,443C,451C,464C ''lstm'':369C ''map'':9C ''model'':87C ''much'':353C ''must'':421C ''n'':470C,481C,502C ''near'':346C ''need'':29C ''net'':15C,553C ''network'':4C,197C,215C ''neural'':3C,214C ''nn'':375C ''nofollow'':585C,595C ''normal'':563C ''notic'':432C ''one'':69C,491C ''one-hot'':490C ''ordinari'':416C ''output'':133C,221C,263C,316C,440C,459C,486C,518C ''p'':1C,142C,572C ''part'':237C ''pass'':130C,212C,402C ''past'':207C ''point'':109C,122C ''pre'':89C ''predict'':218C,229C,297C,550C ''previous'':530C ''prob'':136C ''probabl'':134C,278C,348C ''procedur'':79C,381C ''propag'':192C,251C ''pseudo'':277C ''pseudo-prob'':276C ''pseudocod'':80C ''realli'':27C,413C ''recurr'':2C,374C,429C ''reflect'':230C ''rel'':584C,594C ''rememb'':206C ''respons'':204C ''rnn'':31C,232C,261C,351C,366C,424C ''rnn.forward'':127C ''rnn.input'':101C,114C ''rnn.output.argmax'':139C ''rnn.output.max'':137C ''rnn.output.normalize'':131C ''run'':76C ''sampl'':60C,83C,299C ''say'':554C ''see'':541C ''seen'':243C ''seq'':53C,113C,126C ''sequenc'':11C,35C,100C,149C,390C,474C,512C,533C ''signal'':67C,154C,161C,253C,281C,323C ''similar'':414C ''simpli'':489C ''size'':24C ''softmax'':302C,332C,334C,439C ''squar'':565C ''state'':201C ''suggest'':358C ''suppos'':525C ''t-th'':106C,119C ''take'':33C,292C,360C,547C ''target'':520C ''techniqu'':405C ''temporarili'':225C ''th'':108C,121C,272C,503C ''time'':410C,426C ''timestep'':172C,190C ''toolkit'':363C ''train'':86C,350C,373C ''transform'':20C ''triki'':355C ''unfold'':422C ''uninmport'':226C ''unit'':441C ''unlik'':12C ''updat'':198C ''vector'':25C,175C,248C,317C,460C,487C,493C,519C,543C ''would'':357C ''www.youtube.com'':592C ''www.youtube.com/watch?v=vshmxxqtdds'':591C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8079, 1, '2015-09-12 14:13:22.56', 1, 17, 'Rstudio - package installation problems', '&lt;p&gt;I have some issue, during package installation in R version 3.0.2 (2013-09-25) ubuntu machine:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;install.packages("randomForest")
&lt;/code&gt;&lt;/pre&gt;

&lt;blockquote&gt;
  &lt;p&gt;Installing package into â/usr/local/lib/R/site-libraryâ&lt;br/&gt;
       (as âlibâ is unspecified)&lt;br/&gt;
       Warning in install.packages :&lt;br/&gt;
       ''lib = "/usr/local/lib/R/site-library"'' is not writable&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;How to resolve it?&lt;/p&gt;
', 'r', NULL, '''-09'':19C ''-25'':20C ''/blockquote'':55C ''/code'':28C ''/p'':23C,54C,61C ''/pre'':29C ''/usr/local/lib/r/site-library'':36C,50C ''2013'':18C ''3.0.2'':17C ''blockquot'':30C ''br'':38C,44C,48C ''code'':25C ''instal'':3A,13C,32C ''install.packages'':26C,47C ''issu'':10C ''lib'':49C ''libâ'':41C ''machin'':22C ''p'':6C,31C,56C ''packag'':2A,12C,33C ''pre'':24C ''problem'':4A ''r'':5B,15C ''randomforest'':27C ''resolv'':59C ''rstudio'':1A ''ubuntu'':21C ''unspecifi'':43C ''version'':16C ''warn'':45C ''writabl'':53C ''â'':35C,37C,40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8111, 2, '2015-09-17 11:21:43.797', 0, NULL, NULL, '&lt;p&gt;Naritivly context aware, visual profanity filter.&lt;/p&gt;

&lt;p&gt;In other worlds, rendering physically realistic and thematically/stylistically appropriate clothes on people who are insufficiently dressed, to make the image more family safe.&lt;/p&gt;

&lt;p&gt;Thats the idea, however at the moment it''s both unreliable and when it does work inaccurate.&lt;/p&gt;

&lt;p&gt;However, more tweaking of the parameters of the dream than I have access to, or possibly just using more iterations and a lower "octave" value than I can specify should make the results much more reliable.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Examples:&lt;/p&gt;

&lt;p&gt;Before:
&lt;a href="http://s22.postimg.org/5sjpqbzoh/image.jpg" rel="nofollow"&gt;http://s22.postimg.org/5sjpqbzoh/image.jpg&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;After:
&lt;a href="http://s22.postimg.org/wew6fb3vl/image.jpg" rel="nofollow"&gt;http://s22.postimg.org/wew6fb3vl/image.jpg&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://s13.postimg.org/c4urz139j/image.jpg" rel="nofollow"&gt;http://s13.postimg.org/c4urz139j/image.jpg&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/5sjpqbzoh/image.jpg'':101C ''/5sjpqbzoh/image.jpg&lt;/a&gt;&lt;/p&gt;'':106C ''/c4urz139j/image.jpg'':126C ''/c4urz139j/image.jpg&lt;/a&gt;&lt;/p&gt;'':131C ''/p'':8C,33C,52C,90C,94C,120C ''/wew6fb3vl/image.jpg'':113C ''/wew6fb3vl/image.jpg&lt;/a&gt;&lt;/p&gt;'':118C ''access'':66C ''appropri'':18C ''awar'':4C ''cloth'':19C ''context'':3C ''dream'':62C ''dress'':25C ''exampl'':93C ''famili'':31C ''filter'':7C ''howev'':38C,54C ''hr'':91C ''href'':98C,110C,123C ''idea'':37C ''imag'':29C ''inaccur'':51C ''insuffici'':24C ''iter'':73C ''lower'':76C ''make'':27C,84C ''moment'':41C ''much'':87C ''naritivli'':2C ''nofollow'':103C,115C,128C ''octav'':77C ''p'':1C,9C,34C,53C,92C,95C,107C,119C,121C ''paramet'':59C ''peopl'':21C ''physic'':14C ''possibl'':69C ''profan'':6C ''realist'':15C ''rel'':102C,114C,127C ''reliabl'':89C ''render'':13C ''result'':86C ''s13.postimg.org'':125C,130C ''s13.postimg.org/c4urz139j/image.jpg'':124C ''s13.postimg.org/c4urz139j/image.jpg&lt;/a&gt;&lt;/p&gt;'':129C ''s22.postimg.org'':100C,105C,112C,117C ''s22.postimg.org/5sjpqbzoh/image.jpg'':99C ''s22.postimg.org/5sjpqbzoh/image.jpg&lt;/a&gt;&lt;/p&gt;'':104C ''s22.postimg.org/wew6fb3vl/image.jpg'':111C ''s22.postimg.org/wew6fb3vl/image.jpg&lt;/a&gt;&lt;/p&gt;'':116C ''safe'':32C ''specifi'':82C ''that'':35C ''thematically/stylistically'':17C ''tweak'':56C ''unreli'':45C ''use'':71C ''valu'':78C ''visual'':5C ''work'':50C ''world'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8082, 1, '2015-09-13 13:53:21.273', 1, 12, 'Designing an (RL) agent for a graph-based music improvisation system', '&lt;p&gt;I am trying to implement a simple agent that creates sounds by building up signal building blocks (e.g. sound generators, filters etc.) that can generally be connected in the form of a directed acyclic graph, and each module has quasi-continuous parameter inputs (defined intervals of floating point numbers, e.g. for a frequency, an amplitude etc.). This agent should be able to operate in real-time along with me as a human agent.&lt;/p&gt;

&lt;p&gt;My idea is to use something like &lt;strong&gt;reinforced learning&lt;/strong&gt; as it allows to operate in an exploratory mode where the state space is very large if not infinite, something that I understand is a disadvantage in a learning-by-example (supervised) mode.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Do you consider &lt;strong&gt;RL applicable&lt;/strong&gt; in this scenario?&lt;/li&gt;
&lt;li&gt;How would I go about &lt;strong&gt;defining the state space&lt;/strong&gt;? Is every module (generator or filter) a cell in that space? How about their connections and parameters? Should I introduce a partial ordering of the graph, or radically simplify the state? Perhaps the state should be a feature vector of the actually produced sound instead of the graph structure?&lt;/li&gt;
&lt;li&gt;I am thinking that I have to provide the &lt;strong&gt;rewards manually&lt;/strong&gt; depending on whether the current result is musically meaningful or bad. Does that make sense? Is there any literature for this "human evaluated" RL? I know there are human fitness functions in genetic programming, so I am thinking this would be an analogous situation.&lt;/li&gt;
&lt;li&gt;I am worried about the convergence rate. In automatic learning, I can easily perform thousands of steps, but if I have to assign manual reward and I have to listen to the actual sound production, the agent can only act at a fraction of the speed.&lt;/li&gt;
&lt;/ul&gt;
', 'machine-learning  algorithms  graphs', NULL, '''/li'':154C,216C,275C,323C ''/p'':94C,141C ''/strong'':106C,150C,166C,230C ''/ul'':324C ''abl'':80C ''act'':316C ''actual'':208C,309C ''acycl'':52C ''agent'':4A,26C,77C,93C,313C ''algorithm'':16B ''allow'':109C ''along'':87C ''amplitud'':74C ''analog'':273C ''applic'':149C ''assign'':299C ''automat'':285C ''bad'':241C ''base'':9A ''block'':35C ''build'':31C,34C ''cell'':174C ''connect'':45C,181C ''consid'':146C ''continu'':60C ''converg'':282C ''creat'':28C ''current'':235C ''defin'':63C,162C ''depend'':231C ''design'':1A ''direct'':51C ''disadvantag'':132C ''e.g'':36C,69C ''easili'':289C ''etc'':40C,75C ''evalu'':253C ''everi'':168C ''exampl'':138C ''exploratori'':114C ''featur'':204C ''filter'':39C,172C ''fit'':260C ''float'':66C ''form'':48C ''fraction'':319C ''frequenc'':72C ''function'':261C ''general'':43C ''generat'':38C,170C ''genet'':263C ''go'':159C ''graph'':8A,17B,53C,192C,214C ''graph-bas'':7A ''human'':92C,252C,259C ''idea'':97C ''implement'':23C ''improvis'':11A ''infinit'':125C ''input'':62C ''instead'':211C ''interv'':64C ''introduc'':186C ''know'':256C ''larg'':122C ''learn'':15B,105C,136C,286C ''learning-by-exampl'':135C ''li'':143C,155C,217C,276C ''like'':102C ''listen'':306C ''literatur'':249C ''machin'':14B ''machine-learn'':13B ''make'':244C ''manual'':229C,300C ''meaning'':239C ''mode'':115C,140C ''modul'':56C,169C ''music'':10A,238C ''number'':68C ''oper'':82C,111C ''order'':189C ''p'':18C,95C ''paramet'':61C,183C ''partial'':188C ''perform'':290C ''perhap'':198C ''point'':67C ''produc'':209C ''product'':311C ''program'':264C ''provid'':225C ''quasi'':59C ''quasi-continu'':58C ''radic'':194C ''rate'':283C ''real'':85C ''real-tim'':84C ''reinforc'':104C ''result'':236C ''reward'':228C,301C ''rl'':3A,148C,254C ''scenario'':153C ''sens'':245C ''signal'':33C ''simpl'':25C ''simplifi'':195C ''situat'':274C ''someth'':101C,126C ''sound'':29C,37C,210C,310C ''space'':119C,165C,177C ''speed'':322C ''state'':118C,164C,197C,200C ''step'':293C ''strong'':103C,147C,161C,227C ''structur'':215C ''supervis'':139C ''system'':12A ''think'':220C,268C ''thousand'':291C ''time'':86C ''tri'':21C ''ul'':142C ''understand'':129C ''use'':100C ''vector'':205C ''whether'':233C ''worri'':279C ''would'':157C,270C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8083, 1, '2015-09-13 20:28:07.537', 1, 47, 'Manage x-axis using ggplot()', '&lt;p&gt;Source: &lt;a href="https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip" rel="nofollow"&gt;https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Here is my data prep.  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;NEI &amp;lt;- readRDS("summarySCC_PM25.rds")
Baltimore &amp;lt;- NEI[NEI$fips=="24510", ]
Baltimore$type &amp;lt;- as.factor(Baltimore$type)
Total_Emmisssions &amp;lt;- aggregate(Baltimore$Emissions, 
                               by=list(Baltimore$year, Baltimore$type),
                               FUN=sum)
names(Total_Emmisssions) &amp;lt;- c("Year","Type","Emissions")  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Plot code  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(ggplot2)
g &amp;lt;- ggplot(Total_Emmisssions,aes(x=Year, y=Emissions, colour=Type))
g1 &amp;lt;- g+geom_point()
g2 &amp;lt;- g1+facet_grid(. ~ Type)
g3 &amp;lt;- g2+geom_smooth(method="lm", se=FALSE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However, in this plot, the year is always 2002-2008. How do I change the scale / ticks to show from 1999-2008 at an interval of 3 years. Using &lt;code&gt;scale_x_discrete&lt;/code&gt; does not work and it messes the graph. Please help.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;str(Total_Emmisssions)
''data.frame'':   16 obs. of  3 variables:
 $ Year     : int  1999 2002 2005 2008 1999 2002 2005 2008 1999 2002 ...
 $ Type     : Factor w/ 4 levels "NON-ROAD","NONPOINT",..: 1 1 1 1 2 2 2 2 3 3 ...
 $ Emissions: num  522.9 240.8 248.9 55.8 2107.6 ...
&lt;/code&gt;&lt;/pre&gt;
', 'r  ggplot2', NULL, '''-2008'':122C,134C ''/code'':68C,110C,146C,207C ''/exdata%2fdata%2fnei_data.zip'':15C ''/exdata%2fdata%2fnei_data.zip&lt;/a&gt;&lt;/p&gt;'':20C ''/p'':27C,73C,157C ''/pre'':69C,111C,208C ''1'':190C,191C,192C,193C ''16'':164C ''1999'':133C,171C,175C,179C ''2'':194C,195C,196C,197C ''2002'':121C,172C,176C,180C ''2005'':173C,177C ''2008'':174C,178C ''2107.6'':206C ''240.8'':203C ''24510'':39C ''248.9'':204C ''3'':139C,167C,198C,199C ''4'':184C ''522.9'':202C ''55.8'':205C ''ae'':83C ''aggreg'':49C ''alway'':120C ''as.factor'':43C ''axi'':4A ''baltimor'':34C,40C,44C,50C,54C,56C ''c'':64C ''chang'':126C ''code'':29C,72C,75C,142C,159C ''colour'':88C ''d396qusza40orc.cloudfront.net'':14C,19C ''d396qusza40orc.cloudfront.net/exdata%2fdata%2fnei_data.zip'':13C ''d396qusza40orc.cloudfront.net/exdata%2fdata%2fnei_data.zip&lt;/a&gt;&lt;/p&gt;'':18C ''data'':25C ''data.frame'':163C ''discret'':145C ''emiss'':51C,67C,87C,200C ''emmisss'':47C,62C,82C,162C ''facet'':98C ''factor'':182C ''fals'':109C ''fip'':38C ''fun'':58C ''g'':78C,92C ''g1'':90C,97C ''g2'':95C,103C ''g3'':101C ''geom'':93C,104C ''ggplot'':6A,80C ''ggplot2'':8B,77C ''graph'':154C ''grid'':99C ''help'':156C ''howev'':113C ''href'':12C ''int'':170C ''interv'':137C ''level'':185C ''librari'':76C ''list'':53C ''lm'':107C ''lt'':31C,35C,42C,48C,63C,79C,91C,96C,102C ''manag'':1A ''mess'':152C ''method'':106C ''name'':60C ''nei'':30C,36C,37C ''nofollow'':17C ''non'':187C ''non-road'':186C ''nonpoint'':189C ''num'':201C ''ob'':165C ''p'':9C,21C,70C,112C ''pleas'':155C ''plot'':71C,116C ''point'':94C ''pre'':28C,74C,158C ''prep'':26C ''r'':7B ''readrd'':32C ''rel'':16C ''road'':188C ''scale'':128C,143C ''se'':108C ''show'':131C ''smooth'':105C ''sourc'':10C ''str'':160C ''sum'':59C ''summaryscc_pm25.rds'':33C ''tick'':129C ''total'':46C,61C,81C,161C ''type'':41C,45C,57C,66C,89C,100C,181C ''use'':5A,141C ''variabl'':168C ''w'':183C ''work'':149C ''x'':3A,84C,144C ''x-axi'':2A ''y'':86C ''year'':55C,65C,85C,118C,140C,169C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8084, 1, '2015-09-13 21:50:39.237', 1, 39, 'Solve a pair of coupled nonlinear equations within certain limits', '&lt;p&gt;&lt;a href="http://stackoverflow.com/a/8739526/2623899"&gt;This answer&lt;/a&gt; to &lt;a href="http://stackoverflow.com/q/8739227/2623899"&gt;this question&lt;/a&gt; works only for situations in which the desired solution to the coupled functions is not restricted to a certain range.&lt;/p&gt;

&lt;p&gt;But what if, for example, we wanted a solution such that &lt;code&gt;0 &amp;lt; x &amp;lt; 10&lt;/code&gt; and &lt;code&gt;0 &amp;lt; y &amp;lt; 10&lt;/code&gt;?&lt;/p&gt;

&lt;p&gt;There are functions within &lt;a href="http://docs.scipy.org/doc/scipy/reference/optimize.html" rel="nofollow"&gt;&lt;code&gt;scipy.optimize&lt;/code&gt;&lt;/a&gt; that find roots to a function within a given interval (e.g., &lt;a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.brentq.html#scipy.optimize.brentq" rel="nofollow"&gt;&lt;code&gt;brentq&lt;/code&gt;&lt;/a&gt;), but these work only for functions of &lt;strong&gt;one variable&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Why does &lt;code&gt;scipy&lt;/code&gt; fall short of providing a root solver that works for multi-variable functions within specific ranges?  How might such a solver be implemented?&lt;/p&gt;
', 'python  optimization', NULL, '''/a'':21C,30C,95C,117C ''/a/8739526/2623899'':18C ''/code'':70C,78C,94C,116C,135C ''/doc/scipy/reference/generated/scipy.optimize.brentq.html#scipy.optimize.brentq'':111C ''/doc/scipy/reference/optimize.html'':89C ''/p'':51C,79C,129C,160C ''/q/8739227/2623899'':27C ''/strong'':128C ''0'':65C,73C ''10'':69C,77C ''answer'':20C ''brentq'':115C ''certain'':9A,49C ''code'':64C,72C,92C,114C,133C ''coupl'':5A,42C ''desir'':38C ''docs.scipy.org'':88C,110C ''docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.brentq.html#scipy.optimize.brentq'':109C ''docs.scipy.org/doc/scipy/reference/optimize.html'':87C ''e.g'':106C ''equat'':7A ''exampl'':57C ''fall'':136C ''find'':97C ''function'':43C,83C,101C,123C,149C ''given'':104C ''href'':15C,24C,86C,108C ''implement'':159C ''interv'':105C ''limit'':10A ''lt'':66C,68C,74C,76C ''might'':154C ''multi'':147C ''multi-vari'':146C ''nofollow'':91C,113C ''nonlinear'':6A ''one'':126C ''optim'':12B ''p'':13C,52C,80C,130C ''pair'':3A ''provid'':139C ''python'':11B ''question'':29C ''rang'':50C,152C ''rel'':90C,112C ''restrict'':46C ''root'':98C,141C ''scipi'':134C ''scipy.optimize'':93C ''short'':137C ''situat'':34C ''solut'':39C,61C ''solv'':1A ''solver'':142C,157C ''specif'':151C ''stackoverflow.com'':17C,26C ''stackoverflow.com/a/8739526/2623899'':16C ''stackoverflow.com/q/8739227/2623899'':25C ''strong'':125C ''variabl'':127C,148C ''want'':59C ''within'':8A,84C,102C,150C ''work'':31C,120C,144C ''x'':67C ''y'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8085, 2, '2015-09-13 22:28:37.963', 2, NULL, NULL, '&lt;p&gt;Change &lt;code&gt;Year&lt;/code&gt; to a factor and add &lt;code&gt;group=1&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;g &amp;lt;- ggplot(Total_Emmisssions,aes(x=factor(Year), y=Emissions, colour=Type, group=1))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;you can leave the rest the same (you''ll also prbly want to change the &lt;code&gt;xlab&lt;/code&gt;).&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/1Xbqs.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/1Xbqs.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/1xbqs.png'':60C,67C ''/a'':73C ''/code'':5C,14C,33C,53C ''/p'':15C,54C,74C ''/pre'':34C ''1'':13C,32C ''add'':10C ''ae'':23C ''also'':45C ''alt'':68C ''chang'':2C,49C ''code'':3C,11C,17C,51C ''colour'':29C ''descript'':71C ''emiss'':28C ''emmisss'':22C ''enter'':69C ''factor'':8C,25C ''g'':18C ''ggplot'':20C ''group'':12C,31C ''href'':57C ''i.stack.imgur.com'':59C,66C ''i.stack.imgur.com/1xbqs.png'':58C,65C ''imag'':70C ''img'':63C ''leav'':38C ''ll'':44C ''lt'':19C ''nofollow'':62C ''p'':1C,35C,55C ''prbli'':46C ''pre'':16C ''rel'':61C ''rest'':40C ''src'':64C ''total'':21C ''type'':30C ''want'':47C ''x'':24C ''xlab'':52C ''y'':27C ''year'':4C,26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8088, 2, '2015-09-14 08:05:32.043', 1, NULL, NULL, '&lt;p&gt;As a workaround, you could minimize another function that includes both the objective and the constraints, then check if sol.fun is (numerically) equal to zero.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from scipy.optimize import minimize
import numpy as np

f = lambda x: np.sin(x).sum() #the function to find roots of
L = np.array([-1,-1]) #lower bound for each coordinate
U = np.array([1, 1])  #upper bound for each coordinate
g = lambda x: f(x) **2 +  max(0, (L - x).max(), (x - U).max())
sol = minimize(g, [0.5,0.5])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Also, scipy.optimize seems to have some optimisers that support rectangular bounds, i.e. &lt;a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.differential_evolution.html#scipy.optimize.differential_evolution" rel="nofollow"&gt;differential_evolution&lt;/a&gt; (since version 0.15.0).&lt;/p&gt;
', NULL, NULL, '''-1'':52C,53C ''/a'':111C ''/code'':87C ''/doc/scipy/reference/generated/scipy.optimize.differential_evolution.html#scipy.optimize.differential_evolution'':106C ''/p'':27C,115C ''/pre'':88C ''0'':75C ''0.15.0'':114C ''0.5'':85C,86C ''1'':61C,62C ''2'':73C ''also'':90C ''anoth'':8C ''bound'':55C,64C,100C ''check'':19C ''code'':29C ''constraint'':17C ''coordin'':58C,67C ''could'':6C ''differenti'':109C ''docs.scipy.org'':105C ''docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.differential_evolution.html#scipy.optimize.differential_evolution'':104C ''equal'':24C ''evolut'':110C ''f'':38C,71C ''find'':47C ''function'':9C,45C ''g'':68C,84C ''href'':103C ''i.e'':101C ''import'':32C,34C ''includ'':11C ''l'':50C,76C ''lambda'':39C,69C ''lower'':54C ''max'':74C,78C,81C ''minim'':7C,33C,83C ''nofollow'':108C ''np'':37C ''np.array'':51C,60C ''np.sin'':41C ''numer'':23C ''numpi'':35C ''object'':14C ''optimis'':96C ''p'':1C,89C ''pre'':28C ''rectangular'':99C ''rel'':107C ''root'':48C ''scipy.optimize'':31C,91C ''seem'':92C ''sinc'':112C ''sol'':82C ''sol.fun'':21C ''sum'':43C ''support'':98C ''u'':59C,80C ''upper'':63C ''version'':113C ''workaround'':4C ''x'':40C,42C,70C,72C,77C,79C ''zero'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8089, 2, '2015-09-14 09:28:54.003', 2, NULL, NULL, '&lt;p&gt;Two recent papers use a Deep learning architecture called CharWNN to address this problem. CharWNN was first used to &lt;a href="http://jmlr.org/proceedings/papers/v32/santos14.pdf" rel="nofollow"&gt;get state of the art results&lt;/a&gt; (without handcrafted features) on Part of Speech (POS) tagging on an English corpus.&lt;/p&gt;

&lt;p&gt;The &lt;a href="http://arxiv.org/abs/1505.05008" rel="nofollow"&gt;second paper&lt;/a&gt; by the same author uses the same (or similar) architecture for predicting whether a word belongs to 10 Named Entity classes, with apparent state of the art results.&lt;/p&gt;
', NULL, NULL, '''/a'':34C,60C ''/abs/1505.05008'':55C ''/p'':48C,89C ''/proceedings/papers/v32/santos14.pdf'':25C ''10'':78C ''address'':13C ''appar'':83C ''architectur'':9C,70C ''art'':32C,87C ''arxiv.org'':54C ''arxiv.org/abs/1505.05008'':53C ''author'':64C ''belong'':76C ''call'':10C ''charwnn'':11C,16C ''class'':81C ''corpus'':47C ''deep'':7C ''english'':46C ''entiti'':80C ''featur'':37C ''first'':18C ''get'':28C ''handcraft'':36C ''href'':22C,52C ''jmlr.org'':24C ''jmlr.org/proceedings/papers/v32/santos14.pdf'':23C ''learn'':8C ''name'':79C ''nofollow'':27C,57C ''p'':1C,49C ''paper'':4C,59C ''part'':39C ''pos'':42C ''predict'':72C ''problem'':15C ''recent'':3C ''rel'':26C,56C ''result'':33C,88C ''second'':58C ''similar'':69C ''speech'':41C ''state'':29C,84C ''tag'':43C ''two'':2C ''use'':5C,19C,65C ''whether'':73C ''without'':35C ''word'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8208, 1, '2015-09-25 23:13:55.427', 0, 27, 'Feature importance for random forest classification of a sample', '&lt;p&gt;Using a random forest is it possible to determine which features were the driving features to classify a specific sample as class A?&lt;/p&gt;

&lt;p&gt;I know I can ask which features are more important to perform classification of ANY sample, but can I ask this for a specific sample?  E.g. Why was sample 1 classified as A?  Which of its features were much more like class A than class B?&lt;/p&gt;

&lt;p&gt;Does it even make sense to ask this question of a random forest?&lt;/p&gt;

&lt;p&gt;Bonus points on how to do it with sklearn in python :)&lt;/p&gt;
', 'random-forest  sklearn', NULL, '''/p'':38C,86C,101C,114C ''1'':69C ''ask'':44C,59C,94C ''b'':85C ''bonus'':103C ''class'':36C,81C,84C ''classif'':6A,52C ''classifi'':31C,70C ''determin'':23C ''drive'':28C ''e.g'':65C ''even'':90C ''featur'':1A,25C,29C,46C,76C ''forest'':5A,12B,18C,100C ''import'':2A,49C ''know'':41C ''like'':80C ''make'':91C ''much'':78C ''p'':14C,39C,87C,102C ''perform'':51C ''point'':104C ''possibl'':21C ''python'':113C ''question'':96C ''random'':4A,11B,17C,99C ''random-forest'':10B ''sampl'':9A,34C,55C,64C,68C ''sens'':92C ''sklearn'':13B,111C ''specif'':33C,63C ''use'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8091, 2, '2015-09-13 19:51:12.86', 1, NULL, NULL, '&lt;p&gt;If you are looking for an external storage, then I would suggest you &lt;a href="http://docs.aws.amazon.com/redshift/latest/mgmt/welcome.html" rel="nofollow"&gt;Redshift&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Redshift is a central warehouse and a columnar data store. It allows complex and huge data aggregations and joins; so it is a nice bet for serious Kaggle participants. (I personally use Redshift as data science architecture for kaggle.)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Combinations from some other vendor?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;No, a combination of Redshift and S3 is enough. The data is stored in S3, and then loaded into Redshift using the COPY command.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is there any way to interact with a GUI rather than a command line to set this up?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Yes, there is a CLI interface for Amazon AWS. However, you can use the &lt;a href="http://boto.readthedocs.org/" rel="nofollow"&gt;boto&lt;/a&gt; library on Python for handling this stuff.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Would I have to install Anaconda or some other Python distribution before getting started?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Not a compulsion.  Anaconda is a wrapper of numerical and scientific libraries. You can install them on your own or intall Anaconda.&lt;/p&gt;

&lt;p&gt;Check out &lt;a href="https://spark.apache.org/docs/0.9.0/python-programming-guide.html" rel="nofollow"&gt;PySpark&lt;/a&gt;, which allows you to do handle BigData in Python.&lt;/p&gt;
', NULL, NULL, '''/a'':23C,144C,206C ''/docs/0.9.0/python-programming-guide.html'':202C ''/p'':24C,65C,74C,100C,122C,152C,170C,194C,216C ''/redshift/latest/mgmt/welcome.html'':19C ''/strong'':73C,121C,169C ''aggreg'':42C ''allow'':37C,208C ''amazon'':131C ''anaconda'':160C,175C,193C ''architectur'':62C ''aw'':132C ''bet'':50C ''bigdata'':213C ''boto'':143C ''boto.readthedocs.org'':140C ''central'':29C ''check'':196C ''cli'':128C ''columnar'':33C ''combin'':68C,78C ''command'':99C,115C ''complex'':38C ''compuls'':174C ''copi'':98C ''data'':34C,41C,60C,86C ''distribut'':165C ''docs.aws.amazon.com'':18C ''docs.aws.amazon.com/redshift/latest/mgmt/welcome.html'':17C ''enough'':84C ''extern'':8C ''get'':167C ''gui'':111C ''handl'':149C,212C ''howev'':133C ''href'':16C,139C,199C ''huge'':40C ''instal'':159C,186C ''intal'':192C ''interact'':108C ''interfac'':129C ''join'':44C ''kaggl'':53C,64C ''librari'':145C,183C ''line'':116C ''load'':93C ''look'':5C ''nice'':49C ''nofollow'':21C,142C,204C ''numer'':180C ''p'':1C,25C,66C,75C,101C,123C,153C,171C,195C ''particip'':54C ''person'':56C ''pyspark'':205C ''python'':147C,164C,215C ''rather'':112C ''redshift'':22C,26C,58C,80C,95C ''rel'':20C,141C,203C ''s3'':82C,90C ''scienc'':61C ''scientif'':182C ''serious'':52C ''set'':118C ''spark.apache.org'':201C ''spark.apache.org/docs/0.9.0/python-programming-guide.html'':200C ''start'':168C ''storag'':9C ''store'':35C,88C ''strong'':67C,102C,154C ''stuff'':151C ''suggest'':13C ''use'':57C,96C,136C ''vendor'':72C ''warehous'':30C ''way'':106C ''would'':12C,155C ''wrapper'':178C ''yes'':124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8094, 1, '2015-09-14 22:05:19.697', 0, 67, 'How to make predictions based on correlations?', '&lt;p&gt;I have &lt;code&gt;correlation&lt;/code&gt; values for &lt;code&gt;profit&lt;/code&gt; based on three different attributes - &lt;code&gt;attribute1&lt;/code&gt;,&lt;code&gt;attribute2&lt;/code&gt;,&lt;code&gt;attribute3&lt;/code&gt;. Now I want to make &lt;code&gt;predictions&lt;/code&gt; for the &lt;code&gt;profit&lt;/code&gt; based on these 3 different &lt;code&gt;correlation&lt;/code&gt; values. &lt;/p&gt;

&lt;p&gt;I wanted suggestions for some efficient algorithms that can help me to make predictions  from these &lt;code&gt;correlation&lt;/code&gt; values.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Note:&lt;/strong&gt; I am new to this so I would appreciate some suggestions if there is some lack of understanding in me.   &lt;/p&gt;
', 'algorithms  predictive-modeling  regression  correlation', NULL, '''/code'':19C,24C,32C,35C,38C,46C,51C,59C,81C ''/p'':61C,83C,108C ''/strong'':87C ''3'':55C ''algorithm'':8B,69C ''appreci'':96C ''attribut'':29C ''attribute1'':31C ''attribute2'':34C ''attribute3'':37C ''base'':5A,25C,52C ''code'':17C,22C,30C,33C,36C,44C,49C,57C,79C ''correl'':7A,13B,18C,58C,80C ''differ'':28C,56C ''effici'':68C ''help'':72C ''lack'':103C ''make'':3A,43C,75C ''model'':11B ''new'':90C ''note'':86C ''p'':14C,62C,84C ''predict'':4A,10B,45C,76C ''predictive-model'':9B ''profit'':23C,50C ''regress'':12B ''strong'':85C ''suggest'':65C,98C ''three'':27C ''understand'':105C ''valu'':20C,60C,82C ''want'':41C,64C ''would'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8095, 2, '2015-09-14 23:16:13.443', 0, NULL, NULL, '&lt;p&gt;I''ve read your Post a few Times now. But I''m not absolutely sure if I understood your Experiments fully. But I have a guess what might be happening.&lt;/p&gt;

&lt;p&gt;I think the error is hidden in the part where you achieve this astonishing 0.06 percent. You''re not only generalizing to new data but you predict the future in a way. This should not work, but I think it does because your net is horribly overfitted. So to say your network is still very dumb. It just learned if you show it pictures a, b it will answer c. The second Experiment contains a twist, if I understood you correctly. I assume you still present complete images to the network, but they are built out of the coefficients of their eigenimages. Now it has to spit out coefficients to build the answer from eigenimages. But this would require some understanding of the process, which the network didn''t aquire.&lt;/p&gt;

&lt;p&gt;So I think there are a number of suggestions to improve your experiment:&lt;/p&gt;

&lt;p&gt;The learning of complete inputimages is just an extremely high dimensional problem which requires huge ammount of training data. Consider calculating features from the image.&lt;/p&gt;

&lt;p&gt;I''m not sure if your learning problem is a sound one. Please consider to separate your training data from your prediction data and take more conventional data like handwritten digits.&lt;/p&gt;
', NULL, NULL, '''/p'':32C,164C,179C,206C,239C ''0.06'':48C ''absolut'':15C ''achiev'':45C ''ammount'':196C ''answer'':102C,146C ''aquir'':163C ''assum'':116C ''astonish'':47C ''b'':99C ''build'':144C ''built'':128C ''c'':103C ''calcul'':201C ''coeffici'':132C,142C ''complet'':120C,184C ''consid'':200C,221C ''contain'':107C ''convent'':234C ''correct'':114C ''data'':57C,199C,226C,230C,235C ''didn'':161C ''digit'':238C ''dimension'':191C ''dumb'':89C ''eigenimag'':135C,148C ''error'':37C ''experi'':21C,106C,178C ''extrem'':189C ''featur'':202C ''fulli'':22C ''futur'':62C ''general'':54C ''guess'':27C ''handwritten'':237C ''happen'':31C ''hidden'':39C ''high'':190C ''horribl'':79C ''huge'':195C ''imag'':121C,205C ''improv'':176C ''inputimag'':185C ''learn'':92C,182C,214C ''like'':236C ''m'':13C,209C ''might'':29C ''net'':77C ''network'':85C,124C,160C ''new'':56C ''number'':172C ''one'':219C ''overfit'':80C ''p'':1C,33C,165C,180C,207C ''part'':42C ''percent'':49C ''pictur'':97C ''pleas'':220C ''post'':6C ''predict'':60C,229C ''present'':119C ''problem'':192C,215C ''process'':157C ''re'':51C ''read'':4C ''requir'':152C,194C ''say'':83C ''second'':105C ''separ'':223C ''show'':95C ''sound'':218C ''spit'':140C ''still'':87C,118C ''suggest'':174C ''sure'':16C,211C ''take'':232C ''think'':35C,72C,168C ''time'':9C ''train'':198C,225C ''twist'':109C ''understand'':154C ''understood'':19C,112C ''ve'':3C ''way'':65C ''work'':69C ''would'':151C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8227, 1, '2015-09-28 22:39:45.037', 0, 29, 'Looking for a ''CITY, STATE'' within a body of text (from a CITY-STATE database)', '&lt;p&gt;I''m looking for an optimal way to search a large body of text for a combination of words that resemble any CITY, STATE combination I have in a separate CITY-STATE database.&lt;/p&gt;

&lt;p&gt;My only idea would be to do a separate search against the body of text for each CITY, STATE in the database, but that would require a lot of time considering the amount of CITY, STATE combinations the database has in it. The desired result from this query would be to pull a single CITY, STATE for each body of text I am analyzing to tell the geographical side of the story for this data subset.&lt;/p&gt;

&lt;p&gt;Anyone know of an optimal way/process to do such a query?&lt;/p&gt;
', 'databases  search  parsing  data-indexing-techniques', NULL, '''/p'':59C,137C,150C ''amount'':93C ''analyz'':124C ''anyon'':139C ''bodi'':8A,36C,73C,119C ''citi'':4A,14A,47C,56C,78C,95C,115C ''city-st'':13A,55C ''combin'':41C,49C,97C ''consid'':91C ''data'':21B,135C ''data-indexing-techniqu'':20B ''databas'':16A,17B,58C,82C,99C ''desir'':104C ''geograph'':128C ''idea'':63C ''index'':22B ''know'':140C ''larg'':35C ''look'':1A,27C ''lot'':88C ''m'':26C ''optim'':30C,143C ''p'':24C,60C,138C ''pars'':19B ''pull'':112C ''queri'':108C,149C ''requir'':86C ''resembl'':45C ''result'':105C ''search'':18B,33C,70C ''separ'':54C,69C ''side'':129C ''singl'':114C ''state'':5A,15A,48C,57C,79C,96C,116C ''stori'':132C ''subset'':136C ''techniqu'':23B ''tell'':126C ''text'':10A,38C,75C,121C ''time'':90C ''way'':31C ''way/process'':144C ''within'':6A ''word'':43C ''would'':64C,85C,109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8096, 2, '2015-09-14 23:28:47.893', 1, NULL, NULL, '&lt;p&gt;The correlation coefficient will tell you, how much predictive power the individual attributes posess with respect to profit in a linear sense.
Easier: The correlation coefficient tells you how line-like your data is.
( There might still be a very predictive arc like structure in your data which the correlation coefficient can''t capture )   &lt;/p&gt;

&lt;p&gt;So you can either calculate a regression line for profit with the attribute having the highest correlation coefficient, or you can calculate a hyperplane for all three attributes to get the desired prediction. Regression lines and hyperplanes are usually calculated by least-squares fitting.&lt;/p&gt;

&lt;p&gt;But beware to overestimate predictive power, because it is extrapolation what you do. New data might behave different than old data which makes extrapolation potentially less exact the further you are away from known data.&lt;/p&gt;
', NULL, NULL, '''/p'':57C,104C,141C ''arc'':44C ''attribut'':14C,71C,86C ''away'':137C ''behav'':122C ''bewar'':107C ''calcul'':63C,80C,98C ''captur'':56C ''coeffici'':4C,27C,53C,76C ''correl'':3C,26C,52C,75C ''data'':35C,49C,120C,126C,140C ''desir'':90C ''differ'':123C ''easier'':24C ''either'':62C ''exact'':132C ''extrapol'':115C,129C ''fit'':103C ''get'':88C ''highest'':74C ''hyperplan'':82C,95C ''individu'':13C ''known'':139C ''least'':101C ''least-squar'':100C ''less'':131C ''like'':33C,45C ''line'':32C,66C,93C ''line-lik'':31C ''linear'':22C ''make'':128C ''might'':38C,121C ''much'':9C ''new'':119C ''old'':125C ''overestim'':109C ''p'':1C,58C,105C ''posess'':15C ''potenti'':130C ''power'':11C,111C ''predict'':10C,43C,91C,110C ''profit'':19C,68C ''regress'':65C,92C ''respect'':17C ''sens'':23C ''squar'':102C ''still'':39C ''structur'':46C ''tell'':6C,28C ''three'':85C ''usual'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8097, 2, '2015-09-14 23:37:06.307', 0, NULL, NULL, '&lt;p&gt;If I remember it correctly, classical theory says it is ideal, when the data are gaussians with an identity-covariance matrix. Then it behaves as the Bayesian( Gaussian ) Classifier which is optimal with respect to the 0 - 1 Loss-function.&lt;/p&gt;

&lt;p&gt;Which still doesn''t say anything about the behaviour with a real dataset without infinite data like suggested before.&lt;/p&gt;

&lt;p&gt;The practical answer could be, to try it as a first guess. When it doesn''t work apply PCA to your data ( to decorrelate your features ) and try it again. If it still doesn''t work, try other stuff. &lt;/p&gt;
', NULL, NULL, '''/p'':44C,65C,106C ''0'':39C ''1'':40C ''answer'':69C ''anyth'':51C ''appli'':84C ''bayesian'':29C ''behav'':26C ''behaviour'':54C ''classic'':7C ''classifi'':31C ''correct'':6C ''could'':70C ''covari'':22C ''data'':15C,61C,88C ''dataset'':58C ''decorrel'':90C ''doesn'':48C,81C,100C ''featur'':92C ''first'':77C ''function'':43C ''gaussian'':17C,30C ''guess'':78C ''ideal'':12C ''ident'':21C ''identity-covari'':20C ''infinit'':60C ''like'':62C ''loss'':42C ''loss-funct'':41C ''matrix'':23C ''optim'':34C ''p'':1C,45C,66C ''pca'':85C ''practic'':68C ''real'':57C ''rememb'':4C ''respect'':36C ''say'':9C,50C ''still'':47C,99C ''stuff'':105C ''suggest'':63C ''theori'':8C ''tri'':73C,94C,103C ''without'':59C ''work'':83C,102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8098, 2, '2015-09-15 00:01:23.723', 2, NULL, NULL, '&lt;p&gt;Looks like you could just look for a few seconds of higher then noise derivative.
Just calculate the absolute value of the finite difference from each timestep to the last ( or one of the former ) and wait for a series of high values. That''s when a lane change occurs. &lt;/p&gt;
', NULL, NULL, '''/p'':52C ''absolut'':20C ''calcul'':18C ''chang'':50C ''could'':5C ''deriv'':16C ''differ'':25C ''finit'':24C ''former'':36C ''high'':43C ''higher'':13C ''lane'':49C ''last'':31C ''like'':3C ''look'':2C,7C ''nois'':15C ''occur'':51C ''one'':33C ''p'':1C ''second'':11C ''seri'':41C ''timestep'':28C ''valu'':21C,44C ''wait'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8105, 1, '2015-09-16 13:54:07.77', 0, 170, 'What commercial software should a data scientist purchase?', '&lt;p&gt;I am setting up a work computer and have free reign here. My typical go-to software packages are all freely available, such as Rstudio and Anaconda. &lt;/p&gt;

&lt;p&gt;I have thought about investing in commercial BI software such as Tableau or Spotfire, but nobody else in the group (or myself) is a proficient user. Are there other obvious choices I am missing?&lt;/p&gt;

&lt;p&gt;At the outset, much of what this group is working on will be exploratory. They are needing some justification to invest further into "data science".&lt;/p&gt;
', 'bigdata  software-recommendation', NULL, '''/p'':42C,78C,105C ''anaconda'':41C ''avail'':36C ''bi'':51C ''bigdata'':9B ''choic'':74C ''commerci'':2A,50C ''comput'':20C ''data'':6A,103C ''els'':60C ''exploratori'':93C ''free'':23C ''freeli'':35C ''go'':29C ''go-to'':28C ''group'':63C,87C ''invest'':48C,100C ''justif'':98C ''miss'':77C ''much'':83C ''need'':96C ''nobodi'':59C ''obvious'':73C ''outset'':82C ''p'':13C,43C,79C ''packag'':32C ''profici'':68C ''purchas'':8A ''recommend'':12B ''reign'':24C ''rstudio'':39C ''scienc'':104C ''scientist'':7A ''set'':16C ''softwar'':3A,11B,31C,52C ''software-recommend'':10B ''spotfir'':57C ''tableau'':55C ''thought'':46C ''typic'':27C ''user'':69C ''work'':19C,89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8099, 1, '2015-09-15 03:15:28.163', 3, 132, 'Classifying transactions as malicious', '&lt;p&gt;I have a big data set of &lt;em&gt;fake&lt;/em&gt; transactions for a company. Each row contains the username, credit card number, time, device used, and amount of money in the transaction. I need to classify each transaction as either malicious or not malicious and I am lost for ideas on where to start. Doing it by hand would be silly.&lt;/p&gt;

&lt;p&gt;I was thinking possibly checking for how often a credit card is used, if it is consistently used at a certain time, or if it is used from lots of different devices (iOS AND Android, as an example) would be possible starting places. I''m still fairly new to all this and ML. Would there be some ML algorithm optimal for this problem?&lt;/p&gt;

&lt;p&gt;Also, side question: what would be a good place to host the 600 or so GB of data for cheaps?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'classification  bigdata', NULL, '''/em'':17C ''/p'':69C,134C,156C,159C ''600'':148C ''algorithm'':129C ''also'':136C ''amount'':34C ''android'':105C ''big'':11C ''bigdata'':6B ''card'':28C,81C ''certain'':91C ''cheap'':155C ''check'':75C ''classif'':5B ''classifi'':1A,43C ''compani'':21C ''consist'':87C ''contain'':24C ''credit'':27C,80C ''data'':12C,153C ''devic'':31C,102C ''differ'':101C ''either'':47C ''em'':15C ''exampl'':108C ''fair'':117C ''fake'':16C ''gb'':151C ''good'':143C ''hand'':65C ''host'':146C ''idea'':57C ''io'':103C ''lost'':55C ''lot'':99C ''m'':115C ''malici'':4A,48C,51C ''ml'':123C,128C ''money'':36C ''need'':41C ''new'':118C ''number'':29C ''often'':78C ''optim'':130C ''p'':7C,70C,135C,157C ''place'':113C,144C ''possibl'':74C,111C ''problem'':133C ''question'':138C ''row'':23C ''set'':13C ''side'':137C ''silli'':68C ''start'':61C,112C ''still'':116C ''thank'':158C ''think'':73C ''time'':30C,92C ''transact'':2A,18C,39C,45C ''use'':32C,83C,88C,97C ''usernam'':26C ''would'':66C,109C,124C,140C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8304, 2, '2015-10-04 17:31:11.763', 1, NULL, NULL, '&lt;p&gt;I haven''t the reputation to comment on the original question. This post has a helpful, possibly duplicate answer: &lt;br/&gt;
&lt;a href="http://stats.stackexchange.com/questions/49270/imputation-with-random-forests"&gt;http://stats.stackexchange.com/questions/49270/imputation-with-random-forests&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/questions/49270/imputation-with-random-forests'':26C ''/questions/49270/imputation-with-random-forests&lt;/a&gt;&lt;/p&gt;'':29C ''answer'':20C ''br'':21C ''comment'':8C ''duplic'':19C ''haven'':3C ''help'':17C ''href'':23C ''origin'':11C ''p'':1C ''possibl'':18C ''post'':14C ''question'':12C ''reput'':6C ''stats.stackexchange.com'':25C,28C ''stats.stackexchange.com/questions/49270/imputation-with-random-forests'':24C ''stats.stackexchange.com/questions/49270/imputation-with-random-forests&lt;/a&gt;&lt;/p&gt;'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8230, 1, '2015-09-29 08:00:56.84', 1, 91, 'How could PageRank be used to rank paragraphs in relevance to keywords?', '&lt;p&gt;I have a data set with keywords describing paragraphs in a car manual and the actual paragraphs. I want to rank those paragraphs by that keyword using PageRank algorithm. &lt;/p&gt;

&lt;p&gt;How would I rank these paragraphs in relevance to the keywords?&lt;/p&gt;

&lt;p&gt;The keywords are given an ID number while the paragraphs in the manual are texts. Let''s say there is a keyword with ID number 89 and I have about 15 paragraphs that the keyword describes. How would the paragraphs be ranked in relevance to the keyword? Can I even use PageRank for this problem?&lt;/p&gt;

&lt;p&gt;I understand for ranking websites, backlinks are used to sort the websites into ranks but for paragraphs of texts, how would I link and rank them? &lt;/p&gt;
', 'data-mining  text-mining  ranking', NULL, '''/p'':50C,63C,120C,148C ''15'':95C ''89'':90C ''actual'':36C ''algorithm'':49C ''backlink'':127C ''car'':32C ''could'':2A ''data'':14B,24C ''data-min'':13B ''describ'':28C,100C ''even'':114C ''given'':68C ''id'':70C,88C ''keyword'':12A,27C,46C,62C,66C,86C,99C,111C ''let'':80C ''link'':144C ''manual'':33C,77C ''mine'':15B,18B ''number'':71C,89C ''p'':20C,51C,64C,121C ''pagerank'':3A,48C,116C ''paragraph'':8A,29C,37C,43C,57C,74C,96C,104C,138C ''problem'':119C ''rank'':7A,19B,41C,55C,106C,125C,135C,146C ''relev'':10A,59C,108C ''say'':82C ''set'':25C ''sort'':131C ''text'':17B,79C,140C ''text-min'':16B ''understand'':123C ''use'':5A,47C,115C,129C ''want'':39C ''websit'':126C,133C ''would'':53C,102C,142C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8100, 2, '2015-09-15 04:14:45.927', 7, NULL, NULL, '&lt;p&gt;This problem is popularly called the "&lt;strong&gt;&lt;a href="http://www.azleg.gov/ars/13/02105.htm"&gt;Credit Card Fraud Detection&lt;/a&gt;&lt;/strong&gt;"&lt;/p&gt;

&lt;p&gt;There are several classification algorithms, which aim to tackle this problem. &lt;/p&gt;

&lt;p&gt;With the knowledge of the dataset you possess, the Decision Trees algorithm can be employed for detecting malicious transactions from the non-malicious ones. This &lt;a href="http://www.iaeng.org/publication/IMECS2011/IMECS2011_pp442-447.pdf"&gt;paper&lt;/a&gt; is a nice resource to learn and develop the intuition about fraud detection and the usage of basic classification algorithms like the Decision Trees and the SVMs for solving the problem.&lt;/p&gt;

&lt;p&gt;There are &lt;a href="https://scholar.google.co.in/scholar?hl=en&amp;amp;q=Fraud%20Detection&amp;amp;btnG="&gt;several other papers&lt;/a&gt; which solve this problems employing algorithms like &lt;a href="http://ieeexplore.ieee.org/xpl/login.jsp?tp=&amp;amp;arnumber=4280163&amp;amp;url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel5%2F4280076%2F4280077%2F04280163.pdf%3Farnumber%3D4280163"&gt;Neural Networks&lt;/a&gt;, Logistic Regression, Genetic Algorithms, etc. However, the paper which uses the decision trees algorithm is a nice place to start learning.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;what would be a good place to host the 600 or so GB of data for cheaps?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://what%20would%20be%20a%20good%20place%20to%20host%20the%20600%20or%20so%20GB%20of%20data%20for%20cheaps?"&gt;Aws S3&lt;/a&gt; would be a nice, cheap way to do that. It also integrates nicely with Redshift, in case you want to do complex analytics on the data.&lt;/p&gt;
', NULL, NULL, '''/a'':18C,67C,111C,126C,192C ''/ars/13/02105.htm'':13C ''/p'':20C,33C,99C,148C,169C,219C ''/publication/imecs2011/imecs2011_pp442-447.pdf'':65C ''/scholar?hl=en&amp;amp;q=fraud%20detection&amp;amp;btng='':107C ''/strong'':19C,168C ''/xpl/login.jsp?tp=&amp;amp;arnumber=4280163&amp;amp;url=http%3a%2f%2fieeexplore.ieee.org%2fiel5%2f4280076%2f4280077%2f04280163.pdf%3farnumber%3d4280163'':123C ''20600'':182C ''20a'':176C ''20be'':175C ''20cheaps'':189C ''20data'':187C ''20for'':188C ''20gb'':185C ''20good'':177C ''20host'':180C ''20of'':186C ''20or'':183C ''20place'':178C ''20so'':184C ''20the'':181C ''20to'':179C ''20would'':174C ''600'':160C ''aim'':28C ''algorithm'':26C,46C,87C,117C,130C,140C ''also'':203C ''analyt'':215C ''aw'':190C ''basic'':85C ''call'':6C ''card'':15C ''case'':209C ''cheap'':167C,197C ''classif'':25C,86C ''complex'':214C ''credit'':14C ''data'':165C,218C ''dataset'':40C ''decis'':44C,90C,138C ''detect'':17C,51C,80C ''develop'':75C ''employ'':49C,116C ''etc'':131C ''fraud'':16C,79C ''gb'':163C ''genet'':129C ''good'':155C ''host'':158C ''howev'':132C ''href'':10C,62C,104C,120C,172C ''ieeexplore.ieee.org'':122C ''ieeexplore.ieee.org/xpl/login.jsp?tp=&amp;amp;arnumber=4280163&amp;amp;url=http%3a%2f%2fieeexplore.ieee.org%2fiel5%2f4280076%2f4280077%2f04280163.pdf%3farnumber%3d4280163'':121C ''integr'':204C ''intuit'':77C ''knowledg'':37C ''learn'':73C,147C ''like'':88C,118C ''logist'':127C ''malici'':52C,58C ''network'':125C ''neural'':124C ''nice'':70C,143C,196C,205C ''non'':57C ''non-malici'':56C ''one'':59C ''p'':1C,21C,34C,100C,149C,170C ''paper'':66C,110C,134C ''place'':144C,156C ''popular'':5C ''possess'':42C ''problem'':3C,32C,98C,115C ''redshift'':207C ''regress'':128C ''resourc'':71C ''s3'':191C ''scholar.google.co.in'':106C ''scholar.google.co.in/scholar?hl=en&amp;amp;q=fraud%20detection&amp;amp;btng='':105C ''sever'':24C,108C ''solv'':96C,113C ''start'':146C ''strong'':8C,150C ''svms'':94C ''tackl'':30C ''transact'':53C ''tree'':45C,91C,139C ''usag'':83C ''use'':136C ''want'':211C ''way'':198C ''would'':152C,193C ''www.azleg.gov'':12C ''www.azleg.gov/ars/13/02105.htm'':11C ''www.iaeng.org'':64C ''www.iaeng.org/publication/imecs2011/imecs2011_pp442-447.pdf'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8101, 2, '2015-09-15 09:27:35.667', 0, NULL, NULL, '&lt;p&gt;Try the &lt;a href="https://cran.r-project.org/web/packages/changepoint/changepoint.pdf" rel="nofollow"&gt;changepoint package&lt;/a&gt;. I used it in a similar case.&lt;/p&gt;

&lt;p&gt;Changepoint analysis is the statistical name for methods that detect changes between two "regimes". A car staying in a lane is a line with gradient 0 at the midpoint of a lane. You can easily fit a statistical model to cars driving in lanes. A car changing lane is driving along a line with a gradient that isn''t 0. The model has changed. Changepoint analysis, and the changepoint package, is exactly what you need to determine the point when a model changes from &lt;code&gt;y=a'' (straight and level) to&lt;/code&gt;y=a+bx` (going up or down).&lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/code'':114C ''/p'':21C,122C ''/web/packages/changepoint/changepoint.pdf'':8C ''0'':48C,82C ''along'':73C ''analysi'':24C,88C ''bx'':117C ''car'':38C,63C,68C ''case'':20C ''chang'':33C,69C,86C,105C ''changepoint'':11C,23C,87C,91C ''code'':107C ''cran.r-project.org'':7C ''cran.r-project.org/web/packages/changepoint/changepoint.pdf'':6C ''detect'':32C ''determin'':99C ''drive'':64C,72C ''easili'':57C ''exact'':94C ''fit'':58C ''go'':118C ''gradient'':47C,78C ''href'':5C ''isn'':80C ''lane'':42C,54C,66C,70C ''level'':112C ''line'':45C,75C ''method'':30C ''midpoint'':51C ''model'':61C,84C,104C ''name'':28C ''need'':97C ''nofollow'':10C ''p'':1C,22C ''packag'':12C,92C ''point'':101C ''regim'':36C ''rel'':9C ''similar'':19C ''statist'':27C,60C ''stay'':39C ''straight'':110C ''tri'':2C ''two'':35C ''use'':15C ''y'':108C,115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8102, 2, '2015-09-15 15:34:15.223', 0, NULL, NULL, '&lt;p&gt;Adding to Stephan''s answer (I am unable to comment), there is actually an Optimization task view on CRAN that is even more relevant:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://cran.r-project.org/web/views/Optimization.html" rel="nofollow"&gt;CRAN Task View: Optimization and Mathematical Programming&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':42C ''/p'':26C,43C ''/web/views/optimization.html'':32C ''actual'':14C ''ad'':2C ''answer'':6C ''comment'':11C ''cran'':20C,35C ''cran.r-project.org'':31C ''cran.r-project.org/web/views/optimization.html'':30C ''even'':23C ''href'':29C ''mathemat'':40C ''nofollow'':34C ''optim'':16C,38C ''p'':1C,27C ''program'':41C ''rel'':33C ''relev'':25C ''stephan'':4C ''task'':17C,36C ''unabl'':9C ''view'':18C,37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8107, 2, '2015-09-16 22:47:26.753', 0, NULL, NULL, '&lt;p&gt;I would echo @David. The question is what do you expect the tool to serve. May be SAS or SPSS is the way to go or even MATLAB. Each tool brings something to the table. Depending on what you need, one or more may fit the bill. Also look at your org''s tech road map. If Open Source is a big push, your list may be different. &lt;/p&gt;

&lt;p&gt;Tableau is a good tool (and easy to learn) for Business Users or people who are less technical. Even though it is costly ($2000 for a single user annual license), it has a good reader (free) and once a report is generated, the users can leverage the reader and update without dependence on the license holder. The benefit will lie as long as the reports are stable and standard in nature. &lt;/p&gt;

&lt;p&gt;R has great graphic capabilities and hence users who do get involved in analytic may find it better suited. Similarly python can be a great tool especially if you need a web based solution for your users. &lt;/p&gt;

&lt;p&gt;So think about your needs and strategy.&lt;/p&gt;
', NULL, NULL, '''/p'':70C,143C,182C,191C ''2000'':95C ''also'':49C ''analyt'':158C ''annual'':100C ''base'':177C ''benefit'':129C ''better'':162C ''big'':63C ''bill'':48C ''bring'':32C ''busi'':82C ''capabl'':149C ''cost'':94C ''david'':5C ''depend'':37C,123C ''differ'':69C ''easi'':78C ''echo'':4C ''especi'':171C ''even'':28C,90C ''expect'':12C ''find'':160C ''fit'':46C ''free'':107C ''generat'':113C ''get'':155C ''go'':26C ''good'':75C,105C ''graphic'':148C ''great'':147C,169C ''henc'':151C ''holder'':127C ''involv'':156C ''learn'':80C ''less'':88C ''leverag'':117C ''licens'':101C,126C ''lie'':131C ''list'':66C ''long'':133C ''look'':50C ''map'':57C ''matlab'':29C ''may'':17C,45C,67C,159C ''natur'':142C ''need'':41C,174C,188C ''one'':42C ''open'':59C ''org'':53C ''p'':1C,71C,144C,183C ''peopl'':85C ''push'':64C ''python'':165C ''question'':7C ''r'':145C ''reader'':106C,119C ''report'':111C,136C ''road'':56C ''sas'':19C ''serv'':16C ''similar'':164C ''singl'':98C ''solut'':178C ''someth'':33C ''sourc'':60C ''spss'':21C ''stabl'':138C ''standard'':140C ''strategi'':190C ''suit'':163C ''tabl'':36C ''tableau'':72C ''tech'':55C ''technic'':89C ''think'':185C ''though'':91C ''tool'':14C,31C,76C,170C ''updat'':121C ''user'':83C,99C,115C,152C,181C ''way'':24C ''web'':176C ''without'':122C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8192, 1, '2015-09-24 22:09:40.617', 0, 27, 'differences between plsa vs lsa', '&lt;p&gt;I would like to know what are the main differences between LSA and pLSA, from a non-mathematical point of view (or explained in the most intuitively way).&lt;/p&gt;

&lt;p&gt;I''ve found some explanations &lt;a href="http://www.mblondel.org/journal/2010/06/13/lsa-and-plsa-in-python/" rel="nofollow"&gt;here&lt;/a&gt;, &lt;a href="http://knight.cis.temple.edu/~yates/cis8538/sp11/slides/intro-to-lsa-lda.ppt" rel="nofollow"&gt;here&lt;/a&gt; or &lt;a href="http://stats.stackexchange.com/questions/40723/a-parellel-between-lsa-and-plsa"&gt;here&lt;/a&gt; or with Hoffmann''s paper but I do not understand everything.&lt;/p&gt;

&lt;p&gt;I do understand that LSA is simply a SVD and do not imply probabilities/statistics. I also understand more or less the generative process between pLSA.  &lt;/p&gt;

&lt;p&gt;However, I do not understand the concrete &lt;em&gt;implications&lt;/em&gt;, such as why "LSA cannot handle polysemy, as words are defined by a unique point in a space" or why "Topics are interpretable unlike the arbitrary dimensions of LSA"    &lt;/p&gt;

&lt;p&gt;Are there also major differences in terms of "philosophy" (i.e. not only mathematical)? What are concretely the differences when I run these models in terms of outputs applied to a same text?&lt;/p&gt;

&lt;p&gt;Thanks !&lt;/p&gt;
', 'topic-model', NULL, '''/a'':54C,63C,71C ''/em'':121C ''/journal/2010/06/13/lsa-and-plsa-in-python/'':50C ''/p'':39C,83C,110C,151C,185C,188C ''/questions/40723/a-parellel-between-lsa-and-plsa'':69C ''/~yates/cis8538/sp11/slides/intro-to-lsa-lda.ppt'':59C ''also'':100C,155C ''appli'':180C ''arbitrari'':147C ''cannot'':126C ''concret'':118C,168C ''defin'':132C ''differ'':1A,19C,157C,170C ''dimens'':148C ''em'':119C ''everyth'':82C ''explain'':33C ''explan'':45C ''found'':43C ''generat'':106C ''handl'':127C ''hoffmann'':74C ''howev'':112C ''href'':47C,56C,66C ''i.e'':162C ''impli'':97C ''implic'':120C ''interpret'':144C ''intuit'':37C ''knight.cis.temple.edu'':58C ''knight.cis.temple.edu/~yates/cis8538/sp11/slides/intro-to-lsa-lda.ppt'':57C ''know'':14C ''less'':104C ''like'':12C ''lsa'':5A,21C,89C,125C,150C ''main'':18C ''major'':156C ''mathemat'':28C,165C ''model'':8B,175C ''nofollow'':52C,61C ''non'':27C ''non-mathemat'':26C ''output'':179C ''p'':9C,40C,84C,111C,152C,186C ''paper'':76C ''philosophi'':161C ''plsa'':3A,23C,109C ''point'':29C,136C ''polysemi'':128C ''probabilities/statistics'':98C ''process'':107C ''rel'':51C,60C ''run'':173C ''simpli'':91C ''space'':139C ''stats.stackexchange.com'':68C ''stats.stackexchange.com/questions/40723/a-parellel-between-lsa-and-plsa'':67C ''svd'':93C ''term'':159C,177C ''text'':184C ''thank'':187C ''topic'':7B,142C ''topic-model'':6B ''understand'':81C,87C,101C,116C ''uniqu'':135C ''unlik'':145C ''ve'':42C ''view'':31C ''vs'':4A ''way'':38C ''word'':130C ''would'':11C ''www.mblondel.org'':49C ''www.mblondel.org/journal/2010/06/13/lsa-and-plsa-in-python/'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8108, 2, '2015-09-17 00:58:38.983', 1, NULL, NULL, '&lt;p&gt;If you are looking for a technical package I would definitely go with &lt;a href="http://www.wolfram.com/mathematica/" rel="nofollow"&gt;Mathematica&lt;/a&gt;. It has functions for a very wide &lt;a href="http://reference.wolfram.com/language/" rel="nofollow"&gt;range of disciplines&lt;/a&gt; that come standard with it (no extra packages to buy). This allows you to expand into techniques as you grow without having to change platforms or buy extra packages.  It also has a great interactive format in &lt;a href="http://www.wolfram.com/cdf/" rel="nofollow"&gt;CDF&lt;/a&gt; with both a free and professional reader. The documentation is interactive as well which really helps a lot.&lt;/p&gt;

&lt;p&gt;However, you have to be open to a little bit of structure, typing &amp;amp; chaining functions together, and things like this.  Once you get started it comes very quickly.&lt;/p&gt;
', NULL, NULL, '''/a'':23C,41C,87C ''/cdf/'':83C ''/language/'':35C ''/mathematica/'':19C ''/p'':106C,137C ''allow'':53C ''also'':72C ''amp'':121C ''bit'':117C ''buy'':51C,68C ''cdf'':86C ''chain'':122C ''chang'':65C ''come'':43C,134C ''definit'':12C ''disciplin'':40C ''document'':96C ''expand'':56C ''extra'':48C,69C ''format'':77C ''free'':91C ''function'':26C,123C ''get'':131C ''go'':13C ''great'':75C ''grow'':61C ''help'':103C ''howev'':108C ''href'':16C,32C,80C ''interact'':76C,98C ''like'':127C ''littl'':116C ''look'':5C ''lot'':105C ''mathematica'':22C ''nofollow'':21C,37C,85C ''open'':113C ''p'':1C,107C ''packag'':9C,49C,70C ''platform'':66C ''profession'':93C ''quick'':136C ''rang'':38C ''reader'':94C ''realli'':102C ''reference.wolfram.com'':34C ''reference.wolfram.com/language/'':33C ''rel'':20C,36C,84C ''standard'':44C ''start'':132C ''structur'':119C ''technic'':8C ''techniqu'':58C ''thing'':126C ''togeth'':124C ''type'':120C ''well'':100C ''wide'':30C ''without'':62C ''would'':11C ''www.wolfram.com'':18C,82C ''www.wolfram.com/cdf/'':81C ''www.wolfram.com/mathematica/'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8109, 2, '2015-09-17 07:29:40.237', 1, NULL, NULL, '&lt;p&gt;If you are a data scientist, then there is very little use for pre-packaged, generally inflexible commercial tools. &lt;/p&gt;

&lt;p&gt;Part of the reason OSS is so prevalent and useful in data science is that you will often need to combine and/or modify a procedure to fit the needs at hand -- and then &lt;em&gt;deploy&lt;/em&gt; it without a bunch of lawyers and sales reps getting involved at every step. &lt;/p&gt;

&lt;p&gt;Since data scientists are expected to be proficient programmers, you should be comfortable digging into the source code and adding functionality or making it more user friendly. &lt;/p&gt;

&lt;p&gt;I''ve come close to recommending the purchase of non-free(as in GPL) a couple times only to find that some industrious person has set up a project in Git that provides most if not all of the functionality if commercial software. In the cases where it doesn''t, it at least addresses the core issue and I can modify and extend from it. It''s much easier to modify a prototype than start from scratch.&lt;/p&gt;

&lt;p&gt;Bottom line: be wary of commercial software for data science unless you''ve done your due diligence in the OSS space and can honestly say that you could not find any projects that could be modified to suit your needs. 
Commercial software is not only less flexible but you''re effectively in a business partnership with these folks and that means your fates are somewhat intertwined (at least for the projects that depend in this software).&lt;/p&gt;
', NULL, NULL, '''/em'':59C ''/p'':22C,74C,103C,183C,261C ''ad'':95C ''address'':159C ''and/or'':45C ''bottom'':185C ''bunch'':63C ''busi'':238C ''case'':151C ''close'':108C ''code'':93C ''combin'':44C ''come'':107C ''comfort'':88C ''commerci'':20C,147C,190C,225C ''core'':161C ''could'':212C,218C ''coupl'':121C ''data'':6C,35C,77C,193C ''depend'':257C ''deploy'':58C ''dig'':89C ''dilig'':201C ''doesn'':154C ''done'':198C ''due'':200C ''easier'':174C ''effect'':235C ''em'':57C ''everi'':72C ''expect'':80C ''extend'':168C ''fate'':247C ''find'':125C,214C ''fit'':50C ''flexibl'':231C ''folk'':242C ''free'':116C ''friend'':102C ''function'':96C,145C ''general'':18C ''get'':69C ''git'':136C ''gpl'':119C ''hand'':54C ''honest'':208C ''industri'':128C ''inflex'':19C ''intertwin'':250C ''involv'':70C ''issu'':162C ''lawyer'':65C ''least'':158C,252C ''less'':230C ''line'':186C ''littl'':12C ''make'':98C ''mean'':245C ''modifi'':46C,166C,176C,220C ''much'':173C ''need'':42C,52C,224C ''non'':115C ''non-fre'':114C ''often'':41C ''oss'':28C,204C ''p'':1C,23C,75C,104C,184C ''packag'':17C ''part'':24C ''partnership'':239C ''person'':129C ''pre'':16C ''pre-packag'':15C ''preval'':31C ''procedur'':48C ''profici'':83C ''programm'':84C ''project'':134C,216C,255C ''prototyp'':178C ''provid'':138C ''purchas'':112C ''re'':234C ''reason'':27C ''recommend'':110C ''rep'':68C ''sale'':67C ''say'':209C ''scienc'':36C,194C ''scientist'':7C,78C ''scratch'':182C ''set'':131C ''sinc'':76C ''softwar'':148C,191C,226C,260C ''somewhat'':249C ''sourc'':92C ''space'':205C ''start'':180C ''step'':73C ''suit'':222C ''time'':122C ''tool'':21C ''unless'':195C ''use'':13C,33C ''user'':101C ''ve'':106C,197C ''wari'':188C ''without'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8231, 1, '2015-09-29 08:58:04.893', 1, 35, 'Which value of output should be taken in multiple sessions of training Neural Network', '&lt;p&gt;Suppose I am using Neural Network for a 2 class classification. After training the network with the training set, I want to predict the class label of a dataset with no class label. Now with retraining, the same dataset gives different result. For example in one training session, a sample gave the output of it belonging to class 1 while in the other session it gave the output of it belonging to class 2. Then which value should be taken as the correct one?&lt;/p&gt;
', 'classification  neuralnetwork  supervised-learning', NULL, '''/p'':105C ''1'':79C ''2'':29C,94C ''belong'':76C,91C ''class'':30C,45C,52C,78C,93C ''classif'':15B,31C ''correct'':103C ''dataset'':49C,59C ''differ'':61C ''exampl'':64C ''gave'':71C,86C ''give'':60C ''label'':46C,53C ''learn'':19B ''multipl'':9A ''network'':14A,26C,35C ''neural'':13A,25C ''neuralnetwork'':16B ''one'':66C,104C ''output'':4A,73C,88C ''p'':20C ''predict'':43C ''result'':62C ''retrain'':56C ''sampl'':70C ''session'':10A,68C,84C ''set'':39C ''supervis'':18B ''supervised-learn'':17B ''suppos'':21C ''taken'':7A,100C ''train'':12A,33C,38C,67C ''use'':24C ''valu'':2A,97C ''want'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8168, 1, '2015-09-23 09:45:30.677', 0, 25, 'Multiple confusion matrix for multiple training instances. Which one to take?', '&lt;p&gt;I am using Matlab Neural Network toolbox for a classification problem. Now  considering a single set of data, if the inbuilt neural network is trained and classified with same data multiple number of time, different accuracy and different confusion matrix is obtained. Now which result should I take? Should I take all the vales obtained in all the training instances and average them fix on one particular result?&lt;/p&gt;
', 'neuralnetwork  matlab  confusion-matrix', NULL, '''/p'':86C ''accuraci'':53C ''averag'':79C ''classif'':27C ''classifi'':44C ''confus'':2A,15B,56C ''confusion-matrix'':14B ''consid'':30C ''data'':35C,47C ''differ'':52C,55C ''fix'':81C ''inbuilt'':38C ''instanc'':7A,77C ''matlab'':13B,21C ''matrix'':3A,16B,57C ''multipl'':1A,5A,48C ''network'':23C,40C ''neural'':22C,39C ''neuralnetwork'':12B ''number'':49C ''obtain'':59C,72C ''one'':9A,83C ''p'':17C ''particular'':84C ''problem'':28C ''result'':62C,85C ''set'':33C ''singl'':32C ''take'':11A,65C,68C ''time'':51C ''toolbox'':24C ''train'':6A,42C,76C ''use'':20C ''vale'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8113, 1, '2015-09-17 14:13:54.293', 0, 374, 'SKNN regression problem', '&lt;p&gt;I am trying to learn scikit-neuralnetwork and am comming up against the same problem in regression where no matter the dataset I getting a horizontal straight line for my fit. &lt;/p&gt;

&lt;p&gt;here is an example using the Linear regression example from Scikit learn and then using the SKNN regressor , simple example code from the docs.&lt;/p&gt;

&lt;pre&gt;# -*- coding: utf-8 -*-

# Code source: Jaques Grobler
# http://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html
# License: BSD 3 clause


import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model

# Load the diabetes dataset
diabetes = datasets.load_diabetes()


# Use only one feature
diabetes_X = diabetes.data[:, np.newaxis]
diabetes_X_temp = diabetes_X[:, :, 2]

# Split the data into training/testing sets
diabetes_X_train = diabetes_X_temp[:-20]
diabetes_X_test = diabetes_X_temp[-20:]

# Split the targets into training/testing sets
diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]

# Create linear regression object
regr = linear_model.LinearRegression()

# Train the model using the training sets
regr.fit(diabetes_X_train, diabetes_y_train)

print "Results of Linear Regression...."
print "================================\n"
# The coefficients
print(''Coefficients: '', regr.coef_)
# The mean square error
print("Residual sum of squares: %.2f"
      % np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))
# Explained variance score: 1 is perfect prediction
print(''Variance score: %.2f'' % regr.score(diabetes_X_test, diabetes_y_test))

# Plot outputs
plt.scatter(diabetes_X_test, diabetes_y_test,  color=''black'')
plt.plot(diabetes_X_test, regr.predict(diabetes_X_test), color=''blue'',
         linewidth=3)

plt.xticks(())
plt.yticks(())

plt.show()

# Now using the sknn regressor
# http://scikit-neuralnetwork.readthedocs.org/en/latest/guide_beginners.html
#

from sknn.mlp import Regressor, Layer

nn = Regressor(
    layers=[
        Layer("Rectifier", units=200),
        Layer("Linear")],
    learning_rate=0.02,
    n_iter=10)

nn.fit(diabetes_X_train, diabetes_y_train)
print "Results of SKNN Regression...."
print "==============================\n"


# The coefficients
print(''Coefficients: '', regr.coef_)
# The mean square error
print("Residual sum of squares: %.2f"
      % np.mean((nn.predict(diabetes_X_test) - diabetes_y_test) ** 2))
# Explained variance score: 1 is perfect prediction
print(''Variance score: %.2f'' % nn.score(diabetes_X_test, diabetes_y_test))

# Plot outputs
plt.scatter(diabetes_X_test, diabetes_y_test,  color=''black'')
plt.plot(diabetes_X_test, nn.predict(diabetes_X_test), color=''blue'',
         linewidth=3)

plt.xticks(())
plt.yticks(())

plt.show()

&lt;/pre&gt;

&lt;p&gt;Results of Linear Regression:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(''Coefficients: '', array([ 938.23786125]))
Residual sum of squares: 2548.07
Variance score: 0.47
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;ï¿¼
Results of SKNN Regression:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(''Coefficients: '', array([ 938.23786125]))
Residual sum of squares: 5737.52
Variance score: -0.19
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Changing the number of iterations to 1000 results in a score of -0.15&lt;/p&gt;
', 'python  neuralnetwork  regression', NULL, '''-0.15'':415C ''-0.19'':399C ''-20'':129C,136C,147C,152C ''-8'':70C ''/code'':378C,400C ''/en/latest/guide_beginners.html'':255C ''/p'':40C,66C,364C,386C,416C ''/pre'':358C,379C,401C ''/stable/auto_examples/linear_model/plot_ols.html'':77C ''0.02'':272C ''0.47'':377C ''1'':207C,317C ''10'':275C ''1000'':409C ''2'':116C,203C,313C ''200'':267C ''2548.07'':374C ''2f'':194C,214C,304C,324C ''3'':80C,244C,354C ''5737.52'':396C ''938.23786125'':369C,391C ''array'':368C,390C ''black'':232C,342C ''blue'':242C,352C ''bsd'':79C ''chang'':403C ''claus'':81C ''code'':62C,68C,71C,366C,388C ''coeffici'':181C,183C,291C,293C,367C,389C ''color'':231C,241C,341C,351C ''com'':18C ''creat'':153C ''data'':119C ''dataset'':30C,93C,99C ''datasets.load'':101C ''diabet'':98C,100C,102C,107C,111C,114C,123C,126C,130C,133C,143C,148C,167C,170C,197C,200C,216C,219C,225C,228C,234C,238C,277C,280C,307C,310C,326C,329C,335C,338C,344C,348C ''diabetes.data'':109C ''diabetes.target'':146C,151C ''doc'':65C ''error'':188C,298C ''exampl'':45C,50C,61C ''explain'':204C,314C ''featur'':106C ''fit'':39C ''get'':32C ''grobler'':74C ''horizont'':34C ''import'':82C,86C,92C,258C ''iter'':274C,407C ''jaqu'':73C ''layer'':260C,263C,264C,268C ''learn'':12C,53C,270C ''licens'':78C ''line'':36C ''linear'':48C,94C,154C,176C,269C,362C ''linear_model.linearregression'':158C ''linewidth'':243C,353C ''load'':96C ''matplotlib.pyplot'':83C ''matter'':28C ''mean'':186C,296C ''model'':95C,161C ''n'':179C,273C,289C ''neuralnetwork'':5B,15C ''nn'':261C ''nn.fit'':276C ''nn.predict'':306C,347C ''nn.score'':325C ''np'':89C ''np.mean'':195C,305C ''np.newaxis'':110C ''number'':405C ''numpi'':87C ''object'':156C ''one'':105C ''output'':223C,333C ''p'':7C,41C,359C,380C,402C ''perfect'':209C,319C ''plot'':222C,332C ''plt'':85C ''plt.plot'':233C,343C ''plt.scatter'':224C,334C ''plt.show'':247C,357C ''plt.xticks'':245C,355C ''plt.yticks'':246C,356C ''pre'':67C,365C,387C ''predict'':210C,320C ''print'':173C,178C,182C,189C,211C,283C,288C,292C,299C,321C ''problem'':3A,23C ''python'':4B ''rate'':271C ''rectifi'':265C ''regr'':157C ''regr.coef'':184C,294C ''regr.fit'':166C ''regr.predict'':196C,237C ''regr.score'':215C ''regress'':2A,6B,25C,49C,155C,177C,287C,363C,385C ''regressor'':59C,252C,259C,262C ''residu'':190C,300C,370C,392C ''result'':174C,284C,360C,382C,410C ''scikit'':14C,52C ''scikit-learn.org'':76C ''scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html'':75C ''scikit-neuralnetwork'':13C ''scikit-neuralnetwork.readthedocs.org'':254C ''scikit-neuralnetwork.readthedocs.org/en/latest/guide_beginners.html'':253C ''score'':206C,213C,316C,323C,376C,398C,413C ''set'':122C,142C,165C ''simpl'':60C ''sklearn'':91C ''sknn'':1A,58C,251C,286C,384C ''sknn.mlp'':257C ''sourc'':72C ''split'':117C,137C ''squar'':187C,193C,297C,303C,373C,395C ''straight'':35C ''sum'':191C,301C,371C,393C ''target'':139C ''temp'':113C,128C,135C ''test'':132C,150C,199C,202C,218C,221C,227C,230C,236C,240C,309C,312C,328C,331C,337C,340C,346C,350C ''train'':125C,145C,159C,164C,169C,172C,279C,282C ''training/testing'':121C,141C ''tri'':10C ''unit'':266C ''use'':46C,56C,103C,162C,249C ''utf'':69C ''varianc'':205C,212C,315C,322C,375C,397C ''x'':108C,112C,115C,124C,127C,131C,134C,168C,198C,217C,226C,235C,239C,278C,308C,327C,336C,345C,349C ''y'':144C,149C,171C,201C,220C,229C,281C,311C,330C,339C ''ï'':381C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8286, 1, '2015-10-03 04:09:56.397', 9, 431, 'Are there any tools for feature engineering?', '&lt;p&gt;Specifically what I am looking for are tools with some functionality, which is specific to feature engineering. I would like to be able to easily smooth, visualize, fill gaps, etc. Something similar to MS Excel, but that has R as the underlying language instead of VB.&lt;/p&gt;
', 'feature-selection  feature-extraction  feature-construction', NULL, '''/p'':64C ''abl'':40C ''construct'':16B ''easili'':42C ''engin'':7A,34C ''etc'':47C ''excel'':52C ''extract'':13B ''featur'':6A,9B,12B,15B,33C ''feature-construct'':14B ''feature-extract'':11B ''feature-select'':8B ''fill'':45C ''function'':28C ''gap'':46C ''instead'':61C ''languag'':60C ''like'':37C ''look'':22C ''ms'':51C ''p'':17C ''r'':56C ''select'':10B ''similar'':49C ''smooth'':43C ''someth'':48C ''specif'':18C,31C ''tool'':4A,25C ''under'':59C ''vb'':63C ''visual'':44C ''would'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8114, 1, '2015-09-17 18:11:38.857', 0, 38, 'Classifying text documents using linear/incremental topics', '&lt;p&gt;I''m attempting to classify text documents using a few different dimensions. I''m trying to create arbitrary topics to classify such as size and relevance, which are linear or gradual in nature. For example:&lt;/p&gt;

&lt;p&gt;size: tiny, small, medium, large, huge. 
relevance: bad, ok, good, excellent, awesome&lt;/p&gt;

&lt;p&gt;I am training the classifier by hand. For example, this document represents a ''small'' thing, this other document is discussing a ''large'' thing. When I try multi-label or multi-class SVM for this it does not work well and it also logically doesn''t make sense. &lt;/p&gt;

&lt;p&gt;Which model should I use that would help me predict this linear type of data? I use scikit-learn presently with a tfidf vector of the words.&lt;/p&gt;
', 'classification  scikit', NULL, '''/p'':45C,59C,110C,140C ''also'':104C ''arbitrari'':27C ''attempt'':12C ''awesom'':58C ''bad'':54C ''class'':93C ''classif'':7B ''classifi'':1A,14C,30C,65C ''creat'':26C ''data'':126C ''differ'':20C ''dimens'':21C ''discuss'':80C ''document'':3A,16C,71C,78C ''doesn'':106C ''exampl'':44C,69C ''excel'':57C ''good'':56C ''gradual'':40C ''hand'':67C ''help'':119C ''huge'':52C ''label'':89C ''larg'':51C,82C ''learn'':131C ''linear'':38C,123C ''linear/incremental'':5A ''logic'':105C ''m'':11C,23C ''make'':108C ''medium'':50C ''model'':113C ''multi'':88C,92C ''multi-class'':91C ''multi-label'':87C ''natur'':42C ''ok'':55C ''p'':9C,46C,60C,111C ''predict'':121C ''present'':132C ''relev'':35C,53C ''repres'':72C ''scikit'':8B,130C ''scikit-learn'':129C ''sens'':109C ''size'':33C,47C ''small'':49C,74C ''svm'':94C ''text'':2A,15C ''tfidf'':135C ''thing'':75C,83C ''tini'':48C ''topic'':6A,28C ''train'':63C ''tri'':24C,86C ''type'':124C ''use'':4A,17C,116C,128C ''vector'':136C ''well'':101C ''word'':139C ''work'':100C ''would'':118C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8115, 2, '2015-09-17 18:41:18.71', 1, NULL, NULL, '&lt;p&gt;If you want these output dimensions to be continuous, simply convert your size and relevance metrics to real-valued targets.  Then you can perform &lt;a href="https://en.wikipedia.org/wiki/Regression_analysis" rel="nofollow"&gt;regression&lt;/a&gt; instead of classification, using any of a variety of models.  You could even attempt to train a multi target neural net to predict all of these outputs at once.&lt;/p&gt;

&lt;p&gt;Additionally, you might consider first using  a &lt;a href="https://en.wikipedia.org/wiki/Topic_model" rel="nofollow"&gt;topic model&lt;/a&gt; such as &lt;a href="https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation" rel="nofollow"&gt;LDA&lt;/a&gt; as your feature space.&lt;/p&gt;

&lt;p&gt;Based on the values, it sounds like the "relevance" might be a variable best captured by techniques from &lt;a href="https://en.wikipedia.org/wiki/Sentiment_analysis" rel="nofollow"&gt;sentiment analysis&lt;/a&gt;.  &lt;/p&gt;
', NULL, NULL, '''/a'':35C,83C,94C,128C ''/p'':65C,99C,129C ''/wiki/latent_dirichlet_allocation'':90C ''/wiki/regression_analysis'':31C ''/wiki/sentiment_analysis'':123C ''/wiki/topic_model'':78C ''addit'':67C ''analysi'':127C ''attempt'':49C ''base'':101C ''best'':114C ''captur'':115C ''classif'':38C ''consid'':70C ''continu'':10C ''convert'':12C ''could'':47C ''dimens'':7C ''en.wikipedia.org'':30C,77C,89C,122C ''en.wikipedia.org/wiki/latent_dirichlet_allocation'':88C ''en.wikipedia.org/wiki/regression_analysis'':29C ''en.wikipedia.org/wiki/sentiment_analysis'':121C ''en.wikipedia.org/wiki/topic_model'':76C ''even'':48C ''featur'':97C ''first'':71C ''href'':28C,75C,87C,120C ''instead'':36C ''lda'':93C ''like'':107C ''metric'':17C ''might'':69C,110C ''model'':45C,82C ''multi'':53C ''net'':56C ''neural'':55C ''nofollow'':33C,80C,92C,125C ''output'':6C,62C ''p'':1C,66C,100C ''perform'':26C ''predict'':58C ''real'':20C ''real-valu'':19C ''regress'':34C ''rel'':32C,79C,91C,124C ''relev'':16C,109C ''sentiment'':126C ''simpli'':11C ''size'':14C ''sound'':106C ''space'':98C ''target'':22C,54C ''techniqu'':117C ''topic'':81C ''train'':51C ''use'':39C,72C ''valu'':21C,104C ''variabl'':113C ''varieti'':43C ''want'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8117, 1, '2015-09-17 21:02:33.367', 4, 186, 'How does word2vec handle the input word being in the context?', '&lt;p&gt;If word2vec encounters the same word multiple times in the same window, what occurs?  Obviously it is meaningless to decrease the distance between the vectors for the input word and the target word.  But will the repetition strengthen the relationship between the repeated word and the context words?&lt;/p&gt;
', 'machine-learning  nlp  word-embeddings', NULL, '''/p'':68C ''context'':11A,66C ''decreas'':39C ''distanc'':41C ''embed'':18B ''encount'':22C ''handl'':4A ''input'':6A,47C ''learn'':14B ''machin'':13B ''machine-learn'':12B ''meaningless'':37C ''multipl'':26C ''nlp'':15B ''obvious'':34C ''occur'':33C ''p'':19C ''relationship'':59C ''repeat'':62C ''repetit'':56C ''strengthen'':57C ''target'':51C ''time'':27C ''vector'':44C ''window'':31C ''word'':7A,17B,25C,48C,52C,63C,67C ''word-embed'':16B ''word2vec'':3A,21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8118, 2, '2015-09-17 21:40:51.263', 1, NULL, NULL, '&lt;p&gt;Greyscale to Color&lt;/p&gt;

&lt;p&gt;For example:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://s15.postimg.org/3xq8jx03f/image.jpg" rel="nofollow"&gt;http://s15.postimg.org/3xq8jx03f/image.jpg&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;to&lt;/p&gt;

&lt;p&gt;&lt;a href="http://s15.postimg.org/i5fx8kcsb/image.jpg" rel="nofollow"&gt;http://s15.postimg.org/i5fx8kcsb/image.jpg&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://s15.postimg.org/c5s64wrzv/image.jpg" rel="nofollow"&gt;http://s15.postimg.org/c5s64wrzv/image.jpg&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The tree wood seems unnaturally red but still, it''s not bad. This has worked but less impressively with other greyscale images I''ve tried.&lt;/p&gt;
', NULL, NULL, '''/3xq8jx03f/image.jpg'':15C ''/3xq8jx03f/image.jpg&lt;/a&gt;&lt;/p&gt;'':20C ''/c5s64wrzv/image.jpg'':40C ''/c5s64wrzv/image.jpg&lt;/a&gt;&lt;/p&gt;'':45C ''/i5fx8kcsb/image.jpg'':29C ''/i5fx8kcsb/image.jpg&lt;/a&gt;&lt;/p&gt;'':34C ''/p'':5C,9C,23C,72C ''bad'':58C ''color'':4C ''exampl'':8C ''greyscal'':2C,67C ''href'':12C,26C,37C ''imag'':68C ''impress'':64C ''less'':63C ''nofollow'':17C,31C,42C ''p'':1C,6C,10C,21C,24C,35C,46C ''red'':52C ''rel'':16C,30C,41C ''s15.postimg.org'':14C,19C,28C,33C,39C,44C ''s15.postimg.org/3xq8jx03f/image.jpg'':13C ''s15.postimg.org/3xq8jx03f/image.jpg&lt;/a&gt;&lt;/p&gt;'':18C ''s15.postimg.org/c5s64wrzv/image.jpg'':38C ''s15.postimg.org/c5s64wrzv/image.jpg&lt;/a&gt;&lt;/p&gt;'':43C ''s15.postimg.org/i5fx8kcsb/image.jpg'':27C ''s15.postimg.org/i5fx8kcsb/image.jpg&lt;/a&gt;&lt;/p&gt;'':32C ''seem'':50C ''still'':54C ''tree'':48C ''tri'':71C ''unnatur'':51C ''ve'':70C ''wood'':49C ''work'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8119, 2, '2015-09-17 22:12:50.787', 0, NULL, NULL, '&lt;p&gt;heres another application that is very new &amp;amp; just demonstrated within last few weeks. computers are filtering images to look like paintings in the distinctive style of different artists eg Van Gogh, Picasso, etc... and it seems possible since the technology can encompass different artistic styles it might be used for &lt;a href="https://en.wikipedia.org/wiki/Art_forgery" rel="nofollow"&gt;forgery detection&lt;/a&gt; in the art world at some point. (many very advanced analysis techniques are used in this area historically.) note that filtering methods are very popular on Instagram so it seems likely these will be commercially available at some point.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://qz.com/495614/computers-can-now-paint-like-van-gogh-and-picasso/" rel="nofollow"&gt;Computers can now paint like Van Gogh and Picasso&lt;/a&gt; / Quartz&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.dailymail.co.uk/sciencetech/article-3091562/The-science-Instagram-success-Filtered-photos-21-likely-viewed-warmer-tones-comments.html" rel="nofollow"&gt;The science of Instagram success: Filtered photos are 21% more likely to be viewed - and warmer tones get more comments&lt;/a&gt; / Dailymail&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/495614/computers-can-now-paint-like-van-gogh-and-picasso/'':110C ''/a'':62C,122C,155C ''/li'':125C,158C ''/p'':102C,124C,157C ''/sciencetech/article-3091562/the-science-instagram-success-filtered-photos-21-likely-viewed-warmer-tones-comments.html'':132C ''/ul'':159C ''/wiki/art_forgery'':57C ''21'':143C ''advanc'':72C ''amp'':9C ''analysi'':73C ''anoth'':3C ''applic'':4C ''area'':79C ''art'':65C ''artist'':30C,46C ''avail'':98C ''comment'':154C ''commerci'':97C ''comput'':16C,113C ''dailymail'':156C ''demonstr'':11C ''detect'':61C ''differ'':29C,45C ''distinct'':26C ''eg'':31C ''en.wikipedia.org'':56C ''en.wikipedia.org/wiki/art_forgery'':55C ''encompass'':44C ''etc'':35C ''filter'':18C,83C,140C ''forgeri'':60C ''get'':152C ''gogh'':33C,119C ''here'':2C ''histor'':80C ''href'':54C,107C,129C ''imag'':19C ''instagram'':89C,138C ''last'':13C ''li'':104C,126C ''like'':22C,93C,117C,145C ''look'':21C ''mani'':70C ''method'':84C ''might'':49C ''new'':8C ''nofollow'':59C,112C,134C ''note'':81C ''p'':1C,105C,127C ''paint'':23C,116C ''photo'':141C ''picasso'':34C,121C ''point'':69C,101C ''popular'':87C ''possibl'':39C ''quartz'':123C ''qz.com'':109C ''qz.com/495614/computers-can-now-paint-like-van-gogh-and-picasso/'':108C ''rel'':58C,111C,133C ''scienc'':136C ''seem'':38C,92C ''sinc'':40C ''style'':27C,47C ''success'':139C ''techniqu'':74C ''technolog'':42C ''tone'':151C ''ul'':103C ''use'':51C,76C ''van'':32C,118C ''view'':148C ''warmer'':150C ''week'':15C ''within'':12C ''world'':66C ''www.dailymail.co.uk'':131C ''www.dailymail.co.uk/sciencetech/article-3091562/the-science-instagram-success-filtered-photos-21-likely-viewed-warmer-tones-comments.html'':130C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8120, 1, '2015-09-17 23:06:50.597', 2, 319, 'Can I fine tune the xgboost model instead of re-training it?', '&lt;p&gt;I am using the xgboost library. My system runs a cronjob each night, where it pulls the data from the database and trains the model. However, I would like to remove the re-training of the model again and again, and just fine-tune it with any new data that came in the database. In sklearn''s implemantation (&lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html&lt;/a&gt;) one could use warm_start option, what about xgboost ? &lt;/p&gt;
', 'machine-learning  sklearn', NULL, '''/p'':97C ''/stable/modules/generated/sklearn.ensemble.gradientboostingclassifier.html'':82C ''/stable/modules/generated/sklearn.ensemble.gradientboostingclassifier.html&lt;/a&gt;)'':87C ''came'':70C ''could'':89C ''cronjob'':28C ''data'':35C,68C ''databas'':38C,73C ''fine'':3A,62C ''fine-tun'':61C ''howev'':43C ''href'':79C ''implemant'':77C ''instead'':8A ''learn'':15B ''librari'':23C ''like'':46C ''machin'':14B ''machine-learn'':13B ''model'':7A,42C,55C ''new'':67C ''night'':30C ''nofollow'':84C ''one'':88C ''option'':93C ''p'':17C ''pull'':33C ''re'':11A,51C ''re-train'':10A,50C ''rel'':83C ''remov'':48C ''run'':26C ''scikit-learn.org'':81C,86C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.gradientboostingclassifier.html'':80C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.gradientboostingclassifier.html&lt;/a&gt;)'':85C ''sklearn'':16B,75C ''start'':92C ''system'':25C ''train'':12A,40C,52C ''tune'':4A,63C ''use'':20C,90C ''warm'':91C ''would'':45C ''xgboost'':6A,22C,96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8169, 2, '2015-09-23 09:51:17.85', 0, NULL, NULL, '&lt;p&gt;I am sorry to disappoint you but &lt;a href="https://en.wikipedia.org/wiki/Correlation_does_not_imply_causation" rel="nofollow"&gt;&lt;strong&gt;Correlation does not imply causation&lt;/strong&gt;&lt;/a&gt; !.&lt;/p&gt;

&lt;p&gt;You need to make sure that the data is statically significant (enough data, the difference is not negligible, etc), I would suggest Z-Test, or chi-square, depending on the nature of your data.&lt;/p&gt;
', NULL, NULL, '''/a'':23C ''/p'':24C,62C ''/strong'':22C ''/wiki/correlation_does_not_imply_causation'':13C ''causat'':21C ''chi'':53C ''chi-squar'':52C ''correl'':17C ''data'':33C,38C,61C ''depend'':55C ''differ'':40C ''disappoint'':6C ''en.wikipedia.org'':12C ''en.wikipedia.org/wiki/correlation_does_not_imply_causation'':11C ''enough'':37C ''etc'':44C ''href'':10C ''impli'':20C ''make'':29C ''natur'':58C ''need'':27C ''neglig'':43C ''nofollow'':15C ''p'':1C,25C ''rel'':14C ''signific'':36C ''sorri'':4C ''squar'':54C ''static'':35C ''strong'':16C ''suggest'':47C ''sure'':30C ''test'':50C ''would'':46C ''z'':49C ''z-test'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8121, 2, '2015-09-18 04:06:50.197', 0, NULL, NULL, '&lt;p&gt;My best guess here is that your learning rate is &lt;em&gt;way&lt;/em&gt; too high for the problem. You also probably have far more neurons in your hidden network than you need, seeing as you''re using just one feature. &lt;/p&gt;

&lt;p&gt;Recall that learning rate is controlling the "step size" in gradient descent and that for your dataset, it is likely far too high. I made some minor changes to your code and got better results than linear regression. Notice the use of 2 hidden neurons, a 0.001 learning rate, and 20 iterations.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Now using the sknn regressor
# http://scikit-neuralnetwork.readthedocs.org/en/latest/guide_beginners.html


from sknn.mlp import Regressor, Layer

nn = Regressor(
    layers=[
        Layer("Rectifier", units=2),
        Layer("Linear")],
    learning_rate=0.001,
    n_iter=20)

nn.fit(diabetes_X_train, diabetes_y_train)
print("Results of SKNN Regression....")


# The coefficients
print(''Coefficients: '', regr.coef_)
# The mean square error
print("Residual sum of squares: %.2f"
      % np.mean((nn.predict(diabetes_X_test) - diabetes_y_test) ** 2))
# Explained variance score: 1 is perfect prediction
print(''Variance score: %.2f'' % nn.score(diabetes_X_test, diabetes_y_test))

# Plot outputs
plt.scatter(diabetes_X_test, diabetes_y_test,  color=''black'')
plt.plot(diabetes_X_test, nn.predict(diabetes_X_test), color=''blue'',
         linewidth=3)

plt.xticks(())
plt.yticks(())

plt.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;SKNN regression: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Results of SKNN Regression....
Coefficients:  [ 938.23786125]
Residual sum of squares: 6123.67
Variance score: 0.50
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':207C,229C ''/em'':14C ''/en/latest/guide_beginners.html'':106C ''/p'':42C,96C,212C ''/pre'':208C,230C ''0.001'':90C,123C ''0.50'':228C ''1'':166C ''2'':86C,118C,162C ''20'':94C,126C ''2f'':153C,173C ''3'':203C ''6123.67'':225C ''938.23786125'':220C ''also'':21C ''best'':3C ''better'':77C ''black'':191C ''blue'':201C ''chang'':71C ''code'':74C,98C,214C ''coeffici'':140C,142C,219C ''color'':190C,200C ''control'':49C ''dataset'':60C ''descent'':55C ''diabet'':128C,131C,156C,159C,175C,178C,184C,187C,193C,197C ''em'':12C ''error'':147C ''explain'':163C ''far'':24C,64C ''featur'':41C ''got'':76C ''gradient'':54C ''guess'':4C ''hidden'':29C,87C ''high'':16C,66C ''import'':109C ''iter'':95C,125C ''layer'':111C,114C,115C,119C ''learn'':9C,46C,91C,121C ''like'':63C ''linear'':80C,120C ''linewidth'':202C ''made'':68C ''mean'':145C ''minor'':70C ''n'':124C ''need'':33C ''network'':30C ''neuron'':26C,88C ''nn'':112C ''nn.fit'':127C ''nn.predict'':155C,196C ''nn.score'':174C ''notic'':82C ''np.mean'':154C ''one'':40C ''output'':182C ''p'':1C,43C,209C ''perfect'':168C ''plot'':181C ''plt.plot'':192C ''plt.scatter'':183C ''plt.show'':206C ''plt.xticks'':204C ''plt.yticks'':205C ''pre'':97C,213C ''predict'':169C ''print'':134C,141C,148C,170C ''probabl'':22C ''problem'':19C ''rate'':10C,47C,92C,122C ''re'':37C ''recal'':44C ''rectifi'':116C ''regr.coef'':143C ''regress'':81C,138C,211C,218C ''regressor'':103C,110C,113C ''residu'':149C,221C ''result'':78C,135C,215C ''scikit-neuralnetwork.readthedocs.org'':105C ''scikit-neuralnetwork.readthedocs.org/en/latest/guide_beginners.html'':104C ''score'':165C,172C,227C ''see'':34C ''size'':52C ''sknn'':102C,137C,210C,217C ''sknn.mlp'':108C ''squar'':146C,152C,224C ''step'':51C ''sum'':150C,222C ''test'':158C,161C,177C,180C,186C,189C,195C,199C ''train'':130C,133C ''unit'':117C ''use'':38C,84C,100C ''varianc'':164C,171C,226C ''way'':13C ''x'':129C,157C,176C,185C,194C,198C ''y'':132C,160C,179C,188C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8122, 1, '2015-09-18 05:32:26.25', 0, 91, 'I have few basic questions on R Stem and Leaf Plot', '&lt;p&gt;This data when I plot - &lt;code&gt;60&lt;/code&gt; &lt;code&gt;85&lt;/code&gt; &lt;code&gt;72&lt;/code&gt; &lt;code&gt;59&lt;/code&gt; &lt;code&gt;37&lt;/code&gt; &lt;code&gt;75&lt;/code&gt; &lt;code&gt;93&lt;/code&gt; &lt;code&gt;7&lt;/code&gt; &lt;code&gt;98&lt;/code&gt; &lt;code&gt;63&lt;/code&gt; &lt;code&gt;41&lt;/code&gt; &lt;code&gt;90&lt;/code&gt; &lt;code&gt;5&lt;/code&gt; &lt;code&gt;17&lt;/code&gt; &lt;code&gt;97&lt;/code&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;stem(data)

 0 | 577
 2 | 7
 4 | 19
 6 | 0325
 8 | 50378
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now If I reconstruct data it is &lt;code&gt;5&lt;/code&gt;, &lt;code&gt;7&lt;/code&gt;, &lt;code&gt;7&lt;/code&gt;, &lt;code&gt;27&lt;/code&gt;, &lt;code&gt;60&lt;/code&gt;, &lt;code&gt;63&lt;/code&gt;, &lt;code&gt;62&lt;/code&gt;, &lt;code&gt;65&lt;/code&gt;, &lt;code&gt;85&lt;/code&gt;, &lt;code&gt;80&lt;/code&gt;, &lt;code&gt;83&lt;/code&gt;, &lt;code&gt;87&lt;/code&gt;, &lt;code&gt;88&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;This does not produce the input in reverse.&lt;/p&gt;
', 'r', NULL, '''/code'':21C,24C,27C,30C,33C,36C,39C,42C,45C,48C,51C,54C,57C,60C,63C,79C,91C,94C,97C,100C,103C,106C,109C,112C,115C,118C,121C,124C,127C ''/p'':64C,128C,138C ''/pre'':80C ''0'':69C ''0325'':76C ''17'':59C ''19'':74C ''2'':71C ''27'':99C ''37'':32C ''4'':73C ''41'':50C ''5'':56C,90C ''50378'':78C ''577'':70C ''59'':29C ''6'':75C ''60'':20C,102C ''62'':108C ''63'':47C,105C ''65'':111C ''7'':41C,72C,93C,96C ''72'':26C ''75'':35C ''8'':77C ''80'':117C ''83'':120C ''85'':23C,114C ''87'':123C ''88'':126C ''90'':53C ''93'':38C ''97'':62C ''98'':44C ''basic'':4A ''code'':19C,22C,25C,28C,31C,34C,37C,40C,43C,46C,49C,52C,55C,58C,61C,66C,89C,92C,95C,98C,101C,104C,107C,110C,113C,116C,119C,122C,125C ''data'':15C,68C,86C ''input'':135C ''leaf'':10A ''p'':13C,81C,129C ''plot'':11A,18C ''pre'':65C ''produc'':133C ''question'':5A ''r'':7A,12B ''reconstruct'':85C ''revers'':137C ''stem'':8A,67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8123, 2, '2015-09-18 08:32:19.853', -1, NULL, NULL, '&lt;p&gt;try this
S=c(60, 85, 72, 59, 37, 75, 93, 7, 98, 63, 41, 90 ,5 ,17 ,97)
;stem(S,scale = 2)
A value of scale = 2 will cause the plot to be roughly twice as long as the default.&lt;/p&gt;
', NULL, NULL, '''/p'':43C ''17'':19C ''2'':24C,29C ''37'':10C ''41'':16C ''5'':18C ''59'':9C ''60'':6C ''63'':15C ''7'':13C ''72'':8C ''75'':11C ''85'':7C ''90'':17C ''93'':12C ''97'':20C ''98'':14C ''c'':5C ''caus'':31C ''default'':42C ''long'':39C ''p'':1C ''plot'':33C ''rough'':36C ''scale'':23C,28C ''stem'':21C ''tri'':2C ''twice'':37C ''valu'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8124, 1, '2015-09-18 09:50:09.74', -1, 51, 'Clustering large number of strings based on tags', '&lt;p&gt;I have string representations of text written by users in the form of parts of speech tags like so:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;$NNDN,OVDANPN,PNVRV,^^V,^^!$^OV
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; ^,G,#,!,N,R,$
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;etc. They are separated into two classes (0 or 1).&lt;/p&gt;

&lt;p&gt;I want to be able to cluster these such that I will be able to predict (or try to) what class the user in from their tags using the damerau levenshtein distance.&lt;/p&gt;

&lt;p&gt;The problem is that even a few hundred strings is a huge calculation for any basic clustering that I am aware of (but I am very new to this). &lt;/p&gt;

&lt;p&gt;I''ve tried using the counts of each tag to form a vector but applying SVM, knn classifier and Naive Bayes yielded poor results, even when using a KS test to get the best features. My gut feeling is that this seems like a problem that could be solved in the same way that scientists would compare and cluster genes.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Should I be looking at different machine learning methods?  &lt;/li&gt;
&lt;li&gt;Is there another way of representing the strings that would be more appropriate?&lt;/li&gt;
&lt;li&gt;Is there another way of looking at the problem?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I''m using the scikit-learn library for Python.&lt;/p&gt;
', 'machine-learning  classification  python  clustering', NULL, '''/code'':43C,53C ''/li'':205C,220C,231C ''/p'':35C,47C,66C,100C,131C,193C,244C ''/pre'':44C,54C ''/ul'':232C ''0'':63C ''1'':65C ''abl'':72C,81C ''anoth'':209C,224C ''appli'':147C ''appropri'':219C ''awar'':122C ''base'':6A ''basic'':117C ''bay'':153C ''best'':166C ''calcul'':114C ''class'':62C,88C ''classif'':12B ''classifi'':150C ''cluster'':1A,14B,74C,118C,191C ''code'':37C,49C ''compar'':189C ''could'':179C ''count'':138C ''damerau'':97C ''differ'':201C ''distanc'':99C ''etc'':56C ''even'':106C,157C ''featur'':167C ''feel'':170C ''form'':27C,143C ''g'':50C ''gene'':192C ''get'':164C ''gut'':169C ''huge'':113C ''hundr'':109C ''knn'':149C ''ks'':161C ''larg'':2A ''learn'':11B,203C,240C ''levenshtein'':98C ''li'':195C,206C,221C ''librari'':241C ''like'':33C,175C ''look'':199C,227C ''m'':235C ''machin'':10B,202C ''machine-learn'':9B ''method'':204C ''n'':51C ''naiv'':152C ''new'':128C ''nndn'':38C ''number'':3A ''ov'':42C ''ovdanpn'':39C ''p'':15C,45C,55C,67C,101C,132C,233C ''part'':29C ''pnvrv'':40C ''poor'':155C ''pre'':36C,48C ''predict'':83C ''problem'':103C,177C,230C ''python'':13B,243C ''r'':52C ''repres'':212C ''represent'':19C ''result'':156C ''scientist'':187C ''scikit'':239C ''scikit-learn'':238C ''seem'':174C ''separ'':59C ''solv'':181C ''speech'':31C ''string'':5A,18C,110C,214C ''svm'':148C ''tag'':8A,32C,94C,141C ''test'':162C ''text'':21C ''tri'':85C,135C ''two'':61C ''ul'':194C ''use'':95C,136C,159C,236C ''user'':24C,90C ''v'':41C ''ve'':134C ''vector'':145C ''want'':69C ''way'':185C,210C,225C ''would'':188C,216C ''written'':22C ''yield'':154C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8125, 2, '2015-09-18 10:08:01.81', 1, NULL, NULL, '&lt;p&gt;With the default scale, you see that numbers left of the bar go up by two - hence anything after the 4 is forty-something or fifty-something:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; stem(d,scale=1)

  The decimal point is 1 digit(s) to the right of the |

  0 | 577
  2 | 7
  4 | 19
  6 | 0325
  8 | 50378
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Using &lt;code&gt;scale=2&lt;/code&gt;, you''ll see numbers left of the bar go up by one, so now you can get exact reconstruction of your input, since you input integers:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; stem(d,scale=2)

  The decimal point is 1 digit(s) to the right of the |

  0 | 57
  1 | 7
  2 | 
  3 | 7
  4 | 1
  5 | 9
  6 | 03
  7 | 25
  8 | 5
  9 | 0378
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Going further you can even split it by first and second five within each decade:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; stem(d,scale=4)

  The decimal point is 1 digit(s) to the right of the |

  0 | 57
  1 | 
  1 | 7
  2 | 
  2 | 
  3 | 
  3 | 7
  4 | 1
  4 | 
  5 | 
  5 | 9
  6 | 03
  6 | 
  7 | 2
  7 | 5
  8 | 
  8 | 5
  9 | 03
  9 | 78
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Stem plots &lt;em&gt;do not&lt;/em&gt; always guarantee you can reproduce the data by reversing the process, and that''s not what they''re for.&lt;/p&gt;
', NULL, NULL, '''/code'':61C,68C,134C,202C ''/em'':210C ''/p'':31C,95C,152C,230C ''/pre'':62C,135C,203C ''0'':51C,115C,172C ''03'':127C,189C,199C ''0325'':58C ''0378'':133C ''1'':38C,43C,107C,117C,123C,164C,174C,175C,183C ''19'':56C ''2'':53C,67C,102C,119C,177C,178C,192C ''25'':129C ''3'':120C,179C,180C ''4'':22C,55C,122C,159C,182C,184C ''5'':124C,131C,185C,186C,194C,197C ''50378'':60C ''57'':116C,173C ''577'':52C ''6'':57C,126C,188C,190C ''7'':54C,118C,121C,128C,176C,181C,191C,193C ''78'':201C ''8'':59C,130C,195C,196C ''9'':125C,132C,187C,198C,200C ''alway'':211C ''anyth'':19C ''bar'':13C,76C ''code'':33C,65C,97C,154C ''d'':36C,100C,157C ''data'':217C ''decad'':151C ''decim'':40C,104C,161C ''default'':4C ''digit'':44C,108C,165C ''em'':207C ''even'':141C ''exact'':86C ''fifti'':29C ''fifty-someth'':28C ''first'':145C ''five'':148C ''forti'':25C ''forty-someth'':24C ''get'':85C ''go'':14C,77C,137C ''gt'':34C,98C,155C ''guarante'':212C ''henc'':18C ''input'':90C,93C ''integ'':94C ''left'':10C,73C ''ll'':70C ''number'':9C,72C ''one'':80C ''p'':1C,63C,136C,204C ''plot'':206C ''point'':41C,105C,162C ''pre'':32C,96C,153C ''process'':221C ''re'':228C ''reconstruct'':87C ''reproduc'':215C ''revers'':219C ''right'':48C,112C,169C ''scale'':5C,37C,66C,101C,158C ''second'':147C ''see'':7C,71C ''sinc'':91C ''someth'':26C,30C ''split'':142C ''stem'':35C,99C,156C,205C ''two'':17C ''use'':64C ''within'':149C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8127, 2, '2015-09-18 10:09:53.847', 0, NULL, NULL, '&lt;p&gt;Using the Levenshtein distance does not make a lot of sense in this context, as it is made for comparing distances between words.&lt;/p&gt;

&lt;p&gt;A commonly used representation for texts is the bag-of-words representation, where a text is converted to a vector where every element in the vector represents the count of the corresponding word. In your case you could represent a text as a bag-of-tags.
The vector representation makes calculating distances a lot easier. However, I believe this is not necessary as you can classify the bags of words with Naive Bayes.&lt;/p&gt;

&lt;p&gt;Once you have tried bag-of-words you can try more complicated representations like LDA, word2vec, and the like.&lt;/p&gt;
', NULL, NULL, '''/p'':25C,102C,124C ''bag'':36C,72C,96C,109C ''bag-of-tag'':71C ''bag-of-word'':35C,108C ''bay'':101C ''believ'':86C ''calcul'':79C ''case'':63C ''classifi'':94C ''common'':28C ''compar'':21C ''complic'':116C ''context'':15C ''convert'':44C ''correspond'':59C ''could'':65C ''count'':56C ''distanc'':5C,22C,80C ''easier'':83C ''element'':50C ''everi'':49C ''howev'':84C ''lda'':119C ''levenshtein'':4C ''like'':118C,123C ''lot'':10C,82C ''made'':19C ''make'':8C,78C ''naiv'':100C ''necessari'':90C ''p'':1C,26C,103C ''repres'':54C,66C ''represent'':30C,39C,77C,117C ''sens'':12C ''tag'':74C ''text'':32C,42C,68C ''tri'':107C,114C ''use'':2C,29C ''vector'':47C,53C,76C ''word'':24C,38C,60C,98C,111C ''word2vec'':120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8146, 1, '2015-09-21 18:06:44.903', 2, 142, 'How to train a Text Based data for a Machine Learning problem?', '&lt;ol&gt;
&lt;li&gt;I am working on a project for displaying products to customer by context, based on a search query. For example, I don''t want customers to have to enter a specific product name, instead searching based on functionality (e.g., "walls do not heat much" would return product names such as "Whirlpool NEO IC355 ROY 3S 340 L Double Door Refrigerator")  &lt;/li&gt;
&lt;li&gt;I have a training set comprised of the functionality associated products. I am planning to use Logistic Regression to train a model on these data. How do I process this data in Python or extract features to feed into logistic regression? I have heard of "Bag of words model", but not sure how to use this, or is it even applicable here?&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I know there are plenty of NLTK libraries available. But, I want to implement it from the scratch or using minimum external libraries possible. &lt;/p&gt;

&lt;p&gt;Please help or are there any resources to refer to?&lt;/p&gt;
', 'machine-learning  classification  nlp  logistic-regression', NULL, '''/li'':83C,147C ''/ol'':148C ''/p'':174C,186C ''340'':78C ''3s'':77C ''applic'':145C ''associ'':94C ''avail'':158C ''bag'':130C ''base'':6A,36C,58C ''classif'':16B ''compris'':90C ''context'':35C ''custom'':33C,47C ''data'':7A,109C,115C ''display'':30C ''door'':81C ''doubl'':80C ''e.g'':61C ''enter'':51C ''even'':144C ''exampl'':42C ''extern'':171C ''extract'':119C ''featur'':120C ''feed'':122C ''function'':60C,93C ''heard'':128C ''heat'':65C ''help'':177C ''ic355'':75C ''implement'':163C ''instead'':56C ''know'':151C ''l'':79C ''learn'':11A,15B ''li'':22C,84C ''librari'':157C,172C ''logist'':19B,101C,124C ''logistic-regress'':18B ''machin'':10A,14B ''machine-learn'':13B ''minimum'':170C ''model'':106C,133C ''much'':66C ''name'':55C,70C ''neo'':74C ''nlp'':17B ''nltk'':156C ''ol'':21C ''p'':149C,175C ''plan'':98C ''pleas'':176C ''plenti'':154C ''possibl'':173C ''problem'':12A ''process'':113C ''product'':31C,54C,69C,95C ''project'':28C ''python'':117C ''queri'':40C ''refer'':184C ''refriger'':82C ''regress'':20B,102C,125C ''resourc'':182C ''return'':68C ''roy'':76C ''scratch'':167C ''search'':39C,57C ''set'':89C ''specif'':53C ''sure'':136C ''text'':5A ''train'':3A,88C,104C ''use'':100C,139C,169C ''wall'':62C ''want'':46C,161C ''whirlpool'':73C ''word'':132C ''work'':25C ''would'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8128, 1, '2015-09-18 16:16:42.86', 1, 44, 'Clustering the data if some values in each vector is not applicable', '&lt;p&gt;I have four different categories which the number of variables in each category is 7, 3, 8, and 9 (in total 27). The value of each variable changes from 1 to 5 but some of the variables were not applicable for some observations. In total, there are 20 observations which their data is available for 27 variables in the mentioned context (from 1 to 5 plus not applicable). How can I cluster these four categories for 20 observations to get three clusters for each category?&lt;/p&gt;

&lt;p&gt;&lt;em&gt;edit&lt;/em&gt;: There are 4 categories (A, B, C, and D). Each category has a different vector including 7, 3, 8, and 9 values (from 1 to 5 plus NA). Now, it''s 18 observations for each category of A, B, C, and D. I need to make three groups for each category. How can I find the centroid of each cluster for each category? It means I need three centroid in each category, totally 12(=4 category* 3 groups). Any feedback would be appreciated. Thank you.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/MAdCW.png" alt="enter image description here"&gt;&lt;/p&gt;
', 'classification', NULL, '''/em'':104C ''/madcw.png'':196C ''/p'':100C,190C,202C ''1'':44C,77C,128C ''12'':178C ''18'':136C ''20'':62C,91C ''27'':36C,70C ''3'':30C,122C,181C ''4'':107C,179C ''5'':46C,79C,130C ''7'':29C,121C ''8'':31C,123C ''9'':33C,125C ''alt'':197C ''applic'':12A,54C,82C ''appreci'':187C ''avail'':68C ''b'':110C,143C ''c'':111C,144C ''categori'':19C,27C,89C,99C,108C,115C,140C,155C,167C,176C,180C ''centroid'':161C,173C ''chang'':42C ''classif'':13B ''cluster'':1A,86C,96C,164C ''context'':75C ''d'':113C,146C ''data'':3A,66C ''descript'':200C ''differ'':18C,118C ''edit'':103C ''em'':102C ''enter'':198C ''feedback'':184C ''find'':159C ''four'':17C,88C ''get'':94C ''group'':152C,182C ''i.stack.imgur.com'':195C ''i.stack.imgur.com/madcw.png'':194C ''imag'':199C ''img'':192C ''includ'':120C ''make'':150C ''mean'':169C ''mention'':74C ''na'':132C ''need'':148C,171C ''number'':22C ''observ'':57C,63C,92C,137C ''p'':14C,101C,191C ''plus'':80C,131C ''src'':193C ''thank'':188C ''three'':95C,151C,172C ''total'':35C,59C,177C ''valu'':6A,38C,126C ''variabl'':24C,41C,51C,71C ''vector'':9A,119C ''would'':185C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8129, 1, '2015-09-18 17:44:42.573', 2, 68, 'Generate new images based on a dataset of images', '&lt;p&gt;Is it possible take a training set of one million 120 x 120 pixel tiles, feed these tiles into a machine learning algorithm. And then make it &lt;em&gt;synthesize&lt;/em&gt; images that look like the original training set with a certain, adjustable level of entropy?&lt;/p&gt;

&lt;p&gt;What ML techniques should I consider?&lt;/p&gt;
', 'machine-learning  statistics  image-classification', NULL, '''/em'':47C ''/p'':63C,71C ''120'':28C,30C ''adjust'':59C ''algorithm'':40C ''base'':4A ''certain'':58C ''classif'':16B ''consid'':70C ''dataset'':7A ''em'':45C ''entropi'':62C ''feed'':33C ''generat'':1A ''imag'':3A,9A,15B,48C ''image-classif'':14B ''learn'':12B,39C ''level'':60C ''like'':51C ''look'':50C ''machin'':11B,38C ''machine-learn'':10B ''make'':43C ''million'':27C ''ml'':66C ''new'':2A ''one'':26C ''origin'':53C ''p'':17C,64C ''pixel'':31C ''possibl'':20C ''set'':24C,55C ''statist'':13B ''synthes'':46C ''take'':21C ''techniqu'':67C ''tile'':32C,35C ''train'':23C,54C ''x'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8131, 1, '2015-09-19 04:02:11.133', 1, 135, 'How to start analysing and modelling data for an academic project, when not a statistician or data scientist', '&lt;p&gt;I have collected data for a PhD thesis, and need help understanding how to build a road map to do analytical and statistical analysis. The PhD is not itself in statistics or machine learning, but I would like to understand what are the steps and type of analysis that I have to follow for analysing data for an advanced degree? In general, how should I approach such a problem?  &lt;/p&gt;

&lt;p&gt;In the data I have collected, there are 623 observations including one continuous dependent variable and 13 independent variables (continuous, categorical, and ordinal) that are defined based on the researcher experience and literature review.&lt;/p&gt;

&lt;p&gt;I considered planning to do several regression analysis to predict the dependent variable and study the effective factors (if they are positive, negative, and their magnitude) on it. I''ve tried multiple linear regression including different transformation on independent variables. On the other hand, I''m not sure if I should study each independent variables through the time and  forecast their values in the time horizon? &lt;/p&gt;

&lt;p&gt;Here are the steps in my mind so far:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Plotting the scatter plots of different independent variables vs dependent variable to define outliers and check if the model is linear also with respect to coefficients&lt;/li&gt;
&lt;li&gt;Removing the potential outliers &lt;/li&gt;
&lt;li&gt;&lt;p&gt;Splitting the data into two data sets to build the model and validate it after that. &lt;/p&gt;

&lt;p&gt;If the model is linear then:&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Performing the multiple linear regression &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;Performing the multiple linear regression including different transformations to enhance the model &lt;/li&gt;
&lt;li&gt;Validating the model &lt;/li&gt;
&lt;li&gt;Doing the quantile regression &lt;/li&gt;
&lt;li&gt;Doing supervised learning machine etc. &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;If the model is not linear, I may instead need to use non-linear statistical techniques.&lt;/p&gt;

&lt;p&gt;Any feedback would be highly appreciated. My goal is to build a clear and robust road map for this part of the work.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8134, 2, '2015-09-19 12:45:02.36', 1, NULL, NULL, '&lt;p&gt;First, are your categories independent of each other? If this is really four parallel problems, solve one at a time and it''ll simplify things a bit.&lt;/p&gt;

&lt;p&gt;Second, the heart of clustering is the distance function between any two observations. Because your data has missing values, you need to explicitly decide how to handle them. A common approach is to ignore the problem by throwing away any rows that have any null values, which won''t work at all for your data.&lt;/p&gt;

&lt;p&gt;A simple approach would be to ignore any NA columns, but keep the rest of the columns--for category C, the difference between rows 1 and 2 would be 4 (columns 11, 14, 15, and 16 are all off by one). There''s an issue with this approach that I''ll get to later, but let''s implement it for now.&lt;/p&gt;

&lt;p&gt;For each category, we need three rows of centroid positions (we might as well start off with randomly selected rows that have no missing values in them). We then calculate distance to each centroid using a bunch of summed ifs: if(matrix cell != NA, matrix cell - centroid cell, 0) across all the columns. (If you''re doing this computation in a spreadsheet, I''d use intermediate cells to calculate the pieces instead of dealing with one huge formula.)&lt;/p&gt;

&lt;p&gt;We then optimize the centroids with a standard iterative procedure: each row gets a group membership corresponding to the closest centroid, then each centroid becomes the mean of its group members (and it looks like your mean function automatically excludes NAs), then we repeat until satisfied.&lt;/p&gt;

&lt;p&gt;Why did I specify that we had to seed with a row without NAs? If you allow NA measurements in the centroids, this will fail because a row of all NAs is as central as it gets! If our update step never lets us move to NAs, the NA column will be transient, but what happens when we take the mean of a missing column? (In your sample data, consider what happens if rows 4-12 are a group in the B category.) &lt;/p&gt;

&lt;p&gt;So if we want to allow NAs in the centroids, we need to modify our distance function to something like the following:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;If both cells are numbers, the distance is the difference&lt;/li&gt;
&lt;li&gt;If both cells are NA, the distance is 2&lt;/li&gt;
&lt;li&gt;If only one cell is NA, the distance is 3.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Those numbers are chosen such that for any full column of numbers, a numerical value in the centroid position will be closer, but if the column has sufficiently many NA rows, a NA value in the centroid will be closer. You might want to adjust the values so that the NA threshold looks like what you want it to.&lt;/p&gt;

&lt;p&gt;In order to find the centroid of a set of rows using our distance function, we should use an optimization procedure, but we can cheat: if the number of NAs in the group is above the threshold, we set it to NA, and otherwise we set it to the mean.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8135, 1, '2015-09-19 15:40:54.523', 0, 81, 'Is supervised machine learning by definition predictive?', '&lt;p&gt;I am trying to organize a cheat sheet of sorts for data science, and I am working with the basic distinction between description, inference, and prediction. As examples of the first I see unsupervised methods described, and for the last I see supervised methods. So my question is simply, do these two sets of categories align? Is unsupervised to supervised as description is to prediction?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':77C ''align'':67C ''basic'':31C ''categori'':66C ''cheat'':18C ''data'':23C ''definit'':6A ''describ'':47C ''descript'':34C,73C ''distinct'':32C ''exampl'':39C ''first'':42C ''infer'':35C ''last'':51C ''learn'':4A,10B ''machin'':3A,9B ''machine-learn'':8B ''method'':46C,55C ''organ'':16C ''p'':11C ''predict'':7A,37C,76C ''question'':58C ''scienc'':24C ''see'':44C,53C ''set'':64C ''sheet'':19C ''simpli'':60C ''sort'':21C ''supervis'':2A,54C,71C ''tri'':14C ''two'':63C ''unsupervis'':45C,69C ''work'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8136, 2, '2015-09-19 17:44:53.19', 2, NULL, NULL, '&lt;p&gt;A description as any statistic drawn from your sample data, say the sample mean, quantiles, etc.. Inference is a conclusion drawn from your sample data about the population, e.g., rejecting or accepting some hypothesis or stating that a model is suitable or not for describing your data. Prediction is simply a guess about future observations, which hopefully uses your data and some function/model of the data in a way to formulate that guess.&lt;/p&gt;

&lt;p&gt;Both unsupervised and supervised learning methods aim to learn a function of the data that predicts another variable (typically called y) so both are drawing an inference (i.e., a model is well suited to describe your data, see the first sentence &lt;a href="https://en.wikipedia.org/wiki/Supervised_learning" rel="nofollow"&gt;here&lt;/a&gt;). However, these two methods differ in what data is available. In supervised learning you are able to use an observed sample of y for training your model and in unsupervised learning, y is unobserved.&lt;/p&gt;

&lt;p&gt;Hope that helps!&lt;/p&gt;
', NULL, NULL, '''..'':18C ''/a'':127C ''/p'':76C,162C,167C ''/wiki/supervised_learning'':123C ''abl'':143C ''accept'':34C ''aim'':84C ''anoth'':94C ''avail'':137C ''call'':97C ''conclus'':22C ''data'':11C,27C,49C,62C,68C,91C,114C,135C ''describ'':47C,112C ''descript'':3C ''differ'':132C ''draw'':102C ''drawn'':7C,23C ''e.g'':31C ''en.wikipedia.org'':122C ''en.wikipedia.org/wiki/supervised_learning'':121C ''etc'':17C ''first'':117C ''formul'':73C ''function'':88C ''function/model'':65C ''futur'':56C ''guess'':54C,75C ''help'':166C ''hope'':59C,164C ''howev'':128C ''href'':120C ''hypothesi'':36C ''i.e'':105C ''infer'':19C,104C ''learn'':82C,86C,140C,158C ''mean'':15C ''method'':83C,131C ''model'':41C,107C,154C ''nofollow'':125C ''observ'':57C,147C ''p'':1C,77C,163C ''popul'':30C ''predict'':50C,93C ''quantil'':16C ''reject'':32C ''rel'':124C ''sampl'':10C,14C,26C,148C ''say'':12C ''see'':115C ''sentenc'':118C ''simpli'':52C ''state'':38C ''statist'':6C ''suit'':110C ''suitabl'':43C ''supervis'':81C,139C ''train'':152C ''two'':130C ''typic'':96C ''unobserv'':161C ''unsupervis'':79C,157C ''use'':60C,145C ''variabl'':95C ''way'':71C ''well'':109C ''y'':98C,150C,159C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8143, 2, '2015-09-21 14:32:37.967', 3, NULL, NULL, '&lt;p&gt;You might consider using &lt;a href="https://code.google.com/p/word2vec/" rel="nofollow"&gt;word2vec&lt;/a&gt; to identify &lt;a href="https://radimrehurek.com/gensim/models/phrases.html" rel="nofollow"&gt;phrases&lt;/a&gt; in the corpus.  The presence of a phrase (instead of single tokens) is likely to indicate a ''template.''  &lt;/p&gt;

&lt;p&gt;From here, the tokens most similar to your template phrase are likely to be the values for your parameters.   &lt;/p&gt;
', NULL, NULL, '''/a'':14C,25C ''/gensim/models/phrases.html'':21C ''/p'':44C,65C ''/p/word2vec/'':10C ''code.google.com'':9C ''code.google.com/p/word2vec/'':8C ''consid'':4C ''corpus'':28C ''href'':7C,18C ''identifi'':16C ''indic'':41C ''instead'':34C ''like'':39C,57C ''might'':3C ''nofollow'':12C,23C ''p'':1C,45C ''paramet'':64C ''phrase'':24C,33C,55C ''presenc'':30C ''radimrehurek.com'':20C ''radimrehurek.com/gensim/models/phrases.html'':19C ''rel'':11C,22C ''similar'':51C ''singl'':36C ''templat'':43C,54C ''token'':37C,49C ''use'':5C ''valu'':61C ''word2vec'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8721, 2, '2015-11-04 16:45:26.883', 2, NULL, NULL, '&lt;p&gt;1) Max-Entropy(Logistic Regression) on TFIDF vectors is a good starting point for many NLP classification task.&lt;/p&gt;

&lt;p&gt;2) Word2vec is definitely something worth trying and comparing to model 1. I would suggest using the Doc2Vec flavor for looking at sentences/paragraphs.&lt;/p&gt;

&lt;p&gt;Quoc Le and Tomas Mikolov. Distributed Representations of Sentences and Documents. &lt;a href="http://arxiv.org/pdf/1405.4053v2.pdf" rel="nofollow"&gt;http://arxiv.org/pdf/1405.4053v2.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Gensim(python) has a nice Doc2vec model.&lt;/p&gt;
', NULL, NULL, '''/p'':21C,46C,77C ''/pdf/1405.4053v2.pdf'':63C ''/pdf/1405.4053v2.pdf&lt;/a&gt;&lt;/p&gt;'':68C ''1'':2C,34C ''2'':23C ''arxiv.org'':62C,67C ''arxiv.org/pdf/1405.4053v2.pdf'':61C ''arxiv.org/pdf/1405.4053v2.pdf&lt;/a&gt;&lt;/p&gt;'':66C ''classif'':19C ''compar'':31C ''definit'':26C ''distribut'':53C ''doc2vec'':40C,75C ''document'':58C ''entropi'':5C ''flavor'':41C ''gensim'':70C ''good'':13C ''href'':60C ''le'':49C ''logist'':6C ''look'':43C ''mani'':17C ''max'':4C ''max-entropi'':3C ''mikolov'':52C ''model'':33C,76C ''nice'':74C ''nlp'':18C ''nofollow'':65C ''p'':1C,22C,47C,69C ''point'':15C ''python'':71C ''quoc'':48C ''regress'':7C ''rel'':64C ''represent'':54C ''sentenc'':56C ''sentences/paragraphs'':45C ''someth'':27C ''start'':14C ''suggest'':37C ''task'':20C ''tfidf'':9C ''toma'':51C ''tri'':29C ''use'':38C ''vector'':10C ''word2vec'':24C ''worth'':28C ''would'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8147, 1, '2015-09-21 18:51:35.353', 1, 58, 'Sum of a Series', '&lt;p&gt;I have been trying to find the sum of a series defined by &lt;code&gt;Î£(1+r(i))^1:n&lt;/code&gt; in R where &lt;code&gt;i&lt;/code&gt; and &lt;code&gt;n&lt;/code&gt; is in &lt;code&gt;(1:N)&lt;/code&gt;.  Both &lt;code&gt;i&lt;/code&gt; and &lt;code&gt;n&lt;/code&gt; are of the same length. The solution needs to be two functions, where &lt;code&gt;function 1&lt;/code&gt; calculates the expression &lt;code&gt;(1+r)^n&lt;/code&gt; and the second &lt;code&gt;function 2&lt;/code&gt; recursively calls &lt;code&gt;function 1&lt;/code&gt; to calculate the summation. I am struggling to create a way to call &lt;code&gt;function 1&lt;/code&gt; within &lt;code&gt;function 2&lt;/code&gt; and having it calculate the values recursively.&lt;/p&gt;

&lt;p&gt;Example:  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Rates   Tenor   Printrest  
1.20%   1   1.012  
1.35%   2   1.02718225  
1.90%   3   1.058089859  
2.01%   4   1.082856706  
1.80%   5   1.093298847  
1.70%   6   1.106434521  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Answer  6.379862183&lt;/p&gt;
', 'r', NULL, '''/code'':27C,33C,37C,43C,47C,51C,68C,76C,83C,89C,106C,111C,146C ''/p'':119C,122C,151C ''/pre'':147C ''1'':22C,25C,41C,67C,73C,88C,105C,129C ''1.012'':130C ''1.02718225'':133C ''1.058089859'':136C ''1.082856706'':139C ''1.093298847'':142C ''1.106434521'':145C ''1.20'':128C ''1.35'':131C ''1.70'':143C ''1.80'':140C ''1.90'':134C ''2'':82C,110C,132C ''2.01'':137C ''3'':135C ''4'':138C ''5'':141C ''6'':144C ''6.379862183'':150C ''answer'':149C ''calcul'':69C,91C,115C ''call'':85C,102C ''code'':20C,31C,35C,40C,45C,49C,65C,72C,80C,86C,103C,108C,124C ''creat'':98C ''defin'':18C ''exampl'':121C ''express'':71C ''find'':12C ''function'':63C,66C,81C,87C,104C,109C ''length'':56C ''n'':26C,36C,42C,50C,75C ''need'':59C ''p'':6C,120C,148C ''pre'':123C ''printrest'':127C ''r'':5B,23C,29C,74C ''rate'':125C ''recurs'':84C,118C ''second'':79C ''seri'':4A,17C ''solut'':58C ''struggl'':96C ''sum'':1A,14C ''summat'':93C ''tenor'':126C ''tri'':10C ''two'':62C ''valu'':117C ''way'':100C ''within'':107C ''î'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8138, 1, '2015-09-20 12:40:10.17', 3, 67, 'Identifying templates with parameters in text fragments', '&lt;p&gt;I have a data set with text fragments having a fixed structure that can contain parameters.
Examples are:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; Temperature today is 20 centigrades
 Temperature today is 28 centigrades
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;or&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; Her eyes are blue and hair black.
 Her eyes are green and hair brown.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The first example show a template with one numerical parameter. The second one is a template with two factor parameters.&lt;/p&gt;

&lt;p&gt;The number of templates and the number of parameter is not know. &lt;/p&gt;

&lt;p&gt;The problem is to identify the templates and assign each text fragment to the corresponding template.&lt;/p&gt;

&lt;p&gt;The obvious first idea is to use clustering. The distance measure is defined as a number of non matching words. I.e. the records in example one have distance 1, in example two distance is 2. The distance between the record in example one and two is 7.
This approach works fine, providing the number of clusters is know, which is not the case, so it is not useful.&lt;/p&gt;

&lt;p&gt;I can imagine a programmatic approach scanning the distance matrix searching for records with lot of neighbors in distance 1 (or 2,3,..), but I''m curious if I can apply some unsupervised machine learning algorithm to solve the problem. R is preferred, but not required.&lt;/p&gt;
', 'machine-learning  r  nlp', NULL, '''/code'':45C,66C ''/p'':32C,49C,89C,103C,121C,191C,239C ''/pre'':46C,67C ''1'':151C,212C ''2'':157C,214C ''20'':38C ''28'':43C ''3'':215C ''7'':169C ''algorithm'':228C ''appli'':223C ''approach'':171C,198C ''assign'':113C ''black'':58C ''blue'':55C ''brown'':65C ''case'':185C ''centigrad'':39C,44C ''cluster'':130C,178C ''code'':34C,51C ''contain'':28C ''correspond'':119C ''curious'':219C ''data'':17C ''defin'':135C ''distanc'':132C,150C,155C,159C,201C,211C ''exampl'':30C,71C,147C,153C,164C ''eye'':53C,60C ''factor'':87C ''fine'':173C ''first'':70C,125C ''fix'':24C ''fragment'':7A,21C,116C ''green'':62C ''hair'':57C,64C ''i.e'':143C ''idea'':126C ''identifi'':1A,109C ''imagin'':195C ''know'':102C,180C ''learn'':10B,227C ''lot'':207C ''m'':218C ''machin'':9B,226C ''machine-learn'':8B ''match'':141C ''matrix'':202C ''measur'':133C ''neighbor'':209C ''nlp'':12B ''non'':140C ''number'':92C,97C,138C,176C ''numer'':77C ''obvious'':124C ''one'':76C,81C,148C,165C ''p'':13C,47C,68C,90C,104C,122C,192C ''paramet'':4A,29C,78C,88C,99C ''pre'':33C,50C ''prefer'':235C ''problem'':106C,232C ''programmat'':197C ''provid'':174C ''r'':11B,233C ''record'':145C,162C,205C ''requir'':238C ''scan'':199C ''search'':203C ''second'':80C ''set'':18C ''show'':72C ''solv'':230C ''structur'':25C ''temperatur'':35C,40C ''templat'':2A,74C,84C,94C,111C,120C ''text'':6A,20C,115C ''today'':36C,41C ''two'':86C,154C,167C ''unsupervis'':225C ''use'':129C,190C ''word'':142C ''work'':172C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8139, 2, '2015-09-20 21:42:48.27', 1, NULL, NULL, '&lt;p&gt;Consider the class to be the variable that you are sampling.&lt;/p&gt;

&lt;p&gt;Your "reservoir sample" should still be as good as uniformly drawn from your data.&lt;/p&gt;

&lt;p&gt;Yes, there may be fluctuations, in particular if you have small samples. If you sample a single observation, the class distribution in that sample will be 100% of one class, there is no way around that. ;-)&lt;/p&gt;
', NULL, NULL, '''/p'':13C,29C,67C ''100'':57C ''around'':65C ''class'':4C,50C,60C ''consid'':2C ''data'':28C ''distribut'':51C ''drawn'':25C ''fluctuat'':35C ''good'':22C ''may'':33C ''observ'':48C ''one'':59C ''p'':1C,14C,30C ''particular'':37C ''reservoir'':16C ''sampl'':12C,17C,42C,45C,54C ''singl'':47C ''small'':41C ''still'':19C ''uniform'':24C ''variabl'':8C ''way'':64C ''yes'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8140, 1, '2015-09-21 01:31:19.573', 0, 137, 'Machine Learning algorithm to predict an outcome where output is not known', '&lt;p&gt;I have a very unique problem that I would like to solve using machine learning. I have a set of 90 or so unique options. Each of these options has a unique set of features (5 to be specific) that vary over time and that can be represented mathematically as either scalars or booleans. I want to train a model to tell me what the best option at that moment is given the complete set of inputs.&lt;/p&gt;

&lt;p&gt;At first, I figured a simple feedforward neural network would do the trick but I don''t have a ton of experience with ML models so I ran into the following issue. I know my problem isn''t really a classification problem since I am trying to pick the best option. I figured it could be easily modelled as a regression problem (as some mathematical combination of the inputs) which is fine but then I don''t know what the expected outcome would be. For example, if the expected output is the weighted average of the inputs, I don''t know what the final value should be. All I know is that option B should be suggested over option A.&lt;/p&gt;

&lt;p&gt;I started reading about random forests and they seem like they might help my situation but I wanted to get other opinions first.&lt;/p&gt;
', 'machine-learning  neuralnetwork  predictive-modeling', NULL, '''/p'':98C,220C,245C ''5'':56C ''90'':41C ''algorithm'':3A ''averag'':193C ''b'':213C ''best'':86C,149C ''boolean'':74C ''classif'':140C ''combin'':165C ''complet'':94C ''could'':154C ''easili'':156C ''either'':71C ''exampl'':185C ''expect'':180C,188C ''experi'':121C ''featur'':55C ''feedforward'':106C ''figur'':103C,152C ''final'':203C ''fine'':171C ''first'':101C,244C ''follow'':130C ''forest'':227C ''get'':241C ''given'':92C ''help'':234C ''input'':97C,168C,196C ''isn'':136C ''issu'':131C ''know'':133C,177C,200C,209C ''known'':12A ''learn'':2A,15B,35C ''like'':30C,231C ''machin'':1A,14B,34C ''machine-learn'':13B ''mathemat'':69C,164C ''might'':233C ''ml'':123C ''model'':19B,80C,124C,157C ''moment'':90C ''network'':108C ''neural'':107C ''neuralnetwork'':16B ''opinion'':243C ''option'':45C,49C,87C,150C,212C,218C ''outcom'':7A,181C ''output'':9A,189C ''p'':20C,99C,221C ''pick'':147C ''predict'':5A,18B ''predictive-model'':17B ''problem'':26C,135C,141C,161C ''ran'':127C ''random'':226C ''read'':224C ''realli'':138C ''regress'':160C ''repres'':68C ''scalar'':72C ''seem'':230C ''set'':39C,53C,95C ''simpl'':105C ''sinc'':142C ''situat'':236C ''solv'':32C ''specif'':59C ''start'':223C ''suggest'':216C ''tell'':82C ''time'':63C ''ton'':119C ''train'':78C ''tri'':145C ''trick'':112C ''uniqu'':25C,44C,52C ''use'':33C ''valu'':204C ''vari'':61C ''want'':76C,239C ''weight'':192C ''would'':29C,109C,182C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8141, 2, '2015-09-21 03:51:01', 1, NULL, NULL, '&lt;p&gt;What I was looking for is called ordinal regression or ranking learning (&lt;a href="https://en.wikipedia.org/wiki/Ordinal_regression" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Ordinal_regression&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;Edit: I will elaborate more on why this solved my issue
What I was having a problem with was finding a statistical model to appropriately predict the ordinality of multiple independent options. What I cared about was the order of the options and less so about what the value of the options was.&lt;/p&gt;

&lt;p&gt;Ordered Logit/Probit describe such models. The provided wikipedia link explains them in far more detail than I can.&lt;/p&gt;
', NULL, NULL, '''/p'':78C,98C ''/wiki/ordinal_regression'':18C ''/wiki/ordinal_regression&lt;/a&gt;)&lt;/p&gt;'':23C ''appropri'':49C ''call'':8C ''care'':59C ''describ'':82C ''detail'':94C ''edit'':25C ''elabor'':28C ''en.wikipedia.org'':17C,22C ''en.wikipedia.org/wiki/ordinal_regression'':16C ''en.wikipedia.org/wiki/ordinal_regression&lt;/a&gt;)&lt;/p&gt;'':21C ''explain'':89C ''far'':92C ''find'':44C ''href'':15C ''independ'':55C ''issu'':35C ''learn'':13C ''less'':68C ''link'':88C ''logit/probit'':81C ''look'':5C ''model'':47C,84C ''multipl'':54C ''nofollow'':20C ''option'':56C,66C,76C ''order'':63C,80C ''ordin'':9C,52C ''p'':1C,24C,79C ''predict'':50C ''problem'':41C ''provid'':86C ''rank'':12C ''regress'':10C ''rel'':19C ''solv'':33C ''statist'':46C ''valu'':73C ''wikipedia'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8142, 1, '2015-09-21 11:15:45.813', 1, 56, 'Error Analysis for misclassification of text documents', '&lt;p&gt;I am working on a text classification work. The purpose of this work is to classify whether a particular document belong to class A or Class B. &lt;/p&gt;

&lt;p&gt;I used KNN algorithm and i am able to get some decent results. However I want to know two things.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Why a particular document has been classified as Class A or Class B? What keyword or information that made a document to be classified as such?&lt;/li&gt;
&lt;li&gt;How to perform mis-classification analysis?&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Kindly help.&lt;/p&gt;
', 'data-mining  classification  text-mining', NULL, '''/li'':94C,103C ''/ol'':104C ''/p'':43C,65C,108C ''abl'':52C ''algorithm'':48C ''analysi'':2A,102C ''b'':42C,80C ''belong'':36C ''class'':38C,41C,76C,79C ''classif'':11B,22C,101C ''classifi'':31C,74C,91C ''data'':9B ''data-min'':8B ''decent'':56C ''document'':7A,35C,71C,88C ''error'':1A ''get'':54C ''help'':107C ''howev'':58C ''inform'':84C ''keyword'':82C ''kind'':106C ''knn'':47C ''know'':62C ''li'':67C,95C ''made'':86C ''mine'':10B,14B ''mis'':100C ''mis-classif'':99C ''misclassif'':4A ''ol'':66C ''p'':15C,44C,105C ''particular'':34C,70C ''perform'':98C ''purpos'':25C ''result'':57C ''text'':6A,13B,21C ''text-min'':12B ''thing'':64C ''two'':63C ''use'':46C ''want'':60C ''whether'':32C ''work'':18C,23C,28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8144, 2, '2015-09-21 15:21:13.133', 1, NULL, NULL, '&lt;p&gt;It seems to me that both of your questions could be answered by storing the retrieved neighbours on your test set and giving them a thorough analysis. Assuming you are using a unigram + tf-idf text representation and a cosine similarity distance metric for your K-NN retrieval, it would be trivial once you have a classified document to display the K neighbours and analyze their common unigrams and their respective tf-idf weights in order to see what influenced the classification. Moreover, doing it on your misclassified documents could help you understand which features caused the error.&lt;/p&gt;

&lt;p&gt;I''d be interested to know if there is a more systematized approach to those issues.&lt;/p&gt;
', NULL, NULL, '''/p'':103C,121C ''analysi'':28C ''analyz'':68C ''answer'':13C ''approach'':117C ''assum'':29C ''caus'':100C ''classif'':86C ''classifi'':60C ''common'':70C ''cosin'':42C ''could'':11C,94C ''d'':106C ''display'':63C ''distanc'':44C ''document'':61C,93C ''error'':102C ''featur'':99C ''give'':24C ''help'':95C ''idf'':37C,77C ''influenc'':84C ''interest'':108C ''issu'':120C ''k'':49C,65C ''k-nn'':48C ''know'':110C ''metric'':45C ''misclassifi'':92C ''moreov'':87C ''neighbour'':18C,66C ''nn'':50C ''order'':80C ''p'':1C,104C ''question'':10C ''represent'':39C ''respect'':74C ''retriev'':17C,51C ''see'':82C ''seem'':3C ''set'':22C ''similar'':43C ''store'':15C ''systemat'':116C ''test'':21C ''text'':38C ''tf'':36C,76C ''tf-idf'':35C,75C ''thorough'':27C ''trivial'':55C ''understand'':97C ''unigram'':34C,71C ''use'':32C ''weight'':78C ''would'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8145, 2, '2015-09-21 15:33:29.793', 0, NULL, NULL, '&lt;p&gt;I can''t comment on rfoley''s answer because I lack the reputation, but I''d like to add that Peter Flach has a nice small matrix that describes what you are looking for in his book (Machine Learning, the art and science of algorithms that make sense of data). I''ll reproduce it below (sorry for the bad layout but html tables are not allowed):&lt;/p&gt;

&lt;pre&gt;
                      |     Predictive Model          |   Descriptive Model
------------------------------------------------------------------------------
Supervised learning   | Classification, Regression    |  Subgroup discovery
------------------------------------------------------------------------------
Unsupervised learning | Predictive clustering         |  Descriptive clustering,
                      |                               |  Association rule mining
&lt;/pre&gt;
', NULL, NULL, '''/p'':68C ''/pre'':89C ''add'':20C ''algorithm'':46C ''allow'':67C ''answer'':9C ''art'':42C ''associ'':86C ''bad'':60C ''book'':38C ''classif'':76C ''cluster'':83C,85C ''comment'':5C ''d'':17C ''data'':51C ''describ'':30C ''descript'':72C,84C ''discoveri'':79C ''flach'':23C ''html'':63C ''lack'':12C ''layout'':61C ''learn'':40C,75C,81C ''like'':18C ''ll'':53C ''look'':34C ''machin'':39C ''make'':48C ''matrix'':28C ''mine'':88C ''model'':71C,73C ''nice'':26C ''p'':1C ''peter'':22C ''pre'':69C ''predict'':70C,82C ''regress'':77C ''reproduc'':54C ''reput'':14C ''rfoley'':7C ''rule'':87C ''scienc'':44C ''sens'':49C ''small'':27C ''sorri'':57C ''subgroup'':78C ''supervis'':74C ''tabl'':64C ''unsupervis'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8148, 2, '2015-09-21 21:02:47.13', 3, NULL, NULL, '&lt;p&gt;The basic rationale behind the following suggestion is to associate "eigenvectors" and "templates".&lt;/p&gt;

&lt;p&gt;In particular one could use LSA on the whole corpus based on a a bag-of-words. The resulting eigenvectors would serve as surrogate templates; these eigenvectors should not be directly affected by the number of words in each template. Subsequently the scores could be used to cluster the documents together following a standard procedure (eg. $k$-means in conjunction with AIC).  As an alternative to LSA one could use NNMF. Let me point out that the LSA (or NNMF) would probably need to be done to the transformed TF-IDF rather than the raw word-counts matrix.&lt;/p&gt;
', NULL, NULL, '''/p'':15C,118C ''affect'':49C ''aic'':79C ''altern'':82C ''associ'':11C ''bag'':32C ''bag-of-word'':31C ''base'':27C ''basic'':3C ''behind'':5C ''cluster'':65C ''conjunct'':77C ''corpus'':26C ''could'':20C,61C,86C ''count'':116C ''direct'':48C ''document'':67C ''done'':103C ''eg'':73C ''eigenvector'':12C,37C,44C ''follow'':7C,69C ''idf'':109C ''k'':74C ''let'':89C ''lsa'':22C,84C,95C ''matrix'':117C ''mean'':75C ''need'':100C ''nnmf'':88C,97C ''number'':52C ''one'':19C,85C ''p'':1C,16C ''particular'':18C ''point'':91C ''probabl'':99C ''procedur'':72C ''rather'':110C ''rational'':4C ''raw'':113C ''result'':36C ''score'':60C ''serv'':39C ''standard'':71C ''subsequ'':58C ''suggest'':8C ''surrog'':41C ''templat'':14C,42C,57C ''tf'':108C ''tf-idf'':107C ''togeth'':68C ''transform'':106C ''use'':21C,63C,87C ''whole'':25C ''word'':34C,54C,115C ''word-count'':114C ''would'':38C,98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8149, 2, '2015-09-21 21:50:09.343', 0, NULL, NULL, '&lt;p&gt;For your particular problem I''m not sure that using a supervised Logistic Regression approach is ideal, but I suppose that is a different and larger topic. To answer your question, yes you can use a "bag of words" representation of your text. Python''s &lt;code&gt;sci-kit learn&lt;/code&gt; library offers this functionality via both &lt;code&gt;CountVectorizer&lt;/code&gt; and &lt;code&gt;TfidfVectorizer&lt;/code&gt;. This will result in a sparse matrix representation of n-grams and occurrences of those n-grams in your corpus. From here you basically have 2 options, (1) training your supervised model directly on the sparse matrix or (2) reducing the dimension of your sparse matrix so it be represented as a dense matrix. Luckily sklearn offers functionality for both these, their &lt;code&gt;LogisticRegression&lt;/code&gt; class supports sparse matrices and their &lt;code&gt;TruncatedSVD&lt;/code&gt; implementation of PCA/LSA supports sparse matrices as well.&lt;/p&gt;

&lt;p&gt;That should give what you need from a technical perspective to build a model, but I think the real question is what feature engineering you may be able to do in addition to simply training on the bag of words representation of the text.&lt;/p&gt;
', NULL, NULL, '''/code'':52C,61C,65C,132C,141C ''/p'':150C,196C ''1'':95C ''2'':93C,106C ''abl'':179C ''addit'':183C ''answer'':30C ''approach'':16C ''bag'':38C,189C ''basic'':91C ''build'':163C ''class'':133C ''code'':47C,59C,63C,130C,139C ''corpus'':87C ''countvector'':60C ''dens'':120C ''differ'':25C ''dimens'':109C ''direct'':100C ''engin'':175C ''featur'':174C ''function'':56C,125C ''give'':154C ''gram'':77C,84C ''ideal'':18C ''implement'':142C ''kit'':50C ''larger'':27C ''learn'':51C ''librari'':53C ''logist'':14C ''logisticregress'':131C ''luckili'':122C ''m'':7C ''matric'':136C,147C ''matrix'':72C,104C,113C,121C ''may'':177C ''model'':99C,165C ''n'':76C,83C ''n-gram'':75C,82C ''need'':157C ''occurr'':79C ''offer'':54C,124C ''option'':94C ''p'':1C,151C ''particular'':4C ''pca/lsa'':144C ''perspect'':161C ''problem'':5C ''python'':45C ''question'':32C,171C ''real'':170C ''reduc'':107C ''regress'':15C ''repres'':117C ''represent'':41C,73C,192C ''result'':68C ''sci'':49C ''sci-kit'':48C ''simpli'':185C ''sklearn'':123C ''spars'':71C,103C,112C,135C,146C ''supervis'':13C,98C ''support'':134C,145C ''suppos'':21C ''sure'':9C ''technic'':160C ''text'':44C,195C ''tfidfvector'':64C ''think'':168C ''topic'':28C ''train'':96C,186C ''truncatedsvd'':140C ''use'':11C,36C ''via'':57C ''well'':149C ''word'':40C,191C ''yes'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8193, 1, '2015-09-24 22:16:03.753', 3, 87, 'replicability / reproducibility in topic modeling (LDA)', '&lt;p&gt;If I''m not wrong, topic modeling (LDA) is not replicable, i.e. it gives different results in different runs. Where does this come from (where does this randomness come from and why is it necessary?) and what can be done to solve this issue or gain more stability across runs?&lt;br&gt;
Thanks for the help.&lt;/p&gt;
', 'topic-model  lda', NULL, '''/p'':67C ''across'':60C ''br'':62C ''come'':34C,40C ''differ'':26C,29C ''done'':51C ''gain'':57C ''give'':25C ''help'':66C ''i.e'':23C ''issu'':55C ''lda'':6A,10B,19C ''m'':14C ''model'':5A,9B,18C ''necessari'':46C ''p'':11C ''random'':39C ''replic'':1A,22C ''reproduc'':2A ''result'':27C ''run'':30C,61C ''solv'':53C ''stabil'':59C ''thank'':63C ''topic'':4A,8B,17C ''topic-model'':7B ''wrong'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8151, 2, '2015-09-22 06:55:02.183', 3, NULL, NULL, '&lt;p&gt;I doubt any of the clustering algorithms will work well.&lt;/p&gt;

&lt;p&gt;Instead, you should look into:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;segmentation (yes, this is something different), specifically time series segmentation&lt;/li&gt;
&lt;li&gt;change detection (as you said, there is a rather constant distribution first, then a change, then a rather constant distribution again&lt;/li&gt;
&lt;li&gt;segment-wise regression may also work: try to find the best fit that is constant, linearly changing, and constant again. It''s essentially four parameters to optimize in this restricted model: average before and after + beginning and end of transition.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':32C,55C,98C ''/p'':12C,19C ''/ul'':99C ''algorithm'':8C ''also'':62C ''averag'':89C ''begin'':93C ''best'':68C ''chang'':34C,48C,74C ''cluster'':7C ''constant'':43C,52C,72C,76C ''detect'':35C ''differ'':27C ''distribut'':44C,53C ''doubt'':3C ''end'':95C ''essenti'':80C ''find'':66C ''first'':45C ''fit'':69C ''four'':81C ''instead'':14C ''li'':21C,33C,56C ''linear'':73C ''look'':17C ''may'':61C ''model'':88C ''optim'':84C ''p'':1C,13C ''paramet'':82C ''rather'':42C,51C ''regress'':60C ''restrict'':87C ''said'':38C ''segment'':22C,31C,58C ''segment-wis'':57C ''seri'':30C ''someth'':26C ''specif'':28C ''time'':29C ''transit'':97C ''tri'':64C ''ul'':20C ''well'':11C ''wise'':59C ''work'':10C,63C ''yes'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8152, 2, '2015-09-22 07:42:59.59', 1, NULL, NULL, '&lt;p&gt;Typically, quantitative analysis is planned and performed, based on research study''s goals. Focusing on research goals and corresponding research questions, researcher would propose a model (or several models) and a set of hypotheses, associated with the model(s). Model(s) and its/their elements'' types usually dictate (suggest) quantitative approaches that would make sense in a particular situation. For example, if your model includes latent variables, you would have to use appropriate methods to perform data analysis (i.e., structural equation modeling). Otherwise, you can apply a variety of other methods, such as time series analysis or, as you mentioned, multiple regression and machine learning. For more details on research workflow with latent variables, also see section #3 in &lt;a href="http://datascience.stackexchange.com/a/1006/2452"&gt;my relevant answer&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;One last note: whatever methods you use, pay enough attention to the following two very important aspects - performing full-scale exploratory data analysis (EDA) (see &lt;a href="http://datascience.stackexchange.com/a/5095/2452"&gt;my relevant answer&lt;/a&gt;) and trying to design and perform your analysis in the reproducible research fashion (see &lt;a href="http://datascience.stackexchange.com/a/759/2452"&gt;my relevant answer&lt;/a&gt;).&lt;/p&gt;
', NULL, NULL, '''/a'':128C,165C,188C ''/a/1006/2452'':124C ''/a/5095/2452'':161C ''/a/759/2452'':184C ''/p'':129C,189C ''3'':118C ''also'':115C ''analysi'':4C,78C,96C,154C,173C ''answer'':127C,164C,187C ''appli'':86C ''approach'':51C ''appropri'':73C ''aspect'':147C ''associ'':36C ''attent'':140C ''base'':9C ''correspond'':20C ''data'':77C,153C ''datascience.stackexchange.com'':123C,160C,183C ''datascience.stackexchange.com/a/1006/2452'':122C ''datascience.stackexchange.com/a/5095/2452'':159C ''datascience.stackexchange.com/a/759/2452'':182C ''design'':169C ''detail'':108C ''dictat'':48C ''eda'':155C ''element'':45C ''enough'':139C ''equat'':81C ''exampl'':61C ''exploratori'':152C ''fashion'':178C ''focus'':15C ''follow'':143C ''full'':150C ''full-scal'':149C ''goal'':14C,18C ''href'':121C,158C,181C ''hypothes'':35C ''i.e'':79C ''import'':146C ''includ'':65C ''its/their'':44C ''last'':132C ''latent'':66C,113C ''learn'':105C ''machin'':104C ''make'':54C ''mention'':100C ''method'':74C,91C,135C ''model'':27C,30C,39C,41C,64C,82C ''multipl'':101C ''note'':133C ''one'':131C ''otherwis'':83C ''p'':1C,130C ''particular'':58C ''pay'':138C ''perform'':8C,76C,148C,171C ''plan'':6C ''propos'':25C ''quantit'':3C,50C ''question'':22C ''regress'':102C ''relev'':126C,163C,186C ''reproduc'':176C ''research'':11C,17C,21C,23C,110C,177C ''scale'':151C ''section'':117C ''see'':116C,156C,179C ''sens'':55C ''seri'':95C ''set'':33C ''sever'':29C ''situat'':59C ''structur'':80C ''studi'':12C ''suggest'':49C ''time'':94C ''tri'':167C ''two'':144C ''type'':46C ''typic'':2C ''use'':72C,137C ''usual'':47C ''variabl'':67C,114C ''varieti'':88C ''whatev'':134C ''workflow'':111C ''would'':24C,53C,69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8154, 2, '2015-09-22 10:39:40.333', 1, NULL, NULL, '&lt;p&gt;Your problem looks more like a ranking problem than a classification problem to me. Have you tried a more naive method, like a 1-NN "classifier" with unigram text representation, Tf-Idf term weighting and a cosine similarity metric? It''s far from the state of the art but it tends to give rather good results in retrieval and recommendation tasks.&lt;/p&gt;
', NULL, NULL, '''/p'':64C ''1'':25C ''art'':50C ''classif'':12C ''classifi'':27C ''cosin'':39C ''far'':44C ''give'':55C ''good'':57C ''idf'':34C ''like'':6C,23C ''look'':4C ''method'':22C ''metric'':41C ''naiv'':21C ''nn'':26C ''p'':1C ''problem'':3C,9C,13C ''rank'':8C ''rather'':56C ''recommend'':62C ''represent'':31C ''result'':58C ''retriev'':60C ''similar'':40C ''state'':47C ''task'':63C ''tend'':53C ''term'':35C ''text'':30C ''tf'':33C ''tf-idf'':32C ''tri'':18C ''unigram'':29C ''weight'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8156, 1, '2015-09-22 19:53:12.657', 1, 67, 'Reference of SVM Using Spark', '&lt;p&gt;Can somebody please give me some reference on implementing SVM using PySpark.&lt;/p&gt;
', 'apache-spark', NULL, '''/p'':22C ''apach'':7B ''apache-spark'':6B ''give'':13C ''implement'':18C ''p'':9C ''pleas'':12C ''pyspark'':21C ''refer'':1A,16C ''somebodi'':11C ''spark'':5A,8B ''svm'':3A,19C ''use'':4A,20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8157, 1, '2015-09-22 20:24:47.313', 1, 101, 'How to generate bootstrapping samples in R?', '&lt;p&gt;Here is my original dataset from a voting activity. Each participant voted for one option (A,B,C,D,E,F) listed below. The numerical value is the total number of participants who voted for each option.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/olSQL.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/olSQL.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I want to bootstrap the voting for 1000 times &lt;strong&gt;(sample with replacement)&lt;/strong&gt; and make a comparison between the pre-event and post-event voting for each category using independent sample t-test.&lt;/p&gt;

&lt;p&gt;However, I don''t know how to start with generating the 1000 bootstrapping samples...I have the "Boot" packages installed on R. Could anyone give me a hint what kind of steps I should take in order to generate 1000 bootstrap samples based on the original data I provide above?&lt;/p&gt;
', 'r  statistics  data', NULL, '''/a'':68C ''/olsql.png'':55C,62C ''/p'':49C,69C,108C,160C ''/strong'':84C ''1000'':78C,121C,149C ''activ'':20C ''alt'':63C ''anyon'':133C ''b'':28C ''base'':152C ''boot'':127C ''bootstrap'':4A,74C,122C,150C ''c'':29C ''categori'':101C ''comparison'':88C ''could'':132C ''d'':30C ''data'':10B,156C ''dataset'':16C ''descript'':66C ''e'':31C ''enter'':64C ''event'':93C,97C ''f'':32C ''generat'':3A,119C,148C ''give'':134C ''hint'':137C ''howev'':110C ''href'':52C ''i.stack.imgur.com'':54C,61C ''i.stack.imgur.com/olsql.png'':53C,60C ''imag'':65C ''img'':58C ''independ'':103C ''instal'':129C ''kind'':139C ''know'':114C ''list'':33C ''make'':86C ''nofollow'':57C ''number'':41C ''numer'':36C ''one'':25C ''option'':26C,48C ''order'':146C ''origin'':15C,155C ''p'':11C,50C,70C,109C ''packag'':128C ''particip'':22C,43C ''post'':96C ''post-ev'':95C ''pre'':92C ''pre-ev'':91C ''provid'':158C ''r'':7A,8B,131C ''rel'':56C ''replac'':83C ''sampl'':5A,81C,104C,123C,151C ''src'':59C ''start'':117C ''statist'':9B ''step'':141C ''strong'':80C ''t-test'':105C ''take'':144C ''test'':107C ''time'':79C ''total'':40C ''use'':102C ''valu'':37C ''vote'':19C,23C,45C,76C,98C ''want'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9365, 2, '2015-12-13 17:06:10.41', 1, NULL, NULL, '&lt;p&gt;You could use the caret package to do hyperparameter space search, either through a &lt;a href="http://topepo.github.io/caret/training.html#custom" rel="nofollow"&gt;grid search&lt;/a&gt; , or through &lt;a href="http://topepo.github.io/caret/random.html" rel="nofollow"&gt;random search&lt;/a&gt;. &lt;/p&gt;
', NULL, NULL, '''/a'':25C,37C ''/caret/random.html'':32C ''/caret/training.html#custom'':20C ''/p'':38C ''caret'':6C ''could'':3C ''either'':13C ''grid'':23C ''href'':17C,29C ''hyperparamet'':10C ''nofollow'':22C,34C ''p'':1C ''packag'':7C ''random'':35C ''rel'':21C,33C ''search'':12C,24C,36C ''space'':11C ''topepo.github.io'':19C,31C ''topepo.github.io/caret/random.html'':30C ''topepo.github.io/caret/training.html#custom'':18C ''use'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8159, 2, '2015-09-23 00:01:05.647', 3, NULL, NULL, '&lt;p&gt;The Dataframe Python API exposes the RDD of a Dataframe by calling the following :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;df.rdd # you can save it, perform transformations of course, etc. 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;df.rdd returns the content as an pyspark.RDD of Row.&lt;/p&gt;

&lt;p&gt;You can then map on that RDD of Row transforming every Row into a &lt;code&gt;numpy&lt;/code&gt; vector. I can''t be more specific about the transformation since I don''t know what your vector represents with the information given. &lt;/p&gt;

&lt;p&gt;Note 1: &lt;code&gt;df&lt;/code&gt;is the variable define our Dataframe. &lt;/p&gt;

&lt;p&gt;Note 2: this function is available since Spark 1.3&lt;/p&gt;
', NULL, NULL, '''/code'':29C,59C,89C ''/p'':16C,41C,83C,96C,107C ''/pre'':30C ''1'':86C ''1.3'':106C ''2'':99C ''api'':5C ''avail'':103C ''call'':13C ''code'':18C,57C,87C ''content'':35C ''cours'':27C ''datafram'':3C,11C,95C ''defin'':93C ''df'':88C ''df.rdd'':19C,32C ''etc'':28C ''everi'':53C ''expos'':6C ''follow'':15C ''function'':101C ''given'':82C ''inform'':81C ''know'':74C ''map'':46C ''note'':85C,98C ''numpi'':58C ''p'':1C,31C,42C,84C,97C ''perform'':24C ''pre'':17C ''pyspark.rdd'':38C ''python'':4C ''rdd'':8C,49C ''repres'':78C ''return'':33C ''row'':40C,51C,54C ''save'':22C ''sinc'':70C,104C ''spark'':105C ''specif'':66C ''transform'':25C,52C,69C ''variabl'':92C ''vector'':60C,77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8160, 2, '2015-09-23 03:47:13.19', 2, NULL, NULL, '&lt;p&gt;The &lt;a href="http://spark.apache.org/docs/latest/mllib-linear-methods.html#linear-support-vector-machines-svms" rel="nofollow"&gt;documentation&lt;/a&gt; of Apache Spark''s MLib library has a neat and clear reference to the implementation of linear SVM''s in Python.&lt;/p&gt;

&lt;p&gt;It supports two models: &lt;code&gt;SVMWithSGD&lt;/code&gt; and &lt;code&gt;SVMModel&lt;/code&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':11C ''/code'':41C,45C ''/docs/latest/mllib-linear-methods.html#linear-support-vector-machines-svms'':7C ''/p'':33C,46C ''apach'':13C ''clear'':22C ''code'':39C,43C ''document'':10C ''href'':4C ''implement'':26C ''librari'':17C ''linear'':28C ''mlib'':16C ''model'':38C ''neat'':20C ''nofollow'':9C ''p'':1C,34C ''python'':32C ''refer'':23C ''rel'':8C ''spark'':14C ''spark.apache.org'':6C ''spark.apache.org/docs/latest/mllib-linear-methods.html#linear-support-vector-machines-svms'':5C ''support'':36C ''svm'':29C ''svmmodel'':44C ''svmwithsgd'':40C ''two'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8161, 1, '2015-09-23 06:47:48.473', 1, 89, 'Creating validation data for model comparison', '&lt;p&gt;I am working on building a scoring algorithm for student data,&lt;/p&gt;

&lt;p&gt;say the attributes are :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;name, location, age, class, school_name, skill1, skill2, skill3 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;based on these data I need to create a student score.&lt;/p&gt;

&lt;p&gt;I need to assign weight-ages for age, class, school_name skills and come up with a score for student.&lt;/p&gt;

&lt;p&gt;say I have 2 scoring models like :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;score_1 = x1*location_weight + x2*age_weight + x2*class_weight + x3*school_name_weight + x4*skill1_weight + x5*skill2_weight + x6*skill3_weight

score_2 = y1*location_weight + y2*age_weight + y2*class_weight + y3*school_name_weight + y4*skill1_weight + y5*skill2_weight + y6*skill3_weight  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;now how can I compare these models and evaluate them?&lt;/p&gt;

&lt;p&gt;The problem is I don''t have a test or validation set to prove or compare how accurate each of these model is, so in this case what is the best approach to compare and validate different models? also what is the best ways to build a validation set from scratch?&lt;/p&gt;
', 'dataset  categorical-data  scoring  normalization  weighted-data', NULL, '''/code'':46C,143C ''/p'':28C,34C,60C,83C,92C,156C,209C ''/pre'':47C,144C ''1'':96C ''2'':88C,120C ''accur'':175C ''age'':39C,68C,70C,101C,125C ''algorithm'':24C ''also'':196C ''approach'':189C ''assign'':65C ''attribut'':32C ''base'':49C ''best'':188C,200C ''build'':21C,203C ''case'':184C ''categor'':9B ''categorical-data'':8B ''class'':40C,71C,104C,128C ''code'':36C,94C ''come'':76C ''compar'':150C,173C,191C ''comparison'':6A ''creat'':1A,56C ''data'':3A,10B,15B,27C,52C ''dataset'':7B ''differ'':194C ''evalu'':154C ''like'':91C ''locat'':38C,98C,122C ''model'':5A,90C,152C,179C,195C ''name'':37C,42C,73C,108C,132C ''need'':54C,63C ''normal'':12B ''p'':16C,29C,48C,61C,84C,145C,157C ''pre'':35C,93C ''problem'':159C ''prove'':171C ''say'':30C,85C ''school'':41C,72C,107C,131C ''score'':11B,23C,59C,80C,89C,95C,119C ''scratch'':208C ''set'':169C,206C ''skill'':74C ''skill1'':43C,111C,135C ''skill2'':44C,114C,138C ''skill3'':45C,117C,141C ''student'':26C,58C,82C ''test'':166C ''valid'':2A,168C,193C,205C ''way'':201C ''weight'':14B,67C,99C,102C,105C,109C,112C,115C,118C,123C,126C,129C,133C,136C,139C,142C ''weight-ag'':66C ''weighted-data'':13B ''work'':19C ''x1'':97C ''x2'':100C,103C ''x3'':106C ''x4'':110C ''x5'':113C ''x6'':116C ''y1'':121C ''y2'':124C,127C ''y3'':130C ''y4'':134C ''y5'':137C ''y6'':140C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8162, 1, '2015-09-23 07:37:43.03', 0, 66, 'R plot(surv(), newdata=...) draws same lines many times - why?', '&lt;p&gt;I''m new to R and cannot make plotting work as desired. The problem is that R seems to draw the same four lines over and over again, redundantly. The details of the case I''m having are as follows.&lt;/p&gt;

&lt;p&gt;I have a dataset:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; str(dataset)
''data.frame'':   57641 obs. of  3 variables:
 $ duration : num  3 8 7 2 4 8 2 2 8 8 ...
 $ graduated: logi  FALSE TRUE TRUE FALSE FALSE TRUE ...
 $ group    : num  651 651 671 671 651 651 651 651 651 651 ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then, I fit a Cox proportional hazards regression model to it:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;survObj &amp;lt;- Surv(time = dataset$duration / 2, event = dataset$graduated)
model &amp;lt;- coxph(survObj ~ group, data=dataset)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Next, following &lt;a href="https://rpubs.com/daspringate/survival" rel="nofollow"&gt;this&lt;/a&gt; example, I create a frame that would hopefully group the survival functions by the group number:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;frame &amp;lt;- data.frame(group = dataset$group)

&amp;gt; str(frame)
''data.frame'':   57641 obs. of  1 variable:
 $ group: num  651 651 671 671 651 651 651 651 651 651 ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;There''s four groups in the data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; unique(dataset$group)
[1] 651 671 652 681
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Using this new frame, I create a fitted survival model:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;fitObjGrouped &amp;lt;- survfit(model, newdata = frame)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Finally, I plot the thing:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;color_set &amp;lt;- rainbow(4)
plot(fitObjGrouped, col=color_set)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The result has the correct lines, but drawn many times over each other:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/r5tiz.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/r5tiz.jpg" alt="Survival model plotted - overlapping lines"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;As you can see, there''s two red lines and two blue lines drawn last. They''re the correct ones, one for each category, but closer observation reveals that there''s a green or other color lines underneath each of them. When converting this to PDF the file size is 273 times larger than what it should be!&lt;/p&gt;

&lt;p&gt;So the question is: why is R drawing the lines so many many times and how could I achieve correct model fitting and plotting at the same time?&lt;/p&gt;

&lt;p&gt;Can somebody please help me to better understand the R commands I''m using? Thanks in advance!&lt;/p&gt;
', 'r  survival-analysis', NULL, '''/a'':152C,300C ''/code'':105C,139C,199C,221C,243C,264C ''/daspringate/survival'':148C ''/p'':55C,61C,119C,169C,209C,234C,251C,280C,301C,361C,391C,410C ''/pre'':106C,140C,200C,222C,244C,265C ''/r5tiz.jpg'':286C,293C ''1'':185C,216C ''2'':78C,81C,82C,128C ''273'':353C ''3'':71C,75C ''4'':79C,258C ''57641'':68C,182C ''651'':95C,96C,99C,100C,101C,102C,103C,104C,189C,190C,193C,194C,195C,196C,197C,198C,217C ''652'':219C ''671'':97C,98C,191C,192C,218C ''681'':220C ''7'':77C ''8'':76C,80C,83C,84C ''achiev'':381C ''advanc'':409C ''alt'':294C ''analysi'':13B ''better'':399C ''blue'':314C ''cannot'':21C ''case'':48C ''categori'':326C ''closer'':328C ''code'':63C,121C,171C,211C,236C,253C ''col'':261C ''color'':254C,262C,338C ''command'':403C ''convert'':345C ''correct'':271C,321C,382C ''could'':379C ''cox'':112C ''coxph'':134C ''creat'':155C,229C ''data'':137C,208C ''data.frame'':67C,174C,181C ''dataset'':60C,66C,126C,130C,138C,176C,214C ''desir'':26C ''detail'':45C ''draw'':5A,34C,370C ''drawn'':274C,316C ''durat'':73C,127C ''event'':129C ''exampl'':153C ''fals'':87C,90C,91C ''file'':350C ''final'':246C ''fit'':110C,231C,384C ''fitobjgroup'':237C,260C ''follow'':54C,143C ''four'':37C,204C ''frame'':157C,172C,180C,227C,242C ''function'':164C ''graduat'':85C,131C ''green'':335C ''group'':93C,136C,161C,167C,175C,177C,187C,205C,215C ''gt'':64C,178C,212C ''hazard'':114C ''help'':396C ''hope'':160C ''href'':145C,283C ''i.stack.imgur.com'':285C,292C ''i.stack.imgur.com/r5tiz.jpg'':284C,291C ''img'':289C ''larger'':355C ''last'':317C ''line'':7A,38C,272C,299C,311C,315C,339C,372C ''logi'':86C ''lt'':123C,133C,173C,238C,256C ''m'':16C,50C,405C ''make'':22C ''mani'':8A,275C,374C,375C ''model'':116C,132C,233C,240C,296C,383C ''new'':17C,226C ''newdata'':4A,241C ''next'':142C ''nofollow'':150C,288C ''num'':74C,94C,188C ''number'':168C ''ob'':69C,183C ''observ'':329C ''one'':322C,323C ''overlap'':298C ''p'':14C,56C,107C,141C,201C,223C,245C,266C,281C,302C,362C,392C ''pdf'':348C ''pleas'':395C ''plot'':2A,23C,248C,259C,297C,386C ''pre'':62C,120C,170C,210C,235C,252C ''problem'':28C ''proport'':113C ''question'':365C ''r'':1A,10B,19C,31C,369C,402C ''rainbow'':257C ''re'':319C ''red'':310C ''redund'':43C ''regress'':115C ''rel'':149C,287C ''result'':268C ''reveal'':330C ''rpubs.com'':147C ''rpubs.com/daspringate/survival'':146C ''see'':306C ''seem'':32C ''set'':255C,263C ''size'':351C ''somebodi'':394C ''src'':290C ''str'':65C,179C ''surv'':3A,124C ''survfit'':239C ''surviv'':12B,163C,232C,295C ''survival-analysi'':11B ''survobj'':122C,135C ''thank'':407C ''thing'':250C ''time'':9A,125C,276C,354C,376C,390C ''true'':88C,89C,92C ''two'':309C,313C ''underneath'':340C ''understand'':400C ''uniqu'':213C ''use'':224C,406C ''variabl'':72C,186C ''work'':24C ''would'':159C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8163, 2, '2015-09-23 08:05:21.247', 3, NULL, NULL, '&lt;p&gt;RMSProp is indeed an unpublished method, and in the lecture Geoffrey Hinton gives just the general idea behind RMSProp - to divide the gradient by a moving average of the gradient magnitude. You can watch the lecture here:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://youtu.be/O3sxAc4hxZU" rel="nofollow"&gt;https://youtu.be/O3sxAc4hxZU&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;When this principle is applied to Stochastic Gradient Descent, the update rule you showed is obtained. Since Hinton did not propose an exact algorithm, this principle has been applied to different optimization methods. I agree it''s confusing all these methods call themselves RMSProp.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;climin&lt;/code&gt; implements RMSProp with Nesterov momentum. Momentum methods try to avoid the oscillation that often happens with SGD by slowly changing the current direction of updates. The algorithm given in &lt;code&gt;climin&lt;/code&gt; documentation introduces the $\beta$ parameter that controls how much of the previous update direction is retained. Nesterov momentum is implemented by first taking a step towards the previous update direction $v_t$, calculating gradient at that position, using the gradient to obtain the new update direction $v_{t+1}$, and finally updating the parameters. The &lt;code&gt;climin&lt;/code&gt; implementation also includes the smoothing term $\iota$ inside the square root for stability (1e-8), even though it''s not mentioned in the documentation.&lt;/p&gt;

&lt;p&gt;The implementation in the Downhill library is based on the algorithm described in the paper by A. Graves. It is not mentioned in the article why the square of the average gradient is subtracted from the average square gradient. I''ve seen this done sometimes when RMSProp is implemented with a momentum method, but I''m not sure why.&lt;/p&gt;
', NULL, NULL, '''+1'':181C ''/code'':100C,132C,190C ''/o3sxac4hxzu'':45C ''/o3sxac4hxzu&lt;/a&gt;&lt;/p&gt;'':50C ''/p'':39C,96C,214C,275C ''1e-8'':204C ''agre'':86C ''algorithm'':75C,127C,226C ''also'':192C ''appli'':56C,80C ''articl'':240C ''averag'':28C,246C,252C ''avoid'':110C ''base'':223C ''behind'':19C ''beta'':136C ''calcul'':165C ''call'':93C ''chang'':120C ''climin'':99C,131C,189C ''code'':98C,130C,188C ''confus'':89C ''control'':139C ''current'':122C ''descent'':60C ''describ'':227C ''differ'':82C ''direct'':123C,146C,162C,178C ''divid'':22C ''document'':133C,213C ''done'':259C ''downhil'':220C ''even'':205C ''exact'':74C ''final'':183C ''first'':154C ''general'':17C ''geoffrey'':12C ''give'':14C ''given'':128C ''gradient'':24C,31C,59C,166C,172C,247C,254C ''grave'':233C ''happen'':115C ''hinton'':13C,69C ''href'':42C ''idea'':18C ''implement'':101C,152C,191C,217C,264C ''includ'':193C ''inde'':4C ''insid'':198C ''introduc'':134C ''iota'':197C ''lectur'':11C,37C ''librari'':221C ''m'':271C ''magnitud'':32C ''mention'':210C,237C ''method'':7C,84C,92C,107C,268C ''momentum'':105C,106C,150C,267C ''move'':27C ''much'':141C ''nesterov'':104C,149C ''new'':176C ''nofollow'':47C ''obtain'':67C,174C ''often'':114C ''optim'':83C ''oscil'':112C ''p'':1C,40C,51C,97C,215C ''paper'':230C ''paramet'':137C,186C ''posit'':169C ''previous'':144C,160C ''principl'':54C,77C ''propos'':72C ''rel'':46C ''retain'':148C ''rmsprop'':2C,20C,95C,102C,262C ''root'':201C ''rule'':63C ''seen'':257C ''sgd'':117C ''show'':65C ''sinc'':68C ''slowli'':119C ''smooth'':195C ''sometim'':260C ''squar'':200C,243C,253C ''stabil'':203C ''step'':157C ''stochast'':58C ''subtract'':249C ''sure'':273C ''take'':155C ''term'':196C ''though'':206C ''toward'':158C ''tri'':108C ''unpublish'':6C ''updat'':62C,125C,145C,161C,177C,184C ''use'':170C ''v'':163C,179C ''ve'':256C ''watch'':35C ''youtu.be'':44C,49C ''youtu.be/o3sxac4hxzu'':43C ''youtu.be/o3sxac4hxzu&lt;/a&gt;&lt;/p&gt;'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9554, 1, '2015-12-30 09:37:40.547', 7, 422, 'Open source data science projects to contribute', '&lt;p&gt;Contribution into open source projects is typically a good way to get some practice for newbies, and try a new area for experienced data scientists and analysts.&lt;/p&gt;

&lt;p&gt;Which projects do you contribute? Please provide some intro + link on Github.&lt;/p&gt;
', 'beginner  open-source  github', NULL, '''/p'':41C,55C ''analyst'':40C ''area'':34C ''beginn'':8B ''contribut'':7A,14C,47C ''data'':3A,37C ''experienc'':36C ''get'':25C ''github'':12B,54C ''good'':22C ''intro'':51C ''link'':52C ''new'':33C ''newbi'':29C ''open'':1A,10B,16C ''open-sourc'':9B ''p'':13C,42C ''pleas'':48C ''practic'':27C ''project'':5A,18C,44C ''provid'':49C ''scienc'':4A ''scientist'':38C ''sourc'':2A,11B,17C ''tri'':31C ''typic'':20C ''way'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8164, 1, '2015-09-23 08:16:45.387', 0, 121, 'what is nn.index mean in KNN output', '&lt;p&gt;I am getting &lt;code&gt;attr(, "nn.index")&lt;/code&gt; as part of my KNN output in R. What is meant by that and how is this value getting calculated?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;knn.pred &amp;lt;- knn(tdm.stack.nl_train, tdm.stack.nl_Test, tdm.cand_train)
print(knn.pred)

&amp;gt; knn.pred


[1] Silent      Silent      Silent      Silent      Silent      Silent      Silent     
 [8] Silent      Silent      Silent    


attr(,"nn.index")


      [,1]
[1,]  292  
[2,]  292  
[3,]  343  
[4,]  444  
[5,]  250  
[6,]  445  
[7,]  270  
[8,]  228  
[9,]  302  
[10,]  355  
&lt;/code&gt;&lt;/pre&gt;
', 'r  classification', NULL, '''/code'':17C,89C ''/p'':38C ''/pre'':90C ''1'':54C,68C,69C ''10'':87C ''2'':71C ''228'':84C ''250'':78C ''270'':82C ''292'':70C,72C ''3'':73C ''302'':86C ''343'':74C ''355'':88C ''4'':75C ''444'':76C ''445'':80C ''5'':77C ''6'':79C ''7'':81C ''8'':62C,83C ''9'':85C ''attr'':15C,66C ''calcul'':37C ''classif'':9B ''code'':14C,40C ''get'':13C,36C ''gt'':52C ''knn'':6A,22C,43C ''knn.pred'':41C,51C,53C ''lt'':42C ''mean'':4A ''meant'':28C ''nn.index'':3A,16C,67C ''output'':7A,23C ''p'':10C ''part'':19C ''pre'':39C ''print'':50C ''r'':8B,25C ''silent'':55C,56C,57C,58C,59C,60C,61C,63C,64C,65C ''tdm.cand'':48C ''tdm.stack.nl'':44C,46C ''test'':47C ''train'':45C,49C ''valu'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8165, 2, '2015-09-23 09:26:46.42', -1, NULL, NULL, '&lt;p&gt;I would start by fixing a seed (so that results are reproducible), then selecting a subsample (say, 10pct of data) and saving that as validation set. If you''re using R, check the caret package &lt;/p&gt;

&lt;p&gt;&lt;a href="https://cran.r-project.org/web/packages/caret/index.html" rel="nofollow"&gt;https://cran.r-project.org/web/packages/caret/index.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;and if Python is your weapon of choice, familiarize yourself with the sklearn documentation in 3.1:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/modules/cross_validation.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/cross_validation.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Both of those have multiple functions that might come in handy. If your computational resources allow it, I would suggest a cross-validation procedure (evaluating a model on multiple training-validation splits), both of the links I gave contain references to multiple functions that can help you achieve that. &lt;/p&gt;
', NULL, NULL, '''/p'':37C,66C,130C ''/stable/modules/cross_validation.html'':72C ''/stable/modules/cross_validation.html&lt;/a&gt;&lt;/p&gt;'':77C ''/web/packages/caret/index.html'':43C ''/web/packages/caret/index.html&lt;/a&gt;&lt;/p&gt;'':48C ''10pct'':19C ''3.1'':65C ''achiev'':128C ''allow'':94C ''caret'':35C ''check'':33C ''choic'':57C ''come'':87C ''comput'':92C ''contain'':119C ''cran.r-project.org'':42C,47C ''cran.r-project.org/web/packages/caret/index.html'':41C ''cran.r-project.org/web/packages/caret/index.html&lt;/a&gt;&lt;/p&gt;'':46C ''cross'':101C ''cross-valid'':100C ''data'':21C ''document'':63C ''evalu'':104C ''familiar'':58C ''fix'':6C ''function'':84C,123C ''gave'':118C ''handi'':89C ''help'':126C ''href'':40C,69C ''link'':116C ''might'':86C ''model'':106C ''multipl'':83C,108C,122C ''nofollow'':45C,74C ''p'':1C,38C,49C,67C,78C ''packag'':36C ''procedur'':103C ''python'':52C ''r'':32C ''re'':30C ''refer'':120C ''rel'':44C,73C ''reproduc'':13C ''resourc'':93C ''result'':11C ''save'':23C ''say'':18C ''scikit-learn.org'':71C,76C ''scikit-learn.org/stable/modules/cross_validation.html'':70C ''scikit-learn.org/stable/modules/cross_validation.html&lt;/a&gt;&lt;/p&gt;'':75C ''seed'':8C ''select'':15C ''set'':27C ''sklearn'':62C ''split'':112C ''start'':4C ''subsampl'':17C ''suggest'':98C ''train'':110C ''training-valid'':109C ''use'':31C ''valid'':26C,102C,111C ''weapon'':55C ''would'':3C,97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8170, 2, '2015-09-23 11:34:31.4', 4, NULL, NULL, '&lt;p&gt;Predicting and scoring are two different tasks.&lt;/p&gt;

&lt;p&gt;And according to your answers and comments you are not solving prediction problem. You just want to set to each student a number in range [1,100] according to some rule. This is &lt;strong&gt;ranking&lt;/strong&gt; (or scoring, whatever).&lt;/p&gt;

&lt;p&gt;Therefore, the terms #prediction_model, #accuracy, #validation, #training_set are &lt;strong&gt;out of this scope&lt;/strong&gt;. You don''t need to validate anything. You are not making predictions.&lt;/p&gt;

&lt;p&gt;What you want is to map ranks to students.&lt;/p&gt;

&lt;p&gt;But a problem is that you have mostely categorical data (school name, location etc) that cannot be ''ranked''. Some of them are useless at all: how does the student name refer to his school progress? :)&lt;/p&gt;

&lt;p&gt;If you change it somehow to numerical (e.g. ''Skill_1_level'', ''Skill_2_level'', ''remoteness_of_location'', ''school rank'' etc) than you can do some ranking:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Normalize data: each of your factors&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/cdJkC.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/cdJkC.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;ol start="2"&gt;
&lt;li&gt;Multiple by 100, as you want [0,100] range instead of [0,1]&lt;/li&gt;
&lt;li&gt;Set up weights based on your experience according to factor''s importance. So that the sum of weights is 1.&lt;/li&gt;
&lt;li&gt;And finally build a rank (score):&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;Rank = 0.1 * skill_1_level + 0.2 * skill_2_level + 0.05 * remoteness_of_location + 0.5 * school_rank + ...&lt;/strong&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':185C ''/cdjkc.png'':172C,179C ''/li'':165C,204C,226C,234C ''/ol'':166C,235C ''/p'':9C,50C,80C,91C,128C,156C,186C,255C ''/strong'':46C,67C,254C ''0'':197C,202C ''0.05'':247C ''0.1'':239C ''0.2'':243C ''0.5'':251C ''1'':36C,139C,203C,225C,241C ''100'':37C,193C,198C ''2'':142C,189C,245C ''accord'':12C,38C,213C ''accuraci'':57C ''alt'':180C ''answer'':15C ''anyth'':74C ''base'':209C ''build'':230C ''cannot'':108C ''categor'':101C ''chang'':132C ''comment'':17C ''data'':102C,160C ''descript'':183C ''differ'':7C ''e.g'':137C ''enter'':181C ''etc'':106C,149C ''experi'':212C ''factor'':164C,215C ''final'':229C ''href'':169C ''i.stack.imgur.com'':171C,178C ''i.stack.imgur.com/cdjkc.png'':170C,177C ''imag'':182C ''img'':175C ''import'':217C ''instead'':200C ''level'':140C,143C,242C,246C ''li'':158C,190C,205C,227C ''locat'':105C,146C,250C ''make'':78C ''map'':87C ''model'':56C ''most'':100C ''multipl'':191C ''name'':104C,122C ''need'':71C ''nofollow'':174C ''normal'':159C ''number'':33C ''numer'':136C ''ol'':157C,187C ''p'':1C,10C,51C,81C,92C,129C,167C,236C ''predict'':2C,22C,55C,79C ''problem'':23C,95C ''progress'':127C ''rang'':35C,199C ''rank'':45C,88C,110C,148C,155C,232C,238C,253C ''refer'':123C ''rel'':173C ''remot'':144C,248C ''rule'':41C ''school'':103C,126C,147C,252C ''scope'':66C ''score'':4C,48C,233C ''set'':28C,60C,206C ''skill'':138C,141C,240C,244C ''solv'':21C ''somehow'':134C ''src'':176C ''start'':188C ''strong'':44C,62C,237C ''student'':31C,90C,121C ''sum'':221C ''task'':8C ''term'':54C ''therefor'':52C ''train'':59C ''two'':6C ''useless'':115C ''valid'':58C,73C ''want'':26C,84C,196C ''weight'':208C,223C ''whatev'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8171, 2, '2015-09-23 14:02:10.54', 1, NULL, NULL, '&lt;p&gt;I can''t check at the moment (no Matlab at hand), but I suppose the differences come from the different random seeds used to initialize the neural networks (at least this is the only part which i can think of that has a random component). I would suggest predicting class probabilities, averaging those and then viewing the resulting confusion matrix of the "averaged" prediction. This way you - to a degree - mitigating the effect of randomness resulting from different initializations of the weights.&lt;/p&gt;
', NULL, NULL, '''/p'':84C ''averag'':53C,64C ''check'':5C ''class'':51C ''come'':18C ''compon'':46C ''confus'':60C ''degre'':71C ''differ'':17C,21C,79C ''effect'':74C ''hand'':12C ''initi'':26C,80C ''least'':31C ''matlab'':10C ''matrix'':61C ''mitig'':72C ''moment'':8C ''network'':29C ''neural'':28C ''p'':1C ''part'':36C ''predict'':50C,65C ''probabl'':52C ''random'':22C,45C,76C ''result'':59C,77C ''seed'':23C ''suggest'':49C ''suppos'':15C ''think'':40C ''use'':24C ''view'':57C ''way'':67C ''weight'':83C ''would'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8731, 1, '2015-11-05 17:06:27.647', 3, 107, 'How to model user''s buying behavior on Amazon?', '&lt;p&gt;For our final course project in Data Science, we proposed the following-&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Give the &lt;a href="http://jmcauley.ucsd.edu/data/amazon/" rel="nofollow"&gt;Amazon Reviews Dataset&lt;/a&gt;, we plan to come up with an algorithm (thats roughly based on Personalized PageRank) that determines a strategic position for placing ads on Amazon. For example, there are millions of products on Amazon. And the dataset gives you an idea of what products are related, what products were brought together, viewed together etc. ( We can construct a graph with this info of also viewed and also bought ) It also gives you the reviews associated with each product over 14 years. Using all these info, we will rate/rank products on Amazon. Now, you are a vendor on Amazon who wants to improve traffic to their product page. Our algorithm helps you identify strategic positions in the graph where you can place your ad so that you can derive max traffic.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Now, our Professor''s question is, how will you validate your algorithm without real users? We said-&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;We can model a fixed set of users. Some users follow &lt;code&gt;also_bought&lt;/code&gt; and &lt;code&gt;also_viewed&lt;/code&gt; links to the third hop more often than first or fifth hop. There users'' behavior is normally distributed. Some other users hardly navigate beyond the first hop. This set of users'' behavior is exponentially distributed.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Our Professor''s said - Whatever distribution the users follow, users are navigating using links for similar products. Your ranking algorithm also considers similarity b/w 2 products to rank products. So using this validation algorithm is kinda &lt;code&gt;cheating&lt;/code&gt;. Come with some other user behavior, something more realistic and orthogonal to the algorithm.&lt;/p&gt;

&lt;p&gt;Any ideas on how to model the users'' behavior? I am happy to provide more details about the algo.&lt;/p&gt;
', 'machine-learning  data-mining  dataset  recommendation', NULL, '''/a'':46C ''/blockquote'':177C,255C ''/code'':213C,218C,295C ''/data/amazon/'':40C ''/p'':31C,176C,196C,254C,310C,331C ''14'':125C ''2'':281C ''ad'':68C,168C ''algo'':330C ''algorithm'':54C,154C,190C,276C,290C,309C ''also'':109C,112C,115C,211C,216C,277C ''amazon'':9A,43C,70C,79C,136C,143C ''associ'':120C ''b/w'':280C ''base'':57C ''behavior'':7A,233C,250C,301C,320C ''beyond'':242C ''blockquot'':32C,197C ''bought'':113C,212C ''brought'':95C ''buy'':6A ''cheat'':294C ''code'':210C,215C,293C ''come'':50C,296C ''consid'':278C ''construct'':102C ''cours'':22C ''data'':14B,25C ''data-min'':13B ''dataset'':16B,45C,82C ''deriv'':173C ''detail'':327C ''determin'':62C ''distribut'':236C,253C,262C ''etc'':99C ''exampl'':72C ''exponenti'':252C ''fifth'':229C ''final'':21C ''first'':227C,244C ''fix'':203C ''follow'':30C,209C,265C ''give'':34C,83C,116C ''graph'':104C,162C ''happi'':323C ''hard'':240C ''help'':155C ''hop'':223C,230C,245C ''href'':37C ''idea'':86C,313C ''identifi'':157C ''improv'':147C ''info'':107C,130C ''jmcauley.ucsd.edu'':39C ''jmcauley.ucsd.edu/data/amazon/'':38C ''kinda'':292C ''learn'':12B ''link'':219C,270C ''machin'':11B ''machine-learn'':10B ''max'':174C ''million'':75C ''mine'':15B ''model'':3A,201C,317C ''navig'':241C,268C ''nofollow'':42C ''normal'':235C ''often'':225C ''orthogon'':306C ''p'':18C,33C,178C,198C,256C,311C ''page'':152C ''pagerank'':60C ''person'':59C ''place'':67C,166C ''plan'':48C ''posit'':65C,159C ''product'':77C,89C,93C,123C,134C,151C,273C,282C,285C ''professor'':181C,258C ''project'':23C ''propos'':28C ''provid'':325C ''question'':183C ''rank'':275C,284C ''rate/rank'':133C ''real'':192C ''realist'':304C ''recommend'':17B ''rel'':41C ''relat'':91C ''review'':44C,119C ''rough'':56C ''said'':195C,260C ''scienc'':26C ''set'':204C,247C ''similar'':272C,279C ''someth'':302C ''strateg'':64C,158C ''that'':55C ''third'':222C ''togeth'':96C,98C ''traffic'':148C,175C ''use'':127C,269C,287C ''user'':4A,193C,206C,208C,232C,239C,249C,264C,266C,300C,319C ''valid'':188C,289C ''vendor'':141C ''view'':97C,110C,217C ''want'':145C ''whatev'':261C ''without'':191C ''year'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8172, 1, '2015-09-23 15:02:20.537', 2, 81, 'Propensity Modeling for Retail Marketing: Model Adjustments Based on Consumer Life Changes.', '&lt;p&gt;I built a predictive model using logistic regression for direct marking creatives. I built a âchildrenâsâ model which predicts buying behavior for those that purchase boys, girls and baby apparel.  But one may have a high propensity right now to buy baby clothes but in 12 months, when ones  kid is a toddler, their propensity drops for baby and increases for Girls/Boys.  Iâve been trying to brainstorm ways to take into account that scenario.  My question is, can anyone suggest options for adjustments to the model to based on that additional criteria?   &lt;/p&gt;
', 'predictive-modeling  logistic-regression', NULL, '''/p'':115C ''12'':67C ''account'':94C ''addit'':113C ''adjust'':7A,105C ''anyon'':101C ''apparel'':51C ''babi'':50C,63C,79C ''base'':8A,110C ''behavior'':42C ''boy'':47C ''brainstorm'':89C ''built'':21C,33C ''buy'':41C,62C ''chang'':12A ''childrenâ'':36C ''cloth'':64C ''consum'':10A ''creativ'':31C ''criteria'':114C ''direct'':29C ''drop'':77C ''girl'':48C ''girls/boys'':83C ''high'':57C ''increas'':81C ''iâ'':84C ''kid'':71C ''life'':11A ''logist'':17B,26C ''logistic-regress'':16B ''mark'':30C ''market'':5A ''may'':54C ''model'':2A,6A,15B,24C,38C,108C ''month'':68C ''one'':53C,70C ''option'':103C ''p'':19C ''predict'':14B,23C,40C ''predictive-model'':13B ''propens'':1A,58C,76C ''purchas'':46C ''question'':98C ''regress'':18B,27C ''retail'':4A ''right'':59C ''scenario'':96C ''suggest'':102C ''sâ'':37C ''take'':92C ''toddler'':74C ''tri'':87C ''use'':25C ''ve'':85C ''way'':90C ''â'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8173, 1, '2015-09-23 16:11:07.463', 0, 63, 'Batch convert json links to csv', '&lt;p&gt;I have a batch of links with the following naming convention:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://api.cilabs.net/v1/conferences/ws15/info/attendees?page=1" rel="nofollow"&gt;https://api.cilabs.net/v1/conferences/ws15/info/attendees?page=1&lt;/a&gt;; and the page number incrementing.&lt;/p&gt;

&lt;p&gt;I would like to extract the data from those web pages into a CSV file.&lt;/p&gt;
', 'dataset  csv', NULL, '''/p'':21C,38C,55C ''/v1/conferences/ws15/info/attendees?page=1'':27C ''/v1/conferences/ws15/info/attendees?page=1&lt;/a&gt;;'':32C ''api.cilabs.net'':26C,31C ''api.cilabs.net/v1/conferences/ws15/info/attendees?page=1'':25C ''api.cilabs.net/v1/conferences/ws15/info/attendees?page=1&lt;/a&gt;;'':30C ''batch'':1A,13C ''convent'':20C ''convert'':2A ''csv'':6A,8B,53C ''data'':46C ''dataset'':7B ''extract'':44C ''file'':54C ''follow'':18C ''href'':24C ''increment'':37C ''json'':3A ''like'':42C ''link'':4A,15C ''name'':19C ''nofollow'':29C ''number'':36C ''p'':9C,22C,39C ''page'':35C,50C ''rel'':28C ''web'':49C ''would'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8214, 2, '2015-09-27 05:24:24.88', 2, NULL, NULL, '&lt;p&gt;Unsurprisingly most programs in data science focus mostly on business or marketing applications. I too would like to see more programs focused on Biotechnology, Gaming or Energy for example but I think that in the future those kind of programs will become more and more available.
However there are a few I know of that offer a somewhat different approach.&lt;/p&gt;

&lt;p&gt;Regarding health informatics there are many offers where you can find anything between Public health to Computational Biology and Quantitative Genetics. Take a look at the full list.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.mastersindatascience.org/specialties/health-informatics/" rel="nofollow"&gt;http://www.mastersindatascience.org/specialties/health-informatics/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;On that list there are a few certification programs but there also are full fledged programs that lead to a M.Sc.&lt;/p&gt;

&lt;p&gt;If you are more interested in Marketing Analytics you can find the same list but for marketing related programs. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.mastersindatascience.org/specialties/marketing-analytics/" rel="nofollow"&gt;http://www.mastersindatascience.org/specialties/marketing-analytics/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;other options like Information Systems are also present, just browse that site and you might find something that interests you.&lt;/p&gt;
', NULL, NULL, '''/p'':62C,92C,126C,147C,180C ''/specialties/health-informatics/'':98C ''/specialties/health-informatics/&lt;/a&gt;&lt;/p&gt;'':103C ''/specialties/marketing-analytics/'':153C ''/specialties/marketing-analytics/&lt;/a&gt;&lt;/p&gt;'':158C ''also'':116C,166C ''analyt'':135C ''anyth'':75C ''applic'':14C ''approach'':61C ''avail'':47C ''becom'':43C ''biolog'':81C ''biotechnolog'':25C ''brows'':169C ''busi'':11C ''certif'':112C ''comput'':80C ''data'':6C ''differ'':60C ''energi'':28C ''exampl'':30C ''find'':74C,138C,175C ''fledg'':119C ''focus'':8C,23C ''full'':90C,118C ''futur'':37C ''game'':26C ''genet'':84C ''health'':65C,78C ''howev'':48C ''href'':95C,150C ''inform'':163C ''informat'':66C ''interest'':132C,178C ''kind'':39C ''know'':54C ''lead'':122C ''like'':18C,162C ''list'':91C,107C,141C ''look'':87C ''m.sc'':125C ''mani'':69C ''market'':13C,134C,144C ''might'':174C ''most'':9C ''nofollow'':100C,155C ''offer'':57C,70C ''option'':161C ''p'':1C,63C,93C,104C,127C,148C,159C ''present'':167C ''program'':4C,22C,41C,113C,120C,146C ''public'':77C ''quantit'':83C ''regard'':64C ''rel'':99C,154C ''relat'':145C ''scienc'':7C ''see'':20C ''site'':171C ''someth'':176C ''somewhat'':59C ''system'':164C ''take'':85C ''think'':33C ''unsurpris'':2C ''would'':17C ''www.mastersindatascience.org'':97C,102C,152C,157C ''www.mastersindatascience.org/specialties/health-informatics/'':96C ''www.mastersindatascience.org/specialties/health-informatics/&lt;/a&gt;&lt;/p&gt;'':101C ''www.mastersindatascience.org/specialties/marketing-analytics/'':151C ''www.mastersindatascience.org/specialties/marketing-analytics/&lt;/a&gt;&lt;/p&gt;'':156C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8174, 2, '2015-09-23 16:48:30.63', 1, NULL, NULL, '&lt;p&gt;I guess you are using the &lt;code&gt;fnn&lt;/code&gt; package.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;attr&lt;/code&gt; is a list of attributes which can be used for both &lt;code&gt;nn.index&lt;/code&gt; and &lt;code&gt;nn.dist&lt;/code&gt;. In this case, you are using &lt;code&gt;index&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;So, index returns an n x k matrix for the nearest neighbor indice.&lt;/p&gt;

&lt;p&gt;And the definition of the nearest neighbor index is:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The nearest neighbor index is expressed as the ratio of the observed
  distance divided by the expected distance.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://edndoc.esri.com/arcobjects/9.2/net/shared/geoprocessing/spatial_statistics_tools/average_nearest_neighbor_spatial_statistics_.htm" rel="nofollow"&gt;Definition reference&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':103C ''/arcobjects/9.2/net/shared/geoprocessing/spatial_statistics_tools/average_nearest_neighbor_spatial_statistics_.htm'':98C ''/blockquote'':92C ''/code'':10C,16C,30C,34C,43C ''/p'':12C,44C,59C,70C,91C,104C ''attr'':15C ''attribut'':21C ''blockquot'':71C ''case'':37C ''code'':8C,14C,28C,32C,41C ''definit'':63C,101C ''distanc'':85C,90C ''divid'':86C ''edndoc.esri.com'':97C ''edndoc.esri.com/arcobjects/9.2/net/shared/geoprocessing/spatial_statistics_tools/average_nearest_neighbor_spatial_statistics_.htm'':96C ''expect'':89C ''express'':78C ''fnn'':9C ''guess'':3C ''href'':95C ''index'':42C,47C,68C,76C ''indic'':58C ''k'':52C ''list'':19C ''matrix'':53C ''n'':50C ''nearest'':56C,66C,74C ''neighbor'':57C,67C,75C ''nn.dist'':33C ''nn.index'':29C ''nofollow'':100C ''observ'':84C ''p'':1C,13C,45C,60C,72C,93C ''packag'':11C ''ratio'':81C ''refer'':102C ''rel'':99C ''return'':48C ''use'':6C,25C,40C ''x'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8175, 2, '2015-09-23 17:50:37.233', 1, NULL, NULL, '&lt;p&gt;I guess what you are looking for is a crawler, which can crawl through the web pages and scrape the data for you into a csv file.&lt;/p&gt;

&lt;p&gt;I really don''t like the idea of giving away the full code. So, I would tell you how it''s done. I would use Python for the example.&lt;/p&gt;

&lt;p&gt;Define the url of the web page. Leave the last part (&lt;code&gt;page=&lt;/code&gt;), as it can go into a loop, so that as it needs to be incremented after every page gets scrapped.&lt;/p&gt;

&lt;p&gt;Now, identify the &lt;a href="https://en.wikipedia.org/wiki/XPath" rel="nofollow"&gt;xpaths&lt;/a&gt; of the data which you want to scrape. Store each of them in an array, and keep writing(and appending into a csv)&lt;/p&gt;

&lt;p&gt;This is the &lt;a href="https://github.com/Dawny33/Cricket-Analytics/blob/master/Crawler.py" rel="nofollow"&gt;code of one such scrapper&lt;/a&gt; which I have written for a project of mine. Refer to it in case of a road-block. I have used the &lt;a href="http://lxml.de/" rel="nofollow"&gt;lxml&lt;/a&gt; library of Python. 
You can use other fancy libraries like &lt;a href="http://scrapy.org/" rel="nofollow"&gt;Scrapy&lt;/a&gt;, &lt;a href="http://www.crummy.com/software/BeautifulSoup/" rel="nofollow"&gt;BeautifulSoup&lt;/a&gt;. But, they are just fancy wrappers over lxml, so it''s faster.&lt;/p&gt;
', NULL, NULL, '''/a'':109C,150C,180C,197C,206C ''/code'':75C ''/dawny33/cricket-analytics/blob/master/crawler.py'':142C ''/p'':29C,60C,96C,133C,219C ''/software/beautifulsoup/'':202C ''/wiki/xpath'':105C ''append'':129C ''array'':124C ''away'':40C ''beautifulsoup'':205C ''block'':169C ''case'':164C ''code'':43C,73C,145C ''crawl'':14C ''crawler'':11C ''csv'':27C,132C ''data'':22C,112C ''defin'':62C ''done'':52C ''en.wikipedia.org'':104C ''en.wikipedia.org/wiki/xpath'':103C ''everi'':92C ''exampl'':59C ''fanci'':188C,211C ''faster'':218C ''file'':28C ''full'':42C ''get'':94C ''github.com'':141C ''github.com/dawny33/cricket-analytics/blob/master/crawler.py'':140C ''give'':39C ''go'':79C ''guess'':3C ''href'':102C,139C,175C,192C,199C ''idea'':37C ''identifi'':99C ''increment'':90C ''keep'':126C ''last'':71C ''leav'':69C ''librari'':181C,189C ''like'':35C,190C ''look'':7C ''loop'':82C ''lxml'':179C,214C ''lxml.de'':176C ''mine'':159C ''need'':87C ''nofollow'':107C,144C,178C,195C,204C ''one'':147C ''p'':1C,30C,61C,97C,134C ''page'':18C,68C,74C,93C ''part'':72C ''project'':157C ''python'':56C,183C ''realli'':32C ''refer'':160C ''rel'':106C,143C,177C,194C,203C ''road'':168C ''road-block'':167C ''scrap'':95C ''scrape'':20C,117C ''scrapi'':196C ''scrapper'':149C ''scrapy.org'':193C ''store'':118C ''tell'':47C ''url'':64C ''use'':55C,172C,186C ''want'':115C ''web'':17C,67C ''would'':46C,54C ''wrapper'':212C ''write'':127C ''written'':154C ''www.crummy.com'':201C ''www.crummy.com/software/beautifulsoup/'':200C ''xpath'':108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8176, 2, '2015-09-23 19:28:01.37', 0, NULL, NULL, '&lt;p&gt;You don''t need another editor to change the new line character. In MS Word, ctrl+h opens up the find&amp;amp;replace dialog box and you can change &lt;code&gt;^p&lt;/code&gt; with  &lt;code&gt;&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Of course it may not work if there are some new line characters that you need unchanged. In that case, you need to find a logical structure first (for example, there should be no new line if the next line starts with something) and then it might be possible to translate that structure to regex. For your case, I am not sure if there is any.&lt;/p&gt;
', NULL, NULL, '''/code'':33C,36C ''/p'':37C,105C ''amp'':23C ''anoth'':6C ''box'':26C ''case'':58C,96C ''chang'':9C,30C ''charact'':13C,51C ''code'':31C,35C ''cours'':40C ''ctrl'':17C ''dialog'':25C ''editor'':7C ''exampl'':68C ''find'':22C,62C ''first'':66C ''h'':18C ''line'':12C,50C,74C,78C ''logic'':64C ''may'':42C ''might'':85C ''ms'':15C ''need'':5C,54C,60C ''new'':11C,49C,73C ''next'':77C ''open'':19C ''p'':1C,32C,38C ''possibl'':87C ''regex'':93C ''replac'':24C ''someth'':81C ''start'':79C ''structur'':65C,91C ''sure'':100C ''translat'':89C ''unchang'':55C ''word'':16C ''work'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8195, 1, '2015-09-25 00:47:46.873', 7, 191, 'Difference between training and test data distributions', '&lt;p&gt;A basic assumption in machine learning is that training and test data are drawn from the same population, and thus follow the same distribution. But, in practice, this is highly unlikely. Covariate shift addresses this issue. Can someone clear the following doubts regarding this?&lt;/p&gt;

&lt;p&gt;How does one check whether two distribution are statistically different?
Can kernel density estimate (KDE) be used to estimate the probability distribution to tell the difference?
Let''s say I have 100 images of a specific category. The number of test images is 50, and I''m changing the number of training images from 5 to 50 in steps of 5. Can I say the probability distributions are different when using 5 training images and 50 testing images after estimating them by KDE?&lt;/p&gt;
', 'machine-learning  classification  dataset  image-classification', NULL, '''/p'':61C,146C ''100'':94C ''5'':117C,123C,134C ''50'':106C,119C,138C ''address'':50C ''assumpt'':19C ''basic'':18C ''categori'':99C ''chang'':110C ''check'':66C ''classif'':11B,15B ''clear'':55C ''covari'':48C ''data'':6A,28C ''dataset'':12B ''densiti'':75C ''differ'':1A,72C,88C,131C ''distribut'':7A,40C,69C,84C,129C ''doubt'':58C ''drawn'':30C ''estim'':76C,81C,142C ''follow'':37C,57C ''high'':46C ''imag'':14B,95C,104C,115C,136C,140C ''image-classif'':13B ''issu'':52C ''kde'':77C,145C ''kernel'':74C ''learn'':10B,22C ''let'':89C ''m'':109C ''machin'':9B,21C ''machine-learn'':8B ''number'':101C,112C ''one'':65C ''p'':16C,62C ''popul'':34C ''practic'':43C ''probabl'':83C,128C ''regard'':59C ''say'':91C,126C ''shift'':49C ''someon'':54C ''specif'':98C ''statist'':71C ''step'':121C ''tell'':86C ''test'':5A,27C,103C,139C ''thus'':36C ''train'':3A,25C,114C,135C ''two'':68C ''unlik'':47C ''use'':79C,133C ''whether'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8177, 2, '2015-09-24 01:04:56.403', 3, NULL, NULL, '&lt;h2&gt;Creating samples&lt;/h2&gt;

&lt;p&gt;You can create samples using &lt;code&gt;sample&lt;/code&gt; and &lt;code&gt;replicate&lt;/code&gt; functions.
I create &lt;code&gt;smp&lt;/code&gt; with the first given row.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; smp &amp;lt;- c(rep(''A'', 8), rep(''B'', 4), rep(''C'', 2), rep(''D'', 11), rep(''E'', 21), rep(''F'', 0))
&amp;gt; table(smp)
smp
 A  B  C  D  E 
 8  4  2 11 21 
&amp;gt; 
&amp;gt; samples &amp;lt;- replicate(1000, sample(smp)) # creating 1000 samples
&amp;gt; dim(samples) # 1000 samples of 46 votes
[1]   46 1000
&amp;gt; 
&amp;gt; samples[,1:3]
      [,1] [,2] [,3]
 [1,] "E"  "A"  "E" 
 [2,] "D"  "E"  "E" 
 [3,] "E"  "D"  "C" 
 [4,] "D"  "E"  "E" 
 [5,] "A"  "D"  "D" 
 [6,] "B"  "E"  "E" 
 [7,] "E"  "A"  "D" 
 [8,] "B"  "C"  "D" 
 [9,] "B"  "A"  "E" 
[10,] "E"  "E"  "E" 
[11,] "A"  "E"  "E" 
[12,] "D"  "E"  "D" 
[13,] "D"  "E"  "B" 
[14,] "D"  "B"  "E" 
[15,] "E"  "E"  "D" 
[16,] "E"  "E"  "E" 
[17,] "E"  "B"  "B" 
[18,] "A"  "D"  "E" 
[19,] "E"  "E"  "E" 
[20,] "E"  "E"  "C" 
[21,] "E"  "E"  "E" 
[22,] "E"  "D"  "A" 
[23,] "D"  "D"  "B" 
[24,] "E"  "E"  "E" 
[25,] "D"  "D"  "A" 
[26,] "E"  "E"  "D" 
[27,] "C"  "A"  "E" 
[28,] "D"  "E"  "D" 
[29,] "D"  "E"  "E" 
[30,] "D"  "A"  "E" 
[31,] "A"  "D"  "E" 
[32,] "E"  "E"  "A" 
[33,] "E"  "E"  "D" 
[34,] "E"  "E"  "A" 
[35,] "A"  "E"  "E" 
[36,] "D"  "C"  "A" 
[37,] "A"  "D"  "A" 
[38,] "E"  "D"  "D" 
[39,] "E"  "D"  "E" 
[40,] "A"  "E"  "A" 
[41,] "B"  "A"  "A" 
[42,] "C"  "A"  "D" 
[43,] "A"  "A"  "D" 
[44,] "E"  "B"  "E" 
[45,] "E"  "D"  "B" 
[46,] "E"  "B"  "E" 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If all you need are samples, &lt;code&gt;sample&lt;/code&gt; solves your problem.&lt;/p&gt;

&lt;h2&gt;Bootstraping&lt;/h2&gt;

&lt;p&gt;To bootstrap you need to compute a statistic. For example, I compute the weighted mean for a table of votes.
I''m using &lt;code&gt;boot::boot&lt;/code&gt; to bootsprap.
You must pass the original sample and a handler which receives the original sample &lt;code&gt;s&lt;/code&gt; and a vector with the indexes shuffled (&lt;code&gt;idx&lt;/code&gt;).
This function returns a &lt;code&gt;boot&lt;/code&gt; object that shows the bootstrap statistics.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; boot.smp &amp;lt;- boot::boot(smp, function(s, idx) {
+ tt &amp;lt;- table(s[idx])
+ weighted.mean(tt, as.numeric(factor(names(tt), labels=1:6, levels=LETTERS[1:6])))
+ }, 1000)
&amp;gt; boot.smp

CASE RESAMPLING BOOTSTRAP FOR CENSORED DATA


Call:
boot::boot(data = smp, statistic = function(d, w) {
    tt &amp;lt;- table(d[w])
    weighted.mean(tt, as.numeric(factor(names(tt), labels = 1:6, 
        levels = LETTERS[1:6])))
}, R = 1000)


Bootstrap Statistics :
    original    bias    std. error
t1*     11.4 0.4126077    1.274827
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The &lt;code&gt;boot&lt;/code&gt; object has other methods to compute confidence interval and return much more information.&lt;/p&gt;
', NULL, NULL, '''/code'':13C,17C,23C,282C,293C,328C,347C,357C,364C,449C,455C ''/h2'':4C,300C ''/p'':29C,297C,371C,469C ''/pre'':283C,450C ''0'':53C ''0.4126077'':447C ''1'':87C,93C,95C,98C,395C,399C,431C,435C ''1.274827'':448C ''10'':134C ''1000'':73C,77C,82C,89C,401C,438C ''11'':47C,66C,138C ''11.4'':446C ''12'':142C ''13'':146C ''14'':150C ''15'':154C ''16'':158C ''17'':162C ''18'':166C ''19'':170C ''2'':44C,65C,96C,102C ''20'':174C ''21'':50C,67C,178C ''22'':182C ''23'':186C ''24'':190C ''25'':194C ''26'':198C ''27'':202C ''28'':206C ''29'':210C ''3'':94C,97C,106C ''30'':214C ''31'':218C ''32'':222C ''33'':226C ''34'':230C ''35'':234C ''36'':238C ''37'':242C ''38'':246C ''39'':250C ''4'':41C,64C,110C ''40'':254C ''41'':258C ''42'':262C ''43'':266C ''44'':270C ''45'':274C ''46'':85C,88C,278C ''5'':114C ''6'':118C,396C,400C,432C,436C ''7'':122C ''8'':38C,63C,126C ''9'':130C ''as.numeric'':390C,426C ''b'':40C,59C,119C,127C,131C,149C,152C,164C,165C,189C,259C,272C,277C,280C ''bias'':442C ''boot'':326C,327C,363C,377C,378C,411C,412C,454C ''boot.smp'':375C,403C ''bootsprap'':330C ''bootstrap'':299C,303C,369C,406C,439C ''c'':35C,43C,60C,109C,128C,177C,203C,240C,263C ''call'':410C ''case'':404C ''censor'':408C ''code'':11C,15C,21C,31C,291C,325C,345C,355C,362C,373C,453C ''comput'':307C,313C,461C ''confid'':462C ''creat'':2C,8C,20C,76C ''d'':46C,61C,103C,108C,111C,116C,117C,125C,129C,143C,145C,147C,151C,157C,168C,184C,187C,188C,195C,196C,201C,207C,209C,211C,215C,220C,229C,239C,244C,248C,249C,252C,265C,269C,276C,417C,422C ''data'':409C,413C ''dim'':80C ''e'':49C,62C,99C,101C,104C,105C,107C,112C,113C,120C,121C,123C,133C,135C,136C,137C,140C,141C,144C,148C,153C,155C,156C,159C,160C,161C,163C,169C,171C,172C,173C,175C,176C,179C,180C,181C,183C,191C,192C,193C,199C,200C,205C,208C,212C,213C,217C,221C,223C,224C,227C,228C,231C,232C,236C,237C,247C,251C,253C,256C,271C,273C,275C,279C,281C ''error'':444C ''exampl'':311C ''f'':52C ''factor'':391C,427C ''first'':26C ''function'':18C,359C,380C,416C ''given'':27C ''gt'':32C,54C,68C,69C,79C,90C,91C,374C,402C ''h2'':1C,298C ''handler'':339C ''idx'':356C,382C,387C ''index'':353C ''inform'':468C ''interv'':463C ''label'':394C,430C ''letter'':398C,434C ''level'':397C,433C ''lt'':34C,71C,376C,384C,420C ''m'':323C ''mean'':316C ''method'':459C ''much'':466C ''must'':332C ''name'':392C,428C ''need'':288C,305C ''object'':365C,456C ''origin'':335C,343C,441C ''p'':5C,284C,301C,451C ''pass'':333C ''pre'':30C,372C ''problem'':296C ''r'':437C ''receiv'':341C ''rep'':36C,39C,42C,45C,48C,51C ''replic'':16C,72C ''resampl'':405C ''return'':360C,465C ''row'':28C ''sampl'':3C,9C,12C,70C,74C,78C,81C,83C,92C,290C,292C,336C,344C ''show'':367C ''shuffl'':354C ''smp'':22C,33C,56C,57C,75C,379C,414C ''solv'':294C ''statist'':309C,370C,415C,440C ''std'':443C ''t1'':445C ''tabl'':55C,319C,385C,421C ''tt'':383C,389C,393C,419C,425C,429C ''use'':10C,324C ''vector'':350C ''vote'':86C,321C ''w'':418C,423C ''weight'':315C ''weighted.mean'':388C,424C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8178, 2, '2015-09-24 01:23:20.17', 1, NULL, NULL, '&lt;p&gt;I think &lt;code&gt;within&lt;/code&gt; and &lt;code&gt;cumsum&lt;/code&gt; functions solve your problem.
The last row in &lt;code&gt;comp.acc&lt;/code&gt; column has what you want.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; data.txt &amp;lt;- ''Rates   Tenor   Printrest  
+ 1.20   1   1.012  
+ 1.35   2   1.02718225  
+ 1.90   3   1.058089859  
+ 2.01   4   1.082856706  
+ 1.80   5   1.093298847  
+ 1.70   6   1.106434521''
&amp;gt; 
&amp;gt; df &amp;lt;- read.table(text=data.txt, header = TRUE)
&amp;gt; 
&amp;gt; within(df, {
+   comp &amp;lt;- (1 + Rates/100)^(Tenor)
+   comp.acc &amp;lt;- cumsum(comp)
+ })
  Rates Tenor Printrest comp.acc     comp
1  1.20     1  1.012000 1.012000 1.012000
2  1.35     2  1.027182 2.039182 1.027182
3  1.90     3  1.058090 3.097272 1.058090
4  2.01     4  1.082857 4.180129 1.082857
5  1.80     5  1.093299 5.273428 1.093299
6  1.70     6  1.106435 6.379862 1.106435
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':6C,10C,21C,117C ''/p'':27C ''/pre'':118C ''1'':37C,69C,81C,83C ''1.012'':38C ''1.012000'':84C,85C,86C ''1.027182'':90C,92C ''1.02718225'':41C ''1.058089859'':44C ''1.058090'':96C,98C ''1.082856706'':47C ''1.082857'':102C,104C ''1.093298847'':50C ''1.093299'':108C,110C ''1.106434521'':53C ''1.106435'':114C,116C ''1.20'':36C,82C ''1.35'':39C,88C ''1.70'':51C,112C ''1.80'':48C,106C ''1.90'':42C,94C ''2'':40C,87C,89C ''2.01'':45C,100C ''2.039182'':91C ''3'':43C,93C,95C ''3.097272'':97C ''4'':46C,99C,101C ''4.180129'':103C ''5'':49C,105C,107C ''5.273428'':109C ''6'':52C,111C,113C ''6.379862'':115C ''code'':4C,8C,19C,29C ''column'':22C ''comp'':67C,75C,80C ''comp.acc'':20C,72C,79C ''cumsum'':9C,74C ''data.txt'':31C,60C ''df'':56C,66C ''function'':11C ''gt'':30C,54C,55C,63C,64C ''header'':61C ''last'':16C ''lt'':32C,57C,68C,73C ''p'':1C ''pre'':28C ''printrest'':35C,78C ''problem'':14C ''rate'':33C,76C ''rates/100'':70C ''read.table'':58C ''row'':17C ''solv'':12C ''tenor'':34C,71C,77C ''text'':59C ''think'':3C ''true'':62C ''want'':26C ''within'':5C,65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8181, 2, '2015-09-24 04:17:47.3', 0, NULL, NULL, '&lt;p&gt;I think your last question is worth discussing, but forgive my careless on skipping the details of the model and just leaving a quick answer here :P&lt;/p&gt;

&lt;p&gt;Repeating a sentence in your corpus would definitely change the learning result, and strength the relationship of the words in this sentence, because one of the models behind word2vec is &lt;code&gt;skip-gram&lt;/code&gt;, which assume the center word can be used to predict its surroundings. &lt;/p&gt;

&lt;p&gt;But I have to ask another question coming follows: what is our purpose of using word2vec? &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;To find similar words in semantic and synthetic, which is used to search and information retrieval.&lt;/li&gt;
&lt;li&gt;A skip-gram model is useful for modeling those like click-sequence data, which could be used in recommendation&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/code'':65C ''/li'':115C,138C ''/ol'':139C ''/p'':29C,78C,96C ''anoth'':85C ''answer'':26C ''ask'':84C ''assum'':67C ''behind'':58C ''careless'':13C ''center'':69C ''chang'':39C ''click'':129C ''click-sequ'':128C ''code'':61C ''come'':87C ''corpus'':36C ''could'':133C ''data'':131C ''definit'':38C ''detail'':17C ''discuss'':9C ''find'':100C ''follow'':88C ''forgiv'':11C ''gram'':64C,120C ''inform'':113C ''last'':5C ''learn'':41C ''leav'':23C ''li'':98C,116C ''like'':127C ''model'':20C,57C,121C,125C ''ol'':97C ''one'':54C ''p'':1C,28C,30C,79C ''predict'':75C ''purpos'':92C ''question'':6C,86C ''quick'':25C ''recommend'':137C ''relationship'':46C ''repeat'':31C ''result'':42C ''retriev'':114C ''search'':111C ''semant'':104C ''sentenc'':33C,52C ''sequenc'':130C ''similar'':101C ''skip'':15C,63C,119C ''skip-gram'':62C,118C ''strength'':44C ''surround'':77C ''synthet'':106C ''think'':3C ''use'':73C,94C,109C,123C,135C ''word'':49C,70C,102C ''word2vec'':59C,95C ''worth'':8C ''would'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8186, 1, '2015-09-24 14:58:08.87', 0, 45, 'Big Data and a fwf files reading with filters', '&lt;p&gt;I have searched at all possible areas and haven''t been able to find the solution to my problem. I am hoping that someone here would be able to help me out. If you feel that this question is already answered, I would appreciate the link.&lt;/p&gt;

&lt;p&gt;I am using &lt;code&gt;R3.2.2 64 bit&lt;/code&gt; on a &lt;code&gt;windows 7&lt;/code&gt; machine. I have a &lt;code&gt;fixed width file&lt;/code&gt; of size &lt;code&gt;1.9 GB&lt;/code&gt; and has approx &lt;code&gt;18 million&lt;/code&gt; records in it. I need to read the file based on a filter condition &lt;code&gt;RecordID = "Cast"&lt;/code&gt;. All my attempts to read the &lt;code&gt;file.dat&lt;/code&gt; has failed due to memory constraints. Any help on how to use package &lt;code&gt;ff&lt;/code&gt; for this purpose?  &lt;/p&gt;

&lt;p&gt;I am open to other methods also.&lt;/p&gt;
', 'r  bigdata', NULL, '''/code'':68C,74C,83C,89C,96C,114C,123C,139C ''/p'':59C,143C,152C ''1.9'':87C ''18'':94C ''64'':66C ''7'':73C ''abl'':24C,40C ''alreadi'':52C ''also'':151C ''answer'':53C ''appreci'':56C ''approx'':92C ''area'':19C ''attempt'':117C ''base'':106C ''big'':1A ''bigdata'':11B ''bit'':67C ''cast'':113C ''code'':64C,71C,79C,86C,93C,111C,121C,137C ''condit'':110C ''constraint'':129C ''data'':2A ''due'':126C ''fail'':125C ''feel'':47C ''ff'':138C ''file'':6A,82C,105C ''file.dat'':122C ''filter'':9A,109C ''find'':26C ''fix'':80C ''fwf'':5A ''gb'':88C ''haven'':21C ''help'':42C,131C ''hope'':34C ''link'':58C ''machin'':75C ''memori'':128C ''method'':150C ''million'':95C ''need'':101C ''open'':147C ''p'':12C,60C,144C ''packag'':136C ''possibl'':18C ''problem'':31C ''purpos'':142C ''question'':50C ''r'':10B ''r3.2.2'':65C ''read'':7A,103C,119C ''record'':97C ''recordid'':112C ''search'':15C ''size'':85C ''solut'':28C ''someon'':36C ''use'':63C,135C ''width'':81C ''window'':72C ''would'':38C,55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8187, 2, '2015-09-24 15:19:10.283', 2, NULL, NULL, '&lt;p&gt;Try the &lt;code&gt;LaF&lt;/code&gt; package. See this:
&lt;a href="http://stackoverflow.com/a/24716798/1427069"&gt;http://stackoverflow.com/a/24716798/1427069&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Once you create a &lt;code&gt;laf&lt;/code&gt; object, you can create a data frame or data table using your row filter condition.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(LaF)
laf &amp;lt;- laf_open_fwf("foo.dat",
                    column_widths = colWidths, 
                    column_types=rep("character", length(colWidths)),
                    column_names = colNames)
castData &amp;lt;- laf[RecordID == "Cast", ]
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a/24716798/1427069'':14C ''/a/24716798/1427069&lt;/a&gt;&lt;/p&gt;'':17C ''/code'':6C,25C,69C ''/p'':41C ''/pre'':70C ''cast'':68C ''castdata'':64C ''charact'':58C ''code'':4C,23C,43C ''colnam'':63C ''column'':52C,55C,61C ''colwidth'':54C,60C ''condit'':40C ''creat'':21C,29C ''data'':31C,34C ''filter'':39C ''foo.dat'':51C ''frame'':32C ''fwf'':50C ''href'':11C ''laf'':5C,24C,45C,46C,48C,66C ''length'':59C ''librari'':44C ''lt'':47C,65C ''name'':62C ''object'':26C ''open'':49C ''p'':1C,18C ''packag'':7C ''pre'':42C ''recordid'':67C ''rep'':57C ''row'':38C ''see'':8C ''stackoverflow.com'':13C,16C ''stackoverflow.com/a/24716798/1427069'':12C ''stackoverflow.com/a/24716798/1427069&lt;/a&gt;&lt;/p&gt;'':15C ''tabl'':35C ''tri'':2C ''type'':56C ''use'':36C ''width'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8188, 1, '2015-09-24 16:10:21.187', 0, 44, 'Why use sum and not average for sentiment analysis?', '&lt;p&gt;In Sentiment Analysis, I''ve seen the sentiment of an entire string of words (tweets, paragraphs, sentences, etc.) computed by summing the sentiment of all of the terms in the string.  &lt;/p&gt;

&lt;p&gt;I know there is a monotonic relationship between summing and averaging.  So, if sentences are only ever compared to other sentences, then of course it wouldn''t make a difference.  &lt;/p&gt;

&lt;p&gt;So, why is it that the average is not used instead of the sum?  Especially if there are cases where strings could contain only one word, or many words, it seems to me that an average sentiment would be the way to go.  Is that not true?  &lt;/p&gt;
', 'machine-learning', NULL, '''/p'':45C,77C,126C ''analysi'':9A,16C ''averag'':6A,57C,85C,114C ''case'':97C ''compar'':64C ''comput'':32C ''contain'':101C ''could'':100C ''cours'':70C ''differ'':76C ''entir'':24C ''especi'':93C ''etc'':31C ''ever'':63C ''go'':121C ''instead'':89C ''know'':48C ''learn'':12B ''machin'':11B ''machine-learn'':10B ''make'':74C ''mani'':106C ''monoton'':52C ''one'':103C ''p'':13C,46C,78C ''paragraph'':29C ''relationship'':53C ''seem'':109C ''seen'':19C ''sentenc'':30C,60C,67C ''sentiment'':8A,15C,21C,36C,115C ''string'':25C,44C,99C ''sum'':3A,34C,55C,92C ''term'':41C ''true'':125C ''tweet'':28C ''use'':2A,88C ''ve'':18C ''way'':119C ''word'':27C,104C,107C ''would'':116C ''wouldn'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8194, 1, '2015-09-24 23:46:59.96', 2, 59, 'PhD program in statistics', '&lt;p&gt;I am a first year PhD student in statistics. During this year I have analyzed the scopes of interest of my scientific advisor and found them unpromising. He is majored in &lt;em&gt;mixtures with varying concentrations models&lt;/em&gt; for which I have not found any references to authoritative sources.&lt;/p&gt;

&lt;p&gt;Now I want to change my PhD theme, however, there are no other scientific advisors in my university which majored in statistics. Therefore, I have 2 questions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Is it possible to write at least 5 articles together with a PhD thesis without scientific adviser? If yes, what is a proper way to do this? Here I mean how to choose a theme, where ask for a help and so on.&lt;/li&gt;
&lt;li&gt;Is it possible to find a remote adviser to consult with? If yes, how and where can I find him?&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Also I have no much time for the search. I am interested in statistics, especially in machine learning. I would like my PhD thesis to be of practical value, not pure research one which is popular in my department. Also I have commercial experience in programming (C/C++, R, Python) if that can help.&lt;/p&gt;

&lt;p&gt;Thanks in advance for any help!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8197, 2, '2015-09-25 06:11:52.02', 1, NULL, NULL, '&lt;p&gt;Certain ingredients are needed to give you the best chance of a successful PhD. One of the important ones is that you and your supervisor have mutual interests.&lt;/p&gt;

&lt;p&gt;A second important ingredient, in my opinion, is that you immerse yourself in that environment. It''s important to develop a network of colleagues. It helps to spread ideas, start collaborations, get help when needed, and to explore unthought of opportunities.&lt;/p&gt;

&lt;p&gt;From what you have said, I think you will be missing out on these two important ingredients if you continue in the same place or if you work remotely.&lt;/p&gt;

&lt;p&gt;What is also important is what you to do &lt;em&gt;after&lt;/em&gt; the PhD. PhD is required for academic position. But I think you will be in a weak position to get to the next step (fellowships, faculty positions, etc.) if you do what you proposed. In certain industrial positions it can be looked on favourably, not necessarily for the topic you pursued, but because it says something about your personally. Basically that you can get things gone, rise to a challenge, work independently, work as a team, communicate difficult topics and can bring creativity to solving a problems. &lt;/p&gt;

&lt;p&gt;My advice would be to find a machine learning research group and apply for PhDs. If this is not possible why not consider following the topic of your supervisor and keep machine learning as a hobby? You will become and expert in statistic and so you will find manny concepts will translate between the various statistical disciplines. But only do this if you get along with her/him, and you can see yourself studying this topic.&lt;/p&gt;

&lt;p&gt;Finally you could try a compromise? Are there applications for "mixing statistics" in machine learning? Can you find one? Is there an unexplored opportunity to do something new?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;As a side note I find it ridiculous that PhD supervisor ask the student for topics. This always leads to problems because the student doesn''t really have a clue about the research field. There is room for flexibility but often this hides supervisor laziness.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8189, 2, '2015-09-24 16:44:13.29', 4, NULL, NULL, '&lt;p&gt;Consider the following two sentences:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;My awesome girlfriend bought me a delicious popsicle at the store.
0--1-------0----------0------0--0-1---------0--------0--0---0-----:2:11

My awesome girlfriend, Joyce, drove to the grocery store to buy me a delicious Dole popsicle.
0---1------0-----------0------0-----0--0---0-------0-----0--0---0--0-1---------0----0-------:2:16
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;See how you can sum the values and get &lt;code&gt;2&lt;/code&gt; for both, but average the sentiments and get &lt;code&gt;2/11&lt;/code&gt; vs. &lt;code&gt;2/16&lt;/code&gt;.  There are &lt;a href="http://www.matthewjockers.net/2015/04/28/cumulative-sentiments/" rel="nofollow"&gt;cases where&lt;/a&gt; people have seen better results measuring the total number of good sentiment words rather than letting zero value words cloud the picture.  In this case, I would argue that the sentences have the same sentiment and averaging artificially pulls down the sentiment of the second sentence.  In certain methods like &lt;a href="https://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;&lt;code&gt;TFIDF&lt;/code&gt;&lt;/a&gt;, the word vectors are normalized against are relevant corpus, so this may change things.  In other methods, the &lt;a href="https://en.wikipedia.org/wiki/Stop_words" rel="nofollow"&gt;&lt;code&gt;stop words&lt;/code&gt;&lt;/a&gt; may have already been removed, so this has less effect.&lt;/p&gt;

&lt;p&gt;In twitter examples, users are limited to 140 characters, so one might consider this to be an inherent denominator which makes averaging really averages of averages.  Its very dependent on the specific application and methodology.&lt;/p&gt;

&lt;p&gt;Without a more specific reference, it is difficult to be more specific in my answer.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8190, 1, '2015-09-24 17:17:03.003', 0, 72, '3-way factorization machines', '&lt;p&gt;&lt;a href="http://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle2010FM.pdf" rel="nofollow"&gt;Factorization machines&lt;/a&gt; are a great way to model interactions between different features, in a way that is more efficient than full-blown quadratic interactions. There exist efficient implementations like libFM and Vowpal Wabbit''s &lt;code&gt;--lrq&lt;/code&gt; option. &lt;/p&gt;

&lt;p&gt;My question is about higher order interactions: the FM paper mentions that it is "easy" to extend it to higher orders (Tensor FM?), see. Eq. (5) in &lt;a href="http://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle2010FM.pdf" rel="nofollow"&gt;the paper&lt;/a&gt;. Are there any good implementations of 3-way factorization machines?&lt;/p&gt;

&lt;p&gt;[moved from cross-validated]&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8191, 2, '2015-09-24 17:59:47.897', 2, NULL, NULL, '&lt;p&gt;What you are doing is a typical example of k-fold cross validation.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;XGBoost&lt;/code&gt; is just used for boosting the performance and signifies "distributed gradient boosting".&lt;/p&gt;

&lt;p&gt;First, run the cross-validation step:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;kfld = sklearn.cross_validation.KFold(labels.size, n_folds=10)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then, use the train and test indices in &lt;code&gt;kfld&lt;/code&gt; for constructing the XGBoost matrix and re-scaling weights by looping over them(the indices).&lt;/p&gt;

&lt;p&gt;A very neat implementation has been given as a Kaggle example &lt;a href="https://kaggle2.blob.core.windows.net/forum-message-attachments/44825/1290/test_xgboost.py?sv=2012-02-12&amp;amp;se=2015-12-14T21%3A28%3A18Z&amp;amp;sr=b&amp;amp;sp=r&amp;amp;sig=yQJmvA%2FpUqve9EDdkF1bWAaYjEtNFmcUfjQUHaTTD7U%3D" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;So, cross validation is not done with the &lt;code&gt;xgboost&lt;/code&gt; package, it is done with the &lt;code&gt;cross_validation&lt;/code&gt; module of &lt;code&gt;sklearn&lt;/code&gt;, and then the gradient boosting is done on the indices of the k-fold validation variable results.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8198, 2, '2015-09-25 06:17:41.417', 3, NULL, NULL, '&lt;p&gt;LDA is Bayesian model. This means the desired result is a posterior probability distribution over the random vectors of interest (probability of topics etc. having seen some data). &lt;/p&gt;

&lt;p&gt;Inference for many Bayesian models is done by Markov Chain Monte Carlo. Indeed the &lt;a href="https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation" rel="nofollow"&gt;wiki&lt;/a&gt; on LDA suggests that Gibbs sampling is a popular inference technique for LDA. MCMC draws random samples to provide an approximation of the posterior distribution.&lt;/p&gt;

&lt;p&gt;Variational inference methods should typically be deterministic, but I''m not too familiar with the &lt;a href="https://www.cl.cam.ac.uk/teaching/1011/L101/ml4lp-lect8.pdf" rel="nofollow"&gt;VB inference&lt;/a&gt; for this particular model.&lt;/p&gt;

&lt;p&gt;Also one can typically replicate runs of random algorithms by setting of random number generation seeds (if your purpose is scientific).&lt;/p&gt;

&lt;p&gt;In either case if the results from a Bayesian model show huge variability in the the parameters of interest, it may be telling you that the model is not a good fit for the dataset, or the dataset is not big enough for the model you are fitting.&lt;/p&gt;

&lt;p&gt;EDIT: I don''t know which inference method (Gibbs, VB etc.) the backend of your software is using, so it''s not possible to determine what type (if any) of randomisation is going on. &lt;/p&gt;

&lt;p&gt;For scientific purposes, you''ll probably want to read up some more on Bayesian inference. Standard software (e.g. the LDA in scikits.learn) will give you a summary of the outputs of the inference (e.g. most coders just want the best assignment of docs to topics). There''s more information hanging around behind the scenes which you might be able to get access to, and could be useful.&lt;/p&gt;

&lt;p&gt;E.g. (roughly) for scientific applications of Gibbs sampling methods we''d typically run multiple chains, drop the first N samples generated by each, and check if the resulting samples look like they came from the same distribution. If you''re concerned about dependence on seeds etc. and your backend is the Gibbs sampler you will want to check out MCMC convergence diagnostics for this model.&lt;/p&gt;
', NULL, NULL, '''/a'':54C,106C ''/p'':30C,80C,111C,134C,184C,220C,289C,356C ''/teaching/1011/l101/ml4lp-lect8.pdf'':101C ''/wiki/latent_dirichlet_allocation'':50C ''abl'':280C ''access'':283C ''algorithm'':121C ''also'':113C ''applic'':295C ''approxim'':75C ''around'':272C ''assign'':262C ''backend'':198C,339C ''bayesian'':4C,35C,144C,235C ''behind'':273C ''best'':261C ''big'':176C ''came'':323C ''carlo'':43C ''case'':138C ''chain'':41C,305C ''check'':315C,348C ''coder'':257C ''concern'':331C ''converg'':351C ''could'':286C ''d'':301C ''data'':29C ''dataset'':170C,173C ''depend'':333C ''desir'':9C ''determin'':210C ''determinist'':88C ''diagnost'':352C ''distribut'':15C,79C,327C ''doc'':264C ''done'':38C ''draw'':69C ''drop'':306C ''e.g'':239C,255C,291C ''edit'':186C ''either'':137C ''en.wikipedia.org'':49C ''en.wikipedia.org/wiki/latent_dirichlet_allocation'':48C ''enough'':177C ''etc'':25C,196C,336C ''familiar'':94C ''first'':308C ''fit'':167C,183C ''generat'':127C,311C ''get'':282C ''gibb'':59C,194C,297C,342C ''give'':245C ''go'':218C ''good'':166C ''hang'':271C ''href'':47C,98C ''huge'':147C ''inde'':44C ''infer'':32C,64C,83C,105C,192C,236C,254C ''inform'':270C ''interest'':21C,154C ''know'':190C ''lda'':2C,56C,67C,241C ''like'':321C ''ll'':226C ''look'':320C ''m'':91C ''mani'':34C ''markov'':40C ''may'':156C ''mcmc'':68C,350C ''mean'':7C ''method'':84C,193C,299C ''might'':278C ''model'':5C,36C,110C,145C,162C,180C,355C ''mont'':42C ''multipl'':304C ''n'':309C ''nofollow'':52C,103C ''number'':126C ''one'':114C ''output'':251C ''p'':1C,31C,81C,112C,135C,185C,221C,290C ''paramet'':152C ''particular'':109C ''popular'':63C ''possibl'':208C ''posterior'':13C,78C ''probabl'':14C,22C,227C ''provid'':73C ''purpos'':131C,224C ''random'':18C,70C,120C,125C ''randomis'':216C ''re'':330C ''read'':230C ''rel'':51C,102C ''replic'':117C ''result'':10C,141C,318C ''rough'':292C ''run'':118C,303C ''sampl'':60C,71C,298C,310C,319C ''sampler'':343C ''scene'':275C ''scientif'':133C,223C,294C ''scikits.learn'':243C ''seed'':128C,335C ''seen'':27C ''set'':123C ''show'':146C ''softwar'':201C,238C ''standard'':237C ''suggest'':57C ''summari'':248C ''techniqu'':65C ''tell'':158C ''topic'':24C,266C ''type'':212C ''typic'':86C,116C,302C ''use'':203C,288C ''variabl'':148C ''variat'':82C ''vb'':104C,195C ''vector'':19C ''want'':228C,259C,346C ''wiki'':53C ''www.cl.cam.ac.uk'':100C ''www.cl.cam.ac.uk/teaching/1011/l101/ml4lp-lect8.pdf'':99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8199, 1, '2015-09-25 06:40:34.843', 3, 96, 'Domain-specific data science programs', '&lt;p&gt;I am looking for domain-specific data science programs (as a major, not a minor or specialization). I found programs in bio-statistics (and other disciplines in public health), MS in marketing analytics, MS in learning analytics and MS in urban informatics. Does anyone know any other domain-specific data science field of study?&lt;/p&gt;
', 'data  education', NULL, '''/p'':67C ''analyt'':44C,48C ''anyon'':55C ''bio'':33C ''bio-statist'':32C ''data'':4A,7B,17C,62C ''disciplin'':37C ''domain'':2A,15C,60C ''domain-specif'':1A,14C,59C ''educ'':8B ''field'':64C ''found'':29C ''health'':40C ''informat'':53C ''know'':56C ''learn'':47C ''look'':12C ''major'':22C ''market'':43C ''minor'':25C ''ms'':41C,45C,50C ''p'':9C ''program'':6A,19C,30C ''public'':39C ''scienc'':5A,18C,63C ''special'':27C ''specif'':3A,16C,61C ''statist'':34C ''studi'':66C ''urban'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8200, 2, '2015-09-25 08:23:08.34', 1, NULL, NULL, '&lt;p&gt;When you are using mallet, you can fix a random seed using the command line flag &lt;code&gt;--random-seed&lt;/code&gt;. This makes your results reproducible.&lt;/p&gt;

&lt;p&gt;It does not and cannot remove the fact, that different random seeds produce different topic models.&lt;/p&gt;
', NULL, NULL, '''/code'':22C ''/p'':28C,46C ''cannot'':34C ''code'':18C ''command'':15C ''differ'':39C,43C ''fact'':37C ''fix'':9C ''flag'':17C ''line'':16C ''make'':24C ''mallet'':6C ''model'':45C ''p'':1C,29C ''produc'':42C ''random'':11C,20C,40C ''random-se'':19C ''remov'':35C ''reproduc'':27C ''result'':26C ''seed'':12C,21C,41C ''topic'':44C ''use'':5C,13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8202, 1, '2015-09-25 11:00:38.053', 1, 38, 'Averaging on skewed distribution', '&lt;p&gt;I have to compose a feature which summarizes the blocks area of different sections of cities. (A block is defined as the space contained by streets).&lt;/p&gt;

&lt;p&gt;I could compute the arithmetic average of areas, but the data is often very skewed in the sections. I could compute the arithmetic average of log(areas), which transform the data to a more normal distribution, but the problem is that the data is skewed differently section by section.
How would you create this feature?&lt;/p&gt;
', 'feature-extraction', NULL, '''/p'':35C,92C ''area'':19C,44C,63C ''arithmet'':41C,59C ''averag'':1A,42C,60C ''block'':18C,26C ''citi'':24C ''compos'':12C ''comput'':39C,57C ''contain'':32C ''could'':38C,56C ''creat'':89C ''data'':47C,67C,79C ''defin'':28C ''differ'':21C,82C ''distribut'':4A,72C ''extract'':7B ''featur'':6B,14C,91C ''feature-extract'':5B ''log'':62C ''normal'':71C ''often'':49C ''p'':8C,36C ''problem'':75C ''section'':22C,54C,83C,85C ''skew'':3A,51C,81C ''space'':31C ''street'':34C ''summar'':16C ''transform'':65C ''would'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8203, 1, '2015-09-25 15:28:09.677', -1, 76, 'Beggining in machine learning', '&lt;p&gt;I just want to know which books, courses,videos, links,etc do you recommend me to start in machine learning, neural networks, languajes most commonly used. I want to start from zero, just in the begging of all beacuse I have not experience in this kind of algorithms but it''s something that call my attention. Thank you!&lt;/p&gt;
', 'machine-learning  beginner', NULL, '''/p'':68C ''algorithm'':57C ''attent'':65C ''beacus'':48C ''beg'':45C ''beggin'':1A ''beginn'':8B ''book'':16C ''call'':63C ''common'':34C ''cours'':17C ''etc'':20C ''experi'':52C ''kind'':55C ''know'':14C ''languaj'':32C ''learn'':4A,7B,29C ''link'':19C ''machin'':3A,6B,28C ''machine-learn'':5B ''network'':31C ''neural'':30C ''p'':9C ''recommend'':23C ''someth'':61C ''start'':26C,39C ''thank'':66C ''use'':35C ''video'':18C ''want'':12C,37C ''zero'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8211, 2, '2015-09-26 09:52:25.717', 0, NULL, NULL, '&lt;p&gt;You can try this.
&lt;a href="https://github.com/databricks/spark-redshift" rel="nofollow"&gt;https://github.com/databricks/spark-redshift&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Otherwise use Rahul''s answer, unload data to s3 then load it into SPARK. EMR Spark is on top of YARN and Spark context''s textFile method supports "s3//" natively.&lt;/p&gt;
', NULL, NULL, '''/databricks/spark-redshift'':10C ''/databricks/spark-redshift&lt;/a&gt;&lt;/p&gt;'':15C ''/p'':47C ''answer'':21C ''context'':40C ''data'':23C ''emr'':31C ''github.com'':9C,14C ''github.com/databricks/spark-redshift'':8C ''github.com/databricks/spark-redshift&lt;/a&gt;&lt;/p&gt;'':13C ''href'':7C ''load'':27C ''method'':43C ''nativ'':46C ''nofollow'':12C ''otherwis'':17C ''p'':1C,16C ''rahul'':19C ''rel'':11C ''s3'':25C,45C ''spark'':30C,32C,39C ''support'':44C ''textfil'':42C ''top'':35C ''tri'':4C ''unload'':22C ''use'':18C ''yarn'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8204, 2, '2015-09-25 16:10:13.913', 0, NULL, NULL, '&lt;p&gt;Coursera is currently offering a course on Machine learning with collaboration from MIT. Many says its strongly recommended.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.coursera.org/learn/machine-learning" rel="nofollow"&gt;https://www.coursera.org/learn/machine-learning&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;But I found the below course from Edx more interesting.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.coursera.org/learn/machine-learning" rel="nofollow"&gt;https://www.coursera.org/learn/machine-learning&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;It also provides hands on the Microsofts New exclusive Machine learning platform On Azure. &lt;/p&gt;
', NULL, NULL, '''/learn/machine-learning'':26C,49C ''/learn/machine-learning&lt;/a&gt;&lt;/p&gt;'':31C,54C ''/p'':20C,43C,70C ''also'':57C ''azur'':69C ''collabor'':12C ''cours'':7C,38C ''coursera'':2C ''current'':4C ''edx'':40C ''exclus'':64C ''found'':35C ''hand'':59C ''href'':23C,46C ''interest'':42C ''learn'':10C,66C ''machin'':9C,65C ''mani'':15C ''microsoft'':62C ''mit'':14C ''new'':63C ''nofollow'':28C,51C ''offer'':5C ''p'':1C,21C,32C,44C,55C ''platform'':67C ''provid'':58C ''recommend'':19C ''rel'':27C,50C ''say'':16C ''strong'':18C ''www.coursera.org'':25C,30C,48C,53C ''www.coursera.org/learn/machine-learning'':24C,47C ''www.coursera.org/learn/machine-learning&lt;/a&gt;&lt;/p&gt;'':29C,52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8205, 1, '2015-09-25 17:42:53.69', 1, 40, 'Generate a set of abstract search terms', '&lt;p&gt;I am developing an application for research purposes that uses randomly generated tweets to detect emerging conceptual topics. I need to collect a large amount of them (let''s say, beyond 5 million) and due to the fact that I want the application to detect all kinds of topics, I need to generate a &lt;strong&gt;set of the most general or abstract search terms&lt;/strong&gt; I can find in order to query the stream. The problem is that I can''t figure out how or where should I search for these search terms. Let''s put it that way: if you had a dataset which consisted of the entire knowledge of the human population and you want to generate a representative subset of it, with which search terms would you query this dataset?
Is it going to be something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Technology,
People,
Politics,
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is there a set of the most abstract concepts which I can study?&lt;/p&gt;

&lt;p&gt;P.S.: Of course I know that Twitter hasn''t got the entire knowledge of humans, but you get the picture ;)&lt;/p&gt;
', 'data-mining  dataset  data', NULL, '''/code'':161C ''/p'':155C,177C,199C ''/pre'':162C ''/strong'':78C ''5'':45C ''abstract'':5A,75C,171C ''amount'':38C ''applic'':18C,56C ''beyond'':44C ''code'':157C ''collect'':35C ''concept'':172C ''conceptu'':30C ''consist'':119C ''cours'':181C ''data'':9B,12B ''data-min'':8B ''dataset'':11B,117C,146C ''detect'':28C,58C ''develop'':16C ''due'':48C ''emerg'':29C ''entir'':122C,190C ''fact'':51C ''figur'':95C ''find'':81C ''general'':73C ''generat'':1A,25C,66C,132C ''get'':196C ''go'':149C ''got'':188C ''hasn'':186C ''human'':126C,193C ''kind'':60C ''know'':183C ''knowledg'':123C,191C ''larg'':37C ''let'':41C,107C ''like'':153C ''million'':46C ''mine'':10B ''need'':33C,64C ''order'':83C ''p'':13C,163C,178C ''p.s'':179C ''peopl'':159C ''pictur'':198C ''polit'':160C ''popul'':127C ''pre'':156C ''problem'':89C ''purpos'':21C ''put'':109C ''queri'':85C,144C ''random'':24C ''repres'':134C ''research'':20C ''say'':43C ''search'':6A,76C,102C,105C,140C ''set'':3A,69C,167C ''someth'':152C ''stream'':87C ''strong'':68C ''studi'':176C ''subset'':135C ''technolog'':158C ''term'':7A,77C,106C,141C ''topic'':31C,62C ''tweet'':26C ''twitter'':185C ''use'':23C ''want'':54C,130C ''way'':112C ''would'':142C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8732, 1, '2015-11-05 19:21:27.62', 3, 109, 'Algorithm for gesture classification in a wearable', '&lt;p&gt;I''m fairly new to machine learning so please pardon me if I mess up terms. I''m working on a wearables project that involves hand gesture detection, looking for any pointers on potential algorithms that would be applicable. &lt;/p&gt;

&lt;p&gt;Basically there will be sensors that pick up tendon movement at the wrist to provide input, so for simplicity I''m assuming that would mean 5 features (one for each finger), each with values ranging from say 0-100. &lt;/p&gt;

&lt;p&gt;There are also 5 classes that the gesture could be classified as. The user would do each type of gesture several times to provide training data, and then for each gesture class I''d need an algorithm to determine the best model for it based on that training data. &lt;/p&gt;

&lt;p&gt;After initial training, whenever the user does a gesture it would input the sensor readings into each model to see which one is the closest match.
Additionally, since this is a wearable anything that optimizes for low memory and low processing power would be best.&lt;/p&gt;

&lt;p&gt;If anyone has any input on either the type of models or the algorithm with which to generate them that I should look into I''d appreciate it!&lt;/p&gt;

&lt;p&gt;Edit: Here is some &lt;a href="https://gist.github.com/lfowlie/779ef9025c9d650173f0" rel="nofollow"&gt;sample data&lt;/a&gt; from 4 sensors during 3 different gestures. These are basically just pressure readings from sensors lying along tendons on the wrist.&lt;/p&gt;
', 'machine-learning  algorithms', NULL, '''-100'':92C ''/a'':236C ''/lfowlie/779ef9025c9d650173f0'':231C ''/p'':52C,93C,144C,191C,221C,258C ''0'':91C ''3'':241C ''4'':238C ''5'':79C,98C ''addit'':172C ''algorithm'':1A,11B,47C,131C,206C ''along'':253C ''also'':97C ''anyon'':194C ''anyth'':178C ''applic'':51C ''appreci'':219C ''assum'':75C ''base'':139C ''basic'':54C,246C ''best'':135C,190C ''class'':99C,126C ''classif'':4A ''classifi'':105C ''closest'':170C ''could'':103C ''d'':128C,218C ''data'':120C,143C,235C ''detect'':40C ''determin'':133C ''differ'':242C ''edit'':223C ''either'':199C ''fair'':15C ''featur'':80C ''finger'':84C ''generat'':210C ''gestur'':3A,39C,102C,114C,125C,154C,243C ''gist.github.com'':230C ''gist.github.com/lfowlie/779ef9025c9d650173f0'':229C ''hand'':38C ''href'':228C ''initi'':147C ''input'':69C,157C,197C ''involv'':37C ''learn'':10B,19C ''lie'':252C ''look'':41C,215C ''low'':182C,185C ''m'':14C,30C,74C ''machin'':9B,18C ''machine-learn'':8B ''match'':171C ''mean'':78C ''memori'':183C ''mess'':26C ''model'':136C,163C,203C ''movement'':63C ''need'':129C ''new'':16C ''nofollow'':233C ''one'':81C,167C ''optim'':180C ''p'':12C,53C,94C,145C,192C,222C ''pardon'':22C ''pick'':60C ''pleas'':21C ''pointer'':44C ''potenti'':46C ''power'':187C ''pressur'':248C ''process'':186C ''project'':35C ''provid'':68C,118C ''rang'':88C ''read'':160C,249C ''rel'':232C ''sampl'':234C ''say'':90C ''see'':165C ''sensor'':58C,159C,239C,251C ''sever'':115C ''simplic'':72C ''sinc'':173C ''tendon'':62C,254C ''term'':28C ''time'':116C ''train'':119C,142C,148C ''type'':112C,201C ''user'':108C,151C ''valu'':87C ''wearabl'':7A,34C,177C ''whenev'':149C ''work'':31C ''would'':49C,77C,109C,156C,188C ''wrist'':66C,257C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8207, 1, '2015-09-25 23:04:36.723', 5, 85, 'Predictive models with class value belonging to a set of observations', '&lt;p&gt;I would like to know whether it''s possible to build a predictive model where I could define a set of rows with their attributes, and a class belonging to that set of rows, instead of having the typical model one observation - one class.&lt;/p&gt;

&lt;p&gt;What I''m trying to do is to create a model which should be able to predict the future level of a rider in professional cycling - more likely to be used to forecast young riders'' level . The input data are the results of each rider (race,category of race, type of parcours, position, and so on). The class is represented with the value (from 1 to 10) each "known" rider has been assigned for each skill (mountain, flat, time trial, sprint, etc.), which means that in principle, I would make one predictive model for each skill (what means for each class). For example, for mountain class I would have the following:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Rider   Category    Race Type   Parcours    Distance    Position    Class(Mountain)
-----------------------------------------------------------------------------
Froome  GrandTour   Stage       Mountain    185         1           10
Froome  WorldTour   Stage       MidMountain 210         25          10
Nibali  Contin.Tour Stage       Mountain    170         15          9
Nibali  WorldTour   Classic     MidMountain 245         3           9
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I see here two main challenges, apart from the fact of dealing with both numerical and categorical attributes.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;A rider gathers a lot of results, and each of them will result in the same class value. For example, a victory in a Grand Tour category stage from Chris Froome would be rated with a 10 in mountain, but he would also be rated with a 10 in mountain when he ended up in a bad position in a lower level stage - just as in the example. Ok, it''s supposed that with thousands of rows the model (regression model probably, rather than a classification algorithm, but not sure of that either) should be able to detect the "common" pattern (a good performance in mountain from a good climber like Froome), so it could be overcome. I guess it''d be a matter of testing and finding the best-fitted models.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;I find the predictive part the most problematic one, because what I need isn''t to predict the class value for each observation (which is represented by each result), but the class value for each rider. That''s why I said at the beginning of the thread that I would need to somehow treat all the results of a rider as a unique observation (something like a set or group of observations), and assign the class value to that set. A workaround could be to take all the observations of the rider, predict the class for each of them, and then calculate the average, but it doesn''t look very smart.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;So, any idea of how to build this kind of models?&lt;/p&gt;

&lt;p&gt;Thanks in advance.&lt;/p&gt;
', 'classification  predictive-modeling  regression  feature-selection  feature-construction', NULL, '''/code'':221C ''/li'':380C,495C ''/ol'':496C ''/p'':68C,181C,242C,379C,494C,509C,514C ''/pre'':222C ''1'':134C,198C ''10'':136C,199C,206C,282C,293C ''15'':212C ''170'':211C ''185'':197C ''210'':204C ''245'':218C ''25'':205C ''3'':219C ''9'':213C,220C ''abl'':84C,341C ''advanc'':513C ''algorithm'':332C ''also'':288C ''apart'':230C ''assign'':142C,456C ''attribut'':48C,241C ''averag'':486C ''bad'':302C ''begin'':426C ''belong'':6A,52C ''best'':376C ''best-fit'':375C ''build'':34C,504C ''calcul'':484C ''categor'':240C ''categori'':116C,185C,272C ''challeng'':229C ''chris'':275C ''class'':4A,51C,67C,127C,170C,175C,191C,262C,401C,414C,458C,477C ''classic'':216C ''classif'':12B,331C ''climber'':355C ''code'':183C ''common'':345C ''construct'':22B ''contin.tour'':208C ''could'':40C,360C,465C ''creat'':78C ''cycl'':95C ''d'':366C ''data'':108C ''deal'':235C ''defin'':41C ''detect'':343C ''distanc'':189C ''doesn'':489C ''either'':338C ''end'':298C ''etc'':151C ''exampl'':172C,265C,313C ''fact'':233C ''featur'':18B,21B ''feature-construct'':20B ''feature-select'':17B ''find'':373C,384C ''fit'':377C ''flat'':147C ''follow'':180C ''forecast'':102C ''froom'':193C,200C,276C,357C ''futur'':88C ''gather'':248C ''good'':348C,354C ''grand'':270C ''grandtour'':194C ''group'':452C ''guess'':364C ''idea'':500C ''input'':107C ''instead'':58C ''isn'':396C ''kind'':506C ''know'':28C ''known'':138C ''level'':89C,105C,307C ''li'':244C,381C ''like'':26C,97C,356C,448C ''look'':491C ''lot'':250C ''lower'':306C ''m'':72C ''main'':228C ''make'':159C ''matter'':369C ''mean'':153C,167C ''midmountain'':203C,217C ''model'':2A,15B,37C,63C,80C,162C,324C,326C,378C,508C ''mountain'':146C,174C,192C,196C,210C,284C,295C,351C ''need'':395C,433C ''nibali'':207C,214C ''numer'':238C ''observ'':11A,65C,405C,446C,454C,471C ''ok'':314C ''ol'':243C ''one'':64C,66C,160C,391C ''overcom'':362C ''p'':23C,69C,223C,245C,382C,497C,510C ''parcour'':121C,188C ''part'':387C ''pattern'':346C ''perform'':349C ''posit'':122C,190C,303C ''possibl'':32C ''pre'':182C ''predict'':1A,14B,36C,86C,161C,386C,399C,475C ''predictive-model'':13B ''principl'':156C ''probabl'':327C ''problemat'':390C ''profession'':94C ''race'':115C,118C,186C ''rate'':279C,290C ''rather'':328C ''regress'':16B,325C ''repres'':129C,408C ''result'':111C,252C,258C,411C,439C ''rider'':92C,104C,114C,139C,184C,247C,418C,442C,474C ''row'':45C,57C,322C ''said'':423C ''see'':225C ''select'':19B ''set'':9A,43C,55C,450C,462C ''skill'':145C,165C ''smart'':493C ''somehow'':435C ''someth'':447C ''sprint'':150C ''stage'':195C,202C,209C,273C,308C ''suppos'':317C ''sure'':335C ''take'':468C ''test'':371C ''thank'':511C ''thousand'':320C ''thread'':429C ''time'':148C ''tour'':271C ''treat'':436C ''tri'':73C ''trial'':149C ''two'':227C ''type'':119C,187C ''typic'':62C ''uniqu'':445C ''use'':100C ''valu'':5A,132C,263C,402C,415C,459C ''victori'':267C ''whether'':29C ''workaround'':464C ''worldtour'':201C,215C ''would'':25C,158C,177C,277C,287C,432C ''young'':103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8210, 2, '2015-09-26 02:36:51.623', 0, NULL, NULL, '&lt;p&gt;The answers about Gibbs Sampling are misleading, in my opinion. &lt;/p&gt;

&lt;p&gt;The reason for this is that Gibbs Sampling leads to &lt;em&gt;some&lt;/em&gt; error due to resampling. All Bayesian methods that use MCMC techniques face this issue, and it''s really not that much of a problem, as under standard conditions, you can put bounds on your MCMC error. &lt;/p&gt;

&lt;p&gt;But this is not one of those standard conditions, as Latent Dirichlet Allocation leads to a multi-modal posterior! As such, standard sampling methods will typically find a single mode and sample around that single mode. The sampling techniques may well miss other modes, which may in fact have a higher posterior probability than the one found. Often, in these types of problems, several starts from different initial parameters are run and the solutions are compared. There''s more advanced samplers that attempt to deal with this problem as well, such as a sampler that occasionally attempts to sample far from the current mode in the hopes of hitting another mode. Odds are that if you find a mode, it''s probably a pretty good solution, even if not optimal, but that is certainly not insured!&lt;/p&gt;

&lt;p&gt;In regards to making results replicable, if you want get the same solution, you have to start from the same place, with the same random seed. &lt;/p&gt;
', NULL, NULL, '''/em'':26C ''/p'':12C,63C,202C,230C ''advanc'':145C ''alloc'':77C ''anoth'':175C ''answer'':3C ''around'':98C ''attempt'':148C,162C ''bayesian'':32C ''bound'':58C ''certain'':199C ''compar'':141C ''condit'':54C,73C ''current'':168C ''deal'':150C ''differ'':132C ''dirichlet'':76C ''due'':28C ''em'':24C ''error'':27C,62C ''even'':192C ''face'':38C ''fact'':113C ''far'':165C ''find'':92C,182C ''found'':122C ''get'':213C ''gibb'':5C,20C ''good'':190C ''higher'':116C ''hit'':174C ''hope'':172C ''initi'':133C ''insur'':201C ''issu'':40C ''latent'':75C ''lead'':22C,78C ''make'':207C ''may'':105C,111C ''mcmc'':36C,61C ''method'':33C,89C ''mislead'':8C ''miss'':107C ''modal'':83C ''mode'':95C,101C,109C,169C,176C,184C ''much'':47C ''multi'':82C ''multi-mod'':81C ''occasion'':161C ''odd'':177C ''often'':123C ''one'':69C,121C ''opinion'':11C ''optim'':195C ''p'':1C,13C,64C,203C ''paramet'':134C ''place'':224C ''posterior'':84C,117C ''pretti'':189C ''probabl'':118C,187C ''problem'':50C,128C,153C ''put'':57C ''random'':228C ''realli'':44C ''reason'':15C ''regard'':205C ''replic'':209C ''resampl'':30C ''result'':208C ''run'':136C ''sampl'':6C,21C,88C,97C,103C,164C ''sampler'':146C,159C ''seed'':229C ''sever'':129C ''singl'':94C,100C ''solut'':139C,191C,216C ''standard'':53C,72C,87C ''start'':130C,220C ''techniqu'':37C,104C ''type'':126C ''typic'':91C ''use'':35C ''want'':212C ''well'':106C,155C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8212, 2, '2015-09-26 10:30:28.03', 1, NULL, NULL, '&lt;p&gt;Looks like you have two problems: time-dependant predictive modeling and feature engineering.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;1) Time-dependant data&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Key words:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;create a model which should be able to predict &lt;strong&gt;the future level&lt;/strong&gt; of a rider in professional cycling&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The &lt;em&gt;future&lt;/em&gt; Level of the Rider.&lt;/p&gt;

&lt;p&gt;That means there is &lt;em&gt;current&lt;/em&gt; Level. And Level &lt;em&gt;in the past&lt;/em&gt;. And the history of Level change in time for each Rider.&lt;/p&gt;

&lt;p&gt;The problem you are trying to solve is time dependant. Your source data can look like:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/eNQKD.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/eNQKD.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;2) Build a variable to predict&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Second. You should build the target feature. This Rider Level (Class).&lt;/p&gt;

&lt;p&gt;As far as I understood:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;the overall rider ''level'' can be a function of his ''classes'' in each particular race types&lt;/li&gt;
&lt;li&gt;and... the race ''observation'' class is a kind of function of &lt;code&gt;{Category, Race Type, Parcours, Distance, Position}&lt;/code&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;So the only thing you realy need to predict is race result.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/TAwV6.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/TAwV6.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':128C,235C ''/blockquote'':53C ''/code'':198C ''/em'':58C,71C,79C ''/enqkd.png'':115C,122C ''/li'':178C,199C ''/p'':16C,25C,29C,52C,63C,91C,109C,129C,139C,152C,159C,216C,236C ''/strong'':24C,45C,138C,215C ''/tawv6.png'':222C,229C ''/ul'':200C ''1'':19C ''2'':132C ''abl'':38C ''alt'':123C,230C ''blockquot'':30C ''build'':133C,144C ''categori'':192C ''chang'':85C ''class'':151C,172C,184C ''code'':191C ''creat'':32C ''current'':70C ''cycl'':51C ''data'':23C,105C ''depend'':10C,22C,102C ''descript'':126C,233C ''distanc'':196C ''em'':56C,69C,75C ''engin'':15C ''enter'':124C,231C ''far'':155C ''featur'':14C,147C ''function'':169C,189C ''futur'':43C,57C ''histori'':82C ''href'':112C,219C ''i.stack.imgur.com'':114C,121C,221C,228C ''i.stack.imgur.com/enqkd.png'':113C,120C ''i.stack.imgur.com/tawv6.png'':220C,227C ''imag'':125C,232C ''img'':118C,225C ''key'':27C ''kind'':187C ''level'':44C,59C,72C,74C,84C,150C,165C ''li'':161C,179C ''like'':3C,108C ''look'':2C,107C ''mean'':66C ''model'':12C,34C ''need'':209C ''nofollow'':117C,224C ''observ'':183C ''overal'':163C ''p'':1C,17C,26C,31C,54C,64C,92C,110C,130C,140C,153C,201C,217C ''parcour'':195C ''particular'':175C ''past'':78C ''posit'':197C ''predict'':11C,40C,137C,211C ''problem'':7C,94C ''profession'':50C ''race'':176C,182C,193C,213C ''reali'':208C ''rel'':116C,223C ''result'':214C ''rider'':48C,62C,90C,149C,164C ''second'':141C ''solv'':99C ''sourc'':104C ''src'':119C,226C ''strong'':18C,41C,131C,202C ''target'':146C ''thing'':206C ''time'':9C,21C,87C,101C ''time-depend'':8C,20C ''tri'':97C ''two'':6C ''type'':177C,194C ''ul'':160C ''understood'':158C ''variabl'':135C ''word'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8213, 1, '2015-09-26 11:58:37.963', 5, 150, 'Classifier and Technique to use for large number of categories', '&lt;p&gt;I am designing a scikit learn classifier for a sequence labelling task which has 5000+ categories and training data is at least 80 million and may grow upto an additional 100 million each year. I have already tried with all the categories but it generates classifiers in the order of 100s of GBs binary file. So I think that having one classifier for each category would be helpful and would also help me to fine tune features for each category thereby improving accuracy, but this means 5k+ classifiers for each of these categories. So how to handle this large data requirements and which incremental classifiers to use for this case , considering the fact that I will keep on getting additional training data as well as may discover new categories?&lt;/p&gt;

&lt;p&gt;The number of features are about 100+ and due to the sequence labelling task contiguous sequence of training samples share same features values. The feature values are mostly text based and most are categorical with long text based values  with large cardinality i.e many features may have huge number of possible values. &lt;/p&gt;

&lt;p&gt;The available RAM IS 32gb with 8 core CPU. On a small scale I tried Multinomial NB and linear SGD with sparse matrices which are extremely sparse. Used the scikit learns Dictvectorizer to vectorize the feature dictionary. Also will pandas dataframes help to optimize the overall configuration?&lt;/p&gt;

&lt;p&gt;Due to the scale of data involved I have only used about 20% of the data. And the features itself are 56GB of data from the 500MB input data. For the restricted training data, The precision too is very low does not reach above 12% with a very low  recall too(1-2%). Have gone through vowpal wabbit and  found the SEARN task but it seems that it is now no longer available in the latest version .&lt;/p&gt;
', 'machine-learning  bigdata  dataset  scikit  categorical-data', NULL, '''-2'':307C ''/p'':150C,204C,253C,332C ''1'':306C ''100'':51C,158C ''100s'':71C ''12'':299C ''20'':267C ''32gb'':210C ''5000'':35C ''500mb'':281C ''56gb'':276C ''5k'':107C ''8'':212C ''80'':43C ''accuraci'':103C ''addit'':50C,140C ''alreadi'':57C ''also'':91C,243C ''avail'':207C,327C ''base'':181C,189C ''bigdata'':14B ''binari'':74C ''cardin'':193C ''case'':130C ''categor'':18B,185C ''categori'':10A,36C,62C,85C,100C,113C,149C ''categorical-data'':17B ''classifi'':1A,27C,66C,82C,108C,125C ''configur'':252C ''consid'':131C ''contigu'':166C ''core'':213C ''cpu'':214C ''data'':19B,39C,120C,142C,260C,270C,278C,283C,288C ''datafram'':246C ''dataset'':15B ''design'':23C ''dictionari'':242C ''dictvector'':237C ''discov'':147C ''due'':160C,255C ''extrem'':231C ''fact'':133C ''featur'':97C,155C,173C,176C,196C,241C,273C ''file'':75C ''fine'':95C ''found'':314C ''gbs'':73C ''generat'':65C ''get'':139C ''gone'':309C ''grow'':47C ''handl'':117C ''help'':88C,92C,247C ''huge'':199C ''i.e'':194C ''improv'':102C ''increment'':124C ''input'':282C ''involv'':261C ''keep'':137C ''label'':31C,164C ''larg'':7A,119C,192C ''latest'':330C ''learn'':13B,26C,236C ''least'':42C ''linear'':224C ''long'':187C ''longer'':326C ''low'':294C,303C ''machin'':12B ''machine-learn'':11B ''mani'':195C ''matric'':228C ''may'':46C,146C,197C ''mean'':106C ''million'':44C,52C ''most'':179C ''multinomi'':221C ''nb'':222C ''new'':148C ''number'':8A,153C,200C ''one'':81C ''optim'':249C ''order'':69C ''overal'':251C ''p'':20C,151C,205C,254C ''panda'':245C ''possibl'':202C ''precis'':290C ''ram'':208C ''reach'':297C ''recal'':304C ''requir'':121C ''restrict'':286C ''sampl'':170C ''scale'':218C,258C ''scikit'':16B,25C,235C ''searn'':316C ''seem'':320C ''sequenc'':30C,163C,167C ''sgd'':225C ''share'':171C ''small'':217C ''spars'':227C,232C ''task'':32C,165C,317C ''techniqu'':3A ''text'':180C,188C ''therebi'':101C ''think'':78C ''train'':38C,141C,169C,287C ''tri'':58C,220C ''tune'':96C ''upto'':48C ''use'':5A,127C,233C,265C ''valu'':174C,177C,190C,203C ''vector'':239C ''version'':331C ''vowpal'':311C ''wabbit'':312C ''well'':144C ''would'':86C,90C ''year'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8228, 2, '2015-09-28 23:40:09.62', 0, NULL, NULL, '&lt;p&gt;You should include data when the phone was serviced to create a survival model. These models are commonly used in reliability engineering as well as treatment efficacy. For reliability engineering it is very common to fit your data to a Weibull distribution. Even aircraft manufacturers consider the model to be reliable after calibrating with three to five data points. I can highly recommend the R package ''flexsurv'' package.&lt;/p&gt;

&lt;p&gt;You cannot use typical linear or logistic regressions since some phones in your population will leave your observation period without ever being serviced. Survival models allow for this sort of missing information (this is called censoring). &lt;/p&gt;

&lt;p&gt;Typically you would have the following data&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;|ID| Type       | serviced  | # months_since_purchase
|1 | iphone     | 1         | 12
|2 | iphone     | 0         | 15
|3 | android    | 1         | 2
|4 | android    | 0         | 10
|5 | blackberry | 0         | 5.5
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;With that data you could create the following model in R&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;require(survival)
model &amp;lt;- survfit(Surv(months_since_purchase, serviced) ~ strata(Type) +
 cluster(ID), data = phone_repairs)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The &lt;code&gt;survfit.formula&lt;/code&gt; &lt;code&gt;Surv(months_since_purchase, serviced) ~ strata(Type) + cluster(ID)&lt;/code&gt;
indicates that &lt;code&gt;months_since_purchase&lt;/code&gt; is the time at which an observation was
made, &lt;code&gt;serviced&lt;/code&gt; is 1 if the phone was serviced and 0 otherwise, &lt;code&gt;strata(Type)&lt;/code&gt;
will make sure that you create a different survival model for each phone,
&lt;code&gt;cluster(ID)&lt;/code&gt; will make sure that events relating to the same ID are considered
as a cluster. &lt;/p&gt;

&lt;p&gt;You could extend this model with Joint Models such as &lt;code&gt;JM&lt;/code&gt;.&lt;/p&gt;
', NULL, NULL, '''/code'':146C,180C,186C,197C,204C,216C,230C,247C,277C ''/p'':70C,108C,117C,160C,263C,278C ''/pre'':147C,181C ''0'':132C,140C,144C,225C ''1'':126C,128C,136C,218C ''10'':141C ''12'':129C ''15'':133C ''2'':130C,137C ''3'':134C ''4'':138C ''5'':142C ''5.5'':145C ''aircraft'':45C ''allow'':97C ''android'':135C,139C ''blackberri'':143C ''calibr'':54C ''call'':106C ''cannot'':73C ''censor'':107C ''cluster'':175C,195C,245C,262C ''code'':119C,162C,184C,187C,200C,214C,227C,244C,275C ''common'':19C,35C ''consid'':47C,259C ''could'':153C,266C ''creat'':12C,154C,236C ''data'':5C,39C,59C,116C,151C,177C ''differ'':238C ''distribut'':43C ''efficaci'':28C ''engin'':23C,31C ''even'':44C ''event'':252C ''ever'':92C ''extend'':267C ''fit'':37C ''five'':58C ''flexsurv'':68C ''follow'':115C,156C ''high'':63C ''id'':120C,176C,196C,246C,257C ''includ'':4C ''indic'':198C ''inform'':103C ''iphon'':127C,131C ''jm'':276C ''joint'':271C ''leav'':87C ''linear'':76C ''logist'':78C ''lt'':166C ''made'':213C ''make'':232C,249C ''manufactur'':46C ''miss'':102C ''model'':15C,17C,49C,96C,157C,165C,240C,269C,272C ''month'':123C,169C,189C,201C ''observ'':89C,211C ''otherwis'':226C ''p'':1C,71C,109C,148C,182C,264C ''packag'':67C,69C ''period'':90C ''phone'':8C,82C,178C,221C,243C ''point'':60C ''popul'':85C ''pre'':118C,161C ''purchas'':125C,171C,191C,203C ''r'':66C,159C ''recommend'':64C ''regress'':79C ''relat'':253C ''reliabl'':22C,30C,52C ''repair'':179C ''requir'':163C ''servic'':10C,94C,122C,172C,192C,215C,223C ''sinc'':80C,124C,170C,190C,202C ''sort'':100C ''strata'':173C,193C,228C ''sure'':233C,250C ''surv'':168C,188C ''survfit'':167C ''survfit.formula'':185C ''surviv'':14C,95C,164C,239C ''three'':56C ''time'':207C ''treatment'':27C ''type'':121C,174C,194C,229C ''typic'':75C,110C ''use'':20C,74C ''weibul'':42C ''well'':25C ''without'':91C ''would'':112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8733, 2, '2015-11-06 02:18:46.443', 2, NULL, NULL, '&lt;p&gt;I''ll share what Big Data is like in genomics, in particular de-novo assembly.&lt;/p&gt;

&lt;p&gt;When we sequence your genome (eg: detect novel genes), we take billions of next-generation short reads. Look at the image below, where we try to assemble some reads.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/TaZ5S.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/TaZ5S.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This looks simple? But what if you have billion of those reads? What if those reads contain sequence errors? What if your RAM doesn''t have enough memory to keep the reads? What about repetitive DNA regions, such as the very common &lt;a href="https://en.wikipedia.org/wiki/Alu_element" rel="nofollow"&gt;Alu Element&lt;/a&gt;?&lt;/p&gt;

&lt;p&gt;De-novo assembly is done by constructing a &lt;a href="https://en.wikipedia.org/wiki/De_Bruijn_graph" rel="nofollow"&gt;De-Bruijn graph&lt;/a&gt;:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/juiy3.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/juiy3.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The graph is a clever-mined data-structure to represent overlapping reads. It''s not perfect but it''s better than generating all possible overlaps and store them in an array.&lt;/p&gt;

&lt;p&gt;The assembly process could take days to complete, because there are quite a number of paths that an assembler would need to traverse and collapse.&lt;/p&gt;

&lt;p&gt;In genomics, you have a big data when:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;You can''t brute force all combinations&lt;/li&gt;
&lt;li&gt;Your computer doesn''t have enough physical memory to store the data&lt;/li&gt;
&lt;li&gt;You need to reduce the dimensions (eg: collapsing redundant graph paths)&lt;/li&gt;
&lt;li&gt;You get pissed off because you''d have to wait days to do anything&lt;/li&gt;
&lt;li&gt;You need a special data structure to represent the data&lt;/li&gt;
&lt;li&gt;You need to filter your data-set for errors (eg: sequencing errors) &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/De_Bruijn_graph" rel="nofollow"&gt;https://en.wikipedia.org/wiki/De_Bruijn_graph&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':69C,123C,146C,166C ''/juiy3.png'':153C,160C ''/li'':249C,263C,276C,292C,304C,319C ''/p'':18C,50C,70C,124C,147C,167C,202C,229C,239C ''/taz5s.png'':56C,63C ''/ul'':320C ''/wiki/alu_element'':118C ''/wiki/de_bruijn_graph'':139C,326C ''/wiki/de_bruijn_graph&lt;/a&gt;&lt;/p&gt;'':331C ''alt'':64C,161C ''alu'':121C ''anyth'':291C ''array'':201C ''assembl'':17C,47C,129C,205C,222C ''better'':190C ''big'':6C,236C ''billion'':31C,80C ''bruijn'':144C ''brute'':245C ''clever'':174C ''clever-min'':173C ''collaps'':228C,272C ''combin'':248C ''common'':113C ''complet'':211C ''comput'':252C ''construct'':133C ''contain'':88C ''could'':207C ''d'':284C ''data'':7C,177C,237C,262C,298C,303C,312C ''data-set'':311C ''data-structur'':176C ''day'':209C,288C ''de'':15C,127C,143C ''de-bruijn'':142C ''de-novo'':14C,126C ''descript'':67C,164C ''detect'':26C ''dimens'':270C ''dna'':107C ''doesn'':95C,253C ''done'':131C ''eg'':25C,271C,316C ''element'':122C ''en.wikipedia.org'':117C,138C,325C,330C ''en.wikipedia.org/wiki/alu_element'':116C ''en.wikipedia.org/wiki/de_bruijn_graph'':137C,324C ''en.wikipedia.org/wiki/de_bruijn_graph&lt;/a&gt;&lt;/p&gt;'':329C ''enough'':98C,256C ''enter'':65C,162C ''error'':90C,315C,318C ''filter'':309C ''forc'':246C ''gene'':28C ''generat'':35C,192C ''genom'':11C,24C,232C ''get'':279C ''graph'':145C,170C,274C ''href'':53C,115C,136C,150C,323C ''i.stack.imgur.com'':55C,62C,152C,159C ''i.stack.imgur.com/juiy3.png'':151C,158C ''i.stack.imgur.com/taz5s.png'':54C,61C ''imag'':41C,66C,163C ''img'':59C,156C ''keep'':101C ''li'':241C,250C,264C,277C,293C,305C ''like'':9C ''ll'':3C ''look'':38C,73C ''memori'':99C,258C ''mine'':175C ''need'':224C,266C,295C,307C ''next'':34C ''next-gener'':33C ''nofollow'':58C,120C,141C,155C,328C ''novel'':27C ''novo'':16C,128C ''number'':217C ''overlap'':181C,195C ''p'':1C,19C,51C,71C,125C,148C,168C,203C,230C,321C ''particular'':13C ''path'':219C,275C ''perfect'':186C ''physic'':257C ''piss'':280C ''possibl'':194C ''process'':206C ''quit'':215C ''ram'':94C ''read'':37C,49C,83C,87C,103C,182C ''reduc'':268C ''redund'':273C ''region'':108C ''rel'':57C,119C,140C,154C,327C ''repetit'':106C ''repres'':180C,301C ''sequenc'':22C,89C,317C ''set'':313C ''share'':4C ''short'':36C ''simpl'':74C ''special'':297C ''src'':60C,157C ''store'':197C,260C ''structur'':178C,299C ''take'':30C,208C ''travers'':226C ''tri'':45C ''ul'':240C ''wait'':287C ''would'':223C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (15, 1, '2014-05-14 01:41:23.11', 2, 164, 'What are the advantages and disadvantages of SQL versus NoSQL in data science?', '&lt;p&gt;In which situations would one system be preferred over the other? What are the relative advantages and disadvantages of relational databases versus non-relational databases?&lt;/p&gt;
', 'databases', NULL, '''/p'':42C ''advantag'':4A,31C ''data'':12A ''databas'':14B,36C,41C ''disadvantag'':6A,33C ''non'':39C ''non-rel'':38C ''nosql'':10A ''one'':20C ''p'':15C ''prefer'':23C ''relat'':30C,35C,40C ''scienc'':13A ''situat'':18C ''sql'':8A ''system'':21C ''versus'':9A,37C ''would'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9093, 5, '2015-11-28 14:59:17.057', 0, NULL, NULL, '&lt;p&gt;Finding hidden (statistical) structure in unlabelled data, including clustering and feature extraction for dimensionality reduction&lt;/p&gt;

&lt;p&gt;Because the items are unlabelled, there''s nothing that points toward the "correct" labels, as there is with supervised learning. Unsupervised learning uses methods like clustering and principal components analysis to discover structure.&lt;/p&gt;

&lt;p&gt;Reference:&lt;br&gt;
&lt;a href="http://en.wikipedia.org/wiki/Unsupervised_learning" rel="nofollow"&gt;Wikipedia - Unsupervised learning&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':66C ''/p'':17C,52C,67C ''/wiki/unsupervised_learning'':60C ''analysi'':48C ''br'':55C ''cluster'':10C,44C ''compon'':47C ''correct'':31C ''data'':8C ''dimension'':15C ''discov'':50C ''en.wikipedia.org'':59C ''en.wikipedia.org/wiki/unsupervised_learning'':58C ''extract'':13C ''featur'':12C ''find'':2C ''hidden'':3C ''href'':57C ''includ'':9C ''item'':21C ''label'':32C ''learn'':38C,40C,65C ''like'':43C ''method'':42C ''nofollow'':62C ''noth'':26C ''p'':1C,18C,53C ''point'':28C ''princip'':46C ''reduct'':16C ''refer'':54C ''rel'':61C ''statist'':4C ''structur'':5C,51C ''supervis'':37C ''toward'':29C ''unlabel'':7C,23C ''unsupervis'':39C,64C ''use'':41C ''wikipedia'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8216, 2, '2015-09-27 09:43:24.94', 1, NULL, NULL, '&lt;p&gt;The script below uses LSA with transformed IDF to cut off the parameters from templates. The idea is, that all terms with IDF higher that some threshold are considered as parameters and their frequency is reset to zero.  The threshold can be approximated with the average template occurrence in the corpus.
Eliminating the parameters, the distance of the records with the same template is zero.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; library(tm)
 library(lsa)
 df &amp;lt;- data.frame(TEMPLATE = c(rep("A",3),rep("B",3),rep("C",3)),
 TEXT = c(
 paste("Temperature today is",c(28,24,20),"centigrades"),
 paste("Temperature today is",c(82,75,68),"Fahrenheit"),
 paste("Her eyes are ",c("blue","black","green"), "and hair",c("grey","brown","white"))) , stringsAsFactors=FALSE)
&amp;gt; df     
   TEMPLATE                                TEXT
 1        A Temperature today is 28 centigrades
 2        A Temperature today is 24 centigrades
 3        A Temperature today is 20 centigrades
 4        B  Temperature today is 82 Fahrenheit
 5        B  Temperature today is 75 Fahrenheit
 6        B  Temperature today is 68 Fahrenheit
 7        C    Her eyes are  blue and hair grey
 8        C  Her eyes are  black and hair brown
 9        C  Her eyes are  green and hair white

 corpus &amp;lt;- Corpus(VectorSource(df$TEXT))
 td &amp;lt;- as.matrix(TermDocumentMatrix(corpus,control=list(wordLengths = c(1, Inf)) ))

 &amp;gt; td             Docs
 Terms         1 2 3 4 5 6 7 8 9
   20          0 0 1 0 0 0 0 0 0
   24          0 1 0 0 0 0 0 0 0
   28          1 0 0 0 0 0 0 0 0
   68          0 0 0 0 0 1 0 0 0
   75          0 0 0 0 1 0 0 0 0
   82          0 0 0 1 0 0 0 0 0
   and         0 0 0 0 0 0 1 1 1
   are         0 0 0 0 0 0 1 1 1
   black       0 0 0 0 0 0 0 1 0
   blue        0 0 0 0 0 0 1 0 0
   brown       0 0 0 0 0 0 0 1 0
   centigrades 1 1 1 0 0 0 0 0 0
   eyes        0 0 0 0 0 0 1 1 1
   fahrenheit  0 0 0 1 1 1 0 0 0
   green       0 0 0 0 0 0 0 0 1
   grey        0 0 0 0 0 0 1 0 0
   hair        0 0 0 0 0 0 1 1 1
   her         0 0 0 0 0 0 1 1 1
   is          1 1 1 1 1 1 0 0 0
   temperature 1 1 1 1 1 1 0 0 0
   today       1 1 1 1 1 1 0 0 0
   white       0 0 0 0 0 0 0 0 1

 ## supress terms with idf higher than template frequency
 ## those terms are considered as parameters
 template_freq &amp;lt;- 3
 tdw &amp;lt;- lw_bintf(td) * ifelse(gw_idf(td)&amp;gt; template_freq,0, gw_idf(td))
 dist &amp;lt;- dist(t(as.matrix(tdw)))

 &amp;gt; dist
          1        2        3        4        5        6        7        8
 2 0.000000                                                               
 3 0.000000 0.000000                                                      
 4 3.655689 3.655689 3.655689                                             
 5 3.655689 3.655689 3.655689 0.000000                                    
 6 3.655689 3.655689 3.655689 0.000000 0.000000                           
 7 6.901341 6.901341 6.901341 6.901341 6.901341 6.901341                  
 8 6.901341 6.901341 6.901341 6.901341 6.901341 6.901341 0.000000         
 9 6.901341 6.901341 6.901341 6.901341 6.901341 6.901341 0.000000 0.000000
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The distance matrix clearly shows, that the records 1,2,3 are from the same template (distance = 0, with the synthetic data; in a real case some small threshold should be used). Same is valid for records 4,5,6  and 7,8,9.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8219, 2, '2015-09-27 16:18:25.053', 1, NULL, NULL, '&lt;p&gt;There are alternatives out there. Using cloud capabilities for computing is helpful for many different types of problems and I''ve been using the cloud for a couple years now. There are moments where you need more computational capabilities, for instance if you needed more ram to process data or if you needed GPUs for neural nets computation. The only drawback is the price of some of these services.
The quickest ways to use is just sign up and go through the tutorials on &lt;/p&gt;

&lt;h3&gt;Amazon Machine Learning&lt;/h3&gt;

&lt;p&gt;&lt;a href="https://docs.aws.amazon.com/machine-learning/latest/dg/tutorial.html" rel="nofollow"&gt;https://docs.aws.amazon.com/machine-learning/latest/dg/tutorial.html&lt;/a&gt;
or &lt;/p&gt;

&lt;h3&gt;Azure Machine Learning&lt;/h3&gt;

&lt;p&gt;&lt;a href="https://azure.microsoft.com/en-us/services/machine-learning/" rel="nofollow"&gt;https://azure.microsoft.com/en-us/services/machine-learning/&lt;/a&gt;&lt;/p&gt;

&lt;h3&gt;RStudio on an Amazon instance&lt;/h3&gt;

&lt;p&gt;Another alternative is to set up RStudio on an Amazon instance. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.louisaslett.com/RStudio_AMI/" rel="nofollow"&gt;http://www.louisaslett.com/RStudio_AMI/&lt;/a&gt;&lt;br&gt;
&lt;a href="http://www.louisaslett.com/RStudio_AMI/video_guide.html" rel="nofollow"&gt;http://www.louisaslett.com/RStudio_AMI/video_guide.html&lt;/a&gt;&lt;br&gt;
This tutorial will teach you how to connect to an Amazon instance running R and showing the GUI on your browser.&lt;/p&gt;

&lt;h3&gt;iPython HTML Notebook on Amazon''s AWS&lt;/h3&gt;

&lt;p&gt;The same can be done with python using an IPython notebook to interact with the remote instance.
&lt;a href="https://gist.github.com/iamatypeofwalrus/5183133" rel="nofollow"&gt;https://gist.github.com/iamatypeofwalrus/5183133&lt;/a&gt;&lt;/p&gt;

&lt;h3&gt;Spark on the cloud&lt;/h3&gt;

&lt;p&gt;If you know Apache Spark you can also use 
&lt;a href="https://databricks.com/" rel="nofollow"&gt;https://databricks.com/&lt;/a&gt;&lt;/p&gt;

&lt;h3&gt;Advanced topic: Deep Learning on Amazon EC2 GPU with Python and nolearn&lt;/h3&gt;

&lt;p&gt;Setup a GPU Amazon instance to run deep learning algorithms
&lt;a href="http://www.pyimagesearch.com/2014/10/13/deep-learning-amazon-ec2-gpu-python-nolearn/" rel="nofollow"&gt;http://www.pyimagesearch.com/2014/10/13/deep-learning-amazon-ec2-gpu-python-nolearn/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':244C ''/2014/10/13/deep-learning-amazon-ec2-gpu-python-nolearn/'':274C ''/2014/10/13/deep-learning-amazon-ec2-gpu-python-nolearn/&lt;/a&gt;&lt;/p&gt;'':279C ''/en-us/services/machine-learning/'':115C ''/en-us/services/machine-learning/&lt;/a&gt;&lt;/p&gt;'':120C ''/h3'':91C,109C,127C,192C,226C,258C ''/iamatypeofwalrus/5183133'':215C ''/iamatypeofwalrus/5183133&lt;/a&gt;&lt;/p&gt;'':220C ''/machine-learning/latest/dg/tutorial.html'':97C ''/machine-learning/latest/dg/tutorial.html&lt;/a&gt;'':102C ''/p'':86C,104C,140C,183C ''/rstudio_ami/'':146C ''/rstudio_ami/&lt;/a&gt;&lt;br&gt;'':151C ''/rstudio_ami/video_guide.html'':156C ''/rstudio_ami/video_guide.html&lt;/a&gt;&lt;br&gt;'':161C ''advanc'':246C ''algorithm'':269C ''also'':235C ''altern'':4C,130C ''amazon'':88C,125C,138C,172C,189C,251C,263C ''anoth'':129C ''apach'':231C ''aw'':191C ''azur'':106C ''azure.microsoft.com'':114C,119C ''azure.microsoft.com/en-us/services/machine-learning/'':113C ''azure.microsoft.com/en-us/services/machine-learning/&lt;/a&gt;&lt;/p&gt;'':118C ''browser'':182C ''capabl'':9C,40C ''cloud'':8C,26C,225C ''comput'':11C,39C,59C ''connect'':169C ''coupl'':29C ''data'':50C ''databricks.com'':239C,243C ''databricks.com/&lt;/a&gt;&lt;/p&gt;'':242C ''deep'':248C,267C ''differ'':16C ''docs.aws.amazon.com'':96C,101C ''docs.aws.amazon.com/machine-learning/latest/dg/tutorial.html'':95C ''docs.aws.amazon.com/machine-learning/latest/dg/tutorial.html&lt;/a&gt;'':100C ''done'':198C ''drawback'':62C ''ec2'':252C ''gist.github.com'':214C,219C ''gist.github.com/iamatypeofwalrus/5183133'':213C ''gist.github.com/iamatypeofwalrus/5183133&lt;/a&gt;&lt;/p&gt;'':218C ''go'':81C ''gpu'':253C,262C ''gpus'':55C ''gui'':179C ''h3'':87C,105C,121C,184C,221C,245C ''help'':13C ''href'':94C,112C,143C,153C,212C,238C,271C ''html'':186C ''instanc'':42C,126C,139C,173C,210C,264C ''interact'':206C ''ipython'':185C,203C ''know'':230C ''learn'':90C,108C,249C,268C ''machin'':89C,107C ''mani'':15C ''moment'':34C ''need'':37C,45C,54C ''net'':58C ''neural'':57C ''nofollow'':99C,117C,148C,158C,217C,241C,276C ''nolearn'':257C ''notebook'':187C,204C ''p'':1C,92C,110C,128C,141C,193C,227C,259C ''price'':65C ''problem'':19C ''process'':49C ''python'':200C,255C ''quickest'':72C ''r'':175C ''ram'':47C ''rel'':98C,116C,147C,157C,216C,240C,275C ''remot'':209C ''rstudio'':122C,135C ''run'':174C,266C ''servic'':70C ''set'':133C ''setup'':260C ''show'':177C ''sign'':78C ''spark'':222C,232C ''teach'':165C ''topic'':247C ''tutori'':84C,163C ''type'':17C ''use'':7C,24C,75C,201C,236C ''ve'':22C ''way'':73C ''www.louisaslett.com'':145C,150C,155C,160C ''www.louisaslett.com/rstudio_ami/'':144C ''www.louisaslett.com/rstudio_ami/&lt;/a&gt;&lt;br&gt;'':149C ''www.louisaslett.com/rstudio_ami/video_guide.html'':154C ''www.louisaslett.com/rstudio_ami/video_guide.html&lt;/a&gt;&lt;br&gt;'':159C ''www.pyimagesearch.com'':273C,278C ''www.pyimagesearch.com/2014/10/13/deep-learning-amazon-ec2-gpu-python-nolearn/'':272C ''www.pyimagesearch.com/2014/10/13/deep-learning-amazon-ec2-gpu-python-nolearn/&lt;/a&gt;&lt;/p&gt;'':277C ''year'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8223, 1, '2015-09-28 20:04:57.783', 1, 20, 'Experiments on time spent by the visual cortex', '&lt;p&gt;I''m working on a object detection problem, using convolutional neural networks which have a lot in common with how the human visual cortex works. I''ve previously seen remarks on tasks in computer vision like "if a human can do it in x milliseconds, it can easily be solved by a computer".&lt;/p&gt;

&lt;p&gt;I was thinking of setting up an experiment where I flash an image for a set amount of time and the subject records whether (s)he detected the object or not. I would then have multiple subjects, a large set of images, and a varying flash length. Thus I could kind of quantify how hard my computer vision task is.&lt;/p&gt;

&lt;p&gt;My question is whether you know of any research, papers, books or other resources on this subject? Any resources specifically linked to neural networks and deep learning would be especially helpful.&lt;/p&gt;
', 'experiments  computer-vision', NULL, '''/p'':67C,129C,162C ''amount'':85C ''book'':141C ''common'':31C ''comput'':11B,47C,66C,125C ''computer-vis'':10B ''convolut'':23C ''cortex'':8A,37C ''could'':118C ''deep'':156C ''detect'':20C,95C ''easili'':61C ''especi'':160C ''experi'':1A,9B,76C ''flash'':79C,114C ''hard'':123C ''help'':161C ''human'':35C,52C ''imag'':81C,110C ''kind'':119C ''know'':136C ''larg'':107C ''learn'':157C ''length'':115C ''like'':49C ''link'':151C ''lot'':29C ''m'':15C ''millisecond'':58C ''multipl'':104C ''network'':25C,154C ''neural'':24C,153C ''object'':19C,97C ''p'':13C,68C,130C ''paper'':140C ''previous'':41C ''problem'':21C ''quantifi'':121C ''question'':132C ''record'':91C ''remark'':43C ''research'':139C ''resourc'':144C,149C ''seen'':42C ''set'':73C,84C,108C ''solv'':63C ''specif'':150C ''spent'':4A ''subject'':90C,105C,147C ''task'':45C,127C ''think'':71C ''thus'':116C ''time'':3A,87C ''use'':22C ''vari'':113C ''ve'':40C ''vision'':12B,48C,126C ''visual'':7A,36C ''whether'':92C,134C ''work'':16C,38C ''would'':101C,158C ''x'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8224, 2, '2015-09-28 21:15:42.943', 0, NULL, NULL, '&lt;p&gt;I guess you are not limited to these 100 samples. Generate more, and let each 5th be negative. Then reduce number of positives by random removing 4/5 of them.&lt;/p&gt;

&lt;p&gt;And check this out &lt;a href="http://datascience.stackexchange.com/questions/1107/quick-guide-into-training-highly-imbalanced-data-sets"&gt;Training imbalanced data set&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This is small quantity, you''d better have 50:50 negative vs positive.&lt;/p&gt;
', NULL, NULL, '''/a'':46C ''/p'':31C,47C,62C ''/questions/1107/quick-guide-into-training-highly-imbalanced-data-sets'':41C ''100'':10C ''4/5'':28C ''50'':57C,58C ''5th'':17C ''better'':55C ''check'':34C ''d'':54C ''data'':44C ''datascience.stackexchange.com'':40C ''datascience.stackexchange.com/questions/1107/quick-guide-into-training-highly-imbalanced-data-sets'':39C ''generat'':12C ''guess'':3C ''href'':38C ''imbalanc'':43C ''let'':15C ''limit'':7C ''negat'':19C,59C ''number'':22C ''p'':1C,32C,48C ''posit'':24C,61C ''quantiti'':52C ''random'':26C ''reduc'':21C ''remov'':27C ''sampl'':11C ''set'':45C ''small'':51C ''train'':42C ''vs'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8225, 1, '2015-09-28 21:17:21.15', 3, 133, 'Equipment failure prediction', '&lt;p&gt;I have a system that manages equipments. When these equipments are faulty, they will be serviced. Imagine my dataset looks like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ID
Type
# of times serviced
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Example Data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;|ID| Type       | #serviced |
|1 | iphone     | 1         |
|2 | iphone     | 0         |
|3 | android    | 1         |
|4 | android    | 0         |
|5 | blackberry | 0         |
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What I want to do is I want to predict "of all the equipments that have not been serviced, which ones are likely to be serviced" ? (ie) identify "at risk" equipments.&lt;/p&gt;

&lt;p&gt;The problem is my training data will be #serviced &gt; 0. Any #serviced=0 will not be frozen and dont seem to be valid candidates to include in training. (ie) When it fails, it will be serviced hence the count will go up.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Is this a supervised or unsupervised problem ? (supervised because I have serviced and not-serviced labels, unsupervised because I want to cluster not-serviced with serviced and there by identify at-risk equipments)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;What data should I include in training ?&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Note:&lt;/p&gt;

&lt;p&gt;The example is obviously simplified. In reality I have more features that describe the equipment.&lt;/p&gt;
', 'machine-learning  classification  predictive-modeling  supervised-learning', NULL, '''/code'':45C,71C ''/li'':190C,201C ''/ol'':202C ''/p'':37C,50C,105C,149C,189C,200C,205C,222C ''/pre'':46C,72C ''0'':61C,67C,70C,116C,119C ''1'':56C,58C,64C ''2'':59C ''3'':62C ''4'':65C ''5'':68C ''android'':63C,66C ''at-risk'':185C ''blackberri'':69C ''candid'':130C ''classif'':7B ''cluster'':175C ''code'':39C,52C ''count'':145C ''data'':49C,112C,194C ''dataset'':33C ''describ'':219C ''dont'':125C ''equip'':1A,21C,24C,87C,104C,188C,221C ''exampl'':48C,208C ''fail'':138C ''failur'':2A ''faulti'':26C ''featur'':217C ''frozen'':123C ''go'':147C ''henc'':143C ''id'':40C,53C ''identifi'':101C,184C ''ie'':100C,135C ''imagin'':31C ''includ'':132C,197C ''iphon'':57C,60C ''label'':169C ''learn'':6B,13B ''li'':151C,191C ''like'':35C,96C ''look'':34C ''machin'':5B ''machine-learn'':4B ''manag'':20C ''model'':10B ''not-serv'':166C,176C ''note'':204C ''obvious'':210C ''ol'':150C ''one'':94C ''p'':14C,47C,73C,106C,152C,192C,203C,206C ''pre'':38C,51C ''predict'':3A,9B,83C ''predictive-model'':8B ''problem'':108C,159C ''realiti'':213C ''risk'':103C,187C ''seem'':126C ''servic'':30C,44C,55C,92C,99C,115C,118C,142C,164C,168C,178C,180C ''simplifi'':211C ''supervis'':12B,156C,160C ''supervised-learn'':11B ''system'':18C ''time'':43C ''train'':111C,134C,199C ''type'':41C,54C ''unsupervis'':158C,170C ''valid'':129C ''want'':76C,81C,173C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8226, 2, '2015-09-28 21:43:27.583', 1, NULL, NULL, '&lt;p&gt;This is supervised learning problem. &lt;code&gt;Type&lt;/code&gt; is a predictor. &lt;code&gt;#serviced&lt;/code&gt; classifier is target variable. Model is trained on samples set you already have. Best guess is that any model will not have substantual predictive ability. &lt;code&gt;Type&lt;/code&gt; is not enough.&lt;/p&gt;

&lt;p&gt;Try including more factors (predictors) into the model. Like &lt;code&gt;years_being_in_usage&lt;/code&gt;, &lt;code&gt;equipment_model&lt;/code&gt;, &lt;code&gt;have_been_in_service_before&lt;/code&gt; and so on. The more you get, the better model you can train.&lt;/p&gt;
', NULL, NULL, '''/code'':9C,15C,43C,63C,67C,74C ''/p'':47C,88C ''abil'':40C ''alreadi'':27C ''best'':29C ''better'':83C ''classifi'':16C ''code'':7C,13C,41C,58C,64C,68C ''enough'':46C ''equip'':65C ''factor'':52C ''get'':81C ''guess'':30C ''includ'':50C ''learn'':5C ''like'':57C ''model'':20C,34C,56C,66C,84C ''p'':1C,48C ''predict'':39C ''predictor'':12C,53C ''problem'':6C ''sampl'':24C ''servic'':14C,72C ''set'':25C ''substantu'':38C ''supervis'':4C ''target'':18C ''train'':22C,87C ''tri'':49C ''type'':8C,42C ''usag'':62C ''variabl'':19C ''year'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8250, 2, '2015-09-30 16:00:00.923', 2, NULL, NULL, '&lt;p&gt;I haven''t used a columnar database, but I''ve used an open source columnar file format called Parquet, and I think the benefits are probably the same -- faster processing of data when you only need to query a small subset of a large number of columns. I had a query running on about 50 terabytes of Avro files (a row oriented file format) with 673 columns that took about an hour and a half on a 140 node Hadoop cluster. With Parquet, the same query took about 22 minutes because I only needed 5 columns. &lt;/p&gt;

&lt;p&gt;If you had a small number of columns or were using a large proportion of your columns, I don''t think a columnar database would make much of a difference vs a row oriented one because you would still have to basically scan all of your data. I believe columnar databases store columns separately whereas row oriented databases store rows separately. Your query will be faster any time you''re able to read less data from disk.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Column-oriented_DBMS" rel="nofollow"&gt;This link&lt;/a&gt; explains more of the details.&lt;/p&gt;
', NULL, NULL, '''/a'':188C ''/p'':98C,177C,194C ''/wiki/column-oriented_dbms'':183C ''140'':79C ''22'':90C ''5'':96C ''50'':56C ''673'':67C ''abl'':170C ''avro'':59C ''basic'':141C ''believ'':148C ''benefit'':25C ''call'':19C ''cluster'':82C ''column'':48C,68C,97C,107C,116C,152C ''columnar'':7C,16C,122C,149C ''data'':33C,146C,174C ''databas'':8C,123C,150C,157C ''detail'':193C ''differ'':129C ''disk'':176C ''en.wikipedia.org'':182C ''en.wikipedia.org/wiki/column-oriented_dbms'':181C ''explain'':189C ''faster'':30C,165C ''file'':17C,60C,64C ''format'':18C,65C ''hadoop'':81C ''half'':76C ''haven'':3C ''hour'':73C ''href'':180C ''larg'':45C,112C ''less'':173C ''link'':187C ''make'':125C ''minut'':91C ''much'':126C ''need'':37C,95C ''node'':80C ''nofollow'':185C ''number'':46C,105C ''one'':134C ''open'':14C ''orient'':63C,133C,156C ''p'':1C,99C,178C ''parquet'':20C,84C ''probabl'':27C ''process'':31C ''proport'':113C ''queri'':39C,52C,87C,162C ''re'':169C ''read'':172C ''rel'':184C ''row'':62C,132C,155C,159C ''run'':53C ''scan'':142C ''separ'':153C,160C ''small'':41C,104C ''sourc'':15C ''still'':138C ''store'':151C,158C ''subset'':42C ''terabyt'':57C ''think'':23C,120C ''time'':167C ''took'':70C,88C ''use'':5C,12C,110C ''ve'':11C ''vs'':130C ''wherea'':154C ''would'':124C,137C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9094, 4, '2015-11-28 14:59:17.057', 0, NULL, NULL, 'Finding hidden (statistical) structure in unlabelled data, including clustering and feature extraction for dimensionality reduction.', NULL, NULL, '''cluster'':9C ''data'':7C ''dimension'':14C ''extract'':12C ''featur'':11C ''find'':1C ''hidden'':2C ''includ'':8C ''reduct'':15C ''statist'':3C ''structur'':4C ''unlabel'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8232, 2, '2015-09-29 09:48:38.597', 2, NULL, NULL, '&lt;p&gt;This is normal behaviour of most classifiers. You are not guaranteed 100% accuracy in machine learning, and a direct consequence is that classifiers make mistakes. Different classifiers, even if trained on the same data, can make different mistakes. Neural networks with different starting weights will often converge to slightly different results each time.&lt;/p&gt;

&lt;p&gt;Also, perhaps in your problem the classification is an artificial construct over some spectrum (e.g. "car" vs "van" or "safe" vs "dangerous") in which case the mistake in one case is entirely reasonable and expected?&lt;/p&gt;

&lt;p&gt;You should use the value from the classifier that you trust the most. To establish which one that is, use cross-validation on a hold-out set (where you know the true labels), and use the classifier with the best accuracy, or other metric, such as logloss or area under ROC. Which metric you should prefer depends on the nature of your problem, and the consequences of making a mistake.&lt;/p&gt;

&lt;p&gt;Alternatively, you could look at averaging the class probabilities to determine the best prediction - perhaps one classifier is really confident in the class assignment, and the other is not, so an average will go with the first classifier. Some kind of model aggregation will often boost accuracy, and is common in e.g. Kaggle competitions when you want the highest possible score and don''t mind the extra effort and cost. However, if you want to use aggregation to solve your problem, again you should test your assumptions using validation and a suitable metric so you know whether or not it is really an improvement. &lt;/p&gt;
', NULL, NULL, '''/p'':55C,92C,166C,272C ''100'':13C ''accuraci'':14C,136C,214C ''aggreg'':210C,244C ''also'':57C ''altern'':168C ''area'':144C ''artifici'':66C ''assign'':191C ''assumpt'':254C ''averag'':173C,199C ''behaviour'':5C ''best'':135C,180C ''boost'':213C ''car'':72C ''case'':81C,86C ''class'':175C,190C ''classif'':63C ''classifi'':8C,24C,28C,101C,132C,184C,205C ''common'':217C ''competit'':221C ''confid'':187C ''consequ'':21C,161C ''construct'':67C ''converg'':48C ''cost'':237C ''could'':170C ''cross'':115C ''cross-valid'':114C ''danger'':78C ''data'':35C ''depend'':152C ''determin'':178C ''differ'':27C,38C,43C,51C ''direct'':20C ''e.g'':71C,219C ''effort'':235C ''entir'':88C ''establish'':108C ''even'':29C ''expect'':91C ''extra'':234C ''first'':204C ''go'':201C ''guarante'':12C ''highest'':226C ''hold'':120C ''hold-out'':119C ''howev'':238C ''improv'':271C ''kaggl'':220C ''kind'':207C ''know'':125C,263C ''label'':128C ''learn'':17C ''logloss'':142C ''look'':171C ''machin'':16C ''make'':25C,37C,163C ''metric'':139C,148C,260C ''mind'':232C ''mistak'':26C,39C,83C,165C ''model'':209C ''natur'':155C ''network'':41C ''neural'':40C ''normal'':4C ''often'':47C,212C ''one'':85C,110C,183C ''p'':1C,56C,93C,167C ''perhap'':58C,182C ''possibl'':227C ''predict'':181C ''prefer'':151C ''probabl'':176C ''problem'':61C,158C,248C ''realli'':186C,269C ''reason'':89C ''result'':52C ''roc'':146C ''safe'':76C ''score'':228C ''set'':122C ''slight'':50C ''solv'':246C ''spectrum'':70C ''start'':44C ''suitabl'':259C ''test'':252C ''time'':54C ''train'':31C ''true'':127C ''trust'':104C ''use'':96C,113C,130C,243C,255C ''valid'':116C,256C ''valu'':98C ''van'':74C ''vs'':73C,77C ''want'':224C,241C ''weight'':45C ''whether'':264C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8245, 1, '2015-09-30 12:30:30.29', 0, 21, 'Devices behavior in one continuous variable vs events rate', '&lt;p&gt;I have devices on which I have time series data of one continuous variable. I have to evaluate the relation between the profile of that variable on those devices and "events".&lt;/p&gt;

&lt;p&gt;Those events are given in terms of occurrences on a time period.&lt;/p&gt;

&lt;p&gt;My first intention is to make clusters of similar behavior of that variable and compare those clusters with the low/middle/high events rates.&lt;/p&gt;

&lt;p&gt;I was thinking about doing a K-means with the min, max, quartilles, mean, normal q-q p value, Kurstosis, etc. as dimensions, but I don''t think it''s a good idea because:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Those dimensions are not independant&lt;/li&gt;
&lt;li&gt;It''s "loosing" data and so potentially loosing classification potential&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Do you have some suggestions to group similar devices together?&lt;/p&gt;

&lt;p&gt;Also, do you have other ideas to establish that relationship?&lt;/p&gt;

&lt;p&gt;Sorry, I''m still kind of newbie...&lt;/p&gt;

&lt;p&gt;Thanks a lot and best regards.&lt;/p&gt;

&lt;p&gt;P.S.: Context:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;python3 with the scipy stack&lt;/li&gt;
&lt;li&gt;~ 3000 devices and hundreds of thousands of data per day; 5 months to consider&lt;/li&gt;
&lt;/ul&gt;
', 'machine-learning  clustering  correlation', NULL, '''/li'':131C,143C,197C,213C ''/p'':47C,61C,85C,123C,156C,168C,177C,185C,189C ''/ul'':144C,214C ''3000'':199C ''5'':209C ''also'':158C ''behavior'':2A,72C ''best'':183C ''classif'':141C ''cluster'':13B,69C,79C ''compar'':77C ''consid'':212C ''context'':188C ''continu'':5A,28C ''correl'':14B ''data'':25C,136C,206C ''day'':208C ''devic'':1A,18C,44C,154C,200C ''dimens'':111C,127C ''establish'':165C ''etc'':109C ''evalu'':33C ''event'':8A,46C,50C,83C ''first'':64C ''given'':52C ''good'':120C ''group'':152C ''hundr'':202C ''idea'':121C,163C ''independ'':130C ''intent'':65C ''k'':94C ''k-mean'':93C ''kind'':174C ''kurstosi'':108C ''learn'':12B ''li'':125C,132C,191C,198C ''loos'':135C,140C ''lot'':181C ''low/middle/high'':82C ''m'':172C ''machin'':11B ''machine-learn'':10B ''make'':68C ''max'':99C ''mean'':95C,101C ''min'':98C ''month'':210C ''newbi'':176C ''normal'':102C ''occurr'':56C ''one'':4A,27C ''p'':15C,48C,62C,86C,106C,145C,157C,169C,178C,186C ''p.s'':187C ''per'':207C ''period'':60C ''potenti'':139C,142C ''profil'':38C ''python3'':192C ''q'':104C,105C ''q-q'':103C ''quartill'':100C ''rate'':9A,84C ''regard'':184C ''relat'':35C ''relationship'':167C ''scipi'':195C ''seri'':24C ''similar'':71C,153C ''sorri'':170C ''stack'':196C ''still'':173C ''suggest'':150C ''term'':54C ''thank'':179C ''think'':89C,116C ''thousand'':204C ''time'':23C,59C ''togeth'':155C ''ul'':124C,190C ''valu'':107C ''variabl'':6A,29C,41C,75C ''vs'':7A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8233, 2, '2015-09-29 10:06:26.883', 1, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;So how to handle this large data requirements and which incremental classifiers to use for this case , considering the fact that I will keep on getting additional training data as well as may discover new categories?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I am not familiar with the task you are solving, but have you considered a lazy distance-based learner like k-nn and its variations? It could handle an arbitrary number of categories, learn incrementally and you could keep your model small-ish by using one of the case base maintenance algorithms out there. As for learning new categories, you could use an outlier detection algorithm to see when a new test example doesn''t fit in its category (i.e. when it significantly reduces its intra-class consistency).&lt;/p&gt;
', NULL, NULL, '''/blockquote'':40C ''/p'':39C,134C ''addit'':29C ''algorithm'':96C,110C ''arbitrari'':73C ''base'':60C,94C ''blockquot'':1C ''case'':19C,93C ''categori'':38C,76C,103C,123C ''class'':132C ''classifi'':14C ''consid'':20C,55C ''consist'':133C ''could'':70C,81C,105C ''data'':9C,31C ''detect'':109C ''discov'':36C ''distanc'':59C ''distance-bas'':58C ''doesn'':118C ''exampl'':117C ''fact'':22C ''familiar'':45C ''fit'':120C ''get'':28C ''handl'':6C,71C ''i.e'':124C ''increment'':13C,78C ''intra'':131C ''intra-class'':130C ''ish'':87C ''k'':64C ''k-nn'':63C ''keep'':26C,82C ''larg'':8C ''lazi'':57C ''learn'':77C,101C ''learner'':61C ''like'':62C ''mainten'':95C ''may'':35C ''model'':84C ''new'':37C,102C,115C ''nn'':65C ''number'':74C ''one'':90C ''outlier'':108C ''p'':2C,41C ''reduc'':128C ''requir'':10C ''see'':112C ''signific'':127C ''small'':86C ''small-ish'':85C ''solv'':51C ''task'':48C ''test'':116C ''train'':30C ''use'':16C,89C,106C ''variat'':68C ''well'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8234, 2, '2015-09-29 13:23:16.52', 0, NULL, NULL, '&lt;p&gt;The only thing I can see would be to separate both city and state lists and treat the problem as an automaton: parse your text, run through the n-grams, whenever you detect a CITY token (meaning a n-gram present in your list of cities or close to it in a similarity sense, as there might be misspellings) then look for a STATE token in its neighbourhood (similarly by looking into a list of states, using an edit distance metric to allow for misspellings). If you find one, then you can tag your text with that geographical location.&lt;/p&gt;

&lt;p&gt;Of course, allowing for misspellings will bring some false positives but you could easily bypass that by doing a quick lookup through your corpus to see that "SALAMI, OREGANO" is different from "SALEM, OREGON" (because the frequency of the latter will hopefully be higher than the former)&lt;/p&gt;
', NULL, NULL, '''/p'':103C,152C ''allow'':86C,107C ''automaton'':23C ''bring'':111C ''bypass'':119C ''citi'':13C,37C,49C ''close'':51C ''corpus'':128C ''could'':117C ''cours'':106C ''detect'':35C ''differ'':135C ''distanc'':83C ''easili'':118C ''edit'':82C ''fals'':113C ''find'':91C ''former'':151C ''frequenc'':141C ''geograph'':101C ''gram'':32C,43C ''higher'':148C ''hope'':146C ''latter'':144C ''list'':16C,47C,77C ''locat'':102C ''look'':64C,74C ''lookup'':125C ''mean'':39C ''metric'':84C ''might'':60C ''misspel'':62C,88C,109C ''n'':31C,42C ''n-gram'':30C,41C ''neighbourhood'':71C ''one'':92C ''oregano'':133C ''oregon'':138C ''p'':1C,104C ''pars'':24C ''posit'':114C ''present'':44C ''problem'':20C ''quick'':124C ''run'':27C ''salami'':132C ''salem'':137C ''see'':7C,130C ''sens'':57C ''separ'':11C ''similar'':56C,72C ''state'':15C,67C,79C ''tag'':96C ''text'':26C,98C ''thing'':4C ''token'':38C,68C ''treat'':18C ''use'':80C ''whenev'':33C ''would'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8235, 2, '2015-09-29 17:35:34.363', 1, NULL, NULL, '&lt;p&gt;It seems computer science programs have not changed their curriculum. They just added some elective courses for data science or probably adding a concentration to let students reinforce their skills on data science and big data.  &lt;/p&gt;
', NULL, NULL, '''/p'':38C ''ad'':14C,23C ''big'':36C ''chang'':9C ''comput'':4C ''concentr'':25C ''cours'':17C ''curriculum'':11C ''data'':19C,33C,37C ''elect'':16C ''let'':27C ''p'':1C ''probabl'':22C ''program'':6C ''reinforc'':29C ''scienc'':5C,20C,34C ''seem'':3C ''skill'':31C ''student'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8236, 1, '2015-09-29 19:33:31.053', 4, 86, 'Approach to creating a user profile in music web application', '&lt;p&gt;I am working on a use case, and I''m unsure of the best way to proceed: in order to analyze the behavior of users of a web-based music application, we retain all songs each has played since 2009. We store this information in flat files, each containing the songs played in a day. Each file contains 50M of lines, and we have  19M users. Our entire song catalogue consists of 35M tracks.&lt;/p&gt;

&lt;p&gt;The format of these files is as follows: &lt;/p&gt;

&lt;p&gt;&lt;code&gt;id-user | country | id-artist | id-track&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Question: I would like to represent each user by the songs he or she has played; this profile would be used by the production site. Does anyone have a suggestion for what would be the best way to process the whole chain?&lt;/p&gt;
', 'feature-construction  descriptive-statistics  aggregation', NULL, '''/code'':117C ''/p'':94C,104C,118C,162C ''19m'':84C ''2009'':59C ''35m'':92C ''50m'':78C ''aggreg'':17B ''analyz'':39C ''anyon'':146C ''applic'':10A,50C ''approach'':1A ''artist'':113C ''base'':48C ''behavior'':41C ''best'':32C,155C ''case'':25C ''catalogu'':89C ''chain'':161C ''code'':106C ''consist'':90C ''construct'':13B ''contain'':68C,77C ''countri'':110C ''creat'':3A ''day'':74C ''descript'':15B ''descriptive-statist'':14B ''entir'':87C ''featur'':12B ''feature-construct'':11B ''file'':66C,76C,100C ''flat'':65C ''follow'':103C ''format'':97C ''id'':108C,112C,115C ''id-artist'':111C ''id-track'':114C ''id-us'':107C ''inform'':63C ''like'':123C ''line'':80C ''m'':28C ''music'':8A,49C ''order'':37C ''p'':18C,95C,105C,119C ''play'':57C,71C,135C ''proceed'':35C ''process'':158C ''product'':143C ''profil'':6A,137C ''question'':120C ''repres'':125C ''retain'':52C ''sinc'':58C ''site'':144C ''song'':54C,70C,88C,130C ''statist'':16B ''store'':61C ''suggest'':149C ''track'':93C,116C ''unsur'':29C ''use'':24C,140C ''user'':5A,43C,85C,109C,127C ''way'':33C,156C ''web'':9A,47C ''web-bas'':46C ''whole'':160C ''work'':21C ''would'':122C,138C,152C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8237, 2, '2015-09-29 21:32:45.653', 3, NULL, NULL, '&lt;p&gt;The first question is: what to do you want to see in user profile?&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Top-10 tracks, top-10 artist by user?&lt;/li&gt;
&lt;li&gt;How many tracks/artists a user listens to in a day on average (may be, in last month)?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;May be you want to get some general information related to the whole user base:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Which artist/track is the most popular among users from different countries (top-N of them)?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The second: you store and want to make aggregates for millions of records. It''s not a text file deal. Make a database. Create a table &lt;code&gt;id-user | country | id-artist | id-track&lt;/code&gt;. Create another tables with some aggregates from #1, update it regularly and diplay on the front end.&lt;/p&gt;
', NULL, NULL, '''-10'':20C,23C ''/code'':123C ''/li'':27C,46C,83C ''/p'':16C,64C,141C ''/ul'':47C,84C ''1'':131C ''aggreg'':94C,129C ''among'':73C ''anoth'':125C ''artist'':24C,119C ''artist/track'':68C ''averag'':40C ''base'':63C ''code'':112C ''countri'':77C,116C ''creat'':109C,124C ''databas'':108C ''day'':38C ''deal'':105C ''differ'':76C ''diplay'':136C ''end'':140C ''file'':104C ''first'':3C ''front'':139C ''general'':56C ''get'':54C ''id'':114C,118C,121C ''id-artist'':117C ''id-track'':120C ''id-us'':113C ''inform'':57C ''last'':44C ''li'':18C,28C,66C ''listen'':34C ''make'':93C,106C ''mani'':30C ''may'':41C,49C ''million'':96C ''month'':45C ''n'':80C ''p'':1C,48C,85C ''popular'':72C ''profil'':15C ''question'':4C ''record'':98C ''regular'':134C ''relat'':58C ''second'':87C ''see'':12C ''store'':89C ''tabl'':111C,126C ''text'':103C ''top'':19C,22C,79C ''top-n'':78C ''track'':21C,122C ''tracks/artists'':31C ''ul'':17C,65C ''updat'':132C ''user'':14C,26C,33C,62C,74C,115C ''want'':10C,52C,91C ''whole'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8294, 1, '2015-10-03 18:37:16.597', 4, 154, 'One Hot encoding for large number of values', '&lt;p&gt;How do we use one hot encoding if the number of values which a categorical variable can take is large ?&lt;/p&gt;

&lt;p&gt;In my case it is 56 values. So as per usual method I would have to add 56 columns (56 binary features) in the training dataset which will immensely increase the complexity and hence the training time.&lt;/p&gt;

&lt;p&gt;So how do we deal with such cases ?&lt;/p&gt;
', 'machine-learning  data-mining  classification  dataset  categorical-data', NULL, '''/p'':41C,80C,90C ''56'':48C,60C,62C ''add'':59C ''binari'':63C ''case'':45C,89C ''categor'':18B,35C ''categorical-data'':17B ''classif'':15B ''column'':61C ''complex'':74C ''data'':13B,19B ''data-min'':12B ''dataset'':16B,68C ''deal'':86C ''encod'':3A,27C ''featur'':64C ''henc'':76C ''hot'':2A,26C ''immens'':71C ''increas'':72C ''larg'':5A,40C ''learn'':11B ''machin'':10B ''machine-learn'':9B ''method'':54C ''mine'':14B ''number'':6A,30C ''one'':1A,25C ''p'':20C,42C,81C ''per'':52C ''take'':38C ''time'':79C ''train'':67C,78C ''use'':24C ''usual'':53C ''valu'':8A,32C,49C ''variabl'':36C ''would'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8295, 2, '2015-10-03 19:28:29.77', 1, NULL, NULL, '&lt;p&gt;You could  try  reducing  the dimmension  of the 56 dummy resulting features, if you have some categories that represent a small proportion compared to the majority by labeling them   the same.&lt;/p&gt;
', NULL, NULL, '''/p'':33C ''56'':10C ''categori'':18C ''compar'':24C ''could'':3C ''dimmens'':7C ''dummi'':11C ''featur'':13C ''label'':29C ''major'':27C ''p'':1C ''proport'':23C ''reduc'':5C ''repres'':20C ''result'':12C ''small'':22C ''tri'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8239, 2, '2015-09-30 01:55:58.737', 2, NULL, NULL, '&lt;p&gt;This sounds like a pretty gnarly problem, so a lot of finesse will be needed to solve it.  @JÃ©rÃ©mie Clos has some good points, but I wanted to add some more general thoughts...&lt;/p&gt;

&lt;p&gt;With the size of your problem, you might want to think about a scalable framework like &lt;a href="http://mahout.apache.org/" rel="nofollow"&gt;Mahout&lt;/a&gt; or &lt;a href="http://h2o.ai/" rel="nofollow"&gt;H2O&lt;/a&gt; rather than scikit-learn, which is an awesome shared memory library, but does have &lt;a href="http://scikit-learn.org/stable/auto_examples/neighbors/plot_approximate_nearest_neighbors_scalability.html" rel="nofollow"&gt;scalability limitations&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;With a categorical feature with cardinality of 5000 and 80 million training cases, a uniform distribution will only give you 16,000 training cases for each positive result.  However, &lt;strong&gt;the distribution probably isn''t uniform&lt;/strong&gt;.  I suggest that you eliminate the least common categories until you have thrown out 5% of the data and assess the cardinality of the remaining 95%.  This may give you a better clue about how to proceed i.e. what percent of the cardinality remains? &lt;/p&gt;

&lt;p&gt;Most &lt;a href="http://scikit-learn.org/stable/modules/multiclass.html" rel="nofollow"&gt;multiclass classification algorithms&lt;/a&gt; are just lots of binary classifiers that are combined to produce a composite result, so will essentially do the work for you of splitting up the problem, binarizing the data (aka one-hot encoding), and training the classifiers, so you may not need to explicitly perform this splitting step.  Two exceptions to this, i.e. classifiers that can handle the multi-class problem intrinsically are &lt;a href="http://scikit-learn.org/stable/modules/tree.html" rel="nofollow"&gt;decision trees&lt;/a&gt; and &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html" rel="nofollow"&gt;random forests&lt;/a&gt; (&lt;a href="https://mahout.apache.org/users/classification/partial-implementation.html" rel="nofollow"&gt;Mahout version&lt;/a&gt;). I would try both of these and hold them as benchmarks moving forward.&lt;/p&gt;

&lt;p&gt;In terms of handling the addition of categories as data is added to the system, you will have to retrain sometimes.  This is sometimes referred to as the &lt;a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3732997/" rel="nofollow"&gt;stability-plasticity dilemma&lt;/a&gt;.  The best way to handle this is probably only to make predictions on categories that comprise some threshold of the data and throw out the rest as outlier categories that are not powered well enough to predict on e.g. similar to the sanity check I suggest above.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8240, 1, '2015-09-30 07:13:18.937', 0, 60, 'Detecting if a sentence contains a numeric series', '&lt;p&gt;I have many different strings of text. These strings of text are labels for particular things. But these labels are sloppy, sometimes one label is used for many different things. For example:&lt;/p&gt;

&lt;p&gt;"Brown foxes edition 1999 series 1-6 EDI"&lt;/p&gt;

&lt;p&gt;"Light [old] seasons 1,2,3,4 other gibberish"&lt;/p&gt;

&lt;p&gt;I would like to answer the question: "If the label contains a series, does that serie contain the value N?" For the examples above 6 would be included in the first one, but not the second.&lt;/p&gt;

&lt;p&gt;Initially I thought of using regexes but that quickly grew out of hand. Digits appear everywhere, people can get very creative with separators and the location of the series in the label is not fixed.
There are many different ways the labels denote series.&lt;/p&gt;

&lt;p&gt;What I can do, however, &lt;em&gt;generate&lt;/em&gt; labels with series. I''ll just grab a bunch of separators, a start and an end digit and iterate. This gives me a nice labeled training set.&lt;/p&gt;

&lt;p&gt;Naive Bayes comes to mind for this problem but I''m not sure what good features would be.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;update&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Let me try to clarify. 
Given a label and given a number determine if that number is contained within that label.&lt;/p&gt;

&lt;p&gt;For example: Given I''m looking for season 2 (the number). Does "Pioneer One 2011 seasons 1-3" contain season 2?&lt;/p&gt;
', 'nlp', NULL, '''-3'':252C ''-6'':51C ''/em'':157C ''/p'':43C,53C,64C,102C,148C,185C,205C,210C,233C,256C ''/strong'':209C ''1'':50C,58C,251C ''1999'':48C ''2'':59C,243C,255C ''2011'':249C ''3'':60C ''4'':61C ''6'':90C ''answer'':70C ''appear'':118C ''bay'':188C ''brown'':45C ''bunch'':166C ''clarifi'':216C ''come'':189C ''contain'':5A,76C,82C,229C,253C ''creativ'':124C ''denot'':146C ''detect'':1A ''determin'':224C ''differ'':14C,39C,142C ''digit'':117C,174C ''edi'':52C ''edit'':47C ''em'':155C ''end'':173C ''everywher'':119C ''exampl'':42C,88C,236C ''featur'':202C ''first'':96C ''fix'':138C ''fox'':46C ''generat'':156C ''get'':122C ''gibberish'':63C ''give'':178C ''given'':217C,221C,237C ''good'':201C ''grab'':164C ''grew'':113C ''hand'':116C ''howev'':154C ''includ'':93C ''initi'':104C ''iter'':176C ''label'':23C,29C,34C,75C,135C,145C,158C,182C,219C,232C ''let'':212C ''light'':55C ''like'':68C ''ll'':162C ''locat'':129C ''look'':240C ''m'':197C,239C ''mani'':13C,38C,141C ''mind'':191C ''n'':85C ''naiv'':187C ''nice'':181C ''nlp'':9B ''number'':223C,227C,245C ''numer'':7A ''old'':56C ''one'':33C,97C,248C ''p'':10C,44C,54C,65C,103C,149C,186C,206C,211C,234C ''particular'':25C ''peopl'':120C ''pioneer'':247C ''problem'':194C ''question'':72C ''quick'':112C ''regex'':109C ''season'':57C,242C,250C,254C ''second'':101C ''sentenc'':4A ''separ'':126C,168C ''seri'':8A,49C,78C,81C,132C,147C,160C ''set'':184C ''sloppi'':31C ''sometim'':32C ''start'':170C ''string'':15C,19C ''strong'':207C ''sure'':199C ''text'':17C,21C ''thing'':26C,40C ''thought'':106C ''train'':183C ''tri'':214C ''updat'':208C ''use'':36C,108C ''valu'':84C ''way'':143C ''within'':230C ''would'':67C,91C,203C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8241, 2, '2015-09-30 08:31:50.823', 0, NULL, NULL, '&lt;p&gt;I think your question is badly formulated, but if I understand it you are trying to separate numbers that are "good" numbers (here the number of the tv show season) from "bad" numbers (for example if the text contained the year "1996" and you were looking for "6", or a 6 in any other context).&lt;/p&gt;

&lt;p&gt;Naive Bayes might do the trick, maybe using the neighbourhood of your number as a feature set with a predetermined window? I.e. take the X tokens on both sides of your number as they appear in the text and use them as your feature set. You could even enrich it with their part-of-speech and other contextual evidence.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8242, 1, '2015-09-30 09:34:45.673', 0, 34, 'similarity measure', '&lt;p&gt;I have some question concern similarity measure and i need your help (Iam new in statistics)&lt;/p&gt;

&lt;p&gt;suppose that we have a matrix M where M(i, j) is the similarity measure between user i and user j .&lt;/p&gt;

&lt;p&gt;Question :
1- what similarity measure choose and why?&lt;/p&gt;

&lt;p&gt;2- What is the maximum size of the matrix? What are its properties? How would you choose the store?&lt;/p&gt;

&lt;p&gt;Thank you very much for your help
bests&lt;/p&gt;
', 'statistics  similarity', NULL, '''/p'':22C,45C,55C,76C,86C ''1'':48C ''2'':57C ''best'':85C ''choos'':52C,73C ''concern'':10C ''help'':17C,84C ''iam'':18C ''j'':34C,44C ''m'':30C,32C ''matrix'':29C,65C ''maximum'':61C ''measur'':2A,12C,38C,51C ''much'':81C ''need'':15C ''new'':19C ''p'':5C,23C,46C,56C,77C ''properti'':69C ''question'':9C,47C ''similar'':1A,4B,11C,37C,50C ''size'':62C ''statist'':3B,21C ''store'':75C ''suppos'':24C ''thank'':78C ''user'':40C,43C ''would'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9840, 2, '2016-01-18 20:19:09.4', 0, NULL, NULL, '&lt;p&gt;If you are trying to find both the clusters of weeks and the clusters of hospitals where outbreaks happen then you might be more successful using &lt;/p&gt;

&lt;p&gt;1) a simple moving average filter on the weeks,  playing with the number of weeks you are averaging - trying 3-5 weeks as the averaging window.&lt;br&gt;
2) now look for hospitals that have high moving averages in the same weeks&lt;/p&gt;
', NULL, NULL, '''-5'':50C ''/p'':28C,71C ''1'':30C ''2'':57C ''3'':49C ''averag'':34C,47C,54C,66C ''br'':56C ''cluster'':10C,15C ''filter'':35C ''find'':7C ''happen'':20C ''high'':64C ''hospit'':17C,61C ''look'':59C ''might'':23C ''move'':33C,65C ''number'':42C ''outbreak'':19C ''p'':1C,29C ''play'':39C ''simpl'':32C ''success'':26C ''tri'':5C,48C ''use'':27C ''week'':12C,38C,44C,51C,70C ''window'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8243, 2, '2015-09-30 10:28:10.39', 2, NULL, NULL, '&lt;p&gt;The matrix will be mxm (assuming you have m items) but you don''t need to fill it entirely, since a similarity measure should ideally be symmetric (sim(a,b) = sim(b,a)). &lt;/p&gt;

&lt;p&gt;As to choosing one, it depends entirely on what you store and what you plan to do with it. Assuming you are storing vectors of objects, some popular simple ones are &lt;a href="https://en.wikipedia.org/wiki/Jaccard_index" rel="nofollow"&gt;Jaccard similarity&lt;/a&gt; and &lt;a href="https://en.wikipedia.org/wiki/Cosine_similarity" rel="nofollow"&gt;Cosine similarity&lt;/a&gt;. &lt;a href="http://mines.humanoriented.com/classes/2010/fall/csci568/portfolio_exports/mvoget/similarity/similarity.html" rel="nofollow"&gt;This page&lt;/a&gt; has a bit more information on them (along with some other similarity measures). I would encourage you to search for books and research papers in the area that you are exploring and see what common similarity metrics are used by the community.&lt;/p&gt;
', NULL, NULL, '''/a'':77C,88C,98C ''/classes/2010/fall/csci568/portfolio_exports/mvoget/similarity/similarity.html'':93C ''/p'':35C,141C ''/wiki/cosine_similarity'':83C ''/wiki/jaccard_index'':72C ''along'':106C ''area'':125C ''assum'':7C,56C ''b'':31C,33C ''bit'':101C ''book'':119C ''choos'':39C ''common'':133C ''communiti'':140C ''cosin'':86C ''depend'':42C ''en.wikipedia.org'':71C,82C ''en.wikipedia.org/wiki/cosine_similarity'':81C ''en.wikipedia.org/wiki/jaccard_index'':70C ''encourag'':114C ''entir'':20C,43C ''explor'':129C ''fill'':18C ''href'':69C,80C,90C ''ideal'':26C ''inform'':103C ''item'':11C ''jaccard'':75C ''m'':10C ''matrix'':3C ''measur'':24C,111C ''metric'':135C ''mines.humanoriented.com'':92C ''mines.humanoriented.com/classes/2010/fall/csci568/portfolio_exports/mvoget/similarity/similarity.html'':91C ''mxm'':6C ''need'':16C ''nofollow'':74C,85C,95C ''object'':62C ''one'':40C,66C ''p'':1C,36C ''page'':97C ''paper'':122C ''plan'':51C ''popular'':64C ''rel'':73C,84C,94C ''research'':121C ''search'':117C ''see'':131C ''sim'':29C,32C ''similar'':23C,76C,87C,110C,134C ''simpl'':65C ''sinc'':21C ''store'':47C,59C ''symmetr'':28C ''use'':137C ''vector'':60C ''would'':113C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8246, 1, '2015-09-30 13:04:37.247', 4, 100, 'What is conjugate gradient descent?', '&lt;p&gt;What is Conjugate Gradient Descent of Neural Network? How is it different from Gradient Descent technique? &lt;br&gt;
I came across a &lt;a href="http://matlab.izmiran.ru/help/toolbox/nnet/backpr59.html" rel="nofollow"&gt;resource&lt;/a&gt; but was unable to understand the difference between the two methods. It has mentioned in the procedure that &lt;br&gt; &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;the next search direction is determined so that it is conjugate to previous search directions.&lt;br&gt;
  &lt;br&gt; &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;What does this sentence mean? And what is line search mentioned in the web page?&lt;/p&gt;

&lt;p&gt;Can anyone please explain it with the help of a diagram?&lt;/p&gt;
', 'neuralnetwork  gradient-descent  supervised-learning', NULL, '''/a'':43C ''/blockquote'':84C ''/help/toolbox/nnet/backpr59.html'':39C ''/p'':63C,83C,101C,114C ''across'':33C ''anyon'':104C ''blockquot'':64C ''br'':30C,62C,81C,82C ''came'':32C ''conjug'':3A,16C,76C ''descent'':5A,9B,18C,28C ''determin'':71C ''diagram'':113C ''differ'':25C,50C ''direct'':69C,80C ''explain'':106C ''gradient'':4A,8B,17C,27C ''gradient-desc'':7B ''help'':110C ''href'':36C ''learn'':12B ''line'':94C ''matlab.izmiran.ru'':38C ''matlab.izmiran.ru/help/toolbox/nnet/backpr59.html'':37C ''mean'':90C ''mention'':57C,96C ''method'':54C ''network'':21C ''neural'':20C ''neuralnetwork'':6B ''next'':67C ''nofollow'':41C ''p'':13C,65C,85C,102C ''page'':100C ''pleas'':105C ''previous'':78C ''procedur'':60C ''rel'':40C ''resourc'':42C ''search'':68C,79C,95C ''sentenc'':89C ''supervis'':11B ''supervised-learn'':10B ''techniqu'':29C ''two'':53C ''unabl'':46C ''understand'':48C ''web'':99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8247, 1, '2015-09-30 13:53:34.73', 0, 114, 'Deploying machine learning modules', '&lt;p&gt;I am looking  to find some resources about what I wan''t to do :I wan''t to make some GUI of my machine learning models and  finally deploy them as a web  app.I find R Shiny to be somehow  ok , but it limits me only to use R,and I have also Python things.Looking at the azure ml studio  it brings  somekind of workflow  like in Disco and ProM from process mining, but I am not  sure  how much pain will be to feed everything  like ensembles/grid search etc to those  flow components,also the full paid  version is  very expensive.
Trying to do a web application  from scratch could also not be ok  cause it will be a lot  of pain  when I will modify my  models.
Any suggestions?&lt;/p&gt;
', 'python  scikit  data', NULL, '''/p'':140C ''also'':61C,103C,120C ''app.i'':42C ''applic'':116C ''azur'':66C ''bring'':70C ''caus'':124C ''compon'':102C ''could'':119C ''data'':7B ''deploy'':1A,37C ''disco'':76C ''ensembles/grid'':96C ''etc'':98C ''everyth'':94C ''expens'':110C ''feed'':93C ''final'':36C ''find'':13C,43C ''flow'':101C ''full'':105C ''gui'':29C ''learn'':3A,33C ''like'':74C,95C ''limit'':52C ''look'':11C ''lot'':129C ''machin'':2A,32C ''make'':27C ''mine'':81C ''ml'':67C ''model'':34C,137C ''modifi'':135C ''modul'':4A ''much'':88C ''ok'':49C,123C ''p'':8C ''paid'':106C ''pain'':89C,131C ''process'':80C ''prom'':78C ''python'':5B,62C ''r'':44C,57C ''resourc'':15C ''scikit'':6B ''scratch'':118C ''search'':97C ''shini'':45C ''somehow'':48C ''somekind'':71C ''studio'':68C ''suggest'':139C ''sure'':86C ''things.looking'':63C ''tri'':111C ''use'':56C ''version'':107C ''wan'':19C,24C ''web'':41C,115C ''workflow'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9098, 4, '2015-11-28 15:01:23.757', 0, NULL, NULL, 'The discipline at the crossroads of computing and the biological sciences, which involves organizing, maintaining, and analyzing data from such fields as molecular biology, genetics, and genomics', NULL, NULL, '''analyz'':17C ''biolog'':10C,24C ''comput'':7C ''crossroad'':5C ''data'':18C ''disciplin'':2C ''field'':21C ''genet'':25C ''genom'':27C ''involv'':13C ''maintain'':15C ''molecular'':23C ''organ'':14C ''scienc'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8251, 2, '2015-09-30 16:47:50.043', 1, NULL, NULL, '&lt;p&gt;Correct me if I''m wrong here: &lt;/p&gt;

&lt;p&gt;You''ve already written the code that test/trains the model and you''re looking for a way to put that model on the web somewhere so users can interact with it?&lt;/p&gt;

&lt;p&gt;There''s a couple of way''s you can go about that, but it depends on what you need out of the final product. Is this something clients will be using? Friends? Just you? &lt;a href="http://flask.pocoo.org/" rel="nofollow"&gt;Flask&lt;/a&gt; allows you to prototype quick web apps and still maintain a good amount of control over what the finished product looks like. &lt;a href="https://github.com/adamhajari/spyre" rel="nofollow"&gt;Spyre&lt;/a&gt; is an amazing way to make a web app at near light speed, although it may not be suited well to your use case, depending on what you''re doing. It''s a little harder to make unique-looking things with Spyre, but I always suggest it to people.&lt;/p&gt;

&lt;p&gt;At any rate, the standard thing to do here is to &lt;a href="https://docs.python.org/2/library/pickle.html" rel="nofollow"&gt;Pickle&lt;/a&gt; your model after it''s trained and then load it to memory in a web app. After that you can call the &lt;code&gt;model.predict(X)&lt;/code&gt; on an &lt;code&gt;X&lt;/code&gt; that is sent to your program by a user. &lt;/p&gt;
', NULL, NULL, '''/2/library/pickle.html'':183C ''/a'':84C,115C,187C ''/adamhajari/spyre'':111C ''/code'':213C,218C ''/p'':9C,42C,166C,228C ''allow'':85C ''alreadi'':13C ''although'':129C ''alway'':161C ''amaz'':118C ''amount'':97C ''app'':91C,124C,203C ''call'':208C ''case'':139C ''client'':71C ''code'':16C,210C,216C ''control'':99C ''correct'':2C ''coupl'':47C ''depend'':58C,140C ''docs.python.org'':182C ''docs.python.org/2/library/pickle.html'':181C ''final'':66C ''finish'':103C ''flask'':83C ''flask.pocoo.org'':80C ''friend'':75C ''github.com'':110C ''github.com/adamhajari/spyre'':109C ''go'':53C ''good'':96C ''harder'':150C ''href'':79C,108C,180C ''interact'':39C ''light'':127C ''like'':106C ''littl'':149C ''load'':196C ''look'':24C,105C,155C ''m'':6C ''maintain'':94C ''make'':121C,152C ''may'':131C ''memori'':199C ''model'':20C,31C,189C ''model.predict'':211C ''near'':126C ''need'':62C ''nofollow'':82C,113C,185C ''p'':1C,10C,43C,167C ''peopl'':165C ''pickl'':186C ''product'':67C,104C ''program'':224C ''prototyp'':88C ''put'':29C ''quick'':89C ''rate'':170C ''re'':23C,144C ''rel'':81C,112C,184C ''sent'':221C ''someth'':70C ''somewher'':35C ''speed'':128C ''spyre'':114C,158C ''standard'':172C ''still'':93C ''suggest'':162C ''suit'':134C ''test/trains'':18C ''thing'':156C,173C ''train'':193C ''uniqu'':154C ''unique-look'':153C ''use'':74C,138C ''user'':37C,227C ''ve'':12C ''way'':27C,49C,119C ''web'':34C,90C,123C,202C ''well'':135C ''written'':14C ''wrong'':7C ''x'':212C,217C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8734, 1, '2015-11-06 04:12:26.92', 0, 120, 'PCA on MNIST dataset results in very poor performance', '&lt;p&gt;I am trying to build a model for classifying MNIST dataset using SVM. With raw features I am getting accuracy of around 94% (using linear kernel). &lt;/p&gt;

&lt;p&gt;When I tried it with PCA, with different number of components (35,50,250,500) I am getting accuracy around 11%. &lt;/p&gt;

&lt;p&gt;What can possibly the reason for this?&lt;/p&gt;
', 'svm  performance  multiclass-classification', NULL, '''/p'':42C,65C,74C ''11'':64C ''250'':57C ''35'':55C ''50'':56C ''500'':58C ''94'':38C ''accuraci'':35C,62C ''around'':37C,63C ''build'':20C ''classif'':14B ''classifi'':24C ''compon'':54C ''dataset'':4A,26C ''differ'':51C ''featur'':31C ''get'':34C,61C ''kernel'':41C ''linear'':40C ''mnist'':3A,25C ''model'':22C ''multiclass'':13B ''multiclass-classif'':12B ''number'':52C ''p'':15C,43C,66C ''pca'':1A,49C ''perform'':9A,11B ''poor'':8A ''possibl'':69C ''raw'':30C ''reason'':71C ''result'':5A ''svm'':10B,28C ''tri'':18C,46C ''use'':27C,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8252, 2, '2015-09-30 17:41:35.45', 14, NULL, NULL, '&lt;p&gt;A column-oriented database (=columnar data-store) stores the data of a table column by column on the disk, while a row-oriented database stores the data of a table row by row.&lt;/p&gt;

&lt;p&gt;There are two main advantages of using a column-oriented database in comparison
with a row-oriented database. The first advantage relates to the amount of data oneâs
need to read in case we perform an operation on just a few features. Consider a simple
query:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;SELECT correlation(feature2, feature5)
FROM records
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;A traditional executor would read the entire table (i.e. all the features):&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/4EZHo.png"&gt;&lt;img src="http://i.stack.imgur.com/4EZHo.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Instead, using our column-based approach we just have to read the columns
which are interested in:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/nGTKQ.png"&gt;&lt;img src="http://i.stack.imgur.com/nGTKQ.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The second advantage, which is also very important for large databases, is that column-based storage allows better compression, since the data in
one specific column is indeed homogeneous than across all the columns.&lt;/p&gt;

&lt;p&gt;The main drawback of a column-oriented approach is that manipulating (lookup, update or delete) an entire given row is inefficient. However the situation should
occur rarely in databases for analytics (âwarehousingâ),
which means most operations are read-only, rarely read many attributes in the same
table and writes are only appends.&lt;/p&gt;

&lt;p&gt;Some RDMS offer a column-oriented storage engine option. For example, PostgreSQL
has natively no option to store tables in a column-based fashion, but Greenplum has
created a closed-source one (DBMS2, 2009). Interestingly, Greenplum is also behind
the open-source library for scalable in-database analytics, MADlib (Hellerstein et al.,
2012), which is no coincidence. More recently, CitusDB, a startup working on high speed, analytic database, released their own open-source columnar store extension for
PostgreSQL, CSTORE (Miller, 2014). Googleâs system for large scale machine learning
Sibyl also uses column-oriented data format (Chandra et al., 2010). This trend
reflects the growing interest around column-oriented storage for large-scale analytics.
Stonebraker et al. (2005) further discuss the advantages of column-oriented DBMS.&lt;/p&gt;

&lt;p&gt;Two concrete use cases: &lt;a href="http://qr.ae/RoQxgp"&gt;How are most datasets for large-scale machine learning stored?&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;(most of the answer comes from Appendix C of: &lt;a href="http://francky.me/doc/2015beatdbwaveletsgp.pdf"&gt;BeatDB: An end-to-end approach to unveil saliencies from massive signal data sets. Franck Dernoncourt, S.M, thesis, MIT Dept of EECS&lt;/a&gt;)&lt;/p&gt;
', NULL, NULL, '''/4ezho.png'':119C,124C ''/a'':130C,168C,423C,463C ''/code'':98C ''/doc/2015beatdbwaveletsgp.pdf'':439C ''/ngtkq.png'':157C,162C ''/p'':38C,89C,113C,131C,151C,169C,206C,263C,401C,424C,464C ''/pre'':99C ''/roqxgp'':411C ''2005'':391C ''2009'':301C ''2010'':371C ''2012'':322C ''2014'':351C ''across'':202C ''advantag'':44C,62C,173C,395C ''al'':321C,370C,390C ''allow'':188C ''also'':176C,305C,361C ''alt'':125C,163C ''amount'':66C ''analyt'':239C,317C,336C,387C ''answer'':429C ''append'':262C ''appendix'':432C ''approach'':139C,216C,446C ''around'':378C ''attribut'':253C ''base'':138C,186C,289C ''beatdb'':440C ''behind'':306C ''better'':189C ''c'':433C ''case'':75C,406C ''chandra'':368C ''citusdb'':329C ''close'':297C ''closed-sourc'':296C ''code'':91C ''coincid'':326C ''column'':4C,17C,19C,49C,137C,146C,185C,197C,205C,214C,270C,288C,364C,380C,398C ''column-bas'':136C,184C,287C ''column-ori'':3C,48C,213C,269C,363C,379C,397C ''columnar'':7C,344C ''come'':430C ''comparison'':53C ''compress'':190C ''concret'':404C ''consid'':85C ''correl'':93C ''creat'':294C ''cstore'':349C ''data'':9C,13C,31C,68C,193C,366C,453C ''data-stor'':8C ''databas'':6C,28C,51C,59C,181C,237C,316C,337C ''dataset'':415C ''dbms'':400C ''dbms2'':300C ''delet'':223C ''dept'':460C ''dernoncourt'':456C ''descript'':128C,166C ''discuss'':393C ''disk'':22C ''drawback'':210C ''eec'':462C ''end'':443C,445C ''end-to-end'':442C ''engin'':273C ''enter'':126C,164C ''entir'':107C,225C ''et'':320C,369C,389C ''exampl'':276C ''executor'':103C ''extens'':346C ''fashion'':290C ''featur'':84C,112C ''feature2'':94C ''feature5'':95C ''first'':61C ''format'':367C ''franck'':455C ''francky.me'':438C ''francky.me/doc/2015beatdbwaveletsgp.pdf'':437C ''given'':226C ''googleâ'':352C ''greenplum'':292C,303C ''grow'':376C ''hellerstein'':319C ''high'':334C ''homogen'':200C ''howev'':230C ''href'':116C,154C,408C,436C ''i.e'':109C ''i.stack.imgur.com'':118C,123C,156C,161C ''i.stack.imgur.com/4ezho.png'':117C,122C ''i.stack.imgur.com/ngtkq.png'':155C,160C ''imag'':127C,165C ''img'':120C,158C ''import'':178C ''in-databas'':314C ''inde'':199C ''ineffici'':229C ''instead'':133C ''interest'':149C,302C,377C ''larg'':180C,356C,385C,418C ''large-scal'':384C,417C ''learn'':359C,421C ''librari'':311C ''lookup'':220C ''machin'':358C,420C ''madlib'':318C ''main'':43C,209C ''mani'':252C ''manipul'':219C ''massiv'':451C ''mean'':243C ''miller'':350C ''mit'':459C ''nativ'':279C ''need'':71C ''occur'':234C ''offer'':267C ''one'':195C,299C ''oneâ'':69C ''open'':309C,342C ''open-sourc'':308C,341C ''oper'':79C,245C ''option'':274C,281C ''orient'':5C,27C,50C,58C,215C,271C,365C,381C,399C ''p'':1C,39C,100C,114C,132C,152C,170C,207C,264C,402C,425C ''perform'':77C ''postgresql'':277C,348C ''pre'':90C ''qr.ae'':410C ''qr.ae/roqxgp'':409C ''queri'':88C ''rare'':235C,250C ''rdms'':266C ''read'':73C,105C,144C,248C,251C ''read-on'':247C ''recent'':328C ''record'':97C ''reflect'':374C ''relat'':63C ''releas'':338C ''row'':26C,35C,37C,57C,227C ''row-ori'':25C,56C ''s.m'':457C ''salienc'':449C ''scalabl'':313C ''scale'':357C,386C,419C ''second'':172C ''select'':92C ''set'':454C ''sibyl'':360C ''signal'':452C ''simpl'':87C ''sinc'':191C ''situat'':232C ''sourc'':298C,310C,343C ''specif'':196C ''speed'':335C ''src'':121C,159C ''startup'':331C ''stonebrak'':388C ''storag'':187C,272C,382C ''store'':10C,11C,29C,283C,345C,422C ''system'':354C ''tabl'':16C,34C,108C,257C,284C ''thesi'':458C ''tradit'':102C ''trend'':373C ''two'':42C,403C ''unveil'':448C ''updat'':221C ''use'':46C,134C,362C,405C ''warehousingâ'':241C ''work'':332C ''would'':104C ''write'':259C ''â'':240C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8253, 1, '2015-09-30 17:41:39.737', 1, 219, 'How to binary encode multi-valued categorical variable from Pandas dataframe?', '&lt;p&gt;Suppose we have the following dataframe with multiple values for a certain column:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    categories
0 - ["A", "B"]
1 - ["B", "C", "D"]
2 - ["B", "D"]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;How can we get a table like this?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;   "A"  "B"  "C"  "D"
0 - 1    1    0    0
1 - 0    1    1    1
2 - 0    1    0    1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Note: I don''t necessarily need a new dataframe, I''m wondering how to transform such DataFrames to a format more suitable for machine learning.&lt;/p&gt;
', 'python  pandas', NULL, '''/code'':43C,76C ''/p'':29C,54C,104C ''/pre'':44C,77C ''0'':33C,61C,64C,65C,67C,72C,74C ''1'':36C,62C,63C,66C,68C,69C,70C,73C,75C ''2'':40C,71C ''b'':35C,37C,41C,58C ''binari'':3A ''c'':38C,59C ''categor'':8A ''categori'':32C ''certain'':27C ''code'':31C,56C ''column'':28C ''d'':39C,42C,60C ''datafram'':12A,21C,87C,95C ''encod'':4A ''follow'':20C ''format'':98C ''get'':49C ''learn'':103C ''like'':52C ''m'':89C ''machin'':102C ''multi'':6A ''multi-valu'':5A ''multipl'':23C ''necessarili'':83C ''need'':84C ''new'':86C ''note'':79C ''p'':15C,45C,78C ''panda'':11A,14B ''pre'':30C,55C ''python'':13B ''suitabl'':100C ''suppos'':16C ''tabl'':51C ''transform'':93C ''valu'':7A,24C ''variabl'':9A ''wonder'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8254, 2, '2015-09-30 18:31:26.023', 2, NULL, NULL, '&lt;p&gt;If &lt;code&gt;[0, 1, 2]&lt;/code&gt; are numerical labels and is not the index, then &lt;code&gt;pandas.DataFrame.pivot_table&lt;/code&gt; works:&lt;/p&gt;

&lt;pre&gt;
In []:
data = pd.DataFrame.from_records(
    [[0, ''A''], [0, ''B''], [1, ''B''], [1, ''C''], [1, ''D''], [2, ''B''], [2, ''D'']],
    columns=[''number_label'', ''category''])
data.pivot_table(index=[''number_label''], columns=[''category''], aggfunc=[len], fill_value=0)
&lt;/pre&gt;

&lt;pre&gt;
Out[]:
              len
category      A      B      C      D
number_label                       
0             1      1      0      0
1             0      1      1      1
2             0      1      0      1
&lt;/pre&gt;

&lt;p&gt;This blog post was helpful: &lt;a href="http://pbpython.com/pandas-pivot-table-explained.html" rel="nofollow"&gt;http://pbpython.com/pandas-pivot-table-explained.html&lt;/a&gt;&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;If &lt;code&gt;[0, 1, 2]&lt;/code&gt; is the index, then &lt;code&gt;collections.Counter&lt;/code&gt; is useful:&lt;/p&gt;

&lt;pre&gt;
In []:
data2 = pd.DataFrame.from_dict(
    {''categories'': {0: [''A'', ''B''], 1: [''B'', ''C'', ''D''], 2:[''B'', ''D'']}})
data3 = data2[''categories''].apply(collections.Counter)
pd.DataFrame.from_records(data3).fillna(value=0)
&lt;/pre&gt;

&lt;pre&gt;
Out[]:
       A      B      C      D
0      1      1      0      0
1      0      1      1      1
2      0      1      0      1
&lt;/pre&gt;
', NULL, NULL, '''/code'':7C,20C,108C,115C ''/p'':22C,118C ''/pandas-pivot-table-explained.html'':95C ''/pandas-pivot-table-explained.html&lt;/a&gt;&lt;/p&gt;'':100C ''/pre'':58C,84C,146C,168C ''0'':4C,28C,30C,57C,69C,72C,73C,75C,80C,82C,105C,125C,145C,153C,156C,157C,159C,164C,166C ''1'':5C,32C,34C,36C,70C,71C,74C,76C,77C,78C,81C,83C,106C,128C,154C,155C,158C,160C,161C,162C,165C,167C ''2'':6C,38C,40C,79C,107C,132C,163C ''aggfunc'':53C ''appli'':138C ''b'':31C,33C,39C,64C,127C,129C,133C,150C ''blog'':87C ''c'':35C,65C,130C,151C ''categori'':45C,52C,62C,124C,137C ''code'':3C,17C,104C,113C ''collections.counter'':114C,139C ''column'':42C,51C ''d'':37C,41C,66C,131C,134C,152C ''data'':25C ''data.pivot'':46C ''data2'':121C,136C ''data3'':135C,142C ''dict'':123C ''fill'':55C ''fillna'':143C ''help'':90C ''hr'':101C ''href'':92C ''index'':15C,48C,111C ''label'':10C,44C,50C,68C ''len'':54C,61C ''nofollow'':97C ''number'':43C,49C,67C ''numer'':9C ''p'':1C,85C,102C ''pandas.dataframe.pivot'':18C ''pbpython.com'':94C,99C ''pbpython.com/pandas-pivot-table-explained.html'':93C ''pbpython.com/pandas-pivot-table-explained.html&lt;/a&gt;&lt;/p&gt;'':98C ''pd.dataframe.from'':26C,122C,140C ''post'':88C ''pre'':23C,59C,119C,147C ''record'':27C,141C ''rel'':96C ''tabl'':19C,47C ''use'':117C ''valu'':56C,144C ''work'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8260, 2, '2015-10-01 04:17:22.643', 4, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;What does this sentence mean? &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;It means that the next vector should be perpendicular to all the previous ones with respect to a matrix. It''s like how the &lt;a href="https://en.wikipedia.org/wiki/Standard_basis" rel="nofollow"&gt;natural basis&lt;/a&gt; vectors are &lt;a href="https://en.wikipedia.org/wiki/Orthogonality" rel="nofollow"&gt;perpendicular&lt;/a&gt; to each other, with the added twist of a matrix:&lt;/p&gt;

&lt;p&gt;$\mathrm {x^T A y} = 0$ instead of $\mathrm{x^T y} = 0$&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;And what is line search mentioned in the webpage?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Line_search" rel="nofollow"&gt;Line search&lt;/a&gt; is an optimization method that involves guessing how far along a given direction (i.e., along a line) one should move to best reach the local minimum.&lt;/p&gt;
', NULL, NULL, '''/a'':44C,55C,105C ''/blockquote'':9C,94C ''/p'':8C,66C,81C,93C,132C ''/wiki/line_search'':100C ''/wiki/orthogonality'':51C ''/wiki/standard_basis'':39C ''0'':73C,80C ''ad'':61C ''along'':115C,120C ''basi'':43C ''best'':127C ''blockquot'':1C,82C ''direct'':118C ''en.wikipedia.org'':38C,50C,99C ''en.wikipedia.org/wiki/line_search'':98C ''en.wikipedia.org/wiki/orthogonality'':49C ''en.wikipedia.org/wiki/standard_basis'':37C ''far'':114C ''given'':117C ''guess'':112C ''href'':36C,48C,97C ''i.e'':119C ''instead'':74C ''involv'':111C ''like'':32C ''line'':87C,103C,122C ''local'':130C ''mathrm'':68C,76C ''matrix'':29C,65C ''mean'':7C,12C ''mention'':89C ''method'':109C ''minimum'':131C ''move'':125C ''natur'':42C ''next'':15C ''nofollow'':41C,53C,102C ''one'':24C,123C ''optim'':108C ''p'':2C,10C,67C,83C,95C ''perpendicular'':19C,54C ''previous'':23C ''reach'':128C ''rel'':40C,52C,101C ''respect'':26C ''search'':88C,104C ''sentenc'':6C ''twist'':62C ''vector'':16C,45C ''webpag'':92C ''x'':69C,77C ''y'':72C,79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8292, 2, '2015-10-03 14:27:49.387', 1, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;I was thinking to calculate the average amount of money, say for the
  last ten transactions, and check how far is the next transaction
  amount from the average. Too much deviation would signal an anomaly.
  But this does not sound much, does it?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;A typical outlier detection approach. This would work in most cases. But, as the problem statement deals with credit card fraud detection, the detection technique/algorithm/implementation should be more robust.&lt;/p&gt;

&lt;p&gt;You might want to have a look at the &lt;a href="https://en.wikipedia.org/wiki/Mahalanobis_distance" rel="nofollow"&gt;&lt;strong&gt;Mahalanobis Distance&lt;/strong&gt;&lt;/a&gt; metric for this type of outlier detection.&lt;/p&gt;

&lt;p&gt;Coming to the algorithms for fraud detection, I would point out to the standards used in the industry (as I have no experience in this, but felt these resources would be useful to you).&lt;/p&gt;

&lt;p&gt;Check &lt;a href="http://datascience.stackexchange.com/questions/8099/classifying-transactions-as-malicious/8100#8100"&gt;my answer&lt;/a&gt; for this question. It contains the popular approaches and algorithms used in the domain of fraud detection. The &lt;a href="https://en.wikipedia.org/wiki/Genetic_algorithm" rel="nofollow"&gt;Genetic Algorithm&lt;/a&gt; is the most popular amongst them.&lt;/p&gt;
', NULL, NULL, '''/a'':100C,154C,182C ''/blockquote'':47C ''/p'':46C,78C,108C,144C,189C ''/questions/8099/classifying-transactions-as-malicious/8100#8100'':151C ''/strong'':99C ''/wiki/genetic_algorithm'':177C ''/wiki/mahalanobis_distance'':93C ''algorithm'':113C,164C,181C ''amongst'':187C ''amount'':10C,27C ''anomali'':37C ''answer'':153C ''approach'':53C,162C ''averag'':9C,30C ''blockquot'':1C ''calcul'':7C ''card'':68C ''case'':59C ''check'':20C,146C ''come'':110C ''contain'':159C ''credit'':67C ''datascience.stackexchange.com'':150C ''datascience.stackexchange.com/questions/8099/classifying-transactions-as-malicious/8100#8100'':149C ''deal'':65C ''detect'':52C,70C,72C,107C,116C,171C ''deviat'':33C ''distanc'':98C ''domain'':168C ''en.wikipedia.org'':92C,176C ''en.wikipedia.org/wiki/genetic_algorithm'':175C ''en.wikipedia.org/wiki/mahalanobis_distance'':91C ''experi'':132C ''far'':22C ''felt'':136C ''fraud'':69C,115C,170C ''genet'':180C ''href'':90C,148C,174C ''industri'':127C ''last'':16C ''look'':86C ''mahalanobi'':97C ''metric'':101C ''might'':81C ''money'':12C ''much'':32C,43C ''next'':25C ''nofollow'':95C,179C ''outlier'':51C,106C ''p'':2C,48C,79C,109C,145C ''point'':119C ''popular'':161C,186C ''problem'':63C ''question'':157C ''rel'':94C,178C ''resourc'':138C ''robust'':77C ''say'':13C ''signal'':35C ''sound'':42C ''standard'':123C ''statement'':64C ''strong'':96C ''technique/algorithm/implementation'':73C ''ten'':17C ''think'':5C ''transact'':18C,26C ''type'':104C ''typic'':50C ''use'':124C,141C,165C ''want'':82C ''work'':56C ''would'':34C,55C,118C,139C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8255, 1, '2015-09-30 19:21:44.187', 1, 73, 'Dividing data between test, learn and predict', '&lt;p&gt;I was posting on stats.stackexchange but perhaps I should be posting here.&lt;/p&gt;

&lt;p&gt;Context. Subscription business that charges users a monthly fee for access to the service. Management would like to predict "churn" - subscriptions who are likely to cancel. Management would like to create an email sequence in attempt to prevent high risk accounts from churning, perhaps with a discount code of some sort. So I need to identify those accounts at risk of leaving us.&lt;/p&gt;

&lt;p&gt;I have a dataset with say 50k records. Each line item is an account number along with some variables. One of the variables is "Churned" with a value of "yes" (they cancelled) or "No" (they are active).&lt;/p&gt;

&lt;p&gt;The dataset I have is all data since the beginning of time for the business. About 20k records are active paying customers and about 30k are those who used to be paying customers but who have since cancelled.&lt;/p&gt;

&lt;p&gt;My task is to build a model to predict which of the 20k active customers are currently likely to churn.&lt;/p&gt;

&lt;p&gt;Here is where I have tied my brain in a knott. I need to run the model (Predict) on the 20k records of active customers. &lt;/p&gt;

&lt;p&gt;How do I split my data between training, test and predict?&lt;/p&gt;

&lt;p&gt;Does predict data have to be exclusive of train and test data?&lt;/p&gt;

&lt;p&gt;Can I split the entire dataset of 50k into 0.8 train and 0.2 test, build a model and then predict on the 20k active accounts? That would imply I''m training and testing on data that I''m also going to predict on. Seems "wrong". Is it?&lt;/p&gt;
', 'machine-learning  predictive-modeling  churn', NULL, '''/p'':28C,93C,132C,172C,194C,221C,234C,248C,297C ''0.2'':262C ''0.8'':259C ''20k'':150C,186C,216C,272C ''30k'':158C ''50k'':101C,257C ''access'':40C ''account'':70C,87C,108C,274C ''activ'':131C,153C,187C,219C,273C ''along'':110C ''also'':288C ''attempt'':65C ''begin'':143C ''brain'':203C ''build'':178C,264C ''busi'':32C,148C ''cancel'':55C,126C,171C ''charg'':34C ''churn'':14B,49C,72C,119C,193C ''code'':77C ''context'':30C ''creat'':60C ''current'':190C ''custom'':155C,166C,188C,220C ''data'':2A,140C,228C,238C,247C,284C ''dataset'':98C,135C,255C ''discount'':76C ''divid'':1A ''email'':62C ''entir'':254C ''exclus'':242C ''fee'':38C ''go'':289C ''high'':68C ''identifi'':85C ''impli'':277C ''item'':105C ''knott'':206C ''learn'':5A,10B ''leav'':91C ''like'':46C,53C,58C,191C ''line'':104C ''m'':279C,287C ''machin'':9B ''machine-learn'':8B ''manag'':44C,56C ''model'':13B,180C,212C,266C ''month'':37C ''need'':83C,208C ''number'':109C ''one'':114C ''p'':15C,29C,94C,133C,173C,195C,222C,235C,249C ''pay'':154C,165C ''perhap'':22C,73C ''post'':18C,26C ''predict'':7A,12B,48C,182C,213C,233C,237C,269C,291C ''predictive-model'':11B ''prevent'':67C ''record'':102C,151C,217C ''risk'':69C,89C ''run'':210C ''say'':100C ''seem'':293C ''sequenc'':63C ''servic'':43C ''sinc'':141C,170C ''sort'':80C ''split'':226C,252C ''stats.stackexchange'':20C ''subscript'':31C,50C ''task'':175C ''test'':4A,231C,246C,263C,282C ''tie'':201C ''time'':145C ''train'':230C,244C,260C,280C ''us'':92C ''use'':162C ''user'':35C ''valu'':122C ''variabl'':113C,117C ''would'':45C,57C,276C ''wrong'':294C ''yes'':124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8256, 2, '2015-09-30 21:12:32.543', 1, NULL, NULL, '&lt;p&gt;If you are interested in a very high-level (enterprise architecture) framework, I suggest you to take a look at the &lt;a href="https://en.wikipedia.org/wiki/MIKE2.0_methodology" rel="nofollow"&gt;MIKE2.0 Methodology&lt;/a&gt;. Being an &lt;em&gt;information management&lt;/em&gt; framework, MIKE2.0 has, certainly, much wider coverage than the domain of your interest, but it is a solid, interesting and open (licensed under the Creative Commons Attribution License) framework. A better fit for your focus is the &lt;a href="https://en.wikipedia.org/wiki/Extract,_transform,_load" rel="nofollow"&gt;Extract, transform, load (ETL)&lt;/a&gt; framework, which is extremely popular in contexts of Business Intelligence and Data Warehousing. On a more practical note, you might want to check &lt;a href="https://www.quora.com/Is-there-an-open-source-customer-consumer-and-not-enterprise-customer-master-data-management-solution-preferably-cloud-based-available/answer/Aleksandr-Blekh?srid=OPcS&amp;amp;share=1" rel="nofollow"&gt;my answer&lt;/a&gt; on Quora on open source master data management (MDM) solutions. Pay attention to the Talend solutions (disclaimer: I am not affiliated with this or any company), which cover a wide spectrum of MDM, ETL and &lt;a href="http://www.talend.com/products/data-integration" rel="nofollow"&gt;data integration&lt;/a&gt; domains as open source and commercial offerings.&lt;/p&gt;
', NULL, NULL, '''/a'':33C,88C,121C,166C ''/em'':39C ''/is-there-an-open-source-customer-consumer-and-not-enterprise-customer-master-data-management-solution-preferably-cloud-based-available/answer/aleksandr-blekh?srid=opcs&amp;amp;share=1'':116C ''/p'':174C ''/products/data-integration'':161C ''/wiki/extract,_transform,_load'':81C ''/wiki/mike2.0_methodology'':28C ''affili'':142C ''answer'':120C ''architectur'':13C ''attent'':133C ''attribut'':66C ''better'':70C ''busi'':97C ''certain'':43C ''check'':111C ''commerci'':172C ''common'':65C ''compani'':147C ''context'':95C ''cover'':149C ''coverag'':46C ''creativ'':64C ''data'':100C,128C,164C ''disclaim'':138C ''domain'':49C,167C ''em'':36C ''en.wikipedia.org'':27C,80C ''en.wikipedia.org/wiki/extract,_transform,_load'':79C ''en.wikipedia.org/wiki/mike2.0_methodology'':26C ''enterpris'':12C ''etl'':87C,155C ''extract'':84C ''extrem'':92C ''fit'':71C ''focus'':74C ''framework'':14C,40C,68C,89C ''high'':10C ''high-level'':9C ''href'':25C,78C,113C,158C ''inform'':37C ''integr'':165C ''intellig'':98C ''interest'':5C,52C,58C ''level'':11C ''licens'':61C,67C ''load'':86C ''look'':21C ''manag'':38C,129C ''master'':127C ''mdm'':130C,154C ''methodolog'':32C ''might'':108C ''mike2.0'':31C,41C ''much'':44C ''nofollow'':30C,83C,118C,163C ''note'':106C ''offer'':173C ''open'':60C,125C,169C ''p'':1C ''pay'':132C ''popular'':93C ''practic'':105C ''quora'':123C ''rel'':29C,82C,117C,162C ''solid'':57C ''solut'':131C,137C ''sourc'':126C,170C ''spectrum'':152C ''suggest'':16C ''take'':19C ''talend'':136C ''transform'':85C ''want'':109C ''wareh'':101C ''wide'':151C ''wider'':45C ''www.quora.com'':115C ''www.quora.com/is-there-an-open-source-customer-consumer-and-not-enterprise-customer-master-data-management-solution-preferably-cloud-based-available/answer/aleksandr-blekh?srid=opcs&amp;amp;share=1'':114C ''www.talend.com'':160C ''www.talend.com/products/data-integration'':159C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8257, 2, '2015-09-30 21:15:16.14', 0, NULL, NULL, '&lt;p&gt;The docs from scikit-learn can help with best practices for splitting your data for cross-validation (training, testing, validating): &lt;a href="http://scikit-learn.org/stable/modules/cross_validation.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/cross_validation.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In your case, all of your accounts are labeled as "Churned" with values either "Yes" or "No". If you make a model that predicts Churned=Yes/No, then you''re answering the question "Is a given account currently active?", which I don''t think is the question you want to answer.&lt;/p&gt;

&lt;p&gt;If I understand, I think you want to ask an actionable question about the Churned=No accounts:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;"What is the probability that a Churned=No account will become a Churned=Yes within the next 30 days?"

&lt;ul&gt;
&lt;li&gt;Requires access to time-series data.&lt;/li&gt;
&lt;li&gt;Example approach:

&lt;ul&gt;
&lt;li&gt;Use the 30k Churned=Yes accounts to create a supervised model (since you know when and how these accounts went from Churned=No to Churned=Yes) and apply it to the 20k Churned=No accounts.&lt;/li&gt;
&lt;li&gt;The predicted quantity is the probability of a Churned=No account becoming a Churned=Yes account within the next 30 days.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;"How ''close'' is a Churned=No account to being a Churned=Yes account?"

&lt;ul&gt;
&lt;li&gt;Does not require access to time-series data.&lt;/li&gt;
&lt;li&gt;Example approach:

&lt;ul&gt;
&lt;li&gt;Again use the 30k Churned=Yes accounts to create a supervised model (may require multiple models for different clusters of similar accounts) and apply it to the 20k Churned=No accounts.&lt;/li&gt;
&lt;li&gt;The predicted quantity is a metric (i.e. a risk score) that measures the proximity in parameter space of a Churned=No account to the Churned=Yes accounts.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8258, 2, '2015-10-01 02:43:47.057', 0, NULL, NULL, '&lt;p&gt;If I understand, you have a set of paragraphs, and each paragraph is tagged with keywords. This defines a mapping from paragraph to keyword and vice-versa (an index and a reverse index). You can create a graph of paragraphs and keywords, but all of the edges are undirected (or they all have the same direction). Paragraphs only connect with keywords, and keywords only connect with paragraphs.&lt;/p&gt;

&lt;p&gt;To establish relationships between paragraphs, you could define a transitive equivalence relation such as "all paragraphs that connect to the same keyword are now connected to each other". This would allow you to rank the paragraphs that contain a given keyword by their number of connections to other paragraphs, but it may not be an appropriate measure of relevance.&lt;/p&gt;

&lt;p&gt;For PageRank to be most helpful, I think you need directional relationships (e.g. if "see page 123" was mentioned in a paragraph). To better represent the complexity of your data, creating additional relationships between keywords (e.g. semantic meaning, lexical relationships) and creating additional relationships between paragraphs (e.g. document classification) may help. &lt;a href="https://books.google.com/books?id=KGIbfiiP1i4C" rel="nofollow"&gt;Natural Language Processing with Python&lt;/a&gt; discusses these topics.&lt;/p&gt;
', NULL, NULL, '''/a'':196C ''/books?id=kgibfiip1i4c'':188C ''/p'':70C,131C,200C ''123'':149C ''addit'':164C,175C ''allow'':102C ''appropri'':127C ''better'':156C ''books.google.com'':187C ''books.google.com/books?id=kgibfiip1i4c'':186C ''classif'':181C ''complex'':159C ''connect'':61C,67C,89C,96C,117C ''contain'':109C ''could'':78C ''creat'':38C,163C,174C ''data'':162C ''defin'':19C,79C ''direct'':58C,143C ''discuss'':197C ''document'':180C ''e.g'':145C,168C,179C ''edg'':49C ''equival'':82C ''establish'':73C ''given'':111C ''graph'':40C ''help'':138C,183C ''href'':185C ''index'':31C,35C ''keyword'':17C,25C,44C,63C,65C,93C,112C,167C ''languag'':192C ''lexic'':171C ''map'':21C ''may'':123C,182C ''mean'':170C ''measur'':128C ''mention'':151C ''natur'':191C ''need'':142C ''nofollow'':190C ''number'':115C ''p'':1C,71C,132C ''page'':148C ''pagerank'':134C ''paragraph'':10C,13C,23C,42C,59C,69C,76C,87C,107C,120C,154C,178C ''process'':193C ''python'':195C ''rank'':105C ''rel'':189C ''relat'':83C ''relationship'':74C,144C,165C,172C,176C ''relev'':130C ''repres'':157C ''revers'':34C ''see'':147C ''semant'':169C ''set'':8C ''tag'':15C ''think'':140C ''topic'':199C ''transit'':81C ''understand'':4C ''undirect'':51C ''versa'':29C ''vice'':28C ''vice-versa'':27C ''would'':101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8259, 2, '2015-10-01 03:33:52.937', 2, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Supervised Learning:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Do you have a saved time history of the data?&lt;/strong&gt;  For a supervised learning set you need some churned="No" cases and some churned="Yes" cases, but it sounds like you only have churned="Yes" and the unknown cases e.g. current customers who may or may not churn.  With some time history you can go back in time and definitively label the current customers as churn="No".&lt;/p&gt;

&lt;p&gt;Then it is very easy to split up the data.  And no, you probably don''t want to predict on any data that you trained on since you can only train on it if you already know the solution so it will be a waste of time and throw off any metrics you might use to assess accuracy (precision/recall/F1) in the future.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Unsupervised Learning:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;If you don''t have saved time history of the data&lt;/strong&gt; then this is an unsupervised learning set for which you have churned="yes" and churned="maybe".  You could then employ anomaly or outlier detection on this set. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;novelty detection:&lt;/strong&gt;
    The training data is not polluted by outliers, and we are interested in detecting anomalies in new observations.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;outlier detection:&lt;/strong&gt;
    The training data contains outliers, and we need to fit the central mode of the training data, ignoring the deviant observations.&lt;/p&gt;

&lt;p&gt;You can do either one but novelty is more powerful.  This is kind of a flip around as the novelty here is Churned="No" since all of your data is the confirmed Churn="Yes" cases.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/p'':6C,77C,141C,147C,189C,213C,240C,277C,282C ''/strong'':5C,19C,146C,161C,194C,218C ''accuraci'':136C ''alreadi'':114C ''anomali'':182C,209C ''around'':258C ''assess'':135C ''back'':65C ''case'':30C,35C,48C,276C ''central'':230C ''churn'':28C,33C,43C,57C,75C,173C,176C,264C,274C ''confirm'':273C ''contain'':222C ''could'':179C ''current'':50C,72C ''custom'':51C,73C ''data'':18C,88C,100C,160C,197C,221C,235C,270C ''definit'':69C ''detect'':185C,193C,208C,217C ''deviant'':238C ''e.g'':49C ''easi'':83C ''either'':245C ''employ'':181C ''fit'':228C ''flip'':257C ''futur'':140C ''go'':64C ''help'':281C ''histori'':15C,61C,157C ''hope'':279C ''ignor'':236C ''interest'':206C ''kind'':254C ''know'':115C ''label'':70C ''learn'':4C,23C,145C,167C ''like'':39C ''may'':53C,55C ''mayb'':177C ''metric'':130C ''might'':132C ''mode'':231C ''need'':26C,226C ''new'':211C ''novelti'':192C,248C,261C ''observ'':212C,239C ''one'':246C ''outlier'':184C,202C,216C,223C ''p'':1C,7C,78C,142C,148C,190C,214C,241C,278C ''pollut'':200C ''power'':251C ''precision/recall/f1'':137C ''predict'':97C ''probabl'':92C ''save'':13C,155C ''set'':24C,168C,188C ''sinc'':105C,266C ''solut'':117C ''sound'':38C ''split'':85C ''strong'':2C,8C,143C,149C,191C,215C ''supervis'':3C,22C ''throw'':127C ''time'':14C,60C,67C,125C,156C ''train'':103C,109C,196C,220C,234C ''unknown'':47C ''unsupervis'':144C,166C ''use'':133C ''want'':95C ''wast'':123C ''yes'':34C,44C,174C,275C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8261, 2, '2015-10-01 10:57:42.56', 0, NULL, NULL, '&lt;p&gt;With (d=256, n=10) as well as  with (d=16000, n=1000) you are under the &lt;a href="https://en.wikipedia.org/wiki/Curse_of_dimensionality" rel="nofollow"&gt;curse of dimensionality&lt;/a&gt;. The essence of the curse (quoted from Wikipedia)&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;nearly all of the high-dimensional space is "far away" from the centre. To put it another way, the high-dimensional unit hypercube can be said to consist almost entirely of the "corners" of the hypercube, with almost no "middle".&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;For an intuitive explanation I also found &lt;a href="http://www.visiondummy.com/2014/04/curse-dimensionality-affect-classification/" rel="nofollow"&gt;this&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;IMHO, for your problem with n=10 you can use d&amp;lt;=2, for n=1000 d=3 (maybe 4, at most 5). Why d=3 for n=1000? Roughly speaking  this would correspond to 10 points along each dimension (10^3=1000), which is reasonable to fill the 3D space. For d=5 it is like 4 points in each dimension, which is not so good but not a disaster. &lt;/p&gt;

&lt;p&gt;IMHO, you should try to reformulate your problem and significantly reduce dimensionality (maybe try to use SVD or PCA). This may automatically solve your problem of noisy data.   &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8293, 1, '2015-10-03 18:23:26.623', 0, 10, 'Dummy features-keeping original variable', '&lt;p&gt;When using dummy features, is it a good approach to  keep also  the  initial feature that generated  the dummy  ones, when planning to fit a model that  is doing well on  sparse data?&lt;/p&gt;
', 'feature-construction', NULL, '''/p'':44C ''also'':22C ''approach'':19C ''construct'':9B ''data'':43C ''dummi'':1A,13C,29C ''featur'':3A,8B,14C,25C ''feature-construct'':7B ''features-keep'':2A ''fit'':34C ''generat'':27C ''good'':18C ''initi'':24C ''keep'':4A,21C ''model'':36C ''one'':30C ''origin'':5A ''p'':10C ''plan'':32C ''spars'':42C ''use'':12C ''variabl'':6A ''well'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8598, 1, '2015-10-25 22:05:51.123', 2, 70, 'How is cross validation used to prune a decision tree', '&lt;p&gt;As I understand it one can use cross validation to help find the optimal pruning of a classification or regression tree, for example, implemented here in &lt;a href="http://au.mathworks.com/help/stats/classificationtree.cvloss.html#bunpajd-1" rel="nofollow"&gt;Matlab&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;I do not understand the following about this process. Since each cross validation fold has random data, it may be that the trees fit on each data set may be different and have differing depths. How can one find the best pruning level over all such trees then?&lt;/p&gt;
', 'decision-trees  cross-validation', NULL, '''/a'':52C ''/help/stats/classificationtree.cvloss.html#bunpajd-1'':48C ''/p'':53C,103C ''au.mathworks.com'':47C ''au.mathworks.com/help/stats/classificationtree.cvloss.html#bunpajd-1'':46C ''best'':95C ''classif'':35C ''cross'':3A,15B,25C,66C ''cross-valid'':14B ''data'':71C,81C ''decis'':9A,12B ''decision-tre'':11B ''depth'':89C ''differ'':85C,88C ''exampl'':40C ''find'':29C,93C ''fit'':78C ''fold'':68C ''follow'':60C ''help'':28C ''href'':45C ''implement'':41C ''level'':97C ''matlab'':51C ''may'':73C,83C ''nofollow'':50C ''one'':22C,92C ''optim'':31C ''p'':17C,54C ''process'':63C ''prune'':7A,32C,96C ''random'':70C ''regress'':37C ''rel'':49C ''set'':82C ''sinc'':64C ''tree'':10A,13B,38C,77C,101C ''understand'':20C,58C ''use'':5A,24C ''valid'':4A,16B,26C,67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8262, 2, '2015-10-01 11:02:02.95', 0, NULL, NULL, '&lt;p&gt;&lt;a href="http://stats.stackexchange.com/questions/12046/organizing-a-classification-tree-in-rpart-into-a-set-of-rules"&gt;This question&lt;/a&gt; on CrossValidated sort of answers your question. Rpart classifies via a decision tree, which means that if you could print the tree in one form or another it would be easy for you to take a document and see why it ended up being classified the way it was. The tree in itself is just a hierarchical set of rules applied to new documents.&lt;/p&gt;

&lt;p&gt;The user was advised to use the &lt;a href="https://cran.r-project.org/web/packages/rattle/index.html" rel="nofollow"&gt;rattle&lt;/a&gt; library which also uses decision trees but is able to print the set of rules induced by the tree, which should help you in your task.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8263, 2, '2015-10-01 11:08:47.6', 1, NULL, NULL, '&lt;p&gt;rpart is a decision tree model and as such is very much interpretable.
You should visualize your decision tree. See the examples &lt;a href="http://www.milbo.org/rpart-plot/" rel="nofollow"&gt;here&lt;/a&gt;, one of them is&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/wPG1R.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/wPG1R.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Then for each given document you can follow the tree path and see &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;what were the features on the way responsible for the classification &lt;/li&gt;
&lt;li&gt;what leaf your document arrived at and what is the percentage of correctly classified documents at these leaf.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':32C,56C ''/li'':86C,106C ''/p'':37C,57C,72C ''/rpart-plot/'':28C ''/ul'':107C ''/wpg1r.png'':43C,50C ''alt'':51C ''arriv'':92C ''classif'':85C ''classifi'':101C ''correct'':100C ''decis'':5C,19C ''descript'':54C ''document'':63C,91C,102C ''enter'':52C ''exampl'':23C ''featur'':78C ''follow'':66C ''given'':62C ''href'':25C,40C ''i.stack.imgur.com'':42C,49C ''i.stack.imgur.com/wpg1r.png'':41C,48C ''imag'':53C ''img'':46C ''interpret'':14C ''leaf'':89C,105C ''li'':74C,87C ''model'':7C ''much'':13C ''nofollow'':30C,45C ''one'':33C ''p'':1C,38C,58C ''path'':69C ''percentag'':98C ''rel'':29C,44C ''respons'':82C ''rpart'':2C ''see'':21C,71C ''src'':47C ''tree'':6C,20C,68C ''ul'':73C ''visual'':17C ''way'':81C ''www.milbo.org'':27C ''www.milbo.org/rpart-plot/'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8264, 1, '2015-10-01 15:06:43.927', 4, 104, 'when speed is not a major concern, what is the best ML algorithm for high dimensional data?', '&lt;p&gt;we are trying to build a predicting model using machine learning algorithms.&lt;/p&gt;

&lt;p&gt;I have a use case where the input data have a very high dimension. Each sample point has 20000 features.&lt;/p&gt;

&lt;p&gt;we have a decent training sample set with around 1 million training samples, if necessary, we can acquire more, say, 2 or 3 million.&lt;/p&gt;

&lt;p&gt;We are not very speed sensitive, it''s not like a recommendation system that needs to respond within a second. The application allows us to spend up to a couple of minutes for one prediction. Nevertheless, we hope the algorithm can be run in parallel mode in the future.&lt;/p&gt;

&lt;p&gt;given above description, what kind of algorithm would you suggest? &lt;/p&gt;

&lt;p&gt;Our biggest concern is over fitting, with so many features, it seems that we are doomed to over-fit. &lt;/p&gt;

&lt;p&gt;We were trying to do something alone the line of nearest neighbor, but with these many features, calculating distance sounds like a mission impossible. Maybe we should do PCA to do dimension reduction first?&lt;/p&gt;

&lt;p&gt;Any comment is welcomed!&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':34C,56C,82C,133C,145C,167C,203C,209C ''1'':67C ''2'':78C ''20000'':54C ''3'':80C ''acquir'':75C ''algorithm'':13A,33C,123C,141C ''allow'':106C ''alon'':175C ''applic'':105C ''around'':66C ''best'':11A ''biggest'':148C ''build'':26C ''calcul'':186C ''case'':40C ''comment'':206C ''concern'':7A,149C ''coupl'':113C ''data'':17A,44C ''decent'':61C ''descript'':137C ''dimens'':49C,200C ''dimension'':16A ''distanc'':187C ''doom'':162C ''featur'':55C,156C,185C ''first'':202C ''fit'':152C,166C ''futur'':132C ''given'':135C ''high'':15A,48C ''hope'':121C ''imposs'':192C ''input'':43C ''kind'':139C ''learn'':20B,32C ''like'':93C,189C ''line'':177C ''machin'':19B,31C ''machine-learn'':18B ''major'':6A ''mani'':155C,184C ''mayb'':193C ''million'':68C,81C ''minut'':115C ''mission'':191C ''ml'':12A ''mode'':129C ''model'':29C ''nearest'':179C ''necessari'':72C ''need'':98C ''neighbor'':180C ''nevertheless'':119C ''one'':117C ''over-fit'':164C ''p'':21C,35C,57C,83C,134C,146C,168C,204C ''parallel'':128C ''pca'':197C ''point'':52C ''predict'':28C,118C ''recommend'':95C ''reduct'':201C ''respond'':100C ''run'':126C ''sampl'':51C,63C,70C ''say'':77C ''second'':103C ''seem'':158C ''sensit'':89C ''set'':64C ''someth'':174C ''sound'':188C ''speed'':2A,88C ''spend'':109C ''suggest'':144C ''system'':96C ''train'':62C,69C ''tri'':24C,171C ''us'':107C ''use'':30C,39C ''welcom'':208C ''within'':101C ''would'':142C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8265, 1, '2015-10-01 15:19:58.877', 1, 56, 'similarity measure with two features', '&lt;p&gt;I have some question concern similarity measure&lt;/p&gt;

&lt;p&gt;Suppose that we have a matrix &lt;strong&gt;&lt;em&gt;M&lt;/em&gt;&lt;/strong&gt; where &lt;strong&gt;&lt;em&gt;M(i,j)&lt;/em&gt;&lt;/strong&gt; is the similarity measure between user &lt;strong&gt;&lt;em&gt;i&lt;/em&gt;&lt;/strong&gt; and user &lt;strong&gt;&lt;em&gt;j&lt;/em&gt;&lt;/strong&gt; .&lt;/p&gt;

&lt;p&gt;Each user is characterised by : id-user | country | id-artist | id-track &lt;/p&gt;

&lt;p&gt;For this I choose to use Jaccard similarity metric.&lt;/p&gt;

&lt;p&gt;Jaccard is determined to compute similarity between users based on the tracks that they listened.
My question is : is it possible to take account both id-track &lt;strong&gt;&lt;em&gt;and&lt;/em&gt;&lt;/strong&gt; id-artist to measure the similarity between users?&lt;/p&gt;

&lt;p&gt;Thank you&lt;/p&gt;
', 'similarity', NULL, '''/em'':26C,34C,45C,52C,115C ''/p'':15C,54C,71C,82C,126C,130C ''/strong'':27C,35C,46C,53C,116C ''account'':107C ''artist'':67C,119C ''base'':92C ''characteris'':59C ''choos'':76C ''comput'':88C ''concern'':12C ''countri'':64C ''determin'':86C ''em'':24C,30C,43C,50C,113C ''featur'':5A ''id'':62C,66C,69C,110C,118C ''id-artist'':65C,117C ''id-track'':68C,109C ''id-us'':61C ''j'':33C,51C ''jaccard'':79C,84C ''listen'':98C ''m'':25C,31C ''matrix'':22C ''measur'':2A,14C,39C,121C ''metric'':81C ''p'':7C,16C,55C,72C,83C,127C ''possibl'':104C ''question'':11C,100C ''similar'':1A,6B,13C,38C,80C,89C,123C ''strong'':23C,29C,42C,49C,112C ''suppos'':17C ''take'':106C ''thank'':128C ''track'':70C,95C,111C ''two'':4A ''use'':78C ''user'':41C,48C,57C,63C,91C,125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8266, 1, '2015-10-01 18:40:47.937', 3, 249, 'Is there a library that would perform segmented linear regression in python?', '&lt;p&gt;There is a package named segmented in R. Is there a similar package in python?&lt;/p&gt;
', 'python  regression  linear-regression', NULL, '''/p'':34C ''librari'':4A ''linear'':9A,16B ''linear-regress'':15B ''name'':23C ''p'':18C ''packag'':22C,31C ''perform'':7A ''python'':12A,13B,33C ''r'':26C ''regress'':10A,14B,17B ''segment'':8A,24C ''similar'':30C ''would'':6A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8272, 2, '2015-10-01 21:07:26.54', 8, NULL, NULL, '&lt;p&gt;It depends on &lt;em&gt;what&lt;/em&gt; you do.&lt;/p&gt;

&lt;p&gt;Column stores have two key benefits:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;whole columns can be skipped&lt;/li&gt;
&lt;li&gt;run-length compression works better on columns (for certain data types; in particular with few distinct values)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;However they also have drawbacks:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;many algorithms will need all columns, and only record at a time (e.g. k-means) or may even need to compute a pairwise distance matrix&lt;/li&gt;
&lt;li&gt;compression techniques only work well on sparse data types and factors, but not well on double-valued continuous data&lt;/li&gt;
&lt;li&gt;appends on column stores are expensive, so it is not ideal for streaming / changing data&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Columnar storage is really popular for OLAP aka "stupid analytics" (Michael Stonebraker) and of course for &lt;em&gt;preprocessing&lt;/em&gt; where you may indeed be interested in discarding whole columns (but you would need to have structured data first - you don''t store JSONs in columnar format). Because the columnar layout is really nice for e.g. counting how many apples you have sold last week.&lt;/p&gt;

&lt;p&gt;For much of the scientific / data science use cases, &lt;strong&gt;array databases&lt;/strong&gt; appear to be the way to go (plus, of course, unstructured input data). E.g. SciDB and RasDaMan.&lt;/p&gt;

&lt;p&gt;In many cases (e.g. deep learning), matrixes and arrays are the data types you need, not columns. MapReduce etc. can still be useful in preprocessing, of course. Maybe even column data (but array database usually support a column-like compression, too).&lt;/p&gt;
', NULL, NULL, '''/em'':7C,143C ''/li'':26C,46C,83C,105C,122C ''/p'':10C,18C,54C,189C,221C,265C ''/strong'':203C ''/ul'':47C,123C ''aka'':132C ''algorithm'':58C ''also'':51C ''analyt'':134C ''appear'':204C ''append'':107C ''appl'':183C ''array'':201C,231C,255C ''benefit'':17C ''better'':33C ''case'':199C,225C ''certain'':37C ''chang'':120C ''column'':12C,22C,35C,62C,109C,153C,239C,252C,261C ''column-lik'':260C ''columnar'':125C,169C,173C ''compress'':31C,85C,263C ''comput'':78C ''continu'':103C ''count'':180C ''cours'':139C,213C,249C ''data'':38C,92C,104C,121C,161C,196C,216C,234C,253C ''databas'':202C,256C ''deep'':227C ''depend'':3C ''discard'':151C ''distanc'':81C ''distinct'':44C ''doubl'':101C ''double-valu'':100C ''drawback'':53C ''e.g'':69C,179C,217C,226C ''em'':5C,141C ''etc'':241C ''even'':75C,251C ''expens'':112C ''factor'':95C ''first'':162C ''format'':170C ''go'':210C ''howev'':49C ''ideal'':117C ''inde'':147C ''input'':215C ''interest'':149C ''json'':167C ''k'':71C ''k-mean'':70C ''key'':16C ''last'':187C ''layout'':174C ''learn'':228C ''length'':30C ''li'':20C,27C,56C,84C,106C ''like'':262C ''mani'':57C,182C,224C ''mapreduc'':240C ''matrix'':82C,229C ''may'':74C,146C ''mayb'':250C ''mean'':72C ''michael'':135C ''much'':192C ''need'':60C,76C,157C,237C ''nice'':177C ''olap'':131C ''p'':1C,11C,48C,124C,190C,222C ''pairwis'':80C ''particular'':41C ''plus'':211C ''popular'':129C ''preprocess'':142C,247C ''rasdaman'':220C ''realli'':128C,176C ''record'':65C ''run'':29C ''run-length'':28C ''scidb'':218C ''scienc'':197C ''scientif'':195C ''skip'':25C ''sold'':186C ''spars'':91C ''still'':243C ''stonebrak'':136C ''storag'':126C ''store'':13C,110C,166C ''stream'':119C ''strong'':200C ''structur'':160C ''stupid'':133C ''support'':258C ''techniqu'':86C ''time'':68C ''two'':15C ''type'':39C,93C,235C ''ul'':19C,55C ''unstructur'':214C ''use'':198C,245C ''usual'':257C ''valu'':45C,102C ''way'':208C ''week'':188C ''well'':89C,98C ''whole'':21C,152C ''work'':32C,88C ''would'':156C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8267, 2, '2015-10-01 18:53:46.107', 4, NULL, NULL, '&lt;p&gt;Yes, multiple different ways.&lt;/p&gt;

&lt;p&gt;First, we could consider (id-artist,id-track) items as the elements of our set, and compute the Jaccard similarity by comparing those sets. Note that if the artist''s id gives us no additional information beyond the track id, this will give the same result, whereas it will give different results if a particular track id could be associated with multiple artists.&lt;/p&gt;

&lt;p&gt;Second, we could compute the Jaccard similarity on tracks, and then the Jaccard similarity on artists, and then add the two (probably with some constant coefficient scaling the two). This way two users who listen to the same artists, but none of the same songs by those artists, will be rated as more similar than users who listen to different songs by different artists.&lt;/p&gt;

&lt;p&gt;What coefficient makes sense? Well, you could start with 0.5 for each (i.e. just .5*similarity_artist+.5*similarity_track), see if that''s reasonable, and adjust if it''s not.&lt;/p&gt;
', NULL, NULL, '''/p'':6C,73C,139C,172C ''0.5'':150C ''5'':155C,158C ''add'':93C ''addit'':44C ''adjust'':167C ''artist'':14C,38C,72C,90C,113C,122C,138C,157C ''associ'':69C ''beyond'':46C ''coeffici'':100C,142C ''compar'':31C ''comput'':26C,78C ''consid'':11C ''constant'':99C ''could'':10C,67C,77C,147C ''differ'':4C,60C,134C,137C ''element'':21C ''first'':8C ''give'':41C,52C,59C ''i.e'':153C ''id'':13C,16C,40C,49C,66C ''id-artist'':12C ''id-track'':15C ''inform'':45C ''item'':18C ''jaccard'':28C,80C,87C ''listen'':109C,132C ''make'':143C ''multipl'':3C,71C ''none'':115C ''note'':34C ''p'':1C,7C,74C,140C ''particular'':64C ''probabl'':96C ''rate'':125C ''reason'':165C ''result'':55C,61C ''scale'':101C ''second'':75C ''see'':161C ''sens'':144C ''set'':24C,33C ''similar'':29C,81C,88C,128C,156C,159C ''song'':119C,135C ''start'':148C ''track'':17C,48C,65C,83C,160C ''two'':95C,103C,106C ''us'':42C ''user'':107C,130C ''way'':5C,105C ''well'':145C ''wherea'':56C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8268, 1, '2015-10-01 18:57:42.82', 3, 53, 'Establishing data science programs as an independent discipline', '&lt;p&gt;What are the benefits of establishing data science programs as an independent discipline rather than creating data scientists in different fields? (for students, universities and the society as a whole)&lt;/p&gt;

&lt;p&gt;I am authoring a book on data science and I have mentioned some university programs as models for establishing data science programs in bachelor''s, master''s and doctorate level. I am thinking of some reasons to compare this model with enriching well-established programs and adding data science skills.&lt;/p&gt;
', 'data  education', NULL, '''/p'':42C,95C ''ad'':91C ''author'':46C ''bachelor'':67C ''benefit'':15C ''book'':48C ''compar'':81C ''creat'':27C ''data'':2A,9B,18C,28C,50C,63C,92C ''differ'':31C ''disciplin'':8A,24C ''doctor'':72C ''educ'':10B ''enrich'':85C ''establish'':1A,17C,62C,88C ''field'':32C ''independ'':7A,23C ''level'':73C ''master'':69C ''mention'':55C ''model'':60C,83C ''p'':11C,43C ''program'':4A,20C,58C,65C,89C ''rather'':25C ''reason'':79C ''scienc'':3A,19C,51C,64C,93C ''scientist'':29C ''skill'':94C ''societi'':38C ''student'':34C ''think'':76C ''univers'':35C,57C ''well'':87C ''well-establish'':86C ''whole'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9514, 2, '2015-12-25 11:37:47.143', 2, NULL, NULL, '&lt;p&gt;I think the next option you have to take is to add more features. You have a huge amount of training examples, which is good, but the number of features is very low. Adding more features is one of the most used methods to improve performance in machine learning. &lt;/p&gt;

&lt;p&gt;Furthermore, it would be good to try to understand how your features affect your model. Imagine you have a linear model like &lt;code&gt;y = theta1*feat1 + theta2*feat2 + theta3*feat3&lt;/code&gt;. If theta3 is near to 0, then feat3 is not affecting the model.&lt;/p&gt;
', NULL, NULL, '''/code'':83C ''/p'':51C,97C ''0'':89C ''ad'':35C ''add'':13C ''affect'':65C,94C ''amount'':20C ''code'':75C ''exampl'':23C ''feat1'':78C ''feat2'':80C ''feat3'':82C,91C ''featur'':15C,31C,37C,64C ''furthermor'':53C ''good'':26C,57C ''huge'':19C ''imagin'':68C ''improv'':46C ''learn'':50C ''like'':74C ''linear'':72C ''low'':34C ''machin'':49C ''method'':44C ''model'':67C,73C,96C ''near'':87C ''next'':5C ''number'':29C ''one'':39C ''option'':6C ''p'':1C,52C ''perform'':47C ''take'':10C ''theta1'':77C ''theta2'':79C ''theta3'':81C,85C ''think'':3C ''train'':22C ''tri'':59C ''understand'':61C ''use'':43C ''would'':55C ''y'':76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8270, 2, '2015-10-01 19:38:43.373', 0, NULL, NULL, '&lt;p&gt;Short version: I would not expect PageRank to be the right algorithm to use for this problem, because the thing you''re interested in (relevance) is a feature of the content, not the context (i.e. the graph structure of the links). PageRank works on the web because the context is so closely related to the content.&lt;/p&gt;

&lt;p&gt;Long version: PageRank is a way for weight to flow along directional links. Eventually, a stable distribution is reached where weight flows in and out in equal measure. (Generally, people talk about PageRank measuring "importance" or "popularity" because both of those have natural internal definitions--one item is popular because other popular items like it.)&lt;/p&gt;

&lt;p&gt;You can create something like PageRank with a graph with multiple classes of objects--no ''paragraph'' links to other paragraphs, but it links to ''keyword''s, and no keyword links to other keywords, but it links to other paragraphs. This gives you a minor constraint on the graph structure, which should probably be reflected in the randomized restart probability used by PageRank. Initiate the algorithm with half the weight on paragraphs and half the weight on keywords, and it''ll iterate until it converges. (This is very similar to Samuel Harrold''s solution, except the weighting of the links between paragraphs depends on the number of keywords they have in common.)&lt;/p&gt;

&lt;p&gt;But what problem will that actually solve? Which paragraphs and keywords will be the most popular? The useful work of PageRank is &lt;em&gt;mostly&lt;/em&gt; being done by asymmetric links (knowing that Alice links to Bob but Bob does not link to Alice is evidence that Bob is more popular than Alice), and we only have symmetric links (unless we use different mechanisms to determine what keywords link to paragraphs than what paragraphs link to keywords). &lt;/p&gt;

&lt;p&gt;The remaining work is being done by link scarcity--since a page''s popularity is shared among all its links, a page that links to fewer pages transmits more popularity to them than a page that links to more pages. But this means that paragraphs that only match a few keywords make those keywords more popular, and keywords that only link to a few paragraphs make those paragraphs more popular. It''s not obvious to me that this will have the effect that we want--a paragraph that receives weight from both "spark plug" and "wheel" is going to show up higher in the rankings than a paragraph that receives weight from just "spark plug," which is probably the opposite of what the user will want.&lt;/p&gt;

&lt;p&gt;But you might want to show paragraphs to the user ranked by their weight percentage from a given keyword--if paragraph A gets 100% of its weight from spark plug, paragraph B gets 30% of its weight from spark plug, and C gets 0% of its weight from spark plug, then maybe A is the best result to show for spark plug, followed by B. But is this going to be any different in practice from just ranking pages by what fraction of their total keywords are any particular keyword? It doesn''t seem like it''ll be by much.&lt;/p&gt;
', NULL, NULL, '''/em'':254C ''/p'':58C,115C,228C,306C,434C,536C ''0'':479C ''100'':459C ''30'':469C ''actual'':235C ''algorithm'':13C,181C ''alic'':262C,272C,281C ''along'':70C ''among'':324C ''asymmetr'':258C ''b'':467C,500C ''best'':491C ''bob'':265C,267C,276C ''c'':477C ''class'':128C ''close'':53C ''common'':227C ''constraint'':161C ''content'':32C,57C ''context'':35C,50C ''converg'':200C ''creat'':119C ''definit'':104C ''depend'':218C ''determin'':294C ''differ'':291C,508C ''direct'':71C ''distribut'':76C ''doesn'':527C ''done'':256C,313C ''effect'':389C ''em'':252C ''equal'':86C ''eventu'':73C ''evid'':274C ''except'':210C ''expect'':7C ''featur'':29C ''fewer'':333C ''flow'':69C,81C ''follow'':498C ''fraction'':517C ''general'':88C ''get'':458C,468C,478C ''give'':157C ''given'':453C ''go'':405C,504C ''graph'':38C,125C,164C ''half'':183C,189C ''harrold'':207C ''higher'':409C ''i.e'':36C ''import'':94C ''initi'':179C ''interest'':24C ''intern'':103C ''item'':106C,112C ''iter'':197C ''keyword'':141C,145C,149C,193C,223C,240C,296C,305C,358C,361C,365C,454C,521C,525C ''know'':260C ''like'':113C,121C,530C ''link'':42C,72C,133C,139C,146C,152C,215C,259C,263C,270C,287C,297C,303C,315C,327C,331C,344C,368C ''ll'':196C,532C ''long'':60C ''make'':359C,373C ''match'':355C ''mayb'':487C ''mean'':350C ''measur'':87C,93C ''mechan'':292C ''might'':438C ''minor'':160C ''most'':253C ''much'':535C ''multipl'':127C ''natur'':102C ''number'':221C ''object'':130C ''obvious'':381C ''one'':105C ''opposit'':427C ''p'':1C,59C,116C,229C,307C,435C ''page'':319C,329C,334C,342C,347C,514C ''pagerank'':8C,43C,62C,92C,122C,178C,250C ''paragraph'':132C,136C,155C,187C,217C,238C,299C,302C,352C,372C,375C,394C,415C,442C,456C,466C ''particular'':524C ''peopl'':89C ''percentag'':450C ''plug'':401C,422C,465C,475C,485C,497C ''popular'':96C,108C,111C,245C,279C,321C,337C,363C,377C ''practic'':510C ''probabl'':168C,175C,425C ''problem'':18C,232C ''random'':173C ''rank'':412C,446C,513C ''re'':23C ''reach'':78C ''receiv'':396C,417C ''reflect'':170C ''relat'':54C ''relev'':26C ''remain'':309C ''restart'':174C ''result'':492C ''right'':12C ''samuel'':206C ''scarciti'':316C ''seem'':529C ''share'':323C ''short'':2C ''show'':407C,441C,494C ''similar'':204C ''sinc'':317C ''solut'':209C ''solv'':236C ''someth'':120C ''spark'':400C,421C,464C,474C,484C,496C ''stabl'':75C ''structur'':39C,165C ''symmetr'':286C ''talk'':90C ''thing'':21C ''total'':520C ''transmit'':335C ''unless'':288C ''use'':15C,176C,247C,290C ''user'':431C,445C ''version'':3C,61C ''want'':392C,433C,439C ''way'':65C ''web'':47C ''weight'':67C,80C,185C,191C,212C,397C,418C,449C,462C,472C,482C ''wheel'':403C ''work'':44C,248C,310C ''would'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8271, 1, '2015-10-01 20:22:36.54', 0, 32, 'How to choose the order in which to split a decision tree?', '&lt;p&gt;I know that a decision tree recursively splits along each attribute, greedily minimizing the wrong classifications/deviance at each split.  &lt;/p&gt;

&lt;p&gt;But, what is the order in which the attributes are split?  In other words, for a regression tree in N dimensions, what determines which attribute gets split first?    &lt;/p&gt;
', 'regression  decision-trees', NULL, '''/p'':37C,67C ''along'':26C ''attribut'':28C,47C,63C ''choos'':3A ''classifications/deviance'':33C ''decis'':11A,15B,22C ''decision-tre'':14B ''determin'':61C ''dimens'':59C ''first'':66C ''get'':64C ''greedili'':29C ''know'':19C ''minim'':30C ''n'':58C ''order'':5A,43C ''p'':17C,38C ''recurs'':24C ''regress'':13B,55C ''split'':9A,25C,36C,49C,65C ''tree'':12A,16B,23C,56C ''word'':52C ''wrong'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1085, 2, '2014-09-07 08:02:39.67', 1, NULL, NULL, '&lt;p&gt;If I understand your description correctly, hadoop seems a huge overhead, for the wrong problem. basically you just need a standard distributed architecture: don''t you just have to pass pairs of rows - eg mpi.... ipython.parallel, ...&lt;/p&gt;
', NULL, NULL, '''/p'':38C ''architectur'':24C ''basic'':17C ''correct'':7C ''descript'':6C ''distribut'':23C ''eg'':35C ''hadoop'':8C ''huge'':11C ''ipython.parallel'':37C ''mpi'':36C ''need'':20C ''overhead'':12C ''p'':1C ''pair'':32C ''pass'':31C ''problem'':16C ''row'':34C ''seem'':9C ''standard'':22C ''understand'':4C ''wrong'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8284, 2, '2015-10-02 21:20:40.23', 1, NULL, NULL, '&lt;p&gt;Suppose (prior to one-hot-encoding) you have predictors/fields from a set $Z$ (say movie genre, user gender, and user race).  Suppose further, each predictor $z \in Z$ can take on one of $k_z$ values.  After one-hot encoding, you will have a new set of binary features $X$ of size $K := \sum_{z \in Z}k_z$.&lt;/p&gt;

&lt;p&gt;In a model with all interactions, you must estimate a matrix of interaction coefficients $Q$, which has $K\times (K+1) / 2$ unique terms.&lt;/p&gt;

&lt;p&gt;The factorization machine puts structure on the matrix $Q$, and assumes that $Q \equiv W^{T} W$, where $W$ is of dimension $l \times K$, with $1\le l \le K$ some number specified by the user.  We estimate $W$ instead of $Q$.&lt;/p&gt;

&lt;p&gt;The field-aware factorization machine puts structure on $Q$ as well.  It partitions $Q$ into blocks based on $z$ (the original features).  If $q_{z_i, z_j}$ denotes the $z_i,z_j$ block of $Q$, we assume that $q_{z_i,z_j}$ comes from the $z_i,z_j$ block of $W_j^{T} W_i$, where $W_i$ is of dimension $l \times K$.  As with the FM, we estimate the $W_i$ instead of $Q$.&lt;/p&gt;

&lt;p&gt;The FM factorization of $Q$ has $K \times l $ parameters.  The "feild-aware" FM has $K\times l\times |Z|$ parameters.  A model with all interactions has $K \times (K+1)/2$ parameters.  &lt;/p&gt;
', NULL, NULL, '''+1'':85C,250C ''/2'':251C ''/p'':63C,89C,134C,217C,253C ''1'':117C ''2'':86C ''assum'':101C,175C ''awar'':139C,232C ''base'':153C ''binari'':51C ''block'':152C,171C,189C ''coeffici'':78C ''come'':182C ''denot'':165C ''dimens'':112C,201C ''encod'':8C,43C ''equiv'':104C ''estim'':73C,129C,210C ''factor'':92C,140C,221C ''featur'':52C,158C ''feild'':231C ''feild-awar'':230C ''field'':138C ''field-awar'':137C ''fm'':208C,220C,233C ''gender'':20C ''genr'':18C ''hot'':7C,42C ''instead'':131C,214C ''interact'':70C,77C,245C ''j'':164C,170C,181C,188C,192C ''k'':36C,56C,61C,82C,84C,115C,121C,204C,225C,235C,247C,249C ''l'':113C,119C,202C,227C,237C ''le'':118C,120C ''machin'':93C,141C ''matrix'':75C,98C ''model'':67C,242C ''movi'':17C ''must'':72C ''new'':48C ''number'':123C ''one'':6C,34C,41C ''one-hot'':40C ''one-hot-encod'':5C ''origin'':157C ''p'':1C,64C,90C,135C,218C ''paramet'':228C,240C,252C ''partit'':149C ''predictor'':27C ''predictors/fields'':11C ''prior'':3C ''put'':94C,142C ''q'':79C,99C,103C,133C,145C,150C,160C,173C,177C,216C,223C ''race'':23C ''say'':16C ''set'':14C,49C ''size'':55C ''specifi'':124C ''structur'':95C,143C ''sum'':57C ''suppos'':2C,24C ''take'':32C ''term'':88C ''time'':83C,114C,203C,226C,236C,238C,248C ''uniqu'':87C ''user'':19C,22C,127C ''valu'':38C ''w'':105C,107C,109C,130C,191C,194C,197C,212C ''well'':147C ''x'':53C ''z'':15C,28C,30C,37C,58C,60C,62C,155C,161C,163C,167C,169C,178C,180C,185C,187C,239C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8273, 2, '2015-10-01 23:38:12.95', 5, NULL, NULL, '&lt;p&gt;No, currently there isn''t a package in Python that does segmented linear regression as thoroughly as those in R (e.g. &lt;a href="http://www.r-bloggers.com/r-for-ecologists-putting-together-a-piecewise-regression/" rel="nofollow"&gt;R packages listed in this blog post&lt;/a&gt;). Alternatively, you can use a Bayesian Markov Chain Monte Carlo algorithm in Python to create your segmented model.&lt;/p&gt;

&lt;p&gt;Segmented linear regression, as implemented by all the R packages in the above link, doesn''t permit extra parameter constraints (i.e. priors), and because these packages take a frequentist approach, the resulting model doesn''t give you probability distributions for the model parameters (i.e. breakpoints, slopes, etc). Defining a segmented model in &lt;a href="http://statsmodels.sourceforge.net/" rel="nofollow"&gt;statsmodels&lt;/a&gt;, which is frequentist, is even more restrictive because the model requires a fixed x-coordinate breakpoint.&lt;/p&gt;

&lt;p&gt;You can design a segmented model in Python using the Bayesian Markov Chain Monte Carlo algorithm &lt;a href="http://dan.iel.fm/emcee/current/" rel="nofollow"&gt;emcee&lt;/a&gt;. Jake Vanderplas wrote a useful &lt;a href="http://jakevdp.github.io/blog/2014/06/14/frequentism-and-bayesianism-4-bayesian-in-python/" rel="nofollow"&gt;blog post&lt;/a&gt; and &lt;a href="http://arxiv.org/pdf/1411.5018.pdf" rel="nofollow"&gt;paper&lt;/a&gt; for how to implement emcee with comparisons to PyMC and PyStan.&lt;/p&gt;

&lt;p&gt;Example:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Segmented model with data:&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/QoGKH.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/QoGKH.png" alt="Segmented regression"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Probability distributions of fit parameters:&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/iJ5br.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/iJ5br.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://github.com/stharrold/ARCHIVED_2015_Harrold_SDSSJ1600/blob/master/code/utils.py#L1241-L1546" rel="nofollow"&gt;Link to code for segmented model&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://gist.github.com/stharrold/33120aaf182b66983ee8" rel="nofollow"&gt;Link to (large) ipython notebook&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9339, 2, '2015-12-11 16:13:05.043', 3, NULL, NULL, '&lt;p&gt;This subreddit lists a lot of known Datasets&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.reddit.com/r/datasets" rel="nofollow"&gt;Reddit Datasets&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;There are a lot of dataset requests on that subreddit, several of which have been answered.&lt;/p&gt;
', NULL, NULL, '''/a'':21C ''/p'':10C,22C,40C ''/r/datasets'':16C ''answer'':39C ''dataset'':9C,20C,29C ''href'':13C ''known'':8C ''list'':4C ''lot'':6C,27C ''nofollow'':18C ''p'':1C,11C,23C ''reddit'':19C ''rel'':17C ''request'':30C ''sever'':34C ''subreddit'':3C,33C ''www.reddit.com'':15C ''www.reddit.com/r/datasets'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8274, 2, '2015-10-02 00:00:04.043', 2, NULL, NULL, '&lt;p&gt;You didn''t say whether you were building a regression or classification model, but here goes anyway.&lt;/p&gt;

&lt;p&gt;As ever, it depends ... though several neural network approaches, such as deep learning or RBF networks, have shown promise with high dimensional data.&lt;/p&gt;

&lt;p&gt;It is possible to have KNN approaches which use representative points, either as cluster centres or class boundaries, to reduce the computation burden.&lt;/p&gt;

&lt;p&gt;As a test I tried computing the Euclidean distance between a single vector and  20,000 , 20,000 feature vectors. This took around 7 seconds on a single core of a desktop machine using Mathematica. If you have the RAM and several cores KNN should be feasible in your time scales.&lt;/p&gt;

&lt;p&gt;Feature engineering might gain you speed benefits but you would want to tune that in concert with prediction/classification accuracy.&lt;/p&gt;

&lt;p&gt;There are several methods for achieving regularisation if you suspect you are overfitting,  you might want to explore Ridge Regression/Tikhonov regularisation or early stopping if you are following the neural network path.&lt;/p&gt;

&lt;p&gt;Good luck.&lt;/p&gt;
', NULL, NULL, '''/p'':19C,44C,69C,122C,143C,177C,181C ''000'':86C,88C ''20'':85C,87C ''7'':94C ''accuraci'':142C ''achiev'':150C ''anyway'':18C ''approach'':29C,52C ''around'':93C ''benefit'':130C ''boundari'':63C ''build'':9C ''burden'':68C ''centr'':60C ''class'':62C ''classif'':13C ''cluster'':59C ''comput'':67C,76C ''concert'':139C ''core'':99C,113C ''data'':43C ''deep'':32C ''depend'':24C ''desktop'':102C ''didn'':3C ''dimension'':42C ''distanc'':79C ''earli'':167C ''either'':57C ''engin'':125C ''euclidean'':78C ''ever'':22C ''explor'':162C ''feasibl'':117C ''featur'':89C,124C ''follow'':172C ''gain'':127C ''goe'':17C ''good'':179C ''high'':41C ''knn'':51C,114C ''learn'':33C ''luck'':180C ''machin'':103C ''mathematica'':105C ''method'':148C ''might'':126C,159C ''model'':14C ''network'':28C,36C,175C ''neural'':27C,174C ''overfit'':157C ''p'':1C,20C,45C,70C,123C,144C,178C ''path'':176C ''point'':56C ''possibl'':48C ''prediction/classification'':141C ''promis'':39C ''ram'':110C ''rbf'':35C ''reduc'':65C ''regress'':11C ''regression/tikhonov'':164C ''regularis'':151C,165C ''repres'':55C ''ridg'':163C ''say'':5C ''scale'':121C ''second'':95C ''sever'':26C,112C,147C ''shown'':38C ''singl'':82C,98C ''speed'':129C ''stop'':168C ''suspect'':154C ''test'':73C ''though'':25C ''time'':120C ''took'':92C ''tri'':75C ''tune'':136C ''use'':54C,104C ''vector'':83C,90C ''want'':134C,160C ''whether'':6C ''would'':133C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8276, 4, '2015-10-02 06:35:51.567', 0, NULL, NULL, 'education tag is used for data science programs including university programs and professional certificates.', NULL, NULL, '''certif'':14C ''data'':6C ''educ'':1C ''includ'':9C ''profession'':13C ''program'':8C,11C ''scienc'':7C ''tag'':2C ''univers'':10C ''use'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8310, 2, '2015-10-05 07:54:19.927', 1, NULL, NULL, '&lt;p&gt;What type of sampling method did you use to split the data? I''m guessing, by looking at the ratio of classes in your OOB confusion matrix and your test set confusion matrix that it wasn''t stratified, is this correct? For instance, the class ratio in the OOB data is 4000:800 whereas in the test set it is 7761:43. As a starting point, you may want to split your data using stratified sampling so that the classes are fairly represented (in a statistical sense) in both your training data and test data. The ratio of classes in the test set should be identical or almost the same as the ratio used in the training data.&lt;/p&gt;

&lt;p&gt;I think this would be a good starting point to figuring out the problem.&lt;/p&gt;
', NULL, NULL, '''/p'':120C,136C ''4000'':53C ''43'':63C ''7761'':62C ''800'':54C ''almost'':109C ''class'':23C,46C,81C,100C ''confus'':27C,33C ''correct'':42C ''data'':13C,51C,74C,93C,96C,119C ''fair'':83C ''figur'':132C ''good'':128C ''guess'':16C ''ident'':107C ''instanc'':44C ''look'':18C ''m'':15C ''matrix'':28C,34C ''may'':69C ''method'':6C ''oob'':26C,50C ''p'':1C,121C ''point'':67C,130C ''problem'':135C ''ratio'':21C,47C,98C,114C ''repres'':84C ''sampl'':5C,77C ''sens'':88C ''set'':32C,59C,104C ''split'':11C,72C ''start'':66C,129C ''statist'':87C ''stratifi'':39C,76C ''test'':31C,58C,95C,103C ''think'':123C ''train'':92C,118C ''type'':3C ''use'':9C,75C,115C ''want'':70C ''wasn'':37C ''wherea'':55C ''would'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9984, 4, '2016-01-27 07:56:42.26', 0, NULL, NULL, 'Naive Bayes classifiers makes the naive assumption that the features are independent. They make use of Bayes theorem.', NULL, NULL, '''assumpt'':7C ''bay'':2C,17C ''classifi'':3C ''featur'':10C ''independ'':12C ''make'':4C,14C ''naiv'':1C,6C ''theorem'':18C ''use'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8277, 1, '2015-10-02 08:13:03.78', 1, 223, 'Credit card fraud detection - anomaly detection based on amount of money to be withdrawn?', '&lt;p&gt;I am trying to figure out how the amount of money that a customer would want to withdraw on an ATM tell us if the transaction is fraudulent or not.There are other attributes, of course, but now I would want to hear your views on the amount of money that the customer wants to withdraw.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Data may be of this form:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Let us assume that a customer, for ten consecutive transactions, withdrew the following amounts: &lt;/p&gt;

&lt;p&gt;&lt;code&gt;100.33, 384 , 458, 77.90, 456, 213.55, 500 , 500, 300, 304.&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Questions:&lt;/strong&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;How can we use this data to tell if the next transaction done on this account is fraudulent of not?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Are there specific algorithms that can be used for this classification?&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;What I was thinking:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I was thinking to calculate the average amount of money, say for the last ten transactions, and check how far is the next transaction amount from the average. Too much deviation would signal an anomaly. But this does not sound much, does it?&lt;/p&gt;
', 'machine-learning  algorithms', NULL, '''/code'':114C ''/li'':145C,160C ''/ol'':161C ''/p'':75C,85C,101C,115C,120C,144C,159C,169C,214C ''/strong'':84C,119C,168C ''100.33'':104C ''213.55'':109C ''300'':112C ''304'':113C ''384'':105C ''456'':108C ''458'':106C ''500'':110C,111C ''77.90'':107C ''account'':139C ''algorithm'':18B,151C ''amount'':9A,28C,66C,100C,178C,195C ''anomali'':5A,205C ''assum'':89C ''atm'':40C ''attribut'':52C ''averag'':177C,198C ''base'':7A ''calcul'':175C ''card'':2A ''check'':188C ''classif'':158C ''code'':103C ''consecut'':95C ''cours'':54C ''credit'':1A ''custom'':33C,71C,92C ''data'':78C,129C ''detect'':4A,6A ''deviat'':201C ''done'':136C ''far'':190C ''figur'':24C ''follow'':99C ''form'':83C ''fraud'':3A ''fraudul'':47C,141C ''hear'':61C ''last'':184C ''learn'':17B ''let'':87C ''li'':122C,146C ''machin'':16B ''machine-learn'':15B ''may'':79C ''money'':11A,30C,68C,180C ''much'':200C,211C ''next'':134C,193C ''not.there'':49C ''ol'':121C ''p'':19C,76C,86C,102C,116C,123C,147C,162C,170C ''question'':118C ''say'':181C ''signal'':203C ''sound'':210C ''specif'':150C ''strong'':77C,117C,163C ''tell'':41C,131C ''ten'':94C,185C ''think'':167C,173C ''transact'':45C,96C,135C,186C,194C ''tri'':22C ''us'':42C,88C ''use'':127C,155C ''view'':63C ''want'':35C,59C,72C ''withdraw'':37C,74C ''withdrawn'':14A ''withdrew'':97C ''would'':34C,58C,202C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8278, 1, '2015-10-02 09:49:55.943', 1, 63, 'Covariate shift detection', '&lt;p&gt;Is there any standard approach for detecting the covariate shift between the training and test data ? This would be useful to validate the assumption that covariate shift exists in my database which contains a few hundred images.&lt;/p&gt;
', 'machine-learning  classification  dataset  image-classification', NULL, '''/p'':50C ''approach'':17C ''assumpt'':36C ''classif'':7B,11B ''contain'':45C ''covari'':1A,21C,38C ''data'':28C ''databas'':43C ''dataset'':8B ''detect'':3A,19C ''exist'':40C ''hundr'':48C ''imag'':10B,49C ''image-classif'':9B ''learn'':6B ''machin'':5B ''machine-learn'':4B ''p'':12C ''shift'':2A,22C,39C ''standard'':16C ''test'':27C ''train'':25C ''use'':32C ''valid'':34C ''would'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8279, 2, '2015-10-02 10:23:38.967', 1, NULL, NULL, '&lt;p&gt;One option is to use a simple approach, like just choosing random attributes, or taking one at a time. &lt;/p&gt;

&lt;p&gt;Another option is to use a metric to decide, at each step, the attribute that best splits the data. Different tree algorithms apply different metrics, like for instance Gini impurity (CART algorithm), or information gain (ID3 and C4.5 algorithms).&lt;/p&gt;

&lt;p&gt;You can read a brief description of these methods in the "Decision tree learning" entry on the Wikipedia. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8280, 2, '2015-10-02 12:20:32.303', 0, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;I was thinking to calculate the average amount of money, say for the
  last ten transactions, and check how far is the next transaction
  amount from the average.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This sounds like a good start. I''d look into Local Outlier Probabilities. For a given data point you could calculate the distance from &lt;code&gt;n&lt;/code&gt; nearest neighbors and figure out if the data point under consideration is an outlier. &lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Local_outlier_factor" rel="nofollow"&gt;basic overview can be found here&lt;/a&gt;
I''d also consider the source, destination, volume and frequency of transactions as features. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8281, 1, '2015-10-02 15:11:31.28', 0, 66, 'price elasticity and logistic regression', '&lt;p&gt;I am working on a school enrollment admission project to see how high school students react to scholarship in admission. The purpose is to redesign the scholarship level.&lt;/p&gt;

&lt;p&gt;For example, the original policy has 3 levels(0,2000,4000,6000) and used as training data. The other attributes are like GPA, ACT/SAT, gender,etc.. Y={enrolled, not enrolled}.&lt;/p&gt;

&lt;p&gt;To test the the student''s response to the new levels scholarship, namely the enrollment probability at different scholarship levels, what I did is manually replicating the testing data and each one contains the new levels (0,1000,2000,...8000). And I used logistic regression and regression tree(LOTUS).&lt;/p&gt;

&lt;p&gt;Ideally the probability will increase as the scholarship increases and it will yield a sigmoid or S-curve, but not all the plots shown this. I think the reason is there are no data in the training set has the new levels.
Practically, the college cannot test a certain group of students to see the response of the money changes.&lt;/p&gt;

&lt;p&gt;Later on, I used the generated probabilities times tuition which yields a revenue for school. And a optimization model is made to max the revenue.&lt;/p&gt;

&lt;p&gt;I tried conjoint analysis but I don''t know what does it mean.&lt;/p&gt;

&lt;p&gt;What methods should I use for this or do I miss anything here?&lt;/p&gt;

&lt;p&gt;move from &lt;a href="http://stats.stackexchange.com/questions/172958/price-elasticity-and-logistic-regression"&gt;http://stats.stackexchange.com/questions/172958/price-elasticity-and-logistic-regression&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8282, 2, '2015-10-02 17:50:26.45', 0, NULL, NULL, '&lt;p&gt;Try to make group a factor and you might want to make it a strata,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;model &amp;lt;- coxph(survObj ~ strata(factor(group)), data=dataset)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Not sure whether this would help but it will definitely have impact on how Surv treats your information. Without the factor Surv will consider group a numeric variable where an increase in that number will lead to a higher number of cases. Converting it to a factor and strata will estimate different survival curves for each group.&lt;/p&gt;

&lt;p&gt;Not sure whether this will help with your plot but it will certainly impact the outcome of your fits.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10016, 5, '2016-01-29 06:50:57.143', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10018, 5, '2016-01-29 06:51:48.63', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10020, 5, '2016-01-29 06:52:59.09', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10022, 5, '2016-01-29 06:54:47.96', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10035, 2, '2016-01-30 23:19:11.103', 2, NULL, NULL, '&lt;p&gt;As always clustering is about what is the meaning of distance, since that encodes at least some part of your question. So you question is which channels are similar, where similarity is defined on users. Usually you do not assume some nesting on your instances, but what you have is basically a nesting of users in channels. So you have to incorporate this kind of nesting into the distance / similarity function. &lt;/p&gt;

&lt;p&gt;I would start with some observation on similarity function. We denote similarity between instance $i$ and $j$ with $d(i,j)$. Usually this function obeys the following condition $d(i,j)\ge0$ for any $i$ and $j$. Note that we usually have equality only on identical data points. The main consequence of this observation is that we can have basically an identity at user level. What you want is an identity at channel level.&lt;/p&gt;

&lt;p&gt;One way would be to define the similarity function like:&lt;/p&gt;

&lt;p&gt;$$d_c(i,j) = I_{c_i\ne c_j}d(i,j)$$ &lt;/p&gt;

&lt;p&gt;where $I_{c_i\ne c_j}$ is $1$ when channel of instance $i$ is different that channel of instance $j$. The main effect is that now all the points from the same channel are considered identical since the distance between them is zero. When the identity function is $1$ the distance is given by your real business distance which should be constructed by you and answer your question. &lt;/p&gt;

&lt;p&gt;If you use such kind of function in a hierarchical clustering basically it will start to find first some clusters which are identically with your grouping on channels and later on it will join clusters which are similar. This kind of approach will work even with a kmeans algorithm and perhaps with most of the clustering approaches. &lt;/p&gt;

&lt;p&gt;A slight different approach would be to define your $I$ function to return $1-\lambda$ when cluster is different and $\lambda$ when channels are equal, and have $\lambda$ a positive value close to $0$. This will not guarantee that all the clients will go into the same cluster, but it gives you the benefit that you have a slider which you can use to fine tune the compromise between &lt;em&gt;all user of the same channel goes into the same cluster&lt;/em&gt; and &lt;em&gt;a distance measure which is more robust to outliers&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;A totally different approach from an implementation point of view would be to define a more complex function directly on channel samples. This would be similar with how hierarchical clustering works since in order to joins to clusters it should have a distance function which would measure inter clusters similarity. See more on &lt;a href="https://en.wikipedia.org/wiki/Hierarchical_clustering#Linkage_criteria" rel="nofollow"&gt;linkage-criteria&lt;/a&gt;. For example average linkage clustering. Note that I said that the approach is different only algorithmic. I would bet that the results would be similar with the first approach. &lt;/p&gt;

&lt;p&gt;A totally different approach would be to use a more robust criteria. It is known that sample average is not robust since one point can blow away the estimation. Median instead is much stable. You can use a median or a trimmed mean to have a more robust aggregation values. This would have the advantage that the clustering would be much faster since you would work with channels instead of clients and the running time for computing the clustering would be reduced. &lt;/p&gt;

&lt;p&gt;And finally, another approach which comes to my mind would be to go further with comparing channels, but this time using a distance which would be based on statistical tests. I will give you a scenario to clarify. Suppose that your users have an attribute named &lt;em&gt;Weight&lt;/em&gt;, which as expected would be a continuous variable. How could you define a distance between the &lt;em&gt;Weight&lt;/em&gt; of users of one channel and &lt;em&gt;Weight&lt;/em&gt; of users of other channel? If you can assume a distribution, like a Gaussian on that weight you can build a two sample t-test on the two samples which are the two clusters. If you can''t assume a distribution you can employ a homogeneity / independent test like &lt;a href="https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test#Two-sample_Kolmogorov.E2.80.93Smirnov_test" rel="nofollow"&gt;two sample KS test&lt;/a&gt;. KS test is valuable since does not assume any distribution and is sensitive to both changes is shape and location. If you have nominal attributes you can employ a &lt;a href="https://en.wikipedia.org/wiki/Pearson&amp;#39;s_chi-squared_test#Test_of_independence" rel="nofollow"&gt;chi-square independence test&lt;/a&gt;. Be careful with what you can use from that tests. In order to have equal contribution for each attribute used in distance function you have to you p-values. Also note that if the test is significant it will have a small p-value, since the null hypotheses for both tests is the independence assumption, which can be translated as same marginal for both samples. So, smaller p-values means bigger distance. You can use $1-\text{p_value}$ or even you can try $\frac{1}{\text{p_value}}$.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10136, 5, '2016-02-08 05:49:06.98', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8283, 2, '2015-10-02 20:40:07.63', 1, NULL, NULL, '&lt;p&gt;What''s the underlying model of how much someone requests from an ATM? It doesn''t seem like it''s a simple distribution like a Gaussian, where comparing new amounts to the mean is sensible. Consider a person who always pulls out either \$40 or \$400. Ideally we want to build a distribution of what normal transactions from a user look like, and notice if new datapoints don''t look like they''re sampled from that distribution.&lt;/p&gt;

&lt;p&gt;idclark''s suggestion, to look at the nearest &lt;em&gt;n&lt;/em&gt; datapoints from that user and compute the distance from just them, is a good and fast implementation of that sort of test.&lt;/p&gt;

&lt;p&gt;One other possibility is to try to find similar users, and then aggregate data across users. If I only have 10 withdrawals from each user, I''m not going to be able to reject any new withdrawals with confidence, but if I have seven clusters of users, with a thousand withdrawals per cluster, I can notice when a user who was in a particular cluster deviates from the overall cluster distribution. (This also helps you make use of knowledge about which previous transactions were fraudulent.)&lt;/p&gt;
', NULL, NULL, '''/em'':91C ''/p'':79C,114C,201C ''10'':136C ''40'':45C ''400'':47C ''abl'':147C ''across'':130C ''aggreg'':128C ''also'':188C ''alway'':41C ''amount'':31C ''atm'':14C ''build'':52C ''cluster'':160C,168C,180C,185C ''compar'':29C ''comput'':97C ''confid'':154C ''consid'':37C ''data'':129C ''datapoint'':68C,92C ''deviat'':181C ''distanc'':99C ''distribut'':24C,54C,78C,186C ''doesn'':16C ''either'':44C ''em'':89C ''fast'':107C ''find'':123C ''fraudul'':200C ''gaussian'':27C ''go'':144C ''good'':105C ''help'':189C ''idclark'':81C ''ideal'':48C ''implement'':108C ''knowledg'':194C ''like'':19C,25C,63C,72C ''look'':62C,71C,85C ''m'':142C ''make'':191C ''mean'':34C ''model'':6C ''much'':9C ''n'':90C ''nearest'':88C ''new'':30C,67C,151C ''normal'':57C ''notic'':65C,171C ''one'':116C ''overal'':184C ''p'':1C,80C,115C ''particular'':179C ''per'':167C ''person'':39C ''possibl'':118C ''previous'':197C ''pull'':42C ''re'':74C ''reject'':149C ''request'':11C ''sampl'':75C ''seem'':18C ''sensibl'':36C ''seven'':159C ''similar'':124C ''simpl'':23C ''someon'':10C ''sort'':111C ''suggest'':83C ''test'':113C ''thousand'':165C ''transact'':58C,198C ''tri'':121C ''under'':5C ''use'':192C ''user'':61C,95C,125C,131C,140C,162C,174C ''want'':50C ''withdraw'':137C,152C,166C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8311, 2, '2015-10-05 07:56:16.367', 2, NULL, NULL, '&lt;p&gt;Unfortunately, the highest number of rows in both Numbers and Microsoft excel is 1,048,576. So, if you have a file bigger than this, you are not able to open it.&lt;/p&gt;

&lt;p&gt;Some ideas:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Connect your file in an SQL database and work with it from there.&lt;/li&gt;
&lt;li&gt;Use another tool like &lt;a href="http://delimitware.com/" rel="nofollow"&gt;delimitware&lt;/a&gt; which let you open files up to 2 billions rows.&lt;/li&gt;
&lt;li&gt;Use Python Pandas with &lt;code&gt;pandas.read_csv(''filename'')&lt;/code&gt; and then &lt;code&gt;df.head(5)&lt;/code&gt; to check the first 5 rows.&lt;/li&gt;
&lt;li&gt;Split the file in smaller files with less than 1m rows per each chunk and use Numbers or Microsoft Excel to read it.&lt;/li&gt;
&lt;li&gt;Upload it to Azure ML and work on a new experiment with it. &lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':66C ''/code'':87C,93C ''/li'':54C,77C,100C,125C,140C ''/ol'':141C ''/p'':34C,38C ''048'':16C ''1'':15C ''1m'':111C ''2'':74C ''5'':92C,98C ''576'':17C ''abl'':30C ''anoth'':57C ''azur'':130C ''bigger'':24C ''billion'':75C ''check'':95C ''chunk'':115C ''code'':83C,90C ''connect'':41C ''csv'':85C ''databas'':47C ''delimitwar'':65C ''delimitware.com'':62C ''df.head'':91C ''excel'':13C,121C ''experi'':137C ''file'':23C,43C,71C,104C,107C ''filenam'':86C ''first'':97C ''highest'':4C ''href'':61C ''idea'':37C ''less'':109C ''let'':68C ''li'':40C,55C,78C,101C,126C ''like'':59C ''microsoft'':12C,120C ''ml'':131C ''new'':136C ''nofollow'':64C ''number'':5C,10C,118C ''ol'':39C ''open'':32C,70C ''p'':1C,35C ''panda'':81C ''pandas.read'':84C ''per'':113C ''python'':80C ''read'':123C ''rel'':63C ''row'':7C,76C,99C,112C ''smaller'':106C ''split'':102C ''sql'':46C ''tool'':58C ''unfortun'':2C ''upload'':127C ''use'':56C,79C,117C ''work'':49C,133C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8288, 2, '2015-10-03 08:29:29.853', 0, NULL, NULL, '&lt;p&gt;You should consider checking the &lt;a href="https://azure.microsoft.com/en-us/services/machine-learning/" rel="nofollow"&gt;Azure Machine Learning&lt;/a&gt; platform. It is online and you can use it with a free account.&lt;/p&gt;

&lt;p&gt;Azure ML provides you with a workflow by using modules in a graphic user interface. Many of them are related with Data Munging and you can easily clean your data. If there is something that you cannot do in the GUI, then you can just add a module which let you run custom R or Python script to manipulate your data.&lt;/p&gt;

&lt;p&gt;The nice part of it, is that you can easily visualise your data at any time and check simple stats like the &lt;code&gt;dataframe.describe()&lt;/code&gt; of the R.&lt;/p&gt;
', NULL, NULL, '''/a'':17C ''/code'':120C ''/en-us/services/machine-learning/'':11C ''/p'':31C,94C,124C ''account'':30C ''add'':78C ''azur'':14C,33C ''azure.microsoft.com'':10C ''azure.microsoft.com/en-us/services/machine-learning/'':9C ''cannot'':69C ''check'':5C,113C ''clean'':60C ''code'':118C ''consid'':4C ''custom'':85C ''data'':54C,62C,93C,108C ''dataframe.describe'':119C ''easili'':59C,105C ''free'':29C ''graphic'':45C ''gui'':73C ''href'':8C ''interfac'':47C ''learn'':16C ''let'':82C ''like'':116C ''machin'':15C ''mani'':48C ''manipul'':91C ''ml'':34C ''modul'':42C,80C ''mung'':55C ''nice'':97C ''nofollow'':13C ''onlin'':21C ''p'':1C,32C,95C ''part'':98C ''platform'':18C ''provid'':35C ''python'':88C ''r'':86C,123C ''rel'':12C ''relat'':52C ''run'':84C ''script'':89C ''simpl'':114C ''someth'':66C ''stat'':115C ''time'':111C ''use'':25C,41C ''user'':46C ''visualis'':106C ''workflow'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8289, 2, '2015-10-03 08:44:44.893', 0, NULL, NULL, '&lt;p&gt;I isn''t actually answering your question, but it is an idea of how you can improve it. In my opinion, I don''t believe that you will be able to build a classification model with only those data. And if you do it, it will not have high enough accuracy. In your position, I would start looking for more data to use as features.&lt;/p&gt;

&lt;p&gt;Here are a few examples:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;ATM''s code of the withdrawal. People use most of the time similar ATM in their daily routine. If you know the lat and long of their previous ATM, you can check if one of them is far away and combining it with the other features, you will increase your accuracy.&lt;/li&gt;
&lt;li&gt;Seconds spent on the ATM for each withdrawal. People tend to follow specific patterns when they withdraw money. If all of their previous data are similar on the spending time and then you see lower or higher time on a data point, you will be able to increase the accuracy of the model.&lt;/li&gt;
&lt;li&gt;Labeled data. In models like this, it is far better if you use supervised algorithms instead of unsupervised. Thus, I would seek for labeled data for fraud usages. This will also let you to calculate the actual accuracy of your model.&lt;/li&gt;
&lt;li&gt;Time between the two withdrawals. As I said before, people tends to follow patterns. An "anomaly" on this with a sooner withdrawal than the expected will also raise your accuracy.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;As far as the algorithms are concerned, I am not keen on choosing one, because it is popular. If you have done all the Data Munging and the feature selection, you have the 90% of the job and the algorithm that you will choose is 2-3 lines on the code (in case you are using a language like Python). What I usually do, is to check all the possible algorithms and evaluate their accuracy. Then I either use a combination of them or the one with the highest accuracy.&lt;/p&gt;
', NULL, NULL, '''-3'':307C ''/li'':128C,183C,226C,258C ''/ol'':259C ''/p'':67C,74C,351C ''2'':306C ''90'':294C ''abl'':31C,175C ''accuraci'':52C,127C,179C,222C,257C,335C,350C ''actual'':5C,221C ''algorithm'':199C,265C,300C,331C ''also'':215C,254C ''anomali'':243C ''answer'':6C ''atm'':77C,90C,105C,134C ''away'':115C ''believ'':26C ''better'':194C ''build'':33C ''calcul'':219C ''case'':313C ''check'':108C,327C ''choos'':273C,304C ''classif'':35C ''code'':79C,311C ''combin'':117C,341C ''concern'':267C ''daili'':93C ''data'':40C,62C,153C,170C,186C,209C,285C ''done'':282C ''either'':338C ''enough'':51C ''evalu'':333C ''exampl'':73C ''expect'':252C ''far'':114C,193C,262C ''featur'':66C,122C,289C ''follow'':141C,240C ''fraud'':211C ''high'':50C ''higher'':166C ''highest'':349C ''idea'':13C ''improv'':18C ''increas'':125C,177C ''instead'':200C ''isn'':3C ''job'':297C ''keen'':271C ''know'':97C ''label'':185C,208C ''languag'':318C ''lat'':99C ''let'':216C ''li'':76C,129C,184C,227C ''like'':189C,319C ''line'':308C ''long'':101C ''look'':59C ''lower'':164C ''model'':36C,182C,188C,225C ''money'':147C ''mung'':286C ''ol'':75C ''one'':110C,274C,346C ''opinion'':22C ''p'':1C,68C,260C ''pattern'':143C,241C ''peopl'':83C,138C,237C ''point'':171C ''popular'':278C ''posit'':55C ''possibl'':330C ''previous'':104C,152C ''python'':320C ''question'':8C ''rais'':255C ''routin'':94C ''said'':235C ''second'':130C ''see'':163C ''seek'':206C ''select'':290C ''similar'':89C,155C ''sooner'':248C ''specif'':142C ''spend'':158C ''spent'':131C ''start'':58C ''supervis'':198C ''tend'':139C,238C ''thus'':203C ''time'':88C,159C,167C,228C ''two'':231C ''unsupervis'':202C ''usag'':212C ''use'':64C,84C,197C,316C,339C ''usual'':323C ''withdraw'':82C,137C,146C,232C,249C ''would'':57C,205C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9841, 2, '2016-01-18 23:07:52.433', 1, NULL, NULL, '&lt;p&gt;An embedding layer turns positive integers (indexes) into dense vectors of fixed size. For instance, &lt;code&gt;[[4], [20]] -&amp;gt; [[0.25, 0.1], [0.6, -0.2]]&lt;/code&gt;. This representation conversion is learned automatically with the embedding layer in Keras (see the &lt;a href="http://keras.io/layers/embeddings/" rel="nofollow"&gt;documentation&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;However, it seems that your data does not need any such embedding layer to perform a conversion. Having an unnecessary embedding layer is likely why you cannot get your LSTM to work properly. If that is the case then you should simply remove the embedding layer.&lt;/p&gt;

&lt;p&gt;The first layer in your network should then have the &lt;code&gt;input_shape&lt;/code&gt; argument added with information on the dimensions of your data (see &lt;a href="http://keras.io/examples/" rel="nofollow"&gt;examples&lt;/a&gt;). Note that you can add this argument to any layer - it will not be present in the documentation for any specific layer.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;By the way, hyperparameters are often tuned using random search or Bayesian optimization. I would use RMSProp and focus on tuning batch size (sizes like 32, 64, 128, 256 and 512), gradient clipping (on the interval 0.1-10) and dropout (on the interval of 0.1-0.6). The specifics of course depend on your data and model architecture.&lt;/p&gt;
', NULL, NULL, '''-0.2'':24C ''-0.6'':203C ''-10'':195C ''/a'':48C,132C ''/code'':25C,112C ''/examples/'':128C ''/layers/embeddings/'':44C ''/p'':49C,97C,155C,215C ''0.1'':22C,194C,202C ''0.25'':21C ''0.6'':23C ''128'':185C ''20'':19C ''256'':186C ''32'':183C ''4'':18C ''512'':188C ''64'':184C ''ad'':114C ''add'':137C ''architectur'':214C ''argument'':113C,139C ''automat'':31C ''batch'':179C ''bayesian'':169C ''cannot'':77C ''case'':88C ''clip'':190C ''code'':17C,109C ''convers'':28C,67C ''cours'':207C ''data'':56C,122C,211C ''dens'':10C ''depend'':208C ''dimens'':119C ''document'':47C,150C ''dropout'':197C ''embed'':3C,34C,62C,71C,95C ''exampl'':131C ''first'':100C ''fix'':13C ''focus'':176C ''get'':78C ''gradient'':189C ''gt'':20C ''howev'':51C ''hr'':156C ''href'':41C,125C ''hyperparamet'':161C ''index'':8C ''inform'':116C ''input'':110C ''instanc'':16C ''integ'':7C ''interv'':193C,200C ''kera'':37C ''keras.io'':43C,127C ''keras.io/examples/'':126C ''keras.io/layers/embeddings/'':42C ''layer'':4C,35C,63C,72C,96C,101C,142C,154C ''learn'':30C ''like'':74C,182C ''lstm'':80C ''model'':213C ''need'':59C ''network'':104C ''nofollow'':46C,130C ''note'':133C ''often'':163C ''optim'':170C ''p'':1C,50C,98C,157C ''perform'':65C ''posit'':6C ''present'':147C ''proper'':83C ''random'':166C ''rel'':45C,129C ''remov'':93C ''represent'':27C ''rmsprop'':174C ''search'':167C ''see'':38C,123C ''seem'':53C ''shape'':111C ''simpli'':92C ''size'':14C,180C,181C ''specif'':153C,205C ''tune'':164C,178C ''turn'':5C ''unnecessari'':70C ''use'':165C,173C ''vector'':11C ''way'':160C ''work'':82C ''would'':172C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10137, 4, '2016-02-08 05:49:06.98', 0, NULL, NULL, 'Interpolation is a set of methods to construct intermediate points between known points.', NULL, NULL, '''construct'':8C ''intermedi'':9C ''interpol'':1C ''known'':12C ''method'':6C ''point'':10C,13C ''set'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8599, 1, '2015-10-25 23:37:21.817', 0, 21, 'Yarn timeline recovery not enabled error upgrading via ambari', '&lt;p&gt;Using the automated upgrade, when I try to upgrade I get:&lt;/p&gt;

&lt;p&gt;âYARN Timeline state preserving restart should be enabled
Reason: YARN should have state preserving restart enabled for the Timeline server. The yarn-site.xml property yarn.timeline-service.recovery.enabled should be set to trueâ&lt;/p&gt;

&lt;p&gt;However, using Ambari, whenever I go to change the setting in yarn-site.xml it gets me to create a new configuration group and changes it there. I have moved all of the servers into that config group but the check still fails.&lt;/p&gt;

&lt;p&gt;How do I modify the default yarn-site.xml?
Alternatively, does anyone know the actual script (or how to find it) that is kicked off when the automated upgrade is invoked?&lt;/p&gt;
', 'hadoop', NULL, '''/p'':23C,55C,98C,129C ''actual'':112C ''altern'':107C ''ambari'':9A,59C ''anyon'':109C ''autom'':14C,125C ''chang'':64C,79C ''check'':95C ''config'':91C ''configur'':76C ''creat'':73C ''default'':105C ''enabl'':5A,33C,41C ''error'':6A ''fail'':97C ''find'':117C ''get'':22C,70C ''go'':62C ''group'':77C,92C ''hadoop'':10B ''howev'':57C ''invok'':128C ''kick'':121C ''know'':110C ''modifi'':103C ''move'':84C ''new'':75C ''p'':11C,24C,56C,99C ''preserv'':29C,39C ''properti'':48C ''reason'':34C ''recoveri'':3A ''restart'':30C,40C ''script'':113C ''server'':45C,88C ''set'':52C,66C ''state'':28C,38C ''still'':96C ''timelin'':2A,27C,44C ''tri'':18C ''trueâ'':54C ''upgrad'':7A,15C,20C,126C ''use'':12C,58C ''via'':8A ''whenev'':60C ''yarn'':1A,26C,35C ''yarn-site.xml'':47C,68C,106C ''yarn.timeline-service.recovery.enabled'':49C ''â'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8290, 2, '2015-10-03 10:34:01.96', 2, NULL, NULL, '&lt;p&gt;Advantages of setting up a dedicated data science as an independent discipline, which I can think of:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;There would be more and more &lt;strong&gt;data scientists&lt;/strong&gt; who can cater to the growing demand for them, in companies and industries in almost every domain.&lt;/li&gt;
&lt;li&gt;The concept of statistics, algorithms (ML would be included in this), and Big Data don''t need to exist as chapters in separate domains. Now, they can exist as part of the data science workflow in this program.&lt;/li&gt;
&lt;li&gt;Data Scientists would be more aware how data science would be applicable in the domain of business. The concepts of business knowledge for data scientists is often found wanting. Data scientists with a knowledge of &lt;strong&gt;&lt;a href="https://en.wikipedia.org/wiki/Business_statistics" rel="nofollow"&gt;Business Statistics&lt;/a&gt;&lt;/strong&gt; is in huge demand, and a domain dedicated program would help serving the needs.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':136C ''/li'':49C,89C,152C ''/p'':19C ''/strong'':31C,137C ''/ul'':153C ''/wiki/business_statistics'':131C ''advantag'':2C ''algorithm'':55C ''almost'':46C ''applic'':102C ''awar'':96C ''big'':63C ''busi'':107C,111C,134C ''cater'':34C ''chapter'':71C ''compani'':42C ''concept'':52C,109C ''data'':8C,29C,64C,83C,91C,98C,114C,120C ''dedic'':7C,145C ''demand'':38C,141C ''disciplin'':13C ''domain'':48C,74C,105C,144C ''en.wikipedia.org'':130C ''en.wikipedia.org/wiki/business_statistics'':129C ''everi'':47C ''exist'':69C,78C ''found'':118C ''grow'':37C ''help'':148C ''href'':128C ''huge'':140C ''includ'':59C ''independ'':12C ''industri'':44C ''knowledg'':112C,124C ''li'':21C,50C,90C ''ml'':56C ''need'':67C,151C ''nofollow'':133C ''often'':117C ''p'':1C ''part'':80C ''program'':88C,146C ''rel'':132C ''scienc'':9C,84C,99C ''scientist'':30C,92C,115C,121C ''separ'':73C ''serv'':149C ''set'':4C ''statist'':54C,135C ''strong'':28C,126C ''think'':17C ''ul'':20C ''want'':119C ''workflow'':85C ''would'':23C,57C,93C,100C,147C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8291, 2, '2015-10-03 11:04:10.17', 0, NULL, NULL, '&lt;p&gt;&lt;a href="https://aws.amazon.com/machine-learning/" rel="nofollow"&gt;&lt;strong&gt;Amazon Machine Learning&lt;/strong&gt;&lt;/a&gt; is a tool, which I use for feature engineering some times.&lt;/p&gt;

&lt;p&gt;As Amazon AWS services have shown a lot of promise and standard, I would definitely count on Amazon ML, with it''s prospects and promises for making the workflow of data scientists simpler. But as of now, it''s still small.&lt;/p&gt;

&lt;p&gt;But, as you asked for a tool for feature engineering, so this is one of them.&lt;/p&gt;

&lt;p&gt;Some &lt;a href="https://aws.amazon.com/machine-learning/faqs/" rel="nofollow"&gt;FAQ''s&lt;/a&gt; about/for using Amazon ML.&lt;/p&gt;
', NULL, NULL, '''/a'':14C,99C ''/machine-learning/'':6C ''/machine-learning/faqs/'':94C ''/p'':26C,69C,87C,104C ''/strong'':13C ''about/for'':100C ''amazon'':10C,29C,45C,102C ''ask'':74C ''aw'':30C ''aws.amazon.com'':5C,93C ''aws.amazon.com/machine-learning/'':4C ''aws.amazon.com/machine-learning/faqs/'':92C ''count'':43C ''data'':58C ''definit'':42C ''engin'':23C,80C ''faq'':97C ''featur'':22C,79C ''href'':3C,91C ''learn'':12C ''lot'':35C ''machin'':11C ''make'':54C ''ml'':46C,103C ''nofollow'':8C,96C ''one'':84C ''p'':1C,27C,70C,88C ''promis'':37C,52C ''prospect'':50C ''rel'':7C,95C ''scientist'':59C ''servic'':31C ''shown'':33C ''simpler'':60C ''small'':68C ''standard'':39C ''still'':67C ''strong'':9C ''time'':25C ''tool'':17C,77C ''use'':20C,101C ''workflow'':56C ''would'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8312, 2, '2015-10-05 09:26:13.163', 1, NULL, NULL, '&lt;p&gt;Since you are on OS X you can use the terminal to explore your file without storing everything in memory. The &lt;code&gt;head -5 filename.csv&lt;/code&gt; command for example will display the first 5 lines of your file. You could even do &lt;code&gt;head -10 filename.csv &amp;gt; newfile.csv&lt;/code&gt; to store the first 10 lines in a new file and open it with Apple Numbers to examine what''s inside. &lt;/p&gt;

&lt;p&gt;For anything more than that I''m afraid you would need to write a script to run through each line of the file one by one and compute the relevant descriptive statistics.&lt;/p&gt;
', NULL, NULL, '''-10'':46C ''-5'':25C ''/code'':27C,50C ''/p'':72C,106C ''10'':55C ''5'':35C ''afraid'':81C ''anyth'':75C ''appl'':65C ''code'':23C,44C ''command'':28C ''comput'':101C ''could'':41C ''descript'':104C ''display'':32C ''even'':42C ''everyth'':19C ''examin'':68C ''exampl'':30C ''explor'':14C ''file'':16C,39C,60C,96C ''filename.csv'':26C,47C ''first'':34C,54C ''gt'':48C ''head'':24C,45C ''insid'':71C ''line'':36C,56C,93C ''m'':80C ''memori'':21C ''need'':84C ''new'':59C ''newfile.csv'':49C ''number'':66C ''one'':97C,99C ''open'':62C ''os'':6C ''p'':1C,73C ''relev'':103C ''run'':90C ''script'':88C ''sinc'':2C ''statist'':105C ''store'':18C,52C ''termin'':12C ''use'':10C ''without'':17C ''would'':83C ''write'':86C ''x'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8296, 2, '2015-10-03 20:17:46.67', 0, NULL, NULL, '&lt;p&gt;Firstly you should probably be creating models of classes/segments of users (unsupervised clustering).  Otherwise it is difficult to predict what a given user will do.  (More on that further below.)&lt;/p&gt;

&lt;p&gt;Nextly, I think "deviation from recent transactions" is also fundamentally flawed.  Most likely there are time &lt;em&gt;patterns&lt;/em&gt; (time of day, days of the week, working hours, holidays and so on). To understand how to conceptualise time as useful features, see &lt;a href="http://datascience.stackexchange.com/a/2370/12363"&gt;this excellent answer&lt;/a&gt; on &lt;a href="http://datascience.stackexchange.com/questions/2368/machine-learning-features-engineering-from-date-time-data"&gt;Machine learning - features engineering from date/time data&lt;/a&gt;  And similarly there are amount patterns, partly having to do with practical reasons (eg by withdrawing 38, one can receive 20, 10, 5 and 1 denominations, although this is not possible in some markets, like the USA).&lt;/p&gt;

&lt;p&gt;Modelling the user is more complicated. You will likely not have enough data on each user, but you can build some user models. (Too few, then the system will make similar predictions for all users, without nuance - eg &gt; $400 always detected as fraud. Too many and there will be sparsity, overfitting, and generally the same problems as having no profile models at all, ie one model per actual user - eg fraud incorrectly detected at every time a given user goes to a new ATM.)  This is basically unsupervised clustering.  (Search for user profile categorisation, user models, user model clustering)&lt;/p&gt;

&lt;p&gt;Much depends on the data available to you. Perhaps you can be more specific about the scale and scope.  In any case, I wish you luck - banks/Visa do this very poorly right now.&lt;/p&gt;
', NULL, NULL, '''/a'':84C,98C ''/a/2370/12363'':80C ''/em'':52C ''/p'':32C,136C,237C,272C ''/questions/2368/machine-learning-features-engineering-from-date-time-data'':90C ''1'':123C ''10'':120C ''20'':119C ''38'':115C ''400'':176C ''5'':121C ''actual'':205C ''also'':42C ''although'':125C ''alway'':177C ''amount'':103C ''answer'':83C ''atm'':221C ''avail'':244C ''banks/visa'':265C ''basic'':224C ''build'':157C ''case'':260C ''categoris'':231C ''classes/segments'':10C ''cluster'':14C,226C,236C ''complic'':143C ''conceptualis'':70C ''creat'':7C ''data'':97C,150C,243C ''datascience.stackexchange.com'':79C,89C ''datascience.stackexchange.com/a/2370/12363'':78C ''datascience.stackexchange.com/questions/2368/machine-learning-features-engineering-from-date-time-data'':88C ''date/time'':96C ''day'':55C,56C ''denomin'':124C ''depend'':240C ''detect'':178C,210C ''deviat'':37C ''difficult'':18C ''eg'':112C,175C,207C ''em'':50C ''engin'':94C ''enough'':149C ''everi'':212C ''excel'':82C ''featur'':74C,93C ''first'':2C ''flaw'':44C ''fraud'':180C,208C ''fundament'':43C ''general'':190C ''given'':23C,215C ''goe'':217C ''holiday'':62C ''hour'':61C ''href'':77C,87C ''ie'':201C ''incorrect'':209C ''learn'':92C ''like'':46C,133C,146C ''luck'':264C ''machin'':91C ''make'':167C ''mani'':182C ''market'':132C ''model'':8C,138C,160C,198C,203C,233C,235C ''much'':239C ''new'':220C ''next'':34C ''nuanc'':174C ''one'':116C,202C ''otherwis'':15C ''overfit'':188C ''p'':1C,33C,137C,238C ''part'':105C ''pattern'':51C,104C ''per'':204C ''perhap'':247C ''poor'':269C ''possibl'':129C ''practic'':110C ''predict'':20C,169C ''probabl'':5C ''problem'':193C ''profil'':197C,230C ''reason'':111C ''receiv'':118C ''recent'':39C ''right'':270C ''scale'':255C ''scope'':257C ''search'':227C ''see'':75C ''similar'':100C,168C ''sparsiti'':187C ''specif'':252C ''system'':165C ''think'':36C ''time'':49C,53C,71C,213C ''transact'':40C ''understand'':67C ''unsupervis'':13C,225C ''usa'':135C ''use'':73C ''user'':12C,24C,140C,153C,159C,172C,206C,216C,229C,232C,234C ''week'':59C ''wish'':262C ''withdraw'':114C ''without'':173C ''work'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8297, 1, '2015-10-04 07:00:55.79', 0, 44, 'Use of Correlation score', '&lt;p&gt;How do we use a correlation score between two variables for analysing data?&lt;/p&gt;

&lt;p&gt;I have a set of 20 features and need to predict 21st feature. Now is it necessary that correlation between any two features should be close to 1 ? If I have 2 features with corr score close to -1, then does this mean that they are contradicting and thereby decreasing the accuracy ?&lt;/p&gt;

&lt;p&gt;So how do we use a correlation score in analysis ?&lt;/p&gt;
', 'dataset  feature-selection  correlation', NULL, '''-1'':64C ''/p'':24C,78C,90C ''1'':53C ''2'':57C ''20'':31C ''21st'':37C ''accuraci'':77C ''analys'':22C ''analysi'':89C ''close'':51C,62C ''contradict'':72C ''corr'':60C ''correl'':3A,9B,16C,44C,86C ''data'':23C ''dataset'':5B ''decreas'':75C ''featur'':7B,32C,38C,48C,58C ''feature-select'':6B ''mean'':68C ''necessari'':42C ''need'':34C ''p'':10C,25C,79C ''predict'':36C ''score'':4A,17C,61C,87C ''select'':8B ''set'':29C ''therebi'':74C ''two'':19C,47C ''use'':1A,14C,84C ''variabl'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8298, 1, '2015-10-04 08:02:57.263', 1, 48, 'Correctly interpreting Cosine Angular Distance Similarity &amp; Euclidean Distance Similarity', '&lt;p&gt;As an example, let''s say I have a very simple data set. I am given a csv with three columns, user_id, book_id, rating. The rating can be any number 0-5, where 0 means the user has &lt;em&gt;NOT&lt;/em&gt; rated the book.&lt;/p&gt;

&lt;p&gt;Let''s say I randomly pick three users, and I get these feature/rating vectors.&lt;/p&gt;

&lt;p&gt;Martin: $&amp;lt;3,3,5,1,2,3,2,2,5&amp;gt;$&lt;/p&gt;

&lt;p&gt;Jacob: $&amp;lt;3,3,5,0,0,0,0,0,0&amp;gt;$&lt;/p&gt;

&lt;p&gt;Grant: $&amp;lt;1,1,1,2,2,2,2,2,2&amp;gt;$&lt;/p&gt;

&lt;p&gt;The similarity calculations:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;
+--------------+---------+---------+----------+
|              | M &amp;amp; J   | M &amp;amp; G   | J &amp;amp; G    |
+--------------+---------+---------+----------+
| Euclidean    |   6.85  |    5.91 |    6.92  |
+--------------+---------+---------+----------+
| Cosine       |    .69  |    .83  |      .32 |
+--------------+---------+---------+----------+
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Now, my expectation of similarity is that Martin and Jacob would be the most similar. I would expect this because they have EXACTLY the same ratings for the books that both of them have rated. But we end up finding that Martin and Grant are the most similar. &lt;/p&gt;

&lt;p&gt;I understand mathematically how we get to this conclusion, but I don''t understand how I can rely on Cosine Angular distance or Euclidean distance as a means of calculating similarity, if this type of thing occurs. For what interpretation are Martin and Grant more similar than Martin and Jacob?&lt;/p&gt;

&lt;p&gt;One thought I had was to just calculate Euclidean distance, but ignore all books for which one user hasn''t rated the book.&lt;/p&gt;

&lt;p&gt;I then end up with this&lt;/p&gt;

&lt;p&gt;&lt;code&gt;
+--------------+---------+---------+----------+
|              | M &amp;amp; J   | M &amp;amp; G   | J &amp;amp; G    |
+--------------+---------+---------+----------+
| Euclidean    |   0     |    5.91 |    6.92  |
+--------------+---------+---------+----------+
| Cosine       |    .69  |    .83  |      .32 |
+--------------+---------+---------+----------+
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Of course now I have a Euclidean distance of 0, which fits what I would expect of the recommender system. I see many tutorials and lectures use Cosine Angular distance to ignore the unrated books, rather than use Euclidean and ignore them, so I believe this must not work in general.&lt;/p&gt;

&lt;p&gt;EDIT:&lt;/p&gt;

&lt;p&gt;Just to experiment a little, I adjusted Jacob''s feature vector to be much more similar:&lt;/p&gt;

&lt;p&gt;Jacob: $&amp;lt;3,3,5,1,2,3,2,0,0&amp;gt;$&lt;/p&gt;

&lt;p&gt;When I calculate Cosine Angular distance with Martin, I still only get .82! Still less similar than Martin and Grant, yet by inspection I would expect these two to be very similar.&lt;/p&gt;

&lt;p&gt;Could somebody help explain where my thinking is wrong, and possibly suggest another similarity measure?&lt;/p&gt;
', 'machine-learning  recommendation  beginner', NULL, '''-5'':49C ''/code'':145C,301C ''/em'':58C ''/p'':62C,78C,92C,106C,120C,125C,146C,196C,248C,273C,281C,302C,355C,358C,376C,390C,424C,441C ''0'':48C,51C,99C,100C,101C,102C,103C,104C,294C,313C,387C,388C ''1'':85C,110C,111C,112C,383C ''2'':86C,88C,89C,113C,114C,115C,116C,117C,118C,384C,386C ''3'':82C,83C,87C,96C,97C,380C,381C,385C ''32'':144C,300C ''5'':84C,90C,98C,382C ''5.91'':139C,295C ''6.85'':138C ''6.92'':140C,296C ''69'':142C,298C ''82'':404C ''83'':143C,299C ''adjust'':366C ''amp'':129C,132C,135C,285C,288C,291C ''angular'':4A,218C,332C,396C ''anoth'':438C ''beginn'':14B ''believ'':348C ''book'':39C,61C,176C,263C,272C,338C ''calcul'':124C,227C,257C,394C ''code'':127C,283C ''column'':36C ''conclus'':206C ''correct'':1A ''cosin'':3A,141C,217C,297C,331C,395C ''could'':426C ''cours'':305C ''csv'':33C ''data'':27C ''distanc'':5A,8A,219C,222C,259C,311C,333C,397C ''edit'':357C ''em'':56C ''end'':185C,277C ''euclidean'':7A,137C,221C,258C,293C,310C,342C ''exact'':170C ''exampl'':18C ''expect'':150C,165C,319C,417C ''experi'':362C ''explain'':429C ''featur'':369C ''feature/rating'':76C ''find'':187C ''fit'':315C ''g'':133C,136C,289C,292C ''general'':354C ''get'':74C,203C,403C ''given'':31C ''grant'':108C,191C,241C,411C ''gt'':91C,105C,119C,389C ''hasn'':268C ''help'':428C ''id'':38C,40C ''ignor'':261C,335C,344C ''inspect'':414C ''interpret'':2A,237C ''j'':130C,134C,286C,290C ''jacob'':94C,157C,247C,367C,378C ''learn'':12B ''lectur'':329C ''less'':406C ''let'':19C,64C ''littl'':364C ''lt'':81C,95C,109C,379C ''m'':128C,131C,284C,287C ''machin'':11B ''machine-learn'':10B ''mani'':326C ''martin'':80C,155C,189C,239C,245C,399C,409C ''mathemat'':200C ''mean'':52C,225C ''measur'':440C ''much'':373C ''must'':350C ''number'':47C ''occur'':234C ''one'':250C,266C ''p'':15C,63C,79C,93C,107C,121C,126C,147C,197C,249C,274C,282C,303C,356C,359C,377C,391C,425C ''pick'':69C ''possibl'':436C ''random'':68C ''rate'':41C,43C,59C,173C,182C,270C ''rather'':339C ''recommend'':13B,322C ''reli'':215C ''say'':21C,66C ''see'':325C ''set'':28C ''similar'':6A,9A,123C,152C,162C,195C,228C,243C,375C,407C,423C,439C ''simpl'':26C ''somebodi'':427C ''still'':401C,405C ''suggest'':437C ''system'':323C ''thing'':233C ''think'':432C ''thought'':251C ''three'':35C,70C ''tutori'':327C ''two'':419C ''type'':231C ''understand'':199C,211C ''unrat'':337C ''use'':330C,341C ''user'':37C,54C,71C,267C ''vector'':77C,370C ''work'':352C ''would'':158C,164C,318C,416C ''wrong'':434C ''yet'':412C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8313, 1, '2015-10-05 10:35:09.597', 2, 90, 'How can I infer psychological intentions from email corpus using text mining?', '&lt;p&gt;I''m working with &lt;a href="https://www.cs.cmu.edu/~./enron/" rel="nofollow"&gt;Enron email data set&lt;/a&gt;, and try to get some psychological intentions about email marketing. I know that we can derive &lt;a href="https://en.wikipedia.org/wiki/Sentiment_analysis" rel="nofollow"&gt;Sentimental analysis like emotions and mode&lt;/a&gt; and opinion mining like positive or negative through text data. what else can I do regarding human psychological intentions?&lt;/p&gt;
', 'data-mining  nlp  text-mining', NULL, '''/a'':36C,66C ''/p'':86C ''/wiki/sentiment_analysis'':57C ''/~./enron/'':29C ''analysi'':61C ''corpus'':9A ''data'':14B,34C,76C ''data-min'':13B ''deriv'':52C ''els'':78C ''email'':8A,33C,45C ''emot'':63C ''en.wikipedia.org'':56C ''en.wikipedia.org/wiki/sentiment_analysis'':55C ''enron'':32C ''get'':40C ''href'':26C,54C ''human'':83C ''infer'':4A ''intent'':6A,43C,85C ''know'':48C ''like'':62C,70C ''m'':22C ''market'':46C ''mine'':12A,15B,19B,69C ''mode'':65C ''negat'':73C ''nlp'':16B ''nofollow'':31C,59C ''opinion'':68C ''p'':20C ''posit'':71C ''psycholog'':5A,42C,84C ''regard'':82C ''rel'':30C,58C ''sentiment'':60C ''set'':35C ''text'':11A,18B,75C ''text-min'':17B ''tri'':38C ''use'':10A ''work'':23C ''www.cs.cmu.edu'':28C ''www.cs.cmu.edu/~./enron/'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9524, 2, '2015-12-26 00:34:24.27', 1, NULL, NULL, '&lt;p&gt;I guess the centre of a cluster of two points is a point that is the same distance from each of them. Is there such a point in your system? If not, maybe you should go to the point in your space with the smallest difference in distance from each of your other points, in other words, C such that |d(A, C) - d(A, B)| is minimised, where A and B are the points in your cluster.&lt;/p&gt;

&lt;p&gt;What distance metric are you using?&lt;/p&gt;

&lt;p&gt;Are you planning to generalise this to clusters with more points?&lt;/p&gt;
', NULL, NULL, '''/p'':80C,88C,101C ''b'':67C,73C ''c'':59C,64C ''centr'':5C ''cluster'':8C,79C,97C ''d'':62C,65C ''differ'':47C ''distanc'':19C,49C,83C ''generalis'':94C ''go'':37C ''guess'':3C ''mayb'':34C ''metric'':84C ''minimis'':69C ''p'':1C,81C,89C ''plan'':92C ''point'':11C,14C,28C,40C,55C,76C,100C ''smallest'':46C ''space'':43C ''system'':31C ''two'':10C ''use'':87C ''word'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8303, 1, '2015-10-04 16:49:46.32', 0, 52, 'In image classification/recognition how are images coded?', '&lt;p&gt;I am trying to understand how images are coded for classification/recognition.&lt;/p&gt;

&lt;p&gt;Suppose that I have images that contain animals (e.g. dogs, cats, birds) -- does each image have a label for that animal or does the label only apply to the portion of the image with the animal in it? If the entire images are coded does it take a classifier longer to learn?&lt;/p&gt;
', 'machine-learning  neuralnetwork  image-classification', NULL, '''/p'':27C,81C ''anim'':36C,49C,64C ''appli'':55C ''bird'':40C ''cat'':39C ''classif'':14B ''classifi'':77C ''classification/recognition'':3A,26C ''code'':7A,24C,72C ''contain'':35C ''dog'':38C ''e.g'':37C ''entir'':69C ''imag'':2A,6A,13B,22C,33C,43C,61C,70C ''image-classif'':12B ''label'':46C,53C ''learn'':10B,80C ''longer'':78C ''machin'':9B ''machine-learn'':8B ''neuralnetwork'':11B ''p'':15C,28C ''portion'':58C ''suppos'':29C ''take'':75C ''tri'':18C ''understand'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8299, 2, '2015-10-04 10:47:49.983', 1, NULL, NULL, '&lt;p&gt;After the Data Munging, this is the most difficult task on a prediction model. However, in order to answer it, we need more details. What do you mean by the "best model"? Do you want accuracy and long training time? Do you need something really fast with lower accuracy? Something between the two of them? What are your features? Have you just taken them or have you created new features from them?&lt;/p&gt;

&lt;p&gt;In any case, I suggest that you should spend some time and read this perfect tutorial from &lt;a href="https://azure.microsoft.com/en-us/documentation/articles/machine-learning-algorithm-choice/" rel="nofollow"&gt;Microsoft&lt;/a&gt; about Machine Learning. A part of the tutorial to understand what I mean:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Regression&lt;/strong&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Ordinal regression: Data in rank ordered categories&lt;/li&gt;
&lt;li&gt;Poisson regression: Predicting event counts&lt;/li&gt;
&lt;li&gt;Fast forest quantile regression: Predict a distribution&lt;/li&gt;
&lt;li&gt;Linear regression: Fast training, linear model&lt;/li&gt;
&lt;li&gt;Bayesian linear regression: Linear model, small data sets&lt;/li&gt;
&lt;li&gt;Neural Network regression: Accuracy, long training time&lt;/li&gt;
&lt;li&gt;Decision forest regression: Accuracy, fast training&lt;/li&gt;
&lt;li&gt;Boosted decision tree regression: Accuracy, fast training&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;When I have a similar question and I don''t know which one to choose, I usually end up to 3-4 different algorithms based on the cheat sheet of Microsoft or the one from the &lt;a href="http://scikit-learn.org/stable/tutorial/machine_learning_map/" rel="nofollow"&gt;scikit-learn&lt;/a&gt;, try them all and choose one or two of them with the best results. &lt;/p&gt;
', NULL, NULL, '''-4'':214C ''/a'':101C,239C ''/en-us/documentation/articles/machine-learning-algorithm-choice/'':97C ''/li'':130C,137C,146C,154C,164C,173C,181C,190C ''/ol'':191C ''/p'':74C,115C,120C,254C ''/stable/tutorial/machine_learning_map/'':233C ''/strong'':119C ''3'':213C ''accuraci'':37C,50C,169C,178C,187C ''algorithm'':216C ''answer'':20C ''azure.microsoft.com'':96C ''azure.microsoft.com/en-us/documentation/articles/machine-learning-algorithm-choice/'':95C ''base'':217C ''bayesian'':156C ''best'':32C,252C ''boost'':183C ''case'':78C ''categori'':129C ''cheat'':220C ''choos'':207C,244C ''count'':136C ''creat'':69C ''data'':4C,125C,162C ''decis'':175C,184C ''detail'':25C ''differ'':215C ''difficult'':10C ''distribut'':145C ''end'':210C ''event'':135C ''fast'':47C,139C,150C,179C,188C ''featur'':60C,71C ''forest'':140C,176C ''howev'':16C ''href'':94C,230C ''know'':203C ''learn'':104C,238C ''li'':122C,131C,138C,147C,155C,165C,174C,182C ''linear'':148C,152C,157C,159C ''long'':39C,170C ''lower'':49C ''machin'':103C ''mean'':29C,114C ''microsoft'':100C,223C ''model'':15C,33C,153C,160C ''mung'':5C ''need'':23C,44C ''network'':167C ''neural'':166C ''new'':70C ''nofollow'':99C,235C ''ol'':121C ''one'':205C,226C,245C ''order'':18C,128C ''ordin'':123C ''p'':1C,75C,116C,192C ''part'':106C ''perfect'':90C ''poisson'':132C ''predict'':14C,134C,143C ''quantil'':141C ''question'':198C ''rank'':127C ''read'':88C ''realli'':46C ''regress'':118C,124C,133C,142C,149C,158C,168C,177C,186C ''rel'':98C,234C ''result'':253C ''scikit'':237C ''scikit-learn'':236C ''scikit-learn.org'':232C ''scikit-learn.org/stable/tutorial/machine_learning_map/'':231C ''set'':163C ''sheet'':221C ''similar'':197C ''small'':161C ''someth'':45C,51C ''spend'':84C ''strong'':117C ''suggest'':80C ''taken'':64C ''task'':11C ''time'':41C,86C,172C ''train'':40C,151C,171C,180C,189C ''tree'':185C ''tri'':240C ''tutori'':91C,109C ''two'':54C,247C ''understand'':111C ''usual'':209C ''want'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8491, 1, '2015-10-19 01:57:26.24', 0, 32, 'Returning row and column of ID', '&lt;p&gt;I have a data frame which is a constantly being updated by users on an app. Each section of the app that a user completes online adds an input to their row under a column heading. &lt;/p&gt;

&lt;p&gt;I''d like to be able to use that data frame to report which section (column) has been complete by which user (row). &lt;/p&gt;

&lt;p&gt;This would mean something like:&lt;/p&gt;

&lt;p&gt;when ID in row reaches one before an NA return ID and column. &lt;/p&gt;

&lt;p&gt;I am struggling with putting this into R code. Any pointer would be very helpful.&lt;/p&gt;

&lt;p&gt;Thanks so much,&lt;/p&gt;

&lt;p&gt;Levi&lt;/p&gt;
', 'r', NULL, '''/p'':45C,70C,77C,92C,109C,114C,117C ''abl'':52C ''add'':35C ''app'':24C,29C ''code'':102C ''column'':4A,43C,62C,91C ''complet'':33C,65C ''constant'':17C ''d'':48C ''data'':12C,56C ''frame'':13C,57C ''head'':44C ''help'':108C ''id'':6A,80C,89C ''input'':37C ''levi'':116C ''like'':49C,76C ''mean'':74C ''much'':113C ''na'':87C ''one'':84C ''onlin'':34C ''p'':8C,46C,71C,78C,93C,110C,115C ''pointer'':104C ''put'':98C ''r'':7B,101C ''reach'':83C ''report'':59C ''return'':1A,88C ''row'':2A,40C,69C,82C ''section'':26C,61C ''someth'':75C ''struggl'':96C ''thank'':111C ''updat'':19C ''use'':54C ''user'':21C,32C,68C ''would'':73C,105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8300, 2, '2015-10-04 11:33:32.093', 1, NULL, NULL, '&lt;p&gt;What you described is the Filter Method. One of the three methods of Feature Selection. The most common mistake that one could do is to make the feature selection as an independent step on your whole process and then decide which model he will use. The most appropriate way to do it is to include your Feature Selection process in your accuracy test and try different kind of feature selections. The best way for the feature selection is the domain knowledge. If you don''t have it, then you start working with one of the three methods, like the Filter Method.&lt;/p&gt;

&lt;p&gt;What you need to do is to test the chisquare or mutual information on your features with your label column. This will give you which of the features have low effect on your prediction and you can remove it.&lt;/p&gt;

&lt;p&gt;You can find a briefly detailed article about Feature Selection &lt;a href="http://machinelearningmastery.com/an-introduction-to-feature-selection/" rel="nofollow"&gt;here&lt;/a&gt; and &lt;a href="http://jmlr.csail.mit.edu/papers/volume3/guyon03a/guyon03a.pdf" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':164C,174C ''/an-introduction-to-feature-selection/'':160C ''/p'':103C,144C,175C ''/papers/volume3/guyon03a/guyon03a.pdf'':170C ''accuraci'':63C ''appropri'':49C ''articl'':152C ''best'':73C ''briefli'':150C ''chisquar'':114C ''column'':124C ''common'':19C ''could'':23C ''decid'':41C ''describ'':4C ''detail'':151C ''differ'':67C ''domain'':81C ''effect'':135C ''featur'':15C,29C,58C,70C,77C,120C,132C,154C ''filter'':7C,101C ''find'':148C ''give'':127C ''href'':157C,167C ''includ'':56C ''independ'':33C ''inform'':117C ''jmlr.csail.mit.edu'':169C ''jmlr.csail.mit.edu/papers/volume3/guyon03a/guyon03a.pdf'':168C ''kind'':68C ''knowledg'':82C ''label'':123C ''like'':99C ''low'':134C ''machinelearningmastery.com'':159C ''machinelearningmastery.com/an-introduction-to-feature-selection/'':158C ''make'':27C ''method'':8C,13C,98C,102C ''mistak'':20C ''model'':43C ''mutual'':116C ''need'':107C ''nofollow'':162C,172C ''one'':9C,22C,94C ''p'':1C,104C,145C ''predict'':138C ''process'':38C,60C ''rel'':161C,171C ''remov'':142C ''select'':16C,30C,59C,71C,78C,155C ''start'':91C ''step'':34C ''test'':64C,112C ''three'':12C,97C ''tri'':66C ''use'':46C ''way'':50C,74C ''whole'':37C ''work'':92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8301, 1, '2015-10-04 14:57:05.073', 1, 69, 'How should clickstream data be prepared before user segmentation can be performed?', '&lt;p&gt;I''m interested in doing segmentation/clustering of users in clickstream data and am looking for some good suggestions about how to go about it.&lt;/p&gt;

&lt;p&gt;Lets say my data consists of observations made up of visitors to a website. The data is in clickstream/weblog format and so is made up of users cookie data. Lets say I can identify unique users via their IP address (as a basic example). How should I go about preparing my data so that it can be segmented to find users with similar behaviours? One of my thoughts around this, is that because the data is event-driven, the same user can obviously appear multiple times in the data even though it may all be related to the same session of that user. How are these types of problems tackled so that you can perform segmentation based on user behaviour?&lt;/p&gt;

&lt;p&gt;Thanks for your suggestions!&lt;/p&gt;
', 'data-mining  clustering  preprocessing', NULL, '''/p'':43C,166C,172C ''address'':84C ''appear'':129C ''around'':113C ''base'':162C ''basic'':87C ''behaviour'':108C,165C ''clickstream'':3A,28C ''clickstream/weblog'':63C ''cluster'':16B ''consist'':49C ''cooki'':72C ''data'':4A,14B,29C,48C,60C,73C,96C,119C,134C ''data-min'':13B ''driven'':123C ''even'':135C ''event'':122C ''event-driven'':121C ''exampl'':88C ''find'':104C ''format'':64C ''go'':40C,92C ''good'':35C ''identifi'':78C ''interest'':21C ''ip'':83C ''let'':45C,74C ''look'':32C ''m'':20C ''made'':52C,68C ''may'':138C ''mine'':15B ''multipl'':130C ''observ'':51C ''obvious'':128C ''one'':109C ''p'':18C,44C,167C ''perform'':12A,160C ''prepar'':6A,94C ''preprocess'':17B ''problem'':154C ''relat'':141C ''say'':46C,75C ''segment'':9A,102C,161C ''segmentation/clustering'':24C ''session'':145C ''similar'':107C ''suggest'':36C,171C ''tackl'':155C ''thank'':168C ''though'':136C ''thought'':112C ''time'':131C ''type'':152C ''uniqu'':79C ''user'':8A,26C,71C,80C,105C,126C,148C,164C ''via'':81C ''visitor'':55C ''websit'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8302, 1, '2015-10-04 16:39:09.47', 1, 27, 'Using previous hour''s value in time series data for inclusion in random forest', '&lt;p&gt;I have a training data set, which is something like the following:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;hour   windspeed   pollution
0              5           8
1              6           9
2              3           1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am using scipy''s RandomForestRegressor to estimate &lt;em&gt;pollution&lt;/em&gt; given &lt;em&gt;windspeed, hour&lt;/em&gt;. It is doing "ok", with $r^2 = 0.7$ and $rmse = 35$. However, I would like to include the previous hours &lt;em&gt;pollution values&lt;/em&gt; as additional predictors, as in:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;hour   windspeed   pollution   pollution@t-1  polluion@t-2 ... pollution@t-n
0              5           8              NA           NA                 
1              6           9               8           NA
2              3           1               9           8
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Eventually, I will be applying this to future weather conditions, estimating pollution, where pollution can only be known at time zero:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;hour   windspeed  pollution  p@t-1  p@t-2  p@t-3  p@t-4    ...
1              5          ?     34     28     25     ...   ...         
2              6          ?     NA     34     28      25   ... 
3              3          ?     NA     NA     34      28   ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;How should I best go about including this in a Random Forest? Since RFs don''t tolerate null values, how best to impute them? Since ''0'' is not appropriate (given that 0 is likely very far from the current value), I was thinking that perhaps NaNs could be filled with the nearest known value, as in:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;hour   windspeed  pollution  p@t-1  p@t-2  p@t-3  p@t-4    ...
1              5          ?     34     28     25     ...   ...         
2              6          ?     34     34     28      25   ... 
3              3          ?     34     34     34      28   ...
&lt;/code&gt;&lt;/pre&gt;
', 'python  regression  random-forest', NULL, '''-1'':104C,159C,253C ''-2'':107C,162C,256C ''-3'':165C,259C ''-4'':168C,262C ''/code'':48C,127C,186C,280C ''/em'':61C,66C,90C ''/p'':33C,96C,151C,245C ''/pre'':49C,128C,187C,281C ''0'':39C,112C,214C,220C ''0.7'':74C ''1'':42C,47C,117C,124C,169C,263C ''2'':45C,73C,122C,174C,268C ''25'':173C,179C,267C,273C ''28'':172C,178C,185C,266C,272C,279C ''3'':46C,123C,180C,181C,274C,275C ''34'':171C,177C,184C,265C,270C,271C,276C,277C,278C ''35'':77C ''5'':40C,113C,170C,264C ''6'':43C,118C,175C,269C ''8'':41C,114C,120C,126C ''9'':44C,119C,125C ''addit'':92C ''appli'':134C ''appropri'':217C ''best'':192C,209C ''code'':35C,98C,153C,247C ''condit'':139C ''could'':235C ''current'':227C ''data'':9A,25C ''em'':59C,63C,87C ''estim'':58C,140C ''eventu'':130C ''far'':224C ''fill'':237C ''follow'':32C ''forest'':14A,19B,200C ''futur'':137C ''given'':62C,218C ''go'':193C ''hour'':3A,36C,65C,86C,99C,154C,248C ''howev'':78C ''imput'':211C ''includ'':83C,195C ''inclus'':11A ''known'':147C,241C ''like'':30C,81C,222C ''n'':111C ''na'':115C,116C,121C,176C,182C,183C ''nan'':234C ''nearest'':240C ''null'':206C ''ok'':70C ''p'':20C,50C,129C,157C,160C,163C,166C,188C,251C,254C,257C,260C ''perhap'':233C ''polluion'':105C ''pollut'':38C,60C,88C,101C,102C,108C,141C,143C,156C,250C ''pre'':34C,97C,152C,246C ''predictor'':93C ''previous'':2A,85C ''python'':15B ''r'':72C ''random'':13A,18B,199C ''random-forest'':17B ''randomforestregressor'':56C ''regress'':16B ''rfs'':202C ''rmse'':76C ''scipi'':54C ''seri'':8A ''set'':26C ''sinc'':201C,213C ''someth'':29C ''t-n'':109C ''think'':231C ''time'':7A,149C ''toler'':205C ''train'':24C ''use'':1A,53C ''valu'':5A,89C,207C,228C,242C ''weather'':138C ''windspe'':37C,64C,100C,155C,249C ''would'':80C ''zero'':150C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8305, 1, '2015-10-04 17:34:41.407', 1, 30, 'Case when Out of Bag Error and Test error differs a lot in Random Forest', '&lt;p&gt;I''m using random forest and the out of bag error for the level of one class is very different to its test error. I''m working with a cutt-of equal to c(0.2,0.8). Here''s the case:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;fmla &amp;lt;- as.formula(paste("ex ~ ", paste(names(muestra.fullarbol[,-c(1,2,3,9,10,11,12,17,19,20,21,22,23,24,29,31,32,33,34,35,36,47)]), collapse= "+")))
&amp;gt; bosque &amp;lt;- randomForest(fmla , data=muestra.fullarbol ,mtry=12, ntree=1000  , cutoff=c(0.2,0.8),importance=TRUE)
&amp;gt; bosque

Call:
 randomForest(formula = fmla, data = muestra.fullarbol, mtry = 12,      ntree = 1000, cutoff = c(0.2, 0.8), importance = TRUE) 
               Type of random forest: classification
                     Number of trees: 1000
No. of variables tried at each split: 12

        OOB estimate of  error rate: 15.81%
Confusion matrix:
     No Si class.error
No 3999  1     0.00025
Si  758 42     0.94750
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As we see, the out of bag error for the level "SI" is 0.94750. However If I use the test set to get a sense of the error, the result is very different:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;res.arbol &amp;lt;- predict(bosque,test.fullarbol,type="class")
&amp;gt; summary(res.arbol)
  No   Si 
7761   43 
&amp;gt; table(res.arbol,test.fullarbol$ex)

res.arbol   No   Si
       No 6937  824
       Si    4   39
&amp;gt; prop.table(table(res.arbol,test.fullarbol$ex),1)

res.arbol         No         Si
       No 0.89382811 0.10617189
       Si 0.09302326 0.90697674
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now we see thae error rate for the test set in the class "Si" is very low ,  it''s equal to 0.093 and It doesnÂ´t make sense to me.&lt;/p&gt;

&lt;p&gt;I guess that the cut off is working just to predict out of the sample(muestra.fullarbol), but I''m not sure. What can be the reason of that huge difference?&lt;/p&gt;
', 'machine-learning  predictive-modeling  random-forest  performance', NULL, '''/code'':174C,257C ''/p'':68C,210C,290C,322C ''/pre'':175C,258C ''0.00025'':169C ''0.093'':281C ''0.09302326'':255C ''0.10617189'':253C ''0.2'':62C,116C,134C ''0.8'':63C,117C,135C ''0.89382811'':252C ''0.90697674'':256C ''0.94750'':173C,190C ''1'':80C,168C,247C ''10'':84C ''1000'':113C,131C,146C ''11'':85C ''12'':86C,111C,129C,154C ''15.81'':160C ''17'':87C ''19'':88C ''2'':81C ''20'':89C ''21'':90C ''22'':91C ''23'':92C ''24'':93C ''29'':94C ''3'':82C ''31'':95C ''32'':96C ''33'':97C ''34'':98C ''35'':99C ''36'':100C ''39'':240C ''3999'':167C ''4'':239C ''42'':172C ''43'':226C ''47'':101C ''6937'':236C ''758'':171C ''7761'':225C ''824'':237C ''9'':83C ''as.formula'':73C ''bag'':5A,36C,183C ''bosqu'':104C,121C,216C ''c'':61C,79C,115C,133C ''call'':122C ''case'':1A,67C ''class'':43C,219C,272C ''class.error'':165C ''classif'':142C ''code'':70C,212C ''collaps'':102C ''confus'':161C ''cut'':296C ''cutoff'':114C,132C ''cutt'':57C ''cutt-of'':56C ''data'':108C,126C ''differ'':10A,46C,209C,321C ''doesnâ'':284C ''equal'':59C,279C ''error'':6A,9A,37C,50C,158C,184C,204C,264C ''estim'':156C ''ex'':75C,231C,246C ''fmla'':71C,107C,125C ''forest'':15A,24B,31C,141C ''formula'':124C ''get'':199C ''gt'':103C,120C,220C,227C,241C ''guess'':293C ''howev'':191C ''huge'':320C ''import'':118C,136C ''learn'':18B ''level'':40C,187C ''lot'':12A ''low'':276C ''lt'':72C,105C,214C ''m'':28C,52C,310C ''machin'':17B ''machine-learn'':16B ''make'':286C ''matrix'':162C ''model'':21B ''mtri'':110C,128C ''muestra.fullarbol'':78C,109C,127C,307C ''name'':77C ''ntree'':112C,130C ''number'':143C ''one'':42C ''oob'':155C ''p'':26C,176C,259C,291C ''past'':74C,76C ''perform'':25B ''pre'':69C,211C ''predict'':20B,215C,302C ''predictive-model'':19B ''prop.table'':242C ''random'':14A,23B,30C,140C ''random-forest'':22B ''randomforest'':106C,123C ''rate'':159C,265C ''reason'':317C ''res.arbol'':213C,222C,229C,232C,244C,248C ''result'':206C ''sampl'':306C ''see'':179C,262C ''sens'':201C,287C ''set'':197C,269C ''si'':164C,170C,188C,224C,234C,238C,250C,254C,273C ''split'':153C ''summari'':221C ''sure'':312C ''tabl'':228C,243C ''test'':8A,49C,196C,268C ''test.fullarbol'':217C,230C,245C ''thae'':263C ''tree'':145C ''tri'':150C ''true'':119C,137C ''type'':138C,218C ''use'':29C,194C ''variabl'':149C ''work'':53C,299C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8309, 1, '2015-10-04 23:06:52.52', 1, 184, 'Quick way to visually explore data?', '&lt;p&gt;I have a very large csv file which Apple Numbers won''t open. I can open it with TextEdit, but each row is so long it forms multiple lines in the document and makes the document difficult to understand. Is there a tool for opening a csv and exploring different rows and columns?&lt;/p&gt;
', 'csv', NULL, '''/p'':62C ''appl'':17C ''column'':61C ''csv'':7B,14C,55C ''data'':6A ''differ'':58C ''difficult'':45C ''document'':40C,44C ''explor'':5A,57C ''file'':15C ''form'':35C ''larg'':13C ''line'':37C ''long'':33C ''make'':42C ''multipl'':36C ''number'':18C ''open'':21C,24C,53C ''p'':8C ''quick'':1A ''row'':30C,59C ''textedit'':27C ''tool'':51C ''understand'':47C ''visual'':4A ''way'':2A ''won'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8314, 2, '2015-10-05 13:27:27.03', 0, NULL, NULL, '&lt;p&gt;If you look at the definitions of the two distances, cosine distance is the normalized dot product of the two vectors and euclidian is the square root of the sum of the squared elements of the difference vector.&lt;/p&gt;

&lt;p&gt;The cosine distance between M and J is smaller than between M and G because the normalization factor of M''s vector still includes the numbers for which J didn''t have any ratings. Even if you make J''s vector more similar, like you did, the remaining numbers of M (2 and 5) get you the number you get.
The number for M and G is this high because they both have non-zeroes for all the books. Even though they seem quite different, the normalization factors in the cosine are more "neutralized" by the non-zeroes for corresponding entries in the dot product. Maths don''t lie.&lt;/p&gt;

&lt;p&gt;The books J didn''t rate will be ignored if you make their numbers zero in the computation of the normalization factor for M. Maybe the fault in your thinking is that the books J didn''t rate should be 0 while they shouldn''t be any number.&lt;/p&gt;

&lt;p&gt;Finally, for recommendation systems, I would like to refer to matrix factorization.&lt;/p&gt;
', NULL, NULL, '''/p'':40C,153C,203C,217C ''0'':195C ''2'':93C ''5'':95C ''book'':121C,156C,188C ''comput'':172C ''correspond'':143C ''cosin'':12C,43C,133C ''definit'':7C ''didn'':71C,158C,190C ''differ'':38C,127C ''distanc'':11C,13C,44C ''dot'':17C,147C ''element'':35C ''entri'':144C ''euclidian'':24C ''even'':76C,122C ''factor'':59C,130C,176C,216C ''fault'':181C ''final'':205C ''g'':55C,107C ''get'':96C,101C ''high'':110C ''ignor'':163C ''includ'':65C ''j'':48C,70C,80C,157C,189C ''lie'':152C ''like'':85C,211C ''look'':4C ''m'':46C,53C,61C,92C,105C,178C ''make'':79C,166C ''math'':149C ''matrix'':215C ''mayb'':179C ''neutral'':136C ''non'':116C,140C ''non-zero'':115C,139C ''normal'':16C,58C,129C,175C ''number'':67C,90C,99C,103C,168C,202C ''p'':1C,41C,154C,204C ''product'':18C,148C ''quit'':126C ''rate'':75C,160C,192C ''recommend'':207C ''refer'':213C ''remain'':89C ''root'':28C ''seem'':125C ''shouldn'':198C ''similar'':84C ''smaller'':50C ''squar'':27C,34C ''still'':64C ''sum'':31C ''system'':208C ''think'':184C ''though'':123C ''two'':10C,21C ''vector'':22C,39C,63C,82C ''would'':210C ''zero'':117C,141C,169C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8315, 2, '2015-10-05 13:46:24.837', 2, NULL, NULL, '&lt;p&gt;&lt;strong&gt;&lt;em&gt;DISCLAIMER :&lt;/strong&gt; I''m not sure that this will be the answer you are looking for but I''ll give a try.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;According to Noam Chomsky&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Language is a process of free creation; its laws and principles are
  fixed, but the manner in which the principles of generation are used
  is free and infinitely varied. Even the interpretation and use of
  words involves a process of free creation.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;On one hand, data or text mining the combined large lexical data resources of linguistics and psychology suggests that the linguistic variable of ''word frequency'' should be rehabilitated in psychology.&lt;/p&gt;

&lt;p&gt;But you have to remember that, &lt;strong&gt;&lt;em&gt;correlation does not imply causation&lt;/em&gt;&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;This said, there is not much way to apply cross text mining with psychological intuition. In other words, use cases are not too many.&lt;/p&gt;

&lt;p&gt;You can always try quantitative text analysis. It is a set of techniques stemming from the social sciences where either a human judge or a computer extracts semantic or grammatical relationships between words in order to find out the meaning or stylistic patterns of, usually, a casual personal text for the purpose of psychological profiling etc. &lt;/p&gt;

&lt;p&gt;And by psychological profiling, I''m talking about offender profiling per example. &lt;/p&gt;

&lt;p&gt;You can get more information upon that on the following websites :&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://criminvestresearch.com/" rel="nofollow"&gt;Criminal Investigative Research &amp;amp; Analysis&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.psychlotron.org.uk/" rel="nofollow"&gt;http://www.psychlotron.org.uk/&lt;/a&gt; &lt;a href="http://www.psychlotron.org.uk/newResources/criminological/A2_AQB_crim_whatIsProfiling.pdf" rel="nofollow"&gt;article&lt;/a&gt;  about criminal profiling.&lt;/li&gt;
&lt;li&gt;Here is a very intersting &lt;a href="http://www.newyorker.com/magazine/2007/11/12/dangerous-minds" rel="nofollow"&gt;article&lt;/a&gt; about criminal profiling for beginners.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You can also try to take a look at this &lt;a href="http://begriffs.com/posts/2015-02-25-text-mining-in-r.html" rel="nofollow"&gt;presentation&lt;/a&gt; by Harold Baize who talks about the intersection of text mining in the content analysis of psychology.&lt;/p&gt;

&lt;p&gt;I hope this can help!&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':262C ''/a'':252C,271C,290C,317C ''/blockquote'':79C ''/em'':26C,126C ''/li'':253C,275C,296C ''/magazine/2007/11/12/dangerous-minds'':286C ''/newresources/criminological/a2_aqb_crim_whatisprofiling.pdf'':267C ''/p'':27C,33C,78C,111C,128C,154C,212C,226C,239C,335C,342C ''/posts/2015-02-25-text-mining-in-r.html'':313C ''/strong'':5C,127C ''/ul'':297C ''accord'':29C ''also'':301C ''alway'':158C ''amp'':250C ''analysi'':162C,251C,332C ''answer'':15C ''appli'':138C ''articl'':270C,289C ''baiz'':320C ''beginn'':295C ''begriffs.com'':312C ''begriffs.com/posts/2015-02-25-text-mining-in-r.html'':311C ''blockquot'':34C ''case'':149C ''casual'':202C ''causat'':125C ''chomski'':32C ''combin'':89C ''comput'':181C ''content'':331C ''correl'':121C ''creation'':42C,77C ''crimin'':247C,273C,292C ''criminvestresearch.com'':244C ''cross'':139C ''data'':84C,92C ''disclaim'':4C ''either'':175C ''em'':3C,120C ''etc'':211C ''even'':65C ''exampl'':225C ''extract'':182C ''find'':192C ''fix'':48C ''follow'':237C ''free'':41C,61C,76C ''frequenc'':105C ''generat'':57C ''get'':230C ''give'':23C ''grammat'':185C ''hand'':83C ''harold'':319C ''help'':341C ''hope'':338C ''href'':243C,256C,264C,283C,310C ''human'':177C ''impli'':124C ''infinit'':63C ''inform'':232C ''interpret'':67C ''intersect'':325C ''interst'':281C ''intuit'':144C ''investig'':248C ''involv'':72C ''judg'':178C ''languag'':36C ''larg'':90C ''law'':44C ''lexic'':91C ''li'':241C,254C,276C ''linguist'':95C,101C ''ll'':22C ''look'':18C,306C ''m'':7C,219C ''mani'':153C ''manner'':51C ''mean'':195C ''mine'':87C,141C,328C ''much'':135C ''noam'':31C ''nofollow'':246C,259C,269C,288C,315C ''offend'':222C ''one'':82C ''order'':190C ''p'':1C,28C,35C,80C,112C,129C,155C,213C,227C,298C,336C ''pattern'':198C ''per'':224C ''person'':203C ''present'':316C ''principl'':46C,55C ''process'':39C,74C ''profil'':210C,217C,223C,274C,293C ''psycholog'':97C,110C,143C,209C,216C,334C ''purpos'':207C ''quantit'':160C ''rehabilit'':108C ''rel'':245C,258C,268C,287C,314C ''relationship'':186C ''rememb'':117C ''research'':249C ''resourc'':93C ''said'':131C ''scienc'':173C ''semant'':183C ''set'':166C ''social'':172C ''stem'':169C ''strong'':2C,119C ''stylist'':197C ''suggest'':98C ''sure'':9C ''take'':304C ''talk'':220C,322C ''techniqu'':168C ''text'':86C,140C,161C,204C,327C ''tri'':25C,159C,302C ''ul'':240C ''upon'':233C ''use'':59C,69C,148C ''usual'':200C ''vari'':64C ''variabl'':102C ''way'':136C ''websit'':238C ''word'':71C,104C,147C,188C ''www.newyorker.com'':285C ''www.newyorker.com/magazine/2007/11/12/dangerous-minds'':284C ''www.psychlotron.org.uk'':257C,261C,266C ''www.psychlotron.org.uk/&lt;/a&gt;'':260C ''www.psychlotron.org.uk/newresources/criminological/a2_aqb_crim_whatisprofiling.pdf'':265C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8316, 2, '2015-10-05 14:05:41.85', 0, NULL, NULL, '&lt;p&gt;Actually, there isn''t any limit of what you can do, if you have the right data.&lt;/p&gt;

&lt;p&gt;I can remember a startup that was working on identify anxiety and suicidal thoughts on Tweets and Facebook status. First, they created a system where a user could raise a flag in case of a related Tweet/status about anxiety. They used those data to create a labeled dataset.&lt;/p&gt;

&lt;p&gt;If you have labeled data and you know the psychological intentions from them, then you can just train a model to classify all the other dataset to one of these labels.&lt;/p&gt;

&lt;p&gt;I am not aware if there is any public dataset with already labeled text, but you can either search for one or create your own.&lt;/p&gt;
', NULL, NULL, '''/p'':19C,69C,102C,129C ''actual'':2C ''alreadi'':115C ''anxieti'':31C,59C ''awar'':107C ''case'':53C ''classifi'':92C ''could'':48C ''creat'':42C,65C,126C ''data'':18C,63C,75C ''dataset'':68C,96C,113C ''either'':121C ''facebook'':38C ''first'':40C ''flag'':51C ''identifi'':30C ''intent'':81C ''isn'':4C ''know'':78C ''label'':67C,74C,101C,116C ''limit'':7C ''model'':90C ''one'':98C,124C ''p'':1C,20C,70C,103C ''psycholog'':80C ''public'':112C ''rais'':49C ''relat'':56C ''rememb'':23C ''right'':17C ''search'':122C ''startup'':25C ''status'':39C ''suicid'':33C ''system'':44C ''text'':117C ''thought'':34C ''train'':88C ''tweet'':36C ''tweet/status'':57C ''use'':61C ''user'':47C ''work'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8364, 2, '2015-10-09 09:17:26.153', 1, NULL, NULL, '&lt;p&gt;Each clustering algorithm in my knwledge is based on distance, which is calculated as &lt;em&gt;pairwise differences&lt;/em&gt;. So I''m not sure if it is easy to find a general &lt;em&gt;more elegant approach&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;In you case you can calculate the distance and than override it based on your &lt;em&gt;constraint&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Here an simple example reduced to one numerical dimension with distance threshold 30.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; nlst &amp;lt;- c(1,3,5, 36,39,42, 1001,1003,1005, 1036,1039,1042)

 d &amp;lt;- dist(nlst, method="euclidean")
 dr &amp;lt;- as.dist(ifelse(as.matrix(d) &amp;gt; 30, NA, 0))


     1  2  3  4  5  6  7  8  9 10 11
 2   0                              
 3   0  0                           
 4  NA NA NA                        
 5  NA NA NA  0                     
 6  NA NA NA  0  0                  
 7  NA NA NA NA NA NA               
 8  NA NA NA NA NA NA  0            
 9  NA NA NA NA NA NA  0  0         
 10 NA NA NA NA NA NA NA NA NA      
 11 NA NA NA NA NA NA NA NA NA  0   
 12 NA NA NA NA NA NA NA NA NA  0  0 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;A scan through the points collecting those with distance zero will give the result clustering.&lt;/p&gt;

&lt;p&gt;This should be always possible if your constraints are local (i.e. depending only on the two points for which you calculate the distance).&lt;/p&gt;
', NULL, NULL, '''/code'':195C ''/em'':19C,37C,57C ''/p'':38C,58C,73C,213C,238C ''/pre'':196C ''0'':106C,119C,121C,122C,131C,136C,137C,152C,160C,161C,182C,193C,194C ''1'':79C,107C ''10'':116C,162C ''1001'':85C ''1003'':86C ''1005'':87C ''1036'':88C ''1039'':89C ''1042'':90C ''11'':117C,172C ''12'':183C ''2'':108C,118C ''3'':80C,109C,120C ''30'':72C,104C ''36'':82C ''39'':83C ''4'':110C,123C ''42'':84C ''5'':81C,111C,127C ''6'':112C,132C ''7'':113C,138C ''8'':114C,145C ''9'':115C,153C ''algorithm'':4C ''alway'':218C ''approach'':36C ''as.dist'':99C ''as.matrix'':101C ''base'':9C,52C ''c'':78C ''calcul'':14C,45C,235C ''case'':42C ''cluster'':3C,212C ''code'':75C ''collect'':203C ''constraint'':56C,222C ''d'':91C,102C ''depend'':226C ''differ'':18C ''dimens'':68C ''dist'':93C ''distanc'':11C,47C,70C,206C,237C ''dr'':97C ''easi'':28C ''eleg'':35C ''em'':16C,33C,55C ''euclidean'':96C ''exampl'':63C ''find'':30C ''general'':32C ''give'':209C ''gt'':103C ''i.e'':225C ''ifels'':100C ''knwledg'':7C ''local'':224C ''lt'':77C,92C,98C ''m'':22C ''method'':95C ''na'':105C,124C,125C,126C,128C,129C,130C,133C,134C,135C,139C,140C,141C,142C,143C,144C,146C,147C,148C,149C,150C,151C,154C,155C,156C,157C,158C,159C,163C,164C,165C,166C,167C,168C,169C,170C,171C,173C,174C,175C,176C,177C,178C,179C,180C,181C,184C,185C,186C,187C,188C,189C,190C,191C,192C ''nlst'':76C,94C ''numer'':67C ''one'':66C ''overrid'':50C ''p'':1C,39C,59C,197C,214C ''pairwis'':17C ''point'':202C,231C ''possibl'':219C ''pre'':74C ''reduc'':64C ''result'':211C ''scan'':199C ''simpl'':62C ''sure'':24C ''threshold'':71C ''two'':230C ''zero'':207C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8735, 2, '2015-11-06 05:29:43.86', 0, NULL, NULL, '&lt;p&gt;Firstly, please read up on what exactly is a PCA algorithm and when can it be used and what purpose does it solve. &lt;/p&gt;

&lt;p&gt;Just throwing it on a problem and expect it to improve your results would work most of the time.&lt;/p&gt;

&lt;p&gt;PCA reduces the features in the dataset into dimensions, and it is for the user to select the number of dimensions he/she want to use in the model as features.&lt;/p&gt;

&lt;p&gt;So, &lt;a href="http://stats.stackexchange.com/questions/44060/choosing-number-of-principal-components-to-retain"&gt;choosing the right number of features&lt;/a&gt; to use or eliminate from the model would dictate your accuracy.&lt;/p&gt;
', NULL, NULL, '''/a'':92C ''/p'':25C,46C,78C,104C ''/questions/44060/choosing-number-of-principal-components-to-retain'':85C ''accuraci'':103C ''algorithm'':12C ''choos'':86C ''dataset'':54C ''dictat'':101C ''dimens'':56C,68C ''elimin'':96C ''exact'':8C ''expect'':34C ''featur'':51C,77C,91C ''first'':2C ''he/she'':69C ''href'':82C ''improv'':37C ''model'':75C,99C ''number'':66C,89C ''p'':1C,26C,47C,79C ''pca'':11C,48C ''pleas'':3C ''problem'':32C ''purpos'':21C ''read'':4C ''reduc'':49C ''result'':39C ''right'':88C ''select'':64C ''solv'':24C ''stats.stackexchange.com'':84C ''stats.stackexchange.com/questions/44060/choosing-number-of-principal-components-to-retain'':83C ''throw'':28C ''time'':45C ''use'':18C,72C,94C ''user'':62C ''want'':70C ''work'':41C ''would'':40C,100C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8317, 2, '2015-10-05 17:26:14.55', 1, NULL, NULL, '&lt;p&gt;You may install &lt;a href="https://www.r-project.org/" rel="nofollow"&gt;R&lt;/a&gt; - you are limited only with your RAM&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; ## read csv file
 df &amp;lt;- read.csv("l.csv")

 ## column names
 &amp;gt; colnames(df) 
 [1] "a" "b"

 &amp;gt; head(df)
     a  b
   1 1  1
   2 2  4
 ...

 # quick overview
 &amp;gt; summary(df)
        a                 b            
  Min.   :    1.0   Min.   :        1  
  1st Qu.: 2500.8   1st Qu.:  6253751  
  Median : 5000.5   Median : 25005000  
  Mean   : 5000.5   Mean   : 33338334  
  3rd Qu.: 7500.2   3rd Qu.: 56253750  
  Max.   :10000.0   Max.   :100000000  

 &amp;gt; plot(df2,type=''l'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/WGUcp.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/WGUcp.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Check docu for other graphic and statistics features.&lt;/p&gt;
', NULL, NULL, '''/a'':11C,108C ''/code'':88C ''/p'':19C,109C,119C ''/pre'':89C ''/wgucp.png'':95C,102C ''1'':34C,42C,43C,44C,58C ''1.0'':56C ''10000.0'':80C ''100000000'':82C ''1st'':59C,62C ''2'':45C,46C ''2500.8'':61C ''25005000'':68C ''33338334'':72C ''3rd'':73C,76C ''4'':47C ''5000.5'':66C,70C ''56253750'':78C ''6253751'':64C ''7500.2'':75C ''alt'':103C ''b'':36C,41C,54C ''check'':111C ''code'':21C ''colnam'':32C ''column'':29C ''csv'':23C ''descript'':106C ''df'':25C,33C,39C,52C ''df2'':85C ''docu'':112C ''enter'':104C ''featur'':118C ''file'':24C ''graphic'':115C ''gt'':31C,37C,50C,83C ''head'':38C ''href'':6C,92C ''i.stack.imgur.com'':94C,101C ''i.stack.imgur.com/wgucp.png'':93C,100C ''imag'':105C ''img'':98C ''instal'':4C ''l'':87C ''l.csv'':28C ''limit'':14C ''lt'':26C ''max'':79C,81C ''may'':3C ''mean'':69C,71C ''median'':65C,67C ''min'':55C,57C ''name'':30C ''nofollow'':9C,97C ''overview'':49C ''p'':1C,90C,110C ''plot'':84C ''pre'':20C ''qu'':60C,63C,74C,77C ''quick'':48C ''r'':10C ''ram'':18C ''read'':22C ''read.csv'':27C ''rel'':8C,96C ''src'':99C ''statist'':117C ''summari'':51C ''type'':86C ''www.r-project.org'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8318, 1, '2015-10-05 18:01:37.703', 0, 43, 'Credit Card Fraud detection using Kohenen Self Organizing maps', '&lt;p&gt;I am fairly new to Neural Networks. I want to do a fraud detection system on Automated teller Machines. From what I have read so far I am deciding to use the Kohenen Self Organizing Maps but don''t know how to apply it.&lt;/p&gt;
', 'machine-learning  neuralnetwork  anomaly-detection', NULL, '''/p'':62C ''anomali'':15B ''anomaly-detect'':14B ''appli'':60C ''autom'':34C ''card'':2A ''credit'':1A ''decid'':46C ''detect'':4A,16B,31C ''fair'':20C ''far'':43C ''fraud'':3A,30C ''know'':57C ''kohenen'':6A,50C ''learn'':12B ''machin'':11B,36C ''machine-learn'':10B ''map'':9A,53C ''network'':24C ''neural'':23C ''neuralnetwork'':13B ''new'':21C ''organ'':8A,52C ''p'':17C ''read'':41C ''self'':7A,51C ''system'':32C ''teller'':35C ''use'':5A,48C ''want'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8319, 2, '2015-10-05 20:15:43.173', 4, NULL, NULL, '&lt;p&gt;When you hit the limits of an application like Apple Numbers, or Excel, you need to start using a programming language like Python, R or C. Using a programming language, you write your own application, which is not constrained by arbitrary limits like 2&lt;sup&gt;20&lt;/sup&gt; rows by 2&lt;sup&gt;14&lt;/sup&gt; columns. (Of course, you are still constrained by the physical memory and the way that a particular language addresses it: R and Python tend to be more resource-intensive than C).&lt;/p&gt;

&lt;p&gt;Let me give you an example using Python. You can install Python for your system from the &lt;a href="https://www.python.org/downloads/" rel="nofollow"&gt;official site&lt;/a&gt;. If you install one of the latest versions, &lt;a href="http://python-packaging-user-guide.readthedocs.org/en/latest/installing/" rel="nofollow"&gt;you will already&lt;/a&gt; get &lt;code&gt;pip&lt;/code&gt; as an installer of further Python packages. Install Pandas by starting a terminal (on OS X, there is a ''Terminal'' application, on Windows this used to be a MS-DOS window ...), and typing into that terminal:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;pip install pandas
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You can then start Python in an interactive session by typing &lt;code&gt;python&lt;/code&gt; in a terminal. In the Python shell, you can read the data as mentioned in &lt;strong&gt;@tasos&lt;/strong&gt; ''s answer:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;&amp;gt;&amp;gt; import pandas as pd
&amp;gt;&amp;gt;&amp;gt; df = pd.read_csv(''name_of_your_file.csv'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here you can use the &lt;code&gt;describe&lt;/code&gt; method of the &lt;code&gt;df&lt;/code&gt; DataFrame to get some statistics about the read data. These numbers are for a randomly generated data frame:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;&amp;gt;&amp;gt; df.describe()
          0         1         2
count  3.000000  3.000000  3.000000
mean  -0.150869  0.444380 -0.117066
std    0.751421  0.697880  0.565328
min   -1.017424 -0.356764 -0.506030
25%   -0.386514  0.206466 -0.441313
50%    0.244396  0.769696 -0.376595
75%    0.282409  0.844952  0.077416
max    0.320422  0.920208  0.531427
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then you can generate plots for different columns, one against the other, using the &lt;a href="http://pandas.pydata.org/pandas-docs/stable/visualization.html" rel="nofollow"&gt;Pandas plotting functions&lt;/a&gt;. Check the many examples in the linked documentation. Here is a small example from there:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import matplotlib.pyplot as plt
import pandas as pd
import bumpy as np

df = pd.DataFrame({''a'': np.random.randn(1000) + 1, ''b'': np.random.randn(1000),
                   ''c'': np.random.randn(1000) - 1}, columns=[''a'', ''b'', ''c''])
plt.figure()
df.plot(kind=''hist'', alpha=0.5)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Usually, you will need to use another call more in order to save it to a file:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;plt.save(''output.png'')  ## or *.svg, *.pdf
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In the file you should find something similar to this figure.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://pandas.pydata.org/pandas-docs/stable/_images/hist_new.png" alt=""&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8320, 2, '2015-10-05 20:39:30.453', 0, NULL, NULL, '&lt;p&gt;Given that you run a test against labeled data (the &lt;code&gt;-testFile&lt;/code&gt; option), then Stanford NER will report entity level F1 scores, and a micro-average entity-level F1 at the end of the run.&lt;/p&gt;
', NULL, NULL, '''/code'':14C ''/p'':40C ''averag'':29C ''code'':12C ''data'':10C ''end'':36C ''entiti'':21C,31C ''entity-level'':30C ''f1'':23C,33C ''given'':2C ''label'':9C ''level'':22C,32C ''micro'':28C ''micro-averag'':27C ''ner'':18C ''option'':15C ''p'':1C ''report'':20C ''run'':5C,39C ''score'':24C ''stanford'':17C ''test'':7C ''testfil'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8321, 2, '2015-10-06 07:54:23.917', 0, NULL, NULL, '&lt;p&gt;I agree with the answer by Tasos. Another approach to your work that you may wish to take, is using loglinear analysis on your categorical variables. This is a useful ploy whenever you are dealing with three or more categorical variables. Put simply, it''s basically regression for categorical variables and is very simple to employ.&lt;/p&gt;
', NULL, NULL, '''/p'':58C ''agre'':3C ''analysi'':23C ''anoth'':9C ''answer'':6C ''approach'':10C ''basic'':47C ''categor'':26C,41C,50C ''deal'':36C ''employ'':57C ''loglinear'':22C ''may'':16C ''p'':1C ''ploy'':32C ''put'':43C ''regress'':48C ''simpl'':55C ''simpli'':44C ''take'':19C ''taso'':8C ''three'':38C ''use'':21C,31C ''variabl'':27C,42C,51C ''whenev'':33C ''wish'':17C ''work'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9887, 2, '2016-01-21 17:05:30.203', 0, NULL, NULL, '&lt;p&gt;Yahoo just released a &lt;a href="http://webscope.sandbox.yahoo.com/catalog.php?datatype=l" rel="nofollow"&gt;huge dataset&lt;/a&gt; for research community. Enjoy it!&lt;/p&gt;
', NULL, NULL, '''/a'':15C ''/catalog.php?datatype=l'':10C ''/p'':21C ''communiti'':18C ''dataset'':14C ''enjoy'':19C ''href'':7C ''huge'':13C ''nofollow'':12C ''p'':1C ''rel'':11C ''releas'':4C ''research'':17C ''webscope.sandbox.yahoo.com'':9C ''webscope.sandbox.yahoo.com/catalog.php?datatype=l'':8C ''yahoo'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8338, 1, '2015-10-06 21:55:44.41', 1, 72, 'Measure of correlation for term frequency', '&lt;p&gt;I''m trying to write a framework to compare a set of labels such as (for a sample of 5 yes/no answers to a question)  &lt;code&gt;[0, 1, 1, 1, 0]&lt;/code&gt; to a series of features to determine correlation.  For numerical non-sparse features, like "number of words" or "average word length", I know I can use a variance-covariance matrix and get a sense for whether or not "number of words" or "average word length" is an informative feature for a model to answer the question.&lt;/p&gt;

&lt;p&gt;I''d like to be able to do the same thing for term frequency (let''s say using CountVectorizer in scikit-learn), but the resultant covariance matrix will be rather large and will only indicate whether or not that particular &lt;em&gt;term&lt;/em&gt; is an informative feature.  How do I get some kind of "collapsed" or "aggregate" measure of correlation?  Is this even possible?&lt;/p&gt;
', 'statistics  feature-selection', NULL, '''/code'':43C ''/em'':147C ''/p'':102C,169C ''0'':38C,42C ''1'':39C,40C,41C ''5'':31C ''abl'':109C ''aggreg'':161C ''answer'':33C,99C ''averag'':63C,88C ''code'':37C ''collaps'':159C ''compar'':20C ''correl'':3A,51C,164C ''countvector'':122C ''covari'':74C,130C ''d'':105C ''determin'':50C ''em'':145C ''even'':167C ''featur'':9B,48C,57C,94C,151C ''feature-select'':8B ''framework'':18C ''frequenc'':6A,117C ''get'':77C,155C ''indic'':139C ''inform'':93C,150C ''kind'':157C ''know'':67C ''label'':24C ''larg'':135C ''learn'':126C ''length'':65C,90C ''let'':118C ''like'':58C,106C ''m'':13C ''matrix'':75C,131C ''measur'':1A,162C ''model'':97C ''non'':55C ''non-spars'':54C ''number'':59C,84C ''numer'':53C ''p'':11C,103C ''particular'':144C ''possibl'':168C ''question'':36C,101C ''rather'':134C ''result'':129C ''sampl'':29C ''say'':120C ''scikit'':125C ''scikit-learn'':124C ''select'':10B ''sens'':79C ''seri'':46C ''set'':22C ''spars'':56C ''statist'':7B ''term'':5A,116C,146C ''thing'':114C ''tri'':14C ''use'':70C,121C ''varianc'':73C ''variance-covari'':72C ''whether'':81C,140C ''word'':61C,64C,86C,89C ''write'':16C ''yes/no'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8323, 2, '2015-10-06 12:17:53.297', 0, NULL, NULL, '&lt;p&gt;There is a difference between data with missing values and sparse data. Missing values are generally there because of invalid input, loss or error during data collection or are created when cleaning or processing data. &lt;/p&gt;

&lt;p&gt;If these values are very less in number, the corresponding instances can be ignored or if are around 5-10% of the data, can be filled using various methods (carry forward last observation, fill with mean/median, interpolate the data etc). If you are working in Python, go through Pandas documentation for &lt;em&gt;&lt;a href="http://pandas.pydata.org/pandas-docs/stable/missing_data.html" rel="nofollow"&gt;Working with Missing Values&lt;/a&gt;&lt;/em&gt;, to learn in detail about these options (even if you aren''t working in Python, this is a good read).&lt;/p&gt;

&lt;p&gt;But if your data set has a large number of missing values (say ~ &gt;30% ), then the data is &lt;strong&gt;sparse&lt;/strong&gt;. Such data sets create various bias in your modelling, and there are special ways to deal with them, though I don''t about them much.&lt;/p&gt;
', NULL, NULL, '''-10'':58C ''/a'':102C ''/em'':103C ''/p'':37C,124C,172C ''/pandas-docs/stable/missing_data.html'':95C ''/strong'':146C ''30'':139C ''5'':57C ''aren'':114C ''around'':56C ''bias'':152C ''carri'':68C ''clean'':33C ''collect'':28C ''correspond'':48C ''creat'':31C,150C ''data'':7C,13C,27C,36C,61C,77C,129C,142C,148C ''deal'':162C ''detail'':107C ''differ'':5C ''document'':88C ''em'':90C ''error'':25C ''etc'':78C ''even'':111C ''fill'':64C,72C ''forward'':69C ''general'':17C ''go'':85C ''good'':122C ''href'':92C ''ignor'':52C ''input'':22C ''instanc'':49C ''interpol'':75C ''invalid'':21C ''larg'':133C ''last'':70C ''learn'':105C ''less'':44C ''loss'':23C ''mean/median'':74C ''method'':67C ''miss'':9C,14C,100C,136C ''model'':155C ''much'':171C ''nofollow'':97C ''number'':46C,134C ''observ'':71C ''option'':110C ''p'':1C,38C,125C ''panda'':87C ''pandas.pydata.org'':94C ''pandas.pydata.org/pandas-docs/stable/missing_data.html'':93C ''process'':35C ''python'':84C,118C ''read'':123C ''rel'':96C ''say'':138C ''set'':130C,149C ''spars'':12C,145C ''special'':159C ''strong'':144C ''though'':165C ''use'':65C ''valu'':10C,15C,41C,101C,137C ''various'':66C,151C ''way'':160C ''work'':82C,98C,116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8324, 2, '2015-10-06 12:31:04.54', 2, NULL, NULL, '&lt;p&gt;There are of course other choices to fill in for missing data. The median was already mentioned, and it may work better in certain cases.&lt;/p&gt;

&lt;p&gt;There may even be much better alternatives, which may be very specific to your problem. To find out whether this is the case, you must find out more about the &lt;em&gt;nature&lt;/em&gt; of your missing data. When you understand in detail why data is missing, the probability of coming up with a good solution will be much higher. &lt;/p&gt;

&lt;p&gt;You might want to start your investigation of missing data by finding out whether you have &lt;em&gt;informative&lt;/em&gt; or &lt;em&gt;non-informative&lt;/em&gt; missings. The first category is produced by random data loss; in this case, the observations with missing values are no different from the ones with complete data. As for &lt;em&gt;informative&lt;/em&gt; missing data, this one tells you something about your observation. A simple example is a customer record with a missing contract cancellation date meaning that this customer''s contract has not been cancelled so far. You usually don''t want to fill in informative missings with a mean or a median, but you may want to generate a separate feature from them. &lt;/p&gt;

&lt;p&gt;You may also find out that there are several kinds of missing data, being produced by different mechanisms. In this case, you might want to produce default values in different ways.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8325, 2, '2015-10-06 12:45:01.473', 1, NULL, NULL, '&lt;p&gt;If you have a broad meaning of "intentions" in mind, you might be interested in research showing that a person''s personality (in the sense of the "big five" psychological personality theory) can be inferred with remarkable accuracy from facebook likes. Original research showing this can be found &lt;a href="http://www.pnas.org/content/110/15/5802.full.pdf" rel="nofollow"&gt;here&lt;/a&gt; and &lt;a href="http://www.pnas.org/content/112/4/1036.full.pdf" rel="nofollow"&gt;here&lt;/a&gt;. Obviously, facebook likes are not the same as the textual information you work with, but it may be possible to infer certain likes and dislikes from the text.&lt;/p&gt;
', NULL, NULL, '''/a'':58C,68C ''/content/110/15/5802.full.pdf'':54C ''/content/112/4/1036.full.pdf'':64C ''/p'':97C ''accuraci'':39C ''big'':29C ''broad'':6C ''certain'':90C ''dislik'':93C ''facebook'':41C,70C ''five'':30C ''found'':49C ''href'':51C,61C ''infer'':36C,89C ''inform'':79C ''intent'':9C ''interest'':15C ''like'':42C,71C,91C ''may'':85C ''mean'':7C ''might'':13C ''mind'':11C ''nofollow'':56C,66C ''obvious'':69C ''origin'':43C ''p'':1C ''person'':21C,23C,32C ''possibl'':87C ''psycholog'':31C ''rel'':55C,65C ''remark'':38C ''research'':17C,44C ''sens'':26C ''show'':18C,45C ''text'':96C ''textual'':78C ''theori'':33C ''work'':81C ''www.pnas.org'':53C,63C ''www.pnas.org/content/110/15/5802.full.pdf'':52C ''www.pnas.org/content/112/4/1036.full.pdf'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8326, 2, '2015-10-06 13:01:59.363', 0, NULL, NULL, '&lt;p&gt;Correlation should be as less as possible between different features, because correlated features mean that those features are giving out same kind of information/trend for the predictor to learn. Thus only one of them is actually useful for prediction. &lt;/p&gt;

&lt;p&gt;Keeping more number of uninformative features (correlated features) would result in degraded accuracy if your sample size is similar to you feature set size. Feature selection using Recursive Feature elimination or PCA etc. can help you reduce your feature set to optimal size.&lt;/p&gt;

&lt;p&gt;We calculate correlation score in predictive analysis between features and Target variable. When using linear regression to model a data set, we first see if the plot between different features and target variable values follow an upward (+ve correlation) or downward trend (-ve correlation) and not scattered randomly. If such a relationship exists then regression modelling on the data would work well.&lt;/p&gt;
', NULL, NULL, '''/p'':41C,86C,150C ''accuraci'':55C ''actual'':37C ''analysi'':94C ''calcul'':89C ''correl'':2C,13C,49C,90C,126C,131C ''data'':107C,146C ''degrad'':54C ''differ'':10C,116C ''downward'':128C ''elimin'':72C ''etc'':75C ''exist'':140C ''featur'':11C,14C,18C,48C,50C,64C,67C,71C,81C,96C,117C ''first'':110C ''follow'':122C ''give'':20C ''help'':77C ''information/trend'':25C ''keep'':43C ''kind'':23C ''learn'':30C ''less'':6C ''linear'':102C ''mean'':15C ''model'':105C,143C ''number'':45C ''one'':33C ''optim'':84C ''p'':1C,42C,87C ''pca'':74C ''plot'':114C ''possibl'':8C ''predict'':40C,93C ''predictor'':28C ''random'':135C ''recurs'':70C ''reduc'':79C ''regress'':103C,142C ''relationship'':139C ''result'':52C ''sampl'':58C ''scatter'':134C ''score'':91C ''see'':111C ''select'':68C ''set'':65C,82C,108C ''similar'':61C ''size'':59C,66C,85C ''target'':98C,119C ''thus'':31C ''trend'':129C ''uninform'':47C ''upward'':124C ''use'':38C,69C,101C ''valu'':121C ''variabl'':99C,120C ''ve'':125C,130C ''well'':149C ''work'':148C ''would'':51C,147C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8339, 1, '2015-10-06 23:08:51.327', 1, 86, 'Voting combined results from different classifiers gave bad accuracy', '&lt;p&gt;I used following classifiers along with their accuracies:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Random forest - 85 %&lt;/li&gt;
&lt;li&gt;SVM - 78 %&lt;/li&gt;
&lt;li&gt;Adaboost - 82%&lt;/li&gt;
&lt;li&gt;Logistic regression - 80%&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;When I used voting from above classifiers for final classification, I got lesser accuracy than the case when I used Random forest alone.&lt;/p&gt;

&lt;p&gt;How is this possible? All classifiers are giving more or less same accuracies when used individually, then how does Random Forest outperform their combined result ?&lt;/p&gt;
', 'machine-learning  classification  logistic-regression  random-forest', NULL, '''/li'':35C,39C,43C,48C ''/ol'':49C ''/p'':29C,74C,101C ''78'':38C ''80'':47C ''82'':42C ''85'':34C ''accuraci'':9A,28C,64C,88C ''adaboost'':41C ''alon'':73C ''along'':25C ''bad'':8A ''case'':67C ''classif'':13B,60C ''classifi'':6A,24C,57C,81C ''combin'':2A,99C ''differ'':5A ''final'':59C ''follow'':23C ''forest'':19B,33C,72C,96C ''gave'':7A ''give'':83C ''got'':62C ''individu'':91C ''learn'':12B ''less'':86C ''lesser'':63C ''li'':31C,36C,40C,44C ''logist'':15B,45C ''logistic-regress'':14B ''machin'':11B ''machine-learn'':10B ''ol'':30C ''outperform'':97C ''p'':20C,50C,75C ''possibl'':79C ''random'':18B,32C,71C,95C ''random-forest'':17B ''regress'':16B,46C ''result'':3A,100C ''svm'':37C ''use'':22C,53C,70C,90C ''vote'':1A,54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8329, 2, '2015-10-06 15:24:38.363', 1, NULL, NULL, '&lt;p&gt;When it comes to missing data, there are many different methods of filling these values. However, the imputation method you choose, depends largely on the amount of missing data and the type of variable. For example, you won''t impute the mean value for missing categorical data, you would choose the mode instead. No matter which method you choose, there will be some bias associated with it. One method which does a good job at reducing the bias associated with imputing missing values, is multiple imputation. It can be quite a long-winded approach but it is the most sound approach I''ve seen so far to imputing large amounts of missing values. I believe there may be an R library for multiple imputation. &lt;/p&gt;

&lt;p&gt;Of course, another alternative may be that if variable x has 50% missing data for example, there may be a good explanation as to why this is. Rather than trying to impute it or lose the information associated with the variable, it can sometimes be useful to create a new variable, called variable_x_flag_missing. This would be a binary indicator variable where an observation is coded as 1 if variable x contains a missing value and coded as a 0 if it does not. &lt;/p&gt;
', NULL, NULL, '''/p'':127C,215C ''0'':210C ''1'':198C ''50'':140C ''altern'':132C ''amount'':27C,112C ''anoth'':131C ''approach'':96C,103C ''associ'':66C,80C,166C ''believ'':117C ''bias'':65C,79C ''binari'':189C ''call'':180C ''categor'':47C ''choos'':22C,51C,60C ''code'':196C,207C ''come'':4C ''contain'':202C ''cours'':130C ''creat'':176C ''data'':7C,30C,48C,142C ''depend'':23C ''differ'':11C ''exampl'':37C,144C ''explan'':150C ''far'':108C ''fill'':14C ''flag'':183C ''good'':74C,149C ''howev'':17C ''imput'':19C,41C,82C,87C,110C,126C,160C ''indic'':190C ''inform'':165C ''instead'':54C ''job'':75C ''larg'':24C,111C ''librari'':123C ''long'':94C ''long-wind'':93C ''lose'':163C ''mani'':10C ''matter'':56C ''may'':119C,133C,146C ''mean'':43C ''method'':12C,20C,58C,70C ''miss'':6C,29C,46C,83C,114C,141C,184C,204C ''mode'':53C ''multipl'':86C,125C ''new'':178C ''observ'':194C ''one'':69C ''p'':1C,128C ''quit'':91C ''r'':122C ''rather'':156C ''reduc'':77C ''seen'':106C ''sometim'':172C ''sound'':102C ''tri'':158C ''type'':33C ''use'':174C ''valu'':16C,44C,84C,115C,205C ''variabl'':35C,137C,169C,179C,181C,191C,200C ''ve'':105C ''wind'':95C ''won'':39C ''would'':50C,186C ''x'':138C,182C,201C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8331, 1, '2015-10-06 18:45:53.4', 2, 63, 'Behaviour of Learning Algorithms on Random Data', '&lt;p&gt;Suppose we collect data for 100,000 tosses of a fair coin and record "Heads" or "Tails" as the value for the attribute &lt;em&gt;outcome&lt;/em&gt; and also record the time, temprature and other irrelevant attributes.&lt;/p&gt;

&lt;p&gt;We know that the outcome of each toss is random so there should be no way of predicting future &lt;em&gt;unlabeled&lt;/em&gt; data instances.&lt;/p&gt;

&lt;p&gt;My question is how do learning algorithms (support vector machines, for example) behave when we apply them on random data such as this?&lt;/p&gt;
', 'machine-learning  data-mining  classification  predictive-modeling  svm', NULL, '''/em'':45C,79C ''/p'':56C,82C,107C ''000'':26C ''100'':25C ''algorithm'':4A,90C ''also'':47C ''appli'':99C ''attribut'':42C,55C ''behav'':96C ''behaviour'':1A ''classif'':14B ''coin'':31C ''collect'':22C ''data'':7A,12B,23C,80C,103C ''data-min'':11B ''em'':43C,77C ''exampl'':95C ''fair'':30C ''futur'':76C ''head'':34C ''instanc'':81C ''irrelev'':54C ''know'':59C ''learn'':3A,10B,89C ''machin'':9B,93C ''machine-learn'':8B ''mine'':13B ''model'':17B ''outcom'':44C,62C ''p'':19C,57C,83C ''predict'':16B,75C ''predictive-model'':15B ''question'':85C ''random'':6A,67C,102C ''record'':33C,48C ''support'':91C ''suppos'':20C ''svm'':18B ''tail'':36C ''tempratur'':51C ''time'':50C ''toss'':27C,65C ''unlabel'':78C ''valu'':39C ''vector'':92C ''way'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8332, 1, '2015-10-06 20:03:15.34', 0, 159, 'Is my Lasagne/Theano neural network running too slow?', '&lt;p&gt;So I''m a newcomer to the world of neural networks, and I''ve been getting a little familiar with the field and have started playing with my own networks.&lt;/p&gt;

&lt;p&gt;I''m using &lt;a href="https://github.com/Lasagne/Lasagne" rel="nofollow"&gt;Lasagne&lt;/a&gt;, and I''m finding that the training of my NN is taking an infeasible amount of time.  Maybe the problem I''m looking at is simply biting more than my computer can chew; I just don''t know, because I''m not familiar enough with how these things go.&lt;/p&gt;

&lt;p&gt;I''m trying to train a 1D convolutional NN, which has two 1D convolution layers, and two max-pooling layers, and two fully connected layers. All in all, the NN has around 95,000 parameters (weights + biases).  I''m running this on an Amazon EC2 GPU instance.  What I''m finding is that during training, it''s taking 0.3 sec/sample.  &lt;/p&gt;

&lt;p&gt;By contrast, when I run the Lasagne MNIST CNN example, it''s taking about 0.0003 sec/sample, 1000 times faster!  Now to be fair, my input samples are 18000x3 dimensional, and the MNIST samples are 28x28 dimensional; but the difference there is a factor of 68.  And the MNIST CNN actually has more parameters than mine, with 160,000 or so parameters.&lt;/p&gt;

&lt;p&gt;Does this indicate anything wrong with what I''m doing?  &lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/a'':57C ''/lasagne/lasagne'':53C ''/p'':44C,107C,170C,233C,245C ''0.0003'':186C ''0.3'':168C ''000'':143C,229C ''1000'':188C ''160'':228C ''18000x3'':199C ''1d'':115C,121C ''28x28'':206C ''68'':216C ''95'':142C ''actual'':221C ''amazon'':153C ''amount'':72C ''anyth'':238C ''around'':141C ''bias'':146C ''bite'':84C ''chew'':90C ''cnn'':180C,220C ''comput'':88C ''connect'':133C ''contrast'':173C ''convolut'':116C,122C ''differ'':210C ''dimension'':200C,207C ''ec2'':154C ''enough'':101C ''exampl'':181C ''factor'':214C ''fair'':194C ''familiar'':32C,100C ''faster'':190C ''field'':35C ''find'':61C,160C ''fulli'':132C ''get'':29C ''github.com'':52C ''github.com/lasagne/lasagne'':51C ''go'':106C ''gpu'':155C ''href'':50C ''indic'':237C ''infeas'':71C ''input'':196C ''instanc'':156C ''know'':95C ''lasagn'':56C,178C ''lasagne/theano'':3A ''layer'':123C,129C,134C ''learn'':11B ''littl'':31C ''look'':80C ''m'':16C,47C,60C,79C,98C,110C,148C,159C,243C ''machin'':10B ''machine-learn'':9B ''max'':127C ''max-pool'':126C ''mayb'':75C ''mine'':226C ''mnist'':179C,203C,219C ''network'':5A,24C,43C ''neural'':4A,23C ''neuralnetwork'':12B ''newcom'':18C ''nn'':67C,117C,139C ''nofollow'':55C ''p'':13C,45C,108C,171C,234C ''paramet'':144C,224C,232C ''play'':39C ''pool'':128C ''problem'':77C ''rel'':54C ''run'':6A,149C,176C ''sampl'':197C,204C ''sec/sample'':169C,187C ''simpli'':83C ''slow'':8A ''start'':38C ''take'':69C,167C,184C ''thing'':105C ''time'':74C,189C ''train'':64C,113C,164C ''tri'':111C ''two'':120C,125C,131C ''use'':48C ''ve'':27C ''weight'':145C ''world'':21C ''wrong'':239C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8492, 1, '2015-10-19 02:35:49.983', 1, 76, 'Reason behind choosing Neural Network for classification', '&lt;p&gt;Given a two class multi dimensional classification problem, what reason would you give to choose Artificial Neural Network for carrying out the classification instead of Support Vector Machine or other classification methods?&lt;/p&gt;
', 'classification  dataset  neuralnetwork  svm', NULL, '''/p'':45C ''artifici'':28C ''behind'':2A ''carri'':32C ''choos'':3A,27C ''class'':16C ''classif'':7A,8B,19C,35C,43C ''dataset'':9B ''dimension'':18C ''give'':25C ''given'':13C ''instead'':36C ''machin'':40C ''method'':44C ''multi'':17C ''network'':5A,30C ''neural'':4A,29C ''neuralnetwork'':10B ''p'':12C ''problem'':20C ''reason'':1A,22C ''support'':38C ''svm'':11B ''two'':15C ''vector'':39C ''would'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8337, 2, '2015-10-06 21:43:50.66', 0, NULL, NULL, '&lt;p&gt;If the values are missing at random &lt;em&gt;and&lt;/em&gt; you are sure that your data matrix is of low rank, you can use nuclear norm basis pursuit method (also known as &lt;a href="https://en.wikipedia.org/wiki/Matrix_completion" rel="nofollow"&gt;matrix completion&lt;/a&gt;). The method (among others) is implemented in &lt;a href="http://cvxr.com/tfocs/download/" rel="nofollow"&gt;TFOCS&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In many real-world applications, the data matrix has rarely full rank, so the assumption of low-rank matrix can be acceptable. On the other hand, the values might not be missing truly at random.&lt;/p&gt;

&lt;p&gt;Another approach would be to use Singular Spectrum Analysis (&lt;a href="http://www.jds-online.com/files/JDS-396.pdf" rel="nofollow"&gt;SSA&lt;/a&gt;), also known as the Caterpillar algorithm. It can be used for time-series data with missing values. This algorithm is not very well-known but in literature it is sometimes called "PCA for time-series data".&lt;/p&gt;
', NULL, NULL, '''/a'':43C,59C,118C ''/em'':11C ''/files/jds-396.pdf'':114C ''/p'':60C,99C,158C ''/tfocs/download/'':55C ''/wiki/matrix_completion'':38C ''accept'':85C ''algorithm'':124C,138C ''also'':31C,119C ''among'':46C ''analysi'':109C ''anoth'':101C ''applic'':67C ''approach'':102C ''assumpt'':77C ''basi'':28C ''call'':151C ''caterpillar'':123C ''complet'':42C ''cvxr.com'':54C ''cvxr.com/tfocs/download/'':53C ''data'':17C,69C,133C,157C ''em'':9C ''en.wikipedia.org'':37C ''en.wikipedia.org/wiki/matrix_completion'':36C ''full'':73C ''hand'':89C ''href'':35C,52C,111C ''implement'':49C ''known'':32C,120C,144C ''literatur'':147C ''low'':21C,80C ''low-rank'':79C ''mani'':63C ''matrix'':18C,41C,70C,82C ''method'':30C,45C ''might'':92C ''miss'':6C,95C,135C ''nofollow'':40C,57C,116C ''norm'':27C ''nuclear'':26C ''other'':47C ''p'':1C,61C,100C ''pca'':152C ''pursuit'':29C ''random'':8C,98C ''rank'':22C,74C,81C ''rare'':72C ''real'':65C ''real-world'':64C ''rel'':39C,56C,115C ''seri'':132C,156C ''singular'':107C ''sometim'':150C ''spectrum'':108C ''ssa'':117C ''sure'':14C ''tfoc'':58C ''time'':131C,155C ''time-seri'':130C,154C ''truli'':96C ''use'':25C,106C,128C ''valu'':4C,91C,136C ''well'':143C ''well-known'':142C ''world'':66C ''would'':103C ''www.jds-online.com'':113C ''www.jds-online.com/files/jds-396.pdf'':112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8365, 2, '2015-10-09 12:54:11.923', 0, NULL, NULL, '&lt;p&gt;I''m not sure I understand your question entirely, but are you looking for something like TF-IDF (Term Frequency - Inverse Document Frequency)?&lt;/p&gt;
', NULL, NULL, '''/p'':26C ''document'':24C ''entir'':10C ''frequenc'':22C,25C ''idf'':20C ''invers'':23C ''like'':17C ''look'':14C ''m'':3C ''p'':1C ''question'':9C ''someth'':16C ''sure'':5C ''term'':21C ''tf'':19C ''tf-idf'':18C ''understand'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8346, 2, '2015-10-07 12:20:00.673', 3, NULL, NULL, '&lt;p&gt;The approach you are considering is similar to a multi-class SVM or a &lt;strong&gt;one-vs-the-rest&lt;/strong&gt; approach.&lt;/p&gt;

&lt;p&gt;And here is how I describe the problem. The support vector machine, per example, is fundamentally a two-class classifier. &lt;/p&gt;

&lt;p&gt;In practice, however, we often have to tackle problems involving K &gt; 2 classes. Various methods have therefore been proposed for combining multiple two-class SVMs in order to build a multi-class classifier. &lt;/p&gt;

&lt;p&gt;One commonly used approach (Vapnik, 1998) is to construct K separate SVMs, in which the kth model y_k(x) is trained using the data from class C_k as the positive examples and the data from the remaining K â 1 classes as the negative examples. This is known as the one-versus-the-rest approach where : &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;y(x) = max_k y_k(x)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Unfortunately, this heuristic approach suffers from the problem that the different classifiers were trained on different tasks, and there is no guarantee that the real-valued quantities y_k(x) for different classifiers will have appropriate scales&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Another problem with the one-versus-the-rest approach is that the training sets are imbalanced. For instance, if we have ten classes each with equal numbers of training data points, then the individual classifiers are trained on data sets comprising 90% negative examples and only 10% positive examples, and the symmetry of the original problem is lost.&lt;/p&gt;

&lt;p&gt;Therefor, you got your &lt;em&gt;bad accuracy&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;PS:&lt;/strong&gt; &lt;em&gt;Accuracy&lt;/em&gt;, in most cases, is not a good measure for evaluating a classifier model.&lt;/p&gt;

&lt;p&gt;References :&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Vapnik, V. - Statistical Learning Theory. Wiley-Interscience, New York.&lt;/li&gt;
&lt;li&gt;Christopher M. Bishop - Pattern Recognition and Machine Learning.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/code'':156C ''/em'':269C,277C ''/li'':307C,317C ''/p'':25C,48C,85C,146C,199C,260C,270C,291C,294C ''/pre'':157C ''/strong'':23C,198C,274C ''/ul'':318C ''1'':128C ''10'':248C ''1998'':92C ''2'':61C ''90'':243C ''accuraci'':268C,276C ''anoth'':201C ''approach'':3C,24C,90C,144C,163C,210C ''appropri'':196C ''bad'':267C ''bishop'':311C ''build'':79C ''c'':114C ''case'':280C ''christoph'':309C ''class'':13C,46C,62C,74C,83C,113C,129C,224C ''classifi'':47C,84C,171C,193C,236C,289C ''code'':148C ''combin'':70C ''common'':88C ''compris'':242C ''consid'':6C ''construct'':95C ''data'':111C,122C,231C,240C ''describ'':32C ''differ'':170C,175C,192C ''em'':266C,275C ''equal'':227C ''evalu'':287C ''exampl'':40C,119C,133C,245C,250C ''fundament'':42C ''good'':284C ''got'':264C ''guarante'':181C ''heurist'':162C ''howev'':52C ''imbalanc'':217C ''individu'':235C ''instanc'':219C ''intersci'':304C ''involv'':59C ''k'':60C,96C,105C,115C,126C,152C,154C,189C ''known'':136C ''kth'':102C ''learn'':300C,316C ''li'':296C,308C ''lost'':259C ''m'':310C ''machin'':38C,315C ''max'':151C ''measur'':285C ''method'':64C ''model'':103C,290C ''multi'':12C,82C ''multi-class'':11C,81C ''multipl'':71C ''negat'':132C,244C ''new'':305C ''number'':228C ''often'':54C ''one'':19C,87C,140C,206C ''one-versus-the-rest'':139C,205C ''one-vs-the-rest'':18C ''order'':77C ''origin'':256C ''p'':1C,26C,49C,86C,158C,200C,261C,271C,292C ''pattern'':312C ''per'':39C ''point'':232C ''posit'':118C,249C ''practic'':51C ''pre'':147C ''problem'':34C,58C,167C,202C,257C ''propos'':68C ''ps'':273C ''quantiti'':187C ''real'':185C ''real-valu'':184C ''recognit'':313C ''refer'':293C ''remain'':125C ''rest'':22C,143C,209C ''scale'':197C ''separ'':97C ''set'':215C,241C ''similar'':8C ''statist'':299C ''strong'':17C,159C,272C ''suffer'':164C ''support'':36C ''svm'':14C ''svms'':75C,98C ''symmetri'':253C ''tackl'':57C ''task'':176C ''ten'':223C ''theori'':301C ''therefor'':66C,262C ''train'':108C,173C,214C,230C,238C ''two'':45C,73C ''two-class'':44C,72C ''ul'':295C ''unfortun'':160C ''use'':89C,109C ''v'':298C ''valu'':186C ''vapnik'':91C,297C ''various'':63C ''vector'':37C ''versus'':141C,207C ''vs'':20C ''wiley'':303C ''wiley-intersci'':302C ''x'':106C,150C,155C,190C ''y'':104C,149C,153C,188C ''york'':306C ''â'':127C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8347, 2, '2015-10-07 13:18:16.423', 0, NULL, NULL, '&lt;p&gt;There is a neat online tool called &lt;a href="http://raw.densitydesign.org/" rel="nofollow"&gt;Raw&lt;/a&gt; which will chart your data in your browser.  You drag and drop your file and it will try and make sense of it.  It''s made for web designers, so in the end you export an HTML representation of your visualization, but it''s a fine tool in its own right.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/Kr3Rk.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Kr3Rk.png" alt="enter image description here"&gt;&lt;/a&gt;  &lt;/p&gt;
', NULL, NULL, '''/a'':15C,86C ''/kr3rk.png'':73C,80C ''/p'':67C,87C ''alt'':81C ''browser'':23C ''call'':8C ''chart'':18C ''data'':20C ''descript'':84C ''design'':44C ''drag'':25C ''drop'':27C ''end'':48C ''enter'':82C ''export'':50C ''file'':29C ''fine'':61C ''href'':10C,70C ''html'':52C ''i.stack.imgur.com'':72C,79C ''i.stack.imgur.com/kr3rk.png'':71C,78C ''imag'':83C ''img'':76C ''made'':41C ''make'':35C ''neat'':5C ''nofollow'':13C,75C ''onlin'':6C ''p'':1C,68C ''raw'':14C ''raw.densitydesign.org'':11C ''rel'':12C,74C ''represent'':53C ''right'':66C ''sens'':36C ''src'':77C ''tool'':7C,62C ''tri'':33C ''visual'':56C ''web'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8348, 2, '2015-10-07 18:36:23.627', 5, NULL, NULL, '&lt;p&gt;You do not need additional learning algorithms to perform reinforcement learning in simple systems where you can explore all states. For those, simple iterative &lt;a href="https://en.wikipedia.org/wiki/Q-learning" rel="nofollow"&gt;Q-learning&lt;/a&gt; can do very well. If all your state, action pairs can be enumerated into a table that fits into memory, then you can use a Q-learning technique which is reinforcement learning without any deeper help of neural networks or other models.&lt;/p&gt;

&lt;p&gt;The simplest form of &lt;em&gt;model-free&lt;/em&gt; Q-learning just stores and updates a table of &lt;code&gt;&amp;lt;state, action&amp;gt; =&amp;gt; &amp;lt;estimated reward&amp;gt;&lt;/code&gt; pairs. There is no deeper statistical model inside that. Q-learning relies on estimates of reward from this table in order to take an action and then updates it with a more refined estimate after each action.&lt;/p&gt;

&lt;p&gt;For large, complex problems, which may even have infinite possible states, this is not feasible, and you need good generalised estimates based on some function of the state. In those cases, you can use a neural network to create a function approximator, that can estimate the rewards from similar states to those already seen. The neural network replaces the function of the simple table in Q-Learning. However, the neural network (or other model) does not perform the whole process, you still need an "outer" RL method that explores states and actions in order to provide data for the NN to learn.&lt;/p&gt;
', NULL, NULL, '''/a'':36C ''/code'':111C ''/em'':90C ''/p'':80C,150C,255C ''/wiki/q-learning'':30C ''action'':45C,104C,137C,149C,244C ''addit'':6C ''algorithm'':8C ''alreadi'':204C ''approxim'':193C ''base'':173C ''case'':182C ''code'':101C ''complex'':154C ''creat'':190C ''data'':249C ''deeper'':72C,116C ''em'':86C ''en.wikipedia.org'':29C ''en.wikipedia.org/wiki/q-learning'':28C ''enumer'':49C ''estim'':108C,126C,146C,172C,196C ''even'':158C ''explor'':19C,241C ''feasibl'':166C ''fit'':54C ''form'':84C ''free'':89C ''function'':176C,192C,211C ''generalis'':171C ''good'':170C ''gt'':105C,106C,110C ''help'':73C ''howev'':220C ''href'':27C ''infinit'':160C ''insid'':119C ''iter'':25C ''larg'':153C ''learn'':7C,12C,35C,64C,69C,93C,123C,219C,254C ''lt'':102C,107C ''may'':157C ''memori'':56C ''method'':239C ''model'':79C,88C,118C,226C ''model-fre'':87C ''need'':5C,169C,235C ''network'':76C,188C,208C,223C ''neural'':75C,187C,207C,222C ''nn'':252C ''nofollow'':32C ''order'':133C,246C ''outer'':237C ''p'':1C,81C,151C ''pair'':46C,112C ''perform'':10C,229C ''possibl'':161C ''problem'':155C ''process'':232C ''provid'':248C ''q'':34C,63C,92C,122C,218C ''q-learn'':33C,62C,91C,121C,217C ''refin'':145C ''reinforc'':11C,68C ''rel'':31C ''reli'':124C ''replac'':209C ''reward'':109C,128C,198C ''rl'':238C ''seen'':205C ''similar'':200C ''simpl'':14C,24C,214C ''simplest'':83C ''state'':21C,44C,103C,162C,179C,201C,242C ''statist'':117C ''still'':234C ''store'':95C ''system'':15C ''tabl'':52C,99C,131C,215C ''take'':135C ''techniqu'':65C ''updat'':97C,140C ''use'':60C,185C ''well'':40C ''whole'':231C ''without'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8396, 2, '2015-10-12 07:00:25.807', 0, NULL, NULL, '&lt;p&gt;You can use Decision trees for a single model prediction for both the set of users.&lt;/p&gt;

&lt;p&gt;A good start would be to first &lt;a href="https://en.wikipedia.org/wiki/Decision_tree" rel="nofollow"&gt;read up&lt;/a&gt; on Decision Trees and their applications.&lt;/p&gt;

&lt;p&gt;You can include the offer as a decision(as a boolean in this case). &lt;/p&gt;

&lt;p&gt;&lt;code&gt;buying with offer&lt;/code&gt; and &lt;code&gt;buying without offer&lt;/code&gt; can be the decision criterion.&lt;/p&gt;

&lt;p&gt;You can, in fact go ahead and put in the offer values also. For example, &lt;/p&gt;

&lt;p&gt;&lt;code&gt;offer&amp;gt;10%&lt;/code&gt; and &lt;code&gt;offer &amp;lt;10%&lt;/code&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':36C ''/code'':65C,71C,100C,106C ''/p'':18C,43C,59C,77C,94C,107C ''/wiki/decision_tree'':31C ''10'':99C,105C ''ahead'':84C ''also'':91C ''applic'':42C ''boolean'':55C ''buy'':62C,68C ''case'':58C ''code'':61C,67C,96C,102C ''criterion'':76C ''decis'':5C,38C,52C,75C ''en.wikipedia.org'':30C ''en.wikipedia.org/wiki/decision_tree'':29C ''exampl'':93C ''fact'':82C ''first'':26C ''go'':83C ''good'':21C ''gt'':98C ''href'':28C ''includ'':47C ''lt'':104C ''model'':10C ''nofollow'':33C ''offer'':49C,64C,70C,89C,97C,103C ''p'':1C,19C,44C,60C,78C,95C ''predict'':11C ''put'':86C ''read'':34C ''rel'':32C ''set'':15C ''singl'':9C ''start'':22C ''tree'':6C,39C ''use'':4C ''user'':17C ''valu'':90C ''without'':69C ''would'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8367, 2, '2015-10-09 13:14:55.737', 1, NULL, NULL, '&lt;p&gt;This sounds like a time-related predictive task...i.e. forecasting. The data you have available is event-driven, e.g. data is recorded when a user clicks on a link/refreshes a page/clicks on a Google ad etc. This means that you need to treat your data as if it were a time-series for each user. This would be easier if you had access to ESP (Event Stream Processing) but let''s say that you do not. Therefore, you need to identify each user, aggregate their info/features so that you have features identifying the number of unique pages they visited, the number of sessions they had etc. etc. Then you can segment these users and you may or may not derive useful segments from them. You could even go a step further and do session analysis whereby you identify unique sessions and cluster/segment user sessions looking for nice visual patterns or anomalies. &lt;/p&gt;
', NULL, NULL, '''/p'':156C ''access'':67C ''ad'':38C ''aggreg'':88C ''analysi'':139C ''anomali'':155C ''avail'':17C ''click'':29C ''cluster/segment'':146C ''could'':130C ''data'':14C,23C,48C ''deriv'':124C ''driven'':21C ''e.g'':22C ''easier'':63C ''esp'':69C ''etc'':39C,110C,111C ''even'':131C ''event'':20C,70C ''event-driven'':19C ''featur'':95C ''forecast'':12C ''go'':132C ''googl'':37C ''i.e'':11C ''identifi'':85C,96C,142C ''info/features'':90C ''let'':74C ''like'':4C ''link/refreshes'':32C ''look'':149C ''may'':120C,122C ''mean'':41C ''need'':44C,83C ''nice'':151C ''number'':98C,105C ''p'':1C ''page'':101C ''page/clicks'':34C ''pattern'':153C ''predict'':9C ''process'':72C ''record'':25C ''relat'':8C ''say'':76C ''segment'':115C,126C ''seri'':56C ''session'':107C,138C,144C,148C ''sound'':3C ''step'':134C ''stream'':71C ''task'':10C ''therefor'':81C ''time'':7C,55C ''time-rel'':6C ''time-seri'':54C ''treat'':46C ''uniqu'':100C,143C ''use'':125C ''user'':28C,59C,87C,117C,147C ''visit'':103C ''visual'':152C ''wherebi'':140C ''would'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8493, 2, '2015-10-19 03:52:45.837', 0, NULL, NULL, '&lt;p&gt;SVM is Parametric. Parametric models are something with fixed finite number of parameters independent of dataset size. Anything which is not parametric model is non-parametric model. ANN is non parametric. Also ANN has ''deep architectures" which can represent "intelligent" behaviour/functions etc more efficiently than "shallow architectures" like SVMs. ANN may have any number of outputs, while support vector machines have only one. &lt;/p&gt;
', NULL, NULL, '''/p'':66C ''also'':34C ''ann'':30C,35C,52C ''anyth'':19C ''architectur'':38C,49C ''behaviour/functions'':43C ''dataset'':17C ''deep'':37C ''effici'':46C ''etc'':44C ''finit'':11C ''fix'':10C ''independ'':15C ''intellig'':42C ''like'':50C ''machin'':62C ''may'':53C ''model'':6C,24C,29C ''non'':27C,32C ''non-parametr'':26C ''number'':12C,56C ''one'':65C ''output'':58C ''p'':1C ''paramet'':14C ''parametr'':4C,5C,23C,28C,33C ''repres'':41C ''shallow'':48C ''size'':18C ''someth'':8C ''support'':60C ''svm'':2C ''svms'':51C ''vector'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8349, 1, '2015-10-07 19:14:44.173', 7, 64, 'Classification of vector sequences', '&lt;p&gt;My dataset is comprised of vector sequences. Each vector has 50 real-valued dimensions. The number of vectors in a sequence range from 3-5 to 10-15. In other words, the length of a sequence is not fixed.&lt;/p&gt;

&lt;p&gt;Some fair amount of the sequences (not vectors!) are annotated with a class label. My task is to learn a classifier that given a sequence of vectors, the class label for the whole sequence is computed.&lt;/p&gt;

&lt;p&gt;I cannot tell the exact nature of the data but the nature of sequences is not temporal. Nevertheless, a vector $x_i$ cannot be interchanged with a vector $x_j$ without changing the label ($i \neq j$). In other words, the order of vectors is important. The vectors themselves are comparable, for example it makes sense to compute a dot product and use this similarity value.&lt;/p&gt;

&lt;p&gt;My question is: what are the tools/algorithms that can help to classify such data?&lt;/p&gt;

&lt;p&gt;UPDATE:
The data has such a property that one or very few vectors influence strongly the class label.&lt;/p&gt;

&lt;p&gt;POSSIBLE SOLUTION:
After some research it looks like the Recurrent Neural Networks (RNN) fit the bill pretty naturally. The overarching idea is to pick a context size $k$, concatenate word vectors, do max pooling and feed that through classical NN. At each possible context window position in a sentence, a feature vector is built. The final feature vector is built using max pooling for example. The backpropagation is done to adjust the network''s parameters. I already got some positive results (GPU is a must).&lt;/p&gt;
', 'classification  sequence', NULL, '''-15'':36C ''-5'':33C ''/p'':48C,86C,154C,170C,190C,277C ''10'':35C ''3'':32C ''50'':18C ''adjust'':262C ''alreadi'':268C ''amount'':52C ''annot'':59C ''backpropag'':258C ''bill'':207C ''built'':245C,251C ''cannot'':89C,110C ''chang'':119C ''class'':62C,78C,188C ''classic'':230C ''classif'':1A,5B ''classifi'':70C,167C ''compar'':138C ''compris'':11C ''comput'':85C,145C ''concaten'':220C ''context'':217C,235C ''data'':96C,169C,174C ''dataset'':9C ''dimens'':22C ''done'':260C ''dot'':147C ''exact'':92C ''exampl'':140C,256C ''fair'':51C ''featur'':242C,248C ''feed'':227C ''final'':247C ''fit'':205C ''fix'':47C ''given'':72C ''got'':269C ''gpu'':273C ''help'':165C ''idea'':212C ''import'':133C ''influenc'':185C ''interchang'':112C ''j'':117C,124C ''k'':219C ''label'':63C,79C,121C,189C ''learn'':68C ''length'':41C ''like'':199C ''look'':198C ''make'':142C ''max'':224C,253C ''must'':276C ''natur'':93C,99C,209C ''neq'':123C ''network'':203C,264C ''neural'':202C ''nevertheless'':105C ''nn'':231C ''number'':24C ''one'':180C ''order'':129C ''overarch'':211C ''p'':7C,49C,87C,155C,171C,191C ''paramet'':266C ''pick'':215C ''pool'':225C,254C ''posit'':237C,271C ''possibl'':192C,234C ''pretti'':208C ''product'':148C ''properti'':178C ''question'':157C ''rang'':30C ''real'':20C ''real-valu'':19C ''recurr'':201C ''research'':196C ''result'':272C ''rnn'':204C ''sens'':143C ''sentenc'':240C ''sequenc'':4A,6B,14C,29C,44C,55C,74C,83C,101C ''similar'':152C ''size'':218C ''solut'':193C ''strong'':186C ''task'':65C ''tell'':90C ''tempor'':104C ''tools/algorithms'':162C ''updat'':172C ''use'':150C,252C ''valu'':21C,153C ''vector'':3A,13C,16C,26C,57C,76C,107C,115C,131C,135C,184C,222C,243C,249C ''whole'':82C ''window'':236C ''without'':118C ''word'':39C,127C,221C ''x'':108C,116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8360, 2, '2015-10-09 05:51:46.14', 1, NULL, NULL, '&lt;p&gt;The size of each tree depends very much on its depth. Thus, change the maximal depth (&lt;code&gt;max_depth&lt;/code&gt;). Try to set it to finite number (as opposed to the default "None") and then try to reduce this number. In addition (or as alternative) try to increase &lt;code&gt;min_samples_split&lt;/code&gt; or &lt;code&gt;min_samples_split&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;You can also analyze you features and keep only important ones. The simplest way would be to have a look at the &lt;code&gt;clf.feature_importances_&lt;/code&gt; of your forest. (In general, finding important features is an art and science on itself.) Exclude non-relevant features and rebuild the forest.&lt;/p&gt;
', NULL, NULL, '''/code'':21C,54C,60C,88C ''/p'':61C,113C ''addit'':43C ''also'':65C ''altern'':46C ''analyz'':66C ''art'':99C ''chang'':14C ''clf.feature'':86C ''code'':18C,50C,56C,85C ''default'':33C ''depend'':7C ''depth'':12C,17C,20C ''exclud'':104C ''featur'':68C,96C,108C ''find'':94C ''finit'':27C ''forest'':91C,112C ''general'':93C ''import'':72C,87C,95C ''increas'':49C ''keep'':70C ''look'':82C ''max'':19C ''maxim'':16C ''min'':51C,57C ''much'':9C ''non'':106C ''non-relev'':105C ''none'':34C ''number'':28C,41C ''one'':73C ''oppos'':30C ''p'':1C,62C ''rebuild'':110C ''reduc'':39C ''relev'':107C ''sampl'':52C,58C ''scienc'':101C ''set'':24C ''simplest'':75C ''size'':3C ''split'':53C,59C ''thus'':13C ''tree'':6C ''tri'':22C,37C,47C ''way'':76C ''would'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8350, 2, '2015-10-07 23:16:48.33', 1, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;Voter registration files are public data and are available at the state or county level (since the passage of the Help America Vote Act states are required to maintain lists of eligible voters).&lt;/li&gt;
&lt;li&gt;A popular account of the Obama data analytics is in the book, "The Victory Lab" by Sash Issenberg &lt;a href="http://www.thevictorylab.com/" rel="nofollow"&gt;http://www.thevictorylab.com/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Academic paper, "Political Campaigns and Big Data" &lt;a href="https://www3.nd.edu/~dnickers/files/papers/nickerson_rogers.2014.pdf" rel="nofollow"&gt;https://www3.nd.edu/~dnickers/files/papers/nickerson_rogers.2014.pdf&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/li&gt;'':63C ''/li'':36C ''/ol'':82C ''/~dnickers/files/papers/nickerson_rogers.2014.pdf'':76C ''/~dnickers/files/papers/nickerson_rogers.2014.pdf&lt;/a&gt;&lt;/li&gt;'':81C ''academ'':65C ''account'':40C ''act'':26C ''america'':24C ''analyt'':45C ''avail'':11C ''big'':70C ''book'':49C ''campaign'':68C ''counti'':16C ''data'':8C,44C,71C ''elig'':34C ''file'':5C ''help'':23C ''href'':57C,73C ''issenberg'':55C ''lab'':52C ''level'':17C ''li'':2C,37C,64C ''list'':32C ''maintain'':31C ''nofollow'':60C,78C ''obama'':43C ''ol'':1C ''paper'':66C ''passag'':20C ''polit'':67C ''popular'':39C ''public'':7C ''registr'':4C ''rel'':59C,77C ''requir'':29C ''sash'':54C ''sinc'':18C ''state'':14C,27C ''victori'':51C ''vote'':25C ''voter'':3C,35C ''www.thevictorylab.com'':58C,62C ''www.thevictorylab.com/&lt;/a&gt;&lt;/li&gt;'':61C ''www3.nd.edu'':75C,80C ''www3.nd.edu/~dnickers/files/papers/nickerson_rogers.2014.pdf'':74C ''www3.nd.edu/~dnickers/files/papers/nickerson_rogers.2014.pdf&lt;/a&gt;&lt;/li&gt;'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8351, 2, '2015-10-08 07:04:04.497', 0, NULL, NULL, '&lt;p&gt;Done with K-means clustering with descriptive statistics as features:&lt;/p&gt;

&lt;p&gt;In short, I''ve tried the idea described in the question, even if I was thinking it won''t work. Let the experience talk...&lt;/p&gt;

&lt;p&gt;I initially had a list of devices data. Each element of the list were 2 columns, R rows matrix, and R was different for each device. So, per device:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[
    [mesureValue, timestamp],
    ..., 
    [mesureValue, timestamp],
]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Since I''m only interested in the measureValue distribution, I''ve transformed the inital data to a 8 columns, N rows matrix, where N = number of devices.&lt;/p&gt;

&lt;p&gt;The columns are, computed on the correponding device''s measure value:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Arithmetic mean&lt;/li&gt;
&lt;li&gt;Median&lt;/li&gt;
&lt;li&gt;First quartille&lt;/li&gt;
&lt;li&gt;Third quartille&lt;/li&gt;
&lt;li&gt;Minimum&lt;/li&gt;
&lt;li&gt;Maximum&lt;/li&gt;
&lt;li&gt;Range&lt;/li&gt;
&lt;li&gt;Standard deviation&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;With this matrix, I''ve applied K-means clustering using scikit learn (python).&lt;/p&gt;

&lt;p&gt;I made the link between the matrix line and the physical device by using pandas Data Frames (python) who''s line index are in fact the serial number of the device.&lt;/p&gt;

&lt;p&gt;I''ve tried with 5 clusters, and it works.&lt;/p&gt;

&lt;p&gt;Just in case of, if I need improvements in the future, I''m planning to add other statistics in the columns, especially for deviation vs. normality. So, for example Kurstosis and normal q-q plot p value.&lt;/p&gt;

&lt;p&gt;Best regards.&lt;/p&gt;
', NULL, NULL, '''/code'':77C ''/li'':125C,128C,132C,136C,139C,142C,145C,149C ''/p'':13C,39C,70C,107C,120C,166C,199C,210C,250C,254C ''/pre'':78C ''/ul'':150C ''2'':55C ''5'':205C ''8'':97C ''add'':227C ''appli'':157C ''arithmet'':123C ''best'':252C ''case'':214C ''cluster'':7C,161C,206C ''code'':72C ''column'':56C,98C,110C,232C ''comput'':112C ''correpond'':115C ''data'':48C,94C,183C ''describ'':22C ''descript'':9C ''deviat'':148C,235C ''devic'':47C,66C,69C,106C,116C,179C,198C ''differ'':63C ''distribut'':88C ''done'':2C ''element'':50C ''especi'':233C ''even'':26C ''exampl'':240C ''experi'':37C ''fact'':192C ''featur'':12C ''first'':130C ''frame'':184C ''futur'':222C ''idea'':21C ''improv'':219C ''index'':189C ''init'':93C ''initi'':42C ''interest'':84C ''k'':5C,159C ''k-mean'':4C,158C ''kurstosi'':241C ''learn'':164C ''let'':35C ''li'':122C,126C,129C,133C,137C,140C,143C,146C ''line'':175C,188C ''link'':171C ''list'':45C,53C ''m'':82C,224C ''made'':169C ''matrix'':59C,101C,154C,174C ''maximum'':141C ''mean'':6C,124C,160C ''measur'':118C ''measurevalu'':87C ''median'':127C ''mesurevalu'':73C,75C ''minimum'':138C ''n'':99C,103C ''need'':218C ''normal'':237C,243C ''number'':104C,195C ''p'':1C,14C,40C,79C,108C,151C,167C,200C,211C,248C,251C ''panda'':182C ''per'':68C ''physic'':178C ''plan'':225C ''plot'':247C ''pre'':71C ''python'':165C,185C ''q'':245C,246C ''q-q'':244C ''quartill'':131C,135C ''question'':25C ''r'':57C,61C ''rang'':144C ''regard'':253C ''row'':58C,100C ''scikit'':163C ''serial'':194C ''short'':16C ''sinc'':80C ''standard'':147C ''statist'':10C,229C ''talk'':38C ''think'':30C ''third'':134C ''timestamp'':74C,76C ''transform'':91C ''tri'':19C,203C ''ul'':121C ''use'':162C,181C ''valu'':119C,249C ''ve'':18C,90C,156C,202C ''vs'':236C ''won'':32C ''work'':34C,209C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8352, 2, '2015-10-08 08:21:38.153', 3, NULL, NULL, '&lt;p&gt;As you can''t disclose much detail, I''m forced to be a bit generic in my answer. I hope it will be helpful nevertheless. First of all, I would only consider reducing the sequences before classification (be it by using the dot product or something else) if you can make sure that you don''t lose information you need for classification afterwards. So this approach is only feasible if you have some insight into the nature of the classification. To give a simple example: if the class label is just the number of vectors in your sequence, you won''t be very successfull in predicting the class label from the dot product.&lt;/p&gt;

&lt;p&gt;Hence, I would take the full sequence as an input for classification, and impose a maximum on the sequence length you want to consider. You might do this by first finding the maximum sequence length m in your training set and then turning each sequence of 50-dimensional vectors into one vector of dimension 50*m, possibly with some missing values at the end if your sequence doesn''t have maximum length. You will probably want to get rid of these missing values and you may want to simply replace them by zeros. &lt;/p&gt;

&lt;p&gt;There''s two roads you can go from here: 
1.) You directly apply classification methods known to be suitable for high dimensions. Try something simple that doesn''t need much tuning like naive Bayes. This way you can see whether this approach is feasible without losing too much time if it isn''t.
2.) You try first to reduce the dimension and understand the nature of the classification better. You may want to use something like principal components analysis or analyse correlation/association between each vector component and the class label. If you''re successful, you know how to properly reduce the dimension of your input before applying classification.&lt;/p&gt;

&lt;p&gt;If you would like to follow any of these ideas, please keep in mind that the concrete details of your data and the classification may render any of the ideas proposed above infeasible. So please be careful to check against any details you know but can''t post here before trying to make sure you''re not wasting your time.&lt;/p&gt;
', NULL, NULL, '''/p'':115C,210C,320C,382C ''1'':221C ''2'':265C ''50'':163C,171C ''afterward'':64C ''analys'':292C ''analysi'':290C ''answer'':19C ''appli'':224C,318C ''approach'':67C,253C ''bay'':245C ''better'':280C ''bit'':15C ''care'':358C ''check'':360C ''class'':89C,109C,300C ''classif'':38C,63C,81C,128C,225C,279C,319C,345C ''compon'':289C,297C ''concret'':338C ''consid'':33C,140C ''correlation/association'':293C ''data'':342C ''detail'':8C,339C,363C ''dimens'':170C,233C,272C,313C ''dimension'':164C ''direct'':223C ''disclos'':6C ''doesn'':184C,238C ''dot'':44C,113C ''els'':48C ''end'':180C ''exampl'':86C ''feasibl'':70C,255C ''find'':147C ''first'':27C,146C,268C ''follow'':327C ''forc'':11C ''full'':122C ''generic'':16C ''get'':194C ''give'':83C ''go'':218C ''help'':25C ''henc'':117C ''high'':232C ''hope'':21C ''idea'':331C,351C ''impos'':130C ''infeas'':354C ''inform'':59C ''input'':126C,316C ''insight'':75C ''isn'':263C ''keep'':333C ''know'':307C,365C ''known'':227C ''label'':90C,110C,301C ''length'':136C,151C,188C ''like'':243C,287C,325C ''lose'':58C,257C ''m'':10C,152C,172C ''make'':52C,374C ''maximum'':132C,149C,187C ''may'':202C,282C,346C ''method'':226C ''might'':142C ''mind'':335C ''miss'':176C,198C ''much'':7C,241C,259C ''naiv'':244C ''natur'':78C,276C ''need'':61C,240C ''nevertheless'':26C ''number'':94C ''one'':167C ''p'':1C,116C,211C,321C ''pleas'':332C,356C ''possibl'':173C ''post'':369C ''predict'':107C ''princip'':288C ''probabl'':191C ''product'':45C,114C ''proper'':310C ''propos'':352C ''re'':304C,377C ''reduc'':34C,270C,311C ''render'':347C ''replac'':206C ''rid'':195C ''road'':215C ''see'':250C ''sequenc'':36C,99C,123C,135C,150C,161C,183C ''set'':156C ''simpl'':85C,236C ''simpli'':205C ''someth'':47C,235C,286C ''success'':305C ''successful'':105C ''suitabl'':230C ''sure'':53C,375C ''take'':120C ''time'':260C,381C ''train'':155C ''tri'':234C,267C,372C ''tune'':242C ''turn'':159C ''two'':214C ''understand'':274C ''use'':42C,285C ''valu'':177C,199C ''vector'':96C,165C,168C,296C ''want'':138C,192C,203C,283C ''wast'':379C ''way'':247C ''whether'':251C ''without'':256C ''won'':101C ''would'':31C,119C,324C ''zero'':209C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10129, 4, '2016-02-07 20:58:14.257', 0, NULL, NULL, 'A restricted Boltzmann machine (RBM) is a stochastic neural network.', NULL, NULL, '''boltzmann'':3C ''machin'':4C ''network'':10C ''neural'':9C ''rbm'':5C ''restrict'':2C ''stochast'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8353, 1, '2015-10-08 13:11:56.483', 0, 27, 'How many folds on cross validation', '&lt;p&gt;Cross validation on a dataset with 22680 observations. Want the training set to contain 21420 entries. How many folds can you use for your cross validation?
How do you calculate the folds?&lt;/p&gt;
', 'statistics', NULL, '''/p'':41C ''21420'':23C ''22680'':15C ''calcul'':38C ''contain'':22C ''cross'':5A,9C,33C ''dataset'':13C ''entri'':24C ''fold'':3A,27C,40C ''mani'':2A,26C ''observ'':16C ''p'':8C ''set'':20C ''statist'':7B ''train'':19C ''use'':30C ''valid'':6A,10C,34C ''want'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8354, 2, '2015-10-08 13:27:36.613', 3, NULL, NULL, '&lt;p&gt;The number of folds is nothing you can really calculate. It is more a parameter you choose by good judgement. Typically used values are between 5 and 10, but you can even go up to the so-called leave-one-out cross validation, in which each fold consists of all but one observation. The arguments that guide your decision are typically the following:
- computational cost: the more folds you use, the more computations you need. 
- variance: the more folds you use, the higher the variance of the cross validation result
- bias: the more folds you use, the smaller the bias of the cross validation result.&lt;/p&gt;

&lt;p&gt;In your case, depending on the exact context in which you want to apply cross validation, you will probably want to choose between 5 and 10 folds. &lt;/p&gt;

&lt;p&gt;For more details, you might want to consult &lt;a href="http://ai.stanford.edu/~ronnyk/accEst.pdf" rel="nofollow"&gt;Kohavi''s 1995 paper on cross validation&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':162C ''/p'':109C,138C,163C ''/~ronnyk/accest.pdf'':152C ''10'':29C,136C ''1995'':157C ''5'':27C,134C ''ai.stanford.edu'':151C ''ai.stanford.edu/~ronnyk/accest.pdf'':150C ''appli'':124C ''argument'':58C ''bias'':94C,103C ''calcul'':11C ''call'':40C ''case'':113C ''choos'':18C,132C ''comput'':67C,76C ''consist'':51C ''consult'':147C ''context'':118C ''cost'':68C ''cross'':45C,91C,106C,125C,160C ''decis'':62C ''depend'':114C ''detail'':142C ''even'':33C ''exact'':117C ''fold'':5C,50C,71C,82C,97C,137C ''follow'':66C ''go'':34C ''good'':20C ''guid'':60C ''higher'':86C ''href'':149C ''judgement'':21C ''kohavi'':155C ''leav'':42C ''leave-one-out'':41C ''might'':144C ''need'':78C ''nofollow'':154C ''noth'':7C ''number'':3C ''observ'':56C ''one'':43C,55C ''p'':1C,110C,139C ''paper'':158C ''paramet'':16C ''probabl'':129C ''realli'':10C ''rel'':153C ''result'':93C,108C ''smaller'':101C ''so-cal'':38C ''typic'':22C,64C ''use'':23C,73C,84C,99C ''valid'':46C,92C,107C,126C,161C ''valu'':24C ''varianc'':79C,88C ''want'':122C,130C,145C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8361, 1, '2015-10-09 06:11:18.74', 1, 61, 'What is the difference between SVM and GMM classifier', '&lt;p&gt;What is the difference between support vector machine and Gaussian mixture model classifiers?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':27C ''classifi'':9A,26C ''differ'':4A,17C ''gaussian'':23C ''gmm'':8A ''learn'':12B ''machin'':11B,21C ''machine-learn'':10B ''mixtur'':24C ''model'':25C ''p'':13C ''support'':19C ''svm'':6A ''vector'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8356, 1, '2015-10-08 17:54:38.543', 0, 23, 'Extracting attribute-value from fuzzy text', '&lt;p&gt;I''m using an OCR library to extract products specifications from images. I''m first focusing on notebooks.For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Processor 
Processor model: Intel N3540 
Clock speed: 2.16 GHz 
Memory 
Internal: 4 GB 
Hard disk 
Capacity: 1 TB
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;or:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;TOSHIBA 
SATELLITE C50-5302 
PENTIUM 
TOSHIBA  
DISPLAY 15.6 
4GB 
DDR3 
500 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The OCR is not perfect and sometimes what would be C10 ends up being CIO and other similar things. &lt;/p&gt;

&lt;p&gt;I''d like to extract the attribute-value pairs but I don''t know how to approach this problem.&lt;/p&gt;

&lt;p&gt;I''m thinking about building a file with all the notebooks and microprocessors I can get(because brand, memory and hard drive capacity are pretty limited) and then use a NLP library to extract the entities from text. The problem also is that sometimes there are spelling errors so it''s not as easy as comparing the exact values.&lt;/p&gt;

&lt;p&gt;How would you approach this problem?&lt;/p&gt;
', 'nlp  named-entity-recognition', NULL, '''-5302'':64C ''/code'':54C,72C ''/p'':33C,58C,94C,116C,177C,185C ''/pre'':55C,73C ''1'':52C ''15.6'':68C ''2.16'':43C ''4'':47C ''4gb'':69C ''500'':71C ''also'':158C ''approach'':113C,182C ''attribut'':3A,103C ''attribute-valu'':2A,102C ''brand'':135C ''build'':122C ''c10'':85C ''c50'':63C ''capac'':51C,140C ''cio'':89C ''clock'':41C ''code'':35C,60C ''compar'':173C ''d'':97C ''ddr3'':70C ''disk'':50C ''display'':67C ''drive'':139C ''easi'':171C ''end'':86C ''entiti'':11B,153C ''error'':165C ''exact'':175C ''exampl'':32C ''extract'':1A,21C,100C,151C ''file'':124C ''first'':28C ''focus'':29C ''fuzzi'':6A ''gb'':48C ''get'':133C ''ghz'':44C ''hard'':49C,138C ''imag'':25C ''intel'':39C ''intern'':46C ''know'':110C ''librari'':19C,149C ''like'':98C ''limit'':143C ''m'':15C,27C,119C ''memori'':45C,136C ''microprocessor'':130C ''model'':38C ''n3540'':40C ''name'':10B ''named-entity-recognit'':9B ''nlp'':8B,148C ''notebook'':128C ''notebooks.for'':31C ''ocr'':18C,76C ''p'':13C,56C,74C,95C,117C,178C ''pair'':105C ''pentium'':65C ''perfect'':79C ''pre'':34C,59C ''pretti'':142C ''problem'':115C,157C,184C ''processor'':36C,37C ''product'':22C ''recognit'':12B ''satellit'':62C ''similar'':92C ''sometim'':81C,161C ''specif'':23C ''speed'':42C ''spell'':164C ''tb'':53C ''text'':7A,155C ''thing'':93C ''think'':120C ''toshiba'':61C,66C ''use'':16C,146C ''valu'':4A,104C,176C ''would'':83C,180C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8357, 1, '2015-10-09 00:02:34.463', 5, 136, 'How scientists come up with the correct Hidden Markov Model to use?', '&lt;p&gt;I understand how a Hidden Markov Model is used in genomic sequences, such as finding a gene. But I don''t understand how they they came up with a particular Markov model. I mean, how many states should the model have? How many possible transitions? Should the model have a loop?&lt;/p&gt;

&lt;p&gt;How would they know that their model is optimal? &lt;/p&gt;

&lt;p&gt;Do they imagine, say 10 different models, benchmark those 10 models and publish the best one?&lt;/p&gt;
', 'machine-learning  markov', NULL, '''/p'':69C,80C,98C ''10'':86C,91C ''benchmark'':89C ''best'':96C ''came'':43C ''come'':3A ''correct'':7A ''differ'':87C ''find'':32C ''gene'':34C ''genom'':28C ''hidden'':8A,22C ''imagin'':84C ''know'':74C ''learn'':15B ''loop'':68C ''machin'':14B ''machine-learn'':13B ''mani'':53C,60C ''markov'':9A,16B,23C,48C ''mean'':51C ''model'':10A,24C,49C,57C,65C,77C,88C,92C ''one'':97C ''optim'':79C ''p'':17C,70C,81C ''particular'':47C ''possibl'':61C ''publish'':94C ''say'':85C ''scientist'':2A ''sequenc'':29C ''state'':54C ''transit'':62C ''understand'':19C,39C ''use'':12A,26C ''would'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8358, 1, '2015-10-09 01:54:16.8', 2, 51, 'Prohibitive size of random forest when saved to disk', '&lt;p&gt;When saved to disk using cPickle: &lt;a href="http://stackoverflow.com/questions/20662023/save-python-random-forest-model-to-file"&gt;http://stackoverflow.com/questions/20662023/save-python-random-forest-model-to-file&lt;/a&gt;, my random forest is 6.57 GB. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;with open(''rforest.cpickle'', ''wb'') as f:
    cPickle.dump(rforest, f)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to use the forest itself to make predictions via a python API hosted on Heroku -- of course, that file size is unacceptable. &lt;/p&gt;

&lt;p&gt;Why is the file size so large? There are 500 trees in the ensemble -- all I want to save are the completed trees themselves, since they will be used as prediction. Is it the actual nodes and edges that compose each of the 500 trees that requires nearly 7 GB of space on disk?&lt;/p&gt;

&lt;p&gt;I used scikitlearn''s randomforestregressor:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def buildForest(self, X_train, y_train):
    rf = RandomForestRegressor(n_estimators=500, verbose=1)
    rf.fit_transform(X_train, y_train)
    return rf
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Also, if there is a better way to make my model accessible via API, that would also be good to know.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Update: I reduced it to 100 trees without losing much predictive power, and so now the saved size is 1.3 GB -- much more manageable, but still not great.&lt;/strong&gt;&lt;/p&gt;
', 'python  random-forest', NULL, '''/code'':47C,162C ''/p'':35C,74C,130C,137C,186C,218C ''/pre'':48C,163C ''/questions/20662023/save-python-random-forest-model-to-file'':25C ''/questions/20662023/save-python-random-forest-model-to-file&lt;/a&gt;,'':28C ''/strong'':217C ''1'':153C ''1.3'':208C ''100'':194C ''500'':85C,119C,151C ''6.57'':33C ''7'':124C ''access'':176C ''actual'':110C ''also'':165C,181C ''api'':63C,178C ''better'':170C ''buildforest'':141C ''code'':37C,139C ''complet'':97C ''compos'':115C ''cours'':68C ''cpickl'':20C ''cpickle.dump'':44C ''def'':140C ''disk'':9A,18C,129C ''edg'':113C ''ensembl'':89C ''estim'':150C ''f'':43C,46C ''file'':70C,79C ''forest'':5A,13B,31C,55C ''gb'':34C,125C,209C ''good'':183C ''great'':216C ''heroku'':66C ''host'':64C ''href'':22C ''know'':185C ''larg'':82C ''lose'':197C ''make'':58C,173C ''manag'':212C ''model'':175C ''much'':198C,210C ''n'':149C ''near'':123C ''node'':111C ''open'':39C ''p'':14C,49C,75C,131C,164C,187C ''power'':200C ''pre'':36C,138C ''predict'':59C,106C,199C ''prohibit'':1A ''python'':10B,62C ''random'':4A,12B,30C ''random-forest'':11B ''randomforestregressor'':136C,148C ''reduc'':191C ''requir'':122C ''return'':160C ''rf'':147C,161C ''rf.fit'':154C ''rforest'':45C ''rforest.cpickle'':40C ''save'':7A,16C,94C,205C ''scikitlearn'':134C ''self'':142C ''sinc'':100C ''size'':2A,71C,80C,206C ''space'':127C ''stackoverflow.com'':24C,27C ''stackoverflow.com/questions/20662023/save-python-random-forest-model-to-file'':23C ''stackoverflow.com/questions/20662023/save-python-random-forest-model-to-file&lt;/a&gt;,'':26C ''still'':214C ''strong'':188C ''train'':144C,146C,157C,159C ''transform'':155C ''tree'':86C,98C,120C,195C ''unaccept'':73C ''updat'':189C ''use'':19C,53C,104C,133C ''verbos'':152C ''via'':60C,177C ''want'':51C,92C ''way'':171C ''wb'':41C ''without'':196C ''would'':180C ''x'':143C,156C ''y'':145C,158C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9383, 1, '2015-12-14 21:58:59.8', 2, 14, 'Do I need to include a squared and linear variable in a random forest to achieve a parabolic effect?', '&lt;p&gt;I''m using a random forest model. One of my independent variables almost certainly has a parabolic effect on the dependent variable. In a linear regression, I would include the variable as both linear and squared in order to capture this effect. Should I do the same in a random forest?&lt;/p&gt;

&lt;p&gt;By extension, same question for variables that have logistic effects (age, for example)?&lt;/p&gt;
', 'machine-learning  random-forest  decision-trees', NULL, '''/p'':81C,96C ''achiev'':16A ''age'':93C ''almost'':42C ''captur'':69C ''certain'':43C ''decis'':27B ''decision-tre'':26B ''depend'':50C ''effect'':19A,47C,71C,92C ''exampl'':95C ''extens'':84C ''forest'':14A,25B,35C,80C ''includ'':5A,58C ''independ'':40C ''learn'':22B ''linear'':9A,54C,63C ''logist'':91C ''m'':31C ''machin'':21B ''machine-learn'':20B ''model'':36C ''need'':3A ''one'':37C ''order'':67C ''p'':29C,82C ''parabol'':18A,46C ''question'':86C ''random'':13A,24B,34C,79C ''random-forest'':23B ''regress'':55C ''squar'':7A,65C ''tree'':28B ''use'':32C ''variabl'':10A,41C,51C,60C,88C ''would'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9526, 2, '2015-12-26 04:17:24.98', 1, NULL, NULL, '&lt;p&gt;Certainly. A part of topic modeling output is typically some vector for each document describing it as a distribution over the topics in your model. You can use these as continuous-valued predictors for a classifier trained with the labels you have. I''ve done this before, but, depending on the domain characteristics of your problem, it may not be the best approach, but it is certainly a testable one!&lt;/p&gt;
', NULL, NULL, '''/p'':73C ''approach'':65C ''best'':64C ''certain'':2C,69C ''characterist'':55C ''classifi'':38C ''continu'':33C ''continuous-valu'':32C ''depend'':51C ''describ'':16C ''distribut'':20C ''document'':15C ''domain'':54C ''done'':47C ''label'':42C ''may'':60C ''model'':7C,26C ''one'':72C ''output'':8C ''p'':1C ''part'':4C ''predictor'':35C ''problem'':58C ''testabl'':71C ''topic'':6C,23C ''train'':39C ''typic'':10C ''use'':29C ''valu'':34C ''ve'':46C ''vector'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8359, 1, '2015-10-09 03:44:07.94', 1, 81, 'Clustering with constraints', '&lt;p&gt;I have a dataset of this form:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;chrX posX labelX&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;where &lt;code&gt;chrX&lt;/code&gt; refers to the chromosome number, &lt;code&gt;posX&lt;/code&gt; refers to location, and &lt;code&gt;labelX&lt;/code&gt; is a categorical variable with 3 labels.
For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;chr1 3223 1
chr1 3200 2
chr1 3100  1
chr1 1000 2
chr2 1000 1
chr1 3210 3
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to be cluster these data with certain constraints (thus, this is no longer what ''clustering'' typically is), such that, for each chromosome, the sites which are within, say $\pm 30$ positions are together, the constraint being they should have unique labels.&lt;/p&gt;

&lt;p&gt;For example, an expected output would be:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;chr1 : 3223(1) 3200(2) 3210(3)&lt;/code&gt; Notice how sites like &lt;code&gt;chr1 3100 1&lt;/code&gt; are not part of the ''cluster''.&lt;/p&gt;

&lt;p&gt;One approach would be to simply calculate pairwise differences between all such sites, and grow the cluster taking care of unique labels.&lt;/p&gt;

&lt;p&gt;Are there more elegant approaches to solve such problems?&lt;/p&gt;
', 'machine-learning  clustering  categorical-data', NULL, '''/code'':25C,31C,39C,46C,77C,142C,151C ''/p'':19C,26C,56C,123C,132C,158C,182C,193C ''/pre'':78C ''1'':61C,67C,73C,137C,150C ''1000'':69C,72C ''2'':64C,70C,139C ''3'':52C,76C,141C ''30'':111C ''3100'':66C,149C ''3200'':63C,138C ''3210'':75C,140C ''3223'':60C,136C ''approach'':161C,188C ''calcul'':166C ''care'':178C ''categor'':9B,49C ''categorical-data'':8B ''certain'':88C ''chr1'':59C,62C,65C,68C,74C,135C,148C ''chr2'':71C ''chromosom'':35C,103C ''chrx'':22C,30C ''cluster'':1A,7B,84C,96C,157C,176C ''code'':21C,29C,37C,44C,58C,134C,147C ''constraint'':3A,89C,116C ''data'':10B,86C ''dataset'':15C ''differ'':168C ''eleg'':187C ''exampl'':55C,126C ''expect'':128C ''form'':18C ''grow'':174C ''label'':53C,122C,181C ''labelx'':24C,45C ''learn'':6B ''like'':146C ''locat'':42C ''longer'':94C ''machin'':5B ''machine-learn'':4B ''notic'':143C ''number'':36C ''one'':160C ''output'':129C ''p'':11C,20C,27C,79C,124C,133C,159C,183C ''pairwis'':167C ''part'':154C ''pm'':110C ''posit'':112C ''posx'':23C,38C ''pre'':57C ''problem'':192C ''refer'':32C,40C ''say'':109C ''simpli'':165C ''site'':105C,145C,172C ''solv'':190C ''take'':177C ''thus'':90C ''togeth'':114C ''typic'':97C ''uniqu'':121C,180C ''variabl'':50C ''want'':81C ''within'':108C ''would'':130C,162C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8368, 2, '2015-10-09 14:18:23.56', 3, NULL, NULL, '&lt;p&gt;I''m familiar with three main approaches:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;A priori. You might know that there are four base pairs to pick from, and so allow the HMM to have four states. Or you might know that English has 44 phonemes, and so have 44 states for the hidden phoneme layer in a voice recognition model.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Estimation. The number of states can often be estimated beforehand, perhaps by simple clustering on the observed features of the HMM. If the HMM transition matrix is triangular (which is often the case in failure prediction), the number of states determines the shape of the distribution of total time from the start state to the end state.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Optimization. Like you suggest, either many models are created and fit and the best model selected. One could also adapt the methodology that learns the HMM to allow the model to add or discard states as needed.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':61C,122C,163C ''/ol'':164C ''/p'':9C,60C,121C,162C ''44'':43C,48C ''adapt'':144C ''add'':156C ''allow'':29C,152C ''also'':143C ''approach'':8C ''base'':22C ''beforehand'':73C ''best'':138C ''case'':96C ''cluster'':77C ''could'':142C ''creat'':133C ''determin'':104C ''discard'':158C ''distribut'':109C ''either'':129C ''end'':119C ''english'':41C ''estim'':64C,72C ''failur'':98C ''familiar'':4C ''featur'':81C ''fit'':135C ''four'':21C,34C ''hidden'':52C ''hmm'':31C,84C,87C,150C ''know'':17C,39C ''layer'':54C ''learn'':148C ''li'':11C,62C,123C ''like'':126C ''m'':3C ''main'':7C ''mani'':130C ''matrix'':89C ''methodolog'':146C ''might'':16C,38C ''model'':59C,131C,139C,154C ''need'':161C ''number'':66C,101C ''observ'':80C ''often'':70C,94C ''ol'':10C ''one'':141C ''optim'':125C ''p'':1C,12C,63C,124C ''pair'':23C ''perhap'':74C ''phonem'':44C,53C ''pick'':25C ''predict'':99C ''priori'':14C ''recognit'':58C ''select'':140C ''shape'':106C ''simpl'':76C ''start'':115C ''state'':35C,49C,68C,103C,116C,120C,159C ''suggest'':128C ''three'':6C ''time'':112C ''total'':111C ''transit'':88C ''triangular'':91C ''voic'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8369, 1, '2015-10-09 16:18:05.393', 0, 45, 'Can I consider this pattern of data as a linear and use parametric multiple linear regression?', '&lt;p&gt;In the data, there are 355 observations including one continuous dependent variable (Y: ranges from 15-55) and 12 independent variables (continuous, categorical, and ordinal). The X1 (2 levels) and X6 (3 levels) are considered as categorical variables. Here are some questions that I have:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Can I assume that all the coefficients (except X1 and X6 which are categorical) are linear with respect to Y?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Can I consider X5 as continuous variable; however, it is ordinal and ranges from (1-7)?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Can I get the X7 (year) as continuous variable; however, itâs ordinal and rages from 2002-2006 (In fact, year of data per se does not improve the response; it is the other factors occurring in the same time period which result in improvements and we donât know those factors), does this approach seem logical?. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;In general if I use different transformations on independent variables such as log, squared, square root, and inverse, do I need to standardize the data also?&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Here is the scatter plot:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/GYRpa.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/GYRpa.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Any feedback and insights would be highly appreciated. Thank you&lt;/p&gt;
', 'data-mining', NULL, '''-2006'':131C ''-55'':37C ''-7'':109C ''/a'':229C ''/gyrpa.png'':216C,223C ''/li'':91C,111C,172C,202C ''/ol'':203C ''/p'':66C,90C,110C,171C,201C,210C,230C,242C ''1'':108C ''12'':39C ''15'':36C ''2'':48C ''2002'':130C ''3'':52C ''355'':26C ''also'':200C ''alt'':224C ''appreci'':239C ''approach'':168C ''assum'':72C ''categor'':43C,57C,83C ''coeffici'':76C ''consid'':3A,55C,96C ''continu'':30C,42C,99C,121C ''data'':7A,18B,23C,136C,199C ''data-min'':17B ''depend'':31C ''descript'':227C ''differ'':180C ''donâ'':161C ''enter'':225C ''except'':77C ''fact'':133C ''factor'':148C,165C ''feedback'':233C ''general'':176C ''get'':116C ''high'':238C ''howev'':101C,123C ''href'':213C ''i.stack.imgur.com'':215C,222C ''i.stack.imgur.com/gyrpa.png'':214C,221C ''imag'':226C ''img'':219C ''improv'':141C,158C ''includ'':28C ''independ'':40C,183C ''insight'':235C ''invers'':192C ''itâ'':124C ''know'':163C ''level'':49C,53C ''li'':68C,92C,112C,173C ''linear'':10A,15A,85C ''log'':187C ''logic'':170C ''mine'':19B ''multipl'':14A ''need'':195C ''nofollow'':218C ''observ'':27C ''occur'':149C ''ol'':67C ''one'':29C ''ordin'':45C,104C,126C ''p'':20C,69C,93C,113C,174C,204C,211C,231C ''parametr'':13A ''pattern'':5A ''per'':137C ''period'':154C ''plot'':209C ''question'':62C ''rage'':128C ''rang'':34C,106C ''regress'':16A ''rel'':217C ''respect'':87C ''respons'':143C ''result'':156C ''root'':190C ''scatter'':208C ''se'':138C ''seem'':169C ''squar'':188C,189C ''src'':220C ''standard'':197C ''thank'':240C ''time'':153C ''transform'':181C ''use'':12A,179C ''variabl'':32C,41C,58C,100C,122C,184C ''would'':236C ''x1'':47C,78C ''x5'':97C ''x6'':51C,80C ''x7'':118C ''y'':33C,89C ''year'':119C,134C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8372, 2, '2015-10-09 18:38:53.803', 2, NULL, NULL, '&lt;p&gt;I don''t think "can" is the right question to ask; it''s not going to give you a syntax error. The right question is "what could go wrong?". Any modeling technique will have assumptions that may be broken, and knowing how those assumptions impact the results will help you know what to look for (and how much to care when those assumptions are broken).&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;The best test of whether or not linearity is appropriate is whether the residuals are white or structured. For example, it looks like X9 might have a nonlinear relationship with Y. But that might be an artifact of the interaction between X9 and other variables, especially categorical variables. Fit your full model, then plot the residuals against X9 and see what it looks like.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Treating it as continuous won''t cause serious problems, but you might want to think about what this implies. Is the relationship between 1 and 2 in the same direction and half the strength as the relationship between 2 and 4? If not, you might want to transform this to a scale where you &lt;em&gt;do&lt;/em&gt; think the differences are linear.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Same as 2, except it''s even more reasonable to see time as linear.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Standardization is not necessary for most linear regression techniques, as they contain their own standardization. The primary exception is techniques that use &lt;a href="https://en.wikipedia.org/wiki/Regularization_(mathematics)" rel="nofollow"&gt;regularization&lt;/a&gt;, where the scale of the parameters is relevant.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;It''s also worth pointing out that multivariate linear relationships, while they can capture general trends well, are very poor at capturing logical trends. For example, looking at X3 and X4, it could very well be that there are rules like Y&gt;X3 and Y&gt;X4 in place, which is hinted at but not captured by linear regression.&lt;/p&gt;
', NULL, NULL, '''/a'':253C ''/em'':195C ''/li'':136C,202C,220C,263C ''/ol'':264C ''/p'':67C,135C,201C,219C,262C,324C ''/wiki/regularization_(mathematics)'':249C ''1'':162C ''2'':164C,177C,207C ''4'':179C ''also'':268C ''appropri'':80C ''artifact'':107C ''ask'':12C ''assumpt'':36C,45C,64C ''best'':72C ''broken'':40C,66C ''captur'':279C,287C,320C ''care'':61C ''categor'':117C ''caus'':145C ''contain'':234C ''continu'':142C ''could'':28C,298C ''differ'':198C ''direct'':168C ''em'':193C ''en.wikipedia.org'':248C ''en.wikipedia.org/wiki/regularization_(mathematics)'':247C ''error'':22C ''especi'':116C ''even'':211C ''exampl'':90C,291C ''except'':208C,240C ''fit'':119C ''full'':121C ''general'':280C ''give'':18C ''go'':16C,29C ''half'':170C ''help'':50C ''hint'':316C ''href'':246C ''impact'':46C ''impli'':157C ''interact'':110C ''know'':42C,52C ''li'':69C,137C,203C,221C ''like'':93C,134C,306C ''linear'':78C,200C,218C,229C,274C,322C ''logic'':288C ''look'':55C,92C,133C,292C ''may'':38C ''might'':95C,104C,150C,183C ''model'':32C,122C ''much'':59C ''multivari'':273C ''necessari'':226C ''nofollow'':251C ''nonlinear'':98C ''ol'':68C ''p'':1C,70C,138C,204C,222C,265C ''paramet'':259C ''place'':313C ''plot'':124C ''point'':270C ''poor'':285C ''primari'':239C ''problem'':147C ''question'':10C,25C ''reason'':213C ''regress'':230C,323C ''regular'':252C ''rel'':250C ''relationship'':99C,160C,175C,275C ''relev'':261C ''residu'':84C,126C ''result'':48C ''right'':9C,24C ''rule'':305C ''scale'':190C,256C ''see'':130C,215C ''serious'':146C ''standard'':223C,237C ''strength'':172C ''structur'':88C ''syntax'':21C ''techniqu'':33C,231C,242C ''test'':73C ''think'':5C,153C,196C ''time'':216C ''transform'':186C ''treat'':139C ''trend'':281C,289C ''use'':244C ''variabl'':115C,118C ''want'':151C,184C ''well'':282C,300C ''whether'':75C,82C ''white'':86C ''won'':143C ''worth'':269C ''wrong'':30C ''x3'':294C,308C ''x4'':296C,311C ''x9'':94C,112C,128C ''y'':101C,307C,310C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8373, 1, '2015-10-10 06:14:50.927', 2, 56, 'Fuzzy Cognitive Maps', '&lt;p&gt;I am working on a project for prediction, using Fuzzy Cognitive Maps. I am new to data mining and want to learn about Fuzzy Cognitive Maps and its implementation. I also want to know what tools are required for this. I am beginner in this domain, so I need a little help to get started. I would be very grateful for help with some good resources and advice.&lt;/p&gt;
', 'machine-learning  data-mining', NULL, '''/p'':79C ''advic'':78C ''also'':41C ''beginn'':53C ''cognit'':2A,21C,35C ''data'':8B,27C ''data-min'':7B ''domain'':56C ''fuzzi'':1A,20C,34C ''get'':64C ''good'':75C ''grate'':70C ''help'':62C,72C ''implement'':39C ''know'':44C ''learn'':6B,32C ''littl'':61C ''machin'':5B ''machine-learn'':4B ''map'':3A,22C,36C ''mine'':9B,28C ''need'':59C ''new'':25C ''p'':10C ''predict'':18C ''project'':16C ''requir'':48C ''resourc'':76C ''start'':65C ''tool'':46C ''use'':19C ''want'':30C,42C ''work'':13C ''would'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8381, 1, '2015-10-11 03:41:00.94', 1, 202, 'How to do Machine Learning the right way?', '&lt;p&gt;I have a basic understanding about Machine Learning in general. My question is how it is done in the practical applications of it.&lt;/p&gt;

&lt;p&gt;If I take the following definition of ML&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;A computer program is said to learn from experience E with respect to
  some class of tasks T and performance measure P, if its performance at
  tasks in T, as measured by P, improves with experience E.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;It talks about Experience E. What I understand from the above is the more data you give to the algorithm E increases, and that in turn increases P. &lt;/p&gt;

&lt;p&gt;Now let''s consider a scenario where you build a decision tree model from 10,000 data rows available. Now I have the model, so can I say that my model has learned and just stop there!(use that model for prediction from that point on-wards forever)? &lt;/p&gt;

&lt;p&gt;According to the definition, I need to feed more data so the experience increases, and in turn I get a performance improvement. &lt;/p&gt;

&lt;p&gt;So is Machine Learning a continuous process so that You cannot build the model and just stop there. Do we need to feed more data to the algorithm time by time and improve the model so that the model actually LEARNS? &lt;/p&gt;
', 'machine-learning  data-mining  predictive-modeling', NULL, '''/blockquote'':93C ''/p'':42C,52C,92C,123C,174C,198C,241C ''000'':140C ''10'':139C ''accord'':176C ''actual'':239C ''algorithm'':114C,227C ''applic'':39C ''avail'':143C ''basic'':22C ''blockquot'':53C ''build'':133C,211C ''cannot'':210C ''class'':69C ''comput'':56C ''consid'':128C ''continu'':205C ''data'':13B,109C,141C,185C,224C ''data-min'':12B ''decis'':135C ''definit'':49C,179C ''done'':35C ''e'':64C,91C,99C,115C ''experi'':63C,90C,98C,188C ''feed'':183C,222C ''follow'':48C ''forev'':173C ''general'':28C ''get'':194C ''give'':111C ''improv'':88C,197C,232C ''increas'':116C,121C,189C ''learn'':5A,11B,26C,61C,157C,203C,240C ''let'':126C ''machin'':4A,10B,25C,202C ''machine-learn'':9B ''measur'':75C,85C ''mine'':14B ''ml'':51C ''model'':17B,137C,148C,155C,164C,213C,234C,238C ''need'':181C,220C ''on-ward'':170C ''p'':18C,43C,54C,76C,87C,94C,122C,124C,175C,199C ''perform'':74C,79C,196C ''point'':169C ''practic'':38C ''predict'':16B,166C ''predictive-model'':15B ''process'':206C ''program'':57C ''question'':30C ''respect'':66C ''right'':7A ''row'':142C ''said'':59C ''say'':152C ''scenario'':130C ''stop'':160C,216C ''take'':46C ''talk'':96C ''task'':71C,81C ''time'':228C,230C ''tree'':136C ''turn'':120C,192C ''understand'':23C,102C ''use'':162C ''ward'':172C ''way'':8A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8384, 1, '2015-10-11 08:25:59.45', 1, 73, 'Examples of the Three V''s of Big Data?', '&lt;p&gt;What are some examples of the Three V''s of Big Data? The three V''s stand for: volume, velocity, variety.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Reference:&lt;/strong&gt;&lt;br&gt;
Three V''s of Big Data, provided by Norwegian University of Science and Technology.&lt;br&gt;
&lt;a href="https://www.ntnu.edu/ime/bigdata/what-is" rel="nofollow"&gt;https://www.ntnu.edu/ime/bigdata/what-is&lt;/a&gt;  &lt;/p&gt;
', 'bigdata', NULL, '''/ime/bigdata/what-is'':58C ''/ime/bigdata/what-is&lt;/a&gt;'':63C ''/p'':33C,64C ''/strong'':37C ''big'':8A,22C,43C ''bigdata'':10B ''br'':38C,53C ''data'':9A,23C,44C ''exampl'':1A,15C ''href'':55C ''nofollow'':60C ''norwegian'':47C ''p'':11C,34C ''provid'':45C ''refer'':36C ''rel'':59C ''scienc'':50C ''stand'':28C ''strong'':35C ''technolog'':52C ''three'':4A,18C,25C,39C ''univers'':48C ''v'':5A,19C,26C,40C ''varieti'':32C ''veloc'':31C ''volum'':30C ''www.ntnu.edu'':57C,62C ''www.ntnu.edu/ime/bigdata/what-is'':56C ''www.ntnu.edu/ime/bigdata/what-is&lt;/a&gt;'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8374, 2, '2015-10-10 10:35:22.093', 1, NULL, NULL, '&lt;p&gt;The &lt;a href="https://indico.io/docs#sentiment" rel="nofollow"&gt;Indico.io API&lt;/a&gt; supports Spanish (and
Chinese (Mandarin), Japanese, Italian, French, Russian, Arabic, German, English).&lt;/p&gt;

&lt;p&gt;eg in Python:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;&amp;gt;&amp;gt; import indicoio
&amp;gt;&amp;gt;&amp;gt; indicoio.config.api_key = &amp;lt;YOUR_API_KEY&amp;gt;
&amp;gt;&amp;gt;&amp;gt; indicoio.sentiment("Â¡JamÃ¡s voy a usar esta maldita aplicaciÃ³n!  No funciona para nada.")
0.02919392219306888
&amp;gt;&amp;gt;&amp;gt; indicoio.sentiment("Â¡Es patrÃ³n!  La mejor que he visto.  Punto.")
0.8860221705630639
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If this isn''t your area, then that''s probably the easiest sort of solution to integrate.&lt;/p&gt;
', NULL, NULL, '''/a'':12C ''/code'':82C ''/docs#sentiment'':7C ''/p'':25C,30C,102C ''/pre'':83C ''0.02919392219306888'':66C ''0.8860221705630639'':81C ''api'':11C,45C ''aplicaciã'':60C ''arab'':22C ''area'':90C ''chines'':16C ''code'':32C ''easiest'':96C ''eg'':27C ''english'':24C ''es'':72C ''esta'':58C ''french'':20C ''funciona'':63C ''german'':23C ''gt'':33C,34C,35C,38C,39C,40C,47C,48C,49C,50C,67C,68C,69C ''href'':4C ''import'':36C ''indico.io'':6C,10C ''indico.io/docs#sentiment'':5C ''indicoio'':37C ''indicoio.config.api'':41C ''indicoio.sentiment'':51C,70C ''integr'':101C ''isn'':87C ''italian'':19C ''jamã'':53C ''japanes'':18C ''key'':42C,46C ''la'':75C ''lt'':43C ''maldita'':59C ''mandarin'':17C ''mejor'':76C ''n'':61C,74C ''nada'':65C ''nofollow'':9C ''p'':1C,26C,84C ''para'':64C ''patrã'':73C ''pre'':31C ''probabl'':94C ''punto'':80C ''python'':29C ''que'':77C ''rel'':8C ''russian'':21C ''solut'':99C ''sort'':97C ''spanish'':14C ''support'':13C ''usar'':57C ''visto'':79C ''voy'':55C ''â'':52C,71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8376, 2, '2015-10-10 13:46:02.047', 0, NULL, NULL, '&lt;p&gt;Thanks Matthew.
There is a confusion to me, you mean weâll build the model through multiple linear regression method and then the residuals will be checked VS the significant factors to see if they are structureless. We shouldnât first check whether or not the variables (coefficients) are linear with respect to y.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Since the X9 and X5 were not statistically significant, shall I check the scatter plot of residuals vs those two?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;What are your insights about the scatter plot of residuals vs time, do you think it still needs any transformation? If yes, can you explain the reason?&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Here are the scatterplot of residuals VS significant variables and different Residuals plots vs Y to check if the normality assumptions were met. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/PzyHr.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/PzyHr.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/2pnmb.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/2pnmb.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Please let me know your feedback. On the other hand, can you explain a bit more what your last paragraph meant (note that both X3 and X4 are statistically significant)?
Thank you again.&lt;/p&gt;
', NULL, NULL, '''/2pnmb.png'':164C,171C ''/a'':157C,177C ''/li'':81C,112C ''/ol'':113C ''/p'':56C,80C,111C,138C,158C,178C,213C ''/pzyhr.jpg'':144C,151C ''alt'':152C,172C ''assumpt'':135C ''bit'':194C ''build'':14C ''check'':28C,43C,71C,131C ''coeffici'':49C ''confus'':7C ''descript'':155C,175C ''differ'':125C ''enter'':153C,173C ''explain'':108C,192C ''factor'':32C ''feedback'':185C ''first'':42C ''hand'':189C ''href'':141C,161C ''i.stack.imgur.com'':143C,150C,163C,170C ''i.stack.imgur.com/2pnmb.png'':162C,169C ''i.stack.imgur.com/pzyhr.jpg'':142C,149C ''imag'':154C,174C ''img'':147C,167C ''insight'':87C ''know'':183C ''last'':198C ''let'':181C ''li'':58C,82C ''linear'':19C,51C ''ll'':13C ''matthew'':3C ''mean'':11C ''meant'':200C ''met'':137C ''method'':21C ''model'':16C ''multipl'':18C ''need'':101C ''nofollow'':146C,166C ''normal'':134C ''note'':201C ''ol'':57C ''p'':1C,59C,83C,114C,139C,159C,179C ''paragraph'':199C ''pleas'':180C ''plot'':74C,91C,127C ''reason'':110C ''regress'':20C ''rel'':145C,165C ''residu'':25C,76C,93C,120C,126C ''respect'':53C ''scatter'':73C,90C ''scatterplot'':118C ''see'':34C ''shall'':69C ''shouldnâ'':40C ''signific'':31C,68C,122C,209C ''sinc'':60C ''src'':148C,168C ''statist'':67C,208C ''still'':100C ''structureless'':38C ''thank'':2C,210C ''think'':98C ''time'':95C ''transform'':103C ''two'':79C ''variabl'':48C,123C ''vs'':29C,77C,94C,121C,128C ''weâ'':12C ''whether'':44C ''x3'':204C ''x4'':206C ''x5'':64C ''x9'':62C ''y'':55C,129C ''yes'':105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8387, 2, '2015-10-11 13:13:20.747', 1, NULL, NULL, '&lt;p&gt;You could use &lt;a href="https://www.researchgate.net/profile/Elpiniki_Papageorgiou/publication/220199753_A_new_methodology_for_Decisions_in_Medical_Informatics_using_fuzzy_cognitive_maps_based_on_fuzzy_rule-extraction_techniques/links/53f4c17f0cf2888a74911729.pdf" rel="nofollow"&gt;competitive fuzzy cognitive maps&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':16C ''/p'':17C ''/profile/elpiniki_papageorgiou/publication/220199753_a_new_methodology_for_decisions_in_medical_informatics_using_fuzzy_cognitive_maps_based_on_fuzzy_rule-extraction_techniques/links/53f4c17f0cf2888a74911729.pdf'':9C ''cognit'':14C ''competit'':12C ''could'':3C ''fuzzi'':13C ''href'':6C ''map'':15C ''nofollow'':11C ''p'':1C ''rel'':10C ''use'':4C ''www.researchgate.net'':8C ''www.researchgate.net/profile/elpiniki_papageorgiou/publication/220199753_a_new_methodology_for_decisions_in_medical_informatics_using_fuzzy_cognitive_maps_based_on_fuzzy_rule-extraction_techniques/links/53f4c17f0cf2888a74911729.pdf'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8397, 2, '2015-10-12 08:36:29.197', 1, NULL, NULL, '&lt;p&gt;I don''t think k-means is the correct approach to this problem. You should look at doing topic modelling using latent semantic indexing.&lt;/p&gt;
', NULL, NULL, '''/p'':27C ''approach'':12C ''correct'':11C ''index'':26C ''k'':7C ''k-mean'':6C ''latent'':24C ''look'':18C ''mean'':8C ''model'':22C ''p'':1C ''problem'':15C ''semant'':25C ''think'':5C ''topic'':21C ''use'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8736, 2, '2015-11-06 07:54:07.367', 0, NULL, NULL, '&lt;p&gt;This is one of the nice problem where the scope might vary from an homework assignment to a Google size project.&lt;/p&gt;

&lt;p&gt;Indeed, you can start with co-occurrence of the words (e.g., conditional probability). 
You will discover quickly that you get the list of &lt;a href="https://en.wikipedia.org/wiki/Stop_words" rel="nofollow" title="stop words"&gt;stop words&lt;/a&gt; as related the most of the words simply because they are very popular.
Using the lift of conditional probability will take care of the stop words but will make the relation prone to error in small number (most of your cases).
You might try &lt;a href="https://en.wikipedia.org/wiki/Jaccard_index" rel="nofollow" title="Jaccard"&gt;Jacard&lt;/a&gt; but since it is symmetric there will be many relations it won''t find. &lt;/p&gt;

&lt;p&gt;Then you might consider relations that appear only in short distance from the base word. You can (and should) consider relations base on general corpus''s (e.g., Wikipedia) and user specific (e.g., his emails).&lt;/p&gt;

&lt;p&gt;Very shortly you will have plenty of relatedness measures, when all the measures are good and have some advantage over the others.&lt;/p&gt;

&lt;p&gt;In order to combine such measures, I like to reduce the problem into a classification problem.&lt;/p&gt;

&lt;p&gt;You should build a data set of paris of words and label them as "is related".
In order to build a large labeled dataset you can:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Use sources of known related words (e.g., good old Wikipedia categories) for positives&lt;/li&gt;
&lt;li&gt;Most of the word not known as related are not related.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Then use all the measures you have as features of the pairs.
Now you are in the domain of supervised classification problem.
Build a classifier on the data set, evaluated according to your needs and get a similarity measure that fits your needs.&lt;/p&gt;
', NULL, NULL, '''/a'':61C,116C ''/li'':253C,266C ''/p'':23C,131C,167C,191C,209C,237C,312C ''/ul'':267C ''/wiki/jaccard_index'':110C ''/wiki/stop_words'':53C ''accord'':299C ''advantag'':187C ''appear'':139C ''assign'':17C ''base'':146C,154C ''build'':213C,230C,291C ''care'':83C ''case'':102C ''categori'':250C ''classif'':207C,289C ''classifi'':293C ''co'':31C ''co-occurr'':30C ''combin'':196C ''condit'':37C,79C ''consid'':136C,152C ''corpus'':157C ''data'':215C,296C ''dataset'':234C ''discov'':41C ''distanc'':143C ''domain'':286C ''e.g'':36C,159C,164C,246C ''email'':166C ''en.wikipedia.org'':52C,109C ''en.wikipedia.org/wiki/jaccard_index'':108C ''en.wikipedia.org/wiki/stop_words'':51C ''error'':95C ''evalu'':298C ''featur'':277C ''find'':130C ''fit'':309C ''general'':156C ''get'':45C,304C ''good'':183C,247C ''googl'':20C ''homework'':16C ''href'':50C,107C ''inde'':25C ''jacard'':115C ''jaccard'':114C ''known'':243C,260C ''label'':222C,233C ''larg'':232C ''li'':239C,254C ''lift'':77C ''like'':200C ''list'':47C ''make'':90C ''mani'':125C ''measur'':177C,181C,198C,273C,307C ''might'':12C,104C,135C ''need'':302C,311C ''nice'':7C ''nofollow'':55C,112C ''number'':98C ''occurr'':32C ''old'':248C ''one'':4C ''order'':194C,228C ''other'':190C ''p'':1C,24C,132C,168C,192C,210C,268C ''pair'':280C ''pari'':218C ''plenti'':174C ''popular'':74C ''posit'':252C ''probabl'':38C,80C ''problem'':8C,204C,208C,290C ''project'':22C ''prone'':93C ''quick'':42C ''reduc'':202C ''rel'':54C,111C ''relat'':63C,92C,126C,137C,153C,226C,244C,262C,265C ''related'':176C ''scope'':11C ''set'':216C,297C ''short'':142C,170C ''similar'':306C ''simpli'':69C ''sinc'':118C ''size'':21C ''small'':97C ''sourc'':241C ''specif'':163C ''start'':28C ''stop'':57C,59C,86C ''supervis'':288C ''symmetr'':121C ''take'':82C ''titl'':56C,113C ''tri'':105C ''ul'':238C ''use'':75C,240C,270C ''user'':162C ''vari'':13C ''wikipedia'':160C,249C ''won'':128C ''word'':35C,58C,60C,68C,87C,147C,220C,245C,258C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8377, 2, '2015-10-10 18:12:44.31', 1, NULL, NULL, '&lt;p&gt;Stopwords may be part of the solution at some point, but not the key.  In any case for any major languages good lists of stop words exist, it should not be domain specific.&lt;/p&gt;

&lt;p&gt;I also don''t think that using TD-IDF alone is really correct.  There could be very rare (potentially garbage) words in poor quality strings.&lt;/p&gt;

&lt;p&gt;Instead of trying to guess which exact features are useful: I would start by creating a data set by randomly selecting some of the data and labeling them by hand (as &lt;code&gt;good&lt;/code&gt; or &lt;code&gt;bad&lt;/code&gt;, or on a scale from 0.0 to 1.0).  Then code something up that pulls out many features (length, number of words (tokens), spam score, whether it contains URLs or botish chars, detected language, whether it has a question mark, whether it has proper capitalisation).  Also don''t forget to include non-linguistic features that you may have, like country of the geoIP of the user that made the query, whether the user was logged in, how old the user''s account is.  So at this point you will have a massive table/CSV, and a smaller one with one extra column for the label you''ve added.&lt;/p&gt;

&lt;p&gt;Then train some machine learning package with those labeled examples to build a model that is accurate enough for you.  Then let that model run on the rest of the data.&lt;/p&gt;

&lt;p&gt;If you wish not to code too much, you could even just get those features into CSV form, and give them to the &lt;a href="https://cloud.google.com/prediction/docs/smart_autofill_add_on" rel="nofollow"&gt;Google Prediction API''s spreadsheet interface&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8379, 2, '2015-10-11 00:17:27.447', 0, NULL, NULL, '&lt;p&gt;In fact, I used additive method to calculate the X3 value (X3 is the summation of 21 binary variables) and multiplicative method to calculate X4 (X4 is the multiplication of 3 continues variables). I was wondering if this information will change the way of my calculation.&lt;/p&gt;

&lt;p&gt;Here are the residuals plot versus X7, X5, and X9 plus probability plot of residuals for the X7 (time or year). It can be discovered that the residuals of X7 in each time horizon is normal.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/IacoF.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/IacoF.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/T600s.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/T600s.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;However, I''m still confused about this point, you mean weâll build the model through multiple linear regression and then the residuals will be checked VS the significant or none-significant factors to see if they are structureless and follow the normal distribution. We shouldnât first check whether or not the variables (coefficients) are linear with respect to y.&lt;/p&gt;

&lt;p&gt;Thank you for your feedback.&lt;/p&gt;
', NULL, NULL, '''/a'':105C,125C ''/iacof.jpg'':92C,99C ''/p'':48C,86C,106C,126C,190C,197C ''/t600s.jpg'':112C,119C ''21'':18C ''3'':32C ''addit'':6C ''alt'':100C,120C ''binari'':19C ''build'':140C ''calcul'':9C,25C,47C ''chang'':42C ''check'':153C,177C ''coeffici'':183C ''confus'':132C ''continu'':33C ''descript'':103C,123C ''discov'':74C ''distribut'':172C ''enter'':101C,121C ''fact'':3C ''factor'':161C ''feedback'':196C ''first'':176C ''follow'':169C ''horizon'':83C ''howev'':128C ''href'':89C,109C ''i.stack.imgur.com'':91C,98C,111C,118C ''i.stack.imgur.com/iacof.jpg'':90C,97C ''i.stack.imgur.com/t600s.jpg'':110C,117C ''imag'':102C,122C ''img'':95C,115C ''inform'':40C ''linear'':145C,185C ''ll'':139C ''m'':130C ''mean'':137C ''method'':7C,23C ''model'':142C ''multipl'':22C,30C,144C ''nofollow'':94C,114C ''none'':159C ''none-signific'':158C ''normal'':85C,171C ''p'':1C,49C,87C,107C,127C,191C ''plot'':54C,62C ''plus'':60C ''point'':135C ''probabl'':61C ''regress'':146C ''rel'':93C,113C ''residu'':53C,64C,77C,150C ''respect'':187C ''see'':163C ''shouldnâ'':174C ''signific'':156C,160C ''src'':96C,116C ''still'':131C ''structureless'':167C ''summat'':16C ''thank'':192C ''time'':68C,82C ''use'':5C ''valu'':12C ''variabl'':20C,34C,182C ''versus'':55C ''vs'':154C ''way'':44C ''weâ'':138C ''whether'':178C ''wonder'':37C ''x3'':11C,13C ''x4'':26C,27C ''x5'':57C ''x7'':56C,67C,79C ''x9'':59C ''y'':189C ''year'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8380, 1, '2015-10-11 01:05:04.113', 2, 96, 'Is there a benefit to using hadoop with only one node?', '&lt;p&gt;I just started learning about Hadoop. From what I understand its primary strength is its ability to distribute a task across many nodes. Is there any benefit to using Hadoop with only a single node, other then the potential of expanding to more nodes in the future? What if you have no plans of ever having more then one node? Or is there an expectation that if you are going to use Hadoop you are also going to be setting up a cluster of many nodes?&lt;/p&gt;
', 'hadoop', NULL, '''/p'':100C ''abil'':29C ''across'':34C ''also'':89C ''benefit'':4A,40C ''cluster'':96C ''distribut'':31C ''ever'':68C ''expand'':54C ''expect'':78C ''futur'':60C ''go'':83C,90C ''hadoop'':7A,12B,19C,43C,86C ''learn'':17C ''mani'':35C,98C ''node'':11A,36C,48C,57C,73C,99C ''one'':10A,72C ''p'':13C ''plan'':66C ''potenti'':52C ''primari'':25C ''set'':93C ''singl'':47C ''start'':16C ''strength'':26C ''task'':33C ''understand'':23C ''use'':6A,42C,85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8385, 2, '2015-10-11 09:04:09.813', 1, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Volume:&lt;/strong&gt;&lt;br&gt;
Simply stated, big data is to big to work on one computer. This is a relative definition, as what can''t work on today''s computer will easily work on computers in the future.&lt;br&gt;
 - One Google search uses the computing power of the entire Apollo space mission.&lt;br&gt;
 - Excel used to hold up to 65k rows in a single spreadsheet. Now it holds over a million.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Velocity:&lt;/strong&gt;&lt;br&gt;
Data is coming in extremely fast. Traditional scientific research methods of a few hundred cases could take weeks, months or even years to analyze and publish.&lt;br&gt;
 - Iris flower data set&lt;br&gt;
 - Statistical Programming Language R&lt;br&gt;
 - Twitter Firehose (6,000 tweets per second)      &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Variety:&lt;/strong&gt;&lt;br&gt;
Big Data that is contained in one specific data type or does not fit well within the format of a relational database. This data often comes in the form of unstructured text.&lt;br&gt;
 - Estimated 80% of all enterprise data is unstructured&lt;br&gt;
 - Open Data(Government)&lt;br&gt;
 - noSql Databases&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Iris Data Set:&lt;/strong&gt; &lt;a href="https://en.wikipedia.org/wiki/Iris_flower_data_set" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Iris_flower_data_set&lt;/a&gt;&lt;br&gt;
&lt;strong&gt;Open Data:&lt;/strong&gt; &lt;a href="https://www.data.gov/open-gov/" rel="nofollow"&gt;https://www.data.gov/open-gov/&lt;/a&gt;      &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8386, 1, '2015-10-11 10:57:58.587', 0, 28, 'Heterogeneous Value Difference Metric (HVDM)', '&lt;p&gt;I would like to ask if someone know some examples of the Heterogeneous Value Difference Metric (HVDM) distance ?
also, i would like to ask if there is an implementation of such metric in R?&lt;/p&gt;

&lt;p&gt;I will be grateful if someone can give some useful ressource in such way i could compute this distance manually&lt;/p&gt;
', 'machine-learning  data-mining', NULL, '''/p'':47C,69C ''also'':31C ''ask'':17C,36C ''comput'':65C ''could'':64C ''data'':10B ''data-min'':9B ''differ'':3A,27C ''distanc'':30C,67C ''exampl'':22C ''give'':56C ''grate'':52C ''heterogen'':1A,25C ''hvdm'':5A,29C ''implement'':41C ''know'':20C ''learn'':8B ''like'':15C,34C ''machin'':7B ''machine-learn'':6B ''manual'':68C ''metric'':4A,28C,44C ''mine'':11B ''p'':12C,48C ''r'':46C ''ressourc'':59C ''someon'':19C,54C ''use'':58C ''valu'':2A,26C ''way'':62C ''would'':14C,33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8388, 1, '2015-10-11 17:54:04.907', -1, 45, 'Clustering related areas with k-means', '&lt;p&gt;I am trying to cluster related areas of knowledge based in publications.For example a researcher has 3 keywords in a paper, in another paper he has 5 keywords, but 3 keywords are the same in the both papers. Then these 3 keywords are similar and It could be an area of knowledge. A sample of my file is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;''Domain Ontology,Semantic Web''
''Linked Data,Domain Ontology,Use Case''
''Domain Ontology,Linked Data,Semantic Annotation''
''GIS,Open GIS,Integrated Geo Systems''
''Open GIS,GIS''
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;My file contains 48963 rows and 19000 keywords. I have tried grouping words (like in the sample), using StringToWordVector (STWV), but I don''t have good results. So I tried different K for my cluster since 3 until 13000. When K is greater my log likelihood decrease. In many models my words are grouped perfectly, but the percentages are not really good for some clusters. For example for K=3000, the 2999 have between 0,1% and 1% of the data, but the 3000 have the 21%. I am using WEKA. Anyone know some work related, some advice or any help is helpful...&lt;/p&gt;

&lt;p&gt;Cheers.&lt;/p&gt;
', 'data-mining  clustering  text-mining', NULL, '''/code'':102C ''/p'':74C,208C,211C ''/pre'':103C ''0'':179C ''1'':180C,182C ''13000'':143C ''19000'':111C ''21'':191C ''2999'':176C ''3'':32C,45C,56C,141C ''3000'':174C,188C ''48963'':108C ''5'':42C ''advic'':202C ''annot'':92C ''anoth'':38C ''anyon'':196C ''area'':3A,22C,65C ''base'':25C ''case'':86C ''cheer'':210C ''cluster'':1A,11B,20C,139C,169C ''code'':76C ''contain'':107C ''could'':62C ''data'':9B,82C,90C,185C ''data-min'':8B ''decreas'':151C ''differ'':135C ''domain'':77C,83C,87C ''exampl'':28C,171C ''file'':72C,106C ''geo'':97C ''gis'':93C,95C,100C,101C ''good'':130C,166C ''greater'':147C ''group'':116C,158C ''help'':205C,207C ''integr'':96C ''k'':6A,136C,145C,173C ''k-mean'':5A ''keyword'':33C,43C,46C,57C,112C ''know'':197C ''knowledg'':24C,67C ''like'':118C ''likelihood'':150C ''link'':81C,89C ''log'':149C ''mani'':153C ''mean'':7A ''mine'':10B,14B ''model'':154C ''ontolog'':78C,84C,88C ''open'':94C,99C ''p'':15C,104C,209C ''paper'':36C,39C,53C ''percentag'':162C ''perfect'':159C ''pre'':75C ''publications.for'':27C ''realli'':165C ''relat'':2A,21C,200C ''research'':30C ''result'':131C ''row'':109C ''sampl'':69C,121C ''semant'':79C,91C ''similar'':59C ''sinc'':140C ''stringtowordvector'':123C ''stwv'':124C ''system'':98C ''text'':13B ''text-min'':12B ''tri'':18C,115C,134C ''use'':85C,122C,194C ''web'':80C ''weka'':195C ''word'':117C,156C ''work'':199C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8400, 4, '2015-10-12 09:36:13.85', 0, NULL, NULL, 'A topic model describes text from a large corpus as a probability distribution over topics which are probability distributions over words. There are quantified contributions from all topics to a specific text.', NULL, NULL, '''contribut'':25C ''corpus'':9C ''describ'':4C ''distribut'':13C,19C ''larg'':8C ''model'':3C ''probabl'':12C,18C ''quantifi'':24C ''specif'':31C ''text'':5C,32C ''topic'':2C,15C,28C ''word'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8389, 1, '2015-10-11 20:58:19.907', 1, 38, 'Sampling for multi categorical variable', '&lt;p&gt;My hypothesis &lt;code&gt;h&lt;/code&gt; depends on multiple categorical variables &lt;code&gt;(a,b,c)&lt;/code&gt; each with their corresponding set of possible values &lt;code&gt;(A,B,C)&lt;/code&gt;. Now each of my data point exist in this space where I have no control over the values (observational data). &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;For e.g. Hypothesis to predict user shopping probability say depends
  on (Age, Country, Gender, Devicetype etc.)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;How could I sample the above data set so that it would give me a good representation. The techniques I have learned from the books very well apply to one dimension but that is a rare case in practice. If I sample across one dimension my other dimensions will be heavily skewed towards some values. Is there any standard algorithm to give good sampling? &lt;/p&gt;
', 'sampling  preprocessing', NULL, '''/blockquote'':77C ''/code'':13C,23C,36C ''/p'':57C,76C,143C ''across'':121C ''age'':71C ''algorithm'':138C ''appli'':106C ''b'':21C,34C ''blockquot'':58C ''book'':103C ''c'':22C,35C ''case'':115C ''categor'':4A,17C ''code'':11C,19C,32C ''control'':51C ''correspond'':27C ''could'':80C ''countri'':72C ''data'':41C,56C,85C ''depend'':14C,69C ''devicetyp'':74C ''dimens'':109C,123C,126C ''e.g'':61C ''etc'':75C ''exist'':43C ''gender'':73C ''give'':91C,140C ''good'':94C,141C ''h'':12C ''heavili'':129C ''hypothesi'':10C,62C ''learn'':100C ''multi'':3A ''multipl'':16C ''observ'':55C ''one'':108C,122C ''p'':8C,59C,78C ''point'':42C ''possibl'':30C ''practic'':117C ''predict'':64C ''preprocess'':7B ''probabl'':67C ''rare'':114C ''represent'':95C ''sampl'':1A,6B,82C,120C,142C ''say'':68C ''set'':28C,86C ''shop'':66C ''skew'':130C ''space'':46C ''standard'':137C ''techniqu'':97C ''toward'':131C ''user'':65C ''valu'':31C,54C,133C ''variabl'':5A,18C ''well'':105C ''would'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8390, 1, '2015-10-12 03:31:10.867', 4, 490, 'Sentiment Analysis Tutorial', '&lt;p&gt;I am trying to understand sentiment analysis and how to apply it using any language (R, Python etc). I would like to know if there is a good place on internet for tutorial that I can follow. I googled, but I wasn''t very much satisfied because they were not tutorials but more of theory. I want theory and practical examples.&lt;/p&gt;
', 'machine-learning  reference-request', NULL, '''/p'':72C ''analysi'':2A,17C ''appli'':21C ''etc'':28C ''exampl'':71C ''follow'':47C ''good'':38C ''googl'':49C ''internet'':41C ''know'':33C ''languag'':25C ''learn'':6B ''like'':31C ''machin'':5B ''machine-learn'':4B ''much'':55C ''p'':10C ''place'':39C ''practic'':70C ''python'':27C ''r'':26C ''refer'':8B ''reference-request'':7B ''request'':9B ''satisfi'':56C ''sentiment'':1A,16C ''theori'':65C,68C ''tri'':13C ''tutori'':3A,43C,61C ''understand'':15C ''use'':23C ''want'':67C ''wasn'':52C ''would'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8391, 2, '2015-10-12 04:48:34.71', 3, NULL, NULL, '&lt;p&gt;The &lt;a href="http://www.nltk.org/book/" rel="nofollow"&gt;NLTK book&lt;/a&gt; is by far the best tutorial on basic NLP I have seen(in Python).&lt;/p&gt;

&lt;p&gt;The Coursera &lt;a href="https://www.coursera.org/course/nlp" rel="nofollow"&gt;course&lt;/a&gt; on NLP is also fairly good. It takes off from the basics and takes the student to a novice level.&lt;/p&gt;
', NULL, NULL, '''/a'':12C,39C ''/book/'':7C ''/course/nlp'':35C ''/p'':27C,60C ''also'':43C ''basic'':20C,51C ''best'':17C ''book'':11C ''cours'':38C ''coursera'':30C ''fair'':44C ''far'':15C ''good'':45C ''href'':4C,32C ''level'':59C ''nlp'':21C,41C ''nltk'':10C ''nofollow'':9C,37C ''novic'':58C ''p'':1C,28C ''python'':26C ''rel'':8C,36C ''seen'':24C ''student'':55C ''take'':47C,53C ''tutori'':18C ''www.coursera.org'':34C ''www.coursera.org/course/nlp'':33C ''www.nltk.org'':6C ''www.nltk.org/book/'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8392, 2, '2015-10-12 04:54:03.893', 1, NULL, NULL, '&lt;p&gt;Actually, it is four V''s which define Big Data.&lt;/p&gt;

&lt;p&gt;An &lt;a href="http://stats.stackexchange.com/questions/173060/what-exactly-is-big-data/173065#173065"&gt;answer of mine&lt;/a&gt; on CrossValidated to a &lt;a href="http://stats.stackexchange.com/questions/173060/what-exactly-is-big-data"&gt;similar question&lt;/a&gt; explains the four V''s.&lt;/p&gt;

&lt;p&gt;As @MikeStratton has neatly explained the three V''s, I would explain the fourth V, which is &lt;strong&gt;Veracity&lt;/strong&gt;.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Veracity is the uncertainty in data.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Examples can be poor data quality, inadequate data from surveys, etc.
This makes the fitted models highly biased and error-prone.&lt;/p&gt;

&lt;p&gt;You might also want to have a look at &lt;a href="http://datascience.stackexchange.com/questions/19/how-big-is-big-data"&gt;this discussion&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':23C,35C,115C ''/blockquote'':73C ''/p'':12C,41C,63C,72C,97C,116C ''/questions/173060/what-exactly-is-big-data'':32C ''/questions/173060/what-exactly-is-big-data/173065#173065'':19C ''/questions/19/how-big-is-big-data'':112C ''/strong'':62C ''actual'':2C ''also'':101C ''answer'':20C ''bias'':92C ''big'':10C ''blockquot'':64C ''crossvalid'':25C ''data'':11C,71C,79C,82C ''datascience.stackexchange.com'':111C ''datascience.stackexchange.com/questions/19/how-big-is-big-data'':110C ''defin'':9C ''discuss'':114C ''error'':95C ''error-pron'':94C ''etc'':85C ''exampl'':75C ''explain'':36C,47C,54C ''fit'':89C ''four'':5C,38C ''fourth'':56C ''high'':91C ''href'':16C,29C,109C ''inadequ'':81C ''look'':106C ''make'':87C ''might'':100C ''mikestratton'':44C ''mine'':22C ''model'':90C ''neat'':46C ''p'':1C,13C,42C,65C,74C,98C ''poor'':78C ''prone'':96C ''qualiti'':80C ''question'':34C ''similar'':33C ''stats.stackexchange.com'':18C,31C ''stats.stackexchange.com/questions/173060/what-exactly-is-big-data'':30C ''stats.stackexchange.com/questions/173060/what-exactly-is-big-data/173065#173065'':17C ''strong'':60C ''survey'':84C ''three'':49C ''uncertainti'':69C ''v'':6C,39C,50C,57C ''verac'':61C,66C ''want'':102C ''would'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8393, 2, '2015-10-12 05:27:06.643', 1, NULL, NULL, '&lt;p&gt;As &lt;em&gt;Dawny&lt;/em&gt; said i would suggest &lt;a href="http://www.nltk.org/book/" rel="nofollow"&gt;NLTK Book&lt;/a&gt; too. And in R language &lt;a href="https://cran.r-project.org/web/packages/tm/index.html" rel="nofollow"&gt;''tm''&lt;/a&gt; library is pretty useful. So try &lt;a href="https://rstudio-pubs-static.s3.amazonaws.com/31867_8236987cf0a8444e962ccd2aec46d9c3.html" rel="nofollow"&gt;here&lt;/a&gt; for more related text mining stuff with R. &lt;a href="http://www.r-bloggers.com/text-mining/" rel="nofollow"&gt;r-blog&lt;/a&gt; also worth of consider.
good luck. &lt;/p&gt;
', NULL, NULL, '''/31867_8236987cf0a8444e962ccd2aec46d9c3.html'':44C ''/a'':19C,33C,48C,67C ''/book/'':14C ''/em'':5C ''/p'':74C ''/text-mining/'':61C ''/web/packages/tm/index.html'':29C ''also'':68C ''blog'':66C ''book'':18C ''consid'':71C ''cran.r-project.org'':28C ''cran.r-project.org/web/packages/tm/index.html'':27C ''dawni'':4C ''em'':3C ''good'':72C ''href'':11C,26C,41C,58C ''languag'':24C ''librari'':34C ''luck'':73C ''mine'':53C ''nltk'':17C ''nofollow'':16C,31C,46C,63C ''p'':1C ''pretti'':36C ''r'':23C,56C,65C ''r-blog'':64C ''rel'':15C,30C,45C,62C ''relat'':51C ''rstudio-pubs-static.s3.amazonaws.com'':43C ''rstudio-pubs-static.s3.amazonaws.com/31867_8236987cf0a8444e962ccd2aec46d9c3.html'':42C ''said'':6C ''stuff'':54C ''suggest'':9C ''text'':52C ''tm'':32C ''tri'':39C ''use'':37C ''worth'':69C ''would'':8C ''www.nltk.org'':13C ''www.nltk.org/book/'':12C ''www.r-bloggers.com'':60C ''www.r-bloggers.com/text-mining/'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8394, 2, '2015-10-12 05:49:31.807', 1, NULL, NULL, '&lt;p&gt;The &lt;a href="https://www.coursera.org/course/nlp" rel="nofollow"&gt;Stanford NLP course on Coursera&lt;/a&gt; covers Sentiment Analysis in &lt;a href="https://class.coursera.org/nlp/lecture/preview" rel="nofollow"&gt;week 3&lt;/a&gt;:&lt;br&gt;
- &lt;a href="https://class.coursera.org/nlp/lecture/31" rel="nofollow"&gt;What is Sentiment Analysis?&lt;/a&gt;&lt;br&gt;
- &lt;a href="https://class.coursera.org/nlp/lecture/145" rel="nofollow"&gt;Sentiment Analysis: A baseline algorithm&lt;/a&gt;&lt;br&gt;
- &lt;a href="https://class.coursera.org/nlp/lecture/35" rel="nofollow"&gt;Sentiment Lexicons&lt;/a&gt;&lt;br&gt;
- &lt;a href="https://class.coursera.org/nlp/lecture/144" rel="nofollow"&gt;Learning Sentiment Lexicons&lt;/a&gt;&lt;br&gt;
- &lt;a href="https://class.coursera.org/nlp/lecture/33" rel="nofollow"&gt;Other Sentiment Tasks&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;For coding tutorials see:  &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://streamhacker.com/2010/05/10/text-classification-sentiment-analysis-naive-bayes-classifier/" rel="nofollow"&gt;Stream Hacker''s NLP tutorials&lt;/a&gt; &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://fjavieralba.com/basic-sentiment-analysis-with-python.html" rel="nofollow"&gt;Basic Sentiment Analysis with Python&lt;/a&gt;  &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://andybromberg.com/sentiment-analysis-python/" rel="nofollow"&gt;Andy Bromberg''s Sentiment Analysis tutorials&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.laurentluce.com/posts/twitter-sentiment-analysis-using-python-and-nltk/" rel="nofollow"&gt;Laurent Luce''s Sentiment Analysis tutorials&lt;/a&gt; &lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;These are really basic, so their performance will not be great in all cases.&lt;/p&gt;
', NULL, NULL, '''/2010/05/10/text-classification-sentiment-analysis-naive-bayes-classifier/'':106C ''/a'':15C,29C,42C,56C,67C,79C,91C,114C,131C,149C,167C ''/basic-sentiment-analysis-with-python.html'':123C ''/course/nlp'':7C ''/li'':116C,133C,151C,169C ''/nlp/lecture/144'':73C ''/nlp/lecture/145'':48C ''/nlp/lecture/31'':35C ''/nlp/lecture/33'':85C ''/nlp/lecture/35'':62C ''/nlp/lecture/preview'':24C ''/p'':92C,98C,115C,132C,150C,168C,186C ''/posts/twitter-sentiment-analysis-using-python-and-nltk/'':158C ''/sentiment-analysis-python/'':140C ''/ul'':170C ''3'':28C ''algorithm'':55C ''analysi'':18C,41C,52C,128C,147C,165C ''andi'':143C ''andybromberg.com'':139C ''andybromberg.com/sentiment-analysis-python/'':138C ''baselin'':54C ''basic'':126C,175C ''br'':30C,43C,57C,68C,80C ''bromberg'':144C ''case'':185C ''class.coursera.org'':23C,34C,47C,61C,72C,84C ''class.coursera.org/nlp/lecture/144'':71C ''class.coursera.org/nlp/lecture/145'':46C ''class.coursera.org/nlp/lecture/31'':33C ''class.coursera.org/nlp/lecture/33'':83C ''class.coursera.org/nlp/lecture/35'':60C ''class.coursera.org/nlp/lecture/preview'':22C ''code'':95C ''cours'':12C ''coursera'':14C ''cover'':16C ''fjavieralba.com'':122C ''fjavieralba.com/basic-sentiment-analysis-with-python.html'':121C ''great'':182C ''hacker'':110C ''href'':4C,21C,32C,45C,59C,70C,82C,103C,120C,137C,155C ''laurent'':161C ''learn'':76C ''lexicon'':66C,78C ''li'':100C,117C,134C,152C ''luce'':162C ''nlp'':11C,112C ''nofollow'':9C,26C,37C,50C,64C,75C,87C,108C,125C,142C,160C ''p'':1C,93C,101C,118C,135C,153C,171C ''perform'':178C ''python'':130C ''realli'':174C ''rel'':8C,25C,36C,49C,63C,74C,86C,107C,124C,141C,159C ''see'':97C ''sentiment'':17C,40C,51C,65C,77C,89C,127C,146C,164C ''stanford'':10C ''stream'':109C ''streamhacker.com'':105C ''streamhacker.com/2010/05/10/text-classification-sentiment-analysis-naive-bayes-classifier/'':104C ''task'':90C ''tutori'':96C,113C,148C,166C ''ul'':99C ''week'':27C ''www.coursera.org'':6C ''www.coursera.org/course/nlp'':5C ''www.laurentluce.com'':157C ''www.laurentluce.com/posts/twitter-sentiment-analysis-using-python-and-nltk/'':156C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8395, 1, '2015-10-12 06:36:53.497', 2, 56, 'Predict which user will buy with an offer - discount', '&lt;p&gt;I have historical data from an e-shop transactions. I want to write a prediction model and check if a specific user will buy with or without a discount, so I can do some targeting offers.&lt;/p&gt;

&lt;p&gt;The idea is:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;If a user will buy the regular price, will not have an offer.&lt;/li&gt;
&lt;li&gt;If a user will not buy the regular price, check if he/she will buy with an offer.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;With this way, I will avoid to make an offer to someone who would buy with the regular price.&lt;/p&gt;

&lt;p&gt;So, I am still in the brainstorming and trying to find a way for implementing the 1-2. Should I create two separate models to predict the 1) and then the 2) with the second model? Or should I join both in one prediction model?&lt;/p&gt;
', 'predictive-modeling', NULL, '''-2'':132C ''/li'':72C,91C ''/ol'':92C ''/p'':51C,56C,113C,160C ''1'':131C,142C ''2'':146C ''avoid'':99C ''brainstorm'':121C ''buy'':5A,38C,63C,79C,87C,108C ''check'':32C,83C ''creat'':135C ''data'':17C ''discount'':9A,43C ''e'':21C ''e-shop'':20C ''find'':125C ''he/she'':85C ''histor'':16C ''idea'':54C ''implement'':129C ''join'':154C ''li'':58C,73C ''make'':101C ''model'':12B,30C,138C,150C,159C ''offer'':8A,50C,71C,90C,103C ''ol'':57C ''one'':157C ''p'':13C,52C,93C,114C ''predict'':1A,11B,29C,140C,158C ''predictive-model'':10B ''price'':66C,82C,112C ''regular'':65C,81C,111C ''second'':149C ''separ'':137C ''shop'':22C ''someon'':105C ''specif'':35C ''still'':118C ''target'':49C ''transact'':23C ''tri'':123C ''two'':136C ''user'':3A,36C,61C,76C ''want'':25C ''way'':96C,127C ''without'':41C ''would'':107C ''write'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8399, 5, '2015-10-12 09:36:13.85', 0, NULL, NULL, '&lt;p&gt;A topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats (source: &lt;a href="http://en.wikipedia.org/wiki/Topic_model" rel="nofollow"&gt;wikipedia&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Generative models&lt;/strong&gt; (i.e. the statistical models used for topic modelling)&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Latent Dirichlet Allocation (LDA)&lt;/li&gt;
&lt;li&gt;Hierarchical Dirichlet process (HDP)&lt;/li&gt;
&lt;li&gt;Non-Negative Matrix Factorisation&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Software / Libraries&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://mallet.cs.umass.edu/" rel="nofollow"&gt;Mallet&lt;/a&gt; (Java) &lt;/li&gt;
&lt;li&gt;&lt;a href="http://nlp.stanford.edu/software/tmt/tmt-0.4/" rel="nofollow"&gt;Stanford Topic Modeling Toolbox&lt;/a&gt; (software)  &lt;/li&gt;
&lt;li&gt;&lt;a href="http://radimrehurek.com/gensim/" rel="nofollow"&gt;Gensim â Topic Modelling for Humans&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://cran.r-project.org/web/packages/lda/index.html" rel="nofollow"&gt;lda&lt;/a&gt; (R)&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8407, 2, '2015-10-12 18:49:13.713', 1, NULL, NULL, '&lt;p&gt;To clarify, you have at least one observation in every possible category combination, but you only want to perform analysis on a subset of the total data, and are trying to decide how to choose which points to keep and which points to throw away?&lt;/p&gt;

&lt;p&gt;I think the right approach here will depend strongly on what your hypothesis &lt;code&gt;h&lt;/code&gt; is, what sort of statistical tests you want to run, and what your loss function is. If you''re trying to answer a question which can be answered by the number of datapoints in each combination, for example, or by the mean and stdev of some continuous variable for each combination, reducing the size of your data by sampling will only hurt your analysis.&lt;/p&gt;

&lt;p&gt;If you''re trying to learn a classifier, for example, a classic question is whether to train on a set with equal numbers of all possible classes or with the underlying class distribution found in the wild. The first will train a "superior" classifier, especially if its prior on class membership is later reset to the actual distribution in the wild, by most reasonable loss functions. But is your loss function one of the ones where this is better? &lt;/p&gt;

&lt;p&gt;You might also want to look into &lt;a href="https://en.wikipedia.org/wiki/Design_of_experiments" rel="nofollow"&gt;design of experiments&lt;/a&gt;, &lt;a href="https://en.wikipedia.org/wiki/Combinatorial_design" rel="nofollow"&gt;combinatorial design&lt;/a&gt; in particular, which is trying to solve a symmetrical problem--starting with no data but being able to choose the various values, what set of points should we test to get as much information as possible about the underlying functions?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8401, 1, '2015-10-12 13:30:09.357', 1, 55, '"Relearning" parameters', '&lt;p&gt;If this is a duplicate, I apologize. I''m not really sure what to even search for to try and find a duplicate/answer!&lt;/p&gt;

&lt;p&gt;We are working on a system for providing musical feedback to change the ''mood'' of a subject. There are a number of parameters of the music that we can manipulate.&lt;/p&gt;

&lt;p&gt;At the same time, we measure the physiology of the subject. I''m using a dynamic Bayesian network to reasonably accurately determine the level of frustration of the subject.&lt;/p&gt;

&lt;p&gt;I''m looking for a way to, based on the level of frustration, tell the music generation mechanism "this set of parameters is effective", or not. The music parameters would then adjust automatically and iteratively, as we continue to make judgements of the subject''s level of frustration.&lt;/p&gt;

&lt;p&gt;I''d appreciate any pointers I can get! Please let me know if I can provide any clarification!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8402, 1, '2015-10-12 15:23:01.26', 4, 69, 'Scan-based operations Apache Spark', '&lt;p&gt;Looking at the first paper on RDDs/Apache Spark, I found a statement saying that "RDDs degrade gracefully when there is not enough memory to store them, as long as they are only being used in scan-based operations"&lt;/p&gt;

&lt;p&gt;What are scan-based operations in the context of RDDs and which of the &lt;a href="http://spark.apache.org/docs/latest/programming-guide.html#transformations" rel="nofollow"&gt;Transformations in Spark&lt;/a&gt; are scan-based operations&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8403, 2, '2015-10-12 15:48:35.563', 1, NULL, NULL, '&lt;p&gt;Let me give you some pointers (assuming that I''m right on this, which might not necessarily be true, so proceed with caution :-). First, I''d figure out the applicable terminology. It seems to me that your case can be categorized as &lt;em&gt;multivariate sampling&lt;/em&gt; from a &lt;em&gt;categorical distribution&lt;/em&gt; (see &lt;a href="https://en.wikipedia.org/wiki/Categorical_distribution#Sampling" rel="nofollow"&gt;this section&lt;/a&gt; on categorical distribution sampling). Perhaps, the simplest approach to it is to use &lt;strong&gt;R&lt;/strong&gt; ecosystem''s rich functionality. In particular, standard &lt;code&gt;stats&lt;/code&gt; package contains &lt;code&gt;rmultinom&lt;/code&gt; function (&lt;a href="http://stat.ethz.ch/R-manual/R-devel/library/stats/html/Multinom.html" rel="nofollow"&gt;link&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;If you need more complex types of sampling, there are other packages that might be worth exploring, for example &lt;code&gt;sampling&lt;/code&gt; (&lt;a href="https://cran.r-project.org/web/packages/sampling" rel="nofollow"&gt;link&lt;/a&gt;), &lt;code&gt;miscF&lt;/code&gt; (&lt;a href="https://cran.r-project.org/web/packages/miscF" rel="nofollow"&gt;link&lt;/a&gt;), offering &lt;code&gt;rMultinom&lt;/code&gt; function (&lt;a href="http://www.inside-r.org/packages/cran/miscF/docs/rMultinom" rel="nofollow"&gt;link&lt;/a&gt;). If your complex sampling is focused on survey data, consider reading &lt;a href="http://faculty.washington.edu/tlumley/survey-jsm-nup.pdf" rel="nofollow"&gt;this interesting paper&lt;/a&gt; "Complex Sampling and R" by Thomas Lumley.&lt;/p&gt;

&lt;p&gt;If you use languages other than R, check &lt;code&gt;multinomial&lt;/code&gt; function from Python''s &lt;code&gt;numpy&lt;/code&gt; package and, for Stata, &lt;a href="https://www.kai-arzheimer.com/sampling-from-a-multinomial-distribution-in-stata" rel="nofollow"&gt;this blog post&lt;/a&gt;. Finally, if you are interested in Bayesian statistics, the following two documents seems to be relevant: &lt;a href="https://theclevermachine.wordpress.com/tag/multivariate-sampling-methods" rel="nofollow"&gt;this blog post&lt;/a&gt; and &lt;a href="http://www.stat.ufl.edu/~aa/cda/bayes.pdf" rel="nofollow"&gt;this survey paper&lt;/a&gt;. Hope this helps.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8405, 2, '2015-10-12 16:24:49.857', 0, NULL, NULL, '&lt;p&gt;The "experience" here is the 10000 rows of training data. One expects that with less data, one produces an inferior model (e.g. if there was 1 row of data, or 10 rows).&lt;/p&gt;

&lt;p&gt;In practice, it is not uncommon for models to be specified based on some training set and then used for prediction.&lt;/p&gt;
', NULL, NULL, '''/p'':34C,57C ''1'':27C ''10'':32C ''10000'':7C ''base'':47C ''data'':11C,17C,30C ''e.g'':23C ''expect'':13C ''experi'':3C ''inferior'':21C ''less'':16C ''model'':22C,43C ''one'':12C,18C ''p'':1C,35C ''practic'':37C ''predict'':56C ''produc'':19C ''row'':8C,28C,33C ''set'':51C ''specifi'':46C ''train'':10C,50C ''uncommon'':41C ''use'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8406, 2, '2015-10-12 16:33:04.363', 0, NULL, NULL, '&lt;p&gt;One basic approach would be a hill-climbing algorithm to search for local optima.&lt;/p&gt;

&lt;p&gt;This will be more or less effective depending on the complexity of the space (e.g. convexity: in theory will this find the local optima, but also more practical concerns around the size/dimensionality of the space and easy of specifying neighboring points and the accuracy/precision of the frustration measurement).&lt;/p&gt;

&lt;p&gt;This also answers a slightly different question: the acceptance criteria "this set of parameters is effective" seems linked to some threshold of "frustration" that you would define as opposed to simply a process to "minimize the frustration function with respect to these parameters", although one can view the latter as the way to make the iterative adjustments to search for the former.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10158, 1, '2016-02-09 14:42:16.56', 0, 5, 'Can FTRL be applied on linear least squares? or is it just for logistic regression models?', '&lt;p&gt;I''m exploring follow-the-regularized-leader FTRL proximal gradient descent:
&lt;a href="http://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf" rel="nofollow"&gt;paper&lt;/a&gt;, &lt;a href="https://github.com/mlandry22/kaggle/blob/master/modern_logistic_regression/fast_solution_v3.py" rel="nofollow"&gt;reference implementation&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Everywhere FTRL is mentioned, the loss surface for the gradient decent is the &lt;code&gt;LogLoss&lt;/code&gt;, and the model for prediction is &lt;code&gt;Logistic regression&lt;/code&gt;. &lt;/p&gt;

&lt;p&gt;Can I use the same algorithm for a linear least squares model? I have a problem I want to model with a &lt;code&gt;linear model&lt;/code&gt; and define the loss by &lt;code&gt;least squares&lt;/code&gt;, and then do FTRL to find the optimal solution - do you see any problem with that?&lt;/p&gt;

&lt;p&gt;Thanks. &lt;/p&gt;
', 'logistic-regression  linear-regression  gradient-descent  online-learning', NULL, '''/a'':50C,60C ''/code'':78C,88C,116C,125C ''/mlandry22/kaggle/blob/master/modern_logistic_regression/fast_solution_v3.py'':55C ''/p'':61C,89C,142C,145C ''/~dsculley/papers/ad-click-prediction.pdf'':46C ''algorithm'':96C ''appli'':4A ''code'':76C,85C,113C,122C ''decent'':73C ''defin'':118C ''descent'':25B,41C ''everywher'':63C ''explor'':32C ''find'':131C ''follow'':34C ''follow-the-regularized-lead'':33C ''ftrl'':2A,38C,64C,129C ''github.com'':54C ''github.com/mlandry22/kaggle/blob/master/modern_logistic_regression/fast_solution_v3.py'':53C ''gradient'':24B,40C,72C ''gradient-desc'':23B ''href'':43C,52C ''implement'':59C ''leader'':37C ''learn'':28B ''least'':7A,100C,123C ''linear'':6A,21B,99C,114C ''linear-regress'':20B ''logist'':14A,18B,86C ''logistic-regress'':17B ''logloss'':77C ''loss'':68C,120C ''m'':31C ''mention'':66C ''model'':16A,81C,102C,110C,115C ''nofollow'':48C,57C ''onlin'':27B ''online-learn'':26B ''optim'':133C ''p'':29C,62C,90C,143C ''paper'':49C ''predict'':83C ''problem'':106C,139C ''proxim'':39C ''refer'':58C ''regress'':15A,19B,22B,87C ''regular'':36C ''rel'':47C,56C ''see'':137C ''solut'':134C ''squar'':8A,101C,124C ''surfac'':69C ''thank'':144C ''use'':93C ''want'':108C ''www.eecs.tufts.edu'':45C ''www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9164, 2, '2015-12-02 07:12:16.303', 1, NULL, NULL, '&lt;p&gt;I''m not sure what is the goal of the procedure you are asking about.&lt;/p&gt;

&lt;p&gt;You might be interested in &lt;a href="https://en.wikipedia.org/wiki/Huffman_coding" rel="nofollow"&gt;Huffman coding&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Huffman codes are used for text compression.
They identify high frequency sequences and assign to them short codes, getting a a reduction in the overall text size.&lt;/p&gt;

&lt;p&gt;The tree constructed for the code might help you with what you look for.&lt;/p&gt;

&lt;p&gt;As an extra bonus, it is a classical algorithm so you will be able to find many implementations for it.&lt;/p&gt;
', NULL, NULL, '''/a'':33C ''/p'':17C,34C,63C,79C,101C ''/wiki/huffman_coding'':28C ''abl'':94C ''algorithm'':89C ''ask'':15C ''assign'':49C ''bonus'':84C ''classic'':88C ''code'':32C,37C,53C,70C ''compress'':42C ''construct'':67C ''en.wikipedia.org'':27C ''en.wikipedia.org/wiki/huffman_coding'':26C ''extra'':83C ''find'':96C ''frequenc'':46C ''get'':54C ''goal'':9C ''help'':72C ''high'':45C ''href'':25C ''huffman'':31C,36C ''identifi'':44C ''implement'':98C ''interest'':22C ''look'':77C ''m'':3C ''mani'':97C ''might'':20C,71C ''nofollow'':30C ''overal'':60C ''p'':1C,18C,35C,64C,80C ''procedur'':12C ''reduct'':57C ''rel'':29C ''sequenc'':47C ''short'':52C ''size'':62C ''sure'':5C ''text'':41C,61C ''tree'':66C ''use'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8408, 2, '2015-10-12 19:04:25.647', 1, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;Do we need to feed more data to the algorithm time by time and improve the model so that the model actually LEARNS?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This is called &lt;a href="https://en.wikipedia.org/wiki/Online_machine_learning" rel="nofollow"&gt;online learning&lt;/a&gt;. It is recommended for any application where the underlying relationships might change, and where feedback is quickly available.&lt;/p&gt;

&lt;p&gt;But that isn''t always the case. It may be the case that once a model is learned, we don''t expect it to change, and so this sort of ''continuing education'' is unnecessary, and that feedback could be costly or dangerous. (Imagine playing Rock Paper Scissors with a bot whose updating algorithm you know; you should be able to reliably win by feeding it moves designed to give you an opportunity to know its move and outfox it.)&lt;/p&gt;

&lt;p&gt;Or it may be the case that feedback is available periodically, where my experience has been that people often retrain the model from scratch, rather than trying to do a warm start on the previous model.&lt;/p&gt;
', NULL, NULL, '''/a'':41C ''/blockquote'':27C ''/p'':26C,60C,141C,179C ''/wiki/online_machine_learning'':36C ''abl'':120C ''actual'':24C ''algorithm'':12C,114C ''alway'':66C ''applic'':47C ''avail'':59C,152C ''blockquot'':1C ''bot'':111C ''call'':31C ''case'':68C,73C,148C ''chang'':53C,86C ''continu'':92C ''cost'':101C ''could'':99C ''danger'':103C ''data'':9C ''design'':128C ''educ'':93C ''en.wikipedia.org'':35C ''en.wikipedia.org/wiki/online_machine_learning'':34C ''expect'':83C ''experi'':156C ''feed'':7C,125C ''feedback'':56C,98C,150C ''give'':130C ''href'':33C ''imagin'':104C ''improv'':17C ''isn'':64C ''know'':116C,135C ''learn'':25C,40C,79C ''may'':70C,145C ''might'':52C ''model'':19C,23C,77C,164C,178C ''move'':127C,137C ''need'':5C ''nofollow'':38C ''often'':161C ''onlin'':39C ''opportun'':133C ''outfox'':139C ''p'':2C,28C,61C,142C ''paper'':107C ''peopl'':160C ''period'':153C ''play'':105C ''previous'':177C ''quick'':58C ''rather'':167C ''recommend'':44C ''rel'':37C ''relationship'':51C ''reliabl'':122C ''retrain'':162C ''rock'':106C ''scissor'':108C ''scratch'':166C ''sort'':90C ''start'':174C ''time'':13C,15C ''tri'':169C ''under'':50C ''unnecessari'':95C ''updat'':113C ''warm'':173C ''whose'':112C ''win'':123C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8410, 1, '2015-10-12 20:02:20.073', -1, 35, 'A service to generate examples of context of a word?', '&lt;p&gt;I want to type in "car" and get:&lt;/p&gt;

&lt;p&gt;One day, I came to a car and bought it.
Teacher told us that cars are pricey.
etc. etc. etc..&lt;/p&gt;

&lt;p&gt;Basically just sentences where the word is used.&lt;/p&gt;

&lt;p&gt;Opensource and german preferred.&lt;/p&gt;
', 'nlp', NULL, '''/p'':21C,43C,53C,59C ''basic'':45C ''bought'':31C ''came'':26C ''car'':18C,29C,37C ''context'':7A ''day'':24C ''etc'':40C,41C,42C ''exampl'':5A ''generat'':4A ''german'':57C ''get'':20C ''nlp'':11B ''one'':23C ''opensourc'':55C ''p'':12C,22C,44C,54C ''prefer'':58C ''pricey'':39C ''sentenc'':47C ''servic'':2A ''teacher'':33C ''told'':34C ''type'':16C ''us'':35C ''use'':52C ''want'':14C ''word'':10A,50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8411, 2, '2015-10-12 20:30:50.213', 0, NULL, NULL, '&lt;p&gt;Wiktionary has ever-improving example sentences and open source tools for programmatically using such data.&lt;/p&gt;

&lt;p&gt;Note also that Google Translate does this, but it is not offered in the official API.&lt;/p&gt;
', NULL, NULL, '''/p'':18C,36C ''also'':21C ''api'':35C ''data'':17C ''ever'':5C ''ever-improv'':4C ''exampl'':7C ''googl'':23C ''improv'':6C ''note'':20C ''offer'':31C ''offici'':34C ''open'':10C ''p'':1C,19C ''programmat'':14C ''sentenc'':8C ''sourc'':11C ''tool'':12C ''translat'':24C ''use'':15C ''wiktionari'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8412, 2, '2015-10-12 21:22:43.73', 0, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;The data has such a property that one or very few vectors
  influence strongly the class label.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The best (and easiest) approach would probably be to simply train a classifer on each vector and then average the predictions across the vectors for a given sequence. The important vectors would be strongly influential in their predictions, whereas the predictions for the unimportant vectors would be close to 0.5 (or similar for a non-binary classification problem).&lt;/p&gt;
', NULL, NULL, '''/blockquote'':21C ''/p'':20C,82C ''0.5'':72C ''across'':44C ''approach'':27C ''averag'':41C ''best'':24C ''binari'':79C ''blockquot'':1C ''class'':18C ''classif'':35C,80C ''close'':70C ''data'':4C ''easiest'':26C ''given'':49C ''import'':52C ''influenc'':15C ''influenti'':57C ''label'':19C ''non'':78C ''non-binari'':77C ''one'':10C ''p'':2C,22C ''predict'':43C,60C,63C ''probabl'':29C ''problem'':81C ''properti'':8C ''sequenc'':50C ''similar'':74C ''simpli'':32C ''strong'':16C,56C ''train'':33C ''unimport'':66C ''vector'':14C,38C,46C,53C,67C ''wherea'':61C ''would'':28C,54C,68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8413, 2, '2015-10-12 23:04:44.623', 1, NULL, NULL, '&lt;p&gt;Hadoop, HDFS etc. come at a substantial cost.&lt;/p&gt;

&lt;p&gt;Depending on your application and code quality, the &lt;em&gt;break even&lt;/em&gt; is usually somewhere between 10 and 100 nodes: a beginners Hadoop job may easily run 100x slower than an average non-Hadoop job, so you need this many nodes (and data!) to make up for the overhead.&lt;/p&gt;

&lt;p&gt;This gets much worse when the non-Java APIs are used. The native layer of Hadoop is Java, and running e.g. a Python or R map-reduce job will usually require multiple serializations and deserializations of data into text and back. For simple map jobs such as the famous word-count example, this overhead is more than the actual task.&lt;/p&gt;

&lt;p&gt;As a rule of thumb: &lt;strong&gt;if your data fits into main memory, don''t put it on hadoop&lt;/strong&gt; but process it locally. It may be much more effective to use multithreading or GPUs then. On the other hand, if your data is several TB, and you only need a tiny bit of that data, reading from multiple harddisks in parallel &lt;em&gt;does&lt;/em&gt; offer advantages. That is why a lot of the use cases follow the ETL pattern. Scan a large amount of raw data, extract the required parts, transform them into the desired format, and load them into a different system for further processing.
After that, the data usually is small enough to be processed with "conventional" tools. It''s disappointing, but PCs have just a &lt;em&gt;lot&lt;/em&gt; of RAM these days... and CPU+GPU power is plenty, too.&lt;/p&gt;

&lt;p&gt;Also, &lt;strong&gt;benchmark&lt;/strong&gt;. You''ll be surprised how expensive the network and architecture overheads are.&lt;/p&gt;
', NULL, NULL, '''/em'':23C,193C,260C ''/p'':10C,62C,127C,272C,290C ''/strong'':148C,277C ''10'':28C ''100'':30C ''100x'':39C ''actual'':125C ''advantag'':195C ''also'':274C ''amount'':212C ''api'':73C ''applic'':15C ''architectur'':287C ''averag'':43C ''back'':106C ''beginn'':33C ''benchmark'':276C ''bit'':181C ''break'':21C ''case'':204C ''code'':17C ''come'':5C ''convent'':248C ''cost'':9C ''count'':117C ''cpu'':266C ''data'':55C,102C,137C,171C,184C,215C,239C ''day'':264C ''depend'':12C ''deseri'':100C ''desir'':224C ''differ'':231C ''disappoint'':252C ''e.g'':85C ''easili'':37C ''effect'':158C ''em'':20C,191C,258C ''enough'':243C ''etc'':4C ''etl'':207C ''even'':22C ''exampl'':118C ''expens'':283C ''extract'':216C ''famous'':114C ''fit'':138C ''follow'':205C ''format'':225C ''get'':65C ''gpu'':267C ''gpus'':163C ''hadoop'':2C,34C,46C,80C,147C ''hand'':168C ''harddisk'':188C ''hdfs'':3C ''java'':72C,82C ''job'':35C,47C,93C,110C ''larg'':211C ''layer'':78C ''ll'':279C ''load'':227C ''local'':152C ''lot'':200C,259C ''main'':140C ''make'':57C ''mani'':52C ''map'':91C,109C ''map-reduc'':90C ''may'':36C,154C ''memori'':141C ''much'':66C,156C ''multipl'':97C,187C ''multithread'':161C ''nativ'':77C ''need'':50C,178C ''network'':285C ''node'':31C,53C ''non'':45C,71C ''non-hadoop'':44C ''non-java'':70C ''offer'':194C ''overhead'':61C,120C,288C ''p'':1C,11C,63C,128C,273C ''parallel'':190C ''part'':219C ''pattern'':208C ''pcs'':254C ''plenti'':270C ''power'':268C ''process'':150C,235C,246C ''put'':144C ''python'':87C ''qualiti'':18C ''r'':89C ''ram'':262C ''raw'':214C ''read'':185C ''reduc'':92C ''requir'':96C,218C ''rule'':131C ''run'':38C,84C ''scan'':209C ''serial'':98C ''sever'':173C ''simpl'':108C ''slower'':40C ''small'':242C ''somewher'':26C ''strong'':134C,275C ''substanti'':8C ''surpris'':281C ''system'':232C ''task'':126C ''tb'':174C ''text'':104C ''thumb'':133C ''tini'':180C ''tool'':249C ''transform'':220C ''use'':75C,160C,203C ''usual'':25C,95C,240C ''word'':116C ''word-count'':115C ''wors'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8433, 2, '2015-10-14 19:18:50.54', 0, NULL, NULL, '&lt;p&gt;You need to encode your strings as numeric features that sci-kit can use for the ML algorithms. This functionality is handled in the preprocessing module (e.g.,  see &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder" rel="nofollow"&gt;sklearn.preprocessing.LabelEncoder&lt;/a&gt; for an example).&lt;/p&gt;
', NULL, NULL, '''/a'':39C ''/p'':43C ''/stable/modules/generated/sklearn.preprocessing.labelencoder.html#sklearn.preprocessing.labelencoder'':35C ''algorithm'':20C ''e.g'':29C ''encod'':5C ''exampl'':42C ''featur'':10C ''function'':22C ''handl'':24C ''href'':32C ''kit'':14C ''ml'':19C ''modul'':28C ''need'':3C ''nofollow'':37C ''numer'':9C ''p'':1C ''preprocess'':27C ''rel'':36C ''sci'':13C ''sci-kit'':12C ''scikit-learn.org'':34C ''scikit-learn.org/stable/modules/generated/sklearn.preprocessing.labelencoder.html#sklearn.preprocessing.labelencoder'':33C ''see'':30C ''sklearn.preprocessing.labelencoder'':38C ''string'':7C ''use'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8414, 2, '2015-10-12 23:55:22.95', 2, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Unfortunately, the math simplifies to show that you can''t rigorously justify restricting the cosine similarity comparison of the vectors based on their lengths.&lt;/strong&gt;  &lt;/p&gt;

&lt;p&gt;The key point is that the cosine similarity metric normalizes based on length, so that only the unit vectors are considered.  I know this isn''t necessarily the answer that you wanted, but the math clearly shows that the cosine similarity metrics is agnostic to vector length.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Lets look at the math in more detail:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;You are applying a cosine similarity metric and requiring that that metric be larger than 0.6:&lt;/p&gt;

&lt;p&gt;$$similarity=\cos{(\theta)}=\frac{\mathbf{A}\cdot\mathbf{B}}{||A|| ||B||}\geq0.6$$.&lt;/p&gt;

&lt;p&gt;But the scalar lengths on the bottom can be distributed into the cross products above (distributive property):&lt;/p&gt;

&lt;p&gt;$$\frac{\mathbf{A}\cdot\mathbf{B}}{||A|| ||B||} = \frac{\mathbf{A}}{||A||}\cdot\frac{\mathbf{B}}{||B||}=\hat{\mathbf{A}}\cdot\hat{\mathbf{B}}$$.&lt;/p&gt;

&lt;p&gt;Now $\hat{\mathbf{A}}$ and $\hat{\mathbf{B}}$ are vectors that point in the same direction as $\mathbf{A}$ and $\mathbf{B}$ but they have been normalized to length one.  So the definition of the cosine similarity metric is the take the original vectors, normalize them to length one, and then measure the dot product of the unit vectors.&lt;/p&gt;

&lt;p&gt;Therefor: &lt;/p&gt;

&lt;p&gt;$$similarity=\cos{(\theta)}=\frac{\mathbf{d1}\cdot\mathbf{d2}}{||d1|| ||d2||}=\hat{\mathbf{d1}}\cdot\hat{\mathbf{d2}}\geq0.6$$&lt;/p&gt;

&lt;p&gt;depends only on the orientation of the vectors and not on their magnitude (i.e. length).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Reconciling this with what you are doing:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Despite what the results of the linear algebra show, you might still be seeing a statistically significant result.  Practically speaking you may be finding that the statistics show that length restrictions are valid for your data.  For instance, you might be finding that tweets never share a cosine similarity $\geq0.6$ when compared with Tolstoy''s "War and Peace".  If your statistics look good for using $||d2|| &amp;gt; .8 ||d1||$ and $||d2|| &amp;lt; ||d1|| / .8$ then I suggest you go with it as these type of canopy restrictions are very useful in saving computing time.&lt;/p&gt;

&lt;p&gt;You can perhaps reconcile what you have been doing with distance metrics by also considering the Euclidean distance.  Where as the cosine similarity only returns a value between -1 and 1 based on the angle between the two vectors, the Euclidean distances will return values that depend on the lengths of the two vectors.  In some sense, you are combining aspects of the Euclidean distance with cosine similarity.  &lt;/p&gt;

&lt;p&gt;It makes fairly good sense to require that the relative lengths be within 25% of one another in the sense that this combines an aspect of Euclidean distance to create group-by canopies, which cuts computation time, then length agnostic cosine similarity can be used as the final determinant.&lt;/p&gt;

&lt;p&gt;Note that 1/.8 =1.25, so d2&gt;=.8d1 is a tighter restriction than d2&amp;lt;=d1/.8.  I suggest using d2&gt;=.75d1 and d2&amp;lt;=1.25d1 as this is symmetric.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''-1'':405C ''/p'':28C,77C,89C,107C,121C,140C,166C,227C,230C,251C,268C,279C,375C,445C,497C,528C,533C ''/strong'':27C,88C,278C ''0.6'':106C ''1'':407C ''1.25'':502C,522C ''1/.8'':501C ''25'':460C ''75d1'':518C ''8'':348C,354C ''8d1'':505C ''agnost'':73C,487C ''algebra'':288C ''also'':390C ''angl'':411C ''anoth'':463C ''answer'':58C ''appli'':93C ''aspect'':437C,471C ''b'':117C,119C,147C,149C,157C,158C,165C,175C,189C ''base'':23C,40C,408C ''bottom'':129C ''canopi'':366C,480C ''cdot'':115C,145C,154C,162C,238C,246C ''clear'':65C ''combin'':436C,469C ''compar'':332C ''comparison'':19C ''comput'':373C,483C ''consid'':50C,391C ''cos'':110C,233C ''cosin'':17C,36C,69C,95C,203C,328C,398C,443C,488C ''creat'':476C ''cross'':135C ''cut'':482C ''d1'':237C,241C,245C,349C,353C,523C ''d1/.8'':513C ''d2'':240C,242C,249C,346C,351C,504C,511C,517C,520C ''data'':316C ''definit'':200C ''depend'':253C,423C ''despit'':281C ''detail'':87C ''determin'':496C ''direct'':183C ''distanc'':387C,394C,418C,441C,474C ''distribut'':132C,138C ''dot'':221C ''euclidean'':393C,417C,440C,473C ''fair'':449C ''final'':495C ''find'':304C,322C ''frac'':112C,142C,150C,155C,235C ''geq0.6'':120C,250C,330C ''go'':359C ''good'':343C,450C ''group'':478C ''group-bi'':477C ''gt'':347C ''hat'':159C,163C,169C,173C,243C,247C ''help'':532C ''hope'':530C ''i.e'':266C ''instanc'':318C ''isn'':54C ''justifi'':14C ''key'':31C ''know'':52C ''larger'':104C ''length'':26C,42C,76C,126C,196C,215C,267C,310C,426C,457C,486C ''let'':80C ''linear'':287C ''look'':81C,342C ''lt'':352C,512C,521C ''magnitud'':265C ''make'':448C ''math'':5C,64C,84C ''mathbf'':113C,116C,143C,146C,151C,156C,160C,164C,170C,174C,185C,188C,236C,239C,244C,248C ''may'':302C ''measur'':219C ''metric'':38C,71C,97C,102C,205C,388C ''might'':291C,320C ''necessarili'':56C ''never'':325C ''normal'':39C,194C,212C ''note'':499C ''one'':197C,216C,462C ''orient'':257C ''origin'':210C ''p'':1C,29C,78C,90C,108C,122C,141C,167C,228C,231C,252C,269C,280C,376C,446C,498C,529C ''peac'':338C ''perhap'':379C ''point'':32C,179C ''practic'':299C ''product'':136C,222C ''properti'':139C ''reconcil'':271C,380C ''relat'':456C ''requir'':99C,453C ''restrict'':15C,311C,367C,509C ''result'':284C,298C ''return'':401C,420C ''rigor'':13C ''save'':372C ''scalar'':125C ''see'':294C ''sens'':433C,451C,466C ''share'':326C ''show'':8C,66C,289C,308C ''signific'':297C ''similar'':18C,37C,70C,96C,109C,204C,232C,329C,399C,444C,489C ''simplifi'':6C ''speak'':300C ''statist'':296C,307C,341C ''still'':292C ''strong'':2C,79C,270C ''suggest'':357C,515C ''symmetr'':527C ''take'':208C ''therefor'':229C ''theta'':111C,234C ''tighter'':508C ''time'':374C,484C ''tolstoy'':334C ''tweet'':324C ''two'':414C,429C ''type'':364C ''unfortun'':3C ''unit'':47C,225C ''use'':345C,370C,492C,516C ''valid'':313C ''valu'':403C,421C ''vector'':22C,48C,75C,177C,211C,226C,260C,415C,430C ''want'':61C ''war'':336C ''within'':459C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8422, 1, '2015-10-13 18:15:18.267', 1, 22, 'Visualization techniques best suited for building non-linear models', '&lt;p&gt;What''s the recommended approach for identifying new predictors when building gradient boosting or support vector machine predictive models? I know that visualizations are often used to come up with new predictors and/or transformations for linear models because linear models aren''t able to capture non-linear behaviors or interactions in the same way that gradient boosting can. Are there visualization techniques that provide insight that you wouldn''t get by just adding the candidate predictor to the model and seeing if it improves out-of-sample error?&lt;/p&gt;

&lt;p&gt;I''m just using gradient boosting as an example, but I''m actually interested in 
visualization techniques that could be applied to other powerful models like 
support vector machines too. &lt;/p&gt;
', 'predictive-modeling  visualization', NULL, '''/p'':106C,138C ''abl'':58C ''actual'':120C ''ad'':89C ''and/or'':48C ''appli'':128C ''approach'':20C ''aren'':56C ''behavior'':64C ''best'':3A ''boost'':28C,73C,113C ''build'':6A,26C ''candid'':91C ''captur'':60C ''come'':43C ''could'':126C ''error'':105C ''exampl'':116C ''get'':86C ''gradient'':27C,72C,112C ''identifi'':22C ''improv'':100C ''insight'':81C ''interact'':66C ''interest'':121C ''know'':36C ''like'':133C ''linear'':9A,51C,54C,63C ''m'':109C,119C ''machin'':32C,136C ''model'':10A,13B,34C,52C,55C,95C,132C ''new'':23C,46C ''non'':8A,62C ''non-linear'':7A,61C ''often'':40C ''out-of-sampl'':101C ''p'':15C,107C ''power'':131C ''predict'':12B,33C ''predictive-model'':11B ''predictor'':24C,47C,92C ''provid'':80C ''recommend'':19C ''sampl'':104C ''see'':97C ''suit'':4A ''support'':30C,134C ''techniqu'':2A,78C,124C ''transform'':49C ''use'':41C,111C ''vector'':31C,135C ''visual'':1A,14B,38C,77C,123C ''way'':70C ''wouldn'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9191, 2, '2015-12-03 12:14:27.5', 1, NULL, NULL, '&lt;p&gt;As it states, the following presentation is a ''gentle'' introduction to Gradient Boosting, I found it quite helpful when figuring out Gradient Boosting; there is a fully explained example included. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/slides/gradient_boosting.pdf" rel="nofollow"&gt;http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/slides/gradient_boosting.pdf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/home/vip/teach/mlcourse/4_boosting/slides/gradient_boosting.pdf'':38C ''/home/vip/teach/mlcourse/4_boosting/slides/gradient_boosting.pdf&lt;/a&gt;&lt;/p&gt;'':43C ''/p'':32C ''boost'':14C,24C ''exampl'':30C ''explain'':29C ''figur'':21C ''follow'':6C ''found'':16C ''fulli'':28C ''gentl'':10C ''gradient'':13C,23C ''help'':19C ''href'':35C ''includ'':31C ''introduct'':11C ''nofollow'':40C ''p'':1C,33C ''present'':7C ''quit'':18C ''rel'':39C ''state'':4C ''www.ccs.neu.edu'':37C,42C ''www.ccs.neu.edu/home/vip/teach/mlcourse/4_boosting/slides/gradient_boosting.pdf'':36C ''www.ccs.neu.edu/home/vip/teach/mlcourse/4_boosting/slides/gradient_boosting.pdf&lt;/a&gt;&lt;/p&gt;'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8424, 2, '2015-10-13 21:20:25.58', 2, NULL, NULL, '&lt;p&gt;There might be different ways to do that, like considering implicit ratings like views or clicks.&lt;/p&gt;

&lt;p&gt;But basically, you can consider a rating of 1.0 for each user-item pair you have.&lt;/p&gt;

&lt;p&gt;This way, your prediction will be between 0 and 1 which you can consider similar to a click prediction probability. &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;0 being the probability on not having a click or the item by a user &lt;/li&gt;
&lt;li&gt;1 being the probability that a click would be made. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You can also set a threshold where you can drop a recommendation at a certain limit of your prediction level. &lt;/p&gt;

&lt;p&gt;E.g. Let''s say an user alpha has the following recommendations with a threshold of 0.7&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;i1 , 0.87&lt;/li&gt;
&lt;li&gt;i2, 0,75&lt;/li&gt;
&lt;li&gt;i3, 0,6&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You can drop the i3 recommendation since you don''t consider it good enough. &lt;/p&gt;

&lt;p&gt;Nevertheless, the parameter of your recommendation engine must be determined of course with the help of your evaluation metrics. &lt;/p&gt;

&lt;p&gt;Considering software solutions, I use Apache Spark MLlib with Scala as a base for my recommendation engine algorithms where you can compute item cosine similarity easily per example where you are using in-house implementation or an approximation with the DIMSUM algorithm. &lt;/p&gt;

&lt;p&gt;I hope this helps! &lt;/p&gt;
', NULL, NULL, '''/li'':77C,89C,135C,140C,145C ''/p'':18C,37C,59C,112C,130C,162C,183C,228C,234C ''/ul'':90C,146C ''0'':46C,62C,138C,143C ''0.7'':129C ''0.87'':134C ''1'':48C,79C ''1.0'':28C ''6'':144C ''75'':139C ''algorithm'':202C,227C ''alpha'':120C ''also'':94C ''apach'':190C ''approxim'':223C ''base'':197C ''basic'':21C ''certain'':106C ''click'':17C,56C,70C,85C ''comput'':206C ''consid'':11C,24C,52C,158C,185C ''cosin'':208C ''cours'':175C ''determin'':173C ''differ'':5C ''dimsum'':226C ''drop'':101C,150C ''e.g'':114C ''easili'':210C ''engin'':170C,201C ''enough'':161C ''evalu'':181C ''exampl'':212C ''follow'':123C ''good'':160C ''help'':178C,233C ''hope'':231C ''hous'':219C ''i1'':133C ''i2'':137C ''i3'':142C,152C ''implement'':220C ''implicit'':12C ''in-hous'':217C ''item'':33C,73C,207C ''let'':115C ''level'':111C ''li'':61C,78C,132C,136C,141C ''like'':10C,14C ''limit'':107C ''made'':88C ''metric'':182C ''might'':3C ''mllib'':192C ''must'':171C ''nevertheless'':164C ''p'':1C,19C,38C,91C,113C,147C,163C,184C,229C ''pair'':34C ''paramet'':166C ''per'':211C ''predict'':42C,57C,110C ''probabl'':58C,65C,82C ''rate'':13C,26C ''recommend'':103C,124C,153C,169C,200C ''say'':117C ''scala'':194C ''set'':95C ''similar'':53C,209C ''sinc'':154C ''softwar'':186C ''solut'':187C ''spark'':191C ''threshold'':97C,127C ''ul'':60C,131C ''use'':189C,216C ''user'':32C,76C,119C ''user-item'':31C ''view'':15C ''way'':6C,40C ''would'':86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8417, 2, '2015-10-13 07:50:43.187', 2, NULL, NULL, '&lt;p&gt;There is not really a "right way" to use machine learning outside of running the algorithms, any more than there is a "right way" to use a sorting algorithm, or any other kind of complex automation.&lt;/p&gt;

&lt;p&gt;Some guidelines/thoughts:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;You can start using your model to make predictions, once those predictions are useful to you. This may be once they pass some threshold in performance that would make the predictions the best way of achieving some other goal. That may just mean "better than the last ML model", but could also mean "cheaper and faster than having some people doing it, even if it is less accurate".&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Depending on how your training data is collected, and how the model works, it may be possible to &lt;em&gt;both&lt;/em&gt; use your model for predictions, and to continue training it on new data as it arrives.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;However, there is no &lt;em&gt;requirement&lt;/em&gt; for continuous learning. Learning systems that continuously adapt to new data are useful in some situations (example might be recommendation systems), but might be inappropriate in others (example might be automatic driver for a vehicle, where you don''t want to risk reduction in performance whilst in use).&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;One thing that &lt;em&gt;is&lt;/em&gt; considered very standard practice is to demonstrate how the machine learning system is performing after a period of learning by using a test metric. The simplest variation is to keep some of your precious data to one side, deliberately not training on it, then measure the performance. Often two such sets are kept aside - a &lt;em&gt;cross validation&lt;/em&gt; set is used to help decide automatically between variations of your learning algorithm, and a &lt;em&gt;test&lt;/em&gt; set is used to assess the end result of that selection.&lt;/p&gt;
', NULL, NULL, '''/em'':138C,165C,223C,282C,300C ''/li'':115C,156C,215C ''/p'':38C,42C,114C,155C,214C,312C ''/ul'':216C ''accur'':113C ''achiev'':81C ''adapt'':173C ''algorithm'':17C,30C,295C ''also'':97C ''arriv'':154C ''asid'':277C ''assess'':305C ''autom'':37C ''automat'':196C,289C ''best'':78C ''better'':89C ''cheaper'':99C ''collect'':125C ''complex'':36C ''consid'':224C ''continu'':146C,167C,172C ''could'':96C ''cross'':280C ''data'':123C,151C,176C,258C ''decid'':288C ''deliber'':262C ''demonstr'':230C ''depend'':118C ''driver'':197C ''em'':136C,163C,221C,279C,298C ''end'':307C ''even'':108C ''exampl'':182C,193C ''faster'':101C ''goal'':84C ''guidelines/thoughts'':41C ''help'':287C ''howev'':159C ''inappropri'':190C ''keep'':253C ''kept'':276C ''kind'':34C ''last'':92C ''learn'':12C,168C,169C,234C,242C,294C ''less'':112C ''li'':44C,116C,157C ''machin'':11C,233C ''make'':53C,74C ''may'':63C,86C,132C ''mean'':88C,98C ''measur'':268C ''metric'':247C ''might'':183C,188C,194C ''ml'':93C ''model'':51C,94C,129C,141C ''new'':150C,175C ''often'':271C ''one'':218C,260C ''other'':192C ''outsid'':13C ''p'':1C,39C,45C,117C,158C,217C ''pass'':67C ''peopl'':105C ''perform'':71C,210C,237C,270C ''period'':240C ''possibl'':134C ''practic'':227C ''precious'':257C ''predict'':54C,57C,76C,143C ''realli'':5C ''recommend'':185C ''reduct'':208C ''requir'':164C ''result'':308C ''right'':7C,24C ''risk'':207C ''run'':15C ''select'':311C ''set'':274C,283C,301C ''side'':261C ''simplest'':249C ''situat'':181C ''sort'':29C ''standard'':226C ''start'':48C ''system'':170C,186C,235C ''test'':246C,299C ''thing'':219C ''threshold'':69C ''train'':122C,147C,264C ''two'':272C ''ul'':43C ''use'':10C,27C,49C,59C,139C,178C,213C,244C,285C,303C ''valid'':281C ''variat'':250C,291C ''vehicl'':200C ''want'':205C ''way'':8C,25C,79C ''whilst'':211C ''work'':130C ''would'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8418, 2, '2015-10-13 09:33:02.64', 1, NULL, NULL, '&lt;p&gt;The Leipzig Wortschatz project offers such services on their Web interface, try &lt;a href="http://corpora.informatik.uni-leipzig.de/" rel="nofollow"&gt;http://corpora.informatik.uni-leipzig.de/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You can also download their corpora.&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':21C ''/p'':29C ''also'':25C ''corpora'':28C ''corpora.informatik.uni-leipzig.de'':16C,20C ''corpora.informatik.uni-leipzig.de/&lt;/a&gt;&lt;/p&gt;'':19C ''download'':26C ''href'':15C ''interfac'':12C ''leipzig'':3C ''nofollow'':18C ''offer'':6C ''p'':1C,22C ''project'':5C ''rel'':17C ''servic'':8C ''tri'':13C ''web'':11C ''wortschatz'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8419, 2, '2015-10-13 13:55:36.163', 0, NULL, NULL, '&lt;p&gt;I took an example of Data with two people A and B with wealth of unit 1 and unit 3 respectively. 
Gini Impurity as per Wikipedia = 1 - [ (1/4)^2 + (3/4)^2 ] = 3/8&lt;/p&gt;

&lt;p&gt;Gini coefficient as per Wikipedia would be  ratio of area between red and blue line to the total area under blue line in the following graph
&lt;a href="http://i.stack.imgur.com/HDPUo.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/HDPUo.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Area under red line is 1/2 + 1 + 3/2 = 3&lt;/p&gt;

&lt;p&gt;Total area under blue line = 4&lt;/p&gt;

&lt;p&gt;So Gini coefficient = 3/4&lt;/p&gt;

&lt;p&gt;Clearly the two numbers are different. I will check more cases to see if they are proportional or there is an exact relationship and edit the answer.&lt;/p&gt;
', NULL, NULL, '''/a'':79C ''/hdpuo.jpg'':66C,73C ''/p'':34C,80C,91C,99C,105C,134C ''1'':18C,28C,88C ''1/2'':87C ''1/4'':29C ''2'':30C,32C ''3'':21C,90C ''3/2'':89C ''3/4'':31C,104C ''3/8'':33C ''4'':98C ''alt'':74C ''answer'':133C ''area'':45C,54C,82C,94C ''b'':13C ''blue'':49C,56C,96C ''case'':117C ''check'':115C ''clear'':107C ''coeffici'':37C,103C ''data'':7C ''descript'':77C ''differ'':112C ''edit'':131C ''enter'':75C ''exact'':128C ''exampl'':5C ''follow'':60C ''gini'':23C,36C,102C ''graph'':61C ''href'':63C ''i.stack.imgur.com'':65C,72C ''i.stack.imgur.com/hdpuo.jpg'':64C,71C ''imag'':76C ''img'':69C ''impur'':24C ''line'':50C,57C,85C,97C ''nofollow'':68C ''number'':110C ''p'':1C,35C,81C,92C,100C,106C ''peopl'':10C ''per'':26C,39C ''proport'':123C ''ratio'':43C ''red'':47C,84C ''rel'':67C ''relationship'':129C ''respect'':22C ''see'':119C ''src'':70C ''took'':3C ''total'':53C,93C ''two'':9C,109C ''unit'':17C,20C ''wealth'':15C ''wikipedia'':27C,40C ''would'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8451, 1, '2015-10-15 20:07:41.827', 2, 63, 'Visualizing Latent Features', '&lt;p&gt;In performing ALS and getting an item matrix of latent features, what would be the best method for inferring the possible "meaning" of each latent factor in the item space? And as a corollary, are the any methods for successfully visualizing this data to gain some intuition or understanding?  &lt;/p&gt;
', 'machine-learning  recommendation  visualization', NULL, '''/p'':59C ''al'':12C ''best'':25C ''corollari'':43C ''data'':52C ''factor'':35C ''featur'':3A,20C ''gain'':54C ''get'':14C ''infer'':28C ''intuit'':56C ''item'':16C,38C ''latent'':2A,19C,34C ''learn'':6B ''machin'':5B ''machine-learn'':4B ''matrix'':17C ''mean'':31C ''method'':26C,47C ''p'':9C ''perform'':11C ''possibl'':30C ''recommend'':7B ''space'':39C ''success'':49C ''understand'':58C ''visual'':1A,8B,50C ''would'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8420, 1, '2015-10-13 15:26:31.877', 0, 9, 'Knowledge inside financial errors', '&lt;p&gt;I''m a beginner in DS from Recife, Brazil. &lt;/p&gt;

&lt;p&gt;And there is a initiative called "Recife Open Data"(link: &lt;a href="http://dados.recife.pe.gov.br/" rel="nofollow"&gt;http://dados.recife.pe.gov.br/&lt;/a&gt;) which provides to us some &lt;strong&gt;data from the City Hall&lt;/strong&gt;. &lt;/p&gt;

&lt;p&gt;Anyways, I was looking at the bills of the City and there are bills &lt;strong&gt;&lt;em&gt;who have negative numbers and 0$s!&lt;/em&gt;&lt;/strong&gt;. Besides the fact that I think this has some fraud (or really bad administration on it), I wanted to start a discussion about how this "possible errors" in accountability can have some knowledge?. Does anyone here has any other examples of financial data which you obtained knowledge from errors inside the process or even better, which kind of errors in the administration process you could identify from the data?&lt;/p&gt;

&lt;p&gt;Just, for your information, I''m using Python and Pandas, Numpy, to analyze it.&lt;/p&gt;

&lt;p&gt;Thanks in advance.&lt;/p&gt;
', 'data-mining  data', NULL, '''/&lt;/a&gt;)'':38C ''/em'':76C ''/p'':19C,51C,147C,163C,168C ''/strong'':50C,77C ''0'':74C ''account'':106C ''administr'':91C,139C ''advanc'':167C ''analyz'':161C ''anyon'':112C ''anyway'':53C ''bad'':90C ''beginn'':13C ''besid'':78C ''better'':132C ''bill'':59C,66C ''brazil'':18C ''call'':26C ''citi'':48C,62C ''could'':142C ''dados.recife.pe.gov.br'':33C,37C ''dados.recife.pe.gov.br/&lt;/a&gt;)'':36C ''data'':6B,8B,29C,45C,120C,146C ''data-min'':5B ''discuss'':99C ''ds'':15C ''em'':68C ''error'':4A,104C,126C,136C ''even'':131C ''exampl'':117C ''fact'':80C ''financi'':3A,119C ''fraud'':87C ''hall'':49C ''href'':32C ''identifi'':143C ''inform'':152C ''initi'':25C ''insid'':2A,127C ''kind'':134C ''knowledg'':1A,110C,124C ''link'':30C ''look'':56C ''m'':11C,154C ''mine'':7B ''negat'':71C ''nofollow'':35C ''number'':72C ''numpi'':159C ''obtain'':123C ''open'':28C ''p'':9C,20C,52C,148C,164C ''panda'':158C ''possibl'':103C ''process'':129C,140C ''provid'':40C ''python'':156C ''realli'':89C ''recif'':17C,27C ''rel'':34C ''start'':97C ''strong'':44C,67C ''thank'':165C ''think'':83C ''us'':42C ''use'':155C ''want'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8421, 1, '2015-10-13 16:17:28.133', 1, 61, 'Item Based Collaborative Filtering with No Ratings', '&lt;p&gt;I am building a recommender for web pages. For each web page in our data set, we wish to generate a list of web pages that other users have also visited. &lt;/p&gt;

&lt;p&gt;Our data only shows that a user has either visited a page, or they have not. Users do not provide any ratings of our web pages. This is a good task for item based recommendation. However, most of the algorithms (such as the one in Mahout) requires rating data.&lt;/p&gt;

&lt;p&gt;The first solution I came up with was to use a graph database and write a query which does the following:&lt;/p&gt;

&lt;p&gt;For each page we want recommendations for, we search for all the users who have viewed that page. Then, for each of those users, we look up all other pages they have viewed. We then count the number of users which have viewed each page in this data set, and use those with the highest count as our recommendations.&lt;/p&gt;

&lt;p&gt;While this works pretty well, our data set has grown substantially and scaling the graph database is difficult. The queries become slower as the number of page views in our data set increases. We would like to consider a different implementation before we commit to moving to a distributed graph database.&lt;/p&gt;

&lt;p&gt;In a more traditional item-based recommender (like Mahout''s), is there a good way to ''fake'' the ranking data, or is there a popular open source implementation which does not requires the ranking data?&lt;/p&gt;
', 'recommendation  open-source  apache-mahout', NULL, '''/p'':47C,98C,121C,182C,235C,273C ''algorithm'':88C ''also'':45C ''apach'':13B ''apache-mahout'':12B ''base'':2A,82C,243C ''becom'':204C ''build'':18C ''came'':104C ''collabor'':3A ''commit'':227C ''consid'':221C ''count'':158C,178C ''data'':30C,50C,97C,170C,190C,214C,257C,272C ''databas'':112C,199C,234C ''differ'':223C ''difficult'':201C ''distribut'':232C ''either'':57C ''fake'':254C ''filter'':4A ''first'':101C ''follow'':120C ''generat'':35C ''good'':78C,251C ''graph'':111C,198C,233C ''grown'':193C ''highest'':177C ''howev'':84C ''implement'':224C,265C ''increas'':216C ''item'':1A,81C,242C ''item-bas'':241C ''like'':219C,245C ''list'':37C ''look'':148C ''mahout'':14B,94C,246C ''move'':229C ''number'':160C,208C ''one'':92C ''open'':10B,263C ''open-sourc'':9B ''p'':15C,48C,99C,122C,183C,236C ''page'':23C,27C,40C,60C,74C,125C,140C,152C,167C,210C ''popular'':262C ''pretti'':187C ''provid'':68C ''queri'':116C,203C ''rank'':256C,271C ''rate'':7A,70C,96C ''recommend'':8B,20C,83C,128C,181C,244C ''requir'':95C,269C ''scale'':196C ''search'':131C ''set'':31C,171C,191C,215C ''show'':52C ''slower'':205C ''solut'':102C ''sourc'':11B,264C ''substanti'':194C ''task'':79C ''tradit'':240C ''use'':109C,173C ''user'':43C,55C,65C,135C,146C,162C ''view'':138C,155C,165C,211C ''visit'':46C,58C ''want'':127C ''way'':252C ''web'':22C,26C,39C,73C ''well'':188C ''wish'':33C ''work'':186C ''would'':218C ''write'':114C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8450, 1, '2015-10-15 20:05:36.923', 1, 43, 'tf-idf clustering', '&lt;p&gt;I have over a million text documents that I would like to cluster. I used tf-idf modeling and term vector cosine for identifying similar documents in the corpus, which appeared to work well.&lt;/p&gt;

&lt;p&gt;Some document are annotated with issues labels (e.g., a,b,c,d â twenty in all).  Each issue has as least 100 documents.  &lt;/p&gt;

&lt;p&gt;I would like to compute the average document for each cluster. What would be the best approach to normalization in this context? Right now, I am normalizing using $0.5 + \big{(}0.5 * \frac{term}{\max{term}}\big{)}$. Would it be better to compute the average of the normalized vectors instead? Alternatively, I could compute the sum of all raw term frequencies and then normalize that. What is the best approach to create a normalized term vector for each issue?&lt;/p&gt;
', 'clustering  cosine-distance', NULL, '''/p'':45C,70C,150C ''0.5'':100C,102C ''100'':68C ''altern'':121C ''annot'':50C ''appear'':41C ''approach'':88C,140C ''averag'':78C,115C ''b'':56C ''best'':87C,139C ''better'':111C ''big'':101C,107C ''c'':57C ''cluster'':4A,5B,22C,82C ''comput'':76C,113C,124C ''context'':93C ''corpus'':39C ''cosin'':7B,32C ''cosine-dist'':6B ''could'':123C ''creat'':142C ''d'':58C ''distanc'':8B ''document'':16C,36C,48C,69C,79C ''e.g'':54C ''frac'':103C ''frequenc'':131C ''identifi'':34C ''idf'':3A,27C ''instead'':120C ''issu'':52C,64C,149C ''label'':53C ''least'':67C ''like'':20C,74C ''max'':105C ''million'':14C ''model'':28C ''normal'':90C,98C,118C,134C,144C ''p'':9C,46C,71C ''raw'':129C ''right'':94C ''similar'':35C ''sum'':126C ''term'':30C,104C,106C,130C,145C ''text'':15C ''tf'':2A,26C ''tf-idf'':1A,25C ''twenti'':60C ''use'':24C,99C ''vector'':31C,119C,146C ''well'':44C ''work'':43C ''would'':19C,73C,84C,108C ''â'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8425, 2, '2015-10-13 22:22:02.087', 3, NULL, NULL, '&lt;p&gt;Scan based operations are basically all the operations that require evaluating the predicate on an RDD. &lt;/p&gt;

&lt;p&gt;In other terms, each time your create an RDD or a DataFrame in which you need to compute a &lt;em&gt;predicate&lt;/em&gt; like performing a filter, map on a case class, per example, or even explain method will be considered as a scan based operation. &lt;/p&gt;

&lt;p&gt;To be more clear, let''s review the definition of a predicate.&lt;/p&gt;

&lt;p&gt;A predicate or a functional predicate is a logical symbol that may be applied to an object term to produce another object term. &lt;/p&gt;

&lt;p&gt;Functional predicates are also sometimes called &lt;em&gt;mappings&lt;/em&gt;, but that term can have other meanings as well.&lt;/p&gt;

&lt;p&gt;Example :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;// scan based transformation
rdd.filter(!_.contains("#")) // here the predicate is !_.contains("#")

// another scan based transformation
rdd.filter(myfunc) // myfunc is a boolean function

// a third also trivial scan based transformation followed by a non scan based one.
rdd.map(myfunc2) 
   .reduce(myfunc3)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If you want to understand how spark internals work, I suggest that you watch the &lt;a href="https://spark-summit.org/2014/talk/a-deeper-understanding-of-spark-internals" rel="nofollow"&gt;presentation&lt;/a&gt; made by Databricks about the topics&lt;/p&gt;
', NULL, NULL, '''/2014/talk/a-deeper-understanding-of-spark-internals'':191C ''/a'':195C ''/code'':169C ''/em'':41C,114C ''/p'':18C,65C,79C,104C,124C,127C,202C ''/pre'':170C ''also'':109C,153C ''anoth'':101C,140C ''appli'':94C ''base'':3C,63C,131C,142C,156C,163C ''basic'':6C ''boolean'':149C ''call'':111C ''case'':49C ''class'':50C ''clear'':70C ''code'':129C ''comput'':37C ''consid'':59C ''contain'':134C,139C ''creat'':26C ''databrick'':198C ''datafram'':31C ''definit'':75C ''em'':39C,112C ''evalu'':12C ''even'':54C ''exampl'':52C,126C ''explain'':55C ''filter'':45C ''follow'':158C ''function'':85C,106C,150C ''href'':188C ''intern'':179C ''let'':71C ''like'':42C ''logic'':89C ''made'':196C ''map'':46C,113C ''may'':92C ''mean'':121C ''method'':56C ''myfunc'':145C,146C ''myfunc2'':166C ''myfunc3'':168C ''need'':35C ''nofollow'':193C ''non'':161C ''object'':97C,102C ''one'':164C ''oper'':4C,9C,64C ''p'':1C,19C,66C,80C,105C,125C,171C ''per'':51C ''perform'':43C ''pre'':128C ''predic'':14C,40C,78C,82C,86C,107C,137C ''present'':194C ''produc'':100C ''rdd'':17C,28C ''rdd.filter'':133C,144C ''rdd.map'':165C ''reduc'':167C ''rel'':192C ''requir'':11C ''review'':73C ''scan'':2C,62C,130C,141C,155C,162C ''sometim'':110C ''spark'':178C ''spark-summit.org'':190C ''spark-summit.org/2014/talk/a-deeper-understanding-of-spark-internals'':189C ''suggest'':182C ''symbol'':90C ''term'':22C,98C,103C,117C ''third'':152C ''time'':24C ''topic'':201C ''transform'':132C,143C,157C ''trivial'':154C ''understand'':176C ''want'':174C ''watch'':185C ''well'':123C ''work'':180C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8798, 2, '2015-11-11 12:20:43.383', 0, NULL, NULL, '&lt;p&gt;I think you need to read about &lt;a href="https://en.wikipedia.org/wiki/Online_machine_learning" rel="nofollow"&gt;Online learning&lt;/a&gt;, it refers to learning when new data is being constantly added. In these cases you need an algorithm that can update itself as new data arrives (i.e. it doesn''t need to recalculate itself from scratch). In other words, &lt;em&gt;incrementally&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;There are incremental versions for support vector machines (SVMs) and for neural networks. Also, bayesian networks can be made to work incrementally.&lt;/p&gt;
', NULL, NULL, '''/a'':18C ''/em'':60C ''/p'':61C,85C ''/wiki/online_machine_learning'':13C ''ad'':29C ''algorithm'':36C ''also'':76C ''arriv'':44C ''bayesian'':77C ''case'':32C ''constant'':28C ''data'':25C,43C ''doesn'':47C ''em'':58C ''en.wikipedia.org'':12C ''en.wikipedia.org/wiki/online_machine_learning'':11C ''href'':10C ''i.e'':45C ''increment'':59C,65C,84C ''learn'':17C,22C ''machin'':70C ''made'':81C ''need'':5C,34C,49C ''network'':75C,78C ''neural'':74C ''new'':24C,42C ''nofollow'':15C ''onlin'':16C ''p'':1C,62C ''read'':7C ''recalcul'':51C ''refer'':20C ''rel'':14C ''scratch'':54C ''support'':68C ''svms'':71C ''think'':3C ''updat'':39C ''vector'':69C ''version'':66C ''word'':57C ''work'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8426, 1, '2015-10-13 22:23:39.02', 1, 73, 'Cosine Distance &gt; 1 in scipy', '&lt;p&gt;I am working on a recommendation engine, and I have chosen to use SciPy''s cosine distance as a way of comparing items. &lt;/p&gt;

&lt;p&gt;I have two vectors:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;a = [2.7654870801855078, 0.35995355443076027, 0.016221679989074141, -0.012664358453398751, 0.0036888812311235068]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;b = [-6.2588482809118942, -0.88952297609194686, 0.017336984676103874, -0.0054928004763216964, 0.011122959185936367]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Running the following code will produce an output of ~1.999:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from scipy.spatial import distance
print(distance.cosine(a,b))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is there something wrong with my input values? Anyone know why I am getting a result of &gt;1?&lt;/p&gt;
', 'python  distance  cosine-distance', NULL, '''-0.0054928004763216964'':61C ''-0.012664358453398751'':48C ''-0.88952297609194686'':59C ''-6.2588482809118942'':58C ''/code'':50C,63C,87C ''/p'':35C,41C,54C,76C,108C ''/pre'':51C,64C,88C ''0.0036888812311235068'':49C ''0.011122959185936367'':62C ''0.016221679989074141'':47C ''0.017336984676103874'':60C ''0.35995355443076027'':46C ''1'':3A,107C ''1.999'':75C ''2.7654870801855078'':45C ''anyon'':98C ''b'':57C,86C ''chosen'':22C ''code'':43C,56C,69C,78C ''compar'':33C ''cosin'':1A,9B,27C ''cosine-dist'':8B ''distanc'':2A,7B,10B,28C,82C ''distance.cosine'':84C ''engin'':18C ''follow'':68C ''get'':103C ''import'':81C ''input'':96C ''item'':34C ''know'':99C ''output'':73C ''p'':11C,36C,52C,65C,89C ''pre'':42C,55C,77C ''print'':83C ''produc'':71C ''python'':6B ''recommend'':17C ''result'':105C ''run'':66C ''scipi'':5A,25C ''scipy.spatial'':80C ''someth'':92C ''two'':39C ''use'':24C ''valu'':97C ''vector'':40C ''way'':31C ''work'':14C ''wrong'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8427, 1, '2015-10-14 03:21:48.33', 2, 76, 'What is the best way to split a sentence for a keyword extraction task?', '&lt;p&gt;I''m doing a keyword extraction using TF-IDF on a large number of documents. Currently, I''m splitting each sentence based on n-gram. More particularly, I''m using trigrams. However, this is not the best way to split each sentence into ints constituting keywords. For example a noun phrase like ''triple heart bypass'' may not always get detected as one term. &lt;/p&gt;

&lt;p&gt;The other alternative to chunk each sentence into its constituting elements look to be part of speech tagging and [chunking][1] in [Open NLP][2]. In this approach phrase like ''triple heart bypass'' always gets extracted as a whole but the downside is in TF-IDF the frequency of extracted terms (phrases) dramatically drops. &lt;/p&gt;

&lt;p&gt;Does anyone have any suggestion on either or these two approaches or have any other ideas to improve the quality of the keywords?&lt;/p&gt;
', 'nlp  text-mining', NULL, '''/p'':85C,142C,167C ''1'':107C ''2'':111C ''altern'':89C ''alway'':79C,120C ''anyon'':145C ''approach'':114C,154C ''base'':42C ''best'':4A,58C ''bypass'':76C,119C ''chunk'':91C,106C ''constitut'':66C,96C ''current'':36C ''detect'':81C ''document'':35C ''downsid'':128C ''dramat'':140C ''drop'':141C ''either'':150C ''element'':97C ''exampl'':69C ''extract'':13A,25C,122C,137C ''frequenc'':135C ''get'':80C,121C ''gram'':46C ''heart'':75C,118C ''howev'':53C ''idea'':159C ''idf'':29C,133C ''improv'':161C ''int'':65C ''keyword'':12A,24C,67C,166C ''larg'':32C ''like'':73C,116C ''look'':98C ''m'':21C,38C,50C ''may'':77C ''mine'':18B ''n'':45C ''n-gram'':44C ''nlp'':15B,110C ''noun'':71C ''number'':33C ''one'':83C ''open'':109C ''p'':19C,86C,143C ''part'':101C ''particular'':48C ''phrase'':72C,115C,139C ''qualiti'':163C ''sentenc'':9A,41C,63C,93C ''speech'':103C ''split'':7A,39C,61C ''suggest'':148C ''tag'':104C ''task'':14A ''term'':84C,138C ''text'':17B ''text-min'':16B ''tf'':28C,132C ''tf-idf'':27C,131C ''trigram'':52C ''tripl'':74C,117C ''two'':153C ''use'':26C,51C ''way'':5A,59C ''whole'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8428, 1, '2015-10-14 05:56:06.07', 1, 63, 'Use the output of 2 hidden neurons in the last hidden layer of a NN to visualize the result of a 4-class classification task', '&lt;p&gt;I am working on a 4-class classification task using an Artificial Neural Network. My aim is to visualize how well those 4 classes can be separated, moreover how consistent the data coming from each class is and how far they are "away" from one another.&lt;/p&gt;

&lt;p&gt;My approach is to use 2 neurons in the last hidden layer and plot the output of the neurons in a scatterplot. However my current output usually looks like this where the different colors stand for the actual classes the data is coming from:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/xbb9H.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/xbb9H.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Obviously this is not very useful. First of all I was hoping that the points would not all be all along the same line and second of all I am not sure if this can even be interpreted in the way that I want.&lt;/p&gt;

&lt;p&gt;I am using the neuralnet package in R to train the network using linear output. I am guessing that if this approach can work the choice of activation function would be crucial.&lt;/p&gt;

&lt;p&gt;I am new to Neural Networks and I am not sure whether or not something like this can be done or not and I am hoping to get some comments.&lt;/p&gt;
', 'r  classification  neuralnetwork  visualization', NULL, '''/a'':142C ''/p'':77C,123C,143C,189C,223C,255C ''/xbb9h.png'':129C,136C ''2'':5A,84C ''4'':22A,36C,53C ''activ'':218C ''actual'':116C ''aim'':46C ''along'':165C ''alt'':137C ''anoth'':76C ''approach'':80C,212C ''artifici'':42C ''away'':73C ''choic'':216C ''class'':23A,37C,54C,66C,117C ''classif'':24A,27B,38C ''color'':112C ''come'':63C,121C ''comment'':254C ''consist'':60C ''crucial'':222C ''current'':103C ''data'':62C,119C ''descript'':140C ''differ'':111C ''done'':244C ''enter'':138C ''even'':180C ''far'':70C ''first'':151C ''function'':219C ''get'':252C ''guess'':208C ''hidden'':6A,11A,89C ''hope'':156C,250C ''howev'':101C ''href'':126C ''i.stack.imgur.com'':128C,135C ''i.stack.imgur.com/xbb9h.png'':127C,134C ''imag'':139C ''img'':132C ''interpret'':182C ''last'':10A,88C ''layer'':12A,90C ''like'':107C,240C ''line'':168C ''linear'':204C ''look'':106C ''moreov'':58C ''network'':44C,202C,230C ''neural'':43C,229C ''neuralnet'':195C ''neuralnetwork'':28B ''neuron'':7A,85C,97C ''new'':227C ''nn'':15A ''nofollow'':131C ''obvious'':145C ''one'':75C ''output'':3A,94C,104C,205C ''p'':30C,78C,124C,144C,190C,224C ''packag'':196C ''plot'':92C ''point'':159C ''r'':26B,198C ''rel'':130C ''result'':19A ''scatterplot'':100C ''second'':170C ''separ'':57C ''someth'':239C ''src'':133C ''stand'':113C ''sure'':176C,235C ''task'':25A,39C ''train'':200C ''use'':1A,40C,83C,150C,193C,203C ''usual'':105C ''visual'':17A,29B,49C ''want'':188C ''way'':185C ''well'':51C ''whether'':236C ''work'':33C,214C ''would'':160C,220C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8429, 2, '2015-10-14 07:46:51.447', 5, NULL, NULL, '&lt;p&gt;The cosine distance formula is:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/UmTFw.png"&gt;&lt;img src="http://i.stack.imgur.com/UmTFw.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;And the formula used by the &lt;code&gt;cosine&lt;/code&gt; function of the &lt;code&gt;spatial&lt;/code&gt; class of scipy is:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/KLk4n.png"&gt;&lt;img src="http://i.stack.imgur.com/KLk4n.png" alt="enter image description here"&gt;&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;So, the actual cosine similarity metric is: -0.9998.&lt;/p&gt;

&lt;p&gt;So, it signifies complete dissimilarity.&lt;/p&gt;
', NULL, NULL, '''-0.9998'':73C ''/a'':24C,63C ''/code'':35C,41C ''/klk4n.png'':52C,57C ''/p'':7C,25C,46C,64C,74C,81C ''/umtfw.png'':13C,18C ''actual'':68C ''alt'':19C,58C ''class'':42C ''code'':33C,39C ''complet'':79C ''cosin'':3C,34C,69C ''descript'':22C,61C ''dissimilar'':80C ''distanc'':4C ''enter'':20C,59C ''formula'':5C,29C ''function'':36C ''href'':10C,49C ''i.stack.imgur.com'':12C,17C,51C,56C ''i.stack.imgur.com/klk4n.png'':50C,55C ''i.stack.imgur.com/umtfw.png'':11C,16C ''imag'':21C,60C ''img'':14C,53C ''metric'':71C ''p'':1C,8C,26C,47C,65C,75C ''scipi'':44C ''signifi'':78C ''similar'':70C ''spatial'':40C ''src'':15C,54C ''use'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8432, 2, '2015-10-14 13:32:46.583', 1, NULL, NULL, '&lt;h2&gt;Found the answer&lt;/h2&gt;

&lt;p&gt;I found out why it was going slowly, after doing some profiling and reading &lt;a href="https://groups.google.com/forum/#!topic/theano-users/xX_p8xm3pqg" rel="nofollow"&gt;this&lt;/a&gt;:&lt;/p&gt;

&lt;p&gt;It turns out that if you do a convolution with a stride != 1, then it doesn''t use the GPU for that operation (or perhaps the gradient of that operation), and makes things run much slower.  I was only using a non-1 stride to save execution time anyway, but turns out I''m better off getting rid of the stride.&lt;/p&gt;

&lt;p&gt;Also note that in your convoution layer, you have to completely get rid of &lt;code&gt;stride=X&lt;/code&gt;.  Just having &lt;code&gt;stride=1&lt;/code&gt; is not sufficient.&lt;/p&gt;
', NULL, NULL, '''-1'':74C ''/a'':29C ''/code'':112C,118C ''/forum/#!topic/theano-users/xx_p8xm3pqg'':25C ''/h2'':5C ''/p'':30C,93C,122C ''1'':44C,117C ''also'':95C ''answer'':4C ''anyway'':80C ''better'':86C ''code'':109C,115C ''complet'':105C ''convolut'':40C ''convout'':100C ''doesn'':47C ''execut'':78C ''found'':2C,8C ''get'':88C,106C ''go'':13C ''gpu'':51C ''gradient'':58C ''groups.google.com'':24C ''groups.google.com/forum/#!topic/theano-users/xx_p8xm3pqg'':23C ''h2'':1C ''href'':22C ''layer'':101C ''m'':85C ''make'':63C ''much'':66C ''nofollow'':27C ''non'':73C ''note'':96C ''oper'':54C,61C ''p'':6C,31C,94C ''perhap'':56C ''profil'':18C ''read'':20C ''rel'':26C ''rid'':89C,107C ''run'':65C ''save'':77C ''slower'':67C ''slowli'':14C ''stride'':43C,75C,92C,110C,116C ''suffici'':121C ''thing'':64C ''time'':79C ''turn'':33C,82C ''use'':49C,71C ''x'':111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10232, 1, '2016-02-13 15:57:15.663', 2, 52, 'Decode SQLite database blobs: how to start?', '&lt;p&gt;&lt;em&gt;Updated: see bottom of this question.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;Not sure if I am at the right channel here but I have good hopes someone here might be able to help me.&lt;/p&gt;

&lt;p&gt;I am trying to process and analyse data from a &lt;a href="http://www.rsscan.com/footscan/entry-level/#tab_products" rel="nofollow"&gt;Footscan system&lt;/a&gt; that is exported from the &lt;a href="http://www.rsscan.com/footscan/entry-level/#software-9" rel="nofollow"&gt;Footscan 9 Gait Essentials&lt;/a&gt; software to a .rsdb database. This database is a standard SQLite database with a different file extension.&lt;/p&gt;

&lt;p&gt;I have no problems accessing the data via Python but the most interesting data (the raw data from the sensor) is stored as a ''blob'' (see &lt;a href="https://www.sqlite.org/datatype3.html" rel="nofollow"&gt;sqlite documentation&lt;/a&gt; for more details).&lt;/p&gt;

&lt;p&gt;Exporting this data from SQLitePro to .csv shows that the data in the blob looks like this (although much longer):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;9c4e79cb cbd09de5 c380fc78 a74e819f ... ab2172c7 d9cf311a 357aebac&amp;gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;My question is&lt;/strong&gt;: what would be my first steps to try to discover how this data is encoded? And does anyone maybe recognize this type of encoding?&lt;/p&gt;

&lt;p&gt;Extra hints: The data represents all forces of a force plate with 64x64 force sensors, sampled at 300Hz, so it''s most probably a multidimensional array.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Update&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I am making some progress. As commented below, the values are most probably hexadecimal values, but I''m not sure if the values are base 16: most values seem to be &gt;10^9...&lt;/p&gt;

&lt;p&gt;Furthermore: I found a table in the database called ''Contact'' which linkes to entries in the blob table which contain the most hexadecimal values (&gt;40000). My guess is that these are the entries which are recognized as actual steps. I extracted the data from one of these steps &lt;a href="https://www.dropbox.com/s/4ss303k624ihcos/rsdb_export_24?dl=0" rel="nofollow"&gt;here&lt;/a&gt; (Contact id 2; blob id 24). This blob contains 50792 hexadecimal values.&lt;/p&gt;

&lt;p&gt;This is the entry for this step in ''Contacts'':&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;id: {35d9a73e-608d-4ba3-9823-de0540f11c93}
timestamp: 2
deleted: NULL
FrameWidth: 64
Frames: 24
FrameOffset: 12204
FrameCount: 214
FrameHeight: 64
FrameStoreWithContacts_Contacs: {5d10546e-5ef3-4469-b898-d7b9bd58458f}
OriginalId: {32ae0f73-8022-4151-92e7-510c0e86d09d}
orphan: false
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Full export of the blob table can be downloaded &lt;a href="https://www.dropbox.com/s/ihvpyw3fx2xhoqz/BlobTable.csv?dl=0" rel="nofollow"&gt;here&lt;/a&gt;.
The full .rsdb can be found &lt;a href="https://www.dropbox.com/s/0pb449qbi2pzcnl/Aart_goossens_2016-02-10_20-11-27.rsdb?dl=0" rel="nofollow"&gt;here&lt;/a&gt; (you might have to rename to &lt;code&gt;.sqlite&lt;/code&gt;).
The tables ''Foot'' and ''Region'' in the full database might contain useful data too, but I''m not yet sure how...&lt;/p&gt;
', 'databases  parsing', NULL, '''-4151'':394C ''-510'':396C ''-8022'':393C ''-92e7'':395C ''/a'':65C,82C,137C,334C,420C,435C ''/code'':175C,400C,444C ''/datatype3.html'':132C ''/em'':18C ''/footscan/entry-level/#software-9'':75C ''/footscan/entry-level/#tab_products'':60C ''/p'':19C,44C,100C,141C,163C,207C,235C,241C,276C,347C,358C,466C ''/pre'':176C,401C ''/s/0pb449qbi2pzcnl/aart_goossens_2016-02-10_20-11-27.rsdb?dl=0'':431C ''/s/4ss303k624ihcos/rsdb_export_24?dl=0'':330C ''/s/ihvpyw3fx2xhoqz/blobtable.csv?dl=0'':416C ''/strong'':182C,240C ''10'':274C ''12204'':377C ''16'':268C ''2'':337C,369C ''214'':379C ''24'':340C,375C ''300hz'':226C ''32ae0f73'':392C ''357aebac'':173C ''35d9a73e'':363C ''35d9a73e-608d-4ba3'':362C ''40000'':302C ''4469'':387C ''4ba3'':365C ''50792'':344C ''5d10546e'':385C ''5d10546e-5ef3'':384C ''5ef3'':386C ''608d'':364C ''64'':373C,381C ''64x64'':221C ''9'':79C,275C ''9823'':366C ''9c4e79cb'':167C ''a74e819f'':170C ''ab2172c7'':171C ''abl'':40C ''access'':106C ''actual'':315C ''although'':160C ''analys'':52C ''anyon'':200C ''array'':234C ''b898'':389C ''b898-d7b9bd58458f'':388C ''base'':267C ''blob'':4A,126C,156C,294C,338C,342C,407C ''bottom'':14C ''c0e86d09d'':397C ''c380fc78'':169C ''call'':286C ''cbd09de5'':168C ''channel'':29C ''code'':165C,360C,442C ''comment'':249C ''contac'':383C ''contact'':287C,335C,357C ''contain'':297C,343C,455C ''csv'':149C ''d7b9bd58458f'':390C ''d9cf311a'':172C ''data'':53C,108C,115C,118C,145C,153C,195C,212C,320C,457C ''databas'':3A,8B,87C,89C,94C,285C,453C ''de0540f11c93'':367C ''decod'':1A ''delet'':370C ''detail'':140C ''differ'':97C ''discov'':192C ''document'':136C ''download'':411C ''em'':11C ''encod'':197C,206C ''entri'':291C,310C,352C ''essenti'':81C ''export'':68C,143C,404C ''extens'':99C ''extra'':209C ''extract'':318C ''fals'':399C ''file'':98C ''first'':187C ''foot'':447C ''footscan'':63C,78C ''forc'':215C,218C,222C ''found'':280C,426C ''frame'':374C ''framecount'':378C ''frameheight'':380C ''frameoffset'':376C ''framestorewithcontact'':382C ''framewidth'':372C ''full'':403C,422C,452C ''furthermor'':278C ''gait'':80C ''good'':34C ''gt'':174C ''guess'':304C ''help'':42C ''hexadecim'':256C,300C,345C ''hint'':210C ''hope'':35C ''hr'':236C ''href'':57C,72C,129C,327C,413C,428C ''id'':336C,339C,361C ''interest'':114C ''like'':158C ''link'':289C ''longer'':162C ''look'':157C ''lt'':166C ''m'':260C,461C ''make'':245C ''mayb'':201C ''might'':38C,437C,454C ''much'':161C ''multidimension'':233C ''nofollow'':62C,77C,134C,332C,418C,433C ''null'':371C ''one'':322C ''originalid'':391C ''orphan'':398C ''p'':10C,20C,45C,101C,142C,177C,208C,237C,242C,277C,348C,402C ''pars'':9B ''plate'':219C ''pre'':164C,359C ''probabl'':231C,255C ''problem'':105C ''process'':50C ''progress'':247C ''python'':110C ''question'':17C,180C ''raw'':117C ''recogn'':202C,313C ''region'':449C ''rel'':61C,76C,133C,331C,417C,432C ''renam'':440C ''repres'':213C ''right'':28C ''rsdb'':86C,423C ''sampl'':224C ''see'':13C,127C ''seem'':271C ''sensor'':121C,223C ''show'':150C ''softwar'':83C ''someon'':36C ''sqlite'':2A,93C,135C,443C ''sqlitepro'':147C ''standard'':92C ''start'':7A ''step'':188C,316C,325C,355C ''store'':123C ''strong'':178C,238C ''sure'':22C,262C,464C ''system'':64C ''tabl'':282C,295C,408C,446C ''timestamp'':368C ''tri'':48C,190C ''type'':204C ''updat'':12C,239C ''use'':456C ''valu'':252C,257C,265C,270C,301C,346C ''via'':109C ''would'':184C ''www.dropbox.com'':329C,415C,430C ''www.dropbox.com/s/0pb449qbi2pzcnl/aart_goossens_2016-02-10_20-11-27.rsdb?dl=0'':429C ''www.dropbox.com/s/4ss303k624ihcos/rsdb_export_24?dl=0'':328C ''www.dropbox.com/s/ihvpyw3fx2xhoqz/blobtable.csv?dl=0'':414C ''www.rsscan.com'':59C,74C ''www.rsscan.com/footscan/entry-level/#software-9'':73C ''www.rsscan.com/footscan/entry-level/#tab_products'':58C ''www.sqlite.org'':131C ''www.sqlite.org/datatype3.html'':130C ''yet'':463C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9748, 2, '2016-01-11 13:55:47.207', 0, NULL, NULL, '&lt;p&gt;I don''t think it makes a lot of sense to use HMM''s for this problem. What I would suggest is some kind of text-based classifier. 
If you want to use a cool technique, you could use a neural network to learn based on the text of successful posts.&lt;/p&gt;

&lt;p&gt;On the other hand, If you want to use an easy technique you could make a predictor for the popularity such as a regression model (try to predict upvotes).&lt;/p&gt;
', NULL, NULL, '''/p'':54C,85C ''base'':29C,47C ''classifi'':30C ''cool'':37C ''could'':40C,69C ''easi'':66C ''hand'':59C ''hmm'':14C ''kind'':25C ''learn'':46C ''lot'':9C ''make'':7C,70C ''model'':80C ''network'':44C ''neural'':43C ''p'':1C,55C ''popular'':75C ''post'':53C ''predict'':83C ''predictor'':72C ''problem'':18C ''regress'':79C ''sens'':11C ''success'':52C ''suggest'':22C ''techniqu'':38C,67C ''text'':28C,50C ''text-bas'':27C ''think'':5C ''tri'':81C ''upvot'':84C ''use'':13C,35C,41C,64C ''want'':33C,62C ''would'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8435, 1, '2015-10-14 20:55:21.26', 1, 62, 'How can I vectorize this code in R? Maybe with the apply() function?', '&lt;p&gt;I am really struggling to replicate the output of the dist() function in R code without using 1 or 2 for loops. (If you''re wondering why I''m doing this, it''s so that I can play around with the distance calculation, and also to improve my R skills - so please only solutions that involve R!)&lt;/p&gt;

&lt;p&gt;Overview: matrix is passed to dist(), which calculates the Euclidean distance row-wise and outputs a full distance matrix of the distance between each row (e.g. distance between rows 1 and 50 will be in distancematrix[1, 50] and distancematrix[50, 1]). The fast code looks like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;distancematrix &amp;lt;- as.matrix(dist(myMatrix, method="euclidean", diag = T, upper = T))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have successfully produced the same output in R using the following code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for (i in 1:nrow(myMatrix)) {
for (j in 1:nrow(myMatrix)) {
distancematrix[i, j] &amp;lt;- sum(abs(myMatrix[i,] - myMatrix[j,])) }}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However, using two nested for loops is much slower than using dist(). I have read a lot about using apply() to optimise slower for loops, but I haven''t been able to get my head around it so far. I believe that at least one of the for loops is definitely avoidable by just outputting a vector, and dealing with it at the end. However, I cannot for the life of me work out how to remove both for loops. Does anyone have any thoughts?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'machine-learning  r  programming', NULL, '''/code'':142C,183C ''/p'':77C,128C,158C,271C,274C ''/pre'':143C,184C ''1'':37C,109C,116C,121C,164C,170C ''2'':39C ''50'':111C,117C,120C ''ab'':178C ''abl'':216C ''also'':64C ''anyon'':267C ''appli'':12A,205C ''around'':58C,221C ''as.matrix'':133C ''avoid'':237C ''believ'':226C ''calcul'':62C,86C ''cannot'':252C ''code'':6A,34C,124C,130C,157C,160C ''deal'':244C ''definit'':236C ''diag'':138C ''dist'':30C,84C,134C,197C ''distanc'':61C,89C,97C,101C,106C ''distancematrix'':115C,119C,131C,173C ''e.g'':105C ''end'':249C ''euclidean'':88C,137C ''far'':224C ''fast'':123C ''follow'':156C ''full'':96C ''function'':13A,31C ''get'':218C ''haven'':213C ''head'':220C ''howev'':186C,250C ''improv'':66C ''involv'':75C ''j'':168C,175C,182C ''learn'':16B ''least'':229C ''life'':255C ''like'':126C ''look'':125C ''loop'':41C,191C,210C,234C,265C ''lot'':202C ''lt'':132C,176C ''m'':48C ''machin'':15B ''machine-learn'':14B ''matrix'':80C,98C ''mayb'':9A ''method'':136C ''much'':193C ''mymatrix'':135C,166C,172C,179C,181C ''nest'':189C ''nrow'':165C,171C ''one'':230C ''optimis'':207C ''output'':27C,94C,151C,240C ''overview'':79C ''p'':19C,78C,144C,185C,272C ''pass'':82C ''play'':57C ''pleas'':71C ''pre'':129C,159C ''produc'':148C ''program'':18B ''r'':8A,17B,33C,68C,76C,153C ''re'':44C ''read'':200C ''realli'':22C ''remov'':262C ''replic'':25C ''row'':91C,104C,108C ''row-wis'':90C ''skill'':69C ''slower'':194C,208C ''solut'':73C ''struggl'':23C ''success'':147C ''sum'':177C ''thank'':273C ''thought'':270C ''two'':188C ''upper'':140C ''use'':36C,154C,187C,196C,204C ''vector'':4A,242C ''wise'':92C ''without'':35C ''wonder'':45C ''work'':258C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8437, 2, '2015-10-14 22:49:14.24', 0, NULL, NULL, '&lt;p&gt;A further approach is to sample from models with a countably infinite number of states. The answer to ''how many'' would then emerge by averaging over the samplers. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://mlg.eng.cam.ac.uk/zoubin/papers/ihmm.pdf" rel="nofollow"&gt;http://mlg.eng.cam.ac.uk/zoubin/papers/ihmm.pdf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':30C ''/zoubin/papers/ihmm.pdf'':36C ''/zoubin/papers/ihmm.pdf&lt;/a&gt;&lt;/p&gt;'':41C ''answer'':18C ''approach'':4C ''averag'':26C ''countabl'':12C ''emerg'':24C ''href'':33C ''infinit'':13C ''mani'':21C ''mlg.eng.cam.ac.uk'':35C,40C ''mlg.eng.cam.ac.uk/zoubin/papers/ihmm.pdf'':34C ''mlg.eng.cam.ac.uk/zoubin/papers/ihmm.pdf&lt;/a&gt;&lt;/p&gt;'':39C ''model'':9C ''nofollow'':38C ''number'':14C ''p'':1C,31C ''rel'':37C ''sampl'':7C ''sampler'':29C ''state'':16C ''would'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8439, 1, '2015-10-15 01:33:28.15', 1, 40, 'Learning about chess', '&lt;p&gt;I''m interested to apply machine learning to chess. My aim is to develop a software that understands a chess position and communicate the ideas to humans. For example, I''d want the software to &lt;em&gt;explain&lt;/em&gt; what to do and &lt;em&gt;how&lt;/em&gt; to create a strategic plan. It wouldn''t be a chess-playing engine, more like that chess tutor.&lt;/p&gt;

&lt;p&gt;I know this is a very hard problem, so I''d just limit myself to pattern recognition. For example, where the weak squares are. I believe this has already been done, but not sure where to read the literature.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://chessprogramming.wikispaces.com/Learning" rel="nofollow"&gt;https://chessprogramming.wikispaces.com/Learning&lt;/a&gt; gives me 100+ links, but I don''t know where I should focus (too many papers). Any idea?&lt;/p&gt;
', 'machine-learning  chess  learning', NULL, '''/em'':47C,54C ''/learning'':121C ''/learning&lt;/a&gt;'':126C ''/p'':74C,115C,145C ''100'':129C ''aim'':20C ''alreadi'':104C ''appli'':14C ''believ'':101C ''chess'':3A,7B,18C,29C,66C,72C ''chess-play'':65C ''chessprogramming.wikispaces.com'':120C,125C ''chessprogramming.wikispaces.com/learning'':119C ''chessprogramming.wikispaces.com/learning&lt;/a&gt;'':124C ''communic'':32C ''creat'':56C ''d'':40C,86C ''develop'':23C ''done'':106C ''em'':45C,52C ''engin'':68C ''exampl'':38C,94C ''explain'':46C ''focus'':139C ''give'':127C ''hard'':82C ''href'':118C ''human'':36C ''idea'':34C,144C ''interest'':12C ''know'':77C,135C ''learn'':1A,6B,8B,16C ''like'':70C ''limit'':88C ''link'':130C ''literatur'':114C ''m'':11C ''machin'':5B,15C ''machine-learn'':4B ''mani'':141C ''nofollow'':123C ''p'':9C,75C,116C ''paper'':142C ''pattern'':91C ''plan'':59C ''play'':67C ''posit'':30C ''problem'':83C ''read'':112C ''recognit'':92C ''rel'':122C ''softwar'':25C,43C ''squar'':98C ''strateg'':58C ''sure'':109C ''tutor'':73C ''understand'':27C ''want'':41C ''weak'':97C ''wouldn'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8440, 1, '2015-10-15 02:06:37.107', 0, 64, 'Extract the "path" of a data point through a decision tree in sklearn', '&lt;p&gt;I''m working with decision trees in python''s scikit learn. Unlike many use cases for this, I''m not so much interested in the accuracy of the classifier at this point so much as I am extracting the specific path a data point takes through the tree when I call &lt;code&gt;.predict()&lt;/code&gt; on it. Has anyone done this before? I''d like to build a data frame containing ($X_{i}$, path$_{i}$) pairs for use in a down-stream analysis.&lt;/p&gt;
', 'machine-learning  python  scikit  decision-trees', NULL, '''/code'':76C ''/p'':106C ''accuraci'':48C ''analysi'':105C ''anyon'':80C ''build'':88C ''call'':73C ''case'':37C ''classifi'':51C ''code'':74C ''contain'':92C ''d'':85C ''data'':6A,65C,90C ''decis'':10A,20B,27C ''decision-tre'':19B ''done'':81C ''down-stream'':102C ''extract'':1A,60C ''frame'':91C ''interest'':45C ''learn'':16B,33C ''like'':86C ''m'':24C,41C ''machin'':15B ''machine-learn'':14B ''mani'':35C ''much'':44C,56C ''p'':22C ''pair'':97C ''path'':3A,63C,95C ''point'':7A,54C,66C ''predict'':75C ''python'':17B,30C ''scikit'':18B,32C ''sklearn'':13A ''specif'':62C ''stream'':104C ''take'':67C ''tree'':11A,21B,28C,70C ''unlik'':34C ''use'':36C,99C ''work'':25C ''x'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8441, 2, '2015-10-15 03:26:35.007', 2, NULL, NULL, '&lt;p&gt;I recommend anybody interested in the topic take a look at the &lt;a href="http://arxiv.org/abs/1509.01549" rel="nofollow"&gt;paper&lt;/a&gt; which combines TDL and deep-learning.&lt;/p&gt;

&lt;p&gt;Roughly, you''ll need to make the engine play games against each other. Record the mini-max evaluation for each position. At the end of the game, you''ll get a reward, which is {0,1,-1} for chess. Then you''ll need to adjust your parameters with:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/UPXEl.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/UPXEl.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This equation tells us we should adjust the weights by temporal differences, weighted by how far you should do it. If you have a perfect evaluation, your temporal difference would always be zero, thus you wouldn''t need to make any adjustment.&lt;/p&gt;

&lt;p&gt;Next, you''ll need use the new parameters to play a new game. Repeats until as many games as you can afford or when you think it''s converged.&lt;/p&gt;

&lt;p&gt;Few remarks:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;The paper I quote applies a discount factor. It''s done for the backproportion algorithm for neural network. You don''t need it.&lt;/li&gt;
&lt;li&gt;You''ll need to experiment with the optimal learning rate (alpha in the equation). Too large will make your learning unstable, too little will take longer to converge.
I''ve seen people using 0.70. &lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''-1'':69C ''/a'':22C,100C ''/abs/1509.01549'':18C ''/li'':206C,242C ''/ol'':243C ''/p'':30C,81C,101C,145C,176C,180C ''/upxel.png'':87C,94C ''0'':67C ''0.70'':241C ''1'':68C ''adjust'':77C,109C,144C ''afford'':168C ''algorithm'':197C ''alpha'':218C ''alt'':95C ''alway'':133C ''anybodi'':4C ''appli'':187C ''arxiv.org'':17C ''arxiv.org/abs/1509.01549'':16C ''backproport'':196C ''chess'':71C ''combin'':24C ''converg'':175C,235C ''deep'':28C ''deep-learn'':27C ''descript'':98C ''differ'':114C,131C ''discount'':189C ''done'':193C ''end'':56C ''engin'':39C ''enter'':96C ''equat'':104C,221C ''evalu'':50C,128C ''experi'':212C ''factor'':190C ''far'':118C ''game'':41C,59C,159C,164C ''get'':62C ''href'':15C,84C ''i.stack.imgur.com'':86C,93C ''i.stack.imgur.com/upxel.png'':85C,92C ''imag'':97C ''img'':90C ''interest'':5C ''larg'':223C ''learn'':29C,216C,227C ''li'':182C,207C ''littl'':230C ''ll'':34C,61C,74C,149C,209C ''longer'':233C ''look'':11C ''make'':37C,142C,225C ''mani'':163C ''max'':49C ''mini'':48C ''mini-max'':47C ''need'':35C,75C,140C,150C,204C,210C ''network'':200C ''neural'':199C ''new'':153C,158C ''next'':147C ''nofollow'':20C,89C ''ol'':181C ''optim'':215C ''p'':1C,31C,82C,102C,146C,177C ''paper'':21C,184C ''paramet'':79C,154C ''peopl'':239C ''perfect'':127C ''play'':40C,156C ''posit'':53C ''quot'':186C ''rate'':217C ''recommend'':3C ''record'':45C ''rel'':19C,88C ''remark'':179C ''repeat'':160C ''reward'':64C ''rough'':32C ''seen'':238C ''src'':91C ''take'':9C,232C ''tdl'':25C ''tell'':105C ''tempor'':113C,130C ''think'':172C ''thus'':136C ''topic'':8C ''unstabl'':228C ''us'':106C ''use'':151C,240C ''ve'':237C ''weight'':111C,115C ''would'':132C ''wouldn'':138C ''zero'':135C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8444, 1, '2015-10-15 13:29:08.78', 1, 31, 'Optimal projection for data visualization', '&lt;p&gt;I have 90k points in $\mathbb{R}^{32}$ (i.e., a 90k by 32 real matrix) which I want to visualize. I know I can cluster my points (k-means &amp;amp;c), but I want to select a few "interesting" 2-planes in $\mathbb{R}^{32}$, project the points there and scatter-plot them.&lt;/p&gt;

&lt;p&gt;How do I select the interesting 2-planes to project to?&lt;/p&gt;
', 'r  visualization  dimensionality-reduction', NULL, '''/p'':67C,80C ''2'':52C,75C ''32'':19C,24C,57C ''90k'':14C,22C ''amp'':42C ''c'':43C ''cluster'':36C ''data'':4A ''dimension'':9B ''dimensionality-reduct'':8B ''i.e'':20C ''interest'':51C,74C ''k'':40C ''k-mean'':39C ''know'':33C ''mathbb'':17C,55C ''matrix'':26C ''mean'':41C ''optim'':1A ''p'':11C,68C ''plane'':53C,76C ''plot'':65C ''point'':15C,38C,60C ''project'':2A,58C,78C ''r'':6B,18C,56C ''real'':25C ''reduct'':10B ''scatter'':64C ''scatter-plot'':63C ''select'':48C,72C ''visual'':5A,7B,31C ''want'':29C,46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8446, 2, '2015-10-15 14:48:40.253', 4, NULL, NULL, '&lt;p&gt;There are a few interesting plots and transformations you could start with, each dependent upon the purpose of your analysis. Below are some first steps I might take.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;If simply visually looking for clusters:&lt;br&gt;
If clusters are what you are after, then I would recommend applying a Principal Component Analysis to the dataset and then plotting the dataset with the first 2 principal components as the axes. However, the major downside to PCA is that you will have to go "unpack" the principal components to find the original variables. In other words, you''ll be may be able to identify cool clusters, but it will be a little harder to tie the findings back to your 32 variables.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;If looking for quick relationships between variables to build on:&lt;br&gt;
With "only" 32 variables you could do a pairwise plot. However, a smarter way may be to first identify the relationships mathematically (e.g. correlation) and then plotting those variables.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;If looking for quick relationships in each variable to build on:
Or, look at 32 histograms to start off with. Look out for clear bi-modal (or more) to begin piecing together an understanding of how your variables may contribute to an unsupervised model. If you end up look at 32 unimodal histograms then you can conclude early that no matter how you cluster, you will simply end up with a blob.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Actually, in usual analytics workflows I would go 3 &gt; 2 &gt; 1. But if I was fishing for clusters and just wanted to see if clusters will appear or not, PCA would be a good shortcut.&lt;/p&gt;

&lt;p&gt;Also, feel free to sample your dataset. 90k points on your screen will likely do more harm than good.&lt;/p&gt;
', NULL, NULL, '''/li'':126C,170C,247C ''/ol'':248C ''/p'':30C,125C,169C,246C,285C,306C ''1'':260C ''2'':68C,259C ''3'':258C ''32'':123C,142C,187C,224C ''90k'':294C ''abl'':104C ''actual'':250C ''also'':287C ''analysi'':21C,56C ''analyt'':253C ''appear'':276C ''appli'':52C ''axe'':73C ''back'':120C ''begin'':203C ''bi'':198C ''bi-mod'':197C ''blob'':245C ''br'':40C,139C ''build'':137C,182C ''clear'':196C ''cluster'':39C,42C,108C,237C,267C,274C ''compon'':55C,70C,90C ''conclud'':230C ''contribut'':213C ''cool'':107C ''correl'':163C ''could'':11C,145C ''dataset'':59C,64C,293C ''depend'':15C ''downsid'':77C ''e.g'':162C ''earli'':231C ''end'':220C,241C ''feel'':288C ''find'':92C,119C ''first'':25C,67C,157C ''fish'':265C ''free'':289C ''go'':86C,257C ''good'':283C,305C ''harder'':115C ''harm'':303C ''histogram'':188C,226C ''howev'':74C,150C ''identifi'':106C,158C ''interest'':6C ''li'':32C,127C,171C ''like'':300C ''littl'':114C ''ll'':100C ''look'':37C,130C,174C,185C,193C,222C ''major'':76C ''mathemat'':161C ''matter'':234C ''may'':102C,154C,212C ''might'':28C ''modal'':199C ''model'':217C ''ol'':31C ''origin'':94C ''p'':1C,33C,128C,172C,249C,286C ''pairwis'':148C ''pca'':79C,279C ''piec'':204C ''plot'':7C,62C,149C,166C ''point'':295C ''princip'':54C,69C,89C ''purpos'':18C ''quick'':132C,176C ''recommend'':51C ''relationship'':133C,160C,177C ''sampl'':291C ''screen'':298C ''see'':272C ''shortcut'':284C ''simpli'':35C,240C ''smarter'':152C ''start'':12C,190C ''step'':26C ''take'':29C ''tie'':117C ''togeth'':205C ''transform'':9C ''understand'':207C ''unimod'':225C ''unpack'':87C ''unsupervis'':216C ''upon'':16C ''usual'':252C ''variabl'':95C,124C,135C,143C,168C,180C,211C ''visual'':36C ''want'':270C ''way'':153C ''word'':98C ''workflow'':254C ''would'':50C,256C,280C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8449, 2, '2015-10-15 15:30:11.107', 1, NULL, NULL, '&lt;p&gt;I would view my points as elements of $\mathbb{R}^{n+1}$ and use &lt;a href="https://en.wikipedia.org/wiki/Principal_component_analysis" rel="nofollow"&gt;PCA&lt;/a&gt; to project my points to $\mathbb{R}^3$ and then scale them to $S^2$.&lt;/p&gt;
', NULL, NULL, '''+1'':13C ''/a'':24C ''/p'':40C ''/wiki/principal_component_analysis'':20C ''2'':39C ''3'':32C ''element'':8C ''en.wikipedia.org'':19C ''en.wikipedia.org/wiki/principal_component_analysis'':18C ''href'':17C ''mathbb'':10C,30C ''n'':12C ''nofollow'':22C ''p'':1C ''pca'':23C ''point'':6C,28C ''project'':26C ''r'':11C,31C ''rel'':21C ''scale'':35C ''use'':15C ''view'':4C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8452, 2, '2015-10-15 22:28:32.387', 0, NULL, NULL, '&lt;p&gt;Looks like this is easier to do in R, using the &lt;code&gt;rpart&lt;/code&gt; library in combination with the &lt;code&gt;partykit&lt;/code&gt; library. I''d ideally like to find a way to do this in python, but here''s the code, for anyone who is interested (taken from &lt;a href="http://stackoverflow.com/questions/29618490/get-decision-tree-rule-path-pattern-for-every-row-of-predicted-dataset-for-rpart"&gt;here&lt;/a&gt;):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;pathpred &amp;lt;- function(object, ...){
    ## coerce to "party" object if necessary
    if(!inherits(object, "party")) object &amp;lt;- as.party(object)

    ## get standard predictions (response/prob) and collect in data frame
    rval &amp;lt;- data.frame(response = predict(object, type = "response", ...))
    rval$prob &amp;lt;- predict(object, type = "prob", ...)

    ## get rules for each node
    rls &amp;lt;- partykit:::.list.rules.party(object)

    ## get predicted node and select corresponding rule
    rval$rule &amp;lt;- rls[as.character(predict(object, type = "node", ...))]

    return(rval)
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Illustration using the iris data and rpart():&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library("rpart")
library("partykit")
rp &amp;lt;- rpart(Species ~ ., data = iris)
rp_pred &amp;lt;- pathpred(rp)
rp_pred[c(1, 51, 101), ]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Yielding,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;       response prob.setosa prob.versicolor prob.virginica
 1       setosa  1.00000000      0.00000000     0.00000000
 51  versicolor  0.00000000      0.90740741     0.09259259
 101  virginica  0.00000000      0.02173913     0.97826087
                                           rule
 1                          Petal.Length &amp;lt; 2.45
 51   Petal.Length &amp;gt;= 2.45 &amp;amp; Petal.Width &amp;lt; 1.75
 101 Petal.Length &amp;gt;= 2.45 &amp;amp; Petal.Width &amp;gt;= 1.75
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Which looks to be something I could at least use to derive shared parent node information.&lt;/p&gt;
', NULL, NULL, '''/a'':56C ''/code'':15C,23C,130C,164C,211C ''/p'':57C,140C,168C,230C ''/pre'':131C,165C,212C ''/questions/29618490/get-decision-tree-rule-path-pattern-for-every-row-of-predicted-dataset-for-rpart'':54C ''0.00000000'':178C,179C,182C,187C ''0.02173913'':188C ''0.09259259'':184C ''0.90740741'':183C ''0.97826087'':189C ''1'':161C,175C,191C ''1.00000000'':177C ''1.75'':202C,210C ''101'':163C,185C,203C ''2.45'':194C,198C,206C ''51'':162C,180C,195C ''amp'':199C,207C ''anyon'':44C ''as.character'':123C ''as.party'':76C ''c'':160C ''code'':13C,21C,42C,59C,142C,170C ''coerc'':64C ''collect'':83C ''combin'':18C ''correspond'':117C ''could'':220C ''d'':26C ''data'':85C,137C,151C ''data.frame'':89C ''deriv'':225C ''easier'':6C ''find'':30C ''frame'':86C ''function'':62C ''get'':78C,102C,112C ''gt'':197C,205C,209C ''href'':51C ''ideal'':27C ''illustr'':133C ''inform'':229C ''inherit'':71C ''interest'':47C ''iri'':136C,152C ''least'':222C ''librari'':16C,24C,143C,145C ''like'':3C,28C ''list.rules.party'':110C ''look'':2C,215C ''lt'':61C,75C,88C,97C,108C,121C,148C,155C,193C,201C ''necessari'':69C ''node'':106C,114C,127C,228C ''object'':63C,67C,72C,74C,77C,92C,99C,111C,125C ''p'':1C,132C,166C,213C ''parent'':227C ''parti'':66C,73C ''partykit'':22C,109C,146C ''pathpr'':60C,156C ''petal.length'':192C,196C,204C ''petal.width'':200C,208C ''pre'':58C,141C,169C ''pred'':154C,159C ''predict'':80C,91C,98C,113C,124C ''prob'':96C,101C ''prob.setosa'':172C ''prob.versicolor'':173C ''prob.virginica'':174C ''python'':37C ''r'':10C ''respons'':90C,94C,171C ''response/prob'':81C ''return'':128C ''rls'':107C,122C ''rp'':147C,153C,157C,158C ''rpart'':14C,139C,144C,149C ''rule'':103C,118C,120C,190C ''rval'':87C,95C,119C,129C ''select'':116C ''setosa'':176C ''share'':226C ''someth'':218C ''speci'':150C ''stackoverflow.com'':53C ''stackoverflow.com/questions/29618490/get-decision-tree-rule-path-pattern-for-every-row-of-predicted-dataset-for-rpart'':52C ''standard'':79C ''taken'':48C ''type'':93C,100C,126C ''use'':11C,134C,223C ''versicolor'':181C ''virginica'':186C ''way'':32C ''yield'':167C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8455, 1, '2015-10-15 23:21:30.18', 1, 45, 'Attribute discretization', '&lt;p&gt;I have a dataset where I need to explore using unsupervised technics (clustering and association rules). What are the best strategies to discretize the numeric attributes? Also, does this (attribute discretization) makes sense for clustering (using K-Means)?&lt;/p&gt;

&lt;p&gt;Since I''m using weka, I know that I can just use the Discretize filter, with findNumBins option or useEqualFrequency. But are there other strategies?&lt;/p&gt;

&lt;p&gt;Also, does it makes sense to use discretization on the attributes, and then look for clusters (using K-Means) or not? (I suspect that it doesn''t make that much sense, but I just want to confirm)&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'clustering  k-means  weka', NULL, '''/p'':48C,75C,115C,118C ''also'':35C,77C ''associ'':23C ''attribut'':1A,34C,38C,87C ''best'':28C ''cluster'':3B,21C,43C,92C ''confirm'':114C ''dataset'':12C ''discret'':2A,31C,39C,63C,84C ''doesn'':103C ''explor'':17C ''filter'':64C ''findnumbin'':66C ''k'':5B,46C,95C ''k-mean'':4B,45C,94C ''know'':56C ''look'':90C ''m'':52C ''make'':40C,80C,105C ''mean'':6B,47C,96C ''much'':107C ''need'':15C ''numer'':33C ''option'':67C ''p'':8C,49C,76C,116C ''rule'':24C ''sens'':41C,81C,108C ''sinc'':50C ''strategi'':29C,74C ''suspect'':100C ''technic'':20C ''thank'':117C ''unsupervis'':19C ''use'':18C,44C,53C,61C,83C,93C ''useequalfrequ'':69C ''want'':112C ''weka'':7B,54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8487, 2, '2015-10-18 14:05:16.1', 2, NULL, NULL, '&lt;p&gt;Imagine that one of the column is just random data -- then it''s not informative at all, so no classifier will be improved by including it.&lt;/p&gt;

&lt;p&gt;However, &lt;code&gt;ada&lt;/code&gt;''s stochastic boosting implementations will always have some chance of including that variable in the classifier it generates. As a result, removing it has the potential to improve the classifiers generated.&lt;/p&gt;

&lt;p&gt;(In your case, you might check whether that variable is part of the final model generated.)&lt;/p&gt;
', NULL, NULL, '''/code'':33C ''/p'':28C,65C,83C ''ada'':32C ''alway'':39C ''boost'':36C ''case'':69C ''chanc'':42C ''check'':72C ''classifi'':21C,49C,63C ''code'':31C ''column'':7C ''data'':11C ''final'':80C ''generat'':51C,64C,82C ''howev'':30C ''imagin'':2C ''implement'':37C ''improv'':24C,61C ''includ'':26C,44C ''inform'':16C ''might'':71C ''model'':81C ''one'':4C ''p'':1C,29C,66C ''part'':77C ''potenti'':59C ''random'':10C ''remov'':55C ''result'':54C ''stochast'':35C ''variabl'':46C,75C ''whether'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8799, 1, '2015-11-11 14:27:51.393', 3, 121, 'Boundary conditions for clustering', '&lt;p&gt;I have some data that I would like to cluster with k-means.&lt;/p&gt;

&lt;p&gt;One of the features is the hour of the day.&lt;/p&gt;

&lt;p&gt;The problem is that the hour ''23'' is considered far from the hour ''0''. &lt;/p&gt;

&lt;p&gt;How can I map the data so that the boundary will be cyclic?&lt;/p&gt;
', 'k-means', NULL, '''/p'':23C,35C,51C,66C ''0'':50C ''23'':43C ''boundari'':1A,62C ''cluster'':4A,18C ''condit'':2A ''consid'':45C ''cyclic'':65C ''data'':12C,58C ''day'':34C ''far'':46C ''featur'':28C ''hour'':31C,42C,49C ''k'':6B,21C ''k-mean'':5B,20C ''like'':16C ''map'':56C ''mean'':7B,22C ''one'':25C ''p'':8C,24C,36C,52C ''problem'':38C ''would'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8457, 1, '2015-10-16 04:07:42.02', 4, 253, 'Python library for segmented regression (a.k.a. piecewise regression)', '&lt;p&gt;I am looking for a Python library that can perform &lt;a href="https://en.wikipedia.org/wiki/Segmented_regression#Segmented_linear_regression.2C_two_segments" rel="nofollow"&gt;segmented regression (a.k.a. piecewise regression)&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://onlinecourses.science.psu.edu/stat501/node/310" rel="nofollow"&gt;Example&lt;/a&gt;:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/ZNoPv.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/ZNoPv.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'python  linear-regression  library  software-recommendation', NULL, '''/a'':40C,51C,71C ''/p'':41C,52C,72C ''/stat501/node/310'':47C ''/wiki/segmented_regression#segmented_linear_regression.2c_two_segments'':32C ''/znopv.png'':58C,65C ''a.k.a'':6A,37C ''alt'':66C ''descript'':69C ''en.wikipedia.org'':31C ''en.wikipedia.org/wiki/segmented_regression#segmented_linear_regression.2c_two_segments'':30C ''enter'':67C ''exampl'':50C ''href'':29C,44C,55C ''i.stack.imgur.com'':57C,64C ''i.stack.imgur.com/znopv.png'':56C,63C ''imag'':68C ''img'':61C ''librari'':2A,13B,24C ''linear'':11B ''linear-regress'':10B ''look'':20C ''nofollow'':34C,49C,60C ''onlinecourses.science.psu.edu'':46C ''onlinecourses.science.psu.edu/stat501/node/310'':45C ''p'':17C,42C,53C ''perform'':27C ''piecewis'':7A,38C ''python'':1A,9B,23C ''recommend'':16B ''regress'':5A,8A,12B,36C,39C ''rel'':33C,48C,59C ''segment'':4A,35C ''softwar'':15B ''software-recommend'':14B ''src'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8458, 2, '2015-10-16 05:14:34.893', 1, NULL, NULL, '&lt;p&gt;While previous answers are correct in describing how categorical variables can be encoded with sets of binary variables (&lt;code&gt;binarization&lt;/code&gt; or &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html" rel="nofollow"&gt;&lt;code&gt;one-hot encoding&lt;/code&gt;&lt;/a&gt;), I''m surprised that no one has pointed out the obvious...&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Neither &lt;a href="http://scikit-learn.org/stable/modules/tree.html" rel="nofollow"&gt;&lt;code&gt;decision trees&lt;/code&gt;&lt;/a&gt; or &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html" rel="nofollow"&gt;&lt;code&gt;random-forests&lt;/code&gt;&lt;/a&gt; require one-hot encoding for categorical variables&lt;/strong&gt;.  This is one of the very convenient aspects of both &lt;code&gt;decision tree&lt;/code&gt; and &lt;code&gt;random-forest&lt;/code&gt; classifiers.  &lt;strong&gt;They can operate on integer features, floating point features, ordinal features, and categorical features and a heterogenous combination of them.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;So go ahead and use your categorical variables without encoding them.  Decision trees have the added benefit that you can &lt;a href="http://scikit-learn.org/stable/auto_examples/tree/plot_iris.html#example-tree-plot-iris-py" rel="nofollow"&gt;visualize them&lt;/a&gt; to see how the variables are oriented in the &lt;code&gt;decision surface&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;I know this question is quite old, but I wanted to add so that future readers are aware of this feature.&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', NULL, NULL, '''/a'':37C,64C,78C,162C ''/code'':22C,36C,63C,77C,101C,107C,175C ''/p'':49C,131C,176C,199C,202C ''/stable/auto_examples/tree/plot_iris.html#example-tree-plot-iris-py'':157C ''/stable/modules/generated/sklearn.ensemble.randomforestclassifier.html'':70C ''/stable/modules/generated/sklearn.preprocessing.onehotencoder.html'':28C ''/stable/modules/tree.html'':57C ''/strong'':87C,130C ''ad'':148C ''add'':189C ''ahead'':135C ''answer'':4C ''aspect'':95C ''awar'':195C ''benefit'':149C ''binar'':21C ''binari'':18C ''categor'':10C,85C,122C,139C ''classifi'':108C ''code'':20C,31C,60C,73C,98C,103C,172C ''combin'':127C ''conveni'':94C ''correct'':6C ''decis'':61C,99C,144C,173C ''describ'':8C ''encod'':14C,35C,83C,142C ''featur'':115C,118C,120C,123C,198C ''float'':116C ''forest'':76C,106C ''futur'':192C ''go'':134C ''heterogen'':126C ''hot'':34C,82C ''href'':25C,54C,67C,154C ''integ'':114C ''know'':179C ''m'':39C ''neither'':52C ''nofollow'':30C,59C,72C,159C ''obvious'':48C ''old'':184C ''one'':33C,43C,81C,90C ''one-hot'':32C,80C ''oper'':112C ''ordin'':119C ''orient'':169C ''p'':1C,50C,132C,177C,200C ''point'':45C,117C ''previous'':3C ''question'':181C ''quit'':183C ''random'':75C,105C ''random-forest'':74C,104C ''reader'':193C ''rel'':29C,58C,71C,158C ''requir'':79C ''scikit-learn.org'':27C,56C,69C,156C ''scikit-learn.org/stable/auto_examples/tree/plot_iris.html#example-tree-plot-iris-py'':155C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.randomforestclassifier.html'':68C ''scikit-learn.org/stable/modules/generated/sklearn.preprocessing.onehotencoder.html'':26C ''scikit-learn.org/stable/modules/tree.html'':55C ''see'':164C ''set'':16C ''strong'':51C,109C ''surfac'':174C ''surpris'':40C ''thank'':201C ''tree'':62C,100C,145C ''use'':137C ''variabl'':11C,19C,86C,140C,167C ''visual'':160C ''want'':187C ''without'':141C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8459, 2, '2015-10-16 06:27:14.76', 2, NULL, NULL, '&lt;p&gt;Most clustering algorithms work best with &lt;em&gt;continuous&lt;/em&gt; values (in particular k-means). Many cannot use categorial attributes and won''t work very well with one-hot encodings either.&lt;/p&gt;
', NULL, NULL, '''/em'':10C ''/p'':34C ''algorithm'':4C ''attribut'':21C ''best'':6C ''cannot'':18C ''categori'':20C ''cluster'':3C ''continu'':9C ''either'':33C ''em'':8C ''encod'':32C ''hot'':31C ''k'':15C ''k-mean'':14C ''mani'':17C ''mean'':16C ''one'':30C ''one-hot'':29C ''p'':1C ''particular'':13C ''use'':19C ''valu'':11C ''well'':27C ''won'':23C ''work'':5C,25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8460, 1, '2015-10-16 06:45:35.14', 4, 204, 'Python library to implement Hidden Markov Models', '&lt;p&gt;What &lt;em&gt;stable&lt;/em&gt; Python library can I use to implement Hidden Markov Models? I need it to be reasonably well documented, because I''ve never really used this model before.&lt;/p&gt;

&lt;p&gt;Alternatively, is there a more direct approach to performing a time-series analysis on a data-set using HMM?&lt;/p&gt;
', 'python  time-series  markov-process', NULL, '''/em'':19C ''/p'':47C,70C ''altern'':49C ''analysi'':62C ''approach'':55C ''data'':66C ''data-set'':65C ''direct'':54C ''document'':37C ''em'':17C ''hidden'':5A,27C ''hmm'':69C ''implement'':4A,26C ''librari'':2A,21C ''markov'':6A,13B,28C ''markov-process'':12B ''model'':7A,29C,45C ''need'':31C ''never'':41C ''p'':15C,48C ''perform'':57C ''process'':14B ''python'':1A,8B,20C ''realli'':42C ''reason'':35C ''seri'':11B,61C ''set'':67C ''stabl'':18C ''time'':10B,60C ''time-seri'':9B,59C ''use'':24C,43C,68C ''ve'':40C ''well'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8461, 1, '2015-10-16 07:27:50.5', 0, 15, 'How to extract transformation matrix from scikit''s Linear Discriminant Analysis'' function?', '&lt;p&gt;I am using the LDA mentioned here: &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.lda.LDA.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/generated/sklearn.lda.LDA.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;And, I cannot find a function or variable giving the transformation/weight matrix. The only way I got is find &lt;code&gt;Xnew&lt;/code&gt; from &lt;code&gt;lda.fit__transform(X,y)&lt;/code&gt; and then do &lt;code&gt;Xnew*pinv(X)&lt;/code&gt;, where &lt;code&gt;pinv&lt;/code&gt; is pseudo-inverse. But, I am very afraid to take chances with &lt;code&gt;pinv&lt;/code&gt; as I have to be very sure while executing LDA on some large dataset.&lt;/p&gt;
', 'machine-learning  classification  python  scikit  dimensionality-reduction', NULL, '''/code'':62C,69C,77C,81C,97C ''/p'':112C ''/stable/modules/generated/sklearn.lda.lda.html'':34C ''/stable/modules/generated/sklearn.lda.lda.html&lt;/a&gt;&lt;/p&gt;'':39C ''afraid'':90C ''analysi'':11A ''cannot'':43C ''chanc'':93C ''classif'':16B ''code'':60C,64C,73C,79C,95C ''dataset'':111C ''dimension'':20B ''dimensionality-reduct'':19B ''discrimin'':10A ''execut'':106C ''extract'':3A ''find'':44C,59C ''function'':12A,46C ''give'':49C ''got'':57C ''href'':31C ''invers'':85C ''larg'':110C ''lda'':27C,107C ''lda.fit'':65C ''learn'':15B ''linear'':9A ''machin'':14B ''machine-learn'':13B ''matrix'':5A,52C ''mention'':28C ''nofollow'':36C ''p'':22C,40C ''pinv'':75C,80C,96C ''pseudo'':84C ''pseudo-invers'':83C ''python'':17B ''reduct'':21B ''rel'':35C ''scikit'':7A,18B ''scikit-learn.org'':33C,38C ''scikit-learn.org/stable/modules/generated/sklearn.lda.lda.html'':32C ''scikit-learn.org/stable/modules/generated/sklearn.lda.lda.html&lt;/a&gt;&lt;/p&gt;'':37C ''sure'':104C ''take'':92C ''transform'':4A,66C ''transformation/weight'':51C ''use'':25C ''variabl'':48C ''way'':55C ''x'':67C,76C ''xnew'':61C,74C ''y'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8463, 2, '2015-10-16 13:16:05.703', 2, NULL, NULL, '&lt;p&gt;First of all it should be noted that the code you posted does not actually replicate the output of the &lt;code&gt;dist&lt;/code&gt; function, because the line:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;distancematrix[i, j] &amp;lt;- sum(abs(myMatrix[i,] - myMatrix[j,]))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;does not calculate the Euclidean distance; it should be:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;distancematrix[i, j] &amp;lt;- sqrt(sum((myMatrix[i,] - myMatrix[j,]) ^ 2))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here are two solutions that rely on &lt;code&gt;apply&lt;/code&gt;. They are simplified, and in particular do not take advantage of the symmetry of the distance matrix (which, if considered, would lead to a 2-fold speedup). First, generate some test data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Number of data points
N &amp;lt;- 2000
# Dimensionality
d &amp;lt;- 10
# Generate data
myMatrix = matrix(rnorm(N * d), nrow = N)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For convenience, define:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Wrapper for the distance function
d_fun &amp;lt;- function(x_1, x_2) sqrt(sum((x_1 - x_2) ^ 2))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The first approach is a combination of &lt;code&gt;apply&lt;/code&gt; and &lt;code&gt;sapply&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;system.time(
    D_1 &amp;lt;- 
        apply(myMatrix, 1, function(x_i) 
            sapply(1:nrow(myMatrix), function(j) d_fun(x_i, myMatrix[j, ]))
    )
)

   user  system elapsed 
 14.041   0.100  14.001 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;while the second uses only &lt;code&gt;apply&lt;/code&gt; (but going over the indices, which are paired using &lt;code&gt;expand.grid&lt;/code&gt;):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;system.time(
    D_2 &amp;lt;- 
        matrix(apply(expand.grid(i = 1:nrow(myMatrix), j = 1:nrow(myMatrix)), 1, function(I) 
            d_fun(myMatrix[I[["i"]], ], myMatrix[I[["j"]], ])
        )
    )
)

   user  system elapsed 
 39.313   0.498  39.561 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However, as expected both are much slower than &lt;code&gt;dist&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;system.time(
    distancematrix &amp;lt;- as.matrix(
        dist(myMatrix, method = "euclidean", diag = T, upper = T)
    )
)

   user  system elapsed 
  0.337   0.054   0.388
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':24C,42C,68C,80C,136C,165C,177C,181C,213C,223C,235C,271C,284C,306C ''/p'':29C,54C,113C,142C,182C,236C,285C ''/pre'':43C,69C,137C,166C,214C,272C,307C ''0.054'':304C ''0.100'':211C ''0.337'':303C ''0.388'':305C ''0.498'':269C ''1'':155C,161C,187C,191C,196C,247C,251C,254C ''10'':126C ''14.001'':212C ''14.041'':210C ''2'':67C,105C,157C,163C,164C,241C ''2000'':122C ''39.313'':268C ''39.561'':270C ''ab'':37C ''actual'':16C ''advantag'':90C ''appli'':79C,176C,189C,222C,244C ''approach'':170C ''as.matrix'':291C ''calcul'':47C ''code'':11C,22C,31C,56C,78C,115C,144C,175C,179C,184C,221C,233C,238C,282C,287C ''combin'':173C ''consid'':100C ''conveni'':140C ''d'':124C,133C,150C,186C,201C,240C,257C ''data'':112C,118C,128C ''defin'':141C ''diag'':296C ''dimension'':123C ''dist'':23C,283C,292C ''distanc'':50C,96C,148C ''distancematrix'':32C,57C,289C ''elaps'':209C,267C,302C ''euclidean'':49C,295C ''expand.grid'':234C,245C ''expect'':276C ''first'':2C,108C,169C ''fold'':106C ''fun'':151C,202C,258C ''function'':25C,149C,153C,192C,199C,255C ''generat'':109C,127C ''go'':225C ''howev'':274C ''indic'':228C ''j'':34C,41C,59C,66C,200C,206C,250C,264C ''lead'':102C ''line'':28C ''lt'':35C,60C,121C,125C,152C,188C,242C,290C ''matrix'':97C,130C,243C ''method'':294C ''much'':279C ''mymatrix'':38C,40C,63C,65C,129C,190C,198C,205C,249C,253C,259C,262C,293C ''n'':120C,132C,135C ''note'':8C ''nrow'':134C,197C,248C,252C ''number'':116C ''output'':19C ''p'':1C,44C,70C,138C,167C,215C,273C ''pair'':231C ''particular'':86C ''point'':119C ''post'':13C ''pre'':30C,55C,114C,143C,183C,237C,286C ''reli'':76C ''replic'':17C ''rnorm'':131C ''sappli'':180C,195C ''second'':218C ''simplifi'':83C ''slower'':280C ''solut'':74C ''speedup'':107C ''sqrt'':61C,158C ''sum'':36C,62C,159C ''symmetri'':93C ''system'':208C,266C,301C ''system.time'':185C,239C,288C ''take'':89C ''test'':111C ''two'':73C ''upper'':298C ''use'':219C,232C ''user'':207C,265C,300C ''would'':101C ''wrapper'':145C ''x'':154C,156C,160C,162C,193C,203C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8464, 1, '2015-10-16 13:35:02.133', 3, 90, 'Python + Neural Nets + Large dimension Large Dataset', '&lt;p&gt;I am building a NLP application. My dataset has 0.6M datapoints each of 0.15M dimensions. My feature vector is highly sparse - mostly 0s, atmost 20 1s in any feature vector. I am using a 32 GB machine on AWS.&lt;/p&gt;

&lt;p&gt;Owing to size, Cant load entire dataset in one go in the memory. &lt;strong&gt;I am looking for a python based neural net library that supports&lt;/strong&gt; (if not all then atleast some) of the following [Owing to the dataset size and dimension, i defined below mentioned abilities]: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Ability to train the net on small batches &lt;/li&gt;
&lt;li&gt;library being able to exploit underlying multicore architecture (if available)&lt;/li&gt;
&lt;li&gt;A way to directly feed the feature vectors in compressed representation (like scipy.sparse) [in my case &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I have had a good look at the following libraries - &lt;strong&gt;PyBrain, Pylearn2, NeuroLab, FANN&lt;/strong&gt;. To the best of my understanding, none of them has any of the desired abilities (Pybrain facilitates batch leaning. Even this takes close to 55 hrs).  &lt;/p&gt;

&lt;p&gt;Any suggestions ? (cant reduce dimentionality)  Or in case &lt;em&gt;there is a shortcoming in my understanding and the any of the above libraries have (some) above capabilities&lt;/em&gt;, can you pls point out me to some sample code implementing/exploiting the same ?&lt;/p&gt;
', 'machine-learning  python  neuralnetwork', NULL, '''/em'':220C ''/li'':116C,128C,146C ''/ol'':147C ''/p'':55C,105C,191C,234C ''/strong'':83C,164C ''0.15'':28C ''0.6'':23C ''0s'':38C ''1s'':41C ''20'':40C ''32'':50C ''55'':189C ''abil'':104C,108C,179C ''abl'':120C ''applic'':19C ''architectur'':125C ''atleast'':88C ''atmost'':39C ''avail'':127C ''aw'':54C ''base'':77C ''batch'':115C,182C ''best'':167C ''build'':16C ''cant'':60C,195C ''capabl'':219C ''case'':145C,200C ''close'':187C ''code'':230C ''compress'':139C ''datapoint'':25C ''dataset'':7A,21C,63C,96C ''defin'':101C ''desir'':178C ''dimens'':5A,30C,99C ''diment'':197C ''direct'':133C ''em'':201C ''entir'':62C ''even'':184C ''exploit'':122C ''facilit'':181C ''fann'':163C ''featur'':32C,44C,136C ''feed'':134C ''follow'':92C,157C ''gb'':51C ''go'':66C ''good'':153C ''high'':35C ''hrs'':190C ''implementing/exploiting'':231C ''larg'':4A,6A ''lean'':183C ''learn'':10B ''li'':107C,117C,129C ''librari'':80C,118C,158C,215C ''like'':141C ''load'':61C ''look'':73C,154C ''m'':24C,29C ''machin'':9B,52C ''machine-learn'':8B ''memori'':69C ''mention'':103C ''most'':37C ''multicor'':124C ''net'':3A,79C,112C ''neural'':2A,78C ''neuralnetwork'':12B ''neurolab'':162C ''nlp'':18C ''none'':171C ''ol'':106C ''one'':65C ''owe'':57C,93C ''p'':13C,56C,148C,192C ''pls'':223C ''point'':224C ''pybrain'':160C,180C ''pylearn2'':161C ''python'':1A,11B,76C ''reduc'':196C ''represent'':140C ''sampl'':229C ''scipy.sparse'':142C ''shortcom'':205C ''size'':59C,97C ''small'':114C ''spars'':36C ''strong'':70C,159C ''suggest'':194C ''support'':82C ''take'':186C ''train'':110C ''under'':123C ''understand'':170C,208C ''use'':48C ''vector'':33C,45C,137C ''way'':131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8465, 1, '2015-10-16 16:40:00.123', 0, 31, 'Popular Methods for Multi-Sensor Data Fusion Regression', '&lt;p&gt;There seems to be many high-level data fusion methods for classification problems, but what methods are used for higher level data fusion with regression? I''ve tried low-level sensor fusion but it has worse performance than single-sensor performance. Is there a way to do multisensor fusion with ANNs? Also, are there packages for implementing these methods in R?&lt;/p&gt;

&lt;p&gt;Thank you&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/p'':79C,83C ''also'':69C ''ann'':68C ''classif'':27C ''data'':7A,23C,37C ''fusion'':8A,24C,38C,48C,66C ''high'':21C ''high-level'':20C ''higher'':35C ''implement'':74C ''learn'':12B ''level'':22C,36C,46C ''low'':45C ''low-level'':44C ''machin'':11B ''machine-learn'':10B ''mani'':19C ''method'':2A,25C,31C,76C ''multi'':5A ''multi-sensor'':4A ''multisensor'':65C ''neuralnetwork'':13B ''p'':14C,80C ''packag'':72C ''perform'':53C,58C ''popular'':1A ''problem'':28C ''r'':78C ''regress'':9A,40C ''seem'':16C ''sensor'':6A,47C,57C ''singl'':56C ''single-sensor'':55C ''thank'':81C ''tri'':43C ''use'':33C ''ve'':42C ''way'':62C ''wors'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8468, 2, '2015-10-16 17:33:33.103', 2, NULL, NULL, '&lt;p&gt;For an alternative approach, perhaps even to help foster understanding, you will probably find some utility in doing some analysis via R. Simple time series based tutorials abound for [wannabe] quants that should provide a bootstrap. &lt;a href="http://gekkoquant.com/2014/05/18/hidden-markov-models-model-description-part-1-of-4/" rel="nofollow"&gt;Part 1&lt;/a&gt;, &lt;a href="http://gekkoquant.com/2014/05/26/hidden-markov-models-forward-viterbi-algorithm-part-2-of-4/" rel="nofollow"&gt;Part 2&lt;/a&gt;, &lt;a href="http://gekkoquant.com/2014/09/07/hidden-markov-models-examples-in-r-part-3-of-4/" rel="nofollow"&gt;Part 3&lt;/a&gt;, &lt;a href="http://gekkoquant.com/2015/02/01/hidden-markov-models-trend-following-sharpe-ratio-3-1-part-4-of-4/" rel="nofollow"&gt;Part 4&lt;/a&gt;. These provide sources for data generation/intake as well as manipulation, allowing you to bypass much of the work to be able to see the actual HMM methods at work. There are direct analogues to the Python implementations.&lt;/p&gt;

&lt;p&gt;As a side note, for a more theoretical introduction, perhaps &lt;a href="http://www.robots.ox.ac.uk:5000/~vgg/rg/papers/hmm.pdf" rel="nofollow"&gt;Rabiner&lt;/a&gt; might provide some insights&lt;/p&gt;
', NULL, NULL, '''/2014/05/18/hidden-markov-models-model-description-part-1-of-4/'':42C ''/2014/05/26/hidden-markov-models-forward-viterbi-algorithm-part-2-of-4/'':52C ''/2014/09/07/hidden-markov-models-examples-in-r-part-3-of-4/'':62C ''/2015/02/01/hidden-markov-models-trend-following-sharpe-ratio-3-1-part-4-of-4/'':72C ''/a'':47C,57C,67C,77C,135C ''/p'':115C,140C ''/~vgg/rg/papers/hmm.pdf'':131C ''1'':46C ''2'':56C ''3'':66C ''4'':76C ''abl'':98C ''abound'':29C ''actual'':102C ''allow'':88C ''altern'':4C ''analogu'':110C ''analysi'':21C ''approach'':5C ''base'':27C ''bootstrap'':37C ''bypass'':91C ''data'':82C ''direct'':109C ''even'':7C ''find'':15C ''foster'':10C ''gekkoquant.com'':41C,51C,61C,71C ''gekkoquant.com/2014/05/18/hidden-markov-models-model-description-part-1-of-4/'':40C ''gekkoquant.com/2014/05/26/hidden-markov-models-forward-viterbi-algorithm-part-2-of-4/'':50C ''gekkoquant.com/2014/09/07/hidden-markov-models-examples-in-r-part-3-of-4/'':60C ''gekkoquant.com/2015/02/01/hidden-markov-models-trend-following-sharpe-ratio-3-1-part-4-of-4/'':70C ''generation/intake'':83C ''help'':9C ''hmm'':103C ''href'':39C,49C,59C,69C,128C ''implement'':114C ''insight'':139C ''introduct'':125C ''manipul'':87C ''method'':104C ''might'':136C ''much'':92C ''nofollow'':44C,54C,64C,74C,133C ''note'':120C ''p'':1C,116C ''part'':45C,55C,65C,75C ''perhap'':6C,126C ''probabl'':14C ''provid'':35C,79C,137C ''python'':113C ''quant'':32C ''r'':23C ''rabin'':134C ''rel'':43C,53C,63C,73C,132C ''see'':100C ''seri'':26C ''side'':119C ''simpl'':24C ''sourc'':80C ''theoret'':124C ''time'':25C ''tutori'':28C ''understand'':11C ''util'':17C ''via'':22C ''wannab'':31C ''well'':85C ''work'':95C,106C ''www.robots.ox.ac.uk:5000'':130C ''www.robots.ox.ac.uk:5000/~vgg/rg/papers/hmm.pdf'':129C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8469, 1, '2015-10-16 18:06:11.967', 0, 84, 'Fitting Timeseries data in HMM model from sklearn/hmmlearn', '&lt;p&gt;I have a time series data set as a csv file with following columns-&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ID,TIMESTAMP,MEASUREMENTS[10]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For an ID multiple measurements are there with the associated timestamp when those measurements were made. The column measurements contains a list of 10 measurements. Measurement in one record(associated with a particular timestamp) somehow depends on the previous record.&lt;/p&gt;

&lt;p&gt;eg. of dataset:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ID,TIMESTAMP,MEASUREMENTS
1,0,[123,456,567.....]
1,100,[....]
1,350,[....]
2,0,[....]
2,200,[.....]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Also, the measurement array contains NaNs at some indexes.
Finally, I have some label associated with each ID which is the outcome of the measurements performed till the very last timestamp for that id.
My Objective is to fit this data into an HMM model and then Predict the label for the test dataset which is in the same format.
How to fit this model into a HMM Model from &lt;strong&gt;sklearn/hmmlearn&lt;/strong&gt;? sklearns documentation is not up to the mark for the model, no parameters are explained.&lt;/p&gt;
', 'machine-learning  python  time-series  sklearn  markov-process', NULL, '''/code'':42C,109C ''/p'':35C,85C,90C,200C ''/pre'':43C,110C ''/strong'':184C ''0'':97C,106C ''1'':96C,101C,103C ''10'':41C,68C ''100'':102C ''123'':98C ''2'':105C,107C ''200'':108C ''350'':104C ''456'':99C ''567'':100C ''also'':112C ''array'':115C ''associ'':54C,74C,126C ''code'':37C,92C ''column'':34C,62C ''contain'':64C,116C ''csv'':30C ''data'':3A,26C,152C ''dataset'':89C,165C ''depend'':80C ''document'':186C ''eg'':87C ''explain'':199C ''file'':31C ''final'':121C ''fit'':1A,150C,174C ''follow'':33C ''format'':171C ''hmm'':5A,155C,179C ''id'':38C,47C,93C,129C,145C ''index'':120C ''label'':125C,161C ''last'':141C ''learn'':11B ''list'':66C ''machin'':10B ''machine-learn'':9B ''made'':60C ''mark'':192C ''markov'':18B ''markov-process'':17B ''measur'':40C,49C,58C,63C,69C,70C,95C,114C,136C ''model'':6A,156C,176C,180C,195C ''multipl'':48C ''nan'':117C ''object'':147C ''one'':72C ''outcom'':133C ''p'':20C,44C,86C,111C ''paramet'':197C ''particular'':77C ''perform'':137C ''pre'':36C,91C ''predict'':159C ''previous'':83C ''process'':19B ''python'':12B ''record'':73C,84C ''seri'':15B,25C ''set'':27C ''sklearn'':16B,185C ''sklearn/hmmlearn'':8A,183C ''somehow'':79C ''strong'':182C ''test'':164C ''till'':138C ''time'':14B,24C ''time-seri'':13B ''timeseri'':2A ''timestamp'':39C,55C,78C,94C,142C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8471, 2, '2015-10-16 21:47:34.55', 1, NULL, NULL, '&lt;p&gt;Depending on the item domain You can expect different results. I would try to cluster data using cosinus similarity over item latent features. If number of items is large try DIMSUM (it''s already implemented in Spark).&lt;/p&gt;

&lt;p&gt;For the visualization You can try one of the available implementation of t-SNE. Quick links: &lt;a href="https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding" rel="nofollow"&gt;wiki&lt;/a&gt; and &lt;a href="https://www.youtube.com/watch?v=RJVL80Gg3lA" rel="nofollow"&gt;YT - Google Talk about t-SNE Visualization&lt;/a&gt;. But here I cannot give You any more advice, because I have never tried to visualize it. I looking forward You will share Yours results.  &lt;/p&gt;
', NULL, NULL, '''/a'':66C,83C ''/p'':39C,109C ''/watch?v=rjvl80gg3la'':72C ''/wiki/t-distributed_stochastic_neighbor_embedding'':62C ''advic'':92C ''alreadi'':35C ''avail'':50C ''cannot'':87C ''cluster'':16C ''cosinus'':19C ''data'':17C ''depend'':2C ''differ'':10C ''dimsum'':32C ''domain'':6C ''en.wikipedia.org'':61C ''en.wikipedia.org/wiki/t-distributed_stochastic_neighbor_embedding'':60C ''expect'':9C ''featur'':24C ''forward'':103C ''give'':88C ''googl'':76C ''href'':59C,69C ''implement'':36C,51C ''item'':5C,22C,28C ''larg'':30C ''latent'':23C ''link'':57C ''look'':102C ''never'':96C ''nofollow'':64C,74C ''number'':26C ''one'':47C ''p'':1C,40C ''quick'':56C ''rel'':63C,73C ''result'':11C,108C ''share'':106C ''similar'':20C ''sne'':55C,81C ''spark'':38C ''t-sne'':53C,79C ''talk'':77C ''tri'':14C,31C,46C,97C ''use'':18C ''visual'':43C,82C,99C ''wiki'':65C ''would'':13C ''www.youtube.com'':71C ''www.youtube.com/watch?v=rjvl80gg3la'':70C ''yt'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8472, 2, '2015-10-16 22:29:51.053', 2, NULL, NULL, '&lt;p&gt;I''ve been looking for the same thing, and unfortunately it seems like there isn''t one at this time. Some suggestions for how to proceed can be found in this &lt;a href="http://datascience.stackexchange.com/questions/8266/is-there-a-library-that-would-perform-segmented-linear-regression-in-python"&gt;previous question&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;Alternatively you could look into some R libraries eg segmented, SiZer, strucchange, and if something there works for you try embedding the R code in python with &lt;a href="http://rpy.sourceforge.net/" rel="nofollow"&gt;rpy2&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Editing to add a link to &lt;a href="https://github.com/jcrudy/py-earth" rel="nofollow"&gt;py-earth&lt;/a&gt;, "A Python implementation of Jerome Friedman''s Multivariate Adaptive Regression Splines". &lt;/p&gt;
', NULL, NULL, '''/a'':40C,76C,95C ''/jcrudy/py-earth'':89C ''/p'':41C,77C,107C ''/questions/8266/is-there-a-library-that-would-perform-segmented-linear-regression-in-python'':37C ''adapt'':104C ''add'':81C ''altern'':43C ''code'':66C ''could'':45C ''datascience.stackexchange.com'':36C ''datascience.stackexchange.com/questions/8266/is-there-a-library-that-would-perform-segmented-linear-regression-in-python'':35C ''earth'':94C ''edit'':79C ''eg'':51C ''embed'':63C ''found'':30C ''friedman'':101C ''github.com'':88C ''github.com/jcrudy/py-earth'':87C ''href'':34C,71C,86C ''implement'':98C ''isn'':16C ''jerom'':100C ''librari'':50C ''like'':14C ''link'':83C ''look'':5C,46C ''multivari'':103C ''nofollow'':74C,91C ''one'':18C ''p'':1C,42C,78C ''previous'':38C ''proceed'':27C ''py'':93C ''py-earth'':92C ''python'':68C,97C ''question'':39C ''r'':49C,65C ''regress'':105C ''rel'':73C,90C ''rpy.sourceforge.net'':72C ''rpy2'':75C ''seem'':13C ''segment'':52C ''sizer'':53C ''someth'':57C ''spline'':106C ''strucchang'':54C ''suggest'':23C ''thing'':9C ''time'':21C ''tri'':62C ''unfortun'':11C ''ve'':3C ''work'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8502, 1, '2015-10-19 11:13:06.367', 1, 133, 'Using Machine Learning to Predict Musical Scales', '&lt;p&gt;It''s possible to use Machine Learning techniques to cluster songs into musical-scale groups? I mean: "this song was written in C"... or "this song was written in Am" etc. I made a fast search about the subject and I found no software that can do this. If you know some software, or research (academic papers), related to that subject, could you link it here for me? I''m very interested in that subject but I''m not sure from where I can begin. I have a little experience with Random Forests and Neural Networks, maybe I can accomplish the classification task with one of those algorithms, but, again, I''m not sure which kinda of features I should pass to the algorithm. Thanks in advance.&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/p'':141C ''academ'':69C ''accomplish'':113C ''advanc'':140C ''algorithm'':121C,137C ''begin'':98C ''c'':36C ''classif'':11B,115C ''cluster'':22C ''could'':75C ''etc'':44C ''experi'':103C ''fast'':48C ''featur'':131C ''forest'':106C ''found'':55C ''group'':28C ''interest'':85C ''kinda'':129C ''know'':64C ''learn'':3A,10B,19C ''link'':77C ''littl'':102C ''m'':83C,91C,125C ''machin'':2A,9B,18C ''machine-learn'':8B ''made'':46C ''mayb'':110C ''mean'':30C ''music'':6A,26C ''musical-scal'':25C ''network'':109C ''neural'':108C ''one'':118C ''p'':12C ''paper'':70C ''pass'':134C ''possibl'':15C ''predict'':5A ''random'':105C ''relat'':71C ''research'':68C ''scale'':7A,27C ''search'':49C ''softwar'':57C,66C ''song'':23C,32C,39C ''subject'':52C,74C,88C ''sure'':93C,127C ''task'':116C ''techniqu'':20C ''thank'':138C ''use'':1A,17C ''written'':34C,41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9205, 4, '2015-12-04 12:17:25.853', 0, NULL, NULL, 'Computer Vision is a subfield of computer science which deals with analyzing and understanding images. This includes detection of objects like faces in images or segmenting images.', NULL, NULL, '''analyz'':12C ''comput'':1C,7C ''deal'':10C ''detect'':18C ''face'':22C ''imag'':15C,24C,27C ''includ'':17C ''like'':21C ''object'':20C ''scienc'':8C ''segment'':26C ''subfield'':5C ''understand'':14C ''vision'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8476, 2, '2015-10-17 13:43:30.05', 1, NULL, NULL, '&lt;p&gt;Welcome to the site, @intellion! I can recommend a couple of things for you. First, if you think you''ll continue your interest in data science and will be working on problems like this in the future, it is worthwhile to start familiarizing yourself with the field. There are a variety of introductory textbooks and courses you can choose from. If you have a strong mathematical background, I''d recommend Tibshirani''s "&lt;a href="https://books.google.com/books/about/The_Elements_of_Statistical_Learning.html?id=yPfZBwAAQBAJ&amp;amp;printsec=frontcover&amp;amp;source=kp_read_button&amp;amp;hl=en#v=onepage&amp;amp;q&amp;amp;f=false" rel="nofollow"&gt;The Elements of Statistical Learning&lt;/a&gt;", which is a solid classic in the field. If you''re approaching this from a more application-oriented angle, I''d recommend something written specifically for the language you''d like to work in. If you plan on using R, there''s "&lt;a href="http://rads.stackoverflow.com/amzn/click/1782162143" rel="nofollow"&gt;Machine Learning with R&lt;/a&gt;"; if you plan on using python, there''s "&lt;a href="http://rads.stackoverflow.com/amzn/click/1118961749" rel="nofollow"&gt;Machine Learning in Python&lt;/a&gt;." There are of course a variety of free online resources as well.&lt;/p&gt;

&lt;p&gt;To your question, you could certainly solve your problem with Support Vector Machines (SVMs) or Neural Networks, but I don''t think that''s the first place to start. To me, this looks like a classic multiple regression application situation (for an overview for doing this in R, see &lt;a href="http://www.statmethods.net/stats/regression.html" rel="nofollow"&gt;this&lt;/a&gt; or &lt;a href="https://www.springer.com/us/book/9780387790534" rel="nofollow"&gt;this textbook&lt;/a&gt;). If you really want to try something in machine learning, perhaps decision trees are a good place to start, as they''ll allow you to easily assess the influence of variables on the final model fit (e.g., using &lt;a href="https://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity" rel="nofollow"&gt;Gini Impurity&lt;/a&gt;). I''ve found the &lt;a href="https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf" rel="nofollow"&gt;&lt;code&gt;rpart&lt;/code&gt; library in R&lt;/a&gt; to be quite nice to work with, but it can be a little slow for larger data. You could also use &lt;a href="http://scikit-learn.org/stable/modules/tree.html" rel="nofollow"&gt;sklearn in python&lt;/a&gt;, if that''s your preferred language.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8477, 2, '2015-10-17 17:04:31.537', 1, NULL, NULL, '&lt;p&gt;It looks like your data includes strings and values. I suggest that you start with something simple as Logistic Regression. The strings can be hashed and the real values can be fed in as is.&lt;/p&gt;

&lt;p&gt;Split your dataset randomly in say 70% training and the remaining 30% for testing. Fit a model on the training data and evaluate the performance on the test data. This will give you a rough estimate of your classifier.&lt;/p&gt;

&lt;p&gt;The &lt;a href="https://github.com/JohnLangford/vowpal_wabbit/wiki" rel="nofollow"&gt;Vowpal Wabbit&lt;/a&gt; software package has everything you need to do the classification (and more). The format is simple, the learning is lightning fast - everything you need to experiment.&lt;/p&gt;
', NULL, NULL, '''/a'':90C ''/johnlangford/vowpal_wabbit/wiki'':85C ''/p'':37C,78C,117C ''30'':50C ''70'':45C ''classif'':100C ''classifi'':77C ''data'':6C,59C,67C ''dataset'':41C ''estim'':74C ''evalu'':61C ''everyth'':94C,112C ''experi'':116C ''fast'':111C ''fed'':33C ''fit'':53C ''format'':104C ''github.com'':84C ''github.com/johnlangford/vowpal_wabbit/wiki'':83C ''give'':70C ''hash'':26C ''href'':82C ''includ'':7C ''learn'':108C ''lightn'':110C ''like'':4C ''logist'':20C ''look'':3C ''model'':55C ''need'':96C,114C ''nofollow'':87C ''p'':1C,38C,79C ''packag'':92C ''perform'':63C ''random'':42C ''real'':29C ''regress'':21C ''rel'':86C ''remain'':49C ''rough'':73C ''say'':44C ''simpl'':18C,106C ''softwar'':91C ''someth'':17C ''split'':39C ''start'':15C ''string'':8C,23C ''suggest'':12C ''test'':52C,66C ''train'':46C,58C ''valu'':10C,30C ''vowpal'':88C ''wabbit'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8478, 1, '2015-10-17 18:37:01.587', 1, 29, 'R: Revalue multiple special characters in a data.frame', '&lt;p&gt;R noob here..  &lt;/p&gt;

&lt;p&gt;I have the following data frame  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;data  
 Value Multiplier
1  15    H
2   0    h
3   2    +
4   2    ?
5   2    k
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where the multiplier is of class &lt;code&gt;factor&lt;/code&gt;. The values of &lt;code&gt;K&lt;/code&gt; &amp;amp; &lt;code&gt;k&lt;/code&gt; is &lt;code&gt;3&lt;/code&gt;, &lt;code&gt;+&lt;/code&gt; is &lt;code&gt;5&lt;/code&gt; and &lt;code&gt;?&lt;/code&gt; is &lt;code&gt;2&lt;/code&gt;.  &lt;/p&gt;

&lt;p&gt;I have used &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; data$Multiplier &amp;lt;-  revalue(data$Multiplier, c("+"="5"))
&amp;gt; data$Multiplier &amp;lt;-  revalue(data$Multiplier, c("?"="2"))
&amp;gt; data$Multiplier &amp;lt;-  revalue(data$Multiplier, c("K"="3"))
&amp;gt; data$Multiplier &amp;lt;-  revalue(data$Multiplier, c("k"="3"))  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is there a better way of doing it?&lt;/p&gt;
', 'r  data-cleaning', NULL, '''..'':17C ''/code'':46C,57C,63C,67C,71C,73C,77C,80C,84C,131C ''/p'':18C,26C,85C,90C,142C ''/pre'':47C,132C ''0'':37C ''1'':33C ''15'':34C ''2'':36C,40C,42C,44C,83C,110C ''3'':39C,70C,120C,130C ''4'':41C ''5'':43C,76C,101C ''amp'':64C ''better'':137C ''c'':100C,109C,118C,128C ''charact'':5A ''class'':54C ''clean'':12B ''code'':28C,55C,61C,65C,69C,72C,75C,79C,82C,92C ''data'':11B,24C,30C,94C,98C,103C,107C,112C,116C,122C,126C ''data-clean'':10B ''data.frame'':8A ''factor'':56C ''follow'':23C ''frame'':25C ''gt'':29C,93C,102C,111C,121C ''h'':35C,38C ''k'':45C,62C,66C,119C,129C ''lt'':96C,105C,114C,124C ''multipl'':3A ''multipli'':32C,51C,95C,99C,104C,108C,113C,117C,123C,127C ''noob'':15C ''p'':13C,19C,48C,86C,133C ''pre'':27C,91C ''r'':1A,9B,14C ''revalu'':2A,97C,106C,115C,125C ''special'':4A ''use'':89C ''valu'':31C,59C ''way'':138C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9366, 2, '2015-12-13 23:32:10.927', 0, NULL, NULL, '&lt;p&gt;Record all votes  &lt;/p&gt;

&lt;p&gt;Ratio number of 1 votes when on the front page compared to not   &lt;/p&gt;

&lt;p&gt;Only apply a fraction of number 1 votes while on the first page&lt;br&gt;
Basically remove the page 1 bias based on the page 1 bias as a whole  &lt;/p&gt;

&lt;p&gt;1 vote applied = 1 votes item when on first page * (1 votes second page total / 1 votes first page total) &lt;/p&gt;
', NULL, NULL, '''/p'':5C,20C,51C,73C ''1'':10C,28C,40C,46C,53C,56C,63C,68C ''appli'':23C,55C ''base'':42C ''basic'':36C ''bias'':41C,47C ''br'':35C ''compar'':17C ''first'':33C,61C,70C ''fraction'':25C ''front'':15C ''item'':58C ''number'':8C,27C ''p'':1C,6C,21C,52C ''page'':16C,34C,39C,45C,62C,66C,71C ''ratio'':7C ''record'':2C ''remov'':37C ''second'':65C ''total'':67C,72C ''vote'':4C,11C,29C,54C,57C,64C,69C ''whole'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8480, 1, '2015-10-17 20:41:37.167', 0, 62, 'Why would removing a variable in adaboost decrease error rate?', '&lt;p&gt;I was trying to classify an outcome on some data using adaboost (the ada package in R) and I was playing around with the training data set of descriptors when I realized that removing a column in the descriptor matrix increased the accuracy of the output on the training data. Specifically, the number of false negatives dropped/true positives increased. &lt;/p&gt;

&lt;p&gt;Aside from removing a single column in the descriptors, I left everything else the same, including number of iterations.&lt;/p&gt;
', 'r  classification', NULL, '''/p'':73C,94C ''accuraci'':56C ''ada'':27C ''adaboost'':7A,25C ''around'':35C ''asid'':75C ''classif'':12B ''classifi'':18C ''column'':49C,80C ''data'':23C,39C,63C ''decreas'':8A ''descriptor'':42C,52C,83C ''dropped/true'':70C ''els'':87C ''error'':9A ''everyth'':86C ''fals'':68C ''includ'':90C ''increas'':54C,72C ''iter'':93C ''left'':85C ''matrix'':53C ''negat'':69C ''number'':66C,91C ''outcom'':20C ''output'':59C ''p'':13C,74C ''packag'':28C ''play'':34C ''posit'':71C ''r'':11B,30C ''rate'':10A ''realiz'':45C ''remov'':3A,47C,77C ''set'':40C ''singl'':79C ''specif'':64C ''train'':38C,62C ''tri'':16C ''use'':24C ''variabl'':5A ''would'':2A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8484, 2, '2015-10-18 02:19:57.837', 0, NULL, NULL, '&lt;p&gt;I post an answer but clearly I will award the bonus to someone else&lt;/p&gt;

&lt;p&gt;I think there is a maximum numerator if the document tf is normalized  &lt;/p&gt;

&lt;p&gt;d1âd2/(||d1||||d2||)  &lt;/p&gt;

&lt;p&gt;Assume d1 has same or less terms 
(or just take the d with less terms)&lt;br&gt;
The maximum possible normalized tf is 1&lt;br&gt;
So the maximum possible numerator sum(tf1,i * idf,i * 1 * idf,i) &lt;/p&gt;

&lt;p&gt;||d2|| =  sum(tf1,i * idf,i * 1 * idf,i)  / ||d1|| / .6  &lt;/p&gt;

&lt;p&gt;As for a minimum I am working on that but clearly there is a minimum.&lt;br&gt;
If you are going to match you are going to have ||d||&lt;/p&gt;
', NULL, NULL, '''/p'':16C,31C,37C,76C,89C,119C ''1'':61C,73C,84C ''6'':88C ''answer'':5C ''assum'':39C ''award'':10C ''bonus'':12C ''br'':54C,62C,106C ''clear'':7C,101C ''d'':50C,118C ''d1'':35C,40C,87C ''d1â'':33C ''d2'':34C,36C,78C ''document'':27C ''els'':15C ''go'':110C,115C ''idf'':71C,74C,82C,85C ''less'':44C,52C ''match'':112C ''maximum'':23C,56C,65C ''minimum'':94C,105C ''normal'':30C,58C ''numer'':24C,67C ''p'':1C,17C,32C,38C,77C,90C ''possibl'':57C,66C ''post'':3C ''someon'':14C ''sum'':68C,79C ''take'':48C ''term'':45C,53C ''tf'':28C,59C ''tf1'':69C,80C ''think'':19C ''work'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8485, 2, '2015-10-18 09:24:52.407', 1, NULL, NULL, '&lt;p&gt;There are several models for holding hierarchical data in a relational database. There''s a good summary &lt;a href="http://www.slideshare.net/mobile/billkarwin/models-for-hierarchical-data" rel="nofollow"&gt;here&lt;/a&gt;. In summary they are&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Adjacency list: each row contains a foreign key to its parent. Fast for structure changes. Requires recursive processing to find the base ancestor.&lt;/li&gt;
&lt;li&gt;Path enumeration: each node stores its ancestry. An update requires all children to be updated. For a known node reaching the root requires no further work.&lt;/li&gt;
&lt;li&gt;Nested sets: additional columns hold the ancestry. The cost of structure changes depends on how big the moving subtree is. The root node is fast to find.&lt;/li&gt;
&lt;li&gt;Closure table: store all ancestor-descendant pairs. Structure changes can be expensive. Root node expensive to identify.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;From the given information a path enumeration sounds like your best option.&lt;/p&gt;
', NULL, NULL, '''/a'':27C ''/li'':58C,86C,115C,135C ''/mobile/billkarwin/models-for-hierarchical-data'':23C ''/p'':32C,150C ''/ul'':136C ''addit'':90C ''adjac'':35C ''ancestor'':57C,122C ''ancestor-descend'':121C ''ancestri'':66C,94C ''base'':56C ''best'':148C ''big'':103C ''chang'':49C,99C,126C ''children'':71C ''closur'':117C ''column'':91C ''contain'':39C ''cost'':96C ''data'':9C ''databas'':13C ''depend'':100C ''descend'':123C ''enumer'':61C,144C ''expens'':129C,132C ''fast'':46C,112C ''find'':54C,114C ''foreign'':41C ''given'':140C ''good'':17C ''hierarch'':8C ''hold'':7C,92C ''href'':20C ''identifi'':134C ''inform'':141C ''key'':42C ''known'':77C ''li'':34C,59C,87C,116C ''like'':146C ''list'':36C ''model'':5C ''move'':105C ''nest'':88C ''node'':63C,78C,110C,131C ''nofollow'':25C ''option'':149C ''p'':1C,137C ''pair'':124C ''parent'':45C ''path'':60C,143C ''process'':52C ''reach'':79C ''recurs'':51C ''rel'':24C ''relat'':12C ''requir'':50C,69C,82C ''root'':81C,109C,130C ''row'':38C ''set'':89C ''sever'':4C ''sound'':145C ''store'':64C,119C ''structur'':48C,98C,125C ''subtre'':106C ''summari'':18C,29C ''tabl'':118C ''ul'':33C ''updat'':68C,74C ''work'':85C ''www.slideshare.net'':22C ''www.slideshare.net/mobile/billkarwin/models-for-hierarchical-data'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8486, 1, '2015-10-18 10:06:27.687', 0, 14, 'Fixed effects regression within group variation', '&lt;p&gt;Is there some test or measure of within-group variation that would allow me to say whether it''s sufficient to use a fixed effects model. I am assuming everything else is consistent with the use of a fixed effects model?&lt;/p&gt;
', 'regression', NULL, '''/p'':51C ''allow'':22C ''assum'':38C ''consist'':42C ''effect'':2A,34C,49C ''els'':40C ''everyth'':39C ''fix'':1A,33C,48C ''group'':5A,18C ''measur'':14C ''model'':35C,50C ''p'':8C ''regress'':3A,7B ''say'':25C ''suffici'':29C ''test'':12C ''use'':31C,45C ''variat'':6A,19C ''whether'':26C ''within'':4A,17C ''within-group'':16C ''would'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8488, 2, '2015-10-18 16:42:33.96', 0, NULL, NULL, '&lt;p&gt;Suppose you have &lt;code&gt;(MxN)&lt;/code&gt; sparse matrix, where &lt;code&gt;M&lt;/code&gt; -- stands for number of users who gave recommendations, and &lt;code&gt;N&lt;/code&gt; is the number of items recommended.   The $x_{ij}$ element of the matrix is the recommendation given, with some elements missing, i.e. to be predicted. &lt;/p&gt;

&lt;p&gt;Then your matrix can be "factorized", via introducing &lt;code&gt;K&lt;/code&gt; "latent factors", so that instead of one matrix you have two: &lt;code&gt;(MxK)&lt;/code&gt; --for users, and &lt;code&gt;(KxN)&lt;/code&gt; -- for items, matrix multiplication of which produces the original matrix.&lt;/p&gt;

&lt;p&gt;Finally, to your question: what are latent features in matrix factorization? They are unknown features (&lt;code&gt;K&lt;/code&gt;) in user tastes and recommended items, so that when these two matrices multiplied, they produce matrix of known recommendations. Particular weights (of user preferences towards a particular feature and amount of a feature in a particular item) are defined via so called Alternating Least Squares algo, more about which you can read &lt;a href="http://bugra.github.io/work/notes/2014-04-19/alternating-least-squares-method-for-collaborative-filtering/" rel="nofollow"&gt;here&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':174C ''/code'':7C,13C,25C,63C,77C,83C,113C ''/p'':51C,94C,175C ''/work/notes/2014-04-19/alternating-least-squares-method-for-collaborative-filtering/'':170C ''algo'':159C ''altern'':156C ''amount'':143C ''bugra.github.io'':169C ''bugra.github.io/work/notes/2014-04-19/alternating-least-squares-method-for-collaborative-filtering/'':168C ''call'':155C ''code'':5C,11C,23C,61C,75C,81C,111C ''defin'':152C ''element'':35C,45C ''factor'':58C,65C,106C ''featur'':103C,110C,141C,146C ''final'':96C ''gave'':20C ''given'':42C ''href'':167C ''i.e'':47C ''ij'':34C ''instead'':68C ''introduc'':60C ''item'':30C,85C,119C,150C ''k'':62C,112C ''known'':131C ''kxn'':82C ''latent'':64C,102C ''least'':157C ''m'':12C ''matric'':125C ''matrix'':9C,38C,55C,71C,86C,93C,105C,129C ''miss'':46C ''multipl'':87C ''multipli'':126C ''mxk'':76C ''mxn'':6C ''n'':24C ''nofollow'':172C ''number'':16C,28C ''one'':70C ''origin'':92C ''p'':1C,52C,95C ''particular'':133C,140C,149C ''predict'':50C ''prefer'':137C ''produc'':90C,128C ''question'':99C ''read'':165C ''recommend'':21C,31C,41C,118C,132C ''rel'':171C ''spars'':8C ''squar'':158C ''stand'':14C ''suppos'':2C ''tast'':116C ''toward'':138C ''two'':74C,124C ''unknown'':109C ''user'':18C,79C,115C,136C ''via'':59C,153C ''weight'':134C ''x'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8497, 1, '2015-10-19 05:08:06.053', 1, 32, 'How to determine window size for modeling user behavior', '&lt;p&gt;I am working in mining time series data for modeling user behavior. I collected the data from 37 smartphone users during 42 days(6 weeks). I want to use data during 3 weeks as model and the remaining as test (test performance of my model). The visualization of our data is following below:
&lt;a href="http://i.stack.imgur.com/afLS6.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/afLS6.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In here, I want to ask what is the good &lt;code&gt;window size&lt;/code&gt; for modeling user behavior, is one hour, one day, one week etc? or may be is there any good methods for determining window size?&lt;/p&gt;
', 'data-mining  time-series', NULL, '''/a'':87C ''/afls6.png'':74C,81C ''/code'':103C ''/p'':88C,128C ''3'':48C ''37'':34C ''42'':38C ''6'':40C ''alt'':82C ''ask'':95C ''behavior'':9A,28C,107C ''code'':100C ''collect'':30C ''data'':11B,24C,32C,46C,66C ''data-min'':10B ''day'':39C,112C ''descript'':85C ''determin'':3A,125C ''enter'':83C ''etc'':115C ''follow'':68C ''good'':99C,122C ''hour'':110C ''href'':71C ''i.stack.imgur.com'':73C,80C ''i.stack.imgur.com/afls6.png'':72C,79C ''imag'':84C ''img'':77C ''may'':117C ''method'':123C ''mine'':12B,21C ''model'':7A,26C,51C,61C,105C ''nofollow'':76C ''one'':109C,111C,113C ''p'':16C,89C ''perform'':58C ''rel'':75C ''remain'':54C ''seri'':15B,23C ''size'':5A,102C,127C ''smartphon'':35C ''src'':78C ''test'':56C,57C ''time'':14B,22C ''time-seri'':13B ''use'':45C ''user'':8A,27C,36C,106C ''visual'':63C ''want'':43C,93C ''week'':41C,49C,114C ''window'':4A,101C,126C ''work'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8498, 1, '2015-10-19 06:20:27.753', 1, 48, 'Use the LMS algorithm to train a single perceptron neural network by finding the weights for a given data', '&lt;p&gt;Basically, I have data set contain inputs and outputs for unknown system, I want to use the LMS (Least Mean Square), to train a single perceptron (neuron) NN, and find the weights $ w_1, w_2, ... $, for the given output.&lt;/p&gt;
', 'machine-learning  neuralnetwork  algorithms  optimization', NULL, '''/p'':67C ''1'':60C ''2'':62C ''algorithm'':4A,24B ''basic'':27C ''contain'':32C ''data'':19A,30C ''find'':13A,56C ''given'':18A,65C ''input'':33C ''learn'':22B ''least'':45C ''lms'':3A,44C ''machin'':21B ''machine-learn'':20B ''mean'':46C ''network'':11A ''neural'':10A ''neuralnetwork'':23B ''neuron'':53C ''nn'':54C ''optim'':25B ''output'':35C,66C ''p'':26C ''perceptron'':9A,52C ''set'':31C ''singl'':8A,51C ''squar'':47C ''system'':38C ''train'':6A,49C ''unknown'':37C ''use'':1A,42C ''w'':59C,61C ''want'':40C ''weight'':15A,58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8499, 2, '2015-10-19 09:16:13.503', 2, NULL, NULL, '&lt;p&gt;I wouldn''t give any reason to make an choice &lt;em&gt;a priori&lt;/em&gt;, based on such a broad description.&lt;/p&gt;

&lt;p&gt;Instead I would build models for NN and SVM (or any other that you were considering), train them on the data then compare using cross-validation and a suitable metric for your problem - such as accuracy, &lt;a href="https://en.wikipedia.org/wiki/F1_score" rel="nofollow"&gt;F1 score&lt;/a&gt;, &lt;a href="https://en.wikipedia.org/wiki/Receiver_operating_characteristic" rel="nofollow"&gt;AUROC&lt;/a&gt; or log loss. You may also want to note characteristics other than performance metric if you care about costs such as time to train, amount of computing resource required etc.&lt;/p&gt;

&lt;p&gt;Experience may tell you likely best choices from the problem domain. For instance, if you are classifying photographic or scanned images, then currently a convolutional neural network would be a very strong candidate, as these have repeatedly shown themselves as best performers with real-world image data.&lt;/p&gt;
', NULL, NULL, '''/a'':70C,79C ''/em'':15C ''/p'':22C,110C,160C ''/wiki/f1_score'':65C ''/wiki/receiver_operating_characteristic'':75C ''accuraci'':60C ''also'':85C ''amount'':104C ''auroc'':78C ''base'':16C ''best'':117C,152C ''broad'':20C ''build'':27C ''candid'':144C ''care'':96C ''characterist'':89C ''choic'':11C,118C ''classifi'':128C ''compar'':46C ''comput'':106C ''consid'':39C ''convolut'':136C ''cost'':98C ''cross'':49C ''cross-valid'':48C ''current'':134C ''data'':44C,159C ''descript'':21C ''domain'':122C ''em'':12C ''en.wikipedia.org'':64C,74C ''en.wikipedia.org/wiki/f1_score'':63C ''en.wikipedia.org/wiki/receiver_operating_characteristic'':73C ''etc'':109C ''experi'':112C ''f1'':68C ''give'':5C ''href'':62C,72C ''imag'':132C,158C ''instanc'':124C ''instead'':24C ''like'':116C ''log'':81C ''loss'':82C ''make'':9C ''may'':84C,113C ''metric'':54C,93C ''model'':28C ''network'':138C ''neural'':137C ''nn'':30C ''nofollow'':67C,77C ''note'':88C ''p'':1C,23C,111C ''perform'':92C,153C ''photograph'':129C ''priori'':14C ''problem'':57C,121C ''real'':156C ''real-world'':155C ''reason'':7C ''rel'':66C,76C ''repeat'':148C ''requir'':108C ''resourc'':107C ''scan'':131C ''score'':69C ''shown'':149C ''strong'':143C ''suitabl'':53C ''svm'':32C ''tell'':114C ''time'':101C ''train'':40C,103C ''use'':47C ''valid'':50C ''want'':86C ''world'':157C ''would'':26C,139C ''wouldn'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8500, 1, '2015-10-19 10:14:50.24', 0, 34, 'On fitting a Poisson distribution to make sense of data', '&lt;p&gt;Hi guys I am working with a regular network which has the shape of a square grid and contains &lt;code&gt;100x100=10000&lt;/code&gt; nodes. The edges (links) between these nodes simply follow the shape of a chess table: each node which is not placed in the corner or along the boundary has 4 connections only, all of them involving its nearest neighbors. Accordingly, the nodes on the boundary have 3 connections only, while the nodes in the four corners have 2.&lt;/p&gt;

&lt;p&gt;Now, this brings me to my problem. If you plot such data you will figure out you have 4 nodes with links=2, 98*4 nodes with links=3, and 10000-(98*4)-4 with links=4. This translates into three tuples to plot: &lt;code&gt;(x,y)=(2,4),(3,392),(4,9604)&lt;/code&gt;. The resulting histogram is heavily skewed toward the right hand side:
 &lt;a href="http://i.stack.imgur.com/nYDjE.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/nYDjE.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;My question is: &lt;strong&gt;what kind of distribution do you think would fit this dataset?&lt;/strong&gt; I was thinking of a Poisson distribution (the x-axis values are discrete and not continuous) skewed to the right. &lt;strong&gt;I will appreciate any kind of help/guidance.&lt;/strong&gt; Thank you!&lt;/p&gt;
', 'dataset  neuralnetwork  sampling', NULL, '''-4'':131C ''/a'':180C ''/code'':37C,151C ''/nydje.png'':167C,174C ''/p'':96C,181C,231C ''/strong'':198C,228C ''10000'':36C,128C ''100x100'':35C ''2'':95C,120C,145C ''3'':84C,126C,147C ''392'':148C ''4'':67C,116C,122C,130C,134C,146C,149C ''9604'':150C ''98'':121C,129C ''accord'':77C ''along'':63C ''alt'':175C ''appreci'':223C ''axi'':209C ''boundari'':65C,82C ''bring'':100C ''chess'':51C ''code'':34C,142C ''connect'':68C,85C ''contain'':33C ''continu'':215C ''corner'':61C,93C ''data'':10A,109C ''dataset'':11B,197C ''descript'':178C ''discret'':212C ''distribut'':5A,190C,205C ''edg'':40C ''enter'':176C ''figur'':112C ''fit'':2A,195C ''follow'':46C ''four'':92C ''grid'':31C ''guy'':16C ''hand'':161C ''heavili'':156C ''help/guidance'':227C ''hi'':15C ''histogram'':154C ''href'':164C ''i.stack.imgur.com'':166C,173C ''i.stack.imgur.com/nydje.png'':165C,172C ''imag'':177C ''img'':170C ''involv'':73C ''kind'':188C,225C ''link'':41C,119C,125C,133C ''make'':7A ''nearest'':75C ''neighbor'':76C ''network'':23C ''neuralnetwork'':12B ''node'':38C,44C,54C,79C,89C,117C,123C ''nofollow'':169C ''p'':14C,97C,182C ''place'':58C ''plot'':107C,141C ''poisson'':4A,204C ''problem'':104C ''question'':184C ''regular'':22C ''rel'':168C ''result'':153C ''right'':160C,219C ''sampl'':13B ''sens'':8A ''shape'':27C,48C ''side'':162C ''simpli'':45C ''skew'':157C,216C ''squar'':30C ''src'':171C ''strong'':186C,220C ''tabl'':52C ''thank'':229C ''think'':193C,201C ''three'':138C ''toward'':158C ''translat'':136C ''tupl'':139C ''valu'':210C ''work'':19C ''would'':194C ''x'':143C,208C ''x-axi'':207C ''y'':144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8501, 1, '2015-10-19 11:10:08.813', 1, 30, 'Choosing class labels from annotated data', '&lt;p&gt;For a multi label, multi class categorization on a social media dataset, we have collected around 5000 samples from the dataset and have manually annotated them. 5000 samples are labelled by 3 people, and 1500  of them are same posts.  How should I decide the category label now?&lt;/p&gt;

&lt;p&gt;For example, &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;AnnotatorId  Post  Labels
-------------------------
Annotator1: post1: A,B,C
Annotator2: post1: A,D,E
Annotator3: post1: B,D,E
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Will the label for post1 be &lt;code&gt;A,B,D&lt;/code&gt; through majority voting? Or, is there some better, commonly used approaches?&lt;/p&gt;
', 'machine-learning  text-mining', NULL, '''/code'':87C,100C ''/p'':62C,66C,112C ''/pre'':88C ''1500'':48C ''3'':45C ''5000'':30C,40C ''annot'':5A,38C ''annotator1'':72C ''annotator2'':77C ''annotator3'':82C ''annotatorid'':69C ''approach'':111C ''around'':29C ''b'':75C,84C,98C ''better'':108C ''c'':76C ''categor'':20C ''categori'':59C ''choos'':1A ''class'':2A,19C ''code'':68C,96C ''collect'':28C ''common'':109C ''d'':80C,85C,99C ''data'':6A ''dataset'':25C,34C ''decid'':57C ''e'':81C,86C ''exampl'':65C ''label'':3A,17C,43C,60C,71C,92C ''learn'':9B ''machin'':8B ''machine-learn'':7B ''major'':102C ''manual'':37C ''media'':24C ''mine'':12B ''multi'':16C,18C ''p'':13C,63C,89C ''peopl'':46C ''post'':53C,70C ''post1'':73C,78C,83C,94C ''pre'':67C ''sampl'':31C,41C ''social'':23C ''text'':11B ''text-min'':10B ''use'':110C ''vote'':103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8504, 1, '2015-10-19 11:31:55.477', 2, 65, 'Pick a model from multiple models using a decision tree', '&lt;p&gt;Let us say, I have 4 classification models on a training data set of various &lt;code&gt;examples&lt;/code&gt;. Now, I want to choose which 1 out 4 models (or what combination of the 4 models) should I choose to get the final probability for a particular &lt;code&gt;test example&lt;/code&gt;. So, basically I want to create a &lt;code&gt;meta model&lt;/code&gt; to get ultimate probabilities for all the test examples. I know I can combine all 4 models using regression but I am particularly interested in using a decision tree to combine all 4 models. &lt;/p&gt;

&lt;p&gt;So, the &lt;code&gt;meta decision tree model&lt;/code&gt; will have 4 features/variables (4 model probabilities) as per my current thoughts. Is this a right approach? Should I include more features in it? &lt;/p&gt;

&lt;p&gt;Please explain how do I combine 4 models using a &lt;code&gt;meta decision tree model&lt;/code&gt; to choose the best &lt;code&gt;model out of 4&lt;/code&gt; for a particular &lt;code&gt;test example&lt;/code&gt;.  &lt;/p&gt;
', 'decision-trees  ensemble-modeling', NULL, '''/code'':35C,67C,78C,122C,164C,174C,181C ''/p'':113C,147C,182C ''1'':42C ''4'':23C,44C,51C,94C,111C,125C,127C,155C,173C ''approach'':139C ''basic'':69C ''best'':168C ''choos'':40C,55C,166C ''classif'':24C ''code'':33C,64C,75C,117C,159C,169C,178C ''combin'':48C,92C,109C,154C ''creat'':73C ''current'':133C ''data'':29C ''decis'':9A,12B,106C,119C,161C ''decision-tre'':11B ''ensembl'':15B ''ensemble-model'':14B ''exampl'':34C,66C,87C,180C ''explain'':150C ''featur'':144C ''features/variables'':126C ''final'':59C ''get'':57C,80C ''includ'':142C ''interest'':102C ''know'':89C ''let'':18C ''meta'':76C,118C,160C ''model'':3A,6A,16B,25C,45C,52C,77C,95C,112C,121C,128C,156C,163C,170C ''multipl'':5A ''p'':17C,114C,148C ''particular'':63C,101C,177C ''per'':131C ''pick'':1A ''pleas'':149C ''probabl'':60C,82C,129C ''regress'':97C ''right'':138C ''say'':20C ''set'':30C ''test'':65C,86C,179C ''thought'':134C ''train'':28C ''tree'':10A,13B,107C,120C,162C ''ultim'':81C ''us'':19C ''use'':7A,96C,104C,157C ''various'':32C ''want'':38C,71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8506, 2, '2015-10-19 12:19:46.257', 0, NULL, NULL, '&lt;p&gt;Unlike other graphs, the degree distribution is a function of N. Specifically, the number of nodes with k=2 is constant (4), the  number of nodes with k=3 grows linearly with the length of one side $4l-4$, while the number of nodes with k=4 grows exponentially with the length of a side, $l^2-4l-4$. In the large $l$ (or $N$) limit, the degree distribution is 4. &lt;/p&gt;

&lt;p&gt;You can get the exact distribution by normalizing this piecewise defined function (divide by $l^2=N$). &lt;/p&gt;
', NULL, NULL, '''-4'':40C,59C,61C ''/p'':74C,93C ''2'':20C,58C,91C ''3'':30C ''4'':23C,48C,73C ''4l'':39C ''constant'':22C ''defin'':86C ''degre'':6C,70C ''distribut'':7C,71C,81C ''divid'':88C ''exact'':80C ''exponenti'':50C ''function'':10C,87C ''get'':78C ''graph'':4C ''grow'':31C,49C ''k'':19C,29C,47C ''l'':57C,60C,65C,90C ''larg'':64C ''length'':35C,53C ''limit'':68C ''linear'':32C ''n'':12C,67C,92C ''node'':17C,27C,45C ''normal'':83C ''number'':15C,25C,43C ''one'':37C ''p'':1C,75C ''piecewis'':85C ''side'':38C,56C ''specif'':13C ''unlik'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8519, 1, '2015-10-20 09:24:56.487', 0, 142, 'Extracting list of locations from text using R', '&lt;p&gt;I have a string containing many words [not sentences], I want to know how I can extract all the words that correspond to a location in that string for example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;text&amp;lt;-c("China","Japan","perspective","United Kingdom","formatting","clear","India","Sudan","United States of America","Bagel","Mongolian",...)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The output should be:   &lt;/p&gt;

&lt;pre&gt;&lt;code&gt; &amp;gt; China, Japan, United Kingdom, Mongolian
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;something of the type. Basically I am looking at extracting locative information from random text.
This is a very general problem I am looking for guidance on how to model my solution, is there any dataset or something I can use to compare or extract information from. I don''t want to carry out word by word comparison.&lt;/p&gt;

&lt;p&gt;I have looked up OpenNLP but I am not sure how to use it''s location-models for carrying out Named Entity Recognition in R. In the above example there are only countries but I would like to identify other places, such as provinces, states, counties, cities, etc. as well. 
I am new to machine learning and R-programming, any guidance is greatly appreciated.&lt;/p&gt;
', 'machine-learning  r  information-retrieval  named-entity-recognition', NULL, '''/code'':72C,88C ''/p'':51C,79C,149C,217C ''/pre'':73C,89C ''america'':69C ''appreci'':216C ''bagel'':70C ''basic'':95C ''c'':56C ''carri'':143C,170C ''china'':57C,83C ''citi'':198C ''clear'':63C ''code'':53C,81C ''compar'':133C ''comparison'':148C ''contain'':25C ''correspond'':42C ''counti'':197C ''countri'':184C ''dataset'':126C ''entiti'':18B,173C ''etc'':199C ''exampl'':50C,180C ''extract'':1A,37C,100C,135C ''format'':62C ''general'':110C ''great'':215C ''gt'':82C ''guidanc'':116C,213C ''identifi'':190C ''india'':64C ''inform'':14B,102C,136C ''information-retriev'':13B ''japan'':58C,84C ''kingdom'':61C,86C ''know'':33C ''learn'':11B,207C ''like'':188C ''list'':2A ''locat'':4A,45C,101C,167C ''location-model'':166C ''look'':98C,114C,153C ''lt'':55C ''machin'':10B,206C ''machine-learn'':9B ''mani'':26C ''model'':120C,168C ''mongolian'':71C,87C ''name'':17B,172C ''named-entity-recognit'':16B ''new'':204C ''opennlp'':155C ''output'':76C ''p'':20C,74C,90C,150C ''perspect'':59C ''place'':192C ''pre'':52C,80C ''problem'':111C ''program'':211C ''provinc'':195C ''r'':8A,12B,176C,210C ''r-program'':209C ''random'':104C ''recognit'':19B,174C ''retriev'':15B ''sentenc'':29C ''solut'':122C ''someth'':91C,128C ''state'':67C,196C ''string'':24C,48C ''sudan'':65C ''sure'':160C ''text'':6A,54C,105C ''type'':94C ''unit'':60C,66C,85C ''use'':7A,131C,163C ''want'':31C,141C ''well'':201C ''word'':27C,40C,145C,147C ''would'':187C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8509, 2, '2015-10-19 15:22:32.5', 5, NULL, NULL, '&lt;p&gt;That''s one valid way of approaching the problem. In your final solution, though, it will be helpful to quantify the overall &lt;a href="https://en.wikipedia.org/wiki/Inter-rater_reliability"&gt;inter-rater agreement&lt;/a&gt;. For example, &lt;a href="https://en.wikipedia.org/wiki/Cohen%27s_kappa"&gt;Cohen''s kappa&lt;/a&gt; is a commonly-used metric:
\begin{eqnarray}
\kappa &amp;amp;=&amp;amp; \frac{p_{o}-p_{e}}{1-p_{e}}\\
       &amp;amp;=&amp;amp; 1 - \frac{1-p_{o}}{1-p_{e}},
\end{eqnarray}
where $p_{o}$ and $p_{e}$ are the amount of agreement you observe and that due to chance, respectively. The reason this is important is that the amount of agreement your human annotators achieve is a theoretical upper-bound on the performance of your machine learning solutionâit provides context for interpreting the performance of your algorithmic approach.&lt;/p&gt;
', NULL, NULL, '''/a'':33C,44C ''/p'':135C ''/wiki/cohen%27s_kappa'':40C ''/wiki/inter-rater_reliability'':28C ''1'':61C,66C,68C,71C ''achiev'':109C ''agreement'':32C,86C,105C ''algorithm'':133C ''amount'':84C,103C ''amp'':54C,55C,64C,65C ''annot'':108C ''approach'':8C,134C ''begin'':51C ''bound'':115C ''chanc'':93C ''cohen'':41C ''common'':48C ''commonly-us'':47C ''context'':126C ''due'':91C ''e'':60C,63C,73C,81C ''en.wikipedia.org'':27C,39C ''en.wikipedia.org/wiki/cohen%27s_kappa'':38C ''en.wikipedia.org/wiki/inter-rater_reliability'':26C ''end'':74C ''eqnarray'':52C,75C ''exampl'':35C ''final'':13C ''frac'':56C,67C ''help'':19C ''href'':25C,37C ''human'':107C ''import'':99C ''inter'':30C ''inter-rat'':29C ''interpret'':128C ''kappa'':43C,53C ''learn'':122C ''machin'':121C ''metric'':50C ''o'':58C,70C,78C ''observ'':88C ''one'':4C ''overal'':23C ''p'':1C,57C,59C,62C,69C,72C,77C,80C ''perform'':118C,130C ''problem'':10C ''provid'':125C ''quantifi'':21C ''rater'':31C ''reason'':96C ''respect'':94C ''solut'':14C ''solutionâ'':123C ''theoret'':112C ''though'':15C ''upper'':114C ''upper-bound'':113C ''use'':49C ''valid'':5C ''way'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8510, 2, '2015-10-19 16:35:09.303', 3, NULL, NULL, '&lt;p&gt;From a very high level -- You can convert the song to a spectrogram, there are a large number of implementations to do this. From there you can analyze the sound waves. In the case of the key, for instance, the note A is equal to 440 hz. Look into FFT as well.  Hope this helps get you started. I know spotify trains neural networks on spectrograms of songs to find similar songs based on "sound".&lt;/p&gt;
', NULL, NULL, '''/p'':77C ''440'':47C ''analyz'':29C ''base'':74C ''case'':35C ''convert'':9C ''equal'':45C ''fft'':51C ''find'':71C ''get'':57C ''help'':56C ''high'':5C ''hope'':54C ''hz'':48C ''implement'':21C ''instanc'':40C ''key'':38C ''know'':61C ''larg'':18C ''level'':6C ''look'':49C ''network'':65C ''neural'':64C ''note'':42C ''number'':19C ''p'':1C ''similar'':72C ''song'':11C,69C,73C ''sound'':31C,76C ''spectrogram'':14C,67C ''spotifi'':62C ''start'':59C ''train'':63C ''wave'':32C ''well'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8511, 1, '2015-10-19 16:38:00.863', 4, 96, 'Unsupervised learning to identify most common basketball plays from spatial data', '&lt;p&gt;The NBA has a system called Sports VU that tracks x-y coordinates of every player and the ball every 1/10th of a second for every game of the 2013-2014 and 2014-2015 seasons. With some fancy web scraping I now have access to this data and -- because I''m such an avid fan of the NBA -- I would like to identify each team''s most common plays. Assume I don''t have any knowledge about each team''s plays beforehand (so I don''t think supervised learning would work here). What would be the best unsupervised learning techniques to use? &lt;/p&gt;

&lt;p&gt;If I could trace each player''s path over the course of the play, I imagine the problem would be similar to what you would see with image recognition/classification. Anyways, should I use PCA, some kind of neural  network? I understand this is a very broad question -- I don''t need to know how to code it (I''m a proficient coder and machine learning practitioner); I''m just looking for high level unsupervised machine learning details. &lt;/p&gt;
', 'machine-learning', NULL, '''-2014'':47C ''-2015'':50C ''/p'':119C,198C ''1/10th'':37C ''2013'':46C ''2014'':49C ''access'':60C ''anyway'':150C ''assum'':86C ''avid'':70C ''ball'':35C ''basketbal'':7A ''beforehand'':98C ''best'':113C ''broad'':166C ''call'':21C ''code'':176C ''coder'':182C ''common'':6A,84C ''coordin'':29C ''could'':123C ''cours'':131C ''data'':11A,63C ''detail'':197C ''everi'':31C,36C,42C ''fan'':71C ''fanci'':54C ''game'':43C ''high'':192C ''identifi'':4A,79C ''imag'':148C ''imagin'':136C ''kind'':156C ''know'':173C ''knowledg'':92C ''learn'':2A,14B,105C,115C,185C,196C ''level'':193C ''like'':77C ''look'':190C ''m'':67C,179C,188C ''machin'':13B,184C,195C ''machine-learn'':12B ''nba'':17C,74C ''need'':171C ''network'':159C ''neural'':158C ''p'':15C,120C ''path'':128C ''pca'':154C ''play'':8A,85C,97C,134C ''player'':32C,126C ''practition'':186C ''problem'':138C ''profici'':181C ''question'':167C ''recognition/classification'':149C ''scrape'':56C ''season'':51C ''second'':40C ''see'':146C ''similar'':141C ''spatial'':10A ''sport'':22C ''supervis'':104C ''system'':20C ''team'':81C,95C ''techniqu'':116C ''think'':103C ''trace'':124C ''track'':25C ''understand'':161C ''unsupervis'':1A,114C,194C ''use'':118C,153C ''vu'':23C ''web'':55C ''work'':107C ''would'':76C,106C,110C,139C,145C ''x'':27C ''x-i'':26C ''y'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8512, 2, '2015-10-19 17:02:02.38', 0, NULL, NULL, '&lt;p&gt;I kept searching and I recently found &lt;a href="http://jim.afim-asso.org/jim12/pdf/jim2012_08_p_osmalskyj.pdf" rel="nofollow"&gt;this&lt;/a&gt; and &lt;a href="https://www.academia.edu/351159/Musical_Instruments_in_Random_Forest" rel="nofollow"&gt;this&lt;/a&gt; articles that seems to be very promissory. I think that if I can identify a good number of notes from a music, I will be able to identify the music scale itself. &lt;/p&gt;

&lt;p&gt;If you guys have more material to suggest on this subject I''ll be very grateful. &lt;/p&gt;
', NULL, NULL, '''/351159/musical_instruments_in_random_forest'':23C ''/a'':17C,27C ''/jim12/pdf/jim2012_08_p_osmalskyj.pdf'':13C ''/p'':60C,78C ''abl'':53C ''articl'':28C ''found'':8C ''good'':43C ''grate'':77C ''guy'':64C ''href'':10C,20C ''identifi'':41C,55C ''jim.afim-asso.org'':12C ''jim.afim-asso.org/jim12/pdf/jim2012_08_p_osmalskyj.pdf'':11C ''kept'':3C ''ll'':74C ''materi'':67C ''music'':49C,57C ''nofollow'':15C,25C ''note'':46C ''number'':44C ''p'':1C,61C ''promissori'':34C ''recent'':7C ''rel'':14C,24C ''scale'':58C ''search'':4C ''seem'':30C ''subject'':72C ''suggest'':69C ''think'':36C ''www.academia.edu'':22C ''www.academia.edu/351159/musical_instruments_in_random_forest'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8520, 2, '2015-10-20 10:56:55.36', 0, NULL, NULL, '&lt;p&gt;You can take a look at Music Information Retrieval Evaluation eXchange (MIREX). An annual competition with number of different tasks from MIR. The most relevant to your question being Audio Key Detection. Explanation papers for the used approaches can be found &lt;a href="http://nema.lis.illinois.edu/nema_out/mirex2013/results/akd/" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In terms of library for extracting knowledge from audio signal I''d recommend &lt;a href="http://essentia.upf.edu/documentation/" rel="nofollow"&gt;Essentia&lt;/a&gt;. There are numerous features you can compute over time window in a track including tonal descriptors (key and scale).  &lt;/p&gt;

&lt;p&gt;Once you''ve built discriminative representation of a track for your task you can use any supervised classification model fed with labeled data. There are several musical datasets on the web labeled with track information. Take a look at GTZAN, each track has genre, tempo and key as meta. &lt;/p&gt;
', NULL, NULL, '''/a'':51C,75C ''/documentation/'':71C ''/nema_out/mirex2013/results/akd/'':47C ''/p'':52C,95C,146C ''annual'':15C ''approach'':39C ''audio'':31C,62C ''built'':100C ''classif'':114C ''competit'':16C ''comput'':82C ''d'':65C ''data'':119C ''dataset'':124C ''descriptor'':91C ''detect'':33C ''differ'':20C ''discrimin'':101C ''essentia'':74C ''essentia.upf.edu'':70C ''essentia.upf.edu/documentation/'':69C ''evalu'':11C ''exchang'':12C ''explan'':34C ''extract'':59C ''featur'':79C ''fed'':116C ''found'':42C ''genr'':140C ''gtzan'':136C ''href'':44C,68C ''includ'':89C ''inform'':9C,131C ''key'':32C,92C,143C ''knowledg'':60C ''label'':118C,128C ''librari'':57C ''look'':6C,134C ''meta'':145C ''mir'':23C ''mirex'':13C ''model'':115C ''music'':8C,123C ''nema.lis.illinois.edu'':46C ''nema.lis.illinois.edu/nema_out/mirex2013/results/akd/'':45C ''nofollow'':49C,73C ''number'':18C ''numer'':78C ''p'':1C,53C,96C ''paper'':35C ''question'':29C ''recommend'':66C ''rel'':48C,72C ''relev'':26C ''represent'':102C ''retriev'':10C ''scale'':94C ''sever'':122C ''signal'':63C ''supervis'':113C ''take'':4C,132C ''task'':21C,108C ''tempo'':141C ''term'':55C ''time'':84C ''tonal'':90C ''track'':88C,105C,130C,138C ''use'':38C,111C ''ve'':99C ''web'':127C ''window'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8521, 1, '2015-10-20 11:09:47.047', 1, 100, 'Reduce dimension, then apply SVM', '&lt;p&gt;Just out of curiousity, is it generally a good idea to reduce the dimension of training set before using it to train SVM classifier?&lt;/p&gt;

&lt;p&gt;I have a collection of documents, each of them is represented by a vector with tf-idf weight calculated by scikit-learn''s tfidf_transformer. The number of terms (feature?) is close to 60k, and with my training set that consist of about 2.5mil of documents, it makes the training process to go on forever.&lt;/p&gt;

&lt;p&gt;Besides taking forever to train, the classification also wasn''t accurate most probably due to the wrong model. Just to get an idea of what I am dealing with, I tried finding a way to visualize the data somehow. And I decomposed the document matrix into a (m, 2) matrix using SVD with scikit-learn (wanted to try other methods, but they all crashed halfway).&lt;/p&gt;

&lt;p&gt;So this is what the visualization looks like&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/os28L.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/os28L.png" alt="Result of SVD"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;So is it generally a good practice to reduce the dimension, and then only proceed with SVM? Also in this case what can I do to improve the accuracy of the classifier? I am trying to use sklearn.svm.SVC and &lt;code&gt;kernel=''poly''&lt;/code&gt;, and &lt;code&gt;degree=3&lt;/code&gt; and it is taking a very long time to complete.&lt;/p&gt;
', 'python  svm  scikit', NULL, '''/a'':190C ''/code'':235C,240C ''/os28l.png'':178C,185C ''/p'':34C,94C,162C,172C,191C,251C ''2'':144C ''2.5'':81C ''3'':239C ''60k'':71C ''accur'':106C ''accuraci'':221C ''also'':103C,210C ''alt'':186C ''appli'':4A ''besid'':96C ''calcul'':55C ''case'':213C ''classif'':102C ''classifi'':33C,224C ''close'':69C ''code'':232C,237C ''collect'':39C ''complet'':250C ''consist'':78C ''crash'':160C ''curious'':13C ''data'':133C ''deal'':123C ''decompos'':137C ''degre'':238C ''dimens'':2A,23C,203C ''document'':41C,84C,139C ''due'':109C ''featur'':67C ''find'':127C ''forev'':93C,98C ''general'':16C,196C ''get'':116C ''go'':91C ''good'':18C,198C ''halfway'':161C ''href'':175C ''i.stack.imgur.com'':177C,184C ''i.stack.imgur.com/os28l.png'':176C,183C ''idea'':19C,118C ''idf'':53C ''img'':181C ''improv'':219C ''kernel'':233C ''learn'':59C,151C ''like'':171C ''long'':247C ''look'':170C ''m'':143C ''make'':86C ''matrix'':140C,145C ''method'':156C ''mil'':82C ''model'':113C ''nofollow'':180C ''number'':64C ''p'':9C,35C,95C,163C,173C,192C ''poli'':234C ''practic'':199C ''probabl'':108C ''proceed'':207C ''process'':89C ''python'':6B ''reduc'':1A,21C,201C ''rel'':179C ''repres'':46C ''result'':187C ''scikit'':8B,58C,150C ''scikit-learn'':57C,149C ''set'':26C,76C ''sklearn.svm.svc'':230C ''somehow'':134C ''src'':182C ''svd'':147C,189C ''svm'':5A,7B,32C,209C ''take'':97C,244C ''term'':66C ''tf'':52C ''tf-idf'':51C ''tfidf'':61C ''time'':248C ''train'':25C,31C,75C,88C,100C ''transform'':62C ''tri'':126C,154C,227C ''use'':28C,146C,229C ''vector'':49C ''visual'':131C,169C ''want'':152C ''wasn'':104C ''way'':129C ''weight'':54C ''wrong'':112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10313, 2, '2016-02-19 07:47:26.503', 4, NULL, NULL, '&lt;p&gt;To clarify: you build one random forest on training data and get some results which seems to have no overfit, since CV and test results are similar. The second RF is built on the predictions of the first RF and some other additional features. The error on CV training data is very low and on test data are very high.&lt;/p&gt;

&lt;p&gt;Now we will analyze the performance of the second RF without considering how it was built. Usually if you fit data very well on training set and very poor on test set it means you simply overfit data. This happens when your model learns too much from the data. The point is that in your training data you have two kinds of information. The first one is the real structure of the data, and the second one is the noise around that data, assuming that you have enough data and your model is able to handle the useful structure. The ideal case happens when you learn only the useful structure and when leave out the noise.When that happens you will have close errors in training and test data, which means that what your model learnt is at least almost only useful data. When you learn too much, which means you learn useful and noise together, your training error decreases because the results have the noise structure which is in training data, and on test data the errors grows since the noise from test data does not look like the noise from training data.&lt;/p&gt;

&lt;p&gt;Now the first learner looks like it does not over fit. This of course does not always mean that you can''t do better. It only means that what your learn is a structure which is also found in test data and thus has predictive power. So is understandable that you aim to improve the performance of the first predictor. One way would be to try to improve that predictor, and you can search for that by studying how that model learns. For example you can study if that model improves performance with more data by training repeatedly on samples of increased size and see if stabilize itself or not. There are many approaches. Another approach would be ensembles and a prominent one is stacking. &lt;/p&gt;

&lt;p&gt;But you did not understood the idea of stacking. The whole point of stacking can be expressed in the following statement: explore the model space by building various &lt;em&gt;different&lt;/em&gt; model families and in the end get the best from all of them. So stacking starts with the assumption that since we do not know which would be an ideal model for our data, we should try many imperfect ones. Some of them will behave well in some regions of space, some others in other regions of space, but none of them will be able to handle all the space at high performance. The key point is to have models from different families. Sometimes the same algorithm can produce very different models, and sometimes not. For example two SVMs with very different kernels can produce totally different surfaces. Sometimes even for the same kernel an SVM can produce very different results for very different complexity parameter. Often for RF this is not the case, but it is not impossible to get different surfaces with different parameters on RF. So the conclusion is that stacking aims to explore different ways of learning.&lt;/p&gt;

&lt;p&gt;What you have done is to apply stacking on a single base learner by adding new features. First of all is that in general it does not work. The predictions from the first learner has a string structure, it comes from averaging on regions. So, this predictions compared with additional variables from the second model are more powerful. This means that every wrong thing learned in the first RF becomes very powerful on the second RF, because it uses the same averaging over regions procedure for learning. So the errors from the first learner amplifies in the second learner. I do not know about the additional features, but in this conditions it looks like it is very easy to overfit. &lt;/p&gt;

&lt;p&gt;Turning back to what''s in a data set: this data set has a structured content which you have to learn and an unstructured content (noise) around that. Your purpose of learning is to learn only the structure. The first RF is able to distinguish the structure from noise simply because the noise is not altered. The second RF is not able anymore to make that distinction, since the predictions of the first RF are already smooth in regions, which means it has a powerful structure, at least from the point of view of the second RF, which learns in the same way. &lt;/p&gt;

&lt;p&gt;A last idea is that it looks like what you have done is somehow similar with a boosting procedure. The first learner gives you a structure which is used as a baseline by the second learner. But there is a big difference than in boosting. While in boosting you have a base line which you aim to improve, you improve that baseline by looking again at the original features, which have the noise unstructured. In your case at the second iteration you do not have again that chance and everything is structured. Thus errors amplifies. &lt;/p&gt;

&lt;p&gt;My 2 cents: try multiple learners, eventually using additional features in those learners, try different models for base learners, start with very simple (low complexity or small degrees of freedom for) the stacking learner, like a penalized logistic regression and more further to more complex ones in a controlled fashion. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8513, 1, '2015-10-19 18:05:57.42', 1, 28, 'regression to model exponential distribution', '&lt;p&gt;I have a work order system. I found out that the work order completion times are exponentially distributed. Every ticket has some features. I understand that regression is "a line that best fits the points". In my case, I want the line to be exponential curve. Is it possible ? Or is CDF for exponential distribution better option ? &lt;/p&gt;

&lt;p&gt;My use case is I want to predict the probability of a work order completing by deadline. I was thinking of querying sets of data through "group by" &amp;amp; then compute and store the lambda parameters. When a new ticket comes in, I will pull the corresponding lambda &amp;amp; use CDF to determine %age. I am wondering if an ML algorithm can simplify this process.&lt;/p&gt;

&lt;p&gt;I have .net stack. I use Accord.net. I''m open to other .net libs.&lt;/p&gt;
', 'regression  svm  .net', NULL, '''/p'':67C,134C,150C ''accord.net'':142C ''age'':122C ''algorithm'':129C ''amp'':97C,117C ''best'':41C ''better'':65C ''case'':47C,71C ''cdf'':61C,119C ''come'':109C ''complet'':23C,83C ''comput'':99C ''correspond'':115C ''curv'':55C ''data'':93C ''deadlin'':85C ''determin'':121C ''distribut'':5A,27C,64C ''everi'':28C ''exponenti'':4A,26C,54C,63C ''featur'':32C ''fit'':42C ''found'':17C ''group'':95C ''lambda'':103C,116C ''lib'':149C ''line'':39C,51C ''m'':144C ''ml'':128C ''model'':3A ''net'':8B,138C,148C ''new'':107C ''open'':145C ''option'':66C ''order'':14C,22C,82C ''p'':9C,68C,135C ''paramet'':104C ''point'':44C ''possibl'':58C ''predict'':76C ''probabl'':78C ''process'':133C ''pull'':113C ''queri'':90C ''regress'':1A,6B,36C ''set'':91C ''simplifi'':131C ''stack'':139C ''store'':101C ''svm'':7B ''system'':15C ''think'':88C ''ticket'':29C,108C ''time'':24C ''understand'':34C ''use'':70C,118C,141C ''want'':49C,74C ''wonder'':125C ''work'':13C,21C,81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8514, 1, '2015-10-19 21:48:18.687', 0, 38, 'R code the returns last completed row and colunm', '&lt;p&gt;I am trying to create some code that will loop through a data set and remove the last completed row and column.&lt;/p&gt;

&lt;p&gt;The first column of the data set is the name of the student the rest of the data set represent sections of a program completed. The last column of each row that contains data represent the final section completed by the student.&lt;/p&gt;

&lt;p&gt;Thus the output should be a vector of the following:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;name, sections that are blank and contain NA (i.e. 1_1 or 1_3 etc.)
name, sections that are blank and contain NA
name, sections that are blank and contain NA
etc.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here is the data frame:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;df &amp;lt;- data.frame(list(names = c("John", "Peter", "Steve"),
                      `1_1` = c("yes", "yes", "yes"),
                      `1_2` = c("yes", "yes", ""),
                      `1_3` = c("", "yes", "yes"),
                      `1_4` = c("", "", "yes"),
                      `1_5` = c(NA, NA, NA)))
row.names &amp;lt;- c("1_1", "1_2", "1_3", "1_4", "1_5")
df

# ensures that all blank cell contain NA:
df &amp;lt;- apply(df, 2, function(x) gsub("^$|^ $", NA, x))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here is the code I am attempting to us:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;sapply(df, function(x) which(x==if(df&amp;gt;2) { list(df[,]) }))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The R output I get is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;$John
integer(0)

$Peter
integer(0)

$Steve
integer(0)

$yes
integer(0)

$yes
integer(0)

$yes
integer(0)

$yes
integer(0)

$yes
integer(0)

$&amp;lt;NA&amp;gt;
integer(0)

$&amp;lt;NA&amp;gt;
integer(0)

$yes
integer(0)

$yes
integer(0)

$&amp;lt;NA&amp;gt;
integer(0)

$&amp;lt;NA&amp;gt;
integer(0)

$yes
integer(0)

$&amp;lt;NA&amp;gt;
integer(0)

$&amp;lt;NA&amp;gt;
integer(0)

$&amp;lt;NA&amp;gt;
integer(0)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Thus, does not work at all. Any pointers?&lt;/p&gt;
', 'r', NULL, '''/code'':125C,202C,229C,309C ''/p'':34C,78C,90C,133C,214C,238C,320C ''/pre'':126C,203C,230C,310C ''0'':243C,246C,249C,252C,255C,258C,261C,264C,269C,274C,277C,280C,285C,290C,293C,298C,303C,308C ''1'':102C,103C,105C,145C,146C,151C,156C,161C,165C,174C,175C,176C,178C,180C,182C ''2'':152C,177C,196C,226C ''3'':106C,157C,179C ''4'':162C,181C ''5'':166C,183C ''appli'':194C ''attempt'':211C ''blank'':97C,112C,120C,188C ''c'':141C,147C,153C,158C,163C,167C,173C ''cell'':189C ''code'':2A,18C,92C,135C,208C,216C,240C ''column'':33C,38C,63C ''colunm'':9A ''complet'':6A,30C,60C,74C ''contain'':68C,99C,114C,122C,190C ''creat'':16C ''data'':24C,41C,53C,69C,131C ''data.frame'':138C ''df'':136C,184C,192C,195C,218C,224C,228C ''ensur'':185C ''etc'':107C,124C ''final'':72C ''first'':37C ''follow'':89C ''frame'':132C ''function'':197C,219C ''get'':236C ''gsub'':199C ''gt'':225C,267C,272C,283C,288C,296C,301C,306C ''i.e'':101C ''integ'':242C,245C,248C,251C,254C,257C,260C,263C,268C,273C,276C,279C,284C,289C,292C,297C,302C,307C ''john'':142C,241C ''last'':5A,29C,62C ''list'':139C,227C ''loop'':21C ''lt'':137C,172C,193C,265C,270C,281C,286C,294C,299C,304C ''na'':100C,115C,123C,168C,169C,170C,191C,200C,266C,271C,282C,287C,295C,300C,305C ''name'':45C,93C,108C,116C,140C ''output'':82C,234C ''p'':11C,35C,79C,127C,204C,231C,311C ''peter'':143C,244C ''pointer'':319C ''pre'':91C,134C,215C,239C ''program'':59C ''r'':1A,10B,233C ''remov'':27C ''repres'':55C,70C ''rest'':50C ''return'':4A ''row'':7A,31C,66C ''row.names'':171C ''sappli'':217C ''section'':56C,73C,94C,109C,117C ''set'':25C,42C,54C ''steve'':144C,247C ''student'':48C,77C ''thus'':80C,312C ''tri'':14C ''us'':213C ''vector'':86C ''work'':315C ''x'':198C,201C,220C,222C ''yes'':148C,149C,150C,154C,155C,159C,160C,164C,250C,253C,256C,259C,262C,275C,278C,291C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8515, 2, '2015-10-19 22:42:14.05', 1, NULL, NULL, '&lt;p&gt;For a proper review and definition you may take a look at &lt;a href="http://link.springer.com/chapter/10.1007/11558958_113#page-1" rel="nofollow"&gt;Dimension Reduction vs. Variable Selection&lt;/a&gt; also in the book &lt;a href="https://books.google.ca/books/about/Feature_Extraction.html?id=x5hdbK8bIG0C&amp;amp;hl=en" rel="nofollow"&gt;Feature Extraction Foundations and Applications&lt;/a&gt;
feature extraction is decomposed in to two steps: feature construction and feature selection.&lt;/p&gt;
', NULL, NULL, '''/a'':26C,43C ''/books/about/feature_extraction.html?id=x5hdbk8big0c&amp;amp;hl=en'':35C ''/chapter/10.1007/11558958_113#page-1'':18C ''/p'':57C ''also'':27C ''applic'':42C ''book'':30C ''books.google.ca'':34C ''books.google.ca/books/about/feature_extraction.html?id=x5hdbk8big0c&amp;amp;hl=en'':33C ''construct'':53C ''decompos'':47C ''definit'':7C ''dimens'':21C ''extract'':39C,45C ''featur'':38C,44C,52C,55C ''foundat'':40C ''href'':15C,32C ''link.springer.com'':17C ''link.springer.com/chapter/10.1007/11558958_113#page-1'':16C ''look'':12C ''may'':9C ''nofollow'':20C,37C ''p'':1C ''proper'':4C ''reduct'':22C ''rel'':19C,36C ''review'':5C ''select'':25C,56C ''step'':51C ''take'':10C ''two'':50C ''variabl'':24C ''vs'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8517, 2, '2015-10-20 03:47:05.373', 2, NULL, NULL, '&lt;p&gt;There was an MIT group that got a lot of attention a few years back for predicting twitter trends and doing so with really good &lt;a href="https://en.wikipedia.org/wiki/Receiver_operating_characteristic" rel="nofollow"&gt;ROC-AUC Scores&lt;/a&gt;.  The problem you are trying to solve is kind of reminiscent of the twitter trending problem.  The crux of the solution is that they avoided the hassle of parametric modeling through a grouping of similar time series followed by a classification.&lt;/p&gt;

&lt;p&gt;There is a pretty good writeup of the method for the general public in &lt;a href="https://snikolov.wordpress.com/2012/11/14/early-detection-of-twitter-trends/" rel="nofollow"&gt;Stan Nikolov''s Blog post&lt;/a&gt;.  Notice that there is a &lt;a href="https://www.youtube.com/watch?v=34Q1xP_5QzE" rel="nofollow"&gt;video of a presentation by his thesis advisor also&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The basic idea of the method is to find the most common shapes in the time evolution hold those as the dominant classes.  These would be various exponential shapes in your case (from Nikolov''s &lt;a href="https://snikolov.wordpress.com/2012/11/14/early-detection-of-twitter-trends/" rel="nofollow"&gt;blog post&lt;/a&gt;):
&lt;a href="http://i.stack.imgur.com/Z9obf.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Z9obf.png" alt="Grouping of time series into classes"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Then you can take your training data and choose the best shape for each of your cases.  You can then use this data to train a classification algorithm.  Finally you can use this to predict the outcome of your new data (from Nikolov''s &lt;a href="https://snikolov.wordpress.com/2012/11/14/early-detection-of-twitter-trends/" rel="nofollow"&gt;blog post&lt;/a&gt;):
&lt;a href="http://i.stack.imgur.com/NdJh3.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/NdJh3.png" alt="time series prediction through classification"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The &lt;a href="https://dspace.mit.edu/handle/1721.1/85399" rel="nofollow"&gt;full dissertation is also available online for download&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;There''s actually an &lt;a href="http://blabpredicts.com/" rel="nofollow"&gt;entire company&lt;/a&gt; that is using this methodology and trying to sell it to companies.  They have some pretty cool promotional &lt;a href="https://www.youtube.com/results?search_query=blab+predicts" rel="nofollow"&gt;videos on youtube&lt;/a&gt; also, which won''t teach you the methodology, but show how powerful it can be.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/2012/11/14/early-detection-of-twitter-trends/'':100C,172C,248C ''/a'':38C,108C,130C,177C,197C,253C,272C,291C,305C,334C ''/handle/1721.1/85399'':280C ''/ndjh3.png'':258C,265C ''/p'':80C,131C,198C,273C,292C,350C,355C ''/results?search_query=blab+predicts'':328C ''/watch?v=34q1xp_5qze'':118C ''/wiki/receiver_operating_characteristic'':31C ''/z9obf.png'':182C,189C ''actual'':296C ''advisor'':128C ''algorithm'':227C ''also'':129C,286C,335C ''alt'':190C,266C ''attent'':12C ''auc'':36C ''avail'':287C ''avoid'':63C ''back'':16C ''basic'':134C ''best'':210C ''blabpredicts.com'':300C ''blog'':106C,175C,251C ''case'':164C,216C ''choos'':208C ''class'':155C,196C ''classif'':79C,226C,271C ''common'':144C ''compani'':304C,317C ''cool'':322C ''crux'':56C ''data'':206C,222C,240C ''dissert'':284C ''domin'':154C ''download'':290C ''dspace.mit.edu'':279C ''dspace.mit.edu/handle/1721.1/85399'':278C ''en.wikipedia.org'':30C ''en.wikipedia.org/wiki/receiver_operating_characteristic'':29C ''entir'':303C ''evolut'':149C ''exponenti'':160C ''final'':228C ''find'':141C ''follow'':76C ''full'':283C ''general'':93C ''good'':26C,86C ''got'':8C ''group'':6C,71C,191C ''hassl'':65C ''help'':354C ''hold'':150C ''hope'':352C ''href'':28C,97C,115C,169C,179C,245C,255C,277C,299C,325C ''i.stack.imgur.com'':181C,188C,257C,264C ''i.stack.imgur.com/ndjh3.png'':256C,263C ''i.stack.imgur.com/z9obf.png'':180C,187C ''idea'':135C ''img'':185C,261C ''kind'':47C ''lot'':10C ''method'':90C,138C ''methodolog'':310C,342C ''mit'':5C ''model'':68C ''new'':239C ''nikolov'':104C,166C,242C ''nofollow'':33C,102C,120C,174C,184C,250C,260C,282C,302C,330C ''notic'':109C ''onlin'':288C ''outcom'':236C ''p'':1C,81C,132C,199C,274C,293C,351C ''parametr'':67C ''post'':107C,176C,252C ''power'':346C ''predict'':18C,234C,269C ''present'':124C ''pretti'':85C,321C ''problem'':40C,54C ''promot'':323C ''public'':94C ''realli'':25C ''rel'':32C,101C,119C,173C,183C,249C,259C,281C,301C,329C ''reminisc'':49C ''roc'':35C ''roc-auc'':34C ''score'':37C ''sell'':314C ''seri'':75C,194C,268C ''shape'':145C,161C,211C ''show'':344C ''similar'':73C ''snikolov.wordpress.com'':99C,171C,247C ''snikolov.wordpress.com/2012/11/14/early-detection-of-twitter-trends/'':98C,170C,246C ''solut'':59C ''solv'':45C ''src'':186C,262C ''stan'':103C ''take'':203C ''teach'':339C ''thesi'':127C ''time'':74C,148C,193C,267C ''train'':205C,224C ''trend'':20C,53C ''tri'':43C,312C ''twitter'':19C,52C ''use'':220C,231C,308C ''various'':159C ''video'':121C,331C ''won'':337C ''would'':157C ''writeup'':87C ''www.youtube.com'':117C,327C ''www.youtube.com/results?search_query=blab+predicts'':326C ''www.youtube.com/watch?v=34q1xp_5qze'':116C ''year'':15C ''youtub'':333C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8518, 1, '2015-10-20 07:07:24.123', 0, 39, 'How can i deal with latency in data?', '&lt;p&gt;I am currently creating a model to predict the value of a variable based on years of data from sensors in a system. But, in the physical system, there is some unknown latency--e.g., one sensor might measure a pressure increase, and then 3 minutes later another sensor will measure the same increase. I am trying to predict the value of the sensor at the end of the system. Is there any standard way of dealing with this kind of latency?&lt;/p&gt;
', 'machine-learning  data-mining  predictive-modeling', NULL, '''/p'':100C ''3'':62C ''anoth'':65C ''base'':32C ''creat'':22C ''current'':21C ''data'':8A,13B,36C ''data-min'':12B ''deal'':4A,94C ''e.g'':52C ''end'':84C ''increas'':59C,71C ''kind'':97C ''latenc'':6A,51C,99C ''later'':64C ''learn'':11B ''machin'':10B ''machine-learn'':9B ''measur'':56C,68C ''might'':55C ''mine'':14B ''minut'':63C ''model'':17B,24C ''one'':53C ''p'':18C ''physic'':45C ''predict'':16B,26C,76C ''predictive-model'':15B ''pressur'':58C ''sensor'':38C,54C,66C,81C ''standard'':91C ''system'':41C,46C,87C ''tri'':74C ''unknown'':50C ''valu'':28C,78C ''variabl'':31C ''way'':92C ''year'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8800, 2, '2015-11-11 15:06:18.373', 0, NULL, NULL, '&lt;p&gt;Modular arithmetic. Generally you would do &lt;code&gt;end - start mod 24&lt;/code&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;julia&amp;gt; mod(-23,24)
1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I seem to recall some programming languages treat mod of negative numbers in a different way, so check your implementation first.&lt;/p&gt;
', NULL, NULL, '''-23'':20C ''/code'':13C,23C ''/p'':14C,47C ''/pre'':24C ''1'':22C ''24'':12C,21C ''arithmet'':3C ''check'':43C ''code'':8C,16C ''differ'':40C ''end'':9C ''first'':46C ''general'':4C ''gt'':18C ''implement'':45C ''julia'':17C ''languag'':32C ''mod'':11C,19C,34C ''modular'':2C ''negat'':36C ''number'':37C ''p'':1C,25C ''pre'':15C ''program'':31C ''recal'':29C ''seem'':27C ''start'':10C ''treat'':33C ''way'':41C ''would'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10425, 2, '2016-02-27 10:19:05.907', 3, NULL, NULL, '&lt;p&gt;The object with the lowest mean distance (= the object with the lowest distance sum) is known as the &lt;strong&gt;medoid&lt;/strong&gt; and the basis of k-medoids algorithms such as PAM. Because you must not use k-means with arbitrary distances.&lt;/p&gt;
', NULL, NULL, '''/p'':45C ''/strong'':22C ''algorithm'':30C ''arbitrari'':43C ''basi'':25C ''distanc'':8C,14C,44C ''k'':28C,40C ''k-mean'':39C ''k-medoid'':27C ''known'':17C ''lowest'':6C,13C ''mean'':7C,41C ''medoid'':21C,29C ''must'':36C ''object'':3C,10C ''p'':1C ''pam'':33C ''strong'':20C ''sum'':15C ''use'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10029, 1, '2016-01-29 20:34:48.07', 0, 58, 'Real time face recognition system in python', '&lt;p&gt;I want to build a project on real time facial recognition system in python. The system should work like this : When you run the application, it is ready for capturing the image of a face. Thereafter search this image in the database (like the database of  college students) and can identify whose face is that. Please help me describe what tools to use in python and how to proceed?&lt;/p&gt;
', 'machine-learning  python  image-recognition', NULL, '''/p'':85C ''applic'':40C ''build'':19C ''captur'':45C ''colleg'':62C ''databas'':57C,60C ''describ'':74C ''face'':3A,50C,68C ''facial'':25C ''help'':72C ''identifi'':66C ''imag'':13B,47C,54C ''image-recognit'':12B ''learn'':10B ''like'':34C,58C ''machin'':9B ''machine-learn'':8B ''p'':15C ''pleas'':71C ''proceed'':84C ''project'':21C ''python'':7A,11B,29C,80C ''readi'':43C ''real'':1A,23C ''recognit'':4A,14B,26C ''run'':38C ''search'':52C ''student'':63C ''system'':5A,27C,31C ''thereaft'':51C ''time'':2A,24C ''tool'':76C ''use'':78C ''want'':17C ''whose'':67C ''work'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8522, 2, '2015-10-20 12:04:44.297', 0, NULL, NULL, '&lt;p&gt;I use the function &lt;code&gt;grepl()&lt;/code&gt; for this.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;text&amp;lt;-c("China","Japan","perspective","United Kingdom","formatting","clear",
"India","Sudan","United States of America","Bagel","Mongolian")

#Make a logical vector
arg&amp;lt;-grepl("China",text)|grepl("Japan",text)|grepl("UnitedKingdom",text)|
grepl("Mongolian",text)

#Pick out from text
text[arg]
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':8C,56C ''/p'':11C ''/pre'':57C ''america'':29C ''arg'':36C,55C ''bagel'':30C ''c'':16C ''china'':17C,39C ''clear'':23C ''code'':6C,13C ''format'':22C ''function'':5C ''grepl'':7C,38C,41C,44C,47C ''india'':24C ''japan'':18C,42C ''kingdom'':21C ''logic'':34C ''lt'':15C,37C ''make'':32C ''mongolian'':31C,48C ''p'':1C ''perspect'':19C ''pick'':50C ''pre'':12C ''state'':27C ''sudan'':25C ''text'':14C,40C,43C,46C,49C,53C,54C ''unit'':20C,26C ''unitedkingdom'':45C ''use'':3C ''vector'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8523, 2, '2015-10-20 13:04:23', 1, NULL, NULL, '&lt;p&gt;While performing PCA on your tfidf vectors or &lt;a href="http://www.nltk.org/book/ch03.html" rel="nofollow"&gt;stemming&lt;/a&gt; or eliminating infrequent words might reduce the dimensionality significantly, you might want to try &lt;a href="https://en.wikipedia.org/wiki/Topic_model" rel="nofollow"&gt;topic modeling&lt;/a&gt;.  &lt;/p&gt;

&lt;p&gt;While your initial problem of creating topics and assigning topics to documents will remain in high-dimenional space, the supervised portion will be in topic space.&lt;/p&gt;

&lt;p&gt;There are plenty of fast implementations of &lt;a href="https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation" rel="nofollow"&gt;LDA&lt;/a&gt; and similar topic models out there.&lt;/p&gt;
', NULL, NULL, '''/a'':18C,42C,89C ''/book/ch03.html'':14C ''/p'':43C,72C,96C ''/wiki/latent_dirichlet_allocation'':85C ''/wiki/topic_model'':37C ''assign'':53C ''creat'':50C ''dimenion'':62C ''dimension'':26C ''document'':56C ''elimin'':20C ''en.wikipedia.org'':36C,84C ''en.wikipedia.org/wiki/latent_dirichlet_allocation'':83C ''en.wikipedia.org/wiki/topic_model'':35C ''fast'':78C ''high'':61C ''high-dimenion'':60C ''href'':11C,34C,82C ''implement'':79C ''infrequ'':21C ''initi'':47C ''lda'':88C ''might'':23C,29C ''model'':41C,93C ''nofollow'':16C,39C,87C ''p'':1C,44C,73C ''pca'':4C ''perform'':3C ''plenti'':76C ''portion'':66C ''problem'':48C ''reduc'':24C ''rel'':15C,38C,86C ''remain'':58C ''signific'':27C ''similar'':91C ''space'':63C,71C ''stem'':17C ''supervis'':65C ''tfidf'':7C ''topic'':40C,51C,54C,70C,92C ''tri'':32C ''vector'':8C ''want'':30C ''word'':22C ''www.nltk.org'':13C ''www.nltk.org/book/ch03.html'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8524, 2, '2015-10-20 13:59:07.417', 0, NULL, NULL, '&lt;p&gt;I agree that image classification is the right place to look for inspiration. But instead of viewing the ''image'' as a grid of court positions, with ''color'' being players, I would first see if you get anything useful out of seeing the ''image'' as a grid of players, with ''color'' being the x and y position (and probably also velocity) of the players.&lt;/p&gt;

&lt;p&gt;I think that you need to do some sort of ''clumping'' of data: instead of trying to look at a full game, you want to look at segments that are closer to the length of a play. You can do this with a moving window (the equivalent of an &lt;a href="http://www.cs.toronto.edu/~kyros/courses/320/Lectures.2013s/lecture.2013s.03.pdf" rel="nofollow"&gt;image patch&lt;/a&gt;) or by joining together sufficiently similar player-seconds (the equivalent of &lt;a href="http://ivrl.epfl.ch/research/superpixels" rel="nofollow"&gt;superpixels&lt;/a&gt;) to create a graph that describes the game. My guess is that the moving window approach is the best place to start, as it may inform what sort of features are relevant for joining together pixels to make superpixels.&lt;/p&gt;

&lt;p&gt;It''s likely the case that something like plays will fall out of doing k-means on moving windows of the game, but you''d probably benefit a lot from ensuring that the sort of structure that seems important (say, the distance to the closest enemy player) is already available as a feature to include in hypotheses.&lt;/p&gt;
', NULL, NULL, '''/a'':125C,146C ''/p'':65C,186C,246C ''/research/superpixels'':142C ''/~kyros/courses/320/lectures.2013s/lecture.2013s.03.pdf'':120C ''agre'':3C ''alreadi'':237C ''also'':60C ''anyth'':38C ''approach'':162C ''avail'':238C ''benefit'':215C ''best'':165C ''case'':192C ''classif'':6C ''closer'':97C ''closest'':233C ''clump'':77C ''color'':28C,51C ''court'':25C ''creat'':148C ''d'':213C ''data'':79C ''describ'':152C ''distanc'':230C ''enemi'':234C ''ensur'':219C ''equival'':113C,136C ''fall'':198C ''featur'':176C,241C ''first'':33C ''full'':87C ''game'':88C,154C,210C ''get'':37C ''graph'':150C ''grid'':23C,47C ''guess'':156C ''href'':117C,139C ''hypothes'':245C ''imag'':5C,20C,44C,123C ''import'':227C ''includ'':243C ''inform'':172C ''inspir'':14C ''instead'':16C,80C ''ivrl.epfl.ch'':141C ''ivrl.epfl.ch/research/superpixels'':140C ''join'':128C,180C ''k'':203C ''k-mean'':202C ''length'':100C ''like'':190C,195C ''look'':12C,84C,92C ''lot'':217C ''make'':184C ''may'':171C ''mean'':204C ''move'':110C,160C,206C ''need'':71C ''nofollow'':122C,144C ''p'':1C,66C,187C ''patch'':124C ''pixel'':182C ''place'':10C,166C ''play'':103C,196C ''player'':30C,49C,64C,133C,235C ''player-second'':132C ''posit'':26C,57C ''probabl'':59C,214C ''rel'':121C,143C ''relev'':178C ''right'':9C ''say'':228C ''second'':134C ''see'':34C,42C ''seem'':226C ''segment'':94C ''similar'':131C ''someth'':194C ''sort'':75C,174C,222C ''start'':168C ''structur'':224C ''suffici'':130C ''superpixel'':145C,185C ''think'':68C ''togeth'':129C,181C ''tri'':82C ''use'':39C ''veloc'':61C ''view'':18C ''want'':90C ''window'':111C,161C,207C ''would'':32C ''www.cs.toronto.edu'':119C ''www.cs.toronto.edu/~kyros/courses/320/lectures.2013s/lecture.2013s.03.pdf'':118C ''x'':54C ''y'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8525, 2, '2015-10-20 14:26:45.567', 0, NULL, NULL, '&lt;p&gt;I am not entirely clear on the evaluation criteria that are using but, given your example, you can use %in% to return matching elements in a vector. The %in% statement returns the same Boolean output that Daniel''s grep example does. If you want the actually position of the matching elements you can use %in% and which.    &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;text&amp;lt;-c("China","Japan","perspective","United Kingdom","formatting","clear", "India","Sudan","United States of America","Bagel","Mongolian")
eval &amp;lt;- c("China", "Japan", "United Kingdom", "Mongolian")

# Return matching elements
text[text %in% eval] 

# Return the index (position) of matching elements
which(text %in% eval) 
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':106C ''/p'':59C ''/pre'':107C ''actual'':47C ''america'':77C ''bagel'':78C ''boolean'':35C ''c'':64C,82C ''china'':65C,83C ''clear'':6C,71C ''code'':61C ''criteria'':10C ''daniel'':38C ''element'':25C,52C,90C,101C ''entir'':5C ''eval'':80C,94C,105C ''evalu'':9C ''exampl'':17C,41C ''format'':70C ''given'':15C ''grep'':40C ''index'':97C ''india'':72C ''japan'':66C,84C ''kingdom'':69C,86C ''lt'':63C,81C ''match'':24C,51C,89C,100C ''mongolian'':79C,87C ''output'':36C ''p'':1C ''perspect'':67C ''posit'':48C,98C ''pre'':60C ''return'':23C,32C,88C,95C ''state'':75C ''statement'':31C ''sudan'':73C ''text'':62C,91C,92C,103C ''unit'':68C,74C,85C ''use'':13C,20C,55C ''vector'':28C ''want'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10236, 5, '2016-02-14 02:46:41.803', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10341, 1, '2016-02-21 16:49:54.067', 2, 52, 'Analytical Model for Greedy Scheduler', '&lt;p&gt;I''m trying to create an analytical model for performance of a greedy scheduler in a particular domain. &lt;/p&gt;

&lt;p&gt;I''ve created a preliminary model, but I think it''s very confusing to others and was wondering if someone could provide feedback.&lt;/p&gt;

&lt;p&gt;My problem domain follows. A greedy scheduler has the capacity to schedule c tasks and will select the c highest rewarding tasks. Tasks arrive dynamically online from an arrival pattern, A--that is for every time unit within a time horizon X tasks will arrive, where X is an arbitrary random variable. Task details are all drawn from an arbitrary predefined distribution. Task details include, reward, maximum waiting time before being serviced (i.e if a task is not serviced after m time units, the task will fail), and duration. I''ll label the respective distributions as R for reward, M for maximum waiting time, and D for duration.&lt;/p&gt;

&lt;p&gt;I know this problem is very large, so I would like to focus on a much smaller component of this larger problem. &lt;/p&gt;

&lt;p&gt;Consider a greedy agent with capacity c and no tasks currently scheduled with a task mix of T, where the size of T is larger than c and T''s task''s rewards are representative of the R reward distribution. &lt;/p&gt;

&lt;p&gt;My question is what should be the expected reward (or even better the distribution of rewards) for the c selected tasks from T. This is similar to a k-order statistics problem; however, I''ve been unable to format this problem into a k-order statistics problem due to my basic/limiting understanding of statistics. (I study computer science and only know basic statistical analysis).&lt;/p&gt;

&lt;p&gt;My approach follows; however, I feel it could be improved. In my approach, I try to calculate the lowest reward of the highest rewarding tasks:&lt;/p&gt;

&lt;p&gt;If the scheduler can choose c tasks from T and c &amp;lt; |T|, the scheduler will choose the highest rewarding c tasks. Since T''s task''s rewards are representative of R, The expected reward of a single task will be the expected reward of the upper c / |T| portion of the R distribution.&lt;/p&gt;

&lt;p&gt;To calculate the expected value of a reward distribution, one takes the &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;integral from x= -infinity to x=+infinity of P(R=x)*x
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Similarly, to calculate the expected value of a distribution of the highest c rewards, we take the &lt;/p&gt;

&lt;p&gt;&lt;code&gt;integral from x = Y to x = +infinity of P(R=x)*x&lt;/code&gt;
 where y is the lowest of the highest rewarding task''s rewards.&lt;/p&gt;

&lt;p&gt;To calculate the upper c/ |T| portion of the R distribution, I let the lowest reward of the highest rewarding task be Y and thus c / |T| = 1 - CDF(Y), where CDF is the cumulative distribution function applied to the reward distribution.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;c / |T| = 1 - CDF(Y) states that:
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The proportion of schedulable tasks to total tasks is equal to the proportion of the upper half highest rewarding tasks of the reward distribution.&lt;/p&gt;

&lt;p&gt;By solving for Y, we get that&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Y = inverse_CDF(1 - c/|T|)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;We can then plug the highest rewarding task''s reward into our expected value equation earlier and then the expected value of the highest c rewarding tasks are then the integral from x = Y to x = +infinity of P(R=x)*x.&lt;/p&gt;

&lt;p&gt;Any feedback on how I can improve clarity or improve my analytical equation would be appreciated or if there is a more standard/correct way to describe what I''m doing any suggestions would be appreciated. Also, I''m unsure if this community is the correct community to post this question. If it is not, please provide suggestions as to where I should post this question. I''ve looked at similar work, and they typically use omniscient scheduling and I''m not too understanding of their syntax/approach.&lt;/p&gt;
', 'statistics  predictive-modeling', NULL, '''/code'':410C,445C,512C,557C ''/p'':29C,54C,164C,188C,230C,298C,325C,381C,395C,430C,458C,502C,539C,548C,602C,690C ''/pre'':411C,513C,558C ''1'':487C,507C,554C ''agent'':193C ''also'':639C ''analysi'':297C ''analyt'':1A,17C,615C ''appli'':497C ''appreci'':619C,638C ''approach'':301C,312C ''arbitrari'':105C,115C ''arriv'':79C,84C,100C ''basic'':295C ''basic/limiting'':284C ''better'':243C ''c'':68C,74C,196C,216C,250C,332C,337C,347C,374C,425C,464C,485C,505C,555C,584C ''calcul'':316C,384C,415C,461C ''capac'':65C,195C ''cdf'':488C,491C,508C,553C ''choos'':331C,343C ''clariti'':611C ''code'':397C,432C,504C,550C ''communiti'':645C,649C ''compon'':183C ''comput'':290C ''confus'':43C ''consid'':190C ''correct'':648C ''could'':51C,307C ''creat'':15C,33C ''cumul'':494C ''current'':200C ''d'':161C ''describ'':629C ''detail'':109C,119C ''distribut'':117C,150C,229C,245C,380C,391C,421C,470C,495C,501C,538C ''domain'':28C,58C ''drawn'':112C ''due'':281C ''durat'':144C,163C ''dynam'':80C ''earlier'':575C ''equal'':524C ''equat'':574C,616C ''even'':242C ''everi'':90C ''expect'':239C,360C,369C,386C,417C,572C,579C ''fail'':142C ''feedback'':53C,605C ''feel'':305C ''focus'':178C ''follow'':59C,302C ''format'':271C ''function'':496C ''get'':546C ''greedi'':4A,23C,61C,192C ''half'':531C ''highest'':75C,322C,345C,424C,453C,478C,532C,565C,583C ''horizon'':96C ''howev'':265C,303C ''i.e'':128C ''improv'':309C,610C,613C ''includ'':120C ''infin'':401C,404C,439C,596C ''integr'':398C,433C,590C ''invers'':552C ''k'':261C,277C ''k-order'':260C,276C ''know'':167C,294C ''label'':147C ''larg'':172C ''larger'':186C,214C ''let'':472C ''like'':176C ''ll'':146C ''look'':671C ''lowest'':318C,450C,474C ''lt'':338C ''m'':12C,136C,155C,632C,641C,683C ''maximum'':122C,157C ''mix'':205C ''model'':2A,9B,18C,36C ''much'':181C ''omnisci'':679C ''one'':392C ''onlin'':81C ''order'':262C,278C ''other'':45C ''p'':10C,30C,55C,165C,189C,231C,299C,326C,382C,406C,412C,431C,441C,459C,514C,540C,559C,598C,603C ''particular'':27C ''pattern'':85C ''perform'':20C ''pleas'':658C ''plug'':563C ''portion'':376C,466C ''post'':651C,666C ''pre'':396C,503C,549C ''predefin'':116C ''predict'':8B ''predictive-model'':7B ''preliminari'':35C ''problem'':57C,169C,187C,264C,273C,280C ''proport'':516C,527C ''provid'':52C,659C ''question'':233C,653C,668C ''r'':152C,227C,358C,379C,407C,442C,469C,599C ''random'':106C ''repres'':224C,356C ''respect'':149C ''reward'':76C,121C,154C,222C,228C,240C,247C,319C,323C,346C,354C,361C,370C,390C,426C,454C,457C,475C,479C,500C,533C,537C,566C,569C,585C ''schedul'':5A,24C,62C,67C,201C,329C,341C,518C,680C ''scienc'':291C ''select'':72C,251C ''servic'':127C,134C ''similar'':257C,413C,673C ''sinc'':349C ''singl'':364C ''size'':210C ''smaller'':182C ''solv'':542C ''someon'':50C ''standard/correct'':626C ''state'':510C ''statist'':6B,263C,279C,287C,296C ''studi'':289C ''suggest'':635C,660C ''syntax/approach'':689C ''take'':393C,428C ''task'':69C,77C,78C,98C,108C,118C,131C,140C,199C,204C,220C,252C,324C,333C,348C,352C,365C,455C,480C,519C,522C,534C,567C,586C ''think'':39C ''thus'':484C ''time'':91C,95C,124C,137C,159C ''total'':521C ''tri'':13C,314C ''typic'':677C ''unabl'':269C ''understand'':285C,686C ''unit'':92C,138C ''unsur'':642C ''upper'':373C,463C,530C ''use'':678C ''valu'':387C,418C,573C,580C ''variabl'':107C ''ve'':32C,267C,670C ''wait'':123C,158C ''way'':627C ''within'':93C ''wonder'':48C ''work'':674C ''would'':175C,617C,636C ''x'':97C,102C,400C,403C,408C,409C,435C,438C,443C,444C,592C,595C,600C,601C ''y'':436C,447C,482C,489C,509C,544C,551C,593C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8540, 1, '2015-10-21 11:57:51.553', 1, 34, 'One multilabel classifier or one for each type of label?', '&lt;p&gt;Let''s say I need to classify addresses with scikit-learn, so if I want my classifier to be able to classify addresses by the street name, and post/zip code, should I do a OneVsRest classifier, or separate them into two different classifiers (for the same training set)?&lt;/p&gt;

&lt;p&gt;I have tried both, and it seems like having multiple classifiers might be a better choice, as it feels faster to train multiple smaller classifiers. Is this how it is supposed to be done?&lt;/p&gt;
', 'classification  python  svm  scikit  multilabel-classification', NULL, '''/p'':68C,104C ''abl'':39C ''address'':26C,42C ''better'':84C ''choic'':85C ''classif'':11B,17B ''classifi'':3A,25C,36C,41C,55C,62C,80C,94C ''code'':49C ''differ'':61C ''done'':103C ''faster'':89C ''feel'':88C ''label'':10A ''learn'':30C ''let'':19C ''like'':77C ''might'':81C ''multilabel'':2A,16B ''multilabel-classif'':15B ''multipl'':79C,92C ''name'':46C ''need'':23C ''one'':1A,5A ''onevsrest'':54C ''p'':18C,69C ''post/zip'':48C ''python'':12B ''say'':21C ''scikit'':14B,29C ''scikit-learn'':28C ''seem'':76C ''separ'':57C ''set'':67C ''smaller'':93C ''street'':45C ''suppos'':100C ''svm'':13B ''train'':66C,91C ''tri'':72C ''two'':60C ''type'':8A ''want'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9195, 1, '2015-12-03 21:30:57.907', 2, 25, 'Are there studies which examine dropout vs other regularizations?', '&lt;p&gt;Are there any papers published which show differences of the regularization methods for neural networks, preferably on different domains (or at least different datasets)?&lt;/p&gt;

&lt;p&gt;I am asking because I currently have the feeling that most people seem to use only dropout for regularization in computer vision. I would like to check if there would be a reason (not) to use different ways of regularization.&lt;/p&gt;
', 'machine-learning  neuralnetwork  computer-vision  convnet', NULL, '''/p'':43C,85C ''ask'':47C ''check'':71C ''comput'':15B,65C ''computer-vis'':14B ''convnet'':17B ''current'':50C ''dataset'':42C ''differ'':26C,36C,41C,81C ''domain'':37C ''dropout'':6A,61C ''examin'':5A ''feel'':53C ''learn'':12B ''least'':40C ''like'':69C ''machin'':11B ''machine-learn'':10B ''method'':30C ''network'':33C ''neural'':32C ''neuralnetwork'':13B ''p'':18C,44C ''paper'':22C ''peopl'':56C ''prefer'':34C ''publish'':23C ''reason'':77C ''regular'':9A,29C,63C,84C ''seem'':57C ''show'':25C ''studi'':3A ''use'':59C,80C ''vision'':16B,66C ''vs'':7A ''way'':82C ''would'':68C,74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8526, 2, '2015-10-20 15:14:12.613', 4, NULL, NULL, '&lt;p&gt;For another alternative approach, you can take a look at the PyMC library. 
There is a good gist &lt;a href="https://gist.github.com/fonnesbeck/342989" rel="nofollow"&gt;https://gist.github.com/fonnesbeck/342989&lt;/a&gt; created by Fonnesbeck which walks you through the HMM creation.&lt;/p&gt;

&lt;p&gt;And if you become really eager about the PyMC, there is an awesome open-source book about Bayesian Modeling - &lt;a href="https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers" rel="nofollow"&gt;https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers&lt;/a&gt;. It does not explicitly describe Hidden Markov Processes, but it gives a very good tutorial on the library itself with plenty of examples.&lt;/p&gt;
', NULL, NULL, '''/camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers'':66C ''/camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers&lt;/a&gt;.'':71C ''/fonnesbeck/342989'':24C ''/fonnesbeck/342989&lt;/a&gt;'':29C ''/p'':40C,95C ''altern'':4C ''anoth'':3C ''approach'':5C ''awesom'':54C ''bayesian'':60C ''becom'':45C ''book'':58C ''creat'':30C ''creation'':39C ''describ'':76C ''eager'':47C ''exampl'':94C ''explicit'':75C ''fonnesbeck'':32C ''gist'':19C ''gist.github.com'':23C,28C ''gist.github.com/fonnesbeck/342989'':22C ''gist.github.com/fonnesbeck/342989&lt;/a&gt;'':27C ''github.com'':65C,70C ''github.com/camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers'':64C ''github.com/camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers&lt;/a&gt;.'':69C ''give'':82C ''good'':18C,85C ''hidden'':77C ''hmm'':38C ''href'':21C,63C ''librari'':14C,89C ''look'':10C ''markov'':78C ''model'':61C ''nofollow'':26C,68C ''open'':56C ''open-sourc'':55C ''p'':1C,41C ''plenti'':92C ''process'':79C ''pymc'':13C,50C ''realli'':46C ''rel'':25C,67C ''sourc'':57C ''take'':8C ''tutori'':86C ''walk'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8527, 2, '2015-10-20 16:26:08.09', 1, NULL, NULL, '&lt;p&gt;I''d recommend spending more time thinking about feature selection and representation for your SVM than worrying about the number of dimensions in your model. Generally speaking, SVM tends to be very robust to uninformative features (e.g., see &lt;a href="https://eldorado.tu-dortmund.de/bitstream/2003/2595/1/report23_ps.pdf" rel="nofollow"&gt;Joachims, 1997&lt;/a&gt;, or &lt;a href="https://eldorado.tu-dortmund.de/bitstream/2003/2596/1/report24.pdf" rel="nofollow"&gt;Joachims, 1999&lt;/a&gt; for a nice overview). In my experience, SVM doesn''t often benefit as much from spending time on feature selection as do other algorithms, such as NaÃ¯ve Bayes. The best gains I''ve seen with SVM tend to come from trying to encode your own expert knowledge about the classification domain in a way that is computationally accessible. Say for example that you''re classifying publications on whether they contain information on protein-protein interactions. Something that is lost in the bag of words and tfidf vectorization approaches is the concept of proximityâtwo protein-related words occurring close to each other in a document are more likely to be found in documents dealing with protein-protein interaction. This can sometimes be achieved using $n$-gram modeling, but there are better alternatives that you''ll only be able to use if you think about the characteristics of the types of documents you''re trying to identify. &lt;/p&gt;

&lt;p&gt;If you still want to try doing feature selection, &lt;a href="http://nlp.stanford.edu/IR-book/html/htmledition/feature-selectionchi2-feature-selection-1.html" rel="nofollow"&gt;I''d recommend $\chi^{2}$&lt;/a&gt; (chi-squared) feature selection. To do this, you rank your features with respect to the objective&lt;/p&gt;

&lt;p&gt;\begin{equation}
\chi^{2}(\textbf{D},t,c) = \sum_{e_{t}\in{0,1}}\sum_{e_{c}\in{0,1}}\frac{(N_{e_{t}e_{c}}-E_{e_{t}e_{c}})^{2}}{E_{e_{t}}e_{c}},
\end{equation}
where $N$ is the observed frequency of a term in $\textbf{D}$, $E$ is its expected frequency, and $t$ and $c$ denote term and class, respectively. You can &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html" rel="nofollow"&gt;easily compute this in sklearn&lt;/a&gt;, unless you want the educational experience of coding it yourself $\ddot\smile$&lt;/p&gt;
', NULL, NULL, '''/a'':49C,60C,244C,343C ''/bitstream/2003/2595/1/report23_ps.pdf'':44C ''/bitstream/2003/2596/1/report24.pdf'':55C ''/ir-book/html/htmledition/feature-selectionchi2-feature-selection-1.html'':236C ''/p'':221C,262C,356C ''/stable/modules/generated/sklearn.feature_selection.chi2.html'':335C ''0'':276C,282C ''1'':277C,283C ''1997'':48C ''1999'':59C ''2'':243C,267C,295C ''abl'':202C ''access'':119C ''achiev'':187C ''algorithm'':84C ''altern'':196C ''approach'':150C ''bag'':144C ''bay'':89C ''begin'':264C ''benefit'':72C ''best'':91C ''better'':195C ''c'':271C,280C,289C,294C,300C,323C ''characterist'':210C ''chi'':242C,246C,266C ''chi-squar'':245C ''class'':327C ''classif'':111C ''classifi'':126C ''close'':162C ''code'':351C ''come'':100C ''comput'':118C,339C ''concept'':153C ''contain'':131C ''d'':3C,240C,269C,314C ''ddot'':354C ''deal'':177C ''denot'':324C ''dimens'':23C ''document'':168C,176C,215C ''doesn'':69C ''domain'':112C ''e'':273C,279C,286C,288C,290C,291C,293C,296C,297C,299C,315C ''e.g'':38C ''easili'':338C ''educ'':348C ''eldorado.tu-dortmund.de'':43C,54C ''eldorado.tu-dortmund.de/bitstream/2003/2595/1/report23_ps.pdf'':42C ''eldorado.tu-dortmund.de/bitstream/2003/2596/1/report24.pdf'':53C ''encod'':104C ''end'':301C ''equat'':265C,302C ''exampl'':122C ''expect'':318C ''experi'':67C,349C ''expert'':107C ''featur'':10C,37C,79C,230C,248C,256C ''found'':174C ''frac'':284C ''frequenc'':308C,319C ''gain'':92C ''general'':27C ''gram'':190C ''href'':41C,52C,233C,332C ''identifi'':220C ''inform'':132C ''interact'':137C,182C ''joachim'':47C,58C ''knowledg'':108C ''like'':171C ''ll'':199C ''lost'':141C ''model'':26C,191C ''much'':74C ''n'':189C,285C,304C ''naã'':87C ''nice'':63C ''nlp.stanford.edu'':235C ''nlp.stanford.edu/ir-book/html/htmledition/feature-selectionchi2-feature-selection-1.html'':234C ''nofollow'':46C,57C,238C,337C ''number'':21C ''object'':261C ''observ'':307C ''occur'':161C ''often'':71C ''overview'':64C ''p'':1C,222C,263C ''protein'':135C,136C,158C,180C,181C ''protein-protein'':134C,179C ''protein-rel'':157C ''proximityâ'':155C ''public'':127C ''rank'':254C ''re'':125C,217C ''recommend'':4C,241C ''rel'':45C,56C,237C,336C ''relat'':159C ''represent'':13C ''respect'':258C,328C ''robust'':34C ''say'':120C ''scikit-learn.org'':334C ''scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html'':333C ''see'':39C ''seen'':95C ''select'':11C,80C,231C,249C ''sklearn'':342C ''smile'':355C ''someth'':138C ''sometim'':185C ''speak'':28C ''spend'':5C,76C ''squar'':247C ''still'':225C ''sum'':272C,278C ''svm'':16C,29C,68C,97C ''tend'':30C,98C ''term'':311C,325C ''textbf'':268C,313C ''tfidf'':148C ''think'':8C,207C ''time'':7C,77C ''tri'':102C,218C,228C ''two'':156C ''type'':213C ''uninform'':36C ''unless'':344C ''use'':188C,204C ''ve'':88C,94C ''vector'':149C ''want'':226C,346C ''way'':115C ''whether'':129C ''word'':146C,160C ''worri'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8528, 1, '2015-10-20 17:11:20.453', 2, 68, 'Time Decay Formula for Conversion Attribution', '&lt;h2&gt;Question:&lt;/h2&gt;

&lt;p&gt;What is the Time Decay formula that web analytics packages use to distribute credit across the multiple sessions associated with a user before a conversion?&lt;/p&gt;

&lt;h2&gt;Context:&lt;/h2&gt;

&lt;p&gt;All web analytics packages like Google Analytics rely on the concept of:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;strong&gt;USERS&lt;/strong&gt; &lt;em&gt;visitors, ie unique cookied browsers.&lt;/em&gt; Users have many...&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;SESSIONS&lt;/strong&gt; &lt;em&gt;visits, ie sets of pageviews within close time proximity.&lt;/em&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;CONVERSIONS&lt;/strong&gt;  &lt;em&gt;a success event such as a sign-up which can occur during a session.&lt;/em&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;These analytics packages allow you to distribute the credit of a single conversion across all the preceding sessions because each played a part in leading the user to conversion. One such model is Time Decay, which gives a diminishing share of credit to sessions further from the conversion.&lt;/p&gt;

&lt;p&gt;Time Decay Visual: (1st session) â â â  â â (conversion)&lt;/p&gt;

&lt;p&gt;&lt;a href="https://support.google.com/analytics/answer/1662518?hl=en" rel="nofollow"&gt;more info on attribution models&lt;/a&gt;.&lt;/p&gt;

&lt;h2&gt;Sample Data:&lt;/h2&gt;

&lt;p&gt;I am assuming the data required would be:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;n&lt;/code&gt; - the total number of sessions a user has before a conversion&lt;/li&gt;
&lt;li&gt;&lt;code&gt;t&lt;/code&gt; - the total duration of time from the user''s first session to conversion&lt;/li&gt;
&lt;li&gt;&lt;code&gt;tbc&lt;/code&gt; - the individual time before conversion for each session &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So for three different sample users, data may look like this:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;
UserID  n   t   Session#    Timestamp   TBC Conversion in session?
User1   4   12  1           2015-10-01  12  No
User1   4   12  2           2015-10-06  7   No
User1   4   12  3           2015-10-11  2   No
User1   4   12  4           2015-10-13  0   YES
User2   3   4   1           2015-10-09  4   No
User2   3   4   2           2015-10-09  4   No
User2   3   4   3           2015-10-13  0   YES
User3   1   0   1           2015-10-14  0   YES
&lt;/code&gt;&lt;/p&gt;
', 'consumerweb', NULL, '''-01'':281C ''-06'':290C ''-09'':317C,326C ''-10'':280C,289C,298C,307C,316C,325C,334C,343C ''-11'':299C ''-13'':308C,335C ''-14'':344C ''/a'':186C ''/analytics/answer/1662518?hl=en'':178C ''/code'':206C,222C,240C,347C ''/em'':66C,85C,107C ''/h2'':10C,40C,191C ''/li'':70C,86C,108C,218C,236C,249C ''/ol'':109C ''/p'':37C,54C,159C,172C,187C,201C,263C,348C ''/strong'':59C,74C,90C ''/ul'':250C ''0'':309C,336C,340C,345C ''1'':278C,314C,339C,341C ''12'':277C,282C,286C,295C,304C ''1st'':164C ''2'':287C,300C,323C ''2015'':279C,288C,297C,306C,315C,324C,333C,342C ''3'':296C,312C,321C,330C,332C ''4'':276C,285C,294C,303C,305C,313C,318C,322C,327C,331C ''7'':291C ''across'':26C,124C ''allow'':114C ''analyt'':20C,44C,48C,112C ''associ'':30C ''assum'':195C ''attribut'':6A,184C ''browser'':65C ''close'':82C ''code'':204C,220C,238C,265C ''concept'':52C ''consumerweb'':7B ''context'':39C ''convers'':5A,36C,89C,123C,139C,158C,171C,217C,235C,245C,272C ''cooki'':64C ''credit'':25C,119C,152C ''data'':190C,197C,258C ''decay'':2A,16C,145C,162C ''differ'':255C ''diminish'':149C ''distribut'':24C,117C ''durat'':225C ''em'':60C,75C,91C ''event'':94C ''first'':232C ''formula'':3A,17C ''give'':147C ''googl'':47C ''h2'':8C,38C,188C ''href'':175C ''ie'':62C,77C ''individu'':242C ''info'':182C ''lead'':135C ''li'':56C,71C,87C,203C,219C,237C ''like'':46C,261C ''look'':260C ''mani'':69C ''may'':259C ''model'':142C,185C ''multipl'':28C ''n'':205C,267C ''nofollow'':180C ''number'':209C ''occur'':103C ''ol'':55C ''one'':140C ''p'':11C,41C,110C,160C,173C,192C,251C,264C ''packag'':21C,45C,113C ''pageview'':80C ''part'':133C ''play'':131C ''preced'':127C ''proxim'':84C ''question'':9C ''rel'':179C ''reli'':49C ''requir'':198C ''sampl'':189C,256C ''session'':29C,73C,106C,128C,154C,165C,211C,233C,248C,269C,274C ''set'':78C ''share'':150C ''sign'':99C ''sign-up'':98C ''singl'':122C ''strong'':57C,72C,88C ''success'':93C ''support.google.com'':177C ''support.google.com/analytics/answer/1662518?hl=en'':176C ''tbc'':239C,271C ''three'':254C ''time'':1A,15C,83C,144C,161C,227C,243C ''timestamp'':270C ''total'':208C,224C ''ul'':202C ''uniqu'':63C ''use'':22C ''user'':33C,58C,67C,137C,213C,230C,257C ''user1'':275C,284C,293C,302C ''user2'':311C,320C,329C ''user3'':338C ''userid'':266C ''visit'':76C ''visitor'':61C ''visual'':163C ''web'':19C,43C ''within'':81C ''would'':199C ''yes'':310C,337C,346C ''â'':166C,167C,168C,169C,170C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8529, 2, '2015-10-20 17:28:51.06', 0, NULL, NULL, '&lt;p&gt;This might be better for &lt;a href="http://opendata.stackexchange.com"&gt;opendata&lt;/a&gt;, but nonetheless, you have a few options. One would be to go to &lt;a href="http://www.geohive.com/cntry/" rel="nofollow"&gt;geohive&lt;/a&gt; which has other pages, including &lt;a href="http://www.geohive.com/earth/population_now.aspx" rel="nofollow"&gt;this one&lt;/a&gt;. There is also the UN categorization, available on &lt;a href="https://en.wikipedia.org/wiki/List_of_sovereign_states" rel="nofollow"&gt;wikipedia&lt;/a&gt; which uses membership within the United Nations system divides the 206 listed states into three categories: 193 member states,[1] two observer states, and 11 other states. The sovereignty dispute column indicates states whose sovereignty is undisputed (190 states) and states whose sovereignty is disputed (16 states).&lt;/p&gt;

&lt;p&gt;You can &lt;code&gt;read.table&lt;/code&gt; or &lt;code&gt;rvest&lt;/code&gt; those sources and grab them at runtime.&lt;/p&gt;
', NULL, NULL, '''/a'':11C,33C,48C,65C ''/cntry/'':29C ''/code'':119C,123C ''/earth/population_now.aspx'':43C ''/p'':113C,131C ''/wiki/list_of_sovereign_states'':61C ''1'':85C ''11'':90C ''16'':111C ''190'':103C ''193'':82C ''206'':76C ''also'':51C ''avail'':55C ''better'':5C ''categor'':54C ''categori'':81C ''code'':117C,121C ''column'':96C ''disput'':95C,110C ''divid'':74C ''en.wikipedia.org'':60C ''en.wikipedia.org/wiki/list_of_sovereign_states'':59C ''geohiv'':32C ''go'':23C ''grab'':127C ''href'':8C,26C,40C,58C ''includ'':38C ''indic'':97C ''list'':77C ''member'':83C ''membership'':68C ''might'':3C ''nation'':72C ''nofollow'':31C,45C,63C ''nonetheless'':13C ''observ'':87C ''one'':19C,47C ''opendata'':10C ''opendata.stackexchange.com'':9C ''option'':18C ''p'':1C,114C ''page'':37C ''read.table'':118C ''rel'':30C,44C,62C ''runtim'':130C ''rvest'':122C ''sourc'':125C ''sovereignti'':94C,100C,108C ''state'':78C,84C,88C,92C,98C,104C,106C,112C ''system'':73C ''three'':80C ''two'':86C ''un'':53C ''undisput'':102C ''unit'':71C ''use'':67C ''whose'':99C,107C ''wikipedia'':64C ''within'':69C ''would'':20C ''www.geohive.com'':28C,42C ''www.geohive.com/cntry/'':27C ''www.geohive.com/earth/population_now.aspx'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8571, 2, '2015-10-23 07:41:01.997', 0, NULL, NULL, '&lt;p&gt;The code below from SaturnAPI answers this question. See and run the code at &lt;a href="https://saturnapi.com/artitw/neural-network-decimal-digits-to-binary-bitwise-conversion" rel="nofollow"&gt;https://saturnapi.com/artitw/neural-network-decimal-digits-to-binary-bitwise-conversion&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;% Welcome to Saturn''s MATLAB-Octave API.
% Delete the sample code below these comments and write your own!

% Exercise from http://neuralnetworksanddeeplearning.com/chap1.html
% There is a way of determining the bitwise representation of a digit by adding an extra layer to the three-layer network above. The extra layer converts the output from the previous layer into a binary representation, as illustrated in the figure below. Find a set of weights and biases for the new output layer. Assume that the first 3 layers of neurons are such that the correct output in the third layer (i.e., the old output layer) has activation at least 0.99, and incorrect outputs have activation less than 0.01.

% Inputs from 3rd layer
xj = eye(10,10)

% Weights matrix
wj = [0 0 0 0 0 0 0 0 1 1 ;
      0 0 0 0 1 1 1 1 0 0 ;
      0 0 1 1 0 0 1 1 0 0 ;
      0 1 0 1 0 1 0 1 0 1 ]'';

% Results
wj*xj


% Confirm results
integers = 0:9;
dec2bin(integers)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/artitw/neural-network-decimal-digits-to-binary-bitwise-conversion'':20C ''/artitw/neural-network-decimal-digits-to-binary-bitwise-conversion&lt;/a&gt;&lt;/p&gt;'':25C ''/chap1.html'':51C ''/code'':205C ''/pre'':206C ''0'':155C,156C,157C,158C,159C,160C,161C,162C,165C,166C,167C,168C,173C,174C,175C,176C,179C,180C,183C,184C,185C,187C,189C,191C,193C,201C ''0.01'':143C ''0.99'':135C ''1'':163C,164C,169C,170C,171C,172C,177C,178C,181C,182C,186C,188C,190C,192C,194C ''10'':150C,151C ''3'':112C ''3rd'':146C ''9'':202C ''activ'':132C,140C ''ad'':65C ''answer'':7C ''api'':35C ''assum'':108C ''bias'':102C ''binari'':88C ''bitwis'':59C ''code'':3C,14C,27C,39C ''comment'':42C ''confirm'':198C ''convert'':79C ''correct'':120C ''dec2bin'':203C ''delet'':36C ''determin'':57C ''digit'':63C ''exercis'':47C ''extra'':67C,77C ''eye'':149C ''figur'':94C ''find'':96C ''first'':111C ''href'':17C ''i.e'':126C ''illustr'':91C ''incorrect'':137C ''input'':144C ''integ'':200C,204C ''layer'':68C,73C,78C,85C,107C,113C,125C,130C,147C ''least'':134C ''less'':141C ''matlab'':33C ''matlab-octav'':32C ''matrix'':153C ''network'':74C ''neuralnetworksanddeeplearning.com'':50C ''neuralnetworksanddeeplearning.com/chap1.html'':49C ''neuron'':115C ''new'':105C ''nofollow'':22C ''octav'':34C ''old'':128C ''output'':81C,106C,121C,129C,138C ''p'':1C ''pre'':26C ''previous'':84C ''question'':9C ''rel'':21C ''represent'':60C,89C ''result'':195C,199C ''run'':12C ''sampl'':38C ''saturn'':30C ''saturnapi'':6C ''saturnapi.com'':19C,24C ''saturnapi.com/artitw/neural-network-decimal-digits-to-binary-bitwise-conversion'':18C ''saturnapi.com/artitw/neural-network-decimal-digits-to-binary-bitwise-conversion&lt;/a&gt;&lt;/p&gt;'':23C ''see'':10C ''set'':98C ''third'':124C ''three'':72C ''three-lay'':71C ''way'':55C ''weight'':100C,152C ''welcom'':28C ''wj'':154C,196C ''write'':44C ''xj'':148C,197C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8530, 2, '2015-10-20 17:39:03.5', 0, NULL, NULL, '&lt;p&gt;This is an interesting problem! You''re right in thinking the easiest approach to this is to use a look-up table for identifying locations. Of course, this approach is going to be only as good as your data set and is still going to be somewhat prone to misclassification. One resource I''ve found is the &lt;a href="https://www.maxmind.com/en/free-world-cities-database" rel="nofollow"&gt;Free World Cities Database&lt;/a&gt;. One caveatâthis isn''t still actively maintained, and the countries are listed as country code, which may require further resolution on your part. Another possibility is the &lt;a href="http://www.geonames.org/export/" rel="nofollow"&gt;geonames data set&lt;/a&gt;. There, it looks like you''d want their &lt;code&gt;allCountries.zip&lt;/code&gt; dataset.&lt;/p&gt;
', NULL, NULL, '''/a'':71C,110C ''/code'':121C ''/en/free-world-cities-database'':64C ''/export/'':104C ''/p'':123C ''activ'':78C ''allcountries.zip'':120C ''anoth'':96C ''approach'':14C,31C ''caveatâ'':73C ''citi'':69C ''code'':87C,119C ''countri'':82C,86C ''cours'':29C ''d'':116C ''data'':41C,108C ''databas'':70C ''dataset'':122C ''easiest'':13C ''found'':57C ''free'':67C ''geonam'':107C ''go'':33C,46C ''good'':38C ''href'':61C,101C ''identifi'':26C ''interest'':5C ''isn'':75C ''like'':114C ''list'':84C ''locat'':27C ''look'':22C,113C ''look-up'':21C ''maintain'':79C ''may'':89C ''misclassif'':52C ''nofollow'':66C,106C ''one'':53C,72C ''p'':1C ''part'':95C ''possibl'':97C ''problem'':6C ''prone'':50C ''re'':8C ''rel'':65C,105C ''requir'':90C ''resolut'':92C ''resourc'':54C ''right'':9C ''set'':42C,109C ''somewhat'':49C ''still'':45C,77C ''tabl'':24C ''think'':11C ''use'':19C ''ve'':56C ''want'':117C ''world'':68C ''www.geonames.org'':103C ''www.geonames.org/export/'':102C ''www.maxmind.com'':63C ''www.maxmind.com/en/free-world-cities-database'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8557, 1, '2015-10-22 10:11:39.94', 3, 70, 'Discovering non-interesting attributes', '&lt;p&gt;I would like to ask a question about recommender systems. We are showing some movies to users and they have to decide if they like them or not. These movies have only a few attributes&lt;/p&gt;

&lt;p&gt;Title
Director
Category
Duration
Nationality&lt;/p&gt;

&lt;p&gt;We show the attributes as a block. The users can decide if they like (the block) or not. We aggregate these decisions to recommend them more movies in the future. My question is: Are there any methods that can tell us whether a user is uninterested in a given attribute? For example, if a user is never considering duration when saying yes or no.&lt;/p&gt;
', 'machine-learning  recommendation', NULL, '''/p'':46C,53C,119C ''aggreg'':74C ''ask'':15C ''attribut'':5A,45C,58C,104C ''block'':61C,70C ''categori'':50C ''consid'':112C ''decid'':32C,65C ''decis'':76C ''director'':49C ''discov'':1A ''durat'':51C,113C ''exampl'':106C ''futur'':84C ''given'':103C ''interest'':4A ''learn'':8B ''like'':13C,35C,68C ''machin'':7B ''machine-learn'':6B ''method'':91C ''movi'':25C,40C,81C ''nation'':52C ''never'':111C ''non'':3A ''non-interest'':2A ''p'':10C,47C,54C ''question'':17C,86C ''recommend'':9B,19C,78C ''say'':115C ''show'':23C,56C ''system'':20C ''tell'':94C ''titl'':48C ''uninterest'':100C ''us'':95C ''user'':27C,63C,98C,109C ''whether'':96C ''would'':12C ''yes'':116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8531, 2, '2015-10-20 20:55:19.137', 1, NULL, NULL, '&lt;p&gt;This is a really interesting problem! Like most really interesting problems, you''re unlikely to find an out-of-box solution for this, but I think the field of graph/subgraph similarity has some promise here. I''ll go into more detail, but, at a high level, I think you can view players'' paths during a play as a collection of five traversals through x,y space, with a vertex being any x,y point you have available (presumably there''s some level of time granularity here), and an edge describing a player''s movement from one point in x,y space to the next over time. It should be possible to cluster your data using a similarity metric (e.g., see &lt;a href="https://www.cs.cmu.edu/~jingx/docs/DBreport.pdf" rel="nofollow"&gt;Koutra et al., 2011&lt;/a&gt; for a nice overview). Then, using your own domain expertise, you should be able to identify whether the clusters you''ve derived have some real-world meaning in basketball. &lt;/p&gt;
', NULL, NULL, '''/a'':134C ''/p'':165C ''/~jingx/docs/dbreport.pdf'':127C ''2011'':133C ''abl'':148C ''al'':132C ''avail'':79C ''basketbal'':164C ''box'':22C ''cluster'':114C,153C ''collect'':61C ''data'':116C ''deriv'':156C ''describ'':92C ''detail'':43C ''domain'':143C ''e.g'':121C ''edg'':91C ''et'':131C ''expertis'':144C ''field'':30C ''find'':17C ''five'':63C ''go'':40C ''granular'':87C ''graph/subgraph'':32C ''high'':47C ''href'':124C ''identifi'':150C ''interest'':6C,11C ''koutra'':130C ''level'':48C,84C ''like'':8C ''ll'':39C ''mean'':162C ''metric'':120C ''movement'':96C ''next'':106C ''nice'':137C ''nofollow'':129C ''one'':98C ''out-of-box'':19C ''overview'':138C ''p'':1C ''path'':55C ''play'':58C ''player'':54C,94C ''point'':76C,99C ''possibl'':112C ''presum'':80C ''problem'':7C,12C ''promis'':36C ''re'':14C ''real'':160C ''real-world'':159C ''realli'':5C,10C ''rel'':128C ''see'':122C ''similar'':33C,119C ''solut'':23C ''space'':68C,103C ''think'':28C,50C ''time'':86C,108C ''travers'':64C ''unlik'':15C ''use'':117C,140C ''ve'':155C ''vertex'':71C ''view'':53C ''whether'':151C ''world'':161C ''www.cs.cmu.edu'':126C ''www.cs.cmu.edu/~jingx/docs/dbreport.pdf'':125C ''x'':66C,74C,101C ''y'':67C,75C,102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8532, 2, '2015-10-20 21:34:08.533', 0, NULL, NULL, '&lt;p&gt;I think the tagging approach has some merit here. The frequency drop you''re observing as a consequence of using this is to be expected, I think. After all, keywords are the words that help differentiate a document from others in a corpus. If you have access to an ontology related to the subject matter of your corpus, you could try to map your rare keyword tags to the ontology, and use parent-level information for each to get a slightly more general set of keyword tags! If this is an approach that interests you, Stanford''s open source &lt;a href="http://protege.stanford.edu/" rel="nofollow"&gt;protÃ©gÃ©&lt;/a&gt; system is a good framework for working on this.&lt;/p&gt;
', NULL, NULL, '''/a'':109C ''/p'':119C ''access'':48C ''approach'':6C,94C ''consequ'':19C ''corpus'':44C,59C ''could'':61C ''differenti'':37C ''document'':39C ''drop'':13C ''expect'':26C ''framework'':114C ''frequenc'':12C ''general'':85C ''get'':81C ''good'':113C ''gã'':108C ''help'':36C ''href'':103C ''inform'':77C ''interest'':96C ''keyword'':31C,67C,88C ''level'':76C ''map'':64C ''matter'':56C ''merit'':9C ''nofollow'':106C ''observ'':16C ''ontolog'':51C,71C ''open'':100C ''other'':41C ''p'':1C ''parent'':75C ''parent-level'':74C ''protege.stanford.edu'':104C ''protã'':107C ''rare'':66C ''re'':15C ''rel'':105C ''relat'':52C ''set'':86C ''slight'':83C ''sourc'':101C ''stanford'':98C ''subject'':55C ''system'':110C ''tag'':5C,68C,89C ''think'':3C,28C ''tri'':62C ''use'':21C,73C ''word'':34C ''work'':116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8534, 2, '2015-10-20 23:27:03.737', 0, NULL, NULL, '&lt;p&gt;Complete code in python:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import re
contents = open(''path/to/file'', ''r'').read()
matches = re.findall(r''^(.*?)\n(.*?)$'', contents)
joined = [''\t''.join([i[0] i[1]]) for i in matches]
print ''\n''.join(joined)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://rubular.com/r/Uo0ZOKhk7T" rel="nofollow"&gt;I tested this out on rubular&lt;/a&gt;, and it works on your example. It should generalize to others as well. To walk you through the regular expression, &lt;code&gt;^&lt;/code&gt; and &lt;code&gt;$&lt;/code&gt; match the beginning and end of lines, respectively, while (.*?) uses non-greedy wildcard matching to capture, first, everything up to the newline character, then everything up to the next end of line.&lt;/p&gt;
', NULL, NULL, '''/a'':52C ''/code'':36C,74C,77C ''/p'':6C,111C ''/pre'':37C ''/r/uo0zokhk7t'':43C ''0'':25C ''1'':27C ''begin'':80C ''captur'':94C ''charact'':101C ''code'':3C,8C,73C,76C ''complet'':2C ''content'':11C,20C ''end'':82C,108C ''everyth'':96C,103C ''exampl'':58C ''express'':72C ''first'':95C ''general'':61C ''greedi'':90C ''href'':40C ''import'':9C ''join'':21C,23C,34C,35C ''line'':84C,110C ''match'':16C,31C,78C,92C ''n'':19C,33C ''newlin'':100C ''next'':107C ''nofollow'':45C ''non'':89C ''non-greedi'':88C ''open'':12C ''other'':63C ''p'':1C,38C ''path/to/file'':13C ''pre'':7C ''print'':32C ''python'':5C ''r'':14C,18C ''re'':10C ''re.findall'':17C ''read'':15C ''regular'':71C ''rel'':44C ''respect'':85C ''rubular'':51C ''rubular.com'':42C ''rubular.com/r/uo0zokhk7t'':41C ''test'':47C ''use'':87C ''walk'':67C ''well'':65C ''wildcard'':91C ''work'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8626, 2, '2015-10-28 07:24:42.917', 1, NULL, NULL, '&lt;p&gt;I recently discovered this site: &lt;a href="http://www.librec.net/datagen.html" rel="nofollow"&gt;http://www.librec.net/datagen.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/datagen.html'':11C ''/datagen.html&lt;/a&gt;&lt;/p&gt;'':16C ''discov'':4C ''href'':8C ''nofollow'':13C ''p'':1C ''recent'':3C ''rel'':12C ''site'':6C ''www.librec.net'':10C,15C ''www.librec.net/datagen.html'':9C ''www.librec.net/datagen.html&lt;/a&gt;&lt;/p&gt;'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8536, 2, '2015-10-21 01:08:06.42', 1, NULL, NULL, '&lt;p&gt;This seems to me as the plain old recommendation problem. The Accounts are the USERS and the Symptoms are the ITEMS. Each time an Account shows a particular Symptom your system will increment a count value.&lt;/p&gt;

&lt;p&gt;Creating the following dataset:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;ACCOUNT, SYMPTOM, COUNT&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Now you can use/implement any sort of recommender system (Mahout is only an option, have you seen &lt;a href="http://mymedialite.net/" rel="nofollow"&gt;MyMediaLite&lt;/a&gt;) or you can even implement yours. &lt;/p&gt;

&lt;p&gt;Let''s reuse your ideas:
* You''d like to use a user-based similarity 
* If an Account has shown 2 or more times the same symptom it seems to be important&lt;/p&gt;

&lt;p&gt;So you can filter out the Account, Symptom pairs with less than 2 counts, and with the rest you create the following datasets:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;User, Item dataset:&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;code&gt;ACCOUNT, SYMPTOM&lt;/code&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Table with a unique column containing all Users:&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;code&gt;ACCOUNT&lt;/code&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Table with a unique column containing all Items:&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;code&gt;SYMPTOM&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Now you can use directly the &lt;a href="http://mymedialite.net/documentation/doxygen/class_my_media_lite_1_1_item_recommendation_1_1_user_k_n_n.html" rel="nofollow"&gt;User-KNN algorithm&lt;/a&gt; from MyMediaLite.&lt;/p&gt;

&lt;p&gt;With the recommender model already trained you can pass any ACCOUNT as input and it will give you a ranked list of the most probable SYMPTOMS that might appear.&lt;/p&gt;

&lt;p&gt;Obs.: Initially ignore the time, then you could use it to partition your data in past/future and evaluate the recommendation in a more realistic way. ;-)&lt;/p&gt;
', NULL, NULL, '''/a'':76C,208C ''/code'':50C,154C,171C,188C ''/documentation/doxygen/class_my_media_lite_1_1_item_recommendation_1_1_user_k_n_n.html'':201C ''/li'':148C,166C,183C ''/p'':38C,44C,51C,83C,117C,142C,155C,172C,189C,211C,242C,269C ''/ul'':149C,167C,184C ''2'':105C,131C ''account'':13C,26C,47C,102C,125C,152C,170C,223C ''algorithm'':207C ''alreadi'':217C ''appear'':241C ''base'':98C ''code'':46C,151C,169C,186C ''column'':162C,179C ''contain'':163C,180C ''could'':251C ''count'':36C,49C,132C ''creat'':40C,138C ''d'':91C ''data'':257C ''dataset'':43C,141C,147C ''direct'':195C ''evalu'':261C ''even'':80C ''filter'':122C ''follow'':42C,140C ''give'':229C ''href'':71C,198C ''idea'':89C ''ignor'':246C ''implement'':81C ''import'':116C ''increment'':34C ''initi'':245C ''input'':225C ''item'':22C,146C,182C ''knn'':206C ''less'':129C ''let'':85C ''li'':144C,157C,174C ''like'':92C ''list'':233C ''mahout'':62C ''might'':240C ''model'':216C ''mymedialit'':75C,210C ''mymedialite.net'':72C,200C ''mymedialite.net/documentation/doxygen/class_my_media_lite_1_1_item_recommendation_1_1_user_k_n_n.html'':199C ''nofollow'':74C,203C ''ob'':244C ''old'':9C ''option'':66C ''p'':1C,39C,45C,52C,84C,118C,150C,168C,185C,190C,212C,243C ''pair'':127C ''particular'':29C ''partit'':255C ''pass'':221C ''past/future'':259C ''plain'':8C ''probabl'':237C ''problem'':11C ''rank'':232C ''realist'':267C ''recommend'':10C,60C,215C,263C ''rel'':73C,202C ''rest'':136C ''reus'':87C ''seem'':3C,113C ''seen'':69C ''show'':27C ''shown'':104C ''similar'':99C ''sort'':58C ''symptom'':19C,30C,48C,111C,126C,153C,187C,238C ''system'':32C,61C ''tabl'':158C,175C ''time'':24C,108C,248C ''train'':218C ''ul'':143C,156C,173C ''uniqu'':161C,178C ''use'':94C,194C,252C ''use/implement'':56C ''user'':16C,97C,145C,165C,205C ''user-bas'':96C ''user-knn'':204C ''valu'':37C ''way'':268C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8574, 4, '2015-10-23 15:32:52.94', 0, NULL, NULL, 'Support Vector Machines (SVM) are a popular supervised machine learning algorithm that can be used for classification or regression.', NULL, NULL, '''algorithm'':11C ''classif'':17C ''learn'':10C ''machin'':3C,9C ''popular'':7C ''regress'':19C ''supervis'':8C ''support'':1C ''svm'':4C ''use'':15C ''vector'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8537, 2, '2015-10-21 01:29:59.4', 0, NULL, NULL, '&lt;p&gt;One starting approach is doing the cross-correlation as you''ve said. The correlation will fail to capture the time shifted behavior. But you should calculate it for every pair of Milk/Drug you have.&lt;/p&gt;

&lt;p&gt;The &lt;a href="https://stat.ethz.ch/R-manual/R-devel/library/stats/html/acf.html" rel="nofollow"&gt;&lt;code&gt;ccf()&lt;/code&gt;&lt;/a&gt; function in R does it for you.&lt;/p&gt;

&lt;p&gt;If you''re not successful i suggest you to take a look at the multiple time-series plotted side by side (like &lt;a href="http://stackoverflow.com/questions/19921842/plotting-multiple-time-series-on-the-same-plot-using-ggplot"&gt;here&lt;/a&gt;) and check if really there exists this time-shifted pattern in your customer behavior.&lt;/p&gt;
', NULL, NULL, '''/a'':50C,89C ''/code'':49C ''/p'':37C,58C,105C ''/questions/19921842/plotting-multiple-time-series-on-the-same-plot-using-ggplot'':87C ''/r-manual/r-devel/library/stats/html/acf.html'':44C ''approach'':4C ''behavior'':24C,104C ''calcul'':28C ''captur'':20C ''ccf'':48C ''check'':91C ''code'':47C ''correl'':10C,16C ''cross'':9C ''cross-correl'':8C ''custom'':103C ''everi'':31C ''exist'':95C ''fail'':18C ''function'':51C ''href'':41C,84C ''like'':82C ''look'':71C ''milk/drug'':34C ''multipl'':74C ''nofollow'':46C ''one'':2C ''p'':1C,38C,59C ''pair'':32C ''pattern'':100C ''plot'':78C ''r'':53C ''re'':62C ''realli'':93C ''rel'':45C ''said'':14C ''seri'':77C ''shift'':23C,99C ''side'':79C,81C ''stackoverflow.com'':86C ''stackoverflow.com/questions/19921842/plotting-multiple-time-series-on-the-same-plot-using-ggplot'':85C ''start'':3C ''stat.ethz.ch'':43C ''stat.ethz.ch/r-manual/r-devel/library/stats/html/acf.html'':42C ''success'':64C ''suggest'':66C ''take'':69C ''time'':22C,76C,98C ''time-seri'':75C ''time-shift'':97C ''ve'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8801, 2, '2015-11-11 15:16:22.027', 1, NULL, NULL, '&lt;p&gt;What happens in Spectral Clustering is simply finding some blocks in the data according to the property for which your matrix is defined. If it''s similarity you get the similar data points. &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;The first one is how to introduce weights when performing spectral clustering. Like in the Shi-Malik, Ng-Jordan-Weiss versions, where only the distances are used.&lt;/strong&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Do not get confused. All what you need is to introduce your similarity measure which can be weighted. Just insert all the weights into a classical spectral clustering version (Shi-Malik or Ng or whatever) and there you go!&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;The second one is that my dissimilarity measure does not exists sometimes. In these cases, it has zero weight. How to introduce these cases in the algorithm? I suppose the zero weight solves the problem if I know how to introduce them.&lt;/strong&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I think this should not be a big issue. Just put zero and try your algorithm with a mock data and see what happens. If didn''t work, try to fill missing values by some statistical indicators e.g. mean of the data and see the result.&lt;/p&gt;

&lt;p&gt;Please write a comment about how it worked.&lt;/p&gt;

&lt;p&gt;Good luck :)&lt;/p&gt;
', NULL, NULL, '''/li'':69C,159C ''/p'':35C,112C,208C,218C,222C ''/strong'':68C,158C ''/ul'':70C,160C ''accord'':15C ''algorithm'':142C,177C ''big'':169C ''block'':11C ''case'':130C,139C ''classic'':97C ''cluster'':6C,50C,99C ''comment'':213C ''confus'':75C ''data'':14C,33C,181C,203C ''defin'':24C ''didn'':187C ''dissimilar'':122C ''distanc'':65C ''e.g'':199C ''exist'':126C ''fill'':192C ''find'':9C ''first'':40C ''get'':30C,74C ''go'':111C ''good'':220C ''happen'':3C,185C ''indic'':198C ''insert'':91C ''introduc'':45C,82C,137C,156C ''issu'':170C ''jordan'':59C ''know'':153C ''li'':37C,114C ''like'':51C ''luck'':221C ''malik'':56C,103C ''matrix'':22C ''mean'':200C ''measur'':85C,123C ''miss'':193C ''mock'':180C ''need'':79C ''ng'':58C,105C ''ng-jordan-weiss'':57C ''one'':41C,118C ''p'':1C,71C,161C,209C,219C ''perform'':48C ''pleas'':210C ''point'':34C ''problem'':150C ''properti'':18C ''put'':172C ''result'':207C ''second'':117C ''see'':183C,205C ''shi'':55C,102C ''shi-malik'':54C,101C ''similar'':28C,32C,84C ''simpli'':8C ''solv'':148C ''sometim'':127C ''spectral'':5C,49C,98C ''statist'':197C ''strong'':38C,115C ''suppos'':144C ''think'':163C ''tri'':175C,190C ''ul'':36C,113C ''use'':67C ''valu'':194C ''version'':61C,100C ''weight'':46C,89C,94C,134C,147C ''weiss'':60C ''whatev'':107C ''work'':189C,217C ''write'':211C ''zero'':133C,146C,173C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8538, 2, '2015-10-21 01:46:30.197', 0, NULL, NULL, '&lt;p&gt;All answers above are quite good paths to follow. But if you want to select between multiple algorithms (all in R =D) and understand why selecting them, try this:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Dimensionality_Reduction/Feature_Selection" rel="nofollow"&gt;https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Dimensionality_Reduction/Feature_Selection&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;Last tip: The Feature Ranking approach in that page is exaclty what you want. ;-)&lt;/p&gt;
', NULL, NULL, '''/p'':31C,43C,59C ''/wiki/data_mining_algorithms_in_r/dimensionality_reduction/feature_selection'':37C ''/wiki/data_mining_algorithms_in_r/dimensionality_reduction/feature_selection&lt;/a&gt;'':42C ''algorithm'':19C ''answer'':3C ''approach'':50C ''d'':23C ''en.wikibooks.org'':36C,41C ''en.wikibooks.org/wiki/data_mining_algorithms_in_r/dimensionality_reduction/feature_selection'':35C ''en.wikibooks.org/wiki/data_mining_algorithms_in_r/dimensionality_reduction/feature_selection&lt;/a&gt;'':40C ''exaclti'':55C ''featur'':48C ''follow'':10C ''good'':7C ''href'':34C ''last'':45C ''multipl'':18C ''nofollow'':39C ''p'':1C,32C,44C ''page'':53C ''path'':8C ''quit'':6C ''r'':22C ''rank'':49C ''rel'':38C ''select'':16C,27C ''tip'':46C ''tri'':29C ''understand'':25C ''want'':14C,58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8539, 1, '2015-10-21 11:18:43.607', 3, 101, 'Time dependent Classification problem', '&lt;p&gt;I am trying to solve a decision making problem. In it, information evolves and increases with time for each event observed, and the history of the event may be useful.&lt;/p&gt;

&lt;p&gt;The problem is as follows: in an electric network at time $t_0$ there is signal that automatically triggers the possible need of a technical intervention in a network to correct the problem. The system at $t_0$ has a large set of general indicators describing the state of the network based on which I''d like to determine whether to proceed with an intervention at $t_{0}$. &lt;/p&gt;

&lt;p&gt;It''s possible to carry specific measurements at the point where the problem was detected (this takes some time $\Delta t$). At this stage we can again ask whether the intervention is necessary or not? We can iterate this procedure a few times. Then, my question is, which is the machine learning method best suited for this classification problem? Can it be formulated as a Survival problem?&lt;/p&gt;
', 'machine-learning  classification  survival-analysis', NULL, '''/p'':43C,113C,182C ''0'':57C,82C,112C ''analysi'':11B ''ask'':142C ''automat'':62C ''base'':96C ''best'':168C ''carri'':119C ''classif'':3A,8B,172C ''correct'':75C ''d'':100C ''decis'':19C ''delta'':134C ''depend'':2A ''describ'':90C ''detect'':129C ''determin'':103C ''electr'':52C ''event'':32C,39C ''evolv'':25C ''follow'':49C ''formul'':177C ''general'':88C ''histori'':36C ''increas'':27C ''indic'':89C ''inform'':24C ''intervent'':70C,109C,145C ''iter'':152C ''larg'':85C ''learn'':7B,166C ''like'':101C ''machin'':6B,165C ''machine-learn'':5B ''make'':20C ''may'':40C ''measur'':121C ''method'':167C ''necessari'':147C ''need'':66C ''network'':53C,73C,95C ''observ'':33C ''p'':12C,44C,114C ''point'':124C ''possibl'':65C,117C ''problem'':4A,21C,46C,77C,127C,173C,181C ''procedur'':154C ''proceed'':106C ''question'':160C ''set'':86C ''signal'':60C ''solv'':17C ''specif'':120C ''stage'':138C ''state'':92C ''suit'':169C ''surviv'':10B,180C ''survival-analysi'':9B ''system'':79C ''take'':131C ''technic'':69C ''time'':1A,29C,55C,133C,157C ''tri'':15C ''trigger'':63C ''use'':42C ''whether'':104C,143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8558, 2, '2015-10-22 10:32:16.303', 2, NULL, NULL, '&lt;p&gt;You could use the OpenCV library, specifically, the &lt;a href="http://docs.opencv.org/doc/user_guide/ug_traincascade.html" rel="nofollow"&gt;&lt;strong&gt;Haar-Cascade technique&lt;/strong&gt;&lt;/a&gt; . The OpenCV is a computer vision library that have a python interface. You could take a look in &lt;a href="http://coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html" rel="nofollow"&gt;&lt;strong&gt;this tutorial&lt;/strong&gt;&lt;/a&gt; to start. &lt;/p&gt;
', NULL, NULL, '''/2013/07/22/train-your-own-opencv-haar-classifier.html'':46C ''/a'':23C,53C ''/doc/user_guide/ug_traincascade.html'':14C ''/p'':56C ''/strong'':22C,52C ''cascad'':20C ''coding-robin.de'':45C ''coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html'':44C ''comput'':28C ''could'':3C,37C ''docs.opencv.org'':13C ''docs.opencv.org/doc/user_guide/ug_traincascade.html'':12C ''haar'':19C ''haar-cascad'':18C ''href'':11C,43C ''interfac'':35C ''librari'':7C,30C ''look'':40C ''nofollow'':16C,48C ''opencv'':6C,25C ''p'':1C ''python'':34C ''rel'':15C,47C ''specif'':8C ''start'':55C ''strong'':17C,49C ''take'':38C ''techniqu'':21C ''tutori'':51C ''use'':4C ''vision'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9107, 2, '2015-11-28 16:04:50.477', 2, NULL, NULL, '&lt;p&gt;After reading some more papers, I realize that I misunderstood how the graphs work. The graphs are supposed to contain the conditional probabilities based on their parent(s). &lt;/p&gt;

&lt;p&gt;This clears up the doubts that I had before.&lt;/p&gt;

&lt;p&gt;For more information, see &lt;a href="http://www.csse.monash.edu.au/bai/book/BAI_Chapter2.pdf" rel="nofollow"&gt;this&lt;/a&gt; book chapter.&lt;/p&gt;
', NULL, NULL, '''/a'':55C ''/bai/book/bai_chapter2.pdf'':51C ''/p'':30C,41C,58C ''base'':25C ''book'':56C ''chapter'':57C ''clear'':33C ''condit'':23C ''contain'':21C ''doubt'':36C ''graph'':14C,17C ''href'':48C ''inform'':45C ''misunderstood'':11C ''nofollow'':53C ''p'':1C,31C,42C ''paper'':6C ''parent'':28C ''probabl'':24C ''read'':3C ''realiz'':8C ''rel'':52C ''see'':46C ''suppos'':19C ''work'':15C ''www.csse.monash.edu.au'':50C ''www.csse.monash.edu.au/bai/book/bai_chapter2.pdf'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8542, 1, '2015-10-21 12:30:53.157', 0, 98, 'Appropriate algorithm for string (not document) classification?', '&lt;p&gt;I am trying to classify a large-ish number of small strings (millions) into about 10 disjunct categories. Examples of classes and strings for each class include:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;email: "foo@bar.com"
phone: "55", "22334455"
personName: "John", "Q.", "Public"
organizationName: "Reuters", "IBM"
date: "Dec.", "22.10.2010"
nameAndEmail: "Larsonlarson@gmail.com" (a last name has been concatenated with an email address.)
phoneAndEmail: "55larson@gmail.com" (part of a phone number has been concatenated with an emal address)
separator: ",", "and", "or"
other: stuff that does not fit in other categories.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Carrying out classification via a set of heuristics seems tedious, so I have been experimenting with an SVM. I''m not able to get more than about 80% accuracy without doing a lot of work manually encoding features like "has an ampersand", "is uppercase", "is mostly numeric", "has a colon in the middle", etc., which sort of defeats the purpose.&lt;/p&gt;

&lt;p&gt;I''m wondering whether part of the challenge with using an SVM is that the "salient features" of a given string, like the ''@'' character, are not in a fixed position in the string, so it will not get the same feature index for each string.&lt;/p&gt;

&lt;p&gt;Does anyone have suggestions for a more appropriate approach to this task, or can anyone recommend further reading?&lt;/p&gt;
', 'machine-learning  classification  text-mining  multiclass-classification', NULL, '''/code'':103C ''/p'':47C,166C,213C,233C ''/pre'':104C ''10'':35C ''22.10.2010'':64C ''22334455'':54C ''55'':53C ''55larson@gmail.com'':78C ''80'':133C ''abl'':127C ''accuraci'':134C ''address'':76C,90C ''algorithm'':2A ''ampersand'':147C ''anyon'':216C,229C ''approach'':223C ''appropri'':1A,222C ''carri'':106C ''categori'':37C,102C ''challeng'':175C ''charact'':191C ''class'':40C,45C ''classif'':7A,11B,17B,108C ''classifi'':23C ''code'':49C ''colon'':155C ''concaten'':72C,86C ''date'':62C ''dec'':63C ''defeat'':163C ''disjunct'':36C ''document'':6A ''email'':50C,75C ''emal'':89C ''encod'':142C ''etc'':159C ''exampl'':38C ''experi'':120C ''featur'':143C,184C,208C ''fit'':99C ''fix'':196C ''foo@bar.com'':51C ''get'':129C,205C ''given'':187C ''heurist'':113C ''ibm'':61C ''includ'':46C ''index'':209C ''ish'':27C ''john'':56C ''larg'':26C ''large-ish'':25C ''larsonlarson@gmail.com'':66C ''last'':68C ''learn'':10B ''like'':144C,189C ''lot'':138C ''m'':125C,169C ''machin'':9B ''machine-learn'':8B ''manual'':141C ''middl'':158C ''million'':32C ''mine'':14B ''most'':151C ''multiclass'':16B ''multiclass-classif'':15B ''name'':69C ''nameandemail'':65C ''number'':28C,83C ''numer'':152C ''organizationnam'':59C ''p'':18C,105C,167C,214C ''part'':79C,172C ''personnam'':55C ''phone'':52C,82C ''phoneandemail'':77C ''posit'':197C ''pre'':48C ''public'':58C ''purpos'':165C ''q'':57C ''read'':232C ''recommend'':230C ''reuter'':60C ''salient'':183C ''seem'':114C ''separ'':91C ''set'':111C ''small'':30C ''sort'':161C ''string'':4A,31C,42C,188C,200C,212C ''stuff'':95C ''suggest'':218C ''svm'':123C,179C ''task'':226C ''tedious'':115C ''text'':13B ''text-min'':12B ''tri'':21C ''uppercas'':149C ''use'':177C ''via'':109C ''whether'':171C ''without'':135C ''wonder'':170C ''work'':140C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8543, 2, '2015-10-21 13:29:57.42', 2, NULL, NULL, '&lt;p&gt;You might find it useful to treat n-grams of characters as your feature space. Then you could represent a string as a bag of substrings.  With &lt;code&gt;N = 4&lt;/code&gt; or greater, you would capture things like ".com" in emails, "##.#" in dates, etc.&lt;/p&gt;

&lt;p&gt;It might also help to encode all single digits as one reserved number-only-character.&lt;/p&gt;

&lt;p&gt;An easy way to to this might be to create all the n-gram substrings for each string in your dataset, then simply treat that list of words as a document.  Then you could use &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html#sklearn.feature_extraction.text.CountVectorizer" rel="nofollow"&gt;term frequency&lt;/a&gt; or &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html" rel="nofollow"&gt;tf-idf&lt;/a&gt; vectors in your supervised step.&lt;/p&gt;

&lt;p&gt;For example to create the substring uni-, bi-, and tri-grams for "whatever@gmail.com":&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;a = "whatever@gmail.com" 
b = set([a[x:x+y] for y in range(0,4) for x in range(0,len(a))]) 

set(['''', ''co'', ''ve'', ''ai'', ''eve'', ''r@'', ''at'', ''.co'', ''gm'', ''ev'', ''tev'', ''er'', ''@gm'', ''ver'', ''@g'', ''r@g'', ''ail'', ''il.'', ''gma'', ''.'', ''te'', ''hat'', ''@'', ''wha'', ''om'', ''wh'', ''er@'', ''mai'', ''ma'', ''ha'', ''l.c'', ''a'', ''c'', ''ate'', ''e'', ''g'', ''i'', ''h'', ''.c'', ''m'', ''l'', ''o'', ''l.'', ''r'', ''t'', ''w'', ''v'', ''com'', ''il''])
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':112C,124C ''/code'':33C,218C ''/p'':47C,65C,130C,146C ''/pre'':219C ''/stable/modules/generated/sklearn.feature_extraction.text.countvectorizer.html#sklearn.feature_extraction.text.countvectorizer'':107C ''/stable/modules/generated/sklearn.feature_extraction.text.tfidfvectorizer.html'':118C ''0'':161C,167C ''4'':32C,162C ''ai'':173C ''ail'':187C ''also'':51C ''ate'':202C ''b'':151C ''bag'':26C ''bi'':139C ''c'':201C,207C ''captur'':38C ''charact'':13C,64C ''co'':171C,177C ''code'':30C,148C ''com'':41C,216C ''could'':20C,101C ''creat'':76C,135C ''dataset'':88C ''date'':45C ''digit'':57C ''document'':98C ''e'':203C ''easi'':68C ''email'':43C ''encod'':54C ''er'':181C,195C ''etc'':46C ''ev'':179C ''eve'':174C ''exampl'':133C ''featur'':16C ''find'':4C ''frequenc'':111C ''g'':184C,186C,204C ''gm'':178C,182C ''gma'':189C ''gram'':11C,81C,143C ''greater'':35C ''h'':206C ''ha'':198C ''hat'':191C ''help'':52C ''href'':104C,115C ''idf'':123C ''il'':188C,217C ''l'':209C,211C ''l.c'':199C ''len'':168C ''like'':40C ''list'':93C ''m'':208C ''ma'':197C ''mai'':196C ''might'':3C,50C,73C ''n'':10C,31C,80C ''n-gram'':9C,79C ''nofollow'':109C,120C ''number'':62C ''number-only-charact'':61C ''o'':210C ''om'':193C ''one'':59C ''p'':1C,48C,66C,131C ''pre'':147C ''r'':175C,185C,212C ''rang'':160C,166C ''rel'':108C,119C ''repres'':21C ''reserv'':60C ''scikit-learn.org'':106C,117C ''scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.countvectorizer.html#sklearn.feature_extraction.text.countvectorizer'':105C ''scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.tfidfvectorizer.html'':116C ''set'':152C,170C ''simpli'':90C ''singl'':56C ''space'':17C ''step'':129C ''string'':23C,85C ''substr'':28C,82C,137C ''supervis'':128C ''te'':190C ''term'':110C ''tev'':180C ''tf'':122C ''tf-idf'':121C ''thing'':39C ''treat'':8C,91C ''tri'':142C ''tri-gram'':141C ''uni'':138C ''use'':6C,102C ''v'':215C ''ve'':172C ''vector'':125C ''ver'':183C ''w'':214C ''way'':69C ''wh'':194C ''wha'':192C ''whatever@gmail.com'':145C,150C ''word'':95C ''would'':37C ''x'':154C,155C,164C ''y'':156C,158C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9385, 2, '2015-12-15 01:23:14.193', 0, NULL, NULL, '&lt;p&gt;This question seems really about representation, and not so much about clustering.&lt;/p&gt;

&lt;p&gt;Categorical data is a problem for most algorithms in machine learning. Suppose, for example, you have some categorical variable called "color" that could take on the values red, blue, or yellow. If we simply encode these numerically as 1,2, and 3 respectively, our algorithm will think that red (1) is actually closer to blue (2) than it is to yellow (3). We need to use a representation that lets the computer understand that these things are all actually equally different. &lt;/p&gt;

&lt;p&gt;One simple way is to use what''s called a &lt;a href="https://en.wikipedia.org/wiki/One-hot" rel="nofollow"&gt;one-hot&lt;/a&gt; representation, and it''s exactly what you thought you should do. Rather than having one variable like "color" that can take on three values, we separate it into three variables. These would be "color-red," "color-blue," and "color-yellow," which all can only take on the value 1 or 0. &lt;/p&gt;

&lt;p&gt;This increases the dimensionality of the space, but now you could use any clustering algorithm you like. It does sometimes make sense to zscore or whiten the data after doing this process, but the your idea is definitely reasonable.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2270, 2, '2014-10-13 22:07:51.18', 4, NULL, NULL, '&lt;p&gt;Yes. I believe this is what you are looking for.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.compileonline.com/execute_r_online.php" rel="nofollow"&gt;http://www.compileonline.com/execute_r_online.php&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/execute_r_online.php'':18C ''/execute_r_online.php&lt;/a&gt;&lt;/p&gt;'':23C ''/p'':12C ''believ'':4C ''href'':15C ''look'':10C ''nofollow'':20C ''p'':1C,13C ''rel'':19C ''www.compileonline.com'':17C,22C ''www.compileonline.com/execute_r_online.php'':16C ''www.compileonline.com/execute_r_online.php&lt;/a&gt;&lt;/p&gt;'':21C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8544, 2, '2015-10-21 14:13:34.26', 1, NULL, NULL, '&lt;p&gt;One can copy the ''bag of words'' model for documents, though it becomes a ''bag of characters'' model. (jamesmf extends this to n-grams, which will be more useful.)&lt;/p&gt;

&lt;p&gt;But if you want a feature to be salient to the algorithm, you need to give the algorithm access to that feature. You don''t need to tell it what classes that feature is relevant for--just whether or not the feature is present. For example, the length of the string, number of digits in the string, and number of capital letters in the string are probably very useful for differentiating several of the classes you described, but will not be part of the feature vector unless you put them there. But all you need to do is identify them, assign them numbers, and then let the SVM take care of deciding how relevant they are.&lt;/p&gt;

&lt;p&gt;Similarly, having a binary variable for whether or not the last four characters are &lt;code&gt;.com&lt;/code&gt; (or whether &lt;code&gt;.com&lt;/code&gt; appears in the string if it''s not always at the end) in the feature vector will convey the same amount of helpful information without also giving the algorithm a bunch of worthless information--there may be other 4-grams that are important, but it seems unlikely to me that it''s worth the cost of considering all 4-grams and 3-grams in order to get the known features of &lt;code&gt;.com&lt;/code&gt;, &lt;code&gt;.net&lt;/code&gt;, &lt;code&gt;.org&lt;/code&gt;, and so on.&lt;/p&gt;
', NULL, NULL, '''/code'':168C,173C,247C,250C,253C ''/p'':32C,150C,257C ''3'':235C ''4'':212C,232C ''access'':52C ''algorithm'':45C,51C,202C ''also'':199C ''alway'':182C ''amount'':194C ''appear'':174C ''assign'':134C ''bag'':6C,16C ''becom'':14C ''binari'':155C ''bunch'':204C ''capit'':94C ''care'':143C ''charact'':18C,164C ''class'':64C,108C ''code'':166C,171C,245C,248C,251C ''com'':167C,172C,246C ''consid'':230C ''convey'':191C ''copi'':4C ''cost'':228C ''decid'':145C ''describ'':110C ''differenti'':104C ''digit'':87C ''document'':11C ''end'':185C ''exampl'':79C ''extend'':21C ''featur'':39C,55C,66C,75C,118C,188C,243C ''four'':163C ''get'':240C ''give'':49C,200C ''gram'':26C,213C,233C,236C ''help'':196C ''identifi'':132C ''import'':216C ''inform'':197C,207C ''jamesmf'':20C ''known'':242C ''last'':162C ''length'':81C ''let'':139C ''letter'':95C ''may'':209C ''model'':9C,19C ''n'':25C ''n-gram'':24C ''need'':47C,59C,128C ''net'':249C ''number'':85C,92C,136C ''one'':2C ''order'':238C ''org'':252C ''p'':1C,33C,151C ''part'':115C ''present'':77C ''probabl'':100C ''put'':122C ''relev'':68C,147C ''salient'':42C ''seem'':219C ''sever'':105C ''similar'':152C ''string'':84C,90C,98C,177C ''svm'':141C ''take'':142C ''tell'':61C ''though'':12C ''unless'':120C ''unlik'':220C ''use'':31C,102C ''variabl'':156C ''vector'':119C,189C ''want'':37C ''whether'':71C,158C,170C ''without'':198C ''word'':8C ''worth'':226C ''worthless'':206C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8545, 2, '2015-10-21 15:00:24.857', 3, NULL, NULL, '&lt;p&gt;This is the import you need, and how to get the mean for a column named "RBIs":&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import org.apache.spark.sql.functions._
df.select(avg($"RBIs")).show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For the standard deviation, see
&lt;a href="http://stackoverflow.com/questions/31789939/calculate-the-standard-deviation-of-grouped-data-in-a-spark-dataframe"&gt;scala - Calculate the standard deviation of grouped data in a Spark DataFrame - Stack Overflow&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;For grouping by percentiles, I suggest defining a new column via a user-defined function (UDF), and using groupBy on that column.  See&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://spark.apache.org/docs/latest/sql-programming-guide.html#udf-registration-moved-to-sqlcontextudf-java--scala" rel="nofollow"&gt;Spark SQL and DataFrames - Spark 1.5.1 Documentation - udf registration&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':55C,101C ''/code'':28C ''/docs/latest/sql-programming-guide.html#udf-registration-moved-to-sqlcontextudf-java--scala'':89C ''/li'':102C ''/p'':19C,56C,82C ''/pre'':29C ''/questions/31789939/calculate-the-standard-deviation-of-grouped-data-in-a-spark-dataframe'':40C ''/ul'':103C ''1.5.1'':97C ''avg'':25C ''calcul'':42C ''code'':21C ''column'':16C,67C,80C ''data'':48C ''datafram'':52C,95C ''defin'':64C,72C ''deviat'':34C,45C ''df.select'':24C ''document'':98C ''function'':73C ''get'':11C ''group'':47C,59C ''groupbi'':77C ''href'':37C,86C ''import'':5C,22C ''li'':84C ''mean'':13C ''name'':17C ''need'':7C ''new'':66C ''nofollow'':91C ''org.apache.spark.sql.functions'':23C ''overflow'':54C ''p'':1C,30C,57C ''percentil'':61C ''pre'':20C ''rbis'':18C,26C ''registr'':100C ''rel'':90C ''scala'':41C ''see'':35C,81C ''show'':27C ''spark'':51C,92C,96C ''spark.apache.org'':88C ''spark.apache.org/docs/latest/sql-programming-guide.html#udf-registration-moved-to-sqlcontextudf-java--scala'':87C ''sql'':93C ''stack'':53C ''stackoverflow.com'':39C ''stackoverflow.com/questions/31789939/calculate-the-standard-deviation-of-grouped-data-in-a-spark-dataframe'':38C ''standard'':33C,44C ''suggest'':63C ''udf'':74C,99C ''ul'':83C ''use'':76C ''user'':71C ''user-defin'':70C ''via'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8546, 1, '2015-10-21 15:48:01.713', 2, 79, 'Slow convergence with rpart', '&lt;p&gt;I''m building a decision tree in R using the &lt;code&gt;rpart&lt;/code&gt; function, available in the library of the same name, but am experiencing some serious performance issues when partitioning data using more than one feature (there is roughly 5000 rows in my data set, and the features in question have around 30 unique values each). Running rpart with the one feature completes in a couple hours (running single thread on a CentOS server with about 250GB ram), but, after 6 days, a model with a second feature added has yet to complete!&lt;/p&gt;

&lt;p&gt;I''ve created an approach to extracting the path through the tree new data takes (see my question/solution &lt;a href="http://datascience.stackexchange.com/questions/8440/extract-the-path-of-a-data-point-through-a-decision-tree-in-sklearn"&gt;here&lt;/a&gt;), which is my main motivation here, but the method requires an object that can be type-cast as a &lt;code&gt;party&lt;/code&gt; object. A colleague of mine told me that he''s seen performance boosts using the &lt;code&gt;ctree&lt;/code&gt; function, but the output of this is an inscrutable S4 object that I can''t figure out how to convert. My questions are two:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Does anyone know a way to speed up &lt;code&gt;rpart&lt;/code&gt; beyond what I''ve already done here:&lt;code&gt;clf &amp;lt;- rpart(Y ~ X$var1 + X$var2, rpart.control(maxcompete=1, maxsurrogate=0))&lt;/code&gt;?&lt;/li&gt;
&lt;li&gt;Does anyone know how to convert a ctree object to a party object? Alternatively, does anyone have concurring or contradicting observations on ctree performance?&lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8547, 2, '2015-10-21 16:15:33.81', 0, NULL, NULL, '&lt;p&gt;For a first pass why not use regex?&lt;br&gt;
There are examples of regex for email&lt;br&gt;
You could use regex for email with a digit before @&lt;br&gt;
(and you cannot conclude 55 is a phone number)&lt;br&gt;
From there you would just need to decide if it has a name&lt;br&gt;
And you could use regex to extract the component before the @&lt;/p&gt;

&lt;p&gt;Regex for date  &lt;/p&gt;

&lt;p&gt;Regex for phone number  &lt;/p&gt;

&lt;p&gt;I use regex to extract date and email from documents&lt;br&gt;
It is fast and as accurate as the regex&lt;br&gt;
Every regex is accurate it just may not pick up some edge cases&lt;br&gt;
You expand the regex or pick those up via the algorithm  &lt;/p&gt;

&lt;p&gt;If you also want to also classify invalid email at least you have valid email to compare to  &lt;/p&gt;
', NULL, NULL, '''/p'':65C,70C,76C,124C,144C ''55'':34C ''accur'':94C,102C ''algorithm'':123C ''also'':128C,131C ''br'':10C,18C,29C,39C,53C,88C,98C,112C ''cannot'':32C ''case'':111C ''classifi'':132C ''compar'':142C ''compon'':62C ''conclud'':33C ''could'':20C,56C ''date'':69C,83C ''decid'':47C ''digit'':27C ''document'':87C ''edg'':110C ''email'':17C,24C,85C,134C,140C ''everi'':99C ''exampl'':13C ''expand'':114C ''extract'':60C,82C ''fast'':91C ''first'':4C ''invalid'':133C ''least'':136C ''may'':105C ''name'':52C ''need'':45C ''number'':38C,75C ''p'':1C,66C,71C,77C,125C ''pass'':5C ''phone'':37C,74C ''pick'':107C,118C ''regex'':9C,15C,22C,58C,67C,72C,80C,97C,100C,116C ''use'':8C,21C,57C,79C ''valid'':139C ''via'':121C ''want'':129C ''would'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8741, 2, '2015-11-06 15:11:04.593', 2, NULL, NULL, '&lt;p&gt;This is something you can get in Tableau in 10 minutes.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/HE5go.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/HE5go.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;What you need is year binning, skills weights and length(year_end - year_start) for Gannt chart:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/sTAAt.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/sTAAt.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':32C,71C ''/he5go.png'':19C,26C ''/p'':13C,33C,52C,72C ''/staat.png'':58C,65C ''10'':11C ''alt'':27C,66C ''bin'':40C ''chart'':51C ''descript'':30C,69C ''end'':46C ''enter'':28C,67C ''gannt'':50C ''get'':7C ''href'':16C,55C ''i.stack.imgur.com'':18C,25C,57C,64C ''i.stack.imgur.com/he5go.png'':17C,24C ''i.stack.imgur.com/staat.png'':56C,63C ''imag'':29C,68C ''img'':22C,61C ''length'':44C ''minut'':12C ''need'':37C ''nofollow'':21C,60C ''p'':1C,14C,34C,53C ''rel'':20C,59C ''skill'':41C ''someth'':4C ''src'':23C,62C ''start'':48C ''tableau'':9C ''weight'':42C ''year'':39C,45C,47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8548, 1, '2015-10-21 18:04:07.323', 0, 96, 'Luigi Python package command line', '&lt;p&gt;Using the Luigi package for data pipelines in Python. &lt;/p&gt;

&lt;p&gt;The code runs when I do:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;python ml-pipeline.py EvaluateModel --input-dir text --local-scheduler
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But not when I do:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;luigi --module ml-pipeline EvaluateModel --input-dir text --local-scheduler
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Error is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Traceback (most recent call last):
  File "/Users/franklin/venv/bin/luigi", line 11, in &amp;lt;module&amp;gt;

    sys.exit(luigi_run())

  File "/Users/franklin/venv/lib/python2.7/site-packages/luigi/cmdline.py", line 13, in luigi_run

    luigi.interface.run(argv, use_dynamic_argparse=True)

  File "/Users/franklin/venv/lib/python2.7/site-packages/luigi/interface.py", line 421, in run

    tasks = interface.parse(cmdline_args, main_task_cls=main_task_cls)

  File "/Users/franklin/venv/lib/python2.7/site-packages/luigi/interface.py", line 329, in parse

    __import__(module)

ImportError: No module named ml-pipeline
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Are they not interchangeable? Any idea why the luigi command fails?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8549, 1, '2015-10-21 18:17:22.19', 0, 248, 'How do I set/get heap size for Spark (via Python notebook)', '&lt;p&gt;I''m using Spark (1.5.1) from an IPython notebook on a macbook pro. After installing Spark and Anaconda, I start IPython from a terminal by executing: &lt;code&gt;IPYTHON_OPTS="notebook" pyspark&lt;/code&gt;. This opens a webpage listing all my IPython notebooks. I can select one of them, opening it in a second webpage. SparkContext (sc) is available already, and my first command in the notebook is &lt;code&gt;help(sc)&lt;/code&gt;, which runs fine. The problem I''m having is that I am getting a Java heap space error that I don''t know how to address. How do I view my current Java heap setting, and how do I increase it within the context of my setup. The error message I''m getting follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 19 in stage 247.0 failed 1 times, most recent failure: Lost task 19.0 in stage 247.0 (TID 953, localhost): java.lang.OutOfMemoryError: Java heap space
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8550, 2, '2015-10-21 18:49:41.357', 2, NULL, NULL, '&lt;p&gt;tl;dr: This is not a great candidate for machine learning solutions.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;It is not exactly true that "the strength of an encryption scheme is measured by the randomness (unpredictability, or entropy) of the output".  Instead, strength of an encryption scheme is how resistant it is to &lt;a href="https://en.wikipedia.org/wiki/Cryptanalysis" rel="nofollow"&gt;cryptanalysis&lt;/a&gt;. Yes, input $\rightarrow$ output maps that show patterns are relatively easy to break. But the input $\rightarrow$ output map might be very random (high entropy) from many perspectives but still might be defeated through feasible cryptanalysis.&lt;/p&gt;

&lt;p&gt;You offer another definition: "informally, the strength of an encryption scheme is determined by the number of such input-output pairs needed beyond which it becomes predictable."  This is usefully different from your first definition because it focuses on the information that might be gleaned about the encryption scheme from a stream of input $\rightarrow$ output pairs.  This is different and easier than the usual cryptanalysis problem where all you have is a data set of (encrypted) outputs.&lt;/p&gt;

&lt;p&gt;Framed this way, I think you are heading toward a &lt;a href="https://en.wikipedia.org/wiki/Model_selection" rel="nofollow"&gt;model selection problem.&lt;/a&gt;  While this may be relatively straight forward with weak encryption schemes, it becomes very hard, very quickly with strong schemes.  In fact, I believe you walk right in to the &lt;a href="https://en.wikipedia.org/wiki/No_free_lunch_in_search_and_optimization" rel="nofollow"&gt;No Free Lunch theorem&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;"In formal terms, there is no free lunch when the probability distribution on problem instances is such that all problem solvers have identically distributed results. In the case of search, a problem instance is an objective function, and a result is a sequence of values obtained in evaluation of candidate solutions in the domain of the function. For typical interpretations of results, search is an optimization process. There is no free lunch in search if and only if the distribution on objective functions is invariant under permutation of the space of candidate solutions."&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Translated: if you find yourself needing to search over all possible cryptanalysis methods, there exist input $\rightarrow$ output streams where no method is probabilistically better than any other. This will defeat any model selection method based on optimization of some objective function.&lt;/p&gt;
', NULL, NULL, '''/a'':60C,200C,242C ''/blockquote'':340C ''/p'':14C,97C,178C,243C,339C,384C ''/wiki/cryptanalysis'':56C ''/wiki/model_selection'':194C ''/wiki/no_free_lunch_in_search_and_optimization'':235C ''anoth'':101C ''base'':377C ''becom'':125C,213C ''believ'':224C ''better'':366C ''beyond'':122C ''blockquot'':244C ''break'':73C ''candid'':9C,295C,337C ''case'':273C ''cryptanalysi'':59C,96C,165C,353C ''data'':173C ''defeat'':93C,372C ''definit'':102C,134C ''determin'':111C ''differ'':130C,159C ''distribut'':257C,269C,325C ''domain'':299C ''dr'':3C ''easi'':71C ''easier'':161C ''en.wikipedia.org'':55C,193C,234C ''en.wikipedia.org/wiki/cryptanalysis'':54C ''en.wikipedia.org/wiki/model_selection'':192C ''en.wikipedia.org/wiki/no_free_lunch_in_search_and_optimization'':233C ''encrypt'':27C,44C,108C,147C,176C,210C ''entropi'':36C,85C ''evalu'':293C ''exact'':20C ''exist'':356C ''fact'':222C ''feasibl'':95C ''find'':345C ''first'':133C ''focus'':137C ''formal'':247C ''forward'':207C ''frame'':180C ''free'':239C,252C,316C ''function'':282C,302C,328C,383C ''glean'':144C ''great'':8C ''hard'':215C ''head'':187C ''high'':84C ''hr'':15C ''href'':53C,191C,232C ''ident'':268C ''inform'':103C,140C ''input'':62C,76C,118C,153C,357C ''input-output'':117C ''instanc'':260C,278C ''instead'':40C ''interpret'':305C ''invari'':330C ''learn'':12C ''lunch'':240C,253C,317C ''machin'':11C ''mani'':87C ''map'':65C,79C ''may'':203C ''measur'':30C ''method'':354C,363C,376C ''might'':80C,91C,142C ''model'':197C,374C ''need'':121C,347C ''nofollow'':58C,196C,237C ''number'':114C ''object'':281C,327C,382C ''obtain'':291C ''offer'':100C ''optim'':311C,379C ''output'':39C,64C,78C,119C,155C,177C,359C ''p'':1C,16C,98C,179C,245C,341C ''pair'':120C,156C ''pattern'':68C ''permut'':332C ''perspect'':88C ''possibl'':352C ''predict'':126C ''probabilist'':365C ''probabl'':256C ''problem'':166C,199C,259C,265C,277C ''process'':312C ''quick'':217C ''random'':33C,83C ''rel'':57C,195C,236C ''relat'':70C,205C ''resist'':48C ''result'':270C,285C,307C ''right'':227C ''rightarrow'':63C,77C,154C,358C ''scheme'':28C,45C,109C,148C,211C,220C ''search'':275C,308C,319C,349C ''select'':198C,375C ''sequenc'':288C ''set'':174C ''show'':67C ''solut'':13C,296C,338C ''solver'':266C ''space'':335C ''still'':90C ''straight'':206C ''stream'':151C,360C ''strength'':24C,41C,105C ''strong'':219C ''term'':248C ''theorem'':241C ''think'':184C ''tl'':2C ''toward'':188C ''translat'':342C ''true'':21C ''typic'':304C ''unpredict'':34C ''use'':129C ''usual'':164C ''valu'':290C ''walk'':226C ''way'':182C ''weak'':209C ''yes'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8551, 1, '2015-10-21 19:52:39.08', 0, 33, 'Agglomerative Clustering Stopping Criteria', '&lt;p&gt;I am trying to implement section 3.4 of paper &lt;a href="http://arxiv.org/abs/1505.04803" rel="nofollow"&gt;Predicting Important Objects for Egocentric Video Summarization&lt;/a&gt; where they have created a distance matrix of frame histograms.&lt;/p&gt;

&lt;p&gt;In short, let say &lt;code&gt;â¦&lt;/code&gt; is mean of distances between all frames,&lt;code&gt;DV&lt;/code&gt;is distance matrix.&lt;/p&gt;

&lt;p&gt;I didn''t understand what is meant by this: &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;We next perform complete-link agglomerative clustering
  with DV , grouping frames until the smallest maximum interframe
  distance is larger than two standard deviations beyond
  â¦&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Can this be achieved by setting the &lt;code&gt;cutoff&lt;/code&gt; value to &lt;code&gt;2â¦&lt;/code&gt; in Matlab''s &lt;a href="http://www.mathworks.com/help/stats/clusterdata.html" rel="nofollow"&gt;clusterdata function&lt;/a&gt; ? &lt;/p&gt;
', 'clustering  matlab  computer-vision', NULL, '''/a'':34C,137C ''/abs/1505.04803'':24C ''/blockquote'':108C ''/code'':53C,63C,119C,124C ''/help/stats/clusterdata.html'':132C ''/p'':45C,67C,78C,107C,138C ''2â'':123C ''3.4'':17C ''achiev'':113C ''agglom'':1A,87C ''arxiv.org'':23C ''arxiv.org/abs/1505.04803'':22C ''beyond'':105C ''blockquot'':79C ''cluster'':2A,5B,88C ''clusterdata'':135C ''code'':51C,61C,117C,122C ''complet'':85C ''complete-link'':84C ''comput'':8B ''computer-vis'':7B ''creat'':38C ''criteria'':4A ''cutoff'':118C ''deviat'':104C ''didn'':70C ''distanc'':40C,57C,65C,98C ''dv'':62C,90C ''egocentr'':31C ''frame'':43C,60C,92C ''function'':136C ''group'':91C ''histogram'':44C ''href'':21C,129C ''implement'':15C ''import'':28C ''interfram'':97C ''larger'':100C ''let'':49C ''link'':86C ''matlab'':6B,126C ''matrix'':41C,66C ''maximum'':96C ''mean'':55C ''meant'':75C ''next'':82C ''nofollow'':26C,134C ''object'':29C ''p'':10C,46C,68C,80C,109C ''paper'':19C ''perform'':83C ''predict'':27C ''rel'':25C,133C ''say'':50C ''section'':16C ''set'':115C ''short'':48C ''smallest'':95C ''standard'':103C ''stop'':3A ''summar'':33C ''tri'':13C ''two'':102C ''understand'':72C ''valu'':120C ''video'':32C ''vision'':9B ''www.mathworks.com'':131C ''www.mathworks.com/help/stats/clusterdata.html'':130C ''â'':52C,106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10322, 1, '2016-02-19 17:22:37.827', 1, 65, 'How to plot multiple variables with Pandas and Bokeh', '&lt;p&gt;I''m new to Pandas and Bokeh; I''d to create a bar plot that shows two different variables next to each other for comparison. &lt;/p&gt;

&lt;p&gt;For instance, with the following Pandas data frame, I''d like to see how the amount of Recalled compares to the amount of Recovered for each year.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    year    Recalled    Recovered
0   1994    11.472  10.207
1   1995    11.810  10.326
2   1996    10.632  10.094
3   1997    13.857  12.944
4   1998    13.861  12.588
5   1999    13.375  11.951
6   2000    11.278  nan
7   2001    12.827  nan
8   2002    12.687  nan
9   2003    10.859  nan
10  2004    nan nan
11  2005    11.782  11.047
12  2006    12.089  10.194
13  2007    14.351  13.401
14  2008    14.921  13.886
15  2009    11.759  10.815
16  2010    12.987  11.482
17  2011    13.262  10.730
18  2012    9.980   9.520
19  2013    10.626  9.591
20  2014    12.199  10.270
&lt;/code&gt;&lt;/pre&gt;
', 'python  pandas  barplot', NULL, '''/code'':158C ''/p'':39C,68C ''/pre'':159C ''0'':74C ''1'':78C ''10'':114C ''10.094'':85C ''10.194'':125C ''10.207'':77C ''10.270'':157C ''10.326'':81C ''10.626'':152C ''10.632'':84C ''10.730'':145C ''10.815'':137C ''10.859'':112C ''11'':118C ''11.047'':121C ''11.278'':100C ''11.472'':76C ''11.482'':141C ''11.759'':136C ''11.782'':120C ''11.810'':80C ''11.951'':97C ''12'':122C ''12.089'':124C ''12.199'':156C ''12.588'':93C ''12.687'':108C ''12.827'':104C ''12.944'':89C ''12.987'':140C ''13'':126C ''13.262'':144C ''13.375'':96C ''13.401'':129C ''13.857'':88C ''13.861'':92C ''13.886'':133C ''14'':130C ''14.351'':128C ''14.921'':132C ''15'':134C ''16'':138C ''17'':142C ''18'':146C ''19'':150C ''1994'':75C ''1995'':79C ''1996'':83C ''1997'':87C ''1998'':91C ''1999'':95C ''2'':82C ''20'':154C ''2000'':99C ''2001'':103C ''2002'':107C ''2003'':111C ''2004'':115C ''2005'':119C ''2006'':123C ''2007'':127C ''2008'':131C ''2009'':135C ''2010'':139C ''2011'':143C ''2012'':147C ''2013'':151C ''2014'':155C ''3'':86C ''4'':90C ''5'':94C ''6'':98C ''7'':102C ''8'':106C ''9'':110C ''9.520'':149C ''9.591'':153C ''9.980'':148C ''amount'':56C,62C ''bar'':26C ''barplot'':12B ''bokeh'':9A,20C ''code'':70C ''compar'':59C ''comparison'':38C ''creat'':24C ''d'':22C,50C ''data'':47C ''differ'':31C ''follow'':45C ''frame'':48C ''instanc'':42C ''like'':51C ''m'':15C ''multipl'':4A ''nan'':101C,105C,109C,113C,116C,117C ''new'':16C ''next'':33C ''p'':13C,40C ''panda'':7A,11B,18C,46C ''plot'':3A,27C ''pre'':69C ''python'':10B ''recal'':58C,72C ''recov'':64C,73C ''see'':53C ''show'':29C ''two'':30C ''variabl'':5A,32C ''year'':67C,71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8552, 1, '2015-10-21 20:40:31.937', 2, 56, 'Find most representative image', '&lt;p&gt;I have 1,000 photoshoot-quality images of pianos on white background with very little noise (people in the background, etc). How would I go about finding the piano image that looks most like the others? Alternatively, how would I find five pianos that look most like the "average" piano. I''m particularly looking for python libraries that may be helpful (I''m currently looking at skimage).&lt;/p&gt;
', 'machine-learning  python  image-classification', NULL, '''/p'':81C ''000'':16C ''1'':15C ''altern'':50C ''averag'':62C ''background'':25C,33C ''classif'':11B ''current'':77C ''etc'':34C ''find'':1A,40C,54C ''five'':55C ''go'':38C ''help'':74C ''imag'':4A,10B,20C,43C ''image-classif'':9B ''learn'':7B ''librari'':70C ''like'':47C,60C ''littl'':28C ''look'':45C,58C,67C,78C ''m'':65C,76C ''machin'':6B ''machine-learn'':5B ''may'':72C ''nois'':29C ''other'':49C ''p'':12C ''particular'':66C ''peopl'':30C ''photoshoot'':18C ''photoshoot-qu'':17C ''piano'':22C,42C,56C,63C ''python'':8B,69C ''qualiti'':19C ''repres'':3A ''skimag'':80C ''white'':24C ''would'':36C,52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8554, 2, '2015-10-22 04:50:25.36', 3, NULL, NULL, '&lt;p&gt;Find a low-dimensional emdedding of the images using a nonlinear dimensionality reduction method like autoencoding, then select the image closest to the centroid in the embedding space.&lt;/p&gt;
', NULL, NULL, '''/p'':31C ''autoencod'':18C ''centroid'':26C ''closest'':23C ''dimension'':6C,14C ''embed'':29C ''emded'':7C ''find'':2C ''imag'':10C,22C ''like'':17C ''low'':5C ''low-dimension'':4C ''method'':16C ''nonlinear'':13C ''p'':1C ''reduct'':15C ''select'':20C ''space'':30C ''use'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8556, 2, '2015-10-22 06:55:40.367', 1, NULL, NULL, '&lt;p&gt;Clearly, 2 standard deviations beyond Omega is not the same as twice the mean.&lt;/p&gt;

&lt;p&gt;Apparently, their process is this:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;compute the distance matrix&lt;/li&gt;
&lt;li&gt;compute the mean&lt;/li&gt;
&lt;li&gt;compute the standard deviation&lt;/li&gt;
&lt;li&gt;compute hierarchical clustering with maximum linkage&lt;/li&gt;
&lt;li&gt;cut the tree at mu+2*sigma&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Because complete linkage is in O(n^3), this approach will not scale to longer videos or higher frame rates.&lt;/p&gt;
', NULL, NULL, '''+2'':56C ''/li'':30C,35C,41C,49C,58C ''/ol'':59C ''/p'':16C,23C,81C ''2'':3C ''3'':68C ''appar'':18C ''approach'':70C ''beyond'':6C ''clear'':2C ''cluster'':45C ''complet'':62C ''comput'':26C,32C,37C,43C ''cut'':51C ''deviat'':5C,40C ''distanc'':28C ''frame'':79C ''hierarch'':44C ''higher'':78C ''li'':25C,31C,36C,42C,50C ''linkag'':48C,63C ''longer'':75C ''matrix'':29C ''maximum'':47C ''mean'':15C,34C ''mu'':55C ''n'':67C ''o'':66C ''ol'':24C ''omega'':7C ''p'':1C,17C,60C ''process'':20C ''rate'':80C ''scale'':73C ''sigma'':57C ''standard'':4C,39C ''tree'':53C ''twice'':13C ''video'':76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8560, 1, '2015-10-22 12:54:38.083', 0, 38, 'Can HMM be used as a binary classifier?', '&lt;p&gt;I have some time-series data, which I need to use to predict a binary label for a given time-stamp. I was initially doing it using a Logistic Regression Model but I later figured that a time-series based problem could be better solved using Hidden Markov Models. I might also save on time since this prediction is to be made for ~200,000 time-stamps.&lt;/p&gt;

&lt;p&gt;I have been able to obtain the next &lt;em&gt;n&lt;/em&gt; hidden states using the &lt;a href="http://github.com/hmmlearn/hmmlearn" rel="nofollow"&gt;hmmlearn&lt;/a&gt; implementation, but what I really need are 0/1 labels.&lt;/p&gt;

&lt;p&gt;How can I (if possible) use HMM as a binary classifier?&lt;/p&gt;
', 'classification  time-series  markov-process', NULL, '''/a'':113C ''/em'':100C ''/hmmlearn/hmmlearn'':109C ''/p'':88C,123C,136C ''0/1'':121C ''000'':84C ''200'':83C ''abl'':93C ''also'':71C ''base'':59C ''better'':63C ''binari'':7A,32C,134C ''classif'':9B ''classifi'':8A,135C ''could'':61C ''data'':23C ''em'':98C ''figur'':53C ''github.com'':108C ''github.com/hmmlearn/hmmlearn'':107C ''given'':36C ''hidden'':66C,101C ''hmm'':2A,131C ''hmmlearn'':112C ''href'':106C ''implement'':114C ''initi'':42C ''label'':33C,122C ''later'':52C ''logist'':47C ''made'':81C ''markov'':14B,67C ''markov-process'':13B ''might'':70C ''model'':49C,68C ''n'':99C ''need'':26C,119C ''next'':97C ''nofollow'':111C ''obtain'':95C ''p'':16C,89C,124C ''possibl'':129C ''predict'':30C,77C ''problem'':60C ''process'':15B ''realli'':118C ''regress'':48C ''rel'':110C ''save'':72C ''seri'':12B,22C,58C ''sinc'':75C ''solv'':64C ''stamp'':39C,87C ''state'':102C ''time'':11B,21C,38C,57C,74C,86C ''time-seri'':10B,20C,56C ''time-stamp'':37C,85C ''use'':4A,28C,45C,65C,103C,130C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8561, 1, '2015-10-22 14:48:58.663', 3, 67, 'What causes the error in a RNN to increase late in training?', '&lt;p&gt;I''m training a 2-layer, 1024 node, dropout of 0.5 RNN over natural text. Specifically, I''m using &lt;a href="https://github.com/karpathy/char-rnn" rel="nofollow"&gt;karpathy''s char-rnn&lt;/a&gt; which I found to work quite well for most of my use cases. Sometimes however, late in the training, my errors drastically increase, and I can''t figure out why this is happening.&lt;/p&gt;

&lt;p&gt;The question is specifically asking why am I observing the drastic increase in error, and how this is handled in practice. What can be done to prevent this besides taking the last-known good model?&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/uq6YB.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/uq6YB.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'machine-learning', NULL, '''/a'':48C,139C ''/karpathy/char-rnn'':40C ''/p'':82C,120C,140C ''/uq6yb.png'':126C,133C ''0.5'':27C ''1024'':23C ''2'':21C ''alt'':134C ''ask'':88C ''besid'':112C ''case'':61C ''caus'':2A ''char'':46C ''char-rnn'':45C ''descript'':137C ''done'':108C ''drastic'':70C,94C ''dropout'':25C ''enter'':135C ''error'':4A,69C,97C ''figur'':76C ''found'':51C ''github.com'':39C ''github.com/karpathy/char-rnn'':38C ''good'':118C ''handl'':102C ''happen'':81C ''howev'':63C ''href'':37C,123C ''i.stack.imgur.com'':125C,132C ''i.stack.imgur.com/uq6yb.png'':124C,131C ''imag'':136C ''img'':129C ''increas'':9A,71C,95C ''karpathi'':43C ''known'':117C ''last'':116C ''last-known'':115C ''late'':10A,64C ''layer'':22C ''learn'':15B ''m'':18C,34C ''machin'':14B ''machine-learn'':13B ''model'':119C ''natur'':30C ''node'':24C ''nofollow'':42C,128C ''observ'':92C ''p'':16C,83C,121C ''practic'':104C ''prevent'':110C ''question'':85C ''quit'':54C ''rel'':41C,127C ''rnn'':7A,28C,47C ''sometim'':62C ''specif'':32C,87C ''src'':130C ''take'':113C ''text'':31C ''train'':12A,19C,67C ''use'':35C,60C ''well'':55C ''work'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8563, 2, '2015-10-22 18:21:41.997', 2, NULL, NULL, '&lt;p&gt;As a simple measure, you could simply take the &lt;a href="https://en.wikipedia.org/wiki/Point-biserial_correlation_coefficient" rel="nofollow"&gt;point biserial correlation&lt;/a&gt; between duration and rating for a given user.  If they aren''t correlated (positively or negatively), this variable is likely not important to that user.&lt;/p&gt;

&lt;p&gt;Another way to determine the impact of a variable is by training another model excluding that variable and determining how that affects the quality of the predictions.&lt;/p&gt;
', NULL, NULL, '''/a'':21C ''/p'':47C,76C ''/wiki/point-biserial_correlation_coefficient'':15C ''affect'':70C ''anoth'':49C,61C ''aren'':32C ''biseri'':19C ''correl'':20C,34C ''could'':7C ''determin'':52C,67C ''durat'':23C ''en.wikipedia.org'':14C ''en.wikipedia.org/wiki/point-biserial_correlation_coefficient'':13C ''exclud'':63C ''given'':28C ''href'':12C ''impact'':54C ''import'':43C ''like'':41C ''measur'':5C ''model'':62C ''negat'':37C ''nofollow'':17C ''p'':1C,48C ''point'':18C ''posit'':35C ''predict'':75C ''qualiti'':72C ''rate'':25C ''rel'':16C ''simpl'':4C ''simpli'':8C ''take'':9C ''train'':60C ''user'':29C,46C ''variabl'':39C,57C,65C ''way'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8802, 1, '2015-11-11 15:37:59.103', 2, 23, 'Error on multitask neural nets where all outputs not observed for every example', '&lt;p&gt;Let''s say I have 2 datasets, each from a set of experiments.  Dataset A measures a set of properties X for set S, while dataset B measures properties Y for set T.  X and Y are highly correlated, and S and T have (not perfect) overlap.  To give an example, I might have &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;A   |   ID   |   pKa    |  log P 
    |  cid12 |   3.51   |   1.2
    |  cid51 |   2.32   |   .9

B   |   ID   | Pol Srf A|  cLogP 
    |  cid12 |   48.5   |   1.5
    |  cid88 |   61.2   |   .9
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I would like to use the shared information here (since all of these are physical properties) in a multitask neural net setup.  For examples in both S and T, I have a target vector as long as X union Y.  But for members exclusive to S or T, I am missing values.  &lt;/p&gt;

&lt;p&gt;In the examples, I have &lt;/p&gt;

&lt;pre&gt;&lt;code&gt; C   |   ID   |   pKa    |  log P  | Pol Srf A|  cLogP 
     |  cid12 |   3.51   |   1.2   |   48.5   |   1.5
     |  cid51 |   2.32   |   .9    |    na    |   na
     |  cid88 |    na    |   na    |   61.2   |   .9
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;When I train, do I simply zero fill these outputs, then not backpropagate error coming from these nodes?  So for  cid12, error from all nodes backpropagates, but for cid51, only error from pKa and logP?&lt;/p&gt;

&lt;p&gt;If this is the case, is that a standard procedure implemented in most NN libraries (Theano, for example) and what is that called?&lt;/p&gt;
', 'machine-learning  multitask-learning  backpropagation  theano', NULL, '''/code'':103C,192C ''/p'':77C,158C,165C,230C,255C ''/pre'':104C,193C ''1.2'':87C,179C ''1.5'':99C,181C ''2'':28C ''2.32'':89C,183C ''3.51'':86C,178C ''48.5'':98C,180C ''61.2'':101C,190C ''9'':90C,102C,184C,191C ''b'':49C,91C ''backpropag'':20B,207C,220C ''c'':168C ''call'':254C ''case'':236C ''cid12'':85C,97C,177C,215C ''cid51'':88C,182C,223C ''cid88'':100C,187C ''clogp'':96C,176C ''code'':79C,167C ''come'':209C ''correl'':61C ''dataset'':29C,36C,48C ''error'':1A,208C,216C,225C ''everi'':12A ''exampl'':13A,73C,129C,162C,249C ''exclus'':149C ''experi'':35C ''fill'':202C ''give'':71C ''high'':60C ''id'':81C,92C,169C ''implement'':242C ''inform'':113C ''learn'':16B,19B ''let'':23C ''librari'':246C ''like'':108C ''log'':83C,171C ''logp'':229C ''long'':141C ''machin'':15B ''machine-learn'':14B ''measur'':38C,50C ''member'':148C ''might'':75C ''miss'':156C ''multitask'':3A,18B,124C ''multitask-learn'':17B ''na'':185C,186C,188C,189C ''net'':5A,126C ''neural'':4A,125C ''nn'':245C ''node'':212C,219C ''observ'':10A ''output'':8A,204C ''overlap'':69C ''p'':22C,84C,105C,159C,172C,194C,231C ''perfect'':68C ''physic'':120C ''pka'':82C,170C,227C ''pol'':93C,173C ''pre'':78C,166C ''procedur'':241C ''properti'':42C,51C,121C ''say'':25C ''set'':33C,40C,45C,54C ''setup'':127C ''share'':112C ''simpli'':200C ''sinc'':115C ''srf'':94C,174C ''standard'':240C ''target'':138C ''theano'':21B,247C ''train'':197C ''union'':144C ''use'':110C ''valu'':157C ''vector'':139C ''would'':107C ''x'':43C,56C,143C ''y'':52C,58C,145C ''zero'':201C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8564, 2, '2015-10-22 18:30:05.927', 0, NULL, NULL, '&lt;p&gt;You might train a &lt;a href="https://en.wikipedia.org/wiki/Topic_model" rel="nofollow"&gt;topic model&lt;/a&gt; (like &lt;a href="https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation" rel="nofollow"&gt;LDA&lt;/a&gt;) to approach this problem.  This would allow you to represent your queries (which won''t necessarily contain the exact name of the products desired) in terms of topic vectors.  You can also represent your products in terms of their topic vectors.&lt;/p&gt;

&lt;p&gt;You can then rank the products in terms of &lt;a href="https://en.wikipedia.org/wiki/Metric_%28mathematics%29" rel="nofollow"&gt;similarity&lt;/a&gt; (in topic vector space) to the query.&lt;/p&gt;
', NULL, NULL, '''/a'':15C,25C,86C ''/p'':67C,94C ''/what-is-a-good-explanation-of-latent-dirichlet-allocation'':21C ''/wiki/metric_%28mathematics%29'':82C ''/wiki/topic_model'':10C ''allow'':32C ''also'':57C ''approach'':27C ''contain'':42C ''desir'':49C ''en.wikipedia.org'':9C,81C ''en.wikipedia.org/wiki/metric_%28mathematics%29'':80C ''en.wikipedia.org/wiki/topic_model'':8C ''exact'':44C ''href'':7C,18C,79C ''lda'':24C ''like'':16C ''might'':3C ''model'':14C ''name'':45C ''necessarili'':41C ''nofollow'':12C,23C,84C ''p'':1C,68C ''problem'':29C ''product'':48C,60C,74C ''queri'':37C,93C ''rank'':72C ''rel'':11C,22C,83C ''repres'':35C,58C ''similar'':85C ''space'':90C ''term'':51C,62C,76C ''topic'':13C,53C,65C,88C ''train'':4C ''vector'':54C,66C,89C ''won'':39C ''would'':31C ''www.quora.com'':20C ''www.quora.com/what-is-a-good-explanation-of-latent-dirichlet-allocation'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8608, 2, '2015-10-26 14:22:36.297', 0, NULL, NULL, '&lt;p&gt;In AdaBoost I guess at each step the model is "greedily" updated and given that typically you regularize heavily and there are bounds on model complexity this can have unexpected results.&lt;/p&gt;

&lt;p&gt;For the deterministic AdaBoost I think adding a feature f1 which is random can''t degrade performance if the tree depth has no bounds (so the tree can become as complex as it needs to be-remember we are talking of minimizing the training error so overfitting is ok).&lt;/p&gt;

&lt;p&gt;In actual implementations however the tree depth has an upper bound which might lead to premature pruning of the tree and hence you might end up getting a higher training error.&lt;/p&gt;
', NULL, NULL, '''/p'':33C,85C,118C ''actual'':88C ''ad'':41C ''adaboost'':3C,38C ''be-rememb'':70C ''becom'':63C ''bound'':24C,58C,97C ''complex'':27C,65C ''degrad'':50C ''depth'':55C,93C ''determinist'':37C ''end'':111C ''error'':80C,117C ''f1'':44C ''featur'':43C ''get'':113C ''given'':15C ''greedili'':12C ''guess'':5C ''heavili'':20C ''henc'':108C ''higher'':115C ''howev'':90C ''implement'':89C ''lead'':100C ''might'':99C,110C ''minim'':77C ''model'':10C,26C ''need'':68C ''ok'':84C ''overfit'':82C ''p'':1C,34C,86C ''perform'':51C ''prematur'':102C ''prune'':103C ''random'':47C ''regular'':19C ''rememb'':72C ''result'':32C ''step'':8C ''talk'':75C ''think'':40C ''train'':79C,116C ''tree'':54C,61C,92C,106C ''typic'':17C ''unexpect'':31C ''updat'':13C ''upper'':96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8565, 2, '2015-10-22 21:42:58.66', 1, NULL, NULL, '&lt;p&gt;In &lt;a href="https://en.wikipedia.org/wiki/Third_normal_form" rel="nofollow"&gt;third normal form&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;IndvidualID&lt;br&gt;
CatID&lt;br&gt;
CaseID&lt;br&gt;
Value   &lt;/p&gt;

&lt;p&gt;Primary Key:  IndvidualID, CatID, CaseID&lt;/p&gt;

&lt;p&gt;If you need to constrain CatID and/or CaseID then have a FK   &lt;/p&gt;

&lt;p&gt;From that structure you can easily loop and create long format or multidimensional array.&lt;br&gt;
It is also space efficient as a null is simply no row.&lt;/p&gt;

&lt;p&gt;In sql an avg &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;select CatID, avg(value)  
  from table 
 where CaseID = x 
 group by CatID  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Or you can just loop on those same constraints&lt;br&gt;
Or a tool like LINQ     &lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/code'':94C ''/p'':14C,23C,30C,44C,73C,79C,112C ''/pre'':95C ''/wiki/third_normal_form'':7C ''also'':63C ''and/or'':38C ''array'':59C ''avg'':78C,84C ''br'':17C,19C,21C,60C,106C ''caseid'':20C,29C,39C,89C ''catid'':18C,28C,37C,83C,93C ''code'':81C ''constrain'':36C ''constraint'':105C ''creat'':54C ''easili'':51C ''effici'':65C ''en.wikipedia.org'':6C ''en.wikipedia.org/wiki/third_normal_form'':5C ''fk'':43C ''form'':12C ''format'':56C ''group'':91C ''href'':4C ''indvidualid'':16C,27C ''key'':26C ''like'':110C ''linq'':111C ''long'':55C ''loop'':52C,101C ''multidimension'':58C ''need'':34C ''nofollow'':9C ''normal'':11C ''null'':68C ''p'':1C,15C,24C,31C,45C,74C,96C ''pre'':80C ''primari'':25C ''rel'':8C ''row'':72C ''select'':82C ''simpli'':70C ''space'':64C ''sql'':76C ''structur'':48C ''tabl'':87C ''third'':10C ''tool'':109C ''valu'':22C,85C ''x'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8566, 2, '2015-10-22 21:49:56.613', 3, NULL, NULL, '&lt;p&gt;Both ways are valid and both are commonly used. Sometimes, a classifier that claims to be multilabel may just be separating the labels into multiple OneVsRest classifiers under-the-hood and conveniently joining the results together at the end.&lt;/p&gt;

&lt;p&gt;However, there are cases where the methods are fundamentally different. For instance, in training a neural net with multiple targets (labels), you can setup the structure of the network such that there is shared structure. The shared nodes will end up learning features that are useful for all the targets, which could be very useful.&lt;/p&gt;

&lt;p&gt;For example, if you''re classes (labels) are "cat-pet", "cat-big", and "dog", you may want an algorithm that first learns to distinguish between any cat and any dog, and then in a later step learns to separate cats that are pets from cats that are big (like a lion!). This is called hierarchy, and if your classifier can exploit hierarchy you may gain better accuracy. If your classes are completely independent however, it may not make any difference.&lt;/p&gt;

&lt;p&gt;I suggest you start with the method that is easiest (i.e. OneVsRest), and see if the performance is suitable to your needs, then move to more complicated methods (multilabel, hierarchical methods, etc) only once you need better performance.&lt;/p&gt;
', NULL, NULL, '''/p'':42C,99C,183C,223C ''accuraci'':169C ''algorithm'':121C ''better'':168C,221C ''big'':114C,150C ''call'':156C ''case'':47C ''cat'':110C,113C,129C,142C,147C ''cat-big'':112C ''cat-pet'':109C ''claim'':15C ''class'':106C,172C ''classifi'':13C,28C,161C ''common'':9C ''complet'':174C ''complic'':211C ''conveni'':34C ''could'':95C ''differ'':53C,182C ''distinguish'':126C ''dog'':116C,132C ''easiest'':194C ''end'':41C,83C ''etc'':216C ''exampl'':102C ''exploit'':163C ''featur'':86C ''first'':123C ''fundament'':52C ''gain'':167C ''hierarch'':214C ''hierarchi'':157C,164C ''hood'':32C ''howev'':44C,176C ''i.e'':195C ''independ'':175C ''instanc'':55C ''join'':35C ''label'':24C,64C,107C ''later'':137C ''learn'':85C,124C,139C ''like'':151C ''lion'':153C ''make'':180C ''may'':19C,118C,166C,178C ''method'':50C,191C,212C,215C ''move'':208C ''multilabel'':18C,213C ''multipl'':26C,62C ''need'':206C,220C ''net'':60C ''network'':72C ''neural'':59C ''node'':81C ''onevsrest'':27C,196C ''p'':1C,43C,100C,184C ''perform'':201C,222C ''pet'':111C,145C ''re'':105C ''result'':37C ''see'':198C ''separ'':22C,141C ''setup'':67C ''share'':77C,80C ''sometim'':11C ''start'':188C ''step'':138C ''structur'':69C,78C ''suggest'':186C ''suitabl'':203C ''target'':63C,93C ''togeth'':38C ''train'':57C ''under-the-hood'':29C ''use'':10C,89C,98C ''valid'':5C ''want'':119C ''way'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8568, 1, '2015-10-22 22:32:24.597', 2, 52, 'Does importance of SVM parameters vary for subsample of data?', '&lt;p&gt;I''m training a support vector machine classifier (SVM) on 100.000 observations. I''d like to try different parameter combinations (including kernel types) using cross-validation. However, it is computationally expensive to optimize over these different parameter values. Therefore, I''d like to test them on a subset of 5.000 observations on a validation set and then based on those results choose the parameters for the final model that is trained on 100.000 observations.&lt;/p&gt;

&lt;p&gt;My question is then whether there is something intrinsic in these parameters that will make this approach problematic. More specifically, is there a parameter, where training on the full data set will require a different value for optimal performance?&lt;/p&gt;

&lt;p&gt;I''m using the following kernels with their parameters in parentheses:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;rbf (gamma, C)&lt;/li&gt;
&lt;li&gt;poly (degree, C)&lt;/li&gt;
&lt;li&gt;sigmoid (gamma, C)&lt;/li&gt;
&lt;li&gt;linear (gamma).&lt;/li&gt;
&lt;/ul&gt;
', 'machine-learning  classification  svm', NULL, '''/li'':152C,157C,162C,166C ''/p'':92C,133C,146C ''/ul'':167C ''100.000'':27C,90C ''5.000'':67C ''approach'':110C ''base'':75C ''c'':151C,156C,161C ''choos'':79C ''classif'':14B ''classifi'':24C ''combin'':36C ''comput'':47C ''cross'':42C ''cross-valid'':41C ''d'':30C,58C ''data'':10A,123C ''degre'':155C ''differ'':34C,53C,128C ''expens'':48C ''final'':84C ''follow'':139C ''full'':122C ''gamma'':150C,160C,165C ''howev'':44C ''import'':2A ''includ'':37C ''intrins'':102C ''kernel'':38C,140C ''learn'':13B ''li'':148C,153C,158C,163C ''like'':31C,59C ''linear'':164C ''m'':18C,136C ''machin'':12B,23C ''machine-learn'':11B ''make'':108C ''model'':85C ''observ'':28C,68C,91C ''optim'':50C,131C ''p'':16C,93C,134C ''paramet'':5A,35C,54C,81C,105C,117C,143C ''parenthes'':145C ''perform'':132C ''poli'':154C ''problemat'':111C ''question'':95C ''rbf'':149C ''requir'':126C ''result'':78C ''set'':72C,124C ''sigmoid'':159C ''someth'':101C ''specif'':113C ''subsampl'':8A ''subset'':65C ''support'':21C ''svm'':4A,15B,25C ''test'':61C ''therefor'':56C ''train'':19C,88C,119C ''tri'':33C ''type'':39C ''ul'':147C ''use'':40C,137C ''valid'':43C,71C ''valu'':55C,129C ''vari'':6A ''vector'':22C ''whether'':98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9656, 2, '2016-01-06 20:57:55.773', 1, NULL, NULL, '&lt;p&gt;First of all I think that what you''re trying to do is very difficult. A research paper success depends not only of the words but the math, the time when it was published, the journal, etc. You have many features you should take into account.&lt;/p&gt;

&lt;p&gt;I would try deep learning. For input, I would add all the features above and perhaps more. As the output I would use the number of citations or maybe something more sophisticated like a custom function of popularity. It''s not the same to be published in Nature than in other journals.&lt;/p&gt;
', NULL, NULL, '''/p'':48C,102C ''account'':47C ''add'':59C ''citat'':76C ''custom'':84C ''deep'':53C ''depend'':21C ''difficult'':16C ''etc'':38C ''featur'':42C,62C ''first'':2C ''function'':85C ''input'':56C ''journal'':37C,101C ''learn'':54C ''like'':82C ''mani'':41C ''math'':29C ''mayb'':78C ''natur'':97C ''number'':74C ''output'':69C ''p'':1C,49C ''paper'':19C ''perhap'':65C ''popular'':87C ''publish'':35C,95C ''re'':10C ''research'':18C ''someth'':79C ''sophist'':81C ''success'':20C ''take'':45C ''think'':6C ''time'':31C ''tri'':11C,52C ''use'':72C ''word'':26C ''would'':51C,58C,71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8569, 2, '2015-10-22 22:48:13.96', 1, NULL, NULL, '&lt;p&gt;In theory, if you have a large enough random sample of your data set, it should be representative of the characteristics in the larger population of data that will affect the relationship between parameter values and performance. It seems to me that a $5\%$ sample of your data might be too small for what you''re trying to do. When I''m performing cross-validation on a test/dev data set, I usually set aside somewhere in the neighborhood of $10\%$ for the hold-out test/evaluation data, and use $90\%$ for my development work, but, as you pointed out, this may be too big given the computational constraints you''re up against. Depending on the type of application you''re working on, you could look at feature distribution in your sampled data and compare it to that in the larger population. If the two sets are comparable, then you might be ok to do your dev. work on the smaller data set. I say it depends on the type of application you''re developing, because many statisticians would consider this cheating, in that it''s generally not good model evaluation practice to look at hold-out data at all. Here''s the procedure I''d recommend:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Separate out $10\%$ of your data for hold-out evaluation.&lt;/li&gt;
&lt;li&gt;Divide the remaining $90\%$ into $5\%-10\%$ parameter optimization, and the remainder for final parameter eval.&lt;/li&gt;
&lt;li&gt;Compare the feature distributions of the parameter optimization and final parameter eval data sets, if they''re not comparable, redraw the samples (within the context of the $90\%$ development sample)&lt;/li&gt;
&lt;li&gt;When you have a good subsample, run your parameter optimization experiments on the small parameter optimization data set.&lt;/li&gt;
&lt;li&gt;With your final parameter settings, evaluate with cross-validation on the the $90\%$ combined parameter optimization and final parameter eval data sets.&lt;/li&gt;
&lt;li&gt;Perform final model analysis by training on the $90\%$ data set and classifying on the $10\%$ hold-out evaluation set.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;It might also be worth looking into optimizing your analytics pipeline. For example, is file I/O, feature generation and feature extraction part of the the workflow you''re using, or have you done all of that off-line and are just concerned with the SVM evaluation part?&lt;/p&gt;
', NULL, NULL, '''-10'':232C ''/li'':224C,242C,274C,294C,319C,342C ''/ol'':343C ''/p'':210C,393C ''10'':82C,215C,336C ''5'':45C,231C ''90'':92C,229C,271C,309C,329C ''affect'':31C ''also'':347C ''analysi'':324C ''analyt'':354C ''applic'':120C,173C ''asid'':76C ''big'':106C ''characterist'':22C ''cheat'':183C ''classifi'':333C ''combin'':310C ''compar'':136C,149C,244C,262C ''comput'':109C ''concern'':387C ''consid'':181C ''constraint'':110C ''context'':268C ''could'':126C ''cross'':66C,304C ''cross-valid'':65C,303C ''d'':208C ''data'':14C,28C,49C,71C,89C,134C,163C,200C,218C,256C,292C,317C,330C ''depend'':115C,168C ''dev'':158C ''develop'':95C,176C,272C ''distribut'':130C,247C ''divid'':226C ''done'':377C ''enough'':9C ''eval'':241C,255C,316C ''evalu'':192C,223C,301C,340C,391C ''exampl'':357C ''experi'':286C ''extract'':365C ''featur'':129C,246C,361C,364C ''file'':359C ''final'':239C,253C,298C,314C,322C ''general'':188C ''generat'':362C ''given'':107C ''good'':190C,280C ''hold'':86C,198C,221C,338C ''hold-out'':85C,197C,220C,337C ''i/o'':360C ''larg'':8C ''larger'':25C,142C ''li'':212C,225C,243C,275C,295C,320C ''line'':383C ''look'':127C,195C,350C ''m'':63C ''mani'':178C ''may'':103C ''might'':50C,152C,346C ''model'':191C,323C ''neighborhood'':80C ''off-lin'':381C ''ok'':154C ''ol'':211C ''optim'':234C,251C,285C,291C,312C,352C ''p'':1C,344C ''paramet'':35C,233C,240C,250C,254C,284C,290C,299C,311C,315C ''part'':366C,392C ''perform'':38C,64C,321C ''pipelin'':355C ''point'':100C ''popul'':26C,143C ''practic'':193C ''procedur'':206C ''random'':10C ''re'':57C,112C,122C,175C,260C,372C ''recommend'':209C ''redraw'':263C ''relationship'':33C ''remain'':228C ''remaind'':237C ''repres'':19C ''run'':282C ''sampl'':11C,46C,133C,265C,273C ''say'':166C ''seem'':40C ''separ'':213C ''set'':15C,72C,75C,147C,164C,257C,293C,300C,318C,331C,341C ''small'':53C,289C ''smaller'':162C ''somewher'':77C ''statistician'':179C ''subsampl'':281C ''svm'':390C ''test/dev'':70C ''test/evaluation'':88C ''theori'':3C ''train'':326C ''tri'':58C ''two'':146C ''type'':118C,171C ''use'':91C,373C ''usual'':74C ''valid'':67C,305C ''valu'':36C ''within'':266C ''work'':96C,123C,159C ''workflow'':370C ''worth'':349C ''would'':180C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8570, 2, '2015-10-23 06:43:34.58', 0, NULL, NULL, '&lt;p&gt;Personally, I don''t use vagrant with a local provisioning. I have installed a Spark cluster locally without HDFS which allows me to experiment and develop easily without the overwhelm of a virtual machine. &lt;/p&gt;

&lt;p&gt;HDFS is not a requirement for local clusters, and it''s also a kind of a system administration nightmare if you just need it for local testing.&lt;/p&gt;

&lt;p&gt;Spark works very fine with local file systems which you''ll also have to port when deploy on your Cloud of course.&lt;/p&gt;

&lt;p&gt;Nevertheless, You can use vagrant with AWS provisioning to create a cluster for heavier testing. &lt;/p&gt;

&lt;p&gt;Note: AWS works with S3 and not HDFS. AWS''s HDFS version is ephemeral and if you put down the cluster, you''ll loose all your computations. For persistence, you''ll need to write back to S3. &lt;/p&gt;
', NULL, NULL, '''/p'':36C,65C,89C,106C,145C ''administr'':55C ''allow'':22C ''also'':49C,78C ''aw'':97C,109C,116C ''back'':142C ''cloud'':86C ''cluster'':17C,45C,102C,128C ''comput'':134C ''cours'':88C ''creat'':100C ''deploy'':83C ''develop'':27C ''easili'':28C ''ephemer'':121C ''experi'':25C ''file'':73C ''fine'':70C ''hdfs'':20C,38C,115C,118C ''heavier'':104C ''instal'':14C ''kind'':51C ''ll'':77C,130C,138C ''local'':10C,18C,44C,63C,72C ''loos'':131C ''machin'':35C ''need'':60C,139C ''nevertheless'':91C ''nightmar'':56C ''note'':108C ''overwhelm'':31C ''p'':1C,37C,66C,90C,107C ''persist'':136C ''person'':2C ''port'':81C ''provis'':11C,98C ''put'':125C ''requir'':42C ''s3'':112C,144C ''spark'':16C,67C ''system'':54C,74C ''test'':64C,105C ''use'':6C,94C ''vagrant'':7C,95C ''version'':119C ''virtual'':34C ''without'':19C,29C ''work'':68C,110C ''write'':141C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8575, 2, '2015-10-23 17:08:58.483', 1, NULL, NULL, '

&lt;p&gt;Here''s a naÃ¯ve implementation of your pseudocode in R:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;calculate_cost &amp;lt;- function(kwh){
    if(kwh &amp;lt; 20){
        return(0)
    }else{
         kwh &amp;lt;- kwh - 20
    if(kwh &amp;lt;= 5){
        return(kwh * 0.1)
    }else{
        final_cost &amp;lt;- kwh * 0.1
        kwh &amp;lt;- kwh - 5
        if(kwh &amp;lt;= 100){
            return(final_cost + (kwh * 0.053))
        }else{
            final_cost &amp;lt;- final_cost + (kwh * 0.053)
            kwh &amp;lt;- kwh - 100
            if(kwh &amp;lt;= 875){
                return(final_cost + (kwh * 0.042))
            }else{
                final_cost &amp;lt;- final_cost + (kwh * 0.042)
                kwh &amp;lt;- kwh - 875
                return(final_cost + (kwh * 0.039))
                }
            }
        }
    }
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then, &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; calculate_cost(kwh=625)
[1] 113.3
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':96C,110C ''/p'':13C,100C ''/pre'':97C,111C ''0'':26C ''0.039'':95C ''0.042'':78C,86C ''0.053'':57C,65C ''0.1'':38C,44C ''1'':108C ''100'':52C,69C ''113.3'':109C ''20'':24C,31C ''5'':35C,48C ''625'':107C ''875'':73C,90C ''calcul'':16C,104C ''code'':15C,102C ''cost'':17C,41C,55C,60C,63C,76C,81C,84C,93C,105C ''els'':27C,39C,58C,79C ''final'':40C,54C,59C,62C,75C,80C,83C,92C ''function'':19C ''gt'':103C ''implement'':7C ''kwh'':20C,22C,28C,30C,33C,37C,43C,45C,47C,50C,56C,64C,66C,68C,71C,77C,85C,87C,89C,94C,106C ''lt'':18C,23C,29C,34C,42C,46C,51C,61C,67C,72C,82C,88C ''naã'':5C ''p'':1C,98C ''pre'':14C,101C ''pseudocod'':10C ''r'':12C ''return'':25C,36C,53C,74C,91C ''ve'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8576, 2, '2015-10-23 17:10:12.59', 1, NULL, NULL, '&lt;p&gt;I finally solved it by creating a &lt;strong&gt;spark-defaults.conf&lt;/strong&gt; file in apache-spark/1.5.1/libexec/conf/ and adding the following line to it:
&lt;strong&gt;spark.driver.memory 14g&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;That solved my issue. But then I ran into another issue of "exceeding max result size of 1024MB". The solution was to add another line in the file above:
&lt;strong&gt;spark.dirver.maxResultSize 2g&lt;/strong&gt;&lt;/p&gt;
', NULL, NULL, '''/1.5.1/libexec/conf'':17C ''/p'':29C,64C ''/strong'':11C,28C,63C ''1024mb'':48C ''14g'':27C ''2g'':62C ''ad'':19C ''add'':53C ''anoth'':40C,54C ''apach'':15C ''apache-spark'':14C ''creat'':7C ''exceed'':43C ''file'':12C,58C ''final'':3C ''follow'':21C ''issu'':34C,41C ''line'':22C,55C ''max'':44C ''p'':1C,30C ''ran'':38C ''result'':45C ''size'':46C ''solut'':50C ''solv'':4C,32C ''spark'':16C ''spark-defaults.conf'':10C ''spark.dirver.maxresultsize'':61C ''spark.driver.memory'':26C ''strong'':9C,25C,60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8647, 1, '2015-10-29 12:49:50.05', 0, 17, 'What are probes', '&lt;p&gt;What is probe selection or probing in feature selection? I came across this term in &lt;a href="http://moscow.sci-hub.bz/42a8acd99d095b4a75825e4218ca3dda/10.1089%40cmb.2013.0125.pdf" rel="nofollow"&gt;this&lt;/a&gt; article.. Can someone please give me a hit or explain this in brief?&lt;/p&gt;
', 'machine-learning  neuralnetwork  feature-selection', NULL, '''..'':37C ''/42a8acd99d095b4a75825e4218ca3dda/10.1089%40cmb.2013.0125.pdf'':31C ''/a'':35C ''/p'':50C ''across'':23C ''articl'':36C ''brief'':49C ''came'':22C ''explain'':46C ''featur'':9B,19C ''feature-select'':8B ''give'':41C ''hit'':44C ''href'':28C ''learn'':6B ''machin'':5B ''machine-learn'':4B ''moscow.sci-hub.bz'':30C ''moscow.sci-hub.bz/42a8acd99d095b4a75825e4218ca3dda/10.1089%40cmb.2013.0125.pdf'':29C ''neuralnetwork'':7B ''nofollow'':33C ''p'':11C ''pleas'':40C ''probe'':3A,14C,17C ''rel'':32C ''select'':10B,15C,20C ''someon'':39C ''term'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8577, 1, '2015-10-23 17:27:00.107', 1, 72, 'Recognizing numerical entities', '&lt;p&gt;I''m trying to perform classification on a large dataset with mixed numerical and categorical features. The dataset is all semi-structured text, so everything is a String. Does anyone know of a library I could use to automatically identify numeric features (e.g. "width (in.)", which corresponds to the width in inches)? This is important because numeric and categorical features are encoded/handled differently in my analytics pipeline. I think this is basically a named entity recognition problem wherein the entity is a numeric variable or feature that exists in the data set as a String/text.&lt;/p&gt;
', 'feature-extraction  named-entity-recognition', NULL, '''/p'':108C ''analyt'':78C ''anyon'':42C ''automat'':51C ''basic'':84C ''categor'':26C,71C ''classif'':17C ''correspond'':59C ''could'':48C ''data'':103C ''dataset'':21C,29C ''differ'':75C ''e.g'':55C ''encoded/handled'':74C ''entiti'':3A,9B,87C,92C ''everyth'':37C ''exist'':100C ''extract'':6B ''featur'':5B,27C,54C,72C,98C ''feature-extract'':4B ''identifi'':52C ''import'':67C ''inch'':64C ''know'':43C ''larg'':20C ''librari'':46C ''m'':13C ''mix'':23C ''name'':8B,86C ''named-entity-recognit'':7B ''numer'':2A,24C,53C,69C,95C ''p'':11C ''perform'':16C ''pipelin'':79C ''problem'':89C ''recogn'':1A ''recognit'':10B,88C ''semi'':33C ''semi-structur'':32C ''set'':104C ''string'':40C ''string/text'':107C ''structur'':34C ''text'':35C ''think'':81C ''tri'':14C ''use'':49C ''variabl'':96C ''wherein'':90C ''width'':56C,62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8578, 2, '2015-10-23 17:30:01.997', 1, NULL, NULL, '&lt;h2&gt;Here''s one solution:&lt;/h2&gt;

&lt;p&gt;&lt;em&gt;using the following data:&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;USER-LEVEL:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;fss&lt;/code&gt; &lt;em&gt;first session start (timestamp) - time of first pageview of
first session for that user&lt;/em&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;css&lt;/code&gt; &lt;em&gt;converting session start (timestamp) - time of the first pageview of session where user converted&lt;/em&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;ns&lt;/code&gt; &lt;em&gt;number of sessions (int) - total # of sessions
before conversion for user&lt;/em&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;SESSION-LEVEL:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;ss&lt;/code&gt; &lt;em&gt;session start (timestamp) - timestamp of first pageview in session&lt;/em&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;FORMULA:&lt;/strong&gt; &lt;em&gt;calculated for each session&lt;/em&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;FOR FIRST SESSION: Attribution Credit is just linear: &lt;em&gt;(or really whatever you want to make it)&lt;/em&gt;&lt;br&gt;&lt;br&gt; &lt;img src="https://chart.googleapis.com/chart?cht=tx&amp;amp;chl=sc%5C%20%3D%5C%20%5Cfrac%7B1%7D%7Bns%7D" alt="linear credit"&gt;&lt;br&gt;&lt;br&gt; &lt;/li&gt;
&lt;li&gt;ALL OTHER SESSIONS: 

&lt;ul&gt;
&lt;li&gt;First calculate the SUM of all session''s "time before conversion":&lt;br&gt;&lt;br&gt;&lt;img src="https://chart.googleapis.com/chart?cht=tx&amp;amp;chl=sum(tbc)=%5Cfrac%7B((css-fss)%5C%20%5Ctimes%5C%20ns)-sum(ss)%7D%7Bcss-fss%7D" alt="sum(tbc)"&gt;&lt;br&gt;&lt;/li&gt;
&lt;li&gt;Then Calculate the credit for each session:&lt;br&gt;&lt;br&gt;&lt;img src="https://chart.googleapis.com/chart?cht=tx&amp;amp;chl=sc%5C%20%3D%5C%20%5Cfrac%7B1%5C%20-%5C%20%5Cfrac%7Bcss%5C%20-%5C%20ss%7D%7Bcss%5C%20-%5C%20fss%7D%7D%7Bns%5C%20-%5C%20%5Cfrac%7Bsum(tbc)%7D%7Bcss%5C%20-%5C%20fss%7D%7D%5Ctimes%5C%20%5Cfrac%7Bns-1%7D%7Bns%7D" alt="sc formula"&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;it isn''t perfect because it distributes credit based on the relative position of a session in the User''s lifetime.  Ideally you have a preset halflife (like 7 days) so that comparison is more consistent.&lt;/p&gt;
', NULL, NULL, '''/chart?cht=tx&amp;amp;chl=sc%5c%20%3d%5c%20%5cfrac%7b1%5c%20-%5c%20%5cfrac%7bcss%5c%20-%5c%20ss%7d%7bcss%5c%20-%5c%20fss%7d%7d%7bns%5c%20-%5c%20%5cfrac%7bsum(tbc)%7d%7bcss%5c%20-%5c%20fss%7d%7d%5ctimes%5c%20%5cfrac%7bns-1%7d%7bns%7d'':196C ''/chart?cht=tx&amp;amp;chl=sc%5c%20%3d%5c%20%5cfrac%7b1%7d%7bns%7d'':146C ''/chart?cht=tx&amp;amp;chl=sum(tbc)=%5cfrac%7b((css-fss)%5c%20%5ctimes%5c%20ns)-sum(ss)%7d%7bcss-fss%7d'':176C ''/code'':26C,47C,68C,95C ''/em'':13C,42C,63C,81C,106C,118C,139C ''/h2'':6C ''/li'':43C,64C,82C,107C,152C,181C,200C,202C ''/ol'':203C ''/p'':14C,21C,90C,119C,241C ''/strong'':20C,89C,112C ''/ul'':83C,108C,201C ''7'':233C ''alt'':147C,177C,197C ''attribut'':125C ''base'':213C ''br'':140C,141C,150C,151C,170C,171C,180C,190C,191C ''calcul'':114C,160C,184C ''chart.googleapis.com'':145C,175C,195C ''chart.googleapis.com/chart?cht=tx&amp;amp;chl=sc%5c%20%3d%5c%20%5cfrac%7b1%5c%20-%5c%20%5cfrac%7bcss%5c%20-%5c%20ss%7d%7bcss%5c%20-%5c%20fss%7d%7d%7bns%5c%20-%5c%20%5cfrac%7bsum(tbc)%7d%7bcss%5c%20-%5c%20fss%7d%7d%5ctimes%5c%20%5cfrac%7bns-1%7d%7bns%7d'':194C ''chart.googleapis.com/chart?cht=tx&amp;amp;chl=sc%5c%20%3d%5c%20%5cfrac%7b1%7d%7bns%7d'':144C ''chart.googleapis.com/chart?cht=tx&amp;amp;chl=sum(tbc)=%5cfrac%7b((css-fss)%5c%20%5ctimes%5c%20ns)-sum(ss)%7d%7bcss-fss%7d'':174C ''code'':24C,45C,66C,93C ''comparison'':237C ''consist'':240C ''convers'':78C,169C ''convert'':49C,62C ''credit'':126C,149C,186C,212C ''css'':46C ''data'':12C ''day'':234C ''distribut'':211C ''em'':8C,27C,48C,69C,96C,113C,130C ''first'':28C,34C,37C,56C,102C,123C,159C ''follow'':11C ''formula'':111C,199C ''fss'':25C ''h2'':1C ''halflif'':231C ''ideal'':226C ''img'':142C,172C,192C ''int'':73C ''isn'':206C ''level'':19C,88C ''li'':23C,44C,65C,92C,121C,153C,158C,182C ''lifetim'':225C ''like'':232C ''linear'':129C,148C ''make'':137C ''ns'':67C ''number'':70C ''ol'':120C ''one'':4C ''p'':7C,15C,84C,109C,204C ''pageview'':35C,57C,103C ''perfect'':208C ''posit'':217C ''preset'':230C ''realli'':132C ''relat'':216C ''sc'':198C ''session'':29C,38C,50C,59C,72C,76C,87C,97C,105C,117C,124C,156C,165C,189C,220C ''session-level'':86C ''solut'':5C ''src'':143C,173C,193C ''ss'':94C ''start'':30C,51C,98C ''strong'':16C,85C,110C ''sum'':162C,178C ''tbc'':179C ''time'':32C,53C,167C ''timestamp'':31C,52C,99C,100C ''total'':74C ''ul'':22C,91C,157C ''use'':9C ''user'':18C,41C,61C,80C,223C ''user-level'':17C ''want'':135C ''whatev'':133C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8689, 2, '2015-11-02 13:15:27.973', 1, NULL, NULL, '&lt;p&gt;I don''t know of a well documented python option for collaborative filtering, but one option is to use &lt;a href="http://spark.apache.org/docs/latest/mllib-collaborative-filtering.html" rel="nofollow"&gt;Spark''s MLib&lt;/a&gt;. There is a wrapper that allows you to run it in python (&lt;a href="https://spark.apache.org/docs/0.9.1/python-programming-guide.html" rel="nofollow"&gt;pyspark&lt;/a&gt;) and it is &lt;a href="http://spark.apache.org/docs/latest/spark-standalone.html#spark-standalone-mode" rel="nofollow"&gt;straightforward&lt;/a&gt; to get it running on your local machine. &lt;/p&gt;
', NULL, NULL, '''/a'':31C,52C,64C ''/docs/0.9.1/python-programming-guide.html'':48C ''/docs/latest/mllib-collaborative-filtering.html'':25C ''/docs/latest/spark-standalone.html#spark-standalone-mode'':60C ''/p'':73C ''allow'':37C ''collabor'':13C ''document'':9C ''filter'':14C ''get'':66C ''href'':22C,45C,57C ''know'':5C ''local'':71C ''machin'':72C ''mlib'':30C ''nofollow'':27C,50C,62C ''one'':16C ''option'':11C,17C ''p'':1C ''pyspark'':51C ''python'':10C,43C ''rel'':26C,49C,61C ''run'':40C,68C ''spark'':28C ''spark.apache.org'':24C,47C,59C ''spark.apache.org/docs/0.9.1/python-programming-guide.html'':46C ''spark.apache.org/docs/latest/mllib-collaborative-filtering.html'':23C ''spark.apache.org/docs/latest/spark-standalone.html#spark-standalone-mode'':58C ''straightforward'':63C ''use'':20C ''well'':8C ''wrapper'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8586, 1, '2015-10-24 20:19:43.473', 2, 48, 'Steps in exploratory methods for mild-sized data with mixed categorical and numerical values?', '&lt;p&gt;Experienced in signal/image analysis, and new to data science, I recently was challenged with a relatively simple dataset: 100 to 200 items, about 10-20 numerical variables (in the [0-1] or percentage range), with only one variable used at present time for ranking, and 5 to 10 categorical variables, each with few options. A categorical variable takes about 2 to 4 different values.&lt;/p&gt;

&lt;p&gt;I would like first to get insight on potential structures in such data. I have browsed Agresti''s &lt;a href="http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470082895,subjectCd-STZ0.html" rel="nofollow"&gt;Analysis of Ordinal Categorical Data&lt;/a&gt;, some have advised me to invest on TDA (&lt;a href="https://en.wikipedia.org/wiki/Topological_data_analysis" rel="nofollow"&gt;Topological data Analysis&lt;/a&gt;). Yet I do not know where to start from.&lt;/p&gt;

&lt;p&gt;Do you have guidelines, best practices on such REAL data to gradually address the aforementioned issues, from visualization to genuine processing/inference?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8580, 2, '2015-10-24 08:21:31.12', 1, NULL, NULL, '&lt;p&gt;There are many good libraries for identifying number-like &lt;em&gt;values&lt;/em&gt;, but identifying corresponding &lt;em&gt;fieldnames&lt;/em&gt; is trickier and likely very problem-specific.&lt;/p&gt;

&lt;p&gt;A &lt;strong&gt;purely data driven approach&lt;/strong&gt; might look for co-occurrences with numbers, for example:  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;if [*number-like* capacity] or [capacity: *number-like*] occurs in &amp;gt; x% of 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;the instances where &lt;em&gt;"capacity"&lt;/em&gt; occurs,
    then &lt;em&gt;"capacity"&lt;/em&gt; can be guessed to be a label of number-like things.&lt;/p&gt;

&lt;p&gt;Even the nature of these relationships (eg, whether it should be before, after, capitalised) could be learnt from existing known labels.&lt;/p&gt;

&lt;p&gt;This can be done with &lt;a href="https://cloud.google.com/bigquery/bigquery-web-ui" rel="nofollow"&gt;Google BigQuery&lt;/a&gt; using the public &lt;a href="https://bigquery.cloud.google.com/table/publicdata:samples.trigrams" rel="nofollow"&gt;tri-gram sample data&lt;/a&gt;.  But I assume that''s out of scope and impractical here.&lt;/p&gt;

&lt;p&gt;A &lt;strong&gt;practical approach&lt;/strong&gt;, might aim to classify these strings as &lt;code&gt;numerical_units&lt;/code&gt;, &lt;code&gt;categorical_units&lt;/code&gt; or &lt;code&gt;unsure&lt;/code&gt; or even more classes, and then do human review of the latter.  (There are some very tricky cases, for example, &lt;em&gt;"capacity"&lt;/em&gt; is numerical, but &lt;em&gt;"capacity type"&lt;/em&gt; would not be.)&lt;/p&gt;

&lt;p&gt;As a starting list you can use:&lt;br&gt;
&lt;a href="https://en.wiktionary.org/wiki/Category:en:Units_of_measure" rel="nofollow"&gt;wiktionary:Category:en:Units_of_measure&lt;/a&gt;&lt;br&gt;
&lt;a href="https://en.wiktionary.org/wiki/Category:Symbols_for_SI_units" rel="nofollow"&gt;wiktionary:Category:Symbols_for_SI_units&lt;/a&gt;&lt;br&gt;
&lt;a href="https://en.wiktionary.org/wiki/Category:en:Mathematics" rel="nofollow"&gt;wiktionary:Category:en:Mathematics&lt;/a&gt;&lt;br&gt;
Note that they are in the singular.  For your domain considering finding other such lists.&lt;/p&gt;

&lt;p&gt;If a string is a full match with one of those labels, you can consider it a &lt;code&gt;numerical_unit&lt;/code&gt;.
Your next concern is tolerant, fuzzy matching.  You could treat ''.'' as a wildcard (so &lt;em&gt;"in*"&lt;/em&gt; matches &lt;em&gt;"inch"&lt;/em&gt;), or find the actual abbreviations of units like &lt;em&gt;"inch"&lt;/em&gt;.  These you can label as &lt;code&gt;unsure&lt;/code&gt; and then review.  Likewise if the word is simply contained in the string, eg &lt;em&gt;"arch length"&lt;/em&gt; contains &lt;em&gt;"length"&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;I think once you have done this you can make some refinements and add some special cases.  Without seeing even a sample of your data, it is difficult to say more.  If most of your strings are numerical units, then it may be easier to identify categorical units instead.&lt;/p&gt;
', NULL, NULL, '''/a'':131C,147C,241C,256C,269C ''/bigquery/bigquery-web-ui'':126C ''/code'':66C,176C,180C,184C,308C,348C ''/em'':14C,20C,74C,79C,207C,214C,325C,329C,340C,366C,370C ''/p'':29C,48C,92C,115C,159C,218C,286C,371C,422C ''/pre'':67C ''/strong'':37C,165C ''/table/publicdata:samples.trigrams'':139C ''/wiki/category:en:mathematics'':262C ''/wiki/category:en:units_of_measure'':232C ''/wiki/category:symbols_for_si_units'':247C ''abbrevi'':334C ''actual'':333C ''add'':386C ''aim'':167C ''approach'':36C,164C ''arch'':364C ''assum'':150C ''bigqueri'':130C ''bigquery.cloud.google.com'':138C ''bigquery.cloud.google.com/table/publicdata:samples.trigrams'':137C ''br'':227C,242C,257C,270C ''capac'':55C,57C,73C,78C,206C,212C ''capitalis'':107C ''case'':202C,389C ''categor'':178C,419C ''categori'':236C,251C,266C ''class'':188C ''classifi'':169C ''cloud.google.com'':125C ''cloud.google.com/bigquery/bigquery-web-ui'':124C ''co'':42C ''co-occurr'':41C ''code'':50C,173C,177C,182C,305C,346C ''concern'':311C ''consid'':281C,302C ''contain'':358C,367C ''correspond'':17C ''could'':108C,317C ''data'':34C,146C,397C ''difficult'':400C ''domain'':280C ''done'':120C,378C ''driven'':35C ''easier'':416C ''eg'':100C,362C ''em'':12C,18C,72C,77C,205C,211C,323C,327C,338C,363C,368C ''en'':237C,267C ''en.wiktionary.org'':231C,246C,261C ''en.wiktionary.org/wiki/category:en:mathematics'':260C ''en.wiktionary.org/wiki/category:en:units_of_measure'':230C ''en.wiktionary.org/wiki/category:symbols_for_si_units'':245C ''even'':94C,186C,392C ''exampl'':47C,204C ''exist'':112C ''fieldnam'':19C ''find'':282C,331C ''full'':293C ''fuzzi'':314C ''good'':5C ''googl'':129C ''gram'':144C ''gt'':63C ''guess'':82C ''href'':123C,136C,229C,244C,259C ''human'':192C ''identifi'':8C,16C,418C ''impract'':157C ''inch'':328C,339C ''instanc'':70C ''instead'':421C ''known'':113C ''label'':86C,114C,299C,344C ''latter'':196C ''learnt'':110C ''length'':365C,369C ''librari'':6C ''like'':11C,24C,54C,60C,90C,337C ''likewis'':352C ''list'':223C,285C ''look'':39C ''make'':382C ''mani'':4C ''match'':294C,315C,326C ''mathemat'':268C ''may'':414C ''measur'':240C ''might'':38C,166C ''natur'':96C ''next'':310C ''nofollow'':128C,141C,234C,249C,264C ''note'':271C ''number'':10C,45C,53C,59C,89C ''number-lik'':9C,52C,58C,88C ''numer'':174C,209C,306C,410C ''occur'':61C,75C ''occurr'':43C ''one'':296C ''p'':1C,30C,68C,93C,116C,160C,219C,287C,372C ''practic'':163C ''pre'':49C ''problem'':27C ''problem-specif'':26C ''public'':134C ''pure'':33C ''refin'':384C ''rel'':127C,140C,233C,248C,263C ''relationship'':99C ''review'':193C,351C ''sampl'':145C,394C ''say'':402C ''scope'':155C ''see'':391C ''si'':254C ''simpli'':357C ''singular'':277C ''special'':388C ''specif'':28C ''start'':222C ''string'':171C,290C,361C,408C ''strong'':32C,162C ''symbol'':252C ''thing'':91C ''think'':374C ''toler'':313C ''treat'':318C ''tri'':143C ''tri-gram'':142C ''tricki'':201C ''trickier'':22C ''type'':213C ''unit'':175C,179C,238C,255C,307C,336C,411C,420C ''unsur'':183C,347C ''use'':132C,226C ''valu'':13C ''whether'':101C ''wiktionari'':235C,250C,265C ''wildcard'':321C ''without'':390C ''word'':355C ''would'':215C ''x'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8723, 2, '2015-11-04 17:59:47.003', 2, NULL, NULL, '&lt;p&gt;Slightly variation of the @init-random''s first plot is use of &lt;em&gt;Boxplot&lt;/em&gt;. Here example for the &lt;strong&gt;TotalSize&lt;/strong&gt; using same setup for &lt;code&gt;d&lt;/code&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; xy &amp;lt;- boxplot(TotalSize ~ start.decade    ,   
 data =  d,        
 ylab="sum of skill levels", xlab="start.decade", main= "Skill per Decade - TotalSize")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/sAHNl.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/sAHNl.png" alt="Boxplot"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;But this actually ignores the factor and interprets it as numbers.
To draw a factor frequency in time, you may use &lt;strong&gt;levelplot&lt;/strong&gt; interpreting the third dimension as color.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; library(lattice)
 library(RColorBrewer)
 library(reshape)

 df &amp;lt;- data.frame(d)
 df &amp;lt;- as.data.frame.matrix(table(df[,c("TotalSize","start.decade")]))
 df$TotalSize &amp;lt;- rownames(df)
     library(reshape)
     dfm &amp;lt;- melt(df, id = c("TotalSize"))
     colnames(dfm) &amp;lt;- c("TotalSize", "decade", "count")
     dfm$decade &amp;lt;- as.factor(dfm$decade)
     dfm$TotalSize &amp;lt;- as.factor(dfm$TotalSize)

 xy &amp;lt;- levelplot(count ~ decade + TotalSize , data=dfm, cuts=8, 
 col.regions=brewer.pal(9,"YlOrRd")  ,   
 ylab="Total Skill", xlab="Decade", main= "Total Skill per Decade")
 print (xy)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/TfGOg.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/TfGOg.png" alt="level plot"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':71C,202C ''/code'':31C,54C,184C ''/em'':17C ''/p'':32C,72C,104C,203C ''/pre'':55C,185C ''/sahnl.png'':61C,68C ''/strong'':24C,97C ''/tfgog.png'':191C,198C ''8'':167C ''9'':170C ''actual'':76C ''alt'':69C,199C ''as.data.frame.matrix'':119C ''as.factor'':149C,155C ''boxplot'':16C,37C,70C ''brewer.pal'':169C ''c'':122C,137C,142C ''code'':29C,34C,106C ''col.regions'':168C ''colnam'':139C ''color'':103C ''count'':145C,161C ''cut'':166C ''d'':30C,41C,116C ''data'':40C,164C ''data.frame'':115C ''decad'':52C,144C,147C,151C,162C,176C,181C ''df'':113C,117C,121C,125C,129C,135C ''dfm'':132C,140C,146C,150C,152C,156C,165C ''dimens'':101C ''draw'':86C ''em'':15C ''exampl'':19C ''factor'':79C,88C ''first'':10C ''frequenc'':89C ''href'':58C,188C ''i.stack.imgur.com'':60C,67C,190C,197C ''i.stack.imgur.com/sahnl.png'':59C,66C ''i.stack.imgur.com/tfgog.png'':189C,196C ''id'':136C ''ignor'':77C ''img'':64C,194C ''init'':7C ''init-random'':6C ''interpret'':81C,98C ''lattic'':108C ''level'':46C,200C ''levelplot'':96C,160C ''librari'':107C,109C,111C,130C ''lt'':36C,114C,118C,127C,133C,141C,148C,154C,159C ''main'':49C,177C ''may'':93C ''melt'':134C ''nofollow'':63C,193C ''number'':84C ''p'':1C,56C,73C,186C ''per'':51C,180C ''plot'':11C,201C ''pre'':33C,105C ''print'':182C ''random'':8C ''rcolorbrew'':110C ''rel'':62C,192C ''reshap'':112C,131C ''rownam'':128C ''setup'':27C ''skill'':45C,50C,174C,179C ''slight'':2C ''src'':65C,195C ''start.decade'':39C,48C,124C ''strong'':22C,95C ''sum'':43C ''tabl'':120C ''third'':100C ''time'':91C ''total'':173C,178C ''totals'':23C,38C,53C,123C,126C,138C,143C,153C,157C,163C ''use'':13C,25C,94C ''variat'':3C ''xlab'':47C,175C ''xy'':35C,158C,183C ''ylab'':42C,172C ''ylorrd'':171C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8581, 2, '2015-10-24 09:30:55.723', 2, NULL, NULL, '&lt;p&gt;I have no experience in hmmlearn. &lt;/p&gt;

&lt;p&gt;But if you want to receive the labels for the underlying states of the process you have to do ''decoding'' by using the ''Viterbi'' algorithm. It calculates the most probable state sequence for your data. So if you only have two hidden states it calculates a 0/1 label for each of your time-stamps.&lt;/p&gt;

&lt;p&gt;If instead you''re trying to discern between two different processes and recognize them, you have to train two different HMMs for the two processes and calculate the foward probabilty up to time t. Then you can choose the process with the higher probability. &lt;/p&gt;
', NULL, NULL, '''/p'':8C,65C,111C ''0/1'':56C ''algorithm'':34C ''calcul'':36C,54C,93C ''choos'':104C ''data'':44C ''decod'':29C ''differ'':76C,86C ''discern'':73C ''experi'':5C ''foward'':95C ''hidden'':51C ''higher'':109C ''hmmlearn'':7C ''hmms'':87C ''instead'':68C ''label'':17C,57C ''p'':1C,9C,66C ''probabilti'':96C ''probabl'':39C,110C ''process'':24C,77C,91C,106C ''re'':70C ''receiv'':15C ''recogn'':79C ''sequenc'':41C ''stamp'':64C ''state'':21C,40C,52C ''time'':63C,99C ''time-stamp'':62C ''train'':84C ''tri'':71C ''two'':50C,75C,85C,90C ''under'':20C ''use'':31C ''viterbi'':33C ''want'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8582, 2, '2015-10-24 09:54:27.783', 1, NULL, NULL, '&lt;p&gt;From the description of the problem, you can just choose whatever classification algorithm you like to classify the need of intervention at  $t_{0}$.&lt;/p&gt;

&lt;p&gt;The general rule for choosing a classifier for this is to start simple--e.g., by using nearest neighbor and iterate to more powerful classifiers until you get enough accuracy.&lt;/p&gt;

&lt;p&gt;If additional measurements at $t_{0}$ can be made a classifier can be used to determine the necessity of intervention without this additional data, then based on the probability output of this classifier and a threshold the additional measurements can be triggered. Based on this measurements another probability can be output which hopefully uses more informative features.&lt;/p&gt;

&lt;p&gt;If you care about $\Delta t$, you should think about wether there is a real time dependency. Does the need of intervention depend upon the &lt;strong&gt;series&lt;/strong&gt; of classifications? Is the order of determining interventions as necessary important for you? &lt;/p&gt;

&lt;p&gt;In my opinion, you do not have to care about ordering for this application.
Rather, I would suggest pooling classifications for one instance according to the most frequent label or something like that. &lt;/p&gt;
', NULL, NULL, '''/p'':26C,57C,116C,159C,194C ''/strong'':145C ''0'':25C,64C ''accord'':184C ''accuraci'':56C ''addit'':60C,81C,96C ''algorithm'':14C ''anoth'':105C ''applic'':174C ''base'':84C,101C ''care'':120C,169C ''choos'':11C,32C ''classif'':13C,147C,180C ''classifi'':18C,34C,51C,69C,91C ''data'':82C ''delta'':122C ''depend'':134C,140C ''descript'':4C ''determin'':74C,152C ''e.g'':41C ''enough'':55C ''featur'':115C ''frequent'':188C ''general'':29C ''get'':54C ''hope'':111C ''import'':156C ''inform'':114C ''instanc'':183C ''intervent'':22C,78C,139C,153C ''iter'':47C ''label'':189C ''like'':16C,192C ''made'':67C ''measur'':61C,97C,104C ''nearest'':44C ''necess'':76C ''necessari'':155C ''need'':20C,137C ''neighbor'':45C ''one'':182C ''opinion'':163C ''order'':150C,171C ''output'':88C,109C ''p'':1C,27C,58C,117C,160C ''pool'':179C ''power'':50C ''probabl'':87C,106C ''problem'':7C ''rather'':175C ''real'':132C ''rule'':30C ''seri'':144C ''simpl'':40C ''someth'':191C ''start'':39C ''strong'':143C ''suggest'':178C ''think'':126C ''threshold'':94C ''time'':133C ''trigger'':100C ''upon'':141C ''use'':43C,72C,112C ''wether'':128C ''whatev'':12C ''without'':79C ''would'':177C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8583, 1, '2015-10-24 13:24:09.34', 2, 89, 'How to visualize data of a multidimensional dataset (TIMIT)', '&lt;p&gt;I''ve built a neural network for a speech recognition task using the &lt;a href="https://catalog.ldc.upenn.edu/LDC93S1" rel="nofollow"&gt;timit dataset&lt;/a&gt;. I''ve extracted features using the perceptual linear prediction (PLP_ method. My features space has 39 dimensions (13 PLP values, 13 about first order derivative and 13 about second order derivative).&lt;/p&gt;

&lt;p&gt;I would like to improve my dataset. The only thing I''ve tried thus far is normalizing the dataset using a standard scaler (standardizing features with mean 0 and variance 1).&lt;/p&gt;

&lt;p&gt;My questions are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Since my dataset has high dimensionality, is there a way to visualize? For now, I''ve just plotted the dataset values using a heat map.&lt;/li&gt;
&lt;li&gt;Are there any methods for separating my sample even more, making it easier to differentiate between the classes?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;My heat map is below, representing 20 samples. In this heatmap there are 5 different phonemes, related to vowels, in particular, &lt;em&gt;uh, oy, aw, ix,&lt;/em&gt; and &lt;em&gt;ey.&lt;/em&gt;
As you can see, each phoneme is not really distinguishable from the others. Does anyone know how could I improve it?&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/A1Yby.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/A1Yby.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'machine-learning  neuralnetwork  feature-selection  visualization  preprocessing', NULL, '''/a'':42C,234C ''/a1yby.jpg'':221C,228C ''/em'':189C,193C ''/ldc93s1'':37C ''/li'':140C,160C ''/p'':74C,107C,112C,215C,235C ''/ul'':161C ''0'':103C ''1'':106C ''13'':60C,63C,69C ''20'':169C ''39'':58C ''5'':176C ''alt'':229C ''anyon'':208C ''aw'':187C ''built'':22C ''catalog.ldc.upenn.edu'':36C ''catalog.ldc.upenn.edu/ldc93s1'':35C ''class'':159C ''could'':211C ''data'':4A ''dataset'':8A,41C,82C,94C,117C,134C ''deriv'':67C,73C ''descript'':232C ''differ'':177C ''differenti'':156C ''dimens'':59C ''dimension'':120C ''distinguish'':203C ''easier'':154C ''em'':184C,191C ''enter'':230C ''even'':150C ''extract'':45C ''ey'':192C ''far'':90C ''featur'':15B,46C,55C,100C ''feature-select'':14B ''first'':65C ''heat'':138C,164C ''heatmap'':173C ''high'':119C ''href'':34C,218C ''i.stack.imgur.com'':220C,227C ''i.stack.imgur.com/a1yby.jpg'':219C,226C ''imag'':231C ''img'':224C ''improv'':80C,213C ''ix'':188C ''know'':209C ''learn'':12B ''li'':114C,141C ''like'':78C ''linear'':50C ''machin'':11B ''machine-learn'':10B ''make'':152C ''map'':139C,165C ''mean'':102C ''method'':53C,145C ''multidimension'':7A ''network'':25C ''neural'':24C ''neuralnetwork'':13B ''nofollow'':39C,223C ''normal'':92C ''order'':66C,72C ''other'':206C ''oy'':186C ''p'':19C,75C,108C,162C,216C ''particular'':183C ''perceptu'':49C ''phonem'':178C,199C ''plot'':132C ''plp'':52C,61C ''predict'':51C ''preprocess'':18B ''question'':110C ''realli'':202C ''recognit'':29C ''rel'':38C,222C ''relat'':179C ''repres'':168C ''sampl'':149C,170C ''scaler'':98C ''second'':71C ''see'':197C ''select'':16B ''separ'':147C ''sinc'':115C ''space'':56C ''speech'':28C ''src'':225C ''standard'':97C,99C ''task'':30C ''thing'':85C ''thus'':89C ''timit'':9A,40C ''tri'':88C ''uh'':185C ''ul'':113C ''use'':31C,47C,95C,136C ''valu'':62C,135C ''varianc'':105C ''ve'':21C,44C,87C,130C ''visual'':3A,17B,126C ''vowel'':181C ''way'':124C ''would'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8584, 2, '2015-10-24 15:47:05.65', 1, NULL, NULL, '&lt;p&gt;Like I said in the comment, you''ll need to perform dimension reduction, otherwise you''ll not be able to visualize the $\mathbb{R}^n$ vector space and this is why :&lt;/p&gt;

&lt;p&gt;Visualization of high-dimensional data sets is one of the traditional applications of dimensionality reduction methods such as PCA (Principal components analysis).&lt;/p&gt;

&lt;p&gt;In high-dimensional data, such as experimental data where each dimension corresponds to a different measured variable, dependencies between different dimensions often restrict the data points to a manifold whose dimensionality is much lower than the dimensionality of the data space. &lt;/p&gt;

&lt;p&gt;Many methods are designed for manifold learning, that is, to find and unfold the lower-dimensional manifold. There has been a research boom in manifold learning since 2000, and there now exist many methods that are known to unfold at least certain kinds of manifolds successfully.&lt;/p&gt;

&lt;p&gt;One of the most used methods for dimension reduction is called PCA or Principal component analysis. PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. You can read more on this topics &lt;a href="http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;So once you reduce your high dimensional space into a ${\mathbb{R}^3}$ or ${\mathbb{R}^2}$ space you will able to project it using your adequate visualization method.
References :&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://jmlr.csail.mit.edu/papers/volume11/venna10a/venna10a.pdf" rel="nofollow"&gt;Information Retrieval Perspective to Nonlinear Dimensionality
Reduction for Data Visualization&lt;/a&gt; - Jarkko Venna&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;EDIT:&lt;/strong&gt; &lt;em&gt;To avoid confusion for some concerning PCA and Dimension Reduction&lt;/em&gt;, I add the following details :&lt;/p&gt;

&lt;p&gt;PCA will allow you compute the principal components of your vector model, so the information are not lost but "synthesized".&lt;/p&gt;

&lt;p&gt;Unfortunately there is no other imaginable way to display 39 dimensions on a 2/3 dimension screen. If you wish to analyze correlations between your 39 features, maybe you should consider another visualization technique. &lt;/p&gt;

&lt;p&gt;I would recommend a scatter plot matrix in this case.&lt;/p&gt;
', NULL, NULL, '''/a'':216C,269C ''/cosc453/student_tutorials/principal_components.pdf'':212C ''/em'':289C ''/li'':272C ''/p'':33C,58C,102C,151C,217C,249C,295C,317C,352C,364C ''/papers/volume11/venna10a/venna10a.pdf'':256C ''/strong'':277C ''/ul'':273C ''2'':235C ''2/3'':332C ''2000'':132C ''3'':231C ''39'':328C,343C ''abl'':20C,239C ''add'':291C ''adequ'':245C ''allow'':299C ''analysi'':57C,168C ''analyz'':339C ''anoth'':349C ''applic'':47C ''avoid'':280C ''boom'':127C ''call'':163C,198C ''case'':363C ''certain'':146C ''comment'':7C ''compon'':56C,167C,200C,304C ''comput'':301C ''concern'':284C ''confus'':281C ''consid'':348C ''convert'':180C ''correl'':187C,340C ''correspond'':72C ''data'':40C,64C,68C,85C,100C,267C ''depend'':78C ''design'':107C ''detail'':294C ''differ'':75C,80C ''dimens'':13C,71C,81C,160C,287C,329C,333C ''dimension'':39C,49C,63C,91C,97C,120C,225C,264C ''display'':327C ''edit'':276C ''em'':278C ''exist'':136C ''experiment'':67C ''featur'':344C ''find'':114C ''follow'':293C ''high'':38C,62C,224C ''high-dimension'':37C,61C ''href'':209C,253C ''imagin'':324C ''inform'':259C,311C ''jarkko'':270C ''jmlr.csail.mit.edu'':255C ''jmlr.csail.mit.edu/papers/volume11/venna10a/venna10a.pdf'':254C ''kind'':147C ''known'':141C ''learn'':110C,130C ''least'':145C ''li'':251C ''like'':2C ''linear'':195C ''ll'':9C,17C ''lost'':314C ''lower'':94C,119C ''lower-dimension'':118C ''mani'':104C,137C ''manifold'':89C,109C,121C,129C,149C ''mathbb'':24C,229C,233C ''matrix'':360C ''mayb'':345C ''measur'':76C ''method'':51C,105C,138C,158C,247C ''model'':308C ''much'':93C ''n'':26C ''need'':10C ''nofollow'':214C,258C ''nonlinear'':263C ''observ'':184C ''often'':82C ''one'':43C,153C ''orthogon'':177C ''otherwis'':15C ''p'':1C,34C,59C,103C,152C,218C,274C,296C,318C,353C ''pca'':54C,164C,169C,285C,297C ''perform'':12C ''perspect'':261C ''plot'':359C ''point'':86C ''possibl'':186C ''princip'':55C,166C,199C,303C ''procedur'':173C ''project'':241C ''r'':25C,230C,234C ''read'':203C ''recommend'':356C ''reduc'':222C ''reduct'':14C,50C,161C,265C,288C ''refer'':248C ''rel'':213C,257C ''research'':126C ''restrict'':83C ''retriev'':260C ''said'':4C ''scatter'':358C ''screen'':334C ''set'':41C,182C,191C ''sinc'':131C ''space'':28C,101C,226C,236C ''statist'':172C ''strong'':275C ''success'':150C ''synthes'':316C ''techniqu'':351C ''topic'':207C ''tradit'':46C ''transform'':178C ''ul'':250C ''uncorrel'':196C ''unfold'':116C,143C ''unfortun'':319C ''use'':157C,175C,243C ''valu'':193C ''variabl'':77C,188C,197C ''vector'':27C,307C ''venna'':271C ''visual'':22C,35C,246C,268C,350C ''way'':325C ''whose'':90C ''wish'':337C ''would'':355C ''www.cs.otago.ac.nz'':211C ''www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf'':210C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8605, 2, '2015-10-26 09:01:05.7', 1, NULL, NULL, '&lt;p&gt;Develop a training set, validation set and test set. Training set contains 40% of observations, validation and test set contain 30% each of the observations (assuming you have a fairly large dataset in the first place). 
Develop 5 decision trees, each with differing parameters that you would like to test. Run these decision trees on the training set and then validation set and see which decision tree has the lowest ASE (Average Squared Error) on the validation set. You can use a different validation criterion if you so choose but I prefer the ASE. If you feel that the best performing tree can still perform better given the ASE on the validation set, then go back and tweak the parameters of this tree and re-run on training and validation set. Once you are happy, use your final tree and predict on your test set.   &lt;/p&gt;
', NULL, NULL, '''/p'':148C ''30'':22C ''40'':14C ''5'':39C ''ase'':72C,95C,110C ''assum'':27C ''averag'':73C ''back'':117C ''best'':101C ''better'':107C ''choos'':90C ''contain'':13C,21C ''criterion'':86C ''dataset'':33C ''decis'':40C,54C,67C ''develop'':2C,38C ''differ'':44C,84C ''error'':75C ''fair'':31C ''feel'':98C ''final'':140C ''first'':36C ''given'':108C ''go'':116C ''happi'':137C ''larg'':32C ''like'':49C ''lowest'':71C ''observ'':16C,26C ''p'':1C ''paramet'':45C,121C ''perform'':102C,106C ''place'':37C ''predict'':143C ''prefer'':93C ''re'':127C ''re-run'':126C ''run'':52C,128C ''see'':65C ''set'':5C,7C,10C,12C,20C,59C,63C,79C,114C,133C,147C ''squar'':74C ''still'':105C ''test'':9C,19C,51C,146C ''train'':4C,11C,58C,130C ''tree'':41C,55C,68C,103C,124C,141C ''tweak'':119C ''use'':82C,138C ''valid'':6C,17C,62C,78C,85C,113C,132C ''would'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8587, 1, '2015-10-24 20:32:11.577', 1, 38, 'What are detailed references explaining Hugo Steinhaus early "data science" work?', '&lt;p&gt;Background:  &lt;a href="https://en.wikipedia.org/wiki/Hugo_Steinhaus" rel="nofollow"&gt;Hugo Steinhaus&lt;/a&gt; can be considered as an early father of data science, as his paper &lt;a href="http://www.laurent-duval.eu/Documents/Steinhaus_H_1956_j-bull-acad-polon-sci_division_cmp-k-means.pdf" rel="nofollow"&gt;Sur la division des corps matÃ©riels en parties&lt;/a&gt;, 1956, Bulletin de lâAcadÃ©mie Polonaise des Sciences (that I retranscribed in latex this summer) is one of the first known origins of the $K$-means algorithms (see &lt;a href="http://dx.doi.org/10.1016/j.patrec.2009.09.011" rel="nofollow"&gt;Data Clustering: 50 Years Beyond K-Means&lt;/a&gt;, Anil K. Jain, Pattern recognition letters, 2010 or &lt;a href="http://www.jehps.net/Decembre2008/Bock.pdf" rel="nofollow"&gt;Origins and extensions of the k-means algorithm in cluster analysis&lt;/a&gt;, Hans-Hermann Bock, Electronic Journ@l for History of Probability and Statistics, 2008).&lt;/p&gt;

&lt;p&gt;According to &lt;a href="https://en.wikipedia.org/wiki/Hugo_Steinhaus" rel="nofollow"&gt;his wikipedia page&lt;/a&gt;, he has collaborated with engineers, geologists, economists, physicians, biologists (and even lawyers), and lacking of trustworthy information during World War II, he invented a statistical tool to estimate ''German'' losses, using necrologic news from German soldiers on the front. He notably used the mention that the soldier killed was the first, second or third child from a family.&lt;/p&gt;

&lt;p&gt;What are the references (possibly second-hand) to his applied and "missing value" statistical tool related works? Namely:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Regarding his war lacunary inferences, where is this algorithm described (as I would like to be able to re-implement it)?&lt;/li&gt;
&lt;li&gt;What are specific exemples of his applied works with biologists for instance, were they published?&lt;/li&gt;
&lt;/ol&gt;
', 'clustering  k-means  missing-data  history', NULL, '''/10.1016/j.patrec.2009.09.011'':95C ''/a'':31C,61C,106C,134C,163C ''/decembre2008/bock.pdf'':119C ''/documents/steinhaus_h_1956_j-bull-acad-polon-sci_division_cmp-k-means.pdf'':49C ''/li'':268C,285C ''/ol'':286C ''/p'':149C,222C,243C ''/wiki/hugo_steinhaus'':26C,157C ''1956'':62C ''2008'':148C ''2010'':113C ''50'':100C ''abl'':262C ''acadã'':66C ''accord'':151C ''algorithm'':89C,130C,254C ''analysi'':133C ''anil'':107C ''appli'':234C,276C ''background'':21C ''beyond'':102C ''biologist'':172C,279C ''bock'':138C ''bulletin'':63C ''child'':218C ''cluster'':12B,99C,132C ''collabor'':166C ''consid'':34C ''corp'':56C ''data'':9A,18B,40C,98C ''de'':64C ''des'':55C,69C ''describ'':255C ''detail'':3A ''divis'':54C ''dx.doi.org'':94C ''dx.doi.org/10.1016/j.patrec.2009.09.011'':93C ''earli'':8A,37C ''economist'':170C ''electron'':139C ''en'':59C ''en.wikipedia.org'':25C,156C ''en.wikipedia.org/wiki/hugo_steinhaus'':24C,155C ''engin'':168C ''estim'':191C ''even'':174C ''exempl'':273C ''explain'':5A ''extens'':124C ''famili'':221C ''father'':38C ''first'':82C,214C ''front'':202C ''geologist'':169C ''german'':192C,198C ''han'':136C ''hand'':231C ''hans-hermann'':135C ''hermann'':137C ''histori'':19B,143C ''href'':23C,46C,92C,116C,154C ''hugo'':6A,29C ''ii'':184C ''implement'':266C ''infer'':250C ''inform'':180C ''instanc'':281C ''invent'':186C ''jain'':109C ''journ'':140C ''k'':14B,87C,104C,108C,128C ''k-mean'':13B,103C,127C ''kill'':211C ''known'':83C ''l'':141C ''la'':53C ''lack'':177C ''lacunari'':249C ''latex'':75C ''lawyer'':175C ''letter'':112C ''li'':245C,269C ''like'':259C ''loss'':193C ''lâ'':65C ''matã'':57C ''mean'':15B,88C,105C,129C ''mention'':207C ''mie'':67C ''miss'':17B,236C ''missing-data'':16B ''name'':242C ''necrolog'':195C ''news'':196C ''nofollow'':28C,51C,97C,121C,159C ''notabl'':204C ''ol'':244C ''one'':79C ''origin'':84C,122C ''p'':20C,150C,223C ''page'':162C ''paper'':44C ''parti'':60C ''pattern'':110C ''physician'':171C ''polonais'':68C ''possibl'':228C ''probabl'':145C ''publish'':284C ''re'':265C ''re-impl'':264C ''recognit'':111C ''refer'':4A,227C ''regard'':246C ''rel'':27C,50C,96C,120C,158C ''relat'':240C ''retranscrib'':73C ''riel'':58C ''scienc'':10A,41C,70C ''second'':215C,230C ''second-hand'':229C ''see'':90C ''soldier'':199C,210C ''specif'':272C ''statist'':147C,188C,238C ''steinhaus'':7A,30C ''summer'':77C ''sur'':52C ''third'':217C ''tool'':189C,239C ''trustworthi'':179C ''use'':194C,205C ''valu'':237C ''war'':183C,248C ''wikipedia'':161C ''work'':11A,241C,277C ''world'':182C ''would'':258C ''www.jehps.net'':118C ''www.jehps.net/decembre2008/bock.pdf'':117C ''www.laurent-duval.eu'':48C ''www.laurent-duval.eu/documents/steinhaus_h_1956_j-bull-acad-polon-sci_division_cmp-k-means.pdf'':47C ''year'':101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8588, 2, '2015-10-25 00:10:49.983', 3, NULL, NULL, '&lt;p&gt;You can get a reasonably good approximation of steps for &lt;em&gt;exploratory data analysis (EDA)&lt;/em&gt; by reviewing the &lt;a href="http://www.itl.nist.gov/div898/handbook/eda/eda.htm" rel="nofollow"&gt;EDA section&lt;/a&gt; of the &lt;em&gt;NIST Engineering Statistics Handbook&lt;/em&gt;. Additionally, you might find helpful parts of &lt;a href="http://datascience.stackexchange.com/a/1006/2452"&gt;my related answer&lt;/a&gt; here on &lt;em&gt;Data Science SE&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Methods, related to EDA, are too diverse that it is not feasible to discuss them in a single answer. I will just mention several approaches. If you are interested in applying &lt;em&gt;classification&lt;/em&gt; to your data set, you might find information, mentioned in &lt;a href="http://stats.stackexchange.com/a/135842/31372"&gt;my other answer&lt;/a&gt; helpful. In order to &lt;em&gt;detect structures&lt;/em&gt; in a data set, you can try to apply &lt;a href="https://en.wikipedia.org/wiki/Principal_component_analysis" rel="nofollow"&gt;principal component analysis (PCA)&lt;/a&gt;. If, on the other hand, you are interested in exploring &lt;em&gt;latent structures&lt;/em&gt; in data, consider using &lt;a href="https://en.wikipedia.org/wiki/Exploratory_factor_analysis" rel="nofollow"&gt;exploratory factor analysis (EFA)&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':30C,54C,116C,145C,175C ''/a/1006/2452'':50C ''/a/135842/31372'':112C ''/div898/handbook/eda/eda.htm'':25C ''/em'':17C,38C,61C,97C,124C,159C ''/p'':62C,176C ''/wiki/exploratory_factor_analysis'':168C ''/wiki/principal_component_analysis'':138C ''addit'':39C ''analysi'':15C,143C,173C ''answer'':53C,82C,115C ''appli'':94C,133C ''approach'':88C ''approxim'':8C ''classif'':96C ''compon'':142C ''consid'':162C ''data'':14C,58C,100C,127C,161C ''datascience.stackexchange.com'':49C ''datascience.stackexchange.com/a/1006/2452'':48C ''detect'':122C ''discuss'':77C ''divers'':70C ''eda'':16C,28C,67C ''efa'':174C ''em'':12C,33C,57C,95C,121C,156C ''en.wikipedia.org'':137C,167C ''en.wikipedia.org/wiki/exploratory_factor_analysis'':166C ''en.wikipedia.org/wiki/principal_component_analysis'':136C ''engin'':35C ''explor'':155C ''exploratori'':13C,171C ''factor'':172C ''feasibl'':75C ''find'':42C,104C ''get'':4C ''good'':7C ''hand'':150C ''handbook'':37C ''help'':43C,117C ''href'':22C,47C,109C,135C,165C ''inform'':105C ''interest'':92C,153C ''latent'':157C ''mention'':86C,106C ''method'':64C ''might'':41C,103C ''nist'':34C ''nofollow'':27C,140C,170C ''order'':119C ''p'':1C,63C ''part'':44C ''pca'':144C ''princip'':141C ''reason'':6C ''rel'':26C,139C,169C ''relat'':52C,65C ''review'':19C ''scienc'':59C ''se'':60C ''section'':29C ''set'':101C,128C ''sever'':87C ''singl'':81C ''statist'':36C ''stats.stackexchange.com'':111C ''stats.stackexchange.com/a/135842/31372'':110C ''step'':10C ''structur'':123C,158C ''tri'':131C ''use'':163C ''www.itl.nist.gov'':24C ''www.itl.nist.gov/div898/handbook/eda/eda.htm'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8589, 1, '2015-10-25 03:50:37.68', 0, 16, 'Compare document similarity using Hierarchical Dirichlet process', '&lt;p&gt;I''m trying to build a system that can compare a new document (paragraph) against collection of documents and return most similar document from the collection.&lt;/p&gt;

&lt;p&gt;Is it possible to use Hierarchical Dirichlet process for it ?&lt;/p&gt;
', 'clustering  nlp  similarity', NULL, '''/p'':38C,50C ''build'':16C ''cluster'':8B ''collect'':27C,37C ''compar'':1A,21C ''dirichlet'':6A,46C ''document'':2A,24C,29C,34C ''hierarch'':5A,45C ''m'':13C ''new'':23C ''nlp'':9B ''p'':11C,39C ''paragraph'':25C ''possibl'':42C ''process'':7A,47C ''return'':31C ''similar'':3A,10B,33C ''system'':18C ''tri'':14C ''use'':4A,44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8590, 1, '2015-10-25 05:42:48.993', 0, 23, 'Query related to Matlab Neural Network toolbox', '&lt;p&gt;If anyone has used the Neural Network toolbox in matlab, what does the two values in the performance section means? Like considering this image:&lt;br&gt;
&lt;a href="http://i.stack.imgur.com/c1CoK.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/c1CoK.png" alt="enter image description here"&gt;&lt;/a&gt; &lt;br&gt;
The performance section has the value 0.484 and 1.32e-07. what are these two values. Among them which is the mean squared error value. If one is the mean squared error value, then what is the other value?&lt;/p&gt;
', 'machine-learning  neuralnetwork  algorithms  tools  matlab', NULL, '''/a'':58C ''/c1cok.png'':45C,52C ''/p'':97C ''0.484'':66C ''1.32e-07'':68C ''algorithm'':12B ''alt'':53C ''among'':74C ''anyon'':17C ''br'':40C,59C ''consid'':37C ''descript'':56C ''enter'':54C ''error'':81C,89C ''href'':42C ''i.stack.imgur.com'':44C,51C ''i.stack.imgur.com/c1cok.png'':43C,50C ''imag'':39C,55C ''img'':48C ''learn'':10B ''like'':36C ''machin'':9B ''machine-learn'':8B ''matlab'':4A,14B,25C ''mean'':35C,79C,87C ''network'':6A,22C ''neural'':5A,21C ''neuralnetwork'':11B ''nofollow'':47C ''one'':84C ''p'':15C ''perform'':33C,61C ''queri'':1A ''rel'':46C ''relat'':2A ''section'':34C,62C ''squar'':80C,88C ''src'':49C ''tool'':13B ''toolbox'':7A,23C ''two'':29C,72C ''use'':19C ''valu'':30C,65C,73C,82C,90C,96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8738, 1, '2015-11-06 09:28:56.003', 0, 31, 'Typing error handling n-gram character index and vector space model', '&lt;p&gt;Suppose I apply tri-gram indexing for my document collection, and is implementing a vector-space model to help retrieving the document. In &lt;a href="http://nlp.stanford.edu/IR-book/html/htmledition/k-gram-indexes-for-wildcard-queries-1.html" rel="nofollow"&gt;the text&lt;/a&gt; it is mentioned implementing a trigram will introduce a new step in filtering the result. However, what are the problems that I need to be aware of if I implement tfidf/vector-space model? The reason I am exploring this option is to try handling basic spelling error handling, does it really work in practice?&lt;/p&gt;
', 'information-retrieval', NULL, '''/a'':51C ''/ir-book/html/htmledition/k-gram-indexes-for-wildcard-queries-1.html'':46C ''/p'':105C ''appli'':19C ''awar'':77C ''basic'':95C ''charact'':7A ''collect'':27C ''document'':26C,40C ''error'':2A,97C ''explor'':88C ''filter'':64C ''gram'':6A,22C ''handl'':3A,94C,98C ''help'':37C ''howev'':67C ''href'':43C ''implement'':30C,55C,81C ''index'':8A,23C ''inform'':14B ''information-retriev'':13B ''introduc'':59C ''mention'':54C ''model'':12A,35C,83C ''n'':5A ''n-gram'':4A ''need'':74C ''new'':61C ''nlp.stanford.edu'':45C ''nlp.stanford.edu/ir-book/html/htmledition/k-gram-indexes-for-wildcard-queries-1.html'':44C ''nofollow'':48C ''option'':90C ''p'':16C ''practic'':104C ''problem'':71C ''realli'':101C ''reason'':85C ''rel'':47C ''result'':66C ''retriev'':15B,38C ''space'':11A,34C ''spell'':96C ''step'':62C ''suppos'':17C ''text'':50C ''tfidf/vector-space'':82C ''tri'':21C,93C ''tri-gram'':20C ''trigram'':57C ''type'':1A ''vector'':10A,33C ''vector-spac'':32C ''work'':102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5242, 2, '2015-02-27 08:13:53.847', 0, NULL, NULL, '&lt;p&gt;Check out the e1071 package, here is the manual &lt;a href="http://cran.r-project.org/web/packages/e1071/e1071.pdf" rel="nofollow"&gt;http://cran.r-project.org/web/packages/e1071/e1071.pdf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/web/packages/e1071/e1071.pdf'':15C ''/web/packages/e1071/e1071.pdf&lt;/a&gt;&lt;/p&gt;'':20C ''check'':2C ''cran.r-project.org'':14C,19C ''cran.r-project.org/web/packages/e1071/e1071.pdf'':13C ''cran.r-project.org/web/packages/e1071/e1071.pdf&lt;/a&gt;&lt;/p&gt;'':18C ''e1071'':5C ''href'':12C ''manual'':10C ''nofollow'':17C ''p'':1C ''packag'':6C ''rel'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10442, 2, '2016-02-29 08:39:00.03', 5, NULL, NULL, '&lt;p&gt;I think I can answer that, since I implement such a thing in my own library, even if I really don''t know how it''s implemented in other libraries. Although I am confident that if there are other ways, they don''t differ too much. &lt;/p&gt;

&lt;p&gt;It took my a few weeks to understand how such a graph can be drawn. &lt;/p&gt;

&lt;p&gt;Let''s start with a general function $f:\mathbb{R} \times \mathbb{R} \to \mathbb{R}$. What you want is to draw points with a colour which signifies the value of the function. One way would be to simplify the whole problem and draw one point for each pixel. This would work, but will draw only shaded surfaces and it''s impossible to draw lines with various formats (dotted lines with some colours and line widths. &lt;/p&gt;

&lt;p&gt;The real solution which I found makes two simplifications. The first one would the that instead of colouring with a gradient you can colour bands. Suppose that your function $f$ takes values in $[-1, 1]$. You can split your co-domain into many subintervals like: $[-1, -0.9]$, $[-0.9, -0.8]$ and so on. Now what you have to do would be to paint a polygon filled with appropriate color, for each interval. So your original problem is simplified to draw multiple instances of a simpler problem. Note that when your intervals are small enough it will look like a gradient and even a trained eye would not notice.&lt;/p&gt;

&lt;p&gt;The second simplification would be to split the space which needs to be drawn into a grid of small rectangles. So instead of drawing a polygon on the whole surface, you need to fill each small rectangle with appropriate much simpler polygon. &lt;/p&gt;

&lt;p&gt;If it''s not obvious, the problem is much much simplified. Take for example a one of the rectangle. This rectangle has four corners and you can take an additional point in the center of that rectangle (you might need that point in some certain situations). &lt;/p&gt;

&lt;p&gt;The question is how to fill with appropriate colour the proper region? 
You need to evaluate function in all four corners and in the center. &lt;/p&gt;

&lt;p&gt;There are some specific cases:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;function evaluated in all corners are smaller than the beginning of the interval =&gt; you need to do nothing &lt;/li&gt;
&lt;li&gt;functions evaluated in all corners are greater than the end of the interval =&gt; you need to do nothing&lt;/li&gt;
&lt;li&gt;functions evaluated in all corners are within interval =&gt; fill the whole rectangle with an appropriate color&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You can stop here if you want, but your figures would looks non-smooth. You can go further:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;left-up, left-down, right-down in interval, right-up bigger =&gt; there are two points, one on up side and one on right side which contains the function evaluated at max value from interval =&gt; those two points together with top-right corner forms a triangle which can be filled&lt;/li&gt;
&lt;li&gt;many other cases which requires only common judgement to decide which polygons to be formed and should be filled.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Using this algorithm you can fill polygons or draw lines. In the specific case of SVM you need to know that the line which corresponds with $f(x,y)=0$ is the line which classifies points into positive and negative samples. Also, lines which evaluates the function at $-1$ or $1$ corresponds with the maximal margins of SVM.&lt;/p&gt;

&lt;p&gt;After some time I found that this kind of approach is named iso lines, or iso curves. Perhaps are more similar algorithms like that.&lt;/p&gt;

&lt;p&gt;My implementation is named mesh contour (I did not found a proper name at that time in the beginning) and you can find source &lt;a href="https://github.com/padreati/rapaio/blob/master/src/main/java/rapaio/graphics/plot/plotcomp/MeshContour.java"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Some examples:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/oE7rK.png"&gt;&lt;img src="http://i.stack.imgur.com/oE7rK.png" alt="Mesh curves for grained colour step"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/WQsvq.png"&gt;&lt;img src="http://i.stack.imgur.com/WQsvq.png" alt="Mesh curves for fine colour steps"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/lhlhf.png"&gt;&lt;img src="http://i.stack.imgur.com/lhlhf.png" alt="SVM on synthetic data"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''-0.8'':194C ''-0.9'':192C,193C ''-1'':178C,191C,589C ''/a'':657C,681C,701C,719C ''/lhlhf.png'':708C,713C ''/li'':400C,420C,438C,517C,538C ''/oe7rk.png'':668C,673C ''/p'':48C,65C,143C,253C,297C,345C,372C,379C,460C,599C,625C,658C,662C,682C,702C,720C ''/padreati/rapaio/blob/master/src/main/java/rapaio/graphics/plot/plotcomp/meshcontour.java'':655C ''/ul'':439C,539C ''/wqsvq.png'':688C,693C ''0'':570C ''1'':179C,591C ''addit'':328C ''algorithm'':543C,622C ''also'':582C ''alt'':674C,694C,714C ''although'':32C ''answer'':6C ''approach'':610C ''appropri'':212C,293C,354C,436C ''band'':169C ''begin'':391C,645C ''bigger'':477C ''case'':378C,521C,554C ''center'':332C,371C ''certain'':343C ''classifi'':575C ''co'':185C ''co-domain'':184C ''color'':213C,437C ''colour'':92C,139C,162C,168C,355C,679C,699C ''common'':525C ''confid'':35C ''contain'':492C ''contour'':632C ''corner'':322C,367C,386C,406C,426C,509C ''correspond'':565C,592C ''curv'':617C,676C,696C ''data'':718C ''decid'':528C ''differ'':45C ''domain'':186C ''dot'':135C ''draw'':88C,110C,121C,130C,224C,278C,549C ''drawn'':64C,268C ''end'':411C ''enough'':238C ''evalu'':362C,383C,403C,423C,495C,585C ''even'':18C,246C ''exampl'':312C,661C ''eye'':249C ''f'':74C,174C,567C ''figur'':450C ''fill'':210C,288C,352C,430C,516C,537C,546C ''find'':649C ''fine'':698C ''first'':155C ''form'':510C,533C ''format'':134C ''found'':150C,605C,636C ''four'':321C,366C ''function'':73C,99C,173C,363C,382C,402C,422C,494C,587C ''general'':72C ''github.com'':654C ''github.com/padreati/rapaio/blob/master/src/main/java/rapaio/graphics/plot/plotcomp/meshcontour.java'':653C ''go'':458C ''gradient'':165C,244C ''grain'':678C ''graph'':61C ''greater'':408C ''grid'':271C ''href'':652C,665C,685C,705C ''i.stack.imgur.com'':667C,672C,687C,692C,707C,712C ''i.stack.imgur.com/lhlhf.png'':706C,711C ''i.stack.imgur.com/oe7rk.png'':666C,671C ''i.stack.imgur.com/wqsvq.png'':686C,691C ''img'':669C,689C,709C ''implement'':10C,28C,628C ''imposs'':128C ''instanc'':226C ''instead'':160C,276C ''interv'':216C,235C,394C,414C,429C,473C,500C ''iso'':613C,616C ''judgement'':526C ''kind'':608C ''know'':24C,560C ''left'':464C,467C ''left-down'':466C ''left-up'':463C ''let'':67C ''li'':381C,401C,421C,462C,518C ''librari'':17C,31C ''like'':190C,242C,623C ''line'':131C,136C,141C,550C,563C,573C,583C,614C ''look'':241C,452C ''make'':151C ''mani'':188C,519C ''margin'':596C ''mathbb'':75C,78C,81C ''max'':497C ''maxim'':595C ''mesh'':631C,675C,695C ''might'':337C ''much'':47C,294C,307C,308C ''multipl'':225C ''name'':612C,630C,639C ''need'':265C,286C,338C,360C,396C,416C,558C ''negat'':580C ''non'':454C ''non-smooth'':453C ''note'':231C ''noth'':399C,419C ''notic'':252C ''obvious'':303C ''one'':100C,111C,156C,314C,482C,487C ''origin'':219C ''p'':1C,49C,66C,144C,254C,298C,346C,373C,440C,540C,600C,626C,659C,663C,683C,703C ''paint'':207C ''perhap'':618C ''pixel'':115C ''point'':89C,112C,329C,340C,481C,503C,576C ''polygon'':209C,280C,296C,530C,547C ''posit'':578C ''problem'':108C,220C,230C,305C ''proper'':357C,638C ''question'':348C ''r'':76C,79C,82C ''real'':146C ''realli'':21C ''rectangl'':274C,291C,317C,319C,335C,433C ''region'':358C ''requir'':523C ''right'':470C,475C,489C,508C ''right-down'':469C ''right-up'':474C ''sampl'':581C ''second'':256C ''shade'':123C ''side'':485C,490C ''signifi'':94C ''similar'':621C ''simpler'':229C,295C ''simplif'':153C,257C ''simplifi'':105C,222C,309C ''sinc'':8C ''situat'':344C ''small'':237C,273C,290C ''smaller'':388C ''smooth'':455C ''solut'':147C ''sourc'':650C ''space'':263C ''specif'':377C,553C ''split'':182C,261C ''src'':670C,690C,710C ''start'':69C ''step'':680C,700C ''stop'':443C ''subinterv'':189C ''suppos'':170C ''surfac'':124C,284C ''svm'':556C,598C,715C ''synthet'':717C ''take'':175C,310C,326C ''thing'':13C ''think'':3C ''time'':77C,603C,642C ''togeth'':504C ''took'':51C ''top'':507C ''top-right'':506C ''train'':248C ''triangl'':512C ''two'':152C,480C,502C ''ul'':380C,461C ''understand'':57C ''use'':541C ''valu'':96C,176C,498C ''various'':133C ''want'':85C,447C ''way'':41C,101C ''week'':55C ''whole'':107C,283C,432C ''width'':142C ''within'':428C ''work'':118C ''would'':102C,117C,157C,204C,250C,258C,451C ''x'':568C ''y'':569C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8591, 2, '2015-10-25 06:31:33.783', 0, NULL, NULL, '&lt;p&gt;Document authorship attribution is a  valuable technique in many areas such as plagiarism detection, and lawsuit prosecution. &lt;/p&gt;

&lt;p&gt;we have few research papers on source code authorship attribution. In these papers, we have discussed simple models such as Naive Bayes classifier and few advanced techniques such as Deep Learning. Though, our papers are specifically on source code authorship attribution, you can apply algorithms we discussed in our papers for general document classification problems.&lt;/p&gt;

&lt;p&gt;Good Luck!&lt;/p&gt;

&lt;p&gt;Papers&lt;/p&gt;

&lt;p&gt;[1]. &lt;a href="http://www.ijmlc.org/papers/50-A243.pdf" rel="nofollow"&gt;http://www.ijmlc.org/papers/50-A243.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;[2]. &lt;a href="http://www.sciencedirect.com/science/article/pii/S0167865512003571" rel="nofollow"&gt;http://www.sciencedirect.com/science/article/pii/S0167865512003571&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;[3]. &lt;a href="http://link.springer.com/chapter/10.1007/978-3-642-42042-9_46" rel="nofollow"&gt;http://link.springer.com/chapter/10.1007/978-3-642-42042-9_46&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/chapter/10.1007/978-3-642-42042-9_46'':114C ''/chapter/10.1007/978-3-642-42042-9_46&lt;/a&gt;&lt;/p&gt;'':119C ''/p'':19C,76C,80C,83C ''/papers/50-a243.pdf'':90C ''/papers/50-a243.pdf&lt;/a&gt;&lt;/p&gt;'':95C ''/science/article/pii/s0167865512003571'':102C ''/science/article/pii/s0167865512003571&lt;/a&gt;&lt;/p&gt;'':107C ''1'':85C ''2'':97C ''3'':109C ''advanc'':46C ''algorithm'':65C ''appli'':64C ''area'':11C ''attribut'':4C,30C,61C ''authorship'':3C,29C,60C ''bay'':42C ''classif'':74C ''classifi'':43C ''code'':28C,59C ''deep'':50C ''detect'':15C ''discuss'':36C,67C ''document'':2C,73C ''general'':72C ''good'':78C ''href'':87C,99C,111C ''lawsuit'':17C ''learn'':51C ''link.springer.com'':113C,118C ''link.springer.com/chapter/10.1007/978-3-642-42042-9_46'':112C ''link.springer.com/chapter/10.1007/978-3-642-42042-9_46&lt;/a&gt;&lt;/p&gt;'':117C ''luck'':79C ''mani'':10C ''model'':38C ''naiv'':41C ''nofollow'':92C,104C,116C ''p'':1C,20C,77C,81C,84C,96C,108C ''paper'':25C,33C,54C,70C,82C ''plagiar'':14C ''problem'':75C ''prosecut'':18C ''rel'':91C,103C,115C ''research'':24C ''simpl'':37C ''sourc'':27C,58C ''specif'':56C ''techniqu'':8C,47C ''though'':52C ''valuabl'':7C ''www.ijmlc.org'':89C,94C ''www.ijmlc.org/papers/50-a243.pdf'':88C ''www.ijmlc.org/papers/50-a243.pdf&lt;/a&gt;&lt;/p&gt;'':93C ''www.sciencedirect.com'':101C,106C ''www.sciencedirect.com/science/article/pii/s0167865512003571'':100C ''www.sciencedirect.com/science/article/pii/s0167865512003571&lt;/a&gt;&lt;/p&gt;'':105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8592, 2, '2015-10-25 10:37:43.9', 1, NULL, NULL, '&lt;p&gt;Additional to the content of the document, the form could contain even more valuable information. I can''t comment on the formatting of pdf files, but it should be possible to exploit the pdf metadata. &lt;em&gt;Author, Title, subject&lt;/em&gt; or &lt;em&gt;keywords&lt;/em&gt; could provide some clue pointing to a common origin in the same organization.&lt;/p&gt;
', NULL, NULL, '''/em'':41C,45C ''/p'':59C ''addit'':2C ''author'':38C ''clue'':49C ''comment'':20C ''common'':53C ''contain'':12C ''content'':5C ''could'':11C,46C ''document'':8C ''em'':37C,43C ''even'':13C ''exploit'':33C ''file'':26C ''form'':10C ''format'':23C ''inform'':16C ''keyword'':44C ''metadata'':36C ''organ'':58C ''origin'':54C ''p'':1C ''pdf'':25C,35C ''point'':50C ''possibl'':31C ''provid'':47C ''subject'':40C ''titl'':39C ''valuabl'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8650, 2, '2015-10-29 16:41:20.99', 1, NULL, NULL, '&lt;p&gt;You may use &lt;code&gt;ngram&lt;/code&gt;s to get the frequent sequences of you events.&lt;/p&gt;

&lt;p&gt;Here a little example&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; library(tau)
 seq &amp;lt;- "ababcdcde"
 textcnt(seq, method="ngram", n=3L, decreasing=TRUE)

   _   a  ab   b   c  cd   d  _a _ab aba abc  ba bab  bc bcd cdc cde  dc dcd  de de_   e  e_ 
   2   2   2   2   2   2   2   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Select the &lt;em&gt;longest&lt;/em&gt; and &lt;em&gt;most frequent&lt;/em&gt; ngram, here &lt;strong&gt;ab&lt;/strong&gt; and &lt;strong&gt;cd&lt;/strong&gt;.
You may also trade off the length and frequency to get maximal compression. The &lt;strong&gt;n&lt;/strong&gt; parameter limits the length of the ngram.&lt;/p&gt;
', NULL, NULL, '''/code'':7C,84C ''/em'':91C,96C ''/p'':17C,23C,130C ''/pre'':85C ''/strong'':101C,105C,122C ''1'':68C,69C,70C,71C,72C,73C,74C,75C,76C,77C,78C,79C,80C,81C,82C,83C ''2'':61C,62C,63C,64C,65C,66C,67C ''3l'':36C ''ab'':40C,46C,100C ''aba'':47C ''ababcdcd'':30C ''abc'':48C ''also'':108C ''b'':41C ''ba'':49C ''bab'':50C ''bc'':51C ''bcd'':52C ''c'':42C ''cd'':43C,104C ''cdc'':53C ''cde'':54C ''code'':5C,25C ''compress'':118C ''d'':44C ''dc'':55C ''dcd'':56C ''de'':57C,58C ''decreas'':37C ''e'':59C,60C ''em'':89C,93C ''event'':16C ''exampl'':22C ''frequenc'':114C ''frequent'':12C,95C ''get'':10C,116C ''length'':112C,126C ''librari'':26C ''limit'':124C ''littl'':21C ''longest'':90C ''lt'':29C ''maxim'':117C ''may'':3C,107C ''method'':33C ''n'':35C,121C ''ngram'':6C,34C,97C,129C ''p'':1C,18C,86C ''paramet'':123C ''pre'':24C ''select'':87C ''seq'':28C,32C ''sequenc'':13C ''strong'':99C,103C,120C ''tau'':27C ''textcnt'':31C ''trade'':109C ''true'':38C ''use'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8609, 2, '2015-10-26 14:57:27.84', 1, NULL, NULL, '&lt;p&gt;The only way to be sure is by benchmarking, but for glm Fisher scoring should be faster than coordinate descent.  Fisher scoring is a special case of Newton Raphson, which has a faster &lt;a href="https://en.wikipedia.org/wiki/Rate_of_convergence" rel="nofollow"&gt;rate of convergence&lt;/a&gt; than coordinate descent (Newton-Raphson is quadratically convergent, while coordinate descent is linearly convergent.)  So while the computation of second-derivative information means each step takes more time, it can require many fewer steps than coordinate descent.&lt;/p&gt;

&lt;p&gt;For the lasso, the special form of the penalty term makes it a very special case (and in fact absolute value isn''t differentiable anyway, though sometimes you can finesse this).  For this special problem, coordinate descent proves to be particularly fast.  There are many other optimization problems where in practice Newton-Raphson is faster.&lt;/p&gt;
', NULL, NULL, '''/a'':45C ''/p'':85C,143C ''/wiki/rate_of_convergence'':39C ''absolut'':106C ''anyway'':111C ''benchmark'':10C ''case'':27C,102C ''comput'':64C ''converg'':44C,54C,60C ''coordin'':20C,47C,56C,83C,122C ''deriv'':68C ''descent'':21C,48C,57C,84C,123C ''differenti'':110C ''en.wikipedia.org'':38C ''en.wikipedia.org/wiki/rate_of_convergence'':37C ''fact'':105C ''fast'':128C ''faster'':18C,34C,142C ''fewer'':80C ''finess'':116C ''fisher'':14C,22C ''form'':92C ''glm'':13C ''href'':36C ''inform'':69C ''isn'':108C ''lasso'':89C ''linear'':59C ''make'':97C ''mani'':79C,131C ''mean'':70C ''newton'':29C,50C,139C ''newton-raphson'':49C,138C ''nofollow'':41C ''optim'':133C ''p'':1C,86C ''particular'':127C ''penalti'':95C ''practic'':137C ''problem'':121C,134C ''prove'':124C ''quadrat'':53C ''raphson'':30C,51C,140C ''rate'':42C ''rel'':40C ''requir'':78C ''score'':15C,23C ''second'':67C ''second-deriv'':66C ''sometim'':113C ''special'':26C,91C,101C,120C ''step'':72C,81C ''sure'':7C ''take'':73C ''term'':96C ''though'':112C ''time'':75C ''valu'':107C ''way'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8593, 1, '2015-10-25 12:21:40.167', 0, 64, 'Identifying outliers in an unknown distribution', '&lt;p&gt;I have a sorted sequence of integers, e.g.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1,2,480,1000,1100
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;representing volumes in some categories. The task is to separate the valid data (high volumes) from the outliers (low volumes).&lt;/p&gt;

&lt;p&gt;In the above example I expect following obvious split&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1,2   |   480,1000,1100
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This is of course trivial, if there is a definition of outliers, such as &lt;em&gt;less than .1% of the total volume&lt;/em&gt;, but I''d prefer to have an 
algorithm for calculating this threshold as a result.&lt;/p&gt;

&lt;p&gt;I don''t know anything about the distribution of the values, except for the outliers have low values and the regular categories have high volumes.&lt;/p&gt;

&lt;p&gt;Intuitively, I''d say the approach should pass all possible N-1 splits of the sequence and evaluate some criterion, but I fail to
find a meaningful one (e.g. I distinctly failed comparing the WSS of the sequence with the sum of the WSS of both splits).&lt;/p&gt;

&lt;p&gt;I''d appreciate hints to the algorithm or to transformation of this problem in other well known unsupervised problem.&lt;/p&gt;
', 'machine-learning  outlier  unsupervised-learning', NULL, '''-1'':154C ''/code'':31C,73C ''/em'':98C ''/p'':23C,54C,65C,114C,141C,190C,211C ''/pre'':32C,74C ''1'':26C,68C,93C ''1000'':29C,71C ''1100'':30C,72C ''2'':27C,69C ''480'':28C,70C ''algorithm'':106C,198C ''anyth'':120C ''appreci'':194C ''approach'':148C ''calcul'':108C ''categori'':38C,137C ''code'':25C,67C ''compar'':175C ''cours'':79C ''criterion'':162C ''d'':101C,145C,193C ''data'':46C ''definit'':85C ''distinct'':173C ''distribut'':6A,123C ''e.g'':22C,171C ''em'':90C ''evalu'':160C ''exampl'':59C ''except'':127C ''expect'':61C ''fail'':165C,174C ''find'':167C ''follow'':62C ''high'':47C,139C ''hint'':195C ''identifi'':1A ''integ'':21C ''intuit'':143C ''know'':119C ''known'':208C ''learn'':9B,13B ''less'':91C ''low'':52C,132C ''machin'':8B ''machine-learn'':7B ''meaning'':169C ''n'':153C ''obvious'':63C ''one'':170C ''outlier'':2A,10B,51C,87C,130C ''p'':14C,33C,55C,75C,115C,142C,191C ''pass'':150C ''possibl'':152C ''pre'':24C,66C ''prefer'':102C ''problem'':204C,210C ''regular'':136C ''repres'':34C ''result'':113C ''say'':146C ''separ'':43C ''sequenc'':19C,158C,180C ''sort'':18C ''split'':64C,155C,189C ''sum'':183C ''task'':40C ''threshold'':110C ''total'':96C ''transform'':201C ''trivial'':80C ''unknown'':5A ''unsupervis'':12B,209C ''unsupervised-learn'':11B ''valid'':45C ''valu'':126C,133C ''volum'':35C,48C,53C,97C,140C ''well'':207C ''wss'':177C,186C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8600, 1, '2015-10-25 23:58:44.88', 0, 16, 'In the Cloudera quickstart demo Impala query, why does the second hdfs: URL have a `quickstart` directory but the first does not?', '&lt;p&gt;Step 2 of the Cloudera QuickStart demo has several Impala tables created. The first statement is this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;CREATE EXTERNAL TABLE categories STORED AS AVRO
LOCATION ''hdfs:///user/hive/warehouse/categories''
TBLPROPERTIES (''avro.schema.url''=''hdfs://quickstart/user/examples/sqoop_import_categories.avsc'');
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I understand that the /user/hive/warehouse/categories is created by running &lt;code&gt;sqoop&lt;/code&gt;. I also understand that the &lt;code&gt;.avsc&lt;/code&gt; files are copied to /user/examples/ in the HDFS.  So my question is this---why does the second &lt;code&gt;hdfs:&lt;/code&gt; URL have two slashes and a &lt;code&gt;quickstart&lt;/code&gt; in it, rather than three slashes and begin with &lt;code&gt;user&lt;/code&gt;???  Is &lt;code&gt;quickstart&lt;/code&gt; a username? If so, why is it in the second &lt;code&gt;hdfs:&lt;/code&gt; URL but not in the first?&lt;/p&gt;
', 'hadoop', NULL, '''/code'':55C,69C,77C,97C,106C,118C,122C,135C ''/p'':40C,142C ''/pre'':56C ''/user/examples'':82C ''/user/hive/warehouse/categories'':51C,62C ''2'':24C ''also'':71C ''avro'':49C ''avro.schema.url'':53C ''avsc'':76C ''begin'':114C ''categori'':46C ''cloudera'':3A,27C ''code'':42C,67C,75C,95C,104C,116C,120C,133C ''copi'':80C ''creat'':34C,43C,64C ''demo'':5A,29C ''directori'':17A ''extern'':44C ''file'':78C ''first'':20A,36C,141C ''hadoop'':21B ''hdfs'':12A,85C,96C,134C ''impala'':6A,32C ''locat'':50C ''p'':22C,57C ''pre'':41C ''queri'':7A ''question'':88C ''quickstart'':4A,16A,28C,105C,121C ''quickstart/user/examples/sqoop_import_categories.avsc'':54C ''rather'':109C ''run'':66C ''second'':11A,94C,132C ''sever'':31C ''slash'':101C,112C ''sqoop'':68C ''statement'':37C ''step'':23C ''store'':47C ''tabl'':33C,45C ''tblproperti'':52C ''three'':111C ''two'':100C ''understand'':59C,72C ''url'':13A,98C,136C ''user'':117C ''usernam'':124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8601, 1, '2015-10-26 05:28:37.327', 0, 24, 'JAPE and Python', '&lt;p&gt;I want to extract some named entities and the relationship between them based on some rules. I see that &lt;a href="https://gate.ac.uk/sale/tao/splitch8.html#chap:jape" rel="nofollow"&gt;JAPE&lt;/a&gt; can be used for such purpose, but I''m looking for something similar in Python. Any suggestions?&lt;/p&gt;
', 'python', NULL, '''/a'':33C ''/p'':51C ''/sale/tao/splitch8.html#chap:jape'':29C ''base'':18C ''entiti'':12C ''extract'':9C ''gate.ac.uk'':28C ''gate.ac.uk/sale/tao/splitch8.html#chap:jape'':27C ''href'':26C ''jape'':1A,32C ''look'':43C ''m'':42C ''name'':11C ''nofollow'':31C ''p'':5C ''purpos'':39C ''python'':3A,4B,48C ''rel'':30C ''relationship'':15C ''rule'':21C ''see'':23C ''similar'':46C ''someth'':45C ''suggest'':50C ''use'':36C ''want'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8602, 2, '2015-10-26 06:48:17.057', 0, NULL, NULL, '&lt;p&gt;Run the following from the ambari server''s shell:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;/var/lib/ambari-server/resources/scripts/configs.sh set mace gamma yarn-site "yarn.timeline-service.recovery.enabled" "true"&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Where mace is the ambari server''s host name and gamma is the cluster name&lt;/p&gt;
', NULL, NULL, '''/code'':23C ''/p'':11C,24C,41C ''/var/lib/ambari-server/resources/scripts/configs.sh'':14C ''ambari'':7C,30C ''cluster'':39C ''code'':13C ''follow'':4C ''gamma'':17C,36C ''host'':33C ''mace'':16C,27C ''name'':34C,40C ''p'':1C,12C,25C ''run'':2C ''server'':8C,31C ''set'':15C ''shell'':10C ''site'':20C ''true'':22C ''yarn'':19C ''yarn-sit'':18C ''yarn.timeline-service.recovery.enabled'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8603, 2, '2015-10-26 07:12:40.513', 1, NULL, NULL, '&lt;p&gt;If the high volume and low volume data both appear in non-negligible probability, you may view the problem as a clustering problem with two clusters. Gaussian mixture model or K-means algorithm may help you in this case. &lt;/p&gt;
', NULL, NULL, '''/p'':43C ''algorithm'':36C ''appear'':11C ''case'':42C ''cluster'':24C,28C ''data'':9C ''gaussian'':29C ''help'':38C ''high'':4C ''k'':34C ''k-mean'':33C ''low'':7C ''may'':18C,37C ''mean'':35C ''mixtur'':30C ''model'':31C ''neglig'':15C ''non'':14C ''non-neglig'':13C ''p'':1C ''probabl'':16C ''problem'':21C,25C ''two'':27C ''view'':19C ''volum'':5C,8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8680, 2, '2015-11-01 20:12:32.123', 3, NULL, NULL, '&lt;p&gt;One way of thinking of outlier detection is that you''re creating a predictive model, then you''re checking to see if a point falls within the range of predictions.  From an information-theoretic point of view, you can see how much each observation increases the entropy of your model.&lt;/p&gt;

&lt;p&gt;If you are treating this data as just a collection of numbers, and you don''t have some proposed model for how they''re generated, you might as well just look at the average.  If you''re certain the numbers aren''t normally distributed, you can''t make statements as to how far ''off'' a given number is from the average, but you can just look at it in absolute terms.&lt;/p&gt;

&lt;p&gt;Applying this, you can take the average of all the numbers, then exclude each number and take the average of the others.  Whichever average is most different from the global average is the biggest outlier.  Here''s some python:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def avg(a):
    return sum(a)/len(a)

l = [100, 50, 150, 200, 35, 60 ,50, 20, 500]
m = avg(l)
for idx in range(len(l)):
    print("outlier score of {0}: {1}".format(l[idx], abs(m - avg([elem for i, elem in enumerate(l) if i!=idx]))))
&amp;gt;&amp;gt;
outlier score of 100: 4
outlier score of 50: 10
outlier score of 150: 3
outlier score of 200: 9
outlier score of 35: 12
outlier score of 60: 9
outlier score of 50: 10
outlier score of 20: 14
outlier score of 500: 46 
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':266C ''/len'':176C ''/p'':53C,126C,167C ''/pre'':267C ''0'':201C ''1'':202C ''10'':230C,255C ''100'':179C,224C ''12'':245C ''14'':260C ''150'':181C,234C ''20'':186C,259C ''200'':182C,239C ''3'':235C ''35'':183C,244C ''4'':225C ''46'':265C ''50'':180C,185C,229C,254C ''500'':187C,264C ''60'':184C,249C ''9'':240C,250C ''ab'':206C ''absolut'':124C ''appli'':128C ''aren'':95C ''averag'':88C,115C,134C,146C,151C,158C ''avg'':171C,189C,208C ''biggest'':161C ''certain'':92C ''check'':20C ''code'':169C ''collect'':64C ''creat'':13C ''data'':60C ''def'':170C ''detect'':8C ''differ'':154C ''distribut'':98C ''elem'':209C,212C ''entropi'':49C ''enumer'':214C ''exclud'':140C ''fall'':26C ''far'':107C ''format'':203C ''generat'':79C ''given'':110C ''global'':157C ''gt'':219C,220C ''idx'':192C,205C,218C ''increas'':47C ''inform'':35C ''information-theoret'':34C ''l'':178C,190C,196C,204C,215C ''len'':195C ''look'':85C,120C ''m'':188C,207C ''make'':102C ''might'':81C ''model'':16C,52C,74C ''much'':44C ''normal'':97C ''number'':66C,94C,111C,138C,142C ''observ'':46C ''one'':2C ''other'':149C ''outlier'':7C,162C,198C,221C,226C,231C,236C,241C,246C,251C,256C,261C ''p'':1C,54C,127C ''point'':25C,37C ''pre'':168C ''predict'':15C,31C ''print'':197C ''propos'':73C ''python'':166C ''rang'':29C,194C ''re'':12C,19C,78C,91C ''return'':173C ''score'':199C,222C,227C,232C,237C,242C,247C,252C,257C,262C ''see'':22C,42C ''statement'':103C ''sum'':174C ''take'':132C,144C ''term'':125C ''theoret'':36C ''think'':5C ''treat'':58C ''view'':39C ''way'':3C ''well'':83C ''whichev'':150C ''within'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8604, 1, '2015-10-26 08:50:41.19', 0, 36, 'how often should one sample a dataset?', '&lt;p&gt;I have some archive data that show the variation of a certain quantity X measured over 1-year intervals.&lt;/p&gt;

&lt;p&gt;I''m now facing the problem of understanding how X correlates with another variable Y, that I can measure much more frequently, even daily.&lt;/p&gt;

&lt;p&gt;So my question is, is it really necessary to collect data for Y so often? This seems such a waste of resources.&lt;/p&gt;

&lt;p&gt;So what I would do is to collect data for Y only for 10% of the days of each year and I would select those days randomly. This way I''m sure that the observations are independent because the sample size is not too large and days have been selected randomly.&lt;/p&gt;

&lt;p&gt;What do you think of that? Is that the approach you would follow as well?&lt;/p&gt;
', 'statistics  data', NULL, '''/p'':30C,56C,80C,133C,150C ''1'':27C ''10'':95C ''anoth'':44C ''approach'':144C ''archiv'':14C ''certain'':22C ''collect'':67C,89C ''correl'':42C ''daili'':55C ''data'':9B,15C,68C,90C ''dataset'':7A ''day'':98C,107C,128C ''even'':54C ''face'':35C ''follow'':147C ''frequent'':53C ''independ'':118C ''interv'':29C ''larg'':126C ''m'':33C,112C ''measur'':25C,50C ''much'':51C ''necessari'':65C ''observ'':116C ''often'':2A,72C ''one'':4A ''p'':10C,31C,57C,81C,134C ''problem'':37C ''quantiti'':23C ''question'':60C ''random'':108C,132C ''realli'':64C ''resourc'':79C ''sampl'':5A,121C ''seem'':74C ''select'':105C,131C ''show'':17C ''size'':122C ''statist'':8B ''sure'':113C ''think'':138C ''understand'':39C ''variabl'':45C ''variat'':19C ''wast'':77C ''way'':110C ''well'':149C ''would'':85C,104C,146C ''x'':24C,41C ''y'':46C,70C,92C ''year'':28C,101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9693, 2, '2016-01-08 00:44:24.087', 1, NULL, NULL, '&lt;p&gt;To me, this is a crazy idea because there is absolutely no guarantee your "team" can win anything. You''ll need to place yourself like in the top-three, but remember you don''t have access to the private leaderboard until the end of the competition. Furthermore, the prize money is minimal. The prize money is designed to offer to an individual, not enough for you to make a living.&lt;/p&gt;
', NULL, NULL, '''/p'':73C ''absolut'':12C ''access'':38C ''anyth'':19C ''competit'':48C ''crazi'':7C ''design'':59C ''end'':45C ''enough'':66C ''furthermor'':49C ''guarante'':14C ''idea'':8C ''individu'':64C ''leaderboard'':42C ''like'':26C ''live'':72C ''ll'':21C ''make'':70C ''minim'':54C ''money'':52C,57C ''need'':22C ''offer'':61C ''p'':1C ''place'':24C ''privat'':41C ''prize'':51C,56C ''rememb'':33C ''team'':16C ''three'':31C ''top'':30C ''top-thre'':29C ''win'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8340, 1, '2015-10-06 23:42:44.063', 2, 96, 'What are the best sites to display your data science skills &amp; projects?', '&lt;p&gt;What are the best sites to display your data science skills &amp;amp; projects? Obviously there''s kaggle, but any others that recruiters look at?&lt;/p&gt;
', 'machine-learning  data-mining  beginner', NULL, '''/p'':45C ''amp'':32C ''beginn'':19B ''best'':4A,24C ''data'':9A,17B,29C ''data-min'':16B ''display'':7A,27C ''kaggl'':37C ''learn'':15B ''look'':43C ''machin'':14B ''machine-learn'':13B ''mine'':18B ''obvious'':34C ''other'':40C ''p'':20C ''project'':12A,33C ''recruit'':42C ''scienc'':10A,30C ''site'':5A,25C ''skill'':11A,31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8610, 1, '2015-10-26 15:37:39.17', 0, 59, 'jaccard normalization for a co occurrence matrix', '&lt;p&gt;Hello I have a co occurrence matrix like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;array([[1, 0, 2],
       [0, 3, 4],
       [2, 4, 5]])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Reading the wikipedia article about the &lt;a href="https://en.wikipedia.org/wiki/Jaccard_index" rel="nofollow"&gt;jaccard index&lt;/a&gt; and knowing my matrix is a co occurence one, I should get a matrix like &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;array([[ 1. ,  0. ,  0.5],
       [ 0. ,  1. ,  1. ],
       [ 0.5,  1. ,  1. ]])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is it right? if yes, is there a function in the python/R stack to do this operation? &lt;/p&gt;

&lt;p&gt;I only find scipy.distance.pdist and this function doesn''t throw the correct results.&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'python  recommendation  normalization', NULL, '''/a'':51C ''/code'':33C,80C ''/p'':20C,67C,100C,115C,118C ''/pre'':34C,81C ''/wiki/jaccard_index'':46C ''0'':25C,27C,72C,74C ''0.5'':73C,77C ''1'':24C,71C,75C,76C,78C,79C ''2'':26C,30C ''3'':28C ''4'':29C,31C ''5'':32C ''array'':23C,70C ''articl'':39C ''co'':5A,16C,58C ''code'':22C,69C ''correct'':113C ''doesn'':109C ''en.wikipedia.org'':45C ''en.wikipedia.org/wiki/jaccard_index'':44C ''find'':104C ''function'':91C,108C ''get'':63C ''hello'':12C ''href'':43C ''index'':50C ''jaccard'':1A,49C ''know'':53C ''like'':19C,66C ''matrix'':7A,18C,55C,65C ''nofollow'':48C ''normal'':2A,10B ''occur'':59C ''occurr'':6A,17C ''one'':60C ''oper'':99C ''p'':11C,35C,82C,101C,116C ''pre'':21C,68C ''python'':8B ''python/r'':94C ''read'':36C ''recommend'':9B ''rel'':47C ''result'':114C ''right'':85C ''scipy.distance.pdist'':105C ''stack'':95C ''thank'':117C ''throw'':111C ''wikipedia'':38C ''yes'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8612, 1, '2015-10-26 15:50:28.05', 0, 61, 'Algorithm or technique to find parameters that minimize error on ARIMA model', '&lt;p&gt;I''m currently working on a predictive model, which is supposed to predict the next month''s income for independent workers. I have been using information based on the ARIMA model. After finishing the model, I have several parameters (th$_{\text{outlier}}$, smoothness filter, th$_{\text{autocorrelation}}$, etc.),
which, together, determine the output of the model. 
I''m working with the following scheme: &lt;/p&gt;

&lt;p&gt;Run the model $n$ times (for now, $n = 1000$), with random parameters between a predetermined range.&lt;/p&gt;

&lt;p&gt;Make the prediction model work with the data from the 10$^{th}$ percentile, and output a value. Then, it moves to the 20$^{th}$ percentile and outputs another value, and so on, until 100$^{th}$ percentile. &lt;/p&gt;

&lt;p&gt;Also, I have another column which represents the arithmetic average of the previous outputs from percentiles 10 to 100.
The trouble I''m having is that, until the 80$^{th}$ percentile, the error I get is below 4%, but starting from the 90$^{th}$ percentile it goes up: for the 90$^{th}$ percentile it goes from 5% to 250%, and for the 100$^{th}$ percentile it goes from 10% up to 5000%. The averages go from 3% to 400%. &lt;/p&gt;

&lt;p&gt;What I''ve tried to do is to cut off the values with too many error and then analyze the correlation of each one of the parameters with the output up to the 90$^{th}$ percentile, and then do the same analysis for the average of the outputs. However, the scatter graphics are really messy, and I can''t seem to find a good set of parameters to minimize the error, at least under 10%.
I wanted to ask you if there is an algorithm or machine learning technique to help me find relationships between the parameters and the final error. I have realized that maybe the relationship between the parameters can be dependent of several parameters at once and not just one by one, but I have no idea how to tackle this trouble. Does anyone have a suggestion?&lt;/p&gt;
', 'machine-learning  predictive-modeling  optimization  parameter', NULL, '''/p'':85C,103C,141C,222C,364C ''10'':115C,159C,211C,298C ''100'':138C,161C,205C ''1000'':95C ''20'':127C ''250'':201C ''3'':219C ''4'':180C ''400'':221C ''5'':199C ''5000'':214C ''80'':171C ''90'':185C,193C,257C ''algorithm'':1A,308C ''also'':143C ''analysi'':265C ''analyz'':242C ''anoth'':132C,146C ''anyon'':360C ''arima'':11A,51C ''arithmet'':151C ''ask'':302C ''autocorrel'':68C ''averag'':152C,216C,268C ''base'':48C ''column'':147C ''correl'':244C ''current'':24C ''cut'':232C ''data'':112C ''depend'':337C ''determin'':72C ''error'':9A,175C,239C,294C,324C ''etc'':69C ''filter'':65C ''final'':323C ''find'':5A,285C,316C ''finish'':54C ''follow'':83C ''get'':177C ''go'':217C ''goe'':189C,197C,209C ''good'':287C ''graphic'':275C ''help'':314C ''howev'':272C ''idea'':353C ''incom'':39C ''independ'':41C ''inform'':47C ''learn'':15B,311C ''least'':296C ''m'':23C,79C,165C ''machin'':14B,310C ''machine-learn'':13B ''make'':105C ''mani'':238C ''mayb'':329C ''messi'':278C ''minim'':8A,292C ''model'':12A,18B,29C,52C,56C,77C,89C,108C ''month'':37C ''move'':124C ''n'':90C,94C ''next'':36C ''one'':247C,346C,348C ''optim'':19B ''outlier'':63C ''output'':74C,119C,131C,156C,253C,271C ''p'':21C,86C,104C,142C,223C ''paramet'':6A,20B,60C,98C,250C,290C,320C,334C,340C ''percentil'':117C,129C,140C,158C,173C,187C,195C,207C,259C ''predetermin'':101C ''predict'':17B,28C,34C,107C ''predictive-model'':16B ''previous'':155C ''random'':97C ''rang'':102C ''realiz'':327C ''realli'':277C ''relationship'':317C,331C ''repres'':149C ''run'':87C ''scatter'':274C ''scheme'':84C ''seem'':283C ''set'':288C ''sever'':59C,339C ''smooth'':64C ''start'':182C ''suggest'':363C ''suppos'':32C ''tackl'':356C ''techniqu'':3A,312C ''text'':62C,67C ''th'':61C,66C,116C,128C,139C,172C,186C,194C,206C,258C ''time'':91C ''togeth'':71C ''tri'':227C ''troubl'':163C,358C ''use'':46C ''valu'':121C,133C,235C ''ve'':226C ''want'':300C ''work'':25C,80C,109C ''worker'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8613, 2, '2015-10-26 16:35:02.11', 2, NULL, NULL, '&lt;p&gt;A first derivative, on the surface, would do it.  However, the data you show have a great deal of noise in them, so we need a way to evaluate the first derivative in a somewhat noiseless way, or at least within a frequency domain that eliminates the jitter and preserves the major derivative change.&lt;/p&gt;

&lt;p&gt;Wavelet analysis could achieve this for you, especially if you use the first derivative of a Gaussian as your mother wavelet.  R has some decent wavelet packages (see &lt;a href="https://cran.r-project.org/web/packages/wavelets/wavelets.pdf" rel="nofollow"&gt;r-project.org&lt;/a&gt; for starters).  If you do the wavelet transform at short scales, this will identify the locations of the bits of jitter in the steering.  If you do it at larger scales (i.e. lower frequency), you can likely find just the lane shifts and not the little jitters.&lt;/p&gt;

&lt;p&gt;If you train the transform up with a reasonable data set, you should be able to identify a scale or range of scales that corresponds to lane changes.  But note that if you don''t figure that out, this is something like O(n^2), so try to narrow the scale range down a bit to save compute time.&lt;/p&gt;
', NULL, NULL, '''/a'':94C ''/p'':56C,142C,203C ''/web/packages/wavelets/wavelets.pdf'':90C ''2'':188C ''abl'':158C ''achiev'':61C ''analysi'':59C ''bit'':113C,198C ''chang'':55C,171C ''comput'':201C ''correspond'':168C ''could'':60C ''cran.r-project.org'':89C ''cran.r-project.org/web/packages/wavelets/wavelets.pdf'':88C ''data'':13C,153C ''deal'':19C ''decent'':82C ''deriv'':4C,33C,54C,71C ''domain'':45C ''elimin'':47C ''especi'':65C ''evalu'':30C ''figur'':179C ''find'':132C ''first'':3C,32C,70C ''frequenc'':44C,128C ''gaussian'':74C ''great'':18C ''howev'':11C ''href'':87C ''i.e'':126C ''identifi'':108C,160C ''jitter'':49C,115C,141C ''lane'':135C,170C ''larger'':124C ''least'':41C ''like'':131C,185C ''littl'':140C ''locat'':110C ''lower'':127C ''major'':53C ''mother'':77C ''n'':187C ''narrow'':192C ''need'':26C ''nofollow'':92C ''nois'':21C ''noiseless'':37C ''note'':173C ''o'':186C ''p'':1C,57C,143C ''packag'':84C ''preserv'':51C ''r'':79C ''r-project.org'':93C ''rang'':164C,195C ''reason'':152C ''rel'':91C ''save'':200C ''scale'':105C,125C,162C,166C,194C ''see'':85C ''set'':154C ''shift'':136C ''short'':104C ''show'':15C ''someth'':184C ''somewhat'':36C ''starter'':96C ''steer'':118C ''surfac'':7C ''time'':202C ''train'':146C ''transform'':102C,148C ''tri'':190C ''use'':68C ''wavelet'':58C,78C,83C,101C ''way'':28C,38C ''within'':42C ''would'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8633, 1, '2015-10-28 14:17:49.47', 0, 37, 'Memory efficient structure for membership checking without false positive', '&lt;p&gt;The initial task can be described like this:
I have a requirement to deduplicate HUGE list(potentially billions of items) without storing the original items - it''s simply unaffordable&lt;/p&gt;

&lt;p&gt;All I need to know is answer to the question "Has my system ever seen this element before?"&lt;/p&gt;

&lt;p&gt;The most close data structure I was able to find so far is a bloom filter, but it has false positives which better to avoid in my task as it results in data loss&lt;/p&gt;

&lt;p&gt;For example providing I account to store at least 2^32 items, with positive error rate of just 1%(which means 1% of all urls won''t be visited) I would need at least&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;n = 4,294,967,296, p = 0.01 (1 in 100) â m = 41,167,512,262 (4.79GB), k = 7
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;4.79GB of memory...&lt;/p&gt;

&lt;p&gt;The task itself is a high scale web crawler, so I need to keep track of already visited urls(or sha1 hashes of this urls)&lt;/p&gt;

&lt;p&gt;Any help is welcome&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'bigdata  scalability', NULL, '''/code'':157C ''/p'':42C,62C,98C,134C,164C,191C,197C,200C ''/pre'':158C ''0.01'':143C ''1'':118C,121C,144C ''100'':146C ''167'':150C ''2'':109C ''262'':152C ''294'':139C ''296'':141C ''32'':110C ''4'':138C ''4.79'':153C,160C ''41'':149C ''512'':151C ''7'':156C ''967'':140C ''abl'':71C ''account'':104C ''alreadi'':182C ''answer'':50C ''avoid'':88C ''better'':86C ''bigdata'':10B ''billion'':30C ''bloom'':78C ''check'':6A ''close'':66C ''code'':136C ''crawler'':174C ''data'':67C,96C ''dedupl'':26C ''describ'':18C ''effici'':2A ''element'':60C ''error'':114C ''ever'':57C ''exampl'':101C ''fals'':8A,83C ''far'':75C ''filter'':79C ''find'':73C ''gb'':154C,161C ''hash'':187C ''help'':194C ''high'':171C ''huge'':27C ''initi'':14C ''item'':32C,37C,111C ''k'':155C ''keep'':179C ''know'':48C ''least'':108C,133C ''like'':19C ''list'':28C ''loss'':97C ''m'':148C ''mean'':120C ''membership'':5A ''memori'':1A,163C ''n'':137C ''need'':46C,131C,177C ''origin'':36C ''p'':12C,43C,63C,99C,142C,159C,165C,192C,198C ''posit'':9A,84C,113C ''potenti'':29C ''pre'':135C ''provid'':102C ''question'':53C ''rate'':115C ''requir'':24C ''result'':94C ''scalabl'':11B ''scale'':172C ''seen'':58C ''sha1'':186C ''simpli'':40C ''store'':34C,106C ''structur'':3A,68C ''system'':56C ''task'':15C,91C,167C ''thank'':199C ''track'':180C ''unafford'':41C ''url'':124C,184C,190C ''visit'':128C,183C ''web'':173C ''welcom'':196C ''without'':7A,33C ''won'':125C ''would'':130C ''â'':147C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9305, 2, '2015-12-10 10:09:58.93', 0, NULL, NULL, '&lt;p&gt;I am voting to close it as off-topic.  (StudentT has already given a wonderful answer :) )&lt;/p&gt;

&lt;p&gt;Firstly, it is up to you to take the decision.&lt;/p&gt;

&lt;p&gt;You have to have a passion for data, and willing to get your feet dirty at it.&lt;/p&gt;

&lt;p&gt;It is not the end, if your colleagues are not mentoring you. No one mentored me when I was preparing myself. I have written &lt;a href="https://www.quora.com/How-can-I-find-mentors-to-guide-me-to-become-a-data-scientist-at-Facebook-Google-or-LinkedIn/answer/Jalem-Raj-Rohit" rel="nofollow"&gt;an answer on Quora&lt;/a&gt; about how I disturbed a lot of people till they mentored me. In fact, they were very happy to.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Will it be a wise decision to leave a company where I''ve an
  opportunity to see product from end to end and go to a company where
  I''ll not the same visibility of product ?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The domain of data science is very difficult. Let me cite an example from my team.  Me, being the lead, have an idea of the entire product end-to-end, however the ones in my team working on customer success analytics don''t have the complete idea on the entire product(in fact, they don''t need to), and vice versa.  So, it is completely dependant on the role.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Is my next opportunity a good one if I want to make my career in big
  data space ?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Big data space?  &lt;strong&gt;Yes&lt;/strong&gt;  In data science? Maybe, but you have a &lt;a href="http://nirvacana.com/thoughts/becoming-a-data-scientist/" rel="nofollow"&gt;lot of ground to 
cover&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':87C,269C ''/blockquote'':147C,241C ''/how-can-i-find-mentors-to-guide-me-to-become-a-data-scientist-at-facebook-google-or-linkedin/answer/jalem-raj-rohit'':80C ''/p'':19C,31C,50C,107C,146C,219C,240C,270C ''/strong'':248C ''/thoughts/becoming-a-data-scientist/'':261C ''alreadi'':14C ''analyt'':190C ''answer'':18C,84C ''big'':237C,243C ''blockquot'':108C,220C ''career'':235C ''cite'':159C ''close'':6C ''colleagu'':59C ''compani'':119C,136C ''complet'':195C,214C ''cover'':268C ''custom'':188C ''data'':40C,152C,238C,244C,250C ''decis'':30C,115C ''depend'':215C ''difficult'':156C ''dirti'':47C ''disturb'':91C ''domain'':150C ''end'':56C,129C,131C,177C,179C ''end-to-end'':176C ''entir'':174C,199C ''exampl'':161C ''fact'':101C,202C ''feet'':46C ''first'':21C ''get'':44C ''given'':15C ''go'':133C ''good'':227C ''ground'':266C ''happi'':105C ''howev'':180C ''href'':77C,258C ''idea'':171C,196C ''lead'':168C ''leav'':117C ''let'':157C ''ll'':139C ''lot'':93C,264C ''make'':233C ''mayb'':252C ''mentor'':62C,66C,98C ''need'':206C ''next'':224C ''nirvacana.com'':260C ''nirvacana.com/thoughts/becoming-a-data-scientist/'':259C ''nofollow'':82C,263C ''off-top'':9C ''one'':65C,182C,228C ''opportun'':124C,225C ''p'':1C,20C,32C,51C,109C,148C,221C,242C ''passion'':38C ''peopl'':95C ''prepar'':71C ''product'':127C,145C,175C,200C ''quora'':86C ''rel'':81C,262C ''role'':218C ''scienc'':153C,251C ''see'':126C ''space'':239C,245C ''strong'':246C ''studentt'':12C ''success'':189C ''take'':28C ''team'':164C,185C ''till'':96C ''topic'':11C ''ve'':122C ''versa'':210C ''vice'':209C ''visibl'':143C ''vote'':4C ''want'':231C ''will'':42C ''wise'':114C ''wonder'':17C ''work'':186C ''written'':75C ''www.quora.com'':79C ''www.quora.com/how-can-i-find-mentors-to-guide-me-to-become-a-data-scientist-at-facebook-google-or-linkedin/answer/jalem-raj-rohit'':78C ''yes'':247C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8614, 2, '2015-10-27 02:48:34.59', 1, NULL, NULL, '&lt;p&gt;You could start by learning R or Python and Machine Learning in one of those languages. &lt;/p&gt;

&lt;p&gt;Some other free options for books are &lt;em&gt;The LION Way: Machine Learning plus Intelligent Optimization&lt;/em&gt; and &lt;em&gt;Learning Deep Architectures for AI&lt;/em&gt;, which are both freely available online.&lt;/p&gt;

&lt;p&gt;There''s also the free and open &lt;a href="https://www.edx.org/course/artificial-intelligence-uc-berkeleyx-cs188-1x" rel="nofollow"&gt;Artificial Intelligence course by Berkeley&lt;/a&gt; at edX.&lt;/p&gt;

&lt;p&gt;I have listed all the free books I could find on &lt;a href="http://www.learndatasci.com/free-books/" rel="nofollow"&gt;machine learning and artificial intelligence books&lt;/a&gt; at LearnDataSci, if you''re interested.&lt;/p&gt;
', NULL, NULL, '''/a'':72C,101C ''/course/artificial-intelligence-uc-berkeleyx-cs188-1x'':64C ''/em'':36C,44C ''/free-books/'':92C ''/p'':18C,51C,75C,108C ''ai'':43C ''also'':55C ''architectur'':41C ''artifici'':67C,98C ''avail'':49C ''berkeley'':71C ''book'':25C,83C,100C ''could'':3C,85C ''cours'':69C ''deep'':40C ''edx'':74C ''em'':27C,38C ''find'':86C ''free'':22C,57C,82C ''freeli'':48C ''href'':61C,89C ''intellig'':34C,68C,99C ''interest'':107C ''languag'':17C ''learn'':6C,12C,32C,39C,96C ''learndatasci'':103C ''lion'':29C ''list'':79C ''machin'':11C,31C,95C ''nofollow'':66C,94C ''one'':14C ''onlin'':50C ''open'':59C ''optim'':35C ''option'':23C ''p'':1C,19C,52C,76C ''plus'':33C ''python'':9C ''r'':7C ''re'':106C ''rel'':65C,93C ''start'':4C ''way'':30C ''www.edx.org'':63C ''www.edx.org/course/artificial-intelligence-uc-berkeleyx-cs188-1x'':62C ''www.learndatasci.com'':91C ''www.learndatasci.com/free-books/'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8615, 1, '2015-10-27 05:09:09.397', 0, 22, 'Regarding the hinge loss', '&lt;p&gt;Given a feature vector $x_i \in \mathbb{R}^D$ and the corresponding label vector $y_i \in \{1,\ldots,K\}$, and assuming that there is a classifier $x_i \mapsto f(x_i;\Theta)\in\mathbb{R}^K$ such as the largest component indicates the estimated class for the feature vector $x_i$ (i.e. $\hat{y}_i = \arg\max f(x_i;\Theta)$), I found two different ways to specify the hinge loss. &lt;/p&gt;

&lt;p&gt;The first one is as follows:
$$l(x_i,y_i;\Theta) = \sum_{k=1}^K\max\left\{0,\Delta - (2\mathbb{1}(y_i=k)-1)\left[f(x_i;\Theta)\right]_k\right\}$$
(where $\mathbb{1}(c)$ is 1 if the condition $c$ is true and zero otherwise). My interpretation is that the components corresponding to the wrong classes should be less or equal that $-\Delta$ and the component corresponding to the correct class should be larger than $\Delta$. &lt;/p&gt;

&lt;p&gt;The second formulation is:
$$l(x_i,y_i;\Theta) = \sum_{k=1, k\neq y_i}\max\left\{0,\Delta +\left[f(x_i;\Theta)\right]_k-\left[f(x_i;\Theta)\right]_{y_i}\right\}$$
My interpretation is that the component corresponding to the correct class should be bigger than any of the other components by at least $\Delta$.&lt;/p&gt;

&lt;p&gt;My questions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Are the two formulations equivalent?&lt;/li&gt;
&lt;li&gt;If they are not, are there specific classifiers for which they are (e.g. a linear classifier $f(x;W,b)=Wx+b$?   &lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  loss-function', NULL, '''-1'':114C ''/li'':244C,268C ''/ol'':269C ''/p'':86C,169C,232C,236C ''0'':106C,190C ''1'':30C,102C,110C,125C,128C,183C ''2'':108C ''arg'':70C ''assum'':34C ''b'':265C,267C ''bigger'':221C ''c'':126C,132C ''class'':59C,148C,163C,218C ''classifi'':39C,253C,261C ''compon'':55C,143C,158C,213C,227C ''condit'':131C ''correct'':162C,217C ''correspond'':24C,144C,159C,214C ''d'':21C ''delta'':107C,155C,168C,191C,231C ''differ'':79C ''e.g'':258C ''equal'':153C ''equival'':243C ''estim'':58C ''f'':43C,72C,116C,193C,200C,262C ''featur'':14C,62C ''first'':89C ''follow'':93C ''formul'':173C,242C ''found'':77C ''function'':10B ''given'':12C ''hat'':67C ''hing'':3A,84C ''i.e'':66C ''indic'':56C ''interpret'':139C,209C ''k'':32C,50C,101C,103C,113C,121C,182C,184C,198C ''l'':94C,175C ''label'':25C ''larger'':166C ''largest'':54C ''ldot'':31C ''learn'':7B ''least'':230C ''left'':105C,115C,189C,192C,199C ''less'':151C ''li'':238C,245C ''linear'':260C ''loss'':4A,9B,85C ''loss-funct'':8B ''machin'':6B ''machine-learn'':5B ''mapsto'':42C ''mathbb'':19C,48C,109C,124C ''max'':71C,104C,188C ''neq'':185C ''ol'':237C ''one'':90C ''otherwis'':137C ''p'':11C,87C,170C,233C ''question'':235C ''r'':20C,49C ''regard'':1A ''right'':120C,122C,197C,204C,207C ''second'':172C ''specif'':252C ''specifi'':82C ''sum'':100C,181C ''theta'':46C,75C,99C,119C,180C,196C,203C ''true'':134C ''two'':78C,241C ''vector'':15C,26C,63C ''w'':264C ''way'':80C ''wrong'':147C ''wx'':266C ''x'':16C,40C,44C,64C,73C,95C,117C,176C,194C,201C,263C ''y'':27C,68C,97C,111C,178C,186C,205C ''zero'':136C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8651, 2, '2015-10-29 19:01:21.14', 0, NULL, NULL, '&lt;p&gt;This is a &lt;a href="https://en.wikipedia.org/wiki/Random_walk" rel="nofollow"&gt;random walk&lt;/a&gt; problem.  You could write a fairly simple random walk simulation which ends as soon as the tourist is 60 city blocks away from the starting location.  At any given time you only need to know the tourist''s x,y coordinates.  Then each time-step, you pick a direction and increment either x or y.  You then need to keep track of distance from the origin (0,0).  Presumably this should be Euclidean distance, since it''s all as the crow flies.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;decide direction to move&lt;/li&gt;
&lt;li&gt;update x,y and distance&lt;/li&gt;
&lt;li&gt;check if any of the distance requirements mentioned are met and store the timestep at which it occured&lt;/li&gt;
&lt;li&gt;if distance &gt; 60 blocks, terminate&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':14C ''/li'':105C,112C,132C,139C ''/p'':98C ''/ul'':140C ''/wiki/random_walk'':9C ''0'':82C,83C ''60'':33C,136C ''away'':36C ''block'':35C,137C ''check'':114C ''citi'':34C ''coordin'':55C ''could'':17C ''crow'':96C ''decid'':101C ''direct'':64C,102C ''distanc'':78C,89C,111C,119C,135C ''either'':67C ''en.wikipedia.org'':8C ''en.wikipedia.org/wiki/random_walk'':7C ''end'':26C ''euclidean'':88C ''fair'':20C ''fli'':97C ''given'':43C ''href'':6C ''increment'':66C ''keep'':75C ''know'':49C ''li'':100C,106C,113C,133C ''locat'':40C ''mention'':121C ''met'':123C ''move'':104C ''need'':47C,73C ''nofollow'':11C ''occur'':131C ''origin'':81C ''p'':1C ''pick'':62C ''presum'':84C ''problem'':15C ''random'':12C,22C ''rel'':10C ''requir'':120C ''simpl'':21C ''simul'':24C ''sinc'':90C ''soon'':28C ''start'':39C ''step'':60C ''store'':125C ''termin'':138C ''time'':44C,59C ''time-step'':58C ''timestep'':127C ''tourist'':31C,51C ''track'':76C ''ul'':99C ''updat'':107C ''walk'':13C,23C ''write'':18C ''x'':53C,68C,108C ''y'':54C,70C,109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8638, 1, '2015-10-28 20:10:27.143', 0, 42, 'Unsupervised sequence identification', '&lt;p&gt;I am looking for the best method to go from a sequence of events such as&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;time event
1 a
2 b
3 a
4 b
5 c
6 d
7 c
8 d
9 e
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Where each letter corresponds to a certain event that occurs at a time. I want to reduce the number of events by aggregating frequently occurring events into a new event. A possible solution data set would look like,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1 a''
2 a''
3 a''
4 a'' 
5 b''
6 b''
7 b''
8 b''
9 e
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where the clusters are created because they occur in a sequence following each other. &lt;/p&gt;

&lt;p&gt;I was looking at the text mining algorithms in R with tm or RNA sequenceing
with edgeR. But I have no experience in this so I was hoping that someone can
shed me some light on a common approach for this type of problems.&lt;/p&gt;
', 'feature-extraction  sequence', NULL, '''/code'':48C,110C ''/p'':25C,89C,127C,173C ''/pre'':49C,111C ''1'':30C,92C ''2'':32C,94C ''3'':34C,96C ''4'':36C,98C ''5'':38C,100C ''6'':40C,102C ''7'':42C,104C ''8'':44C,106C ''9'':46C,108C ''aggreg'':73C ''algorithm'':136C ''approach'':167C ''b'':33C,37C,101C,103C,105C,107C ''best'':14C ''c'':39C,43C ''certain'':57C ''cluster'':115C ''code'':27C,91C ''common'':166C ''correspond'':54C ''creat'':117C ''d'':41C,45C ''data'':84C ''e'':47C,109C ''edger'':145C ''event'':22C,29C,58C,71C,76C,80C ''experi'':150C ''extract'':6B ''featur'':5B ''feature-extract'':4B ''follow'':124C ''frequent'':74C ''go'':17C ''hope'':156C ''identif'':3A ''letter'':53C ''light'':163C ''like'':88C ''look'':11C,87C,131C ''method'':15C ''mine'':135C ''new'':79C ''number'':69C ''occur'':60C,75C,120C ''p'':8C,50C,112C,128C ''possibl'':82C ''pre'':26C,90C ''problem'':172C ''r'':138C ''reduc'':67C ''rna'':142C ''sequenc'':2A,7B,20C,123C,143C ''set'':85C ''shed'':160C ''solut'':83C ''someon'':158C ''text'':134C ''time'':28C,63C ''tm'':140C ''type'':170C ''unsupervis'':1A ''want'':65C ''would'':86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8616, 1, '2015-10-27 09:55:31.54', 2, 42, 'ngram and RNN prediction rate wrt word index', '&lt;p&gt;I tried to plot the rate of correct predictions (for top 1 shortlist) with relation to word''s position in sentence : &lt;a href="http://i.stack.imgur.com/NjMvs.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/NjMvs.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I was expecting to see a plateau sooner on the ngram setup since it need less context. However one thing I wasn''t expecting was that the prediction rate drops.
In my understanding since we already have a context of 3 words, the plateau should converge asymptotically to its highest value.
But both the recurrent network and the Ngram models are experiencing this drop.
I have no idea why it would be.&lt;/p&gt;

&lt;p&gt;(Note RNNLM is name of the framework used to build the recurrent neural net, it uses 500 neurones and 100M direct connections, RNN25 is the same setup but with a training base divided by for)&lt;/p&gt;

&lt;p&gt;Here is the sentence size distribution :
&lt;a href="http://i.stack.imgur.com/MxSY1.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/MxSY1.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Thanks in advance.&lt;/p&gt;
', 'neuralnetwork  statistics  nlp', NULL, '''/a'':51C,188C ''/mxsy1.png'':175C,182C ''/njmvs.png'':38C,45C ''/p'':52C,126C,163C,189C,194C ''1'':24C ''100m'':147C ''3'':94C ''500'':144C ''advanc'':193C ''alreadi'':89C ''alt'':46C,183C ''asymptot'':100C ''base'':159C ''build'':137C ''connect'':149C ''context'':70C,92C ''converg'':99C ''correct'':20C ''descript'':49C,186C ''direct'':148C ''distribut'':170C ''divid'':160C ''drop'':83C,117C ''enter'':47C,184C ''expect'':56C,77C ''experienc'':115C ''framework'':134C ''highest'':103C ''howev'':71C ''href'':35C,172C ''i.stack.imgur.com'':37C,44C,174C,181C ''i.stack.imgur.com/mxsy1.png'':173C,180C ''i.stack.imgur.com/njmvs.png'':36C,43C ''idea'':121C ''imag'':48C,185C ''img'':41C,178C ''index'':8A ''less'':69C ''model'':113C ''name'':131C ''need'':68C ''net'':141C ''network'':109C ''neural'':140C ''neuralnetwork'':9B ''neuron'':145C ''ngram'':1A,64C,112C ''nlp'':11B ''nofollow'':40C,177C ''note'':128C ''one'':72C ''p'':12C,53C,127C,164C,190C ''plateau'':60C,97C ''plot'':16C ''posit'':31C ''predict'':4A,21C,81C ''rate'':5A,18C,82C ''recurr'':108C,139C ''rel'':39C,176C ''relat'':27C ''rnn'':3A ''rnn25'':150C ''rnnlm'':129C ''see'':58C ''sentenc'':33C,168C ''setup'':65C,154C ''shortlist'':25C ''sinc'':66C,87C ''size'':169C ''sooner'':61C ''src'':42C,179C ''statist'':10B ''thank'':191C ''thing'':73C ''top'':23C ''train'':158C ''tri'':14C ''understand'':86C ''use'':135C,143C ''valu'':104C ''wasn'':75C ''word'':7A,29C,95C ''would'':124C ''wrt'':6A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (836, 2, '2014-07-25 04:54:07.787', 14, NULL, NULL, '&lt;p&gt;Here some resources that might be helpful:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Recommenderlab&lt;/strong&gt; - a framework and open source software for developing and testing recommendation algorithms: &lt;a href="http://lyle.smu.edu/IDA/recommenderlab"&gt;http://lyle.smu.edu/IDA/recommenderlab&lt;/a&gt;. Corresponding &lt;code&gt;R&lt;/code&gt; package &lt;code&gt;recommenderlab&lt;/code&gt;: &lt;a href="http://cran.r-project.org/package=recommenderlab"&gt;http://cran.r-project.org/package=recommenderlab&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;The following blog post illustrates the use of &lt;code&gt;recommenderlab&lt;/code&gt; package (which IMHO can be generalized for any open source recommendation engine) for building &lt;strong&gt;movie recommendation&lt;/strong&gt; application, based on &lt;em&gt;collaborative filtering&lt;/em&gt;: &lt;a href="http://scn.sap.com/community/developer-center/hana/blog/2013/11/06/movie-recommendation-by-leveraging-r"&gt;http://scn.sap.com/community/developer-center/hana/blog/2013/11/06/movie-recommendation-by-leveraging-r&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Research on recommender systems&lt;/strong&gt; - a nice webpage with resources on the topic, maintained by Recommenderlab''s lead developer Michael Hahsler: &lt;a href="http://michael.hahsler.net/research/recommender"&gt;http://michael.hahsler.net/research/recommender&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Mortar Recommendation Engine&lt;/strong&gt; - an open source customizable recommendation engine for &lt;code&gt;Hadoop&lt;/code&gt; and &lt;code&gt;Pig&lt;/code&gt;, written in &lt;code&gt;Python&lt;/code&gt; and &lt;code&gt;Java&lt;/code&gt;: &lt;a href="https://github.com/mortardata/mortar-recsys"&gt;https://github.com/mortardata/mortar-recsys&lt;/a&gt;. Company, sponsoring the development of this project, &lt;strong&gt;Mortar Data&lt;/strong&gt;, offers general commercial &lt;em&gt;cloud platform&lt;/em&gt; for development and hosting &lt;em&gt;data science&lt;/em&gt; software projects, including ones based on the &lt;code&gt;Mortar Recommendation Engine&lt;/code&gt; (development and hosting of public projects are free): &lt;a href="http://www.mortardata.com"&gt;http://www.mortardata.com&lt;/a&gt;. Mortar Data provides help in form of &lt;strong&gt;public Q&amp;amp;A forum&lt;/strong&gt; (&lt;a href="https://answers.mortardata.com"&gt;https://answers.mortardata.com&lt;/a&gt;) as well as a &lt;strong&gt;comprehensive tutorial&lt;/strong&gt; on building recommendation engine using open technologies (&lt;a href="http://help.mortardata.com/data_apps/recommendation_engine"&gt;http://help.mortardata.com/data_apps/recommendation_engine&lt;/a&gt;).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;"Introduction to Recommender Systems"&lt;/strong&gt; - a relevant Coursera course (MOOC), which content and description provide additional resources on the topic: &lt;a href="https://www.coursera.org/course/recsys"&gt;https://www.coursera.org/course/recsys&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;PredictionIO&lt;/strong&gt; - an open source machine learning server software, which allows building &lt;em&gt;data science applications&lt;/em&gt;, including &lt;em&gt;recommendation systems&lt;/em&gt;: &lt;a href="http://prediction.io"&gt;http://prediction.io&lt;/a&gt; (source code is available on GitHub: &lt;a href="https://github.com/PredictionIO"&gt;https://github.com/PredictionIO&lt;/a&gt;). &lt;code&gt;PredictionIO&lt;/code&gt; includes a built-in &lt;em&gt;recommendation engine&lt;/em&gt; (&lt;a href="http://docs.prediction.io/current/engines/itemrec/index.html"&gt;http://docs.prediction.io/current/engines/itemrec/index.html&lt;/a&gt;) and supports a wide range of programming languages and frameworks via &lt;code&gt;RESTful&lt;/code&gt; APIs as well as SDKs/plug-ins. PredictionIO maintains an &lt;code&gt;Amazon Machine Image&lt;/code&gt; on &lt;strong&gt;AWS Marketplace&lt;/strong&gt; for deploying applications on the AWS infrastructure: &lt;a href="https://aws.amazon.com/marketplace/pp/B00ECGJYGE"&gt;https://aws.amazon.com/marketplace/pp/B00ECGJYGE&lt;/a&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Additional open source software projects&lt;/strong&gt;, relevant to the topic (discovered via &lt;code&gt;MLOSS&lt;/code&gt; website on &lt;em&gt;machine learning&lt;/em&gt; open source software: &lt;a href="http://www.mloss.org"&gt;http://www.mloss.org&lt;/a&gt;):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Jubatus&lt;/strong&gt;: &lt;a href="http://jubat.us/en"&gt;http://jubat.us/en&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;MyMediaLite&lt;/strong&gt;: &lt;a href="http://mymedialite.net"&gt;http://mymedialite.net&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;TBEEF&lt;/strong&gt;: &lt;a href="https://github.com/ChrisRackauckas/TBEEF"&gt;https://github.com/ChrisRackauckas/TBEEF&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;PREA&lt;/strong&gt;: &lt;a href="http://prea.gatech.edu"&gt;http://prea.gatech.edu&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;CofiRank&lt;/strong&gt;: &lt;a href="http://www.cofirank.org"&gt;http://www.cofirank.org&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;The following relevant &lt;code&gt;R&lt;/code&gt; &lt;strong&gt;blog posts&lt;/strong&gt; are also interesting:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;"Simple tools for building a recommendation engine" (&lt;a href="http://blog.revolutionanalytics.com/2012/04/simple-tools-for-building-a-recommendation-engine.html"&gt;http://blog.revolutionanalytics.com/2012/04/simple-tools-for-building-a-recommendation-engine.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;"Recommendation System in R" (&lt;a href="http://blog.yhathq.com/posts/recommender-system-in-r.html"&gt;http://blog.yhathq.com/posts/recommender-system-in-r.html&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (951, 2, '2014-08-08 20:46:58.493', 2, NULL, NULL, '&lt;p&gt;Not sure anybody have worked with &lt;em&gt;all&lt;/em&gt; these tools, so I''m going to share my experience with some of them and let others share their experience with the others. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;MongoDB&lt;/strong&gt; addresses problems that involve heterogeneous and nested objects, while data mining mostly works with simple tabular data. MongoDB is neither fast with this type of data, nor provide any advanced tools for analysis (correct me if you know any). So I can think of a very few applications for Mongo in data mining. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Hadoop&lt;/strong&gt; is a large ecosystem, containing dozens of different tools. I will assume that you mean core Hadoop features - HDFS and MapReduce. HDFS provides flexible way to store large amounts of data, while MapReduce gives basis for processing them. It has its clear advantages for processing multi-terabyte datasets, but it also has significant drawbacks. In particular, because of intensive disk IO during MapReduce tasks (that slows down computations a lot) it is terrible for interactive development, iterative algorithms and working with not-that-big datasets. For more details see my &lt;a href="http://datascience.stackexchange.com/a/863/1279"&gt;earlier answer&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Many algorithms in Hadoop require multiple MapReduce jobs with complicated data flow. This is where &lt;strong&gt;Scalding&lt;/strong&gt; gets shiny. Scalding (and underlying Java''s &lt;a href="http://www.cascading.org/" rel="nofollow"&gt;Cascading&lt;/a&gt;) provides much simpler API, but at the moment uses same MapReduce as its runtime and thus holds all the same issues.  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Spark&lt;/strong&gt; addresses exactly these issues. It drops Hadoop''s MapReduce and offers completely new computational framework based on distributed in-memory collections and delayed evaluations. Its API is somewhat similar to Scalding''s with all MR complexity removed, so it''s really easy to get started with it. Spark is also the first in this list that comes with data mining library - &lt;a href="https://spark.apache.org/docs/latest/mllib-guide.html" rel="nofollow"&gt;MLlib&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;But Spark doesn''t reinvent things like basic linear algebra. For this purpose it uses &lt;strong&gt;Breeze&lt;/strong&gt;. To my opinion, Breeze is far in quality from scientific packages like SciPy, Octave or Julia, but it is still good enough for most practical use cases. &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Mahout&lt;/strong&gt; relies on Hadoop''s MapReduce and thus is terrible for iterative algorithms. &lt;strong&gt;Spire&lt;/strong&gt; and &lt;strong&gt;Saddle&lt;/strong&gt; look cute and probably have their niche, but seem to be much less well-known than Breeze. I couldn''t find much information about &lt;strong&gt;H2O&lt;/strong&gt;, so it doesn''t look like a big player here (comments from people who used it are welcome).&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Some quick summary. Spark seems to be the most simple, flexible and fast-growing project for large-scale data processing. It facilitates a number of new projects (e.g. Shark or Spark SQL) and penetrates into existing (including &lt;a href="http://www.concurrentinc.com/2014/05/cascading-now-supports-tez-spark-and-storm-up-next/" rel="nofollow"&gt;Cascading&lt;/a&gt; and &lt;a href="https://mahout.apache.org/users/sparkbindings/home.html" rel="nofollow"&gt;Mahout&lt;/a&gt;). Spark knows how to utilize HDFS API, and thus scales to terabytes of data easily. And for data mining downshifters who don''t want to bother with a cluster setup there''s always pure Breeze. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8617, 2, '2015-10-27 13:53:26.117', 0, NULL, NULL, '&lt;p&gt;I don''t see it explicitly discussed in the documentation, but it appears that the numbers on the right side of the screen are the stopping criteria.  That run appears to have stopped because the error on the validation set increased six times (explained &lt;a href="http://www.mathworks.com/help/nnet/ref/plotperform.html" rel="nofollow"&gt;here&lt;/a&gt;).  I''m guessing it would also stop after 1000 epochs, when 0 MSE was achieved, the gradient was below 1e-6, or after 4 resets.&lt;/p&gt;

&lt;p&gt;Thus the number to the left of the bar should be the MSE on the epoch with the best validation error*.  I''m guessing the value of the blue bar is the lowest that MSE got during the iterations that were actually increasing the validation error.&lt;/p&gt;

&lt;p&gt;From the docs:
"In the default setup, the training stops after six consecutive increases in validation error, and the best performance is taken from the epoch with the lowest validation error."&lt;/p&gt;
', NULL, NULL, '''/a'':54C ''/help/nnet/ref/plotperform.html'':50C ''/p'':79C,127C,160C ''0'':66C ''1000'':63C ''1e-6'':74C ''4'':77C ''achiev'':69C ''actual'':122C ''also'':60C ''appear'':14C,31C ''bar'':89C,110C ''best'':99C,148C ''blue'':109C ''consecut'':141C ''criteria'':28C ''default'':134C ''discuss'':8C ''doc'':131C ''document'':11C ''epoch'':64C,96C,154C ''error'':37C,101C,126C,145C,159C ''explain'':45C ''explicit'':7C ''got'':116C ''gradient'':71C ''guess'':57C,104C ''href'':47C ''increas'':42C,123C,142C ''iter'':119C ''left'':86C ''lowest'':113C,157C ''m'':56C,103C ''mse'':67C,93C,115C ''nofollow'':52C ''number'':17C,83C ''p'':1C,80C,128C ''perform'':149C ''rel'':51C ''reset'':78C ''right'':20C ''run'':30C ''screen'':24C ''see'':5C ''set'':41C ''setup'':135C ''side'':21C ''six'':43C,140C ''stop'':27C,34C,61C,138C ''taken'':151C ''thus'':81C ''time'':44C ''train'':137C ''valid'':40C,100C,125C,144C,158C ''valu'':106C ''would'':59C ''www.mathworks.com'':49C ''www.mathworks.com/help/nnet/ref/plotperform.html'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8618, 1, '2015-10-27 14:09:44.397', 0, 37, 'Understanding the Hypothesis Space of the Bayes Optimal Classifier', '&lt;p&gt;This is part of the play tennis data set. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt; Outlook | Temp | Humidity |   Wind | Play Tennis?
    Rain    Hot       High     Weak             No
   Sunny    Hot       High   Strong             No
Overcast    Hot       High     Weak            Yes 
    Rain   Mild       High     Weak            Yes
    Rain   Cool     Normal     Weak            Yes
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Given this data $P(PlayTennis=Yes | Outlook=Rain)$ would be&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;prior = 3/5
likelihood = 2/3
data = 3/5
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;$\frac{prior * likelihood}{data}=0.66$ My understanding is that PlayTennis=Yes is the hypothesis and Outlook=Rain is the data or evidence, i.e. $P(h|D)$. Similarly, $P(PlayTennis=No | Outlook=Rain)=0.5$. In the Tom Mitchell Machine Learning book it indicates that the $h_{MAP}$ here would be PlayTennis=Yes as the most likely hypothesis. There is then a distinction made that finding the most likely hypothesis is not the same as the most likely classification. The latter is defined as &lt;/p&gt;

&lt;p&gt;$argmax_{v_j \in V} \sum_{h_i \in H}P(v_j|h_i)P(h_i|D)$&lt;/p&gt;

&lt;p&gt;which is the Bayes Optimal Classifier. Is it not the case that there are only two hypotheses, Yes and No? When I see descriptions of Bayes Optimal&lt;/p&gt;

&lt;p&gt;&lt;a href="http://jmvidal.cse.sc.edu/talks/bayesianlearning/bayesopclass.xml" rel="nofollow"&gt;http://jmvidal.cse.sc.edu/talks/bayesianlearning/bayesopclass.xml&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;there appears to be more hypotheses than the two Yes/No for the PlayTennis target. If we are using the given data set and no extra knowledge to define priors how is it that we can have alternative hypotheses? If we are using additional knowledge to determine prior, how would we then find the likelihood in our data set?&lt;/p&gt;
', 'classification', NULL, '''/code'':55C,77C ''/p'':21C,68C,162C,183C,212C,283C ''/pre'':56C,78C ''/talks/bayesianlearning/bayesopclass.xml'':218C ''/talks/bayesianlearning/bayesopclass.xml&lt;/a&gt;&lt;/p&gt;'':223C ''0.5'':112C ''0.66'':84C ''2/3'':74C ''3/5'':72C,76C ''addit'':267C ''altern'':261C ''appear'':226C ''argmax'':164C ''bay'':7A,188C,210C ''book'':119C ''case'':195C ''classif'':10B,156C ''classifi'':9A,190C ''code'':23C,70C ''cool'':51C ''d'':105C,182C ''data'':19C,60C,75C,83C,99C,245C,281C ''defin'':160C,252C ''descript'':208C ''determin'':270C ''distinct'':140C ''evid'':101C ''extra'':249C ''find'':143C,276C ''frac'':80C ''given'':58C,244C ''h'':104C,124C,170C,173C,177C,180C ''high'':32C,37C,42C,47C ''hot'':31C,36C,41C ''href'':215C ''humid'':26C ''hypothes'':201C,230C,262C ''hypothesi'':3A,93C,135C,147C ''i.e'':102C ''indic'':121C ''j'':166C,176C ''jmvidal.cse.sc.edu'':217C,222C ''jmvidal.cse.sc.edu/talks/bayesianlearning/bayesopclass.xml'':216C ''jmvidal.cse.sc.edu/talks/bayesianlearning/bayesopclass.xml&lt;/a&gt;&lt;/p&gt;'':221C ''knowledg'':250C,268C ''latter'':158C ''learn'':118C ''like'':134C,146C,155C ''likelihood'':73C,82C,278C ''machin'':117C ''made'':141C ''map'':125C ''mild'':46C ''mitchel'':116C ''nofollow'':220C ''normal'':52C ''optim'':8A,189C,211C ''outlook'':24C,64C,95C,110C ''overcast'':40C ''p'':11C,57C,61C,79C,103C,107C,163C,174C,179C,184C,213C,224C ''part'':14C ''play'':17C,28C ''playtenni'':62C,89C,108C,129C,237C ''pre'':22C,69C ''prior'':71C,81C,253C,271C ''rain'':30C,45C,50C,65C,96C,111C ''rel'':219C ''see'':207C ''set'':20C,246C,282C ''similar'':106C ''space'':4A ''strong'':38C ''sum'':169C ''sunni'':35C ''target'':238C ''temp'':25C ''tenni'':18C,29C ''tom'':115C ''two'':200C,233C ''understand'':1A,86C ''use'':242C,266C ''v'':165C,168C,175C ''weak'':33C,43C,48C,53C ''wind'':27C ''would'':66C,127C,273C ''yes'':44C,49C,54C,63C,90C,130C,202C ''yes/no'':234C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8619, 2, '2015-10-27 15:15:57.083', 1, NULL, NULL, '&lt;p&gt;In order to show a correlation between X and Y they should be aggregated over the same time period. Otherwise, there there may be seasonality or other features in Y that may have been averaged out in X. Is it possible re-index X to get monthly aggregates? I would not sub-sample Y. Find a common time period for both X and Y to make your comparisons.&lt;/p&gt;
', NULL, NULL, '''/p'':72C ''aggreg'':15C,50C ''averag'':36C ''common'':60C ''comparison'':71C ''correl'':7C ''featur'':29C ''find'':58C ''get'':48C ''index'':45C ''make'':69C ''may'':24C,33C ''month'':49C ''order'':3C ''otherwis'':21C ''p'':1C ''period'':20C,62C ''possibl'':42C ''re'':44C ''re-index'':43C ''sampl'':56C ''season'':26C ''show'':5C ''sub'':55C ''sub-sampl'':54C ''time'':19C,61C ''would'':52C ''x'':9C,39C,46C,65C ''y'':11C,31C,57C,67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8620, 1, '2015-10-27 15:29:35.667', 0, 19, 'How to model time series data for the purpose of understanding mechanisms of posting behaviors', '&lt;p&gt;I''ve got a data set on post volume with regard to a variety of keywords in Sina Weibo over about a year''s time. The data involve different aspects of microblogging behaviors (e.g., &lt;em&gt;retweet&lt;/em&gt;, &lt;em&gt;original post&lt;/em&gt;, &lt;em&gt;posting links&lt;/em&gt;, &lt;em&gt;posting pictures&lt;/em&gt;, as well as flags indicating posts by verified and non-verified users. I want to understand how external (such as URL links) and internal factors (such as discussion by influential members in the community) influence non-verified members'' posting volume, or on the burst attention of non-verified members. Â 
I''m looking for ideas for approaches to building models and conducting hypothesis testing. Does anyone have ideas on an approach to take for this problem?&lt;/p&gt;
', 'bigdata  time-series  research', NULL, '''/em'':58C,62C,66C,70C ''/p'':150C ''anyon'':139C ''approach'':130C,144C ''aspect'':51C ''attent'':117C ''behavior'':15A,54C ''bigdata'':16B ''build'':132C ''burst'':116C ''communiti'':105C ''conduct'':135C ''data'':6A,26C,48C ''differ'':50C ''discuss'':99C ''e.g'':55C ''em'':56C,59C,63C,67C ''extern'':89C ''factor'':96C ''flag'':74C ''got'':24C ''hypothesi'':136C ''idea'':128C,141C ''indic'':75C ''influenc'':106C ''influenti'':101C ''intern'':95C ''involv'':49C ''keyword'':37C ''link'':65C,93C ''look'':126C ''m'':125C ''mechan'':12A ''member'':102C,110C,122C ''microblog'':53C ''model'':3A,133C ''non'':81C,108C,120C ''non-verifi'':80C,107C,119C ''origin'':60C ''p'':21C ''pictur'':69C ''post'':14A,29C,61C,64C,68C,76C,111C ''problem'':149C ''purpos'':9A ''regard'':32C ''research'':20B ''retweet'':57C ''seri'':5A,19B ''set'':27C ''sina'':39C ''take'':146C ''test'':137C ''time'':4A,18B,46C ''time-seri'':17B ''understand'':11A,87C ''url'':92C ''user'':83C ''varieti'':35C ''ve'':23C ''verifi'':78C,82C,109C,121C ''volum'':30C,112C ''want'':85C ''weibo'':40C ''well'':72C ''year'':44C ''â'':123C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8743, 2, '2015-11-06 16:33:56.42', 2, NULL, NULL, '&lt;p&gt;Trigram models can be more powerful for document retrieval than unigram models, but if you want to handle spelling errors, they will not be of much help.  You need some form of &lt;a href="https://en.wikipedia.org/wiki/Approximate_string_matching" rel="nofollow"&gt;fuzzy matching&lt;/a&gt; for that.&lt;/p&gt;

&lt;p&gt;For example the string, "I like dosg too" would fool a unigram model because "dosg" is likely "dogs" misspelled, and it will encode it as &lt;code&gt;"dosg" : 1&lt;/code&gt;.  But you have the same problem in a trigram model.  It will encode &lt;code&gt;"I like dosg" : 1&lt;/code&gt;, &lt;code&gt;"like dosg too" : 1&lt;/code&gt;.  Which is not really better, as it will still not match any trigrams with the word "dogs" in it.&lt;/p&gt;
', NULL, NULL, '''/a'':43C ''/code'':76C,95C,101C ''/p'':46C,121C ''/wiki/approximate_string_matching'':38C ''1'':75C,94C,100C ''better'':106C ''code'':73C,90C,96C ''document'':9C ''dog'':65C,118C ''dosg'':54C,62C,74C,93C,98C ''en.wikipedia.org'':37C ''en.wikipedia.org/wiki/approximate_string_matching'':36C ''encod'':70C,89C ''error'':21C ''exampl'':49C ''fool'':57C ''form'':32C ''fuzzi'':41C ''handl'':19C ''help'':28C ''href'':35C ''like'':53C,64C,92C,97C ''match'':42C,112C ''misspel'':66C ''model'':3C,13C,60C,86C ''much'':27C ''need'':30C ''nofollow'':40C ''p'':1C,47C ''power'':7C ''problem'':82C ''realli'':105C ''rel'':39C ''retriev'':10C ''spell'':20C ''still'':110C ''string'':51C ''trigram'':2C,85C,114C ''unigram'':12C,59C ''want'':17C ''word'':117C ''would'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1065, 2, '2014-08-30 18:36:07.49', 5, NULL, NULL, '&lt;p&gt;State of the art as in: used in practise or worked on in theory?&lt;/p&gt;

&lt;p&gt;APRIORI is used everywhere, except in developing new frequent itemset algorithms. It''s easy to implement, and easy to reuse in very different domains. You''ll find hundreds of APRIORI implementations of varying quality. And it''s easy to get APRIORI wrong, actually.&lt;/p&gt;

&lt;p&gt;FPgrowth is much harder to implement, but also much more interesting. So from an academic point of view, everybody tries to improve FPgrowth - getting work based on APRIORI accepted will be very hard by now.&lt;/p&gt;

&lt;p&gt;If you have a good implementation, every algorithm has it''s good and it''s bad situations in my opinion. A good APRIORI implementation will &lt;em&gt;only&lt;/em&gt; need to scan the database &lt;em&gt;k&lt;/em&gt; times to find all frequent itemsets of length &lt;em&gt;k&lt;/em&gt;. In particular if your data fits into main memory this is cheap. What can kill APRIORI is too many frequent 2-itemsets (in particular when you don''t use a Trie and similar acceleration techniques etc.). It works best on large data with a low number of frequent itemsets.&lt;/p&gt;

&lt;p&gt;Eclat works on columns; but it needs to read each column much more often. There is some work on diffsets to reduce this work. If your data does not fit into main memory, Eclat suffers probably more than Apriori. By going depth first, it will also be able to return a first interesting result much earlier than Apriori, and you can use these results to adjust parameters; so you need less iterations to find good parameters. But by design, it cannot exploit pruning as neatly as Apriori did.&lt;/p&gt;

&lt;p&gt;FPGrowth compresses the data set into the tree. This works best when you have lots of duplicate records. You could probably reap of quite some gains for Apriori and Eclat too if you can presort your data and merge duplicates into weighted vectors. FPGrowth does this at an extreme level. The drawback is that the implementation is much harder; and once this tree does not fit into memory anymore it gets a mess to implement.&lt;/p&gt;

&lt;p&gt;As for performance results and benchmarks - don''t trust them. There are so many things to implement incorrectly. Try 10 different implementations, and you get 10 very different performance results. In particular for APRIORI, I have the impression that most implementations are broken in the sense of missing some of the main contributions of APRIORI... and of those that have these parts right, the quality of optimizations varies a lot.&lt;/p&gt;

&lt;p&gt;There are actually even papers on how to implement these algorithms efficiently:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Efficient Implementations of Apriori and Eclat.&lt;br /&gt; Christian Borgelt&lt;br /&gt;Workshop of Frequent Item Set Mining Implementations (FIMI 2003, Melbourne, FL, USA).&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;You may also want to read these surveys on this domain:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;blockquote&gt;
  &lt;p&gt;Goethals, Bart. "Survey on frequent pattern mining." Univ. of Helsinki (2003).&lt;/p&gt;
&lt;/blockquote&gt;&lt;/li&gt;
&lt;li&gt;&lt;blockquote&gt;
  &lt;p&gt;Ferenc Bodon, A Survey on Frequent Itemset Mining, Technical Report, Budapest University of Technology and Economic, 2006, &lt;/p&gt;
&lt;/blockquote&gt;&lt;/li&gt;
&lt;li&gt;&lt;blockquote&gt;
  &lt;p&gt;Frequent Item Set Mining&lt;br /&gt;Christian Borgelt&lt;br /&gt;Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2(6):437-456. 2012&lt;/p&gt;
&lt;/blockquote&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''-456'':552C ''/blockquote'':475C,505C,528C,555C ''/em'':127C,135C,146C ''/li'':506C,529C,556C ''/p'':16C,61C,98C,196C,286C,363C,435C,449C,474C,488C,504C,527C,554C ''/ul'':557C ''10'':384C,390C ''2'':167C,549C ''2003'':470C,503C ''2006'':526C ''2012'':553C ''437'':551C ''6'':550C ''abl'':245C ''academ'':77C ''acceler'':180C ''accept'':91C ''actual'':60C,439C ''adjust'':263C ''algorithm'':28C,107C,447C ''also'':70C,243C,479C ''anymor'':356C ''apriori'':18C,47C,58C,90C,122C,162C,236C,255C,284C,315C,398C,419C,455C ''art'':5C ''bad'':115C ''bart'':494C ''base'':88C ''benchmark'':370C ''best'':185C,298C ''blockquot'':450C,491C,508C,531C ''bodon'':511C ''borgelt'':460C,539C ''br'':458C,461C,537C,540C ''broken'':407C ''budapest'':520C ''cannot'':278C ''cheap'':158C ''christian'':459C,538C ''column'':201C,208C ''compress'':289C ''contribut'':417C ''could'':307C ''data'':151C,188C,224C,291C,324C,544C ''databas'':132C ''depth'':239C ''design'':276C ''develop'':24C ''differ'':40C,385C,392C ''diffset'':217C ''discoveri'':548C ''domain'':41C,487C ''drawback'':339C ''duplic'':304C,327C ''earlier'':253C ''easi'':31C,35C,55C ''eclat'':198C,231C,317C,457C ''econom'':525C ''effici'':448C,452C ''em'':125C,133C,144C ''etc'':182C ''even'':440C ''everi'':106C ''everybodi'':81C ''everywher'':21C ''except'':22C ''exploit'':279C ''extrem'':336C ''ferenc'':510C ''fimi'':469C ''find'':44C,138C,271C ''first'':240C,249C ''fit'':152C,227C,353C ''fl'':472C ''fpgrowth'':63C,85C,288C,331C ''frequent'':26C,140C,166C,194C,464C,497C,515C,533C ''gain'':313C ''get'':57C,86C,358C,389C ''go'':238C ''goethal'':493C ''good'':104C,111C,121C,272C ''hard'':95C ''harder'':66C,346C ''helsinki'':502C ''hundr'':45C ''implement'':33C,48C,68C,105C,123C,343C,362C,381C,386C,405C,445C,453C,468C ''impress'':402C ''improv'':84C ''incorrect'':382C ''interdisciplinari'':542C ''interest'':73C,250C ''item'':465C,534C ''itemset'':27C,141C,168C,195C,516C ''iter'':269C ''k'':134C,145C ''kill'':161C ''knowledg'':547C ''larg'':187C ''length'':143C ''less'':268C ''level'':337C ''li'':490C,507C,530C ''ll'':43C ''lot'':302C,434C ''low'':191C ''main'':154C,229C,416C ''mani'':165C,378C ''may'':478C ''melbourn'':471C ''memori'':155C,230C,355C ''merg'':326C ''mess'':360C ''mine'':467C,499C,517C,536C,545C ''miss'':412C ''much'':65C,71C,209C,252C,345C ''neat'':282C ''need'':128C,204C,267C ''new'':25C ''number'':192C ''often'':211C ''opinion'':119C ''optim'':431C ''p'':1C,17C,62C,99C,197C,287C,364C,436C,451C,476C,492C,509C,532C ''paper'':441C ''paramet'':264C,273C ''part'':426C ''particular'':148C,170C,396C ''pattern'':498C ''perform'':367C,393C ''point'':78C ''practis'':10C ''presort'':322C ''probabl'':233C,308C ''prune'':280C ''qualiti'':51C,429C ''quit'':311C ''read'':206C,482C ''reap'':309C ''record'':305C ''reduc'':219C ''report'':519C ''result'':251C,261C,368C,394C ''return'':247C ''reus'':37C ''review'':543C ''right'':427C ''scan'':130C ''sens'':410C ''set'':292C,466C,535C ''similar'':179C ''situat'':116C ''state'':2C ''suffer'':232C ''survey'':484C,495C,513C ''technic'':518C ''techniqu'':181C ''technolog'':523C ''theori'':15C ''thing'':379C ''time'':136C ''tree'':295C,350C ''tri'':82C,383C ''trie'':177C ''trust'':373C ''ul'':489C ''univ'':500C ''univers'':521C ''usa'':473C ''use'':8C,20C,175C,259C ''vari'':50C,432C ''vector'':330C ''view'':80C ''want'':480C ''weight'':329C ''wiley'':541C ''work'':12C,87C,184C,199C,215C,221C,297C ''workshop'':462C ''wrong'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1080, 1, '2014-09-05 16:34:55.17', 2, 106, 'ANOVA RBF kernel returns very poor results', '&lt;p&gt;I was curious about the ANOVA RBF kernel provided by kernlab package available in R.&lt;/p&gt;

&lt;p&gt;I tested it with a numeric dataSet of 34 input variables and one output variable. For each variable I have 700 different values. Comparing with other kernels, I got very bad results with this kernel.
For example using the simple RBF kernel I could predict with 0,88 R2 however with the anova RBF I could only get 0,33 R2.
I thought that ANOVA RBF would be a very good kernel. Any thoughts? Thanks&lt;/p&gt;

&lt;p&gt;The code is as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;set.seed(100) #use the same seed to train different models
svrFitanovaacv &amp;lt;- train(R ~ .,
                       data = trainSet,
                       method = SVManova,
                       preProc = c("center", "scale"),
                       trControl = ctrl, tuneLength = 10) #By default, RMSE and R2 are computed for regression (in all cases, selects the tunning and cross-val model with best value) , metric = "ROC"
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;define custom model in caret package:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(caret)
#RBF ANOVA KERNEL
SVManova &amp;lt;- list(type = "Regression", library = "kernlab", loop = NULL)
prmanova &amp;lt;- data.frame(parameter = c("C", "sigma", "degree", "epsilon"),
                     class = rep("numeric", 4),
                     label = c("Cost", "Sigma", "Degree", "Epsilon"))
SVManova$parameters &amp;lt;- prmanova
svmGridanova &amp;lt;- function(x, y, len = NULL) {
library(kernlab)
sigmas &amp;lt;- sigest(as.matrix(x), na.action = na.omit, scaled = TRUE, frac = 1)
expand.grid(sigma = mean(sigmas[-2]), epsilon = 0.000001,
            C = 2^(-40:len), degree = 1:2) # len = tuneLength in train
}
SVManova$grid &amp;lt;- svmGridanova
svmFitanova &amp;lt;- function(x, y, wts, param, lev, last, weights, classProbs, ...) {
  ksvm(x = as.matrix(x), y = y,
       kernel = "anovadot",
       kpar = list(sigma = param$sigma, degree = param$degree),
       C = param$C, epsilon = param$epsilon,
       prob.model = classProbs,
       ...) #default type = "eps-svr"
}
SVManova$fit &amp;lt;- svmFitanova
svmPredanova &amp;lt;- function(modelFit, newdata, preProc = NULL, submodels = NULL)
  predict(modelFit, newdata)
SVManova$predict &amp;lt;- svmPredanova
svmProb &amp;lt;- function(modelFit, newdata, preProc = NULL, submodels = NULL)
  predict(modelFit, newdata, type="probabilities")
SVManova$prob &amp;lt;- svmProb
svmSortanova &amp;lt;- function(x) x[order(x$C), ]
SVManova$sort &amp;lt;- svmSortanova
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;load data:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dataA2&amp;lt;-read.csv("C:/results/A2.txt",header = TRUE, 
                             blank.lines.skip = TRUE,sep = ",")
set.seed(1)
inTrainSet &amp;lt;- createDataPartition(dataA2$R, p = 0.75, list = FALSE) #[[1]]
trainSet &amp;lt;- dataA2[inTrainSet,]
testSet &amp;lt;- dataA2[-inTrainSet,]
#-----------------------------------------------------------------------------
#K-folds resampling method for fitting svr
ctrl &amp;lt;- trainControl(method = "repeatedcv", number = 10, repeats = 10,
                     allowParallel = TRUE) #10 separate 10-fold cross-validations
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;link to data:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;wuala.com/jpcgandre/Documents/Data%20SVR/?key=BOD9NTINzRHG
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1084, 2, '2014-09-07 01:08:04.33', 8, NULL, NULL, '&lt;p&gt;Let''s work it out from the ground up. Classification (also known as categorization) is an example of &lt;strong&gt;supervised learning&lt;/strong&gt;. In supervised learning you have: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;model&lt;/strong&gt; - something that approximates internal structure in your data, enabling you to reason about it and make useful predictions (e.g. predict class of an object); normally model has parameters that you want to "learn"&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;training&lt;/strong&gt; and &lt;strong&gt;testing datasets&lt;/strong&gt; - sets of objects that you use for training your model (finding good values for parameters) and further evaluating &lt;/li&gt;
&lt;li&gt;&lt;strong&gt;training&lt;/strong&gt; and &lt;strong&gt;classification algorithms&lt;/strong&gt; - first describes how to learn model from training dataset, second shows how to derive class of a new object given trained model&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Now let''s take a simple case of spam classification. Your training dataset is a corpus of emails + corresponding labels - "spam" or "not spam". Testing dataset has the same structure, but made from some independent emails (normally one just splits his dataset and makes, say, 9/10 of it to be used for training and 1/10 - for testing). One way to model emails is to represent each of them as a set (bag) of words. If we assume that words are independent of each other, we can use &lt;strong&gt;Naive Bayes classifier&lt;/strong&gt;, that is, calculate prior probabilities for each word and each class (training algorithm) and then apply Bayes theorem to find posterior probability of a new document to belong to particular class. &lt;/p&gt;

&lt;p&gt;So, basically we have: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;raw model + training set + training algorithm -&amp;gt; trained model
trained model + classification algorithm + new object -&amp;gt; object label
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now note that we represented our objects (documents) as a bag of words. But is the only way? In fact, we can extract much more from raw text. For example, instead of words as is we can use their &lt;a href="http://nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html"&gt;stems or lemmas&lt;/a&gt;, throw out noisy &lt;a href="http://en.wikipedia.org/wiki/Stop_words"&gt;stop words&lt;/a&gt;, add &lt;a href="http://nlp.stanford.edu/software/tagger.shtml"&gt;POS tags&lt;/a&gt; of words, extract &lt;a href="http://nlp.stanford.edu/software/CRF-NER.shtml"&gt;named entities&lt;/a&gt; or even explore HTML structure of the document. In fact, more general representation of a document (and, in general, any object) is a &lt;strong&gt;feature vector&lt;/strong&gt;. E.g. for text: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;actor, analogue, bad, burn, ..., NOUN, VERB, ADJ, ..., in_bold, ... | label
    0,        0,   1,    1, ...,    5,    7,   2, ...,       2, ... | not spam
    0,        1,   0,    0, ...,    3,   12,  10, ...,       0, ... | spam
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here first row is a list of possible features and subsequent rows show how many times that feature occurs in a document. E.g. in first document there''s no occurrences of word "actor", 1 occurrence of word "burn", 5 nouns, 2 adjectives and 2 pieces of text in bold. Last column corresponds to a resulting class label. &lt;/p&gt;

&lt;p&gt;Using feature vector you can incorporate any properties of your texts. Though finding good set of features may take some time. &lt;/p&gt;

&lt;p&gt;And what about model and algorithms? Are we bound to Naive Bayes. Not at all. &lt;strong&gt;logistic regression&lt;/strong&gt;, &lt;strong&gt;SVM&lt;/strong&gt;, &lt;strong&gt;decision trees&lt;/strong&gt; - just to mention few popular classifiers. (Note, that we say "classifier" in most cases we mean model + corresponding algorithms for training and classification). &lt;/p&gt;

&lt;p&gt;As for implementation, you can divide task into 2 parts: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Features extraction - transforming raw texts into feature vectors. &lt;/li&gt;
&lt;li&gt;Object classification - building and applying model.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;First point is well worked out in many &lt;a href="http://stackoverflow.com/questions/4115526/natural-language-processing"&gt;NLP libraries&lt;/a&gt;. Second is about machine learning, so, depending on your dataset, you can use either &lt;a href="http://www.cs.waikato.ac.nz/ml/weka/"&gt;Weka&lt;/a&gt;, or &lt;a href="https://spark.apache.org/docs/latest/mllib-guide.html"&gt;MLlib&lt;/a&gt;. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8621, 1, '2015-10-27 16:58:05.237', 0, 37, 'Test/Train Set Splits (Critical Value in Test Doesn''t Appear in Train) - Using Random Decision Forest', '&lt;p&gt;I have a group of 10 sources with similar fields.&lt;/p&gt;

&lt;p&gt;My training set is made up of 9 of the 10 sources and my test set is the 10th source. The training set is more than 10 times the test set.&lt;/p&gt;

&lt;p&gt;My training set has a field, (for this example, field 1) that is almost always blank.&lt;/p&gt;

&lt;p&gt;My test set has the same field (also called field 1) which is always populated.&lt;/p&gt;

&lt;p&gt;There is a particular value that appears in the test set''s field 1 which is significant and needs to be flagged. This value never appears in the training set and we cannot integrate the values from the test set into the training set.&lt;/p&gt;

&lt;p&gt;How can I integrate the critical value from my test set as significant in my training set (which doesn''t contain it) so when my test set is being evaluated against my training set, it will be deemed significant?&lt;/p&gt;

&lt;p&gt;Any help would be greatly appreciated!&lt;/p&gt;
', 'machine-learning  dataset  feature-selection  random-forest', NULL, '''/p'':38C,71C,89C,106C,152C,193C,201C ''1'':83C,101C,121C ''10'':33C,50C,66C ''10th'':58C ''9'':47C ''almost'':86C ''also'':98C ''alway'':87C,104C ''appear'':10A,114C,133C ''appreci'':200C ''blank'':88C ''call'':99C ''cannot'':140C ''contain'':174C ''critic'':4A,159C ''dataset'':20B ''decis'':15A ''deem'':191C ''doesn'':8A,172C ''evalu'':183C ''exampl'':81C ''featur'':22B ''feature-select'':21B ''field'':37C,78C,82C,97C,100C,120C ''flag'':129C ''forest'':16A,26B ''great'':199C ''group'':31C ''help'':196C ''integr'':141C,157C ''learn'':19B ''machin'':18B ''machine-learn'':17B ''made'':44C ''need'':126C ''never'':132C ''p'':27C,39C,72C,90C,107C,153C,194C ''particular'':111C ''popul'':105C ''random'':14A,25B ''random-forest'':24B ''select'':23B ''set'':2A,42C,55C,62C,70C,75C,93C,118C,137C,147C,151C,164C,170C,180C,187C ''signific'':124C,166C,192C ''similar'':36C ''sourc'':34C,51C,59C ''split'':3A ''test'':7A,54C,69C,92C,117C,146C,163C,179C ''test/train'':1A ''time'':67C ''train'':12A,41C,61C,74C,136C,150C,169C,186C ''use'':13A ''valu'':5A,112C,131C,143C,160C ''would'':197C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8668, 2, '2015-10-31 09:28:22', 0, NULL, NULL, '&lt;p&gt;I''d probably do stats; but that''s because it''s the choice I made ;)&lt;/p&gt;

&lt;p&gt;If your undergrad was in pure math, maybe doing data science would be a good bet.&lt;/p&gt;

&lt;p&gt;Otherwise doing MSc stats is a good way to get better at math. Getting better at math is way more difficult to do outside of academia than getting better at programming.&lt;/p&gt;

&lt;p&gt;It partly depends what kind of data scientist you''re aiming to be and what kind of work you want to do. If you''re leaning towards doing visualisations rather than modelling the data science programmes might be more relvevant.&lt;/p&gt;

&lt;p&gt;I''d say beware of a focus on learning particular computer tools as this is a rapidly changing area and everything could be different in five years.&lt;/p&gt;
', NULL, NULL, '''/p'':17C,35C,68C,110C,139C ''academia'':62C ''aim'':80C ''area'':130C ''bet'':34C ''better'':47C,51C,65C ''bewar'':115C ''chang'':129C ''choic'':14C ''comput'':122C ''could'':133C ''d'':3C,113C ''data'':28C,76C,103C ''depend'':72C ''differ'':135C ''difficult'':57C ''everyth'':132C ''five'':137C ''focus'':118C ''get'':46C,50C,64C ''good'':33C,43C ''kind'':74C,85C ''lean'':95C ''learn'':120C ''made'':16C ''math'':25C,49C,53C ''mayb'':26C ''might'':106C ''model'':101C ''msc'':39C ''otherwis'':37C ''outsid'':60C ''p'':1C,18C,36C,69C,111C ''part'':71C ''particular'':121C ''probabl'':4C ''program'':67C ''programm'':105C ''pure'':24C ''rapid'':128C ''rather'':99C ''re'':79C,94C ''relvev'':109C ''say'':114C ''scienc'':29C,104C ''scientist'':77C ''stat'':6C,40C ''tool'':123C ''toward'':96C ''undergrad'':21C ''visualis'':98C ''want'':89C ''way'':44C,55C ''work'':87C ''would'':30C ''year'':138C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8622, 1, '2015-10-27 17:16:08.807', 1, 56, 'Tool to Generate 2D Data via Mouse Clicking', '&lt;p&gt;Often when I am learning new machine learning methods or experimenting with a data analysis algorithm I need to generate a series of 2D points. Teachers also do this often when making a lesson or tutorial.&lt;/p&gt;

&lt;p&gt;In some cases I just create a function, add some noise, and plot it, but there are many times when I wish I could just click my mouse on a graph to generate points. For instance, when I want to generate a fairly complex relationship between x and y, it''s a hassle to think of the right formulation to generate the points programmatically.&lt;/p&gt;

&lt;p&gt;Does there exist a tool that will allow me to generate data points using my mouse, with an option to export to CSV (or other simple format)?&lt;/p&gt;

&lt;p&gt;For example, I am currently learning how to use mutual information and entropy as a metric of dependence between variables. I''d like to see what happens when I have data that is clearly dependent but does not have a linear relationship, so I drew this image:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/PtwUR.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/PtwUR.png" alt="Non-linear dependence"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Now I just need a way to export the coordinates of the points to CSV. I realize this is a simple program and I could write my own, but surely someone has already done this and created a tool to do so? It could be a website, an .exe, Python source, or any other application.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8631, 1, '2015-10-28 13:09:49.287', 0, 57, 'Python - Download a 20 gb of dataset/datadump from Oracle server to my local disk drive using Python', '&lt;p&gt;want help for the following task :&lt;/p&gt;

&lt;p&gt;I want to download a &lt;strong&gt;20gb&lt;/strong&gt; of dataset/datadump from Oracle server (oracle 11g database) to my local disk drive (i.e. E:/python/). I want to achieve this using Python 3.4 (windows64 bit ; I''m using Anaconda - spyder IDE)&lt;/p&gt;

&lt;p&gt;I normaly use SAS for the task using following query:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; LIBNAME ORACLE ODBC DSN= oracle UID= user PWD= password; #CONNECTION TO SERVER
 LIBNAME LOCAL "E:/PYTHON"; #SETTING LOACAL PATH FOR DATA STORE

 CREATE TABLE LOCAL.MYnewTable AS
 SELECT * FROM ORACLE.DOWLOAD_TABLE
 ;QUIT;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Above query will download 20GB of datadump from the server to my local E:/ drive using SAS. How to do the same in Python?? My RAM is only &lt;strong&gt;4gb&lt;/strong&gt; so downloading entire 20gb dataset in the Pandas'' data frame will eat up the RAM (i believe!! I may be wrong). SAS does this task very easily. Please suggest the query for Python. Request you all to share the code.&lt;/p&gt;

&lt;p&gt;Thanks!!&lt;/p&gt;

&lt;p&gt;p.s. I am able to connect to the server and import few rows in Dataframe. Its the big &lt;strong&gt;20-40 GB&lt;/strong&gt; data that i am concerned about.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8744, 1, '2015-11-06 17:41:28.683', 2, 47, 'Does Naive Bayes Classifier require assuming a specific distribution for training?', '&lt;p&gt;Since NB is a generative classifier, we assume that the data points are all generated from a distribution, right?&lt;/p&gt;

&lt;p&gt;But since we can compute the MLE of &lt;strong&gt;p(x_i|y)&lt;/strong&gt; by counting (MLE of p(x_i|y) = # of (x_i,y) / # of y), do we really need a specific distribution, if at all, to model the likelihood(p(x_i|y))?&lt;/p&gt;
', 'machine-learning  naive-bayes-classifier', NULL, '''/p'':39C,87C ''/strong'':54C ''assum'':6A,27C ''bay'':3A,17B ''classifi'':4A,18B,25C ''comput'':45C ''count'':56C ''data'':30C ''distribut'':9A,37C,75C ''generat'':24C,34C ''learn'':14B ''likelihood'':82C ''machin'':13B ''machine-learn'':12B ''mle'':47C,57C ''model'':80C ''naiv'':2A,16B ''naive-bayes-classifi'':15B ''nb'':21C ''need'':72C ''p'':19C,40C,50C,59C,83C ''point'':31C ''realli'':71C ''requir'':5A ''right'':38C ''sinc'':20C,42C ''specif'':8A,74C ''strong'':49C ''train'':11A ''x'':51C,60C,64C,84C ''y'':53C,62C,66C,68C,86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1127, 2, '2014-09-16 14:29:41.52', 4, NULL, NULL, '&lt;p&gt;&lt;strong&gt;One class learning&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I wouldn''t be too quick to throw out one-class classification methods (option 2) - the key is to model the positive (minority) class with the one-class model.  &lt;/p&gt;

&lt;p&gt;There has been research demonstrating cases where one-class classification out-performed other approaches like sampling for highly imbalanced data as often seen with protein classification tasks.&lt;/p&gt;

&lt;p&gt;I couldn''t find the research I recalled, but I did find some other comparisons, showing using one-class classifiers (typically modeling the minority class) achieved as good or better performance than binary classification typically with sampled "negatives" from the large set of proteins not known to be positive.  &lt;/p&gt;

&lt;p&gt;Additionally this approach also gives the advantage of much improved run-time - since you only need to train the classifier on the smaller, positive set.  A couple papers:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://journal.imbio.de/articles/pdf/jib-77.pdf" rel="nofollow"&gt;"Prediction of protein-protein interactions using one-class classification methods and integrating diverse biological data"&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.inf.u-szeged.hu/~busarobi/PDFs/BaBuKe09.pdf" rel="nofollow"&gt;"A One-Class Classification Approach for Protein Sequences and Structures"&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;At the very least I would try some one-class methods and compare the performance using validation with your binary/multi-class classification approaches.  There are also open source implementations for many of these so it shouldn''t be too costly to try them out, for example LibSVM has a one-class SVM implementation.  Additionally, it might prove valuable for use in an ensemble with binary classifiers, since there may be more disagreement in their predictions.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Higher level representation embedding / clustering&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Along the lines of what you were thinking with (1) and the other post suggesting PCA, approaches like clustering, sparse coding, or even topic modeling - treating each protein as a document string and different protein families as different topics - could yield a representation that might make classifying the proteins straightforward.  &lt;/p&gt;

&lt;p&gt;I.e., you could identify which group/cluster a protein belongs to or classify the cluster memberships / embedded representations.  &lt;/p&gt;

&lt;p&gt;E.g., such embedding approaches as sparse coding can yield representations that reveal which cluster a protein belongs too - so that some sets of features are only active (non-zero) for proteins in the same cluster - which can make classifying them much easier. &lt;/p&gt;

&lt;p&gt;Additionally class labels or known cluster membership can be incorporated in the embedding process for most methods.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Ensemble&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Ensembles of multiple classifiers tend to work best - especially when the classifiers are very diverse and can achieve comparable performance individually.  &lt;/p&gt;

&lt;p&gt;There are at least two ways use ensembles for this problem.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;You can build an ensemble of binary classifiers by sampling multiple different same-size negative sets and training a classifier on each.&lt;/li&gt;
&lt;li&gt;You can build an ensemble from different approaches, such as binary classifiers with different negative samples, combined with a one-class classification approach, combined with classification models trained on the embedded data. &lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1135, 1, '2014-09-17 13:13:59.147', 6, 197, 'Statistical Commute Analysis in Java', '&lt;p&gt;I have a rather large commute every day - it ranges between about an hour and about an hour and half of driving.&lt;/p&gt;

&lt;p&gt;I have been tracking my driving times, and want to continue to do so. I am capturing the date, my time of departure, my time of arrival, the route I took (there are two or three possible ones), weather conditions (wet/dry and clear/hazy/foggy), and whether I stopped (and if so, for what reason - fuel/toilet break/food break, and for how long) for every journey to and from work.&lt;/p&gt;

&lt;p&gt;I would like to create a system to analyse this data and suggest an optimal departure time (for the next journey) based on day of the week, weather conditions, and whether i need to stop.&lt;/p&gt;

&lt;p&gt;Anecdotally, I can see that Tuesday mornings are worse than other mornings, the earlier I leave the more likely I am to take a toilet break or a food break, and obviously that the journey takes longer on rainy or foggy days than on clear and dry days - but I would like the system to empirically tell me that!&lt;/p&gt;

&lt;p&gt;I assume this is a machine-learning and statistical analysis problem.&lt;/p&gt;

&lt;p&gt;However, I have absolutely no knowledge of machine-learning, or statistical methods.&lt;/p&gt;

&lt;p&gt;What statistical methods should I use to do this kind of analysis to the point where the data will lead to suggestions like "tomorrow is Tuesday and it is going to rain, so you must leave home between 7.50 and 8.00, and take route XYZ, to get the optimal driving time. Oh and chances are you will need a toilet break - and I have factored that in"? (assume that I manually enter tomorrowâs weather forecast - Iâll look into integrating with a weather service later)&lt;/p&gt;

&lt;p&gt;Note that this is life-hacking for me, trying to optimise the hell out of a tedious process, and it is very personal - specific to me and my habits, specific to this route, and specific to the morning/evening commute times. Google Maps with Traffic, TomTom with IQ, and Waze do very well in the more open-ended situations of ad-hoc driving-time prediction. Even Apple is happy to tell me on my iPhone notification screen how long it will take me to get home if I leave right now.&lt;/p&gt;

&lt;p&gt;Also note, it appears to me that traffic is not a consideration - that is to say, I do not think I need to know the actual traffic conditions - traffic is a function of day of the week and weather. For example, there are more people on the roads on Monday and Tuesday mornings, and people drive more slowly, and more people are in cars (opting to drive instead of cycle or take public transport) when it rains.&lt;/p&gt;

&lt;p&gt;To what extent can I let the data do all the talking? I have a somewhat ambiguous hidden agenda which may not be apparent from the data;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;I should be at work at 9.30 (i.,e. 9.15 +/- 15 minutes) every day, but the occasional 10am arrival is OK&lt;/li&gt;
&lt;li&gt;I want to leave home as late as possible, and yet arrive at work as early as possible&lt;/li&gt;
&lt;li&gt;I want to leave work as early as possible, and yet have done at least 8 hoursâ work&lt;/li&gt;
&lt;li&gt;it is OK for me to, say, leave half an hour early on one day but stay late on another to compensate&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I think I can come up with a procedural formula that can encompass all of these rules, but my gut feeling is that statistical analysis can make it a lot smarter.&lt;/p&gt;

&lt;p&gt;Apart from the methods of analysis, the technology stack is not an issue. Java is my language of choice - I am quite familiar with programming in it, and in creating web applications.&lt;/p&gt;

&lt;p&gt;Assuming that it is possible, are there Java libraries that can provide the requisite methods?&lt;/p&gt;

&lt;p&gt;What limitations are there? I want to keep capturing more and more data every day, making the data set bigger, hopefully, making the prediction more accurate.&lt;/p&gt;

&lt;p&gt;What other ways are there to do it? Can I push this data into, say, Wolfram Programming Cloud, or maybe something Google provides to get the desired results?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1169, 2, '2014-09-26 11:39:07.313', 11, NULL, NULL, '&lt;p&gt;First of all, &lt;strong&gt;dimensionality reduction&lt;/strong&gt; is used when you have &lt;strong&gt;many covariated dimensions&lt;/strong&gt; and want to reduce problem size by rotating data points into new orthogonal basis and taking only axes with largest variance. With 8 variables (columns) your space is already low-dimensional, reducing number of variables further is unlikely to solve technical issues with memory size, but may affect dataset quality a lot. In you concrete case it''s more promising to take a look at &lt;a href="http://en.wikipedia.org/wiki/Online_machine_learning"&gt;&lt;strong&gt;online learning&lt;/strong&gt;&lt;/a&gt; methods. Roughly speaking, instead of working with the whole dataset, these methods take a little part of them (often referred to as "mini-batches") at a time and build a model incrementally. (I personally like to interpret word "online" as a reference to some infinitely long source of data from Internet like Twitter feed, where you just can''t load the whole dataset at once). &lt;/p&gt;

&lt;p&gt;But what if you really wanted to apply dimensionality reduction technique like PCA to a dataset that doesn''t fit into a memory? Normally dataset is represented as a data matrix &lt;em&gt;X&lt;/em&gt; of size &lt;em&gt;n&lt;/em&gt; x &lt;em&gt;m&lt;/em&gt;, where &lt;em&gt;n&lt;/em&gt; is number of observations (rows) and &lt;em&gt;m&lt;/em&gt; is a number of variables (columns). Typically problem with memory comes from only one of these two numbers. &lt;/p&gt;

&lt;h2&gt;Too many observations (n &gt;&gt; m)&lt;/h2&gt;

&lt;p&gt;When you have &lt;strong&gt;too many observations&lt;/strong&gt;, but number of variables is from small to moderate, you can &lt;strong&gt;build covariance matrix incrementally&lt;/strong&gt;. Indeed, typical PCA consists of constructing covariance matrix of size &lt;em&gt;m&lt;/em&gt; x &lt;em&gt;m&lt;/em&gt; and applying singular value decomposition to it. With &lt;em&gt;m&lt;/em&gt;=1000 variables of type float64 covariance matrix has size 1000*1000*8 ~ 8Mb, which easily fits into memory and may be used with SVD. So you need only to build covariance matrix without loading entire dataset into memory - &lt;a href="http://rebcabin.github.io/blog/2013/01/22/covariance-matrices/"&gt;pretty tractable task&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;Alternatively, you can select small representative sample from your dataset and &lt;strong&gt;approximate covariance matrix&lt;/strong&gt;. This matrix will have all the same properties as normal, just a little bit less accurate. &lt;/p&gt;

&lt;h2&gt;Too many variables (n &amp;lt;&amp;lt; m)&lt;/h2&gt;

&lt;p&gt;On another hand, sometimes, when you have &lt;strong&gt;too many variables&lt;/strong&gt;, covariance matrix itself will not fit into memory. E.g. if you work with 640x480 images, every observation has 640*480=307200 variables, which results in 703Gb covariance matrix! That''s definitely not what you would like to keep in memory of your computer. Or even in memory of your cluster. So we need to reduce dimensions without building covariance matrix at all. &lt;/p&gt;

&lt;p&gt;My favourite method for doing it is &lt;a href="http://web.stanford.edu/~hastie/Papers/Ping/KDD06_rp.pdf"&gt;&lt;strong&gt;Random Projection&lt;/strong&gt;&lt;/a&gt;. In short, if you have dataset &lt;em&gt;X&lt;/em&gt; of size &lt;em&gt;n&lt;/em&gt; x &lt;em&gt;m&lt;/em&gt;, you can multiply it by some sparse random matrix &lt;em&gt;R&lt;/em&gt; of size &lt;em&gt;m&lt;/em&gt; x &lt;em&gt;k&lt;/em&gt; (with &lt;em&gt;k&lt;/em&gt; &amp;lt;&amp;lt; &lt;em&gt;m&lt;/em&gt;) and obtain new matrix &lt;em&gt;X''&lt;/em&gt; of a much smaller size &lt;em&gt;n&lt;/em&gt; x &lt;em&gt;k&lt;/em&gt; with &lt;em&gt;approximately same properties&lt;/em&gt; as original one. Why it works? Well, you should know that PCA aims to find set of orthogonal axes (principal components) and project your data onto first &lt;em&gt;k&lt;/em&gt; of them. It turns out, that sparse random vectors are &lt;em&gt;nearly orthogonal&lt;/em&gt; and thus may also be used as a new basis. &lt;/p&gt;

&lt;p&gt;And, of course, you don''t have to multiply the whole dataset &lt;em&gt;X&lt;/em&gt; by &lt;em&gt;R&lt;/em&gt; - you can translate every observation &lt;em&gt;x&lt;/em&gt; into new basis separately or in mini-batches.&lt;/p&gt;

&lt;p&gt;There''s also somewhat similar algorithm called &lt;strong&gt;Random SVD&lt;/strong&gt;. I don''t have any real experience with it, but you can find example code with explanations &lt;a href="http://stats.stackexchange.com/a/11934/3305"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;As a bottom line, here''s short check list for dimensionality reduction of big datasets: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;If you have not that many dimensions (variables), simply use online learning algorithms. &lt;/li&gt;
&lt;li&gt;If there are many observations, but moderate number of variables (covariance matrix fits into memory), construct matrix incrementally and use normal SVD. &lt;/li&gt;
&lt;li&gt;If number of variables is too high, use incremental algorithms. &lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3723, 2, '2014-12-18 13:30:54.21', 4, NULL, NULL, '&lt;p&gt;For this answer, I have assumed that you prefer &lt;strong&gt;open source solutions&lt;/strong&gt; to &lt;em&gt;big data visualization&lt;/em&gt;. This assumption is based on budgetary details from your question. However, there is one &lt;em&gt;exclusion&lt;/em&gt; to this - below I will add a reference to one commercial product, which I believe might be beneficial in your case (provided that you could afford that). I also assume that &lt;em&gt;browser-based solutions are acceptable&lt;/em&gt; (I would even prefer them, unless you have specific contradictory requirements).&lt;/p&gt;

&lt;p&gt;Naturally, the first candidate as a solution to your problem I would consider &lt;strong&gt;D3.js JavaScript library&lt;/strong&gt;: &lt;a href="http://d3js.org" rel="nofollow"&gt;http://d3js.org&lt;/a&gt;. However, despite &lt;em&gt;flexibility&lt;/em&gt; and other &lt;em&gt;benefits&lt;/em&gt;, I think that this solution is &lt;em&gt;too low-level&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Therefore, I would recommend you to take a look at the following &lt;strong&gt;open source projects for big data visualization&lt;/strong&gt;, which are &lt;em&gt;powerful&lt;/em&gt; and &lt;em&gt;flexible&lt;/em&gt; enough, but operate at a &lt;em&gt;higher level of abstraction&lt;/em&gt; (some of them are based on D3.js foundation and sometimes are referred to as D3.js &lt;a href="http://schoolofdata.org/2013/08/12/climbing-the-d3-js-visualisation-stack" rel="nofollow"&gt;visualization stack&lt;/a&gt;).&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Bokeh&lt;/strong&gt; - Python-based interactive visualization library, which supports big data and streaming data: &lt;a href="http://bokeh.pydata.org" rel="nofollow"&gt;http://bokeh.pydata.org&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Flot&lt;/strong&gt; - JavaScript-based interactive visualization library, focused on jQuery: &lt;a href="http://www.flotcharts.org" rel="nofollow"&gt;http://www.flotcharts.org&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;NodeBox&lt;/strong&gt; - unique rapid data visualization system (not browser-based, but multi-language and multi-platform), based on generative design and visual functional programming: &lt;a href="https://www.nodebox.net" rel="nofollow"&gt;https://www.nodebox.net&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Processing&lt;/strong&gt; - complete software development system with its own programming language, libraries, plug-ins, etc., oriented to visual content: &lt;a href="https://www.processing.org" rel="nofollow"&gt;https://www.processing.org&lt;/a&gt; (allows executing Processing programs in a browser via &lt;a href="http://processingjs.org" rel="nofollow"&gt;http://processingjs.org&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Crossfilter&lt;/strong&gt; - JavaScript-based interactive visualization library for big data by Square (very fast visualization of large multivariate data sets): &lt;a href="http://square.github.io/crossfilter" rel="nofollow"&gt;http://square.github.io/crossfilter&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;bigvis&lt;/strong&gt; - an R package for big data exploratory analysis (not a visualization library per se, but could be useful to process large data sets /aggregating, smoothing/ prior to visualization, using various R graphics options): &lt;a href="https://github.com/hadley/bigvis" rel="nofollow"&gt;https://github.com/hadley/bigvis&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;prefuse&lt;/strong&gt; - Java-based interactive visualization library: &lt;a href="http://prefuse.org" rel="nofollow"&gt;http://prefuse.org&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Lumify&lt;/strong&gt; - big data integration, analysis and visualization platform (interesting feature: supports Semantic Web): &lt;a href="http://lumify.io" rel="nofollow"&gt;http://lumify.io&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Separately, I''d like to mention two open source &lt;em&gt;big data analysis and visualization projects&lt;/em&gt;, focused on &lt;strong&gt;graph/network data&lt;/strong&gt; (with some support for &lt;em&gt;streaming data&lt;/em&gt; of that type): &lt;a href="http://www.cytoscape.org" rel="nofollow"&gt;Cytoscape&lt;/a&gt; and &lt;a href="https://gephi.github.io" rel="nofollow"&gt;Gephi&lt;/a&gt;. If you are interested in some other, &lt;em&gt;more specific&lt;/em&gt; (&lt;em&gt;maps&lt;/em&gt; support, etc.) or &lt;em&gt;commercial&lt;/em&gt; (basic free tiers), projects and products, please see this &lt;strong&gt;awesome compilation&lt;/strong&gt;, which I thoroughly &lt;em&gt;curated&lt;/em&gt; to come up with the main list above and &lt;em&gt;analyzed&lt;/em&gt;: &lt;a href="http://blog.profitbricks.com/39-data-visualization-tools-for-big-data" rel="nofollow"&gt;http://blog.profitbricks.com/39-data-visualization-tools-for-big-data&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Finally, as I promised in the beginning, &lt;strong&gt;Zoomdata&lt;/strong&gt; - a commercial product, which I thought you might want to take a look at: &lt;a href="http://www.zoomdata.com" rel="nofollow"&gt;http://www.zoomdata.com&lt;/a&gt;. The reason I made an exclusion for it from my open source software compilation is due to its &lt;strong&gt;built-in support for big data platforms&lt;/strong&gt;. In particular, Zoomdata provides &lt;em&gt;data connectors&lt;/em&gt; for Cloudera Impala, Amazon Redshift, MongoDB, Spark and Hadoop, plus search engines, major database engines and streaming data.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Disclaimer:&lt;/strong&gt; I have no affiliation with Zoomdata whatsoever - I was just impressed by their &lt;em&gt;range of connectivity options&lt;/em&gt; (which might &lt;strong&gt;cost&lt;/strong&gt; you dearly, but that''s another &lt;em&gt;aspect&lt;/em&gt; of this topic''s analysis).&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3789, 5, '2014-12-31 15:34:17.397', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8623, 1, '2015-10-27 20:02:55.957', 0, 73, 'Classification when one class is other', '&lt;p&gt;I am working on a litigation support application using the &lt;a href="https://www.cs.cmu.edu/~./enron/" rel="nofollow"&gt;Enron corpus&lt;/a&gt;, which contains about 600,000 unique text documents.&lt;/p&gt;

&lt;p&gt;In litigation, one is often concerned with whether a document is responsive or non-responsive. One produces responsive documents to the opposing side, unless they are privileged (e.g., attorney client communication).  &lt;/p&gt;

&lt;p&gt;Here, I have sample sets of over 200 responsive and non-responsive documents.  &lt;/p&gt;

&lt;p&gt;The challenge here is that the topic of the responsive documents is about one thing, whereas the non-responsive documents could be about any number of topics,  ranging from spam, to soccer practice, to business documents, etc.  The non-responsive is what I would call &lt;em&gt;diluted&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;I don''t know what those non-responsive classes are up front, and there is no purpose or value to breaking them out up front. If a document is non-responsive, then it needs to be quickly (and cheaply) dismissed.&lt;/p&gt;

&lt;p&gt;If the samples are random when the classification is applied to the corpus my customers expect the split between corpus responsive and non-responsive to be close to the split of the sample.&lt;/p&gt;

&lt;p&gt;What is the best approach to classify responsive versus non-responsive in this situation?    &lt;/p&gt;

&lt;p&gt;Below I briefly describe what I have tried. If there is a better approach, please share.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Using tf-idf, create an average vector for each document class (responsive and non-responsive).&lt;/li&gt;
&lt;li&gt;Take the first 1000 terms (sorted by weight), such that each vector is the same length. &lt;/li&gt;
&lt;li&gt;&lt;p&gt;Normalized the vectors.&lt;br&gt;
Note: these two vectors are only a 0.03 cosine similarity to each other.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;For each document in the responsive sample set, calculate the cosine similarity to the single average vector.&lt;br&gt;
Note: the average is 0.06.  It is a small number, as documents in the sample set have around 40 terms, as compared to an average vector of 1000. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;In the non-responsive perform the same analysis (compare each non-responsive sample document to the non-responsive average vector).&lt;br&gt;
In the non-responsive that same comparison is 0.03. Basically, the non-responsive average vector is diluted.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Based on this approach, I cannot really conclude if document has a higher cosine similarity to responsive compared to non-responsive then it is responsive.  By that measure 1/3 of the documents in the non-responsive sample set would be classified as responsive.    &lt;/p&gt;

&lt;p&gt;The non-responsive needs to be handicapped.  In this approach, how do I handicap it?  Is there another approach that would accomplish the same thing? &lt;/p&gt;
', 'classification', NULL, '''/a'':26C ''/em'':136C ''/li'':272C,290C,311C,361C,408C ''/ol'':409C ''/p'':35C,69C,85C,137C,181C,217C,234C,252C,310C,360C,407C,456C,484C ''/~./enron/'':21C ''0.03'':304C,397C ''0.06'':336C ''000'':31C ''1/3'':440C ''1000'':277C,359C ''200'':78C ''40'':350C ''600'':30C ''accomplish'':480C ''analysi'':372C ''anoth'':476C ''appli'':192C ''applic'':14C ''approach'':223C,249C,414C,468C,477C ''around'':349C ''attorney'':66C ''averag'':261C,329C,334C,356C,385C,403C ''base'':411C ''basic'':398C ''best'':222C ''better'':248C ''br'':296C,331C,387C ''break'':160C ''briefli'':238C ''busi'':122C ''calcul'':322C ''call'':133C ''cannot'':416C ''challeng'':88C ''cheapli'':179C ''class'':4A,148C,266C ''classif'':1A,5B,190C ''classifi'':225C,453C ''client'':67C ''close'':210C ''communic'':68C ''compar'':353C,373C,428C ''comparison'':395C ''concern'':42C ''conclud'':418C ''contain'':28C ''corpus'':25C,195C,202C ''cosin'':305C,324C,424C ''could'':108C ''creat'':259C ''custom'':197C ''describ'':239C ''dilut'':135C,406C ''dismiss'':180C ''document'':34C,46C,56C,84C,97C,107C,123C,167C,265C,316C,343C,379C,420C,443C ''e.g'':65C ''em'':134C ''enron'':24C ''etc'':124C ''expect'':198C ''first'':276C ''front'':151C,164C ''handicap'':465C,472C ''higher'':423C ''href'':18C ''idf'':258C ''know'':142C ''length'':289C ''li'':254C,273C,291C,312C,362C ''litig'':12C,38C ''measur'':439C ''need'':174C,462C ''nofollow'':23C ''non'':51C,82C,105C,127C,146C,170C,206C,229C,270C,367C,376C,383C,391C,401C,431C,447C,460C ''non-respons'':50C,81C,104C,126C,145C,169C,205C,228C,269C,366C,375C,382C,390C,400C,430C,446C,459C ''normal'':293C ''note'':297C,332C ''number'':112C,341C ''often'':41C ''ol'':253C ''one'':3A,39C,53C,100C ''oppos'':59C ''p'':6C,36C,70C,86C,138C,182C,218C,235C,292C,313C,363C,410C,457C ''perform'':369C ''pleas'':250C ''practic'':120C ''privileg'':64C ''produc'':54C ''purpos'':156C ''quick'':177C ''random'':187C ''rang'':115C ''realli'':417C ''rel'':22C ''respons'':48C,52C,55C,79C,83C,96C,106C,128C,147C,171C,203C,207C,226C,230C,267C,271C,319C,368C,377C,384C,392C,402C,427C,432C,436C,448C,455C,461C ''sampl'':74C,185C,216C,320C,346C,378C,449C ''set'':75C,321C,347C,450C ''share'':251C ''side'':60C ''similar'':306C,325C,425C ''singl'':328C ''situat'':233C ''small'':340C ''soccer'':119C ''sort'':279C ''spam'':117C ''split'':200C,213C ''support'':13C ''take'':274C ''term'':278C,351C ''text'':33C ''tf'':257C ''tf-idf'':256C ''thing'':101C,483C ''topic'':93C,114C ''tri'':243C ''two'':299C ''uniqu'':32C ''unless'':61C ''use'':15C,255C ''valu'':158C ''vector'':262C,285C,295C,300C,330C,357C,386C,404C ''versus'':227C ''weight'':281C ''wherea'':102C ''whether'':44C ''work'':9C ''would'':132C,451C,479C ''www.cs.cmu.edu'':20C ''www.cs.cmu.edu/~./enron/'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8624, 2, '2015-10-28 00:42:02.707', 0, NULL, NULL, '&lt;p&gt;According to that &lt;a href="http://hmmlearn.github.io/hmmlearn/hmm.html#building-hmm-and-generating-samples" rel="nofollow"&gt;same documentation you provided&lt;/a&gt;, the way to use custom emission probabilities is:&lt;/p&gt;

&lt;p&gt;"&lt;strong&gt;1.1.3. Implementing HMMs with custom emission probabilities&lt;/strong&gt; &lt;br&gt;
If you want to implement other emission probability (e.g. Poisson), you have to implement a new HMM class by inheriting the _BaseHMM and overriding the methods &lt;strong&gt;init&lt;/strong&gt;, _compute_log_likelihood, _set and _get for additional parameters, _initialize_sufficient_statistics, _accumulate_sufficient_statistics and _do_mstep."&lt;/p&gt;

&lt;p&gt;NOTE: The documentation pages are for hmm.learn version 0.1.* which is no longer the most recent one. That is, it seems that the documentation page available at any given time might not correspond to the most recent version of hmmlearn. &lt;a href="https://github.com/hmmlearn/hmmlearn/issues/54" rel="nofollow"&gt;For example&lt;/a&gt;, sample code for hmm  0.1.* does not run with hmm learn 0.20.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8625, 1, '2015-10-28 02:21:19.663', 2, 150, 'Multivariate linear regression in Python', '&lt;p&gt;I''m looking for a Python package that implements multivariate linear regression.&lt;/p&gt;

&lt;p&gt;(Terminological note: &lt;em&gt;multivariate&lt;/em&gt; regression deals with the case where there are more than one dependent variables while &lt;em&gt;multiple&lt;/em&gt; regression deals with the case where there is one dependent variable but more than one independent variables.)&lt;/p&gt;
', 'python  regression  library  software-recommendation', NULL, '''/em'':31C,48C ''/p'':25C,66C ''case'':36C,53C ''deal'':33C,50C ''depend'':43C,58C ''em'':29C,46C ''implement'':21C ''independ'':64C ''librari'':8B ''linear'':2A,23C ''look'':15C ''m'':14C ''multipl'':47C ''multivari'':1A,22C,30C ''note'':28C ''one'':42C,57C,63C ''p'':12C,26C ''packag'':19C ''python'':5A,6B,18C ''recommend'':11B ''regress'':3A,7B,24C,32C,49C ''softwar'':10B ''software-recommend'':9B ''terminolog'':27C ''variabl'':44C,59C,65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8639, 1, '2015-10-28 21:04:45.53', 0, 36, 'Does my Naive Bayes training data need to be proportional?', '&lt;p&gt;I''ll use spam classification as an example. The canonical approach would be to hand-classify a random sampling of emails and use them to train the NB classifier.&lt;/p&gt;

&lt;p&gt;Great, now say I added a bunch of archived emails that I know are not spam. Would this skew my classifier results because now the proportion of spam:not spam is no longer representative? The two ways I could think of this happening:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;The features become too non-spam heavy.&lt;/li&gt;
&lt;li&gt;The algorithm implicitly uses probability(spam) in its classification (in the same way that probability(medical condition) is devalued by the rarity of the medical condition even when the test is positive.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In general, more training data is better than less, so I''d like to add it if it doesn''t break the algorithm.&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/li'':102C,136C ''/p'':46C,91C,162C ''/ul'':137C ''ad'':52C ''add'':153C ''algorithm'':105C,161C ''approach'':26C ''archiv'':56C ''bay'':4A ''becom'':96C ''better'':145C ''break'':159C ''bunch'':54C ''canon'':25C ''classif'':14B,20C,112C ''classifi'':32C,45C,68C ''condit'':120C,129C ''could'':86C ''d'':150C ''data'':6A,143C ''devalu'':122C ''doesn'':157C ''email'':37C,57C ''even'':130C ''exampl'':23C ''featur'':95C ''general'':140C ''great'':48C ''hand'':31C ''hand-classifi'':30C ''happen'':90C ''heavi'':101C ''implicit'':106C ''know'':60C ''learn'':13B ''less'':147C ''li'':93C,103C ''like'':151C ''ll'':17C ''longer'':80C ''machin'':12B ''machine-learn'':11B ''medic'':119C,128C ''naiv'':3A ''nb'':44C ''need'':7A ''non'':99C ''non-spam'':98C ''p'':15C,47C,138C ''posit'':135C ''probabl'':108C,118C ''proport'':10A,73C ''random'':34C ''rariti'':125C ''repres'':81C ''result'':69C ''sampl'':35C ''say'':50C ''skew'':66C ''spam'':19C,63C,75C,77C,100C,109C ''test'':133C ''think'':87C ''train'':5A,42C,142C ''two'':83C ''ul'':92C ''use'':18C,39C,107C ''way'':84C,116C ''would'':27C,64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8745, 1, '2015-11-06 22:25:43.853', 0, 131, 'Are there any machine learning algorithms that focus on comparing items, rather than classification or regression?', '&lt;p&gt;This is more of a hypothetical than something I''m actively trying to solve. It just struck me that a machine learning algorithm that specifically looked at two pieces of data and had to label one as greater than the other or something of that nature might be inherently different than classifying each separately and comparing the strength of the classifications.&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':82C ''activ'':31C ''algorithm'':6A,43C ''classif'':14A,81C ''classifi'':72C ''compar'':10A,76C ''data'':51C ''differ'':70C ''focus'':8A ''greater'':58C ''hypothet'':26C ''inher'':69C ''item'':11A ''label'':55C ''learn'':5A,19B,42C ''look'':46C ''m'':30C ''machin'':4A,18B,41C ''machine-learn'':17B ''might'':67C ''natur'':66C ''one'':56C ''p'':20C ''piec'':49C ''rather'':12A ''regress'':16A ''separ'':74C ''solv'':34C ''someth'':28C,63C ''specif'':45C ''strength'':78C ''struck'':37C ''tri'':32C ''two'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4888, 2, '2015-01-16 01:10:21.24', 18, NULL, NULL, '&lt;h3&gt;Why to use deep networks?&lt;/h3&gt;

&lt;p&gt;Let''s first try to solve very simple classification task. Say, you moderate a web forum which is sometimes flooded with spam messages. These messages are easily identifiable - most often they contain specific words like "buy", "porn", etc. and a URL to outer resources. You want to create filter that will alert you about such suspecious messages. It turns to be pretty easy - you get list of features (e.g. list of suspicious words and presence of a URL) and train simple logistic regression (a.k.a. perceptron), i.e. model like: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;g(w0 + w1*x1 + w2*x2 + ... + wnxn)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where &lt;code&gt;x1..xn&lt;/code&gt; are your features (either presence of specific word or a URL), &lt;code&gt;w0..wn&lt;/code&gt; - learned coefficients and &lt;code&gt;g()&lt;/code&gt; is a &lt;a href="http://en.wikipedia.org/wiki/Logistic_function"&gt;logistic function&lt;/a&gt; to make result be between 0 and 1. It''s very simple classifier, but for this simple task it may give very good results, creating linear decision boundary. Assuming you used only 2 features, this boundary may look something like this: &lt;/p&gt;

&lt;p&gt;&lt;img src="http://www.mblondel.org/images/perceptron_linear.png" alt="linear boundary"&gt;&lt;/p&gt;

&lt;p&gt;Here 2 axes represent features (e.g. number of occurrences of specific word in a message, normalized around zero), red points stay for spam and blue points - for normal messages, while black line shows separation line. &lt;/p&gt;

&lt;p&gt;But soon you notice that some good messages contain a lot of occurrences of word "buy", but no URLs, or extended discussion of &lt;a href="http://stackoverflow.com/questions/713247/what-is-the-best-way-to-programatically-detect-porn-images"&gt;porn detection&lt;/a&gt;, not actually refferring to porn movies. Linear decision boundary simply cannot handle such situations. Instead you need something like this: &lt;/p&gt;

&lt;p&gt;&lt;img src="http://openclassroom.stanford.edu/MainFolder/courses/MachineLearning/exercises/ex8materials/ex8b_100.png" alt="non-linear boundary"&gt;&lt;/p&gt;

&lt;p&gt;This new non-linear decision boundary is much more &lt;strong&gt;flexible&lt;/strong&gt;, i.e. it can fit the data much closer. There are many ways to achieve this non-linearity - you can use polynomial features (e.g. &lt;code&gt;x1^2&lt;/code&gt;) or their combination (e.g. &lt;code&gt;x1*x2&lt;/code&gt;) or project them out to a higher dimension like in &lt;a href="http://en.wikipedia.org/wiki/Kernel_method"&gt;kernel methods&lt;/a&gt;. But in neural networks it''s common to solve it by &lt;strong&gt;combining perceptrons&lt;/strong&gt; or, in other words, by building &lt;a href="http://en.wikipedia.org/wiki/Multilayer_perceptron"&gt;&lt;strong&gt;multilayer perceptron&lt;/strong&gt;&lt;/a&gt;. Non-linearity here comes from logistic function between layers. The more layers, the more sophisticated patterns may be covered by MLP. Single layer (perceptron) can handle simple spam detection, network with 2-3 layers can catch tricky combinations of features, and networks of 5-9 layers, used by large research labs and companies like Google, may model the whole language or detect cats on images. &lt;/p&gt;

&lt;p&gt;This is essential reason to have &lt;strong&gt;deep architectures&lt;/strong&gt; - they can &lt;em&gt;model more sophisticated patterns&lt;/em&gt;. &lt;/p&gt;

&lt;h3&gt;Why deep networks are hard to train?&lt;/h3&gt;

&lt;p&gt;With only one feature and linear decision boundary it''s in fact enough to have only 2 training examples - one positive and one negative. With several features and/or non-linear decision boundary you need several orders more examples to cover all possible cases (e.g. you need not only find examples with &lt;code&gt;word1&lt;/code&gt;, &lt;code&gt;word2&lt;/code&gt; and &lt;code&gt;word3&lt;/code&gt;, but also with all possible their combinations). And in real life you need to deal with hundreds and thousands of features (e.g. words in a language or pixels in an image) and at least several layers to have enough non-linearity. Size of a data set, needed to fully train such networks, easily exceeds 10^30 examples, making it totally impossible to get enough data. In other words, with many features and many layers our decision function becomes &lt;strong&gt;too flexible&lt;/strong&gt; to be able to learn it &lt;strong&gt;precisely&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;There are, however, ways to learn it &lt;em&gt;approximately&lt;/em&gt;. For example, if we were working in probabilistic settings, then instead of learning frequencies of all combinations of all features we could assume that they are independent and learn only individual frequencies, reducing full and unconstrained &lt;a href="http://en.wikipedia.org/wiki/Bayes_classifier"&gt;Bayes classifier&lt;/a&gt; to a &lt;a href="http://en.wikipedia.org/wiki/Naive_Bayes_classifier"&gt;Naive Bayes&lt;/a&gt; and thus requiring much, much less data to learn. &lt;/p&gt;

&lt;p&gt;In neural networks there were several attempts to (meaningfully) reduce complexity (flexibility) of decision function. For example, convolutional networks, extensively used in image classification, assume only local connections between nearby pixels and thus try only learn combinations of pixels inside small "windows" (say, 16x16 pixels = 256 input neurons) as opposed to full images (say, 100x100 pixels = 10000 input neurons). Other approaches include feature engineering, i.e. searching for specific, human-discovered descriptors of input data. &lt;/p&gt;

&lt;p&gt;Manually discovered features are very promising actually. In natural language processing, for example, it''s sometimes helpful to use special dictionaries (like those containing spam-specific words) or catch negation (e.g. "&lt;em&gt;not&lt;/em&gt; good"). And in computer vision things like &lt;a href="http://en.wikipedia.org/wiki/SURF"&gt;SURF descriptors&lt;/a&gt; or &lt;a href="http://en.wikipedia.org/wiki/Haar-like_features"&gt;Haar-like features&lt;/a&gt; are almost irreplaceable. &lt;/p&gt;

&lt;p&gt;But the problem with manual feature engineering is that it takes literally years to come up with good descriptors. Moreover, these features are often specific &lt;/p&gt;

&lt;h3&gt;Unsupervised pretraining&lt;/h3&gt;

&lt;p&gt;But it turns out that we can &lt;em&gt;obtain good features automatically&lt;/em&gt; right from the data using such algorithms as &lt;strong&gt;autoencoders&lt;/strong&gt; and &lt;strong&gt;restricted Boltzmann machines&lt;/strong&gt;. I described them in detail in my other &lt;a href="http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma/117188#117188"&gt;answer&lt;/a&gt;, but in short they allow to &lt;strong&gt;find repeated patterns in the input data&lt;/strong&gt; and transform it into higher-level features. For example, given only row pixel values as an input, these algorithms may identify and pass higher whole edges, then from these edges construct figures and so on, until you get really high-level descriptors like variations in faces. &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/oGBRR.jpg" alt="deep learning"&gt;&lt;/p&gt;

&lt;p&gt;After such (unsupervised) pretraining network is usually converted into MLP and used for normal supervised training. Note, that pretraining is done layer-wise. This significantly reduces solution space for learning algorithm (and thus number of training examples needed) as it only needs to learn parameters &lt;em&gt;inside&lt;/em&gt; each layer without taking into account other layers. &lt;/p&gt;

&lt;h3&gt;And beyond...&lt;/h3&gt;

&lt;p&gt;Unsupervised pretraining have been here for some time now, but recently other algorithms were found to improve learning both - together with pretraining and without it. One notable example of such algorithms is &lt;a href="http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf"&gt;&lt;strong&gt;dropout&lt;/strong&gt;&lt;/a&gt; - simple technique, that randomly "drops out" some neurons during training, creatig some distortion and preventing networks of following data too closely. This is still a hot research topic, so I leave this to a reader. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8628, 2, '2015-10-28 07:47:26.29', 1, NULL, NULL, '&lt;p&gt;Separate the operational and the training scenarios.&lt;/p&gt;

&lt;p&gt;The operational scenario is the one in which your classifier will be measure on.
This is where you should perform well.
Use should have a dataset that is representative of this scenario.&lt;/p&gt;

&lt;p&gt;The training scenario is whatever you are doing in order to build a classifier that will perform well on the operational scenario.&lt;/p&gt;

&lt;p&gt;Many time the datasets in both scenarios are of the same nature so there is no need to distinct them.
For example, you have some online store so you use for training past usage in order to perform well on the future usage. 
 However, while training you can use a different dataset than a one that represents the operational scenario.
Actually, if you sleep, dream of a classifier, validate it on your operational scenario (this step should be done after waking up), you are just as good as after going the usual machine learning path.&lt;/p&gt;

&lt;p&gt;The distinction between operational and training scenarios becomes important when the dataset is imbalanced. Most algorithm won''t perform well on such a dataset.&lt;/p&gt;

&lt;p&gt;So, don''t hesitate to use two datasets -
You can use a balanced dataset for the training.
Once you are done, validate your classifier of the operational dataset.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8630, 1, '2015-10-28 11:09:51.18', 0, 14, 'Maximum utility in Half Life (Breese''s) metric', '&lt;p&gt;I want to implement the half life metric (described in &lt;a href="http://arxiv.org/abs/1301.7363v1" rel="nofollow"&gt;arXiv:1301.7363v1&lt;/a&gt;, Â§3.1) in order to evaluate a recommender, but am having troubles computing the normalization term.&lt;br&gt;
Citing from the paper&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;[it] is the maximum achievable utility if all observed items had been
  at the top of the ranked list, ordered by vote value.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I assume that observed items are the items in the test set.  &lt;/p&gt;

&lt;p&gt;The problem is that I get values higher than 1; for example, when the first two predicted items have rating 5 and 4, and in the test set those items have rating 4 and 2 in lower positions.&lt;br&gt;
I don''t think this is correct because recommenders which predict high ratings will have a larger metric value, therefore as normalization I always choose the maximum available rating.  &lt;/p&gt;

&lt;p&gt;Is this correct or am I missing something?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8632, 2, '2015-10-28 13:58:41.143', 2, NULL, NULL, '&lt;p&gt;You can still use &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression" rel="nofollow"&gt;sklearn.linear_model.LinearRegression&lt;/a&gt;.  Simply make the output &lt;code&gt;y&lt;/code&gt; a matrix with as many columns as you have dependent variables.  If you want something &lt;a href="http://scikit-learn.org/stable/modules/linear_model.html#polynomial-regression-extending-linear-models-with-basis-functions" rel="nofollow"&gt;non-linear&lt;/a&gt;, you can try different basis functions, use polynomial features, or use a different method for regression (like a NN).&lt;/p&gt;
', NULL, NULL, '''/a'':14C,47C ''/code'':21C ''/p'':67C ''/stable/modules/generated/sklearn.linear_model.linearregression.html#sklearn.linear_model.linearregression'':10C ''/stable/modules/linear_model.html#polynomial-regression-extending-linear-models-with-basis-functions'':41C ''basi'':52C ''code'':19C ''column'':27C ''depend'':31C ''differ'':51C,60C ''featur'':56C ''function'':53C ''href'':7C,38C ''like'':64C ''linear'':46C ''make'':16C ''mani'':26C ''matrix'':23C ''method'':61C ''nn'':66C ''nofollow'':12C,43C ''non'':45C ''non-linear'':44C ''output'':18C ''p'':1C ''polynomi'':55C ''regress'':63C ''rel'':11C,42C ''scikit-learn.org'':9C,40C ''scikit-learn.org/stable/modules/generated/sklearn.linear_model.linearregression.html#sklearn.linear_model.linearregression'':8C ''scikit-learn.org/stable/modules/linear_model.html#polynomial-regression-extending-linear-models-with-basis-functions'':39C ''simpli'':15C ''sklearn.linear_model.linearregression'':13C ''someth'':36C ''still'':4C ''tri'':50C ''use'':5C,54C,58C ''variabl'':32C ''want'':35C ''y'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8634, 2, '2015-10-28 14:23:47.867', 1, NULL, NULL, '&lt;p&gt;As a first step you should try &lt;a href="https://en.wikipedia.org/wiki/Logistic_regression" rel="nofollow"&gt;logistic regression&lt;/a&gt; on your tf-idf vectors.  It is simple to implement and would provide a good baseline for comparison.  You can find an implementation in whatever language you''re using.  You could also try some kind of (&lt;a href="https://www.cs.princeton.edu/~blei/papers/BleiMcAuliffe2007.pdf" rel="nofollow"&gt;perhaps supervised&lt;/a&gt;) &lt;a href="https://en.wikipedia.org/wiki/Topic_model" rel="nofollow"&gt;topic modeling&lt;/a&gt; to create a better feature space, but that would be more involved.&lt;/p&gt;
', NULL, NULL, '''/a'':18C,65C,75C ''/p'':88C ''/wiki/logistic_regression'':13C ''/wiki/topic_model'':70C ''/~blei/papers/bleimcauliffe2007.pdf'':60C ''also'':51C ''baselin'':35C ''better'':79C ''comparison'':37C ''could'':50C ''creat'':77C ''en.wikipedia.org'':12C,69C ''en.wikipedia.org/wiki/logistic_regression'':11C ''en.wikipedia.org/wiki/topic_model'':68C ''featur'':80C ''find'':40C ''first'':4C ''good'':34C ''href'':10C,57C,67C ''idf'':23C ''implement'':29C,42C ''involv'':87C ''kind'':54C ''languag'':45C ''logist'':16C ''model'':74C ''nofollow'':15C,62C,72C ''p'':1C ''perhap'':63C ''provid'':32C ''re'':47C ''regress'':17C ''rel'':14C,61C,71C ''simpl'':27C ''space'':81C ''step'':5C ''supervis'':64C ''tf'':22C ''tf-idf'':21C ''topic'':73C ''tri'':8C,52C ''use'':48C ''vector'':24C ''whatev'':44C ''would'':31C,84C ''www.cs.princeton.edu'':59C ''www.cs.princeton.edu/~blei/papers/bleimcauliffe2007.pdf'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9306, 2, '2015-12-10 10:40:41.863', 1, NULL, NULL, '&lt;p&gt;Reading this short free eBook might give you some insights on what to do:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://shop.oreilly.com/product/0636920029014.do" rel="nofollow"&gt;Analyzing the Analyzers&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':28C ''/p'':16C,29C ''/product/0636920029014.do'':22C ''analyz'':25C,27C ''ebook'':6C ''free'':5C ''give'':8C ''href'':19C ''insight'':11C ''might'':7C ''nofollow'':24C ''p'':1C,17C ''read'':2C ''rel'':23C ''shop.oreilly.com'':21C ''shop.oreilly.com/product/0636920029014.do'':20C ''short'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4954, 2, '2015-01-27 01:37:13.737', 3, NULL, NULL, '&lt;p&gt;Google AdWords.  That has absolute search volumes. &lt;/p&gt;
', NULL, NULL, '''/p'':9C ''absolut'':6C ''adword'':3C ''googl'':2C ''p'':1C ''search'':7C ''volum'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5078, 2, '2015-02-07 07:37:14.04', 1, NULL, NULL, '&lt;p&gt;The reason your OCR program is not recognizing anything is that the font size is very small. If you resize your image to increase its size, you will obtain better results. For example, using ImageMagick to apply a fixed threshold to remove the background on your first image and increase its size:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;convert -density 500 -threshold 40% 29-1891a.gif -resize 250% output.tiff&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;After this, tesseract does a reasonable job:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;tesseract output.tiff output test.config&lt;/code&gt; &lt;/p&gt;

&lt;p&gt;I included a configuration file &lt;code&gt;test.config&lt;/code&gt; to tesseract in order to restrict the permitted characters. In this way, we don''t obtain mistaken unicode characters in our text.&lt;/p&gt;

&lt;p&gt;test.config&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;tessedit_char_whitelist ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789-
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This is the result:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;HOWARD COUNTY 10011607

NALL JOHN E 0-686196 1 LT F05 DALE MARVIN E 30037267 PVT DON SANSJNG DAVID 10505969 PFC
NIXSON EONOND R 10077072 CPL KIA DANIEL NILBORN N 30637064 PVT FOB SAUNDER6 NEWTON L 0-401709 CAPT
NOBLES STEPNEN E 30502791 PVT 0N0 DAVIS IR D 30433764 PVT 0N0 SNELTON CHARLES R 14001742 VT
OSBURN SILLY F 02008518 2 LT DNB BAVIS JINKIE L 18214208 5 SC KIA SHELTON CHARLIE 33423953 7335
PELTON J L 30105473 5 SC RNA 0 DEAN CHARLES 02066314 3 LT 0N0 SMERAN CARLTON A JR 0-510441 2 LT
GUEZADA FRANK 5 10014939 PFC KIA IDIXON JOHN N 6202114 PVT 00R SIBLEV JESSE 30037241 PFC
RAMIREZ JOSE T 30347914 PPC KIA DREADIN RAYMOND 6950964 PVT KIA SLATER JERONE E JR 0-605266 1 LT
OSE THO S 0 30345100 PPC NTA - DUNN GARLAND J 30037202 PVT 0N0 SMITH 00 SE 30509104 PPC
R035 ORREN C 16015376 PVT ONE 1 ELDER GERALD P 0-387706 2 LT KIA SNITN JASPER 7 33037355 7 SC
RUTLEDCE CARL R 38107116 PVTELPNAA JEARHER MARVIN R 30279097 CPL DON SMITN JERALO D 10005000 CPL
SCUDDAY BERNIE L 0-603906 1 LT KIA - FINDLEY PAUL A 50536010 PFC OMB SMITH NELDON A 5550735 PFC
SNITH ROBERT L 10015524 507 ONE- ELINC ROY T I 0-724730 CAPT F05 S THEY 4295431 5 5
SNITH TRAVIS L 30670530 PVT IDUN FORD HERRELL E 0-665672 CAPT E00 STEPN NSON JESSIE P 10006200 PVT
SNEED ROY A 13076927 S 80 ONE GARRETT TOMMY 30220694 PFC KIA SULLIVAN 31 01525125 2 L
SOUTN CARL 0 JR 30343379 PVT OMB CATLOR R T 20012665 SOT KIA SNINDELL VERL O 30037252 7005
STEVENS JANESCO 10015557 PVT DNR CLASSCOCN CNARLES J 6571000 5 SO ONO TAYLOR ARTHUR V 33531479 PFC
STENA D JO 0-723036 2 LT POL 7 00 A 10007027 PVT DOM ITOHN E N A JR 30035251 5 SO
UTTON ARVI 6360431 AV C 0N6 COSSETT JANES N 30436055 CPL KIAI ALKER J NEE H 30117597 SOT
IALBOTT CHARLES 9 38342083 PFC KIA GRESN J 18126923 5 SO KIA NALKER RALPH L 203126071PPC
TUCKER JAMES 30848763 PVT MIA CRIFFIS WILLIAM J 6270119 PFC 0N0 NALLACE JESSE A 30043713 T SC
-TUCKER STERLING P 33341143 RPCI KIA CROSS ELERY C 0-390713 3 LT DNR NARREN CHARLES D 30110253 5 50
NAOSNORTN PAUL P 30345079 5 SC ONE HAKNONBS ROBERT M 38433940 3 SC KIA MASHINCTON HENRY 30299100 PFC
NALNER JAN S H JR 0-696023 2 LT P0 HANDLSY JAMES J JR 0-417955 1 LT ONE EEMS NINPRED E 20017930 CPL
NEBB GLEN 30343104 PVT -K1 7 NANET FARRELL 8 30012597 SCT KIA NNITE DE NIS 10124415 PFC
NRAT JAMES H 30067743 TE05 KIA NARCIE PRANCNARD 6370053 1 SC NIA NMITE MARVIN J 30430974 PFC
NRIOHT NAILAND 0 30609570 PFC KIA NARKEY VEWCEN 30424190 PVT KIA NOODARD BILLY E 510217472 T 5
HARRIS PRBS 38111537 PFC OKIA NRICNT EILL 0-725355 CAPT
I HARRISO DUKE N JR 18055432 AV 0 ONE YOST TNURNAN R 6273153 PVT
HENDRIX JOHN N JR 10217563 PVT 0N8
HICKERSDN JACK 04431540 1 LT 9N8
HUDSPETH COUNTY 3536305354 A CHEW 55555 W
9 0776 A -
- JOHNSTON LONNIE O 0-754906 3 LT KIA H INSON COUNTY
ONES GEORBE N 01173315 1 LT ONE UTCH
JUMPER ISAAC H 30605960 PVT KIA I
A9515 HAN C -7 7 3 LT N5 LONG OSCAR D 30812573 5 SC KIA ALEXANDER BOYD A 0-690739 2 LT
EARDNERNE3SEPN H 3L5E3355 3 L7 335 LVTLE JOHN E 30433946 PVT KIA EALDNIN JANES H 0-407114 CAPT
NORALES ALFREDO L 18015539 3 CG P05 MACK HULET 00057345 1 LT NIA EICCERSTAPP C N 34530001 PC
54N1952 LOCAS N 39441440 PFC K14 NAJORS TRUSTT J 00410046 2 LT KIA BRITTON JAMES H 18036279 8 SC
ROBLES VICENTE 30570733 PFC 0N5 MASON DICE 30203437 CPL NIA EULLARD CAR 5 30607623 PVT
SANCNE2 ANGEL R 38310341 PVT ONE MASON HALTER P 0-671673 3 LT NIA C IN ROBE T E 33105799 TECS
VALLES RENICIO 0 30441430 PIC DON NASSEV JESSE D 30435424 PVT KIA COHA K LL03 N 10077256 PVT
NC CLENDON J H 30117530 PPC ONE EVANS L 0 37392406 TECS
NC HNORTER C R 30300391 PVT KIA FIRLEY MARVIN L 38572402 PVT
4 HILLNAN ODEAN R T-000345 FL 0 DNB IPIELDS JAKES 35711546 PVT
HIL10N JACKSON 7 13030591 PFC 933 ORADDY ROY L 39112920 PFC
HUNT COUNTY HOORE ARLON D 6295620 9 SC KIA GRANT 30572401 PVT
MORRISON DURHARD D 0-519411 3 LT KIA HANNA EVERETT T 10104243 S SC
- LNULLINS GERALD D 6295622 T 50 NIA HANSARD SAMUEL 2 04754619 1 LT
NEAL HOMER M 30609200 PVT KIA THARVEY JOHN B 0-602116 2 LT
ALANIS VICENTE 30894642 VT KIA MEAL RAVN 39 9 1759550 3 NECOAL MARTIN J 6396940 PFC
ALLEN TRUHAN L 0-123936 1 LT KIA NELSON 1 0 30431073 PPET ONE HILL RAYMONO 8 30050142 PVT
ALLEY NILBUR K 01703993 2 LT KIA NELSON TRAVIS C 38204649 PVT DOH HOP JACK H D 30342393 PFC
BENCH CLARENCE A 30003600 PFC KIA INEWLAND OTIS T T-000346 FL 0 F051 HDFF ROBERT C 30345133 T 50
BENNETT EVERETT N 0-692130 LT 0N5 NICNOLSON DALE- 10005042 PVT KIA JONES JANES 0 30343505 PVT
I I
A NIXON LOYD 5041674 TECS KIA KAPPELMN MC 0-360687 CAPT
BLACKHELL E C 38357321 307 KIA PARKER N 33130N V 52959524 3 L7 KIA KECANS TIM JR 38342898 FC
ERITT BASIL JR 36002307 PFC K14 PATTERSON THOMAS H 18136913 SCT 9N5 KENNINER EARL 0 30401408 SGT
BROHN SHOE 8 38634396 PFC KIA PERRI JA 5 0-562333 1 LT KIA LANTRON EDWARD L 19190302 5 SO
BURNS VIRCIL P 38035701 PRC KIA PETTICREH FRED 0 16215700 S 50 NIA LESNER ROLLIE H 30050735 AV C
BUTLER GEORGE A 37259433 1 SC KIA PHILLIPS C L JR 0-431139 CAPT KIA NC CARTY TOURNAN P 30711042 PFC
CAMERON VANDSLL C 30634570 PPCI KIA PILCRTN CLYDE 30115026 V NIA NC GLENDON JACK 30330395 PFC
CARTER LEO RD K 30119959 AV 0 ONE PO D EUCENE N 4 30431074 PRC KIA NC NINNET K E 30340151 PVT
CARTER OILLIAH F 38685535 PFC 0N8 PRESLEY NILLIAN H 30037547 PVT 0N5 NC QUEEN JAMES Y 02055059 2 LT
CREEK LOTD 6379416 A SC DNB PRICE PREDRICK P 30409331 CPL 100W NOTEN GEORGE N 30304695 PFC
CLARK JOHN 18317907 CPL KIA PURCELL SAMUEL N 30017905 PVT 0N5 PANNELL NOLENIA 6227623 1 SC
COLLINS RAYMOND N 30137040 PFC OOH RAILIPF NARDEN A 30037006 CPL 00H PIERCE FELIX 0 30335390 5 SC
CREAKER SAH BL N 18006101 PVT KIA HAYNES NILLIAN T 7-954739 PL 0 9N5 PIETZSCH JAI E 0-426961 2 LT
CRIDER NARLAN 0 10154794 SCT KIA REED JDRN L 30529414 PVT POL RTER NALLACE N JR 38193560 CPL
DALE CHARLEY 8 I 38049500 3 SC KIA ROI NILLIS 5 01393956 1 LT KIA PRESCOTT DAVID L JR 0-707191 2 LT
05 231



D KZXXN D
NNNNH 2
XXLA-A- Q

2
33

UR R
2055
35
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As you can see, there are many mistakes, but at least we are obtaining reasonable results. In any case, you don''t want to do this because even if you were able to get perfect recognition, some of your lines would be placed in the wrong county because tesseract is looking at your document as a big paragraph. I recommend you to use the vertical lines to segment your image into 3 parts and then preprocess each one of them. You can even try to concatenate the parts vertically and perform OCR in a single page. This also applies to the second image.&lt;/p&gt;

&lt;p&gt;By the way, the resolution of your image is not great, so if you can get a better image, that is going to make a big difference. &lt;/p&gt;
', NULL, NULL, '''-000345'':850C ''-000346'':985C ''-123936'':941C ''-360687'':1023C ''-387706'':296C ''-390713'':514C ''-401709'':173C ''-407114'':728C ''-417955'':557C ''-426961'':1248C ''-431139'':1112C ''-510441'':229C ''-519411'':885C ''-562333'':1071C ''-602116'':918C ''-603906'':326C ''-605266'':264C ''-665672'':370C ''-671673'':792C ''-686196'':143C ''-690739'':709C ''-692130'':999C ''-696023'':548C ''-7'':693C ''-707191'':1289C ''-723036'':435C ''-724730'':352C ''-725355'':625C ''-754906'':668C ''-954739'':1240C ''/code'':66C,83C,93C,126C,1309C ''/p'':54C,67C,76C,84C,116C,119C,133C,1415C,1444C ''/pre'':127C,1310C ''0'':142C,172C,217C,228C,263C,270C,295C,325C,351C,369C,398C,434C,513C,547C,556C,602C,624C,634C,667C,708C,727C,791C,805C,830C,852C,884C,917C,940C,947C,987C,998C,1009C,1022C,1058C,1070C,1089C,1111C,1143C,1225C,1242C,1247C,1253C,1288C ''00'':281C,440C ''00057345'':739C ''00410046'':757C ''00h'':1222C ''00r'':243C ''01173315'':678C ''01393956'':1280C ''01525125'':393C ''01703993'':959C ''02008518'':195C ''02055059'':1175C ''02066314'':220C ''04431540'':650C ''04754619'':905C ''05'':1292C ''0776'':662C ''0n0'':180C,186C,223C,279C,497C ''0n5'':771C,1001C,1170C,1205C ''0n6'':458C ''0n8'':647C,1164C ''1'':144C,265C,291C,327C,558C,592C,651C,679C,740C,906C,942C,946C,1072C,1104C,1209C,1281C ''10005000'':320C ''10005042'':1004C ''10006200'':377C ''10007027'':442C ''10011607'':138C ''10014939'':235C ''10015524'':344C ''10015557'':416C ''10077072'':160C ''10077256'':819C ''100w'':1189C ''10104243'':892C ''10124415'':581C ''10154794'':1254C ''10217563'':645C ''10505969'':155C ''13030591'':861C ''13076927'':382C ''14001742'':190C ''16015376'':288C ''16215700'':1090C ''1759550'':930C ''18006101'':1233C ''18015539'':733C ''18036279'':764C ''18055432'':632C ''18126923'':479C ''18136913'':1053C ''18214208'':202C ''18317907'':1197C ''19190302'':1078C ''2'':196C,230C,297C,394C,436C,549C,710C,758C,904C,919C,960C,1176C,1249C,1290C,1298C,1303C ''20012665'':406C ''20017930'':564C ''203126071ppc'':486C ''2055'':1307C ''231'':1293C ''250'':64C ''29-1891a.gif'':62C ''3'':221C,515C,535C,669C,695C,715C,734C,793C,886C,931C,1036C,1274C,1383C ''30003600'':978C ''30012597'':575C ''30017905'':1203C ''30035251'':450C ''30037006'':1220C ''30037202'':277C ''30037241'':246C ''30037252'':412C ''30037267'':150C ''30037547'':1168C ''30043713'':501C ''30050142'':954C ''30050735'':1097C ''30067743'':586C ''30105473'':213C ''30110253'':521C ''30115026'':1129C ''30117530'':825C ''30117597'':469C ''30119959'':1141C ''30137040'':1214C ''30203437'':774C ''30220694'':388C ''30279097'':314C ''30299100'':540C ''30300391'':837C ''30304695'':1193C ''30330395'':1135C ''30335390'':1226C ''30340151'':1157C ''30342393'':973C ''30343104'':568C ''30343379'':400C ''30343505'':1010C ''30345079'':527C ''30345100'':271C ''30345133'':992C ''30347914'':251C ''30401408'':1059C ''30409331'':1187C ''30424190'':608C ''30430974'':598C ''30431073'':948C ''30431074'':1150C ''30433764'':184C ''30433946'':721C ''30435424'':812C ''30436055'':462C ''30441430'':806C ''30502791'':178C ''30509104'':283C ''30529414'':1260C ''30570733'':769C ''30572401'':879C ''30605960'':686C ''30607623'':780C ''30609200'':911C ''30609570'':603C ''30634570'':1124C ''30637064'':166C ''30670530'':363C ''307'':1029C ''30711042'':1119C ''30812573'':701C ''30848763'':489C ''30894642'':923C ''31'':392C ''33'':1304C ''33037355'':303C ''33105799'':801C ''33130n'':1033C ''33341143'':507C ''33423953'':208C ''335'':717C ''33531479'':429C ''34530001'':746C ''35'':1308C ''3536305354'':656C ''35711546'':856C ''36002307'':1047C ''37259433'':1103C ''37392406'':831C ''38035701'':1084C ''38049500'':1273C ''38107116'':309C ''38111537'':619C ''38193560'':1267C ''38204649'':966C ''38310341'':785C ''38342083'':474C ''38342898'':1042C ''38357321'':1028C ''38433940'':534C ''38572402'':843C ''38634396'':1064C ''38685535'':1162C ''39'':928C ''39112920'':867C ''39441440'':751C ''3l5e3355'':714C ''4'':845C,1149C ''40'':61C ''4295431'':357C ''5'':203C,214C,234C,358C,359C,423C,451C,480C,522C,528C,616C,702C,779C,1069C,1079C,1227C,1279C ''50'':523C,900C,994C,1092C ''500'':59C ''5041674'':1017C ''50536010'':333C ''507'':345C ''510217472'':614C ''52959524'':1035C ''54n1952'':748C ''5550735'':339C ''55555'':659C ''6202114'':241C ''6227623'':1208C ''6270119'':495C ''6273153'':639C ''6295620'':874C ''6295622'':898C ''6360431'':455C ''6370053'':591C ''6379416'':1180C ''6396940'':935C ''6571000'':422C ''6950964'':256C ''7'':302C,304C,439C,571C,694C,860C,1239C ''7005'':413C ''7335'':209C ''8'':574C,765C,953C,1063C,1271C ''80'':384C ''9'':473C,661C,875C,929C ''933'':863C ''9n5'':1055C,1243C ''9n8'':653C ''a9515'':690C ''abcdefghijklmnopqrstuvwxyz0123456789'':125C ''abl'':1343C ''alani'':921C ''alexand'':705C ''alfredo'':731C ''alker'':465C ''allen'':937C ''alley'':956C ''also'':1409C ''angel'':783C ''anyth'':10C ''appli'':38C,1410C ''arlon'':872C ''arthur'':427C ''arvi'':454C ''av'':456C,633C,1098C,1142C ''b'':916C ''background'':45C ''basil'':1045C ''bavi'':199C ''bench'':975C ''bennett'':995C ''berni'':323C ''better'':31C,1434C ''big'':1368C,1442C ''billi'':612C ''bl'':1231C ''blackhel'':1025C ''boyd'':706C ''britton'':761C ''brohn'':1061C ''burn'':1081C ''butler'':1100C ''c'':287C,457C,512C,692C,744C,796C,835C,965C,991C,1027C,1099C,1108C,1123C ''cameron'':1121C ''capt'':174C,353C,371C,626C,729C,1024C,1113C ''car'':778C ''carl'':307C,397C ''carlton'':225C ''carter'':1137C,1159C ''carti'':1116C ''case'':1330C ''catlor'':403C ''cg'':735C ''char'':123C ''charact'':102C,112C ''charl'':188C,219C,472C,519C ''charley'':1270C ''charli'':207C ''chew'':658C ''clarenc'':976C ''clark'':1195C ''classcocn'':419C ''clendon'':822C ''clyde'':1128C ''cnarl'':420C ''code'':56C,78C,91C,121C,135C ''coha'':815C ''collin'':1211C ''concaten'':1397C ''configur'':89C ''convert'':57C ''cossett'':459C ''counti'':137C,655C,674C,870C,1358C ''cpl'':161C,315C,321C,463C,565C,775C,1188C,1198C,1221C,1268C ''creaker'':1229C ''creek'':1178C ''crider'':1251C ''criffi'':492C ''cross'':510C ''d'':183C,319C,432C,520C,700C,811C,873C,883C,897C,972C,1146C,1294C,1296C ''dale'':147C,1003C,1269C ''daniel'':163C ''davi'':181C ''david'':154C,1285C ''de'':579C ''dean'':218C ''densiti'':58C ''dice'':773C ''differ'':1443C ''dnb'':198C,853C,1183C ''dnr'':418C,517C ''document'':1365C ''doh'':968C ''dom'':444C ''dreadin'':254C ''duke'':629C ''dunn'':274C ''durhard'':882C ''e'':141C,149C,177C,261C,368C,446C,563C,613C,720C,800C,1026C,1156C,1246C ''e00'':372C ''ealdnin'':724C ''eardnerne3sepn'':712C ''earl'':1057C ''edward'':1076C ''eem'':561C ''eiccerstapp'':743C ''eill'':623C ''elder'':292C ''eleri'':511C ''elinc'':347C ''eonond'':158C ''eritt'':1044C ''eucen'':1147C ''eullard'':777C ''evan'':828C ''even'':1339C,1394C ''everett'':890C,996C ''exampl'':34C ''f'':194C,1161C ''f05'':146C,354C ''f051'':988C ''farrel'':573C ''fc'':1043C ''felix'':1224C ''file'':90C ''findley'':330C ''firley'':840C ''first'':48C ''fix'':40C ''fl'':851C,986C ''fob'':168C ''font'':14C ''ford'':366C ''frank'':233C ''fred'':1088C ''garland'':275C ''garrett'':386C ''georb'':676C ''georg'':1101C,1191C ''gerald'':293C,896C ''get'':1345C,1432C ''glen'':567C ''glendon'':1133C ''go'':1438C ''grant'':878C ''great'':1427C ''gresn'':477C ''guezada'':232C ''h'':468C,545C,585C,672C,685C,713C,726C,763C,824C,971C,1052C,1096C,1167C ''haknonb'':531C ''halter'':789C ''han'':691C ''handlsi'':552C ''hanna'':889C ''hansard'':902C ''harri'':617C ''harriso'':628C ''hayn'':1236C ''hdff'':989C ''hendrix'':641C ''henri'':539C ''herrel'':367C ''hickersdn'':648C ''hil10n'':858C ''hill'':951C ''hillnan'':846C ''hnorter'':834C ''homer'':909C ''hoor'':871C ''hop'':969C ''howard'':136C ''hudspeth'':654C ''hulet'':738C ''hunt'':869C ''ialbott'':471C ''idixon'':238C ''idun'':365C ''imag'':23C,49C,1381C,1414C,1424C,1435C ''imagemagick'':36C ''includ'':87C ''increas'':25C,51C ''inewland'':981C ''inson'':673C ''ipield'':854C ''ir'':182C ''isaac'':684C ''itohn'':445C ''j'':211C,276C,421C,466C,478C,494C,554C,597C,756C,823C,934C ''ja'':1068C ''jack'':649C,970C,1134C ''jackson'':859C ''jai'':1245C ''jake'':855C ''jame'':488C,553C,584C,762C,1173C ''jan'':543C ''jane'':460C,725C,1008C ''janesco'':415C ''jasper'':301C ''jdrn'':1258C ''jearher'':311C ''jeralo'':318C ''jeron'':260C ''jess'':245C,499C,810C ''jessi'':375C ''jinki'':200C ''jo'':433C ''job'':75C ''john'':140C,239C,642C,719C,915C,1196C ''johnston'':664C ''jone'':1007C ''jose'':249C ''jr'':227C,262C,399C,449C,546C,555C,631C,644C,1041C,1046C,1110C,1266C,1287C ''jumper'':683C ''k'':816C,958C,1140C,1155C ''k1'':570C ''k14'':753C,1049C ''kappelmn'':1020C ''kecan'':1039C ''kennin'':1056C ''kia'':162C,205C,237C,253C,258C,299C,329C,390C,408C,476C,482C,509C,537C,577C,588C,605C,610C,671C,688C,704C,723C,760C,814C,839C,877C,888C,913C,925C,944C,962C,980C,1006C,1019C,1030C,1038C,1066C,1074C,1086C,1106C,1114C,1126C,1152C,1199C,1235C,1256C,1276C,1283C ''kiai'':464C ''kzxxn'':1295C ''l'':171C,201C,212C,324C,343C,362C,395C,485C,732C,829C,842C,866C,939C,1077C,1109C,1259C,1286C ''l7'':716C,1037C ''lantron'':1075C ''least'':1322C ''leo'':1138C ''lesner'':1094C ''line'':1351C,1377C ''ll03'':817C ''lnullin'':895C ''loca'':749C ''long'':698C ''lonni'':665C ''look'':1362C ''lotd'':1179C ''loyd'':1016C ''lt'':145C,197C,222C,231C,266C,298C,328C,437C,516C,550C,559C,652C,670C,680C,696C,711C,741C,759C,794C,887C,907C,920C,943C,961C,1000C,1073C,1177C,1250C,1282C,1291C ''lvtle'':718C ''m'':533C,910C ''mack'':737C ''make'':1440C ''mani'':1318C ''martin'':933C ''marvin'':148C,312C,596C,841C ''mashincton'':538C ''mason'':772C,788C ''mc'':1021C ''meal'':926C ''mia'':491C ''mistak'':1319C ''mistaken'':110C ''morrison'':881C ''n'':165C,240C,447C,461C,630C,643C,677C,745C,750C,818C,997C,1032C,1148C,1192C,1202C,1213C,1232C,1265C ''n5'':697C ''nailand'':601C ''najor'':754C ''nalker'':483C ''nall'':139C ''nallac'':498C,1264C ''nalner'':542C ''nanet'':572C ''naosnortn'':524C ''narci'':589C ''narden'':1218C ''narkey'':606C ''narlan'':1252C ''narren'':518C ''nassev'':809C ''nc'':821C,833C,1115C,1132C,1153C,1171C ''neal'':908C ''nebb'':566C ''necoal'':932C ''nee'':467C ''neldon'':337C ''nelson'':945C,963C ''newton'':170C ''nia'':594C,742C,776C,795C,901C,1093C,1131C ''nicnolson'':1002C ''nilborn'':164C ''nilbur'':957C ''nilli'':1278C ''nillian'':1166C,1237C ''ninnet'':1154C ''ninpr'':562C ''nis'':580C ''nixon'':1015C ''nixson'':157C ''nmite'':595C ''nnite'':578C ''nnnnh'':1297C ''nobl'':175C ''nolenia'':1207C ''noodard'':611C ''noral'':730C ''noten'':1190C ''nrat'':583C ''nricnt'':622C ''nrioht'':600C ''nson'':374C ''nta'':273C ''o'':411C,666C ''obtain'':30C,109C,1325C ''ocr'':5C,1403C ''odean'':847C ''oilliah'':1160C ''okia'':621C ''omb'':335C,402C ''one'':290C,346C,385C,530C,560C,635C,675C,681C,787C,827C,950C,1144C,1389C ''ono'':425C ''ooh'':1216C ''oraddi'':864C ''order'':97C ''orren'':286C ''osburn'':192C ''oscar'':699C ''ose'':267C ''oti'':982C ''output'':81C ''output.tiff'':65C,80C ''p'':1C,55C,68C,77C,85C,117C,128C,294C,376C,506C,526C,790C,1083C,1118C,1186C,1311C,1416C ''p0'':551C ''p05'':736C ''page'':1407C ''pannel'':1206C ''paragraph'':1369C ''parker'':1031C ''part'':1384C,1399C ''patterson'':1050C ''paul'':331C,525C ''pc'':747C ''pelton'':210C ''perfect'':1346C ''perform'':1402C ''permit'':101C ''perri'':1067C ''petticreh'':1087C ''pfc'':156C,236C,247C,334C,340C,389C,430C,475C,496C,541C,582C,599C,604C,620C,752C,770C,862C,868C,936C,974C,979C,1048C,1065C,1120C,1136C,1163C,1194C,1215C ''phillip'':1107C ''pic'':807C ''pierc'':1223C ''pietzsch'':1244C ''pilcrtn'':1127C ''pl'':1241C ''place'':1354C ''po'':1145C ''pol'':438C,1262C ''ppc'':252C,272C,284C,826C ''ppci'':1125C ''ppet'':949C ''prancnard'':590C ''prbs'':618C ''prc'':1085C,1151C ''pre'':120C,134C ''predrick'':1185C ''preprocess'':1387C ''prescott'':1284C ''presley'':1165C ''price'':1184C ''program'':6C ''purcel'':1200C ''pvt'':151C,167C,179C,185C,242C,257C,278C,289C,364C,378C,401C,417C,443C,490C,569C,609C,640C,646C,687C,722C,781C,786C,813C,820C,838C,844C,857C,880C,912C,955C,967C,1005C,1011C,1158C,1169C,1204C,1234C,1261C ''pvtelpnaa'':310C ''q'':1302C ''queen'':1172C ''r'':159C,189C,308C,313C,404C,638C,784C,836C,848C,1306C ''r035'':285C ''railipf'':1217C ''ralph'':484C ''ramirez'':248C ''ravn'':927C ''raymond'':255C,1212C ''raymono'':952C ''rd'':1139C ''reason'':3C,74C,1326C ''recogn'':9C ''recognit'':1347C ''recommend'':1371C ''reed'':1257C ''remov'':43C ''renicio'':804C ''resiz'':21C,63C ''resolut'':1421C ''restrict'':99C ''result'':32C,132C,1327C ''rna'':216C ''robe'':798C ''robert'':342C,532C,990C ''robl'':767C ''roi'':1277C ''rolli'':1095C ''roy'':348C,380C,865C ''rpci'':508C ''rter'':1263C ''rutledc'':306C ''sah'':1230C ''samuel'':903C,1201C ''sancne2'':782C ''sansjng'':153C ''saunder6'':169C ''sc'':204C,215C,305C,503C,529C,536C,593C,703C,766C,876C,894C,1105C,1182C,1210C,1228C,1275C ''sct'':576C,1054C,1255C ''scudday'':322C ''se'':282C ''second'':1413C ''see'':1315C ''segment'':1379C ''sgt'':1060C ''shelton'':206C ''shoe'':1062C ''siblev'':244C ''silli'':193C ''singl'':1406C ''size'':15C,27C,53C ''slater'':259C ''small'':18C ''smeran'':224C ''smith'':280C,336C ''smitn'':317C ''sneed'':379C ''snelton'':187C ''snindel'':409C ''snith'':341C,360C ''snitn'':300C ''sot'':407C,470C ''soutn'':396C ''stena'':431C ''stepn'':373C ''stepnen'':176C ''sterl'':505C ''steven'':414C ''sullivan'':391C ''taylor'':426C ''te05'':587C ''tec'':802C,832C,1018C ''tessedit'':122C ''tesseract'':71C,79C,95C,1360C ''test.config'':82C,92C,118C ''text'':115C ''tharvey'':914C ''tho'':268C ''thoma'':1051C ''threshold'':41C,60C ''tim'':1040C ''tnurnan'':637C ''tommi'':387C ''tournan'':1117C ''travi'':361C,964C ''tri'':1395C ''truhan'':938C ''trustt'':755C ''tucker'':487C,504C ''unicod'':111C ''ur'':1305C ''use'':35C,1374C ''utch'':682C ''utton'':453C ''v'':428C,1034C,1130C ''vall'':803C ''vandsll'':1122C ''verl'':410C ''vertic'':1376C,1400C ''vewcen'':607C ''vicent'':768C,922C ''vircil'':1082C ''vt'':191C,924C ''w'':660C ''want'':1334C ''way'':105C,1419C ''whitelist'':124C ''william'':493C ''would'':1352C ''wrong'':1357C ''xxla'':1300C ''xxla-a'':1299C ''y'':1174C ''yost'':636C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8635, 2, '2015-10-28 14:28:10.89', 2, NULL, NULL, '&lt;p&gt;In R:&lt;/p&gt;

&lt;p&gt;First set up a blank plot with whatever x and y scale limits you need:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;plot(NA, xlim=c(11,20),ylim=c(10,99))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then click click click with mouse-button 1 and end with mouse-button 2 (probably):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;pts = data.frame(locator(type="p"))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then save as a CSV file:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;write.csv(pts,"pts.csv",row.names=FALSE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;producing:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;"x","y"
20.9461142167608,54.0921852908633
11.6463003491398,24.5409354249845
14.4239385175408,44.1769632963908
14.7755382856928,29.5957544809901
14.7931182741004,62.8409105801038
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':34C,62C,79C,98C ''/p'':4C,21C,54C,71C,83C ''/pre'':35C,63C,80C,99C ''1'':45C ''10'':32C ''11'':28C ''11.6463003491398'':90C ''14.4239385175408'':92C ''14.7755382856928'':94C ''14.7931182741004'':96C ''2'':52C ''20'':29C ''20.9461142167608'':88C ''24.5409354249845'':91C ''29.5957544809901'':95C ''44.1769632963908'':93C ''54.0921852908633'':89C ''62.8409105801038'':97C ''99'':33C ''blank'':10C ''button'':44C,51C ''c'':27C,31C ''click'':38C,39C,40C ''code'':23C,56C,73C,85C ''csv'':69C ''data.frame'':58C ''end'':47C ''fals'':78C ''file'':70C ''first'':6C ''limit'':18C ''locat'':59C ''mous'':43C,50C ''mouse-button'':42C,49C ''na'':25C ''need'':20C ''p'':1C,5C,36C,61C,64C,81C ''plot'':11C,24C ''pre'':22C,55C,72C,84C ''probabl'':53C ''produc'':82C ''pts'':57C,75C ''pts.csv'':76C ''r'':3C ''row.names'':77C ''save'':66C ''scale'':17C ''set'':7C ''type'':60C ''whatev'':13C ''write.csv'':74C ''x'':14C,86C ''xlim'':26C ''y'':16C,87C ''ylim'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8636, 2, '2015-10-28 15:21:19.96', 2, NULL, NULL, '&lt;p&gt;For web crawler scale why not use a distributed database like Apache Cassandra? Lookups on indexes are efficient and no false positives.&lt;/p&gt;
', NULL, NULL, '''/p'':24C ''apach'':13C ''cassandra'':14C ''crawler'':4C ''databas'':11C ''distribut'':10C ''effici'':19C ''fals'':22C ''index'':17C ''like'':12C ''lookup'':15C ''p'':1C ''posit'':23C ''scale'':5C ''use'':8C ''web'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8641, 1, '2015-10-29 09:48:39.013', 2, 44, 'How do I learn experimental methodology? When is it relevant?', '&lt;p&gt;I just graduated in Computer Science, with a very theoretical background but without any kind of Data Science or Artificial Intelligence experience, and I working on my own to discover those two fields. More precisely, I try to work on a toy subspace clustering k-means-like algorithm, and I think I successfully learned basic optimization techniques.&lt;/p&gt;

&lt;p&gt;So now I have some ad-hoc subspace clustering algorithm, and... I''m stuck. How do I validate it? During my studies, I studied a lot of formal proving or model checking techniques, but I feel they are totally irrelevant here. I read several AI papers, and it seems that there is a strong tradition of experimental validation in Clustering, with a lot of validity measures. My problem is that they mean nothing to me. I don''t understand what they prove, or even why they are relevant. I would be very interested in a general course about the experimental validation methodology (if it actually is a thing!), if possible with theoretical justification.&lt;/p&gt;

&lt;p&gt;Moreover, I don''t really know what are the good properties people are looking for in clustering algorithms. What general theorems should I aim for? I understand this may be a question too related to what I am working on; a generic answer for k-means-like algorithms would be enough.&lt;/p&gt;

&lt;p&gt;Thanks for reading. I apologize deeply if the question isn''t clear, or in the wrong section.&lt;/p&gt;
', 'clustering  experiments  books', NULL, '''/p'':73C,189C,243C,262C ''actual'':180C ''ad'':81C ''ad-hoc'':80C ''ai'':120C ''aim'':214C ''algorithm'':63C,85C,208C,239C ''answer'':233C ''apolog'':249C ''artifici'':34C ''background'':25C ''basic'':70C ''book'':13B ''check'':107C ''clear'':256C ''cluster'':11B,58C,84C,135C,207C ''comput'':19C ''cours'':172C ''data'':31C ''deepli'':250C ''discov'':44C ''enough'':242C ''even'':159C ''experi'':12B,36C ''experiment'':5A,132C,175C ''feel'':111C ''field'':47C ''formal'':103C ''general'':171C,210C ''generic'':232C ''good'':200C ''graduat'':17C ''hoc'':82C ''intellig'':35C ''interest'':168C ''irrelev'':115C ''isn'':254C ''justif'':188C ''k'':60C,236C ''k-means-lik'':59C,235C ''kind'':29C ''know'':196C ''learn'':4A,69C ''like'':62C,238C ''look'':204C ''lot'':101C,138C ''m'':88C ''may'':219C ''mean'':61C,147C,237C ''measur'':141C ''methodolog'':6A,177C ''model'':106C ''moreov'':191C ''noth'':148C ''optim'':71C ''p'':14C,74C,190C,244C ''paper'':121C ''peopl'':202C ''possibl'':185C ''precis'':49C ''problem'':143C ''properti'':201C ''prove'':104C,157C ''question'':222C,253C ''read'':118C,247C ''realli'':195C ''relat'':224C ''relev'':10A,163C ''scienc'':20C,32C ''section'':261C ''seem'':124C ''sever'':119C ''strong'':129C ''stuck'':89C ''studi'':97C,99C ''subspac'':57C,83C ''success'':68C ''techniqu'':72C,108C ''thank'':245C ''theorem'':211C ''theoret'':24C,187C ''thing'':183C ''think'':66C ''total'':114C ''toy'':56C ''tradit'':130C ''tri'':51C ''two'':46C ''understand'':154C,217C ''valid'':93C,133C,140C,176C ''without'':27C ''work'':39C,53C,229C ''would'':165C,240C ''wrong'':260C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8684, 2, '2015-11-02 05:38:00.973', 0, NULL, NULL, '&lt;p&gt;Data Scientist differs from Data Engineer as it is "science vs engineering".&lt;/p&gt;

&lt;p&gt;Scientific methods (such as statistics, validation, experimental design, etc) is what separates data scientists from data engineers. While the program skills are still essential, it is the "tool" for solving problem instead of "problem solving" itself - we use programming tools to gather, process and analyze data but it is the scientific methods that solidify the results.&lt;/p&gt;

&lt;p&gt;I would recommend you to learn more about statistics. I was a computer science graduate as well and I personally learn statistics from Udacity (and other sources afterwards). I feel it''s a nice place to start.&lt;/p&gt;
', NULL, NULL, '''/p'':14C,72C,111C ''afterward'':101C ''analyz'':60C ''comput'':86C ''data'':2C,6C,28C,31C,61C ''design'':23C ''differ'':4C ''engin'':7C,13C,32C ''essenti'':39C ''etc'':24C ''experiment'':22C ''feel'':103C ''gather'':57C ''graduat'':88C ''instead'':47C ''learn'':79C,94C ''method'':17C,67C ''nice'':107C ''p'':1C,15C,73C ''person'':93C ''place'':108C ''problem'':46C,49C ''process'':58C ''program'':35C,54C ''recommend'':76C ''result'':71C ''scienc'':11C,87C ''scientif'':16C,66C ''scientist'':3C,29C ''separ'':27C ''skill'':36C ''solidifi'':69C ''solv'':45C,50C ''sourc'':100C ''start'':110C ''statist'':20C,82C,95C ''still'':38C ''tool'':43C,55C ''udac'':97C ''use'':53C ''valid'':21C ''vs'':12C ''well'':90C ''would'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8640, 2, '2015-10-29 09:11:50.207', 2, NULL, NULL, '&lt;p&gt;Simply combining by voting some classifiers can naturally give bag results. Consider a toy example like having a set of data with $100$ instances. Suppose you have $3$ classifiers. Let''s say on first $70$ instances all classifiers match perfectly. Than on next $10$ first classifier is good, the others are bad, on the next $10$, the 2nd is good, others bad, and on the last $10$ the 3rd is good, others are bad. All three models goes on with $0.80$ accuracy. Voting them would lead to $0.70$.&lt;/p&gt;

&lt;p&gt;There are also some other potential difficulties like unscaled scores for base learners (it often happen with SVM, if it is not scaled by a logistic or whatever). Another reason is the model family provided by different learners. Bagging (or bootstrap aggregating which is used in RF for example) work simply because they assume that the model comes from the same family (it is the same tree model, or statistical speaking it is the majority class over a region), and only the samples are drawn randomly. Thus bootstrapping which requires independent and identically distributed sub-samples does not work in your case, since your models (which can be considered functions over samples) are not identically distributed.  &lt;/p&gt;

&lt;p&gt;There are however solutions to this problem, one of them is stacked generalization (or simply stacking). The simplest way is stacking with a logistic regressor. The idea is to train some base classifiers (which you already have), and take their results as input variables for a logistic regression. The top logistic regression would be able to find the ''proper balance'' between the predictions and usually you get some increased accuracy as a benefit among others. &lt;/p&gt;

&lt;p&gt;For further references see: &lt;a href="https://en.wikipedia.org/wiki/Ensemble_learning#Stacking" rel="nofollow"&gt;Wikipedia page on ensemble learning&lt;/a&gt;. Search on Google for ''stacked generalization''. For papers start with the seminal paper of &lt;a href="http://machine-learning.martinsewell.com/ensembles/stacking/Wolpert1992.pdf" rel="nofollow"&gt;David Wolpert - Stacked Generalization - 1992&lt;/a&gt; which started all the discussion on this topic.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5095, 2, '2015-02-10 05:36:05.493', 1, NULL, NULL, '&lt;p&gt;While @Ben''s answer is nice and partially introduces what should be done first with a newly cleaned data set, I feel that the approach is important enough to have its name presented loud and clear: &lt;em&gt;exploratory data analysis (EDA)&lt;/em&gt;. Therefore, the short answer to your question is that the &lt;strong&gt;first step should be EDA&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;This suggestion is supported by most researchers, regardless of their knowledge domain or type of study. Here is how the father of EDA presents his thoughts on the subject (Tukey, 1977, p. 1-3; emphasis mine):&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Exploratory Data Analysis (EDA) is detective work â numerical
  detective work â or counting detective work â or graphical detective
  work ... unless exploratory data analysis uncovers indications,
  usually quantitative ones, there is likely to be nothing for
  confirmatory data analysis to consider ... [it] can never be the whole
  story, but &lt;strong&gt;nothing else can serve as the foundation stone - as the
  first step&lt;/strong&gt;.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;There is an enormous amount of information on approaches, guidelines and procedures for performing EDA. Potential starting points might include &lt;a href="http://www.itl.nist.gov/div898/handbook/eda/eda.htm" rel="nofollow"&gt;EDA page&lt;/a&gt; on the NIST''s Engineering Statistics Handbook website, &lt;a href="http://www.epa.gov/caddis/da_exploratory_0.html" rel="nofollow"&gt;EDA pages&lt;/a&gt; on the EPA''s website, &lt;a href="http://www.stat.cmu.edu/~hseltman/309/Book/chapter4.pdf" rel="nofollow"&gt;corresponding chapter&lt;/a&gt;  from the book "Experimental Design for Behavioral and Social Sciences" and a &lt;a href="http://cll.stanford.edu/~langley/cogsys/behrens97pm.pdf" rel="nofollow"&gt;survey research paper on EDA&lt;/a&gt; by Begrens (1997), among many others. It is interesting to note that some sources include less traditional methods into EDA toolset, such as dimensionality reduction and clustering (for example, see the description of &lt;a href="http://www.ischool.berkeley.edu/courses/i290-eda" rel="nofollow"&gt;this research seminar&lt;/a&gt;). While some of the EDA approaches and methods are relatively simple, overall EDA is both &lt;strong&gt;art and science&lt;/strong&gt;, as it &lt;a href="http://community.lithium.com/t5/Science-of-Social-blog/Exploratory-Data-Analysis-Playing-with-Big-Data/ba-p/71632" rel="nofollow"&gt;combines unstructured (creative) and structured approaches&lt;/a&gt;. This aspect is especially important to recognize, as &lt;em&gt;big data&lt;/em&gt; exponentially increases complexity of data analyses, including EDA.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Behrens, J. T. (1997). Principles and procedures of exploratory data analysis. &lt;em&gt;Psychological Methods, 2&lt;/em&gt;(2), 131-160.&lt;/p&gt;

&lt;p&gt;Tukey, J. W. (1977). &lt;em&gt;Exploratory Data Analysis.&lt;/em&gt; Addison-Wesley.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;NOTES:&lt;/strong&gt; For those interested in the Tukey''s classic, it is &lt;a href="http://rads.stackoverflow.com/amzn/click/0201076160" rel="nofollow"&gt;available on Amazon&lt;/a&gt;. Various MOOCs on EDA are also available, for example &lt;a href="https://www.coursera.org/course/exdata" rel="nofollow"&gt;this one&lt;/a&gt; and &lt;a href="https://www.udacity.com/course/ud651" rel="nofollow"&gt;this one&lt;/a&gt; (both are R-focused).&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5367, 1, '2015-03-21 17:05:07.51', 0, 122, 'Best way to format data for supervised machine learning ranking predictions', '&lt;p&gt;I''m fairly new to machine learning, but I''m doing my best to learn as much as possible.&lt;/p&gt;

&lt;p&gt;I am curious about how predicting athlete performance (runners in particular) in a race of a specific starting lineup. For instance, if RunnerA, RunnerB, RunnerC, and RunnerD are all racing a 400 meter race, I want to best predict whether &lt;strong&gt;RunnerA&lt;/strong&gt; will beat &lt;strong&gt;RunnerB&lt;/strong&gt; based on past race result information (which I have at my disposal). However, I have many cases where &lt;strong&gt;RunnerA&lt;/strong&gt; has never raced against &lt;strong&gt;RunnerB&lt;/strong&gt;; yet I do have data showing &lt;strong&gt;RunnerA&lt;/strong&gt; has beat &lt;strong&gt;RunnerC&lt;/strong&gt; in the past, and &lt;strong&gt;RunnerC&lt;/strong&gt; has beat &lt;strong&gt;RunnerB&lt;/strong&gt; in the past. This logic extends deeper as well. So, it would seem that &lt;strong&gt;RunnerA&lt;/strong&gt; &lt;em&gt;should&lt;/em&gt; beat &lt;strong&gt;RunnerB&lt;/strong&gt;, given this information. My real concern is when it gets more complicated than this as I add more features (multiple runners, different distances, etc), and so I''m turing to ML algorithms to help my predictions.&lt;/p&gt;

&lt;p&gt;However, I am having difficulty figuring out how to include this in my row data that I can train (after all, correctly formatting data is 99% of proper machine learning), and I am hoping that someone here might have thought along the same lines in the past and might be able to shed some light. &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Example:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I am currently trying to include &lt;strong&gt;RunnerX&lt;/strong&gt;-&lt;strong&gt;RunnerY&lt;/strong&gt; past race data by counting all the races that &lt;strong&gt;RunnerX&lt;/strong&gt; and &lt;strong&gt;RunnerY&lt;/strong&gt; have run together and normalizing them on a scale from &lt;code&gt;-1&lt;/code&gt; to &lt;code&gt;1&lt;/code&gt;; &lt;code&gt;-1&lt;/code&gt; indicating &lt;strong&gt;RunnerX&lt;/strong&gt; lost all past races against RunnerY; and &lt;code&gt;+1&lt;/code&gt; indicating that RunnerX has  won all past races against RunnerY; and &lt;code&gt;+1&lt;/code&gt; indicating. And &lt;code&gt;0&lt;/code&gt; indicating an equal number of wins and losses (or no past races against each other).&lt;/p&gt;

&lt;p&gt;For instance, if &lt;strong&gt;RunnerA&lt;/strong&gt; is racing &lt;strong&gt;RunnerB&lt;/strong&gt;, and &lt;strong&gt;RunnerA&lt;/strong&gt; has beat &lt;strong&gt;RunnerB&lt;/strong&gt; in the past, then I want the algorithm to know that (denoted by a &lt;code&gt;+1&lt;/code&gt; on the &lt;strong&gt;RunnerB&lt;/strong&gt; column of row &lt;strong&gt;RunnerA&lt;/strong&gt;); same for vice versa. Taking it another step further, If &lt;strong&gt;RunnerA&lt;/strong&gt; is racing &lt;strong&gt;RunnerC&lt;/strong&gt; (but the two have never raced each other in the past), and &lt;strong&gt;RunnerA&lt;/strong&gt; has beat &lt;strong&gt;RunnerD&lt;/strong&gt; in a past race, and &lt;strong&gt;RunnerD&lt;/strong&gt; has beat &lt;strong&gt;RunnerC&lt;/strong&gt; in a past race, then I want the algorithm to learn that &lt;strong&gt;RunnerA&lt;/strong&gt; should beat &lt;strong&gt;RunnerC&lt;/strong&gt;. I say &lt;em&gt;beat&lt;/em&gt; here, but I mean an "average beat" for any &lt;strong&gt;RunnerX&lt;/strong&gt;-&lt;strong&gt;RunnerY&lt;/strong&gt; combinations when data for more than 1 past race is available.&lt;/p&gt;

&lt;p&gt;I have set my data up as:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;name     track   surface  distance  age    RunnerA   RunnerB   RunnerC   RunnerD
RunnerA  Home    2        400       11     0         1         0         1
RunnerC  Away    2        400       12     0         0         0         -1
RunnerD  Home    2        400       10     0         0         1         0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;which shows that RunnerA has beat &lt;strong&gt;RunnerB&lt;/strong&gt; and &lt;strong&gt;RunnerD&lt;/strong&gt; in the past. RunnerC has lost to &lt;strong&gt;RunnerD&lt;/strong&gt;. And &lt;strong&gt;RunnerD&lt;/strong&gt; has beat &lt;strong&gt;RunnerC&lt;/strong&gt;.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;The problem:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The problem is that I don''t really think this is a correct display of the information for an ML algorithm.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;From what I understand, ML data should be row independent. And this data isn''t because row 1 (&lt;strong&gt;RunnerA&lt;/strong&gt;) has beat &lt;strong&gt;RunnerD&lt;/strong&gt;, yet the data indicating &lt;strong&gt;RunnerD&lt;/strong&gt; has beat &lt;strong&gt;RunnerC&lt;/strong&gt; is in row 3.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;Does anyone have any ideas how I might be able to incorporate this past win-percentage-for-runner-pair-combination data??? I''m totally stuck here. I''ve read a lot about some algorithms that estimate the win loss by simply totaling win statistics, but those don''t say anything about the actual probability of a particular runner to beat another particular runner.&lt;/p&gt;

&lt;p&gt;Any pointers would be super helpful.&lt;/p&gt;

&lt;p&gt;Thanks!!!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5388, 2, '2015-03-25 12:52:43.58', 7, NULL, NULL, '&lt;p&gt;Shallow &lt;strong&gt;N&lt;/strong&gt;atural &lt;strong&gt;L&lt;/strong&gt;anguage &lt;strong&gt;P&lt;/strong&gt;rocessing technique can be used to extract concepts from sentence.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;-------------------------------------------&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Shallow NLP technique steps:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;1) Convert the sentence to lowercase&lt;/p&gt;

&lt;p&gt;2) Remove stopwords (these are common words found in a language. Words like for, very, and, of, are, etc, are common stop words)&lt;/p&gt;

&lt;p&gt;3) Extract n-gram i.e., a contiguous sequence ofÂ nÂ items from a given sequenceÂ of text (simply increasing n, model can be used to store more context)&lt;/p&gt;

&lt;p&gt;4) Assign a syntactic label (noun, verb etc.)&lt;/p&gt;

&lt;p&gt;5) Knowledge extraction from text through semantic/syntactic analysis approach i.e., try to retain words that hold higher weight in a sentence like Noun/Verb&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;-------------------------------------------&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Lets examine the results of applying the above steps to your given sentence &lt;code&gt;Complimentary gym access for two for the length of stay ($12 value per person per day)&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;1-gram Results:&lt;/strong&gt; gym, access, length, stay, value, person, day&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Summary of step 1 through 4 of shallow NLP:

1-gram          PoS_Tag   Stopword (Yes/No)?    PoS Tag Description
-------------------------------------------------------------------    
Complimentary   NNP                             Proper noun, singular
gym             NN                              Noun, singular or mass
access          NN                              Noun, singular or mass
for             IN         Yes                  Preposition or subordinating conjunction
two             CD                              Cardinal number
for             IN         Yes                  Preposition or subordinating conjunction
the             DT         Yes                  Determiner
length          NN                              Noun, singular or mass
of              IN         Yes                  Preposition or subordinating conjunction
stay            NN                              Noun, singular or mass
($12            CD                              Cardinal number
value           NN                              Noun, singular or mass
per             IN                              Preposition or subordinating conjunction
person          NN                              Noun, singular or mass
per             IN                              Preposition or subordinating conjunction
day)            NN                              Noun, singular or mass

Step 4: Retaining only the Noun/Verbs we end up with gym, access, length, stay, value, person, day
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Lets increase n to store more context and remove stopwords.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;2-gram Results:&lt;/strong&gt; complimentary gym, gym access, length stay, stay value&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Summary of step 1 through 4 of shallow NLP:

2-gram              Pos Tag
---------------------------
access two          NN CD
complimentary gym   NNP NN
gym access          NN NN
length stay         NN NN
per day             IN NN
per person          IN NN
person per          NN IN
stay value          NN NN
two length          CD NN
value per           NN IN

Step 5: Retaining only the Noun/Verb combination we end up with complimentary gym, gym access, length stay, stay value
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;3-gram Results:&lt;/strong&gt; complimentary gym access, length stay value, person per day&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Summary of step 1 through 4 of shallow NLP:

3-gram                      Pos Tag
-------------------------------------
access two length           NN CD NN
complimentary gym access    NNP NN NN
gym access two              NN NN CD
length stay value           NN NN NN
per person per              IN NN IN
person per day              NN IN NN
stay value per              NN NN IN
two length stay             CD NN NN
value per person            NN IN NN


Step 5: Retaining only the Noun/Verb combination we end up with complimentary gym access, length stay value, person per day
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Things to remember:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Refer the Penn tree bank to understand PoS tag description &lt;a href="https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html"&gt;https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Depending on your &lt;em&gt;data and the business context&lt;/em&gt; you can decide the n value to extract n-grams from sentence&lt;/li&gt;
&lt;li&gt;Adding domain specific stop words would increase the quality of concept/theme extraction&lt;/li&gt;
&lt;li&gt;Deep NLP technique will give better results i.e., rather than n-gram, detect relationships within the sentences and represent/express as complex construction to retain the context. For additional info, please refer &lt;a href="http://stats.stackexchange.com/a/133680/66708"&gt;http://stats.stackexchange.com/a/133680/66708&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Tools:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;You can consider using OpenNLP / StanfordNLP for Part of Speech tagging. Most of the programming language have supporting library for OpenNLP/StanfordNLP. You can choose the language based on your comfort. Below is the sample R code I used for PoS tagging.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Sample R code:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Sys.setenv(JAVA_HOME=''C:\\Program Files\\Java\\jre7'') # for 32-bit version
library(rJava)
require("openNLP")
require("NLP")

s &amp;lt;- paste("Complimentary gym access for two for the length of stay $12 value per person per day")

tagPOS &amp;lt;-  function(x, ...) {
  s &amp;lt;- as.String(x)
    word_token_annotator &amp;lt;- Maxent_Word_Token_Annotator()
    a2 &amp;lt;- Annotation(1L, "sentence", 1L, nchar(s))
    a2 &amp;lt;- annotate(s, word_token_annotator, a2)
    a3 &amp;lt;- annotate(s, Maxent_POS_Tag_Annotator(), a2)
    a3w &amp;lt;- a3[a3$type == "word"]
        POStags &amp;lt;- unlist(lapply(a3w$features, `[[`, "POS"))
    POStagged &amp;lt;- paste(sprintf("%s/%s", s[a3w], POStags), collapse = " ")
    list(POStagged = POStagged, POStags = POStags)
  }

  tagged_str &amp;lt;-  tagPOS(s)
  tagged_str

#$POStagged
    #[1] "Complimentary/NNP gym/NN access/NN for/IN two/CD for/IN the/DT length/NN of/IN stay/NN $/$ 12/CD value/NN per/IN     person/NN per/IN day/NN"
    #
    #$POStags
#[1] "NNP" "NN"  "NN"  "IN"  "CD"  "IN"  "DT"  "NN"  "IN"  "NN"  "$"   "CD" 
#[13] "NN"  "IN"  "NN"  "IN"  "NN" 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Additional readings on Shallow &amp;amp; Deep NLP:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Shallow and Deep NLP Processing for ontology learning: a Quick Overview &lt;a href="http://azouaq.athabascau.ca/publications/Conferences,%20Workshops,%20Books/%5BBC2%5D_KDW_2010.pdf"&gt;Click Here&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Integrating Shallow and Deep NLP for Information Extraction &lt;a href="http://www.dfki.de/~feiyu/WHIES.pdf"&gt;Click Here&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8642, 1, '2015-10-29 10:30:39.64', 2, 85, 'What is the difference between concept learning and classification?', '&lt;p&gt;I''ve recently read about concept learning in a machine learning class. They defined a concept as (translated from German):&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Concept:&lt;/p&gt;
  
  &lt;ul&gt;
  &lt;li&gt;Describes a subset of objects or events defined on a bigger set.&lt;/li&gt;
  &lt;li&gt;Boolean function defined on bigger set&lt;/li&gt;
  &lt;/ul&gt;
  
  &lt;p&gt;Given: Examples which are labeled as members or not-members&lt;/p&gt;
  
  &lt;p&gt;Searched: Automatically infer the definition of the underlying concept&lt;/p&gt;
  
  &lt;p&gt;Definition of concept learning: Infering a boolean-valued function from training data of its input and its output.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I would call this a 2-class classification problem. Is there a difference?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8643, 1, '2015-10-29 12:15:40.24', 0, 91, 'Training time too high for MNIST classification using scikit-learn', '&lt;p&gt;I am performing classification on MNIST dataset using sklearn''s SVM library. The training data size is 60,000 * 784. While training it is taking more than 1.5 hours of time. &lt;/p&gt;

&lt;p&gt;Is it a general issue or is it some hardware problem on my side? My laptop''s hardware configuration is 6GB RAM and i3 core processor. &lt;/p&gt;
', 'svm  scikit', NULL, '''/p'':46C,74C ''000'':33C ''1.5'':42C ''60'':32C ''6gb'':68C ''784'':34C ''classif'':7A,18C ''configur'':66C ''core'':72C ''data'':29C ''dataset'':21C ''general'':51C ''hardwar'':57C,65C ''high'':4A ''hour'':43C ''i3'':71C ''issu'':52C ''laptop'':63C ''learn'':11A ''librari'':26C ''mnist'':6A,20C ''p'':14C,47C ''perform'':17C ''problem'':58C ''processor'':73C ''ram'':69C ''scikit'':10A,13B ''scikit-learn'':9A ''side'':61C ''size'':30C ''sklearn'':23C ''svm'':12B,25C ''take'':39C ''time'':2A,45C ''train'':1A,28C,36C ''use'':8A,22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8644, 2, '2015-10-29 12:15:41.227', 1, NULL, NULL, '&lt;p&gt;On the metrics. A common technique is to make simulated data with known labels. Then see if your algorithm can reproduce the clustering; if it can''t there''s a problem, if it can there may still be problems but you''re off the starting blocks. Typical measures would include ROC and AUC (essentially how many times does the algo correctly place object i and j in the same class, balanced against false positives).&lt;/p&gt;

&lt;p&gt;Another tactic is to use well known real datasets to benchmark against. E.g. the classic iris dataset. See how your algo performs in terms of speed and accuracy versus other algos.&lt;/p&gt;

&lt;p&gt;As to what people are looking for this could mean a multitude of things. You might do well to consider where your algo is suited, find an application in that area, and find out whether it performs for that purpose.&lt;/p&gt;
', NULL, NULL, '''/p'':76C,109C,151C ''accuraci'':105C ''algo'':61C,98C,108C,133C ''algorithm'':20C ''anoth'':78C ''applic'':138C ''area'':141C ''auc'':54C ''balanc'':72C ''benchmark'':88C ''block'':47C ''class'':71C ''classic'':92C ''cluster'':24C ''common'':6C ''consid'':130C ''correct'':62C ''could'':119C ''data'':12C ''dataset'':86C,94C ''e.g'':90C ''essenti'':55C ''fals'':74C ''find'':136C,143C ''includ'':51C ''iri'':93C ''j'':67C ''known'':14C,84C ''label'':15C ''look'':116C ''make'':10C ''mani'':57C ''may'':37C ''mean'':120C ''measur'':49C ''metric'':4C ''might'':126C ''multitud'':122C ''object'':64C ''p'':1C,77C,110C ''peopl'':114C ''perform'':99C,147C ''place'':63C ''posit'':75C ''problem'':32C,40C ''purpos'':150C ''re'':43C ''real'':85C ''reproduc'':22C ''roc'':52C ''see'':17C,95C ''simul'':11C ''speed'':103C ''start'':46C ''still'':38C ''suit'':135C ''tactic'':79C ''techniqu'':7C ''term'':101C ''thing'':124C ''time'':58C ''typic'':48C ''use'':82C ''versus'':106C ''well'':83C,128C ''whether'':145C ''would'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8645, 1, '2015-10-29 12:33:07.31', 2, 158, 'Python Seaborn: how are error bars computed in barplots?', '&lt;p&gt;I''m using seaborn to generate bar plots in python. I''m wondering what statistics are used to compute the error bars, but can''t find any reference to this in the &lt;a href="https://stanford.edu/~mwaskom/software/seaborn/generated/seaborn.barplot.html" rel="nofollow"&gt;documentation&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I know the bar values are computed based on mean in my case (the default option), and I assume the error bars are computed based on a Normal distribution 95% confidence interval, but I''d like to be sure.&lt;/p&gt;
', 'python  barplot', NULL, '''/a'':53C ''/p'':54C,95C ''/~mwaskom/software/seaborn/generated/seaborn.barplot.html'':49C ''95'':85C ''assum'':74C ''bar'':6A,19C,34C,59C,77C ''barplot'':9A,11B ''base'':63C,80C ''case'':68C ''comput'':7A,31C,62C,79C ''confid'':86C ''d'':90C ''default'':70C ''distribut'':84C ''document'':52C ''error'':5A,33C,76C ''find'':38C ''generat'':18C ''href'':46C ''interv'':87C ''know'':57C ''like'':91C ''m'':14C,24C ''mean'':65C ''nofollow'':51C ''normal'':83C ''option'':71C ''p'':12C,55C ''plot'':20C ''python'':1A,10B,22C ''refer'':40C ''rel'':50C ''seaborn'':2A,16C ''stanford.edu'':48C ''stanford.edu/~mwaskom/software/seaborn/generated/seaborn.barplot.html'':47C ''statist'':27C ''sure'':94C ''use'':15C,29C ''valu'':60C ''wonder'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9341, 2, '2015-12-11 18:09:06.853', 1, NULL, NULL, '&lt;p&gt;Your problem is going a lot of directions.&lt;/p&gt;

&lt;p&gt;If you have known worker attributes, like doctor, engineer, or accountant, you could have mirror document classification, and use machine learning to classify.   &lt;/p&gt;

&lt;p&gt;If you are going to measure performance, you will need to get a sample from each user. For example, take the top $\frac{1}{4}$ performance and have a target vector for the user.  Map a document to a user''s strength.  If that user is over-queued, pick the next highest $\frac{1}{4}$ and so on.  You could use cosine similarity to give you a score. This can get tricky, as you may want to normalize on productivity.  You may get a document that a high productivity user is better at, but a low productivity user is OK at. By way of analogy, an american football player may be the best receiver and half back, but you might have a good backup receiver and no good backup halfback, so you let your backup play receiver.&lt;/p&gt;

&lt;p&gt;Static parameters would be different, and I am not sure you should use them if you have a performance measure. My guess would be to queue before you have performance for a worker.  But even at 4 documents, you can select a top $\frac{1}{4}$.&lt;/p&gt;

&lt;p&gt;You could also create clusters and measure a user''s performance versus the cluster. This is the easiest approach to queuing, as you just take the next document out of the cluster that is a user''s best cluster.  You only need a few cross-cluster workers to level queues. The downside is, if you have 20 clusters, it would take a while to get user performance versus cluster, so you would need to use static attributes until you get a valid sampling.&lt;/p&gt;
', NULL, NULL, '''/p'':10C,35C,175C,223C,309C ''1'':60C,91C,221C ''20'':282C ''4'':61C,92C,213C,222C ''account'':22C ''also'':227C ''american'':144C ''analog'':142C ''approach'':243C ''attribut'':17C,302C ''back'':154C ''backup'':161C,166C,172C ''best'':150C,262C ''better'':129C ''classif'':28C ''classifi'':34C ''cluster'':229C,238C,256C,263C,271C,283C,294C ''cosin'':99C ''could'':24C,97C,226C ''creat'':228C ''cross'':270C ''cross-clust'':269C ''differ'':181C ''direct'':9C ''doctor'':19C ''document'':27C,73C,122C,214C,252C ''downsid'':277C ''easiest'':242C ''engin'':20C ''even'':211C ''exampl'':55C ''footbal'':145C ''frac'':59C,90C,220C ''get'':48C,108C,120C,290C,305C ''give'':102C ''go'':5C,40C ''good'':160C,165C ''guess'':198C ''half'':153C ''halfback'':167C ''high'':125C ''highest'':89C ''known'':15C ''learn'':32C ''let'':170C ''level'':274C ''like'':18C ''lot'':7C ''low'':133C ''machin'':31C ''map'':71C ''may'':112C,119C,147C ''measur'':42C,196C,231C ''might'':157C ''mirror'':26C ''need'':46C,266C,298C ''next'':88C,251C ''normal'':115C ''ok'':137C ''over-queu'':83C ''p'':1C,11C,36C,176C,224C ''paramet'':178C ''perform'':43C,62C,195C,206C,235C,292C ''pick'':86C ''play'':173C ''player'':146C ''problem'':3C ''product'':117C,126C,134C ''queu'':85C,245C ''queue'':202C,275C ''receiv'':151C,162C,174C ''sampl'':50C,308C ''score'':105C ''select'':217C ''similar'':100C ''static'':177C,301C ''strength'':78C ''sure'':186C ''take'':56C,249C,286C ''target'':66C ''top'':58C,219C ''tricki'':109C ''use'':30C,98C,189C,300C ''user'':53C,70C,76C,81C,127C,135C,233C,260C,291C ''valid'':307C ''vector'':67C ''versus'':236C,293C ''want'':113C ''way'':140C ''worker'':16C,209C,272C ''would'':179C,199C,285C,297C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (18, 4, '2014-05-14 02:49:14.58', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (108, 5, '2014-05-16 20:24:38.98', 1, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (141, 5, '2014-05-18 14:36:16.35', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (177, 5, '2014-05-21 07:05:44.78', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (202, 2, '2014-05-23 13:47:54.603', 7, NULL, NULL, '&lt;p&gt;The Dirichlet distribution is a multivariate distribution. We can denote the parameters of the Dirichlet as a vector of size K of the form ~ 1/B(a) * Product(x_i ^ (a_i-1)), where a is the vector of size K of the parameters, and sum of x_i = 1.&lt;/p&gt;

&lt;p&gt;Now the LDA uses some constructs like:
- a document can have multiple topics (because of this multiplicity, we need the Dirichlet distribution); and there is a Dirichlet distribution which models this relation
- words can also belong to multiple topics, when you consider them outside of a document; so here we need another Dirichlet to model this&lt;/p&gt;

&lt;p&gt;The previous two are distributions which you do not really see from data, this is why is called latent, or hidden.&lt;/p&gt;

&lt;p&gt;Now, in Bayesian inference you use the Bayes rule to infer the posterior probability. For simplicity, let''s say you have data &lt;em&gt;x&lt;/em&gt; and you have a model for this data governed by some parameters theta. In order to infer values for this parameters, in full Bayesian inference you will infer the posterior probability of these parameters using Bayes'' rule with &lt;em&gt;p(theta|x) = p(x|theta)p(theta|alpha)/p(x|alpha)&lt;/em&gt;. In plain words is &lt;em&gt;posterior probability = likelihood x prior probability / marginal likelihood&lt;/em&gt;. Note that here comes an &lt;em&gt;alpha&lt;/em&gt;. This is your initial belief about this distribution, and is the parameter of the prior distribution. Usually this is chosen in such a way that will have a conjugate prior (so the distribution of the posterior is the same with the distribution of the prior) and often to encode some knowledge if you have one or to have maximum entropy if you know nothing.&lt;/p&gt;

&lt;p&gt;The parameters of the prior are called &lt;em&gt;hyperparameters&lt;/em&gt;. So, in LDA, both topic distributions, over documents and over words have also correspondent priors, which are denoted usually with alpha and beta, and because are the parameters of the prior distributions are called hyperparameters. &lt;/p&gt;

&lt;p&gt;Now about choosing priors. If you plot some Dirichlet distributions you will note that if the individual parameters &lt;em&gt;alpha_k&lt;/em&gt; have the same value, the pdf is symmetric in the simplex defined by the &lt;em&gt;x&lt;/em&gt; values, which is the minimum or maximum for pdf is at the center. &lt;/p&gt;

&lt;p&gt;If all the alpha_k have values lower than unit the maximum is found at corners&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/5khZE.png" width="200" height="200"&gt;&lt;/p&gt;

&lt;p&gt;or can if all values alpha_k are the same and greater than 1 the maximum will be found in center like &lt;/p&gt;

&lt;p&gt;&lt;img src="http://research.microsoft.com/en-us/um/people/cmbishop/prml/prmlfigs-png/Figure2.5c.png" width="200" height="200"&gt;&lt;/p&gt;

&lt;p&gt;It is easy to see that if values for alpha_k are not equal the symmetry is broken and the maximum will be found near bigger values. &lt;/p&gt;

&lt;p&gt;Additional, please note that values for priors parameters produce smooth pdfs of the distribution as the values of the parameters are near 1. So if you have great confidence that something is clearly distributed in a way you know, with a high degree of confidence, than values far from 1 in absolute value are to be used, if you do not have such kind of knowledge than values near 1 would be encode this lack of knowledge. It is easy to see why 1 plays such a role in Dirichlet distribution from the formula of the distribution itself.&lt;/p&gt;

&lt;p&gt;Another way to understand this is to see that prior encode prior-knowledge. In the same time you might think that prior encode some prior seen data. This data was not saw by the algorithm itself, it was saw by you, you learned something, and you can model prior according to what you know (learned). So in the prior parameters (hyperparameters) you encode also how big this data set you apriori saw, because the sum of alpha_k can be that also as the size of this more or less imaginary data set. So the bigger the prior data set, the bigger is the confidence, the bigger the values of alpha_k you can choose, the sharper the surface near maximum value, which means also less doubts. &lt;/p&gt;

&lt;p&gt;Hope it helped.&lt;/p&gt;

&lt;p&gt;PS: It''s a hell to write something without LaTeX notation. I hope moderators/administrators will do something.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (308, 2, '2014-06-11 06:49:04.07', 12, NULL, NULL, '&lt;p&gt;News outlets tend to use "Big Data" pretty loosely.  Vendors usually provide case studies surrounding their specific products.  There aren''t a lot out there for open source implementations, but they do get mentioned.  For instance, Apache isn''t going to spend a lot of time building a case study on hadoop, but vendors like Cloudera and Hortonworks probably will.&lt;/p&gt;

&lt;p&gt;Here''s an &lt;a href="http://www.cloudera.com/content/cloudera/en/resources/library/casestudy/joint-success-story-major-retail-bank-case-study-datameer.html"&gt;example case study from Cloudera&lt;/a&gt; in the finance sector.&lt;/p&gt;

&lt;p&gt;Quoting the study:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;One major global financial services conglomerate uses Cloudera and Datameer to help
  identify rogue trading activity. Teams within the firmâs asset management group are
  performing ad hoc analysis on daily feeds of price, position, and order information. Having
  ad hoc analysis to all of the detailed data allows the group to detect anomalies across
  certain asset classes and identify suspicious behavior. Users previously relied solely on
  desktop spreadsheet tools. Now, with Datameer and Cloudera, users have a powerful
  platform that allows them to sift through more data more quickly and avert potential
  losses before they begin.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;A leading retail bank is using Cloudera and Datameer to validate data accuracy and quality
  as required by the Dodd-Frank Act and other regulations. Integrating loan and branch data
  as well as wealth management data, the bankâs data quality initiative is responsible for
  ensuring that every record is accurate. The process includes subjectingthe data to over
  50 data sanity and quality checks. The results of those checks are trended over time to
  ensure that the tolerances for data corruption and data domains arenât changing adversely
  and that the risk profiles being reported to investors and regulatory agencies are prudent
  and in compliance with regulatory requirements. The results are reported through a data
  quality dashboard to the Chief Risk Officer and Chief Financial Officer, who are ultimately
  responsible for ensuring the accuracy of regulatory compliance reporting as well as
  earnings forecasts to investors&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I didn''t see any other finance related studies at Cloudera, but I didn''t search very hard.  You can have a look at &lt;a href="http://www.cloudera.com/content/cloudera/en/resources/library.html?q=bank"&gt;their library&lt;/a&gt; here.&lt;/p&gt;

&lt;p&gt;Also, Hortonworks has a &lt;a href="http://hortonworks.com/blog/building-stock-trading-strategies-20-faster-with-hadoop/"&gt;case study on Trading Strategies&lt;/a&gt; where they saw a 20% decrease in the time it took to develop a strategy by leveraging K-means, Hadoop, and R.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/qX3Qy.png" alt="Each color indicates a group of strategies with similar probability of a profit and loss"&gt;&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Sj8U9.png" alt="how the trading system was improved by using Hadoop (Hortonworks Data Platform), and the k-means algorithm"&gt;&lt;/p&gt;

&lt;p&gt;These don''t answer all of your questions.  I''m pretty sure both of these studies covered most of them. I don''t see anything about tool selection specifically.  I imagine sales reps had a lot to do with getting the overall product in the door, but the data scientists themselves leveraged the tools they were most comfortable with.  I don''t have a lot of insight into that area in the big data space.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (331, 2, '2014-06-12 10:09:23.887', 9, NULL, NULL, '&lt;p&gt;Some additional thoughts.&lt;/p&gt;

&lt;p&gt;The programming language ''per se'' is only a tool. All languages were designed to make some type of constructs more easy to build than others. And the knowledge and mastery of a programming language is more important and effective than the features of that language compared to others.   &lt;/p&gt;

&lt;p&gt;As far as I can see there are two dimensions of this question. The first dimension is the ability to explore, build proof of concepts or models at a fast pace, eventually having at hand enough tools to study what is going on (like statistical tests, graphics, measurement tools, etc). This kind of activity is usually preferred by researchers and data scientists (I always wonder what that means, but I use this term for its loose definition). They tend to rely on well-known and verified instruments, which can be used for proofs or arguments.&lt;/p&gt;

&lt;p&gt;The second dimension is the ability to extend, change, improve or even create tools, algorithms or models. In order to achieve that you need a proper programming language. Roughly all of them are the same. If you work for a company, than you depend a lot on the company''s infrastructure, internal culture and your choices diminish significantly. Also, when you want to implement an algorithm for production use, you have to trust the implementation. And implementing in another language which you do not master will not help you much.&lt;/p&gt;

&lt;p&gt;I tend to favor for the first type of activity the R ecosystem. You have a great community, a huge set of tools, proofs that these tools works as expected. Also, you can consider Python, Octave (to name a few), which are reliable candidates.&lt;/p&gt;

&lt;p&gt;For the second task, you have to think before at what you really want. If you want robust production ready tools, then C/C++, Java, C# are great candidates. I consider Python as a second citizen in this category, together with Scala and friends. I do not want to start a flame war, it''s my opinion only. But after more than 17 years as a developer, I tend to prefer a strict contract and my knowledge, than the freedom to do whatever you might think of (like it happens with a lot of dynamic languages).&lt;/p&gt;

&lt;p&gt;Personally, I want to learn as much as possible. I decided that I have to choose the hard way, which means to implement myself everything from scratch. I use R as a model and inspiration. It has great treasures in libraries and a lot of experience distilled. However, as a programming language R, for me at least is a nightmare. So I decided to use Java, and use no additional library. That is only because of my experience, and nothing else.&lt;/p&gt;

&lt;p&gt;If you have time, the best thing you can do is to spend some time with all these things. In this way you will earn for yourself the best answer possible, fitted for you. Dijkstra said once that the tools influence the way you think, so it is advisable to know your tools before letting them to model how you think. You can read more about that in his famous paper called &lt;a href="http://www.cs.utexas.edu/~EWD/transcriptions/EWD03xx/EWD340.html"&gt;The Humble Programmer&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (497, 1, '2014-06-20 03:18:59.477', 15, 281, 'What statistical model should I use to analyze the likelihood that a single event influenced longitudinal data', '&lt;p&gt;I am trying to find a formula, method, or model to use to analyze the likelihood that a specific event influenced some longitudinal data. I am having difficultly figuring out what to search for on Google.&lt;/p&gt;

&lt;p&gt;Here is an example scenario:&lt;/p&gt;

&lt;p&gt;Image you own a business that has an average of 100 walk-in customers every day. One day, you decide you want to increase the number of walk-in customers arriving at your store each day, so you pull a crazy stunt outside your store to get attention. Over the next week, you see on average 125 customers a day.&lt;/p&gt;

&lt;p&gt;Over the next few months, you again decide that you want to get some more business, and perhaps sustain it a bit longer, so you try some other random things to get more customers in your store. Unfortunately, you are not the best marketer, and some of your tactics have little or no effect, and others even have a negative impact.&lt;/p&gt;

&lt;p&gt;What methodology could I use to determine the probability that any one individual event positively or negatively impacted the number of walk-in customers? I am fully aware that correlation does not necessarily equal causation, but what methods could I use to determine the likely increase or decrease in your business''s daily walk in client''s following a specific event?&lt;/p&gt;

&lt;p&gt;I am not interested in analyzing whether or not there is a correlation between your attempts to increase the number of walk-in customers, but rather whether or not any one single event, independent of all others, was impactful.&lt;/p&gt;

&lt;p&gt;I realize that this example is rather contrived and simplistic, so I will also give you a brief description of the actual data that I am using:&lt;/p&gt;

&lt;p&gt;I am attempting to determine the impact that a particular marketing agency has on their client''s website when they publish new content, perform social media campaigns, etc. For any one specific agency, they may have anywhere from 1 to 500 clients. Each client has websites ranging in size from 5 pages to well over 1 million. Over the course of the past 5 year, each agency has annotated all of their work for each client, including the type of work that was done, the number of webpages on a website that were influenced, the number of hours spent, etc.&lt;/p&gt;

&lt;p&gt;Using the above data, which I have assembled into a data warehouse (placed into a bunch of star/snowflake schemas), I need to determine how likely it was that any one piece of work (any one event in time) had an impact on the traffic hitting any/all pages influenced by a specific piece of work. I have created models for 40 different types of content that are found on a website that describes the typical traffic pattern a page with said content type might experience from launch date until present. Normalized relative to the appropriate model, I need to determine the highest and lowest number of increased or decreased visitors a specific page received as the result of a specific piece of work.&lt;/p&gt;

&lt;p&gt;While I have experience with basic data analysis (linear and multiple regression, correlation, etc), I am at a loss for how to approach solving this problem. Whereas in the past I have typically analyzed data with multiple measurements for a given axis (for example temperature vs thirst vs animal and determined the impact on thirst that increased temperate has across animals), I feel that above, I am attempting to analyze the impact of a single event at some point in time for a non-linear, but predictable (or at least model-able), longitudinal dataset. I am stumped :(&lt;/p&gt;

&lt;p&gt;Any help, tips, pointers, recommendations, or directions would be extremely helpful and I would be eternally grateful!&lt;/p&gt;
', 'machine-learning  data-mining  statistics', NULL, '''/p'':62C,69C,133C,196C,260C,302C,331C,433C,557C,658C,677C ''1'':371C,388C ''100'':81C ''125'':129C ''40'':494C ''5'':383C,396C ''500'':373C ''abl'':652C ''across'':618C ''actual'':325C ''agenc'':344C,365C,399C ''also'':317C ''analysi'':566C ''analyz'':8A,39C,267C,592C,628C ''anim'':607C,619C ''annot'':401C ''any/all'':480C ''anywher'':369C ''approach'':581C ''appropri'':528C ''arriv'':103C ''assembl'':442C ''attempt'':277C,335C,626C ''attent'':120C ''averag'':79C,128C ''awar'':226C ''axi'':600C ''basic'':564C ''best'':177C ''bit'':156C ''brief'':321C ''bunch'':450C ''busi'':75C,150C,249C ''campaign'':359C ''causat'':233C ''client'':254C,348C,374C,376C,408C ''content'':355C,498C,515C ''contriv'':311C ''correl'':228C,274C,571C ''could'':200C,237C ''cours'':392C ''crazi'':113C ''creat'':491C ''custom'':85C,102C,130C,168C,222C,286C ''daili'':251C ''data'':17A,22B,49C,326C,438C,445C,565C,593C ''data-min'':21B ''dataset'':654C ''date'':521C ''day'':87C,89C,108C,132C ''decid'':91C,142C ''decreas'':246C,542C ''describ'':506C ''descript'':322C ''determin'':204C,241C,337C,457C,533C,609C ''differ'':495C ''difficult'':53C ''direct'':666C ''done'':416C ''effect'':188C ''equal'':232C ''etc'':360C,432C,572C ''etern'':675C ''even'':191C ''event'':14A,45C,211C,259C,295C,470C,634C ''everi'':86C ''exampl'':67C,308C,602C ''experi'':518C,562C ''extrem'':669C ''feel'':621C ''figur'':54C ''find'':30C ''follow'':256C ''formula'':32C ''found'':501C ''fulli'':225C ''get'':119C,147C,166C ''give'':318C ''given'':599C ''googl'':61C ''grate'':676C ''help'':661C,670C ''highest'':535C ''hit'':479C ''hour'':430C ''imag'':71C ''impact'':195C,215C,301C,339C,475C,611C,630C ''includ'':409C ''increas'':95C,244C,279C,540C,615C ''independ'':296C ''individu'':210C ''influenc'':15A,46C,426C,482C ''interest'':265C ''launch'':520C ''learn'':20B ''least'':649C ''like'':243C,459C ''likelihood'':10A,41C ''linear'':567C,644C ''littl'':185C ''longer'':157C ''longitudin'':16A,48C,653C ''loss'':577C ''lowest'':537C ''machin'':19B ''machine-learn'':18B ''market'':178C,343C ''may'':367C ''measur'':596C ''media'':358C ''method'':33C,236C ''methodolog'':199C ''might'':517C ''million'':389C ''mine'':23B ''model'':3A,35C,492C,529C,651C ''model-'':650C ''month'':139C ''multipl'':569C,595C ''necessarili'':231C ''need'':455C,531C ''negat'':194C,214C ''new'':354C ''next'':123C,137C ''non'':643C ''non-linear'':642C ''normal'':524C ''number'':97C,217C,281C,418C,428C,538C ''one'':88C,209C,293C,363C,464C,469C ''other'':190C,299C ''outsid'':115C ''p'':25C,63C,70C,134C,197C,261C,303C,332C,434C,558C,659C ''page'':384C,481C,512C,546C ''particular'':342C ''past'':395C,588C ''pattern'':510C ''perform'':356C ''perhap'':152C ''piec'':465C,486C,554C ''place'':447C ''point'':637C ''pointer'':663C ''posit'':212C ''predict'':646C ''present'':523C ''probabl'':206C ''problem'':584C ''publish'':353C ''pull'':111C ''random'':163C ''rang'':379C ''rather'':288C,310C ''realiz'':305C ''receiv'':547C ''recommend'':664C ''regress'':570C ''relat'':525C ''result'':550C ''said'':514C ''scenario'':68C ''schema'':453C ''search'':58C ''see'':126C ''simplist'':313C ''singl'':13A,294C,633C ''size'':381C ''social'':357C ''solv'':582C ''specif'':44C,258C,364C,485C,545C,553C ''spent'':431C ''star/snowflake'':452C ''statist'':2A,24B ''store'':106C,117C,171C ''stump'':657C ''stunt'':114C ''sustain'':153C ''tactic'':183C ''temper'':616C ''temperatur'':603C ''thing'':164C ''thirst'':605C,613C ''time'':472C,639C ''tip'':662C ''traffic'':478C,509C ''tri'':28C,160C ''type'':411C,496C,516C ''typic'':508C,591C ''unfortun'':172C ''use'':6A,37C,202C,239C,330C,435C ''visitor'':543C ''vs'':604C,606C ''walk'':83C,100C,220C,252C,284C ''walk-in'':82C,99C,219C,283C ''want'':93C,145C ''warehous'':446C ''webpag'':420C ''websit'':350C,378C,423C,504C ''week'':124C ''well'':386C ''wherea'':585C ''whether'':268C,289C ''work'':405C,413C,467C,488C,556C ''would'':667C,673C ''year'':397C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3749, 2, '2014-12-22 23:55:11.037', 0, NULL, NULL, '&lt;p&gt;Try ''arrange(Data.frame.name, ID)'' function from package ''dplyr''&lt;/p&gt;
', NULL, NULL, '''/p'':10C ''arrang'':3C ''data.frame.name'':4C ''dplyr'':9C ''function'':6C ''id'':5C ''p'':1C ''packag'':8C ''tri'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8646, 1, '2015-10-29 12:42:35.213', 1, 83, 'Predictive analysis of rare events', '&lt;p&gt;I''m trying to predict rare events, meaning less than 1% of positive cases. I basically try to predict if a subject will have 0, 1, 2 ... , 6, &gt; 6 failures (there are cases in all those categories).&lt;/p&gt;

&lt;p&gt;I''ve tried several algorithms:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;decision trees&lt;/li&gt;
&lt;li&gt;random forest&lt;/li&gt;
&lt;li&gt;adaboost&lt;/li&gt;
&lt;li&gt;grouping using k-means clustering and finding associations with failures (which group has most failure)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In any case, learning either goes to no failure or has too much variance (leading poor reasults on C.V. set).&lt;/p&gt;

&lt;p&gt;Do you know any machine learning algorithms which are better suited for rare events?&lt;/p&gt;

&lt;p&gt;Or is it surprising that I get those bad results using those algorithms, which means that my features list is not good?&lt;/p&gt;

&lt;p&gt;Thanks a lot.&lt;/p&gt;
', 'machine-learning  algorithms  predictive-modeling  random-forest  k-means', NULL, '''/li'':69C,73C,76C,94C ''/p'':57C,64C,117C,133C,157C,162C ''/ul'':95C ''0'':44C ''1'':30C,45C ''2'':46C ''6'':47C,48C ''adaboost'':75C ''algorithm'':9B,63C,125C,147C ''analysi'':2A ''associ'':86C ''bad'':143C ''basic'':35C ''better'':128C ''c.v'':115C ''case'':33C,52C,99C ''categori'':56C ''cluster'':83C ''decis'':67C ''either'':101C ''event'':5A,26C,132C ''failur'':49C,88C,93C,105C ''featur'':152C ''find'':85C ''forest'':15B,72C ''get'':141C ''goe'':102C ''good'':156C ''group'':78C,90C ''k'':17B,81C ''k-mean'':16B,80C ''know'':121C ''lead'':111C ''learn'':8B,100C,124C ''less'':28C ''li'':66C,70C,74C,77C ''list'':153C ''lot'':161C ''m'':21C ''machin'':7B,123C ''machine-learn'':6B ''mean'':18B,27C,82C,149C ''model'':12B ''much'':109C ''p'':19C,58C,96C,118C,134C,158C ''poor'':112C ''posit'':32C ''predict'':1A,11B,24C,38C ''predictive-model'':10B ''random'':14B,71C ''random-forest'':13B ''rare'':4A,25C,131C ''reasult'':113C ''result'':144C ''set'':116C ''sever'':62C ''subject'':41C ''suit'':129C ''surpris'':138C ''thank'':159C ''tree'':68C ''tri'':22C,36C,61C ''ul'':65C ''use'':79C,145C ''varianc'':110C ''ve'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8685, 2, '2015-11-02 05:58:23.197', 1, NULL, NULL, '&lt;p&gt;X and Y, Size, Texture, Shape, Orientation, Color are some common encodings for data visualization. Some of them have implied orders (X and Y, Size). In your case, I feel that you may use an unordered encoding such as color.&lt;/p&gt;

&lt;p&gt;FYR. The following website have a rather comprehensive explanation.
&lt;a href="https://www.safaribooksonline.com/library/view/designing-data-visualizations/9781449314774/ch04.html" rel="nofollow"&gt;https://www.safaribooksonline.com/library/view/designing-data-visualizations/9781449314774/ch04.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/library/view/designing-data-visualizations/9781449314774/ch04.html'':57C ''/library/view/designing-data-visualizations/9781449314774/ch04.html&lt;/a&gt;&lt;/p&gt;'':62C ''/p'':42C ''case'':29C ''color'':9C,41C ''common'':12C ''comprehens'':51C ''data'':15C ''encod'':13C,38C ''explan'':52C ''feel'':31C ''follow'':46C ''fyr'':44C ''href'':54C ''impli'':21C ''may'':34C ''nofollow'':59C ''order'':22C ''orient'':8C ''p'':1C,43C ''rather'':50C ''rel'':58C ''shape'':7C ''size'':5C,26C ''textur'':6C ''unord'':37C ''use'':35C ''visual'':16C ''websit'':47C ''www.safaribooksonline.com'':56C,61C ''www.safaribooksonline.com/library/view/designing-data-visualizations/9781449314774/ch04.html'':55C ''www.safaribooksonline.com/library/view/designing-data-visualizations/9781449314774/ch04.html&lt;/a&gt;&lt;/p&gt;'':60C ''x'':2C,23C ''y'':4C,25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8652, 2, '2015-10-29 19:21:27.53', 2, NULL, NULL, '&lt;p&gt;When you have an unbalanced data set, the algorithm is going to weight its success on each data point equally, meaning the majority class comes out as much more important than the minority class. The typical solution is to sample down the majority class until it''s the same size as the minority class, and an alternate (similar) solution is to adjust the cost function so that the minority class is weighted appropriately.&lt;/p&gt;

&lt;p&gt;See these similar questions for more:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/810/should-i-go-for-a-balanced-dataset-or-a-representative-dataset/"&gt;Should I go for a ''balanced'' dataset or a ''representative'' dataset?&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/1107/quick-guide-into-training-highly-imbalanced-data-sets/"&gt;Quick Guide into training highly imbalanced data sets&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/454/what-are-the-implications-for-training-a-tree-ensemble-with-highly-biased-datase"&gt;What are the implications for training a tree ensemble with highly biased datasets?&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/736/skewed-multi-class-data/"&gt;Skewed multi-class data&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/6939/ratio-of-positive-to-negative-sample-in-data-set-for-best-classification/"&gt;Ratio of positive to negative sample in data set for best classification&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':102C,118C,139C,152C,172C ''/li'':103C,119C,140C,153C,173C ''/p'':75C,83C ''/questions/1107/quick-guide-into-training-highly-imbalanced-data-sets/'':109C ''/questions/454/what-are-the-implications-for-training-a-tree-ensemble-with-highly-biased-datase'':125C ''/questions/6939/ratio-of-positive-to-negative-sample-in-data-set-for-best-classification/'':159C ''/questions/736/skewed-multi-class-data/'':146C ''/questions/810/should-i-go-for-a-balanced-dataset-or-a-representative-dataset/'':90C ''/ul'':174C ''adjust'':63C ''algorithm'':10C ''altern'':58C ''appropri'':74C ''balanc'':96C ''best'':170C ''bias'':137C ''class'':25C,35C,45C,55C,71C,150C ''classif'':171C ''come'':26C ''cost'':65C ''data'':7C,19C,116C,151C,167C ''datascience.stackexchange.com'':89C,108C,124C,145C,158C ''datascience.stackexchange.com/questions/1107/quick-guide-into-training-highly-imbalanced-data-sets/'':107C ''datascience.stackexchange.com/questions/454/what-are-the-implications-for-training-a-tree-ensemble-with-highly-biased-datase'':123C ''datascience.stackexchange.com/questions/6939/ratio-of-positive-to-negative-sample-in-data-set-for-best-classification/'':157C ''datascience.stackexchange.com/questions/736/skewed-multi-class-data/'':144C ''datascience.stackexchange.com/questions/810/should-i-go-for-a-balanced-dataset-or-a-representative-dataset/'':88C ''dataset'':97C,101C,138C ''ensembl'':134C ''equal'':21C ''function'':66C ''go'':12C,93C ''guid'':111C ''high'':114C,136C ''href'':87C,106C,122C,143C,156C ''imbalanc'':115C ''implic'':129C ''import'':31C ''li'':85C,104C,120C,141C,154C ''major'':24C,44C ''mean'':22C ''minor'':34C,54C,70C ''much'':29C ''multi'':149C ''multi-class'':148C ''negat'':164C ''p'':1C,76C ''point'':20C ''posit'':162C ''question'':80C ''quick'':110C ''ratio'':160C ''repres'':100C ''sampl'':41C,165C ''see'':77C ''set'':8C,117C,168C ''similar'':59C,79C ''size'':51C ''skew'':147C ''solut'':38C,60C ''success'':16C ''train'':113C,131C ''tree'':133C ''typic'':37C ''ul'':84C ''unbalanc'':6C ''weight'':14C,73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8654, 1, '2015-10-29 22:02:46.53', 1, 71, 'Classifier Chains', '&lt;p&gt;My question is about creating &lt;a href="https://en.wikipedia.org/wiki/Classifier_chains" rel="nofollow"&gt;classifier chains&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The problem I am working with is a multiclass problem where I have to assign one of 7 possible classes. &lt;/p&gt;

&lt;p&gt;I started by training a single random forest based classifier for this purpose and the results were not that great. The expectation is about 95%+ for Precision, whereas I was at 79.&lt;/p&gt;

&lt;p&gt;So I started working with training 7 different classifiers for each class. When I take class 1, for the purpose of training, I convert the class labels of other classes to âOtherâ.&lt;/p&gt;

&lt;p&gt;By doing this process, the performance of the individual classifiers registered ~ 95%+.&lt;/p&gt;

&lt;p&gt;Then I thought, may be lets chain the classifiers (in a waterfall manner) in a way that the most accurate model gets first attempt at classification, the next most accurate gets left over rows to make prediction on, the third most accurate one goes after that so on and so forth. The training process was still the same(as for 7 individual classifiers).&lt;/p&gt;

&lt;p&gt;Concretely, let us say classifier 1 predicts on 100 rows and finds 10 to be of Class 1, the other 90 will be âOtherâ. Classifier 2 will then do predictions on the remaining 90 which will be marked as âOtherâ. Classifier 3 will then go on the left over and so on and so forth. &lt;/p&gt;

&lt;p&gt;Problem is, the results are nowhere near the when they go individually. It is down to high 70s.&lt;/p&gt;

&lt;p&gt;I am  trying to figure out what might be the reason for the steep decline in performance and wondering if this is expected or I need to modify something to achieve somewhat comparative performance. &lt;/p&gt;

&lt;p&gt;I appreciate any input/suggestions/comments.&lt;/p&gt;
', 'machine-learning  multiclass-classification  classifier-chains', NULL, '''/a'':27C ''/p'':28C,50C,84C,119C,133C,198C,257C,277C,313C,319C ''/wiki/classifier_chains'':22C ''1'':102C,205C,217C ''10'':212C ''100'':208C ''2'':226C ''3'':243C ''7'':47C,92C,195C ''70s'':276C ''79'':83C ''90'':220C,234C ''95'':76C,132C ''accur'':154C,164C,176C ''achiev'':309C ''appreci'':316C ''assign'':44C ''attempt'':158C ''base'':60C ''chain'':2A,11B,26C,141C ''class'':49C,97C,101C,111C,115C,216C ''classif'':8B,160C ''classifi'':1A,10B,25C,61C,94C,130C,143C,197C,204C,225C,242C ''classifier-chain'':9B ''compar'':311C ''concret'':200C ''convert'':109C ''creat'':17C ''declin'':293C ''differ'':93C ''en.wikipedia.org'':21C ''en.wikipedia.org/wiki/classifier_chains'':20C ''expect'':73C,301C ''figur'':283C ''find'':211C ''first'':157C ''forest'':59C ''forth'':185C,256C ''get'':156C,165C ''go'':246C,269C ''goe'':178C ''great'':71C ''high'':275C ''href'':19C ''individu'':129C,196C,270C ''input/suggestions/comments'':318C ''label'':112C ''learn'':5B ''left'':166C,249C ''let'':140C,201C ''machin'':4B ''machine-learn'':3B ''make'':170C ''manner'':147C ''mark'':238C ''may'':138C ''might'':286C ''model'':155C ''modifi'':306C ''multiclass'':7B,38C ''multiclass-classif'':6B ''near'':265C ''need'':304C ''next'':162C ''nofollow'':24C ''nowher'':264C ''one'':45C,177C ''otherâ'':118C,224C,241C ''p'':12C,29C,51C,85C,120C,134C,199C,258C,278C,314C ''perform'':126C,295C,312C ''possibl'':48C ''precis'':78C ''predict'':171C,206C,230C ''problem'':31C,39C,259C ''process'':124C,188C ''purpos'':64C,105C ''question'':14C ''random'':58C ''reason'':289C ''regist'':131C ''rel'':23C ''remain'':233C ''result'':67C,262C ''row'':168C,209C ''say'':203C ''singl'':57C ''someth'':307C ''somewhat'':310C ''start'':53C,88C ''steep'':292C ''still'':190C ''take'':100C ''third'':174C ''thought'':137C ''train'':55C,91C,107C,187C ''tri'':281C ''us'':202C ''waterfal'':146C ''way'':150C ''wherea'':79C ''wonder'':297C ''work'':34C,89C ''â'':117C,223C,240C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (67, 5, '2014-05-14 18:48:42.263', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (68, 4, '2014-05-14 18:48:42.263', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (541, 2, '2014-06-23 15:48:10.033', 2, NULL, NULL, '&lt;p&gt;The #1 most important thing is to &lt;em&gt;explicitly document your process&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Different people working on different data make different choices. Any scientists who claim that their work is entirely objective and rational are in denial about the nature of science; every single model we create is informed by our biases and perspectives. The key is that we each have different biases and perspectives, which is one reason that science is the pursuit of a society, not of individuals, and underscores the importance of peer review.&lt;/p&gt;

&lt;p&gt;You have already identified some trade-offs between an algorithmic solution and individual/case-by-case corrections. If we have training as scientists, we may be used to thinking that everything has to be applied as an overall rule, so we may bend over backwards to find correlations and fit curves to data that just won''t reasonably accept it. Applying an algorithm to the entire data set can be powerful when it works, but it''s not the only tool at your disposal. Sometimes you need to use your judgment as the scientist.&lt;/p&gt;

&lt;p&gt;But, if you use your individual judgment to make exceptions and to manually correct your data, be prepared to defend your judgment and argue your case. Be prepared to show that you considered all the data. If you''re going to manually correct 8 observations out of a set of 100,000, you need to do more than justify those 8 manual corrections - you also need to justify &lt;strong&gt;not&lt;/strong&gt; correcting the other 99,992 observations.&lt;/p&gt;

&lt;p&gt;You can still take a methodical approach, perhaps by classifying your data in a systematic way, and choosing which subset of the data to apply your cleaning algorithm to based on that system of classification. And when you do this, you document it, you make your case, and you respect the judgment of your colleagues in the field.&lt;/p&gt;

&lt;p&gt;On the other hand, why do all this extra work before you know it''s necessary? Plenty of "dirty" data sets will still produce useful results. Perhaps 0.5% of your data is "dirty" in a way that you know will have a bad influence on your end product. Well, any analysis is subject to error, and the more you obsess over that 0.5% the more you will start to think of it and treat it like it was 5%, or 25% - much more significant than it truly is. Try to apply your analysis while admitting you know there is some error, and only do extra work to clean the data once you can show for certain that your analysis fails or is not useful otherwise.&lt;/p&gt;

&lt;h2&gt;For example...&lt;/h2&gt;

&lt;p&gt;Say you have a set of test results from a survey of wells, showing the concentrations of certain dissolved substances, hourly over the course of a year. And you observe in this set certain spikes, of short duration, and orders of magnitude higher than the surrounding data. If they are few, and obvious, and you know that the sensors used to produce the data set occasionally malfunction, then there''s no reason to apply an algorithmic solution to the entire data set. You have a choice between excluding some data or modifying it. &lt;/p&gt;

&lt;p&gt;I recommend the exclusion route whenever possible, since you are making fewer assumptions when you don''t have to additionally choose a "correct" value. But if your analysis will absolutely fail with a discontinuity, there are many options. You could choose to replace "bad" values via linear interpolation. You could hold constant the previous value. If there is some great previous literature to apply, and you really have a strong case that the "bad" data is purely due to equipment malfunction, perhaps there''s an established model that you can apply to fill in those regions.&lt;/p&gt;

&lt;p&gt;There are a great many approaches, which is why the most important thing is to document your process, explicitly and exhaustively. Arguing your case is important too, but not strictly necessary; the community is just more likely to ignore you if you don''t make a full effort to engage the review process.&lt;/p&gt;
', NULL, NULL, '''/em'':14C ''/h2'':455C ''/p'':15C,91C,185C,264C,324C,451C,550C,648C,703C ''/strong'':257C ''0.5'':353C,388C ''000'':238C ''1'':3C ''100'':237C ''25'':406C ''5'':404C ''8'':230C,247C ''99'':261C ''992'':262C ''absolut'':581C ''accept'':149C ''addit'':571C ''admit'':420C ''algorithm'':103C,153C,293C,532C ''alreadi'':95C ''also'':251C ''analysi'':376C,418C,444C,579C ''appli'':125C,151C,290C,416C,530C,615C,642C ''approach'':272C,655C ''argu'':210C,671C ''assumpt'':564C ''backward'':135C ''bad'':368C,595C,625C ''base'':295C ''bend'':133C ''bias'':55C,66C ''case'':212C,312C,622C,673C ''certain'':441C,474C,490C ''choic'':25C,542C ''choos'':283C,572C,592C ''claim'':29C ''classif'':300C ''classifi'':275C ''clean'':292C,433C ''colleagu'':320C ''communiti'':682C ''concentr'':472C ''consid'':219C ''constant'':603C ''correct'':107C,200C,229C,249C,258C,574C ''correl'':138C ''could'':591C,601C ''cours'':480C ''creat'':50C ''curv'':141C ''data'':22C,143C,157C,202C,222C,277C,288C,345C,356C,435C,503C,520C,537C,546C,626C ''defend'':206C ''denial'':40C ''differ'':17C,21C,24C,65C ''dirti'':344C,358C ''discontinu'':585C ''dispos'':174C ''dissolv'':475C ''document'':11C,307C,665C ''due'':629C ''durat'':494C ''effort'':697C ''em'':9C ''end'':372C ''engag'':699C ''entir'':34C,156C,536C ''equip'':631C ''error'':380C,426C ''establish'':637C ''everi'':46C ''everyth'':121C ''exampl'':454C ''except'':196C ''exclud'':544C ''exclus'':555C ''exhaust'':670C ''explicit'':10C,668C ''extra'':334C,430C ''fail'':445C,582C ''fewer'':563C ''field'':323C ''fill'':644C ''find'':137C ''fit'':140C ''full'':696C ''go'':226C ''great'':611C,653C ''h2'':452C ''hand'':329C ''higher'':499C ''hold'':602C ''hour'':477C ''identifi'':96C ''ignor'':688C ''import'':5C,87C,661C,675C ''individu'':83C,192C ''individual/case-by-case'':106C ''influenc'':369C ''inform'':52C ''interpol'':599C ''judgment'':181C,193C,208C,317C ''justifi'':245C,254C ''key'':59C ''know'':338C,364C,422C,512C ''like'':401C,686C ''linear'':598C ''literatur'':613C ''magnitud'':498C ''make'':23C,195C,310C,562C,694C ''malfunct'':523C,632C ''mani'':588C,654C ''manual'':199C,228C,248C ''may'':115C,132C ''method'':271C ''model'':48C,638C ''modifi'':548C ''much'':407C ''natur'':43C ''necessari'':341C,680C ''need'':177C,240C,252C ''object'':35C ''observ'':231C,263C,486C ''obsess'':385C ''obvious'':509C ''occasion'':522C ''off'':100C ''one'':71C ''option'':589C ''order'':496C ''otherwis'':450C ''overal'':128C ''p'':1C,16C,92C,186C,265C,325C,456C,551C,649C ''peer'':89C ''peopl'':18C ''perhap'':273C,352C,633C ''perspect'':57C,68C ''plenti'':342C ''possibl'':558C ''power'':161C ''prepar'':204C,214C ''previous'':605C,612C ''process'':13C,667C,702C ''produc'':349C,518C ''product'':373C ''pure'':628C ''pursuit'':77C ''ration'':37C ''re'':225C ''realli'':618C ''reason'':72C,148C,528C ''recommend'':553C ''region'':647C ''replac'':594C ''respect'':315C ''result'':351C,464C ''review'':90C,701C ''rout'':556C ''rule'':129C ''say'':457C ''scienc'':45C,74C ''scientist'':27C,113C,184C ''sensor'':515C ''set'':158C,235C,346C,461C,489C,521C,538C ''short'':493C ''show'':216C,439C,470C ''signific'':409C ''sinc'':559C ''singl'':47C ''societi'':80C ''solut'':104C,533C ''sometim'':175C ''spike'':491C ''start'':393C ''still'':268C,348C ''strict'':679C ''strong'':255C,621C ''subject'':378C ''subset'':285C ''substanc'':476C ''surround'':502C ''survey'':467C ''system'':298C ''systemat'':280C ''take'':269C ''test'':463C ''thing'':6C,662C ''think'':119C,395C ''tool'':171C ''trade'':99C ''trade-off'':98C ''train'':111C ''treat'':399C ''tri'':414C ''truli'':412C ''underscor'':85C ''use'':117C,179C,190C,350C,449C,516C ''valu'':575C,596C,606C ''via'':597C ''way'':281C,361C ''well'':374C,469C ''whenev'':557C ''won'':146C ''work'':19C,32C,164C,335C,431C ''year'':483C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (330, 2, '2014-06-12 09:57:39.89', 2, NULL, NULL, '&lt;p&gt;You can also checkout the seaborn package for statistical charts.&lt;/p&gt;
', NULL, NULL, '''/p'':12C ''also'':4C ''chart'':11C ''checkout'':5C ''p'':1C ''packag'':8C ''seaborn'':7C ''statist'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (219, 2, '2014-05-29 13:51:48.82', 1, NULL, NULL, '&lt;p&gt;Since you have 39,949 variables, the covariance matrix would have about 1.6 billion elements (39,949 * 39,949 = 1,595,922,601).  That is likely why you are getting that error.&lt;/p&gt;
', NULL, NULL, '''/p'':34C ''1'':21C ''1.6'':14C ''39'':5C,17C,19C ''595'':22C ''601'':24C ''922'':23C ''949'':6C,18C,20C ''billion'':15C ''covari'':9C ''element'':16C ''error'':33C ''get'':31C ''like'':27C ''matrix'':10C ''p'':1C ''sinc'':2C ''variabl'':7C ''would'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (390, 2, '2014-06-15 16:07:35.543', 4, NULL, NULL, '&lt;p&gt;When categorical variables are in the mix, I reach for Random Decision Forests, as it handles categorical variables directly without the 1-of-n encoding transformation. This loses less information.&lt;/p&gt;
', NULL, NULL, '''/p'':33C ''1'':23C ''categor'':3C,18C ''decis'':13C ''direct'':20C ''encod'':27C ''forest'':14C ''handl'':17C ''inform'':32C ''less'':31C ''lose'':30C ''mix'':8C ''n'':26C ''of-n'':24C ''p'':1C ''random'':12C ''reach'':10C ''transform'':28C ''variabl'':4C,19C ''without'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (866, 1, '2014-07-30 11:45:08.313', 10, 294, 'Predicting next medical condition from past conditions in claims data', '&lt;p&gt;I am, admittedly, very new to data science. I have spent the last 8 months or so learning as much as I can about the field and its methods.  I am having issues choosing which methods to apply.&lt;/p&gt;

&lt;p&gt;I am currently working with a large set of health insurance claims data that includes some laboratory and pharmacy claims. The most consistent information in the data set, however, is made up of diagnosis (ICD-9CM) and procedure codes (CPT, HCSPCS, ICD-9CM).&lt;/p&gt;

&lt;p&gt;My goals are to:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Identify the most influential precursor conditions (comorbidities) for a medical condition like chronic kidney disease;&lt;/li&gt;
&lt;li&gt;Identify the likelihood (or probability) that a patient will develop a medical condition based on the conditions they have had in the past;&lt;/li&gt;
&lt;li&gt;Do the same as 1 and 2, but with procedures and/or diagnoses.&lt;/li&gt;
&lt;li&gt;Preferably, the results would be interpretable by a doctor&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I have looked at things like the &lt;a href="https://www.heritagehealthprize.com/c/hhp/details/milestone-winners"&gt;Heritage Health Prize Milestone papers&lt;/a&gt; and have learned a lot from them, but they are focused on predicting hospitalizations.&lt;/p&gt;

&lt;p&gt;I have thrown a number of algorithms at the problem (random forests, logistic regression, CART, Cox regressions) and it''s been an amazing learning experience.  I have not been able to decide on what "works" or "doesn''t work," if you know what I mean.  I have enough knowledge and skills to be misled by my own excitement and naivete; what I need is to be able to get excited about something real.&lt;/p&gt;

&lt;p&gt;So here are my questions: What methods do you think work well for problems like this? And, what resources would be most useful for learning about data science applications and methods relevant to healthcare and clinical medicine?&lt;/p&gt;

&lt;p&gt;EDIT #2 to add plaintext table:&lt;/p&gt;

&lt;p&gt;CKD is the target condition, "chronic kidney disease", ".any" denotes that they have acquired that condition at any time, ".isbefore.ckd" means they had that condition before their frist diagnosis of CKD.  The other abbreviations correspond with other conditions identified by ICD-9CM code groupings.  This grouping occurs in SQL during the import process. Each variable, with the exception of patient_age, is binary.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  gender patient_age anx.any art.any ast.any bpa.any can.any cer.any chf.any ckd.any dep.any dia.any end.any flu.any hrt.any hyp.any inf.any men.any ren.any sdp.any
1   Male          31       1       0       1       1       0       0       0       0       1       0       1       1       1       1       0       1       0       0
2 Female          29       1       0       1       1       0       0       0       0       1       0       1       0       0       0       0       0       0       0
3 Female          31       0       1       1       1       0       0       0       0       0       1       0       0       0       0       0       1       0       0
4 Female          53       1       1       1       1       1       0       0       0       1       0       1       0       1       0       1       0       0       0
5   Male          47       0       1       0       0       0       0       0       0       0       0       0       0       0       1       0       1       0       0
6 Female          48       0       1       1       1       1       0       0       0       0       0       1       0       1       0       1       0       0       0
  skn.any tra.any anx.isbefore.ckd art.isbefore.ckd ast.isbefore.ckd bpa.isbefore.ckd can.isbefore.ckd cer.isbefore.ckd chf.isbefore.ckd ckd.isbefore.ckd
1       1       1                0                0                0                0                0                0                0                0
2       0       1                0                0                0                0                0                0                0                0
3       0       0                0                0                0                0                0                0                0                0
4       1       0                0                0                0                0                0                0                0                0
5       0       1                0                0                0                0                0                0                0                0
6       1       0                0                0                0                0                0                0                0                0
  dep.isbefore.ckd dia.isbefore.ckd end.isbefore.ckd flu.isbefore.ckd hrt.isbefore.ckd hyp.isbefore.ckd inf.isbefore.ckd men.isbefore.ckd ren.isbefore.ckd
1                0                0                0                0                0                0                0                0                0
2                0                0                0                0                0                0                0                0                0
3                0                0                0                0                0                0                0                0                0
4                0                0                0                0                0                0                0                0                0
5                0                0                0                0                0                0                0                0                0
6                0                0                0                0                0                0                0                0                0
  sdp.isbefore.ckd skn.isbefore.ckd tra.isbefore.ckd
1                0                0                0
2                0                0                0
3                0                0                0
4                0                0                0
5                0                0                0
6                0                0                0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;EDIT to add sample data frame:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;structure(list(gender = structure(c(1L, 2L, 2L, 2L, 1L, 2L), .Label = c("Male",         "Female"), class = "factor"), patient_age = c(31, 29, 31, 53, 
47, 48), anx.any = c(1, 1, 0, 1, 0, 0), art.any = c(0, 0, 1, 
1, 1, 1), ast.any = c(1, 1, 1, 1, 0, 1), bpa.any = c(1, 1, 1, 
1, 0, 1), can.any = c(0, 0, 0, 1, 0, 1), cer.any = c(0, 0, 0, 
0, 0, 0), chf.any = c(0, 0, 0, 0, 0, 0), ckd.any = c(0, 0, 0, 
0, 0, 0), dep.any = c(1, 1, 0, 1, 0, 0), dia.any = c(0, 0, 1, 
0, 0, 0), end.any = c(1, 1, 0, 1, 0, 1), flu.any = c(1, 0, 0, 
0, 0, 0), hrt.any = c(1, 0, 0, 1, 0, 1), hyp.any = c(1, 0, 0, 
0, 1, 0), inf.any = c(0, 0, 0, 1, 0, 1), men.any = c(1, 0, 1, 
0, 1, 0), ren.any = c(0, 0, 0, 0, 0, 0), sdp.any = c(0, 0, 0, 
0, 0, 0), skn.any = c(1, 0, 0, 1, 0, 1), tra.any = c(1, 1, 0, 
0, 1, 0), anx.isbefore.ckd = c(0, 0, 0, 0, 0, 0), art.isbefore.ckd = c(0, 
0, 0, 0, 0, 0), ast.isbefore.ckd = c(0, 0, 0, 0, 0, 0), bpa.isbefore.ckd = c(0, 
0, 0, 0, 0, 0), can.isbefore.ckd = c(0, 0, 0, 0, 0, 0), cer.isbefore.ckd = c(0, 
0, 0, 0, 0, 0), chf.isbefore.ckd = c(0, 0, 0, 0, 0, 0), ckd.isbefore.ckd = c(0, 
0, 0, 0, 0, 0), dep.isbefore.ckd = c(0, 0, 0, 0, 0, 0), dia.isbefore.ckd = c(0, 
0, 0, 0, 0, 0), end.isbefore.ckd = c(0, 0, 0, 0, 0, 0), flu.isbefore.ckd = c(0, 
0, 0, 0, 0, 0), hrt.isbefore.ckd = c(0, 0, 0, 0, 0, 0), hyp.isbefore.ckd = c(0, 
0, 0, 0, 0, 0), inf.isbefore.ckd = c(0, 0, 0, 0, 0, 0), men.isbefore.ckd = c(0, 
0, 0, 0, 0, 0), ren.isbefore.ckd = c(0, 0, 0, 0, 0, 0), sdp.isbefore.ckd = c(0, 
0, 0, 0, 0, 0), skn.isbefore.ckd = c(0, 0, 0, 0, 0, 0), tra.isbefore.ckd = c(0, 
0, 0, 0, 0, 0)), .Names = c("gender", "patient_age", "anx.any", 
"art.any", "ast.any", "bpa.any", "can.any", "cer.any", "chf.any", 
"ckd.any", "dep.any", "dia.any", "end.any", "flu.any", "hrt.any", 
"hyp.any", "inf.any", "men.any", "ren.any", "sdp.any", "skn.any", 
"tra.any", "anx.isbefore.ckd", "art.isbefore.ckd", "ast.isbefore.ckd", 
"bpa.isbefore.ckd", "can.isbefore.ckd", "cer.isbefore.ckd", "chf.isbefore.ckd", 
"ckd.isbefore.ckd", "dep.isbefore.ckd", "dia.isbefore.ckd", "end.isbefore.ckd", 
"flu.isbefore.ckd", "hrt.isbefore.ckd", "hyp.isbefore.ckd", "inf.isbefore.ckd", 
"men.isbefore.ckd", "ren.isbefore.ckd", "sdp.isbefore.ckd", "skn.isbefore.ckd", 
"tra.isbefore.ckd"), row.names = c(NA, 6L), class = "data.frame")
&lt;/code&gt;&lt;/pre&gt;
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''tra.any'':550C,911C,1103C ''tra.isbefore.ckd'':696C,1071C,1123C ''use'':310C ''variabl'':390C ''well'':299C ''work'':60C,247C,251C,298C ''would'':170C,307C ''www.heritagehealthprize.com'':189C ''www.heritagehealthprize.com/c/hhp/details/milestone-winners'':188C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5332, 1, '2015-03-17 04:25:34.463', 0, 40, 'does pruning a decision tree always make it more general?', '&lt;p&gt;If I prune a decision tree, does that make the resulting decision tree always more general than the original decision tree?&lt;/p&gt;

&lt;p&gt;Are there examples where this is not the case?&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/p'':37C,48C ''alway'':6A,29C ''case'':47C ''classif'':14B ''decis'':4A,20C,27C,35C ''exampl'':41C ''general'':10A,31C ''learn'':13B ''machin'':12B ''machine-learn'':11B ''make'':7A,24C ''origin'':34C ''p'':15C,38C ''prune'':2A,18C ''result'':26C ''tree'':5A,21C,28C,36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (461, 1, '2014-06-18 22:10:58.497', 8, 263, 'Preference Matching Algorithm', '&lt;p&gt;There''s this side project I''m working on where I need to structure a solution to the following problem.&lt;/p&gt;

&lt;p&gt;I have two groups of people (clients). Group &lt;code&gt;A&lt;/code&gt; intends to buy and group &lt;code&gt;B&lt;/code&gt; intends to sell a determined product &lt;code&gt;X&lt;/code&gt;. The product has a series of attributes &lt;code&gt;x_i&lt;/code&gt;, and my objective is to facilitate the transaction between &lt;code&gt;A&lt;/code&gt; and &lt;code&gt;B&lt;/code&gt; by matching their preferences. The main idea is to point out to each member of &lt;code&gt;A&lt;/code&gt; a corresponding in &lt;code&gt;B&lt;/code&gt; whose product better suits his needs, and vice versa. &lt;/p&gt;

&lt;p&gt;Some complicating aspects of the problem:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;The list of attributes is not finite. The buyer might be interested in a very particular characteristic or some kind of design, which is rare among the population and I can''t predict. Can''t previously list all the attributes;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Attributes might be continuous, binary or non-quantifiable (ex: price, functionality, design);&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Any suggestion on how to approach this problem and solve it in an automated way? The idea is to really think out of the box here, so feel free to "go wild" on your suggestions.&lt;/p&gt;

&lt;p&gt;I would also appreciate some references to other similar problems if possible. &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Great suggestions! Many similarities in to the way Iâm thinking of approaching the problem.&lt;/p&gt;

&lt;p&gt;The main issue on mapping the attributes is that the level of detail to which the product should be described depends on each buyers. Letâs take an example of a car. The product âcarâ has lots and lots of attributes that range from its performance, mechanical structure, price etc.&lt;/p&gt;

&lt;p&gt;Suppose I just want a cheap car, or an electric car. Ok, thatâs easy to map because they represent main features of this product. But letâs say, for instance, that I want a car with Dual-Clutch transmission or Xenon headlights. Well there might be many cars on the data base with this attributes but I wouldnât ask the seller to fill in this level of detail to their product prior to the information that there is someone looking them.  Such a procedure would require every seller fill a complex, very detailed, form just try to sell his car on the platform. Just wouldnât work. &lt;/p&gt;

&lt;p&gt;But still, my challenge is to try to be as detailed as necessary in the search to make a good match. So the way Iâm thinking is mapping main aspects of the product, those that are probably relevant to everyone, to narrow down de group of potential sellers.
Next step would be a ârefined searchâ. In order to avoid creating a too detailed form I could ask buyers and sellers to write a free text of their specification. And then use some word matching algorithm to find possible matches. Although I understand that this is not a proper solution to the problem because the seller cannot âguessâ what the buyer needs.  But might get me close.  &lt;/p&gt;

&lt;p&gt;The weighting criteria suggested is great.  It allows me to quantify the level to which the seller matches the buyerâs needs.  The scaling part might be a problem though, because the importance of each attribute varies from client to client. Iâm thinking of using some kind of pattern recognition or just asking de buyer to input the level of importance of each attribute. &lt;/p&gt;

&lt;p&gt;Iâm not a native English speaker so hope Iâm being able to express my ideas properly. &lt;/p&gt;
', 'bigdata  text-mining  recommendation', NULL, '''/code'':42C,50C,59C,70C,82C,86C,104C,110C ''/li'':173C,190C ''/ol'':191C ''/p'':30C,120C,128C,172C,189C,228C,242C,260C,313C,425C,546C,613C,633C ''abl'':627C ''algorithm'':3A,513C ''allow'':555C ''also'':232C ''although'':518C ''among'':157C ''appreci'':233C ''approach'':198C,257C ''ask'':376C,495C,601C ''aspect'':124C,457C ''attribut'':66C,135C,171C,176C,268C,303C,371C,583C,612C ''autom'':206C ''avoid'':487C ''b'':49C,85C,109C ''base'':368C ''better'':113C ''bigdata'':4B ''binari'':180C ''box'':217C ''buy'':45C ''buyer'':140C,285C,496C,539C,603C ''buyerâ'':567C ''cannot'':534C ''car'':293C,321C,325C,350C,364C,417C ''carâ'':297C ''challeng'':430C ''characterist'':148C ''cheap'':320C ''client'':38C,586C,588C ''close'':545C ''clutch'':354C ''code'':40C,48C,57C,67C,80C,84C,102C,108C ''complex'':408C ''complic'':123C ''continu'':179C ''correspond'':106C ''could'':494C ''creat'':488C ''criteria'':550C ''data'':367C ''de'':471C,602C ''depend'':282C ''describ'':281C ''design'':153C,188C ''detail'':274C,385C,410C,437C,491C ''determin'':55C ''dual'':353C ''dual-clutch'':352C ''easi'':329C ''electr'':324C ''english'':620C ''etc'':312C ''everi'':404C ''everyon'':467C ''ex'':185C ''exampl'':290C ''express'':629C ''facilit'':76C ''featur'':336C ''feel'':220C ''fill'':380C,406C ''find'':515C ''finit'':138C ''follow'':28C ''form'':411C,492C ''free'':221C,502C ''function'':187C ''get'':543C ''go'':223C ''good'':446C ''great'':245C,553C ''group'':35C,39C,47C,472C ''guessâ'':536C ''headlight'':358C ''hope'':623C ''hr'':243C ''idea'':93C,209C,631C ''import'':580C,609C ''inform'':392C ''input'':605C ''instanc'':345C ''intend'':43C,51C ''interest'':143C ''issu'':264C ''iâ'':253C,451C,589C,615C,624C ''kind'':151C,595C ''letâ'':286C,341C ''level'':272C,383C,560C,607C ''li'':130C,174C ''list'':133C,168C ''look'':397C ''lot'':299C,301C ''m'':16C,254C,452C,590C,616C,625C ''main'':92C,263C,335C,456C ''make'':444C ''mani'':247C,363C ''map'':266C,331C,455C ''match'':2A,88C,447C,512C,517C,565C ''mechan'':309C ''member'':100C ''might'':141C,177C,361C,542C,573C ''mine'':7B ''narrow'':469C ''nativ'':619C ''necessari'':439C ''need'':21C,116C,540C,569C ''next'':476C ''non'':183C ''non-quantifi'':182C ''object'':73C ''ok'':326C ''ol'':129C ''order'':485C ''p'':9C,31C,121C,131C,175C,192C,229C,244C,261C,314C,426C,547C,614C ''part'':572C ''particular'':147C ''pattern'':597C ''peopl'':37C ''perform'':308C ''platform'':420C ''point'':96C ''popul'':159C ''possibl'':241C,516C ''potenti'':474C ''predict'':164C ''prefer'':1A,90C ''previous'':167C ''price'':186C,311C ''prior'':389C ''probabl'':464C ''problem'':29C,127C,200C,239C,259C,530C,576C ''procedur'':401C ''product'':56C,61C,112C,278C,295C,339C,388C,460C ''project'':14C ''proper'':526C,632C ''quantifi'':184C,558C ''rang'':305C ''rare'':156C ''realli'':212C ''recognit'':598C ''recommend'':8B ''refer'':235C ''refin'':482C ''relev'':465C ''repres'':334C ''requir'':403C ''say'':343C ''scale'':571C ''search'':442C ''searchâ'':483C ''sell'':53C,415C ''seller'':378C,405C,475C,498C,533C,564C ''seri'':64C ''side'':13C ''similar'':238C,248C ''solut'':25C,527C ''solv'':202C ''someon'':396C ''speaker'':621C ''specif'':506C ''step'':477C ''still'':428C ''structur'':23C,310C ''suggest'':194C,227C,246C,551C ''suit'':114C ''suppos'':315C ''take'':288C ''text'':6B,503C ''text-min'':5B ''thatâ'':327C ''think'':213C,255C,453C,591C ''though'':577C ''transact'':78C ''transmiss'':355C ''tri'':413C,433C ''two'':34C ''understand'':520C ''use'':509C,593C ''vari'':584C ''versa'':119C ''vice'':118C ''want'':318C,348C ''way'':207C,252C,450C ''weight'':549C ''well'':359C ''whose'':111C ''wild'':224C ''word'':511C ''work'':17C,424C ''would'':231C,402C,478C ''wouldnâ'':374C,422C ''write'':500C ''x'':58C,68C ''xenon'':357C ''â'':296C,481C,535C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (506, 2, '2014-06-20 16:09:41.637', 7, NULL, NULL, '&lt;p&gt;For the record, I think this is the type of question that''s perfect for the data science Stack Exchange. I hope we get a bunch of real world examples of data problems and several perspectives on how best to solve them.&lt;/p&gt;

&lt;p&gt;I would encourage you &lt;em&gt;not&lt;/em&gt; to use p-values as they can be pretty misleading (&lt;a href="http://andrewgelman.com/2013/03/12/misunderstanding-the-p-value/"&gt;1&lt;/a&gt;, &lt;a href="http://occamstypewriter.org/boboh/2008/08/19/why_p_values_are_evil/"&gt;2&lt;/a&gt;). My approach hinges on you being able to summarize traffic on a given page before and after some intervention. What you care about is the difference in the &lt;em&gt;rate&lt;/em&gt; before and after the intervention. That is, how does the number of hits per day change? Below, I explain a first stab approach with some simulated example data. I will then explain one potential pitfall (and what I would do about it).&lt;/p&gt;

&lt;p&gt;First, let''s think about one page before and after an intervention. Pretend the intervention increases hits per day by roughly 15%:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

def simulate_data(true_diff=0):
    #First choose a number of days between [1, 1000] before the intervention
    num_before = np.random.randint(1, 1001)

    #Next choose a number of days between [1, 1000] after the intervention
    num_after = np.random.randint(1, 1001)

    #Next choose a rate for before the intervention. How many views per day on average?
    rate_before = np.random.randint(50, 151)

    #The intervention causes a `true_diff` increase on average (but is also random)
    rate_after = np.random.normal(1 + true_diff, .1) * rate_before

    #Simulate viewers per day:
    vpd_before = np.random.poisson(rate_before, size=num_before)
    vpd_after = np.random.poisson(rate_after, size=num_after)

    return vpd_before, vpd_after

vpd_before, vpd_after = simulate_data(.15)

plt.hist(vpd_before, histtype="step", bins=20, normed=True, lw=2)
plt.hist(vpd_after, histtype="step", bins=20, normed=True, lw=2)
plt.legend(("before", "after"))
plt.title("Views per day before and after intervention")
plt.xlabel("Views per day")
plt.ylabel("Frequency")
plt.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/FJJqD.png" alt="Distribution of hits per day before and after the intervention"&gt;&lt;/p&gt;

&lt;p&gt;We can clearly see that the intervention increased the number of hits per day, on average. But in order to quantify the difference in rates, we should use one company''s intervention for multiple pages. Since the underlying rate will be different for each page, we should compute the percent change in rate (again, the rate here is hits per day).&lt;/p&gt;

&lt;p&gt;Now, let''s pretend we have data for &lt;code&gt;n = 100&lt;/code&gt; pages, each of which received an intervention from the same company. To get the percent difference we take (mean(hits per day before) - mean(hits per day after)) / mean(hits per day before):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;n = 100

pct_diff = np.zeros(n)

for i in xrange(n):
    vpd_before, vpd_after = simulate_data(.15)
    # % difference. Note: this is the thing we want to infer
    pct_diff[i] = (vpd_after.mean() - vpd_before.mean()) / vpd_before.mean()

plt.hist(pct_diff)
plt.title("Distribution of percent change")
plt.xlabel("Percent change")
plt.ylabel("Frequency")
plt.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/CAitf.png" alt="Distribution of percent change"&gt;&lt;/p&gt;

&lt;p&gt;Now we have the distribution of our parameter of interest! We can query this result in different ways. For example, we might want to know the mode, or (approximation of) the most likely value for this percent change:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def mode_continuous(x, num_bins=None):
    if num_bins is None:
        counts, bins = np.histogram(x)
    else:
        counts, bins = np.histogram(x, bins=num_bins)

    ndx = np.argmax(counts)
    return bins[ndx:(ndx+1)].mean()

mode_continuous(pct_diff, 20)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;When I ran this I got 0.126, which is not bad, considering our true percent change is 15. We can also see the number of positive changes, which approximates the probability that a given company''s intervention improves hits per day:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(pct_diff &amp;gt; 0).mean()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here, my result is 0.93, so we could say there''s a pretty good chance that this company is effective.&lt;/p&gt;

&lt;p&gt;Finally, a potential pitfall: Each page probably has some underlying trend that you should probably account for. That is, even without the intervention, hits per day may increase. To account for this, I would estimate a simple linear regression where the outcome variable is hits per day and the independent variable is day (start at day=0 and simply increment for all the days in your sample). Then subtract the estimate, y_hat, from each number of hits per day to de-trend your data. Then you can do the above procedure and be confident that a positive percent difference is not due to the underlying trend. Of course, the trend may not be linear, so use discretion! Good luck!&lt;/p&gt;
', NULL, NULL, '''+1'':611C ''/2013/03/12/misunderstanding-the-p-value/'':68C ''/a'':70C,77C ''/boboh/2008/08/19/why_p_values_are_evil/'':75C ''/caitf.png'':531C ''/code'':344C,439C,524C,618C,670C ''/em'':52C,108C ''/fjjqd.png'':351C ''/p'':44C,151C,175C,363C,426C,473C,537C,577C,662C,693C,816C ''/pre'':345C,525C,619C,671C ''0'':195C,668C,751C ''0.126'':627C ''0.93'':677C ''1'':69C,203C,211C,220C,228C,266C,269C ''100'':438C,477C ''1000'':204C,221C ''1001'':212C,229C ''15'':174C,303C,493C,638C ''151'':249C ''2'':76C,314C,325C ''20'':310C,321C,617C ''50'':248C ''abl'':84C ''account'':710C,724C ''also'':261C,641C ''alt'':352C,532C ''andrewgelman.com'':67C ''andrewgelman.com/2013/03/12/misunderstanding-the-p-value/'':66C ''approach'':79C,131C ''approxim'':567C,649C ''averag'':244C,258C,380C ''bad'':631C ''best'':40C ''bin'':309C,320C,585C,589C,593C,598C,601C,603C,608C ''bunch'':27C ''care'':99C ''caus'':252C ''chanc'':687C ''chang'':124C,415C,517C,520C,536C,576C,636C,647C ''choos'':197C,214C,231C ''clear'':367C ''code'':177C,436C,475C,579C,664C ''compani'':394C,450C,655C,690C ''comput'':412C ''confid'':790C ''consid'':632C ''continu'':582C,614C ''could'':680C ''count'':592C,597C,606C ''cours'':804C ''data'':18C,33C,136C,192C,302C,434C,492C,780C ''day'':123C,171C,201C,218C,242C,275C,332C,340C,357C,378C,425C,461C,466C,471C,661C,720C,741C,747C,750C,758C,774C ''de'':777C ''de-trend'':776C ''def'':190C,580C ''diff'':194C,255C,268C,479C,505C,512C,616C,666C ''differ'':103C,387C,406C,455C,494C,555C,795C ''discret'':813C ''distribut'':353C,514C,533C,543C ''due'':798C ''effect'':692C ''els'':596C ''em'':50C,106C ''encourag'':48C ''estim'':729C,765C ''even'':714C ''exampl'':31C,135C,558C ''exchang'':21C ''explain'':127C,140C ''final'':695C ''first'':129C,153C,196C ''frequenc'':342C,522C ''get'':25C,452C ''given'':90C,654C ''good'':686C,814C ''got'':626C ''gt'':667C ''hat'':767C ''hing'':80C ''histtyp'':307C,318C ''hit'':121C,169C,355C,376C,423C,459C,464C,469C,659C,718C,739C,772C ''hope'':23C ''href'':65C,72C ''i.stack.imgur.com'':350C,530C ''i.stack.imgur.com/caitf.png'':529C ''i.stack.imgur.com/fjjqd.png'':349C ''img'':347C,527C ''import'':178C,182C,186C ''improv'':658C ''increas'':168C,256C,372C,722C ''increment'':754C ''independ'':744C ''infer'':503C ''interest'':548C ''intervent'':96C,113C,164C,167C,207C,224C,237C,251C,336C,362C,371C,396C,446C,657C,717C ''know'':563C ''let'':154C,429C ''like'':571C ''linear'':732C,810C ''luck'':815C ''lw'':313C,324C ''mani'':239C ''matplotlib.pyplot'':183C ''may'':721C,807C ''mean'':458C,463C,468C,612C,669C ''might'':560C ''mislead'':63C ''mode'':565C,581C,613C ''multipl'':398C ''n'':437C,476C,481C,486C ''ndx'':604C,609C,610C ''next'':213C,230C ''none'':586C,591C ''norm'':311C,322C ''note'':495C ''np'':181C ''np.argmax'':605C ''np.histogram'':594C,599C ''np.random.normal'':265C ''np.random.poisson'':278C,286C ''np.random.randint'':210C,227C,247C ''np.zeros'':480C ''num'':208C,225C,282C,290C,584C,588C,602C ''number'':119C,199C,216C,374C,644C,770C ''numpi'':179C ''occamstypewriter.org'':74C ''occamstypewriter.org/boboh/2008/08/19/why_p_values_are_evil/'':73C ''one'':141C,158C,393C ''order'':383C ''outcom'':736C ''p'':1C,45C,56C,152C,346C,364C,427C,526C,538C,620C,672C,694C ''p-valu'':55C ''page'':91C,159C,399C,409C,440C,700C ''paramet'':546C ''pct'':478C,504C,511C,615C,665C ''per'':122C,170C,241C,274C,331C,339C,356C,377C,424C,460C,465C,470C,660C,719C,740C,773C ''percent'':414C,454C,516C,519C,535C,575C,635C,794C ''perfect'':15C ''perspect'':37C ''pitfal'':143C,698C ''plt'':185C ''plt.hist'':304C,315C,510C ''plt.legend'':326C ''plt.show'':343C,523C ''plt.title'':329C,513C ''plt.xlabel'':337C,518C ''plt.ylabel'':341C,521C ''posit'':646C,793C ''potenti'':142C,697C ''pre'':176C,474C,578C,663C ''pretend'':165C,431C ''pretti'':62C,685C ''probabl'':651C,701C,709C ''problem'':34C ''procedur'':787C ''quantifi'':385C ''queri'':551C ''question'':12C ''ran'':623C ''random'':262C ''rate'':107C,233C,245C,263C,270C,279C,287C,389C,403C,417C,420C ''real'':29C ''receiv'':444C ''record'':4C ''regress'':733C ''result'':553C,675C ''return'':292C,607C ''rough'':173C ''sampl'':761C ''say'':681C ''scienc'':19C ''seaborn'':187C ''see'':368C,642C ''sever'':36C ''simpl'':731C ''simpli'':753C ''simul'':134C,191C,272C,301C,491C ''sinc'':400C ''size'':281C,289C ''sns'':189C ''solv'':42C ''src'':348C,528C ''stab'':130C ''stack'':20C ''start'':748C ''step'':308C,319C ''subtract'':763C ''summar'':86C ''take'':457C ''thing'':499C ''think'':6C,156C ''traffic'':87C ''trend'':705C,778C,802C,806C ''true'':193C,254C,267C,312C,323C,634C ''type'':10C ''under'':402C,704C,801C ''use'':54C,392C,812C ''valu'':57C,572C ''variabl'':737C,745C ''view'':240C,330C,338C ''viewer'':273C ''vpd'':276C,284C,293C,295C,297C,299C,305C,316C,487C,489C ''vpd_after.mean'':507C ''vpd_before.mean'':508C,509C ''want'':501C,561C ''way'':556C ''without'':715C ''world'':30C ''would'':47C,147C,728C ''x'':583C,595C,600C ''xrang'':485C ''y'':766C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (507, 2, '2014-06-20 16:29:27.673', 7, NULL, NULL, '&lt;p&gt;Back in my data analyst days this type of problem was pretty typical.  Basically, everyone in marketing would come up with a crazy idea that the sold to higher ups as the single event that would boost KPI''s by 2000%.  The higher ups would approve them and then they would begin their "test".  Results would come back, and management would dump it on the data analysts to determine what worked and who did it.&lt;/p&gt;

&lt;p&gt;The short answer is you cant really know if it wasn''t run as a random A/B style test on like time periods.  But I am very aware of how deficient that answer is, especially if the fact that a pure answer doesn''t exist is irrelevant to the urgency of future business decisions.  Here are some of the techniques I would use to salvage the analysis in this situation, bear in mind this is more of an art then a science.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Handles&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;A handle is something that exists in the data that you can hold onto.  From what you are telling me in your situation you have a lot of info on who the marketing agency is, when they tried a tactic, and to which site they applied it to.  These are your starting point and information like this going to be the corner stone of your analysis.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Methodology&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The methodology is going to probably hold the strongest impact on which agencies are given credit for any and all gains so you are going to need to make sure that it is clearly outlines and all stake holders agree that it makes sense.  If you cant do that it is going to be difficult for people to trust your analysis.  &lt;/p&gt;

&lt;p&gt;An example of this are conversions. Say the marketing department purchases some leads and they arrive at our landing page, we would track them for 3 days, if they made a purchase within that time we would count them as having been converted.  Why 3 days, why not 5 or 1?  Thats not important as long as everyone agrees, you now have a definition you can build off of.  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Comparisons&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;In an ideal would you would have a nice A/B test to prove a definitive relationship, I am going to assume that you are running short on those, still, you can learn something from a simple comparison of like data.  When companies are trying to determine the efficacy of radio advertising they will often run ads on offset months in the same market, or for several months in one market and compare that with the results in a separate but similar market.  Its doesn''t pass for science, but even with all that noise a strong results will almost always be noticeable.&lt;/p&gt;

&lt;p&gt;I would combine these in your case to determine how long an event is given to register an effect.  Once you have the data from that time period run it against your modeled out traffic prediction, week over week growth, month over month etc. Which, can then allow a meaningful comparison between agencies, and across time periods.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Pragmatism&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The aspiration is to be able to provide a deep understanding of cause and effect, but it is probably not realistic.  Because of how messy outside factors make your analysis, you are constantly going to run up against the question over and over again: Did this event raise volume/sales/click throughs, or would doing anything at all have had the same effect?  The best advise I can give for this is set very realistic goals for what you are looking to measure.  A good starting point is, within the methodology you have, which event had the largest impact.  Once you have those open your aperture from there.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Summary&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Once you have reasoned out all of these aspects you can go about building a general solution which can then be automated.  The advantage to designing your solution in this manner is that the business logic is already built in.  This will make your results much more approachable and intuitive to non-technical business leaders.&lt;/p&gt;
', NULL, NULL, '''/p'':77C,161C,166C,234C,239C,302C,373C,378C,482C,541C,546C,654C,659C,717C ''/strong'':165C,238C,377C,545C,658C ''1'':354C ''2000'':42C ''3'':329C,348C ''5'':352C ''a/b'':95C,389C ''abl'':553C ''across'':538C ''ad'':435C ''advantag'':684C ''advertis'':430C ''advis'':611C ''agenc'':201C,253C,536C ''agre'':280C,362C ''allow'':531C ''almost'':478C ''alreadi'':698C ''alway'':479C ''analysi'':145C,233C,301C,577C ''analyst'':6C,68C ''answer'':81C,111C,120C ''anyth'':601C ''apertur'':651C ''appli'':213C ''approach'':708C ''approv'':47C ''arriv'':319C ''art'':157C ''aspect'':669C ''aspir'':549C ''assum'':400C ''autom'':682C ''awar'':106C ''back'':2C,59C ''basic'':15C ''bear'':149C ''begin'':53C ''best'':610C ''boost'':38C ''build'':370C,674C ''built'':699C ''busi'':131C,695C,715C ''cant'':84C,287C ''case'':490C ''caus'':560C ''clear'':274C ''combin'':486C ''come'':20C,58C ''compani'':421C ''compar'':451C ''comparison'':376C,416C,534C ''constant'':580C ''convers'':309C ''convert'':346C ''corner'':229C ''count'':341C ''crazi'':24C ''credit'':256C ''data'':5C,67C,176C,419C,507C ''day'':7C,330C,349C ''decis'':132C ''deep'':557C ''defici'':109C ''definit'':367C,394C ''depart'':313C ''design'':686C ''determin'':70C,425C,492C ''difficult'':295C ''doesn'':121C,463C ''dump'':63C ''effect'':502C,562C,608C ''efficaci'':427C ''especi'':113C ''etc'':527C ''even'':469C ''event'':35C,496C,594C,640C ''everyon'':16C,361C ''exampl'':305C ''exist'':123C,173C ''fact'':116C ''factor'':574C ''futur'':130C ''gain'':261C ''general'':676C ''give'':614C ''given'':255C,498C ''go'':225C,244C,265C,292C,398C,581C,672C ''goal'':621C ''good'':630C ''growth'':523C ''handl'':164C,169C ''higher'':30C,44C ''hold'':180C,247C ''holder'':279C ''idea'':25C ''ideal'':382C ''impact'':250C,644C ''import'':357C ''info'':196C ''inform'':222C ''intuit'':710C ''irrelev'':125C ''know'':86C ''kpi'':39C ''land'':322C ''largest'':643C ''lead'':316C ''leader'':716C ''learn'':411C ''like'':99C,223C,418C ''logic'':696C ''long'':359C,494C ''look'':626C ''lot'':194C ''made'':333C ''make'':269C,283C,575C,703C ''manag'':61C ''manner'':691C ''market'':18C,200C,312C,442C,449C,461C ''meaning'':533C ''measur'':628C ''messi'':572C ''methodolog'':237C,242C,636C ''mind'':151C ''model'':516C ''month'':438C,446C,524C,526C ''much'':706C ''need'':267C ''nice'':388C ''nois'':473C ''non'':713C ''non-techn'':712C ''notic'':481C ''offset'':437C ''often'':433C ''one'':448C ''onto'':181C ''open'':649C ''outlin'':275C ''outsid'':573C ''p'':1C,78C,162C,167C,235C,240C,303C,374C,379C,483C,542C,547C,655C,660C ''page'':323C ''pass'':465C ''peopl'':297C ''period'':101C,511C,540C ''point'':220C,632C ''pragmat'':544C ''predict'':519C ''pretti'':13C ''probabl'':246C,566C ''problem'':11C ''prove'':392C ''provid'':555C ''purchas'':314C,335C ''pure'':119C ''question'':587C ''radio'':429C ''rais'':595C ''random'':94C ''realist'':568C,620C ''realli'':85C ''reason'':664C ''regist'':500C ''relationship'':395C ''result'':56C,455C,476C,705C ''run'':91C,404C,434C,512C,583C ''salvag'':143C ''say'':310C ''scienc'':160C,467C ''sens'':284C ''separ'':458C ''set'':618C ''sever'':445C ''short'':80C,405C ''similar'':460C ''simpl'':415C ''singl'':34C ''site'':211C ''situat'':148C,190C ''sold'':28C ''solut'':677C,688C ''someth'':171C,412C ''stake'':278C ''start'':219C,631C ''still'':408C ''stone'':230C ''strong'':163C,236C,375C,475C,543C,656C ''strongest'':249C ''style'':96C ''summari'':657C ''sure'':270C ''tactic'':207C ''technic'':714C ''techniqu'':138C ''tell'':186C ''test'':55C,97C,390C ''that'':355C ''through'':597C ''time'':100C,338C,510C,539C ''track'':326C ''traffic'':518C ''tri'':205C,423C ''trust'':299C ''type'':9C ''typic'':14C ''understand'':558C ''up'':31C,45C ''urgenc'':128C ''use'':141C ''volume/sales/click'':596C ''wasn'':89C ''week'':520C,522C ''within'':336C,634C ''work'':72C ''would'':19C,37C,46C,52C,57C,62C,140C,325C,340C,383C,385C,485C,599C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5299, 2, '2015-03-09 21:56:00.827', 3, NULL, NULL, '&lt;p&gt;The similarity is regression.
NNs can be used for regression and the fourier transform is in its heart just a curve fit of multiple sin and cos functions to some data.&lt;/p&gt;
', NULL, NULL, '''/p'':33C ''cos'':28C ''curv'':22C ''data'':32C ''fit'':23C ''fourier'':14C ''function'':29C ''heart'':19C ''multipl'':25C ''nns'':6C ''p'':1C ''regress'':5C,11C ''similar'':3C ''sin'':26C ''transform'':15C ''use'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (663, 2, '2014-07-03 17:06:13.143', 6, NULL, NULL, '&lt;p&gt;Not enough rep for a comment, but check out: &lt;a href="http://www.rdatamining.com/examples/text-mining"&gt;http://www.rdatamining.com/examples/text-mining&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Here, they will take you through loading unstructured text to creating a wordcloud.  You can adapt this strategy and instead of creating a wordcloud, you can create a frequency matrix of terms used.  The idea is to take the unstructured text and structure it somehow.  You change everything to lowercase (or uppercase), remove stop words, and find frequent terms for each job function, via Document Term Matrices.  You also have the option of stemming the words.  If you stem words you will be able to detect different forms of words as the same word.  For example, ''programed'' and ''programming'' could be stemmed to ''program''.  You can possibly add the occurrence of these frequent terms as a weighted feature in your ML model training.&lt;/p&gt;

&lt;p&gt;You can also adapt this to frequent phrases, finding common groups of 2-3 words for each job function.&lt;/p&gt;

&lt;p&gt;Example:&lt;/p&gt;

&lt;p&gt;1) Load libraries and build the example data&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(tm)
library(SnowballC)

doc1 = "I am highly skilled in Java Programming.  I have spent 5 years developing bug-tracking systems and creating data managing system applications in C."
job1 = "Software Engineer"
doc2 = "Tested new software releases for major program enhancements.  Designed and executed test procedures and worked with relational databases.  I helped organize and lead meetings and work independently and in a group setting."
job2 = "Quality Assurance"
doc3 = "Developed large and complex web applications for client service center. Lead projects for upcoming releases and interact with consumers.  Perform database design and debugging of current releases."
job3 = "Software Engineer"
jobInfo = data.frame("text" = c(doc1,doc2,doc3),
                     "job" = c(job1,job2,job3))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;2) Now we do some text structuring. I am positive there are quicker/shorter ways to do the following.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Convert to lowercase
jobInfo$text = sapply(jobInfo$text,tolower)

# Remove Punctuation
jobInfo$text = sapply(jobInfo$text,function(x) gsub("[[:punct:]]"," ",x))

# Remove extra white space
jobInfo$text = sapply(jobInfo$text,function(x) gsub("[ ]+"," ",x))

# Remove stop words
jobInfo$text = sapply(jobInfo$text, function(x){
  paste(setdiff(strsplit(x," ")[[1]],stopwords()),collapse=" ")
})

# Stem words (Also try without stemming?)
jobInfo$text = sapply(jobInfo$text, function(x)  {
  paste(setdiff(wordStem(strsplit(x," ")[[1]]),""),collapse=" ")
})
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;3) Make a corpus source and document term matrix.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Create Corpus Source
jobCorpus = Corpus(VectorSource(jobInfo$text))

# Create Document Term Matrix
jobDTM = DocumentTermMatrix(jobCorpus)

# Create Term Frequency Matrix
jobFreq = as.matrix(jobDTM)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now we have the frequency matrix, jobFreq, that is a (3 by x) matrix, 3 entries and X number of words.&lt;/p&gt;

&lt;p&gt;Where you go from here is up to you.  You can keep only specific (more common) words and use them as features in your model.  Another way is to keep it simple and have a percentage of words used in each job description, say "java" would have 80% occurrence in ''software engineer'' and only 50% occurrence in ''quality assurance''.&lt;/p&gt;

&lt;p&gt;Now it''s time to go look up why ''assurance'' has 1 ''r'' and ''occurrence'' has 2 ''r''s.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (759, 2, '2014-07-17 06:02:04.813', 23, NULL, NULL, '&lt;p&gt;The topic of &lt;em&gt;reproducible research&lt;/em&gt; (RR) is &lt;strong&gt;very popular&lt;/strong&gt; today and, consequently, is &lt;strong&gt;huge&lt;/strong&gt;, but I hope that my answer will be &lt;strong&gt;comprehensive enough&lt;/strong&gt; as an answer and will provide enough information for &lt;strong&gt;further research&lt;/strong&gt;, should you decide to do so.&lt;/p&gt;

&lt;p&gt;While Python-specific tools for RR certainly exist out there, I think it makes more sense to focus on more &lt;strong&gt;universal tools&lt;/strong&gt; (you never know for sure what programming languages and computing environments you will be working with in the future). Having said that, let''s take a look what tools are available per your list.&lt;/p&gt;

&lt;p&gt;1) &lt;strong&gt;Tools for data version control&lt;/strong&gt;. Unless you plan to work with (very) &lt;em&gt;big data&lt;/em&gt;, I guess, it would make sense to use the same &lt;code&gt;git&lt;/code&gt;, which you use for source code version control. The infrastructure is already there. Even if your files are binary and big, this advice might be helpful: &lt;a href="http://stackoverflow.com/questions/540535/managing-large-binary-files-with-git"&gt;http://stackoverflow.com/questions/540535/managing-large-binary-files-with-git&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;2) &lt;strong&gt;Tools for managing RR workflows and experiments&lt;/strong&gt;. Here''s a list of most popular tools in this category, to the best of my knowledge (in the descending order of popularity):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;Taverna Workflow Management System&lt;/em&gt; (&lt;a href="http://www.taverna.org.uk"&gt;http://www.taverna.org.uk&lt;/a&gt;) - very solid, if a little too complex, set of tools. The major tool is a Java-based desktop software. However, it is compatible with online workflow repository portal &lt;em&gt;myExperiment&lt;/em&gt; (&lt;a href="http://www.myexperiment.org"&gt;http://www.myexperiment.org&lt;/a&gt;), where user can store and share their RR workflows. Web-based RR portal, fully compatible with &lt;em&gt;Taverna&lt;/em&gt; is called &lt;em&gt;Taverna Online&lt;/em&gt;, but it is being developed and maintained by totally different organization in Russia (referred there to as &lt;em&gt;OnlineHPC&lt;/em&gt;: &lt;a href="http://onlinehpc.com"&gt;http://onlinehpc.com&lt;/a&gt;).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;The Kepler Project&lt;/em&gt; (&lt;a href="https://kepler-project.org"&gt;https://kepler-project.org&lt;/a&gt;)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;VisTrails&lt;/em&gt; (&lt;a href="http://vistrails.org"&gt;http://vistrails.org&lt;/a&gt;)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;Madagascar&lt;/em&gt; (&lt;a href="http://www.reproducibility.org"&gt;http://www.reproducibility.org&lt;/a&gt;)&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;EXAMPLE&lt;/strong&gt;. Here''s an interesting article on scientific workflows with an example of the &lt;strong&gt;real&lt;/strong&gt; workflow design and data analysis, based on using &lt;em&gt;Kepler&lt;/em&gt; and &lt;em&gt;myExperiment&lt;/em&gt; projects: &lt;a href="http://f1000research.com/articles/3-110/v1"&gt;http://f1000research.com/articles/3-110/v1&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;There are many RR tools that implement &lt;em&gt;literate programming&lt;/em&gt; paradigm, exemplified by &lt;code&gt;LaTeX&lt;/code&gt; software family. Tools that help in report generation and presentation is also a large category, where &lt;code&gt;Sweave&lt;/code&gt; and &lt;code&gt;knitr&lt;/code&gt; are probably the most well-known ones. &lt;code&gt;Sweave&lt;/code&gt; is a tool, focused on R, but it can be integrated with Python-based projects, albeit with some additional effort (&lt;a href="http://stackoverflow.com/questions/2161152/sweave-for-python"&gt;http://stackoverflow.com/questions/2161152/sweave-for-python&lt;/a&gt;). I think that &lt;code&gt;knitr&lt;/code&gt; might be a better option, as it''s modern, has extensive support by popular tools (such as &lt;code&gt;RStudio&lt;/code&gt;) and is language-neutral (&lt;a href="http://yihui.name/knitr/demo/engines"&gt;http://yihui.name/knitr/demo/engines&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;3) &lt;strong&gt;Protocol and suggested directory structure&lt;/strong&gt;. If I understood correctly what you implied by using term &lt;em&gt;protocol&lt;/em&gt; (&lt;em&gt;workflow&lt;/em&gt;), generally I think that standard RR data analysis workflow consists of the following sequential phases: &lt;em&gt;data collection&lt;/em&gt; =&gt; &lt;em&gt;data preparation&lt;/em&gt; (cleaning, transformation, merging, sampling) =&gt; &lt;em&gt;data analysis&lt;/em&gt; =&gt; &lt;em&gt;presentation of results&lt;/em&gt; (generating reports and/or presentations). Nevertheless, every workflow is project-specific and, thus, some specific tasks might require adding additional steps.&lt;/p&gt;

&lt;p&gt;For sample directory structure, you may take a look at documentation for R package &lt;code&gt;ProjectTemplate&lt;/code&gt; (&lt;a href="http://projecttemplate.net"&gt;http://projecttemplate.net&lt;/a&gt;), as an attempt to automate data analysis workflows and projects:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/0B2vo.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;4) &lt;strong&gt;Automated build/run tools&lt;/strong&gt;. Since my answer is focused on universal (language-neutral) RR tools, the most popular tools is &lt;code&gt;make&lt;/code&gt;. Read the following article for some reasons to use &lt;code&gt;make&lt;/code&gt; as the preferred RR workflow automation tool: &lt;a href="http://bost.ocks.org/mike/make"&gt;http://bost.ocks.org/mike/make&lt;/a&gt;. Certainly, there are other &lt;strong&gt;similar&lt;/strong&gt; tools, which either improve some aspects of &lt;code&gt;make&lt;/code&gt;, or add some additional features. For example: &lt;code&gt;ant&lt;/code&gt; (officially, Apache Ant: &lt;a href="http://ant.apache.org"&gt;http://ant.apache.org&lt;/a&gt;), &lt;code&gt;Maven&lt;/code&gt; ("next generation &lt;code&gt;ant&lt;/code&gt;": &lt;a href="http://maven.apache.org"&gt;http://maven.apache.org&lt;/a&gt;), &lt;code&gt;rake&lt;/code&gt; (&lt;a href="https://github.com/ruby/rake"&gt;https://github.com/ruby/rake&lt;/a&gt;), &lt;code&gt;Makepp&lt;/code&gt; (&lt;a href="http://makepp.sourceforge.net"&gt;http://makepp.sourceforge.net&lt;/a&gt;). For a comprehensive list of such tools, see Wikipedia: &lt;a href="http://en.wikipedia.org/wiki/List_of_build_automation_software"&gt;http://en.wikipedia.org/wiki/List_of_build_automation_software&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (729, 2, '2014-07-11 23:08:16.267', 5, NULL, NULL, '&lt;p&gt;I thought this was an interesting problem, so I wrote a sample data set and a linear slope estimator in R.  I hope it helps you with your problem.  I''m going to make some assumptions, the biggest is that you want to estimate a constant slope, given by some segments in your data.  Another assumption to separate the blocks of linear data is that the natural ''reset'' will be found by comparing consecutive differences and finding ones that are X-standard deviations below the mean. (I chose 4 sd''s, but this can be changed)&lt;/p&gt;

&lt;p&gt;Here is a plot of the data, and the code to generating it is at the bottom.
&lt;img src="http://i.stack.imgur.com/2dC1w.png" alt="Sample Data"&gt;&lt;/p&gt;

&lt;p&gt;For starters, we find the breaks and fit each set of y-values and record the slopes.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Find the differences between adjacent points
diffs = y_data[-1] - y_data[-length(y_data)]
# Find the break points (here I use 4 s.d.''s)
break_points = c(0,which(diffs &amp;lt; (mean(diffs) - 4*sd(diffs))),length(y_data))
# Create the lists of y-values
y_lists = sapply(1:(length(break_points)-1),function(x){
  y_data[(break_points[x]+1):(break_points[x+1])]
})
# Create the lists of x-values
x_lists = lapply(y_lists,function(x) 1:length(x))
#Find all the slopes for the lists of points
slopes = unlist(lapply(1:length(y_lists), function(x) lm(y_lists[[x]] ~ x_lists[[x]])$coefficients[2]))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here are the slopes:
(3.309110, 4.419178, 3.292029, 4.531126, 3.675178, 4.294389)&lt;/p&gt;

&lt;p&gt;And we can just take the mean to find the expected slope (3.920168).&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Edit: Predicting when series reaches 120&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I realized I didn''t finish predicted when series reaches 120.  If we estimate the slope to be m and we see a reset at time t to a value x (x&amp;lt;120), we can predict how much longer it would take to reach 120 by some simple algebra.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/DixZv.gif" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Here, t is the time it would take to reach 120 after a reset, x is what it resets to, and m is the estimated slope.  I''m not going to even touch the subject of units here, but it''s good practice to work them out and make sure everything makes sense.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Edit: Creating The Sample Data&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The sample data will consist of 100 points, random noise with a slope of 4 (Hopefully we will estimate this).  When the y-values reach a cutoff, they reset to 50.  The cutoff is randomly chosen between 115 and 120 for each reset.  Here is the R code to create the data set.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Create Sample Data
set.seed(1001)
x_data = 1:100 # x-data
y_data = rep(0,length(x_data)) # Initialize y-data
y_data[1] = 50 
reset_level = sample(115:120,1) # Select initial cutoff
for (i in x_data[-1]){ # Loop through rest of x-data
  if(y_data[i-1]&amp;gt;reset_level){ # check if y-value is above cutoff
    y_data[i] = 50             # Reset if it is and
    reset_level = sample(115:120,1) # rechoose cutoff
  }else {
    y_data[i] = y_data[i-1] + 4 + (10*runif(1)-5) # Or just increment y with random noise
  }
}
plot(x_data,y_data) # Plot data
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (910, 2, '2014-08-05 09:07:42.393', 9, NULL, NULL, '&lt;p&gt;Here are the basic Natural Language Processing capabilities (or annotators) that are usually necessary to extract language units from textual data for sake of search and other applications:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Sentence_boundary_disambiguation" rel="nofollow"&gt;Sentence breaker&lt;/a&gt; - to split text (usually, text paragraphs) to sentences. Even in English it can be hard for some cases like "Mr. and Mrs. Brown stay in room no. 20."&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Tokenization" rel="nofollow"&gt;Tokenizer&lt;/a&gt; - to split text or sentences to words or word-level units, including punctuation. This task is not trivial for languages with no spaces and no stable understanding of word boundaries (e.g. Chinese, Japanese)&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/POS_tagger" rel="nofollow"&gt;Part-of-speech Tagger&lt;/a&gt; - to guess part of speech of each word in the context of sentence; usually each word is assigned a so-called POS-tag from a tagset developed in advance to serve your final task (for example, parsing).&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Lemmatization" rel="nofollow"&gt;Lemmatizer&lt;/a&gt; - to convert a given word into its canonical form (&lt;a href="http://en.wikipedia.org/wiki/Lemma_(morphology)" rel="nofollow"&gt;lemma&lt;/a&gt;). Usually you need to know the word''s POS-tag. For example, word "heating" as gerund must be converted to "heat", but as noun it must be left unchanged.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Parser" rel="nofollow"&gt;Parser&lt;/a&gt; - to perform syntactic analysis of the sentence and build a syntactic tree or graph. There''re two main ways to represent syntactic structure of sentence: via &lt;a href="http://en.wikipedia.org/wiki/Dependency_grammar#Dependency_vs._constituency" rel="nofollow"&gt;constituency or dependency&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Automatic_summarization" rel="nofollow"&gt;Summarizer&lt;/a&gt; - to generate a short summary of the text by selecting a set of top informative sentences of the document, representing its main idea. However can be done in more intelligent manner than just selecting the sentences from existing ones.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Named-entity_recognition" rel="nofollow"&gt;Named Entity Recognition&lt;/a&gt; - to extract so-called named entities from the text. Named entities are the chunks of words from text, which refer to an entity of certain type. The types may include: geographic locations (countries, cities, rivers, ...), person names, organization names etc. Before going into NER task you must understand what do you want to get and, possible, predefine a taxonomy of named entity types to resolve.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Coreference_resolution" rel="nofollow"&gt;Coreference Resolution&lt;/a&gt; - to group named entities (or, depending on your task, any other text units) into clusters corresponding to a single real object/meaning. For example, "B. Gates", "William Gates", "Founder of Microsoft" etc. in one text may mean the same person, referenced by using different expressions.&lt;/p&gt;

&lt;p&gt;There''re many other interesting NLP applications/annotators (see &lt;a href="http://en.wikipedia.org/wiki/Category:Tasks_of_natural_language_processing" rel="nofollow"&gt;NLP tasks category&lt;/a&gt;), sentiment analysis, machine translation etc.). There''re many books on this, the classical book: "Speech and Language Processing" by Daniel Jurafsky and James H. Martin., but it can be too detailed for you.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1006, 2, '2014-08-20 05:43:08.61', 7, NULL, NULL, '&lt;p&gt;I will try to answer your questions, but before I''d like to note that using term "large dataset" is misleading, as "large" is a &lt;em&gt;relative&lt;/em&gt; concept. You have to provide more details. If you''re dealing with &lt;strong&gt;bid data&lt;/strong&gt;, then this fact will most likely affect selection of preferred &lt;em&gt;tools&lt;/em&gt;, &lt;em&gt;approaches&lt;/em&gt; and &lt;em&gt;algorithms&lt;/em&gt; for your &lt;strong&gt;data analysis&lt;/strong&gt;. I hope that the following thoughts of mine on data analysis address your sub-questions. Please note that the numbering of my points does not match the numbering of your sub-questions. However, I believe that it better reflects general &lt;strong&gt;data analysis workflow&lt;/strong&gt;, at least, how I understand it.&lt;/p&gt;

&lt;p&gt;1) Firstly, I think that you need to have at least some kind of &lt;strong&gt;conceptual model&lt;/strong&gt; in mind (or, better, on paper). This model should guide you in your &lt;em&gt;exploratory data analysis (EDA)&lt;/em&gt;. A presence of a &lt;em&gt;dependent variable (DV)&lt;/em&gt; in the model means that in your &lt;em&gt;machine learning (ML)&lt;/em&gt; phase later in the analysis you will deal with so called supervised ML, as opposed to unsupervised ML in the absence of an identified DV.&lt;/p&gt;

&lt;p&gt;2) Secondly, &lt;strong&gt;EDA&lt;/strong&gt; is a crucial part. IMHO, EDA should include &lt;strong&gt;multiple iterations&lt;/strong&gt; of producing &lt;em&gt;descriptive statistics&lt;/em&gt; and &lt;em&gt;data visualization&lt;/em&gt;, as you refine your understanding about the data. Not only this phase will give you valuable insights about your datasets, but it will feed your next important phase - &lt;strong&gt;data cleaning and transformation&lt;/strong&gt;. Just throwing your raw data into a statistical software package won''t give much - for any &lt;strong&gt;valid&lt;/strong&gt; statistical analysis, data should be &lt;em&gt;clean, correct and consistent&lt;/em&gt;. This is often the most time- and effort-consuming, but absolutely necessary part. For more details on this topic, read these nice papers: &lt;a href="http://vita.had.co.nz/papers/tidy-data.pdf"&gt;http://vita.had.co.nz/papers/tidy-data.pdf&lt;/a&gt; (by Hadley Wickham) and &lt;a href="http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf"&gt;http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf&lt;/a&gt; (by Edwin de Jonge and Mark van der Loo).&lt;/p&gt;

&lt;p&gt;3) Now, as you''re hopefully done with &lt;em&gt;EDA&lt;/em&gt; as well as data cleaning and transformation, your ready to start some more statistically-involved phases. One of such phases is &lt;em&gt;exploratory factor analysis (EFA)&lt;/em&gt;, which will allow you to extract the underlying &lt;strong&gt;structure&lt;/strong&gt; of your data. For datasets with large number of variables, the positive side effect of EFA is &lt;em&gt;dimensionality reduction&lt;/em&gt;. And, while in that sense EFA is similar to &lt;em&gt;principal components analysis (PCA)&lt;/em&gt; and other dimensionality reduction approaches, I think that EFA is more important as it allows to refine your conceptual model of the phenomena that your data "describe", thus making sense of your datasets. Of course, in addition to EFA, you can/should perform &lt;strong&gt;regression analysis&lt;/strong&gt; as well as apply &lt;strong&gt;machine learning techniques&lt;/strong&gt;, based on your findings in previous phases.&lt;/p&gt;

&lt;p&gt;Finally, a note on &lt;strong&gt;software tools&lt;/strong&gt;. In my opinion, current state of statistical software packages is at such point that practically any major software packages have comparable offerings feature-wise. If you study or work in an organization that have certain policies and preferences in term of software tools, then you are &lt;em&gt;constrained&lt;/em&gt; by them. However, if that is not the case, I would heartily recommend &lt;strong&gt;open source&lt;/strong&gt; statistical software, based on your comfort with its specific &lt;em&gt;programming language&lt;/em&gt;, &lt;em&gt;learning curve&lt;/em&gt; and your &lt;em&gt;career perspectives&lt;/em&gt;. My current platform of choice is &lt;strong&gt;R Project&lt;/strong&gt;, which offers mature, powerful, flexible, extensive and open statistical software, along with amazing ecosystem of packages, experts and enthusiasts. Other nice choices include &lt;em&gt;Python&lt;/em&gt;, &lt;em&gt;Julia&lt;/em&gt; and specific open source software for processing &lt;strong&gt;big data&lt;/strong&gt;, such as &lt;em&gt;Hadoop&lt;/em&gt;, &lt;em&gt;Spark&lt;/em&gt;, &lt;em&gt;NoSQL&lt;/em&gt; databases, &lt;em&gt;WEKA&lt;/em&gt;. For more examples of open source software for &lt;strong&gt;data mining&lt;/strong&gt;, which include general and specific statistical and ML software, see this section of a Wikipedia page: &lt;a href="http://en.wikipedia.org/wiki/Data_mining#Free_open-source_data_mining_software_and_applications"&gt;http://en.wikipedia.org/wiki/Data_mining#Free_open-source_data_mining_software_and_applications&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;UPDATE: Forgot to mention &lt;em&gt;Rattle&lt;/em&gt; (&lt;a href="http://rattle.togaware.com"&gt;http://rattle.togaware.com&lt;/a&gt;), which is also a very popular open source R-oriented GUI software for data mining.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1103, 2, '2014-09-12 00:40:07.877', 1, NULL, NULL, '&lt;p&gt;Your best best is to train your own models on the kind of data you''re going to be working with.&lt;/p&gt;
', NULL, NULL, '''/p'':23C ''best'':3C,4C ''data'':15C ''go'':18C ''kind'':13C ''model'':10C ''p'':1C ''re'':17C ''train'':7C ''work'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1236, 1, '2014-10-09 12:26:01.643', 1, 28, 'how to get the Polysemes of a word in wordnet or any other api?', '&lt;p&gt;how to get the Polysemes of a word in wordnet or any other api. I am looking for any api. with java any idea is appreciated?&lt;/p&gt;
', 'nlp', NULL, '''/p'':43C ''api'':14A,30C,36C ''appreci'':42C ''get'':3A,19C ''idea'':40C ''java'':38C ''look'':33C ''nlp'':15B ''p'':16C ''polysem'':5A,21C ''word'':8A,24C ''wordnet'':10A,26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2385, 2, '2014-10-31 22:40:24.513', 3, NULL, NULL, '&lt;p&gt;&lt;a href="http://www.kaggle.com/competitions" rel="nofollow"&gt;Kaggle&lt;/a&gt; has a bunch of good practice datasets and basic tutorials.&lt;/p&gt;
', NULL, NULL, '''/a'':10C ''/competitions'':6C ''/p'':21C ''basic'':19C ''bunch'':13C ''dataset'':17C ''good'':15C ''href'':3C ''kaggl'':9C ''nofollow'':8C ''p'':1C ''practic'':16C ''rel'':7C ''tutori'':20C ''www.kaggle.com'':5C ''www.kaggle.com/competitions'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2587, 1, '2014-12-03 11:05:59.49', 2, 245, 'N - fold cross validation in mahout', '&lt;p&gt;Is there a method/class available in Apache Mahout to perform n-fold cross validation?
If yes how it can be done?&lt;/p&gt;
', 'machine-learning  data-mining  java  apache-mahout', NULL, '''/p'':40C ''apach'':15B,24C ''apache-mahout'':14B ''avail'':22C ''cross'':3A,31C ''data'':11B ''data-min'':10B ''done'':39C ''fold'':2A,30C ''java'':13B ''learn'':9B ''machin'':8B ''machine-learn'':7B ''mahout'':6A,16B,25C ''method/class'':21C ''mine'':12B ''n'':1A,29C ''n-fold'':28C ''p'':17C ''perform'':27C ''valid'':4A,32C ''yes'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2630, 2, '2014-12-06 13:02:19.373', 4, NULL, NULL, '&lt;p&gt;I get asked this question all the time, so earlier this year I wrote an article (&lt;a href="http://oss4ds.com/what-is-data-science.html" rel="nofollow"&gt;&lt;strong&gt;What is Data Science?&lt;/strong&gt;&lt;/a&gt;) based on a presentation I''ve given a few times. Here''s the gist...&lt;/p&gt;

&lt;p&gt;First, a few definitions of data science offered by others:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://twitter.com/josh_wills/status/198093512149958656" rel="nofollow"&gt;&lt;strong&gt;Josh Wills&lt;/strong&gt; from &lt;strong&gt;Cloudera&lt;/strong&gt; says&lt;/a&gt; a data scientist is someone "who is better at statistics than any software engineer and better at software engineering than any statistician."&lt;/p&gt;

&lt;p&gt;&lt;a href="https://twitter.com/nivertech/status/180109930139893761" rel="nofollow"&gt;A frequently-heard &lt;strong&gt;joke&lt;/strong&gt;&lt;/a&gt; is that a "Data Scientist" is a Data Analyst who lives in California.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://twitter.com/BigDataBorat/status/372350993255518208" rel="nofollow"&gt;According to &lt;strong&gt;Big Data Borat&lt;/strong&gt;&lt;/a&gt;, Data Science is statistics on a Mac.&lt;/p&gt;

&lt;p&gt;In &lt;a href="http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram" rel="nofollow"&gt;&lt;strong&gt;Drew Conway''s&lt;/strong&gt; famous Data Science Venn Diagram&lt;/a&gt;, it''s the intersection of Hacking Skills, Math &amp;amp; Statistics Knowledge, and Substantive Expertise.&lt;/p&gt;

&lt;p&gt;Here''s another good definition I found on the &lt;a href="http://www.itproportal.com/2014/02/11/how-to-pick-a-data-scientist-the-right-way/" rel="nofollow"&gt;&lt;strong&gt;ITProPortal&lt;/strong&gt; blog&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;"A data scientist is someone who understands the domains of programming, machine learning, data mining, statistics, and hacking"&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Here''s how we define Data Science at &lt;strong&gt;Altamira&lt;/strong&gt; (my current employer):&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/YAUZy.png" alt="data science diagram"&gt;&lt;/p&gt;

&lt;p&gt;The bottom four rows are the &lt;strong&gt;table stakes&lt;/strong&gt; -- the cost of admission just to play the game. These are foundational skills that all aspiring data scientists must obtain. Every data scientist must be a &lt;strong&gt;competent programmer&lt;/strong&gt;. He or she must also have a solid grasp of math, statistics, and &lt;strong&gt;analytic methodology&lt;/strong&gt;. Data science and "&lt;strong&gt;big data&lt;/strong&gt;" go hand-in-hand, so all data scientists need to be familiar with frameworks for distributed computing. Finally, data scientists must have a basic understanding of the domains in which they operate, as well as excellent communications skills and the ability to &lt;strong&gt;tell a good story with data&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;With these basics covered, the next step is to develop &lt;strong&gt;deep expertise&lt;/strong&gt; in one or more of the vertical areas. "Data Science" is really an umbrella term for a collection of interrelated techniques and approaches taken from a variety of disciplines, including mathematics, statistics, computer science, and software engineering. The goal of these diverse methods is to &lt;strong&gt;extract actionable intelligence&lt;/strong&gt; from data of all kinds, enabling clients to make better &lt;strong&gt;data-driven decisions&lt;/strong&gt;. No one person can ever possibly master all aspects of data science; doing so would require multiple lifetimes of training and experience. The best data scientists are therefore "&lt;strong&gt;T-shaped&lt;/strong&gt;" individuals -- that is, they possess a breadth of knowledge across all areas of data science, along with deep expertise in at least one. Accordingly, the best &lt;strong&gt;data science teams&lt;/strong&gt; bring together a set of individuals with complementary skillsets spanning the &lt;strong&gt;entire spectrum&lt;/strong&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3737, 2, '2014-12-21 11:43:35.1', 1, NULL, NULL, '&lt;p&gt;This is a &lt;strong&gt;tricky&lt;/strong&gt; question, because it''s easy and difficult at the same time. &lt;em&gt;Easy&lt;/em&gt;, because there is a lot of resources that potentially can help you make a decision on the topic. &lt;em&gt;Difficult&lt;/em&gt;, because the situation is very different for a particular person (not to mention that their interest might change at any time), which makes extremely difficult for other people to give you a &lt;strong&gt;good&lt;/strong&gt; advice and for you to make the &lt;strong&gt;right&lt;/strong&gt; decision.&lt;/p&gt;

&lt;p&gt;As for &lt;strong&gt;data science career&lt;/strong&gt; options, you can certainly consider a &lt;strong&gt;degree path&lt;/strong&gt; (MS or MS + PhD), but you need to be aware of &lt;strong&gt;other options&lt;/strong&gt;. For a comprehensive resource, dedicated to &lt;strong&gt;data science and related degree programs&lt;/strong&gt; (both &lt;em&gt;traditional&lt;/em&gt; and &lt;em&gt;online&lt;/em&gt;), please visit this page: &lt;a href="http://www.kdnuggets.com/education/index.html" rel="nofollow"&gt;http://www.kdnuggets.com/education/index.html&lt;/a&gt;. A &lt;strong&gt;comprehensive&lt;/strong&gt; review of &lt;strong&gt;all&lt;/strong&gt; these offerings is IMHO an enormous task and is &lt;strong&gt;far&lt;/strong&gt; beyond an answer here on Stack Exchange or, even, a lengthy blog post.&lt;/p&gt;

&lt;p&gt;However, nowadays one is not limited to &lt;em&gt;traditional educational options&lt;/em&gt; and I think it''s important to be aware of &lt;strong&gt;other educational options&lt;/strong&gt;. One of the other options include &lt;strong&gt;certifications&lt;/strong&gt; (linked at the above-mentioned page, but, in my opinion, the only certification worth considering is the &lt;a href="https://www.informs.org/Certification-Continuing-Ed/Analytics-Certification" rel="nofollow"&gt;Certified Analytics Professional&lt;/a&gt; as a &lt;em&gt;solid&lt;/em&gt; and &lt;em&gt;vendor-neutral&lt;/em&gt; certification from a reputable INFORMS). Another option is recently booming &lt;strong&gt;data science intensive educational offerings&lt;/strong&gt;, from &lt;em&gt;short-term&lt;/em&gt; (and often too commercial, to put it lightly) &lt;em&gt;bootcamps&lt;/em&gt; to &lt;em&gt;more solid offerings&lt;/em&gt;, including free, but competitive, ones, such as &lt;a href="http://insightdatascience.com" rel="nofollow"&gt;Insight Data Science Fellows Program&lt;/a&gt;, where one needs to be a PhD to apply, or its sister program &lt;a href="http://www.insightdataengineering.com" rel="nofollow"&gt;Insight Data Engineering Fellows Program&lt;/a&gt;, which doesn''t have such requirement. Finally, there is yet another option: &lt;strong&gt;self-study&lt;/strong&gt;. It partially intersects with the certificate option, if one uses &lt;em&gt;massive open online courses (MOOC)&lt;/em&gt; (a review of which deserves a separate comprehensive post), but there are &lt;strong&gt;open curricula&lt;/strong&gt; that might suit one better, such as the &lt;em&gt;Open Source Data Science Masters&lt;/em&gt; curriculum, linked in my &lt;a href="http://datascience.stackexchange.com/a/742/2452"&gt;earlier relevant answer&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;P.S.&lt;/strong&gt; While your question focuses on data science, I think that it may be wise to at least consider another career path, given your &lt;em&gt;math background&lt;/em&gt;. I''m talking about &lt;strong&gt;operations research&lt;/strong&gt; field, which is not that far away from data science (and even somewhat intersects with it). While similar, data science is IMHO more statistics-focused, whereas operations research is more math-focused, at least that''s how I see it. Despite all the popularity and "gold rush" of data science, operations research career is a solid one, just not as hot. Of course, if you''re excited about things like artificial intelligence, machine learning and, especially, deep learning, data science career is the way to go. Whatever you will choose, the good thing is that with your math background it will be easy to change focus, should you decide to. Hope this helps. Good luck!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4889, 2, '2015-01-16 04:44:23.923', 0, NULL, NULL, '&lt;p&gt;If you have existing properly tagged chat transcripts, you can try treating it as a supervised learning problem. If you''re starting from a blank slate, that won''t work. &lt;/p&gt;
', NULL, NULL, '''/p'':32C ''blank'':26C ''chat'':8C ''exist'':5C ''learn'':18C ''p'':1C ''problem'':19C ''proper'':6C ''re'':22C ''slate'':27C ''start'':23C ''supervis'':17C ''tag'':7C ''transcript'':9C ''treat'':13C ''tri'':12C ''won'':29C ''work'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4906, 5, '2015-01-19 19:11:09.507', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4907, 4, '2015-01-19 19:11:09.507', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4909, 4, '2015-01-19 19:17:36.82', 0, NULL, NULL, 'Methods and principles of building "computer systems that automatically improve with experience."&#x0d;
', NULL, NULL, '''automat'':9C ''build'':5C ''comput'':6C ''experi'':12C ''improv'':10C ''method'':1C ''principl'':3C ''system'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4929, 2, '2015-01-23 10:22:36.327', 11, NULL, NULL, '&lt;p&gt;If you are looking for a VM with a bunch of tools preinstalled, try the &lt;a href="http://datasciencetoolbox.org/"&gt;Data Science Toolbox&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':23C ''/p'':24C ''bunch'':11C ''data'':20C ''datasciencetoolbox.org'':19C ''href'':18C ''look'':5C ''p'':1C ''preinstal'':14C ''scienc'':21C ''tool'':13C ''toolbox'':22C ''tri'':15C ''vm'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4946, 2, '2015-01-26 08:00:14.63', 7, NULL, NULL, '&lt;p&gt;This is very &lt;strong&gt;broad&lt;/strong&gt; question, which I think it''s impossible to cover &lt;em&gt;comprehensively&lt;/em&gt; in a single answer. Therefore, I think that it would be more beneficial to provide some pointers to relevant answers and/or resources. This is exactly what I will do by providing the following information and thoughts of mine.&lt;/p&gt;

&lt;p&gt;First of all, I should mention the excellent and comprehensive &lt;a href="http://research.microsoft.com/pubs/150728/FnT_dimensionReduction.pdf"&gt;tutorial on dimensionality reduction&lt;/a&gt; by Burges (2009) from Microsoft Research. He touches on &lt;em&gt;high-dimensional aspects&lt;/em&gt; of data frequently throughout the monograph. This work, referring to &lt;em&gt;dimensionality reduction&lt;/em&gt; as &lt;em&gt;dimension reduction&lt;/em&gt;, presents a theoretical introduction into &lt;strong&gt;the problem&lt;/strong&gt;, suggests a &lt;strong&gt;taxonomy&lt;/strong&gt; of dimensionality reduction methods, consisting of &lt;em&gt;projective methods&lt;/em&gt; and &lt;em&gt;manifold modeling methods&lt;/em&gt;, as well as provides an &lt;strong&gt;overview&lt;/strong&gt; of multiple methods in each category.&lt;/p&gt;

&lt;p&gt;The "&lt;strong&gt;projective&lt;/strong&gt; pursuit" methods reviewed include &lt;em&gt;independent component analysis (ICA)&lt;/em&gt;, &lt;em&gt;principal component analysis (PCA)&lt;/em&gt; and its variations, such as &lt;em&gt;kernel PCA&lt;/em&gt; and &lt;em&gt;probabilistic PCA&lt;/em&gt;, &lt;em&gt;canonical correlation analysis (CCA)&lt;/em&gt; and its &lt;em&gt;kernel CCA&lt;/em&gt; variation, &lt;em&gt;linear discriminant analysis (LDA)&lt;/em&gt;, &lt;em&gt;kernel dimension reduction (KDR)&lt;/em&gt; and some others. The &lt;strong&gt;manifold&lt;/strong&gt; methods reviewed include &lt;em&gt;multidimensional scaling (MDS)&lt;/em&gt; and its &lt;em&gt;landmark MDS&lt;/em&gt; variation, &lt;em&gt;Isomap&lt;/em&gt;, &lt;em&gt;Locally Linear Embedding&lt;/em&gt; and graphical methods, such as &lt;em&gt;Laplacian eigenmaps&lt;/em&gt; and &lt;em&gt;spectral clustering&lt;/em&gt;. I''m listing the most of the reviewed methods here in case, if the original publication is inaccessible for you, either &lt;em&gt;online&lt;/em&gt; (link above), or &lt;em&gt;offline&lt;/em&gt; (References).&lt;/p&gt;

&lt;p&gt;There is a &lt;strong&gt;caveat&lt;/strong&gt; for the term "comprehensive" that I''ve applied to the above-mentioned work. While it is indeed rather comprehensive, this is relative, as some of the approaches to dimensionality reduction are not discussed in the monograph, in particular, the ones, focused on &lt;em&gt;unobservable (latent) variables&lt;/em&gt;. Some of them are mentioned, though, with references to another source - a book on dimensionality reduction.&lt;/p&gt;

&lt;p&gt;Now, I will briefly cover several narrower aspects of the topic in question by referring to my relevant or related answers. In regard to &lt;em&gt;nearest neighbors (NN)-type approaches&lt;/em&gt; to high-dimensional data, please see my answers &lt;a href="http://datascience.stackexchange.com/a/975/2452"&gt;here&lt;/a&gt; (I especially recommend to check the paper #4 in my list). One of the effects of the &lt;em&gt;curse of dimensionality&lt;/em&gt; is that high-dimensional data is frequently &lt;strong&gt;sparse&lt;/strong&gt;. Considering this fact, I believe that my relevant answers &lt;a href="http://datascience.stackexchange.com/a/918/2452"&gt;here&lt;/a&gt; and &lt;a href="http://stats.stackexchange.com/a/130665/31372"&gt;here&lt;/a&gt; on &lt;strong&gt;regression&lt;/strong&gt; and &lt;strong&gt;PCA&lt;/strong&gt; for &lt;em&gt;sparse and high-dimensional data&lt;/em&gt; might be helpful.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Burges, C. J. C. (2010). Dimension reduction: A guided tour. &lt;em&gt;Foundations and TrendsÂ® in Machine Learning, 2&lt;/em&gt;(4), 275-365. doi:10.1561/2200000002&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (4964, 2, '2015-01-28 06:41:28.65', 1, NULL, NULL, '&lt;p&gt;Dryad is an academic project, whereas Spark is widely deployed in production, and now has a company behind it for support. Just focus on Spark.&lt;/p&gt;
', NULL, NULL, '''/p'':27C ''academ'':5C ''behind'':19C ''compani'':18C ''deploy'':11C ''dryad'':2C ''focus'':24C ''p'':1C ''product'':13C ''project'':6C ''spark'':8C,26C ''support'':22C ''wherea'':7C ''wide'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5027, 2, '2015-02-03 19:01:59.827', 3, NULL, NULL, '&lt;p&gt;Amazon Kinesis might be another choice for stream processing, if you don''t want to set up the clusters by yourself.&lt;/p&gt;
', NULL, NULL, '''/p'':23C ''amazon'':2C ''anoth'':6C ''choic'':7C ''cluster'':20C ''kinesi'':3C ''might'':4C ''p'':1C ''process'':10C ''set'':17C ''stream'':9C ''want'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5653, 1, '2015-04-30 11:50:44.76', 0, 29, 'International Journal', '&lt;p&gt;I would like to post a paper in International Conference on Soft Computing. I want to know whether the journal is a reputed journal. &lt;/p&gt;
', 'data-mining  nlp', NULL, '''/p'':32C ''comput'':20C ''confer'':17C ''data'':4B ''data-min'':3B ''intern'':1A,16C ''journal'':2A,27C,31C ''know'':24C ''like'':10C ''mine'':5B ''nlp'':6B ''p'':7C ''paper'':14C ''post'':12C ''reput'':30C ''soft'':19C ''want'':22C ''whether'':25C ''would'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5232, 2, '2015-02-25 17:08:09.83', 1, NULL, NULL, '&lt;p&gt;&lt;strong&gt;&lt;em&gt;First, some caveats&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I''m not sure why you can''t use your preferred programming (sub-)paradigm*, &lt;em&gt;Inductive Logic Programming (ILP)&lt;/em&gt;, or what it is that you''re trying to classify. Giving more detail would probably lead to a much better answer; especially as it''s a little unusual to approach selection of classification algorithms on the basis of the programming paradigm with which they''re associated. If your real world example is confidential, then simply make up a fictional-but-analogous example.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Big Data Classification without ILP&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Having said that, after ruling out ILP we have 4 other logic programming paradigms in our consideration set:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Abductive&lt;/li&gt;
&lt;li&gt;Answer Set&lt;/li&gt;
&lt;li&gt;Constraint&lt;/li&gt;
&lt;li&gt;Functional&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;in addition to the dozens of paradigms and sub-paradigms outside of logic programming.&lt;/p&gt;

&lt;p&gt;Within &lt;em&gt;Functional Logic Programming&lt;/em&gt; for instance,  there exists extensions of ILP called &lt;em&gt;Inductive Functional Logic Programming&lt;/em&gt;, which is based on inversion narrowing (i.e. inversion of the narrowing mechanism). This approach overcomes several limitations of ILP and (&lt;a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.225.4755&amp;amp;rep=rep1&amp;amp;type=pdf" rel="nofollow"&gt;according to some scholars, at least&lt;/a&gt;) is as suitable for application in terms of representation and has the benefit of allowing problems to be expressed in a more natural way. &lt;/p&gt;

&lt;p&gt;Without knowing more about the specifics of your database and the barriers you face to using ILP, I can''t know if this solves your problem or suffers from the same problems. As such, I''ll throw out a completely different approach as well.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://books.google.com/books?hl=en&amp;amp;lr=&amp;amp;id=xzVD8C2YpnQC&amp;amp;oi=fnd&amp;amp;pg=PR11&amp;amp;dq=Predictive%20Data-Mining.%20A%20Practical%20Guide.%20&amp;amp;ots=IN48sFFcSQ&amp;amp;sig=d6TOKBx_6qmitB8BSTk4_nLO9ak" rel="nofollow"&gt;ILP is contrasted with "classical" or "propositional" approaches to data mining&lt;/a&gt;. Those approaches include the meat and bones of Machine Learning like decision trees, neural networks, regression, bagging and other statistical methods. Rather than give up on these approaches due to the size of your data, you can join the ranks of many Data Scientists, Big Data engineers and statisticians who utilize High Performance Computing (HPC) to employ these methods on with massive data sets (there are also sampling and other statistical techniques you may choose to utilize to reduce the computational resources and time required to analyze the Big Data in your relational database). &lt;/p&gt;

&lt;p&gt;HPC includes things like utilizing multiple CPU cores, scaling up your analysis with elastic use of servers with high memory and large numbers of fast CPU cores, using high-performance data warehouse appliances, employing clusters or other forms of parallel computing, etc. I''m not sure what language or statistical suite you''re analyzing your data with, but as an example this &lt;a href="http://cran.r-project.org/web/views/HighPerformanceComputing.html" rel="nofollow"&gt;CRAN Task View&lt;/a&gt; lists many HPC resources for the R language which would allow you to scale up a propositional algorithm.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5234, 2, '2015-02-25 22:41:45.663', 1, NULL, NULL, '&lt;p&gt;I don''t think that you will learn much about &lt;em&gt;data science&lt;/em&gt; (meaning, acquire understanding and skills) by using software tools like Tableau. Such tools are targeting mainly &lt;em&gt;advanced users&lt;/em&gt; (not &lt;em&gt;data scientists&lt;/em&gt;), for example analysts and other subject matter experts, who use &lt;em&gt;graphical user interface (GUI)&lt;/em&gt; to &lt;em&gt;analyze&lt;/em&gt; and (mostly) &lt;em&gt;visualize&lt;/em&gt; data. Having said that, software tools like Tableau might be good enough to perform initial phase of data science &lt;strong&gt;workflow&lt;/strong&gt;: &lt;em&gt;exploratory data analysis (EDA)&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;In terms of data science &lt;strong&gt;self-education&lt;/strong&gt;, there are several popular online courses (MOOCs) that you can choose from (most come in both free and paid versions). In addition to the one on Udacity that you''ve mentioned (&lt;a href="https://www.udacity.com/course/ud359" rel="nofollow"&gt;https://www.udacity.com/course/ud359&lt;/a&gt;), there are two data science courses on Coursera: &lt;em&gt;Introduction to Data Science&lt;/em&gt; by University of Washington (&lt;a href="https://www.coursera.org/course/datasci" rel="nofollow"&gt;https://www.coursera.org/course/datasci&lt;/a&gt;) and a set of courses from &lt;em&gt;Data Science specialization&lt;/em&gt; by Johns Hopkins University (&lt;a href="https://www.coursera.org/specialization/jhudatascience/1" rel="nofollow"&gt;https://www.coursera.org/specialization/jhudatascience/1&lt;/a&gt;). Note that you can take specialization''s individual courses for free at your convenience. There are several other, albeit less popular, data science MOOCs.&lt;/p&gt;

&lt;p&gt;In terms of &lt;strong&gt;data sources&lt;/strong&gt;, I''m not sure what do you mean by "Dummy Data", but there is a wealth of &lt;em&gt;open data sets&lt;/em&gt;, including many in the area of &lt;em&gt;public health&lt;/em&gt;. You can review corresponding resources, listed on KDnuggets (&lt;a href="http://www.kdnuggets.com/datasets/index.html" rel="nofollow"&gt;http://www.kdnuggets.com/datasets/index.html&lt;/a&gt;) and choose ones that you''re interested in. For a country-level analysis, the fastest way to obtain data is finding and visiting corresponding &lt;em&gt;open data&lt;/em&gt; government websites. For example, for public health data in US, I would go to &lt;a href="http://www.healthdata.gov" rel="nofollow"&gt;http://www.healthdata.gov&lt;/a&gt; and &lt;a href="http://www.data.gov" rel="nofollow"&gt;http://www.data.gov&lt;/a&gt; (the latter - for corresponding non-medical data that you might want to include in your analysis).&lt;/p&gt;

&lt;p&gt;In regard to &lt;strong&gt;research papers&lt;/strong&gt; in the area of public health, I have two comments: 1) most &lt;em&gt;empirical research&lt;/em&gt; in that (or any other) area IMHO can be considered a data science study/project; 2) you need to perform a &lt;em&gt;literature review&lt;/em&gt; in the area or on the topic of your interest, so you''re on your own in that sense.&lt;/p&gt;

&lt;p&gt;Finally, a note on &lt;strong&gt;software tools&lt;/strong&gt;. If you''re serious about data science, I would suggest to invest some time in learning either R, or Python (if you don''t know them already), as those are two most popular open source tools among data scientists nowadays. Both have a variety of feature-rich &lt;em&gt;development environments&lt;/em&gt; as well as large &lt;em&gt;ecosystems&lt;/em&gt; of packages/libraries and users/developers all over the world.&lt;/p&gt;

&lt;p&gt;You might also find useful some of my other &lt;strong&gt;related answers&lt;/strong&gt; here on Data Science StackExchange site. For example, I recommend you to read &lt;a href="http://datascience.stackexchange.com/a/742/2452"&gt;this answer&lt;/a&gt;, &lt;a href="http://datascience.stackexchange.com/a/843/2452"&gt;this answer&lt;/a&gt; and &lt;a href="http://datascience.stackexchange.com/a/759/2452"&gt;this answer&lt;/a&gt;. Good luck!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5404, 1, '2015-03-27 08:41:13.68', 2, 1363, 'How does SQL Server Analysis Services compare to R?', '&lt;p&gt;This may be too broad of a question with heavy opinions, but I really am finding it hard to seek information about running various algorithms using SQL Server Analysis Service Data Mining projects versus using R. This is mainly because all the data science guys I work with don''t have any idea about SSAS because no one seems to use it. :)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;The Database Guy&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Before I start, let me clarify. I am a database guy and not a data scientist. I work with people who are data scientist who mainly use R. I assist these guys with creating large data sets where they can analyze and crunch data.&lt;/p&gt;

&lt;p&gt;My objective here is to leverage a tool that came with SQL Server that no one is really leveraging because no one seems to have a clue about how it works in comparison to other methods and tools such as R, SAS, SSPS and so forth in my camp.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;SSAS&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I have never really used SQL Server Analysis Services (SSAS) outside of creating OLAP cubes. Those who know SSAS, you can also perform data mining tasks on cubes or directly on the data in SQL Server. &lt;/p&gt;

&lt;p&gt;SSAS Data Mining comes with a range of algorithm types:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Classification algorithms&lt;/strong&gt; predict one or more discrete variables,
based on the other attributes in the dataset.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Regression algorithms&lt;/strong&gt; predict one or more continuous variables, such
as profit or loss, based on other attributes in the dataset.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Segmentation algorithms&lt;/strong&gt; divide data into groups, or clusters, of
items that have similar properties.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Association algorithms&lt;/strong&gt; find correlations between different attributes
in a dataset. The most common application of this kind of algorithm
is for creating association rules, which can be used in a market
basket analysis.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Sequence analysis algorithms&lt;/strong&gt; summarize frequent sequences or episodes
in data, such as a Web path flow.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Predicting Discrete Columns&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;With these different algorithm options, I can start making general predictions from the data such as finding out simply who is going to buy a bike based on a predictable column, Bike Buyers, against an input column, Age. The histogram shows that the age of a person helps distinguish whether that person will purchase a bicycle.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/X64Y1.gif" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Predicting Continuous Columns&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;When the Microsoft Decision Trees algorithm builds a tree based on a continuous predictable column, each node contains a regression formula. A split occurs at a point of non-linearity in the regression formula. For example, consider the following diagram.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/6MkTt.gif" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Comparison&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;With some of that said, it seems I can run a range of algorithms on the data and also have various functions available to me in SSAS to run against the data. It also seems I can develop my own algorithms in Visual Studio and deploy them to SSAS (if I''m not mistaken).&lt;/p&gt;

&lt;p&gt;So, what am I missing here in regards to languages and tools from R? Is it just that they have more flexibility to deploy and edit complex algorithms versus SSAS etc?&lt;/p&gt;
', 'data-mining  r  algorithms', NULL, '''/6mktt.gif'':491C ''/li'':258C,282C,300C,337C,357C ''/p'':78C,85C,131C,182C,187C,225C,237C,365C,423C,435C,442C,485C,497C,502C,558C,591C ''/strong'':84C,186C,243C,263C,287C,305C,343C,364C,441C,501C ''/ul'':358C ''/x64y1.gif'':429C ''age'':404C,410C ''algorithm'':14B,40C,235C,242C,262C,286C,304C,322C,342C,370C,449C,517C,544C,587C ''also'':210C,522C,537C ''alt'':430C,492C ''analysi'':5A,44C,196C,336C,341C ''analyz'':127C ''applic'':317C ''assist'':116C ''associ'':303C,326C ''attribut'':254C,278C,310C ''avail'':526C ''base'':250C,275C,393C,453C ''basket'':335C ''bicycl'':422C ''bike'':392C,398C ''broad'':20C ''build'':450C ''buy'':390C ''buyer'':399C ''came'':142C ''camp'':181C ''clarifi'':92C ''classif'':241C ''clue'':159C ''cluster'':293C ''column'':363C,397C,403C,440C,458C ''come'':230C ''common'':316C ''compar'':7A ''comparison'':165C,500C ''complex'':586C ''consid'':481C ''contain'':461C ''continu'':268C,439C,456C ''correl'':307C ''creat'':120C,201C,325C ''crunch'':129C ''cube'':203C,216C ''data'':11B,46C,58C,101C,109C,122C,130C,212C,221C,228C,289C,350C,380C,520C,535C ''data-min'':10B ''databas'':82C,96C ''dataset'':257C,281C,313C ''decis'':447C ''deploy'':549C,583C ''descript'':433C,495C ''develop'':541C ''diagram'':484C ''differ'':309C,369C ''direct'':218C ''discret'':248C,362C ''distinguish'':415C ''divid'':288C ''edit'':585C ''enter'':431C,493C ''episod'':348C ''etc'':590C ''exampl'':480C ''find'':31C,306C,383C ''flexibl'':581C ''flow'':356C ''follow'':483C ''formula'':464C,478C ''forth'':178C ''frequent'':345C ''function'':525C ''general'':376C ''go'':388C ''group'':291C ''guy'':60C,83C,97C,118C ''hard'':33C ''heavi'':25C ''help'':414C ''histogram'':406C ''i.stack.imgur.com'':428C,490C ''i.stack.imgur.com/6mktt.gif'':489C ''i.stack.imgur.com/x64y1.gif'':427C ''idea'':68C ''imag'':432C,494C ''img'':425C,487C ''inform'':36C ''input'':402C ''item'':295C ''kind'':320C ''know'':206C ''languag'':569C ''larg'':121C ''let'':90C ''leverag'':138C,151C ''li'':239C,259C,283C,301C,338C ''linear'':474C ''loss'':274C ''m'':555C ''main'':54C,112C ''make'':375C ''market'':334C ''may'':17C ''method'':168C ''microsoft'':446C ''mine'':12B,47C,213C,229C ''miss'':564C ''mistaken'':557C ''never'':191C ''node'':460C ''non'':473C ''non-linear'':472C ''object'':134C ''occur'':467C ''olap'':202C ''one'':73C,148C,154C,245C,265C ''opinion'':26C ''option'':371C ''outsid'':199C ''p'':15C,79C,86C,132C,183C,188C,226C,359C,366C,424C,436C,443C,486C,498C,503C,559C ''path'':355C ''peopl'':106C ''perform'':211C ''person'':413C,418C ''point'':470C ''predict'':244C,264C,361C,377C,396C,438C,457C ''profit'':272C ''project'':48C ''properti'':299C ''purchas'':420C ''question'':23C ''r'':9A,13B,51C,114C,173C,573C ''rang'':233C,515C ''realli'':29C,150C,192C ''regard'':567C ''regress'':261C,463C,477C ''rule'':327C ''run'':38C,513C,532C ''said'':508C ''sas'':174C ''scienc'':59C ''scientist'':102C,110C ''seek'':35C ''seem'':74C,155C,510C,538C ''segment'':285C ''sequenc'':340C,346C ''server'':4A,43C,145C,195C,224C ''servic'':6A,45C,197C ''set'':123C ''show'':407C ''similar'':298C ''simpli'':385C ''split'':466C ''sql'':3A,42C,144C,194C,223C ''src'':426C,488C ''ssas'':70C,185C,198C,207C,227C,530C,552C,589C ''ssps'':175C ''start'':89C,374C ''strong'':80C,184C,240C,260C,284C,302C,339C,360C,437C,499C ''studio'':547C ''summar'':344C ''task'':214C ''tool'':140C,170C,571C ''tree'':448C,452C ''type'':236C ''ul'':238C ''use'':41C,50C,76C,113C,193C,331C ''variabl'':249C,269C ''various'':39C,524C ''versus'':49C,588C ''visual'':546C ''web'':354C ''whether'':416C ''work'':62C,104C,163C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5516, 1, '2015-04-13 10:43:43.617', 0, 9, 'SAS PROC anova (how do better?)', '&lt;p&gt;PROC anova data=a;&lt;br&gt;
class b; 
model var1=b; 
run;&lt;/p&gt;

&lt;p&gt;And what to do if I have a few continuous variable: var1, var2, var3.&lt;/p&gt;
', 'processing', NULL, '''/p'':20C,36C ''anova'':3A,10C ''b'':15C,18C ''better'':6A ''br'':13C ''class'':14C ''continu'':31C ''data'':11C ''model'':16C ''p'':8C,21C ''proc'':2A,9C ''process'':7B ''run'':19C ''sas'':1A ''var1'':17C,33C ''var2'':34C ''var3'':35C ''variabl'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6388, 2, '2015-07-08 07:51:00.077', 1, NULL, NULL, '&lt;p&gt;One reason for sure is, that you can easialy open it in spreadsheet viewer.&lt;/p&gt;
', NULL, NULL, '''/p'':16C ''easiali'':10C ''one'':2C ''open'':11C ''p'':1C ''reason'':3C ''spreadsheet'':14C ''sure'':5C ''viewer'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5620, 2, '2015-04-27 07:24:13.68', -2, NULL, NULL, '&lt;p&gt;Use library(reshape) for most of the pivot &amp;amp; filter operations. Should resolve your problem.&lt;/p&gt;
', NULL, NULL, '''/p'':17C ''amp'':10C ''filter'':11C ''librari'':3C ''oper'':12C ''p'':1C ''pivot'':9C ''problem'':16C ''reshap'':4C ''resolv'':14C ''use'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5651, 2, '2015-04-30 08:18:14.52', -1, NULL, NULL, '&lt;p&gt;That just means count. #(w) is the number of times w occurs in the corpus.&lt;/p&gt;
', NULL, NULL, '''/p'':17C ''corpus'':16C ''count'':5C ''mean'':4C ''number'':9C ''occur'':13C ''p'':1C ''time'':11C ''w'':6C,12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5695, 1, '2015-05-06 10:26:56.647', 0, 116, 'Neural networks with non-negative weights', '&lt;p&gt;Could you tell me, are there any techniques for building neural networks with non-negative weights?&lt;/p&gt;
', 'neuralnetwork', NULL, '''/p'':27C ''build'':19C ''could'':10C ''negat'':6A,25C ''network'':2A,21C ''neural'':1A,20C ''neuralnetwork'':8B ''non'':5A,24C ''non-neg'':4A,23C ''p'':9C ''techniqu'':17C ''tell'':12C ''weight'':7A,26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5817, 1, '2015-05-17 20:12:48.76', 8, 213, 'Airline Fares - What analysis should be used to detect competitive price-setting behavior and price correlations?', '&lt;p&gt;I want to investigate price-setting behavior of airlines -- specifically how airlines react to competitors pricing.&lt;/p&gt;

&lt;p&gt;As I would say my knowledge about more complex analysis is quite limited I''ve done mostly all basic methods to gather a overall view of the data. This includes simple graphs which already help to identify similar patterns. I''am also using SAS Enterprise 9.4.&lt;/p&gt;

&lt;p&gt;However I''am looking for a more number based approach.&lt;/p&gt;

&lt;h2&gt;Data Set&lt;/h2&gt;

&lt;p&gt;The (self) collected data set I''am using contain around ~54.000 fares.
All fares were collected within a 60 day time window, on a daily basis (every night at 00:00).&lt;img src="http://i.stack.imgur.com/q5bcT.png" alt="Collection Method"&gt;&lt;/p&gt;

&lt;p&gt;Hence, every fare within that time window occurs $n$ times subject to the availability of the fare as well as the departure date of the flight, when it is passed by the collection date of the fare.
&lt;em&gt;(You cant collect a fare for a flight when the departure date of the flight is in the past)&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;The unformatted that looks basically like this: (fake data)&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;+--------------------+-----------+--------------------+--------------------------+---------------+
| requestDate        | price| tripStartDeparture | tripDestinationDeparture | flightCarrier |
+--------------------+-----------+--------------------+--------------------------+---------------+
| 14APR2015:00:00:00 | 725.32    | 16APR2015:10:50:02 | 23APR2015:21:55:04       | XA            |
+--------------------+-----------+--------------------+--------------------------+---------------+
| 14APR2015:00:00:00 | 966.32    | 16APR2015:13:20:02 | 23APR2015:19:00:04       | XY            |
+--------------------+-----------+--------------------+--------------------------+---------------+
| 14APR2015:00:00:00 | 915.32    | 16APR2015:13:20:02 | 23APR2015:21:55:04       | XH            |
+--------------------+-----------+--------------------+--------------------------+---------------+
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;"DaysBeforeDeparture" is calculated via $I=s-c$ where&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;I &amp;amp; interval (days before departure)&lt;/li&gt;
&lt;li&gt;s &amp;amp; date of the fare (flight departure)&lt;/li&gt;
&lt;li&gt;c &amp;amp; date of which the fare was collected&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Here is a example of grouped data set by I (DaysBeforeDep.) (fake data!):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;+-----------------+------------------+------------------+------------------+------------------+
| DaysBefDeparture | AVG_of_sale | MIN_of_sale | MAX_of_sale | operatingCarrier |
+-----------------+------------------+------------------+------------------+------------------+
| 0               | 880.68           | 477.99           | 2,245.23         | DL           |
+-----------------+------------------+------------------+------------------+------------------+
| 0               | 904.89           | 477.99           | 2,534.55         | DL           |
+-----------------+------------------+------------------+------------------+------------------+
| 0               | 1,044.39         | 920.99           | 2,119.09         | LH               |
+-----------------+------------------+------------------+------------------+------------------+
&lt;/code&gt;&lt;/pre&gt;

&lt;h2&gt;What I came up with so far&lt;/h2&gt;

&lt;p&gt;Looking at the line graphs I can already estimate that several lines will have a high correlation factor. Hence, I tried to use correlation analysis first on the grouped data. But is that the correct way? Basically I try now to make correlations on the averages rather then on the individual prices?
Is there an other way?&lt;/p&gt;

&lt;p&gt;I''am unsure wich regression model fits here, as the prices do not move in any linear form and appear non-linear. Would I need to fit a model to each of price developments of an airline&lt;/p&gt;

&lt;p&gt;PS: This is a long text-wall. If I need to clarify anything let me know. I''am new to this sub.&lt;/p&gt;

&lt;p&gt;Anyone a clue? :-)&lt;/p&gt;
', 'data-mining  dataset  regression  correlation  visualization', NULL, '''/code'':269C,360C ''/em'':207C ''/h2'':107C,370C ''/li'':290C,300C,311C ''/p'':43C,91C,103C,148C,208C,219C,281C,327C,429C,469C,494C,499C ''/pre'':270C,361C ''/q5bct.png'':144C ''/ul'':312C ''0'':341C,347C,353C ''00'':138C,139C,228C,229C,230C,242C,243C,244C,252C,256C,257C,258C ''02'':235C,249C,263C ''04'':239C,253C,267C ''044.39'':355C ''1'':354C ''10'':233C ''119.09'':358C ''13'':247C,261C ''14apr2015'':227C,241C,255C ''16apr2015'':232C,246C,260C ''19'':251C ''2'':344C,350C,357C ''20'':248C,262C ''21'':237C,265C ''23apr2015'':236C,250C,264C ''245.23'':345C ''477.99'':343C,349C ''50'':234C ''534.55'':351C ''54.000'':119C ''55'':238C,266C ''60'':127C ''725.32'':231C ''880.68'':342C ''9.4'':90C ''904.89'':348C ''915.32'':259C ''920.99'':356C ''966.32'':245C ''airlin'':1A,35C,38C,468C ''alreadi'':78C,379C ''also'':86C ''alt'':145C ''amp'':285C,293C,303C ''analysi'':4A,54C,396C ''anyon'':496C ''anyth'':484C ''appear'':450C ''approach'':102C ''around'':118C ''avail'':163C ''averag'':417C ''avg'':331C ''base'':101C ''basi'':134C ''basic'':63C,214C,408C ''behavior'':14A,33C ''c'':279C,302C ''calcul'':274C ''came'':365C ''cant'':189C ''clarifi'':483C ''clue'':498C ''code'':221C,329C ''collect'':111C,124C,146C,182C,190C,310C ''competit'':10A ''competitor'':41C ''complex'':53C ''contain'':117C ''correct'':406C ''correl'':17A,23B,388C,395C,414C ''daili'':133C ''data'':19B,72C,105C,112C,218C,320C,326C,401C ''data-min'':18B ''dataset'':21B ''date'':172C,183C,199C,294C,304C ''day'':128C,287C ''daysbefdepartur'':330C ''daysbeforedep'':324C ''daysbeforedepartur'':272C ''departur'':171C,198C,289C,299C ''detect'':9A ''develop'':465C ''dl'':346C,352C ''done'':60C ''em'':187C ''enterpris'':89C ''estim'':380C ''everi'':135C,151C ''exampl'':317C ''factor'':389C ''fake'':217C,325C ''far'':369C ''fare'':2A,120C,122C,152C,166C,186C,192C,297C,308C ''first'':397C ''fit'':437C,458C ''flight'':175C,195C,202C,298C ''flightcarri'':226C ''form'':448C ''gather'':66C ''graph'':76C,376C ''group'':319C,400C ''h2'':104C,362C ''help'':79C ''henc'':150C,390C ''high'':387C ''howev'':93C ''i.stack.imgur.com'':143C ''i.stack.imgur.com/q5bct.png'':142C ''identifi'':81C ''img'':140C ''includ'':74C ''individu'':422C ''interv'':286C ''investig'':29C ''know'':487C ''knowledg'':50C ''let'':485C ''lh'':359C ''li'':283C,291C,301C ''like'':215C ''limit'':57C ''line'':375C,383C ''linear'':447C,453C ''long'':475C ''look'':96C,213C,372C ''make'':413C ''max'':337C ''method'':64C,147C ''min'':334C ''mine'':20B ''model'':436C,460C ''most'':61C ''move'':444C ''n'':158C ''need'':456C,481C ''new'':490C ''night'':136C ''non'':452C ''non-linear'':451C ''number'':100C ''occur'':157C ''operatingcarri'':340C ''overal'':68C ''p'':25C,44C,92C,108C,149C,209C,271C,313C,371C,430C,470C,495C ''pass'':179C ''past'':206C ''pattern'':83C ''pre'':220C,328C ''price'':12A,16A,31C,42C,223C,423C,441C,464C ''price-set'':11A,30C ''ps'':471C ''quit'':56C ''rather'':418C ''react'':39C ''regress'':22B,435C ''requestd'':222C ''s-c'':277C ''sale'':333C,336C,339C ''sas'':88C ''say'':48C ''self'':110C ''set'':13A,32C,106C,113C,321C ''sever'':382C ''similar'':82C ''simpl'':75C ''specif'':36C ''src'':141C ''sub'':493C ''subject'':160C ''text'':477C ''text-wal'':476C ''time'':129C,155C,159C ''tri'':392C,410C ''tripdestinationdepartur'':225C ''tripstartdepartur'':224C ''ul'':282C ''unformat'':211C ''unsur'':433C ''use'':7A,87C,116C,394C ''ve'':59C ''via'':275C ''view'':69C ''visual'':24B ''wall'':478C ''want'':27C ''way'':407C,428C ''well'':168C ''wich'':434C ''window'':130C,156C ''within'':125C,153C ''would'':47C,454C ''xa'':240C ''xh'':268C ''xy'':254C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9250, 5, '2015-12-07 16:43:44.823', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5830, 1, '2015-05-19 02:09:13.7', 1, 86, 'How do I best parse ECOG scores in cancer clinical trial results in java?', '&lt;p&gt;I was informed of 5 java NLP libraries.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Apache cTAKESâ¢&lt;/li&gt;
&lt;li&gt;MetaMap&lt;/li&gt;
&lt;li&gt;LexEVS (&lt;a href="http://lexsrv3.nlm.nih.gov/LexSysGroup/Projects/lvg/current/web/download.html" rel="nofollow"&gt;http://lexsrv3.nlm.nih.gov/LexSysGroup/Projects/lvg/current/web/download.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;Apache OpenNLP&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I also plan to parallelize an NLP library via map-reduce with hadoop.
However, I''m new to natural language processing, so I don''t know how to approach the problem.&lt;/p&gt;

&lt;p&gt;The goal is to download a set of clinical trials on cancer from www.clinicaltrials.gov and parse eligibility criteria (both inclusion and exclusion criteria), identify the ECOG scores and annotate each CTA with the allowed ECOG scores.&lt;/p&gt;

&lt;p&gt;For example, in the following document, if ECOG is specified in inclusion criteria, it is not negated. If ECOG is specified in exclusion criteria, it is negated. If both exclusion criteria and inclusion criteria are not mentioned, then ECOG is not negated.&lt;/p&gt;

&lt;p&gt;Document: &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Eligibility criteria of CTA "NCT01572038"&lt;/p&gt;
  
  &lt;p&gt;Inclusion Criteria:&lt;/p&gt;
  
  &lt;ul&gt;
  &lt;li&gt;Patients must have histological proof of a primary non-small cell lung cancer
  (bronchoalveolar carcinomas presenting as a discrete solitary radiological mass or
  nodule are eligible)&lt;/li&gt;
  &lt;li&gt;Patients must be classified post-operatively as stage IB, II or IIIA on the basis of
  pathologic criteria&lt;/li&gt;
  &lt;li&gt;At the time of resection a complete mediastinal lymph node resection or at least
  lymph node sampling should have been attempted; if a complete mediastinal lymph node
  resection or lymph node sampling was not undertaken, any mediastinal lymph node which
  measured 1.5 cm or more on the pre-surgical computed tomography (CT)/magnetic
  resonance imaging (MRI) scan or any area of increased uptake in the mediastinum on a
  pre-surgical positron emission tomography (PET) scan must have been biopsied; note: a
  pre-surgical PET scan is not mandatory
  &lt;ul&gt;
  &lt;li&gt;The nodal tissue must be labelled according to the recommendations of the
  American Thoracic Society; surgeons are encouraged to dissect or sample all
  accessible nodal levels; the desirable levels for biopsy are:&lt;/li&gt;
  &lt;li&gt;Right upper lobe: 4, 7, 10&lt;/li&gt;
  &lt;li&gt;Right middle lobe: 4, 7, 10&lt;/li&gt;
  &lt;li&gt;Right lower lobe: 4, 7, 9, 10&lt;/li&gt;
  &lt;li&gt;Left upper lobe: 5, 6, 7, 10&lt;/li&gt;
  &lt;li&gt;Left lower lobe: 7, 9, 10&lt;/li&gt;
  &lt;/ul&gt;&lt;/li&gt;
  &lt;li&gt;Surgery may consist of lobectomy, sleeve resection, bilobectomy or pneumonectomy as
  determined by the attending surgeon based on the intraoperative findings; patients
  who have had only segmentectomies or wedge resections are not eligible for this
  study; all gross disease must have been removed at the end of surgery; all surgical
  margins of resection must be negative for tumor&lt;/li&gt;
  &lt;li&gt;No more than 16 weeks may have elapsed between surgery and randomization; for
  patients who received post-operative adjuvant platinum-based chemotherapy, no more
  than 26 weeks may have elapsed between surgery and randomization&lt;/li&gt;
  &lt;li&gt;Patient must consent to provision of and investigator(s) must agree to submit a
  representative formalin fixed paraffin block of tumor tissue at the request of the
  Central Tumor Bank in order that the specific EGFR correlative marker assays may be
  conducted&lt;/li&gt;
  &lt;li&gt;The patient must have an Eastern Cooperative Oncology Group (ECOG) performance status
  of 0, 1 or 2&lt;/li&gt;
  &lt;li&gt;Leukocytes &amp;gt;= 3.0 x 10^9/L or &amp;gt;= 3000/ul&lt;/li&gt;
  &lt;li&gt;Absolute granulocyte count &amp;gt;= 1.5 x 10^9/L or &amp;gt;= 1,500/ul&lt;/li&gt;
  &lt;li&gt;Platelets &amp;gt;= 100 x 10^9/L or &amp;gt;= 100,000/ul&lt;/li&gt;
  &lt;li&gt;Total bilirubin within normal institutional limits&lt;/li&gt;
  &lt;li&gt;Alkaline phosphatase =&amp;lt; 2.5 x institutional upper limit of normal; if alkaline
  phosphatase is greater than the institutional upper limit of normal (UNL) but less
  than the maximum allowed, an abdominal (including liver) ultrasound, CT or MRI scan
  and a radionuclide bone scan must be performed prior to randomization to rule out
  metastatic disease; if the values are greater than the maximum allowed, patients will
  not be considered eligible regardless of findings on any supplementary imaging&lt;/li&gt;
  &lt;li&gt;Aspartate aminotransferase (AST) (serum glutamic oxaloacetic transaminase [SGOT])
  and/or alanine aminotransferase (ALT) (serum glutamate pyruvate transaminase [SGPT])
  =&amp;lt; 2.5 x institutional upper limit of normal; if AST (SGOT) or ALT (SGPT) are greater
  than the institutional upper limit of normal (UNL) but less than the maximum allowed,
  an abdominal (including liver) ultrasound, CT or MRI scan must be performed prior to
  randomization to rule out metastatic disease; if the values are greater than the
  maximum allowed, patients will not be considered eligible regardless of findings on
  any supplementary imaging&lt;/li&gt;
  &lt;li&gt;Patient must have a chest x-ray done within 14 days prior to randomization; patient
  must have a CT or MRI scan of the chest done within 90 days prior to surgical
  resection if at least one of the following was undertaken:
  &lt;ul&gt;
  &lt;li&gt;A complete mediastinal lymph node resection; or&lt;/li&gt;
  &lt;li&gt;Biopsy of all desired levels of lymph nodes - as specified above; or&lt;/li&gt;
  &lt;li&gt;A pre-surgical PET scan within 60 days prior to surgical resection If none of
  the above was undertaken then the CT or MRI scan of the chest must have been
  performed within 60 days prior to surgical resection Note: a pre-surgical PET
  scan is not mandatory&lt;/li&gt;
  &lt;/ul&gt;&lt;/li&gt;
  &lt;li&gt;Patient must have an electrocardiogram (EKG) done within 14 days prior to
  randomization&lt;/li&gt;
  &lt;li&gt;Women of childbearing age and men must agree to use adequate contraception (hormonal
  or barrier method of birth control) prior to study entry and while taking study
  medication and for a period of three months after final dose; should a woman become
  pregnant or suspect she is pregnant while she or her male partner are participating
  in this study, she should inform her treating physician immediately&lt;/li&gt;
  &lt;li&gt;Patients may receive post-operative radiation therapy; patients must have completed
  radiation at least 3 weeks prior to randomization and have recovered from all
  radiation-induced toxicity; patients who have received radiation therapy should also
  be randomized within 16 weeks of surgery&lt;/li&gt;
  &lt;li&gt;Patient consent must be obtained according to local institutional and/or University
  Human Experimentation Committee requirements; it will be the responsibility of the
  local participating investigators to obtain the necessary local clearance, and to
  indicate in writing to either the National Cancer Institute of Canada (NCIC) Clinical
  Trials Group (CTG) study coordinator (for NCIC CTG centers) or the Cancer Trials
  Support Unit (CTSU) (for all other investigators), that such clearance has been
  obtained, before the trial can commence in that center; a standard consent form for
  the trial will not be provided, but a sample form is given; this sample consent form
  has been approved by the National Cancer Institute (NCI) Central Institutional Review
  Board (IRB) and must be used unaltered by those CTSI centers which operate under CIRB
  authority; for NCIC CTG centers, a copy of the initial full board Research Ethics
  Board (REB) approval and approved consent form must be sent to the NCIC CTG central
  office; please note that the consent form for this study must contain a statement
  which gives permission for the government agencies, NCI, NCIC CTG and monitoring
  agencies to review patient records
  &lt;ul&gt;
  &lt;li&gt;NCIC-CTG Centers: the patient must have the ability to understand and the
  willingness to sign a written informed consent document; the patient must sign
  the consent form prior to randomization&lt;/li&gt;
  &lt;li&gt;CTSU Centers: the patient, or in the case of a mentally incompetent patient his
  or her legally authorized and qualified representative, must have the ability to
  understand and the willingness to sign a written informed consent document; the
  consent form must be signed prior to randomization&lt;/li&gt;
  &lt;/ul&gt;&lt;/li&gt;
  &lt;li&gt;Patients must be accessible for treatment and follow-up; investigators must assure
  themselves that patients registered on this trial will be available for complete
  documentation of the treatment administered, toxicity and follow-up&lt;/li&gt;
  &lt;li&gt;Initiation of protocol treatment must begin within 10 working days of patient
  randomization&lt;/li&gt;
  &lt;li&gt;Patients may have received post-operative adjuvant platinum-based chemotherapy;
  patients must have completed chemotherapy at least 3 weeks prior to randomization and
  have recovered from all chemotherapy-induced toxicity; patients who have received
  adjuvant chemotherapy should also be randomized within 26 weeks of surgery&lt;/li&gt;
  &lt;/ul&gt;
  
  &lt;p&gt;Exclusion Criteria:&lt;/p&gt;
  
  &lt;ul&gt;
  &lt;li&gt;Prior or concurrent malignancies; patients who have had a previous diagnosis of
  cancer, if they remain free of recurrence and metastases five years or more following
  the end of treatment and, in the opinion of the treating physician do not have a
  substantial risk of recurrence of the prior malignancy, are eligible for the study;
  patients who have been adequately treated for non-melanomatous skin cancer or
  carcinoma in situ of the cervix are eligible irrespective of when that treatment was
  given&lt;/li&gt;
  &lt;li&gt;A combination of small cell and non-small cell carcinomas or a pulmonary carcinoid
  tumor&lt;/li&gt;
  &lt;li&gt;More than one discrete area of apparent primary cancer (even if within the same lobe,
  T4, IIIB)&lt;/li&gt;
  &lt;li&gt;Clinically significant or untreated ophthalmologic (e.g. Sjogren''s etc.) or
  gastrointestinal conditions (e.g. Crohn''s disease, ulcerative colitis)&lt;/li&gt;
  &lt;li&gt;Any active pathological condition that would render the protocol treatment dangerous
  such as: uncontrolled congestive heart failure, angina, or arrhythmias, active
  uncontrolled infection, or others&lt;/li&gt;
  &lt;li&gt;A history of psychiatric or neurological disorder that would make the obtainment of
  informed consent problematic or that would limit compliance with study requirements&lt;/li&gt;
  &lt;li&gt;Patient, if female, is pregnant or breast-feeding&lt;/li&gt;
  &lt;li&gt;Neoadjuvant chemotherapy or immunotherapy for NSCLC; however, patients may have
  received pre-operative limited field, low dose (less than 1000 cGy) external beam
  radiation therapy or endobronchial brachytherapy or laser therapy for short term
  control of hemoptysis or lobar obstruction; full dose pre-operative radiotherapy of
  curative intent is a cause for exclusion; patients may have received post-operative
  adjuvant platinum-based chemotherapy however non-platinum-based chemotherapy is a
  cause for exclusion&lt;/li&gt;
  &lt;li&gt;History of allergic reactions attributed to compounds of similar chemical or biologic
  composition to the agents used on this trial; patients with ongoing use of phenytoin,
  carbamazepine, barbiturates, rifampicin, or St John''s Wort are excluded&lt;/li&gt;
  &lt;li&gt;Incomplete healing from previous oncologic or other major surgery&lt;/li&gt;
  &lt;/ul&gt;
&lt;/blockquote&gt;

&lt;p&gt;I want to find documents with a query as below.&lt;/p&gt;

&lt;p&gt;Input &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;''Patients with Eastern cooperative Oncology Group (ECOG) performance status &gt; 2'' will be excluded&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Output &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;CTA: NCT01572038 
Labels: ECOG 0, ECOG 1, ECOG 2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What is the simplest way to approach this problem?&lt;/p&gt;
', 'nlp  java', NULL, '''/blockquote'':1749C,1781C ''/code'':1796C ''/lexsysgroup/projects/lvg/current/web/download.html'':41C ''/lexsysgroup/projects/lvg/current/web/download.html&lt;/a&gt;)&lt;/li&gt;'':46C ''/li'':31C,34C,50C,212C,233C,360C,368C,376C,385C,394C,402C,404C,464C,502C,546C,565C,576C,590C,602C,610C,688C,779C,833C,847C,899C,901C,916C,984C,1030C,1254C,1302C,1304C,1341C,1356C,1406C,1497C,1515C,1534C,1554C,1581C,1607C,1618C,1698C,1736C,1747C ''/magnetic'':288C ''/p'':26C,85C,123C,167C,170C,178C,182C,1411C,1761C,1764C,1780C,1784C,1808C ''/pre'':1797C ''/ul'':51C,403C,900C,1303C,1407C,1748C ''0'':561C,1791C ''000/ul'':601C ''1'':562C,588C,1793C ''1.5'':276C,582C ''10'':367C,375C,384C,393C,401C,571C,584C,596C,1350C ''100'':594C,600C ''1000'':1640C ''14'':791C,911C ''16'':469C,1026C ''2'':564C,1776C,1795C ''2.5'':615C,708C ''26'':493C,1402C ''3'':1001C,1377C ''3.0'':569C ''3000/ul'':575C ''4'':365C,373C,381C ''5'':22C,390C ''500/ul'':589C ''6'':391C ''60'':856C,883C ''7'':366C,374C,382C,392C,399C ''9'':383C,400C ''9/l'':572C,585C,597C ''90'':809C ''abdomin'':642C,738C ''abil'':1231C,1280C ''absolut'':578C ''access'':351C,1309C ''accord'':334C,1037C ''activ'':1557C,1576C ''adequ'':928C,1473C ''adjuv'':485C,1365C,1395C,1682C ''administ'':1335C ''age'':921C ''agenc'':1209C,1215C ''agent'':1715C ''agre'':514C,925C ''alanin'':699C ''alkalin'':612C,623C ''allerg'':1702C ''allow'':120C,640C,674C,736C,765C ''also'':54C,1022C,1398C ''alt'':701C,719C ''american'':340C ''aminotransferas'':691C,700C ''and/or'':698C,1041C ''angina'':1573C ''annot'':115C ''apach'':29C,48C ''appar'':1523C ''approach'':82C,1805C ''approv'':1135C,1176C,1178C ''area'':295C,1521C ''arrhythmia'':1575C ''aspart'':690C ''assay'':542C ''assur'':1318C ''ast'':692C,716C ''attempt'':255C ''attend'':420C ''attribut'':1704C ''author'':1160C,1273C ''avail'':1328C ''bank'':533C ''barbitur'':1727C ''barrier'':932C ''base'':422C,488C,1368C,1685C,1691C ''basi'':229C ''beam'':1643C ''becom'':959C ''begin'':1348C ''best'':4A ''bilirubin'':605C ''bilobectomi'':413C ''biolog'':1711C ''biopsi'':315C,358C,835C ''birth'':935C ''block'':522C ''blockquot'':171C,1765C ''board'':1145C,1171C,1174C ''bone'':653C ''brachytherapi'':1648C ''breast'':1616C ''breast-feed'':1615C ''bronchoalveolar'':199C ''canada'':1075C ''cancer'':9A,98C,198C,1072C,1089C,1139C,1426C,1480C,1525C ''carbamazepin'':1726C ''carcinoid'':1513C ''carcinoma'':200C,1482C,1509C ''case'':1263C ''caus'':1672C,1695C ''cell'':196C,1503C,1508C ''center'':1086C,1111C,1155C,1164C,1225C,1257C ''central'':531C,1142C,1188C ''cervix'':1487C ''cgi'':1641C ''chemic'':1709C ''chemotherapi'':489C,1369C,1374C,1388C,1396C,1621C,1686C,1692C ''chemotherapy-induc'':1387C ''chest'':785C,806C,877C ''childbear'':920C ''cirb'':1159C ''classifi'':217C ''clearanc'':1062C,1100C ''clinic'':10A,95C,1077C,1536C ''cm'':277C ''code'':1786C ''coliti'':1553C ''combin'':1500C ''commenc'':1108C ''committe'':1045C ''complet'':241C,258C,827C,997C,1330C,1373C ''complianc'':1603C ''composit'':1712C ''compound'':1706C ''comput'':285C ''concurr'':1416C ''condit'':1547C,1559C ''conduct'':545C ''congest'':1570C ''consent'':506C,1033C,1114C,1131C,1179C,1194C,1242C,1249C,1291C,1294C,1597C ''consid'':679C,770C ''consist'':408C ''contain'':1200C ''contracept'':929C ''control'':936C,1655C ''cooper'':554C,1770C ''coordin'':1082C ''copi'':1166C ''correl'':540C ''count'':580C ''criteria'':104C,109C,137C,148C,155C,158C,174C,181C,232C,1410C ''crohn'':1549C ''ct'':287C,646C,742C,800C,871C ''cta'':117C,176C,1787C ''ctakesâ'':30C ''ctg'':1080C,1085C,1163C,1187C,1212C,1224C ''ctsi'':1154C ''ctsu'':1093C,1256C ''curat'':1668C ''danger'':1566C ''day'':792C,810C,857C,884C,912C,1352C ''desir'':355C,838C ''determin'':417C ''diagnosi'':1424C ''discret'':204C,1520C ''diseas'':444C,665C,756C,1551C ''disord'':1589C ''dissect'':347C ''document'':130C,169C,1243C,1292C,1331C,1755C ''done'':789C,807C,909C ''dose'':955C,1637C,1662C ''download'':91C ''e.g'':1541C,1548C 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''lexev'':36C ''lexsrv3.nlm.nih.gov'':40C,45C ''lexsrv3.nlm.nih.gov/lexsysgroup/projects/lvg/current/web/download.html'':39C ''lexsrv3.nlm.nih.gov/lexsysgroup/projects/lvg/current/web/download.html&lt;/a&gt;)&lt;/li&gt;'':44C ''li'':28C,32C,35C,47C,184C,213C,234C,327C,361C,369C,377C,386C,395C,405C,465C,503C,547C,566C,577C,591C,603C,611C,689C,780C,825C,834C,848C,902C,917C,985C,1031C,1221C,1255C,1305C,1342C,1357C,1413C,1498C,1516C,1535C,1555C,1582C,1608C,1619C,1699C,1737C ''librari'':25C,60C ''limit'':609C,619C,631C,712C,727C,1602C,1634C ''liver'':644C,740C ''lobar'':1659C ''lobe'':364C,372C,380C,389C,398C,1531C ''lobectomi'':410C ''local'':1039C,1054C,1061C ''low'':1636C ''lower'':379C,397C ''lt'':614C,707C ''lung'':197C ''lymph'':243C,249C,260C,264C,272C,829C,841C ''m'':69C ''major'':1745C ''make'':1592C ''male'':970C ''malign'':1417C,1463C ''mandatori'':325C,898C ''map'':63C ''map-reduc'':62C ''margin'':456C ''marker'':541C ''mass'':207C ''maximum'':639C,673C,735C,764C ''may'':407C,471C,495C,543C,987C,1359C,1628C,1676C ''measur'':275C ''mediastin'':242C,259C,271C,828C ''mediastinum'':301C ''medic'':945C ''melanomat'':1478C ''men'':923C ''mental'':1266C ''mention'':161C ''metamap'':33C ''metastas'':1434C ''metastat'':664C,755C ''method'':933C ''middl'':371C ''monitor'':1214C ''month'':952C ''mri'':291C,648C,744C,802C,873C ''must'':186C,215C,312C,331C,445C,459C,505C,513C,550C,655C,746C,782C,797C,878C,904C,924C,995C,1034C,1148C,1181C,1199C,1228C,1246C,1277C,1296C,1307C,1317C,1347C,1371C ''nation'':1071C,1138C ''natur'':72C ''nci'':1141C,1210C ''ncic'':1076C,1084C,1162C,1186C,1211C,1223C ''ncic-ctg'':1222C ''nct01572038'':177C,1788C ''necessari'':1060C ''negat'':141C,151C,166C,461C ''neoadjuv'':1620C ''neurolog'':1588C ''new'':70C ''nlp'':15B,24C,59C ''nodal'':329C,352C ''node'':244C,250C,261C,265C,273C,830C,842C ''nodul'':209C ''nofollow'':43C ''non'':194C,1477C,1506C,1689C ''non-melanomat'':1476C ''non-platinum-bas'':1688C ''non-smal'':193C,1505C ''none'':863C ''normal'':607C,621C,633C,714C,729C ''note'':316C,889C,1191C ''nsclc'':1625C ''obstruct'':1660C ''obtain'':1036C,1058C,1103C,1594C ''offic'':1189C ''oncolog'':555C,1742C,1771C ''one'':818C,1519C ''ongo'':1722C ''opennlp'':49C ''oper'':220C,484C,991C,1157C,1364C,1633C,1665C,1681C ''ophthalmolog'':1540C ''opinion'':1447C ''order'':535C ''other'':1580C ''output'':1783C ''oxaloacet'':695C ''p'':17C,52C,86C,124C,168C,172C,179C,1408C,1750C,1762C,1766C,1782C,1798C ''paraffin'':521C ''parallel'':57C ''pars'':5A,102C ''particip'':973C,1055C ''partner'':971C ''patholog'':231C,1558C ''patient'':185C,214C,427C,479C,504C,549C,675C,766C,781C,796C,903C,986C,994C,1015C,1032C,1218C,1227C,1245C,1259C,1268C,1306C,1321C,1354C,1358C,1370C,1391C,1418C,1469C,1609C,1627C,1675C,1720C,1767C ''perform'':558C,657C,748C,881C,1774C ''period'':949C ''permiss'':1205C ''pet'':310C,321C,853C,894C ''phenytoin'':1725C ''phosphatas'':613C,624C ''physician'':982C,1451C ''plan'':55C ''platelet'':592C ''platinum'':487C,1367C,1684C,1690C ''platinum-bas'':486C,1366C,1683C ''pleas'':1190C ''pneumonectomi'':415C ''positron'':307C ''post'':219C,483C,990C,1363C,1680C ''post-op'':218C,482C,989C,1362C,1679C ''pre'':283C,305C,319C,851C,892C,1632C,1664C,1785C ''pre-op'':1631C,1663C ''pre-surg'':282C,304C,318C,850C,891C ''pregnant'':960C,965C,1613C ''present'':201C ''previous'':1423C,1741C ''primari'':192C,1524C ''prior'':658C,749C,793C,811C,858C,885C,913C,937C,1003C,1251C,1299C,1379C,1414C,1462C ''problem'':84C,1807C ''problemat'':1598C ''process'':74C ''proof'':189C ''protocol'':1345C,1564C ''provid'':1122C ''provis'':508C ''psychiatr'':1586C ''pulmonari'':1512C ''pyruv'':704C ''qualifi'':1275C ''queri'':1758C ''radiat'':992C,998C,1012C,1019C,1644C ''radiation-induc'':1011C ''radiolog'':206C ''radionuclid'':652C ''radiotherapi'':1666C ''random'':477C,501C,660C,751C,795C,915C,1005C,1024C,1253C,1301C,1355C,1381C,1400C ''ray'':788C ''reaction'':1703C ''reb'':1175C 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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5857, 2, '2015-05-21 19:00:55.473', 3, NULL, NULL, '&lt;p&gt;Right now, I only have time for a very brief answer, but I''ll try to expand on it later on.&lt;/p&gt;

&lt;p&gt;What you want to do is a &lt;strong&gt;clustering&lt;/strong&gt;, since you want to discover some labels for your data.  (As opposed to a classification, where you would have labels for at least some of the data and you would like to label the rest).&lt;/p&gt;

&lt;p&gt;In order to perform a clustering on your users, you need to have them as some kind of points in an abstract space.  Then you will measure distances between points, and say that points that are "near" are "similar", and label them according to their place in that space.&lt;/p&gt;

&lt;p&gt;You need to transform your data into something that looks like a user profile, i.e.: a user ID, followed by a vector of numbers that represent the features of this user.  In your case, each feature could be a "category of website" or a "category of product", and the number could be the amount of dollars spent in that feature.  Or a feature could be a combination of web and product, of course.&lt;/p&gt;

&lt;p&gt;As an example, let us imagine the user profile with just three features:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;dollars spent in "techy" webs, &lt;/li&gt;
&lt;li&gt;dollars spent on "fashion" products, &lt;/li&gt;
&lt;li&gt;and dollars spent on "aggressive" video games on "family-oriented" webs (who knows).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In order to build those profiles, you need to map the "categories" and "keywords" that you have, which are too plentiful, into the features you think are relevant.  Look into &lt;a href="http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf.html" rel="nofollow"&gt;topic modeling&lt;/a&gt; or &lt;a href="http://en.wikipedia.org/wiki/Semantic_similarity" rel="nofollow"&gt;semantic similarity&lt;/a&gt; to do so.  Once that map is built, it will state that all dollars spent on webs with keywords "gadget", "electronics", "programming", and X others, should all be aggregated into our first feature; and so on.&lt;/p&gt;

&lt;p&gt;Do not be afraid of "imposing" the features!  You will need to refine them and maybe completely change them once you have clustered the users.&lt;/p&gt;

&lt;p&gt;Once you have user profiles, proceed to cluster them using &lt;a href="http://en.wikipedia.org/wiki/K-means_clustering" rel="nofollow"&gt;k-means&lt;/a&gt; or whatever else you think is interesting.  Whatever technique you use, you will be interested in getting the "representative" point for each cluster.  This is usually the geometric "center" of the points in that cluster.&lt;/p&gt;

&lt;p&gt;Plot those "representative" points, and also plot how they compare to other clusters.  Using a &lt;a href="http://en.wikipedia.org/wiki/Radar_chart" rel="nofollow"&gt;radar chart&lt;/a&gt; is very useful here.  Wherever there is a salient feature (something in the representative that is very marked, and is also very prominent in its comparison to other clusters) is a good candidate to help you label the cluster with some catchy phrase ("nerds", "fashionistas", "aggressive moms" ...).&lt;/p&gt;

&lt;p&gt;Remember that a clustering problem is an open problem, so there is no "right" solution!  And I think my answer is quite long already; check also about normalization of the profiles and filtering outliers.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9268, 1, '2015-12-08 16:37:35.857', 2, 235, 'What is finite precision? Why is finite precision a problem in machine learning?', '&lt;p&gt;Can you explain what is finite precision? 
Why is finite precision a problem in machine learning?&lt;/p&gt;
', 'machine-learning  predictive-modeling', NULL, '''/p'':37C ''explain'':23C ''finit'':3A,7A,26C,30C ''learn'':13A,16B,36C ''machin'':12A,15B,35C ''machine-learn'':14B ''model'':19B ''p'':20C ''precis'':4A,8A,27C,31C ''predict'':18B ''predictive-model'':17B ''problem'':10A,33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6565, 2, '2015-07-24 02:21:36.687', 1, NULL, NULL, '&lt;p&gt;This is a post that starts from data science and walks you through options for completing common tasks:  &lt;a href="http://www.drbunsen.org/explorations-in-unix/" rel="nofollow"&gt;http://www.drbunsen.org/explorations-in-unix/&lt;/a&gt;.  &lt;/p&gt;
', NULL, NULL, '''/explorations-in-unix/'':24C ''/explorations-in-unix/&lt;/a&gt;.'':29C ''/p'':30C ''common'':18C ''complet'':17C ''data'':9C ''href'':21C ''nofollow'':26C ''option'':15C ''p'':1C ''post'':5C ''rel'':25C ''scienc'':10C ''start'':7C ''task'':19C ''walk'':12C ''www.drbunsen.org'':23C,28C ''www.drbunsen.org/explorations-in-unix/'':22C ''www.drbunsen.org/explorations-in-unix/&lt;/a&gt;.'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6235, 1, '2015-06-27 03:41:16.967', 2, 55, 'Data repositories like UCI', '&lt;p&gt;Are there any other data repositories like UCI and mlData, for biological data?? I want to know about mostly biological data set.&lt;/p&gt;
', 'machine-learning  data-mining  dataset  databases', NULL, '''/p'':36C ''biolog'':25C,33C ''data'':1A,9B,18C,26C,34C ''data-min'':8B ''databas'':12B ''dataset'':11B ''know'':30C ''learn'':7B ''like'':3A,20C ''machin'':6B ''machine-learn'':5B ''mine'':10B ''mldata'':23C ''most'':32C ''p'':13C ''repositori'':2A,19C ''set'':35C ''uci'':4A,21C ''want'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6237, 2, '2015-06-27 08:46:32.01', 2, NULL, NULL, '&lt;p&gt;I do biological network analysis and &lt;a href="http://biit.cs.ut.ee/graphweb/welcome.cgi?t=examples" rel="nofollow"&gt;here&lt;/a&gt; are some networks.&lt;/p&gt;
', NULL, NULL, '''/a'':16C ''/graphweb/welcome.cgi?t=examples'':12C ''/p'':20C ''analysi'':6C ''biit.cs.ut.ee'':11C ''biit.cs.ut.ee/graphweb/welcome.cgi?t=examples'':10C ''biolog'':4C ''href'':9C ''network'':5C,19C ''nofollow'':14C ''p'':1C ''rel'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6780, 4, '2015-08-12 23:15:20.783', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6547, 1, '2015-07-22 14:26:58.66', 13, 3315, 'Open source Anomaly Detection in Python', '&lt;p&gt;&lt;strong&gt;Problem Background:&lt;/strong&gt;
I am working on a project that involves log files similar to those found in the IT monitoring space (to my best understanding of IT space). These log files are time-series data, organized into hundreds/thousands of rows of various parameters. Each parameter is numeric (float) and there is a non-trivial/non-error value for each time point. My task is to monitor said log files for anomaly detection (spikes, falls, unusual patterns with some parameters being out of sync, strange 1st/2nd/etc. derivative behavior, etc.). &lt;/p&gt;

&lt;p&gt;On a similar assignment, I have tried Splunk with Prelert, but I am exploring open-source options at the moment.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Constraints:&lt;/strong&gt;
I am limiting myself to Python because I know it well, and would like to delay the switch to R and the associated learning curve. Unless there seems to be overwhelming support for R (or other languages/software), I would like to stick to Python for this task.&lt;/p&gt;

&lt;p&gt;Also, I am working in a Windows environment for the moment. I would like to continue to sandbox in Windows on small-sized log files but can move to Linux environment if needed. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Resources:&lt;/strong&gt;
I have checked out the following with dead-ends as results:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/questions/5193/python-or-r-for-implementing-machine-learning-algorithms-for-fraud-detection"&gt;Python or R for implementing machine learning algorithms for fraud detection&lt;/a&gt;. Some info here is helpful, but unfortunately, I am struggling to find the right package because:&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Twitter''s "AnomalyDetection" is in R, and I want to stick to Python. Furthermore, the Python port &lt;a href="https://github.com/nicolasmiller/pyculiarity" rel="nofollow"&gt;pyculiarity&lt;/a&gt; seems to cause issues in implementing in Windows environment for me. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Skyline, my next attempt, seems to have been pretty much discontinued (from &lt;a href="https://github.com/etsy/skyline/issues/119" rel="nofollow"&gt;github issues&lt;/a&gt;). I haven''t dived deep into this, given how little support there seems to be online.  &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;scikit-learn I am still exploring, but this seems to be much more manual. The down-in-the-weeds approach is OK by me, but my background in learning tools is weak, so would like something like a black box for the technical aspects like algorithms, similar to Splunk+Prelert.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;Problem Definition and Questions:&lt;/strong&gt;
I am looking for open-source software that can help me with automating the process of anomaly detection from time-series log files in Python via packages or libraries. &lt;/p&gt;

&lt;ol start="5"&gt;
&lt;li&gt;Do such things exist to assist with my immediate task, or are they imaginary in my mind? &lt;/li&gt;
&lt;li&gt;Can anyone assist with concrete steps to help me to my goal, including background fundamentals or concepts? &lt;/li&gt;
&lt;li&gt;Is this the best StackExchange community to ask in, or is Stats, Math, or even Security or Stackoverflow the better options?&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;EDIT [2015-07-23]&lt;/strong&gt; 
Note that the latest update to &lt;a href="https://github.com/nicolasmiller/pyculiarity" rel="nofollow"&gt;pyculiarity&lt;/a&gt; seems to be &lt;a href="https://github.com/nicolasmiller/pyculiarity/issues/1" rel="nofollow"&gt;fixed&lt;/a&gt; for the Windows environment! I have yet to confirm, but should be another useful tool for the community.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT [2016-01-19]&lt;/strong&gt;
A minor update. I had not time to work on this and research, but I am taking a step back to understand the fundamentals of this problem before continuing to research in specific details. For example, two concrete steps that I am taking are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Starting with the Wikipedia articles for anomaly detection [&lt;a href="https://en.wikipedia.org/wiki/Anomaly_detection" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Anomaly_detection&lt;/a&gt; ], understanding fully, and then either moving up or down in concept hierarchy of other linked Wikipedia articles, such as [&lt;a href="https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm" rel="nofollow"&gt;https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm&lt;/a&gt; ], and then to [&lt;a href="https://en.wikipedia.org/wiki/Machine_learning" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Machine_learning&lt;/a&gt; ].&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Exploring techniques in the great surveys done by Chandola et al 2009 "Anomaly Detection: A Survey"[&lt;a href="http://www-users.cs.umn.edu/~banerjee/papers/09/anomaly.pdf" rel="nofollow"&gt;http://www-users.cs.umn.edu/~banerjee/papers/09/anomaly.pdf&lt;/a&gt; ] and Hodge et al 2004 "A Survey of Outlier Detection Methodologies"[&lt;a href="http://eprints.whiterose.ac.uk/767/1/hodgevj4.pdf" rel="nofollow"&gt;http://eprints.whiterose.ac.uk/767/1/hodgevj4.pdf&lt;/a&gt; ]. &lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Once the concepts are better understood (I hope to play around with toy examples as I go to develop the practical side as well), I hope to understand which open source Python tools are better suited for my problems.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6605, 2, '2015-07-28 09:09:21.467', 0, NULL, NULL, '&lt;p&gt;&lt;code&gt;N&lt;/code&gt; is  function  and represents the Normal, or Gaussian, distribution. &lt;/p&gt;
', NULL, NULL, '''/code'':4C ''/p'':14C ''code'':2C ''distribut'':13C ''function'':6C ''gaussian'':12C ''n'':3C ''normal'':10C ''p'':1C ''repres'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6647, 2, '2015-07-31 14:21:03.577', -1, NULL, NULL, '&lt;p&gt;AWS does "support" Mac OS. You can use any SSH client from your Mac to access the GPU instance. &lt;/p&gt;
', NULL, NULL, '''/p'':21C ''access'':17C ''aw'':2C ''client'':12C ''gpu'':19C ''instanc'':20C ''mac'':5C,15C ''os'':6C ''p'':1C ''ssh'':11C ''support'':4C ''use'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6637, 4, '2015-07-30 17:48:36.43', 0, NULL, NULL, 'Questions asking for specific applications that perform a given task or work with a given file format.', NULL, NULL, '''applic'':5C ''ask'':2C ''file'':16C ''format'':17C ''given'':9C,15C ''perform'':7C ''question'':1C ''specif'':4C ''task'':10C ''work'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6663, 2, '2015-08-02 18:39:25.623', 0, NULL, NULL, '&lt;p&gt;There has been an answer on this question, please check it : &lt;/p&gt;

&lt;p&gt;&lt;a href="http://stats.stackexchange.com/questions/51062/test-for-bimodal-distribution"&gt;Test for bimodal distribution&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':24C ''/p'':13C,25C ''/questions/51062/test-for-bimodal-distribution'':19C ''answer'':6C ''bimod'':22C ''check'':11C ''distribut'':23C ''href'':16C ''p'':1C,14C ''pleas'':10C ''question'':9C ''stats.stackexchange.com'':18C ''stats.stackexchange.com/questions/51062/test-for-bimodal-distribution'':17C ''test'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6689, 2, '2015-08-04 21:09:48.26', 1, NULL, NULL, '&lt;p&gt;You should first define what your churn event is which you have started. Is it global or individual? Is it has not gambled for 3 months or has changed his pattern? Global is better to model. You can use survival models for this. &lt;/p&gt;
', NULL, NULL, '''/p'':45C ''3'':26C ''better'':35C ''chang'':30C ''churn'':8C ''defin'':5C ''event'':9C ''first'':4C ''gambl'':24C ''global'':17C,33C ''individu'':19C ''model'':37C,42C ''month'':27C ''p'':1C ''pattern'':32C ''start'':14C ''surviv'':41C ''use'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6995, 1, '2015-09-03 13:45:43.01', 2, 115, 'NLTK: Tuning LinearSVC classifier accuracy? - Looking for better approaches/advices', '&lt;p&gt;&lt;strong&gt;Problem/Main objective/TLDR:&lt;/strong&gt; Train a classifier, then feed it a random review and get the correspondent predicted review rating (number of stars from 1 to 5) - only 60% accuracy! :(&lt;/p&gt;

&lt;p&gt;I have a big dataset with around 48000 tech product reviews (from many different writers and from different products - here this is not so important (?)) and corresponding ratings (1 to 5 stars) I randomly selected some reviews within each class:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1 star: 173 reviews (could not pick 1000 because there were 173)
2 stars: 1000 reviews
3 stars: 1000 reviews
4 stars: 1000 reviews
5 stars: 1000 reviews
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Total: 4173 reviews - this data is organized in one file (all_reviews_labeled.txt) in tuple format, one review and rating for line:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(âreview textâ, âx starâ)
(âreview textâ, âx starâ)
(âreview textâ, âx starâ)
(âreview textâ, âx starâ)
â¦
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;My 1st âdummieâ aproach was:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Tokenize review text
POS tagging

Get most frequent bigrams that folowing some POS tags rules for most frequent trigrams (I have seen this rules - using this POS patterns in âAutomatic Star-rating Generation from Text Reviewsâ - pag.7 - paper from Chong-U Lim, Pablo Ortiz and Sang-Woo Jun):

for (w1,t1), (w2,t2), (w3,t3) in nltk.trigrams(text):
    if (t1 == ''JJ'' or t1 == ''JJS'' or t1 == ''JJR'') and (t2 == ''NN'' or t2 == ''NNS''):
        bi = unicode(w1 + '' '' + w2).encode(''utf-8'')
        bigrams.append(bi)
    elif (t1 == ''RB'' or t1 == ''RBR'' or t1 == ''RBS'') and (t2 == ''JJ'' or t2 == ''JJS'' or t2 == ''JJR'') and (t3 != ''NN'' or t3 != ''NNS''):
        bi = unicode(w1 + '' '' + w2).encode(''utf-8'')
        bigrams.append(bi)
    elif (t1 == ''JJ'' or t1 == ''JJS'' or t1 == ''JJR'') and (t2 == ''JJ'' or t2 == ''JJS'' or t2 == ''JJRS'') and (t3 != ''NN'' or t3 != ''NNS''):
        bi = unicode(w1 + '' '' + w2).encode(''utf-8'')
        bigrams.append(bi)
    elif (t1 == ''NN'' or t1 == ''NNS'') and (t2 == ''JJ'' or t2 == ''JJS'' or t2 == ''JJRS'') and (t3 != ''NN'' or t3 != ''NNS''):
        bi = unicode(w1 + '' '' + w2).encode(''utf-8'')
        bigrams.append(bi)
    elif (t1 == ''RB'' or t1 == ''RBR'' or t1 == ''RBS'') and (t2 == ''VB'' or t2 == ''VBD'' or t2 == ''VBN'' or t2 == ''VBG''):
        bi = unicode(w1 + '' '' + w2).encode(''utf-8'')
        bigrams.append(bi)
    elif (t1 == ''DT'') and (t2 == ''JJ'' or t2 == ''JJS'' or t2 == ''JJRS''):
        bi = unicode(w1 + '' '' + w2).encode(''utf-8'')
        bigrams.append(bi)
    elif (t1 == ''VBZ'') and (t2 == ''JJ'' or t2 == ''JJS'' or t2 == ''JJRS''):
        bi = unicode(w1 + '' '' + w2).encode(''utf-8'')
        bigrams.append(bi)
    else:
        continue

Extract features (here is where I have more doubts - should I only look for this two features?):

features={}
for bigram,freq in word_features:
    features[''contains(%s)'' % unicode(bigram).encode(''utf-8'')] = True
    features["count({})".format(unicode(bigram).encode(''utf-8''))] = freq
return features

featuresets = [(review_features(review), rating) for (review, rating) in tuples_labeled_reviews]

Splits the training data into training size and testing size (90% training - 10% testing):

numtrain = int(len(tuples_labeled_reviews) * 90 / 100)
train_set, test_set = featuresets[:numtrain], featuresets[numtrain:]

Train SVMc:

classifier = nltk.classify.SklearnClassifier(LinearSVC())
classifier.train(train_set)

Evaluate the classifier:

errors = 0
correct = 0
for review, rating in test_set:
    tagged_rating = classifier.classify(review)
    if tagged_rating == rating:
        correct += 1
        print("Correct")
        print "Guess: ", tagged_rating
        print "Correct: ", rating
    else:
        errors += 1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So far I get only 60% accuracyâ¦ What can I do to improve my prediction results? Is something before, some text/reviews preprocessing (like removing stopwords/punctuation?) that is missing? Could you suggest me some other approaches? I am still a bit confused if is really a classification problem or a regression one... :/&lt;/p&gt;

&lt;p&gt;Please simple explanations, or give me a link to âmachine learning for dummiesâ, or be my mentor, I promise to learn fast! My background in machine learning/language processing/data mining is very light, I have played a couple of times with weka (Java), but now I need to stick with Python (nltk + scikit-learn)!&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT:&lt;/strong&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Now I am also extracting unigrams as features, unigrams POS-tagged as ''JJ'', ''NN'',''VB'' and ''RB''. It improved a little the accuracy to 65%.&lt;/li&gt;
&lt;li&gt;I applied also do stemming and lemmatization in text before POS tagging. It improved the accuracy to +70%.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;EDIT 2:&lt;/strong&gt;&lt;/p&gt;

&lt;ol start="3"&gt;
&lt;li&gt;&lt;p&gt;I have feed the classifier all my reviews, the 48000, split into 90% training and 10% testing and the accuracy was 91%.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Now I have 32000 new reviews (also labeled) and feed them all for testing and the mean accuracy was 62 % ... my confusion matrix is something like this image below (i divided by equal errors of +1/-1 star point, +2/-2, +3/-3 - because it is just an illustration):&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/efSpy.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/efSpy.png" alt="enter image description here"&gt;&lt;/a&gt;
What is happening? Why accuracy drops so much at 3 and 5 stars?&lt;/p&gt;
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''realli'':617C ''regress'':623C ''rel'':833C ''remov'':597C ''result'':589C ''return'':484C ''review'':32C,38C,62C,88C,98C,110C,114C,118C,122C,128C,141C,150C,156C,162C,168C,187C,487C,489C,492C,497C,517C,544C,552C,756C,780C ''reviewsâ'':223C ''rule'':200C,209C ''sang'':235C ''sang-woo'':234C ''scikit'':18B,680C ''scikit-learn'':679C ''seen'':207C ''select'':86C ''set'':521C,523C,535C,548C ''simpl'':628C ''size'':504C,507C ''someth'':591C,799C ''split'':498C,759C ''src'':836C ''star'':42C,83C,96C,108C,112C,116C,120C,218C,812C,858C ''star-rat'':217C ''start'':745C ''starâ'':154C,160C,166C,172C ''stem'':722C ''stick'':675C ''still'':611C ''stopwords/punctuation'':598C ''strong'':20C,684C,739C ''suggest'':604C ''svmc'':529C ''t1'':240C,249C,252C,255C,273C,276C,279C,306C,309C,312C,339C,342C,369C,372C,375C,399C,420C ''t2'':242C,258C,261C,282C,285C,288C,315C,318C,321C,345C,348C,351C,378C,381C,384C,387C,402C,405C,408C,423C,426C,429C ''t3'':244C,291C,294C,324C,327C,354C,357C ''tag'':190C,199C,549C,554C,563C,701C,729C ''tech'':60C ''test'':506C,511C,522C,547C,765C,788C ''text'':188C,222C,247C,726C ''text/reviews'':594C ''textâ'':151C,157C,163C,169C ''time'':666C ''token'':186C ''total'':126C ''train'':24C,500C,503C,509C,520C,528C,534C,762C ''trigram'':204C ''true'':474C ''tune'':2A ''tupl'':138C,495C,515C ''two'':457C ''u'':229C ''unicod'':264C,297C,330C,360C,390C,411C,432C,469C,478C ''unigram'':695C,698C ''use'':210C ''utf'':268C,301C,334C,364C,394C,415C,436C,472C,481C ''vb'':379C,705C ''vbd'':382C ''vbg'':388C ''vbn'':385C ''vbz'':421C ''w1'':239C,265C,298C,331C,361C,391C,412C,433C ''w2'':241C,266C,299C,332C,362C,392C,413C,434C ''w3'':243C ''weka'':668C ''within'':89C ''woo'':236C ''word'':464C ''writer'':66C ''x'':153C,159C,165C,171C ''â'':149C,152C,155C,158C,161C,164C,167C,170C,173C,179C,215C,636C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6878, 2, '2015-08-23 18:59:54.223', 0, NULL, NULL, '&lt;p&gt;Use google search, which has some very sophisticated similarity algorithms,  to lookup the company names and see if the point to the same web site addresses using python, mathematica or import.io or a tool of your choice.&lt;/p&gt;
', NULL, NULL, '''/p'':39C ''address'':27C ''algorithm'':11C ''choic'':38C ''compani'':15C ''googl'':3C ''import.io'':32C ''lookup'':13C ''mathematica'':30C ''name'':16C ''p'':1C ''point'':21C ''python'':29C ''search'':4C ''see'':18C ''similar'':10C ''site'':26C ''sophist'':9C ''tool'':35C ''use'':2C,28C ''web'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6983, 4, '2015-09-02 09:01:54.15', 0, NULL, NULL, 'ELK (also called ELK stack) is combination of elasticsearch, logstash and kibana.', NULL, NULL, '''also'':2C ''call'':3C ''combin'':7C ''elasticsearch'':9C ''elk'':1C,4C ''kibana'':12C ''logstash'':10C ''stack'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6925, 1, '2015-08-28 01:07:41.31', 0, 58, 'What are some challenges with big data?', '&lt;p&gt;How did the phrase "big data" come about? What were some of the new challenges that arose? How does big data differ from a small amount of data and how do we distinguish the two? &lt;/p&gt;
', 'bigdata', NULL, '''/p'':45C ''amount'':35C ''aros'':26C ''big'':6A,14C,29C ''bigdata'':8B ''challeng'':4A,24C ''come'':16C ''data'':7A,15C,30C,37C ''differ'':31C ''distinguish'':42C ''new'':23C ''p'':9C ''phrase'':13C ''small'':34C ''two'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8045, 1, '2015-09-09 12:10:46.467', 2, 112, 'Binary classification with unexplained data', '&lt;p&gt;My apologies for cross-posting to stackoverflow and cross validated. Not really sure which one is the most relevant place.&lt;/p&gt;

&lt;p&gt;Please shed some light on me with this task.&lt;/p&gt;

&lt;h2&gt;Description&lt;/h2&gt;

&lt;p&gt;Assuming the training data looks like&lt;/p&gt;

&lt;pre&gt;
0 | X773579,Y2640,Y2072,Z4,Z15
1 | X374616379,X773579,X344420902,Y1940,Y1705,Z4,Z15,Z26
...
&lt;/pre&gt;

&lt;p&gt;One would like to predict 0/1 from those comma separated values, without further explanation about what exactly the X, Y, and Z with numbers mean. Some rare cases even have un-numbered X or Y (possibly just noises though). The length of comma separated values varies, but each row contains no repeated values. Also, the length of numbers seem correlated to X, Y, and Z.&lt;/p&gt;

&lt;p&gt;The data size is 1 million rows.&lt;/p&gt;

&lt;h2&gt;Feature&lt;/h2&gt;

&lt;p&gt;In LibSVM format&lt;/p&gt;

&lt;pre&gt;
Label | Value_ID:1...
&lt;/pre&gt;

&lt;p&gt;Example&lt;/p&gt;

&lt;pre&gt;
0 1:1 2:1 3:1 4:1 5:1
1 1:1 4:1 5:1 14:1 36:1 37:1 38:1 39:1
&lt;/pre&gt;

&lt;p&gt;I''ve tried treating those values categorically and applying several different models of Spark MLlib, namely linear regression, logistic regression, decision tree, random forests, gradient-boosted trees, and SVM, along with LibSVM and Conditional Random Fields. Their 5-fold cross-validations got only 75%~79% accuracy.&lt;/p&gt;

&lt;p&gt;I really want to know if there''s any formal way to discover the signal from the data, build up models, extract and even select features accordingly. Admittedly I haven''t done ANOVA yet.&lt;/p&gt;

&lt;p&gt;Thank you!&lt;/p&gt;

&lt;h2&gt;Background (self-disclosure)&lt;/h2&gt;

&lt;p&gt;This is a private interview question and the employer didn''t share any details (except "no offer" response :p). Although I specifically asked twice that I would like to learn from it, no reply so far.&lt;/p&gt;

&lt;h2&gt;Book studying/studied&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;Pattern classification&lt;/li&gt;
&lt;li&gt;Pattern recognition&lt;/li&gt;
&lt;li&gt;Foundations of Statistical Natural Language Processing&lt;/li&gt;
&lt;li&gt;Speech and Language Processing&lt;/li&gt;
&lt;/ul&gt;

&lt;h2&gt;What I may try next&lt;/h2&gt;

&lt;p&gt;Based on a discussion with @NeilSlater :&lt;/p&gt;

&lt;p&gt;I have done some quick frequent pattern analysis and found certain n-grams. The problem was that they seemed associated with both labels 0 and 1. I will run some feature templates of bi-gram and tri-gram, and then get back to you. One thing that is also intriguing to me is, in NLP, sometimes labels also got dependencies, namely some 1 may be followed by certain patterns of 0 and vice versa.&lt;/p&gt;

&lt;h2&gt;Updates&lt;/h2&gt;

&lt;p&gt;In terms of linear/logistic regressions and conditional random fields:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;n-gram is slightly harmful: tried adjacent bigram/trigram &amp;amp; pair/triple combinations, all got worse about 1%&lt;/li&gt;
&lt;li&gt;occurrences of X/Y/Z &amp;amp; mean/max/min/sum/avg of X/Y/Z can only reach 69%.&lt;/li&gt;
&lt;li&gt;X or Y or Z solely as categorical feature got 70%~75%. Y is the most useful category.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Other models listed in the original description all got far worse results.&lt;/p&gt;
', 'machine-learning  self-study', NULL, '''/h2'':48C,153C,293C,335C,366C,456C ''/li'':340C,344C,352C,358C,487C,500C,520C ''/ol'':521C ''/p'':34C,45C,56C,141C,150C,158C,167C,247C,283C,287C,331C,374C,453C,467C,535C ''/pre'':73C,164C,197C ''/ul'':359C ''0'':58C,169C,400C,449C ''0/1'':80C ''1'':64C,147C,163C,170C,171C,173C,175C,177C,179C,180C,181C,182C,184C,186C,188C,190C,192C,194C,196C,402C,441C,486C ''14'':187C ''2'':172C ''3'':174C ''36'':189C ''37'':191C ''38'':193C ''39'':195C ''4'':176C,183C ''5'':178C,185C,237C ''69'':499C ''70'':512C ''75'':244C,513C ''79'':245C ''accord'':275C ''accuraci'':246C ''adjac'':477C ''admit'':276C ''along'':229C ''also'':129C,427C,436C ''although'':314C ''amp'':479C,492C ''analysi'':383C ''anova'':281C ''apolog'':14C ''appli'':207C ''ask'':317C ''associ'':396C ''assum'':50C ''back'':420C ''background'':289C ''base'':368C ''bi'':411C ''bi-gram'':410C ''bigram/trigram'':478C ''binari'':1A ''book'':333C ''boost'':225C ''build'':267C ''case'':102C ''categor'':205C,509C ''categori'':519C ''certain'':386C,446C ''classif'':2A,339C ''combin'':481C ''comma'':83C,118C ''condit'':233C,464C ''contain'':125C ''correl'':135C ''cross'':17C,22C,240C ''cross-post'':16C ''cross-valid'':239C ''data'':5A,53C,144C,266C ''decis'':219C ''depend'':438C ''descript'':47C,529C ''detail'':308C ''didn'':304C ''differ'':209C ''disclosur'':292C ''discov'':261C ''discuss'':371C ''done'':280C,378C ''employ'':303C ''even'':103C,272C ''exact'':91C ''exampl'':166C ''except'':309C ''explan'':88C ''extract'':270C ''far'':330C,532C ''featur'':152C,274C,407C,510C ''field'':235C,466C ''fold'':238C ''follow'':444C ''forest'':222C ''formal'':258C ''format'':157C ''found'':385C ''foundat'':346C ''frequent'':381C ''get'':419C ''got'':242C,437C,483C,511C,531C ''gradient'':224C ''gradient-boost'':223C ''gram'':389C,412C,416C,472C ''h2'':46C,151C,288C,332C,360C,454C ''harm'':475C ''haven'':278C ''id'':162C ''interview'':299C ''intrigu'':428C ''know'':253C ''label'':160C,399C,435C ''languag'':350C,356C ''learn'':8B,324C ''length'':116C,131C ''li'':337C,341C,345C,353C,469C,488C,501C ''libsvm'':156C,231C ''light'':39C ''like'':55C,77C,322C ''linear'':215C ''linear/logistic'':461C ''list'':525C ''logist'':217C ''look'':54C ''machin'':7B ''machine-learn'':6B ''may'':363C,442C ''mean'':99C ''mean/max/min/sum/avg'':493C ''million'':148C ''mllib'':213C ''model'':210C,269C,524C ''n'':388C,471C ''n-gram'':387C,470C ''name'':214C,439C ''natur'':349C ''neilslat'':373C ''next'':365C ''nlp'':433C ''nois'':113C ''number'':98C,107C,133C ''occurr'':489C ''offer'':311C ''ol'':468C ''one'':28C,75C,423C ''origin'':528C ''p'':12C,35C,49C,74C,142C,154C,165C,198C,248C,284C,294C,313C,367C,375C,457C,522C ''pair/triple'':480C ''pattern'':338C,342C,382C,447C ''place'':33C ''pleas'':36C ''possibl'':111C ''post'':18C ''pre'':57C,159C,168C ''predict'':79C ''privat'':298C ''problem'':391C ''process'':351C,357C ''question'':300C ''quick'':380C ''random'':221C,234C,465C ''rare'':101C ''reach'':498C ''realli'':25C,250C ''recognit'':343C ''regress'':216C,218C,462C ''relev'':32C ''repeat'':127C ''repli'':328C ''respons'':312C ''result'':534C ''row'':124C,149C ''run'':405C ''seem'':134C,395C ''select'':273C ''self'':10B,291C ''self-disclosur'':290C ''self-studi'':9B ''separ'':84C,119C ''sever'':208C ''share'':306C ''shed'':37C ''signal'':263C ''size'':145C ''slight'':474C ''sole'':507C ''sometim'':434C ''spark'':212C ''specif'':316C ''speech'':354C ''stackoverflow'':20C ''statist'':348C ''studi'':11B ''studying/studied'':334C ''sure'':26C ''svm'':228C ''task'':44C ''templat'':408C ''term'':459C ''thank'':285C ''thing'':424C ''though'':114C ''train'':52C ''treat'':202C ''tree'':220C,226C ''tri'':201C,364C,415C,476C ''tri-gram'':414C ''twice'':318C ''ul'':336C ''un'':106C ''un-numb'':105C ''unexplain'':4A ''updat'':455C ''use'':518C ''valid'':23C,241C ''valu'':85C,120C,128C,161C,204C ''vari'':121C ''ve'':200C ''versa'':452C ''vice'':451C ''want'':251C ''way'':259C ''without'':86C ''wors'':484C,533C ''would'':76C,321C ''x'':93C,108C,137C,502C ''x/y/z'':491C,495C ''x344420902'':67C ''x374616379'':65C ''x773579'':59C,66C ''y'':94C,110C,138C,504C,514C ''y1705'':69C ''y1940'':68C ''y2072'':61C ''y2640'':60C ''yet'':282C ''z'':96C,140C,506C ''z15'':63C,71C ''z26'':72C ''z4'':62C,70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8058, 2, '2015-09-10 08:07:49.92', 3, NULL, NULL, '&lt;p&gt;I found this page, but the R package seems not to be open source :
&lt;a href="http://www.cs.colostate.edu/~idfah/main/projects/rnn" rel="nofollow"&gt;link.&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':24C ''/p'':25C ''/~idfah/main/projects/rnn'':20C ''found'':3C ''href'':17C ''link'':23C ''nofollow'':22C ''open'':14C ''p'':1C ''packag'':9C ''page'':5C ''r'':8C ''rel'':21C ''seem'':10C ''sourc'':15C ''www.cs.colostate.edu'':19C ''www.cs.colostate.edu/~idfah/main/projects/rnn'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9099, 5, '2015-11-28 15:02:56', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8179, 2, '2015-09-24 02:41:53.323', -2, NULL, NULL, '&lt;p&gt;might want to checkout snowflake. It can handle both structured and semi-structured data together. www.snowflake.net&lt;/p&gt;
', NULL, NULL, '''/p'':19C ''checkout'':5C ''data'':16C ''handl'':9C ''might'':2C ''p'':1C ''semi'':14C ''semi-structur'':13C ''snowflak'':6C ''structur'':11C,15C ''togeth'':17C ''want'':3C ''www.snowflake.net'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8244, 1, '2015-09-30 10:43:15.157', 14, 495, 'What makes columnar databases suitable for data science?', '&lt;p&gt;What are some of the advantages of columnar data-stores which make them more suitable for data science and analytics?&lt;/p&gt;
', 'databases', NULL, '''/p'':32C ''advantag'':16C ''analyt'':31C ''columnar'':3A,18C ''data'':7A,20C,28C ''data-stor'':19C ''databas'':4A,9B ''make'':2A,23C ''p'':10C ''scienc'':8A,29C ''store'':21C ''suitabl'':5A,26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8495, 5, '2015-10-19 04:07:31.743', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8496, 4, '2015-10-19 04:07:31.743', 0, NULL, NULL, 'Word2vec is a group of algorithms as well as an open-source tool that create word embeddings.', NULL, NULL, '''algorithm'':6C ''creat'':16C ''embed'':18C ''group'':4C ''open'':12C ''open-sourc'':11C ''sourc'':13C ''tool'':14C ''well'':8C ''word'':17C ''word2vec'':1C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8573, 5, '2015-10-23 15:32:52.94', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9196, 2, '2015-12-04 00:28:11.3', 1, NULL, NULL, '&lt;p&gt;Extract color histograms from each image. Then cluster them with ELKI, which has a number of relevant similarity measures for images, such as histogram intersection distance.
Maybe start with hierarchical clustering first, then also try OPTICS.&lt;/p&gt;
', NULL, NULL, '''/p'':38C ''also'':35C ''cluster'':9C,32C ''color'':3C ''distanc'':27C ''elki'':12C ''extract'':2C ''first'':33C ''hierarch'':31C ''histogram'':4C,25C ''imag'':7C,22C ''intersect'':26C ''mayb'':28C ''measur'':20C ''number'':16C ''optic'':37C ''p'':1C ''relev'':18C ''similar'':19C ''start'':29C ''tri'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8674, 4, '2015-10-31 16:59:23.013', 0, NULL, NULL, 'Autoencoders are a type of neural network that learns a useful encoding for data.', NULL, NULL, '''autoencod'':1C ''data'':14C ''encod'':12C ''learn'':9C ''network'':7C ''neural'':6C ''type'':4C ''use'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8858, 2, '2015-11-16 11:30:44.943', 1, NULL, NULL, '&lt;p&gt;It seems you are lucky and the section headings are XML-tagged. So you can use XSLT transformations or Python lxml.etree to get rid of them or to extract them.&lt;/p&gt;
', NULL, NULL, '''/p'':33C ''extract'':31C ''get'':25C ''head'':10C ''lucki'':6C ''lxml.etree'':23C ''p'':1C ''python'':22C ''rid'':26C ''section'':9C ''seem'':3C ''tag'':14C ''transform'':20C ''use'':18C ''xml'':13C ''xml-tag'':12C ''xslt'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8898, 2, '2015-11-19 02:21:26.98', 3, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Since you accepted another answer, which says this can''t be done, I am editing this to include an example of it being done.  Hope this helps!&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Original Answer:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The most logical way to transform hour is into two variables that swing back and forth out of sink. Imagine the position of the end of the hour hand of a 24-hour clock. The &lt;code&gt;x&lt;/code&gt; position swings back and forth out of sink with the &lt;code&gt;y&lt;/code&gt; position. For a 24-hour clock you can accomplish this with &lt;code&gt;x=sin(2pi*hour/24)&lt;/code&gt;,&lt;code&gt;y=cos(2pi*hour/24)&lt;/code&gt;.  &lt;/p&gt;

&lt;p&gt;You need both variables or the proper movement through time is lost.  This is due to the fact that the derivative of either sin or cos changes in time where as the &lt;code&gt;(x,y)&lt;/code&gt; position varies smoothly as it travels around the unit circle.&lt;/p&gt;

&lt;p&gt;This method works really well for clustering and for keeping the distance between 15 minutes after midnight and 5 minutes before midnight "close" in Euclidean space.  All of the modulo suggestions don''t accomplish this and the cyclic representation that they do accomplish is pretty clumsy.&lt;/p&gt;

&lt;p&gt;Finally, consider whether it is worthwhile to add a third feature to trace linear time, which can be constructed by hours (or minutes or seconds) from the start of the first record or a Unix time stamp or something similar.  These three features then provide proxies for both the cyclic and linear progression of time e.g. you can pull out cyclic phenomenon like sleep cycles in people''s movement and also linear growth like population vs. time.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Example of if being accomplished:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Enable inline plotting
%matplotlib inline

#Import everything I need...

import numpy as np
import matplotlib as mp

import matplotlib.pyplot as plt
import pandas as pd

# Grab some random times from here: https://www.random.org/clock-times/
# put them into a csv.
from pandas import DataFrame, read_csv
df = read_csv(''/Users/angus/Machine_Learning/ipython_notebooks/times.csv'',delimiter='':'')
df[''hourfloat'']=df.hour+df.minute/60.0
df[''x'']=np.sin(2.*np.pi*df.hourfloat/24.)
df[''y'']=np.cos(2.*np.pi*df.hourfloat/24.)

df
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/1UTYj.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/1UTYj.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def kmeansshow(k,X):

    from sklearn import cluster
    from matplotlib import pyplot
    import numpy as np

    kmeans = cluster.KMeans(n_clusters=k)
    kmeans.fit(X)

    labels = kmeans.labels_
    centroids = kmeans.cluster_centers_
    #print centroids

    for i in range(k):
        # select only data observations with cluster label == i
        ds = X[np.where(labels==i)]
        # plot the data observations
        pyplot.plot(ds[:,0],ds[:,1],''o'')
        # plot the centroids
        lines = pyplot.plot(centroids[i,0],centroids[i,1],''kx'')
        # make the centroid x''s bigger
        pyplot.setp(lines,ms=15.0)
        pyplot.setp(lines,mew=2.0)
    pyplot.show()
    return centroids
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now lets try it out:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;kmeansshow(6,df[[''x'', ''y'']].values)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/MkVNg.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/MkVNg.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You can just barely see that there are some after midnight times included with the before midnight green cluster.  Now lets reduce the number of clusters and show that before and after midnight can be connected in a single cluster in more detail:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;kmeansshow(3,df[[''x'', ''y'']].values)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/bWg2B.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/bWg2B.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;See how the blue cluster contains times that are from before and after midnight that are clustered together in the same cluster...&lt;/p&gt;

&lt;p&gt;QED!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8957, 5, '2015-11-21 06:25:13.837', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8990, 4, '2015-11-23 10:59:40.87', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9051, 4, '2015-11-26 09:58:43.803', 0, NULL, NULL, 'SAS is a proprietary cross-platform general-purpose statistical software package. [Official Website](http://www.sas.com/index.html)', NULL, NULL, '''/index.html)'':18C ''cross'':6C ''cross-platform'':5C ''general'':9C ''general-purpos'':8C ''offici'':14C ''packag'':13C ''platform'':7C ''proprietari'':4C ''purpos'':10C ''sas'':1C ''softwar'':12C ''statist'':11C ''websit'':15C ''www.sas.com'':17C ''www.sas.com/index.html)'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9057, 4, '2015-11-26 10:00:43.78', 0, NULL, NULL, 'Recommendations for software or libraries.', NULL, NULL, '''librari'':5C ''recommend'':1C ''softwar'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9043, 4, '2015-11-26 09:53:45.897', 0, NULL, NULL, 'A confusion matrix is a special contingency table used to evaluate the predictive accuracy of a classifier. Predicted classes are listed in rows and actual classes in columns, with counts of respective cases in each cell.', NULL, NULL, '''accuraci'':14C ''actual'':25C ''case'':33C ''cell'':36C ''class'':19C,26C ''classifi'':17C ''column'':28C ''confus'':2C ''conting'':7C ''count'':30C ''evalu'':11C ''list'':21C ''matrix'':3C ''predict'':13C,18C ''respect'':32C ''row'':23C ''special'':6C ''tabl'':8C ''use'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9201, 2, '2015-12-04 11:05:08.427', -1, NULL, NULL, '&lt;p&gt;When there are large number of categorical variables, it is advisable to do one versus rest.&lt;/p&gt;
', NULL, NULL, '''/p'':18C ''advis'':12C ''categor'':8C ''larg'':5C ''number'':6C ''one'':15C ''p'':1C ''rest'':17C ''variabl'':9C ''versus'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9116, 2, '2015-11-29 09:51:01.64', 1, NULL, NULL, '&lt;p&gt;Startup.ml conference has had a couple good conferences. The talks are done by experts in the field and I found more technical and practical than other conferences. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://conf.startup.ml" rel="nofollow"&gt;http://conf.startup.ml&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':37C ''/p'':29C,38C ''conf.startup.ml'':33C,36C ''confer'':3C,9C,28C ''coupl'':7C ''done'':13C ''expert'':15C ''field'':18C ''found'':21C ''good'':8C ''href'':32C ''nofollow'':35C ''p'':1C,30C ''practic'':25C ''rel'':34C ''startup.ml'':2C ''talk'':11C ''technic'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9175, 1, '2015-12-02 21:53:17.183', 7, 142, 'How do subsequent convolution layers work?', '&lt;p&gt;This question boils down to "how do convolution layers &lt;em&gt;exactly&lt;/em&gt; work.&lt;/p&gt;

&lt;p&gt;Suppose I have an $n \times m$ greyscale image. So the image has one channel.
In the first layer, I apply a $3\times 3$ convolution with $k_1$ filters and padding. Then I have another convolution layer with $5 \times 5$ convolutions and $k_2$ filters. How many feature maps do I have?&lt;/p&gt;

&lt;h2&gt;Type 1 convolution&lt;/h2&gt;

&lt;p&gt;The first layer gets executed. After that, I have $k_1$ feature maps (one for each filter). Each of those has the size $n \times m$. Every single pixel was created by taking $3 \cdot 3 = 9$ pixels from the padded input image.&lt;/p&gt;

&lt;p&gt;Then the second layer gets applied. Every single filter gets applied separately to &lt;strong&gt;each of the feature maps&lt;/strong&gt;. This results in $k_2$ feature maps for every of the $k_1$ feature maps. So there are $k_1 \times k_2$ feature maps after the second layer. Every single pixel of each of the new feature maps got created by taking $5 \cdot 5 = 25$ "pixels" of the padded feature map from before.&lt;/p&gt;

&lt;p&gt;The system has to learn $k_1 \cdot 3 \cdot 3 + k_2 \cdot 5 \cdot 5$ parameters.&lt;/p&gt;

&lt;h2&gt;Type 2.1 convolution&lt;/h2&gt;

&lt;p&gt;Like before: The first layer gets executed. After that, I have $k_1$ feature maps (one for each filter). Each of those has the size $n \times m$. Every single pixel was created by taking $3 \cdot 3 = 9$ pixels from the padded input image.&lt;/p&gt;

&lt;p&gt;Unlike before: Then the second layer gets applied. Every single filter gets applied to the same region, but &lt;strong&gt;all feature maps&lt;/strong&gt; from before. This results in $k_2$ feature maps in total after the second layer got executed. Every single pixel of each of the new feature maps got created by taking $k_2 \cdot 5 \cdot 5 = 25 \cdot k_2$ "pixels" of the padded feature maps from before.&lt;/p&gt;

&lt;p&gt;The system has to learn $k_1 \cdot 3 \cdot 3 + k_2 \cdot 5 \cdot 5$ parameters.&lt;/p&gt;

&lt;h2&gt;Type 2.2 convolution&lt;/h2&gt;

&lt;p&gt;Like above, but instead of having $5 \cdot 5 = 25$ parameters per filter which have to be learned and get simply copied for the other input feature maps, you have $k_1 \cdot 3 \cdot 3 + k_2 \cdot k_1 \cdot 5 \cdot 5$ paramters which have to be learned.&lt;/p&gt;

&lt;h2&gt;Question&lt;/h2&gt;

&lt;ol&gt;
&lt;li&gt;Is type 1 or type 2 typically used?&lt;/li&gt;
&lt;li&gt;Which type is used in &lt;a href="http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf"&gt;Alexnet&lt;/a&gt;?&lt;/li&gt;
&lt;li&gt;Which type is used in &lt;a href="http://arxiv.org/abs/1409.4842"&gt;GoogLeNet&lt;/a&gt;?

&lt;ul&gt;
&lt;li&gt;If you say type 1: Why do $1 \times 1$ convolutions make any sense? Don''t they only multiply the data with a constant?&lt;/li&gt;
&lt;li&gt;If you say type 2: Please explain the quadratic cost ("For example, in a deep vision network, if two convolutional layers are chained,
any uniform increase in the number of their filters results in a quadratic increase of computation")&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;For all answers, please give some evidence (papers, textbooks, documentation of frameworks) that your answer is correct.&lt;/p&gt;

&lt;h2&gt;Bonus question 1&lt;/h2&gt;

&lt;p&gt;Is the pooling applied always only per feature map or is it also done over multiple feature maps?&lt;/p&gt;

&lt;h2&gt;Bonus question 2&lt;/h2&gt;

&lt;p&gt;I''m relatively sure that type 1 is correct and I got something wrong with the GoogLe paper. But there a 3D convolutions, too. Lets say you have 1337 feature maps of size $42 \times 314$ and you apply a $3 \times 4 \times 5$ filter. How do you slide the filter over the feature maps? (Left to right, top to bottom, first feature map to last feature map?) Does it matter as long as you do it consistantly?&lt;/p&gt;

&lt;h2&gt;My research&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;I''ve read the two papers from above, but I''m still not sure what is used.&lt;/li&gt;
&lt;li&gt;I''ve read the &lt;a href="http://lasagne.readthedocs.org/en/latest/modules/layers/conv.html"&gt;lasagne documentation&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;I''ve read the &lt;a href="http://deeplearning.net/software/theano/library/tensor/nnet/conv.html"&gt;theano documentation&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;I''ve read the answers on &lt;a href="http://stats.stackexchange.com/q/85767/25741"&gt;Understanding convolutional neural networks&lt;/a&gt; (without following all links)&lt;/li&gt;
&lt;li&gt;I''ve read &lt;a href="http://deeplearning.net/tutorial/lenet.html"&gt;Convolutional Neural Networks (LeNet)&lt;/a&gt;. Especially figure 1 makes me relatively sure that Type 2.1 is the right one. This would also fit to the "quadratic cost" comment in GoogLe Net and to some practical experience I had with Caffee.&lt;/li&gt;
&lt;/ul&gt;
', 'neuralnetwork  convnet', NULL, '''/a'':457C,471C,709C,723C,741C,760C ''/abs/1409.4842'':469C ''/em'':21C ''/en/latest/modules/layers/conv.html'':706C ''/h2'':84C,231C,377C,433C,566C,591C,676C ''/li'':444C,458C,498C,539C,541C,696C,710C,724C,746C,796C ''/ol'':542C ''/p'':23C,79C,129C,206C,226C,278C,352C,372C,430C,561C,586C,672C ''/q/85767/25741'':736C ''/software/theano/library/tensor/nnet/conv.html'':720C ''/strong'':150C,302C ''/tutorial/lenet.html'':755C ''/ul'':540C,797C ''/~fritz/absps/imagenet.pdf'':455C ''1'':53C,82C,96C,163C,170C,214C,245C,360C,410C,419C,438C,478C,481C,483C,565C,599C,763C ''1337'':621C ''2'':70C,155C,173C,220C,309C,335C,343C,366C,416C,441C,504C,590C ''2.1'':229C,770C ''2.2'':375C ''25'':197C,340C,388C ''3'':47C,49C,119C,121C,216C,218C,268C,270C,362C,364C,412C,414C,633C ''314'':628C ''3d'':614C ''4'':635C ''42'':626C ''5'':64C,66C,194C,196C,222C,224C,337C,339C,368C,370C,385C,387C,421C,423C,637C ''9'':122C,271C ''alexnet'':456C ''also'':580C,777C ''alway'':572C ''anoth'':60C ''answer'':546C,558C,730C ''appli'':45C,136C,141C,287C,292C,571C,631C ''arxiv.org'':468C ''arxiv.org/abs/1409.4842'':467C ''boil'':12C ''bonus'':563C,588C ''bottom'':654C ''caffe'':795C ''cdot'':120C,195C,215C,217C,221C,223C,269C,336C,338C,341C,361C,363C,367C,369C,386C,411C,413C,417C,420C,422C ''chain'':522C ''channel'':39C ''comment'':783C ''comput'':538C ''consist'':671C ''constant'':497C ''convnet'':8B ''convolut'':4A,17C,50C,61C,67C,83C,230C,376C,484C,519C,615C,738C,756C ''copi'':400C ''correct'':560C,601C ''cost'':509C,782C ''creat'':116C,191C,265C,331C ''data'':494C ''deep'':514C ''deeplearning.net'':719C,754C ''deeplearning.net/software/theano/library/tensor/nnet/conv.html'':718C ''deeplearning.net/tutorial/lenet.html'':753C ''document'':553C,708C,722C ''done'':581C ''em'':19C ''especi'':761C ''everi'':112C,137C,159C,180C,261C,288C,320C ''evid'':550C ''exact'':20C ''exampl'':511C ''execut'':90C,239C,319C ''experi'':791C ''explain'':506C ''featur'':74C,97C,148C,156C,164C,174C,188C,202C,246C,300C,310C,328C,348C,405C,575C,584C,622C,647C,656C,660C ''figur'':762C ''filter'':54C,71C,102C,139C,251C,290C,391C,531C,638C,644C ''first'':42C,87C,236C,655C ''fit'':778C ''follow'':743C ''framework'':555C ''get'':89C,135C,140C,238C,286C,291C,398C ''give'':548C ''googl'':609C,785C ''googlenet'':470C ''got'':190C,318C,330C,604C ''greyscal'':32C ''h2'':80C,227C,373C,431C,562C,587C,673C ''href'':452C,466C,703C,717C,733C,752C ''imag'':33C,36C,128C,277C ''increas'':525C,536C ''input'':127C,276C,404C ''instead'':382C ''k'':52C,69C,95C,154C,162C,169C,172C,213C,219C,244C,308C,334C,342C,359C,365C,409C,415C,418C ''lasagn'':707C ''lasagne.readthedocs.org'':705C ''lasagne.readthedocs.org/en/latest/modules/layers/conv.html'':704C ''last'':659C ''layer'':5A,18C,43C,62C,88C,134C,179C,237C,285C,317C,520C ''learn'':212C,358C,396C,429C ''left'':649C ''lenet'':759C ''let'':617C ''li'':435C,445C,459C,473C,499C,678C,697C,711C,725C,747C ''like'':233C,379C ''link'':745C ''long'':666C ''m'':31C,111C,260C,594C,689C ''make'':485C,764C ''mani'':73C ''map'':75C,98C,149C,157C,165C,175C,189C,203C,247C,301C,311C,329C,349C,406C,576C,585C,623C,648C,657C,661C ''matter'':664C ''multipl'':583C ''multipli'':492C ''n'':29C,109C,258C ''net'':786C ''network'':516C,740C,758C ''neural'':739C,757C ''neuralnetwork'':7B ''new'':187C,327C ''number'':528C ''ol'':434C ''one'':38C,99C,248C,774C ''p'':9C,24C,85C,130C,207C,232C,279C,353C,378C,543C,567C,592C ''pad'':56C,126C,201C,275C,347C ''paper'':551C,610C,684C ''paramet'':225C,371C,389C ''paramt'':424C ''per'':390C,574C ''pixel'':114C,123C,182C,198C,263C,272C,322C,344C ''pleas'':505C,547C ''pool'':570C ''practic'':790C ''quadrat'':508C,535C,781C ''question'':11C,432C,564C,589C ''read'':681C,700C,714C,728C,750C ''region'':296C ''relat'':595C,766C ''research'':675C ''result'':152C,306C,532C ''right'':651C,773C ''say'':476C,502C,618C ''second'':133C,178C,284C,316C ''sens'':487C ''separ'':142C ''simpli'':399C ''singl'':113C,138C,181C,262C,289C,321C ''size'':108C,257C,625C ''slide'':642C ''someth'':605C ''stats.stackexchange.com'':735C ''stats.stackexchange.com/q/85767/25741'':734C ''still'':690C ''strong'':144C,298C ''subsequ'':3A ''suppos'':25C ''sure'':596C,692C,767C ''system'':209C,355C ''take'':118C,193C,267C,333C ''textbook'':552C ''theano'':721C ''time'':30C,48C,65C,110C,171C,259C,482C,627C,634C,636C ''top'':652C ''total'':313C ''two'':518C,683C ''type'':81C,228C,374C,437C,440C,447C,461C,477C,503C,598C,769C ''typic'':442C ''ul'':472C,677C ''understand'':737C ''uniform'':524C ''unlik'':280C ''use'':443C,449C,463C,695C ''ve'':680C,699C,713C,727C,749C ''vision'':515C ''without'':742C ''work'':6A,22C ''would'':776C ''wrong'':606C ''www.cs.toronto.edu'':454C ''www.cs.toronto.edu/~fritz/absps/imagenet.pdf'':453C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9212, 1, '2015-12-04 15:45:40.557', 1, 47, 'Is Maxout the same as max pooling?', '&lt;p&gt;I''ve recently read about maxout in slides of a lecture and in the paper.&lt;/p&gt;

&lt;p&gt;Is maxout the same as max pooling?&lt;/p&gt;
', 'machine-learning  convnet', NULL, '''/p'':28C,37C ''convnet'':11B ''learn'':10B ''lectur'':23C ''machin'':9B ''machine-learn'':8B ''max'':6A,35C ''maxout'':2A,18C,31C ''p'':12C,29C ''paper'':27C ''pool'':7A,36C ''read'':16C ''recent'':15C ''slide'':20C ''ve'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9321, 1, '2015-12-10 22:03:16.547', 0, 63, 'Multi-class logistic regression', '&lt;p&gt;I am trying to understand logistic regression for a multi-class example and I have the following code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy
import theano
import theano.tensor as T
rng = numpy.random

num_classes = 3
#N = number of examples
N = 100
#feats = number of input neurons
feats = 784
#training rate
tr_rate = 0.1 

D = (rng.randn(N, feats), rng.randint(size=N, low=0, high=num_classes))
training_steps = 1000

# Declare Theano symbolic variables
x = T.matrix("x")
y = T.ivector("y")

w = theano.shared(rng.randn(feats), name="w")
b = theano.shared(0.01, name="b")

# Construct Theano expression graph
sigma = T.nnet.softmax(T.dot(x,w) + b) 
prediction = T.argmax(sigma, axis=1)          # The class with highest probability

# Cross-entropy loss function
xent = -T.mean(T.log(sigma)[T.arange(y.shape[0]), y])

cost = xent.mean() + 0.01 * (w ** 2).sum()      # Regularisation 
gw, gb = T.grad(cost, [w, b])                   # Compute the gradient of the cost 

# Compile
train = theano.function(
          inputs=[x,y],
          outputs=[xent],
          updates=((w, w - tr_rate * gw), (b, b - tr_rate * gb)),
          allow_input_downcast=True)
predict = theano.function(inputs=[x], outputs=prediction)

# Train
for i in range(training_steps):
    train(D[0], D[1])      
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However at this point the code gives me the following error:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ValueError                                Traceback (most recent call last)
&amp;lt;ipython-input-246-c4753ce8ccc7&amp;gt; in &amp;lt;module&amp;gt;()
      1 # Train
      2 for i in range(training_steps):
----&amp;gt; 3     train(D[0], D[1])

/Users/vzocca/gitProjects/Theano/theano/compile/function_module.pyc in __call__(self, *args, **kwargs)
    869                     node=self.fn.nodes[self.fn.position_of_error],
    870                     thunk=thunk,
--&amp;gt; 871                     storage_map=getattr(self.fn, ''storage_map'', None))
    872             else:
    873                 # old-style linkers raise their own exceptions

/Users/vzocca/gitProjects/Theano/theano/gof/link.pyc in raise_with_op(node, thunk, exc_info, storage_map)
    312         # extra long error message in that case.
    313         pass
--&amp;gt; 314     reraise(exc_type, exc_value, exc_trace)
    315 
    316 

/Users/vzocca/gitProjects/Theano/theano/compile/function_module.pyc in __call__(self, *args, **kwargs)
    857         t0_fn = time.time()
    858         try:
--&amp;gt; 859             outputs = self.fn()
    860         except Exception:
    861             if hasattr(self.fn, ''position_of_error''):

ValueError: number of rows in x (1) does not match length of y (100)
Apply node that caused the error: CrossentropySoftmaxArgmax1HotWithBias(Elemwise{Add}[(0, 0)].0, Alloc.0, y)
Toposort index: 12
Inputs types: [TensorType(float64, row), TensorType(float64, vector), TensorType(int32, vector)]
Inputs shapes: [(1, 100), (100,), (100,)]
Inputs strides: [(800, 8), (8,), (4,)]
Inputs values: [''not shown'', ''not shown'', ''not shown'']
Outputs clients: [[Sum{acc_dtype=float64}(CrossentropySoftmaxArgmax1HotWithBias.0)], [], []]

Backtrace when the node is created:
  File "&amp;lt;ipython-input-244-834f04b2fb36&amp;gt;", line 16, in &amp;lt;module&amp;gt;
    xent = -T.mean(T.log(sigma)[T.arange(y.shape[0]), y])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;HINT: Use the Theano flag ''exception_verbosity=high'' for a debugprint and storage map footprint of this apply node.&lt;/p&gt;

&lt;p&gt;Can anyone help understand what is wrong?
Thank you! &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9338, 2, '2015-12-11 13:07:06.583', 0, NULL, NULL, '&lt;p&gt;I see that in the current version of python wrapper of xgboost you can specify file name or existing xgboost model (class Booster) in train function.&lt;/p&gt;
', NULL, NULL, '''/p'':28C ''booster'':24C ''class'':23C ''current'':7C ''exist'':20C ''file'':17C ''function'':27C ''model'':22C ''name'':18C ''p'':1C ''python'':10C ''see'':3C ''specifi'':16C ''train'':26C ''version'':8C ''wrapper'':11C ''xgboost'':13C,21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9462, 2, '2015-12-21 12:43:00.493', 1, NULL, NULL, '&lt;p&gt;Note that I am cross-posting this answer because the question is cross-posted, and one of the questions will likely be deleted. If this is a breach of etiquette, please let me know. Cross-post is &lt;a href="http://stats.stackexchange.com/questions/187723/how-to-build-a-supervised-artificial-neural-network"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;This question has two parts, at least implicitly.  The second part is, generally speaking, how to train and predict from neural networks (in Python).  The first part is (implicitly) how do we build a model that predicts (correctly) which person won a presidency based on demographics.&lt;/p&gt;

&lt;p&gt;The answer to the second question is quite easy to state, but harder to do.  It boils down to the rather unhelpful statement:  &lt;strong&gt;learn about neural networks, and use that knowledge&lt;/strong&gt;.  Google for tutorials on neural networks. I would make recommendations, but I don''t know your mathematical, statistical, and computer science background, and thus cannot really make suggestions.&lt;/p&gt;

&lt;p&gt;The first question, however, I can answer, although likely not in the way you''d like.  Just using the data you''ve listed, it''s highly unlikely you''ll get any sort of accuracy out of your neural networks, or any other model you fit.  Why?  For several reasons.&lt;/p&gt;

&lt;p&gt;The first is that unless you can always classify the president into one of two (or more) distinct groups, as you can in the US (Democrat, Republican), your data are changing all the time.  You can''t classify anything if it doesn''t have a class, and that''s difficult in some countries.&lt;/p&gt;

&lt;p&gt;Secondly, even if there are distinct classes, demographic data is likely not a set of good predictors.  Demographic data changes depending on how good the local governance is, as well as the overall governance.  You might, might be able to code that in somehow.  But demographic data also changes due to a lot of other things. When it comes down to voting, people choose to vote based on a bunch of things, and their demographic status is only one factor.&lt;/p&gt;

&lt;p&gt;The third reason is more a caution than anything.  Neural networks and all kinds of machine learning/statistical modelling methods can give good predictions with the right kind of data.  Something like predicting a president is relatively difficult, especially in smaller countries.  (Nate Silver did it well in the US, but there was a lot more data there.)  And even if you''ve got great data and a great model, there''s invariably something you miss.  George Box said it right:  "All models are wrong, but some are useful."  When you mention predicting &lt;strong&gt;correctly&lt;/strong&gt;, all sorts of alarm bells go off in my head.  The problem with trying for "correct" prediction is that there are no guarantees your prediction will be correct, often even in simple cases.  No model is perfect.&lt;/p&gt;
', NULL, NULL, '''/a'':48C ''/p'':49C,97C,160C,211C,266C,348C,482C ''/questions/187723/how-to-build-a-supervised-artificial-neural-network'':46C ''/strong'':131C,444C ''abl'':306C ''accuraci'':195C ''alarm'':448C ''also'':315C ''although'':169C ''alway'':220C ''answer'':10C,100C,168C ''anyth'':251C,358C ''background'':153C ''base'':94C,334C ''bell'':449C ''boil'':115C ''box'':426C ''breach'':31C ''build'':83C ''bunch'':337C ''cannot'':156C ''case'':477C ''caution'':356C ''chang'':243C,287C,316C ''choos'':331C ''class'':258C,274C ''classifi'':221C,250C ''code'':308C ''come'':326C ''comput'':151C ''correct'':88C,443C,460C,472C ''countri'':265C,390C ''cross'':7C,16C,39C ''cross-post'':6C,15C,38C ''d'':176C ''data'':181C,241C,276C,286C,314C,378C,405C,414C ''delet'':26C ''democrat'':238C ''demograph'':96C,275C,285C,313C,342C ''depend'':288C ''difficult'':262C,386C ''distinct'':230C,273C ''doesn'':254C ''due'':317C ''easi'':107C ''especi'':387C ''etiquett'':33C ''even'':269C,408C,474C ''factor'':347C ''first'':76C,163C,214C ''fit'':206C ''general'':63C ''georg'':425C ''get'':191C ''give'':370C ''go'':450C ''good'':283C,291C,371C ''googl'':132C ''got'':412C ''govern'':294C,301C ''great'':413C,417C ''group'':231C ''guarante'':467C ''harder'':111C ''head'':454C ''high'':187C ''howev'':165C ''href'':43C ''implicit'':58C,79C ''invari'':421C ''kind'':363C,376C ''know'':37C,146C ''knowledg'':130C ''learn'':123C ''learning/statistical'':366C ''least'':57C ''let'':35C ''like'':24C,170C,177C,278C,380C ''list'':184C ''ll'':190C ''local'':293C ''lot'':320C,403C ''machin'':365C ''make'':140C,158C ''mathemat'':148C ''mention'':440C ''method'':368C ''might'':303C,304C ''miss'':424C ''model'':85C,204C,367C,418C,431C,479C ''nate'':391C ''network'':72C,126C,137C,200C,360C ''neural'':71C,125C,136C,199C,359C ''note'':2C ''often'':473C ''one'':19C,225C,346C ''overal'':300C ''p'':1C,50C,98C,161C,212C,267C,349C ''part'':55C,61C,77C ''peopl'':330C ''perfect'':481C ''person'':90C ''pleas'':34C ''post'':8C,17C,40C ''predict'':69C,87C,372C,381C,441C,461C,469C ''predictor'':284C ''presid'':93C,223C,383C ''problem'':456C ''python'':74C ''question'':13C,22C,52C,104C,164C ''quit'':106C ''rather'':119C ''realli'':157C ''reason'':210C,352C ''recommend'':141C ''relat'':385C ''republican'':239C ''right'':375C,429C ''said'':427C ''scienc'':152C ''second'':60C,103C,268C ''set'':281C ''sever'':209C ''silver'':392C ''simpl'':476C ''smaller'':389C ''somehow'':311C ''someth'':379C,422C ''sort'':193C,446C ''speak'':64C ''state'':109C ''statement'':121C ''statist'':149C ''stats.stackexchange.com'':45C ''stats.stackexchange.com/questions/187723/how-to-build-a-supervised-artificial-neural-network'':44C ''status'':343C ''strong'':122C,442C ''suggest'':159C ''thing'':323C,339C ''third'':351C ''thus'':155C ''time'':246C ''train'':67C ''tri'':458C ''tutori'':134C ''two'':54C,227C ''unhelp'':120C ''unless'':217C ''unlik'':188C ''us'':237C,398C ''use'':128C,179C,437C ''ve'':183C,411C ''vote'':329C,333C ''way'':174C ''well'':297C,395C ''won'':91C ''would'':139C ''wrong'':433C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9488, 1, '2015-12-22 17:19:37.87', 4, 105, 'xgboost: give more importance to recent samples', '&lt;p&gt;Is there a way to add more importance to points which are more recent when analyzing data with xgboost? &lt;/p&gt;
', 'xgboost  weighted-data', NULL, '''/p'':32C ''add'':18C ''analyz'':28C ''data'':11B,29C ''give'':2A ''import'':4A,20C ''p'':12C ''point'':22C ''recent'':6A,26C ''sampl'':7A ''way'':16C ''weight'':10B ''weighted-data'':9B ''xgboost'':1A,8B,31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9495, 1, '2015-12-23 06:48:19.68', 1, 98, 'How should a neural network for unbound function approximation be structured?', '&lt;p&gt;I''ve heard that a multilayer perceptron can approximate any function arbitrarily exact, given enough neurons. I wanted to try it, so I wrote the following code:&lt;/p&gt;



&lt;pre&gt;&lt;code&gt;#!/usr/bin/env python

"""Example for learning a regression."""


import tensorflow as tf
import numpy


def plot(xs, ys_truth, ys_pred):
    """
    Plot the true values and the predicted values.

    Parameters
    ----------
    xs : list
        Numeric values
    ys_truth : list
        Numeric values, same length as `xs`
    ys_pred : list
        Numeric values, same length as `xs`
    """
    import matplotlib.pyplot as plt
    truth_plot, = plt.plot(xs, ys_truth, ''-o'', color=''#00ff00'')
    pred_plot, = plt.plot(xs, ys_pred, ''-o'', color=''#ff0000'')
    plt.legend([truth_plot, pred_plot],
               [''Truth'', ''Prediction''],
               loc=''upper center'')
    plt.savefig(''plot.png'')


# Parameters
learning_rate = 0.1
momentum = 0.6
training_epochs = 1000
display_step = 100

# Generate training data
train_X = []
train_Y = []

# First simple test: a linear function
f = lambda x: x+4

# Second, more complicated test: x^2
# f = lambda x: x**2

for x in range(-20, 20):
    train_X.append(float(x))
    train_Y.append(f(x))
train_X = numpy.asarray(train_X)
train_Y = numpy.asarray(train_Y)
n_samples = train_X.shape[0]

# Graph input
X = tf.placeholder(tf.float32)
reshaped_X = tf.reshape(X, [-1, 1])
Y = tf.placeholder("float")

# Create Model
W1 = tf.Variable(tf.truncated_normal([1, 100], stddev=0.1), name="weight")
b1 = tf.Variable(tf.constant(0.1, shape=[1, 100]), name="bias")
mul = tf.matmul(reshaped_X, W1)
h1 = tf.nn.sigmoid(mul) + b1
W2 = tf.Variable(tf.truncated_normal([100, 100], stddev=0.1), name="weight")
b2 = tf.Variable(tf.constant(0.1, shape=[100]), name="bias")
h2 = tf.nn.sigmoid(tf.matmul(h1, W2)) + b2
W3 = tf.Variable(tf.truncated_normal([100, 1], stddev=0.1), name="weight")
b3 = tf.Variable(tf.constant(0.1, shape=[1]), name="bias")
# identity as activation to get arbitrary output
activation = tf.matmul(h2, W3) + b3

# Minimize the squared errors
l2_loss = tf.reduce_sum(tf.pow(activation-Y, 2))/(2*n_samples)
optimizer = tf.train.MomentumOptimizer(learning_rate, momentum).minimize(l2_loss)

# Initializing the variables
init = tf.initialize_all_variables()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    # Fit all training data
    for epoch in range(training_epochs):
        for (x, y) in zip(train_X, train_Y):
            sess.run(optimizer, feed_dict={X: x, Y: y})

        # Display logs per epoch step
        if epoch % display_step == 0:
            cost = sess.run(l2_loss, feed_dict={X: train_X, Y: train_Y})
            print("cost=%s\nW1=%s" % (cost, sess.run(W1)))

    print("Optimization Finished!")
    print("cost=%s W1=%s" %
          (sess.run(l2_loss, feed_dict={X: train_X, Y: train_Y}),
           sess.run(W1)))  # "b2=", sess.run(b2)

    # Get output and plot it
    ys_pred = []
    ys_truth = []

    test_X = []
    for x in range(-40, 40):
        test_X.append(float(x))

    for x in test_X:
        ret = sess.run(activation, feed_dict={X: x})
        ys_pred.append(list(ret)[0][0])
        ys_truth.append(f(x))
    plot(train_X.tolist(), ys_truth, ys_pred)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This kind of works for linear functions (at least for the training data, not so much for the testing data outside of the range):&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/cIRSR.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/cIRSR.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;However, it doesn''t work at all for non-linear function $x^2$:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/q6JbH.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/q6JbH.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Why does this neural network not work for such simple function approximation? What do I have to change to make the same network topology work for both functions?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9709, 2, '2016-01-09 03:23:24.01', 0, NULL, NULL, '&lt;p&gt;Ok , It''s my misunderstanding. K-Means is not converged with non-convex shape data set .Nothing relate with convex function,sorry.&lt;/p&gt;
', NULL, NULL, '''/p'':26C ''converg'':12C ''convex'':16C,23C ''data'':18C ''function'':24C ''k'':8C ''k-mean'':7C ''mean'':9C ''misunderstand'':6C ''non'':15C ''non-convex'':14C ''noth'':20C ''ok'':2C ''p'':1C ''relat'':21C ''set'':19C ''shape'':17C ''sorri'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9771, 2, '2016-01-13 00:37:33.207', 2, NULL, NULL, '&lt;p&gt;It seems like you do not have fixed numbers of centroid(clusters) so centroid based clustering for example k-means can not be used in your case. However, you can use density based clustering for example DBSCAN. &lt;/p&gt;
', NULL, NULL, '''/p'':40C ''base'':16C,35C ''case'':29C ''centroid'':12C,15C ''cluster'':13C,17C,36C ''dbscan'':39C ''densiti'':34C ''exampl'':19C,38C ''fix'':9C ''howev'':30C ''k'':21C ''k-mean'':20C ''like'':4C ''mean'':22C ''number'':10C ''p'':1C ''seem'':3C ''use'':26C,33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9815, 1, '2016-01-17 08:51:12.87', 0, 10, 'How to define a suitbale number of centroids for fuzzy c-means?', '&lt;p&gt;In Fuzzy c-means, we have to put how many centers(centroids) in the code. I am wondering how many centroids are suitbale ? &lt;/p&gt;

&lt;p&gt;How to define a suitbale number of centroids for fuzzy c-means? &lt;/p&gt;
', 'clustering', NULL, '''/p'':40C,55C ''c'':12A,19C,53C ''c-mean'':11A,18C,52C ''center'':27C ''centroid'':8A,28C,37C,49C ''cluster'':14B ''code'':31C ''defin'':3A,44C ''fuzzi'':10A,17C,51C ''mani'':26C,36C ''mean'':13A,20C,54C ''number'':6A,47C ''p'':15C,41C ''put'':24C ''suitbal'':5A,39C,46C ''wonder'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9850, 1, '2016-01-19 11:48:29.337', 5, 178, 'Neural networks: which cost function to use?', '&lt;p&gt;I am using &lt;a href="https://en.wikipedia.org/wiki/TensorFlow"&gt;TensorFlow&lt;/a&gt; for experiments mainly with neural networks. Although I have done quite some experiments (XOR-Problem, MNIST, some Regression stuff, ...) now, I struggle with choosing the "correct" cost function for specific problems because overall I could be considered a beginner.&lt;/p&gt;

&lt;p&gt;Before coming to TensorFlow I coded some fully-connected MLPs and some recurrent networks on my own with &lt;a href="https://en.wikipedia.org/wiki/Python_(programming_language)"&gt;Python&lt;/a&gt; and &lt;a href="https://en.wikipedia.org/wiki/NumPy"&gt;NumPy&lt;/a&gt; but mostly I had problems where a simple squared error and a simple gradient descient was sufficient.&lt;/p&gt;

&lt;p&gt;However, since TensorFlow offers quite a lot of cost functions itself as well as building custom cost functions, I would like to know if there is some kind of tutorial maybe specifically for cost functions on neural networks? (I''ve already done like half of the official TensorFlow tutorials but they''re not really explaining &lt;strong&gt;why&lt;/strong&gt; specific cost functions or learners are used for specific problems - at least not for beginners)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;To give some examples:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_output, y_train))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I guess it applies the softmax function on both inputs so that the sum of one vector equals 1. But what exactly is cross entropy with logits? I thought it sums up the values and calculates the cross entropy...so some metric measurement?! Wouldn''t this be very much the same if I normalize the output, sum it up and take the squared error?
Additionally, why is this used e.g. for MNIST (or even much harder problems)? When I want to classify like 10 or maybe even 1000 classes, doesn''t summing up the values completely destroy any information about &lt;em&gt;which&lt;/em&gt; class actually was the output?&lt;/p&gt;

&lt;p&gt;&lt;code&gt;cost = tf.nn.l2_loss(vector)&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;What is this for? I thought l2 loss is pretty much the squared error but TensorFlow''s API tells that it''s input is just one tensor. Doesn''t get the idea at all?!&lt;/p&gt;

&lt;p&gt;Besides I saw this for &lt;strong&gt;cross entropy&lt;/strong&gt; pretty often:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;cross_entropy = -tf.reduce_sum(y_train * tf.log(y_output))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;...but why is this used? Isn''t the loss in cross entropy mathematically this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;-1/n * sum(y_train * log(y_output) + (1 - y_train) * log(1 - y_output))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Where is the &lt;code&gt;(1 - y_train) * log(1 - y_output)&lt;/code&gt; part in most TensorFlow examples? Isn''t it missing?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Answers:&lt;/strong&gt; I know this question is quite open, but I do not expect to get like 10 pages with every single problem/cost function listed in detail. I just need a short summary about when to use which cost function (in general or in TensorFlow, doesn''t matter much to me) and some explanation about this topic. And/or some source(s) for beginners ;)&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9912, 2, '2016-01-22 23:15:09.463', 4, NULL, NULL, '&lt;p&gt;It is natural that RBM requires some time to converge, but if you see that the reconstruction is decent, then your algorithm works well.&lt;/p&gt;

&lt;p&gt;I don''t understand the idea of optimizing the model by your objective function:
&lt;code&gt;cost = T.sum((data - vis)**2).mean()&lt;/code&gt;.
I think you just cannot train RBM (or any other stochastic model) that way because vis is a nonlinear function of the data. If you omit sampling of the hidden vector you might use this formula and end up having model similar to the feed forward network that learns an identity transformation. It fact it can be used as a feature extractor.&lt;/p&gt;

&lt;p&gt;Also notice that your second network no longer will be a proper generative model, because its learning rule doesn''t follow the gradient of RBM''s cost function. Hence it won''t properly model either an energy function or a joint probability P(v, h) of your visible and hidden vector.&lt;/p&gt;

&lt;p&gt;You may also be interested in reading &lt;a href="https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf" rel="nofollow"&gt;this&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Although it is convenient, the reconstruction error is actually a very poor measure of the progress of learning.  It is not the function that CD learning is approximately optimizing, [...] and it systematically confounds two different quantities that are changing during the learning.  The
  frst  is  the  difference  between  the  empirical  distribution  of  the  training  data  and  the  equilibrium
  distribution of the RBM. The second is the mixing rate of the alternating Gibbs Markov chain.  If
  the mixing rate is very low, the reconstruction error will be very small even when the distributions of
  the data and the model are very different.  As the weights increase the mixing rate falls, so decreases
  in reconstruction error do not necessarily mean that the model is improving and, conversely, small
  increases do not necessarily mean the model is getting worse.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;So what is the cost function that RBM tries to optimize? &lt;a href="http://deeplearning.net/tutorial/rbm.html" rel="nofollow"&gt;good description can be found here&lt;/a&gt;
$$-\frac{\partial log(p(x))}{\partial \theta }=\frac{\partial F(x)}{\partial \theta}-\sum_{\tilde{x}}p(\tilde{x})\frac{\partial F(\tilde{x})}{\partial \theta}$$
On the left hand side we have minus log probability of the data which we must minimize in order to increase the probability of the data. On the right hand side we have two terms. The function $F(x)$ coresponds to the free energy of the network given the data $x$ and current weights $\theta$ so minimization of this function increases the probability of our data. On the other hand we must take into account the normalizing factor $Z$- its role is to give correct probability distribution of the input vectors and this sum in the equation corresponds to the normalizing factor. In other words we must maximize the free energy (decrease probability) over all set of inputs proportionally to its current probability $p(\tilde{x})$. If we omit to maximize this sum our model won''t converge to the optimal solution. Contrastive Divergence is only crude approximation of this objective funtion but it works very well in practice. I recommend that you watch &lt;a href="https://www.youtube.com/watch?v=AyzOUbkUf3M" rel="nofollow"&gt;this film by Geoffrey Hinton&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10023, 4, '2016-01-29 06:54:47.96', 0, NULL, NULL, 'The Natural Language Toolkit is a Python library for computational linguistics.', NULL, NULL, '''comput'':10C ''languag'':3C ''librari'':8C ''linguist'':11C ''natur'':2C ''python'':7C ''toolkit'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10043, 2, '2016-01-31 22:37:43.727', -1, NULL, NULL, '&lt;p&gt;First of all you got the condition wrong. It is m&gt;n, not n&gt;m. In other words, matrix A has to have more rows than columns.&lt;/p&gt;

&lt;p&gt;There is no way to relax this requirement for SVD. &lt;/p&gt;

&lt;p&gt;You could drop some columns to satisfy this requirement.&lt;/p&gt;
', NULL, NULL, '''/p'':29C,41C,52C ''column'':28C,47C ''condit'':8C ''could'':44C ''drop'':45C ''first'':2C ''got'':6C ''m'':12C,16C ''matrix'':20C ''n'':13C,15C ''p'':1C,30C,42C ''relax'':36C ''requir'':38C,51C ''row'':26C ''satisfi'':49C ''svd'':40C ''way'':34C ''word'':19C ''wrong'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10099, 1, '2016-02-05 21:26:05.873', 0, 33, 'How to get the mu+0.1sigma from a normal distribution?', '&lt;p&gt;I have a normal distribution and I would like to know what is the confidence level of mu+0.1sigma (where mu is the mean and sigma the standard deviation). How do i get the confidence level?&lt;/p&gt;

&lt;p&gt;Using python or R&lt;/p&gt;
', 'r  python  statistics', NULL, '''+0.1'':6A,34C ''/p'':53C,59C ''confid'':30C,51C ''deviat'':45C ''distribut'':11A,20C ''get'':3A,49C ''know'':26C ''level'':31C,52C ''like'':24C ''mean'':40C ''mu'':5A,33C,37C ''normal'':10A,19C ''p'':15C,54C ''python'':13B,56C ''r'':12B,58C ''sigma'':7A,35C,42C ''standard'':44C ''statist'':14B ''use'':55C ''would'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10144, 1, '2016-02-08 19:55:06.057', 1, 34, 'Markov switching models', '&lt;p&gt;What are some reference sources for understanding Markov switching models?&lt;/p&gt;
', 'time-series  markov-process  reference-request', NULL, '''/p'':24C ''markov'':1A,8B,21C ''markov-process'':7B ''model'':3A,23C ''p'':13C ''process'':9B ''refer'':11B,17C ''reference-request'':10B ''request'':12B ''seri'':6B ''sourc'':18C ''switch'':2A,22C ''time'':5B ''time-seri'':4B ''understand'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10171, 2, '2016-02-10 00:23:19.837', 0, NULL, NULL, '&lt;p&gt;I can safely conclude you cannot.&lt;/p&gt;
', NULL, NULL, '''/p'':8C ''cannot'':7C ''conclud'':5C ''p'':1C ''safe'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10245, 1, '2016-02-15 02:51:17.527', 0, 16, 'Objective function for classification problem?', '&lt;p&gt;Why is cross entropy function a better objective function than Squared Sum Error function in case of Classification problem.&lt;/p&gt;
', 'multiclass-classification', NULL, '''/p'':29C ''better'':16C ''case'':25C ''classif'':4A,8B,27C ''cross'':12C ''entropi'':13C ''error'':22C ''function'':2A,14C,18C,23C ''multiclass'':7B ''multiclass-classif'':6B ''object'':1A,17C ''p'':9C ''problem'':5A,28C ''squar'':20C ''sum'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10237, 4, '2016-02-14 02:46:41.803', 0, NULL, NULL, 'gensim is the python library for topic modelling. multi-dimensional vector representation of words or sentences which preserves semantic meaning is computed through word2vec and doc2vec models.', NULL, NULL, '''comput'':23C ''dimension'':11C ''doc2vec'':27C ''gensim'':1C ''librari'':5C ''mean'':21C ''model'':8C,28C ''multi'':10C ''multi-dimension'':9C ''preserv'':19C ''python'':4C ''represent'':13C ''semant'':20C ''sentenc'':17C ''topic'':7C ''vector'':12C ''word'':15C ''word2vec'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10266, 2, '2016-02-16 13:49:00.593', 0, NULL, NULL, '&lt;p&gt;When you have a constant function &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;f(x) = c, for each x \in \R^n
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;the correlation between the input and output is 0. In other terms, you can change x as you want, but in output we''ll always have 0. The correlation exists and it is zero.&lt;/p&gt;
', NULL, NULL, '''/code'':20C ''/p'':8C,57C ''/pre'':21C ''0'':31C,49C ''alway'':47C ''c'':13C ''chang'':37C ''code'':10C ''constant'':6C ''correl'':24C,51C ''exist'':52C ''f'':11C ''function'':7C ''input'':27C ''ll'':46C ''n'':19C ''output'':29C,44C ''p'':1C,22C ''pre'':9C ''r'':18C ''term'':34C ''want'':41C ''x'':12C,16C,38C ''zero'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10335, 4, '2016-02-21 05:39:00.67', 0, NULL, NULL, 'For questions which concern getting started in Data Science or any of its related subdomains.', NULL, NULL, '''concern'':4C ''data'':8C ''get'':5C ''question'':2C ''relat'':14C ''scienc'':9C ''start'':6C ''subdomain'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8655, 1, '2015-10-30 01:03:04.533', 2, 84, 'Implementation of Tree Kernels in Python', '&lt;p&gt;I''m looking to use a tree kernel (e.g. Zelenko et al., 2003, Moschitti 2006, etc.) for text classification in Python. However, I''m still a little unclear on the implementation. Is there an existing implementation (in any language) that I could use as a reference, or is there a 3rd-party tree kernel library I could use?&lt;/p&gt;
', 'machine-learning  python  statistics  nlp', NULL, '''/p'':73C ''2003'':26C ''2006'':28C ''3rd'':65C ''3rd-party'':64C ''al'':25C ''classif'':32C ''could'':55C,71C ''e.g'':22C ''et'':24C ''etc'':29C ''exist'':48C ''howev'':35C ''implement'':1A,44C,49C ''kernel'':4A,21C,68C ''languag'':52C ''learn'':9B ''librari'':69C ''littl'':40C ''look'':16C ''m'':15C,37C ''machin'':8B ''machine-learn'':7B ''moschitti'':27C ''nlp'':12B ''p'':13C ''parti'':66C ''python'':6A,10B,34C ''refer'':59C ''statist'':11B ''still'':38C ''text'':31C ''tree'':3A,20C,67C ''unclear'':41C ''use'':18C,56C,72C ''zelenko'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10505, 2, '2016-03-03 17:38:03.55', 0, NULL, NULL, '&lt;p&gt;All in all, what you are trying to reach is Pareto efficiency.&lt;/p&gt;

&lt;p&gt;In order to make it cooperative you need to define a single reward function that is shared by all the players (it could be a function that combines in some way individual reward functions). &lt;/p&gt;

&lt;p&gt;Somehow, you need to weight the rewards that you obtain from one product with respect to the others.&lt;/p&gt;
', NULL, NULL, '''/p'':14C,50C,70C ''combin'':43C ''cooper'':21C ''could'':38C ''defin'':25C ''effici'':13C ''function'':29C,41C,49C ''individu'':47C ''make'':19C ''need'':23C,54C ''obtain'':61C ''one'':63C ''order'':17C ''other'':69C ''p'':1C,15C,51C ''pareto'':12C ''player'':36C ''product'':64C ''reach'':10C ''respect'':66C ''reward'':28C,48C,58C ''share'':32C ''singl'':27C ''somehow'':52C ''tri'':8C ''way'':46C ''weight'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9596, 1, '2016-01-03 23:32:48.993', 3, 119, 'Machine learning algorithm to classify blog posts', '&lt;p&gt;So I have a large collection of blog posts containing &lt;code&gt;title&lt;/code&gt;, &lt;code&gt;content&lt;/code&gt;, &lt;code&gt;category&lt;/code&gt;, &lt;code&gt;tags&lt;/code&gt; and &lt;code&gt;geo-location&lt;/code&gt; fields and I''m looking to achieve three things:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Assign a category (or multiple categories) to all the posts and any new ones. I have a strict vocabulary of categories.&lt;/li&gt;
&lt;li&gt;Add new tags to the posts that might be relevant to the post.&lt;/li&gt;
&lt;li&gt;Mark the post if it contains information about a place. For example: &lt;code&gt;Lorem ipsum dolor sit amet San Francisco, consectetur adipiscing elit&lt;/code&gt;.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I''ve been looking into different machine learning algorithms, most recently decision trees, but I don''t feel that is the best algorithm to work out the problems above (or that I haven''t understood them enough).&lt;/p&gt;

&lt;p&gt;Many of these posts already contain &lt;code&gt;categories&lt;/code&gt;, &lt;code&gt;tags&lt;/code&gt; and &lt;code&gt;geo-location&lt;/code&gt; data. Some do not contain any information and some have only a few details.&lt;/p&gt;

&lt;p&gt;What would be the best machine learning algorithm to look into to solve each of the three areas?&lt;/p&gt;
', 'machine-learning  data-mining  classification  algorithms', NULL, '''/code'':29C,32C,35C,38C,44C,118C,169C,172C,178C ''/li'':78C,93C,119C ''/ol'':120C ''/p'':54C,159C,193C,213C ''achiev'':51C ''add'':80C ''adipisc'':116C ''algorithm'':3A,15B,130C,144C,202C ''alreadi'':165C ''amet'':112C ''area'':212C ''assign'':57C ''best'':143C,199C ''blog'':6A,24C ''categori'':34C,59C,62C,77C,168C ''classif'':14B ''classifi'':5A ''code'':27C,30C,33C,36C,40C,107C,167C,170C,174C ''collect'':22C ''consectetur'':115C ''contain'':26C,100C,166C,183C ''content'':31C ''data'':12B,179C ''data-min'':11B ''decis'':133C ''detail'':192C ''differ'':127C ''dolor'':110C ''elit'':117C ''enough'':158C ''exampl'':106C ''feel'':139C ''field'':45C ''francisco'':114C ''geo'':42C,176C ''geo-loc'':41C,175C ''haven'':154C ''inform'':101C,185C ''ipsum'':109C ''larg'':21C ''learn'':2A,10B,129C,201C ''li'':56C,79C,94C ''locat'':43C,177C ''look'':49C,125C,204C ''lorem'':108C ''m'':48C ''machin'':1A,9B,128C,200C ''machine-learn'':8B ''mani'':161C ''mark'':95C ''might'':87C ''mine'':13B ''multipl'':61C ''new'':69C,81C ''ol'':55C ''one'':70C ''p'':16C,121C,160C,194C ''place'':104C ''post'':7A,25C,66C,85C,92C,97C,164C ''problem'':149C ''recent'':132C ''relev'':89C ''san'':113C ''sit'':111C ''solv'':207C ''strict'':74C ''tag'':37C,82C,171C ''thing'':53C ''three'':52C,211C ''titl'':28C ''tree'':134C ''understood'':156C ''ve'':123C ''vocabulari'':75C ''work'':146C ''would'':196C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8657, 1, '2015-10-30 04:21:27.737', 0, 157, 'Trying to understand Logistic Regression Implementation', '&lt;p&gt;I''m currently using the following code as a starting point to deepen my understanding of regularized logistic regression.  As a first pass I''m just trying to do a binary classification on part of the iris data set.  &lt;/p&gt;

&lt;p&gt;One problem I think I have encountered is that the negative log-loss (computed with loss and stored in loss_vec) doesn''t change much from one iteration to the next.&lt;/p&gt;

&lt;p&gt;Another challenge I am facing is trying to figure out how to plot the decision boundary once I have learned the logistic regression coefficients. Using the coefficients to plot the 0.5 decision boundary is way off.  This makes me think I have made a mistake somewhere&lt;/p&gt;

&lt;p&gt;&lt;a href="http://fa.bianp.net/blog/2013/numerical-optimizers-for-logistic-regression/" rel="nofollow"&gt;http://fa.bianp.net/blog/2013/numerical-optimizers-for-logistic-regression/&lt;/a&gt;&lt;/p&gt;



&lt;pre&gt;&lt;code&gt;import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model, datasets


iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
Y = iris.target


X = X[:100,:]
Y = Y[:100]

def phi(t):
    # logistic function, returns 1 / (1 + exp(-t))
    idx = t &amp;gt; 0
    out = np.empty(t.size, dtype=np.float)
    out[idx] = 1. / (1 + np.exp(-t[idx]))
    exp_t = np.exp(t[~idx])
    out[~idx] = exp_t / (1. + exp_t)
    return out


def loss(x0, X, y, alpha):
    # logistic loss function, returns Sum{-log(phi(t))}
    #x0 is the weight vector w are the paramaters, c is the bias term
    w, c = x0[:X.shape[1]], x0[-1]
    z = X.dot(w) + c
    yz = y * z
    idx = yz &amp;gt; 0
    out = np.zeros_like(yz)
    out[idx] = np.log(1 + np.exp(-yz[idx]))
    out[~idx] = (-yz[~idx] + np.log(1 + np.exp(yz[~idx])))
    out = out.sum() / X.shape[0] + .5 * alpha * w.dot(w)
    return out


def gradient(x0, X, y, alpha):
    # gradient of the logistic loss
    w, c = x0[:X.shape[1]], x0[-1]
    z = X.dot(w) + c
    z = phi(y * z)
    z0 = (z - 1) * y
    grad_w = X.T.dot(z0) / X.shape[0] + alpha * w
    grad_c = z0.sum() / X.shape[0]
    return np.concatenate((grad_w, [grad_c]))


def bgd(X, y, alpha, max_iter):
    step_sizes = np.array([100,10,1,.1,.01,.001,.0001,.00001])
    iter_no = 0
    x0 = np.random.random(X.shape[1] + 1) #initialize weight vector

    #placeholder for coefficients to test against the loss function
    next_thetas = np.zeros((step_sizes.shape[0],X.shape[1]+1) )   
    J = loss(x0,X,y,alpha)
    running_loss = []
    while iter_no &amp;lt; max_iter:
        grad = gradient(x0,X,y,alpha)
        next_thetas = -(np.outer(step_sizes,grad)-x0)
        loss_vec = []
        for i in range(np.shape(next_thetas)[0]):
            loss_vec.append(loss(next_thetas[i],X,y,alpha))
        ind = np.argmin(loss_vec)
        x0 = next_thetas[ind]
        if iter_no % 500 == 0:
            running_loss.append(loss(x0,X,y,alpha))
        iter_no += 1  
    return next_thetas
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8679, 2, '2015-11-01 14:50:27.32', 4, NULL, NULL, '&lt;p&gt;You can use BoxPlot for outlier analysis. I would show you how to do that in Python:&lt;/p&gt;

&lt;p&gt;Consider your data as an array:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;a = [100, 50, 150, 200, 35, 60 ,50, 20, 500]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now, use seaborn to plot the boxplot:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import seaborn as sn
sn.boxplot(a)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So, you would get a plot which looks somewhat like this:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/cQv7b.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/cQv7b.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Seems like 500 is the only outlier to me.  But, it all depends on the analysis and the tolerance level of the analyst or the statistician and also the problem statement.&lt;/p&gt;

&lt;p&gt;You can have a look at &lt;a href="http://stats.stackexchange.com/a/178106/84191"&gt;one of my answers&lt;/a&gt; on the CrossValidated SE for more tests.&lt;/p&gt;

&lt;p&gt;And there are several nice questions on outliers and the algorithms and techniques for detecting them.&lt;/p&gt;

&lt;p&gt;My personal favourite is the &lt;a href="http://stackoverflow.com/questions/9935271/one-dimensional-mahalanobis-distance-in-python"&gt;Mahalanobis distance technique&lt;/a&gt;. &lt;/p&gt;
', NULL, NULL, '''/a'':92C,143C,184C ''/a/178106/84191'':138C ''/code'':40C,59C ''/cqv7b.png'':79C,86C ''/p'':19C,27C,50C,73C,93C,126C,151C,169C,185C ''/pre'':41C,60C ''/questions/9935271/one-dimensional-mahalanobis-distance-in-python'':180C ''100'':31C ''150'':33C ''20'':38C ''200'':34C ''35'':35C ''50'':32C,37C ''500'':39C,97C ''60'':36C ''algorithm'':163C ''also'':122C ''alt'':87C ''analysi'':8C,110C ''analyst'':117C ''answer'':142C ''array'':26C ''boxplot'':5C,49C ''code'':29C,52C ''consid'':21C ''crossvalid'':146C ''data'':23C ''depend'':107C ''descript'':90C ''detect'':167C ''distanc'':182C ''enter'':88C ''favourit'':173C ''get'':65C ''href'':76C,135C,177C ''i.stack.imgur.com'':78C,85C ''i.stack.imgur.com/cqv7b.png'':77C,84C ''imag'':89C ''img'':82C ''import'':53C ''level'':114C ''like'':71C,96C ''look'':69C,132C ''mahalanobi'':181C ''nice'':157C ''nofollow'':81C ''one'':139C ''outlier'':7C,101C,160C ''p'':1C,20C,42C,61C,74C,94C,127C,152C,170C ''person'':172C ''plot'':47C,67C ''pre'':28C,51C ''problem'':124C ''python'':18C ''question'':158C ''rel'':80C ''se'':147C ''seaborn'':45C,54C ''seem'':95C ''sever'':156C ''show'':11C ''sn'':56C ''sn.boxplot'':57C ''somewhat'':70C ''src'':83C ''stackoverflow.com'':179C ''stackoverflow.com/questions/9935271/one-dimensional-mahalanobis-distance-in-python'':178C ''statement'':125C ''statistician'':120C ''stats.stackexchange.com'':137C ''stats.stackexchange.com/a/178106/84191'':136C ''techniqu'':165C,183C ''test'':150C ''toler'':113C ''use'':4C,44C ''would'':10C,64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8659, 2, '2015-10-30 12:35:16.94', 1, NULL, NULL, '&lt;p&gt;So there are many ways to denote a series.  How are you going to parse the series down to determine the values if you don''t know the format?  &lt;/p&gt;

&lt;p&gt;Determining if the label has a series does not get you to the specific numbers in the series. &lt;/p&gt;

&lt;p&gt;2,3,5,7  parses out to 4 numbers&lt;/p&gt;

&lt;p&gt;Is 6 in 1996?  I assume that is one number and 1996 != 6&lt;/p&gt;

&lt;p&gt;"55,56,57" is series with a 6 but but not the number 6 &lt;/p&gt;

&lt;p&gt;Does 7-9 parse out to 2 numbers or 3 number&lt;br&gt;
Is 6 in 7-9?&lt;br&gt;
If 6 is in 7-9 identifying that as a series does not answer that question.   &lt;/p&gt;

&lt;p&gt;How many ways can there be represent a series that regex got out of hand?  For each format of series you also need to parse the values.  You need to know the format of the series to parse out the numbers.  I would have a set of regex mapped to set of parsers.  &lt;/p&gt;

&lt;p&gt;Maybe use machine learning to identity new series formats but you are still going to need to parse out the series. &lt;/p&gt;
', NULL, NULL, '''-9'':97C,111C,118C ''/p'':31C,51C,62C,77C,93C,129C,184C,207C ''1996'':67C,75C ''2'':53C,101C ''3'':54C,104C ''4'':60C ''5'':55C ''55'':79C ''56'':80C ''57'':81C ''6'':65C,76C,86C,92C,108C,114C ''7'':56C,96C,110C,117C ''also'':152C ''answer'':126C ''assum'':69C ''br'':106C,112C ''denot'':8C ''determin'':21C,33C ''format'':30C,148C,163C,194C ''get'':42C ''go'':14C,199C ''got'':142C ''hand'':145C ''ident'':191C ''identifi'':119C ''know'':28C,161C ''label'':36C ''learn'':189C ''machin'':188C ''mani'':5C,132C ''map'':179C ''mayb'':186C ''need'':153C,159C,201C ''new'':192C ''number'':47C,61C,73C,91C,102C,105C,171C ''one'':72C ''p'':1C,32C,52C,63C,78C,94C,130C,185C ''pars'':16C,57C,98C,155C,168C,203C ''parser'':183C ''question'':128C ''regex'':141C,178C ''repres'':137C ''seri'':10C,18C,39C,50C,83C,123C,139C,150C,166C,193C,206C ''set'':176C,181C ''specif'':46C ''still'':198C ''use'':187C ''valu'':23C,157C ''way'':6C,133C ''would'':173C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8660, 1, '2015-10-30 15:59:38.85', 4, 67, 'Transforming AutoEncoders', '&lt;p&gt;I''ve just read Jeff Hintons paper on transforming autoencoders &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Hinton, Krizhevsky and Wang: &lt;a href="http://www.cs.toronto.edu/~fritz/absps/transauto6.pdf" rel="nofollow"&gt;Transforming Auto-encoders&lt;/a&gt;. In Artificial Neural Networks and Machine Learning, 2011.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;and would quite like to play around with something like this. But having read it I couldn''t get enough detail from the paper on how I might actually implement it. &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Does anyone know how the mapping between input pixels to capsules should work? &lt;/li&gt;
&lt;li&gt;What exactly should be happening in the recognition units?&lt;/li&gt;
&lt;li&gt;How it should be trained? Is it just standard back prop between every connection?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Even better would be a link to some source code for this or something similar.&lt;/p&gt;
', 'neuralnetwork  autoencoder', NULL, '''/a'':34C ''/blockquote'':44C ''/li'':93C,104C,120C ''/p'':16C,43C,77C,138C ''/ul'':121C ''/~fritz/absps/transauto6.pdf'':27C ''2011'':42C ''actual'':74C ''anyon'':81C ''around'':52C ''artifici'':36C ''auto'':32C ''auto-encod'':31C ''autoencod'':2A,4B,15C ''back'':115C ''better'':124C ''blockquot'':17C ''capsul'':90C ''code'':132C ''connect'':119C ''couldn'':62C ''detail'':66C ''encod'':33C ''enough'':65C ''even'':123C ''everi'':118C ''exact'':96C ''get'':64C ''happen'':99C ''hinton'':11C,19C ''href'':24C ''implement'':75C ''input'':87C ''jeff'':10C ''know'':82C ''krizhevski'':20C ''learn'':41C ''li'':79C,94C,105C ''like'':49C,55C ''link'':128C ''machin'':40C ''map'':85C ''might'':73C ''network'':38C ''neural'':37C ''neuralnetwork'':3B ''nofollow'':29C ''p'':5C,18C,45C,122C ''paper'':12C,69C ''pixel'':88C ''play'':51C ''prop'':116C ''quit'':48C ''read'':9C,59C ''recognit'':102C ''rel'':28C ''similar'':137C ''someth'':54C,136C ''sourc'':131C ''standard'':114C ''train'':110C ''transform'':1A,14C,30C ''ul'':78C ''unit'':103C ''ve'':7C ''wang'':22C ''work'':92C ''would'':47C,125C ''www.cs.toronto.edu'':26C ''www.cs.toronto.edu/~fritz/absps/transauto6.pdf'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8661, 2, '2015-10-30 17:40:08.8', 1, NULL, NULL, '&lt;p&gt;I guess, a nice place to start is seminal work of John Kleinberg:  &lt;a href="https://www.cs.cornell.edu/home/kleinber/bhs.pdf" rel="nofollow"&gt;https://www.cs.cornell.edu/home/kleinber/bhs.pdf&lt;/a&gt;
Yes, this paper is outdated, but anyway it''s still very influential and simple. Also, digging articles, citing this paper, could be very helpful. By the way, these guys solve very similar task ( and they even use Weibo logs as well!): &lt;a href="http://www.csce.uark.edu/~xintaowu/publ/pakdd15.pdf" rel="nofollow"&gt;http://www.csce.uark.edu/~xintaowu/publ/pakdd15.pdf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/home/kleinber/bhs.pdf'':19C ''/home/kleinber/bhs.pdf&lt;/a&gt;'':24C ''/~xintaowu/publ/pakdd15.pdf'':70C ''/~xintaowu/publ/pakdd15.pdf&lt;/a&gt;&lt;/p&gt;'':75C ''also'':39C ''anyway'':31C ''articl'':41C ''cite'':42C ''could'':45C ''dig'':40C ''even'':60C ''guess'':3C ''guy'':53C ''help'':48C ''href'':16C,67C ''influenti'':36C ''john'':13C ''kleinberg'':14C ''log'':63C ''nice'':5C ''nofollow'':21C,72C ''outdat'':29C ''p'':1C ''paper'':27C,44C ''place'':6C ''rel'':20C,71C ''semin'':10C ''similar'':56C ''simpl'':38C ''solv'':54C ''start'':8C ''still'':34C ''task'':57C ''use'':61C ''way'':51C ''weibo'':62C ''well'':65C ''work'':11C ''www.cs.cornell.edu'':18C,23C ''www.cs.cornell.edu/home/kleinber/bhs.pdf'':17C ''www.cs.cornell.edu/home/kleinber/bhs.pdf&lt;/a&gt;'':22C ''www.csce.uark.edu'':69C,74C ''www.csce.uark.edu/~xintaowu/publ/pakdd15.pdf'':68C ''www.csce.uark.edu/~xintaowu/publ/pakdd15.pdf&lt;/a&gt;&lt;/p&gt;'':73C ''yes'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8677, 2, '2015-11-01 11:04:00.17', 3, NULL, NULL, '&lt;h2&gt;There is no one-size-fits-all&lt;/h2&gt;

&lt;p&gt;Also, data dimensionality is not the same everywhere. Text data, which is inherently sparse, has a very different &lt;strong&gt;intrinsic dimensionality&lt;/strong&gt; than e.g. random Gaussians.&lt;/p&gt;

&lt;p&gt;For text data, linear SVMs are known to work very well.&lt;/p&gt;

&lt;p&gt;RBF kernels do not work well with high-dimensional data, because they are distance-based at the core, and choosing the sigma parameter becomes next to impossible.&lt;/p&gt;

&lt;p&gt;If you can "fold" dimensions, you also get very different behavior. Im image recognition, you usually have thousands of pixels. However, you &lt;em&gt;never look at all of them at once&lt;/em&gt;. Instead, you use convolutional kernels that move over the data space, and they may have only say 32x32 pixels. That is still 1024 dimensions, but not millions anymore.&lt;/p&gt;
', NULL, NULL, '''/em'':115C ''/h2'':10C ''/p'':38C,51C,82C,144C ''/strong'':33C ''1024'':138C ''32x32'':133C ''also'':12C,90C ''anymor'':143C ''base'':69C ''becom'':78C ''behavior'':94C ''choos'':74C ''convolut'':119C ''core'':72C ''data'':13C,21C,42C,63C,125C ''differ'':29C,93C ''dimens'':88C,139C ''dimension'':14C,32C,62C ''distanc'':68C ''distance-bas'':67C ''e.g'':35C ''em'':106C ''everywher'':19C ''fit'':8C ''fold'':87C ''gaussian'':37C ''get'':91C ''h2'':1C ''high'':61C ''high-dimension'':60C ''howev'':104C ''im'':95C ''imag'':96C ''imposs'':81C ''inher'':24C ''instead'':116C ''intrins'':31C ''kernel'':54C,120C ''known'':46C ''linear'':43C ''look'':108C ''may'':129C ''million'':142C ''move'':122C ''never'':107C ''next'':79C ''one'':6C ''one-size-fits-al'':5C ''p'':11C,39C,52C,83C ''paramet'':77C ''pixel'':103C,134C ''random'':36C ''rbf'':53C ''recognit'':97C ''say'':132C ''sigma'':76C ''size'':7C ''space'':126C ''spars'':25C ''still'':137C ''strong'':30C ''svms'':44C ''text'':20C,41C ''thousand'':101C ''use'':118C ''usual'':99C ''well'':50C,58C ''work'':48C,57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8681, 1, '2015-11-02 04:12:53.367', 2, 100, 'Clustering for mixed numeric and nominal discrete data', '&lt;p&gt;My data includes survey responses that are binary (numeric) and nominal / categorical. All responses are discrete and at individuals level. &lt;a href="http://i.stack.imgur.com/3jIFZ.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/3jIFZ.png" alt="Snapshot of data"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Data is of shape (n=7219, p=105). &lt;/p&gt;

&lt;p&gt;Couple things:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;I am trying to identify a clustering technique with a similarity measure that would work for categorical and numeric binary data. There are techniques in R kmodes clustering and kprototype that are designed for this type of problem, but I am using Python and need a technique from sklearn clustering that works well with this type of problems.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;I want to build profiles of segments of individuals. meaning this group of individuals care more about these set of features. &lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', 'clustering  k-means  sklearn  categorical-data', NULL, '''/3jifz.png'':42C,49C ''/a'':54C ''/li'':132C,157C ''/p'':55C,65C,69C,131C,156C ''/ul'':158C ''105'':64C ''7219'':62C ''alt'':50C ''binari'':25C,92C ''build'':138C ''care'':149C ''categor'':15B,29C,89C ''categorical-data'':14B ''cluster'':1A,9B,79C,100C,122C ''coupl'':67C ''data'':8A,16B,19C,53C,57C,93C ''design'':105C ''discret'':7A,33C ''featur'':155C ''group'':146C ''href'':39C ''i.stack.imgur.com'':41C,48C ''i.stack.imgur.com/3jifz.png'':40C,47C ''identifi'':77C ''img'':45C ''includ'':20C ''individu'':36C,143C,148C ''k'':11B ''k-mean'':10B ''kmode'':99C ''kprototyp'':102C ''level'':37C ''li'':71C,133C ''mean'':12B,144C ''measur'':84C ''mix'':3A ''n'':61C ''need'':117C ''nofollow'':44C ''nomin'':6A,28C ''numer'':4A,26C,91C ''p'':17C,56C,63C,66C,72C,134C ''problem'':110C,130C ''profil'':139C ''python'':115C ''r'':98C ''rel'':43C ''respons'':22C,31C ''segment'':141C ''set'':153C ''shape'':60C ''similar'':83C ''sklearn'':13B,121C ''snapshot'':51C ''src'':46C ''survey'':21C ''techniqu'':80C,96C,119C ''thing'':68C ''tri'':75C ''type'':108C,128C ''ul'':70C ''use'':114C ''want'':136C ''well'':125C ''work'':87C,124C ''would'':86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8682, 2, '2015-11-02 05:15:53.35', 1, NULL, NULL, '&lt;p&gt;The objective is rather different.
For classification problems, we want to know: GIVEN X, WHAT WILL BE Y?
For concept-learning problems, we are asking: WHAT KIND OF X WILL GIVE US Y?&lt;/p&gt;

&lt;p&gt;It may seem similar, but for classification problems, we may use a model that is unable to generalize and answer "WHAT KIND OF X WILL GIVE US Y" until you plug X into the model (e.g. K-nearest neighbor).&lt;/p&gt;

&lt;p&gt;On the other hand, concept-learning problems, the main issue is to generalize and answer what can be X specifically.&lt;/p&gt;
', NULL, NULL, '''/p'':36C,78C,101C ''answer'':57C,95C ''ask'':27C ''classif'':8C,44C ''concept'':22C,85C ''concept-learn'':21C,84C ''differ'':6C ''e.g'':73C ''general'':55C,93C ''give'':33C,63C ''given'':14C ''hand'':83C ''issu'':90C ''k'':75C ''k-nearest'':74C ''kind'':29C,59C ''know'':13C ''learn'':23C,86C ''main'':89C ''may'':39C,47C ''model'':50C,72C ''nearest'':76C ''neighbor'':77C ''object'':3C ''p'':1C,37C,79C ''plug'':68C ''problem'':9C,24C,45C,87C ''rather'':5C ''seem'':40C ''similar'':41C ''specif'':100C ''unabl'':53C ''us'':34C,64C ''use'':48C ''want'':11C ''x'':15C,31C,61C,69C,99C ''y'':19C,35C,65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8683, 2, '2015-11-02 05:25:24.69', 0, NULL, NULL, '&lt;p&gt;I think the dataset is generated using dynamic network generator made available at &lt;a href="http://mlg.ucd.ie/snam/" rel="nofollow"&gt;this address&lt;/a&gt; which is dynamic version of static LFR. It''s an excellent dynamic networked data generator.&lt;/p&gt;
', NULL, NULL, '''/a'':24C ''/p'':40C ''/snam/'':19C ''address'':23C ''avail'':13C ''data'':38C ''dataset'':5C ''dynam'':9C,27C,36C ''excel'':35C ''generat'':7C,11C,39C ''href'':16C ''lfr'':31C ''made'':12C ''mlg.ucd.ie'':18C ''mlg.ucd.ie/snam/'':17C ''network'':10C,37C ''nofollow'':21C ''p'':1C ''rel'':20C ''static'':30C ''think'':3C ''use'':8C ''version'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8692, 1, '2015-11-02 13:45:50.947', 0, 59, 'Algorithm to predict the best variable where dependent variable is unkown', '&lt;p&gt;The problem seems to be somewhat similar one I have now. But this us little tricky&lt;/p&gt;

&lt;p&gt;I have an issue in analyzing some data. I have to predict the best company and rank it based on some variables we considered. &lt;/p&gt;

&lt;p&gt;The issue here is we dont have any dependent variable to do a regression. All variable are independent. So I have done Z-score to all the variable and generated a score for each company. Now i''m considering the score as dependent variable and applied multiple regression analysis between the dependent variable(score generated using z-score) and other independent variables.&lt;/p&gt;

&lt;p&gt;But the generated Predicted values are unreliable. How do I proceed further.&lt;/p&gt;

&lt;p&gt;Thanks,
NS&lt;/p&gt;
', 'bigdata', NULL, '''/p'':30C,56C,122C,136C,140C ''algorithm'':1A ''analysi'':107C ''analyz'':37C ''appli'':104C ''base'':50C ''best'':5A,45C ''bigdata'':12B ''compani'':46C,93C ''consid'':55C,97C ''data'':39C ''depend'':8A,66C,101C,110C ''done'':79C ''dont'':63C ''generat'':88C,113C,126C ''independ'':75C,120C ''issu'':35C,59C ''littl'':28C ''m'':96C ''multipl'':105C ''ns'':139C ''one'':21C ''p'':13C,31C,57C,123C,137C ''predict'':3A,43C,127C ''problem'':15C ''proceed'':134C ''rank'':48C ''regress'':71C,106C ''score'':82C,90C,99C,112C,117C ''seem'':16C ''similar'':20C ''somewhat'':19C ''thank'':138C ''tricki'':29C ''unkown'':11A ''unreli'':130C ''us'':27C ''use'':114C ''valu'':128C ''variabl'':6A,9A,53C,67C,73C,86C,102C,111C,121C ''z'':81C,116C ''z-score'':80C,115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8695, 2, '2015-11-02 22:05:06.603', 1, NULL, NULL, '&lt;p&gt;Have you considered using &lt;a href="http://dato.com/products/create/" rel="nofollow"&gt;GraphLab Create&lt;/a&gt;? It''s a Python tool for doing many different types of Machine Learning.&lt;/p&gt;

&lt;p&gt;General info about their Recommender System can be found here:
&lt;a href="https://dato.com/solutions/use-cases/recommendation-engine.html" rel="nofollow"&gt;https://dato.com/solutions/use-cases/recommendation-engine.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;A hands-on coding example can be found here: &lt;a href="https://dato.com/learn/gallery/notebooks/basic_recommender_functionalities.html" rel="nofollow"&gt;https://dato.com/learn/gallery/notebooks/basic_recommender_functionalities.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Using this product you should be able to create a Recommender System in as few as five lines of code.&lt;/p&gt;

&lt;p&gt;One downside, if you''re using it for a commercial purpose, you''ll need to get a license for the product. Although they do have a free trial period. &lt;/p&gt;
', NULL, NULL, '''/a'':15C ''/learn/gallery/notebooks/basic_recommender_functionalities.html'':66C ''/learn/gallery/notebooks/basic_recommender_functionalities.html&lt;/a&gt;&lt;/p&gt;'':71C ''/p'':29C,93C,124C ''/products/create/'':10C ''/solutions/use-cases/recommendation-engine.html'':45C ''/solutions/use-cases/recommendation-engine.html&lt;/a&gt;&lt;/p&gt;'':50C ''abl'':79C ''although'':116C ''code'':56C,92C ''commerci'':104C ''consid'':4C ''creat'':14C,81C ''dato.com'':9C,44C,49C,65C,70C ''dato.com/learn/gallery/notebooks/basic_recommender_functionalities.html'':64C ''dato.com/learn/gallery/notebooks/basic_recommender_functionalities.html&lt;/a&gt;&lt;/p&gt;'':69C ''dato.com/products/create/'':8C ''dato.com/solutions/use-cases/recommendation-engine.html'':43C ''dato.com/solutions/use-cases/recommendation-engine.html&lt;/a&gt;&lt;/p&gt;'':48C ''differ'':24C ''downsid'':96C ''exampl'':57C ''five'':89C ''found'':39C,60C ''free'':121C ''general'':31C ''get'':110C ''graphlab'':13C ''hand'':54C ''hands-on'':53C ''href'':7C,42C,63C ''info'':32C ''learn'':28C ''licens'':112C ''line'':90C ''ll'':107C ''machin'':27C ''mani'':23C ''need'':108C ''nofollow'':12C,47C,68C ''one'':95C ''p'':1C,30C,51C,72C,94C ''period'':123C ''product'':75C,115C ''purpos'':105C ''python'':19C ''re'':99C ''recommend'':35C,83C ''rel'':11C,46C,67C ''system'':36C,84C ''tool'':20C ''trial'':122C ''type'':25C ''use'':5C,73C,100C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8696, 2, '2015-11-03 02:21:53.237', 0, NULL, NULL, '&lt;p&gt;Try running &lt;em&gt;all&lt;/em&gt; of the models for each of the predictions. You will need get the probability of the prediction. Take as the classification the individual model which gives the maximum probability. Using your proposed "waterfall" does not allow the other models to have a say in the prediction of each new input. Also, are you saying that when you switched form one model with 7 classes to 7 models with 2 classes each the accuracy went from ~70 to ~95? That is interesting. Maybe you should look to see which features are the strongest predictors in each of your binary models (see variable importance).&lt;/p&gt;
', NULL, NULL, '''/em'':6C ''/p'':109C ''2'':75C ''7'':69C,72C ''70'':82C ''95'':84C ''accuraci'':79C ''allow'':42C ''also'':57C ''binari'':104C ''class'':70C,76C ''classif'':27C ''em'':4C ''featur'':95C ''form'':65C ''get'':18C ''give'':32C ''import'':108C ''individu'':29C ''input'':56C ''interest'':87C ''look'':91C ''maximum'':34C ''mayb'':88C ''model'':9C,30C,45C,67C,73C,105C ''need'':17C ''new'':55C ''one'':66C ''p'':1C ''predict'':14C,23C,52C ''predictor'':99C ''probabl'':20C,35C ''propos'':38C ''run'':3C ''say'':49C,60C ''see'':93C,106C ''strongest'':98C ''switch'':64C ''take'':24C ''tri'':2C ''use'':36C ''variabl'':107C ''waterfal'':39C ''went'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8697, 1, '2015-11-03 06:26:49.093', 1, 29, 'about the error additivity', '&lt;p&gt;In machine learning, one can use Euclidean distance to measure a cluster&lt;/p&gt;

&lt;p&gt;$$
\mu\in R^k,
$$ &lt;/p&gt;

&lt;p&gt;over data points 
$$
\{x_i\}^N_{i=1}\in R^{k\times N},
$$ 
with the measure 
$$
\text{error}_i = ||\mu-x_i||^2_2
$$
The total error can be calculated as
$$
\text{error} = \sum^N_{i=1}error_i
$$ 
To estimate the parameters, one formulates the optimization problem
$$
\min_{\mu_{new}} \big{(}\text{error}\big{)}
$$
This has two underlying assumptions: first, that samples are independent, and, second, that the dimensions are independent for each sample. &lt;/p&gt;

&lt;p&gt;Can anyone explain how I should analyze when either or both assumptions are not satisfied?&lt;/p&gt;
', 'machine-learning  data-mining', NULL, '''/p'':24C,30C,108C,125C ''1'':39C,69C ''2'':55C,56C ''addit'':4A ''analyz'':116C ''anyon'':111C ''assumpt'':92C,121C ''big'':84C,87C ''calcul'':62C ''cluster'':23C ''data'':9B,33C ''data-min'':8B ''dimens'':102C ''distanc'':19C ''either'':118C ''error'':3A,49C,59C,65C,70C,86C ''estim'':73C ''euclidean'':18C ''explain'':112C ''first'':93C ''formul'':77C ''independ'':97C,104C ''k'':29C,42C ''learn'':7B,14C ''machin'':6B,13C ''machine-learn'':5B ''measur'':21C,47C ''min'':81C ''mine'':10B ''mu'':26C,52C,82C ''mu-x'':51C ''n'':37C,44C,67C ''new'':83C ''one'':15C,76C ''optim'':79C ''p'':11C,25C,31C,109C ''paramet'':75C ''point'':34C ''problem'':80C ''r'':28C,41C ''sampl'':95C,107C ''satisfi'':124C ''second'':99C ''sum'':66C ''text'':48C,64C,85C ''time'':43C ''total'':58C ''two'':90C ''under'':91C ''use'':17C ''x'':35C,53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8704, 1, '2015-11-04 04:25:31.113', 1, 30, 'Is Vector in Cosine Similarity the same as vector in Physics?', '&lt;p&gt;I''m new to Data Science. I''m trying to understand cosine similarity and it seems like the equation is about finding the distance between two vectors. From what I''ve Googled, a vector needs to have magnitude and direction. But in CS, it seems like it''s a 1-dimensional array. Is vector in CS the same as vector in Physics? If so, what is the direction of a vector. And if a vector is like this &lt;code&gt;[1, 0, 1, 0]&lt;/code&gt; what is the magnitude of this vector? &lt;/p&gt;
', 'cosine-distance', NULL, '''/code'':99C ''/p'':107C ''0'':96C,98C ''1'':65C,95C,97C ''array'':67C ''code'':94C ''cosin'':4A,13B,27C ''cosine-dist'':12B ''cs'':58C,71C ''data'':20C ''dimension'':66C ''direct'':55C,83C ''distanc'':14B,39C ''equat'':34C ''find'':37C ''googl'':47C ''like'':32C,61C,92C ''m'':17C,23C ''magnitud'':53C,103C ''need'':50C ''new'':18C ''p'':15C ''physic'':11A,77C ''scienc'':21C ''seem'':31C,60C ''similar'':5A,28C ''tri'':24C ''two'':41C ''understand'':26C ''ve'':46C ''vector'':2A,9A,42C,49C,69C,75C,86C,90C,106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8709, 1, '2015-11-04 08:18:22.727', 0, 28, 'Using non-negative matrix factorization (NMF) for facial recognition', '&lt;p&gt;I''m interested to find out how to implement NMF for facial recognition. I understand that the NMF works by taking V, which is a matrix of face images (n resolution x m persons), and factorize V = WH, where we get r basis vectors. I presume that this step is done using the training set. But I don''t know how to proceed from here.&lt;/p&gt;

&lt;p&gt;With PCA, after building the eigenfaces from the training set, we project the test image onto the face-space, then classify the image by comparing the weight coefficients between the test image and training images. But what about NMF? Do I use the basis vectors? What do I do with them?&lt;/p&gt;
', 'object-recognition', NULL, '''/p'':80C,134C ''basi'':57C,126C ''build'':85C ''classifi'':103C ''coeffici'':110C ''compar'':107C ''done'':65C ''eigenfac'':87C ''face'':42C,100C ''face-spac'':99C ''facial'':9A,26C ''factor'':6A,50C ''find'':19C ''get'':55C ''imag'':43C,96C,105C,114C,117C ''implement'':23C ''interest'':17C ''know'':74C ''m'':16C,47C ''matrix'':5A,40C ''n'':44C ''negat'':4A ''nmf'':7A,24C,32C,121C ''non'':3A ''non-neg'':2A ''object'':12B ''object-recognit'':11B ''onto'':97C ''p'':14C,81C ''pca'':83C ''person'':48C ''presum'':60C ''proceed'':77C ''project'':93C ''r'':56C ''recognit'':10A,13B,27C ''resolut'':45C ''set'':69C,91C ''space'':101C ''step'':63C ''take'':35C ''test'':95C,113C ''train'':68C,90C,116C ''understand'':29C ''use'':1A,66C,124C ''v'':36C,51C ''vector'':58C,127C ''weight'':109C ''wh'':52C ''work'':33C ''x'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8702, 2, '2015-11-03 21:33:06.7', 0, NULL, NULL, '&lt;p&gt;This is more what I did then an answer to the stated question.  &lt;/p&gt;

&lt;p&gt;What I also want is a rank.  Clearly a similarity of 0.9 versus 0.1 is a higher rank than 0.5 versus 0.4.   My problem is 0.43 non-responsive is versus 0.45 responsive is actually non-responsive (other).  &lt;/p&gt;

&lt;p&gt;What I did for the raw score was the harmonic mean of the difference and log of the ratio.  I could get .35 versus a .20 and 0.1 versus a 0.01.  Using the log of the ratio seem to give absolute differences more even ground with relative differences.  Then the harmonic mean kept the bigger number from dominating as much.   &lt;/p&gt;

&lt;p&gt;Then I went down the ordered by the exact ratio of the sample set and used that as the offset so a score of 0 was don''t know.&lt;/p&gt;

&lt;p&gt;This rank seems to be effective0 as the top 1/2 by rank had zero errors when looking at sample set versus the classification of the sample set.  And the stuff right in the middle had the most errors.   &lt;/p&gt;
', NULL, NULL, '''/p'':15C,56C,118C,149C,189C ''0'':144C ''0.01'':88C ''0.1'':30C,85C ''0.4'':38C ''0.43'':42C ''0.45'':48C ''0.5'':36C ''0.9'':28C ''1/2'':160C ''20'':83C ''35'':80C ''absolut'':98C ''actual'':51C ''also'':19C ''answer'':10C ''bigger'':112C ''classif'':173C ''clear'':24C ''could'':78C ''differ'':71C,99C,105C ''domin'':115C ''effective0'':156C ''error'':165C,188C ''even'':101C ''exact'':128C ''get'':79C ''give'':97C ''ground'':102C ''harmon'':67C,108C ''higher'':33C ''kept'':110C ''know'':148C ''log'':73C,91C ''look'':167C ''mean'':68C,109C ''middl'':184C ''much'':117C ''non'':44C,53C ''non-respons'':43C,52C ''number'':113C ''offset'':139C ''order'':125C ''p'':1C,16C,57C,119C,150C ''problem'':40C ''question'':14C ''rank'':23C,34C,152C,162C ''ratio'':76C,94C,129C ''raw'':63C ''relat'':104C ''respons'':45C,49C,54C ''right'':181C ''sampl'':132C,169C,176C ''score'':64C,142C ''seem'':95C,153C ''set'':133C,170C,177C ''similar'':26C ''state'':13C ''stuff'':180C ''top'':159C ''use'':89C,135C ''versus'':29C,37C,47C,81C,86C,171C ''want'':20C ''went'':122C ''zero'':164C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8719, 2, '2015-11-04 14:50:14.17', 3, NULL, NULL, '&lt;p&gt;Spark only recently implemented &lt;a href="https://spark.apache.org/docs/latest/api/java/org/apache/spark/ml/feature/CountVectorizer.html" rel="nofollow"&gt;CountVectorizer&lt;/a&gt;, which will take the labels (as strings) and encode them as your 100-dimensional vector (assuming all 100 labels show up somewhere in your dataset).  Once you have those vectors, it should be a simple step to threshold them to make them 0/1 instead of a frequency.&lt;/p&gt;
', NULL, NULL, '''/a'':14C ''/docs/latest/api/java/org/apache/spark/ml/feature/countvectorizer.html'':10C ''/p'':62C ''0/1'':57C ''100'':27C,32C ''assum'':30C ''countvector'':13C ''dataset'':39C ''dimension'':28C ''encod'':23C ''frequenc'':61C ''href'':7C ''implement'':5C ''instead'':58C ''label'':19C,33C ''make'':55C ''nofollow'':12C ''p'':1C ''recent'':4C ''rel'':11C ''show'':34C ''simpl'':49C ''somewher'':36C ''spark'':2C ''spark.apache.org'':9C ''spark.apache.org/docs/latest/api/java/org/apache/spark/ml/feature/countvectorizer.html'':8C ''step'':50C ''string'':21C ''take'':17C ''threshold'':52C ''vector'':29C,44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8710, 2, '2015-11-04 09:27:04', 1, NULL, NULL, '&lt;p&gt;Yes, they are the same. &lt;/p&gt;

&lt;p&gt;The array &lt;code&gt;[1, 0, 1, 0]&lt;/code&gt; represents a vector in $4$ dimensional euclidean space ($\mathbb{R}^4$) with tails at &lt;code&gt;[0, 0 , 0 ,0]&lt;/code&gt; and head at  &lt;code&gt;[1, 0, 1, 0]&lt;/code&gt;. So your vector is "pointing" in the direction of your given set of coordinates.&lt;/p&gt;

&lt;p&gt;This might be easier to visualize in $2$ dimensions: for example, the array &lt;code&gt;[1,0]&lt;/code&gt; corresponds to the unit vector along the $x$-axis centered at the origin.  &lt;/p&gt;
', NULL, NULL, '''/code'':16C,36C,45C,78C ''/p'':7C,60C,92C ''0'':13C,15C,32C,33C,34C,35C,42C,44C,77C ''1'':12C,14C,41C,43C,76C ''2'':69C ''4'':21C,27C ''along'':84C ''array'':10C,74C ''axi'':87C ''center'':88C ''code'':11C,31C,40C,75C ''coordin'':59C ''correspond'':79C ''dimens'':70C ''dimension'':22C ''direct'':53C ''easier'':65C ''euclidean'':23C ''exampl'':72C ''given'':56C ''head'':38C ''mathbb'':25C ''might'':63C ''origin'':91C ''p'':1C,8C,61C ''point'':50C ''r'':26C ''repres'':17C ''set'':57C ''space'':24C ''tail'':29C ''unit'':82C ''vector'':19C,48C,83C ''visual'':67C ''x'':86C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8711, 2, '2015-11-04 11:15:29.96', 0, NULL, NULL, '&lt;p&gt;In this situation, I use RapidMiner (&lt;a href="http://rapidminer.com/" rel="nofollow"&gt;RapidMiner&lt;/a&gt;). There a multiple solutions such as Hadoop and Radoop or RapidMiner cloud service etc.&lt;/p&gt;
', NULL, NULL, '''/a'':14C ''/p'':29C ''cloud'':26C ''etc'':28C ''hadoop'':21C ''href'':9C ''multipl'':17C ''nofollow'':12C ''p'':1C ''radoop'':23C ''rapidmin'':7C,13C,25C ''rapidminer.com'':10C ''rel'':11C ''servic'':27C ''situat'':4C ''solut'':18C ''use'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8703, 1, '2015-11-04 03:50:42.33', 0, 77, 'Using the GA R package to optimize the weights of a MLP neural network', '&lt;p&gt;The neural network I am trying to evolve uses the tanh as an activation function in each neuron and has a topology of 1-5-1, so I need at least 5 weights. The solution of the GA is a real-number vector of length 5, which represents the weights of the network and each weight should take values between -5 to 5. I wrote an R function to use as a fitness function which returns the mean squared error (MSE) of the output data in comparison to the desired output. I want it to learn the cubic function. The input data I am using is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;input&amp;lt;-seq(-1,1,0.02)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and the output data is its cubic function&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;des_out&amp;lt;-input^3
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The evaluation function is the following&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;evalnn&amp;lt;-function(x){

mse&amp;lt;-0
for(i in 1:length(input)){

     nn.out &amp;lt;- tanh(x[1]*input[i]) + tanh(x[2]*input[i]) + tanh(x[3]*input[i]) + tanh(x[4]*input[i]) + tanh(x[5]*input[i]) 
     mse &amp;lt;- (des.out[i] - nn.out)^2 + mse

}
return(-(mse)/length(input))
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have set the return value to negative, because I want the smallest value to be thought as the best fit.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;gann&amp;lt;-ga(type="real-valued", fitness=evalnn,min=c(-5,-5,-5,-5,-5),max=c(5,5,5,5,5),popSize=100,maxiter=150,pmutation=0.01,pcrossover=0.8)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What I always get back from the GA are weights that make up a linear function although I have been experimenting with the ga''s parameters quite a lot, i.e. I have tried all crossover methods and mutations. &lt;a href="https://cran.r-project.org/web/packages/GA/GA.pdf" rel="nofollow"&gt;https://cran.r-project.org/web/packages/GA/GA.pdf&lt;/a&gt;.
&lt;a href="http://i.stack.imgur.com/iJVXi.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/iJVXi.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;br&gt;
The cubic function plotted together with my output function.&lt;/p&gt;

&lt;p&gt;I have used a linear function as training data before the cubic with this implentation and worked. It has trouble with the non-linear.&lt;/p&gt;

&lt;p&gt;If anybody could figure out why do I get this, is there something I have missed. 
Thank you&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8713, 1, '2015-11-04 11:41:05.777', 1, 11, 'Handling covariance matrices of changing size', '&lt;p&gt;In the context of a gaussian mixture model, we can place a prior on the covariance of the cluster means. In an infinite GMM, the problem then arises that changing the number of clusters also changes the size of the covariance matrix. &lt;/p&gt;

&lt;p&gt;I''m doing MCMC sampling and I would like to be able to propose a split or merge move, where I can add (or remove, but that''s easy, right?) rows and columns on the fly. Is there some way of drawing the elements of the extra row and column from some distribution?&lt;/p&gt;

&lt;p&gt;Essentially, I need an infinite dimensional prior covariance for the infinite number of gaussians, where I can ignore any dimensions corresponding to empty clusters.&lt;/p&gt;
', 'clustering  statistics', NULL, '''/p'':52C,107C,133C ''abl'':65C ''add'':76C ''also'':44C ''aris'':37C ''chang'':5A,39C,45C ''cluster'':7B,28C,43C,132C ''column'':86C,103C ''context'':12C ''correspond'':129C ''covari'':2A,25C,50C,116C ''dimens'':128C ''dimension'':114C ''distribut'':106C ''draw'':95C ''easi'':82C ''element'':97C ''empti'':131C ''essenti'':109C ''extra'':100C ''fli'':89C ''gaussian'':15C,122C ''gmm'':33C ''handl'':1A ''ignor'':126C ''infinit'':32C,113C,119C ''like'':62C ''m'':55C ''matric'':3A ''matrix'':51C ''mcmc'':57C ''mean'':29C ''merg'':71C ''mixtur'':16C ''model'':17C ''move'':72C ''need'':111C ''number'':41C,120C ''p'':9C,53C,108C ''place'':20C ''prior'':22C,115C ''problem'':35C ''propos'':67C ''remov'':78C ''right'':83C ''row'':84C,101C ''sampl'':58C ''size'':6A,47C ''split'':69C ''statist'':8B ''way'':93C ''would'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8727, 1, '2015-11-05 12:55:59.507', 0, 24, 'Is it possible to do clustering with moving points in MOA?', '&lt;p&gt;I have a set of points whose feature values change continously. I want to cluster those points using an incremental machine learning tool like MOA. &lt;a href="http://moa.cms.waikato.ac.nz/" rel="nofollow"&gt;MOA is an incremental machine learning tool&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;The problem is MOA expects a stream of instances where each instance is a new separate point. Every new instance (row of data) is a new separate point for MOA. But in my case the values of the points change. &lt;/p&gt;

&lt;p&gt;So, I cannot pass each updated point as a new instance to MOA. Instead I should maybe remove the existing point first from MOA''s time window and pass the updated values as a new instance.&lt;/p&gt;

&lt;p&gt;Do you have any suggestion how to solve this problem using MOA API or a similar tool?&lt;/p&gt;

&lt;p&gt;For example, this is a point: &lt;code&gt;(1,15,25)&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;The first variable is the id of the point. &lt;/p&gt;

&lt;p&gt;Then, the point is updated: &lt;code&gt;(1,15,26)&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Now, the previous point should be replaced with this new point and clustering should be rerun.&lt;/p&gt;
', 'clustering  data-stream-mining', NULL, '''/a'':55C ''/code'':167C,190C ''/p'':56C,98C,136C,155C,168C,179C,191C,209C ''1'':164C,187C ''15'':165C,188C ''25'':166C ''26'':189C ''api'':150C ''cannot'':102C ''case'':91C ''chang'':27C,97C ''cluster'':6A,12B,32C,205C ''code'':163C,186C ''contin'':28C ''data'':14B,80C ''data-stream-min'':13B ''everi'':75C ''exampl'':158C ''exist'':119C ''expect'':62C ''featur'':25C ''first'':121C,171C ''href'':44C ''id'':175C ''increment'':37C,51C ''instanc'':66C,69C,77C,110C,135C ''instead'':113C ''learn'':39C,53C ''like'':41C ''machin'':38C,52C ''mayb'':116C ''mine'':16B ''moa'':11A,42C,48C,61C,87C,112C,123C,149C ''moa.cms.waikato.ac.nz'':45C ''move'':8A ''new'':72C,76C,83C,109C,134C,202C ''nofollow'':47C ''p'':17C,57C,99C,137C,156C,169C,180C,192C ''pass'':103C,128C ''point'':9A,23C,34C,74C,85C,96C,106C,120C,162C,178C,183C,196C,203C ''possibl'':3A ''previous'':195C ''problem'':59C,147C ''rel'':46C ''remov'':117C ''replac'':199C ''rerun'':208C ''row'':78C ''separ'':73C,84C ''set'':21C ''similar'':153C ''solv'':145C ''stream'':15B,64C ''suggest'':142C ''time'':125C ''tool'':40C,54C,154C ''updat'':105C,130C,185C ''use'':35C,148C ''valu'':26C,93C,131C ''variabl'':172C ''want'':30C ''whose'':24C ''window'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8729, 1, '2015-11-05 15:02:35.457', 2, 37, 'Does it makes sense to apply feature scaling on timestamp', '&lt;p&gt;I was wondering if it makes sense to apply normal standardization on a feature like timestamp ?
The data that I process are network packets.&lt;/p&gt;

&lt;p&gt;Thank you&lt;/p&gt;
', 'feature-scaling  preprocessing', NULL, '''/p'':40C,44C ''appli'':6A,24C ''data'':33C ''featur'':7A,12B,29C ''feature-sc'':11B ''like'':30C ''make'':3A,21C ''network'':38C ''normal'':25C ''p'':15C,41C ''packet'':39C ''preprocess'':14B ''process'':36C ''scale'':8A,13B ''sens'':4A,22C ''standard'':26C ''thank'':42C ''timestamp'':10A,31C ''wonder'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8730, 2, '2015-11-05 15:53:49.797', 2, NULL, NULL, '&lt;p&gt;For time series analysis.  &lt;strong&gt;Yes&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;But, turning data into a computable object for using in the ML computation?  &lt;strong&gt;No&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Using the data as a feature.  Then, &lt;strong&gt;Yes&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I would give you a general time series example:&lt;/p&gt;

&lt;p&gt;Consider the number of days in months. The irregularity causes friction while analyzing the model.&lt;/p&gt;

&lt;p&gt;Consider this:
&lt;a href="http://i.stack.imgur.com/oMTB6.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/oMTB6.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;So, such type of transformations would be helpful which analyzing time series'', which can help reduce friction in the models sensibly.
&lt;a href="http://stats.stackexchange.com/questions/165166/number-of-days-in-a-monthly-forecast/165169#165169"&gt;Link of the explanation&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':88C,121C ''/omtb6.png'':75C,82C ''/p'':9C,27C,39C,50C,67C,89C,122C ''/questions/165166/number-of-days-in-a-monthly-forecast/165169#165169'':116C ''/strong'':8C,26C,38C ''alt'':83C ''analysi'':5C ''analyz'':64C,100C ''caus'':61C ''comput'':16C,23C ''consid'':52C,69C ''data'':13C,31C ''day'':56C ''descript'':86C ''enter'':84C ''exampl'':49C ''explan'':120C ''featur'':34C ''friction'':62C,107C ''general'':46C ''give'':43C ''help'':98C,105C ''href'':72C,113C ''i.stack.imgur.com'':74C,81C ''i.stack.imgur.com/omtb6.png'':73C,80C ''imag'':85C ''img'':78C ''irregular'':60C ''link'':117C ''ml'':22C ''model'':66C,110C ''month'':58C ''nofollow'':77C ''number'':54C ''object'':17C ''p'':1C,10C,28C,40C,51C,68C,90C ''reduc'':106C ''rel'':76C ''sensibl'':111C ''seri'':4C,48C,102C ''src'':79C ''stats.stackexchange.com'':115C ''stats.stackexchange.com/questions/165166/number-of-days-in-a-monthly-forecast/165169#165169'':114C ''strong'':6C,24C,36C ''time'':3C,47C,101C ''transform'':95C ''turn'':12C ''type'':93C ''use'':19C,29C ''would'':42C,96C ''yes'':7C,37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8737, 2, '2015-11-06 08:15:59.4', 0, NULL, NULL, '&lt;p&gt;It is useful to have in you bag of tools the family of &lt;a href="https://en.wikipedia.org/wiki/Locality-sensitive_hashing" rel="nofollow"&gt;locality sensitive hashing algorithms&lt;/a&gt;.
This family is not semantic at all. 
Actually is consider the text as a sequence of bits.
I find it useful in dirty data sets when the same text appears many times with slight differences.&lt;/p&gt;

&lt;p&gt;You can use &lt;a href="http://ssdeep.sourceforge.net/usage.html" rel="nofollow" title="ssdeep"&gt;ssdeep&lt;/a&gt; (which is based on &lt;a href="https://en.wikipedia.org/wiki/Nilsimsa_Hash" rel="nofollow"&gt;Nilsimsa hash&lt;/a&gt;) for identifying such documents.
Ssdeep was originally planned for the domain of spam. Spammers often do small changes in the message (add a space) in order to prevent detection by exact signature (e.g., &lt;a href="https://en.wikipedia.org/wiki/MD5" rel="nofollow"&gt;md5&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;Since many version of almost the same document in the same data set will cause havoc to statistical methods that will be applied on it, doing such a cleanup can be very beneficial.&lt;/p&gt;
', NULL, NULL, '''/a'':26C,77C,91C,133C ''/p'':62C,134C,169C ''/usage.html'':71C ''/wiki/locality-sensitive_hashing'':19C ''/wiki/md5'':129C ''/wiki/nilsimsa_hash'':86C ''actual'':34C ''add'':113C ''algorithm'':25C ''almost'':140C ''appear'':56C ''appli'':158C ''bag'':9C ''base'':80C ''benefici'':168C ''bit'':43C ''caus'':150C ''chang'':109C ''cleanup'':164C ''consid'':36C ''data'':50C,147C ''detect'':120C ''differ'':61C ''dirti'':49C ''document'':95C,143C ''domain'':102C ''e.g'':124C ''en.wikipedia.org'':18C,85C,128C ''en.wikipedia.org/wiki/locality-sensitive_hashing'':17C ''en.wikipedia.org/wiki/md5'':127C ''en.wikipedia.org/wiki/nilsimsa_hash'':84C ''exact'':122C ''famili'':13C,28C ''find'':45C ''hash'':24C,90C ''havoc'':151C ''href'':16C,68C,83C,126C ''identifi'':93C ''local'':22C ''mani'':57C,137C ''md5'':132C ''messag'':112C ''method'':154C ''nilsimsa'':89C ''nofollow'':21C,73C,88C,131C ''often'':106C ''order'':117C ''origin'':98C ''p'':1C,63C,135C ''plan'':99C ''prevent'':119C ''rel'':20C,72C,87C,130C ''semant'':31C ''sensit'':23C ''sequenc'':41C ''set'':51C,148C ''signatur'':123C ''sinc'':136C ''slight'':60C ''small'':108C ''space'':115C ''spam'':104C ''spammer'':105C ''ssdeep'':75C,76C,96C ''ssdeep.sourceforge.net'':70C ''ssdeep.sourceforge.net/usage.html'':69C ''statist'':153C ''text'':38C,55C ''time'':58C ''titl'':74C ''tool'':11C ''use'':4C,47C,66C ''version'':138C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10240, 1, '2016-02-14 21:03:06.543', 0, 2, 'what dependencies do spark slaves require?', '&lt;p&gt;I know how to set up a spark standalone cluster on one machine. (Install,java, install git, install scala, install spark, configure environment variables and PATH, passwordless ssh login-ability).&lt;/p&gt;

&lt;p&gt;But what is needed in the slave machines. Obviously JAVA since SPARK prob behaves like hadoop, shipping the jar file to the various slave machines where the RDD lives. But do the slaves need scala and spark as well?&lt;/p&gt;
', 'apache-spark  java', NULL, '''/p'':43C,84C ''abil'':42C ''apach'':8B ''apache-spark'':7B ''behav'':58C ''cluster'':21C ''configur'':33C ''depend'':2A ''environ'':34C ''file'':64C ''git'':28C ''hadoop'':60C ''instal'':25C,27C,29C,31C ''jar'':63C ''java'':10B,26C,54C ''know'':13C ''like'':59C ''live'':73C ''login'':41C ''login-'':40C ''machin'':24C,52C,69C ''need'':48C,78C ''obvious'':53C ''one'':23C ''p'':11C,44C ''passwordless'':38C ''path'':37C ''prob'':57C ''rdd'':72C ''requir'':6A ''scala'':30C,79C ''set'':16C ''ship'':61C ''sinc'':55C ''slave'':5A,51C,68C,77C ''spark'':4A,9B,19C,32C,56C,81C ''ssh'':39C ''standalon'':20C ''variabl'':35C ''various'':67C ''well'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8749, 2, '2015-11-07 16:04:53.32', 0, NULL, NULL, '&lt;p&gt;Not sure if I read the question correctly, but I have implemented multi-variate sub-sample optimisation routines using the Kolmogrov-Smirnov test? &lt;/p&gt;

&lt;p&gt;The advantage is that if two distributions exactly track each other (so would be significantly correlated) but are at quite different scales (e.g. one is half the value of the other), they would still rank as very different distributions at similar scales. Only when both distribution and scale match is the ranking high.&lt;/p&gt;

&lt;p&gt;Spatially per-pixel comparison algorithms are also quite common in raster analysis?&lt;/p&gt;
', NULL, NULL, '''/p'':28C,83C,98C ''advantag'':31C ''algorithm'':90C ''also'':92C ''analysi'':97C ''common'':94C ''comparison'':89C ''correct'':9C ''correl'':45C ''differ'':50C,67C ''distribut'':36C,68C,75C ''e.g'':52C ''exact'':37C ''half'':55C ''high'':82C ''implement'':13C ''kolmogrov'':25C ''kolmogrov-smirnov'':24C ''match'':78C ''multi'':15C ''multi-vari'':14C ''one'':53C ''optimis'':20C ''p'':1C,29C,84C ''per'':87C ''per-pixel'':86C ''pixel'':88C ''question'':8C ''quit'':49C,93C ''rank'':64C,81C ''raster'':96C ''read'':6C ''routin'':21C ''sampl'':19C ''scale'':51C,71C,77C ''signific'':44C ''similar'':70C ''smirnov'':26C ''spatial'':85C ''still'':63C ''sub'':18C ''sub-sampl'':17C ''sure'':3C ''test'':27C ''track'':38C ''two'':35C ''use'':22C ''valu'':57C ''variat'':16C ''would'':42C,62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8750, 2, '2015-11-07 16:46:45.183', 0, NULL, NULL, '&lt;p&gt;Been a while since I used SAS but I think you could use a data step where you make an ID var on which you can aggregate, then vars for each "cat1-4." You could then use proc transpose or do a proc SQL sum() with a "groupby" statement on the ID variable.&lt;/p&gt;

&lt;p&gt;So first step is to get to:&lt;/p&gt;

&lt;blockquote&gt;
&lt;pre&gt;&lt;code&gt;''Topic_ID'' | ''Cat1'' | ''Cat2'' | ''Cat3'' | ''Cat4''
     1     |   1.5  |    0   |    0   |    0
     1     |    0   |    3   |    0   |    0
     1     |    0   |    0   |    1   |    0
     1     |    0   |    0   |    0   |    4
     2     |    3   |    0   |    0   |    0
    ...
&lt;/code&gt;&lt;/pre&gt;
&lt;/blockquote&gt;

&lt;p&gt;If Topic_number isn''t explicit in your dataset, you can always calculate it for each observation by floor(obs_#/4).&lt;/p&gt;

&lt;p&gt;Then by performing a Proc SQL sum() using &lt;a href="http://www.w3schools.com/sql/sql_groupby.asp" rel="nofollow"&gt;group by&lt;/a&gt;, you can reduce the data to look like this&lt;/p&gt;

&lt;blockquote&gt;
&lt;pre&gt;&lt;code&gt;''Topic_ID'' | ''Cat1'' | ''Cat2'' | ''Cat3'' | ''Cat4''
     1     |   1.5  |    3   |    1   |    4
     2     |    3   |   ...  |  ...   |  ...
&lt;/code&gt;&lt;/pre&gt;
&lt;/blockquote&gt;

&lt;p&gt;This isn''t necessarily the most efficient method, but it''s simple to implement with SAS.&lt;/p&gt;
', NULL, NULL, '''-4'':34C ''/4'':123C ''/a'':143C ''/blockquote'':101C,172C ''/code'':99C,170C ''/p'':55C,64C,124C,153C,190C ''/pre'':100C,171C ''/sql/sql_groupby.asp'':138C ''0'':76C,77C,78C,80C,82C,83C,85C,86C,88C,90C,91C,92C,96C,97C,98C ''1'':74C,79C,84C,87C,89C,163C,166C ''1.5'':75C,164C ''2'':94C,168C ''3'':81C,95C,165C,169C ''4'':93C,167C ''aggreg'':28C ''alway'':114C ''blockquot'':65C,154C ''calcul'':115C ''cat1'':33C,70C,159C ''cat2'':71C,160C ''cat3'':72C,161C ''cat4'':73C,162C ''code'':67C,156C ''could'':13C,36C ''data'':16C,148C ''dataset'':111C ''effici'':180C ''explicit'':108C ''first'':58C ''floor'':121C ''get'':62C ''group'':141C ''groupbi'':49C ''href'':135C ''id'':22C,53C,69C,158C ''implement'':187C ''isn'':106C,175C ''like'':151C ''look'':150C ''make'':20C ''method'':181C ''necessarili'':177C ''nofollow'':140C ''number'':105C ''ob'':122C ''observ'':119C ''p'':1C,56C,102C,125C,173C ''perform'':128C ''pre'':66C,155C ''proc'':39C,44C,130C ''reduc'':146C ''rel'':139C ''sas'':8C,189C ''simpl'':185C ''sinc'':5C ''sql'':45C,131C ''statement'':50C ''step'':17C,59C ''sum'':46C,132C ''think'':11C ''topic'':68C,104C,157C ''transpos'':40C ''use'':7C,14C,38C,133C ''var'':23C,30C ''variabl'':54C ''www.w3schools.com'':137C ''www.w3schools.com/sql/sql_groupby.asp'':136C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8752, 2, '2015-11-08 01:26:47.457', 0, NULL, NULL, '&lt;p&gt;I finally got the results I was looking for.&lt;/p&gt;

&lt;p&gt;Apparently a single hidden layer neural network cannot learn the cubic function. Instead of just evolving the weights, I included the topology of the neural network into the chromosome. My neural network can have maximum 5 hidden layers with maximum 5 neurons in each layer and the chromosome contains information for the structure of the network and the selection of the weights. &lt;/p&gt;

&lt;p&gt;When I ran the GA with population of 200 and for 100 iterations I got a full topology of:&lt;/p&gt;

&lt;p&gt;1(input) - 4-2-3-2-2 - 1(output), a mean squared error of 0.0051 and the following plot&lt;a href="http://i.stack.imgur.com/87WsZ.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/87WsZ.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I would be glad if anyone has any comments or questions.&lt;/p&gt;
', NULL, NULL, '''-2'':101C,103C,104C ''-3'':102C ''/87wsz.png'':121C,128C ''/a'':134C ''/p'':11C,75C,96C,135C,148C ''0.0051'':112C ''1'':98C,105C ''100'':88C ''200'':85C ''4'':100C ''5'':48C,53C ''alt'':129C ''anyon'':142C ''appar'':13C ''cannot'':20C ''chromosom'':41C,60C ''comment'':145C ''contain'':61C ''cubic'':23C ''descript'':132C ''enter'':130C ''error'':110C ''evolv'':28C ''final'':3C ''follow'':115C ''full'':93C ''function'':24C ''ga'':81C ''glad'':140C ''got'':4C,91C ''hidden'':16C,49C ''href'':118C ''i.stack.imgur.com'':120C,127C ''i.stack.imgur.com/87wsz.png'':119C,126C ''imag'':131C ''img'':124C ''includ'':32C ''inform'':62C ''input'':99C ''instead'':25C ''iter'':89C ''layer'':17C,50C,57C ''learn'':21C ''look'':9C ''maximum'':47C,52C ''mean'':108C ''network'':19C,38C,44C,68C ''neural'':18C,37C,43C ''neuron'':54C ''nofollow'':123C ''output'':106C ''p'':1C,12C,76C,97C,136C ''plot'':116C ''popul'':83C ''question'':147C ''ran'':79C ''rel'':122C ''result'':6C ''select'':71C ''singl'':15C ''squar'':109C ''src'':125C ''structur'':65C ''topolog'':34C,94C ''weight'':30C,74C ''would'':138C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8753, 1, '2015-11-08 04:17:56.45', 2, 105, 'What is a better input for Word2Vec?', '&lt;p&gt;This is more like a general NLP question. 
What is the appropriate input to train a word embedding namely Word2Vec? Should all sentences belonging to an article be a separate document in a corpus? Or should each article be a document in said corpus?
This is just an example using python and gensim.&lt;/p&gt;

&lt;p&gt;Corpus split by sentence&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;SentenceCorpus = [["first", "sentence", "of", "the", "first", "article."], ["second", "sentence", "of", "the", "first", "article."], ["first", "sentence", "of", "the", "second", "article."], ["second", "sentence", "of", "the", "second", "article."]]&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Corpus split by article&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;ArticleCorpus = [["first", "sentence", "of", "the", "first", "article.", "second", "sentence", "of", "the", "first", "article."], ["first", "sentence", "of", "the", "second", "article.", "second", "sentence", "of", "the", "second", "article."]]&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Training Word2Vec in Python&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;from gensim.models import Word2Vec&lt;/p&gt;
  
  &lt;p&gt;wikiWord2Vec = Word2Vec(ArticleCorpus)&lt;/p&gt;
&lt;/blockquote&gt;
', 'nlp  word-embeddings', NULL, '''/blockquote'':101C,136C,155C ''/p'':66C,72C,100C,107C,135C,142C,149C,154C ''appropri'':24C ''articl'':39C,50C,81C,87C,93C,99C,106C,116C,122C,128C,134C ''articlecorpus'':110C,153C ''belong'':36C ''better'':4A ''blockquot'':73C,108C,143C ''corpus'':46C,56C,68C,103C ''document'':43C,53C ''embed'':11B,30C ''exampl'':61C ''first'':76C,80C,86C,88C,111C,115C,121C,123C ''general'':18C ''gensim'':65C ''gensim.models'':146C ''import'':147C ''input'':5A,25C ''like'':16C ''name'':31C ''nlp'':8B,19C ''p'':12C,67C,74C,102C,109C,137C,144C,150C ''python'':63C,141C ''question'':20C ''said'':55C ''second'':82C,92C,94C,98C,117C,127C,129C,133C ''sentenc'':35C,71C,77C,83C,89C,95C,112C,118C,124C,130C ''sentencecorpus'':75C ''separ'':42C ''split'':69C,104C ''train'':27C,138C ''use'':62C ''wikiword2vec'':151C ''word'':10B,29C ''word-embed'':9B ''word2vec'':7A,32C,139C,148C,152C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8754, 1, '2015-11-08 05:22:06.33', 0, 28, 'Display Fisherface after reducing dimensions using PCA in Matlab', '&lt;p&gt;I''m learning to implement the Fisherface algorithm using Matlab. I understand that to deal with the singularity issue of Sw, I can project Sb and Sw onto the PCA space of r dimensions, with r â¤ rank(Sw) and compute the Fisherfaces in this PCA space. If I set r = 1000, what are my Fisherfaces'' dimensions, and how do I display them as images? Do I need to reconstruct them back to their original dimensions? If so, how do I do this?&lt;/p&gt;
', 'matlab', NULL, '''/p'':95C ''1000'':63C ''algorithm'':19C ''back'':83C ''comput'':52C ''deal'':26C ''dimens'':5A,45C,68C,87C ''display'':1A,73C ''fisherfac'':2A,18C,54C,67C ''imag'':76C ''implement'':16C ''issu'':30C ''learn'':14C ''m'':13C ''matlab'':9A,10B,21C ''need'':79C ''onto'':39C ''origin'':86C ''p'':11C ''pca'':7A,41C,57C ''project'':35C ''r'':44C,47C,62C ''rank'':49C ''reconstruct'':81C ''reduc'':4A ''sb'':36C ''set'':61C ''singular'':29C ''space'':42C,58C ''sw'':32C,38C,50C ''understand'':23C ''use'':6A,20C ''â'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8755, 1, '2015-11-08 07:01:32.58', 5, 115, 'Predicting New Data with Naive Bayes', '&lt;p&gt;Say I had the following training set for a Naive Bayes algorithm. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;   Outlook    Person    Play Golf?
   -------    ------    ----------
   Sunny      Joe       Yes
   Sunny      Mary      Yes
   Raining    Joe       Yes
   Raining    Mary      No
   Raining    Harry     Yes
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If try to predict whether Harry will play golf on a sunny day (which I have no data for). 
Would it be correct to exclude the person attribute and use the remaining outlook attribute to calculate the probability of this happening? Or could that potentially cause problems with a larger data set that I''m unaware of?&lt;/p&gt;
', 'machine-learning  classification  naive-bayes-classifier', NULL, '''/code'':50C ''/p'':28C,110C ''/pre'':51C ''algorithm'':27C ''attribut'':80C,86C ''bay'':6A,13B,26C ''calcul'':88C ''caus'':98C ''classif'':10B ''classifi'':14B ''code'':30C ''correct'':75C ''could'':95C ''data'':3A,70C,103C ''day'':65C ''exclud'':77C ''follow'':20C ''golf'':34C,61C ''happen'':93C ''harri'':48C,58C ''joe'':36C,42C ''larger'':102C ''learn'':9B ''m'':107C ''machin'':8B ''machine-learn'':7B ''mari'':39C,45C ''naiv'':5A,12B,25C ''naive-bayes-classifi'':11B ''new'':2A ''outlook'':31C,85C ''p'':15C,52C ''person'':32C,79C ''play'':33C,60C ''potenti'':97C ''pre'':29C ''predict'':1A,56C ''probabl'':90C ''problem'':99C ''rain'':41C,44C,47C ''remain'':84C ''say'':16C ''set'':22C,104C ''sunni'':35C,38C,64C ''train'':21C ''tri'':54C ''unawar'':108C ''use'':82C ''whether'':57C ''would'':72C ''yes'':37C,40C,43C,49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8757, 1, '2015-11-08 20:26:21.163', 3, 46, 'Introducing weights into spectral clustering', '&lt;p&gt;Suppose I have a data set with points $x_i$ and a dissimilarity measure $d_{ij}$ between each pair, as well as a weight $w_{ij}$ that qualifies the quality of this dissimilarity. I have two problems:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;The first one is how to introduce weights when performing spectral clustering. Like in the Shi-Malik, Ng-Jordan-Weiss versions, where only the distances are used.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;The second one is that my dissimilarity measure does not exists sometimes. In these cases, it has zero weight. How to introduce these cases in the algorithm? I suppose the zero weight solves the problem if I know how to introduce them.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;An idea I had is to perform the similarity transformation as:
$$
s_{ij} = w_{ij}  e^{-\frac{d^2_{ij}}{\sigma^2}}
$$
but I feel it does not make sense since the weigth should reflect the importance of the measure, not the measure itself.&lt;/p&gt;

&lt;p&gt;The data is not Euclidean, so I can use only this dissimilarity measure. Any reference is appreciated.&lt;/p&gt;
', 'clustering', NULL, '''/li'':79C,125C ''/p'':45C,78C,124C,172C,191C ''/ul'':126C ''2'':146C,149C ''algorithm'':108C ''appreci'':190C ''case'':96C,105C ''cluster'':5A,6B,60C ''d'':22C,145C ''data'':12C,175C ''dissimilar'':20C,40C,88C,185C ''distanc'':75C ''e'':143C ''euclidean'':178C ''exist'':92C ''feel'':152C ''first'':50C ''frac'':144C ''idea'':129C ''ij'':23C,33C,140C,142C,147C ''import'':164C ''introduc'':1A,55C,103C,122C ''jordan'':69C ''know'':119C ''li'':47C,80C ''like'':61C ''make'':156C ''malik'':66C ''measur'':21C,89C,167C,170C,186C ''ng'':68C ''ng-jordan-weiss'':67C ''one'':51C,84C ''p'':7C,48C,81C,127C,173C ''pair'':26C ''perform'':58C,134C ''point'':15C ''problem'':44C,116C ''qualifi'':35C ''qualiti'':37C ''refer'':188C ''reflect'':162C ''second'':83C ''sens'':157C ''set'':13C ''shi'':65C ''shi-malik'':64C ''sigma'':148C ''similar'':136C ''sinc'':158C ''solv'':114C ''sometim'':93C ''spectral'':4A,59C ''suppos'':8C,110C ''transform'':137C ''two'':43C ''ul'':46C ''use'':77C,182C ''version'':71C ''w'':32C,141C ''weight'':2A,31C,56C,100C,113C ''weigth'':160C ''weiss'':70C ''well'':28C ''x'':16C ''zero'':99C,112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8762, 1, '2015-11-09 09:40:19.857', 2, 402, 'How to define a distance measure between two IP addresses?', '&lt;p&gt;I have IP addresses as feature and I would like to know how much two IP addresses are similar to each other to use the difference in an Euclidean distance measure (in order to quantify the similarities of my data points). What tactic can I use for this?&lt;/p&gt;
', 'feature-extraction  preprocessing  featurization', NULL, '''/p'':65C ''address'':10A,20C,33C ''data'':56C ''defin'':3A ''differ'':42C ''distanc'':5A,46C ''euclidean'':45C ''extract'':13B ''featur'':12B,15B,22C ''feature-extract'':11B ''ip'':9A,19C,32C ''know'':28C ''like'':26C ''measur'':6A,47C ''much'':30C ''order'':49C ''p'':16C ''point'':57C ''preprocess'':14B ''quantifi'':51C ''similar'':35C,53C ''tactic'':59C ''two'':8A,31C ''use'':40C,62C ''would'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8763, 2, '2015-11-09 09:54:22.597', 1, NULL, NULL, '&lt;p&gt;IP (v4) addresses are a 32-bit integer, which trivially gives you a metric. However, it may not be a particularly &lt;em&gt;useful&lt;/em&gt; metric - 10.255.255.255 and 11.0.0.0 are almost certainly significantly more different than 192.168.1.1 and 192.168.1.2.&lt;/p&gt;
', NULL, NULL, '''/em'':25C ''/p'':40C ''10.255.255.255'':27C ''11.0.0.0'':29C ''192.168.1.1'':37C ''192.168.1.2'':39C ''32'':7C ''address'':4C ''almost'':31C ''bit'':8C ''certain'':32C ''differ'':35C ''em'':23C ''give'':12C ''howev'':16C ''integ'':9C ''ip'':2C ''may'':18C ''metric'':15C,26C ''p'':1C ''particular'':22C ''signific'':33C ''trivial'':11C ''use'':24C ''v4'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8764, 2, '2015-11-09 10:09:25.23', 3, NULL, NULL, '&lt;p&gt;That''s a very interesting question. Similarity here should be computed component-wise, but the thing is from a "business logic" perspective, the similarity of the last 3 numbers doesn''t matter if the other 3 sets of numbers are not the same. Keeping that in mind, I would probably do something like the following (there is probably a more elegant way of doing it, and I don''t have much time to think about it so forgive me if it doesn''t answer your question and for the poor formatting).&lt;/p&gt;

&lt;p&gt;Assuming IPv4 of the form aaa.bbb.ccc.ddd, I would so something like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;If aaa_1 == aaa_2:
  If bbb_1 == bbb_2:
    If ccc_1 == ccc_2:
        If ddd_1 == ddd_2:
            Dist = 1;
        Else:
            Dist = (3 + distance(ddd_1,ddd_2))/4;
        End if;
    Else:
        Dist = (2 + distance(ccc_1,ccc_2))/4;
    End if;
  Else:
    Dist = (1 + distance(bbb_1,bbb_2))/4;
  End if;
 Else:
  Dist = distance(aaa_1,aaa_2);
  Return 1/Dist;
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/4'':140C,151C,162C ''/code'':174C ''/p'':94C,107C ''/pre'':175C ''1'':112C,117C,122C,127C,131C,137C,148C,156C,159C,169C ''1/dist'':173C ''2'':114C,119C,124C,129C,139C,145C,150C,161C,171C ''3'':30C,38C,134C ''aaa'':111C,113C,168C,170C ''aaa.bbb.ccc.ddd'':101C ''answer'':86C ''assum'':96C ''bbb'':116C,118C,158C,160C ''busi'':22C ''ccc'':121C,123C,147C,149C ''code'':109C ''compon'':14C ''component-wis'':13C ''comput'':12C ''ddd'':126C,128C,136C,138C ''dist'':130C,133C,144C,155C,166C ''distanc'':135C,146C,157C,167C ''doesn'':32C,84C ''eleg'':63C ''els'':132C,143C,154C,165C ''end'':141C,152C,163C ''follow'':57C ''forgiv'':80C ''form'':100C ''format'':93C ''interest'':6C ''ipv4'':97C ''keep'':46C ''last'':29C ''like'':55C,106C ''logic'':23C ''matter'':34C ''mind'':49C ''much'':73C ''number'':31C,41C ''p'':1C,95C ''perspect'':24C ''poor'':92C ''pre'':108C ''probabl'':52C,60C ''question'':7C,88C ''return'':172C ''set'':39C ''similar'':8C,26C ''someth'':54C,105C ''thing'':18C ''think'':76C ''time'':74C ''way'':64C ''wise'':15C ''would'':51C,103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8765, 2, '2015-11-09 11:06:56.353', 0, NULL, NULL, '&lt;p&gt;From what you describe, it seems that something like &lt;a href="https://en.wikipedia.org/wiki/Pointwise_mutual_information" rel="nofollow"&gt;Pointwise Mutual Information&lt;/a&gt; could satisfy your requirements. It''s often used in text mining and opinion analysis to analyse the degree of correlation between specific terms and classes (e.g. a term in a product review and the "positive reviews" class).&lt;/p&gt;
', NULL, NULL, '''/a'':21C ''/p'':59C ''/wiki/pointwise_mutual_information'':15C ''analys'':37C ''analysi'':35C ''class'':46C,58C ''correl'':41C ''could'':22C ''degre'':39C ''describ'':5C ''e.g'':47C ''en.wikipedia.org'':14C ''en.wikipedia.org/wiki/pointwise_mutual_information'':13C ''href'':12C ''inform'':20C ''like'':10C ''mine'':32C ''mutual'':19C ''nofollow'':17C ''often'':28C ''opinion'':34C ''p'':1C ''pointwis'':18C ''posit'':56C ''product'':52C ''rel'':16C ''requir'':25C ''review'':53C,57C ''satisfi'':23C ''seem'':7C ''someth'':9C ''specif'':43C ''term'':44C,49C ''text'':31C ''use'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8794, 1, '2015-11-11 06:34:17.577', 0, 23, 'Understanding convolutional pooling sizes (deep learning)', '&lt;p&gt;I''m dumb but still trying to understand the code provided from this &lt;a href="http://neuralnetworksanddeeplearning.com/chap6.html" rel="nofollow"&gt;e-book&lt;/a&gt; on deep learning, but it doesn''t explain where the &lt;code&gt;n_in=40*4*4&lt;/code&gt; comes from. &lt;code&gt;40&lt;/code&gt; is from the 40 previous feature maps, but what about the &lt;code&gt;4*4&lt;/code&gt;?&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/AfDJj.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/AfDJj.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;&amp;gt;&amp;gt; net = Network([
        ConvPoolLayer(image_shape=(mini_batch_size, 1, 28, 28), 
                      filter_shape=(20, 1, 5, 5), 
                      poolsize=(2, 2), 
                      activation_fn=ReLU),
        ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12), 
                      filter_shape=(40, 20, 5, 5), 
                      poolsize=(2, 2), 
                      activation_fn=ReLU),
        FullyConnectedLayer(
            n_in=40*4*4, n_out=1000, activation_fn=ReLU, p_dropout=0.5),
        FullyConnectedLayer(
            n_in=1000, n_out=1000, activation_fn=ReLU, p_dropout=0.5),
        SoftmaxLayer(n_in=1000, n_out=10, p_dropout=0.5)], 
        mini_batch_size)
&amp;gt;&amp;gt;&amp;gt; net.SGD(expanded_training_data, 40, mini_batch_size, 0.03, 
            validation_data, test_data)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For instance, what if I do a similar analysis in 1D as shown below, which should that &lt;code&gt;n_in&lt;/code&gt; term be?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;&amp;gt;&amp;gt; net = Network([
        ConvPoolLayer(image_shape=(mini_batch_size, 1, 81, 1), 
                      filter_shape=(20, 1, 5, 1), 
                      poolsize=(2, 1), 
                      activation_fn=ReLU),
        ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 1), 
                      filter_shape=(40, 20, 5, 1), 
                      poolsize=(2, 1), 
                      activation_fn=ReLU),
        FullyConnectedLayer(
            n_in=40*???, n_out=1000, activation_fn=ReLU, p_dropout=0.5),
        FullyConnectedLayer(
            n_in=1000, n_out=1000, activation_fn=ReLU, p_dropout=0.5),
        SoftmaxLayer(n_in=1000, n_out=10, p_dropout=0.5)], 
        mini_batch_size)
&amp;gt;&amp;gt;&amp;gt; net.SGD(expanded_training_data, 40, mini_batch_size, 0.03, 
            validation_data, test_data)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'neuralnetwork  deep-learning', NULL, '''/a'':35C,92C ''/afdjj.png'':79C,86C ''/chap6.html'':29C ''/code'':52C,57C,72C,200C,223C,331C ''/p'':73C,93C,226C,335C ''/pre'':201C,332C ''0.03'':195C,326C ''0.5'':157C,170C,180C,288C,301C,311C ''1'':107C,113C,240C,242C,246C,248C,251C,263C,269C,272C ''10'':177C,308C ''1000'':151C,161C,164C,174C,282C,292C,295C,305C ''12'':129C,130C,262C ''1d'':213C ''2'':117C,118C,138C,139C,250C,271C ''20'':112C,128C,134C,245C,261C,267C ''28'':108C,109C ''4'':50C,51C,70C,71C,147C,148C ''40'':49C,56C,61C,133C,146C,191C,266C,279C,322C ''5'':114C,115C,135C,136C,247C,268C ''81'':241C ''activ'':119C,140C,152C,165C,252C,273C,283C,296C ''alt'':87C ''analysi'':211C ''batch'':105C,126C,182C,193C,238C,259C,313C,324C ''book'':34C ''code'':21C,46C,55C,69C,95C,220C,228C ''come'':53C ''convolut'':2A ''convpoollay'':101C,122C,234C,255C ''data'':190C,197C,199C,321C,328C,330C ''deep'':5A,9B,37C ''deep-learn'':8B ''descript'':90C ''doesn'':41C ''dropout'':156C,169C,179C,287C,300C,310C ''dumb'':14C ''e'':33C ''e-book'':32C ''enter'':88C ''expand'':188C,319C ''explain'':43C ''featur'':63C ''filter'':110C,131C,243C,264C ''fn'':120C,141C,153C,166C,253C,274C,284C,297C ''fullyconnectedlay'':143C,158C,276C,289C ''gt'':96C,97C,98C,184C,185C,186C,229C,230C,231C,315C,316C,317C ''href'':26C,76C ''i.stack.imgur.com'':78C,85C ''i.stack.imgur.com/afdjj.png'':77C,84C ''imag'':89C,102C,123C,235C,256C ''img'':82C ''instanc'':204C ''learn'':6A,10B,38C ''m'':13C ''map'':64C ''mini'':104C,125C,181C,192C,237C,258C,312C,323C ''n'':47C,144C,149C,159C,162C,172C,175C,221C,277C,280C,290C,293C,303C,306C ''net'':99C,232C ''net.sgd'':187C,318C ''network'':100C,233C ''neuralnetwork'':7B ''neuralnetworksanddeeplearning.com'':28C ''neuralnetworksanddeeplearning.com/chap6.html'':27C ''nofollow'':31C,81C ''p'':11C,74C,155C,168C,178C,202C,286C,299C,309C,333C ''pool'':3A ''poolsiz'':116C,137C,249C,270C ''pre'':94C,227C ''previous'':62C ''provid'':22C ''rel'':30C,80C ''relu'':121C,142C,154C,167C,254C,275C,285C,298C ''shape'':103C,111C,124C,132C,236C,244C,257C,265C ''shown'':215C ''similar'':210C ''size'':4A,106C,127C,183C,194C,239C,260C,314C,325C ''softmaxlay'':171C,302C ''src'':83C ''still'':16C ''term'':224C ''test'':198C,329C ''thank'':334C ''train'':189C,320C ''tri'':17C ''understand'':1A,19C ''valid'':196C,327C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8769, 2, '2015-11-09 23:11:48.677', 2, NULL, NULL, '&lt;p&gt;Yes I think so. Just by looking at &lt;a href="https://en.wikipedia.org/wiki/Feature_learning" rel="nofollow"&gt;Feature Learning&lt;/a&gt; and &lt;a href="https://en.wikipedia.org/wiki/Feature_extraction" rel="nofollow"&gt;Feature extraction&lt;/a&gt; you can see it''s a different problem.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Feature extraction&lt;/strong&gt; is just transforming your raw data into a sequence of feature vectors (e.g. a dataframe) that you can work on. &lt;/p&gt;

&lt;p&gt;In &lt;strong&gt;feature learning&lt;/strong&gt;, you don''t know what feature you can extract from your data. In fact, you will probably apply machine learning techniques just to  discover what are good features to extract from your dataset. Then you can extract them them apply machine learning to the extracted features. Deep learning techniques are one example of this.&lt;/p&gt;

&lt;p&gt;In the &lt;strong&gt;word2vec&lt;/strong&gt; toolkit, for instance, you extract vectors from documents which can''t be easily interpreted by a human, you can''t look at it and tell what &lt;em&gt;features&lt;/em&gt; have been extracted at all. It''s just a mass of vectors which, for some reason, give good empirical results.&lt;/p&gt;
', NULL, NULL, '''/a'':19C,30C ''/em'':161C ''/p'':39C,65C,126C,182C ''/strong'':44C,71C,132C ''/wiki/feature_extraction'':25C ''/wiki/feature_learning'':14C ''appli'':89C,111C ''data'':50C,83C ''datafram'':59C ''dataset'':104C ''deep'':118C ''differ'':37C ''discov'':95C ''document'':140C ''e.g'':57C ''easili'':145C ''em'':159C ''empir'':180C ''en.wikipedia.org'':13C,24C ''en.wikipedia.org/wiki/feature_extraction'':23C ''en.wikipedia.org/wiki/feature_learning'':12C ''exampl'':123C ''extract'':29C,43C,80C,101C,108C,116C,137C,164C ''fact'':85C ''featur'':17C,28C,42C,55C,69C,77C,99C,117C,160C ''give'':178C ''good'':98C,179C ''href'':11C,22C ''human'':149C ''instanc'':135C ''interpret'':146C ''know'':75C ''learn'':18C,70C,91C,113C,119C ''look'':8C,153C ''machin'':90C,112C ''mass'':171C ''nofollow'':16C,27C ''one'':122C ''p'':1C,40C,66C,127C ''probabl'':88C ''problem'':38C ''raw'':49C ''reason'':177C ''rel'':15C,26C ''result'':181C ''see'':33C ''sequenc'':53C ''strong'':41C,68C,130C ''techniqu'':92C,120C ''tell'':157C ''think'':4C ''toolkit'':133C ''transform'':47C ''vector'':56C,138C,173C ''word2vec'':131C ''work'':63C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8770, 2, '2015-11-09 23:14:37.53', 1, NULL, NULL, '&lt;p&gt;Not sure whether it''s been mentioned yet, but there''s also &lt;a href="https://github.com/JohnLangford/vowpal_wabbit" rel="nofollow"&gt;vowpal wabbit&lt;/a&gt; but it might be specific to certain kinds of problem only.&lt;/p&gt;
', NULL, NULL, '''/a'':23C ''/johnlangford/vowpal_wabbit'':18C ''/p'':35C ''also'':13C ''certain'':30C ''github.com'':17C ''github.com/johnlangford/vowpal_wabbit'':16C ''href'':15C ''kind'':31C ''mention'':8C ''might'':26C ''nofollow'':20C ''p'':1C ''problem'':33C ''rel'':19C ''specif'':28C ''sure'':3C ''vowpal'':21C ''wabbit'':22C ''whether'':4C ''yet'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9423, 2, '2015-12-17 09:30:24.847', 2, NULL, NULL, '&lt;p&gt;I decided to expand a bit on my comment and make a full answer. &lt;/p&gt;

&lt;p&gt;So the reason why somebody may say that performing the hashing trick can destroy interactions is because it may map different features to the same bucket. But usually it''s not a big deal. &lt;/p&gt;

&lt;p&gt;Note that &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html" rel="nofollow"&gt;DictVectorizer&lt;/a&gt; doesn''t perform the hashing trick:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;When feature values are strings, this transformer will do a binary one-hot (aka one-of-K) coding: one boolean-valued feature is constructed for each of the possible string values that the feature can take on. For instance, a feature âfâ that can take on the values âhamâ and âspamâ will become two features in the output, one signifying âf=hamâ, the other âf=spamâ.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;To do it, you need to use a different vectorizer: &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.HashingVectorizer.html" rel="nofollow"&gt;HashingVectorizer&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':64C,169C ''/blockquote'':149C ''/p'':16C,52C,71C,148C,170C ''/stable/modules/generated/sklearn.feature_extraction.dictvectorizer.html'':60C ''/stable/modules/generated/sklearn.feature_extraction.text.hashingvectorizer.html'':165C ''aka'':88C ''answer'':15C ''becom'':132C ''big'':50C ''binari'':84C ''bit'':7C ''blockquot'':72C ''boolean'':96C ''boolean-valu'':95C ''bucket'':43C ''code'':93C ''comment'':10C ''construct'':100C ''deal'':51C ''decid'':3C ''destroy'':31C ''dictvector'':63C ''differ'':38C,159C ''doesn'':65C ''expand'':5C ''f'':141C,146C ''featur'':39C,75C,98C,110C,117C,134C ''full'':14C ''fâ'':119C ''hamâ'':127C,142C ''hash'':28C,69C ''hashingvector'':168C ''hot'':87C ''href'':57C,162C ''instanc'':115C ''interact'':32C ''k'':92C ''make'':12C ''map'':37C ''may'':23C,36C ''need'':155C ''nofollow'':62C,167C ''note'':54C ''one'':86C,90C,94C,138C ''one-hot'':85C ''one-of-k'':89C ''output'':137C ''p'':1C,17C,53C,73C,150C ''perform'':26C,67C ''possibl'':105C ''reason'':20C ''rel'':61C,166C ''say'':24C ''scikit-learn.org'':59C,164C ''scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.dictvectorizer.html'':58C ''scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.hashingvectorizer.html'':163C ''signifi'':139C ''somebodi'':22C ''spamâ'':130C,147C ''string'':78C,106C ''take'':112C,122C ''transform'':80C ''trick'':29C,70C ''two'':133C ''use'':157C ''usual'':45C ''valu'':76C,97C,107C,125C ''vector'':160C ''â'':118C,126C,129C,140C,145C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8771, 2, '2015-11-09 23:48:53.397', 2, NULL, NULL, '&lt;p&gt;20 years ago, I would have suggested to use the &lt;strong&gt;length of the shared prefix&lt;/strong&gt; as similarity measure.&lt;/p&gt;

&lt;p&gt;So you take two IPs. In their 32 bit representation, not the "pretty printed" x.y.z.w form; the real "int" reoresentation your network stack uses. Then XOR them, count the leading zeros, and you get&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;distance = 32 - leadingZeros(ip1 XOR ip2)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However, we have exhausted the IPv4 namespace long ago. The last 10 years, the few remaining netblock have been more or less "randomly" (at least from a similarity perspective) been distributed. &lt;strong&gt;IP ranges have been relocated&lt;/strong&gt; and so on.&lt;/p&gt;

&lt;p&gt;A lomg time ago, people would have told you routing happens on trees, based on their prefix. So If you wanted to read an IP 10.2.3.4 it would go to 10.0.0.0 then 10.2.0.0 then 10.2.3.0. But that was just the theory. If you &lt;em&gt;manually&lt;/em&gt; configured your router, that is what you would do. 10.2 isthe second building, 10.2.3. is the third floor router.
History.
Within networks, IPs are assigned by DHCP, often first-come-first-served. Within intranet, you have mostly switches not routers. And on the global level, the &lt;strong&gt;BGP&lt;/strong&gt; is responsible for taking care of the &lt;strong&gt;big mess&lt;/strong&gt; of todays routing tables.&lt;/p&gt;

&lt;p&gt;In other words:
use some database like GeoIP to map the IPs to (approximate) coordinates. Best you can do.
IP based similarity is mostly useful on a /24 prefix, but a binary yes/no similarity won''t make you happy I guess.&lt;/p&gt;
', NULL, NULL, '''/24'':253C ''/code'':67C ''/em'':158C ''/p'':22C,58C,111C,224C,267C ''/pre'':68C ''/strong'':18C,107C,208C,219C ''10'':81C ''10.0.0.0'':143C ''10.2'':168C ''10.2.0.0'':145C ''10.2.3'':172C ''10.2.3.0'':147C ''10.2.3.4'':138C ''20'':2C ''32'':31C,62C ''ago'':4C,78C,116C ''approxim'':239C ''assign'':183C ''base'':126C,246C ''best'':241C ''bgp'':207C ''big'':217C ''binari'':257C ''bit'':32C ''build'':171C ''care'':213C ''code'':60C ''come'':189C ''configur'':159C ''coordin'':240C ''count'':51C ''databas'':231C ''dhcp'':185C ''distanc'':61C ''distribut'':100C ''em'':156C ''exhaust'':73C ''first'':188C,190C ''first-come-first-serv'':187C ''floor'':176C ''form'':39C ''geoip'':233C ''get'':57C ''global'':203C ''go'':141C ''guess'':266C ''happen'':123C ''happi'':264C ''histori'':178C ''howev'':70C ''int'':42C ''intranet'':193C ''ip'':28C,102C,137C,181C,237C,245C ''ip1'':64C ''ip2'':66C ''ipv4'':75C ''isth'':169C ''last'':80C ''lead'':53C ''leadingzero'':63C ''least'':94C ''length'':13C ''less'':91C ''level'':204C ''like'':232C ''lomg'':114C ''long'':77C ''make'':262C ''manual'':157C ''map'':235C ''measur'':21C ''mess'':218C ''most'':196C,249C ''namespac'':76C ''netblock'':86C ''network'':45C,180C ''often'':186C ''p'':1C,23C,69C,112C,225C ''peopl'':117C ''perspect'':98C ''pre'':59C ''prefix'':17C,129C,254C ''pretti'':36C ''print'':37C ''random'':92C ''rang'':103C ''read'':135C ''real'':41C ''reloc'':106C ''remain'':85C ''reoresent'':43C ''represent'':33C ''respons'':210C ''rout'':122C,222C ''router'':161C,177C,199C ''second'':170C ''serv'':191C ''share'':16C ''similar'':20C,97C,247C,259C ''stack'':46C ''strong'':12C,101C,206C,216C ''suggest'':8C ''switch'':197C ''tabl'':223C ''take'':26C,212C ''theori'':153C ''third'':175C ''time'':115C ''today'':221C ''told'':120C ''tree'':125C ''two'':27C ''use'':10C,47C,229C,250C ''want'':133C ''within'':179C,192C ''won'':260C ''word'':228C ''would'':6C,118C,140C,166C ''x.y.z.w'':38C ''xor'':49C,65C ''year'':3C,82C ''yes/no'':258C ''zero'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8778, 2, '2015-11-10 13:09:21.747', 0, NULL, NULL, '&lt;p&gt;For the numbers you mention, I think all alternatives should work (read: you''ll be able to finish your analysis in reasonable time).
I recommend on a design that can lead to significantly faster results.&lt;/p&gt;

&lt;p&gt;As answered before, in general postgresql is faster than mongo, some times more than 4 times faster.
See for example:
&lt;a href="http://www.enterprisedb.com/postgres-plus-edb-blog/marc-linster/postgres-outperforms-mongodb-and-ushers-new-developer-reality" rel="nofollow"&gt;http://www.enterprisedb.com/postgres-plus-edb-blog/marc-linster/postgres-outperforms-mongodb-and-ushers-new-developer-reality&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You said that you are interested in improving performance in joins.
I assume that you are interested in calculating similarities among the entities (e.g., post, author) so you''ll mainly join the table with it self (e.g., by post or author) and aggregate.&lt;/p&gt;

&lt;p&gt;Add to that the fact that after the initial loading your database will be read only, what make the problem very suitable to index usage. You won''t pay for index update since you won''t have any and I guess you have the extra storage for the index.&lt;/p&gt;

&lt;p&gt;I would have use postgres and store the data in two tables:&lt;/p&gt;

&lt;p&gt;create table posts(
post_id integer,
url varchar(255),
author_id integer
)
;&lt;/p&gt;

&lt;p&gt;-- Load data and then create the indices.
-- That will lead to a faster load and better indices
alter table posts add constraint posts_pk primary key(post_id);
create  index post_author on posts(author_id);&lt;/p&gt;

&lt;p&gt;create table comments(
comment_id integer,
post_id integer,
author_id integer,
comment varchar(255)
)
;
alter table comments add constraint comments_pk primary key(comment_id);
create  index comment_author on comments(author_id);
create  index comment_post on comments(post_id);&lt;/p&gt;

&lt;p&gt;Then you can compute author similarity based on comments in queries like
select
m. author_id as m_author_id,
a. author_id as a_author_id,
count(distinct m.post_id) as posts
from
comments as m 
join
comments as a
using
(post_id)
group by
m.author_id
, a. author_id&lt;/p&gt;

&lt;p&gt;In case you are interested in tokenzing the words in the comment for nlp, add another table for that but remember that it will increase the volume of your data significantly.Usually it is better not to represent the entire tokenization in the database.&lt;/p&gt;
', NULL, NULL, '''/p'':37C,113C,164C,178C,192C,230C,274C,327C,372C ''/postgres-plus-edb-blog/marc-linster/postgres-outperforms-mongodb-and-ushers-new-developer-reality'':63C ''/postgres-plus-edb-blog/marc-linster/postgres-outperforms-mongodb-and-ushers-new-developer-reality&lt;/a&gt;&lt;/p&gt;'':68C ''255'':188C,246C ''4'':53C ''abl'':17C ''add'':115C,214C,250C,343C ''aggreg'':112C ''alter'':211C,247C ''altern'':10C ''among'':90C ''analysi'':21C ''anoth'':344C ''answer'':40C ''assum'':82C ''author'':95C,110C,189C,225C,228C,241C,261C,264C,280C,290C,294C,297C,301C,325C ''base'':282C ''better'':209C,362C ''calcul'':88C ''case'':330C ''comment'':234C,235C,244C,249C,252C,256C,260C,263C,268C,271C,284C,310C,314C,340C ''comput'':279C ''constraint'':215C,251C ''count'':303C ''creat'':180C,198C,222C,232C,258C,266C ''data'':174C,195C,358C ''databas'':126C,371C ''design'':29C ''distinct'':304C ''e.g'':93C,106C ''entir'':367C ''entiti'':92C ''exampl'':58C ''extra'':159C ''fact'':119C ''faster'':35C,46C,55C,206C ''finish'':19C ''general'':43C ''group'':320C ''guess'':155C ''href'':60C ''id'':184C,190C,221C,229C,236C,239C,242C,257C,265C,273C,291C,295C,298C,302C,306C,319C,323C,326C ''improv'':77C ''increas'':353C ''index'':138C,145C,163C,223C,259C,267C ''indic'':200C,210C ''initi'':123C ''integ'':185C,191C,237C,240C,243C ''interest'':75C,86C,333C ''join'':80C,100C,313C ''key'':219C,255C ''lead'':32C,203C ''like'':287C ''ll'':15C,98C ''load'':124C,194C,207C ''m'':289C,293C,312C ''m.author'':322C ''m.post'':305C ''main'':99C ''make'':132C ''mention'':6C ''mongo'':48C ''nlp'':342C ''nofollow'':65C ''number'':4C ''p'':1C,38C,69C,114C,165C,179C,193C,231C,275C,328C ''pay'':143C ''perform'':78C ''pk'':217C,253C ''post'':94C,108C,182C,183C,213C,216C,220C,224C,227C,238C,269C,272C,308C,318C ''postgr'':170C ''postgresql'':44C ''primari'':218C,254C ''problem'':134C ''queri'':286C ''read'':13C,129C ''reason'':23C ''recommend'':26C ''rel'':64C ''rememb'':349C ''repres'':365C ''result'':36C ''said'':71C ''see'':56C ''select'':288C ''self'':105C ''signific'':34C ''significantly.usually'':359C ''similar'':89C,281C ''sinc'':147C ''storag'':160C ''store'':172C ''suitabl'':136C ''tabl'':102C,177C,181C,212C,233C,248C,345C ''think'':8C ''time'':24C,50C,54C ''token'':368C ''tokenz'':335C ''two'':176C ''updat'':146C ''url'':186C ''usag'':139C ''use'':169C,317C ''varchar'':187C,245C ''volum'':355C ''won'':141C,149C ''word'':337C ''work'':12C ''would'':167C ''www.enterprisedb.com'':62C,67C ''www.enterprisedb.com/postgres-plus-edb-blog/marc-linster/postgres-outperforms-mongodb-and-ushers-new-developer-reality'':61C ''www.enterprisedb.com/postgres-plus-edb-blog/marc-linster/postgres-outperforms-mongodb-and-ushers-new-developer-reality&lt;/a&gt;&lt;/p&gt;'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8772, 1, '2015-11-10 00:31:29.863', -1, 35, 'How do I count every combination of numbers from left to right?', '&lt;p&gt;Let''s say you have the array of numbers listed below of n &amp;lt; n + 1:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  YourArray = [1, 2, 3, 4, 5]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;...and you wanted to count every combination of these numbers from left to right.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1) [1, 2], [1, 2, 3], [1, 2, 3, 4], [1, 2, 3, 4, 5]
2) [2, 3], [2, 3, 4], [2, 3, 4, 5]
3) [3, 4], [3, 4, 5]
4) [4, 5]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;...how would you go about counting the combinations to get 10?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Possible Answer&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Forgive me, I''m a complete moron when it comes to math. Looking at what I wrote in plain text, I can see maybe the answer is...&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;abs(1 - 5) = 4
abs(2 - 5) = 3
abs(3 - 5) = 2
abs(4 - 5) = 1
--------------
    Total = 10
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;But What If?&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1) [2, 3], [2, 3, 4,], [2, 3, 4, 10]
2) [3, 4], [3, 4, 10]
3) [4, 10]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In this case, would it be a good approach to ignore the values and just focus on the total numbers of values? So, instead of it being &lt;/p&gt;

&lt;pre&gt;&lt;code&gt; [2, 3], [2, 3, 4]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I should convert it to...&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; [1, 2], [1, 2, 3]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then do the above method of abs(1 - 4) = 3?&lt;/p&gt;

&lt;p&gt;&lt;em&gt;(Note: abs() = absolute value)&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Purpose&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I''m trying to break a bigger problem into parts. Maybe this is a dumb approach, but any help is appreciated. &lt;/p&gt;

&lt;p&gt;No, this is not for a class or anything. Just playing with random problems to learn new things.&lt;/p&gt;
', 'algorithms', NULL, '''/code'':40C,94C,164C,194C,232C,248C ''/em'':268C ''/p'':31C,57C,108C,114C,143C,172C,224C,240C,261C,269C,274C,297C,317C ''/pre'':41C,95C,165C,195C,233C,249C ''/strong'':113C,171C,273C ''1'':30C,35C,60C,61C,63C,66C,70C,147C,161C,175C,243C,245C,258C ''10'':107C,163C,184C,190C,193C ''2'':36C,62C,64C,67C,71C,75C,76C,78C,81C,151C,157C,176C,178C,181C,185C,227C,229C,244C,246C ''3'':37C,65C,68C,72C,77C,79C,82C,85C,86C,88C,153C,155C,177C,179C,182C,186C,188C,191C,228C,230C,247C,260C ''4'':38C,69C,73C,80C,83C,87C,89C,91C,92C,149C,159C,180C,183C,187C,189C,192C,231C,259C ''5'':39C,74C,84C,90C,93C,148C,152C,156C,160C ''ab'':146C,150C,154C,158C,257C,265C ''absolut'':266C ''algorithm'':13B ''answer'':112C,141C ''anyth'':307C ''appreci'':296C ''approach'':205C,291C ''array'':21C ''bigger'':282C ''break'':280C ''case'':199C ''class'':305C ''code'':33C,59C,145C,174C,226C,242C ''combin'':6A,49C,104C ''come'':125C ''complet'':121C ''convert'':237C ''count'':4A,47C,102C ''dumb'':290C ''em'':263C ''everi'':5A,48C ''focus'':212C ''forgiv'':116C ''get'':106C ''go'':100C ''good'':204C ''help'':294C ''ignor'':207C ''instead'':220C ''learn'':314C ''left'':10A,54C ''let'':15C ''list'':24C ''look'':128C ''lt'':28C ''m'':119C,277C ''math'':127C ''mayb'':139C,286C ''method'':255C ''moron'':122C ''n'':27C,29C ''new'':315C ''note'':264C ''number'':8A,23C,52C,216C ''p'':14C,42C,96C,109C,115C,166C,196C,234C,250C,262C,270C,275C,298C ''part'':285C ''plain'':134C ''play'':309C ''possibl'':111C ''pre'':32C,58C,144C,173C,225C,241C ''problem'':283C,312C ''purpos'':272C ''random'':311C ''right'':12A,56C ''say'':17C ''see'':138C ''strong'':110C,167C,271C ''text'':135C ''thing'':316C ''total'':162C,215C ''tri'':278C ''valu'':209C,218C,267C ''want'':45C ''would'':98C,200C ''wrote'':132C ''yourarray'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8776, 2, '2015-11-10 11:46:16.91', 2, NULL, NULL, '&lt;p&gt;Among Naive Bayes assumptions the main one is that features are conditionally independent. For our problem we would have:&lt;/p&gt;

&lt;p&gt;$$P(Play|Outlook,Person) \propto P(Play)P(Outlook|Play)P(Name|Play)$$&lt;/p&gt;

&lt;p&gt;To address question &lt;em&gt;is Harry going to play on a sunny day?&lt;/em&gt;, you have to compute the following:&lt;/p&gt;

&lt;p&gt;$$P(Yes|Sunny,Harry) = P(Yes)P(Sunny|Yes)P(Harry|Yes)$$
$$P(No|Sunny,Harry) = P(No)P(Sunny|No)P(Harry|No)$$&lt;/p&gt;

&lt;p&gt;and choose the probability with bigger value. &lt;/p&gt;

&lt;p&gt;That is what theory says. To address your question I will rephrase the main assumption of Naive Bayes. The assumptions that features are independent given the output means basically that the information given by joint distribution can be obtained by product of marginals. In plain English: assume you can find if Harry plays on sunny days if you only know how much Harry plays in general and how much anybody plays on sunny days. As you can see, &lt;em&gt;you simply you would not use the fact that Harry plays on sunny days&lt;/em&gt; &lt;strong&gt;even if you would have had that record in your data&lt;/strong&gt;. Simply because Naive Bayes assumes there is no useful information in the interaction between the features, and this is the precise meaning of conditional independence, which Naive Bayes relies upon. &lt;/p&gt;

&lt;p&gt;That said if you would want to use the interaction of features than you would have either to use a different model, or simply add a new &lt;em&gt;combined&lt;/em&gt; feature like a concatenation of factors of names and outlook. &lt;/p&gt;

&lt;p&gt;As a conclusion when you do not include names in your input features you will have a &lt;em&gt;general wisdom&lt;/em&gt; classifier like &lt;em&gt;everybody plays no matter outlook&lt;/em&gt;, since most of the instances have play=yes. If you include the name in your input variables you allow to alter that &lt;em&gt;general wisdom&lt;/em&gt; with something specific to player. So your classifier &lt;em&gt;wisdom&lt;/em&gt; would look like &lt;em&gt;players prefer in general to play, no matter outlook, but Marry like less to play less on Rainy&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;There is however a potential problem with Naive Bayes on your data set. This problem is related with the potential big number of levels for variable Name. In order to approximate the probability there is a general thing that happens: more data, better estimates. This probably would happen with variable Outlook, since there are two levels and adding more data would probably not increase number of levels. So the estimates for Outlook would be probably better with more data. However for name you will not have the same situation. Adding more instances would be possible perhaps only by adding more names. Which means that on average the number of instances for each name would be relatively stable. And if you would have a single instance, like it is the case for Harry, you do not have enough data to estimate $P(Harry|No)$. &lt;/p&gt;

&lt;p&gt;As it happens this problem can be alleviated using smoothing. Perhaps Laplace smoothing (or a more general for like Lindstone) is very helpful. The reason is that estimates based on maximum likelihood have big problems with cases like that.&lt;/p&gt;

&lt;p&gt;I hope it answers at least partially your question.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8777, 2, '2015-11-10 12:59:30.27', 1, NULL, NULL, '&lt;p&gt;First of all, you will not be able to prove anything with a model, you will have false positives/negatives. With a good model you may be able show what variables may be an indicator of component failure. &lt;/p&gt;

&lt;p&gt;In problems like this feature generation can have the most important influence on the accuracy of the model. The time stamps can be used for aggregation. For example, you may aggregate metrics per device per hour. The metrics/features that you might create for input into the model might be average/max temperature or fan speed, rate of change in temperature or fan speed, number of seconds device was above some threshold temperature or fan speed, boolean indicator of voltage spike, etc. There could be any number of features you may create. You can then find which features are not strong predictors and remove these columns to reduce noise, if need be.&lt;/p&gt;
', NULL, NULL, '''/p'':39C,151C ''abl'':9C,28C ''accuraci'':55C ''aggreg'':66C,71C ''anyth'':12C ''average/max'':90C ''boolean'':115C ''chang'':97C ''column'':144C ''compon'':37C ''could'':122C ''creat'':82C,130C ''devic'':74C,106C ''etc'':120C ''exampl'':68C ''failur'':38C ''fals'':19C ''fan'':93C,101C,113C ''featur'':45C,127C,136C ''find'':134C ''first'':2C ''generat'':46C ''good'':23C ''hour'':76C ''import'':51C ''indic'':35C,116C ''influenc'':52C ''input'':84C ''like'':43C ''may'':26C,32C,70C,129C ''metric'':72C ''metrics/features'':78C ''might'':81C,88C ''model'':15C,24C,58C,87C ''need'':149C ''nois'':147C ''number'':103C,125C ''p'':1C,40C ''per'':73C,75C ''positives/negatives'':20C ''predictor'':140C ''problem'':42C ''prove'':11C ''rate'':95C ''reduc'':146C ''remov'':142C ''second'':105C ''show'':29C ''speed'':94C,102C,114C ''spike'':119C ''stamp'':61C ''strong'':139C ''temperatur'':91C,99C,111C ''threshold'':110C ''time'':60C ''use'':64C ''variabl'':31C ''voltag'':118C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8805, 2, '2015-11-11 19:37:13.993', 0, NULL, NULL, '&lt;p&gt;Deep Learning has been around for a long time. CNN,RNN,Boltzmann Machines sure look like new techniques but they were developed long time back. Check the history of deep learning &lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Deep_learning#History" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Deep_learning#History&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The resurgence of Deep Learning is due to the fact that the computational powers increased exponentially from that time. With a laptop equipped with GPUs you can train a complex Deep Learning model is a very small time as compared to its previous days. Deep learning models are also very efficient empirically. Current state of the art in Images,Speech and many fields is Deep Learning models.&lt;/p&gt;

&lt;p&gt;I believe due to these factors we can see lot of NLP/ML community has shifted its focus on Deep Learning.    &lt;/p&gt;
', NULL, NULL, '''/p'':33C,113C,135C ''/wiki/deep_learning#history'':39C ''/wiki/deep_learning#history&lt;/a&gt;&lt;/p&gt;'':44C ''also'':94C ''around'':6C ''art'':102C ''back'':26C ''believ'':116C ''boltzmann'':13C ''check'':27C ''cnn'':11C ''communiti'':127C ''compar'':85C ''complex'':75C ''comput'':58C ''current'':98C ''day'':89C ''deep'':2C,31C,49C,76C,90C,110C,133C ''develop'':23C ''due'':52C,117C ''effici'':96C ''empir'':97C ''en.wikipedia.org'':38C,43C ''en.wikipedia.org/wiki/deep_learning#history'':37C ''en.wikipedia.org/wiki/deep_learning#history&lt;/a&gt;&lt;/p&gt;'':42C ''equip'':68C ''exponenti'':61C ''fact'':55C ''factor'':120C ''field'':108C ''focus'':131C ''gpus'':70C ''histori'':29C ''href'':36C ''imag'':104C ''increas'':60C ''laptop'':67C ''learn'':3C,32C,50C,77C,91C,111C,134C ''like'':17C ''long'':9C,24C ''look'':16C ''lot'':124C ''machin'':14C ''mani'':107C ''model'':78C,92C,112C ''new'':18C ''nlp/ml'':126C ''nofollow'':41C ''p'':1C,34C,45C,114C ''power'':59C ''previous'':88C ''rel'':40C ''resurg'':47C ''rnn'':12C ''see'':123C ''shift'':129C ''small'':82C ''speech'':105C ''state'':99C ''sure'':15C ''techniqu'':19C ''time'':10C,25C,64C,83C ''train'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8779, 2, '2015-11-10 13:11:38.847', 0, NULL, NULL, '&lt;p&gt;First you need to distinguish private and public addresses (check wikipedia IP_address#Private_addresses).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Private IPs:&lt;/strong&gt; the best you can do is compute if 2 addresses COULD be on the same network or not, then you need clues from other features to KNOW if it is the case or no.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Public IPs:&lt;/strong&gt;
For geographic distance, you may want to check web services/API that try to map IP and geographical locations (one google search turned this one for instance &lt;a href="https://www.db-ip.com/" rel="nofollow"&gt;enter link description here&lt;/a&gt;). &lt;/p&gt;

&lt;p&gt;Another point which could be interesting is the "organisational distance", from the IP address you can try to identify the owner of the address (the ISP), check ARIN for instance &lt;a href="http://whois.arin.net/rest/net/NET-8-8-8-0-1/pft?s=8.8.8.8" rel="nofollow"&gt;http://whois.arin.net/rest/net/NET-8-8-8-0-1/pft?s=8.8.8.8&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;From there you can try to figure out if two addresses belong to the same organisation or not. You will have to find some way to tell if the organisation is an ISP with private customers or a company with their own network. Please be also aware that some organisations have started to resell blocks from their IPV4 addresses to other companies for money with the effect that addresses that used to be in the same organisation/location can now be thousands of miles away in different companies.&lt;/p&gt;

&lt;p&gt;I think it would be wise to consider these informations as probabilities only.&lt;/p&gt;
', NULL, NULL, '''/a'':98C ''/p'':17C,57C,99C,141C,230C,245C ''/rest/net/net-8-8-8-0-1/pft?s=8.8.8.8'':135C ''/rest/net/net-8-8-8-0-1/pft?s=8.8.8.8&lt;/a&gt;.'':140C ''/strong'':22C,62C ''2'':31C ''address'':10C,14C,16C,32C,114C,124C,153C,201C,211C ''also'':188C ''anoth'':101C ''arin'':128C ''awar'':189C ''away'':226C ''belong'':154C ''best'':24C ''block'':197C ''case'':54C ''check'':11C,70C,127C ''clue'':44C ''compani'':181C,204C,229C ''comput'':29C ''consid'':239C ''could'':33C,104C ''custom'':178C ''descript'':96C ''differ'':228C ''distanc'':65C,110C ''distinguish'':6C ''effect'':209C ''enter'':94C ''featur'':47C ''figur'':149C ''find'':165C ''first'':2C ''geograph'':64C,79C ''googl'':82C ''href'':90C,132C ''identifi'':119C ''inform'':241C ''instanc'':88C,130C ''interest'':106C ''ip'':13C,21C,61C,77C,113C ''ipv4'':200C ''isp'':126C,175C ''know'':49C ''link'':95C ''locat'':80C ''map'':76C ''may'':67C ''mile'':225C ''money'':206C ''need'':4C,43C ''network'':38C,185C ''nofollow'':93C,137C ''one'':81C,86C ''organis'':109C,158C,172C,192C ''organisation/location'':219C ''owner'':121C ''p'':1C,18C,58C,100C,142C,231C ''pleas'':186C ''point'':102C ''privat'':7C,15C,20C,177C ''probabl'':243C ''public'':9C,60C ''rel'':92C,136C ''resel'':196C ''search'':83C ''services/api'':72C ''start'':194C ''strong'':19C,59C ''tell'':169C ''think'':233C ''thousand'':223C ''tri'':74C,117C,147C ''turn'':84C ''two'':152C ''use'':213C ''want'':68C ''way'':167C ''web'':71C ''whois.arin.net'':134C,139C ''whois.arin.net/rest/net/net-8-8-8-0-1/pft?s=8.8.8.8'':133C ''whois.arin.net/rest/net/net-8-8-8-0-1/pft?s=8.8.8.8&lt;/a&gt;.'':138C ''wikipedia'':12C ''wise'':237C ''would'':235C ''www.db-ip.com'':91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10310, 1, '2016-02-18 22:06:24.44', 1, 44, 'Which classification algorithm tackles heavily overlapped data points of different lables?', '&lt;p&gt;I am running into this problem and seeking for some advice. It seems tree models are feasible and also SVM is another possible solution. are there any other possible ''existing'' algos that tackle this type of problem? Thanks!&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/p'':55C ''advic'':27C ''algo'':47C ''algorithm'':3A ''also'':35C ''anoth'':38C ''classif'':2A,15B ''data'':7A ''differ'':10A ''exist'':46C ''feasibl'':33C ''heavili'':5A ''labl'':11A ''learn'':14B ''machin'':13B ''machine-learn'':12B ''model'':31C ''overlap'':6A ''p'':16C ''point'':8A ''possibl'':39C,45C ''problem'':22C,53C ''run'':19C ''seek'':24C ''seem'':29C ''solut'':40C ''svm'':36C ''tackl'':4A,49C ''thank'':54C ''tree'':30C ''type'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8789, 1, '2015-11-10 20:02:19.077', 2, 55, 'How to model compositional data?', '&lt;p&gt;&lt;/p&gt;

&lt;p&gt;What is the best way to model compositional data problems?&lt;/p&gt;

&lt;p&gt;Compositional data is when each example or sample is a vector that sums to 1 (or 100%). In my case, I am interested in the composition of minerals in a rock and I have sensors that tell me the sum of the minerals but not the components that make up the sum.&lt;/p&gt;

&lt;p&gt;For example, lets say I have two minerals, $m_1$ and $m_2$, that are made up of 3 elements (like copper and other elements from the periodic table) which form a vector of length 3:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;m1 = [0.1, 0.3, 0.6]
m2 = [0.6, 0.2, 0.2]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If a rock has 25% of $m_1$ and 75% of $m_2$, the sensor reading produces the sum of the two minerals (shown in the plot below): &lt;/p&gt;

&lt;p&gt;$$0.25*m_1 + 0.75*m_2 = [0.475, 0.225, 0.3]$$&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/LHbDs.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/LHbDs.png" alt="Mineral composition example"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I would like to know how to model and solve the problem of unmixing a composition into its underlying components, where the sum of the elements is normalized to 100% (e.g. $0.25m_1 + 0.75m_2$ has the same composition as $0.50m_1 + 1.50m_2$). &lt;/p&gt;

&lt;p&gt;Furthermore, my example is simplistic; in reality a composition can have more than just 2 minerals (up to 3000) and each mineral is made up of 118 elements, not just 3 (all the elements of periodic table - though many elements will be zero). The elemental composition of a mineral is assumed to be known (definition of $m_1$ and $m_2$ in the example). Also, the sensor reading is noisy - each element of the observed composition is assumed to have Gaussian noise.&lt;/p&gt;
', 'regression', NULL, '''/a'':183C ''/code'':123C ''/lhbds.png'':171C,178C ''/p'':8C,20C,74C,112C,154C,165C,184C,234C,318C ''/pre'':124C ''0.1'':116C ''0.2'':121C,122C ''0.225'':163C ''0.25'':156C,217C ''0.3'':117C,164C ''0.475'':162C ''0.50'':228C ''0.6'':118C,120C ''0.75'':159C,220C ''1'':36C,85C,133C,158C,219C,230C,293C ''1.50'':231C ''100'':38C,215C ''118'':262C ''2'':88C,138C,161C,222C,233C,250C,296C ''25'':130C ''3'':94C,111C,266C ''3000'':254C ''75'':135C ''also'':300C ''alt'':179C ''assum'':286C,313C ''best'':13C ''case'':41C ''code'':114C ''compon'':68C,205C ''composit'':4A,17C,22C,47C,181C,201C,226C,244C,281C,311C ''copper'':97C ''data'':5A,18C,23C ''definit'':290C ''e.g'':216C ''element'':95C,100C,211C,263C,269C,275C,280C,307C ''exampl'':27C,77C,182C,238C,299C ''form'':106C ''furthermor'':236C ''gaussian'':316C ''href'':168C ''i.stack.imgur.com'':170C,177C ''i.stack.imgur.com/lhbds.png'':169C,176C ''img'':174C ''interest'':44C ''know'':190C ''known'':289C ''length'':110C ''let'':78C ''like'':96C,188C ''m'':84C,87C,132C,137C,157C,160C,218C,221C,229C,232C,292C,295C ''m1'':115C ''m2'':119C ''made'':91C,259C ''make'':70C ''mani'':274C ''miner'':49C,64C,83C,148C,180C,251C,257C,284C ''model'':3A,16C,193C ''nofollow'':173C ''nois'':317C ''noisi'':305C ''normal'':213C ''observ'':310C ''p'':7C,9C,21C,75C,125C,155C,166C,185C,235C ''period'':103C,271C ''plot'':152C ''pre'':113C ''problem'':19C,197C ''produc'':142C ''read'':141C,303C ''realiti'':242C ''regress'':6B ''rel'':172C ''rock'':52C,128C ''sampl'':29C ''say'':79C ''sensor'':56C,140C,302C ''shown'':149C ''simplist'':240C ''solv'':195C ''src'':175C ''sum'':34C,61C,73C,144C,208C ''tabl'':104C,272C ''tell'':58C ''though'':273C ''two'':82C,147C ''under'':204C ''unmix'':199C ''vector'':32C,108C ''way'':14C ''would'':187C ''zero'':278C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8784, 1, '2015-11-10 17:00:58.527', 1, 36, 'how to make new class from the test data', '&lt;p&gt;I have a list of accounts as data set and I need to group the accounts that refer to the same user using many features.&lt;/p&gt;

&lt;p&gt;I''m thinking to use machine learning( but I''m new in this domain), because I know the group of each account for the training data set.&lt;br&gt;
ex of training data:&lt;br&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;account-id   Feature1    Feature2    class(Group)
1            T1          P4          Gr1
2            T2          P4          Gr1
3            T3          P2          Gr2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The problem is in the testing of data and when a new account arrive for a new group not learned before in the training set.
&lt;br&gt;
ex of testing data:&lt;br&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;account-id   Feature1   Feature2
4             T5         P5
5             T6         P5
6             T3         P2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The groups of the testing data should be as following:&lt;br&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;account-id   Feature1   Feature2   class(Group)
4             T5         P5         Gr3
5             T6         P5         Gr3
6             T3         P2         Gr2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The accounts 4 and 5 are in a new group (Gr3) which is not learned before in the training data.&lt;/p&gt;

&lt;p&gt;My question is how could I group the new data under a new class that is not defined before in the learning phase ? and which algorithm can I use to solve this issue ?&lt;/p&gt;
', 'machine-learning  classification  multiclass-classification  multilabel-classification', NULL, '''/code'':103C,154C,190C ''/p'':46C,81C,137C,168C,213C,248C ''/pre'':104C,155C,191C ''1'':91C ''2'':95C ''3'':99C ''4'':145C,178C,195C ''5'':148C,182C,197C ''6'':151C,186C ''account'':26C,36C,69C,85C,118C,141C,172C,194C ''account-id'':84C,140C,171C ''algorithm'':240C ''arriv'':119C ''br'':75C,80C,131C,136C,167C ''class'':5A,89C,176C,228C ''classif'':13B,16B,19B ''code'':83C,139C,170C ''could'':219C ''data'':9A,28C,73C,79C,113C,135C,162C,212C,224C ''defin'':232C ''domain'':61C ''ex'':76C,132C ''featur'':45C ''feature1'':87C,143C,174C ''feature2'':88C,144C,175C ''follow'':166C ''gr1'':94C,98C ''gr2'':102C,189C ''gr3'':181C,185C,203C ''group'':34C,66C,90C,123C,158C,177C,202C,221C ''id'':86C,142C,173C ''issu'':247C ''know'':64C ''learn'':12B,54C,125C,207C,236C ''list'':24C ''m'':49C,57C ''machin'':11B,53C ''machine-learn'':10B ''make'':3A ''mani'':44C ''multiclass'':15B ''multiclass-classif'':14B ''multilabel'':18B ''multilabel-classif'':17B ''need'':32C ''new'':4A,58C,117C,122C,201C,223C,227C ''p'':20C,47C,105C,156C,192C,214C ''p2'':101C,153C,188C ''p4'':93C,97C ''p5'':147C,150C,180C,184C ''phase'':237C ''pre'':82C,138C,169C ''problem'':107C ''question'':216C ''refer'':38C ''set'':29C,74C,130C ''solv'':245C ''t1'':92C ''t2'':96C ''t3'':100C,152C,187C ''t5'':146C,179C ''t6'':149C,183C ''test'':8A,111C,134C,161C ''think'':50C ''train'':72C,78C,129C,211C ''use'':43C,52C,243C ''user'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8785, 1, '2015-11-10 18:16:17.677', 1, 87, 'What is a benchmark model?', '&lt;p&gt;I am working on a breast cancer dataset (&lt;a href="http://kdd.org/kdd-cup/view/kdd-cup-2008" rel="nofollow"&gt;http://kdd.org/kdd-cup/view/kdd-cup-2008&lt;/a&gt;). I need to perform classification on the data using C4.5 algorithm, after doing any necessary pre-processing. &lt;/p&gt;

&lt;p&gt;A section of the report that I have to write is "benchmark models" and I have no idea what that means. I googled the term and it doesn''t seem to be something well defined in data mining. Any idea what that means?&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'data-mining  classification  decision-trees  preprocessing', NULL, '''/kdd-cup/view/kdd-cup-2008'':27C ''/kdd-cup/view/kdd-cup-2008&lt;/a&gt;).'':32C ''/p'':51C,96C,99C ''algorithm'':43C ''benchmark'':4A,64C ''breast'':20C ''c4.5'':42C ''cancer'':21C ''classif'':9B,37C ''data'':7B,40C,89C ''data-min'':6B ''dataset'':22C ''decis'':11B ''decision-tre'':10B ''defin'':87C ''doesn'':80C ''googl'':75C ''href'':24C ''idea'':70C,92C ''kdd.org'':26C,31C ''kdd.org/kdd-cup/view/kdd-cup-2008'':25C ''kdd.org/kdd-cup/view/kdd-cup-2008&lt;/a&gt;).'':30C ''mean'':73C,95C ''mine'':8B,90C ''model'':5A,65C ''necessari'':47C ''need'':34C ''nofollow'':29C ''p'':14C,52C,97C ''perform'':36C ''pre'':49C ''pre-process'':48C ''preprocess'':13B ''process'':50C ''rel'':28C ''report'':57C ''section'':54C ''seem'':82C ''someth'':85C ''term'':77C ''thank'':98C ''tree'':12B ''use'':41C ''well'':86C ''work'':17C ''write'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8786, 2, '2015-11-10 18:28:17.97', 3, NULL, NULL, '&lt;p&gt;I wrote up this post detailing some of my personal favorites:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stackabuse.com/the-best-machine-learning-libraries-in-python/" rel="nofollow"&gt;The Best Machine Learning Libraries in Python&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Since over 30 different libraries are mentioned, I won''t post them all here, but these are among the most popular:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://github.com/tensorflow/tensorflow" rel="nofollow"&gt;Tensorflow&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://github.com/scikit-learn/scikit-learn" rel="nofollow"&gt;scikit-learn&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://github.com/Theano/Theano" rel="nofollow"&gt;Theano&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://github.com/lisa-lab/pylearn2" rel="nofollow"&gt;Pylearn2&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://github.com/perone/Pyevolve" rel="nofollow"&gt;Pyevolve&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://github.com/numenta/nupic" rel="nofollow"&gt;NuPIC&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://github.com/clips/pattern" rel="nofollow"&gt;Pattern&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://github.com/BVLC/caffe" rel="nofollow"&gt;Caffe&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strike&gt;(Sorry, can''t link to the Github repos since my rep is still &amp;lt; 10...)&lt;/strike&gt;&lt;/p&gt;

&lt;p&gt;Edit: Added links to Github repos.&lt;/p&gt;
', NULL, NULL, '''/a'':29C,64C,77C,88C,99C,110C,121C,132C,143C ''/bvlc/caffe'':139C ''/clips/pattern'':128C ''/li'':65C,78C,89C,100C,111C,122C,133C,144C ''/lisa-lab/pylearn2'':95C ''/numenta/nupic'':117C ''/p'':13C,30C,53C,164C,172C ''/perone/pyevolve'':106C ''/scikit-learn/scikit-learn'':71C ''/strike'':163C ''/tensorflow/tensorflow'':60C ''/the-best-machine-learning-libraries-in-python/'':19C ''/theano/theano'':84C ''/ul'':145C ''10'':162C ''30'':34C ''ad'':167C ''among'':49C ''best'':23C ''caff'':142C ''detail'':7C ''differ'':35C ''edit'':166C ''favorit'':12C ''github'':154C,170C ''github.com'':59C,70C,83C,94C,105C,116C,127C,138C ''github.com/bvlc/caffe'':137C ''github.com/clips/pattern'':126C ''github.com/lisa-lab/pylearn2'':93C ''github.com/numenta/nupic'':115C ''github.com/perone/pyevolve'':104C ''github.com/scikit-learn/scikit-learn'':69C ''github.com/tensorflow/tensorflow'':58C ''github.com/theano/theano'':82C ''href'':16C,57C,68C,81C,92C,103C,114C,125C,136C ''learn'':25C,76C ''li'':55C,66C,79C,90C,101C,112C,123C,134C ''librari'':26C,36C ''link'':151C,168C ''lt'':161C ''machin'':24C ''mention'':38C ''nofollow'':21C,62C,73C,86C,97C,108C,119C,130C,141C ''nupic'':120C ''p'':1C,14C,31C,146C,165C ''pattern'':131C ''person'':11C ''popular'':52C ''post'':6C,42C ''pyevolv'':109C ''pylearn2'':98C ''python'':28C ''rel'':20C,61C,72C,85C,96C,107C,118C,129C,140C ''rep'':158C ''repo'':155C,171C ''scikit'':75C ''scikit-learn'':74C ''sinc'':32C,156C ''sorri'':148C ''stackabuse.com'':18C ''stackabuse.com/the-best-machine-learning-libraries-in-python/'':17C ''still'':160C ''strike'':147C ''tensorflow'':63C ''theano'':87C ''ul'':54C ''won'':40C ''wrote'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8787, 2, '2015-11-10 18:34:19.713', 3, NULL, NULL, '&lt;p&gt;Benchmarking is the process of comparing your result to existing methods. You may compare to published results using another paper, for example. If there is no other obvious methodology against which you can benchmark, you might compare to the best naive solution (guessing the mean, guessing the majority class, etc) or a very simple model (a simple regression, K Nearest Neighbors).  If the field is well studied, you should probably benchmark against the current published state of the art (and possibly against human performance when relevant).&lt;/p&gt;
', NULL, NULL, '''/p'':88C ''anoth'':20C ''art'':80C ''benchmark'':2C,35C,72C ''best'':41C ''class'':50C ''compar'':7C,15C,38C ''current'':75C ''etc'':51C ''exampl'':23C ''exist'':11C ''field'':65C ''guess'':44C,47C ''human'':84C ''k'':60C ''major'':49C ''may'':14C ''mean'':46C ''method'':12C ''methodolog'':30C ''might'':37C ''model'':56C ''naiv'':42C ''nearest'':61C ''neighbor'':62C ''obvious'':29C ''p'':1C ''paper'':21C ''perform'':85C ''possibl'':82C ''probabl'':71C ''process'':5C ''publish'':17C,76C ''regress'':59C ''relev'':87C ''result'':9C,18C ''simpl'':55C,58C ''solut'':43C ''state'':77C ''studi'':68C ''use'':19C ''well'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8815, 2, '2015-11-12 13:17:55.257', 2, NULL, NULL, '&lt;p&gt;Online learning for SVMs is supported only for &lt;a href="http://stackoverflow.com/questions/23056460/does-the-svm-in-sklearn-support-incremental-online-learning"&gt;certain SVM options&lt;/a&gt; (a linear SVM with SGD can easily update for example).  &lt;/p&gt;

&lt;p&gt;There are implementations out there to address the issue of online learning for all SVMs.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stats.stackexchange.com/questions/26041/can-svm-do-stream-learning-one-example-at-a-time"&gt;http://stats.stackexchange.com/questions/26041/can-svm-do-stream-learning-one-example-at-a-time&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If your model isn''t terribly large it also probably isn''t too bad to train a new one on all data periodically.&lt;/p&gt;
', NULL, NULL, '''/a'':18C ''/p'':29C,46C,80C ''/questions/23056460/does-the-svm-in-sklearn-support-incremental-online-learning'':14C ''/questions/26041/can-svm-do-stream-learning-one-example-at-a-time'':52C ''/questions/26041/can-svm-do-stream-learning-one-example-at-a-time&lt;/a&gt;&lt;/p&gt;'':55C ''address'':37C ''also'':65C ''bad'':70C ''certain'':15C ''data'':78C ''easili'':25C ''exampl'':28C ''href'':11C,49C ''implement'':33C ''isn'':60C,67C ''issu'':39C ''larg'':63C ''learn'':3C,42C ''linear'':20C ''model'':59C ''new'':74C ''one'':75C ''onlin'':2C,41C ''option'':17C ''p'':1C,30C,47C,56C ''period'':79C ''probabl'':66C ''sgd'':23C ''stackoverflow.com'':13C ''stackoverflow.com/questions/23056460/does-the-svm-in-sklearn-support-incremental-online-learning'':12C ''stats.stackexchange.com'':51C,54C ''stats.stackexchange.com/questions/26041/can-svm-do-stream-learning-one-example-at-a-time'':50C ''stats.stackexchange.com/questions/26041/can-svm-do-stream-learning-one-example-at-a-time&lt;/a&gt;&lt;/p&gt;'':53C ''support'':7C ''svm'':16C,21C ''svms'':5C,45C ''terribl'':62C ''train'':72C ''updat'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8790, 2, '2015-11-10 20:44:50.153', 1, NULL, NULL, '&lt;p&gt;If I understand correctly, in your problem the mineral-element vector $m1$ and $m2$ is &lt;em&gt;known&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;If that is the case, I would suggest LASSO regression. Suppose you have $n$ minerals $m_1$, $m_2$, ..., $m_n\in\mathbb{R}^p$ arranged into a matrix $M=[m_1;m_2,...,m_n]\in\mathbb{R}^{n\times p}$, and an unknown mixing vector $x\in\mathbb{R}^n$ (in your case, $x=[0.25; 0.75]$). Your sensor measures $y=M^\top x\in\mathbb{R}^n$ and you want to recover $x$ from $y$.&lt;/p&gt;

&lt;p&gt;The LASSO regression aims to solve $\hat{x}=\arg\min_x ||y-M^\top x||_2 + \lambda||x||_1$. The $\ell_1$ penalty promotes sparsity in $x$ and could be beneficial when you have a lot of candidate minerals.&lt;/p&gt;

&lt;p&gt;I don''t think you need any special handling due to the compositional nature of the data, other than normalizing the final $\hat{x}$ if needed.&lt;/p&gt;
', NULL, NULL, '''/em'':20C ''/p'':21C,100C,142C,170C ''0.25'':79C ''0.75'':80C ''1'':39C,54C,121C,124C ''2'':41C,56C,118C ''aim'':105C ''arg'':110C ''arrang'':48C ''benefici'':133C ''candid'':140C ''case'':27C,77C ''composit'':156C ''correct'':5C ''could'':131C ''data'':160C ''due'':153C ''element'':12C ''ell'':123C ''em'':18C ''final'':165C ''handl'':152C ''hat'':108C,166C ''known'':19C ''lambda'':119C ''lasso'':31C,103C ''lot'':138C ''m'':38C,40C,42C,52C,53C,55C,57C,85C,115C ''m1'':14C ''m2'':16C ''mathbb'':45C,60C,72C,89C ''matrix'':51C ''measur'':83C ''min'':111C ''miner'':11C,37C,141C ''mineral-el'':10C ''mix'':68C ''n'':36C,43C,58C,62C,74C,91C ''natur'':157C ''need'':149C,169C ''normal'':163C ''p'':1C,22C,47C,64C,101C,143C ''penalti'':125C ''problem'':8C ''promot'':126C ''r'':46C,61C,73C,90C ''recov'':96C ''regress'':32C,104C ''sensor'':82C ''solv'':107C ''sparsiti'':127C ''special'':151C ''suggest'':30C ''suppos'':33C ''think'':147C ''time'':63C ''top'':86C,116C ''understand'':4C ''unknown'':67C ''vector'':13C,69C ''want'':94C ''would'':29C ''x'':70C,78C,87C,97C,109C,112C,117C,120C,129C,167C ''y'':84C,99C,114C ''y-m'':113C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8791, 2, '2015-11-10 20:45:53.72', 1, NULL, NULL, '&lt;p&gt;First normalize the result vector. E.g. &lt;code&gt;[.95, .45, .6]&lt;/code&gt; by dividing by 2 (sum of the members); giving &lt;code&gt;[.445, .225, .3]&lt;/code&gt;. &lt;/p&gt;

&lt;p&gt;Let $x$ be the share of the first mineral, than $(x-1)$ is the share of the second mineral.&lt;/p&gt;

&lt;p&gt;Solve the three linear equation which must give the same result.&lt;/p&gt;

&lt;p&gt;$.1 * x + .6 * (1-x) = .475$&lt;/p&gt;

&lt;p&gt;$.3 * x + .2 * (1-x) = .225$&lt;/p&gt;

&lt;p&gt;$.6 * x + .2 * (1-x) = .3$&lt;/p&gt;

&lt;p&gt;Result is $x = 1/4$ as expected. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The above proposed solution works of course only if the number of minerals is less or equal
the number of elements.
In the update of the question is clearly stated, that this is not the case (3000 minerals and 118 elements).&lt;/p&gt;

&lt;p&gt;Lets simulate this oposite case on a small example with 3 minarals and 2 elements.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;m1 &amp;lt;- c(0.2, 0.8)  
m2 &amp;lt;- c(0.4, 0.6)  
m3 &amp;lt;- c(0.9, 0.1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and the with the mix of minerals&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; x &amp;lt;- c(.25, .65, .1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;which produce a measurement of&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; t(matrix(c(m1,m2,m3),3,2, byrow= T))  %*% x

 [,1]
 [1,]  0.4
 [2,]  0.6
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This gives following linear equations &lt;/p&gt;

&lt;p&gt;$m_1 + m_2 + m_3 = 1$&lt;/p&gt;

&lt;p&gt;$.2 m_1 + .4 m_2 + .9 m_3 = .4$&lt;/p&gt;

&lt;p&gt;$.8 m_1 + .6 m_2 + .1 m_3 = .6$&lt;/p&gt;

&lt;p&gt;The solution of the equations is not unique &lt;/p&gt;

&lt;p&gt;$m_3 \in &amp;lt;0, 1 / 3.5&amp;gt;$&lt;/p&gt;

&lt;p&gt;$m_1 = 2 - 2 * m_2 - 4.5 * m_3$&lt;/p&gt;

&lt;p&gt;$m_2 = 1 - 3.5 * m_3$&lt;/p&gt;

&lt;p&gt;Some alternative solutions are provided below&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  0 1 0
  0.125 0.825 0.050
  0.375 0.475 0.150
  0.5 0.3 0.2
  0.625 0.125 0.250
  0.71428571 0.00000000 0.28571429
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This is of course oversimplified example, but shows that you should carefully select the optimization goal,
as there could be more "equaly good" solutions.
For example with promoting sparsity you will find the solution &lt;code&gt;[0 1 0]&lt;/code&gt; which is far from our used mix.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8792, 1, '2015-11-11 01:32:56.953', 4, 32, 'Difference between multinomia logistic regression and N 1 vs all binary', '&lt;p&gt;Is there a fundamental difference between building a set on N logistic regressions in 1 vs all fashion, as compared to training a single mutlinomial logistic regression? Put another way, are there any optimization techniques that treat the 1 to N classes logistic regression problem in a way that''s markedly different from N independent regressions? &lt;/p&gt;

&lt;p&gt;Intuitively is seems like the answer ought to be yes, since there should be a lot of information sharing between various problems if two classes are similar. But since I''m not entirely well versed on how  common 1 to N solvers actually work, I can''t tell if I''m right or these problems are treated in ways that fundamentally the same. &lt;/p&gt;

&lt;p&gt;I think that we can see that there could be a difference between the two models, but I''m not entirely sure. Googling the matter revealed a few arcane discussing about the subject, but I was unable to find an authoritative discussion of the matter. &lt;/p&gt;
', 'logistic-regression  multiclass-classification', NULL, '''/p'':75C,140C,187C ''1'':8A,33C,57C,115C ''actual'':119C ''anoth'':47C ''answer'':82C ''arcan'':170C ''authorit'':182C ''binari'':11A ''build'':25C ''class'':60C,101C ''classif'':17B ''common'':114C ''compar'':38C ''could'':150C ''differ'':1A,23C,70C,153C ''discuss'':171C,183C ''entir'':109C,162C ''fashion'':36C ''find'':180C ''fundament'':22C,137C ''googl'':164C ''independ'':73C ''inform'':94C ''intuit'':77C ''like'':80C ''logist'':4A,13B,30C,44C,61C ''logistic-regress'':12B ''lot'':92C ''m'':107C,127C,160C ''mark'':69C ''matter'':166C,186C ''model'':157C ''multiclass'':16B ''multiclass-classif'':15B ''multinomia'':3A ''mutlinomi'':43C ''n'':7A,29C,59C,72C,117C ''optim'':52C ''ought'':83C ''p'':18C,76C,141C ''problem'':63C,98C,131C ''put'':46C ''regress'':5A,14B,31C,45C,62C,74C ''reveal'':167C ''right'':128C ''see'':147C ''seem'':79C ''set'':27C ''share'':95C ''similar'':103C ''sinc'':87C,105C ''singl'':42C ''solver'':118C ''subject'':174C ''sure'':163C ''techniqu'':53C ''tell'':124C ''think'':143C ''train'':40C ''treat'':55C,133C ''two'':100C,156C ''unabl'':178C ''various'':97C ''vers'':111C ''vs'':9A,34C ''way'':48C,66C,135C ''well'':110C ''work'':120C ''yes'':86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8793, 2, '2015-11-11 01:44:01.247', 1, NULL, NULL, '&lt;p&gt;Quote from &lt;em&gt;Alan, Agresti. "Categorical data analysis." A John Wiley and Sons, Inc. Publication, Hoboken, New Jersey, USA (2002)&lt;/em&gt;, page 273:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The separate-fitting estimates differ from the ML estimates for simultaneous fitting of the J-1 logits. They are less efficient, tending to have larger standard errors. However, Begg and Gray 1984 showed that the efficiency loss is minor when the response category having highest prevalence is the baseline.&lt;/p&gt;
&lt;/blockquote&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8796, 2, '2015-11-11 07:32:15.217', 0, NULL, NULL, '&lt;p&gt;I''d like to refer to how you should choose a performance measure.
Before that I''ll refer to the specific question of accuracy and AUC.&lt;/p&gt;

&lt;p&gt;As answered before, on imbalanced dataset using the majority run as a classifier will lead to high accuracy what will make it a misleading measure.
AUC aggregate over confidence threshold, for good and bad.
For good, you get a weight result for all confidence level.
The bad is that you are usually care only about the confidence level you will actually use and the rest are irrelevant.&lt;/p&gt;

&lt;p&gt;However, I want to remark about choosing a proper performance measure for a model.
You should compare a model by its goal. The goal of a model is not a question os machine learning or statistic, in is question of the business domain and its needs.&lt;/p&gt;

&lt;p&gt;If you are digging for gold (a scenario in which you have huge benefit from a true positive, not too high cost of a false positive) then recall is a good measure.&lt;/p&gt;

&lt;p&gt;If you are trying to decide whether to perform a complex medical procedure on people (high cost of false positive, hopefully a low cost of false negative), precision is the measure you should use.&lt;/p&gt;

&lt;p&gt;There are plenty of measures you can use.
You can also combine them in various ways.&lt;/p&gt;

&lt;p&gt;However, there is no universal "best" measure.
There is the best model for your needs, the one that maximizing it will maximize your benefit.&lt;/p&gt;
', NULL, NULL, '''/p'':28C,97C,145C,179C,215C,233C,259C ''accuraci'':25C,47C ''actual'':90C ''aggreg'':56C ''also'':227C ''answer'':31C ''auc'':27C,55C ''bad'':63C,76C ''benefit'':160C,258C ''best'':240C,245C ''busi'':140C ''care'':82C ''choos'':11C,105C ''classifi'':42C ''combin'':228C ''compar'':115C ''complex'':191C ''confid'':58C,73C,86C ''cost'':168C,197C,204C ''d'':3C ''dataset'':35C ''decid'':186C ''dig'':150C ''domain'':141C ''fals'':171C,199C,206C ''get'':67C ''goal'':120C,122C ''gold'':152C ''good'':61C,65C,177C ''high'':46C,167C,196C ''hope'':201C ''howev'':99C,235C ''huge'':159C ''imbalanc'':34C ''irrelev'':96C ''lead'':44C ''learn'':132C ''level'':74C,87C ''like'':4C ''ll'':18C ''low'':203C ''machin'':131C ''major'':38C ''make'':50C ''maxim'':253C,256C ''measur'':14C,54C,109C,178C,211C,221C,241C ''medic'':192C ''mislead'':53C ''model'':112C,117C,125C,246C ''need'':144C,249C ''negat'':207C ''one'':251C ''os'':130C ''p'':1C,29C,98C,146C,180C,216C,234C ''peopl'':195C ''perform'':13C,108C,189C ''plenti'':219C ''posit'':164C,172C,200C ''precis'':208C ''procedur'':193C ''proper'':107C ''question'':23C,129C,137C ''recal'':174C ''refer'':6C,19C ''remark'':103C ''rest'':94C ''result'':70C ''run'':39C ''scenario'':154C ''specif'':22C ''statist'':134C ''threshold'':59C ''tri'':184C ''true'':163C ''univers'':239C ''use'':36C,91C,214C,224C ''usual'':81C ''various'':231C ''want'':101C ''way'':232C ''weight'':69C ''whether'':187C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8814, 2, '2015-11-12 13:10:01.687', 1, NULL, NULL, '&lt;p&gt;You''re right, a basic linear regression is unlikely to fit this data.  You need some form of &lt;a href="https://en.m.wikipedia.org/wiki/Nonlinear_regression" rel="nofollow"&gt;non-linear regression&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Basic forms of this (including exponential regression, mentioned by @Dawny33)  can be found in most spreadsheet software, including Excel. Packages like scikit learn and others will allow for more flexibility.&lt;/p&gt;
', NULL, NULL, '''/a'':31C ''/p'':32C,64C ''/wiki/nonlinear_regression'':24C ''allow'':60C ''basic'':6C,34C ''data'':14C ''dawny33'':43C ''en.m.wikipedia.org'':23C ''en.m.wikipedia.org/wiki/nonlinear_regression'':22C ''excel'':52C ''exponenti'':39C ''fit'':12C ''flexibl'':63C ''form'':18C,35C ''found'':46C ''href'':21C ''includ'':38C,51C ''learn'':56C ''like'':54C ''linear'':7C,29C ''mention'':41C ''need'':16C ''nofollow'':26C ''non'':28C ''non-linear'':27C ''other'':58C ''p'':1C,33C ''packag'':53C ''re'':3C ''regress'':8C,30C,40C ''rel'':25C ''right'':4C ''scikit'':55C ''softwar'':50C ''spreadsheet'':49C ''unlik'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8806, 1, '2015-11-11 19:52:07.653', 0, 59, 'On interpreting the statistical significance of R squared', '&lt;p&gt;I have performed a linear regression analysis to two series of data, each of which has &lt;code&gt;50&lt;/code&gt; values. I did the analysis in SPSS and as a result got a table which says that my &lt;code&gt;adjusted R squared&lt;/code&gt; is &lt;code&gt;0.145&lt;/code&gt; and its significance is &lt;code&gt;0.004&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Being &lt;code&gt;0.004 &amp;lt; 0.05&lt;/code&gt;, I assume my &lt;code&gt;adjusted R squared&lt;/code&gt; is significant.&lt;/p&gt;

&lt;p&gt;1) Does it mean my &lt;code&gt;adjusted R squared&lt;/code&gt; is &lt;em&gt;credible&lt;/em&gt;?&lt;/p&gt;

&lt;p&gt;2) What does happen if you get a significance which is &lt;code&gt;&amp;gt; 0.05&lt;/code&gt;? Does it imply the &lt;code&gt;adjusted R squared&lt;/code&gt; can be trusted with credibility but also that the two datasets are not or poorly correlated?&lt;/p&gt;
', 'statistics  correlation', NULL, '''/code'':30C,53C,57C,64C,72C,80C,94C,115C,124C ''/em'':98C ''/p'':65C,83C,99C,141C ''0.004'':63C,69C ''0.05'':71C,114C ''0.145'':56C ''1'':85C ''2'':101C ''50'':29C ''adjust'':50C,77C,91C,121C ''also'':131C ''analysi'':18C,35C ''assum'':74C ''code'':28C,49C,55C,62C,68C,76C,90C,112C,120C ''correl'':10B,140C ''credibl'':97C,129C ''data'':23C ''dataset'':135C ''em'':96C ''get'':107C ''got'':42C ''gt'':113C ''happen'':104C ''impli'':118C ''interpret'':2A ''linear'':16C ''lt'':70C ''mean'':88C ''p'':11C,66C,84C,100C ''perform'':14C ''poor'':139C ''r'':7A,51C,78C,92C,122C ''regress'':17C ''result'':41C ''say'':46C ''seri'':21C ''signific'':5A,60C,82C,109C ''spss'':37C ''squar'':8A,52C,79C,93C,123C ''statist'':4A,9B ''tabl'':44C ''trust'':127C ''two'':20C,134C ''valu'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8807, 2, '2015-11-11 20:09:31.253', 2, NULL, NULL, '&lt;p&gt;The p-value is the strength of evidence against the null hypotheses. In this case the null is that the coefficient is equal to zero. So your p-value says that this is very weak evidence against the null so you model is likely to be describing the underlying system of the data.&lt;/p&gt;

&lt;p&gt;R-squared describes the percent of variation that is explained by the model. Your value is very low; 14.5%. Of all the "activity" in the data your model is only explaining 14.5% of it.&lt;/p&gt;

&lt;p&gt;So you have a situation were model is most likely explaining variation in data but not explaining very much of it.  I would suggest altering the model and refitting. &lt;/p&gt;
', NULL, NULL, '''/p'':57C,94C,125C ''14.5'':78C,91C ''activ'':82C ''alter'':120C ''case'':17C ''coeffici'':23C ''data'':56C,85C,109C ''describ'':50C,62C ''equal'':25C ''evid'':10C,39C ''explain'':69C,90C,106C,112C ''hypothes'':14C ''like'':47C,105C ''low'':77C ''model'':45C,72C,87C,102C,122C ''much'':114C ''null'':13C,19C,42C ''p'':1C,4C,31C,58C,95C ''p-valu'':3C,30C ''percent'':64C ''r'':60C ''r-squar'':59C ''refit'':124C ''say'':33C ''situat'':100C ''squar'':61C ''strength'':8C ''suggest'':119C ''system'':53C ''under'':52C ''valu'':5C,32C,74C ''variat'':66C,107C ''weak'':38C ''would'':118C ''zero'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8809, 1, '2015-11-12 00:11:39.77', 2, 74, 'What is an interpretation of the $\,f''\!\left(\sum_i w_{ij}y_i\right)$ factor in the in the $\delta$-rule in back propagation?', '&lt;p&gt;In the $\delta$-rule which is used for error back propagation in neural networks, there is a factor $f''\!\left(\sum_i w_{ij}y_i\right)$, often written as $f''(\text{net}_i)$, which is just the derivative of the activation or transfer function evaluated at the weighted sum of the input. The derivative of $\tanh$ is bell-shaped so that values $\left|x\right|\geqslant3$ it is almost zero:&lt;/p&gt;

&lt;p&gt;Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â &lt;img src="https://i.imgur.com/A1dWTIY.png" alt=""&gt;&lt;/p&gt;

&lt;p&gt;This means that units with a strong activation in either way have a more limited scope of action, where as values close to zero have more leeway. If I''m understanding it correctly, it keeps neurons that have already strongly decided one way or the other in their configuration. What is the purpose of this for weight updating? How would the networks behave differently if one would remove or change this factor?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;As a reminder, the full $\delta$-rule looks like this for hidden neurons and output neurons:&lt;/p&gt;

&lt;p&gt;$$\Delta w_{ij} = \eta\;\delta_j\;y_i$$&lt;/p&gt;

&lt;p&gt;$$\delta_j = (\hat y_j - y_j)\;f''(\text{net}_j) \quad \text{or} \quad \delta_j = \sum\limits_k^K\left(w_{jk}\delta_k\right)\;f''(\text{net}_j)$$&lt;/p&gt;

&lt;p&gt;where $\Delta w_{ij}$ is the change of the weight that connects the neuron $i^{\text{th}}$ neuron in the preceding layer with the $j^{\text{th}}$ neuron in the layer we are currently considering; $\eta$ is the learning rate, $y_j$ is the output of the current neuron, $\hat y_j$ is the teacher value for the $j^{\text{th}}$ output, $y_i$ is the output of the $i^{\text{th}}$, and $k$ is an index in the subsequent layer.&lt;/p&gt;
', 'neuralnetwork', NULL, '''/a1dwtiy.png'':121C ''/p'':100C,123C,197C,216C,226C,259C,342C ''action'':142C ''activ'':69C,132C ''almost'':98C ''alreadi'':163C ''alt'':122C ''back'':24A,37C ''behav'':187C ''bell'':87C ''bell-shap'':86C ''chang'':194C,267C ''close'':146C ''configur'':173C ''connect'':272C ''consid'':295C ''correct'':157C ''current'':294C,308C ''decid'':165C ''delta'':21A,30C,205C,218C,222C,228C,243C,252C,262C ''deriv'':66C,82C ''differ'':188C ''either'':134C ''error'':36C ''eta'':221C,296C ''evalu'':73C ''f'':7A,46C,58C,235C,255C ''factor'':16A,45C,196C ''full'':204C ''function'':72C ''geqslant3'':95C ''hat'':230C,310C ''hidden'':211C ''hr'':198C ''i.imgur.com'':120C ''i.imgur.com/a1dwtiy.png'':119C ''ij'':12A,51C,220C,264C ''img'':117C ''index'':337C ''input'':80C ''interpret'':4A ''j'':223C,229C,232C,234C,238C,244C,258C,285C,302C,312C,319C ''jk'':251C ''k'':247C,248C,253C,334C ''keep'':159C ''layer'':282C,291C,341C ''learn'':299C ''leeway'':151C ''left'':8A,47C,92C,249C ''like'':208C ''limit'':139C,246C ''look'':207C ''m'':154C ''mean'':126C ''net'':60C,237C,257C ''network'':41C,186C ''neural'':40C ''neuralnetwork'':26B ''neuron'':160C,212C,215C,274C,278C,288C,309C ''often'':55C ''one'':166C,190C ''output'':214C,305C,322C,327C ''p'':27C,101C,124C,199C,217C,227C,260C ''preced'':281C ''propag'':25A,38C ''purpos'':177C ''quad'':239C,242C ''rate'':300C ''remind'':202C ''remov'':192C ''right'':15A,54C,94C,254C ''rule'':22A,31C,206C ''scope'':140C ''shape'':88C ''src'':118C ''strong'':131C,164C ''subsequ'':340C ''sum'':9A,48C,77C,245C ''tanh'':84C ''teacher'':315C ''text'':59C,236C,240C,256C,276C,286C,320C,331C ''th'':277C,287C,321C,332C ''transfer'':71C ''understand'':155C ''unit'':128C ''updat'':182C ''use'':34C ''valu'':91C,145C,316C ''w'':11A,50C,219C,250C,263C ''way'':135C,167C ''weight'':76C,181C,270C ''would'':184C,191C ''written'':56C ''x'':93C ''y'':13A,52C,224C,231C,233C,301C,311C,323C ''zero'':99C,148C ''â'':102C,103C,104C,105C,106C,107C,108C,109C,110C,111C,112C,113C,114C,115C,116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8810, 2, '2015-11-12 02:24:41.69', 1, NULL, NULL, '&lt;p&gt;NMF is not a classifier -- it is a transformation (or, more specifically, a factorization).  We have $V \sim W H$, where (depending on your perspective) each column of $H$ corresponds to some "proto-image", and each row of $W$ corresponds to the amount that the corresponding row of $V$ is made up of each image.&lt;/p&gt;

&lt;p&gt;One approach that I might suggest (and this is not the only approach) is to take your lower-dimensional data $W$ and use some other classification method on top of that.  From this perspective there is no need for the basis vectors in $H$; you operate only with $W$.  So this is like seeing NMF as a preprocessing dimensionality reduction step before some out-of-the-box classifier.  An advantage here is that the rank of your decomposition is often much lower than your original dimensionality, which can (among other things) reduce the necessary training time.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8811, 1, '2015-11-12 07:16:06.783', 1, 75, 'Improving SVM binary classification model on new dataset', '&lt;p&gt;I am working to create a SVM binary classifier for classification of Tweets based on news class "Crime" and "Non Crime". I have downloaded a dataset of 6400 rows from various sources and training my model on it. While I am able to achieve over 95% accuracy, what concerns me is about its performance on new datasets. The system will be deployed on live streaming tweets, so how can I put in a feedback system such as my model is continuously updates itself? As in, say for new crime type news source which were not covered in training model, how to incorporate them further?&lt;/p&gt;

&lt;p&gt;I am asking a broad methodology question, not some specific program related question as I would like to figure out the implementation myself :)&lt;/p&gt;
', 'data-mining  classification  text-mining  svm', NULL, '''/p'':122C,147C ''6400'':45C ''95'':63C ''abl'':59C ''accuraci'':64C ''achiev'':61C ''ask'':126C ''base'':31C ''binari'':3A,25C ''broad'':128C ''class'':34C ''classif'':4A,12B,28C ''classifi'':26C ''concern'':66C ''continu'':98C ''cover'':113C ''creat'':22C ''crime'':35C,38C,106C ''data'':10B ''data-min'':9B ''dataset'':8A,43C,74C ''deploy'':79C ''download'':41C ''feedback'':91C ''figur'':142C ''implement'':145C ''improv'':1A ''incorpor'':119C ''like'':140C ''live'':81C ''methodolog'':129C ''mine'':11B,15B ''model'':5A,53C,96C,116C ''new'':7A,73C,105C ''news'':33C,108C ''non'':37C ''p'':17C,123C ''perform'':71C ''program'':134C ''put'':88C ''question'':130C,136C ''relat'':135C ''row'':46C ''say'':103C ''sourc'':49C,109C ''specif'':133C ''stream'':82C ''svm'':2A,16B,24C ''system'':76C,92C ''text'':14B ''text-min'':13B ''train'':51C,115C ''tweet'':30C,83C ''type'':107C ''updat'':99C ''various'':48C ''work'':20C ''would'':139C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8813, 1, '2015-11-12 09:44:13.153', 3, 126, 'Non-linear regression line fit', '&lt;p&gt;I performed a regression analysis with two datasets, each of which has size &lt;code&gt;50&lt;/code&gt;. One dataset is called &lt;code&gt;Spatial %&lt;/code&gt; and the other &lt;code&gt;Min values&lt;/code&gt;, and I wanted to check whether the two are correlated. I did the analysis in &lt;code&gt;SPSS&lt;/code&gt; and the resulting scatterplot is as follows:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/GO49G.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/GO49G.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I am not that much experienced but it seems to me that a line is not the perfect fit for this scatterplot. Would a power line fit better? Or what else do you suggest?&lt;/p&gt;
', 'regression  correlation', NULL, '''/a'':84C ''/code'':25C,32C,39C,57C ''/go49g.png'':71C,78C ''/p'':65C,85C,121C ''50'':24C ''alt'':79C ''analysi'':14C,53C ''better'':114C ''call'':29C ''check'':44C ''code'':23C,30C,36C,55C ''correl'':8B,49C ''dataset'':17C,27C ''descript'':82C ''els'':117C ''enter'':80C ''experienc'':92C ''fit'':6A,105C,113C ''follow'':64C ''href'':68C ''i.stack.imgur.com'':70C,77C ''i.stack.imgur.com/go49g.png'':69C,76C ''imag'':81C ''img'':74C ''line'':5A,100C,112C ''linear'':3A ''min'':37C ''much'':91C ''nofollow'':73C ''non'':2A ''non-linear'':1A ''one'':26C ''p'':9C,66C,86C ''perfect'':104C ''perform'':11C ''power'':111C ''regress'':4A,7B,13C ''rel'':72C ''result'':60C ''scatterplot'':61C,108C ''seem'':95C ''size'':22C ''spatial'':31C ''spss'':56C ''src'':75C ''suggest'':120C ''two'':16C,47C ''valu'':38C ''want'':42C ''whether'':45C ''would'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8816, 2, '2015-11-12 14:09:13.323', 1, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Yes&lt;/strong&gt; Linear Regression is not a nice fit for this problem. &lt;/p&gt;

&lt;p&gt;Non-linear regression, as @jamesmf suggested, can be a nice option.&lt;/p&gt;

&lt;p&gt;But, this looks like a nice fit for &lt;a href="https://onlinecourses.science.psu.edu/stat501/node/372" rel="nofollow"&gt;exponential regression&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The graph of exponential regression looks something like this:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/5QjCl.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/5QjCl.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;So, adjusting the parameters to fit your data should go good.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;The &lt;a href="http://onlinestatbook.com/2/transformations/box-cox.html" rel="nofollow"&gt;Box-Cox transformation&lt;/a&gt; can also be used for fitting the plot.&lt;/p&gt;

&lt;p&gt;I have taken a sample data set, and fitted a Box-Plot transformation, with relevant parameters for transforming it to look somewhat like the plot of your data:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/FpgnL.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/FpgnL.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Sorry for the noise, as it is a quick and dirty implementation.  But, yeah a &lt;a href="http://onlinestatbook.com/2/transformations/box-cox.html" rel="nofollow"&gt;Box Cox transformation&lt;/a&gt; should also be a nice way to fit.&lt;/p&gt;

&lt;p&gt;R code for the above plot:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;lambda = +9.6 
plot(BoxCox(elec, lambda))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="https://cran.r-project.org/web/packages/fpp/fpp.pdf" rel="nofollow"&gt;elec&lt;/a&gt; is a sample data set.&lt;/p&gt;
', NULL, NULL, '''+9.6'':216C ''/2/transformations/box-cox.html'':101C,189C ''/5qjcl.jpg'':66C,73C ''/a'':48C,79C,108C,167C,195C,232C ''/code'':221C ''/fpgnl.png'':154C,161C ''/p'':15C,29C,49C,60C,80C,93C,117C,148C,168C,204C,212C,238C ''/pre'':222C ''/stat501/node/372'':43C ''/strong'':4C ''/web/packages/fpp/fpp.pdf'':228C ''adjust'':83C ''also'':110C,197C ''alt'':74C,162C ''box'':105C,130C,192C ''box-cox'':104C ''box-plot'':129C ''boxcox'':218C ''code'':207C,214C ''cox'':106C,193C ''cran.r-project.org'':227C ''cran.r-project.org/web/packages/fpp/fpp.pdf'':226C ''data'':89C,124C,147C,236C ''descript'':77C,165C ''dirti'':180C ''elec'':219C,231C ''enter'':75C,163C ''exponenti'':46C,54C ''fit'':11C,37C,87C,114C,127C,203C ''go'':91C ''good'':92C ''graph'':52C ''hr'':94C ''href'':40C,63C,98C,151C,186C,225C ''i.stack.imgur.com'':65C,72C,153C,160C ''i.stack.imgur.com/5qjcl.jpg'':64C,71C ''i.stack.imgur.com/fpgnl.png'':152C,159C ''imag'':76C,164C ''img'':69C,157C ''implement'':181C ''jamesmf'':22C ''lambda'':215C,220C ''like'':34C,58C,142C ''linear'':5C,19C ''look'':33C,56C,140C ''nice'':10C,27C,36C,200C ''nofollow'':45C,68C,103C,156C,191C,230C ''nois'':173C ''non'':18C ''non-linear'':17C ''onlinecourses.science.psu.edu'':42C ''onlinecourses.science.psu.edu/stat501/node/372'':41C ''onlinestatbook.com'':100C,188C ''onlinestatbook.com/2/transformations/box-cox.html'':99C,187C ''option'':28C ''p'':1C,16C,30C,50C,61C,81C,95C,118C,149C,169C,205C,223C ''paramet'':85C,135C ''plot'':116C,131C,144C,211C,217C ''pre'':213C ''problem'':14C ''quick'':178C ''r'':206C ''regress'':6C,20C,47C,55C ''rel'':44C,67C,102C,155C,190C,229C ''relev'':134C ''sampl'':123C,235C ''set'':125C,237C ''someth'':57C ''somewhat'':141C ''sorri'':170C ''src'':70C,158C ''strong'':2C ''suggest'':23C ''taken'':121C ''transform'':107C,132C,137C,194C ''use'':112C ''way'':201C ''yeah'':183C ''yes'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8819, 2, '2015-11-12 15:56:47.663', 0, NULL, NULL, '&lt;p&gt;You should look at Open Multilingual Wordnet (&lt;a href="http://compling.hss.ntu.edu.sg/omw/" rel="nofollow"&gt;http://compling.hss.ntu.edu.sg/omw/&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;They used wiktionary (not wikipedia, because wikipedia lemmas are nouns only) to get translations and synsets. EDIT: In the extended version, there is also a Hindi wordnet with 3,593 synsets, see &lt;a href="http://compling.hss.ntu.edu.sg/omw/summx.html" rel="nofollow"&gt;http://compling.hss.ntu.edu.sg/omw/summx.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;There is a non-open Hindi wordnet at &lt;a href="https://catalog.ldc.upenn.edu/LDC2008L02" rel="nofollow"&gt;https://catalog.ldc.upenn.edu/LDC2008L02&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/ldc2008l02'':76C ''/ldc2008l02&lt;/a&gt;.&lt;/p&gt;'':81C ''/omw/'':13C ''/omw/&lt;/a&gt;).&lt;/p&gt;'':18C ''/omw/summx.html'':56C ''/omw/summx.html&lt;/a&gt;&lt;/p&gt;'':61C ''3'':48C ''593'':49C ''also'':43C ''catalog.ldc.upenn.edu'':75C,80C ''catalog.ldc.upenn.edu/ldc2008l02'':74C ''catalog.ldc.upenn.edu/ldc2008l02&lt;/a&gt;.&lt;/p&gt;'':79C ''compling.hss.ntu.edu.sg'':12C,17C,55C,60C ''compling.hss.ntu.edu.sg/omw/'':11C ''compling.hss.ntu.edu.sg/omw/&lt;/a&gt;).&lt;/p&gt;'':16C ''compling.hss.ntu.edu.sg/omw/summx.html'':54C ''compling.hss.ntu.edu.sg/omw/summx.html&lt;/a&gt;&lt;/p&gt;'':59C ''edit'':36C ''extend'':39C ''get'':32C ''hindi'':45C,69C ''href'':10C,53C,73C ''lemma'':27C ''look'':4C ''multilingu'':7C ''nofollow'':15C,58C,78C ''non'':67C ''non-open'':66C ''noun'':29C ''open'':6C,68C ''p'':1C,19C,62C ''rel'':14C,57C,77C ''see'':51C ''synset'':35C,50C ''translat'':33C ''use'':21C ''version'':40C ''wikipedia'':24C,26C ''wiktionari'':22C ''wordnet'':8C,46C,70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8820, 1, '2015-11-12 16:09:57.543', 3, 120, 'Unbalanced classes -- How to minimize false negatives?', '&lt;p&gt;I have a dataset that has a binary class attribute. There are 623 instances with class +1 (cancer positive) and 101,671 instances with class -1 (cancer negative).&lt;/p&gt;

&lt;p&gt;I''ve tried various algorithms (Naive Bayes, Random Forest, AODE, C4.5) and all of them have unacceptable false negative ratios. Random Forest has the highest overall prediction accuracy (99.5%) and the lowest false negative ratio, but still misses 79% of positive classes (i.e. fails to detect 79% of malignant tumors).&lt;/p&gt;

&lt;p&gt;Any ideas how I can improve this situation?&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'classification  random-forest  decision-trees  unbalanced-classes', NULL, '''+1'':35C ''-1'':44C ''/p'':47C,99C,109C,112C ''101'':39C ''623'':31C ''671'':40C ''79'':87C,95C ''99.5'':77C ''accuraci'':76C ''algorithm'':53C ''aod'':58C ''attribut'':28C ''bay'':55C ''binari'':26C ''c4.5'':59C ''cancer'':36C,45C ''class'':2A,17B,27C,34C,43C,90C ''classif'':8B ''dataset'':22C ''decis'':13B ''decision-tre'':12B ''detect'':94C ''fail'':92C ''fals'':6A,66C,81C ''forest'':11B,57C,70C ''highest'':73C ''i.e'':91C ''idea'':102C ''improv'':106C ''instanc'':32C,41C ''lowest'':80C ''malign'':97C ''minim'':5A ''miss'':86C ''naiv'':54C ''negat'':7A,46C,67C,82C ''overal'':74C ''p'':18C,48C,100C,110C ''posit'':37C,89C ''predict'':75C ''random'':10B,56C,69C ''random-forest'':9B ''ratio'':68C,83C ''situat'':108C ''still'':85C ''thank'':111C ''tree'':14B ''tri'':51C ''tumor'':98C ''unaccept'':65C ''unbalanc'':1A,16B ''unbalanced-class'':15B ''various'':52C ''ve'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8821, 2, '2015-11-12 16:21:22.213', 2, NULL, NULL, '&lt;p&gt;&lt;a href="http://www.cs.cmu.edu/~qyj/IR-Lab/ImbalancedSummary.html" rel="nofollow"&gt;Class imbalance&lt;/a&gt; is a very common problem.  You can either oversample the positive class (or undersample the negative) or add class weights.&lt;/p&gt;

&lt;p&gt;Another thing to remember in this case is that accuracy is not a very useful metric here.  You might consider &lt;a href="http://www.r-bloggers.com/illustrated-guide-to-roc-and-auc/" rel="nofollow"&gt;AUC&lt;/a&gt; or &lt;a href="https://en.wikipedia.org/wiki/F1_score" rel="nofollow"&gt;F1 score&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Changing your decision threshold may seem appealing, but will obviously lead to (in this case likely drastically) increased false positives (though perhaps FPs aren''t as bad as FNs in the case of medical diagnosis, if tests aren''t prohibitively expensive).&lt;/p&gt;

&lt;p&gt;A more in-depth analysis of options in the case of class imbalance is provided &lt;a href="http://www.cs.ox.ac.uk/people/vasile.palade/papers/Class-Imbalance-SVM.pdf" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':11C,62C,73C,143C ''/illustrated-guide-to-roc-and-auc/'':58C ''/p'':32C,74C,117C,144C ''/people/vasile.palade/papers/class-imbalance-svm.pdf'':139C ''/wiki/f1_score'':68C ''/~qyj/ir-lab/imbalancedsummary.html'':6C ''accuraci'':43C ''add'':29C ''analysi'':124C ''anoth'':34C ''appeal'':82C ''aren'':99C,113C ''auc'':61C ''bad'':102C ''case'':40C,90C,107C,129C ''chang'':76C ''class'':9C,23C,30C,131C ''common'':15C ''consid'':53C ''decis'':78C ''depth'':123C ''diagnosi'':110C ''drastic'':92C ''either'':19C ''en.wikipedia.org'':67C ''en.wikipedia.org/wiki/f1_score'':66C ''expens'':116C ''f1'':71C ''fals'':94C ''fns'':104C ''fps'':98C ''href'':3C,55C,65C,136C ''imbal'':10C,132C ''in-depth'':121C ''increas'':93C ''lead'':86C ''like'':91C ''may'':80C ''medic'':109C ''metric'':49C ''might'':52C ''negat'':27C ''nofollow'':8C,60C,70C,141C ''obvious'':85C ''option'':126C ''oversampl'':20C ''p'':1C,33C,75C,118C ''perhap'':97C ''posit'':22C,95C ''problem'':16C ''prohibit'':115C ''provid'':134C ''rel'':7C,59C,69C,140C ''rememb'':37C ''score'':72C ''seem'':81C ''test'':112C ''thing'':35C ''though'':96C ''threshold'':79C ''undersampl'':25C ''use'':48C ''weight'':31C ''www.cs.cmu.edu'':5C ''www.cs.cmu.edu/~qyj/ir-lab/imbalancedsummary.html'':4C ''www.cs.ox.ac.uk'':138C ''www.cs.ox.ac.uk/people/vasile.palade/papers/class-imbalance-svm.pdf'':137C ''www.r-bloggers.com'':57C ''www.r-bloggers.com/illustrated-guide-to-roc-and-auc/'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8823, 2, '2015-11-12 18:00:35.987', 0, NULL, NULL, '&lt;p&gt;You can have a look at &lt;a href="http://datascience.stackexchange.com/q/8646/3024"&gt;this question&lt;/a&gt; where I got interesting replies for this problem.&lt;/p&gt;

&lt;p&gt;Best regards&lt;/p&gt;
', NULL, NULL, '''/a'':15C ''/p'':24C,28C ''/q/8646/3024'':12C ''best'':26C ''datascience.stackexchange.com'':11C ''datascience.stackexchange.com/q/8646/3024'':10C ''got'':18C ''href'':9C ''interest'':19C ''look'':6C ''p'':1C,25C ''problem'':23C ''question'':14C ''regard'':27C ''repli'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8824, 2, '2015-11-12 19:01:19.127', 1, NULL, NULL, '&lt;p&gt;Even though the answer in reality is always &lt;code&gt;true&lt;/code&gt; or &lt;code&gt;false&lt;/code&gt;, you can make your class attributes not labels but floating point numbers, ie 1.0 and 0.0 (or 100 and 0).  That is, you can frame it is a &lt;em&gt;regression&lt;/em&gt; problem, not classification problem.&lt;/p&gt;

&lt;p&gt;Then the predicted output will likewise be numbers on that spectrum, ie &lt;em&gt;probabilities&lt;/em&gt; not labels.  Your current implementation is essentially equivalent to a regression model with a threshold of 0.5.&lt;/p&gt;

&lt;p&gt;With such an output, you or your can client can define a threshold that is acceptable (eg 0.3).  Of course there will be more false positives then, but for some applications, like detecting cancer, that is optimal.&lt;/p&gt;

&lt;p&gt;Oversampling (the positives) or undersampling (the negatives) are also ways to fix this, but must be done thoughtfully, can sacrifice accuracy, and still sacrifices the control to move the threshold after creating the data and training.&lt;/p&gt;
', NULL, NULL, '''/code'':12C,16C ''/em'':47C,68C ''/p'':52C,86C,125C,163C ''0'':36C ''0.0'':32C ''0.3'':105C ''0.5'':85C ''1.0'':30C ''100'':34C ''accept'':103C ''accuraci'':147C ''also'':135C ''alway'':9C ''answer'':5C ''applic'':118C ''attribut'':22C ''cancer'':121C ''class'':21C ''classif'':50C ''client'':96C ''code'':10C,14C ''control'':152C ''cours'':107C ''creat'':158C ''current'':72C ''data'':160C ''defin'':98C ''detect'':120C ''done'':143C ''eg'':104C ''em'':45C,66C ''equival'':76C ''essenti'':75C ''even'':2C ''fals'':15C,112C ''fix'':138C ''float'':26C ''frame'':41C ''ie'':29C,65C ''implement'':73C ''label'':24C,70C ''like'':119C ''likewis'':59C ''make'':19C ''model'':80C ''move'':154C ''must'':141C ''negat'':133C ''number'':28C,61C ''optim'':124C ''output'':57C,91C ''oversampl'':127C ''p'':1C,53C,87C,126C ''point'':27C ''posit'':113C,129C ''predict'':56C ''probabl'':67C ''problem'':48C,51C ''realiti'':7C ''regress'':46C,79C ''sacrific'':146C,150C ''spectrum'':64C ''still'':149C ''though'':3C ''thought'':144C ''threshold'':83C,100C,156C ''train'':162C ''true'':11C ''undersampl'':131C ''way'':136C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8825, 1, '2015-11-13 04:36:59.007', 4, 59, 'Make use of relationships on recommendation systems', '&lt;p&gt;I have a data set of user rating for movie as&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;user_name, product_name, user_rating
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and I am using this data to recommend new movie to user (collaborative filtering). &lt;/p&gt;

&lt;p&gt;I also have another data set which show relationships between user like&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;user_1, CO_WORKER, user_2
user_1, FATHER_OF, user_3
user_3, FRIEND_OF, user_4 etc
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now what are the ways to include ''relationship data'' along with ''ratings data'' to produce better recommendations ?&lt;/p&gt;

&lt;p&gt;example : user_1 might like the movies which user_2 likes since they are co_workers&lt;/p&gt;
', 'machine-learning  data-mining  python  recommendation', NULL, '''/code'':37C,90C ''/p'':28C,54C,68C,110C,128C ''/pre'':38C,91C ''1'':72C,78C,114C ''2'':76C,121C ''3'':82C,84C ''4'':88C ''along'':102C ''also'':57C ''anoth'':59C ''better'':108C ''co'':73C,126C ''code'':30C,70C ''collabor'':52C ''data'':12B,20C,45C,60C,101C,105C ''data-min'':11B ''etc'':89C ''exampl'':112C ''father'':79C ''filter'':53C ''friend'':85C ''includ'':99C ''learn'':10B ''like'':67C,116C,122C ''machin'':9B ''machine-learn'':8B ''make'':1A ''might'':115C ''mine'':13B ''movi'':26C,49C,118C ''name'':32C,34C ''new'':48C ''p'':16C,39C,55C,92C,111C ''pre'':29C,69C ''produc'':107C ''product'':33C ''python'':14B ''rate'':24C,36C,104C ''recommend'':6A,15B,47C,109C ''relationship'':4A,64C,100C ''set'':21C,61C ''show'':63C ''sinc'':123C ''system'':7A ''use'':2A,43C ''user'':23C,31C,35C,51C,66C,71C,75C,77C,81C,83C,87C,113C,120C ''way'':97C ''worker'':74C,127C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9535, 2, '2015-12-27 16:31:59.26', 2, NULL, NULL, '&lt;p&gt;The model I would use is the one that minimizes the accumulated quadratic error. Both models you are using, linear and quadratic, looks good. You can compute which one has the lowest error.&lt;/p&gt;

&lt;p&gt;If you want to use an advanced method you can use &lt;a href="https://en.m.wikipedia.org/wiki/RANSAC" rel="nofollow"&gt;RANSAC&lt;/a&gt;. It is an iterative method for regression that assumes that there are outliers and remove them from the optimization. So your model should be more accurate that just using the first approach I told you.&lt;/p&gt;
', NULL, NULL, '''/a'':56C ''/p'':35C,92C ''/wiki/ransac'':52C ''accumul'':13C ''accur'':82C ''advanc'':43C ''approach'':88C ''assum'':65C ''comput'':28C ''en.m.wikipedia.org'':51C ''en.m.wikipedia.org/wiki/ransac'':50C ''error'':15C,34C ''first'':87C ''good'':25C ''href'':49C ''iter'':60C ''linear'':21C ''look'':24C ''lowest'':33C ''method'':44C,61C ''minim'':11C ''model'':3C,17C,78C ''nofollow'':54C ''one'':9C,30C ''optim'':75C ''outlier'':69C ''p'':1C,36C ''quadrat'':14C,23C ''ransac'':55C ''regress'':63C ''rel'':53C ''remov'':71C ''told'':90C ''use'':6C,20C,41C,47C,85C ''want'':39C ''would'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8826, 2, '2015-11-13 05:26:22.873', 1, NULL, NULL, '&lt;p&gt;There are a number of ways to make use of this information, including various graph-based methods of recommendation.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://ieeexplore.ieee.org/xpl/login.jsp?tp=&amp;amp;arnumber=6825613&amp;amp;url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6825613" rel="nofollow"&gt;http://ieeexplore.ieee.org/xpl/login.jsp?tp=&amp;amp;arnumber=6825613&amp;amp;url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6825613&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://snap.stanford.edu/class/cs224w-2013/projects2013/cs224w-038-final.pdf" rel="nofollow"&gt;http://snap.stanford.edu/class/cs224w-2013/projects2013/cs224w-038-final.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;It''s also common to use multiple approaches to build a hybrid recommendation system.&lt;/p&gt;
', NULL, NULL, '''/class/cs224w-2013/projects2013/cs224w-038-final.pdf'':39C ''/class/cs224w-2013/projects2013/cs224w-038-final.pdf&lt;/a&gt;&lt;/p&gt;'':44C ''/p'':22C,60C ''/xpl/login.jsp?tp=&amp;amp;arnumber=6825613&amp;amp;url=http%3a%2f%2fieeexplore.ieee.org%2fxpls%2fabs_all.jsp%3farnumber%3d6825613'':28C ''/xpl/login.jsp?tp=&amp;amp;arnumber=6825613&amp;amp;url=http%3a%2f%2fieeexplore.ieee.org%2fxpls%2fabs_all.jsp%3farnumber%3d6825613&lt;/a&gt;&lt;/p&gt;'':33C ''also'':48C ''approach'':53C ''base'':18C ''build'':55C ''common'':49C ''graph'':17C ''graph-bas'':16C ''href'':25C,36C ''hybrid'':57C ''ieeexplore.ieee.org'':27C,32C ''ieeexplore.ieee.org/xpl/login.jsp?tp=&amp;amp;arnumber=6825613&amp;amp;url=http%3a%2f%2fieeexplore.ieee.org%2fxpls%2fabs_all.jsp%3farnumber%3d6825613'':26C ''ieeexplore.ieee.org/xpl/login.jsp?tp=&amp;amp;arnumber=6825613&amp;amp;url=http%3a%2f%2fieeexplore.ieee.org%2fxpls%2fabs_all.jsp%3farnumber%3d6825613&lt;/a&gt;&lt;/p&gt;'':31C ''includ'':14C ''inform'':13C ''make'':9C ''method'':19C ''multipl'':52C ''nofollow'':30C,41C ''number'':5C ''p'':1C,23C,34C,45C ''recommend'':21C,58C ''rel'':29C,40C ''snap.stanford.edu'':38C,43C ''snap.stanford.edu/class/cs224w-2013/projects2013/cs224w-038-final.pdf'':37C ''snap.stanford.edu/class/cs224w-2013/projects2013/cs224w-038-final.pdf&lt;/a&gt;&lt;/p&gt;'':42C ''system'':59C ''use'':10C,51C ''various'':15C ''way'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8827, 2, '2015-11-13 07:17:47.38', 0, NULL, NULL, '&lt;p&gt;It depends. If you need a fast way to mine streams of data and use adaptative training of data sets, the best tool is SAMOA, because it could be easily integrated with Storm or S4 stream processing engines. If you need only to process batch data in a fast and distributed manner, the Spark MLLib would be the best solution among them.&lt;/p&gt;
', NULL, NULL, '''/p'':64C ''adapt'':17C ''among'':62C ''batch'':46C ''best'':23C,60C ''could'':29C ''data'':14C,20C,47C ''depend'':3C ''distribut'':52C ''easili'':31C ''engin'':39C ''fast'':8C,50C ''integr'':32C ''manner'':53C ''mine'':11C ''mllib'':56C ''need'':6C,42C ''p'':1C ''process'':38C,45C ''s4'':36C ''samoa'':26C ''set'':21C ''solut'':61C ''spark'':55C ''storm'':34C ''stream'':12C,37C ''tool'':24C ''train'':18C ''use'':16C ''way'':9C ''would'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8835, 2, '2015-11-13 17:57:42.067', 0, NULL, NULL, '&lt;p&gt;The types of methods that would work depend on the nature of the data being captured in relation to the output classification.  If the gestures are linearly separable in the 5-dimensional input space, a simple logistic regression is cheap and fast.  &lt;/p&gt;

&lt;p&gt;Alternatively a weak but very cheap method would be to find the mean input vector for each of the gestures, and simply decide which gesture vector the input is closest to at any point.  The downside there you might need another class for "not gesturing." &lt;/p&gt;

&lt;p&gt;It''s also possible to gather a lot of test data from many users to pre-train a model, and long as readings don''t vary too drastically between users,you wouldn''t need to train on a by-user basis (on the wearable itself).  In most cases prediction is efficient, so you wouldn''t be too limited by computational power. &lt;/p&gt;
', NULL, NULL, '''/p'':44C,91C,156C ''5'':32C ''also'':95C ''altern'':46C ''anoth'':86C ''basi'':135C ''by-us'':132C ''captur'':17C ''case'':142C ''cheap'':41C,51C ''class'':87C ''classif'':23C ''closest'':75C ''comput'':154C ''data'':15C,103C ''decid'':68C ''depend'':9C ''dimension'':33C ''downsid'':81C ''drastic'':121C ''effici'':145C ''fast'':43C ''find'':56C ''gather'':98C ''gestur'':26C,65C,70C,90C ''input'':34C,59C,73C ''limit'':152C ''linear'':28C ''logist'':38C ''long'':114C ''lot'':100C ''mani'':105C ''mean'':58C ''method'':5C,52C ''might'':84C ''model'':112C ''natur'':12C ''need'':85C,127C ''output'':22C ''p'':1C,45C,92C ''point'':79C ''possibl'':96C ''power'':155C ''pre'':109C ''pre-train'':108C ''predict'':143C ''read'':116C ''regress'':39C ''relat'':19C ''separ'':29C ''simpl'':37C ''simpli'':67C ''space'':35C ''test'':102C ''train'':110C,129C ''type'':3C ''user'':106C,123C,134C ''vari'':119C ''vector'':60C,71C ''weak'':48C ''wearabl'':138C ''work'':8C ''would'':7C,53C ''wouldn'':125C,148C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8836, 2, '2015-11-13 18:49:00.407', 0, NULL, NULL, '&lt;p&gt;If you evaluate the effectiveness of your classifier using a &lt;a href="https://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf" rel="nofollow"&gt;ROC curve&lt;/a&gt;, you don''t have to worry so much about class skew:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;ROC curves have an attractive property: they are insensitive to
  changes in class distribution. If the proportion of positive to
  negative instances changes in a test set, the ROC curves will not
  change.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;If you are using R, the &lt;a href="https://rocr.bioinf.mpi-sb.mpg.de/" rel="nofollow"&gt;ROCR package&lt;/a&gt; is excellent for constructing ROC curves (and many other metrics, too). &lt;/p&gt;
', NULL, NULL, '''/a'':21C,85C ''/blockquote'':70C ''/p'':33C,69C,97C ''/workshops/mir2009/references/rocintro.pdf'':16C ''attract'':40C ''blockquot'':34C ''ccrma.stanford.edu'':15C ''ccrma.stanford.edu/workshops/mir2009/references/rocintro.pdf'':14C ''chang'':46C,58C,68C ''class'':31C,48C ''classifi'':9C ''construct'':89C ''curv'':20C,37C,65C,91C ''distribut'':49C ''effect'':6C ''evalu'':4C ''excel'':87C ''href'':13C,79C ''insensit'':44C ''instanc'':57C ''mani'':93C ''metric'':95C ''much'':29C ''negat'':56C ''nofollow'':18C,82C ''p'':1C,35C,71C ''packag'':84C ''posit'':54C ''properti'':41C ''proport'':52C ''r'':76C ''rel'':17C,81C ''roc'':19C,36C,64C,90C ''rocr'':83C ''rocr.bioinf.mpi-sb.mpg.de'':80C ''set'':62C ''skew'':32C ''test'':61C ''use'':10C,75C ''worri'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8837, 1, '2015-11-13 19:16:49.497', 1, 53, 'How can I figure out section headings in a document?', '&lt;p&gt;I have a large number of different XML documents with associated style sheets (SEC financial filings). Are there any natural language processing (or other) tools that I can use to try to automatically extract section headings?&lt;/p&gt;
', 'nlp  text-mining', NULL, '''/p'':52C ''associ'':26C ''automat'':48C ''differ'':22C ''document'':10A,24C ''extract'':49C ''figur'':4A ''file'':31C ''financi'':30C ''head'':7A,51C ''languag'':36C ''larg'':19C ''mine'':14B ''natur'':35C ''nlp'':11B ''number'':20C ''p'':15C ''process'':37C ''sec'':29C ''section'':6A,50C ''sheet'':28C ''style'':27C ''text'':13B ''text-min'':12B ''tool'':40C ''tri'':46C ''use'':44C ''xml'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8838, 1, '2015-11-13 21:41:18.97', 0, 21, 'Why do we need to use sysfunc when we call a SAS function inside a SAS macro', '&lt;p&gt;I saw this piece of code in my project:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;%let num = test;
%let x=%sysfunc(trim(&amp;amp;num));
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Why could not  I write: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;%let x= %trim(&amp;amp;num);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Why did I need to use sysfunc?&lt;/p&gt;

&lt;p&gt;Under what circumstances can I call a function inside a macro without using sysfunc?&lt;/p&gt;
', 'sas', NULL, '''/code'':41C,57C ''/p'':29C,49C,67C,83C ''/pre'':42C,58C ''amp'':39C,55C ''call'':10A,74C ''circumst'':71C ''code'':25C,31C,51C ''could'':45C ''function'':13A,76C ''insid'':14A,77C ''let'':32C,35C,52C ''macro'':17A,79C ''need'':4A,63C ''num'':33C,40C,56C ''p'':19C,43C,59C,68C ''piec'':23C ''pre'':30C,50C ''project'':28C ''sas'':12A,16A,18B ''saw'':21C ''sysfunc'':7A,37C,66C,82C ''test'':34C ''trim'':38C,54C ''use'':6A,65C,81C ''without'':80C ''write'':48C ''x'':36C,53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8839, 2, '2015-11-13 23:03:59.497', 1, NULL, NULL, '&lt;p&gt;Without the sysfunc(), the expression will not be evaluated. You will not be assigning the value of the expression trim(&amp;amp;num) to the macrovariable, but rather the whole expression.&lt;/p&gt;

&lt;p&gt;If you want to store the result of an expression, you need to execute that function with sysfunc()&lt;/p&gt;

&lt;p&gt;&lt;a href="http://support.sas.com/documentation/cdl/en/mcrolref/61885/HTML/default/viewer.htm#z3514sysfunc.htm" rel="nofollow"&gt;http://support.sas.com/documentation/cdl/en/mcrolref/61885/HTML/default/viewer.htm#z3514sysfunc.htm&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/documentation/cdl/en/mcrolref/61885/html/default/viewer.htm#z3514sysfunc.htm'':58C ''/documentation/cdl/en/mcrolref/61885/html/default/viewer.htm#z3514sysfunc.htm&lt;/a&gt;&lt;/p&gt;'':63C ''/p'':32C,52C ''amp'':22C ''assign'':15C ''evalu'':10C ''execut'':47C ''express'':6C,20C,31C,43C ''function'':49C ''href'':55C ''macrovari'':26C ''need'':45C ''nofollow'':60C ''num'':23C ''p'':1C,33C,53C ''rather'':28C ''rel'':59C ''result'':40C ''store'':38C ''support.sas.com'':57C,62C ''support.sas.com/documentation/cdl/en/mcrolref/61885/html/default/viewer.htm#z3514sysfunc.htm'':56C ''support.sas.com/documentation/cdl/en/mcrolref/61885/html/default/viewer.htm#z3514sysfunc.htm&lt;/a&gt;&lt;/p&gt;'':61C ''sysfunc'':4C,51C ''trim'':21C ''valu'':17C ''want'':36C ''whole'':30C ''without'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8840, 1, '2015-11-14 05:51:38.297', 1, 54, 'How many features do you generally use for your ML Model?', '&lt;p&gt;I am working on a certain kaggle competition and users there say that they are using &gt;5000 features and training a XGBoost or Random Forest on it. &lt;/p&gt;

&lt;p&gt;The mentioned post is here: &lt;a href="https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/forums/t/17258/feature-counts" rel="nofollow"&gt;https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/forums/t/17258/feature-counts&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I tried doing so myself after exploding the feature space and creating interaction features, but it takes forever to run on my 16GB macbook. 
For example: (96000,1000) data took around 5 hrs to finish to train an RF with n_estimators=1000,max_depth=40, cv=3.&lt;/p&gt;

&lt;p&gt;Tuning such a model would take weeks and it only uses 1000 predictors....&lt;/p&gt;

&lt;p&gt;Now here are some of my doubts:
1. Is my python scikit environment properly loaded. I use anaconda distribution. Are these running times normal?&lt;/p&gt;
', 'scikit', NULL, '''/c/walmart-recruiting-trip-type-classification/forums/t/17258/feature-counts'':52C ''/c/walmart-recruiting-trip-type-classification/forums/t/17258/feature-counts&lt;/a&gt;&lt;/p&gt;'':57C ''/p'':41C,107C,122C,148C ''1'':131C ''1000'':86C,101C,120C ''16gb'':81C ''3'':106C ''40'':104C ''5'':90C ''5000'':30C ''96000'':85C ''anaconda'':141C ''around'':89C ''certain'':19C ''competit'':21C ''creat'':70C ''cv'':105C ''data'':87C ''depth'':103C ''distribut'':142C ''doubt'':130C ''environ'':136C ''estim'':100C ''exampl'':84C ''explod'':65C ''featur'':3A,31C,67C,72C ''finish'':93C ''forest'':38C ''forev'':76C ''general'':6A ''href'':49C ''hrs'':91C ''interact'':71C ''kaggl'':20C ''load'':138C ''macbook'':82C ''mani'':2A ''max'':102C ''mention'':44C ''ml'':10A ''model'':11A,112C ''n'':99C ''nofollow'':54C ''normal'':147C ''p'':13C,42C,58C,108C,123C ''post'':45C ''predictor'':121C ''proper'':137C ''python'':134C ''random'':37C ''rel'':53C ''rf'':97C ''run'':78C,145C ''say'':25C ''scikit'':12B,135C ''space'':68C ''take'':75C,114C ''time'':146C ''took'':88C ''train'':33C,95C ''tri'':60C ''tune'':109C ''use'':7A,29C,119C,140C ''user'':23C ''week'':115C ''work'':16C ''would'':113C ''www.kaggle.com'':51C,56C ''www.kaggle.com/c/walmart-recruiting-trip-type-classification/forums/t/17258/feature-counts'':50C ''www.kaggle.com/c/walmart-recruiting-trip-type-classification/forums/t/17258/feature-counts&lt;/a&gt;&lt;/p&gt;'':55C ''xgboost'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8841, 1, '2015-11-14 05:58:56.993', 2, 66, 'oversampling plus down sampling using smote not working on random forests', '&lt;p&gt;I am trying to solve a classification problem on a highly imbalanced data set. I am using SMOTE to over sample the minority samples and down sample the majority ones. After creating a balanced data set, I applied the random forest model. But, the prediction error for the minority class is extremely high even after using a balanced data set. What could possibly be going wrong?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(DMwR)
new.data &amp;lt;- SMOTE(Clicked ~ ., train, perc.over = 600, perc.under =  80)
table(new.data$Clicked)

rand.forest &amp;lt;- randomForest(Clicked ~., data=new.data, mtry = 7,
                            importance = TRUE, proximity=TRUE, ntree = 1000
                            )


#confusion matrix
table(yhat.rf, test$Clicked)

yhat.rf   0   1
      0 889  47
      1  57   7
&lt;/code&gt;&lt;/pre&gt;
', 'machine-learning  classification  r  unbalanced-classes', NULL, '''/code'':133C ''/p'':87C ''/pre'':134C ''0'':125C,127C ''1'':126C,130C ''1000'':117C ''47'':129C ''57'':131C ''600'':98C ''7'':111C,132C ''80'':100C ''889'':128C ''appli'':58C ''balanc'':54C,78C ''class'':19B,70C ''classif'':15B,27C ''click'':95C,103C,107C,123C ''code'':89C ''confus'':118C ''could'':82C ''creat'':52C ''data'':33C,55C,79C,108C ''dmwr'':91C ''error'':66C ''even'':74C ''extrem'':72C ''forest'':11A,61C ''go'':85C ''high'':31C,73C ''imbalanc'':32C ''import'':112C ''learn'':14B ''librari'':90C ''lt'':93C,105C ''machin'':13B ''machine-learn'':12B ''major'':49C ''matrix'':119C ''minor'':43C,69C ''model'':62C ''mtri'':110C ''new.data'':92C,102C,109C ''ntree'':116C ''one'':50C ''oversampl'':1A ''p'':20C ''perc.over'':97C ''perc.under'':99C ''plus'':2A ''possibl'':83C ''pre'':88C ''predict'':65C ''problem'':28C ''proxim'':114C ''r'':16B ''rand.forest'':104C ''random'':10A,60C ''randomforest'':106C ''sampl'':4A,41C,44C,47C ''set'':34C,56C,80C ''smote'':6A,38C,94C ''solv'':25C ''tabl'':101C,120C ''test'':122C ''train'':96C ''tri'':23C ''true'':113C,115C ''unbalanc'':18B ''unbalanced-class'':17B ''use'':5A,37C,76C ''work'':8A ''wrong'':86C ''yhat.rf'':121C,124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8842, 2, '2015-11-14 06:22:14.19', 0, NULL, NULL, '&lt;p&gt;Balancing your dataset does not guarantee an even prediction split.  Imagine the case where your features cannot separate between positive and negative examples at all. In this case, even if you balance the dataset, you will learn a decision boundary that essentially randomly guesses on each example. You would therefore expect that your prediction error would mirror the distribution of the majority/minority classes.&lt;/p&gt;

&lt;p&gt;In this scenario you might but have strongly predictive features or you may have insufficient data.&lt;/p&gt;
', NULL, NULL, '''/p'':65C,83C ''balanc'':2C,33C ''boundari'':41C ''cannot'':18C ''case'':14C,29C ''class'':64C ''data'':82C ''dataset'':4C,35C ''decis'':40C ''distribut'':60C ''error'':56C ''essenti'':43C ''even'':9C,30C ''exampl'':24C,48C ''expect'':52C ''featur'':17C,76C ''guarante'':7C ''guess'':45C ''imagin'':12C ''insuffici'':81C ''learn'':38C ''majority/minority'':63C ''may'':79C ''might'':71C ''mirror'':58C ''negat'':23C ''p'':1C,66C ''posit'':21C ''predict'':10C,55C,75C ''random'':44C ''scenario'':69C ''separ'':19C ''split'':11C ''strong'':74C ''therefor'':51C ''would'':50C,57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8846, 1, '2015-11-14 19:49:28.633', 1, 46, 'Does anyone have any thought how come the fuzzy rules can be verified and validated?', '&lt;p&gt;Fuzzy logic was utilized to derive a performance indicator of some manufacturing facilities in an uncertain environment. There are some historical data obtained from some manufacturing facilitiesâ rigorous assessments. How come can I use this data to verify and validate the fuzzy rules? Any insights and feedback would be appreciated.   &lt;/p&gt;
', 'data-mining  reference-request  fuzzy-logic', NULL, '''/p'':76C ''anyon'':2A ''appreci'':75C ''assess'':54C ''come'':7A,56C ''data'':17B,47C,61C ''data-min'':16B ''deriv'':31C ''environ'':42C ''facil'':38C ''facilitiesâ'':52C ''feedback'':72C ''fuzzi'':9A,23B,26C,67C ''fuzzy-log'':22B ''histor'':46C ''indic'':34C ''insight'':70C ''logic'':24B,27C ''manufactur'':37C,51C ''mine'':18B ''obtain'':48C ''p'':25C ''perform'':33C ''refer'':20B ''reference-request'':19B ''request'':21B ''rigor'':53C ''rule'':10A,68C ''thought'':5A ''uncertain'':41C ''use'':59C ''util'':29C ''valid'':15A,65C ''verifi'':13A,63C ''would'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8847, 1, '2015-11-15 00:45:03.647', 4, 346, 'Feature extraction of images in Python', '&lt;p&gt;In my class I have to create an application using two classifiers to decide whether an object in an image is an example of phylum porifera (seasponge) or some other object.&lt;/p&gt;

&lt;p&gt;However, I am completely lost when it comes to feature extraction techniques in python. My advisor convinced me to use images which haven''t been covered in class.&lt;/p&gt;

&lt;p&gt;Can anyone direct me towards meaningful documentation or reading or suggest methods to consider?&lt;/p&gt;
', 'python  feature-extraction  image-recognition', NULL, '''/p'':46C,76C,92C ''advisor'':63C ''anyon'':79C ''applic'':23C ''class'':17C,75C ''classifi'':26C ''come'':55C ''complet'':51C ''consid'':91C ''convinc'':64C ''cover'':73C ''creat'':21C ''decid'':28C ''direct'':80C ''document'':84C ''exampl'':37C ''extract'':2A,10B,58C ''featur'':1A,9B,57C ''feature-extract'':8B ''haven'':70C ''howev'':48C ''imag'':4A,12B,34C,68C ''image-recognit'':11B ''lost'':52C ''meaning'':83C ''method'':89C ''object'':31C,45C ''p'':14C,47C,77C ''phylum'':39C ''porifera'':40C ''python'':6A,7B,61C ''read'':86C ''recognit'':13B ''seaspong'':41C ''suggest'':88C ''techniqu'':59C ''toward'':82C ''two'':25C ''use'':24C,67C ''whether'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8848, 2, '2015-11-15 06:53:23.637', 5, NULL, NULL, '&lt;p&gt;This great tutorial covers the basics of convolutional neuraltworks, which are currently achieving state of the art performance in most vision tasks:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://deeplearning.net/tutorial/lenet.html"&gt;http://deeplearning.net/tutorial/lenet.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;There are a number of options for CNNs in python, including Theano and the libraries built on top of it (I found keras to be easy to use).  &lt;/p&gt;

&lt;p&gt;If you prefer to avoid deep learning, you might look into OpenCV, which can learn many other types of features, line Haar cascades and SIFT features.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_feature2d/py_table_of_contents_feature2d/py_table_of_contents_feature2d.html"&gt;http://opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_feature2d/py_table_of_contents_feature2d/py_table_of_contents_feature2d.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/en/latest/py_tutorials/py_feature2d/py_table_of_contents_feature2d/py_table_of_contents_feature2d.html'':97C ''/en/latest/py_tutorials/py_feature2d/py_table_of_contents_feature2d/py_table_of_contents_feature2d.html&lt;/a&gt;&lt;/p&gt;'':100C ''/p'':24C,63C,91C ''/tutorial/lenet.html'':30C ''/tutorial/lenet.html&lt;/a&gt;&lt;/p&gt;'':33C ''achiev'':14C ''art'':18C ''avoid'':69C ''basic'':7C ''built'':50C ''cascad'':87C ''cnns'':42C ''convolut'':9C ''cover'':5C ''current'':13C ''deep'':70C ''deeplearning.net'':29C,32C ''deeplearning.net/tutorial/lenet.html'':28C ''deeplearning.net/tutorial/lenet.html&lt;/a&gt;&lt;/p&gt;'':31C ''easi'':60C ''featur'':84C,90C ''found'':56C ''great'':3C ''haar'':86C ''href'':27C,94C ''includ'':45C ''kera'':57C ''learn'':71C,79C ''librari'':49C ''line'':85C ''look'':74C ''mani'':80C ''might'':73C ''neuraltwork'':10C ''number'':38C ''opencv'':76C ''opencv-python-tutroals.readthedocs.org'':96C,99C ''opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_feature2d/py_table_of_contents_feature2d/py_table_of_contents_feature2d.html'':95C ''opencv-python-tutroals.readthedocs.org/en/latest/py_tutorials/py_feature2d/py_table_of_contents_feature2d/py_table_of_contents_feature2d.html&lt;/a&gt;&lt;/p&gt;'':98C ''option'':40C ''p'':1C,25C,34C,64C,92C ''perform'':19C ''prefer'':67C ''python'':44C ''sift'':89C ''state'':15C ''task'':23C ''theano'':46C ''top'':52C ''tutori'':4C ''type'':82C ''use'':62C ''vision'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8849, 2, '2015-11-15 07:34:27.787', 3, NULL, NULL, '&lt;p&gt;In images, some frequently used techniques for feature extraction are &lt;strong&gt;binarizing&lt;/strong&gt; and &lt;strong&gt;blurring&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Binarizing:&lt;/strong&gt; converts the image array into 1s and 0s. This is done while converting the image to a 2D image. Even gray-scaling can also be used. It gives you a numerical matrix of the image. Grayscale takes much lesser space when stored on Disc.&lt;/p&gt;

&lt;p&gt;This is how you do it in Python:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from PIL import Image

%matplotlib inline  

#Import an image
image = Image.open("xyz.jpg")

image
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Example Image: &lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/mkf97.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/mkf97.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Now, convert into gray-scale:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;im = image.convert(''L'')

im
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;will return you this image:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/AGxy6.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/AGxy6.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;And the matrix can be seen by running this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;array(im)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The array would look something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;array([[213, 213, 213, ..., 176, 176, 176],
       [213, 213, 213, ..., 176, 176, 176],
       [213, 213, 213, ..., 175, 175, 175],
       ..., 
       [173, 173, 173, ..., 204, 204, 204],
       [173, 173, 173, ..., 205, 205, 204],
       [173, 173, 173, ..., 205, 205, 205]], dtype=uint8)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now, use a histogram plot and/or a contour plot to have a look at the image features:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from pylab import *

# create a new figure
figure()
gray()
# show contours with origin upper left corner
contour(im, origin=''image'')
axis(''equal'')
axis(''off'')


figure()


hist(im_array.flatten(), 128)

show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This would return you a plot, which looks something like this:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/56A0K.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/56A0K.png" alt="enter image description here"&gt;&lt;/a&gt;
&lt;a href="http://i.stack.imgur.com/tWpNy.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/tWpNy.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Blurring:&lt;/strong&gt; Blurring algorithm takes weighted average of neighbouring pixels to incorporate surroundings color into every pixel. It enhances the contours better and helps in understanding the features and their importance better.&lt;/p&gt;

&lt;p&gt;And this is how you do it in Python:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from PIL import *


figure()
p = image.convert("L").filter(ImageFilter.GaussianBlur(radius = 2))
p.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And the blurred image is:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/0Dx8q.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/0Dx8q.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;So, these are some ways in which you can do feature engineering. And for advanced methods, you have to understand the basics of Computer Vision and neural networks, and also the different types of filters and their significance and the math behind them.&lt;/p&gt;
', NULL, NULL, '''/0dx8q.jpg'':410C,417C ''/56a0k.png'':303C,310C ''/a'':119C,162C,316C,334C,423C ''/agxy6.png'':149C,156C ''/code'':95C,135C,179C,231C,283C,396C ''/mkf97.jpg'':106C,113C ''/p'':19C,69C,79C,100C,120C,128C,143C,163C,174C,189C,251C,297C,335C,370C,381C,404C,424C,469C ''/pre'':96C,136C,180C,232C,284C,397C ''/strong'':14C,18C,23C,339C ''/twpny.png'':321C,328C ''0s'':31C ''128'':281C ''173'':211C,212C,213C,217C,218C,219C,223C,224C,225C ''175'':208C,209C,210C ''176'':196C,197C,198C,202C,203C,204C ''1s'':29C ''2'':394C ''204'':214C,215C,216C,222C ''205'':220C,221C,226C,227C,228C ''213'':193C,194C,195C,199C,200C,201C,205C,206C,207C ''2d'':41C ''advanc'':440C ''algorithm'':341C ''also'':48C,455C ''alt'':114C,157C,311C,329C,418C ''and/or'':239C ''array'':27C,177C,183C,192C ''averag'':344C ''axi'':274C,276C ''basic'':447C ''behind'':467C ''better'':359C,369C ''binar'':13C,22C ''blur'':17C,338C,340C,401C ''code'':81C,130C,176C,191C,253C,383C ''color'':351C ''comput'':449C ''contour'':241C,264C,270C,358C ''convert'':24C,36C,123C ''corner'':269C ''creat'':257C ''descript'':117C,160C,314C,332C,421C ''differ'':457C ''disc'':68C ''done'':34C ''dtype'':229C ''engin'':437C ''enhanc'':356C ''enter'':115C,158C,312C,330C,419C ''equal'':275C ''even'':43C ''everi'':353C ''exampl'':98C ''extract'':10C ''featur'':9C,250C,365C,436C ''figur'':260C,261C,278C,387C ''filter'':391C,460C ''frequent'':5C ''give'':52C ''gray'':45C,126C,262C ''gray-scal'':44C,125C ''grayscal'':60C ''help'':361C ''hist'':279C ''histogram'':237C ''href'':103C,146C,300C,318C,407C ''i.stack.imgur.com'':105C,112C,148C,155C,302C,309C,320C,327C,409C,416C ''i.stack.imgur.com/0dx8q.jpg'':408C,415C ''i.stack.imgur.com/56a0k.png'':301C,308C ''i.stack.imgur.com/agxy6.png'':147C,154C ''i.stack.imgur.com/mkf97.jpg'':104C,111C ''i.stack.imgur.com/twpny.png'':319C,326C ''im'':131C,134C,178C,271C ''im_array.flatten'':280C ''imag'':3C,26C,38C,42C,59C,85C,90C,91C,94C,99C,116C,142C,159C,249C,273C,313C,331C,402C,420C ''image.convert'':132C,389C ''image.open'':92C ''imagefilter.gaussianblur'':392C ''img'':109C,152C,306C,324C,413C ''import'':84C,88C,256C,368C,386C ''incorpor'':349C ''inlin'':87C ''l'':133C,390C ''left'':268C ''lesser'':63C ''like'':187C,295C ''look'':185C,246C,293C ''math'':466C ''matplotlib'':86C ''matrix'':56C,167C ''method'':441C ''much'':62C ''neighbour'':346C ''network'':453C ''neural'':452C ''new'':259C ''nofollow'':108C,151C,305C,323C,412C ''numer'':55C ''origin'':266C,272C ''p'':1C,20C,70C,97C,101C,121C,137C,144C,164C,181C,233C,285C,298C,336C,371C,388C,398C,405C,425C ''p.show'':395C ''pil'':83C,385C ''pixel'':347C,354C ''plot'':238C,242C,291C ''pre'':80C,129C,175C,190C,252C,382C ''pylab'':255C ''python'':78C,380C ''radius'':393C ''rel'':107C,150C,304C,322C,411C ''return'':139C,288C ''run'':172C ''scale'':46C,127C ''seen'':170C ''show'':263C,282C ''signific'':463C ''someth'':186C,294C ''space'':64C ''src'':110C,153C,307C,325C,414C ''store'':66C ''strong'':12C,16C,21C,337C ''surround'':350C ''take'':61C,342C ''techniqu'':7C ''type'':458C ''uint8'':230C ''understand'':363C,445C ''upper'':267C ''use'':6C,50C,235C ''vision'':450C ''way'':430C ''weight'':343C ''would'':184C,287C ''xyz.jpg'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8867, 2, '2015-11-16 22:12:09.917', 0, NULL, NULL, '&lt;p&gt;It is possible that most of the variance in the dataset exists between input images (or between input and output images). In that case, the most informative principal components serve to separate input examples or to separate the input from the output. &lt;/p&gt;

&lt;p&gt;If only the less informative PCs describe the variance b/w outputs,  it will be harder to distinguish between  outputs than it was in the original feature space.&lt;/p&gt;

&lt;p&gt;That said,  PCA in images is rarely all that helpful. In this example I can see it being appealing, but it would probably much more meaningful and memory-friendly to learn a different lower dimensional feature representation. You could try a simple autoencoder, SIFT/SURF features, or haar-like features.&lt;/p&gt;
', NULL, NULL, '''/p'':44C,73C,126C ''appeal'':93C ''autoencod'':118C ''b/w'':55C ''case'':25C ''compon'':30C ''could'':114C ''dataset'':12C ''describ'':52C ''differ'':108C ''dimension'':110C ''distinguish'':62C ''exampl'':35C,87C ''exist'':13C ''featur'':71C,111C,120C,125C ''friend'':104C ''haar'':123C ''haar-lik'':122C ''harder'':60C ''help'':84C ''imag'':16C,22C,79C ''inform'':28C,50C ''input'':15C,19C,34C,40C ''learn'':106C ''less'':49C ''like'':124C ''lower'':109C ''meaning'':100C ''memori'':103C ''memory-friend'':102C ''much'':98C ''origin'':70C ''output'':21C,43C,56C,64C ''p'':1C,45C,74C ''pca'':77C ''pcs'':51C ''possibl'':4C ''princip'':29C ''probabl'':97C ''rare'':81C ''represent'':112C ''said'':76C ''see'':90C ''separ'':33C,38C ''serv'':31C ''sift/surf'':119C ''simpl'':117C ''space'':72C ''tri'':115C ''varianc'':9C,54C ''would'':96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8850, 1, '2015-11-15 15:06:41.813', 2, 112, 'Python distributed machine learning', '&lt;p&gt;I occasionally train neural nets for my research, and they usually take quite a long time to run (especially when I''m working on my laptop).&lt;/p&gt;

&lt;p&gt;I''m looking for a way to build the model on any computer and send it up to a server for training and have it return the graphs/accuracies/weights etc. I know there are paid solutions for this but I''m looking for a distributed solution I can run myself.&lt;/p&gt;

&lt;p&gt;I have a server set up at home which is about to get a CPU and GPU upgrade. I''d like to be able to set it up so that when I''m working on the LAN, or when I''m working remotely on my laptop, I can send code to the server and have it train the model and return to me the results (or save the results if the sender machine is switched off)&lt;/p&gt;

&lt;p&gt;Are there any existing solutions to accomplish something like this? I''m not tied to any specific library, but would prefer to stick with Python if possible&lt;/p&gt;
', 'machine-learning  python  neuralnetwork  distributed', NULL, '''/p'':38C,89C,167C,196C ''abl'':114C ''accomplish'':175C ''build'':47C ''code'':140C ''comput'':52C ''cpu'':105C ''d'':110C ''distribut'':2A,10B,83C ''especi'':30C ''etc'':68C ''exist'':172C ''get'':103C ''gpu'':107C ''graphs/accuracies/weights'':67C ''home'':98C ''know'':70C ''lan'':127C ''laptop'':37C,136C ''learn'':4A,7B ''librari'':186C ''like'':111C,177C ''long'':26C ''look'':42C,80C ''m'':33C,41C,79C,123C,131C,180C ''machin'':3A,6B,163C ''machine-learn'':5B ''model'':49C,149C ''net'':16C ''neural'':15C ''neuralnetwork'':9B ''occasion'':13C ''p'':11C,39C,90C,168C ''paid'':73C ''possibl'':195C ''prefer'':189C ''python'':1A,8B,193C ''quit'':24C ''remot'':133C ''research'':19C ''result'':155C,159C ''return'':65C,151C ''run'':29C,87C ''save'':157C ''send'':54C,139C ''sender'':162C ''server'':59C,94C,143C ''set'':95C,116C ''solut'':74C,84C,173C ''someth'':176C ''specif'':185C ''stick'':191C ''switch'':165C ''take'':23C ''tie'':182C ''time'':27C ''train'':14C,61C,147C ''upgrad'':108C ''usual'':22C ''way'':45C ''work'':34C,124C,132C ''would'':188C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8866, 2, '2015-11-16 18:48:32.353', 10, NULL, NULL, '&lt;p&gt;Bagging and dropout do not achieve quite the same thing, though both are types of model averaging.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Bootstrap_aggregating"&gt;Bagging&lt;/a&gt; is an operation across your entire dataset which trains models on a subset of the training data.  Thus some training examples are not shown to a given model.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf"&gt;Dropout&lt;/a&gt;, by contrast, is applied to features within each training example.  It is true that the result is functionally equivalent to training exponentially many networks (with shared weights!) and then equally weighting their outputs.  But dropout works on the feature space, causing certain features to be unavailable to the network, not full examples.  Because each neuron cannot completely rely on one input, representations in these networks tend to be more distributed and the network is less likely to overfit.&lt;/p&gt;
', NULL, NULL, '''/a'':27C,64C ''/p'':19C,56C,143C ''/wiki/bootstrap_aggregating'':25C ''/~hinton/absps/jmlrdropout.pdf'':62C ''achiev'':7C ''across'':31C ''appli'':68C ''averag'':18C ''bag'':2C,26C ''cannot'':120C ''caus'':105C ''certain'':106C ''complet'':121C ''contrast'':66C ''data'':44C ''dataset'':34C ''distribut'':134C ''dropout'':4C,63C,99C ''en.wikipedia.org'':24C ''en.wikipedia.org/wiki/bootstrap_aggregating'':23C ''entir'':33C ''equal'':94C ''equival'':83C ''exampl'':48C,74C,116C ''exponenti'':86C ''featur'':70C,103C,107C ''full'':115C ''function'':82C ''given'':54C ''href'':22C,59C ''input'':125C ''less'':139C ''like'':140C ''mani'':87C ''model'':17C,37C,55C ''network'':88C,113C,129C,137C ''neuron'':119C ''one'':124C ''oper'':30C ''output'':97C ''overfit'':142C ''p'':1C,20C,57C ''quit'':8C ''reli'':122C ''represent'':126C ''result'':80C ''share'':90C ''shown'':51C ''space'':104C ''subset'':40C ''tend'':130C ''thing'':11C ''though'':12C ''thus'':45C ''train'':36C,43C,47C,73C,85C ''true'':77C ''type'':15C ''unavail'':110C ''weight'':91C,95C ''within'':71C ''work'':100C ''www.cs.toronto.edu'':61C ''www.cs.toronto.edu/~hinton/absps/jmlrdropout.pdf'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8851, 2, '2015-11-15 20:27:56.33', 3, NULL, NULL, '&lt;p&gt;It might be really useful to provide a bit more details regarding the actual data, produced by your sensor. Can you provide a simple example for any simple gesture? &lt;/p&gt;

&lt;p&gt;Anyway, let''s try to address the problem in the following way: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;sensor can be represented as a function &lt;code&gt;f(t)&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;range of the function will be &lt;code&gt;{1,2,3,..100}&lt;/code&gt; &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So we can draw something like the following (can we?): &lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/okVvh.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/okVvh.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You can extract various high-level features out of that data, but I would suggest to go with inflection points as the most straightforward option:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/qBHIA.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/qBHIA.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Those points, basically, define a pattern. I''ll use a figure from the &lt;a href="http://stackoverflow.com/a/19071980/1435741"&gt;different answer&lt;/a&gt; to illustrate the idea. For example, the naive pattern below is used to identify a circle. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/qzN7M.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/qzN7M.png" alt="enter image description here"&gt;&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;In your case situation is a bit simpler, because sensor output is the only data you can work with. Naturally, you can combine multiple sensors in order to define a bit more complex interaction scenarios:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/MKJn1.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/MKJn1.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Also you can take into account &lt;code&gt;f''(t)&lt;/code&gt; in order to distinguish between slow and fast movements, etc.  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Update&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Thanks for the data. It does look like these three gestures looks pretty much the same from the data perspective (see visualization below). &lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/uhdcR.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/uhdcR.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;It really looks like several features are required to identify the gesture.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8855, 1, '2015-11-16 08:15:55.44', 2, 60, 'Can the size of a pooling layer be learned?', '&lt;p&gt;As far as I understood it, the pooling layer doesn''t learn anything. It has several parameters, most important its &lt;code&gt;pool_size&lt;/code&gt; and &lt;code&gt;stride&lt;/code&gt; (see &lt;a href="http://lasagne.readthedocs.org/en/latest/modules/layers/pool.html#lasagne.layers.MaxPool2DLayer" rel="nofollow"&gt;Lasagne documentation&lt;/a&gt; for more), but none of those is learned.&lt;/p&gt;

&lt;p&gt;Is it possible to learn these two (or one of those) parameters? Are there papers about it?&lt;/p&gt;

&lt;p&gt;(I would guess that it is not possible to add this to the objective function in a meaningful way... but I''d like to see if people thought about it.)&lt;/p&gt;
', 'machine-learning  neuralnetwork  convnet', NULL, '''/a'':54C ''/code'':39C,43C ''/en/latest/modules/layers/pool.html#lasagne.layers.maxpool2dlayer'':49C ''/p'':63C,82C,114C ''add'':93C ''anyth'':28C ''code'':36C,41C ''convnet'':14B ''d'':105C ''document'':53C ''doesn'':25C ''far'':17C ''function'':98C ''guess'':86C ''href'':46C ''import'':34C ''lasagn'':52C ''lasagne.readthedocs.org'':48C ''lasagne.readthedocs.org/en/latest/modules/layers/pool.html#lasagne.layers.maxpool2dlayer'':47C ''layer'':7A,24C ''learn'':9A,12B,27C,62C,69C ''like'':106C ''machin'':11B ''machine-learn'':10B ''meaning'':101C ''neuralnetwork'':13B ''nofollow'':51C ''none'':58C ''object'':97C ''one'':73C ''p'':15C,64C,83C ''paper'':79C ''paramet'':32C,76C ''peopl'':110C ''pool'':6A,23C,37C ''possibl'':67C,91C ''rel'':50C ''see'':44C,108C ''sever'':31C ''size'':3A,38C ''stride'':42C ''thought'':111C ''two'':71C ''understood'':20C ''way'':102C ''would'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8856, 2, '2015-11-16 10:17:28.597', 4, NULL, NULL, '&lt;p&gt;No it is not possible to learn those meta parameters from a data set using a learning algorithm. There is no way to calculate a gradient of any objective function with respect to pool_size and stride params. Even if there were, they are typically discrete values and relatively small integers, so cannot be updated in the same way as e.g. the weights are.&lt;/p&gt;

&lt;p&gt;What you can do is search through different values looking for optimal ones, using cross-validation to pick the best. The size and stride meta parameters can also have a large effect on memory use and speed of learning, so it may be the case you are looking to find a good compromise. With stride in lower layers for instance, it may be more effective (in terms of training time required for similar level of accuracy) to have a higher stride value but increase the overall training set size by adding random x/y translations to the training set images.&lt;/p&gt;
', NULL, NULL, '''/p'':66C,168C ''accuraci'':144C ''ad'':159C ''algorithm'':19C ''also'':96C ''best'':88C ''calcul'':25C ''cannot'':54C ''case'':113C ''compromis'':121C ''cross'':83C ''cross-valid'':82C ''data'':14C ''differ'':75C ''discret'':47C ''e.g'':62C ''effect'':100C,133C ''even'':40C ''find'':118C ''function'':31C ''good'':120C ''gradient'':27C ''higher'':148C ''imag'':167C ''increas'':152C ''instanc'':128C ''integ'':52C ''larg'':99C ''layer'':126C ''learn'':8C,18C,107C ''level'':142C ''look'':77C,116C ''lower'':125C ''may'':110C,130C ''memori'':102C ''meta'':10C,93C ''object'':30C ''one'':80C ''optim'':79C ''overal'':154C ''p'':1C,67C ''param'':39C ''paramet'':11C,94C ''pick'':86C ''pool'':35C ''possibl'':6C ''random'':160C ''relat'':50C ''requir'':139C ''respect'':33C ''search'':73C ''set'':15C,156C,166C ''similar'':141C ''size'':36C,90C,157C ''small'':51C ''speed'':105C ''stride'':38C,92C,123C,149C ''term'':135C ''time'':138C ''train'':137C,155C,165C ''translat'':162C ''typic'':46C ''updat'':56C ''use'':16C,81C,103C ''valid'':84C ''valu'':48C,76C,150C ''way'':23C,60C ''weight'':64C ''x/y'':161C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8865, 2, '2015-11-16 17:45:01.217', 2, NULL, NULL, '&lt;p&gt;There are a group of algorithms (or techniques) called the &lt;a href="https://en.wikipedia.org/wiki/Multi-armed_bandit" rel="nofollow"&gt;Bandit algorithms&lt;/a&gt;, which deal especially with the problem statement, which is the optimization of Click-through rates of advertisements.&lt;/p&gt;

&lt;p&gt;The problem is framed in a setting of multiple bandits with vending machines. There are various strategies which can be implemented:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Epsilon-greedy strategy&lt;/li&gt;
&lt;li&gt;Epsilon-first strategy&lt;/li&gt;
&lt;li&gt;Epsilon-decreasing strategy&lt;/li&gt;
&lt;li&gt;Contextual Epsilon strategy&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="https://www.chrisstucchio.com/blog/2012/bandit_algorithms_vs_ab.html" rel="nofollow"&gt;Reference on why Bandit algorithms are better than A/B testing frameworks.&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':21C,108C ''/blog/2012/bandit_algorithms_vs_ab.html'':94C ''/li'':70C,76C,82C,87C ''/p'':40C,63C,109C ''/ul'':88C ''/wiki/multi-armed_bandit'':16C ''a/b'':105C ''advertis'':39C ''algorithm'':7C,20C,101C ''bandit'':19C,51C,100C ''better'':103C ''call'':10C ''click'':35C ''click-through'':34C ''contextu'':84C ''deal'':23C ''decreas'':80C ''en.wikipedia.org'':15C ''en.wikipedia.org/wiki/multi-armed_bandit'':14C ''epsilon'':67C,73C,79C,85C ''epsilon-decreas'':78C ''epsilon-first'':72C ''epsilon-greedi'':66C ''especi'':24C ''first'':74C ''frame'':45C ''framework'':107C ''greedi'':68C ''group'':5C ''href'':13C,91C ''implement'':62C ''li'':65C,71C,77C,83C ''machin'':54C ''multipl'':50C ''nofollow'':18C,96C ''optim'':32C ''p'':1C,41C,89C ''problem'':27C,43C ''rate'':37C ''refer'':97C ''rel'':17C,95C ''set'':48C ''statement'':28C ''strategi'':58C,69C,75C,81C,86C ''techniqu'':9C ''test'':106C ''ul'':64C ''various'':57C ''vend'':53C ''www.chrisstucchio.com'':93C ''www.chrisstucchio.com/blog/2012/bandit_algorithms_vs_ab.html'':92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8857, 1, '2015-11-16 10:57:53.013', 4, 93, 'Dealing with training set of questionable quality', '&lt;p&gt;Most of the material I have read in the past usually assumes that the training set is flawless. However that doesn''t seem to be the case here with what I am given. The data that is meant to send into the training set is often questionable (I don''t even know how to start separating good from bad ones). I have spent much more time trying to pre-process the files in order to increase the validity of the data, than actually building the prototype.&lt;/p&gt;

&lt;p&gt;I have only dumped a part of the data that is claimed to be better. There are still data from other sources to complement the missing part in the first set of training data. Given the amount of data, it is practically impossible for me to spend too much time on them.&lt;/p&gt;

&lt;p&gt;So the question is, how do people deal with really horribly, inconsistent data in real life (assuming I am working with addresses, where people often write the wrong postcode due to bad city planning or lack of clear instruction, misspell the name of neighbourhood, plus data got "autocorrected" by excel such that "1-2" becomes "1-Feb", etc. etc.)? Specifically, how do I ensure the quality of the train classifier given the training set (and cross validation set) has questionable quality?&lt;/p&gt;
', 'data-cleaning  preprocessing', NULL, '''-2'':209C ''/p'':100C,154C,237C ''1'':208C,211C ''actual'':96C ''address'':177C ''amount'':138C ''assum'':24C,172C ''autocorrect'':203C ''bad'':71C,187C ''becom'':210C ''better'':116C ''build'':97C ''case'':39C ''citi'':188C ''claim'':113C ''classifi'':225C ''clean'':10B ''clear'':193C ''complement'':125C ''cross'':231C ''data'':9B,47C,94C,110C,120C,135C,140C,168C,201C ''data-clean'':8B ''deal'':1A,163C ''doesn'':33C ''due'':185C ''dump'':105C ''ensur'':219C ''etc'':213C,214C ''even'':63C ''excel'':205C ''feb'':212C ''file'':85C ''first'':131C ''flawless'':30C ''given'':45C,136C,226C ''good'':69C ''got'':202C ''horribl'':166C ''howev'':31C ''imposs'':144C ''inconsist'':167C ''increas'':89C ''instruct'':194C ''know'':64C ''lack'':191C ''life'':171C ''materi'':16C ''meant'':50C ''miss'':127C ''misspel'':195C ''much'':76C,150C ''name'':197C ''neighbourhood'':199C ''often'':58C,180C ''one'':72C ''order'':87C ''p'':12C,101C,155C ''part'':107C,128C ''past'':22C ''peopl'':162C,179C ''plan'':189C ''plus'':200C ''postcod'':184C ''practic'':143C ''pre'':82C ''pre-process'':81C ''preprocess'':11B ''process'':83C ''prototyp'':99C ''qualiti'':7A,221C,236C ''question'':6A,59C,158C,235C ''read'':19C ''real'':170C ''realli'':165C ''seem'':35C ''send'':52C ''separ'':68C ''set'':4A,28C,56C,132C,229C,233C ''sourc'':123C ''specif'':215C ''spend'':148C ''spent'':75C ''start'':67C ''still'':119C ''time'':78C,151C ''train'':3A,27C,55C,134C,224C,228C ''tri'':79C ''usual'':23C ''valid'':91C,232C ''work'':175C ''write'':181C ''wrong'':183C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10150, 2, '2016-02-09 09:06:10.08', 2, NULL, NULL, '&lt;p&gt;Recurrent Neural Networks can deal with variable length data. You might want to have a look at:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Andrej Karpathy: &lt;a href="http://karpathy.github.io/2015/05/21/rnn-effectiveness/" rel="nofollow"&gt;The Unreasonable Effectiveness of Recurrent Neural Networks&lt;/a&gt;.&lt;/li&gt;
&lt;li&gt;Christopher Olah: &lt;a href="http://colah.github.io/posts/2015-08-Understanding-LSTMs/" rel="nofollow"&gt;Understanding LSTM Networks&lt;/a&gt;.&lt;/li&gt;
&lt;li&gt;Hochreiter, Schmidthuber: &lt;a href="http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6795963" rel="nofollow"&gt;Long short-term memory&lt;/a&gt;.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Another idea (which I have not tested so far and  just came to my mind) is using a histogram approach: You could probably make fixed-size windows, get the data from those windows and make it discrete (e.g. vector quantization, k-means). After that, you can make a histogram of how often those vectors appeared.&lt;/p&gt;

&lt;p&gt;You could also use &lt;a href="https://en.wikipedia.org/wiki/Hidden_Markov_model" rel="nofollow"&gt;HMMs&lt;/a&gt; for recognition of variable length data.&lt;/p&gt;

&lt;p&gt;Transformations (e.g. &lt;a href="https://en.wikipedia.org/wiki/Fourier_transform" rel="nofollow"&gt;Fourier transform&lt;/a&gt; from the time domain in the &lt;a href="https://en.wikipedia.org/wiki/Frequency_domain" rel="nofollow"&gt;frequency domain&lt;/a&gt;) might also come in handy.&lt;/p&gt;
', NULL, NULL, '''/2015/05/21/rnn-effectiveness/'':28C ''/a'':38C,53C,70C,145C,165C,181C ''/li'':39C,54C,71C ''/p'':19C,131C,152C,187C ''/posts/2015-08-understanding-lstms/'':47C ''/ul'':72C ''/wiki/fourier_transform'':160C ''/wiki/frequency_domain'':176C ''/wiki/hidden_markov_model'':141C ''/xpl/freeabs_all.jsp?arnumber=6795963'':62C ''also'':135C,183C ''andrej'':22C ''anoth'':74C ''appear'':130C ''approach'':93C ''came'':85C ''christoph'':41C ''colah.github.io'':46C ''colah.github.io/posts/2015-08-understanding-lstms/'':45C ''come'':184C ''could'':95C,134C ''data'':10C,104C,151C ''deal'':6C ''discret'':111C ''domain'':169C,180C ''e.g'':112C,155C ''effect'':33C ''en.wikipedia.org'':140C,159C,175C ''en.wikipedia.org/wiki/fourier_transform'':158C ''en.wikipedia.org/wiki/frequency_domain'':174C ''en.wikipedia.org/wiki/hidden_markov_model'':139C ''far'':82C ''fix'':99C ''fixed-s'':98C ''fourier'':163C ''frequenc'':179C ''get'':102C ''handi'':186C ''histogram'':92C,124C ''hmms'':144C ''hochreit'':56C ''href'':25C,44C,59C,138C,157C,173C ''idea'':75C ''ieeexplore.ieee.org'':61C ''ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6795963'':60C ''k'':116C ''k-mean'':115C ''karpathi'':23C ''karpathy.github.io'':27C ''karpathy.github.io/2015/05/21/rnn-effectiveness/'':26C ''length'':9C,150C ''li'':21C,40C,55C ''long'':65C ''look'':17C ''lstm'':51C ''make'':97C,109C,122C ''mean'':117C ''memori'':69C ''might'':12C,182C ''mind'':88C ''network'':4C,37C,52C ''neural'':3C,36C ''nofollow'':30C,49C,64C,143C,162C,178C ''often'':127C ''olah'':42C ''p'':1C,73C,132C,153C ''probabl'':96C ''quantiz'':114C ''recognit'':147C ''recurr'':2C,35C ''rel'':29C,48C,63C,142C,161C,177C ''schmidthub'':57C ''short'':67C ''short-term'':66C ''size'':100C ''term'':68C ''test'':80C ''time'':168C ''transform'':154C,164C ''ul'':20C ''understand'':50C ''unreason'':32C ''use'':90C,136C ''variabl'':8C,149C ''vector'':113C,129C ''want'':13C ''window'':101C,107C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8862, 1, '2015-11-16 15:40:51.043', 3, 54, 'how to find probability of one or more events to happen from an incomplete data set', '&lt;p&gt;I have a dataset that gives information of a population. For instance, I know the fraction of people that are males (&lt;code&gt;M&lt;/code&gt;) and that are within a certain age range (&lt;code&gt;A&lt;/code&gt;), &lt;code&gt;P(M &amp;amp; A)&lt;/code&gt;, and then I know the fraction of males that live in a certain area (&lt;code&gt;L&lt;/code&gt;), &lt;code&gt;P(M &amp;amp; L)&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;What I''m interested in computing is &lt;code&gt;P(M &amp;amp; A &amp;amp; L)&lt;/code&gt;, which is the fraction of people that are males, are within a certain age range and live in a certain area. &lt;/p&gt;

&lt;p&gt;Using Baye''s formula I can say that&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;P(M &amp;amp; A &amp;amp; L) = P(M &amp;amp; A | L) P(L)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But my dataset only gives &lt;code&gt;P(L)&lt;/code&gt; and not &lt;code&gt;P(M &amp;amp; A | L)&lt;/code&gt;. However, if I assumed that &lt;code&gt;M &amp;amp; A&lt;/code&gt; and &lt;code&gt;L&lt;/code&gt; are independent I have&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;P(M &amp;amp; A | L) = P(M &amp;amp; A) P(L)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;How large is the error on &lt;code&gt;P(M &amp;amp; A | L)&lt;/code&gt; if I make this assumption. Do you know of any other method I could use to estimate &lt;code&gt;P(M &amp;amp; A | L)&lt;/code&gt; without assuming independence?&lt;/p&gt;
', 'statistics', NULL, '''/code'':42C,53C,59C,76C,82C,99C,147C,158C,167C,177C,181C,200C,215C,239C ''/p'':83C,121C,131C,186C,243C ''/pre'':148C,201C ''age'':49C,113C ''amp'':57C,80C,95C,97C,136C,138C,142C,164C,175C,191C,196C,212C,236C ''area'':73C,120C ''assum'':171C,241C ''assumpt'':220C ''bay'':124C ''certain'':48C,72C,112C,119C ''code'':40C,51C,54C,74C,77C,92C,133C,155C,161C,173C,179C,188C,209C,233C ''comput'':90C ''could'':229C ''data'':15A ''dataset'':22C,152C ''error'':207C ''estim'':232C ''event'':9A ''find'':3A ''formula'':126C ''fraction'':34C,65C,103C ''give'':24C,154C ''happen'':11A ''howev'':168C ''incomplet'':14A ''independ'':183C,242C ''inform'':25C ''instanc'':30C ''interest'':88C ''know'':32C,63C,223C ''l'':75C,81C,98C,139C,144C,146C,157C,166C,180C,193C,199C,214C,238C ''larg'':204C ''live'':69C,116C ''m'':41C,56C,79C,87C,94C,135C,141C,163C,174C,190C,195C,211C,235C ''make'':218C ''male'':39C,67C,108C ''method'':227C ''one'':6A ''p'':18C,55C,78C,84C,93C,122C,134C,140C,145C,149C,156C,162C,189C,194C,198C,202C,210C,234C ''peopl'':36C,105C ''popul'':28C ''pre'':132C,187C ''probabl'':4A ''rang'':50C,114C ''say'':129C ''set'':16A ''statist'':17B ''use'':123C,230C ''within'':46C,110C ''without'':240C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8871, 1, '2015-11-17 07:38:41.22', 3, 76, 'Features &amp; Models to compute the probability of certain customer accepting an offer/product from a bank?', '&lt;p&gt;What are the features &amp;amp; models that can be used to compute the probability of a certain customer accepting an offer/product from a bank? &lt;/p&gt;

&lt;p&gt;After some research, I came to know of what is called ''&lt;a href="https://en.wikipedia.org/wiki/Propensity_score_matching" rel="nofollow"&gt;Propensity Scoring&lt;/a&gt;'' and it''s definition is very relevant to what I am trying to find. &lt;/p&gt;

&lt;p&gt;But I failed to find any technical papers going into any appreciable detail. I tried Google Scholar but reached a dead end because the term ''Propensity modeling'' is actually a marketing term not exactly academic.&lt;/p&gt;

&lt;p&gt;I also know from my search that a broader term is ''Campaign Response Models'' but this turns out to be too broad and I am looking for something very specific. Any references/links that contain some technical details about the modeling process, the features or the choice of models will be very helpful.&lt;/p&gt;
', 'predictive-modeling  reference-request', NULL, '''/a'':69C ''/p'':47C,84C,121C,175C ''/wiki/propensity_score_matching'':64C ''academ'':120C ''accept'':10A,41C ''actual'':114C ''also'':124C ''amp'':27C ''appreci'':97C ''bank'':15A,46C ''broad'':144C ''broader'':131C ''call'':59C ''came'':53C ''campaign'':134C ''certain'':8A,39C ''choic'':168C ''comput'':4A,34C ''contain'':156C ''custom'':9A,40C ''dead'':106C ''definit'':73C ''detail'':98C,159C ''en.wikipedia.org'':63C ''en.wikipedia.org/wiki/propensity_score_matching'':62C ''end'':107C ''exact'':119C ''fail'':88C ''featur'':1A,26C,165C ''find'':83C,90C ''go'':94C ''googl'':101C ''help'':174C ''href'':61C ''know'':55C,125C ''look'':148C ''market'':116C ''model'':2A,18B,28C,112C,136C,162C,170C ''nofollow'':66C ''offer/product'':12A,43C ''p'':22C,48C,85C,122C ''paper'':93C ''predict'':17B ''predictive-model'':16B ''probabl'':6A,36C ''process'':163C ''propens'':67C,111C ''reach'':104C ''refer'':20B ''reference-request'':19B ''references/links'':154C ''rel'':65C ''relev'':76C ''request'':21B ''research'':51C ''respons'':135C ''scholar'':102C ''score'':68C ''search'':128C ''someth'':150C ''specif'':152C ''technic'':92C,158C ''term'':110C,117C,132C ''tri'':81C,100C ''turn'':139C ''use'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8863, 2, '2015-11-16 16:50:46.557', 3, NULL, NULL, '&lt;p&gt;Using a word-based metric would explicitly favor word-level retrieval methods.  The theory is that (just as you suggest with dwell time), the URL-level metric measures more directly the desired result.  &lt;/p&gt;

&lt;p&gt;More concretely, consider a search of "alcohol from potatoes." Assume we have two pages: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1) A page that is simply a grocery list (containing "alcohol" and "potatoes") 
2) A page that describes vodka or moonshine which only uses the words "ethanol"
   and "potatoes" but not "alcohol."
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;A word-level metric would prefer the first result, since it contains both alcohol and potatoes..  A URL-level metric would likely label the second result as more relevant, which would allow a model to form associations beyond simple word-matching (i.e. it could learn that "alcohol" and "vodka" are associated).&lt;/p&gt;
', NULL, NULL, '''..'':109C ''/code'':89C ''/p'':38C,54C,147C ''/pre'':90C ''1'':57C ''2'':70C ''alcohol'':46C,67C,88C,106C,142C ''allow'':126C ''associ'':131C,146C ''assum'':49C ''base'':6C ''beyond'':132C ''code'':56C ''concret'':41C ''consid'':42C ''contain'':66C,104C ''could'':139C ''describ'':74C ''desir'':36C ''direct'':34C ''dwell'':25C ''ethanol'':83C ''explicit'':9C ''favor'':10C ''first'':100C ''form'':130C ''groceri'':64C ''i.e'':137C ''label'':117C ''learn'':140C ''level'':13C,30C,95C,113C ''like'':116C ''list'':65C ''match'':136C ''measur'':32C ''method'':15C ''metric'':7C,31C,96C,114C ''model'':128C ''moonshin'':77C ''p'':1C,39C,91C ''page'':53C,59C,72C ''potato'':48C,69C,85C,108C ''pre'':55C ''prefer'':98C ''relev'':123C ''result'':37C,101C,120C ''retriev'':14C ''search'':44C ''second'':119C ''simpl'':133C ''simpli'':62C ''sinc'':102C ''suggest'':23C ''theori'':17C ''time'':26C ''two'':52C ''url'':29C,112C ''url-level'':28C,111C ''use'':2C,80C ''vodka'':75C,144C ''word'':5C,12C,82C,94C,135C ''word-bas'':4C ''word-level'':11C,93C ''word-match'':134C ''would'':8C,97C,115C,125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8869, 1, '2015-11-17 03:06:51.357', 3, 71, 'Best way to search for a similar document given the ngram', '&lt;p&gt;I have a database of about 200 documents who''s ngrams I have extracted. I want to find the document in my database that is most similar to a query document. In otherwords, I want to find the document in the database that shares the most number of ngrams with the query document. Right now, I can go through each one and compare it one by one, but this will take O(N) time and is expensive if N is very large. I was wondering if there are any efficient data structures or methods in doing efficient similarity search. Thanks&lt;/p&gt;
', 'nlp  similarity  search  information-retrieval', NULL, '''/p'':119C ''200'':25C ''best'':1A ''compar'':81C ''data'':109C ''databas'':22C,41C,60C ''document'':8A,26C,38C,49C,57C,71C ''effici'':108C,115C ''expens'':95C ''extract'':32C ''find'':36C,55C ''given'':9A ''go'':76C ''inform'':16B ''information-retriev'':15B ''larg'':100C ''method'':112C ''n'':91C,97C ''ngram'':11A,29C,67C ''nlp'':12B ''number'':65C ''o'':90C ''one'':79C,83C,85C ''otherword'':51C ''p'':18C ''queri'':48C,70C ''retriev'':17B ''right'':72C ''search'':4A,14B,117C ''share'':62C ''similar'':7A,13B,45C,116C ''structur'':110C ''take'':89C ''thank'':118C ''time'':92C ''want'':34C,53C ''way'':2A ''wonder'':103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8870, 2, '2015-11-17 07:21:41.843', 0, NULL, NULL, '&lt;p&gt;The data structure typically uses is &lt;a href="https://en.wikipedia.org/wiki/Inverted_index" rel="nofollow"&gt;inverted index&lt;/a&gt; (e.g., in databases).&lt;/p&gt;

&lt;p&gt;Please note that matching all ngram is a good heuristic but you might want to improve it. &lt;/p&gt;

&lt;p&gt;Taking into account the probability of each term and &lt;a href="https://en.wikipedia.org/wiki/Stemming" rel="nofollow"&gt;stemming&lt;/a&gt; are directions you might benefit from.&lt;/p&gt;
', NULL, NULL, '''/a'':17C,59C ''/p'':21C,40C,66C ''/wiki/inverted_index'':12C ''/wiki/stemming'':55C ''account'':44C ''benefit'':64C ''data'':3C ''databas'':20C ''direct'':61C ''e.g'':18C ''en.wikipedia.org'':11C,54C ''en.wikipedia.org/wiki/inverted_index'':10C ''en.wikipedia.org/wiki/stemming'':53C ''good'':31C ''heurist'':32C ''href'':9C,52C ''improv'':38C ''index'':16C ''invert'':15C ''match'':26C ''might'':35C,63C ''ngram'':28C ''nofollow'':14C,57C ''note'':24C ''p'':1C,22C,41C ''pleas'':23C ''probabl'':46C ''rel'':13C,56C ''stem'':58C ''structur'':4C ''take'':42C ''term'':49C ''typic'':5C ''use'':6C ''want'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8872, 2, '2015-11-17 09:21:08.463', 1, NULL, NULL, '&lt;p&gt;table  &lt;/p&gt;

&lt;p&gt;ngram&lt;br&gt;
docID  &lt;/p&gt;

&lt;p&gt;PK (primary key) ngram, docID  &lt;/p&gt;

&lt;p&gt;depending the database may change a bit but this is for TSQL&lt;br&gt;
x is the document you are matching &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;select top(1) with ties *    
from 
(  select tm.docID, count(*) as count 
     from table td
     join table tm
       on tm.docID &amp;lt;&amp;gt; td.docID 
      and tm.ngram = td.ngram 
      and td.docID = x
    group by tm.docID 
) tt 
order by count desc
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The join is on an index (PK) so this is very fast. I do this on a million documents in just a few seconds (with more advanced conditions).     &lt;/p&gt;

&lt;p&gt;This is going to favor larger documents but that is what you asked for.  &lt;/p&gt;

&lt;p&gt;Question seems to be changing  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;declare table @query (varchar ngram);
insert into @query values (''ng1''), (''ng2''), (''ng3'');
select top(10) with ties *    
from 
(  select tm.docID, count(*) as count 
     from table td
     join @query
       on tm.ngram = @query.ngram
    group by tm.docID 
) tt 
order by count desc
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':76C,172C ''/p'':3C,8C,15C,37C,107C,123C,130C ''/pre'':77C,173C ''1'':42C ''10'':147C ''advanc'':105C ''ask'':121C ''bit'':23C ''br'':6C,29C ''chang'':21C,129C ''code'':39C,132C ''condit'':106C ''count'':48C,50C,74C,153C,155C,170C ''databas'':19C ''declar'':133C ''depend'':17C ''desc'':75C,171C ''docid'':7C,14C ''document'':33C,97C,115C ''fast'':90C ''favor'':113C ''go'':111C ''group'':68C,164C ''gt'':60C ''index'':84C ''insert'':138C ''join'':54C,80C,159C ''key'':12C ''larger'':114C ''lt'':59C ''match'':36C ''may'':20C ''million'':96C ''ng1'':142C ''ng2'':143C ''ng3'':144C ''ngram'':5C,13C,137C ''order'':72C,168C ''p'':1C,4C,9C,16C,78C,108C,124C ''pk'':10C,85C ''pre'':38C,131C ''primari'':11C ''queri'':135C,140C,160C ''query.ngram'':163C ''question'':125C ''second'':102C ''seem'':126C ''select'':40C,46C,145C,151C ''tabl'':2C,52C,55C,134C,157C ''td'':53C,158C ''td.docid'':61C,66C ''td.ngram'':64C ''tie'':44C,149C ''tm'':56C ''tm.docid'':47C,58C,70C,152C,166C ''tm.ngram'':63C,162C ''top'':41C,146C ''tsql'':28C ''tt'':71C,167C ''valu'':141C ''varchar'':136C ''x'':30C,67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8873, 1, '2015-11-17 16:10:20', 6, 104, 'User-product positive (click data) available. How to generate negative (no-click data)?', '&lt;p&gt;Its very common in recommender that we have user product data which have label as an e.g. "click". In order to learn the model, I need click and no-click data.&lt;/p&gt;

&lt;p&gt;Simplest approach to generate is to take user-products pairs which are not found in click data. However, that may be misleading. Example: &lt;/p&gt;

&lt;p&gt;&lt;code&gt;user1, product1 (click)
 user2, product2 (click)
 user2, product3 (click)
 user3, product2 (click)&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;I can take user1 with all products except product1 and label them as "no_click" and so on. But this may not be true. Maybe user1 would have clicked product2 if he was shown product2. But just because he was shown other sets of products - he had no opportunity to decide to click/no-click product2.&lt;/p&gt;

&lt;p&gt;So how to address unary data problem?&lt;/p&gt;
', 'machine-learning  data-mining', NULL, '''/code'':96C ''/p'':55C,81C,97C,153C,162C ''address'':158C ''approach'':58C ''avail'':7A ''click'':5A,14A,40C,49C,53C,73C,86C,89C,92C,95C,113C,127C ''click/no-click'':151C ''code'':83C ''common'':25C ''data'':6A,15A,20B,33C,54C,74C,160C ''data-min'':19B ''decid'':149C ''e.g'':39C ''exampl'':80C ''except'':106C ''found'':71C ''generat'':10A,60C ''howev'':75C ''label'':36C,109C ''learn'':18B,44C ''machin'':17B ''machine-learn'':16B ''may'':77C,119C ''mayb'':123C ''mine'':21B ''mislead'':79C ''model'':46C ''need'':48C ''negat'':11A ''no-click'':12A,51C ''opportun'':147C ''order'':42C ''p'':22C,56C,82C,98C,154C ''pair'':67C ''posit'':4A ''problem'':161C ''product'':3A,32C,66C,105C,143C ''product1'':85C,107C ''product2'':88C,94C,128C,133C,152C ''product3'':91C ''recommend'':27C ''set'':141C ''shown'':132C,139C ''simplest'':57C ''take'':63C,101C ''true'':122C ''unari'':159C ''user'':2A,31C,65C ''user-product'':1A,64C ''user1'':84C,102C,124C ''user2'':87C,90C ''user3'':93C ''would'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8874, 2, '2015-11-17 16:59:45.853', 1, NULL, NULL, '&lt;p&gt;Reproducing the accepted answer from CrossValidated here, as it is the most complete and the best answer to this question.&lt;/p&gt;

&lt;p&gt;In order to understand this, remember the "ingredients" of random forest classifier (there are some modifications, but this is the general pipeline):&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;At each step of building individual tree we find the &lt;strong&gt;best split&lt;/strong&gt; of data&lt;/li&gt;
&lt;li&gt;While building a tree we use not the whole dataset, but &lt;strong&gt;bootstrap sample&lt;/strong&gt;&lt;/li&gt;
&lt;li&gt;We aggregate the individual tree outputs by &lt;strong&gt;averaging&lt;/strong&gt; (actually 2 and 3 means together more general &lt;a href="http://en.wikipedia.org/wiki/Bootstrap_aggregating" rel="nofollow"&gt;bagging procedure&lt;/a&gt;).&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Assume first point. It is not always possible to find the best split. For example in the following dataset each split will give exactly one misclassified object.
&lt;img src="http://i.stack.imgur.com/aG3Ps.png" alt="Example of the dataset with no best split"&gt;&lt;/p&gt;

&lt;p&gt;And I think that exactly this point can be confusing: indeed, the behaviour of the individual split is somehow similar to the behaviour of Naive Bayes classifier: if the variables are dependent - there is no better split for Decision Trees and Naive Bayes classifier also fails (just to remind: independent variables is the main assumption that we make in Naive Bayes classifier; all other assumptions come from the probabilistic model that we choose).&lt;/p&gt;

&lt;p&gt;But here comes the great advantage of decision trees: we take &lt;strong&gt;any&lt;/strong&gt; split and &lt;strong&gt;continue&lt;/strong&gt; splitting further. And for the following splits we will find a perfect separation (in red).
&lt;img src="http://i.stack.imgur.com/ptLS4.png" alt="Example of the decision boundary"&gt;&lt;/p&gt;

&lt;p&gt;And as we have no probabilistic model, but just binary split, we don''t need to make any assumption at all.&lt;/p&gt;

&lt;p&gt;That was about Decision Tree, but it also applies for Random Forest. The difference is that for Random Forest we use Bootstrap Aggregation. It has no model underneath, and the only assumption that it relies is that &lt;strong&gt;sampling is representative&lt;/strong&gt;. But this is usually a common assumption. For example, if one class consist of two components and in our dataset one component is represented by 100 samples, and another component is represented by 1 sample - probably most individual decision trees will see only the first component and Random Forest will misclassify the second one.
&lt;img src="http://i.stack.imgur.com/Ndk6t.png" alt="Example of weakly represented second component"&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':111C ''/ag3ps.png'':146C ''/li'':65C,82C,112C ''/ndk6t.png'':403C ''/ol'':113C ''/p'':22C,46C,156C,231C,278C,301C,411C ''/ptls4.png'':271C ''/strong'':62C,81C,93C,246C,251C,344C ''/wiki/bootstrap_aggregating'':106C ''1'':378C ''100'':370C ''2'':95C ''3'':97C ''accept'':4C ''actual'':94C ''advantag'':238C ''aggreg'':85C,325C ''also'':202C,310C ''alt'':147C,272C,404C ''alway'':121C ''anoth'':373C ''answer'':5C,18C ''appli'':311C ''assum'':115C ''assumpt'':212C,222C,298C,334C,351C ''averag'':92C ''bag'':109C ''bay'':183C,200C,218C ''behaviour'':170C,180C ''best'':17C,60C,126C,154C ''better'':193C ''binari'':289C ''bootstrap'':79C,324C ''boundari'':277C ''build'':53C,68C ''choos'':230C ''class'':356C ''classifi'':35C,184C,201C,219C ''come'':223C,235C ''common'':350C ''complet'':14C ''compon'':360C,366C,374C,390C,410C ''confus'':167C ''consist'':357C ''continu'':250C ''crossvalid'':7C ''data'':64C ''dataset'':76C,133C,151C,364C ''decis'':196C,240C,276C,306C,383C ''depend'':189C ''differ'':316C ''en.wikipedia.org'':105C ''en.wikipedia.org/wiki/bootstrap_aggregating'':104C ''exact'':138C,162C ''exampl'':129C,148C,273C,353C,405C ''fail'':203C ''find'':57C,124C,261C ''first'':116C,389C ''follow'':132C,257C ''forest'':34C,314C,321C,393C ''general'':44C,101C ''give'':137C ''great'':237C ''href'':103C ''i.stack.imgur.com'':145C,270C,402C ''i.stack.imgur.com/ag3ps.png'':144C ''i.stack.imgur.com/ndk6t.png'':401C ''i.stack.imgur.com/ptls4.png'':269C ''img'':142C,267C,399C ''inde'':168C ''independ'':207C ''individu'':54C,87C,173C,382C ''ingredi'':31C ''li'':48C,66C,83C ''main'':211C ''make'':215C,296C ''mean'':98C ''misclassifi'':140C,395C ''model'':227C,286C,329C ''modif'':39C ''naiv'':182C,199C,217C ''need'':294C ''nofollow'':108C ''object'':141C ''ol'':47C ''one'':139C,355C,365C,398C ''order'':25C ''output'':89C ''p'':1C,23C,114C,157C,232C,279C,302C ''perfect'':263C ''pipelin'':45C ''point'':117C,164C ''possibl'':122C ''probabilist'':226C,285C ''probabl'':380C ''procedur'':110C ''question'':21C ''random'':33C,313C,320C,392C ''red'':266C ''rel'':107C ''reli'':337C ''rememb'':29C ''remind'':206C ''repres'':343C,368C,376C,408C ''reproduc'':2C ''sampl'':80C,341C,371C,379C ''second'':397C,409C ''see'':386C ''separ'':264C ''similar'':177C ''somehow'':176C ''split'':61C,127C,135C,155C,174C,194C,247C,252C,258C,290C ''src'':143C,268C,400C ''step'':51C ''strong'':59C,78C,91C,244C,249C,340C ''take'':243C ''think'':160C ''togeth'':99C ''tree'':55C,70C,88C,197C,241C,307C,384C ''two'':359C ''underneath'':330C ''understand'':27C ''use'':72C,323C ''usual'':348C ''variabl'':187C,208C ''weak'':407C ''whole'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8875, 1, '2015-11-17 18:56:02.027', 1, 41, 'Finding the top K most similar sets', '&lt;p&gt;I have a database containing sets of words. So for example, I have a database that has:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;{happy, birthday, to, you}
{how, are, you}
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Given a query set, lets say {how, was your, birthday}, I want to find the top K sets in the database that is most similar to my query. Similarity metric used can be something like Jaccard''s Index. Right now, I go through the database one by one and calculate the Jaccard Index and keep track of the top K scores found so far. I was wondering if there are any data structures or methods that would allow me to more efficiently find the top K scores. Right now it''s a linear search. Thanks &lt;/p&gt;
', 'similarity  information-retrieval', NULL, '''/code'':40C ''/p'':30C,138C ''/pre'':41C ''allow'':120C ''birthday'':34C,52C ''calcul'':92C ''code'':32C ''contain'':17C ''data'':114C ''databas'':16C,27C,63C,87C ''effici'':124C ''exampl'':23C ''far'':106C ''find'':1A,56C,125C ''found'':104C ''given'':43C ''go'':84C ''happi'':33C ''index'':80C,95C ''inform'':10B ''information-retriev'':9B ''jaccard'':78C,94C ''k'':4A,59C,102C,128C ''keep'':97C ''let'':47C ''like'':77C ''linear'':135C ''method'':117C ''metric'':72C ''one'':88C,90C ''p'':12C,42C ''pre'':31C ''queri'':45C,70C ''retriev'':11B ''right'':81C,130C ''say'':48C ''score'':103C,129C ''search'':136C ''set'':7A,18C,46C,60C ''similar'':6A,8B,67C,71C ''someth'':76C ''structur'':115C ''thank'':137C ''top'':3A,58C,101C,127C ''track'':98C ''use'':73C ''want'':54C ''wonder'':109C ''word'':20C ''would'':119C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8876, 2, '2015-11-17 20:59:01.573', 2, NULL, NULL, '&lt;p&gt;Do you have any information about your data set? Is it sparse, will most similarities be zero? Is the total dictionary small? You could consider a inverted index. For example&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;word  query_id
W1    [1, 3, 6]
W2    [2, 5]
W3    [1, 3, 4]
W4    [2, 3, 4]
W5    [2, 3, 6]

query_id  query
1         W1 W3
2         W2 W4 W4
3         W1 W3 W4 W5
4         W3 W4
5         W2
6         W1 W5
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here W_i is a word, e.g. birthday and query_id is the id of the query in the database. e.g. {how, are, you} might have id 22. Now you get a query {W1 W3 W5}. Aggregate counts on the inverted index. W1 was seen in queries 1, 3, and 6. W3 in 1, 3, and 4, etc.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;query_id  count
1         2
2         1
3         3
4         1
6         2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The count will the number of words in common with the incoming query, this is the numerator of the jaccard similarity. So, to find the top k you can start with the queries with the highest count. query_id 3 has the highest count and its similarity is 3/4.&lt;/p&gt;

&lt;p&gt;If you have a massive database there are techniques like locality sensitive hashing which will basically reduce the search space into a small bucket. The incoming query gets hashed and lands in a bucket. You can then do a linear search with all the queries in this bucket to find the nearest k.&lt;/p&gt;
', NULL, NULL, '''/code'':80C,157C ''/p'':32C,141C,209C,264C ''/pre'':81C,158C ''1'':39C,46C,60C,130C,136C,147C,150C,154C ''2'':43C,50C,54C,63C,148C,149C,156C ''22'':110C ''3'':40C,47C,51C,55C,67C,131C,137C,151C,152C,199C ''3/4'':208C ''4'':48C,52C,72C,139C,153C ''5'':44C,75C ''6'':41C,56C,77C,133C,155C ''aggreg'':119C ''basic'':226C ''birthday'':90C ''bucket'':234C,244C,258C ''code'':34C,143C ''common'':168C ''consid'':26C ''could'':25C ''count'':120C,146C,161C,196C,203C ''data'':9C ''databas'':102C,216C ''dictionari'':22C ''e.g'':89C,103C ''etc'':140C ''exampl'':31C ''find'':183C,260C ''get'':113C,238C ''hash'':223C,239C ''highest'':195C,202C ''id'':37C,58C,93C,96C,109C,145C,198C ''incom'':171C,236C ''index'':29C,124C ''inform'':6C ''invert'':28C,123C ''jaccard'':179C ''k'':186C,263C ''land'':241C ''like'':220C ''linear'':250C ''local'':221C ''massiv'':215C ''might'':107C ''nearest'':262C ''number'':164C ''numer'':176C ''p'':1C,82C,159C,210C ''pre'':33C,142C ''queri'':36C,57C,59C,92C,99C,115C,129C,144C,172C,192C,197C,237C,255C ''reduc'':227C ''search'':229C,251C ''seen'':127C ''sensit'':222C ''set'':10C ''similar'':16C,180C,206C ''small'':23C,233C ''space'':230C ''spars'':13C ''start'':189C ''techniqu'':219C ''top'':185C ''total'':21C ''w'':84C ''w1'':38C,61C,68C,78C,116C,125C ''w2'':42C,64C,76C ''w3'':45C,62C,69C,73C,117C,134C ''w4'':49C,65C,66C,70C,74C ''w5'':53C,71C,79C,118C ''word'':35C,88C,166C ''zero'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8877, 2, '2015-11-17 22:41:32.403', 1, NULL, NULL, '&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Bayes%27_theorem" rel="nofollow"&gt;Bayes Theorem&lt;/a&gt; applies to conditional probabilities. 
$
     P(A|B) = \frac{P(A).P(B|A)}{P(B)} 
    $&lt;/p&gt;

&lt;p&gt;The question you have posed is one of multiple events occurring together. If the events can be considered to be independent of each other, then,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;P(A &amp;amp; B &amp;amp; C) = P(A).P(B).P(C)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In your question above, &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;P(M &amp;amp; A &amp;amp; L) = P(M &amp;amp; A).P(L) = P(M).P(A).P(L)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Based on the data provided by you, you should have these probabilities or can derive them by appropriate summations.&lt;/p&gt;

&lt;p&gt;Let''s say that .1 probability of males are between 1-10 years of age. Further, probability of males in location L is 0.2. So the probability of males in 1-10 years of age in Location L is 0.1 X 0.2 = 0.02 . This is assuming that all locations have the same probability distribution of age ranges. &lt;/p&gt;
', NULL, NULL, '''-10'':132C,152C ''/a'':11C ''/code'':69C,97C ''/p'':27C,54C,76C,119C,178C ''/pre'':70C,98C ''/wiki/bayes%27_theorem'':6C ''0.02'':163C ''0.1'':160C ''0.2'':144C,162C ''1'':125C,131C,151C ''age'':135C,155C,176C ''amp'':59C,61C,81C,83C,87C ''appli'':12C ''appropri'':117C ''assum'':166C ''b'':18C,23C,26C,60C,66C ''base'':100C ''bay'':9C ''c'':62C,68C ''code'':56C,78C ''condit'':14C ''consid'':46C ''data'':103C ''deriv'':114C ''distribut'':174C ''en.wikipedia.org'':5C ''en.wikipedia.org/wiki/bayes%27_theorem'':4C ''event'':38C,43C ''frac'':19C ''href'':3C ''independ'':49C ''l'':84C,90C,96C,142C,158C ''let'':121C ''locat'':141C,157C,169C ''m'':80C,86C,92C ''male'':128C,139C,149C ''multipl'':37C ''nofollow'':8C ''occur'':39C ''one'':35C ''p'':1C,16C,20C,22C,25C,28C,57C,63C,65C,67C,71C,79C,85C,89C,91C,93C,95C,99C,120C ''pose'':33C ''pre'':55C,77C ''probabl'':15C,111C,126C,137C,147C,173C ''provid'':104C ''question'':30C,74C ''rang'':177C ''rel'':7C ''say'':123C ''summat'':118C ''theorem'':10C ''togeth'':40C ''x'':161C ''year'':133C,153C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8881, 1, '2015-11-18 05:47:48.48', 2, 40, 'Content based recommendation on Mahout', '&lt;p&gt;Is it possible to get  recommendation on similar product using Mahout ?&lt;/p&gt;

&lt;p&gt;eg :&lt;/p&gt;

&lt;p&gt;I have data set of movies with following attributes&lt;/p&gt;

&lt;p&gt;Movie_name, Actor_1, Actor_2, Actress_1, Actress_2, Director, Theme, Language&lt;/p&gt;

&lt;p&gt;Now given a Movie_name the system should  recommend top 3 similar movies based on the attributes .&lt;/p&gt;

&lt;p&gt;Can this be done using Mahout. If yes how ?&lt;/p&gt;
', 'python  recommendation  apache-mahout', NULL, '''/p'':23C,26C,37C,52C,71C,82C ''1'':42C,46C ''2'':44C,48C ''3'':64C ''actor'':41C,43C ''actress'':45C,47C ''apach'':9B ''apache-mahout'':8B ''attribut'':36C,70C ''base'':2A,67C ''content'':1A ''data'':30C ''director'':49C ''done'':76C ''eg'':25C ''follow'':35C ''get'':16C ''given'':55C ''languag'':51C ''mahout'':5A,10B,22C,78C ''movi'':33C,39C,57C,66C ''name'':40C,58C ''p'':11C,24C,27C,38C,53C,72C ''possibl'':14C ''product'':20C ''python'':6B ''recommend'':3A,7B,17C,62C ''set'':31C ''similar'':19C,65C ''system'':60C ''theme'':50C ''top'':63C ''use'':21C,77C ''yes'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8883, 1, '2015-11-18 09:12:27.92', 5, 87, 'What to do when testing data has less features than training data?', '&lt;p&gt;Let''s say we are predicting the sales of a shop and my training data has two sets of features:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;One about the store sales with the dates (the field "Store" is not unique)&lt;/li&gt;
&lt;li&gt;One about the store types (the field "Store" is unique here)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So the matrix would look something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;+-------+-----------+------------+---------+-----------+------+-------+--------------+
| Store | DayOfWeek |    Date    |  Sales  | Customers | Open | Promo | StateHoliday |
+-------+-----------+------------+---------+-----------+------+-------+--------------+
|   1   |     5     | 2015-07-31 |  5263.0 |   555.0   |  1   |   1   |      0       |
|   2   |     5     | 2015-07-31 |  6064.0 |   625.0   |  1   |   1   |      0       |
|   3   |     5     | 2015-07-31 |  8314.0 |   821.0   |  1   |   1   |      0       |
|   4   |     5     | 2015-07-31 | 13995.0 |   1498.0  |  1   |   1   |      0       |
|   5   |     5     | 2015-07-31 |  4822.0 |   559.0   |  1   |   1   |      0       |
|   6   |     5     | 2015-07-31 |  5651.0 |   589.0   |  1   |   1   |      0       |
|   7   |     5     | 2015-07-31 | 15344.0 |   1414.0  |  1   |   1   |      0       |
|   8   |     5     | 2015-07-31 |  8492.0 |   833.0   |  1   |   1   |      0       |
|   9   |     5     | 2015-07-31 |  8565.0 |   687.0   |  1   |   1   |      0       |
|   10  |     5     | 2015-07-31 |  7185.0 |   681.0   |  1   |   1   |      0       |
+-------+-----------+------------+---------+-----------+------+-------+--------------+
[986159 rows x 4 columns]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;+-------+-----------+------------+---------------------+
| Store | StoreType | Assortment | CompetitionDistance |
+-------+-----------+------------+---------------------+
|   1   |     c     |     a      |         1270        |
|   2   |     a     |     a      |         570         |
|   3   |     a     |     a      |        14130        |
|   4   |     c     |     c      |         620         |
|   5   |     a     |     a      |        29910        |
|   6   |     a     |     a      |         310         |
|   7   |     a     |     c      |        24000        |
|   8   |     a     |     a      |         7520        |
|   9   |     a     |     c      |         2030        |
|   10  |     a     |     a      |         3160        |
+-------+-----------+------------+---------------------+
[1115 rows x 4 columns]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The second matrix describes the store type, the assortment groups of item each of them sell and the distance from the nearest competitor store.&lt;/p&gt;

&lt;p&gt;But in my test data, I only have information in the first matrix without the &lt;code&gt;Customers&lt;/code&gt; and &lt;code&gt;Sales&lt;/code&gt; fields. The aim is to predict the sales field given the &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Store&lt;/li&gt;
&lt;li&gt;DayofWeek&lt;/li&gt;
&lt;li&gt;Date&lt;/li&gt;
&lt;li&gt;Open (whether the store is open)&lt;/li&gt;
&lt;li&gt;Promo (whether the store is having a promotion)&lt;/li&gt;
&lt;li&gt;StateHoliday (whether it is a state holiday)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I can easily train a classifier based on the bulleted fields above to predict &lt;code&gt;Sales&lt;/code&gt; but &lt;strong&gt;how can I make use of the second matrix in my training data that I would not get in test data?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is it logical to assume that the second matrix about the Store types is static and I can easily join it to the test data?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;What happens if there are holes in my test data feature set, let''s say for some rows in the test data, I don''t have the "Promo" values.&lt;/p&gt;
', 'machine-learning  regression  feature-selection  feature-construction  missing-data', NULL, '''-07'':102C,112C,122C,132C,142C,152C,162C,172C,182C,192C ''-31'':103C,113C,123C,133C,143C,153C,163C,173C,183C,193C ''/code'':204C,260C,306C,310C,378C ''/li'':64C,77C,326C,329C,332C,340C,350C,359C ''/p'':47C,88C,208C,287C,322C,403C,432C,463C ''/pre'':205C,261C ''/strong'':402C,431C ''/ul'':78C,360C ''0'':108C,118C,128C,138C,148C,158C,168C,178C,188C,198C ''1'':99C,106C,107C,116C,117C,126C,127C,136C,137C,146C,147C,156C,157C,166C,167C,176C,177C,186C,187C,196C,197C,215C ''10'':189C,251C ''1115'':255C ''1270'':218C ''13995.0'':134C ''14130'':226C ''1414.0'':165C ''1498.0'':135C ''15344.0'':164C ''2'':109C,219C ''2015'':101C,111C,121C,131C,141C,151C,161C,171C,181C,191C ''2030'':250C ''24000'':242C ''29910'':234C ''3'':119C,223C ''310'':238C ''3160'':254C ''4'':129C,202C,227C,258C ''4822.0'':144C ''5'':100C,110C,120C,130C,139C,140C,150C,160C,170C,180C,190C,231C ''5263.0'':104C ''555.0'':105C ''559.0'':145C ''5651.0'':154C ''570'':222C ''589.0'':155C ''6'':149C,235C ''6064.0'':114C ''620'':230C ''625.0'':115C ''681.0'':195C ''687.0'':185C ''7'':159C,239C ''7185.0'':194C ''7520'':246C ''8'':169C,243C ''821.0'':125C ''8314.0'':124C ''833.0'':175C ''8492.0'':174C ''8565.0'':184C ''9'':179C,247C ''986159'':199C ''aim'':313C ''assort'':213C,271C ''assum'':410C ''base'':368C ''bullet'':371C ''c'':216C,228C,229C,241C,249C ''classifi'':367C ''code'':90C,210C,304C,308C,376C ''column'':203C,259C ''competitiondist'':214C ''competitor'':285C ''construct'':22B ''custom'':95C,305C ''data'':6A,12A,25B,41C,293C,393C,401C,430C,443C,455C ''date'':57C,93C,331C ''dayofweek'':92C,328C ''describ'':266C ''distanc'':281C ''easili'':364C,424C ''featur'':9A,18B,21B,46C,444C ''feature-construct'':20B ''feature-select'':17B ''field'':59C,72C,311C,319C,372C ''first'':300C ''get'':398C ''given'':320C ''group'':272C ''happen'':435C ''hole'':439C ''holiday'':358C ''inform'':297C ''item'':274C ''join'':425C ''learn'':15B ''less'':8A ''let'':27C,446C ''li'':49C,65C,324C,327C,330C,333C,341C,351C ''like'':86C ''logic'':408C ''look'':84C ''machin'':14B ''machine-learn'':13B ''make'':384C ''matrix'':82C,265C,301C,389C,414C ''miss'':24B ''missing-data'':23B ''nearest'':284C ''one'':50C,66C ''open'':96C,334C,339C ''p'':26C,79C,206C,262C,288C,361C,404C,433C ''pre'':89C,209C ''predict'':32C,316C,375C ''promo'':97C,342C,461C ''promot'':349C ''regress'':16B ''row'':200C,256C,451C ''sale'':34C,54C,94C,309C,318C,377C ''say'':29C,448C ''second'':264C,388C,413C ''select'':19B ''sell'':278C ''set'':44C,445C ''shop'':37C ''someth'':85C ''state'':357C ''stateholiday'':98C,352C ''static'':420C ''store'':53C,60C,69C,73C,91C,211C,268C,286C,325C,337C,345C,417C ''storetyp'':212C ''strong'':380C,405C ''test'':5A,292C,400C,429C,442C,454C ''train'':11A,40C,365C,392C ''two'':43C ''type'':70C,269C,418C ''ul'':48C,323C ''uniqu'':63C,75C ''use'':385C ''valu'':462C ''whether'':335C,343C,353C ''without'':302C ''would'':83C,396C ''x'':201C,257C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8916, 1, '2015-11-19 10:48:24.853', 2, 25, 'Using classification to find the best support and confidence measure in associative rule mining', '&lt;p&gt;I have been trying the find the best support and confidence values for associative rules mining. I came across the following approach from an &lt;a href="https://www.quora.com/How-do-I-pick-appropriate-support-confidence-value-when-doing-basket-analysis-with-Apriori-algorithm/answer/Boxun-Zhang" rel="nofollow"&gt;answer on Quora&lt;/a&gt; - &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Picking the "appropriate" values for support and confidence can be
  difficult, as it is very much an unsupervised process.&lt;/p&gt;
  
  &lt;p&gt;However, if you transform the output of Apriori algorithm (association
  rules) into features for a supervised machine learning algorithm, you
  can examine the effect of having different support and confidences
  values (while having other features fixed) on the performance of that
  supervised model (ROC, RMSE, and etc.). Then, the best values for
  support and confidence are the values that maximize, or minimize, the
  performance metric.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Being a rookie in this field, I am not sure on how to do this. Could you please help me on this?&lt;/p&gt;
', 'data-mining  classification  unsupervised-learning', NULL, '''/a'':57C ''/blockquote'':148C ''/how-do-i-pick-appropriate-support-confidence-value-when-doing-basket-analysis-with-apriori-algorithm/answer/boxun-zhang'':51C ''/p'':58C,80C,147C,172C ''across'':41C ''algorithm'':90C,100C ''answer'':54C ''approach'':44C ''appropri'':63C ''apriori'':89C ''associ'':12A,36C,91C ''best'':6A,30C,131C ''blockquot'':59C ''came'':40C ''classif'':2A,18B ''confid'':9A,33C,68C,111C,136C ''could'':165C ''data'':16B ''data-min'':15B ''differ'':108C ''difficult'':71C ''effect'':105C ''etc'':128C ''examin'':103C ''featur'':94C,116C ''field'':155C ''find'':4A,28C ''fix'':117C ''follow'':43C ''help'':168C ''howev'':82C ''href'':48C ''learn'':21B,99C ''machin'':98C ''maxim'':141C ''measur'':10A ''metric'':146C ''mine'':14A,17B,38C ''minim'':143C ''model'':124C ''much'':76C ''nofollow'':53C ''output'':87C ''p'':22C,60C,81C,149C ''perform'':120C,145C ''pick'':61C ''pleas'':167C ''process'':79C ''quora'':56C ''rel'':52C ''rmse'':126C ''roc'':125C ''rooki'':152C ''rule'':13A,37C,92C ''supervis'':97C,123C ''support'':7A,31C,66C,109C,134C ''sure'':159C ''transform'':85C ''tri'':26C ''unsupervis'':20B,78C ''unsupervised-learn'':19B ''use'':1A ''valu'':34C,64C,112C,132C,139C ''www.quora.com'':50C ''www.quora.com/how-do-i-pick-appropriate-support-confidence-value-when-doing-basket-analysis-with-apriori-algorithm/answer/boxun-zhang'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8884, 5, '2015-11-18 11:18:18.503', 0, NULL, NULL, '&lt;p&gt;Neural networks traditionally refer to a network or circuit of biological neurons. The modern usage of the term often refers to &lt;a href="https://en.wikipedia.org/wiki/Artificial_neural_network" rel="nofollow"&gt;artificial neural networks&lt;/a&gt; (ANN), which are composed of artificial neurons or nodes - programming constructs that mimic the properties of biological neurons. A set of weighted connections between the neurons allows information to propagate through the network to solve artificial intelligence problems without the network designer having had a model of a real system. The goal is good, or human-like, predictive ability.&lt;/p&gt;
', NULL, NULL, '''/a'':33C ''/p'':94C ''/wiki/artificial_neural_network'':27C ''abil'':93C ''allow'':60C ''ann'':34C ''artifici'':30C,39C,69C ''biolog'':12C,50C ''circuit'':10C ''compos'':37C ''connect'':56C ''construct'':44C ''design'':75C ''en.wikipedia.org'':26C ''en.wikipedia.org/wiki/artificial_neural_network'':25C ''goal'':85C ''good'':87C ''href'':24C ''human'':90C ''human-lik'':89C ''inform'':61C ''intellig'':70C ''like'':91C ''mimic'':46C ''model'':79C ''modern'':15C ''network'':3C,8C,32C,66C,74C ''neural'':2C,31C ''neuron'':13C,40C,51C,59C ''node'':42C ''nofollow'':29C ''often'':20C ''p'':1C ''predict'':92C ''problem'':71C ''program'':43C ''propag'':63C ''properti'':48C ''real'':82C ''refer'':5C,21C ''rel'':28C ''set'':53C ''solv'':68C ''system'':83C ''term'':19C ''tradit'':4C ''usag'':16C ''weight'':55C ''without'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8885, 4, '2015-11-18 11:18:18.503', 0, NULL, NULL, 'Artificial neural networks (ANN), are composed of ''neurons'' - programming constructs that mimic the properties of biological neurons. A set of weighted connections between the neurons allows information to propagate through the network to solve artificial intelligence problems without the network designer having had a model of a real system.', NULL, NULL, '''allow'':26C ''ann'':4C ''artifici'':1C,35C ''biolog'':16C ''compos'':6C ''connect'':22C ''construct'':10C ''design'':41C ''inform'':27C ''intellig'':36C ''mimic'':12C ''model'':45C ''network'':3C,32C,40C ''neural'':2C ''neuron'':8C,17C,25C ''problem'':37C ''program'':9C ''propag'':29C ''properti'':14C ''real'':48C ''set'':19C ''solv'':34C ''system'':49C ''weight'':21C ''without'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9064, 2, '2015-11-26 13:32:00.8', 0, NULL, NULL, '&lt;p&gt;Have you tried the XML package? In a similar question here, in SE, the most upvoted answer suggested using some packages for that.&lt;/p&gt;

&lt;p&gt;Here: &lt;a href="http://stackoverflow.com/questions/3195522/is-there-a-simple-way-in-r-to-extract-only-the-text-elements-of-an-html-page"&gt;http://stackoverflow.com/questions/3195522/is-there-a-simple-way-in-r-to-extract-only-the-text-elements-of-an-html-page&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Here you find further instructions: &lt;a href="http://stackoverflow.com/questions/1844829/how-can-i-read-and-parse-the-contents-of-a-webpage-in-r"&gt;http://stackoverflow.com/questions/1844829/how-can-i-read-and-parse-the-contents-of-a-webpage-in-r&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':25C ''/questions/1844829/how-can-i-read-and-parse-the-contents-of-a-webpage-in-r'':46C ''/questions/1844829/how-can-i-read-and-parse-the-contents-of-a-webpage-in-r&lt;/a&gt;&lt;/p&gt;'':49C ''/questions/3195522/is-there-a-simple-way-in-r-to-extract-only-the-text-elements-of-an-html-page'':32C ''/questions/3195522/is-there-a-simple-way-in-r-to-extract-only-the-text-elements-of-an-html-page&lt;/a&gt;&lt;/p&gt;'':35C ''answer'':18C ''find'':39C ''href'':29C,43C ''instruct'':41C ''p'':1C,26C,36C ''packag'':7C,22C ''question'':11C ''se'':14C ''similar'':10C ''stackoverflow.com'':31C,34C,45C,48C ''stackoverflow.com/questions/1844829/how-can-i-read-and-parse-the-contents-of-a-webpage-in-r'':44C ''stackoverflow.com/questions/1844829/how-can-i-read-and-parse-the-contents-of-a-webpage-in-r&lt;/a&gt;&lt;/p&gt;'':47C ''stackoverflow.com/questions/3195522/is-there-a-simple-way-in-r-to-extract-only-the-text-elements-of-an-html-page'':30C ''stackoverflow.com/questions/3195522/is-there-a-simple-way-in-r-to-extract-only-the-text-elements-of-an-html-page&lt;/a&gt;&lt;/p&gt;'':33C ''suggest'':19C ''tri'':4C ''upvot'':17C ''use'':20C ''xml'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8886, 5, '2015-11-18 11:20:47.033', 0, NULL, NULL, '&lt;p&gt;"Regression" is a general term for a wide variety of techniques to analyze the relationship between one (or more) dependent variables and independent variables. Typically the dependent variables are modeled with probability distributions whose parameters are assumed to vary (deterministically) with the independent variables.&lt;/p&gt;

&lt;p&gt;Ordinary least squares (OLS) regression affords a simple example in which the expectation of one dependent variable is assumed to depend linearly on the independent variables. The unknown coefficients in the assumed linear function are estimated by choosing values for them that minimize the sum of squared differences between the values of the dependent variable and the corresponding fitted values.&lt;/p&gt;
', NULL, NULL, '''/p'':46C,108C ''afford'':53C ''analyz'':14C ''assum'':38C,66C,79C ''choos'':85C ''coeffici'':76C ''correspond'':105C ''depend'':21C,28C,63C,68C,101C ''determinist'':41C ''differ'':95C ''distribut'':34C ''estim'':83C ''exampl'':56C ''expect'':60C ''fit'':106C ''function'':81C ''general'':5C ''independ'':24C,44C,72C ''least'':49C ''linear'':69C,80C ''minim'':90C ''model'':31C ''ol'':51C ''one'':18C,62C ''ordinari'':48C ''p'':1C,47C ''paramet'':36C ''probabl'':33C ''regress'':2C,52C ''relationship'':16C ''simpl'':55C ''squar'':50C,94C ''sum'':92C ''techniqu'':12C ''term'':6C ''typic'':26C ''unknown'':75C ''valu'':86C,98C,107C ''vari'':40C ''variabl'':22C,25C,29C,45C,64C,73C,102C ''varieti'':10C ''whose'':35C ''wide'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8887, 4, '2015-11-18 11:20:47.033', 0, NULL, NULL, 'Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.&#x0d;
', NULL, NULL, '''analyz'':3C ''depend'':10C ''independ'':13C ''one'':7C ''relationship'':5C ''techniqu'':1C ''variabl'':11C,14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8962, 2, '2015-11-21 07:50:45.983', 3, NULL, NULL, '&lt;p&gt;Annual UseR! conference:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;link: &lt;a href="http://user2015.math.aau.dk/" rel="nofollow"&gt;UseR!2015&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;link: &lt;a href="http://user2016.org/" rel="nofollow"&gt;UseR!2016&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Data lead has an annual conference. Last year in the US, this year in Paris:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.datalead2015.com/index.php" rel="nofollow"&gt;http://www.datalead2015.com/index.php&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;H2O has a conference &lt;a href="http://h2oworld.h2o.ai/" rel="nofollow"&gt;H2O world&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Since Data Science is a very broad subject:&lt;/p&gt;

&lt;p&gt;Effective applications of the R language:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.earl-conference.com/" rel="nofollow"&gt;http://www.earl-conference.com/&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Strata + Hadoop World:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://conferences.oreilly.com/strata" rel="nofollow"&gt;http://conferences.oreilly.com/strata&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Joint Statistical Meetings:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://www.amstat.org/meetings/jsm.cfm" rel="nofollow"&gt;https://www.amstat.org/meetings/jsm.cfm&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;And of course your local &lt;a href="http://www.meetup.com/" rel="nofollow"&gt;meetups&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/li&gt;'':101C ''/a'':16C,27C,72C,151C ''/index.php'':53C ''/index.php&lt;/a&gt;&lt;/li&gt;'':58C ''/li'':17C,28C ''/meetings/jsm.cfm'':132C ''/meetings/jsm.cfm&lt;/a&gt;&lt;/li&gt;'':137C ''/p'':5C,46C,73C,83C,91C,107C,125C,152C ''/strata'':114C ''/strata&lt;/a&gt;&lt;/li&gt;'':119C ''/ul'':29C,59C,102C,120C,138C ''2015'':15C ''2016'':26C ''annual'':2C,35C ''applic'':86C ''broad'':81C ''confer'':4C,36C,64C ''conferences.oreilly.com'':113C,118C ''conferences.oreilly.com/strata'':112C ''conferences.oreilly.com/strata&lt;/a&gt;&lt;/li&gt;'':117C ''cours'':142C ''data'':31C,76C ''effect'':85C ''h2o'':61C,70C ''h2oworld.h2o.ai'':67C ''hadoop'':105C ''href'':10C,21C,50C,66C,95C,111C,129C,146C ''joint'':122C ''languag'':90C ''last'':37C ''lead'':32C ''li'':7C,18C,48C,93C,109C,127C ''link'':8C,19C ''local'':144C ''meet'':124C ''meetup'':150C ''nofollow'':13C,24C,55C,69C,98C,116C,134C,149C ''p'':1C,30C,60C,74C,84C,103C,121C,139C ''pari'':45C ''r'':89C ''rel'':12C,23C,54C,68C,97C,115C,133C,148C ''scienc'':77C ''sinc'':75C ''statist'':123C ''strata'':104C ''subject'':82C ''ul'':6C,47C,92C,108C,126C ''us'':41C ''user'':3C,14C,25C ''user2015.math.aau.dk'':11C ''user2016.org'':22C ''world'':71C,106C ''www.amstat.org'':131C,136C ''www.amstat.org/meetings/jsm.cfm'':130C ''www.amstat.org/meetings/jsm.cfm&lt;/a&gt;&lt;/li&gt;'':135C ''www.datalead2015.com'':52C,57C ''www.datalead2015.com/index.php'':51C ''www.datalead2015.com/index.php&lt;/a&gt;&lt;/li&gt;'':56C ''www.earl-conference.com'':96C,100C ''www.earl-conference.com/&lt;/a&gt;&lt;/li&gt;'':99C ''www.meetup.com'':147C ''year'':38C,43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8891, 2, '2015-11-18 12:14:08.953', 1, NULL, NULL, '&lt;h2&gt;K-means uses the &lt;em&gt;mean&lt;/em&gt;.&lt;/h2&gt;

&lt;p&gt;K-means is designed for least-squares. It only works reliably with (variants of) squared Euclidean distance (= sum of squared deviations).&lt;/p&gt;

&lt;h3&gt;Counterexample:&lt;/h3&gt;

&lt;p&gt;Assume you have the two hours 0 and 23.&lt;/p&gt;

&lt;p&gt;If they get assigned to the same cluster, k-means will compute the &lt;strong&gt;mean&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;The mean of the two values is 11.5. It is &lt;em&gt;not&lt;/em&gt; 23.5.&lt;/p&gt;

&lt;p&gt;Abusing k-means with a cyclic "distance" may &lt;strong&gt;no longer converge&lt;/strong&gt;, and will return nonsense results.&lt;/p&gt;

&lt;p&gt;But there are more cases where the concept of a &lt;em&gt;cluster center&lt;/em&gt; is not viable on cyclic data. For example, &lt;strong&gt;given an event on every full hour, what is the center&lt;/strong&gt;? The arithmetic mean is 12 - but if you take cyclic space into account &lt;strong&gt;every hour is an equally good choice&lt;/strong&gt; in cyclic space. Therefore, the concept of a "center" in cyclic space is fragile.&lt;/p&gt;

&lt;h2&gt;Alternate clustering algorithms&lt;/h2&gt;

&lt;p&gt;You can try e.g. PAM or DBSCAN instead, with an appropriate similarity measure.&lt;/p&gt;

&lt;h2&gt;Projection techniques&lt;/h2&gt;

&lt;p&gt;As pointed out by other answers, you &lt;em&gt;can&lt;/em&gt; project the time to the unit circle via sin/cos(time/24*2pi). By computing the angle of the centroids, you can map this back to a point in time. But once you want additional attributes it gets hard to meaningfully normalize the data (to combine attributes), and you can get undefined time (e.g. if there are two points in a cluster, one at 6 and one at 18).
I didn''t discuss this because I wanted to point out that modifying the distance function is &lt;em&gt;not&lt;/em&gt; a good idea for k-means.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8892, 2, '2015-11-18 12:47:07.477', 1, NULL, NULL, '&lt;p&gt;Factorization machines - &lt;a href="http://www.libfm.org/" rel="nofollow"&gt;libfm&lt;/a&gt; open source software has been used widely among top companies in recommender applications. &lt;/p&gt;

&lt;p&gt;Check it out. It contains various algorithms to play with (e.g. stochastic gradient descent).&lt;/p&gt;

&lt;p&gt;I guess your data has like "click/view" label for each pair of user-product or say cookie-advertisement saying &lt;em&gt;"whether user clicked or just viewed product"&lt;/em&gt;. You can treat this as binary classification with two classes click or just view.&lt;/p&gt;
', NULL, NULL, '''/a'':10C ''/em'':71C ''/p'':24C,40C,86C ''advertis'':61C ''algorithm'':32C ''among'':18C ''applic'':23C ''binari'':77C ''check'':26C ''class'':81C ''classif'':78C ''click'':66C,82C ''click/view'':48C ''compani'':20C ''contain'':30C ''cooki'':60C ''cookie-advertis'':59C ''data'':45C ''descent'':39C ''e.g'':36C ''em'':63C ''factor'':2C ''gradient'':38C ''guess'':43C ''href'':5C ''label'':49C ''libfm'':9C ''like'':47C ''machin'':3C ''nofollow'':8C ''open'':11C ''p'':1C,25C,41C ''pair'':52C ''play'':34C ''product'':56C,70C ''recommend'':22C ''rel'':7C ''say'':58C,62C ''softwar'':13C ''sourc'':12C ''stochast'':37C ''top'':19C ''treat'':74C ''two'':80C ''use'':16C ''user'':55C,65C ''user-product'':54C ''various'':31C ''view'':69C,85C ''whether'':64C ''wide'':17C ''www.libfm.org'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8893, 2, '2015-11-18 12:48:39.317', 1, NULL, NULL, '&lt;p&gt;I found a comparison of the two kind of nets in &lt;a href="http://jmlr.csail.mit.edu/proceedings/papers/v28/goodfellow13.pdf" rel="nofollow"&gt;Max Out Networks&lt;/a&gt; which says:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Dropout training is similar to bagging (Breiman, 1994), where many different models are trained on different subsets of the data. Dropout training differs from bagging in that each model is trained for only one step and all of the models share parameters. For this training procedure (dropout) to behave as if it is training an ensemble rather than a single model, each update must have a large effect, so that it makes the sub-model induced by that Âµ fit the current input v well.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Hope it will be useful.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8894, 2, '2015-11-18 14:48:24.85', 5, NULL, NULL, '&lt;p&gt;So, there are two problems.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Recording impressions (shows)&lt;/li&gt;
&lt;li&gt;How to deal with non-impressions&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;For (1) you &lt;em&gt;should&lt;/em&gt; be recording this information. If it is not currently being recorded you should start recording this information. Given that you do not have this information you want to provide recommendations. Fortunately, with just click data you can still create a utility matrix, see 9.1.1.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stanford.edu/~ullman/mmds/ch9.pdf"&gt;http://i.stanford.edu/~ullman/mmds/ch9.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You could then use user- or item-based collaborative filtering as described in the paper. This is basically an exercise in populating the utility matrix and trying to find "scores" for unclicked items. Your recommendation would be an unclicked item with the highest score.&lt;/p&gt;

&lt;p&gt;For (2) you will still make recommendations on unclicked items. So, that alone is not an issue. You will want to optimize your impressions however. You also cannot have full knowledge where a user can see all possible options. You need to record impressions and understand a number of things.  &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;show rate of an item&lt;/li&gt;
&lt;li&gt;click rate of an item&lt;/li&gt;
&lt;li&gt;how to incorporate new items&lt;/li&gt;
&lt;li&gt;how to optimize which items to show&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;This is a huge topic and basically this is the problem domain of online advertising. However, a recommendation engine tries to find items of interest in the long tail, which is a bit different from ad optimization. This is a feedback loop to evaluate your recommendation. A/B tests are common. You will want to test click rates and recommendation errors between your current system and new system.&lt;/p&gt;

&lt;p&gt;Also see here.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://cs.brynmawr.edu/Courses/cs380/fall2006/Herlocker2004.pdf"&gt;http://cs.brynmawr.edu/Courses/cs380/fall2006/Herlocker2004.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf"&gt;http://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8896, 2, '2015-11-18 22:38:55.063', 3, NULL, NULL, '&lt;p&gt;For &lt;code&gt;1)&lt;/code&gt; and &lt;code&gt;2)&lt;/code&gt;, you want to &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1) choose a model that performs well on data distributed as you 
   expect the real data will be 
2) evaluate the model on data distributed the same way
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So for those datasets, you shouldn''t need to balance the classes.&lt;/p&gt;

&lt;p&gt;You might also try using class weights instead of under/oversampling, as this takes care of this decision for you.&lt;/p&gt;

&lt;p&gt;For &lt;code&gt;3)&lt;/code&gt; you likely want to optimize using whatever metric you will be scored on (if it''s a competition).  But if that isn''t a consideration, all those models are fine choices.  F1 may be influenced by the low precision, but you want that to be captured.  It''s precisely when naive models (like guessing the majority class) can score well by some metrics that scores like F1 are relevant.&lt;/p&gt;

&lt;p&gt;As for &lt;code&gt;4)&lt;/code&gt; there is nothing wrong with showing whichever metric you end up optimizing on.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8985, 5, '2015-11-23 10:57:43.013', 0, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Overview&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;A Markov process is any stochastic process $Y_{t}$ such that the future is conditionally independent of the past, given the present; the distribution of the process only depends on where the process is, not where it has been: 
$$ P(Y_{t+1}=y_{t+1} |Y_t = y_{t}, Y_{t-1} = y_{t-1}, ..., Y_{1} = y_{1}) =  P(Y_{t+1}=y_{t+1} |Y_t = y_{t}) $$
This property is known as the Markov property.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The following threads on &lt;a href="http://math.stackexchange.com/"&gt;math.se&lt;/a&gt; provide references to resources on Markov processes:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://math.stackexchange.com/q/15431/84650"&gt;Good introductory book for Markov processes&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://math.stackexchange.com/q/27514/84650"&gt;Nice references on Markov chains/processes?&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8935, 5, '2015-11-20 11:14:26.57', 0, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Feature selection&lt;/strong&gt;, also called &lt;strong&gt;attribute selection&lt;/strong&gt; or &lt;strong&gt;feature reduction&lt;/strong&gt;, refers to techniques for identifying a subset of features of a data set that are relevant to a given problem. By removing irrelevant and redundant features, successful feature selection can avoid the &lt;a href="http://en.wikipedia.org/wiki/Curse_of_dimensionality" rel="nofollow"&gt;curse of dimensionality&lt;/a&gt; and improve the performance, speed, and interpretability of subsequent models.  &lt;/p&gt;

&lt;p&gt;Feature selection includes &lt;strong&gt;manual methods&lt;/strong&gt; (such those those based on domain knowledge) and automatic methods.  &lt;strong&gt;Automatic methods&lt;/strong&gt; are often categorized into &lt;strong&gt;filter&lt;/strong&gt;, &lt;strong&gt;wrapper&lt;/strong&gt;, and &lt;strong&gt;embedded&lt;/strong&gt; approaches.  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Filter approaches&lt;/strong&gt; perform feature selection as a separate preprocessing step before the learning algorithm.  Filter approaches thus look only at the intrinsic properties of the data. Filter methods include Wilcoxon rank sum tests and &lt;a href="http://en.wikipedia.org/wiki/Feature_selection#Correlation_feature_selection" rel="nofollow"&gt;Correlation based tests&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Wrapper approaches&lt;/strong&gt; uses performance of a learning algorithm to select features. A search algorithm is âwrappedâ around the learning algorithm to ensure the space of feature subsets is adequately searched.  As such, wrapper methods can be seen as conducting the model hypothesis search within the feature subset search. Examples of wrapper approaches are &lt;a href="http://en.wikipedia.org/wiki/Simulated_annealing" rel="nofollow"&gt;simulated annealing&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/Beam_search" rel="nofollow"&gt;beam search&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Embedded approaches&lt;/strong&gt; incorporate variable selection as a part of the training process, with feature relevance obtained analytically from the objective of the learning model. Embedded methods can be seen as a search in the combined space of feature subsets and hypotheses.  Examples of embedded approaches are &lt;a href="http://en.wikipedia.org/wiki/Boosting_%28machine_learning%29" rel="nofollow"&gt;boosting&lt;/a&gt; and recursive ridge regression.&lt;/p&gt;
', NULL, NULL, '''/a'':59C,156C,224C,235C,294C ''/p'':70C,108C,157C,236C,299C ''/strong'':5C,11C,16C,78C,92C,99C,102C,106C,113C,162C,241C ''/wiki/beam_search'':230C ''/wiki/boosting_%28machine_learning%29'':290C ''/wiki/curse_of_dimensionality'':53C ''/wiki/feature_selection#correlation_feature_selection'':150C ''/wiki/simulated_annealing'':219C ''adequ'':190C ''algorithm'':125C,168C,174C,181C ''also'':6C ''analyt'':256C ''anneal'':223C ''approach'':107C,112C,127C,161C,213C,240C,284C ''around'':178C ''attribut'':9C ''automat'':87C,90C ''avoid'':47C ''base'':82C,154C ''beam'':233C ''boost'':293C ''call'':7C ''categor'':95C ''combin'':274C ''conduct'':200C ''correl'':153C ''curs'':56C ''data'':28C,137C ''dimension'':58C ''domain'':84C ''embed'':105C,239C,264C,283C ''en.wikipedia.org'':52C,149C,218C,229C,289C ''en.wikipedia.org/wiki/beam_search'':228C ''en.wikipedia.org/wiki/boosting_%28machine_learning%29'':288C ''en.wikipedia.org/wiki/curse_of_dimensionality'':51C ''en.wikipedia.org/wiki/feature_selection#correlation_feature_selection'':148C ''en.wikipedia.org/wiki/simulated_annealing'':217C ''ensur'':183C ''exampl'':210C,281C ''featur'':3C,14C,25C,42C,44C,72C,115C,171C,187C,207C,253C,277C ''filter'':98C,111C,126C,138C ''given'':35C ''href'':50C,147C,216C,227C,287C ''hypothes'':280C ''hypothesi'':203C ''identifi'':21C ''improv'':61C ''includ'':74C,140C ''incorpor'':242C ''interpret'':66C ''intrins'':133C ''irrelev'':39C ''knowledg'':85C ''learn'':124C,167C,180C,262C ''look'':129C ''manual'':76C ''method'':77C,88C,91C,139C,195C,265C ''model'':69C,202C,263C ''nofollow'':55C,152C,221C,232C,292C ''object'':259C ''obtain'':255C ''often'':94C ''p'':1C,71C,109C,158C,237C ''part'':247C ''perform'':63C,114C,164C ''preprocess'':120C ''problem'':36C ''process'':251C ''properti'':134C ''rank'':142C ''recurs'':296C ''reduct'':15C ''redund'':41C ''refer'':17C ''regress'':298C ''rel'':54C,151C,220C,231C,291C ''relev'':32C,254C ''remov'':38C ''ridg'':297C ''search'':173C,191C,204C,209C,234C,271C ''seen'':198C,268C ''select'':4C,10C,45C,73C,116C,170C,244C ''separ'':119C ''set'':29C ''simul'':222C ''space'':185C,275C ''speed'':64C ''step'':121C ''strong'':2C,8C,13C,75C,89C,97C,100C,104C,110C,159C,238C ''subsequ'':68C ''subset'':23C,188C,208C,278C ''success'':43C ''sum'':143C ''techniqu'':19C ''test'':144C,155C ''thus'':128C ''train'':250C ''use'':163C ''variabl'':243C ''wilcoxon'':141C ''within'':205C ''wrappedâ'':177C ''wrapper'':101C,160C,194C,212C ''â'':176C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8897, 2, '2015-11-19 02:05:24.543', 3, NULL, NULL, '&lt;p&gt;Great question...  Here are some specific answers to your numbered questions:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;1)&lt;/code&gt; You should cross validate on B not B`.  Otherwise, you won''t know how well your class balancing is working.  It couldn''t hurt to cross validate on both B &lt;strong&gt;and&lt;/strong&gt; B` and will be useful based on the answer to 4 below.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;2)&lt;/code&gt; You should test on both C and C` based on 4 below.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;3)&lt;/code&gt; I would stick with F1 and it could be useful to use ROC-AUC and this provides a good sanity check.  Both tend to be useful with unbalanced classes.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;4)&lt;/code&gt; This gets really tricky.  The problem with this is that the best method requires that you reinterpret what the learning curves should look like or use both the re-sampled and original data sets.  &lt;/p&gt;

&lt;p&gt;The classic interpretation of learning curves is: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Overfit&lt;/strong&gt; - Lines don''t quite come together; &lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Underfit&lt;/strong&gt; - Lines come together but at too low an F1 score;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Just Right&lt;/strong&gt; - Lines come together with a reasonable F1 score.  &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Now, if you are training on A` and testing on C, the lines will never completely come together.  If you are training on A` and testing on C` the results won''t be meaningful in the context of the original problem.  So what do you do?  &lt;/p&gt;

&lt;p&gt;The answer is to train on A` and test on B`, but also test on B.  Get the F1 score for B` where you want it to be, then check the F1 score for B.  Then do your testing and generate learning curves for C.  The curves won''t ever come together, but you will have a sense of the acceptable bias... its the difference between F1(B) and F1(B`).&lt;/p&gt;

&lt;p&gt;Now, the new interpretation of your learning curves is: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Overfit&lt;/strong&gt; - Lines don''t come together and are farther apart than F1(B`)-F1(B);&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Underfit&lt;/strong&gt; - Lines don''t come together but the difference is less than F1(B`)-F1(B) and the F1(C) score is under F1(B);&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Just right&lt;/strong&gt; - Lines don''t come together but the difference is less than F1(B`)-F1(B) with an F1(C) score similar to F1(B).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;General&lt;/strong&gt;: I strenuously suggest that for unbalanced classes you first try adjusting your class weights in your learning algorithm instead of over/under-sampling as it avoids all of the rigor moral that we''ve outlined above.  Its very easy in libraries like scikit-learn and pretty easy to hand code in anything that uses a sigmoid function or a majority vote.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/code'':17C,67C,84C,119C ''/li'':176C,191C,205C,358C,387C,417C ''/p'':13C,63C,80C,115C,155C,164C,254C,327C,338C,483C,488C ''/strong'':50C,169C,180C,196C,343C,362C,392C,422C ''/ul'':206C,418C ''1'':16C ''2'':66C ''3'':83C ''4'':61C,78C,118C ''accept'':316C ''adjust'':433C ''algorithm'':440C ''also'':268C ''answer'':8C,59C,257C ''anyth'':473C ''apart'':352C ''auc'':99C ''avoid'':446C ''b'':23C,25C,47C,51C,266C,271C,277C,290C,323C,326C,355C,357C,375C,377C,386C,405C,407C,416C ''balanc'':35C ''base'':56C,76C ''best'':131C ''bias'':317C ''c'':73C,75C,218C,235C,300C,381C,411C ''check'':106C,285C ''class'':34C,114C,429C,435C ''classic'':158C ''code'':15C,65C,82C,117C,471C ''come'':174C,182C,198C,224C,306C,347C,366C,396C ''complet'':223C ''context'':244C ''could'':92C ''couldn'':39C ''cross'':20C,43C ''curv'':140C,162C,298C,302C,336C ''data'':153C ''differ'':320C,370C,400C ''easi'':459C,468C ''ever'':305C ''f1'':89C,189C,203C,274C,287C,322C,325C,354C,356C,374C,376C,380C,385C,404C,406C,410C,415C ''farther'':351C ''first'':431C ''function'':478C ''general'':421C ''generat'':296C ''get'':121C,272C ''good'':104C ''great'':2C ''hand'':470C ''help'':487C ''hope'':485C ''hurt'':41C ''instead'':441C ''interpret'':159C,332C ''know'':30C ''learn'':139C,161C,297C,335C,439C,465C ''less'':372C,402C ''li'':166C,177C,192C,340C,359C,388C ''librari'':461C ''like'':143C,462C ''line'':170C,181C,197C,220C,344C,363C,393C ''look'':142C ''low'':187C ''major'':481C ''meaning'':241C ''method'':132C ''moral'':451C ''never'':222C ''new'':331C ''number'':11C ''origin'':152C,247C ''otherwis'':26C ''outlin'':455C ''over/under-sampling'':443C ''overfit'':168C,342C ''p'':1C,14C,64C,81C,116C,156C,207C,255C,328C,419C,484C ''pretti'':467C ''problem'':125C,248C ''provid'':102C ''question'':3C,12C ''quit'':173C ''re'':149C ''re-sampl'':148C ''realli'':122C ''reason'':202C ''reinterpret'':136C ''requir'':133C ''result'':237C ''right'':195C,391C ''rigor'':450C ''roc'':98C ''roc-auc'':97C ''sampl'':150C ''saniti'':105C ''scikit'':464C ''scikit-learn'':463C ''score'':190C,204C,275C,288C,382C,412C ''sens'':313C ''set'':154C ''sigmoid'':477C ''similar'':413C ''specif'':7C ''stick'':87C ''strenuous'':424C ''strong'':48C,167C,178C,193C,341C,360C,389C,420C ''suggest'':425C ''tend'':108C ''test'':70C,216C,233C,264C,269C,294C ''togeth'':175C,183C,199C,225C,307C,348C,367C,397C ''train'':212C,229C,260C ''tri'':432C ''tricki'':123C ''ul'':165C,339C ''unbalanc'':113C,428C ''underfit'':179C,361C ''use'':55C,94C,96C,111C,145C,475C ''valid'':21C,44C ''ve'':454C ''vote'':482C ''want'':280C ''weight'':436C ''well'':32C ''won'':28C,238C,303C ''work'':37C ''would'':86C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8899, 5, '2015-11-19 02:45:13.793', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8900, 4, '2015-11-19 02:45:13.793', 0, NULL, NULL, 'Refers to a subset of data mining concerned with extracting information from data in the form of text by recognizing patterns. The goal of text mining is often to classify a given document into one of a number of categories in an automatic way, and to improve this performance dynamically, making it an example of machine learning. One example of this type of text mining are spam filters used for email.', NULL, NULL, '''automat'':43C ''categori'':40C ''classifi'':30C ''concern'':8C ''data'':6C,13C ''document'':33C ''dynam'':50C ''email'':71C ''exampl'':54C,59C ''extract'':10C ''filter'':68C ''form'':16C ''given'':32C ''goal'':23C ''improv'':47C ''inform'':11C ''learn'':57C ''machin'':56C ''make'':51C ''mine'':7C,26C,65C ''number'':38C ''often'':28C ''one'':35C,58C ''pattern'':21C ''perform'':49C ''recogn'':20C ''refer'':1C ''spam'':67C ''subset'':4C ''text'':18C,25C,64C ''type'':62C ''use'':69C ''way'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8901, 5, '2015-11-19 02:46:12.347', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8902, 4, '2015-11-19 02:46:12.347', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8903, 5, '2015-11-19 02:47:23.013', 0, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Overview&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Time series are data observed over time (either in continuous time or at discrete time periods).&lt;/p&gt;

&lt;p&gt;Time series analysis includes trend identification, temporal pattern recognition, spectral analysis, and forecasting future values based on the past.&lt;/p&gt;

&lt;p&gt;The salient characteristic of methods of time series analysis (as opposed to more general methods to analyze relationships among data) is accounting for the possibility of &lt;em&gt;serial correlation&lt;/em&gt; (also known as temporal correlation) among the data.  Positive serial correlation means successive observations in time tend to be close to one another, whereas negative serial correlation means successive observations tend to oscillate between extremes.  Time series analysis also differs from analyses of more general stochastic processes by focusing on the &lt;em&gt;inherent direction&lt;/em&gt; of time, creating a potential asymmetry between past and future.&lt;/p&gt;
', NULL, NULL, '''/em'':75C,130C ''/p'':5C,23C,44C,141C ''/strong'':4C ''account'':67C ''also'':76C,114C ''among'':64C,81C ''analys'':117C ''analysi'':27C,35C,54C,113C ''analyz'':62C ''anoth'':98C ''asymmetri'':136C ''base'':40C ''characterist'':48C ''close'':95C ''continu'':16C ''correl'':74C,80C,86C,102C ''creat'':133C ''data'':10C,65C,83C ''differ'':115C ''direct'':129C ''discret'':20C ''either'':14C ''em'':72C,127C ''extrem'':110C ''focus'':124C ''forecast'':37C ''futur'':38C,140C ''general'':59C,120C ''identif'':30C ''includ'':28C ''inher'':128C ''known'':77C ''mean'':87C,103C ''method'':50C,60C ''negat'':100C ''observ'':11C,89C,105C ''one'':97C ''oppos'':56C ''oscil'':108C ''overview'':3C ''p'':1C,6C,24C,45C ''past'':43C,138C ''pattern'':32C ''period'':22C ''posit'':84C ''possibl'':70C ''potenti'':135C ''process'':122C ''recognit'':33C ''relationship'':63C ''salient'':47C ''seri'':8C,26C,53C,112C ''serial'':73C,85C,101C ''spectral'':34C ''stochast'':121C ''strong'':2C ''success'':88C,104C ''tempor'':31C,79C ''tend'':92C,106C ''time'':7C,13C,17C,21C,25C,52C,91C,111C,132C ''trend'':29C ''valu'':39C ''wherea'':99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8904, 4, '2015-11-19 02:47:23.013', 0, NULL, NULL, 'Time series are data observed over time (either in continuous time or at discrete time periods).&#x0d;
', NULL, NULL, '''continu'':10C ''data'':4C ''discret'':14C ''either'':8C ''observ'':5C ''period'':16C ''seri'':2C ''time'':1C,7C,11C,15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9351, 1, '2015-12-12 15:57:51.297', 0, 31, 'What is the most common approach used to calculate a score for ranking items when there are two parameters?', '&lt;p&gt;One parameter is a $ value and can take negative values to very large positive values.
The second one is a % value and can be negative and greater than 100 also.&lt;/p&gt;

&lt;p&gt;Both parameters are of equal importance.
Items with better ranks should have higher values on first parameter and lower values on parameter 2. &lt;/p&gt;

&lt;p&gt;Also let me know if the approach remains same if a third parameter, another $ value is added, and this should be high for higher rank.&lt;/p&gt;
', 'ranking  scoring', NULL, '''/p'':53C,78C,105C ''100'':51C ''2'':77C ''ad'':96C ''also'':52C,80C ''anoth'':93C ''approach'':6A,86C ''better'':63C ''calcul'':9A ''common'':5A ''equal'':59C ''first'':70C ''greater'':49C ''high'':101C ''higher'':67C,103C ''import'':60C ''item'':14A,61C ''know'':83C ''larg'':35C ''let'':81C ''lower'':73C ''negat'':31C,47C ''one'':23C,40C ''p'':22C,54C,79C ''paramet'':19A,24C,56C,71C,76C,92C ''posit'':36C ''rank'':13A,20B,64C,104C ''remain'':87C ''score'':11A,21B ''second'':39C ''take'':30C ''third'':91C ''two'':18A ''use'':7A ''valu'':27C,32C,37C,43C,68C,74C,94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8905, 5, '2015-11-19 02:47:56.697', 0, NULL, NULL, '&lt;p&gt;"Regression" is a general term for a wide variety of techniques to analyze the relationship between one (or more) dependent variables and independent variables. Typically the dependent variables are modeled with probability distributions whose parameters are assumed to vary (deterministically) with the independent variables.&lt;/p&gt;

&lt;p&gt;Ordinary least squares (OLS) regression affords a simple example in which the expectation of one dependent variable is assumed to depend linearly on the independent variables. The unknown coefficients in the assumed linear function are estimated by choosing values for them that minimize the sum of squared differences between the values of the dependent variable and the corresponding fitted values.&lt;/p&gt;
', NULL, NULL, '''/p'':46C,108C ''afford'':53C ''analyz'':14C ''assum'':38C,66C,79C ''choos'':85C ''coeffici'':76C ''correspond'':105C ''depend'':21C,28C,63C,68C,101C ''determinist'':41C ''differ'':95C ''distribut'':34C ''estim'':83C ''exampl'':56C ''expect'':60C ''fit'':106C ''function'':81C ''general'':5C ''independ'':24C,44C,72C ''least'':49C ''linear'':69C,80C ''minim'':90C ''model'':31C ''ol'':51C ''one'':18C,62C ''ordinari'':48C ''p'':1C,47C ''paramet'':36C ''probabl'':33C ''regress'':2C,52C ''relationship'':16C ''simpl'':55C ''squar'':50C,94C ''sum'':92C ''techniqu'':12C ''term'':6C ''typic'':26C ''unknown'':75C ''valu'':86C,98C,107C ''vari'':40C ''variabl'':22C,25C,29C,45C,64C,73C,102C ''varieti'':10C ''whose'':35C ''wide'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8906, 4, '2015-11-19 02:47:56.697', 0, NULL, NULL, 'Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.&#x0d;
', NULL, NULL, '''analyz'':3C ''depend'':10C ''independ'':13C ''one'':7C ''relationship'':5C ''techniqu'':1C ''variabl'':11C,14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8915, 2, '2015-11-19 10:37:11.273', 4, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;MXNet Julia Package - flexible and efficient deep learning in Julia&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="https://github.com/dmlc/MXNet.jl" rel="nofollow"&gt;https://github.com/dmlc/MXNet.jl&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Pros&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Fast&lt;/li&gt;
&lt;li&gt;Scales up to multi GPUs and distributed setting with auto parallelism.&lt;/li&gt;
&lt;li&gt;Lightweight, memory efficient and portable to smart devices.&lt;/li&gt;
&lt;li&gt;Automatic Differentiation&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Cons&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Doesn''t have yet low level operations for algorithm implementation. But they are working on this issue (&lt;a href="https://github.com/dmlc/mxnet/issues/586" rel="nofollow"&gt;https://github.com/dmlc/mxnet/issues/586&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/blockquote'':14C ''/dmlc/mxnet.jl'':20C ''/dmlc/mxnet.jl&lt;/a&gt;&lt;/p&gt;'':25C ''/dmlc/mxnet/issues/586'':87C ''/dmlc/mxnet/issues/586&lt;/a&gt;)&lt;/li&gt;'':92C ''/li'':32C,45C,55C,59C ''/p'':13C,28C,63C ''/ul'':60C,93C ''algorithm'':74C ''auto'':43C ''automat'':57C ''blockquot'':1C ''con'':62C ''deep'':9C ''devic'':54C ''differenti'':58C ''distribut'':40C ''doesn'':66C ''effici'':8C,49C ''fast'':31C ''flexibl'':6C ''github.com'':19C,24C,86C,91C ''github.com/dmlc/mxnet.jl'':18C ''github.com/dmlc/mxnet.jl&lt;/a&gt;&lt;/p&gt;'':23C ''github.com/dmlc/mxnet/issues/586'':85C ''github.com/dmlc/mxnet/issues/586&lt;/a&gt;)&lt;/li&gt;'':90C ''gpus'':38C ''href'':17C,84C ''implement'':75C ''issu'':82C ''julia'':4C,12C ''learn'':10C ''level'':71C ''li'':30C,33C,46C,56C,65C ''lightweight'':47C ''low'':70C ''memori'':48C ''multi'':37C ''mxnet'':3C ''nofollow'':22C,89C ''oper'':72C ''p'':2C,15C,26C,61C ''packag'':5C ''parallel'':44C ''portabl'':51C ''pros'':27C ''rel'':21C,88C ''scale'':34C ''set'':41C ''smart'':53C ''ul'':29C,64C ''work'':79C ''yet'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9692, 1, '2016-01-07 22:39:43.08', -1, 71, 'create a company to make money by winning Kaggle competition', '&lt;p&gt;I am thinking of creating a data science company (team) that aims to make money by winning Kaggle competitions. It will be actually a onsite data science learning service but I want to create the company''s brand identity like a sport team instead of as a "school." Have you ever heard of such an idea?&lt;/p&gt;
', 'data  kaggle', NULL, '''/p'':70C ''actual'':36C ''aim'':25C ''brand'':51C ''compani'':3A,22C,49C ''competit'':10A,32C ''creat'':1A,18C,47C ''data'':11B,20C,39C ''ever'':64C ''heard'':65C ''idea'':69C ''ident'':52C ''instead'':57C ''kaggl'':9A,12B,31C ''learn'':41C ''like'':53C ''make'':5A,27C ''money'':6A,28C ''onsit'':38C ''p'':13C ''school'':61C ''scienc'':21C,40C ''servic'':42C ''sport'':55C ''team'':23C,56C ''think'':16C ''want'':45C ''win'':8A,30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8917, 2, '2015-11-19 11:07:35.66', 0, NULL, NULL, '&lt;p&gt;&lt;a href="http://lxml.de/" rel="nofollow"&gt;lxml&lt;/a&gt; is a nice web scrapping library in Python. Beautiful Soup is a wrapper over lxml.  So, lxml is faster than both scrapy and beautiful soup and has a much easier learning curve.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/Dawny33/Cricket-Analytics/blob/master/Crawler.py" rel="nofollow"&gt;This&lt;/a&gt; is an example of a scraper which I built with it for a personal project, which can iterate over web pages.&lt;/p&gt;
', NULL, NULL, '''/a'':8C,51C ''/dawny33/cricket-analytics/blob/master/crawler.py'':47C ''/p'':41C,73C ''beauti'':17C,32C ''built'':60C ''curv'':40C ''easier'':38C ''exampl'':54C ''faster'':27C ''github.com'':46C ''github.com/dawny33/cricket-analytics/blob/master/crawler.py'':45C ''href'':3C,44C ''iter'':69C ''learn'':39C ''librari'':14C ''lxml'':7C,23C,25C ''lxml.de'':4C ''much'':37C ''nice'':11C ''nofollow'':6C,49C ''p'':1C,42C ''page'':72C ''person'':65C ''project'':66C ''python'':16C ''rel'':5C,48C ''scrap'':13C ''scraper'':57C ''scrapi'':30C ''soup'':18C,33C ''web'':12C,71C ''wrapper'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8918, 2, '2015-11-19 11:34:07.25', 0, NULL, NULL, '&lt;p&gt;Decision trees are non linear. Unlike Linear regression there is no equation  to express relationship between independent and dependent variables.&lt;/p&gt;

&lt;p&gt;Ex:&lt;/p&gt;

&lt;p&gt;Linear regression - 
Price of fruit = b0 + b1*Freshness + b2*Size&lt;/p&gt;

&lt;p&gt;Decision tree - 
Nodes:
Ripe - Yes or no | 
Fresh -  Yes or No | 
Size - &amp;lt;5, &gt;5 but &amp;lt;10 and &gt;10 |&lt;/p&gt;

&lt;p&gt;In the second case there is no linear relationship between independent and dependent variables. &lt;/p&gt;
', NULL, NULL, '''/p'':22C,25C,37C,59C,75C ''10'':56C,58C ''5'':52C,53C ''b0'':32C ''b1'':33C ''b2'':35C ''case'':64C ''decis'':2C,39C ''depend'':20C,73C ''equat'':13C ''ex'':24C ''express'':15C ''fresh'':34C,46C ''fruit'':31C ''independ'':18C,71C ''linear'':6C,8C,27C,68C ''lt'':51C,55C ''node'':41C ''non'':5C ''p'':1C,23C,26C,38C,60C ''price'':29C ''regress'':9C,28C ''relationship'':16C,69C ''ripe'':42C ''second'':63C ''size'':36C,50C ''tree'':3C,40C ''unlik'':7C ''variabl'':21C,74C ''yes'':43C,47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8919, 2, '2015-11-19 11:43:50.75', 0, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;Recently a friend of mine was asked whether decision tree algorithm a
  linear or nonlinear algorithm in an interview&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Decision trees is a non-linear classifier like the neural networks, etc. It is generally used for classifying non-linearly separable data.&lt;/p&gt;

&lt;p&gt;Even when you consider the regression example, decision tree is non-linear.&lt;/p&gt;

&lt;p&gt;For example, a linear regression line would look somewhat like this:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/GXj8T.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/GXj8T.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The red dots are the data points.&lt;/p&gt;

&lt;p&gt;And a decision tree regression plot would look something like this:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/JvuRJ.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/JvuRJ.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;So, clearly &lt;strong&gt;decision trees in non-linear&lt;/strong&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':96C,138C ''/blockquote'':23C ''/gxj8t.png'':83C,90C ''/jvurj.png'':125C,132C ''/p'':22C,49C,64C,77C,97C,106C,119C,139C,151C ''/strong'':150C ''algorithm'':13C,18C ''alt'':91C,133C ''ask'':9C ''blockquot'':1C ''classifi'':32C,43C ''clear'':142C ''consid'':54C ''data'':48C,104C ''decis'':11C,25C,58C,110C,144C ''descript'':94C,136C ''dot'':101C ''enter'':92C,134C ''etc'':37C ''even'':51C ''exampl'':57C,67C ''friend'':5C ''general'':40C ''href'':80C,122C ''i.stack.imgur.com'':82C,89C,124C,131C ''i.stack.imgur.com/gxj8t.png'':81C,88C ''i.stack.imgur.com/jvurj.png'':123C,130C ''imag'':93C,135C ''img'':86C,128C ''interview'':21C ''like'':33C,75C,117C ''line'':71C ''linear'':15C,31C,46C,63C,69C,149C ''look'':73C,115C ''mine'':7C ''network'':36C ''neural'':35C ''nofollow'':85C,127C ''non'':30C,45C,62C,148C ''non-linear'':29C,44C,61C,147C ''nonlinear'':17C ''p'':2C,24C,50C,65C,78C,98C,107C,120C,140C ''plot'':113C ''point'':105C ''recent'':3C ''red'':100C ''regress'':56C,70C,112C ''rel'':84C,126C ''separ'':47C ''someth'':116C ''somewhat'':74C ''src'':87C,129C ''strong'':143C ''tree'':12C,26C,59C,111C,145C ''use'':41C ''whether'':10C ''would'':72C,114C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8920, 1, '2015-11-19 12:04:51.34', 2, 64, 'Can I apply Hidden Markov Models this way?', '&lt;p&gt;I have just gotten my feet wet with Hidden Markov Models. Now I want to apply them to tell whether a transaction from an ATM is suspicious or not. I have great confusion in defining my Hidden States.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;My attempt:&lt;/strong&gt; A transaction is either suspicious or non-suspicious. Since I cannot tell, &lt;code&gt;("this is hidden to me")&lt;/code&gt;, whether a transaction is suspicious or not before analyzing it, therefore my &lt;code&gt;Hidden States(Suspicious, Non-Suspicious)&lt;/code&gt;. &lt;strong&gt;Does this hold&lt;/strong&gt;? &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;My great confusion:&lt;/strong&gt; When I read about &lt;a href="https://en.wikipedia.org/wiki/Hidden_Markov_model" rel="nofollow"&gt;Hidden Markov Models on Weather example&lt;/a&gt;, I understood it this way: "weather can be &lt;code&gt;rainy&lt;/code&gt; or &lt;code&gt;sunny&lt;/code&gt; - I mean its the ''same'' weather that is switching between the two states". In my &lt;code&gt;transaction&lt;/code&gt; case - one transaction cannot switch between being suspicious and non-suspicious. &lt;em&gt;So, does my hidden states hold&lt;/em&gt;?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Questions:&lt;/strong&gt; Does my hidden states hold? Can I apply the HMM that way? Please advice, thanks.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;P.S&lt;/strong&gt;: I had my Observations as location, time and amount of money but as of now I want clarification on Hidden states.&lt;/p&gt;
', 'machine-learning  markov-process', NULL, '''/a'':129C ''/code'':79C,99C,140C,144C,162C ''/em'':182C ''/p'':54C,105C,183C,203C,229C ''/strong'':59C,104C,111C,187C,207C ''/wiki/hidden_markov_model'':120C ''advic'':201C ''amount'':216C ''analyz'':88C ''appli'':3A,31C,195C ''atm'':40C ''attempt'':58C ''cannot'':71C,166C ''case'':163C ''clarif'':225C ''code'':73C,92C,138C,142C,160C ''confus'':48C,110C ''defin'':50C ''either'':63C ''em'':175C ''en.wikipedia.org'':119C ''en.wikipedia.org/wiki/hidden_markov_model'':118C ''exampl'':128C ''feet'':21C ''gotten'':19C ''great'':47C,109C ''hidden'':4A,24C,52C,76C,93C,123C,179C,190C,227C ''hmm'':197C ''hold'':103C,181C,192C ''href'':117C ''learn'':11B ''locat'':213C ''machin'':10B ''machine-learn'':9B ''markov'':5A,13B,25C,124C ''markov-process'':12B ''mean'':146C ''model'':6A,26C,125C ''money'':218C ''nofollow'':122C ''non'':67C,97C,173C ''non-suspici'':66C,96C,172C ''observ'':211C ''one'':164C ''p'':15C,55C,106C,184C,204C ''p.s'':206C ''pleas'':200C ''process'':14B ''question'':186C ''raini'':139C ''read'':114C ''rel'':121C ''sinc'':69C ''state'':53C,94C,157C,180C,191C,228C ''strong'':56C,100C,107C,185C,205C ''sunni'':143C ''suspici'':42C,64C,68C,84C,95C,98C,170C,174C ''switch'':153C,167C ''tell'':34C,72C ''thank'':202C ''therefor'':90C ''time'':214C ''transact'':37C,61C,82C,161C,165C ''two'':156C ''understood'':131C ''want'':29C,224C ''way'':8A,134C,199C ''weather'':127C,135C,150C ''wet'':22C ''whether'':35C,80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8936, 4, '2015-11-20 11:14:26.57', 0, NULL, NULL, 'Methods and principles of selecting a subset of attributes for use in further modelling&#x0d;
', NULL, NULL, '''attribut'':9C ''method'':1C ''model'':14C ''principl'':3C ''select'':5C ''subset'':7C ''use'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9009, 2, '2015-11-24 21:07:46.043', 1, NULL, NULL, '&lt;p&gt;A repeated &lt;a href="https://en.wikipedia.org/wiki/Cross-sectional_study" rel="nofollow"&gt;cross-sectional study&lt;/a&gt;. Cross-sectional refers to data collected at one specific point in time or over a short period. Repeated since you compare at least two time frames. If there are several time frames, one could talk about &lt;a href="http://as.nyu.edu/docs/IO/2576/beck.pdf" rel="nofollow"&gt;time-series cross-sectional (TSCS)&lt;/a&gt; data. Repeated cross-sectional studies may become pseudolongitudinal, or &lt;a href="https://en.wikipedia.org/wiki/Longitudinal_study" rel="nofollow"&gt;longitudinal&lt;/a&gt; if carried over a longer lapse of time.&lt;/p&gt;
', NULL, NULL, '''/a'':15C,67C,86C ''/docs/io/2576/beck.pdf'':57C ''/p'':95C ''/wiki/cross-sectional_study'':8C ''/wiki/longitudinal_study'':82C ''as.nyu.edu'':56C ''as.nyu.edu/docs/io/2576/beck.pdf'':55C ''becom'':75C ''carri'':88C ''collect'':22C ''compar'':37C ''could'':50C ''cross'':12C,17C,64C,71C ''cross-sect'':11C,16C,63C,70C ''data'':21C,68C ''en.wikipedia.org'':7C,81C ''en.wikipedia.org/wiki/cross-sectional_study'':6C ''en.wikipedia.org/wiki/longitudinal_study'':80C ''frame'':42C,48C ''href'':5C,54C,79C ''laps'':92C ''least'':39C ''longer'':91C ''longitudin'':85C ''may'':74C ''nofollow'':10C,59C,84C ''one'':24C,49C ''p'':1C ''period'':33C ''point'':26C ''pseudolongitudin'':76C ''refer'':19C ''rel'':9C,58C,83C ''repeat'':3C,34C,69C ''section'':13C,18C,65C,72C ''seri'':62C ''sever'':46C ''short'':32C ''sinc'':35C ''specif'':25C ''studi'':14C,73C ''talk'':51C ''time'':28C,41C,47C,61C,94C ''time-seri'':60C ''tscs'':66C ''two'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8924, 2, '2015-11-19 13:52:28.787', 3, NULL, NULL, '&lt;p&gt;For instance:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; rs&amp;lt;-c("copyright @ The Society of mo","I want you to meet me @ the coffeshop")
 s&amp;lt;-gsub("@.*","",rs)
 s
 [1] "copyright "             "I want you to meet me "
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Or, if you want to keep the @ character:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; s&amp;lt;-gsub("(@).*","\\1",rs)
 s
 [1] "copyright @"             "I want you to meet me @"
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;EDIT: If what you want is to remove everything from the last @ on you just have to follow this previous example with the appropriate regex. Example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;rs&amp;lt;-c("copyright @ The Society of mo located @ my house","I want you to meet me @ the coffeshop")
s&amp;lt;-gsub("(.*)@.*","\\1",rs)
s
[1] "copyright @ The Society of mo located " "I want you to meet me "
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Given the matching we are looking for, both sub and gsub will give you the same answer.&lt;/p&gt;
', NULL, NULL, '''/code'':36C,64C,134C ''/p'':4C,47C,93C,154C ''/pre'':37C,65C,135C ''1'':28C,53C,56C,118C,121C ''answer'':153C ''appropri'':90C ''c'':9C,98C ''charact'':46C ''code'':6C,49C,95C ''coffeshop'':22C,114C ''copyright'':10C,29C,57C,99C,122C ''edit'':67C ''everyth'':75C ''exampl'':87C,92C ''follow'':84C ''give'':149C ''given'':137C ''gsub'':25C,52C,117C,147C ''hous'':106C ''instanc'':3C ''keep'':44C ''last'':78C ''locat'':104C,127C ''look'':142C ''lt'':8C,24C,51C,97C,116C ''match'':139C ''meet'':19C,34C,62C,111C,132C ''mo'':14C,103C,126C ''p'':1C,38C,66C,136C ''pre'':5C,48C,94C ''previous'':86C ''regex'':91C ''remov'':74C ''rs'':7C,26C,54C,96C,119C ''societi'':12C,101C,124C ''sub'':145C ''want'':16C,31C,42C,59C,71C,108C,129C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8926, 1, '2015-11-19 15:19:35.033', 3, 73, 'Impact of unlabelled documents for label prediction via SVM', '&lt;p&gt;I have a corpus of text documents, some of which are labelled by analysts with label L. I am using this data to train an SVM for predicting if a new document should have label L.&lt;/p&gt;

&lt;p&gt;So far it''s straight-forward, but there is an issue: The analysts have not evaluated all documents in the training set, so there are in fact three classes of documents:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Documents labeled L&lt;/li&gt;
&lt;li&gt;Documents the analysts have looked at, and chosen not label L (so you could say they''re labelled not-L)&lt;/li&gt;
&lt;li&gt;Documents the analysts have not looked at.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Unfortunately, at training time, I have no way to separate documents in 2. and 3, or not-L and unlabelled documents. I believe this is a problem, because a not-L label gives information to the SVM, but an unlabelled document is more "neutral".&lt;/p&gt;

&lt;p&gt;How can I estimate the impact of this issue on predicting if a new document should have label L?&lt;/p&gt;
', 'machine-learning  statistics  svm  multilabel-classification', NULL, '''/li'':95C,118C,127C ''/ol'':128C ''/p'':55C,89C,176C,197C ''2'':142C ''3'':144C ''analyst'':32C,70C,99C,122C ''believ'':153C ''chosen'':104C ''class'':86C ''classif'':17B ''corpus'':22C ''could'':110C ''data'':40C ''document'':4A,25C,50C,75C,88C,92C,97C,120C,140C,151C,172C,192C ''estim'':181C ''evalu'':73C ''fact'':84C ''far'':58C ''forward'':63C ''give'':164C ''impact'':1A,183C ''inform'':165C ''issu'':68C,186C ''l'':35C,54C,94C,107C,117C,148C,162C,196C ''label'':6A,30C,34C,53C,93C,106C,114C,163C,195C ''learn'':12B ''li'':91C,96C,119C ''look'':101C,125C ''machin'':11B ''machine-learn'':10B ''multilabel'':16B ''multilabel-classif'':15B ''neutral'':175C ''new'':49C,191C ''not-l'':115C,146C,160C ''ol'':90C ''p'':18C,56C,129C,177C ''predict'':7A,46C,188C ''problem'':157C ''re'':113C ''say'':111C ''separ'':139C ''set'':79C ''statist'':13B ''straight'':62C ''straight-forward'':61C ''svm'':9A,14B,44C,168C ''text'':24C ''three'':85C ''time'':133C ''train'':42C,78C,132C ''unfortun'':130C ''unlabel'':3A,150C,171C ''use'':38C ''via'':8A ''way'':137C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8927, 2, '2015-11-19 16:34:49.927', 1, NULL, NULL, '&lt;p&gt;The &lt;a href="http://ghmm.org/" rel="nofollow"&gt;ghmm&lt;/a&gt; library might be the one which you are looking for. &lt;/p&gt;

&lt;p&gt;As it is said in their website:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;It is used for implementing efficient data structures and algorithms
  for basic and extended HMMs with discrete and continuous emissions. It
  comes with Python wrappers which provide a much nicer interface and
  added functionality.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;It also has a nice documentation and a step-by-step tutorial for getting your feet wet.&lt;/p&gt;
', NULL, NULL, '''/a'':9C ''/blockquote'':67C ''/p'':20C,29C,66C,87C ''ad'':64C ''algorithm'':41C ''also'':70C ''basic'':43C ''blockquot'':30C ''come'':53C ''continu'':50C ''data'':38C ''discret'':48C ''document'':74C ''effici'':37C ''emiss'':51C ''extend'':45C ''feet'':85C ''function'':65C ''get'':83C ''ghmm'':8C ''ghmm.org'':5C ''hmms'':46C ''href'':4C ''implement'':36C ''interfac'':62C ''librari'':10C ''look'':18C ''might'':11C ''much'':60C ''nice'':73C ''nicer'':61C ''nofollow'':7C ''one'':14C ''p'':1C,21C,31C,68C ''provid'':58C ''python'':55C ''rel'':6C ''said'':25C ''step'':78C,80C ''step-by-step'':77C ''structur'':39C ''tutori'':81C ''use'':34C ''websit'':28C ''wet'':86C ''wrapper'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8928, 2, '2015-11-20 00:37:55.537', 1, NULL, NULL, '&lt;p&gt;This is a reasonable application for a hidden markov model only if you have reason to believe that the transactions have the markov property - that the next state depends only on the present state, and not on any of the previous ones. This often makes sense for models in time series (like in the weather example, in which the fact that it''s already raining makes it more likely that in the next time step it will still be raining). Of course, the model can be useful even if it''s not a perfect fit.&lt;/p&gt;

&lt;p&gt;The way you''ve formulated the model is saying that the hidden state (suspicious/non-suspicious) of an ATM may be predictive of the emitted, or observed states (location, time, and amount of money). At least for the first two, this feels a bit off, particularly if there are multiple ATMs involved, as is suggested by the presence of a location variable. I''ll try to explain what I think the way you''ve formulated the model suggests in terms of observed variables and how it might be nonsensical, and suggest some alternatives.&lt;/p&gt;

&lt;p&gt;First, this seems to suggest that suspicious/non-suspicious is a shared state of all ATMs, and that that state somehow influences where an ATM is being used (each hidden state has a particular distribution from the location is drawn). It may make more sense to have a separate model for each ATM location. Maybe this is already what you were intending. If that is the case, location wouldn''t really be an observed variable. Instead of thinking of the transactions switching between states, which doesn''t seem to make much sense, as you say, perhaps you want to be thinking of the ATMs switching states.&lt;/p&gt;

&lt;p&gt;Second, it''s not clear how time would be a random variable that would be emitted based on a state of suspiciousness. Instead, time would probably make more sense as the way of ordering the observations you feed into the model.&lt;/p&gt;

&lt;p&gt;If you do want to combine all transactions into a single model, maybe an SVM would be more straightforward as a binary classifier.&lt;/p&gt;
', NULL, NULL, '''/p'':97C,190C,296C,339C,364C ''alreadi'':65C,247C ''altern'':189C ''amount'':128C ''applic'':6C ''atm'':115C,147C,205C,214C,242C,293C ''base'':314C ''believ'':18C ''binari'':362C ''bit'':140C ''case'':256C ''classifi'':363C ''clear'':302C ''combin'':346C ''cours'':83C ''depend'':30C ''distribut'':224C ''doesn'':275C ''drawn'':229C ''emit'':121C,313C ''even'':89C ''exampl'':57C ''explain'':163C ''fact'':61C ''feed'':335C ''feel'':138C ''first'':135C,192C ''fit'':96C ''formul'':103C,171C ''hidden'':9C,110C,219C ''influenc'':211C ''instead'':265C,320C ''intend'':251C ''involv'':148C ''least'':132C ''like'':53C,70C ''ll'':160C ''locat'':125C,157C,227C,243C,257C ''make'':46C,67C,232C,279C,324C ''markov'':10C,24C ''may'':116C,231C ''mayb'':244C,353C ''might'':183C ''model'':11C,49C,85C,105C,173C,239C,338C,352C ''money'':130C ''much'':280C ''multipl'':146C ''next'':28C,74C ''nonsens'':185C ''observ'':123C,178C,263C,333C ''often'':45C ''one'':43C ''order'':331C ''p'':1C,98C,191C,297C,340C ''particular'':142C,223C ''perfect'':95C ''perhap'':285C ''predict'':118C ''presenc'':154C ''present'':34C ''previous'':42C ''probabl'':323C ''properti'':25C ''rain'':66C,81C ''random'':308C ''realli'':260C ''reason'':5C,16C ''say'':107C,284C ''second'':298C ''seem'':194C,277C ''sens'':47C,234C,281C,326C ''separ'':238C ''seri'':52C ''share'':201C ''singl'':351C ''somehow'':210C ''state'':29C,35C,111C,124C,202C,209C,220C,273C,295C,317C ''step'':76C ''still'':79C ''straightforward'':359C ''suggest'':151C,174C,187C,196C ''suspici'':319C ''suspicious/non-suspicious'':112C,198C ''svm'':355C ''switch'':271C,294C ''term'':176C ''think'':166C,267C,290C ''time'':51C,75C,126C,304C,321C ''transact'':21C,270C,348C ''tri'':161C ''two'':136C ''use'':88C,217C ''variabl'':158C,179C,264C,309C ''ve'':102C,170C ''want'':287C,344C ''way'':100C,168C,329C ''weather'':56C ''would'':305C,311C,322C,356C ''wouldn'':258C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8929, 2, '2015-11-20 03:16:58.08', 0, NULL, NULL, '&lt;p&gt;Echoing @James Allen''s suggestion, the total amount of data at any point in time is important to know. It will tell you how much disk you need. Also important is the rate at which new data will be received and old data deleted as this will be limited by the IOPS of your hardware&lt;/p&gt;

&lt;p&gt;From your use case any popular DBMS on commodity hardware should suffice. Something like PostgreSQL can handle terabytes of data at hundreds of transactions per second on a run-of-the-mill four core server.&lt;/p&gt;

&lt;p&gt;Have a look at the &lt;a href="https://www.r-project.org/" rel="nofollow"&gt;R programming language&lt;/a&gt; for analysis. SQL is great for reads and writes but a histogram or linear regression is very tricky in comparison.&lt;/p&gt;
', NULL, NULL, '''/a'':110C ''/p'':57C,95C,131C ''allen'':4C ''also'':30C ''amount'':9C ''analysi'':112C ''case'':62C ''commod'':67C ''comparison'':130C ''core'':93C ''data'':11C,38C,44C,78C ''dbms'':65C ''delet'':45C ''disk'':27C ''echo'':2C ''four'':92C ''great'':115C ''handl'':75C ''hardwar'':56C,68C ''histogram'':122C ''href'':103C ''hundr'':80C ''import'':18C,31C ''iop'':53C ''jame'':3C ''know'':20C ''languag'':109C ''like'':72C ''limit'':50C ''linear'':124C ''look'':99C ''mill'':91C ''much'':26C ''need'':29C ''new'':37C ''nofollow'':106C ''old'':43C ''p'':1C,58C,96C ''per'':83C ''point'':14C ''popular'':64C ''postgresql'':73C ''program'':108C ''r'':107C ''rate'':34C ''read'':117C ''receiv'':41C ''regress'':125C ''rel'':105C ''run'':88C ''run-of-the-mil'':87C ''second'':84C ''server'':94C ''someth'':71C ''sql'':113C ''suffic'':70C ''suggest'':6C ''tell'':23C ''terabyt'':76C ''time'':16C ''total'':8C ''transact'':82C ''tricki'':128C ''use'':61C ''write'':119C ''www.r-project.org'':104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8930, 2, '2015-11-20 09:05:33.487', 5, NULL, NULL, '&lt;p&gt;There are ranking algorithms based on machine learning that are aimed to build ranking models. Training data for these models is given in the form of partial ordering between each pair of elements in a sample. A brief description, together with a list of useful references, is given in the corresponding &lt;a href="https://en.wikipedia.org/wiki/Learning_to_rank"&gt;Wikipedia page&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':60C ''/p'':61C ''/wiki/learning_to_rank'':57C ''aim'':12C ''algorithm'':5C ''base'':6C ''brief'':39C ''build'':14C ''correspond'':52C ''data'':18C ''descript'':40C ''element'':34C ''en.wikipedia.org'':56C ''en.wikipedia.org/wiki/learning_to_rank'':55C ''form'':26C ''given'':23C,49C ''href'':54C ''learn'':9C ''list'':44C ''machin'':8C ''model'':16C,21C ''order'':29C ''p'':1C ''page'':59C ''pair'':32C ''partial'':28C ''rank'':4C,15C ''refer'':47C ''sampl'':37C ''togeth'':41C ''train'':17C ''use'':46C ''wikipedia'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8932, 4, '2015-11-20 11:11:34.763', 0, NULL, NULL, 'An algorithm is a set of one or more computations that will produce a calculated result. All statistics methods are algorithms. Algorithms can be simple, such as calculating a percentage, or can be very complex and require a computer for fast and accurate results.&#x0d;
', NULL, NULL, '''accur'':43C ''algorithm'':2C,21C,22C ''calcul'':15C,28C ''complex'':35C ''comput'':10C,39C ''fast'':41C ''method'':19C ''one'':7C ''percentag'':30C ''produc'':13C ''requir'':37C ''result'':16C,44C ''set'':5C ''simpl'':25C ''statist'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8933, 5, '2015-11-20 11:13:45.567', 0, NULL, NULL, '&lt;p&gt;The &lt;em&gt;logistic function&lt;/em&gt; is $$ f(x) = \frac{1}{1+e^{-x}}, $$ which maps real numbers to $(0,1)$. One common use of the logistic function is &lt;a href="http://en.wikipedia.org/wiki/Logistic_regression" rel="nofollow"&gt;logistic regression&lt;/a&gt;, which is a standard method of quantifying the effect of a set of predictors $\{X_1, ..., X_p\}$ on a binary outcome, $Y$. The model can be written as 
$$ P(Y=1|X) = f(\beta_0 + \beta_1X_1 + ... + \beta_p X_p)$$ The logistic regression model has the nice property that the exponentiated regression coefficients can be interpreted as &lt;a href="http://en.wikipedia.org/wiki/Odds_ratio" rel="nofollow"&gt;odds ratios&lt;/a&gt; associated with a one unit increase in the predictor. &lt;/p&gt;

&lt;p&gt;A second use of the logistic function (but unrelated to logistic regression) is the logistic distribution, which has $f(x)$ as its quantile function. &lt;/p&gt;
', NULL, NULL, '''/a'':39C,109C ''/em'':6C ''/p'':119C,145C ''/wiki/logistic_regression'':34C ''/wiki/odds_ratio'':104C ''0'':20C,75C ''1'':11C,12C,21C,55C,71C,78C ''1x'':77C ''associ'':110C ''beta'':74C,76C,79C ''binari'':60C ''coeffici'':95C ''common'':23C ''distribut'':136C ''e'':13C ''effect'':48C ''em'':3C ''en.wikipedia.org'':33C,103C ''en.wikipedia.org/wiki/logistic_regression'':32C ''en.wikipedia.org/wiki/odds_ratio'':102C ''exponenti'':93C ''f'':8C,73C,139C ''frac'':10C ''function'':5C,28C,127C,144C ''href'':31C,101C ''increas'':115C ''interpret'':98C ''logist'':4C,27C,37C,84C,126C,131C,135C ''map'':16C ''method'':44C ''model'':64C,86C ''nice'':89C ''nofollow'':36C,106C ''number'':18C ''odd'':107C ''one'':22C,113C ''outcom'':61C ''p'':1C,57C,69C,80C,82C,120C ''predictor'':53C,118C ''properti'':90C ''quantifi'':46C ''quantil'':143C ''ratio'':108C ''real'':17C ''regress'':38C,85C,94C,132C ''rel'':35C,105C ''second'':122C ''set'':51C ''standard'':43C ''unit'':114C ''unrel'':129C ''use'':24C,123C ''written'':67C ''x'':9C,14C,54C,56C,72C,81C,140C ''y'':62C,70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8934, 4, '2015-11-20 11:13:45.567', 0, NULL, NULL, 'Refers generally to statistical procedures that utilize the logistic function, most commonly various forms of logistic regression&#x0d;
', NULL, NULL, '''common'':12C ''form'':14C ''function'':10C ''general'':2C ''logist'':9C,16C ''procedur'':5C ''refer'':1C ''regress'':17C ''statist'':4C ''util'':7C ''various'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8946, 1, '2015-11-20 20:52:08.42', 2, 65, 'Feature Scaling and Mean Normalization', '&lt;p&gt;In my machine learning class these two methods were discussed and mentioned that both should be used. I have a couple questions about this:&lt;/p&gt;

&lt;p&gt;1) Can I mix and match these two approaches? e.g. Feature Scale x1 and Mean Normalize x2?&lt;/p&gt;

&lt;p&gt;2) How do you determine which of these options to apply? It seems that either could accomplish the task of increasing your convergence rates... I suppose you just need to know your data set to understand which will reliably reduce your values while leaving as few outliers as possible?&lt;/p&gt;
', 'data-cleaning  gradient-descent  linear-regression', NULL, '''/p'':40C,59C,110C ''1'':42C ''2'':61C ''accomplish'':77C ''appli'':71C ''approach'':50C ''class'':20C ''clean'':8B ''converg'':83C ''could'':76C ''coupl'':36C ''data'':7B,93C ''data-clean'':6B ''descent'':11B ''determin'':65C ''discuss'':25C ''e.g'':51C ''either'':75C ''featur'':1A,52C ''gradient'':10B ''gradient-desc'':9B ''increas'':81C ''know'':91C ''learn'':19C ''leav'':104C ''linear'':13B ''linear-regress'':12B ''machin'':18C ''match'':47C ''mean'':4A,56C ''mention'':27C ''method'':23C ''mix'':45C ''need'':89C ''normal'':5A,57C ''option'':69C ''outlier'':107C ''p'':15C,41C,60C ''possibl'':109C ''question'':37C ''rate'':84C ''reduc'':100C ''regress'':14B ''reliabl'':99C ''scale'':2A,53C ''seem'':73C ''set'':94C ''suppos'':86C ''task'':79C ''two'':22C,49C ''understand'':96C ''use'':32C ''valu'':102C ''x1'':54C ''x2'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9945, 1, '2016-01-25 15:57:55.647', 4, 101, 'R: machine learning on GPU', '&lt;p&gt;Are there any machine learning packages for R that can make use of the GPU to improve training speed? Something like theano from the python world&lt;/p&gt;

&lt;p&gt;I see that there is a package called gputools which allows execution of code on the gpu, but I''m looking for a more complete library for machine learning&lt;/p&gt;
', 'machine-learning  r', NULL, '''/p'':37C,68C ''allow'':49C ''call'':46C ''code'':52C ''complet'':63C ''execut'':50C ''gpu'':5A,25C,55C ''gputool'':47C ''improv'':27C ''learn'':3A,8B,15C,67C ''librari'':64C ''like'':31C ''look'':59C ''m'':58C ''machin'':2A,7B,14C,66C ''machine-learn'':6B ''make'':21C ''p'':10C,38C ''packag'':16C,45C ''python'':35C ''r'':1A,9B,18C ''see'':40C ''someth'':30C ''speed'':29C ''theano'':32C ''train'':28C ''use'':22C ''world'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9159, 1, '2015-12-02 01:06:28.243', 1, 60, 'When to choose linear regression or Decision Tree or Random Forest regression?', '&lt;p&gt;I am working on a project and I am having difficulty in deciding which algorithm to choose for &lt;code&gt;regression&lt;/code&gt;. I want to know under what conditions should one choose a &lt;code&gt;linear regression&lt;/code&gt; or &lt;code&gt;Decision Tree regression&lt;/code&gt; or &lt;code&gt;Random Forest regression&lt;/code&gt;? Are there any specific characteristics of the data that would make the decision to go towards a specific algorithm amongst the tree mentioned above? What are those characteristics that I should look in my dataset to make the decision? And are there some reasons that would make one choose a &lt;code&gt;decision tree&lt;/code&gt; or &lt;code&gt;random forest&lt;/code&gt; algorithm even if the same correctness can be achieved by &lt;code&gt;linear regression&lt;/code&gt;? &lt;/p&gt;
', 'machine-learning  algorithms  random-forest  linear-regression  decision-trees', NULL, '''/code'':47C,62C,68C,74C,128C,133C,147C ''/p'':148C ''achiev'':142C ''algorithm'':16B,41C,93C,134C ''amongst'':94C ''characterist'':79C,102C ''choos'':3A,43C,57C,123C ''code'':45C,59C,64C,70C,125C,130C,144C ''condit'':54C ''correct'':139C ''data'':82C ''dataset'':109C ''decid'':39C ''decis'':7A,24B,65C,87C,113C,126C ''decision-tre'':23B ''difficulti'':37C ''even'':135C ''forest'':11A,19B,72C,132C ''go'':89C ''know'':51C ''learn'':15B ''linear'':4A,21B,60C,145C ''linear-regress'':20B ''look'':106C ''machin'':14B ''machine-learn'':13B ''make'':85C,111C,121C ''mention'':97C ''one'':56C,122C ''p'':26C ''project'':32C ''random'':10A,18B,71C,131C ''random-forest'':17B ''reason'':118C ''regress'':5A,12A,22B,46C,61C,67C,73C,146C ''specif'':78C,92C ''toward'':90C ''tree'':8A,25B,66C,96C,127C ''want'':49C ''work'':29C ''would'':84C,120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8937, 5, '2015-11-20 11:15:01.42', 0, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Overview&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Random forests are an ensemble learning method for classification (and regression) that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes output by individual trees. The randomness in the decision trees follows from (a) using a new bootstrapped version of the original sample for each tree and (b) using a random subsample of the explanatory variables at each node of each tree. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm" rel="nofollow"&gt;Random Forest page&lt;/a&gt; maintained by Leo Breiman and Adele Cutler, the creators of the algorithm.&lt;/li&gt;
&lt;li&gt;Wikipedia pages on &lt;a href="http://en.wikipedia.org/wiki/Random_tree_%28disambiguation%29" rel="nofollow"&gt;Random Trees&lt;/a&gt;, &lt;a href="http://en.wikipedia.org/wiki/Random_forest" rel="nofollow"&gt;Random Forest&lt;/a&gt; and &lt;a href="http://%20http://en.wikipedia.org/wiki/Ensemble_learning" rel="nofollow"&gt;Ensemble Learning&lt;/a&gt;.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':100C,127C,137C,147C ''/en.wikipedia.org/wiki/ensemble_learning'':142C ''/li'':113C,148C ''/p'':5C,82C,87C ''/strong'':4C,86C ''/ul'':149C ''/wiki/random_forest'':132C ''/wiki/random_tree_%28disambiguation%29'':122C ''/~breiman/randomforests/cc_home.htm'':94C ''20http'':141C ''adel'':106C ''algorithm'':112C ''b'':67C ''bootstrap'':57C ''breiman'':104C ''class'':33C,40C ''classif'':15C ''construct'':21C ''creator'':109C ''cutler'':107C ''decis'':25C,49C ''en.wikipedia.org'':121C,131C ''en.wikipedia.org/wiki/random_forest'':130C ''en.wikipedia.org/wiki/random_tree_%28disambiguation%29'':120C ''ensembl'':11C,145C ''explanatori'':74C ''follow'':51C ''forest'':8C,98C,136C ''href'':91C,119C,129C,140C ''individu'':43C ''learn'':12C,146C ''leo'':103C ''li'':89C,114C ''maintain'':101C ''method'':13C ''mode'':37C ''multitud'':23C ''new'':56C ''node'':78C ''nofollow'':96C,124C,134C,144C ''oper'':19C ''origin'':61C ''output'':31C,41C ''overview'':3C ''p'':1C,6C,83C ''page'':99C,116C ''random'':7C,46C,70C,97C,125C,135C ''refer'':85C ''regress'':17C ''rel'':95C,123C,133C,143C ''sampl'':62C ''strong'':2C,84C ''subsampl'':71C ''time'':29C ''train'':28C ''tree'':26C,44C,50C,65C,81C,126C ''ul'':88C ''use'':54C,68C ''variabl'':75C ''version'':58C ''wikipedia'':115C ''www.stat.berkeley.edu'':93C ''www.stat.berkeley.edu/~breiman/randomforests/cc_home.htm'':92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8938, 4, '2015-11-20 11:15:01.42', 0, NULL, NULL, 'Random forest is a machine-learning classifier based on choosing random subsets of variables for each tree and using the most frequent tree output as the overall classification.&#x0d;
', NULL, NULL, '''base'':9C ''choos'':11C ''classif'':29C ''classifi'':8C ''forest'':2C ''frequent'':23C ''learn'':7C ''machin'':6C ''machine-learn'':5C ''output'':25C ''overal'':28C ''random'':1C,12C ''subset'':13C ''tree'':18C,24C ''use'':20C ''variabl'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8939, 5, '2015-11-20 11:15:36.97', 0, NULL, NULL, '&lt;p&gt;Data cleaning is a preliminary step to statistical analysis in which the data-set is edited to correct errors and to put it into a form suitable for processing by statistical software. Exploratory data analysis techniques are often used to identify problems.&lt;/p&gt;
', NULL, NULL, '''/p'':45C ''analysi'':10C,37C ''clean'':3C ''correct'':20C ''data'':2C,15C,36C ''data-set'':14C ''edit'':18C ''error'':21C ''exploratori'':35C ''form'':28C ''identifi'':43C ''often'':40C ''p'':1C ''preliminari'':6C ''problem'':44C ''process'':31C ''put'':24C ''set'':16C ''softwar'':34C ''statist'':9C,33C ''step'':7C ''suitabl'':29C ''techniqu'':38C ''use'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8940, 4, '2015-11-20 11:15:36.97', 0, NULL, NULL, 'Data cleaning is a preliminary step to statistical analysis in which the data-set is edited to correct errors and to put it into a form suitable for processing by statistical software.&#x0d;
', NULL, NULL, '''analysi'':9C ''clean'':2C ''correct'':19C ''data'':1C,14C ''data-set'':13C ''edit'':17C ''error'':20C ''form'':27C ''preliminari'':5C ''process'':30C ''put'':23C ''set'':15C ''softwar'':33C ''statist'':8C,32C ''step'':6C ''suitabl'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8941, 1, '2015-11-20 11:21:03.5', 8, 1613, 'Data Science conferences?', '&lt;p&gt;This is a similar question like the &lt;a href="http://stats.stackexchange.com/questions/1904/statistics-conferences"&gt;Statistics Conferences question at CrossValidated&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;What are the most significant annual Data Science conferences?&lt;/p&gt;

&lt;p&gt;Rules:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Include a link to the conference&lt;/li&gt;
&lt;li&gt;Please include links for the talks (be it youtube, the conference site or some other video streaming site)&lt;/li&gt;
&lt;/ul&gt;
', 'conferences', NULL, '''/a'':23C ''/li'':47C,67C ''/p'':24C,35C,38C ''/questions/1904/statistics-conferences'':17C ''/ul'':68C ''annual'':31C ''confer'':3A,4B,19C,34C,46C,59C ''crossvalid'':22C ''data'':1A,32C ''href'':14C ''includ'':41C,50C ''li'':40C,48C ''like'':11C ''link'':43C,51C ''p'':5C,25C,36C ''pleas'':49C ''question'':10C,20C ''rule'':37C ''scienc'':2A,33C ''signific'':30C ''similar'':9C ''site'':60C,66C ''statist'':18C ''stats.stackexchange.com'':16C ''stats.stackexchange.com/questions/1904/statistics-conferences'':15C ''stream'':65C ''talk'':54C ''ul'':39C ''video'':64C ''youtub'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8988, 4, '2015-11-23 10:58:13.177', 0, NULL, NULL, 'Refers to general procedures that attempt to determine the generalizability of a statistical result. Cross-validation arises frequently in the context of assessing how a particular model fit predicts future observations. Methods for cross-validation usually involve withholding a random subset of the data during model fitting and quantifying how accurate the withheld data are predicted and repeating this process to get a measure of prediction accuracy.', NULL, NULL, '''accur'':53C ''accuraci'':69C ''aris'':18C ''assess'':24C ''attempt'':6C ''context'':22C ''cross'':16C,36C ''cross-valid'':15C,35C ''data'':46C,56C ''determin'':8C ''fit'':29C,49C ''frequent'':19C ''futur'':31C ''general'':3C ''generaliz'':10C ''get'':64C ''involv'':39C ''measur'':66C ''method'':33C ''model'':28C,48C ''observ'':32C ''particular'':27C ''predict'':30C,58C,68C ''procedur'':4C ''process'':62C ''quantifi'':51C ''random'':42C ''refer'':1C ''repeat'':60C ''result'':14C ''statist'':13C ''subset'':43C ''usual'':38C ''valid'':17C,37C ''withheld'':55C ''withhold'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8989, 5, '2015-11-23 10:59:40.87', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8942, 2, '2015-11-20 13:31:29.41', 1, NULL, NULL, '&lt;p&gt;I don''t think anyone can help you with features without more information about your particular domain, but you could do worse than logistic regression for modeling:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Discrete_choice" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Discrete_choice&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Logistic_regression" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Logistic_regression&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':29C ''/wiki/discrete_choice'':35C ''/wiki/discrete_choice&lt;/a&gt;&lt;/p&gt;'':40C ''/wiki/logistic_regression'':46C ''/wiki/logistic_regression&lt;/a&gt;&lt;/p&gt;'':51C ''anyon'':6C ''could'':21C ''domain'':18C ''en.wikipedia.org'':34C,39C,45C,50C ''en.wikipedia.org/wiki/discrete_choice'':33C ''en.wikipedia.org/wiki/discrete_choice&lt;/a&gt;&lt;/p&gt;'':38C ''en.wikipedia.org/wiki/logistic_regression'':44C ''en.wikipedia.org/wiki/logistic_regression&lt;/a&gt;&lt;/p&gt;'':49C ''featur'':11C ''help'':8C ''href'':32C,43C ''inform'':14C ''logist'':25C ''model'':28C ''nofollow'':37C,48C ''p'':1C,30C,41C ''particular'':17C ''regress'':26C ''rel'':36C,47C ''think'':5C ''without'':12C ''wors'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8963, 1, '2015-11-21 07:58:22.937', 4, 231, 'data science / machine learning resources?', '&lt;p&gt;In a few weeks I''m starting a new job that will be involved in machine learning and data science.&lt;/p&gt;

&lt;p&gt;I have a masters degree in probability / mathematics but I have no knowledge of machine learning and data science.&lt;/p&gt;

&lt;p&gt;Are there any online resources like courses or books that could help me prepare for my new job? Basically I''m trying to get a head start on my new job. &lt;/p&gt;

&lt;p&gt;Right now the only things I really know related to this fields are regression methods like linear regression and logistic regression, and I have a decent grasp of mathematical concepts so I''d prefer something that is not totally dumbed-down&lt;/p&gt;
', 'machine-learning  reference-request', NULL, '''/p'':33C,54C,87C,131C ''basic'':74C ''book'':64C ''concept'':118C ''could'':66C ''cours'':62C ''d'':121C ''data'':1A,31C,52C ''decent'':114C ''degre'':39C ''dumb'':129C ''dumbed-down'':128C ''field'':100C ''get'':79C ''grasp'':115C ''head'':81C ''help'':67C ''involv'':26C ''job'':22C,73C,86C ''know'':96C ''knowledg'':47C ''learn'':4A,8B,29C,50C ''like'':61C,104C ''linear'':105C ''logist'':108C ''m'':18C,76C ''machin'':3A,7B,28C,49C ''machine-learn'':6B ''master'':38C ''mathemat'':42C,117C ''method'':103C ''new'':21C,72C,85C ''onlin'':59C ''p'':12C,34C,55C,88C ''prefer'':122C ''prepar'':69C ''probabl'':41C ''realli'':95C ''refer'':10B ''reference-request'':9B ''regress'':102C,106C,109C ''relat'':97C ''request'':11B ''resourc'':5A,60C ''right'':89C ''scienc'':2A,32C,53C ''someth'':123C ''start'':19C,82C ''thing'':93C ''total'':127C ''tri'':77C ''week'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8951, 5, '2015-11-21 06:21:43.267', 0, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Overview&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Data visualization refers to techniques for presenting results in graphical form, such as histograms, scatterplots, or boxplots. Data visualization is a special challenge for data with high dimensionality.&lt;/p&gt;

&lt;p&gt;If your question is only about how to get particular software to produce a specific effect, then it is likely not on topic here.  Programming questions (for example, in Python, or in &lt;code&gt;R&lt;/code&gt; with &lt;code&gt;ggplot&lt;/code&gt;, etc.) for which you can supply a reproducible example are usually welcomed on &lt;a href="http://stackoverflow.com/"&gt;StackOverflow&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The following question contains references to data visualization resources:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/q/22434/27581"&gt;Modern successor to Exploratory Data Analysis by Tukey?&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':93C,126C ''/code'':71C,75C ''/li'':127C ''/p'':5C,35C,94C,99C,110C ''/q/22434/27581'':117C ''/strong'':4C,98C ''/ul'':128C ''analysi'':123C ''boxplot'':23C ''challeng'':29C ''code'':69C,73C ''contain'':104C ''data'':7C,24C,31C,107C,122C ''dimension'':34C ''effect'':52C ''etc'':76C ''exampl'':64C,84C ''exploratori'':121C ''follow'':102C ''form'':17C ''get'':45C ''ggplot'':74C ''graphic'':16C ''high'':33C ''histogram'':20C ''href'':90C,114C ''li'':112C ''like'':56C ''modern'':118C ''overview'':3C ''p'':1C,6C,36C,95C,100C ''particular'':46C ''present'':13C ''produc'':49C ''program'':61C ''python'':66C ''question'':39C,62C,103C ''r'':70C ''refer'':9C,97C,105C ''reproduc'':83C ''resourc'':109C ''result'':14C ''scatterplot'':21C ''softwar'':47C ''special'':28C ''specif'':51C ''stackoverflow'':92C ''stackoverflow.com'':91C ''stats.stackexchange.com'':116C ''stats.stackexchange.com/q/22434/27581'':115C ''strong'':2C,96C ''successor'':119C ''suppli'':81C ''techniqu'':11C ''topic'':59C ''tukey'':125C ''ul'':111C ''usual'':86C ''visual'':8C,25C,108C ''welcom'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8952, 4, '2015-11-21 06:21:43.267', 0, NULL, NULL, 'Constructing meaningful and useful graphical representations of data. (If your question is only about how to get particular software to produce a specific effect, then it is likely not on topic here.)&#x0d;
', NULL, NULL, '''construct'':1C ''data'':8C ''effect'':24C ''get'':17C ''graphic'':5C ''like'':28C ''meaning'':2C ''particular'':18C ''produc'':21C ''question'':11C ''represent'':6C ''softwar'':19C ''specif'':23C ''topic'':31C ''use'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8954, 4, '2015-11-21 06:22:47.047', 0, NULL, NULL, 'In statistics this refers to selecting an estimator of a parameter by maximizing or minimizing some function of the data. One very common example is choosing an estimator which maximizes the joint density (or mass function) of the observed data referred to as Maximum Likelihood Estimation (MLE).&#x0d;
', NULL, NULL, '''choos'':26C ''common'':23C ''data'':20C,40C ''densiti'':33C ''estim'':8C,28C,46C ''exampl'':24C ''function'':17C,36C ''joint'':32C ''likelihood'':45C ''mass'':35C ''maxim'':13C,30C ''maximum'':44C ''minim'':15C ''mle'':47C ''observ'':39C ''one'':21C ''paramet'':11C ''refer'':4C,41C ''select'':6C ''statist'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8956, 4, '2015-11-21 06:23:57.42', 0, NULL, NULL, 'a new area of Machine Learning research concerned with the technologies used for learning hierarchical representations of data, mainly done with deep neural networks (i.e. networks with two or more hidden layers), but also with some sorts of Probabilistic Graphical Models.', NULL, NULL, '''also'':34C ''area'':3C ''concern'':8C ''data'':18C ''deep'':22C ''done'':20C ''graphic'':40C ''hidden'':31C ''hierarch'':15C ''i.e'':25C ''layer'':32C ''learn'':6C,14C ''machin'':5C ''main'':19C ''model'':41C ''network'':24C,26C ''neural'':23C ''new'':2C ''probabilist'':39C ''represent'':16C ''research'':7C ''sort'':37C ''technolog'':11C ''two'':28C ''use'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8958, 4, '2015-11-21 06:25:13.837', 0, NULL, NULL, 'k-means is a family of cluster analysis methods in which you specify the number of clusters you expect. This is as opposed to hierarchical cluster analysis methods.&#x0d;
', NULL, NULL, '''analysi'':9C,28C ''cluster'':8C,18C,27C ''expect'':20C ''famili'':6C ''hierarch'':26C ''k'':2C ''k-mean'':1C ''mean'':3C ''method'':10C,29C ''number'':16C ''oppos'':24C ''specifi'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8959, 5, '2015-11-21 06:26:15.18', 0, NULL, NULL, '&lt;p&gt;From &lt;a href="http://en.wikipedia.org/wiki/Database" rel="nofollow"&gt;Wikipedia&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;A database is an organized collection of data. The data is typically
  organized to model relevant aspects of reality (for example, the
  availability of rooms in hotels), in a way that supports processes
  requiring this information (for example, finding a hotel with
  vacancies).&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;A large proportion of websites and applications rely on databases. They are a crucial component of telecommunications systems, banking systems, video games, and just about any other software system or electronic device that maintains some amount of persistent information. In addition to persistence, database systems provide a number of other properties that make them exceptionally useful and convenient: reliability, efficiency, scalability, concurrency control, data abstraction, and high-level query languages. Databases are so ubiquitous and important that computer science graduates frequently cite their database class as the one most useful to them in their industry or graduate-school careers.&lt;sup&gt;&lt;a href="http://en.wikipedia.org/wiki/Database_management_system" rel="nofollow"&gt;2&lt;/a&gt;&lt;/sup&gt;&lt;/p&gt;

&lt;p&gt;The term &lt;em&gt;database&lt;/em&gt; should not be confused with &lt;em&gt;&lt;a href="http://en.wikipedia.org/wiki/Database_management_system" rel="nofollow"&gt;Database Management System&lt;/a&gt;&lt;/em&gt; (DBMS). A DBMS is the system software used to create and manage databases and provide users and applications with access to the database(s). A database is to a DBMS as a document is to a word processor.&lt;/p&gt;

&lt;p&gt;Some useful references:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://dba.stackexchange.com/questions/18700/what-is-a-database/18702#18702"&gt;What is database?&lt;/a&gt; from Database Administrators SE.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Database_design" rel="nofollow"&gt;design&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.tldp.org/HOWTO/Sybase-ASA-HOWTO/manage.html" rel="nofollow"&gt;managing a database&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Database_tuning" rel="nofollow"&gt;tuning of a database&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Database_security" rel="nofollow"&gt;database security&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Database_connection" rel="nofollow"&gt;Database connections&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Database_normalization" rel="nofollow"&gt;Database normalization&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':11C,171C,196C,252C,267C,280C,294C,306C,318C,330C ''/blockquote'':59C ''/em'':179C,197C ''/howto/sybase-asa-howto/manage.html'':274C ''/li'':257C,268C,281C,295C,307C,319C,331C ''/p'':12C,58C,173C,236C,241C ''/questions/18700/what-is-a-database/18702#18702'':248C ''/sup'':172C ''/ul'':332C ''/wiki/database'':7C ''/wiki/database_connection'':313C ''/wiki/database_design'':263C ''/wiki/database_management_system'':167C,190C ''/wiki/database_normalization'':325C ''/wiki/database_security'':301C ''/wiki/database_tuning'':287C ''2'':170C ''abstract'':125C ''access'':217C ''addit'':101C ''administr'':255C ''amount'':96C ''applic'':67C,215C ''aspect'':31C ''avail'':37C ''bank'':79C ''blockquot'':13C ''career'':161C ''cite'':143C ''class'':146C ''collect'':20C ''compon'':75C ''comput'':139C ''concurr'':122C ''confus'':183C ''connect'':317C ''control'':123C ''conveni'':118C ''creat'':207C ''crucial'':74C ''data'':22C,24C,124C ''databas'':16C,70C,104C,132C,145C,178C,193C,210C,220C,223C,251C,254C,279C,293C,304C,316C,328C ''dba.stackexchange.com'':247C ''dba.stackexchange.com/questions/18700/what-is-a-database/18702#18702'':246C ''dbms'':198C,200C,227C ''design'':266C ''devic'':92C ''document'':230C ''effici'':120C ''electron'':91C ''em'':177C,185C ''en.wikipedia.org'':6C,166C,189C,262C,286C,300C,312C,324C ''en.wikipedia.org/wiki/database'':5C ''en.wikipedia.org/wiki/database_connection'':311C ''en.wikipedia.org/wiki/database_design'':261C ''en.wikipedia.org/wiki/database_management_system'':165C,188C ''en.wikipedia.org/wiki/database_normalization'':323C ''en.wikipedia.org/wiki/database_security'':299C ''en.wikipedia.org/wiki/database_tuning'':285C ''exampl'':35C,52C ''except'':115C ''find'':53C ''frequent'':142C ''game'':82C ''graduat'':141C,159C ''graduate-school'':158C ''high'':128C ''high-level'':127C ''hotel'':41C,55C ''href'':4C,164C,187C,245C,260C,271C,284C,298C,310C,322C ''import'':137C ''industri'':156C ''inform'':50C,99C ''languag'':131C ''larg'':62C ''level'':129C ''li'':243C,258C,269C,282C,296C,308C,320C ''maintain'':94C ''make'':113C ''manag'':194C,209C,277C ''model'':29C ''nofollow'':9C,169C,192C,265C,276C,289C,303C,315C,327C ''normal'':329C ''number'':108C ''one'':149C ''organ'':19C,27C ''p'':1C,14C,60C,174C,237C ''persist'':98C,103C ''process'':47C ''processor'':235C ''properti'':111C ''proport'':63C ''provid'':106C,212C ''queri'':130C ''realiti'':33C ''refer'':240C ''rel'':8C,168C,191C,264C,275C,288C,302C,314C,326C ''relev'':30C ''reli'':68C ''reliabl'':119C ''requir'':48C ''room'':39C ''scalabl'':121C ''school'':160C ''scienc'':140C ''se'':256C ''secur'':305C ''softwar'':88C,204C ''sup'':162C ''support'':46C ''system'':78C,80C,89C,105C,195C,203C ''telecommun'':77C ''term'':176C ''tune'':290C ''typic'':26C ''ubiquit'':135C ''ul'':242C ''use'':116C,151C,205C,239C ''user'':213C ''vacanc'':57C ''video'':81C ''way'':44C ''websit'':65C ''wikipedia'':10C ''word'':234C ''www.tldp.org'':273C ''www.tldp.org/howto/sybase-asa-howto/manage.html'':272C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8960, 4, '2015-11-21 06:26:15.18', 0, NULL, NULL, 'A comprehensive collection of related data organized for convenient access, generally associated with software to update and query the data.&#x0d;
', NULL, NULL, '''access'':10C ''associ'':12C ''collect'':3C ''comprehens'':2C ''conveni'':9C ''data'':6C,20C ''general'':11C ''organ'':7C ''queri'':18C ''relat'':5C ''softwar'':14C ''updat'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8965, 2, '2015-11-21 09:19:31.403', 10, NULL, NULL, '&lt;p&gt;Start with the &lt;a href="https://www.coursera.org/learn/machine-learning" rel="nofollow"&gt;Coursera''s Machine Learning&lt;/a&gt; course. It does a really good job in introducing the student to the domain of Machine Learning and helps you lay a solid foundation in the concepts.&lt;/p&gt;

&lt;p&gt;In case, you feel that the math is a bit dumbed down in that course, you can take &lt;a href="https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599" rel="nofollow"&gt;this course&lt;/a&gt;, taught by the same professor and is more math-intensive than the former.&lt;/p&gt;

&lt;p&gt;Now, you would have a clear intuition about the basic concepts of Machine Learning. Take &lt;a href="https://www.youtube.com/playlist?list=PLD63A284B7615313A" rel="nofollow"&gt;this course&lt;/a&gt;, which can be said as a follow-up or a supplement to the course of Andrew Ng.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mltut.htm" rel="nofollow"&gt;This resource from IAPR&lt;/a&gt; has in-depth notes on a lot of ML concepts like cross-validation, regularization, etc.&lt;/p&gt;

&lt;p&gt;You can also have a look at these &lt;a href="https://www.quora.com/profile/Eren-Golge/Machine-Learning/Some-of-useful-machine-learning-resources-from-beginner-to-intermediate" rel="nofollow"&gt;amazing list of resources&lt;/a&gt; compiled into a blog on Quora.&lt;/p&gt;

&lt;p&gt;Now, for diving into advanced concepts of neural networks and deep learning, you can make use of &lt;a href="http://neuralnetworksanddeeplearning.com/" rel="nofollow"&gt;this free book&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Finally, the free e-book: &lt;a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/" rel="nofollow"&gt;Elements of Statistical Learning&lt;/a&gt; is a wonderful book for beginners in ML or Statistical Learning.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8967, 1, '2015-11-21 23:02:02.99', 2, 84, 'Plotting libraries for Scala on Zeppelin', '&lt;p&gt;My main question is it looks like &lt;a href="http://zeppelin-project.org/" rel="nofollow"&gt;Zeppelin&lt;/a&gt; limit the display of the results to on 1000, I know that I can change this number but when I change it Zeppelin become slow. And it looks like the default plotting tool of Zeppelin also plot the first 1000 results.
Is there a configuration or a way to make the plotting tool plot all the data?&lt;/p&gt;

&lt;p&gt;If no, is there any equivalent for Matlibplot to Scala on Zeppelin?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8968, 5, '2015-11-22 05:40:44.037', 0, NULL, NULL, '&lt;p&gt;The &lt;a href="http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient" rel="nofollow"&gt;correlation&lt;/a&gt; between two random variables $X$ and $Y$ is defined as &lt;/p&gt;

&lt;p&gt;$$ {\rm cor}(X,Y) = \frac{ E(XY) - E(X)E(Y) }{ \sqrt{ {\rm var}(X) {\rm var}(Y) } }$$&lt;/p&gt;

&lt;p&gt;and is bounded between $-1$ (perfect negative linear relationship) and $1$ (perfect positive linear relationship). The numerator of ${\rm cor}(X,Y)$ is known as the &lt;a href="http://en.wikipedia.org/wiki/Covariance" rel="nofollow"&gt;covariance&lt;/a&gt; between $X$ and $Y$. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8970, 5, '2015-11-22 05:41:19.62', 0, NULL, NULL, '&lt;p&gt;&lt;em&gt;From &lt;a href="http://spark.apache.org/" rel="nofollow"&gt;http://spark.apache.org/&lt;/a&gt;:&lt;/em&gt; &lt;/p&gt;

&lt;p&gt;Apache Spark is an open source cluster computing system that aims to make data analytics fast â both fast to run and fast to write.&lt;/p&gt;

&lt;p&gt;To run programs faster, Spark offers a general execution model that can optimize arbitrary operator graphs, and supports in-memory computing, which lets it query data faster than disk-based engines like &lt;a href="/questions/tagged/hadoop" class="post-tag" title="show questions tagged &amp;#39;hadoop&amp;#39;" rel="tag"&gt;hadoop&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Spark is not tied to the two-stage &lt;a href="/questions/tagged/mapreduce" class="post-tag" title="show questions tagged &amp;#39;mapreduce&amp;#39;" rel="tag"&gt;mapreduce&lt;/a&gt; paradigm, and promises performance up to 100 times faster than Hadoop MapReduce&lt;/p&gt;

&lt;p&gt;Spark provides primitives for in-memory cluster computing that allows user programs to load data into a cluster''s memory and query it repeatedly, making it well suited to machine learning algorithms.
To make programming faster, Spark provides clean, concise APIs in &lt;a href="/questions/tagged/scala" class="post-tag" title="show questions tagged &amp;#39;scala&amp;#39;" rel="tag"&gt;scala&lt;/a&gt;, &lt;a href="/questions/tagged/java" class="post-tag" title="show questions tagged &amp;#39;java&amp;#39;" rel="tag"&gt;java&lt;/a&gt; and &lt;a href="/questions/tagged/python" class="post-tag" title="show questions tagged &amp;#39;python&amp;#39;" rel="tag"&gt;python&lt;/a&gt;. You can also use Spark interactively from the &lt;a href="/questions/tagged/scala" class="post-tag" title="show questions tagged &amp;#39;scala&amp;#39;" rel="tag"&gt;scala&lt;/a&gt; and &lt;a href="/questions/tagged/python" class="post-tag" title="show questions tagged &amp;#39;python&amp;#39;" rel="tag"&gt;python&lt;/a&gt; shells to rapidly query big datasets.&lt;/p&gt;

&lt;p&gt;Spark runs on &lt;a href="/questions/tagged/yarn" class="post-tag" title="show questions tagged &amp;#39;yarn&amp;#39;" rel="tag"&gt;yarn&lt;/a&gt;, &lt;a href="/questions/tagged/mesos" class="post-tag" title="show questions tagged &amp;#39;mesos&amp;#39;" rel="tag"&gt;mesos&lt;/a&gt;, standalone, or in the cloud. It can access diverse data sources including &lt;a href="/questions/tagged/hdfs" class="post-tag" title="show questions tagged &amp;#39;hdfs&amp;#39;" rel="tag"&gt;hdfs&lt;/a&gt;, &lt;a href="/questions/tagged/cassandra" class="post-tag" title="show questions tagged &amp;#39;cassandra&amp;#39;" rel="tag"&gt;cassandra&lt;/a&gt;, &lt;a href="/questions/tagged/hbase" class="post-tag" title="show questions tagged &amp;#39;hbase&amp;#39;" rel="tag"&gt;hbase&lt;/a&gt;, and &lt;a href="/questions/tagged/amazon-s3" class="post-tag" title="show questions tagged &amp;#39;amazon-s3&amp;#39;" rel="tag"&gt;amazon-s3&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Recommended reference sources:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://spark.apache.org/documentation.html" rel="nofollow"&gt;Spark Documentation&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://shop.oreilly.com/product/0636920028512.do" rel="nofollow"&gt;Learning Spark&lt;/a&gt; - Lightning-Fast Big Data Analysis&lt;/p&gt;

&lt;p&gt;&lt;a href="http://ampcamp.berkeley.edu/5/" rel="nofollow"&gt;AMP Camp 5&lt;/a&gt; (Berkeley, CA, November 20-21, 2014) &lt;/p&gt;

&lt;p&gt;&lt;a href="http://ampcamp.berkeley.edu/4/" rel="nofollow"&gt;AMP Camp 4&lt;/a&gt; (Strata Santa Clara, Feb 2014) â focus on BlinkDB, MLlib, GraphX, Tachyon&lt;/p&gt;

&lt;p&gt;&lt;a href="http://ampcamp.berkeley.edu/3/" rel="nofollow"&gt;AMP Camp 3&lt;/a&gt; (Berkeley, CA, Aug 2013)&lt;/p&gt;

&lt;p&gt;&lt;a href="http://ampcamp.berkeley.edu/amp-camp-two-strata-2013/" rel="nofollow"&gt;AMP Camp 2&lt;/a&gt; (Strata Santa Clara, Feb 2013)&lt;/p&gt;

&lt;p&gt;&lt;a href="http://ampcamp.berkeley.edu/agenda-2012/" rel="nofollow"&gt;AMP Camp 1&lt;/a&gt; (Berkeley, CA, Aug 2012)&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8971, 4, '2015-11-22 05:41:19.62', 0, NULL, NULL, 'Apache Spark is an open source cluster computing system that aims to make data analytics fast â both fast to run and fast to write, originally developed in the AMPLab at UC Berkeley.', NULL, NULL, '''aim'':11C ''amplab'':30C ''analyt'':15C ''apach'':1C ''berkeley'':33C ''cluster'':7C ''comput'':8C ''data'':14C ''develop'':27C ''fast'':16C,19C,23C ''make'':13C ''open'':5C ''origin'':26C ''run'':21C ''sourc'':6C ''spark'':2C ''system'':9C ''uc'':32C ''write'':25C ''â'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8998, 2, '2015-11-24 09:04:15.257', 0, NULL, NULL, '&lt;p&gt;Geographically weighted regression would be one way to see what factors correlate with price and how that model changes over space.&lt;/p&gt;

&lt;p&gt;Used with caution it can also help predict expected prices in the intervening locations i.e. if the data points are suitably distributed.&lt;/p&gt;

&lt;p&gt;GWR is available in e.g. R and ArcGIS&lt;/p&gt;
', NULL, NULL, '''/p'':23C,47C,57C ''also'':30C ''arcgi'':56C ''avail'':51C ''caution'':27C ''chang'':20C ''correl'':13C ''data'':42C ''distribut'':46C ''e.g'':53C ''expect'':33C ''factor'':12C ''geograph'':2C ''gwr'':49C ''help'':31C ''i.e'':39C ''interven'':37C ''locat'':38C ''model'':19C ''one'':7C ''p'':1C,24C,48C ''point'':43C ''predict'':32C ''price'':15C,34C ''r'':54C ''regress'':4C ''see'':10C ''space'':22C ''suitabl'':45C ''use'':25C ''way'':8C ''weight'':3C ''would'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8992, 4, '2015-11-23 11:01:16.99', 0, NULL, NULL, 'Comma-Separated Values are a list of plain text values delimited by commas, or a file containing one or more lists in that format.', NULL, NULL, '''comma'':2C,14C ''comma-separ'':1C ''contain'':18C ''delimit'':12C ''file'':17C ''format'':25C ''list'':7C,22C ''one'':19C ''plain'':9C ''separ'':3C ''text'':10C ''valu'':4C,11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8979, 2, '2015-11-22 17:00:41.07', 0, NULL, NULL, '&lt;p&gt;Yes, it is a full-strength Machine Learning paradigm.&lt;/p&gt;

&lt;p&gt;XGBoost is basically Extreme Gradient Boosting.&lt;/p&gt;

&lt;p&gt;It only takes in numeric matrix data. So, you might want to convert your data such that it is compatible with XGBoost.&lt;/p&gt;

&lt;p&gt;The &lt;a href="http://xgboost.readthedocs.org/en/latest/parameter.html" rel="nofollow"&gt;wide range of parameters&lt;/a&gt; of the xgboost paradigm is what makes it so diverse. Boosting can be done on trees and linear models, and then more parameters can be defined depending on the model you have selected.&lt;/p&gt;

&lt;p&gt;So, yes it is a complete paradigm in itself. But, when you want more than the limitations of xgboost like linear and tree models, then you can use the &lt;a href="https://en.wikipedia.org/wiki/Ensemble_learning" rel="nofollow"&gt;concept of ensembling&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;In the case of ensembles, the tools/libraries which can be used depends on the data scientist who is conducting the experiment. It can be Keras or Theano or TensorFlow, or anything which he/she is comfortable with. (opinion-based)&lt;/p&gt;
', NULL, NULL, '''/a'':58C,133C ''/en/latest/parameter.html'':51C ''/p'':12C,20C,44C,92C,134C,175C ''/wiki/ensemble_learning'':127C ''anyth'':166C ''base'':174C ''basic'':16C ''boost'':19C,69C ''case'':138C ''comfort'':170C ''compat'':41C ''complet'':99C ''concept'':130C ''conduct'':154C ''convert'':34C ''data'':28C,36C,150C ''defin'':84C ''depend'':85C,147C ''divers'':68C ''done'':72C ''en.wikipedia.org'':126C ''en.wikipedia.org/wiki/ensemble_learning'':125C ''ensembl'':132C,140C ''experi'':156C ''extrem'':17C ''full'':7C ''full-strength'':6C ''gradient'':18C ''he/she'':168C ''href'':48C,124C ''kera'':160C ''learn'':10C ''like'':113C ''limit'':110C ''linear'':76C,114C ''machin'':9C ''make'':65C ''matrix'':27C ''might'':31C ''model'':77C,88C,117C ''nofollow'':53C,129C ''numer'':26C ''opinion'':173C ''opinion-bas'':172C ''p'':1C,13C,21C,45C,93C,135C ''paradigm'':11C,62C,100C ''paramet'':57C,81C ''rang'':55C ''rel'':52C,128C ''scientist'':151C ''select'':91C ''strength'':8C ''take'':24C ''tensorflow'':164C ''theano'':162C ''tools/libraries'':142C ''tree'':74C,116C ''use'':121C,146C ''want'':32C,106C ''wide'':54C ''xgboost'':14C,43C,61C,112C ''xgboost.readthedocs.org'':50C ''xgboost.readthedocs.org/en/latest/parameter.html'':49C ''yes'':2C,95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8980, 2, '2015-11-22 17:42:25.77', 3, NULL, NULL, '&lt;p&gt;Just went to this one last week (Open Data Science Conference): &lt;a href="http://odsc.com/" rel="nofollow"&gt;http://odsc.com/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;It was really enjoyable. Heavy focus on open source technologies. Speakers from high profile (within the community) projects. And they have posted a lot of the videos from it too.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.youtube.com/playlist?list=PLB2SCq-tZtVl2qWH4CjizIb-IrkT_RA1f" rel="nofollow"&gt;Playlist&lt;/a&gt; of the talk video.&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':20C ''/a'':62C ''/p'':52C,67C ''/playlist?list=plb2scq-tztvl2qwh4cjizib-irkt_ra1f'':58C ''communiti'':38C ''confer'':12C ''data'':10C ''enjoy'':25C ''focus'':27C ''heavi'':26C ''high'':34C ''href'':14C,55C ''last'':7C ''lot'':45C ''nofollow'':17C,60C ''odsc.com'':15C,19C ''odsc.com/&lt;/a&gt;&lt;/p&gt;'':18C ''one'':6C ''open'':9C,29C ''p'':1C,21C,53C ''playlist'':61C ''post'':43C ''profil'':35C ''project'':39C ''realli'':24C ''rel'':16C,59C ''scienc'':11C ''sourc'':30C ''speaker'':32C ''talk'':65C ''technolog'':31C ''video'':48C,66C ''week'':8C ''went'':3C ''within'':36C ''www.youtube.com'':57C ''www.youtube.com/playlist?list=plb2scq-tztvl2qwh4cjizib-irkt_ra1f'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8983, 1, '2015-11-23 09:40:01.58', 1, 141, 'What''s cooking Kaggle - Improve model', '&lt;p&gt;I''m participating in a Kaggle contest "What''s cooking". The goal is to know wich kind of cuisine we have, depending on some ingredients. So it''s a multiclass classification problem. I have an existing model, and I have been trying to improve it for 2 weeks without result. I am using scikit learn, and my existing model is &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;pipelineLR = Pipeline([
(''vect'', CountVectorizer(max_df=0.75)),
(''lr'', LogisticRegression(C=0.4,solver=''lbfgs'',multi_class=''ovr'',fit_intercept=True,warm_start=True)),
])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;It''s really basic. But the problem of this dataset is that we have unbalanced data. Also we have a a huge sparse matrix because of the CountVectorizer. I tried a lot of different model, with and without TF-IDF and this model is the best I can get. I have also confusion matrix, classification report and learning curve for it. &lt;/p&gt;

&lt;p&gt;What I can notice is that is have high-variance. I think I can do something by preprocessing the data but I am really stuck. I don''t know if my model is good enough to be improved, or if I have to find a totally new approach to it. Also what I can get from the classification report is that the model is working well for classes where I have a lot of data, but it''s making a lot of mistakes for classes with less data. &lt;/p&gt;

&lt;p&gt;I am very new to data science and I am trying to learn by myself. But I think I really need to have another point of view with this problem, because I can''t manage to improve my score.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/8LrDq.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/8LrDq.png" alt="Classification report"&gt;&lt;/a&gt;
&lt;a href="http://i.stack.imgur.com/JBUEP.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/JBUEP.png" alt="Learning curves"&gt;&lt;/a&gt;&lt;/p&gt;
', 'kaggle', NULL, '''/8lrdq.png'':297C,304C ''/a'':308C,324C ''/code'':94C ''/jbuep.png'':313C,320C ''/p'':69C,159C,250C,291C,325C ''/pre'':95C ''0.4'':82C ''0.75'':78C ''2'':55C ''also'':113C,149C,212C ''alt'':305C,321C ''anoth'':275C ''approach'':209C ''basic'':100C ''best'':143C ''c'':81C ''class'':86C,229C,246C ''classif'':39C,152C,219C,306C ''code'':71C ''confus'':150C ''contest'':15C ''cook'':3A,18C ''countvector'':75C,124C ''cuisin'':27C ''curv'':156C,323C ''data'':112C,181C,236C,249C,257C ''dataset'':106C ''depend'':30C ''df'':77C ''differ'':130C ''enough'':196C ''exist'':44C,66C ''find'':205C ''fit'':88C ''get'':146C,216C ''goal'':20C ''good'':195C ''high'':170C ''high-vari'':169C ''href'':294C,310C ''huge'':118C ''i.stack.imgur.com'':296C,303C,312C,319C ''i.stack.imgur.com/8lrdq.png'':295C,302C ''i.stack.imgur.com/jbuep.png'':311C,318C ''idf'':137C ''img'':300C,316C ''improv'':5A,52C,199C,288C ''ingredi'':33C ''intercept'':89C ''kaggl'':4A,7B,14C ''kind'':25C ''know'':23C,190C ''lbfgs'':84C ''learn'':63C,155C,264C,322C ''less'':248C ''logisticregress'':80C ''lot'':128C,234C,242C ''lr'':79C ''m'':10C ''make'':240C ''manag'':286C ''matrix'':120C,151C ''max'':76C ''mistak'':244C ''model'':6A,45C,67C,131C,140C,193C,224C ''multi'':85C ''multiclass'':38C ''need'':272C ''new'':208C,255C ''nofollow'':299C,315C ''notic'':164C ''ovr'':87C ''p'':8C,96C,160C,251C,292C ''particip'':11C ''pipelin'':73C ''pipelinelr'':72C ''point'':276C ''pre'':70C ''preprocess'':179C ''problem'':40C,103C,281C ''realli'':99C,185C,271C ''rel'':298C,314C ''report'':153C,220C,307C ''result'':58C ''scienc'':258C ''scikit'':62C ''score'':290C ''solver'':83C ''someth'':177C ''spars'':119C ''src'':301C,317C ''start'':92C ''stuck'':186C ''tf'':136C ''tf-idf'':135C ''think'':173C,269C ''total'':207C ''tri'':50C,126C,262C ''true'':90C,93C ''unbalanc'':111C ''use'':61C ''varianc'':171C ''vect'':74C ''view'':278C ''warm'':91C ''week'':56C ''well'':227C ''wich'':24C ''without'':57C,134C ''work'':226C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8984, 2, '2015-11-23 10:42:18.23', 1, NULL, NULL, '&lt;p&gt;Lots of nice conferences already mentioned by the existing answers. Here are some which I think deserve a place in the top conferences list:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://mlconf.com/" rel="nofollow"&gt;MLConf&lt;/a&gt;:  As the name puts: &lt;em&gt;THE MACHINE LEARNING CONFERENCE&lt;/em&gt;.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Purpose of the conference:  &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;MLconf gathers communities to discuss the recent research and
  application of Algorithms, Tools, and Platforms to solve the hard
  problems that exist within organizing and analyzing massive and noisy
  data sets.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="https://www.youtube.com/channel/UCjeM1xxYb_37bZfyparLS3Q" rel="nofollow"&gt;Videos link&lt;/a&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.baylearn.org/" rel="nofollow"&gt;Baylearn&lt;/a&gt;: The Bay Area Machine Learning symposium.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Purpose:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The BayLearn Symposium aims at gathering scientists in machine
  learning from the San Francisco Bay Area. While it promotes community
  building between local researchers from academic and industrial
  institutions, it also welcomes visitors. This one-day event combines
  invited talks and posters to foster exchange of ideas.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="https://www.youtube.com/channel/UCnf609gsCjoJd_GvQ31SFVA" rel="nofollow"&gt;Link to videos&lt;/a&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://fifthelephant.in" rel="nofollow"&gt;Fifth Elephant&lt;/a&gt;: &lt;/li&gt;
&lt;/ul&gt;

&lt;blockquote&gt;
  &lt;p&gt;The Fifth Elephant is a community-powered conference on the big data
  ecosystem, covering the technology and its applications in a range of
  industries.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="https://hasgeek.tv/fifthelephant/" rel="nofollow"&gt;Link to videos&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':35C,99C,109C,184C,195C,238C ''/blockquote'':88C,172C,226C ''/channel/ucjem1xxyb_37bzfyparls3q'':94C ''/channel/ucnf609gscjojd_gvq31sfva'':178C ''/em'':45C ''/fifthelephant/'':232C ''/li'':46C,116C,196C ''/p'':26C,53C,87C,100C,120C,171C,185C,225C,239C ''/ul'':47C,117C,197C ''academ'':148C ''aim'':126C ''algorithm'':67C ''alreadi'':6C ''also'':153C ''analyz'':81C ''answer'':11C ''applic'':65C,219C ''area'':112C,138C ''bay'':111C,137C ''baylearn'':108C,124C ''big'':211C ''blockquot'':54C,121C,198C ''build'':143C ''combin'':161C ''communiti'':58C,142C,206C ''community-pow'':205C ''confer'':5C,24C,44C,52C,208C ''cover'':214C ''data'':85C,212C ''day'':159C ''deserv'':18C ''discuss'':60C ''ecosystem'':213C ''eleph'':194C,202C ''em'':40C ''event'':160C ''exchang'':168C ''exist'':10C,77C ''fifth'':193C,201C ''fifthelephant.in'':190C ''foster'':167C ''francisco'':136C ''gather'':57C,128C ''hard'':74C ''hasgeek.tv'':231C ''hasgeek.tv/fifthelephant/'':230C ''href'':30C,91C,104C,175C,189C,229C ''idea'':170C ''industri'':150C,224C ''institut'':151C ''invit'':162C ''learn'':43C,114C,132C ''li'':28C,102C,187C ''link'':98C,181C,235C ''list'':25C ''local'':145C ''lot'':2C ''machin'':42C,113C,131C ''massiv'':82C ''mention'':7C ''mlconf'':34C,56C ''mlconf.com'':31C ''name'':38C ''nice'':4C ''nofollow'':33C,96C,107C,180C,192C,234C ''noisi'':84C ''one'':158C ''one-day'':157C ''organ'':79C ''p'':1C,48C,55C,89C,118C,122C,173C,199C,227C ''place'':20C ''platform'':70C ''poster'':165C ''power'':207C ''problem'':75C ''promot'':141C ''purpos'':49C,119C ''put'':39C ''rang'':222C ''recent'':62C ''rel'':32C,95C,106C,179C,191C,233C ''research'':63C,146C ''san'':135C ''scientist'':129C ''set'':86C ''solv'':72C ''symposium'':115C,125C ''talk'':163C ''technolog'':216C ''think'':17C ''tool'':68C ''top'':23C ''ul'':27C,101C,186C ''video'':97C,183C,237C ''visitor'':155C ''welcom'':154C ''within'':78C ''www.baylearn.org'':105C ''www.youtube.com'':93C,177C ''www.youtube.com/channel/ucjem1xxyb_37bzfyparls3q'':92C ''www.youtube.com/channel/ucnf609gscjojd_gvq31sfva'':176C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9049, 4, '2015-11-26 09:56:54.963', 0, NULL, NULL, 'Ranking is ordering data from highest to lowest or &#x0d;
*vice versa.* For questions about *constructing* scores to use in ranking, please use the "valuation" tag, too.', NULL, NULL, '''construct'':15C ''data'':4C ''highest'':6C ''lowest'':8C ''order'':3C ''pleas'':21C ''question'':13C ''rank'':1C,20C ''score'':16C ''tag'':25C ''use'':18C,22C ''valuat'':24C ''versa'':11C ''vice'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8991, 5, '2015-11-23 11:01:16.99', 0, NULL, NULL, '&lt;p&gt;CSV is a file format describing a plain text file with information separated by commas (&lt;code&gt;,&lt;/code&gt;). &lt;/p&gt;

&lt;p&gt;While technically plain text files (&lt;code&gt;.txt&lt;/code&gt;), these files are given the &lt;code&gt;.csv&lt;/code&gt; extension to indicate that the data is delimited by commas and can be afforded special parsing.&lt;/p&gt;

&lt;p&gt;The MIME type for CSV files is &lt;code&gt;text/csv&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Information is often stored as CSV format to make it easy to transfer tables of data between applications. Each row of a table is represented as a list of plain text (human-readable) values with commas as delimiters between each discrete piece of data. Values may be enclosed in quotes, especially if they contain spaces (which might otherwise be parsed as delimiters), or if the data itself contains commas or line breaks. The first row of data often contains the headings of the table columns, which describe the meaning of the data in each column.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Example:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Tabular format:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;+-------+-------------+----------+----------------------+
| Time  | Temperature | Humidity | Description          |
+-------|-------------|----------|----------------------+
| 08:00 |     70      |    35    | Sunny and Clear      |
| 11:45 |     94      |    90    | Hazy, Hot, and Humid |
| 14:30 |     18      |          | Freezing             |
+-------+-------------+----------+----------------------+
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;CSV format:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Time,Temperature,Humidity,Description
08:00,70,35,"Sunny and Clear"
11:45,94,90,"Hazy, Hot, and Humid"
14:30,18,,Freezing
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In this example, the first row of CSV data serves as the "header" which describes the corresponding data below it. Each successive line of the CSV file would then neatly fit into the same field as the first line. There is no inherent way to describe within a CSV file whether the first row is a header row or not.&lt;/p&gt;

&lt;p&gt;Note that empty fields (fields with no available data, such as the third field in the last line) are place-held with commas so that the fields that follow may be correctly placed.&lt;/p&gt;

&lt;p&gt;Questions tagged &lt;a href="/questions/tagged/csv" class="post-tag" title="show questions tagged &amp;#39;csv&amp;#39;" rel="tag"&gt;csv&lt;/a&gt; are expected to relate to programming in some way, for example, parsing/importing CSV files or creating them programmatically.&lt;/p&gt;

&lt;p&gt;Related links:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://stackoverflow.com/q/1382604/190829"&gt;What to set as mimetype for csv files to open in spreadsheet applications&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://tools.ietf.org/html/rfc4180" rel="nofollow"&gt;RFC 4180&lt;/a&gt; (describes official CSV format)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Reference: &lt;a href="http://stackoverflow.com/tags/csv/info"&gt;Stack Overflow SE&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':349C,393C,405C,422C ''/code'':18C,28C,36C,64C,198C,229C ''/html/rfc4180'':400C ''/li'':394C,410C ''/p'':19C,53C,65C,163C,168C,172C,203C,292C,328C,368C,372C,423C ''/pre'':199C,230C ''/q/1382604/190829'':379C ''/questions/tagged/csv'':334C ''/strong'':167C ''/tags/csv/info'':418C ''/ul'':411C ''00'':180C,211C ''08'':179C,210C ''11'':186C,217C ''14'':194C,225C ''18'':196C,227C ''30'':195C,226C ''35'':182C,213C ''39'':343C,345C ''4180'':404C ''45'':187C,218C ''70'':181C,212C ''90'':189C,220C ''94'':188C,219C ''afford'':50C ''applic'':84C,392C ''avail'':301C ''break'':139C ''class'':335C ''clear'':185C,216C ''code'':17C,26C,34C,62C,174C,205C ''column'':152C,162C ''comma'':16C,46C,103C,136C,317C ''contain'':121C,135C,146C ''correct'':326C ''correspond'':248C ''creat'':365C ''csv'':2C,35C,59C,72C,201C,239C,257C,280C,344C,348C,362C,386C,408C ''data'':42C,82C,111C,133C,144C,159C,240C,249C,302C ''delimit'':44C,105C,129C ''describ'':7C,154C,246C,277C,406C ''descript'':178C,209C ''discret'':108C ''easi'':77C ''empti'':296C ''enclos'':115C ''especi'':118C ''exampl'':166C,234C,360C ''expect'':351C ''extens'':37C ''field'':266C,297C,298C,307C,321C ''file'':5C,11C,25C,30C,60C,258C,281C,363C,387C ''first'':141C,236C,269C,284C ''fit'':262C ''follow'':323C ''format'':6C,73C,171C,202C,409C ''freez'':197C,228C ''given'':32C ''hazi'':190C,221C ''head'':148C ''header'':244C,288C ''held'':315C ''hot'':191C,222C ''href'':333C,376C,397C,415C ''human'':99C ''human-read'':98C ''humid'':177C,193C,208C,224C ''indic'':39C ''inform'':13C,67C ''inher'':274C ''last'':310C ''li'':374C,395C ''line'':138C,254C,270C,311C ''link'':371C ''list'':94C ''make'':75C ''may'':113C,324C ''mean'':156C ''might'':124C ''mime'':56C ''mimetyp'':384C ''neat'':261C ''nofollow'':402C ''note'':294C ''offici'':407C ''often'':69C,145C ''open'':389C ''otherwis'':125C ''overflow'':420C ''p'':1C,20C,54C,66C,164C,169C,200C,231C,293C,329C,369C,412C ''pars'':52C,127C ''parsing/importing'':361C ''piec'':109C ''place'':314C,327C ''place-held'':313C ''plain'':9C,23C,96C ''post'':337C ''post-tag'':336C ''pre'':173C,204C ''program'':355C ''programmat'':367C ''question'':330C,341C ''quot'':117C ''readabl'':100C ''refer'':413C ''rel'':346C,401C ''relat'':353C,370C ''repres'':91C ''rfc'':403C ''row'':86C,142C,237C,285C,289C ''se'':421C ''separ'':14C ''serv'':241C ''set'':382C ''show'':340C ''space'':122C ''special'':51C ''spreadsheet'':391C ''stack'':419C ''stackoverflow.com'':378C,417C ''stackoverflow.com/q/1382604/190829'':377C ''stackoverflow.com/tags/csv/info'':416C ''store'':70C ''strong'':165C ''success'':253C ''sunni'':183C,214C ''tabl'':80C,89C,151C ''tabular'':170C ''tag'':331C,338C,342C,347C ''technic'':22C ''temperatur'':176C,207C ''text'':10C,24C,97C ''text/csv'':63C ''third'':306C ''time'':175C,206C ''titl'':339C ''tools.ietf.org'':399C ''tools.ietf.org/html/rfc4180'':398C ''transfer'':79C ''txt'':27C ''type'':57C ''ul'':373C ''valu'':101C,112C ''way'':275C,358C ''whether'':282C ''within'':278C ''would'':259C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8994, 2, '2015-11-23 20:39:48.233', 1, NULL, NULL, '&lt;p&gt;&lt;a href="http://passbaconference.com/2016/Home.aspx" rel="nofollow"&gt;PASS Analytics&lt;/a&gt; - The Professional Association for SQL Server has an analytics focused conference. While some of the conference tracks may not be for highly qualified data scientists, many are specifically relevant as Microsoft continues to support Hadoop and integrates R into SQL Server among many other things.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Whether youâre just starting out in the data analytics field, are a
  data pro who sees the value in expanding your skillset, or are a
  seasoned business analytics pro, the PASS Business Analytics
  Conference is the place to be.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/2016/home.aspx'':6C ''/a'':11C ''/blockquote'':101C ''/p'':57C,100C ''among'':53C ''analyt'':10C,20C,69C,88C,93C ''associ'':14C ''blockquot'':58C ''busi'':87C,92C ''confer'':22C,27C,94C ''continu'':43C ''data'':35C,68C,73C ''expand'':80C ''field'':70C ''focus'':21C ''hadoop'':46C ''high'':33C ''href'':3C ''integr'':48C ''mani'':37C,54C ''may'':29C ''microsoft'':42C ''nofollow'':8C ''p'':1C,59C ''pass'':9C,91C ''passbaconference.com'':5C ''passbaconference.com/2016/home.aspx'':4C ''place'':97C ''pro'':74C,89C ''profession'':13C ''qualifi'':34C ''r'':49C ''re'':62C ''rel'':7C ''relev'':40C ''scientist'':36C ''season'':86C ''see'':76C ''server'':17C,52C ''skillset'':82C ''specif'':39C ''sql'':16C,51C ''start'':64C ''support'':45C ''thing'':56C ''track'':28C ''valu'':78C ''whether'':60C ''youâ'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8997, 2, '2015-11-24 00:56:24.3', 0, NULL, NULL, '&lt;p&gt;Feature Selection is a pretty good starting point. U can use statistical or information theoretic approaches (e.g. variance, entropy, ...) to choose &lt;em&gt;k&lt;/em&gt; best features which influence the data.&lt;/p&gt;

&lt;p&gt;Clustering seems attractive here as well. Cluster houses based on different properties to disclose hidden pattern e.g. the relation between geographic location and price as a simple one. &lt;/p&gt;

&lt;p&gt;You can also build a model from which one can predict the price of a house given its features.&lt;/p&gt;
', NULL, NULL, '''/em'':25C ''/p'':32C,62C,83C ''also'':66C ''approach'':17C ''attract'':36C ''base'':42C ''best'':26C ''build'':67C ''choos'':22C ''cluster'':34C,40C ''data'':31C ''differ'':44C ''disclos'':47C ''e.g'':18C,50C ''em'':23C ''entropi'':20C ''featur'':2C,27C,82C ''geograph'':54C ''given'':80C ''good'':7C ''hidden'':48C ''hous'':41C,79C ''influenc'':29C ''inform'':15C ''k'':24C ''locat'':55C ''model'':69C ''one'':61C,72C ''p'':1C,33C,63C ''pattern'':49C ''point'':9C ''predict'':74C ''pretti'':6C ''price'':57C,76C ''properti'':45C ''relat'':52C ''seem'':35C ''select'':3C ''simpl'':60C ''start'':8C ''statist'':13C ''theoret'':16C ''u'':10C ''use'':12C ''varianc'':19C ''well'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8999, 1, '2015-11-24 09:13:14.75', 2, 156, 'Deconvolutional Network in Semantic Segmentation', '&lt;p&gt;I recently came across a paper about doing semantic segmentation using deconvolutional network: &lt;strong&gt;Learning Deconvolution Network for Semantic Segmentation&lt;/strong&gt; &lt;a href="http://arxiv.org/abs/1505.04366" rel="nofollow"&gt;http://arxiv.org/abs/1505.04366&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The basic structure of the network is like this:
&lt;a href="http://i.stack.imgur.com/lm5wv.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/lm5wv.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The goal is to generate a probability map in the end. I''m having trouble figuring out how to realize the deconvolution layer. In the paper, it says:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The output of an unpooling layer is an enlarged, yet sparse activation map. The deconvolution layers densify the sparse activations obtained by unpooling through convolution-like operations with multiple learned filters. However, contrary to convolutional layers, which connect multiple input activations within a filter window to a single activation, deconvolutional layers associate a single input activation with multiple outputs.&lt;/p&gt;
  
  &lt;p&gt;The output of the deconvolutional layer is an enlarged and dense activation map. We crop the boundary of the enlarged activation map to keep the size of the output map identical to the one from the preceding unpooling layer.&lt;/p&gt;
  
  &lt;p&gt;The learned filters in deconvolutional layers correspond to bases to reconstruct shape of an input object. Therefore, similar to the convolution network, a hierarchical structure of deconvolutional layers are used to capture different level of shape details. The filters in lower layers tend to capture overall shape of an object while the class-specific fine details are encoded in the filters in higher layers. In this way, the network directly takes class-specific shape information into account for semantic segmentation.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Can anyone explain how the deconvolution works? I''m guessing it''s not a simple interpolation.&lt;/p&gt;
', 'neuralnetwork  deep-learning  image-classification', NULL, '''/a'':72C ''/abs/1505.04366'':39C ''/abs/1505.04366&lt;/a&gt;.&lt;/p&gt;'':44C ''/blockquote'':292C ''/lm5wv.png'':59C,66C ''/p'':73C,103C,166C,207C,291C,310C ''/strong'':34C ''account'':287C ''across'':17C ''activ'':117C,125C,147C,155C,162C,179C,188C ''alt'':67C ''anyon'':295C ''arxiv.org'':38C,43C ''arxiv.org/abs/1505.04366'':37C ''arxiv.org/abs/1505.04366&lt;/a&gt;.&lt;/p&gt;'':42C ''associ'':158C ''base'':217C ''basic'':47C ''blockquot'':104C ''boundari'':184C ''came'':16C ''captur'':240C,253C ''class'':262C,282C ''class-specif'':261C,281C ''classif'':12B ''connect'':144C ''contrari'':139C ''convolut'':131C,141C,229C ''convolution-lik'':130C ''correspond'':215C ''crop'':182C ''deconvolut'':1A,25C,29C,96C,120C,156C,172C,213C,235C,299C ''deep'':8B ''deep-learn'':7B ''dens'':178C ''densifi'':122C ''descript'':70C ''detail'':245C,265C ''differ'':241C ''direct'':279C ''encod'':267C ''end'':85C ''enlarg'':114C,176C,187C ''enter'':68C ''explain'':296C ''figur'':90C ''filter'':137C,150C,211C,247C,270C ''fine'':264C ''generat'':79C ''goal'':76C ''guess'':303C ''hierarch'':232C ''higher'':272C ''howev'':138C ''href'':36C,56C ''i.stack.imgur.com'':58C,65C ''i.stack.imgur.com/lm5wv.png'':57C,64C ''ident'':198C ''imag'':11B,69C ''image-classif'':10B ''img'':62C ''inform'':285C ''input'':146C,161C,223C ''interpol'':309C ''keep'':191C ''layer'':97C,111C,121C,142C,157C,173C,206C,214C,236C,250C,273C ''learn'':9B,28C,136C,210C ''level'':242C ''like'':53C,132C ''lower'':249C ''m'':87C,302C ''map'':82C,118C,180C,189C,197C ''multipl'':135C,145C,164C ''network'':2A,26C,30C,51C,230C,278C ''neuralnetwork'':6B ''nofollow'':41C,61C ''object'':224C,258C ''obtain'':126C ''one'':201C ''oper'':133C ''output'':107C,165C,169C,196C ''overal'':254C ''p'':13C,45C,74C,105C,167C,208C,293C ''paper'':19C,100C ''preced'':204C ''probabl'':81C ''realiz'':94C ''recent'':15C ''reconstruct'':219C ''rel'':40C,60C ''say'':102C ''segment'':5A,23C,33C,290C ''semant'':4A,22C,32C,289C ''shape'':220C,244C,255C,284C ''similar'':226C ''simpl'':308C ''singl'':154C,160C ''size'':193C ''spars'':116C,124C ''specif'':263C,283C ''src'':63C ''strong'':27C ''structur'':48C,233C ''take'':280C ''tend'':251C ''therefor'':225C ''troubl'':89C ''unpool'':110C,128C,205C ''use'':24C,238C ''way'':276C ''window'':151C ''within'':148C ''work'':300C ''yet'':115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9000, 1, '2015-11-24 10:36:18.83', 12, 2426, 'Data Science Podcasts?', '&lt;p&gt;What are some podcasts which are related to data science?&lt;/p&gt;

&lt;p&gt;This is a similar question to the &lt;a href="http://stats.stackexchange.com/questions/927/statistical-podcasts"&gt;reference request question on CrossValidated&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Details/rules:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;The podcasts (the theme and the episodes) should be related to data science. (For example: A podcast which is about some other domain, with an episode which speaks about data science in that domain, is not a good reference/answer.)&lt;/li&gt;
&lt;li&gt;Personal opinions/reviews (if any) would be very helpful too.&lt;/li&gt;
&lt;/ul&gt;
', 'reference-request', NULL, '''/a'':37C ''/li'':83C,94C ''/p'':18C,38C,41C ''/questions/927/statistical-podcasts'':31C ''/ul'':95C ''crossvalid'':36C ''data'':1A,16C,55C,73C ''details/rules'':40C ''domain'':66C,77C ''episod'':50C,69C ''exampl'':58C ''good'':81C ''help'':92C ''href'':28C ''li'':43C,84C ''opinions/reviews'':86C ''p'':7C,19C,39C ''person'':85C ''podcast'':3A,11C,45C,60C ''question'':24C,34C ''refer'':5B,32C ''reference-request'':4B ''reference/answer'':82C ''relat'':14C,53C ''request'':6B,33C ''scienc'':2A,17C,56C,74C ''similar'':23C ''speak'':71C ''stats.stackexchange.com'':30C ''stats.stackexchange.com/questions/927/statistical-podcasts'':29C ''theme'':47C ''ul'':42C ''would'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9011, 2, '2015-11-24 23:47:14.847', 2, NULL, NULL, '&lt;p&gt;&lt;a href="https://soundcloud.com/nssd-podcast" rel="nofollow"&gt;Not So Standard Deviations&lt;/a&gt; by Hilary Parker and Roger Peng! Fun little podcast where they talk about a variety of things. They only have 4 episodes out currently, but they seem to be releasing one every two weeks, so there''s that.&lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/nssd-podcast'':6C ''/p'':52C ''4'':34C ''current'':37C ''deviat'':12C ''episod'':35C ''everi'':45C ''fun'':20C ''hilari'':15C ''href'':3C ''littl'':21C ''nofollow'':8C ''one'':44C ''p'':1C ''parker'':16C ''peng'':19C ''podcast'':22C ''rel'':7C ''releas'':43C ''roger'':18C ''seem'':40C ''soundcloud.com'':5C ''soundcloud.com/nssd-podcast'':4C ''standard'':11C ''talk'':25C ''thing'':30C ''two'':46C ''varieti'':28C ''week'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9110, 2, '2015-11-28 19:43:43.387', 0, NULL, NULL, '&lt;p&gt;I think , if you are  not   in a particular  interest  to use  Java(which is not  a deditcated  language for machine learning) you could  find a lot  of  references in R and Python:
&lt;a href="https://pymc-devs.github.io/pymc/" rel="nofollow"&gt;https://pymc-devs.github.io/pymc/&lt;/a&gt;
&lt;a href="http://www.bayespy.org/index.html" rel="nofollow"&gt;http://www.bayespy.org/index.html&lt;/a&gt;
&lt;a href="http://www.bnlearn.com/examples/" rel="nofollow"&gt;http://www.bnlearn.com/examples/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/examples/'':59C ''/examples/&lt;/a&gt;&lt;/p&gt;'':64C ''/index.html'':49C ''/index.html&lt;/a&gt;'':54C ''/pymc/'':39C ''/pymc/&lt;/a&gt;'':44C ''could'':25C ''deditc'':19C ''find'':26C ''href'':36C,46C,56C ''interest'':11C ''java'':14C ''languag'':20C ''learn'':23C ''lot'':28C ''machin'':22C ''nofollow'':41C,51C,61C ''p'':1C ''particular'':10C ''pymc-devs.github.io'':38C,43C ''pymc-devs.github.io/pymc/'':37C ''pymc-devs.github.io/pymc/&lt;/a&gt;'':42C ''python'':34C ''r'':32C ''refer'':30C ''rel'':40C,50C,60C ''think'':3C ''use'':13C ''www.bayespy.org'':48C,53C ''www.bayespy.org/index.html'':47C ''www.bayespy.org/index.html&lt;/a&gt;'':52C ''www.bnlearn.com'':58C,63C ''www.bnlearn.com/examples/'':57C ''www.bnlearn.com/examples/&lt;/a&gt;&lt;/p&gt;'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9032, 2, '2015-11-25 21:16:44.9', 0, NULL, NULL, '&lt;p&gt;In a neural network model, you can use &lt;a href="https://en.wikipedia.org/wiki/Autoencoder" rel="nofollow"&gt;autoencoders&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The basic idea of an autoencoder is to learn a hidden layer of features by creating a network that simply copies the input vector at the output. So the training features and training "labels" are initially identical, no supervised labels are required. This can work using a classic &lt;em&gt;triangular&lt;/em&gt; network architecture with progressively smaller layers that capture a compressed and hopefully useful set of derived features. The network''s hidden layers learn representations based on the larger unsupervised data set. These layers can then be used to initialise a regular supervised learning network to be trained using the actual labels. &lt;/p&gt;

&lt;p&gt;A similar idea is pre-training layers using a &lt;a href="https://en.wikipedia.org/wiki/Restricted_Boltzmann_machine" rel="nofollow"&gt;Restricted Boltzmann Machine&lt;/a&gt;, which can be used in a very similar way, although based on different principles. &lt;/p&gt;
', NULL, NULL, '''/a'':18C,145C ''/em'':71C ''/p'':19C,123C,160C ''/wiki/autoencoder'':14C ''/wiki/restricted_boltzmann_machine'':139C ''actual'':121C ''although'':155C ''architectur'':73C ''autoencod'':17C,26C ''base'':96C,156C ''basic'':22C ''boltzmann'':143C ''captur'':79C ''classic'':68C ''compress'':81C ''copi'':41C ''creat'':36C ''data'':101C ''deriv'':87C ''differ'':158C ''em'':69C ''en.wikipedia.org'':13C,138C ''en.wikipedia.org/wiki/autoencoder'':12C ''en.wikipedia.org/wiki/restricted_boltzmann_machine'':137C ''featur'':34C,51C,88C ''hidden'':31C,92C ''hope'':83C ''href'':11C,136C ''idea'':23C,127C ''ident'':57C ''initi'':56C ''initialis'':110C ''input'':43C ''label'':54C,60C,122C ''larger'':99C ''layer'':32C,77C,93C,104C,132C ''learn'':29C,94C,114C ''machin'':144C ''model'':6C ''network'':5C,38C,72C,90C,115C ''neural'':4C ''nofollow'':16C,141C ''output'':47C ''p'':1C,20C,124C ''pre'':130C ''pre-train'':129C ''principl'':159C ''progress'':75C ''regular'':112C ''rel'':15C,140C ''represent'':95C ''requir'':62C ''restrict'':142C ''set'':85C,102C ''similar'':126C,153C ''simpli'':40C ''smaller'':76C ''supervis'':59C,113C ''train'':50C,53C,118C,131C ''triangular'':70C ''unsupervis'':100C ''use'':9C,66C,84C,108C,119C,133C,149C ''vector'':44C ''way'':154C ''work'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9198, 2, '2015-12-04 07:59:25.257', 3, NULL, NULL, '&lt;p&gt;You should distinct between a &lt;em&gt;time series&lt;/em&gt; prediction, where from a known history of some attribute the future is predicted and &lt;em&gt;model prediction&lt;/em&gt; where based on the predictor variables the target variable is calculated.&lt;/p&gt;

&lt;p&gt;In your case you could combine both approaches, i.e. use time series prediction on the customer balances and apply the regression model to calculate the profit on the result.&lt;/p&gt;
', NULL, NULL, '''/em'':10C,28C ''/p'':40C,71C ''appli'':60C ''approach'':49C ''attribut'':19C ''balanc'':58C ''base'':30C ''calcul'':39C,65C ''case'':44C ''combin'':47C ''could'':46C ''custom'':57C ''distinct'':4C ''em'':7C,25C ''futur'':21C ''histori'':16C ''i.e'':50C ''known'':15C ''model'':26C,63C ''p'':1C,41C ''predict'':11C,23C,27C,54C ''predictor'':33C ''profit'':67C ''regress'':62C ''result'':70C ''seri'':9C,53C ''target'':36C ''time'':8C,52C ''use'':51C ''variabl'':34C,37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9002, 1, '2015-11-24 11:01:43.763', 1, 45, 'Consolidating values for predictive modelling through Random Forest', '&lt;p&gt;I am working on developing a predictive model using Random Forest. There are a lot of users that log in to the site but only a fraction of them actually monetize on that day. I am trying to predict the probability of monetization of a user during a login. The predictor variables have been selected. Now the issue is that one user can log in on multiple days and have different values of predictor variables and the label (Monetized/ Not Monetized) for each log in.&lt;/p&gt;

&lt;p&gt;If I try to train the data on the last 30 days'' data, some users would occur multiple times and others would only occur one time. This may lead to a higher weight-age being given to the users who logged in on more days.&lt;/p&gt;

&lt;p&gt;How can I ensure that equal weightage is assigned to all the users even though the users might have different numbers of logins and hence different counts of data points.&lt;/p&gt;
', 'machine-learning  r  predictive-modeling  random-forest', NULL, '''/p'':105C,153C,185C ''30'':117C ''actual'':49C ''age'':141C ''assign'':163C ''consolid'':1A ''count'':181C ''data'':113C,119C,183C ''day'':53C,87C,118C,152C ''develop'':24C ''differ'':90C,174C,180C ''ensur'':158C ''equal'':160C ''even'':168C ''forest'':8A,18B,30C ''fraction'':46C ''given'':143C ''henc'':179C ''higher'':138C ''issu'':77C ''label'':97C ''last'':116C ''lead'':135C ''learn'':11B ''log'':38C,83C,103C,148C ''login'':68C,177C ''lot'':34C ''machin'':10B ''machine-learn'':9B ''may'':134C ''might'':172C ''model'':5A,15B,27C ''monet'':50C,62C,98C,100C ''multipl'':86C,124C ''number'':175C ''occur'':123C,130C ''one'':80C,131C ''other'':127C ''p'':19C,106C,154C ''point'':184C ''predict'':4A,14B,26C,58C ''predictive-model'':13B ''predictor'':70C,93C ''probabl'':60C ''r'':12B ''random'':7A,17B,29C ''random-forest'':16B ''select'':74C ''site'':42C ''though'':169C ''time'':125C,132C ''train'':111C ''tri'':56C,109C ''use'':28C ''user'':36C,65C,81C,121C,146C,167C,171C ''valu'':2A,91C ''variabl'':71C,94C ''weight'':140C ''weight-ag'':139C ''weightag'':161C ''work'':22C ''would'':122C,128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9003, 1, '2015-11-24 11:50:46.29', 3, 74, 'When do I have to use aucPR instead of auROC? (and vice versa)', '&lt;p&gt;I''m wondering if sometimes, to validate a model, it''s not better to use aucPR instead of aucROC? Do these cases only depend on the "domain  &amp;amp; business understanding" ? &lt;/p&gt;

&lt;p&gt;Especially, I''m thinking about the "unbalanced class problem" where, it seems more logical to use the aucPR because recall and precision are well-used metrics for this problem.&lt;/p&gt;
', 'machine-learning  data-mining  cross-validation  evaluation', NULL, '''/p'':55C,87C ''amp'':52C ''aucpr'':7A,40C,74C ''aucroc'':43C ''auroc'':10A ''better'':37C ''busi'':53C ''case'':46C ''class'':64C ''cross'':21B ''cross-valid'':20B ''data'':18B ''data-min'':17B ''depend'':48C ''domain'':51C ''especi'':57C ''evalu'':23B ''instead'':8A,41C ''learn'':16B ''logic'':70C ''m'':26C,59C ''machin'':15B ''machine-learn'':14B ''metric'':83C ''mine'':19B ''model'':33C ''p'':24C,56C ''precis'':78C ''problem'':65C,86C ''recal'':76C ''seem'':68C ''sometim'':29C ''think'':60C ''unbalanc'':63C ''understand'':54C ''use'':6A,39C,72C,82C ''valid'':22B,31C ''versa'':13A ''vice'':12A ''well'':81C ''well-us'':80C ''wonder'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9004, 2, '2015-11-24 12:27:30.943', 9, NULL, NULL, '&lt;p&gt;I strongly suggest &lt;a href="http://www.thetalkingmachines.com"&gt;Talking Machines&lt;/a&gt;. It''s a very well put together podcast from a professor at Harvard. They cater to both machine learning experts and enthusiasts.&lt;/p&gt;

&lt;p&gt;Their interviews are often done from NIPS, and the guests are usually top tier practitioners.&lt;/p&gt;
', NULL, NULL, '''/a'':10C ''/p'':33C,50C ''cater'':25C ''done'':39C ''enthusiast'':32C ''expert'':30C ''guest'':44C ''harvard'':23C ''href'':6C ''interview'':36C ''learn'':29C ''machin'':9C,28C ''nip'':41C ''often'':38C ''p'':1C,34C ''podcast'':18C ''practition'':49C ''professor'':21C ''put'':16C ''strong'':3C ''suggest'':4C ''talk'':8C ''tier'':48C ''togeth'':17C ''top'':47C ''usual'':46C ''well'':15C ''www.thetalkingmachines.com'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9059, 4, '2015-11-26 10:01:14.917', 0, NULL, NULL, 'Indicates questions asking about the use and meaning of specific technical words/concepts in statistics.', NULL, NULL, '''ask'':3C ''indic'':1C ''mean'':8C ''question'':2C ''specif'':10C ''statist'':14C ''technic'':11C ''use'':6C ''words/concepts'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9258, 2, '2015-12-08 03:16:43.023', 1, NULL, NULL, '&lt;p&gt;It looks like you want to create dummy variable from a pandas dataframe column. Fortunately, pandas has a special method for it: &lt;code&gt;get_dummies()&lt;/code&gt;. 
Here is a code snippet that you can adapt for your need:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas as pd
data = pd.read_clipboard(sep='','')

#get the names of the first 3 columns
colN = data.columns.values[:3]

#make a copy of the dataframe
data_transformed = data

#the get_dummies method is doing the job for you
for column_name in colN:
    dummies = pd.get_dummies(data_transformed[column_name], prefix=''value'', prefix_sep=''_'')
    col_names_dummies = dummies.columns.values

    #then you can append new columns to the dataframe
    for i,value in enumerate(col_names_dummies):
        data_transformed[value] = dummies.iloc[:,i]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here is the output of &lt;code&gt;data_transformed&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;         dx1    dx2    dx3   dx4    dx5    dx6    dx7  value_25041  value_25061  0  25041  40391   5856     0  V4511  V5867  30000            1            0   
    1  25041  40391  25081  5856   5363   3572      0            1            0   
    2  25041  40391  42822     0   5856      0      0            1            0   
    3  25061  40391      0     0      0      0      0            0            1   
    4  25041  40391      0  5856  25081  V4511  25051            1            0   

      value_40391  value_0  value_5856  value_25081  value_42822  
    0            1        0           1            0            0  
    1            1        0           0            1            0  
    2            1        0           0            0            1  
    3            1        1           0            0            0  
    4            1        1           0            0            0  
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':27C,123C,134C,239C ''/p'':40C,135C ''/pre'':124C,240C ''0'':149C,153C,158C,166C,168C,173C,175C,176C,178C,182C,183C,184C,185C,186C,187C,192C,198C,202C,209C,211C,213C,214C,217C,218C,220C,223C,224C,225C,230C,231C,232C,236C,237C,238C ''1'':157C,159C,167C,177C,188C,197C,210C,212C,215C,216C,219C,222C,226C,228C,229C,234C,235C ''2'':169C,221C ''25041'':146C,150C,160C,170C,190C ''25051'':196C ''25061'':148C,180C ''25081'':162C,194C,206C ''3'':57C,61C,179C,227C ''30000'':156C ''3572'':165C ''4'':189C,233C ''40391'':151C,161C,171C,181C,191C,200C ''42822'':172C,208C ''5363'':164C ''5856'':152C,163C,174C,193C,204C ''adapt'':36C ''append'':104C ''clipboard'':49C ''code'':24C,31C,42C,131C,137C ''col'':97C,115C ''coln'':59C,85C ''column'':15C,58C,82C,91C,106C ''copi'':64C ''creat'':8C ''data'':47C,68C,70C,89C,118C,132C ''data.columns.values'':60C ''datafram'':14C,67C,109C ''dummi'':9C,26C,73C,86C,88C,99C,117C ''dummies.columns.values'':100C ''dummies.iloc'':121C ''dx1'':138C ''dx2'':139C ''dx3'':140C ''dx4'':141C ''dx5'':142C ''dx6'':143C ''dx7'':144C ''enumer'':114C ''first'':56C ''fortun'':16C ''get'':25C,51C,72C ''import'':43C ''job'':78C ''like'':4C ''look'':3C ''make'':62C ''method'':21C,74C ''name'':53C,83C,92C,98C,116C ''need'':39C ''new'':105C ''output'':129C ''p'':1C,125C ''panda'':13C,17C,44C ''pd'':46C ''pd.get'':87C ''pd.read'':48C ''pre'':41C,136C ''prefix'':93C,95C ''sep'':50C,96C ''snippet'':32C ''special'':20C ''transform'':69C,90C,119C,133C ''v4511'':154C,195C ''v5867'':155C ''valu'':94C,112C,120C,145C,147C,199C,201C,203C,205C,207C ''variabl'':10C ''want'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9005, 2, '2015-11-24 13:50:29.083', 5, NULL, NULL, '&lt;p&gt;Yes, you are correct that the dominant difference between the area under the curve of a receiver operator characteristic curve (&lt;a href="https://en.wikipedia.org/wiki/Receiver_operating_characteristic"&gt;ROC-AUC&lt;/a&gt;) and the area under the curve of a Precision-Recall curve (&lt;a href="http://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html"&gt;PR-AUC&lt;/a&gt;) lies in its tractability for unbalanced classes.  They are very similar and have been shown to contain essentially the same information, however PR curves are slightly more finicky, but a well drawn curve gives a more complete picture.  The issue with PR-AUC is that its difficult to interpolate between points in the PR curve and thus numerical integration to achieve an area under the curve becomes more difficult.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://pages.cs.wisc.edu/~jdavis/davisgoadrichcamera2.pdf"&gt;Check out this discussion of the differences and similarities.&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Quoting Davis'' 2006 abstract:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Receiver Operator Characteristic (ROC)
  curves are commonly used to present results
  for binary decision problems in machine
  learning. However, when dealing
  with highly skewed datasets, Precision-Recall
  (PR) curves give a more informative picture
  of an algorithmâs performance. We show that
  a deep connection exists between ROC space
  and PR space, such that a curve dominates
  in ROC space if and only if it dominates
  in PR space. A corollary is the notion of
  an achievable PR curve, which has properties
  much like the convex hull in ROC space;
  we show an efficient algorithm for computing
  this curve. Finally, we also note differences
  in the two types of curves are significant for
  algorithm design. For example, in PR space
  it is incorrect to linearly interpolate between
  points. Furthermore, algorithms that optimize
  the area under the ROC curve are not
  guaranteed to optimize the area under the
  PR curve.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="https://www.kaggle.com/forums/f/15/kaggle-forum/t/7517/precision-recall-auc-vs-roc-auc-for-class-imbalance-problems/41179"&gt;This was also discussed on Kaggle recently.&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stats.stackexchange.com/questions/7207/roc-vs-precision-and-recall-curves"&gt;There is also some useful discussion on Cross Validated.&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':30C,51C,138C,313C,330C ''/blockquote'':299C ''/forums/f/15/kaggle-forum/t/7517/precision-recall-auc-vs-roc-auc-for-class-imbalance-problems/41179'':305C ''/p'':122C,139C,145C,298C,314C,331C ''/questions/7207/roc-vs-precision-and-recall-curves'':320C ''/stable/auto_examples/model_selection/plot_precision_recall.html'':47C ''/wiki/receiver_operating_characteristic'':26C ''/~jdavis/davisgoadrichcamera2.pdf'':128C ''2006'':143C ''abstract'':144C ''achiev'':113C,225C ''algorithm'':243C,262C,278C ''algorithmâ'':185C ''also'':250C,308C,323C ''area'':12C,33C,115C,282C,293C ''auc'':29C,50C,95C ''becom'':119C ''binari'':160C ''blockquot'':146C ''characterist'':20C,150C ''check'':129C ''class'':58C ''common'':154C ''complet'':88C ''comput'':245C ''connect'':193C ''contain'':68C ''convex'':234C ''corollari'':219C ''correct'':5C ''cross'':328C ''curv'':15C,21C,36C,42C,75C,84C,107C,118C,152C,177C,204C,227C,247C,258C,286C,297C ''dataset'':172C ''davi'':142C ''deal'':168C ''decis'':161C ''deep'':192C ''design'':263C ''differ'':9C,135C,252C ''difficult'':99C,121C ''discuss'':132C,309C,326C ''domin'':8C,205C,214C ''drawn'':83C ''effici'':242C ''en.wikipedia.org'':25C ''en.wikipedia.org/wiki/receiver_operating_characteristic'':24C ''essenti'':69C ''exampl'':265C ''exist'':194C ''final'':248C ''finicki'':79C ''furthermor'':277C ''give'':85C,178C ''guarante'':289C ''high'':170C ''howev'':73C,166C ''href'':23C,44C,125C,302C,317C ''hull'':235C ''incorrect'':271C ''inform'':72C,181C ''integr'':111C ''interpol'':101C,274C ''issu'':91C ''kaggl'':311C ''learn'':165C ''lie'':52C ''like'':232C ''linear'':273C ''machin'':164C ''much'':231C ''note'':251C ''notion'':222C ''numer'':110C ''oper'':19C,149C ''optim'':280C,291C ''p'':1C,123C,140C,147C,300C,315C ''pages.cs.wisc.edu'':127C ''pages.cs.wisc.edu/~jdavis/davisgoadrichcamera2.pdf'':126C ''perform'':187C ''pictur'':89C,182C ''point'':103C,276C ''pr'':49C,74C,94C,106C,176C,199C,216C,226C,267C,296C ''pr-auc'':48C,93C ''precis'':40C,174C ''precision-recal'':39C,173C ''present'':157C ''problem'':162C ''properti'':230C ''quot'':141C ''recal'':41C,175C ''receiv'':18C,148C ''recent'':312C ''result'':158C ''roc'':28C,151C,196C,207C,237C,285C ''roc-auc'':27C ''scikit-learn.org'':46C ''scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html'':45C ''show'':189C,240C ''shown'':66C ''signific'':260C ''similar'':62C,137C ''skew'':171C ''slight'':77C ''space'':197C,200C,208C,217C,238C,268C ''stats.stackexchange.com'':319C ''stats.stackexchange.com/questions/7207/roc-vs-precision-and-recall-curves'':318C ''thus'':109C ''tractabl'':55C ''two'':255C ''type'':256C ''unbalanc'':57C ''use'':155C,325C ''valid'':329C ''well'':82C ''www.kaggle.com'':304C ''www.kaggle.com/forums/f/15/kaggle-forum/t/7517/precision-recall-auc-vs-roc-auc-for-class-imbalance-problems/41179'':303C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9006, 2, '2015-11-24 14:00:52.92', 4, NULL, NULL, '&lt;p&gt;Some which I regularly hear to, are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://fivethirtyeight.com/tag/whats-the-point/" rel="nofollow"&gt;What''s the point&lt;/a&gt; by FiveThirtyEight&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It is a very nice podcast, where not only the concepts, but also the applications of data science to a wide range of domains, are discussed.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://www.udacity.com/podcasts/linear-digressions" rel="nofollow"&gt;Linear digressions&lt;/a&gt; by Udacity&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It focuses more on ML and concepts of data science. Hosted by two really nice data scientists Katie and Ben.  All their episodes are fun to watch.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Partially Derivative  (Already mentioned by agold)&lt;/li&gt;
&lt;li&gt;Data Skeptic  (Already mentioned by agold)&lt;/li&gt;
&lt;li&gt;Talking Machine  (Already mentioned by jamesmf)&lt;/li&gt;
&lt;li&gt;&lt;a href="http://freakonomics.com/radio/freakonomics-radio-podcast-archive/" rel="nofollow"&gt;Freakonomics&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It is a fun podcast which focuses on data and it''s importance in various domains and how to make sense of it.&lt;/p&gt;
', NULL, NULL, '''/a'':23C,67C,135C ''/li'':26C,70C,109C,117C,125C,136C ''/p'':9C,55C,100C,162C ''/podcasts/linear-digressions'':62C ''/radio/freakonomics-radio-podcast-archive/'':131C ''/tag/whats-the-point/'':16C ''/ul'':27C,71C,137C ''agold'':108C,116C ''alreadi'':105C,113C,121C ''also'':41C ''applic'':43C ''ben'':92C ''concept'':39C,79C ''data'':45C,81C,88C,111C,147C ''deriv'':104C ''digress'':66C ''discuss'':54C ''domain'':52C,154C ''episod'':95C ''fivethirtyeight'':25C ''fivethirtyeight.com'':15C ''fivethirtyeight.com/tag/whats-the-point/'':14C ''focus'':74C,145C ''freakonom'':134C ''freakonomics.com'':130C ''freakonomics.com/radio/freakonomics-radio-podcast-archive/'':129C ''fun'':97C,142C ''hear'':6C ''host'':83C ''href'':13C,59C,128C ''import'':151C ''jamesmf'':124C ''kati'':90C ''li'':11C,57C,102C,110C,118C,126C ''linear'':65C ''machin'':120C ''make'':158C ''mention'':106C,114C,122C ''ml'':77C ''nice'':33C,87C ''nofollow'':18C,64C,133C ''p'':1C,28C,72C,138C ''partial'':103C ''podcast'':34C,143C ''point'':22C ''rang'':50C ''realli'':86C ''regular'':5C ''rel'':17C,63C,132C ''scienc'':46C,82C ''scientist'':89C ''sens'':159C ''skeptic'':112C ''talk'':119C ''two'':85C ''udac'':69C ''ul'':10C,56C,101C ''various'':153C ''watch'':99C ''wide'':49C ''www.udacity.com'':61C ''www.udacity.com/podcasts/linear-digressions'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9007, 2, '2015-11-24 16:33:41.683', 1, NULL, NULL, '&lt;p&gt;It''s a slight variation on your problem as described, but if your goal is to build a good model for predicting documents labeled $L$, I would initially formulate this as a recommender system problem, until you''ve reached a desirable point in the learning curve of your system. I did exactly this in &lt;a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3872296/" rel="nofollow"&gt;a publication a few years back&lt;/a&gt;. In my approach, all labeled documents are used to train the SVM, while all those unlabeled are used for classifying. I use the signed margin distance of each classified document for ranking, and take the top-$n$ as the next $n$ documents the reviewers should assign a label $\in(L, L_{not})$. A side effect of this process is that you get to iteratively evaluate the performance of your classifier as you add new labeled documents to the model, which should get you what you need. If this approach sounds useful to you, I recommend looking over my paper, as I describe some performance metrics useful for this, in addition to outlining the specific procedure to use that will ensure you''re not biasing your model. &lt;/p&gt;
', NULL, NULL, '''/a'':69C ''/p'':196C ''/pmc/articles/pmc3872296/'':60C ''add'':142C ''addit'':179C ''approach'':72C,158C ''assign'':115C ''back'':68C ''bias'':193C ''build'':18C ''classifi'':89C,98C,139C ''curv'':47C ''describ'':11C,171C ''desir'':42C ''distanc'':95C ''document'':24C,75C,99C,111C,145C ''effect'':124C ''ensur'':189C ''evalu'':134C ''exact'':53C ''formul'':30C ''get'':131C,151C ''goal'':15C ''good'':20C ''href'':57C ''initi'':29C ''iter'':133C ''l'':26C,119C,120C ''label'':25C,74C,117C,144C ''learn'':46C ''look'':165C ''margin'':94C ''metric'':174C ''model'':21C,148C,195C ''n'':106C,110C ''need'':155C ''new'':143C ''next'':109C ''nofollow'':62C ''outlin'':181C ''p'':1C ''paper'':168C ''perform'':136C,173C ''point'':43C ''predict'':23C ''problem'':9C,36C ''procedur'':184C ''process'':127C ''public'':64C ''rank'':101C ''re'':191C ''reach'':40C ''recommend'':34C,164C ''rel'':61C ''review'':113C ''side'':123C ''sign'':93C ''slight'':5C ''sound'':159C ''specif'':183C ''svm'':81C ''system'':35C,50C ''take'':103C ''top'':105C ''train'':79C ''unlabel'':85C ''use'':77C,87C,91C,160C,175C,186C ''variat'':6C ''ve'':39C ''would'':28C ''www.ncbi.nlm.nih.gov'':59C ''www.ncbi.nlm.nih.gov/pmc/articles/pmc3872296/'':58C ''year'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9008, 1, '2015-11-24 19:09:10.28', 0, 33, 'term for data that compares a day to same day of a week/month/year ago', '&lt;p&gt;What is the technical term for a report or dataset that compares data of an interval of time with that of the same interval in a previous week, month, or year?  &lt;/p&gt;
', 'terminology', NULL, '''/p'':48C ''ago'':14A ''compar'':5A,28C ''data'':3A,29C ''dataset'':26C ''day'':7A,10A ''interv'':32C,40C ''month'':45C ''p'':16C ''previous'':43C ''report'':24C ''technic'':20C ''term'':1A,21C ''terminolog'':15B ''time'':34C ''week'':44C ''week/month/year'':13A ''year'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9050, 5, '2015-11-26 09:58:43.803', 0, NULL, NULL, '&lt;p&gt;SAS is a proprietary cross-platform general-purpose statistical software package (&lt;a href="http://www.sas.com/index.html" rel="nofollow"&gt;Official Website&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;Various Resources for SAS include&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;The &lt;a href="http://support.sas.com/documentation/" rel="nofollow"&gt;online documentation&lt;/a&gt; includes the SAS User''s Guide which contains quite detailed information on SAS procedures, including syntax, theoretical details, and examples.&lt;/li&gt;
&lt;li&gt;The &lt;a href="http://support.sas.com/events/sasglobalforum/previous/online.html" rel="nofollow"&gt;proceedings of SAS Global Forum&lt;/a&gt;. The proceedings were formerly referred to by the acronym of SUGI, which stood for SAS User Group International, but in 2007 the group was renamed to SAS Global Forum. The proceedings include short papers related to programming aspects of SAS as well as statistical analysis.&lt;/li&gt;
&lt;li&gt;The UCLA Academic Technology Services provides &lt;a href="http://www.ats.ucla.edu/stat/sas/default.htm" rel="nofollow"&gt;various tutorials&lt;/a&gt; on conducting statistical analysis and programming in SAS&lt;/li&gt;
&lt;li&gt;Chris Hemedinger on his blog, &lt;a href="http://blogs.sas.com/content/sasdummy/" rel="nofollow"&gt;The SAS Dummy&lt;/a&gt;, has &lt;a href="http://blogs.sas.com/content/sasdummy/2011/09/07/google-reader-bundles-for-sas-related-blogs/" rel="nofollow"&gt;posted a listing of various SAS blogs&lt;/a&gt; he follows (including his own) in two Google reader bundles. Even if you don''t use Google reader it is possible to follow the trail to the various webpages.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.lexjansen.com/" rel="nofollow"&gt;Lex Jansen&lt;/a&gt; has indexed all of SUGI/SGF as well as many SAS regional.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Other Forums in which you can ask SAS specific programming questions (in descending order of activity)&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;The &lt;a href="http://listserv.uga.edu/archives/sas-l.html" rel="nofollow"&gt;SAS-L&lt;/a&gt; listserve&lt;/li&gt;
&lt;li&gt;Stackoverflow has (as of 12/14/2011) over 400 &lt;a href="http://stackoverflow.com/questions/tagged/sas"&gt;questions tagged with sas&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.runsubmit.com/" rel="nofollow"&gt;RunSubmit&lt;/a&gt; (a Stack-alike site specifically for SAS)&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':24C,45C,81C,144C,170C,186C,225C,270C,290C,299C ''/archives/sas-l.html'':264C ''/content/sasdummy/'':164C ''/content/sasdummy/2011/09/07/google-reader-bundles-for-sas-related-blogs/'':176C ''/documentation/'':40C ''/events/sasglobalforum/previous/online.html'':73C ''/index.html'':19C ''/li'':66C,127C,153C,216C,237C,272C,291C,308C ''/p'':25C,32C,256C ''/questions/tagged/sas'':285C ''/stat/sas/default.htm'':139C ''/ul'':238C,309C ''12/14/2011'':278C ''2007'':102C ''400'':280C ''academ'':131C ''acronym'':90C ''activ'':255C ''alik'':303C ''analysi'':126C,148C ''ask'':246C ''aspect'':119C ''blog'':159C,185C ''blogs.sas.com'':163C,175C ''blogs.sas.com/content/sasdummy/'':162C ''blogs.sas.com/content/sasdummy/2011/09/07/google-reader-bundles-for-sas-related-blogs/'':174C ''bundl'':196C ''chris'':155C ''conduct'':146C ''contain'':53C ''cross'':7C ''cross-platform'':6C ''descend'':252C ''detail'':55C,63C ''document'':44C ''dummi'':169C ''even'':197C ''exampl'':65C ''follow'':188C,209C ''former'':85C ''forum'':80C,110C,241C ''general'':10C ''general-purpos'':9C ''global'':79C,109C ''googl'':194C,203C ''group'':98C,104C ''guid'':51C ''hemeding'':156C ''href'':16C,37C,70C,136C,161C,173C,219C,261C,282C,294C ''includ'':31C,46C,60C,113C,189C ''index'':227C ''inform'':56C ''intern'':99C ''jansen'':224C ''l'':269C ''lex'':223C ''li'':34C,67C,128C,154C,217C,258C,273C,292C ''list'':181C ''listserv'':271C ''listserv.uga.edu'':263C ''listserv.uga.edu/archives/sas-l.html'':262C ''mani'':234C ''nofollow'':21C,42C,75C,141C,166C,178C,222C,266C,297C ''offici'':22C ''onlin'':43C ''order'':253C ''p'':1C,26C,239C ''packag'':14C ''paper'':115C ''platform'':8C ''possibl'':207C ''post'':179C ''procedur'':59C ''proceed'':76C,83C,112C ''program'':118C,150C,249C ''proprietari'':5C ''provid'':134C ''purpos'':11C ''question'':250C,286C ''quit'':54C ''reader'':195C,204C ''refer'':86C ''region'':236C ''rel'':20C,41C,74C,140C,165C,177C,221C,265C,296C ''relat'':116C ''renam'':106C ''resourc'':28C ''runsubmit'':298C ''sas'':2C,30C,48C,58C,78C,96C,108C,121C,152C,168C,184C,235C,247C,268C,289C,307C ''sas-l'':267C ''servic'':133C ''short'':114C ''site'':304C ''softwar'':13C ''specif'':248C,305C ''stack'':302C ''stack-alik'':301C ''stackoverflow'':274C ''stackoverflow.com'':284C ''stackoverflow.com/questions/tagged/sas'':283C ''statist'':12C,125C,147C ''stood'':94C ''sugi'':92C ''sugi/sgf'':230C ''support.sas.com'':39C,72C ''support.sas.com/documentation/'':38C ''support.sas.com/events/sasglobalforum/previous/online.html'':71C ''syntax'':61C ''tag'':287C ''technolog'':132C ''theoret'':62C ''trail'':211C ''tutori'':143C ''two'':193C ''ucla'':130C ''ul'':33C,257C ''use'':202C ''user'':49C,97C ''various'':27C,142C,183C,214C ''webpag'':215C ''websit'':23C ''well'':123C,232C ''www.ats.ucla.edu'':138C ''www.ats.ucla.edu/stat/sas/default.htm'':137C ''www.lexjansen.com'':220C ''www.runsubmit.com'':295C ''www.sas.com'':18C ''www.sas.com/index.html'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9010, 2, '2015-11-24 22:24:44.447', 1, NULL, NULL, '&lt;p&gt;There are two main functions they undo.&lt;/p&gt;

&lt;p&gt;The &lt;a href="http://deeplearning.net/tutorial/lenet.html#maxpooling" rel="nofollow"&gt;pooling layers&lt;/a&gt; in the convolutional neural network downsample the image by (usually) taking the maximum value within the receptive field.  Each &lt;code&gt;rxr&lt;/code&gt; image region is downsampled to a single value.  What this implementation does is store which unit had the maximum activation in each of these pooling steps.  Then, at each "unpooling" layer in the deconvolutional network, they upsample back to a &lt;code&gt;rxr&lt;/code&gt; image region, only propagating the activation to the location that produced the original max-pooled value.&lt;/p&gt;

&lt;p&gt;Thus, "the output of an unpooling layer is an enlarged, yet sparse activation map."&lt;/p&gt;

&lt;p&gt;Convolutional layers learn a filter for each image region that maps from a region of size &lt;code&gt;r x r&lt;/code&gt; to a single value, where &lt;code&gt;r&lt;/code&gt; is the receptive field size. The point of the deconvolutional layer is to learn the opposite filter.  This filter is a set of weights that projects an &lt;code&gt;rxr&lt;/code&gt; input into a space of size &lt;code&gt;sxs&lt;/code&gt;, where s is the size of the next convolutional layer.  These filters are learned in the same way that as regular convolutional layers are.&lt;/p&gt;

&lt;p&gt;As the mirror image of a deep CNN, the low-level features of the network are really high-level, class-specific features.  Each layer in the network then localizes them, enhancing class-specific features while minimizing noise.&lt;/p&gt;
', NULL, NULL, '''/a'':21C ''/code'':43C,86C,142C,150C,180C,189C ''/p'':9C,104C,120C,214C,257C ''/tutorial/lenet.html#maxpooling'':16C ''activ'':63C,92C,118C ''back'':81C ''class'':238C,251C ''class-specif'':237C,250C ''cnn'':223C ''code'':41C,84C,138C,148C,178C,187C ''convolut'':24C,122C,198C,211C ''deconvolut'':77C,160C ''deep'':222C ''deeplearning.net'':15C ''deeplearning.net/tutorial/lenet.html#maxpooling'':14C ''downsampl'':27C,47C ''enhanc'':249C ''enlarg'':115C ''featur'':228C,240C,253C ''field'':39C,154C ''filter'':126C,167C,169C,201C ''function'':6C ''high'':235C ''high-level'':234C ''href'':13C ''imag'':29C,44C,87C,129C,219C ''implement'':54C ''input'':181C ''layer'':20C,74C,112C,123C,161C,199C,212C,242C ''learn'':124C,164C,203C ''level'':227C,236C ''local'':247C ''locat'':95C ''low'':226C ''low-level'':225C ''main'':5C ''map'':119C,132C ''max'':101C ''max-pool'':100C ''maximum'':34C,62C ''minim'':255C ''mirror'':218C ''network'':26C,78C,231C,245C ''neural'':25C ''next'':197C ''nofollow'':18C ''nois'':256C ''opposit'':166C ''origin'':99C ''output'':108C ''p'':1C,10C,105C,121C,215C ''point'':157C ''pool'':19C,68C,102C ''produc'':97C ''project'':176C ''propag'':90C ''r'':139C,141C,149C ''realli'':233C ''recept'':38C,153C ''region'':45C,88C,130C,135C ''regular'':210C ''rel'':17C ''rxr'':42C,85C,179C ''set'':172C ''singl'':50C,145C ''size'':137C,155C,186C,194C ''space'':184C ''spars'':117C ''specif'':239C,252C ''step'':69C ''store'':57C ''sxs'':188C ''take'':32C ''thus'':106C ''two'':4C ''undo'':8C ''unit'':59C ''unpool'':73C,111C ''upsampl'':80C ''usual'':31C ''valu'':35C,51C,103C,146C ''way'':207C ''weight'':174C ''within'':36C ''x'':140C ''yet'':116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9012, 1, '2015-11-25 00:03:55.7', 1, 68, 'Reading a CSV using R', '&lt;p&gt;I have a CSV file that, by all appearances, is totally normal except that each line ends with &lt;code&gt;,^M&lt;/code&gt;.  Not sure if that has anything to do with my issue or not, but I try reading in the file in R with the usual command &lt;/p&gt;

&lt;p&gt;&lt;code&gt;df &amp;lt;- read.csv(''file.csv'')&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;and then when I try to inspect it by typing &lt;code&gt;df&lt;/code&gt; it prints out this result that I don''t know how to interpret:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;function (x, df1, df2, ncp, log = FALSE) 
{
    if (missing(ncp)) 
        .External(C_df, x, df1, df2, log)
    else .External(C_dnf, x, df1, df2, ncp, log)
}
&amp;lt;bytecode: 0x000000000b083720&amp;gt;
&amp;lt;environment: namespace:stats&amp;gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Anybody know what''s going on here?  I''ve read in CSV files before and had no problem.&lt;/p&gt;
', 'r  csv', NULL, '''/code'':29C,63C,78C,130C ''/p'':56C,64C,92C,151C ''/pre'':131C ''0x000000000b083720'':123C ''anybodi'':133C ''anyth'':35C ''appear'':17C ''bytecod'':122C ''c'':106C,114C ''code'':27C,58C,76C,94C ''command'':55C ''csv'':3A,7B,12C,144C ''df'':59C,77C,107C ''df1'':97C,109C,117C ''df2'':98C,110C,118C ''dnf'':115C ''els'':112C ''end'':25C ''environ'':126C ''except'':21C ''extern'':105C,113C ''fals'':101C ''file'':13C,49C,145C ''file.csv'':62C ''function'':95C ''go'':137C ''gt'':124C,129C ''inspect'':72C ''interpret'':91C ''issu'':40C ''know'':88C,134C ''line'':24C ''log'':100C,111C,120C ''lt'':60C,121C,125C ''m'':28C ''miss'':103C ''namespac'':127C ''ncp'':99C,104C,119C ''normal'':20C ''p'':8C,57C,65C,132C ''pre'':93C ''print'':80C ''problem'':150C ''r'':5A,6B,51C ''read'':1A,46C,142C ''read.csv'':61C ''result'':83C ''stat'':128C ''sure'':31C ''total'':19C ''tri'':45C,70C ''type'':75C ''use'':4A ''usual'':54C ''ve'':141C ''x'':96C,108C,116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9013, 2, '2015-11-25 01:30:54.147', 0, NULL, NULL, '&lt;p&gt;The ^M looks like the file came from a computer with a different OS from yours - maybe Unix and you''re on Windows? Look on Google for a Unix2DOS utility, or maybe you can open it in Excel and then re-export to fix it.&lt;/p&gt;
', NULL, NULL, '''/p'':48C ''came'':8C ''comput'':11C ''differ'':14C ''excel'':39C ''export'':44C ''file'':7C ''fix'':46C ''googl'':27C ''like'':5C ''look'':4C,25C ''m'':3C ''mayb'':18C,33C ''open'':36C ''os'':15C ''p'':1C ''re'':22C,43C ''re-export'':42C ''unix'':19C ''unix2dos'':30C ''util'':31C ''window'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9015, 2, '2015-11-25 04:58:15.01', 0, NULL, NULL, '&lt;p&gt;Sorry, never mind, I just realized that I thought I was running the command to set my working directory to the correct file and it turns out I wasn''t so when I was importing the file I was importing the wrong one.  All fixed.&lt;/p&gt;
', NULL, NULL, '''/p'':47C ''command'':15C ''correct'':23C ''directori'':20C ''file'':24C,38C ''fix'':46C ''import'':36C,41C ''mind'':4C ''never'':3C ''one'':44C ''p'':1C ''realiz'':7C ''run'':13C ''set'':17C ''sorri'':2C ''thought'':10C ''turn'':27C ''wasn'':30C ''work'':19C ''wrong'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9016, 1, '2015-11-25 06:01:48.613', 1, 51, 'How do I allow user to run a R script without making a website or web application?', '&lt;p&gt;I have data &amp;amp; a R script that creates a report from the data. I can''t expose the data to internet. Also, I cant expose my script to internet / user. But I would like to eliminate myself from the work, and allow couple of users (yeah, only three users that use this script to generate reports, but they do it weekly) to run the script and generate report for them. 
I would like the community to suggest me how I can achieve this. Any information helps. &lt;/p&gt;
', 'r  data-product', NULL, '''/p'':110C ''achiev'':105C ''allow'':4A,65C ''also'':45C ''amp'':26C ''applic'':17A ''cant'':47C ''communiti'':98C ''coupl'':66C ''creat'':31C ''data'':20B,25C,36C,42C ''data-product'':19B ''elimin'':59C ''expos'':40C,48C ''generat'':78C,90C ''help'':109C ''inform'':108C ''internet'':44C,52C ''like'':57C,96C ''make'':12A ''p'':22C ''product'':21B ''r'':9A,18B,28C ''report'':33C,79C,91C ''run'':7A,86C ''script'':10A,29C,50C,76C,88C ''suggest'':100C ''three'':71C ''use'':74C ''user'':5A,53C,68C,72C ''web'':16A ''websit'':14A ''week'':84C ''without'':11A ''work'':63C ''would'':56C,95C ''yeah'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9018, 1, '2015-11-25 08:44:38.9', 0, 12, 'Text classification: Training set for conseptually indistinct categories', '&lt;p&gt;I''m in a process of choosing the right training set for a supervised training algorithm. Description of the data set:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Documents are natural language&lt;/li&gt;
&lt;li&gt;Concepts of the categories are overlapping so there are few distinctions to be made manually&lt;/li&gt;
&lt;li&gt;The content of the data is short in most cases and may contain many generic words&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;What I have done so far:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Picked some terms manually after the training process&lt;/li&gt;
&lt;li&gt;Eliminated the longest and the shortest documents from the corpus&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Are there any frameworks or models how this kind of data should be dealt with and distinctions should be made between this kind of categories?&lt;/p&gt;
', 'supervised-learning', NULL, '''/li'':41C,58C,76C,96C,108C ''/p'':34C,85C,136C ''/ul'':77C,109C ''algorithm'':28C ''case'':69C ''categori'':8A,46C,135C ''choos'':19C ''classif'':2A ''concept'':43C ''conseptu'':6A ''contain'':72C ''content'':61C ''corpus'':107C ''data'':32C,64C,121C ''dealt'':124C ''descript'':29C ''distinct'':53C,127C ''document'':37C,104C ''done'':82C ''elimin'':98C ''far'':84C ''framework'':114C ''generic'':74C ''indistinct'':7A ''kind'':119C,133C ''languag'':40C ''learn'':11B ''li'':36C,42C,59C,87C,97C ''longest'':100C ''m'':14C ''made'':56C,130C ''mani'':73C ''manual'':57C,91C ''may'':71C ''model'':116C ''natur'':39C ''overlap'':48C ''p'':12C,78C,110C ''pick'':88C ''process'':17C,95C ''right'':21C ''set'':4A,23C,33C ''short'':66C ''shortest'':103C ''supervis'':10B,26C ''supervised-learn'':9B ''term'':90C ''text'':1A ''train'':3A,22C,27C,94C ''ul'':35C,86C ''word'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9019, 2, '2015-11-25 08:56:04.333', 2, NULL, NULL, '&lt;p&gt;Here are two other podcasts not mentioned previously:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.ocdqblog.com/podcast/" rel="nofollow"&gt;OCDQ&lt;/a&gt;: for some interesting discussions on data quality and Big Data.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.learningmachines101.com/" rel="nofollow"&gt;Learning Machine 101&lt;/a&gt;: for begginer level discussion on machine learning.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':21C,42C ''/li'':32C,50C ''/p'':10C ''/podcast/'':17C ''/ul'':51C ''101'':41C ''beggin'':44C ''big'':30C ''data'':27C,31C ''discuss'':25C,46C ''href'':14C,35C ''interest'':24C ''learn'':39C,49C ''level'':45C ''li'':12C,33C ''machin'':40C,48C ''mention'':8C ''nofollow'':19C,38C ''ocdq'':20C ''p'':1C ''podcast'':6C ''previous'':9C ''qualiti'':28C ''rel'':18C,37C ''two'':4C ''ul'':11C ''www.learningmachines101.com'':36C ''www.ocdqblog.com'':16C ''www.ocdqblog.com/podcast/'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9199, 2, '2015-12-04 10:57:55.683', 0, NULL, NULL, '&lt;p&gt;One way to generate a set of predictor variables is by adding noise.
In your case, this might work well unless there is a lot of variation due to uncontrolled conditions like financial crisis.
You have to be careful about adding noise. It should thoroughly test your model for robustness. One way would be to add fractions of the variance of each attribute. &lt;/p&gt;
', NULL, NULL, '''/p'':65C ''ad'':13C,42C ''add'':57C ''attribut'':64C ''care'':40C ''case'':17C ''condit'':32C ''crisi'':35C ''due'':29C ''financi'':34C ''fraction'':58C ''generat'':5C ''like'':33C ''lot'':26C ''might'':19C ''model'':49C ''nois'':14C,43C ''one'':2C,52C ''p'':1C ''predictor'':9C ''robust'':51C ''set'':7C ''test'':47C ''thorough'':46C ''uncontrol'':31C ''unless'':22C ''variabl'':10C ''varianc'':61C ''variat'':28C ''way'':3C,53C ''well'':21C ''work'':20C ''would'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9020, 1, '2015-11-25 11:11:25.923', 1, 18, 'Do I have to standardize my new polynomial features?', '&lt;p&gt;I have a vector X with n features previously standardized.&lt;/p&gt;

&lt;p&gt;If I want to generate new polynomial features (let say adding square features), do I need to do another standardization on these new features after the computing ? &lt;/p&gt;

&lt;p&gt;Because knowing that my original features are already between 0 and 1, the new polynomial features will also be between 0 and 1. But the higher the degree is, the higher the "ratio" between the original one and the polynomial one will be.&lt;/p&gt;

&lt;p&gt;Also,  would that be better to do the square computing on the values of the non standardized features, and then standardize these new features ?&lt;/p&gt;

&lt;p&gt;By the way, when I say "standardization", I m thinking about substrat each value by the mean and divide it by the std.&lt;/p&gt;

&lt;p&gt;Thanks in advance,&lt;/p&gt;

&lt;p&gt;Regards.&lt;/p&gt;
', 'machine-learning  dataset  data-cleaning  data', NULL, '''/p'':29C,58C,103C,129C,154C,159C,162C ''0'':69C,80C ''1'':71C,82C ''ad'':41C ''advanc'':158C ''alreadi'':67C ''also'':77C,105C ''anoth'':49C ''better'':109C ''clean'':16B ''comput'':57C,114C ''data'':15B,17B ''data-clean'':14B ''dataset'':13B ''degre'':87C ''divid'':149C ''featur'':9A,26C,38C,43C,54C,65C,75C,122C,128C ''generat'':35C ''higher'':85C,90C ''know'':61C ''learn'':12B ''let'':39C ''m'':139C ''machin'':11B ''machine-learn'':10B ''mean'':147C ''n'':25C ''need'':46C ''new'':7A,36C,53C,73C,127C ''non'':120C ''one'':96C,100C ''origin'':64C,95C ''p'':18C,30C,59C,104C,130C,155C,160C ''polynomi'':8A,37C,74C,99C ''previous'':27C ''ratio'':92C ''regard'':161C ''say'':40C,136C ''squar'':42C,113C ''standard'':5A,28C,50C,121C,125C,137C ''std'':153C ''substrat'':142C ''thank'':156C ''think'':140C ''valu'':117C,144C ''vector'':22C ''want'':33C ''way'':133C ''would'':106C ''x'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9021, 1, '2015-11-25 11:18:32.557', 0, 13, 'To check vailidity of model if P value is not given', '&lt;p&gt;I am running a linear model where price is the dependent variable, and weight is the independent variable. While checking the output of the model under the parameter estimates i have the slope as 18381.22 and standard error as 141.74. However, the model does not show the significance value or the p-value. What could be the reason?
As per my understanding i cannot call out this model as a fit model. 
Also, regression standardized residual plot shows outliers and the data is not normally distributed which also violates the assumption of a linear model. 
Am I ok with my assumptions?&lt;/p&gt;
', 'statistics  predictive-modeling  linear-regression', NULL, '''/p'':122C ''141.74'':59C ''18381.22'':54C ''also'':93C,108C ''assumpt'':111C,121C ''call'':85C ''cannot'':84C ''check'':2A,39C ''could'':75C ''data'':102C ''depend'':30C ''distribut'':106C ''error'':57C ''estim'':48C ''fit'':91C ''given'':11A ''howev'':60C ''independ'':36C ''linear'':17B,24C,114C ''linear-regress'':16B ''model'':5A,15B,25C,44C,62C,88C,92C,115C ''normal'':105C ''ok'':118C ''outlier'':99C ''output'':41C ''p'':7A,19C,72C ''p-valu'':71C ''paramet'':47C ''per'':80C ''plot'':97C ''predict'':14B ''predictive-model'':13B ''price'':27C ''reason'':78C ''regress'':18B,94C ''residu'':96C ''run'':22C ''show'':65C,98C ''signific'':67C ''slope'':52C ''standard'':56C,95C ''statist'':12B ''understand'':82C ''vailid'':3A ''valu'':8A,68C,73C ''variabl'':31C,37C ''violat'':109C ''weight'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9054, 5, '2015-11-26 10:00:06.537', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9022, 1, '2015-11-25 12:13:29.793', 2, 71, 'pandas count values for last 7 days from each date', '&lt;p&gt;There are two Dataframes. First is like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;print df1

        id        date    month  is_buy
     0  17  2015-01-16  2015-01       1
     1  17  2015-01-26  2015-01       1
     2  17  2015-01-27  2015-01       1
     3  17  2015-02-11  2015-02       1
     4  17  2015-03-14  2015-03       1
     5  18  2015-01-28  2015-01       1
     6  18  2015-02-12  2015-02       1
     7  18  2015-02-25  2015-02       1
     8  18  2015-03-04  2015-03       1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In second data frame there are some aggregated data by month from the first one:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;df2 = df1[df1[''is_buy''] == 1].groupby([''id'', ''month'']).agg({''is_buy'': np.sum})

print df2

        id    month       buys
     0  17  2015-01          3
     1  17  2015-02          1
     2  17  2015-03          1
     3  18  2015-01          1
     4  18  2015-02          2
     5  18  2015-03          1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m trying to get new df2 column named ''last_week_buys'' with aggregated buys by last 7 days from first day of each df1[''month'']. In other words, I want to get this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;        id    month       buys    last_week_buys
     0  17  2015-01          3               NaN
     1  17  2015-02          1                 2
     2  17  2015-03          1                 0
     3  18  2015-01          1               NaN
     4  18  2015-02          2                 1
     5  18  2015-03          1                 1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Are there any ideas to get this column?&lt;/p&gt;
', 'python  pandas', NULL, '''-01'':35C,38C,43C,46C,51C,54C,75C,78C,146C,161C,222C,240C ''-02'':59C,62C,83C,86C,91C,94C,151C,166C,228C,246C ''-03'':67C,70C,99C,102C,156C,171C,234C,252C ''-04'':100C ''-11'':60C ''-12'':84C ''-14'':68C ''-16'':36C ''-25'':92C ''-26'':44C ''-27'':52C ''-28'':76C ''/code'':104C,173C,255C ''/p'':22C,122C,210C,266C ''/pre'':105C,174C,256C ''0'':32C,143C,219C,236C ''1'':39C,40C,47C,55C,63C,71C,79C,87C,95C,103C,130C,148C,152C,157C,162C,172C,225C,229C,235C,241C,248C,253C,254C ''17'':33C,41C,49C,57C,65C,144C,149C,154C,220C,226C,232C ''18'':73C,81C,89C,97C,159C,164C,169C,238C,244C,250C ''2'':48C,153C,167C,230C,231C,247C ''2015'':34C,37C,42C,45C,50C,53C,58C,61C,66C,69C,74C,77C,82C,85C,90C,93C,98C,101C,145C,150C,155C,160C,165C,170C,221C,227C,233C,239C,245C,251C ''3'':56C,147C,158C,223C,237C ''4'':64C,163C,243C ''5'':72C,168C,249C ''6'':80C ''7'':6A,88C,193C ''8'':96C ''agg'':134C ''aggreg'':114C,189C ''buy'':31C,129C,136C,142C,187C,190C,215C,218C ''code'':24C,124C,212C ''column'':183C,265C ''count'':2A ''data'':109C,115C ''datafram'':17C ''date'':10A,28C ''day'':7A,194C,197C ''df1'':26C,126C,127C,200C ''df2'':125C,139C,182C ''first'':18C,120C,196C ''frame'':110C ''get'':180C,208C,263C ''groupbi'':131C ''id'':27C,132C,140C,213C ''idea'':261C ''last'':5A,185C,192C,216C ''like'':20C ''m'':177C ''month'':29C,117C,133C,141C,201C,214C ''name'':184C ''nan'':224C,242C ''new'':181C ''np.sum'':137C ''one'':121C ''p'':13C,106C,175C,257C ''panda'':1A,12B ''pre'':23C,123C,211C ''print'':25C,138C ''python'':11B ''second'':108C ''tri'':178C ''two'':16C ''valu'':3A ''want'':206C ''week'':186C,217C ''word'':204C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9023, 1, '2015-11-25 14:28:46.053', 1, 76, 'Which algorithm would you use for text categorisation using clustering?', '&lt;p&gt;If you are asked to do text categorization using clustering. Which algorithm would you use and why?&lt;/p&gt;
', 'data-mining  clustering', NULL, '''/p'':33C ''algorithm'':2A,27C ''ask'':19C ''categor'':23C ''categoris'':8A ''cluster'':10A,14B,25C ''data'':12B ''data-min'':11B ''mine'':13B ''p'':15C ''text'':7A,22C ''use'':5A,9A,24C,30C ''would'':3A,28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9024, 2, '2015-11-25 15:00:16.763', 1, NULL, NULL, '&lt;p&gt;I do not believe classic SVM is the right tool in the situation simply because you have no negatives. I''d try to use one of the following: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Semi-supervised approach: if you data can be clustered well enough, then you can determine which of the clusters corresponds to the positive examples using your data and use this as your classifier&lt;/li&gt;
&lt;li&gt;Use 1-class SVM&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':67C,73C ''/ol'':74C ''/p'':30C ''1'':70C ''approach'':36C ''believ'':5C ''class'':71C ''classic'':6C ''classifi'':66C ''cluster'':42C,52C ''correspond'':53C ''d'':22C ''data'':39C,60C ''determin'':48C ''enough'':44C ''exampl'':57C ''follow'':29C ''li'':32C,68C ''negat'':20C ''ol'':31C ''one'':26C ''p'':1C ''posit'':56C ''right'':10C ''semi'':34C ''semi-supervis'':33C ''simpli'':15C ''situat'':14C ''supervis'':35C ''svm'':7C,72C ''tool'':11C ''tri'':23C ''use'':25C,58C,62C,69C ''well'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9025, 1, '2015-11-25 15:00:50.553', 1, 92, 'How do I perform Naive Bayes Classification with a Bayesian Belief Network?', '&lt;p&gt;I''ve been writing a java library that I want to use to build Bayesian Belief Networks. I have classes that I use to build a Directed Graph&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;public class Node{
    private String label;
    private List&amp;lt;Node&amp;gt; adjacencyList = new ArrayList&amp;lt;Node&amp;gt;();
    private Frequency&amp;lt;String&amp;gt; distribution = new Frequency&amp;lt;String&amp;gt;();

    public String getLabel() {
        return label;
    }

    public void setLabel(String label) {
        this.label = label;
    }

    public List&amp;lt;Node&amp;gt; getAdjacencyList(){
        return adjacencyList;
    }

    public void addNeighbour(Node neighbour){
        adjacencyList.add(neighbour);
    }

    public void setDistribution(List&amp;lt;String&amp;gt; data){

        for(String s:data){
            distribution.addValue(s);
        }
    }

    public double getDistributionValue(String value){
        return distribution.getPct(value);
    }
} 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Graph&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;public class DirectedGraph {

Map&amp;lt;String,Node&amp;gt; graph = new HashMap&amp;lt;String,Node&amp;gt;();

public void addVertex(String label){
    Node vertex = new Node();
    vertex.setLabel(label);
    graph.put(label, vertex);
}

public void addEdge(String here, String there){
    Node nHere = graph.get(here);
    Node nThere = graph.get(there);
    nThere.addNeighbour(nHere);
    graph.put(there,nThere);
}

public List&amp;lt;Node&amp;gt; getNeighbors(String vertex){
    return graph.get(vertex).getAdjacencyList();
}

public int degree(String vertex){
    return graph.get(vertex).getAdjacencyList().size();
}

public boolean hasVertex(String vertex){
    return graph.containsKey(vertex);
}

public boolean hasEdge(String here, String there){
    Set&amp;lt;Node&amp;gt; nThere = new HashSet&amp;lt;Node&amp;gt;(graph.get(there).getAdjacencyList());
    boolean thereConHere = nThere.contains(here);
    return (thereConHere);
}
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have a class that I use to keep track of the probability distribution of a data set&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;public class Frequency&amp;lt;T extends Comparable&amp;lt;T&amp;gt;&amp;gt; {

private Multiset event = HashMultiset.create();
private Multimap event2 = LinkedListMultimap.create();

public void addValue(T data){
    if(event2.containsKey(data) == false){
        event2.put(data,data);
    }

    event.add(data);
}

public void clear(){

    this.event = null;
    this.event2 = null;

    this.event = HashMultiset.create();
    this.event2 = LinkedListMultimap.create();
}

public double getPct(T data){
    int numberOfIndElements = event.count(data);
    int totalNumOfElements = event.size();
    return (double) numberOfIndElements/totalNumOfElements;
}

public int getNum(T data){
    int numberOfIndElements = event.count(data);
    return numberOfIndElements;
}

public int getSumFreq(){
    return event.size();
}

public int getUniqueCount(){
    return event.entrySet().size();
}

public String[] getKeys(){
    Set&amp;lt;String&amp;gt; test = event2.keySet();
    Object[] keys = test.toArray();
    String[] keysAsStrings = new String[keys.length];

    for(int i=0;i&amp;lt;keys.length;i++){
        keysAsStrings[i] = (String) keys[i];
    }

    return keysAsStrings;
}
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;as well as another function that I can use to calculate conditional probabilities&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;public double conditionalProbability(List&amp;lt;String&amp;gt; interestedSet,
                                     List&amp;lt;String&amp;gt; reducingSet,
                                     String interestedClass,
                                     String reducingClass){
    List&amp;lt;Integer&amp;gt; conditionalData = new LinkedList&amp;lt;Integer&amp;gt;();
    double returnProb = 0;
    iFrequency.clear();
    rFrequency.clear();

    this.setInterestedFrequency(interestedSet);
    this.setReducingFrequency(reducingSet);


    for(int i = 0;i&amp;lt;reducingSet.size();i++){
        if(reducingSet.get(i).equalsIgnoreCase(reducingClass)){
            if(interestedSet.get(i).equalsIgnoreCase(interestedClass)){
                conditionalData.add(i);
            }
        }
    }

    int numerator = conditionalData.size();
    int denominator = this.rFrequency.getNum(reducingClass);

    if(denominator !=0){
        returnProb = (double)numerator/denominator;
    }

    iFrequency.clear();
    rFrequency.clear();
    return returnProb;
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However, I''m still not sure how to hook everything up in order to perform classification.&lt;/p&gt;

&lt;p&gt;I was reading over a paper entitled &lt;a href="http://arxiv.org/pdf/1301.6684.pdf" rel="nofollow"&gt;Comparing Bayesian Network Classifiers &lt;/a&gt; to try and get an understanding. &lt;/p&gt;

&lt;p&gt;Let''s say that I am trying to predict a person''s sex based on the attributes height, weight and shoe size. My understanding is that I would have Sex as my parent/classification node and height, weight and shoe size would by my child nodes.&lt;/p&gt;

&lt;p&gt;This is what I''m confused about. The various classification nodes only keep track of the probability distribution of their respective attributes, but I''d need the conditional probabilities in order to perform classification.&lt;/p&gt;

&lt;p&gt;I have an older version of Naive Bayes that I wrote&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;public void naiveBayes(Data data,List&amp;lt;String&amp;gt; targetClass, BayesOption bayesOption,boolean headers){
    //intialize variables
    int numOfClasses = data.getNumOfKeys();//.getHeaders().size();
    String[] keyNames = data.getKeys();//  data.getHeaders().toArray();
    double conditionalProb = 1.0;
    double prob = 1.0;
    String[] rClass;
    String priorName;


    iFrequency.clear();
    rFrequency.clear();

    if(bayesOption.compareTo(BayesOption.TRAIN) == 0){
        this.setInterestedFrequency(targetClass);
        this.targetClassKeys = Util.convertToStringArray(iFrequency.getKeys());

        for(int i=0;i&amp;lt;this.targetClassKeys.length;i++){
            priors.put(this.targetClassKeys[i],iFrequency.getPct(this.targetClassKeys[i]));
        }

    }


    //for each classification in the target class
    for(int i=0;i&amp;lt;this.targetClassKeys.length;i++){

        //get all of the different classes for that variable
        for(int j=0;j&amp;lt;numOfClasses;j++){

            String reducingKey = Util.convertToString(keyNames[j]);
            List&amp;lt;String&amp;gt; reducingClass = data.dataColumn(reducingKey,DataOption.GET,true);// new ArrayList(data.getData().get(reducingKey));
            this.setReducingFrequency(reducingClass);
            Object[] reducingClassKeys = rFrequency.getKeys();
            rClass = Util.convertToStringArray(reducingClassKeys);


            for(int k=0;k&amp;lt;reducingClassKeys.length;k++){

                if(bayesOption.compareTo(BayesOption.TRAIN) == 0){
                    conditionalProb = conditionalProbability(targetClass, reducingClass, this.targetClassKeys[i], rClass[k]);
                    priorName = this.targetClassKeys[i]+"|"+rClass[k];
                    priors.put(priorName,conditionalProb);
                }

                if(bayesOption.compareTo(BayesOption.PREDICT) == 0){
                    priorName = this.targetClassKeys[i]+"|"+rClass[k];
                    prob = prob * priors.get(priorName);

                }
            }
            rFrequency.clear();

        }

        if(BayesOption.PREDICT.compareTo(bayesOption) == 0){
            prob = prob * priors.get(this.targetClassKeys[i]);
            Pair&amp;lt;String,Double&amp;gt; pred = new Pair&amp;lt;String, Double&amp;gt;(this.targetClassKeys[i],prob);
            this.predictions.add(pred);
        }

    }

    this.iFrequency.clear();
    this.rFrequency.clear();

}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So I generally understand how the math works, but I''m not quite sure how I''m supposed to get things to work with this specific architecture. &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;How do I calculate the conditional probabilities?&lt;/p&gt;
  
  &lt;p&gt;Can somebody explain this discrepancy to me please? &lt;/p&gt;
&lt;/blockquote&gt;
', 'machine-learning  data-mining  classification  statistics  predictive-modeling', NULL, '''/.getheaders'':638C ''/a'':513C ''/blockquote'':860C ''/code'':133C,245C,382C,474C,809C ''/for'':680C ''/get'':695C ''/intialize'':633C ''/p'':53C,137C,266C,398C,493C,520C,567C,603C,616C,839C,849C,859C ''/pdf/1301.6684.pdf'':506C ''/pre'':134C,246C,383C,475C,810C ''0'':370C,430C,440C,466C,660C,669C,690C,707C,742C,750C,770C,784C ''1.0'':647C,650C ''addedg'':171C ''addneighbour'':106C ''addvalu'':290C ''addvertex'':157C ''adjacencylist'':67C,103C ''adjacencylist.add'':109C ''anoth'':388C ''architectur'':838C ''arraylist'':69C,727C ''arxiv.org'':505C ''arxiv.org/pdf/1301.6684.pdf'':504C ''attribut'':538C,590C ''base'':535C ''bay'':6A,612C ''bayesian'':10A,39C,510C ''bayesopt'':629C,630C,783C ''bayesoption.compareto'':658C,748C,768C ''bayesoption.predict'':769C ''bayesoption.predict.compareto'':782C ''bayesoption.train'':659C,749C ''belief'':11A,40C ''blockquot'':840C 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''event.add'':300C ''event.count'':320C,335C ''event.entryset'':348C ''event.size'':324C,343C ''event2'':286C ''event2.containskey'':294C ''event2.keyset'':358C ''event2.put'':297C ''everyth'':486C ''explain'':853C ''extend'':274C ''fals'':296C ''frequenc'':74C,80C,271C ''function'':389C ''general'':814C ''get'':517C,729C,831C ''getadjacencylist'':101C,200C,209C,238C ''getdistributionvalu'':127C ''getkey'':352C ''getlabel'':86C ''getneighbor'':194C ''getnum'':330C ''getpct'':315C ''getsumfreq'':341C ''getuniquecount'':346C ''graph'':52C,136C,148C ''graph.containskey'':217C ''graph.get'':178C,182C,198C,207C,236C ''graph.put'':166C,186C ''gt'':66C,72C,77C,83C,100C,117C,147C,154C,193C,229C,235C,278C,279C,356C,407C,412C,421C,427C,627C,720C,794C,801C ''hasedg'':221C ''hashmap'':150C ''hashmultiset.create'':283C,310C ''hashset'':232C ''hasvertex'':213C ''header'':632C ''height'':539C,557C ''hook'':485C ''howev'':477C ''href'':503C ''ifrequency.clear'':431C,470C,655C ''ifrequency.getkeys'':665C ''ifrequency.getpct'':677C ''int'':202C,318C,322C,329C,333C,340C,345C,368C,438C,457C,460C,635C,667C,688C,705C,740C ''integ'':420C,426C ''interestedclass'':415C,454C ''interestedset'':408C,434C ''interestedset.get'':451C ''j'':706C,708C,711C,716C ''java'':30C ''k'':741C,743C,746C,758C,763C,775C ''keep'':256C,581C ''key'':360C,378C ''keynam'':641C,715C ''keys.length'':366C,373C ''keysasstr'':363C,375C,381C ''label'':61C,88C,93C,95C,159C,165C,167C ''learn'':15B ''let'':522C ''librari'':31C ''linkedlist'':424C ''linkedlistmultimap.create'':287C,312C ''list'':63C,97C,114C,190C,404C,409C,418C,624C,717C ''lt'':64C,70C,75C,81C,98C,115C,144C,151C,191C,227C,233C,272C,276C,354C,372C,405C,410C,419C,425C,442C,625C,671C,692C,709C,718C,744C,791C,798C ''m'':479C,573C,822C,828C ''machin'':14B ''machine-learn'':13B ''map'':143C ''math'':818C ''mine'':18B ''model'':23B ''multimap'':285C ''multiset'':281C ''naiv'':5A,611C ''naivebay'':621C ''need'':594C ''neighbour'':108C,110C 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''public'':56C,84C,89C,96C,104C,111C,125C,140C,155C,169C,189C,201C,211C,219C,269C,288C,302C,313C,328C,339C,344C,350C,401C,619C ''quit'':824C ''rclass'':652C,736C,757C,762C,774C ''read'':497C ''reducingclass'':417C,449C,463C,721C,732C,754C ''reducingclasskey'':734C,738C ''reducingclasskeys.length'':745C ''reducingkey'':713C,723C,730C ''reducingset'':413C,436C ''reducingset.get'':446C ''reducingset.size'':443C ''rel'':507C ''respect'':589C ''return'':87C,102C,130C,197C,206C,216C,243C,325C,337C,342C,347C,380C,472C ''returnprob'':429C,467C,473C ''rfrequency.clear'':432C,471C,656C,780C ''rfrequency.getkeys'':735C ''say'':524C ''set'':226C,265C,353C ''setdistribut'':113C ''setlabel'':91C ''sex'':534C,551C ''shoe'':542C,560C ''size'':210C,349C,543C,561C,639C ''somebodi'':852C ''specif'':837C ''statist'':20B ''still'':480C ''string'':60C,76C,82C,85C,92C,116C,120C,128C,145C,152C,158C,172C,174C,195C,204C,214C,222C,224C,351C,355C,362C,365C,377C,406C,411C,414C,416C,626C,640C,651C,653C,712C,719C,792C,799C ''suppos'':829C ''sure'':482C,825C ''target'':685C ''targetclass'':628C,662C,753C ''test'':357C ''test.toarray'':361C ''thereconher'':240C,244C ''thing'':832C ''this.event'':305C,309C ''this.event2'':307C,311C ''this.ifrequency.clear'':807C ''this.label'':94C ''this.predictions.add'':805C ''this.rfrequency.clear'':808C ''this.rfrequency.getnum'':462C ''this.setinterestedfrequency'':433C,661C ''this.setreducingfrequency'':435C,731C ''this.targetclasskeys'':663C,675C,678C,755C,760C,772C,788C,802C ''this.targetclasskeys.length'':672C,693C ''toarray'':644C ''totalnumofel'':323C ''track'':257C,582C ''tri'':515C,528C ''true'':725C ''understand'':519C,545C,815C ''use'':36C,47C,254C,393C ''util.converttostring'':714C ''util.converttostringarray'':664C,737C ''valu'':129C,132C ''variabl'':634C,703C ''various'':577C ''ve'':26C ''version'':609C ''vertex'':161C,168C,196C,199C,205C,208C,215C,218C ''vertex.setlabel'':164C ''void'':90C,105C,112C,156C,170C,289C,303C,620C ''want'':34C ''weight'':540C,558C ''well'':386C ''work'':819C,834C ''would'':549C,562C ''write'':28C ''wrote'':615C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9026, 2, '2015-11-25 15:04:04.387', 1, NULL, NULL, '&lt;p&gt;Text categorization using clustering can be done in a lot of ways. Some of which are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Using the &lt;a href="http://www.codeproject.com/Articles/439890/Text-Documents-Clustering-using-K-Means-Algorithm" rel="nofollow"&gt;K-means algorithm&lt;/a&gt;  (&lt;a href="http://scikit-learn.org/stable/auto_examples/text/document_clustering.html" rel="nofollow"&gt;Another link&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;a href="http://dl.acm.org/citation.cfm?id=985318" rel="nofollow"&gt;Feature Projections&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In fact, most(or almost all) the &lt;a href="https://en.wikipedia.org/wiki/Category:Data_clustering_algorithms" rel="nofollow"&gt;clustering algorithms&lt;/a&gt; can be applied for classifying text through clustering.&lt;/p&gt;
', NULL, NULL, '''/a'':34C,44C,56C,76C ''/articles/439890/text-documents-clustering-using-k-means-algorithm'':27C ''/citation.cfm?id=985318'':51C ''/li'':45C,57C ''/p'':18C,85C ''/stable/auto_examples/text/document_clustering.html'':39C ''/ul'':58C ''/wiki/category:data_clustering_algorithms'':71C ''algorithm'':33C,75C ''almost'':64C ''anoth'':42C ''appli'':79C ''categor'':3C ''classifi'':81C ''cluster'':5C,74C,84C ''dl.acm.org'':50C ''dl.acm.org/citation.cfm?id=985318'':49C ''done'':8C ''en.wikipedia.org'':70C ''en.wikipedia.org/wiki/category:data_clustering_algorithms'':69C ''fact'':61C ''featur'':54C ''href'':24C,36C,48C,68C ''k'':31C ''k-mean'':30C ''li'':20C,46C ''link'':43C ''lot'':11C ''mean'':32C ''nofollow'':29C,41C,53C,73C ''p'':1C,59C ''project'':55C ''rel'':28C,40C,52C,72C ''scikit-learn.org'':38C ''scikit-learn.org/stable/auto_examples/text/document_clustering.html'':37C ''text'':2C,82C ''ul'':19C ''use'':4C,21C ''way'':13C ''www.codeproject.com'':26C ''www.codeproject.com/articles/439890/text-documents-clustering-using-k-means-algorithm'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9027, 1, '2015-11-25 15:14:36.44', 1, 36, 'Theoretical treatment of unlabeled samples', '&lt;p&gt;In a typical supervised learning setting with a few positive and a few negative examples, it is clear that unlabeled data carries some information that can benefit learning and that is not captured in the labeled data. For example one can estimate mean values, bounds and some other geometrical characteristics of the data-set with much higher precision if you do not discard the (massive) unlabeled data. &lt;/p&gt;

&lt;p&gt;On the other hand, the most common ML algorithms from Neural Networks to SVM do not take advantage of this information (at least in their standard, most common form). My question:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Is there any theoretical framework where unlabeled data is treated in the supervised setting?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I can think of semi-supervised ways to approach this (first cluster and then label the clusters). Are there any other?&lt;/p&gt;
', 'machine-learning  data-mining  supervised-learning  unsupervised-learning', NULL, '''/li'':137C ''/p'':87C,120C,162C ''/ul'':138C ''advantag'':106C ''algorithm'':97C ''approach'':149C ''benefit'':45C ''bound'':63C ''captur'':51C ''carri'':40C ''characterist'':68C ''clear'':36C ''cluster'':152C,157C ''common'':95C,116C ''data'':10B,39C,55C,72C,86C,130C ''data-min'':9B ''data-set'':71C ''discard'':82C ''estim'':60C ''exampl'':33C,57C ''first'':151C ''form'':117C ''framework'':127C ''geometr'':67C ''hand'':92C ''higher'':76C ''inform'':42C,109C ''label'':54C,155C ''learn'':8B,14B,17B,23C,46C ''least'':111C ''li'':122C ''machin'':7B ''machine-learn'':6B ''massiv'':84C ''mean'':61C ''mine'':11B ''ml'':96C ''much'':75C ''negat'':32C ''network'':100C ''neural'':99C ''one'':58C ''p'':18C,88C,139C ''posit'':28C ''precis'':77C ''question'':119C ''sampl'':5A ''semi'':145C ''semi-supervis'':144C ''set'':24C,73C,136C ''standard'':114C ''supervis'':13B,22C,135C,146C ''supervised-learn'':12B ''svm'':102C ''take'':105C ''theoret'':1A,126C ''think'':142C ''treat'':132C ''treatment'':2A ''typic'':21C ''ul'':121C ''unlabel'':4A,38C,85C,129C ''unsupervis'':16B ''unsupervised-learn'':15B ''valu'':62C ''way'':147C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9028, 2, '2015-11-25 15:36:27.167', 0, NULL, NULL, '&lt;p&gt;You already dropped the key word "semi-supervised" in your question. Indeed, semi-supervised learning is the answer to your question. Search for this term with your favourite search engine or library catalog to find out about algorithms for semi-supervised learning.&lt;/p&gt;
', NULL, NULL, '''/p'':47C ''algorithm'':41C ''alreadi'':3C ''answer'':21C ''catalog'':36C ''drop'':4C ''engin'':33C ''favourit'':31C ''find'':38C ''inde'':14C ''key'':6C ''learn'':18C,46C ''librari'':35C ''p'':1C ''question'':13C,24C ''search'':25C,32C ''semi'':9C,16C,44C ''semi-supervis'':8C,15C,43C ''supervis'':10C,17C,45C ''term'':28C ''word'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9029, 2, '2015-11-25 16:22:36.677', 1, NULL, NULL, '&lt;p&gt;&lt;a href="https://en.m.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;tf-idf vectors&lt;/a&gt; are an easy start, but it can be hard to cluster very high dimensional data.&lt;/p&gt;

&lt;p&gt;You might try &lt;a href="https://en.m.wikipedia.org/wiki/Topic_model" rel="nofollow"&gt;topic modeling&lt;/a&gt; (LDA, LSI for example) to reduce the dimensionality of your features. &lt;/p&gt;

&lt;p&gt;A newer approach is &lt;a href="https://www.google.com/url?sa=t&amp;amp;source=web&amp;amp;rct=j&amp;amp;url=https://cs.stanford.edu/~quocle/paragraph_vector.pdf&amp;amp;ved=0ahUKEwiAz52Ija3JAhXLKCYKHRdeAcIQFgghMAA&amp;amp;usg=AFQjCNE-2SsR07iQm78dQqVV-6_Y3ERurA&amp;amp;sig2=O64Artz1ekwZ2KqjFCMabQ" rel="nofollow"&gt;paragraph vectors&lt;/a&gt;, which learn a distributed representation of arbitrary-length text. &lt;a href="https://radimrehurek.com/gensim/models/doc2vec.html" rel="nofollow"&gt;Here&lt;/a&gt; is an implementation in python.&lt;/p&gt;

&lt;p&gt;Learning a reasonable, lower dimensional representation of the text can help with the issues that arise trying to cluster high dimensional data.&lt;/p&gt;
', NULL, NULL, '''/a'':13C,43C,70C,89C ''/gensim/models/doc2vec.html'':85C ''/p'':29C,55C,95C,119C ''/url?sa=t&amp;amp;source=web&amp;amp;rct=j&amp;amp;url=https://cs.stanford.edu/~quocle/paragraph_vector.pdf&amp;amp;ved=0ahukewiaz52ija3jahxlkcykhrdeaciqfgghmaa&amp;amp;usg=afqjcne-2ssr07iqm78dqqvv-6_y3erura&amp;amp;sig2=o64artz1ekwz2kqjfcmabq'':65C ''/wiki/tf%e2%80%93idf'':6C ''/wiki/topic_model'':38C ''approach'':59C ''arbitrari'':78C ''arbitrary-length'':77C ''aris'':112C ''cluster'':24C,115C ''data'':28C,118C ''dimension'':27C,51C,101C,117C ''distribut'':74C ''easi'':16C ''en.m.wikipedia.org'':5C,37C ''en.m.wikipedia.org/wiki/tf%e2%80%93idf'':4C ''en.m.wikipedia.org/wiki/topic_model'':36C ''exampl'':47C ''featur'':54C ''hard'':22C ''help'':107C ''high'':26C,116C ''href'':3C,35C,62C,82C ''idf'':11C ''implement'':92C ''issu'':110C ''lda'':44C ''learn'':72C,97C ''length'':79C ''lower'':100C ''lsi'':45C ''might'':32C ''model'':42C ''newer'':58C ''nofollow'':8C,40C,67C,87C ''p'':1C,30C,56C,96C ''paragraph'':68C ''python'':94C ''radimrehurek.com'':84C ''radimrehurek.com/gensim/models/doc2vec.html'':83C ''reason'':99C ''reduc'':49C ''rel'':7C,39C,66C,86C ''represent'':75C,102C ''start'':17C ''text'':80C,105C ''tf'':10C ''tf-idf'':9C ''topic'':41C ''tri'':33C,113C ''vector'':12C,69C ''www.google.com'':64C ''www.google.com/url?sa=t&amp;amp;source=web&amp;amp;rct=j&amp;amp;url=https://cs.stanford.edu/~quocle/paragraph_vector.pdf&amp;amp;ved=0ahukewiaz52ija3jahxlkcykhrdeaciqfgghmaa&amp;amp;usg=afqjcne-2ssr07iqm78dqqvv-6_y3erura&amp;amp;sig2=o64artz1ekwz2kqjfcmabq'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9030, 2, '2015-11-25 16:37:48.423', 1, NULL, NULL, '&lt;p&gt;It will depend on the purpose and the text.  Many options this is what I have used.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/K-means_clustering" rel="nofollow"&gt;k-means clustering&lt;/a&gt; with &lt;a href="http://tf%E2%80%93idf" rel="nofollow"&gt;TF-IDF&lt;/a&gt;  &lt;/p&gt;

&lt;p&gt;What I did was limit the cluster and any document vector to the top 1000 terms sorted by weight.  This not only results in faster processing but you get some multi modal clusters and will have thousands and thousands of terms.  What happens is those really long vectors dilute the similarity when compared to a even a long single document.  I think you also get faster convergence.  If you want to keep documents out of multi modal clusters then don''t truncate the cluster vector.&lt;/p&gt;
', NULL, NULL, '''/a'':32C,45C ''/p'':19C,46C,133C ''/wiki/k-means_clustering'':25C ''1000'':62C ''80'':38C ''93idf'':39C ''also'':111C ''cluster'':31C,54C,80C,125C,131C ''compar'':100C ''converg'':114C ''depend'':4C ''dilut'':96C ''document'':57C,107C,120C ''e2'':37C ''en.wikipedia.org'':24C ''en.wikipedia.org/wiki/k-means_clustering'':23C ''even'':103C ''faster'':72C,113C ''get'':76C,112C ''happen'':90C ''href'':22C,35C ''idf'':44C ''k'':29C ''k-mean'':28C ''keep'':119C ''limit'':52C ''long'':94C,105C ''mani'':11C ''mean'':30C ''modal'':79C,124C ''multi'':78C,123C ''nofollow'':27C,41C ''option'':12C ''p'':1C,20C,47C ''process'':73C ''purpos'':7C ''realli'':93C ''rel'':26C,40C ''result'':70C ''similar'':98C ''singl'':106C ''sort'':64C ''term'':63C,88C ''text'':10C ''tf'':36C,43C ''tf-idf'':42C ''think'':109C ''thousand'':84C,86C ''top'':61C ''truncat'':129C ''use'':18C ''vector'':58C,95C,132C ''want'':117C ''weight'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9055, 4, '2015-11-26 10:00:06.537', 0, NULL, NULL, '"References" is our generic tag for questions seeking information about books, papers, presentations, videos of lectures, on-line tutorials, etc., regarding any subject matter that is on-topic for Data Science.', NULL, NULL, '''book'':11C ''data'':32C ''etc'':21C ''generic'':4C ''inform'':9C ''lectur'':16C ''line'':19C ''matter'':25C ''on-lin'':17C ''on-top'':28C ''paper'':12C ''present'':13C ''question'':7C ''refer'':1C ''regard'':22C ''scienc'':33C ''seek'':8C ''subject'':24C ''tag'':5C ''topic'':30C ''tutori'':20C ''video'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9031, 2, '2015-11-25 20:39:36.057', 2, NULL, NULL, '&lt;p&gt;The main obstacle is figuring out whether a date is within the last 7 days of the month. I''d recommend something hacky like the following:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from datetime import datetime, date, timedelta
def last7(datestr):
    orig = datetime.strptime(datestr,''%Y-%m-%d'')
    plus7 = orig+timedelta(7)
    return plus7.month != orig.month
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Once you have that, it''s relatively simple to adapt your previous code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;df3 = df1[df1[''is_buy''] == 1 &amp;amp;&amp;amp; last7(df1[''date''])].groupby([''id'', ''month'']).agg({''is_buy'': np.sum})
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now we just join together &lt;code&gt;df2&lt;/code&gt; and &lt;code&gt;df3&lt;/code&gt; and we''re done.&lt;/p&gt;
', NULL, NULL, '''/code'':53C,90C,100C,104C ''/p'':28C,69C,109C ''/pre'':54C,91C ''1'':77C ''7'':15C,49C ''adapt'':65C ''agg'':86C ''amp'':78C,79C ''buy'':76C,88C ''code'':30C,68C,71C,98C,102C ''d'':21C,45C ''date'':10C,35C,82C ''datestr'':39C,42C ''datetim'':32C,34C ''datetime.strptime'':41C ''day'':16C ''def'':37C ''df1'':73C,74C,81C ''df2'':99C ''df3'':72C,103C ''done'':108C ''figur'':6C ''follow'':27C ''groupbi'':83C ''hacki'':24C ''id'':84C ''import'':33C ''join'':96C ''last'':14C ''last7'':38C,80C ''like'':25C ''m'':44C ''main'':3C ''month'':19C,85C ''np.sum'':89C ''obstacl'':4C ''orig'':40C,47C ''orig.month'':52C ''p'':1C,55C,92C ''plus7'':46C ''plus7.month'':51C ''pre'':29C,70C ''previous'':67C ''re'':107C ''recommend'':22C ''relat'':62C ''return'':50C ''simpl'':63C ''someth'':23C ''timedelta'':36C,48C ''togeth'':97C ''whether'':8C ''within'':12C ''y'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9034, 2, '2015-11-25 22:03:57.35', 2, NULL, NULL, '&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/modules/model_evaluation.html#cohen-s-kappa" rel="nofollow"&gt;Cohen''s kappa&lt;/a&gt; was introduced in scikit-learn 0.17.&lt;/p&gt;

&lt;p&gt;You can wrap it in &lt;a href="http://scikit-learn.org/stable/modules/model_evaluation.html#scoring" rel="nofollow"&gt;make_scorer&lt;/a&gt; for use in GridSearchCV.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from sklearn.metrics import cohen_kappa_score, make_scorer
from sklearn.grid_search import GridSearchCV
from sklearn.svm import LinearSVC

kappa_scorer = make_scorer(cohen_kappa_score)
grid = GridSearchCV(LinearSVC(), param_grid={''C'': [1, 10]}, scoring=kappa_scorer)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':12C,36C ''/code'':79C ''/p'':20C,41C ''/pre'':80C ''/stable/modules/model_evaluation.html#cohen-s-kappa'':6C ''/stable/modules/model_evaluation.html#scoring'':31C ''0.17'':19C ''1'':74C ''10'':75C ''c'':73C ''code'':43C ''cohen'':9C,47C,65C ''grid'':68C,72C ''gridsearchcv'':40C,56C,69C ''href'':3C,28C ''import'':46C,55C,59C ''introduc'':14C ''kappa'':11C,48C,61C,66C,77C ''learn'':18C ''linearsvc'':60C,70C ''make'':34C,50C,63C ''nofollow'':8C,33C ''p'':1C,21C ''param'':71C ''pre'':42C ''rel'':7C,32C ''scikit'':17C ''scikit-learn'':16C ''scikit-learn.org'':5C,30C ''scikit-learn.org/stable/modules/model_evaluation.html#cohen-s-kappa'':4C ''scikit-learn.org/stable/modules/model_evaluation.html#scoring'':29C ''score'':49C,67C,76C ''scorer'':35C,51C,62C,64C,78C ''search'':54C ''sklearn.grid'':53C ''sklearn.metrics'':45C ''sklearn.svm'':58C ''use'':38C ''wrap'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9036, 1, '2015-11-26 00:10:57.433', 2, 56, 'Result Dithering? Why randomly shuffle results?', '&lt;p&gt;Most of us want to build a recommendation engine as accurate as possible, however, an experienced chief data scientist believes a &lt;em&gt;practical&lt;/em&gt; machine learning algorithm should randomly shuffle the results, therefore non-optimal results. This is known as "Results Dithering". Slide 15 at:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://cikm2013.org/slides/ted.pptx" rel="nofollow"&gt;http://cikm2013.org/slides/ted.pptx&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;While I understand &lt;strong&gt;how&lt;/strong&gt; to do it (adding a small fixed Gaussian noise), but &lt;strong&gt;why&lt;/strong&gt; would we do it to make the performance worse.&lt;/p&gt;
', 'machine-learning', NULL, '''/em'':34C ''/p'':57C,98C ''/slides/ted.pptx'':63C ''/slides/ted.pptx&lt;/a&gt;&lt;/p&gt;'':68C ''/strong'':75C,88C ''15'':55C ''accur'':21C ''ad'':79C ''algorithm'':37C ''believ'':30C ''build'':16C ''chief'':27C ''cikm2013.org'':62C,67C ''cikm2013.org/slides/ted.pptx'':61C ''cikm2013.org/slides/ted.pptx&lt;/a&gt;&lt;/p&gt;'':66C ''data'':28C ''dither'':2A,53C ''em'':32C ''engin'':19C ''experienc'':26C ''fix'':82C ''gaussian'':83C ''howev'':24C ''href'':60C ''known'':50C ''learn'':9B,36C ''machin'':8B,35C ''machine-learn'':7B ''make'':94C ''nofollow'':65C ''nois'':84C ''non'':45C ''non-optim'':44C ''optim'':46C ''p'':10C,58C,69C ''perform'':96C ''possibl'':23C ''practic'':33C ''random'':4A,39C ''recommend'':18C ''rel'':64C ''result'':1A,6A,42C,47C,52C ''scientist'':29C ''shuffl'':5A,40C ''slide'':54C ''small'':81C ''strong'':73C,86C ''therefor'':43C ''understand'':72C ''us'':13C ''want'':14C ''wors'':97C ''would'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9037, 1, '2015-11-26 03:45:19.333', 0, 53, 'How to evaluate the clustering result when cluster numbers are not equal to data set class', '&lt;p&gt;When apply clustering algorithm with the multi-class data set and class numbers are not equal to the result cluster numbers(For example , When we use K-Means algorithm by setting K = 3 apply with "yeast" data set that has 14 classes).&lt;/p&gt;

&lt;p&gt;What is the proper way to evaluate the result in this situation ? &lt;/p&gt;
', 'clustering  multiclass-classification  evaluation  unsupervised-learning', NULL, '''/p'':70C,84C ''14'':68C ''3'':60C ''algorithm'':29C,56C ''appli'':27C,61C ''class'':16A,34C,38C,69C ''classif'':20B ''cluster'':5A,8A,17B,28C,46C ''data'':14A,35C,64C ''equal'':12A,42C ''evalu'':3A,21B,78C ''exampl'':49C ''k'':54C,59C ''k-mean'':53C ''learn'':24B ''mean'':55C ''multi'':33C ''multi-class'':32C ''multiclass'':19B ''multiclass-classif'':18B ''number'':9A,39C,47C ''p'':25C,71C ''proper'':75C ''result'':6A,45C,80C ''set'':15A,36C,58C,65C ''situat'':83C ''unsupervis'':23B ''unsupervised-learn'':22B ''use'':52C ''way'':76C ''yeast'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9038, 1, '2015-11-26 04:28:17.827', 11, 400, 'Purpose of visualizing high dimensional data?', '&lt;p&gt;There are many techniques for visualizing high dimension datasets, such as T-SNE, isomap, PCA, supervised PCA, etc. And we go through the motions of projecting the data down to a 2D or 3D space, so we have a "pretty pictures". Some of these embedding (manifold learning) methods are described &lt;a href="http://scikit-learn.org/stable/modules/manifold.html"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/H3FBv.png"&gt;&lt;img src="http://i.stack.imgur.com/H3FBv.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;But is this "pretty picture" actually meaningful? What possible insights can someone grab by trying to visualize this embedded space?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I ask because the projection down to this embedded space is usually meaningless. For example, if you project your data down to principal components generated by PCA, those principal components (eiganvectors) don''t correspond to features in the dataset; they''re their own feature space. &lt;/p&gt;

&lt;p&gt;Similarly, t-SNE projects your data down to a space, where items are near each other if they minimize some KL divergence. This isn''t the original feature space anymore. (Correct me if I''m wrong, but I don''t even think there is a large effort by the ML community to use t-SNE to aid classification; that''s a different problem than data visualization though.)&lt;/p&gt;

&lt;p&gt;I''m just very largely confused why people make such a big deal about some of these visualizations. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9272, 2, '2015-12-09 02:27:46.88', 0, NULL, NULL, '&lt;p&gt;In the vein of bayesian optimization, I prefer Hyperopt, available on github at &lt;a href="https://github.com/hyperopt/hyperopt" rel="nofollow"&gt;https://github.com/hyperopt/hyperopt&lt;/a&gt; or through pip, homepage of author at &lt;a href="https://github.com/hyperopt/hyperopt" rel="nofollow"&gt;https://github.com/hyperopt/hyperopt&lt;/a&gt;. The tree-parzen-estimator algorithm behind it is described in the paper at &lt;a href="http://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf" rel="nofollow"&gt;http://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf&lt;/a&gt; . &lt;/p&gt;

&lt;p&gt;You can define an arbitrary nested search space, and then tell it to find the optimum of a black-box function. &lt;/p&gt;

&lt;p&gt;Hyperopt also supports a built-in sklearn search space out of the box, but I have not used that, I''ve typically defined my own. &lt;/p&gt;

&lt;p&gt;There''s also a few other bayesian optimization schemes around. I can''t claim to know which one of them will come out on top as the "best" in the long run. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9039, 2, '2015-11-26 06:28:37.397', 1, NULL, NULL, '&lt;p&gt;The first step would be to train a couple of different algorithms/classifiers to get a rough idea of how good your results can be without changing your data.&lt;/p&gt;

&lt;p&gt;Then, once you do your kmeans, you can use that information to possibly improve your data and then run it through your classifiers again to see if your results improve.&lt;/p&gt;

&lt;p&gt;When I say "use that information to possibly improve your data", here''s what I mean:&lt;/p&gt;

&lt;p&gt;Say your data has 5 features(columns)...see following totally made up data...&lt;/p&gt;

&lt;p&gt;2,5,6,3,2&lt;/p&gt;

&lt;p&gt;4,5,2,3,7&lt;/p&gt;

&lt;p&gt;...&lt;/p&gt;

&lt;p&gt;5,8,2,3,5&lt;/p&gt;

&lt;p&gt;It is this data that you ran through your kmeans...using 3 clusters, each row of your data will ''belong'' to a cluster...so add it to your data and your data becomes:&lt;/p&gt;

&lt;p&gt;2,5,6,3,2,1,0,0  belongs to cluster 1&lt;/p&gt;

&lt;p&gt;4,5,2,3,7,0,1,0  belongs to cluster 2&lt;/p&gt;

&lt;p&gt;...&lt;/p&gt;

&lt;p&gt;5,8,2,3,5,0,0,1  belongs to cluster 3&lt;/p&gt;

&lt;p&gt;You could just add one column and make it 1,2,3 but some algorithms prefer the 0/1 method (one-hot encoding, dummy variable)&lt;/p&gt;

&lt;p&gt;See, now you have a bit more information in your data than you started with. Having the before and after comparison models will let you know if it is actually useful information!  &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9040, 2, '2015-11-26 07:01:38.963', 0, NULL, NULL, '&lt;p&gt;In addition to the link in the existing answer, there is also a Scikit-Learn laboratory, where methods and algorithms are being experimented.&lt;/p&gt;

&lt;p&gt;In case you are okay with working with bleeding edge code, &lt;a href="http://skll.readthedocs.org/en/latest/index.html" rel="nofollow"&gt;this library&lt;/a&gt; would be a nice reference.&lt;/p&gt;

&lt;p&gt;The Cohen''s Kappa is &lt;a href="http://skll.readthedocs.org/en/latest/api/metrics.html#skll.metrics.kappa" rel="nofollow"&gt;also one of the metrics in the library&lt;/a&gt;, which takes in &lt;code&gt;true labels&lt;/code&gt;, &lt;code&gt;predicted labels&lt;/code&gt;, &lt;code&gt;weights&lt;/code&gt; and &lt;code&gt;allowing one off?&lt;/code&gt; as the input parameters. Obviously, the metric would range from &lt;code&gt;[-1, 1]&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;You can also have a look at the &lt;a href="http://skll.readthedocs.org/en/latest/_modules/skll/metrics.html" rel="nofollow"&gt;implementation code&lt;/a&gt;, just in case you would like to contribute.&lt;/p&gt;

&lt;p&gt;Note: Cohen''s Kappa is also &lt;a href="https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/metrics/classification.py#L260-L308" rel="nofollow"&gt;implemented in Scikit-Learn&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Yes, &lt;a href="http://machinelearningmastery.com/classification-accuracy-is-not-enough-more-performance-measures-you-can-use/" rel="nofollow"&gt;there&lt;/a&gt; &lt;a href="http://blog.dato.com/how-to-evaluate-machine-learning-models-part-2a-classification-metrics" rel="nofollow"&gt;are&lt;/a&gt; &lt;a href="http://scikit-learn.org/stable/modules/model_evaluation.html" rel="nofollow"&gt;alternatives&lt;/a&gt; to the Cohen Kappa metric.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9041, 2, '2015-11-26 07:07:15.133', 1, NULL, NULL, '&lt;p&gt;Even if the number of clusters were equal, kt rarely holds that there is a one on one match of clusters to classes. For example, one class may be split into two clusters, and two classes may be indiscernible and thus in the same cluster.&lt;/p&gt;

&lt;p&gt;In fact &lt;strong&gt;clustering is most interesting if it &lt;em&gt;differs&lt;/em&gt; from your classes&lt;/strong&gt;!&lt;/p&gt;

&lt;p&gt;To compare clusterings (and classes) a number of metrics have been developed. Most are based on &lt;strong&gt;pair counting&lt;/strong&gt;, and the Rand index is one of the most popular.&lt;/p&gt;

&lt;p&gt;Instead of objects, you look at &lt;strong&gt;pairs of objects&lt;/strong&gt;: are they in the same cluster and same class? Then the pair is "true positive".&lt;/p&gt;

&lt;p&gt;See Wikipedia for more details.&lt;/p&gt;
', NULL, NULL, '''/em'':60C ''/p'':47C,65C,97C,125C,132C ''/strong'':64C,86C,109C ''base'':81C ''class'':24C,28C,37C,63C,71C,118C ''cluster'':7C,22C,34C,46C,52C,69C,115C ''compar'':68C ''count'':85C ''detail'':131C ''develop'':78C ''differ'':59C ''em'':58C ''equal'':9C ''even'':2C ''exampl'':26C ''fact'':50C ''hold'':12C ''index'':90C ''indiscern'':40C ''instead'':99C ''interest'':55C ''kt'':10C ''look'':103C ''match'':20C ''may'':29C,38C ''metric'':75C ''number'':5C,73C ''object'':101C,108C ''one'':17C,19C,27C,92C ''p'':1C,48C,66C,98C,126C ''pair'':84C,106C,121C ''popular'':96C ''posit'':124C ''rand'':89C ''rare'':11C ''see'':127C ''split'':31C ''strong'':51C,83C,105C ''thus'':42C ''true'':123C ''two'':33C,36C ''wikipedia'':128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9042, 5, '2015-11-26 09:53:45.897', 0, NULL, NULL, '&lt;p&gt;A &lt;a href="https://en.wikipedia.org/wiki/Confusion_matrix" rel="nofollow"&gt;confusion matrix&lt;/a&gt; is a special contingency table used to evaluate the predictive accuracy of a classifier.  Predicted classes are listed in rows (or columns) &amp;amp; actual classes in columns (or rows), with counts of the cases in each combination listed in each cell.  All cases represented along the main diagonal are accurately classified, while the off-diagonal elements are misclassified.  Inspection of the confusion matrix can identify which classes tend to be ''confused'' for each other.  The confusion matrix also allows the calculation of model performance metrics such as sensitivity and specificity, precision and recall, positive and negative predictive value, etc.  &lt;/p&gt;

&lt;p&gt;Here is an example confusion matrix from a model of Fisher''s iris data with good accuracy.  The model occasionally confuses &lt;code&gt;versicolor&lt;/code&gt; and &lt;code&gt;virginica&lt;/code&gt;, but never misclassified either as &lt;code&gt;setosa&lt;/code&gt;.  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;                      Actual:
             setosa versicolor virginica 
Predicted:
  setosa         50          0         0  
  versicolor      0         47         3  
  virginica       0          4        46 
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':12C ''/code'':138C,142C,150C,171C ''/p'':113C,151C ''/pre'':172C ''/wiki/confusion_matrix'':7C ''0'':161C,162C,164C,168C ''3'':166C ''4'':169C ''46'':170C ''47'':165C ''50'':160C ''accur'':62C ''accuraci'':23C,131C ''actual'':36C,154C ''allow'':92C ''along'':57C ''also'':91C ''amp'':35C ''calcul'':94C ''case'':46C,55C ''cell'':53C ''class'':28C,37C,80C ''classifi'':26C,63C ''code'':136C,140C,148C,153C ''column'':34C,39C ''combin'':49C ''confus'':10C,75C,84C,89C,119C,135C ''conting'':16C ''count'':43C ''data'':128C ''diagon'':60C,68C ''either'':146C ''element'':69C ''en.wikipedia.org'':6C ''en.wikipedia.org/wiki/confusion_matrix'':5C ''etc'':112C ''evalu'':20C ''exampl'':118C ''fisher'':125C ''good'':130C ''href'':4C ''identifi'':78C ''inspect'':72C ''iri'':127C ''list'':30C,50C ''main'':59C ''matrix'':11C,76C,90C,120C ''metric'':98C ''misclassifi'':71C,145C ''model'':96C,123C,133C ''negat'':109C ''never'':144C ''nofollow'':9C ''occasion'':134C ''off-diagon'':66C ''p'':1C,114C ''perform'':97C ''posit'':107C ''pre'':152C ''precis'':104C ''predict'':22C,27C,110C,158C ''recal'':106C ''rel'':8C ''repres'':56C ''row'':32C,41C ''sensit'':101C ''setosa'':149C,155C,159C ''special'':15C ''specif'':103C ''tabl'':17C ''tend'':81C ''use'':18C ''valu'':111C ''versicolor'':137C,156C,163C ''virginica'':141C,157C,167C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9045, 4, '2015-11-26 09:54:27.533', 0, NULL, NULL, 'Survival analysis is concerned with modelling the time before subjects change state, typically time until death or failure. One key feature of such data is that they can be censored, that is, some subjects will not have changed state before the study ends. ', NULL, NULL, '''analysi'':2C ''censor'':30C ''chang'':11C,38C ''concern'':4C ''data'':24C ''death'':16C ''end'':43C ''failur'':18C ''featur'':21C ''key'':20C ''model'':6C ''one'':19C ''state'':12C,39C ''studi'':42C ''subject'':10C,34C ''surviv'':1C ''time'':8C,14C ''typic'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9046, 5, '2015-11-26 09:55:44.8', 0, NULL, NULL, '&lt;p&gt;Supervised learning is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal).&lt;/p&gt;
', NULL, NULL, '''/p'':53C ''also'':48C ''call'':49C ''consist'':20C,35C ''data'':16C,19C ''desir'':45C ''exampl'':26C,31C ''function'':12C ''infer'':10C ''input'':38C ''label'':14C ''learn'':3C,7C,29C ''machin'':6C ''object'':39C ''output'':46C ''p'':1C ''pair'':34C ''set'':23C ''signal'':52C ''supervis'':2C,28C ''supervisori'':51C ''task'':8C ''train'':15C,18C,25C ''typic'':40C ''valu'':47C ''vector'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9111, 2, '2015-11-28 20:58:34.853', 0, NULL, NULL, '&lt;p&gt;Short answer: &lt;/p&gt;

&lt;p&gt;No, there is no difference between labelling and classification. &lt;/p&gt;

&lt;p&gt;Class - a set or category of things having some property or attribute in common and differentiated from others by kind, type or quality. See ''category''.&lt;/p&gt;

&lt;p&gt;Label - word or phrase indicating that what follows belongs in a particular category or class.&lt;/p&gt;

&lt;p&gt;To classify something is to label it, they are the necessarily the same thing. The term labelling probably evolved because "label" allows you to avoid saying "class" which has other connotations in Computer Science. &lt;/p&gt;

&lt;p&gt;Label is much simpler, and in all cases, classification is just the act of putting labels on objects (or learning to correctly do so).&lt;/p&gt;

&lt;p&gt;The discrepancy you''re seeing in the use of labelling/classification comes from the simple fact that a title like:&lt;/p&gt;

&lt;p&gt;"Semantic Classification: Classification of 3D Entities Based on Spatial Feature Descriptors"
or
"Knowledge-based classification and tissue classification of MR images of human brain"&lt;/p&gt;

&lt;p&gt;Sound really awkward.&lt;/p&gt;

&lt;p&gt;Like most academic paper titles, these are just overly complex descriptions of what is in the paper that explain exactly what is going on without sounding redundant.&lt;/p&gt;

&lt;p&gt;TL;DR - Don''t get hung up on terminology!&lt;/p&gt;
', NULL, NULL, '''/p'':4C,15C,42C,59C,95C,121C,142C,169C,174C,203C,214C ''3d'':148C ''academ'':178C ''act'':109C ''allow'':82C ''answer'':3C ''attribut'':28C ''avoid'':85C ''awkward'':173C ''base'':150C,158C ''belong'':52C ''brain'':168C ''case'':104C ''categori'':21C,41C,56C ''class'':17C,58C,87C ''classif'':14C,105C,145C,146C,159C,162C ''classifi'':62C ''come'':133C ''common'':30C ''complex'':185C ''comput'':93C ''connot'':91C ''correct'':118C ''descript'':186C ''descriptor'':154C ''differ'':10C ''differenti'':32C ''discrep'':124C ''dr'':206C ''entiti'':149C ''evolv'':79C ''exact'':195C ''explain'':194C ''fact'':137C ''featur'':153C ''follow'':51C ''get'':209C ''go'':198C ''human'':167C ''hung'':210C ''imag'':165C ''indic'':48C ''kind'':36C ''knowledg'':157C ''knowledge-bas'':156C ''label'':12C,44C,66C,77C,81C,97C,112C ''labelling/classification'':132C ''learn'':116C ''like'':141C,176C ''mr'':164C ''much'':99C ''necessarili'':71C ''object'':114C ''other'':34C ''over'':184C ''p'':1C,5C,16C,43C,60C,96C,122C,143C,170C,175C,204C ''paper'':179C,192C ''particular'':55C ''phrase'':47C ''probabl'':78C ''properti'':26C ''put'':111C ''qualiti'':39C ''re'':126C ''realli'':172C ''redund'':202C ''say'':86C ''scienc'':94C ''see'':40C,127C ''semant'':144C ''set'':19C ''short'':2C ''simpl'':136C ''simpler'':100C ''someth'':63C ''sound'':171C,201C ''spatial'':152C ''term'':76C ''terminolog'':213C ''thing'':23C,74C ''tissu'':161C ''titl'':140C,180C ''tl'':205C ''type'':37C ''use'':130C ''without'':200C ''word'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9048, 5, '2015-11-26 09:56:54.963', 0, NULL, NULL, '&lt;p&gt;Ranking means ordering data from highest to lowest or &lt;em&gt;vice versa&lt;/em&gt;.  Statistical "ranks" are normally given the values 1, 2, 3, ..., up to the sample size, assigned (respectively) to values or attributes which have the highest (or lowest) rank, the second highest (/lowest) rank, and so on.&lt;/p&gt;

&lt;p&gt;A standard notation is to bracket subscripts when denoting ranks: that is, for a batch of numbers $(x_1, x_2, \ldots, x_n)$ (written in any order), the sorted values are written $(x_{[1]}, x_{[2]}, \ldots, x_{[n]})$.&lt;/p&gt;

&lt;p&gt;Since the original values being ranked can sometimes be tied (have a &lt;em&gt;weak order&lt;/em&gt;), tied ranks can occur where more than one rank is to be assigned to indistinguishable values. This issue is resolved in various ways, but commonly in statistics the average rank allocated to the tied (indistinguishable) observations is assigned to each of them.  Allocating &lt;em&gt;average ranks&lt;/em&gt; is sometimes called &lt;em&gt;fractional ranking&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;Reference: &lt;a href="http://en.wikipedia.org/wiki/Ranking#Ranking_in_statistics" rel="nofollow"&gt;Wikipedia: Ranking in statistics&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':179C ''/em'':14C,110C,157C,164C ''/lowest'':46C ''/p'':51C,93C,165C,180C ''/wiki/ranking#ranking_in_statistics'':172C ''1'':22C,71C,87C ''2'':23C,73C,89C ''3'':24C ''alloc'':141C,153C ''assign'':30C,123C,148C ''attribut'':35C ''averag'':139C,155C ''batch'':67C ''bracket'':58C ''call'':160C ''common'':135C ''data'':5C ''denot'':61C ''em'':11C,107C,154C,161C ''en.wikipedia.org'':171C ''en.wikipedia.org/wiki/ranking#ranking_in_statistics'':170C ''fraction'':162C ''given'':19C ''highest'':7C,39C,45C ''href'':169C ''indistinguish'':125C,145C ''issu'':128C ''ldot'':74C,90C ''lowest'':9C,41C ''mean'':3C ''n'':76C,92C ''nofollow'':174C ''normal'':18C ''notat'':55C ''number'':69C ''observ'':146C ''occur'':114C ''one'':118C ''order'':4C,80C,109C ''origin'':97C ''p'':1C,52C,94C,166C ''rank'':2C,16C,42C,47C,62C,100C,112C,119C,140C,156C,163C,176C ''refer'':167C ''rel'':173C ''resolv'':130C ''respect'':31C ''sampl'':28C ''second'':44C ''sinc'':95C ''size'':29C ''sometim'':102C,159C ''sort'':82C ''standard'':54C ''statist'':15C,137C,178C ''subscript'':59C ''tie'':104C,111C,144C ''valu'':21C,33C,83C,98C,126C ''various'':132C ''versa'':13C ''vice'':12C ''way'':133C ''weak'':108C ''wikipedia'':175C ''written'':77C,85C ''x'':70C,72C,75C,86C,88C,91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9066, 2, '2015-11-26 16:21:28.057', 2, NULL, NULL, '&lt;p&gt;I assume you have inspected your topic-by-word matrix, and that is why you say you have ''trash'' topics.  If no words are strongly associated with a given topic, it may not be a useful topic.  If you find many of these, you can re-run the experiment with fewer topics.  &lt;/p&gt;

&lt;p&gt;If instead there are words strongly associated with these topics, you just don''t see the cohesive meaning behind the words associated with the topic, then these are not exactly "trash" topics.  They are still capturing the latent structure of the data, it just isn''t something easily deemed a "topic" in the traditional sense of the word.&lt;/p&gt;

&lt;p&gt;Another thing to remember is that the hyperparameters also control the shape of your resulting distributions.  &lt;a href="http://stats.stackexchange.com/a/37444/95325"&gt;This answer&lt;/a&gt; does a great job explaining alpha and beta.&lt;/p&gt;
', NULL, NULL, '''/a'':141C ''/a/37444/95325'':138C ''/p'':56C,116C,150C ''alpha'':147C ''also'':126C ''anoth'':118C ''answer'':140C ''associ'':28C,64C,79C ''assum'':3C ''behind'':76C ''beta'':149C ''captur'':93C ''cohes'':74C ''control'':127C ''data'':99C ''deem'':106C ''distribut'':133C ''easili'':105C ''exact'':87C ''experi'':52C ''explain'':146C ''fewer'':54C ''find'':42C ''given'':31C ''great'':144C ''href'':135C ''hyperparamet'':125C ''inspect'':6C ''instead'':59C ''isn'':102C ''job'':145C ''latent'':95C ''mani'':43C ''matrix'':12C ''may'':34C ''mean'':75C ''p'':1C,57C,117C ''re'':49C ''re-run'':48C ''rememb'':121C ''result'':132C ''run'':50C ''say'':18C ''see'':72C ''sens'':112C ''shape'':129C ''someth'':104C ''stats.stackexchange.com'':137C ''stats.stackexchange.com/a/37444/95325'':136C ''still'':92C ''strong'':27C,63C ''structur'':96C ''thing'':119C ''topic'':9C,22C,32C,39C,55C,67C,82C,89C,108C ''topic-by-word'':8C ''tradit'':111C ''trash'':21C,88C ''use'':38C ''word'':11C,25C,62C,78C,115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9060, 1, '2015-11-25 10:30:37.023', 1, 64, 'Dealing with big data', '&lt;p&gt;I am on a project dealing with a lot of data in the form of images and videos (Data related to wind engineering). My requirement is to build a predictive algorithm based on the data I have. I have found many tools with which I can analyse the data where each tool has its own advantages and disadvantages. Big data being really new to me, I find it very difficult to choose a platform to start with. There should be other people here who should have dealt with similar situations.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;What criteria should I mainly take into account before selecting a
tool for analysing big data?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Some of the Criteria that I have taken into account : Visualization, Interaction, Security, Data Access and integration, Speed of response, Integrated Data Mining, Pattern Matching, Ease of use etc. As you can see the list that I have made for the criteria comes from the extensive reading of different articles on the topic. But I can''t narrow down the list nor find the individual contribution of these criteria in the various tools available for analysis.&lt;/p&gt;

&lt;p&gt;Let me also list some of the tools that I found after googling : Knime, Statistica 2, Rapidminer, Orange, WEKA, KEEL, R and RATTLE. &lt;/p&gt;

&lt;p&gt;On what basis could I choose a tool from a list of tools that perform similar tasks?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE based on Comment&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Aim : To develop a software that analyses the data coming from the wind mills and generate reports. The software should be able to predict when a wind mill can fail based on the analysis.&lt;/p&gt;

&lt;p&gt;The project is still in the phase of gathering User Requirements. Maybe i am so early to come into conclusions about what tool should be used.&lt;/p&gt;

&lt;p&gt;Someone else suggested that I should be finalise the requirements and then think about a tool that can help me get things done. So is it possible that I find what and how things should be analysed before finding a tool? And is it also possible that I find an algorithm for predictive analysis without knowing what would be the results of the tool after analysis. &lt;/p&gt;
', 'bigdata  software-recommendation', NULL, '''/li'':119C ''/p'':100C,198C,223C,242C,250C,286C,314C,382C ''/strong'':249C ''/ul'':120C ''2'':215C ''abl'':273C ''access'':136C ''account'':110C,131C ''advantag'':65C ''aim'':252C ''algorithm'':40C,366C ''also'':202C,360C ''analys'':56C,116C,258C,352C ''analysi'':197C,285C,369C,381C ''articl'':171C ''avail'':195C ''base'':41C,246C,282C ''basi'':227C ''big'':3A,68C,117C ''bigdata'':5B ''build'':37C ''choos'':81C,230C ''come'':164C,261C,305C ''comment'':248C ''conclus'':307C ''contribut'':187C ''could'':228C ''criteria'':104C,125C,163C,190C ''data'':4A,20C,28C,44C,58C,69C,118C,135C,143C,260C ''deal'':1A,15C ''dealt'':96C ''develop'':254C ''differ'':170C ''difficult'':79C ''disadvantag'':67C ''done'':338C ''earli'':303C ''eas'':147C ''els'':317C ''engin'':32C ''etc'':150C ''extens'':167C ''fail'':281C ''finalis'':323C ''find'':76C,184C,345C,354C,364C ''form'':23C ''found'':49C,210C ''gather'':296C ''generat'':267C ''get'':336C ''googl'':212C ''help'':334C ''imag'':25C ''individu'':186C ''integr'':138C,142C ''interact'':133C ''keel'':219C ''knime'':213C ''know'':371C ''let'':200C ''li'':102C ''list'':156C,182C,203C,235C ''lot'':18C ''made'':160C ''main'':107C ''mani'':50C ''match'':146C ''mayb'':299C ''mill'':265C,279C ''mine'':144C ''narrow'':179C ''new'':72C ''orang'':217C ''p'':9C,121C,199C,224C,243C,251C,287C,315C ''pattern'':145C ''peopl'':91C ''perform'':239C ''phase'':294C ''platform'':83C ''possibl'':342C,361C ''predict'':39C,275C,368C ''project'':14C,289C ''r'':220C ''rapidmin'':216C ''rattl'':222C ''read'':168C ''realli'':71C ''recommend'':8B ''relat'':29C ''report'':268C ''requir'':34C,298C,325C ''respons'':141C ''result'':376C ''secur'':134C ''see'':154C ''select'':112C ''similar'':98C,240C ''situat'':99C ''softwar'':7B,256C,270C ''software-recommend'':6B ''someon'':316C ''speed'':139C ''start'':85C ''statistica'':214C ''still'':291C ''strong'':244C ''suggest'':318C ''take'':108C ''taken'':129C ''task'':241C ''thing'':337C,349C ''think'':328C ''tool'':51C,61C,114C,194C,207C,232C,237C,310C,331C,356C,379C ''topic'':174C ''ul'':101C ''updat'':245C ''use'':149C,313C ''user'':297C ''various'':193C ''video'':27C ''visual'':132C ''weka'':218C ''wind'':31C,264C,278C ''without'':370C ''would'':373C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9061, 2, '2015-11-25 14:02:38.9', 3, NULL, NULL, '&lt;p&gt;Syncfusion provides a &lt;a href="http://www.syncfusion.com/products/big-data" rel="nofollow"&gt;Big data platform&lt;/a&gt; which is an easy to use Hadoop distribution for Windows. It can help you get started quickly. Syncfusion also provides a &lt;a href="http://www.syncfusion.com/products/data-science/predictive-analytics" rel="nofollow"&gt;PMML processing library&lt;/a&gt; using which you can execute predictive analytic models. There is a &lt;a href="http://www.syncfusion.com/products/dashboard" rel="nofollow"&gt;Dashboard platform&lt;/a&gt; also available that can help visualize the data.&lt;/p&gt;

&lt;p&gt;All of the above are available for free through the &lt;a href="http://www.syncfusion.com/products/communitylicense" rel="nofollow"&gt;community license&lt;/a&gt; if you qualify.&lt;/p&gt;

&lt;p&gt;Note: I work for Syncfusion.&lt;/p&gt;
', NULL, NULL, '''/a'':15C,47C,68C,98C ''/p'':77C,102C,109C ''/products/big-data'':9C ''/products/communitylicense'':93C ''/products/dashboard'':63C ''/products/data-science/predictive-analytics'':41C ''also'':34C,69C ''analyt'':54C ''avail'':70C,84C ''big'':12C ''communiti'':96C ''dashboard'':66C ''data'':13C,76C ''distribut'':23C ''easi'':19C ''execut'':52C ''free'':86C ''get'':30C ''hadoop'':22C ''help'':28C,73C ''href'':6C,38C,60C,90C ''librari'':46C ''licens'':97C ''model'':55C ''nofollow'':11C,43C,65C,95C ''note'':104C ''p'':1C,78C,103C ''platform'':14C,67C ''pmml'':44C ''predict'':53C ''process'':45C ''provid'':3C,35C ''qualifi'':101C ''quick'':32C ''rel'':10C,42C,64C,94C ''start'':31C ''syncfus'':2C,33C,108C ''use'':21C,48C ''visual'':74C ''window'':25C ''work'':106C ''www.syncfusion.com'':8C,40C,62C,92C ''www.syncfusion.com/products/big-data'':7C ''www.syncfusion.com/products/communitylicense'':91C ''www.syncfusion.com/products/dashboard'':61C ''www.syncfusion.com/products/data-science/predictive-analytics'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9104, 4, '2015-11-28 15:04:14.043', 0, NULL, NULL, 'Descriptive statistics summarize features of a sample, such as mean and standard deviations, median and quartiles, the maximum and minimum. With multiple variables, may include correlations and crosstabs. Can include visual displays - boxplots, histograms, scatterplots and so on.&#x0d;
', NULL, NULL, '''boxplot'':33C ''correl'':26C ''crosstab'':28C ''descript'':1C ''deviat'':13C ''display'':32C ''featur'':4C ''histogram'':34C ''includ'':25C,30C ''maximum'':18C ''may'':24C ''mean'':10C ''median'':14C ''minimum'':20C ''multipl'':22C ''quartil'':16C ''sampl'':7C ''scatterplot'':35C ''standard'':12C ''statist'':2C ''summar'':3C ''variabl'':23C ''visual'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9069, 1, '2015-11-27 02:54:52.78', 3, 56, 'Which Spark MLlib regression algorithm is suitable for numeric predictions based on non-numeric features?', '&lt;p&gt;I am working on &lt;code&gt;Spark MLlib&lt;/code&gt; and have a project where I have to make predictions for numeric data based on non-numeric features. I am a bit confused about which &lt;code&gt;regression algorithm&lt;/code&gt; to use from &lt;code&gt;Spark MLlib&lt;/code&gt; library primarily due to being new at this. The algorithms present in Spark MLlib library are:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;-linear models (SVMs, logistic regression, linear regression)
-naive Bayes
-decision trees
-ensembles of trees (Random Forests and Gradient-Boosted Trees)
-isotonic regression 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Can anyone provide me some guidance as to which algorithm will be suitable for predictions for numeric data based on non-numeric features?&lt;/p&gt;
', 'machine-learning  regression  apache-spark', NULL, '''/code'':32C,62C,69C,112C ''/p'':86C,139C ''/pre'':113C ''algorithm'':5A,61C,79C,124C ''anyon'':116C ''apach'':22B ''apache-spark'':21B ''base'':11A,46C,133C ''bay'':97C ''bit'':55C ''boost'':108C ''code'':29C,59C,66C,88C ''confus'':56C ''data'':45C,132C ''decis'':98C ''due'':72C ''ensembl'':100C ''featur'':16A,51C,138C ''forest'':104C ''gradient'':107C ''gradient-boost'':106C ''guidanc'':120C ''isoton'':110C ''learn'':19B ''librari'':70C,84C ''linear'':89C,94C ''logist'':92C ''machin'':18B ''machine-learn'':17B ''make'':41C ''mllib'':3A,31C,68C,83C ''model'':90C ''naiv'':96C ''new'':75C ''non'':14A,49C,136C ''non-numer'':13A,48C,135C ''numer'':9A,15A,44C,50C,131C,137C ''p'':24C,114C ''pre'':87C ''predict'':10A,42C,129C ''present'':80C ''primarili'':71C ''project'':36C ''provid'':117C ''random'':103C ''regress'':4A,20B,60C,93C,95C,111C ''spark'':2A,23B,30C,67C,82C ''suitabl'':7A,127C ''svms'':91C ''tree'':99C,102C,109C ''use'':64C ''work'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9068, 2, '2015-11-26 17:31:49.537', 2, NULL, NULL, '&lt;p&gt;Both the answers from @Emre and @Madison May make good points about the issue at hand.  The problem is one of representing your string as a feature vector for input to the NN.  &lt;/p&gt;

&lt;p&gt;First, the problem depends on the size of the string you want to process.  Long strings containing may tokens (usually words) are often called documents in this setting.  There are separate methods for dealing with individual tokens/words.&lt;/p&gt;

&lt;p&gt;There are a number of ways to represent documents.  Many of them make the &lt;a href="https://en.wikipedia.org/wiki/Bag-of-words_model" rel="nofollow"&gt;bag-of-words&lt;/a&gt; assumption.  The simplest types represent the document as a vector of the counts of words, or term frequency (tf).  In order to eliminate the effects of document length, usually people prefer to normalize by the number of documents a term shows up in, document frequency (&lt;a href="https://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;tf-idf&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;Another approach is topic modeling, which learns a latent lower-dimensional representation of the data.  &lt;a href="https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation" rel="nofollow"&gt;LDA&lt;/a&gt; and &lt;a href="https://en.wikipedia.org/wiki/Latent_semantic_indexing" rel="nofollow"&gt;LSI/LSA&lt;/a&gt; are typical choices, but it''s important to remember this is unsupervised.  The representation learned will not necessarily be ideal for whatever supervised learning you''re doing with your NN.  If you want to do topic modeling, you might also try &lt;a href="https://www.cs.princeton.edu/~blei/papers/BleiMcAuliffe2007.pdf" rel="nofollow"&gt;supervised topic models&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;For individual words, you can use &lt;a href="https://code.google.com/p/word2vec/" rel="nofollow"&gt;word2vec&lt;/a&gt;, which leverages NNs to embed words into an arbitrary-sized space.  Similarity between two word vectors in this learned space tends to correspond to semantic similarity. &lt;/p&gt;

&lt;p&gt;A more recently pioneered approach is that of &lt;a href="https://cs.stanford.edu/~quocle/paragraph_vector.pdf" rel="nofollow"&gt;paragraph vectors&lt;/a&gt;, which first learns a word2vec-like word model, then builds on that representation to learn a distributed representation of sets of words (documents of any size).  This has shown state-of-the-art results in many applications. &lt;/p&gt;

&lt;p&gt;When using NNs in NLP, people often use different architectures, like &lt;a href="https://en.wikipedia.org/wiki/Recurrent_neural_network" rel="nofollow"&gt;Recurrent Neural Nets&lt;/a&gt; (like &lt;a href="https://en.wikipedia.org/wiki/Recurrent_neural_network#Long_short_term_memory_network" rel="nofollow"&gt;Long Short Term Memory&lt;/a&gt; networks).  In &lt;a href="http://www.aclweb.org/anthology/C14-1008" rel="nofollow"&gt;some cases&lt;/a&gt; people have even used &lt;a href="http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/" rel="nofollow"&gt;Convolutional Neural Networks&lt;/a&gt; on text.&lt;/p&gt;
', NULL, NULL, '''/a'':101C,157C,184C,194C,246C,263C,310C,373C,386C,398C,413C ''/anthology/c14-1008'':393C ''/p'':35C,74C,158C,247C,291C,350C,416C ''/p/word2vec/'':259C ''/tutorial/supervised/convolutionalneuralnetwork/'':407C ''/wiki/bag-of-words_model'':94C ''/wiki/latent_dirichlet_allocation'':180C ''/wiki/latent_semantic_indexing'':190C ''/wiki/recurrent_neural_network'':367C ''/wiki/recurrent_neural_network#long_short_term_memory_network'':379C ''/wiki/tf%e2%80%93idf'':151C ''/~blei/papers/bleimcauliffe2007.pdf'':240C ''/~quocle/paragraph_vector.pdf'':305C ''also'':234C ''anoth'':160C ''answer'':4C ''applic'':349C ''approach'':161C,297C ''arbitrari'':273C ''arbitrary-s'':272C ''architectur'':361C ''art'':345C ''assumpt'':102C ''bag'':98C ''bag-of-word'':97C ''build'':321C ''call'':60C ''case'':397C ''choic'':197C ''code.google.com'':258C ''code.google.com/p/word2vec/'':257C ''contain'':53C ''convolut'':410C ''correspond'':287C ''count'':114C ''cs.stanford.edu'':304C ''cs.stanford.edu/~quocle/paragraph_vector.pdf'':303C ''data'':175C ''deal'':70C ''depend'':40C ''differ'':360C ''dimension'':171C ''distribut'':328C ''document'':61C,84C,108C,128C,139C,145C,334C ''effect'':126C ''elimin'':124C ''emb'':268C ''emr'':6C ''en.wikipedia.org'':93C,150C,179C,189C,366C,378C ''en.wikipedia.org/wiki/bag-of-words_model'':92C ''en.wikipedia.org/wiki/latent_dirichlet_allocation'':178C ''en.wikipedia.org/wiki/latent_semantic_indexing'':188C ''en.wikipedia.org/wiki/recurrent_neural_network'':365C ''en.wikipedia.org/wiki/recurrent_neural_network#long_short_term_memory_network'':377C ''en.wikipedia.org/wiki/tf%e2%80%93idf'':149C ''even'':401C ''featur'':28C ''first'':37C,312C ''frequenc'':119C,146C ''good'':11C ''hand'':17C ''href'':91C,148C,177C,187C,237C,256C,302C,364C,376C,390C,404C ''ideal'':214C ''idf'':156C ''import'':201C ''individu'':72C,250C ''input'':31C ''issu'':15C ''latent'':168C ''lda'':183C ''learn'':166C,209C,218C,283C,313C,326C ''length'':129C ''leverag'':265C ''like'':317C,362C,374C ''long'':51C,382C ''lower'':170C ''lower-dimension'':169C ''lsi/lsa'':193C ''madison'':8C ''make'':10C,88C ''mani'':85C,348C ''may'':9C,54C ''memori'':385C ''method'':68C ''might'':233C ''model'':164C,231C,245C,319C ''necessarili'':212C ''net'':372C ''network'':387C,412C ''neural'':371C,411C ''nlp'':356C ''nn'':34C,224C ''nns'':266C,354C ''nofollow'':96C,153C,182C,192C,242C,261C,307C,369C,381C,395C,409C ''normal'':134C ''number'':79C,137C ''often'':59C,358C ''one'':21C ''order'':122C ''p'':1C,36C,75C,159C,248C,292C,351C ''paragraph'':308C ''peopl'':131C,357C,399C ''pioneer'':296C ''point'':12C ''prefer'':132C ''problem'':19C,39C ''process'':50C ''re'':220C ''recent'':295C ''recurr'':370C ''rel'':95C,152C,181C,191C,241C,260C,306C,368C,380C,394C,408C ''rememb'':203C ''repres'':23C,83C,106C ''represent'':172C,208C,324C,329C ''result'':346C ''semant'':289C ''separ'':67C ''set'':64C,331C ''short'':383C ''show'':142C ''shown'':340C ''similar'':276C,290C ''simplest'':104C ''size'':43C,274C,337C ''space'':275C,284C ''state'':342C ''state-of-the-art'':341C ''string'':25C,46C,52C ''supervis'':217C,243C ''tend'':285C ''term'':118C,141C,384C ''text'':415C ''tf'':120C,155C ''tf-idf'':154C ''token'':55C ''tokens/words'':73C ''topic'':163C,230C,244C ''tri'':235C ''two'':278C ''type'':105C ''typic'':196C ''ufldl.stanford.edu'':406C ''ufldl.stanford.edu/tutorial/supervised/convolutionalneuralnetwork/'':405C ''unsupervis'':206C ''use'':254C,353C,359C,402C ''usual'':56C,130C ''vector'':29C,111C,280C,309C ''want'':48C,227C ''way'':81C ''whatev'':216C ''word'':57C,100C,116C,251C,269C,279C,318C,333C ''word2vec'':262C,316C ''word2vec-like'':315C ''www.aclweb.org'':392C ''www.aclweb.org/anthology/c14-1008'':391C ''www.cs.princeton.edu'':239C ''www.cs.princeton.edu/~blei/papers/bleimcauliffe2007.pdf'':238C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9070, 2, '2015-11-27 03:48:58.973', 1, NULL, NULL, '&lt;p&gt;I would suggest &lt;code&gt;-ensembles of trees (Random Forests and Gradient-Boosted Trees)&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stats.stackexchange.com/questions/20416/how-should-decision-tree-splits-be-implemented-when-predicting-continuous-variab"&gt;Here&lt;/a&gt; is a nice reference for handling such data with decision trees.&lt;/p&gt;
', NULL, NULL, '''/a'':25C ''/code'':16C ''/p'':17C,37C ''/questions/20416/how-should-decision-tree-splits-be-implemented-when-predicting-continuous-variab'':23C ''boost'':14C ''code'':5C ''data'':33C ''decis'':35C ''ensembl'':6C ''forest'':10C ''gradient'':13C ''gradient-boost'':12C ''handl'':31C ''href'':20C ''nice'':28C ''p'':1C,18C ''random'':9C ''refer'':29C ''stats.stackexchange.com'':22C ''stats.stackexchange.com/questions/20416/how-should-decision-tree-splits-be-implemented-when-predicting-continuous-variab'':21C ''suggest'':4C ''tree'':8C,15C,36C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9071, 2, '2015-11-27 04:15:23.57', 0, NULL, NULL, '&lt;p&gt;A linear regression would work, but the real issue here is &lt;a href="http://spark.apache.org/docs/latest/mllib-feature-extraction.html" rel="nofollow"&gt;feature extraction&lt;/a&gt;. You have to encode your &lt;a href="https://en.wikipedia.org/wiki/Categorical_variable#Categorical_variables_and_regression" rel="nofollow"&gt;categorical features&lt;/a&gt; somehow, likely by vectorizing them. You can one-hot encode your features, treat them as text and countVectorize them, etc.&lt;/p&gt;
', NULL, NULL, '''/a'':22C,37C ''/docs/latest/mllib-feature-extraction.html'':17C ''/p'':59C ''/wiki/categorical_variable#categorical_variables_and_regression'':32C ''categor'':35C ''countvector'':56C ''en.wikipedia.org'':31C ''en.wikipedia.org/wiki/categorical_variable#categorical_variables_and_regression'':30C ''encod'':26C,48C ''etc'':58C ''extract'':21C ''featur'':20C,36C,50C ''hot'':47C ''href'':14C,29C ''issu'':10C ''like'':39C ''linear'':3C ''nofollow'':19C,34C ''one'':46C ''one-hot'':45C ''p'':1C ''real'':9C ''regress'':4C ''rel'':18C,33C ''somehow'':38C ''spark.apache.org'':16C ''spark.apache.org/docs/latest/mllib-feature-extraction.html'':15C ''text'':54C ''treat'':51C ''vector'':41C ''work'':6C ''would'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9074, 1, '2015-11-27 09:05:46.147', 3, 149, 'Is there a difference between "classification" and "labeling"?', '&lt;p&gt;Until recently, I thought that "labeling" and "classification" are synonyms. But when I started another question about &lt;a href="http://stackoverflow.com/q/33947823/562769"&gt;terminology in computer vision&lt;/a&gt; I thought about it: Is there a difference between "labeling" and "classification"?&lt;/p&gt;

&lt;p&gt;I thought that the "class" is the concept you want to detect and "label" is what you assign to data. So the "class" is a concept which leads to the data and "label" is only the name. Hence "labeling" would be the same as "classification" as both want to make a statement about the underlying class which lead to the data.&lt;/p&gt;

&lt;h2&gt;Articles&lt;/h2&gt;

&lt;p&gt;A quick search via Google Scholar revealed that some articles use both terms in the title:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Markus Eich, Malgorzata Dabrowska, and Frank Kirchner: "Semantic Labeling: Classification of 3D Entities Based on Spatial Feature Descriptors"&lt;/li&gt;
&lt;li&gt;Chunlin Li, Dmitry B. Goldgof, and Lawrence 0. Hall: "Knowledge-based classification and tissue labeling of MR images of human brain"&lt;/li&gt;
&lt;li&gt;Ray Blanchard: "The classification and labeling of nonhomosexual gender dysphorias" - another research area but probably it is the same difference between the two words?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So I guess there is a difference between "labeling" and "classification". What is the difference?&lt;/p&gt;

&lt;h2&gt;Google N-Gram&lt;/h2&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/zpn9y.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/zpn9y.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;classification seems to be a much boarder term.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9200, 2, '2015-12-04 11:01:26.237', 0, NULL, NULL, '&lt;p&gt;I guess classification is a better term here.
One idea would be to calculate histogram of an image.
Make 32 bins from the histogram.
So if an image has Red, Blue, Green channels, each having 256 values, you will get a vector of 32*3=96 dimensions.
You can run SVM or Naive Bayes on it.&lt;/p&gt;
', NULL, NULL, '''/p'':58C ''256'':37C ''3'':46C ''32'':21C,45C ''96'':47C ''bay'':55C ''better'':7C ''bin'':22C ''blue'':32C ''calcul'':15C ''channel'':34C ''classif'':4C ''dimens'':48C ''get'':41C ''green'':33C ''guess'':3C ''histogram'':16C,25C ''idea'':11C ''imag'':19C,29C ''make'':20C ''naiv'':54C ''one'':10C ''p'':1C ''red'':31C ''run'':51C ''svm'':52C ''term'':8C ''valu'':38C ''vector'':43C ''would'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9092, 2, '2015-11-28 09:07:14.723', 3, NULL, NULL, '&lt;p&gt;No, this is not in general true. For a map-only job, or map and reduce, MapReduce is a bit faster. It''s more optimized for this pattern and a lot easier to scale / tune. However, the problem is that few problems are just one or two operations. Once you have a chain of them to execute, considering the entire DAG and execution plan is a win even if memory is not used for persistence.&lt;/p&gt;

&lt;p&gt;Developer productivity also related to efficiency. If I don''t have to spend my time writing lower-level code then I can spend more time designing a more sophisticated distributed application that could be faster.&lt;/p&gt;

&lt;p&gt;You can see this in Crunch, for example, which gives you a high-level language similar to Spark Java on MapReduce, and I''d say you can write more efficient Crunch jobs in a fixed time than M/R.&lt;/p&gt;
', NULL, NULL, '''/p'':78C,116C,157C ''also'':82C ''applic'':111C ''bit'':22C ''chain'':55C ''code'':99C ''consid'':60C ''could'':113C ''crunch'':123C,149C ''d'':142C ''dag'':63C ''design'':106C ''develop'':80C ''distribut'':110C ''easier'':34C ''effici'':85C,148C ''entir'':62C ''even'':70C ''exampl'':125C ''execut'':59C,65C ''faster'':23C,115C ''fix'':153C ''general'':7C ''give'':127C ''high'':131C ''high-level'':130C ''howev'':38C ''java'':137C ''job'':14C,150C ''languag'':133C ''level'':98C,132C ''lot'':33C ''lower'':97C ''lower-level'':96C ''m/r'':156C ''map'':12C,16C ''map-on'':11C ''mapreduc'':19C,139C ''memori'':72C ''one'':47C ''oper'':50C ''optim'':27C ''p'':1C,79C,117C ''pattern'':30C ''persist'':77C ''plan'':66C ''problem'':40C,44C ''product'':81C ''reduc'':18C ''relat'':83C ''say'':143C ''scale'':36C ''see'':120C ''similar'':134C ''sophist'':109C ''spark'':136C ''spend'':92C,103C ''time'':94C,105C,154C ''true'':8C ''tune'':37C ''two'':49C ''use'':75C ''win'':69C ''write'':95C,146C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9085, 1, '2015-11-27 17:44:20.98', 6, 190, 'Reducing the effect of down voters with rating system', '&lt;p&gt;I  have a site in which users rate things in a 1-5 star system. Once an item reaches the top of the charts, some users tend to start rating it 1 star even though it got a majority of 4-5 stars to get where it''s at. It''s not rampant, I would say 10-20% of the new votes are 1''s. Clearly they are trying to manipulate the rating system, and I want to prevent that.&lt;/p&gt;

&lt;p&gt;The current way I am doing that is by having a "reasonable window" of what I consider to be a legitimate vote.&lt;/p&gt;

&lt;p&gt;For items with less than 10 votes;
I currently do nothing and take the mean as it''s rating. &lt;/p&gt;

&lt;p&gt;Once an item starts getting more than 10 votes I tie them to a window of their mean. This window is defined as&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Window = 4.5 - Log(TotalVotes, 10);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So a reasonable vote range is then &lt;code&gt;(Mean - Window) thru (Mean + Window)&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Once the reasonable vote range is found, the "Rating" is just the mean of all the reasonable votes (those who fall in the reasonable range).&lt;/p&gt;

&lt;p&gt;This means an item with a real mean of 4.2 with 100 votes would have a window of &lt;code&gt;4.5-Log(100,10) = 2.5&lt;/code&gt;, so if that item gets a 1 star vote, it will be ignored in the rating. However, the 1 star will still effect the underlying mean.&lt;/p&gt;

&lt;p&gt;This has worked good in general but the issue is when an item''s &lt;code&gt;Mean - Window&lt;/code&gt; is just at the brink of 1.0, as soon as it dips below 1.0 every 1 star vote now is included in the rating and the rating drops significantly even the difference before and after may have just been one more 1 star rating.&lt;/p&gt;

&lt;p&gt;I need a better system/way of accomplishing to filter out these 1 star ratings, and not just them but handle the situation in where someone may get their friends to upvote an item 10 votes and all 5 stars, where its true rating may be more 3 star.&lt;/p&gt;

&lt;p&gt;Looking for any recommendations of how to handle user-driven rating systems and normalizing outlier votes.&lt;/p&gt;
', 'statistics  normalization', NULL, '''-20'':70C ''-5'':25C,54C ''/code'':171C,187C,241C,287C ''/p'':93C,117C,138C,163C,188C,215C,268C,332C,382C,401C ''/pre'':172C ''1'':24C,44C,76C,248C,260C,303C,329C,345C ''1.0'':294C,301C ''10'':69C,124C,147C,170C,239C,367C ''100'':228C,238C ''2.5'':240C ''3'':380C ''4'':53C ''4.2'':226C ''4.5'':167C,236C ''5'':371C ''accomplish'':340C ''better'':337C ''brink'':292C ''chart'':36C ''clear'':78C ''code'':165C,181C,235C,284C ''consid'':111C ''current'':96C,127C ''defin'':161C ''differ'':319C ''dip'':299C ''driven'':394C ''drop'':315C ''effect'':3A,264C ''even'':46C,317C ''everi'':302C ''fall'':210C ''filter'':342C ''found'':196C ''friend'':362C ''general'':275C ''get'':57C,144C,246C,360C ''good'':273C ''got'':49C ''handl'':353C,391C ''howev'':258C ''ignor'':254C ''includ'':308C ''issu'':278C ''item'':30C,120C,142C,220C,245C,282C,366C ''legitim'':115C ''less'':122C ''log'':168C,237C ''look'':384C ''major'':51C ''manipul'':83C ''may'':323C,359C,377C ''mean'':133C,157C,182C,185C,202C,218C,224C,267C,285C ''need'':335C ''new'':73C ''normal'':11B,398C ''noth'':129C ''one'':327C ''outlier'':399C ''p'':12C,94C,118C,139C,173C,189C,216C,269C,333C,383C ''pre'':164C ''prevent'':91C ''rampant'':65C ''rang'':178C,194C,214C ''rate'':8A,20C,42C,85C,137C,198C,257C,311C,314C,331C,347C,376C,395C ''reach'':31C ''real'':223C ''reason'':106C,176C,192C,206C,213C ''recommend'':387C ''reduc'':1A ''say'':68C ''signific'':316C ''site'':16C ''situat'':355C ''someon'':358C ''soon'':296C ''star'':26C,45C,55C,249C,261C,304C,330C,346C,372C,381C ''start'':41C,143C ''statist'':10B ''still'':263C ''system'':9A,27C,86C,396C ''system/way'':338C ''take'':131C ''tend'':39C ''thing'':21C ''though'':47C ''thru'':184C ''tie'':150C ''top'':33C ''totalvot'':169C ''tri'':81C ''true'':375C ''under'':266C ''upvot'':364C ''user'':19C,38C,393C ''user-driven'':392C ''vote'':74C,116C,125C,148C,177C,193C,207C,229C,250C,305C,368C,400C ''voter'':6A ''want'':89C ''way'':97C ''window'':107C,154C,159C,166C,183C,186C,233C,286C ''work'':272C ''would'':67C,230C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9086, 1, '2015-11-27 18:11:03.323', 3, 257, 'Server log analysis using machine learning', '&lt;p&gt;I was assigned this task to analyze the server logs of our application which contains exception logs, database logs event logs etc. I am new to machine learning, we use Spark with elastic search and Sparks MLlib(or PredictionIO).An example of the desired result would be to be able to predict based on the exception logs gathered to be able to predict which user is more likely to cause the next exception and at which feature(and bunch of other stuff to keep track and improve optimization of the application).&lt;/p&gt;

&lt;p&gt;I have successfully been able to ingest data from ElasticSearch into spark and create DataFrames and map the data needed. What I would like to know is how do I approach the Machine Learning aspect of my implementation. I''ve been through articles and papers that talk about Data preprocessing, training the data models and creating labels and then generating predictions. &lt;/p&gt;

&lt;p&gt;The questions I have are&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;How do I approach transforming the exiting log data into numerical vectors which can be used to datasets to be trained.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;What algorithms do I use to train my dataset(with the limited knowledge i''ve gathered the past couple days, i was thinking bout implementing linear regression, please suggest which implementation would be best)&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Just looking for suggestions on how to approach this problem. &lt;/p&gt;

&lt;p&gt;Thank You.&lt;/p&gt;
', 'machine-learning  predictive-modeling  apache-spark', NULL, '''/li'':204C,242C ''/p'':108C,171C,178C,203C,241C,255C,259C ''/ul'':243C ''abl'':66C,77C,114C ''algorithm'':208C ''analysi'':3A ''analyz'':23C ''apach'':14B ''apache-spark'':13B ''applic'':29C,107C ''approach'':140C,185C,252C ''articl'':152C ''aspect'':144C ''assign'':19C ''base'':69C ''best'':240C ''bout'':230C ''bunch'':95C ''caus'':86C ''contain'':31C ''coupl'':225C ''creat'':123C,165C ''data'':117C,128C,158C,162C,190C ''databas'':34C ''datafram'':124C ''dataset'':199C,215C ''day'':226C ''desir'':60C ''elast'':49C ''elasticsearch'':119C ''etc'':38C ''event'':36C ''exampl'':57C ''except'':32C,72C,89C ''exit'':188C ''featur'':93C ''gather'':74C,222C ''generat'':169C ''implement'':147C,231C,237C ''improv'':103C ''ingest'':116C ''keep'':100C ''know'':135C ''knowledg'':219C ''label'':166C ''learn'':6A,9B,44C,143C ''li'':180C,205C ''like'':84C,133C ''limit'':218C ''linear'':232C ''log'':2A,26C,33C,35C,37C,73C,189C ''look'':246C ''machin'':5A,8B,43C,142C ''machine-learn'':7B ''map'':126C ''mllib'':53C ''model'':12B,163C ''need'':129C ''new'':41C ''next'':88C ''numer'':192C ''optim'':104C ''p'':16C,109C,172C,181C,206C,244C,256C ''paper'':154C ''past'':224C ''pleas'':234C ''predict'':11B,68C,79C,170C ''predictionio'':55C ''predictive-model'':10B ''preprocess'':159C ''problem'':254C ''question'':174C ''regress'':233C ''result'':61C ''search'':50C ''server'':1A,25C ''spark'':15B,47C,52C,121C ''stuff'':98C ''success'':112C ''suggest'':235C,248C ''talk'':156C ''task'':21C ''thank'':257C ''think'':229C ''track'':101C ''train'':160C,202C,213C ''transform'':186C ''ul'':179C ''use'':4A,46C,197C,211C ''user'':81C ''ve'':149C,221C ''vector'':193C ''would'':62C,132C,238C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9087, 1, '2015-11-27 19:04:28.73', 1, 40, 'Correlation and Naive Bayes', '&lt;p&gt;I would like to ask if the Pearson correlation between fields (but not the class field) of a dataset affects somehow the performance of Naive Bayes when applying it to the dataset in order to predict the class field.&lt;/p&gt;
', 'predictive-modeling  correlation', NULL, '''/p'':49C ''affect'':29C ''appli'':37C ''ask'':14C ''bay'':4A,35C ''class'':24C,47C ''correl'':1A,8B,18C ''dataset'':28C,41C ''field'':20C,25C,48C ''like'':12C ''model'':7B ''naiv'':3A,34C ''order'':43C ''p'':9C ''pearson'':17C ''perform'':32C ''predict'':6B,45C ''predictive-model'':5B ''somehow'':30C ''would'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9088, 2, '2015-11-27 21:45:39.147', 1, NULL, NULL, '&lt;p&gt;When you script out steps for spark on an RDD, it does not begin executing the operations until the data needs to be accessed.  Instead of actually manipulating the data, spark builds a graph of how to go from the data, to the desired result set, and will not store intermediate data.  For example, if you enter the following into a shell&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;my_rdd = sc.parallelize(some_data)
mapped_data = my_rdd.map(...)
filtered_data = mapped_data.filter(...)
filtered_data.take(3)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;you''ll notice that when you enter lines 2 and 3, no execution occurs.  This is because spark isn''t creating mapped_data or filtered_data, but rather, is building a graph of how to get from my_rdd to filtered data.  The only reason that you would really write it in this way, is if you need to access mapped_data at a later state, in which case you would want to use a cache.&lt;/p&gt;
', NULL, NULL, '''/code'':80C ''/p'':64C,159C ''/pre'':81C ''2'':91C ''3'':79C,93C ''access'':25C,142C ''actual'':28C ''begin'':15C ''build'':33C,112C ''cach'':158C ''case'':151C ''code'':66C ''creat'':103C ''data'':21C,31C,42C,53C,71C,73C,76C,105C,108C,124C,144C ''desir'':45C ''enter'':58C,89C ''exampl'':55C ''execut'':16C,95C ''filter'':75C,107C,123C ''filtered_data.take'':78C ''follow'':60C ''get'':118C ''go'':39C ''graph'':35C,114C ''instead'':26C ''intermedi'':52C ''isn'':101C ''later'':147C ''line'':90C ''ll'':84C ''manipul'':29C ''map'':72C,104C,143C ''mapped_data.filter'':77C ''my_rdd.map'':74C ''need'':22C,140C ''notic'':85C ''occur'':96C ''oper'':18C ''p'':1C,82C ''pre'':65C ''rather'':110C ''rdd'':11C,68C,121C ''realli'':131C ''reason'':127C ''result'':46C ''sc.parallelize'':69C ''script'':4C ''set'':47C ''shell'':63C ''spark'':8C,32C,100C ''state'':148C ''step'':6C ''store'':51C ''use'':156C ''want'':154C ''way'':136C ''would'':130C,153C ''write'':132C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10351, 2, '2016-02-22 18:54:45.957', 1, NULL, NULL, '&lt;p&gt;I hope this &lt;a href="http://www.modelingonlineauctions.com/datasets" rel="nofollow"&gt;data set might&lt;/a&gt; suits you.This contains details of eBay Online Auctions (2012). very similar to your bank customer database you looking for. And all CSV format. Therefore you can import it to SQL also.Just Tryout.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;File Format&lt;/strong&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;All files are available in comma separated format (CSV).&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This dataset contain details of:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Data Field Description&lt;/strong&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;em&gt;auctionid&lt;/em&gt; - unique identifier of an auction&lt;/p&gt;
  
  &lt;p&gt;&lt;em&gt;bid&lt;/em&gt; - the proxy bid placed by a bidder&lt;/p&gt;
  
  &lt;p&gt;&lt;em&gt;bidtime&lt;/em&gt; - the time (in days) that the bid was placed, from the start of the auction&lt;/p&gt;
  
  &lt;p&gt;&lt;em&gt;bidder&lt;/em&gt; - eBay username of the bidder&lt;/p&gt;
  
  &lt;p&gt;&lt;em&gt;bidderrate&lt;/em&gt; - eBay feedback rating of the bidder&lt;/p&gt;
  
  &lt;p&gt;openbid - the opening bid set by the seller&lt;/p&gt;
  
  &lt;p&gt;&lt;em&gt;price&lt;/em&gt; - the closing price that the item sold for (equivalent to the second highest bid + an increment)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Or&lt;/strong&gt; 
My personal favorite &lt;a href="https://code.google.com/archive/p/open-advertising-dataset/downloads" rel="nofollow"&gt;Google Adwords dataset&lt;/a&gt;. You need to really check it to know how much information it has...&lt;/p&gt;
', NULL, NULL, '''/a'':15C,195C ''/archive/p/open-advertising-dataset/downloads'':189C ''/blockquote'':67C,177C ''/datasets'':9C ''/em'':86C,96C,108C,128C,138C,159C ''/p'':48C,54C,66C,74C,81C,92C,104C,124C,134C,145C,155C,176C,209C ''/strong'':53C,80C,181C ''2012'':24C ''adword'':193C ''also.just'':46C ''auction'':23C,91C,123C ''auctionid'':85C ''avail'':60C ''bank'':29C ''bid'':95C,99C,115C,150C,173C ''bidder'':103C,127C,133C,144C ''bidderr'':137C ''bidtim'':107C ''blockquot'':55C,82C ''check'':200C ''close'':161C ''code.google.com'':188C ''code.google.com/archive/p/open-advertising-dataset/downloads'':187C ''comma'':62C ''contain'':18C,71C ''csv'':37C,65C ''custom'':30C ''data'':12C,77C ''databas'':31C ''dataset'':70C,194C ''day'':112C ''descript'':79C ''detail'':19C,72C ''ebay'':21C,129C,139C ''em'':84C,94C,106C,126C,136C,157C ''equival'':168C ''favorit'':184C ''feedback'':140C ''field'':78C ''file'':51C,58C ''format'':38C,52C,64C ''googl'':192C ''highest'':172C ''hope'':3C ''href'':6C,186C ''identifi'':88C ''import'':42C ''increment'':175C ''inform'':206C ''item'':165C ''know'':203C ''look'':33C ''might'':14C ''much'':205C ''need'':197C ''nofollow'':11C,191C ''onlin'':22C ''open'':149C ''openbid'':147C ''p'':1C,49C,56C,68C,75C,83C,93C,105C,125C,135C,146C,156C,178C ''person'':183C ''place'':100C,117C ''price'':158C,162C ''proxi'':98C ''rate'':141C ''realli'':199C ''rel'':10C,190C ''second'':171C ''seller'':154C ''separ'':63C ''set'':13C,151C ''similar'':26C ''sold'':166C ''sql'':45C ''start'':120C ''strong'':50C,76C,179C ''suit'':16C ''therefor'':39C ''time'':110C ''tryout'':47C ''uniqu'':87C ''usernam'':130C ''www.modelingonlineauctions.com'':8C ''www.modelingonlineauctions.com/datasets'':7C ''you.this'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9089, 1, '2015-11-28 01:40:58.53', 1, 30, 'Modelling population changes with years on a network graph', '&lt;p&gt;This may be a silly question, apologies if it is. And further apologies if I''ve said something wrong.&lt;/p&gt;

&lt;p&gt;Network graphs, or visualisations look somewhat like &lt;a href="https://flowingdata.com/wp-content/uploads/2013/06/revere-network-reduced1-620x574.png" rel="nofollow"&gt;this&lt;/a&gt;, with each node representing a thing connected to another thing. So for my example, I''d like to model population changes for refugees from their origin country to their destination country.&lt;/p&gt;

&lt;p&gt;I''ve been tasked to create a graph which shows a similar change over a period of years, but I am really not sure how years could be modelled? e.g. how would a graph like the example above, show a change in years?&lt;/p&gt;

&lt;p&gt;Edit: preferably using the NodeXL plugin&lt;/p&gt;
', 'visualization  nodexl', NULL, '''/a'':49C ''/p'':32C,81C,126C,134C ''/wp-content/uploads/2013/06/revere-network-reduced1-620x574.png'':45C ''anoth'':58C ''apolog'':19C,25C ''chang'':3A,70C,95C,123C ''connect'':56C ''could'':109C ''countri'':76C,80C ''creat'':88C ''d'':65C ''destin'':79C ''e.g'':112C ''edit'':128C ''exampl'':63C,119C ''flowingdata.com'':44C ''flowingdata.com/wp-content/uploads/2013/06/revere-network-reduced1-620x574.png'':43C ''graph'':9A,35C,90C,116C ''href'':42C ''like'':40C,66C,117C ''look'':38C ''may'':14C ''model'':1A,68C,111C ''network'':8A,34C ''node'':52C ''nodexl'':11B,132C ''nofollow'':47C ''origin'':75C ''p'':12C,33C,82C,127C ''period'':98C ''plugin'':133C ''popul'':2A,69C ''prefer'':129C ''question'':18C ''realli'':104C ''refuge'':72C ''rel'':46C ''repres'':53C ''said'':29C ''show'':92C,121C ''silli'':17C ''similar'':94C ''someth'':30C ''somewhat'':39C ''sure'':106C ''task'':86C ''thing'':55C,59C ''use'':130C ''ve'':28C,84C ''visual'':10B ''visualis'':37C ''would'':114C ''wrong'':31C ''year'':5A,100C,108C,125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9106, 4, '2015-11-28 15:12:29.987', 0, NULL, NULL, 'Given multivariate data split into several subsamples (classes) the analysis finds linear combinations of variables, called discriminant functions, which discriminate between classes and are uncorrelated. The functions are applied then to assign old or new observations to the classes. Discriminant analysis is both dimensionality reduction and classification technique.', NULL, NULL, '''analysi'':10C,41C ''appli'':29C ''assign'':32C ''call'':16C ''class'':8C,22C,39C ''classif'':47C ''combin'':13C ''data'':3C ''dimension'':44C ''discrimin'':17C,20C,40C ''find'':11C ''function'':18C,27C ''given'':1C ''linear'':12C ''multivari'':2C ''new'':35C ''observ'':36C ''old'':33C ''reduct'':45C ''sever'':6C ''split'':4C ''subsampl'':7C ''techniqu'':48C ''uncorrel'':25C ''variabl'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9090, 2, '2015-11-28 02:48:00.047', 1, NULL, NULL, '&lt;p&gt;The &lt;a href="https://en.wikipedia.org/wiki/Sankey_diagram" rel="nofollow"&gt;Sankey diagram&lt;/a&gt; would be nicely suited for your problem statement.&lt;/p&gt;

&lt;p&gt;The flow width can be the number of refugees migrated, and the end nodes can be the destinations (from and to) of the refugees, and the flow width would be the magnitude of refugee migration.&lt;/p&gt;

&lt;p&gt;If you want to model the graph on a geographical map, it can look something like this:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/VA78L.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/VA78L.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I am not sure whether it can be done in NodeXL or not.  But, a google search has returned me this &lt;a href="http://djjr-courses.wikidot.com/ppol225:sankey-diagram" rel="nofollow"&gt;link&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':12C,96C,128C ''/p'':21C,58C,77C,97C,129C ''/ppol225:sankey-diagram'':124C ''/va78l.png'':83C,90C ''/wiki/sankey_diagram'':7C ''alt'':91C ''descript'':94C ''destin'':40C ''diagram'':11C ''djjr-courses.wikidot.com'':123C ''djjr-courses.wikidot.com/ppol225:sankey-diagram'':122C ''done'':107C ''en.wikipedia.org'':6C ''en.wikipedia.org/wiki/sankey_diagram'':5C ''end'':35C ''enter'':92C ''flow'':24C,49C ''geograph'':69C ''googl'':114C ''graph'':66C ''href'':4C,80C,121C ''i.stack.imgur.com'':82C,89C ''i.stack.imgur.com/va78l.png'':81C,88C ''imag'':93C ''img'':86C ''like'':75C ''link'':127C ''look'':73C ''magnitud'':54C ''map'':70C ''migrat'':32C,57C ''model'':64C ''nice'':15C ''node'':36C ''nodexl'':109C ''nofollow'':9C,85C,126C ''number'':29C ''p'':1C,22C,59C,78C,98C ''problem'':19C ''refuge'':31C,46C,56C ''rel'':8C,84C,125C ''return'':117C ''sankey'':10C ''search'':115C ''someth'':74C ''src'':87C ''statement'':20C ''suit'':16C ''sure'':102C ''want'':62C ''whether'':103C ''width'':25C,50C ''would'':13C,51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9091, 2, '2015-11-28 04:21:48.2', 0, NULL, NULL, '&lt;p&gt;It seems that our first issue is to model the graph before visualizing it. I would recommend &lt;a href="http://neo4j.com/" rel="nofollow"&gt;Neo4j&lt;/a&gt; and its &lt;a href="http://neo4j.com/developer/cypher-query-language/" rel="nofollow"&gt;Cypher&lt;/a&gt; query language.&lt;/p&gt;

&lt;p&gt;For the data modeling, states can be the nodes with a population property (or even population for each year), relationship could be the directed migration with the number of refugees and a year property.&lt;/p&gt;

&lt;p&gt;Now with such a graph at hand you can easily retrieve the total number of refugees per year by using the year property from the nodes or the edges. &lt;/p&gt;
', NULL, NULL, '''/a'':25C,36C ''/developer/cypher-query-language/'':32C ''/p'':39C,75C,106C ''could'':61C ''cypher'':35C ''data'':43C ''direct'':64C ''easili'':86C ''edg'':105C ''even'':55C ''first'':6C ''graph'':12C,81C ''hand'':83C ''href'':20C,29C ''issu'':7C ''languag'':38C ''migrat'':65C ''model'':10C,44C ''neo4j'':24C ''neo4j.com'':21C,31C ''neo4j.com/developer/cypher-query-language/'':30C ''node'':49C,102C ''nofollow'':23C,34C ''number'':68C,90C ''p'':1C,40C,76C ''per'':93C ''popul'':52C,56C ''properti'':53C,74C,99C ''queri'':37C ''recommend'':18C ''refuge'':70C,92C ''rel'':22C,33C ''relationship'':60C ''retriev'':87C ''seem'':3C ''state'':45C ''total'':89C ''use'':96C ''visual'':14C ''would'':17C ''year'':59C,73C,94C,98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9148, 2, '2015-12-01 16:14:33.33', 1, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Totally disagree with @Derek Janni.&lt;/strong&gt; Be careful about notation however you should not get lost in terminology. Those papers you mentioned used the term "labeling" literally but in Machine Learning/Data Mining community, &lt;strong&gt;labeling is the process of preparing data for supervised learning (classification)! It has nothing to do with the ML task!&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Those papers used the term to show that after supervised learning they can recognize different labels of different objects so they used the term labeling but you probably can not find in any literature that they use these two terms as synonyms.&lt;/p&gt;
', NULL, NULL, '''/p'':58C,102C ''/strong'':8C,57C ''care'':10C ''classif'':47C ''communiti'':35C ''data'':43C ''derek'':6C ''differ'':74C,77C ''disagre'':4C ''find'':90C ''get'':17C ''howev'':13C ''janni'':7C ''label'':28C,37C,75C,84C ''learn'':46C,70C ''learning/data'':33C ''liter'':29C ''literatur'':93C ''lost'':18C ''machin'':32C ''mention'':24C ''mine'':34C ''ml'':55C ''notat'':12C ''noth'':50C ''object'':78C ''p'':1C,59C ''paper'':22C,61C ''prepar'':42C ''probabl'':87C ''process'':40C ''recogn'':73C ''show'':66C ''strong'':2C,36C ''supervis'':45C,69C ''synonym'':101C ''task'':56C ''term'':27C,64C,83C,99C ''terminolog'':20C ''total'':3C ''two'':98C ''use'':25C,62C,81C,96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9101, 5, '2015-11-28 15:03:42.977', 0, NULL, NULL, '&lt;p&gt;The expectation maximization (EM) algorithm is an optimization algorithm. It is an instance of a broader class of optimization algorithms known as Majorization-Minimization (MM). It is a cornerstone of statistics since it particularly suitable for "skipping" local maxima which often arise in likelihood maximization problems, especially in the presence of missing data.&lt;/p&gt;

&lt;p&gt;More specifically, the &lt;a href="http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm" rel="nofollow"&gt;EM algorithm&lt;/a&gt; is an iterative method for finding maximum likelihood estimates. The typical form of the EM algorithm works as follows:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;em&gt;Expectation Step&lt;/em&gt;: Compute the expected value of the log-likelihood function based on the current estimate.&lt;/li&gt;
&lt;li&gt;&lt;em&gt;Maximization Step&lt;/em&gt;: Update the estimate by maximizing the result of the expectation step.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':70C ''/em'':96C,117C ''/li'':112C,129C ''/p'':56C,90C ''/ul'':130C ''/wiki/expectation%e2%80%93maximization_algorithm'':65C ''algorithm'':6C,10C,21C,69C,86C ''aris'':44C ''base'':107C ''broader'':17C ''class'':18C ''comput'':97C ''cornerston'':31C ''current'':110C ''data'':55C ''em'':5C,68C,85C,93C,114C ''en.wikipedia.org'':64C ''en.wikipedia.org/wiki/expectation%e2%80%93maximization_algorithm'':63C ''especi'':49C ''estim'':79C,111C,120C ''expect'':3C,94C,99C,127C ''find'':76C ''follow'':89C ''form'':82C ''function'':106C ''href'':62C ''instanc'':14C ''iter'':73C ''known'':22C ''li'':92C,113C ''likelihood'':46C,78C,105C ''local'':40C ''log'':104C ''log-likelihood'':103C ''major'':25C ''majorization-minim'':24C ''maxim'':4C,47C,115C,122C ''maxima'':41C ''maximum'':77C ''method'':74C ''minim'':26C ''miss'':54C ''mm'':27C ''nofollow'':67C ''often'':43C ''optim'':9C,20C ''p'':1C,57C ''particular'':36C ''presenc'':52C ''problem'':48C ''rel'':66C ''result'':124C ''sinc'':34C ''skip'':39C ''specif'':59C ''statist'':33C ''step'':95C,116C,128C ''suitabl'':37C ''typic'':81C ''ul'':91C ''updat'':118C ''valu'':100C ''work'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9102, 4, '2015-11-28 15:03:42.977', 0, NULL, NULL, 'An optimization algorithm often used for maximum-likelihood estimation in the presence of missing data. ', NULL, NULL, '''algorithm'':3C ''data'':16C ''estim'':10C ''likelihood'':9C ''maximum'':8C ''maximum-likelihood'':7C ''miss'':15C ''often'':4C ''optim'':2C ''presenc'':13C ''use'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9103, 5, '2015-11-28 15:04:14.043', 0, NULL, NULL, '&lt;p&gt;Descriptive statistics summarize features of a sample. &lt;/p&gt;

&lt;p&gt;Common descriptive statistics include mean and standard deviations, particular quantiles like the median and quartiles, the maximum and minimum, range and interquartile range, five number summaries and so on, but with multiple variables, may include correlations and crosstabs. &lt;/p&gt;

&lt;p&gt;Descriptive statistics may include visual displays such as boxplots, histograms and scatterplots.&lt;/p&gt;
', NULL, NULL, '''/p'':9C,49C,63C ''boxplot'':59C ''common'':11C ''correl'':46C ''crosstab'':48C ''descript'':2C,12C,51C ''deviat'':18C ''display'':56C ''featur'':5C ''five'':34C ''histogram'':60C ''includ'':14C,45C,54C ''interquartil'':32C ''like'':21C ''maximum'':27C ''may'':44C,53C ''mean'':15C ''median'':23C ''minimum'':29C ''multipl'':42C ''number'':35C ''p'':1C,10C,50C ''particular'':19C ''quantil'':20C ''quartil'':25C ''rang'':30C,33C ''sampl'':8C ''scatterplot'':62C ''standard'':17C ''statist'':3C,13C,52C ''summar'':4C ''summari'':36C ''variabl'':43C ''visual'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9108, 2, '2015-11-28 19:19:51.47', 0, NULL, NULL, '&lt;p&gt;The way I view it: ''Classification'' (in the context of machine learning) is &lt;strong&gt;a type of problem&lt;/strong&gt; in which you assign a ''label'' to an object. Formally, ''Classification'' is a type of problem whereas labeling is a function from an object to a set of labels (maybe infinite).&lt;/p&gt;

&lt;p&gt;Much the same way Regression is a type of problem where you, again, assign a label to an object only this time the label is some real number.&lt;/p&gt;

&lt;p&gt;Both in Classification and in Regression you are attempting to find the ''best'' labeling function with respect to some metric/loss function.&lt;/p&gt;
', NULL, NULL, '''/p'':52C,82C,105C ''/strong'':20C ''assign'':24C,67C ''attempt'':92C ''best'':96C ''classif'':7C,31C,86C ''context'':10C ''find'':94C ''formal'':30C ''function'':41C,98C,104C ''infinit'':51C ''label'':26C,38C,49C,69C,77C,97C ''learn'':13C ''machin'':12C ''mayb'':50C ''metric/loss'':103C ''much'':54C ''number'':81C ''object'':29C,44C,72C ''p'':1C,53C,83C ''problem'':19C,36C,63C ''real'':80C ''regress'':58C,89C ''respect'':100C ''set'':47C ''strong'':15C ''time'':75C ''type'':17C,34C,61C ''view'':5C ''way'':3C,57C ''wherea'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9208, 1, '2015-12-04 14:24:09.31', 7, 191, 'Pylearn2 vs TensorFlow', '&lt;p&gt;I am about to dive into a long NN research project and wanted a push in the direction of Pylearn2 or TensorFlow?  As of Dec 2015 has the community started to lean one direction or another?&lt;/p&gt;

&lt;p&gt;This &lt;a href="http://deeplearning4j.org/compare-dl4j-torch7-pylearn.html"&gt;link&lt;/a&gt; has given me concern about getting tied to TenserFlow.&lt;/p&gt;
', 'machine-learning  python  neuralnetwork', NULL, '''/a'':55C ''/compare-dl4j-torch7-pylearn.html'':53C ''/p'':46C,65C ''2015'':35C ''anoth'':45C ''communiti'':38C ''concern'':59C ''dec'':34C ''deeplearning4j.org'':52C ''deeplearning4j.org/compare-dl4j-torch7-pylearn.html'':51C ''direct'':27C,43C ''dive'':14C ''get'':61C ''given'':57C ''href'':50C ''lean'':41C ''learn'':6B ''link'':54C ''long'':17C ''machin'':5B ''machine-learn'':4B ''neuralnetwork'':8B ''nn'':18C ''one'':42C ''p'':9C,47C ''project'':20C ''push'':24C ''pylearn2'':1A,29C ''python'':7B ''research'':19C ''start'':39C ''tenserflow'':64C ''tensorflow'':3A,31C ''tie'':62C ''vs'':2A ''want'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9109, 1, '2015-11-28 19:36:54.963', 2, 41, 'Genetic Algorithm to find best parameter values of an estimaor', '&lt;p&gt;I am making some stochastic training ensemble classes in Python, and I want to get hyperparameters values. Grid search will take too long for moderate data sets, because in my stochastic training I train different learners on different dropouts, as in cross-validation, then I average or weight-average on best error for dropout. Having this set, I want to get best parameter values at every iteration, so I thought to use a genetic algorithm approach. &lt;/p&gt;

&lt;p&gt;I found one example on Github, but failed to install, and I am looking for more references of genetic algorithms for doing this. Can anyone help me?&lt;/p&gt;
', 'optimization  hyperparameter  genetic-algorithms', NULL, '''/p'':95C,124C ''algorithm'':2A,15B,93C,116C ''anyon'':121C ''approach'':94C ''averag'':63C,67C ''best'':5A,69C,80C ''class'':24C ''cross'':59C ''cross-valid'':58C ''data'':42C ''differ'':51C,54C ''dropout'':55C,72C ''ensembl'':23C ''error'':70C ''estimaor'':10A ''everi'':84C ''exampl'':100C ''fail'':104C ''find'':4A ''found'':98C ''genet'':1A,14B,92C,115C ''genetic-algorithm'':13B ''get'':31C,79C ''github'':102C ''grid'':34C ''help'':122C ''hyperparamet'':12B,32C ''instal'':106C ''iter'':85C ''learner'':52C ''long'':39C ''look'':110C ''make'':19C ''moder'':41C ''one'':99C ''optim'':11B ''p'':16C,96C ''paramet'':6A,81C ''python'':26C ''refer'':113C ''search'':35C ''set'':43C,75C ''stochast'':21C,47C ''take'':37C ''thought'':88C ''train'':22C,48C,50C ''use'':90C ''valid'':60C ''valu'':7A,33C,82C ''want'':29C,77C ''weight'':66C ''weight-averag'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10352, 2, '2016-02-22 23:19:26.997', 0, NULL, NULL, '&lt;p&gt;The problem is with how you are training the problem.  If you use Timestamp1 and Timestamp2 as training parameters, they will carry 100% predictive power, and the algorithm will completely disregard any location parameters.  If you want to make predictions based on only an origin and destination, you need to train your model using only those parameters.&lt;/p&gt;
', NULL, NULL, '''/p'':59C ''100'':24C ''algorithm'':29C ''base'':42C ''carri'':23C ''complet'':31C ''destin'':48C ''disregard'':32C ''locat'':34C ''make'':40C ''model'':54C ''need'':50C ''origin'':46C ''p'':1C ''paramet'':20C,35C,58C ''power'':26C ''predict'':25C,41C ''problem'':3C,11C ''timestamp1'':15C ''timestamp2'':17C ''train'':9C,19C,52C ''use'':14C,55C ''want'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9112, 2, '2015-11-28 22:25:05.03', 2, NULL, NULL, '&lt;p&gt;As you probably know, naive here implies that the "fields" are independent. So your question boils down to does correlation imply dependence. Yes, it does. See here.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stats.stackexchange.com/questions/113417/does-non-zero-correlation-imply-dependence"&gt;http://stats.stackexchange.com/questions/113417/does-non-zero-correlation-imply-dependence&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;So, if your features show correlation then this will have an adverse effect on the naive assumption. Despite this fact, Naive Bayes has been shown to be robust against this assumption. If your model still suffers from this, however, you could consider transforming the space to be independent with methods such as PCA.  &lt;/p&gt;
', NULL, NULL, '''/p'':29C,93C ''/questions/113417/does-non-zero-correlation-imply-dependence'':35C ''/questions/113417/does-non-zero-correlation-imply-dependence&lt;/a&gt;&lt;/p&gt;'':38C ''advers'':51C ''assumpt'':56C,70C ''bay'':61C ''boil'':17C ''consid'':81C ''correl'':21C,45C ''could'':80C ''depend'':23C ''despit'':57C ''effect'':52C ''fact'':59C ''featur'':43C ''field'':11C ''howev'':78C ''href'':32C ''impli'':8C,22C ''independ'':13C,87C ''know'':5C ''method'':89C ''model'':73C ''naiv'':6C,55C,60C ''p'':1C,30C,39C ''pca'':92C ''probabl'':4C ''question'':16C ''robust'':67C ''see'':27C ''show'':44C ''shown'':64C ''space'':84C ''stats.stackexchange.com'':34C,37C ''stats.stackexchange.com/questions/113417/does-non-zero-correlation-imply-dependence'':33C ''stats.stackexchange.com/questions/113417/does-non-zero-correlation-imply-dependence&lt;/a&gt;&lt;/p&gt;'':36C ''still'':74C ''suffer'':75C ''transform'':82C ''yes'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9114, 2, '2015-11-29 08:09:29.833', 2, NULL, NULL, '&lt;p&gt;I tried to estimate a few of your data values from the scatter plot you provided.&lt;/p&gt;

&lt;p&gt;Then I performed a power model regression and came up with&lt;/p&gt;

&lt;p&gt;$y = (5.777 \cdot 10^{-16}) \cdot 1.404^{x}.$&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/ni7RV.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/ni7RV.png" alt="Graph of model"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The estimated values I used are below.&lt;/p&gt;

&lt;p&gt;$(70, 0.01), (75, 0.012), (80, 0.015), (90, 0.025), (95, 0.075), (98, 0.15), (99, 0.20), (99.5, 0.25), (99.9, 0.32)$&lt;/p&gt;

&lt;p&gt;Of course, your actual model will differ because you have the actual data set. I just eyeballed a few points so I could test the power fit.&lt;/p&gt;
', NULL, NULL, '''-16'':37C ''/a'':59C ''/ni7rv.png'':47C,54C ''/p'':18C,31C,41C,60C,69C,89C,118C ''0.01'':72C ''0.012'':74C ''0.015'':76C ''0.025'':78C ''0.075'':80C ''0.15'':82C ''0.20'':84C ''0.25'':86C ''0.32'':88C ''1.404'':39C ''10'':36C ''5.777'':34C ''70'':71C ''75'':73C ''80'':75C ''90'':77C ''95'':79C ''98'':81C ''99'':83C ''99.5'':85C ''99.9'':87C ''actual'':94C,102C ''alt'':55C ''came'':28C ''cdot'':35C,38C ''could'':113C ''cours'':92C ''data'':10C,103C ''differ'':97C ''estim'':5C,63C ''eyebal'':107C ''fit'':117C ''graph'':56C ''href'':44C ''i.stack.imgur.com'':46C,53C ''i.stack.imgur.com/ni7rv.png'':45C,52C ''img'':50C ''model'':25C,58C,95C ''nofollow'':49C ''p'':1C,19C,32C,42C,61C,70C,90C ''perform'':22C ''plot'':15C ''point'':110C ''power'':24C,116C ''provid'':17C ''regress'':26C ''rel'':48C ''scatter'':14C ''set'':104C ''src'':51C ''test'':114C ''tri'':3C ''use'':66C ''valu'':11C,64C ''x'':40C ''y'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9115, 2, '2015-11-29 08:50:05.273', 1, NULL, NULL, '&lt;p&gt;I suspect that your $x$ values, as they are percentages, lie in $[0,100[$. Regarding $y$ values, they seem positive. But many are very close to $0$. So I would first decide whether $y$s below a threshold should be put aside first as outliers, as they will have a huge influence on the first basic fits. You can reintroduce them afterward with robust fitting procedures.&lt;/p&gt;

&lt;p&gt;An important question is: are $y$s bounded or not? The slope seems very steep, so you have to guess if the derivative is infinite at $x=100$, to help you chose models. &lt;/p&gt;

&lt;p&gt;I believe a first idea is to perform a change of variable on the $x$ axis, with $x'' = \frac{1}{{(100-x)}^\alpha}$ and try some $\alpha $ values, to see if clearer patterns appear.&lt;/p&gt;
', NULL, NULL, '''/p'':68C,103C,139C ''0'':14C,28C ''1'':124C ''100'':15C,97C,125C ''afterward'':63C ''alpha'':127C,131C ''appear'':138C ''asid'':43C ''axi'':120C ''basic'':57C ''believ'':106C ''bound'':77C ''chang'':114C ''chose'':101C ''clearer'':136C ''close'':26C ''decid'':33C ''deriv'':92C ''first'':32C,44C,56C,108C ''fit'':58C,66C ''frac'':123C ''guess'':89C ''help'':99C ''huge'':52C ''idea'':109C ''import'':71C ''infinit'':94C ''influenc'':53C ''lie'':12C ''mani'':23C ''model'':102C ''outlier'':46C ''p'':1C,69C,104C ''pattern'':137C ''percentag'':11C ''perform'':112C ''posit'':21C ''procedur'':67C ''put'':42C ''question'':72C ''regard'':16C ''reintroduc'':61C ''robust'':65C ''see'':134C ''seem'':20C,82C ''slope'':81C ''steep'':84C ''suspect'':3C ''threshold'':39C ''tri'':129C ''valu'':7C,18C,132C ''variabl'':116C ''whether'':34C ''would'':31C ''x'':6C,96C,119C,122C,126C ''y'':17C,35C,75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9117, 1, '2015-11-29 10:18:27.317', 0, 36, 'Subset of training set produces good results while full training set produces poor results', '&lt;p&gt;I have an extremely unbalanced data set: around 200 positive samples and 70,000 negative samples. To overcome this problem I have tried to over-sample the minority class as suggested in previous questions here.&lt;/p&gt;

&lt;p&gt;Since training the classifier (in my case I am using SVM) takes a long time on my computer I first trained a classifier on a small subset of my data picked randomly, the classifier gave solid results when tested on the test set, yet when I trained a classifier using the full training data the classifier produced very poor results. Does anyone have insight into why this is happening and what should i do? I have also tried using weights but it did not produce a different result.&lt;/p&gt;
', 'svm  unbalanced-classes  cross-validation', NULL, '''/p'':59C,149C ''000'':36C ''200'':31C ''70'':35C ''also'':137C ''anyon'':122C ''around'':30C ''case'':67C ''class'':18B,52C ''classifi'':64C,83C,94C,109C,116C ''comput'':78C ''cross'':20B ''cross-valid'':19B ''data'':28C,90C,114C ''differ'':147C ''extrem'':26C ''first'':80C ''full'':9A,112C ''gave'':95C ''good'':6A ''happen'':129C ''insight'':124C ''long'':74C ''minor'':51C ''negat'':37C ''over-sampl'':47C ''overcom'':40C ''p'':22C,60C ''pick'':91C ''poor'':13A,119C ''posit'':32C ''previous'':56C ''problem'':42C ''produc'':5A,12A,117C,145C ''question'':57C ''random'':92C ''result'':7A,14A,97C,120C,148C ''sampl'':33C,38C,49C ''set'':4A,11A,29C,103C ''sinc'':61C ''small'':86C ''solid'':96C ''subset'':1A,87C ''suggest'':54C ''svm'':15B,71C ''take'':72C ''test'':99C,102C ''time'':75C ''train'':3A,10A,62C,81C,107C,113C ''tri'':45C,138C ''unbalanc'':17B,27C ''unbalanced-class'':16B ''use'':70C,110C,139C ''valid'':21B ''weight'':140C ''yet'':104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9329, 1, '2015-12-11 06:13:25.48', 1, 36, 'Using predictive modelling for temperature data set', '&lt;p&gt;I am absolutely new to this area of predictive modelling in data science. I am not able to understand how and what modelling techniques do we use? Does it depend on the data type? Does it depend on size of data?&lt;/p&gt;

&lt;p&gt;To be specific to the title, I have to predict missing values in a given temperature data set and i am unaware of anything that i can use. Could someone guide me through?&lt;/p&gt;
', 'data-mining  missing-data  data-imputation', NULL, '''/p'':59C,94C ''abl'':34C ''absolut'':20C ''anyth'':84C ''area'':24C ''could'':89C ''data'':6A,9B,13B,15B,29C,50C,58C,77C ''data-imput'':14B ''data-min'':8B ''depend'':47C,54C ''given'':75C ''guid'':91C ''imput'':16B ''mine'':10B ''miss'':12B,71C ''missing-data'':11B ''model'':3A,27C,40C ''new'':21C ''p'':17C,60C ''predict'':2A,26C,70C ''scienc'':30C ''set'':7A,78C ''size'':56C ''someon'':90C ''specif'':63C ''techniqu'':41C ''temperatur'':5A,76C ''titl'':66C ''type'':51C ''unawar'':82C ''understand'':36C ''use'':1A,44C,88C ''valu'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9118, 2, '2015-11-29 11:18:31.997', 5, NULL, NULL, '&lt;p&gt;You should look into other estimators of location.&lt;/p&gt;

&lt;p&gt;What you want is a &lt;em&gt;robust&lt;/em&gt; estimator, with a &lt;em&gt;high break-down point&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;The extreme approach would be the median.&lt;/p&gt;

&lt;p&gt;But you may get more numerically interesting results with a &lt;strong&gt;trimmed mean&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;You define a threshold, say 2%. Then you remove the top 2% of votes, and the bottom 2% of votes, and take the mean only of the remaining entries. An app with 98% 5 stars will still get a 5.0&lt;/p&gt;

&lt;p&gt;But to prevent manipulation, I would look into other signals. Such as clustered votes from a single region, for example.&lt;/p&gt;
', NULL, NULL, '''/em'':19C,29C ''/p'':10C,30C,39C,55C,97C,119C ''/strong'':54C ''2'':62C,68C,74C ''5'':90C ''5.0'':96C ''98'':89C ''app'':87C ''approach'':34C ''bottom'':73C ''break'':26C ''break-down'':25C ''cluster'':111C ''defin'':58C ''em'':17C,23C ''entri'':85C ''estim'':7C,20C ''exampl'':118C ''extrem'':33C ''get'':44C,94C ''high'':24C ''interest'':47C ''locat'':9C ''look'':4C,105C ''manipul'':102C ''may'':43C ''mean'':53C,80C ''median'':38C ''numer'':46C ''p'':1C,11C,31C,40C,56C,98C ''point'':28C ''prevent'':101C ''region'':116C ''remain'':84C ''remov'':65C ''result'':48C ''robust'':18C ''say'':61C ''signal'':108C ''singl'':115C ''star'':91C ''still'':93C ''strong'':51C ''take'':78C ''threshold'':60C ''top'':67C ''trim'':52C ''vote'':70C,76C,112C ''want'':14C ''would'':35C,104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9119, 1, '2015-11-29 12:51:16.56', 3, 41, 'Hub removal from graphs', '&lt;p&gt;I have a graph with vertices that represent some entities and the edges are weighted as the correlation between two such entities.&lt;/p&gt;

&lt;p&gt;I would like to break this graph into several subgraphs with high inner-correlation.&lt;/p&gt;

&lt;p&gt;My problem is that I have a few ''hubs'', with high correlation to a lot of different entities.&lt;/p&gt;

&lt;p&gt;How can I detect such hubs in order to remove them?&lt;/p&gt;
', 'graphs', NULL, '''/p'':29C,46C,66C,79C ''break'':35C ''correl'':24C,45C,59C ''detect'':71C ''differ'':64C ''edg'':19C ''entiti'':16C,28C,65C ''graph'':4A,5B,10C,37C ''high'':42C,58C ''hub'':1A,56C,73C ''inner'':44C ''inner-correl'':43C ''like'':33C ''lot'':62C ''order'':75C ''p'':6C,30C,47C,67C ''problem'':49C ''remov'':2A,77C ''repres'':14C ''sever'':39C ''subgraph'':40C ''two'':26C ''vertic'':12C ''weight'':21C ''would'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9120, 2, '2015-11-29 15:59:03.413', 2, NULL, NULL, '&lt;p&gt;Excellent question. In chapter 4 of "Illuminating the Path, The Research and Development Agenda for Visual Analytics" by James J. Thomas and Kristin A. Cook is a discussion on data representations and data transformations. In my research I have approached this question in the context of PCA and factor analysis. My brief answer is that the visualizations are useful if one has the data transformation to move from the visualization space to the original data space. This would additionally be conducted within a visual analytics framework.&lt;/p&gt;
', NULL, NULL, '''/p'':88C ''4'':6C ''addit'':80C ''agenda'':15C ''analysi'':51C ''analyt'':18C,86C ''answer'':54C ''approach'':41C ''brief'':53C ''chapter'':5C ''conduct'':82C ''context'':46C ''cook'':26C ''data'':31C,34C,65C,76C ''develop'':14C ''discuss'':29C ''excel'':2C ''factor'':50C ''framework'':87C ''illumin'':8C ''j'':21C ''jame'':20C ''kristin'':24C ''move'':68C ''one'':62C ''origin'':75C ''p'':1C ''path'':10C ''pca'':48C ''question'':3C,43C ''represent'':32C ''research'':12C,38C ''space'':72C,77C ''thoma'':22C ''transform'':35C,66C ''use'':60C ''visual'':17C,58C,71C,85C ''within'':83C ''would'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9161, 2, '2015-12-02 02:28:11.103', 0, NULL, NULL, '&lt;p&gt;There are methods like the &lt;a href="https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence" rel="nofollow"&gt;Kullback-Leibler divergence model&lt;/a&gt;, the &lt;a href="http://www.itl.nist.gov/div898/handbook/eda/section3/eda35d.htm" rel="nofollow"&gt;Wald-Wolfowitz test&lt;/a&gt; for detecting non-randomness and covariance shift. &lt;/p&gt;

&lt;p&gt;A simple test for quick analysis of covariance test would be to build a machine learning model, where the model is repeatedly tested with inputting training data and the production data. &lt;/p&gt;

&lt;p&gt;In case, the model can make out the difference between the training and production datasets then it can be a sign of covariance shift.&lt;/p&gt;
', NULL, NULL, '''/a'':19C,32C ''/div898/handbook/eda/section3/eda35d.htm'':25C ''/p'':41C,74C,100C ''/wiki/kullback%e2%80%93leibler_divergence'':11C ''analysi'':48C ''build'':55C ''case'':77C ''covari'':39C,50C,98C ''data'':69C,73C ''dataset'':90C ''detect'':34C ''differ'':84C ''diverg'':17C ''en.wikipedia.org'':10C ''en.wikipedia.org/wiki/kullback%e2%80%93leibler_divergence'':9C ''href'':8C,22C ''input'':67C ''kullback'':15C ''kullback-leibl'':14C ''learn'':58C ''leibler'':16C ''like'':5C ''machin'':57C ''make'':81C ''method'':4C ''model'':18C,59C,62C,79C ''nofollow'':13C,27C ''non'':36C ''non-random'':35C ''p'':1C,42C,75C ''product'':72C,89C ''quick'':47C ''random'':37C ''rel'':12C,26C ''repeat'':64C ''shift'':40C,99C ''sign'':96C ''simpl'':44C ''test'':31C,45C,51C,65C ''train'':68C,87C ''wald'':29C ''wald-wolfowitz'':28C ''wolfowitz'':30C ''would'':52C ''www.itl.nist.gov'':24C ''www.itl.nist.gov/div898/handbook/eda/section3/eda35d.htm'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9136, 1, '2015-11-30 17:45:34.93', 1, 34, 'Detecting boilerplate in text samples', '&lt;p&gt;I have a corpus of unstructured text that, due to a concatenation from different sources, has boilerplate metadata that I would like to remove. For example:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;DESCRIPTION PROVIDED BY AUTHOR: The goal of my ...&lt;/p&gt;
  
  &lt;p&gt;Author provided: The goal of my ...&lt;/p&gt;
  
  &lt;p&gt;The goal of my ... END OF TRANSCRIPT&lt;/p&gt;
  
  &lt;p&gt;The goal of my ... END, SPONSORED BY COMPANY XYZ&lt;/p&gt;
  
  &lt;p&gt;The goal of my ... SPONSORED: COMPANY XYZ, All rights reserved, date: 10/21&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This boilerplate can be assumed to occur in beginning or end of each sample. What are some robust methods for wrangling this out of the data?&lt;/p&gt;
', 'text-mining  data-cleaning  data-wrangling', NULL, '''/blockquote'':96C ''/p'':42C,53C,61C,70C,81C,95C,124C ''10/21'':94C ''assum'':102C ''author'':48C,55C ''begin'':106C ''blockquot'':43C ''boilerpl'':2A,32C,99C ''clean'':11B ''compani'':79C,88C ''concaten'':27C ''corpus'':19C ''data'':10B,13B,123C ''data-clean'':9B ''data-wrangl'':12B ''date'':93C ''descript'':45C ''detect'':1A ''differ'':29C ''due'':24C ''end'':67C,76C,108C ''exampl'':41C ''goal'':50C,58C,64C,73C,84C ''like'':37C ''metadata'':33C ''method'':116C ''mine'':8B ''occur'':104C ''p'':15C,44C,54C,62C,71C,82C,97C ''provid'':46C,56C ''remov'':39C ''reserv'':92C ''right'':91C ''robust'':115C ''sampl'':5A,111C ''sourc'':30C ''sponsor'':77C,87C ''text'':4A,7B,22C ''text-min'':6B ''transcript'':69C ''unstructur'':21C ''would'':36C ''wrangl'':14B,118C ''xyz'':80C,89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9230, 2, '2015-12-06 00:53:21.6', 2, NULL, NULL, '&lt;p&gt;It''s better to do the square computing on the values of the non standardized features, and then standardize these new features. &lt;/p&gt;

&lt;p&gt;The point of standardizing features is to put features in a similar scale, e.g. 0-1. If you didn''t standardize these new features after the computing, although your new features are between 0-1, your new features will be one or more magnitude smaller than your old features. &lt;/p&gt;
', NULL, NULL, '''-1'':41C,60C ''/p'':24C,75C ''0'':40C,59C ''although'':53C ''better'':4C ''comput'':9C,52C ''didn'':44C ''e.g'':39C ''featur'':17C,23C,30C,34C,49C,56C,63C,74C ''magnitud'':69C ''new'':22C,48C,55C,62C ''non'':15C ''old'':73C ''one'':66C ''p'':1C,25C ''point'':27C ''put'':33C ''scale'':38C ''similar'':37C ''smaller'':70C ''squar'':8C ''standard'':16C,20C,29C,46C ''valu'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9126, 2, '2015-11-30 06:05:30.84', 7, NULL, NULL, '&lt;p&gt;First of all your explanation about the methods are right. The point is that Embedding algorithms are not to only visualize but basically reducing the dimentionality to cope with two main problems in Statistical Data Analysis, namely &lt;strong&gt;Curse of Dimentionaliy&lt;/strong&gt; and &lt;strong&gt;Low-Sample Size Problem&lt;/strong&gt; so that they are not supposed to depict physically understood features and they are not only &lt;em&gt;meaningful&lt;/em&gt; but also necessary for data analysis!&lt;/p&gt;

&lt;p&gt;Actually the visualization is almost the last usage of embedding methods. Projecting high-dimensional data into a lower-dimension space helps to preserve the actual pair-wise distances (mainly Euclidean one) which get distorted in the high dimensions or capturing the most information embedded in the variance of different features.&lt;/p&gt;
', NULL, NULL, '''/em'':70C ''/p'':77C,132C ''/strong'':43C,51C ''actual'':79C,105C ''algorithm'':17C ''almost'':83C ''also'':72C ''analysi'':37C,76C ''basic'':24C ''captur'':121C ''cope'':29C ''curs'':40C ''data'':36C,75C,94C ''depict'':59C ''differ'':130C ''dimens'':99C,119C ''dimension'':93C ''diment'':27C ''dimentionaliy'':42C ''distanc'':109C ''distort'':115C ''em'':68C ''embed'':16C,88C,125C ''euclidean'':111C ''explan'':6C ''featur'':62C,131C ''first'':2C ''get'':114C ''help'':101C ''high'':92C,118C ''high-dimension'':91C ''inform'':124C ''last'':85C ''low'':47C ''low-sampl'':46C ''lower'':98C ''lower-dimens'':97C ''main'':32C,110C ''meaning'':69C ''method'':9C,89C ''name'':38C ''necessari'':73C ''one'':112C ''p'':1C,78C ''pair'':107C ''pair-wis'':106C ''physic'':60C ''point'':13C ''preserv'':103C ''problem'':33C,50C ''project'':90C ''reduc'':25C ''right'':11C ''sampl'':48C ''size'':49C ''space'':100C ''statist'':35C ''strong'':39C,45C ''suppos'':57C ''two'':31C ''understood'':61C ''usag'':86C ''varianc'':128C ''visual'':22C,81C ''wise'':108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9128, 2, '2015-11-30 11:30:14.44', 2, NULL, NULL, '&lt;p&gt;Based on the statements and the discussions, I think there is an important point to distinct. A transformation to a lower dimensional space may &lt;em&gt;reduce&lt;/em&gt; the information, which is something different from making the information &lt;em&gt;meaningless&lt;/em&gt;. Let me use a following analogy:&lt;/p&gt;

&lt;p&gt;Observing (2D) pictures of our world (3D) is a usual practice. A visualization method provides only different âglassesâ to see a high dimensional space.&lt;/p&gt;

&lt;p&gt;A good thing to âtrustâ a visualization method is to understand the internals. My favourite example is the &lt;a href="https://en.wikipedia.org/wiki/Multidimensional_scaling" rel="nofollow"&gt;MDS&lt;/a&gt;  . It is easy possible to implement this method at your own using some optimization tool (e.g. R &lt;em&gt;optim&lt;/em&gt;). So you can &lt;em&gt;see&lt;/em&gt; how the method words, you may &lt;em&gt;measure the error&lt;/em&gt; of the result etc.&lt;/p&gt;

&lt;p&gt;At the end you get a picture preserving the similarity of the original data with some degree of precision. Not more, but not less.&lt;/p&gt;
', NULL, NULL, '''/a'':104C ''/em'':28C,41C,124C,130C,141C ''/p'':48C,75C,146C,172C ''/wiki/multidimensional_scaling'':100C ''2d'':51C ''3d'':56C ''analog'':47C ''base'':2C ''data'':161C ''degre'':164C ''differ'':34C,66C ''dimension'':23C,73C ''discuss'':8C ''distinct'':17C ''e.g'':120C ''easi'':107C ''em'':26C,39C,122C,128C,137C ''en.wikipedia.org'':99C ''en.wikipedia.org/wiki/multidimensional_scaling'':98C ''end'':150C ''error'':140C ''etc'':145C ''exampl'':93C ''favourit'':92C ''follow'':46C ''get'':152C ''glassesâ'':68C ''good'':78C ''high'':72C ''href'':97C ''implement'':110C ''import'':14C ''inform'':30C,38C ''intern'':90C ''less'':171C ''let'':42C ''lower'':22C ''make'':36C ''may'':25C,136C ''mds'':103C ''meaningless'':40C ''measur'':138C ''method'':63C,85C,112C,133C ''nofollow'':102C ''observ'':50C ''optim'':118C,123C ''origin'':160C ''p'':1C,49C,76C,147C ''pictur'':52C,154C ''point'':15C ''possibl'':108C ''practic'':60C ''precis'':166C ''preserv'':155C ''provid'':64C ''r'':121C ''reduc'':27C ''rel'':101C ''result'':144C ''see'':70C,129C ''similar'':157C ''someth'':33C ''space'':24C,74C ''statement'':5C ''thing'':79C ''think'':10C ''tool'':119C ''transform'':19C ''trustâ'':82C ''understand'':88C ''use'':44C,116C ''usual'':59C ''visual'':62C,84C ''word'':134C ''world'':55C ''â'':67C,81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9129, 1, '2015-11-30 11:31:55.753', 1, 62, 'Regression: Scatterplot with low R squared and high p-values', '&lt;p&gt;Based on three datasets, I have produced the scatterplot below in Python:
&lt;a href="http://i.stack.imgur.com/C6oMN.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/C6oMN.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I am trying to fit a line on each dataset, but when I check the metrics this is what I get:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Set 1 (red):     R squared=0.002, p-value=0.651 &lt;/li&gt;
&lt;li&gt;Set 2 (purple):  R squared=0.008, p-value=0.378&lt;/li&gt;
&lt;li&gt;Set 3 (blue):    R squared=0.001, p-value=0.714&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;My question:&lt;/strong&gt; are such data sets impossible to fit? Is there any kind of data transformation I could apply, based on the scatterplot shape?&lt;/p&gt;

&lt;p&gt;The goal is to predict from regression.&lt;/p&gt;
', 'dataset  regression', NULL, '''/a'':44C ''/c6omn.png'':31C,38C ''/li'':81C,93C,105C ''/p'':45C,68C,134C,143C ''/strong'':111C ''/ul'':106C ''0.001'':100C ''0.002'':76C ''0.008'':88C ''0.378'':92C ''0.651'':80C ''0.714'':104C ''1'':72C ''2'':84C ''3'':96C ''alt'':39C ''appli'':128C ''base'':15C,129C ''blue'':97C ''check'':60C ''could'':127C ''data'':114C,124C ''dataset'':12B,18C,56C ''descript'':42C ''enter'':40C ''fit'':51C,118C ''get'':67C ''goal'':137C ''high'':8A ''href'':28C ''i.stack.imgur.com'':30C,37C ''i.stack.imgur.com/c6omn.png'':29C,36C ''imag'':41C ''img'':34C ''imposs'':116C ''kind'':122C ''li'':70C,82C,94C ''line'':53C ''low'':4A ''metric'':62C ''nofollow'':33C ''p'':10A,14C,46C,78C,90C,102C,107C,135C ''p-valu'':9A,77C,89C,101C ''predict'':140C ''produc'':21C ''purpl'':85C ''python'':26C ''question'':110C ''r'':5A,74C,86C,98C ''red'':73C ''regress'':1A,13B,142C ''rel'':32C ''scatterplot'':2A,23C,132C ''set'':71C,83C,95C,115C ''shape'':133C ''squar'':6A,75C,87C,99C ''src'':35C ''strong'':108C ''three'':17C ''transform'':125C ''tri'':49C ''ul'':69C ''valu'':11A,79C,91C,103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9131, 1, '2015-11-30 14:32:56.113', 1, 62, 'How can I weigh observations differently that were provided for a time horizon?', '&lt;p&gt;I have 623 observations with one continuous dependent variable and 13 independent variables (continuous, categorical, and ordinal), defined based on researcher experience and literature review.
I considered planning to do several regression analysis to estimate the dependent variable and study the predictive factors (if they are positive, negative, and their magnitude) on it. Data are provided for 10 years. Since the latest observations are more important, Iâm interested in using weighted observations. How can I approach this problem and validate my approach?&lt;/p&gt;
', 'data-mining  dataset', NULL, '''/p'':102C ''10'':76C ''13'':29C ''623'':21C ''analysi'':51C ''approach'':95C,101C ''base'':37C ''categor'':33C ''consid'':45C ''continu'':25C,32C ''data'':15B,72C ''data-min'':14B ''dataset'':17B ''defin'':36C ''depend'':26C,55C ''differ'':6A ''estim'':53C ''experi'':40C ''factor'':61C ''horizon'':13A ''import'':84C ''independ'':30C ''interest'':87C ''iâ'':85C ''latest'':80C ''literatur'':42C ''m'':86C ''magnitud'':69C ''mine'':16B ''negat'':66C ''observ'':5A,22C,81C,91C ''one'':24C ''ordin'':35C ''p'':18C ''plan'':46C ''posit'':65C ''predict'':60C ''problem'':97C ''provid'':9A,74C ''regress'':50C ''research'':39C ''review'':43C ''sever'':49C ''sinc'':78C ''studi'':58C ''time'':12A ''use'':89C ''valid'':99C ''variabl'':27C,31C,56C ''weigh'':4A ''weight'':90C ''year'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9133, 2, '2015-11-30 15:59:29.73', 0, NULL, NULL, '&lt;p&gt;"I now declare you man and wife", an example of utterance by means of which the speaker performs a particular act, is an instance of a &lt;strong&gt;performative&lt;/strong&gt; act, which changes the thing being modeled and thus changes the concept. Reflexive, iterative, recursive, retroactive, &lt;a href="http://dictionary.reference.com/browse/self-generating" rel="nofollow"&gt;self-generating&lt;/a&gt;, are related terms.&lt;/p&gt;
', NULL, NULL, '''/a'':57C ''/browse/self-generating'':51C ''/p'':61C ''/strong'':30C ''act'':22C,31C ''chang'':33C,40C ''concept'':42C ''declar'':4C ''dictionary.reference.com'':50C ''dictionary.reference.com/browse/self-generating'':49C ''exampl'':10C ''generat'':56C ''href'':48C ''instanc'':25C ''iter'':44C ''man'':6C ''mean'':14C ''model'':37C ''nofollow'':53C ''p'':1C ''particular'':21C ''perform'':19C,29C ''recurs'':45C ''reflex'':43C ''rel'':52C ''relat'':59C ''retroact'':46C ''self'':55C ''self-gener'':54C ''speaker'':18C ''strong'':28C ''term'':60C ''thing'':35C ''thus'':39C ''utter'':12C ''wife'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9137, 2, '2015-12-01 02:41:58.287', 0, NULL, NULL, '&lt;p&gt;Converting the date into an integer timestamp will put a higher weight on the more recent observation.&lt;/p&gt;

&lt;p&gt;When you write :&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Since the latest observations are more important, Iâm interested in using weighted observations.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Do you mean that you know already that the date will be a predictive factor in your analysis, or that you want to artificially make this variable a predictive factor for the regression?&lt;/p&gt;

&lt;p&gt;If it is the former, then the integer conversion above could do the trick.
If it is the latter, you need to combine the date with your target variable (by  multiplication for example).&lt;/p&gt;
', NULL, NULL, '''/blockquote'':42C ''/p'':19C,24C,41C,77C,112C ''alreadi'':50C ''analysi'':61C ''artifici'':67C ''blockquot'':25C ''combin'':101C ''convers'':87C ''convert'':2C ''could'':89C ''date'':4C,53C,103C ''exampl'':111C ''factor'':58C,73C ''former'':83C ''higher'':12C ''import'':33C ''integ'':7C,86C ''interest'':36C ''iâ'':34C ''know'':49C ''latest'':29C ''latter'':97C ''m'':35C ''make'':68C ''mean'':46C ''multipl'':109C ''need'':99C ''observ'':18C,30C,40C ''p'':1C,20C,26C,43C,78C ''predict'':57C,72C ''put'':10C ''recent'':17C ''regress'':76C ''sinc'':27C ''target'':106C ''timestamp'':8C ''trick'':92C ''use'':38C ''variabl'':70C,107C ''want'':65C ''weight'':13C,39C ''write'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9138, 1, '2015-12-01 04:53:16.017', 2, 84, 'how to extract high frequency phrase', '&lt;p&gt;This is the corpus&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;abcdef
abcd
xabcd
b
bcef
ef
aef
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;what I want to extract is &lt;code&gt;abcd&lt;/code&gt;, &lt;code&gt;ef&lt;/code&gt;, then I try to count all n-grams:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;abcdef 1
abcd 3
abc 3
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;My question is how to distinguish &lt;code&gt;abcd&lt;/code&gt; with &lt;code&gt;abcdef&lt;/code&gt; and &lt;code&gt;abc&lt;/code&gt;, since &lt;code&gt;abcdef&lt;/code&gt; just appear once, and &lt;code&gt;abc&lt;/code&gt; is the sub-string of ''abcd''. Of course, I can filter with this two conditions, but just want to know any existing algorithm target on solving this problem.&lt;/p&gt;
', 'data-mining  dataset  statistics  text-mining', NULL, '''/code'':30C,41C,44C,63C,74C,78C,82C,86C,93C ''/p'':20C,54C,123C ''/pre'':31C,64C ''1'':58C ''3'':60C,62C ''abc'':61C,81C,92C ''abcd'':24C,40C,59C,73C,100C ''abcdef'':23C,57C,77C,85C ''aef'':29C ''algorithm'':117C ''appear'':88C ''b'':26C ''bcef'':27C ''code'':22C,39C,42C,56C,72C,76C,80C,84C,91C ''condit'':109C ''corpus'':19C ''count'':49C ''cours'':102C ''data'':8B ''data-min'':7B ''dataset'':10B ''distinguish'':71C ''ef'':28C,43C ''exist'':116C ''extract'':3A,37C ''filter'':105C ''frequenc'':5A ''gram'':53C ''high'':4A ''know'':114C ''mine'':9B,14B ''n'':52C ''n-gram'':51C ''p'':15C,32C,65C ''phrase'':6A ''pre'':21C,55C ''problem'':122C ''question'':67C ''sinc'':83C ''solv'':120C ''statist'':11B ''string'':98C ''sub'':97C ''sub-str'':96C ''target'':118C ''text'':13B ''text-min'':12B ''tri'':47C ''two'':108C ''want'':35C,112C ''xabcd'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9530, 2, '2015-12-26 21:18:35.373', 1, NULL, NULL, '&lt;p&gt;I''d sugest first vizualize your data.&lt;/p&gt;

&lt;p&gt;The &lt;em&gt;time to archive a partition&lt;/em&gt; could be defined as a difference between the partition start (first possible insert) and archiving time (last possible update).&lt;/p&gt;

&lt;p&gt;Plot the total distribution of this data for some time interval - check if it follows some distribution. Is the average, median or sd meaningfull?&lt;/p&gt;

&lt;p&gt;Make a boxplot for each hour to see inter quartile interval and the outliers.&lt;/p&gt;

&lt;p&gt;Plot it as a time series - does it have a trend or daily / weekly pattern?&lt;/p&gt;

&lt;p&gt;In your simple set up (with one predictor and one response) this will give you impression about the accuracy that you can expect from a model. The choose of the model depends on the result of the visualization.&lt;/p&gt;

&lt;p&gt;I personaly would start with simpliest models&lt;/p&gt;

&lt;p&gt;1) all partition archived after N hours&lt;/p&gt;

&lt;p&gt;2) independent model for each hour predicting say .9 quantile of some history interval&lt;/p&gt;

&lt;p&gt;3) more advances 2) considering the weekday and/or trend and/or ignoring outlier.&lt;/p&gt;

&lt;p&gt;always check the accuracy and stopp if expectations are met.&lt;/p&gt;
', NULL, NULL, '''/em'':18C ''/p'':9C,38C,64C,80C,97C,137C,146C,155C,171C,185C,197C ''1'':148C ''2'':157C,176C ''3'':173C ''9'':165C ''accuraci'':117C,190C ''advanc'':175C ''alway'':187C ''and/or'':180C,182C ''archiv'':15C,33C,151C ''averag'':59C ''boxplot'':68C ''check'':51C,188C ''choos'':126C ''consid'':177C ''could'':19C ''d'':3C ''daili'':94C ''data'':8C,46C ''defin'':21C ''depend'':130C ''differ'':24C ''distribut'':43C,56C ''em'':12C ''expect'':121C,194C ''first'':5C,29C ''follow'':54C ''give'':112C ''histori'':169C ''hour'':71C,154C,162C ''ignor'':183C ''impress'':114C ''independ'':158C ''insert'':31C ''inter'':74C ''interv'':50C,76C,170C ''last'':35C ''make'':66C ''meaningful'':63C ''median'':60C ''met'':196C ''model'':124C,129C,145C,159C ''n'':153C ''one'':105C,108C ''outlier'':79C,184C ''p'':1C,10C,39C,65C,81C,98C,138C,147C,156C,172C,186C ''partit'':17C,27C,150C ''pattern'':96C ''personali'':140C ''plot'':40C,82C ''possibl'':30C,36C ''predict'':163C ''predictor'':106C ''quantil'':166C ''quartil'':75C ''respons'':109C ''result'':133C ''say'':164C ''sd'':62C ''see'':73C ''seri'':87C ''set'':102C ''simpl'':101C ''simpliest'':144C ''start'':28C,142C ''stopp'':192C ''sugest'':4C ''time'':13C,34C,49C,86C ''total'':42C ''trend'':92C,181C ''updat'':37C ''visual'':136C ''vizual'':6C ''week'':95C ''weekday'':179C ''would'':141C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9140, 1, '2015-12-01 06:13:35.21', 1, 53, 'How to cluster Houses on the basis of similarity of features+location?', '&lt;p&gt;I have a dataset of houses like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;HouseID  Latitude Longitude PriceIndex
  1          1.4     103.120    1.21
  2          1.42    103.112    2.01 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to find houses which are similar to each other both on the basis of their position as well as their price index.[Also would need to Rank in order of similarities, given one house] I tried using hclust package in R  and was able to extract 9 classes. However the groups don''t seem to have any interpretable similarities (for example the points are spread all across the city etc). I haven''t done clustering based projects before so any help in the right direction will be helpful. Thanks!&lt;/p&gt;

&lt;p&gt;Edit: Removing the price index column from the clustering data-set actually clusters spatially. But adding the price shows only price-based clustering&lt;/p&gt;
', 'r  clustering  similarity', NULL, '''/code'':40C ''/p'':25C,134C,161C ''/pre'':41C ''1'':32C ''1.21'':35C ''1.4'':33C ''1.42'':37C ''103.112'':38C ''103.120'':34C ''2'':36C ''2.01'':39C ''9'':91C ''abl'':88C ''across'':111C ''actual'':148C ''ad'':152C ''also'':67C ''base'':120C,159C ''basi'':7A,57C ''citi'':113C ''class'':92C ''cluster'':3A,14B,119C,144C,149C,160C ''code'':27C ''column'':141C ''data'':146C ''data-set'':145C ''dataset'':20C ''direct'':129C ''done'':118C ''edit'':136C ''etc'':114C ''exampl'':105C ''extract'':90C ''featur'':11A ''find'':46C ''given'':76C ''group'':95C ''haven'':116C ''hclust'':82C ''help'':125C,132C ''hous'':4A,22C,47C,78C ''houseid'':28C ''howev'':93C ''index'':66C,140C ''interpret'':102C ''latitud'':29C ''like'':23C ''locat'':12A ''longitud'':30C ''need'':69C ''one'':77C ''order'':73C ''p'':16C,42C,135C ''packag'':83C ''point'':107C ''posit'':60C ''pre'':26C ''price'':65C,139C,154C,158C ''price-bas'':157C ''priceindex'':31C ''project'':121C ''r'':13B,85C ''rank'':71C ''remov'':137C ''right'':128C ''seem'':98C ''set'':147C ''show'':155C ''similar'':9A,15B,50C,75C,103C ''spatial'':150C ''spread'':109C ''thank'':133C ''tri'':80C ''use'':81C ''want'':44C ''well'':62C ''would'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9141, 2, '2015-12-01 07:33:56.7', 2, NULL, NULL, '&lt;p&gt;I don''t think you necessarily need to convert the individual log entries into vectors for use in an algorithm.  I would guess that what you are interested in is a sequence of log entries, which represent a series of events, ordered in time, which together make up a series of ''cases''.  Here the relationship between a series of collected log entries is important.&lt;/p&gt;

&lt;p&gt;If this is the case then you might want to consider using &lt;a href="http://www.processmining.org/start" rel="nofollow"&gt;Process Mining&lt;/a&gt; techniques.  This allows you to build models of your process (the use of your application) and determine patterns of process steps, along with errors and rework steps.&lt;/p&gt;

&lt;p&gt;There is a good introduction course on Coursera, &lt;a href="https://www.coursera.org/course/procmin" rel="nofollow"&gt;here&lt;/a&gt;.  There are even some developed, commercial packages like ''disco'' to help you with the analysis and visualisation&lt;/p&gt;
', NULL, NULL, '''/a'':89C,135C ''/course/procmin'':131C ''/p'':66C,117C,153C ''/start'':84C ''algorithm'':21C ''allow'':92C ''along'':111C ''analysi'':150C ''applic'':104C ''build'':95C ''case'':53C,72C ''collect'':61C ''commerci'':141C ''consid'':78C ''convert'':10C ''cours'':124C ''coursera'':126C ''determin'':106C ''develop'':140C ''disco'':144C ''entri'':14C,36C,63C ''error'':113C ''even'':138C ''event'':42C ''good'':122C ''guess'':24C ''help'':146C ''href'':81C,128C ''import'':65C ''individu'':12C ''interest'':29C ''introduct'':123C ''like'':143C ''log'':13C,35C,62C ''make'':48C ''might'':75C ''mine'':88C ''model'':96C ''necessarili'':7C ''need'':8C ''nofollow'':86C,133C ''order'':43C ''p'':1C,67C,118C ''packag'':142C ''pattern'':107C ''process'':87C,99C,109C ''rel'':85C,132C ''relationship'':56C ''repres'':38C ''rework'':115C ''sequenc'':33C ''seri'':40C,51C,59C ''step'':110C,116C ''techniqu'':90C ''think'':5C ''time'':45C ''togeth'':47C ''use'':18C,79C,101C ''vector'':16C ''visualis'':152C ''want'':76C ''would'':23C ''www.coursera.org'':130C ''www.coursera.org/course/procmin'':129C ''www.processmining.org'':83C ''www.processmining.org/start'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9142, 1, '2015-12-01 08:39:25.037', 0, 70, 'How to use a trained model to make a prediction from a given input?', '&lt;p&gt;I am following a &lt;a href="http://neuralnetworksanddeeplearning.com/chap6.html" rel="nofollow"&gt;tutorial&lt;/a&gt; with a public &lt;a href="https://github.com/mnielsen/neural-networks-and-deep-learning/blob/master/src/network3.py" rel="nofollow"&gt;code repo&lt;/a&gt; available. I have been banging my head trying to figure out how to apply the model after it has been trained. Since the tutorial only covers the training, testing and validation, I''m not sure how to use it on new unclassified data. Here''s what I''ve tried:&lt;/p&gt;

&lt;p&gt;(1) Under the main class "Network" in &lt;a href="https://github.com/mnielsen/neural-networks-and-deep-learning/blob/master/src/network3.py" rel="nofollow"&gt;network3.py&lt;/a&gt; used to construct and train networks, I added the following method on &lt;code&gt;line 182&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def predict(self, input):
    self.predict = theano.function([self.x, self.y], self.layers[-1].y_out, on_unused_input=''warn'')
    print(self.predict(input))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;(2) From the Python interactive console (iPython notebook), I imported the data and trained the model as usual:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;net = Network([
        FullyConnectedLayer(n_in=59, n_out=100),
        SoftmaxLayer(n_in=100, n_out=10)], mini_batch_size)

net.SGD(training_data, 10, mini_batch_size, 0.03, validation_data, test_data)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;(3) I then issued the command:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;net.predict((data[0,1:], data[0,0]))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;which caused the following error&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;ValueError: (''Bad input argument to theano function with name
  "network3.py:181"  at index 0(0-based)'', ''setting an array element
  with a sequence.'')&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;It looks like my inputs are not defined or interpreted as I expected. The data I used for training, testing, validating and predicting are all the same format, i.e. col 0 is the classification and cols 1-59 are numerical inputs. In the code above, I just used the training data to do the prediction. I would greatly appreciate any help or advice to steer me in the right direction. Thanks!&lt;/p&gt;
', 'python  neuralnetwork  deep-learning  theano', NULL, '''-1'':143C ''-59'':300C ''/a'':34C,47C,114C ''/blockquote'':261C ''/chap6.html'':30C ''/code'':130C,153C,208C,227C ''/mnielsen/neural-networks-and-deep-learning/blob/master/src/network3.py'':42C,110C ''/p'':97C,131C,174C,217C,235C,260C,334C ''/pre'':154C,209C,228C ''0'':222C,225C,226C,250C,251C,293C ''0.03'':203C ''1'':99C,223C,299C ''10'':192C,199C ''100'':185C,189C ''182'':129C ''2'':156C ''3'':211C ''59'':182C ''ad'':122C ''advic'':325C ''appli'':61C ''appreci'':321C ''argument'':241C ''array'':255C ''avail'':48C ''bad'':239C ''bang'':52C ''base'':252C ''batch'':194C,201C ''blockquot'':236C ''caus'':231C ''class'':103C ''classif'':296C ''code'':45C,127C,133C,176C,219C,306C ''col'':292C,298C ''command'':216C ''consol'':161C ''construct'':117C ''cover'':73C ''data'':90C,167C,198C,205C,207C,221C,224C,277C,313C ''deep'':18B ''deep-learn'':17B ''def'':134C ''defin'':270C ''direct'':332C ''element'':256C ''error'':234C ''expect'':275C ''figur'':57C ''follow'':24C,124C,233C ''format'':290C ''fullyconnectedlay'':179C ''function'':244C ''github.com'':41C,109C ''github.com/mnielsen/neural-networks-and-deep-learning/blob/master/src/network3.py'':40C,108C ''given'':13A ''great'':320C ''head'':54C ''help'':323C ''href'':27C,39C,107C ''i.e'':291C ''import'':165C ''index'':249C ''input'':14A,137C,148C,152C,240C,267C,303C ''interact'':160C ''interpret'':272C ''ipython'':162C ''issu'':214C ''learn'':19B ''like'':265C ''line'':128C ''look'':264C ''m'':80C ''main'':102C ''make'':8A ''method'':125C ''mini'':193C,200C ''model'':6A,63C,171C ''n'':180C,183C,187C,190C ''name'':246C ''net'':177C ''net.predict'':220C ''net.sgd'':196C ''network'':104C,120C,178C ''network3.py'':113C ''network3.py:181'':247C ''neuralnetwork'':16B ''neuralnetworksanddeeplearning.com'':29C ''neuralnetworksanddeeplearning.com/chap6.html'':28C ''new'':88C ''nofollow'':32C,44C,112C ''notebook'':163C ''numer'':302C ''p'':21C,98C,155C,210C,229C,237C,262C ''pre'':132C,175C,218C ''predict'':10A,135C,285C,317C ''print'':150C ''public'':37C ''python'':15B,159C ''rel'':31C,43C,111C ''repo'':46C ''right'':331C ''self'':136C ''self.layers'':142C ''self.predict'':138C,151C ''self.x'':140C ''self.y'':141C ''sequenc'':259C ''set'':253C ''sinc'':69C ''size'':195C,202C ''softmaxlay'':186C ''steer'':327C ''sure'':82C ''test'':76C,206C,282C ''thank'':333C ''theano'':20B,243C ''theano.function'':139C ''train'':5A,68C,75C,119C,169C,197C,281C,312C ''tri'':55C,96C ''tutori'':33C,71C ''unclassifi'':89C ''unus'':147C ''use'':3A,85C,115C,279C,310C ''usual'':173C ''valid'':78C,204C,283C ''valueerror'':238C ''ve'':95C ''warn'':149C ''would'':319C ''y'':144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9147, 2, '2015-12-01 14:13:41.883', 1, NULL, NULL, '&lt;p&gt;This uses python and NLTK&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import nltk

text = """abcdef
abcd
xabcd
b
bcef
ef
aef
"""

def tokens(lines):
    for line in lines:
        for n in range(1, len(line)):
            ts = nltk.ngrams(line, n)
            for t in ts: yield str.join('''', t) 
        yield line

ts = tokens(text.splitlines())
freq = nltk.FreqDist(ts)
print(freq.most_common())
# [(''b'', 5), (''c'', 4), (''ef'', 4), (''bc'', 4), (''e'', 4)...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;There are other solutions, of course, but I think this meets your criteria. You may need to optimize if your lines are huge as this will create every possible n-gram.&lt;/p&gt;
', NULL, NULL, '''/code'':66C ''/p'':7C,101C ''/pre'':67C ''1'':31C ''4'':59C,61C,63C,65C ''5'':57C ''abcd'':14C ''abcdef'':13C ''aef'':19C ''b'':16C,56C ''bc'':62C ''bcef'':17C ''c'':58C ''code'':9C ''common'':55C ''cours'':74C ''creat'':95C ''criteria'':81C ''def'':20C ''e'':64C ''ef'':18C,60C ''everi'':96C ''freq'':50C ''freq.most'':54C ''gram'':100C ''huge'':91C ''import'':10C ''len'':32C ''line'':22C,24C,26C,33C,36C,46C,89C ''may'':83C ''meet'':79C ''n'':28C,37C,99C ''n-gram'':98C ''need'':84C ''nltk'':6C,11C ''nltk.freqdist'':51C ''nltk.ngrams'':35C ''optim'':86C ''p'':1C,68C ''possibl'':97C ''pre'':8C ''print'':53C ''python'':4C ''rang'':30C ''solut'':72C ''str.join'':43C ''text'':12C ''text.splitlines'':49C ''think'':77C ''token'':21C,48C ''ts'':34C,41C,47C,52C ''use'':3C ''xabcd'':15C ''yield'':42C,45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9149, 2, '2015-12-01 17:24:11.44', 1, NULL, NULL, '&lt;p&gt;This might get you started. Phrase length is determined by the range() function. Basically this tokenizes and creates n-grams. Then it counts each token. Tokens with a high mean over all documents (occurs often across documents) is printed out in the last line.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from sklearn.feature_extraction.text import CountVectorizer
import numpy as np
import nltk

text = """DESCRIPTION PROVIDED BY AUTHOR: The goal of my a...
Author provided: The goal of my b...
The goal of my c... END OF TRANSCRIPT
The goal of my d... END SPONSORED BY COMPANY XYZ
The goal of my e... SPONSORED: COMPANY XYZ All rights reserved date: 10/21
"""

def todocuments(lines):
    for line in lines:
        words = line.lower().split('' '')
        doc = ""
        for n in range(3, 6):
            ts = nltk.ngrams(words, n)
            for t in ts: doc = doc + " " + str.join(''_'', t) 
        yield doc

cv = CountVectorizer(min_df=.5)

fit = cv.fit_transform(todocuments(text.splitlines()))
vocab_idx = {b: a for a, b in cv.vocabulary_.items()}

means = fit.mean(axis=0)
arr = np.squeeze(np.asarray(means))
[vocab_idx[idx] for idx in np.where(arr &amp;gt; .95)[0]]
# [''goal_of_my'', ''the_goal_of'', ''the_goal_of_my'']
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':188C ''/p'':47C ''/pre'':189C ''0'':162C,177C ''10/21'':107C ''3'':123C ''5'':143C ''6'':124C ''95'':176C ''across'':38C ''arr'':163C,174C ''author'':64C,70C ''axi'':161C ''b'':76C,151C,155C ''basic'':15C ''c'':81C ''code'':49C ''compani'':93C,101C ''count'':25C ''countvector'':53C,140C ''creat'':19C ''cv'':139C ''cv.fit'':145C ''cv.vocabulary'':157C ''d'':89C ''date'':106C ''def'':108C ''descript'':61C ''determin'':10C ''df'':142C ''doc'':118C,133C,134C,138C ''document'':35C,39C ''e'':99C ''end'':82C,90C ''fit'':144C ''fit.mean'':160C ''function'':14C ''get'':4C ''goal'':66C,73C,78C,86C,96C,178C,182C,185C ''gram'':22C ''gt'':175C ''high'':31C ''idx'':150C,168C,169C,171C ''import'':52C,54C,58C ''item'':158C ''last'':45C ''length'':8C ''line'':46C,110C,112C,114C ''line.lower'':116C ''mean'':32C,159C,166C ''might'':3C ''min'':141C ''n'':21C,120C,128C ''n-gram'':20C ''nltk'':59C ''nltk.ngrams'':126C ''np'':57C ''np.asarray'':165C ''np.squeeze'':164C ''np.where'':173C ''numpi'':55C ''occur'':36C ''often'':37C ''p'':1C ''phrase'':7C ''pre'':48C ''print'':41C ''provid'':62C,71C ''rang'':13C,122C ''reserv'':105C ''right'':104C ''sklearn.feature_extraction.text'':51C ''split'':117C ''sponsor'':91C,100C ''start'':6C ''str.join'':135C ''text'':60C ''text.splitlines'':148C ''todocu'':109C,147C ''token'':17C,27C,28C ''transcript'':84C ''transform'':146C ''ts'':125C,132C ''vocab'':149C,167C ''word'':115C,127C ''xyz'':94C,102C ''yield'':137C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9531, 2, '2015-12-27 00:34:21.437', 1, NULL, NULL, '&lt;p&gt;The training corpus needs to have all the words of which you want to find similarity.&lt;/p&gt;
', NULL, NULL, '''/p'':18C ''corpus'':4C ''find'':16C ''need'':5C ''p'':1C ''similar'':17C ''train'':3C ''want'':14C ''word'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9150, 2, '2015-12-01 20:08:28.413', 1, NULL, NULL, '&lt;p&gt;Having used genetic algorithms extensively in both professional projects and for my own entertainment, I think it is best to just code your own set of functions/classes for the specific project.  As you have seen, there are quite a few well-intended open-source GA packages out there. However, many of the fine details and tweaks necessary for a good GA are problem-specific.  It is also more convenient to work directly with your existing data structures, rather than adapt to someone else''s worldview.  In other words, cost-benefit does not seem to work out when applying someone else''s GA package.  You need a function for creating a random instance, a function for checking it is valid, a function for scoring it, and a mating function.  That''s pretty much it!     &lt;/p&gt;
', NULL, NULL, '''/p'':139C ''adapt'':84C ''algorithm'':5C ''also'':71C ''appli'':103C ''benefit'':95C ''best'':20C ''check'':121C ''code'':23C ''conveni'':73C ''cost'':94C ''cost-benefit'':93C ''creat'':114C ''data'':80C ''detail'':57C ''direct'':76C ''els'':87C,105C ''entertain'':15C ''exist'':79C ''extens'':6C ''fine'':56C ''function'':112C,119C,126C,133C ''functions/classes'':28C ''ga'':48C,64C,107C ''genet'':4C ''good'':63C ''howev'':52C ''instanc'':117C ''intend'':44C ''mani'':53C ''mate'':132C ''much'':137C ''necessari'':60C ''need'':110C ''open'':46C ''open-sourc'':45C ''p'':1C ''packag'':49C,108C ''pretti'':136C ''problem'':67C ''problem-specif'':66C ''profession'':9C ''project'':10C,32C ''quit'':39C ''random'':116C ''rather'':82C ''score'':128C ''seem'':98C ''seen'':36C ''set'':26C ''someon'':86C,104C ''sourc'':47C ''specif'':31C,68C ''structur'':81C ''think'':17C ''tweak'':59C ''use'':3C ''valid'':124C ''well'':43C ''well-intend'':42C ''word'':92C ''work'':75C,100C ''worldview'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9151, 2, '2015-12-01 20:08:57.823', 1, NULL, NULL, '&lt;p&gt;For the 200 positive samples, are there many different "types"? In other words, will future positive samples look nothing like the current positive samples? If this is the case, you may try anomaly detection in machine learning. &lt;/p&gt;

&lt;p&gt;From a lecture by Andrew Ng in Coursera, if you have small number of positive samples (e.g., 20), large number of negative samples (e.g., 10000), and it''s hard for any algorithm to learn from the positive samples, you should use anomaly detection, otherwise, you can use supervised learning.  &lt;/p&gt;
', NULL, NULL, '''/p'':39C,90C ''10000'':65C ''20'':58C ''200'':4C ''algorithm'':72C ''andrew'':45C ''anomali'':34C,82C ''case'':30C ''coursera'':48C ''current'':23C ''detect'':35C,83C ''differ'':10C ''e.g'':57C,64C ''futur'':16C ''hard'':69C ''larg'':59C ''learn'':38C,74C,89C ''lectur'':43C ''like'':21C ''look'':19C ''machin'':37C ''mani'':9C ''may'':32C ''negat'':62C ''ng'':46C ''noth'':20C ''number'':53C,60C ''otherwis'':84C ''p'':1C,40C ''posit'':5C,17C,24C,55C,77C ''sampl'':6C,18C,25C,56C,63C,78C ''small'':52C ''supervis'':88C ''tri'':33C ''type'':11C ''use'':81C,87C ''word'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9152, 2, '2015-12-01 21:07:45.98', 2, NULL, NULL, '&lt;p&gt;Sometimes, it is meaningful to visualize high dimensional data since it may tell us physics. &lt;/p&gt;

&lt;p&gt;There is at least one example in astrophysics where you project your data down to principal components generated by PCA and those principal components correspond to much physical insight about the galaxies. For detail, see the last figure in &lt;a href="http://www.astroml.org/sklearn_tutorial/dimensionality_reduction.html#id2" rel="nofollow"&gt;http://www.astroml.org/sklearn_tutorial/dimensionality_reduction.html#id2&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;and the paper in &lt;/p&gt;

&lt;p&gt;&lt;a href="http://iopscience.iop.org/article/10.1086/425626/pdf" rel="nofollow"&gt;http://iopscience.iop.org/article/10.1086/425626/pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Here is the basic idea. The authors apply PCA to many spectra (e.g., 10,000) from a telescope. Each spectrum has ~1000 attributes. Since this data set has large dimensions, it''s difficult to visualize it. However, the first 4 components from PCA reveal much physics about the spectra (see sections 4.1-4.4 in the paper above). &lt;/p&gt;
', NULL, NULL, '''-4.4'':139C ''/article/10.1086/425626/pdf'':80C ''/article/10.1086/425626/pdf&lt;/a&gt;&lt;/p&gt;'':85C ''/p'':17C,68C,74C,144C ''/sklearn_tutorial/dimensionality_reduction.html#id2'':62C ''/sklearn_tutorial/dimensionality_reduction.html#id2&lt;/a&gt;'':67C ''000'':101C ''10'':100C ''1000'':108C ''4'':126C ''4.1'':138C ''appli'':94C ''astrophys'':26C ''attribut'':109C ''author'':93C ''basic'':90C ''compon'':35C,42C,127C ''correspond'':43C ''data'':10C,31C,112C ''detail'':52C ''difficult'':119C ''dimens'':116C ''dimension'':9C ''e.g'':99C ''exampl'':24C ''figur'':56C ''first'':125C ''galaxi'':50C ''generat'':36C ''high'':8C ''howev'':123C ''href'':59C,77C ''idea'':91C ''insight'':47C ''iopscience.iop.org'':79C,84C ''iopscience.iop.org/article/10.1086/425626/pdf'':78C ''iopscience.iop.org/article/10.1086/425626/pdf&lt;/a&gt;&lt;/p&gt;'':83C ''larg'':115C ''last'':55C ''least'':22C ''mani'':97C ''may'':13C ''meaning'':5C ''much'':45C,131C ''nofollow'':64C,82C ''one'':23C ''p'':1C,18C,69C,75C,86C ''paper'':72C,142C ''pca'':38C,95C,129C ''physic'':16C,46C,132C ''princip'':34C,41C ''project'':29C ''rel'':63C,81C ''reveal'':130C ''section'':137C ''see'':53C,136C ''set'':113C ''sinc'':11C,110C ''sometim'':2C ''spectra'':98C,135C ''spectrum'':106C ''telescop'':104C ''tell'':14C ''us'':15C ''visual'':7C,121C ''www.astroml.org'':61C,66C ''www.astroml.org/sklearn_tutorial/dimensionality_reduction.html#id2'':60C ''www.astroml.org/sklearn_tutorial/dimensionality_reduction.html#id2&lt;/a&gt;'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9333, 2, '2015-12-11 07:41:24.76', 0, NULL, NULL, '&lt;p&gt;I found my own answer. I had defined &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;w = theano.shared(rng.randn(feats), name="w")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and that was wrong.
The correct definition is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;w = theano.shared(rng.randn(feats, num_classes), name="w")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;since the weights link ''feats''-number of input neuron to ''num_classes''-number of output neurons.&lt;/p&gt;
', NULL, NULL, '''/code'':19C,41C ''/p'':10C,30C,60C ''/pre'':20C,42C ''answer'':6C ''class'':38C,55C ''code'':12C,32C ''correct'':27C ''defin'':9C ''definit'':28C ''feat'':16C,36C,48C ''found'':3C ''input'':51C ''link'':47C ''name'':17C,39C ''neuron'':52C,59C ''num'':37C,54C ''number'':49C,56C ''output'':58C ''p'':1C,21C,43C ''pre'':11C,31C ''rng.randn'':15C,35C ''sinc'':44C ''theano.shared'':14C,34C ''w'':13C,18C,33C,40C ''weight'':46C ''wrong'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9162, 2, '2015-12-02 06:41:14.46', 0, NULL, NULL, '&lt;p&gt;As far as I know, there is not a rule to say which algorithm works for which dataset. Just make sure your dataset and variables of interest fulfill the pre-assumptions of running each algorithm and give it a try. For example, linear regression has some pre-assumptions such as normality of resuduals, homoscedasticity (the variability in the response variable is the same at all levels of the explanatory variable) and so on. Just check these for your variables and give the algorithm a try.&lt;/p&gt;

&lt;p&gt;You can use a point and click software to see the results without getting involved in the code and parameter setting. If you are an R user, rattle package will be a very useful tool at this stage. You do your job in point and click mode and you have access to the code behind it.&lt;/p&gt;
', NULL, NULL, '''/p'':89C,147C ''access'':141C ''algorithm'':15C,37C,86C ''assumpt'':33C,51C ''behind'':145C ''check'':78C ''click'':97C,136C ''code'':108C,144C ''dataset'':19C,24C ''exampl'':44C ''explanatori'':72C ''far'':3C ''fulfil'':29C ''get'':104C ''give'':39C,84C ''homoscedast'':57C ''interest'':28C ''involv'':105C ''job'':132C ''know'':6C ''level'':69C ''linear'':45C ''make'':21C ''mode'':137C ''normal'':54C ''p'':1C,90C ''packag'':119C ''paramet'':110C ''point'':95C,134C ''pre'':32C,50C ''pre-assumpt'':31C,49C ''r'':116C ''rattl'':118C ''regress'':46C ''respons'':62C ''resudu'':56C ''result'':102C ''rule'':11C ''run'':35C ''say'':13C ''see'':100C ''set'':111C ''softwar'':98C ''stage'':128C ''sure'':22C ''tool'':125C ''tri'':42C,88C ''use'':93C,124C ''user'':117C ''variabl'':26C,59C,63C,73C,82C ''without'':103C ''work'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9165, 1, '2015-12-02 08:50:48.877', 2, 31, 'Regression: how to interpret different linear relations?', '&lt;p&gt;I have three datasets, let''s call them &lt;code&gt;X&lt;/code&gt; and &lt;code&gt;Y1&lt;/code&gt; and &lt;code&gt;Y2&lt;/code&gt;. A scatterplot is produced out of them, with &lt;code&gt;Y1&lt;/code&gt; and &lt;code&gt;Y2&lt;/code&gt; sharing them same &lt;code&gt;X&lt;/code&gt; dataset (or support).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;My question:&lt;/strong&gt; if the two regression lines are different in both slope and intercept, is there a way to evaluate if the &lt;code&gt;X&lt;/code&gt; dataset has more influence on &lt;code&gt;Y1&lt;/code&gt; or &lt;code&gt;Y2&lt;/code&gt;? &lt;/p&gt;

&lt;p&gt;Based on the image below, this is to say - &lt;strong&gt;which Y dataset is more influenced by the X dataset?&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Blue slope (Y1): -112&lt;/li&gt;
&lt;li&gt;Red slope (Y2): -90&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;EDIT&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;It is visible that an increase in X produces a decrease in both Y1 and Y2. My question could be interpreted as follows: &lt;strong&gt;which Y dataset decreases the most, given the increase in X? Is the slope everything I need?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Image:
&lt;a href="http://i.stack.imgur.com/AaHEa.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/AaHEa.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'predictive-modeling  regression', NULL, '''-112'':126C ''-90'':132C ''/a'':203C ''/aahea.png'':190C,197C ''/code'':23C,27C,31C,42C,46C,52C,84C,92C,96C ''/li'':127C,133C ''/p'':56C,97C,120C,139C,183C,204C ''/strong'':61C,119C,138C,182C ''/ul'':134C ''alt'':198C ''base'':99C ''blue'':123C ''call'':19C ''code'':21C,25C,29C,40C,44C,50C,82C,90C,94C ''could'':159C ''dataset'':16C,53C,85C,111C,118C,167C ''decreas'':151C,168C ''descript'':201C ''differ'':5A,68C ''edit'':137C ''enter'':199C ''evalu'':79C ''everyth'':179C ''follow'':163C ''given'':171C ''href'':187C ''i.stack.imgur.com'':189C,196C ''i.stack.imgur.com/aahea.png'':188C,195C ''imag'':102C,185C,200C ''img'':193C ''increas'':146C,173C ''influenc'':88C,114C ''intercept'':73C ''interpret'':4A,161C ''let'':17C ''li'':122C,128C ''line'':66C ''linear'':6A ''model'':10B ''need'':181C ''nofollow'':192C ''p'':12C,57C,98C,135C,140C,184C ''predict'':9B ''predictive-model'':8B ''produc'':35C,149C ''question'':60C,158C ''red'':129C ''regress'':1A,11B,65C ''rel'':191C ''relat'':7A ''say'':107C ''scatterplot'':33C ''share'':47C ''slope'':71C,124C,130C,178C ''src'':194C ''strong'':58C,108C,136C,164C ''support'':55C ''three'':15C ''two'':64C ''ul'':121C ''visibl'':143C ''way'':77C ''x'':22C,51C,83C,117C,148C,175C ''y'':110C,166C ''y1'':26C,41C,91C,125C,154C ''y2'':30C,45C,95C,131C,156C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9269, 1, '2015-12-08 17:49:04.497', 2, 29, 'Performance metric in recommender systems with implicit feedback', '&lt;p&gt;&lt;a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4781121" rel="nofollow"&gt;This paper&lt;/a&gt; describes a technique for making recommendations when the feedback is implicit, that is, $r_{ui}$ is only a guess. The recommendation problems boils down to the following optimisation problem:
$$
\min_{x^*, y^*}\ \ \sum_{u,i}c_{ui}(p_{ui}-x_i^Ty_i)^2+\lambda \left(\sum_u\|x_u\|^2+\|y_u\|^2 \right) 
$$
where $c_{ui} = 1+\alpha r_{ui}$.   &lt;/p&gt;

&lt;p&gt;I have the following concerns:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;The algorithm above approximates the binary indicator function $p_{ui}$, weighted by the term $c_{ui}$. However, most people use RMSE between $r_{ui}$ and $x_i^Ty_i$ for assessing performance. I am not sure why that would make sense. &lt;/li&gt;
&lt;li&gt;Why using RMSE at all? With implicit feedback, it would make more sense to me to use rank-based metrics. I would imagine RMSE is more suitable in machine learning competition where the objective metric is usually RMSE.  &lt;/li&gt;
&lt;/ol&gt;
', 'bigdata  recommendation  optimization', NULL, '''/a'':22C ''/li'':136C,177C ''/ol'':178C ''/p'':85C,92C ''/xpl/articledetails.jsp?arnumber=4781121'':17C ''1'':81C ''2'':66C,73C,76C ''algorithm'':96C ''alpha'':82C ''approxim'':98C ''assess'':125C ''base'':157C ''bigdata'':9B ''binari'':100C ''boil'':45C ''c'':58C,79C,109C ''competit'':169C ''concern'':91C ''describ'':23C ''feedback'':8A,31C,145C ''follow'':49C,90C ''function'':102C ''guess'':41C ''howev'':111C ''href'':14C ''ieeexplore.ieee.org'':16C ''ieeexplore.ieee.org/xpl/articledetails.jsp?arnumber=4781121'':15C ''imagin'':161C ''implicit'':7A,33C,144C ''indic'':101C ''lambda'':67C ''learn'':168C ''left'':68C ''li'':94C,137C ''machin'':167C ''make'':27C,134C,148C ''metric'':2A,158C,173C ''min'':52C ''nofollow'':19C ''object'':172C ''ol'':93C ''optim'':11B ''optimis'':50C ''p'':12C,60C,86C,103C ''paper'':21C ''peopl'':113C ''perform'':1A,126C ''problem'':44C,51C ''r'':36C,83C,117C ''rank'':156C ''rank-bas'':155C ''recommend'':4A,10B,28C,43C ''rel'':18C ''right'':77C ''rmse'':115C,140C,162C,176C ''sens'':135C,150C ''suitabl'':165C ''sum'':55C,69C ''sure'':130C ''system'':5A ''techniqu'':25C ''term'':108C ''ty'':64C,122C ''u'':56C,70C,72C,75C ''ui'':37C,59C,61C,80C,84C,104C,110C,118C ''use'':114C,139C,154C ''usual'':175C ''weight'':105C ''would'':133C,147C,160C ''x'':53C,62C,71C,120C ''y'':54C,74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9166, 2, '2015-12-02 09:01:48.113', 2, NULL, NULL, '&lt;p&gt;What you are looking for is the Analysis of Covariance (&lt;a href="https://en.wikipedia.org/wiki/Analysis_of_covariance" rel="nofollow"&gt;ANCOVA&lt;/a&gt;) analysis, which is used to compare two or more regression lines by testing the effect of a categorical factor on a dependent variable (y-var) while controlling for the effect of a continuous co-variable (x-var).&lt;/p&gt;

&lt;p&gt;&lt;a href="http://r-eco-evo.blogspot.in/2011/08/comparing-two-regression-slopes-by.html" rel="nofollow"&gt;Here&lt;/a&gt; is an example for carrying out the ANCOVA analysis using R.&lt;/p&gt;
', NULL, NULL, '''/2011/08/comparing-two-regression-slopes-by.html'':67C ''/a'':20C,71C ''/p'':61C,83C ''/wiki/analysis_of_covariance'':16C ''analysi'':9C,21C,80C ''ancova'':19C,79C ''carri'':76C ''categor'':38C ''co'':56C ''co-vari'':55C ''compar'':26C ''continu'':54C ''control'':48C ''covari'':11C ''depend'':42C ''effect'':35C,51C ''en.wikipedia.org'':15C ''en.wikipedia.org/wiki/analysis_of_covariance'':14C ''exampl'':74C ''factor'':39C ''href'':13C,64C ''line'':31C ''look'':5C ''nofollow'':18C,69C ''p'':1C,62C ''r'':82C ''r-eco-evo.blogspot.in'':66C ''r-eco-evo.blogspot.in/2011/08/comparing-two-regression-slopes-by.html'':65C ''regress'':30C ''rel'':17C,68C ''test'':33C ''two'':27C ''use'':24C,81C ''var'':46C,60C ''variabl'':43C,57C ''x'':59C ''x-var'':58C ''y'':45C ''y-var'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9167, 1, '2015-12-02 11:38:06.48', 1, 31, 'What does RMSE points about performance of a model in machine learning?', '&lt;p&gt;I am working on &lt;code&gt;Decision Tree&lt;/code&gt; algorithm and at the end I calculate &lt;code&gt;RMSE&lt;/code&gt; value based on actual labels and predicted values (for regression). Now what I am having difficulty in is in understanding the significance of the &lt;code&gt;RMSE&lt;/code&gt; value that I get. I know that the lower the &lt;code&gt;RMSE&lt;/code&gt; better is the performance of the model but what &lt;code&gt;RMSE&lt;/code&gt; value is considered reasonable low or high? Suppose my &lt;code&gt;RMSE&lt;/code&gt; value comes as &lt;code&gt;20&lt;/code&gt; so does that mean the performance of my model is &lt;code&gt;80%&lt;/code&gt; (means 80% of predictions are correct)?&lt;/p&gt;
', 'machine-learning  performance', NULL, '''/code'':25C,35C,62C,75C,87C,99C,105C,118C ''/p'':125C ''20'':104C ''80'':117C,120C ''actual'':39C ''algorithm'':26C ''base'':37C ''better'':76C ''calcul'':32C ''code'':22C,33C,60C,73C,85C,97C,103C,116C ''come'':101C ''consid'':90C ''correct'':124C ''decis'':23C ''difficulti'':51C ''end'':30C ''get'':66C ''high'':94C ''know'':68C ''label'':40C ''learn'':12A,15B ''low'':92C ''lower'':71C ''machin'':11A,14B ''machine-learn'':13B ''mean'':109C,119C ''model'':9A,82C,114C ''p'':17C ''perform'':6A,16B,79C,111C ''point'':4A ''predict'':42C,122C ''reason'':91C ''regress'':45C ''rmse'':3A,34C,61C,74C,86C,98C ''signific'':57C ''suppos'':95C ''tree'':24C ''understand'':55C ''valu'':36C,43C,63C,88C,100C ''work'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9169, 1, '2015-12-02 13:56:10.107', 2, 91, 'What are the best ways to tune multiple parameters?', '&lt;p&gt;When building a model in Machine Learning, it''s more than common to have several "parameters" (I''m thinking of real parameter like the step of gradient descent, or things like features) to tune. We validate these parameters on a validating set.&lt;/p&gt;

&lt;p&gt;My question is: what is the best way of tuning these multiple parameters? For example, let say we have 3 parameters A, B and C that take 3 values each:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;A = [ A1, A2, A3 ]&lt;/li&gt;
&lt;li&gt;B = [ B1, B2, B3 ]&lt;/li&gt;
&lt;li&gt;C = [ C1, C2, C3 ]&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Two methods come to my mind.&lt;/p&gt;

&lt;p&gt;Method 1:&lt;/p&gt;

&lt;p&gt;Vary all the parameters at the same time and test different combinations randomly, such as:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Test1 = [A1,B1,C1]&lt;/li&gt;
&lt;li&gt;Test2 = [A2,B2,C2]&lt;/li&gt;
&lt;li&gt;Test3 = [A3,B3,C3]&lt;/li&gt;
&lt;li&gt;Test4 = [A1,B2,C3]&lt;/li&gt;
&lt;li&gt;Test5= [A3,B1,C2]&lt;/li&gt;
&lt;li&gt;etc..&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Method 2:&lt;/p&gt;

&lt;p&gt;Fix all the parameters except one:
 - TestA1 = [A1,B1,C1]
 - TestA2 = [A2,B1,C1]
 - TestA3 = [A3,B1,C1]
In that way, we can find the best value for parameter A, then we fix this value and use it to find the best value for B, and finally the best for C.&lt;/p&gt;

&lt;p&gt;It seems more logical to me to use the method 2 which seems more organized. But may be we will miss a combination which can be found only in method 1 that doesnât appear in method 2, such as [A1,B2,C3] for example. &lt;/p&gt;

&lt;p&gt;Which method is the best? Is there another method more accurate for tuning multiple parameters?&lt;/p&gt;

&lt;p&gt;Thanks in advance.&lt;/p&gt;

&lt;p&gt;Regards.&lt;/p&gt;
', 'data-mining  dataset  data-cleaning  parameter', NULL, '''/li'':100C,106C,112C,149C,155C,161C,167C,173C,176C ''/p'':61C,93C,121C,125C,142C,181C,234C,281C,298C,303C,306C ''/ul'':113C,177C ''1'':124C,266C ''2'':180C,246C,273C ''3'':82C,90C ''a1'':97C,146C,164C,190C,276C ''a2'':98C,152C,194C ''a3'':99C,158C,170C,198C ''accur'':293C ''advanc'':302C ''anoth'':290C ''appear'':270C ''b'':85C,102C,227C ''b1'':103C,147C,171C,191C,195C,199C ''b2'':104C,153C,165C,277C ''b3'':105C,159C ''best'':4A,69C,208C,224C,231C,287C ''build'':20C ''c'':87C,108C,233C ''c1'':109C,148C,192C,196C,200C ''c2'':110C,154C,172C ''c3'':111C,160C,166C,278C ''clean'':16B ''combin'':138C,258C ''come'':117C ''common'':30C ''data'':11B,15B ''data-clean'':14B ''data-min'':10B ''dataset'':13B ''descent'':46C ''differ'':137C ''doesnâ'':268C ''etc'':175C ''exampl'':77C,280C ''except'':187C ''featur'':50C ''final'':229C ''find'':206C,222C ''fix'':183C,215C ''found'':262C ''gradient'':45C ''learn'':25C ''let'':78C ''li'':95C,101C,107C,144C,150C,156C,162C,168C,174C ''like'':41C,49C ''logic'':239C ''m'':36C ''machin'':24C ''may'':252C ''method'':116C,123C,179C,245C,265C,272C,284C,291C ''mind'':120C ''mine'':12B ''miss'':256C ''model'':22C ''multipl'':8A,74C,296C ''one'':188C ''organ'':250C ''p'':18C,62C,114C,122C,126C,178C,182C,235C,282C,299C,304C ''paramet'':9A,17B,34C,40C,56C,75C,83C,130C,186C,211C,297C ''question'':64C ''random'':139C ''real'':39C ''regard'':305C ''say'':79C ''seem'':237C,248C ''set'':60C ''sever'':33C ''step'':43C ''take'':89C ''test'':136C ''test1'':145C ''test2'':151C ''test3'':157C ''test4'':163C ''test5'':169C ''testa1'':189C ''testa2'':193C ''testa3'':197C ''thank'':300C ''thing'':48C ''think'':37C ''time'':134C ''tune'':7A,52C,72C,295C ''two'':115C ''ul'':94C,143C ''use'':219C,243C ''valid'':54C,59C ''valu'':91C,209C,217C,225C ''vari'':127C ''way'':5A,70C,203C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9172, 1, '2015-12-02 16:16:08.317', 1, 77, 'Can k-means clustering get shells as clusters?', '&lt;p&gt;Imagine you have $k$ classes. Every class $i$ has points which are follow a probability distribution, such that their distance to 0 is $i$ in mean, but this distance follows a normal distribution. The direction is uniformly distributed. So all classes are in shells around the origin 0.&lt;/p&gt;

&lt;p&gt;Can $k$-means get these shells when you choose the "right" distance metric?
(Obviously it can''t find it if you take the euclidean metric, but I wonder if there is any metric at all or if this problem is inherently unsolvable by $k$-means, even if you know the number of clusters $k$)&lt;/p&gt;
', 'clustering  k-means  distance', NULL, '''/p'':64C,120C ''0'':37C,63C ''around'':60C ''choos'':74C ''class'':20C,22C,56C ''cluster'':5A,9A,10B,118C ''direct'':50C ''distanc'':14B,35C,44C,77C ''distribut'':31C,48C,53C ''euclidean'':89C ''even'':111C ''everi'':21C ''find'':83C ''follow'':28C,45C ''get'':6A,69C ''imagin'':16C ''inher'':106C ''k'':3A,12B,19C,67C,109C,119C ''k-mean'':2A,11B ''know'':114C ''mean'':4A,13B,41C,68C,110C ''metric'':78C,90C,98C ''normal'':47C ''number'':116C ''obvious'':79C ''origin'':62C ''p'':15C,65C ''point'':25C ''probabl'':30C ''problem'':104C ''right'':76C ''shell'':7A,59C,71C ''take'':87C ''uniform'':52C ''unsolv'':107C ''wonder'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9173, 2, '2015-12-02 16:48:12.337', 0, NULL, NULL, '&lt;p&gt;There are multiple factors to consider, but the first thing to realize is that in regression, you don''t want to think about whether an example is "correct" or "incorrect" but rather how close it was to the true target value.  Therefore you can ignore your original intuition about "80% of predictions are ''correct''." &lt;/p&gt;

&lt;p&gt;Second remember that &lt;a href="https://www.kaggle.com/wiki/RootMeanSquaredError" rel="nofollow"&gt;RMSE&lt;/a&gt; is in the same space as your target values.  So it is relative to the variance in your target values.  The benchmark of random guessing should get you an &lt;code&gt;RMSE = standard_deviation&lt;/code&gt;.  So lower than this, your model is demonstrating some ability to learn; above that number, you haven''t even learned to guess the mean correctly.&lt;/p&gt;

&lt;p&gt;There isn''t a cutoff for "my model is doing well" in RMSE space, just like with other metrics.  Everything is relative to a naive solution/benchmark or the state-of-the-art.&lt;/p&gt;
', NULL, NULL, '''/a'':69C ''/code'':103C ''/p'':56C,129C,164C ''/wiki/rootmeansquarederror'':65C ''80'':51C ''abil'':113C ''art'':163C ''benchmark'':91C ''close'':35C ''code'':99C ''consid'':7C ''correct'':29C,55C,128C ''cutoff'':135C ''demonstr'':111C ''deviat'':102C ''even'':122C ''everyth'':150C ''exampl'':27C ''factor'':5C ''first'':10C ''get'':96C ''guess'':94C,125C ''haven'':120C ''href'':62C ''ignor'':46C ''incorrect'':31C ''intuit'':49C ''isn'':132C ''learn'':115C,123C ''like'':146C ''lower'':105C ''mean'':127C ''metric'':149C ''model'':109C,138C ''multipl'':4C ''naiv'':155C ''nofollow'':67C ''number'':118C ''origin'':48C ''p'':1C,57C,130C ''predict'':53C ''random'':93C ''rather'':33C ''realiz'':13C ''regress'':17C ''rel'':66C ''relat'':82C,152C ''rememb'':59C ''rmse'':68C,100C,143C ''second'':58C ''solution/benchmark'':156C ''space'':74C,144C ''standard'':101C ''state'':160C ''state-of-the-art'':159C ''target'':41C,77C,88C ''therefor'':43C ''thing'':11C ''think'':23C ''true'':40C ''valu'':42C,78C,89C ''varianc'':85C ''want'':21C ''well'':141C ''whether'':25C ''www.kaggle.com'':64C ''www.kaggle.com/wiki/rootmeansquarederror'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9174, 2, '2015-12-02 16:59:16.86', 5, NULL, NULL, '&lt;p&gt;Generally people perform a &lt;a href="http://scikit-learn.org/stable/modules/grid_search.html"&gt;grid search&lt;/a&gt;, which in its simplest "exhaustive" form is similar to Method 1.  However there are also more ''intelligent'' ways to choose what to explore, which optimize in parameter space in a fashion similar to how each individual model is optimized. It can be tricky to do greedy optimization in this space, as it is often strongly non-convex.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Hyperparameter_optimization"&gt;This page&lt;/a&gt; describes the basics of optimizing model parameters.&lt;/p&gt;
', NULL, NULL, '''/a'':13C,81C ''/p'':72C,89C ''/stable/modules/grid_search.html'':10C ''/wiki/hyperparameter_optimization'':78C ''1'':24C ''also'':28C ''basic'':84C ''choos'':33C ''convex'':71C ''describ'':82C ''en.wikipedia.org'':77C ''en.wikipedia.org/wiki/hyperparameter_optimization'':76C ''exhaust'':18C ''explor'':36C ''fashion'':44C ''form'':19C ''general'':2C ''greedi'':59C ''grid'':11C ''howev'':25C ''href'':7C,75C ''individu'':49C ''intellig'':30C ''method'':23C ''model'':50C,87C ''non'':70C ''non-convex'':69C ''often'':67C ''optim'':38C,52C,60C,86C ''p'':1C,73C ''page'':80C ''paramet'':40C,88C ''peopl'':3C ''perform'':4C ''scikit-learn.org'':9C ''scikit-learn.org/stable/modules/grid_search.html'':8C ''search'':12C ''similar'':21C,45C ''simplest'':17C ''space'':41C,63C ''strong'':68C ''tricki'':56C ''way'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9176, 2, '2015-12-02 22:16:52.933', 2, NULL, NULL, '&lt;p&gt;You cannot just use arbitrary distance functions with k-means.&lt;/p&gt;

&lt;p&gt;because the algorithm is &lt;strong&gt;not based on metric properties but on variance&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stats.stackexchange.com/q/81481/7828"&gt;http://stats.stackexchange.com/q/81481/7828&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Fact is that k-means minimizes the &lt;strong&gt;sum of squares&lt;/strong&gt;. This does not even give you the "smallest distances" but only the smallest &lt;em&gt;squared&lt;/em&gt; distances. This is &lt;em&gt;not&lt;/em&gt; the same (see: difference between median and mean) - if you want to minimize Euclidean distances, use k-medians or if you want other distances PAM (k-medoids).&lt;/p&gt;

&lt;p&gt;You can generalize k-means to use a few more distances known as "Bergman divergences" and you can do some variant of the kernel trick. But that is not very powerful, because you don''t have labels for optimizing the kernel parameters! Still, that could be what this exercise question is up to... If your "shells" are indeed centered at 0, then you can transform your data (read: kernel trick done wrong) to angle+distance from origin, and k-means may be able to cluster the projected data (dependent on the not well defined scaling of the axes).
Or the textbook did &lt;em&gt;not&lt;/em&gt; realize that a kernel k-means has been proposed long ago. Then the argument is probably this: &lt;strong&gt;the mean of each shell is 0, and thus the shells cannot be distinguished&lt;/strong&gt;. This clearly holds for &lt;em&gt;unmodified&lt;/em&gt; k-means.&lt;/p&gt;
', NULL, NULL, '''/em'':68C,74C,212C,253C ''/p'':13C,29C,104C,257C ''/q/81481/7828'':35C ''/q/81481/7828&lt;/a&gt;&lt;/p&gt;'':38C ''/strong'':28C,52C,246C ''0'':167C,238C ''abl'':190C ''ago'':224C ''algorithm'':17C ''angl'':180C ''arbitrari'':6C ''argument'':227C ''axe'':205C ''base'':21C ''bergman'':120C ''cannot'':3C,243C ''center'':165C ''clear'':248C ''cluster'':192C ''could'':151C ''data'':173C,195C ''defin'':201C ''depend'':196C ''differ'':78C ''distanc'':7C,61C,69C,89C,99C,117C,181C ''distinguish'':245C ''diverg'':121C ''done'':177C ''em'':66C,72C,210C,251C ''euclidean'':88C ''even'':56C ''exercis'':155C ''fact'':40C ''function'':8C ''general'':108C ''give'':57C ''hold'':249C ''href'':32C ''inde'':164C ''k'':11C,44C,92C,102C,110C,186C,218C,255C ''k-mean'':10C,43C,109C,185C,217C,254C ''k-median'':91C ''k-medoid'':101C ''kernel'':130C,147C,175C,216C ''known'':118C ''label'':143C ''long'':223C ''may'':188C ''mean'':12C,45C,82C,111C,187C,219C,233C,256C ''median'':80C,93C ''medoid'':103C ''metric'':23C ''minim'':46C,87C ''optim'':145C ''origin'':183C ''p'':1C,14C,30C,39C,105C ''pam'':100C ''paramet'':148C ''power'':137C ''probabl'':229C ''project'':194C ''properti'':24C ''propos'':222C ''question'':156C ''read'':174C ''realiz'':213C ''scale'':202C ''see'':77C ''shell'':162C,236C,242C ''smallest'':60C,65C ''squar'':51C,67C ''stats.stackexchange.com'':34C,37C ''stats.stackexchange.com/q/81481/7828'':33C ''stats.stackexchange.com/q/81481/7828&lt;/a&gt;&lt;/p&gt;'':36C ''still'':149C ''strong'':19C,48C,231C ''sum'':49C ''textbook'':208C ''thus'':240C ''transform'':171C ''trick'':131C,176C ''unmodifi'':252C ''use'':5C,90C,113C ''varianc'':27C ''variant'':127C ''want'':85C,97C ''well'':200C ''wrong'':178C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9660, 2, '2016-01-06 23:17:43.363', 2, NULL, NULL, '&lt;p&gt;&lt;a href="http://appliedpredictivemodeling.com/" rel="nofollow"&gt;Applied Predictive Modeling book&lt;/a&gt; has a &lt;a href="https://www.kaggle.com/c/unimelb" rel="nofollow"&gt;case study of acceptance rate of grant proposals, which was a Kaggle competition&lt;/a&gt;. You can get many good ideas from there. &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;from the dataset, what kinds of features you would look at&lt;/li&gt;
&lt;li&gt;from the book, step-by-step explanations of how to build models&lt;/li&gt;
&lt;li&gt;from the Kaggle competition, approaches by the contestants&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I think it is very hard to predict the quality of the paper from the text of the paper, but you might have much more luck by including features such as academic rank of the author(s), their past publishing history and success, acceptance rate of the journals the paper was submitted to, and things such as even the length of the paper. Such proxy features for paper quality actually might be very successful... &lt;/p&gt;
', NULL, NULL, '''/a'':11C,34C ''/c/unimelb'':18C ''/li'':57C,72C,82C ''/p'':43C,159C ''/ul'':83C ''academ'':116C ''accept'':24C,128C ''actual'':154C ''appli'':7C ''appliedpredictivemodeling.com'':4C ''approach'':78C ''author'':120C ''book'':10C,61C ''build'':70C ''case'':21C ''competit'':33C,77C ''contest'':81C ''dataset'':48C ''even'':142C ''explan'':66C ''featur'':52C,113C,150C ''get'':37C ''good'':39C ''grant'':27C ''hard'':90C ''histori'':125C ''href'':3C,15C ''idea'':40C ''includ'':112C ''journal'':132C ''kaggl'':32C,76C ''kind'':50C ''length'':144C ''li'':45C,58C,73C ''look'':55C ''luck'':110C ''mani'':38C ''might'':106C,155C ''model'':9C,71C ''much'':108C ''nofollow'':6C,20C ''p'':1C,84C ''paper'':97C,103C,134C,147C,152C ''past'':123C ''predict'':8C,92C ''propos'':28C ''proxi'':149C ''publish'':124C ''qualiti'':94C,153C ''rank'':117C ''rate'':25C,129C ''rel'':5C,19C ''step'':63C,65C ''step-by-step'':62C ''studi'':22C ''submit'':136C ''success'':127C,158C ''text'':100C ''thing'':139C ''think'':86C ''ul'':44C ''would'':54C ''www.kaggle.com'':17C ''www.kaggle.com/c/unimelb'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9181, 2, '2015-12-03 06:03:56.253', 0, NULL, NULL, '&lt;p&gt;Yes, there are libraries for TensorFlow and Theano.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/Theano/Theano" rel="nofollow"&gt;Theano Library&lt;/a&gt;:  An &lt;a href="http://www.deeplearning.net/software/theano/" rel="nofollow"&gt;extensive tutorial&lt;/a&gt; for the Theano library is also available online.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/tensorflow/tensorflow" rel="nofollow"&gt;TensorFlow library&lt;/a&gt;: An in-depth &lt;a href="http://www.tensorflow.org/tutorials/index.html" rel="nofollow"&gt;official tutorial&lt;/a&gt; of TensorFlow.  (Highly recommend, if you''re going with TensorFlow)&lt;/p&gt;

&lt;p&gt;In addition, I would also like to suggest you to have a look at this &lt;a href="http://datascience.stackexchange.com/q/694/11097"&gt;question&lt;/a&gt; regarding open source Python deep learning libraries.&lt;/p&gt;
', NULL, NULL, '''/a'':21C,32C,52C,66C,100C ''/p'':10C,41C,77C,108C ''/q/694/11097'':98C ''/software/theano/'':27C ''/tensorflow/tensorflow'':47C ''/theano/theano'':16C ''/tutorials/index.html'':61C ''addit'':80C ''also'':38C,83C ''avail'':39C ''datascience.stackexchange.com'':97C ''datascience.stackexchange.com/q/694/11097'':96C ''deep'':105C ''depth'':56C ''extens'':30C ''github.com'':15C,46C ''github.com/tensorflow/tensorflow'':45C ''github.com/theano/theano'':14C ''go'':74C ''high'':69C ''href'':13C,24C,44C,58C,95C ''in-depth'':54C ''learn'':106C ''librari'':5C,20C,36C,51C,107C ''like'':84C ''look'':91C ''nofollow'':18C,29C,49C,63C ''offici'':64C ''onlin'':40C ''open'':102C ''p'':1C,11C,42C,78C ''python'':104C ''question'':99C ''re'':73C ''recommend'':70C ''regard'':101C ''rel'':17C,28C,48C,62C ''sourc'':103C ''suggest'':86C ''tensorflow'':7C,50C,68C,76C ''theano'':9C,19C,35C ''tutori'':31C,65C ''would'':82C ''www.deeplearning.net'':26C ''www.deeplearning.net/software/theano/'':25C ''www.tensorflow.org'':60C ''www.tensorflow.org/tutorials/index.html'':59C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9234, 2, '2015-12-06 11:57:37.6', 1, NULL, NULL, '&lt;p&gt;A good answer to this question has to rely on the specific dataset / domain.&lt;/p&gt;

&lt;p&gt;The questions I would ask myself are (in this order):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Can I solve my classification task without those features? â Just remove those features&lt;/li&gt;
&lt;li&gt;Do I know of a relationship between features I know and the (partially) missing feature? â Find hard rules to fill those missing ones&lt;/li&gt;
&lt;li&gt;Otherwise: Train a classifier / regression model to fill those missing features&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':45C,71C,84C ''/p'':16C,28C ''/ul'':85C ''answer'':4C ''ask'':22C ''classif'':35C ''classifi'':76C ''dataset'':14C ''domain'':15C ''featur'':39C,44C,54C,61C,83C ''fill'':67C,80C ''find'':63C ''good'':3C ''hard'':64C ''know'':49C,56C ''li'':30C,46C,72C ''miss'':60C,69C,82C ''model'':78C ''one'':70C ''order'':27C ''otherwis'':73C ''p'':1C,17C ''partial'':59C ''question'':7C,19C ''regress'':77C ''relationship'':52C ''reli'':10C ''remov'':42C ''rule'':65C ''solv'':33C ''specif'':13C ''task'':36C ''train'':74C ''ul'':29C ''without'':37C ''would'':21C ''â'':40C,62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9358, 2, '2015-12-13 03:02:27.553', 2, NULL, NULL, '&lt;p&gt;The concept is the same but you are getting confused by the type of data. Spectral Clustering as &lt;a href="http://ai.stanford.edu/~ang/papers/nips01-spectral.pdf" rel="nofollow"&gt;Ng et al.&lt;/a&gt; explain is about clustering standard data while the Laplacian matrix is a graph derived matrix used in algebraic graph theory.&lt;/p&gt;

&lt;p&gt;So the point is that whenever you encode the similarity of your objects into a matrix, this matrix could be used for spectral clustering. &lt;/p&gt;

&lt;p&gt;If you have standard data i.e. a sample-feature matrix, you can find the proximity or affinity or whatever you want to call it as a matrix and apply spectral clustering.&lt;/p&gt;

&lt;p&gt;If you have a graph, this affinity would be anything like adjacency matrix, distance matrix or Laplacialn matrix and solving the eigenfunction for such a matrix gives you the corresponding result. &lt;/p&gt;

&lt;p&gt;The point about using Laplacian rather than adjacency is to keep the so-called affinity matrix positive semi-definite (and normalized Laplacian matrix is a better choice as it gives you normalized eigenvalues between 0 and 2 and reveals the structure of graph much better).&lt;/p&gt;

&lt;p&gt;So the long story short is that as long as you have a matrix containing the affinity of your data you can use spectral clustering in general. The difference is in details (i.g. the property of normalized Laplacian I just mentioned)&lt;/p&gt;
', NULL, NULL, '''/a'':30C ''/p'':51C,77C,111C,144C,193C,236C ''/~ang/papers/nips01-spectral.pdf'':24C ''0'':182C ''2'':184C ''adjac'':124C,153C ''affin'':96C,119C,161C,211C ''ai.stanford.edu'':23C ''ai.stanford.edu/~ang/papers/nips01-spectral.pdf'':22C ''al'':29C ''algebra'':48C ''anyth'':122C ''appli'':108C ''better'':173C,192C ''call'':102C,160C ''choic'':174C ''cluster'':18C,34C,76C,110C,219C ''concept'':3C ''confus'':11C ''contain'':209C ''correspond'':142C ''could'':71C ''data'':16C,36C,83C,214C ''definit'':166C ''deriv'':44C ''detail'':226C ''differ'':223C ''distanc'':126C ''eigenfunct'':134C ''eigenvalu'':180C ''encod'':60C ''et'':28C ''explain'':31C ''featur'':88C ''find'':92C ''general'':221C ''get'':10C ''give'':139C,177C ''graph'':43C,49C,117C,190C ''href'':21C ''i.e'':84C ''i.g'':227C ''keep'':156C ''laplacialn'':129C ''laplacian'':39C,150C,169C,232C ''like'':123C ''long'':197C,203C ''matrix'':40C,45C,68C,70C,89C,106C,125C,127C,130C,138C,162C,170C,208C ''mention'':235C ''much'':191C ''ng'':27C ''nofollow'':26C ''normal'':168C,179C,231C ''object'':65C ''p'':1C,52C,78C,112C,145C,194C ''point'':55C,147C ''posit'':163C ''properti'':229C ''proxim'':94C ''rather'':151C ''rel'':25C ''result'':143C ''reveal'':186C ''sampl'':87C ''sample-featur'':86C ''semi'':165C ''semi-definit'':164C ''short'':199C ''similar'':62C ''so-cal'':158C ''solv'':132C ''spectral'':17C,75C,109C,218C ''standard'':35C,82C ''stori'':198C ''structur'':188C ''theori'':50C ''type'':14C ''use'':46C,73C,149C,217C ''want'':100C ''whatev'':98C ''whenev'':58C ''would'':120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9182, 5, '2015-12-03 06:24:50.837', 0, NULL, NULL, '&lt;h2&gt;Interface&lt;/h2&gt;

&lt;p&gt;TensorFlow provides a Python API for building and executing data flow graphs. There is also a C++ API for executing graphs, and adding new operation kernels. Other languages can invoke TensorFlow using its low-level C API.&lt;/p&gt;

&lt;p&gt;TensorFlow also includes TensorBoard, which is a web-based visualization tool for understanding performance and network features/parameters.&lt;/p&gt;

&lt;h2&gt;Speed&lt;/h2&gt;

&lt;p&gt;TensorFlow achieves its performance by parallelizing execution across multiple CPUs and GPUs. The kernel of each operation is implemented in C++, using efficient libraries such as Eigen and cuDNN for the best performance.&lt;/p&gt;
', NULL, NULL, '''/h2'':3C,66C ''/p'':43C,63C,101C ''achiev'':69C ''across'':75C ''ad'':27C ''also'':19C,46C ''api'':9C,22C,42C ''base'':54C ''best'':99C ''build'':11C ''c'':21C,41C,88C ''cpus'':77C ''cudnn'':96C ''data'':14C ''effici'':90C ''eigen'':94C ''execut'':13C,24C,74C ''features/parameters'':62C ''flow'':15C ''gpus'':79C ''graph'':16C,25C ''h2'':1C,64C ''implement'':86C ''includ'':47C ''interfac'':2C ''invok'':34C ''kernel'':30C,81C ''languag'':32C ''level'':40C ''librari'':91C ''low'':39C ''low-level'':38C ''multipl'':76C ''network'':61C ''new'':28C ''oper'':29C,84C ''p'':4C,44C,67C ''parallel'':73C ''perform'':59C,71C,100C ''provid'':6C ''python'':8C ''speed'':65C ''tensorboard'':48C ''tensorflow'':5C,35C,45C,68C ''tool'':56C ''understand'':58C ''use'':36C,89C ''visual'':55C ''web'':53C ''web-bas'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9183, 4, '2015-12-03 06:24:50.837', 0, NULL, NULL, 'TensorFlow is an open source library for machine learning and machine intelligence.  TensorFlow uses data flow graphs with tensors flowing along edges. For details, see https://www.tensorflow.org.  TensorFlow is released under an Apache 2.0 License.', NULL, NULL, '''2.0'':33C ''along'':21C ''apach'':32C ''data'':15C ''detail'':24C ''edg'':22C ''flow'':16C,20C ''graph'':17C ''intellig'':12C ''learn'':9C ''librari'':6C ''licens'':34C ''machin'':8C,11C ''open'':4C ''releas'':29C ''see'':25C ''sourc'':5C ''tensor'':19C ''tensorflow'':1C,13C,27C ''use'':14C ''www.tensorflow.org'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9190, 2, '2015-12-03 11:39:54.64', 1, NULL, NULL, '&lt;p&gt;I would impressed if there is already a dedicated 9gag clustering :P&lt;/p&gt;

&lt;p&gt;However, you can read this blog post about &lt;a href="http://www.janeriksolem.net/2009/04/hierarchical-clustering-in-python.html" rel="nofollow"&gt;Hierarchical clustering in Python&lt;/a&gt; for images, which is close to what you want. The problem is that the author uses the average color of the image as a feature and it could proved crude and not inefficient. You may find something more interesting to use. But in the end, you need to experiment a lot with your own dataset.&lt;/p&gt;
', NULL, NULL, '''/2009/04/hierarchical-clustering-in-python.html'':28C ''/a'':35C ''/p'':14C,91C ''9gag'':11C ''alreadi'':8C ''author'':50C ''averag'':53C ''blog'':21C ''close'':40C ''cluster'':12C,32C ''color'':54C ''could'':63C ''crude'':65C ''dataset'':90C ''dedic'':10C ''end'':80C ''experi'':84C ''featur'':60C ''find'':71C ''hierarch'':31C ''howev'':16C ''href'':25C ''imag'':37C,57C ''impress'':4C ''ineffici'':68C ''interest'':74C ''lot'':86C ''may'':70C ''need'':82C ''nofollow'':30C ''p'':1C,13C,15C ''post'':22C ''problem'':46C ''prove'':64C ''python'':34C ''read'':19C ''rel'':29C ''someth'':72C ''use'':51C,76C ''want'':44C ''would'':3C ''www.janeriksolem.net'':27C ''www.janeriksolem.net/2009/04/hierarchical-clustering-in-python.html'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9184, 5, '2015-12-03 06:28:53.41', 0, NULL, NULL, '&lt;p&gt;&lt;a href="http://pandas.pydata.org/" rel="nofollow"&gt;&lt;code&gt;pandas&lt;/code&gt;&lt;/a&gt; is a &lt;a href="https://www.python.org/" rel="nofollow"&gt;&lt;code&gt;python&lt;/code&gt;&lt;/a&gt; library for PAN-el DA-ta manipulation and analysis, i.e. multidimensional time series and cross-sectional data sets commonly found in statistics, experimental science results, econometrics, or finance.  &lt;code&gt;pandas&lt;/code&gt; is implemented primarily using &lt;a href="http://www.numpy.org/" rel="nofollow"&gt;&lt;code&gt;numpy&lt;/code&gt;&lt;/a&gt; and &lt;a href="http://cython.org/" rel="nofollow"&gt;Cython&lt;/a&gt;; it is intended to be able to integrate very easily with other &lt;a href="http://www.numpy.org/" rel="nofollow"&gt;&lt;code&gt;numpy&lt;/code&gt;&lt;/a&gt;-based scientific libraries, such as &lt;a href="http://statsmodels.sourceforge.net/" rel="nofollow"&gt;&lt;code&gt;statsmodels&lt;/code&gt;&lt;/a&gt;.&lt;/p&gt;

&lt;h3&gt;Main Features:&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;Data structures: for 1, 2, and 3 dimensional labeled data sets (respectively &lt;a href="http://pandas.pydata.org/pandas-docs/stable/dsintro.html#series" rel="nofollow"&gt;&lt;code&gt;Series&lt;/code&gt;&lt;/a&gt;, &lt;a href="http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe" rel="nofollow"&gt;&lt;code&gt;DataFrames&lt;/code&gt;&lt;/a&gt; and &lt;a href="http://pandas.pydata.org/pandas-docs/stable/dsintro.html#panel" rel="nofollow"&gt;&lt;code&gt;Panels&lt;/code&gt;&lt;/a&gt;). Some of their main features include:

&lt;ul&gt;
&lt;li&gt;Automatically aligning data and interpolation&lt;/li&gt;
&lt;li&gt;Handling missing observations in calculations&lt;/li&gt;
&lt;li&gt;Convenient slicing and reshaping ("reindexing") functions&lt;/li&gt;
&lt;li&gt;&lt;a href="http://pandas.pydata.org/pandas-docs/stable/categorical.html" rel="nofollow"&gt;Categorical data&lt;/a&gt; types &lt;/li&gt;
&lt;li&gt;Provide ''group by'' aggregation or transformation functionality&lt;/li&gt;
&lt;li&gt;Tools for merging / joining together data sets&lt;/li&gt;
&lt;li&gt;Simple &lt;a href="http://matplotlib.sourceforge.net/" rel="nofollow"&gt;&lt;code&gt;matplotlib&lt;/code&gt;&lt;/a&gt; integration for plotting&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;Date tools: objects for expressing date offsets or generating date ranges; some functionality similar to scikits.timeseries.  Dates can be aligned to a specific timezone and converted / compared at-will&lt;/li&gt;
&lt;li&gt;Statistical models: convenient ordinary least squares and panel OLS implementations for in-sample or rolling time series / cross-sectional regressions. These will hopefully be the starting point for implementing other models&lt;/li&gt;
&lt;li&gt;Intelligent Cython offloading; complex computations are performed rapidly due to these optimizations.&lt;/li&gt;
&lt;li&gt;Static and moving statistical tools: mean, standard deviation, correlation, covariance&lt;/li&gt;
&lt;li&gt;&lt;a href="http://pandas.pydata.org/pandas-docs/stable/" rel="nofollow"&gt;Rich User Documentation&lt;/a&gt;, using &lt;a href="http://sphinx.pocoo.org/" rel="nofollow"&gt;Sphinx&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;h3&gt;Resources:&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://pandas.pydata.org/pandas-docs/stable/" rel="nofollow"&gt;Docs&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://github.com/pydata/pandas" rel="nofollow"&gt;Github&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://pandas.pydata.org/" rel="nofollow"&gt;Homepage&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;h3&gt;Books:&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://shop.oreilly.com/product/0636920023784.do" rel="nofollow"&gt;Python for Data Analysis&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.packtpub.com/learning-ipython-for-interactive-computing-and-data-visualization/book" rel="nofollow"&gt;Learning IPython for Interactive Computing and Data Visualization&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.packtpub.com/big-data-and-business-intelligence/mastering-pandas-finance" rel="nofollow"&gt;Mastering pandas for Finance&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9185, 4, '2015-12-03 06:28:53.41', 0, NULL, NULL, 'pandas is a python library for Panel Data manipulation and analysis, e.g. multidimensional time series and cross-sectional data sets commonly found in statistics, experimental science results, econometrics, or finance.', NULL, NULL, '''analysi'':11C ''common'':22C ''cross'':18C ''cross-sect'':17C ''data'':8C,20C ''e.g'':12C ''econometr'':29C ''experiment'':26C ''financ'':31C ''found'':23C ''librari'':5C ''manipul'':9C ''multidimension'':13C ''panda'':1C ''panel'':7C ''python'':4C ''result'':28C ''scienc'':27C ''section'':19C ''seri'':15C ''set'':21C ''statist'':25C ''time'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9186, 2, '2015-12-03 08:34:51.567', 1, NULL, NULL, '&lt;p&gt;I think the OP was asking for a specific library &lt;em&gt;for&lt;/em&gt; Theano or TensorFlow that provides off-the-shelf functionality for object identification.&lt;/p&gt;

&lt;p&gt;I would look first into &lt;code&gt;scikit image&lt;/code&gt;, an image processing library for Python in the Scipy/Numpy stack. It has all sorts of edge detectors, blob detectors, shape identifications, color separations... Look at their gallery of examples: &lt;a href="http://scikit-image.org/docs/dev/auto_examples/" rel="nofollow"&gt;http://scikit-image.org/docs/dev/auto_examples/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Btw, counting humans in a crowd may not be so trivial to automate...&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9209, 1, '2015-12-04 14:53:13.657', 0, 29, 'Empirical Research from a Survey', '&lt;p&gt;I''m looking for some help carrying out empirical research that I need to do for an assignment. The data is based on a sub-sample of a survey given to undergraduate students, and I''m meant to come up with a question I''ll answer based on the data.&lt;/p&gt;

&lt;p&gt;The initial question I was going to answer was: "To what extent is academic performance a result of family background?"&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;DV - Average First Year Score (0-100)  &lt;/li&gt;
&lt;li&gt;IV - Mothers Educucation (Where College or Below=0 and Bachelors or Above=1)  &lt;/li&gt;
&lt;li&gt;IV - Fathers Education (Where College or Below=0 and Bachelors or Above=1)  &lt;/li&gt;
&lt;li&gt;IV - Part Time Job (Hours worked)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Now, I can find summary statistics on a student where their Mother''s Educ=1, etc. 
But what other things can I find out from just the dummy variables 0 and 1? How can I perform a regression on this and see the strengths of the coefficients? And, if the strength of the coefficients are rather weak, can I still use this as a topic for research and argue that Academic performance is &lt;strong&gt;not&lt;/strong&gt; a result of family background? And then I would carry out the same procedures looking at fathers education. Finally, I was going to see if there is a relationship between those having to work part times jobs achieving lower grades than those who do not/work very little. Again, I''m stuck on how to bring all these together to make a strong point.&lt;/p&gt;

&lt;p&gt;I feel the relationship between these variables are going to be too weak, so I may have to pick a different question. &lt;/p&gt;

&lt;p&gt;Any help would be greatly appreciated as I''m just going round in circles with this assignment! I keep jumping from one question to another when there''s no strong correlation and just generally getting stressed out!&lt;/p&gt;
', 'statistics  regression  descriptive-statistics', NULL, '''-100'':94C ''/li'':95C,110C,125C,133C ''/p'':63C,85C,275C,299C,338C ''/strong'':211C ''/ul'':134C ''0'':93C,104C,119C,165C ''1'':109C,124C,150C,167C ''academ'':78C,206C ''achiev'':249C ''anoth'':325C ''answer'':58C,72C ''appreci'':306C ''argu'':204C ''assign'':29C,317C ''averag'':89C ''bachelor'':106C,121C ''background'':84C,216C ''base'':33C,59C ''bring'':266C ''carri'':18C,221C ''circl'':314C ''coeffici'':182C,189C ''colleg'':101C,116C ''come'':51C ''correl'':331C ''data'':31C,62C ''descript'':9B ''descriptive-statist'':8B ''differ'':297C ''dummi'':163C ''dv'':88C ''educ'':114C,149C,229C ''educuc'':99C ''empir'':1A,20C ''etc'':151C ''extent'':76C ''famili'':83C,215C ''father'':113C,228C ''feel'':278C ''final'':230C ''find'':139C,158C ''first'':90C ''general'':334C ''get'':335C ''given'':42C ''go'':70C,233C,285C,311C ''grade'':251C ''great'':305C ''help'':17C,302C ''hour'':131C ''initi'':66C ''iv'':97C,112C,127C ''job'':130C,248C ''jump'':320C ''keep'':319C ''li'':87C,96C,111C,126C ''littl'':258C ''ll'':57C ''look'':14C,226C ''lower'':250C ''m'':13C,48C,261C,309C ''make'':271C ''may'':292C ''meant'':49C ''mother'':98C,147C ''need'':24C ''not/work'':256C ''one'':322C ''p'':11C,64C,135C,276C,300C ''part'':128C,246C ''perform'':79C,171C,207C ''pick'':295C ''point'':274C ''procedur'':225C ''question'':55C,67C,298C,323C ''rather'':191C ''regress'':7B,173C ''relationship'':240C,280C ''research'':2A,21C,202C ''result'':81C,213C ''round'':312C ''sampl'':38C ''score'':92C ''see'':177C,235C ''statist'':6B,10B,141C ''still'':195C ''strength'':179C,186C ''stress'':336C ''strong'':209C,273C,330C ''stuck'':262C ''student'':45C,144C ''sub'':37C ''sub-sampl'':36C ''summari'':140C ''survey'':5A,41C ''thing'':155C ''time'':129C,247C ''togeth'':269C ''topic'':200C ''ul'':86C ''undergradu'':44C ''use'':196C ''variabl'':164C,283C ''weak'':192C,289C ''work'':132C,245C ''would'':220C,303C ''year'':91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9188, 1, '2015-12-03 10:33:27.963', 1, 62, 'Cluster Similar Images into Folders', '&lt;p&gt;I wasn''t sure where to ask this question so forgive me if the question seems out of place (and please guide me where to ask it !)&lt;/p&gt;

&lt;p&gt;I have an archive of 9GAG images and I want to Cluster them based on their content and their similarity... 9GAG images are mostly memes so it''s natural that you''d find many of them pretty similar to each other...&lt;/p&gt;

&lt;p&gt;I couldn''t find any application which does this out of the box (if there is, could you please refer me to it ?) and what I found was a vast number of Papers about Image Clustering but no real application based on them...&lt;/p&gt;

&lt;p&gt;I was wondering if there is a Ruby, Python or Java Program which could simply get the directory of the Images and Cluster them into groups (folders) based on their Similarity to each other ?&lt;/p&gt;

&lt;p&gt;Thank you very much...&lt;/p&gt;
', 'clustering  image-classification', NULL, '''/p'':38C,81C,126C,162C,168C ''9gag'':45C,60C ''applic'':88C,122C ''archiv'':43C ''ask'':17C,36C ''base'':53C,123C,155C ''box'':95C ''classif'':9B ''cluster'':1A,6B,51C,118C,150C ''content'':56C ''could'':99C,141C ''couldn'':84C ''d'':71C ''directori'':145C ''find'':72C,86C ''folder'':5A,154C ''forgiv'':21C ''found'':109C ''get'':143C ''group'':153C ''guid'':32C ''imag'':3A,8B,46C,61C,117C,148C ''image-classif'':7B ''java'':138C ''mani'':73C ''meme'':64C ''most'':63C ''much'':167C ''natur'':68C ''number'':113C ''p'':10C,39C,82C,127C,163C ''paper'':115C ''place'':29C ''pleas'':31C,101C ''pretti'':76C ''program'':139C ''python'':136C ''question'':19C,25C ''real'':121C ''refer'':102C ''rubi'':135C ''seem'':26C ''similar'':2A,59C,77C,158C ''simpli'':142C ''sure'':14C ''thank'':164C ''vast'':112C ''want'':49C ''wasn'':12C ''wonder'':130C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9202, 1, '2015-12-04 11:36:40.26', 1, 47, 'Categorical and ordinal feature data representation in regression analysis?', '&lt;p&gt;I am trying to fully understand difference between categorical and ordinal data when doing regression analysis. For now, what is clear:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Categorical feature and data example:&lt;/strong&gt;&lt;br&gt; 
Color: red, white, black &lt;br&gt; 
Why categorical: &lt;code&gt;red &amp;lt; white &amp;lt; black&lt;/code&gt; is logically &lt;strong&gt;incorrect&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Ordinal feature and data example:&lt;/strong&gt;&lt;br&gt; 
Condition: old, renovated, new &lt;br&gt; 
Why ordinal: &lt;code&gt;old &amp;lt; renovated &amp;lt; new&lt;/code&gt; is logically &lt;strong&gt;correct&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Categorical-to-numeric and ordinal-to-numeric encoding methods: &lt;br&gt;&lt;/strong&gt;
One-Hot encoding for categorical data &lt;br&gt;
Arbitrary numbers for ordinal data&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Categorical data to numeric:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data = {''color'': [''blue'', ''green'', ''green'', ''red'']}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Numeric format after One-Hot encoding:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;   color_blue  color_green  color_red
0           1            0          0
1           0            1          0
2           0            1          0
3           0            0          1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Ordinal data to numeric:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data = {''con'': [''old'', ''new'', ''new'', ''renovated'']}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Numeric format after using mapping: Old &amp;lt; renovated &amp;lt; new â 0, 1, 2&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;0    0
1    2
2    2
3    1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In my data I have ''color'' feature. As color changes from white to black price increases. From above mentioned rules I probably have to use one-hot encoding for categorical ''color'' data. But why I cannot use ordinal representation. Below I provided my observations from where my question arised.&lt;/p&gt;

&lt;p&gt;Let me start with introducing formula for linear regression:
&lt;a href="http://i.stack.imgur.com/mbiK6.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/mbiK6.jpg" alt="enter image description here"&gt;&lt;/a&gt;
&lt;br&gt;Let have a look at data representations for color:
&lt;a href="http://i.stack.imgur.com/Veb54.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Veb54.jpg" alt="enter image description here"&gt;&lt;/a&gt;
Let''s predict price for 1-st and 2-nd item using formula for both data representations:&lt;br&gt;
&lt;strong&gt;One-hot encoding:&lt;/strong&gt; 
In this case different thetas for different colors will exist. I assume that thetas already derived from regression (20, 50 and 100). Prediction will be: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Price (1 item) = 0 + 20*1 + 50*0 + 100*0 = 20$  (thetas are assumed for example)
    Price (2 item) = 0 + 20*0 + 50*1 + 100*0 = 50$  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Ordinal encoding for color:&lt;/strong&gt;
In this case all colors will have 1 common theta but  my assigned multipliers (10, 20, 30) differ:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Price (1 item) = 0 + 20*10 = 200$  (theta assumed for example)
    Price (2 item) = 0 + 20*20 = 400$  (theta assumed for example)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In my model White &amp;lt; Red &amp;lt; Black in prices. Seem to be that correlation works correctly and it is logical predictions in both cases. For ordinal and categorical representations. So I can use any encoding for my regression regardless of the data type (categorical or ordinal)? This division in data representations is just a matter of conventions and software-oriented representations rather than a matter of regression logic?&lt;/p&gt;
', 'machine-learning  regression  categorical-data  linear-regression', NULL, '''/a'':308C,336C ''/code'':65C,94C,146C,181C,199C,227C,416C,468C ''/mbik6.jpg'':295C,302C ''/p'':42C,71C,100C,129C,137C,156C,190C,216C,280C,386C,443C,541C ''/pre'':147C,182C,200C,228C,417C,469C ''/strong'':50C,70C,79C,99C,115C,136C,189C,360C,424C ''/veb54.jpg'':323C,330C ''0'':165C,167C,168C,170C,172C,174C,176C,178C,179C,213C,219C,220C,392C,396C,398C,408C,410C,414C,449C,460C ''1'':166C,169C,171C,175C,180C,214C,221C,226C,342C,390C,394C,412C,432C,447C ''10'':439C,451C ''100'':382C,397C,413C ''2'':173C,215C,222C,223C,224C,345C,406C,458C ''20'':379C,393C,399C,409C,440C,450C,461C,462C ''200'':452C ''3'':177C,225C ''30'':441C ''400'':463C ''50'':380C,395C,411C,415C ''alreadi'':375C ''alt'':303C,331C ''analysi'':9A,36C ''arbitrari'':124C ''aris'':279C ''assign'':437C ''assum'':372C,402C,454C,465C ''black'':55C,64C,243C,478C ''blue'':142C,160C ''br'':51C,56C,80C,85C,114C,123C,309C,354C ''cannot'':266C ''case'':363C,427C,495C ''categor'':1A,15B,29C,45C,58C,104C,121C,132C,260C,499C,515C ''categorical-data'':14B ''categorical-to-numer'':103C ''chang'':239C ''clear'':41C ''code'':59C,88C,139C,158C,192C,218C,388C,445C ''color'':52C,141C,159C,161C,163C,235C,238C,261C,318C,368C,423C,429C ''common'':433C ''con'':194C ''condit'':81C ''convent'':528C ''correct'':98C,487C ''correl'':485C ''data'':5A,16B,32C,48C,77C,122C,128C,133C,140C,186C,193C,232C,262C,315C,352C,513C,521C ''deriv'':376C ''descript'':306C,334C ''differ'':27C,364C,367C,442C ''divis'':519C ''encod'':112C,119C,155C,258C,359C,421C,506C ''enter'':304C,332C ''exampl'':49C,78C,404C,456C,467C ''exist'':370C ''featur'':4A,46C,75C,236C ''format'':150C,203C ''formula'':287C,349C ''fulli'':25C ''green'':143C,144C,162C ''hot'':118C,154C,257C,358C ''href'':292C,320C ''i.stack.imgur.com'':294C,301C,322C,329C ''i.stack.imgur.com/mbik6.jpg'':293C,300C ''i.stack.imgur.com/veb54.jpg'':321C,328C ''imag'':305C,333C ''img'':298C,326C ''incorrect'':69C ''increas'':245C ''introduc'':286C ''item'':347C,391C,407C,448C,459C ''learn'':12B ''let'':282C,310C,337C ''linear'':18B,289C ''linear-regress'':17B ''logic'':67C,96C,491C,540C ''look'':313C ''lt'':61C,63C,90C,92C,208C,210C,475C,477C ''machin'':11B ''machine-learn'':10B ''map'':206C ''matter'':526C,537C ''mention'':248C ''method'':113C ''model'':473C ''multipli'':438C ''nd'':346C ''new'':84C,93C,196C,197C,211C ''nofollow'':297C,325C ''number'':125C ''numer'':106C,111C,135C,149C,188C,202C ''observ'':274C ''old'':82C,89C,195C,207C ''one'':117C,153C,256C,357C ''one-hot'':116C,152C,255C,356C ''ordin'':3A,31C,74C,87C,109C,127C,185C,268C,420C,497C,517C ''ordinal-to-numer'':108C ''orient'':532C ''p'':20C,43C,72C,101C,130C,148C,183C,201C,229C,281C,418C,470C ''pre'':138C,157C,191C,217C,387C,444C ''predict'':339C,383C,492C ''price'':244C,340C,389C,405C,446C,457C,480C ''probabl'':251C ''provid'':272C ''question'':278C ''rather'':534C ''red'':53C,60C,145C,164C,476C ''regardless'':510C ''regress'':8A,13B,19B,35C,290C,378C,509C,539C ''rel'':296C,324C ''renov'':83C,91C,198C,209C ''represent'':6A,269C,316C,353C,500C,522C,533C ''rule'':249C ''seem'':481C ''softwar'':531C ''software-ori'':530C ''src'':299C,327C ''st'':343C ''start'':284C ''strong'':44C,68C,73C,97C,102C,131C,184C,355C,419C ''theta'':365C,374C,400C,434C,453C,464C ''tri'':23C ''type'':514C ''understand'':26C ''use'':205C,254C,267C,348C,504C ''white'':54C,62C,241C,474C ''work'':486C ''â'':212C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9207, 1, '2015-12-04 14:11:04.62', 2, 39, 'Finding Patterns in continuous data', '&lt;p&gt;I''d like to find frequent patterns in data that has been created by an accelerometer of a Smart Watch.
The algorithm should return the parts of the data that occur after a pattern.
In the best case, the algorithm finds the movement automatically that shows how we open doors, grab our phone etc.&lt;/p&gt;

&lt;p&gt;Unfortunately, until now I only was able to find algorithms that find frequent &lt;strong&gt;item sets&lt;/strong&gt; in data.&lt;/p&gt;

&lt;p&gt;Does anyone know a solution on how to find those patterns in &lt;strong&gt;continuous&lt;/strong&gt; data?&lt;/p&gt;

&lt;p&gt;Thank you very much! :)&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.imgur.com/KCIQ2fG.png?1" alt="patterns"&gt;&lt;/p&gt;
', 'machine-learning', NULL, '''/kciq2fg.png?1'':115C ''/p'':64C,85C,103C,109C,118C ''/strong'':82C,101C ''abl'':72C ''acceleromet'':25C ''algorithm'':31C,49C,75C ''alt'':116C ''anyon'':88C ''automat'':53C ''best'':46C ''case'':47C ''continu'':4A,100C ''creat'':22C ''d'':11C ''data'':5A,18C,38C,84C,102C ''door'':59C ''etc'':63C ''find'':1A,14C,50C,74C,77C,95C ''frequent'':15C,78C ''grab'':60C ''i.imgur.com'':114C ''i.imgur.com/kciq2fg.png?1'':113C ''img'':111C ''item'':80C ''know'':89C ''learn'':8B ''like'':12C ''machin'':7B ''machine-learn'':6B ''movement'':52C ''much'':108C ''occur'':40C ''open'':58C ''p'':9C,65C,86C,104C,110C ''part'':35C ''pattern'':2A,16C,43C,97C,117C ''phone'':62C ''return'':33C ''set'':81C ''show'':55C ''smart'':28C ''solut'':91C ''src'':112C ''strong'':79C,99C ''thank'':105C ''unfortun'':66C ''watch'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9210, 2, '2015-12-04 15:42:09.467', 3, NULL, NULL, '&lt;p&gt;As far as I know, the existing (almost all) libraries in Python can handle very complex models of Neural Networks.&lt;/p&gt;

&lt;p&gt;TensorFlow is however not polished as of now. It still has a long way to grow before getting accepted as a mainstream library for ML. &lt;/p&gt;

&lt;p&gt;So, going ahead with the existing libraries like PyLearn/Keras/Torch, etc makes sense as of now (also as they have wide and dedicated communities already), as you need to concentrate on research rather than worrying on bugs and technical problems of a library.&lt;/p&gt;
', NULL, NULL, '''/p'':22C,49C,93C ''accept'':42C ''ahead'':53C ''almost'':9C ''alreadi'':74C ''also'':66C ''bug'':86C ''communiti'':73C ''complex'':17C ''concentr'':79C ''dedic'':72C ''etc'':60C ''exist'':8C,56C ''far'':3C ''get'':41C ''go'':52C ''grow'':39C ''handl'':15C ''howev'':26C ''know'':6C ''librari'':11C,46C,57C,92C ''like'':58C ''long'':36C ''mainstream'':45C ''make'':61C ''ml'':48C ''model'':18C ''need'':77C ''network'':21C ''neural'':20C ''p'':1C,23C,50C ''polish'':28C ''problem'':89C ''pylearn/keras/torch'':59C ''python'':13C ''rather'':82C ''research'':81C ''sens'':62C ''still'':33C ''technic'':88C ''tensorflow'':24C ''way'':37C ''wide'':70C ''worri'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9211, 2, '2015-12-04 15:44:19.133', 1, NULL, NULL, '&lt;p&gt;The distinction between ordinal and categorical does matter. If in truth the difference between white and red was drastically different from red and black, your (10,20,30) ordinal model would not have performed well.&lt;/p&gt;

&lt;p&gt;One hot encoding can learn the relationship between the ordinal values more finely, but throws out the information that the variables are related. Similarly, with insufficient data it is more likely to overfit.&lt;/p&gt;

&lt;p&gt;Ordinal variables lessen those  problems but at the cost of forcing you to define the interval. There are a number of methods for defining the values of your ordinal variables, like rologit.&lt;/p&gt;
', NULL, NULL, '''/p'':37C,72C,106C ''10'':27C ''20'':28C ''30'':29C ''black'':25C ''categor'':7C ''cost'':82C ''data'':65C ''defin'':87C,97C ''differ'':14C,21C ''distinct'':3C ''drastic'':20C ''encod'':41C ''fine'':51C ''forc'':84C ''hot'':40C ''inform'':56C ''insuffici'':64C ''interv'':89C ''learn'':43C ''lessen'':76C ''like'':69C,104C ''matter'':9C ''method'':95C ''model'':31C ''number'':93C ''one'':39C ''ordin'':5C,30C,48C,74C,102C ''overfit'':71C ''p'':1C,38C,73C ''perform'':35C ''problem'':78C ''red'':18C,23C ''relat'':61C ''relationship'':45C ''rologit'':105C ''similar'':62C ''throw'':53C ''truth'':12C ''valu'':49C,99C ''variabl'':59C,75C,103C ''well'':36C ''white'':16C ''would'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9213, 2, '2015-12-04 15:45:40.557', 0, NULL, NULL, '&lt;p&gt;They are almost identical:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The second key reason that maxout performs well is
  that it improves the bagging style training phase of
  dropout. Note that the arguments in section 7 motivating
  the use of maxout also apply equally to rectified
  linear units (Salinas &amp;amp; Abbott, 1996; Hahnloser, 1998;
  Glorot et al., 2011). &lt;strong&gt;The only difference between maxout
  and max pooling over a set of rectified linear units
  is that maxout does not include a 0 in the max&lt;/strong&gt;.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Source: &lt;a href="http://arxiv.org/pdf/1302.4389v4.pdf" rel="nofollow"&gt;Maxout Networks&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':98C ''/blockquote'':86C ''/p'':6C,85C,99C ''/pdf/1302.4389v4.pdf'':93C ''/strong'':84C ''0'':80C ''1996'':50C ''1998'':52C ''2011'':56C ''7'':34C ''abbott'':49C ''al'':55C ''almost'':4C ''also'':40C ''amp'':48C ''appli'':41C ''argument'':31C ''arxiv.org'':92C ''arxiv.org/pdf/1302.4389v4.pdf'':91C ''bag'':22C ''blockquot'':7C ''differ'':60C ''dropout'':27C ''equal'':42C ''et'':54C ''glorot'':53C ''hahnlos'':51C ''href'':90C ''ident'':5C ''improv'':20C ''includ'':78C ''key'':11C ''linear'':45C,71C ''max'':64C,83C ''maxout'':14C,39C,62C,75C,96C ''motiv'':35C ''network'':97C ''nofollow'':95C ''note'':28C ''p'':1C,8C,87C ''perform'':15C ''phase'':25C ''pool'':65C ''reason'':12C ''rectifi'':44C,70C ''rel'':94C ''salina'':47C ''second'':10C ''section'':33C ''set'':68C ''sourc'':88C ''strong'':57C ''style'':23C ''train'':24C ''unit'':46C,72C ''use'':37C ''well'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9215, 2, '2015-12-04 17:35:20.373', 0, NULL, NULL, '&lt;p&gt;That paper of Goodfellow is a little bit cryptic. For what I understand, the max out networks let the net to find the best activation function for the specific problem. This is done through a winner take all algorithm in  intermediate layers. Accidentally this kind of networks compress the information in term of paths. This means that the information is stored in the net in term weights and paths that the net create. In my opinion this is not the same as the max pooling, even though we use a winner take all in both cases, we perform a max pool in a layer to reduce the dimensionality of the problem. &lt;/p&gt;
', NULL, NULL, '''/p'':113C ''accident'':44C ''activ'':26C ''algorithm'':40C ''best'':25C ''bit'':9C ''case'':97C ''compress'':49C ''creat'':74C ''cryptic'':10C ''dimension'':109C ''done'':34C ''even'':87C ''find'':23C ''function'':27C ''goodfellow'':5C ''inform'':51C,60C ''intermedi'':42C ''kind'':46C ''layer'':43C,105C ''let'':19C ''littl'':8C ''max'':16C,85C,101C ''mean'':57C ''net'':21C,65C,73C ''network'':18C,48C ''opinion'':77C ''p'':1C ''paper'':3C ''path'':55C,70C ''perform'':99C ''pool'':86C,102C ''problem'':31C,112C ''reduc'':107C ''specif'':30C ''store'':62C ''take'':38C,93C ''term'':53C,67C ''though'':88C ''understand'':14C ''use'':90C ''weight'':68C ''winner'':37C,92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9327, 2, '2015-12-11 03:39:17.913', 1, NULL, NULL, '&lt;p&gt;Since you have multiple repetitions in A and B, and these two vectors are of different lengths (5913 and 3733), it is expected that vector C and D are of different size. However, C and D might contain the same unique elements.&lt;/p&gt;

&lt;p&gt;Let''s take the example proposed by Biswajit Banerjee:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;A = c(1,1,2,2,3,3,4,4,5,5,6,6,9,11,15,17,19,19)
B = c(1,2,2,2,3,3,3,4,5,6,7,8,8,8)

C= A[(A %in% B)]
D = B[(B %in% A)]

all(unique(C) == unique(D))
TRUE
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You could verify that your vectors C and D of length 2007 and 126 contains the same unique values. Does it solve your problem ?&lt;/p&gt;
', NULL, NULL, '''/code'':110C ''/p'':44C,55C,137C ''/pre'':111C ''1'':60C,61C,80C ''11'':73C ''126'':126C ''15'':74C ''17'':75C ''19'':76C,77C ''2'':62C,63C,81C,82C,83C ''2007'':124C ''3'':64C,65C,84C,85C,86C ''3733'':21C ''4'':66C,67C,87C ''5'':68C,69C,88C ''5913'':19C ''6'':70C,71C,89C ''7'':90C ''8'':91C,92C,93C ''9'':72C ''b'':10C,78C,98C,100C,101C ''banerje'':54C ''biswajit'':53C ''c'':27C,35C,59C,79C,94C,106C,119C ''code'':57C ''contain'':39C,127C ''could'':114C ''d'':29C,37C,99C,108C,121C ''differ'':17C,32C ''element'':43C ''exampl'':50C ''expect'':24C ''howev'':34C ''length'':18C,123C ''let'':46C ''might'':38C ''multipl'':5C ''p'':1C,45C,112C ''pre'':56C ''problem'':136C ''propos'':51C ''repetit'':6C ''sinc'':2C ''size'':33C ''solv'':134C ''take'':48C ''true'':109C ''two'':13C ''uniqu'':42C,105C,107C,130C ''valu'':131C ''vector'':14C,26C,118C ''verifi'':115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9216, 1, '2015-12-04 17:50:56.987', 1, 8, 'Change raw count to a proportion based on a filter in Tableau', '&lt;p&gt;In Tableau, I commonly want to carry out a task like distinguishing 2 groups by various measures.&lt;/p&gt;

&lt;p&gt;The example below shows the difference in the count of 2 treatment and control for variable levels of a factor (measure):&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/6xuNV.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/6xuNV.jpg" alt="Tableau"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This view is misleading though, because the overall group sizes of treatment and control are not equal. &lt;/p&gt;

&lt;p&gt;What I''d like to do is to represent this as a proportion for each levels of the factor (measure) instead of using just a count. For instance, perhaps 25% of the treatment group is in Business Development whereas only 10% of the control group is in Business Development. &lt;/p&gt;

&lt;p&gt;Is this possible to do in Tableau, or does it strictly require pre-processing?&lt;/p&gt;
', 'tableau', NULL, '''/6xunv.jpg'':61C,68C ''/a'':71C ''/p'':32C,55C,72C,91C,142C,159C ''10'':133C ''2'':27C,44C ''25'':122C ''alt'':69C ''base'':7A ''busi'':129C,140C ''carri'':21C ''chang'':1A ''common'':18C ''control'':47C,87C,136C ''count'':3A,42C,118C ''d'':95C ''develop'':130C,141C ''differ'':39C ''distinguish'':26C ''equal'':90C ''exampl'':35C ''factor'':53C,111C ''filter'':10A ''group'':28C,82C,126C,137C ''href'':58C ''i.stack.imgur.com'':60C,67C ''i.stack.imgur.com/6xunv.jpg'':59C,66C ''img'':64C ''instanc'':120C ''instead'':113C ''level'':50C,108C ''like'':25C,96C ''measur'':31C,54C,112C ''mislead'':77C ''nofollow'':63C ''overal'':81C ''p'':14C,33C,56C,73C,92C,143C ''perhap'':121C ''possibl'':146C ''pre'':157C ''pre-process'':156C ''process'':158C ''proport'':6A,105C ''raw'':2A ''rel'':62C ''repres'':101C ''requir'':155C ''show'':37C ''size'':83C ''src'':65C ''strict'':154C ''tableau'':12A,13B,16C,70C,150C ''task'':24C ''though'':78C ''treatment'':45C,85C,125C ''use'':115C ''variabl'':49C ''various'':30C ''view'':75C ''want'':19C ''wherea'':131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9217, 2, '2015-12-04 18:11:57.24', 2, NULL, NULL, '&lt;p&gt;Taking a slightly different approach than the other great answers here, the "pretty picture" is worth a thousand words. Ultimately, you will need to convey your findings to someone who is not as statistically literate, or who simply does not have the time, interest, or whatever, to grasp the full situation. That doesn''t mean we cannot help the person to understand, at least a general concept or a piece of the reality. This is what books like Freakonomics do - there''s little to no math, no data sets, and yet the findings are still presented.&lt;/p&gt;

&lt;p&gt;From the arts, look at &lt;a href="https://en.wikipedia.org/wiki/Adolphe_Yvon#/media/File:Adolphe_Yvon_%281817-1893%29_-_Marshall_Ney_at_retreat_in_Russia.jpg" rel="nofollow"&gt;Marshal Ney at Retreat in Russia&lt;/a&gt;. This massive oversimplification of the Napoleonic wars nevertheless conveys great meaning and allows people with even the most ignorant knowledge of the war to understand the brutality, the climate, the landscape, the death, and decorum that permeated the invasion of Russia.&lt;/p&gt;

&lt;p&gt;Ultimately the charts are simply communication, and for better or worse, human communication is often times focused on conflation, simplification, and brevity.&lt;/p&gt;
', NULL, NULL, '''/a'':118C ''/p'':98C,160C,184C ''/wiki/adolphe_yvon#/media/file:adolphe_yvon_%281817-1893%29_-_marshall_ney_at_retreat_in_russia.jpg'':109C ''allow'':131C ''answer'':11C ''approach'':6C ''art'':102C ''better'':170C ''book'':78C ''breviti'':183C ''brutal'':145C ''cannot'':58C ''chart'':164C ''climat'':147C ''communic'':167C,174C ''concept'':68C ''conflat'':180C ''convey'':26C,127C ''data'':89C ''death'':151C ''decorum'':153C ''differ'':5C ''doesn'':54C ''en.wikipedia.org'':108C ''en.wikipedia.org/wiki/adolphe_yvon#/media/file:adolphe_yvon_%281817-1893%29_-_marshall_ney_at_retreat_in_russia.jpg'':107C ''even'':134C ''find'':28C,94C ''focus'':178C ''freakonom'':80C ''full'':51C ''general'':67C ''grasp'':49C ''great'':10C,128C ''help'':59C ''href'':106C ''human'':173C ''ignor'':137C ''interest'':45C ''invas'':157C ''knowledg'':138C ''landscap'':149C ''least'':65C ''like'':79C ''liter'':36C ''littl'':84C ''look'':103C ''marshal'':112C ''massiv'':120C ''math'':87C ''mean'':56C,129C ''napoleon'':124C ''need'':24C ''nevertheless'':126C ''ney'':113C ''nofollow'':111C ''often'':176C ''oversimplif'':121C ''p'':1C,99C,161C ''peopl'':132C ''permeat'':155C ''person'':61C ''pictur'':15C ''piec'':71C ''present'':97C ''pretti'':14C ''realiti'':74C ''rel'':110C ''retreat'':115C ''russia'':117C,159C ''set'':90C ''simpli'':39C,166C ''simplif'':181C ''situat'':52C ''slight'':4C ''someon'':30C ''statist'':35C ''still'':96C ''take'':2C ''thousand'':19C ''time'':44C,177C ''ultim'':21C,162C ''understand'':63C,143C ''war'':125C,141C ''whatev'':47C ''word'':20C ''wors'':172C ''worth'':17C ''yet'':92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9299, 2, '2015-12-10 03:26:52.917', 0, NULL, NULL, '&lt;p&gt;&lt;strong&gt;An Introduction to Statistical Learning by Hastie&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;It is like a bible for all those who want to get started at statistical analysis and Machine Learning.  It gives an excellent intuition about the theory behind all those models and algorithms, enough to pursue research and/or dive into another advanced ML book like the &lt;code&gt;Applied Predictive Modeling by Kuhn and Johnson&lt;/code&gt;&lt;/p&gt;
', NULL, NULL, '''/code'':67C ''/p'':11C,68C ''/strong'':10C ''advanc'':54C ''algorithm'':45C ''analysi'':28C ''and/or'':50C ''anoth'':53C ''appli'':60C ''behind'':40C ''bibl'':17C ''book'':56C ''code'':59C ''dive'':51C ''enough'':46C ''excel'':35C ''get'':24C ''give'':33C ''hasti'':9C ''introduct'':4C ''intuit'':36C ''johnson'':66C ''kuhn'':64C ''learn'':7C,31C ''like'':15C,57C ''machin'':30C ''ml'':55C ''model'':43C,62C ''p'':1C,12C ''predict'':61C ''pursu'':48C ''research'':49C ''start'':25C ''statist'':6C,27C ''strong'':2C ''theori'':39C ''want'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9661, 2, '2016-01-07 01:31:41.43', 1, NULL, NULL, '&lt;p&gt;You''ve just described the matrix factorization model, which works well. In fact, it even works without explicit features; i.e., any properties of the items. But you can introduce prior information by augmenting the feature vectors, e.g., with the document embedding, as you describe. I do not see any reason why it should not work, esp. since I''ve seen it done.&lt;/p&gt;
', NULL, NULL, '''/p'':64C ''augment'':34C ''describ'':5C,45C ''document'':41C ''done'':63C ''e.g'':38C ''embed'':42C ''esp'':57C ''even'':16C ''explicit'':19C ''fact'':14C ''factor'':8C ''featur'':20C,36C ''i.e'':21C ''inform'':32C ''introduc'':30C ''item'':26C ''matrix'':7C ''model'':9C ''p'':1C ''prior'':31C ''properti'':23C ''reason'':51C ''see'':49C ''seen'':61C ''sinc'':58C ''ve'':3C,60C ''vector'':37C ''well'':12C ''without'':18C ''work'':11C,17C,56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9221, 2, '2015-12-05 05:53:41.113', 3, NULL, NULL, '&lt;p&gt;I just want to add more information about these  more "intelligent" ways to pick hyperparameters. One in particular that''s becoming more and more popular. &lt;a href="http://arxiv.org/pdf/1206.2944.pdf" rel="nofollow"&gt;BAYESIAN  OPTIMIZATION  OF  MACHINE  LEARNING ALGORITHMS&lt;/a&gt; by Jasper Snoek, Hugo Larochelle and Ryan P. Adam. Which has proven effective in algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks.&lt;/p&gt;

&lt;p&gt;Algorithm on Github
&lt;a href="https://github.com/JasperSnoek/spearmint" rel="nofollow"&gt;https://github.com/JasperSnoek/spearmint&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This method is particularly useful when the algorithm is complicated (leaning towards a black box model) and/or the data is massive and it''s computationally expensive to train several times a model.&lt;/p&gt;
', NULL, NULL, '''/a'':40C ''/jaspersnoek/spearmint'':75C ''/jaspersnoek/spearmint&lt;/a&gt;&lt;/p&gt;'':80C ''/p'':66C,114C ''/pdf/1206.2944.pdf'':31C ''adam'':49C ''add'':6C ''algorithm'':39C,55C,68C,89C ''alloc'':59C ''and/or'':98C ''arxiv.org'':30C ''arxiv.org/pdf/1206.2944.pdf'':29C ''bayesian'':34C ''becom'':22C ''black'':95C ''box'':96C ''complic'':91C ''comput'':106C ''convolut'':63C ''data'':100C ''dirichlet'':58C ''effect'':53C ''expens'':107C ''github'':70C ''github.com'':74C,79C ''github.com/jaspersnoek/spearmint'':73C ''github.com/jaspersnoek/spearmint&lt;/a&gt;&lt;/p&gt;'':78C ''href'':28C,72C ''hugo'':44C ''hyperparamet'':16C ''includ'':56C ''inform'':8C ''intellig'':12C ''jasper'':42C ''larochell'':45C ''latent'':57C ''lean'':92C ''learn'':38C ''machin'':37C ''massiv'':102C ''method'':83C ''model'':97C,113C ''network'':65C ''neural'':64C ''nofollow'':33C,77C ''one'':17C ''optim'':35C ''p'':1C,48C,67C,81C ''particular'':19C,85C ''pick'':15C ''popular'':26C ''proven'':52C ''rel'':32C,76C ''ryan'':47C ''sever'':110C ''snoek'':43C ''structur'':60C ''svms'':61C ''time'':111C ''toward'':93C ''train'':109C ''use'':86C ''want'':4C ''way'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9232, 1, '2015-12-06 10:56:34.09', 2, 25, 'How to test for Overall Significance?', '&lt;p&gt;I have two models: one a simple linear regression and the other a multi linear regression.&lt;/p&gt;

&lt;p&gt;The strongest relationship is between the two variables in the simple linear regression. The multi linear regression includes two extra variables that can be connected with the dependent, however, statistically they are insignificant (P &gt; 0).&lt;/p&gt;

&lt;p&gt;How do I interpret whether the best model to use is the simple linear regression or the multi-linear regression?&lt;/p&gt;

&lt;p&gt;Here are some values from the regression:&lt;/p&gt;

&lt;p&gt;Simple Linear : F(1, 77) = 21.07 , Prob &gt; F = 0.0000 , Rsquared = 0.2148, Adj Rsquared = 0.2046&lt;/p&gt;

&lt;p&gt;Multi Linear: F(3, 75) = 7.29 , Prob &gt; F = 0.0002, Rsquared = 0.2258, Adj Rsquared = 0.1948&lt;/p&gt;
', 'statistics  linear-regression', NULL, '''/p'':28C,65C,88C,97C,113C,129C ''0'':64C ''0.0000'':107C ''0.0002'':123C ''0.1948'':128C ''0.2046'':112C ''0.2148'':109C ''0.2258'':125C ''1'':102C ''21.07'':104C ''3'':118C ''7.29'':120C ''75'':119C ''77'':103C ''adj'':110C,126C ''best'':73C ''connect'':54C ''depend'':57C ''extra'':49C ''f'':101C,106C,117C,122C ''howev'':58C ''includ'':47C ''insignific'':62C ''interpret'':70C ''linear'':9B,19C,26C,41C,45C,80C,86C,100C,116C ''linear-regress'':8B ''model'':15C,74C ''multi'':25C,44C,85C,115C ''multi-linear'':84C ''one'':16C ''overal'':5A ''p'':11C,29C,63C,66C,89C,98C,114C ''prob'':105C,121C ''regress'':10B,20C,27C,42C,46C,81C,87C,96C ''relationship'':32C ''rsquar'':108C,111C,124C,127C ''signific'':6A ''simpl'':18C,40C,79C,99C ''statist'':7B,59C ''strongest'':31C ''test'':3A ''two'':14C,36C,48C ''use'':76C ''valu'':93C ''variabl'':37C,50C ''whether'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9239, 2, '2015-12-07 01:03:55.753', 5, NULL, NULL, '&lt;p&gt;You might want to take into consideration that &lt;a href="https://github.com/lisa-lab/pylearn2/pull/1565"&gt;Pylearn2 has no more developer&lt;/a&gt;, and now &lt;a href="https://github.com/lisa-lab/pylearn2/commit/48380daba195db8cf2a53ec86139a1ae7af89d7b#diff-88b99bb28683bd5b7e3a204826ead112"&gt;points to other Theano-based libraries&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;There are other machine learning frameworks built on top of Theano that could interest you, such as: Blocks, Keras and Lasagne.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;As Dawny33 says, TensorFlow is just getting started, but it is interesting to note that the number of questions on TensorFlow (244) on Stack Overflow already surpasses Torch (166) and will probably catch up with Theano (672) in  2016.&lt;/p&gt;
', NULL, NULL, '''/a'':20C,35C ''/blockquote'':61C ''/lisa-lab/pylearn2/commit/48380daba195db8cf2a53ec86139a1ae7af89d7b#diff-88b99bb28683bd5b7e3a204826ead112'':27C ''/lisa-lab/pylearn2/pull/1565'':14C ''/p'':36C,60C,102C ''166'':91C ''2016'':101C ''244'':84C ''672'':99C ''alreadi'':88C ''base'':33C ''block'':56C ''blockquot'':37C ''built'':45C ''catch'':95C ''consider'':8C ''could'':51C ''dawny33'':64C ''develop'':19C ''framework'':44C ''get'':69C ''github.com'':13C,26C ''github.com/lisa-lab/pylearn2/commit/48380daba195db8cf2a53ec86139a1ae7af89d7b#diff-88b99bb28683bd5b7e3a204826ead112'':25C ''github.com/lisa-lab/pylearn2/pull/1565'':12C ''href'':11C,24C ''interest'':52C,74C ''kera'':57C ''lasagn'':59C ''learn'':43C ''librari'':34C ''machin'':42C ''might'':3C ''note'':76C ''number'':79C ''overflow'':87C ''p'':1C,38C,62C ''point'':28C ''probabl'':94C ''pylearn2'':15C ''question'':81C ''say'':65C ''stack'':86C ''start'':70C ''surpass'':89C ''take'':6C ''tensorflow'':66C,83C ''theano'':32C,49C,98C ''theano-bas'':31C ''top'':47C ''torch'':90C ''want'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9222, 1, '2015-12-05 10:24:32.533', 1, 124, 'How does strided deconvolution works?', '&lt;p&gt;I am trying to understand how the shape of the image changes after deconvolution ?
I am trying to understand the example code of &lt;a href="https://github.com/NervanaSystems/neon/blob/master/examples/conv_autoencoder.py" rel="nofollow"&gt;convolutional autoencoder from neon&lt;/a&gt;.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;layers = [Conv((4, 4, 8), init=init_uni, activation=Rectlin()),
          Pooling(2),
          Conv((4, 4, 32), init=init_uni, activation=Rectlin()),
          Pooling(2),
          Deconv(fshape=(3, 3, 8), init=init_uni, strides=2, padding=1),
          Deconv(fshape=(3, 3, 8), init=init_uni, strides=2, padding=1),
          Deconv(fshape=(4, 4, 1), init=init_uni, strides=2, padding=0)]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The input_shapes and output_shapes of each layer are as follows&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Convolution Layer ''ConvolutionLayer'': 1 x (28x28) inputs, 8 x (25x25) outputs, padding 0, stride 1
Pooling Layer ''PoolingLayer'': 8 x (25x25) inputs, 8 x (12x12) outputs
Convolution Layer ''ConvolutionLayer'': 8 x (12x12) inputs, 32 x (9x9) outputs, padding 0, stride 1
Pooling Layer ''PoolingLayer'': 32 x (9x9) inputs, 32 x (4x4) outputs
Deconvolution Layer ''DeconvolutionLayer'': 32 x (4x4) inputs, 8 x (7x7) outputs
Deconvolution Layer ''DeconvolutionLayer'': 8 x (7x7) inputs, 8 x (13x13) outputs
Deconvolution Layer ''DeconvolutionLayer'': 8 x (13x13) inputs, 1 x (28x28) outputs
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I understand how the shapes change after convolution (''valid''). (Thanks to &lt;a href="http://cs231n.github.io/convolutional-networks/" rel="nofollow"&gt;http://cs231n.github.io/convolutional-networks/&lt;/a&gt;)
How does the stride affect the size of matrix when deconvolution (full convolution) is used ? &lt;/p&gt;
', 'deep-learning  convnet  autoencoder', NULL, '''/a'':46C ''/code'':109C,212C ''/convolutional-networks/'':230C ''/convolutional-networks/&lt;/a&gt;)'':235C ''/nervanasystems/neon/blob/master/examples/conv_autoencoder.py'':39C ''/p'':47C,124C,251C ''/pre'':110C,213C ''0'':108C,139C,165C ''1'':84C,96C,101C,130C,141C,167C,208C ''12x12'':151C,158C ''13x13'':199C,206C ''2'':61C,72C,82C,94C,106C ''25x25'':136C,147C ''28x28'':132C,210C ''3'':75C,76C,87C,88C ''32'':65C,160C,171C,175C,182C ''4'':52C,53C,63C,64C,99C,100C ''4x4'':177C,184C ''7x7'':188C,195C ''8'':54C,77C,89C,134C,145C,149C,156C,186C,193C,197C,204C ''9x9'':162C,173C ''activ'':58C,69C ''affect'':240C ''autoencod'':10B,43C ''chang'':23C,220C ''code'':33C,49C,126C ''conv'':51C,62C ''convnet'':9B ''convolut'':42C,127C,153C,222C,248C ''convolutionlay'':129C,155C ''cs231n.github.io'':229C,234C ''cs231n.github.io/convolutional-networks/'':228C ''cs231n.github.io/convolutional-networks/&lt;/a&gt;)'':233C ''deconv'':73C,85C,97C ''deconvolut'':4A,25C,179C,190C,201C,246C ''deconvolutionlay'':181C,192C,203C ''deep'':7B ''deep-learn'':6B ''exampl'':32C ''follow'':123C ''fshape'':74C,86C,98C ''full'':247C ''github.com'':38C ''github.com/nervanasystems/neon/blob/master/examples/conv_autoencoder.py'':37C ''href'':36C,227C ''imag'':22C ''init'':55C,56C,66C,67C,78C,79C,90C,91C,102C,103C ''input'':113C,133C,148C,159C,174C,185C,196C,207C ''layer'':50C,120C,128C,143C,154C,169C,180C,191C,202C ''learn'':8B ''matrix'':244C ''neon'':45C ''nofollow'':41C,232C ''output'':116C,137C,152C,163C,178C,189C,200C,211C ''p'':11C,111C,214C ''pad'':83C,95C,107C,138C,164C ''pool'':60C,71C,142C,168C ''poolinglay'':144C,170C ''pre'':48C,125C ''rectlin'':59C,70C ''rel'':40C,231C ''shape'':19C,114C,117C,219C ''size'':242C ''stride'':3A,81C,93C,105C,140C,166C,239C ''thank'':224C ''tri'':14C,28C ''understand'':16C,30C,216C ''uni'':57C,68C,80C,92C,104C ''use'':250C ''valid'':223C ''work'':5A ''x'':131C,135C,146C,150C,157C,161C,172C,176C,183C,187C,194C,198C,205C,209C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9224, 1, '2015-12-05 15:04:43.81', 4, 47, 'Predicting app usage on mobile phone', '&lt;p&gt;I''m currently building an app that strives to predict how the users uses different apps and give the user a suggestion based on which apps it think the user will currently use (a ranked list based on the user''s current conditions). I''ve been collecting some data over the past week now, and I''m not really sure which approach to take. I''ve been thinking about using multiple seasonalities (correct me if I''m using the wrong terminology) such as time of day, day of week, week of month, month and quarter. I also want to use location, and other sensor data (such as the user state "walking" or "sitting" later on).&lt;/p&gt;

&lt;p&gt;I''ve summarised the usage over the last week on an hourly level for some apps. 
The bars represents each time the app was opened during that time, and the green line is a weighted moving average which has a weight of 0.5 on its closest neighbours.&lt;/p&gt;

&lt;p&gt;Now I see several challenges in front of me and would greatly appreciate some input from others, or some good resources to find further information.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Do you think my model is a good one for this problem&lt;/li&gt;
&lt;li&gt;How do I account for ageing data?&lt;/li&gt;
&lt;li&gt;How do add up the different seasonalities/states/location? Multiply them?&lt;/li&gt;
&lt;li&gt;Does it make sense smoothening the curve as I do?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Here some data for the last week on an hourly basis:
&lt;a href="http://i.stack.imgur.com/N4fjl.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/N4fjl.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'machine-learning  predictive-modeling', NULL, '''/a'':283C ''/li'':220C,229C,240C,252C ''/n4fjl.png'':270C,277C ''/p'':129C,178C,205C,284C ''/ul'':253C ''0.5'':173C ''account'':225C ''add'':233C ''age'':227C ''also'':110C ''alt'':278C ''app'':2A,19C,29C,39C,146C,153C ''appreci'':192C ''approach'':75C ''averag'':167C ''bar'':148C ''base'':36C,50C ''basi'':265C ''build'':17C ''challeng'':184C ''closest'':176C ''collect'':60C ''condit'':56C ''correct'':86C ''current'':16C,45C,55C ''curv'':248C ''data'':62C,118C,228C,257C ''day'':99C,100C ''descript'':281C ''differ'':28C,236C ''enter'':279C ''find'':202C ''front'':186C ''give'':31C ''good'':199C,215C ''great'':191C ''green'':161C ''hour'':142C,264C ''href'':267C ''i.stack.imgur.com'':269C,276C ''i.stack.imgur.com/n4fjl.png'':268C,275C ''imag'':280C ''img'':273C ''inform'':204C ''input'':194C ''last'':138C,260C ''later'':127C ''learn'':9B ''level'':143C ''li'':207C,221C,230C,241C ''line'':162C ''list'':49C ''locat'':114C ''m'':15C,70C,90C ''machin'':8B ''machine-learn'':7B ''make'':244C ''mobil'':5A ''model'':12B,212C ''month'':105C,106C ''move'':166C ''multipl'':84C ''multipli'':238C ''neighbour'':177C ''nofollow'':272C ''one'':216C ''open'':155C ''other'':196C ''p'':13C,130C,179C,254C ''past'':65C ''phone'':6A ''predict'':1A,11B,23C ''predictive-model'':10B ''problem'':219C ''quarter'':108C ''rank'':48C ''realli'':72C ''rel'':271C ''repres'':149C ''resourc'':200C ''season'':85C ''seasonalities/states/location'':237C ''see'':182C ''sens'':245C ''sensor'':117C ''sever'':183C ''sit'':126C ''smoothen'':246C ''src'':274C ''state'':123C ''strive'':21C ''suggest'':35C ''summaris'':133C ''sure'':73C ''take'':77C ''terminolog'':94C ''think'':41C,81C,210C ''time'':97C,151C,158C ''ul'':206C ''usag'':3A,135C ''use'':27C,46C,83C,91C,113C ''user'':26C,33C,43C,53C,122C ''ve'':58C,79C,132C ''walk'':124C ''want'':111C ''week'':66C,102C,103C,139C,261C ''weight'':165C,171C ''would'':190C ''wrong'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9227, 2, '2015-12-05 20:18:42.887', 6, NULL, NULL, '&lt;p&gt;The idea with Neural Networks is that they need little pre-processing since the heavy lifting is done by the algorithm which is the one in charge of learning the features.&lt;/p&gt;

&lt;p&gt;The winners of the Data Science Bowl 2015 have a great write-up regarding their approach, so most of this answer''s content was taken from:
&lt;a href="https://benanne.github.io/2015/03/17/plankton.html" rel="nofollow"&gt;Classifying plankton with deep neural networks&lt;/a&gt;. I suggest you read it, specially the part about &lt;strong&gt;Pre-processing and data augmentation&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;- Resize Images&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;As for different sizes, resolutions or distances you can do the following. You can simply rescale the largest side of each image to a fixed length.&lt;/p&gt;

&lt;p&gt;Another option is to use openCV or scipy.
and this will resize the image to have 100 cols (width) and 50 rows (height):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;resized_image = cv2.resize(image, (100, 50)) 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Yet another option is to use scipy module, by using:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;small = scipy.misc.imresize(image, 0.5)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;- Data Augmentation&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Data Augmentation always improves performance though the amount depends on the dataset. If you want to augmented the data to artificially increase the size of the dataset you can do the following if the case applies (it wouldn''t apply if for example were images of houses or people where if you rotate them 180degrees they would lose all information but not if you flip them like a mirror does):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;rotation: random with angle between 0Â° and 360Â° (uniform)&lt;/li&gt;
&lt;li&gt;translation: random with shift between -10 and 10 pixels (uniform)&lt;/li&gt;
&lt;li&gt;rescaling: random with scale factor between 1/1.6 and 1.6
(log-uniform)&lt;/li&gt;
&lt;li&gt;flipping: yes or no (bernoulli)&lt;/li&gt;
&lt;li&gt;shearing: random with angle between -20Â° and 20Â° (uniform)&lt;/li&gt;
&lt;li&gt;stretching: random with stretch factor between 1/1.3 and 1.3
(log-uniform)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You can see the results on the Data Science bowl images.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Pre-processed images&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/0S0Y0.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/0S0Y0.png" alt="Pre-processed images"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;augmented versions of the same images&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/KJXZK.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/KJXZK.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;-Other techniques&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;These will deal with other image properties like lighting and are already related to the main algorithm more like a simple pre-processing step. Check the full list on: &lt;a href="http://ufldl.stanford.edu/tutorial/unsupervised/PCAWhitening/" rel="nofollow"&gt;UFLDL Tutorial&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''-10'':280C ''-20'':313C ''/0s0y0.png'':360C,367C ''/2015/03/17/plankton.html'':67C ''/a'':76C,373C,403C,451C ''/code'':162C,182C ''/kjxzk.png'':390C,397C ''/li'':273C,285C,299C,306C,318C,332C ''/p'':34C,94C,100C,128C,153C,175C,189C,261C,346C,354C,374C,384C,404C,410C,452C ''/pre'':163C,183C ''/strong'':93C,99C,188C,353C,383C,409C ''/tutorial/unsupervised/pcawhitening/'':446C ''/ul'':333C ''0.5'':181C ''0â'':269C ''1.3'':328C ''1.6'':295C ''1/1.3'':326C ''1/1.6'':293C ''10'':282C ''100'':146C,160C ''180degrees'':245C ''2015'':43C ''20â'':316C ''360â'':271C ''50'':150C,161C ''algorithm'':23C,428C ''alreadi'':423C ''alt'':368C,398C ''alway'':193C ''amount'':198C ''angl'':267C,311C ''anoth'':130C,166C ''answer'':57C ''appli'':226C,230C ''approach'':52C ''artifici'':211C ''augment'':92C,187C,192C,207C,377C ''benanne.github.io'':66C ''benanne.github.io/2015/03/17/plankton.html'':65C ''bernoulli'':305C ''bowl'':42C,344C ''case'':225C ''charg'':29C ''check'':437C ''classifi'':70C ''code'':155C,177C ''col'':147C ''content'':59C ''cv2.resize'':158C ''data'':40C,91C,186C,191C,209C,342C ''dataset'':202C,217C ''deal'':414C ''deep'':73C ''depend'':199C ''descript'':401C ''differ'':104C ''distanc'':108C ''done'':20C ''enter'':399C ''exampl'':233C ''factor'':291C,324C ''featur'':33C ''fix'':126C ''flip'':255C,301C ''follow'':113C,222C ''full'':439C ''great'':46C ''heavi'':17C ''height'':152C ''hous'':237C ''href'':64C,357C,387C,443C ''i.stack.imgur.com'':359C,366C,389C,396C ''i.stack.imgur.com/0s0y0.png'':358C,365C ''i.stack.imgur.com/kjxzk.png'':388C,395C ''idea'':3C ''imag'':98C,123C,143C,157C,159C,180C,235C,345C,352C,372C,382C,400C,417C ''img'':363C,393C ''improv'':194C ''increas'':212C ''inform'':250C ''largest'':119C ''learn'':31C ''length'':127C ''li'':263C,274C,286C,300C,307C,319C ''lift'':18C ''light'':420C ''like'':257C,419C,430C ''list'':440C ''littl'':11C ''log'':297C,330C ''log-uniform'':296C,329C ''lose'':248C ''main'':427C ''mirror'':259C ''modul'':172C ''need'':10C ''network'':6C,75C ''neural'':5C,74C ''nofollow'':69C,362C,392C,448C ''one'':27C ''opencv'':135C ''option'':131C,167C ''p'':1C,35C,95C,101C,129C,164C,184C,190C,334C,347C,355C,375C,385C,405C,411C ''part'':84C ''peopl'':239C ''perform'':195C ''pixel'':283C ''plankton'':71C ''pre'':13C,88C,154C,176C,350C,370C,434C ''pre-process'':12C,87C,349C,369C,433C ''process'':14C,89C,351C,371C,435C ''properti'':418C ''random'':265C,276C,288C,309C,321C ''read'':80C ''regard'':50C ''rel'':68C,361C,391C,447C ''relat'':424C ''rescal'':117C,287C ''resiz'':97C,141C,156C ''resolut'':106C ''result'':339C ''rotat'':243C,264C ''row'':151C ''scale'':290C ''scienc'':41C,343C ''scipi'':137C,171C ''scipy.misc.imresize'':179C ''see'':337C ''shear'':308C ''shift'':278C ''side'':120C ''simpl'':432C ''simpli'':116C ''sinc'':15C ''size'':105C,214C ''small'':178C ''special'':82C ''src'':364C,394C ''step'':436C ''stretch'':320C,323C ''strong'':86C,96C,185C,348C,376C,406C ''suggest'':78C ''taken'':61C ''techniqu'':408C ''though'':196C ''translat'':275C ''tutori'':450C ''ufldl'':449C ''ufldl.stanford.edu'':445C ''ufldl.stanford.edu/tutorial/unsupervised/pcawhitening/'':444C ''ul'':262C ''uniform'':272C,284C,298C,317C,331C ''use'':134C,170C,174C ''version'':378C ''want'':205C ''width'':148C ''winner'':37C ''would'':247C ''wouldn'':228C ''write'':48C ''write-up'':47C ''yes'':302C ''yet'':165C ''â'':314C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9229, 2, '2015-12-06 00:09:57.89', 3, NULL, NULL, '&lt;p&gt;They serve different purposes. &lt;/p&gt;

&lt;p&gt;KNN is unsupervised, Decision Tree (DT) supervised.&lt;/p&gt;

&lt;p&gt;KNN is used for clustering, DT for classification.&lt;/p&gt;

&lt;p&gt;KNN determines neighborhoods, so there must be a distance metric. This implies that all features must be numeric. Distance metrics may be effected by varying scales between attributes and also high-dimensional space.&lt;/p&gt;

&lt;p&gt;DT on the other hand predicts a class for a given input vector. The attributes may be numeric or nominal.&lt;/p&gt;

&lt;p&gt;So, if you want to find similar examples you could use KNN. If you want to classify examples you could use DT.&lt;/p&gt;
', NULL, NULL, '''/p'':6C,15C,25C,61C,83C,107C ''also'':56C ''attribut'':54C,77C ''class'':70C ''classif'':24C ''classifi'':101C ''cluster'':21C ''could'':94C,104C ''decis'':11C ''determin'':28C ''differ'':4C ''dimension'':59C ''distanc'':35C,45C ''dt'':13C,22C,63C,106C ''effect'':49C ''exampl'':92C,102C ''featur'':41C ''find'':90C ''given'':73C ''hand'':67C ''high'':58C ''high-dimension'':57C ''impli'':38C ''input'':74C ''knn'':8C,17C,27C,96C ''may'':47C,78C ''metric'':36C,46C ''must'':32C,42C ''neighborhood'':29C ''nomin'':82C ''numer'':44C,80C ''p'':1C,7C,16C,26C,62C,84C ''predict'':68C ''purpos'':5C ''scale'':52C ''serv'':3C ''similar'':91C ''space'':60C ''supervis'':14C ''tree'':12C ''unsupervis'':10C ''use'':19C,95C,105C ''vari'':51C ''vector'':75C ''want'':88C,99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9233, 1, '2015-12-06 11:41:30.75', 7, 154, '(Why) do activation functions have to be monotonic?', '&lt;p&gt;I am currently preparing for an exam in neural networks. In several protocols from former exams I read that activation functions of neurons (in multilayer perceptrons) have to be monotonic.&lt;/p&gt;

&lt;p&gt;I understand that activation functions should be differentiable, have a derivative which is not 0 on most points and non-linear. I do not undertand why being monotonic is important / helpful.&lt;/p&gt;

&lt;p&gt;I know the following activation functions and that they are monotonic:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;ReLU&lt;/li&gt;
&lt;li&gt;Sigmoid&lt;/li&gt;
&lt;li&gt;Tanh&lt;/li&gt;
&lt;li&gt;Softmax&lt;/li&gt;
&lt;li&gt;Softplus&lt;/li&gt;
&lt;li&gt;(Identity)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;However, I still can''t see any reason why for example $\varphi(x) = x^2$.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;(Why) do activation functions have to be monotonic?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;(Related side question: Is there any reason why the logarithm / exponential function is not used as an activation function?)&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/li'':95C,98C,101C,104C,107C,110C ''/p'':44C,78C,91C,128C,140C,161C ''/strong'':139C ''/ul'':111C ''0'':60C ''2'':127C ''activ'':3A,33C,49C,84C,133C,159C ''current'':16C ''deriv'':56C ''differenti'':53C ''exam'':20C,29C ''exampl'':123C ''exponenti'':152C ''follow'':83C ''former'':28C ''function'':4A,34C,50C,85C,134C,153C,160C ''help'':77C ''howev'':113C ''ident'':109C ''import'':76C ''know'':81C ''learn'':11B ''li'':93C,96C,99C,102C,105C,108C ''linear'':67C ''logarithm'':151C ''machin'':10B ''machine-learn'':9B ''monoton'':8A,43C,74C,90C,138C ''multilay'':38C ''network'':23C ''neural'':22C ''neuralnetwork'':12B ''neuron'':36C ''non'':66C ''non-linear'':65C ''p'':13C,45C,79C,112C,129C,141C ''perceptron'':39C ''point'':63C ''prepar'':17C ''protocol'':26C ''question'':144C ''read'':31C ''reason'':120C,148C ''relat'':142C ''relu'':94C ''see'':118C ''sever'':25C ''side'':143C ''sigmoid'':97C ''softmax'':103C ''softplus'':106C ''still'':115C ''strong'':130C ''tanh'':100C ''ul'':92C ''understand'':47C ''undertand'':71C ''use'':156C ''varphi'':124C ''x'':125C,126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9244, 1, '2015-12-07 07:53:11.75', 2, 12, 'GridSearch returns different result than metrics.precision_score', '&lt;p&gt;I have a quite simple text classification setup where i need to optimize the precision score. I use scikit-learn with a LinearSVC and a TfidfVectorizer. To find the optimal parameters, i use a GridSearchCV as in the &lt;a href="http://scikit-learn.org/stable/auto_examples/model_selection/grid_search_digits.html" rel="nofollow" title="scikit-learn example"&gt;scikit-learn example&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;My data set consists of 3400 text samples, from which 450 are labeled as 1. Therefore, i set the class_weight parameter of the SVM to ''auto'', as is suggested in the &lt;a href="http://scikit-learn.org/0.15/modules/generated/sklearn.svm.LinearSVC.html" rel="nofollow"&gt;documentation&lt;/a&gt; (it has been renamed to ''balanced'' in the latest version of scikit).&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;training = load_training_data(some_file.json)

d_train = training[''data'']
d_test = training[''target'']

x_train, x_test, y_train, y_test = train_test_split(
    d_train, d_test, test_size=0.33)

vectorizer = TfidfVectorizer()

X_train = vectorizer.fit_transform(x_train)
X_test = vectorizer.transform(x_test)

param_grid = {
    ''C'': [0.01, 0.1, 1, 10, 100, 1000],
}

grid = GridSearchCV(
    LinearSVC(class_weight=''auto''),
    param_grid=param_grid,
    scoring=''precision'',
    cv=5
)

grid.fit(X_train, y_train)
pred = grid.predict(X_test)

print(grid.best_score_)                      # returns 0.829
print(metrics.precision_score(y_test, pred)) # returns 0.768
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;now from my understanding, shouldn''t the last 2 values be the same? shouldn''t grid.best_score_ return the best precision found and that should be equal to the precision_score calculated by the metrics module? The values actually differ quite a bit and i am still trying to figure out why.&lt;/p&gt;

&lt;p&gt;I have asked this question on &lt;a href="http://stats.stackexchange.com/questions/184658/gridsearch-returns-different-result-than-metrics-precision-score"&gt;CrossValidated&lt;/a&gt;, but it doesn''t seem to be as active as this subsite.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9387, 2, '2015-12-15 01:43:06.89', 2, NULL, NULL, '&lt;p&gt;Actually, it shouldn''t really matter what classification algorithm you use. The whole point of machine learning is that the algorithm learns how to combine the available features to achieve the desired result. If one feature has the ability to ''turn the others off,'' the algorithm will learn that (It''ll also learn lots of things that you probably aren''t aware of). &lt;/p&gt;

&lt;p&gt;So in short, no, modifying the data this way probably won''t affect classification performance. Not needing to incorporate these kinds of things into the training set is part of what makes machine learning so cool!&lt;/p&gt;
', NULL, NULL, '''/p'':65C,103C ''abil'':40C ''achiev'':31C ''actual'':2C ''affect'':79C ''algorithm'':10C,22C,47C ''also'':53C ''aren'':61C ''avail'':28C ''awar'':63C ''classif'':9C,80C ''combin'':26C ''cool'':102C ''data'':73C ''desir'':33C ''featur'':29C,37C ''incorpor'':85C ''kind'':87C ''learn'':18C,23C,49C,54C,100C ''ll'':52C ''lot'':55C ''machin'':17C,99C ''make'':98C ''matter'':7C ''modifi'':71C ''need'':83C ''one'':36C ''other'':44C ''p'':1C,66C ''part'':95C ''perform'':81C ''point'':15C ''probabl'':60C,76C ''realli'':6C ''result'':34C ''set'':93C ''short'':69C ''shouldn'':4C ''thing'':57C,89C ''train'':92C ''turn'':42C ''use'':12C ''way'':75C ''whole'':14C ''won'':77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9235, 2, '2015-12-06 12:17:40.473', 1, NULL, NULL, '&lt;h2&gt;SOMs&lt;/h2&gt;

&lt;p&gt;Self-organizing maps (SOMs) are one specific type of neural networks. In contrast to multilayer perceptrons (MLPs; they are used much more often) the SOMs neurons have a position on a regular grid.&lt;/p&gt;

&lt;h2&gt;Training of SOMs&lt;/h2&gt;

&lt;p&gt;SOMs are usually trained in a stochastic way (source: [1]). This means one training example is used at a time. After every single training example the network learns something.&lt;/p&gt;

&lt;p&gt;In contrast, batch learning means the network is presented the complete batch of all training-examples (or a big part of usually 128 examples; then you call it mini-batch). The parameters are only updated after all of the examples were presented to the network.&lt;/p&gt;

&lt;p&gt;In contrast to MLPs, you don''t apply gradient descent (as far as I know; I''ve never used them, but only read about SOMs). I wouldn''t even know to which function you would apply the gradient descent algorithm, because there is no error function in SOMs. (At least no standard error function).&lt;/p&gt;

&lt;p&gt;For MLPs, I know that the advantage of batch training compared to stochastic training is that the weights are not jumping as much. So you rather go in the right direction. However, the advantage of stochastic training is that you make updates much more often. You want to get the best compromise, so you make mini-batch training.&lt;/p&gt;

&lt;h2&gt;Online Learning&lt;/h2&gt;

&lt;p&gt;"Online" has quite a lot of different meanings:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;"online" as in "in the internet".&lt;/li&gt;
&lt;li&gt;"online" as in "&lt;a href="https://en.wikipedia.org/wiki/Online_algorithm" rel="nofollow"&gt;online algorithms&lt;/a&gt;" which get input piece-by-piece and have to make a decision before knowing all of the input (e.g. when you have severe time constraints or when you can''t store all the data like in CERN where you have to throw most of the data away right after you get it)&lt;/li&gt;
&lt;li&gt;"online" in handwriting recognition in contrast to the offline OCR methods (see &lt;a href="http://arxiv.org/abs/1511.09030" rel="nofollow"&gt;On-line Recognition of Handwritten Mathematical Symbols&lt;/a&gt; if you''re interested). Online methods use the way how symbols were written, offline methods only use the image.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;According to &lt;a href="https://en.wikipedia.org/wiki/Online_machine_learning" rel="nofollow"&gt;Wikipedia&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Online machine learning is used when data becomes available in a sequential order to determine a mapping from data set corresponding labels. The difference between online learning and batch learning (or "offline" learning) techniques, is that in online learning the mapping is updated after the arrival of every new data point in a scale fashion, whereas batch techniques are used when one has access to the entire training data set at once.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;h2&gt;Sources&lt;/h2&gt;

&lt;p&gt;[1] Andries P. Engelbrecht: Computational Intelligence: An Introduction. Page 62.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9236, 1, '2015-12-06 17:35:35.983', 3, 40, 'How to draw a hyperplane using the weights calculated', '&lt;p&gt;In the simple example where I have &lt;strong&gt;n&lt;/strong&gt; input neurons, I can consider this to be a point in a n-dimensional space.&lt;/p&gt;

&lt;p&gt;If the output layer is just one neuron with value &lt;strong&gt;0&lt;/strong&gt; or &lt;strong&gt;1&lt;/strong&gt;, if I get convergence, the neural net should define a hyperplane dividing the two classes of points - those mapped to &lt;strong&gt;0&lt;/strong&gt; and those mapped to &lt;strong&gt;1&lt;/strong&gt;. How do I calculate this hyperplane using the &lt;strong&gt;(n,1)&lt;/strong&gt; matrix of weights I calculated?&lt;/p&gt;
', 'neuralnetwork', NULL, '''/p'':38C,104C ''/strong'':21C,52C,56C,79C,86C,98C ''0'':51C,78C ''1'':55C,85C,97C ''calcul'':9A,90C,103C ''class'':71C ''consid'':26C ''converg'':60C ''defin'':65C ''dimension'':36C ''divid'':68C ''draw'':3A ''exampl'':15C ''get'':59C ''hyperplan'':5A,67C,92C ''input'':22C ''layer'':43C ''map'':75C,82C ''matrix'':99C ''n'':20C,35C,96C ''n-dimension'':34C ''net'':63C ''neural'':62C ''neuralnetwork'':10B ''neuron'':23C,47C ''one'':46C ''output'':42C ''p'':11C,39C ''point'':31C,73C ''simpl'':14C ''space'':37C ''strong'':19C,50C,54C,77C,84C,95C ''two'':70C ''use'':6A,93C ''valu'':49C ''weight'':8A,101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9237, 2, '2015-12-06 18:23:15.39', 6, NULL, NULL, '&lt;p&gt;The monotonicity criterion helps the neural network to converge easier into an more accurate classifier. 
See this &lt;a href="http://cs.stackexchange.com/questions/45281/should-activation-function-be-monotonic-in-neural-networks"&gt;stackexchange answer&lt;/a&gt; and &lt;a href="https://en.wikipedia.org/wiki/Activation_function"&gt;wikipedia article&lt;/a&gt; for further details and reasons.&lt;/p&gt;

&lt;p&gt;However, the monotonicity criterion is not mandatory for an activation function - It is also possible to train neural nets with non-monotonic activation functions. It just gets harder to optimize the neural network. 
See &lt;a href="https://www.quora.com/Can-non-monotonic-activation-function-neural-networks-be-trained-using-the-same-backpropagation-algorithm-as-monotonic-activation-function-neural-networks"&gt;Yoshua Bengio''s answer&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':26C,35C,87C ''/can-non-monotonic-activation-function-neural-networks-be-trained-using-the-same-backpropagation-algorithm-as-monotonic-activation-function-neural-networks'':82C ''/p'':41C,88C ''/questions/45281/should-activation-function-be-monotonic-in-neural-networks'':23C ''/wiki/activation_function'':32C ''accur'':15C ''activ'':52C,66C ''also'':56C ''answer'':25C,86C ''articl'':34C ''bengio'':84C ''classifi'':16C ''converg'':10C ''criterion'':4C,46C ''cs.stackexchange.com'':22C ''cs.stackexchange.com/questions/45281/should-activation-function-be-monotonic-in-neural-networks'':21C ''detail'':38C ''easier'':11C ''en.wikipedia.org'':31C ''en.wikipedia.org/wiki/activation_function'':30C ''function'':53C,67C ''get'':70C ''harder'':71C ''help'':5C ''howev'':43C ''href'':20C,29C,79C ''mandatori'':49C ''monoton'':3C,45C,65C ''net'':61C ''network'':8C,76C ''neural'':7C,60C,75C ''non'':64C ''non-monoton'':63C ''optim'':73C ''p'':1C,42C ''possibl'':57C ''reason'':40C ''see'':17C,77C ''stackexchang'':24C ''train'':59C ''wikipedia'':33C ''www.quora.com'':81C ''www.quora.com/can-non-monotonic-activation-function-neural-networks-be-trained-using-the-same-backpropagation-algorithm-as-monotonic-activation-function-neural-networks'':80C ''yoshua'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9238, 2, '2015-12-06 20:48:55.103', 3, NULL, NULL, '&lt;p&gt;I think a better to way to think about a neural net as you have it written is that it is a function.&lt;/p&gt;

&lt;p&gt;$$f_{nn} : \mathbb{R}^n \rightarrow \{0, 1\}$$&lt;/p&gt;

&lt;p&gt;which is generally a non-linear decision boundary. You did not indicate your network structure and activation function. If your data is linearly separable then a single layer perceptron will define weights for you in the input layer, plus a bias input node for the intercept. &lt;/p&gt;

&lt;p&gt;If you specifically want a separating hyperplane you could consider support vector machines. See here.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stackoverflow.com/a/8021219/431895"&gt;http://stackoverflow.com/a/8021219/431895&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a/8021219/431895'':107C ''/a/8021219/431895&lt;/a&gt;&lt;/p&gt;'':110C ''/p'':25C,35C,84C,101C ''0'':33C ''1'':34C ''activ'':54C ''better'':5C ''bias'':78C ''boundari'':45C ''consid'':95C ''could'':94C ''data'':58C ''decis'':44C ''defin'':68C ''f'':27C ''function'':24C,55C ''general'':39C ''href'':104C ''hyperplan'':92C ''indic'':49C ''input'':74C,79C ''intercept'':83C ''layer'':65C,75C ''linear'':43C,60C ''machin'':98C ''mathbb'':29C ''n'':31C ''net'':13C ''network'':51C ''neural'':12C ''nn'':28C ''node'':80C ''non'':42C ''non-linear'':41C ''p'':1C,26C,36C,85C,102C ''perceptron'':66C ''plus'':76C ''r'':30C ''rightarrow'':32C ''see'':99C ''separ'':61C,91C ''singl'':64C ''specif'':88C ''stackoverflow.com'':106C,109C ''stackoverflow.com/a/8021219/431895'':105C ''stackoverflow.com/a/8021219/431895&lt;/a&gt;&lt;/p&gt;'':108C ''structur'':52C ''support'':96C ''think'':3C,9C ''vector'':97C ''want'':89C ''way'':7C ''weight'':69C ''written'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9245, 1, '2015-12-07 11:08:27.253', 1, 12, 'which model for ordinal scaled dependent variable', '&lt;p&gt;I want to use several models to find a relation btw. life satisfaction and several independent variables like children, unemployment, marriage and so on.&lt;/p&gt;

&lt;p&gt;My dependent var has an ordinal scale [1:10] and the independent vars have nominal and ordinal scales but no metric ones.&lt;/p&gt;

&lt;p&gt;I would set up an ordered model like an ordered probit, but I do not know what else I could use. Could you give me some hints?&lt;/p&gt;
', 'data-mining', NULL, '''/p'':36C,60C,89C ''1'':45C ''10'':46C ''btw'':22C ''children'':30C ''could'':81C,83C ''data'':9B ''data-min'':8B ''depend'':6A,39C ''els'':79C ''find'':19C ''give'':85C ''hint'':88C ''independ'':27C,49C ''know'':77C ''life'':23C ''like'':29C,69C ''marriag'':32C ''metric'':58C ''mine'':10B ''model'':2A,17C,68C ''nomin'':52C ''one'':59C ''order'':67C,71C ''ordin'':4A,43C,54C ''p'':11C,37C,61C ''probit'':72C ''relat'':21C ''satisfact'':24C ''scale'':5A,44C,55C ''set'':64C ''sever'':16C,26C ''unemploy'':31C ''use'':15C,82C ''var'':40C,50C ''variabl'':7A,28C ''want'':13C ''would'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9240, 2, '2015-12-07 01:10:34.357', 2, NULL, NULL, '&lt;p&gt;&lt;a href="https://github.com/NervanaSystems/neon" rel="nofollow"&gt;neon&lt;/a&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Python based&lt;/li&gt;
&lt;li&gt;Open source (Apache 2.0 License)&lt;/li&gt;
&lt;li&gt;Developed by &lt;a href="http://www.nervanasys.com/" rel="nofollow"&gt;Nervana Systems&lt;/a&gt; (if you don''t know them: &lt;a href="http://venturebeat.com/2015/06/04/deep-learning-startup-nervana-raises-20-5m/" rel="nofollow"&gt;Deep learning startup Nervana raises $20.5M&lt;/a&gt;;  &lt;a href="https://gigaom.com/2014/08/21/nervana-systems-raises-3-3m-to-build-hardware-designed-for-deep-learning/" rel="nofollow"&gt;Nervana Systems raises 3.3M USD to build hardware designed for deep learning&lt;/a&gt; )&lt;/li&gt;
&lt;li&gt;Supports CPU and GPU &lt;/li&gt;
&lt;li&gt;Basic automatic differentiation support&lt;/li&gt;
&lt;li&gt;Framework for visualization&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/2014/08/21/nervana-systems-raises-3-3m-to-build-hardware-designed-for-deep-learning/'':60C ''/2015/06/04/deep-learning-startup-nervana-raises-20-5m/'':45C ''/a'':10C,34C,55C,76C ''/li'':16C,23C,77C,83C,89C,94C ''/nervanasystems/neon'':6C ''/p'':11C ''/ul'':95C ''2.0'':21C ''20.5'':53C ''3.3'':66C ''apach'':20C ''automat'':86C ''base'':15C ''basic'':85C ''build'':70C ''cpu'':80C ''deep'':48C,74C ''design'':72C ''develop'':25C ''differenti'':87C ''framework'':91C ''gigaom.com'':59C ''gigaom.com/2014/08/21/nervana-systems-raises-3-3m-to-build-hardware-designed-for-deep-learning/'':58C ''github.com'':5C ''github.com/nervanasystems/neon'':4C ''gpu'':82C ''hardwar'':71C ''href'':3C,28C,42C,57C ''know'':39C ''learn'':49C,75C ''li'':13C,17C,24C,78C,84C,90C ''licens'':22C ''m'':54C,67C ''neon'':9C ''nervana'':32C,51C,63C ''nofollow'':8C,31C,47C,62C ''open'':18C ''p'':1C ''python'':14C ''rais'':52C,65C ''rel'':7C,30C,46C,61C ''sourc'':19C ''startup'':50C ''support'':79C,88C ''system'':33C,64C ''ul'':12C ''usd'':68C ''venturebeat.com'':44C ''venturebeat.com/2015/06/04/deep-learning-startup-nervana-raises-20-5m/'':43C ''visual'':93C ''www.nervanasys.com'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9247, 1, '2015-12-07 12:10:37.997', 1, 17, 'Convergence line for oscillating trend', '&lt;p&gt;I am working with an algorithm studying the response of a system to an external factor (Phenomenon A). The system can either respond perfectly, or can be completely disrupted.&lt;/p&gt;

&lt;p&gt;I am plotting the system''s response in the following graph, which shows kind of an &lt;code&gt;oscillating trend&lt;/code&gt;. High &lt;code&gt;y&lt;/code&gt; values imply poor responses (upper limit: &lt;code&gt;100&lt;/code&gt;, &lt;code&gt;total collapse&lt;/code&gt;), whereas low &lt;code&gt;y&lt;/code&gt; values mean very good responses (lower limit: &lt;code&gt;0&lt;/code&gt;, &lt;code&gt;no influence&lt;/code&gt; whatsoever).&lt;/p&gt;

&lt;p&gt;I am running this experiment based on an arbitrary number of simulations - in this case, 1000.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;My question:&lt;/strong&gt; is there a metric which can help me identify the most appropriate number of simulations? I was thinking of something which could associate an optimal number of runs with a reduction in oscillation (&lt;code&gt;convergence&lt;/code&gt;). What metric/formula could be useful here?&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/nwxgI.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/nwxgI.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'predictive-modeling  simulation', NULL, '''/a'':184C ''/code'':61C,65C,74C,78C,83C,93C,97C,158C ''/nwxgi.png'':171C,178C ''/p'':40C,99C,117C,165C,185C ''/strong'':122C ''0'':92C ''100'':73C ''1000'':116C ''algorithm'':16C ''alt'':179C ''appropri'':134C ''arbitrari'':109C ''associ'':145C ''base'':106C ''case'':115C ''code'':58C,63C,72C,75C,81C,91C,94C,156C ''collaps'':77C ''complet'':38C ''converg'':1A,157C ''could'':144C,161C ''descript'':182C ''disrupt'':39C ''either'':32C ''enter'':180C ''experi'':105C ''extern'':25C ''factor'':26C ''follow'':51C ''good'':87C ''graph'':52C ''help'':129C ''high'':62C ''href'':168C ''i.stack.imgur.com'':170C,177C ''i.stack.imgur.com/nwxgi.png'':169C,176C ''identifi'':131C ''imag'':181C ''img'':174C ''impli'':67C ''influenc'':96C ''kind'':55C ''limit'':71C,90C ''line'':2A ''low'':80C ''lower'':89C ''mean'':85C ''metric'':126C ''metric/formula'':160C ''model'':8B ''nofollow'':173C ''number'':110C,135C,148C ''optim'':147C ''oscil'':4A,59C,155C ''p'':10C,41C,100C,118C,166C ''perfect'':34C ''phenomenon'':27C ''plot'':44C ''poor'':68C ''predict'':7B ''predictive-model'':6B ''question'':121C ''reduct'':153C ''rel'':172C ''respond'':33C ''respons'':19C,48C,69C,88C ''run'':103C,150C ''show'':54C ''simul'':9B,112C,137C ''someth'':142C ''src'':175C ''strong'':119C ''studi'':17C ''system'':22C,30C,46C ''think'':140C ''total'':76C ''trend'':5A,60C ''upper'':70C ''use'':163C ''valu'':66C,84C ''whatsoev'':98C ''wherea'':79C ''work'':13C ''y'':64C,82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9242, 2, '2015-12-07 06:16:39.233', 0, NULL, NULL, '&lt;p&gt;Note: This is my question, and I''m really thankful for the wonderful answers here, which helped me grasp the concept. &lt;/p&gt;

&lt;p&gt;So, I would explain the concept the way which I have understood:&lt;/p&gt;

&lt;p&gt;Generally, the data in the databases are stored in the memory in the following formats:&lt;/p&gt;

&lt;p&gt;Consider this datum:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;           X1         X2
1   0.7091409 -1.4061361
2  -1.1334614 -0.1973846
3   2.3343391 -0.4385071
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In a relational row-based store, it is stored like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1, 0.7091409, -1.4061361, 2, -1.1334614, -0.1973846, 3, 2.3343391, -0.4385071 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;in the form of rows.&lt;/p&gt;

&lt;p&gt;In the columnar store, it would be stored like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1, 2, 3, 0.7091409 ,-1.1334614, 2.3343391, -1.4061361, -0.1973846, -0.4385071
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;in the form of columns.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;So, what does this mean?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This means that insertion(and updating) and deletions are fast in the row-based column store as it is just removal of the last few values or the first few values.  However, it is not the case in columnar stores as the value in each block store needs to be removed.&lt;/p&gt;

&lt;p&gt;However, when there is the need for columnar aggregates and operations, the columnar stores have an edge over their row-based counterparts, as they are stored column-wise, and as a result, accessing individual columns is very easy.&lt;/p&gt;
', NULL, NULL, '''-0.1973846'':69C,96C,130C ''-0.4385071'':72C,99C,131C ''-1.1334614'':68C,95C,127C ''-1.4061361'':66C,93C,129C ''/code'':73C,100C,132C ''/p'':23C,37C,54C,59C,88C,108C,120C,140C,149C,203C,245C ''/pre'':74C,101C,133C ''/strong'':148C ''0.7091409'':65C,92C,126C ''1'':64C,91C,123C ''2'':67C,94C,124C ''2.3343391'':71C,98C,128C ''3'':70C,97C,125C ''access'':239C ''aggreg'':213C ''answer'':15C ''base'':81C,165C,226C ''block'':197C ''case'':188C ''code'':61C,90C,122C ''column'':139C,166C,233C,241C ''column-wis'':232C ''columnar'':112C,190C,212C,217C ''concept'':22C,30C ''consid'':56C ''counterpart'':227C ''data'':41C ''databas'':44C ''datum'':58C ''delet'':158C ''easi'':244C ''edg'':221C ''explain'':28C ''fast'':160C ''first'':180C ''follow'':52C ''form'':105C,137C ''format'':53C ''general'':39C ''grasp'':20C ''help'':18C ''howev'':183C,205C ''individu'':240C ''insert'':154C ''last'':175C ''like'':86C,118C ''m'':9C ''mean'':147C,152C ''memori'':49C ''need'':199C,210C ''note'':2C ''oper'':215C ''p'':1C,24C,38C,55C,75C,102C,109C,134C,141C,150C,204C ''pre'':60C,89C,121C ''question'':6C ''realli'':10C ''relat'':78C ''remov'':172C,202C ''result'':238C ''row'':80C,107C,164C,225C ''row-bas'':79C,163C,224C ''store'':46C,82C,85C,113C,117C,167C,191C,198C,218C,231C ''strong'':142C ''thank'':11C ''understood'':36C ''updat'':156C ''valu'':177C,182C,194C ''way'':32C ''wise'':234C ''wonder'':14C ''would'':27C,115C ''x1'':62C ''x2'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9243, 2, '2015-12-07 07:08:39.933', 2, NULL, NULL, '&lt;p&gt;Your problem belongs to the framework of &lt;a href="https://en.wikipedia.org/wiki/One-class_classification" rel="nofollow"&gt;PU learning&lt;/a&gt; (only positives, a lot of unlabelled).&lt;/p&gt;

&lt;p&gt;It is also close to the more common frameworks of  &lt;a href="https://en.wikipedia.org/wiki/Semi-supervised_learning" rel="nofollow"&gt;Semi supervised learning&lt;/a&gt; (few positives and negatives, a lot of unlabeled).&lt;/p&gt;

&lt;p&gt;There are many survey papers that you can look up on the field.&lt;/p&gt;

&lt;p&gt;A classical method in the field, that was also tested on spam as in your case, is &lt;a href="https://en.wikipedia.org/wiki/Co-training" rel="nofollow"&gt;co-training&lt;/a&gt;
In co training you build two independent learners (e.g, one based on the mail content and one based on the sending scheme) and you use the results of one of the to train the other and vice versa.&lt;/p&gt;
', NULL, NULL, '''/a'':18C,47C,100C ''/p'':25C,56C,71C,139C ''/wiki/co-training'':94C ''/wiki/one-class_classification'':13C ''/wiki/semi-supervised_learning'':41C ''also'':29C,81C ''base'':111C,118C ''belong'':4C ''build'':105C ''case'':88C ''classic'':74C ''close'':30C ''co'':98C,102C ''co-train'':97C ''common'':34C ''content'':115C ''e.g'':109C ''en.wikipedia.org'':12C,40C,93C ''en.wikipedia.org/wiki/co-training'':92C ''en.wikipedia.org/wiki/one-class_classification'':11C ''en.wikipedia.org/wiki/semi-supervised_learning'':39C ''field'':70C,78C ''framework'':7C,35C ''href'':10C,38C,91C ''independ'':107C ''learn'':17C,46C ''learner'':108C ''look'':66C ''lot'':22C,53C ''mail'':114C ''mani'':60C ''method'':75C ''negat'':51C ''nofollow'':15C,43C,96C ''one'':110C,117C,129C ''p'':1C,26C,57C,72C ''paper'':62C ''posit'':20C,49C ''problem'':3C ''pu'':16C ''rel'':14C,42C,95C ''result'':127C ''scheme'':122C ''semi'':44C ''send'':121C ''spam'':84C ''supervis'':45C ''survey'':61C ''test'':82C ''train'':99C,103C,133C ''two'':106C ''unlabel'':24C,55C ''use'':125C ''versa'':138C ''vice'':137C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9246, 1, '2015-12-07 12:02:31.137', 0, 58, 'Error using Machine Learning Functions "fitcsvm" and "fitcecoc" in Matlab', '&lt;p&gt;I''m trying to devise a Machine Learning system in Matlab. I have collected and preprocessed the data (features and variables). I have created a 270x17 Table where columns(1-16) have the features and column 17 consists of a categorical array. There are 9 classes (each class = 30 rows). But when I try to Input this data into the "fitcecoc" function in Matlab (2015) to train a model, the following error occurs.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Error using
classreg.learning.FullClassificationRegressionModel.prepareDataCR
(line 129)
X must be a numeric matrix.

Error in
classreg.learning.classif.FullClassificationModel.prepareData (line
455)
            [X,Y,W,dataSummary] = ...

Error in classreg.learning.FitTemplate/fit (line 205)
                [X,Y,dataPrepOut{1:this.NDataPrepOut}] = ...

Error in fitcecoc (line 224)
obj = fit(temp,X,Y);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have even tried the "fitcsvm" and a siimilar error occurs. 
I have solved the problem by converting the table to simple matrix and using a separate variable to hold the "Categorical" labels. (But this is very cumbersome as I have to train a lot of models and have to change a lot of code.)&lt;br&gt;
But, according to the documentation, "fitcecoc" and "fitcsvm" are perfectly capable of handling "table" datatypes.
&lt;a href="http://www.mathworks.com/help/stats/fitcecoc.html?refresh=true#inputarg_tbl" rel="nofollow"&gt;http://www.mathworks.com/help/stats/fitcecoc.html?refresh=true#inputarg_tbl&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;What am I doing wrong?&lt;/p&gt;

&lt;p&gt;P.S. I''ve searched Mathworks'' answer pages a lot for a few days to no avail. Please help if you know the answer. &lt;/p&gt;
', 'machine-learning  dataset  matlab', NULL, '''-16'':47C ''/code'':135C ''/fit'':117C ''/help/stats/fitcecoc.html?refresh=true#inputarg_tbl'':213C ''/help/stats/fitcecoc.html?refresh=true#inputarg_tbl&lt;/a&gt;&lt;/p&gt;'':218C ''/p'':90C,225C,250C ''/pre'':136C ''1'':46C,123C ''129'':97C ''17'':53C ''2015'':81C ''205'':119C ''224'':129C ''270x17'':42C ''30'':65C ''455'':108C ''9'':61C ''accord'':195C ''answer'':232C,249C ''array'':58C ''avail'':242C ''br'':193C ''capabl'':204C ''categor'':57C,169C ''chang'':188C ''class'':62C,64C ''classreg.learning.classif.fullclassificationmodel.preparedata'':106C ''classreg.learning.fittemplate'':116C ''classreg.learning.fittemplate/fit'':115C ''classreg.learning.fullclassificationregressionmodel.preparedatacr'':95C ''code'':92C,192C ''collect'':30C ''column'':45C,52C ''consist'':54C ''convert'':155C ''creat'':40C ''cumbersom'':175C ''data'':34C,74C ''dataprepout'':122C ''dataset'':14B ''datasummari'':112C ''datatyp'':208C ''day'':239C ''devis'':21C ''document'':198C ''error'':1A,88C,93C,104C,113C,125C,147C ''even'':140C ''featur'':35C,50C ''fit'':131C ''fitcecoc'':8A,77C,127C,199C ''fitcsvm'':6A,143C,201C ''follow'':87C ''function'':5A,78C ''handl'':206C ''help'':244C ''hold'':167C ''href'':210C ''input'':72C ''know'':247C ''label'':170C ''learn'':4A,13B,24C ''line'':96C,107C,118C,128C ''lot'':182C,190C,235C ''m'':18C ''machin'':3A,12B,23C ''machine-learn'':11B ''mathwork'':231C ''matlab'':10A,15B,27C,80C ''matrix'':103C,160C ''model'':85C,184C ''must'':99C ''nofollow'':215C ''numer'':102C ''obj'':130C ''occur'':89C,148C ''p'':16C,137C,219C,226C ''p.s'':227C ''page'':233C ''perfect'':203C ''pleas'':243C ''pre'':91C ''preprocess'':32C ''problem'':153C ''rel'':214C ''row'':66C ''search'':230C ''separ'':164C ''siimilar'':146C ''simpl'':159C ''solv'':151C ''system'':25C ''tabl'':43C,157C,207C ''temp'':132C ''this.ndataprepout'':124C ''train'':83C,180C ''tri'':19C,70C,141C ''use'':2A,94C,162C ''variabl'':37C,165C ''ve'':229C ''w'':111C ''wrong'':224C ''www.mathworks.com'':212C,217C ''www.mathworks.com/help/stats/fitcecoc.html?refresh=true#inputarg_tbl'':211C ''www.mathworks.com/help/stats/fitcecoc.html?refresh=true#inputarg_tbl&lt;/a&gt;&lt;/p&gt;'':216C ''x'':98C,109C,120C,133C ''y'':110C,121C,134C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9274, 2, '2015-12-09 03:02:02.42', 0, NULL, NULL, '&lt;p&gt;For the former, gensim has the Word2Vec class. For the latter, Doc2Vec.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://rare-technologies.com/doc2vec-tutorial/" rel="nofollow"&gt;http://rare-technologies.com/doc2vec-tutorial/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/doc2vec-tutorial/'':20C ''/doc2vec-tutorial/&lt;/a&gt;&lt;/p&gt;'':25C ''/p'':14C ''class'':9C ''doc2vec'':13C ''former'':4C ''gensim'':5C ''href'':17C ''latter'':12C ''nofollow'':22C ''p'':1C,15C ''rare-technologies.com'':19C,24C ''rare-technologies.com/doc2vec-tutorial/'':18C ''rare-technologies.com/doc2vec-tutorial/&lt;/a&gt;&lt;/p&gt;'':23C ''rel'':21C ''word2vec'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9248, 1, '2015-12-07 15:40:19.207', 1, 65, 'How to find similarity/distance matrix with mixed Continuous and Categorical data?', '&lt;p&gt;Say I have a dataset like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Hotel  HasPool AvgPrice
 1        1      $123
 2        0      $234 
 3        1      $200     
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Currently I have broken down the dataset into 2 (one containing all continuous, other all categorical). The continuous one, I am calculating euclidean distance b/w all observation whereas for the categorical one I calculate Cosine distance . Is there a way to combine both scores effectively. Or, is there a distance function which works for both data types ?
My final output should look like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Hotel  1   2   3
  1    1  0.3  0.7 
  2    0.3  1  0.5 
  3    0.7  0.5 1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Any help will be appreciated! Thanks&lt;/p&gt;
', 'similarity  cosine-distance', NULL, '''/code'':39C,125C ''/p'':24C,106C,134C ''/pre'':40C,126C ''0'':34C ''0.3'':115C,118C ''0.5'':120C,123C ''0.7'':116C,122C ''1'':30C,31C,37C,110C,113C,114C,119C,124C ''123'':32C ''2'':33C,50C,111C,117C ''200'':38C ''234'':35C ''3'':36C,112C,121C ''appreci'':132C ''avgpric'':29C ''b/w'':66C ''broken'':45C ''calcul'':63C,75C ''categor'':10A,57C,72C ''code'':26C,108C ''combin'':83C ''contain'':52C ''continu'':8A,54C,59C ''cosin'':14B,76C ''cosine-dist'':13B ''current'':42C ''data'':11A,97C ''dataset'':21C,48C ''distanc'':15B,65C,77C,91C ''effect'':86C ''euclidean'':64C ''final'':100C ''find'':3A ''function'':92C ''haspool'':28C ''help'':129C ''hotel'':27C,109C ''like'':22C,104C ''look'':103C ''matrix'':5A ''mix'':7A ''observ'':68C ''one'':51C,60C,73C ''output'':101C ''p'':16C,41C,127C ''pre'':25C,107C ''say'':17C ''score'':85C ''similar'':12B ''similarity/distance'':4A ''thank'':133C ''type'':98C ''way'':81C ''wherea'':69C ''work'':94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9254, 1, '2015-12-07 18:38:50.513', 1, 28, 'What data mining methods are appropriate for knowledge discovery in vehicle trajectory data?', '&lt;h1&gt;Data&lt;/h1&gt;

&lt;p&gt;I have vehicle trajectory data (for US-101 segment in LA, California) collected for 3 time periods representing the build-up of congestion and congested traffic. These data include the Vehicle ID, Time Frame #, Longitudinal position of vehicle from a fixed starting point, lateral position of vehicle from the left edge of the road, velocity, acceleration and gap from the lead vehicle in same lane, etc. Each vehicle is recorded since it entered the study area until it exited at every 0.1 second (1 frame duration). And there are about 2000 vehicles. These data are available from &lt;a href="http://ngsim-community.org/" rel="nofollow"&gt;NGSIM project website&lt;/a&gt;.  &lt;/p&gt;

&lt;h1&gt;Goals of Modeling&lt;/h1&gt;

&lt;p&gt;I want to model the acceleration that a vehicle applies, while traveling in the same lane, as a function of other factors such as the gap to lead vehicle, current speed, difference in speed from lead vehicle, etc.&lt;br&gt;
There are some conditions that I have to take into account e.g. 1) the driver might be applying max acceleration because the distance to lead vehicle is very large, 2) driver might be subconsciously following the lead vehicle based on his sensitivity to the accelerations/deccelerations of lead vehicle, 3) driver might be approaching a slow/stopped vehicle. In each of these conditions, the driver''s behavior is expected to be different and resulting models are also different.&lt;/p&gt;

&lt;h1&gt;Appropriate algorithms&lt;/h1&gt;

&lt;p&gt;Many different ''car-following'' models exist that capture the driver behaviors and resulting accelerations. However, I want to develop a data-driven model.&lt;br&gt;
The first thing that I want to do is to identify whether a vehicle is driving free, following or approaching because the data points are not labelled with this info. I am new to data mining and don''t know what available algorithms are better suited for this task. Correlation of some kind between the trajectories of 2 vehicles? Fourier transform? Time-series segmentation?&lt;br&gt;
The second task is to model the acceleration in each driving process as a function of other variables. What kind of regression is appropriate in this setting?&lt;br&gt;
Please guide me to appropriate algorithms and resources where I could learn about them. Preferably, online courses, websites and books. &lt;/p&gt;
', 'data-mining  time-series', NULL, '''-101'':31C ''/a'':130C ''/h1'':22C,136C,257C ''/p'':131C,253C,399C ''0.1'':106C ''1'':108C,189C ''2'':206C,342C ''2000'':115C ''3'':38C,225C ''acceler'':80C,143C,196C,273C,358C ''accelerations/deccelerations'':221C ''account'':187C ''algorithm'':256C,327C,384C ''also'':251C ''appli'':147C,194C ''approach'':229C,304C ''appropri'':6A,255C,374C,383C ''area'':100C ''avail'':120C,326C ''base'':215C ''behavior'':241C,270C ''better'':329C ''book'':398C ''br'':176C,284C,350C,378C ''build'':44C ''build-up'':43C ''california'':35C ''captur'':267C ''car'':262C ''car-follow'':261C ''collect'':36C ''condit'':180C,237C ''congest'':47C,49C ''correl'':334C ''could'':389C ''cours'':395C ''current'':167C ''data'':2A,13A,15B,21C,28C,52C,118C,281C,307C,319C ''data-driven'':280C ''data-min'':14B ''develop'':278C ''differ'':169C,246C,252C,260C ''discoveri'':9A ''distanc'':199C ''drive'':300C,361C ''driven'':282C ''driver'':191C,207C,226C,239C,269C ''durat'':110C ''e.g'':188C ''edg'':75C ''enter'':97C ''etc'':90C,175C ''everi'':105C ''exist'':265C ''exit'':103C ''expect'':243C ''factor'':159C ''first'':286C ''fix'':65C ''follow'':211C,263C,302C ''fourier'':344C ''frame'':58C,109C ''free'':301C ''function'':156C,365C ''gap'':82C,163C ''goal'':133C ''guid'':380C ''h1'':20C,132C,254C ''howev'':274C ''href'':123C ''id'':56C ''identifi'':295C ''includ'':53C ''info'':314C ''kind'':337C,370C ''know'':324C ''knowledg'':8A ''la'':34C ''label'':311C ''lane'':89C,153C ''larg'':205C ''later'':68C ''lead'':85C,165C,173C,201C,213C,223C ''learn'':390C ''left'':74C ''longitudin'':59C ''mani'':259C ''max'':195C ''method'':4A ''might'':192C,208C,227C ''mine'':3A,16B,320C ''model'':135C,141C,249C,264C,283C,356C ''new'':317C ''ngsim'':127C ''ngsim-community.org'':124C ''nofollow'':126C ''onlin'':394C ''p'':23C,137C,258C ''period'':40C ''pleas'':379C ''point'':67C,308C ''posit'':60C,69C ''prefer'':393C ''process'':362C ''project'':128C ''record'':94C ''regress'':372C ''rel'':125C ''repres'':41C ''resourc'':386C ''result'':248C,272C ''road'':78C ''second'':107C,352C ''segment'':32C,349C ''sensit'':218C ''seri'':19B,348C ''set'':377C ''sinc'':95C ''slow/stopped'':231C ''speed'':168C,171C ''start'':66C ''studi'':99C ''subconsci'':210C ''suit'':330C ''take'':185C ''task'':333C,353C ''thing'':287C ''time'':18B,39C,57C,347C ''time-seri'':17B,346C ''traffic'':50C ''trajectori'':12A,27C,340C ''transform'':345C ''travel'':149C ''us'':30C ''variabl'':368C ''vehicl'':11A,26C,55C,62C,71C,86C,92C,116C,146C,166C,174C,202C,214C,224C,232C,298C,343C ''veloc'':79C ''want'':139C,276C,290C ''websit'':129C,396C ''whether'':296C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9255, 1, '2015-12-07 21:39:27.877', 2, 287, 'Creating new columns by iterating over rows in pandas dataframe', '&lt;p&gt;I have a pandas data frame (X11) like this: In actual I have 99 columns up to dx99&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    dx1      dx2    dx3    dx4
0   25041   40391   5856    0
1   25041   40391   25081   5856
2   25041   40391   42822   0
3   25061   40391   0       0
4   25041   40391   0       5856
5   40391   25002   5856    3569
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to create additional column(s) for cell values like 25041,40391,5856 etc. So there will be a column 25041 with value as 1 or 0 if 25041 occurs in that particular row in any dxs columns. I am using this code and it works when number of rows are less.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;mat = X11.as_matrix(columns=None)
values, counts = np.unique(mat.astype(str), return_counts=True)

for x in values:
    X11[x] = X11.isin([x]).any(1).astype(int)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am getting result like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dx1     dx2     dx3    dx4  0   25002   25041   25061   25081   3569    40391   42822   5856
25041   40391   5856    0   0   0       1       0       0       0          1        0       1
25041   40391   25081  5856 0   0       1       0       1       0            1      0       1
25041   40391   42822   0   0   0       1       0       0       0           1       1       0
25061   40391   0       0   0   0       0       1       0       0          1        0       0
25041   40391   0    5856   0   0       1       0       0       0          1        0       1
40391   25002 5856   3569   0   1       0       0       0       1          1        0       1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;When number of rows are many thousands or in millions, it hangs and takes forever and I am not getting any result. Please see that cell values are not unique to column, instead repeating in multi columns. For ex, 40391 is occurring in dx1 as well as in dx2 and so on for 0 and 5856 etc. Any idea how to improve the logic mentioned above?&lt;/p&gt;
', 'python  data-cleaning  pandas  anaconda', NULL, '''/code'':73C,157C,260C ''/p'':36C,129C,166C,329C ''/pre'':74C,158C,261C ''0'':43C,47C,57C,61C,62C,66C,103C,173C,185C,186C,187C,189C,190C,191C,193C,199C,200C,202C,204C,206C,211C,212C,213C,215C,216C,217C,220C,223C,224C,225C,226C,227C,229C,230C,232C,233C,236C,238C,239C,241C,242C,243C,245C,251C,253C,254C,255C,258C,316C ''1'':48C,101C,154C,188C,192C,194C,201C,203C,205C,207C,214C,218C,219C,228C,231C,240C,244C,246C,252C,256C,257C,259C ''2'':53C ''25002'':70C,174C,248C ''25041'':44C,49C,54C,64C,87C,97C,105C,175C,182C,195C,208C,234C ''25061'':59C,176C,221C ''25081'':51C,177C,197C ''3'':58C ''3569'':72C,178C,250C ''4'':63C ''40391'':45C,50C,55C,60C,65C,69C,88C,179C,183C,196C,209C,222C,235C,247C,302C ''42822'':56C,180C,210C ''5'':68C ''5856'':46C,52C,67C,71C,89C,181C,184C,198C,237C,249C,318C ''99'':31C ''actual'':28C ''addit'':80C ''anaconda'':16B ''astyp'':155C ''cell'':84C,288C ''clean'':14B ''code'':38C,119C,131C,168C ''column'':3A,32C,81C,96C,114C,135C,294C,299C ''count'':138C,143C ''creat'':1A,79C ''data'':13B,22C ''data-clean'':12B ''datafram'':10A ''dx1'':39C,169C,306C ''dx2'':40C,170C,311C ''dx3'':41C,171C ''dx4'':42C,172C ''dx99'':35C ''dxs'':113C ''etc'':90C,319C ''ex'':301C ''forev'':277C ''frame'':23C ''get'':162C,282C ''hang'':274C ''idea'':321C ''improv'':324C ''instead'':295C ''int'':156C ''iter'':5A ''less'':128C ''like'':25C,86C,164C ''logic'':326C ''mani'':268C ''mat'':132C ''mat.astype'':140C ''matrix'':134C ''mention'':327C ''million'':272C ''multi'':298C ''new'':2A ''none'':136C ''np.unique'':139C ''number'':124C,264C ''occur'':106C,304C ''p'':17C,75C,159C,262C ''panda'':9A,15B,21C ''particular'':109C ''pleas'':285C ''pre'':37C,130C,167C ''python'':11B ''repeat'':296C ''result'':163C,284C ''return'':142C ''row'':7A,110C,126C,266C ''see'':286C ''str'':141C ''take'':276C ''thousand'':269C ''true'':144C ''uniqu'':292C ''use'':117C ''valu'':85C,99C,137C,148C,289C ''want'':77C ''well'':308C ''work'':122C ''x'':146C,150C,152C ''x11'':24C,149C ''x11.as'':133C ''x11.isin'':151C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9256, 2, '2015-12-07 23:29:01.027', 1, NULL, NULL, '&lt;p&gt;Depending on the language of your choice, you may or may not have to normalise the data yourself - for example the &lt;code&gt;e1071&lt;/code&gt; package in &lt;code&gt;R&lt;/code&gt; does this automatically for you. As this is built off the ''libsvm'' library, it might be that this is also the case there - the library documentation is your best source. As for the normalisation values, you should definitely use the same [min,max] values from the &lt;strong&gt;training&lt;/strong&gt; set for the test set as well. Also, I would reccomend reducing the dimensionality of your data first, than normalising before running a svm. hth &lt;/p&gt;
', NULL, NULL, '''/code'':25C,30C ''/p'':105C ''/strong'':79C ''also'':50C,87C ''automat'':33C ''best'':59C ''built'':39C ''case'':52C ''choic'':8C ''code'':23C,28C ''data'':18C,96C ''definit'':68C ''depend'':2C ''dimension'':93C ''document'':56C ''e1071'':24C ''exampl'':21C ''first'':97C ''hth'':104C ''languag'':5C ''librari'':43C,55C ''libsvm'':42C ''max'':73C ''may'':10C,12C ''might'':45C ''min'':72C ''normalis'':16C,64C,99C ''p'':1C ''packag'':26C ''r'':29C ''reccomend'':90C ''reduc'':91C ''run'':101C ''set'':80C,84C ''sourc'':60C ''strong'':77C ''svm'':103C ''test'':83C ''train'':78C ''use'':69C ''valu'':65C,74C ''well'':86C ''would'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9259, 2, '2015-12-08 04:02:56.427', 1, NULL, NULL, '&lt;p&gt;Are your inputs &lt;a href="http://stackoverflow.com/questions/4674623/why-do-we-have-to-normalize-the-input-for-an-artificial-neural-network"&gt;scaled&lt;/a&gt;? Not doing so can cause the weights to immediately blow up. &lt;/p&gt;

&lt;p&gt;It''s common practice to preprocess your input to be between -1 and 1 or at least in that range. Otherwise you are running the risk of your gradients exploding or vanishing (covered &lt;a href="http://neuralnetworksanddeeplearning.com/chap5.html" rel="nofollow"&gt;here&lt;/a&gt;).&lt;/p&gt;
', NULL, NULL, '''-1'':36C ''/a'':11C,66C ''/chap5.html'':62C ''/p'':23C,67C ''/questions/4674623/why-do-we-have-to-normalize-the-input-for-an-artificial-neural-network'':9C ''1'':38C ''blow'':21C ''caus'':16C ''common'':27C ''cover'':57C ''explod'':54C ''gradient'':53C ''href'':6C,59C ''immedi'':20C ''input'':4C,32C ''least'':41C ''neuralnetworksanddeeplearning.com'':61C ''neuralnetworksanddeeplearning.com/chap5.html'':60C ''nofollow'':64C ''otherwis'':45C ''p'':1C,24C ''practic'':28C ''preprocess'':30C ''rang'':44C ''rel'':63C ''risk'':50C ''run'':48C ''scale'':10C ''stackoverflow.com'':8C ''stackoverflow.com/questions/4674623/why-do-we-have-to-normalize-the-input-for-an-artificial-neural-network'':7C ''vanish'':56C ''weight'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9260, 2, '2015-12-08 06:07:07.413', 6, NULL, NULL, '&lt;p&gt;If I were given the option, &lt;strong&gt;I would go with Theano&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Reasons:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;State of the art RNN implementations and API''s&lt;/strong&gt;, which TensorFlow unfortunately doesn''t possess, and &lt;a href="https://github.com/tensorflow/tensorflow/issues/7" rel="nofollow"&gt;has a long way to go&lt;/a&gt;. And in a domain where RNN''s are the trending rage, Theano has a big edge there.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Very wide range of implementations&lt;/strong&gt;. TensorFlow has a long way to catch up. A lot of recent ML models have been done with the help of Theano, so it is something like a standard when it comes to neural networks.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Optimized and &lt;a href="http://deeplearning.net/software/theano/tutorial/loop.html" rel="nofollow"&gt;improved looping&lt;/a&gt;:&lt;/strong&gt; Theano''s scan is a wonderful way for looping in neural networks, which makes use of the awesome map-reduce framework. But, I''m pretty sure TensorFlow would improve on this as its creator Jeff Dean is the daddy of Map Reduce.  However, as of now; it''s Theano&lt;/li&gt;
&lt;li&gt;Huge edge when it comes to video analytics.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;However, TensorFlow supports both the cpp and Python interfaces which might be an advantage with the cpp community. But, when it comes to ML and data science products, Python has been the standard, so it wouldn''t be a huge edge IMO.&lt;/p&gt;

&lt;p&gt;But, model deployment and ease of use in production is where TensorFlow has the real advantage. As it uses Eigen for improved and easy deployment, it would be a darling for engineers. If it gets compatible with Windows, then you''ll see a huge migration. But, I''ve got used to the Python overhead, I can wait till it gets more polished.&lt;/p&gt;

&lt;p&gt;So, Theano for now. I can happily wait for TensorFlow to catch up. &lt;/p&gt;

&lt;p&gt;If you are deploying simple to average complexity neural networks, go with Tensorflow.  If deep learning, then Theano.&lt;/p&gt;
', NULL, NULL, '''/a'':52C,128C ''/li'':70C,114C,180C,190C ''/p'':15C,18C,235C,299C,314C,334C ''/software/theano/tutorial/loop.html'':123C ''/strong'':14C,31C,78C,129C ''/tensorflow/tensorflow/issues/7'':43C ''/ul'':191C ''advantag'':206C,252C ''analyt'':189C ''api'':29C ''art'':25C ''averag'':322C ''awesom'':147C ''big'':67C ''catch'':85C,312C ''come'':110C,186C,214C ''communiti'':210C ''compat'':272C ''complex'':323C ''cpp'':198C,209C ''creator'':164C ''daddi'':169C ''darl'':266C ''data'':218C ''dean'':166C ''deep'':330C ''deeplearning.net'':122C ''deeplearning.net/software/theano/tutorial/loop.html'':121C ''deploy'':239C,261C,319C ''doesn'':35C ''domain'':56C ''done'':95C ''eas'':241C ''easi'':260C ''edg'':68C,183C,233C ''eigen'':256C ''engin'':268C ''framework'':151C ''get'':271C,296C ''github.com'':42C ''github.com/tensorflow/tensorflow/issues/7'':41C ''given'':5C ''go'':11C,51C,326C ''got'':285C ''happili'':307C ''help'':98C ''howev'':173C,193C ''href'':40C,120C ''huge'':182C,232C,280C ''imo'':234C ''implement'':27C,77C ''improv'':126C,159C,258C ''interfac'':201C ''jeff'':165C ''learn'':331C ''li'':20C,71C,115C,181C ''like'':105C ''ll'':277C ''long'':48C,82C ''loop'':127C,138C ''lot'':88C ''m'':154C ''make'':143C ''map'':149C,171C ''map-reduc'':148C ''might'':203C ''migrat'':281C ''ml'':91C,216C ''model'':92C,238C ''network'':113C,141C,325C ''neural'':112C,140C,324C ''nofollow'':45C,125C ''optim'':117C ''option'':7C ''overhead'':290C ''p'':1C,16C,192C,236C,300C,315C ''polish'':298C ''possess'':37C ''pretti'':155C ''product'':220C,245C ''python'':200C,221C,289C ''rage'':63C ''rang'':75C ''real'':251C ''reason'':17C ''recent'':90C ''reduc'':150C,172C ''rel'':44C,124C ''rnn'':26C,58C ''scan'':132C ''scienc'':219C ''see'':278C ''simpl'':320C ''someth'':104C ''standard'':107C,225C ''state'':22C ''strong'':8C,21C,72C,116C ''support'':195C ''sure'':156C ''tensorflow'':33C,79C,157C,194C,248C,310C,328C ''theano'':13C,64C,100C,130C,179C,302C,333C ''till'':294C ''trend'':62C ''ul'':19C ''unfortun'':34C ''use'':144C,243C,255C,286C ''ve'':284C ''video'':188C ''wait'':293C,308C ''way'':49C,83C,136C ''wide'':74C ''window'':274C ''wonder'':135C ''would'':10C,158C,263C ''wouldn'':228C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9261, 2, '2015-12-08 06:12:59.66', 1, NULL, NULL, '&lt;p&gt;There''s a much more pythonic solution in pandas...&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;This takes less than a second on 10 Million rows on my laptop:&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for x in X11.E.unique():
    X11[x]=(X11.E==x).astype(int)
X11
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Here are the details laid out:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Simple small dataframe - &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
import pandas as pd

X11 = pd.DataFrame(np.random.randn(6,4), columns=list(''ABCD''))
X11[''E''] = [25223, 112233,25223,14333,14333,112233]
X11
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/DBgSz.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/DBgSz.png" alt="simple small dataframe"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Binarization method - &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for x in X11.E.unique():
    X11[x]=(X11.E==x).astype(int)
X11
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/NANqV.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/NANqV.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Dataframe with 10 Million rows - &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;pd.set_option("display.max_rows",20)
X12 = pd.DataFrame(np.random.randn(10000000,4), columns=list(''ABCD''))
foo = [25223, 112233,25223,14333,14333,112233]
bar=[]
import random
for x in range(10000000):
    bar.append(random.choice(foo))
X12[''E''] = bar
X12
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/hJAAV.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/hJAAV.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Timed binarization (aka one-hot encoding) on 10 million row dataframe - &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import time
start = time.clock()

for x in X12.E.unique():
    X12[x]=(X12.E==x).astype(int)
elapsed = (time.clock() - start)

print "This is the time that this took in seconds: ",elapsed

X12
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/vFkob.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/vFkob.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/a'':105C,144C,210C,277C ''/code'':42C,86C,124C,190C,257C ''/dbgsz.png'':93C,100C ''/hjaav.png'':197C,204C ''/nanqv.png'':131C,138C ''/p'':11C,28C,53C,58C,106C,110C,145C,152C,211C,225C,278C,283C ''/pre'':43C,87C,125C,191C,258C ''/strong'':27C,52C ''/vfkob.png'':264C,271C ''10'':21C,149C,221C ''10000000'':163C,182C ''112233'':80C,84C,170C,174C ''14333'':82C,83C,172C,173C ''20'':159C ''25223'':79C,81C,169C,171C ''4'':73C,164C ''6'':72C ''abcd'':76C,167C ''aka'':215C ''alt'':101C,139C,205C,272C ''astyp'':39C,121C,240C ''bar'':175C,188C ''bar.append'':183C ''binar'':108C,214C ''code'':30C,60C,112C,154C,227C ''column'':74C,165C ''datafram'':57C,104C,147C,224C ''descript'':142C,208C,275C ''detail'':49C ''display.max'':157C ''e'':78C,187C ''elaps'':242C,255C ''encod'':219C ''enter'':140C,206C,273C ''foo'':168C,185C ''help'':282C ''hope'':280C ''hot'':218C ''href'':90C,128C,194C,261C ''i.stack.imgur.com'':92C,99C,130C,137C,196C,203C,263C,270C ''i.stack.imgur.com/dbgsz.png'':91C,98C ''i.stack.imgur.com/hjaav.png'':195C,202C ''i.stack.imgur.com/nanqv.png'':129C,136C ''i.stack.imgur.com/vfkob.png'':262C,269C ''imag'':141C,207C,274C ''img'':96C,134C,200C,267C ''import'':61C,65C,176C,228C ''int'':40C,122C,241C ''laid'':50C ''laptop'':26C ''less'':16C ''list'':75C,166C ''method'':109C ''million'':22C,150C,222C ''much'':5C ''nofollow'':95C,133C,199C,266C ''np'':64C ''np.random.randn'':71C,162C ''numpi'':62C ''one'':217C ''one-hot'':216C ''option'':156C ''p'':1C,12C,44C,54C,88C,107C,126C,146C,192C,212C,259C,279C ''panda'':10C,66C ''pd'':68C ''pd.dataframe'':70C,161C ''pd.set'':155C ''pre'':29C,59C,111C,153C,226C ''print'':245C ''python'':7C ''random'':177C ''random.choice'':184C ''rang'':181C ''rel'':94C,132C,198C,265C ''row'':23C,151C,158C,223C ''second'':19C,254C ''simpl'':55C,102C ''small'':56C,103C ''solut'':8C ''src'':97C,135C,201C,268C ''start'':230C,244C ''strong'':13C,45C ''take'':15C ''time'':213C,229C,249C ''time.clock'':231C,243C ''took'':252C ''x'':32C,36C,38C,114C,118C,120C,179C,233C,237C,239C ''x11'':35C,41C,69C,77C,85C,117C,123C ''x11.e'':37C,119C ''x11.e.unique'':34C,116C ''x12'':160C,186C,189C,236C,256C ''x12.e'':238C ''x12.e.unique'':235C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9307, 1, '2015-12-10 12:50:10.27', 1, 362, 'R: comparison (6) is possible only for atomic and list types', '&lt;p&gt;I am getting the error in the subject line for the following piece of code in R.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;r&amp;lt;- matrix(c(2,4, 1,5,5,3, 4,5,1,2,3,4), nrow=3)
for (u in 1:nrow(r))
  for (x in 1:ncol(r))
    r_hat&amp;lt;- function(u,x) {


      if (r_hat &amp;gt;r[u,x]) {
        print(r[u,x]-r_hat)
      }
    }
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;How can I get rid of the error? Thanks&lt;/p&gt;
', 'r', NULL, '''/code'':83C ''/p'':31C,95C ''/pre'':84C ''1'':40C,46C,55C,61C ''2'':38C,47C ''3'':43C,48C,51C ''4'':39C,44C,49C ''5'':41C,42C,45C ''6'':3A ''atom'':8A ''c'':37C ''code'':28C,33C ''comparison'':2A ''error'':18C,93C ''follow'':25C ''function'':67C ''get'':16C,89C ''gt'':73C ''hat'':65C,72C,82C ''line'':22C ''list'':10A ''lt'':35C,66C ''matrix'':36C ''ncol'':62C ''nrow'':50C,56C ''p'':13C,85C ''piec'':26C ''possibl'':5A ''pre'':32C ''print'':77C ''r'':1A,12B,30C,34C,57C,63C,64C,71C,74C,78C,81C ''rid'':90C ''subject'':21C ''thank'':94C ''type'':11A ''u'':53C,68C,75C,79C ''x'':59C,69C,76C,80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9262, 1, '2015-12-08 10:37:44.05', 3, 131, 'Calculating KL Divergence in Python', '&lt;p&gt;I am rather new to this and can''t say I have a complete understanding of the theoretical concepts behind this. I am trying to calculate the KL Divergence between several lists of points in Python. I am using &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html&lt;/a&gt; to try and do this. The problem that I''m running into is that the value returned is the same for any 2 lists of numbers (its 1.3862943611198906). I have a feeling that I''m making some sort of theoretical mistake here but can''t spot it.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;values1 = [1.346112,1.337432,1.246655]
values2 = [1.033836,1.082015,1.117323]
metrics.mutual_info_score(values1,values2)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;That is an example of what I''m running - just that I''m getting the same output for any 2 input. Any advice/help would be appreciated!&lt;/p&gt;
', 'python  clustering  sklearn', NULL, '''/code'':122C ''/p'':106C,151C ''/pre'':123C ''/stable/modules/generated/sklearn.metrics.mutual_info_score.html'':53C ''/stable/modules/generated/sklearn.metrics.mutual_info_score.html&lt;/a&gt;'':58C ''1.033836'':114C ''1.082015'':115C ''1.117323'':116C ''1.246655'':112C ''1.337432'':111C ''1.346112'':110C ''1.3862943611198906'':86C ''2'':81C,144C ''advice/help'':147C ''appreci'':150C ''behind'':29C ''calcul'':1A,35C ''cluster'':7B ''code'':108C ''complet'':23C ''concept'':28C ''diverg'':3A,38C ''exampl'':128C ''feel'':90C ''get'':138C ''href'':50C ''info'':118C ''input'':145C ''kl'':2A,37C ''list'':41C,82C ''m'':68C,93C,132C,137C ''make'':94C ''metrics.mutual'':117C ''mistak'':99C ''new'':13C ''nofollow'':55C ''number'':84C ''output'':141C ''p'':9C,124C ''point'':43C ''pre'':107C ''problem'':65C ''python'':5A,6B,45C ''rather'':12C ''rel'':54C ''return'':75C ''run'':69C,133C ''say'':19C ''scikit-learn.org'':52C,57C ''scikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html'':51C ''scikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html&lt;/a&gt;'':56C ''score'':119C ''sever'':40C ''sklearn'':8B ''sort'':96C ''spot'':104C ''theoret'':27C,98C ''tri'':33C,60C ''understand'':24C ''use'':48C ''valu'':74C ''values1'':109C,120C ''values2'':113C,121C ''would'':148C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9263, 2, '2015-12-08 10:38:48', 0, NULL, NULL, '&lt;p&gt;Check out this link: &lt;a href="http://blog.minitab.com/blog/adventures-in-statistics/what-is-the-f-test-of-overall-significance-in-regression-analysis" rel="nofollow"&gt;http://blog.minitab.com/blog/adventures-in-statistics/what-is-the-f-test-of-overall-significance-in-regression-analysis&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;(Sorry i don''t hve enough reputations to add this as a comment)&lt;/p&gt;
', NULL, NULL, '''/blog/adventures-in-statistics/what-is-the-f-test-of-overall-significance-in-regression-analysis'':10C ''/blog/adventures-in-statistics/what-is-the-f-test-of-overall-significance-in-regression-analysis&lt;/a&gt;&lt;/p&gt;'':15C ''/p'':30C ''add'':25C ''blog.minitab.com'':9C,14C ''blog.minitab.com/blog/adventures-in-statistics/what-is-the-f-test-of-overall-significance-in-regression-analysis'':8C ''blog.minitab.com/blog/adventures-in-statistics/what-is-the-f-test-of-overall-significance-in-regression-analysis&lt;/a&gt;&lt;/p&gt;'':13C ''check'':2C ''comment'':29C ''enough'':22C ''href'':7C ''hve'':21C ''link'':5C ''nofollow'':12C ''p'':1C,16C ''rel'':11C ''reput'':23C ''sorri'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9264, 2, '2015-12-08 10:59:33.703', 2, NULL, NULL, '&lt;p&gt;I''m not sure with the ScikitLearn implementation, but here is a quick implementation of the KL divergence in Python:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np

def KL(a, b):
    a = np.asarray(a, dtype=np.float)
    b = np.asarray(b, dtype=np.float)

    return np.sum(np.where(a != 0, a * np.log(a / b), 0))


values1 = [1.346112,1.337432,1.246655]
values2 = [1.033836,1.082015,1.117323]

print KL(values1, values2)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Output:  &lt;code&gt;0.775279624079&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;There might be &lt;a href="http://datascience.stackexchange.com/a/8429/11097"&gt;conflict of implementation&lt;/a&gt; in some libraries, so make sure you read their docs before using.&lt;/p&gt;
', NULL, NULL, '''/a'':85C ''/a/8429/11097'':81C ''/code'':65C,71C ''/p'':22C,72C,98C ''/pre'':66C ''0'':47C,52C ''0.775279624079'':70C ''1.033836'':58C ''1.082015'':59C ''1.117323'':60C ''1.246655'':56C ''1.337432'':55C ''1.346112'':54C ''b'':32C,38C,40C,51C ''code'':24C,69C ''conflict'':82C ''datascience.stackexchange.com'':80C ''datascience.stackexchange.com/a/8429/11097'':79C ''def'':29C ''diverg'':19C ''doc'':95C ''dtype'':36C,41C ''href'':78C ''implement'':9C,15C,84C ''import'':25C ''kl'':18C,30C,62C ''librari'':88C ''m'':3C ''make'':90C ''might'':75C ''np'':28C ''np.asarray'':34C,39C ''np.float'':37C,42C ''np.log'':49C ''np.sum'':44C ''np.where'':45C ''numpi'':26C ''output'':68C ''p'':1C,67C,73C ''pre'':23C ''print'':61C ''python'':21C ''quick'':14C ''read'':93C ''return'':43C ''scikitlearn'':8C ''sure'':5C,91C ''use'':97C ''values1'':53C,63C ''values2'':57C,64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9265, 2, '2015-12-08 13:19:10.36', 3, NULL, NULL, '&lt;p&gt;Scipy''s &lt;a href="http://scipy.github.io/devdocs/generated/scipy.stats.entropy.html" rel="nofollow"&gt;entropy function&lt;/a&gt; will calculate KL divergence if feed two vectors p and q, each representing a probability distribution. If the two vectors aren''t pdfs, it will normalize then first.&lt;/p&gt;

&lt;p&gt;Mutual information is &lt;a href="https://en.wikipedia.org/wiki/Mutual_information" rel="nofollow"&gt;related to, but not the same&lt;/a&gt; as KL Divergence.&lt;/p&gt;

&lt;p&gt;"This weighted mutual information is a form of weighted KL-Divergence, which is known to take negative values for some inputs, and there are examples where the weighted mutual information also takes negative values"&lt;/p&gt;
', NULL, NULL, '''/a'':13C,60C ''/devdocs/generated/scipy.stats.entropy.html'':8C ''/p'':42C,64C,101C ''/wiki/mutual_information'':51C ''also'':97C ''aren'':34C ''calcul'':15C ''distribut'':29C ''diverg'':17C,63C,77C ''en.wikipedia.org'':50C ''en.wikipedia.org/wiki/mutual_information'':49C ''entropi'':11C ''exampl'':91C ''feed'':19C ''first'':41C ''form'':72C ''function'':12C ''href'':5C,48C ''inform'':45C,69C,96C ''input'':87C ''kl'':16C,62C,76C ''kl-diverg'':75C ''known'':80C ''mutual'':44C,68C,95C ''negat'':83C,99C ''nofollow'':10C,53C ''normal'':39C ''p'':1C,22C,43C,65C ''pdfs'':36C ''probabl'':28C ''q'':24C ''rel'':9C,52C ''relat'':54C ''repres'':26C ''scipi'':2C ''scipy.github.io'':7C ''scipy.github.io/devdocs/generated/scipy.stats.entropy.html'':6C ''take'':82C,98C ''two'':20C,32C ''valu'':84C,100C ''vector'':21C,33C ''weight'':67C,74C,94C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9266, 1, '2015-12-08 14:12:05.66', 2, 78, 'Theano logistic regression example', '&lt;p&gt;I am trying to understand some simple neural net case using theano.
The deeplearning.net site gives the following simple code for implementing a logistic regression application to a simple case:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy
import theano
import theano.tensor as T
rng = numpy.random

N = 400
feats = 784
D = (rng.randn(N, feats), rng.randint(size=N, low=0, high=2))
training_steps = 10000

# Declare Theano symbolic variables
x = T.matrix("x")
y = T.vector("y")
w = theano.shared(rng.randn(feats), name="w")
b = theano.shared(0., name="b")
print("Initial model:")
print(w.get_value())
print(b.get_value())

# Construct Theano expression graph
p_1 = 1 / (1 + T.exp(-T.dot(x, w) - b))   # Probability that target = 1
prediction = p_1 &amp;gt; 0.5                    # The prediction thresholded
xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1) # Cross-entropy loss function
cost = xent.mean() + 0.01 * (w ** 2).sum()# The cost to minimize
gw, gb = T.grad(cost, [w, b])             # Compute the gradient of the cost
                                          # (we shall return to this in a
                                          # following section of this tutorial)

# Compile
train = theano.function(
          inputs=[x,y],
          outputs=[prediction, xent],
          updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb)))
predict = theano.function(inputs=[x], outputs=prediction)

# Train
for i in range(training_steps):
    pred, err = train(D[0], D[1])

print("Final model:")
print(w.get_value())
print(b.get_value())
print("target values for D:")
print(D[1])
print("prediction on D:")
print(predict(D[0]))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I understand most of it, p_1 is the logistic regression function, the prediction is whether the value will be in the 0 class or 1 class, xent is the loss function, i.e. how far from correct is our prediction. I do not understand the next line, the cost. Shouldn''t the cost be equal to the xent, i.e. the loss? 
What is the cost function representing here?
Also, why is the bias initially set to 0 and not a random number like the weights?&lt;/p&gt;
', 'python  logistic-regression  theano', NULL, '''/code'':240C ''/p'':41C,328C ''/pre'':241C ''0'':66C,90C,212C,239C,265C,319C ''0.01'':145C ''0.1'':189C,193C ''0.5'':123C ''1'':107C,108C,109C,118C,121C,131C,132C,135C,137C,214C,231C,249C,268C ''10000'':71C ''2'':68C,147C ''400'':55C ''784'':57C ''also'':311C ''applic'':36C ''b'':88C,92C,114C,158C,191C,192C ''b.get'':100C,222C ''bias'':315C ''case'':20C,40C ''class'':266C,269C ''code'':30C,43C ''compil'':177C ''comput'':159C ''construct'':102C ''correct'':279C ''cost'':143C,150C,156C,164C,291C,295C,307C ''cross'':139C ''cross-entropi'':138C ''d'':58C,211C,213C,228C,230C,235C,238C ''declar'':72C ''deeplearning.net'':24C ''entropi'':140C ''equal'':297C ''err'':209C ''exampl'':4A ''express'':104C ''far'':277C ''feat'':56C,61C,85C ''final'':216C ''follow'':28C,172C ''function'':142C,254C,274C,308C ''gb'':154C,194C ''give'':26C ''gradient'':161C ''graph'':105C ''gt'':122C ''gw'':153C,190C ''high'':67C ''i.e'':275C,301C ''implement'':32C ''import'':44C,46C,48C ''initi'':94C,316C ''input'':180C,197C ''like'':325C ''line'':289C ''logist'':2A,7B,34C,252C ''logistic-regress'':6B ''loss'':141C,273C,303C ''low'':65C ''minim'':152C ''model'':95C,217C ''n'':54C,60C,64C ''name'':86C,91C ''net'':19C ''neural'':18C ''next'':288C ''number'':324C ''numpi'':45C ''numpy.random'':53C ''output'':183C,199C ''p'':10C,106C,120C,130C,136C,242C,248C ''pre'':42C ''pred'':208C ''predict'':119C,125C,184C,195C,200C,233C,237C,256C,282C ''print'':93C,96C,99C,215C,218C,221C,224C,229C,232C,236C ''probabl'':115C ''python'':5B ''random'':323C ''rang'':205C ''regress'':3A,8B,35C,253C ''repres'':309C ''return'':167C ''rng'':52C ''rng.randint'':62C ''rng.randn'':59C,84C ''section'':173C ''set'':317C ''shall'':166C ''shouldn'':292C ''simpl'':17C,29C,39C ''site'':25C ''size'':63C ''step'':70C,207C ''sum'':148C ''symbol'':74C ''t.dot'':111C ''t.exp'':110C ''t.grad'':155C ''t.log'':129C,134C ''t.matrix'':77C ''t.vector'':80C ''target'':117C,225C ''theano'':1A,9B,22C,47C,73C,103C ''theano.function'':179C,196C ''theano.shared'':83C,89C ''theano.tensor'':49C ''threshold'':126C ''train'':69C,178C,201C,206C,210C ''tri'':13C ''tutori'':176C ''understand'':15C,244C,286C ''updat'':186C ''use'':21C ''valu'':98C,101C,220C,223C,226C,260C ''variabl'':75C ''w'':82C,87C,113C,146C,157C,187C,188C ''w.get'':97C,219C ''weight'':327C ''whether'':258C ''x'':76C,78C,112C,181C,198C ''xent'':127C,185C,270C,300C ''xent.mean'':144C ''y'':79C,81C,128C,133C,182C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9267, 2, '2015-12-08 14:40:38.523', 5, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;I do not understand the next line, the cost. Shouldn''t the cost be equal to the xent, i.e. the loss? What is the cost function representing here? &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The cost is the error (xent.mean()) + some regularization (0.01 * (w ** 2).sum())&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Why is the bias initially set to 0 and not a random number like the weights?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;It is possible and common to initialize the biases to be zero, since the asymmetry breaking is provided by the small random numbers in the weights. &lt;/p&gt;

&lt;p&gt;More details &lt;a href="http://cs231n.github.io/neural-networks-2/#init"&gt;here&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':104C ''/blockquote'':32C,66C ''/neural-networks-2/#init'':102C ''/p'':31C,46C,65C,94C,105C ''0'':56C ''0.01'':42C ''2'':44C ''asymmetri'':82C ''bias'':52C,76C ''blockquot'':1C,47C ''break'':83C ''common'':72C ''cost'':11C,15C,27C,35C ''cs231n.github.io'':101C ''cs231n.github.io/neural-networks-2/#init'':100C ''detail'':97C ''equal'':17C ''error'':38C ''function'':28C ''href'':99C ''i.e'':21C ''initi'':53C,74C ''like'':62C ''line'':9C ''loss'':23C ''next'':8C ''number'':61C,90C ''p'':2C,33C,48C,67C,95C ''possibl'':70C ''provid'':85C ''random'':60C,89C ''regular'':41C ''repres'':29C ''set'':54C ''shouldn'':12C ''sinc'':80C ''small'':88C ''sum'':45C ''understand'':6C ''w'':43C ''weight'':64C,93C ''xent'':20C ''xent.mean'':39C ''zero'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9271, 2, '2015-12-08 22:29:45.167', 3, NULL, NULL, '&lt;p&gt;First of all, &lt;code&gt;sklearn.metrics.mutual_info_score&lt;/code&gt; implements &lt;em&gt;mutual information&lt;/em&gt; for evaluating clustering results, not &lt;em&gt;pure&lt;/em&gt; Kullback-Leibler divergence!&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;This is equal to the Kullback-Leibler divergence of the joint distribution with the product distribution of the marginals.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;KL divergence (and any other such measure) expects the input data to &lt;strong&gt;have a sum of 1&lt;/strong&gt;. Otherwise, they are &lt;em&gt;not&lt;/em&gt; proper &lt;strong&gt;probability distributions&lt;/strong&gt;. If your data does not have a sum of 1, most likely it is usually not proper to use KL divergence! (In some cases, it may be admissible to have a sum of less than 1, e.g. in the case of missing data.)&lt;/p&gt;

&lt;p&gt;Also note that it is common to use base 2 logarithms. This only yields a constant scaling factor in difference, but base 2 logarithms are easier to interpret and have a more intuitive scale (0 to 1 instead of 0 to log2=0.69314..., measuring the information in bits instead of nats).&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; sklearn.metrics.mutual_info_score([0,1],[1,0])
0.69314718055994529
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;as we can clearly see, the MI result of sklearn is scaled using natural logarithms instead of log2. This is an unfortunate choice, as explained above.&lt;/p&gt;

&lt;p&gt;Kullback-Leibler divergence is fragile, unfortunately. On above example it is not well-defined: &lt;code&gt;KL([0,1],[1,0])&lt;/code&gt; causes a division by zero, and tends to infinity. It is also &lt;em&gt;asymmetric&lt;/em&gt;.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':51C ''/code'':9C,191C,244C ''/em'':14C,22C,77C,259C ''/p'':27C,50C,126C,179C,220C,260C ''/pre'':192C ''/strong'':71C,82C ''0'':162C,167C,186C,189C,240C,243C ''0.69314'':170C ''0.69314718055994529'':190C ''1'':70C,92C,118C,164C,187C,188C,241C,242C ''2'':137C,150C ''admiss'':110C ''also'':128C,256C ''asymmetr'':258C ''base'':136C,149C ''bit'':175C ''blockquot'':28C ''case'':106C,122C ''caus'':245C ''choic'':216C ''clear'':197C ''cluster'':17C ''code'':5C,181C,238C ''common'':133C ''constant'':143C ''data'':63C,85C,125C ''defin'':237C ''differ'':147C ''distribut'':42C,46C,81C ''diverg'':26C,38C,54C,103C,225C ''divis'':247C ''e.g'':119C ''easier'':153C ''em'':11C,20C,75C,257C ''equal'':32C ''evalu'':16C ''exampl'':231C ''expect'':60C ''explain'':218C ''factor'':145C ''first'':2C ''fragil'':227C ''gt'':182C ''implement'':10C ''infin'':253C ''info'':7C,184C ''inform'':13C,173C ''input'':62C ''instead'':165C,176C,209C ''interpret'':155C ''intuit'':160C ''joint'':41C ''kl'':53C,102C,239C ''kullback'':24C,36C,223C ''kullback-leibl'':23C,35C,222C ''leibler'':25C,37C,224C ''less'':116C ''like'':94C ''log2'':169C,211C ''logarithm'':138C,151C,208C ''margin'':49C ''may'':108C ''measur'':59C,171C ''mi'':200C ''miss'':124C ''mutual'':12C ''nat'':178C ''natur'':207C ''note'':129C ''otherwis'':72C ''p'':1C,29C,52C,127C,193C,221C ''pre'':180C ''probabl'':80C ''product'':45C ''proper'':78C,99C ''pure'':21C ''result'':18C,201C ''scale'':144C,161C,205C ''score'':8C,185C ''see'':198C ''sklearn'':203C ''sklearn.metrics.mutual'':6C,183C ''strong'':65C,79C ''sum'':68C,90C,114C ''tend'':251C ''unfortun'':215C,228C ''use'':101C,135C,206C ''usual'':97C ''well'':236C ''well-defin'':235C ''yield'':141C ''zero'':249C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9342, 1, '2015-12-11 19:59:49.92', 3, 60, 'How do write the following piece of code in R?', '&lt;p&gt;I am trying to write a piece of code for R to fetch data from an api with the following command:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data &amp;lt;- read.csv("https://...../api/dataservice?sql = select * from db where name IN (?)")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The parenthesis are where I need to input 218 different name tags. How can I do this programmatically? It is possible to write down few of them, but over 200 is not possible. Is there a way this? Can I use a character vector containing the names?&lt;/p&gt;
', 'r  sql', NULL, '''/api/dataservice'':41C ''/code'':49C ''/p'':35C,99C ''/pre'':50C ''200'':81C ''218'':60C ''api'':30C ''charact'':94C ''code'':8A,22C,37C ''command'':34C ''contain'':96C ''data'':27C,38C ''db'':45C ''differ'':61C ''fetch'':26C ''follow'':5A,33C ''input'':59C ''lt'':39C ''name'':47C,62C,98C ''need'':57C ''p'':13C,51C ''parenthesi'':53C ''piec'':6A,20C ''possibl'':72C,84C ''pre'':36C ''programmat'':69C ''r'':10A,11B,24C ''read.csv'':40C ''select'':43C ''sql'':12B,42C ''tag'':63C ''tri'':16C ''use'':92C ''vector'':95C ''way'':88C ''write'':3A,18C,74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9273, 2, '2015-12-09 02:41:37.34', 3, NULL, NULL, '&lt;p&gt;Finite precision is decimal representation of a number which has been rounded or truncated. There many cases where this may be necessary or appropriate. For example 1/3 and the transcendental numbers $e$ and $\pi$ all have infinite decimal representations. In the programming language C, a double value is 8 bit and precise to approximately 16 digits. See here.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.learncpp.com/cpp-tutorial/25-floating-point-numbers/" rel="nofollow"&gt;http://www.learncpp.com/cpp-tutorial/25-floating-point-numbers/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;To concretely represent one of these numbers on a (finite) computer there must be some sort of compromise. We could write 1/3 to 9 digits as .333333333 which is less than 1/3. &lt;/p&gt;

&lt;p&gt;These compromises are compounded with arithmetic operations. Unstable algorithms are prone to arithmetic errors. This is why SVD is often used to compute PCA (instability of the covariance matrix). &lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.sandia.gov/~smartin/presentations/SMartin_Stability.pdf" rel="nofollow"&gt;http://www.sandia.gov/~smartin/presentations/SMartin_Stability.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Numerical_stability" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Numerical_stability&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In the naive bayes classifier you will often see multiplication transformed into a sum of logarithms, which is less prone to rounding errors.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/cpp-tutorial/25-floating-point-numbers/'':66C ''/cpp-tutorial/25-floating-point-numbers/&lt;/a&gt;&lt;/p&gt;'':71C ''/p'':60C,105C,136C,183C ''/wiki/naive_bayes_classifier#multinomial_naive_bayes'':189C ''/wiki/naive_bayes_classifier#multinomial_naive_bayes&lt;/a&gt;&lt;/p&gt;'':194C ''/wiki/numerical_stability'':153C ''/wiki/numerical_stability&lt;/a&gt;&lt;/p&gt;'':158C ''/~smartin/presentations/smartin_stability.pdf'':142C ''/~smartin/presentations/smartin_stability.pdf&lt;/a&gt;&lt;/p&gt;'':147C ''1/3'':28C,94C,104C ''16'':56C ''333333333'':99C ''8'':50C ''9'':96C ''algorithm'':115C ''appropri'':25C ''approxim'':55C ''arithmet'':112C,119C ''bay'':163C ''bit'':51C ''c'':45C ''case'':18C ''classifi'':164C ''compound'':110C ''compromis'':90C,108C ''comput'':83C,129C ''concret'':74C ''could'':92C ''covari'':134C ''decim'':5C,39C ''digit'':57C,97C ''doubl'':47C ''e'':33C ''en.wikipedia.org'':152C,157C,188C,193C ''en.wikipedia.org/wiki/naive_bayes_classifier#multinomial_naive_bayes'':187C ''en.wikipedia.org/wiki/naive_bayes_classifier#multinomial_naive_bayes&lt;/a&gt;&lt;/p&gt;'':192C ''en.wikipedia.org/wiki/numerical_stability'':151C ''en.wikipedia.org/wiki/numerical_stability&lt;/a&gt;&lt;/p&gt;'':156C ''error'':120C,182C ''exampl'':27C ''finit'':2C,82C ''href'':63C,139C,150C,186C ''infinit'':38C ''instabl'':131C ''languag'':44C ''less'':102C,178C ''logarithm'':175C ''mani'':17C ''matrix'':135C ''may'':21C ''multipl'':169C ''must'':85C ''naiv'':162C ''necessari'':23C ''nofollow'':68C,144C,155C,191C ''number'':9C,32C,79C ''often'':126C,167C ''one'':76C ''oper'':113C ''p'':1C,61C,72C,106C,137C,148C,159C,184C ''pca'':130C ''pi'':35C ''precis'':3C,53C ''program'':43C ''prone'':117C,179C ''rel'':67C,143C,154C,190C ''repres'':75C ''represent'':6C,40C ''round'':13C,181C ''see'':58C,168C ''sort'':88C ''sum'':173C ''svd'':124C ''transcendent'':31C ''transform'':170C ''truncat'':15C ''unstabl'':114C ''use'':127C ''valu'':48C ''write'':93C ''www.learncpp.com'':65C,70C ''www.learncpp.com/cpp-tutorial/25-floating-point-numbers/'':64C ''www.learncpp.com/cpp-tutorial/25-floating-point-numbers/&lt;/a&gt;&lt;/p&gt;'':69C ''www.sandia.gov'':141C,146C ''www.sandia.gov/~smartin/presentations/smartin_stability.pdf'':140C ''www.sandia.gov/~smartin/presentations/smartin_stability.pdf&lt;/a&gt;&lt;/p&gt;'':145C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9275, 1, '2015-12-09 04:10:02.643', 2, 77, 'Is $R^2$ an appropriate evaluation metric for k-Nearest Neighbors?', '&lt;p&gt;I found a source that stated that $R^2$ is the âpercentage of the response variable variation that is explained by a linear model.â
(&lt;a href="http://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit" rel="nofollow"&gt;Source&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;Since kNN is not a linear model (it is nonparametric), is it appropriate to use $R^2$ to evaluate it? If not, what error metric would be appropriate? I''m using the kNN classifier provided by scikit-learn in python.&lt;/p&gt;
', 'classification  python  scikit  correlation', NULL, '''/a'':52C ''/blog/adventures-in-statistics/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit'':48C ''/p'':53C,96C ''2'':3A,26C,71C ''appropri'':5A,67C,82C ''blog.minitab.com'':47C ''blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit'':46C ''classif'':13B ''classifi'':88C ''correl'':16B ''error'':78C ''evalu'':6A,73C ''explain'':38C ''found'':19C ''href'':45C ''k'':10A ''k-nearest'':9A ''knn'':56C,87C ''learn'':93C ''linear'':41C,60C ''m'':84C ''metric'':7A,79C ''model'':42C,61C ''nearest'':11A ''neighbor'':12A ''nofollow'':50C ''nonparametr'':64C ''p'':17C,54C ''percentag'':30C ''provid'':89C ''python'':14B,95C ''r'':2A,25C,70C ''rel'':49C ''respons'':33C ''scikit'':15B,92C ''scikit-learn'':91C ''sinc'':55C ''sourc'':21C,51C ''state'':23C ''use'':69C,85C ''variabl'':34C ''variat'':35C ''would'':80C ''â'':29C,43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9276, 2, '2015-12-09 04:51:55.55', 0, NULL, NULL, '&lt;p&gt;To combine the cosine distance and euclidian distance, I would first normalize the euclidian distance on the same scale as the cosine distance [o-1], and then combine them by average. But I am sure you already try that approach. Thus I am wondering what is your definition of "combine both scores effectively" ? Do you need to weight differently the two metrics? How did you came to the final matrix you are showing? Is it intuition? If so, you can investigate this further to better define the weights you want to put on each feature and it associated metrics.&lt;/p&gt;
', NULL, NULL, '''-1'':26C ''/p'':101C ''alreadi'':38C ''approach'':41C ''associ'':99C ''averag'':32C ''better'':86C ''came'':67C ''combin'':3C,29C,51C ''cosin'':5C,23C ''defin'':87C ''definit'':49C ''differ'':60C ''distanc'':6C,9C,16C,24C ''effect'':54C ''euclidian'':8C,15C ''featur'':96C ''final'':70C ''first'':12C ''intuit'':77C ''investig'':82C ''matrix'':71C ''metric'':63C,100C ''need'':57C ''normal'':13C ''o'':25C ''p'':1C ''put'':93C ''scale'':20C ''score'':53C ''show'':74C ''sure'':36C ''thus'':42C ''tri'':39C ''two'':62C ''want'':91C ''weight'':59C,89C ''wonder'':45C ''would'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9277, 2, '2015-12-09 04:54:45.217', 3, NULL, NULL, '&lt;p&gt;If you have only an input layer, one set of weights, and an output layer, you can solve this directly with $$ X \cdot w = threshold $$&lt;/p&gt;

&lt;p&gt;However if you add in hidden layers, you no longer necessarily have a &lt;a href="https://en.wikipedia.org/wiki/Hyperplane" rel="nofollow"&gt;hyperplane&lt;/a&gt;, as in order to be a hyperplane it must be able to be expressed as the "solution of a single algebraic equation of degree 1."&lt;/p&gt;

&lt;p&gt;Even if you can''t solve directly, you can still get a sense for the response surface by evaluating your network''s output over a wide range of inputs. &lt;/p&gt;
', NULL, NULL, '''/a'':50C ''/p'':27C,76C,107C ''/wiki/hyperplane'':46C ''1'':75C ''abl'':61C ''add'':32C ''algebra'':71C ''cdot'':24C ''degre'':74C ''direct'':21C,84C ''en.wikipedia.org'':45C ''en.wikipedia.org/wiki/hyperplane'':44C ''equat'':72C ''evalu'':96C ''even'':78C ''express'':64C ''get'':88C ''hidden'':34C ''howev'':29C ''href'':43C ''hyperplan'':49C,57C ''input'':7C,106C ''layer'':8C,16C,35C ''longer'':38C ''must'':59C ''necessarili'':39C ''network'':98C ''nofollow'':48C ''one'':9C ''order'':53C ''output'':15C,100C ''p'':1C,28C,77C ''rang'':104C ''rel'':47C ''respons'':93C ''sens'':90C ''set'':10C ''singl'':70C ''solut'':67C ''solv'':19C,83C ''still'':87C ''surfac'':94C ''threshold'':26C ''w'':25C ''weight'':12C ''wide'':103C ''x'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9278, 2, '2015-12-09 06:19:59.753', 3, NULL, NULL, '&lt;p&gt;The answer to this question is that &lt;em&gt;it depends&lt;/em&gt;. The primary approach is to pass in the tokenized sentences (so &lt;code&gt;SentenceCorpus&lt;/code&gt; in your example), but depending on what your goal is and what the corpus is you''re looking at, you might want to instead use the entire article to learn the embeddings. This is something you might not know ahead of time -- so you''ll have to think about how you want to evaluate the quality of the embeddings, and do some experiments to see which ''kind'' of embeddings are more useful for your task(s).&lt;/p&gt;
', NULL, NULL, '''/code'':26C ''/em'':12C ''/p'':103C ''ahead'':66C ''answer'':3C ''approach'':15C ''articl'':54C ''code'':24C ''corpus'':40C ''depend'':11C,31C ''em'':9C ''embed'':58C,85C,95C ''entir'':53C ''evalu'':80C ''exampl'':29C ''experi'':89C ''goal'':35C ''instead'':50C ''kind'':93C ''know'':65C ''learn'':56C ''ll'':71C ''look'':44C ''might'':47C,63C ''p'':1C ''pass'':18C ''primari'':14C ''qualiti'':82C ''question'':6C ''re'':43C ''see'':91C ''sentenc'':22C ''sentencecorpus'':25C ''someth'':61C ''task'':101C ''think'':74C ''time'':68C ''token'':21C ''use'':51C,98C ''want'':48C,78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9389, 1, '2015-12-15 03:10:26.323', -2, 40, 'How should I analyze this data from reddit (sample text included)', '&lt;p&gt;I have downloaded some data to learn about machine learning and distributed computing. I used WinZip to uncompress a .bz2. Now I have text (opened in Notepad) which looks like this (this file is long, this is just a sample):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    {"parent_id":"t3_5yba3","created_utc":"1192450635","ups":1,"controversiality":0,"distinguished":null,"subreddit_id":"t5_6","id":"c0299an","downs":0,"archived":true,"link_id":"t3_5yba3","score":1,"author":"bostich","score_hidden":false,"body":"test","gilded":0,"author_flair_text":null,"subreddit":"reddit.com","edited":false,"author_flair_css_class":null,"name":"t1_c0299an","retrieved_on":1427426409}
    {"score_hidden":false,"body":"much smoother.\r\n\r\nIm just glad reddit is back, #reddit in mIRC was entertaining but I had no idea how addicted I had become. Thanks for making the detox somewhat short.","author_flair_text":null,"gilded":0,"link_id":"t3_5yba3","score":2,"author":"igiveyoumylife","author_flair_css_class":null,"name":"t1_c0299ao","retrieved_on":1427426409,"edited":false,"subreddit":"reddit.com","ups":2,"controversiality":0,"parent_id":"t3_5yba3","created_utc":"1192450639","downs":0,"archived":true,"distinguished":null,"subreddit_id":"t5_6","id":"c0299ao"}
{"author":"Arve","link_id":"t3_5yba3","score":0,"body":"Can we please deprecate the word \"Ajax\" now? \r\n\r\n(But yeah, this _is_ much nicer)","score_hidden":false,"author_flair_text":null,"gilded":0,"subreddit":"reddit.com","edited":false,"author_flair_css_class":null,"retrieved_on":1427426409,"name":"t1_c0299ap","created_utc":"1192450643","parent_id":"t1_c02999p","controversiality":0,"ups":0,"distinguished":null,"id":"c0299ap","subreddit_id":"t5_6","downs":0,"archived":true}
{"author_flair_text":null,"gilded":0,"score_hidden":false,"body":"[deleted]","score":1,"link_id":"t3_5yba3","author":"[deleted]","name":"t1_c0299aq","retrieved_on":1427426409,"author_flair_css_class":null,"subreddit":"reddit.com","edited":false,"ups":1,"controversiality":0,"created_utc":"1192450646","parent_id":"t3_5yba3","archived":true,"downs":0,"subreddit_id":"t5_6","id":"c0299aq","distinguished":null}
{"edited":false,"subreddit":"reddit.com","author_flair_css_class":null,"name":"t1_c0299ar","retrieved_on":1427426409,"author":"gigaquack","link_id":"t3_5yba3","score":3,"body":"Oh, I see. Fancy schmancy \"submitting....\"","score_hidden":false,"author_flair_text":null,"gilded":0,"distinguished":null,"subreddit_id":"t5_6","id":"c0299ar","downs":0,"archived":true,"created_utc":"1192450646","parent_id":"t1_c0299ah","controversiality":0,"ups":3}
{"author_flair_text":null,"gilded":0,"body":"testing ...","score_hidden":false,"author":"Percept","score":1,"link_id":"t3_5yba3","retrieved_on":1427426409,"name":"t1_c0299as","author_flair_css_class":null,"edited":false,"subreddit":"reddit.com","controversiality":0,"ups":1,"parent_id":"t3_5yba3","created_utc":"1192450656","archived":true,"downs":0,"subreddit_id":"t5_6","id":"c0299as","distinguished":null}
{"ups":3,"controversiality":0,"parent_id":"t1_c0299ar","created_utc":"1192450658","archived":true,"downs":0,"subreddit_id":"t5_6","id":"c0299at","distinguished":null,"author_flair_text":null,"gilded":0,"score_hidden":false,"body":"I like it. One more time...","score":3,"link_id":"t3_5yba3","author":"gigaquack","name":"t1_c0299at","retrieved_on":1427426409,"author_flair_css_class":null,"subreddit":"reddit.com","edited":false}
{"ups":2,"controversiality":0,"parent_id":"t1_c02999j","created_utc":"1192450662","archived":true,"downs":0,"id":"c0299au","subreddit_id":"t5_6","distinguished":null,"gilded":0,"author_flair_text":null,"score_hidden":false,"body":" try refreshing yor cache, that worked for me \n//edit: trying to edit","score":2,"link_id":"t3_5yba3","author":"mcm69","retrieved_on":1427426409,"name":"t1_c0299au","author_flair_css_class":null,"edited":false,"subreddit":"reddit.com"}
{"controversiality":0,"ups":3,"parent_id":"t1_c0299at","created_utc":"1192450670","downs":0,"archived":true,"distinguished":null,"subreddit_id":"t5_6","id":"c0299av","body":"K. I lied. Just one more...","score_hidden":false,"author_flair_text":null,"gilded":0,"author":"gigaquack","score":3,"link_id":"t3_5yba3","author_flair_css_class":null,"name":"t1_c0299av","retrieved_on":1427426409,"edited":false,"subreddit":"reddit.com"}
{"edited":false,"subreddit":"reddit.com","author_flair_css_class":null,"retrieved_on":1427426409,"name":"t1_c0299aw","author":"deki","score":2,"link_id":"t3_5yba3","body":"I also wonder what the differences are...","score_hidden":false,"gilded":0,"author_flair_text":null,"distinguished":null,"id":"c0299aw","subreddit_id":"t5_6","downs":0,"archived":true,"parent_id":"t3_5yba3","created_utc":"1192450681","controversiality":0,"ups":2}
{"created_utc":"1192450683","parent_id":"t1_c0299av","controversiality":0,"ups":3,"subreddit_id":"t5_6","id":"c0299ax","distinguished":null,"archived":true,"downs":0,"author":"gigaquack","score":3,"link_id":"t3_5yba3","author_flair_text":null,"gilded":0,"score_hidden":false,"body":"So addictive...","edited":false,"subreddit":"reddit.com","name":"t1_c0299ax","retrieved_on":1427426409,"author_flair_css_class":null}
{"downs":0,"archived":true,"distinguished":null,"subreddit_id":"t5_6","id":"c0299ay","controversiality":0,"ups":1,"created_utc":"1192450687","parent_id":"t3_5yba1","author_flair_css_class":null,"retrieved_on":1427426409,"name":"t1_c0299ay","subreddit":"reddit.com","edited":false,"body":"I can''t post a story to proggit - I got \"you are trying to submit too fast\" on my first submission.","score_hidden":false,"author_flair_text":null,"gilded":0,"author":"llimllib","score":1,"link_id":"t3_5yba1"}
{"gilded":0,"author_flair_text":null,"score_hidden":false,"body":"Alright I''m done.","author":"gigaquack","score":3,"link_id":"t3_5yba3","name":"t1_c0299az","retrieved_on":1427426409,"author_flair_css_class":null,"subreddit":"reddit.com","edited":false,"controversiality":0,"ups":3,"parent_id":"t1_c0299ax","created_utc":"1192450691","archived":true,"downs":0,"subreddit_id":"t5_6","id":"c0299az","distinguished":null}
{"distinguished":null,"subreddit_id":"t5_6","id":"c0299b0","downs":0,"archived":true,"created_utc":"1192450696","parent_id":"t3_5yba3","ups":12,"controversiality":0,"edited":false,"subreddit":"reddit.com","author_flair_css_class":null,"retrieved_on":1427426409,"name":"t1_c0299b0","score":12,"link_id":"t3_5yba3","author":"igiveyoumylife","score_hidden":false,"body":"Is anyone else''s \"recommended\" page completely empty? Mine is, and it is usually jam-packed.","author_flair_text":null,"gilded":0}
{"downs":0,"archived":true,"distinguished":null,"id":"c0299b1","subreddit_id":"t5_6","ups":1,"controversiality":0,"parent_id":"t1_c02999j","created_utc":"1192450700","author_flair_css_class":null,"name":"t1_c0299b1","retrieved_on":1427426409,"subreddit":"reddit.com","edited":false,"body":"[deleted]","score_hidden":false,"author_flair_text":null,"gilded":0,"link_id":"t3_5yba3","score":1,"author":"[deleted]"}
{"parent_id":"t3_5yba3","created_utc":"1192450705","controversiality":0,"ups":2,"id":"c0299b2","subreddit_id":"t5_6","distinguished":null,"archived":true,"downs":0,"author":"[deleted]","score":2,"link_id":"t3_5yba3","author_flair_text":null,"gilded":0,"score_hidden":false,"body":"Ok, I guess we need to submit comments to test? ","edited":false,"subreddit":"reddit.com","retrieved_on":1427426409,"name":"t1_c0299b2","author_flair_css_class":null}
{"name":"t1_c0299b3","retrieved_on":1427426409,"author_flair_css_class":null,"subreddit":"reddit.com","edited":false,"author_flair_text":null,"gilded":0,"body":"I can''t submit any stories -- even the first time, i get, \"you are trying to submit too fast\" .. anyone else seeing this?\n\nEdit: This appears to be fixed.","score_hidden":false,"author":"raldi","score":5,"link_id":"t3_5yba3","archived":true,"downs":0,"id":"c0299b3","subreddit_id":"t5_6","distinguished":null,"controversiality":0,"ups":5,"parent_id":"t3_5yba3","created_utc":"1192450709"}
{"archived":true,"downs":0,"subreddit_id":"t5_6","id":"c0299b4","distinguished":null,"controversiality":0,"ups":1,"created_utc":"1192450737","parent_id":"t1_c0299a5","retrieved_on":1427426409,"name":"t1_c0299b4","author_flair_css_class":null,"subreddit":"reddit.com","edited":false,"author_flair_text":null,"gilded":0,"body":"Working fine with normal Adblock.","score_hidden":false,"author":"deki","score":1,"link_id":"t3_5yba3"}
{"subreddit_id":"t5_6","id":"c0299b5","distinguished":null,"archived":true,"downs":0,"parent_id":"t3_5yba3","created_utc":"1192450739","controversiality":0,"ups":12,"subreddit":"reddit.com","edited":false,"retrieved_on":1427426409,"name":"t1_c0299b5","author_flair_css_class":null,"author":"jezmck","link_id":"t3_5yba3","score":12,"author_flair_text":null,"gilded":0,"score_hidden":false,"body":"can''t see beta.reddit.com from here..."}
{"downs":0,"archived":true,"distinguished":null,"id":"c0299b6","subreddit_id":"t5_6","ups":2,"controversiality":0,"parent_id":"t1_c02999j","created_utc":"1192450748","author_flair_css_class":null,"retrieved_on":1427426409,"name":"t1_c0299b6","subreddit":"reddit.com","edited":false,"body":"I had a problem as well, with my comment being lost. Then I did a \"super-refresh\" (shift-Reload) and it seemed to work.","score_hidden":false,"gilded":0,"author_flair_text":null,"score":2,"link_id":"t3_5yba3","author":"sickofthisshit"}
{"distinguished":null,"id":"c0299b7","subreddit_id":"t5_6","downs":0,"archived":true,"parent_id":"t3_5yba3","created_utc":"1192450748","ups":1,"controversiality":0,"edited":false,"subreddit":"reddit.com","author_flair_css_class":null,"name":"t1_c0299b7","retrieved_on":1427426409,"score":1,"link_id":"t3_5yba3","author":"hfaber","body":"hm?","score_hidden":false,"author_flair_text":null,"gilded":0}
{"subreddit":"reddit.com","edited":false,"retrieved_on":1427426409,"name":"t1_c0299b8","author_flair_css_class":null,"score":1,"link_id":"t3_5yba3","author":"paternoster","author_flair_text":null,"gilded":0,"score_hidden":false,"body":"I think so...","id":"c0299b8","subreddit_id":"t5_6","distinguished":null,"archived":true,"downs":0,"parent_id":"t1_c0299b2","created_utc":"1192450770","ups":1,"controversiality":0}
{"distinguished":null,"id":"c0299b9","subreddit_id":"t5_6","downs":0,"archived":true,"parent_id":"t1_c0299ac","created_utc":"1192450771","ups":2,"controversiality":0,"subreddit":"reddit.com","edited":false,"author_flair_css_class":null,"name":"t1_c0299b9","retrieved_on":1427426409,"link_id":"t3_5yba3","score":2,"author":"igiveyoumylife","score_hidden":false,"body":"well... I read Ron Paul''s website.","gilded":0,"author_flair_text":null}
{"parent_id":"t1_c0299ap","created_utc":"1192450772","controversiality":0,"ups":2,"distinguished":null,"subreddit_id":"t5_6","id":"c0299ba","downs":0,"archived":true,"author":"zoomzoom83","link_id":"t3_5yba3","score":2,"score_hidden":false,"body":"No","gilded":0,"author_flair_text":null,"edited":false,"subreddit":"reddit.com","author_flair_css_class":null,"name":"t1_c0299ba","retrieved_on":1427426409}
{"gilded":0,"author_flair_text":null,"body":"Cry me a river.  Most patents and\r\ncopyrights these days are theft  of\r\npublic domain under the color of\r\nlaw, to an absurd degree.\r\n\r\nMickey Mouse copyright extended to\r\n100 years as a favor to everyone''s\r\nfavorite political donor, Will Eisner.\r\nAmazon''s one click patent.\r\n\r\nIt''s probably been 20 years \r\nsince the Constitutional authority\r\nto grant patents and copyrights\r\nactually served, on the whole, to\r\n\"advance the arts and commerce\",\r\nrather than to stifle them.\r\n","score_hidden":false,"link_id":"t3_2zxa6","score":1,"author":"donh","retrieved_on":1427426409,"name":"t1_c0299bb","author_flair_css_class":null,"subreddit":"reddit.com","edited":false,"ups":1,"controversiality":0,"parent_id":"t3_2zxa6","created_utc":"1192450781","archived":true,"downs":0,"id":"c0299bb","subreddit_id":"t5_6","distinguished":null}
{"parent_id":"t1_c0299b2","created_utc":"1192450789","controversiality":0,"ups":1,"distinguished":null,"subreddit_id":"t5_6","id":"c0299bc","downs":0,"archived":true,"author":"tashbarg","link_id":"t3_5yba3","score":1,"body":"jup","score_hidden":false,"author_flair_text":null,"gilded":0,"edited":false,"subreddit":"reddit.com","author_flair_css_class":null,"name":"t1_c0299bc","retrieved_on":1427426409}
{"retrieved_on":1427426409,"name":"t1_c0299bd","author_flair_css_class":null,"subreddit":"reddit.com","edited":false,"gilded":0,"author_flair_text":null,"body":"As long as we can still say \"Web 2.0\"...","score_hidden":false,"link_id":"t3_5yba3","score":2,"author":"newton_dave","archived":true,"downs":0,"subreddit_id":"t5_6","id":"c0299bd","distinguished":null,"ups":2,"controversiality":0,"parent_id":"t1_c0299ap","created_utc":"1192450790"}
{"ups":3,"controversiality":0,"parent_id":"t1_c0299b7","created_utc":"1192450797","downs":0,"archived":true,"distinguished":null,"id":"c0299be","subreddit_id":"t5_6","score_hidden":false,"body":"what''s new except the asynchronous submit (which alone isn''t really worth being dubbed ''new comment system'')?","author_flair_text":null,"gilded":0,"link_id":"t3_5yba3","score":3,"author":"hfaber","author_flair_css_class":null,"name":"t1_c0299be","retrieved_on":1427426409,"edited":false,"subreddit":"reddit.com"}
{"subreddit":"reddit.com","edited":false,"author_flair_css_class":null,"retrieved_on":1427426409,"name":"t1_c0299bf","score":6,"link_id":"t3_5yba3","author":"paternoster","score_hidden":false,"body":"Yep","gilded":0,"author_flair_text":null,"distinguished":null,"id":"c0299bf","subreddit_id":"t5_6","downs":0,"archived":true,"parent_id":"t1_c0299b0","created_utc":"1192450809","ups":6,"controversiality":0}
{"name":"t1_c0299bg","retrieved_on":1427426409,"author_flair_css_class":null,"subreddit":"reddit.com","edited":false,"author_flair_text":null,"gilded":0,"score_hidden":false,"body":"[deleted]","score":1,"link_id":"t3_5yba3","author":"[deleted]","archived":true,"downs":0,"id":"c0299bg","subreddit_id":"t5_6","distinguished":null,"ups":1,"controversiality":0,"parent_id":"t3_5yba3","created_utc":"1192450827"}
{"subreddit_id":"t5_6","id":"c0299bh","distinguished":null,"archived":true,"downs":0,"created_utc":"1192450831","parent_id":"t1_c0299az","controversiality":0,"ups":2,"subreddit":"reddit.com","edited":false,"retrieved_on":1427426409,"name":"t1_c0299bh","author_flair_css_class":null,"author":"newton_dave","link_id":"t3_5yba3","score":2,"author_flair_text":null,"gilded":0,"body":"You can stop anytime you want.\n\nReally.","score_hidden":false}
{"distinguished":null,"id":"c0299bi","subreddit_id":"t5_6","downs":0,"archived":true,"created_utc":"1192450849","parent_id":"t1_c02999p","controversiality":0,"ups":3,"subreddit":"reddit.com","edited":false,"author_flair_css_class":null,"retrieved_on":1427426409,"name":"t1_c0299bi","author":"lyon","link_id":"t3_5yba3","score":3,"body":"hmm only worked after refresh...","score_hidden":false,"gilded":0,"author_flair_text":null}
{"body":"okay, dokay, lets see how the comment work now ...  PING !!!","score_hidden":false,"author_flair_text":null,"gilded":0,"link_id":"t3_5yba3","score":2,"author":"joe24pack","author_flair_css_class":null,"name":"t1_c0299bj","retrieved_on":1427426409,"subreddit":"reddit.com","edited":false,"ups":2,"controversiality":0,"parent_id":"t3_5yba3","created_utc":"1192450869","downs":0,"archived":true,"distinguished":null,"id":"c0299bj","subreddit_id":"t5_6"}
{"distinguished":null,"subreddit_id":"t5_6","id":"c0299bk","downs":0,"archived":true,"created_utc":"1192450870","parent_id":"t3_5yba3","controversiality"
&lt;/code&gt;&lt;/pre&gt;
', 'data-cleaning', NULL, '''/code'':2306C ''/edit'':559C ''/p'':56C ''/pre'':2307C ''0'':69C,79C,96C,158C,185C,194C,212C,240C,264C,266C,276C,284C,316C,327C,374C,384C,395C,403C,433C,446C,458C,469C,483C,520C,531C,541C,587C,598C,624C,683C,697C,708C,719C,733C,747C,770C,782C,837C,847C,884C,897C,915C,928C,978C,980C,994C,1027C,1044C,1058C,1072C,1122C,1168C,1178C,1191C,1201C,1231C,1260C,1269C,1300C,1312C,1326C,1380C,1402C,1415C,1449C,1478C,1497C,1508C,1518C,1531C,1567C,1580C,1592C,1609C,1630C,1763C,1774C,1791C,1803C,1824C,1856C,1886C,1898C,1909C,1918C,1956C,2009C,2023C,2036C,2057C,2074C,2086C,2105C,2114C,2146C,2168C,2179C,2215C,2239C,2266C,2275C,2295C ''1'':67C,87C,291C,314C,412C,435C,784C,841C,992C,1033C,1203C,1244C,1413C,1432C,1466C,1506C,1742C,1761C,1793C,1813C,2064C,2084C ''1192450635'':65C ''1192450639'':192C ''1192450643'':258C ''1192450646'':319C,389C ''1192450656'':442C ''1192450658'':465C ''1192450662'':527C ''1192450670'':596C ''1192450681'':706C ''1192450683'':713C ''1192450687'':787C ''1192450691'':893C ''1192450696'':920C ''1192450700'':1001C ''1192450705'':1042C ''1192450709'':1187C ''1192450737'':1206C ''1192450739'':1267C ''1192450748'':1333C,1411C ''1192450770'':1504C ''1192450771'':1527C ''1192450772'':1578C ''1192450781'':1770C ''1192450789'':1789C ''1192450790'':1905C ''1192450797'':1916C ''1192450809'':2032C ''1192450827'':2093C ''1192450831'':2108C ''1192450849'':2173C ''1192450869'':2273C ''1192450870'':2300C ''12'':926C,945C,1271C,1294C ''1427426409'':115C,177C,252C,303C,350C,419C,507C,573C,643C,659C,763C,799C,873C,940C,1012C,1093C,1107C,1213C,1278C,1341C,1430C,1456C,1546C,1628C,1747C,1839C,1842C,1975C,1991C,2042C,2123C,2193C,2258C ''2'':164C,183C,518C,564C,666C,710C,1046C,1062C,1324C,1386C,1529C,1552C,1582C,1602C,1879C,1896C,2116C,2140C,2245C,2264C ''2.0'':1870C ''20'':1699C ''2zxa6'':1740C,1767C ''3'':358C,397C,456C,495C,589C,628C,721C,737C,863C,886C,1907C,1962C,2181C,2204C ''5'':1160C,1180C ''5yba1'':791C,845C 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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9279, 2, '2015-12-09 10:08:11.757', 2, NULL, NULL, '&lt;p&gt;I''ve tried the following algorithms:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;&lt;a href="http://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf" rel="nofollow"&gt;Factorization Machines&lt;/a&gt; by Steffen Rendle&lt;/strong&gt; - 
Really good algorithm for sparse feature sets, in construct to polynomial-regression.
Measures the interaction between features. Model that in addition to learning linear weights on features, learn a vector space for each feature to learn pairing interactions between features in this new space. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Field-Aware Factorization Machines&lt;/strong&gt; - an improvement of the FM model.
Recently, have been used to win two Kaggle''s click-through rate prediction competitions.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;FTRL - "Follow The (Proximally) Regularized Leader" algorithm&lt;/strong&gt; - regularized online logistic regression. Equivalent to Online (Stochastic) Gradient Descent when no regularization is used. Very easy to implement. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;&lt;a href="http://research.microsoft.com/apps/pubs/?id=122779" rel="nofollow"&gt;AdPredictor&lt;/a&gt; algorithm by Microsoft&lt;/strong&gt; 
The algorithm is based on a probit regression model that maps discrete or real-valued input features to probabilities. It maintains Gaussian beliefs over weights of the model and performs Gaussian online updates derived from approximate message passing.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':22C,149C ''/apps/pubs/?id=122779'':145C ''/li'':73C,105C,137C,194C ''/ol'':195C ''/p'':8C,72C,104C,136C,193C ''/strong'':26C,82C,116C,153C ''/~b97053/paper/rendle2010fm.pdf'':17C ''addit'':48C ''adpredictor'':148C ''algorithm'':7C,29C,115C,150C,155C ''approxim'':190C ''awar'':79C ''base'':157C ''belief'':177C ''click'':99C ''click-through'':98C ''competit'':103C ''construct'':35C ''deriv'':188C ''descent'':126C ''discret'':165C ''easi'':133C ''equival'':121C ''factor'':20C,80C ''featur'':32C,44C,54C,61C,67C,171C ''field'':78C ''field-awar'':77C ''fm'':87C ''follow'':6C,110C ''ftrl'':109C ''gaussian'':176C,185C ''good'':28C ''gradient'':125C ''href'':14C,142C ''implement'':135C ''improv'':84C ''input'':170C ''interact'':42C,65C ''kaggl'':96C ''leader'':114C ''learn'':50C,55C,63C ''li'':10C,74C,106C,138C ''linear'':51C ''logist'':119C ''machin'':21C,81C ''maintain'':175C ''map'':164C ''measur'':40C ''messag'':191C ''microsoft'':152C ''model'':45C,88C,162C,182C ''new'':70C ''nofollow'':19C,147C ''ol'':9C ''onlin'':118C,123C,186C ''p'':1C,11C,75C,107C,139C ''pair'':64C ''pass'':192C ''perform'':184C ''polynomi'':38C ''polynomial-regress'':37C ''predict'':102C ''probabl'':173C ''probit'':160C ''proxim'':112C ''rate'':101C ''real'':168C ''real-valu'':167C ''realli'':27C ''recent'':89C ''regress'':39C,120C,161C ''regular'':113C,117C,129C ''rel'':18C,146C ''rendl'':25C ''research.microsoft.com'':144C ''research.microsoft.com/apps/pubs/?id=122779'':143C ''set'':33C ''space'':58C,71C ''spars'':31C ''steffen'':24C ''stochast'':124C ''strong'':12C,76C,108C,140C ''tri'':4C ''two'':95C ''updat'':187C ''use'':92C,131C ''valu'':169C ''ve'':3C ''vector'':57C ''weight'':52C,179C ''win'':94C ''www.csie.ntu.edu.tw'':16C ''www.csie.ntu.edu.tw/~b97053/paper/rendle2010fm.pdf'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9280, 2, '2015-12-09 10:26:11.503', 0, NULL, NULL, '&lt;p&gt;For simple extrapolation, use the &lt;code&gt;predict()&lt;/code&gt; function; call it with  the &lt;code&gt;newdata=&lt;/code&gt; Argument. This is a dataframe with the attribute values you for the next n years.&lt;/p&gt;

&lt;p&gt;Otherwise, check the &lt;code&gt;forecast()&lt;/code&gt; function from the &lt;code&gt;forecast&lt;/code&gt; package. It is recommended by the R cheatsheet &lt;a href="http://%20https://s3.amazonaws.com/quandl-static-content/Documents/Quandl+-+R+Cheat+Sheet.pdf" rel="nofollow"&gt;here&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':65C ''/code'':9C,17C,40C,46C ''/p'':33C,66C ''/s3.amazonaws.com/quandl-static-content/documents/quandl'':58C ''20https'':57C ''argument'':18C ''attribut'':25C ''call'':11C ''cheat'':60C ''cheatsheet'':54C ''check'':36C ''code'':7C,15C,38C,44C ''datafram'':22C ''extrapol'':4C ''forecast'':39C,45C ''function'':10C,41C ''href'':56C ''n'':31C ''newdata'':16C ''next'':30C ''nofollow'':63C ''otherwis'':35C ''p'':1C,34C ''packag'':47C ''predict'':8C ''r'':53C,59C ''recommend'':50C ''rel'':62C ''sheet.pdf'':61C ''simpl'':3C ''use'':5C ''valu'':26C ''year'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9501, 1, '2015-12-24 00:35:23.427', 1, 15, 'Include label for each time step to allow gradient flow in LSTM', '&lt;p&gt;Say I want to do a &lt;a href="http://karpathy.github.io/2015/05/21/rnn-effectiveness/" rel="nofollow"&gt;many-to-one&lt;/a&gt; classification task such as sentiment analysis with a sequence of ~100 elements using a &lt;a href="http://deeplearning.net/tutorial/lstm.html" rel="nofollow"&gt;LSTM&lt;/a&gt;. Might it help to train the network using a many-to-many setup instead to allow the gradients to flow more easily to the initial elements in the long sequence? Each $y_t$ would just be a replica of the true value $y$.&lt;/p&gt;
', 'machine-learning  neuralnetwork  deep-learning', NULL, '''/2015/05/21/rnn-effectiveness/'':31C ''/a'':38C,61C ''/p'':106C ''/tutorial/lstm.html'':57C ''100'':49C ''allow'':8A,78C ''analysi'':44C ''classif'':39C ''deep'':18B ''deep-learn'':17B ''deeplearning.net'':56C ''deeplearning.net/tutorial/lstm.html'':55C ''easili'':84C ''element'':50C,88C ''flow'':10A,82C ''gradient'':9A,80C ''help'':64C ''href'':28C,54C ''includ'':1A ''initi'':87C ''instead'':76C ''karpathy.github.io'':30C ''karpathy.github.io/2015/05/21/rnn-effectiveness/'':29C ''label'':2A ''learn'':15B,19B ''long'':91C ''lstm'':12A,60C ''machin'':14B ''machine-learn'':13B ''mani'':35C,72C,74C ''many-to-mani'':71C ''many-to-on'':34C ''might'':62C ''network'':68C ''neuralnetwork'':16B ''nofollow'':33C,59C ''one'':37C ''p'':20C ''rel'':32C,58C ''replica'':100C ''say'':21C ''sentiment'':43C ''sequenc'':47C,92C ''setup'':75C ''step'':6A ''task'':40C ''time'':5A ''train'':66C ''true'':103C ''use'':51C,69C ''valu'':104C ''want'':23C ''would'':96C ''y'':94C,105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9281, 1, '2015-12-09 11:29:23.027', 1, 22, 'Minimum Error Rate Training using Powell Search for Machine Translation', '&lt;p&gt;From the tutorial slides: &lt;a href="http://mt-class.org/jhu/slides/lecture-tuning.pdf" rel="nofollow"&gt;http://mt-class.org/jhu/slides/lecture-tuning.pdf&lt;/a&gt;, (slide 37) the powell search algorithm goes as such:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Input: sentences with n-best list of translations, initial parameter values
1: repeat
2:   for all parameter do
3:     set of threshold points T = {}
4:     for all sentence do
5:       for all translation do
6:         compute line l: parameter value â score
7:       end for
8:       find line l with steepest descent
9:       while find line l2 that intersects with l first do
10:        add parameter value at intersection to set of threshold points T
11:        l = l2
12:      end while
13:    end for
14:    sort set of threshold points T by parameter value
15:    compute score for value before first threshold point
16:    for all threshold point t â T do
17:      compute score for value after threshold point t
18:      if highest do record max score and threshold point t
19:    end for
20:    if max score is higher than current do update parameter value
21:    end for
22: until no changes to parameter values applied
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And on slide 31, there''s a formula to &lt;code&gt;compute the line l&lt;/code&gt; which is used for line 6 of the pseudo code above:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;p(ei|f) = Î»ai + bi
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However, it''s unclear what should the values be for &lt;code&gt;ai&lt;/code&gt; and &lt;code&gt;bi&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Given a sentence from the nbest list as such:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;0 ||| including options , ÑÑÐ¾Ð±Ñ buy 20 Ð±Ð¾Ð»ÑÑÐµ planes , ÑÐ¾Ð¾ÑÐ½Ð¾ÑÐµÐ½Ð¸Ð¸ volume - Ð² ÑÐ¾Ð¼ 26 Ð¼Ð¸Ð»Ð»Ð¸Ð°ÑÐ´Ð¾Ð² Ð´Ð¾Ð»Ð»Ð°ÑÐ¾Ð² .  ||| LexicalReordering0= -3.10978 0 0 -6.72232 0 0 Distortion0= 0 LM0= -111.207 WordPenalty0= -18 PhrasePenalty0= 16 TranslationModel0= -13.1426 -8.45991 -12.0762 -11.3076 ||| -746.351
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;When computing the score for this line for the first parameter of &lt;code&gt;LexicalReordering0&lt;/code&gt;, is the &lt;code&gt;p(ei|f)&lt;/code&gt; this?:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;p(ei|f) = Î»*-3.10978 + bi
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But what is the value of &lt;code&gt;bi&lt;/code&gt; in this case?&lt;/p&gt;

&lt;p&gt;Is &lt;code&gt;p(ei|f) = -746.351&lt;/code&gt; for this sentence ?&lt;/p&gt;

&lt;p&gt;Does the &lt;code&gt;â score&lt;/code&gt; on line 6 of the pseudo code mean -746.351 from the nbest list or does it mean the MT evaluation metric score for this translation with respect to the reference translation (e.g. BLEU)?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;What should line 6 of the pseudo code do?&lt;/strong&gt; &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9282, 2, '2015-12-09 14:30:16.7', 2, NULL, NULL, '&lt;p&gt;You can see nice algorithms in kaggle competitions about CTR:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://www.kaggle.com/c/avito-context-ad-clicks" rel="nofollow"&gt;https://www.kaggle.com/c/avito-context-ad-clicks&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.kaggle.com/c/avazu-ctr-prediction" rel="nofollow"&gt;https://www.kaggle.com/c/avazu-ctr-prediction&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Just go to forum of each competition and search for winning solutions ;)&lt;/p&gt;
', NULL, NULL, '''/c/avazu-ctr-prediction'':30C ''/c/avazu-ctr-prediction&lt;/a&gt;&lt;/li&gt;'':35C ''/c/avito-context-ad-clicks'':19C ''/c/avito-context-ad-clicks&lt;/a&gt;&lt;/li&gt;'':24C ''/p'':12C,50C ''/ul'':36C ''algorithm'':6C ''competit'':9C,44C ''ctr'':11C ''forum'':41C ''go'':39C ''href'':16C,27C ''kaggl'':8C ''li'':14C,25C ''nice'':5C ''nofollow'':21C,32C ''p'':1C,37C ''rel'':20C,31C ''search'':46C ''see'':4C ''solut'':49C ''ul'':13C ''win'':48C ''www.kaggle.com'':18C,23C,29C,34C ''www.kaggle.com/c/avazu-ctr-prediction'':28C ''www.kaggle.com/c/avazu-ctr-prediction&lt;/a&gt;&lt;/li&gt;'':33C ''www.kaggle.com/c/avito-context-ad-clicks'':17C ''www.kaggle.com/c/avito-context-ad-clicks&lt;/a&gt;&lt;/li&gt;'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9283, 2, '2015-12-09 14:43:35.64', 0, NULL, NULL, '&lt;p&gt;I think that it depends on your data set. There are many ways to handle unbalanced data sets, just search, for example &lt;a href="https://www.quora.com/In-classification-how-do-you-handle-an-unbalanced-training-set" rel="nofollow"&gt;https://www.quora.com/In-classification-how-do-you-handle-an-unbalanced-training-set&lt;/a&gt; . I think that the simplest way is to use the same distribution of classes in the train and test sets.&lt;/p&gt;

&lt;p&gt;If you have really small amount of minority class you can try &lt;a href="https://en.wikipedia.org/wiki/One-class_classification" rel="nofollow"&gt;one-class classification&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':79C ''/in-classification-how-do-you-handle-an-unbalanced-training-set'':28C ''/in-classification-how-do-you-handle-an-unbalanced-training-set&lt;/a&gt;'':33C ''/p'':54C,80C ''/wiki/one-class_classification'':72C ''amount'':61C ''class'':47C,64C,77C ''classif'':78C ''data'':9C,18C ''depend'':6C ''distribut'':45C ''en.wikipedia.org'':71C ''en.wikipedia.org/wiki/one-class_classification'':70C ''exampl'':23C ''handl'':16C ''href'':25C,69C ''mani'':13C ''minor'':63C ''nofollow'':30C,74C ''one'':76C ''one-class'':75C ''p'':1C,55C ''realli'':59C ''rel'':29C,73C ''search'':21C ''set'':10C,19C,53C ''simplest'':38C ''small'':60C ''test'':52C ''think'':3C,35C ''train'':50C ''tri'':67C ''unbalanc'':17C ''use'':42C ''way'':14C,39C ''www.quora.com'':27C,32C ''www.quora.com/in-classification-how-do-you-handle-an-unbalanced-training-set'':26C ''www.quora.com/in-classification-how-do-you-handle-an-unbalanced-training-set&lt;/a&gt;'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9284, 2, '2015-12-09 14:49:27.51', 0, NULL, NULL, '&lt;p&gt;$R^2$ is for data with a numerical range. Classification by k Nearest Neighbours assigns class labels that are just labels (even if you choose them to be numbers, they aren''t like real numbers). &lt;/p&gt;

&lt;p&gt;You use kNN in a supervised setting, typical quality assessment consists in splitting up your data in training and test sets (n-fold cross validation) and determining precision, recall, and F-score.&lt;/p&gt;
', NULL, NULL, '''/p'':37C,73C ''2'':3C ''aren'':32C ''assess'':48C ''assign'':16C ''choos'':26C ''class'':17C ''classif'':11C ''consist'':49C ''cross'':63C ''data'':6C,54C ''determin'':66C ''even'':23C ''f'':71C ''f-score'':70C ''fold'':62C ''k'':13C ''knn'':41C ''label'':18C,22C ''like'':34C ''n'':61C ''n-fold'':60C ''nearest'':14C ''neighbour'':15C ''number'':30C,36C ''numer'':9C ''p'':1C,38C ''precis'':67C ''qualiti'':47C ''r'':2C ''rang'':10C ''real'':35C ''recal'':68C ''score'':72C ''set'':45C,59C ''split'':51C ''supervis'':44C ''test'':58C ''train'':56C ''typic'':46C ''use'':40C ''valid'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9285, 1, '2015-12-09 17:10:27.163', 3, 30, 'Term for calculated values that lose pertinence when changing scale', '&lt;p&gt;I''m trying to find the term for a type of calculations or values that cannot be simpply added or multiplied when zooming in or out from a temporal scale. I know it''s not very clear, if it were I would just google the question.&lt;/p&gt;

&lt;p&gt;Here''s an example: say you''re comparing 2014 vs 2015 sales of ice cream and temperature at day''s level. If you want to do an analysis by week, you can just add the sales for seven days, and compare weeks between them, the information still makes sense: there''s a fact (amount of money deposited in the bank that week) that is grounded in reality that relate to the new figure. You could also calculate an average by day from the total by week.&lt;/p&gt;

&lt;p&gt;But, adding temperatures for a total would not make any sense in reality, you need to calculate an average for that kind of data.&lt;/p&gt;

&lt;p&gt;So is there a term for this type of values that can only make sense as average and never be added or multiplied (except as an intermediate step for calculating an average)?&lt;/p&gt;
', 'terminology', NULL, '''/p'':59C,147C,173C,207C ''2014'':69C ''2015'':71C ''ad'':31C,150C,195C ''add'':94C ''also'':136C ''amount'':114C ''analysi'':88C ''averag'':139C,167C,191C,206C ''bank'':120C ''calcul'':3A,24C,137C,165C,204C ''cannot'':28C ''chang'':9A ''clear'':49C ''compar'':68C,101C ''could'':135C ''cream'':75C ''data'':172C ''day'':79C,99C,141C ''deposit'':117C ''exampl'':64C ''except'':198C ''fact'':113C ''figur'':133C ''find'':17C ''googl'':56C ''ground'':125C ''ice'':74C ''inform'':106C ''intermedi'':201C ''kind'':170C ''know'':44C ''level'':81C ''lose'':6A ''m'':14C ''make'':108C,157C,188C ''money'':116C ''multipli'':33C,197C ''need'':163C ''never'':193C ''new'':132C ''p'':12C,60C,148C,174C ''pertin'':7A ''question'':58C ''re'':67C ''realiti'':127C,161C ''relat'':129C ''sale'':72C,96C ''say'':65C ''scale'':10A,42C ''sens'':109C,159C,189C ''seven'':98C ''simppli'':30C ''step'':202C ''still'':107C ''temperatur'':77C,151C ''tempor'':41C ''term'':1A,19C,179C ''terminolog'':11B ''total'':144C,154C ''tri'':15C ''type'':22C,182C ''valu'':4A,26C,184C ''vs'':70C ''want'':84C ''week'':90C,102C,122C,146C ''would'':54C,155C ''zoom'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9287, 2, '2015-12-09 18:56:08.837', 2, NULL, NULL, '&lt;p&gt;I found a PhD in Energy Informatics at &lt;a href="http://www.energystatusdata.kit.edu/index.php" rel="nofollow"&gt;Karlsruhe Institute of Technology (KIT)&lt;/a&gt; and PhD in Learning Analytics at &lt;a href="http://utscic.edu.au/research/phd/" rel="nofollow"&gt;University of Technology Sydney&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':22C,40C ''/index.php'':14C ''/p'':41C ''/research/phd/'':33C ''analyt'':27C ''energi'':7C ''found'':3C ''href'':11C,30C ''informat'':8C ''institut'':18C ''karlsruh'':17C ''kit'':21C ''learn'':26C ''nofollow'':16C,35C ''p'':1C ''phd'':5C,24C ''rel'':15C,34C ''sydney'':39C ''technolog'':20C,38C ''univers'':36C ''utscic.edu.au'':32C ''utscic.edu.au/research/phd/'':31C ''www.energystatusdata.kit.edu'':13C ''www.energystatusdata.kit.edu/index.php'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9300, 2, '2015-12-10 03:30:23.02', 1, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;I have attribute that contain string or null. i want to change the
  record of attribute to 0 if null and 1 if not null. What preprocessing
  step name?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This step is &lt;a href="http://www.ats.ucla.edu/stat/mult_pkg/faq/general/dummy.htm" rel="nofollow"&gt;dummy coding&lt;/a&gt;.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;i have attribute that contain 200 tweet of twitter user. i make new
  attribute that describe URL ratio in 200 tweet (number of URL in 200
  tweet / 200). is it aggregation or feature creation or else?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Feature aggregation&lt;/p&gt;
', NULL, NULL, '''/a'':47C ''/blockquote'':33C,88C ''/p'':32C,48C,87C,92C ''/stat/mult_pkg/faq/general/dummy.htm'':42C ''0'':20C ''1'':24C ''200'':56C,70C,76C,78C ''aggreg'':81C,91C ''attribut'':5C,18C,53C,64C ''blockquot'':1C,49C ''chang'':14C ''code'':46C ''contain'':7C,55C ''creation'':84C ''describ'':66C ''dummi'':45C ''els'':86C ''featur'':83C,90C ''href'':39C ''make'':62C ''name'':31C ''new'':63C ''nofollow'':44C ''null'':10C,22C,27C ''number'':72C ''p'':2C,34C,50C,89C ''preprocess'':29C ''ratio'':68C ''record'':16C ''rel'':43C ''step'':30C,36C ''string'':8C ''tweet'':57C,71C,77C ''twitter'':59C ''url'':67C,74C ''user'':60C ''want'':12C ''www.ats.ucla.edu'':41C ''www.ats.ucla.edu/stat/mult_pkg/faq/general/dummy.htm'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9288, 1, '2015-12-09 19:26:25.173', 1, 35, 'Which Machine Learning book to choose (APM, MLAP or ISL)?', '&lt;p&gt;I''m searching a book as a refresher in machine learning (I have taken a lecture in machine learning sometime ago). I will be applying machine learning in a project.&lt;/p&gt;

&lt;p&gt;I have searched a lot of books and found the following three the one which best fits me:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Applied Predictive Modeling by Kuhn and Johnson&lt;/li&gt;
&lt;li&gt;Machine learning an Algorithmic Perspective (second edition) by Marsland&lt;/li&gt;
&lt;li&gt;An Introduction to Statistical Learning by Hastie&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Applied Predictive Modeling and An Introduction to Statistical Learning uses R as examples and Machine learning an Algorithmic Perspective uses Python. The used language does not matter that much because I''m not really interested in language examples (I''m using Matlab) and I will probably skip such examples.&lt;/p&gt;

&lt;p&gt;The advantage of Applied Predictive Modeling seems to be that it covers the whole machine learning procedure (feature selection etc.) and seems to be very well written. The advantage of Machine learning an Algorithmic Perspective (second edition) is that it covers more topics (ensemble learning, graphical models, gaussian processes) and has perhaps a bit more math.&lt;/p&gt;

&lt;p&gt;Which one of these three books would you recommend (and why)?&lt;/p&gt;
', 'machine-learning  reference-request  books', NULL, '''/li'':79C,90C,99C ''/p'':49C,69C,151C,209C,222C ''/ul'':100C ''advantag'':154C,181C ''ago'':39C ''algorithm'':84C,119C,186C ''apm'':7A ''appli'':43C,72C,102C,156C ''best'':66C ''bit'':206C ''book'':4A,17B,23C,57C,216C ''choos'':6A ''cover'':164C,193C ''edit'':87C,189C ''ensembl'':196C ''etc'':172C ''exampl'':114C,139C,150C ''featur'':170C ''fit'':67C ''follow'':61C ''found'':59C ''gaussian'':200C ''graphic'':198C ''hasti'':98C ''interest'':136C ''introduct'':93C,107C ''isl'':10A ''johnson'':78C ''kuhn'':76C ''languag'':125C,138C ''learn'':3A,13B,29C,37C,45C,82C,96C,110C,117C,168C,184C,197C ''lectur'':34C ''li'':71C,80C,91C ''lot'':55C ''m'':20C,133C,141C ''machin'':2A,12B,28C,36C,44C,81C,116C,167C,183C ''machine-learn'':11B ''marsland'':89C ''math'':208C ''matlab'':143C ''matter'':128C ''mlap'':8A ''model'':74C,104C,158C,199C ''much'':130C ''one'':64C,212C ''p'':18C,50C,101C,152C,210C ''perhap'':204C ''perspect'':85C,120C,187C ''predict'':73C,103C,157C ''probabl'':147C ''procedur'':169C ''process'':201C ''project'':48C ''python'':122C ''r'':112C ''realli'':135C ''recommend'':219C ''refer'':15B ''reference-request'':14B ''refresh'':26C ''request'':16B ''search'':21C,53C ''second'':86C,188C ''seem'':159C,174C ''select'':171C ''skip'':148C ''sometim'':38C ''statist'':95C,109C ''taken'':32C ''three'':62C,215C ''topic'':195C ''ul'':70C ''use'':111C,121C,124C,142C ''well'':178C ''whole'':166C ''would'':217C ''written'':179C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9508, 2, '2015-12-24 21:11:01.72', 1, NULL, NULL, '&lt;p&gt;Narrowing down your question is a binary classification task. Neural networks and svm are equally sufficient for the job. However, only decision trees use "scores" and actually show you the reasoning behind the classification while the other two are a black box. What you are after is called uplifting modelling. Also   it is worth checking feature selection and sensitivity due to the nature of the task.&lt;/p&gt;
', NULL, NULL, '''/p'':68C ''actual'':28C ''also'':52C ''behind'':33C ''binari'':8C ''black'':42C ''box'':43C ''call'':49C ''check'':56C ''classif'':9C,35C ''decis'':23C ''due'':61C ''equal'':16C ''featur'':57C ''howev'':21C ''job'':20C ''model'':51C ''narrow'':2C ''natur'':64C ''network'':12C ''neural'':11C ''p'':1C ''question'':5C ''reason'':32C ''score'':26C ''select'':58C ''sensit'':60C ''show'':29C ''suffici'':17C ''svm'':14C ''task'':10C,67C ''tree'':24C ''two'':39C ''uplift'':50C ''use'':25C ''worth'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9289, 2, '2015-12-09 19:31:14.81', 0, NULL, NULL, '&lt;p&gt;I don''t get the assertion that a sum of temperature loses pertinence&lt;br&gt;
$$
\text{avg}(T)  = \frac{\text{sum}(T)}{n}
$$
$$
\frac{\text{avg}(\text{Revenue})}{\text{avg}(\text{Temp})} = \frac{\frac{\text{sum}(\text{Revenue})}{ n}}{\frac{\text{sum}(\text{Temp})}{n}} = \frac{\text{sum}(\text{Revenue})}{\text{sum}(\text{Temp}) }
$$
Now multiply does lack meaning if the value is not 0 based&lt;br&gt;
200 degrees Â°F (Fahrenheit) is not twice as hot 100 Â°F&lt;br&gt;
As Â°F is not zero based&lt;br&gt;
You need to translate to absolute - Â°R (Rankine)&lt;br&gt;
(200 + 459.67) / (100 + 459.67) = 1.17867672&lt;/p&gt;

&lt;p&gt;Not sure what term you want to call it but I think you are describing linear zero based properties   &lt;/p&gt;

&lt;p&gt;Like $\log$ is not linear $\frac{\log(a)}{\log(b)} \ne \frac{a}{b}$ (even if $a$ and $b$ are zero based)  &lt;/p&gt;

&lt;p&gt;Spacial data does not add, subtract, or average in a simple linear manner.&lt;/p&gt;
', NULL, NULL, '''/p'':105C,126C,150C,165C ''0'':65C ''1.17867672'':104C ''100'':78C,102C ''200'':68C,100C ''459.67'':101C,103C ''absolut'':95C ''add'':156C ''assert'':7C ''averag'':159C ''avg'':17C,26C,30C ''b'':137C,141C,146C ''base'':66C,88C,124C,149C ''br'':15C,67C,81C,89C,99C ''call'':114C ''data'':153C ''degre'':69C ''describ'':121C ''even'':142C ''f'':71C,80C,84C ''fahrenheit'':72C ''frac'':19C,24C,33C,34C,40C,46C,133C,139C ''get'':5C ''hot'':77C ''lack'':58C ''like'':128C ''linear'':122C,132C,163C ''log'':129C,134C,136C ''lose'':13C ''manner'':164C ''mean'':59C ''multipli'':56C ''n'':23C,39C,45C ''ne'':138C ''need'':91C ''p'':1C,106C,127C,151C ''pertin'':14C ''properti'':125C ''r'':97C ''rankin'':98C ''revenu'':28C,38C,50C ''simpl'':162C ''spacial'':152C ''subtract'':157C ''sum'':10C,21C,36C,42C,48C,52C ''sure'':108C ''temp'':32C,44C,54C ''temperatur'':12C ''term'':110C ''text'':16C,20C,25C,27C,29C,31C,35C,37C,41C,43C,47C,49C,51C,53C ''think'':118C ''translat'':93C ''twice'':75C ''valu'':62C ''want'':112C ''zero'':87C,123C,148C ''â'':70C,79C,83C,96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9290, 1, '2015-12-09 19:34:03.51', 2, 37, 'Scikit Learn''s RandomForestRegressor is not giving results on large data set', '&lt;p&gt;I have a Pandas dataframe X(20346, 4116). All independent columns have binary variable as 0 or 1. Whereas dependent column has continuous variable. When I execute below code using the scikit-learn library, it takes for ever and I am not getting any result.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;X_train, X_test, y_train, y_test = train_test_split(X13, y, train_size=0.8)

rf = RandomForestRegressor(n_estimators=100,  max_features=''auto'', oob_score=True,
                            n_jobs=1, verbose=0)
rf.fit(X_train, y_train) 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am using this for feature selection. In case of less number of rows I can get result. But when number of rows go up then this problem starts happening. Do you have any idea how to make it work?&lt;/p&gt;
', 'python  random-forest  scikit', NULL, '''/code'':105C ''/p'':65C,148C ''/pre'':106C ''0'':34C,99C ''0.8'':83C ''1'':36C,97C ''100'':88C ''20346'':25C ''4116'':26C ''auto'':91C ''binari'':31C ''case'':116C ''code'':47C,67C ''column'':29C,39C ''continu'':41C ''data'':11A ''datafram'':23C ''depend'':38C ''estim'':87C ''ever'':57C ''execut'':45C ''featur'':90C,113C ''forest'':16B ''get'':62C,124C ''give'':7A ''go'':131C ''happen'':137C ''idea'':142C ''independ'':28C ''job'':96C ''larg'':10A ''learn'':2A,52C ''less'':118C ''librari'':53C ''make'':145C ''max'':89C ''n'':86C,95C ''number'':119C,128C ''oob'':92C ''p'':18C,107C ''panda'':22C ''pre'':66C ''problem'':135C ''python'':13B ''random'':15B ''random-forest'':14B ''randomforestregressor'':4A,85C ''result'':8A,64C,125C ''rf'':84C ''rf.fit'':100C ''row'':121C,130C ''scikit'':1A,17B,51C ''scikit-learn'':50C ''score'':93C ''select'':114C ''set'':12A ''size'':82C ''split'':78C ''start'':136C ''take'':55C ''test'':71C,75C,77C ''train'':69C,73C,76C,81C,102C,104C ''true'':94C ''use'':48C,110C ''variabl'':32C,42C ''verbos'':98C ''wherea'':37C ''work'':147C ''x'':24C,68C,70C,101C ''x13'':79C ''y'':72C,74C,80C,103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9292, 1, '2015-12-09 22:12:27.573', 2, 37, 'Combining Linear Regression and Time Series', '&lt;p&gt;Does anyone know of a predictive model that can combine the linear regression model and time series model?&lt;/p&gt;

&lt;p&gt;I have some data about some products. The data has two parts, some attributes about the products themselves, and some time series data of month-by-month sales amounts for each product. We want to predict future month-by-month sales amount for each product. with the products attributes data, we can build a regression model, or an svm model, to predict sales amounts. With the time series sales amount, we can build a time series model to predict sales amount, but is there any predictive model that can combine the regression, svm, and time series models to do the prediction?&lt;/p&gt;
', 'time-series  regression  svm', NULL, '''/p'':31C,136C ''amount'':62C,76C,98C,104C,115C ''anyon'':14C ''attribut'':46C,83C ''build'':87C,107C ''combin'':1A,22C,124C ''data'':36C,41C,55C,84C ''futur'':70C ''know'':15C ''linear'':2A,24C ''model'':19C,26C,30C,90C,94C,111C,121C,131C ''month'':58C,60C,72C,74C ''month-by-month'':57C,71C ''p'':12C,32C ''part'':44C ''predict'':18C,69C,96C,113C,120C,135C ''product'':39C,49C,65C,79C,82C ''regress'':3A,10B,25C,89C,126C ''sale'':61C,75C,97C,103C,114C ''seri'':6A,9B,29C,54C,102C,110C,130C ''svm'':11B,93C,127C ''time'':5A,8B,28C,53C,101C,109C,129C ''time-seri'':7B ''two'':43C ''want'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9293, 2, '2015-12-09 22:17:04.55', 0, NULL, NULL, '&lt;p&gt;Like someone else mentioned, treating IPs as int automatically gives higher bits higher weights.
I''ve used variance of IPs which is log scaled.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;math.log(np.std([IP(ip).int() for ip in ips]))
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':38C ''/p'':26C ''/pre'':39C ''automat'':10C ''bit'':13C ''code'':28C ''els'':4C ''give'':11C ''higher'':12C,14C ''int'':9C,33C ''ip'':7C,21C,31C,32C,35C,37C ''like'':2C ''log'':24C ''math.log'':29C ''mention'':5C ''np.std'':30C ''p'':1C ''pre'':27C ''scale'':25C ''someon'':3C ''treat'':6C ''use'':18C ''varianc'':19C ''ve'':17C ''weight'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9294, 1, '2015-12-09 22:24:43.523', 1, 65, 'What is the appropriate evaluation metric for RandomForest with probability in R?', '&lt;p&gt;In order to build a predict model with two categories (buy or not buy),I want to use RandomForest and predict with type=''prob'', so I can have a prob of someone buy or not buy. So, with this outcome I can clusterize and make groups, like this:&lt;/p&gt;

&lt;p&gt;group A: costumer who has [100 to 80]% of buy.
group B: costumer who has [81 to 60]% of buy.
...&lt;/p&gt;

&lt;p&gt;But I don''t know the appropriate evaluation metric to measure the accuracy of this model. I guess that I can''t use a confusion matrix.&lt;/p&gt;

&lt;p&gt;Maybe I can use a ROC curve, and or measure KS between the buy group with the not buy group. But I''m not sure about this metrics. &lt;/p&gt;
', 'r  predictive-modeling  random-forest  probability', NULL, '''/p'':70C,92C,120C,150C ''100'':77C ''60'':89C ''80'':79C ''81'':87C ''accuraci'':106C ''appropri'':4A,100C ''b'':83C ''build'':25C ''buy'':32C,35C,54C,57C,81C,91C,135C,140C ''categori'':31C ''cluster'':64C ''confus'':118C ''costum'':74C,84C ''curv'':128C ''evalu'':5A,101C ''forest'':19B ''group'':67C,72C,82C,136C,141C ''guess'':111C ''know'':98C ''ks'':132C ''like'':68C ''m'':144C ''make'':66C ''matrix'':119C ''mayb'':122C ''measur'':104C,131C ''metric'':6A,102C,149C ''model'':16B,28C,109C ''order'':23C ''outcom'':61C ''p'':21C,71C,93C,121C ''predict'':15B,27C,42C ''predictive-model'':14B ''prob'':45C,51C ''probabl'':10A,20B ''r'':12A,13B ''random'':18B ''random-forest'':17B ''randomforest'':8A,40C ''roc'':127C ''someon'':53C ''sure'':146C ''two'':30C ''type'':44C ''use'':39C,116C,125C ''want'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9295, 2, '2015-12-09 22:48:12.113', 0, NULL, NULL, '&lt;p&gt;Group the entires by the probability score you assigned them (e.g. 100-95%, 95-90%,....) and then measure the likelihood of their purchases from ground truth.  You can then perform a Chi-squared calculation between your predicted and measured outcomes.&lt;/p&gt;
', NULL, NULL, '''-90'':16C ''-95'':14C ''/p'':43C ''100'':13C ''95'':15C ''assign'':10C ''calcul'':36C ''chi'':34C ''chi-squar'':33C ''e.g'':12C ''entir'':4C ''ground'':26C ''group'':2C ''likelihood'':21C ''measur'':19C,41C ''outcom'':42C ''p'':1C ''perform'':31C ''predict'':39C ''probabl'':7C ''purchas'':24C ''score'':8C ''squar'':35C ''truth'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9296, 1, '2015-12-10 00:53:03.687', 1, 37, 'Data preprocessing : Aggregation, feature creation, or else?', '&lt;p&gt;I have a problem to name data processing step.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;I have an attribute that contain string or null. I want to change the record of an attribute to 0 if null and 1 if not null. What preprocessing step name?&lt;/li&gt;
&lt;li&gt;I have an attribute that contain 200 tweets of a twitter user. I make a new attribute that describe URL ratio in 200 tweets (number of URL in 200 tweet / 200). is it aggregation or feature creation or else?&lt;/li&gt;
&lt;/ol&gt;
', 'data-mining  preprocessing', NULL, '''/li'':56C,97C ''/ol'':98C ''/p'':22C ''0'':44C ''1'':48C ''200'':64C,80C,86C,88C ''aggreg'':3A,91C ''attribut'':28C,42C,61C,74C ''chang'':37C ''contain'':30C,63C ''creation'':5A,94C ''data'':1A,9B,19C ''data-min'':8B ''describ'':76C ''els'':7A,96C ''featur'':4A,93C ''li'':24C,57C ''make'':71C ''mine'':10B ''name'':18C,55C ''new'':73C ''null'':33C,46C,51C ''number'':82C ''ol'':23C ''p'':12C ''preprocess'':2A,11B,53C ''problem'':16C ''process'':20C ''ratio'':78C ''record'':39C ''step'':21C,54C ''string'':31C ''tweet'':65C,81C,87C ''twitter'':68C ''url'':77C,84C ''user'':69C ''want'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9297, 2, '2015-12-10 01:08:47.27', 1, NULL, NULL, '&lt;p&gt;You are looking for names to attribute to the two items listed? For (1) I would just call it a transformation as it is a straight mapping with no change in distribution of the feature. When working with data, make sure you make copies of your data transformation and do not alter the original data set.&lt;/p&gt;

&lt;p&gt;For (2), since it is a single number per group, where group here is the full data set I would call it an aggregation. Likewise if you did a similar calculation per user. If however, you computed a new value from existing features for each record, this would be feature generation or creation. &lt;/p&gt;
', NULL, NULL, '''/p'':58C,113C ''1'':15C ''2'':61C ''aggreg'':83C ''alter'':53C ''attribut'':8C ''calcul'':90C ''call'':19C,80C ''chang'':31C ''comput'':96C ''copi'':45C ''creation'':112C ''data'':40C,48C,56C,76C ''distribut'':33C ''exist'':101C ''featur'':36C,102C,109C ''full'':75C ''generat'':110C ''group'':69C,71C ''howev'':94C ''item'':12C ''likewis'':84C ''list'':13C ''look'':4C ''make'':41C,44C ''map'':28C ''name'':6C ''new'':98C ''number'':67C ''origin'':55C ''p'':1C,59C ''per'':68C,91C ''record'':105C ''set'':57C,77C ''similar'':89C ''sinc'':62C ''singl'':66C ''straight'':27C ''sure'':42C ''transform'':22C,49C ''two'':11C ''user'':92C ''valu'':99C ''work'':38C ''would'':17C,79C,107C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9298, 2, '2015-12-10 01:20:42.543', 0, NULL, NULL, '&lt;p&gt;Have you tried increasing  n_jobs or setting oob_score to False? I am not sure what the logic is in sklearn to default max_features to auto (=n_features). Using sqrt is common in random forest and should gain efficiency.&lt;/p&gt;
', NULL, NULL, '''/p'':43C ''auto'':29C ''common'':35C ''default'':25C ''effici'':42C ''fals'':13C ''featur'':27C,31C ''forest'':38C ''gain'':41C ''increas'':5C ''job'':7C ''logic'':20C ''max'':26C ''n'':6C,30C ''oob'':10C ''p'':1C ''random'':37C ''score'':11C ''set'':9C ''sklearn'':23C ''sqrt'':33C ''sure'':17C ''tri'':4C ''use'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9308, 1, '2015-12-10 13:41:33.53', 4, 70, 'Web services to mine the social web?', '&lt;p&gt;Are there any web services that can be used to analyse data in social networks with respect to a specific research question (e.g. mentioning of certain products in social media discussions)?&lt;/p&gt;
', 'data-mining  social-network-analysis', NULL, '''/p'':47C ''analys'':26C ''analysi'':14B ''certain'':41C ''data'':9B,27C ''data-min'':8B ''discuss'':46C ''e.g'':38C ''media'':45C ''mention'':39C ''mine'':4A,10B ''network'':13B,30C ''p'':15C ''product'':42C ''question'':37C ''research'':36C ''respect'':32C ''servic'':2A,20C ''social'':6A,12B,29C,44C ''social-network-analysi'':11B ''specif'':35C ''use'':24C ''web'':1A,7A,19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9301, 1, '2015-12-10 05:25:41.537', 1, 48, 'How to visualize (make plot) of regression output against categorical input variable?', '&lt;p&gt;I am doing linear regression with multiple variables. In my data I have &lt;strong&gt;n = 143&lt;/strong&gt; features and &lt;strong&gt;m = 13000&lt;/strong&gt; training examples. Some of my features are continuous (ordinal) variables (area, year, number of rooms). But I also have categorical variables (district, color, type). For now I visualized some of my feautures against predicted price. For example here is the plot of &lt;code&gt;area&lt;/code&gt; against predicted &lt;code&gt;price&lt;/code&gt;:
&lt;a href="http://i.stack.imgur.com/n3Mc7.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/n3Mc7.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Since &lt;code&gt;area&lt;/code&gt; is continuous ordinal variable I had no troubles visualizing the data. But now I wanted to somehow visualize dependency of my categorical variables (such as district) on predicted price.
For categorical variables I used one-hot (dummy) encoding. 
&lt;br&gt;For example that kind of data:&lt;br&gt;
&lt;a href="http://i.stack.imgur.com/hoFqt.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/hoFqt.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;turned to this format:
&lt;a href="http://i.stack.imgur.com/jQBuN.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/jQBuN.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If I were using ordinal encoding for districts this way:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;DistrictA - 1
DistrictB - 2
DistrictC - 3
DistrictD - 4
DistrictE - 5
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I would plot this values against predicted price pretty easy by putting 1-5 to X axis and price to Y axis.&lt;/p&gt;

&lt;p&gt;But I used dummy coding and now I do not know how can I show (visualize) dependency between price and categorical variable ''District'' represented as series of zeros and ones.&lt;/p&gt;

&lt;p&gt;How can I make a plot showing a regression line of districts against predicted price in case of using dummy coding?&lt;/p&gt;
', 'machine-learning  visualization  categorical-data  linear-regression', NULL, '''-5'':251C ''/a'':114C,185C,209C ''/code'':91C,96C,120C,235C ''/hofqt.jpg'':172C,179C ''/jqbun.jpg'':196C,203C ''/n3mc7.jpg'':101C,108C ''/p'':115C,186C,210C,222C,260C,292C,315C ''/pre'':236C ''/strong'':40C,46C ''1'':226C,250C ''13000'':45C ''143'':39C ''2'':228C ''3'':230C ''4'':232C ''5'':234C ''also'':64C ''alt'':109C,180C,204C ''area'':57C,90C,119C ''axi'':254C,259C ''br'':160C,167C ''case'':310C ''categor'':10A,18B,66C,142C,151C,282C ''categorical-data'':17B ''code'':89C,94C,118C,224C,266C,314C ''color'':69C ''continu'':54C,122C ''data'':19B,34C,131C,166C ''depend'':139C,278C ''descript'':112C,183C,207C ''district'':68C,146C,219C,233C,284C,305C ''districta'':225C ''districtb'':227C ''districtc'':229C ''districtd'':231C ''dummi'':158C,265C,313C ''easi'':247C ''encod'':159C,217C ''enter'':110C,181C,205C ''exampl'':48C,83C,162C ''featur'':41C,52C ''feautur'':78C ''format'':191C ''hot'':157C ''href'':98C,169C,193C ''i.stack.imgur.com'':100C,107C,171C,178C,195C,202C ''i.stack.imgur.com/hofqt.jpg'':170C,177C ''i.stack.imgur.com/jqbun.jpg'':194C,201C ''i.stack.imgur.com/n3mc7.jpg'':99C,106C ''imag'':111C,182C,206C ''img'':104C,175C,199C ''input'':11A ''kind'':164C ''know'':272C ''learn'':15B ''line'':303C ''linear'':21B,27C ''linear-regress'':20B ''m'':44C ''machin'':14B ''machine-learn'':13B ''make'':4A,297C ''multipl'':30C ''n'':38C ''nofollow'':103C,174C,198C ''number'':59C ''one'':156C,291C ''one-hot'':155C ''ordin'':55C,123C,216C ''output'':8A ''p'':23C,116C,187C,211C,237C,261C,293C ''plot'':5A,87C,240C,299C ''pre'':223C ''predict'':80C,93C,148C,244C,307C ''pretti'':246C ''price'':81C,95C,149C,245C,256C,280C,308C ''put'':249C ''regress'':7A,22B,28C,302C ''rel'':102C,173C,197C ''repres'':285C ''room'':61C ''seri'':287C ''show'':276C,300C ''sinc'':117C ''somehow'':137C ''src'':105C,176C,200C ''strong'':37C,43C ''train'':47C ''troubl'':128C ''turn'':188C ''type'':70C ''use'':154C,215C,264C,312C ''valu'':242C ''variabl'':12A,31C,56C,67C,124C,143C,152C,283C ''visual'':3A,16B,74C,129C,138C,277C ''want'':135C ''way'':221C ''would'':239C ''x'':253C ''y'':258C ''year'':58C ''zero'':289C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9302, 1, '2015-12-10 06:22:48.927', 7, 168, 'The cross-entropy error function in neural networks', '&lt;p&gt;In the &lt;a href="https://www.tensorflow.org/versions/master/tutorials/mnist/beginners/index.html"&gt;MNIST For ML Beginners&lt;/a&gt; they define cross-entropy as&lt;/p&gt;

&lt;p&gt;$$H_{y''} (y) := - \sum_{i} y_{i}'' \log (y_i)$$&lt;/p&gt;

&lt;p&gt;$y_i$ is the predicted probability distribution of the class $i$ (?) and $y_i''$ is the true probability for that class.&lt;/p&gt;

&lt;h2&gt;Question 1&lt;/h2&gt;

&lt;p&gt;Isn''t it a problem that $y_i$ (in $\log(y_i)$) could be 0? This would mean that we have a really bad classifier, of course. But think of an error in our dataset, e.g. an "obvious" &lt;code&gt;1&lt;/code&gt; labeled as &lt;code&gt;3&lt;/code&gt;. Would it simply crash? Does the model we chose (softmax activation at the end) basically never give the probability 0 for the correct class?&lt;/p&gt;

&lt;h2&gt;Question 2&lt;/h2&gt;

&lt;p&gt;I''ve learned that cross-entropy is defined as &lt;/p&gt;

&lt;p&gt;$$H_{y''}(y) := - \sum_{i} ({y_i'' \log(y_i) + (1-y_i'') \log (1-y_i)})$$&lt;/p&gt;

&lt;p&gt;What is correct? Do you have any textbook references for either version? How do those functions differ in their properties (as error functions for neural networks)?&lt;/p&gt;
', 'machine-learning  tensorflow', NULL, '''/a'':26C ''/code'':114C,119C ''/h2'':72C,148C ''/p'':33C,45C,68C,144C,160C,179C,207C ''/versions/master/tutorials/mnist/beginners/index.html'':21C ''0'':88C,139C ''1'':71C,113C,172C,176C ''2'':147C ''3'':118C ''activ'':130C ''bad'':97C ''basic'':134C ''beginn'':25C ''chose'':128C ''class'':56C,67C,143C ''classifi'':98C ''code'':112C,117C ''correct'':142C,183C ''could'':86C ''cours'':100C ''crash'':123C ''cross'':3A,30C,155C ''cross-entropi'':2A,29C,154C ''dataset'':108C ''defin'':28C,158C ''differ'':197C ''distribut'':53C ''e.g'':109C ''either'':191C ''end'':133C ''entropi'':4A,31C,156C ''error'':5A,105C,202C ''function'':6A,196C,203C ''give'':136C ''h'':35C,162C ''h2'':69C,145C ''href'':18C ''isn'':74C ''label'':115C ''learn'':12B,152C ''log'':42C,83C,169C,175C ''machin'':11B ''machine-learn'':10B ''mean'':91C ''ml'':24C ''mnist'':22C ''model'':126C ''network'':9A,206C ''neural'':8A,205C ''never'':135C ''obvious'':111C ''p'':14C,34C,46C,73C,149C,161C,180C ''predict'':51C ''probabl'':52C,64C,138C ''problem'':78C ''properti'':200C ''question'':70C,146C ''realli'':96C ''refer'':189C ''simpli'':122C ''softmax'':129C ''sum'':38C,165C ''tensorflow'':13B ''textbook'':188C ''think'':102C ''true'':63C ''ve'':151C ''version'':192C ''would'':90C,120C ''www.tensorflow.org'':20C ''www.tensorflow.org/versions/master/tutorials/mnist/beginners/index.html'':19C ''y'':36C,37C,40C,43C,47C,59C,80C,84C,163C,164C,167C,170C,173C,177C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9303, 1, '2015-12-10 08:53:58.557', 0, 43, 'Seeking Career guidance', '&lt;p&gt;I am a recent graduate.Currently I work in a late stage startup (product company) as a Software Engineer where my responsibilities are to interact with customers, understand their environment and write code so that they can use our product. I work on C#, web services and relational databases. I also work rarely on Big Data space to migrate some small components of our product to big data platform. I''ve got an opportunity to work in a large firm as a big data engineer to work on apache projects like yarn and hadoop and solve customer issues. And also to contribute to open source community. In curreny company, there is no one to review my work and mentor me. 
Will it be a wise decision to leave a company where I''ve an opportunity to see product from end to end and go to a company where I''ll not the same visibility of product ?&lt;/p&gt;

&lt;p&gt;Is my next opportunity a good one if I want to make my career in big data space ?&lt;/p&gt;

&lt;p&gt;Please share your inputs to help me take my decision ?&lt;/p&gt;
', 'bigdata', NULL, '''/p'':161C,181C,193C ''also'':55C,104C ''apach'':93C ''big'':59C,71C,87C,178C ''bigdata'':4B ''c'':48C ''career'':2A,176C ''code'':37C ''communiti'':110C ''compani'':19C,113C,134C,151C ''compon'':66C ''contribut'':106C ''curreni'':112C ''custom'':31C,101C ''data'':60C,72C,88C,179C ''databas'':53C ''decis'':130C,192C ''end'':144C,146C ''engin'':23C,89C ''environ'':34C ''firm'':84C ''go'':148C ''good'':168C ''got'':76C ''graduate.currently'':10C ''guidanc'':3A ''hadoop'':98C ''help'':188C ''input'':186C ''interact'':29C ''issu'':102C ''larg'':83C ''late'':15C ''leav'':132C ''like'':95C ''ll'':154C ''make'':174C ''mentor'':123C ''migrat'':63C ''next'':165C ''one'':117C,169C ''open'':108C ''opportun'':78C,139C,166C ''p'':5C,162C,182C ''platform'':73C ''pleas'':183C ''product'':18C,44C,69C,142C,160C ''project'':94C ''rare'':57C ''recent'':9C ''relat'':52C ''respons'':26C ''review'':119C ''see'':141C ''seek'':1A ''servic'':50C ''share'':184C ''small'':65C ''softwar'':22C ''solv'':100C ''sourc'':109C ''space'':61C,180C ''stage'':16C ''startup'':17C ''take'':190C ''understand'':32C ''use'':42C ''ve'':75C,137C ''visibl'':158C ''want'':172C ''web'':49C ''wise'':129C ''work'':12C,46C,56C,80C,91C,121C ''write'':36C ''yarn'':96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9304, 2, '2015-12-10 09:25:34.18', 3, NULL, NULL, '&lt;p&gt;I think your question is off-topic, but I''ll still try to answer because it might help you. Getting a job in data science isn''t something everybody can do, it''s a competitive field. Well done, for landing yourself in data science. However, data science is a big area, so you''ll need to think what you want to do and plan ahead.&lt;/p&gt;

&lt;p&gt;Do you want to get yourself into machine learning? Do you want to get yourself into statistics? Right now, you''re more like a software engineer in the field, still not quite being a data scientist. Do you want to become a real data scientist? Or are you happy being a software engineer in the field? If you''re planning to become a data scientist, you should train yourself hard in statistics, machine learning etc. Being a Hadoop user is simply not enough to step yourself fully into the field.&lt;/p&gt;

&lt;p&gt;I give you my own experience. I was a software engineer myself. It wasn''t easy to get a job in data science. I had to do a postgraduate degree in mathematics. Trained myself as much as possible. In the end, I landed myself a job as a bioinformatician.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Recommendation&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Don''t leave just because nobody mentors you. Learn as much as possible and prepare yourself. Once you''re comfortable, move on.&lt;/p&gt;
', NULL, NULL, '''/p'':68C,160C,211C,216C,240C ''/strong'':215C ''ahead'':67C ''answer'':16C ''area'':53C ''becom'':110C,131C ''big'':52C ''bioinformatician'':210C ''comfort'':237C ''competit'':37C ''data'':26C,45C,48C,104C,113C,133C,183C ''degre'':191C ''done'':40C ''easi'':177C ''end'':202C ''engin'':95C,122C,172C ''enough'':152C ''etc'':144C ''everybodi'':31C ''experi'':167C ''field'':38C,98C,125C,159C ''fulli'':156C ''get'':22C,74C,83C,179C ''give'':163C ''hadoop'':147C ''happi'':118C ''hard'':139C ''help'':20C ''howev'':47C ''isn'':28C ''job'':24C,181C,207C ''land'':42C,204C ''learn'':78C,143C,226C ''leav'':220C ''like'':92C ''ll'':12C,56C ''machin'':77C,142C ''mathemat'':193C ''mentor'':224C ''might'':19C ''move'':238C ''much'':197C,228C ''need'':57C ''nobodi'':223C ''off-top'':7C ''p'':1C,69C,161C,212C,217C ''plan'':66C,129C ''possibl'':199C,230C ''postgradu'':190C ''prepar'':232C ''question'':5C ''quit'':101C ''re'':90C,128C,236C ''real'':112C ''recommend'':214C ''right'':87C ''scienc'':27C,46C,49C,184C ''scientist'':105C,114C,134C ''simpli'':150C ''softwar'':94C,121C,171C ''someth'':30C ''statist'':86C,141C ''step'':154C ''still'':13C,99C ''strong'':213C ''think'':3C,59C ''topic'':9C ''train'':137C,194C ''tri'':14C ''user'':148C ''want'':62C,72C,81C,108C ''wasn'':175C ''well'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9376, 2, '2015-12-14 17:17:18.263', 0, NULL, NULL, '&lt;p&gt;I think that your first and last approach are the same - as a result you have sparse feature vector. In your last approach you just don''t list features with value 0. &lt;/p&gt;

&lt;p&gt;It is hard to say if you should use categorical or continuous values - it depends on each feature.&lt;/p&gt;
', NULL, NULL, '''/p'':34C,54C ''0'':33C ''approach'':9C,24C ''categor'':45C ''continu'':47C ''depend'':50C ''featur'':19C,30C,53C ''first'':6C ''hard'':38C ''last'':8C,23C ''list'':29C ''p'':1C,35C ''result'':15C ''say'':40C ''spars'':18C ''think'':3C ''use'':44C ''valu'':32C,48C ''vector'':20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9377, 2, '2015-12-14 17:19:44.423', 0, NULL, NULL, '&lt;p&gt;PyLearn2 is built on top of Theano. If you have Theano snippets you can run them with PyLearn2 snippets.&lt;/p&gt;
', NULL, NULL, '''/p'':21C ''built'':4C ''p'':1C ''pylearn2'':2C,19C ''run'':16C ''snippet'':13C,20C ''theano'':8C,12C ''top'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9309, 2, '2015-12-10 14:08:05.68', 2, NULL, NULL, '&lt;p&gt;Those issues are handled by the tutorial''s use of softmax. &lt;/p&gt;

&lt;p&gt;For 1) you''re correct that softmax guarantees a non-zero output because it exponentiates it''s input. For activations that do not give this guarantee (like relu), it''s simple to add a very small positive term to every output to avoid that problem.&lt;/p&gt;

&lt;p&gt;As for 2), they aren''t the same obviously, but I the softmax formulation they gave takes care of the the issue. If you didn''t use softmax, this would cause you to learn huge bias terms that guess 1 for every class for any input. But since they normalize the softmax across all classes, the only way to maximize the output of the correct class is for it to be large relative to the incorrect classes.&lt;/p&gt;
', NULL, NULL, '''/p'':13C,61C,140C ''1'':16C,102C ''2'':65C ''across'':115C ''activ'':35C ''add'':48C ''aren'':67C ''avoid'':58C ''bias'':98C ''care'':80C ''caus'':93C ''class'':105C,117C,128C,139C ''correct'':19C,127C ''didn'':87C ''everi'':55C,104C ''exponenti'':30C ''formul'':76C ''gave'':78C ''give'':39C ''guarante'':22C,41C ''guess'':101C ''handl'':5C ''huge'':97C ''incorrect'':138C ''input'':33C,108C ''issu'':3C,84C ''larg'':134C ''learn'':96C ''like'':42C ''maxim'':122C ''non'':25C ''non-zero'':24C ''normal'':112C ''obvious'':71C ''output'':27C,56C,124C ''p'':1C,14C,62C ''posit'':52C ''problem'':60C ''re'':18C ''relat'':135C ''relu'':43C ''simpl'':46C ''sinc'':110C ''small'':51C ''softmax'':12C,21C,75C,90C,114C ''take'':79C ''term'':53C,99C ''tutori'':8C ''use'':10C,89C ''way'':120C ''would'':92C ''zero'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9310, 2, '2015-12-10 15:48:15.77', 3, NULL, NULL, '&lt;p&gt;Twitter''s API is one of the best sources of social network data. You can extract off twitter pretty much everything you can imagine, you just need an account and a developer ID. The documentation is rather big so I will let you navigate it.
&lt;a href="https://dev.twitter.com/overview/documentation" rel="nofollow"&gt;https://dev.twitter.com/overview/documentation&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;As usual there are wrappers that make your life easier.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://github.com/bear/python-twitter" rel="nofollow"&gt;python-twitter&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://cran.r-project.org/web/packages/twitteR/index.html" rel="nofollow"&gt;twitteR&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;There are also companies who offer detailed twitter analytics and historic datasets for a fee.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://gnip.com/historical/" rel="nofollow"&gt;Gnip&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datasift.com/products/stream-for-social-data/" rel="nofollow"&gt;Datasift&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Check them out!&lt;/p&gt;
', NULL, NULL, '''/a'':81C,92C,122C,133C ''/bear/python-twitter'':75C ''/historical/'':118C ''/li'':82C,93C,123C,134C ''/overview/documentation'':51C ''/overview/documentation&lt;/a&gt;&lt;/p&gt;'':56C ''/p'':68C,111C,140C ''/products/stream-for-social-data/'':129C ''/ul'':94C,135C ''/web/packages/twitter/index.html'':88C ''account'':30C ''also'':98C ''analyt'':104C ''api'':4C ''best'':9C ''big'':39C ''check'':137C ''compani'':99C ''cran.r-project.org'':87C ''cran.r-project.org/web/packages/twitter/index.html'':86C ''data'':14C ''dataset'':107C ''datasift'':132C ''datasift.com'':128C ''datasift.com/products/stream-for-social-data/'':127C ''detail'':102C ''dev.twitter.com'':50C,55C ''dev.twitter.com/overview/documentation'':49C ''dev.twitter.com/overview/documentation&lt;/a&gt;&lt;/p&gt;'':54C ''develop'':33C ''document'':36C ''easier'':67C ''everyth'':22C ''extract'':17C ''fee'':110C ''github.com'':74C ''github.com/bear/python-twitter'':73C ''gnip'':121C ''gnip.com'':117C ''gnip.com/historical/'':116C ''histor'':106C ''href'':48C,72C,85C,115C,126C ''id'':34C ''imagin'':25C ''let'':43C ''li'':70C,83C,113C,124C ''life'':66C ''make'':64C ''much'':21C ''navig'':45C ''need'':28C ''network'':13C ''nofollow'':53C,77C,90C,120C,131C ''offer'':101C ''one'':6C ''p'':1C,57C,95C,136C ''pretti'':20C ''python'':79C ''python-twitt'':78C ''rather'':38C ''rel'':52C,76C,89C,119C,130C ''social'':12C ''sourc'':10C ''twitter'':2C,19C,80C,91C,103C ''ul'':69C,112C ''usual'':59C ''wrapper'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9311, 2, '2015-12-10 16:10:21.97', 3, NULL, NULL, '&lt;p&gt;The first logloss formula you are using is for multiclass log loss, where the $i$ subscript enumerates the different classes in an example. The formula &lt;em&gt;assumes&lt;/em&gt; that a single $y_i''$ in each example is 1, and the rest are all 0.&lt;/p&gt;

&lt;p&gt;That means the formula only captures error on the target class. It discards any notion of errors that you might consider "false positive" and does not care how predicted probabilities are distributed other than predicted probability of the true class.&lt;/p&gt;

&lt;p&gt;Another assumption is that $\sum_i y_i = 1$ for the predictions of each example. A softmax layer does this automatically - if you use something different you will need to scale the outputs to meet that constraint.&lt;/p&gt;

&lt;h2&gt;Question 1&lt;/h2&gt;

&lt;blockquote&gt;
  &lt;p&gt;Isn''t it a problem that the $y_i$ (in $log(y_i)$) could be 0?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Yes that can be a problem, but it is usually not a practical one. A randomly-initialised softmax layer is extremely unlikely to output an exact &lt;code&gt;0&lt;/code&gt; in any class. But it is possible, so worth allowing for it. First, don''t evaluate $log(y_i)$ for any $y_i''=0$, because the negative classes always contribute 0 to the error. Second, in practical code you can limit the value to something like &lt;code&gt;log( max( y_predict, 1e-15 ) )&lt;/code&gt; for numerical stability - in many cases it is not required, but this is sensible defensive programming.&lt;/p&gt;

&lt;h2&gt;Question 2&lt;/h2&gt;

&lt;blockquote&gt;
  &lt;p&gt;I''ve learned that cross-entropy is defined as $H_{y''}(y) := - \sum_{i} ({y_i'' \log(y_i) + (1-y_i'') \log (1-y_i)})$&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This formulation is often used for a network with one output predicting two classes (usually positive class membership for 1 and negative for 0 output). In that case $i$ may only have one value - you can lose the sum over $i$.&lt;/p&gt;

&lt;p&gt;If you modify such a network to have two opposing outputs and use softmax plus the first logloss definition, then you can see that in fact it &lt;em&gt;is the same error measurement&lt;/em&gt; but folding the error metric for two classes into a single output.&lt;/p&gt;

&lt;p&gt;If there is more than one class to predict membership of, and the classes are &lt;em&gt;not exclusive&lt;/em&gt; i.e. an example could be any or all of the classes at the same time, then you will need to use this second formulation. For digit recognition that is not the case (a written digit should only have one "true" class)&lt;/p&gt;
', NULL, NULL, '''/blockquote'':151C,287C ''/code'':182C,235C ''/em'':29C,365C,398C ''/h2'':131C,256C ''/p'':46C,88C,127C,150C,252C,286C,330C,378C,440C ''0'':45C,149C,181C,206C,213C,312C ''1'':39C,98C,130C,279C,283C,308C ''1e-15'':234C ''2'':255C ''allow'':192C ''alway'':211C ''anoth'':90C ''assum'':28C ''assumpt'':91C ''automat'':110C ''blockquot'':132C,257C ''captur'':53C ''care'':74C ''case'':241C,316C,430C ''class'':21C,58C,87C,185C,210C,302C,305C,373C,386C,393C,409C,439C ''code'':180C,220C,229C ''consid'':68C ''constraint'':126C ''contribut'':212C ''could'':147C,402C ''cross'':264C ''cross-entropi'':263C ''defens'':250C ''defin'':267C ''definit'':350C ''differ'':20C,115C ''digit'':424C,433C ''discard'':60C ''distribut'':79C ''em'':27C,359C,395C ''entropi'':265C ''enumer'':18C ''error'':54C,64C,216C,363C,369C ''evalu'':198C ''exact'':179C ''exampl'':24C,37C,104C,401C ''exclus'':397C ''extrem'':174C ''fact'':357C ''fals'':69C ''first'':3C,195C,348C ''fold'':367C ''formul'':290C,422C ''formula'':5C,26C,51C ''h'':269C ''h2'':128C,253C ''i.e'':399C ''initialis'':170C ''isn'':134C ''layer'':107C,172C ''learn'':261C ''like'':228C ''limit'':223C ''log'':12C,144C,199C,230C,276C,282C ''logloss'':4C,349C ''lose'':325C ''loss'':13C ''mani'':240C ''max'':231C ''may'':318C ''mean'':49C ''measur'':364C ''meet'':124C ''membership'':306C,389C ''metric'':370C ''might'':67C ''modifi'':334C ''multiclass'':11C ''need'':118C,417C ''negat'':209C,310C ''network'':296C,337C ''notion'':62C ''numer'':237C ''often'':292C ''one'':166C,298C,321C,385C,437C ''oppos'':341C ''output'':122C,177C,299C,313C,342C,377C ''p'':1C,47C,89C,133C,152C,258C,288C,331C,379C ''plus'':346C ''posit'':70C,304C ''possibl'':189C ''practic'':165C,219C ''predict'':76C,82C,101C,233C,300C,388C ''probabl'':77C,83C ''problem'':138C,158C ''program'':251C ''question'':129C,254C ''random'':169C ''randomly-initialis'':168C ''recognit'':425C ''requir'':245C ''rest'':42C ''scale'':120C ''second'':217C,421C ''see'':354C ''sensibl'':249C ''singl'':32C,376C ''softmax'':106C,171C,345C ''someth'':114C,227C ''stabil'':238C ''subscript'':17C ''sum'':94C,272C,327C ''target'':57C ''time'':413C ''true'':86C,438C ''two'':301C,340C,372C ''unlik'':175C ''use'':8C,113C,293C,344C,419C ''usual'':162C,303C ''valu'':225C,322C ''ve'':260C ''worth'':191C ''written'':432C ''y'':33C,96C,141C,145C,200C,204C,232C,270C,271C,274C,277C,280C,284C ''yes'':153C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9313, 2, '2015-12-10 17:02:37.683', 0, NULL, NULL, '&lt;p&gt;You should select some threshold, let''s say 0.5 and treat customers with probability below threshold as not buy and above as buy. Based on this you can compute accuracy of your model. You can also check the ROC curve of the model. You can check various metric for binary classifier &lt;a href="https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers" rel="nofollow"&gt;here&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':61C ''/p'':62C ''/wiki/evaluation_of_binary_classifiers'':57C ''0.5'':10C ''accuraci'':31C ''also'':37C ''base'':25C ''binari'':51C ''buy'':20C,24C ''check'':38C,47C ''classifi'':52C ''comput'':30C ''curv'':41C ''custom'':13C ''en.wikipedia.org'':56C ''en.wikipedia.org/wiki/evaluation_of_binary_classifiers'':55C ''href'':54C ''let'':7C ''metric'':49C ''model'':34C,44C ''nofollow'':59C ''p'':1C ''probabl'':15C ''rel'':58C ''roc'':40C ''say'':9C ''select'':4C ''threshold'':6C,17C ''treat'':12C ''various'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9364, 1, '2015-12-13 14:19:54.51', 3, 792, 'Hypertuning XGBoost parameters', '&lt;p&gt;XGBoost have been doing a great job, when it comes to dealing with both categorical and continuous dependant variables. But, how do I select the optimized parameters for an XGBoost problem?&lt;/p&gt;

&lt;p&gt;This is how I applied the parameters for a recent Kaggle problem:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;param &amp;lt;- list(  objective           = "reg:linear", 
                booster = "gbtree",
                eta                 = 0.02, # 0.06, #0.01,
                max_depth           = 10, #changed from default of 8
                subsample           = 0.5, # 0.7
                colsample_bytree    = 0.7, # 0.7
                num_parallel_tree   = 5
                # alpha = 0.0001, 
                # lambda = 1
)


clf &amp;lt;- xgb.train(   params              = param, 
                    data                = dtrain, 
                    nrounds             = 3000, #300, #280, #125, #250, # changed from 300
                    verbose             = 0,
                    early.stop.round    = 100,
                    watchlist           = watchlist,
                    maximize            = FALSE,
                    feval=RMPSE
)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;All I do to experiment is randomly select (with intuition) another set of parameters for improving on the result.&lt;/p&gt;

&lt;p&gt;Is there anyway I automate the selection of optimized(best) set of parameters?&lt;/p&gt;

&lt;p&gt;(Answers can be in any language. I''m just looking for the technique)&lt;/p&gt;
', 'r  python  xgboost', NULL, '''/code'':117C ''/p'':39C,53C,139C,154C,169C ''/pre'':118C ''0'':108C ''0.0001'':88C ''0.01'':67C ''0.02'':65C ''0.06'':66C ''0.5'':77C ''0.7'':78C,81C,82C ''1'':90C ''10'':70C ''100'':110C ''125'':102C ''250'':103C ''280'':101C ''300'':100C,106C ''3000'':99C ''5'':86C ''8'':75C ''alpha'':87C ''anoth'':130C ''answer'':156C ''anyway'':143C ''appli'':45C ''autom'':145C ''best'':150C ''booster'':62C ''bytre'':80C ''categor'':22C ''chang'':71C,104C ''clf'':91C ''code'':55C ''colsampl'':79C ''come'':17C ''continu'':24C ''data'':96C ''deal'':19C ''default'':73C ''depend'':25C ''depth'':69C ''dtrain'':97C ''early.stop.round'':109C ''eta'':64C ''experi'':124C ''fals'':114C ''feval'':115C ''gbtree'':63C ''great'':13C ''hypertun'':1A ''improv'':135C ''intuit'':129C ''job'':14C ''kaggl'':51C ''lambda'':89C ''languag'':161C ''linear'':61C ''list'':58C ''look'':165C ''lt'':57C,92C ''m'':163C ''max'':68C ''maxim'':113C ''nround'':98C ''num'':83C ''object'':59C ''optim'':33C,149C ''p'':7C,40C,119C,140C,155C ''parallel'':84C ''param'':56C,94C,95C ''paramet'':3A,34C,47C,133C,153C ''pre'':54C ''problem'':38C,52C ''python'':5B ''r'':4B ''random'':126C ''recent'':50C ''reg'':60C ''result'':138C ''rmpse'':116C ''select'':31C,127C,147C ''set'':131C,151C ''subsampl'':76C ''techniqu'':168C ''tree'':85C ''variabl'':26C ''verbos'':107C ''watchlist'':111C,112C ''xgb.train'':93C ''xgboost'':2A,6B,8C,37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9314, 2, '2015-12-10 17:45:32.63', 5, NULL, NULL, '&lt;p&gt;&lt;a href="https://github.com/tweepy/tweepy"&gt;Tweepy&lt;/a&gt; is one of the best libraries for analyzing and hacking around with the Twitter API.  (Being a contributor for tweepy, I can vouch for it''s stability and quality)&lt;/p&gt;

&lt;p&gt;For a Python wrapper for the Facebook graph API, you can use the &lt;a href="https://github.com/debrouwere/facebook-insights"&gt;Facebook-Insights library&lt;/a&gt;, which is well-maintained and neat documentation.&lt;/p&gt;

&lt;p&gt;There are services out there which can mine you information, but they are limited to the complexity of the query.&lt;/p&gt;

&lt;p&gt;For example: "How many people tweeted about banana on Monday?" can be answered from existing tools&lt;/p&gt;

&lt;p&gt;"How many people happy people tweeted about banana on Monday" can also be done, but would require more efforts and the software or tool should be able to detect emotion.&lt;/p&gt;

&lt;p&gt;So, if you are into research, I would advise you to go with the API''s and good ol'' programming!&lt;/p&gt;
', NULL, NULL, '''/a'':8C,62C ''/debrouwere/facebook-insights'':57C ''/p'':38C,71C,93C,111C,143C,165C ''/tweepy/tweepy'':6C ''abl'':139C ''advis'':153C ''also'':124C ''analyz'':16C ''answer'':107C ''api'':23C,48C,159C ''around'':19C ''banana'':102C,120C ''best'':13C ''complex'':89C ''contributor'':26C ''detect'':141C ''document'':70C ''done'':126C ''effort'':131C ''emot'':142C ''exampl'':96C ''exist'':109C ''facebook'':46C,59C ''facebook-insight'':58C ''github.com'':5C,56C ''github.com/debrouwere/facebook-insights'':55C ''github.com/tweepy/tweepy'':4C ''go'':156C ''good'':162C ''graph'':47C ''hack'':18C ''happi'':116C ''href'':3C,54C ''inform'':82C ''insight'':60C ''librari'':14C,61C ''limit'':86C ''maintain'':67C ''mani'':98C,114C ''mine'':80C ''monday'':104C,122C ''neat'':69C ''ol'':163C ''one'':10C ''p'':1C,39C,72C,94C,112C,144C ''peopl'':99C,115C,117C ''program'':164C ''python'':42C ''qualiti'':37C ''queri'':92C ''requir'':129C ''research'':150C ''servic'':75C ''softwar'':134C ''stabil'':35C ''tool'':110C,136C ''tweepi'':7C,28C ''tweet'':100C,118C ''twitter'':22C ''use'':51C ''vouch'':31C ''well'':66C ''well-maintain'':65C ''would'':128C,152C ''wrapper'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9315, 2, '2015-12-10 20:02:49.097', 1, NULL, NULL, '&lt;p&gt;Let us think of sales ($S$) as a property of a system.  You could imagine Week 1 to be a system and Week 2 to be another system.  Call the Week 1 system $W_1$ and the Week 2 system $W_2$.  Then,
$$
  S(W_1) + S(W_2) = S(W_1 + W_2)
$$
A property that has this additive character is called &lt;strong&gt;extensive&lt;/strong&gt;.  &lt;/p&gt;

&lt;p&gt;Now consider the temperature ($T$) as the property with values $T(W_1)$ and $T(W_2)$ for weeks 1 and 2.  Assume that the two $T$ values are identical.  Merge the Week 1 and Week 2 systems together to get another system $W$ for the two weeks together.  Then
$$
  T(W) = T(W_1+W_2)
$$ 
Properties that follow this rule are called &lt;strong&gt;intensive&lt;/strong&gt; and cannot be added.&lt;/p&gt;

&lt;p&gt;These definitions were invented for physical properties and systems and one has to be careful in applying the definitions to general situations.  I''m not sure how much thought along these lines has gone into the social sciences. &lt;/p&gt;

&lt;p&gt;For more, see &lt;a href="https://en.wikipedia.org/wiki/Intensive_and_extensive_properties" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Intensive_and_extensive_properties&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/p'':68C,141C,181C ''/strong'':67C,136C ''/wiki/intensive_and_extensive_properties'':190C ''/wiki/intensive_and_extensive_properties&lt;/a&gt;.&lt;/p&gt;'':195C ''1'':18C,33C,36C,47C,53C,82C,89C,103C,124C ''2'':25C,40C,43C,50C,55C,86C,91C,106C,126C ''ad'':140C ''addit'':61C ''along'':172C ''anoth'':28C,111C ''appli'':159C ''assum'':92C ''call'':30C,64C,133C ''cannot'':138C ''care'':157C ''charact'':62C ''consid'':71C ''could'':15C ''definit'':144C,161C ''en.wikipedia.org'':189C,194C ''en.wikipedia.org/wiki/intensive_and_extensive_properties'':188C ''en.wikipedia.org/wiki/intensive_and_extensive_properties&lt;/a&gt;.&lt;/p&gt;'':193C ''extens'':66C ''follow'':129C ''general'':163C ''get'':110C ''gone'':176C ''href'':187C ''ident'':99C ''imagin'':16C ''intens'':135C ''invent'':146C ''let'':2C ''line'':174C ''m'':166C ''merg'':100C ''much'':170C ''nofollow'':192C ''one'':153C ''p'':1C,69C,142C,182C ''physic'':148C ''properti'':10C,57C,77C,127C,149C ''rel'':191C ''rule'':131C ''sale'':6C ''scienc'':180C ''see'':185C ''situat'':164C ''social'':179C ''strong'':65C,134C ''sure'':168C ''system'':13C,22C,29C,34C,41C,107C,112C,151C ''temperatur'':73C ''think'':4C ''thought'':171C ''togeth'':108C,118C ''two'':95C,116C ''us'':3C ''valu'':79C,97C ''w'':35C,42C,46C,49C,52C,54C,81C,85C,113C,121C,123C,125C ''week'':17C,24C,32C,39C,88C,102C,105C,117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9335, 1, '2015-12-11 09:07:07.943', 2, 39, 'R: substituting only whole statements (not parts of them)', '&lt;p&gt;I have a data set DS.
I want to substitute the statements contained with others following a pattern-replacement schema like the following one:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/0YZe5.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/0YZe5.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I have used the followng function:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from_distincts_to_origins &amp;lt;- function(patternST, replacementST,originalFile){

  #---------------------------------------
  patternST&amp;lt;-paste0(''\\b'',patternST,''\\b'')
  #---------------------------------------
  # we have to point back to the original data set
  file_origin_DF &amp;lt;- as.data.frame(originalFile)

  file_output &amp;lt;- file_origin_DF
  for(i in 1:length(patternST))   { 
    where &amp;lt;- file_output[[1]]#vector
    file_output &amp;lt;- apply(file_output,  
                         2,
                         function(x) sub(patternST[i], 
                                         replacementST[i], 
                                         where, 
                                         #ignored.case=FALSE, 
                                         perl=FALSE, 
                                         fixed=FALSE, useBytes=FALSE)
    )

    file_output &amp;lt;- as.data.frame(file_output, stringsAsFactors =F) # in order to work
  }       

  return(file_output)
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What will happen is that R will start substituting all the "I like football" with "football". When it will pass to the second row it will look for "I like football, W Lazio" but it will only find "&lt;strong&gt;football, W Lazio&lt;/strong&gt;", so those rows won''t be substituted with "Lazio".&lt;/p&gt;

&lt;p&gt;One possible solution is to order the Pattern such to have the longest strings at the top, but it would only &lt;em&gt;reduce&lt;/em&gt; the substitution missings.&lt;/p&gt;

&lt;p&gt;Is there another method in R to do that?&lt;/p&gt;
', 'r  text-mining', NULL, '''/0yze5.jpg'':44C,51C ''/a'':57C ''/code'':156C ''/em'':236C ''/p'':38C,58C,66C,211C,240C,251C ''/pre'':157C ''/strong'':201C ''1'':109C,116C ''2'':124C ''alt'':52C ''anoth'':244C ''appli'':121C ''as.data.frame'':98C,144C ''b'':81C,83C ''back'':88C ''code'':68C ''contain'':25C ''data'':16C,92C ''descript'':55C ''df'':96C,105C ''distinct'':70C ''ds'':18C ''em'':234C ''enter'':53C ''f'':148C ''fals'':134C,136C,138C,140C ''file'':94C,100C,103C,114C,118C,122C,141C,145C,154C ''find'':196C ''fix'':137C ''follow'':28C,36C ''followng'':64C ''footbal'':172C,174C,189C,198C ''function'':65C,74C,125C ''happen'':161C ''href'':41C ''i.stack.imgur.com'':43C,50C ''i.stack.imgur.com/0yze5.jpg'':42C,49C ''ignored.case'':133C ''imag'':54C ''img'':47C ''lazio'':191C,200C,210C ''length'':110C ''like'':34C,171C,188C ''longest'':225C ''look'':185C ''lt'':73C,79C,97C,102C,113C,120C,143C ''method'':245C ''mine'':11B ''miss'':239C ''nofollow'':46C ''one'':37C,213C ''order'':150C,218C ''origin'':72C,91C,95C,104C ''originalfil'':77C,99C ''other'':27C ''output'':101C,115C,119C,123C,142C,146C,155C ''p'':12C,39C,59C,158C,212C,241C ''part'':7A ''pass'':178C ''paste0'':80C ''pattern'':31C,220C ''pattern-replac'':30C ''patternst'':75C,78C,82C,111C,128C ''perl'':135C ''point'':87C ''possibl'':214C ''pre'':67C ''r'':1A,8B,164C,247C ''reduc'':235C ''rel'':45C ''replac'':32C ''replacementst'':76C,130C ''return'':153C ''row'':182C,204C ''schema'':33C ''second'':181C ''set'':17C,93C ''solut'':215C ''src'':48C ''start'':166C ''statement'':5A,24C ''string'':226C ''stringsasfactor'':147C ''strong'':197C ''sub'':127C ''substitut'':2A,22C,167C,208C,238C ''text'':10B ''text-min'':9B ''top'':229C ''use'':62C ''usebyt'':139C ''vector'':117C ''w'':190C,199C ''want'':20C ''whole'':4A ''won'':205C ''work'':152C ''would'':232C ''x'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9336, 2, '2015-12-11 10:04:33.21', 1, NULL, NULL, '&lt;p&gt;Does &lt;code&gt;C &amp;lt;- intersect(unique(A), unique(B))&lt;/code&gt; work? &lt;/p&gt;

&lt;p&gt;Then you can do &lt;code&gt;A[A %in% C]&lt;/code&gt; to get them from  &lt;code&gt;A&lt;/code&gt; with duplicates.&lt;/p&gt;
', NULL, NULL, '''/code'':11C,24C,31C ''/p'':13C,34C ''b'':10C ''c'':4C,23C ''code'':3C,19C,29C ''duplic'':33C ''get'':26C ''intersect'':6C ''lt'':5C ''p'':1C,14C ''uniqu'':7C,9C ''work'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9316, 1, '2015-12-10 20:22:54.097', 1, 30, 'How to determine Nonnegativity in Matrix Factorization?', '&lt;p&gt;We have information about what the user likes in our app and we want to recommend content to similar users even those who may not have explicitly like a particular content but are similar to those who have explicitly liked that content.&lt;/p&gt;

&lt;p&gt;I was planning to use the implicit feedback &lt;a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4781121" rel="nofollow"&gt;variation&lt;/a&gt; of Alternating Least Squares using Apache Spark to implement a recommender system which will find me the additional users that I can target.&lt;/p&gt;

&lt;p&gt;One of the tuning parameters is a boolean flag &lt;code&gt;nonnegative&lt;/code&gt; for non-negative matrix factorization. &lt;/p&gt;

&lt;p&gt;I will be counting the number of likes per user and tag so I won''t have negative values. Can I say &lt;code&gt;nonnegative=true&lt;/code&gt; or does it mean something completely different. My understanding is that the nonnegative constraint is for the values inside the two matrices which the algorithm will decompose my original matrix to,  but I don''t have the knowledge to know if those values will be non-negative for my scenario.&lt;/p&gt;

&lt;p&gt;Ref:
&lt;a href="http://spark.apache.org/docs/latest/mllib-collaborative-filtering.html" rel="nofollow"&gt;Spark ALS Algorithm&lt;/a&gt;. Not exactly since there is another API but good enough for this question.&lt;/p&gt;
', 'recommendation  apache-spark', NULL, '''/a'':73C,209C ''/code'':110C,144C ''/docs/latest/mllib-collaborative-filtering.html'':203C ''/p'':55C,97C,117C,196C,223C ''/xpl/articledetails.jsp?arnumber=4781121'':69C ''addit'':91C ''al'':207C ''algorithm'':169C,208C ''altern'':75C ''anoth'':215C ''apach'':10B,79C ''apache-spark'':9B ''api'':216C ''app'':23C ''boolean'':106C ''code'':108C,141C ''complet'':150C ''constraint'':158C ''content'':29C,43C,54C ''count'':122C ''decompos'':171C ''determin'':3A ''differ'':151C ''enough'':219C ''even'':33C ''exact'':211C ''explicit'':39C,51C ''factor'':7A,116C ''feedback'':64C ''find'':88C ''flag'':107C ''good'':218C ''href'':66C,200C ''ieeexplore.ieee.org'':68C ''ieeexplore.ieee.org/xpl/articledetails.jsp?arnumber=4781121'':67C ''implement'':82C ''implicit'':63C ''inform'':15C ''insid'':163C ''know'':184C ''knowledg'':182C ''least'':76C ''like'':20C,40C,52C,126C ''matric'':166C ''matrix'':6A,115C,174C ''may'':36C ''mean'':148C ''negat'':114C,136C,192C ''nofollow'':71C,205C ''non'':113C,191C ''non-neg'':112C,190C ''nonneg'':4A,109C,142C,157C ''number'':124C ''one'':99C ''origin'':173C ''p'':12C,56C,98C,118C,197C ''paramet'':103C ''particular'':42C ''per'':127C ''plan'':59C ''question'':222C ''recommend'':8B,28C,84C ''ref'':198C ''rel'':70C,204C ''say'':140C ''scenario'':195C ''similar'':31C,46C ''sinc'':212C ''someth'':149C ''spark'':11B,80C,206C ''spark.apache.org'':202C ''spark.apache.org/docs/latest/mllib-collaborative-filtering.html'':201C ''squar'':77C ''system'':85C ''tag'':130C ''target'':96C ''true'':143C ''tune'':102C ''two'':165C ''understand'':153C ''use'':61C,78C ''user'':19C,32C,92C,128C ''valu'':137C,162C,187C ''variat'':72C ''want'':26C ''won'':133C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9340, 1, '2015-12-11 16:59:33.75', 2, 86, 'How can I preprocess multi-page image inputs in a theano/lasagne network?', '&lt;p&gt;I am trying to classify multi-page documents using a convolutional neural network (CNN). The content of each page in the corpus contains only text (i.e., no  photographs or icons), and different documents may have a different shape (height and width). I''d like my classification approach to use all pages in each document, rather than just the first page. &lt;/p&gt;

&lt;p&gt;As far as I know, the input to a CNN (like theano) needs a standardized shape. My first thought was to create a single image array that has all the pages concatenated. But I would then have to resize/zero pad all concatenated pages to match the length of the document with the largest page count, and use that as my lowest height and width for the set of concatenated pages. If I don''t use this strategy I risk losing resolution on the words in the image, but that is a giant input vector.&lt;/p&gt;

&lt;p&gt;I feel like splitting each document''s pages into separate samples would be a better approach to standardize input preprocessing, but I''m at a loss for how to classify the whole document if I''m just training on loose pages of each document. Can anyone advise on me on this?&lt;/p&gt;
', 'neuralnetwork  image-classification  preprocessing  theano', NULL, '''/p'':82C,178C,231C ''advis'':226C ''anyon'':225C ''approach'':68C,195C ''array'':109C ''better'':194C ''classif'':17B,67C ''classifi'':25C,209C ''cnn'':35C,93C ''concaten'':115C,125C,152C ''contain'':44C ''content'':37C ''convolut'':32C ''corpus'':43C ''count'':138C ''creat'':105C ''d'':64C ''differ'':53C,58C ''document'':29C,54C,75C,133C,185C,212C,223C ''far'':85C ''feel'':181C ''first'':80C,101C ''giant'':175C ''height'':60C,145C ''i.e'':47C ''icon'':51C ''imag'':8A,16B,108C,170C ''image-classif'':15B ''input'':9A,90C,176C,198C ''know'':88C ''largest'':136C ''length'':130C ''like'':65C,94C,182C ''loos'':219C ''lose'':163C ''loss'':205C ''lowest'':144C ''m'':202C,215C ''match'':128C ''may'':55C ''multi'':6A,27C ''multi-pag'':5A,26C ''need'':96C ''network'':13A,34C ''neural'':33C ''neuralnetwork'':14B ''p'':20C,83C,179C ''pad'':123C ''page'':7A,28C,40C,72C,81C,114C,126C,137C,153C,187C,220C ''photograph'':49C ''preprocess'':4A,18B,199C ''rather'':76C ''resize/zero'':122C ''resolut'':164C ''risk'':162C ''sampl'':190C ''separ'':189C ''set'':150C ''shape'':59C,99C ''singl'':107C ''split'':183C ''standard'':98C,197C ''strategi'':160C ''text'':46C ''theano'':19B,95C ''theano/lasagne'':12A ''thought'':102C ''train'':217C ''tri'':23C ''use'':30C,70C,140C,158C ''vector'':177C ''whole'':211C ''width'':62C,147C ''word'':167C ''would'':118C,191C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9317, 1, '2015-12-10 21:07:40.313', 2, 31, 'How to get common values between two multi sets?', '&lt;p&gt;I am working in R. I have two vectors; A and B of lengths 5913 and 3733 respectively (with entries are repeated). I want to extract those values (with repetitions) that are present in both A and B. 
I have done (A %in% B) (lets call it C) and (B %in% A) (lets call it D). 
The length of C is 3906 and that of D is 3607 (so 2007 elements in A are not in B and 126 elements in B are not in A). &lt;/p&gt;

&lt;p&gt;But How do I find the common values ? I don''t think I can use intersect() method as it expects vectors with no duplicate values. I have many many repetitions. &lt;/p&gt;

&lt;p&gt;Note: Due to the length of these vectors, I couldn''t mention them here. &lt;/p&gt;

&lt;p&gt;Any help would be greatly appreciated. &lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'r', NULL, '''/p'':98C,130C,146C,154C,157C ''126'':90C ''2007'':81C ''3607'':79C ''3733'':28C ''3906'':73C ''5913'':26C ''appreci'':153C ''b'':23C,49C,55C,61C,88C,93C ''c'':59C,71C ''call'':57C,65C ''common'':4A,106C ''couldn'':141C ''d'':67C,77C ''done'':52C ''due'':133C ''duplic'':123C ''element'':82C,91C ''entri'':31C ''expect'':119C ''extract'':37C ''find'':104C ''get'':3A ''great'':152C ''help'':149C ''intersect'':115C ''length'':25C,69C,136C ''let'':56C,64C ''mani'':127C,128C ''mention'':143C ''method'':116C ''multi'':8A ''note'':132C ''p'':11C,99C,131C,147C,155C ''present'':44C ''r'':10B,16C ''repeat'':33C ''repetit'':41C,129C ''respect'':29C ''set'':9A ''thank'':156C ''think'':111C ''two'':7A,19C ''use'':114C ''valu'':5A,39C,107C,124C ''vector'':20C,120C,139C ''want'':35C ''work'':14C ''would'':150C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9318, 2, '2015-12-10 21:45:17.327', 1, NULL, NULL, '&lt;p&gt;The code works and leads to no error. The result is a definition of one variable &lt;code&gt;r&lt;/code&gt;and one function &lt;code&gt;r_hat&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;You get the same with this snippet (I removed the loop as not relevant, what you do is creating the same function repeatedly in a loop)&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; r&amp;lt;- matrix(c(2,4, 1,5,5,3, 4,5,1,2,3,4), nrow=3)
&amp;gt; 
&amp;gt; r_hat&amp;lt;- function(u,x) {
+       if (r_hat &amp;gt;r[u,x]) {
+         print(r[u,x]-r_hat)
+       }
+ } 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The problem is in the function, after calling it you get the mentioned error.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; r_hat(0,0)   
Error in r_hat &amp;gt; r[u, x] : 
  comparison (6) is possible only for atomic and list types
&amp;gt; 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You reference &lt;code&gt;r_hat&lt;/code&gt; within the function, are you trying to define a recursive function? Than the recursive call should have the parameters and the function some return value...&lt;/p&gt;

&lt;p&gt;My guess is you tries to compare the &lt;em&gt;function definition&lt;/em&gt; with an element of a matrix, which leads to this error.&lt;/p&gt;

&lt;p&gt;If this is not enough for you and you explains what the code should deliver, I''ll try to complete my answer.&lt;/p&gt;
', NULL, NULL, '''/code'':20C,27C,99C,143C,151C ''/em'':190C ''/p'':28C,56C,116C,177C,202C,226C ''/pre'':100C,144C ''0'':122C,123C ''1'':66C,72C ''2'':64C,73C ''3'':69C,74C,77C ''4'':65C,70C,75C ''5'':67C,68C,71C ''6'':133C ''answer'':225C ''atom'':138C ''c'':63C ''call'':109C,166C ''code'':3C,18C,24C,58C,118C,148C,216C ''compar'':185C ''comparison'':132C ''complet'':223C ''creat'':48C ''defin'':159C ''definit'':14C,189C ''deliv'':218C ''element'':193C ''em'':187C ''enough'':208C ''error'':9C,115C,124C,201C ''explain'':213C ''function'':23C,51C,83C,107C,154C,162C,173C,188C ''get'':31C,112C ''gt'':59C,78C,79C,89C,119C,128C,142C ''guess'':180C ''hat'':26C,81C,88C,98C,121C,127C,150C ''lead'':6C,198C ''list'':140C ''ll'':220C ''loop'':40C,55C ''lt'':61C,82C ''matrix'':62C,196C ''mention'':114C ''nrow'':76C ''one'':16C,22C ''p'':1C,29C,101C,145C,178C,203C ''paramet'':170C ''possibl'':135C ''pre'':57C,117C ''print'':93C ''problem'':103C ''r'':19C,25C,60C,80C,87C,90C,94C,97C,120C,126C,129C,149C ''recurs'':161C,165C ''refer'':147C ''relev'':43C ''remov'':38C ''repeat'':52C ''result'':11C ''return'':175C ''snippet'':36C ''tri'':157C,183C,221C ''type'':141C ''u'':84C,91C,95C,130C ''valu'':176C ''variabl'':17C ''within'':152C ''work'':4C ''x'':85C,92C,96C,131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9319, 2, '2015-12-10 21:49:06.973', 2, NULL, NULL, '&lt;p&gt;Feature selection is about choosing some of features based on some statistical score but feature extraction is using techniques to extract some second layer information from the data e.g. interesting frequencies of a signal using Fourier transform.&lt;/p&gt;

&lt;p&gt;Dimensionality reduction is all about transforming data into a low-dimensional space in which data preserves its euclidean structure but does not suffer from curse of dimensionality.
For instance assume you extract some word features $[x_1,...,x_n]$ from a data set where each document can be modeled as a point in n-dimensional space and n is too large (a toy example). In this case many algorithms do not work according to the distance distortion of high-dimensional space. Now you need to reduce dimensionality by either selecting most informative features or transforming them into a low-dimensional manifold using dimensionality reduction methods e.g. PCA, LLE, etc.&lt;/p&gt;
', NULL, NULL, '''/p'':39C,154C ''1'':78C ''accord'':115C ''algorithm'':111C ''assum'':71C ''base'':10C ''case'':109C ''choos'':6C ''curs'':66C ''data'':29C,47C,56C,83C ''dimension'':41C,52C,68C,97C,123C,130C,144C,147C ''distanc'':118C ''distort'':119C ''document'':87C ''e.g'':30C,150C ''either'':132C ''etc'':153C ''euclidean'':59C ''exampl'':106C ''extract'':17C,22C,73C ''featur'':2C,9C,16C,76C,136C ''fourier'':37C ''frequenc'':32C ''high'':122C ''high-dimension'':121C ''inform'':26C,135C ''instanc'':70C ''interest'':31C ''larg'':103C ''layer'':25C ''lle'':152C ''low'':51C,143C ''low-dimension'':50C,142C ''mani'':110C ''manifold'':145C ''method'':149C ''model'':90C ''n'':80C,96C,100C ''n-dimension'':95C ''need'':127C ''p'':1C,40C ''pca'':151C ''point'':93C ''preserv'':57C ''reduc'':129C ''reduct'':42C,148C ''score'':14C ''second'':24C ''select'':3C,133C ''set'':84C ''signal'':35C ''space'':53C,98C,124C ''statist'':13C ''structur'':60C ''suffer'':64C ''techniqu'':20C ''toy'':105C ''transform'':38C,46C,138C ''use'':19C,36C,146C ''word'':75C ''work'':114C ''x'':77C,79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9320, 2, '2015-12-10 22:02:30.91', 1, NULL, NULL, '&lt;p&gt;Go for network analysis! make a network in which the links are relations and try to make network analysis to get your answer. Tones of tutorials on the web. You can have a look at GraphLab Create as a start.&lt;/p&gt;
', NULL, NULL, '''/p'':42C ''analysi'':5C,20C ''answer'':24C ''creat'':38C ''get'':22C ''go'':2C ''graphlab'':37C ''link'':12C ''look'':35C ''make'':6C,18C ''network'':4C,8C,19C ''p'':1C ''relat'':14C ''start'':41C ''tone'':25C ''tri'':16C ''tutori'':27C ''web'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9324, 2, '2015-12-11 00:25:06.727', 0, NULL, NULL, '&lt;p&gt;GraphLab is a good choice.&lt;/p&gt;

&lt;p&gt;Check out &lt;a href="https://networkx.github.io/" rel="nofollow"&gt;NetworkX&lt;/a&gt;. You could make this really easy on yourself by eliminating the father of/friend of/etc. distinctions and just treat every relationship as fundamentally the same. Then its as simple as building a graph that tracks the connections between your users. After that you could limit collaborative filtering to people in this person''s close circle of friends.&lt;/p&gt;

&lt;p&gt;You could also make distinct Friend/Family/Coworker implementations, but unless you have a LOT of data (and a lot of Family members) collaborative filtering could be a little disappointing.&lt;/p&gt;
', NULL, NULL, '''/a'':17C ''/p'':7C,74C,104C ''also'':78C ''build'':47C ''check'':9C ''choic'':6C ''circl'':71C ''close'':70C ''collabor'':62C,97C ''connect'':53C ''could'':19C,60C,77C,99C ''data'':90C ''disappoint'':103C ''distinct'':32C,80C ''easi'':23C ''elimin'':27C ''everi'':36C ''famili'':95C ''father'':29C ''filter'':63C,98C ''friend'':73C ''friend/family/coworker'':81C ''fundament'':39C ''good'':5C ''graph'':49C ''graphlab'':2C ''href'':12C ''implement'':82C ''limit'':61C ''littl'':102C ''lot'':88C,93C ''make'':20C,79C ''member'':96C ''networkx'':16C ''networkx.github.io'':13C ''nofollow'':15C ''of/etc'':31C ''of/friend'':30C ''p'':1C,8C,75C ''peopl'':65C ''person'':68C ''realli'':22C ''rel'':14C ''relationship'':37C ''simpl'':45C ''track'':51C ''treat'':35C ''unless'':84C ''user'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9326, 2, '2015-12-11 03:04:26.597', 2, NULL, NULL, '&lt;p&gt;I agree the code may be hard to read at the first glance, but I find the &lt;a href="http://deeplearning.net/tutorial/code/logistic_sgd.py" rel="nofollow"&gt;comment&lt;/a&gt; pretty clear:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;y.shape[0]&lt;/code&gt; is (symbolically) the number of rows in &lt;code&gt;y&lt;/code&gt;, i.e., number of examples (call it &lt;code&gt;n&lt;/code&gt;) in the minibatch &lt;/li&gt;
&lt;li&gt;&lt;code&gt;T.arange(y.shape[0])&lt;/code&gt; is a symbolic vector which will contain &lt;code&gt;[0,1,2,... n-1]&lt;/code&gt;.&lt;/li&gt;
&lt;li&gt;&lt;code&gt;T.log(self.p_y_given_x)&lt;/code&gt; is a matrix of Log-Probabilities (call it &lt;code&gt;LP&lt;/code&gt;) with one row per example and one column per class &lt;/li&gt;
&lt;li&gt;&lt;code&gt;LP[T.arange(y.shape[0]),y]&lt;/code&gt; is a vector v containing &lt;code&gt;[LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ..., LP[n-1,y[n-1]]]&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;T.mean(LP[T.arange(y.shape[0]),y])&lt;/code&gt; is the mean (across minibatch examples) of the elements in v, i.e., the mean log-likelihood across the minibatch. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Everything is Python notation (but sometimes using Theano functions).&lt;/p&gt;
', NULL, NULL, '''-1'':78C,137C,140C ''/a'':27C ''/code'':36C,46C,55C,65C,79C,85C,97C,116C,141C,151C ''/li'':59C,80C,108C,142C,172C ''/p'':30C,184C ''/tutorial/code/logistic_sgd.py'':23C ''/ul'':173C ''0'':35C,64C,74C,114C,124C,126C,149C ''1'':75C,128C,130C ''2'':76C,132C,134C ''across'':155C,169C ''agre'':3C ''call'':51C,93C ''class'':107C ''clear'':29C ''code'':5C,33C,44C,53C,61C,73C,82C,95C,110C,122C,144C ''column'':105C ''comment'':26C ''contain'':72C,121C ''deeplearning.net'':22C ''deeplearning.net/tutorial/code/logistic_sgd.py'':21C ''element'':160C ''everyth'':175C ''exampl'':50C,102C,157C ''find'':17C ''first'':13C ''function'':183C ''glanc'':14C ''hard'':8C ''href'':20C ''i.e'':47C,163C ''li'':32C,60C,81C,109C,143C ''likelihood'':168C ''log'':91C,167C ''log-likelihood'':166C ''log-prob'':90C ''lp'':96C,111C,123C,127C,131C,135C,146C ''matrix'':88C ''may'':6C ''mean'':154C,165C ''minibatch'':58C,156C,171C ''n'':54C,77C,136C,139C ''nofollow'':25C ''notat'':178C ''number'':40C,48C ''one'':99C,104C ''p'':1C,174C ''per'':101C,106C ''pretti'':28C ''probabl'':92C ''python'':177C ''read'':10C ''rel'':24C ''row'':42C,100C ''self.p_y_given_x'':84C ''sometim'':180C ''symbol'':38C,68C ''t.arange'':62C,112C,147C ''t.log'':83C ''t.mean'':145C ''theano'':182C ''ul'':31C ''use'':181C ''v'':120C,162C ''vector'':69C,119C ''y'':45C,115C,125C,129C,133C,138C,150C ''y.shape'':34C,63C,113C,148C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9322, 1, '2015-12-10 22:13:30.213', 2, 52, 'Theano logistic regression understanding code', '&lt;p&gt;I am used to C/Java like programming, and sometimes I am getting a headache on understanding the Python notation.&lt;/p&gt;

&lt;p&gt;On the logistic regression code available online, I am trying to understand this line of code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;-T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;It is basically saying perform this average:
$\begin{align}
J(\theta) = - \left[ \sum_{i=1}^{b} \sum_{k=1}^{K}  1\left\{y^{(i)} = k\right\} \log \frac{\exp(\theta^{(k)\top} x^{(i)})}{\sum_{j=1}^K \exp(\theta^{(j)\top} x^{(i)})}\right]
\end{align}$ where $b$ is the batch size.&lt;/p&gt;

&lt;p&gt;So is it related to theano code, or it is just a python notation ?
I am interested exactly on this piece of code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;T.log(self.p_y_given_x)[T.arange(y.shape[0]), y]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If you could please give me some explanation.&lt;/p&gt;
', 'python  theano', NULL, '''/code'':56C,146C ''/p'':28C,46C,112C,137C,157C ''/pre'':57C,147C ''0'':54C,144C ''1'':73C,77C,79C,95C ''align'':67C,105C ''avail'':35C ''averag'':65C ''b'':74C,107C ''basic'':61C ''batch'':110C ''begin'':66C ''c/java'':13C ''code'':5A,34C,45C,48C,120C,136C,139C ''could'':151C ''end'':104C ''exact'':131C ''exp'':87C,97C ''explan'':156C ''frac'':86C ''get'':20C ''give'':153C ''headach'':22C ''interest'':130C ''j'':68C,94C,99C ''k'':76C,78C,83C,89C,96C ''left'':70C,80C ''like'':14C ''line'':43C ''log'':85C ''logist'':2A,32C ''notat'':27C,127C ''onlin'':36C ''p'':8C,29C,58C,113C,148C ''perform'':63C ''piec'':134C ''pleas'':152C ''pre'':47C,138C ''program'':15C ''python'':6B,26C,126C ''regress'':3A,33C ''relat'':117C ''right'':84C,103C ''say'':62C ''self.p_y_given_x'':51C,141C ''size'':111C ''sometim'':17C ''sum'':71C,75C,93C ''t.arange'':52C,142C ''t.log'':50C,140C ''t.mean'':49C ''theano'':1A,7B,119C ''theta'':69C,88C,98C ''top'':90C,100C ''tri'':39C ''understand'':4A,24C,41C ''use'':11C ''x'':91C,101C ''y'':55C,81C,145C ''y.shape'':53C,143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9331, 2, '2015-12-11 06:41:49.733', 2, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;I am not able to understand how and what modelling techniques do we
  use?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Every data science workflow has the follwing steps:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Pre-processing (data cleaning and wrangling)&lt;/li&gt;
&lt;li&gt;Exploratory analytics&lt;/li&gt;
&lt;li&gt;Model selection&lt;/li&gt;
&lt;li&gt;Prediction and testing.  (And re-iteration)&lt;/li&gt;
&lt;li&gt;(Optional) Reporting the workflow&lt;/li&gt;
&lt;/ol&gt;

&lt;blockquote&gt;
  &lt;p&gt;Does it depend on the data type?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Yes, the entire workflow is dependent on the type and features of the data.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Does it depend on size of data?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Size of data makes a difference in the tools and sometimes(very rarely) the algorithms used.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;I have to predict missing values in a given temperature data set and i
  am unaware of anything that i can use&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;There is a &lt;a href="http://datascience.stackexchange.com/questions/tagged/missing-data?sort=newest&amp;amp;pageSize=50"&gt;lot of&lt;/a&gt; &lt;a href="http://stats.stackexchange.com/questions/tagged/missing-data"&gt;material&lt;/a&gt; and algorithms on how to impute missing data, which you can refer to and use them accordingly depending on the type of data and the problem statement.&lt;/p&gt;
', NULL, NULL, '''/a'':156C,163C ''/blockquote'':18C,73C,100C,144C ''/li'':38C,42C,46C,55C,61C ''/ol'':62C ''/p'':17C,28C,72C,89C,99C,118C,143C,191C ''/questions/tagged/missing-data'':161C ''/questions/tagged/missing-data?sort=newest&amp;amp;pagesize=50'':153C ''abl'':6C ''accord'':180C ''algorithm'':116C,165C ''analyt'':41C ''anyth'':138C ''blockquot'':1C,63C,90C,119C ''clean'':35C ''data'':21C,34C,70C,88C,98C,104C,131C,171C,186C ''datascience.stackexchange.com'':152C ''datascience.stackexchange.com/questions/tagged/missing-data?sort=newest&amp;amp;pagesize=50'':151C ''depend'':67C,80C,94C,181C ''differ'':107C ''entir'':77C ''everi'':20C ''exploratori'':40C ''featur'':85C ''follw'':26C ''given'':129C ''href'':150C,158C ''imput'':169C ''iter'':54C ''li'':30C,39C,43C,47C,56C ''lot'':154C ''make'':105C ''materi'':162C ''miss'':125C,170C ''model'':12C,44C ''ol'':29C ''option'':57C ''p'':2C,19C,64C,74C,91C,101C,120C,145C ''pre'':32C ''pre-process'':31C ''predict'':48C,124C ''problem'':189C ''process'':33C ''rare'':114C ''re'':53C ''re-iter'':52C ''refer'':175C ''report'':58C ''scienc'':22C ''select'':45C ''set'':132C ''size'':96C,102C ''sometim'':112C ''statement'':190C ''stats.stackexchange.com'':160C ''stats.stackexchange.com/questions/tagged/missing-data'':159C ''step'':27C ''techniqu'':13C ''temperatur'':130C ''test'':50C ''tool'':110C ''type'':71C,83C,184C ''unawar'':136C ''understand'':8C ''use'':16C,117C,142C,178C ''valu'':126C ''workflow'':23C,60C,78C ''wrangl'':37C ''yes'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9323, 2, '2015-12-10 22:42:41.48', 1, NULL, NULL, '&lt;p&gt;One possible first step is to convert the data back to the original coding.
This is called in SQL &lt;em&gt;unpivot&lt;/em&gt;, in R &lt;em&gt;melt&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Here an R example&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; my.df &amp;lt;- read.table(
+ text = "DistrictA     DistrictB    DistrictC    DistrictD     DistrictE     Price
+         1             0            0            0             0             10000
+         0             1            0            0             0             20000
+         0             0            1            0             0             30000
+         0             0            0            1             0             40000
+         0             0            0            0             1             50000"
+      , header = TRUE)
&amp;gt; my.df
  DistrictA DistrictB DistrictC DistrictD DistrictE Price
1         1         0         0         0         0 10000
2         0         1         0         0         0 20000
3         0         0         1         0         0 30000
4         0         0         0         1         0 40000
5         0         0         0         0         1 50000

&amp;gt; library(reshape)
&amp;gt; subset(melt(my.df, id="Price", variable = "District"),value == 1)[,c(1,2)]
   Price  District
1  10000 DistrictA
7  20000 DistrictB
13 30000 DistrictC
19 40000 DistrictD
25 50000 DistrictE
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;After that you plot the Price dependent on a factor variable. You may additionally consider to order the factor based on the predicted price.&lt;/p&gt;

&lt;p&gt;I provide no details, as you don''t tagged your tool, but I would recommend additional to a scatter plot to consider a box plot and/or density plot - always combined with the prediction value from the model for each factor level.&lt;/p&gt;
', NULL, NULL, '''/code'':157C ''/em'':23C,28C ''/p'':29C,35C,184C,227C ''/pre'':158C ''0'':50C,51C,52C,53C,55C,57C,58C,59C,61C,62C,64C,65C,67C,68C,69C,71C,73C,74C,75C,76C,91C,92C,93C,94C,97C,99C,100C,101C,104C,105C,107C,108C,111C,112C,113C,115C,118C,119C,120C,121C ''1'':49C,56C,63C,70C,77C,89C,90C,98C,106C,114C,122C,136C,138C,142C ''10000'':54C,95C,143C ''13'':148C ''19'':151C ''2'':96C,139C ''20000'':60C,102C,146C ''25'':154C ''3'':103C ''30000'':66C,109C,149C ''4'':110C ''40000'':72C,116C,152C ''5'':117C ''50000'':78C,123C,155C ''7'':145C ''addit'':173C,201C ''alway'':214C ''and/or'':211C ''back'':11C ''base'':179C ''box'':209C ''c'':137C ''call'':18C ''code'':15C,37C ''combin'':215C ''consid'':174C,207C ''convert'':8C ''data'':10C ''densiti'':212C ''depend'':166C ''detail'':189C ''district'':47C,87C,134C,141C,156C ''districta'':43C,83C,144C ''districtb'':44C,84C,147C ''districtc'':45C,85C,150C ''districtd'':46C,86C,153C ''em'':21C,26C ''exampl'':34C ''factor'':169C,178C,225C ''first'':4C ''gt'':38C,81C,124C,127C ''header'':79C ''id'':131C ''level'':226C ''librari'':125C ''lt'':40C ''may'':172C ''melt'':27C,129C ''model'':222C ''my.df'':39C,82C,130C ''one'':2C ''order'':176C ''origin'':14C ''p'':1C,30C,159C,185C ''plot'':163C,205C,210C,213C ''possibl'':3C ''pre'':36C ''predict'':182C,218C ''price'':48C,88C,132C,140C,165C,183C ''provid'':187C ''r'':25C,33C ''read.table'':41C ''recommend'':200C ''reshap'':126C ''scatter'':204C ''sql'':20C ''step'':5C ''subset'':128C ''tag'':194C ''text'':42C ''tool'':196C ''true'':80C ''unpivot'':22C ''valu'':135C,219C ''variabl'':133C,170C ''would'':199C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9441, 1, '2015-12-19 16:57:31.583', 1, 34, 'Code Vectorization of gsub in R', '&lt;p&gt;How can I vectorize this code in R?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data &amp;lt;- data.frame(A = rep(5, 5), B = rep(0, 5))
data$abstract &amp;lt;- c("no abstract available", "A", "A", "B", "no abstract available")

for (row in (1:nrow(data))){
  data [row,"abstract"] &amp;lt;- gsub("no abstract available"," ",data[row,"abstract"])
}
&lt;/code&gt;&lt;/pre&gt;
', 'r  data-cleaning', NULL, '''/code'':64C ''/p'':20C ''/pre'':65C ''0'':32C ''1'':50C ''5'':28C,29C,33C ''abstract'':35C,39C,45C,55C,59C,63C ''avail'':40C,46C,60C ''b'':30C,43C ''c'':37C ''clean'':10B ''code'':1A,17C,22C ''data'':9B,23C,34C,52C,53C,61C ''data-clean'':8B ''data.frame'':25C ''gsub'':4A,57C ''lt'':24C,36C,56C ''nrow'':51C ''p'':11C ''pre'':21C ''r'':6A,7B,19C ''rep'':27C,31C ''row'':48C,54C,62C ''vector'':2A,15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9325, 1, '2015-12-11 02:54:26.433', 1, 23, 'Python library that can compute the confusion matrix for multi-label classification', '&lt;p&gt;I''m looking for a Python library that can compute the &lt;a href="http://stats.stackexchange.com/q/21551/12359"&gt;confusion matrix for multi-label classification&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;(scikit-learn &lt;a href="https://github.com/scikit-learn/scikit-learn/issues/3452" rel="nofollow"&gt;doesn''t&lt;/a&gt; support multi-label for confusion matrix)&lt;/p&gt;
', 'python  software-recommendation  multilabel-classification', NULL, '''/a'':45C,60C ''/p'':46C,68C ''/q/21551/12359'':37C ''/scikit-learn/scikit-learn/issues/3452'':55C ''classif'':13A,20B,44C ''comput'':5A,31C ''confus'':7A,38C,66C ''doesn'':58C ''github.com'':54C ''github.com/scikit-learn/scikit-learn/issues/3452'':53C ''href'':34C,52C ''label'':12A,43C,64C ''learn'':50C ''librari'':2A,28C ''look'':24C ''m'':23C ''matrix'':8A,39C,67C ''multi'':11A,42C,63C ''multi-label'':10A,41C,62C ''multilabel'':19B ''multilabel-classif'':18B ''nofollow'':57C ''p'':21C,47C ''python'':1A,14B,27C ''recommend'':17B ''rel'':56C ''scikit'':49C ''scikit-learn'':48C ''softwar'':16B ''software-recommend'':15B ''stats.stackexchange.com'':36C ''stats.stackexchange.com/q/21551/12359'':35C ''support'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9328, 1, '2015-12-11 06:02:34.457', 2, 47, 'Machine learning worker performance features for optimum allocation of tasks to workers', '&lt;p&gt;Suppose we have an English to French translation task in a company, and there are 100s of workers who are proficient in doing this task, but each worker has its own unique attributes which enables them to do certain translations in a better way than the others (e.g., a translator who is a doctor would do better translations of medical documents). Consider 1000s of documents for translation received per day, and each document takes one day to translate. So, there is a two-fold problem:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Allocating the tasks to available translators using a queuing model or any other efficient allocation mechanism.&lt;/li&gt;
&lt;li&gt;Learning the worker model based on the past performance of each (e.g., correctness scores, static parameters like skill set, qualifications, experience, etc.) using a machine learning system.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Is there a setup/system/model which solves both of the above problems in one cohesive system? From my reading, this problem calls for the application of the assignment problem, user modelling, queuing, and machine learning optimization. I am searching for existing models or frameworks which integrate all of this, can anyone give me some direction for this task of optimum allocation?&lt;/p&gt;
', 'machine-learning  predictive-modeling  optimization', NULL, '''/li'':126C,155C ''/ol'':156C ''/p'':107C,218C ''1000s'':83C ''100s'':36C ''alloc'':8A,110C,124C,217C ''anyon'':207C ''applic'':181C ''assign'':184C ''attribut'':53C ''avail'':114C ''base'':132C ''better'':63C,77C ''call'':178C ''certain'':59C ''cohes'':171C ''compani'':32C ''consid'':82C ''correct'':140C ''day'':90C,96C ''direct'':211C ''doctor'':74C ''document'':81C,85C,93C ''e.g'':68C,139C ''effici'':123C ''enabl'':55C ''english'':25C ''etc'':149C ''exist'':197C ''experi'':148C ''featur'':5A ''fold'':105C ''framework'':200C ''french'':27C ''give'':208C ''integr'':202C ''learn'':2A,15B,128C,153C,191C ''li'':109C,127C ''like'':144C ''machin'':1A,14B,152C,190C ''machine-learn'':13B ''mechan'':125C ''medic'':80C ''model'':18B,119C,131C,187C,198C ''ol'':108C ''one'':95C,170C ''optim'':19B,192C ''optimum'':7A,216C ''other'':67C ''p'':20C,157C ''paramet'':143C ''past'':135C ''per'':89C ''perform'':4A,136C ''predict'':17B ''predictive-model'':16B ''problem'':106C,168C,177C,185C ''profici'':41C ''qualif'':147C ''queu'':118C,188C ''read'':175C ''receiv'':88C ''score'':141C ''search'':195C ''set'':146C ''setup/system/model'':161C ''skill'':145C ''solv'':163C ''static'':142C ''suppos'':21C ''system'':154C,172C ''take'':94C ''task'':10A,29C,45C,112C,214C ''translat'':28C,60C,70C,78C,87C,98C,115C ''two'':104C ''two-fold'':103C ''uniqu'':52C ''use'':116C,150C ''user'':186C ''way'':64C ''worker'':3A,12A,38C,48C,130C ''would'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9343, 2, '2015-12-11 20:02:25.103', 1, NULL, NULL, '&lt;p&gt;If you already have the names in R, you can iterate over the vector of names, and use the &lt;code&gt;paste&lt;/code&gt; function. &lt;a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/paste.html" rel="nofollow"&gt;Here''s the help page for it&lt;/a&gt;. &lt;code&gt;sprintf&lt;/code&gt; would be &lt;a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/sprintf.html" rel="nofollow"&gt;another option&lt;/a&gt;, if you prefer that approach.&lt;/p&gt;

&lt;p&gt;An example of this would be &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;names &amp;lt;- c("a", "b", "c")
querystring_a &amp;lt;- "https://...../api/dataservice?sql = select * from db where name IN "
querystring_b &amp;lt;- ''\\''
querystring_c &amp;lt;- "\''"
for(n in names){
    cat(paste(querystring_a, querystring_b, querystring_c, n, querystring_c, sep=""), sep="\n")
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Return:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;https://...../api/dataservice?sql = select * from db where name IN \''a''
https://...../api/dataservice?sql = select * from db where name IN \''b''
https://...../api/dataservice?sql = select * from db where name IN \''c''
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':39C,54C ''/api/dataservice'':80C,119C,128C,137C ''/code'':23C,42C,112C,146C ''/p'':60C,68C,116C ''/pre'':113C,147C ''/r-manual/r-devel/library/base/html/paste.html'':29C ''/r-manual/r-devel/library/base/html/sprintf.html'':49C ''alreadi'':4C ''anoth'':52C ''approach'':59C ''b'':75C,89C,103C,136C ''c'':73C,76C,92C,105C,108C,145C ''cat'':98C ''code'':21C,40C,70C,118C ''db'':84C,123C,132C,141C ''exampl'':63C ''function'':24C ''help'':35C ''href'':26C,46C ''iter'':12C ''lt'':72C,79C,90C,93C ''n'':95C,106C,111C ''name'':7C,17C,71C,86C,97C,125C,134C,143C ''nofollow'':31C,51C ''option'':53C ''p'':1C,61C,114C ''page'':36C ''past'':22C,99C ''pre'':69C,117C ''prefer'':57C ''querystr'':77C,88C,91C,100C,102C,104C,107C ''r'':9C ''rel'':30C,50C ''return'':115C ''select'':82C,121C,130C,139C ''sep'':109C,110C ''sprintf'':41C ''sql'':81C,120C,129C,138C ''stat.ethz.ch'':28C,48C ''stat.ethz.ch/r-manual/r-devel/library/base/html/paste.html'':27C ''stat.ethz.ch/r-manual/r-devel/library/base/html/sprintf.html'':47C ''use'':19C ''vector'':15C ''would'':43C,66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9344, 1, '2015-12-11 21:29:32.397', 1, 36, 'How to build the following the string in R?', '&lt;p&gt;I am trying to build a long string of the form.....&lt;code&gt;\''name1\'',\''name2\'',\''name3\'',..etc&lt;/code&gt;. How do I do it ? I am trying to do it using R ? By hand is impossible as there are currently over 200 such names and in future I may have more. So I need to automate the process. However, every time I tried to use R''s paste0 function I am getting a different result. Here is what I did:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;t &amp;lt;- c()
for( nms in name){ t &amp;lt;- paste0(t, paste0("\\''",nms,"\\''", sep = ",") ) }
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However, the result turns out to include double backslashes instead of one, like this &lt;code&gt;"\\''name1\\'',\\''name2\\'',\\''name3\\''&lt;/code&gt; etc. I couldn''t get a single backslash as I want. &lt;/p&gt;

&lt;p&gt;Any help would be appreciated.&lt;/p&gt;

&lt;p&gt;Thanks. &lt;/p&gt;
', 'r', NULL, '''/code'':28C,107C,128C ''/p'':90C,140C,147C,150C ''/pre'':108C ''200'':51C ''appreci'':146C ''autom'':65C ''backslash'':118C,136C ''build'':3A,16C ''c'':95C ''code'':23C,92C,124C ''couldn'':131C ''current'':49C ''differ'':83C ''doubl'':117C ''etc'':27C,129C ''everi'':69C ''follow'':5A ''form'':22C ''function'':78C ''futur'':56C ''get'':81C,133C ''hand'':43C ''help'':143C ''howev'':68C,110C ''imposs'':45C ''includ'':116C ''instead'':119C ''like'':122C ''long'':18C ''lt'':94C,101C ''may'':58C ''name'':53C,99C ''name1'':24C,125C ''name2'':25C,126C ''name3'':26C,127C ''need'':63C ''nms'':97C,105C ''one'':121C ''p'':11C,109C,141C,148C ''paste0'':77C,102C,104C ''pre'':91C ''process'':67C ''r'':9A,10B,41C,75C ''result'':84C,112C ''sep'':106C ''singl'':135C ''string'':7A,19C ''thank'':149C ''time'':70C ''tri'':14C,36C,72C ''turn'':113C ''use'':40C,74C ''want'':139C ''would'':144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9345, 1, '2015-12-11 23:01:37.85', 2, 29, 'Central Probability Interval', '&lt;p&gt;So, I''m having problems learning for a data analysis lecture. The topic is the construction of the "central interval" as our lecturer calls it. The concept shall path the way to understand confidence &amp;amp; credible intervals. But I don''t even get his concept and therefore wanted to ask for help. 
I will give an excerpt of the passage I don''t get (it is in the context of a bionmial distribution where r is the possible outcome of a bernoulli experiment):&lt;/p&gt;

&lt;p&gt;&lt;em&gt;"To this end, we define the &lt;strong&gt;Central Probability Interval&lt;/strong&gt; and Smallest
Probability Interval containing a probability at least 1-$\alpha$.
This  interval  is  constructed  by  defining  a  smallest  member  of  the  set  of possible outcomes, $r_{1}$, and a largest member, $r_{2}$, subject to the criterion that
$$P(r&amp;lt;r_{1}) &amp;lt;= \alpha/2$$and $$P(r&amp;gt;r_{2}) &amp;lt;= \alpha/2$$ while maximizing these probablities."&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;First of all, $\alpha$ seems to be an arbitrary factor because it isn''t mentioned anytime before. After choosing such an $\alpha$, we look for two numbers $r_{1,2}$. These numbers are limits which, when r surpasses these number, will yield a probability smaller than $\alpha$/2. But what about the maximizing of the probability? What probability?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;"Stating this precisely, we write:
$$
r_{1} = 0 \quad\text{if}\quad P(r=0|N,p) &amp;gt; \alpha/2\quad\text{ else}\quad r_{1} =  \sup_{r\in0,...,N}[\sum_{i=0}^{r}P(i|N,p) &amp;lt;= \alpha/2]+1
$$
and
$$
r_{2} = N\quad\text{if}\quad P(r=N|N,p) &amp;gt; \alpha/2\quad\text{ else}\quad r_{2} =  \sup_{r\in0,...,N}[\sum_{i=r}^{N}P(i|N,p) &amp;lt;= \alpha/2]-1
$$
The set of our results in our Central Interval is
$$
O^{C}_{1-\alpha}=\{ r_{1}, r_{1}+1,...r_{2}\}.
$$"&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Now, I cannot make anything of the equations simply because I don''t know how to read and process them properly. Maybe someone has an idea or can give me a good example?
Your help is much appreciated.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit:&lt;/strong&gt;
I found a hint online and I think, now I get it.
We construct an interval which contains a prbability 1-$\alpha$. Therefore, the probability of all r values outside the interval [$r_{1}$, $r_{2}$] has to be $\alpha$. If you split this symmetric you get the criterion stated above.&lt;/p&gt;

&lt;p&gt;Now as for the equations. They add up all the probabilities which contribute to an outcome smaller than $\alpha$/2. Taking the $\sup_{r \in 0,...,N}$ now gives the r value for which this sum is maximized. Then you have to add the +1 in order to take the next r which then is $r_{1}$.&lt;/p&gt;
', 'statistics  data', NULL, '''+1'':267C,322C,470C ''-1'':303C ''/2'':213C,445C ''/em'':162C,325C ''/p'':90C,163C,225C,326C,368C,424C,483C ''/strong'':103C,224C,367C,372C ''0'':235C,242C,259C,451C ''1'':113C,131C,147C,194C,234C,252C,316C,319C,321C,393C,406C,482C ''2'':137C,155C,195C,270C,288C,324C,408C ''add'':432C,468C ''alpha'':114C,169C,187C,212C,317C,394C,412C,444C ''alpha/2'':149C,157C,246C,266C,282C,302C ''amp'':41C ''analysi'':16C ''anyth'':333C ''anytim'':181C ''appreci'':366C ''arbitrari'':174C ''ask'':56C ''bernoulli'':88C ''bionmial'':78C ''c'':315C ''call'':30C ''cannot'':331C ''central'':1A,25C,100C,311C ''choos'':184C ''concept'':33C,51C ''confid'':40C ''construct'':22C,118C,386C ''contain'':108C,390C ''context'':75C ''contribut'':438C ''credibl'':42C ''criterion'':141C,421C ''data'':5B,15C ''defin'':97C,120C ''distribut'':79C ''edit'':371C ''els'':249C,285C ''em'':92C,227C ''end'':95C ''equat'':336C,430C ''even'':48C ''exampl'':361C ''excerpt'':63C ''experi'':89C ''factor'':175C ''first'':166C ''found'':374C ''get'':49C,70C,383C,419C ''give'':61C,357C,454C ''good'':360C ''gt'':153C,245C,281C ''help'':58C,363C ''hint'':376C ''idea'':354C ''in0'':255C,291C ''interv'':3A,26C,43C,102C,107C,116C,312C,388C,404C ''isn'':178C ''know'':342C ''largest'':134C ''learn'':12C ''least'':112C ''lectur'':17C,29C ''limit'':199C ''look'':189C ''lt'':145C,148C,156C,265C,301C ''m'':9C ''make'':332C ''maxim'':159C,218C,463C ''mayb'':350C ''member'':123C,135C ''mention'':180C ''much'':365C ''n'':243C,256C,263C,271C,278C,279C,292C,296C,299C,452C ''next'':476C ''number'':192C,197C,205C ''o'':314C ''onlin'':377C ''order'':472C ''outcom'':85C,129C,441C ''outsid'':402C ''p'':6C,91C,143C,151C,164C,226C,240C,244C,261C,264C,276C,280C,297C,300C,327C,369C,425C ''passag'':66C ''path'':35C ''possibl'':84C,128C ''prbabil'':392C ''precis'':230C ''probabl'':2A,101C,106C,110C,161C,209C,221C,223C,397C,436C ''problem'':11C ''process'':347C ''proper'':349C ''quad'':236C,239C,247C,250C,272C,275C,283C,286C ''r'':81C,130C,136C,144C,146C,152C,154C,193C,202C,233C,241C,251C,254C,260C,269C,277C,287C,290C,295C,318C,320C,323C,400C,405C,407C,449C,456C,477C,481C ''read'':345C ''result'':308C ''seem'':170C ''set'':126C,305C ''shall'':34C ''simpli'':337C ''smaller'':210C,442C ''smallest'':105C,122C ''someon'':351C ''split'':415C ''state'':228C,422C ''statist'':4B ''strong'':99C,165C,328C,370C ''subject'':138C ''sum'':257C,293C,461C ''sup'':253C,289C,448C ''surpass'':203C ''symmetr'':417C ''take'':446C,474C ''text'':237C,248C,273C,284C ''therefor'':53C,395C ''think'':380C ''topic'':19C ''two'':191C ''understand'':39C ''valu'':401C,457C ''want'':54C ''way'':37C ''write'':232C ''yield'':207C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9346, 1, '2015-12-11 23:43:50.733', 8, 162, 'How do AI''s learn to act when the problem space is too big', '&lt;p&gt;I learn best through experimentation and example. I''m learning about neural networks and have (what I think) is a pretty good understanding of classification and regression and also supervised and unsupervised learning, but I''ve stumbled upon something I can''t quiet figure out;&lt;/p&gt;

&lt;p&gt;If I wanted to train an AI to play a complicated game; I''m thinking something like a RTS (eg. Age of Empires, Empire Earth etc.). In these types of games there is typically a number of entities controlled by the player (units, buildings) each with different capabilities. It seem like the problem of that the AI does would be classification (eg. choose that unit, and that action), however since the number of units is a variable how does one handle a classification problem in this way?&lt;/p&gt;

&lt;p&gt;The only thing I can think of is multiple networks that do different stages (one for overall strategy, one for controlling this type of unit, one for that type of building etc.); but this seems like I''m making the problem to complicated.&lt;/p&gt;

&lt;p&gt;Are there any good example of machine learning/neural networks learning complex games (not specifically RTS, but more complicated the &lt;a href="https://www.google.co.uk/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;cad=rja&amp;amp;uact=8&amp;amp;ved=0ahUKEwjoy7Cf_tTJAhWKtxQKHfYjDCwQyCkIJDAA&amp;amp;url=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3Dqv6UVOQ0F44&amp;amp;usg=AFQjCNFHQQt6wIfZEm4jzRCo8B0CWEF65w&amp;amp;sig2=ur7NzjxwjqTtVUmOX4Hcow"&gt;Mario&lt;/a&gt;)?&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/a'':226C ''/p'':65C,154C,199C,227C ''/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;cad=rja&amp;amp;uact=8&amp;amp;ved=0ahukewjoy7cf_ttjahwktxqkhfyjdcwqyckijdaa&amp;amp;url=https%3a%2f%2fwww.youtube.com%2fwatch%3fv%3dqv6uvoq0f44&amp;amp;usg=afqjcnfhqqt6wifzem4jzrco8b0cwef65w&amp;amp;sig2=ur7nzjxwjqttvumox4hcow'':224C ''act'':7A ''action'':134C ''age'':87C ''ai'':3A,73C,123C ''also'':48C ''best'':22C ''big'':14A ''build'':110C,186C ''capabl'':114C ''choos'':129C ''classif'':44C,127C,149C ''complex'':211C ''complic'':77C,198C,218C ''control'':105C,176C ''differ'':113C,168C ''earth'':91C ''eg'':86C,128C ''empir'':89C,90C ''entiti'':104C ''etc'':92C,187C ''exampl'':26C,205C ''experiment'':24C ''figur'':63C ''game'':78C,97C,212C ''good'':41C,204C ''handl'':147C ''howev'':135C ''href'':221C ''learn'':5A,17B,21C,29C,52C,210C ''learning/neural'':208C ''like'':83C,117C,191C ''m'':28C,80C,193C ''machin'':16B,207C ''machine-learn'':15B ''make'':194C ''mario'':225C ''multipl'':164C ''network'':32C,165C,209C ''neural'':31C ''neuralnetwork'':18B ''number'':102C,138C ''one'':146C,170C,174C,181C ''overal'':172C ''p'':19C,66C,155C,200C ''play'':75C ''player'':108C ''pretti'':40C ''problem'':10A,119C,150C,196C ''quiet'':62C ''regress'':46C ''rts'':85C,215C ''seem'':116C,190C ''sinc'':136C ''someth'':58C,82C ''space'':11A ''specif'':214C ''stage'':169C ''strategi'':173C ''stumbl'':56C ''supervis'':49C ''thing'':158C ''think'':37C,81C,161C ''train'':71C ''type'':95C,178C,184C ''typic'':100C ''understand'':42C ''unit'':109C,131C,140C,180C ''unsupervis'':51C ''upon'':57C ''variabl'':143C ''ve'':55C ''want'':69C ''way'':153C ''would'':125C ''www.google.co.uk'':223C ''www.google.co.uk/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;cad=rja&amp;amp;uact=8&amp;amp;ved=0ahukewjoy7cf_ttjahwktxqkhfyjdcwqyckijdaa&amp;amp;url=https%3a%2f%2fwww.youtube.com%2fwatch%3fv%3dqv6uvoq0f44&amp;amp;usg=afqjcnfhqqt6wifzem4jzrco8b0cwef65w&amp;amp;sig2=ur7nzjxwjqttvumox4hcow'':222C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9347, 1, '2015-12-12 13:15:44.547', 2, 112, 'How to create Self learning data product', '&lt;p&gt;I am trying to build price recommendation solution for clients in a scalable manner. I have two choices as below. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Professional service&lt;/strong&gt;: Statistician involvement to build regression model or any other kind of predictive model that fits specifically to client data and can be used.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Issue:&lt;/em&gt;&lt;/strong&gt; So on the long run there will be issues around scalability as one analyst cannot build model simultaneously for hundreds of clients who want to come on board and use this service. Hiring 1 analyst for 100 clients is not an option for me as i do not run this operation.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Product&lt;/strong&gt;: Plug and play with least human intervention. i.e. Some kind of self learning/artificial algorithm that works for any and every client. Additionally, it adjusts to new trends that appear over time which might not have been captured in the existing model. &lt;strong&gt;Personally, i will be biased towards this kind of solution.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Issue&lt;/em&gt;&lt;/strong&gt;: This is the ideal solution, however i am not sure how to build this and how to approach in this direction.&lt;/p&gt;

&lt;p&gt;To give context, a client in Europe might have same data structure in their sql database in terms of name, variable type in comparison to sql database of a client who is based in Singapore. However, Singapore data might have different trend, seasonality and pattern in comparison to data in Europe. Is there any way that a single Machine Learning or Artificial Intelligence or Deep Learning or Neural Net algorithm can be built and used for all regions and clients without having to update that model manually for any new client?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;My background:&lt;/strong&gt; i have previously worked in a professional service landscape and have statistics/predictive modelling background.&lt;/p&gt;
', 'machine-learning  recommendation  deep-learning  scalability  data-product', NULL, '''/em'':74C,190C ''/p'':40C,69C,127C,185C,213C,306C,326C ''/strong'':45C,75C,131C,184C,191C,311C ''1'':109C ''100'':112C ''addit'':153C ''adjust'':155C ''algorithm'':145C,284C ''analyst'':89C,110C ''appear'':160C ''approach'':209C ''around'':85C ''artifici'':276C ''background'':310C,325C ''base'':247C ''bias'':178C ''board'':103C ''build'':24C,49C,91C,204C ''built'':287C ''cannot'':90C ''captur'':168C ''choic'':37C ''client'':29C,63C,97C,113C,152C,219C,244C,294C,305C ''come'':101C ''comparison'':238C,261C ''context'':217C ''creat'':3A ''data'':6A,17B,64C,225C,252C,263C ''data-product'':16B ''databas'':230C,241C ''deep'':13B,279C ''deep-learn'':12B ''differ'':255C ''direct'':212C ''em'':72C,188C ''europ'':221C,265C ''everi'':151C ''exist'':171C ''fit'':60C ''give'':216C ''hire'':108C ''howev'':197C,250C ''human'':137C ''hundr'':95C ''i.e'':139C ''ideal'':195C ''intellig'':277C ''intervent'':138C ''involv'':47C ''issu'':73C,84C,189C ''kind'':55C,141C,181C ''landscap'':320C ''learn'':5A,10B,14B,274C,280C ''learning/artificial'':144C ''least'':136C ''long'':79C ''machin'':9B,273C ''machine-learn'':8B ''manner'':33C ''manual'':301C ''might'':164C,222C,253C ''model'':51C,58C,92C,172C,300C,324C ''name'':234C ''net'':283C ''neural'':282C ''new'':157C,304C ''one'':88C ''oper'':126C ''option'':117C ''p'':19C,41C,70C,128C,186C,214C,307C ''pattern'':259C ''person'':174C ''play'':134C ''plug'':132C ''predict'':57C ''previous'':314C ''price'':25C ''product'':7A,18B,130C ''profession'':43C,318C ''recommend'':11B,26C ''region'':292C ''regress'':50C ''run'':80C,124C ''scalabl'':15B,32C,86C ''season'':257C ''self'':4A,143C ''servic'':44C,107C,319C ''simultan'':93C ''singapor'':249C,251C ''singl'':272C ''solut'':27C,183C,196C ''specif'':61C ''sql'':229C,240C ''statistician'':46C ''statistics/predictive'':323C ''strong'':42C,71C,129C,173C,187C,308C ''structur'':226C ''sure'':201C ''term'':232C ''time'':162C ''toward'':179C ''trend'':158C,256C ''tri'':22C ''two'':36C ''type'':236C ''updat'':298C ''use'':68C,105C,289C ''variabl'':235C ''want'':99C ''way'':269C ''without'':295C ''work'':147C,315C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9352, 1, '2015-12-12 16:14:27.67', 0, 56, 'Stemming text in R', '&lt;p&gt;I am stemming my text data in R. I am using a solution proposed by Yanchang Zhao for the latest version of tm package but found this very slow. I am looking for an alternative way.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;myCorpusCopy &amp;lt;- myCorpus

stemCompletion2 &amp;lt;- function(x, dictionary) {
x &amp;lt;- unlist(strsplit(as.character(x), " "))
x &amp;lt;- x[x != ""]
x &amp;lt;- stemCompletion(x, dictionary=dictionary)
x &amp;lt;- paste(x, sep="", collapse=" ") 
PlainTextDocument(stripWhitespace(x)) }


 myCorpus &amp;lt;- lapply(myCorpus, stemCompletion2, dictionary=myCorpusCopy)

 myCorpus &amp;lt;- Corpus(VectorSource(myCorpus))
&lt;/code&gt;&lt;/pre&gt;
', 'r  text-mining', NULL, '''/code'':94C ''/p'':46C ''/pre'':95C ''altern'':44C ''as.character'':61C ''code'':48C ''collaps'':78C ''corpus'':91C ''data'':15C ''dictionari'':56C,71C,72C,87C ''found'':35C ''function'':54C ''lappli'':84C ''latest'':29C ''look'':41C ''lt'':50C,53C,58C,64C,68C,74C,83C,90C ''mine'':8B ''mycorpus'':51C,82C,85C,89C,93C ''mycorpuscopi'':49C,88C ''p'':9C ''packag'':33C ''past'':75C ''plaintextdocu'':79C ''pre'':47C ''propos'':23C ''r'':4A,5B,17C ''sep'':77C ''slow'':38C ''solut'':22C ''stem'':1A,12C ''stemcomplet'':69C ''stemcompletion2'':52C,86C ''stripwhitespac'':80C ''strsplit'':60C ''text'':2A,7B,14C ''text-min'':6B ''tm'':32C ''unlist'':59C ''use'':20C ''vectorsourc'':92C ''version'':30C ''way'':45C ''x'':55C,57C,62C,63C,65C,66C,67C,70C,73C,76C,81C ''yanchang'':25C ''zhao'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9353, 2, '2015-12-12 16:45:36.38', 1, NULL, NULL, '&lt;p&gt;Apply &lt;strong&gt;stemming&lt;/strong&gt; during preprocessing to reduce words to their basic form.&lt;/p&gt;

&lt;p&gt;Porter stemmer is okay for English, and rather fast.&lt;/p&gt;
', NULL, NULL, '''/p'':15C,26C ''/strong'':5C ''appli'':2C ''basic'':13C ''english'':22C ''fast'':25C ''form'':14C ''okay'':20C ''p'':1C,16C ''porter'':17C ''preprocess'':7C ''rather'':24C ''reduc'':9C ''stem'':4C ''stemmer'':18C ''strong'':3C ''word'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9355, 1, '2015-12-12 17:16:45.253', 1, 44, 'Early-Stopping for logistic regression. Theano', '&lt;p&gt;I am trying to understand the code for the logistic regression on the official documentation, but I am struggling to understand the logic behind this code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# early-stopping parameters
patience = 5000  # look as this many examples regardless
patience_increase = 2     # wait this much longer when a new best is
                              # found
improvement_threshold = 0.995  # a relative improvement of this much is
                               # considered significant
validation_frequency = min(n_train_batches, patience/2)
                              # go through this many
                              # minibatches before checking the network
                              # on the validation set; in this case we
                              # check every epoch

best_params = None
best_validation_loss = numpy.inf
test_score = 0.
start_time = time.clock()

done_looping = False
epoch = 0
while (epoch &amp;lt; n_epochs) and (not done_looping):
    # Report "1" for first epoch, "n_epochs" for last epoch
    epoch = epoch + 1
    for minibatch_index in xrange(n_train_batches):

        d_loss_wrt_params = ... # compute gradient
        params -= learning_rate * d_loss_wrt_params # gradient descent

        # iteration number. We want it to start at 0.
        iter = (epoch - 1) * n_train_batches + minibatch_index
        # note that if we do `iter % validation_frequency` it will be
        # true for iter = 0 which we do not want. We want it true for
        # iter = validation_frequency - 1.
        if (iter + 1) % validation_frequency == 0:

            this_validation_loss = ... # compute zero-one loss on validation set

            if this_validation_loss &amp;lt; best_validation_loss:

                # improve patience if loss improvement is good enough
                if this_validation_loss &amp;lt; best_validation_loss * improvement_threshold:

                    patience = max(patience, iter * patience_increase)
                best_params = copy.deepcopy(params)
                best_validation_loss = this_validation_loss

        if patience &amp;lt;= iter:
            done_looping = True
            break
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Could any one, explain to me, what do the variables: patience, patience_increase, improvement_threshold, validation_frequency, iter,  represent ?&lt;/p&gt;

&lt;p&gt;What this condition do ?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;if (iter + 1) % validation_frequency == 0:
&lt;/code&gt;&lt;/pre&gt;
', 'logistic-regression  theano', NULL, '''/code'':282C,319C ''/p'':39C,304C,310C ''/pre'':283C,320C ''0'':115C,123C,177C,200C,220C,318C ''0.995'':69C ''1'':134C,145C,180C,214C,217C,315C ''2'':56C ''5000'':47C ''batch'':84C,153C,183C ''behind'':36C ''best'':64C,106C,109C,237C,253C,264C,268C ''break'':281C ''case'':101C ''check'':92C,103C ''code'':19C,38C,41C,312C ''comput'':158C,224C ''condit'':308C ''consid'':77C ''copy.deepcopy'':266C ''could'':285C ''d'':154C,163C ''descent'':168C ''document'':27C ''done'':119C,131C,278C ''earli'':2A,43C ''early-stop'':1A,42C ''enough'':247C ''epoch'':105C,122C,125C,128C,137C,139C,142C,143C,144C,179C ''everi'':104C ''exampl'':52C ''explain'':288C ''fals'':121C ''first'':136C ''found'':66C ''frequenc'':80C,193C,213C,219C,301C,317C ''go'':86C ''good'':246C ''gradient'':159C,167C ''improv'':67C,72C,240C,244C,256C,298C ''increas'':55C,263C,297C ''index'':148C,185C ''iter'':169C,178C,191C,199C,211C,216C,261C,277C,302C,314C ''last'':141C ''learn'':161C ''logic'':35C ''logist'':5A,9B,22C ''logistic-regress'':8B ''longer'':60C ''look'':48C ''loop'':120C,132C,279C ''loss'':111C,155C,164C,223C,228C,235C,239C,243C,251C,255C,270C,273C ''lt'':126C,236C,252C,276C ''mani'':51C,89C ''max'':259C ''min'':81C ''minibatch'':90C,147C,184C ''much'':59C,75C ''n'':82C,127C,138C,151C,181C ''network'':94C ''new'':63C ''none'':108C ''note'':186C ''number'':170C ''numpy.inf'':112C ''offici'':26C ''one'':227C,287C ''p'':12C,284C,305C ''param'':107C,157C,160C,166C,265C,267C ''paramet'':45C ''patienc'':46C,54C,241C,258C,260C,262C,275C,295C,296C ''patience/2'':85C ''pre'':40C,311C ''rate'':162C ''regardless'':53C ''regress'':6A,10B,23C ''relat'':71C ''report'':133C ''repres'':303C ''score'':114C ''set'':98C,231C ''signific'':78C ''start'':116C,175C ''stop'':3A,44C ''struggl'':31C ''test'':113C ''theano'':7A,11B ''threshold'':68C,257C,299C ''time'':117C ''time.clock'':118C ''train'':83C,152C,182C ''tri'':15C ''true'':197C,209C,280C ''understand'':17C,33C ''valid'':79C,97C,110C,192C,212C,218C,222C,230C,234C,238C,250C,254C,269C,272C,300C,316C ''variabl'':294C ''wait'':57C ''want'':172C,205C,207C ''wrt'':156C,165C ''xrang'':150C ''zero'':226C ''zero-on'':225C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9356, 1, '2015-12-12 17:48:00.66', 0, 19, 'Survival Analisys - Calculating life time expectancy', '&lt;p&gt;How to calculate life time expectancy when not all patients have died.
Kalan Meir provides survival curve which is similar to cumulative distribution function but not the actual expectancy.&lt;/p&gt;

&lt;p&gt;For reference: &lt;a href="http://lifelines.readthedocs.org/en/latest/Quickstart.html" rel="nofollow"&gt;http://lifelines.readthedocs.org/en/latest/Quickstart.html&lt;/a&gt;&lt;/p&gt;
', 'survival-analysis', NULL, '''/en/latest/quickstart.html'':48C ''/en/latest/quickstart.html&lt;/a&gt;&lt;/p&gt;'':53C ''/p'':40C ''actual'':38C ''analisi'':2A ''analysi'':9B ''calcul'':3A,13C ''cumul'':32C ''curv'':27C ''die'':22C ''distribut'':33C ''expect'':6A,16C,39C ''function'':34C ''href'':45C ''kalan'':23C ''life'':4A,14C ''lifelines.readthedocs.org'':47C,52C ''lifelines.readthedocs.org/en/latest/quickstart.html'':46C ''lifelines.readthedocs.org/en/latest/quickstart.html&lt;/a&gt;&lt;/p&gt;'':51C ''meir'':24C ''nofollow'':50C ''p'':10C,41C ''patient'':20C ''provid'':25C ''refer'':43C ''rel'':49C ''similar'':30C ''surviv'':1A,8B,26C ''survival-analysi'':7B ''time'':5A,15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9357, 2, '2015-12-12 23:10:13.957', 1, NULL, NULL, '&lt;p&gt;&lt;code&gt;Patience&lt;/code&gt; is the number of training batches to do before you stop. &lt;code&gt;iter&lt;/code&gt; is the number of training batches you''ve seen. Each iteration, you decide whether or not your validation is lower than your previous best. The improvement is only stored if the score is lower than &lt;code&gt;improvement_threshold * validation_score&lt;/code&gt;.  &lt;/p&gt;

&lt;p&gt;It appears that &lt;code&gt;patience_increase&lt;/code&gt; is a multiplier.  Every time you have a new lowest score, you up the total number or training batches to &lt;code&gt;iter*patience_increase&lt;/code&gt;, but not below the current value of &lt;code&gt;patience&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;validation_frequency&lt;/code&gt; is just the number of batches between times you check the validation score.&lt;/p&gt;
', NULL, NULL, '''/code'':4C,18C,59C,68C,93C,103C,109C ''/p'':60C,104C,123C ''appear'':63C ''batch'':10C,24C,87C,115C ''best'':42C ''check'':119C ''code'':2C,16C,54C,65C,89C,101C,106C ''current'':98C ''decid'':31C ''everi'':72C ''frequenc'':108C ''improv'':44C,55C ''increas'':67C,92C ''iter'':17C,29C,90C ''lower'':38C,52C ''lowest'':78C ''multipli'':71C ''new'':77C ''number'':7C,21C,84C,113C ''p'':1C,61C,105C ''patienc'':3C,66C,91C,102C ''previous'':41C ''score'':50C,58C,79C,122C ''seen'':27C ''stop'':15C ''store'':47C ''threshold'':56C ''time'':73C,117C ''total'':83C ''train'':9C,23C,86C ''valid'':36C,57C,107C,121C ''valu'':99C ''ve'':26C ''whether'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9363, 2, '2015-12-13 10:39:27.733', 3, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Linear Regression&lt;/strong&gt; is used for predicting continuous variables.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Logistic Regression&lt;/strong&gt; is used for predicting variables which has only limited values.&lt;/p&gt;

&lt;p&gt;Let me quote a nice example which can help you make the difference between the both:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;For instance, if X contains the area in square feet of houses, and Y
  contains the corresponding sale price of those houses, you could use
  linear regression to predict selling price as a function of house
  size. While the possible selling price may not actually be any, there
  are so many possible values that a linear regression model would be
  chosen.&lt;/p&gt;
  
  &lt;p&gt;If, instead, you wanted to predict, based on size, whether a house
  would sell for more than 200K, you would use logistic regression. The
  possible outputs are either Yes, the house will sell for more than
  $200K, or No, the house will not.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/blockquote'':156C ''/p'':12C,28C,46C,110C,155C ''/strong'':5C,17C ''200k'':129C,148C ''actual'':93C ''area'':55C ''base'':118C ''blockquot'':47C ''chosen'':109C ''contain'':53C,63C ''continu'':10C ''correspond'':65C ''could'':72C ''differ'':42C ''either'':139C ''exampl'':35C ''feet'':58C ''function'':82C ''help'':38C ''hous'':60C,70C,84C,123C,142C,152C ''instanc'':50C ''instead'':113C ''let'':30C ''limit'':26C ''linear'':3C,74C,104C ''logist'':15C,133C ''make'':40C ''mani'':99C ''may'':91C ''model'':106C ''nice'':34C ''output'':137C ''p'':1C,13C,29C,48C,111C ''possibl'':88C,100C,136C ''predict'':9C,21C,77C,117C ''price'':67C,79C,90C ''quot'':32C ''regress'':4C,16C,75C,105C,134C ''sale'':66C ''sell'':78C,89C,125C,144C ''size'':85C,120C ''squar'':57C ''strong'':2C,14C ''use'':7C,19C,73C,132C ''valu'':27C,101C ''variabl'':11C,22C ''want'':115C ''whether'':121C ''would'':107C,124C,131C ''x'':52C ''y'':62C ''yes'':140C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9359, 1, '2015-12-13 05:04:15.86', 0, 50, 'Should i pick this as a career?', '&lt;p&gt;I''m mainly interested in creative things, but also interested in the science behind viewership and marketing a piece of media. Think about having a stable career in data science, where do you think I should begin? Are media analytics as big as others like business intelligence? Ive checked online courses, like google analytics academy and others like these:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.edx.org/xseries/data-science-analytics-context" rel="nofollow"&gt;https://www.edx.org/xseries/data-science-analytics-context&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.coursera.org/specializations/jhudatascience" rel="nofollow"&gt;https://www.coursera.org/specializations/jhudatascience&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Is this the right path to take? And is it easy to find jobs in such a field even if the person is a beginner in programming?&lt;/p&gt;
', 'bigdata  predictive-modeling  beginner', NULL, '''/p'':73C,124C ''/specializations/jhudatascience'':90C ''/specializations/jhudatascience&lt;/a&gt;&lt;/p&gt;'':95C ''/xseries/data-science-analytics-context'':79C ''/xseries/data-science-analytics-context&lt;/a&gt;&lt;/p&gt;'':84C ''academi'':68C ''also'':22C ''analyt'':53C,67C ''begin'':50C ''beginn'':12B,121C ''behind'':27C ''big'':55C ''bigdata'':8B ''busi'':59C ''career'':7A,40C ''check'':62C ''cours'':64C ''creativ'':19C ''data'':42C ''easi'':107C ''even'':115C ''field'':114C ''find'':109C ''googl'':66C ''href'':76C,87C ''intellig'':60C ''interest'':17C,23C ''ive'':61C ''job'':110C ''like'':58C,65C,71C ''m'':15C ''main'':16C ''market'':30C ''media'':34C,52C ''model'':11B ''nofollow'':81C,92C ''onlin'':63C ''other'':57C,70C ''p'':13C,74C,85C,96C ''path'':101C ''person'':118C ''pick'':3A ''piec'':32C ''predict'':10B ''predictive-model'':9B ''program'':123C ''rel'':80C,91C ''right'':100C ''scienc'':26C,43C ''stabl'':39C ''take'':103C ''thing'':20C ''think'':35C,47C ''viewership'':28C ''www.coursera.org'':89C,94C ''www.coursera.org/specializations/jhudatascience'':88C ''www.coursera.org/specializations/jhudatascience&lt;/a&gt;&lt;/p&gt;'':93C ''www.edx.org'':78C,83C ''www.edx.org/xseries/data-science-analytics-context'':77C ''www.edx.org/xseries/data-science-analytics-context&lt;/a&gt;&lt;/p&gt;'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9360, 2, '2015-12-13 05:52:33.113', 1, NULL, NULL, '&lt;p&gt;Data Science is all about creativity and do/see things what makes sense. &lt;/p&gt;

&lt;p&gt;I would describe Data Scientist career in couple words: Take this path only and only if you are passionate about science, working with data and are curious. Do not take this way if you are thinking about money or job security ... this field is not so simple and you stay weeks and months experimenting and try to find something in that datasets ... 
One example: as data scientist you are mostly starting to look at data and try to find something inside and solve problems, some dta does not provide any info and after weeks of "magic" is put on the side and moved to other project, after a while you returning and look into data to find information on other business problems. &lt;/p&gt;

&lt;p&gt;Look at Data Science as pure science and only after that about money, also most of the data scientist''s primary goal is not remuneration ... media is promoting salaries but not the real work what we doing ... how much we love data ...&lt;/p&gt;
', NULL, NULL, '''/p'':14C,138C,182C ''also'':153C ''busi'':136C ''career'':21C ''coupl'':23C ''creativ'':7C ''curious'':42C ''data'':2C,19C,39C,81C,90C,130C,142C,157C,181C ''dataset'':77C ''describ'':18C ''do/see'':9C ''dta'':101C ''exampl'':79C ''experi'':69C ''field'':58C ''find'':73C,94C,132C ''goal'':161C ''info'':106C ''inform'':133C ''insid'':96C ''job'':55C ''look'':88C,128C,140C ''love'':180C ''magic'':111C ''make'':12C ''media'':165C ''money'':53C,152C ''month'':68C ''most'':85C ''move'':118C ''much'':178C ''one'':78C ''p'':1C,15C,139C ''passion'':34C ''path'':27C ''primari'':160C ''problem'':99C,137C ''project'':121C ''promot'':167C ''provid'':104C ''pure'':145C ''put'':113C ''real'':172C ''remuner'':164C ''return'':126C ''salari'':168C ''scienc'':3C,36C,143C,146C ''scientist'':20C,82C,158C ''secur'':56C ''sens'':13C ''side'':116C ''simpl'':62C ''solv'':98C ''someth'':74C,95C ''start'':86C ''stay'':65C ''take'':25C,45C ''thing'':10C ''think'':51C ''tri'':71C,92C ''way'':47C ''week'':66C,109C ''word'':24C ''work'':37C,173C ''would'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9449, 2, '2015-12-20 06:02:38.193', 0, NULL, NULL, '&lt;p&gt;In weka, you could assign the weights to your learning instances. Assign the weights in inverse proportion to the class frequency weight and you should be good to go.
Another way would be to play around with sampling.&lt;/p&gt;
', NULL, NULL, '''/p'':40C ''anoth'':31C ''around'':37C ''assign'':6C,13C ''class'':21C ''could'':5C ''frequenc'':22C ''go'':30C ''good'':28C ''instanc'':12C ''invers'':17C ''learn'':11C ''p'':1C ''play'':36C ''proport'':18C ''sampl'':39C ''way'':32C ''weight'':8C,15C,23C ''weka'':3C ''would'':33C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9361, 2, '2015-12-13 06:43:55.51', 2, NULL, NULL, '&lt;p&gt;From Sebastian Raschka''s &lt;em&gt;&lt;a href="https://www.packtpub.com/big-data-and-business-intelligence/python-machine-learning" rel="nofollow"&gt;Python Machine Learning&lt;/a&gt;&lt;/em&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The main advantage of such a memory-based approach [the KNN] is that
  the classifier immediately adapts as we collect new training data.
  However, the downside is that the computational complexity for
  classifying new samples grows linearly with the number of samples in
  the training dataset in the worst-case scenarioâunless the dataset has
  very few dimensions (features) and the algorithm has been implemented
  using efficient data structures such as KD-trees. J. H. Friedman, J.
  L. Bentley, and R. A. Finkel. An algorithm for finding best matches in
  logarithmic expected time. ACM Transactions on Mathematical Software
  (TOMS), 3(3):209â226, 1977. Furthermore, we can''t discard training
  samples since no training step is involved. Thus, storage space can
  become a challenge if we are working with large datasets.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The decision tree, however, can rapidly classify new examples. You''re just running a series of boolean comparisons.&lt;/p&gt;
', NULL, NULL, '''/a'':17C ''/big-data-and-business-intelligence/python-machine-learning'':11C ''/blockquote'':157C ''/em'':18C ''/p'':19C,156C,177C ''1977'':128C ''209â'':126C ''226'':127C ''3'':124C,125C ''acm'':118C ''adapt'':39C ''advantag'':24C ''algorithm'':85C,109C ''approach'':31C ''base'':30C ''becom'':146C ''bentley'':103C ''best'':112C ''blockquot'':20C ''boolean'':175C ''case'':73C ''challeng'':148C ''classifi'':37C,55C,165C ''collect'':42C ''comparison'':176C ''complex'':53C ''comput'':52C ''data'':45C,91C ''dataset'':68C,77C,155C ''decis'':160C ''dimens'':81C ''discard'':133C ''downsid'':48C ''effici'':90C ''em'':6C ''exampl'':167C ''expect'':116C ''featur'':82C ''find'':111C ''finkel'':107C ''friedman'':100C ''furthermor'':129C ''grow'':58C ''h'':99C ''howev'':46C,162C ''href'':8C ''immedi'':38C ''implement'':88C ''involv'':141C ''j'':98C,101C ''kd'':96C ''kd-tree'':95C ''knn'':33C ''l'':102C ''larg'':154C ''learn'':16C ''linear'':59C ''logarithm'':115C ''machin'':15C ''main'':23C ''match'':113C ''mathemat'':121C ''memori'':29C ''memory-bas'':28C ''new'':43C,56C,166C ''nofollow'':13C ''number'':62C ''p'':1C,21C,158C ''python'':14C ''r'':105C ''rapid'':164C ''raschka'':4C ''re'':169C ''rel'':12C ''run'':171C ''sampl'':57C,64C,135C ''scenarioâ'':74C ''sebastian'':3C ''seri'':173C ''sinc'':136C ''softwar'':122C ''space'':144C ''step'':139C ''storag'':143C ''structur'':92C ''thus'':142C ''time'':117C ''tom'':123C ''train'':44C,67C,134C,138C ''transact'':119C ''tree'':97C,161C ''unless'':75C ''use'':89C ''work'':152C ''worst'':72C ''worst-cas'':71C ''www.packtpub.com'':10C ''www.packtpub.com/big-data-and-business-intelligence/python-machine-learning'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9362, 1, '2015-12-13 09:26:04.977', 2, 73, 'When to use linear or logistic regression?', '&lt;p&gt;Still I have no clear concept when I should choose linear OR Logistic regression. 
&lt;br&gt; In addition, when I can say either linear or logistic regression performing better? &lt;/p&gt;

&lt;p&gt;In particular, I want to know When should I choose Linear Regression and When to choose Logistic regression? &lt;/p&gt;
', 'logistic-regression  linear-regression  model-selection', NULL, '''/p'':46C,66C ''addit'':34C ''better'':45C ''br'':32C ''choos'':27C,57C,63C ''clear'':22C ''concept'':23C ''either'':39C ''know'':53C ''linear'':4A,12B,28C,40C,58C ''linear-regress'':11B ''logist'':6A,9B,30C,42C,64C ''logistic-regress'':8B ''model'':15B ''model-select'':14B ''p'':17C,47C ''particular'':49C ''perform'':44C ''regress'':7A,10B,13B,31C,43C,59C,65C ''say'':38C ''select'':16B ''still'':18C ''use'':3A ''want'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9386, 2, '2015-12-15 01:36:15.547', 0, NULL, NULL, '&lt;p&gt;I appended my answer to &lt;a href="http://datascience.stackexchange.com/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data"&gt;this&lt;/a&gt; question below - you guys essentially asked the same thing.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;This question seems really about representation, and not so much about clustering.&lt;/p&gt;

&lt;p&gt;Categorical data is a problem for most algorithms in machine learning. Suppose, for example, you have some categorical variable called "color" that could take on the values red, blue, or yellow. If we simply encode these numerically as 1,2, and 3 respectively, our algorithm will think that red (1) is actually closer to blue (2) than it is to yellow (3). We need to use a representation that lets the computer understand that these things are all actually equally different.&lt;/p&gt;

&lt;p&gt;One simple way is to use what''s called a one-hot representation, and it''s exactly what you thought you should do. Rather than having one variable like "color" that can take on three values, we separate it into three variables. These would be "color-red," "color-blue," and "color-yellow," which all can only take on the value 1 or 0.&lt;/p&gt;

&lt;p&gt;This increases the dimensionality of the space, but now you could use any clustering algorithm you like. It does sometimes make sense to zscore or whiten the data after doing this process, but the your idea is definitely reasonable.&lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/p'':23C,38C,121C,190C,231C ''/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data'':11C ''0'':189C ''1'':78C,89C,187C ''2'':79C,95C ''3'':81C,101C ''actual'':91C,118C ''algorithm'':47C,84C,206C ''answer'':5C ''append'':3C ''ask'':19C ''blue'':68C,94C,174C ''call'':59C,131C ''categor'':40C,57C ''closer'':92C ''cluster'':37C,205C ''color'':60C,153C,170C,173C,177C ''color-blu'':172C ''color-r'':169C ''color-yellow'':176C ''comput'':111C ''could'':62C,202C ''data'':41C,219C ''datascience.stackexchange.com'':10C ''datascience.stackexchange.com/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data'':9C ''definit'':229C ''differ'':120C ''dimension'':195C ''encod'':74C ''equal'':119C ''essenti'':18C ''exact'':140C ''exampl'':53C ''guy'':17C ''hot'':135C ''hr'':24C ''href'':8C ''idea'':227C ''increas'':193C ''learn'':50C ''let'':109C ''like'':152C,208C ''machin'':49C ''make'':212C ''much'':35C ''need'':103C ''numer'':76C ''one'':123C,134C,150C ''one-hot'':133C ''p'':1C,25C,39C,122C,191C ''problem'':44C ''process'':223C ''question'':14C,27C ''rather'':147C ''realli'':29C ''reason'':230C ''red'':67C,88C,171C ''represent'':31C,107C,136C ''respect'':82C ''seem'':28C ''sens'':213C ''separ'':161C ''simpl'':124C ''simpli'':73C ''sometim'':211C ''space'':198C ''suppos'':51C ''take'':63C,156C,183C ''thing'':22C,115C ''think'':86C ''thought'':143C ''three'':158C,164C ''understand'':112C ''use'':105C,128C,203C ''valu'':66C,159C,186C ''variabl'':58C,151C,165C ''way'':125C ''whiten'':217C ''would'':167C ''yellow'':70C,100C,178C ''zscore'':215C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9420, 1, '2015-12-17 02:44:52.977', 7, 91, 'What''s the relationship between an SVM and hinge loss?', '&lt;p&gt;My colleague and I are trying to wrap our heads around the difference between logistic regression and an SVM. Clearly they are optimizing different objective functions. Is an SVM as simple as saying it''s a discriminative classifier that simply optimizes the hinge loss? Or is it more complex than that? How do the support vectors come into play? What about the slack variables? Why can''t you have deep SVM''s the way you can''t you have a deep neural network with sigmoid activation functions?&lt;/p&gt;
', 'svm  logistic-regression  hinge-loss', NULL, '''/p'':106C ''activ'':104C ''around'':29C ''classifi'':56C ''clear'':38C ''colleagu'':20C ''come'':75C ''complex'':67C ''deep'':88C,99C ''differ'':31C,42C ''discrimin'':55C ''function'':44C,105C ''head'':28C ''hing'':9A,16B,61C ''hinge-loss'':15B ''logist'':13B,33C ''logistic-regress'':12B ''loss'':10A,17B,62C ''network'':101C ''neural'':100C ''object'':43C ''optim'':41C,59C ''p'':18C ''play'':77C ''regress'':14B,34C ''relationship'':4A ''say'':51C ''sigmoid'':103C ''simpl'':49C ''simpli'':58C ''slack'':81C ''support'':73C ''svm'':7A,11B,37C,47C,89C ''tri'':24C ''variabl'':82C ''vector'':74C ''way'':92C ''wrap'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9370, 1, '2015-12-14 10:33:28.333', 3, 75, 'Extremely dominant feature?', '&lt;p&gt;I''m new to datascience. I was wondering how one should treat an extremely dominant feature.&lt;/p&gt;

&lt;p&gt;For example, one of the features is "on"/"off", and when it''s "off", none of the other features matter and the output will just always be 0. So should I drop all rows where it''s "off" in my train/test data sets? I feel like I would get a better fit that way.&lt;/p&gt;

&lt;p&gt;If I delete those rows, I''m concerned about how I would handle those rows in the test set. For example, I''d have to write code to loop through the data and put a 0 in the prediction column for those rows, as well as make sure everything else lines up. (This is all Kaggle related, so the training set is several columns of features and a y_column, whereas the test set doesn''t have the y_column and we''re supposed to predict it.)&lt;/p&gt;

&lt;p&gt;I''m using Python and Scikit Learn''s random forest, if that matters.&lt;/p&gt;
', 'random-forest  feature-selection  scikit', NULL, '''/p'':28C,84C,173C,188C ''0'':57C,121C ''alway'':55C ''better'':80C ''code'':112C ''column'':125C,149C,155C,165C ''concern'':93C ''d'':108C ''data'':71C,117C ''datasci'':16C ''delet'':88C ''doesn'':160C ''domin'':2A,26C ''drop'':61C ''els'':135C ''everyth'':134C ''exampl'':31C,106C ''extrem'':1A,25C ''featur'':3A,8B,27C,35C,48C,151C ''feature-select'':7B ''feel'':74C ''fit'':81C ''forest'':6B,184C ''get'':78C ''handl'':98C ''kaggl'':141C ''learn'':181C ''like'':75C ''line'':136C ''loop'':114C ''m'':13C,92C,176C ''make'':132C ''matter'':49C,187C ''new'':14C ''none'':44C ''one'':21C,32C ''output'':52C ''p'':11C,29C,85C,174C ''predict'':124C,171C ''put'':119C ''python'':178C ''random'':5B,183C ''random-forest'':4B ''re'':168C ''relat'':142C ''row'':63C,90C,100C,128C ''scikit'':10B,180C ''select'':9B ''set'':72C,104C,146C,159C ''sever'':148C ''suppos'':169C ''sure'':133C ''test'':103C,158C ''train'':145C ''train/test'':70C ''treat'':23C ''use'':177C ''way'':83C ''well'':130C ''wherea'':156C ''wonder'':19C ''would'':77C,97C ''write'':111C ''y'':154C,164C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9371, 2, '2015-12-14 12:51:44.017', 2, NULL, NULL, '&lt;p&gt;If you are using RandomForest, then I am sure it will pick up this rule given that $\text{off} \rightarrow 0$ provides complete node purity. However, your intuition is correct, it is not uncommon to preprocess data. You can think of this rule as a single-level decision tree or &lt;a href="https://en.wikipedia.org/wiki/Decision_stump" rel="nofollow"&gt;decision stump&lt;/a&gt;, (aka 1-rule algorithm). Basically, you would remove these records from test and training to reduce noise. During classification you would also preprocess, if the input vector matches the rule then classify as 0 otherwise classify input vector with your model. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9399, 2, '2015-12-15 13:57:06.523', 1, NULL, NULL, '&lt;p&gt;My two cents...&lt;/p&gt;

&lt;p&gt;Not 100% accurate, but can give you a rough idea...&lt;/p&gt;

&lt;p&gt;Linear regression makes a linearization of a problem where $y = f(x)$, with $x$ and $y$ are continuous variables.&lt;/p&gt;

&lt;p&gt;Now imagine that you want to predict a kind of boolean behavior (yes/no) based on a $x$ value. For example, based on your salary, are you happy or not.&lt;/p&gt;

&lt;p&gt;You can say happy = 1 and not happy = 0. You can make a scatter plot with all pairs (salary, happy) (happy in the vertical axis).&lt;/p&gt;

&lt;p&gt;You can try to make a line to separate the happy people from the unhappy ones, but you''ll see quickly that''s not working well (what is a value in the middle, etc.).&lt;/p&gt;

&lt;p&gt;A better idea would be to draw a kind of s curve which will pass as best through the points you have.&lt;/p&gt;

&lt;p&gt;That''s what the logistics regression makes. It basically makes linear the s curve by transforming the $y$ values: this is the Logit function.&lt;/p&gt;

&lt;p&gt;We then say that we predict "True" if the predicted logit is higher than a threshold. This threshold corresponds often to 0.5, which is the inflexion point of the curve (some tools are only allowing to use 0.5). That threshold is in fact the probability.&lt;/p&gt;

&lt;p&gt;When we have more than yes/no possibilities, one solution is to make the logistics regression for all the possibilities (e.g. if you have A, B, C, that will be A/not A, B/not B, C/not C) and take the possibility that give you highest probability. This is called the "one-vs-all" approach.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9425, 1, '2015-12-17 16:32:03.567', 4, 80, 'Handling many missing values', '&lt;p&gt;I have a dataset I want to perform multivariate linear regression to it. The dimensions of the dataset are 832085 rows and 11 columns. The data are quite messy and given the size and my lack of experience I am confused on how to clean them. &lt;/p&gt;

&lt;p&gt;First of all, 6 out of 11 columns have more than 277000 NA values. In that case I know that I cannot remove them because they are a big portion of the dataset so I have to impute them. Usually I would do mean substitution but I''ve read that this approach might create bias in the data and I wouldn''t like to have that. I have tried the packages Amelia and mice in R, mice couldn''t run properly and after a while it was giving me an error, Amelia was very fast and completed 5 imputations but it introduced many negative values in the dataset. &lt;/p&gt;

&lt;p&gt;Anyone with similar experience on that?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9375, 1, '2015-12-14 16:58:41.163', 3, 71, 'Random Forest Regression. How to represent really long list of categories for processing', '&lt;p&gt;I''m trying to build a model to solve a regression task. Simplified, the data look like:
&lt;code&gt;
7.16, A 1, B 4, C 15, D 3
8.21, C 3, D 2, G  7, M 4, Y 9
1.85, D 3, N 1, L  1
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;First column is a label, consequent columns are a class and respective number (for the first record there is one A, four B, fifteen C and three D - such a configuration gives us 7.16). The question is how to represent my data so that I can fit a Random Forest Regression model? I''m thinking of the following approaches, not sure what''s the best one (or what''s at least make sense):&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Figure out whole list of classes (there are 26 letters, but I have way more classes) and put zeros for all of the unknown classes into each record. Vector.sparse() might help here as it can reduce amount of required memory.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Figure out whole list of classes, correspond a number starting from 0 and convert each record into a vector (i.e. the first one would look): 0 1 1 4 2 15 3 3. Then each column is treated as a separate feature, but in this case I have to specify that some of my features are categorical and that''s not possible with Spark since different records are of different length (In Spark I have to provide columns of features, which are categorical with number of classes. In the example that would be a map(0 -&gt; 26, 2 -&gt; 26, 4 -&gt; 26) - like every second column is a class with 26 categories).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;It''s possible to build features like A:1, B:4, C:15, D:3 and treat all of them as categorical. Or move decision burden (categorical vs continuous) to Spark itself.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9384, 2, '2015-12-14 22:21:15.12', 2, NULL, NULL, '&lt;p&gt;I am not sure about the alternatives described above, but the commonly used methodology is:&lt;/p&gt;

&lt;p&gt;Before the application of the non-linearity, each filter output depends linearly on all of the feature maps before within the patch, so you end up with $k_2$ filters after the second layers. The overall number of parameters is $3 \dot{} 3\dot{}k_1 + k_1\dot{} 5 \dot{} 5 \dot{} k_2$.&lt;/p&gt;

&lt;p&gt;Bonus 1: Pooling is done per feature map, separately.&lt;/p&gt;

&lt;p&gt;Bonus 2: The order of "sliding" does not matter. In fact, each output is computed based on the previous layer, so the output filter responses do not depend on each other. They can be computed in parallel.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9378, 1, '2015-12-14 17:21:21.047', 1, 50, 'How to update weights in a neural network using gradient descent with mini-batches?', '&lt;p&gt;[I''ve cross-posted it to cross.validated because I''m not sure where it fits best]&lt;/p&gt;

&lt;p&gt;How does gradient descent work for training a neural network if I choose mini-batch (i.e., sample a subset of the training set)? I have thought of three different possibilities:&lt;/p&gt;

&lt;p&gt;Epoch starts. We sample and feedforward one minibatch only, get the error and backprop it, i.e. update the weights. Epoch over.&lt;/p&gt;

&lt;p&gt;Epoch starts. We sample and feedforward a minibatch, get the error and backprop it, i.e. update the weights. We repeat this until we have sampled the full data set. Epoch over.&lt;/p&gt;

&lt;p&gt;Epoch starts. We sample and feedforward a minibatch, get the error and store it. We repeat this until we have sampled the full data set. We somehow average the errors and backprop them by updating the weights. Epoch over.&lt;/p&gt;
', 'machine-learning  neuralnetwork  gradient-descent  backpropagation', NULL, '''/p'':42C,75C,98C,131C,172C ''averag'':160C ''backprop'':90C,112C,164C ''backpropag'':23B ''batch'':15A,59C ''best'':41C ''choos'':56C ''cross'':28C ''cross-post'':27C ''cross.validated'':32C ''data'':127C,156C ''descent'':11A,22B,47C ''differ'':73C ''epoch'':77C,96C,100C,129C,133C,170C ''error'':88C,110C,143C,162C ''feedforward'':82C,105C,138C ''fit'':40C ''full'':126C,155C ''get'':86C,108C,141C ''gradient'':10A,21B,46C ''gradient-desc'':20B ''i.e'':60C,92C,114C ''learn'':18B ''m'':35C ''machin'':17B ''machine-learn'':16B ''mini'':14A,58C ''mini-batch'':13A,57C ''minibatch'':84C,107C,140C ''network'':8A,53C ''neural'':7A,52C ''neuralnetwork'':19B ''one'':83C ''p'':24C,43C,76C,99C,132C ''possibl'':74C ''post'':29C ''repeat'':119C,148C ''sampl'':61C,80C,103C,124C,136C,153C ''set'':67C,128C,157C ''somehow'':159C ''start'':78C,101C,134C ''store'':145C ''subset'':63C ''sure'':37C ''thought'':70C ''three'':72C ''train'':50C,66C ''updat'':3A,93C,115C,167C ''use'':9A ''ve'':26C ''weight'':4A,95C,117C,169C ''work'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9379, 2, '2015-12-14 17:45:18.323', 1, NULL, NULL, '&lt;p&gt;When you train with mini-batches then you have the second option, network is updated after each mini-batch, and epoch ends after presenting all samples.&lt;/p&gt;

&lt;p&gt;Please see these &lt;a href="http://stackoverflow.com/questions/4752626/epoch-vs-iteration-when-training-neural-networks"&gt;responses&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/a'':41C ''/p'':30C,42C ''/questions/4752626/epoch-vs-iteration-when-training-neural-networks'':39C ''batch'':8C,22C ''end'':25C ''epoch'':24C ''href'':36C ''mini'':7C,21C ''mini-batch'':6C,20C ''network'':15C ''option'':14C ''p'':1C,31C ''pleas'':32C ''present'':27C ''respons'':40C ''sampl'':29C ''second'':13C ''see'':33C ''stackoverflow.com'':38C ''stackoverflow.com/questions/4752626/epoch-vs-iteration-when-training-neural-networks'':37C ''train'':4C ''updat'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9426, 2, '2015-12-17 18:48:27.133', 0, NULL, NULL, '&lt;p&gt;This is not a technical question. You should decide which one is more important for you: the columns or the rows that contain these 277000 NA values. Probably the columns are more important.&lt;/p&gt;

&lt;p&gt;I recommend deleting the rows at first, building  a model (starting from one variable and adding more variables gradually), calculating the model measures (such as adjusted R-Squared) . At this point you have some baseline.&lt;/p&gt;

&lt;p&gt;Then restore these deleted rows and see how your model measures change. Maybe these 6 columns are not predictors of your response variable and you are worried for nothing. There is no magic to say which method works for your data. Give it a try. This is exactly what is done in statistics and machine learning.&lt;/p&gt;

&lt;p&gt;After understanding your data better, you can decide to fill your missing values or not. Keep in mind that data analysis is an iterative process.&lt;/p&gt;
', NULL, NULL, '''/p'':35C,73C,131C,158C ''277000'':26C ''6'':89C ''ad'':52C ''adjust'':62C ''analysi'':153C ''baselin'':72C ''better'':137C ''build'':44C ''calcul'':56C ''chang'':86C ''column'':19C,31C,90C ''contain'':24C ''data'':115C,136C,152C ''decid'':10C,140C ''delet'':39C,78C ''done'':125C ''exact'':122C ''fill'':142C ''first'':43C ''give'':116C ''gradual'':55C ''import'':15C,34C ''iter'':156C ''keep'':148C ''learn'':130C ''machin'':129C ''magic'':107C ''mayb'':87C ''measur'':59C,85C ''method'':111C ''mind'':150C ''miss'':144C ''model'':46C,58C,84C ''na'':27C ''noth'':103C ''one'':12C,49C ''p'':1C,36C,74C,132C ''point'':68C ''predictor'':93C ''probabl'':29C ''process'':157C ''question'':7C ''r'':64C ''r-squar'':63C ''recommend'':38C ''respons'':96C ''restor'':76C ''row'':22C,41C,79C ''say'':109C ''see'':81C ''squar'':65C ''start'':47C ''statist'':127C ''technic'':6C ''tri'':119C ''understand'':134C ''valu'':28C,145C ''variabl'':50C,54C,97C ''work'':112C ''worri'':101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9390, 1, '2015-12-15 03:30:28.397', 1, 41, 'Algorithm to construct similarity structure from hash lookup table', '&lt;p&gt;I have constructed a lookup table using locality-sensitive hashing for comparing nearly similar documents/records. If two records (columns) have the same hash value in a row, they are considered to be similar. For example, the structure is &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;   R1 R2 R3 R4 R5
b1 a1 a2 a3 a2 a5 .....
b2 a2 a4 a1 a4 a4 ..... 
b3 a3 a5 a3 a7 a4....
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Since each band holds similar records, similar record sets will be&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;S1 = {R2,R4}
S2 = {R2, R4, R5}
S3 = {R1,R3}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And the merged similarity structures will be &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;S1'' = {R2,R4,R5}
S2'' = {R1,R3}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to span the matrix and map all similarity structures so I have buckets of similar records. The dimensionality of the hash table is large, hence a naive approach is not likely to work. What types of algorithms should I consider to achieve this efficiently?&lt;/p&gt;

&lt;p&gt;Edit 1:
Question updated to clarify more on the objective &lt;/p&gt;
', 'algorithms  graphs  named-entity-recognition', NULL, '''/code'':82C,109C,129C ''/p'':56C,96C,119C,178C,190C ''/pre'':83C,110C,130C ''1'':181C ''a1'':65C,73C ''a2'':66C,68C,71C ''a3'':67C,77C,79C ''a4'':72C,74C,75C,81C ''a5'':69C,78C ''a7'':80C ''achiev'':175C ''algorithm'':1A,10B,170C ''approach'':161C ''b1'':64C ''b2'':70C ''b3'':76C ''band'':87C ''bucket'':146C ''clarifi'':185C ''code'':58C,98C,121C ''column'':36C ''compar'':29C ''consid'':47C,173C ''construct'':3A,19C ''dimension'':151C ''documents/records'':32C ''edit'':180C ''effici'':177C ''entiti'':14B ''exampl'':52C ''graph'':11B ''hash'':7A,27C,40C,154C ''henc'':158C ''hold'':88C ''larg'':157C ''like'':164C ''local'':25C ''locality-sensit'':24C ''lookup'':8A,21C ''map'':139C ''matrix'':137C ''merg'':114C ''naiv'':160C ''name'':13B ''named-entity-recognit'':12B ''near'':30C ''object'':189C ''p'':16C,84C,111C,131C,179C ''pre'':57C,97C,120C ''question'':182C ''r1'':59C,107C,127C ''r2'':60C,100C,103C,123C ''r3'':61C,108C,128C ''r4'':62C,101C,104C,124C ''r5'':63C,105C,125C ''recognit'':15B ''record'':35C,90C,92C,149C ''row'':44C ''s1'':99C,122C ''s2'':102C,126C ''s3'':106C ''sensit'':26C ''set'':93C ''similar'':4A,31C,50C,89C,91C,115C,141C,148C ''sinc'':85C ''span'':135C ''structur'':5A,54C,116C,142C ''tabl'':9A,22C,155C ''two'':34C ''type'':168C ''updat'':183C ''use'':23C ''valu'':41C ''want'':133C ''work'':166C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9391, 2, '2015-12-15 03:42:50.74', 2, NULL, NULL, '&lt;p&gt;You seem to be using the Random Forest model. &lt;/p&gt;

&lt;p&gt;I don''t see how that feature would influence the model. It actually doesn''t make a difference, as random forest divides the sample space iteratively, and your sample space would be divided as &lt;code&gt;switch = 0&lt;/code&gt; and &lt;code&gt;switch = 1&lt;/code&gt;. &lt;/p&gt;

&lt;p&gt;So, the presence of those sample points do not affect the model.&lt;/p&gt;
', NULL, NULL, '''/code'':50C,55C ''/p'':11C,56C,70C ''0'':49C ''1'':54C ''actual'':25C ''affect'':67C ''code'':47C,52C ''differ'':30C ''divid'':34C,45C ''doesn'':26C ''featur'':19C ''forest'':9C,33C ''influenc'':21C ''iter'':38C ''make'':28C ''model'':10C,23C,69C ''p'':1C,12C,57C ''point'':64C ''presenc'':60C ''random'':8C,32C ''sampl'':36C,41C,63C ''see'':16C ''seem'':3C ''space'':37C,42C ''switch'':48C,53C ''use'':6C ''would'':20C,43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9392, 1, '2015-12-15 05:54:18.307', 2, 27, 'Handling a feature with multiple categorical values for the same instance value', '&lt;p&gt;I have data in the following form:&lt;/p&gt;

&lt;p&gt;table 1&lt;br&gt;
id, feature1, predict &lt;br&gt;
1, xyz,yes&lt;br&gt;
2, abc, yes&lt;br&gt;&lt;/p&gt;

&lt;p&gt;table2&lt;br&gt;
id, feature2&lt;br&gt;
1, class1&lt;br&gt;
1, class2&lt;br&gt;
1, class3&lt;br&gt;
2, class2&lt;/p&gt;

&lt;p&gt;I could perform a one many join and train on the resultant set- which is one way to go about it. But If I rather wanted to maintain the length of the resultant set equal length of table 1, what is the technique?&lt;/p&gt;
', 'machine-learning  dataset  data-cleaning  feature-extraction', NULL, '''/p'':31C,48C,66C,111C ''1'':34C,40C,55C,58C,61C,106C ''2'':44C,64C ''abc'':45C ''br'':35C,39C,43C,47C,51C,54C,57C,60C,63C ''categor'':6A ''class1'':56C ''class2'':59C,65C ''class3'':62C ''clean'':19B ''could'':69C ''data'':18B,26C ''data-clean'':17B ''dataset'':16B ''equal'':102C ''extract'':22B ''featur'':3A,21B ''feature-extract'':20B ''feature1'':37C ''feature2'':53C ''follow'':29C ''form'':30C ''go'':86C ''handl'':1A ''id'':36C,52C ''instanc'':11A ''join'':74C ''learn'':15B ''length'':97C,103C ''machin'':14B ''machine-learn'':13B ''maintain'':95C ''mani'':73C ''multipl'':5A ''one'':72C,83C ''p'':23C,32C,49C,67C ''perform'':70C ''predict'':38C ''rather'':92C ''result'':79C,100C ''set'':80C,101C ''tabl'':33C,105C ''table2'':50C ''techniqu'':110C ''train'':76C ''valu'':7A,12A ''want'':93C ''way'':84C ''xyz'':41C ''yes'':42C,46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9428, 1, '2015-12-18 01:31:06.703', 2, 13, 'Conditional logit in Stata', '&lt;p&gt;&lt;strong&gt;Question&lt;/strong&gt;: Can Stata present or output the actual betas/coefficients in a datasheet when it performs the conditional logit model?&lt;/p&gt;

&lt;p&gt;I understand that Stata has a "predict" capability, but I need to store the betas/coefficients and use them later, after new factor values have been calculated.&lt;/p&gt;
', 'predictive-modeling  logistic-regression  stata', NULL, '''/p'':34C,62C ''/strong'':15C ''actual'':22C ''betas/coefficients'':23C,50C ''calcul'':61C ''capabl'':43C ''condit'':1A,31C ''datasheet'':26C ''factor'':57C ''later'':54C ''logist'':9B ''logistic-regress'':8B ''logit'':2A,32C ''model'':7B,33C ''need'':46C ''new'':56C ''output'':20C ''p'':12C,35C ''perform'':29C ''predict'':6B,42C ''predictive-model'':5B ''present'':18C ''question'':14C ''regress'':10B ''stata'':4A,11B,17C,39C ''store'':48C ''strong'':13C ''understand'':37C ''use'':52C ''valu'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9393, 2, '2015-12-15 07:26:34.523', 1, NULL, NULL, '&lt;p&gt;Similarity measures are subjective and so are they ways to combine them.
You should decide what is your subjective definition of similarity and then find a way to combine them that fit your definition.&lt;/p&gt;

&lt;p&gt;In general, I like to reduce similarity problems into classification problems. Given the dataset of items you have, create a new dataset of item pairs. The concept should be whether the two items in a pair are similar. Each similarity measure you have is a feature of the pair. Note that now you are in the good old classification framework. You can evaluate the similarity measures by computing the mutual information/accuracy/your chosen metric given the concept. &lt;/p&gt;

&lt;p&gt;In your case, I would build the dataset this way. First choose pairs of items on which both your measures agree on. Choose pairs that are close by both the Euclidian distance and the cosine distance or pairs that are far by both measures. Note that since the similarity measures agree on these pair, combining them usually  leads to agreement too and not that important. However, you should use such pairs so they will stabilize your weighting function. Other than that, you can easily generate these pair, which is always an advantage. &lt;/p&gt;

&lt;p&gt;Next, you should choose pair on which your similarity function disagree. Choose some that are considered close by the first and far by the second and vice versa. Manually label the pairs in order to decide whether they are close or not.&lt;/p&gt;

&lt;p&gt;Once you built the dataset, you can present your subjective definition of similarity as a set of pairs and distances. Now run any classifier (in your case a linear regression might fit what you look for) and use the result as the combined similarity measure. &lt;/p&gt;
', NULL, NULL, '''/p'':36C,114C,208C,252C,299C ''advantag'':207C ''agre'':136C,166C ''agreement'':175C ''alway'':205C ''build'':121C ''built'':256C ''case'':118C,280C ''choos'':127C,138C,213C,221C ''chosen'':109C ''classif'':47C,96C ''classifi'':277C ''close'':142C,226C,249C ''combin'':12C,30C,170C,296C ''comput'':105C ''concept'':64C,113C ''consid'':225C ''cosin'':150C ''creat'':56C ''dataset'':51C,59C,123C,258C ''decid'':16C,245C ''definit'':21C,35C,264C ''disagre'':220C ''distanc'':147C,151C,273C ''easili'':199C ''euclidian'':146C ''evalu'':100C ''far'':156C,231C ''featur'':83C ''find'':26C ''first'':126C,229C ''fit'':33C,285C ''framework'':97C ''function'':193C,219C ''general'':39C ''generat'':200C ''given'':49C,111C ''good'':94C ''howev'':181C ''import'':180C ''information/accuracy/your'':108C ''item'':53C,61C,70C,130C ''label'':239C ''lead'':173C ''like'':41C ''linear'':282C ''look'':288C ''manual'':238C ''measur'':3C,78C,103C,135C,159C,165C,298C ''metric'':110C ''might'':284C ''mutual'':107C ''new'':58C ''next'':210C ''note'':87C,160C ''old'':95C ''order'':243C ''p'':1C,37C,115C,209C,253C ''pair'':62C,73C,86C,128C,139C,153C,169C,186C,202C,214C,241C,271C ''present'':261C ''problem'':45C,48C ''reduc'':43C ''regress'':283C ''result'':293C ''run'':275C ''second'':234C ''set'':269C ''similar'':2C,23C,44C,75C,77C,102C,164C,218C,266C,297C ''sinc'':162C ''stabil'':190C ''subject'':5C,20C,263C ''two'':69C ''use'':184C,291C ''usual'':172C ''versa'':237C ''vice'':236C ''way'':10C,28C,125C ''weight'':192C ''whether'':67C,246C ''would'':120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9521, 2, '2015-12-25 16:40:10.677', 2, NULL, NULL, '&lt;p&gt;I think there might be a problem in the way you are stating the problem. You say that you test data doesn''t have two fields, but that can not be correct.&lt;/p&gt;

&lt;p&gt;You have to take all your data and split it into 2 groups, the training set and the test set. In a proportion of 80%-20% or 70%-30%. Then you train your algorithm with the data in the training set, and test the accuracy of the model with the data in the test set. &lt;/p&gt;

&lt;p&gt;The accuracy you get is the probability that your model is correct. Or said in another way, the next time you use your model to predict a sale, the accuracy is the probability that your prediction is real&lt;/p&gt;
', NULL, NULL, '''-20'':61C ''-30'':64C ''/p'':34C,91C,131C ''2'':47C ''70'':63C ''80'':60C ''accuraci'':80C,94C,122C ''algorithm'':69C ''anoth'':108C ''correct'':33C,104C ''data'':22C,42C,72C,86C ''doesn'':23C ''field'':27C ''get'':96C ''group'':48C ''might'':5C ''model'':83C,102C,116C ''next'':111C ''p'':1C,35C,92C ''predict'':118C,128C ''probabl'':99C,125C ''problem'':8C,16C ''proport'':58C ''real'':130C ''said'':106C ''sale'':120C ''say'':18C ''set'':51C,55C,76C,90C ''split'':44C ''state'':14C ''take'':39C ''test'':21C,54C,78C,89C ''think'':3C ''time'':112C ''train'':50C,67C,75C ''two'':26C ''use'':114C ''way'':11C,109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9394, 2, '2015-12-15 08:23:17.867', 2, NULL, NULL, '&lt;p&gt;One possible approach is to perform an encoding, where each level of the feature2 corresponds to a new feature (column).
This way you may describe the 1:N relation between the feature 1 and 2&lt;/p&gt;

&lt;p&gt;Here a small example in R&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; table1  &amp;lt;- data.frame(id = c(1,2),  feature1 = c("xyz","abc"), predict = c(T,T))
&amp;gt; table2  &amp;lt;- data.frame(id = c(1,1,1,2), feature2 = c("class1", "class2", "class3", "class2"))
&amp;gt; 
&amp;gt; ## encoding
&amp;gt; table(table2)
   feature2
id  class1 class2 class3
  1      1      1      1
  2      0      1      0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The new object contains the (now unique) &lt;code&gt;id&lt;/code&gt; and setting of the feature2.
You need only to merge (join) the result to the &lt;code&gt;table1&lt;/code&gt; (basically same task a DB join - which variance: inner, outer or full depends on your requirements).&lt;/p&gt;
', NULL, NULL, '''/code'':99C,111C,129C ''/p'':37C,45C,146C ''/pre'':100C ''0'':96C,98C ''1'':28C,34C,54C,70C,71C,72C,91C,92C,93C,94C,97C ''2'':36C,55C,73C,95C ''abc'':59C ''approach'':4C ''basic'':130C ''c'':53C,57C,61C,69C,75C ''class1'':76C,88C ''class2'':77C,79C,89C ''class3'':78C,90C ''code'':47C,109C,127C ''column'':21C ''contain'':105C ''correspond'':16C ''data.frame'':51C,67C ''db'':134C ''depend'':142C ''describ'':26C ''encod'':9C,82C ''exampl'':42C ''featur'':20C,33C ''feature1'':56C ''feature2'':15C,74C,86C,116C ''full'':141C ''gt'':48C,64C,80C,81C,83C ''id'':52C,68C,87C,110C ''inner'':138C ''join'':122C,135C ''level'':12C ''lt'':50C,66C ''may'':25C ''merg'':121C ''n'':29C ''need'':118C ''new'':19C,103C ''object'':104C ''one'':2C ''outer'':139C ''p'':1C,38C,101C ''perform'':7C ''possibl'':3C ''pre'':46C ''predict'':60C ''r'':44C ''relat'':30C ''requir'':145C ''result'':124C ''set'':113C ''small'':41C ''tabl'':84C ''table1'':49C,128C ''table2'':65C,85C ''task'':132C ''uniqu'':108C ''varianc'':137C ''way'':23C ''xyz'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9395, 2, '2015-12-15 08:24:10.153', 1, NULL, NULL, '&lt;p&gt;I like @Anony-Mousse''s answer. Using robust estimators is good.&lt;/p&gt;

&lt;p&gt;I want to add a different direction to cope with the problem.
It seems that there are some "malicious" users casting these down votes so you might want to identify them.&lt;/p&gt;

&lt;p&gt;Create a dataset of the users and use "casted &lt;strong&gt;unjustified&lt;/strong&gt; down vote on leading item" as the label. You can use "casted down vote on leading item" as the default value and then manually modify them and make the rule more delicate like "casted more than twice  down vote on leading item after the  item reached the top charts"
I guess that features like number of low votes, number of low votes to leading items, etc will be useful.&lt;/p&gt;

&lt;p&gt;Now you are in a supervised learning framework. Once you identify malicious users, ignore their votes and avoid the manipulations.&lt;/p&gt;
', NULL, NULL, '''/p'':14C,47C,130C,152C ''/strong'':60C ''add'':19C ''anoni'':5C ''anony-mouss'':4C ''answer'':8C ''avoid'':149C ''cast'':36C,57C,72C,94C ''chart'':109C ''cope'':24C ''creat'':49C ''dataset'':51C ''default'':80C ''delic'':92C ''differ'':21C ''direct'':22C ''estim'':11C ''etc'':126C ''featur'':113C ''framework'':139C ''good'':13C ''guess'':111C ''identifi'':45C,142C ''ignor'':145C ''item'':65C,77C,102C,105C,125C ''label'':68C ''lead'':64C,76C,101C,124C ''learn'':138C ''like'':3C,93C,114C ''low'':117C,121C ''make'':88C ''malici'':34C,143C ''manipul'':151C ''manual'':84C ''might'':42C ''modifi'':85C ''mouss'':6C ''number'':115C,119C ''p'':1C,15C,48C,131C ''problem'':27C ''reach'':106C ''robust'':10C ''rule'':90C ''seem'':29C ''strong'':58C ''supervis'':137C ''top'':108C ''twice'':97C ''unjustifi'':59C ''use'':9C,56C,71C,129C ''user'':35C,54C,144C ''valu'':81C ''vote'':39C,62C,74C,99C,118C,122C,147C ''want'':17C,43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9396, 2, '2015-12-15 09:11:31.15', 1, NULL, NULL, '&lt;p&gt;Data Science is not an algorithm to run on your data. It is the process that helps you answer a specific question. The key to be a data scientist is to ask the right questions. So, first, since you want to be familiar with machine learning, examine your data and try to understand what they can answer for you.&lt;/p&gt;

&lt;p&gt;Examples:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Can I cluster the reddit questions in categories?&lt;/li&gt;
&lt;li&gt;Can you predict the score of a question based on the number of comments?&lt;/li&gt;
&lt;li&gt;Can you predict the sub-reddit that a user will ask a question?&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':75C,91C,107C ''/ol'':108C ''/p'':61C,64C ''algorithm'':7C ''answer'':20C,58C ''ask'':33C,104C ''base'':85C ''categori'':74C ''cluster'':69C ''comment'':90C ''data'':2C,12C,29C,50C ''examin'':48C ''exampl'':63C ''familiar'':44C ''first'':38C ''help'':18C ''key'':25C ''learn'':47C ''li'':66C,76C,92C ''machin'':46C ''number'':88C ''ol'':65C ''p'':1C,62C ''predict'':79C,95C ''process'':16C ''question'':23C,36C,72C,84C,106C ''reddit'':71C,99C ''right'':35C ''run'':9C ''scienc'':3C ''scientist'':30C ''score'':81C ''sinc'':39C ''specif'':22C ''sub'':98C ''sub-reddit'':97C ''tri'':52C ''understand'':54C ''user'':102C ''want'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9397, 1, '2015-12-15 10:12:37.867', 0, 78, 'Handling categorical features in Factorization Machines algorithm - Feature Hashing vs. One-Hot encoding', '&lt;p&gt;For solving a prediction problem I''m willing to use the &lt;strong&gt;Factorization Machines&lt;/strong&gt;, a model that in addition to learning linear weights on features, learn a vector space for each feature to learn pairing interactions between features in this new space. &lt;/p&gt;

&lt;p&gt;I was told that performing the hashing trick to convert categorical features to 1-of-k binary features (using sklearnâs &lt;strong&gt;DictVectorizer&lt;/strong&gt;, which returns sparse matrix) can destroy feature interaction and I should try regular &lt;strong&gt;one-hot encoding&lt;/strong&gt; instead.&lt;/p&gt;

&lt;p&gt;Can anyone explain why? &lt;/p&gt;
', 'python  categorical-data  sklearn  feature-engineering', NULL, '''/p'':67C,114C,120C ''/strong'':38C,93C,112C ''1'':82C ''addit'':43C ''algorithm'':7A ''anyon'':117C ''binari'':86C ''categor'':2A,17B,79C ''categorical-data'':16B ''convert'':78C ''data'':18B ''destroy'':99C ''dictvector'':92C ''encod'':14A,111C ''engin'':22B ''explain'':118C ''factor'':5A,36C ''featur'':3A,8A,21B,49C,56C,62C,80C,87C,100C ''feature-engin'':20B ''handl'':1A ''hash'':9A,75C ''hot'':13A,110C ''instead'':113C ''interact'':60C,101C ''k'':85C ''learn'':45C,50C,58C ''linear'':46C ''m'':30C ''machin'':6A,37C ''matrix'':97C ''model'':40C ''new'':65C ''of-k'':83C ''one'':12A,109C ''one-hot'':11A,108C ''p'':23C,68C,115C ''pair'':59C ''perform'':73C ''predict'':27C ''problem'':28C ''python'':15B ''regular'':106C ''return'':95C ''sklearn'':19B ''sklearnâ'':89C ''solv'':25C ''space'':53C,66C ''spars'':96C ''strong'':35C,91C,107C ''told'':71C ''tri'':105C ''trick'':76C ''use'':33C,88C ''vector'':52C ''vs'':10A ''weight'':47C ''will'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9398, 1, '2015-12-15 13:25:39.84', 1, 33, 'Regression problem - too complex for gradient descent', '&lt;p&gt;I try to predict temperatures values as function of time and different parameters. The temperature curve look like a "ramp" with some "gauss peaks" on regular intervals.&lt;/p&gt;

&lt;p&gt;So, I try to build a regression model for the following equation:&lt;/p&gt;

&lt;p&gt;$$
\hat{T}_{a, \mu, \sigma, s, c, m, p}\left(t\right) = a \frac{1}{\sigma\sqrt{2 \pi}} e^{-\frac{\left(\sin\left(\frac{t-s}{c}\right) - \mu\right)^2}{2 \sigma^2}} + m t + p
$$&lt;/p&gt;

&lt;p&gt;My goal is to establish the values of $a$, $\mu$, $\sigma$, $s$, $c$, $m$, $p$.&lt;/p&gt;

&lt;p&gt;My first intention was to use the gradient descent, but I''m pretty sure that the derivative of the RSS based cost function won''t be convex, so I might get stuck at local mimina.&lt;/p&gt;

&lt;p&gt;I''m also wondering if there is no better alternative to solve that problem. It concerns especially methods not involving minimizing derivatives. I think (but not sure) that neuronal networks can help me with that.&lt;/p&gt;

&lt;p&gt;So, in short, how would you solve the following?&lt;/p&gt;

&lt;p&gt;\begin{equation}
 \left[\begin{array}{l}
   a \\
   \mu \\
   \sigma \\
   s \\
   c \\
   m \\
   p
 \end{array}\right] \leftarrow \textrm{argmin}_{a, \mu, \sigma, s, c, m, p}\frac{1}{2N} \sum_{i=1}^N{\left(a \frac{1}{\sigma\sqrt{2 \pi}} e^{-\frac{\left(\sin\left(\frac{t_i-s}{c}\right) - \mu\right)^2}{2 \sigma^2}} + m t + p - T_i\right)^2}
\end{equation}&lt;/p&gt;

&lt;p&gt;Thanks a lot for your help.&lt;/p&gt;

&lt;p&gt;P.S.: I''m using python 3 with scipy stack&lt;/p&gt;
', 'predictive-modeling  regression  optimization  gradient-descent', NULL, '''/p'':44C,58C,99C,116C,153C,190C,201C,271C,279C,290C ''1'':74C,230C,234C,239C ''2'':77C,92C,93C,95C,242C,258C,259C,261C,268C ''2n'':231C ''3'':286C ''also'':157C ''altern'':164C ''argmin'':221C ''array'':207C,217C ''base'':138C ''begin'':203C,206C ''better'':163C ''build'':50C ''c'':66C,88C,113C,213C,226C,254C ''complex'':4A ''concern'':170C ''convex'':144C ''cost'':139C ''curv'':32C ''deriv'':134C,176C ''descent'':7A,15B,126C ''differ'':28C ''e'':79C,244C ''end'':216C,269C ''equat'':57C,204C,270C ''especi'':171C ''establish'':105C ''first'':119C ''follow'':56C,200C ''frac'':73C,80C,84C,229C,238C,245C,249C ''function'':24C,140C ''gauss'':39C ''get'':148C ''goal'':102C ''gradient'':6A,14B,125C ''gradient-desc'':13B ''hat'':60C ''help'':186C,278C ''i-'':251C ''intent'':120C ''interv'':43C ''involv'':174C ''l'':208C ''left'':69C,81C,83C,205C,236C,246C,248C ''leftarrow'':219C ''like'':34C ''local'':151C ''look'':33C ''lot'':275C ''m'':67C,96C,114C,129C,156C,214C,227C,262C,283C ''method'':172C ''might'':147C ''mimina'':152C ''minim'':175C ''model'':10B,53C ''mu'':63C,90C,110C,210C,223C,256C ''n'':235C ''network'':184C ''neuron'':183C ''optim'':12B ''p'':16C,45C,59C,68C,98C,100C,115C,117C,154C,191C,202C,215C,228C,264C,272C,280C ''p.s'':281C ''paramet'':29C ''peak'':40C ''pi'':78C,243C ''predict'':9B,20C ''predictive-model'':8B ''pretti'':130C ''problem'':2A,168C ''python'':285C ''ramp'':36C ''regress'':1A,11B,52C ''regular'':42C ''right'':71C,89C,91C,218C,255C,257C,267C ''rss'':137C ''scipi'':288C ''short'':194C ''sigma'':64C,75C,94C,111C,211C,224C,240C,260C ''sin'':82C,247C ''solv'':166C,198C ''sqrt'':76C,241C ''stack'':289C ''stuck'':149C ''sum'':232C ''sure'':131C,181C ''t-s'':85C ''temperatur'':21C,31C ''textrm'':220C ''thank'':273C ''think'':178C ''time'':26C ''tri'':18C,48C ''use'':123C,284C ''valu'':22C,107C ''won'':141C ''wonder'':158C ''would'':196C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9400, 2, '2015-12-15 13:59:18.51', 2, NULL, NULL, '&lt;p&gt;Rather than discarding the dominant feature (which will discard information), try reducing the number of features randomly selected when making each partition. In scikit''s syntax this is &lt;code&gt;max_features&lt;/code&gt; (&lt;code&gt;mtry&lt;/code&gt; in R''s randomForest). By default this is set to compare the square root of all features (&lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html&lt;/a&gt;), instead try much smaller (perhaps even 2 aka decision stump). You''ll probably also need many more trees vs. higher settings. This will allow you to extract information from more subtle features without losing all that great information provided by the dominant feature.&lt;/p&gt;
', NULL, NULL, '''/code'':33C,36C ''/p'':107C ''/stable/modules/generated/sklearn.ensemble.randomforestclassifier.html'':58C ''/stable/modules/generated/sklearn.ensemble.randomforestclassifier.html&lt;/a&gt;),'':63C ''2'':70C ''aka'':71C ''allow'':87C ''also'':77C ''code'':30C,34C ''compar'':47C ''decis'':72C ''default'':42C ''discard'':4C,10C ''domin'':6C,105C ''even'':69C ''extract'':90C ''featur'':7C,17C,32C,53C,95C,106C ''great'':100C ''higher'':83C ''href'':55C ''inform'':11C,91C,101C ''instead'':64C ''ll'':75C ''lose'':97C ''make'':21C ''mani'':79C ''max'':31C ''mtri'':35C ''much'':66C ''need'':78C ''nofollow'':60C ''number'':15C ''p'':1C ''partit'':23C ''perhap'':68C ''probabl'':76C ''provid'':102C ''r'':38C ''random'':18C ''randomforest'':40C ''rather'':2C ''reduc'':13C ''rel'':59C ''root'':50C ''scikit'':25C ''scikit-learn.org'':57C,62C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.randomforestclassifier.html'':56C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.randomforestclassifier.html&lt;/a&gt;),'':61C ''select'':19C ''set'':45C,84C ''smaller'':67C ''squar'':49C ''stump'':73C ''subtl'':94C ''syntax'':27C ''tree'':81C ''tri'':12C,65C ''vs'':82C ''without'':96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9403, 2, '2015-12-15 19:21:12.943', 1, NULL, NULL, '&lt;p&gt;The algorithm you are asking about is very straightforward. &lt;/p&gt;

&lt;p&gt;What you do, you are looking for &lt;a href="https://en.wikipedia.org/wiki/Connected_component_%28graph_theory%29" rel="nofollow"&gt;connected components&lt;/a&gt; in a certain graph where edges are determined by matching hash values. You can implement this with a modification of the &lt;a href="https://en.wikipedia.org/wiki/Disjoint-set_data_structure" rel="nofollow"&gt;disjoint-set data structure&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;Your particular variation would be that apart from keeping track of vertices in each component, you have to keep track of m (number of rows) sets of hash values that have been found for each component.&lt;/p&gt;
', NULL, NULL, '''/a'':29C,63C ''/p'':11C,64C,102C ''/wiki/connected_component_%28graph_theory%29'':24C ''/wiki/disjoint-set_data_structure'':55C ''algorithm'':3C ''apart'':72C ''ask'':6C ''certain'':32C ''compon'':28C,80C,101C ''connect'':27C ''data'':61C ''determin'':37C ''disjoint'':59C ''disjoint-set'':58C ''edg'':35C ''en.wikipedia.org'':23C,54C ''en.wikipedia.org/wiki/connected_component_%28graph_theory%29'':22C ''en.wikipedia.org/wiki/disjoint-set_data_structure'':53C ''found'':98C ''graph'':33C ''hash'':40C,93C ''href'':21C,52C ''implement'':44C ''keep'':74C,84C ''look'':18C ''m'':87C ''match'':39C ''modif'':48C ''nofollow'':26C,57C ''number'':88C ''p'':1C,12C,65C ''particular'':67C ''rel'':25C,56C ''row'':90C ''set'':60C,91C ''straightforward'':10C ''structur'':62C ''track'':75C,85C ''valu'':41C,94C ''variat'':68C ''vertic'':77C ''would'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9404, 2, '2015-12-15 19:31:07.267', 3, NULL, NULL, '&lt;p&gt;Let us say that the output of one neural network given it''s parameters is $$f(x;w)$$
Let us define the loss function as the squared L2 loss (in this case).
$$L(X,y;w) = \frac{1}{2n}\sum_{i=0}^{n}[f(X_i;w)-y_i]^2$$
In this case the batchsize will be denoted as $n$. Essentially what this means is that we iterate over a finite subset of samples with the size of the subset being equal to your batch-size, and use the gradient normalized under this batch. We do this until we have exhausted every data-point in the dataset. Then the epoch is over. The gradient in this case:
$$\frac{\partial L(X,y;w)}{\partial w} = \frac{1}{n}\sum_{i=0}^{n}[f(X_i;w)-y_i]\frac{\partial f(X_i;w)}{\partial w}$$
Using batch gradient descent normalizes your gradient, so the updates are not as sporadic as if you have used stochastic gradient descent. &lt;/p&gt;
', NULL, NULL, '''/p'':172C ''0'':43C,134C ''1'':39C,130C ''2'':51C ''2n'':40C ''batch'':87C,96C,151C ''batch-siz'':86C ''batchsiz'':56C ''case'':33C,54C,120C ''data'':106C ''data-point'':105C ''dataset'':110C ''defin'':22C ''denot'':59C ''descent'':153C,171C ''epoch'':113C ''equal'':83C ''essenti'':62C ''everi'':104C ''exhaust'':103C ''f'':17C,45C,136C,144C ''finit'':72C ''frac'':38C,121C,129C,142C ''function'':25C ''given'':12C ''gradient'':92C,117C,152C,156C,170C ''iter'':69C ''l'':34C,123C ''l2'':29C ''let'':2C,20C ''loss'':24C,30C ''mean'':65C ''n'':44C,61C,131C,135C ''network'':11C ''neural'':10C ''normal'':93C,154C ''one'':9C ''output'':7C ''p'':1C ''paramet'':15C ''partial'':122C,127C,143C,148C ''point'':107C ''sampl'':75C ''say'':4C ''size'':78C,88C ''sporad'':163C ''squar'':28C ''stochast'':169C ''subset'':73C,81C ''sum'':41C,132C ''updat'':159C ''us'':3C,21C ''use'':90C,150C,168C ''w'':19C,37C,48C,126C,128C,139C,147C,149C ''x'':18C,35C,46C,124C,137C,145C ''y'':36C,49C,125C,140C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9405, 2, '2015-12-16 09:37:25.713', 1, NULL, NULL, '&lt;p&gt;Sounds like you want something like &lt;a href="http://spark.apache.org/docs/latest/index.html" rel="nofollow"&gt;Apache Spark with its Python API&lt;/a&gt;. It is designed to run locally, or on a single server, OR in a distributed way, and its "distributedness" is  hidden away from you as much as possible. &lt;/p&gt;

&lt;p&gt;It has a big community - I took this &lt;a href="https://www.edx.org/course/introduction-big-data-apache-spark-uc-berkeleyx-cs100-1x" rel="nofollow"&gt;MOOC&lt;/a&gt; in summer 2015 together with 80000 participants, and it was a great introduction to the topic. It is still open for enrollments but unmaintained by the instructors.&lt;/p&gt;
', NULL, NULL, '''/a'':21C,68C ''/course/introduction-big-data-apache-spark-uc-berkeleyx-cs100-1x'':64C ''/docs/latest/index.html'':12C ''/p'':50C,96C ''2015'':71C ''80000'':74C ''apach'':15C ''api'':20C ''away'':43C ''big'':55C ''communiti'':56C ''design'':24C ''distribut'':36C ''distributed'':40C ''enrol'':90C ''great'':80C ''hidden'':42C ''href'':9C,61C ''instructor'':95C ''introduct'':81C ''like'':3C,7C ''local'':27C ''mooc'':67C ''much'':47C ''nofollow'':14C,66C ''open'':88C ''p'':1C,51C ''particip'':75C ''possibl'':49C ''python'':19C ''rel'':13C,65C ''run'':26C ''server'':32C ''singl'':31C ''someth'':6C ''sound'':2C ''spark'':16C ''spark.apache.org'':11C ''spark.apache.org/docs/latest/index.html'':10C ''still'':87C ''summer'':70C ''togeth'':72C ''took'':58C ''topic'':84C ''unmaintain'':92C ''want'':5C ''way'':37C ''www.edx.org'':63C ''www.edx.org/course/introduction-big-data-apache-spark-uc-berkeleyx-cs100-1x'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9406, 1, '2015-12-16 11:39:09.137', 4, 56, 'Can regression trees predict continuously?', '&lt;p&gt;Suppose I have a smooth function like $f(x, y) = x^2+y^2$. I have a training set $D \subsetneq \{((x, y), f(x,y)) | (x,y) \in \mathbb{R}^2\}$ and, of course, I don''t know $f$ although I can evaluate $f$ wherever I want.&lt;/p&gt;

&lt;p&gt;Are regression trees capable of finding a smooth model of the function (hence a tiny change in the input should only give a tiny change in the output)?&lt;/p&gt;

&lt;p&gt;From what I''ve read in &lt;a href="http://www.stat.cmu.edu/~cshalizi/350-2006/lecture-10.pdf" rel="nofollow"&gt;Lecture 10: Regression Trees&lt;/a&gt; it seems to me that regression trees basically put the function values into bins:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;For classic regression trees, the model in each cell is just a constant estimate of
  Y .&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;As they write "classic" I guess there is a variant where the cells do something more interesting?&lt;/p&gt;
', 'predictive-modeling  regression  decision-trees', NULL, '''/a'':111C ''/blockquote'':146C ''/p'':62C,92C,126C,145C,165C ''/~cshalizi/350-2006/lecture-10.pdf'':104C ''10'':108C ''2'':25C,27C,45C ''although'':54C ''basic'':119C ''bin'':125C ''blockquot'':127C ''capabl'':67C ''cell'':137C,160C ''chang'':79C,88C ''classic'':130C,151C ''constant'':141C ''continu'':5A ''cours'':48C ''d'':33C ''decis'':11B ''decision-tre'':10B ''estim'':142C ''evalu'':57C ''f'':21C,37C,53C,58C ''find'':69C ''function'':19C,75C,122C ''give'':85C ''guess'':153C ''henc'':76C ''href'':101C ''input'':82C ''interest'':164C ''know'':52C ''lectur'':107C ''like'':20C ''mathbb'':43C ''model'':8B,72C,134C ''nofollow'':106C ''output'':91C ''p'':13C,63C,93C,128C,147C ''predict'':4A,7B ''predictive-model'':6B ''put'':120C ''r'':44C ''read'':98C ''regress'':2A,9B,65C,109C,117C,131C ''rel'':105C ''seem'':113C ''set'':32C ''smooth'':18C,71C ''someth'':162C ''subsetneq'':34C ''suppos'':14C ''tini'':78C,87C ''train'':31C ''tree'':3A,12B,66C,110C,118C,132C ''valu'':123C ''variant'':157C ''ve'':97C ''want'':61C ''wherev'':59C ''write'':150C ''www.stat.cmu.edu'':103C ''www.stat.cmu.edu/~cshalizi/350-2006/lecture-10.pdf'':102C ''x'':22C,24C,35C,38C,40C ''y'':23C,26C,36C,39C,41C,144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9407, 1, '2015-12-16 12:18:51.933', 0, 41, 'Logistic regression program does not give statistically correct result', '&lt;p&gt;I wrote a very simple and compact logistic regression program using theano. I am initialising my data randomly, and I have restricted the number of training steps to 1, so since the weights are also random, I should expect the result to follow a standard distribution. I have ten classes and 10 initial examples, so I am expecting my accuracy to be close to 10, most of the time, however I am often (too often) getting results that are quite far from 10 (like 0 or 49). I should expect to get those results sometimes, but not so often. Is there something wrong in my code?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import theano
import theano.tensor as T
import numpy as np
rng = np.random
#rng = np.random.RandomState(0)

ninputs = 100
nfeatures = 784
noutputs = 10
nhiddens = 50

#training rate
tr_rate = 0.1 
training_steps = 1

D = (rng.randn(ninputs, nfeatures), rng.randint(size=ninputs, low=0, high=noutputs))

# Feed-forward
x = T.dmatrix(''x'')
t = T.ivector(''t'')

w = theano.shared(rng.randn(nfeatures, noutputs), name="w")
b = theano.shared(np.full(noutputs, 0.01), name="b")
sigma = T.nnet.softmax(T.dot(x,w) + b) 
prediction = T.argmax(sigma, axis=1)   


parameters = [w, b]

xent = -T.mean(T.log(sigma)[T.arange(t.shape[0]), t])          # Cost function

cost = xent.mean() + 0.01 * (w ** 2).sum()                     # Regularisation 

gparameters = [T.grad(cost, param) for param in parameters]    # Compute the gradient of the cost 

pars = zip(parameters, gparameters)

train = theano.function(
          inputs=[x,t],
          outputs=[xent],
          updates = [(param, param - tr_rate * gparam) for param, gparam in zip(parameters, gparameters)],
          allow_input_downcast = True )


predict = theano.function(inputs=[x], outputs=prediction)

# Train
for i in range(training_steps):
    train(D[0], D[1])


result = predict(D[0]) - D[1]

N = ninputs

error = 0
for index in result:
    if result[index] != 0:
        error += 1

correct_guesses = N - error
accuracy = (N - error)*100/N

print
print "correct predictions = %f over %i examples" % (correct_guesses, N)
print "accuracy = %i%%" % accuracy
&lt;/code&gt;&lt;/pre&gt;
', 'python  logistic-regression  theano', NULL, '''/code'':324C ''/p'':122C ''/pre'':325C ''0'':100C,139C,164C,210C,278C,284C,290C,298C ''0.01'':187C,216C ''0.1'':152C ''1'':44C,155C,200C,280C,286C,300C ''10'':67C,80C,98C,145C ''100'':141C ''100/n'':308C ''2'':218C ''49'':102C ''50'':147C ''784'':143C ''accuraci'':75C,305C,321C,323C ''allow'':259C ''also'':50C ''axi'':199C ''b'':183C,189C,195C,203C ''class'':65C ''close'':78C ''code'':121C,124C ''compact'':22C ''comput'':229C ''correct'':8A,301C,311C,317C ''cost'':212C,214C,223C,234C ''d'':156C,277C,279C,283C,285C ''data'':32C ''distribut'':61C ''downcast'':261C ''error'':289C,299C,304C,307C ''exampl'':69C,316C ''expect'':54C,73C,105C ''f'':313C ''far'':96C ''feed'':168C ''feed-forward'':167C ''follow'':58C ''forward'':169C ''function'':213C ''get'':91C,107C ''give'':6A ''gparam'':251C,254C ''gparamet'':221C,238C,258C ''gradient'':231C ''guess'':302C,318C ''high'':165C ''howev'':85C ''import'':125C,127C,131C ''index'':292C,297C ''initi'':68C ''initialis'':30C ''input'':241C,260C,265C ''like'':99C ''logist'':1A,12B,23C ''logistic-regress'':11B ''low'':163C ''n'':287C,303C,306C,319C ''name'':181C,188C ''nfeatur'':142C,159C,179C ''nhidden'':146C ''ninput'':140C,158C,162C,288C ''noutput'':144C,166C,180C,186C ''np'':134C ''np.full'':185C ''np.random'':136C ''np.random.randomstate'':138C ''number'':39C ''numpi'':132C ''often'':88C,90C,114C ''output'':244C,267C ''p'':15C ''par'':235C ''param'':224C,226C,247C,248C,253C ''paramet'':201C,228C,237C,257C ''pre'':123C ''predict'':196C,263C,268C,282C,312C ''print'':309C,310C,320C ''program'':3A,25C ''python'':10B ''quit'':95C ''random'':33C,51C ''rang'':273C ''rate'':149C,151C,250C ''regress'':2A,13B,24C ''regularis'':220C ''restrict'':37C ''result'':9A,56C,92C,109C,281C,294C,296C ''rng'':135C,137C ''rng.randint'':160C ''rng.randn'':157C,178C ''sigma'':190C,198C,207C ''simpl'':20C ''sinc'':46C ''size'':161C ''someth'':117C ''sometim'':110C ''standard'':60C ''statist'':7A ''step'':42C,154C,275C ''sum'':219C ''t.arange'':208C ''t.argmax'':197C ''t.dmatrix'':171C ''t.dot'':192C ''t.grad'':222C ''t.ivector'':174C ''t.log'':206C ''t.mean'':205C ''t.nnet.softmax'':191C ''t.shape'':209C ''ten'':64C ''theano'':14B,27C,126C ''theano.function'':240C,264C ''theano.shared'':177C,184C ''theano.tensor'':128C ''time'':84C ''tr'':150C,249C ''train'':41C,148C,153C,239C,269C,274C,276C ''true'':262C ''updat'':246C ''use'':26C ''w'':176C,182C,194C,202C,217C ''weight'':48C ''wrong'':118C ''wrote'':17C ''x'':170C,172C,193C,242C,266C ''xent'':204C,245C ''xent.mean'':215C ''zip'':236C,256C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9409, 2, '2015-12-16 14:20:18.467', 0, NULL, NULL, '&lt;p&gt;While it is technically probably easiest to implement one of the above solutions, I think you should also consider disincentivising the voters to downvote. For example, if the downvotes come from a minority of users who are clearly abusing the system, their repeated downvotes should count (negatively) toward their reputation - like this site. &lt;/p&gt;
', NULL, NULL, '''/p'':55C ''abus'':40C ''also'':19C ''clear'':39C ''come'':31C ''consid'':20C ''count'':47C ''disincentivis'':21C ''downvot'':25C,30C,45C ''easiest'':7C ''exampl'':27C ''implement'':9C ''like'':52C ''minor'':34C ''negat'':48C ''one'':10C ''p'':1C ''probabl'':6C ''repeat'':44C ''reput'':51C ''site'':54C ''solut'':14C ''system'':42C ''technic'':5C ''think'':16C ''toward'':49C ''user'':36C ''voter'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9410, 2, '2015-12-16 14:41:34.937', 1, NULL, NULL, '&lt;p&gt;If you initialized the weights randomly and predicted immediately, you should probably expect &lt;code&gt;1/num_classes&lt;/code&gt; accuracy. &lt;/p&gt;

&lt;p&gt;But you are training first. The data may be random, but it is still possible to overfit it. With a sample that small and wide, you could imagine some degree of separability of the classes by chance. In that scenario, your network can learn weights that are better than random (far from perfect in only one iteration).&lt;/p&gt;

&lt;p&gt;The effect would likely be lessened by using much more data or holding out/using new random data to test accuracy.&lt;/p&gt;
', NULL, NULL, '''/code'':17C ''/p'':19C,78C,100C ''1/num_classes'':16C ''accuraci'':18C,99C ''better'':68C ''chanc'':57C ''class'':55C ''code'':15C ''could'':47C ''data'':27C,90C,96C ''degre'':50C ''effect'':81C ''expect'':14C ''far'':71C ''first'':25C ''hold'':92C ''imagin'':48C ''immedi'':10C ''initi'':4C ''iter'':77C ''learn'':64C ''lessen'':85C ''like'':83C ''may'':28C ''much'':88C ''network'':62C ''new'':94C ''one'':76C ''out/using'':93C ''overfit'':37C ''p'':1C,20C,79C ''perfect'':73C ''possibl'':35C ''predict'':9C ''probabl'':13C ''random'':7C,30C,70C,95C ''sampl'':41C ''scenario'':60C ''separ'':52C ''small'':43C ''still'':34C ''test'':98C ''train'':24C ''use'':87C ''weight'':6C,65C ''wide'':45C ''would'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9411, 1, '2015-12-16 14:55:27.443', 3, 84, 'SPARK, ML: Naive Bayes classifier often assigns 1 as probability prediction', '&lt;p&gt;Hi I am using Spark ML to optimise a Naive Bayes multi-class classifier.&lt;/p&gt;

&lt;p&gt;I have about 300 categories and I am classifying text documents. The training set is balanced enough and there is about 300 training examples for each category.&lt;/p&gt;

&lt;p&gt;All looks good and the classifier is working with acceptable precision on unseen documents. But what I am noticing that often when classifying a new document, the classifier is often assigning a very high probability to one of the categories (the prediction probability is almost equal to 1), while the other categories receive very low probabilities (close to zero).&lt;/p&gt;

&lt;p&gt;What are the possible reasons for this phenomenon?&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;I can think of one possible reason, which is all the words in some documents did not appear with some categories in the training dataset? but I am not totally convinced of that especially that for most of the results, there is always one category who has very high probability and all the others have very low probability.&lt;/li&gt;
&lt;li&gt;Is there any other explanations or reasons?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I would like to add that in SPARK ML there is something called "raw prediction" and when I look at it, I can see negative numbers but they have more or less comparable magnitude, so even the category with the high probability has comparable raw prediction score, but I am finding difficulties in interpreting this scores.&lt;/p&gt;
', 'classification  apache-spark  multiclass-classification  naive-bayes-classifier', NULL, '''/li'':200C,209C ''/p'':39C,68C,129C,139C,268C ''/ul'':210C ''1'':8A,117C ''300'':44C,62C ''accept'':79C ''add'':216C ''almost'':114C ''alway'':184C ''apach'':14B ''apache-spark'':13B ''appear'':159C ''assign'':7A,100C ''balanc'':56C ''bay'':4A,21B,34C ''call'':224C ''categori'':45C,67C,109C,121C,162C,186C,249C ''class'':37C ''classif'':12B,18B ''classifi'':5A,22B,38C,49C,75C,92C,97C ''close'':126C ''compar'':244C,255C ''convinc'':172C ''dataset'':166C ''difficulti'':263C ''document'':51C,83C,95C,156C ''enough'':57C ''equal'':115C ''especi'':175C ''even'':247C ''exampl'':64C ''explan'':206C ''find'':262C ''good'':72C ''hi'':24C ''high'':103C,190C,252C ''interpret'':265C ''less'':243C ''li'':141C,201C ''like'':214C ''look'':71C,230C ''low'':124C,198C ''magnitud'':245C ''ml'':2A,29C,220C ''multi'':36C ''multi-class'':35C ''multiclass'':17B ''multiclass-classif'':16B ''naiv'':3A,20B,33C ''naive-bayes-classifi'':19B ''negat'':236C ''new'':94C ''notic'':88C ''number'':237C ''often'':6A,90C,99C ''one'':106C,146C,185C ''optimis'':31C ''other'':195C ''p'':23C,40C,69C,130C,211C ''phenomenon'':138C ''possibl'':134C,147C ''precis'':80C ''predict'':11A,111C,226C,257C ''probabl'':10A,104C,112C,125C,191C,199C,253C ''raw'':225C,256C ''reason'':135C,148C,208C ''receiv'':122C ''result'':181C ''score'':258C,267C ''see'':235C ''set'':54C ''someth'':223C ''spark'':1A,15B,28C,219C ''text'':50C ''think'':144C ''total'':171C ''train'':53C,63C,165C ''ul'':140C ''unseen'':82C ''use'':27C ''word'':153C ''work'':77C ''would'':213C ''zero'':128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9412, 2, '2015-12-16 16:06:42.623', 0, NULL, NULL, '&lt;p&gt;In classic regression trees you have a one value in the leaf, but in the leaf you can have a linear regression model, check &lt;a href="http://stats.stackexchange.com/questions/78563/regression-tree-algorithm-with-linear-regression-models-in-each-leaf"&gt;this&lt;/a&gt; ticket.&lt;/p&gt;

&lt;p&gt;You can also use ensemble of trees (Random Forest or Gradient Boosting Machines) to have continuous output value.&lt;/p&gt;
', NULL, NULL, '''/a'':32C ''/p'':34C,54C ''/questions/78563/regression-tree-algorithm-with-linear-regression-models-in-each-leaf'':30C ''also'':38C ''boost'':47C ''check'':25C ''classic'':3C ''continu'':51C ''ensembl'':40C ''forest'':44C ''gradient'':46C ''href'':27C ''leaf'':13C,17C ''linear'':22C ''machin'':48C ''model'':24C ''one'':9C ''output'':52C ''p'':1C,35C ''random'':43C ''regress'':4C,23C ''stats.stackexchange.com'':29C ''stats.stackexchange.com/questions/78563/regression-tree-algorithm-with-linear-regression-models-in-each-leaf'':28C ''ticket'':33C ''tree'':5C,42C ''use'':39C ''valu'':10C,53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9413, 1, '2015-12-16 16:13:42.943', 2, 54, 'How to analyse move kindness in python (Logistic regression, neural network, etc.)?', '&lt;p&gt;With a team of researchers we were given the assignment to make a scale for the &lt;em&gt;move kindness&lt;/em&gt; (how inviting a room or place is for exercise--e.g., a gym). In order to get objective results we were asked to make a measurement device. This device receives input through various sensors and maybe some true/false questions, and then analyze it with training information in mind. After the analysis it returns a number from, say: 1 to 10, indicating the move kindness. So a gym gets, for example an eight and a classroom a three. The problem with this is, that move kindness is very subjective, so we have conducted some surveys. For example one of the criteria is the temperature of the room/place. While the survey was being conducted we measured the temperature:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;From a scale from 1 to 10, what is your opinion about the temperature?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;And then we measured the temperature. We have put all these information into some  spreadsheets:&lt;/p&gt;

&lt;p&gt;Rating (Move Kindness): 8&lt;br&gt;
Temperature: 18 degrees Celsius&lt;/p&gt;

&lt;p&gt;At the end of this survey we asked them to give the move kindness a rating.
So we have this, for example: &lt;br&gt;
Temperature: 8, 18 &lt;br&gt;
Light: 7, 300 &lt;br&gt;
Humidity: 8, 50 &lt;br&gt;
.... &lt;br&gt;
Rating (Move Kindness): 8&lt;/p&gt;

&lt;p&gt;So my question is, what''s the best way to analyse these data for a reliable measurement device using python?
We were thinking of using neural networks, because they can be trained, but logistic regression or some other machine learning algorithm is also an option. Can anyone give me some direction on this?&lt;/p&gt;
', 'machine-learning  python  neuralnetwork  logistic-regression', NULL, '''/blockquote'':175C ''/em'':41C ''/p'':157C,174C,192C,203C,245C,300C ''1'':98C,164C ''10'':100C,166C ''18'':200C,230C ''300'':234C ''50'':238C ''7'':233C ''8'':197C,229C,237C,244C ''algorithm'':287C ''also'':289C ''analys'':3A,257C ''analysi'':91C ''analyz'':82C ''anyon'':293C ''ask'':62C,212C ''assign'':31C ''best'':254C ''blockquot'':158C ''br'':198C,227C,231C,235C,239C,240C ''celsius'':202C ''classroom'':115C ''conduct'':132C,152C ''criteria'':140C ''data'':259C ''degre'':201C ''devic'':67C,69C,264C ''direct'':297C ''e.g'':51C ''eight'':112C ''em'':38C ''end'':207C ''etc'':12A ''exampl'':110C,136C,226C ''exercis'':50C ''get'':57C,108C ''give'':215C,294C ''given'':29C ''gym'':53C,107C ''humid'':236C ''indic'':101C ''inform'':86C,188C ''input'':71C ''invit'':43C ''kind'':5A,40C,104C,125C,196C,218C,243C ''learn'':15B,286C ''light'':232C ''logist'':8A,19B,280C ''logistic-regress'':18B ''machin'':14B,285C ''machine-learn'':13B ''make'':33C,64C ''mayb'':76C ''measur'':66C,154C,180C,263C ''mind'':88C ''move'':4A,39C,103C,124C,195C,217C,242C ''network'':11A,273C ''neural'':10A,272C ''neuralnetwork'':17B ''number'':95C ''object'':58C ''one'':137C ''opinion'':170C ''option'':291C ''order'':55C ''p'':21C,159C,176C,193C,204C,246C ''place'':47C ''problem'':119C ''put'':185C ''python'':7A,16B,266C ''question'':79C,249C ''rate'':194C,220C,241C ''receiv'':70C ''regress'':9A,20B,281C ''reliabl'':262C ''research'':26C ''result'':59C ''return'':93C ''room'':45C ''room/place'':146C ''say'':97C ''scale'':35C,162C ''sensor'':74C ''spreadsheet'':191C ''subject'':128C ''survey'':134C,149C,210C ''team'':24C ''temperatur'':143C,156C,173C,182C,199C,228C ''think'':269C ''three'':117C ''train'':85C,278C ''true/false'':78C ''use'':265C,271C ''various'':73C ''way'':255C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9452, 1, '2015-12-20 10:09:26.6', 0, 33, 'Inspecting Term Document Matrix in R', '&lt;p&gt;I am doing Text Mining in R. I have a problem in inspecting the Term Document Matrix. I have columns that have no names instead of having the index of the documents:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/bfDjm.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/bfDjm.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;My term Document Matrix has been built using the following code in tm package:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;myTdmBase &amp;lt;- TermDocumentMatrix(myCorpus, 
       control=list(wordLengths=c(3,Inf),local=c(10,Inf),weighting=weightTfIdf))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Unfortunately my problem is not reproducible. I have no problem with test data. What can I do?&lt;/p&gt;
', 'r  text-mining', NULL, '''/a'':63C ''/bfdjm.jpg'':50C,57C ''/code'':99C ''/p'':44C,64C,80C,119C ''/pre'':100C ''10'':95C ''3'':91C ''alt'':58C ''built'':72C ''c'':90C,94C ''code'':76C,82C ''column'':31C ''control'':87C ''data'':114C ''descript'':61C ''document'':3A,27C,43C,68C ''enter'':59C ''follow'':75C ''href'':47C ''i.stack.imgur.com'':49C,56C ''i.stack.imgur.com/bfdjm.jpg'':48C,55C ''imag'':60C ''img'':53C ''index'':40C ''inf'':92C,96C ''inspect'':1A,24C ''instead'':36C ''list'':88C ''local'':93C ''lt'':84C ''matrix'':4A,28C,69C ''mine'':10B,16C ''mycorpus'':86C ''mytdmbas'':83C ''name'':35C ''nofollow'':52C ''p'':11C,45C,65C,101C ''packag'':79C ''pre'':81C ''problem'':22C,104C,111C ''r'':6A,7B,18C ''rel'':51C ''reproduc'':107C ''src'':54C ''term'':2A,26C,67C ''termdocumentmatrix'':85C ''test'':113C ''text'':9B,15C ''text-min'':8B ''tm'':78C ''unfortun'':102C ''use'':73C ''weight'':97C ''weighttfidf'':98C ''wordlength'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9414, 2, '2015-12-16 17:48:22.55', 1, NULL, NULL, '&lt;p&gt;Okay, so from what I understand, you have a regression problem taking into account a variety of physical features. The reason I say that this is a regression problem, verses a classification problem is because the scale you are trying to predict is an ordinal scale.&lt;/p&gt;

&lt;p&gt;There are a couple approaches to this. If your features are discriminative and linear enough, a simple least squares linear regression might work. If you believe the problem you have is to complicated for linear regressions, a simple vanilla neural network with one single output. I would recommend using the scikit-learn library in python for all models that are not neural networks. Here is a link to the generalized linear regression page:&lt;br&gt;
&lt;a href="http://scikit-learn.org/stable/modules/linear_model.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/linear_model.html&lt;/a&gt;&lt;br&gt;
 That link has code samples and mathematical explanations. If you decide to use neural networks, and you don''t have a great amount of samples or a need to use the GPU, the pyBrain library is great. &lt;a href="http://pybrain.org/" rel="nofollow"&gt;http://pybrain.org/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I wouldn''t recommend using a logistic regression (since you mentioned it in your question), simply because a logistic regression is a classification problem, and I believe you would be better off approaching this from a regression standpoint.     &lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':179C ''/p'':48C,219C ''/stable/modules/linear_model.html'':129C ''/stable/modules/linear_model.html&lt;/a&gt;&lt;br&gt;'':134C ''account'':15C ''amount'':157C ''approach'':54C,213C ''believ'':75C,207C ''better'':211C ''br'':124C ''classif'':33C,203C ''code'':138C ''complic'':82C ''coupl'':53C ''decid'':145C ''discrimin'':61C ''enough'':64C ''explan'':142C ''featur'':20C,59C ''general'':120C ''gpu'':166C ''great'':156C,171C ''href'':126C,173C ''learn'':102C ''least'':67C ''librari'':103C,169C ''linear'':63C,69C,84C,121C ''link'':117C,136C ''logist'':187C,199C ''mathemat'':141C ''mention'':191C ''might'':71C ''model'':108C ''need'':162C ''network'':90C,113C,149C ''neural'':89C,112C,148C ''nofollow'':131C,176C ''okay'':2C ''one'':92C ''ordin'':46C ''output'':94C ''p'':1C,49C,180C ''page'':123C ''physic'':19C ''predict'':43C ''problem'':12C,30C,34C,77C,204C ''pybrain'':168C ''pybrain.org'':174C,178C ''pybrain.org/&lt;/a&gt;&lt;/p&gt;'':177C ''python'':105C ''question'':195C ''reason'':22C ''recommend'':97C,184C ''regress'':11C,29C,70C,85C,122C,188C,200C,217C ''rel'':130C,175C ''sampl'':139C,159C ''say'':24C ''scale'':38C,47C ''scikit'':101C ''scikit-learn'':100C ''scikit-learn.org'':128C,133C ''scikit-learn.org/stable/modules/linear_model.html'':127C ''scikit-learn.org/stable/modules/linear_model.html&lt;/a&gt;&lt;br&gt;'':132C ''simpl'':66C,87C ''simpli'':196C ''sinc'':189C ''singl'':93C ''squar'':68C ''standpoint'':218C ''take'':13C ''tri'':41C ''understand'':7C ''use'':98C,147C,164C,185C ''vanilla'':88C ''varieti'':17C ''vers'':31C ''work'':72C ''would'':96C,209C ''wouldn'':182C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9415, 1, '2015-12-16 21:20:08.39', 3, 24, 'Is it advisable to rerun LASSO multiple (2) times?', '&lt;p&gt;I have a dataset with large (4500 variables) and 3000 observations (n &amp;lt; p case). I am using LASSO (from the glmnet package in R) to reduce dimensionality of the problem. I have couple of questions. &lt;/p&gt;

&lt;p&gt;1) After I ran LASSO the first time, the number of variables with non-zero coefficients goes down to ~80 (which seems reasonable). But the correlation matrix shows that some of these variables are very highly correlated themselves (FORMS distinct groups if you plot it using the &lt;code&gt;corplot&lt;/code&gt; package. So I ran LASSO exclusively on these variables whose coefficients were non-zero during the 1st run. This time, the algorithm picked up even less number of variables.I was expecting that they would remain more or less similar. I want to know if running LASSO multiple times is advisable or it introduces bias of any form in anyway that interferes with interpretation/prediction ? &lt;/p&gt;

&lt;p&gt;2) My second question is about the magnitude of the coefficients. Some of the coefficients, in absolute value, turns out to be very close to 0 (~1E-14) while some are larger. I mean the spread is very large. Does it indicate that the variables with this small coefficients are making the model worse in any way. Is it advisable to get rid off those variables and rerun LASSO ? &lt;/p&gt;

&lt;p&gt;Thoughts/comments are most welcome. &lt;/p&gt;

&lt;p&gt;Thanks &lt;/p&gt;
', 'machine-learning  data-mining  r', NULL, '''/code'':106C ''/p'':54C,171C,241C,247C,250C ''0'':198C ''1'':56C ''1e-14'':199C ''1st'':124C ''2'':8A,173C ''3000'':27C ''4500'':24C ''80'':76C ''absolut'':189C ''advis'':3A,157C,231C ''algorithm'':129C ''anyway'':166C ''bias'':161C ''case'':32C ''close'':196C ''code'':104C ''coeffici'':72C,117C,183C,187C,220C ''corplot'':105C ''correl'':82C,93C ''coupl'':51C ''data'':14B ''data-min'':13B ''dataset'':21C ''dimension'':45C ''distinct'':96C ''even'':132C ''exclus'':112C ''expect'':138C ''first'':62C ''form'':95C,164C ''get'':233C ''glmnet'':39C ''goe'':73C ''group'':97C ''high'':92C ''indic'':213C ''interfer'':168C ''interpretation/prediction'':170C ''introduc'':160C ''know'':150C ''larg'':23C,210C ''larger'':203C ''lasso'':6A,36C,60C,111C,153C,240C ''learn'':12B ''less'':133C,145C ''lt'':30C ''machin'':11B ''machine-learn'':10B ''magnitud'':180C ''make'':222C ''matrix'':83C ''mean'':205C ''mine'':15B ''model'':224C ''multipl'':7A,154C ''n'':29C ''non'':70C,120C ''non-zero'':69C,119C ''number'':65C,134C ''observ'':28C ''p'':17C,31C,55C,172C,242C,248C ''packag'':40C,107C ''pick'':130C ''plot'':100C ''problem'':48C ''question'':53C,176C ''r'':16B,42C ''ran'':59C,110C ''reason'':79C ''reduc'':44C ''remain'':142C ''rerun'':5A,239C ''rid'':234C ''run'':125C,152C ''second'':175C ''seem'':78C ''show'':84C ''similar'':146C ''small'':219C ''spread'':207C ''thank'':249C ''thoughts/comments'':243C ''time'':9A,63C,127C,155C ''turn'':191C ''use'':35C,102C ''valu'':190C ''variabl'':25C,67C,89C,115C,216C,237C ''variables.i'':136C ''want'':148C ''way'':228C ''welcom'':246C ''whose'':116C ''wors'':225C ''would'':141C ''zero'':71C,121C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9416, 1, '2015-12-16 21:21:35.683', 0, 42, 'Importance of variables in RandomForest in R', '&lt;p&gt;I''m using varImpPlot() to evaluate the importance of variables from my rf model, and I can''t decide wich variable I have to delete from my model. I''m not sure about the diference between "MeanDecreaseAccuracy" and "MeanDecreaseGini", I guess the last one can measure the purity of the tree nodes, but as an example of my model: I have a variable that has a MeanDecreaseGini of about 100 and at the same time a MeanDecreaseAccuracy of about zero.! Because of this I can''t decide if this variable is good or bad.&lt;/p&gt;
', 'r  random-forest  ensemble-modeling', NULL, '''/p'':110C ''100'':85C ''bad'':109C ''decid'':34C,102C ''delet'':40C ''difer'':50C ''ensembl'':13B ''ensemble-model'':12B ''evalu'':21C ''exampl'':71C ''forest'':11B ''good'':107C ''guess'':56C ''import'':1A,23C ''last'':58C ''m'':17C,45C ''meandecreaseaccuraci'':52C,92C ''meandecreasegini'':54C,82C ''measur'':61C ''model'':14B,29C,43C,74C ''node'':67C ''one'':59C ''p'':15C ''puriti'':63C ''r'':7A,8B ''random'':10B ''random-forest'':9B ''randomforest'':5A ''rf'':28C ''sure'':47C ''time'':90C ''tree'':66C ''use'':18C ''variabl'':3A,25C,36C,78C,105C ''varimpplot'':19C ''wich'':35C ''zero'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9417, 1, '2015-12-16 21:49:31.96', 0, 59, 'Why in deep learning or another AI technique python is used rather than other languages?', '&lt;p&gt;I want to know, why python? Instead of C#, Java, C\C++ and so on?&lt;/p&gt;
', 'machine-learning  python  java', NULL, '''/p'':37C ''ai'':7A ''anoth'':6A ''c'':30C,32C,33C ''deep'':3A ''instead'':28C ''java'':20B,31C ''know'':25C ''languag'':15A ''learn'':4A,18B ''machin'':17B ''machine-learn'':16B ''p'':21C ''python'':9A,19B,27C ''rather'':12A ''techniqu'':8A ''use'':11A ''want'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9418, 1, '2015-12-17 01:42:57.98', 2, 19, 'Is it common to preprocess image data before sending it through a deep net?', '&lt;p&gt;I''m curious as how convolutional neural network are used in practice for object recognition. Is it common to perform data preprocessing before providing the data to the input layer ? If so, what types of preprocessing are common ?&lt;/p&gt;
', 'image-classification  preprocessing  object-recognition', NULL, '''/p'':61C ''classif'':17B ''common'':3A,40C,60C ''convolut'':28C ''curious'':25C ''data'':7A,43C,48C ''deep'':13A ''imag'':6A,16B ''image-classif'':15B ''input'':51C ''layer'':52C ''m'':24C ''net'':14A ''network'':30C ''neural'':29C ''object'':20B,36C ''object-recognit'':19B ''p'':22C ''perform'':42C ''practic'':34C ''preprocess'':5A,18B,44C,58C ''provid'':46C ''recognit'':21B,37C ''send'':9A ''type'':56C ''use'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9419, 2, '2015-12-17 01:55:19.17', 2, NULL, NULL, '&lt;p&gt;Short answers&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Tooling.&lt;/strong&gt; Python has fantastic math, statistics, and linear algebra libraries.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Less Code, Same Result.&lt;/strong&gt; Python provides quick and simple ways of achieving programming solutions compared to C#, Java, C++, etc. This means you''ll write less code and achieve the same result.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':21C,59C ''/ol'':60C ''/p'':4C,20C,58C ''/strong'':10C,29C ''achiev'':37C,54C ''algebra'':18C ''answer'':3C ''c'':42C,44C ''code'':26C,52C ''compar'':40C ''etc'':45C ''fantast'':13C ''java'':43C ''less'':25C,51C ''li'':6C,22C ''librari'':19C ''linear'':17C ''ll'':49C ''math'':14C ''mean'':47C ''ol'':5C ''p'':1C,7C,23C ''program'':38C ''provid'':31C ''python'':11C,30C ''quick'':32C ''result'':28C,57C ''short'':2C ''simpl'':34C ''solut'':39C ''statist'':15C ''strong'':8C,24C ''tool'':9C ''way'':35C ''write'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9422, 1, '2015-12-17 07:58:13.057', 1, 23, 'Pig is not able to read the complete data', '&lt;p&gt;I am trying to load a huge dataset of around 3.4 TB with approximately 1.4 million files in Pig on Amazon EMR.
The operations on the data are simple (JOIN and STORE), but the data is not getting loaded completely, and the program is terminating with a java outofmemory exception.&lt;/p&gt;

&lt;p&gt;I''ve tried increasing the Pig Heap size to 8192, but that hasn''t worked, however my code works fine if I use only 25% of the dataset.&lt;/p&gt;

&lt;p&gt;This is the last line of the complete log file:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;20124550 [Service Thread] DEBUG &lt;br&gt;
  org.apache.pig.impl.util.SpillableMemoryManager  - memory handler call - &lt;br&gt;Collection threshold init = 374341632(365568K) used =
  698749896(682372K) committed = 698875904(682496K) max =
  698875904(682496K)&lt;br&gt;&lt;/p&gt;
  
  &lt;p&gt;java.lang.OutOfMemoryError: Java heap space
  -XX:OnOutOfMemoryError="kill -9 %p&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Here''s my code for loading the dataset:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;log  = LOAD s3://some-path/{20151101..20151130}/{00,01,02,03,04,05,06,07,08,09,10,11,12,13,14,15,16,17,18,19,20,21,22,23}/'' USING PigStorage (''\n'') AS (line:chararray);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''ve specified each date individually, so that is not the issue.&lt;/p&gt;

&lt;p&gt;Also I''ve tried using 35 machines in the cluster, but that hasn''t helped. I could potentially add more, but that is not the type of solution I am looking for.&lt;/p&gt;

&lt;p&gt;Can anyone can suggest better approach to coding this problem, or some other solution?&lt;/p&gt;
', 'hadoop  map-reduce  apache-pig', NULL, '''-9'':147C ''/blockquote'':150C ''/code'':199C ''/p'':68C,98C,110C,138C,149C,160C,214C,248C,264C ''/pre'':200C ''/some-path'':166C ''00'':169C ''01'':170C ''02'':171C ''03'':172C ''04'':173C ''05'':174C ''06'':175C ''07'':176C ''08'':177C ''09'':178C ''1.4'':32C ''10'':179C ''11'':180C ''12'':181C ''13'':182C ''14'':183C ''15'':184C ''16'':185C ''17'':186C ''18'':187C ''19'':188C ''20'':189C ''20124550'':113C ''20151101'':167C ''20151130'':168C ''21'':190C ''22'':191C ''23'':192C ''25'':94C ''3.4'':28C ''35'':221C ''365568k'':127C ''374341632'':126C ''682372k'':130C ''682496k'':133C,136C ''698749896'':129C ''698875904'':132C,135C ''8192'':79C ''abl'':4A ''add'':234C ''also'':216C ''amazon'':38C ''anyon'':251C ''apach'':15B ''apache-pig'':14B ''approach'':255C ''approxim'':31C ''around'':27C ''better'':254C ''blockquot'':111C ''br'':117C,122C,137C ''call'':121C ''chararray'':198C ''cluster'':225C ''code'':87C,155C,162C,257C ''collect'':123C ''commit'':131C ''complet'':8A,57C,107C ''could'':232C ''data'':9A,44C,52C ''dataset'':25C,97C,159C ''date'':206C ''debug'':116C ''emr'':39C ''except'':67C ''file'':34C,109C ''fine'':89C ''get'':55C ''hadoop'':10B ''handler'':120C ''hasn'':82C,228C ''heap'':76C,142C ''help'':230C ''howev'':85C ''huge'':24C ''increas'':73C ''individu'':207C ''init'':125C ''issu'':213C ''java'':65C,141C ''java.lang.outofmemoryerror'':140C ''join'':47C ''kill'':146C ''last'':103C ''line'':104C,197C ''load'':22C,56C,157C,164C ''log'':108C,163C ''look'':246C ''machin'':222C ''map'':12B ''map-reduc'':11B ''max'':134C ''memori'':119C ''million'':33C ''n'':195C ''onoutofmemoryerror'':145C ''oper'':41C ''org.apache.pig.impl.util.spillablememorymanager'':118C ''outofmemori'':66C ''p'':17C,69C,99C,112C,139C,148C,151C,201C,215C,249C ''pig'':1A,16B,36C,75C ''pigstorag'':194C ''potenti'':233C ''pre'':161C ''problem'':259C ''program'':60C ''read'':6A ''reduc'':13B ''s3'':165C ''servic'':114C ''simpl'':46C ''size'':77C ''solut'':243C,263C ''space'':143C ''specifi'':204C ''store'':49C ''suggest'':253C ''tb'':29C ''termin'':62C ''thread'':115C ''threshold'':124C ''tri'':20C,72C,219C ''type'':241C ''use'':92C,128C,193C,220C ''ve'':71C,203C,218C ''work'':84C,88C ''xx'':144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9424, 1, '2015-12-17 10:52:10.013', 3, 48, 'SPARK Mllib: Multiclass logistic regression, how to get the probabilities of all classes rather than the top one?', '&lt;p&gt;I am using &lt;code&gt;LogisticRegressionWithLBFGS&lt;/code&gt; to train a multi-class classifier.&lt;/p&gt;

&lt;p&gt;Is there a way to get the probability of all classes (not only the top candidate class) when I test the model on new unseen samples?&lt;/p&gt;

&lt;p&gt;P.S. I am not necessarily obliged to use the LBFGS classifier but I would like to use the logistic regression in my problem. So if there is a solution by using another LR classifier type, I would go for it.&lt;/p&gt;
', 'classification  apache-spark  multiclass-classification', NULL, '''/code'':32C ''/p'':40C,68C,110C ''anoth'':101C ''apach'':21B ''apache-spark'':20B ''candid'':57C ''class'':13A,38C,52C,58C ''classif'':19B,25B ''classifi'':39C,80C,103C ''code'':30C ''get'':8A,47C ''go'':107C ''lbfgs'':79C ''like'':84C ''logist'':4A,88C ''logisticregressionwithlbfg'':31C ''lr'':102C ''mllib'':2A ''model'':63C ''multi'':37C ''multi-class'':36C ''multiclass'':3A,24B ''multiclass-classif'':23B ''necessarili'':74C ''new'':65C ''oblig'':75C ''one'':18A ''p'':26C,41C,69C ''p.s'':70C ''probabl'':10A,49C ''problem'':92C ''rather'':14A ''regress'':5A,89C ''sampl'':67C ''solut'':98C ''spark'':1A,22B ''test'':61C ''top'':17A,56C ''train'':34C ''type'':104C ''unseen'':66C ''use'':29C,77C,86C,100C ''way'':45C ''would'':83C,106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9522, 1, '2015-12-25 21:09:39.49', 0, 29, 'Getting back dummy features to 1 dimmensional space', '&lt;p&gt;I am facing the next issue :
I have some  features  expanded to an R^m space  with  values {0,1} where  m is the  dimmension of the  cardinaliaty of the  categories in the original 1 space.I am looking for a python(or R) method  to  get my dummy expansion  back  with little  effort.&lt;/p&gt;
', 'feature-selection', NULL, '''/p'':66C ''0'':31C ''1'':6A,32C,47C ''back'':2A,62C ''cardinaliati'':40C ''categori'':43C ''dimmens'':37C ''dimmension'':7A ''dummi'':3A,60C ''effort'':65C ''expand'':23C ''expans'':61C ''face'':15C ''featur'':4A,10B,22C ''feature-select'':9B ''get'':1A,58C ''issu'':18C ''littl'':64C ''look'':50C ''m'':27C,34C ''method'':56C ''next'':17C ''origin'':46C ''p'':12C ''python'':53C ''r'':26C,55C ''select'':11B ''space'':8A,28C ''space.i'':48C ''valu'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9429, 2, '2015-12-18 07:05:01.303', 1, NULL, NULL, '&lt;p&gt;There are a number of different ways to impute data when you have missing values. Far to many people just delete those records, which often contain useful training data.  Further, some people advocate for simple methods like using the mean of the feature when it is missing.  I don''t like this method as it seems too simplistic.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.amstat.org/sections/srms/webinarfiles/ModernMethodWebinarMay2012.pdf" rel="nofollow"&gt;Here is a good presentation&lt;/a&gt; detailing a couple of methods and particularly drawing attention to imputation through maximum likelihood.&lt;/p&gt;

&lt;p&gt;However, my favorite method is collaborative filtering, which is &lt;a href="http://papersdb.cs.ualberta.ca/~papersdb/uploaded_files/916/paper_SAC08_DM101.pdf" rel="nofollow"&gt;detailed in this paper&lt;/a&gt;. The nice thing about collaborative filtering is that it is easy to write from scratch and is also very popular, so is usually contained in most machine learning libraries.&lt;/p&gt;

&lt;p&gt;Also think about taking a look at the imputation techniques that might be available in the specific machine learning library that you may be using.&lt;/p&gt;

&lt;p&gt;Finally, a word of caution... imputing data adds some linear dependence, so you should expect that you might have to increase your regularization slightly in order to avoid high variance scenarios. I would experiment with (cross validate) removing features, removing records, then &lt;a href="http://papersdb.cs.ualberta.ca/~papersdb/uploaded_files/967/paper_08_WI.pdf" rel="nofollow"&gt;imputing data using collaborative filtering&lt;/a&gt; and see what produces the best results.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/a'':74C,111C,224C ''/p'':60C,89C,141C,168C,232C,237C ''/sections/srms/webinarfiles/modernmethodwebinarmay2012.pdf'':66C ''/~papersdb/uploaded_files/916/paper_sac08_dm101.pdf'':104C ''/~papersdb/uploaded_files/967/paper_08_wi.pdf'':216C ''add'':177C ''advoc'':34C ''also'':129C,143C ''attent'':83C ''avail'':156C ''avoid'':197C ''best'':230C ''caution'':174C ''collabor'':96C,116C,222C ''contain'':27C,135C ''coupl'':77C ''cross'':205C ''data'':11C,30C,176C,220C ''delet'':22C ''depend'':180C ''detail'':75C,107C ''differ'':7C ''draw'':82C ''easi'':122C ''expect'':184C ''experi'':203C ''far'':17C ''favorit'':93C ''featur'':44C,208C ''filter'':97C,117C,223C ''final'':170C ''good'':72C ''help'':236C ''high'':198C ''hope'':234C ''howev'':91C ''href'':63C,101C,213C ''imput'':10C,85C,151C,175C,219C ''increas'':190C ''learn'':139C,161C ''librari'':140C,162C ''like'':38C,52C ''likelihood'':88C ''linear'':179C ''look'':148C ''machin'':138C,160C ''mani'':19C ''maximum'':87C ''may'':165C ''mean'':41C ''method'':37C,54C,79C,94C ''might'':154C,187C ''miss'':15C,48C ''nice'':113C ''nofollow'':68C,106C,218C ''number'':5C ''often'':26C ''order'':195C ''p'':1C,61C,90C,142C,169C,233C ''paper'':110C ''papersdb.cs.ualberta.ca'':103C,215C ''papersdb.cs.ualberta.ca/~papersdb/uploaded_files/916/paper_sac08_dm101.pdf'':102C ''papersdb.cs.ualberta.ca/~papersdb/uploaded_files/967/paper_08_wi.pdf'':214C ''particular'':81C ''peopl'':20C,33C ''popular'':131C ''present'':73C ''produc'':228C ''record'':24C,210C ''regular'':192C ''rel'':67C,105C,217C ''remov'':207C,209C ''result'':231C ''scenario'':200C ''scratch'':126C ''see'':226C ''seem'':57C ''simpl'':36C ''simplist'':59C ''slight'':193C ''specif'':159C ''take'':146C ''techniqu'':152C ''thing'':114C ''think'':144C ''train'':29C ''use'':28C,39C,167C,221C ''usual'':134C ''valid'':206C ''valu'':16C ''varianc'':199C ''way'':8C ''word'':172C ''would'':202C ''write'':124C ''www.amstat.org'':65C ''www.amstat.org/sections/srms/webinarfiles/modernmethodwebinarmay2012.pdf'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9430, 2, '2015-12-18 07:54:21.37', 2, NULL, NULL, '&lt;p&gt;A regular SVM with default values uses a radial basis function as the SVM kernel.  This is basically a Gaussian kernel aka bell-curve.  Meaning that the no man''s land between different classes is created with a Gaussian function.  The linear-SVM uses a linear kernel for the basis function, so you can think of this as a ^ shaped function.  It is much less tunable and is basically just a linear interpolation.&lt;/p&gt;

&lt;p&gt;People are kind of hammering this question because you didn''t provide a lot of information, but looking deeply into what you posted... this hits on some fundamental aspects of really understanding the details of bias and variance and the difference between linear and nonlinear basis functions in SVM.&lt;/p&gt;

&lt;p&gt;Check out &lt;a href="http://blog.fliptop.com/blog/2015/03/02/bias-variance-and-overfitting-machine-learning-overview/" rel="nofollow"&gt;this image&lt;/a&gt; describing the four regions of high and low bias and high and low variance.  Obviously the best place to be is  low variance and low bias.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/jyHqx.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/jyHqx.png" alt="darts bias-variance image"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;First lets assess variance -&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Now take a look at your plots:
&lt;a href="http://i.stack.imgur.com/fIVsR.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/fIVsR.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The nonlinear basis function has higher variance.  See how it is noisier than the linear kernel!  The linear kernel has lower variance.  See how it is less noisy!  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Now lets assess bias -&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Which kernel is more accurate?  We can add the errors that you provided.  The nonlinear kernel has a total error of ~550+325=~875.  The linear kernel has an error of ~690+~50=~740.  So the linear kernel &lt;strong&gt;seems&lt;/strong&gt; to do better overall, but they are pretty close overall.  This is were things get tricky!  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Putting it all together&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;See how the linear kernel did a poor job on 1''s and a really great job on 0''s.  This is pretty unbalanced.  Where as the nonlinear kernel is more balanced.  It kind of seems like the sweet spot might be to create a balanced model that doesn''t have such high variance.  How do we control for high variance?  Bingo - regularization.  We can add regularization to the nonlinear model and we will probably see much better results.  This is the C parameter in scikit learn SVMs, which you will want to increase from the default.  We could also play with the gamma parameter.  Gamma controlls the width of the Gaussian.  Maybe try increasing that one slightly to get less noisy results e.g. a larger no-man''s land between classes.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''+325'':286C ''/a'':141C,188C,223C ''/blog/2015/03/02/bias-variance-and-overfitting-machine-learning-overview/'':136C ''/fivsr.png'':210C,217C ''/jyhqx.png'':174C,181C ''/p'':77C,128C,168C,189C,197C,224C,254C,262C,321C,329C,464C,469C ''/strong'':196C,261C,304C,328C ''0'':349C ''1'':341C ''50'':296C ''550'':285C ''690'':295C ''740'':297C ''875'':287C ''accur'':268C ''add'':271C,396C ''aka'':23C ''also'':430C ''alt'':182C,218C ''aspect'':107C ''assess'':194C,259C ''balanc'':362C,376C ''basi'':11C,53C,124C,228C ''basic'':19C,72C ''bell'':25C ''bell-curv'':24C ''best'':158C ''better'':307C,408C ''bias'':114C,150C,167C,185C,260C ''bias-vari'':184C ''bingo'':392C ''blog.fliptop.com'':135C ''blog.fliptop.com/blog/2015/03/02/bias-variance-and-overfitting-machine-learning-overview/'':134C ''c'':413C ''check'':130C ''class'':36C,463C ''close'':313C ''control'':388C,437C ''could'':429C ''creat'':38C,374C ''curv'':26C ''dart'':183C ''deepli'':97C ''default'':6C,427C ''describ'':142C ''descript'':221C ''detail'':112C ''didn'':88C ''differ'':35C,119C ''doesn'':379C ''e.g'':454C ''enter'':219C ''error'':273C,283C,293C ''first'':192C ''four'':144C ''function'':12C,42C,54C,64C,125C,229C ''fundament'':106C ''gamma'':434C,436C ''gaussian'':21C,41C,442C ''get'':319C,450C ''great'':346C ''hammer'':83C ''help'':468C ''high'':147C,152C,383C,390C ''higher'':231C ''hit'':103C ''hope'':466C ''href'':133C,171C,207C ''i.stack.imgur.com'':173C,180C,209C,216C ''i.stack.imgur.com/fivsr.png'':208C,215C ''i.stack.imgur.com/jyhqx.png'':172C,179C ''imag'':140C,187C,220C ''img'':177C,213C ''increas'':424C,445C ''inform'':94C ''interpol'':76C ''job'':339C,347C ''kernel'':16C,22C,50C,241C,244C,265C,279C,290C,301C,335C,359C ''kind'':81C,364C ''land'':33C,461C ''larger'':456C ''learn'':417C ''less'':68C,252C,451C ''let'':193C,258C ''like'':367C ''linear'':45C,49C,75C,121C,240C,243C,289C,300C,334C ''linear-svm'':44C ''look'':96C,202C ''lot'':92C ''low'':149C,154C,163C,166C ''lower'':246C ''man'':31C,459C ''mayb'':443C ''mean'':27C ''might'':371C ''model'':377C,401C ''much'':67C,407C ''no-man'':457C ''nofollow'':138C,176C,212C ''noisi'':253C,452C ''noisier'':237C ''nonlinear'':123C,227C,278C,358C,400C ''obvious'':156C ''one'':447C ''overal'':308C,314C ''p'':1C,78C,129C,169C,190C,198C,225C,255C,263C,322C,330C,465C ''paramet'':414C,435C ''peopl'':79C ''place'':159C ''play'':431C ''plot'':205C ''poor'':338C ''post'':101C ''pretti'':312C,353C ''probabl'':405C ''provid'':90C,276C ''put'':324C ''question'':85C ''radial'':10C ''realli'':109C,345C ''region'':145C ''regular'':3C,393C,397C ''rel'':137C,175C,211C ''result'':409C,453C ''scikit'':416C ''see'':233C,248C,331C,406C ''seem'':303C,366C ''shape'':63C ''slight'':448C ''spot'':370C ''src'':178C,214C ''strong'':191C,256C,302C,323C ''svm'':4C,15C,46C,127C ''svms'':418C ''sweet'':369C ''take'':200C ''thing'':318C ''think'':58C ''togeth'':327C ''total'':282C ''tri'':444C ''tricki'':320C ''tunabl'':69C ''unbalanc'':354C ''understand'':110C ''use'':8C,47C ''valu'':7C ''varianc'':116C,155C,164C,186C,195C,232C,247C,384C,391C ''want'':422C ''width'':439C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9543, 2, '2015-12-28 11:46:45.243', 5, NULL, NULL, '&lt;p&gt;There are several ways to make this big number trainable:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Use &lt;a href="https://en.wikipedia.org/wiki/Convolutional_neural_network"&gt;CNNs&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Auto-Encoders (see &lt;a href="https://www.cs.toronto.edu/~hinton/science.pdf"&gt;Reducing the Dimensionality of Data with Neural Networks&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;

&lt;h2&gt;Dimensionality reduction of the input&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;Scale the image down&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Principal_component_analysis"&gt;PCA&lt;/a&gt; / &lt;a href="https://en.wikipedia.org/wiki/Linear_discriminant_analysis"&gt;LDA&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;h2&gt;Troll-Answer&lt;/h2&gt;

&lt;p&gt;If you really meant "only a few neurons" then you might want to have a look at &lt;a href="https://en.wikipedia.org/wiki/Spiking_neural_network"&gt;Spiking neural networks&lt;/a&gt;. Those are incredibly computationally intensive, need a lot of hand-crafting and still get worse performance than normal neural networks for most tasks ... but you only need very little of them.&lt;/p&gt;
', NULL, NULL, '''/a'':22C,42C,66C,73C,107C ''/h2'':51C,80C ''/li'':23C,43C,58C,74C ''/p'':12C,140C ''/ul'':44C,75C ''/wiki/convolutional_neural_network'':20C ''/wiki/linear_discriminant_analysis'':71C ''/wiki/principal_component_analysis'':64C ''/wiki/spiking_neural_network'':103C ''/~hinton/science.pdf'':33C ''answer'':79C ''auto'':26C ''auto-encod'':25C ''big'':9C ''cnns'':21C ''comput'':111C ''craft'':119C ''data'':38C ''dimension'':36C,46C ''en.wikipedia.org'':19C,63C,70C,102C ''en.wikipedia.org/wiki/convolutional_neural_network'':18C ''en.wikipedia.org/wiki/linear_discriminant_analysis'':69C ''en.wikipedia.org/wiki/principal_component_analysis'':62C ''en.wikipedia.org/wiki/spiking_neural_network'':101C ''encod'':27C ''get'':122C ''h2'':45C,76C ''hand'':118C ''hand-craft'':117C ''href'':17C,30C,61C,68C,100C ''imag'':56C ''incred'':110C ''input'':50C ''intens'':112C ''lda'':72C ''li'':14C,24C,53C,59C ''littl'':137C ''look'':97C ''lot'':115C ''make'':7C ''meant'':85C ''might'':92C ''need'':113C,135C ''network'':41C,106C,128C ''neural'':40C,105C,127C ''neuron'':89C ''normal'':126C ''number'':10C ''p'':1C,81C ''pca'':65C ''perform'':124C ''realli'':84C ''reduc'':34C ''reduct'':47C ''scale'':54C ''see'':28C ''sever'':4C ''spike'':104C ''still'':121C ''task'':131C ''trainabl'':11C ''troll'':78C ''troll-answ'':77C ''ul'':13C,52C ''use'':15C ''want'':93C ''way'':5C ''wors'':123C ''www.cs.toronto.edu'':32C ''www.cs.toronto.edu/~hinton/science.pdf'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9431, 2, '2015-12-18 10:50:59.48', 12, NULL, NULL, '&lt;p&gt;Let''s review how the ReLU (Rectified Linear Unit) looks like :
&lt;a href="http://i.stack.imgur.com/8CGlM.png"&gt;&lt;img src="http://i.stack.imgur.com/8CGlM.png" alt=""&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The input to the rectifier for some input $x_n$ is 
$$z_n=\sum_{i=0}^k w_i a^n_i$$ for weights $w_i$, and activations from the previous layer $a^n_i$ for that particular input $x_n$. The rectifier neuron function is $ReLU = max(0,z_n)$&lt;/p&gt;

&lt;p&gt;Assuming a very simple error measure &lt;/p&gt;

&lt;p&gt;$$error = ReLU - y$$&lt;/p&gt;

&lt;p&gt;the rectifier has only 2 possible gradient values for the deltas of backpropagation algorithm:
$$\frac{\partial error}{\partial z_n} = \delta_n = \left\{
\begin{array}{c l}     
    1 &amp;amp; z_n \geq 0\\
    0 &amp;amp; z_n &amp;lt; 0
\end{array}\right.$$
(if we use a proper error measure, then the 1 will become something else, but the 0 will stay the same)
and so for a certain weight $w_j$ :
$$\nabla error = \frac{\partial error}{\partial w_j}=\frac{\partial error}{\partial z_n} \times \frac{\partial z_n}{\partial w_j} = \delta_n \times a_j^n = \left\{
\begin{array}{c 1}
    a_j^n &amp;amp; z_n \geq 0\\
    0 &amp;amp; z_n &amp;lt; 0
\end{array}\right.$$&lt;/p&gt;

&lt;p&gt;One question that comes to mind is how actually ReLU works "at all" with the gradient $=$ 0 on the left side. What if, for the input $x_n$, the current weights put the ReLU on the left flat side while it optimally should be on the right side for this particular input ? The gradient is 0 and so the weight will not be updated, not even a tiny bit, so where is "learning" in this case? &lt;/p&gt;

&lt;p&gt;The essence of the answer lies in the fact that Stochastic Gradient Descent will not only consider a single input $x_n$, but many of them, and the hope is that not all inputs will put the ReLU on the flat side, so the gradient will be non-zero for &lt;strong&gt;some&lt;/strong&gt; inputs (it may be +ve or -ve though). If at least one input $x_*$ has our ReLU on the steep side, then the ReLU is still &lt;strong&gt;alive&lt;/strong&gt;  because there''s still learning going on and weights getting updated for this neuron. If all inputs put the ReLU on the flat side, there''s no hope that the weights change at all and the neuron is &lt;strong&gt;dead&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;A ReLU may be alive then die due to the gradient step for some &lt;strong&gt;input batch&lt;/strong&gt; driving the weights to smaller values, making $z_n &amp;lt; 0$ for all inputs. A large learning rate amplifies this problem.&lt;/p&gt;

&lt;p&gt;As @Neil Slater mentioned, a fix is to modify the flat side to have a small gradient, so that it becomes $ReLU=max(0.1x,x)$ as below, which is called LeakyReLU.
&lt;a href="http://i.stack.imgur.com/LZk6i.png"&gt;&lt;img src="http://i.stack.imgur.com/LZk6i.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9432, 1, '2015-12-18 10:54:15.307', 4, 73, 'Alternatives to Graph theory?', '&lt;ul&gt;
&lt;li&gt;&lt;p&gt;I was planning to use a graph theoretic and page-rank approach to finding the most influential person in an organization.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Influential person is someone who drives a lot of activity in the
  organization. When he assigns a task, most people do it. When he sends
  a mail, most people reply to him. When he assigns some training, most
  people do it.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;But there is one drawback to using graph theory here. For example, say Person A is very influential, and Person B has just joined an organization and works under A. B puts in a leave request and sends it A for approval. A approves the leave request. This seems contradictory, since A is very influential but A responded to B. According to graph theory, wouldn''t this make Person B also very influential? This approach would give very bad results.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How can i overcome this limitation? Can anyone suggest an alternative approach?&lt;/strong&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;hr&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;I am using number of interactions to calculate an &lt;code&gt;affinity scores&lt;/code&gt; between two users. Based on this, I suggest what content should be shown to a user (like FaceBook does). User A responds to User B most of the time.  so (A-&gt;B) has a high affinity score. So, in user B''s newsfeed I intend to show content coming from User A. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;is there a better way than just counting number of interactions?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;If two users A &amp;amp; C have same affinity scores with B, if A is more influential than B, A''s content will be shown first to user B.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9542, 2, '2015-12-28 10:28:41.26', 1, NULL, NULL, '&lt;p&gt;You want to spread out the initial clusters, the article argues that this gives (on average) faster convergence and lower error. This means that points with a higher distance to the closest center are likely to be better candidates for initial clusters. So the probability distribution for a new initial clustering point is:&lt;/p&gt;

&lt;p&gt;$\mathbb{P}(C = c) = \frac{D(c)^2}{\sum_{\forall x\in X}D(x)^2}$&lt;/p&gt;

&lt;p&gt;Where $X$ is the collection of all candidate points and $C$ the chosen point distribution.&lt;/p&gt;

&lt;p&gt;This means the higher the distance, the higher the chance it will be picked as initial point.&lt;/p&gt;
', NULL, NULL, '''/p'':55C,73C,90C,109C ''2'':64C,72C ''argu'':12C ''articl'':11C ''averag'':17C ''better'':39C ''c'':59C,60C,63C,85C ''candid'':40C,82C ''center'':34C ''chanc'':101C ''chosen'':87C ''closest'':33C ''cluster'':9C,43C,52C ''collect'':79C ''converg'':19C ''d'':62C,70C ''distanc'':30C,97C ''distribut'':47C,89C ''error'':22C ''faster'':18C ''foral'':66C ''frac'':61C ''give'':15C ''higher'':29C,95C,99C ''initi'':8C,42C,51C,107C ''like'':36C ''lower'':21C ''mathbb'':57C ''mean'':24C,93C ''new'':50C ''p'':1C,56C,58C,74C,91C ''pick'':105C ''point'':26C,53C,83C,88C,108C ''probabl'':46C ''spread'':5C ''sum'':65C ''want'':3C ''x'':67C,69C,71C,76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9678, 2, '2016-01-07 12:04:25.113', 3, NULL, NULL, '&lt;p&gt;Machine Learning is a really big field. Depending on what exactly you want to do, there might be huge differences.&lt;/p&gt;

&lt;p&gt;Having said that, the following skills are helpful:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Programming, especially Python / C++&lt;/li&gt;
&lt;li&gt;Frameworks like &lt;a href="http://tensorflow.org" rel="nofollow"&gt;TensorFlow&lt;/a&gt;, &lt;a href="http://scikit-learn.org/stable/" rel="nofollow"&gt;sklearn&lt;/a&gt;, &lt;a href="http://torch.ch/" rel="nofollow"&gt;Torch&lt;/a&gt;, ...&lt;/li&gt;
&lt;li&gt;Algorithms like neural networks (especially gradient descent), SVM, decision trees, clustering algorithms, Q-Learning, ...&lt;/li&gt;
&lt;li&gt;Mathematics (e.g. probability theory / statistics, linear algebra, analysis for calculating gradients&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Knowing how to program the GPU directly (CUDA) is probably not always necessary, but certainly a big bonus.&lt;/p&gt;

&lt;p&gt;In case you''re interested what you learn at KIT (Karlsruhe, Germany) in an introductory machine learning course, see &lt;a href="http://martin-thoma.com/machine-learning-1-course/" rel="nofollow"&gt;my blog post for exam preparation&lt;/a&gt; (I''m sorry, it is mostly in German).&lt;/p&gt;

&lt;h2&gt;Online Courses&lt;/h2&gt;

&lt;p&gt;There are lots of online material for machine learning. For example an &lt;a href="https://www.coursera.org/learn/machine-learning" rel="nofollow"&gt;Coursera ML course&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/a'':49C,58C,65C,151C,188C ''/h2'':164C ''/learn/machine-learning'':182C ''/li'':39C,66C,83C,96C ''/machine-learning-1-course/'':142C ''/p'':22C,32C,117C,160C,189C ''/stable/'':54C ''/ul'':97C ''algebra'':91C ''algorithm'':68C,79C ''alway'':110C ''analysi'':92C ''big'':7C,115C ''blog'':146C ''bonus'':116C ''c'':38C ''calcul'':94C ''case'':120C ''certain'':113C ''cluster'':78C ''cours'':136C,163C,187C ''coursera'':185C ''cuda'':106C ''decis'':76C ''depend'':9C ''descent'':74C ''differ'':21C ''direct'':105C ''e.g'':86C ''especi'':36C,72C ''exact'':12C ''exam'':149C ''exampl'':176C ''field'':8C ''follow'':28C ''framework'':41C ''german'':159C ''germani'':130C ''gpu'':104C ''gradient'':73C,95C ''h2'':161C ''help'':31C ''href'':44C,51C,60C,139C,179C ''huge'':20C ''interest'':123C ''introductori'':133C ''karlsruh'':129C ''kit'':128C ''know'':99C ''learn'':3C,82C,126C,135C,174C ''li'':34C,40C,67C,84C ''like'':42C,69C ''linear'':90C ''lot'':168C ''m'':153C ''machin'':2C,134C,173C ''martin-thoma.com'':141C ''martin-thoma.com/machine-learning-1-course/'':140C ''materi'':171C ''mathemat'':85C ''might'':18C ''ml'':186C ''most'':157C ''necessari'':111C ''network'':71C ''neural'':70C ''nofollow'':47C,56C,63C,144C,184C ''onlin'':162C,170C ''p'':1C,23C,98C,118C,165C ''post'':147C ''prepar'':150C ''probabl'':87C,108C ''program'':35C,102C ''python'':37C ''q'':81C ''q-learn'':80C ''re'':122C ''realli'':6C ''rel'':46C,55C,62C,143C,183C ''said'':25C ''scikit-learn.org'':53C ''scikit-learn.org/stable/'':52C ''see'':137C ''skill'':29C ''sklearn'':57C ''sorri'':154C ''statist'':89C ''svm'':75C ''tensorflow'':48C ''tensorflow.org'':45C ''theori'':88C ''torch'':64C ''torch.ch'':61C ''tree'':77C ''ul'':33C ''want'':14C ''www.coursera.org'':181C ''www.coursera.org/learn/machine-learning'':180C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9433, 1, '2015-12-18 16:19:00.96', 1, 24, 'Sampling with replacement, specify the probabilities', '&lt;p&gt;I am trying to do sampling with replacement in Scala/Spark, defining the probabilities for each class. &lt;/p&gt;

&lt;p&gt;This is how I would do it in R.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Vector to sample from
x &amp;lt;- c("User1","User2","User3","User4","User5")

# Occurenciens from which to obtain sampling probabilities
y &amp;lt;- c(2,4,4,3,2)

# Calculate sampling probabilities
p &amp;lt;- y / sum(y)

# Draw sample with replacement of size 10
s &amp;lt;- sample(x, 10, replace = TRUE, prom = p)

# Which yields (for example):
[1] "User5" "User1" "User1" "User5" "User2" "User4" "User4" "User2" "User1" "User3"
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;How can I do the same in Scala / Spark?&lt;/p&gt;
', 'r  apache-spark  scala', NULL, '''/code'':109C ''/p'':29C,40C,121C ''/pre'':110C ''1'':98C ''10'':84C,89C ''2'':65C,69C ''3'':68C ''4'':66C,67C ''apach'':9B ''apache-spark'':8B ''c'':49C,64C ''calcul'':70C ''class'':28C ''code'':42C ''defin'':23C ''draw'':78C ''exampl'':97C ''lt'':48C,63C,74C,86C ''obtain'':59C ''occurencien'':55C ''p'':12C,30C,73C,93C,111C ''pre'':41C ''probabl'':6A,25C,61C,72C ''prom'':92C ''r'':7B,39C ''replac'':3A,20C,81C,90C ''sampl'':1A,18C,45C,60C,71C,79C,87C ''scala'':11B,119C ''scala/spark'':22C ''size'':83C ''spark'':10B,120C ''specifi'':4A ''sum'':76C ''tri'':15C ''true'':91C ''user1'':50C,100C,101C,107C ''user2'':51C,103C,106C ''user3'':52C,108C ''user4'':53C,104C,105C ''user5'':54C,99C,102C ''vector'':43C ''would'':35C ''x'':47C,88C ''y'':62C,75C,77C ''yield'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9454, 1, '2015-12-21 05:06:49.21', 1, 21, 'How to deal with a sparse matrix when using a perceptron based recommender system?', '&lt;p&gt;Let''s assume for now that I''m constrained to use a perceptron based method . say I have a user-item matrix filled with rating data on scale of 1 to 5 like this, with around 50% of the matrix with no data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;r&amp;lt;- matrix(c(2,4, NA,5,NA,3, NA,5,NA,1,NA,3,NA,5,NA,4,4,NA,NA,NA,1,1,2,NA,1,1,1,1,NA,NA,NA,NA,2,3,4,2,NA,NA,NA,NA,3,4,5,1,NA,NA,2,3,NA), nrow=7).
#one row represents one user, hile one column represents one item
r
    [,1] [,2] [,3] [,4] [,5] [,6] [,7]
[1,]    2    5   NA    1   NA    2    5
[2,]    4   NA    4    2   NA   NA    1
[3,]   NA    1    4   NA   NA   NA   NA
[4,]    5   NA   NA    1   NA   NA   NA
[5,]   NA    3   NA    1    2   NA    2
[6,]    3   NA   NA    1    3    3    3
[7,]   NA    5    1    1    4    4   NA
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m recoding the NA''s to 0''s, which makes the perceptron give out only 2 classes (one containing all the 1''s and 0''s , and the other containing all the 2,3,4 and 5. Which is understandable I guess.) How do I deal with this? I tried mean imputation but the results are not good (20% accuracy)&lt;/p&gt;

&lt;p&gt;(Sorry I can''t give exact code due to this being a proprietery code of my company, but any perceptron based method should work for this example)&lt;/p&gt;

&lt;p&gt;Any help would be appreciated. Thanks&lt;/p&gt;
', 'machine-learning  recommendation', NULL, '''/code'':197C ''/p'':64C,261C,290C,298C ''/pre'':198C ''0'':207C,225C ''1'':50C,80C,91C,92C,95C,96C,97C,98C,114C,134C,141C,145C,156C,159C,169C,177C,185C,192C,193C,222C ''2'':71C,93C,103C,106C,117C,135C,142C,147C,149C,153C,178C,180C,216C,233C ''20'':259C ''3'':76C,82C,104C,111C,118C,136C,157C,175C,182C,186C,187C,188C,234C ''4'':72C,86C,87C,105C,112C,137C,150C,152C,160C,165C,194C,195C,235C ''5'':52C,74C,78C,84C,113C,138C,143C,148C,166C,173C,191C,237C ''50'':57C ''6'':139C,181C ''7'':121C,140C,189C ''accuraci'':260C ''appreci'':296C ''around'':56C ''assum'':22C ''base'':12A,33C,283C ''c'':70C ''class'':217C ''code'':66C,269C,276C ''column'':129C ''compani'':279C ''constrain'':28C ''contain'':219C,230C ''data'':46C,63C ''deal'':3A,246C ''due'':270C ''exact'':268C ''exampl'':289C ''fill'':43C ''give'':213C,267C ''good'':258C ''guess'':242C ''help'':293C ''hile'':127C ''imput'':252C ''item'':41C,132C ''learn'':17B ''let'':20C ''like'':53C ''lt'':68C ''m'':27C,201C ''machin'':16B ''machine-learn'':15B ''make'':210C ''matrix'':7A,42C,60C,69C ''mean'':251C ''method'':34C,284C ''na'':73C,75C,77C,79C,81C,83C,85C,88C,89C,90C,94C,99C,100C,101C,102C,107C,108C,109C,110C,115C,116C,119C,144C,146C,151C,154C,155C,158C,161C,162C,163C,164C,167C,168C,170C,171C,172C,174C,176C,179C,183C,184C,190C,196C,204C ''nrow'':120C ''one'':122C,125C,128C,131C,218C ''p'':19C,199C,262C,291C ''perceptron'':11A,32C,212C,282C ''pre'':65C ''proprieteri'':275C ''r'':67C,133C ''rate'':45C ''recod'':202C ''recommend'':13A,18B ''repres'':124C,130C ''result'':255C ''row'':123C ''say'':35C ''scale'':48C ''sorri'':263C ''spars'':6A ''system'':14A ''thank'':297C ''tri'':250C ''understand'':240C ''use'':9A,30C ''user'':40C,126C ''user-item'':39C ''work'':286C ''would'':294C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9435, 1, '2015-12-18 19:31:32.01', 9, 411, 'How to self-learn data science?', '&lt;p&gt;I am a self-taught web developer and am interested in teaching myself data science, but I''m unsure of how to begin. In particular, I''m wondering:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;What fields are there within data science? (e.g., Artificial Intelligence, machine learning, data analysis, etc.)&lt;/li&gt;
&lt;li&gt;Are there online classes people can recommend?&lt;/li&gt;
&lt;li&gt;Are there projects available out there that I can practice on (e.g., open datasets).&lt;/li&gt;
&lt;li&gt;Are there certifications I can apply for or complete?&lt;/li&gt;
&lt;/ol&gt;
', 'beginner  self-study', NULL, '''/li'':60C,69C,85C,96C ''/ol'':97C ''/p'':42C ''analysi'':58C ''appli'':92C ''artifici'':53C ''avail'':74C ''begin'':36C ''beginn'':8B ''certif'':89C ''class'':65C ''complet'':95C ''data'':6A,27C,50C,57C ''dataset'':84C ''develop'':20C ''e.g'':52C,82C ''etc'':59C ''field'':46C ''intellig'':54C ''interest'':23C ''learn'':5A,56C ''li'':44C,61C,70C,86C ''m'':31C,40C ''machin'':55C ''ol'':43C ''onlin'':64C ''open'':83C ''p'':12C ''particular'':38C ''peopl'':66C ''practic'':80C ''project'':73C ''recommend'':68C ''scienc'':7A,28C,51C ''self'':4A,10B,17C ''self-learn'':3A ''self-studi'':9B ''self-taught'':16C ''studi'':11B ''taught'':18C ''teach'':25C ''unsur'':32C ''web'':19C ''within'':49C ''wonder'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9436, 2, '2015-12-18 20:50:13.463', 9, NULL, NULL, '&lt;p&gt;Welcome to the site, Martin! That''s a pretty broad question, so you''re probably going to get a variety of answers. Here''s my take.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;em&gt;Data Science&lt;/em&gt; is an interdisciplinary field generally thought to combine classical statistics, machine learning, and computer science (again, this depends on who you ask, but other might include business intelligence here, and possible information visualization or knowledge discovery as well; for example, &lt;a href="https://en.wikipedia.org/wiki/Data_science"&gt;the wikipedia article on data science&lt;/a&gt;). A good data scientist is also skilled at picking up on the domain-specific characteristics of the domain in which they working, as well. For example, a data scientist working on analytics for hospital records is much more effective if they have a background in Biomedical Informatics.&lt;/li&gt;
&lt;li&gt;There are many options here, depending on the type of analytics you''re interested in. &lt;a href="https://www.coursera.org/learn/machine-learning"&gt;Andrew Ng''s coursera course is the first resource mentioned by most&lt;/a&gt;, and rightly so. If you''re interested in machine learning, that''s a great starting place. If you want an in-depth exploration of the mathematics involved, &lt;a href="http://rads.stackoverflow.com/amzn/click/0387848576"&gt;Tibshirani''s The Elements of Statistical Learning&lt;/a&gt; is excellent, but fairly advanced text. There are many online courses available on coursera in addition to Ng''s, but you should select them with a mind for the type of analytics you want to focus on, and/or the domain in which you plan on working.&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.kaggle.com/"&gt;Kaggle&lt;/a&gt;. Start with kaggle, if you want to dive in on some real-world analytics problems. Depending on your level of expertise, it might be good to start of simpler, though. &lt;a href="https://projecteuler.net/"&gt;Project Euler&lt;/a&gt; is a great resource for one-off practice problems that I still use as warm-up work.&lt;/li&gt;
&lt;li&gt;Again, this probably depends on the domain you wish to work in. However, I know Coursera offers a data science certificate, if you complete a series of data science-related courses. This is probably a good place to start.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Good luck! If you have any other specific questions, feel free to ask me in the comments, and I''ll do my best to help!&lt;/p&gt;
', NULL, NULL, '''/a'':86C,169C,210C,263C,300C ''/amzn/click/0387848576'':202C ''/em'':34C ''/learn/machine-learning'':156C ''/li'':135C,257C,320C,362C ''/ol'':363C ''/p'':28C,390C ''/wiki/data_science'':79C ''addit'':226C ''advanc'':215C ''also'':92C ''analyt'':119C,147C,242C,278C ''and/or'':248C ''andrew'':157C ''answer'':23C ''articl'':82C ''ask'':56C,377C ''avail'':222C ''background'':131C ''best'':387C ''biomed'':133C ''broad'':11C ''busi'':61C ''certif'':342C ''characterist'':102C ''classic'':43C ''combin'':42C ''comment'':381C ''complet'':345C ''comput'':48C ''cours'':161C,221C,353C ''coursera'':160C,224C,337C ''data'':32C,84C,89C,115C,340C,349C ''depend'':52C,142C,280C,325C ''depth'':192C ''discoveri'':70C ''dive'':271C ''domain'':100C,105C,250C,328C ''domain-specif'':99C ''effect'':126C ''element'':206C ''em'':31C ''en.wikipedia.org'':78C ''en.wikipedia.org/wiki/data_science'':77C ''euler'':299C ''exampl'':74C,113C ''excel'':212C ''expertis'':285C ''explor'':193C ''fair'':214C ''feel'':374C ''field'':38C ''first'':164C ''focus'':246C ''free'':375C ''general'':39C ''get'':19C ''go'':17C ''good'':88C,289C,358C,365C ''great'':183C,303C ''help'':389C ''hospit'':121C ''howev'':334C ''href'':76C,153C,199C,260C,296C ''in-depth'':190C ''includ'':60C ''inform'':66C ''informat'':134C ''intellig'':62C ''interdisciplinari'':37C ''interest'':150C,176C ''involv'':197C ''kaggl'':262C,266C ''know'':336C ''knowledg'':69C ''learn'':46C,179C,209C ''level'':283C ''li'':30C,136C,258C,321C ''ll'':384C ''luck'':366C ''machin'':45C,178C ''mani'':139C,219C ''martin'':6C ''mathemat'':196C ''mention'':166C ''might'':59C,287C ''mind'':237C ''much'':124C ''ng'':158C,228C ''offer'':338C ''ol'':29C ''one'':307C ''one-off'':306C ''onlin'':220C ''option'':140C ''p'':1C,364C ''pick'':95C ''place'':185C,359C ''plan'':254C ''possibl'':65C ''practic'':309C ''pretti'':10C ''probabl'':16C,324C,356C ''problem'':279C,310C ''project'':298C ''projecteuler.net'':297C ''question'':12C,373C ''rads.stackoverflow.com'':201C ''rads.stackoverflow.com/amzn/click/0387848576'':200C ''re'':15C,149C,175C ''real'':276C ''real-world'':275C ''record'':122C ''relat'':352C ''resourc'':165C,304C ''right'':171C ''scienc'':33C,49C,85C,341C,351C ''science-rel'':350C ''scientist'':90C,116C ''select'':233C ''seri'':347C ''simpler'':293C ''site'':5C ''skill'':93C ''specif'':101C,372C ''start'':184C,264C,291C,361C ''statist'':44C,208C ''still'':313C ''take'':27C ''text'':216C ''though'':294C ''thought'':40C ''tibshirani'':203C ''type'':145C,240C ''use'':314C ''varieti'':21C ''visual'':67C ''want'':188C,244C,269C ''warm'':317C ''warm-up'':316C ''welcom'':2C ''well'':72C,111C ''wikipedia'':81C ''wish'':330C ''work'':109C,117C,256C,319C,332C ''world'':277C ''www.coursera.org'':155C ''www.coursera.org/learn/machine-learning'':154C ''www.kaggle.com'':261C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9438, 2, '2015-12-19 04:30:36.62', 1, NULL, NULL, '&lt;p&gt;I recommend starting from Coursera specializations in data science.  The data science specialization by Johns Hopkins is the oldest running specialization. I do not recommend books and kaggle. They only confuse you in the beginning. Keep in mind that coding is the easiest part of data science and you have to learn a lot.
For getting an idea about the field, &lt;a href="http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram" rel="nofollow"&gt;this Venn Diagram&lt;/a&gt; is a good start.&lt;/p&gt;
', NULL, NULL, '''/a'':73C ''/p'':78C ''/zia/2013/3/26/the-data-science-venn-diagram'':67C ''begin'':36C ''book'':27C ''code'':41C ''confus'':32C ''coursera'':6C ''data'':9C,12C,47C ''diagram'':72C ''drewconway.com'':66C ''drewconway.com/zia/2013/3/26/the-data-science-venn-diagram'':65C ''easiest'':44C ''field'':62C ''get'':57C ''good'':76C ''hopkin'':17C ''href'':64C ''idea'':59C ''john'':16C ''kaggl'':29C ''keep'':37C ''learn'':53C ''lot'':55C ''mind'':39C ''nofollow'':69C ''oldest'':20C ''p'':1C ''part'':45C ''recommend'':3C,26C ''rel'':68C ''run'':21C ''scienc'':10C,13C,48C ''special'':7C,14C,22C ''start'':4C,77C ''venn'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9440, 1, '2015-12-19 07:52:15.327', 0, 18, 'How to customise cost function in Scikit learn''s modelï¼', '&lt;p&gt;For example, when I have a problem that false negative should be penalised more, how can I incorporate that requirement in the algorithm such as SVM?&lt;/p&gt;
', 'machine-learning  python  scikit', NULL, '''/p'':43C ''algorithm'':39C ''cost'':4A ''customis'':3A ''exampl'':18C ''fals'':25C ''function'':5A ''incorpor'':34C ''learn'':8A,13B ''machin'':12B ''machine-learn'':11B ''modelï'':10A ''negat'':26C ''p'':16C ''penalis'':29C ''problem'':23C ''python'':14B ''requir'':36C ''scikit'':7A,15B ''svm'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9442, 2, '2015-12-19 18:42:13.853', 1, NULL, NULL, '&lt;p&gt;You have plenty of alternatives for this problem&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Using sapply&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data$abstract &amp;lt;- sapply(data$abstract, 
                        function(x){gsub(pattern = "no abstract available",
                        replacement = " ", x)})
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Using mapply&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data$abstract &amp;lt;- mapply(gsub, pattern = "no abstract available",
                        replacement = " ", data$abstract)
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Using the stringr package&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(stringr)
data$abstract &amp;lt;- str_replace(data$abstract, "no abstract available", 
                             " ")
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Also, check this &lt;a href="https://stackoverflow.com/questions/19424709/r-gsub-pattern-vector-and-replacement-vector"&gt;this question on StackOverflow&lt;/a&gt; for more information, like solutions with match and the qdap package.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9443, 1, '2015-12-19 19:30:35.527', 2, 38, 'When to use One Hot Encoding vs LabelEncoder vs DictVectorizor?', '&lt;p&gt;I have been building models with categorical data for a while now and when in this situation I basically default to using scikit-learn''s LabelEncoder function to transform this data prior to building a model.&lt;/p&gt;

&lt;p&gt;I understand the difference between OHE, LabelEncoder and DictVectorizor in terms of what they are doing to the data, but what isn''t clear to me is when you might choose to employ one technique over another.  Are there certain algorithms or situations in which one has advantages/disadvantages with respect to the others?&lt;/p&gt;

&lt;p&gt;Many thanks.&lt;/p&gt;
', 'scikit  categorical-data  feature-engineering', NULL, '''/p'':56C,111C,115C ''advantages/disadvantages'':105C ''algorithm'':98C ''anoth'':94C ''basic'':37C ''build'':22C,53C ''categor'':13B,25C ''categorical-data'':12B ''certain'':97C ''choos'':88C ''clear'':81C ''data'':14B,26C,50C,76C ''default'':38C ''dictvectorizor'':10A,66C ''differ'':61C ''employ'':90C ''encod'':6A ''engin'':17B ''featur'':16B ''feature-engin'':15B ''function'':46C ''hot'':5A ''isn'':79C ''labelencod'':8A,45C,64C ''learn'':43C ''mani'':113C ''might'':87C ''model'':23C,55C ''ohe'':63C ''one'':4A,91C,103C ''other'':110C ''p'':18C,57C,112C ''prior'':51C ''respect'':107C ''scikit'':11B,42C ''scikit-learn'':41C ''situat'':35C,100C ''techniqu'':92C ''term'':68C ''thank'':114C ''transform'':48C ''understand'':59C ''use'':3A,40C ''vs'':7A,9A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9444, 1, '2015-12-19 19:35:24.46', 0, 22, 'Neural network with multiple kinds of output', '&lt;p&gt;&lt;strong&gt;Introduction:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I am trying to build a neural network similar to the following one &lt;a href="http://www.nature.com/articles/srep11476" rel="nofollow"&gt;http://www.nature.com/articles/srep11476&lt;/a&gt; to predict two different features at the same time:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Secondary structure: discrete label with three classes (Helix, Sheet and Coil)&lt;/li&gt;
&lt;li&gt;Psi/Phi angles: continuous value (-pi to pi in rad or -180/180 in degrees)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;To do so, I use an output layer with softmax activation for the secondary structure and a tanh layer for the angles to predict sin(phi), cos(phi), sin(psi) and cos(psi), which are subsequently converted back to angles phi/psi. Note that this is done to avoid problems with the periodicity of angles.&lt;/p&gt;

&lt;p&gt;The cost for the individual features is a cross entropy for the secondary structure and a mean square error for the angles.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Problem&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I want to predict both features at the same time by having two different output layers and then train the network with a cost that takes into account both features.&lt;/p&gt;

&lt;p&gt;Question 1: the mean square error cost should be done at the sin/cos level or at the angle level (rad or deg)? sin/cos is constrained from -1/1, the rad from -pi/pi and the deg from -180 to 180&lt;/p&gt;

&lt;p&gt;Question 2: how can I merge the cross entropy cost for the secondary structure with the mean squared error for the angles in order to make a single cost? I cannot use a simple sum since they have different ranges.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9446, 2, '2015-12-20 00:21:27.72', 2, NULL, NULL, '&lt;p&gt;$R^{2}$ is a cursory measure of goodness of fit of a linear model to the data and it is used in regression analysis. It appears to be a popular choice when deciding between linear and non-linear regression models.&lt;/p&gt;

&lt;p&gt;It seems you intend to use kNN for classification, which has different evaluation metrics than regression. Scikit-learn provides ''accuracy'', ''true-positive'', ''false-positive'', etc (TP,FP,TN,FN), ''precision'', ''recall'', ''F1 score'', etc. for evaluating performance of a classifier.&lt;/p&gt;

&lt;p&gt;bonus: kNN are simple classifiers that are used when data shows local structure but not global structure, i.e. data with same label are found in multiple localized clusters in the data space. The key parameter to tune is ''k'', which should be based on the size of these localized clusters as you would stand to achieve considerably different results with different values of ''k''. You can do a grid-search (also in scikit-learn) to tune your parameters (including ''k'' of course).&lt;/p&gt;

&lt;p&gt;evaluation metric: accuracy or ROC or mean Average Precision or F1-score: There are several metrics to choose from and the choice is based on the task you are performing and what your priorities are. (you can look up these metrics for more details) but you can begin with accuracy for starters.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9447, 2, '2015-12-20 00:29:13.517', 4, NULL, NULL, '&lt;p&gt;There are some cases where LabelEncoder or DictVectorizor are useful, but these are quite limited in my opinion due to ordinality.&lt;/p&gt;

&lt;p&gt;LabelEncoder can turn [dog,cat,dog,mouse,cat] into [1,2,1,3,2], but then the imposed ordinality means that the average of dog and mouse is cat.  Still there are algorithms like decision trees and random forests that can work with categorical variables just fine and LabelEncoder can be used to store values using less disk space.&lt;/p&gt;

&lt;p&gt;One-Hot-Encoding has a the advantage that the result is binary rather than ordinal and that everything sits in an orthogonal vector space.  The disadvantage is that for high cardinality, the feature space can really blow up quickly and you start fighting with the curse of dimensionality. In these cases, I typically employ one-hot-encoding followed by PCA for dimensionality reduction.  I find that the judicious combination of one-hot plus PCA can seldom be beat by other encoding schemes.  PCA finds the linear overlap, so will naturally tend to group similar features into the same feature.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/p'':23C,84C,188C,193C ''1'':34C,36C ''2'':35C,38C ''3'':37C ''advantag'':93C ''algorithm'':57C ''averag'':47C ''beat'':166C ''binari'':98C ''blow'':123C ''cardin'':117C ''case'':5C,137C ''cat'':29C,32C,53C ''categor'':68C ''combin'':156C ''curs'':132C ''decis'':59C ''dictvectorizor'':9C ''dimension'':134C,149C ''disadvantag'':112C ''disk'':82C ''dog'':28C,30C,49C ''due'':20C ''employ'':140C ''encod'':89C,144C,169C ''everyth'':104C ''featur'':119C,183C,187C ''fight'':129C ''find'':152C,172C ''fine'':71C ''follow'':145C ''forest'':63C ''group'':181C ''help'':192C ''high'':116C ''hope'':190C ''hot'':88C,143C,160C ''impos'':42C ''judici'':155C ''labelencod'':7C,25C,73C ''less'':81C ''like'':58C ''limit'':16C ''linear'':174C ''mean'':44C ''mous'':31C,51C ''natur'':178C ''one'':87C,142C,159C ''one-hot'':158C ''one-hot-encod'':86C,141C ''opinion'':19C ''ordin'':22C,43C,101C ''orthogon'':108C ''overlap'':175C ''p'':1C,24C,85C,189C ''pca'':147C,162C,171C ''plus'':161C ''quick'':125C ''quit'':15C ''random'':62C ''rather'':99C ''realli'':122C ''reduct'':150C ''result'':96C ''scheme'':170C ''seldom'':164C ''similar'':182C ''sit'':105C ''space'':83C,110C,120C ''start'':128C ''still'':54C ''store'':78C ''tend'':179C ''tree'':60C ''turn'':27C ''typic'':139C ''use'':11C,76C,80C ''valu'':79C ''variabl'':69C ''vector'':109C ''work'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9448, 2, '2015-12-20 01:09:49.76', 1, NULL, NULL, '&lt;p&gt;(In addition to the excellent answer by Tim Goodman)&lt;/p&gt;

&lt;p&gt;The choice of k-modes is definitely the way to go for stability of the clustering algorithm used.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;The clustering algorithm is free to choose any distance metric / similarity score. Euclidean is the most popular. But any other metric can be used that scales according to the data distribution in each dimension /attribute, for example the Mahalanobis metric.
&lt;a href="http://i.stack.imgur.com/fjv5L.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/fjv5L.jpg" alt="Illustrating the distance of data points from the center based on the distance metric used."&gt;&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;With regards to mixed (numerical and categorical) clustering a good paper that might help is: &lt;a href="http://users.cis.fiu.edu/~lzhen001/activities/KDD2011Program/docs/p1127.pdf" rel="nofollow"&gt;INCONCO: Interpretable Clustering of Numerical and Categorical Objects&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Beyond k-means: Since plain vanilla k-means has already been ruled out as an appropriate approach to this problem, I''ll venture beyond to the idea of thinking of clustering as a model fitting problem. Different measures, like information-theoretic metric: Kullback-Liebler divergence work well when trying to converge a parametric model towards the data distribution.
(Of course parametric clustering techniques like GMM are slower than Kmeans, so there are drawbacks to consider)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Fuzzy k-modes clustering also sounds appealing since fuzzy logic techniques were developed to deal with something like categorical data. See &lt;a href="http://biosoft.kaist.ac.kr/~dhlee/pubs/papers/2004FuzzyClustgeringCategorical.pdf" rel="nofollow"&gt;Fuzzy clustering of categorical data using fuzzy centroids&lt;/a&gt; for more information.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Also check out: &lt;a href="https://www.cis.upenn.edu/~sudipto/mypapers/categorical.pdf" rel="nofollow"&gt;ROCK: A Robust Clustering Algorithm for Categorical Attributes&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9458, 1, '2015-12-21 10:26:19.633', 1, 66, 'nlp - opinion mining vs sentiment analysis', '&lt;p&gt;I have been told that nlp possibly holds the key for allowing researchers to infer the affective state of a person when writing. For instance, by using nlp analysis on online note taking you could infer whether a student is stressed. I work in educational cognitive science, so if this were the case it would be a valuable resource for me, however I am struggling to find evidence that this is indeed the case.&lt;/p&gt;

&lt;p&gt;I have begun preliminary research and am trying to learn a bit about the technical aspects of nlp - I''m taking an &lt;a href="https://www.coursera.org/course/nlp" rel="nofollow"&gt;online course by Jurafsky and Manning&lt;/a&gt;, &lt;a href="https://www.coursera.org/course/nlangp" rel="nofollow"&gt;another by Michael Collins&lt;/a&gt; - and I have been reading about what what can be inferred using nlp, specifically around opinion mining and sentiment analysis.&lt;/p&gt;

&lt;p&gt;My question is two part:
Firstly, most resources I have come across say something along the lines of ''nlp can be used for opinion mining and sentiment analysis and we will talk about the implications for opinion mining''. Can someone point me in the direction of more sentiment analysis oriented resources?&lt;/p&gt;

&lt;p&gt;Secondly, as I understand it, sentiment analysis is deriving the &lt;em&gt;stated&lt;/em&gt; sentiment within text, e.g. I was happy with, I loved, was tasty, enjoyed, hated, frustrated, etc. Can nlp, or something else, be used to derive &lt;em&gt;unstated&lt;/em&gt; affect? And is this the same thing as sentiment analysis?&lt;/p&gt;

&lt;p&gt;[Apologies if this is asked in the wrong exchange. I''ve been trying to find the best fit, but there were a few candidates and I wasn''t sure which would be most appropriate.]&lt;/p&gt;
', 'nlp', NULL, '''/a'':120C,132C ''/course/nlangp'':125C ''/course/nlp'':111C ''/em'':220C,248C ''/p'':83C,153C,206C,259C,295C ''across'':166C ''affect'':25C,249C ''allow'':20C ''along'':169C ''analysi'':6A,37C,152C,182C,203C,214C,258C ''anoth'':128C ''apolog'':261C ''appropri'':294C ''around'':147C ''ask'':265C ''aspect'':100C ''begun'':87C ''best'':277C ''bit'':96C ''candid'':284C ''case'':61C,82C ''cognit'':54C ''collin'':131C ''come'':165C ''could'':43C ''cours'':115C ''deriv'':216C,245C ''direct'':199C ''e.g'':224C ''educ'':53C ''els'':241C ''em'':218C,246C ''enjoy'':233C ''etc'':236C ''evid'':76C ''exchang'':269C ''find'':75C,275C ''first'':160C ''fit'':278C ''frustrat'':235C ''happi'':227C ''hate'':234C ''hold'':16C ''howev'':70C ''href'':108C,122C ''implic'':189C ''inde'':80C ''infer'':23C,44C,143C ''instanc'':33C ''jurafski'':117C ''key'':18C ''learn'':94C ''line'':171C ''love'':230C ''m'':104C ''man'':119C ''michael'':130C ''mine'':3A,149C,179C,192C ''nlp'':1A,7B,14C,36C,102C,145C,173C,238C ''nofollow'':113C,127C ''note'':40C ''onlin'':39C,114C ''opinion'':2A,148C,178C,191C ''orient'':204C ''p'':8C,84C,154C,207C,260C ''part'':159C ''person'':29C ''point'':195C ''possibl'':15C ''preliminari'':88C ''question'':156C ''read'':137C ''rel'':112C,126C ''research'':21C,89C ''resourc'':67C,162C,205C ''say'':167C ''scienc'':55C ''second'':208C ''sentiment'':5A,151C,181C,202C,213C,221C,257C ''someon'':194C ''someth'':168C,240C ''specif'':146C ''state'':26C,219C ''stress'':49C ''struggl'':73C ''student'':47C ''sure'':289C ''take'':41C,105C ''talk'':186C ''tasti'':232C ''technic'':99C ''text'':223C ''thing'':255C ''told'':12C ''tri'':92C,273C ''two'':158C ''understand'':211C ''unstat'':247C ''use'':35C,144C,176C,243C ''valuabl'':66C ''ve'':271C ''vs'':4A ''wasn'':287C ''whether'':45C ''within'':222C ''work'':51C ''would'':63C,291C ''write'':31C ''wrong'':268C ''www.coursera.org'':110C,124C ''www.coursera.org/course/nlangp'':123C ''www.coursera.org/course/nlp'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9453, 1, '2015-12-20 17:09:48.473', 4, 87, 'Linear kernel in SVM performing much worse than RBF or Poly', '&lt;p&gt;When trying to train a SVM on some Kaggle data, I have encountered a situation where the linear kernel fails to give any results.&lt;/p&gt;

&lt;p&gt;This doesn''t make sense to me because the RBF kernel works just fine, and my understanding is that a linear kernel is a strictly simpler version that doesn''t map the data into higher dimensions.  In fact, &lt;a href="http://stats.stackexchange.com/questions/73032/linear-kernel-and-non-linear-kernel-for-support-vector-machine"&gt;all&lt;/a&gt; &lt;a href="http://stackoverflow.com/questions/20566869/where-is-it-best-to-use-svm-with-linear-kernel"&gt;my&lt;/a&gt; &lt;a href="https://www.quora.com/How-does-one-decide-on-which-kernel-to-choose-for-an-SVM-RBF-vs-linear-vs-poly-kernel" rel="nofollow"&gt;research&lt;/a&gt; suggests that not only is a linear kernel possible, but that in most cases, it should be faster to converge (with the trade-off that the results might not be as accurate).  &lt;/p&gt;

&lt;p&gt;However, this has not proved to be the case.  While the RBF kernel was able to produce a result during cross-validation after ~6 minutes, the linear kernel just sat there with no output after ~6 hours.  After this point, I just force quit the training and moved onto testing other SVM parameters.&lt;/p&gt;

&lt;p&gt;A breakdown of some facts:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Roughly 60,000 examples, around 120 features (Sometimes cutting down to 30 features helps, and linear kernel will produce a result after many hours)&lt;/li&gt;
&lt;li&gt;I''m using SKLearn GridSearchCV to perform my training&lt;/li&gt;
&lt;li&gt;I have tested this used &lt;code&gt;SVC(kernel=''linear'')&lt;/code&gt; as well as &lt;code&gt;LinearSVC()&lt;/code&gt;, both with the same outcome&lt;/li&gt;
&lt;li&gt;This issue has come up with multiple data sets across different competitions&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;My current hypothesis is that the training stops possibly because the data is not linearly separable.  I might be missing something else obvious though.  Any guidance is appreciated, thanks!&lt;/p&gt;
', 'svm  sklearn  cross-validation', NULL, '''/a'':88C,95C,104C ''/code'':250C,256C ''/how-does-one-decide-on-which-kernel-to-choose-for-an-svm-rbf-vs-linear-vs-poly-kernel'':100C ''/li'':228C,239C,262C,276C ''/p'':42C,138C,194C,201C,308C ''/questions/20566869/where-is-it-best-to-use-svm-with-linear-kernel'':93C ''/questions/73032/linear-kernel-and-non-linear-kernel-for-support-vector-machine'':86C ''/ul'':277C ''000'':206C ''120'':209C ''30'':215C ''6'':164C,176C ''60'':205C ''abl'':154C ''accur'':137C ''across'':273C ''appreci'':306C ''around'':208C ''breakdown'':197C ''case'':118C,148C ''code'':246C,254C ''come'':267C ''competit'':275C ''converg'':124C ''cross'':15B,161C ''cross-valid'':14B,160C ''current'':280C ''cut'':212C ''data'':27C,76C,271C,290C ''differ'':274C ''dimens'':79C ''doesn'':45C,72C ''els'':300C ''encount'':30C ''exampl'':207C ''fact'':81C,200C ''fail'':37C ''faster'':122C ''featur'':210C,216C ''fine'':57C ''forc'':183C ''give'':39C ''gridsearchcv'':234C ''guidanc'':304C ''help'':217C ''higher'':78C ''hour'':177C,227C ''howev'':140C ''href'':83C,90C,97C ''hypothesi'':281C ''issu'':265C ''kaggl'':26C ''kernel'':2A,36C,54C,65C,112C,152C,168C,220C,248C ''li'':203C,229C,240C,263C ''linear'':1A,35C,64C,111C,167C,219C,249C,293C ''linearsvc'':255C ''m'':231C ''make'':47C ''mani'':226C ''map'':74C ''might'':133C,296C ''minut'':165C ''miss'':298C ''move'':188C ''much'':6A ''multipl'':270C ''nofollow'':102C ''obvious'':301C ''onto'':189C ''outcom'':261C ''output'':174C ''p'':17C,43C,139C,195C,278C ''paramet'':193C ''perform'':5A,236C ''point'':180C ''poli'':11A ''possibl'':113C,287C ''produc'':156C,222C ''prove'':144C ''quit'':184C ''rbf'':9A,53C,151C ''rel'':101C ''research'':103C ''result'':41C,132C,158C,224C ''rough'':204C ''sat'':170C ''sens'':48C ''separ'':294C ''set'':272C ''simpler'':69C ''situat'':32C ''sklearn'':13B,233C ''someth'':299C ''sometim'':211C ''stackoverflow.com'':92C ''stackoverflow.com/questions/20566869/where-is-it-best-to-use-svm-with-linear-kernel'':91C ''stats.stackexchange.com'':85C ''stats.stackexchange.com/questions/73032/linear-kernel-and-non-linear-kernel-for-support-vector-machine'':84C ''stop'':286C ''strict'':68C ''suggest'':105C ''svc'':247C ''svm'':4A,12B,23C,192C ''test'':190C,243C ''thank'':307C ''though'':302C ''trade'':128C ''trade-off'':127C ''train'':21C,186C,238C,285C ''tri'':19C ''ul'':202C ''understand'':60C ''use'':232C,245C ''valid'':16B,162C ''version'':70C ''well'':252C ''work'':55C ''wors'':7A ''www.quora.com'':99C ''www.quora.com/how-does-one-decide-on-which-kernel-to-choose-for-an-svm-rbf-vs-linear-vs-poly-kernel'':98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9455, 2, '2015-12-21 05:40:40.41', 1, NULL, NULL, '&lt;p&gt;One quick note: gridsearch gets &lt;em&gt;very&lt;/em&gt; slow as your hyperparameter space grows. Randomized search often leads to just-as-good hyperparameters, and is much faster:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/auto_examples/model_selection/randomized_search.html" rel="nofollow"&gt;http://scikit-learn.org/stable/auto_examples/model_selection/randomized_search.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/em'':9C ''/p'':30C ''/stable/auto_examples/model_selection/randomized_search.html'':36C ''/stable/auto_examples/model_selection/randomized_search.html&lt;/a&gt;&lt;/p&gt;'':41C ''em'':7C ''faster'':29C ''get'':6C ''good'':24C ''gridsearch'':5C ''grow'':15C ''href'':33C ''hyperparamet'':13C,25C ''just-as-good'':21C ''lead'':19C ''much'':28C ''nofollow'':38C ''note'':4C ''often'':18C ''one'':2C ''p'':1C,31C ''quick'':3C ''random'':16C ''rel'':37C ''scikit-learn.org'':35C,40C ''scikit-learn.org/stable/auto_examples/model_selection/randomized_search.html'':34C ''scikit-learn.org/stable/auto_examples/model_selection/randomized_search.html&lt;/a&gt;&lt;/p&gt;'':39C ''search'':17C ''slow'':10C ''space'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9456, 1, '2015-12-21 06:50:07.703', 1, 27, 'Dense text representations', '&lt;p&gt;I did a lot of Googling but could not find a paper that presents an algorithm which will produce dense feature vectors for short text input. I would be happy to find feature extraction algorithm which would perform at least as good as sparse word unigram and bigram feature vectors.&lt;/p&gt;

&lt;p&gt;Currently I am exploring the idea of using LDA (Latent Dirichlet Allocation) but there are problems with processing short text (2-7 words per document).&lt;/p&gt;

&lt;p&gt;The task at hand is short text classification. The number of classes for my data ranges from 10 to 20 classes. The classes are fairly well represented and the word unigram and bigram features work well. I would like to compute dense feature vectors for other experiments.&lt;/p&gt;

&lt;p&gt;Any pointers to papers, preferrably simple to implement, would be appreciated.&lt;/p&gt;
', 'classification  text', NULL, '''-7'':80C ''/p'':57C,84C,133C,146C ''10'':103C ''2'':79C ''20'':105C ''algorithm'':22C,41C ''alloc'':70C ''appreci'':145C ''bigram'':54C,118C ''class'':97C,106C,108C ''classif'':4B,93C ''comput'':126C ''could'':14C ''current'':59C ''data'':100C ''dens'':1A,26C,127C ''dirichlet'':69C ''document'':83C ''experi'':132C ''explor'':62C ''extract'':40C ''fair'':110C ''featur'':27C,39C,55C,119C,128C ''find'':16C,38C ''good'':48C ''googl'':12C ''hand'':89C ''happi'':36C ''idea'':64C ''implement'':142C ''input'':32C ''latent'':68C ''lda'':67C ''least'':46C ''like'':124C ''lot'':10C ''number'':95C ''p'':6C,58C,85C,134C ''paper'':18C,138C ''per'':82C ''perform'':44C ''pointer'':136C ''preferr'':139C ''present'':20C ''problem'':74C ''process'':76C ''produc'':25C ''rang'':101C ''repres'':112C ''represent'':3A ''short'':30C,77C,91C ''simpl'':140C ''spars'':50C ''task'':87C ''text'':2A,5B,31C,78C,92C ''unigram'':52C,116C ''use'':66C ''vector'':28C,56C,129C ''well'':111C,121C ''word'':51C,81C,115C ''work'':120C ''would'':34C,43C,123C,143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9457, 1, '2015-12-21 07:39:44.953', 4, 84, 'How to build a supervised artificial neural network?', '&lt;p&gt;I am trying to build and train a machine learning data science algorithm that correctly predicts what president won in what county. I have the following information for training data.&lt;/p&gt;

&lt;p&gt;Total population Median age % BachelorsDeg or higher Unemployment rate Per capita income Total households Average household size % Owner occupied housing % Renter occupied housing % Vacant housing Median home value Population growth House hold growth Per capita income growth Winner&lt;/p&gt;

&lt;p&gt;I am new to the field and this is my first time building an artificial neural network and I don''t know what to do to start (so forgive me if my question is extremely broad). What I have done so far is read the wikipedia page for artificial neural networks. What I want to do next is implement supervised learning using the training data.&lt;/p&gt;

&lt;p&gt;I would appreciate any help getting started. Please let me know if you have used any good tutorials or libraries to help build something similar. I was thinking of using the library Lasagne.&lt;/p&gt;

&lt;p&gt;A few specific questions I have as I tried to use Lasagne - What is the error I am to calculate when building an algorithm using training data? How many layers do I need and what does each layer signify?&lt;/p&gt;
', 'machine-learning  neuralnetwork  algorithms  supervised-learning', NULL, '''/p'':48C,88C,155C,190C,231C ''age'':53C ''algorithm'':13B,30C,215C ''appreci'':159C ''artifici'':6A,104C,138C ''averag'':64C ''bachelorsdeg'':54C ''broad'':125C ''build'':3A,22C,102C,179C,213C ''calcul'':211C ''capita'':60C,84C ''correct'':32C ''counti'':39C ''data'':28C,47C,154C,218C ''done'':129C ''error'':207C ''extrem'':124C ''far'':131C ''field'':95C ''first'':100C ''follow'':43C ''forgiv'':118C ''get'':162C ''good'':173C ''growth'':79C,82C,86C ''help'':161C,178C ''higher'':56C ''hold'':81C ''home'':76C ''hous'':69C,72C,74C,80C ''household'':63C,65C ''implement'':148C ''incom'':61C,85C ''inform'':44C ''know'':111C,167C ''lasagn'':189C,203C ''layer'':221C,229C ''learn'':11B,16B,27C,150C ''let'':165C ''librari'':176C,188C ''machin'':10B,26C ''machine-learn'':9B ''mani'':220C ''median'':52C,75C ''need'':224C ''network'':8A,106C,140C ''neural'':7A,105C,139C ''neuralnetwork'':12B ''new'':92C ''next'':146C ''occupi'':68C,71C ''owner'':67C ''p'':17C,49C,89C,156C,191C ''page'':136C ''per'':59C,83C ''pleas'':164C ''popul'':51C,78C ''predict'':33C ''presid'':35C ''question'':122C,195C ''rate'':58C ''read'':133C ''renter'':70C ''scienc'':29C ''signifi'':230C ''similar'':181C ''size'':66C ''someth'':180C ''specif'':194C ''start'':116C,163C ''supervis'':5A,15B,149C ''supervised-learn'':14B ''think'':184C ''time'':101C ''total'':50C,62C ''train'':24C,46C,153C,217C ''tri'':20C,200C ''tutori'':174C ''unemploy'':57C ''use'':151C,171C,186C,202C,216C ''vacant'':73C ''valu'':77C ''want'':143C ''wikipedia'':135C ''winner'':87C ''won'':36C ''would'':158C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9476, 2, '2015-12-21 21:46:56.083', 2, NULL, NULL, '&lt;p&gt;Generally, this is done using &lt;code&gt;spark-rowsimilarity&lt;/code&gt; algorithm - it is a class of content based recommendation. However, the actual process of doing this is quite simple. Here are the steps:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;For each movie, convert your categorical variables into columns. For lets say that actor_1 has Brad Pitt, Daniel Craig, and Vin Diesel for different movies. This will become three columns with a 1 denoting which movies have each actor. Your movie matrix will look something like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Movie Name, Has_Brad_Pitt, Has_Daniel_Craig, Has_Vin_Diesel, ...
MI-6      ,     1        ,       0         ,     0         , ...
Fast&amp;amp;Furios,    0        ,       0         ,     1         , ...
Casino Royale,  0        ,       1         ,     0         , ...
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Now, to find similarity score of movies, you can just compute the cross product of the two vectors. Higher the value, more they are similar. &lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;This can be done by the &lt;a href="https://github.com/apache/mahout/blob/master/spark/src/main/scala/org/apache/mahout/drivers/RowSimilarityDriver.scala" rel="nofollow"&gt;&lt;code&gt;spark-rowsimilarity&lt;/code&gt;&lt;/a&gt; algorithm in one shot. You may have to do some work in encoding categorical variables.&lt;/p&gt;
', NULL, NULL, '''-6'':101C ''/a'':168C ''/apache/mahout/blob/master/spark/src/main/scala/org/apache/mahout/drivers/rowsimilaritydriver.scala'':160C ''/code'':11C,116C,167C ''/li'':118C,147C ''/ol'':148C ''/p'':35C,86C,146C,184C ''/pre'':117C ''0'':103C,104C,108C,109C,113C,115C ''1'':53C,72C,102C,110C,114C ''actor'':52C,78C ''actual'':23C ''algorithm'':12C,169C ''amp'':106C ''base'':19C ''becom'':67C ''brad'':55C,92C ''casino'':111C ''categor'':44C,182C ''class'':16C ''code'':7C,88C,163C ''column'':47C,69C ''comput'':131C ''content'':18C ''convert'':42C ''craig'':58C,96C ''cross'':133C ''daniel'':57C,95C ''denot'':73C ''diesel'':61C,99C ''differ'':63C ''done'':5C,153C ''encod'':181C ''fast'':105C ''find'':123C ''furio'':107C ''general'':2C ''github.com'':159C ''github.com/apache/mahout/blob/master/spark/src/main/scala/org/apache/mahout/drivers/rowsimilaritydriver.scala'':158C ''higher'':139C ''howev'':21C ''href'':157C ''let'':49C ''li'':37C,119C ''like'':85C ''look'':83C ''matrix'':81C ''may'':174C ''mi'':100C ''movi'':41C,64C,75C,80C,89C,127C ''name'':90C ''nofollow'':162C ''ol'':36C ''one'':171C ''p'':1C,38C,120C,149C ''pitt'':56C,93C ''pre'':87C ''process'':24C ''product'':134C ''quit'':29C ''recommend'':20C ''rel'':161C ''rowsimilar'':10C,166C ''royal'':112C ''say'':50C ''score'':125C ''shot'':172C ''similar'':124C,145C ''simpl'':30C ''someth'':84C ''spark'':9C,165C ''spark-rowsimilar'':8C,164C ''step'':34C ''three'':68C ''two'':137C ''use'':6C ''valu'':141C ''variabl'':45C,183C ''vector'':138C ''vin'':60C,98C ''work'':179C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9459, 1, '2015-12-21 10:38:45.997', 3, 111, 'Recognition human in images through HOG descriptor and SVM classifier performs poorly', '&lt;p&gt;I''m using a HOG descriptor, coupled with a SVM classifier, to recognise humans in pictures. I''m using the Python wrappers for OpenCV.
I''ve used the excellent tutorial at &lt;a href="http://www.pyimagesearch.com/2015/11/09/pedestrian-detection-opencv/" rel="nofollow"&gt;pymagesearch&lt;/a&gt;, which explains what the algorithm does and furnishes hints on how to set the parameters of the &lt;em&gt;detectMultiScale&lt;/em&gt; method. &lt;/p&gt;

&lt;p&gt;Specifically, I do&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# initialize the HOG descriptor
hog = cv2.HOGDescriptor()

# Set the support vector machine to be pre-trained for people detection
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

# Detect people in the image
(rects, weights) = hog.detectMultiScale(image,
                                        winStride=(4, 4),
                                        padding=(8, 8),
                                        scale=1.05)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The parameters are chosen according to a fine tuning on both accuracy and performance, following the explanations in the tutorial itself.&lt;/p&gt;

&lt;p&gt;My problem is that this method, which seems like the currently best method to recognise humans in a picture according to literature (the original &lt;a href="http://lear.inrialpes.fr/people/triggs/pubs/Dalal-cvpr05.pdf" rel="nofollow"&gt;paper&lt;/a&gt; is dated 2005) seems to perform pretty poorly on my images. 
I have images containing clothes, both with a model and without it and I''m trying this approach to recognise those with the model. On a subset of 300 images which I manually scanned to tag them for containing the model or not, the method fails 30% of the times.&lt;/p&gt;

&lt;p&gt;These are some images as examples.&lt;/p&gt;

&lt;p&gt;Here it detected a missing human:
&lt;a href="http://i.stack.imgur.com/aaR8D.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/aaR8D.jpg" alt=""&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Here it did not get the full human: 
&lt;a href="http://i.stack.imgur.com/uExXC.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/uExXC.jpg" alt=""&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Here it did not recognise it at all:
&lt;a href="http://i.stack.imgur.com/qPjs5.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/qPjs5.jpg" alt=""&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I understand that the detector works for upright humans. Should they also be full-figure? 
My images encompass half-figure, figures with no head or no feet.&lt;/p&gt;

&lt;p&gt;Before this, I have tried a Haar-feature based cascade classifier to recognise the face in an image and the accuracy on the same set of image has been 90%, so I was trying to improve on this. Also, I''m interested in understanding why things are not working here.&lt;/p&gt;
', 'python  computer-vision  object-recognition', NULL, '''/2015/11/09/pedestrian-detection-opencv/'':56C ''/a'':60C,187C,278C,302C,326C ''/aar8d.jpg'':269C,276C ''/code'':129C ''/em'':80C ''/p'':82C,87C,153C,249C,257C,279C,303C,327C,358C,411C ''/people/triggs/pubs/dalal-cvpr05.pdf'':183C ''/pre'':130C ''/qpjs5.jpg'':317C,324C ''/uexxc.jpg'':293C,300C ''1.05'':128C ''2005'':190C ''30'':245C ''300'':227C ''4'':122C,123C ''8'':125C,126C ''90'':390C ''accord'':136C,174C ''accuraci'':143C,381C ''algorithm'':65C ''also'':340C,399C ''alt'':277C,301C,325C ''approach'':216C ''base'':369C ''best'':166C ''cascad'':370C ''chosen'':135C ''classifi'':10A,31C,371C ''cloth'':203C ''code'':89C ''comput'':15B ''computer-vis'':14B ''contain'':202C,237C ''coupl'':27C ''current'':165C ''cv2.hogdescriptor'':95C,110C ''date'':189C ''descriptor'':7A,26C,93C ''detect'':108C,112C,261C ''detectmultiscal'':79C ''detector'':333C ''em'':78C ''encompass'':347C ''exampl'':256C ''excel'':49C ''explain'':62C ''explan'':148C ''face'':375C ''fail'':244C ''featur'':368C ''feet'':357C ''figur'':344C,350C,351C ''fine'':139C ''follow'':146C ''full'':287C,343C ''full-figur'':342C ''furnish'':68C ''get'':285C ''getdefaultpeopledetector'':111C ''haar'':367C ''haar-featur'':366C ''half'':349C ''half-figur'':348C ''head'':354C ''hint'':69C ''hog'':6A,25C,92C,94C ''hog.detectmultiscale'':119C ''hog.setsvmdetector'':109C ''href'':53C,180C,266C,290C,314C ''human'':2A,34C,170C,264C,288C,337C ''i.stack.imgur.com'':268C,275C,292C,299C,316C,323C ''i.stack.imgur.com/aar8d.jpg'':267C,274C ''i.stack.imgur.com/qpjs5.jpg'':315C,322C ''i.stack.imgur.com/uexxc.jpg'':291C,298C ''imag'':4A,116C,120C,198C,201C,228C,254C,346C,378C,387C ''img'':272C,296C,320C ''improv'':396C ''initi'':90C ''interest'':402C ''lear.inrialpes.fr'':182C ''lear.inrialpes.fr/people/triggs/pubs/dalal-cvpr05.pdf'':181C ''like'':163C ''literatur'':176C ''m'':22C,38C,213C,401C ''machin'':100C ''manual'':231C ''method'':81C,160C,167C,243C ''miss'':263C ''model'':207C,222C,239C ''nofollow'':58C,185C,271C,295C,319C ''object'':18B ''object-recognit'':17B ''opencv'':44C ''origin'':178C ''p'':20C,83C,131C,154C,250C,258C,280C,304C,328C,359C ''pad'':124C ''paper'':186C ''paramet'':75C,133C ''peopl'':107C,113C ''perform'':11A,145C,193C ''pictur'':36C,173C ''poor'':12A,195C ''pre'':88C,104C ''pre-train'':103C ''pretti'':194C ''problem'':156C ''pymagesearch'':59C ''python'':13B,41C ''recognis'':33C,169C,218C,309C,373C ''recognit'':1A,19B ''rect'':117C ''rel'':57C,184C,270C,294C,318C ''scale'':127C ''scan'':232C ''seem'':162C,191C ''set'':73C,96C,385C ''specif'':84C ''src'':273C,297C,321C ''subset'':225C ''support'':98C ''svm'':9A,30C ''tag'':234C ''thing'':406C ''time'':248C ''train'':105C ''tri'':214C,364C,394C ''tune'':140C ''tutori'':50C,151C ''understand'':330C,404C ''upright'':336C ''use'':23C,39C,47C ''ve'':46C ''vector'':99C ''vision'':16B ''weight'':118C ''winstrid'':121C ''without'':209C ''work'':334C,409C ''wrapper'':42C ''www.pyimagesearch.com'':55C ''www.pyimagesearch.com/2015/11/09/pedestrian-detection-opencv/'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9964, 1, '2016-01-26 03:02:13.593', 1, 45, 'stock market analysis', '&lt;p&gt;I''m starting my Ph.D. this year in Computer Science  and for that I need a research topic. I am interested in Predictive Analytics in the area of stock market data. In this field, what are the unexplored topics that can help me choose a strong problem? Thanks.&lt;/p&gt;
', 'machine-learning  data-mining  finance', NULL, '''/p'':60C ''analysi'':3A ''analyt'':35C ''area'':38C ''choos'':55C ''comput'':20C ''data'':8B,42C ''data-min'':7B ''field'':45C ''financ'':10B ''help'':53C ''interest'':32C ''learn'':6B ''m'':13C ''machin'':5B ''machine-learn'':4B ''market'':2A,41C ''mine'':9B ''need'':26C ''p'':11C ''ph.d'':16C ''predict'':34C ''problem'':58C ''research'':28C ''scienc'':21C ''start'':14C ''stock'':1A,40C ''strong'':57C ''thank'':59C ''topic'':29C,50C ''unexplor'':49C ''year'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9460, 1, '2015-12-21 11:18:31.08', 1, 101, 'Pandas: how can I create multi-level columns', '&lt;p&gt;I have a pandas DataFrame which has the following columns:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;n_0&lt;/li&gt;
&lt;li&gt;n_1&lt;/li&gt;
&lt;li&gt;p_0&lt;/li&gt;
&lt;li&gt;p_1&lt;/li&gt;
&lt;li&gt;e_0&lt;/li&gt;
&lt;li&gt;e_1&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I want to transform it to have columns and sub-columns:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;0

&lt;ul&gt;
&lt;li&gt;n&lt;/li&gt;
&lt;li&gt;p&lt;/li&gt;
&lt;li&gt;e&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;1

&lt;ul&gt;
&lt;li&gt;n&lt;/li&gt;
&lt;li&gt;p&lt;/li&gt;
&lt;li&gt;e&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I''ve searched in the documentation, and I''m completely lost on how to implement this. Does anyone have any suggestions?&lt;/p&gt;
', 'pandas', NULL, '''/li'':27C,31C,35C,39C,43C,47C,69C,72C,75C,77C,83C,86C,89C,91C ''/p'':22C,62C,115C ''/ul'':48C,76C,90C,92C ''0'':26C,34C,42C,65C ''1'':30C,38C,46C,79C ''anyon'':111C ''column'':9A,21C,57C,61C ''complet'':103C ''creat'':5A ''datafram'':16C ''document'':99C ''e'':41C,45C,74C,88C ''follow'':20C ''implement'':108C ''level'':8A ''li'':24C,28C,32C,36C,40C,44C,64C,67C,70C,73C,78C,81C,84C,87C ''lost'':104C ''m'':102C ''multi'':7A ''multi-level'':6A ''n'':25C,29C,68C,82C ''p'':11C,33C,37C,49C,71C,85C,93C ''panda'':1A,10B,15C ''search'':96C ''sub'':60C ''sub-column'':59C ''suggest'':114C ''transform'':53C ''ul'':23C,63C,66C,80C ''ve'':95C ''want'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9461, 2, '2015-12-21 12:25:11.593', 0, NULL, NULL, '&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Sentiment_analysis" rel="nofollow"&gt;&lt;em&gt;Sentiment analysis&lt;/em&gt;, &lt;em&gt;sentiment detection&lt;/em&gt; and &lt;em&gt;opinion mining&lt;/em&gt;&lt;/a&gt; all cover a set of problems, and can generally be considered to be one and the same.&lt;/p&gt;

&lt;p&gt;The term &lt;em&gt;sentiment analysis&lt;/em&gt; seems to be more popular in the press and in industry.  In practice, as of 2015, it is mostly about giving a score, to text, between 0.0 and 1.0, for negative to positive sentiment.  (Strictly speaking that is only a subproblem and one of many possible formulations.)&lt;/p&gt;

&lt;p&gt;But whenever any of the terms is used, you should define or ask for definition of the exact problem.&lt;/p&gt;
', NULL, NULL, '''/a'':22C ''/em'':12C,16C,21C,47C ''/p'':40C,95C,116C ''/wiki/sentiment_analysis'':6C ''0.0'':74C ''1.0'':76C ''2015'':63C ''analysi'':11C,46C ''ask'':109C ''consid'':33C ''cover'':24C ''defin'':107C ''definit'':111C ''detect'':15C ''em'':9C,13C,18C,44C ''en.wikipedia.org'':5C ''en.wikipedia.org/wiki/sentiment_analysis'':4C ''exact'':114C ''formul'':94C ''general'':31C ''give'':68C ''href'':3C ''industri'':58C ''mani'':92C ''mine'':20C ''most'':66C ''negat'':78C ''nofollow'':8C ''one'':36C,90C ''opinion'':19C ''p'':1C,41C,96C ''popular'':52C ''posit'':80C ''possibl'':93C ''practic'':60C ''press'':55C ''problem'':28C,115C ''rel'':7C ''score'':70C ''seem'':48C ''sentiment'':10C,14C,45C,81C ''set'':26C ''speak'':83C ''strict'':82C ''subproblem'':88C ''term'':43C,102C ''text'':72C ''use'':104C ''whenev'':98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9463, 2, '2015-12-21 12:44:47.847', 1, NULL, NULL, '&lt;p&gt;Finally, I found a solution.&lt;/p&gt;

&lt;p&gt;You can find the example script below.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;#!/usr/bin/env python3
import pickle
import pandas as pd
import itertools
import numpy as np

data = pd.DataFrame(np.random.randn(10, 5), columns=(''0_n'', ''1_n'', ''0_p'', ''1_p'', ''x''))

indices = set()
groups = set()
others = set()
for c in data.columns:
    if ''_'' in c:
        (i, g) = c.split(''_'')
        c2 = pd.MultiIndex.from_tuples((i, g),)
        indices.add(int(i))
        groups.add(g)
    else:
        others.add(c)
columns = list(itertools.product(groups, indices))
columns = pd.MultiIndex.from_tuples(columns)
ret = pd.DataFrame(columns=columns)
for c in columns:
    ret[c] = data[''%d_%s'' % (int(c[1]), c[0])]
for c in others:
    ret[c] = data[''%s'' % c]
ret.rename(columns={''total'': ''total_indices''}, inplace=True)

print("Before:")
print(data)
print("")
print("After:")
print(ret)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Sorry for this...&lt;/p&gt;
', NULL, NULL, '''/code'':129C ''/p'':7C,16C,135C ''/pre'':130C ''/usr/bin/env'':19C ''0'':39C,43C,103C ''1'':41C,45C,101C ''10'':36C ''5'':37C ''c'':55C,60C,76C,91C,95C,100C,102C,105C,109C,112C ''c.split'':63C ''c2'':64C ''code'':18C ''column'':38C,77C,82C,85C,88C,89C,93C,114C ''d'':97C ''data'':33C,96C,110C,123C ''data.columns'':57C ''els'':74C ''exampl'':13C ''final'':2C ''find'':11C ''found'':4C ''g'':62C,68C,73C ''group'':50C,80C ''groups.add'':72C ''import'':21C,23C,27C,29C ''indic'':48C,81C,117C ''indices.add'':69C ''inplac'':118C ''int'':70C,99C ''itertool'':28C ''itertools.product'':79C ''list'':78C ''n'':40C,42C ''np'':32C ''np.random.randn'':35C ''numpi'':30C ''other'':52C,107C ''others.add'':75C ''p'':1C,8C,44C,46C,131C ''panda'':24C ''pd'':26C ''pd.dataframe'':34C,87C ''pd.multiindex.from'':65C,83C ''pickl'':22C ''pre'':17C ''print'':120C,122C,124C,125C,127C ''python3'':20C ''ret'':86C,94C,108C,128C ''ret.rename'':113C ''script'':14C ''set'':49C,51C,53C ''solut'':6C ''sorri'':132C ''total'':115C,116C ''true'':119C ''tupl'':66C,84C ''x'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9544, 1, '2015-12-28 13:32:36.103', -1, 49, 'How to convert a 2D matrix into a graph object in R', '&lt;pre&gt;&lt;code&gt;set.seed(42)
iris_matrix &amp;lt;- as.matrix(iris[,1:4])
image(iris_matrix)
g&amp;lt;-cor(iris_matrix)
g
library(igraph)
independent.vertex.sets(g, min=NULL, max=NULL)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Error&lt;/p&gt;

&lt;p&gt;Error in independent.vertex.sets(g, min = NULL, max = NULL) : 
       Not a graph object&lt;/p&gt;

&lt;p&gt;Trying to find independent sets but problems in converting the correlation  matrix into a graph object.&lt;/p&gt;
', 'r  graphs', NULL, '''/code'':43C ''/p'':47C,61C,79C ''/pre'':44C ''1'':24C ''2d'':5A ''4'':25C ''42'':18C ''as.matrix'':22C ''code'':16C ''convert'':3A,71C ''cor'':31C ''correl'':73C ''error'':46C,49C ''find'':65C ''g'':29C,34C,38C,52C ''graph'':9A,14B,59C,77C ''igraph'':36C ''imag'':26C ''independ'':66C ''independent.vertex.sets'':37C,51C ''iri'':19C,23C,27C,32C ''librari'':35C ''lt'':21C,30C ''matrix'':6A,20C,28C,33C,74C ''max'':41C,55C ''min'':39C,53C ''null'':40C,42C,54C,56C ''object'':10A,60C,78C ''p'':45C,48C,62C ''pre'':15C ''problem'':69C ''r'':12A,13B ''set'':67C ''set.seed'':17C ''tri'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9576, 2, '2016-01-01 09:19:31.513', 3, NULL, NULL, '&lt;p&gt;Just to add some more resources. Recently there was a paper studying the differences between several packages of neural networks and deep neural networks. &lt;/p&gt;

&lt;p&gt;Here you can find the &lt;a href="https://github.com/zer0n/deepframeworks/blob/master/README.md" rel="nofollow"&gt;information&lt;/a&gt;. It looks that &lt;a href="https://github.com/torch/torch7" rel="nofollow"&gt;Torch&lt;/a&gt; and &lt;a href="https://github.com/tensorflow/tensorflow" rel="nofollow"&gt;TensorFlow&lt;/a&gt; are the winners. &lt;/p&gt;

&lt;p&gt;Note: not all of them are in python. However, I posted it to open the discussion.&lt;/p&gt;
', NULL, NULL, '''/a'':41C,53C,63C ''/p'':26C,67C,85C ''/tensorflow/tensorflow'':59C ''/torch/torch7'':49C ''/zer0n/deepframeworks/blob/master/readme.md'':37C ''add'':4C ''deep'':23C ''differ'':15C ''discuss'':84C ''find'':31C ''github.com'':36C,48C,58C ''github.com/tensorflow/tensorflow'':57C ''github.com/torch/torch7'':47C ''github.com/zer0n/deepframeworks/blob/master/readme.md'':35C ''howev'':77C ''href'':34C,46C,56C ''inform'':40C ''look'':43C ''network'':21C,25C ''neural'':20C,24C ''nofollow'':39C,51C,61C ''note'':69C ''open'':82C ''p'':1C,27C,68C ''packag'':18C ''paper'':12C ''post'':79C ''python'':76C ''recent'':8C ''rel'':38C,50C,60C ''resourc'':7C ''sever'':17C ''studi'':13C ''tensorflow'':62C ''torch'':52C ''winner'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9469, 1, '2015-12-21 17:26:36.72', 2, 41, 'Forward propagation in complicated neural networks', '&lt;p&gt;In a typical neural network you have a certain number of neurons in each layer, all neurons have incoming connections from each neuron in the previous layer (and visa-versa). This I have no problem figuring out how to propagate.&lt;/p&gt;

&lt;p&gt;However when you have a network that doesn''t have the neurons in layers (I''m looking into the NEAT algorithm); where neurons are just connected based on genetic algorithm (random creation - then testing of fitness) there isn''t a simple way of figuring how to forward propagate, for example;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1 input neuron - connected to neurons 2 and 3
Neuron 2 is also connected to neuron 3
Neuron 3 is connected to the output.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now in this case, if I just go through numerically then everything works out just fine. However with this subtle change;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1 input neuron - connected to neurons 2 and 3
Neuron **3** is also connected to neuron **2**
Neuron **2** is connected to the output.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The going through; 1, 2, then 3 would mean neuron 2 doesn''t include the input from neuron 3. I can see that there''s a brute force way through this, but is there a better, more efficient way?&lt;/p&gt;
', 'neuralnetwork', NULL, '''/code'':129C,180C ''/p'':50C,102C,153C,222C ''/pre'':130C,181C ''1'':105C,156C,186C ''2'':111C,115C,162C,172C,174C,187C,193C ''3'':113C,121C,123C,164C,166C,189C,201C ''algorithm'':72C,81C ''also'':117C,168C ''base'':78C ''better'':218C ''brute'':209C ''case'':135C ''certain'':17C ''chang'':152C ''code'':104C,155C ''complic'':4A ''connect'':28C,77C,108C,118C,125C,159C,169C,176C ''creation'':83C ''doesn'':59C,194C ''effici'':220C ''everyth'':143C ''exampl'':101C ''figur'':45C,95C ''fine'':147C ''fit'':87C ''forc'':210C ''forward'':1A,98C ''genet'':80C ''go'':139C,184C ''howev'':52C,148C ''includ'':196C ''incom'':27C ''input'':106C,157C,198C ''isn'':89C ''layer'':23C,35C,65C ''look'':68C ''m'':67C ''mean'':191C ''neat'':71C ''network'':6A,13C,57C ''neural'':5A,12C ''neuralnetwork'':7B ''neuron'':20C,25C,31C,63C,74C,107C,110C,114C,120C,122C,158C,161C,165C,171C,173C,192C,200C ''number'':18C ''numer'':141C ''output'':128C,179C ''p'':8C,51C,131C,182C ''pre'':103C,154C ''previous'':34C ''problem'':44C ''propag'':2A,49C,99C ''random'':82C ''see'':204C ''simpl'':92C ''subtl'':151C ''test'':85C ''typic'':11C ''versa'':39C ''visa'':38C ''visa-versa'':37C ''way'':93C,211C,221C ''work'':144C ''would'':190C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9464, 2, '2015-12-21 14:36:31.693', 1, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;Can someone point me in the direction of more sentiment analysis
  oriented resources?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;There are &lt;a href="https://www.google.co.in/search?q=top%20research%20papers%20on%20sentiment%20analysis&amp;amp;oq=top%20research%20papers%20on%20sentiment%20analysis&amp;amp;aqs=chrome..69i57.12126j0j4&amp;amp;sourceid=chrome&amp;amp;es_sm=91&amp;amp;ie=UTF-8#q=sentiment%20analysis%20site%3Aarxiv.org" rel="nofollow"&gt;tonnes of exceptionally good papers&lt;/a&gt; on sentiment analysis for your reference. Especially the &lt;a href="https://www.google.co.in/search?q=top%20research%20papers%20on%20sentiment%20analysis&amp;amp;oq=top%20research%20papers%20on%20sentiment%20analysis&amp;amp;aqs=chrome..69i57.12126j0j4&amp;amp;sourceid=chrome&amp;amp;es_sm=91&amp;amp;ie=UTF-8#q=sentiment%20analysis%20site%3Aarxiv.org" rel="nofollow"&gt;ones&lt;/a&gt; on social network sites like Twitter.&lt;/p&gt;

&lt;p&gt;However, I think the book by Manning and his course are a nice start as references to NLP and opinion mining.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Can nlp, or something else, be used to derive unstated affect?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Yes, it can be. This would be my approach:&lt;/p&gt;

&lt;p&gt;(Consider I already have a hand-classified training data) Using the bag of words approach, the most used words and phrases for a mood (stressful, jovial, etc) can be captured, and the subsequent training data can be ranked accordingly as (60% stressful, 40% jovial).&lt;/p&gt;

&lt;p&gt;Additionally, you can also define thresholds for getting a single mood. Like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;if mood[score] &amp;gt; .5:
    current_mood = mood
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9465, 2, '2015-12-21 14:41:34.36', 2, NULL, NULL, '&lt;p&gt;This depends very much on your dataset: There is data, which can &lt;em&gt;not&lt;/em&gt; be separated using a linear kernel. It is impossible, and you won''t get any reasonable result. Take for example the simple XOR function. The plot of the XOR looks like this:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/YXSMs.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/YXSMs.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;No matter how hard you try, you will never be able to separate this using a linear SVM, or any linear method. &lt;/p&gt;

&lt;p&gt;Here''s some short MATLAB code which illustrates the problem:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;x = [0 0; 0 1; 1 0; 1 1];    % training points
y = [0; 1; 1; 0];            % target: XOR
linearSvm = fitcsvm(x,y);    % train with linear kernel
linearSvm.predict(x)

&amp;gt;&amp;gt; ans = 
        0
        0
        0
        0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The SVM has no chance of finding a solution. Now if we use a kernel, we create a mapping to a higher-dimensional space. The kernel trick allows us to implicitly do this mapping, by calculating a kernel function, which returns the result of a scalar product in this high-dimensional space, without needing to map the input values to this space. The RBF kernel is special, as it corresponds to an infinite-dimensional space. 
Now if we specify a RBF kernel and run the same example again, then:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;gaussSvm = fitcsvm(x,y,''KernelFunction'',''rbf'');  % RBF kernel
gaussSvm.predict(x)

&amp;gt;&amp;gt; ans = 
        0
        1
        1
        0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;the SVM easily finds the correct result. The same result (in this XOR case) is also found when using a polynomial kernel. There are many more examples, where data simply can''t be separated linearly, for example:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/viKHY.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/viKHY.png" alt="circles"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Of course, if your data is almost linearly separable, then you can (and probably will) get good results with a linear kernel. Using soft-margin SVMs, which allow for a few misclassifications, often leads to good results, as they can ignore outliers. &lt;/p&gt;

&lt;p&gt;If the linear kernel gives no result, you will have to accept that your data is not (well) linearly separable. You''ll either have to switch to a kernel, or allow for more misclassifications in the soft-margin SVM.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9466, 2, '2015-12-21 15:31:56.623', 2, NULL, NULL, '&lt;p&gt;Assuming that LDA produced a list of topics and put a score against each topic for each document, you could represent the document and it''s scores as a vector:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Document | Prime | Minister | Controversial | TopicN | ...
   1       0.041    0.042      0.041          ...
   2       0.052    0.011      0.042          ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;To get the scores for each document, you can run the document. as a bag of words, through a trained LDA model. From the gensim documentation:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;&amp;gt;&amp;gt; lda = LdaModel(corpus, num_topics=100)  # train model
&amp;gt;&amp;gt;&amp;gt; print(lda[doc_bow]) # get topic probability distribution for a document
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then, you could run the k-means on this matrix and it should group documents that are similar together. 
K-means by default is a hard clustering algorithm implying that it classifies each document into one cluster. You could use soft clustering mechanisms that will give you a probability score that a document fits within a cluster - this is called &lt;a href="https://mahout.apache.org/users/clustering/fuzzy-k-means.html" rel="nofollow"&gt;fuzzy k-means&lt;/a&gt;. 
 &lt;a href="https://gist.github.com/mblondel/1451300" rel="nofollow"&gt;https://gist.github.com/mblondel/1451300&lt;/a&gt; is a Python gist showing how you can do it with scikit learn.&lt;/p&gt;

&lt;p&gt;ps: I cant post more than 2 links&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9467, 1, '2015-12-21 16:21:25.913', 2, 33, 'R: lattice equivalent of density2d in ggplot?', '&lt;p&gt;What would be the equivalent of geom_density2d in lattice? In essence I''m trying to create this graph with lattice:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/5hoQj.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/5hoQj.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I don''t think contourplot or levelplot is what i want and when trying it, it gives me a blank plot?&lt;/p&gt;
', 'r  ggplot2', NULL, '''/5hoqj.png'':38C,45C ''/a'':51C ''/p'':32C,52C,75C ''alt'':46C ''blank'':73C ''contourplot'':58C ''creat'':27C ''density2d'':5A,18C ''descript'':49C ''enter'':47C ''equival'':3A,15C ''essenc'':22C ''geom'':17C ''ggplot'':7A ''ggplot2'':9B ''give'':70C ''graph'':29C ''href'':35C ''i.stack.imgur.com'':37C,44C ''i.stack.imgur.com/5hoqj.png'':36C,43C ''imag'':48C ''img'':41C ''lattic'':2A,20C,31C ''levelplot'':60C ''m'':24C ''nofollow'':40C ''p'':10C,33C,53C ''plot'':74C ''r'':1A,8B ''rel'':39C ''src'':42C ''think'':57C ''tri'':25C,67C ''want'':64C ''would'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9474, 2, '2015-12-21 21:07:12.467', 1, NULL, NULL, '&lt;p&gt;There is a good tutorial here:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.unal.edu.co/diracad/einternacional/Weka.pdf" rel="nofollow"&gt;http://www.unal.edu.co/diracad/einternacional/Weka.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Basically you want to use a StringToWordVector filter to convert your string into word vectors (!) and then train a Bayes classifier on that data.&lt;/p&gt;
', NULL, NULL, '''/diracad/einternacional/weka.pdf'':14C ''/diracad/einternacional/weka.pdf&lt;/a&gt;&lt;/p&gt;'':19C ''/p'':8C,45C ''basic'':21C ''bay'':40C ''classifi'':41C ''convert'':30C ''data'':44C ''filter'':28C ''good'':5C ''href'':11C ''nofollow'':16C ''p'':1C,9C,20C ''rel'':15C ''string'':32C ''stringtowordvector'':27C ''train'':38C ''tutori'':6C ''use'':25C ''vector'':35C ''want'':23C ''word'':34C ''www.unal.edu.co'':13C,18C ''www.unal.edu.co/diracad/einternacional/weka.pdf'':12C ''www.unal.edu.co/diracad/einternacional/weka.pdf&lt;/a&gt;&lt;/p&gt;'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9478, 2, '2015-12-22 01:17:41.407', 0, NULL, NULL, '&lt;p&gt;First of all, I would think that plot is useful. The classes do seen fairly separable in that space. &lt;/p&gt;

&lt;p&gt;A first pass option for visualization should be PCA, though as pointed out in other answers, it will consider only linear combinations of features.&lt;/p&gt;

&lt;p&gt;If you want to visualize the separability of the classes in the context of how well your classifier is doing, the plot you made is a good option. It shows that while there is some confusion (for green in particular), you could fairly easily group each class on that plot.&lt;/p&gt;

&lt;p&gt;If instead you want to visualize the natural separability of the classes, PCA or a low-dimensional autoencoder is the way to go. &lt;/p&gt;

&lt;p&gt;The difference is that if what you care about is the former, it''s a good thing that your representation is optimized to separate the classes. If you care about the latter, the unsupervised nature of autoencoders is more appropriate.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9468, 2, '2015-12-21 17:13:49.767', 1, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;Please let me know if you have used any good tutorials or libraries to
  help build something similar.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I would highly recommend the online book of Michael Nielsen: &lt;a href="http://neuralnetworksanddeeplearning.com/" rel="nofollow"&gt;Neural Networks and Deep Learning&lt;/a&gt;. Coming to the libraries suggestion, I found &lt;a href="http://keras.io/" rel="nofollow"&gt;Keras&lt;/a&gt; extremely useful and easy when I was learning neural networks. However, Lasagna is also a nice choice.&lt;/p&gt;

&lt;p&gt;You can learn about more libraries from &lt;a href="http://datascience.stackexchange.com/q/694/11097"&gt;this question&lt;/a&gt;.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;What is the error I am to calculate when building an algorithm using
  training data?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The most commonly used error function is the squared error function, which is also used in the BackPropogation algorithm, which is used for optimizing the error function.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;How many layers do I need and what does each layer signify?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Layers add sophistication, and help in finding subtle patterns in the data. However, you have to make sure that the network don''t &lt;a href="https://en.wikipedia.org/wiki/Overfitting" rel="nofollow"&gt;overfit&lt;/a&gt;. So, as long as the number of layers don''t overfit the model, you are good to go. For ensuring that, you need to know what is &lt;a href="https://en.wikipedia.org/wiki/Cross-validation_(statistics)" rel="nofollow"&gt;cross-validation&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9511, 2, '2015-12-25 08:59:31.813', 2, NULL, NULL, '&lt;p&gt;In &lt;a href="http://goo.gl/JSaqb4" rel="nofollow"&gt;Sepp Hochreiter''s original paper on the LSTM &lt;/a&gt;  where he introduces the algorithm and method to the scientific community, he explains that the long term memory refers to the learned weights and the short term memory refers to the gated cell state values that change with each step through time &lt;em&gt;t&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;edit:&lt;/strong&gt; quote from paper &lt;em&gt;"Recurrent networks can in principle use their feedback connections to store representations of recent input events in the form of activations ("short-term memory", as opposed to "long-term memory embodied by slowly changing weights)&lt;/em&gt;"&lt;/p&gt;
', NULL, NULL, '''/a'':18C ''/em'':63C,110C ''/jsaqb4'':7C ''/p'':64C,111C ''/strong'':68C ''activ'':93C ''algorithm'':23C ''cell'':51C ''chang'':55C,108C ''communiti'':29C ''connect'':81C ''edit'':67C ''em'':61C,72C ''embodi'':105C ''event'':88C ''explain'':31C ''feedback'':80C ''form'':91C ''gate'':50C ''goo.gl'':6C ''goo.gl/jsaqb4'':5C ''hochreit'':11C ''href'':4C ''input'':87C ''introduc'':21C ''learn'':40C ''long'':34C,102C ''long-term'':101C ''lstm'':17C ''memori'':36C,46C,97C,104C ''method'':25C ''network'':74C ''nofollow'':9C ''oppos'':99C ''origin'':13C ''p'':1C,65C ''paper'':14C,71C ''principl'':77C ''quot'':69C ''recent'':86C ''recurr'':73C ''refer'':37C,47C ''rel'':8C ''represent'':84C ''scientif'':28C ''sepp'':10C ''short'':44C,95C ''short-term'':94C ''slowli'':107C ''state'':52C ''step'':58C ''store'':83C ''strong'':66C ''term'':35C,45C,96C,103C ''time'':60C ''use'':78C ''valu'':53C ''weight'':41C,109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9471, 1, '2015-12-21 19:40:44.957', 1, 30, 'Asking a specific question from a general one', '&lt;h1&gt;Setting&lt;/h1&gt;

&lt;p&gt;I have data about a board game which has been played multiple times with different players per game (though players reoccur in all kinds of different constellations). During the game one can score points in one of seven categories. The general question is: What is the best strategy? On what category should players concentrate?&lt;/p&gt;

&lt;h1&gt;Details about the data and performed transformations&lt;/h1&gt;

&lt;p&gt;The original data has the variables:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;game - the number of the game&lt;/li&gt;
&lt;li&gt;name - the name of the player&lt;/li&gt;
&lt;li&gt;military points&lt;/li&gt;
&lt;li&gt;money points&lt;/li&gt;
&lt;li&gt;world wonder points&lt;/li&gt;
&lt;li&gt;... points&lt;/li&gt;
&lt;li&gt;sum of points&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;which I transferred into&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;game&lt;/li&gt;
&lt;li&gt;name&lt;/li&gt;
&lt;li&gt;sum of points (a little repetitive, since its the same for a game and name)&lt;/li&gt;
&lt;li&gt;place in game (a little repetitive, since its the same for a game and name)&lt;/li&gt;
&lt;li&gt;type of points (one of the seven categories)&lt;/li&gt;
&lt;li&gt;number of points&lt;/li&gt;
&lt;li&gt;percentage this type of points contributed to the total amount of points of this player for this game&lt;/li&gt;
&lt;/ul&gt;

&lt;h1&gt;My Question&lt;/h1&gt;

&lt;p&gt;Asking the right question is been said to be the most important thing in Data Science and in most situations I managed to find a good concrete (programmable) question, but this one seems tough to me.&lt;/p&gt;

&lt;p&gt;So: &lt;strong&gt;How do I ask a specific question to answer the general question?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Of course I have already asked/answered simple questions regarding the winning players of each game. For example: "What is the type of points that contributes most to the total points for winning players (whose success proved them right)?"&lt;/p&gt;

&lt;p&gt;Now I''d like to make analyses over the entire field of players.&lt;/p&gt;

&lt;p&gt;An even more advanced question would consider that player reoccur across games.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;em&gt;I have no idea what tag to use here.&lt;/em&gt;&lt;/p&gt;
', 'simulation', NULL, '''/em'':348C ''/h1'':12C,77C,211C ''/li'':94C,102C,106C,110C,115C,118C,123C,134C,137C,154C,171C,181C,186C,206C ''/p'':68C,85C,130C,249C,266C,306C,321C,335C,349C ''/strong'':265C ''/ul'':124C,207C ''across'':333C ''advanc'':326C ''alreadi'':272C ''amount'':197C ''analys'':314C ''answer'':261C ''ask'':1A,213C,256C ''asked/answered'':273C ''best'':60C ''board'':19C ''categori'':52C,64C,180C ''concentr'':67C ''concret'':239C ''consid'':329C ''constel'':40C ''contribut'':193C,292C ''cours'':269C ''d'':310C ''data'':16C,73C,81C,227C ''detail'':70C ''differ'':28C,39C ''em'':338C ''entir'':317C ''even'':324C ''exampl'':284C ''field'':318C ''find'':236C ''game'':20C,31C,43C,88C,93C,133C,151C,158C,168C,205C,282C,334C ''general'':7A,54C,263C ''good'':238C ''h1'':10C,69C,208C ''hr'':336C ''idea'':342C ''import'':224C ''kind'':37C ''li'':87C,95C,103C,107C,111C,116C,119C,132C,135C,138C,155C,172C,182C,187C ''like'':311C ''littl'':143C,160C ''make'':313C ''manag'':234C ''militari'':104C ''money'':108C ''multipl'':25C ''name'':96C,98C,136C,153C,170C ''number'':90C,183C ''one'':8A,44C,49C,176C,244C ''origin'':80C ''p'':13C,78C,125C,212C,250C,267C,307C,322C,337C ''per'':30C ''percentag'':188C ''perform'':75C ''place'':156C ''play'':24C ''player'':29C,33C,66C,101C,202C,279C,300C,320C,331C ''point'':47C,105C,109C,114C,117C,122C,141C,175C,185C,192C,199C,290C,297C ''programm'':240C ''prove'':303C ''question'':4A,55C,210C,216C,241C,259C,264C,275C,327C ''regard'':276C ''reoccur'':34C,332C ''repetit'':144C,161C ''right'':215C,305C ''said'':219C ''scienc'':228C ''score'':46C ''seem'':245C ''set'':11C ''seven'':51C,179C ''simpl'':274C ''simul'':9B ''sinc'':145C,162C ''situat'':232C ''specif'':3A,258C ''strategi'':61C ''strong'':252C ''success'':302C ''sum'':120C,139C ''tag'':344C ''thing'':225C ''though'':32C ''time'':26C ''total'':196C,296C ''tough'':246C ''transfer'':128C ''transform'':76C ''type'':173C,190C,288C ''ul'':86C,131C ''use'':346C ''variabl'':84C ''whose'':301C ''win'':278C,299C ''wonder'':113C ''world'':112C ''would'':328C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9472, 1, '2015-12-21 19:53:49.627', 1, 30, 'Which text clustering algorithms should I use to group free text fields in Python?', '&lt;p&gt;I have a data set in which users have input free text (think "APPLE", "Apple", "apple", "appl") and I''m trying to group the input by response type (e.g. organize all of the preceding examples by "Apple"). What''s the best way to do this in Python? &lt;/p&gt;
', 'machine-learning  python  nlp', NULL, '''/p'':68C ''algorithm'':4A ''appl'':34C,35C,36C,37C,57C ''best'':61C ''cluster'':3A ''data'':24C ''e.g'':49C ''exampl'':55C ''field'':12A ''free'':10A,31C ''group'':9A,43C ''input'':30C,45C ''learn'':17B ''m'':40C ''machin'':16B ''machine-learn'':15B ''nlp'':19B ''organ'':50C ''p'':20C ''preced'':54C ''python'':14A,18B,67C ''respons'':47C ''set'':25C ''text'':2A,11A,32C ''think'':33C ''tri'':41C ''type'':48C ''use'':7A ''user'':28C ''way'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9473, 1, '2015-12-21 20:14:42.187', 2, 46, 'What are the advantages/disadvantages of neural net packages in R?', '&lt;p&gt;I''ve seen that there are four neural net packages in R:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;neural&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;neuralnet&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;nnet&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;RSNNS&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;H2O&lt;/code&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;What are the advantages/disadvantages of those compared with each other?&lt;/p&gt;

&lt;p&gt;As I found out &lt;code&gt;NeuralNetTools&lt;/code&gt; only provides additional tools.&lt;/p&gt;
', 'r  neuralnetwork', NULL, '''/code'':31C,36C,41C,46C,51C,73C ''/li'':32C,37C,42C,47C,52C ''/p'':26C,65C,78C ''/ul'':53C ''addit'':76C ''advantages/disadvantages'':4A,58C ''code'':29C,34C,39C,44C,49C,71C ''compar'':61C ''found'':69C ''four'':20C ''h2o'':50C ''li'':28C,33C,38C,43C,48C ''net'':7A,22C ''neural'':6A,21C,30C ''neuralnet'':35C ''neuralnettool'':72C ''neuralnetwork'':12B ''nnet'':40C ''p'':13C,54C,66C ''packag'':8A,23C ''provid'':75C ''r'':10A,11B,25C ''rsnns'':45C ''seen'':16C ''tool'':77C ''ul'':27C ''ve'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9475, 2, '2015-12-21 21:42:06.333', 2, NULL, NULL, '&lt;p&gt;I''m somewhat new to the topic as well, but I think what you are looking for is an autoencoder.  I have only used it in the h2o deeplearning package, but it seems to work well.&lt;/p&gt;

&lt;p&gt;The idea behind an auto-encoder is to begin with your inputs, encode down to less nodes (two is nice for visual representation), then decode back to your original outputs (ass accurately as possible).&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/Zwsmz.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Zwsmz.png" alt="Autoencoder"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If the auto-encoder can decode back to an accurate representation of your inputs, then there was sufficient important information withheld in those two nodes.  You can then plot the features that those two nodes held in a two-dimensional plot that would be analogous to a bounded PCA plot.&lt;/p&gt;

&lt;p&gt;Also - the auto-encoder is not constrained to linearity like the PCA is.  Below is a quick snippet of some code, hope it helps.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(h2o)
localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE, min_mem_size = "3g", max_mem_size = "4g", nthreads = -1)
dat_h2o &amp;lt;- as.h2o(train.df)

unsupervised &amp;lt;- 
  h2o.deeplearning(x = 24:356,  # column numbers to use
                   training_frame = dat_h2o, # data in H2O format
                   autoencoder = TRUE, ## unsupervised autoencoding
                   activation = "Tanh", # or ''Tanh'' ''Rectifier'' ''WithDropout'' node activation function, Tanh seems to work best for autoencoding
                   hidden = c(5,2,5), # three layers of nodes, with 5/2/5 nodes, respectively
                   epochs = 1) # max. no. of epochs 

## layer 1 corresponds to hidden[1], so it will reduce to 5 variables
## below is roughly similar to using predict(pca.object, newdata)
training_data &amp;lt;- h2o.deepfeatures(unsupervised, dat_h2o, layer = 1)
testing_data &amp;lt;- h2o.deepfeatures(unsupervised, test_h2o, layer = 1)

## explore the second layer with 2 nodes, similar to exploring PC1 vs PC2
train_supervised_features2 = h2o.deepfeatures(unsupervised, dat_h2o, layer=2)

plotdata2 = as.data.frame(train_supervised_features2)
plotdata2$label = as.character(as.vector(dat_h2o[,364]))

## L2 corresponds to layer 2, so use L2
qplot(DF.L2.C1, DF.L2.C2, data = plotdata2, color = label, main = ''Neural network: 5-2-5'')
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9477, 2, '2015-12-21 23:06:24.26', 1, NULL, NULL, '&lt;p&gt;Use a custom panel function in which the density is estimated (e.g. using &lt;code&gt;MASS::kde2d()&lt;/code&gt;):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library("lattice")
library("MASS")

set.seed(1);
dat &amp;lt;- data.frame(x=rnorm(100), y=rt(100, 5))

xyplot(y~x, data=dat,
       panel=function(x, y, ...) {
         dens &amp;lt;- kde2d(x=dat$x, y=dat$y, n=50)
         tmp &amp;lt;- data.frame(x=dens$x,
                           y=rep(dens$y, each=length(dens$x)),
                           z=as.vector(dens$z))
         panel.levelplot(tmp$x, tmp$y, tmp$z, contour=TRUE,
                         subscripts=1:nrow(tmp), region=FALSE, ...)
         panel.xyplot(x, y, ...)
       })
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/d2rSi.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/d2rSi.png" alt="lattice plot"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':112C ''/code'':18C,94C ''/d2rsi.png'':101C,108C ''/p'':19C,113C ''/pre'':95C ''1'':27C,86C ''100'':33C,36C ''5'':37C ''50'':57C ''alt'':109C ''as.vector'':73C ''code'':15C,21C ''contour'':83C ''custom'':4C ''dat'':28C,42C,51C,54C ''data'':41C ''data.frame'':30C,60C ''den'':47C,62C,66C,70C,74C ''densiti'':10C ''e.g'':13C ''estim'':12C ''fals'':90C ''function'':6C,44C ''href'':98C ''i.stack.imgur.com'':100C,107C ''i.stack.imgur.com/d2rsi.png'':99C,106C ''img'':104C ''kde2d'':17C,49C ''lattic'':23C,110C ''length'':69C ''librari'':22C,24C ''lt'':29C,48C,59C ''mass'':16C,25C ''n'':56C ''nofollow'':103C ''nrow'':87C ''p'':1C,96C ''panel'':5C,43C ''panel.levelplot'':76C ''panel.xyplot'':91C ''plot'':111C ''pre'':20C ''region'':89C ''rel'':102C ''rep'':65C ''rnorm'':32C ''rt'':35C ''set.seed'':26C ''src'':105C ''subscript'':85C ''tmp'':58C,77C,79C,81C,88C ''true'':84C ''use'':2C,14C ''x'':31C,45C,50C,52C,61C,63C,71C,78C,92C ''xyplot'':38C ''y'':34C,39C,46C,53C,55C,64C,67C,80C,93C ''z'':72C,75C,82C ''~x'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9479, 2, '2015-12-22 01:42:09.48', 1, NULL, NULL, '&lt;p&gt;I think the key is that most &lt;a href="http://karpathy.github.io/2015/05/21/rnn-effectiveness/" rel="nofollow"&gt;Recurrent Neural Networks&lt;/a&gt; problems are formulated in terms of either a regression (with low values indicating negative sentiment, and high values positive) or a binary classification (is this text positive?).&lt;/p&gt;

&lt;p&gt;What you seem to be interested in is a much more nuanced definition of sentiment. This doesn''t present any inherent problem, as the same algorithms might well work to predict more complex sentiments. The issue is simply labeled data. Because this kind of classification is difficult even for humans, it isn''t easy to reliably gather data on, say, how stressed a writer is.&lt;/p&gt;

&lt;p&gt;However if you''re interested in assembling a dataset of that nature, you''d be able to apply the same methods (&lt;a href="http://karpathy.github.io/2015/05/21/rnn-effectiveness/" rel="nofollow"&gt;Recurrent Neural Networks&lt;/a&gt; are a popular option) to do the classification. Many researchers in the field use &lt;a href="https://www.mturk.com/mturk/welcome" rel="nofollow"&gt;Amazon Mechanical Turk&lt;/a&gt; or something similar to gather labeled data at a reasonable cost.&lt;/p&gt;
', NULL, NULL, '''/2015/05/21/rnn-effectiveness/'':13C,141C ''/a'':19C,147C,172C ''/mturk/welcome'':166C ''/p'':47C,114C,184C ''abl'':131C ''algorithm'':74C ''amazon'':169C ''appli'':133C ''assembl'':122C ''binari'':41C ''classif'':42C,93C,155C ''complex'':81C ''cost'':183C ''d'':129C ''data'':88C,106C,179C ''dataset'':124C ''definit'':61C ''difficult'':95C ''doesn'':65C ''easi'':102C ''either'':26C ''even'':96C ''field'':160C ''formul'':22C ''gather'':105C,177C ''high'':36C ''howev'':116C ''href'':10C,138C,163C ''human'':98C ''indic'':32C ''inher'':69C ''interest'':54C,120C ''isn'':100C ''issu'':84C ''karpathy.github.io'':12C,140C ''karpathy.github.io/2015/05/21/rnn-effectiveness/'':11C,139C ''key'':5C ''kind'':91C ''label'':87C,178C ''low'':30C ''mani'':156C ''mechan'':170C ''method'':136C ''might'':75C ''much'':58C ''natur'':127C ''negat'':33C ''network'':18C,146C ''neural'':17C,145C ''nofollow'':15C,143C,168C ''nuanc'':60C ''option'':151C ''p'':1C,48C,115C ''popular'':150C ''posit'':38C,46C ''predict'':79C ''present'':67C ''problem'':20C,70C ''re'':119C ''reason'':182C ''recurr'':16C,144C ''regress'':28C ''rel'':14C,142C,167C ''reliabl'':104C ''research'':157C ''say'':108C ''seem'':51C ''sentiment'':34C,63C,82C ''similar'':175C ''simpli'':86C ''someth'':174C ''stress'':110C ''term'':24C ''text'':45C ''think'':3C ''turk'':171C ''use'':161C ''valu'':31C,37C ''well'':76C ''work'':77C ''writer'':112C ''www.mturk.com'':165C ''www.mturk.com/mturk/welcome'':164C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9480, 1, '2015-12-22 04:11:21.253', 0, 21, 'Given the files and their parsed content how can I reverse engineer the file format?', '&lt;p&gt;I have access to a few thousand files binary files and I don''t have the program that generated them. The files have been parsed and I also have access to the database where the parsed fields are stored. What techniques can be used to figure out the format of the files, given this information?&lt;/p&gt;
', 'data-formats', NULL, '''/p'':75C ''access'':22C,49C ''also'':47C ''binari'':28C ''content'':7A ''data'':17B ''data-format'':16B ''databas'':52C ''engin'':12A ''field'':56C ''figur'':65C ''file'':3A,14A,27C,29C,41C,71C ''format'':15A,18B,68C ''generat'':38C ''given'':1A,72C ''inform'':74C ''p'':19C ''pars'':6A,44C,55C ''program'':36C ''revers'':11A ''store'':58C ''techniqu'':60C ''thousand'':26C ''use'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9481, 1, '2015-12-22 09:36:33.997', 3, 187, 'What kind of statistical analyses can I do with my data?', '&lt;p&gt;I''m trying to analyze &lt;em&gt;human intentions in clicking google ad word keywords&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;In &lt;a href="https://code.google.com/p/open-advertising-dataset/downloads/list" rel="nofollow"&gt;this dataset&lt;/a&gt; I have the usual adword details, for example &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;CTR = Clicks / Impressions
CPC = Cost / Clicks
CPA = Cost / Converted Clicks
ROI = Total Conversion Value / Cost
CVR = Converted Clicks / Clicks
Weighted Avg Pos = WAP / Impressions
and Ad position placement.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Basically, I can imagine a relationship between Keywords and CTR. I can do some psychological analysis, like opinion mining and emotion detection, like a vice. Still, I need to show different types of analysis. &lt;/p&gt;

&lt;p&gt;So what kind of advanced statistical analysis methods can be applied to this data set? My data looks like this:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Keyword |Average Position|    Average CPC|    Clicks| CTR|    Cost|   Impressions&lt;/p&gt;
  
  &lt;p&gt;android app developers    ,1  ,0.5,   21.79,  8.7%,   10.99,  250  &lt;/p&gt;
&lt;/blockquote&gt;
', 'data-mining  statistics  csv', NULL, '''/a'':45C ''/blockquote'':168C ''/code'':85C ''/em'':32C ''/p'':33C,54C,122C,144C,156C,167C ''/p/open-advertising-dataset/downloads/list'':40C ''/pre'':86C ''0.5'':162C ''1'':161C ''10.99'':165C ''21.79'':163C ''250'':166C ''8.7'':164C ''ad'':29C,82C ''advanc'':128C ''adword'':50C ''analys'':5A ''analysi'':103C,121C,130C ''analyz'':22C ''android'':158C ''app'':159C ''appli'':134C ''averag'':148C,150C ''avg'':77C ''basic'':88C ''blockquot'':145C ''click'':27C,58C,62C,66C,74C,75C,152C ''code'':56C ''code.google.com'':39C ''code.google.com/p/open-advertising-dataset/downloads/list'':38C ''convers'':69C ''convert'':65C,73C ''cost'':61C,64C,71C,154C ''cpa'':63C ''cpc'':60C,151C ''csv'':16B ''ctr'':57C,97C,153C ''cvr'':72C ''data'':11A,13B,137C,140C ''data-min'':12B ''dataset'':44C ''detail'':51C ''detect'':109C ''develop'':160C ''differ'':118C ''em'':23C ''emot'':108C ''exampl'':53C ''googl'':28C ''href'':37C ''human'':24C ''imagin'':91C ''impress'':59C,80C,155C ''intent'':25C ''keyword'':31C,95C,147C ''kind'':2A,126C ''like'':104C,110C,142C ''look'':141C ''m'':19C ''method'':131C ''mine'':14B,106C ''need'':115C ''nofollow'':42C ''opinion'':105C ''p'':17C,34C,87C,123C,146C,157C ''placement'':84C ''pos'':78C ''posit'':83C,149C ''pre'':55C ''psycholog'':102C ''rel'':41C ''relationship'':93C ''roi'':67C ''set'':138C ''show'':117C ''statist'':4A,15B,129C ''still'':113C ''total'':68C ''tri'':20C ''type'':119C ''usual'':49C ''valu'':70C ''vice'':112C ''wap'':79C ''weight'':76C ''word'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9532, 1, '2015-12-27 02:19:02.473', 0, 10, 'spikes in the cost vs iteration graph using ReLU activation function', '&lt;p&gt;The cost vs iteration graph while using the ReLU activation function has number of spikes &lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/Zz7U1.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Zz7U1.png" alt="ReLU activation function"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The accuracy on MNIST dataset (both train and test) is around 95%, also on using sigmoid as an activation function I get a smooth downward sloping curve so I think the implementation is correct. Are these spikes expected for ReLU? How would you explain this property?&lt;/p&gt;
', 'neuralnetwork', NULL, '''/a'':47C ''/p'':29C,48C,96C ''/zz7u1.png'':35C,42C ''95'':61C ''accuraci'':51C ''activ'':10A,23C,45C,68C ''also'':62C ''alt'':43C ''around'':60C ''correct'':83C ''cost'':4A,15C ''curv'':76C ''dataset'':54C ''downward'':74C ''expect'':87C ''explain'':93C ''function'':11A,24C,46C,69C ''get'':71C ''graph'':7A,18C ''href'':32C ''i.stack.imgur.com'':34C,41C ''i.stack.imgur.com/zz7u1.png'':33C,40C ''img'':38C ''implement'':81C ''iter'':6A,17C ''mnist'':53C ''neuralnetwork'':12B ''nofollow'':37C ''number'':26C ''p'':13C,30C,49C ''properti'':95C ''rel'':36C ''relu'':9A,22C,44C,89C ''sigmoid'':65C ''slope'':75C ''smooth'':73C ''spike'':1A,28C,86C ''src'':39C ''test'':58C ''think'':79C ''train'':56C ''use'':8A,20C,64C ''vs'':5A,16C ''would'':91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9482, 1, '2015-12-22 10:24:35.447', 1, 20, 'Recognition of digit of size other than those in the training set using DNNs', '&lt;p&gt;[cross-posted from cross-validated]&lt;/p&gt;

&lt;p&gt;I have a DNN trained on MNIST data that recognizes images from the test set with high precision. 
&lt;a href="http://i.stack.imgur.com/vY8dI.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/vY8dI.jpg" alt="enter image description here"&gt;&lt;/a&gt;    &lt;/p&gt;

&lt;p&gt;Each digit is centered and all of them are roughly of the equal size. Will it also recognize image of the same, but transformed (smaller) digit?
&lt;a href="http://i.stack.imgur.com/Vu46T.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Vu46T.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;How about the same digit in a different location?
&lt;a href="http://i.stack.imgur.com/5W1WE.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/5W1WE.jpg" alt="enter image description here"&gt;&lt;/a&gt;
And multiple images? 
&lt;a href="http://i.stack.imgur.com/xYdi6.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/xYdi6.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The size of the input space is the same (e.g. 28x28 pixels).&lt;/p&gt;

&lt;p&gt;I think, in general, the answer is no. The input in the neural network is intensity of every pixel, so during training, as the hidden layers learn the features, the weights between the input layer and the first hidden layer are updated based on the value of the particular pixel (think backprop equation), so, for example, the features of the digit ''4'' will depend on the intensity of the pixels are are most active when this observation is given to the DNN. &lt;/p&gt;

&lt;p&gt;Hence, in case of image 3, the activated pixels are very far from those trained with MNIST and they will active completely different neurons, that will combine into unrelated features (if anything sensible at all) and badly misclassify. Something similar should happen in case of image 3. In other words, during testing the features that are associated with this specific digit (''4'') will stay quite inactive. If the observation is reasonably close to the pixels of observations in the dataset (image 2), then it can accidentally trigger features from other digits and misclassify too.     &lt;/p&gt;

&lt;p&gt;A possible solution, I guess, would be some CNN, but I''m not sure how it will know what to transform (and to what size) and when. Any suggestions? &lt;/p&gt;
', 'neuralnetwork  deep-learning  image-classification', NULL, '''/5w1we.jpg'':129C,136C ''/a'':67C,113C,142C,163C ''/p'':30C,68C,114C,164C,178C,262C,358C,389C ''/vu46t.jpg'':100C,107C ''/vy8di.jpg'':54C,61C ''/xydi6.jpg'':150C,157C ''2'':345C ''28x28'':176C ''3'':269C,310C ''4'':241C,325C ''accident'':349C ''activ'':253C,271C,284C ''also'':86C ''alt'':62C,108C,137C,158C ''answer'':185C ''anyth'':295C ''associ'':320C ''backprop'':231C ''bad'':300C ''base'':222C ''case'':266C,307C ''center'':73C ''classif'':21B ''close'':335C ''cnn'':368C ''combin'':290C ''complet'':285C ''cross'':24C,28C ''cross-post'':23C ''cross-valid'':27C ''data'':39C ''dataset'':343C ''deep'':17B ''deep-learn'':16B ''depend'':243C ''descript'':65C,111C,140C,161C ''differ'':123C,286C ''digit'':3A,71C,95C,120C,240C,324C,354C ''dnn'':35C,261C ''dnns'':14A ''e.g'':175C ''enter'':63C,109C,138C,159C ''equal'':82C ''equat'':232C ''everi'':197C ''exampl'':235C ''far'':275C ''featur'':208C,237C,293C,317C,351C ''first'':217C ''general'':183C ''given'':258C ''guess'':364C ''happen'':305C ''henc'':264C ''hidden'':204C,218C ''high'':48C ''href'':51C,97C,126C,147C ''i.stack.imgur.com'':53C,60C,99C,106C,128C,135C,149C,156C ''i.stack.imgur.com/5w1we.jpg'':127C,134C ''i.stack.imgur.com/vu46t.jpg'':98C,105C ''i.stack.imgur.com/vy8di.jpg'':52C,59C ''i.stack.imgur.com/xydi6.jpg'':148C,155C ''imag'':20B,42C,64C,88C,110C,139C,145C,160C,268C,309C,344C ''image-classif'':19B ''img'':57C,103C,132C,153C ''inact'':329C ''input'':170C,189C,213C ''intens'':195C,246C ''know'':377C ''layer'':205C,214C,219C ''learn'':18B,206C ''locat'':124C ''m'':371C ''misclassifi'':301C,356C ''mnist'':38C,280C ''multipl'':144C ''network'':193C ''neural'':192C ''neuralnetwork'':15B ''neuron'':287C ''nofollow'':56C,102C,131C,152C ''observ'':256C,332C,340C ''p'':22C,31C,69C,115C,165C,179C,263C,359C ''particular'':228C ''pixel'':177C,198C,229C,249C,272C,338C ''possibl'':361C ''post'':25C ''precis'':49C ''quit'':328C ''reason'':334C ''recogn'':41C,87C ''recognit'':1A ''rel'':55C,101C,130C,151C ''rough'':79C ''sensibl'':296C ''set'':12A,46C ''similar'':303C ''size'':5A,83C,167C,384C ''smaller'':94C ''solut'':362C ''someth'':302C ''space'':171C ''specif'':323C ''src'':58C,104C,133C,154C ''stay'':327C ''suggest'':388C ''sure'':373C ''test'':45C,315C ''think'':181C,230C ''train'':11A,36C,201C,278C ''transform'':93C,380C ''trigger'':350C ''unrel'':292C ''updat'':221C ''use'':13A ''valid'':29C ''valu'':225C ''weight'':210C ''word'':313C ''would'':365C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9483, 1, '2015-12-22 11:34:37.877', 3, 291, 'XGBoost Linear Regression output incorrect', '&lt;p&gt;I am a newbie to XGBoost so pardon my ignorance. Here is the python code : &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas as pd
import xgboost as xgb

df = pd.DataFrame({''x'':[1,2,3], ''y'':[10,20,30]})
X_train = df.drop(''y'',axis=1)
Y_train = df[''y'']
T_train_xgb = xgb.DMatrix(X_train, Y_train)

params = {"objective": "reg:linear"}
gbm = xgb.train(dtrain=T_train_xgb,params=params)
Y_pred = gbm.predict(xgb.DMatrix(pd.DataFrame({''x'':[4,5]})))
print Y_pred
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Output is :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[ 24.126194  24.126194]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As you can see the input data is simply a straight line. So the output I expect is &lt;code&gt;[40,50]&lt;/code&gt;.  What am I doing wrong here?&lt;/p&gt;
', 'python  linear-regression  xgboost', NULL, '''/code'':89C,99C,123C ''/p'':27C,94C,130C ''/pre'':90C,100C ''1'':41C,53C ''10'':45C ''2'':42C ''20'':46C ''24.126194'':97C,98C ''3'':43C ''30'':47C ''4'':84C ''40'':121C ''5'':85C ''50'':122C ''axi'':52C ''code'':26C,29C,96C,120C ''data'':108C ''df'':38C,56C ''df.drop'':50C ''dtrain'':72C ''expect'':118C ''gbm'':70C ''gbm.predict'':80C ''ignor'':21C ''import'':30C,34C ''incorrect'':5A ''input'':107C ''line'':113C ''linear'':2A,8B,69C ''linear-regress'':7B ''newbi'':15C ''object'':67C ''output'':4A,92C,116C ''p'':11C,91C,101C ''panda'':31C ''param'':66C,76C,77C ''pardon'':19C ''pd'':33C ''pd.dataframe'':39C,82C ''pre'':28C,95C ''pred'':79C,88C ''print'':86C ''python'':6B,25C ''reg'':68C ''regress'':3A,9B ''see'':105C ''simpli'':110C ''straight'':112C ''train'':49C,55C,59C,63C,65C,74C ''wrong'':128C ''x'':40C,48C,62C,83C ''xgb'':37C,60C,75C ''xgb.dmatrix'':61C,81C ''xgb.train'':71C ''xgboost'':1A,10B,17C,35C ''y'':44C,51C,54C,57C,64C,78C,87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9484, 1, '2015-12-22 12:56:20.123', 1, 46, 'Neural network with multiple layer: learning function', '&lt;p&gt;Here is my code to implement the learning of my neural network using the backpropagation learning. The algorithms is stable but I don''t learn correctly the output. Do you see anything wrong in my learning process?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;//### Parameter ###
#define Nb_entry 2
#define coeff_app 0.01 
#define par_momentum 0.8 
#define par_nb_test 0.2
#define para_tolerence 0.05
#define para_stop_learning 1000
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here are my learning functions&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;void fonction_neuron(double * input, Neuron * neuron_info)
{
    int i=0; // loop variable
    double net=0;

    // Computation of the net value type: net = w[0]*bias + sum(w[i]*imp[i])
    net=(*neuron_info).weight[0]; //bias = 1
    for(i=1;i&amp;lt;(*neuron_info).nb_input;i++) net+=(*neuron_info).weight[i]*input[i];

    (*neuron_info).output=net;
    //print_stat_neuron(neuron_info);
}

void fonction_network(Neuron N_network[][10], int nb_layer, int *nb_neuron_per_layer,double *input)
{
    int i,j; //loop variable
    double previous_layer_output[10]={0};

    //Propagation of the signal into the neural network
    for(i=0;i&amp;lt;nb_layer;i++)
    {
        if(i!=0) for(j=0;j&amp;lt;nb_neuron_per_layer[i-1];j++) 
        {
            previous_layer_output[j]=N_network[i-1][j].output;//save previous layer output
            //printf("previous_layer_output[%d]=%f\n",j,previous_layer_output[j]); 
        } 

        for(j=0;j&amp;lt;nb_neuron_per_layer[i];j++)
        {
            //printf(" i=%d j=%d \n", i,j);
            N_network[i][j].old_output=N_network[i][j].output; //save previous value
            if(i==0)fonction_neuron(input, &amp;amp;(N_network[i][j])); //first layer               
            else fonction_neuron(previous_layer_output, &amp;amp;(N_network[i][j])); //other layer using the previous layer output
        }
    }
}
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9533, 2, '2015-12-27 10:16:56.583', 5, NULL, NULL, '&lt;p&gt;&lt;strong&gt;First problem:&lt;/strong&gt; Minimizing $\|w\|$ or $\|w\|^2$:&lt;/p&gt;

&lt;p&gt;It is correct that one wants to maximize the margin. This is actually done by maximizing $\frac{2}{\|w\|}$. This would be the "correct" way of doing it, but it is rather inconvenient. Let''s first drop the $2$, as it is just a constant. Now if $\frac{1}{\|w\|}$ is maximal, $\|w\|$ will have to be as small as possible. We can thus find the identical solution by &lt;em&gt;minimizing&lt;/em&gt; $\|w\|$. &lt;/p&gt;

&lt;p&gt;$\|w\|$ can be calculated by $\sqrt{w^T w}$. As the square root is a monotonic function, any point $x$ which maximizes $\sqrt{f(x)}$ will also maximize $f(x)$. To find this point $x$ we thus don''t have to calculate the square root and can minimize $w^T w = \|w\|^2$.&lt;/p&gt;

&lt;p&gt;Finally, as we often have to calculate derivatives, we multiply the whole expression by a factor $\frac{1}{2}$. This is done very often, because if we derive $\frac{d}{dx} x^2 = 2 x$ and thus $\frac{d}{dx} \frac{1}{2} x^2 = x$. 
This is how we end up with the problem: minimize $\frac{1}{2} \|w\|^2$.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;tl;dr&lt;/em&gt;: yes, minimizing $\|w\|$ instead of $\frac{1}{2} \|w\|^2$ would work.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Second problem:&lt;/strong&gt; $\geq 0$ or $\geq 1$:&lt;/p&gt;

&lt;p&gt;As already stated in the question, $y_i \left( \langle w,x_i \rangle + b \right) \geq 0$ means that the point has to be on the correct side of the hyperplane. However this isn''t enough: we want the point to be at least as far away as the margin (then the point is a support vector), or even further away.&lt;/p&gt;

&lt;p&gt;Remember the definition of the hyperplane,&lt;/p&gt;

&lt;p&gt;$\mathcal{H} = \{ x \mid \langle w,x \rangle + b = 0\}$.&lt;/p&gt;

&lt;p&gt;This description however is not unique: if we scale $w$ and $b$ by a constant $c$, then we get an equivalent description of this hyperplane. To make sure our optimization algorithm doesn''t just scale $w$ and $b$ by constant factors to get a higher margin, we define that the distance of a support vector from the hyperplane is always $1$, i.e. the margin is $\frac{1}{\|w\|}$. A support vector is thus characterized by $y_i \left( \langle w,x_i \rangle + b \right) = 1 $. &lt;/p&gt;

&lt;p&gt;As already mentioned earlier, we want all points to be either a support vector, or even further away from the hyperplane. In training, we thus add the constraint $y_i \left( \langle w,x_i \rangle + b \right) \geq 1$, which ensures eactly that.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;tl;dr&lt;/em&gt;: Training points don''t only need to be correct, they have to be on the margin or further away.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9528, 1, '2015-12-26 19:42:51.46', 4, 80, 'Where exactly does $\geq 1$ come from in SVMs optimization problem constraint?', '&lt;p&gt;I''ve understood that SVMs are binary, linear classifiers (without the kernel trick). They have training data $(x_i, y_i)$ where $x_i$ is a vector and $y_i \in \{-1, 1\}$ is the class. As they are binary, linear classifiers the task is to find a hyperplane which separates the data points with the label $-1$ from the data points with the label $+1$.&lt;/p&gt;

&lt;p&gt;Assume for now, that the data points are linearly separable and we don''t need slack variables.&lt;/p&gt;

&lt;p&gt;Now I''ve read that the training problem is now the following optimization problem:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;${\min_{w, b} \frac{1}{2} \|w\|^2}$&lt;/li&gt;
&lt;li&gt;s.t. $y_i ( \langle w, x_i \rangle + b) \geq 1$&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I think I got that minizmizing $\|w\|^2$ means maximizing the margin (however, I don''t understand why it is the square here. Would anything change if one would try to minimize $\|w\|$?).&lt;/p&gt;

&lt;p&gt;I also understood that $y_i ( \langle w, x_i \rangle + b) \geq 0$ means that the model has to be correct on the training data. However, there is a $1$ and not a $0$. Why?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10360, 1, '2016-02-23 17:00:22.933', 1, 32, 'Left Join with b.key being NULL in R', '&lt;p&gt;I am trying to replicate the below sql query in R &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;select a.*, b.key from Table1 a 
LEFT OUTER JOIN Table2 b 
on a.key = b.key where b.key is null
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have read through this post however I am still struggling to code my specific case. &lt;a href="http://stackoverflow.com/questions/1299871/how-to-join-merge-data-frames-inner-outer-left-right"&gt;http://stackoverflow.com/questions/1299871/how-to-join-merge-data-frames-inner-outer-left-right&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I have tried the below but the result doesnot allow me to filter for &lt;code&gt;b.key IS NULL&lt;/code&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;LoansToInsert_stg1 &amp;lt;- merge(x = Prior_stg2, y = BankLoans_stg2, 
                            by = "Account_ID", all.x = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Any insights?&lt;/p&gt;

&lt;p&gt;Example:  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Key1 &amp;lt;- c("A1","A2","A3","A4","A5")  
Key2 &amp;lt;- c("A1","A2","A3","B4","B5")  
BV1 &amp;lt;- c(100, 200, 300, 400, 500)  
BV2 &amp;lt;- c(150, 250, 350, 450, 550)  
df1 &amp;lt;- as.data.frame(cbind(Key1, BV1))  
df2 &amp;lt;- as.data.frame(cbind(Key2, BV2))  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Expected Output as a new df:  &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Key1 BV1 Key2 BV2
A1 100 A1 150
A2 200 A2 250
A3 300 A3 350
A4 400 NA NA
A5 500 NA NA
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9490, 2, '2015-12-22 18:18:07.993', 1, NULL, NULL, '&lt;p&gt;Here are some questions to help with the implementation of the algorithm:&lt;/p&gt;

&lt;p&gt;1- You did the sum, but where/what is the activation function (sigmoid, hyperbolic tangent, Gaussian, etc.)?&lt;/p&gt;

&lt;p&gt;2- Is multilayer? How did you forward? Did you use the sum along with activation of each neuron past for the other layers?&lt;/p&gt;

&lt;p&gt;3- Have you stored the derivatives of activations to the backpropagation?&lt;/p&gt;

&lt;p&gt;4- Will you use the traditional backpropagation algorithm or genetic algorithm to adjust the network weights?&lt;/p&gt;

&lt;p&gt;5- If you choose the sigmoid and backpropagation, &lt;a href="http://image.slidesharecdn.com/2-150507064358-lva1-app6891/95/25-backpropagation-14-638.jpg?cb=1430981260" rel="nofollow"&gt;here is an example algorithm&lt;/a&gt; (this algorithm the derivative of the sigmoid is already calculated $O_{j}$ $(1-O_{j})$.&lt;/p&gt;
', NULL, NULL, '''/2-150507064358-lva1-app6891/95/25-backpropagation-14-638.jpg?cb=1430981260'':102C ''/a'':110C ''/p'':14C,32C,57C,70C,88C,126C ''1'':16C,123C ''2'':34C ''3'':59C ''4'':72C ''5'':90C ''activ'':25C,48C,66C ''adjust'':84C ''algorithm'':13C,79C,82C,109C,112C ''along'':46C ''alreadi'':119C ''backpropag'':69C,78C,97C ''calcul'':120C ''choos'':93C ''deriv'':64C,114C ''etc'':31C ''exampl'':108C ''forward'':40C ''function'':26C ''gaussian'':30C ''genet'':81C ''help'':7C ''href'':99C ''hyperbol'':28C ''image.slidesharecdn.com'':101C ''image.slidesharecdn.com/2-150507064358-lva1-app6891/95/25-backpropagation-14-638.jpg?cb=1430981260'':100C ''implement'':10C ''j'':122C,125C ''layer'':56C ''multilay'':36C ''network'':86C ''neuron'':51C ''nofollow'':104C ''o'':121C,124C ''p'':1C,15C,33C,58C,71C,89C ''past'':52C ''question'':5C ''rel'':103C ''sigmoid'':27C,95C,117C ''store'':62C ''sum'':20C,45C ''tangent'':29C ''tradit'':77C ''use'':43C,75C ''weight'':87C ''where/what'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9491, 1, '2015-12-22 18:26:05.8', 2, 11, 'Has anyone defined a spline function (i.e. defineFuction) in PMML?', '&lt;p&gt;Has anyone defined a spline function (i.e. defineFuction) in PMML?&lt;/p&gt;

&lt;p&gt;There are quite a few parameters that need to be defined, with a fairly lengthy math. For example, for a predictor with a simple 3-knot restrictive cubic spline, I have seven parameters (including two coefficients, three knots, one reference point and one predictor name). The math includes a bit of exponential, max statements, etc.&lt;/p&gt;

&lt;p&gt;For that matter, is there a library of functions other than those described on the PMML website documentation? It seems like it would be helpful to have a library of descriptions for even relatively simple functions like polynomials.&lt;/p&gt;
', 'predictive-modeling  algorithms  regression', NULL, '''/p'':27C,84C,124C ''3'':53C ''algorithm'':14B ''anyon'':2A,18C ''bit'':78C ''coeffici'':64C ''cubic'':56C ''defin'':3A,19C,39C ''definefuct'':8A,24C ''describ'':98C ''descript'':116C ''document'':103C ''etc'':83C ''even'':118C ''exampl'':46C ''exponenti'':80C ''fair'':42C ''function'':6A,22C,94C,121C ''help'':110C ''i.e'':7A,23C ''includ'':62C,76C ''knot'':54C,66C ''lengthi'':43C ''librari'':92C,114C ''like'':106C,122C ''math'':44C,75C ''matter'':88C ''max'':81C ''model'':13B ''name'':73C ''need'':36C ''one'':67C,71C ''p'':16C,28C,85C ''paramet'':34C,61C ''pmml'':10A,26C,101C ''point'':69C ''polynomi'':123C ''predict'':12B ''predictive-model'':11B ''predictor'':49C,72C ''quit'':31C ''refer'':68C ''regress'':15B ''relat'':119C ''restrict'':55C ''seem'':105C ''seven'':60C ''simpl'':52C,120C ''spline'':5A,21C,57C ''statement'':82C ''three'':65C ''two'':63C ''websit'':102C ''would'':108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9492, 2, '2015-12-22 23:22:26.827', 1, NULL, NULL, '&lt;p&gt;Data Science is so broad, there''s many different paths to get into it. It is usually split into 4 or 5 different types for example:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/P3ePHl.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/P3ePHl.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You could see from the other posts in this topic people coming from an Applied Statistics background (applying the right algorithm), Programming background (participating in Kaggle), and others applying it to a business background&lt;/p&gt;

&lt;p&gt;Savvy companies could refer to a programming skewed person as a "Data Engineer". Big companies also use each type for their data science team, so demonstrating good T-shaped skills would be a good thing.&lt;/p&gt;
', NULL, NULL, '''/a'':47C ''/p'':28C,48C,84C,122C ''/p3ephl.png'':34C,41C ''4'':21C ''5'':23C ''algorithm'':70C ''also'':101C ''alt'':42C ''appli'':64C,67C,78C ''background'':66C,72C,83C ''big'':99C ''broad'':6C ''busi'':82C ''come'':61C ''compani'':87C,100C ''could'':51C,88C ''data'':2C,97C,107C ''demonstr'':111C ''descript'':45C ''differ'':10C,24C ''engin'':98C ''enter'':43C ''exampl'':27C ''get'':13C ''good'':112C,120C ''href'':31C ''i.stack.imgur.com'':33C,40C ''i.stack.imgur.com/p3ephl.png'':32C,39C ''imag'':44C ''img'':37C ''kaggl'':75C ''mani'':9C ''nofollow'':36C ''other'':77C ''p'':1C,29C,49C,85C ''particip'':73C ''path'':11C ''peopl'':60C ''person'':94C ''post'':56C ''program'':71C,92C ''refer'':89C ''rel'':35C ''right'':69C ''savvi'':86C ''scienc'':3C,108C ''see'':52C ''shape'':115C ''skew'':93C ''skill'':116C ''split'':19C ''src'':38C ''statist'':65C ''t-shape'':113C ''team'':109C ''thing'':121C ''topic'':59C ''type'':25C,104C ''use'':102C ''usual'':18C ''would'':117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9493, 2, '2015-12-23 00:38:44.573', 1, NULL, NULL, '&lt;p&gt;Just add weights based on your time labels to your xgb.DMatrix.
The following example is written in R but the same principle applies to xgboost on Python or Julia.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data &amp;lt;- data.frame(feature = rep(5, 5),
                   year = seq(2011, 2015), 
                   target = c(1, 0, 1, 0, 0))
weightsData &amp;lt;- 1 + (data$year - max(data$year)) * 5 * 0.01

#Now create the xgboost matrix with your data and weights
xgbMatrix &amp;lt;- xgb.DMatrix(as.matrix(data$feature), 
                         label = data$target, 
                         weight = weightsData)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':83C ''/p'':31C ''/pre'':84C ''0'':48C,50C,51C ''0.01'':61C ''1'':47C,49C,54C ''2011'':43C ''2015'':44C ''5'':39C,40C,60C ''add'':3C ''appli'':24C ''as.matrix'':75C ''base'':5C ''c'':46C ''code'':33C ''creat'':63C ''data'':34C,55C,58C,69C,76C,79C ''data.frame'':36C ''exampl'':15C ''featur'':37C,77C ''follow'':14C ''julia'':30C ''label'':9C,78C ''lt'':35C,53C,73C ''matrix'':66C ''max'':57C ''p'':1C ''pre'':32C ''principl'':23C ''python'':28C ''r'':19C ''rep'':38C ''seq'':42C ''target'':45C,80C ''time'':8C ''weight'':4C,71C,81C ''weightsdata'':52C,82C ''written'':17C ''xgb.dmatrix'':12C,74C ''xgbmatrix'':72C ''xgboost'':26C,65C ''year'':41C,56C,59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9534, 2, '2015-12-27 13:01:57.797', 3, NULL, NULL, '&lt;p&gt;Network Repository (&lt;a href="http://networkrepository.com" rel="nofollow"&gt;http://networkrepository.com&lt;/a&gt;) has tons of social networks, web graphs, bio and brain networks, etc. Best of all, they also have interactive visual analytic tools to compare/explore the various social networks.&lt;/p&gt;
', NULL, NULL, '''/a'':10C ''/p'':39C ''also'':27C ''analyt'':31C ''best'':23C ''bio'':18C ''brain'':20C ''compare/explore'':34C ''etc'':22C ''graph'':17C ''href'':5C ''interact'':29C ''network'':2C,15C,21C,38C ''networkrepository.com'':6C,9C ''nofollow'':8C ''p'':1C ''rel'':7C ''repositori'':3C ''social'':14C,37C ''ton'':12C ''tool'':32C ''various'':36C ''visual'':30C ''web'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9498, 1, '2015-12-23 23:11:25.907', 1, 14, 'You are in charge of investing Chipotle''s E-Coli source/s, what methods do you use?', '&lt;p&gt;With the amount of geographic diversity that is popping up over time with Chipotle and the outbreak of e-coli, if you were investigating the source of all these issues, and where it could occur again in the future, where would you begin? &lt;/p&gt;

&lt;p&gt;While I initially thought it would be very simple to narrow it down, the most recent CDC articles indicate they have suspicions but nothing for sure yet to indicate how to track it down. &lt;/p&gt;

&lt;p&gt;I am relatively new to machine learning algorithms, but I would think a feature classification that would take into account the ingredients involved in positive cases, the sources of the ingredients, and possibly even the machinery used or methods of cooking to help classify the inputs that relate to a positive or negative output would be a good start. &lt;/p&gt;
', 'machine-learning  feature-selection', NULL, '''/p'':69C,105C,166C ''account'':126C ''algorithm'':114C ''amount'':27C ''articl'':88C ''begin'':68C ''case'':132C ''cdc'':87C ''charg'':4A ''chipotl'':7A,38C ''classif'':121C ''classifi'':150C ''coli'':11A,45C ''cook'':147C ''could'':59C ''divers'':30C ''e'':10A,44C ''e-coli'':9A,43C ''even'':140C ''featur'':22B,120C ''feature-select'':21B ''futur'':64C ''geograph'':29C ''good'':164C ''help'':149C ''indic'':89C,99C ''ingredi'':128C,137C ''initi'':73C ''input'':152C ''invest'':6A ''investig'':49C ''involv'':129C ''issu'':55C ''learn'':20B,113C ''machin'':19B,112C ''machine-learn'':18B ''machineri'':142C ''method'':14A,145C ''narrow'':81C ''negat'':159C ''new'':110C ''noth'':94C ''occur'':60C ''outbreak'':41C ''output'':160C ''p'':24C,70C,106C ''pop'':33C ''posit'':131C,157C ''possibl'':139C ''recent'':86C ''relat'':109C,154C ''select'':23B ''simpl'':79C ''sourc'':51C,134C ''source/s'':12A ''start'':165C ''sure'':96C ''suspicion'':92C ''take'':124C ''think'':118C ''thought'':74C ''time'':36C ''track'':102C ''use'':17A,143C ''would'':66C,76C,117C,123C,161C ''yet'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9499, 1, '2015-12-24 00:13:09.11', 1, 44, 'Binary Neural Network Classification or Multiclass Neural Network Classification?', '&lt;p&gt;I am confused about the difference between a binary and multiclass neural network classification. If I am writing an algorithm that has 2 output classes (Obama or Romney), but not yes or no (so not like Obama or not Obama), then is it a binary neural network or a multi class (2 class) neural network classification?&lt;/p&gt;

&lt;p&gt;What I do know:
A binary neural network classification outputs 1 unit. If a multi class classification neural network has k classes, then it has k outputs.&lt;/p&gt;

&lt;p&gt;What I think:&lt;/p&gt;

&lt;p&gt;I think it is a binary neural network classification because I am really only trying to output one thing, whether a county votes for Romney or Obama. I am confused because I thought binary was more like Romney or not Romney classification and I am not sure if that is the same as Romney or Obama. Just wanted to double check and clarify my understanding.&lt;/p&gt;
', 'neuralnetwork  multiclass-classification  binary', NULL, '''/p'':72C,101C,106C,173C ''1'':84C ''2'':38C,67C ''algorithm'':35C ''binari'':1A,14B,24C,60C,79C,113C,141C ''check'':168C ''clarifi'':170C ''class'':40C,66C,68C,89C,95C ''classif'':4A,9A,13B,29C,71C,82C,90C,116C,149C ''confus'':18C,137C ''counti'':129C ''differ'':21C ''doubl'':167C ''k'':94C,99C ''know'':77C ''like'':51C,144C ''multi'':65C,88C ''multiclass'':6A,12B,26C ''multiclass-classif'':11B ''network'':3A,8A,28C,62C,70C,81C,92C,115C ''neural'':2A,7A,27C,61C,69C,80C,91C,114C ''neuralnetwork'':10B ''obama'':41C,52C,55C,134C,163C ''one'':125C ''output'':39C,83C,100C,124C ''p'':15C,73C,102C,107C ''realli'':120C ''romney'':43C,132C,145C,148C,161C ''sure'':154C ''thing'':126C ''think'':105C,109C ''thought'':140C ''tri'':122C ''understand'':172C ''unit'':85C ''vote'':130C ''want'':165C ''whether'':127C ''write'':33C ''yes'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9502, 1, '2015-12-24 00:54:52.5', 5, 66, 'Properties for building a Multilayer Perceptron Neural Network using Keras?', '&lt;p&gt;I am trying to build and train a multilayer perceptron neural network that correctly predicts what president won in what county for the first time. I have the following information for training data.&lt;/p&gt;

&lt;p&gt;Total population Median age % BachelorsDeg or higher Unemployment rate Per capita income Total households Average household size % Owner occupied housing % Renter occupied housing % Vacant housing Median home value Population growth House hold growth Per capita income growth Winner&lt;/p&gt;

&lt;p&gt;That''s 14 columns of training data and the 15th column is what the output should be.&lt;/p&gt;

&lt;p&gt;I am trying to use Keras to build a multilayer perceptron neural network, but I need some help understanding a few properties and the pros of cons of choosing different options for these properties.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;ACTIVATION FUNCTION&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I know my first step is to come up with an activation function. I always studied neural networks used sigmoid activation functions. Is a sigmoid activation function the best? How do you know which one to use? Keras additionally gives the options of using a softmax, softplus, relu, tanh, linear, or hard_sigmoid activation function. I''m okay with using whatever, but I just want to be able to understand why and the pros and cons.&lt;/p&gt;

&lt;ol start="2"&gt;
&lt;li&gt;PROBABILITY INITIALIZAIONS&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I know initializations define the probability distribution used to set the initial random weights of Keras layers. The options Keras gives are uniform
lecun_uniform, normal, identity, orthogonal, zero, glorot_normal, glorot_uniform, he_normal, and he_uniform. How does my selection here impact my end result or model? Shouldn''t it not matter because we are "training" whatever random model we start with and come up with a more optimal weighting of the layers anyways?&lt;/p&gt;
', 'neuralnetwork  keras', NULL, '''/li'':147C,233C ''/ol'':148C,234C ''/p'':47C,87C,106C,142C,226C,312C ''14'':91C ''15th'':98C ''2'':229C ''abl'':217C ''activ'':145C,161C,170C,175C,203C ''addit'':188C ''age'':52C ''alway'':164C ''anyway'':311C ''averag'':63C ''bachelorsdeg'':53C ''best'':178C ''build'':3A,18C,115C ''capita'':59C,83C ''choos'':136C ''column'':92C,99C ''come'':157C,301C ''con'':134C,225C ''correct'':27C ''counti'':34C ''data'':46C,95C ''defin'':239C ''differ'':137C ''distribut'':242C ''end'':281C ''first'':37C,153C ''follow'':42C ''function'':146C,162C,171C,176C,204C ''give'':189C,256C ''glorot'':265C,267C ''growth'':78C,81C,85C ''hard'':201C ''help'':125C ''higher'':55C ''hold'':80C ''home'':75C ''hous'':68C,71C,73C,79C ''household'':62C,64C ''ident'':262C ''impact'':279C ''incom'':60C,84C ''inform'':43C ''initi'':238C,247C ''initializaion'':232C ''kera'':10A,12B,113C,187C,251C,255C ''know'':151C,182C,237C ''layer'':252C,310C ''lecun'':259C ''li'':144C,230C ''linear'':199C ''m'':206C ''matter'':289C ''median'':51C,74C ''model'':284C,296C ''multilay'':5A,22C,117C ''need'':123C ''network'':8A,25C,120C,167C ''neural'':7A,24C,119C,166C ''neuralnetwork'':11B ''normal'':261C,266C,270C ''occupi'':67C,70C ''okay'':207C ''ol'':143C,227C ''one'':184C ''optim'':306C ''option'':138C,191C,254C ''orthogon'':263C ''output'':103C ''owner'':66C ''p'':13C,48C,88C,107C,149C,235C ''per'':58C,82C ''perceptron'':6A,23C,118C ''popul'':50C,77C ''predict'':28C ''presid'':30C ''probabl'':231C,241C ''properti'':1A,129C,141C ''pros'':132C,223C ''random'':248C,295C ''rate'':57C ''relu'':197C ''renter'':69C ''result'':282C ''select'':277C ''set'':245C ''shouldn'':285C ''sigmoid'':169C,174C,202C ''size'':65C ''softmax'':195C ''softplus'':196C ''start'':228C,298C ''step'':154C ''studi'':165C ''tanh'':198C ''time'':38C ''total'':49C,61C ''train'':20C,45C,94C,293C ''tri'':16C,110C ''understand'':126C,219C ''unemploy'':56C ''uniform'':258C,260C,268C,273C ''use'':9A,112C,168C,186C,193C,209C,243C ''vacant'':72C ''valu'':76C ''want'':214C ''weight'':249C,307C ''whatev'':210C,294C ''winner'':86C ''won'':31C ''zero'':264C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9545, 1, '2015-12-28 16:41:01.15', 1, 116, 'Algorithmically predicting football scores in Python', '&lt;p&gt;I am a beginner in machine learning. I was thinking about creating an algorithmic approach to score prediction of football matches with machine learning. Which algorithms are recommended for this type of problem? I want to use Python for my approach, so any Python library recommendations would be appreciated!&lt;/p&gt;
', 'machine-learning  python  predictive-modeling  sports', NULL, '''/p'':65C ''algorithm'':1A,29C,41C ''appreci'':64C ''approach'':30C,56C ''beginn'':19C ''creat'':27C ''footbal'':3A,35C ''learn'':9B,22C,39C ''librari'':60C ''machin'':8B,21C,38C ''machine-learn'':7B ''match'':36C ''model'':13B ''p'':15C ''predict'':2A,12B,33C ''predictive-model'':11B ''problem'':48C ''python'':6A,10B,53C,59C ''recommend'':43C,61C ''score'':4A,32C ''sport'':14B ''think'':25C ''type'':46C ''use'':52C ''want'':50C ''would'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9503, 2, '2015-12-24 09:50:39.853', 4, NULL, NULL, '&lt;p&gt;I think you are making things more confusing then they are. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Binary&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;In this case you have two possible outputs: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Obama = 1. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Not-Obama (who in this case can only be Romney) = 0. &lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Multi-Class&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;In this case you have &lt;em&gt;k&lt;/em&gt; possible outputs, for example when &lt;em&gt;k = 4&lt;/em&gt;: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;k = 0&lt;/em&gt;: Obama&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;k = 1&lt;/em&gt;: Romney&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;k = 2&lt;/em&gt;: Clinton&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;k = 3&lt;/em&gt;: Bush&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;There are approaches to tackle multi-class classification as binary classification which are called &lt;em&gt;One-vs-rest classification&lt;/em&gt; and &lt;em&gt;One-vs-one classification&lt;/em&gt;. &lt;/p&gt;
', NULL, NULL, '''/em'':68C,77C,85C,94C,103C,112C,139C,147C ''/li'':35C,51C,88C,97C,106C,115C ''/p'':13C,18C,28C,34C,50C,59C,78C,87C,96C,105C,114C,148C ''/strong'':17C,58C ''/ul'':52C,116C ''0'':49C,84C ''1'':33C,93C ''2'':102C ''3'':111C ''4'':76C ''approach'':120C ''binari'':16C,128C ''bush'':113C ''call'':132C ''case'':22C,44C,63C ''class'':57C,125C ''classif'':126C,129C,138C,146C ''clinton'':104C ''confus'':9C ''em'':66C,74C,82C,91C,100C,109C,133C,141C ''exampl'':72C ''k'':67C,75C,83C,92C,101C,110C ''li'':30C,36C,80C,89C,98C,107C ''make'':6C ''multi'':56C,124C ''multi-class'':55C,123C ''not-obama'':38C ''obama'':32C,40C,86C ''one'':135C,143C,145C ''one-vs-on'':142C ''one-vs-rest'':134C ''output'':27C,70C ''p'':1C,14C,19C,31C,37C,53C,60C,81C,90C,99C,108C,117C ''possibl'':26C,69C ''rest'':137C ''romney'':48C,95C ''strong'':15C,54C ''tackl'':122C ''thing'':7C ''think'':3C ''two'':25C ''ul'':29C,79C ''vs'':136C,144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9504, 2, '2015-12-24 15:18:53.523', 4, NULL, NULL, '&lt;p&gt;1) Activation is an architecture choice, which boils down to a hyperparameter choice. You can make a theoretical argument for using any function, but the best way to determine this is to try several and evaluate on a validation set. It''s also important to remember you can mix and match activations of various layers.&lt;/p&gt;

&lt;p&gt;2) In theory yes, many random initializations would be the same if your data was extremely well behaved and your network ideal. But in practice initializations seek to ensure the gradient starts off reasonable and the signal can be backpropagated correctly. Likely in this case any of those initializations would perform similarly, but the best approach is to try them out, switching if you get undesirable results.&lt;/p&gt;
', NULL, NULL, '''/p'':57C,126C ''1'':2C ''2'':59C ''activ'':3C,53C ''also'':44C ''approach'':114C ''architectur'':6C ''argument'':20C ''backpropag'':98C ''behav'':76C ''best'':27C,113C ''boil'':9C ''case'':103C ''choic'':7C,14C ''correct'':99C ''data'':72C ''determin'':30C ''ensur'':87C ''evalu'':37C ''extrem'':74C ''function'':24C ''get'':123C ''gradient'':89C ''hyperparamet'':13C ''ideal'':80C ''import'':45C ''initi'':65C,84C,107C ''layer'':56C ''like'':100C ''make'':17C ''mani'':63C ''match'':52C ''mix'':50C ''network'':79C ''p'':1C,58C ''perform'':109C ''practic'':83C ''random'':64C ''reason'':92C ''rememb'':47C ''result'':125C ''seek'':85C ''set'':41C ''sever'':35C ''signal'':95C ''similar'':110C ''start'':90C ''switch'':120C ''theoret'':19C ''theori'':61C ''tri'':34C,117C ''undesir'':124C ''use'':22C ''valid'':40C ''various'':55C ''way'':28C ''well'':75C ''would'':66C,108C ''yes'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9505, 1, '2015-12-24 17:21:26.85', 2, 217, 'Improve a regression model and feature selection', '&lt;p&gt;I am working on Azure ML Studio and try to create a regression model to predict a numerical value. I will try to describe my features and what I have done until now.&lt;/p&gt;

&lt;p&gt;My data with about 3 million rows :&lt;/p&gt;

&lt;p&gt;Features:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;8 integer features from 1 to 25&lt;/li&gt;
&lt;li&gt;2 boolean features with 0 and 1&lt;/li&gt;
&lt;li&gt;3 integer features from 1 to 10&lt;/li&gt;
&lt;li&gt;2 integer feature from 0 to 500.000 (and 1.000.000 respectively) with about 4.500 unique values&lt;/li&gt;
&lt;li&gt;1 integer feature from 20 to 50&lt;/li&gt;
&lt;li&gt;1 integer feature from 1 to 15&lt;/li&gt;
&lt;li&gt;1 integer feature from 0 to 100&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Label:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Integer from 10.000 to 100.000.000 with about 5.000 unique values&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;What I have done:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Split the dataset to 80% (train) and 20% (test). Then I split the training dataset again to 60% (actual train) and 40% (validation).&lt;/li&gt;
&lt;li&gt;Normalize the features with many unique values (4th bullet in the above list)&lt;/li&gt;
&lt;li&gt;Train a model of Boosted Decision Tree Regression.&lt;/li&gt;
&lt;li&gt;Use the Sweep Parameters module to find the best combination&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I tried also Neural Networks, Bayesian Linear Regression, but BDTR gave the best score.&lt;/p&gt;

&lt;p&gt;I tried to exclude columns and start with only a few (based on what I think it will affect the model) and then add more columns one by one. &lt;/p&gt;

&lt;p&gt;However, the least MSE I could achieved was 1.500.000 (plus I had many negative scored values)&lt;/p&gt;

&lt;p&gt;So, I was thinking what other techniques I could use to improve the model. &lt;/p&gt;
', 'predictive-modeling  regression  feature-selection  feature-engineering  azure-ml', NULL, '''/li'':77C,86C,95C,112C,121C,130C,139C,156C,189C,204C,214C,226C ''/p'':55C,64C,67C,143C,163C,243C,274C,292C,308C ''/ul'':140C,157C,227C ''0'':83C,101C,136C ''1'':74C,85C,92C,114C,123C,127C,132C ''1.000.000'':105C ''1.500.000'':284C ''10'':94C ''10.000'':148C ''100'':138C ''100.000.000'':150C ''15'':129C ''2'':79C,97C ''20'':118C,173C ''25'':76C ''3'':61C,88C ''4.500'':109C ''40'':187C ''4th'':198C ''5.000'':153C ''50'':120C ''500.000'':103C ''60'':183C ''8'':70C ''80'':170C ''achiev'':282C ''actual'':184C ''add'':268C ''affect'':263C ''also'':231C ''azur'':19B,26C ''azure-ml'':18B ''base'':256C ''bayesian'':234C ''bdtr'':238C ''best'':224C,241C ''boolean'':80C ''boost'':210C ''bullet'':199C ''column'':249C,270C ''combin'':225C ''could'':281C,302C ''creat'':32C ''data'':58C ''dataset'':168C,180C ''decis'':211C ''describ'':45C ''done'':52C,162C ''engin'':17B ''exclud'':248C ''featur'':6A,13B,16B,47C,66C,72C,81C,90C,99C,116C,125C,134C,193C ''feature-engin'':15B ''feature-select'':12B ''find'':222C ''gave'':239C ''howev'':276C ''improv'':1A,305C ''integ'':71C,89C,98C,115C,124C,133C,146C ''label'':142C ''least'':278C ''li'':69C,78C,87C,96C,113C,122C,131C,145C,165C,190C,205C,215C ''linear'':235C ''list'':203C ''mani'':195C,288C ''million'':62C ''ml'':20B,27C ''model'':4A,10B,35C,208C,265C,307C ''modul'':220C ''mse'':279C ''negat'':289C ''network'':233C ''neural'':232C ''normal'':191C ''numer'':39C ''one'':271C,273C ''p'':21C,56C,65C,141C,158C,228C,244C,275C,293C ''paramet'':219C ''plus'':285C ''predict'':9B,37C ''predictive-model'':8B ''regress'':3A,11B,34C,213C,236C ''respect'':106C ''row'':63C ''score'':242C,290C ''select'':7A,14B ''split'':166C,177C ''start'':251C ''studio'':28C ''sweep'':218C ''techniqu'':300C ''test'':174C ''think'':260C,297C ''train'':171C,179C,185C,206C ''tree'':212C ''tri'':30C,43C,230C,246C ''ul'':68C,144C,164C ''uniqu'':110C,154C,196C ''use'':216C,303C ''valid'':188C ''valu'':40C,111C,155C,197C,291C ''work'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9506, 2, '2015-12-24 20:13:18.98', 0, NULL, NULL, '&lt;p&gt;THere is little known by useful &lt;code&gt;dask&lt;/code&gt; library for parallel numpy/pandas jobs&lt;/p&gt;

&lt;p&gt;Link: &lt;a href="https://github.com/blaze/dask" rel="nofollow"&gt;https://github.com/blaze/dask&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/blaze/dask'':23C ''/blaze/dask&lt;/a&gt;&lt;/p&gt;'':28C ''/code'':10C ''/p'':16C ''code'':8C ''dask'':9C ''github.com'':22C,27C ''github.com/blaze/dask'':21C ''github.com/blaze/dask&lt;/a&gt;&lt;/p&gt;'':26C ''href'':20C ''job'':15C ''known'':5C ''librari'':11C ''link'':18C ''littl'':4C ''nofollow'':25C ''numpy/pandas'':14C ''p'':1C,17C ''parallel'':13C ''rel'':24C ''use'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9507, 1, '2015-12-24 21:09:29.9', 4, 81, 'Machine Learning: Single input to variable number of outputs', '&lt;p&gt;Is there a machine learning algorithm that maps a single input to an output list of variable length? If so, are there any implementations of the algorithm for public use? If not, what do you recommend as a workaround?&lt;/p&gt;

&lt;p&gt;In my case, the input is a single scalar and the output is a list of scalars with variable length. For example, suppose I wanted to output a list of ones given the length of the list as input. Then &amp;lt;input, output&amp;gt; could be &amp;lt;1, &amp;#91;1&amp;#93;&gt;, &amp;lt;2, [1, 1]&gt;, etc. A small tweak would be providing the square root of the length in which case &amp;lt;2, [1, 1, 1, 1]&gt; would be an answer. Note: the input need not be tied directly to the output.
&lt;hr&gt;
For a more complex example, suppose I want to learn the &lt;a href="https://en.wikipedia.org/wiki/Look-and-say_sequence" rel="nofollow" title="Look-and-say sequence"&gt;look-and-say sequence&lt;/a&gt;. Valid &amp;lt;input, output&amp;gt; pairs would be: &amp;lt;1, &amp;#91;1&amp;#93;&gt;, &amp;lt;2, [1, 1]&gt;, &amp;lt;3, [2, 1]&gt;, &amp;lt;4, [1, 2, 1, 1]&gt;, &amp;lt;5, [1, 1, 1, 2, 2, 1]&gt;, etc. My problem is also similar in that I can generate more examples; I am not restricted to a finite set of examples.&lt;/p&gt;
', 'machine-learning', NULL, '''/a'':177C ''/p'':53C,236C ''/wiki/look-and-say_sequence'':163C ''1'':102C,104C,108C,109C,128C,129C,130C,131C,187C,189C,193C,194C,198C,201C,203C,204C,207C,208C,209C,212C ''2'':107C,127C,192C,197C,202C,210C,211C ''3'':196C ''4'':200C ''5'':206C ''91'':103C,188C ''93'':105C,190C ''algorithm'':19C,40C ''also'':217C ''answer'':135C ''case'':57C,125C ''complex'':151C ''could'':99C ''direct'':143C ''en.wikipedia.org'':162C ''en.wikipedia.org/wiki/look-and-say_sequence'':161C ''etc'':110C,213C ''exampl'':76C,152C,225C,235C ''finit'':232C ''generat'':223C ''given'':86C ''gt'':98C,182C ''hr'':147C ''href'':160C ''implement'':37C ''input'':4A,24C,59C,93C,96C,138C,180C ''learn'':2A,12B,18C,157C ''length'':31C,74C,88C,122C ''list'':28C,69C,83C,91C ''look'':168C,173C ''look-and-say'':167C,172C ''lt'':95C,101C,106C,126C,179C,186C,191C,195C,199C,205C ''machin'':1A,11B,17C ''machine-learn'':10B ''map'':21C ''need'':139C ''nofollow'':165C ''note'':136C ''number'':7A ''one'':85C ''output'':9A,27C,66C,81C,97C,146C,181C ''p'':13C,54C ''pair'':183C ''problem'':215C ''provid'':116C ''public'':42C ''recommend'':49C ''rel'':164C ''restrict'':229C ''root'':119C ''say'':170C,175C ''scalar'':63C,71C ''sequenc'':171C,176C ''set'':233C ''similar'':218C ''singl'':3A,23C,62C ''small'':112C ''squar'':118C ''suppos'':77C,153C ''tie'':142C ''titl'':166C ''tweak'':113C ''use'':43C ''valid'':178C ''variabl'':6A,30C,73C ''want'':79C,155C ''workaround'':52C ''would'':114C,132C,184C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9509, 1, '2015-12-25 07:40:52.577', 1, 20, 'Predicting when a partition in Oracle database will be archived', '&lt;p&gt;We have an Oracle database in which the main table "A1" is partitioned by the hour of insertion of the row. A row once inserted, may be updated depending upon data in 6 other tables (B,C,D,E,F,G) in the same database.&lt;/p&gt;

&lt;p&gt;A partition in table A1 typically gets updated for a few hours after it starts receiving data. After the updates to the partition are finished, the partition gets archived, i.e, it is moved from production to an archive database. The time of creation of a partition and the time of its archival are logged.&lt;/p&gt;

&lt;p&gt;Now, my question is given the past history of creation and archival times of partitions, how can I predict when a partition created now in A1 will be archived? What kind of model (regression, etc.) is the best to answer this question? How can I set up a model that tracks the dependence of the archival time of a partition on the attributes in other tables (B,C,D,E,F,G)?&lt;/p&gt;
', 'predictive-modeling  regression', NULL, '''/p'':61C,117C,191C ''6'':48C ''a1'':26C,67C,144C ''answer'':158C ''archiv'':10A,91C,100C,114C,130C,147C,174C ''attribut'':181C ''b'':51C,185C ''best'':156C ''c'':52C,186C ''creat'':141C ''creation'':105C,128C ''d'':53C,187C ''data'':46C,79C ''databas'':7A,20C,60C,101C ''depend'':44C,171C ''e'':54C,188C ''etc'':153C ''f'':55C,189C ''finish'':87C ''g'':56C,190C ''get'':69C,90C ''given'':123C ''histori'':126C ''hour'':31C,74C ''i.e'':92C ''insert'':33C,40C ''kind'':149C ''log'':116C ''main'':24C ''may'':41C ''model'':13B,151C,167C ''move'':95C ''oracl'':6A,19C ''p'':15C,62C,118C ''partit'':4A,28C,64C,85C,89C,108C,133C,140C,178C ''past'':125C ''predict'':1A,12B,137C ''predictive-model'':11B ''product'':97C ''question'':121C,160C ''receiv'':78C ''regress'':14B,152C ''row'':36C,38C ''set'':164C ''start'':77C ''tabl'':25C,50C,66C,184C ''time'':103C,111C,131C,175C ''track'':169C ''typic'':68C ''updat'':43C,70C,82C ''upon'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9510, 1, '2015-12-25 08:08:22.43', 1, 42, 'Where does the name ''LSTM'' come from?', '&lt;p&gt;Long short-term memory is a recurrent neural network architecture introduced in the paper &lt;a href="http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf" rel="nofollow"&gt;Long short-term memory&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Can you please tell me where the name comes from?&lt;/p&gt;

&lt;p&gt;("Memory", as the network can store information because of the recurrence - but where does the "Long short-term" come from?)&lt;/p&gt;
', 'machine-learning  neuralnetwork  terminology', NULL, '''/a'':40C ''/p'':41C,53C,76C ''/pdfs/hochreiter97_lstm.pdf'':32C ''architectur'':23C ''come'':6A,51C,74C ''deeplearning.cs.cmu.edu'':31C ''deeplearning.cs.cmu.edu/pdfs/hochreiter97_lstm.pdf'':30C ''href'':29C ''inform'':61C ''introduc'':24C ''learn'':9B ''long'':13C,35C,70C ''lstm'':5A ''machin'':8B ''machine-learn'':7B ''memori'':17C,39C,55C ''name'':4A,50C ''network'':22C,58C ''neural'':21C ''neuralnetwork'':10B ''nofollow'':34C ''p'':12C,42C,54C ''paper'':27C ''pleas'':45C ''recurr'':20C,65C ''rel'':33C ''short'':15C,37C,72C ''short-term'':14C,36C,71C ''store'':60C ''tell'':46C ''term'':16C,38C,73C ''terminolog'':11B');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9515, 2, '2015-12-25 11:54:24.15', 3, NULL, NULL, '&lt;p&gt;That is a good question and many scientists around the world are asking the same. Well, first a game like Age of Empires is not considered to have a really big solution space, there are not so many things you can do. It''s the same in games like Mario Bros. The problem of learning in easy games like Atari games was solve by the guys of DeepMind (here the &lt;a href="https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf" rel="nofollow"&gt;paper&lt;/a&gt;), that was acquired by Google. They used an implementation of Reinforcement Learning with Deep Learning.&lt;/p&gt;

&lt;p&gt;Going back to your question. A really big problem is how to imitate the amount of decisions a human being takes every day. Wake up, have breakfast, take a shower, leave your house... All these action need a really high level of intelligence and many actions to develop. &lt;/p&gt;

&lt;p&gt;There are many people working on this problem, I''m one of them. I don''t know the solution but I can tell you in which way I''m looking. I follow the theories of Marvin Minsky, he is one of the fathers of AI. This book, the Emotion Machine, tells a very good view of the problem. He suggested that the way to create a machine that imitates the human behavior is not by constructing an unified compact theory of artificial intelligence. On the contrary, he argues that our brain contains resources that compete
between each other to satisfy different goals at the same moment. They called this &lt;strong&gt;Ways to Think&lt;/strong&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':80C ''/p'':96C,146C,262C ''/strong'':261C ''/~vmnih/docs/dqn.pdf'':76C ''acquir'':83C ''action'':133C,143C ''age'':22C ''ai'':192C ''amount'':112C ''argu'':235C ''around'':10C ''artifici'':229C ''ask'':14C ''atari'':61C ''back'':99C ''behavior'':219C ''big'':32C,105C ''book'':194C ''brain'':238C ''breakfast'':124C ''bros'':52C ''call'':255C ''compact'':226C ''compet'':242C ''consid'':27C ''construct'':223C ''contain'':239C ''contrari'':233C ''creat'':212C ''day'':120C ''decis'':114C ''deep'':94C ''deepmind'':69C ''develop'':145C ''differ'':248C ''easi'':58C ''emot'':196C ''empir'':24C ''everi'':119C ''father'':190C ''first'':18C ''follow'':179C ''game'':20C,49C,59C,62C ''go'':98C ''goal'':249C ''good'':5C,201C ''googl'':85C ''guy'':67C ''high'':137C ''hous'':130C ''href'':73C ''human'':116C,218C ''imit'':110C,216C ''implement'':89C ''intellig'':140C,230C ''know'':164C ''learn'':56C,92C,95C ''leav'':128C ''level'':138C ''like'':21C,50C,60C ''look'':177C ''m'':157C,176C ''machin'':197C,214C ''mani'':8C,39C,142C,150C ''mario'':51C ''marvin'':183C ''minski'':184C ''moment'':253C ''need'':134C ''nofollow'':78C ''one'':158C,187C ''p'':1C,97C,147C ''paper'':79C ''peopl'':151C ''problem'':54C,106C,155C,205C ''question'':6C,102C ''realli'':31C,104C,136C ''reinforc'':91C ''rel'':77C ''resourc'':240C ''satisfi'':247C ''scientist'':9C ''shower'':127C ''solut'':33C,166C ''solv'':64C ''space'':34C ''strong'':257C ''suggest'':207C ''take'':118C,125C ''tell'':170C,198C ''theori'':181C,227C ''thing'':40C ''think'':260C ''unifi'':225C ''use'':87C ''view'':202C ''wake'':121C ''way'':174C,210C,258C ''well'':17C ''work'':152C ''world'':12C ''www.cs.toronto.edu'':75C ''www.cs.toronto.edu/~vmnih/docs/dqn.pdf'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9516, 2, '2015-12-25 12:12:17.34', 0, NULL, NULL, '&lt;p&gt;You have to wonder what is what you want to improve in your page. In my opinion, the most important analysis in every page is the conversion, this is people that enter the page versus people that buys something (or interact). Everything in the page should be optimized to increase this value. &lt;/p&gt;

&lt;p&gt;Another important thing would be cost versus conversion. To see how the cost affects your conversion. &lt;/p&gt;

&lt;p&gt;If your content can be share it is important also number of visits versus number of shares.&lt;/p&gt;
', NULL, NULL, '''/p'':54C,72C,91C ''affect'':69C ''also'':83C ''analysi'':22C ''anoth'':56C ''buy'':39C ''content'':76C ''convers'':28C,63C,71C ''cost'':61C,68C ''enter'':33C ''everi'':24C ''everyth'':43C ''import'':21C,57C,82C ''improv'':12C ''increas'':51C ''interact'':42C ''number'':84C,88C ''opinion'':18C ''optim'':49C ''p'':1C,55C,73C ''page'':15C,25C,35C,46C ''peopl'':31C,37C ''see'':65C ''share'':79C,90C ''someth'':40C ''thing'':58C ''valu'':53C ''versus'':36C,62C,87C ''visit'':86C ''want'':10C ''wonder'':5C ''would'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9517, 2, '2015-12-25 12:16:45.9', 2, NULL, NULL, '&lt;p&gt;You are using the training set that opencv is giving you which it doesn''t correspond to the kind of images you are using. The data you are using comes from ''getDefaultPeopleDetector'' and the kind of images that the default detector uses are pictures of many people, not a female model from a fashion ecommerce. &lt;/p&gt;

&lt;p&gt;If you want to distinguish between models and garments you can try to train your own classifier with HOG or other features. &lt;/p&gt;

&lt;p&gt;Another path you can take is to detect if there is a face or not. You could use haar cascades for that.&lt;/p&gt;
', NULL, NULL, '''/p'':57C,81C,105C ''anoth'':83C ''cascad'':102C ''classifi'':75C ''come'':31C ''correspond'':17C ''could'':99C ''data'':27C ''default'':41C ''detect'':90C ''detector'':42C ''distinguish'':63C ''doesn'':15C ''ecommerc'':56C ''face'':95C ''fashion'':55C ''featur'':80C ''femal'':51C ''garment'':67C ''getdefaultpeopledetector'':33C ''give'':11C ''haar'':101C ''hog'':77C ''imag'':22C,38C ''kind'':20C,36C ''mani'':47C ''model'':52C,65C ''opencv'':9C ''p'':1C,58C,82C ''path'':84C ''peopl'':48C ''pictur'':45C ''set'':7C ''take'':87C ''train'':6C,72C ''tri'':70C ''use'':4C,25C,30C,43C,100C ''want'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9559, 1, '2015-12-30 17:13:44.007', 0, 15, 'Exploratory Logistic Regression with SPSS', '&lt;p&gt;Is there a way to use SPSS to look for potential models for a logistic regression?  I have a dataset with hundreds of social, economic and race variables and one dichotomous response variable.  Other software I have used called this an exploratory regression but perhaps SPSS calls it something else.&lt;/p&gt;
', 'logistic-regression', NULL, '''/p'':60C ''call'':48C,56C ''dataset'':29C ''dichotom'':40C ''econom'':34C ''els'':59C ''exploratori'':1A,51C ''hundr'':31C ''logist'':2A,7B,24C ''logistic-regress'':6B ''look'':18C ''model'':21C ''one'':39C ''p'':9C ''perhap'':54C ''potenti'':20C ''race'':36C ''regress'':3A,8B,25C,52C ''respons'':41C ''social'':33C ''softwar'':44C ''someth'':58C ''spss'':5A,16C,55C ''use'':15C,47C ''variabl'':37C,42C ''way'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9518, 1, '2015-12-25 13:05:53.903', 0, 12, 'How do I create an interactive visualization of the recycling sector activities in my country?', '&lt;p&gt;I''m working on a project aiming to upgrade the solid waste management / Recycling sector in my country and make it sustainable through a value-chain linkage.&lt;/p&gt;

&lt;p&gt;So many linkages across the country: example, a factory takes in glass waste, melts them into artifacts, which are then picked up by an artisan shop in another city which in turn exports. The way I see it, I need some visualization platform that shows the amount of waste picked up by the factory then an arrow indicating the recycled glass moving to another city, then another arrow showing export. In this example, arrows size and color should indicate the type of glass and amount, markers indicating a factory or a collection facility should be of different colors... etc...&lt;/p&gt;

&lt;p&gt;What''s a good tool for all this ?&lt;/p&gt;
', 'visualization  data', NULL, '''/p'':47C,148C,158C ''across'':52C ''activ'':12A ''aim'':25C ''amount'':95C,133C ''anoth'':76C,112C,115C ''arrow'':105C,116C,122C ''artifact'':65C ''artisan'':73C ''chain'':45C ''citi'':77C,113C ''collect'':140C ''color'':125C,146C ''countri'':15A,36C,54C ''creat'':4A ''data'':17B ''differ'':145C ''etc'':147C ''exampl'':55C,121C ''export'':81C,118C ''facil'':141C ''factori'':57C,102C,137C ''glass'':60C,109C,131C ''good'':153C ''indic'':106C,127C,135C ''interact'':6A ''linkag'':46C,51C ''m'':20C ''make'':38C ''manag'':31C ''mani'':50C ''marker'':134C ''melt'':62C ''move'':110C ''need'':88C ''p'':18C,48C,149C ''pick'':69C,98C ''platform'':91C ''project'':24C ''recycl'':10A,32C,108C ''sector'':11A,33C ''see'':85C ''shop'':74C ''show'':93C,117C ''size'':123C ''solid'':29C ''sustain'':40C ''take'':58C ''tool'':154C ''turn'':80C ''type'':129C ''upgrad'':27C ''valu'':44C ''value-chain'':43C ''visual'':7A,16B,90C ''wast'':30C,61C,97C ''way'':83C ''work'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9529, 1, '2015-12-26 20:10:00.473', 1, 44, 'How to select regression algorithm for noisy (scattered) data?', '&lt;p&gt;I am going to do regression analysis with multiple variables. In my data I have &lt;strong&gt;n = 23&lt;/strong&gt; features and &lt;strong&gt;m = 13000&lt;/strong&gt; training examples. Here is the plot of my training data (area of houses against price):&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/9lggy.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/9lggy.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;There are 13000 training examples on the plot. As you can see it is relatively noisy data. My question is which regression algorithm is more appropriate and reasonable to use in my case. I mean is it more logical to use simple linear regression or some nonlinear regression algorithm.&lt;/p&gt;

&lt;p&gt;To be more clear I provide some examples.&lt;br&gt;
Here is some unrelated example of linear regression fit:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/DyTKO.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/DyTKO.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;And some unrelated example of nonlinear regression fit:
&lt;a href="http://i.stack.imgur.com/uB7pm.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/uB7pm.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;And now I provide some hypothetic regression lines for my data:
&lt;a href="http://i.stack.imgur.com/q8FzC.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/q8FzC.png" alt="enter image description here"&gt;&lt;/a&gt;
AFAIK primitive linear regression for my data will generate very high error cost because it is very noisy and scattered data. On the other hand, there is no apparent nonlinear pattern (for example sinusoidal). What regression algorithm will be more reasonable to use in my case (house prices data) in order to get more or less appropriate houses'' price prediction and why this algorithm (linear or nonlinear) is more reasonable? &lt;/p&gt;
', 'machine-learning  regression  linear-regression', NULL, '''/9lggy.png'':64C,71C ''/a'':77C,168C,196C,227C ''/dytko.png'':155C,162C ''/p'':58C,78C,129C,149C,169C,197C,298C ''/q8fzc.png'':214C,221C ''/strong'':36C,42C ''/ub7pm.png'':183C,190C ''13000'':41C,82C ''23'':35C ''afaik'':228C ''algorithm'':5A,102C,128C,264C,291C ''alt'':72C,163C,191C,222C ''analysi'':24C ''appar'':256C ''appropri'':105C,284C ''area'':53C ''br'':139C ''case'':112C,273C ''clear'':134C ''cost'':240C ''data'':9A,30C,52C,96C,209C,234C,248C,276C ''descript'':75C,166C,194C,225C ''enter'':73C,164C,192C,223C ''error'':239C ''exampl'':44C,84C,138C,144C,174C,260C ''featur'':37C ''fit'':148C,178C ''generat'':236C ''get'':280C ''go'':20C ''hand'':252C ''high'':238C ''hous'':55C,274C,285C ''href'':61C,152C,180C,211C ''hypothet'':204C ''i.stack.imgur.com'':63C,70C,154C,161C,182C,189C,213C,220C ''i.stack.imgur.com/9lggy.png'':62C,69C ''i.stack.imgur.com/dytko.png'':153C,160C ''i.stack.imgur.com/q8fzc.png'':212C,219C ''i.stack.imgur.com/ub7pm.png'':181C,188C ''imag'':74C,165C,193C,224C ''img'':67C,158C,186C,217C ''learn'':12B ''less'':283C ''line'':206C ''linear'':15B,122C,146C,230C,292C ''linear-regress'':14B ''logic'':118C ''m'':40C ''machin'':11B ''machine-learn'':10B ''mean'':114C ''multipl'':26C ''n'':34C ''nofollow'':66C,157C,185C,216C ''noisi'':7A,95C,245C ''nonlinear'':126C,176C,257C,294C ''order'':278C ''p'':17C,59C,79C,130C,150C,170C,198C ''pattern'':258C ''plot'':48C,87C ''predict'':287C ''price'':57C,275C,286C ''primit'':229C ''provid'':136C,202C ''question'':98C ''reason'':107C,268C,297C ''regress'':4A,13B,16B,23C,101C,123C,127C,147C,177C,205C,231C,263C ''rel'':65C,156C,184C,215C ''relat'':94C ''scatter'':8A,247C ''see'':91C ''select'':3A ''simpl'':121C ''sinusoid'':261C ''src'':68C,159C,187C,218C ''strong'':33C,39C ''train'':43C,51C,83C ''unrel'':143C,173C ''use'':109C,120C,270C ''variabl'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9519, 1, '2015-12-25 15:10:51.277', 0, 49, 'Definition - center of the cluster with non-Euclidean distance', '&lt;p&gt;I have cluster consisting of two points and I am working with non-Euclidean distance. I wonder if it is appropriate to talk about center of cluster in this case and how we can interpret it? We can start with simpler case when distance is weighted Euclidean.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Update:&lt;/strong&gt; Here is clarification. Assume we are using Manhattan distance to find centroid of our 2 point cluster. As far as I understand centroid is not unique in this case if we use PAM algorithm. We need to work with whole set of centroids  for one cluster. Hence, clustering might produce random results on each iteration. It looks to me that problem is not well posed. &lt;/p&gt;
', 'clustering  distance', NULL, '''/p'':62C,132C ''/strong'':66C ''2'':81C ''algorithm'':100C ''appropri'':35C ''assum'':70C ''case'':44C,56C,95C ''center'':2A,39C ''centroid'':78C,89C,109C ''clarif'':69C ''cluster'':5A,11B,16C,41C,83C,112C,114C ''consist'':17C ''definit'':1A ''distanc'':10A,12B,29C,58C,75C ''euclidean'':9A,28C,61C ''far'':85C ''find'':77C ''henc'':113C ''interpret'':49C ''iter'':121C ''look'':123C ''manhattan'':74C ''might'':115C ''need'':102C ''non'':8A,27C ''non-euclidean'':7A,26C ''one'':111C ''p'':13C,63C ''pam'':99C ''point'':20C,82C ''pose'':131C ''problem'':127C ''produc'':116C ''random'':117C ''result'':118C ''set'':107C ''simpler'':55C ''start'':53C ''strong'':64C ''talk'':37C ''two'':19C ''understand'':88C ''uniqu'':92C ''updat'':65C ''use'':73C,98C ''weight'':60C ''well'':130C ''whole'':106C ''wonder'':31C ''work'':24C,104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9520, 2, '2015-12-25 16:21:29.437', 1, NULL, NULL, '&lt;p&gt;A good way to measure the difference between two probabilistic distributions is &lt;a href="https://en.m.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence" rel="nofollow"&gt;Kullbak-Liebler&lt;/a&gt;. You have to take into account that the distribution has integrate to one. Also you have to take into account that it''s not a distance because it''s not symmetric. KL(A,B) not equal to KL(B,A)&lt;/p&gt;
', NULL, NULL, '''/a'':24C ''/p'':65C ''/wiki/kullback%e2%80%93leibler_divergence'':18C ''account'':30C,44C ''also'':38C ''b'':58C,63C ''differ'':8C ''distanc'':50C ''distribut'':12C,33C ''en.m.wikipedia.org'':17C ''en.m.wikipedia.org/wiki/kullback%e2%80%93leibler_divergence'':16C ''equal'':60C ''good'':3C ''href'':15C ''integr'':35C ''kl'':56C,62C ''kullbak'':22C ''kullbak-liebl'':21C ''liebler'':23C ''measur'':6C ''nofollow'':20C ''one'':37C ''p'':1C ''probabilist'':11C ''rel'':19C ''symmetr'':55C ''take'':28C,42C ''two'':10C ''way'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9727, 1, '2016-01-10 17:30:52.217', 1, 26, 'ZeroR Classifier', '&lt;p&gt;I''ve got a program that analyses some data based on a bunch of values then spits out a true/false value if it was correct or not. I''m using the Weka software to graph this all up and I just want to show the ROC curve for my data. &lt;/p&gt;

&lt;p&gt;If I use the ZeroR classifier does it change the data in any way or does it just display the data? &lt;/p&gt;

&lt;p&gt;I want to just show the results and get the ROC without changing/inferring any data - can I use ZeroR for this?&lt;/p&gt;
', 'classification  predictive-modeling  weka', NULL, '''/p'':59C,82C,105C ''analys'':15C ''base'':18C ''bunch'':21C ''chang'':69C ''changing/inferring'':96C ''classif'':3B ''classifi'':2A,66C ''correct'':33C ''curv'':55C ''data'':17C,58C,71C,81C,98C ''display'':79C ''get'':92C ''got'':11C ''graph'':43C ''m'':37C ''model'':6B ''p'':8C,60C,83C ''predict'':5B ''predictive-model'':4B ''program'':13C ''result'':90C ''roc'':54C,94C ''show'':52C,88C ''softwar'':41C ''spit'':25C ''true/false'':28C ''use'':38C,63C,101C ''valu'':23C,29C ''ve'':10C ''want'':50C,85C ''way'':74C ''weka'':7B,40C ''without'':95C ''zeror'':1A,65C,102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9523, 2, '2015-12-25 23:35:34.56', 3, NULL, NULL, '&lt;p&gt;The easiest solution for a non-Euclidean cluster center is the &lt;em&gt;medoid&lt;/em&gt;, as in the algorithm PAM. This works with arbitrary metrics, but unfortunately for a 2-element cluster the result by definition is random (by metric properties, each point is an equally good medoid).&lt;/p&gt;

&lt;p&gt;Or you step back and rethink what the &lt;em&gt;center&lt;/em&gt; is. It is the point which has the &lt;em&gt;least squared error&lt;/em&gt;. So "all" you need to do is solve some math for your distance function, how to optimize this least-squares estimation. People have been solving such problems since Gauss. For example with the deviation abs(x-mu) the least-squares objective is minimized by the arithmetic mean, an the one dimensional result carries over to the n-dimensional case and thus Euclidean distance!&lt;/p&gt;

&lt;p&gt;Obviously, this &lt;em&gt;needs&lt;/em&gt; to be done for every distance function again.&lt;/p&gt;

&lt;p&gt;So if you want to &lt;em&gt;interpolate&lt;/em&gt; a center for an other distance function, solve that optimization problem (efficiently, but try to find an existing solution first, of course).&lt;/p&gt;
', NULL, NULL, '''/em'':16C,62C,75C,149C,167C ''/p'':50C,143C,158C,190C ''2'':31C ''ab'':111C ''algorithm'':20C ''arbitrari'':25C ''arithmet'':124C ''back'':55C ''carri'':131C ''case'':138C ''center'':11C,61C,169C ''cluster'':10C,33C ''cours'':189C ''definit'':37C ''deviat'':110C ''dimension'':129C,137C ''distanc'':88C,142C,155C,173C ''done'':152C ''easiest'':3C ''effici'':179C ''element'':32C ''em'':14C,60C,71C,147C,165C ''equal'':47C ''error'':74C ''estim'':97C ''euclidean'':9C,141C ''everi'':154C ''exampl'':107C ''exist'':185C ''find'':183C ''first'':187C ''function'':89C,156C,174C ''gauss'':105C ''good'':48C ''interpol'':166C ''least'':72C,95C,117C ''least-squar'':94C,116C ''math'':85C ''mean'':125C ''medoid'':15C,49C ''metric'':26C,41C ''minim'':121C ''mu'':114C ''n'':136C ''n-dimension'':135C ''need'':79C,148C ''non'':8C ''non-euclidean'':7C ''object'':119C ''obvious'':145C ''one'':128C ''optim'':92C,177C ''p'':1C,51C,144C,159C ''pam'':21C ''peopl'':98C ''point'':44C,67C ''problem'':103C,178C ''properti'':42C ''random'':39C ''result'':35C,130C ''rethink'':57C ''sinc'':104C ''solut'':4C,186C ''solv'':83C,101C,175C ''squar'':73C,96C,118C ''step'':54C ''thus'':140C ''tri'':181C ''unfortun'':28C ''want'':163C ''work'':23C ''x'':113C ''x-mu'':112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9527, 1, '2015-12-26 19:31:41.54', 1, 47, 'How to replace the missing values in Age column from Titanic/kaggle project', '&lt;p&gt;The code below doesn''t give any errors but it doesn''t iterate after the first observation, so it''s useless. I tried using sapply without any improvement and I don''t know if it''s a smart approach to use it in conjunctions with the other functions...
Any idea how to improve my code is much appreciated.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for (i in 1:nrow(train)) {
  if (is.na(train[i,5])) {
    if (grepl("Master", train$Name, fixed=TRUE) == TRUE) {
          train$Age[i] = round(mean(train$Age[train$Name == "Master"], na.rm = TRUE), digits = 2)
        } else if (grepl("Miss",train$Name, fixed=TRUE) == TRUE) {
      train$Age[i] = round(mean(train$Age[train$Name == "Miss"], na.rm = TRUE), digits = 2)
        } else if (grepl("Mrs",train$Name, fixed=TRUE) == TRUE) {
      train$Age[i] = round(mean(train$Age[train$Name == "Mrs"], na.rm = TRUE), digits = 2)
        } else if (grepl("Mr",train$Name, fixed=TRUE) == TRUE) {
      train$Age[i] = round(mean(train$Age[train$Name == "Mr"], na.rm = TRUE), digits = 2)
        } else if (grepl("Dr",train$Name, fixed=TRUE) == TRUE) {
      train$Age[i] = round(mean(train$Age[train$Name == "Dr"], na.rm = TRUE), digits = 2)
    } 
  }
}
&lt;/code&gt;&lt;/pre&gt;
', 'r  feature-engineering', NULL, '''/code'':204C ''/p'':76C ''/pre'':205C ''1'':82C ''2'':111C,134C,157C,180C,203C ''5'':89C ''age'':8A,99C,104C,122C,127C,145C,150C,168C,173C,191C,196C ''appreci'':75C ''approach'':56C ''code'':19C,72C,78C ''column'':9A ''conjunct'':61C ''digit'':110C,133C,156C,179C,202C ''doesn'':21C,28C ''dr'':184C,199C ''els'':112C,135C,158C,181C ''engin'':16B ''error'':25C ''featur'':15B ''feature-engin'':14B ''first'':33C ''fix'':95C,118C,141C,164C,187C ''function'':65C ''give'':23C ''grepl'':91C,114C,137C,160C,183C ''idea'':67C ''improv'':45C,70C ''is.na'':86C ''iter'':30C ''know'':50C ''master'':92C,107C ''mean'':102C,125C,148C,171C,194C ''miss'':5A,115C,130C ''mr'':161C,176C ''mrs'':138C,153C ''much'':74C ''na.rm'':108C,131C,154C,177C,200C ''name'':94C,106C,117C,129C,140C,152C,163C,175C,186C,198C ''nrow'':83C ''observ'':34C ''p'':17C ''pre'':77C ''project'':12A ''r'':13B ''replac'':3A ''round'':101C,124C,147C,170C,193C ''sappli'':42C ''smart'':55C ''titanic/kaggle'':11A ''train'':84C,87C,93C,98C,103C,105C,116C,121C,126C,128C,139C,144C,149C,151C,162C,167C,172C,174C,185C,190C,195C,197C ''tri'':40C ''true'':96C,97C,109C,119C,120C,132C,142C,143C,155C,165C,166C,178C,188C,189C,201C ''use'':41C,58C ''useless'':38C ''valu'':6A ''without'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9537, 2, '2015-12-27 17:38:30.03', 3, NULL, NULL, '&lt;p&gt;Every algorithm that deals with text data has a vocabulary. In the case of word2vec, the vocabulary is comprised of all words in the input corpus, or at least those above the minimum-frequency threshold.&lt;/p&gt;

&lt;p&gt;Algorithms tend to ignore words that are outside their vocabulary. However there are ways to reframe your problem such that there are essentially no Out-Of-Vocabulary words.&lt;/p&gt;

&lt;p&gt;Remember that words are simply "tokens" in word2vec. They could be ngrams or they could be letters. One way to define your vocabulary is to say that every word that occurs at least X times is in your vocabulary. Then the most common "syllables" (ngrams of letters) are added to your vocabulary. Then you add individual letters to your vocabulary.&lt;/p&gt;

&lt;p&gt;In this way you can define any word as either &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;A word in your vocabulary&lt;/li&gt;
&lt;li&gt;A set of syllables in your vocabulary&lt;/li&gt;
&lt;li&gt;A combined set of letters and syllables in your vocabulary&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':151C,160C,172C ''/ol'':173C ''/p'':38C,69C,131C,143C ''ad'':119C ''add'':125C ''algorithm'':3C,40C ''case'':14C ''combin'':163C ''common'':113C ''compris'':20C ''corpus'':27C ''could'':80C,85C ''data'':8C ''deal'':5C ''defin'':91C,138C ''either'':142C ''essenti'':62C ''everi'':2C,98C ''frequenc'':36C ''howev'':50C ''ignor'':43C ''individu'':126C ''input'':26C ''least'':30C,103C ''letter'':87C,117C,127C,166C ''li'':145C,152C,161C ''minimum'':35C ''minimum-frequ'':34C ''ngram'':82C,115C ''occur'':101C ''ol'':144C ''one'':88C ''out-of-vocabulari'':64C ''outsid'':47C ''p'':1C,39C,70C,132C ''problem'':57C ''refram'':55C ''rememb'':71C ''say'':96C ''set'':154C,164C ''simpli'':75C ''syllabl'':114C,156C,168C ''tend'':41C ''text'':7C ''threshold'':37C ''time'':105C ''token'':76C ''vocabulari'':11C,18C,49C,67C,93C,109C,122C,130C,150C,159C,171C ''way'':53C,89C,135C ''word'':23C,44C,68C,73C,99C,140C,147C ''word2vec'':16C,78C ''x'':104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9538, 1, '2015-12-27 23:41:51.893', 1, 19, 'Significance shifts when cases are excluded', '&lt;p&gt;I have a county level dataset with a dichotomous variable which represents whether or not any charter schools exist in a county.  I am performing a nested logistic regression. That variable is the response and the explanatory variables are social factors such as education level, language spoken at home, household type, etc.  When I include all counties in the U.S. (Including ten states which have no charter authorization laws and thus every county in those states is represented as a zero in the response variable) my results show significant associations between the response and the explanatory variables.  When I exclude those ten states the significance disappears.  &lt;/p&gt;

&lt;p&gt;Any ideas as to why this might be happening?  &lt;/p&gt;
', 'logistic-regression', NULL, '''/p'':117C,128C ''associ'':100C ''author'':78C ''case'':4A ''charter'':27C,77C ''counti'':14C,32C,67C,83C ''dataset'':16C ''dichotom'':19C ''disappear'':116C ''educ'':54C ''etc'':62C ''everi'':82C ''exclud'':6A,110C ''exist'':29C ''explanatori'':47C,106C ''factor'':51C ''happen'':127C ''home'':59C ''household'':60C ''idea'':120C ''includ'':65C,71C ''languag'':56C ''law'':79C ''level'':15C,55C ''logist'':8B,38C ''logistic-regress'':7B ''might'':125C ''nest'':37C ''p'':10C,118C ''perform'':35C ''regress'':9B,39C ''repres'':22C,88C ''respons'':44C,94C,103C ''result'':97C ''school'':28C ''shift'':2A ''show'':98C ''signific'':1A,99C,115C ''social'':50C ''spoken'':57C ''state'':73C,86C,113C ''ten'':72C,112C ''thus'':81C ''type'':61C ''u.s'':70C ''variabl'':20C,41C,48C,95C,107C ''whether'':23C ''zero'':91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9539, 1, '2015-12-28 07:15:56.377', 0, 33, 'Using sklearn DictVectorizer in real-time systems', '&lt;p&gt;Any binary one-hot encoding is aware of only values seen in training, so features not encountered during fitting will be silently ignored. For real time, where you have millions of records in a second, and features have very high cardinality, you need to keep your hasher/mapper updated with the data.&lt;/p&gt;

&lt;p&gt;How can we do an incremental update to the hasher (rather calculating the entire fit() every time we incounter a new feature-value pair)? What is the suggested approach here the tackle this?&lt;/p&gt;
', 'python  categorical-data  sklearn', NULL, '''/p'':67C,103C ''approach'':98C ''awar'':22C ''binari'':16C ''calcul'':80C ''cardin'':56C ''categor'':11B ''categorical-data'':10B ''data'':12B,66C ''dictvector'':3A ''encod'':20C ''encount'':32C ''entir'':82C ''everi'':84C ''featur'':30C,52C,91C ''feature-valu'':90C ''fit'':34C,83C ''hasher'':78C ''hasher/mapper'':62C ''high'':55C ''hot'':19C ''ignor'':38C ''incount'':87C ''increment'':74C ''keep'':60C ''million'':45C ''need'':58C ''new'':89C ''one'':18C ''one-hot'':17C ''p'':14C,68C ''pair'':93C ''python'':9B ''rather'':79C ''real'':6A,40C ''real-tim'':5A ''record'':47C ''second'':50C ''seen'':26C ''silent'':37C ''sklearn'':2A,13B ''suggest'':97C ''system'':8A ''tackl'':101C ''time'':7A,41C,85C ''train'':28C ''updat'':63C,75C ''use'':1A ''valu'':25C,92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9540, 2, '2015-12-28 07:17:28.6', 1, NULL, NULL, '&lt;p&gt;Sounds like you came across &lt;a href="https://en.wikipedia.org/wiki/Simpson%27s_paradox" rel="nofollow"&gt;Simpson''s Paradox&lt;/a&gt;. Roughly speaking you have a result in one group of data but not when you look at different groups or subsets. This often happens when the variables depend on hidden dimensions in the data. &lt;/p&gt;

&lt;p&gt;The &lt;a href="https://en.wikipedia.org/wiki/Simpson%27s_paradox" rel="nofollow"&gt;wikipedia page&lt;/a&gt; explains it a bit better and in more detail.&lt;/p&gt;
', NULL, NULL, '''/a'':17C,64C ''/p'':52C,74C ''/wiki/simpson%27s_paradox'':11C,59C ''across'':6C ''better'':69C ''bit'':68C ''came'':5C ''data'':28C,51C ''depend'':45C ''detail'':73C ''differ'':35C ''dimens'':48C ''en.wikipedia.org'':10C,58C ''en.wikipedia.org/wiki/simpson%27s_paradox'':9C,57C ''explain'':65C ''group'':26C,36C ''happen'':41C ''hidden'':47C ''href'':8C,56C ''like'':3C ''look'':33C ''nofollow'':13C,61C ''often'':40C ''one'':25C ''p'':1C,53C ''page'':63C ''paradox'':16C ''rel'':12C,60C ''result'':23C ''rough'':18C ''simpson'':14C ''sound'':2C ''speak'':19C ''subset'':38C ''variabl'':44C ''wikipedia'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9541, 1, '2015-12-28 10:15:19.117', 3, 75, 'Details of the k-means++ algorithm that is used to seed k-means', '&lt;p&gt;Regard to K-Means++ algorithm, which is an algorithm for choosing the initial values (or "seeds") for the k-means clustering algorithm.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/K-means%2B%2B" rel="nofollow"&gt;K-Means++ algorithm in Wikipedia&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The exact algorithm is as follows:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Choose one center uniformly at random from among the data points.&lt;/li&gt;
&lt;li&gt;For each data point x, compute D(x), the distance between x and the nearest center that has already been chosen.&lt;/li&gt;
&lt;li&gt;Choose one new data point at random as a new center, using a weighted probability distribution where a point x is chosen with probability proportional to D(x)2.&lt;/li&gt;
&lt;li&gt;Repeat Steps 2 and 3 until k centers have been chosen.&lt;/li&gt;
&lt;li&gt;Now that the initial centers have been chosen, proceed using standard k-means clustering.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I dont understand step 3&lt;/p&gt;

&lt;p&gt;"Choose one new data point at random as a new center, using a weighted probability distribution where a point x is chosen with probability proportional to D(x)^2."&lt;/p&gt;

&lt;p&gt;What is probability proportional  ?&lt;/p&gt;

&lt;p&gt;If I do not misunderstand  . . .&lt;/p&gt;

&lt;p&gt;The next centroid x I choose must the distance&lt;/p&gt;

&lt;p&gt;D(x) = D(x)^2 / summation of all distances from all data points square &lt;/p&gt;

&lt;p&gt;Is that right ?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;I still wonder about implementation. I try this in java but it does not work , the chance is very low and it make the selection distort. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;public static double euclidean(Data a, Data b) {

    double accumValue = 0;
    double res;

    for (int i = 0; i &amp;lt; 72; i++) {

        res = a.features[i] - b.features[i];

        res = Math.pow(res, 2);

        accumValue += res;

    }

    double finalRes = Math.sqrt(accumValue);

    return finalRes;

}

public static double accumeratedSqrDistanceCal(ArrayList&amp;lt;Data&amp;gt; dataList, ArrayList&amp;lt;Data&amp;gt; centroids) {

    double[] squareDistanceCollection = new double[dataList.size()];

    for (int i = 0; i &amp;lt; dataList.size(); i++) {

        double minDistance = minDistanceFromClosetCentroidsCal(dataList.get(i), centroids);

        squareDistanceCollection[i] = Math.pow(minDistance, 2);

    }

    double accumerateDistance = 0;

    for (int i = 0; i &amp;lt; dataList.size(); i++) {

        accumerateDistance += squareDistanceCollection[i];

    }

    return accumerateDistance;

}

public static double minDistanceFromClosetCentroidsCal(Data d, ArrayList&amp;lt;Data&amp;gt; centroids) {

    double minDistance = 100000;

    for (int i = 0; i &amp;lt; centroids.size(); i++) {

        double distance = euclidean(d, centroids.get(i));

        if (distance &amp;lt; minDistance) {

            minDistance = distance;
        }

    }

    return minDistance;

}
public static void main(String[] args) {

for (int i = 0; i &amp;lt; CENTROIDS_SIZE; i++) {

            for (int j = 0; j &amp;lt; dataList.size(); j++) {

                double accumerateDistance = accumeratedSqrDistanceCal(dataList, centroids);

                double rand = Math.random();

                double distance = minDistanceFromClosetCentroidsCal(dataList.get(j), centroids);

                double distanceSquare = Math.pow(distance, 2);

                double chance = distanceSquare / accumerateDistance;

                if (chance &amp;gt; rand) {

                    centroids.add(dataList.get(j));
                    break;

                }

            }
        }

}
&lt;/code&gt;&lt;/pre&gt;
', 'clustering  k-means', NULL, '''/a'':60C ''/code'':465C ''/li'':83C,106C,137C,150C,167C ''/ol'':168C ''/p'':45C,61C,69C,175C,206C,212C,219C,230C,246C,251C,280C ''/pre'':466C ''/wiki/k-means%2b%2b'':51C ''0'':293C,299C,343C,361C,365C,392C,420C,429C ''100000'':388C ''2'':136C,141C,205C,236C,312C,358C,452C ''3'':143C,174C ''72'':302C ''a.features'':305C ''accumeratedist'':360C,370C,374C,435C,456C ''accumeratedsqrdistancec'':324C,436C ''accumvalu'':292C,313C,318C ''algorithm'':7A,26C,30C,44C,57C,65C ''alreadi'':103C ''among'':79C ''arg'':416C ''arraylist'':325C,330C,381C ''b'':290C ''b.features'':307C ''break'':464C ''center'':74C,100C,118C,146C,156C,187C ''centroid'':223C,334C,353C,385C,423C,438C,447C ''centroids.add'':461C ''centroids.get'':401C ''centroids.size'':395C ''chanc'':270C,454C,458C ''choos'':32C,72C,108C,177C,226C ''chosen'':105C,129C,149C,159C,198C ''cluster'':16B,43C,166C ''code'':282C ''comput'':90C ''d'':91C,134C,203C,232C,234C,380C,400C ''data'':81C,87C,111C,180C,243C,287C,289C,327C,332C,379C,383C ''datalist'':329C,437C ''datalist.get'':351C,445C,462C ''datalist.size'':339C,346C,368C,432C ''detail'':1A ''distanc'':94C,229C,240C,398C,404C,408C,443C,451C ''distancesquar'':449C,455C ''distort'':279C ''distribut'':123C,192C ''dont'':171C ''doubl'':285C,291C,294C,315C,323C,335C,338C,348C,359C,377C,386C,397C,434C,439C,442C,448C,453C ''en.wikipedia.org'':50C ''en.wikipedia.org/wiki/k-means%2b%2b'':49C ''euclidean'':286C,399C ''exact'':64C ''finalr'':316C,320C ''follow'':68C ''gt'':328C,333C,384C,459C ''hr'':252C ''href'':48C ''implement'':258C ''initi'':34C,155C ''int'':297C,341C,363C,390C,418C,427C ''j'':428C,430C,433C,446C,463C ''java'':263C ''k'':5A,14A,18B,24C,41C,55C,145C,164C ''k-mean'':4A,13A,17B,23C,40C,54C,163C ''li'':71C,84C,107C,138C,151C ''low'':273C ''lt'':301C,326C,331C,345C,367C,382C,394C,405C,422C,431C ''main'':414C ''make'':276C ''math.pow'':310C,356C,450C ''math.random'':441C ''math.sqrt'':317C ''mean'':6A,15A,19B,25C,42C,56C,165C ''mindist'':349C,357C,387C,406C,407C,410C ''mindistancefromclosetcentroidsc'':350C,378C,444C ''misunderstand'':218C ''must'':227C ''nearest'':99C ''new'':110C,117C,179C,186C,337C ''next'':222C ''nofollow'':53C ''ol'':70C ''one'':73C,109C,178C ''p'':20C,46C,62C,169C,176C,207C,213C,220C,231C,247C,253C ''point'':82C,88C,112C,126C,181C,195C,244C ''pre'':281C ''probabl'':122C,131C,191C,200C,210C ''proceed'':160C ''proport'':132C,201C,211C ''public'':283C,321C,375C,411C ''rand'':440C,460C ''random'':77C,114C,183C ''regard'':21C ''rel'':52C ''repeat'':139C ''res'':295C,304C,309C,311C,314C ''return'':319C,373C,409C ''right'':250C ''seed'':12A,37C ''select'':278C ''size'':424C ''squar'':245C ''squaredistancecollect'':336C,354C,371C ''standard'':162C ''static'':284C,322C,376C,412C ''step'':140C,173C ''still'':255C ''string'':415C ''summat'':237C ''tri'':260C ''understand'':172C ''uniform'':75C ''use'':10A,119C,161C,188C ''valu'':35C ''void'':413C ''weight'':121C,190C ''wikipedia'':59C ''wonder'':256C ''work'':268C ''x'':89C,92C,96C,127C,135C,196C,204C,224C,233C,235C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9546, 2, '2015-12-28 19:16:05.84', 0, NULL, NULL, '&lt;p&gt;First you might be interested about the model.
There is a number of good answers on this site:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/265/can-machine-learning-algorithms-predict-sports-scores-or-plays"&gt;Can machine learning algorithms predict sports scores or plays?&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://datascience.stackexchange.com/questions/6201/resources-on-data-science-for-football-soccer"&gt;Resources on Data Science for Football / Soccer?&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Finding implementation of anything in Python is trivial:
&lt;a href="http://scikit-learn.org/" rel="nofollow"&gt;http://scikit-learn.org/&lt;/a&gt; will be enough.&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':71C ''/a'':37C,52C ''/li'':38C,53C ''/p'':20C,75C ''/questions/265/can-machine-learning-algorithms-predict-sports-scores-or-plays'':27C ''/questions/6201/resources-on-data-science-for-football-soccer'':44C ''/ul'':54C ''algorithm'':31C ''answer'':16C ''anyth'':59C ''data'':47C ''datascience.stackexchange.com'':26C,43C ''datascience.stackexchange.com/questions/265/can-machine-learning-algorithms-predict-sports-scores-or-plays'':25C ''datascience.stackexchange.com/questions/6201/resources-on-data-science-for-football-soccer'':42C ''enough'':74C ''find'':56C ''first'':2C ''footbal'':50C ''good'':15C ''href'':24C,41C,65C ''implement'':57C ''interest'':6C ''learn'':30C ''li'':22C,39C ''machin'':29C ''might'':4C ''model'':9C ''nofollow'':68C ''number'':13C ''p'':1C,55C ''play'':36C ''predict'':32C ''python'':61C ''rel'':67C ''resourc'':45C ''scienc'':48C ''scikit-learn.org'':66C,70C ''scikit-learn.org/&lt;/a&gt;'':69C ''score'':34C ''site'':19C ''soccer'':51C ''sport'':33C ''trivial'':63C ''ul'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9547, 1, '2015-12-28 19:38:08.663', 1, 29, 'Stacked features not helping', '&lt;p&gt;I am wondering why my stacked features do not help me to improve against my loss metric. Here''s what I''m doing: I am adding new features which are simple the predictions originated from train, predict of other   models to the original train/test features. Every time I have tried this method, it has failed. I am curious  what the issue could be the with this. Can anyone give me some advice?&lt;/p&gt;
', 'ensemble-modeling', NULL, '''/p'':81C ''ad'':34C ''advic'':80C ''anyon'':76C ''could'':70C ''curious'':66C ''ensembl'':6B ''ensemble-model'':5B ''everi'':54C ''fail'':63C ''featur'':2A,15C,36C,53C ''give'':77C ''help'':4A,18C ''improv'':21C ''issu'':69C ''loss'':24C ''m'':30C ''method'':60C ''metric'':25C ''model'':7B,48C ''new'':35C ''origin'':42C,51C ''p'':8C ''predict'':41C,45C ''simpl'':39C ''stack'':1A,14C ''time'':55C ''train'':44C ''train/test'':52C ''tri'':58C ''wonder'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9556, 2, '2015-12-30 14:38:51.157', 5, NULL, NULL, '&lt;p&gt;There are plenty of them available. I do not know if I am allowed to do this (please let me know if it is wrong), but I develop one and it has already over 2 years on git hub (it actually started one years before github). 
The project is called rapaio, is on git hub &lt;a href="https://github.com/padreati/rapaio"&gt;here&lt;/a&gt; and recently I started to write a manual for it (some of my friends asked me about that). The manual can be found &lt;a href="https://aureliantutuianu.gitbooks.io/rapaio-manual/content/"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;It fits your needs if you are willing to develop in Java 8, if you like to do yourself any tool and if you like to experiment. There are only two principles which I enforce. The first one is &lt;em&gt;write something only when you need it&lt;/em&gt;. That is because I strongly believe that only when you need a tool you also know what you really want from it in terms of output, performance, information. The second principle is &lt;em&gt;you depend only on jdk, if you need something you will write it&lt;/em&gt;. I can agree that I am old fashioned, but you can tailor any feature for your purpose in this way. &lt;/p&gt;

&lt;p&gt;If I am not allowed to do that as an aswer, again, please let me know. Although, since it''s an open source initiative, a &lt;em&gt;give something back to the people with no profit&lt;/em&gt; type of project I see not reason why I could not do it. &lt;/p&gt;
', NULL, NULL, '''/a'':63C,93C ''/em'':143C,190C,248C ''/p'':94C,211C,262C ''/padreati/rapaio'':61C ''/rapaio-manual/content/'':91C ''2'':36C ''8'':108C ''actual'':42C ''agre'':193C ''allow'':15C,217C ''alreadi'':34C ''also'':158C ''although'':229C ''ask'':78C ''aswer'':223C ''aureliantutuianu.gitbooks.io'':90C ''aureliantutuianu.gitbooks.io/rapaio-manual/content/'':89C ''avail'':7C ''back'':241C ''believ'':149C ''call'':51C ''could'':258C ''depend'':178C ''develop'':29C,105C ''em'':135C,176C,238C ''enforc'':130C ''experi'':122C ''fashion'':198C ''featur'':204C ''first'':132C ''fit'':97C ''found'':86C ''friend'':77C ''git'':39C,55C ''github'':47C ''github.com'':60C ''github.com/padreati/rapaio'':59C ''give'':239C ''href'':58C,88C ''hub'':40C,56C ''inform'':171C ''initi'':236C ''java'':107C ''jdk'':181C ''know'':11C,22C,159C,228C ''let'':20C,226C ''like'':111C,120C ''manual'':71C,83C ''need'':99C,141C,154C,184C ''old'':197C ''one'':30C,44C,133C ''open'':234C ''output'':169C ''p'':1C,95C,212C ''peopl'':244C ''perform'':170C ''pleas'':19C,225C ''plenti'':4C ''principl'':127C,174C ''profit'':247C ''project'':49C,251C ''purpos'':207C ''rapaio'':52C ''realli'':162C ''reason'':255C ''recent'':65C ''second'':173C ''see'':253C ''sinc'':230C ''someth'':137C,185C,240C ''sourc'':235C ''start'':43C,67C ''strong'':148C ''tailor'':202C ''term'':167C ''tool'':116C,156C ''two'':126C ''type'':249C ''want'':163C ''way'':210C ''will'':103C ''write'':69C,136C,188C ''wrong'':26C ''year'':37C,45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9548, 2, '2015-12-28 21:30:18.467', 1, NULL, NULL, '&lt;p&gt;If you''re doing anything at the &lt;em&gt;millions of records per second&lt;/em&gt; level, you''re going to need something more high-performance than Python. Spark/PySpark is ideal for this since it interfaces with your existing Python knowledge and supports HDFS/distributed technologies that are made to take on the millions per second sized tasks.&lt;/p&gt;

&lt;p&gt;As for sklearn: you might be out of luck here. The &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html" rel="nofollow"&gt;fit_transform() or fit()&lt;/a&gt; methods necessarily have to consider every record that you pass to them to keep the hash up to date, so if you have millions of data points coming in you''re going to have to do millions of computations to keep moving, whether you do them one at a time or in a batch. The source code for the &lt;a href="https://github.com/scikit-learn/scikit-learn/blob/c957249/sklearn/feature_extraction/dict_vectorizer.py#L269" rel="nofollow"&gt;transform()&lt;/a&gt; method should convince you that doing either is equivalent.&lt;/p&gt;

&lt;p&gt;One caveat is that if you have predictable features, e.g. maybe you''re receiving counts of foo''s bar''s and baz''s, like in the first link:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;D = [{''foo'': 1, ''bar'': 2}, {''foo'': 3, ''baz'': 1}]
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Then you don''t need to update the hash every time, all you have to do is transform &lt;code&gt;D&lt;/code&gt; into the vectorized format and add it to your collection. As long as you don''t need to fit new features this can be done relatively fast. However, if you''re doing something less structured like NLP this won''t give you hardly any useful information.&lt;/p&gt;
', NULL, NULL, '''/a'':82C,150C ''/code'':202C,225C ''/em'':15C ''/p'':58C,160C,190C,203C,272C ''/scikit-learn/scikit-learn/blob/c957249/sklearn/feature_extraction/dict_vectorizer.py#l269'':146C ''/stable/modules/generated/sklearn.feature_extraction.dictvectorizer.html'':75C ''1'':195C,201C ''2'':197C ''3'':199C ''add'':231C ''anyth'':6C ''bar'':180C,196C ''batch'':136C ''baz'':183C,200C ''caveat'':163C ''code'':139C,192C,223C ''collect'':235C ''come'':110C ''comput'':121C ''consid'':87C ''convinc'':153C ''count'':176C ''d'':193C,224C ''data'':108C ''date'':101C ''done'':250C ''e.g'':171C ''either'':157C ''em'':9C ''equival'':159C ''everi'':88C,214C ''exist'':39C ''fast'':252C ''featur'':170C,246C ''first'':188C ''fit'':78C,81C,244C ''foo'':178C,194C,198C ''format'':229C ''github.com'':145C ''github.com/scikit-learn/scikit-learn/blob/c957249/sklearn/feature_extraction/dict_vectorizer.py#l269'':144C ''give'':266C ''go'':19C,114C ''hard'':268C ''hash'':98C,213C ''hdfs/distributed'':44C ''high'':25C ''high-perform'':24C ''howev'':253C ''href'':72C,143C ''ideal'':31C ''inform'':271C ''interfac'':36C ''keep'':96C,123C ''knowledg'':41C ''less'':259C ''level'':16C ''like'':185C,261C ''link'':189C ''long'':237C ''luck'':68C ''made'':48C ''mayb'':172C ''method'':83C,151C ''might'':64C ''million'':10C,53C,106C,119C ''move'':124C ''necessarili'':84C ''need'':21C,209C,242C ''new'':245C ''nlp'':262C ''nofollow'':77C,148C ''one'':129C,162C ''p'':1C,59C,161C,191C,204C ''pass'':92C ''per'':13C,54C ''perform'':26C ''point'':109C ''predict'':169C ''python'':28C,40C ''re'':4C,18C,113C,174C,256C ''receiv'':175C ''record'':12C,89C ''rel'':76C,147C ''relat'':251C ''scikit-learn.org'':74C ''scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.dictvectorizer.html'':73C ''second'':14C,55C ''sinc'':34C ''size'':56C ''sklearn'':62C ''someth'':22C,258C ''sourc'':138C ''spark/pyspark'':29C ''structur'':260C ''support'':43C ''take'':50C ''task'':57C ''technolog'':45C ''time'':132C,215C ''transform'':79C,149C,222C ''updat'':211C ''use'':270C ''vector'':228C ''whether'':125C ''won'':264C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9550, 2, '2015-12-29 09:52:09.657', 2, NULL, NULL, '&lt;p&gt;As far as I understood stacking does not add features to the original data set. The point is to train several models on the training data and use their predictions on training data as input features to another model. &lt;/p&gt;

&lt;p&gt;First such kind of construction used logistic regression as a final ensemble and and class probabilities from each base learner as input features. Now, what I have described is a technical layout, the intuition behind is the following: considering that there are no models which are good over all joint probability space of features, one can combine their results in order to get the best from each one. In other words we can state the we explore the richness of models (seen as function spaces) to get a combined thing. This strategy does not work always but often it works. &lt;/p&gt;

&lt;p&gt;I think you do something wrong. I think is better to use original features only for base learners. Be careful to use scores or probabilities if possible, instead of final classifications from base learners, it gives more space for improvements. Often is better to stack learners from different families, not the same model with different parameters (better to use a gradient boost and random forest than two gradient boosts). All of those advices are not rules which cannot be broken, and even if you take them all there is no guarantee that there will be improvements.  &lt;/p&gt;
', NULL, NULL, '''/p'':41C,143C,241C ''add'':10C ''advic'':217C ''alway'':138C ''anoth'':39C ''base'':61C,161C,177C ''behind'':77C ''best'':107C ''better'':154C,187C,201C ''boost'':206C,213C ''broken'':224C ''cannot'':222C ''care'':164C ''class'':57C ''classif'':175C ''combin'':99C,131C ''consid'':81C ''construct'':47C ''data'':15C,27C,34C ''describ'':70C ''differ'':192C,199C ''ensembl'':54C ''even'':226C ''explor'':119C ''famili'':193C ''far'':3C ''featur'':11C,37C,65C,96C,158C ''final'':53C,174C ''first'':43C ''follow'':80C ''forest'':209C ''function'':126C ''get'':105C,129C ''give'':180C ''good'':89C ''gradient'':205C,212C ''guarante'':235C ''improv'':184C,240C ''input'':36C,64C ''instead'':172C ''intuit'':76C ''joint'':92C ''kind'':45C ''layout'':74C ''learner'':62C,162C,178C,190C ''logist'':49C ''model'':23C,40C,86C,123C,197C ''often'':140C,185C ''one'':97C,110C ''order'':103C ''origin'':14C,157C ''p'':1C,42C,144C ''paramet'':200C ''point'':18C ''possibl'':171C ''predict'':31C ''probabl'':58C,93C,169C ''random'':208C ''regress'':50C ''result'':101C ''rich'':121C ''rule'':220C ''score'':167C ''seen'':124C ''set'':16C ''sever'':22C ''someth'':149C ''space'':94C,127C,182C ''stack'':7C,189C ''state'':116C ''strategi'':134C ''take'':229C ''technic'':73C ''thing'':132C ''think'':146C,152C ''train'':21C,26C,33C ''two'':211C ''understood'':6C ''use'':29C,48C,156C,166C,203C ''word'':113C ''work'':137C,142C ''wrong'':150C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9558, 1, '2015-12-30 16:11:20.963', 1, 71, 'Neural Networks for Predictive typing', '&lt;p&gt;I don''t have a background in neural networks. But, various studies has been proved that neural networks (feed forward / Recurrent) outperformed n-gram language modeling for predicting words in a sequence. But, in an application to text messaging or any text-based conversation, where the language which is most likely used will be more informal or colloquial. Can still a neural networks perform well than n-gram LM? Considering the data to be fed are the text messages (colloquial phrases). If so, please enlighten me, thanks.&lt;/p&gt;
', 'machine-learning  neuralnetwork  nlp  language-model', NULL, '''/p'':105C ''applic'':51C ''background'':20C ''base'':59C ''colloqui'':74C,97C ''consid'':87C ''convers'':60C ''data'':89C ''enlighten'':102C ''fed'':92C ''feed'':33C ''forward'':34C ''gram'':39C,85C ''inform'':72C ''languag'':12B,40C,63C ''language-model'':11B ''learn'':8B ''like'':67C ''lm'':86C ''machin'':7B ''machine-learn'':6B ''messag'':54C,96C ''model'':13B,41C ''n'':38C,84C ''n-gram'':37C,83C ''network'':2A,23C,32C,79C ''neural'':1A,22C,31C,78C ''neuralnetwork'':9B ''nlp'':10B ''outperform'':36C ''p'':14C ''perform'':80C ''phrase'':98C ''pleas'':101C ''predict'':4A,43C ''prove'':29C ''recurr'':35C ''sequenc'':47C ''still'':76C ''studi'':26C ''text'':53C,58C,95C ''text-bas'':57C ''thank'':104C ''type'':5A ''use'':68C ''various'':25C ''well'':81C ''word'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9551, 2, '2015-12-29 22:02:40.703', 0, NULL, NULL, '&lt;p&gt;I would try to set a &lt;a href="http://scikit-learn.org/stable/modules/multiclass.html" rel="nofollow"&gt;multilabel classification algorithm&lt;/a&gt; and make the output standard by adding zeros. So if your data is like this: &amp;lt;1, &lt;a href="http://scikit-learn.org/stable/modules/multiclass.html" rel="nofollow"&gt;1&lt;/a&gt;&gt;, &amp;lt;2, [1, 1]&gt;, &amp;lt;3, [2, 1]&gt;, &amp;lt;4, [1, 2, 1, 1]&gt;, &amp;lt;5, [1, 1, 1, 2, 2, 1]&gt;. The maximum number of output is 6. So you could transform your data into something like: 
&amp;lt;1, [1,0,0,0,0,0]&gt;, &amp;lt;2, [1, 1,0,0,0,0]&gt;, &amp;lt;3, [2, 1,0,0,0,0]&gt;, &amp;lt;4, [1, 2, 1, 1,0,0]&gt;, &amp;lt;5, [1, 1, 1, 2, 2, 1]&gt;&lt;/p&gt;

&lt;p&gt;Another option that occurs to me is to add the limit dynamically. Let say you have your training and test set. You can search for the biggest length and create an algorithm that adds the zeros to both datasets. Then let''s say a new data you want to predict has a bigger length, then you''ll need to recompute all training and test with for this new prediction. You can even check how extending the limit affects your model.&lt;/p&gt;
', NULL, NULL, '''/a'':18C,44C ''/p'':123C,205C ''/stable/modules/multiclass.html'':12C,40C ''0'':86C,87C,88C,89C,90C,95C,96C,97C,98C,103C,104C,105C,106C,113C,114C ''1'':35C,43C,47C,48C,52C,55C,57C,58C,61C,62C,63C,66C,84C,85C,93C,94C,102C,109C,111C,112C,117C,118C,119C,122C ''2'':46C,51C,56C,64C,65C,92C,101C,110C,120C,121C ''3'':50C,100C ''4'':54C,108C ''5'':60C,116C ''6'':73C ''ad'':25C ''add'':133C,158C ''affect'':202C ''algorithm'':17C,156C ''anoth'':125C ''bigger'':177C ''biggest'':151C ''check'':197C ''classif'':16C ''could'':76C ''creat'':154C ''data'':30C,79C,170C ''dataset'':163C ''dynam'':136C ''even'':196C ''extend'':199C ''href'':9C,37C ''length'':152C,178C ''let'':137C,165C ''like'':32C,82C ''limit'':135C,201C ''ll'':181C ''lt'':34C,45C,49C,53C,59C,83C,91C,99C,107C,115C ''make'':20C ''maximum'':68C ''model'':204C ''multilabel'':15C ''need'':182C ''new'':169C,192C ''nofollow'':14C,42C ''number'':69C ''occur'':128C ''option'':126C ''output'':22C,71C ''p'':1C,124C ''predict'':174C,193C ''recomput'':184C ''rel'':13C,41C ''say'':138C,167C ''scikit-learn.org'':11C,39C ''scikit-learn.org/stable/modules/multiclass.html'':10C,38C ''search'':148C ''set'':6C,145C ''someth'':81C ''standard'':23C ''test'':144C,188C ''train'':142C,186C ''transform'':77C ''tri'':4C ''want'':172C ''would'':3C ''zero'':26C,160C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9552, 2, '2015-12-29 23:06:19.167', 1, NULL, NULL, '&lt;p&gt;I would suggest &lt;a href="https://en.wikipedia.org/wiki/Recurrent_neural_network" rel="nofollow"&gt;Recurrent Neural Nets&lt;/a&gt;. They are good for time series, however they need a huge dataset to get good performance. Here you can find an &lt;a href="https://github.com/Element-Research/rnn" rel="nofollow"&gt;implementation in torch&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':15C,47C ''/element-research/rnn'':41C ''/p'':48C ''/wiki/recurrent_neural_network'':9C ''dataset'':27C ''en.wikipedia.org'':8C ''en.wikipedia.org/wiki/recurrent_neural_network'':7C ''find'':35C ''get'':29C ''github.com'':40C ''github.com/element-research/rnn'':39C ''good'':18C,30C ''howev'':22C ''href'':6C,38C ''huge'':26C ''implement'':44C ''need'':24C ''net'':14C ''neural'':13C ''nofollow'':11C,43C ''p'':1C ''perform'':31C ''recurr'':12C ''rel'':10C,42C ''seri'':21C ''suggest'':4C ''time'':20C ''torch'':46C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9553, 1, '2015-12-30 02:46:00.233', 1, 31, 'Tokenizing words of length 1, what would happen if I do topic modeling?', '&lt;p&gt;Suppose my dataset contains some very small documents (about 20 words each). And each of them may have words in at least two languages (combination of malay and english, for instance). Also there are some numbers inside each of them.&lt;/p&gt;

&lt;p&gt;Just out of curiosity, while usually customizable, why are some tokenizers choose to ignore tokens that are just numbers by default, or anything that doesn''t meet certain length? For example, the &lt;a href="http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html" rel="nofollow"&gt;CountVectorizer&lt;/a&gt; in scikit-learn ignores words that do not have more than 1 alphanumeric. And the &lt;a href="http://radimrehurek.com/gensim/utils.html#gensim.utils.tokenize" rel="nofollow"&gt;tokenizer&lt;/a&gt; utility in gensim ignores words with digits.&lt;/p&gt;

&lt;p&gt;I used CountVectorizer in the end and made it to accept words containing digits and words with length 1 as well. This is because I needed exact match, as slight differences in those words of length 1 may point to a different document.&lt;/p&gt;

&lt;p&gt;I am currently trying topic modeling (&lt;a href="http://radimrehurek.com/gensim/models/lsimodel.html" rel="nofollow"&gt;gensim''s LSI&lt;/a&gt;) to perform topic analysis, but the main intention of doing that is to be able to reduce the dimension so I can feed it to spotify''s &lt;a href="https://github.com/spotify/annoy" rel="nofollow"&gt;annoy library&lt;/a&gt; for quick approximate searching (from 58k features, to 500 topics). Also I expect it to reduce the time and memory taken to compute classifier models.&lt;/p&gt;

&lt;p&gt;So the question is really, if I tokenize words of length 1, would it makes sense to perform topic modeling? Even if I do a brute force search by just comparing cosine similarity (no fancy ANN), would it affect the precision or accuracy (i.e. would it be able to recognize the slight change in words of length 1 in query and retrieve the right document)?&lt;/p&gt;
', 'information-retrieval  lsi', NULL, '''/a'':101C,126C,197C,234C ''/gensim/models/lsimodel.html'':191C ''/gensim/utils.html#gensim.utils.tokenize'':122C ''/p'':59C,134C,179C,260C,327C ''/spotify/annoy'':229C ''/stable/tutorial/text_analytics/working_with_text_data.html'':97C ''1'':5A,114C,154C,172C,273C,319C ''20'':28C ''500'':243C ''58k'':240C ''abl'':212C,309C ''accept'':146C ''accuraci'':304C ''affect'':300C ''alphanumer'':115C ''also'':50C,245C ''analysi'':201C ''ann'':297C ''annoy'':232C ''anyth'':83C ''approxim'':237C ''brute'':287C ''certain'':88C ''chang'':314C ''choos'':72C ''classifi'':258C ''combin'':43C ''compar'':292C ''comput'':257C ''contain'':22C,148C ''cosin'':293C ''countvector'':100C,138C ''curios'':64C ''current'':183C ''customiz'':67C ''dataset'':21C ''default'':81C ''differ'':166C,177C ''digit'':133C,149C ''dimens'':216C ''document'':26C,178C,326C ''doesn'':85C ''end'':141C ''english'':47C ''even'':282C ''exact'':162C ''exampl'':91C ''expect'':247C ''fanci'':296C ''featur'':241C ''feed'':220C ''forc'':288C ''gensim'':129C,194C ''github.com'':228C ''github.com/spotify/annoy'':227C ''happen'':8A ''href'':94C,119C,188C,226C ''i.e'':305C ''ignor'':74C,106C,130C ''inform'':15B ''information-retriev'':14B ''insid'':55C ''instanc'':49C ''intent'':205C ''languag'':42C ''learn'':105C ''least'':40C ''length'':4A,89C,153C,171C,272C,318C ''librari'':233C ''lsi'':17B,196C ''made'':143C ''main'':204C ''make'':276C ''malay'':45C ''match'':163C ''may'':35C,173C ''meet'':87C ''memori'':254C ''model'':13A,186C,259C,281C ''need'':161C ''nofollow'':99C,124C,193C,231C ''number'':54C,79C ''p'':18C,60C,135C,180C,261C ''perform'':199C,279C ''point'':174C ''precis'':302C ''queri'':321C ''question'':264C ''quick'':236C ''radimrehurek.com'':121C,190C ''radimrehurek.com/gensim/models/lsimodel.html'':189C ''radimrehurek.com/gensim/utils.html#gensim.utils.tokenize'':120C ''realli'':266C ''recogn'':311C ''reduc'':214C,250C ''rel'':98C,123C,192C,230C ''retriev'':16B,323C ''right'':325C ''scikit'':104C ''scikit-learn'':103C ''scikit-learn.org'':96C ''scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html'':95C ''search'':238C,289C ''sens'':277C ''similar'':294C ''slight'':165C,313C ''small'':25C ''spotifi'':223C ''suppos'':19C ''taken'':255C ''time'':252C ''token'':1A,71C,75C,125C,269C ''topic'':12A,185C,200C,244C,280C ''tri'':184C ''two'':41C ''use'':137C ''usual'':66C ''util'':127C ''well'':156C ''word'':2A,29C,37C,107C,131C,147C,151C,169C,270C,316C ''would'':7A,274C,298C,306C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9555, 1, '2015-12-30 10:11:43.473', 0, 14, 'How to create neighbour object in R for spatial autoregression modelling?', '&lt;p&gt;I am building a OLS regression model to explain rent of properties in India by using features like size of the properties and the amenities available.&lt;/p&gt;

&lt;p&gt;In the process I read about SAR models giving better results then OLS.&lt;/p&gt;

&lt;p&gt;I need to do Moran''s I test to determine if there is spatial pattern in the error of OLS.&lt;/p&gt;

&lt;p&gt;Material I read about above problem uses a neighbour object and that is created by "spdep" package in r.&lt;/p&gt;

&lt;p&gt;I am not able to understand how can I create that object of class nb in R to proceed with SAR modeling.&lt;/p&gt;

&lt;p&gt;I have lat and long for every property which is a row in my dataset.&lt;/p&gt;

&lt;p&gt;Please tell me how can I create neighbour list in R to perform Moran''s I test?&lt;/p&gt;
', 'machine-learning  r  regression  linear-regression', NULL, '''/p'':47C,62C,84C,105C,129C,146C,165C ''abl'':110C ''amen'':45C ''autoregress'':10A ''avail'':46C ''better'':58C ''build'':23C ''class'':120C ''creat'':3A,99C,116C,154C ''dataset'':145C ''determin'':73C ''error'':81C ''everi'':137C ''explain'':29C ''featur'':37C ''give'':57C ''india'':34C ''lat'':133C ''learn'':14B ''like'':38C ''linear'':18B ''linear-regress'':17B ''list'':156C ''long'':135C ''machin'':13B ''machine-learn'':12B ''materi'':86C ''model'':11A,27C,56C,128C ''moran'':68C,161C ''nb'':121C ''need'':65C ''neighbour'':4A,94C,155C ''object'':5A,95C,118C ''ol'':25C,61C,83C ''p'':20C,48C,63C,85C,106C,130C,147C ''packag'':102C ''pattern'':78C ''perform'':160C ''pleas'':148C ''problem'':91C ''proceed'':125C ''process'':51C ''properti'':32C,42C,138C ''r'':7A,15B,104C,123C,158C ''read'':53C,88C ''regress'':16B,19B,26C ''rent'':30C ''result'':59C ''row'':142C ''sar'':55C,127C ''size'':39C ''spatial'':9A,77C ''spdep'':101C ''tell'':149C ''test'':71C,164C ''understand'':112C ''use'':36C,92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9557, 2, '2015-12-30 15:28:37.507', 8, NULL, NULL, '&lt;p&gt;The &lt;a href="https://github.com/JuliaLang/julia"&gt;Julia project&lt;/a&gt; is one which I actively contribute to, including the advanced computing and XGBoost libraries. So, I can definitely vouch for it''s maintenence and the quality of the community.&lt;/p&gt;

&lt;p&gt;Some really good open source data science projects where even the beginners can contribute are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md"&gt;Sklearn&lt;/a&gt;: Always developing at a rapid pace, the sklearn community is always open to new developers and contributors.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://h2o.ai/"&gt;H2O&lt;/a&gt;: H2O is another fast growing data science projects, working on scalable machine learning and Deep Learning solutions. &lt;/li&gt;
&lt;li&gt;&lt;a href="https://github.com/datasciencemasters/go"&gt;Go&lt;/a&gt;: Open source data science road map and resources. Not really a technical project, but is very helpful for absolute beginners and aspiring analysts.&lt;/li&gt;
&lt;li&gt;&lt;a href="https://github.com/lisa-lab/pylearn2"&gt;Pylearn2&lt;/a&gt;: Another fast growing Machine Learning and Deep Learning project. &lt;/li&gt;
&lt;li&gt;&lt;a href="https://github.com/JohnLangford/vowpal_wabbit/wiki"&gt;Vowpal Wabbit&lt;/a&gt;: The Vowpal Wabbit (VW) project is a fast out-of-core learning system sponsored by Microsoft Research and (previously) Yahoo! Research.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Here is a &lt;a href="https://www.quora.com/What-are-some-interesting-open-source-data-science-projects-to-contribute-on-GitHub"&gt;Quora discussion&lt;/a&gt; on such projects and some more which are not mentioned in this answer. &lt;/p&gt;

&lt;p&gt;Here is a another &lt;a href="http://www.kdnuggets.com/2015/06/top-20-python-machine-learning-open-source-projects.html"&gt;nice discussion&lt;/a&gt; about open source Data Science and ML projects in Python.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9607, 1, '2016-01-04 12:51:14.62', 0, 8, 'Open cv and computer vision', '&lt;p&gt;I''m new to computer vision, and I''m looking for a good place to start from, what''s better between open cv in python or open cv in c++&lt;/p&gt;
', 'computer-vision', NULL, '''/p'':40C ''better'':29C ''c'':39C ''comput'':4A,7B,14C ''computer-vis'':6B ''cv'':2A,32C,37C ''good'':22C ''look'':19C ''m'':11C,18C ''new'':12C ''open'':1A,31C,36C ''p'':9C ''place'':23C ''python'':34C ''start'':25C ''vision'':5A,8B,15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9560, 1, '2015-12-30 19:32:09.643', 3, 36, 'Research in random forest algorithms able to switch data sets', '&lt;p&gt;I''m curious as to whether research been done into random forests that combine unsupervised with supervised learning in a way allowing a single algorithm to find patterns in, and work with, multiple different data sets. I have googled every possible way to find research on this, and have come up empty. Can anyone point me in the right direction?&lt;/p&gt;
', 'random-forest  supervised-learning  unsupervised-learning', NULL, '''/p'':81C ''abl'':6A ''algorithm'':5A,45C ''allow'':42C ''anyon'':74C ''combin'':34C ''come'':70C ''curious'':23C ''data'':9A,55C ''differ'':54C ''direct'':80C ''done'':29C ''empti'':72C ''everi'':60C ''find'':47C,64C ''forest'':4A,13B,32C ''googl'':59C ''learn'':16B,19B,38C ''m'':22C ''multipl'':53C ''p'':20C ''pattern'':48C ''point'':75C ''possibl'':61C ''random'':3A,12B,31C ''random-forest'':11B ''research'':1A,27C,65C ''right'':79C ''set'':10A,56C ''singl'':44C ''supervis'':15B,37C ''supervised-learn'':14B ''switch'':8A ''unsupervis'':18B,35C ''unsupervised-learn'':17B ''way'':41C,62C ''whether'':26C ''work'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9572, 1, '2016-01-01 00:30:14.717', 6, 120, 'Software Testing for Data Science in R', '&lt;p&gt;I often use &lt;a href="http://docs.python-guide.org/en/latest/writing/tests/"&gt;Nose, Tox or Unittest&lt;/a&gt; when testing my python code, specially when it has to be integrated with other modules or other pieces of code. However, now that I''ve found myself using R more than python for ML modelling and development. I realized that I don''t really test my R code (And more importantly I really don''t know how to do it well). So my question is, what are good packages that allow you to test R code in a similar manner as Nose, Tox or Unittest do in Python. Additional references such as tutorials will be greatly appreciated as well.&lt;/p&gt;

&lt;p&gt;Bonus points for packages in R similar to &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://hypothesis.readthedocs.org/en/latest/"&gt;Hypothesis&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;or&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://feature-forge.readthedocs.org/en/latest/"&gt;Feature Forge&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;Related Talk:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.youtube.com/watch?v=GEqM9uJi64Q"&gt;Trey Causey: Testing for Data Scientists&lt;/a&gt; &lt;/p&gt;
', 'r  software-development', NULL, '''/a'':25C,145C,159C,180C ''/en/latest/'':143C,156C ''/en/latest/writing/tests/'':20C ''/li'':150C,160C ''/ol'':161C ''/p'':125C,135C,146C,149C,167C,181C ''/strong'':166C ''/watch?v=geqm9uji64q'':173C ''addit'':114C ''allow'':96C ''appreci'':122C ''bonus'':127C ''causey'':175C ''code'':30C,45C,73C,101C ''data'':4A,178C ''develop'':11B,62C ''docs.python-guide.org'':19C ''docs.python-guide.org/en/latest/writing/tests/'':18C ''featur'':157C ''feature-forge.readthedocs.org'':155C ''feature-forge.readthedocs.org/en/latest/'':154C ''forg'':158C ''found'':51C ''good'':93C ''great'':121C ''howev'':46C ''href'':17C,140C,153C,170C ''hypothesi'':144C ''hypothesis.readthedocs.org'':142C ''hypothesis.readthedocs.org/en/latest/'':141C ''import'':76C ''integr'':37C ''know'':81C ''li'':137C,151C ''manner'':105C ''ml'':59C ''model'':60C ''modul'':40C ''nose'':21C,107C ''often'':14C ''ol'':136C ''p'':12C,126C,138C,147C,162C,168C ''packag'':94C,130C ''piec'':43C ''point'':128C ''python'':29C,57C,113C ''question'':89C ''r'':7A,8B,54C,72C,100C,132C ''realiz'':64C ''realli'':69C,78C ''refer'':115C ''relat'':164C ''scienc'':5A ''scientist'':179C ''similar'':104C,133C ''softwar'':1A,10B ''software-develop'':9B ''special'':31C ''strong'':163C ''talk'':165C ''test'':2A,27C,70C,99C,176C ''tox'':22C,108C ''trey'':174C ''tutori'':118C ''unittest'':24C,110C ''use'':15C,53C ''ve'':50C ''well'':86C,124C ''www.youtube.com'':172C ''www.youtube.com/watch?v=geqm9uji64q'':171C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9561, 2, '2015-12-31 00:17:17.063', 4, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Semi-Supervised Learning&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The combination of unsupervised learning and supervised learning is referred to as &lt;strong&gt;&lt;code&gt;semi-supervised learning&lt;/code&gt;&lt;/strong&gt;, which is the concept that I believe you are searching for.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.slideshare.net/dav009/label-propagation-semisupervised-learning-with-applications-to-nlp" rel="nofollow"&gt;&lt;code&gt;Label propagation&lt;/code&gt;&lt;/a&gt; is often cited when outlining the heuristics of &lt;code&gt;semi-supervised learning&lt;/code&gt;.  The essence is to employ clustering, but to use a tiny set of known cases in order to derive (or propogate) the labels of the clusters.  Hence one is able to use a small set of labeled cases to classify a much larger set of unsupervised data.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Here are some references:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Semi-supervised_learning" rel="nofollow"&gt;Wikipedia has an entry on the &lt;code&gt;semi-supervised learning&lt;/code&gt;&lt;/a&gt;.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://scikit-learn.org/stable/modules/label_propagation.html" rel="nofollow"&gt;The scikit learn User Guide is often a useful starting point and has a label propogation routine&lt;/a&gt;.&lt;/li&gt;
&lt;li&gt;There are, in fact, papers treating &lt;a href="http://www.icg.tugraz.at/publications/pdf/srf.pdf" rel="nofollow"&gt;&lt;code&gt;semi-supervised&lt;/code&gt; &lt;code&gt;random forest&lt;/code&gt; models&lt;/a&gt;. &lt;/li&gt;
&lt;li&gt;&lt;a href="http://research.microsoft.com/pubs/155552/decisionForests_MSR_TR_2011_114.pdf" rel="nofollow"&gt;Another one here&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/a'':54C,146C,173C,199C,212C ''/code'':28C,53C,68C,145C,193C,197C ''/dav009/label-propagation-semisupervised-learning-with-applications-to-nlp'':47C ''/li'':147C,174C,200C,213C ''/p'':8C,41C,116C,124C,219C ''/publications/pdf/srf.pdf'':186C ''/pubs/155552/decisionforests_msr_tr_2011_114.pdf'':206C ''/stable/modules/label_propagation.html'':153C ''/strong'':7C,29C,123C ''/ul'':214C ''/wiki/semi-supervised_learning'':131C ''abl'':98C ''anoth'':209C ''believ'':36C ''case'':83C,106C ''cite'':57C ''classifi'':108C ''cluster'':74C,94C ''code'':23C,50C,63C,140C,189C,194C ''combin'':11C ''concept'':33C ''data'':115C ''deriv'':87C ''employ'':73C ''en.wikipedia.org'':130C ''en.wikipedia.org/wiki/semi-supervised_learning'':129C ''entri'':137C ''essenc'':70C ''fact'':179C ''forest'':196C ''guid'':160C ''help'':218C ''henc'':95C ''heurist'':61C ''hope'':216C ''href'':44C,128C,150C,183C,203C ''known'':82C ''label'':51C,91C,105C,170C ''larger'':111C ''learn'':6C,14C,17C,27C,67C,144C,158C ''li'':126C,148C,175C,201C ''model'':198C ''much'':110C ''nofollow'':49C,133C,155C,188C,208C ''often'':56C,162C ''one'':96C,210C ''order'':85C ''outlin'':59C ''p'':1C,9C,42C,117C,215C ''paper'':180C ''point'':166C ''propag'':52C ''propog'':89C,171C ''random'':195C ''refer'':19C,122C ''rel'':48C,132C,154C,187C,207C ''research.microsoft.com'':205C ''research.microsoft.com/pubs/155552/decisionforests_msr_tr_2011_114.pdf'':204C ''routin'':172C ''scikit'':157C ''scikit-learn.org'':152C ''scikit-learn.org/stable/modules/label_propagation.html'':151C ''search'':39C ''semi'':4C,25C,65C,142C,191C ''semi-supervis'':3C,24C,64C,141C,190C ''set'':80C,103C,112C ''small'':102C ''start'':165C ''strong'':2C,22C,118C ''supervis'':5C,16C,26C,66C,143C,192C ''tini'':79C ''treat'':181C ''ul'':125C ''unsupervis'':13C,114C ''use'':77C,100C,164C ''user'':159C ''wikipedia'':134C ''www.icg.tugraz.at'':185C ''www.icg.tugraz.at/publications/pdf/srf.pdf'':184C ''www.slideshare.net'':46C ''www.slideshare.net/dav009/label-propagation-semisupervised-learning-with-applications-to-nlp'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9562, 1, '2015-12-31 03:53:38.003', 1, 91, 'Using R and Python together', '&lt;p&gt;I''m new in this field and I started working with data by using R. Because of that I find R much easier to approach a data project. But apparently an employer wants you to know an object-oriented? language like Python. 
So it would be smart to think I can use Python just when I need to deal with a complex programming process, like replacing the na''s in Titanic/kaggle project with the average based on name and to use R for anything else? So use them both interchangeably?&lt;br&gt;
Beside the fact that Python is more programming oriented I don''t see why somebody would use it over R...&lt;/p&gt;
', 'r  python', NULL, '''/p'':121C ''anyth'':93C ''appar'':38C ''approach'':33C ''averag'':84C ''base'':85C ''besid'':101C ''br'':100C ''complex'':71C ''data'':20C,35C ''deal'':68C ''easier'':31C ''els'':94C ''employ'':40C ''fact'':103C ''field'':14C ''find'':28C ''interchang'':99C ''know'':44C ''languag'':49C ''like'':50C,74C ''m'':10C ''much'':30C ''na'':77C ''name'':87C ''need'':66C ''new'':11C ''object'':47C ''object-ori'':46C ''orient'':48C,109C ''p'':8C ''process'':73C ''program'':72C,108C ''project'':36C,81C ''python'':4A,7B,51C,62C,105C ''r'':2A,6B,23C,29C,91C,120C ''replac'':75C ''see'':113C ''smart'':56C ''somebodi'':115C ''start'':17C ''think'':58C ''titanic/kaggle'':80C ''togeth'':5A ''use'':1A,22C,61C,90C,96C,117C ''want'':41C ''work'':18C ''would'':54C,116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9563, 2, '2015-12-31 05:08:46.897', 3, NULL, NULL, '&lt;p&gt;Several clarifications: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;you can program with object-oriented (OOP) concepts in R, even though OOP in R has slightly different syntax from other languages. Methods do not bind to objects. In R, different method versions will be involved based on the input argument classes (and types). (Ref: &lt;a href="http://adv-r.had.co.nz/OO-essentials.html" rel="nofollow"&gt;Advanced R&lt;/a&gt;)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;you can also replace nans with mean / any stat. / value in R using a mask if you store the data in a dataframe (See &lt;a href="http://stackoverflow.com/questions/25835643/replacing-missing-values-in-r-with-column-mean"&gt;SO post&lt;/a&gt;)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;There is no problem using them interchangeably. I use R from Python using the package RPy2. I assume it is equally easy to do it the other way round. &lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;At the end of the day, any language is only as good as how much the users know about it. Use one that you are more familiar with and try to learn it properly using the vast online resources online.&lt;/p&gt;
', NULL, NULL, '''/a'':63C,99C ''/li'':65C,101C,134C ''/oo-essentials.html'':58C ''/p'':4C,64C,100C,133C,177C ''/questions/25835643/replacing-missing-values-in-r-with-column-mean'':96C ''/ul'':135C ''adv-r.had.co.nz'':57C ''adv-r.had.co.nz/oo-essentials.html'':56C ''advanc'':61C ''also'':70C ''argument'':49C ''assum'':121C ''base'':45C ''bind'':34C ''clarif'':3C ''class'':50C ''concept'':16C ''data'':87C ''datafram'':90C ''day'':142C ''differ'':26C,39C ''easi'':125C ''end'':139C ''equal'':124C ''even'':19C ''familiar'':163C ''good'':148C ''href'':55C,93C ''input'':48C ''interchang'':110C ''involv'':44C ''know'':154C ''languag'':30C,144C ''learn'':168C ''li'':6C,66C,102C ''mask'':82C ''mean'':74C ''method'':31C,40C ''much'':151C ''nan'':72C ''nofollow'':60C ''object'':13C,36C ''object-ori'':12C ''one'':158C ''onlin'':174C,176C ''oop'':15C,21C ''orient'':14C ''p'':1C,7C,67C,103C,136C ''packag'':118C ''post'':98C ''problem'':107C ''program'':10C ''proper'':170C ''python'':115C ''r'':18C,23C,38C,62C,79C,113C ''ref'':53C ''rel'':59C ''replac'':71C ''resourc'':175C ''round'':132C ''rpy2'':119C ''see'':91C ''sever'':2C ''slight'':25C ''stackoverflow.com'':95C ''stackoverflow.com/questions/25835643/replacing-missing-values-in-r-with-column-mean'':94C ''stat'':76C ''store'':85C ''syntax'':27C ''though'':20C ''tri'':166C ''type'':52C ''ul'':5C ''use'':80C,108C,112C,116C,157C,171C ''user'':153C ''valu'':77C ''vast'':173C ''version'':41C ''way'':131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9564, 2, '2015-12-31 10:24:18.86', 2, NULL, NULL, '&lt;p&gt;Convolutional neural nets share weights across the whole input image and this drastically reduces the number of weights. For example you could have a 3x3 grid where each point has its own weight, this scans over each of the 3 channels in the 1024x1024 image. Only 9 weights are used for each channel making 27 weights. If you have, say 10 of these grids then that''s only 270 weights! &lt;/p&gt;

&lt;p&gt;Furthermore by sharing weights you create some translation invariance over the input space which is a desirable property in things like object recognition. &lt;/p&gt;
', NULL, NULL, '''/p'':72C,97C ''10'':62C ''1024x1024'':45C ''27'':56C ''270'':70C ''3'':41C ''3x3'':26C ''9'':48C ''across'':7C ''channel'':42C,54C ''convolut'':2C ''could'':23C ''creat'':79C ''desir'':90C ''drastic'':14C ''exampl'':21C ''furthermor'':74C ''grid'':27C,65C ''imag'':11C,46C ''input'':10C,85C ''invari'':82C ''like'':94C ''make'':55C ''net'':4C ''neural'':3C ''number'':17C ''object'':95C ''p'':1C,73C ''point'':30C ''properti'':91C ''recognit'':96C ''reduc'':15C ''say'':61C ''scan'':36C ''share'':5C,76C ''space'':86C ''thing'':93C ''translat'':81C ''use'':51C ''weight'':6C,19C,34C,49C,57C,71C,77C ''whole'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9565, 2, '2015-12-31 12:14:42.187', 1, NULL, NULL, '&lt;p&gt;You have to &lt;strong&gt;convert your matrix into a graph&lt;/strong&gt;.
For example, started from your "g" :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;G &amp;lt;- as.undirected(graph.adjacency(g, weighted = T))
plot(G, edge.color=c("red","green")[sign(E(G)$weight)/2 + 1.5], 
     edge.width = 3 *abs(E(G)$weight))
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/2'':39C ''/code'':47C ''/p'':19C ''/pre'':48C ''/strong'':12C ''1.5'':40C ''3'':42C ''ab'':43C ''as.undirected'':24C ''c'':32C ''code'':21C ''convert'':6C ''e'':36C,44C ''edge.color'':31C ''edge.width'':41C ''exampl'':14C ''g'':18C,22C,26C,30C,37C,45C ''graph'':11C ''graph.adjacency'':25C ''green'':34C ''lt'':23C ''matrix'':8C ''p'':1C ''plot'':29C ''pre'':20C ''red'':33C ''sign'':35C ''start'':15C ''strong'':5C ''weight'':27C,38C,46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9566, 2, '2015-12-31 12:37:01.817', 3, NULL, NULL, '&lt;p&gt;A neural network is in principle a good choice when you have A LOT of similar data and classification tasks. Predicting the next character (or word... which is just multiple characters) is such a szenario. I don''t think it really matters which kind of language you have, as long as you have enough training data of the same kind.&lt;/p&gt;

&lt;p&gt;See &lt;a href="http://karpathy.github.io/2015/05/21/rnn-effectiveness/" rel="nofollow"&gt;The Unreasonable Effectiveness of Recurrent Neural Networks&lt;/a&gt; for a nice article where a recurrent neural network (RNN) was used as a character predictor to write complete texts. They also have code on &lt;a href="https://github.com/karpathy/char-rnn" rel="nofollow"&gt;github.com/karpathy/char-rnn&lt;/a&gt; ready to train / go. You can feed it with a start string and ask for the next characters / words.&lt;/p&gt;
', NULL, NULL, '''/2015/05/21/rnn-effectiveness/'':69C ''/a'':79C ''/karpathy/char-rnn'':109C ''/karpathy/char-rnn&lt;/a&gt;'':114C ''/p'':62C,134C ''also'':101C ''articl'':83C ''ask'':128C ''charact'':25C,32C,94C,132C ''choic'':10C ''classif'':20C ''code'':103C ''complet'':98C ''data'':18C,57C ''effect'':74C ''enough'':55C ''feed'':121C ''github.com'':108C,113C ''github.com/karpathy/char-rnn'':107C ''github.com/karpathy/char-rnn&lt;/a&gt;'':112C ''go'':118C ''good'':9C ''href'':66C,106C ''karpathy.github.io'':68C ''karpathy.github.io/2015/05/21/rnn-effectiveness/'':67C ''kind'':45C,61C ''languag'':47C ''long'':51C ''lot'':15C ''matter'':43C ''multipl'':31C ''network'':4C,78C,88C ''neural'':3C,77C,87C ''next'':24C,131C ''nice'':82C ''nofollow'':71C,111C ''p'':1C,63C ''predict'':22C ''predictor'':95C ''principl'':7C ''readi'':115C ''realli'':42C ''recurr'':76C,86C ''rel'':70C,110C ''rnn'':89C ''see'':64C ''similar'':17C ''start'':125C ''string'':126C ''szenario'':36C ''task'':21C ''text'':99C ''think'':40C ''train'':56C,117C ''unreason'':73C ''use'':91C ''word'':27C,133C ''write'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9567, 2, '2015-12-31 17:19:42.423', 0, NULL, NULL, '&lt;p&gt;Labels come up in conjunction with classification when the object does not belong to a single class but to a broader set, hence the term "multilabel learning" or "multilabel classification". Since they refer to discrete classes, they can be used synonymously, but I would recommend using the traditional terminology (&lt;em&gt;classification&lt;/em&gt; when a single class is to be assigned) to avoid confusion.&lt;/p&gt;
', NULL, NULL, '''/em'':53C ''/p'':65C ''assign'':61C ''avoid'':63C ''belong'':14C ''broader'':22C ''class'':18C,37C,57C ''classif'':8C,31C,52C ''come'':3C ''confus'':64C ''conjunct'':6C ''discret'':36C ''em'':51C ''henc'':24C ''label'':2C ''learn'':28C ''multilabel'':27C,30C ''object'':11C ''p'':1C ''recommend'':46C ''refer'':34C ''set'':23C ''sinc'':32C ''singl'':17C,56C ''synonym'':42C ''term'':26C ''terminolog'':50C ''tradit'':49C ''use'':41C,47C ''would'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9568, 1, '2015-12-31 19:18:24.28', 3, 38, 'Database options for JSON storage, queried with Apache Drill', '&lt;p&gt;I am planning to set up a JSON storage system. It will store tens of millions of JSON records, all in the same format. I''d like to be able to query the data using Apache Drill.&lt;/p&gt;

&lt;p&gt;It looks like there is Drill support for MongoDB and Postgres. However, I''m unsure of the pros and cons of each, and how I''d structure the schema if I''d choose Postgres.&lt;/p&gt;
', 'bigdata  mongodb  json  apache-drill', NULL, '''/p'':54C,90C ''abl'':46C ''apach'':8A,14B,52C ''apache-dril'':13B ''bigdata'':10B ''choos'':88C ''con'':75C ''d'':42C,81C,87C ''data'':50C ''databas'':1A ''drill'':9A,15B,53C,61C ''format'':40C ''howev'':67C ''json'':4A,12B,24C,34C ''like'':43C,58C ''look'':57C ''m'':69C ''million'':32C ''mongodb'':11B,64C ''option'':2A ''p'':16C,55C ''plan'':19C ''postgr'':66C,89C ''pros'':73C ''queri'':6A,48C ''record'':35C ''schema'':84C ''set'':21C ''storag'':5A,25C ''store'':29C ''structur'':82C ''support'':62C ''system'':26C ''ten'':30C ''unsur'':70C ''use'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9573, 1, '2016-01-01 08:11:03.38', 2, 21, 'supervised learning and labels', '&lt;p&gt;In &lt;a href="https://en.wikipedia.org/wiki/Support_vector_machine" rel="nofollow"&gt;this&lt;/a&gt; wiki page, I came across with the following phrase.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;When data is not labeled, a supervised learning is not possible, and
  an unsupervised learning is required&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I cannot figure out why supervised learning is not possible?&lt;/p&gt;

&lt;p&gt;Appreciate any help to resolve this ambiguity.&lt;/p&gt;
', 'supervised-learning  unsupervised-learning', NULL, '''/a'':21C ''/blockquote'':52C ''/p'':31C,51C,64C,73C ''/wiki/support_vector_machine'':17C ''across'':26C ''ambigu'':72C ''appreci'':66C ''blockquot'':32C ''came'':25C ''cannot'':55C ''data'':35C ''en.wikipedia.org'':16C ''en.wikipedia.org/wiki/support_vector_machine'':15C ''figur'':56C ''follow'':29C ''help'':68C ''href'':14C ''label'':4A,38C ''learn'':2A,7B,10B,41C,48C,60C ''nofollow'':19C ''p'':11C,33C,53C,65C ''page'':23C ''phrase'':30C ''possibl'':44C,63C ''rel'':18C ''requir'':50C ''resolv'':70C ''supervis'':1A,6B,40C,59C ''supervised-learn'':5B ''unsupervis'':9B,47C ''unsupervised-learn'':8B ''wiki'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9574, 2, '2016-01-01 08:54:20.33', 2, NULL, NULL, '&lt;p&gt;The main difference between supervised and unsupervised learning is the following:&lt;/p&gt;

&lt;p&gt;In supervised learning you have a set of labelled data, meaning that you have the values of the inputs and the outputs. What you try to achieve with machine learning is to find the &lt;strong&gt;true&lt;/strong&gt; relationship between them, what we usually call &lt;strong&gt;the model&lt;/strong&gt; in math. There are many different algorithms in machine learning that allow you to obtain a model of the data. The objective that you seek, and how you can use machine learning, is to predict the output given a new input, once you know the model.&lt;/p&gt;

&lt;p&gt;In unsupervised learning you don''t have the data labelled. You can say that you have the inputs but not the outputs. And the objective is to find some kind of pattern in your data. You can find groups or &lt;strong&gt;clusters&lt;/strong&gt; that you think that belong to the same group or output. Here you also have to obtain a model. And again, the objective you seek is to be able to predict the output given a new input. &lt;/p&gt;

&lt;p&gt;Finally, going back to your question, if you don''t have labels you can not use supervised learning, you have to use unsupervised learning.&lt;/p&gt;
', NULL, NULL, '''/p'':13C,109C,191C,217C ''/strong'':51C,62C,153C ''abl'':182C ''achiev'':41C ''algorithm'':69C ''allow'':74C ''also'':167C ''back'':195C ''belong'':158C ''call'':58C ''cluster'':152C ''data'':24C,82C,119C,145C ''differ'':4C,68C ''final'':193C ''find'':47C,138C,148C ''follow'':12C ''given'':100C,187C ''go'':194C ''group'':149C,162C ''input'':33C,103C,128C,190C ''kind'':140C ''know'':106C ''label'':23C,120C,204C ''learn'':9C,17C,44C,72C,94C,113C,210C,216C ''machin'':43C,71C,93C ''main'':3C ''mani'':67C ''math'':64C ''mean'':25C ''model'':61C,79C,108C,172C ''new'':102C,189C ''object'':84C,135C,176C ''obtain'':77C,170C ''output'':36C,99C,132C,164C,186C ''p'':1C,14C,110C,192C ''pattern'':142C ''predict'':97C,184C ''question'':198C ''relationship'':52C ''say'':123C ''seek'':87C,178C ''set'':21C ''strong'':49C,59C,151C ''supervis'':6C,16C,209C ''think'':156C ''tri'':39C ''true'':50C ''unsupervis'':8C,112C,215C ''use'':92C,208C,214C ''usual'':57C ''valu'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9575, 2, '2016-01-01 09:09:22.127', 4, NULL, NULL, '&lt;p&gt;Packages for unit testing and assertive testing that are actively maintained:
Packages for unit testing&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;testthat: more information on how to use you can find &lt;a href="http://r-pkgs.had.co.nz/tests.html" rel="nofollow"&gt;here&lt;/a&gt; or on &lt;a href="https://github.com/hadley/testthat" rel="nofollow"&gt;github&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Runit: &lt;a href="https://cran.r-project.org/web/packages/RUnit/index.html" rel="nofollow"&gt;Cran page&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Packages for assertions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;assertthat: info on &lt;a href="https://github.com/hadley/assertthat" rel="nofollow"&gt;github&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;assertive: Assertive has a lot of subpackages available in case you do not need all of them. check on cran&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;assertr: info on &lt;a href="https://github.com/tonyfischetti/assertr" rel="nofollow"&gt;github&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;ensurer: info on &lt;a href="https://github.com/smbache/ensurer" rel="nofollow"&gt;github&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;tester: info on &lt;a href="https://github.com/gastonstat/tester" rel="nofollow"&gt;github&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;It is a matter of preference what you want to use for assertions. 
Read &lt;a href="http://bioconductor.org/developers/unitTesting-guidelines/" rel="nofollow"&gt;this bioconductor&lt;/a&gt; page for more info on the difference between RUnit and testthat.&lt;/p&gt;
', NULL, NULL, '''/a'':38C,49C,62C,84C,124C,140C,156C,184C ''/developers/unittesting-guidelines/'':179C ''/gastonstat/tester'':152C ''/hadley/assertthat'':80C ''/hadley/testthat'':45C ''/li'':50C,63C,86C,110C,126C,142C,158C ''/ol'':64C,159C ''/p'':17C,69C,85C,109C,125C,141C,157C,196C ''/smbache/ensurer'':136C ''/tests.html'':34C ''/tonyfischetti/assertr'':120C ''/web/packages/runit/index.html'':57C ''activ'':11C ''assert'':7C,68C,89C,90C,173C ''assertr'':113C ''assertthat'':73C ''avail'':96C ''bioconductor'':183C ''bioconductor.org'':178C ''bioconductor.org/developers/unittesting-guidelines/'':177C ''case'':98C ''check'':106C ''cran'':60C,108C ''cran.r-project.org'':56C ''cran.r-project.org/web/packages/runit/index.html'':55C ''differ'':191C ''ensur'':129C ''find'':29C ''github'':48C,83C,123C,139C,155C ''github.com'':44C,79C,119C,135C,151C ''github.com/gastonstat/tester'':150C ''github.com/hadley/assertthat'':78C ''github.com/hadley/testthat'':43C ''github.com/smbache/ensurer'':134C ''github.com/tonyfischetti/assertr'':118C ''href'':31C,42C,54C,77C,117C,133C,149C,176C ''info'':74C,114C,130C,146C,188C ''inform'':22C ''li'':19C,51C,71C,87C,111C,127C,143C ''lot'':93C ''maintain'':12C ''matter'':164C ''need'':102C ''nofollow'':36C,47C,59C,82C,122C,138C,154C,181C ''ol'':18C,70C ''p'':1C,65C,72C,88C,112C,128C,144C,160C ''packag'':2C,13C,66C ''page'':61C,185C ''prefer'':166C ''r-pkgs.had.co.nz'':33C ''r-pkgs.had.co.nz/tests.html'':32C ''read'':174C ''rel'':35C,46C,58C,81C,121C,137C,153C,180C ''runit'':52C,193C ''subpackag'':95C ''test'':5C,8C,16C ''tester'':145C ''testthat'':20C,195C ''unit'':4C,15C ''use'':26C,171C ''want'':169C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9577, 1, '2016-01-01 09:45:59.92', 0, 24, 'The implementation of overlapping clustering (NEO-K-Means)', '&lt;p&gt;I am researching on overlapping clustering (Clusters are non-disjoint).I found that &lt;a href="http://bigdata.ices.utexas.edu/publication/non-exhaustive-overlapping-k-means/" rel="nofollow"&gt;Neo-K-Means&lt;/a&gt; is probably the state-of-the-art now.But, when I tried  implementing the algorithm with the multi-label data set (music-emotion/scene).I hadn''t got the high result as declared in the paper (My results are around 0.4 F-measure , the paper declare 0.55 for music and 0.626 for scene ). In spite of , I had initialized the experiments with the best situation for K-Means (centroid seeds are means of each class and the total cluster assignments are equal to reality) . I wonder what wrong with my implementation method , orDo I have to do something extra for getting higher result ? &lt;/p&gt;

&lt;p&gt;PS. I have found the &lt;a href="http://dl.acm.org/citation.cfm?id=2783398" rel="nofollow"&gt;further research of Neo-Kmeans&lt;/a&gt; , but I think I should clear this point before go further.&lt;/p&gt;

&lt;p&gt;This is my code &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;while (count &amp;lt; TIMES) {

            DC = new ArrayList&amp;lt;DistanceCollection&amp;gt;();

            for (int i = 0; i &amp;lt; K; i++) {

                cluster[i] = new Cluster();

            }

            for (int i = 0; i &amp;lt; dataList.size(); i++) {

                for (int j = 0; j &amp;lt; K; j++) {

                    DistanceCollection dist = new DistanceCollection();
                    dist.dataNum = dataList.get(i).dataNum;
                    dist.clusterNum = j;
                    dist.distanceFromCluster = euclidean(centroids[j], dataList.get(i));
                    DC.add(dist);

                }

            }

            // sort the distances for argmin(i,j) checking 
            Collections.sort(DC, new DistanceCollectionComparator());



            int totalAssignment = 2585;
            int assignedCluster = -1;
            int assignmentCount = 0;
            int[] dataSelectionCheck = new int[2407];
            int dataMatrix[][] = new int[6][2407];

            for (int i = 0;  assignmentCount &amp;lt; dataList.size(); i++) {

                int clusterNum = DC.get(i).clusterNum;
                int dataNum = DC.get(i).dataNum;

                if (dataMatrix[clusterNum][dataNum] == 0 &amp;amp;&amp;amp; dataSelectionCheck[dataNum] == 0) {

                    cluster[clusterNum].dataMembers.add(dataList.get(dataNum));
                    dataMatrix[clusterNum][dataNum] = 1;
                    dataSelectionCheck[dataNum] = 1;

                    assignmentCount++;

                }

            }

            for (int i = 0; assignmentCount &amp;lt; totalAssignment; i++) {

                int clusterNum = DC.get(i).clusterNum;
                int dataNum = DC.get(i).dataNum;

                if (dataMatrix[clusterNum][dataNum] == 0) {

                    cluster[clusterNum].dataMembers.add(dataList.get(dataNum));
                    dataMatrix[clusterNum][dataNum] = 1; 

                    assignmentCount++;

                }

            }



            for (int i = 0; i &amp;lt; K; i++) {

                if (cluster[i].dataMembers.size() &amp;gt; 0) {

                    for (int j = 0; j &amp;lt; centroids[i].features.length ; j++) {

                        double accumFeaturesValue = 0;

                        for (int k = 0; k &amp;lt; cluster[i].dataMembers.size(); k++) {

                            accumFeaturesValue += cluster[i].dataMembers.get(k).features[j];

                        }

                        centroids[i].features[j] = accumFeaturesValue / cluster[i].dataMembers.size();

                    }
                }

            }


            count++ ;

        }
&lt;/code&gt;&lt;/pre&gt;
', 'machine-learning  data-mining  clustering  k-means  research', NULL, '''-1'':269C ''/a'':47C,175C ''/citation.cfm?id=2783398'':166C ''/code'':411C ''/p'':155C,187C,193C ''/pre'':412C ''/publication/non-exhaustive-overlapping-k-means/'':40C ''/scene'':73C ''0'':209C,221C,229C,272C,287C,306C,311C,328C,347C,361C,371C,375C,384C,388C ''0.4'':90C ''0.55'':97C ''0.626'':101C ''1'':320C,323C,356C ''2407'':277C,283C ''2585'':266C ''6'':282C ''accumfeaturesvalu'':383C,395C,406C ''algorithm'':62C ''amp'':307C,308C ''argmin'':256C ''around'':89C ''arraylist'':202C ''art'':55C ''assign'':131C ''assignedclust'':268C ''assignmentcount'':271C,288C,324C,329C,357C ''best'':114C ''bigdata.ices.utexas.edu'':39C ''bigdata.ices.utexas.edu/publication/non-exhaustive-overlapping-k-means/'':38C ''centroid'':120C,246C,378C,402C ''check'':259C ''class'':126C ''clear'':181C ''cluster'':5A,16B,27C,28C,130C,214C,217C,312C,348C,367C,391C,396C,407C ''clusternum'':293C,296C,304C,313C,318C,334C,337C,345C,349C,354C ''code'':192C,195C ''collections.sort'':260C ''count'':197C,410C ''data'':14B,68C ''data-min'':13B ''datalist.get'':239C,248C,315C,351C ''datalist.size'':224C,290C ''datamatrix'':279C,303C,317C,344C,353C ''datamembers.add'':314C,350C ''datamembers.get'':398C ''datamembers.size'':369C,393C,409C ''datanum'':241C,298C,301C,305C,310C,316C,319C,322C,339C,342C,346C,352C,355C ''dataselectioncheck'':274C,309C,321C ''dc'':200C,261C ''dc.add'':250C ''dc.get'':294C,299C,335C,340C ''declar'':82C,96C ''disjoint'':32C ''dist'':235C,251C ''dist.clusternum'':242C ''dist.datanum'':238C ''dist.distancefromcluster'':244C ''distanc'':254C ''distancecollect'':204C,234C,237C ''distancecollectioncompar'':263C ''dl.acm.org'':165C ''dl.acm.org/citation.cfm?id=2783398'':164C ''doubl'':382C ''emot'':72C ''equal'':133C ''euclidean'':245C ''experi'':111C ''extra'':150C ''f'':92C ''f-measur'':91C ''featur'':400C,404C ''features.length'':380C ''found'':34C,160C ''get'':152C ''go'':185C ''got'':77C ''gt'':205C,370C ''hadn'':75C ''high'':79C ''higher'':153C ''href'':37C,163C ''implement'':2A,60C,142C ''initi'':109C ''int'':207C,219C,227C,264C,267C,270C,273C,276C,278C,281C,285C,292C,297C,326C,333C,338C,359C,373C,386C ''j'':228C,230C,233C,243C,247C,258C,374C,376C,381C,401C,405C ''k'':8A,18B,45C,118C,212C,232C,364C,387C,389C,394C,399C ''k-mean'':17B,117C ''kmean'':174C ''label'':67C ''learn'':12B ''lt'':198C,203C,211C,223C,231C,289C,330C,363C,377C,390C ''machin'':11B ''machine-learn'':10B ''mean'':9A,19B,46C,119C,123C ''measur'':93C ''method'':143C ''mine'':15B ''multi'':66C ''multi-label'':65C ''music'':71C,99C ''music-emot'':70C ''neo'':7A,44C,173C ''neo-k-mean'':6A,43C ''neo-kmean'':172C ''new'':201C,216C,236C,262C,275C,280C ''nofollow'':42C,168C ''non'':31C ''non-disjoint'':30C ''now.but'':56C ''ordo'':144C ''overlap'':4A,26C ''p'':21C,156C,188C ''paper'':85C,95C ''point'':183C ''pre'':194C ''probabl'':49C ''ps'':157C ''realiti'':135C ''rel'':41C,167C ''research'':20B,24C,170C ''result'':80C,87C,154C ''scene'':103C ''seed'':121C ''set'':69C ''situat'':115C ''someth'':149C ''sort'':252C ''spite'':105C ''state'':52C ''state-of-the-art'':51C ''think'':178C ''time'':199C ''total'':129C ''totalassign'':265C,331C ''tri'':59C ''wonder'':137C ''wrong'':139C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9578, 2, '2016-01-01 11:39:24.2', 1, NULL, NULL, '&lt;p&gt;I have never heard of NEO-k-means. So I wouldn''t call it "state of the art": which tools does include it?&lt;/p&gt;

&lt;p&gt;Plenty of methods are published every week and have zero impact because they are redundant, badly written, only work on a single data set, hard to parameterize, or outright irreproducible (As I have not read NEO-k-Means, I''m not claiming any of this applies to this particular algorithm!)&lt;/p&gt;

&lt;p&gt;Nevertheless, it is not the first to do non-overlapping clustering.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Fuzzy c-means (FCM): a soft-asssignment k-means variant&lt;/li&gt;
&lt;li&gt;EM (Gaussian Mixture Modeling) is even more powerful, and has a sound theorertical backing&lt;/li&gt;
&lt;li&gt;Subspace and correlation clusterings are usually overlapping, too.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I''d start with GMM and FCM, as these have been successfully used by many people.&lt;/p&gt;
', NULL, NULL, '''/li'':109C,125C,135C ''/p'':26C,79C,93C,154C ''/ul'':136C ''algorithm'':78C ''appli'':74C ''art'':20C ''asssign'':104C ''back'':124C ''bad'':43C ''c'':98C ''c-mean'':97C ''call'':15C ''claim'':70C ''cluster'':92C,130C ''correl'':129C ''d'':139C ''data'':50C ''em'':111C ''even'':116C ''everi'':33C ''fcm'':100C,144C ''first'':86C ''fuzzi'':96C ''gaussian'':112C ''gmm'':142C ''hard'':52C ''heard'':5C ''impact'':38C ''includ'':24C ''irreproduc'':57C ''k'':9C,65C,106C ''k-mean'':105C ''li'':95C,110C,126C ''m'':68C ''mani'':152C ''mean'':10C,66C,99C,107C ''method'':30C ''mixtur'':113C ''model'':114C ''neo'':8C,64C ''neo-k-mean'':7C,63C ''never'':4C ''nevertheless'':81C ''non'':90C ''non-overlap'':89C ''outright'':56C ''overlap'':91C,133C ''p'':1C,27C,80C,137C ''parameter'':54C ''particular'':77C ''peopl'':153C ''plenti'':28C ''power'':118C ''publish'':32C ''read'':62C ''redund'':42C ''set'':51C ''singl'':49C ''soft'':103C ''soft-asssign'':102C ''sound'':122C ''start'':140C ''state'':17C ''subspac'':127C ''success'':149C ''theorert'':123C ''tool'':22C ''ul'':94C ''use'':150C ''usual'':132C ''variant'':108C ''week'':34C ''work'':46C ''wouldn'':13C ''written'':44C ''zero'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9579, 1, '2016-01-01 18:32:56.21', 0, 32, 'How to interface with Higher Dimension Data', '&lt;p&gt;I''m not sure whether this is the right place to ask this question but I try.&lt;/p&gt;

&lt;p&gt;In order to process visual data, I had to project the data into a higher dimension for example to do clustering.  What would be ways to interface with this data so that&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;A human being could aid in a mostly unsupervised training process and,&lt;/li&gt;
&lt;li&gt;At the same time allow programs to control the robot autonomously&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Is there an available cognitive engine for this purpose or one that can be adapted?&lt;/p&gt;

&lt;p&gt;Need inputs to set me on the right path so any help is welcome.&lt;/p&gt;

&lt;p&gt;Thanks in advance&lt;/p&gt;
', 'machine-learning', NULL, '''/li'':78C,91C ''/ol'':92C ''/p'':29C,63C,109C,125C,130C ''adapt'':108C ''advanc'':129C ''aid'':70C ''allow'':84C ''ask'':23C ''autonom'':90C ''avail'':97C ''cluster'':51C ''cognit'':98C ''control'':87C ''could'':69C ''data'':7A,36C,42C,60C ''dimens'':6A,46C ''engin'':99C ''exampl'':48C ''help'':122C ''higher'':5A,45C ''human'':67C ''input'':112C ''interfac'':3A,57C ''learn'':10B ''li'':65C,79C ''m'':13C ''machin'':9B ''machine-learn'':8B ''most'':73C ''need'':111C ''ol'':64C ''one'':104C ''order'':32C ''p'':11C,30C,93C,110C,126C ''path'':119C ''place'':21C ''process'':34C,76C ''program'':85C ''project'':40C ''purpos'':102C ''question'':25C ''right'':20C,118C ''robot'':89C ''set'':114C ''sure'':15C ''thank'':127C ''time'':83C ''train'':75C ''tri'':28C ''unsupervis'':74C ''visual'':35C ''way'':55C ''welcom'':124C ''whether'':16C ''would'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9618, 2, '2016-01-04 20:12:13.473', 2, NULL, NULL, '&lt;p&gt;The model likely won''t have much predictive power if the input is a single day. No weather patterns longer than one day can be captured that way.&lt;/p&gt;

&lt;p&gt;Instead you should aggregate the days together. You can come up with different features that describe your larger, aggregated unit of time (months, year). For example mean precipitation is a very simple one. Binning the data and using counts within those bins would also work.&lt;/p&gt;

&lt;p&gt;More advanced options would roll the time all the way up to a full year and learn a feature set at that level.&lt;/p&gt;
', NULL, NULL, '''/p'':30C,77C,102C ''advanc'':80C ''aggreg'':35C,50C ''also'':75C ''bin'':65C,73C ''captur'':27C ''come'':41C ''count'':70C ''data'':67C ''day'':17C,24C,37C ''describ'':47C ''differ'':44C ''exampl'':57C ''featur'':45C,97C ''full'':92C ''input'':13C ''instead'':32C ''larger'':49C ''learn'':95C ''level'':101C ''like'':4C ''longer'':21C ''mean'':58C ''model'':3C ''month'':54C ''much'':8C ''one'':23C,64C ''option'':81C ''p'':1C,31C,78C ''pattern'':20C ''power'':10C ''precipit'':59C ''predict'':9C ''roll'':83C ''set'':98C ''simpl'':63C ''singl'':16C ''time'':53C,85C ''togeth'':38C ''unit'':51C ''use'':69C ''way'':29C,88C ''weather'':19C ''within'':71C ''won'':5C ''work'':76C ''would'':74C,82C ''year'':55C,93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10342, 2, '2016-02-21 18:36:25.06', 5, NULL, NULL, '&lt;p&gt;I think that &lt;code&gt;caret&lt;/code&gt; and &lt;code&gt;e1071&lt;/code&gt; serve difference purposes. First lets discuss &lt;code&gt;caret&lt;/code&gt;, its closest competitor is the &lt;code&gt;mlr&lt;/code&gt; package. Both are meta packages that allow to optimize models across parameters. Take for example a problem where you are not sure whether you would want to use Lasso or Ridge to create a model. As explained &lt;a href="https://rstudio-pubs-static.s3.amazonaws.com/22067_48fad02fb1a944e9a8fb1d56c55119ef.html#ridge-regression-and-lasso"&gt;here&lt;/a&gt; &lt;code&gt;caret&lt;/code&gt; allows to choose the optimal lambda based on different type of cross validations. &lt;/p&gt;

&lt;p&gt;The &lt;code&gt;e1071&lt;/code&gt; package instead is a bag of functions developed by the TU Wien. It is probably one of the most popular packages for support vector machines which are used by &lt;code&gt;caret&lt;/code&gt;. But its goal is very different than &lt;code&gt;caret&lt;/code&gt;, it implements learning algorithms and other functions whereas &lt;code&gt;caret&lt;/code&gt; seeks to find the best parameter in learning models.&lt;/p&gt;
', NULL, NULL, '''/22067_48fad02fb1a944e9a8fb1d56c55119ef.html#ridge-regression-and-lasso'':70C ''/a'':72C ''/code'':7C,11C,20C,28C,75C,94C,126C,136C,147C ''/p'':89C,157C ''across'':39C ''algorithm'':140C ''allow'':35C,76C ''bag'':99C ''base'':82C ''best'':152C ''caret'':6C,19C,74C,125C,135C,146C ''choos'':78C ''closest'':22C ''code'':5C,9C,18C,26C,73C,92C,124C,134C,145C ''competitor'':23C ''creat'':61C ''cross'':87C ''develop'':102C ''differ'':13C,84C,132C ''discuss'':17C ''e1071'':10C,93C ''exampl'':43C ''explain'':65C ''find'':150C ''first'':15C ''function'':101C,143C ''goal'':129C ''href'':67C ''implement'':138C ''instead'':96C ''lambda'':81C ''lasso'':57C ''learn'':139C,155C ''let'':16C ''machin'':119C ''meta'':32C ''mlr'':27C ''model'':38C,63C,156C ''one'':110C ''optim'':37C,80C ''p'':1C,90C ''packag'':29C,33C,95C,115C ''paramet'':40C,153C ''popular'':114C ''probabl'':109C ''problem'':45C ''purpos'':14C ''ridg'':59C ''rstudio-pubs-static.s3.amazonaws.com'':69C ''rstudio-pubs-static.s3.amazonaws.com/22067_48fad02fb1a944e9a8fb1d56c55119ef.html#ridge-regression-and-lasso'':68C ''seek'':148C ''serv'':12C ''support'':117C ''sure'':50C ''take'':41C ''think'':3C ''tu'':105C ''type'':85C ''use'':56C,122C ''valid'':88C ''vector'':118C ''want'':54C ''wherea'':144C ''whether'':51C ''wien'':106C ''would'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9595, 2, '2016-01-03 21:24:47.673', 1, NULL, NULL, '&lt;p&gt;You are unlikely to find a tool that will truly do this for you. Various bits of software may produce some nice graphs. However we''re not at a stage where tools can generate interesting questions to ask, ask them, and then explain the results to you.&lt;/p&gt;

&lt;p&gt;If this is commercial, your best bet is to hire someone with an appropriate background and set them some questions. Tell them what your biggest goals and costs are, explain the data to them, and see what happens.  So the answer is a contractor.&lt;/p&gt;
', NULL, NULL, '''/p'':49C,95C ''answer'':91C ''appropri'':64C ''ask'':39C,40C ''background'':65C ''best'':56C ''bet'':57C ''biggest'':75C ''bit'':17C ''commerci'':54C ''contractor'':94C ''cost'':78C ''data'':82C ''explain'':44C,80C ''find'':6C ''generat'':35C ''goal'':76C ''graph'':24C ''happen'':88C ''hire'':60C ''howev'':25C ''interest'':36C ''may'':20C ''nice'':23C ''p'':1C,50C ''produc'':21C ''question'':37C,70C ''re'':27C ''result'':46C ''see'':86C ''set'':67C ''softwar'':19C ''someon'':61C ''stage'':31C ''tell'':71C ''tool'':8C,33C ''truli'':11C ''unlik'':4C ''various'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9582, 1, '2016-01-02 04:45:46.13', 2, 17, 'Sentiment Analysis of comments to understand suport on a topic', '&lt;p&gt;I have downloaded comments from a website which asked people whether they supported or opposed the implementation of a certain political policy related to immigration. I would like resources/ideas on how to extract aggregate support/opposition to this policy. In particular, I need a method that correctly identifies that all of the following comments are "anti-immigrant" in some sense:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;We need to give Americans first priority in the job market&lt;/li&gt;
&lt;li&gt;We should not let Americans suffer more unemployment on account of immigrants&lt;/li&gt;
&lt;li&gt;Immigrants should not be allowed to take American jobs&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Similarly the method should be able to identify "pro-immigrant" comments, such as:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Providing this service to immigrants will be good for the economy&lt;/li&gt;
&lt;li&gt;The American economy will suffer if immigrants aren''t allowed to continue to work here&lt;/li&gt;
&lt;li&gt;I do not think that passing this law will be detrimental to American jobs.&lt;/li&gt;
&lt;/ol&gt;
', 'text-mining', NULL, '''/li'':89C,103C,114C,145C,162C,178C ''/ol'':115C,179C ''/p'':75C,131C ''abl'':122C ''account'':100C ''aggreg'':48C ''allow'':109C,156C ''american'':82C,95C,112C,148C,176C ''analysi'':2A ''anti'':70C ''anti-immigr'':69C ''aren'':154C ''ask'':23C ''certain'':34C ''comment'':4A,18C,67C,128C ''continu'':158C ''correct'':60C ''detriment'':174C ''download'':17C ''economi'':144C,149C ''extract'':47C ''first'':83C ''follow'':66C ''give'':81C ''good'':141C ''identifi'':61C,124C ''immigr'':39C,71C,102C,105C,127C,138C,153C ''implement'':31C ''job'':87C,113C,177C ''law'':171C ''let'':94C ''li'':77C,90C,104C,133C,146C,163C ''like'':42C ''market'':88C ''method'':58C,119C ''mine'':13B ''need'':56C,79C ''ol'':76C,132C ''oppos'':29C ''p'':14C,116C ''particular'':54C ''pass'':169C ''peopl'':24C ''polici'':36C,52C ''polit'':35C ''prioriti'':84C ''pro'':126C ''pro-immigr'':125C ''provid'':134C ''relat'':37C ''resources/ideas'':43C ''sens'':74C ''sentiment'':1A ''servic'':136C ''similar'':117C ''suffer'':96C,151C ''suport'':7A ''support'':27C ''support/opposition'':49C ''take'':111C ''text'':12B ''text-min'':11B ''think'':167C ''topic'':10A ''understand'':6A ''unemploy'':98C ''websit'':21C ''whether'':25C ''work'':160C ''would'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9583, 1, '2016-01-02 09:01:43.163', -2, 88, 'What are the top 10 problems yet to solve in machine learning?', '&lt;p&gt;Can somebody answer that? It would be good if the answer comes with evidences or some research paper. I''m not asking for opinions&lt;/p&gt;
', 'machine-learning  ranking', NULL, '''/p'':42C ''10'':5A ''answer'':20C,28C ''ask'':39C ''come'':29C ''evid'':31C ''good'':25C ''learn'':12A,15B ''m'':37C ''machin'':11A,14B ''machine-learn'':13B ''opinion'':41C ''p'':17C ''paper'':35C ''problem'':6A ''rank'':16B ''research'':34C ''solv'':9A ''somebodi'':19C ''top'':4A ''would'':23C ''yet'':7A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9584, 2, '2016-01-02 10:15:40.907', 1, NULL, NULL, '&lt;p&gt;The libraries usually exclude 1-length tokens and tokens with no alpha-numeric characters because typically they are noise and do not have any descriptive power. That is, these tokens are usually not helpful, say, in distinguishing between relevant vs not relevant documents. &lt;/p&gt;

&lt;p&gt;However, if in your domain you feel like 1-length tokens can be helpful, feel free to use them as well. For example, if all the document that contain &lt;code&gt;1&lt;/code&gt; belong to the same topic, it may be a good idea to preserve this token. &lt;code&gt;1&lt;/code&gt; has descriptive power in this case: it can help distinguish between one particular topic and the rest.&lt;/p&gt;

&lt;p&gt;Now, your next question is about LSI. For LSI there is no difference if a column in the document-term matrix corresponds to a 1-char token or to a 5-char token. So you can use LSI in your analysis. &lt;/p&gt;
', NULL, NULL, '''/code'':79C,97C ''/p'':46C,115C,159C ''1'':6C,56C,78C,96C,142C ''5'':148C ''alpha'':14C ''alpha-numer'':13C ''analysi'':158C ''belong'':80C ''case'':103C ''char'':143C,149C ''charact'':16C ''code'':77C,95C ''column'':132C ''contain'':76C ''correspond'':139C ''descript'':27C,99C ''differ'':129C ''distinguish'':39C,107C ''document'':45C,74C,136C ''document-term'':135C ''domain'':52C ''exampl'':70C ''exclud'':5C ''feel'':54C,62C ''free'':63C ''good'':89C ''help'':36C,61C,106C ''howev'':48C ''idea'':90C ''length'':7C,57C ''librari'':3C ''like'':55C ''lsi'':123C,125C,155C ''matrix'':138C ''may'':86C ''next'':119C ''nois'':21C ''numer'':15C ''one'':109C ''p'':1C,47C,116C ''particular'':110C ''power'':28C,100C ''preserv'':92C ''question'':120C ''relev'':41C,44C ''rest'':114C ''say'':37C ''term'':137C ''token'':8C,10C,32C,58C,94C,144C,150C ''topic'':84C,111C ''typic'':18C ''use'':65C,154C ''usual'':4C,34C ''vs'':42C ''well'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9585, 1, '2016-01-02 17:02:04.953', 1, 22, 'How do a real brain actually learn?', '&lt;p&gt;In the biology class we''ve learned, that the neurons are connected. And if two or more neurons interact with each other often, then the connection gets stronger and stronger, and new connections may form. But if a connection is unused for a while, then it gets weaker and weaker, and it may lost forever. That is how the human brain/memory learns and forgets something.&lt;/p&gt;

&lt;p&gt;I am a bit confused now. Because I''ve started to learn about artificial neurons, and the method of learning is very different. We analyze, how efficient the network was, and before restarting it, we update the weights a bit, based on the relative difference between the returned and the expected value.&lt;/p&gt;

&lt;p&gt;So my question is: How is a real-world brain capable of learning? And does it have a default configuration?&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/p'':78C,132C,155C ''actual'':6A ''analyz'':104C ''artifici'':93C ''base'':120C ''biolog'':15C ''bit'':83C,119C ''brain'':5A,144C ''brain/memory'':73C ''capabl'':145C ''class'':16C ''configur'':154C ''confus'':84C ''connect'':24C,38C,45C,51C ''default'':153C ''differ'':102C,124C ''effici'':106C ''expect'':130C ''forev'':67C ''forget'':76C ''form'':47C ''get'':39C,59C ''human'':72C ''interact'':31C ''learn'':7A,10B,19C,74C,91C,99C,147C ''lost'':66C ''machin'':9B ''machine-learn'':8B ''may'':46C,65C ''method'':97C ''network'':108C ''neuralnetwork'':11B ''neuron'':22C,30C,94C ''new'':44C ''often'':35C ''p'':12C,79C,133C ''question'':136C ''real'':4A,142C ''real-world'':141C ''relat'':123C ''restart'':112C ''return'':127C ''someth'':77C ''start'':89C ''stronger'':40C,42C ''two'':27C ''unus'':53C ''updat'':115C ''valu'':131C ''ve'':18C,88C ''weaker'':60C,62C ''weight'':117C ''world'':143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9586, 2, '2016-01-02 17:09:31.627', 1, NULL, NULL, '&lt;p&gt;I found a similar question to yours from 2013 on Quora. To my knowledge, the problems given in the post are still mostly unsolved. Here you can find the &lt;a href="http://www.quora.com/What-were-the-Top-10-problems-in-Machine-Learning-for-2013" rel="nofollow"&gt;link&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I think #9 is perhaps the greatest obstacle.  How can we create a machine that hears, sees, and learns like a human?  Just like a human child starts at zero and learns from his/her senses, perhaps that is the best way to create a truly intelligent machine.         &lt;/p&gt;
', NULL, NULL, '''/a'':39C ''/p'':40C,89C ''/what-were-the-top-10-problems-in-machine-learning-for-2013'':35C ''2013'':10C ''9'':44C ''best'':81C ''child'':68C ''creat'':53C,84C ''find'':29C ''found'':3C ''given'':18C ''greatest'':48C ''hear'':57C ''his/her'':75C ''href'':32C ''human'':63C,67C ''intellig'':87C ''knowledg'':15C ''learn'':60C,73C ''like'':61C,65C ''link'':38C ''machin'':55C,88C ''most'':24C ''nofollow'':37C ''obstacl'':49C ''p'':1C,41C ''perhap'':46C,77C ''post'':21C ''problem'':17C ''question'':6C ''quora'':12C ''rel'':36C ''see'':58C ''sens'':76C ''similar'':5C ''start'':69C ''still'':23C ''think'':43C ''truli'':86C ''unsolv'':25C ''way'':82C ''www.quora.com'':34C ''www.quora.com/what-were-the-top-10-problems-in-machine-learning-for-2013'':33C ''zero'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9587, 2, '2016-01-02 21:40:25.217', 1, NULL, NULL, '&lt;p&gt;Artificial Neural Networks are algorithms loosely based in how a brain functions so they shouldn''t be treated as the equivalent of brain learning.&lt;/p&gt;

&lt;p&gt;The science that studies all the details of how neurons and brains learn is &lt;a href="http://www.scholarpedia.org/article/Category:Computational_neuroscience" rel="nofollow"&gt;Computational Neuroscience&lt;/a&gt; and the question you ask is pretty much an open question but it has many great hypothesis. &lt;/p&gt;

&lt;p&gt;The method you described about how artificial neural networks update their "weights" is called backpropagation and some authors included O''Reilly, Randall; Munakata, Yuko (2000) in Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain argue that backpropagation is actually the algorithm that drives learning in the brain. In later publications they propose architectures that include the Hippocampus region to re-create short term memory.&lt;/p&gt;

&lt;p&gt;However in recent years, with the advent of mainstream deep learning new ideas have arrived along with old ideas with new approaches. The ones that look to replicate the brain and its function or at least approximate it as much as we currently can are: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Spiking_neural_network" rel="nofollow"&gt;Spiking neural networks&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Hierarchical_temporal_memory" rel="nofollow"&gt;Hierarchical temporal memory / Numenta&lt;/a&gt; (They also have resources and solutions for data science problems like anomaly detection and classification)&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.scholarpedia.org/article/Category:Computational_Intelligence" rel="nofollow"&gt;Other theories in Computational Intelligence&lt;/a&gt; &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Spiking neural networks are computationally expensive and hard to tune, their performance isn''t close to what deep learning can do, however natural brains use some form of Spiking neural networks which tells you about the potential of these kind of models.&lt;/p&gt;

&lt;p&gt;Another interesting mention is &lt;a href="http://arxiv.org/abs/1410.5401" rel="nofollow"&gt;Neural Turing Machines&lt;/a&gt;. It''s an interesting approach on how certain brain inspired processes can be used to compute diverse problems.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9588, 1, '2016-01-03 02:10:10.643', 0, 30, 'How to select particular column in Spark(pyspark)?', '&lt;p&gt;testPassengerId = test.select(''PassengerId'').map(lambda x: x.PassengerId)&lt;/p&gt;

&lt;p&gt;I want to select PassengerId column and make RDD of it. But .select is not working. It says ''RDD'' object has no attribute ''select''&lt;/p&gt;
', 'apache-spark', NULL, '''/p'':20C,46C ''apach'':10B ''apache-spark'':9B ''attribut'':44C ''column'':5A,27C ''lambda'':17C ''make'':29C ''map'':16C ''object'':41C ''p'':12C,21C ''particular'':4A ''passengerid'':15C,26C ''pyspark'':8A ''rdd'':30C,40C ''say'':39C ''select'':3A,25C,34C,45C ''spark'':7A,11B ''test.select'':14C ''testpassengerid'':13C ''want'':23C ''work'':37C ''x'':18C ''x.passengerid'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9589, 1, '2016-01-03 02:57:04.017', 0, 21, 'Ways to improve AI Song Composer algorithm', '&lt;p&gt;I am working on designing a program for algorithmic music generation where it would take input midi files and through machine learning, it would analyze music patterns and generate its own original music (with midi file as output).&lt;/p&gt;

&lt;p&gt;Originally I tried a LSTM (Long Short Term Memory) neural network approach and managed to get decent results.&lt;/p&gt;

&lt;p&gt;Right now I am trying a different approach where I am using a combination of Recurrent Neural Networks and Restricted Boltzmann Machines (from the Theano documentation) and wish to add some more algorithms to improve it in order to make the melodies it produces more melodic.&lt;/p&gt;

&lt;p&gt;I was thinking of looking into musical methods. For example, I was considering on using hidden markov models to deal with chord progression. Is there any other algorithms or musical tactics that I should consider to make my machine learning model produce more melodic results?&lt;/p&gt;
', 'machine-learning  neuralnetwork  algorithms  markov', NULL, '''/p'':53C,73C,121C,168C ''add'':104C ''ai'':4A ''algorithm'':7A,12B,23C,107C,150C ''analyz'':39C ''approach'':66C,82C ''boltzmann'':95C ''chord'':144C ''combin'':88C ''compos'':6A ''consid'':135C,157C ''deal'':142C ''decent'':71C ''design'':19C ''differ'':81C ''document'':100C ''exampl'':132C ''file'':32C,50C ''generat'':25C,43C ''get'':70C ''hidden'':138C ''improv'':3A,109C ''input'':30C ''learn'':10B,36C,162C ''long'':60C ''look'':127C ''lstm'':59C ''machin'':9B,35C,96C,161C ''machine-learn'':8B ''make'':114C,159C ''manag'':68C ''markov'':13B,139C ''melod'':120C,166C ''melodi'':116C ''memori'':63C ''method'':130C ''midi'':31C,49C ''model'':140C,163C ''music'':24C,40C,47C,129C,152C ''network'':65C,92C ''neural'':64C,91C ''neuralnetwork'':11B ''order'':112C ''origin'':46C,55C ''output'':52C ''p'':14C,54C,74C,122C ''pattern'':41C ''produc'':118C,164C ''program'':21C ''progress'':145C ''recurr'':90C ''restrict'':94C ''result'':72C,167C ''right'':75C ''short'':61C ''song'':5A ''tactic'':153C ''take'':29C ''term'':62C ''theano'':99C ''think'':125C ''tri'':57C,79C ''use'':86C,137C ''way'':1A ''wish'':102C ''work'':17C ''would'':28C,38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9630, 2, '2016-01-05 09:06:51.717', 1, NULL, NULL, '&lt;p&gt;Line 7 is what you need:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;set.seed(42)
iris_matrix &amp;lt;- as.matrix(iris[,1:4])
image(iris_matrix)
g&amp;lt;-cor(iris_matrix)
library(igraph)
R&amp;lt;-graph.adjacency(g, mode=c("undirected"), weighted=TRUE, diag=FALSE, add.colnames=NULL, add.rownames=NULL)
R[R &amp;gt; thresh] &amp;lt;- 0 
plot(R)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':53C ''/p'':8C ''/pre'':54C ''0'':50C ''1'':18C ''4'':19C ''42'':12C ''7'':3C ''add.colnames'':41C ''add.rownames'':43C ''as.matrix'':16C ''c'':35C ''code'':10C ''cor'':25C ''diag'':39C ''fals'':40C ''g'':23C,33C ''graph.adjacency'':32C ''gt'':47C ''igraph'':29C ''imag'':20C ''iri'':13C,17C,21C,26C ''librari'':28C ''line'':2C ''lt'':15C,24C,31C,49C ''matrix'':14C,22C,27C ''mode'':34C ''need'':7C ''null'':42C,44C ''p'':1C ''plot'':51C ''pre'':9C ''r'':30C,45C,46C,52C ''set.seed'':11C ''thresh'':48C ''true'':38C ''undirect'':36C ''weight'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9590, 1, '2016-01-03 11:29:14.937', 3, 84, 'What tool to find expected and hidden patterns in data?', '&lt;p&gt;I have no background AT ALL in data science/stats/mathematics. However, I''ve always been interested in what data shows.&lt;/p&gt;

&lt;p&gt;I have a huge dataset right now - daily attendance figures for a factory of ~300 for the past 10 years. I''m interested in finding out answers to questions like is there a pattern of leaves correlated with public holidays? For example, around which holiday (+/- 2 days) are workers most likely to take a leave? This is an expected pattern. Or, was there a significant increase (+10%) in on-time reporting after bonuses were issued. Maybe there are hidden patterns which an algorithm can find.&lt;/p&gt;

&lt;p&gt;Is there a tool I can plug this data in which can help me find these patterns? Google tells me there''s a tool &lt;a href="http://www.i-programmer.info/news/84-database/3501-mine-finding-patterns-in-big-data.html" rel="nofollow"&gt;http://www.i-programmer.info/news/84-database/3501-mine-finding-patterns-in-big-data.html&lt;/a&gt; but I''m not sure if this is the right direction for me.&lt;/p&gt;

&lt;p&gt;I''d appreciate any advice!&lt;/p&gt;
', 'data-mining', NULL, '''+10'':102C ''/news/84-database/3501-mine-finding-patterns-in-big-data.html'':152C ''/news/84-database/3501-mine-finding-patterns-in-big-data.html&lt;/a&gt;'':157C ''/p'':34C,122C,171C,178C ''10'':54C ''2'':81C ''300'':50C ''advic'':177C ''algorithm'':119C ''alway'':27C ''answer'':62C ''appreci'':175C ''around'':78C ''attend'':44C ''background'':18C ''bonus'':109C ''correl'':72C ''d'':174C ''daili'':43C ''data'':10A,12B,22C,32C,132C ''data-min'':11B ''dataset'':40C ''day'':82C ''direct'':168C ''exampl'':77C ''expect'':5A,94C ''factori'':48C ''figur'':45C ''find'':4A,60C,121C,138C ''googl'':141C ''help'':136C ''hidden'':7A,115C ''holiday'':75C,80C ''howev'':24C ''href'':149C ''huge'':39C ''increas'':101C ''interest'':29C,58C ''issu'':111C ''leav'':71C,90C ''like'':65C,86C ''m'':57C,160C ''mayb'':112C ''mine'':13B ''nofollow'':154C ''on-tim'':104C ''p'':14C,35C,123C,172C ''past'':53C ''pattern'':8A,69C,95C,116C,140C ''plug'':130C ''public'':74C ''question'':64C ''rel'':153C ''report'':107C ''right'':41C,167C ''science/stats/mathematics'':23C ''show'':33C ''signific'':100C ''sure'':162C ''take'':88C ''tell'':142C ''time'':106C ''tool'':2A,127C,147C ''ve'':26C ''worker'':84C ''www.i-programmer.info'':151C,156C ''www.i-programmer.info/news/84-database/3501-mine-finding-patterns-in-big-data.html'':150C ''www.i-programmer.info/news/84-database/3501-mine-finding-patterns-in-big-data.html&lt;/a&gt;'':155C ''year'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9594, 1, '2016-01-03 18:31:14.72', 0, 13, 'Spike Slab in r, bad output', '&lt;p&gt;I''ve successfully used Spike Slab in the past, but with this data it seems like something is going wrong. My code is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;require(spikeslab)
&amp;gt;set.seed(2)
&amp;gt;model1_ss&amp;lt;-with(data_use,spikeslab(gdeaths_per_100_thousand~gdealers_per_100_thousand+Physically.Unhealthy.Days+Mentally.Unhealthy.Days, na.rm=TRUE))
&amp;gt;summary(model1_ss)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;the first 10 lines of data_use is &lt;/p&gt;

&lt;pre&gt;&lt;code&gt; Physically.Unhealthy.Days  Mentally.Unhealthy.Days gdeaths_per_100_thousand    gdealers_per_100_thousand
 5.2        4.1     19.70210418  30.11828855
 4.9        4.1     21.6723146   32.83851989
 5.1        4.3     23.52217417  39.4076506
 5.1        3.6     NA           38.03625359
 3.4        4.1     17.78983533  27.28191219
 3.4        4.2     19.4576324   32.2906761
 3.3        3.8     13.9248167   40.75855495
 3.3        3.8     12.05513916  36.2194827
 5.9        3.9     NA           31.52340694
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And it returns this, which is totally unlike what I expect from from Spike Slab:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    Length Class      Mode     
summary           4  data.frame list     
verbose          10  -none-     list     
terms             3  terms      call     
sigma.hat         1  -none-     numeric  
y              2132  -none-     numeric  
xnew          12792  -none-     numeric  
x             12792  -none-     numeric  
y.center          1  -none-     numeric  
x.center          6  -none-     numeric  
x.scale           6  -none-     numeric  
names             6  -none-     character
bma               6  -none-     numeric  
bma.scale         6  -none-     numeric  
gnet              6  -none-     numeric  
gnet.scale        6  -none-     numeric  
gnet.path         2  -none-     list     
gnet.obj         16  lars       list     
gnet.obj.vars     6  -none-     numeric  
gnet.parms        6  -none-     numeric  
phat              1  -none-     numeric  
complexity      500  -none-     numeric  
ridge           500  -none-     list     
model           500  -none-     list 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is there a way to interpret this that I don''t know about? Or is there something wrong with how I''m running it? Thanks so much for any help, I have no idea what to try.&lt;/p&gt;
', 'r', NULL, '''/code'':62C,122C,238C ''/p'':32C,73C,140C,278C ''/pre'':63C,123C,239C ''1'':159C,175C,223C ''10'':67C,151C ''100'':51C,80C,84C ''12.05513916'':116C ''12792'':167C,171C ''13.9248167'':112C ''16'':211C ''17.78983533'':104C ''19.4576324'':108C ''19.70210418'':88C ''2'':40C,207C ''21.6723146'':92C ''2132'':163C ''23.52217417'':96C ''27.28191219'':105C ''3'':155C ''3.3'':110C,114C ''3.4'':102C,106C ''3.6'':99C ''3.8'':111C,115C ''3.9'':119C ''30.11828855'':89C ''31.52340694'':121C ''32.2906761'':109C ''32.83851989'':93C ''36.2194827'':117C ''38.03625359'':101C ''39.4076506'':97C ''4'':147C ''4.1'':87C,91C,103C ''4.2'':107C ''4.3'':95C ''4.9'':90C ''40.75855495'':113C ''5.1'':94C,98C ''5.2'':86C ''5.9'':118C ''500'':227C,231C,235C ''6'':179C,183C,187C,191C,195C,199C,203C,215C,219C ''bad'':5A ''bma'':190C ''bma.scale'':194C ''call'':157C ''charact'':189C ''class'':144C ''code'':30C,34C,75C,142C ''complex'':226C ''data'':21C,46C,70C ''data.frame'':148C ''expect'':135C ''first'':66C ''gdealer'':82C ''gdeath'':49C,78C ''gnet'':198C ''gnet.obj'':210C ''gnet.obj.vars'':214C ''gnet.parms'':218C ''gnet.path'':206C ''gnet.scale'':202C ''go'':27C ''gt'':35C,38C,41C,58C ''help'':270C ''idea'':274C ''interpret'':246C ''know'':252C ''lar'':212C ''length'':143C ''like'':24C ''line'':68C ''list'':149C,153C,209C,213C,233C,237C ''lt'':44C ''m'':262C ''mentally.unhealthy.days'':55C,77C ''mode'':145C ''model'':234C ''model1'':42C,60C ''much'':267C ''na'':100C,120C ''na.rm'':56C ''name'':186C ''none'':152C,160C,164C,168C,172C,176C,180C,184C,188C,192C,196C,200C,204C,208C,216C,220C,224C,228C,232C,236C ''numer'':161C,165C,169C,173C,177C,181C,185C,193C,197C,201C,205C,217C,221C,225C,229C ''output'':6A ''p'':8C,64C,124C,240C ''past'':17C ''per'':50C,79C,83C ''phat'':222C ''physically.unhealthy.days'':54C,76C ''pre'':33C,74C,141C ''r'':4A,7B ''requir'':36C ''return'':127C ''ridg'':230C ''run'':263C ''seem'':23C ''set.seed'':39C ''sigma.hat'':158C ''slab'':2A,14C,139C ''someth'':25C,257C ''spike'':1A,13C,138C ''spikeslab'':37C,48C ''ss'':43C,61C ''success'':11C ''summari'':59C,146C ''term'':154C,156C ''thank'':265C ''thousand'':52C,81C,85C ''total'':131C ''tri'':277C ''true'':57C ''unlik'':132C ''use'':12C,47C,71C ''ve'':10C ''verbos'':150C ''way'':244C ''wrong'':28C,258C ''x'':170C ''x.center'':178C ''x.scale'':182C ''xnew'':166C ''y'':162C ''y.center'':174C ''~gdealers_per_100_thousand'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9721, 2, '2016-01-10 09:17:00.887', 1, NULL, NULL, '&lt;p&gt;I am not sure you would get any such tutorial regarding the same. It is more like asking how to build a software.&lt;/p&gt;

&lt;p&gt;For example, consider you have to build an ML model for doing Sentiment Analysis before a post comes onto your feed, this is how you need to build the system:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;The ML model would constitute the backend of the software, as it is just another algorithm we are using for building the software.&lt;/li&gt;
&lt;li&gt;The plots or the feed you want to display to the end user would constitute the front end, which would include the templates and the views. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The above can be achieved through an MVC framework. The data can be sent back and forth the server and the client through a protocol.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.quora.com/How-can-I-implement-machine-learning-algorithms-in-a-web-application" rel="nofollow"&gt;Useful link&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In case, you want to integrate it with an external system, then create an API for the above system which you have built and let the other systems use your software/app by calling your api.&lt;/p&gt;

&lt;p&gt;Now, create a simple SlackBot which can interact with your API with some certain commands, and display the relevant results in the group.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/kengz/jarvis" rel="nofollow"&gt;Example of such a system&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':150C,227C ''/how-can-i-implement-machine-learning-algorithms-in-a-web-application'':145C ''/kengz/jarvis'':219C ''/li'':83C,111C ''/p'':25C,57C,139C,151C,188C,213C,228C ''/ul'':112C ''achiev'':118C ''algorithm'':75C ''analysi'':40C ''anoth'':74C ''api'':167C,187C,200C ''ask'':19C ''back'':128C ''backend'':66C ''build'':22C,33C,54C,80C ''built'':175C ''call'':185C ''case'':154C ''certain'':203C ''client'':135C ''come'':44C ''command'':204C ''consid'':29C ''constitut'':64C,99C ''creat'':165C,191C ''data'':124C ''display'':93C,206C ''end'':96C,102C ''exampl'':28C,222C ''extern'':162C ''feed'':47C,89C ''forth'':130C ''framework'':122C ''front'':101C ''get'':8C ''github.com'':218C ''github.com/kengz/jarvis'':217C ''group'':212C ''href'':142C,216C ''includ'':105C ''integr'':158C ''interact'':197C ''let'':177C ''li'':59C,84C ''like'':18C ''link'':149C ''ml'':35C,61C ''model'':36C,62C ''mvc'':121C ''need'':52C ''nofollow'':147C,221C ''onto'':45C ''p'':1C,26C,113C,140C,152C,189C,214C ''plot'':86C ''post'':43C ''protocol'':138C ''regard'':12C ''rel'':146C,220C ''relev'':208C ''result'':209C ''sent'':127C ''sentiment'':39C ''server'':132C ''simpl'':193C ''slackbot'':194C ''softwar'':24C,69C,82C ''software/app'':183C ''sure'':5C ''system'':56C,163C,171C,180C,226C ''templat'':107C ''tutori'':11C ''ul'':58C ''use'':78C,148C,181C ''user'':97C ''view'':110C ''want'':91C,156C ''would'':7C,63C,98C,104C ''www.quora.com'':144C ''www.quora.com/how-can-i-implement-machine-learning-algorithms-in-a-web-application'':143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9598, 1, '2016-01-04 00:17:58.2', 4, 207, 'Building a machine learning model to predict crop yields based on environmental data', '&lt;p&gt;I have a dataset containing data on temperature, precipitation and soybean yields for a farm for 10 years (2005 - 2014). I would like to predict yields for 2015 based on this data.&lt;/p&gt;

&lt;p&gt;Please note that the dataset has DAILY values for temperature and precipitation, but only 1 value per year for the yield, since harvesting of crop happens at end of growing season of crop.&lt;/p&gt;

&lt;p&gt;I want to build a regression or some other machine learning based model to predict 2015 yields, based on a regression/some other model derived by studying the relation between yields and temperature and precipitation in previous years.&lt;/p&gt;

&lt;p&gt;I am familiar with performing machine learning using scikit-learn. However, not sure how to represent this problem. The tricky part here is that temperature and precipitation are daily but yield is just 1 value per year.&lt;/p&gt;

&lt;p&gt;How do I approach this?&lt;/p&gt;
', 'python  scikit  pandas', NULL, '''/p'':50C,85C,124C,164C,171C ''1'':66C,160C ''10'':34C ''2005'':36C ''2014'':37C ''2015'':45C,102C ''approach'':169C ''base'':10A,46C,98C,104C ''build'':1A,90C ''contain'':22C ''crop'':8A,76C,84C ''daili'':58C,155C ''data'':13A,23C,49C ''dataset'':21C,56C ''deriv'':110C ''end'':79C ''environment'':12A ''familiar'':128C ''farm'':32C ''grow'':81C ''happen'':77C ''harvest'':74C ''howev'':137C ''learn'':4A,97C,132C,136C ''like'':40C ''machin'':3A,96C,131C ''model'':5A,99C,109C ''note'':53C ''p'':17C,51C,86C,125C,165C ''panda'':16B ''part'':147C ''per'':68C,162C ''perform'':130C ''pleas'':52C ''precipit'':26C,63C,120C,153C ''predict'':7A,42C,101C ''previous'':122C ''problem'':144C ''python'':14B ''regress'':92C ''regression/some'':107C ''relat'':114C ''repres'':142C ''scikit'':15B,135C ''scikit-learn'':134C ''season'':82C ''sinc'':73C ''soybean'':28C ''studi'':112C ''sure'':139C ''temperatur'':25C,61C,118C,151C ''tricki'':146C ''use'':133C ''valu'':59C,67C,161C ''want'':88C ''would'':39C ''year'':35C,69C,123C,163C ''yield'':9A,29C,43C,72C,103C,116C,157C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9599, 2, '2016-01-04 00:24:37.313', 2, NULL, NULL, '&lt;p&gt;For starters, you can predict the yield for the upcoming year based on the daily data for the previous year. You can estimate the model parameters by considering each year''s worth of data as one "point", then validate the model using cross-validation. You can extend this model by considering more than the past year, but look back too far and you''ll have trouble validating your model and overfit.&lt;/p&gt;
', NULL, NULL, '''/p'':74C ''back'':61C ''base'':13C ''consid'':29C,53C ''cross'':45C ''cross-valid'':44C ''daili'':16C ''data'':17C,35C ''estim'':24C ''extend'':49C ''far'':63C ''ll'':66C ''look'':60C ''model'':26C,42C,51C,71C ''one'':37C ''overfit'':73C ''p'':1C ''paramet'':27C ''past'':57C ''point'':38C ''predict'':6C ''previous'':20C ''starter'':3C ''troubl'':68C ''upcom'':11C ''use'':43C ''valid'':40C,46C,69C ''worth'':33C ''year'':12C,21C,31C,58C ''yield'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9600, 2, '2016-01-04 01:29:57.74', 0, NULL, NULL, '&lt;p&gt;You might want to look at &lt;a href="https://en.wikipedia.org/wiki/Naive_Bayes_classifier" rel="nofollow"&gt;Naive Bayes classifier&lt;/a&gt; or have a look at this page on &lt;a href="http://stackoverflow.com/questions/13788229/very-simple-text-classification-by-machine-learning"&gt;Very simple text classification by machine learning&lt;/a&gt;. You could also look at &lt;a href="http://nlp.stanford.edu:8080/ner/process" rel="nofollow"&gt;Stanford Named Entity Tagger&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':18C,39C,56C ''/ner/process'':49C ''/p'':57C ''/questions/13788229/very-simple-text-classification-by-machine-learning'':31C ''/wiki/naive_bayes_classifier'':12C ''also'':42C ''bay'':16C ''classif'':35C ''classifi'':17C ''could'':41C ''en.wikipedia.org'':11C ''en.wikipedia.org/wiki/naive_bayes_classifier'':10C ''entiti'':54C ''href'':9C,28C,46C ''learn'':38C ''look'':6C,22C,43C ''machin'':37C ''might'':3C ''naiv'':15C ''name'':53C ''nlp.stanford.edu:8080'':48C ''nlp.stanford.edu:8080/ner/process'':47C ''nofollow'':14C,51C ''p'':1C ''page'':25C ''rel'':13C,50C ''simpl'':33C ''stackoverflow.com'':30C ''stackoverflow.com/questions/13788229/very-simple-text-classification-by-machine-learning'':29C ''stanford'':52C ''tagger'':55C ''text'':34C ''want'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9608, 2, '2016-01-04 13:05:35.4', 0, NULL, NULL, '&lt;p&gt;Depends on your programming skills. Here is the summary:&lt;/p&gt;

&lt;p&gt;OpenCV is a great tool originally developed in C++ and after a while a Python interface was added. In industry (and also many academic research group) C++ is the popular language for Computer Vision as the nature of data needs efficiency in computation. So starting with python is an easy way to learn Computer Vision and OpenCV capabilities but when you are applying for a job in industry or academia, they most probably need you to get your hands dirty with C++.&lt;/p&gt;
', NULL, NULL, '''/p'':11C,95C ''academ'':36C ''academia'':82C ''ad'':30C ''also'':34C ''appli'':75C ''c'':21C,39C,94C ''capabl'':70C ''comput'':45C,55C,66C ''data'':51C ''depend'':2C ''develop'':19C ''dirti'':92C ''easi'':62C ''effici'':53C ''get'':89C ''great'':16C ''group'':38C ''hand'':91C ''industri'':32C,80C ''interfac'':28C ''job'':78C ''languag'':43C ''learn'':65C ''mani'':35C ''natur'':49C ''need'':52C,86C ''opencv'':13C,69C ''origin'':18C ''p'':1C,12C ''popular'':42C ''probabl'':85C ''program'':5C ''python'':27C,59C ''research'':37C ''skill'':6C ''start'':57C ''summari'':10C ''tool'':17C ''vision'':46C,67C ''way'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9632, 1, '2016-01-05 10:57:35.867', 3, 71, 'Simple example of genetic alg minimization', '&lt;p&gt;I have been looking for a while for examples of how I could find the points at which a function achieves its minimum using a genetic algorithm approach in Python. I looked at  DEAP documentation, but the examples there were pretty hard for me to follow. For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def  function(x,y):
     return x*y+3*x-x**2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I  am looking  for some references on how  I can make a genetic algorithm in which I can feed some initial random values for both x and y (not coming from the same dimensions). Can someone with experience creating and using genetic algorithms give me some guidance on this?&lt;/p&gt;
', 'python  optimization  genetic-algorithms', NULL, '''+3'':71C ''/code'':76C ''/p'':61C,128C ''/pre'':77C ''2'':75C ''achiev'':33C ''alg'':5A ''algorithm'':11B,39C,92C,121C ''approach'':40C ''code'':63C ''come'':108C ''could'':25C ''creat'':117C ''deap'':46C ''def'':64C ''dimens'':112C ''document'':47C ''exampl'':2A,21C,50C,60C ''experi'':116C ''feed'':97C ''find'':26C ''follow'':58C ''function'':32C,65C ''genet'':4A,10B,38C,91C,120C ''genetic-algorithm'':9B ''give'':122C ''guidanc'':125C ''hard'':54C ''initi'':99C ''look'':16C,44C,81C ''make'':89C ''minim'':6A ''minimum'':35C ''optim'':8B ''p'':12C,78C ''point'':28C ''pre'':62C ''pretti'':53C ''python'':7B,42C ''random'':100C ''refer'':84C ''return'':68C ''simpl'':1A ''someon'':114C ''use'':36C,119C ''valu'':101C ''x'':66C,69C,73C,74C,104C ''x-x'':72C ''y'':67C,70C,106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9614, 1, '2016-01-04 16:43:21.4', 1, 39, 'How can I use data on customer interactions to drive communication strategy?', '&lt;p&gt;I have data on my customers (age, location, gender) and number of interactions with customer by channel (#calls, #sms, #letters) and data on whether they have bought a product (yes/no).&lt;/p&gt;

&lt;p&gt;I would like to predict what combination of interaction (e.g. 3 calls, 2 letters) is the best approach when contacting different types of customers. What would be an appropriate statistical/ machine learning technique to use to calculate this?&lt;/p&gt;
', 'statistics  marketing', NULL, '''/p'':46C,86C ''2'':60C ''3'':58C ''age'':22C ''approach'':65C ''appropri'':76C ''best'':64C ''bought'':42C ''calcul'':84C ''call'':33C,59C ''channel'':32C ''combin'':54C ''communic'':11A ''contact'':67C ''custom'':7A,21C,30C,71C ''data'':5A,18C,37C ''differ'':68C ''drive'':10A ''e.g'':57C ''gender'':24C ''interact'':8A,28C,56C ''learn'':79C ''letter'':35C,61C ''like'':50C ''locat'':23C ''machin'':78C ''market'':14B ''number'':26C ''p'':15C,47C ''predict'':52C ''product'':44C ''sms'':34C ''statist'':13B,77C ''strategi'':12A ''techniqu'':80C ''type'':69C ''use'':4A,82C ''whether'':39C ''would'':49C,73C ''yes/no'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9602, 1, '2016-01-04 04:49:51.893', 4, 27, 'How to deal with analyzing optional survey data', '&lt;p&gt;Consider this scenario. There is a website that ranks every type of sandwich. For each type of sandwich, users fill out a survey about the following: A) how tasty it is (on a scale from 1-5); B) how filling it is (on a scale from 1 to 5); C*) if it is spicy (-1 or 1). &lt;/p&gt;

&lt;p&gt;The website displays the average value of the answers to Question A, the average value of the answers to Question B, and the sum of the answers to Question C.&lt;/p&gt;

&lt;p&gt;Question A and Question B are mandatory. Question C, however, is optional. I have the number of user-submitted ratings for each sandwich, and I have the sum of the answers to C. &lt;/p&gt;

&lt;p&gt;I want to compare the spiciness of different sandwiches. Since different sandwiches have different rankings, I cannot simply compare the sum of the answers to Question C. I also cannot weight it as "Sum of Answers to Question C/Number of Ratings" since Question C is optional and some people won''t answer it. &lt;/p&gt;

&lt;p&gt;What would be the best way to go about comparing the spiciness of different sandwiches under these constraints?&lt;/p&gt;
', 'statistics  data', NULL, '''-1'':66C ''-5'':48C ''/p'':69C,101C,137C,192C,212C ''1'':47C,58C,68C ''5'':60C ''also'':167C ''analyz'':5A ''answer'':79C,88C,97C,134C,162C,174C,190C ''averag'':75C,84C ''b'':49C,91C,107C ''best'':198C ''c'':61C,100C,111C,136C,165C,182C ''c/number'':177C ''cannot'':155C,168C ''compar'':142C,157C,203C ''consid'':12C ''constraint'':211C ''data'':8A,10B ''deal'':3A ''differ'':146C,149C,152C,207C ''display'':73C ''everi'':21C ''fill'':31C,51C ''follow'':37C ''go'':201C ''howev'':112C ''mandatori'':109C ''number'':118C ''option'':6A,114C,184C ''p'':11C,70C,102C,138C,193C ''peopl'':187C ''question'':81C,90C,99C,103C,106C,110C,164C,176C,181C ''rank'':20C,153C ''rate'':123C,179C ''sandwich'':24C,29C,126C,147C,150C,208C ''scale'':45C,56C ''scenario'':14C ''simpli'':156C ''sinc'':148C,180C ''spici'':65C,144C,205C ''statist'':9B ''submit'':122C ''sum'':94C,131C,159C,172C ''survey'':7A,34C ''tasti'':40C ''type'':22C,27C ''user'':30C,121C ''user-submit'':120C ''valu'':76C,85C ''want'':140C ''way'':199C ''websit'':18C,72C ''weight'':169C ''won'':188C ''would'':195C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9603, 2, '2016-01-04 07:03:09.92', 1, NULL, NULL, '&lt;p&gt;Check &lt;a href="http://web.maths.unsw.edu.au/~adelle/Garvan/Assays/GoodnessOfFit.html" rel="nofollow"&gt;this&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;A value closer to 0 indicates that the model has a smaller random error component, and that the fit will be more useful for prediction.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/a'':11C ''/blockquote'':41C ''/p'':12C,40C ''/~adelle/garvan/assays/goodnessoffit.html'':7C ''0'':19C ''blockquot'':13C ''check'':2C ''closer'':17C ''compon'':29C ''error'':28C ''fit'':33C ''href'':4C ''indic'':20C ''model'':23C ''nofollow'':9C ''p'':1C,14C ''predict'':39C ''random'':27C ''rel'':8C ''smaller'':26C ''use'':37C ''valu'':16C ''web.maths.unsw.edu.au'':6C ''web.maths.unsw.edu.au/~adelle/garvan/assays/goodnessoffit.html'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9604, 1, '2016-01-04 10:38:13.97', 3, 48, 'How to prepare colored images for neural networks?', '&lt;p&gt;I have seen many examples online regarding the MNIST dataset, but it''s all in black and white. In that case, a 2D array can be constructed where the values at each array element represent the intensity of the corresponding pixel. However, what if I want to do colored images? What''s the best way to represent the RGB data? &lt;/p&gt;

&lt;p&gt;There''s a very brief discussion of it &lt;a href="http://neuralnetworksanddeeplearning.com/chap6.html#exercise_683491" rel="nofollow"&gt;here&lt;/a&gt;, which I quote below. However, I still don''t get how the RGB data should be organized. Additionally, is there some OpenCV library/command we should use to preprocess the colored images?&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;the feature detectors in the second convolutional-pooling layer have
  access to all the features from the previous layer, but only within
  their particular local receptive field*&lt;/p&gt;
  
  &lt;p&gt;*This issue would have arisen in the first layer if the input images were in color. In that case we''d have 3 input features for each pixel,
  corresponding to red, green and blue channels in the input image. So
  we''d allow the feature detectors to have access to all color
  information, but only within a given local receptive field.&lt;/p&gt;
&lt;/blockquote&gt;
', 'neuralnetwork  deep-learning  image-classification', NULL, '''/a'':95C ''/blockquote'':222C ''/chap6.html#exercise_683491'':91C ''/p'':77C,127C,158C,221C ''2d'':39C ''3'':182C ''access'':141C,208C ''addit'':113C ''allow'':202C ''arisen'':164C ''array'':40C,49C ''best'':70C ''black'':32C ''blockquot'':128C ''blue'':193C ''brief'':83C ''case'':37C,178C ''channel'':194C ''classif'':15B ''color'':4A,65C,125C,175C,211C ''construct'':43C ''convolut'':137C ''convolutional-pool'':136C ''correspond'':56C,188C ''d'':180C,201C ''data'':76C,109C ''dataset'':26C ''deep'':11B ''deep-learn'':10B ''detector'':132C,205C ''discuss'':84C ''element'':50C ''exampl'':21C ''featur'':131C,145C,184C,204C ''field'':157C,220C ''first'':167C ''get'':105C ''given'':217C ''green'':191C ''howev'':58C,100C ''href'':88C ''imag'':5A,14B,66C,126C,172C,198C ''image-classif'':13B ''inform'':212C ''input'':171C,183C,197C ''intens'':53C ''issu'':161C ''layer'':139C,149C,168C ''learn'':12B ''library/command'':118C ''local'':155C,218C ''mani'':20C ''mnist'':25C ''network'':8A ''neural'':7A ''neuralnetwork'':9B ''neuralnetworksanddeeplearning.com'':90C ''neuralnetworksanddeeplearning.com/chap6.html#exercise_683491'':89C ''nofollow'':93C ''onlin'':22C ''opencv'':117C ''organ'':112C ''p'':16C,78C,129C,159C ''particular'':154C ''pixel'':57C,187C ''pool'':138C ''prepar'':3A ''preprocess'':123C ''previous'':148C ''quot'':98C ''recept'':156C,219C ''red'':190C ''regard'':23C ''rel'':92C ''repres'':51C,73C ''rgb'':75C,108C ''second'':135C ''seen'':19C ''still'':102C ''use'':121C ''valu'':46C ''want'':62C ''way'':71C ''white'':34C ''within'':152C,215C ''would'':162C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9605, 2, '2016-01-04 10:38:29.7', 2, NULL, NULL, '&lt;p&gt;Check this project on github: &lt;a href="https://github.com/josephmisiti/awesome-machine-learning" rel="nofollow"&gt;https://github.com/josephmisiti/awesome-machine-learning&lt;/a&gt;. It contains a comprehensive list of open source projects grouped by language, with some short descriptions. I think you can find there some of them which meet you needs. &lt;/p&gt;
', NULL, NULL, '''/josephmisiti/awesome-machine-learning'':11C ''/josephmisiti/awesome-machine-learning&lt;/a&gt;.'':16C ''/p'':46C ''check'':2C ''comprehens'':20C ''contain'':18C ''descript'':32C ''find'':37C ''github'':6C ''github.com'':10C,15C ''github.com/josephmisiti/awesome-machine-learning'':9C ''github.com/josephmisiti/awesome-machine-learning&lt;/a&gt;.'':14C ''group'':26C ''href'':8C ''languag'':28C ''list'':21C ''meet'':43C ''need'':45C ''nofollow'':13C ''open'':23C ''p'':1C ''project'':4C,25C ''rel'':12C ''short'':31C ''sourc'':24C ''think'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9606, 2, '2016-01-04 12:05:47.32', 2, NULL, NULL, '&lt;h1&gt;Question 1: Category prediction&lt;/h1&gt;

&lt;p&gt;To predict the category of a new blog post, you could do the following:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Build a MLP (multilayer Perceptron, a very simple neural network). Each category gets an output node, each tag is a binary input node. However, this will only work if the number of tags is very little. As soon as you add new tags, you will have to retrain the network.&lt;/li&gt;
&lt;li&gt;Build a MLP with "important words" as features.&lt;/li&gt;
&lt;li&gt;If you have internal links, you might want to have a look at "On Node Classification in Dynamic Content-based Networks". In case you''re German, you might also like &lt;a href="http://arxiv.org/abs/1512.04469" rel="nofollow"&gt;Ãber die Klassifizierung von Knoten in dynamischen Netzwerken mit Inhalt&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;You could take all words you currently have, see those as a vector space. Fix that vocabulary (and probably remove some meaningless words like "with", "a", "an" - this is commonly called a "stopword"). For each text, you count the different words you have in your vocabulary. A new blog post is point in this space. Use $k$ nearest neighbor for classification.&lt;/li&gt;
&lt;li&gt;Use combinations of different predictiors by letting each predictor give a vote for a classification.&lt;/li&gt;
&lt;/ul&gt;

&lt;h2&gt;See also&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;Yiming Yang, Jan O. Pedersen: &lt;a href="http://www.surdeanu.info/mihai/teaching/ista555-spring15/readings/yang97comparative.pdf" rel="nofollow"&gt;A Comparative Study on Feature Selection in Text Categorization&lt;/a&gt;, 1997.&lt;/li&gt;
&lt;li&gt;Scikit-learn: &lt;a href="http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html" rel="nofollow"&gt;Working With Text Data&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;h1&gt;Question 2: Tagging texts&lt;/h1&gt;

&lt;p&gt;This can be treated the same way like question 1.&lt;/p&gt;

&lt;h1&gt;Question 3: Finding locations&lt;/h1&gt;

&lt;p&gt;Download a database of countries / cities (e.g. &lt;a href="https://www.maxmind.com/de/free-world-cities-database" rel="nofollow"&gt;maxmind&lt;/a&gt;) and just search for a match.&lt;/p&gt;
', NULL, NULL, '''/a'':136C,246C,264C,307C ''/abs/1512.04469'':122C ''/de/free-world-cities-database'':303C ''/h1'':6C,272C,290C ''/h2'':222C ''/li'':75C,85C,137C,200C,217C,248C,265C ''/mihai/teaching/ista555-spring15/readings/yang97comparative.pdf'':234C ''/p'':22C,284C,314C ''/stable/tutorial/text_analytics/working_with_text_data.html'':257C ''/ul'':218C,266C ''1'':3C,283C ''1997'':247C ''2'':269C ''3'':287C ''add'':65C ''also'':116C,221C ''arxiv.org'':121C ''arxiv.org/abs/1512.04469'':120C ''base'':107C ''ber'':126C ''binari'':45C ''blog'':15C,187C ''build'':25C,77C ''call'':169C ''case'':110C ''categor'':245C ''categori'':4C,11C,36C ''citi'':297C ''classif'':102C,199C,216C ''combin'':203C ''common'':168C ''compar'':238C ''content'':106C ''content-bas'':105C ''could'':18C,140C ''count'':176C ''countri'':296C ''current'':145C ''data'':263C ''databas'':294C ''die'':127C ''differ'':178C,205C ''download'':292C ''dynam'':104C ''dynamischen'':132C ''e.g'':298C ''featur'':84C,241C ''find'':288C ''fix'':153C ''follow'':21C ''german'':113C ''get'':37C ''give'':211C ''h1'':1C,267C,285C ''h2'':219C ''howev'':48C ''href'':119C,231C,254C,300C ''import'':81C ''inhalt'':135C ''input'':46C ''intern'':90C ''jan'':227C ''k'':195C ''klassifizierung'':128C ''knoten'':130C ''learn'':252C ''let'':208C ''li'':24C,76C,86C,138C,201C,224C,249C ''like'':117C,162C,281C ''link'':91C ''littl'':60C ''locat'':289C ''look'':98C ''match'':313C ''maxmind'':306C ''meaningless'':160C ''might'':93C,115C ''mit'':134C ''mlp'':27C,79C ''multilay'':28C ''nearest'':196C ''neighbor'':197C ''network'':34C,74C,108C ''netzwerken'':133C ''neural'':33C ''new'':14C,66C,186C ''node'':40C,47C,101C ''nofollow'':124C,236C,259C,305C ''number'':55C ''o'':228C ''output'':39C ''p'':7C,273C,291C ''pedersen'':229C ''perceptron'':29C ''point'':190C ''post'':16C,188C ''predict'':5C,9C ''predictior'':206C ''predictor'':210C ''probabl'':157C ''question'':2C,268C,282C,286C ''re'':112C ''rel'':123C,235C,258C,304C ''remov'':158C ''retrain'':72C ''scikit'':251C ''scikit-learn'':250C ''scikit-learn.org'':256C ''scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html'':255C ''search'':310C ''see'':147C,220C ''select'':242C ''simpl'':32C ''soon'':62C ''space'':152C,193C ''stopword'':171C ''studi'':239C ''tag'':42C,57C,67C,270C ''take'':141C ''text'':174C,244C,262C,271C ''treat'':277C ''ul'':23C,223C ''use'':194C,202C ''vector'':151C ''vocabulari'':155C,184C ''von'':129C ''vote'':213C ''want'':94C ''way'':280C ''word'':82C,143C,161C,179C ''work'':52C,260C ''www.maxmind.com'':302C ''www.maxmind.com/de/free-world-cities-database'':301C ''www.surdeanu.info'':233C ''www.surdeanu.info/mihai/teaching/ista555-spring15/readings/yang97comparative.pdf'':232C ''yang'':226C ''yime'':225C ''ã'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9609, 2, '2016-01-04 13:20:15.52', 1, NULL, NULL, '&lt;h2&gt;According to graph theory, wouldn''t this make Person B also very influential?&lt;/h2&gt;

&lt;p&gt;No. You need to think about how to set up your graph first and then go further. In this case your graph is not simple but both &lt;strong&gt;weighted&lt;/strong&gt; and &lt;strong&gt;directed&lt;/strong&gt; thus handling a request of B by A neither reduces A''s influence nor improves B''s. Because the weights of directed edges are determining the influence.&lt;/p&gt;

&lt;h2&gt;Is there a better way than just counting number of interactions?&lt;/h2&gt;

&lt;p&gt;&lt;a href="http://arxiv.org/abs/1511.01523" rel="nofollow"&gt;&lt;em&gt;Community Detection&lt;/em&gt;&lt;/a&gt; or what computer scientists call &lt;a href="http://dollar.biz.uiowa.edu/~street/graphClustering.pdf" rel="nofollow"&gt;&lt;em&gt;Graph Clustering&lt;/em&gt;&lt;/a&gt;. If you do not have to limit yourself to hierarchies, you can extract communities who have the most interaction and share suggestions inside the community. &lt;/p&gt;
', NULL, NULL, '''/a'':104C,121C ''/abs/1511.01523'':97C ''/em'':103C,120C ''/h2'':15C,91C ''/p'':78C,147C ''/strong'':46C,50C ''/~street/graphclustering.pdf'':114C ''accord'':2C ''also'':12C ''arxiv.org'':96C ''arxiv.org/abs/1511.01523'':95C ''b'':11C,56C,66C ''better'':83C ''call'':109C ''case'':36C ''cluster'':119C ''communiti'':101C,135C,146C ''comput'':107C ''count'':87C ''detect'':102C ''determin'':75C ''direct'':49C,72C ''dollar.biz.uiowa.edu'':113C ''dollar.biz.uiowa.edu/~street/graphclustering.pdf'':112C ''edg'':73C ''em'':100C,117C ''extract'':134C ''first'':29C ''go'':32C ''graph'':4C,28C,38C,118C ''h2'':1C,79C ''handl'':52C ''hierarchi'':131C ''href'':94C,111C ''improv'':65C ''influenc'':63C,77C ''influenti'':14C ''insid'':144C ''interact'':90C,140C ''limit'':128C ''make'':9C ''need'':19C ''neither'':59C ''nofollow'':99C,116C ''number'':88C ''p'':16C,92C ''person'':10C ''reduc'':60C ''rel'':98C,115C ''request'':54C ''scientist'':108C ''set'':25C ''share'':142C ''simpl'':41C ''strong'':44C,48C ''suggest'':143C ''theori'':5C ''think'':21C ''thus'':51C ''way'':84C ''weight'':45C,70C ''wouldn'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9658, 2, '2016-01-06 21:30:26.947', 1, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;=run a maximum of n tests or &lt;strong&gt;stop earlier if a good improvement is found&lt;/strong&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Don''t do that. Don''t stop before n tests have been run if you like what you''ve seen so far....&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.evanmiller.org/how-not-to-run-an-ab-test.html" rel="nofollow"&gt;http://www.evanmiller.org/how-not-to-run-an-ab-test.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In general, however, figuring out how big an N you need given an expected effect size and alpha level is the domain of power analysis. Read more here:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.uta.edu/faculty/sawasthi/Statistics/stpowan.html" rel="nofollow"&gt;http://www.uta.edu/faculty/sawasthi/Statistics/stpowan.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/blockquote'':21C ''/faculty/sawasthi/statistics/stpowan.html'':92C ''/faculty/sawasthi/statistics/stpowan.html&lt;/a&gt;&lt;/p&gt;'':97C ''/how-not-to-run-an-ab-test.html'':51C ''/how-not-to-run-an-ab-test.html&lt;/a&gt;&lt;/p&gt;'':56C ''/p'':20C,45C,86C ''/strong'':19C ''alpha'':75C ''analysi'':82C ''big'':64C ''blockquot'':1C ''domain'':79C ''earlier'':12C ''effect'':72C ''expect'':71C ''far'':44C ''figur'':61C ''found'':18C ''general'':59C ''given'':69C ''good'':15C ''howev'':60C ''href'':48C,89C ''improv'':16C ''level'':76C ''like'':38C ''maximum'':5C ''n'':7C,31C,66C ''need'':68C ''nofollow'':53C,94C ''p'':2C,22C,46C,57C,87C ''power'':81C ''read'':83C ''rel'':52C,93C ''run'':3C,35C ''seen'':42C ''size'':73C ''stop'':11C,29C ''strong'':10C ''test'':8C,32C ''ve'':41C ''www.evanmiller.org'':50C,55C ''www.evanmiller.org/how-not-to-run-an-ab-test.html'':49C ''www.evanmiller.org/how-not-to-run-an-ab-test.html&lt;/a&gt;&lt;/p&gt;'':54C ''www.uta.edu'':91C,96C ''www.uta.edu/faculty/sawasthi/statistics/stpowan.html'':90C ''www.uta.edu/faculty/sawasthi/statistics/stpowan.html&lt;/a&gt;&lt;/p&gt;'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9655, 1, '2016-01-06 20:18:26.413', 2, 68, 'Deep Learning for Recommender System', '&lt;p&gt;I read about Recursive Neural Networks that they can convert Documents to distributed word representation. &lt;/p&gt;

&lt;p&gt;In the context of new article recommendation, I am thinking to use this model to convert all news articles to vectors and then recommend to a  particular user, news articles similar to ones he browsed. &lt;/p&gt;

&lt;p&gt;In vector space this will boil down to finding ''similar'' vectors to a given vector(user''s news read). &lt;/p&gt;

&lt;p&gt;How likely is that this model will work well in practice? Any comments and/or suggestions?&lt;/p&gt;
', 'recommendation  deep-learning', NULL, '''/p'':26C,63C,84C,101C ''and/or'':99C ''articl'':33C,46C,57C ''boil'':70C ''brows'':62C ''comment'':98C ''context'':30C ''convert'':20C,43C ''deep'':1A,8B ''deep-learn'':7B ''distribut'':23C ''document'':21C ''find'':73C ''given'':78C ''learn'':2A,9B ''like'':87C ''model'':41C,91C ''network'':16C ''neural'':15C ''new'':32C ''news'':45C,56C,82C ''one'':60C ''p'':10C,27C,64C,85C ''particular'':54C ''practic'':96C ''read'':12C,83C ''recommend'':4A,6B,34C,51C ''recurs'':14C ''represent'':25C ''similar'':58C,74C ''space'':67C ''suggest'':100C ''system'':5A ''think'':37C ''use'':39C ''user'':55C,80C ''vector'':48C,66C,75C,79C ''well'':94C ''word'':24C ''work'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9610, 2, '2016-01-04 14:38:12.23', 0, NULL, NULL, '&lt;p&gt;There seems to be no &lt;code&gt;summary&lt;/code&gt; method for objects of class &lt;code&gt;spikeslab&lt;/code&gt;, so the default summary method is used which prints the internal list elements.&lt;/p&gt;

&lt;p&gt;Just print the object to get a nice summary - maybe that''s what you want? Is this the output you expected?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; model1_ss
------------------------------------------------------------------- 
Variable selection method     : AIC 
Big p small n                 : FALSE 
Screen variables              : FALSE 
Fast processing               : TRUE 
Sample size                   : 7 
No. predictors                : 3 
No. burn-in values            : 500 
No. sampled values            : 500 
Estimated mse                 : 7.5765 
Model size                    : 2 


---&amp;gt; Top variables:
                            bma  gnet bma.scale gnet.scale
Mentally.Unhealthy.Days   2.290 2.331    13.003     13.236
Physically.Unhealthy.Days 0.926 0.951     1.084      1.113
------------------------------------------------------------------- 
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':9C,17C,114C ''/p'':31C,54C ''/pre'':115C ''0.926'':110C ''0.951'':111C ''1.084'':112C ''1.113'':113C ''13.003'':107C ''13.236'':108C ''2'':96C ''2.290'':105C ''2.331'':106C ''3'':80C ''500'':86C,90C ''7'':77C ''7.5765'':93C ''aic'':63C ''big'':64C ''bma'':100C ''bma.scale'':102C ''burn'':83C ''burn-in'':82C ''class'':14C ''code'':7C,15C,56C ''default'':20C ''element'':30C ''estim'':91C ''expect'':53C ''fals'':68C,71C ''fast'':72C ''get'':38C ''gnet'':101C ''gnet.scale'':103C ''gt'':57C,97C ''intern'':28C ''list'':29C ''mayb'':42C ''mentally.unhealthy.days'':104C ''method'':10C,22C,62C ''model'':94C ''model1'':58C ''mse'':92C ''n'':67C ''nice'':40C ''object'':12C,36C ''output'':51C ''p'':1C,32C,65C ''physically.unhealthy.days'':109C ''pre'':55C ''predictor'':79C ''print'':26C,34C ''process'':73C ''sampl'':75C,88C ''screen'':69C ''seem'':3C ''select'':61C ''size'':76C,95C ''small'':66C ''spikeslab'':16C ''ss'':59C ''summari'':8C,21C,41C ''top'':98C ''true'':74C ''use'':24C ''valu'':85C,89C ''variabl'':60C,70C,99C ''want'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9611, 2, '2016-01-04 14:39:34.63', 2, NULL, NULL, '&lt;p&gt;Your best bet is to change the way you ask question C and the way you measure it. Giving a yes / no response for C will not let you gauge the actual spiciness so you need to adjust it to a scale and allow individuals to rank it. While all of your questions are fairly subjective, C is the most so as tolerance to spice plays a big role in if one thinks it spicy or not.&lt;/p&gt;

&lt;p&gt;I suggest using a 1-5 scale, as you have for the others, and give comparative suggestions for each level of the scale e.g. 1 is eating bread while 5 is equivalent to eating a habnero pepper.&lt;/p&gt;
', NULL, NULL, '''-5'':86C ''/p'':79C,118C ''1'':85C,105C ''5'':110C ''actual'':33C ''adjust'':39C ''allow'':45C ''ask'':11C ''best'':3C ''bet'':4C ''big'':69C ''bread'':108C ''c'':13C,26C,58C ''chang'':7C ''compar'':96C ''e.g'':104C ''eat'':107C,114C ''equival'':112C ''fair'':56C ''gaug'':31C ''give'':20C,95C ''habnero'':116C ''individu'':46C ''let'':29C ''level'':100C ''measur'':18C ''need'':37C ''one'':73C ''other'':93C ''p'':1C,80C ''pepper'':117C ''play'':67C ''question'':12C,54C ''rank'':48C ''respons'':24C ''role'':70C ''scale'':43C,87C,103C ''spice'':66C ''spici'':34C,76C ''subject'':57C ''suggest'':82C,97C ''think'':74C ''toler'':64C ''use'':83C ''way'':9C,16C ''yes'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9612, 1, '2016-01-04 15:17:44.63', 2, 33, 'Assigning values to missing target vector values in scikit-learn', '&lt;p&gt;I have a dataset containing data on temperature, precipitation and soybean yields for a farm for 10 years (2005 - 2014). I would like to predict yields for 2015 based on this data.&lt;/p&gt;

&lt;p&gt;Please note that the dataset has DAILY values for temperature and precipitation, but only 1 value per year for the yield, since harvesting of crop happens at end of growing season of crop.&lt;/p&gt;

&lt;p&gt;I want to build a regression or some other machine learning based model to predict 2015 yields, based on a regression/some other model derived by studying the relation between yields and temperature and precipitation in previous years.&lt;/p&gt;

&lt;p&gt;As per, &lt;a href="http://datascience.stackexchange.com/questions/9598/building-a-machine-learning-model-to-predict-crop-yields-based-on-environmental"&gt;Building a machine learning model to predict crop yields based on environmental data&lt;/a&gt;, I am using sklearn.cross_validation.LabelKFold to assign each year the same label.&lt;/p&gt;

&lt;p&gt;The question is that since I have a single target value per year, do I need to interpolate to fill in target values for all the other days of the year? Should I just use the same target value for each day of the year?&lt;/p&gt;
', 'python  scikit  pandas', NULL, '''/a'':144C ''/p'':48C,83C,122C,156C,203C ''/questions/9598/building-a-machine-learning-model-to-predict-crop-yields-based-on-environmental'':130C ''1'':64C ''10'':32C ''2005'':34C ''2014'':35C ''2015'':43C,100C ''assign'':1A,150C ''base'':44C,96C,102C,140C ''build'':88C,131C ''contain'':20C ''crop'':74C,82C,138C ''daili'':56C ''data'':21C,47C,143C ''datascience.stackexchange.com'':129C ''datascience.stackexchange.com/questions/9598/building-a-machine-learning-model-to-predict-crop-yields-based-on-environmental'':128C ''dataset'':19C,54C ''day'':185C,199C ''deriv'':108C ''end'':77C ''environment'':142C ''farm'':30C ''fill'':177C ''grow'':79C ''happen'':75C ''harvest'':72C ''href'':127C ''interpol'':175C ''label'':155C ''learn'':11A,95C,134C ''like'':38C ''machin'':94C,133C ''miss'':4A ''model'':97C,107C,135C ''need'':173C ''note'':51C ''p'':15C,49C,84C,123C,157C ''panda'':14B ''per'':66C,125C,169C ''pleas'':50C ''precipit'':24C,61C,118C ''predict'':40C,99C,137C ''previous'':120C ''python'':12B ''question'':159C ''regress'':90C ''regression/some'':105C ''relat'':112C ''scikit'':10A,13B ''scikit-learn'':9A ''season'':80C ''sinc'':71C,162C ''singl'':166C ''sklearn.cross_validation.labelkfold'':148C ''soybean'':26C ''studi'':110C ''target'':5A,167C,179C,195C ''temperatur'':23C,59C,116C ''use'':147C,192C ''valu'':2A,7A,57C,65C,168C,180C,196C ''vector'':6A ''want'':86C ''would'':37C ''year'':33C,67C,121C,152C,170C,188C,202C ''yield'':27C,41C,70C,101C,114C,139C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9613, 2, '2016-01-04 15:55:41.277', 1, NULL, NULL, '&lt;p&gt;You can use Bayesian Belief Network for prediction. Her is a link for basic explanation.
&lt;a href="http://math.stackexchange.com/questions/93195/bayesian-network-for-dummies"&gt;Bayesian Network&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':24C ''/p'':25C ''/questions/93195/bayesian-network-for-dummies'':21C ''basic'':15C ''bayesian'':5C,22C ''belief'':6C ''explan'':16C ''href'':18C ''link'':13C ''math.stackexchange.com'':20C ''math.stackexchange.com/questions/93195/bayesian-network-for-dummies'':19C ''network'':7C,23C ''p'':1C ''predict'':9C ''use'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9615, 1, '2016-01-04 18:15:35.28', 1, 35, 'Which of the NIPS 2015 papers represent the most interesting advances, and why?', '&lt;p&gt;Many of the &lt;a href="http://papers.nips.cc/book/advances-in-neural-information-processing-systems-28-2015" rel="nofollow"&gt;NIPS 2015 papers&lt;/a&gt; are fascinating, but it is difficult to evaluate which ones represent the greatest progress over previous approaches.&lt;/p&gt;

&lt;p&gt;Which papers are most significant and are likely to have a major impact?&lt;/p&gt;
', 'machine-learning  deep-learning  state-of-the-art', NULL, '''/a'':37C ''/book/advances-in-neural-information-processing-systems-28-2015'':31C ''/p'':55C,70C ''2015'':5A,35C ''advanc'':11A ''approach'':54C ''art'':22B ''deep'':16B ''deep-learn'':15B ''difficult'':43C ''evalu'':45C ''fascin'':39C ''greatest'':50C ''href'':28C ''impact'':69C ''interest'':10A ''learn'':14B,17B ''like'':64C ''machin'':13B ''machine-learn'':12B ''major'':68C ''mani'':24C ''nip'':4A,34C ''nofollow'':33C ''one'':47C ''p'':23C,56C ''paper'':6A,36C,58C ''papers.nips.cc'':30C ''papers.nips.cc/book/advances-in-neural-information-processing-systems-28-2015'':29C ''previous'':53C ''progress'':51C ''rel'':32C ''repres'':7A,48C ''signific'':61C ''state'':19B ''state-of-the-art'':18B');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9616, 1, '2016-01-04 18:35:57.54', 2, 57, 'How to create US state heatmap', '&lt;p&gt;I have a value associated with each US state (let''s pretend it''s the average temperature in January for each state). I want to display this data as a heat map of the United States. To be clear, it would be a map of the US with each state having a color from a color gradient that corresponds to a quantitative value. I don''t know what this plot type is called, so I''m having trouble figuring out how to do this in MATLAB or another program, but does anyone know an easy way to do this? I have a vector of data (of length 50) and just need to make a US map plot.&lt;/p&gt;
', 'data', NULL, '''/p'':126C ''50'':116C ''anoth'':96C ''anyon'':100C ''associ'':13C ''averag'':24C ''call'':81C ''clear'':47C ''color'':61C,64C ''correspond'':67C ''creat'':3A ''data'':7B,36C,113C ''display'':34C ''easi'':103C ''figur'':87C ''gradient'':65C ''heat'':39C ''heatmap'':6A ''januari'':27C ''know'':75C,101C ''length'':115C ''let'':18C ''m'':84C ''make'':121C ''map'':40C,52C,124C ''matlab'':94C ''need'':119C ''p'':8C ''plot'':78C,125C ''pretend'':20C ''program'':97C ''quantit'':70C ''state'':5A,17C,30C,44C,58C ''temperatur'':25C ''troubl'':86C ''type'':79C ''unit'':43C ''us'':4A,16C,55C,123C ''valu'':12C,71C ''vector'':111C ''want'':32C ''way'':104C ''would'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9617, 1, '2016-01-04 19:14:51.783', 2, 59, 'What is the relationship between Markov Random Fields and Conditional Random Fields?', '&lt;p&gt;In &lt;a href="https://www.youtube.com/watch?v=ZYUnyyVgtyA" rel="nofollow"&gt;Neural networks [3.8] : Conditional random fields - Markov network&lt;/a&gt; by Hugo Larochelle it seems to me that a Markov Random Field is a special case of a CRF.&lt;/p&gt;

&lt;p&gt;However, in the Wikipedia article &lt;a href="https://en.wikipedia.org/w/index.php?title=Markov_random_field&amp;amp;oldid=695876526#Conditional_random_fields" rel="nofollow"&gt;Markov random field&lt;/a&gt; it says:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;One notable variant of a Markov random field is a conditional random field, in which each random variable may also be conditioned upon a set of global observations o.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This would mean that CRFs are a special case of MRFs.&lt;/p&gt;

&lt;h2&gt;Definitions&lt;/h2&gt;

&lt;h3&gt;Markov Random Field&lt;/h3&gt;

&lt;p&gt;Again, according to Wikipedia&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Given an undirected graph $G=(V,E)$, a set of random variables $X = (X_v)_{v\in V}$ indexed by $V$  form a Markov random field with respect to $G$  if they satisfy the local Markov properties:&lt;/p&gt;
  
  &lt;p&gt;&lt;strong&gt;Pairwise Markov property&lt;/strong&gt;: Any two non-adjacent variables are conditionally independent given all other variables:
  $X_u \perp\!\!\!\perp X_v \mid X_{V \setminus \{u,v\}} \quad \text{if } \{u,v\} \notin E$&lt;/p&gt;
  
  &lt;p&gt;&lt;strong&gt;Local Markov property&lt;/strong&gt;: A variable is conditionally independent of all other variables given its neighbors:
  $X_v \perp\!\!\!\perp X_{V\setminus \operatorname{cl}(v)} \mid X_{\operatorname{ne}(v)}$
  where ${\textstyle \operatorname{ne}(v)}$ is the set of neighbors of $v$, and $\operatorname{cl}(v) = v \cup \operatorname{ne}(v)$ is the closed neighbourhood of $v$.&lt;/p&gt;
  
  &lt;p&gt;&lt;strong&gt;Global Markov property&lt;/strong&gt;: Any two subsets of variables are conditionally independent given a separating subset:
  $X_A \perp\!\!\!\perp X_B \mid X_S$
  where every path from a node in $A$ to a node in $B$ passes through $S$.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Please note if you know a citable source which gives a good definition.&lt;/p&gt;

&lt;h3&gt;Conditional Random Fields&lt;/h3&gt;

&lt;p&gt;According to &lt;a href="https://en.wikipedia.org/w/index.php?title=Conditional_random_field&amp;amp;oldid=695232246#Description" rel="nofollow"&gt;Wikipedia&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Lafferty, McCallum and Pereira[1] define a CRF on observations $\boldsymbol{X}$ and random variables $\boldsymbol{Y}$ as follows:&lt;/p&gt;
  
  &lt;blockquote&gt;
    &lt;p&gt;Let $G = (V , E)$ be a graph such that&lt;/p&gt;
    
    &lt;p&gt;$\boldsymbol{Y} = (\boldsymbol{Y}_v)_{v\in V}$, so that $\boldsymbol{Y}$ is indexed by the vertices of $G$. Then $(\boldsymbol{X}, \boldsymbol{Y})$ is a conditional random field when the random variables $\boldsymbol{Y}_v$, conditioned on $\boldsymbol{X}$, obey the Markov property with respect to the graph: $$p(\boldsymbol{Y}_v |\boldsymbol{X}, \boldsymbol{Y}_w, w \neq v) = p(\boldsymbol{Y}_v |\boldsymbol{X}, \boldsymbol{Y}_w, w \sim v)$$ where $\mathit{w} \sim v$ means that $w$ and $v$ are neighbors in G.&lt;/p&gt;
  &lt;/blockquote&gt;
  
  &lt;p&gt;What this means is that a CRF is an undirected graphical model whose nodes can be divided into exactly two disjoint sets $\boldsymbol{X}$ and $\boldsymbol{Y}$, the observed and output variables, respectively; the conditional distribution $p(\boldsymbol{Y}|\boldsymbol{X})$ is then modeled.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;h2&gt;Question&lt;/h2&gt;

&lt;p&gt;What is the relationship between Markov Random Fields and Conditional Random Fields?&lt;/p&gt;
', 'machine-learning  markov  pgm', NULL, '''/a'':35C,72C,352C ''/blockquote'':108C,320C,477C,524C ''/h2'':124C,527C ''/h3'':129C,340C ''/p'':55C,75C,107C,121C,135C,175C,214C,275C,319C,335C,353C,375C,387C,476C,523C,541C ''/strong'':181C,220C,281C ''/w/index.php?title=conditional_random_field&amp;amp;oldid=695232246#description'':348C ''/w/index.php?title=markov_random_field&amp;amp;oldid=695876526#conditional_random_fields'':66C ''/watch?v=zyunyyvgtya'':24C ''1'':360C ''3.8'':29C ''accord'':132C,342C ''adjac'':186C ''also'':97C ''articl'':61C ''b'':299C,315C ''blockquot'':76C,136C,354C,376C ''boldsymbol'':366C,371C,389C,391C,399C,409C,411C,422C,427C,439C,442C,444C,451C,454C,456C,501C,504C,516C,518C ''case'':51C,118C ''citabl'':328C ''cl'':241C,262C ''close'':271C ''condit'':10A,30C,88C,99C,189C,224C,288C,337C,415C,425C,513C,538C ''crf'':54C,363C,485C ''crfs'':114C ''cup'':265C ''defin'':361C ''definit'':123C,334C ''disjoint'':499C ''distribut'':514C ''divid'':495C ''e'':144C,213C,381C ''en.wikipedia.org'':65C,347C ''en.wikipedia.org/w/index.php?title=conditional_random_field&amp;amp;oldid=695232246#description'':346C ''en.wikipedia.org/w/index.php?title=markov_random_field&amp;amp;oldid=695876526#conditional_random_fields'':64C ''everi'':304C ''exact'':497C ''field'':8A,12A,32C,47C,71C,85C,90C,128C,163C,339C,417C,536C,540C ''follow'':374C ''form'':159C ''g'':142C,167C,379C,407C,475C ''give'':331C ''given'':138C,191C,230C,290C ''global'':104C,278C ''good'':333C ''graph'':141C,384C,437C ''graphic'':489C ''h2'':122C,525C ''h3'':125C,336C ''howev'':57C ''href'':21C,63C,345C ''hugo'':37C ''independ'':190C,225C,289C ''index'':156C,402C ''know'':326C ''lafferti'':356C ''larochell'':38C ''learn'':15B ''let'':378C ''local'':172C,217C ''machin'':14B ''machine-learn'':13B ''markov'':6A,16B,33C,45C,69C,83C,126C,161C,173C,179C,218C,279C,431C,534C ''mathit'':463C ''may'':96C ''mccallum'':357C ''mean'':112C,467C,481C ''mid'':201C,243C,300C ''model'':490C,522C ''mrfs'':120C ''ne'':246C,251C,267C ''neighbor'':232C,257C,473C ''neighbourhood'':272C ''neq'':448C ''network'':28C,34C ''neural'':27C ''node'':308C,313C,492C ''nofollow'':26C,68C,350C ''non'':185C ''non-adjac'':184C ''notabl'':79C ''note'':323C ''notin'':212C ''o'':106C ''obey'':429C ''observ'':105C,365C,507C ''one'':78C ''operatornam'':240C,245C,250C,261C,266C ''output'':509C ''p'':18C,56C,77C,109C,130C,137C,176C,215C,276C,321C,341C,355C,377C,388C,438C,450C,478C,515C,528C ''pairwis'':178C ''pass'':316C ''path'':305C ''pereira'':359C ''perp'':197C,198C,235C,236C,296C,297C ''pgm'':17B ''pleas'':322C ''properti'':174C,180C,219C,280C,432C ''quad'':207C ''question'':526C ''random'':7A,11A,31C,46C,70C,84C,89C,94C,127C,148C,162C,338C,369C,416C,420C,535C,539C ''rel'':25C,67C,349C ''relationship'':4A,532C ''respect'':165C,434C,511C ''satisfi'':170C ''say'':74C ''seem'':40C ''separ'':292C ''set'':102C,146C,255C,500C ''setminus'':204C,239C ''sim'':460C,465C ''sourc'':329C ''special'':50C,117C ''strong'':177C,216C,277C ''subset'':284C,293C ''text'':208C ''textstyl'':249C ''two'':183C,283C,498C ''u'':196C,205C,210C ''undirect'':140C,488C ''upon'':100C ''v'':143C,152C,153C,155C,158C,200C,203C,206C,211C,234C,238C,242C,247C,252C,259C,263C,264C,268C,274C,380C,393C,394C,396C,424C,441C,449C,453C,461C,466C,471C ''variabl'':95C,149C,187C,194C,222C,229C,286C,370C,421C,510C ''variant'':80C ''vertic'':405C ''w'':446C,447C,458C,459C,464C,469C ''whose'':491C ''wikipedia'':60C,134C,351C ''would'':111C ''www.youtube.com'':23C ''www.youtube.com/watch?v=zyunyyvgtya'':22C ''x'':150C,151C,195C,199C,202C,233C,237C,244C,294C,298C,301C,367C,410C,428C,443C,455C,502C,519C ''y'':372C,390C,392C,400C,412C,423C,440C,445C,452C,457C,505C,517C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9669, 2, '2016-01-07 10:03:23.543', 1, NULL, NULL, '&lt;p&gt;If you don''t want to dig into much NLP in that task, I suggest you to generate a set of most frequent NGrams (of lengths 2-5) from your documents and find the most distinct ngrams for each category using TF*IDF metric as sense importance of a particular ngram (normalizing measure by word count) and selecting those Ngrams that are used in a particular category and are not (or rarely) used in others.&lt;/p&gt;
', NULL, NULL, '''-5'':29C ''/p'':77C ''2'':28C ''categori'':41C,68C ''count'':57C ''dig'':8C ''distinct'':37C ''document'':32C ''find'':34C ''frequent'':24C ''generat'':19C ''idf'':44C ''import'':48C ''length'':27C ''measur'':54C ''metric'':45C ''much'':10C ''ngram'':25C,38C,52C,61C ''nlp'':11C ''normal'':53C ''other'':76C ''p'':1C ''particular'':51C,67C ''rare'':73C ''select'':59C ''sens'':47C ''set'':21C ''suggest'':16C ''task'':14C ''tf'':43C ''use'':42C,64C,74C ''want'':6C ''word'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9679, 2, '2016-01-07 14:38:02.57', 1, NULL, NULL, '&lt;p&gt;I have already answered similar questions &lt;a href="http://datascience.stackexchange.com/a/9439/11097"&gt;here&lt;/a&gt; and &lt;a href="http://datascience.stackexchange.com/a/8965/11097"&gt;here&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;So, that would help you get an idea of how you need to design your own learning path.&lt;/p&gt;

&lt;p&gt;The reason why I said that this question is opinion-based is:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Data Science is a huge domain in itself, and attaining complete
  knowledge of everything in it, is close to impossible.&lt;/p&gt;
  
  &lt;p&gt;So, if I am interested in pursuing the concept of Neural Networks
  alone, and subsequently Deep Learning; then my learning path would be
  completely different to someone who wants to learn a different concept like time series.&lt;/p&gt;
  
  &lt;p&gt;And so does that of someone who wants to learn some other concept. The
  concepts might be intertwined, but something which I absolutely
  require in my learning path might not be necessary to someone else''s.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;So, those above resources are a rough guide to get started.&lt;/p&gt;

&lt;p&gt;In addition to that, here is a list of &lt;a href="http://datascience.stackexchange.com/q/8941/11097"&gt;conferences&lt;/a&gt; and &lt;a href="http://datascience.stackexchange.com/q/9000/11097"&gt;podcasts&lt;/a&gt; of Data Science, which help you with your learning.&lt;/p&gt;

&lt;p&gt;And also keep an eye on the &lt;a href="/questions/tagged/beginner" class="post-tag" title="show questions tagged &amp;#39;beginner&amp;#39;" rel="tag"&gt;beginner&lt;/a&gt; tag here.&lt;/p&gt;

&lt;p&gt;Additional reading:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stats.stackexchange.com/a/174140/84191"&gt;Applying machine learning in a real world example&lt;/a&gt;
&lt;a href="http://neuralnetworksanddeeplearning.com/" rel="nofollow"&gt;A really good neural networks and deep learning book&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':14C,22C,189C,197C,233C,255C,270C ''/a/174140/84191'':246C ''/a/8965/11097'':20C ''/a/9439/11097'':12C ''/blockquote'':159C ''/p'':23C,43C,58C,81C,120C,158C,172C,207C,236C,240C,271C ''/q/8941/11097'':187C ''/q/9000/11097'':195C ''/questions/tagged/beginner'':218C ''39'':227C,229C ''absolut'':144C ''addit'':175C,238C ''alon'':95C ''alreadi'':4C ''also'':210C ''answer'':5C ''appli'':247C ''attain'':70C ''base'':56C ''beginn'':228C,232C ''blockquot'':59C ''book'':269C ''class'':219C ''close'':78C ''complet'':71C,106C ''concept'':91C,116C,134C,136C ''confer'':188C ''data'':61C,199C ''datascience.stackexchange.com'':11C,19C,186C,194C ''datascience.stackexchange.com/a/8965/11097'':18C ''datascience.stackexchange.com/a/9439/11097'':10C ''datascience.stackexchange.com/q/8941/11097'':185C ''datascience.stackexchange.com/q/9000/11097'':193C ''deep'':98C,267C ''design'':38C ''differ'':107C,115C ''domain'':66C ''els'':156C ''everyth'':74C ''exampl'':254C ''eye'':213C ''get'':30C,170C ''good'':263C ''guid'':168C ''help'':28C,202C ''href'':9C,17C,184C,192C,217C,243C,257C ''huge'':65C ''idea'':32C ''imposs'':80C ''interest'':87C ''intertwin'':139C ''keep'':211C ''knowledg'':72C ''learn'':41C,99C,102C,113C,131C,148C,206C,249C,268C ''like'':117C ''list'':181C ''machin'':248C ''might'':137C,150C ''necessari'':153C ''need'':36C ''network'':94C,265C ''neural'':93C,264C ''neuralnetworksanddeeplearning.com'':258C ''nofollow'':260C ''opinion'':55C ''opinion-bas'':54C ''p'':1C,24C,44C,60C,82C,121C,160C,173C,208C,237C,241C ''path'':42C,103C,149C ''podcast'':196C ''post'':221C ''post-tag'':220C ''pursu'':89C ''question'':7C,52C,225C ''read'':239C ''real'':252C ''realli'':262C ''reason'':46C ''rel'':230C,259C ''requir'':145C ''resourc'':164C ''rough'':167C ''said'':49C ''scienc'':62C,200C ''seri'':119C ''show'':224C ''similar'':6C ''someon'':109C,127C,155C ''someth'':141C ''start'':171C ''stats.stackexchange.com'':245C ''stats.stackexchange.com/a/174140/84191'':244C ''subsequ'':97C ''tag'':222C,226C,231C,234C ''time'':118C ''titl'':223C ''want'':111C,129C ''world'':253C ''would'':27C,104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9694, 2, '2016-01-08 01:23:37.253', 4, NULL, NULL, '&lt;p&gt;&lt;strong&gt;It will have very little effect&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The answer most will give is that it will have no effect, but adding one more feature will decrease the ratio of records to features so will slightly increase the bias and will hence make your model slightly less accurate.  Unless, of course, you have overfit your model , in which case it will make your model slightly more accurate (a good data scientist would never do this because they understand the importance of cross-validation :-).&lt;/p&gt;

&lt;p&gt;If you normalize your data and then attempt some sort of dimensionality reduction, your algorithm will immediately eliminate the feature that you added since it is perfectly negatively (linearly) correlated with the first feature.  &lt;strong&gt;In this case it will have no effect&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Please also consider the following:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I always see big red flags when someone asks a very fundamental data science question with the words &lt;code&gt;neural network&lt;/code&gt; included.  Neural networks are very powerful and receive a great deal of attention in the media and on Kaggle, but they take more data to train, are difficult to configure, and require much more computing power.  If you are just starting out, I suggest getting a foundation in linear regression, logistic regression, clustering, SVMs, decision trees, random forests, and naive Bayes before delving into artificial neural networks.  Just some food for thought.&lt;/p&gt;

&lt;p&gt;Hope this helps! &lt;/p&gt;
', NULL, NULL, '''/code'':166C ''/p'':10C,88C,134C,143C,239C,244C ''/strong'':9C,133C,142C ''accur'':51C,70C ''ad'':25C,112C ''algorithm'':104C ''also'':138C ''alway'':146C ''answer'':13C ''artifici'':231C ''ask'':153C ''attempt'':97C ''attent'':179C ''bay'':227C ''bias'':42C ''big'':148C ''case'':62C,127C ''cluster'':219C ''code'':163C ''comput'':201C ''configur'':196C ''consid'':139C ''correl'':119C ''cours'':54C ''cross'':86C ''cross-valid'':85C ''data'':73C,94C,157C,190C ''deal'':177C ''decis'':221C ''decreas'':30C ''delv'':229C ''difficult'':194C ''dimension'':101C ''effect'':8C,23C,132C ''elimin'':107C ''featur'':28C,36C,109C,123C ''first'':122C ''flag'':150C ''follow'':141C ''food'':236C ''forest'':224C ''foundat'':213C ''fundament'':156C ''get'':211C ''give'':16C ''good'':72C ''great'':176C ''help'':243C ''henc'':45C ''hope'':241C ''immedi'':106C ''import'':83C ''includ'':167C ''increas'':40C ''kaggl'':185C ''less'':50C ''linear'':118C,215C ''littl'':7C ''logist'':217C ''make'':46C,65C ''media'':182C ''model'':48C,59C,67C ''much'':199C ''naiv'':226C ''negat'':117C ''network'':165C,169C,233C ''neural'':164C,168C,232C ''never'':76C ''normal'':92C ''one'':26C ''overfit'':57C ''p'':1C,11C,89C,135C,144C,240C ''perfect'':116C ''pleas'':137C ''power'':172C,202C ''question'':159C ''random'':223C ''ratio'':32C ''receiv'':174C ''record'':34C ''red'':149C ''reduct'':102C ''regress'':216C,218C ''requir'':198C ''scienc'':158C ''scientist'':74C ''see'':147C ''sinc'':113C ''slight'':39C,49C,68C ''someon'':152C ''sort'':99C ''start'':207C ''strong'':2C,124C,136C ''suggest'':210C ''svms'':220C ''take'':188C ''thought'':238C ''train'':192C ''tree'':222C ''understand'':81C ''unless'':52C ''valid'':87C ''word'':162C ''would'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9619, 1, '2016-01-04 20:22:46.043', 4, 80, 'How can I predict the acceptance of an article by publisher?', '&lt;p&gt;I am working on this project where I have to predict the acceptance probability of a literary article, let''s say a research paper by a publisher, which in this case would be a Journal. &lt;/p&gt;

&lt;p&gt;I would like to use a machine learning algorithm for this project which would learn from the pool of articles/research papers published by the Publisher/Journal till date. Later I want to use the current article/research paper at hand to predict the probability of acceptance of this article/research paper on the insights that the algorithm has learnt. Here are my queries:-&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;What algorithm are there to tackle such task?&lt;/li&gt;
&lt;li&gt;How should I make these algorithms learn the word vector, grammars, topics etc?&lt;/li&gt;
&lt;li&gt;How do I convert the article into a feature set from which the algorithms can learn?&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  data-mining  nlp  predictive-modeling', NULL, '''/li'':131C,146C,164C ''/ol'':165C ''/p'':58C,120C ''accept'':6A,35C,103C ''algorithm'':68C,113C,124C,138C,161C ''articl'':9A,40C,153C ''article/research'':94C,106C ''articles/research'':79C ''case'':53C ''convert'':151C ''current'':93C ''data'':16B ''data-min'':15B ''date'':86C ''etc'':145C ''featur'':156C ''grammar'':143C ''hand'':97C ''insight'':110C ''journal'':57C ''later'':87C ''learn'':14B,67C,74C,139C,163C ''learnt'':115C ''let'':41C ''li'':122C,132C,147C ''like'':62C ''literari'':39C ''machin'':13B,66C ''machine-learn'':12B ''make'':136C ''mine'':17B ''model'':21B ''nlp'':18B ''ol'':121C ''p'':22C,59C ''paper'':46C,80C,95C,107C ''pool'':77C ''predict'':4A,20B,33C,99C ''predictive-model'':19B ''probabl'':36C,101C ''project'':28C,71C ''publish'':11A,49C,81C ''publisher/journal'':84C ''queri'':119C ''research'':45C ''say'':43C ''set'':157C ''tackl'':128C ''task'':130C ''till'':85C ''topic'':144C ''use'':64C,91C ''vector'':142C ''want'':89C ''word'':141C ''work'':25C ''would'':54C,61C,73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9620, 2, '2016-01-04 20:48:57.71', 3, NULL, NULL, '&lt;p&gt;Plotly in Python &lt;a href="https://plot.ly/python/choropleth-maps/" rel="nofollow"&gt;provides a nice solution&lt;/a&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import plotly.plotly as py
import pandas as pd

df = pd.read_csv(''https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv'')

for col in df.columns:
    df[col] = df[col].astype(str)

scl = [[0.0, ''rgb(242,240,247)''],[0.2, ''rgb(218,218,235)''],[0.4, ''rgb(188,189,220)''],\
            [0.6, ''rgb(158,154,200)''],[0.8, ''rgb(117,107,177)''],[1.0, ''rgb(84,39,143)'']]

df[''text''] = df[''state''] + ''&amp;lt;br&amp;gt;'' +\
    ''Beef ''+df[''beef'']+'' Dairy ''+df[''dairy'']+''&amp;lt;br&amp;gt;''+\
    ''Fruits ''+df[''total fruits'']+'' Veggies '' + df[''total veggies'']+''&amp;lt;br&amp;gt;''+\
    ''Wheat ''+df[''wheat'']+'' Corn ''+df[''corn'']

data = [ dict(
        type=''choropleth'',
        colorscale = scl,
        autocolorscale = False,
        locations = df[''code''],
        z = df[''total exports''].astype(float),
        locationmode = ''USA-states'',
        text = df[''text''],
        marker = dict(
            line = dict (
                color = ''rgb(255,255,255)'',
                width = 2
            )
        ),
        colorbar = dict(
            title = "Millions USD"
        )
    ) ]

layout = dict(
        title = ''2011 US Agriculture Exports by State&amp;lt;br&amp;gt;(Hover for breakdown)'',
        geo = dict(
            scope=''usa'',
            projection=dict( type=''albers usa'' ),
            showlakes = True,
            lakecolor = ''rgb(255, 255, 255)'',
        ),
    )

fig = dict( data=data, layout=layout )

url = py.plot( fig, filename=''d3-cloropleth-map'' )
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/uhhaw.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/uhhaw.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':16C,213C ''/code'':193C ''/p'':17C,214C ''/plotly/datasets/master/2011_us_ag_exports.csv'''')'':33C ''/pre'':194C ''/python/choropleth-maps/'':9C ''/uhhaw.png'':200C,207C ''0.0'':45C ''0.2'':50C ''0.4'':55C ''0.6'':60C ''0.8'':65C ''1.0'':70C ''107'':68C ''117'':67C ''143'':74C ''154'':63C ''158'':62C ''177'':69C ''188'':57C ''189'':58C ''2'':142C ''200'':64C ''2011'':151C ''218'':52C,53C ''220'':59C ''235'':54C ''240'':48C ''242'':47C ''247'':49C ''255'':138C,139C,140C,176C,177C,178C ''39'':73C ''84'':72C ''agricultur'':153C ''alber'':170C ''alt'':208C ''astyp'':42C,123C ''autocolorscal'':114C ''beef'':82C,84C ''br'':80C,89C,100C,158C ''breakdown'':162C ''choropleth'':111C ''cloropleth'':191C ''code'':19C,118C ''col'':35C,39C,41C ''color'':136C ''colorbar'':143C ''colorscal'':112C ''corn'':105C,107C ''csv'':30C ''d3'':190C ''d3-cloropleth-map'':189C ''dairi'':85C,87C ''data'':108C,181C,182C ''descript'':211C ''df'':28C,38C,40C,75C,77C,83C,86C,92C,96C,103C,106C,117C,120C,130C ''df.columns'':37C ''dict'':109C,133C,135C,144C,149C,164C,168C,180C ''enter'':209C ''export'':122C,154C ''fals'':115C ''fig'':179C,187C ''filenam'':188C ''float'':124C ''fruit'':91C,94C ''geo'':163C ''gt'':81C,90C,101C,159C ''hover'':160C ''href'':6C,197C ''i.stack.imgur.com'':199C,206C ''i.stack.imgur.com/uhhaw.png'':198C,205C ''imag'':210C ''img'':203C ''import'':20C,24C ''lakecolor'':174C ''layout'':148C,183C,184C ''line'':134C ''locat'':116C ''locationmod'':125C ''lt'':79C,88C,99C,157C ''map'':192C ''marker'':132C ''million'':146C ''nice'':14C ''nofollow'':11C,202C ''p'':1C,195C ''panda'':25C ''pd'':27C ''pd.read'':29C ''plot'':2C ''plot.ly'':8C ''plot.ly/python/choropleth-maps/'':7C ''plotly.plotly'':21C ''pre'':18C ''project'':167C ''provid'':12C ''py'':23C ''py.plot'':186C ''python'':4C ''raw.githubusercontent.com'':32C ''raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv'''')'':31C ''rel'':10C,201C ''rgb'':46C,51C,56C,61C,66C,71C,137C,175C ''scl'':44C,113C ''scope'':165C ''showlak'':172C ''solut'':15C ''src'':204C ''state'':78C,128C,156C ''str'':43C ''text'':76C,129C,131C ''titl'':145C,150C ''total'':93C,97C,121C ''true'':173C ''type'':110C,169C ''url'':185C ''us'':152C ''usa'':127C,166C,171C ''usa-st'':126C ''usd'':147C ''veggi'':95C,98C ''wheat'':102C,104C ''width'':141C ''z'':119C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9622, 2, '2016-01-04 22:47:13.593', 0, NULL, NULL, '&lt;p&gt;I would do the following:&lt;/p&gt;

&lt;p&gt;First, cluster your customers into several groups, based on age, location, gender etc. Second, for each group, use different combination of interaction as features to predict ''yes/no'', and select which combination of interaction performs best.&lt;/p&gt;
', NULL, NULL, '''/p'':7C,44C ''age'':18C ''base'':16C ''best'':43C ''cluster'':10C ''combin'':28C,39C ''custom'':12C ''differ'':27C ''etc'':21C ''featur'':32C ''first'':9C ''follow'':6C ''gender'':20C ''group'':15C,25C ''interact'':30C,41C ''locat'':19C ''p'':1C,8C ''perform'':42C ''predict'':34C ''second'':22C ''select'':37C ''sever'':14C ''use'':26C ''would'':3C ''yes/no'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9623, 2, '2016-01-04 22:49:17.717', 4, NULL, NULL, '&lt;p&gt;Your R,G, and B pixel values can be broken into 3 separate channels (and in most cases this is done for you). These channels are treated no differently than feature maps in higher levels of the network. Convolution extends naturally to more than 2 dimensions.&lt;/p&gt;

&lt;p&gt;Imagine the greyscale, single-channel example. Say you have N feature maps to learn in the first layer. Then the output of this layer (and therefore the input to the second layer) will be comprised of N channels, each of which is the result of convolving a feature map with each window in your image. Having 3 channels in your first layer is no different. &lt;/p&gt;

&lt;p&gt;This tutorial does a nice job on convolution in general.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://deeplearning.net/tutorial/lenet.html" rel="nofollow"&gt;http://deeplearning.net/tutorial/lenet.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':48C,116C,128C ''/tutorial/lenet.html'':134C ''/tutorial/lenet.html&lt;/a&gt;&lt;/p&gt;'':139C ''2'':46C ''3'':13C,107C ''b'':6C ''broken'':11C ''case'':19C ''channel'':15C,26C,55C,88C,108C ''compris'':85C ''convolut'':40C,125C ''convolv'':96C ''deeplearning.net'':133C,138C ''deeplearning.net/tutorial/lenet.html'':132C ''deeplearning.net/tutorial/lenet.html&lt;/a&gt;&lt;/p&gt;'':137C ''differ'':30C,115C ''dimens'':47C ''done'':22C ''exampl'':56C ''extend'':41C ''featur'':32C,61C,98C ''first'':67C,111C ''g'':4C ''general'':127C ''greyscal'':52C ''higher'':35C ''href'':131C ''imag'':105C ''imagin'':50C ''input'':78C ''job'':123C ''layer'':68C,74C,82C,112C ''learn'':64C ''level'':36C ''map'':33C,62C,99C ''n'':60C,87C ''natur'':42C ''network'':39C ''nice'':122C ''nofollow'':136C ''output'':71C ''p'':1C,49C,117C,129C ''pixel'':7C ''r'':3C ''rel'':135C ''result'':94C ''say'':57C ''second'':81C ''separ'':14C ''singl'':54C ''single-channel'':53C ''therefor'':76C ''treat'':28C ''tutori'':119C ''valu'':8C ''window'':102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9626, 1, '2016-01-05 04:02:38.443', 2, 83, 'Information Extraction from Free-form text to create Transactions', '&lt;p&gt;I''m working on a use case where the user will be provided a text box to enter the details of the transaction application.&lt;br&gt;
For Example user might enter the below text and I have to parse the data and accordingly create the transaction. (I took basic example as a startup)&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Ex 1 : Transfer $100 from Account 1 into Account 2&lt;br&gt;
  Ex 2: Move Money of total 400 dollars into account owned by Mr.xxxx from Acct#&lt;br&gt;
  Ex 3: Deposit an amount of five thousand dollars into acct#&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;For above examples I have to parse and convert into a tabular format like 
Trx_Type (Deposit or Transfer), Amt, From_Acct#, To_Acct#, Acct_Holder_Name,etc.&lt;/p&gt;

&lt;p&gt;I''m trying to look options by doing Named Entity (NER) or Classifier modelling using NLTK in python. Can someone please share ideas from where to start and guide me with a draft way to proceed.&lt;br&gt;
Looking forward for a response. Thanks!&lt;/p&gt;
', 'python  nlp  information-retrieval', NULL, '''/blockquote'':111C ''/p'':69C,110C,141C,186C ''1'':73C,78C ''100'':75C ''2'':81C,84C ''3'':100C ''400'':89C ''accord'':58C ''account'':77C,80C,92C ''acct'':97C,109C,134C,136C,137C ''amount'':103C ''amt'':132C ''applic'':40C ''basic'':64C ''blockquot'':70C ''box'':32C ''br'':41C,82C,98C,179C ''case'':23C ''classifi'':155C ''convert'':121C ''creat'':9A,59C ''data'':56C ''deposit'':101C,129C ''detail'':36C ''dollar'':90C,107C ''draft'':175C ''enter'':34C,46C ''entiti'':152C ''etc'':140C ''ex'':72C,83C,99C ''exampl'':43C,65C,115C ''extract'':2A ''five'':105C ''form'':6A ''format'':125C ''forward'':181C ''free'':5A ''free-form'':4A ''guid'':171C ''holder'':138C ''idea'':165C ''inform'':1A,14B ''information-retriev'':13B ''like'':126C ''look'':147C,180C ''m'':18C,144C ''might'':45C ''model'':156C ''money'':86C ''move'':85C ''mr.xxxx'':95C ''name'':139C,151C ''ner'':153C ''nlp'':12B ''nltk'':158C ''option'':148C ''own'':93C ''p'':16C,71C,112C,142C ''pars'':54C,119C ''pleas'':163C ''proceed'':178C ''provid'':29C ''python'':11B,160C ''respons'':184C ''retriev'':15B ''share'':164C ''someon'':162C ''start'':169C ''startup'':68C ''tabular'':124C ''text'':7A,31C,49C ''thank'':185C ''thousand'':106C ''took'':63C ''total'':88C ''transact'':10A,39C,61C ''transfer'':74C,131C ''tri'':145C ''trx'':127C ''type'':128C ''use'':22C,157C ''user'':26C,44C ''way'':176C ''work'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9627, 1, '2016-01-05 04:59:01.667', 2, 36, 'How to make edges appear in a graph when adjacency matrix value is beyond a particular threshold?', '&lt;p&gt;I''ve created a graph from an adjacency matrix using the igraph package but I want edges to appear in the graph only when the value in the adjacency matrix is beyond a particular threshold. Using the present method I am getting all the edges which have non zero weights.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;set.seed(42)
iris_matrix &amp;lt;- as.matrix(iris[,1:4])
image(iris_matrix)
g&amp;lt;-cor(iris_matrix)
library(igraph)
R&amp;lt;-graph.adjacency(g, mode=c("undirected"), weighted=TRUE, diag=FALSE, add.colnames=NULL, add.rownames=NULL)
plot(R)
&lt;/code&gt;&lt;/pre&gt;
', 'r  graphs', NULL, '''/code'':110C ''/p'':71C ''/pre'':111C ''1'':81C ''4'':82C ''42'':75C ''add.colnames'':104C ''add.rownames'':106C ''adjac'':10A,28C,49C ''appear'':5A,39C ''as.matrix'':79C ''beyond'':14A,52C ''c'':98C ''code'':73C ''cor'':88C ''creat'':23C ''diag'':102C ''edg'':4A,37C,65C ''fals'':103C ''g'':86C,96C ''get'':62C ''graph'':8A,19B,25C,42C ''graph.adjacency'':95C ''igraph'':32C,92C ''imag'':83C ''iri'':76C,80C,84C,89C ''librari'':91C ''lt'':78C,87C,94C ''make'':3A ''matrix'':11A,29C,50C,77C,85C,90C ''method'':59C ''mode'':97C ''non'':68C ''null'':105C,107C ''p'':20C ''packag'':33C ''particular'':16A,54C ''plot'':108C ''pre'':72C ''present'':58C ''r'':18B,93C,109C ''set.seed'':74C ''threshold'':17A,55C ''true'':101C ''undirect'':99C ''use'':30C,56C ''valu'':12A,46C ''ve'':22C ''want'':36C ''weight'':70C,100C ''zero'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9628, 2, '2016-01-05 08:50:35.083', 0, NULL, NULL, '&lt;p&gt;You have 10 data points with each data point having 365 (temperature for each day) + 365 (precipitation for each day) dimensions. Ideally, I would first reduce dimensions via machine learning methods, e.g. PCA. Then use machine learning methods to build a prediction model. However, due to the small dataset, I don''t think machine learning techniques are appropriate to your problem.&lt;/p&gt;
', NULL, NULL, '''/p'':63C ''10'':4C ''365'':12C,17C ''appropri'':59C ''build'':41C ''data'':5C,9C ''dataset'':50C ''day'':16C,21C ''dimens'':22C,28C ''due'':46C ''e.g'':33C ''first'':26C ''howev'':45C ''ideal'':23C ''learn'':31C,38C,56C ''machin'':30C,37C,55C ''method'':32C,39C ''model'':44C ''p'':1C ''pca'':34C ''point'':6C,10C ''precipit'':18C ''predict'':43C ''problem'':62C ''reduc'':27C ''small'':49C ''techniqu'':57C ''temperatur'':13C ''think'':54C ''use'':36C ''via'':29C ''would'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9629, 2, '2016-01-05 08:50:56.897', 0, NULL, NULL, '&lt;p&gt;This is how you can approach this problem:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;strong&gt;Customer Segmentation:&lt;/strong&gt; Segment each customer as a separate data point, and if possible have another project for clustering the customers depending on various factors (Example: Depending on geographical regions, age, etc). This would help in simplifying the problem statement and narrowing it too.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Now choose the best combination:&lt;/strong&gt; Now after segmentation, choose the best(average) combination of interaction mechanisms needed to be implemented for customer conversion. The mean would answer you the question: &lt;code&gt;What is the average number of interactions by different media taken by a customer before he/she is converted&lt;/code&gt; &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;Bonus&lt;/strong&gt;: In addition to calculating the mean, calculating the median also helps, as it helps in fixing skew due to overly compulsive customer reps who interact a bit more frequently than some, and difficult customers.  &lt;a href="http://datascience.stackexchange.com/a/8865/11097"&gt;Useful reference&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':159C ''/a/8865/11097'':156C ''/code'':111C ''/li'':58C,112C ''/ol'':113C ''/p'':10C,160C ''/strong'':16C,66C,117C ''addit'':119C ''age'':44C ''also'':127C ''anoth'':29C ''answer'':88C ''approach'':7C ''averag'':73C,96C ''best'':64C,72C ''bit'':144C ''bonus'':116C ''calcul'':121C,124C ''choos'':62C,70C ''cluster'':32C ''code'':92C ''combin'':65C,74C ''compuls'':138C ''convers'':84C ''convert'':110C ''custom'':14C,19C,34C,83C,106C,139C,151C ''data'':23C ''datascience.stackexchange.com'':155C ''datascience.stackexchange.com/a/8865/11097'':154C ''depend'':35C,40C ''differ'':101C ''difficult'':150C ''due'':135C ''etc'':45C ''exampl'':39C ''factor'':38C ''fix'':133C ''frequent'':146C ''geograph'':42C ''he/she'':108C ''help'':48C,128C,131C ''href'':153C ''implement'':81C ''interact'':76C,99C,142C ''li'':12C,59C ''mean'':86C,123C ''mechan'':77C ''media'':102C ''median'':126C ''narrow'':55C ''need'':78C ''number'':97C ''ol'':11C ''over'':137C ''p'':1C,114C ''point'':24C ''possibl'':27C ''problem'':9C,52C ''project'':30C ''question'':91C ''refer'':158C ''region'':43C ''rep'':140C ''segment'':15C,17C,69C ''separ'':22C ''simplifi'':50C ''skew'':134C ''statement'':53C ''strong'':13C,60C,115C ''taken'':103C ''use'':157C ''various'':37C ''would'':47C,87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9633, 1, '2016-01-05 12:28:49.013', 1, 39, 'Score probability groups with Random Forest in R', '&lt;p&gt;I would like to use RF in order to get a score probability of a binary outcome, in order to know groups with higher probability of buy something. Like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;fit&amp;lt;-randomForest(formula = buy ~.  , data = pract,importance = TRUE ,ntree =1000) 


pract$PREDICTION &amp;lt;- as.vector(predict(fit,type=''prob'',newdata=pract)[,2])

test$PREDICTION &amp;lt;- as.vector(predict(fit,type=''prob'',newdata=test)[,2])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;First Question, When I perfom auc of the pract$PREDICTION I get 1! It is because overfiting? Should I use out of bag instead of pract, like this?:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;pract$PREDICTION &amp;lt;- as.vector(predict(fit,type=''prob'')[,2]) 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Second Question:&lt;/p&gt;

&lt;p&gt;With test$PREDICTION I want to make groups ordered by the score prob, like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;test$score[test$PREDICTION &amp;gt;= 0 &amp;amp; test$PREDICTION &amp;lt;=0.3] &amp;lt;-  1
    test$score[test$PREDICTION &amp;gt; 0.3 &amp;amp; test$PREDICTION &amp;lt;=0.6] &amp;lt;- 2
...
test$score[test$PREDICTION &amp;gt; 0.9 &amp;amp; test$PREDICTION &amp;lt;=1.0] &amp;lt;- 10
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;How can I measure how good my model is? by doing the same segmentation group in the pract$PREDICTION and then comparing how right and how wrong both tests are in each group ? This make any sense?&lt;/p&gt;
', 'r  predictive-modeling  random-forest  probability', NULL, '''/code'':84C,127C,191C ''/p'':48C,115C,132C,149C,231C ''/pre'':85C,128C,192C ''0'':157C ''0.3'':162C,170C ''0.6'':175C ''0.9'':183C ''1'':99C,164C ''1.0'':188C ''10'':190C ''1000'':61C ''2'':72C,83C,126C,177C ''amp'':158C,171C,184C ''as.vector'':65C,76C,121C ''auc'':92C ''bag'':109C ''binari'':33C ''buy'':44C,55C ''code'':50C,117C,151C ''compar'':215C ''data'':56C ''first'':87C ''fit'':51C,67C,78C,123C ''forest'':6A,15B ''formula'':54C ''get'':27C,98C ''good'':199C ''group'':3A,39C,141C,208C,226C ''gt'':156C,169C,182C ''higher'':41C ''import'':58C ''instead'':110C ''know'':38C ''like'':20C,46C,113C,147C ''lt'':52C,64C,75C,120C,161C,163C,174C,176C,187C,189C ''make'':140C,228C ''measur'':197C ''model'':12B,201C ''newdata'':70C,81C ''ntree'':60C ''order'':25C,36C,142C ''outcom'':34C ''overfit'':103C ''p'':17C,86C,129C,133C,193C ''perfom'':91C ''pract'':57C,62C,71C,95C,112C,118C,211C ''pre'':49C,116C,150C ''predict'':11B,63C,66C,74C,77C,96C,119C,122C,136C,155C,160C,168C,173C,181C,186C,212C ''predictive-model'':10B ''prob'':69C,80C,125C,146C ''probabl'':2A,16B,30C,42C ''question'':88C,131C ''r'':8A,9B ''random'':5A,14B ''random-forest'':13B ''randomforest'':53C ''rf'':23C ''right'':217C ''score'':1A,29C,145C,153C,166C,179C ''second'':130C ''segment'':207C ''sens'':230C ''someth'':45C ''test'':73C,82C,135C,152C,154C,159C,165C,167C,172C,178C,180C,185C,222C ''true'':59C ''type'':68C,79C,124C ''use'':22C,106C ''want'':138C ''would'':19C ''wrong'':220C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9635, 2, '2016-01-05 13:26:27.517', 3, NULL, NULL, '&lt;p&gt;&lt;a href="http://elki.dbs.ifi.lmu.de/" rel="nofollow"&gt;ELKI&lt;/a&gt; (also on &lt;a href="https://github.com/elki-project/elki" rel="nofollow"&gt;GitHub&lt;/a&gt;) is data mining and data science open-source project. It is unique with respect to its modular architecture: you can combine algorithms, distance functions, and indexes for acceleration with very few limitations (of course, algorithms that do not use distances cannot be combined with distances). It is not the easiest code because of efficiency. For data mining, you need to be careful about memory - using &lt;code&gt;ArrayList&amp;lt;Integer&amp;gt;&lt;/code&gt; is a no-go if you want scalability.&lt;/p&gt;

&lt;p&gt;Because of the modular architecture, it is easy to contribute just small modules, like a single distance function or algorithm.&lt;/p&gt;

&lt;p&gt;We keep a list of &lt;a href="http://elki.dbs.ifi.lmu.de/wiki/ProjectIdeas" rel="nofollow"&gt;data mining project ideas&lt;/a&gt;, roughly grouped by difficulty. Most projects are the implementation of some variant of an algorithm. ELKI aims at allowing comparative studies of algorithms, so we try to allow any combination, and cover also variants of algorithms. For example with k-means, we not only have Lloyds algorithm, but 10 variants of the general k-means theme. Over 220 articles have been (at least partially) reimplemented in ELKI.&lt;/p&gt;

&lt;p&gt;By implementing everything in the same tool, we get much more comparable results. If you use R for benchmarking, you are usually comparing apples and oranges. k-means in R itself is actually an old Fortran program, and very fast. k-means in R but in the "flexclust" package is 100x slower, because it is written in real R code. So don''t trust a benchmark in R... also, R modules tend to be incompatible, so you often can''t use distance A from modules A with algorithm B from module B. in ELKI we try to share as much code as possible across implementations to reduce such artifacts (it will, of course, never be possible to have a 100% fair benchmark - there is always room for optimization), but also to allow combining modules easily.&lt;/p&gt;

&lt;p&gt;You could start with something small such as the Hartigan&amp;amp;Wong k-means variant, and then continue into spherical k-means (which is meant for sparse data, where different performance optimizations may become necessary) and continue into adding better support for categorical data; or adding indexing functionality.&lt;/p&gt;

&lt;p&gt;I''d also &lt;strong&gt;love to see a better UI for ELKI&lt;/strong&gt;, but that is a major effort.&lt;/p&gt;
', NULL, NULL, '''/a'':8C,19C,141C ''/code'':91C ''/elki-project/elki'':15C ''/p'':101C,123C,211C,350C,402C,423C ''/strong'':416C ''/wiki/projectideas'':134C ''10'':191C ''100'':334C ''100x'':265C ''220'':201C ''acceler'':48C ''across'':318C ''actual'':246C ''ad'':392C,399C ''aim'':158C ''algorithm'':42C,55C,122C,156C,164C,177C,189C,302C ''allow'':160C,169C,346C ''also'':9C,174C,283C,344C,406C ''alway'':339C ''amp'':362C ''appl'':236C ''architectur'':38C,107C ''arraylist'':87C ''articl'':202C ''artifact'':323C ''b'':303C,306C ''becom'':387C ''benchmark'':231C,280C,336C ''better'':393C,412C ''cannot'':61C ''care'':82C ''categor'':396C ''code'':71C,86C,274C,315C ''combin'':41C,63C,171C,347C ''compar'':161C,224C,235C ''continu'':370C,390C ''contribut'':112C ''could'':353C ''cours'':54C,327C ''cover'':173C ''d'':405C ''data'':21C,24C,76C,137C,381C,397C ''differ'':383C ''difficulti'':145C ''distanc'':43C,60C,65C,119C,296C ''easi'':110C ''easiest'':70C ''easili'':349C ''effici'':74C ''effort'':422C ''elki'':7C,157C,210C,308C,415C ''elki.dbs.ifi.lmu.de'':4C,133C ''elki.dbs.ifi.lmu.de/wiki/projectideas'':132C ''everyth'':215C ''exampl'':179C ''fair'':335C ''fast'':253C ''flexclust'':262C ''fortran'':249C ''function'':44C,120C,401C ''general'':195C ''get'':221C ''github'':18C ''github.com'':14C ''github.com/elki-project/elki'':13C ''go'':96C ''group'':143C ''gt'':90C ''hartigan'':361C ''href'':3C,12C,131C ''idea'':140C ''implement'':150C,214C,319C ''incompat'':289C ''index'':46C,400C ''integ'':89C ''k'':182C,197C,240C,255C,365C,374C ''k-mean'':181C,196C,239C,254C,364C,373C ''keep'':126C ''least'':206C ''like'':116C ''limit'':52C ''list'':128C ''lloyd'':188C ''love'':408C ''lt'':88C ''major'':421C ''may'':386C ''mean'':183C,198C,241C,256C,366C,375C ''meant'':378C ''memori'':84C ''mine'':22C,77C,138C ''modul'':115C,285C,299C,305C,348C ''modular'':37C,106C ''much'':222C,314C ''necessari'':388C ''need'':79C ''never'':328C ''no-go'':94C ''nofollow'':6C,17C,136C ''often'':292C ''old'':248C ''open'':27C ''open-sourc'':26C ''optim'':342C,385C ''orang'':238C ''p'':1C,102C,124C,212C,351C,403C ''packag'':263C ''partial'':207C ''perform'':384C ''possibl'':317C,330C ''program'':250C ''project'':29C,139C,147C ''r'':229C,243C,258C,273C,282C,284C ''real'':272C ''reduc'':321C ''reimplement'':208C ''rel'':5C,16C,135C ''respect'':34C ''result'':225C ''room'':340C ''rough'':142C ''scalabl'':100C ''scienc'':25C ''see'':410C ''share'':312C ''singl'':118C ''slower'':266C ''small'':114C,357C ''someth'':356C ''sourc'':28C ''spars'':380C ''spheric'':372C ''start'':354C ''strong'':407C ''studi'':162C ''support'':394C ''tend'':286C ''theme'':199C ''tool'':219C ''tri'':167C,310C ''trust'':278C ''ui'':413C ''uniqu'':32C ''use'':59C,85C,228C,295C ''usual'':234C ''variant'':153C,175C,192C,367C ''want'':99C ''wong'':363C ''written'':270C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9636, 1, '2016-01-05 13:27:19.253', 2, 21, 'Mine webshop history for clusters', '&lt;p&gt;I''ve no experience in data science so this will be one of &lt;em&gt;those&lt;/em&gt; questions...&lt;/p&gt;

&lt;p&gt;I have data from &gt;100k purchases made via a webshop regarding a catalogue of around &gt;100 items. The history of purchases flattened out looks like&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Item1 Item2 ... ItemN Sex State
    5     0         0   M    NY
   25    15         0   F    IL
    0     1         1   ?    NY
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;By playing around with the data, I can deduce simple facts like "90% of all purchases include at least 3 Item1", "If there are at least 4 of Item2, it is likely that Item3 is 0" or "60% of all customers from NY are male, but only 40% of those from IL are". Given the amount of combinations and data there is, the most obvious question: How can I approach wringing out more information from a data set like the above? I''m mostly interested in how one item does or does not entails inclusion of another... &lt;/p&gt;
', 'data-mining  classification  clustering', NULL, '''/code'':78C ''/em'':27C ''/p'':29C,56C,178C ''/pre'':79C ''0'':65C,66C,71C,74C,116C ''1'':75C,76C ''100'':46C ''100k'':35C ''15'':70C ''25'':69C ''3'':100C ''4'':107C ''40'':128C ''5'':64C ''60'':118C ''90'':93C ''amount'':136C ''anoth'':177C ''approach'':150C ''around'':45C,83C ''catalogu'':43C ''classif'':9B ''cluster'':5A,10B ''code'':58C ''combin'':138C ''custom'':121C ''data'':7B,17C,33C,86C,140C,157C ''data-min'':6B ''deduc'':89C ''em'':25C ''entail'':174C ''experi'':15C ''f'':72C ''fact'':91C ''flatten'':52C ''given'':134C ''histori'':3A,49C ''il'':73C,132C ''includ'':97C ''inclus'':175C ''inform'':154C ''interest'':165C ''item'':47C,169C ''item1'':59C,101C ''item2'':60C,109C ''item3'':114C ''itemn'':61C ''least'':99C,106C ''like'':55C,92C,112C,159C ''look'':54C ''m'':67C,163C ''made'':37C ''male'':125C ''mine'':1A,8B ''most'':164C ''ny'':68C,77C,123C ''obvious'':145C ''one'':23C,168C ''p'':11C,30C,80C ''play'':82C ''pre'':57C ''purchas'':36C,51C,96C ''question'':28C,146C ''regard'':41C ''scienc'':18C ''set'':158C ''sex'':62C ''simpl'':90C ''state'':63C ''ve'':13C ''via'':38C ''webshop'':2A,40C ''wring'':151C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9637, 1, '2016-01-05 13:28:18.94', 3, 172, 'How to teach neural network a policy for a board game using reinforcement learning?', '&lt;p&gt;I need to use reinforcement learning to teach a neural net a policy for a board game. I chose Q-learining as the specific alghoritm.&lt;/p&gt;

&lt;p&gt;I''d like a neural net to have the following structure:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;layer - &lt;code&gt;rows * cols + 1&lt;/code&gt; neurons - input - values of consecutive fields on the board (&lt;code&gt;0&lt;/code&gt; for empty, &lt;code&gt;1&lt;/code&gt; or &lt;code&gt;2&lt;/code&gt; representing a player), action (natural number) in that state&lt;/li&gt;
&lt;li&gt;layer - (??) neurons - hidden&lt;/li&gt;
&lt;li&gt;layer - 1 neuron - output - value of action in given state (float)&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;My first idea was to begin with creating a map of states, actions and values, and then try to teach neural network. If the teaching process would not succeed I could increase the number of neurons and start over.&lt;/p&gt;

&lt;p&gt;However, &lt;a href="http://stackoverflow.com/questions/34597386/how-to-improve-performance-of-a-script-operating-on-large-amount-of-data"&gt;I quickly ran into performance problems&lt;/a&gt;. Firstly, I needed to switch from simple in-memory Python &lt;code&gt;dict&lt;/code&gt; to a database (not enough RAM). Now the database seems to be a bottleneck (simply speaking there are so many possible states that the retrieval of actions'' values is taking a noticeable amount of time). Calculations would take weeks.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;I guess it would be possible to teach neural network on the fly, without the layer of a map in the middle. But how would I choose a right number of neurons on the hidden layer? How could I figure out that I''m loosing periously saved (learned) data?&lt;/strong&gt;&lt;/p&gt;
', 'machine-learning  neuralnetwork  reinforcement-learning  q-learning', NULL, '''/a'':178C ''/code'':73C,85C,90C,94C,192C ''/li'':104C,109C,122C ''/ol'':123C ''/p'':52C,65C,164C,232C,285C ''/questions/34597386/how-to-improve-performance-of-a-script-operating-on-large-amount-of-data'':171C ''/strong'':284C ''0'':84C ''1'':72C,89C,112C ''2'':93C ''action'':98C,117C,137C,219C ''alghoritm'':51C ''amount'':225C ''begin'':130C ''board'':10A,41C,82C ''bottleneck'':206C ''calcul'':228C ''choos'':261C ''chose'':44C ''code'':69C,83C,88C,92C,190C ''col'':71C ''consecut'':78C ''could'':155C,272C ''creat'':132C ''d'':55C ''data'':283C ''databas'':195C,201C ''dict'':191C ''empti'':87C ''enough'':197C ''field'':79C ''figur'':274C ''first'':126C,179C ''fli'':247C ''float'':121C ''follow'':63C ''game'':11A,42C ''given'':119C ''guess'':236C ''hidden'':108C,269C ''howev'':166C ''href'':168C ''idea'':127C ''in-memori'':186C ''increas'':156C ''input'':75C ''layer'':68C,106C,111C,250C,270C ''learin'':47C ''learn'':14A,17B,21B,24B,31C,282C ''li'':67C,105C,110C ''like'':56C ''loos'':279C ''m'':278C ''machin'':16B ''machine-learn'':15B ''mani'':212C ''map'':134C,253C ''memori'':188C ''middl'':256C ''natur'':99C ''need'':27C,181C ''net'':36C,59C ''network'':5A,146C,244C ''neural'':4A,35C,58C,145C,243C ''neuralnetwork'':18B ''neuron'':74C,107C,113C,160C,266C ''notic'':224C ''number'':100C,158C,264C ''ol'':66C ''output'':114C ''p'':25C,53C,124C,165C,233C ''perform'':176C ''perious'':280C ''player'':97C ''polici'':7A,38C ''possibl'':213C,240C ''problem'':177C ''process'':150C ''python'':189C ''q'':23B,46C ''q-learin'':45C ''q-learn'':22B ''quick'':173C ''ram'':198C ''ran'':174C ''reinforc'':13A,20B,30C ''reinforcement-learn'':19B ''repres'':95C ''retriev'':217C ''right'':263C ''row'':70C ''save'':281C ''seem'':202C ''simpl'':185C ''simpli'':207C ''speak'':208C ''specif'':50C ''stackoverflow.com'':170C ''stackoverflow.com/questions/34597386/how-to-improve-performance-of-a-script-operating-on-large-amount-of-data'':169C ''start'':162C ''state'':103C,120C,136C,214C ''strong'':234C ''structur'':64C ''succeed'':153C ''switch'':183C ''take'':222C,230C ''teach'':3A,33C,144C,149C,242C ''time'':227C ''tri'':142C ''use'':12A,29C ''valu'':76C,115C,139C,220C ''week'':231C ''without'':248C ''would'':151C,229C,238C,259C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9638, 2, '2016-01-05 14:42:39.35', 1, NULL, NULL, '&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Association_rule_learning" rel="nofollow"&gt;Frequent Item-Set Mining&lt;/a&gt; is what you are looking for. You can see the tree structure of your frequent itemsets and the association rules afterwards.&lt;/p&gt;

&lt;p&gt;For your data I''d suggest to look at the whole for a while to get a sense on what you have in hand. Playing with concepts like Probability Distributions, &lt;a href="https://en.wikipedia.org/wiki/Entropy_%28information_theory%29" rel="nofollow"&gt;Entropy&lt;/a&gt;, etc would be really helpful in case you can reduce the size of your features. &lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Principal_component_analysis" rel="nofollow"&gt;PCA&lt;/a&gt; also gives you the opportunity of projecting your data into a low-dimenstional space and you can see also plots showing first several PCs in 2-D or 3-D and get an impression about your data.&lt;/p&gt;

&lt;p&gt;Before all above I strongly suggest to see if you have &lt;a href="https://www.utexas.edu/cola/prc/_files/cs/Missing-Data.pdf" rel="nofollow"&gt;Missing Values&lt;/a&gt; and if yes try to cope with them.&lt;/p&gt;
', NULL, NULL, '''/a'':14C,76C,102C,163C ''/cola/prc/_files/cs/missing-data.pdf'':158C ''/p'':36C,92C,141C,172C ''/wiki/association_rule_learning'':6C ''/wiki/entropy_%28information_theory%29'':72C ''/wiki/principal_component_analysis'':98C ''2'':129C ''3'':132C ''afterward'':35C ''also'':103C,122C ''associ'':33C ''case'':83C ''concept'':64C ''cope'':169C ''d'':42C,130C,133C ''data'':40C,111C,140C ''dimenst'':116C ''distribut'':67C ''en.wikipedia.org'':5C,71C,97C ''en.wikipedia.org/wiki/association_rule_learning'':4C ''en.wikipedia.org/wiki/entropy_%28information_theory%29'':70C ''en.wikipedia.org/wiki/principal_component_analysis'':96C ''entropi'':75C ''etc'':77C ''featur'':91C ''first'':125C ''frequent'':9C,29C ''get'':53C,135C ''give'':104C ''hand'':61C ''help'':81C ''href'':3C,69C,95C,155C ''impress'':137C ''item'':11C ''item-set'':10C ''itemset'':30C ''like'':65C ''look'':19C,45C ''low'':115C ''low-dimenst'':114C ''mine'':13C ''miss'':161C ''nofollow'':8C,74C,100C,160C ''opportun'':107C ''p'':1C,37C,93C,142C ''pca'':101C ''pcs'':127C ''play'':62C ''plot'':123C ''probabl'':66C ''project'':109C ''realli'':80C ''reduc'':86C ''rel'':7C,73C,99C,159C ''rule'':34C ''see'':23C,121C,150C ''sens'':55C ''set'':12C ''sever'':126C ''show'':124C ''size'':88C ''space'':117C ''strong'':147C ''structur'':26C ''suggest'':43C,148C ''tree'':25C ''tri'':167C ''valu'':162C ''whole'':48C ''would'':78C ''www.utexas.edu'':157C ''www.utexas.edu/cola/prc/_files/cs/missing-data.pdf'':156C ''yes'':166C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9639, 2, '2016-01-05 17:17:03.907', 1, NULL, NULL, '&lt;p&gt;Conditinal Random Fields (CRFs) are a special case of Markov Random Fields (MRFs).&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;1.5.4 Conditional Random Field&lt;/p&gt;
  
  &lt;p&gt;A Conditional Random Field (CRF) is a form of MRF that defines a posterior for
  variables x given data z, as with the hidden MRF above. Unlike the hidden MRF,
  however, the factorization into the data distribution P (x|z) and the prior P
  (x) is not made explicit [288]. This allows complex dependencies of x on z to
  be written directly in the posterior distribution, without the factorization
  being made explicit. (Given P (x|z), such factorizations always exist,
  howeverâinfinitely many of them, in factâso there is no suggestion that the CRF
  is more general than the hidden MRF, only that it may be more convenient to
  deal with.)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Source: Blake, Kohli and Rother: Markov random fields for vision and image processing. 2011.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;A conditional random field or CRF (Lafferty et al. 2001), sometimes a
  discriminative random field (Kumar and Hebert 2003), is just a version of an
  MRF where all the clique potentials are conditioned on input features:
  [...]&lt;/p&gt;
  
  &lt;p&gt;The advantage of a CRF over an MRF is analogous to the advantage of a discriminative
  classifier over a generative classifier (see Section 8.6), namely, we donât need to âwaste resourcesâ
  modeling things that we always observe. [...]&lt;/p&gt;
  
  &lt;p&gt;The disadvantage of CRFs over MRFs is that they require labeled training data, and they are slower to train[...]&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Source: Kevin P. Murphy: &lt;a href="https://www.cs.ubc.ca/~murphyk/MLbook/pml-print3-ch19.pdf" rel="nofollow"&gt;Machine Learning: A Probabilistic Perspective&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9642, 2, '2016-01-06 04:20:10.207', 1, NULL, NULL, '&lt;p&gt;If I understand correctly, you essentially have two forms of features for your models. (1) Text data that you have represented as a sparse bag of words and (2) more traditional dense features. If that is the case then there are 3 common approaches:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Perform dimensionality reduction (such as LSA via &lt;code&gt;TruncatedSVD&lt;/code&gt;) on your sparse data to make it dense and combine the features into a single dense matrix to train your model(s). &lt;/li&gt;
&lt;li&gt;Add your few dense features to your sparse matrix using something like scipy''s &lt;code&gt;hstack&lt;/code&gt; into a single sparse matrix to train your model(s).&lt;/li&gt;
&lt;li&gt;Create a model using only your sparse text data and then combine its predictions (probabilities if it''s classification) as a dense feature with your other dense features to create a model (ie: ensembling via stacking). If you go this route remember to only use CV predictions as features to train your model otherwise you''ll likely overfit quite badly (you can make a quite class to do this all within a single &lt;code&gt;Pipeline&lt;/code&gt; if desired). &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;All three approaches are valid and have their own pros and cons. Personally, I find (1) to typically be the worst because it is, relatively speaking, extremely slow. I also find (3) to usually be the best, being both sufficiently fast and resulting in very good predictions. You can obviously do a combination of them as well if you''re willing to do some more extensive ensembling.&lt;/p&gt;

&lt;p&gt;As for the algorithms you use, they can essentially all fit within that framework. Logistic regression performs surprisingly well most of the time, but others may do better depending on the problem at hand and how well you tune them. I''m partial to GBMs myself, but the bottom line is that you can try as many algorithms as you would like and even doing simple weighted ensembles of their predictions will almost always lead to a better overall solution.&lt;/p&gt;
', NULL, NULL, '''/code'':58C,99C,187C ''/li'':81C,110C,190C ''/ol'':191C ''/p'':46C,260C,342C ''1'':16C,208C ''2'':30C ''3'':43C,224C ''add'':83C ''algorithm'':265C,319C ''almost'':334C ''also'':222C ''alway'':335C ''approach'':45C,195C ''bad'':171C ''bag'':26C ''best'':229C ''better'':289C,339C ''bottom'':310C ''case'':39C ''class'':177C ''classif'':130C ''code'':56C,97C,185C ''combin'':68C,123C,245C ''common'':44C ''con'':204C ''correct'':5C ''creat'':112C,141C ''cv'':157C ''data'':18C,62C,120C ''dens'':33C,66C,74C,86C,133C,138C ''depend'':290C ''desir'':189C ''dimension'':50C ''ensembl'':145C,259C,329C ''essenti'':7C,270C ''even'':325C ''extens'':258C ''extrem'':219C ''fast'':233C ''featur'':12C,34C,70C,87C,134C,139C,160C ''find'':207C,223C ''fit'':272C ''form'':10C ''framework'':275C ''gbms'':306C ''go'':150C ''good'':238C ''hand'':295C ''hstack'':98C ''ie'':144C ''lead'':336C ''li'':48C,82C,111C ''like'':94C,168C,323C ''line'':311C ''ll'':167C ''logist'':276C ''lsa'':54C ''m'':303C ''make'':64C,174C ''mani'':318C ''matrix'':75C,91C,104C ''may'':287C ''model'':15C,79C,108C,114C,143C,164C ''obvious'':242C ''ol'':47C ''other'':286C ''otherwis'':165C ''overal'':340C ''overfit'':169C ''p'':1C,192C,261C ''partial'':304C ''perform'':49C,278C ''person'':205C ''pipelin'':186C ''predict'':125C,158C,239C,332C ''probabl'':126C ''problem'':293C ''pros'':202C ''quit'':170C,176C ''re'':252C ''reduct'':51C ''regress'':277C ''relat'':217C ''rememb'':153C ''repres'':22C ''result'':235C ''rout'':152C ''scipi'':95C ''simpl'':327C ''singl'':73C,102C,184C ''slow'':220C ''solut'':341C ''someth'':93C ''spars'':25C,61C,90C,103C,118C ''speak'':218C ''stack'':147C ''suffici'':232C ''surpris'':279C ''text'':17C,119C ''three'':194C ''time'':284C ''tradit'':32C ''train'':77C,106C,162C ''tri'':316C ''truncatedsvd'':57C ''tune'':300C ''two'':9C ''typic'':210C ''understand'':4C ''use'':92C,115C,156C,267C ''usual'':226C ''valid'':197C ''via'':55C,146C ''weight'':328C ''well'':249C,280C,298C ''will'':253C ''within'':182C,273C ''word'':28C ''worst'':213C ''would'':322C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9644, 1, '2016-01-06 08:39:14.273', 1, 20, 'Is the binary matrix of transaction database low-rank in the problem of association rules mining?', '&lt;p&gt;In the problem of association rules mining, the transaction database with $N$ transactions and $M$ items can be represented by a binary matrix $D$ with dimension $N \times M$. My questions are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Is $D$ low-rank or approximately low-rank? Equivalently, are there only few large singular values dominate the overall singular values of $D$?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Intuitively, only few items on average are contained in each transaction because a customer generally buy only few items. However, is that true?&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;It will be much appreciated if someone familiar this field could answer my questions or even only provide any clues.  &lt;/p&gt;
', 'data-mining  bigdata', NULL, '''/li'':85C,112C ''/ol'':113C ''/p'':55C,84C,111C,135C ''answer'':126C ''appreci'':119C ''approxim'':65C ''associ'':15A,27C ''averag'':93C ''bigdata'':21B ''binari'':3A,44C ''buy'':103C ''clue'':134C ''contain'':95C ''could'':125C ''custom'':101C ''d'':46C,60C,83C ''data'':19B ''data-min'':18B ''databas'':7A,32C ''dimens'':48C ''domin'':77C ''equival'':69C ''even'':130C ''familiar'':122C ''field'':124C ''general'':102C ''howev'':107C ''intuit'':88C ''item'':38C,91C,106C ''larg'':74C ''li'':57C,86C ''low'':9A,62C,67C ''low-rank'':8A,61C,66C ''m'':37C,51C ''matrix'':4A,45C ''mine'':17A,20B,29C ''much'':118C ''n'':34C,49C ''ol'':56C ''overal'':79C ''p'':22C,58C,87C,114C ''problem'':13A,25C ''provid'':132C ''question'':53C,128C ''rank'':10A,63C,68C ''repres'':41C ''rule'':16A,28C ''singular'':75C,80C ''someon'':121C ''time'':50C ''transact'':6A,31C,35C,98C ''true'':110C ''valu'':76C,81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9703, 2, '2016-01-08 12:18:48.24', 0, NULL, NULL, '&lt;p&gt;A counterexample in dimension 1: take three points: 1, 3 and 4 on the x axis. When you sample two points at random, the expected sizes of the bucket each point falls in are 4/3, 2 and 5/3 respectively.&lt;/p&gt;

&lt;p&gt;But maybe your hypothesis is "approximately true" for data from some good enough distribution (for example, uniformly random points on a sphere).&lt;/p&gt;
', NULL, NULL, '''/p'':41C,65C ''1'':6C,10C ''2'':37C ''3'':11C ''4'':13C ''4/3'':36C ''5/3'':39C ''approxim'':48C ''axi'':17C ''bucket'':30C ''counterexampl'':3C ''data'':51C ''dimens'':5C ''distribut'':56C ''enough'':55C ''exampl'':58C ''expect'':26C ''fall'':33C ''good'':54C ''hypothesi'':46C ''mayb'':44C ''p'':1C,42C ''point'':9C,22C,32C,61C ''random'':24C,60C ''respect'':40C ''sampl'':20C ''size'':27C ''sphere'':64C ''take'':7C ''three'':8C ''true'':49C ''two'':21C ''uniform'':59C ''x'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9648, 2, '2016-01-06 12:16:03.76', 1, NULL, NULL, '&lt;p&gt;R, with &lt;a href="https://cran.r-project.org/web/packages/googleVis/vignettes/googleVis_examples.html" rel="nofollow"&gt;googlevis package&lt;/a&gt;, also provides great solution (interactive html map):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;require(datasets)
states &amp;lt;- data.frame(state.name, state.x77)
GeoStates &amp;lt;- gvisGeoChart(states, "state.name", "Illiteracy",
                          options=list(region="US", 
                                       displayMode="regions", 
                                       resolution="provinces",
                                       width=600, height=400))
plot(GeoStates)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/WsaPX.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/WsaPX.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':13C,71C ''/code'':51C ''/p'':21C,72C ''/pre'':52C ''/web/packages/googlevis/vignettes/googlevis_examples.html'':8C ''/wsapx.png'':58C,65C ''400'':48C ''600'':46C ''also'':14C ''alt'':66C ''code'':23C ''cran.r-project.org'':7C ''cran.r-project.org/web/packages/googlevis/vignettes/googlevis_examples.html'':6C ''data.frame'':28C ''dataset'':25C ''descript'':69C ''displaymod'':41C ''enter'':67C ''geostat'':31C,50C ''googlevi'':11C ''great'':16C ''gvisgeochart'':33C ''height'':47C ''href'':5C,55C ''html'':19C ''i.stack.imgur.com'':57C,64C ''i.stack.imgur.com/wsapx.png'':56C,63C ''illiteraci'':36C ''imag'':68C ''img'':61C ''interact'':18C ''list'':38C ''lt'':27C,32C ''map'':20C ''nofollow'':10C,60C ''option'':37C ''p'':1C,53C ''packag'':12C ''plot'':49C ''pre'':22C ''provid'':15C ''provinc'':44C ''r'':2C ''region'':39C,42C ''rel'':9C,59C ''requir'':24C ''resolut'':43C ''solut'':17C ''src'':62C ''state'':26C,34C ''state.name'':29C,35C ''state.x77'':30C ''us'':40C ''width'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9649, 2, '2016-01-06 12:52:19.833', 4, NULL, NULL, '&lt;p&gt;You might also want to take a look at &lt;a href="https://flink.apache.org/" rel="nofollow" title="Apache Flink"&gt;Apache Flink&lt;/a&gt;. It is a new contender to Apache Spark as it can do both, realtime and batch analysis. It also claims to be faster than Apache Spark, but I think mostly in depends on your usecase.&lt;/p&gt;

&lt;p&gt;Good thing is: Apache Flink project has some Storm compatibility layer in its development version. In this way you can take your existing Storm project and simply run it inside Flink. For me it was at least 2 times faster than Storm in a very simple topology. So give it a try! :)&lt;/p&gt;
', NULL, NULL, '''/a'':21C ''/p'':57C,111C ''2'':96C ''also'':4C,40C ''analysi'':38C ''apach'':17C,19C,28C,46C,62C ''batch'':37C ''claim'':41C ''compat'':68C ''contend'':26C ''depend'':53C ''develop'':72C ''exist'':81C ''faster'':44C,98C ''flink'':18C,20C,63C,89C ''flink.apache.org'':13C ''give'':107C ''good'':59C ''href'':12C ''insid'':88C ''layer'':69C ''least'':95C ''look'':9C ''might'':3C ''most'':51C ''new'':25C ''nofollow'':15C ''p'':1C,58C ''project'':64C,83C ''realtim'':35C ''rel'':14C ''run'':86C ''simpl'':104C ''simpli'':85C ''spark'':29C,47C ''storm'':67C,82C,100C ''take'':7C,79C ''thing'':60C ''think'':50C ''time'':97C ''titl'':16C ''topolog'':105C ''tri'':110C ''usecas'':56C ''version'':73C ''want'':5C ''way'':76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9650, 2, '2016-01-06 13:04:21.167', 1, NULL, NULL, '&lt;p&gt;My answer is based on couple of assumptions:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;user input is more or less standard, so there won''t be "Ex 20000"&lt;/li&gt;
&lt;li&gt;you have at least majority of forms of input covered&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In every representative example of transaction description you would need to mark words of interest, be it name of holder and account number. You can start small with 10-20 examples for start, and then, when you have all required fields marked, you can train a sequence labelling model, or, say it, custom Named Entity Recognition model, which will parse new text and extract required data for you.&lt;/p&gt;

&lt;p&gt;How to train actual model is answered in that question: &lt;a href="http://datascience.stackexchange.com/questions/4980/help-regarding-ner-in-nltk"&gt;Help regarding NER in NLTK&lt;/a&gt; , and how to mark up the data - is more of a question you should answer, since only you do know, what should be marked as account number, account holder etc.&lt;/p&gt;

&lt;p&gt;If you would be digging into NER training, I would advise you not to rely only on current word features, but add some regexp-alike features and dictionaries as features, since task seems very limited in terms of context variation.&lt;/p&gt;
', NULL, NULL, '''-20'':71C ''/a'':132C ''/li'':27C,39C ''/p'':10C,110C,163C,206C ''/questions/4980/help-regarding-ner-in-nltk'':126C ''/ul'':40C ''10'':70C ''20000'':26C ''account'':63C,158C,160C ''actual'':115C ''add'':186C ''advis'':175C ''alik'':190C ''answer'':3C,118C,147C ''assumpt'':9C ''base'':5C ''context'':204C ''coupl'':7C ''cover'':38C ''current'':182C ''custom'':94C ''data'':107C,139C ''datascience.stackexchange.com'':125C ''datascience.stackexchange.com/questions/4980/help-regarding-ner-in-nltk'':124C ''descript'':48C ''dictionari'':193C ''dig'':169C ''entiti'':96C ''etc'':162C ''everi'':43C ''ex'':25C ''exampl'':45C,72C ''extract'':105C ''featur'':184C,191C,195C ''field'':82C ''form'':35C ''help'':127C ''holder'':61C,161C ''href'':123C ''input'':14C,37C ''interest'':56C ''know'':152C ''label'':89C ''least'':32C ''less'':18C ''li'':12C,28C ''limit'':200C ''major'':33C ''mark'':53C,83C,136C,156C ''model'':90C,98C,116C ''name'':59C,95C ''need'':51C ''ner'':129C,171C ''new'':102C ''nltk'':131C ''number'':64C,159C ''p'':1C,41C,111C,164C ''pars'':101C ''question'':121C,144C ''recognit'':97C ''regard'':128C ''regexp'':189C ''regexp-alik'':188C ''reli'':179C ''repres'':44C ''requir'':81C,106C ''say'':92C ''seem'':198C ''sequenc'':88C ''sinc'':148C,196C ''small'':68C ''standard'':19C ''start'':67C,74C ''task'':197C ''term'':202C ''text'':103C ''train'':86C,114C,172C ''transact'':47C ''ul'':11C ''user'':13C ''variat'':205C ''won'':22C ''word'':54C,183C ''would'':50C,167C,174C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9680, 1, '2016-01-07 15:11:21.47', 3, 42, 'Approximating density of test set in train', '&lt;p&gt;I am looking for a method to approximate how similar a test set (i.e., test set  features) to a train set. For example, something like, for each row in test: is there a similar enough data point in train? I''ve been thinking about using a mixture model approach, but I haven''t been able to find a good reference on this. Can anyone suggest a good approach, or provide good references for how to use mixture models for this application?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':93C ''abl'':66C ''anyon'':75C ''applic'':92C ''approach'':60C,79C ''approxim'':1A,19C ''data'':47C ''densiti'':2A ''enough'':46C ''exampl'':34C ''featur'':28C ''find'':68C ''good'':70C,78C,82C ''haven'':63C ''i.e'':25C ''learn'':10B ''like'':36C ''look'':14C ''machin'':9B ''machine-learn'':8B ''method'':17C ''mixtur'':58C,88C ''model'':59C,89C ''p'':11C ''point'':48C ''provid'':81C ''refer'':71C,83C ''row'':39C ''set'':5A,24C,27C,32C ''similar'':21C,45C ''someth'':35C ''suggest'':76C ''test'':4A,23C,26C,41C ''think'':54C ''train'':7A,31C,50C ''use'':56C,87C ''ve'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9651, 1, '2016-01-06 14:21:05.26', 2, 34, 'Pandas: access fields within field in a DataFrame', '&lt;p&gt;Suppose I have such a JSON file:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[
  {
    "id": "0",
    "name": "name0", 
    "first_sent": "date0",  
    "analytics": [
        {
            "a": 1,
            ...
        }, 
        {
            "a": 2, 
            ...
        }
    ]
  }
]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and I want to parse it with Pandas. So I load it with&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;df = pd.read_json(''file.son'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;It''s all good until I try to access and count the number of dictionaries in the "analytics" field for each item, for which task I haven''t found any better way than &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for i in range(df.shape[0]):
    num = len(df[i:i+1][''analytics''][i])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But this looks totally non-elegant and it''s missing the point of using Pandas in the first place. I need to be able to access the fields within "analytics" for each item.
The question is how to use Pandas to access fields within a field (which maps to a Series object), without reverting to non-Pandas approaches.&lt;/p&gt;

&lt;p&gt;A head of the DataFrame looks like this (only fields ''id'' and ''analytics'' reported):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;0    [{u''a'': 0.0, u''b...
1    [{u''a'': 0.01, u''b...
2    [{u''a'': 0.4, u''b...
3    [{u''a'': 0.2, u''b...
Name: analytics, dtype: object
0      ''0''
1      ''1''
2      ''2''
3      ''3''
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The first number is obviously the index, the string is the ''id'', and it is clear that ''analytics'' appears as a Series.&lt;/p&gt;
', 'python  pandas', NULL, '''+1'':107C ''/code'':34C,57C,110C,228C ''/p'':19C,50C,93C,173C,189C,253C ''/pre'':35C,58C,111C,229C ''0'':23C,101C,192C,220C,221C ''0.0'':195C ''0.01'':201C ''0.2'':213C ''0.4'':207C ''1'':31C,198C,222C,223C ''2'':33C,204C,224C,225C ''3'':210C,226C,227C ''abl'':137C ''access'':2A,68C,139C,155C ''analyt'':29C,77C,108C,143C,187C,217C,248C ''appear'':249C ''approach'':172C ''b'':197C,203C,209C,215C ''better'':90C ''clear'':246C ''code'':21C,52C,95C,191C ''count'':70C ''datafram'':8A,179C ''date0'':28C ''df'':53C,104C ''df.shape'':100C ''dictionari'':74C ''dtype'':218C ''eleg'':119C ''field'':3A,5A,78C,141C,156C,159C,184C ''file'':18C ''file.son'':56C ''first'':26C,131C,232C ''found'':88C ''good'':63C ''haven'':86C ''head'':176C ''id'':22C,185C,242C ''index'':237C ''item'':81C,146C ''json'':17C,55C ''len'':103C ''like'':181C ''load'':47C ''look'':115C,180C ''map'':161C ''miss'':123C ''name'':24C,216C ''name0'':25C ''need'':134C ''non'':118C,170C ''non-eleg'':117C ''non-panda'':169C ''num'':102C ''number'':72C,233C ''object'':165C,219C ''obvious'':235C ''p'':11C,36C,59C,112C,174C,230C ''panda'':1A,10B,44C,128C,153C,171C ''pars'':41C ''pd.read'':54C ''place'':132C ''point'':125C ''pre'':20C,51C,94C,190C ''python'':9B ''question'':148C ''rang'':99C ''report'':188C ''revert'':167C ''sent'':27C ''seri'':164C,252C ''string'':239C ''suppos'':12C ''task'':84C ''total'':116C ''tri'':66C ''u'':193C,196C,199C,202C,205C,208C,211C,214C ''use'':127C,152C ''want'':39C ''way'':91C ''within'':4A,142C,157C ''without'':166C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9652, 1, '2016-01-06 15:07:08.563', 2, 52, 'Writing custom data analysis program', '&lt;p&gt;I have a number of large datasets (10GBs) each with data fetched from a NoSQL database that I have remotely downloaded on my desktop. I would like to write a Python program to run some custom data analysis (plots - preferably interactive) and export custom reports in html or pdf.&lt;/p&gt;

&lt;p&gt;I was wondering how people do the following:&lt;/p&gt;

&lt;p&gt;1) Store the data. For the moment I have plain text files (each file has rows of a fixed number of columns - most of the data are categorical). Would it make sense to save those in some database (SQL) or hdf5? Any hints on which is preferrable?&lt;/p&gt;

&lt;p&gt;2) Which plotting library would you propose for the graphs? I have seen about bookeh and matplotlib supports interactive widgets but I don''t know what people normally use.&lt;/p&gt;

&lt;p&gt;3) Could I export the analysis results in an IPython notebook and then in html programmatically?&lt;/p&gt;
', 'python  bigdata', NULL, '''/p'':58C,68C,117C,148C,166C ''1'':70C ''10gbs'':16C ''2'':119C ''3'':150C ''analysi'':4A,46C,155C ''bigdata'':7B ''bookeh'':133C ''categor'':97C ''column'':91C ''could'':151C ''custom'':2A,44C,52C ''data'':3A,19C,45C,73C,95C ''databas'':24C,107C ''dataset'':15C ''desktop'':32C ''download'':29C ''export'':51C,153C ''fetch'':20C ''file'':81C,83C ''fix'':88C ''follow'':67C ''graph'':128C ''hdf5'':110C ''hint'':112C ''html'':55C,164C ''interact'':49C,137C ''ipython'':159C ''know'':143C ''larg'':14C ''librari'':122C ''like'':35C ''make'':100C ''matplotlib'':135C ''moment'':76C ''normal'':146C ''nosql'':23C ''notebook'':160C ''number'':12C,89C ''p'':8C,59C,69C,118C,149C ''pdf'':57C ''peopl'':64C,145C ''plain'':79C ''plot'':47C,121C ''prefer'':48C ''preferr'':116C ''program'':5A,40C ''programmat'':165C ''propos'':125C ''python'':6B,39C ''remot'':28C ''report'':53C ''result'':156C ''row'':85C ''run'':42C ''save'':103C ''seen'':131C ''sens'':101C ''sql'':108C ''store'':71C ''support'':136C ''text'':80C ''use'':147C ''widget'':138C ''wonder'':62C ''would'':34C,98C,123C ''write'':1A,37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9653, 2, '2016-01-06 15:48:31.523', 2, NULL, NULL, '&lt;p&gt;There are &lt;a href="https://www.google.com/search?q=choropleth+map+r" rel="nofollow"&gt;many ways&lt;/a&gt; to plot &lt;a href="https://en.wikipedia.org/wiki/Choropleth_map" rel="nofollow"&gt;choropleth maps&lt;/a&gt; in R. Here''s another one that uses the popular grammar of graphics library ggplot:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(ggplot2)
library(dplyr)
choro &amp;lt;- left_join(
  map_data("state"), 
  USArrests %&amp;gt;% 
    add_rownames("region") %&amp;gt;% 
    mutate(region=tolower(region))
)
ggplot(choro, aes(long, lat)) +
  geom_polygon(aes(group = group, fill = Assault)) + 
  coord_quickmap()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/zSKVa.gif" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/zSKVa.gif" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':13C,25C,99C ''/code'':79C ''/p'':41C,100C ''/pre'':80C ''/search?q=choropleth+map+r'':8C ''/wiki/choropleth_map'':20C ''/zskva.gif'':86C,93C ''add'':57C ''ae'':67C,72C ''alt'':94C ''anoth'':30C ''assault'':76C ''choro'':48C,66C ''choropleth'':23C ''code'':43C ''coord'':77C ''data'':53C ''descript'':97C ''dplyr'':47C ''en.wikipedia.org'':19C ''en.wikipedia.org/wiki/choropleth_map'':18C ''enter'':95C ''fill'':75C ''geom'':70C ''ggplot'':40C,65C ''ggplot2'':45C ''grammar'':36C ''graphic'':38C ''group'':73C,74C ''gt'':56C,60C ''href'':5C,17C,83C ''i.stack.imgur.com'':85C,92C ''i.stack.imgur.com/zskva.gif'':84C,91C ''imag'':96C ''img'':89C ''join'':51C ''lat'':69C ''left'':50C ''librari'':39C,44C,46C ''long'':68C ''lt'':49C ''mani'':11C ''map'':24C,52C ''mutat'':61C ''nofollow'':10C,22C,88C ''one'':31C ''p'':1C,81C ''plot'':15C ''polygon'':71C ''popular'':35C ''pre'':42C ''quickmap'':78C ''r'':27C ''region'':59C,62C,64C ''rel'':9C,21C,87C ''rownam'':58C ''src'':90C ''state'':54C ''tolow'':63C ''usarrest'':55C ''use'':33C ''way'':12C ''www.google.com'':7C ''www.google.com/search?q=choropleth+map+r'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9654, 2, '2016-01-06 16:30:57.22', 0, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;1) Store the data. For the moment I have plain text files (each file
  has rows of a fixed number of columns - most of the data are
  categorical). Would it make sense to save those in some database (SQL)
  or hdf5? Any hints on which is preferable?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Yes,&lt;/strong&gt; it would make sense to store in a local database, rather than using large csv/text files. As you say that the data is derived from a NoSQL source, I assume unstructured data. So, using a SQL/relational store is out of question. As you say you are using Python, I would suggest you use &lt;a href="http://tinydb.readthedocs.org/en/latest/" rel="nofollow"&gt;TinyDB&lt;/a&gt;, which is both light-weight and easy to handle.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;2) Which plotting library would you propose for the graphs? I have
  seen about bookeh and matplotlib supports interactive widgets but I
  don''t know what people normally use.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Matplotlib would be good enough. Actually, this question is more opinion-based than anything else. There are a lot of visualization libraries you can use, like Bokeh, Seaborn, etc.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;3) Could I export the analysis results in an IPython notebook and then
  in html programmatically?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Yes&lt;/strong&gt;, you can do the analytics directly in an Ipython notebook(Jupyter), which also supports Markdown and HTML cells.&lt;/p&gt;

&lt;p&gt;In addition, you can also use &lt;a href="https://github.com/ipython/ipywidgets" rel="nofollow"&gt;widgets and interactive visualization with Jupyter Ipy notebooks&lt;/a&gt; and Matplotlib.  &lt;a href="https://github.com/ipython/ipywidgets/blob/master/examples/notebooks/Index.ipynb" rel="nofollow"&gt;Tutorials for the same&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':117C,259C,273C ''/blockquote'':51C,161C,213C ''/en/latest/'':113C ''/ipython/ipywidgets'':248C ''/ipython/ipywidgets/blob/master/examples/notebooks/index.ipynb'':266C ''/p'':50C,128C,160C,193C,212C,236C,274C ''/strong'':55C,217C ''1'':3C ''2'':131C ''3'':196C ''actual'':168C ''addit'':239C ''also'':230C,242C ''analysi'':201C ''analyt'':222C ''anyth'':177C ''assum'':85C ''base'':175C ''blockquot'':1C,129C,194C ''bokeh'':190C ''bookeh'':145C ''categor'':30C ''cell'':235C ''column'':24C ''could'':197C ''csv/text'':70C ''data'':6C,28C,77C,87C ''databas'':40C,65C ''deriv'':79C ''direct'':223C ''easi'':125C ''els'':178C ''enough'':167C ''etc'':192C ''export'':199C ''file'':14C,16C,71C ''fix'':21C ''github.com'':247C,265C ''github.com/ipython/ipywidgets'':246C ''github.com/ipython/ipywidgets/blob/master/examples/notebooks/index.ipynb'':264C ''good'':166C ''graph'':140C ''handl'':127C ''hdf5'':43C ''hint'':45C ''href'':110C,245C,263C ''html'':210C,234C ''interact'':149C,253C ''ipi'':257C ''ipython'':205C,226C ''jupyt'':228C,256C ''know'':155C ''larg'':69C ''librari'':134C,185C ''light'':122C ''light-weight'':121C ''like'':189C ''local'':64C ''lot'':182C ''make'':33C,58C ''markdown'':232C ''matplotlib'':147C,163C,261C ''moment'':9C ''nofollow'':115C,250C,268C ''normal'':158C ''nosql'':82C ''notebook'':206C,227C,258C ''number'':22C ''opinion'':174C ''opinion-bas'':173C ''p'':2C,52C,130C,162C,195C,214C,237C ''peopl'':157C ''plain'':12C ''plot'':133C ''prefer'':49C ''programmat'':211C ''propos'':137C ''python'':103C ''question'':96C,170C ''rather'':66C ''rel'':114C,249C,267C ''result'':202C ''row'':18C ''save'':36C ''say'':74C,99C ''seaborn'':191C ''seen'':143C ''sens'':34C,59C ''sourc'':83C ''sql'':41C ''sql/relational'':91C ''store'':4C,61C,92C ''strong'':53C,215C ''suggest'':106C ''support'':148C,231C ''text'':13C ''tinydb'':116C ''tinydb.readthedocs.org'':112C ''tinydb.readthedocs.org/en/latest/'':111C ''tutori'':269C ''unstructur'':86C ''use'':68C,89C,102C,108C,159C,188C,243C ''visual'':184C,254C ''weight'':123C ''widget'':150C,251C ''would'':31C,57C,105C,135C,164C ''yes'':54C,216C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9657, 2, '2016-01-06 21:19:02.2', 2, NULL, NULL, '&lt;p&gt;Multi-indexing &lt;em&gt;might&lt;/em&gt; be helpful. See this:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://pandas.pydata.org/pandas-docs/stable/advanced.html" rel="nofollow"&gt;http://pandas.pydata.org/pandas-docs/stable/advanced.html&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;But the below was the immediate solution that came to mind. I think it''s a little more elegant than what you came up with (fewer obscure numbers, more interpretable natural language):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas as pd
df = pd.read_json(''test_file.json'')
df = df.append(df) # just to give us an extra row to loop through below
df.reset_index(inplace=True) # not really important except to distinguish your rows
for _ , row in df.iterrows():
    currNumbDict = len(row[''analytics''])
    print(currNumbDict)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':105C ''/em'':7C ''/p'':12C,24C,58C ''/pandas-docs/stable/advanced.html'':18C ''/pandas-docs/stable/advanced.html&lt;/a&gt;'':23C ''/pre'':106C ''analyt'':102C ''came'':34C,48C ''code'':60C ''currnumbdict'':99C,104C ''df'':65C,69C,71C ''df.append'':70C ''df.iterrows'':98C ''df.reset'':83C ''distinguish'':92C ''eleg'':44C ''em'':5C ''except'':90C ''extra'':77C ''fewer'':51C ''give'':74C ''help'':9C ''href'':15C ''immedi'':31C ''import'':61C,89C ''index'':4C,84C ''inplac'':85C ''interpret'':55C ''json'':67C ''languag'':57C ''len'':100C ''littl'':42C ''loop'':80C ''might'':6C ''mind'':36C ''multi'':3C ''multi-index'':2C ''natur'':56C ''nofollow'':20C ''number'':53C ''obscur'':52C ''p'':1C,13C,25C ''panda'':62C ''pandas.pydata.org'':17C,22C ''pandas.pydata.org/pandas-docs/stable/advanced.html'':16C ''pandas.pydata.org/pandas-docs/stable/advanced.html&lt;/a&gt;'':21C ''pd'':64C ''pd.read'':66C ''pre'':59C ''print'':103C ''realli'':88C ''rel'':19C ''row'':78C,94C,96C,101C ''see'':10C ''solut'':32C ''test_file.json'':68C ''think'':38C ''true'':86C ''us'':75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9659, 2, '2016-01-06 22:45:56.38', 1, NULL, NULL, '&lt;p&gt;There are many ways to calculate similarity between articles. I have not seen anybody doing a vector conversion and running comparisons. However, there is a text mining strategy called "Term-Frequency / Inverse-Document-Frequency" which is a clever way to find unique words and phrases in documents. You can run this on multiple documents, and compare the extracted keywords to match them for recommendations.&lt;/p&gt;

&lt;p&gt;Check out my ebook for more details: &lt;a href="https://lizrush.gitbooks.io/algorithms-for-webdevs-ebook/content/chapters/tf-idf.html" rel="nofollow"&gt;https://lizrush.gitbooks.io/algorithms-for-webdevs-ebook/content/chapters/tf-idf.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you want to leverage this technique on web documents (like a blog), there is a free service to do so: &lt;a href="https://algorithmia.com/recommends" rel="nofollow"&gt;https://algorithmia.com/recommends&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/algorithms-for-webdevs-ebook/content/chapters/tf-idf.html'':81C ''/algorithms-for-webdevs-ebook/content/chapters/tf-idf.html&lt;/a&gt;&lt;/p&gt;'':86C ''/p'':68C ''/recommends'':113C ''/recommends&lt;/a&gt;&lt;/p&gt;'':118C ''algorithmia.com'':112C,117C ''algorithmia.com/recommends'':111C ''algorithmia.com/recommends&lt;/a&gt;&lt;/p&gt;'':116C ''anybodi'':15C ''articl'':10C ''blog'':100C ''calcul'':7C ''call'':30C ''check'':70C ''clever'':41C ''compar'':59C ''comparison'':22C ''convers'':19C ''detail'':76C ''document'':36C,50C,57C,97C ''ebook'':73C ''extract'':61C ''find'':44C ''free'':104C ''frequenc'':33C,37C ''howev'':23C ''href'':78C,110C ''invers'':35C ''inverse-document-frequ'':34C ''keyword'':62C ''leverag'':92C ''like'':98C ''lizrush.gitbooks.io'':80C,85C ''lizrush.gitbooks.io/algorithms-for-webdevs-ebook/content/chapters/tf-idf.html'':79C ''lizrush.gitbooks.io/algorithms-for-webdevs-ebook/content/chapters/tf-idf.html&lt;/a&gt;&lt;/p&gt;'':84C ''mani'':4C ''match'':64C ''mine'':28C ''multipl'':56C ''nofollow'':83C,115C ''p'':1C,69C,87C ''phrase'':48C ''recommend'':67C ''rel'':82C,114C ''run'':21C,53C ''seen'':14C ''servic'':105C ''similar'':8C ''strategi'':29C ''techniqu'':94C ''term'':32C ''term-frequ'':31C ''text'':27C ''uniqu'':45C ''vector'':18C ''want'':90C ''way'':5C,42C ''web'':96C ''word'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9683, 1, '2016-01-07 16:49:38.773', 2, 46, 'How data representation affects neural networks?', '&lt;p&gt;Suppose A''s possible values are ON or OFF.&lt;/p&gt;

&lt;p&gt;Suppose I represent it as: if A ON then feature f=1 else f=0&lt;/p&gt;

&lt;p&gt;Or, suppose I represent it with 2 features, where: &lt;/p&gt;

&lt;p&gt;-if A is ON then f1=1 and f2=0 &lt;/p&gt;

&lt;p&gt;-if A is OFF then f1=0 and f2=1&lt;/p&gt;

&lt;p&gt;How this kind of representation affects neural networks?&lt;/p&gt;
', 'neuralnetwork', NULL, '''/p'':18C,35C,46C,58C,70C,80C ''0'':34C,57C,66C ''1'':31C,54C,69C ''2'':43C ''affect'':4A,77C ''data'':2A ''els'':32C ''f'':30C,33C ''f1'':53C,65C ''f2'':56C,68C ''featur'':29C,44C ''kind'':74C ''network'':6A,79C ''neural'':5A,78C ''neuralnetwork'':7B ''p'':8C,19C,36C,47C,59C,71C ''possibl'':12C ''repres'':22C,40C ''represent'':3A,76C ''suppos'':9C,20C,38C ''valu'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9662, 1, '2016-01-07 02:33:51.253', 3, 110, 'Tools to perform SQL analytics on 350TB of csv data', '&lt;p&gt;In short, what would be the best method/tricks/techniques/tools for performing ad hoc sql (style) queries on 350TB of csv data? Would there be other options, tool wise that would do it faster if we dropped the "sql" requirement?&lt;/p&gt;

&lt;p&gt;Is my best option Hive and as many servers I can''t get my hands on? Would spark be of any benefit since this size of dataset wouldn''t come close to fitting into memory? I have other related questions /ideas but don''t want to bog this down.&lt;/p&gt;

&lt;p&gt;Background&lt;/p&gt;

&lt;p&gt;The data is originally stored in a binary format that gets converted into an ASCII. The ASCII file is getting turned into CSV (actually tab separated but whatever). 1 binary file = 1 ASCII file = 1 CSV file. The data format is pretty simple, 200 to 500 header columns, each row is a sample of an attribute in 1-second intervals.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Example (first row is header):
t,attribute1,attribute2...&amp;lt;attributeX&amp;gt;
1,val1,val2...&amp;lt;valx&amp;gt;
2,val1,val2...&amp;lt;valx&amp;gt;
3,val1,val2...&amp;lt;valx&amp;gt;
...
X,val1,val2...&amp;lt;valx&amp;gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Example queries that could be ran. &lt;/p&gt;

&lt;p&gt;1) Take t value (t is non-unique, as each file starts at 1 and ends at some random time) and compare different attribute values between t value of 100-1000 for all other attributes that were sampled at 100-1000. &lt;/p&gt;

&lt;p&gt;2)There is UTC value as an attribute value (one of the columns), and same idea as #1 we might want to compare all attributes between X dates and compare those attributes to Y data that is from a different UTC range. &lt;/p&gt;

&lt;p&gt;3)We might want to figure out a single particular attribute and trend that from the earliest time we saw that attribute for a different particular attribute. &lt;/p&gt;

&lt;p&gt;These are my best examples as provided to me but other types of queries could exist. If you do have a question of why don''t we cut out the middle man of the conversions of the binary data to CSV data, we could theoretically. However, the program that converts the files is quite old and is very involved. &lt;/p&gt;
', 'bigdata  dataset  data-wrangling', NULL, '''-1000'':251C,261C ''/code'':209C ''/ideas'':97C ''/p'':55C,106C,109C,171C,218C,262C,306C,335C,396C ''/pre'':210C ''1'':139C,142C,145C,168C,185C,220C,234C,281C ''100'':250C,260C ''2'':191C,264C ''200'':154C ''3'':197C,308C ''350tb'':7A,33C ''500'':156C ''actual'':134C ''ad'':27C ''analyt'':5A ''ascii'':125C,127C,143C ''attribut'':166C,244C,255C,271C,288C,295C,318C,329C,334C ''attribute1'':180C ''attribute2'':181C ''attributex'':183C ''background'':108C ''benefit'':78C ''best'':23C,59C,340C ''bigdata'':11B ''binari'':118C,140C,374C ''bog'':103C ''close'':87C ''code'':173C ''column'':158C,276C ''come'':86C ''compar'':242C,286C,293C ''convers'':371C ''convert'':122C,386C ''could'':215C,351C,380C ''csv'':9A,35C,133C,146C,377C ''cut'':364C ''data'':10A,14B,36C,112C,149C,298C,375C,378C ''data-wrangl'':13B ''dataset'':12B,83C ''date'':291C ''differ'':243C,303C,332C ''drop'':51C ''earliest'':324C ''end'':236C ''exampl'':174C,212C,341C ''exist'':352C ''faster'':48C ''figur'':313C ''file'':128C,141C,144C,147C,231C,388C ''first'':175C ''fit'':89C ''format'':119C,150C ''get'':69C,121C,130C ''gt'':184C,190C,196C,202C,208C ''hand'':71C ''header'':157C,178C ''hive'':61C ''hoc'':28C ''howev'':382C ''idea'':279C ''interv'':170C ''involv'':395C ''lt'':182C,188C,194C,200C,206C ''man'':368C ''mani'':64C ''memori'':91C ''method/tricks/techniques/tools'':24C ''middl'':367C ''might'':283C,310C ''non'':227C ''non-uniqu'':226C ''old'':391C ''one'':273C ''option'':41C,60C ''origin'':114C ''p'':16C,56C,107C,110C,211C,219C,263C,307C,336C ''particular'':317C,333C ''perform'':3A,26C ''pre'':172C ''pretti'':152C ''program'':384C ''provid'':343C ''queri'':31C,213C,350C ''question'':96C,358C ''quit'':390C ''ran'':217C ''random'':239C ''rang'':305C ''relat'':95C ''requir'':54C ''row'':160C,176C ''sampl'':163C,258C ''saw'':327C ''second'':169C ''separ'':136C ''server'':65C ''short'':18C ''simpl'':153C ''sinc'':79C ''singl'':316C ''size'':81C ''spark'':74C ''sql'':4A,29C,53C ''start'':232C ''store'':115C ''style'':30C ''tab'':135C ''take'':221C ''theoret'':381C ''time'':240C,325C ''tool'':1A,42C ''trend'':320C ''turn'':131C ''type'':348C ''uniqu'':228C ''utc'':267C,304C ''val1'':186C,192C,198C,204C ''val2'':187C,193C,199C,205C ''valu'':223C,245C,248C,268C,272C ''valx'':189C,195C,201C,207C ''want'':101C,284C,311C ''whatev'':138C ''wise'':43C ''would'':20C,37C,45C,73C ''wouldn'':84C ''wrangl'':15B ''x'':203C,290C ''y'':297C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9672, 1, '2016-01-07 10:26:48.333', 2, 73, 'How exactly does adding a new unit work in Cascade Correlation?', '&lt;p&gt;I''ve just read &lt;a href="http://papers.nips.cc/paper/207-the-cascade-correlation-learning-architecture.pdf" rel="nofollow"&gt;The Cascade-Correlation Learning Architecture&lt;/a&gt; by Scott E. Fahlman and Christian Lebiere.&lt;/p&gt;

&lt;p&gt;I think I''ve got the overall concept (or at least the "cascade" part - a &lt;a href="https://www.youtube.com/watch?v=1E3XZr-bzZ4" rel="nofollow"&gt;4min YouTube video&lt;/a&gt; how I think it works):&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Start with a minimal network with input and output units only&lt;/li&gt;
&lt;li&gt;Learn those weights with standard algorithms (e.g. gradient descent - they seem to use another training objective which I don''t quite understand, so it is gradient ascent in the paper)&lt;/li&gt;
&lt;li&gt;When the network doesn''t improve, add a single new hidden unit. This unit gets input from all input nodes and all hidden nodes which were added before. Its output goes to all output nodes only.&lt;/li&gt;
&lt;li&gt;Repeat step 3&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;However, I don''t understand the details of step 3: The input weights to hidden units are frozen (indicated by boxes in the paper). When exactly do they get frozen? Are they just initialized by random and never learned at all?&lt;/p&gt;

&lt;p&gt;I also don''t understand this paragraph:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;To create a new hidden unit, we begin with a candidate unit that receives
  trainable input connections from all of the network''s external inputs and from
  all pre-existing hidden units. The output of this candidate unit is not yet
  connected to the active network. We run a number of passes over the examples of
  the training set, adjusting the candidate unit''s input weights after each pass.
  The goal of this adjustment is to maximize $S$, the sum over all output units $o$
  of the magnitude of the correlation (or, more precisely, the covariance)
  between $V$, the candidate unit''s value, and Eo, the residual output error
  observed at unit o. We define S as&lt;/p&gt;
  
  &lt;p&gt;$$S = \sum_{o} | \sum_p (V_p - \bar V) (E_{p,o} - \bar{E_o}) |$$&lt;/p&gt;
  
  &lt;p&gt;where $o$ is the network output at which the error is measured and p is the
  training pattern. The quantities $\bar V$ and $\bar{E_o}$ are the values of
  $V$ and $E_o$ averaged over all patterns.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;What is an "residual output error"? Is $V_p$ simply the activation of the unit given the pattern $p$? What does the term $S$ mean and why do we want to maximize it?&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/a'':34C,69C ''/blockquote'':396C ''/li'':89C,121C,159C,164C ''/ol'':165C ''/p'':42C,75C,208C,217C,338C,355C,395C,431C ''/paper/207-the-cascade-correlation-learning-architecture.pdf'':25C ''/watch?v=1e3xzr-bzz4'':63C ''3'':163C,176C ''4min'':66C ''activ'':265C,409C ''ad'':4A,149C ''add'':129C ''adjust'':280C,294C ''algorithm'':96C ''also'':211C ''anoth'':104C ''architectur'':33C ''ascent'':117C ''averag'':391C ''bar'':347C,352C,377C,380C ''begin'':227C ''blockquot'':218C ''box'':187C ''candid'':230C,257C,282C,320C ''cascad'':10A,30C,56C ''cascade-correl'':29C ''christian'':40C ''concept'':51C ''connect'':236C,262C ''correl'':11A,31C,311C ''covari'':316C ''creat'':221C ''defin'':335C ''descent'':99C ''detail'':173C ''doesn'':126C ''e'':37C,349C,353C,381C,389C ''e.g'':97C ''eo'':325C ''error'':329C,366C,403C ''exact'':2A,192C ''exampl'':275C ''exist'':250C ''extern'':243C ''fahlman'':38C ''frozen'':184C,196C ''get'':137C,195C ''given'':413C ''goal'':291C ''goe'':153C ''got'':48C ''gradient'':98C,116C ''hidden'':133C,145C,181C,224C,251C ''howev'':167C ''href'':22C,60C ''improv'':128C ''indic'':185C ''initi'':200C ''input'':84C,138C,141C,178C,235C,244C,285C ''learn'':14B,32C,91C,205C ''least'':54C ''lebier'':41C ''li'':77C,90C,122C,160C ''machin'':13B ''machine-learn'':12B ''magnitud'':308C ''maxim'':297C,429C ''mean'':422C ''measur'':368C ''minim'':81C ''network'':82C,125C,241C,266C,361C ''neuralnetwork'':15B ''never'':204C ''new'':6A,132C,223C ''node'':142C,146C,157C ''nofollow'':27C,65C ''number'':270C ''o'':305C,333C,342C,351C,354C,358C,382C,390C ''object'':106C ''observ'':330C ''ol'':76C ''output'':86C,152C,156C,254C,303C,328C,362C,402C ''overal'':50C ''p'':16C,43C,166C,209C,219C,339C,344C,346C,350C,356C,370C,397C,406C,416C ''paper'':120C,190C ''papers.nips.cc'':24C ''papers.nips.cc/paper/207-the-cascade-correlation-learning-architecture.pdf'':23C ''paragraph'':216C ''part'':57C ''pass'':272C,289C ''pattern'':374C,394C,415C ''pre'':249C ''pre-exist'':248C ''precis'':314C ''quantiti'':376C ''quit'':111C ''random'':202C ''read'':20C ''receiv'':233C ''rel'':26C,64C ''repeat'':161C ''residu'':327C,401C ''run'':268C ''scott'':36C ''seem'':101C ''set'':279C ''simpli'':407C ''singl'':131C ''standard'':95C ''start'':78C ''step'':162C,175C ''sum'':300C,341C,343C ''term'':420C ''think'':45C,72C ''train'':105C,278C,373C ''trainabl'':234C ''understand'':112C,171C,214C ''unit'':7A,87C,134C,136C,182C,225C,231C,252C,258C,283C,304C,321C,332C,412C ''use'':103C ''v'':318C,345C,348C,378C,387C,405C ''valu'':323C,385C ''ve'':18C,47C ''video'':68C ''want'':427C ''weight'':93C,179C,286C ''work'':8A,74C ''www.youtube.com'':62C ''www.youtube.com/watch?v=1e3xzr-bzz4'':61C ''yet'':261C ''youtub'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9663, 1, '2016-01-07 04:28:45.337', 2, 55, 'How to give name to topics created using LDA?', '&lt;p&gt;I have categorized 800,000 documents into 500 categories using the Mahout topic modelling.&lt;/p&gt;

&lt;p&gt;Instead of representing the topic using the top 5/10 words for each topics, I want to infer a generic name for the group using any existing algorithm.
For the time being, I have used the following algorithm to arrive at the name for the topic:&lt;/p&gt;

&lt;p&gt;For each topic&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Take all the documents belonging to the topic (using the document-topic distribution output)&lt;/li&gt;
&lt;li&gt;Run python nltk to get the noun phrases&lt;/li&gt;
&lt;li&gt;Create the TF file from the output&lt;/li&gt;
&lt;li&gt;name for the topic is the phrase (limited towards max 5 words)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Please suggest a approach to arrive at more relevant name for the topics.&lt;/p&gt;
', 'machine-learning  data-mining  nlp  text-mining  topic-model', NULL, '''/li'':108C,118C,127C,141C ''/p'':38C,85C,90C,157C ''/ul'':142C ''000'':28C ''5'':139C ''5/10'':48C ''500'':31C ''800'':27C ''algorithm'':66C,76C ''approach'':147C ''arriv'':78C,149C ''belong'':97C ''categor'':26C ''categori'':32C ''creat'':7A,120C ''data'':14B ''data-min'':13B ''distribut'':106C ''document'':29C,96C,104C ''document-top'':103C ''exist'':65C ''file'':123C ''follow'':75C ''generic'':58C ''get'':114C ''give'':3A ''group'':62C ''infer'':56C ''instead'':40C ''lda'':9A ''learn'':12B ''li'':92C,109C,119C,128C ''limit'':136C ''machin'':11B ''machine-learn'':10B ''mahout'':35C ''max'':138C ''mine'':15B,19B ''model'':22B,37C ''name'':4A,59C,81C,129C,153C ''nlp'':16B ''nltk'':112C ''noun'':116C ''output'':107C,126C ''p'':23C,39C,86C,143C ''phrase'':117C,135C ''pleas'':144C ''python'':111C ''relev'':152C ''repres'':42C ''run'':110C ''suggest'':145C ''take'':93C ''text'':18B ''text-min'':17B ''tf'':122C ''time'':69C ''top'':47C ''topic'':6A,21B,36C,44C,52C,84C,89C,100C,105C,132C,156C ''topic-model'':20B ''toward'':137C ''ul'':91C ''use'':8A,33C,45C,63C,73C,101C ''want'':54C ''word'':49C,140C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9664, 1, '2016-01-07 06:01:43.687', 2, 35, 'The problem of K-Means with non-convex function', '&lt;p&gt;In all articles I''ve read say "K-means is not converge with non-convex function".I just know that convex function is the function that able to find the global minimum. However , I do not understand how this relate to K-Means algorithm.&lt;/p&gt;

&lt;p&gt;I need to ask&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Which situation cause the non-convex problem ? (If possible please visualize it and show the equation)&lt;/li&gt;
&lt;li&gt;In some articles say that the bad initialization( the poor seeds) cause the not-convex problem, How are they related?&lt;/li&gt;
&lt;li&gt;I''m not quite sure the referred function is subject to which variables.Are that clusters and their centroid ? &lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  data-mining  clustering  k-means', NULL, '''/li'':96C,119C,139C ''/ol'':140C ''/p'':70C,76C ''abl'':51C ''algorithm'':69C ''articl'':25C,100C ''ask'':75C ''bad'':104C ''caus'':81C,109C ''centroid'':138C ''cluster'':18B,135C ''converg'':35C ''convex'':10A,39C,45C,85C,113C ''data'':16B ''data-min'':15B ''equat'':95C ''find'':53C ''function'':11A,40C,46C,49C,128C ''global'':55C ''howev'':57C ''initi'':105C ''k'':5A,20B,31C,67C ''k-mean'':4A,19B,30C,66C ''know'':43C ''learn'':14B ''li'':78C,97C,120C ''m'':122C ''machin'':13B ''machine-learn'':12B ''mean'':6A,21B,32C,68C ''mine'':17B ''minimum'':56C ''need'':73C ''non'':9A,38C,84C ''non-convex'':8A,37C,83C ''not-convex'':111C ''ol'':77C ''p'':22C,71C ''pleas'':89C ''poor'':107C ''possibl'':88C ''problem'':2A,86C,114C ''quit'':124C ''read'':28C ''refer'':127C ''relat'':64C,118C ''say'':29C,101C ''seed'':108C ''show'':93C ''situat'':80C ''subject'':130C ''sure'':125C ''understand'':61C ''variables.are'':133C ''ve'':27C ''visual'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9668, 1, '2016-01-07 08:51:03.393', 4, 86, 'Same SVM configuration, same input data gives different output using Matlab and scikit-learn implementation of SVM, in a classification problem', '&lt;p&gt;I have a classification problem with 60 data points in a 2-dimensional feature space. The data originally is divided into 2 classes. Earlier I was using Statistics Toolbox of Matlab so it was giving me fairly good results.It was giving 1 false negative and no false positive.&lt;/p&gt;

&lt;p&gt;I used the following code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;SVMstruct = svmtrain(point(1:60,:),T(1:60),''Kernel_Function'',''polynomial'',''polyorder'',11,''Showplot'',true);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am using a polynomial kernel and with polynomial order of 11. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/XSWKG.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/XSWKG.jpg" alt="Boundries Generated after training data using svmTrain from Matlab"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;But when I use same kernel configuration in scikit-learn SVC it does not gives the same result rather it gives very undesirable result with classifying all of them to single class.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/ftFJj.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/ftFJj.png" alt="Boundries Generated after training data using SVC from Scikit-learn in python"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I am using it as &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;svc = svm.SVC(kernel=''poly'', degree=11, C=10)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have used with many values of C too. No major difference.&lt;/p&gt;

&lt;p&gt;Why there is so much difference in results ?
How can I get same result as I got using Matlab ?
For me it is compulsory to do with python-scikit.&lt;/p&gt;
', 'classification  svm  scikit  matlab', NULL, '''/a'':140C,204C ''/code'':101C,223C ''/ftfjj.png'':182C,189C ''/p'':76C,83C,116C,141C,176C,205C,212C,238C,270C ''/pre'':102C,224C ''/xswkg.jpg'':122C,129C ''1'':69C,89C,92C ''10'':222C ''11'':98C,115C,220C ''2'':39C,49C ''60'':34C,90C,93C ''alt'':130C,190C ''boundri'':131C,191C ''c'':221C,233C ''class'':50C,175C ''classif'':21A,23B,31C ''classifi'':169C ''code'':82C,85C,214C ''compulsori'':263C ''configur'':3A,149C ''data'':6A,35C,44C,135C,195C ''degre'':219C ''differ'':8A,237C,245C ''dimension'':40C ''divid'':47C ''earlier'':51C ''fair'':64C ''fals'':70C,74C ''featur'':41C ''follow'':81C ''function'':95C ''generat'':132C,192C ''get'':251C ''give'':7A,62C,68C,158C,164C ''good'':65C ''got'':256C ''href'':119C,179C ''i.stack.imgur.com'':121C,128C,181C,188C ''i.stack.imgur.com/ftfjj.png'':180C,187C ''i.stack.imgur.com/xswkg.jpg'':120C,127C ''img'':125C,185C ''implement'':16A ''input'':5A ''kernel'':94C,109C,148C,217C ''learn'':15A,153C,201C ''major'':236C ''mani'':230C ''matlab'':11A,26B,58C,139C,258C ''much'':244C ''negat'':71C ''nofollow'':124C,184C ''order'':113C ''origin'':45C ''output'':9A ''p'':27C,77C,103C,117C,142C,177C,206C,225C,239C ''point'':36C,88C ''poli'':218C ''polynomi'':96C,108C,112C ''polyord'':97C ''posit'':75C ''pre'':84C,213C ''problem'':22A,32C ''python'':203C,268C ''python-scikit'':267C ''rather'':162C ''rel'':123C,183C ''result'':161C,167C,247C,253C ''results.it'':66C ''scikit'':14A,25B,152C,200C,269C ''scikit-learn'':13A,151C,199C ''showplot'':99C ''singl'':174C ''space'':42C ''src'':126C,186C ''statist'':55C ''svc'':154C,197C,215C ''svm'':2A,18A,24B ''svm.svc'':216C ''svmstruct'':86C ''svmtrain'':87C,137C ''toolbox'':56C ''train'':134C,194C ''true'':100C ''undesir'':166C ''use'':10A,54C,79C,106C,136C,146C,196C,209C,228C,257C ''valu'':231C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9670, 1, '2016-01-07 10:04:14.607', 1, 26, 'Python: validating the existence of NLTK data with database search', '&lt;p&gt;I need to pull the names of companies out of resumes. Thousands of them. I was thinking of using NLTK to create a list of possible companies, and then cross-referencing the list of strings with something like SEC.gov.&lt;/p&gt;

&lt;p&gt;I''ve already been able to successfully pull the candidate''s name, and contact info off of the resumes with some RegEx, but this one has me quite stumped.&lt;/p&gt;

&lt;p&gt;What I''m thinking is that I could use NLTK to create a list of strings of proper nouns from the resume''s, and then search SEC.gov, or some other database.&lt;/p&gt;

&lt;p&gt;This is a link to the SEC page I would be searching: SEC company search page&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Read Resume1
Get all potential company names as list of strings potentialCompanies
    IF searching for string1 in SEC gets result, THEN add to candidateCompanies
        ELSE remove from potentialCompanies, go to next string
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;My Questions&lt;/p&gt;

&lt;p&gt;To people that have used NLTK, would there be a better way of getting the potential companies from the text besides using proper nouns?&lt;/p&gt;

&lt;p&gt;Would there be a better place to search for companies than the SEC site?&lt;/p&gt;

&lt;p&gt;I have never done any web scraping before, and don''t really know where to start if it is needed.&lt;/p&gt;

&lt;p&gt;(I had posted this on Stack Overflow but they told me that it might be better suited for here...)&lt;/p&gt;
', 'python  nlp  nltk', NULL, '''/code'':172C ''/p'':55C,86C,119C,137C,177C,203C,219C,241C,262C ''/pre'':173C ''abl'':61C ''add'':161C ''alreadi'':59C ''besid'':199C ''better'':189C,209C,258C ''candid'':66C ''candidatecompani'':163C ''code'':139C ''compani'':22C,41C,134C,145C,195C,214C ''contact'':70C ''could'':95C ''creat'':36C,99C ''cross'':45C ''cross-referenc'':44C ''data'':7A ''databas'':9A,118C ''done'':224C ''els'':164C ''exist'':4A ''get'':142C,158C,192C ''go'':168C ''info'':71C ''know'':233C ''like'':53C ''link'':124C ''list'':38C,48C,101C,148C ''m'':90C ''might'':256C ''name'':20C,68C,146C ''need'':16C,240C ''never'':223C ''next'':170C ''nlp'':12B ''nltk'':6A,13B,34C,97C,184C ''noun'':106C,202C ''one'':81C ''overflow'':249C ''p'':14C,56C,87C,120C,174C,178C,204C,220C,242C ''page'':128C,136C ''peopl'':180C ''place'':210C ''possibl'':40C ''post'':245C ''potenti'':144C,194C ''potentialcompani'':151C,167C ''pre'':138C ''proper'':105C,201C ''pull'':18C,64C ''python'':1A,11B ''question'':176C ''quit'':84C ''read'':140C ''realli'':232C ''referenc'':46C ''regex'':78C ''remov'':165C ''result'':159C ''resum'':25C,75C,109C ''resume1'':141C ''scrape'':227C ''search'':10A,113C,132C,135C,153C,212C ''sec'':127C,133C,157C,217C ''sec.gov'':54C,114C ''site'':218C ''someth'':52C ''stack'':248C ''start'':236C ''string'':50C,103C,150C,171C ''string1'':155C ''stump'':85C ''success'':63C ''suit'':259C ''text'':198C ''think'':31C,91C ''thousand'':26C ''told'':252C ''use'':33C,96C,183C,200C ''valid'':2A ''ve'':58C ''way'':190C ''web'':226C ''would'':130C,185C,205C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9671, 2, '2016-01-07 10:14:52.51', 1, NULL, NULL, '&lt;p&gt;NLTK has a built-in NER model that would extract potential Organizations from text, you can read about it here (and see examples) &lt;a href="http://www.nltk.org/book/ch07.html" rel="nofollow"&gt;NLTK book&lt;/a&gt; (look for section "5 Named Entity Recognition").&lt;/p&gt;

&lt;p&gt;However, if your input text has organizations in a very specific context that wasn''t seen by NLTK NER model, performance might be quite low. In that case you should be looking into training your own NER model, what would extract company names. For that you would require to manually markup a small amount of your dataset.&lt;/p&gt;
', NULL, NULL, '''/a'':35C ''/book/ch07.html'':30C ''/p'':43C,102C ''5'':39C ''amount'':98C ''book'':34C ''built'':6C ''built-in'':5C ''case'':72C ''compani'':86C ''context'':56C ''dataset'':101C ''entiti'':41C ''exampl'':25C ''extract'':12C,85C ''howev'':45C ''href'':27C ''input'':48C ''look'':36C,76C ''low'':69C ''manual'':94C ''markup'':95C ''might'':66C ''model'':9C,64C,82C ''name'':40C,87C ''ner'':8C,63C,81C ''nltk'':2C,33C,62C ''nofollow'':32C ''organ'':14C,51C ''p'':1C,44C ''perform'':65C ''potenti'':13C ''quit'':68C ''read'':19C ''recognit'':42C ''rel'':31C ''requir'':92C ''section'':38C ''see'':24C ''seen'':60C ''small'':97C ''specif'':55C ''text'':16C,49C ''train'':78C ''wasn'':58C ''would'':11C,84C,91C ''www.nltk.org'':29C ''www.nltk.org/book/ch07.html'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9682, 2, '2016-01-07 16:35:46.12', 4, NULL, NULL, '&lt;p&gt;Check out &lt;a href="https://support.rstudio.com/hc/en-us/articles/200484568-Code-Folding-and-Sections" rel="nofollow"&gt;Code Folding and Sections&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;strong&gt;Code sections allow you to break a larger source file into a set of
  discrete regions for easy navigation between them&lt;/strong&gt;. Code sections are
  automatically foldableâfor example, the following source file has
  three sections (one expanded and the other two folded):&lt;/p&gt;
  
  &lt;p&gt;To insert a new code section you can use the Code -&gt; Insert Section
  command. Alternatively, any comment line which includes at least four
  trailing dashes (-), equal signs (=), or pound signs (#) automatically
  creates a code section. For example, all of the following lines create
  code sections:&lt;/p&gt;
  
  &lt;p&gt;# Section One ---------------------------------&lt;br&gt;
   # Section Two =================================&lt;br&gt;
   ### Section Three ############################# &lt;/p&gt;
  
  &lt;p&gt;Note that as illustrated above the line can start with any number of
  pound signs (#) so long as &lt;strong&gt;it ends with four or more -, =, or #
  characters&lt;/strong&gt;.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;(highlights by myself)&lt;/p&gt;
', NULL, NULL, '''/a'':15C ''/blockquote'':151C ''/hc/en-us/articles/200484568-code-folding-and-sections'':8C ''/p'':16C,63C,110C,120C,150C,156C ''/strong'':41C,149C ''allow'':22C ''altern'':79C ''automat'':45C,95C ''blockquot'':17C ''br'':114C,117C ''break'':25C ''charact'':148C ''check'':2C ''code'':11C,20C,42C,69C,75C,98C,108C ''command'':78C ''comment'':81C ''creat'':96C,107C ''dash'':89C ''discret'':34C ''easi'':37C ''end'':142C ''equal'':90C ''exampl'':48C,101C ''expand'':57C ''file'':29C,52C ''fold'':12C,62C ''foldableâ'':46C ''follow'':50C,105C ''four'':87C,144C ''highlight'':153C ''href'':5C ''illustr'':125C ''includ'':84C ''insert'':66C,76C ''larger'':27C ''least'':86C ''line'':82C,106C,128C ''long'':138C ''navig'':38C ''new'':68C ''nofollow'':10C ''note'':122C ''number'':133C ''one'':56C,113C ''p'':1C,18C,64C,111C,121C,152C ''pound'':93C,135C ''region'':35C ''rel'':9C ''section'':14C,21C,43C,55C,70C,77C,99C,109C,112C,115C,118C ''set'':32C ''sign'':91C,94C,136C ''sourc'':28C,51C ''start'':130C ''strong'':19C,140C ''support.rstudio.com'':7C ''support.rstudio.com/hc/en-us/articles/200484568-code-folding-and-sections'':6C ''three'':54C,119C ''trail'':88C ''two'':61C,116C ''use'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9685, 2, '2016-01-07 17:51:11.043', 3, NULL, NULL, '&lt;p&gt;You have to be sure that the algorithm is the same and the kernel functions are really the same. 
If you look at this python documentation page for &lt;a href="http://scikit-learn.org/stable/modules/svm.html#svm-kernels" rel="nofollow"&gt;kernels in scikit learn&lt;/a&gt; you will see there a description of poly kernel.&lt;/p&gt;

&lt;p&gt;Notice that you have a gamma and a degree. Gamma is by default ''auto'' which is evaluated at 1/n_samples. For the same kernel you have ''coef0'' (a great name for a parameter), which is used in poly as the free term. I do not know how matlab put this values as defaults, but the usual formula for poly kernel in literature I found it to be $poly(x_1, x_2) = (&amp;lt;x_1,x_2&amp;gt; + 1)^{d}$. So no gamma and the free term is $1$. I think matlab uses that. (Anyway I found the ''improvements'' in scikit learn to have a not so good smell).&lt;/p&gt;

&lt;p&gt;Also in this &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html" rel="nofollow"&gt;SVC documentation page&lt;/a&gt; they state that there is a parameter called shrinking. I really do not know it''s effect, but its auto, which means is enabled. Might be an issue. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Later edit&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;I found this documentation page for &lt;a href="http://uk.mathworks.com/discovery/support-vector-machine.html" rel="nofollow"&gt;svm in matlab&lt;/a&gt; which describes the kernel in the way I stated (no degree, free term with $1$). Also it states that ''SMO'' is used by default, make it sure you use ''SMO'' in python also.&lt;/p&gt;

&lt;p&gt;On the other hand you have to understand that these kind of algorithms are solved by optimization methods which are usually iterative and to save some memory, or cycles their implementations can be different in small details, which will almost produce different results. I agree however that the results should be similar.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9686, 1, '2016-01-07 19:11:01.153', 3, 124, 'Mathematics major for data science', '&lt;p&gt;So I''m a recent transfer 2nd year student from Computer Science major to Mathematics major. Though I do have a bit of an issue here. I can choose between the applied mathematics, pure mathematics and statistics concentrations.&lt;/p&gt;

&lt;p&gt;Along with this major, I''m doing a minor in Data Science with courses focused on Economics and Statistics.&lt;/p&gt;

&lt;p&gt;In the future, I''m interested in doing a master''s degree in Data Science and a career in data analysis for businesses.&lt;/p&gt;

&lt;p&gt;Although I know any degree can be used to get into graduate school, I still want to which would be most beneficial for education in the future as well as opportunities for internships, research, and jobs.&lt;/p&gt;

&lt;p&gt;Thank you!&lt;/p&gt;
', 'data', NULL, '''/p'':46C,67C,92C,130C,134C ''2nd'':14C ''along'':48C ''although'':94C ''analysi'':89C ''appli'':39C ''benefici'':115C ''bit'':29C ''busi'':91C ''career'':86C ''choos'':36C ''comput'':18C ''concentr'':45C ''cours'':61C ''data'':4A,6B,58C,82C,88C ''degre'':80C,98C ''econom'':64C ''educ'':117C ''focus'':62C ''futur'':71C,120C ''get'':103C ''graduat'':105C ''interest'':74C ''internship'':126C ''issu'':32C ''job'':129C ''know'':96C ''m'':10C,53C,73C ''major'':2A,20C,23C,51C ''master'':78C ''mathemat'':1A,22C,40C,42C ''minor'':56C ''opportun'':124C ''p'':7C,47C,68C,93C,131C ''pure'':41C ''recent'':12C ''research'':127C ''school'':106C ''scienc'':5A,19C,59C,83C ''statist'':44C,66C ''still'':108C ''student'':16C ''thank'':132C ''though'':24C ''transfer'':13C ''use'':101C ''want'':109C ''well'':122C ''would'':112C ''year'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9687, 2, '2016-01-07 19:36:58.41', 5, NULL, NULL, '&lt;p&gt;If you know that you want to become a data scientist, you can pretty much rule out pure mathematics.  Note, I''m not saying that pure mathematicians cannot become data scientists, but it''s not the most natural transition.  Between the other two branches, stats is probably the most natural path.  Both will having you think about applying math to answer real world problems, but stats is very specifically geared towards focusing on larger scale data analysis.&lt;/p&gt;

&lt;p&gt;EDIT:
@rocinante mentions the need for software skills, and suggests CS over econ as a minor.  I would say this really depends; if you are in a bigger data science team, you''ll probably work alongside dedicated programers, who will be able to implement your analytics more efficiently than you would be expected to as an analyst.  If you know that you want to apply D.S. techniques to either finance or business, domain specific knowledge is helpful.  Additionally, if you know that it is your desire to end up in data science, you''ll be able to look for ways to use programing along the way, and keep those skills sharp.&lt;/p&gt;
', NULL, NULL, '''/p'':79C,192C ''abl'':122C,176C ''addit'':158C ''along'':184C ''alongsid'':116C ''analysi'':78C ''analyst'':137C ''analyt'':126C ''answer'':62C ''appli'':59C,145C ''becom'':9C,30C ''bigger'':108C ''branch'':45C ''busi'':152C ''cannot'':29C ''cs'':91C ''d.s'':146C ''data'':11C,31C,77C,109C,171C ''dedic'':117C ''depend'':102C ''desir'':166C ''domain'':153C ''econ'':93C ''edit'':81C ''effici'':128C ''either'':149C ''end'':168C ''expect'':133C ''financ'':150C ''focus'':73C ''gear'':71C ''help'':157C ''implement'':124C ''keep'':188C ''know'':4C,140C,161C ''knowledg'':155C ''larger'':75C ''ll'':113C,174C ''look'':178C ''m'':23C ''math'':60C ''mathemat'':20C ''mathematician'':28C ''mention'':83C ''minor'':96C ''much'':16C ''natur'':39C,51C ''need'':85C ''note'':21C ''p'':1C,80C ''path'':52C ''pretti'':15C ''probabl'':48C,114C ''problem'':65C ''program'':118C,183C ''pure'':19C,27C ''real'':63C ''realli'':101C ''rocinant'':82C ''rule'':17C ''say'':25C,99C ''scale'':76C ''scienc'':110C,172C ''scientist'':12C,32C ''sharp'':191C ''skill'':88C,190C ''softwar'':87C ''specif'':70C,154C ''stat'':46C,67C ''suggest'':90C ''team'':111C ''techniqu'':147C ''think'':57C ''toward'':72C ''transit'':40C ''two'':44C ''use'':182C ''want'':7C,143C ''way'':180C,186C ''work'':115C ''world'':64C ''would'':98C,131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9688, 2, '2016-01-07 20:21:59.707', 2, NULL, NULL, '&lt;p&gt;I agree with Gartner about the importance of statics. However, if your data science minor courses focus on economics, I would re-evaluate going through with it. Instead, I would switch to a computer science minor, or use up your electives to take programming courses. &lt;/p&gt;

&lt;p&gt;As a person who did applied math and stats, I felt that my programming knowledge was really deficient when it came to handling real life huge, messy data sets. All my program required was two Java courses, so I ended up having to spend an inordinate amount of time learning more about scripting, useful programming languages, etc.&lt;/p&gt;
', NULL, NULL, '''/p'':48C,107C ''agre'':3C ''amount'':96C ''appli'':55C ''came'':70C ''comput'':36C ''cours'':17C,47C,86C ''data'':14C,77C ''defici'':67C ''econom'':20C ''elect'':43C ''end'':89C ''etc'':106C ''evalu'':25C ''felt'':60C ''focus'':18C ''gartner'':5C ''go'':26C ''handl'':72C ''howev'':11C ''huge'':75C ''import'':8C ''inordin'':95C ''instead'':30C ''java'':85C ''knowledg'':64C ''languag'':105C ''learn'':99C ''life'':74C ''math'':56C ''messi'':76C ''minor'':16C,38C ''p'':1C,49C ''person'':52C ''program'':46C,63C,81C,104C ''re'':24C ''re-evalu'':23C ''real'':73C ''realli'':66C ''requir'':82C ''scienc'':15C,37C ''script'':102C ''set'':78C ''spend'':93C ''stat'':58C ''static'':10C ''switch'':33C ''take'':45C ''time'':98C ''two'':84C ''use'':40C,103C ''would'':22C,32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9743, 2, '2016-01-11 11:07:23.76', 3, NULL, NULL, '&lt;p&gt;This procedure exists and is called &lt;em&gt;stacked generalization&lt;/em&gt; or simply &lt;em&gt;stacking&lt;/em&gt;. See stacking section from &lt;a href="https://en.wikipedia.org/wiki/Ensemble_learning" rel="nofollow"&gt;wikipedia page&lt;/a&gt;. Strating from there you can red more by following references from the page. The first paper on subject was published by Wolpert in 1992. &lt;/p&gt;

&lt;p&gt;[Later edit]&lt;/p&gt;

&lt;p&gt;Do not combine the results with original features, but keep only the predictions combined only with the target. &lt;/p&gt;
', NULL, NULL, '''/a'':30C ''/em'':11C,16C ''/p'':55C,59C,79C ''/wiki/ensemble_learning'':25C ''1992'':54C ''call'':7C ''combin'':63C,74C ''edit'':58C ''em'':8C,14C ''en.wikipedia.org'':24C ''en.wikipedia.org/wiki/ensemble_learning'':23C ''exist'':4C ''featur'':68C ''first'':45C ''follow'':39C ''general'':10C ''href'':22C ''keep'':70C ''later'':57C ''nofollow'':27C ''origin'':67C ''p'':1C,56C,60C ''page'':29C,43C ''paper'':46C ''predict'':73C ''procedur'':3C ''publish'':50C ''red'':36C ''refer'':40C ''rel'':26C ''result'':65C ''section'':19C ''see'':17C ''simpli'':13C ''stack'':9C,15C,18C ''strate'':31C ''subject'':48C ''target'':78C ''wikipedia'':28C ''wolpert'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9689, 2, '2016-01-07 21:39:33.3', 1, NULL, NULL, '&lt;p&gt;I recently answered in &lt;a href="http://math.stackexchange.com/questions/1592421/why-does-topology-rarely-come-up-outside-of-topology/1592463#1592463"&gt;Why does topology rarely come up outside of topology?&lt;/a&gt; that pure mathematics (topology) can be useful in data science. I believe that mixed skills are required for data science: programming (computer science, algorithmics), practicing (statistics, signal processing), theorizing (logic, mathematics). You can get a &lt;a href="https://bringdata.wordpress.com/2013/11/14/data-science-metromap/" rel="nofollow"&gt;metromap of data science skills&lt;/a&gt;, which are numerous.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://bringdata.files.wordpress.com/2013/11/roadtodatascientist1-1024x831.png?w=512&amp;amp;h=415" rel="nofollow"&gt;metro map of data scientist skills&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I believe that it can be better to learn first what is more difficult, and what you cannot easily learn by yourself or with online lecture. A little bit of math seems interesting, it can give you (if properly chosen) fuel for the future, by learning abstractions you rarely learn elsewhere.&lt;/p&gt;
', NULL, NULL, '''/2013/11/14/data-science-metromap/'':60C ''/2013/11/roadtodatascientist1-1024x831.png?w=512&amp;amp;h=415'':78C ''/a'':20C,68C,87C ''/p'':72C,88C,141C ''/questions/1592421/why-does-topology-rarely-come-up-outside-of-topology/1592463#1592463'':10C ''abstract'':136C ''algorithm'':44C ''answer'':4C ''believ'':32C,91C ''better'':96C ''bit'':118C ''bringdata.files.wordpress.com'':77C ''bringdata.files.wordpress.com/2013/11/roadtodatascientist1-1024x831.png?w=512&amp;amp;h=415'':76C ''bringdata.wordpress.com'':59C ''bringdata.wordpress.com/2013/11/14/data-science-metromap/'':58C ''cannot'':107C ''chosen'':129C ''come'':15C ''comput'':42C ''data'':29C,39C,65C,84C ''difficult'':103C ''easili'':108C ''elsewher'':140C ''first'':99C ''fuel'':130C ''futur'':133C ''get'':54C ''give'':125C ''href'':7C,57C,75C ''interest'':122C ''learn'':98C,109C,135C,139C ''lectur'':115C ''littl'':117C ''logic'':50C ''map'':82C ''math'':120C ''math.stackexchange.com'':9C ''math.stackexchange.com/questions/1592421/why-does-topology-rarely-come-up-outside-of-topology/1592463#1592463'':8C ''mathemat'':23C,51C ''metro'':81C ''metromap'':63C ''mix'':34C ''nofollow'':62C,80C ''numer'':71C ''onlin'':114C ''outsid'':17C ''p'':1C,73C,89C ''practic'':45C ''process'':48C ''program'':41C ''proper'':128C ''pure'':22C ''rare'':14C,138C ''recent'':3C ''rel'':61C,79C ''requir'':37C ''scienc'':30C,40C,43C,66C ''scientist'':85C ''seem'':121C ''signal'':47C ''skill'':35C,67C,86C ''statist'':46C ''theoriz'':49C ''topolog'':13C,19C,24C ''use'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9696, 1, '2016-01-08 04:26:22.997', 1, 60, 'How can I predict the post popularity of reddit.com with hidden markov model(HMM)?', '&lt;p&gt;If I get some posts on reddict.com, how can I predict whether this post will (trending/hot/popular) in the future or not? I would like to use the hidden markov model to predict it, but I don`t know how to define the hidden states and observation sequence...can anyone give me any suggestion? Thanks~&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/io9IX.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/io9IX.png" alt="I only have these time series data, such asu comments.."&gt;&lt;/a&gt; &lt;/p&gt;
', 'markov', NULL, '''/a'':96C ''/io9ix.png'':77C,84C ''/p'':71C,97C ''alt'':85C ''anyon'':65C ''asu'':94C ''comment'':95C ''data'':92C ''defin'':57C ''futur'':35C ''get'':19C ''give'':66C ''hidden'':11A,44C,59C ''hmm'':14A ''href'':74C ''i.stack.imgur.com'':76C,83C ''i.stack.imgur.com/io9ix.png'':75C,82C ''img'':80C ''know'':54C ''like'':40C ''markov'':12A,15B,45C ''model'':13A,46C ''nofollow'':79C ''observ'':62C ''p'':16C,72C ''popular'':7A ''post'':6A,21C,30C ''predict'':4A,27C,48C ''reddict.com'':23C ''reddit.com'':9A ''rel'':78C ''sequenc'':63C ''seri'':91C ''src'':81C ''state'':60C ''suggest'':69C ''thank'':70C ''time'':90C ''trending/hot/popular'':32C ''use'':42C ''whether'':28C ''would'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9698, 2, '2016-01-08 10:45:16.107', 1, NULL, NULL, '&lt;p&gt;I believe it will help if you add the exact regex anchors to the pattern you are trying to match. In your case adding &lt;code&gt;"^I like football$"&lt;/code&gt; as the pattern you are trying to match will look for strings starting and ending exactly like that. That way you have:   &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;grepl("^I like football$", "I like football, W Lazio")
[1] FALSE
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This will eliminate at least the problem you are facing now.&lt;br&gt;
 I am not sure if I understand the rest of the question though...as I get it, you have some &lt;em&gt;other&lt;/em&gt; character vector called &lt;code&gt;text&lt;/code&gt; for example, and inside you are trying to look for elements of the vector &lt;code&gt;pattern&lt;/code&gt; and, if matched, replace them with the corresponding vector element from &lt;code&gt;replacement&lt;/code&gt; right?&lt;/p&gt;
', NULL, NULL, '''/code'':30C,68C,111C,128C,142C ''/em'':105C ''/p'':53C,144C ''/pre'':69C ''1'':66C ''ad'':25C ''add'':9C ''anchor'':13C ''believ'':3C ''br'':82C ''call'':108C ''case'':24C ''charact'':106C ''code'':26C,55C,109C,126C,140C ''correspond'':136C ''element'':122C,138C ''elimin'':73C ''em'':103C ''end'':45C ''exact'':11C,46C ''exampl'':113C ''face'':80C ''fals'':67C ''footbal'':29C,60C,63C ''get'':98C ''grepl'':57C ''gt'':56C ''help'':6C ''insid'':115C ''lazio'':65C ''least'':75C ''like'':28C,47C,59C,62C ''look'':40C,120C ''match'':21C,38C,131C ''p'':1C,70C ''pattern'':16C,33C,127C ''pre'':54C ''problem'':77C ''question'':94C ''regex'':12C ''replac'':132C,141C ''rest'':91C ''right'':143C ''start'':43C ''string'':42C ''sure'':86C ''text'':110C ''though'':95C ''tri'':19C,36C,118C ''understand'':89C ''vector'':107C,125C,137C ''w'':64C ''way'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9699, 1, '2016-01-08 10:58:31.153', 1, 24, 'Expectation number of points in initial clustering for LSH', '&lt;p&gt;I have a very skewed, 10-dimensional data set. I need approximate nearest neighbours for my use case and I was looking into Locality senstive hashing. However after scaling and randomly generating hyperplanes through the origin and coding the data points like that I got very skewed buckets because of the nature of the data. After thinking about it for a bit I came up with an idea to take random points from the data and use these as cluster centers for the hash. Every point will be mapped to the ID of the closest randomly picked center. My question is if the expected size of the bucket of a specific point is the same for all the other data points. I think that is the case but others say it shouldn''t. My rationale is that more dense areas have more randomly decided cluster points while outliers will not be picked very often. I can''t find anything about this in literature.&lt;/p&gt;

&lt;p&gt;Edit: I did run some tests and they seem to support my hypothesis somewhat but variance is relatively high because there is a high dependence between sizes of clusters (data points wise)&lt;/p&gt;
', 'clustering', NULL, '''/p'':175C,209C ''10'':17C ''anyth'':170C ''approxim'':23C ''area'':151C ''bit'':73C ''bucket'':59C,119C ''came'':75C ''case'':29C,138C ''center'':92C,109C ''closest'':106C ''cluster'':7A,10B,91C,156C,205C ''code'':49C ''data'':19C,51C,66C,86C,131C,206C ''decid'':155C ''dens'':150C ''depend'':201C ''dimension'':18C ''edit'':177C ''everi'':96C ''expect'':1A,115C ''find'':169C ''generat'':43C ''got'':56C ''hash'':37C,95C ''high'':195C,200C ''howev'':38C ''hyperplan'':44C ''hypothesi'':189C ''id'':103C ''idea'':79C ''initi'':6A ''like'':53C ''literatur'':174C ''local'':35C ''look'':33C ''lsh'':9A ''map'':100C ''natur'':63C ''nearest'':24C ''need'':22C ''neighbour'':25C ''number'':2A ''often'':165C ''origin'':47C ''other'':140C ''outlier'':159C ''p'':11C,176C ''pick'':108C,163C ''point'':4A,52C,83C,97C,123C,132C,157C,207C ''question'':111C ''random'':42C,82C,107C,154C ''rational'':146C ''relat'':194C ''run'':180C ''say'':141C ''scale'':40C ''seem'':185C ''senstiv'':36C ''set'':20C ''shouldn'':143C ''size'':116C,203C ''skew'':16C,58C ''somewhat'':190C ''specif'':122C ''support'':187C ''take'':81C ''test'':182C ''think'':68C,134C ''use'':28C,88C ''varianc'':192C ''wise'':208C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9700, 2, '2016-01-08 11:12:37.197', 0, NULL, NULL, '&lt;p&gt;It''s not a proof, but I just found a paper that claims this empirically, and also had the exact same idea for the same problem.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://web.kaist.ac.kr/~kyomin/2012BigLearn.pdf" rel="nofollow"&gt;http://web.kaist.ac.kr/~kyomin/2012BigLearn.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If someone does happen to have a proof for this, I''m still very interested though.&lt;/p&gt;
', NULL, NULL, '''/p'':28C,57C ''/~kyomin/2012biglearn.pdf'':34C ''/~kyomin/2012biglearn.pdf&lt;/a&gt;&lt;/p&gt;'':39C ''also'':18C ''claim'':14C ''empir'':16C ''exact'':21C ''found'':10C ''happen'':44C ''href'':31C ''idea'':23C ''interest'':55C ''m'':52C ''nofollow'':36C ''p'':1C,29C,40C ''paper'':12C ''problem'':27C ''proof'':6C,48C ''rel'':35C ''someon'':42C ''still'':53C ''though'':56C ''web.kaist.ac.kr'':33C,38C ''web.kaist.ac.kr/~kyomin/2012biglearn.pdf'':32C ''web.kaist.ac.kr/~kyomin/2012biglearn.pdf&lt;/a&gt;&lt;/p&gt;'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9710, 1, '2016-01-09 03:48:10.907', 0, 10, 'How to train a linear model using ANFIS function in MATLAB?', '&lt;p&gt;As the title says,I do not know how can Iãdetermined the parameters trained? Now after training using ANFIS in MATLAB, I only can get some structures info. including input, output, rules etc.And next what shou I do ? can someone else can give me a detail help? Thanks a lot!!!&lt;/p&gt;
', 'machine-learning  matlab  supervised-learning  fuzzy-logic', NULL, '''/p'':74C ''anfi'':8A,42C ''detail'':69C ''determin'':34C ''els'':64C ''etc.and'':56C ''function'':9A ''fuzzi'':20B ''fuzzy-log'':19B ''get'':48C ''give'':66C ''help'':70C ''includ'':52C ''info'':51C ''input'':53C ''iã'':33C ''know'':30C ''learn'':14B,18B ''linear'':5A ''logic'':21B ''lot'':73C ''machin'':13B ''machine-learn'':12B ''matlab'':11A,15B,44C ''model'':6A ''next'':57C ''output'':54C ''p'':22C ''paramet'':36C ''rule'':55C ''say'':26C ''shou'':59C ''someon'':63C ''structur'':50C ''supervis'':17B ''supervised-learn'':16B ''thank'':71C ''titl'':25C ''train'':3A,37C,40C ''use'':7A,41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9746, 1, '2016-01-11 12:36:55.43', 2, 102, 'Simple Explanation of Apache Kafka', '&lt;p&gt;Can anybody explain Apache Kafka for me in a plain language? I''d appreciate an explanation with a practical example instead of abstract theoretical definitions, then I can understand better. &lt;/p&gt;

&lt;p&gt;What is it used for? What does messaging mean? Messaging between what?!! At which stage of a BigData analysis is it used?&lt;/p&gt;

&lt;p&gt;And what are prerequisites for learning it?&lt;/p&gt;

&lt;p&gt;PS: 
Please explain as you would explain for a non-technical person&lt;/p&gt;
', 'bigdata  apache-kafka', NULL, '''/p'':41C,65C,74C,89C ''abstract'':33C ''analysi'':61C ''anybodi'':12C ''apach'':4A,8B,14C ''apache-kafka'':7B ''appreci'':24C ''better'':40C ''bigdata'':6B,60C ''d'':23C ''definit'':35C ''exampl'':30C ''explain'':13C,78C,82C ''explan'':2A,26C ''instead'':31C ''kafka'':5A,9B,15C ''languag'':21C ''learn'':72C ''mean'':51C ''messag'':50C,52C ''non'':86C ''non-techn'':85C ''p'':10C,42C,66C,75C ''person'':88C ''plain'':20C ''pleas'':77C ''practic'':29C ''prerequisit'':70C ''ps'':76C ''simpl'':1A ''stage'':57C ''technic'':87C ''theoret'':34C ''understand'':39C ''use'':46C,64C ''would'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10169, 2, '2016-02-09 21:35:25.743', 2, NULL, NULL, '&lt;p&gt;For a complete decision tree, either of your proposed models would be able to represent the same set of concepts since a decision tree can be decomposed into a disjunction of conjunctions. I don''t think the fact that the bins have different sizes is really an issue because placing any significance on the exact location of splits occurring "inside" a bin is probably not a good idea anyway. From a performance perspective, what probably matters most is how your data are distributed (i.e., does your concept split the data between a single pair of adjacent bins or is it more complicated).&lt;/p&gt;

&lt;p&gt;Since you have the "random-forest" tag associated with your question, I''m inclined to agree with you that keeping them ordered is probably better because if you are generating many trees, the ordered representation will likely result in more terse/smaller trees.&lt;/p&gt;
', NULL, NULL, '''/p'':104C,149C ''abl'':14C ''adjac'':97C ''agre'':122C ''anyway'':70C ''associ'':114C ''better'':131C ''bin'':42C,63C,98C ''complet'':4C ''complic'':103C ''concept'':21C,88C ''conjunct'':33C ''data'':82C,91C ''decis'':5C,24C ''decompos'':28C ''differ'':44C ''disjunct'':31C ''distribut'':84C ''either'':7C ''exact'':56C ''fact'':39C ''forest'':112C ''generat'':136C ''good'':68C ''i.e'':85C ''idea'':69C ''inclin'':120C ''insid'':61C ''issu'':49C ''keep'':126C ''like'':143C ''locat'':57C ''m'':119C ''mani'':137C ''matter'':77C ''model'':11C ''occur'':60C ''order'':128C,140C ''p'':1C,105C ''pair'':95C ''perform'':73C ''perspect'':74C ''place'':51C ''probabl'':65C,76C,130C ''propos'':10C ''question'':117C ''random'':111C ''random-forest'':110C ''realli'':47C ''repres'':16C ''represent'':141C ''result'':144C ''set'':19C ''signific'':53C ''sinc'':22C,106C ''singl'':94C ''size'':45C ''split'':59C,89C ''tag'':113C ''terse/smaller'':147C ''think'':37C ''tree'':6C,25C,138C,148C ''would'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9702, 1, '2016-01-08 11:57:50.097', 2, 69, 'Export weights (formula) from Random Forest Regressor in Scikit-Learn', '&lt;p&gt;I trained a prediction model with Scikit Learn in Python (Random Forest Regressor) and I want to extract somehow the weights of each feature to create an excel tool for manual prediction.&lt;/p&gt;

&lt;p&gt;The only thing that I found is the &lt;code&gt;model.feature_importances_&lt;/code&gt; but it doesn''t help.&lt;/p&gt;

&lt;p&gt;Is there any way to achieve it?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def performRandomForest(X_train, y_train, X_test, y_test):

    ''''''Perform Random Forest Regression''''''

    from sklearn.ensemble  import  RandomForestRegressor

    model  =  RandomForestRegressor()
    model.fit( X_train , y_train )

    #make predictions
    expected  = y_test
    predicted  = model.predict( X_test )

    #summarize the fit of the model
    mse  = np.mean(( predicted - expected )** 2)
    accuracy = ( model.score ( X_train , y_train ))

    return model, mse, accuracy
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;At the moment, I use the &lt;code&gt;model.predict([features])&lt;/code&gt; to do it, but I need it in an excel file.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9719, 1, '2016-01-10 07:48:55.2', 1, 44, 'Embedding a Machine Learning Model into a Web Application', '&lt;p&gt;I''m looking  to  find some  tutorials  involving embedding a Machine Learning Model into a  Web Application eventualy with some storage and database behind( with things like slack  etc)  .&lt;/p&gt;
', 'machine-learning  consumerweb', NULL, '''/p'':44C ''applic'':9A,31C ''behind'':38C ''consumerweb'':13B ''databas'':37C ''embed'':1A,23C ''etc'':43C ''eventuali'':32C ''find'':19C ''involv'':22C ''learn'':4A,12B,26C ''like'':41C ''look'':17C ''m'':16C ''machin'':3A,11B,25C ''machine-learn'':10B ''model'':5A,27C ''p'':14C ''slack'':42C ''storag'':35C ''thing'':40C ''tutori'':21C ''web'':8A,30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9704, 2, '2016-01-08 16:42:36.143', 1, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Statsmodels:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://statsmodels.sourceforge.net/" rel="nofollow"&gt;Statsmodels&lt;/a&gt; is your best bet for a python library that includes ARIMA.  I have used it fairly extensively and am quite happy with it. But, its certainly not as well tested as R based ARIMA models. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;R:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;If you want something "well-tested" then your best bet is likely to use &lt;a href="http://rpy.sourceforge.net/" rel="nofollow"&gt;Rpy2&lt;/a&gt; to call an &lt;a href="https://cran.r-project.org/" rel="nofollow"&gt;R&lt;/a&gt; based &lt;a href="https://stat.ethz.ch/R-manual/R-devel/library/stats/html/arima.html" rel="nofollow"&gt;ARIMA library&lt;/a&gt; from python. Rpy2 can be a bit tricky based on version reconciliation between python, R and Rpy2.&lt;/p&gt;

&lt;p&gt;Here''s a &lt;a href="https://sites.google.com/site/aslugsguidetopython/data-analysis/pandas/calling-r-from-python" rel="nofollow"&gt;turorial on calling R from python using Rpy2&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/a'':13C,77C,87C,98C,136C ''/p'':5C,49C,54C,116C,137C,142C ''/r-manual/r-devel/library/stats/html/arima.html'':93C ''/site/aslugsguidetopython/data-analysis/pandas/calling-r-from-python'':125C ''/strong'':4C,53C ''arima'':24C,47C,96C ''base'':46C,88C,107C ''best'':16C,65C ''bet'':17C,66C ''bit'':105C ''call'':79C,130C ''certain'':39C ''cran.r-project.org'':83C ''extens'':30C ''fair'':29C ''happi'':34C ''help'':141C ''hope'':139C ''href'':8C,72C,82C,90C,122C ''includ'':23C ''librari'':21C,97C ''like'':68C ''model'':48C ''nofollow'':11C,75C,85C,95C,127C ''p'':1C,6C,50C,55C,117C,138C ''python'':20C,100C,112C,133C ''quit'':33C ''r'':45C,52C,86C,113C,131C ''reconcili'':110C ''rel'':10C,74C,84C,94C,126C ''rpy.sourceforge.net'':73C ''rpy2'':76C,101C,115C,135C ''sites.google.com'':124C ''sites.google.com/site/aslugsguidetopython/data-analysis/pandas/calling-r-from-python'':123C ''someth'':59C ''stat.ethz.ch'':92C ''stat.ethz.ch/r-manual/r-devel/library/stats/html/arima.html'':91C ''statsmodel'':3C,12C ''statsmodels.sourceforge.net'':9C ''strong'':2C,51C ''test'':43C,62C ''tricki'':106C ''turori'':128C ''use'':27C,70C,134C ''version'':109C ''want'':58C ''well'':42C,61C ''well-test'':60C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9706, 2, '2016-01-08 19:54:12.367', 4, NULL, NULL, '&lt;p&gt;The approach that comes to mind, is to calculate the kullback-leibler divergence between the kernel density estimations of your train dataset and of your test dataset. &lt;/p&gt;

&lt;p&gt;The kernel density estimation of each of your datasets will give you an approximation to the pdf''s of your datasets. The kullback-leibler divergence will give you a number that will represent the divergence in bits from one distribution to another (if you use base 2 for your logarithm). Below are some references I think you would fine useful. &lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Kernel_density_estimation" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Kernel_density_estimation&lt;/a&gt;
&lt;a href="https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence&lt;/a&gt;
&lt;a href="https://jakevdp.github.io/blog/2013/12/01/kernel-density-estimation/" rel="nofollow"&gt;https://jakevdp.github.io/blog/2013/12/01/kernel-density-estimation/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you would like me to show the math behind this method. Feel free to ask.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT: Added math as asked by author of question&lt;/strong&gt;    &lt;/p&gt;

&lt;p&gt;Let $\hat x_1, \hat x_2,\hat x_3...,\hat x_n$ be your training dataset while $x_1,x_2,x_3...,x_n$ is your testing dataset, where both $\hat x_i$ and $x_i$ belong to $\mathbb{R}^d$.
$$\hat f(x;H)=\frac{1}{n} \sum_{i=1}^{n}K(x-\hat x_i;H)$$
$$f(x;H)=\frac{1}{n} \sum_{i=1}^{n}K(x-x_i;H)$$
$\hat f$and $f$ represent the kernel density estimation for training set and testing set respectively. The parameter $H$ represents the bandwidth parameter and is a symmetric positive definite $d \times d$ matrix. $K(u;H)$ can be rewritten as $$|H|^{-\frac{1}{2}} K(H^{\frac{1}{2}} u)$$ where K can be any kernel function. I would recommend for simplicity purposes the standard multivariate normal kernel. Okay so now that we have the kernel density estimations of both our training and our testing dataset, we can use the kullback-leibler divergence in order to estimate the difference between the two.&lt;br&gt;
Optimally we would like to calculate the kullback-leibler divergence with respect to every point in our space. Mathematically speaking.  &lt;/p&gt;

&lt;p&gt;$$\int_X f(x;H) \ log_2(\frac{f(x;H)}{\hat f(x;H)})dx$$&lt;/p&gt;

&lt;p&gt;But this is computationally unpractical to compute. We can approximate this integral by sampling a set of points from the $f(x;H)$ and then computing the discrete sum.
$$\sum_{x \in X} f(x;H) \ log_2(\frac{f(x;H)}{\hat f(x;H)})$$&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Quick Note:&lt;/strong&gt; To sample from a kernel density estimator, uniformly randomly select a point from the respective dataset. Then sample from the kernel of choice centered around the point chosen. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9708, 1, '2016-01-09 01:16:15.027', 1, 31, 'Confidence range for individual prediction from multiple regression', '&lt;p&gt;I''ve built a multivariate regression model for predicting the price of an item given a couple of other factors about that item - age, condition, etc. So, given values for age and condition I can return a single value for price.&lt;/p&gt;

&lt;p&gt;But what I want to do is return a price &lt;em&gt;range&lt;/em&gt;, representing, say, 95% confidence that the true value will fall within this range.&lt;/p&gt;

&lt;p&gt;I''ve googled a bunch, but I can''t find a clear description of how to achieve this. The closest I''ve come is what''s described in this paper: &lt;a href="http://pareonline.net/getvn.asp?v=7&amp;amp;n=2" rel="nofollow"&gt;http://pareonline.net/getvn.asp?v=7&amp;amp;n=2&lt;/a&gt;, in the section "Calculating a Predicted Score, and Confidence Intervals Around That Score".&lt;/p&gt;

&lt;p&gt;Given that the confidence of a regression model "bows" in around the mean for each value, I feel like this is more complex than just taking +/-X% of the predicted value. The range should be greater for items that are at the extremes of age and condition, right?&lt;/p&gt;

&lt;p&gt;Grateful for any help.&lt;/p&gt;
', 'machine-learning  regression', NULL, '''/em'':69C ''/getvn.asp?v=7&amp;amp;n=2'':119C ''/getvn.asp?v=7&amp;amp;n=2&lt;/a&gt;,'':124C ''/p'':55C,83C,138C,188C,194C ''95'':72C ''achiev'':101C ''age'':37C,44C,184C ''around'':135C,150C ''bow'':148C ''built'':16C ''bunch'':89C ''calcul'':128C ''clear'':96C ''closest'':104C ''come'':107C ''complex'':162C ''condit'':38C,46C,186C ''confid'':1A,73C,133C,143C ''coupl'':30C ''describ'':111C ''descript'':97C ''em'':67C ''etc'':39C ''extrem'':182C ''factor'':33C ''fall'':79C ''feel'':157C ''find'':94C ''given'':28C,41C,140C ''googl'':87C ''grate'':190C ''greater'':175C ''help'':193C ''href'':116C ''individu'':4A ''interv'':134C ''item'':27C,36C,177C ''learn'':11B ''like'':158C ''machin'':10B ''machine-learn'':9B ''mean'':152C ''model'':20C,147C ''multipl'':7A ''multivari'':18C ''nofollow'':121C ''p'':13C,56C,84C,139C,189C ''paper'':114C ''pareonline.net'':118C,123C ''pareonline.net/getvn.asp?v=7&amp;amp;n=2'':117C ''pareonline.net/getvn.asp?v=7&amp;amp;n=2&lt;/a&gt;,'':122C ''predict'':5A,22C,130C,169C ''price'':24C,54C,66C ''rang'':2A,68C,82C,172C ''regress'':8A,12B,19C,146C ''rel'':120C ''repres'':70C ''return'':49C,64C ''right'':187C ''say'':71C ''score'':131C,137C ''section'':127C ''singl'':51C ''take'':165C ''true'':76C ''valu'':42C,52C,77C,155C,170C ''ve'':15C,86C,106C ''want'':60C ''within'':80C ''x'':166C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9713, 1, '2016-01-09 14:43:38.303', 1, 27, 'Prediction in the training sample with randomforest in r', '&lt;p&gt;I''m using a Random Forest algorithm in order to construct a classification model, and I HAVE to check the accuracy of my rf model in the training sample, but as you can see in this answers : &lt;/p&gt;

&lt;p&gt;&lt;a href="http://stats.stackexchange.com/a/112052/90446"&gt;http://stats.stackexchange.com/a/112052/90446&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stats.stackexchange.com/a/66546/90446"&gt;http://stats.stackexchange.com/a/66546/90446&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;you can''t evaluate the accuracy considering the training samples like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;predict(model, data=train)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m not confortable with the idea of use OOB to get accuracy of the training sample, because the OOB was not used to build the model, how could this be right? I don''t know what should I do to predict the fit in the training sample and get the accuracy of the training sample, is it possible or make any sense? When a check the AUC of the prediction of my training sample I get something near of 0.98, but the AUC of the test sample is about 0.7. Is this  due to the limitations of prediction at the training sample or due to Overfitting? &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9715, 1, '2016-01-09 16:07:08.427', 4, 83, 'is neural networks an online algorithm by nature?', '&lt;p&gt;I have been doing machine learning for a while, but bits and pieces come together even after some time of practicing.&lt;/p&gt;

&lt;p&gt;In neural networks, you adjust the weights by doing one pass (forward pass), and then computing the partial derivatives for the weights (backward pass) after each training example - and subtracting those partial derivatives from the initial weights.&lt;/p&gt;

&lt;p&gt;in turn, the calculation of the new weights is mathematically complex (you need to compute the partial derivative of the weights, for which you compute the error at every layer of the neural net - but the input layer).&lt;/p&gt;

&lt;p&gt;Is that not by definition an online algorithm, where cost and new weights are calculated after each training example?&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'machine-learning  neuralnetwork  online-learning', NULL, '''/p'':38C,77C,117C,138C,141C ''adjust'':44C ''algorithm'':6A,126C ''backward'':62C ''bit'':27C ''calcul'':82C,133C ''come'':30C ''complex'':89C ''comput'':55C,93C,103C ''cost'':128C ''definit'':123C ''deriv'':58C,72C,96C ''error'':105C ''even'':32C ''everi'':107C ''exampl'':67C,137C ''forward'':51C ''initi'':75C ''input'':115C ''layer'':108C,116C ''learn'':11B,15B,22C ''machin'':10B,21C ''machine-learn'':9B ''mathemat'':88C ''natur'':8A ''need'':91C ''net'':112C ''network'':3A,42C ''neural'':2A,41C,111C ''neuralnetwork'':12B ''new'':85C,130C ''one'':49C ''onlin'':5A,14B,125C ''online-learn'':13B ''p'':16C,39C,78C,118C,139C ''partial'':57C,71C,95C ''pass'':50C,52C,63C ''piec'':29C ''practic'':37C ''subtract'':69C ''thank'':140C ''time'':35C ''togeth'':31C ''train'':66C,136C ''turn'':80C ''weight'':46C,61C,76C,86C,99C,131C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9716, 1, '2016-01-09 16:47:45.16', 1, 36, 'Artificial neural network on ternary content addressable memory', '&lt;p&gt;One of the applications of Ternary content addressable memory(TCAM) is artificial neural network. How does TCAM do prediction or recognition? How TCAM is related to artificial neural network?&lt;/p&gt;
', 'machine-learning  data-mining  bigdata  predictive-modeling  object-recognition', NULL, '''/p'':52C ''address'':7A,30C ''applic'':26C ''artifici'':1A,34C,49C ''bigdata'':15B ''content'':6A,29C ''data'':13B ''data-min'':12B ''learn'':11B ''machin'':10B ''machine-learn'':9B ''memori'':8A,31C ''mine'':14B ''model'':18B ''network'':3A,36C,51C ''neural'':2A,35C,50C ''object'':20B ''object-recognit'':19B ''one'':23C ''p'':22C ''predict'':17B,41C ''predictive-model'':16B ''recognit'':21B,43C ''relat'':47C ''tcam'':32C,39C,45C ''ternari'':5A,28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9717, 2, '2016-01-09 16:48:25.53', 1, NULL, NULL, '&lt;p&gt;You can train after each example, or after each epoch. This is the difference between stochastic gradient descent, and batch gradient descent. See pg 84 of Sebastian Raschka''s Python Machine Learning book for more.&lt;/p&gt;
', NULL, NULL, '''/p'':37C ''84'':26C ''batch'':21C ''book'':34C ''descent'':19C,23C ''differ'':15C ''epoch'':11C ''exampl'':7C ''gradient'':18C,22C ''learn'':33C ''machin'':32C ''p'':1C ''pg'':25C ''python'':31C ''raschka'':29C ''sebastian'':28C ''see'':24C ''stochast'':17C ''train'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9718, 2, '2016-01-10 01:31:18.25', 1, NULL, NULL, '&lt;p&gt;There are many tools that are able to support such queries (as you mentioned Hive or Spark), and it is really up to your requirements in terms of number of queries, number of people who are going to query the data, what kind of BI or reporting tools you might want to use with it, etc. &lt;/p&gt;

&lt;p&gt;More than that, your requirements are probably going to change in the future. If you provide easier ways to query the data, the more people around you are going to use it. &lt;/p&gt;

&lt;p&gt;For example, if you choose to load that data into Amazon Redshift (&lt;a href="https://aws.amazon.com/redshift/" rel="nofollow"&gt;https://aws.amazon.com/redshift/&lt;/a&gt;), it will give you a full SQL flexibility, very fast performance and the ability to connect to several BI, visualization and reporting tools. When you COPY the data into Redshift it is being compressed (column based encoding) 5-8 times, and you can put all your data in a cluster of 6 nodes of DS2.8XL of Redshift, for a couple of weeks for your POC. &lt;/p&gt;

&lt;p&gt;The concept of compression can be applied in different solutions as well. For example, instead of using CSV format, you can convert it to Parquet (&lt;a href="https://parquet.apache.org/" rel="nofollow"&gt;https://parquet.apache.org/&lt;/a&gt;) format, and save on your storage and increase the performance as you need to move around and scan less bytes of data. Once you have your data in Parquet, you can query it for example with Presto, like Netflix are doing - &lt;a href="http://techblog.netflix.com/2014/10/using-presto-in-our-big-data-platform.html" rel="nofollow"&gt;http://techblog.netflix.com/2014/10/using-presto-in-our-big-data-platform.html&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;Regarding Spark, it can help even if you don''t fit all the data into memory. Spark can also help you with the usage of Zeppelin (&lt;a href="https://zeppelin.incubator.apache.org/" rel="nofollow"&gt;https://zeppelin.incubator.apache.org/&lt;/a&gt;) as an interactive notebook, with nice visualization features, as well as write code with Scala, Python or even R with SparkR. &lt;/p&gt;
', NULL, NULL, '''-8'':154C ''/&lt;/a&gt;)'':215C,302C ''/2014/10/using-presto-in-our-big-data-platform.html'':261C ''/2014/10/using-presto-in-our-big-data-platform.html&lt;/a&gt;'':266C ''/p'':58C,92C,181C,267C,324C ''/redshift/'':110C ''/redshift/&lt;/a&gt;),'':115C ''5'':153C ''6'':167C ''abil'':129C ''abl'':8C ''also'':287C ''amazon'':104C ''appli'':189C ''around'':85C,231C ''aws.amazon.com'':109C,114C ''aws.amazon.com/redshift/'':108C ''aws.amazon.com/redshift/&lt;/a&gt;),'':113C ''base'':151C ''bi'':46C,134C ''byte'':235C ''chang'':69C ''choos'':98C ''cluster'':165C ''code'':315C ''column'':150C ''compress'':149C,186C ''concept'':184C ''connect'':131C ''convert'':204C ''copi'':141C ''coupl'':175C ''csv'':200C ''data'':42C,81C,102C,143C,162C,237C,242C,282C ''differ'':191C ''ds2.8xl'':170C ''easier'':76C ''encod'':152C ''etc'':57C ''even'':274C,320C ''exampl'':95C,196C,250C ''fast'':125C ''featur'':310C ''fit'':279C ''flexibl'':123C ''format'':201C,216C ''full'':121C ''futur'':72C ''give'':118C ''go'':38C,67C,88C ''help'':273C,288C ''hive'':16C ''href'':107C,209C,258C,296C ''increas'':223C ''instead'':197C ''interact'':305C ''kind'':44C ''less'':234C ''like'':253C ''load'':100C ''mani'':4C ''memori'':284C ''mention'':15C ''might'':51C ''move'':230C ''need'':228C ''netflix'':254C ''nice'':308C ''node'':168C ''nofollow'':112C,212C,263C,299C ''notebook'':306C ''number'':30C,33C ''p'':1C,59C,93C,182C,268C ''parquet'':207C,244C ''parquet.apache.org'':210C,214C ''parquet.apache.org/&lt;/a&gt;)'':213C ''peopl'':35C,84C ''perform'':126C,225C ''poc'':180C ''presto'':252C ''probabl'':66C ''provid'':75C ''put'':159C ''python'':318C ''queri'':12C,32C,40C,79C,247C ''r'':321C ''realli'':22C ''redshift'':105C,145C,172C ''regard'':269C ''rel'':111C,211C,262C,298C ''report'':48C,137C ''requir'':26C,64C ''save'':218C ''scala'':317C ''scan'':233C ''sever'':133C ''solut'':192C ''spark'':18C,270C,285C ''sparkr'':323C ''sql'':122C ''storag'':221C ''support'':10C ''techblog.netflix.com'':260C,265C ''techblog.netflix.com/2014/10/using-presto-in-our-big-data-platform.html'':259C ''techblog.netflix.com/2014/10/using-presto-in-our-big-data-platform.html&lt;/a&gt;'':264C ''term'':28C ''time'':155C ''tool'':5C,49C,138C ''usag'':292C ''use'':54C,90C,199C ''visual'':135C,309C ''want'':52C ''way'':77C ''week'':177C ''well'':194C,312C ''write'':314C ''zeppelin'':294C ''zeppelin.incubator.apache.org'':297C,301C ''zeppelin.incubator.apache.org/&lt;/a&gt;)'':300C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9722, 1, '2016-01-10 10:49:20.447', 5, 77, 'applying word2vec on small text files', '&lt;p&gt;I''m totally new to word2vec so pls bear it with me. I have a set of text files each containing a set of tweets, between 1000-3000. I have chosen a common keyword ("kw1") and wants to find semantically relevant terms for "kw1" using word2vec. For example if the keyword is "apple" I would expect to see related terms such as "ipad" "os" "mac"... based on the input file. So this set of related terms for "kw1" would be different for each input file as word2vec would be trained on individual files (eg., 5 input files, run word2vec 5 times on each file). &lt;/p&gt;

&lt;p&gt;My goal is to find sets of related terms for each input file given the common keyword ("kw1"), which would be used for some other purposes. &lt;/p&gt;

&lt;p&gt;My questions/doubts are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Does it make sense to use word2vec for a task like this? is it technically right to use considering the small size of an input file?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I have downloaded the code from code.google.com: &lt;a href="https://code.google.com/p/word2vec/"&gt;https://code.google.com/p/word2vec/&lt;/a&gt; and have just given it a dry run as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; time ./word2vec -train $file -output vectors.bin -cbow 1 -size 200 -window 10 -negative 25 -hs 1 -sample 1e-3 -threads 12 -binary 1 -iter 50

./distance vectors.bin 
&lt;/code&gt;&lt;/pre&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;From my results I saw I''m getting many noisy terms (stopwords) when I''m using the ''distance'' tool to get related terms to "kw1". So I did remove stopwords and other noisy terms such as user mentions. But I haven''t seen anywhere that word2vec requires cleaned input data...?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;How do you choose right parameters? I see the results (from running the distance tool) varies greatly when i change parameters such as ''-window'', ''-iter''. Which technique should I use to find the correct values for the parameters. (manual trial and error is not possible for me as I''ll be scaling up the dataset). &lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9723, 2, '2016-01-10 12:36:27.09', 3, NULL, NULL, '&lt;p&gt;There are three training modes for neural networks&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;stochastic gradient descent&lt;/strong&gt;: Adjust the weights after every single training example&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;batch training&lt;/strong&gt;: Adjust the weights after going through all data (an epoch)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;mini-batch training&lt;/strong&gt;: Adjust the weights after going through a &lt;em&gt;mini-batch&lt;/em&gt;. This is usually 128 training examples.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Most of the time, mini-batch training seems to be used.&lt;/p&gt;

&lt;p&gt;So the answer is:&lt;/p&gt;

&lt;p&gt;No, the neural network learning algorithm is not online algorithm.&lt;/p&gt;
', NULL, NULL, '''/em'':61C ''/li'':26C,42C,68C ''/p'':10C,83C,89C,101C ''/strong'':17C,31C,49C ''/ul'':69C ''128'':65C ''adjust'':18C,32C,50C ''algorithm'':96C,100C ''answer'':87C ''batch'':29C,47C,60C,77C ''data'':39C ''descent'':16C ''em'':57C ''epoch'':41C ''everi'':22C ''exampl'':25C,67C ''go'':36C,54C ''gradient'':15C ''learn'':95C ''li'':12C,27C,43C ''mini'':46C,59C,76C ''mini-batch'':45C,58C,75C ''mode'':6C ''network'':9C,94C ''neural'':8C,93C ''onlin'':99C ''p'':1C,70C,84C,90C ''seem'':79C ''singl'':23C ''stochast'':14C ''strong'':13C,28C,44C ''three'':4C ''time'':74C ''train'':5C,24C,30C,48C,66C,78C ''ul'':11C ''use'':82C ''usual'':64C ''weight'':20C,34C,52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9724, 1, '2016-01-10 14:11:47.697', 2, 73, 'Deciding about dimensionality reduction, classification and clustering?', '&lt;p&gt;Could you please help me to understand it because I''m not sure if I got it correctly.&lt;/p&gt;

&lt;p&gt;Let''s say I have a dataset, of persons, with 100 features, various characteristics like height, weight, age, etc. I want to classify if are normal or abnormal. By abnormal I mean if a 20 years old man is 170cm and 150kg to identify it as abnormal.&lt;/p&gt;

&lt;p&gt;Should I use Self Organising Map to reduce the dimensionality (these 100 features) and then K-means to classify them into normal and abnormal? Is that a correct approach? Or I can use just K-NN to classify them into normal - abnormal without any dimensionality reduction?&lt;/p&gt;

&lt;p&gt;How many features can I use with K-NN? All the examples I''ve found so far use just two.&lt;/p&gt;

&lt;p&gt;And if later I''d like to find why this person has included into the abnormal class how can I find that this happened because of these two features, his weight according to his height?&lt;/p&gt;
', 'machine-learning  classification  clustering  dimensionality-reduction', NULL, '''/p'':35C,84C,134C,157C,194C ''100'':47C,97C ''150kg'':78C ''170cm'':76C ''20'':71C ''abnorm'':64C,66C,83C,110C,129C,174C ''accord'':190C ''age'':54C ''approach'':115C ''characterist'':50C ''class'':175C ''classif'':5A,11B ''classifi'':59C,105C,125C ''cluster'':7A,12B ''correct'':34C,114C ''could'':17C ''d'':163C ''dataset'':43C ''decid'':1A ''dimension'':3A,14B,95C,132C ''dimensionality-reduct'':13B ''etc'':55C ''exampl'':148C ''far'':153C ''featur'':48C,98C,138C,187C ''find'':166C,179C ''found'':151C ''got'':32C ''happen'':182C ''height'':52C,193C ''help'':20C ''identifi'':80C ''includ'':171C ''k'':102C,122C,144C ''k-mean'':101C ''k-nn'':121C,143C ''later'':161C ''learn'':10B ''let'':37C ''like'':51C,164C ''m'':27C ''machin'':9B ''machine-learn'':8B ''man'':74C ''mani'':137C ''map'':91C ''mean'':68C,103C ''nn'':123C,145C ''normal'':62C,108C,128C ''old'':73C ''organis'':90C ''p'':16C,36C,85C,135C,158C ''person'':45C,169C ''pleas'':19C ''reduc'':93C ''reduct'':4A,15B,133C ''say'':39C ''self'':89C ''sure'':29C ''two'':156C,186C ''understand'':23C ''use'':88C,119C,141C,154C ''various'':49C ''ve'':150C ''want'':57C ''weight'':53C,189C ''without'':130C ''year'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9725, 2, '2016-01-10 14:41:39.533', 1, NULL, NULL, '&lt;p&gt;The issue is that pyspark is not on os sys path by default. After several failed attempt to add it manually to my config files/paths/etc, I came across this GitHub repository called findspark. &lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/minrk/findspark" rel="nofollow"&gt;https://github.com/minrk/findspark&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I cloned this repository using
git clone &lt;a href="https://github.com/minrk/findspark.git" rel="nofollow"&gt;https://github.com/minrk/findspark.git&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Then I ran "pip install findspark" from the findspark root.&lt;/p&gt;

&lt;p&gt;Started a Jupyter notebook, created a new Python3 notebook and added&lt;/p&gt;

&lt;p&gt;import findspark&lt;br&gt;
import os&lt;br&gt;
findspark.init()&lt;br&gt;
import pyspark&lt;br&gt;
sc = pyspark.SparkContext()  &lt;/p&gt;

&lt;p&gt;Before findspark.init(), import pyspark came back with an error.&lt;/p&gt;

&lt;p&gt;To test I just typed sc and got back:&lt;br&gt;
pyspark.context.SparkContext at 0x4526d30&lt;/p&gt;

&lt;p&gt;All working for me now.&lt;/p&gt;
', NULL, NULL, '''/minrk/findspark'':41C ''/minrk/findspark&lt;/a&gt;&lt;/p&gt;'':46C ''/minrk/findspark.git'':59C ''/minrk/findspark.git&lt;/a&gt;&lt;/p&gt;'':64C ''/p'':35C,76C,89C,104C,115C,130C,137C ''0x4526d30'':129C ''across'':29C ''ad'':88C ''add'':20C ''attempt'':18C ''back'':111C,125C ''br'':93C,96C,98C,101C,126C ''call'':33C ''came'':28C,110C ''clone'':49C,54C ''config'':25C ''creat'':82C ''default'':14C ''error'':114C ''fail'':17C ''files/paths/etc'':26C ''findspark'':34C,71C,74C,92C ''findspark.init'':97C,107C ''git'':53C ''github'':31C ''github.com'':40C,45C,58C,63C ''github.com/minrk/findspark'':39C ''github.com/minrk/findspark&lt;/a&gt;&lt;/p&gt;'':44C ''github.com/minrk/findspark.git'':57C ''github.com/minrk/findspark.git&lt;/a&gt;&lt;/p&gt;'':62C ''got'':124C ''href'':38C,56C ''import'':91C,94C,99C,108C ''instal'':70C ''issu'':3C ''jupyt'':80C ''manual'':22C ''new'':84C ''nofollow'':43C,61C ''notebook'':81C,86C ''os'':10C,95C ''p'':1C,36C,47C,65C,77C,90C,105C,116C,131C ''path'':12C ''pip'':69C ''pyspark'':6C,100C,109C ''pyspark.context.sparkcontext'':127C ''pyspark.sparkcontext'':103C ''python3'':85C ''ran'':68C ''rel'':42C,60C ''repositori'':32C,51C ''root'':75C ''sc'':102C,122C ''sever'':16C ''start'':78C ''sys'':11C ''test'':118C ''type'':121C ''use'':52C ''work'':133C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9729, 1, '2016-01-10 19:44:59.887', 3, 77, 'Image clustering by similarity measurement (CW-SSIM)', '&lt;p&gt;I''m trying to use &lt;a href="http://scikit-learn.org/" rel="nofollow"&gt;scikit-learn&lt;/a&gt; and &lt;a href="https://github.com/jterrace/pyssim" rel="nofollow"&gt;pyssim&lt;/a&gt; for clustering a set of images - less than 100.&lt;/p&gt;

&lt;p&gt;The end goal is to place the images into several buckets (clusters) according to the calculated similarity measures - CW-SSIM.&lt;/p&gt;

&lt;p&gt;The task seems to be trivial, but I can''t figure out the best way to handle "similarity based" clustering in scikit-learn. K-Means clustering looks like a good choice, but it doesn''t accept any "comparison functions" or custom distance functions.&lt;/p&gt;

&lt;p&gt;So how to handle the comparison based (similarity based) clustering in scikit-learn?&lt;/p&gt;

&lt;p&gt;I was thinking about "comparison matrices" with 1 (similar) or 0 (not similar) per cell according to the calculated CW-SSIM similarity values. This matrix will be used for fitting into K-Means clustering. But then we will face the scalability issue, because such matrix will have dimensions equal to the amount of images ... which might grow to 1+ million in the future.&lt;/p&gt;

&lt;p&gt;If there is an easier option in R than in Python, then I''m ready to review as well.&lt;/p&gt;

&lt;p&gt;Thanks in advance. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE from Jan 18, 2016&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I''ve created some code on GitHub about this topic: &lt;a href="https://github.com/llvll/imgcluster" rel="nofollow"&gt;https://github.com/llvll/imgcluster&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This project also includes IP[y] Notebook with step-by-step instructions and extra comments: &lt;a href="https://github.com/llvll/imgcluster/blob/master/ip%5By%5D/imgcluster.ipynb" rel="nofollow"&gt;https://github.com/llvll/imgcluster/blob/master/ip%5By%5D/imgcluster.ipynb&lt;/a&gt;&lt;/p&gt;
', 'machine-learning  r  python  scikit  k-means', NULL, '''/a'':32C,42C ''/jterrace/pyssim'':38C ''/llvll/imgcluster'':256C ''/llvll/imgcluster&lt;/a&gt;&lt;/p&gt;'':261C ''/llvll/imgcluster/blob/master/ip%5by%5d/imgcluster.ipynb'':283C ''/llvll/imgcluster/blob/master/ip%5by%5d/imgcluster.ipynb&lt;/a&gt;&lt;/p&gt;'':288C ''/p'':52C,75C,122C,138C,205C,226C,231C,240C ''/strong'':239C ''0'':150C ''1'':147C,200C ''100'':51C ''18'':237C ''2016'':238C ''accept'':114C ''accord'':66C,155C ''advanc'':230C ''also'':265C ''amount'':193C ''base'':95C,130C,132C ''best'':90C ''bucket'':64C ''calcul'':69C,158C ''cell'':154C ''choic'':109C ''cluster'':2A,44C,65C,96C,104C,133C,175C ''code'':246C ''comment'':278C ''comparison'':116C,129C,144C ''creat'':244C ''custom'':119C ''cw'':7A,73C,160C ''cw-ssim'':6A,72C,159C ''dimens'':189C ''distanc'':120C ''doesn'':112C ''easier'':211C ''end'':55C ''equal'':190C ''extra'':277C ''face'':180C ''figur'':87C ''fit'':170C ''function'':117C,121C ''futur'':204C ''github'':248C ''github.com'':37C,255C,260C,282C,287C ''github.com/jterrace/pyssim'':36C ''github.com/llvll/imgcluster'':254C ''github.com/llvll/imgcluster&lt;/a&gt;&lt;/p&gt;'':259C ''github.com/llvll/imgcluster/blob/master/ip%5by%5d/imgcluster.ipynb'':281C ''github.com/llvll/imgcluster/blob/master/ip%5by%5d/imgcluster.ipynb&lt;/a&gt;&lt;/p&gt;'':286C ''goal'':56C ''good'':108C ''grow'':198C ''handl'':93C,127C ''href'':25C,35C,253C,280C ''imag'':1A,48C,61C,195C ''includ'':266C ''instruct'':275C ''ip'':267C ''issu'':183C ''jan'':236C ''k'':16B,102C,173C ''k-mean'':15B,101C,172C ''learn'':11B,31C,100C,137C ''less'':49C ''like'':106C ''look'':105C ''m'':20C,220C ''machin'':10B ''machine-learn'':9B ''matric'':145C ''matrix'':165C,186C ''mean'':17B,103C,174C ''measur'':5A,71C ''might'':197C ''million'':201C ''nofollow'':28C,40C,258C,285C ''notebook'':269C ''option'':212C ''p'':18C,53C,76C,123C,139C,206C,227C,232C,241C,262C ''per'':153C ''place'':59C ''project'':264C ''pyssim'':41C ''python'':13B,217C ''r'':12B,214C ''readi'':221C ''rel'':27C,39C,257C,284C ''review'':223C ''scalabl'':182C ''scikit'':14B,30C,99C,136C ''scikit-learn'':29C,98C,135C ''scikit-learn.org'':26C ''seem'':79C ''set'':46C ''sever'':63C ''similar'':4A,70C,94C,131C,148C,152C,162C ''ssim'':8A,74C,161C ''step'':272C,274C ''step-by-step'':271C ''strong'':233C ''task'':78C ''thank'':228C ''think'':142C ''topic'':251C ''tri'':21C ''trivial'':82C ''updat'':234C ''use'':23C,168C ''valu'':163C ''ve'':243C ''way'':91C ''well'':225C ''y'':268C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9744, 2, '2016-01-11 12:11:36.337', 1, NULL, NULL, '&lt;p&gt;These weights should be introduced by a user. With a weight you tell the K-means algorithm, that one feature is more important than the other.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;[0] These might represent a measure of importance, a frequency count, or some other information. The intent is that a point with a weight of 5.0 is twice as "important" as a point with a weight of 2.5, for instance. This gives rise to the "weighted" K-Means problem. &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;[0] &lt;a href="http://people.sc.fsu.edu/~jburkardt/m_src/kmeans/kmeans.html" rel="nofollow"&gt;http://people.sc.fsu.edu/~jburkardt/m_src/kmeans/kmeans.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/blockquote'':83C ''/p'':29C,82C ''/~jburkardt/m_src/kmeans/kmeans.html'':90C ''/~jburkardt/m_src/kmeans/kmeans.html&lt;/a&gt;&lt;/p&gt;'':95C ''0'':32C,85C ''2.5'':69C ''5.0'':57C ''algorithm'':19C ''blockquot'':30C ''count'':42C ''featur'':22C ''frequenc'':41C ''give'':73C ''href'':87C ''import'':25C,39C,61C ''inform'':46C ''instanc'':71C ''intent'':48C ''introduc'':6C ''k'':17C,79C ''k-mean'':16C,78C ''mean'':18C,80C ''measur'':37C ''might'':34C ''nofollow'':92C ''one'':21C ''p'':1C,31C,84C ''people.sc.fsu.edu'':89C,94C ''people.sc.fsu.edu/~jburkardt/m_src/kmeans/kmeans.html'':88C ''people.sc.fsu.edu/~jburkardt/m_src/kmeans/kmeans.html&lt;/a&gt;&lt;/p&gt;'':93C ''point'':52C,64C ''problem'':81C ''rel'':91C ''repres'':35C ''rise'':74C ''tell'':14C ''twice'':59C ''user'':9C ''weight'':3C,12C,55C,67C,77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9730, 2, '2016-01-10 21:29:01.78', 2, NULL, NULL, '&lt;p&gt;I would use a regular clustering algorithm and replace the objective function, which is usually the MSE, with a differentiable loss function of your choice. Another way is to learn an embedding that optimizes your similarity metric using a neural network and just cluster that.&lt;/p&gt;

&lt;p&gt;If you would rather do similarity-based clustering, here are some papers:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;A Similarity-Based Robust Clustering Method&lt;/li&gt;
&lt;li&gt;A Discriminative Framework for Clustering via Similarity Functions&lt;/li&gt;
&lt;li&gt;Similarity-Based Clustering by Left-Stochastic Matrix Factorization&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;sklearn implements two similarity clustering methods: &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.cluster.AffinityPropagation.html#sklearn.cluster.AffinityPropagation" rel="nofollow"&gt;Affinity propagation&lt;/a&gt;, and &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralClustering.html#sklearn.cluster.SpectralClustering" rel="nofollow"&gt;spectral clustering&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':112C,123C ''/li'':72C,82C,94C ''/p'':47C,62C,124C ''/stable/modules/generated/sklearn.cluster.affinitypropagation.html#sklearn.cluster.affinitypropagation'':107C ''/stable/modules/generated/sklearn.cluster.spectralclustering.html#sklearn.cluster.spectralclustering'':118C ''/ul'':95C ''affin'':110C ''algorithm'':8C ''anoth'':27C ''base'':56C,68C,86C ''choic'':26C ''cluster'':7C,45C,57C,70C,78C,87C,101C,122C ''differenti'':21C ''discrimin'':75C ''embed'':33C ''factor'':93C ''framework'':76C ''function'':13C,23C,81C ''href'':104C,115C ''implement'':98C ''learn'':31C ''left'':90C ''left-stochast'':89C ''li'':64C,73C,83C ''loss'':22C ''matrix'':92C ''method'':71C,102C ''metric'':38C ''mse'':18C ''network'':42C ''neural'':41C ''nofollow'':109C,120C ''object'':12C ''optim'':35C ''p'':1C,48C,96C ''paper'':61C ''propag'':111C ''rather'':52C ''regular'':6C ''rel'':108C,119C ''replac'':10C ''robust'':69C ''scikit-learn.org'':106C,117C ''scikit-learn.org/stable/modules/generated/sklearn.cluster.affinitypropagation.html#sklearn.cluster.affinitypropagation'':105C ''scikit-learn.org/stable/modules/generated/sklearn.cluster.spectralclustering.html#sklearn.cluster.spectralclustering'':116C ''similar'':37C,55C,67C,80C,85C,100C ''similarity-bas'':54C,66C,84C ''sklearn'':97C ''spectral'':121C ''stochast'':91C ''two'':99C ''ul'':63C ''use'':4C,39C ''usual'':16C ''via'':79C ''way'':28C ''would'':3C,51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9731, 1, '2016-01-11 01:07:17.193', 1, 27, 'Torch and conceptual question about neural nets backpropagation', '&lt;p&gt;First a theoretical question and then a practical one.&lt;/p&gt;

&lt;p&gt;Neural nets backpropagation is the computation of the weights derivatives or the computation of the new weights (that is, the original weights minus  the weight derivatives times the learning rate - simplified)?&lt;/p&gt;

&lt;p&gt;It may well be a semantics issue but important nevertheless.&lt;/p&gt;

&lt;p&gt;Also, if anyone is familiar with Torch, nn class&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;gradInput = module:backward(input, gradOutput)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;is gradinput the weight set for the next forward pass or is it the derivatives of the weights of the previous forward pass?&lt;/p&gt;

&lt;p&gt;Thanks!&lt;/p&gt;
', 'neuralnetwork  backpropagation', NULL, '''/code'':85C ''/p'':21C,54C,66C,77C,111C,114C ''/pre'':86C ''also'':68C ''anyon'':70C ''backpropag'':8A,10B,25C ''backward'':82C ''class'':76C ''code'':79C ''comput'':28C,35C ''conceptu'':3A ''deriv'':32C,48C,102C ''familiar'':72C ''first'':12C ''forward'':96C,109C ''gradinput'':80C,89C ''gradoutput'':84C ''import'':64C ''input'':83C ''issu'':62C ''learn'':51C ''may'':57C ''minus'':45C ''modul'':81C ''net'':7A,24C ''neural'':6A,23C ''neuralnetwork'':9B ''nevertheless'':65C ''new'':38C ''next'':95C ''nn'':75C ''one'':20C ''origin'':43C ''p'':11C,22C,55C,67C,87C,112C ''pass'':97C,110C ''practic'':19C ''pre'':78C ''previous'':108C ''question'':4A,15C ''rate'':52C ''semant'':61C ''set'':92C ''simplifi'':53C ''thank'':113C ''theoret'':14C ''time'':49C ''torch'':1A,74C ''weight'':31C,39C,44C,47C,91C,105C ''well'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9734, 1, '2016-01-11 05:56:19.68', 4, 41, 'What is a Recurrent Heavy Subgraph?', '&lt;p&gt;I recently came across this term &lt;code&gt;recurrent heavy subgraph&lt;/code&gt; in a talk. I don''t seem to understand what it means and Google doesn''t seem to show any good results. Can someone explain what this means in detail.&lt;/p&gt;
', 'data-mining  graphs  terminology', NULL, '''/code'':23C ''/p'':54C ''across'':16C ''came'':15C ''code'':19C ''data'':8B ''data-min'':7B ''detail'':53C ''doesn'':38C ''explain'':48C ''good'':44C ''googl'':37C ''graph'':10B ''heavi'':5A,21C ''mean'':35C,51C ''mine'':9B ''p'':12C ''recent'':14C ''recurr'':4A,20C ''result'':45C ''seem'':30C,40C ''show'':42C ''someon'':47C ''subgraph'':6A,22C ''talk'':26C ''term'':18C ''terminolog'':11B ''understand'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9755, 2, '2016-01-12 05:59:35.087', -1, NULL, NULL, '&lt;p&gt;It might be easier for you to take a look at the events streaming services that are offered in Amazon Web Services (AWS), mainly Kinesis. &lt;/p&gt;

&lt;p&gt;The easiest of them in Kinesis Firehose (&lt;a href="http://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html" rel="nofollow"&gt;http://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html&lt;/a&gt;), which gives you an endpoint where you can write your events to, and they will appear in Amazon Redshift, which is a data warehouse service that can scale to huge sizes (&lt;a href="https://aws.amazon.com/redshift/" rel="nofollow"&gt;https://aws.amazon.com/redshift/&lt;/a&gt;). &lt;/p&gt;

&lt;p&gt;AWS also announced that they will add Kinesis Analytics, which will allow you to write SQL code on top of the events stream of Kinesis. You can calculate different statistics such as moving agerages and other relevant statistic on your incoming events. &lt;/p&gt;

&lt;p&gt;These events that are processed in near real time, can be clicks on your web site, purchases in your e-commerce site, security events that should be monitored for potential intrusion, etc. &lt;/p&gt;
', NULL, NULL, '''/firehose/latest/dev/what-is-this-service.html'':40C ''/firehose/latest/dev/what-is-this-service.html&lt;/a&gt;),'':45C ''/p'':27C,87C,131C,166C ''/redshift/'':81C ''/redshift/&lt;/a&gt;).'':86C ''add'':95C ''agerag'':122C ''allow'':100C ''also'':90C ''amazon'':21C,63C ''analyt'':97C ''announc'':91C ''appear'':61C ''aw'':24C,89C ''aws.amazon.com'':80C,85C ''aws.amazon.com/redshift/'':79C ''aws.amazon.com/redshift/&lt;/a&gt;).'':84C ''calcul'':116C ''click'':144C ''code'':105C ''commerc'':154C ''data'':68C ''differ'':117C ''docs.aws.amazon.com'':39C,44C ''docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html'':38C ''docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html&lt;/a&gt;),'':43C ''e'':153C ''e-commerc'':152C ''easier'':5C ''easiest'':30C ''endpoint'':50C ''etc'':165C ''event'':14C,56C,110C,130C,134C,157C ''firehos'':35C ''give'':47C ''href'':37C,78C ''huge'':75C ''incom'':129C ''intrus'':164C ''kinesi'':26C,34C,96C,113C ''look'':11C ''main'':25C ''might'':3C ''monitor'':161C ''move'':121C ''near'':139C ''nofollow'':42C,83C ''offer'':19C ''p'':1C,28C,88C,132C ''potenti'':163C ''process'':137C ''purchas'':149C ''real'':140C ''redshift'':64C ''rel'':41C,82C ''relev'':125C ''scale'':73C ''secur'':156C ''servic'':16C,23C,70C ''site'':148C,155C ''size'':76C ''sql'':104C ''statist'':118C,126C ''stream'':15C,111C ''take'':9C ''time'':141C ''top'':107C ''warehous'':69C ''web'':22C,147C ''write'':54C,103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9735, 1, '2016-01-11 07:07:23.557', 5, 63, 'what is the difference between "fully developed decision trees" and "shallow decision trees"?', '&lt;p&gt;As reading &lt;a href="http://scikit-learn.org/stable/modules/ensemble.html" rel="nofollow"&gt;Ensemble methods&lt;/a&gt; on scikit-learn docs, it says that&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;bagging methods work best with strong and complex models (e.g., fully
  developed decision trees), in contrast with boosting methods which
  usually work best with weak models (e.g., shallow decision trees).&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;But search on google it always return information about &lt;code&gt;Decision Tree&lt;/code&gt;.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;I''d like to know the detail of the two trees mentioned in the doc, what''s the &lt;code&gt;fully developed&lt;/code&gt; and &lt;code&gt;shallow&lt;/code&gt; meanings.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;Update:&lt;/strong&gt;&lt;/p&gt;

&lt;ol start="2"&gt;
&lt;li&gt;About why &lt;code&gt;bagging&lt;/code&gt; work best with &lt;code&gt;fully developed&lt;/code&gt; and why &lt;code&gt;boosting&lt;/code&gt; work best with &lt;code&gt;shallow&lt;/code&gt;. &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;First, I think &lt;code&gt;complex models (e.g., fully developed decision trees)&lt;/code&gt; means such a data set has a complex format be called as &lt;code&gt;fully developed decision trees&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;After I read above quote over 20 times and rapaio''s answer, I think my poor English lead me to the wrong road(misunderstand). I also mistake &lt;code&gt;shallow&lt;/code&gt; as &lt;code&gt;shadow&lt;/code&gt; , which make me confusing a long time....Now I understand the meanings of &lt;code&gt;fully developed&lt;/code&gt; and &lt;code&gt;shallow&lt;/code&gt; .&lt;/p&gt;

&lt;p&gt;Now, I think the quote is saying &lt;code&gt;bagging&lt;/code&gt; work best with a models(already trained) which algorithm is complex. And &lt;code&gt;boosting&lt;/code&gt; only need simple model.
Both &lt;code&gt;bagging&lt;/code&gt; and &lt;code&gt;boosting&lt;/code&gt; need  many &lt;code&gt;estimators&lt;/code&gt;, as &lt;code&gt;n_estimators=100&lt;/code&gt; in scikit-learn examples.&lt;/p&gt;

&lt;p&gt;If &lt;code&gt;n_estimators=100&lt;/code&gt; :&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;bagging&lt;/code&gt; need 100 &lt;code&gt;fully developed decision trees&lt;/code&gt; estimators(models)&lt;/li&gt;
&lt;li&gt;&lt;code&gt;boosting&lt;/code&gt; need 100 &lt;code&gt;shallow decision trees&lt;/code&gt; estimators(models)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Does my thoughts is right?
Hope my update can help non-native speakers.&lt;/p&gt;

&lt;ol start="3"&gt;
&lt;li&gt;&lt;code&gt;e.g.&lt;/code&gt; means for example, so there are other models can use for bagging and boosting. How about changing the model to &lt;code&gt;svm&lt;/code&gt; or something else? Or both of they need a tree base model?&lt;/li&gt;
&lt;/ol&gt;
', 'scikit  decision-trees  ensemble-modeling', NULL, '''/a'':33C ''/blockquote'':76C ''/code'':90C,115C,119C,136C,143C,148C,154C,169C,187C,219C,223C,240C,244C,256C,271C,279C,283C,288C,294C,307C,313C,321C,328C,335C,362C,385C ''/li'':121C,155C,324C,338C,398C ''/ol'':122C,156C,399C ''/p'':42C,75C,91C,127C,188C,245C,300C,308C,355C ''/stable/modules/ensemble.html'':28C ''/strong'':126C ''/ul'':339C ''100'':293C,306C,315C,330C ''2'':130C ''20'':196C ''3'':358C ''algorithm'':265C ''alreadi'':262C ''also'':215C ''alway'':83C ''answer'':201C ''bag'':45C,135C,255C,278C,312C,374C ''base'':396C ''best'':48C,67C,138C,150C,258C ''blockquot'':43C ''boost'':62C,147C,270C,282C,327C,376C ''call'':180C ''chang'':379C ''code'':87C,112C,117C,134C,140C,146C,152C,161C,182C,217C,221C,237C,242C,254C,269C,277C,281C,286C,290C,303C,311C,316C,326C,331C,360C,383C ''complex'':52C,162C,177C,267C ''confus'':227C ''contrast'':60C ''d'':95C ''data'':173C ''decis'':8A,12A,16B,57C,73C,88C,167C,185C,319C,333C ''decision-tre'':15B ''detail'':100C ''develop'':7A,56C,114C,142C,166C,184C,239C,318C ''differ'':4A ''doc'':38C,108C ''e.g'':54C,71C,164C,361C ''els'':388C ''english'':206C ''ensembl'':19B,31C ''ensemble-model'':18B ''estim'':287C,292C,305C,322C,336C ''exampl'':299C,365C ''first'':158C ''format'':178C ''fulli'':6A,55C,113C,141C,165C,183C,238C,317C ''googl'':81C ''help'':350C ''hope'':346C ''href'':25C ''inform'':85C ''know'':98C ''lead'':207C ''learn'':37C,298C ''li'':93C,131C,310C,325C,359C ''like'':96C ''long'':229C ''make'':225C ''mani'':285C ''mean'':120C,170C,235C,363C ''mention'':105C ''method'':32C,46C,63C ''mistak'':216C ''misunderstand'':213C ''model'':20B,53C,70C,163C,261C,275C,323C,337C,370C,381C,397C ''n'':291C,304C ''nativ'':353C ''need'':273C,284C,314C,329C,393C ''nofollow'':30C ''non'':352C ''non-nat'':351C ''ol'':92C,128C,356C ''p'':21C,44C,77C,123C,157C,189C,246C,301C,340C ''poor'':205C ''quot'':194C,251C ''rapaio'':199C ''read'':23C,192C ''rel'':29C ''return'':84C ''right'':345C ''road'':212C ''say'':40C,253C ''scikit'':14B,36C,297C ''scikit-learn'':35C,296C ''scikit-learn.org'':27C ''scikit-learn.org/stable/modules/ensemble.html'':26C ''search'':79C ''set'':174C ''shadow'':222C ''shallow'':11A,72C,118C,153C,218C,243C,332C ''simpl'':274C ''someth'':387C ''speaker'':354C ''start'':129C,357C ''strong'':50C,124C ''svm'':384C ''think'':160C,203C,249C ''thought'':343C ''time'':197C,230C ''train'':263C ''tree'':9A,13A,17B,58C,74C,89C,104C,168C,186C,320C,334C,395C ''two'':103C ''ul'':309C ''understand'':233C ''updat'':125C,348C ''use'':372C ''usual'':65C ''weak'':69C ''work'':47C,66C,137C,149C,257C ''wrong'':211C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9966, 1, '2016-01-26 09:00:35.757', 1, 30, 'Knime : Scatter Plot', '&lt;p&gt;I have a sample dataset containing around 77k of rows and I have clustered them by using k mean clustering and colored the clusters using color manager. Now i would like to view them, However I could not set the No. of rows to display as 77k in Scatter Plot and Is there any way to visualize the clusters in Knime? &lt;/p&gt;
', 'data-mining  visualization', NULL, '''/p'':70C ''77k'':16C,55C ''around'':15C ''cluster'':22C,28C,32C,67C ''color'':30C,34C ''contain'':14C ''could'':45C ''data'':5B ''data-min'':4B ''dataset'':13C ''display'':53C ''howev'':43C ''k'':26C ''knime'':1A,69C ''like'':39C ''manag'':35C ''mean'':27C ''mine'':6B ''p'':8C ''plot'':3A,58C ''row'':18C,51C ''sampl'':12C ''scatter'':2A,57C ''set'':47C ''use'':25C,33C ''view'':41C ''visual'':7B,65C ''way'':63C ''would'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9736, 1, '2016-01-11 07:16:58.747', 10, 219, 'What kinds of learning problems are suitable for Support Vector Machines?', '&lt;p&gt;What are the hallmarks or properties that indicate that a certain learning problem can be tackled using support vector machines? &lt;/p&gt;

&lt;p&gt;In other words, what is it that, when you see a learning problem, makes you go "oh I should definitely use SVMs for this'''' rather than Neural networks or Decision trees or anything else?&lt;/p&gt;
', 'machine-learning  svm  supervised-learning  unsupervised-learning', NULL, '''/p'':43C,79C ''anyth'':77C ''certain'':33C ''decis'':74C ''definit'':64C ''els'':78C ''go'':60C ''hallmark'':26C ''indic'':30C ''kind'':2A ''learn'':4A,14B,18B,21B,34C,56C ''machin'':11A,13B,42C ''machine-learn'':12B ''make'':58C ''network'':72C ''neural'':71C ''oh'':61C ''p'':22C,44C ''problem'':5A,35C,57C ''properti'':28C ''rather'':69C ''see'':54C ''suitabl'':7A ''supervis'':17B ''supervised-learn'':16B ''support'':9A,40C ''svm'':15B ''svms'':66C ''tackl'':38C ''tree'':75C ''unsupervis'':20B ''unsupervised-learn'':19B ''use'':39C,65C ''vector'':10A,41C ''word'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9738, 1, '2016-01-11 09:24:36.577', 2, 47, 'Question about train example code for TensorFlow', '&lt;p&gt;I am trying to learn TensorFlow, and I could understand how it uses the &lt;em&gt;batch&lt;/em&gt; in &lt;a href="https://www.tensorflow.org/versions/master/tutorials/mnist/pros/index.html#start-tensorflow-interactivesession" rel="nofollow"&gt;this&lt;/a&gt; example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
sess.run(tf.initialize_all_variables())
for i in range(20000):
  batch = mnist.train.next_batch(50)
  if i%100 == 0:
    train_accuracy = accuracy.eval(feed_dict={
        x:batch[0], y_: batch[1], keep_prob: 1.0})
    print("step %d, training accuracy %g"%(i, train_accuracy))
  train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

print("test accuracy %g"%accuracy.eval(feed_dict={
    x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;My question is, why it get a batch of 50 training data, but only use the first one for training. Maybe I did not understand the code correctly.&lt;/p&gt;
', 'tensorflow', NULL, '''/a'':36C ''/code'':139C ''/em'':26C ''/p'':38C,170C ''/pre'':140C ''/versions/master/tutorials/mnist/pros/index.html#start-tensorflow-interactivesession'':32C ''0'':89C,97C,118C ''0.5'':124C ''1'':62C,65C,100C,121C ''1.0'':103C,138C ''100'':88C ''1e-4'':52C ''20000'':81C ''50'':85C,151C ''accuraci'':66C,91C,108C,112C,127C ''accuracy.eval'':92C,129C ''batch'':25C,82C,84C,96C,99C,117C,120C,149C ''code'':5A,40C,168C ''conv'':48C,61C ''correct'':56C,70C,169C ''could'':18C ''cross'':41C,54C ''d'':106C ''data'':153C ''dict'':94C,115C,131C ''em'':24C ''entropi'':42C,55C ''exampl'':4A,37C ''feed'':93C,114C,130C ''first'':158C ''float'':72C ''g'':109C,128C ''get'':147C ''href'':29C ''keep'':101C,122C,136C ''learn'':14C ''mayb'':162C ''mean'':68C ''minim'':53C ''mnist.test.images'':133C ''mnist.test.labels'':135C ''mnist.train.next'':83C ''nofollow'':34C ''one'':159C ''p'':9C,141C ''pre'':39C ''predict'':57C,71C ''print'':104C,125C ''prob'':102C,123C,137C ''question'':1A,143C ''rang'':80C ''rel'':33C ''sess.run'':73C ''step'':50C,105C ''sum'':44C ''tensorflow'':7A,8B,15C ''test'':126C ''tf.argmax'':59C,63C ''tf.cast'':69C ''tf.equal'':58C ''tf.initialize'':74C ''tf.log'':46C ''tf.reduce'':43C,67C ''tf.train.adamoptimizer'':51C ''train'':3A,49C,90C,107C,111C,152C,161C ''train_step.run'':113C ''tri'':12C ''understand'':19C,166C ''use'':22C,156C ''variabl'':76C ''www.tensorflow.org'':31C ''www.tensorflow.org/versions/master/tutorials/mnist/pros/index.html#start-tensorflow-interactivesession'':30C ''x'':95C,116C,132C ''y'':45C,47C,60C,64C,98C,119C,134C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9742, 2, '2016-01-11 11:00:13.157', 1, NULL, NULL, '&lt;p&gt;ZeroR classifier uses only the target (dependent variable) to build a majority classifier. As a consequence, it does not fit your purpose. You can built it from code as described here: &lt;a href="https://weka.wikispaces.com/Generating+ROC+curve" rel="nofollow"&gt;Generating ROC curve example&lt;/a&gt;. Basically you do not need a classifier, you need only the results and an instance of &lt;a href="http://weka.sourceforge.net/doc.dev/weka/classifiers/evaluation/ThresholdCurve.html" rel="nofollow"&gt;ThresholdCurve&lt;/a&gt; class (this is how it is named in weka library).&lt;/p&gt;
', NULL, NULL, '''/a'':44C,69C ''/doc.dev/weka/classifiers/evaluation/thresholdcurve.html'':65C ''/generating+roc+curve'':37C ''/p'':80C ''basic'':45C ''build'':11C ''built'':26C ''class'':70C ''classifi'':3C,14C,51C ''code'':29C ''consequ'':17C ''curv'':42C ''depend'':8C ''describ'':31C ''exampl'':43C ''fit'':21C ''generat'':40C ''href'':34C,62C ''instanc'':59C ''librari'':79C ''major'':13C ''name'':76C ''need'':49C,53C ''nofollow'':39C,67C ''p'':1C ''purpos'':23C ''rel'':38C,66C ''result'':56C ''roc'':41C ''target'':7C ''thresholdcurv'':68C ''use'':4C ''variabl'':9C ''weka'':78C ''weka.sourceforge.net'':64C ''weka.sourceforge.net/doc.dev/weka/classifiers/evaluation/thresholdcurve.html'':63C ''weka.wikispaces.com'':36C ''weka.wikispaces.com/generating+roc+curve'':35C ''zeror'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9749, 2, '2016-01-11 14:39:49.243', 4, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;Can anybody explain Apache Kafka for me in a plain language?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;As the official documentation of Kafka says:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Apache Kafka is publish-subscribe messaging rethought as a distributed
  commit log. It follows the publish-subscribe messaging style, with speed and durability built in.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Now, let me give a dumbed down explanation of the same:&lt;/p&gt;

&lt;p&gt;A publish-subscribe(commonly called pub-sub) architecture basically contains a publisher(who sends the messages, for example: A twitter stream) and a subscriber(who receives the messages Ex: the analyst/the user''s activity stream)&lt;/p&gt;

&lt;p&gt;A clearer explanation would be:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;In software architecture, publishâsubscribe is a messaging pattern
  where senders of messages, called publishers, do not program the
  messages to be sent directly to specific receivers, called
  subscribers, but instead characterize published messages into classes
  without knowledge of which subscribers, if any, there may be.
  Similarly, subscribers express interest in one or more classes and
  only receive messages that are of interest, without knowledge of which
  publishers, if any, there are.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;And keep in mind that Kafka is a &lt;strong&gt;distributed&lt;/strong&gt; pub-sub messaging system, designed to scale.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/gcBD0.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/gcBD0.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Now, &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;At which stage of a BigData analysis is it used?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;It is basically used in the &lt;strong&gt;Extraction&lt;/strong&gt; step of the ETL pipeline. It can contain high volumes and speed of data flow (we call it high throughput in technical terms).&lt;/p&gt;

&lt;p&gt;It can store messages also, but it is a persistent store; which means that the data is not stored forever. The stored data has an expiry date.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;And what are prerequisites for learning it?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This is a tough question, as everyone who can program can get started with it, but if you really want to implement this in your architecture, then these are the pre-requisites which I can think of:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Know what is a persistent data store and where to use them&lt;/li&gt;
&lt;li&gt;Know what a pub-sub messaging system is&lt;/li&gt;
&lt;li&gt;Know what an extraction step is, in ETL. And also know how to deal with data with huge volume coming at you at high velocity. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Further reading:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Kafka has an &lt;a href="http://kafka.apache.org/documentation.html" rel="nofollow"&gt;excellent documentation&lt;/a&gt;, which can help to get started with it.&lt;/p&gt;
', NULL, NULL, '''/a'':230C,436C ''/blockquote'':15C,55C,190C,248C,322C ''/documentation.html'':431C ''/gcbd0.png'':217C,224C ''/li'':377C,388C,415C ''/p'':14C,24C,54C,68C,107C,114C,189C,211C,231C,234C,247C,282C,311C,321C,362C,422C,445C ''/strong'':202C,258C,421C ''/ul'':416C ''activ'':105C ''also'':288C,399C ''alt'':225C ''analysi'':243C ''analyst/the'':102C ''anybodi'':4C ''apach'':6C,27C ''architectur'':79C,119C,349C ''basic'':80C,252C ''bigdata'':242C ''blockquot'':1C,25C,115C,235C,312C ''built'':52C ''call'':75C,130C,144C,275C ''character'':148C ''class'':152C,171C ''clearer'':110C ''come'':409C ''commit'':38C ''common'':74C ''contain'':81C,266C ''data'':272C,299C,306C,370C,405C ''date'':310C ''deal'':403C ''descript'':228C ''design'':208C ''direct'':140C ''distribut'':37C,201C ''document'':20C,435C ''dumb'':62C ''durabl'':51C ''enter'':226C ''etl'':262C,397C ''everyon'':330C ''ex'':100C ''exampl'':89C ''excel'':434C ''expiri'':309C ''explain'':5C ''explan'':64C,111C ''express'':165C ''extract'':257C,393C ''flow'':273C ''follow'':41C ''forev'':303C ''get'':335C,441C ''give'':60C ''help'':439C ''high'':267C,277C,413C ''href'':214C,428C ''huge'':407C ''i.stack.imgur.com'':216C,223C ''i.stack.imgur.com/gcbd0.png'':215C,222C ''imag'':227C ''img'':220C ''implement'':345C ''instead'':147C ''interest'':166C,179C ''kafka'':7C,22C,28C,197C,424C ''kafka.apache.org'':430C ''kafka.apache.org/documentation.html'':429C ''keep'':193C ''know'':365C,379C,390C,400C ''knowledg'':154C,181C ''languag'':13C ''learn'':319C ''let'':58C ''li'':364C,378C,389C ''log'':39C ''may'':161C ''mean'':296C ''messag'':33C,46C,87C,99C,124C,129C,136C,150C,175C,206C,287C,385C ''mind'':195C ''nofollow'':219C,433C ''offici'':19C ''one'':168C ''p'':2C,16C,26C,56C,69C,108C,116C,191C,212C,232C,236C,249C,283C,313C,323C,417C,423C ''pattern'':125C ''persist'':293C,369C ''pipelin'':263C ''plain'':12C ''pre'':355C ''pre-requisit'':354C ''prerequisit'':317C ''program'':134C,333C ''pub'':77C,204C,383C ''pub-sub'':76C,203C,382C ''publish'':31C,44C,72C,83C,131C,149C,184C ''publish-subscrib'':30C,43C,71C ''publishâ'':120C ''question'':328C ''read'':420C ''realli'':342C ''receiv'':97C,143C,174C ''rel'':218C,432C ''requisit'':356C ''rethought'':34C ''say'':23C ''scale'':210C ''send'':85C ''sender'':127C ''sent'':139C ''similar'':163C ''softwar'':118C ''specif'':142C ''speed'':49C,270C ''src'':221C ''stage'':239C ''start'':336C,442C ''step'':259C,394C ''store'':286C,294C,302C,305C,371C ''stream'':92C,106C ''strong'':200C,256C,418C ''style'':47C ''sub'':78C,205C,384C ''subscrib'':32C,45C,73C,95C,121C,145C,157C,164C ''system'':207C,386C ''technic'':280C ''term'':281C ''think'':360C ''throughput'':278C ''tough'':327C ''twitter'':91C ''ul'':363C ''use'':246C,253C,375C ''user'':103C ''veloc'':414C ''volum'':268C,408C ''want'':343C ''without'':153C,180C ''would'':112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9750, 2, '2016-01-11 18:35:57.99', 4, NULL, NULL, '&lt;p&gt;&lt;strong&gt;What is it used for?&lt;/strong&gt; &lt;/p&gt;

&lt;p&gt;Data ingestion into a distributed datastore (e.g. HDFS). See image (I did not make the image and am only including the image for visual aid). There are other tools that will help you with the ingestion of data as well (Storm and Sqoop are mentioned). &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;At which stage of a BigData analysis is it used?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;It is used for data ingestion into your distributed datastore (e.g. HDFS). So for example a webserver is running logging information into /var/logs/webserver.log. Apache Flume can look at that file, grab what it needs out of it and send it to HDFS. Once the data gets put into your datastore you can then utilize other tools to analyze the imported data (e.g. Hive, Pig, MR, etc.)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;And what are prerequisites for learning it?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Understanding of how to write scripts, edit configuration settings, and your way around Linux would be the absolute minimum to get started. This set of instructions are old but a starting point would be to look at the hortwonworks tutorial on flume. &lt;a href="http://hortonworks.com/hadoop-tutorial/how-to-refine-and-visualize-server-log-data/" rel="nofollow"&gt;http://hortonworks.com/hadoop-tutorial/how-to-refine-and-visualize-server-log-data/&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;If you would like me to elaborate more I would be glad to but I wanted to try and meet your requirement of a simple, short explanation.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/raU0I.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/raU0I.jpg" alt="HDP Data Lake Solution Architecture"&gt;&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9761, 1, '2016-01-12 13:01:58.427', 1, 31, 'Feauture selection for clustering regarding zero-correlated feature', '&lt;p&gt;I want to cluster a 5 feature data-set. Firstly to explore the data I did a correlation matrix to see if some features where highly correlated so I could reduce them. Then I saw a feature that have close to zero correlation against all the other features. This got me wondering if I should exclude this parameter since it acts as a kind of "noise" relatively to all the other features. What''s your opinion? &lt;/p&gt;
', 'clustering  feature-selection  correlation', NULL, '''/p'':93C ''5'':21C ''act'':77C ''close'':56C ''cluster'':4A,10B,19C ''correl'':8A,14B,34C,43C,59C ''could'':46C ''data'':24C,30C ''data-set'':23C ''exclud'':72C ''explor'':28C ''featur'':9A,12B,22C,40C,53C,64C,88C ''feature-select'':11B ''feautur'':1A ''first'':26C ''got'':66C ''high'':42C ''kind'':80C ''matrix'':35C ''nois'':82C ''opinion'':92C ''p'':15C ''paramet'':74C ''reduc'':47C ''regard'':5A ''relat'':83C ''saw'':51C ''see'':37C ''select'':2A,13B ''set'':25C ''sinc'':75C ''want'':17C ''wonder'':68C ''zero'':7A,58C ''zero-correl'':6A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9751, 2, '2016-01-11 19:04:01.747', 4, NULL, NULL, '&lt;p&gt;SVM can be used for classification (distinguishing between several groups or classes) and regression (obtaining a mathematical model to predict something). They can be applied to both linear and non linear problems. &lt;/p&gt;

&lt;p&gt;Until 2006 they were the best general purpose algorithm for machine learning.  I was trying to find a paper that compared many implementations of the most known algorithms: svm, neural nets, trees, etc. I couldn''t find it sorry (you will have to believe me, bad thing). In the paper the algorithm that got the best performance was svm, with the library libsvm.&lt;/p&gt;

&lt;p&gt;In 2006 Hinton came up with deep learning and neural nets. He improved the current state of the art by at least 30%, which is a huge advancement. However deep learning only get good performance for huge training sets. If you have a small training set I would suggest to use svm.&lt;/p&gt;

&lt;p&gt;Furthermore you can find here a useful infographic about &lt;a href="http://scikit-learn.org/stable/tutorial/machine_learning_map/" rel="nofollow"&gt;when to use different machine learning algorithms&lt;/a&gt; by scikit-learn. However, to the best of my knowledge there is no agreement among the scientific community about if a problem has X,Y and Z features then it''s better to use svm. I would suggest to try different methods. Also, please don''t forget that svm or neural nets is just a method to compute a model. It is very important as well the features you use.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9752, 2, '2016-01-11 19:05:27.367', 3, NULL, NULL, '&lt;p&gt;I noticed you are asking several questions related to the Hadoop ecosystem. It is hard to judge how much you know while explaining the things you are asking in a very simplistic manner without knowing that. Might be worthwhile giving some background of what you are trying to understand. But I digress.&lt;/p&gt;

&lt;p&gt;Unless you are building an application that requires coordination (think race conditions) you more than likely won''t need Zookeeper directly. Many tools within the Hadoop ecosystem do use it for this exact purpose (e.g. HBase).&lt;/p&gt;

&lt;p&gt;This is probably the best and most simplistic answer I can give. &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;In a nutshell, ZooKeeper gives you the tools to help build distributed applications. For example, suppose you have a distributed web server application running on 10 nodes. Say, you want to get total real-time hit count. One way to do this is to write an application which connects to the 10 nodes, gets count from each and present the sum. Alternatively, you can have each web server application write their hit counts to ZooKeeper on regular intervals and then query ZooKeeper to get the count.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Source: &lt;a href="https://10kloc.wordpress.com/2013/12/21/quick-intro-to-apache-zookeeper/" rel="nofollow"&gt;https://10kloc.wordpress.com/2013/12/21/quick-intro-to-apache-zookeeper/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;ZooKeeper Quorum is "A replicated group of servers in the same application is called a quorum, and in replicated mode, all servers in the quorum have copies of the same configuration file."&lt;/p&gt;

&lt;p&gt;Source: &lt;a href="https://zookeeper.apache.org/doc/r3.1.2/zookeeperStarted.html" rel="nofollow"&gt;https://zookeeper.apache.org/doc/r3.1.2/zookeeperStarted.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;A more detailed answer of what Zookeeper is and how it is beneficial can be found on SO (&lt;a href="http://stackoverflow.com/questions/3662995/explaining-apache-zookeeper"&gt;http://stackoverflow.com/questions/3662995/explaining-apache-zookeeper&lt;/a&gt;).&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9763, 2, '2016-01-12 14:41:46.947', 1, NULL, NULL, '&lt;p&gt;Lack of correlation with other features is not a reason to omit a feature. On the contrary, it is usually a reason to keep the feature because it may provide unique information. Typically, highly correlated features provide redundant information and feature reduction techniques (e.g., &lt;a href="https://en.wikipedia.org/wiki/Principal_component_analysis" rel="nofollow"&gt;Principal Components Analysis&lt;/a&gt;) are used to remove the redundancy.&lt;/p&gt;

&lt;p&gt;While it is &lt;em&gt;possible&lt;/em&gt; that the uncorrelated feature is noise, you should not make that assumption. It could be that the uncorrelated feature is the &lt;em&gt;only&lt;/em&gt; one containing information and the other 4 features are all correlated noise.&lt;/p&gt;
', NULL, NULL, '''/a'':56C ''/em'':70C,94C ''/p'':63C,107C ''/wiki/principal_component_analysis'':50C ''4'':101C ''analysi'':55C ''assumpt'':82C ''compon'':54C ''contain'':96C ''contrari'':18C ''correl'':4C,36C,105C ''could'':84C ''e.g'':45C ''em'':68C,92C ''en.wikipedia.org'':49C ''en.wikipedia.org/wiki/principal_component_analysis'':48C ''featur'':7C,15C,27C,37C,42C,74C,89C,102C ''high'':35C ''href'':47C ''inform'':33C,40C,97C ''keep'':25C ''lack'':2C ''make'':80C ''may'':30C ''nofollow'':52C ''nois'':76C,106C ''omit'':13C ''one'':95C ''p'':1C,64C ''possibl'':69C ''princip'':53C ''provid'':31C,38C ''reason'':11C,23C ''reduct'':43C ''redund'':39C,62C ''rel'':51C ''remov'':60C ''techniqu'':44C ''typic'':34C ''uncorrel'':73C,88C ''uniqu'':32C ''use'':58C ''usual'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9757, 2, '2016-01-12 07:44:43.37', 2, NULL, NULL, '&lt;p&gt;Let''s assume that we are in a classification setting. &lt;/p&gt;

&lt;p&gt;For &lt;code&gt;svm&lt;/code&gt; feature engineering is cornerstone:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;the sets have to be linearly separable. Otherwise the data needs to be transformed (eg using Kernels). This is not done by the algo itself and might blow out the number of features.&lt;/li&gt;
&lt;li&gt;I would say that &lt;code&gt;svm&lt;/code&gt; performance suffers as we increase the number of dimensions faster than other methodologies (tree ensemble). This is due to the constrained optimization problem that backs &lt;code&gt;svm&lt;/code&gt;s. Sometimes feature reduction is feasible, sometimes not and this is when we can''t really pave the way for an effective use of &lt;code&gt;svm&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;svm&lt;/code&gt; will likely struggle with a dataset where the number of features is much larger than the number of observations. This, again, can be understood by looking at the constrained optimizatiom problem.&lt;/li&gt;
&lt;li&gt;categorical variables are not handled out of the box by the &lt;code&gt;svm&lt;/code&gt; algorithm. &lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/code'':17C,66C,94C,121C,126C,173C ''/li'':58C,122C,158C,175C ''/p'':12C,22C ''/ul'':176C ''algo'':48C ''algorithm'':174C ''assum'':4C ''back'':91C ''blow'':52C ''box'':168C ''categor'':160C ''classif'':10C ''code'':15C,64C,92C,119C,124C,171C ''constrain'':87C,155C ''cornerston'':21C ''data'':34C ''dataset'':132C ''dimens'':75C ''done'':45C ''due'':84C ''effect'':116C ''eg'':39C ''engin'':19C ''ensembl'':81C ''faster'':76C ''feasibl'':100C ''featur'':18C,57C,97C,137C ''handl'':164C ''increas'':71C ''kernel'':41C ''larger'':140C ''let'':2C ''li'':24C,59C,123C,159C ''like'':128C ''linear'':30C ''look'':152C ''methodolog'':79C ''might'':51C ''much'':139C ''need'':35C ''number'':55C,73C,135C,143C ''observ'':145C ''optim'':88C ''optimizatiom'':156C ''otherwis'':32C ''p'':1C,13C ''pave'':111C ''perform'':67C ''problem'':89C,157C ''realli'':110C ''reduct'':98C ''say'':62C ''separ'':31C ''set'':11C,26C ''sometim'':96C,101C ''struggl'':129C ''suffer'':68C ''svm'':16C,65C,93C,120C,125C,172C ''transform'':38C ''tree'':80C ''ul'':23C ''understood'':150C ''use'':40C,117C ''variabl'':161C ''way'':113C ''would'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9758, 1, '2016-01-12 09:57:38.883', 6, 143, 'Overfitting/Underfitting with Data set size', '&lt;p&gt;In the below graph, &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;x-axis =&gt; &lt;strong&gt;Data set Size&lt;/strong&gt;&lt;/li&gt;
&lt;li&gt;y-axis =&gt; &lt;strong&gt;Cross validation Score&lt;/strong&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/4VAFr.jpg"&gt;&lt;img src="http://i.stack.imgur.com/4VAFr.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Red line&lt;/strong&gt; is for Training Data&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Green line&lt;/strong&gt; is for Testing Data&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In a tutorial that I''m referring to, the author says that the point where the &lt;strong&gt;red line&lt;/strong&gt; and the &lt;strong&gt;green line&lt;/strong&gt; overlap means,&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Collecting more data is unlikely to increase the generalization
  performance and &lt;strong&gt;we''re in a region that we are likely to underfit the
  data. Therefore it makes sense to try out with a model with more capacity&lt;/strong&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I cannot quite understand meaning of the &lt;strong&gt;bold&lt;/strong&gt; phrase and how it happens.&lt;/p&gt;

&lt;p&gt;Appreciate any help.&lt;/p&gt;
', 'machine-learning  cross-validation', NULL, '''/4vafr.jpg'':45C,50C ''/a'':56C ''/blockquote'':156C ''/li'':28C,38C,70C,82C ''/p'':17C,57C,69C,81C,113C,155C,173C,178C ''/strong'':27C,37C,64C,76C,104C,110C,154C,167C ''/ul'':39C,83C ''alt'':51C ''appreci'':175C ''author'':94C ''axi'':22C,32C ''blockquot'':114C ''bold'':166C ''cannot'':159C ''capac'':153C ''collect'':116C ''cross'':10B,34C ''cross-valid'':9B ''data'':3A,24C,68C,80C,118C,140C ''descript'':54C ''enter'':52C ''general'':124C ''graph'':16C ''green'':74C,108C ''happen'':172C ''help'':177C ''href'':42C ''i.stack.imgur.com'':44C,49C ''i.stack.imgur.com/4vafr.jpg'':43C,48C ''imag'':53C ''img'':46C ''increas'':122C ''learn'':8B ''li'':19C,29C,59C,71C ''like'':136C ''line'':63C,75C,103C,109C ''m'':90C ''machin'':7B ''machine-learn'':6B ''make'':143C ''mean'':112C,162C ''model'':150C ''overfitting/underfitting'':1A ''overlap'':111C ''p'':12C,40C,60C,72C,84C,115C,157C,174C ''perform'':125C ''phrase'':168C ''point'':98C ''quit'':160C ''re'':129C ''red'':62C,102C ''refer'':91C ''region'':132C ''say'':95C ''score'':36C ''sens'':144C ''set'':4A,25C ''size'':5A,26C ''src'':47C ''strong'':23C,33C,61C,73C,101C,107C,127C,165C ''test'':79C ''therefor'':141C ''train'':67C ''tri'':146C ''tutori'':87C ''ul'':18C,58C ''underfit'':138C ''understand'':161C ''unlik'':120C ''valid'':11B,35C ''x'':21C ''x-axi'':20C ''y'':31C ''y-axi'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9760, 2, '2016-01-12 12:09:19.85', 4, NULL, NULL, '&lt;p&gt;So, the underfitting means that you still have capacity for improving your learning while overfitting means that you have used a capacity more than needed for learning.&lt;/p&gt;

&lt;p&gt;Green area is where testing error is rising i.e. you should continue providing capacity (either data points or model complexity) to gain better results. More green line goes, more flat it becomes i.e. you are reaching the point where the provided capacity (which is data) is enough and better to try providing the other type of capacity which is model complexity. &lt;/p&gt;

&lt;p&gt;If it does not improve your test score or even reduce it that means that the combination of Data-Complexity was somehow optimal and you can stop training.&lt;/p&gt;
', NULL, NULL, '''/p'':29C,92C,123C ''area'':32C ''becom'':62C ''better'':53C,79C ''capac'':10C,23C,44C,72C,87C ''combin'':110C ''complex'':50C,91C,114C ''continu'':42C ''data'':46C,75C,113C ''data-complex'':112C ''either'':45C ''enough'':77C ''error'':36C ''even'':103C ''flat'':60C ''gain'':52C ''goe'':58C ''green'':31C,56C ''i.e'':39C,63C ''improv'':12C,98C ''learn'':14C,28C ''line'':57C ''mean'':5C,17C,107C ''model'':49C,90C ''need'':26C ''optim'':117C ''overfit'':16C ''p'':1C,30C,93C ''point'':47C,68C ''provid'':43C,71C,82C ''reach'':66C ''reduc'':104C ''result'':54C ''rise'':38C ''score'':101C ''somehow'':116C ''still'':8C ''stop'':121C ''test'':35C,100C ''train'':122C ''tri'':81C ''type'':85C ''underfit'':4C ''use'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9762, 1, '2016-01-12 13:54:34.18', 5, 127, 'Best regression model to use for sales prediction', '&lt;p&gt;I have the following variables along with sales data going back a few years:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;date # simple date, can be split in year, month etc &lt;/li&gt;
&lt;li&gt;shipping_time  (0-6 weeks) # 0 weeks means in stock, more weeks means the product is out of stock but a shipment is on the way to the warehouse. Longer shipping times have a siginificant impact on sales. &lt;/li&gt;
&lt;li&gt;sales # amount of products sold&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I need to predict the sales (which vary seasonally) while taking into account the shipping time. What would be a simple regression model that would produce reasonable results? I tried linear regression with only date and sales, but this does not account for seasonality, so the prediction is rather weak.&lt;/p&gt;

&lt;p&gt;Edit: As a measure of accuracy, I will withold a random sample of data from the input and compare against the result.&lt;/p&gt;

&lt;p&gt;Extra points if it can be easily done in python/scipy&lt;/p&gt;

&lt;p&gt;Data can look like this&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;--------------------------------------------------
| date           | delivery_time| sales          |
--------------------------------------------------
| 2015-01-01     | 0            |10              |
--------------------------------------------------
| 2015-01-01     | 7            |2               |
--------------------------------------------------
| 2015-01-02     | 7            |3               |
...
&lt;/code&gt;&lt;/pre&gt;
', 'predictive-modeling  regression', NULL, '''-01'':192C,193C,197C,198C,202C ''-02'':203C ''-6'':46C ''/code'':206C ''/li'':41C,81C,88C ''/p'':28C,141C,165C,177C,184C ''/pre'':207C ''/ul'':89C ''0'':45C,48C,194C ''10'':195C ''2'':200C ''2015'':191C,196C,201C ''3'':205C ''7'':199C,204C ''account'':103C,132C ''accuraci'':148C ''along'':19C ''amount'':84C ''back'':24C ''best'':1A ''code'':186C ''compar'':161C ''data'':22C,156C,179C ''date'':31C,33C,125C,187C ''deliveri'':188C ''done'':174C ''easili'':173C ''edit'':143C ''etc'':40C ''extra'':167C ''follow'':17C ''go'':23C ''impact'':78C ''input'':159C ''li'':30C,42C,82C ''like'':182C ''linear'':121C ''longer'':72C ''look'':181C ''mean'':50C,55C ''measur'':146C ''model'':3A,11B,113C ''month'':39C ''need'':92C ''p'':13C,90C,142C,166C,178C ''point'':168C ''pre'':185C ''predict'':8A,10B,94C,137C ''predictive-model'':9B ''produc'':116C ''product'':57C,86C ''python/scipy'':176C ''random'':153C ''rather'':139C ''reason'':117C ''regress'':2A,12B,112C,122C ''result'':118C,164C ''sale'':7A,21C,80C,83C,96C,127C,190C ''sampl'':154C ''season'':99C,134C ''ship'':43C,73C,105C ''shipment'':64C ''siginific'':77C ''simpl'':32C,111C ''sold'':87C ''split'':36C ''stock'':52C,61C ''take'':101C ''time'':44C,74C,106C,189C ''tri'':120C ''ul'':29C ''use'':5A ''vari'':98C ''variabl'':18C ''warehous'':71C ''way'':68C ''weak'':140C ''week'':47C,49C,54C ''withold'':151C ''would'':108C,115C ''year'':27C,38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10032, 1, '2016-01-30 18:41:55.73', 5, 108, 'What is the novelty in AlphaGo, Google Deepmind''s Go playing system?', '&lt;p&gt;Recently researchers at Google DeepMind published a &lt;a href="http://deepmind.com/alpha-go.html"&gt;paper&lt;/a&gt;, where they described a Go playing system that beat the best current computer programs and the human European champion.&lt;/p&gt;

&lt;p&gt;I had a quick look at that paper, and it seems it is using many interesting ideas from previous papers. What did they do differently that allowed them to achieve this spectacular improvement? &lt;/p&gt;
', 'reinforcement-learning', NULL, '''/a'':30C ''/alpha-go.html'':28C ''/p'':50C,85C ''achiev'':81C ''allow'':78C ''alphago'':6A ''beat'':39C ''best'':41C ''champion'':49C ''comput'':43C ''current'':42C ''deepmind'':8A,21C ''deepmind.com'':27C ''deepmind.com/alpha-go.html'':26C ''describ'':33C ''differ'':76C ''european'':48C ''go'':10A,35C ''googl'':7A,20C ''href'':25C ''human'':47C ''idea'':68C ''improv'':84C ''interest'':67C ''learn'':15B ''look'':56C ''mani'':66C ''novelti'':4A ''p'':16C,51C ''paper'':29C,59C,71C ''play'':11A,36C ''previous'':70C ''program'':44C ''publish'':22C ''quick'':55C ''recent'':17C ''reinforc'':14B ''reinforcement-learn'':13B ''research'':18C ''seem'':62C ''spectacular'':83C ''system'':12A,37C ''use'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9768, 2, '2016-01-12 21:30:25.1', 0, NULL, NULL, '&lt;p&gt;If you''re just looking for a data model, this one should handle your stated requirements:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    Person
    -------------------
    PersonId
    Age
    Gender
    Country
    Etc
    --------------------

    Person_Conversation
    --------------------
    PersonId
    ConversationId
    --------------------

    Conversation
    --------------------
    ConversationId
    ....
    --------------------

    ---- if a sentence can be part of more than one conversation
    ---- use:

    Conversation_Sentence 
    --------------------
    ConversationId
    SentenceId
    --------------------

    Sentence
    --------------------
    SentenceId
    Keyword
    Polarity
    --------------------

    ----Otherwise use:

    Sentence
    --------------------
    SentenceId
    ConversationId
    Keyword
    Polarity
    --------------------
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':60C ''/p'':18C ''/pre'':61C ''age'':23C ''code'':20C ''convers'':28C,31C,43C,45C ''conversationid'':30C,32C,47C,57C ''countri'':25C ''data'':9C ''etc'':26C ''gender'':24C ''handl'':14C ''keyword'':51C,58C ''look'':6C ''model'':10C ''one'':12C,42C ''otherwis'':53C ''p'':1C ''part'':38C ''person'':21C,27C ''personid'':22C,29C ''polar'':52C,59C ''pre'':19C ''re'':4C ''requir'':17C ''sentenc'':35C,46C,49C,55C ''sentenceid'':48C,50C,56C ''state'':16C ''use'':44C,54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9764, 2, '2016-01-12 16:20:16.453', 3, NULL, NULL, '&lt;p&gt;The term may best be expressed as a &lt;strong&gt;Recurrent&lt;/strong&gt;, &lt;strong&gt;Heavy&lt;/strong&gt; &lt;strong&gt;Subgraph&lt;/strong&gt;. That is, a subgraph which is both &lt;strong&gt;Recurrent&lt;/strong&gt; &lt;em&gt;and&lt;/em&gt; &lt;strong&gt;Heavy&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Heaviness&lt;/strong&gt; of a subgraph refers to heavily connected vertices- that is, nodes which are connected many times ("many" being relative to the network in question). &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Recurrent&lt;/strong&gt; refers to the propensity of a subgraph to occur more than once. &lt;/p&gt;

&lt;p&gt;Thus, a &lt;strong&gt;Recurrent Heavy Subgraph&lt;/strong&gt;  is a densely connected set of vertices which occurs several times in the overall network.&lt;/p&gt;

&lt;p&gt;These subgraphs are often used to determine properties of a network. &lt;/p&gt;

&lt;p&gt;For example: In a network of emails interactions within a company organized into 4-person teams with one member acting as the lead, each team''s email activity (if they email between themselves sufficiently to be considered "heavy") could be described as a &lt;strong&gt;Heavy&lt;/strong&gt; &lt;strong&gt;Subgraph&lt;/strong&gt;. The fact that these subgraphs occur many times in the network make them &lt;strong&gt;Recurrent Heavy Subgraphs&lt;/strong&gt;. If one was searching for structure in the network, noticing that these recurrent, heavy subgraphs exist would go a long way toward determining the organization of the network as a whole.&lt;/p&gt;
', NULL, NULL, '''/em'':31C ''/p'':35C,64C,81C,105C,118C,218C ''/strong'':12C,15C,18C,28C,34C,39C,68C,89C,165C,168C,186C ''4'':133C ''act'':139C ''activ'':147C ''best'':5C ''compani'':130C ''connect'':46C,53C,93C ''consid'':156C ''could'':158C ''dens'':92C ''describ'':160C ''determin'':113C,209C ''em'':29C ''email'':126C,146C,150C ''exampl'':121C ''exist'':202C ''express'':7C ''fact'':170C ''go'':204C ''heavi'':14C,33C,38C,87C,157C,164C,184C,200C ''heavili'':45C ''interact'':127C ''lead'':142C ''long'':206C ''make'':180C ''mani'':54C,56C,175C ''may'':4C ''member'':138C ''network'':61C,104C,117C,124C,179C,195C,214C ''node'':50C ''notic'':196C ''occur'':77C,98C,174C ''often'':110C ''one'':137C,188C ''organ'':131C,211C ''overal'':103C ''p'':1C,36C,65C,82C,106C,119C ''person'':134C ''propens'':72C ''properti'':114C ''question'':63C ''recurr'':11C,27C,67C,86C,183C,199C ''refer'':43C,69C ''relat'':58C ''search'':190C ''set'':94C ''sever'':99C ''strong'':10C,13C,16C,26C,32C,37C,66C,85C,163C,166C,182C ''structur'':192C ''subgraph'':17C,22C,42C,75C,88C,108C,167C,173C,185C,201C ''suffici'':153C ''team'':135C,144C ''term'':3C ''thus'':83C ''time'':55C,100C,176C ''toward'':208C ''use'':111C ''vertic'':47C,96C ''way'':207C ''whole'':217C ''within'':128C ''would'':203C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9765, 2, '2016-01-12 17:53:48.103', 4, NULL, NULL, '&lt;p&gt;While Kasra Manshaei gives a good general answer (+1), I would like to give an easy to understand example.&lt;/p&gt;

&lt;p&gt;Think of a very simple problem: Fitting a function $f:[0, 1] \rightarrow \mathbb{R}$. To do so, you take a model out of the polynomial class. For the sake of argument, let''s say you take a polynomial of degree 0. This models &lt;strong&gt;capacity&lt;/strong&gt; is very limited as it can only fit constants. It will basically guess the mean value (depends on the error function, of course, but keep it simple). So relatively quick you will have a pretty good estimate of what the best parameters for this kind of model are. Your test- and training error will be almost identical, no matter how many examples you add. The problem is not that you don''t have enough data, the problem is that your model is not powerful enough: You &lt;strong&gt;underfit&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;So lets go the other way around: Say you have 1000 data points. Knowing a bit of math, you choose a polynomial of degree 999. Now you can fit the training data perfectly. However, your data might just fit the data too perfectly. For example, see (from &lt;a href="http://martin-thoma.com/spline-interpolation/" rel="nofollow"&gt;my blog&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/fYSlo.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/fYSlo.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In this case, you have other models which also fit the data perfectly. Obviously, the blue model seems kind of unnatural between the datapoints. The model itself might not be able to capture the kind of distribution well, so restricting the model to something simpler might actually help it. This can be an example of &lt;strong&gt;overfitting&lt;/strong&gt;.&lt;/p&gt;
', NULL, NULL, '''+1'':10C ''/a'':217C,237C ''/fyslo.png'':224C,231C ''/p'':21C,159C,218C,238C,298C ''/spline-interpolation/'':212C ''/strong'':69C,158C,297C ''0'':33C,64C ''1'':34C ''1000'':171C ''999'':185C ''abl'':270C ''actual'':286C ''add'':133C ''almost'':125C ''also'':248C ''alt'':232C ''answer'':9C ''argument'':54C ''around'':167C ''basic'':81C ''best'':110C ''bit'':176C ''blog'':216C ''blue'':255C ''capac'':68C ''captur'':272C ''case'':242C ''choos'':180C ''class'':49C ''constant'':78C ''cours'':92C ''data'':144C,172C,192C,196C,201C,251C ''datapoint'':263C ''degre'':63C,184C ''depend'':86C ''descript'':235C ''distribut'':276C ''easi'':17C ''enough'':143C,154C ''enter'':233C ''error'':89C,122C ''estim'':106C ''exampl'':20C,131C,205C,293C ''f'':32C ''fit'':29C,77C,189C,199C,249C ''function'':31C,90C ''general'':8C ''give'':5C,15C ''go'':163C ''good'':7C,105C ''guess'':82C ''help'':287C ''howev'':194C ''href'':209C,221C ''i.stack.imgur.com'':223C,230C ''i.stack.imgur.com/fyslo.png'':222C,229C ''ident'':126C ''imag'':234C ''img'':227C ''kasra'':3C ''keep'':94C ''kind'':114C,258C,274C ''know'':174C ''let'':55C,162C ''like'':13C ''limit'':72C ''mani'':130C ''manshaei'':4C ''martin-thoma.com'':211C ''martin-thoma.com/spline-interpolation/'':210C ''math'':178C ''mathbb'':36C ''matter'':128C ''mean'':84C ''might'':197C,267C,285C ''model'':44C,66C,116C,150C,246C,256C,265C,281C ''nofollow'':214C,226C ''obvious'':253C ''overfit'':296C ''p'':1C,22C,160C,219C,239C ''paramet'':111C ''perfect'':193C,203C,252C ''point'':173C ''polynomi'':48C,61C,182C ''power'':153C ''pretti'':104C ''problem'':28C,135C,146C ''quick'':99C ''r'':37C ''rel'':213C,225C ''relat'':98C ''restrict'':279C ''rightarrow'':35C ''sake'':52C ''say'':57C,168C ''see'':206C ''seem'':257C ''simpl'':27C,96C ''simpler'':284C ''someth'':283C ''src'':228C ''strong'':67C,156C,295C ''take'':42C,59C ''test'':119C ''think'':23C ''train'':121C,191C ''underfit'':157C ''understand'':19C ''unnatur'':260C ''valu'':85C ''way'':166C ''well'':277C ''would'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9766, 2, '2016-01-12 20:49:00.64', 4, NULL, NULL, '&lt;p&gt;This is a pretty classic &lt;strong&gt;ARIMA&lt;/strong&gt; dataset. &lt;strong&gt;ARIMA&lt;/strong&gt; is implemented in the StatsModels package for Python, the documentation for which is available &lt;a href="http://statsmodels.sourceforge.net/stable/index.html" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;An &lt;strong&gt;ARIMA&lt;/strong&gt; model with seasonal adjustment may be the simplest reasonably successful forecast for a complex time series such as sales forecasting. It may (probably will) be that you need to combine the method with an additional model layer to detect additional fluctuation beyond the auto-regressive function of your sales trend.&lt;/p&gt;

&lt;p&gt;Unfortunately, simple linear regression models tend to fare quite poorly on time series data.&lt;/p&gt;
', NULL, NULL, '''/a'':36C ''/p'':37C,94C,110C ''/stable/index.html'':32C ''/strong'':9C,13C,42C ''addit'':77C,82C ''adjust'':46C ''arima'':8C,12C,41C ''auto'':87C ''auto-regress'':86C ''avail'':27C ''beyond'':84C ''classic'':6C ''combin'':72C ''complex'':56C ''data'':109C ''dataset'':10C ''detect'':81C ''document'':23C ''fare'':103C ''fluctuat'':83C ''forecast'':53C,62C ''function'':89C ''href'':29C ''implement'':15C ''layer'':79C ''linear'':98C ''may'':47C,64C ''method'':74C ''model'':43C,78C,100C ''need'':70C ''nofollow'':34C ''p'':1C,38C,95C ''packag'':19C ''poor'':105C ''pretti'':5C ''probabl'':65C ''python'':21C ''quit'':104C ''reason'':51C ''regress'':88C,99C ''rel'':33C ''sale'':61C,92C ''season'':45C ''seri'':58C,108C ''simpl'':97C ''simplest'':50C ''statsmodel'':18C ''statsmodels.sourceforge.net'':31C ''statsmodels.sourceforge.net/stable/index.html'':30C ''strong'':7C,11C,40C ''success'':52C ''tend'':101C ''time'':57C,107C ''trend'':93C ''unfortun'':96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9774, 2, '2016-01-13 06:02:40.97', 0, NULL, NULL, '&lt;p&gt;I am clueless about the difference coming both the implementation yet, but found how to find a decision boundary for the problem.&lt;/p&gt;

&lt;p&gt;At First step I scaled the data to a range [0,100] which earlier was [0,1] for the input. 
(Still do not know why scaling creates a difference)&lt;/p&gt;

&lt;p&gt;And then used a RBF kernal with low gamma and high C value.&lt;/p&gt;

&lt;p&gt;svc = svm.SVC(kernel=''rbf'', gamma=.004, C=1000).&lt;/p&gt;

&lt;p&gt;This gives me the following result.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/ZLppV.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/ZLppV.png" alt="Separation boundary created using above mentioned kernal"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':110C ''/p'':24C,55C,70C,80C,88C,111C ''/zlppv.png'':94C,101C ''0'':36C,41C ''004'':77C ''1'':42C ''100'':37C ''1000'':79C ''alt'':102C ''boundari'':20C,104C ''c'':68C,78C ''clueless'':4C ''come'':8C ''creat'':52C,105C ''data'':32C ''decis'':19C ''differ'':7C,54C ''earlier'':39C ''find'':17C ''first'':27C ''follow'':86C ''found'':14C ''gamma'':65C,76C ''give'':83C ''high'':67C ''href'':91C ''i.stack.imgur.com'':93C,100C ''i.stack.imgur.com/zlppv.png'':92C,99C ''img'':97C ''implement'':11C ''input'':45C ''kernal'':62C,109C ''kernel'':74C ''know'':49C ''low'':64C ''mention'':108C ''nofollow'':96C ''p'':1C,25C,56C,71C,81C,89C ''problem'':23C ''rang'':35C ''rbf'':61C,75C ''rel'':95C ''result'':87C ''scale'':30C,51C ''separ'':103C ''src'':98C ''step'':28C ''still'':46C ''svc'':72C ''svm.svc'':73C ''use'':59C,106C ''valu'':69C ''yet'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9780, 1, '2016-01-13 16:20:48.9', 1, 30, 'Combining K-means clustering with Agglomerative clustering', '&lt;p&gt;I am trying to identify 6 clusters in a graph of authors. Authors are connected with an edge when they have co-authored on a paper. I have already created a clustering with K-means (6-Means) and with Agglomerative clustering. Now, I want to combine these two clustering approaches in order to get more accurate clusters.
I am able to find corresponding clusters, but I have no clue on how to decide to which cluster an author belongs when the algorithms do not agree. &lt;/p&gt;

&lt;p&gt;Possibly I could use some form of a linear combination, but I do not know how to apply this properly. Other methods might be possible, but I am not sure on that.&lt;/p&gt;
', 'clustering  k-means', NULL, '''/p'':101C,135C ''6'':19C,51C ''abl'':75C ''accur'':71C ''agglom'':7A,55C ''agre'':100C ''algorithm'':97C ''alreadi'':43C ''appli'':120C ''approach'':65C ''author'':25C,26C,37C,93C ''belong'':94C ''clue'':84C ''cluster'':5A,8A,9B,20C,46C,56C,64C,72C,79C,91C ''co'':36C ''co-author'':35C ''combin'':1A,61C,112C ''connect'':28C ''correspond'':78C ''could'':105C ''creat'':44C ''decid'':88C ''edg'':31C ''find'':77C ''form'':108C ''get'':69C ''graph'':23C ''identifi'':18C ''k'':3A,11B,49C ''k-mean'':2A,10B,48C ''know'':117C ''linear'':111C ''mean'':4A,12B,50C,52C ''method'':124C ''might'':125C ''order'':67C ''p'':13C,102C ''paper'':40C ''possibl'':103C,127C ''proper'':122C ''sure'':132C ''tri'':16C ''two'':63C ''use'':106C ''want'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9769, 1, '2016-01-12 23:39:55.8', 4, 78, 'Why is Reconstruction in Autoencoders Using the Same Activation Function as Forward Activation, and not the Inverse?', '&lt;p&gt;Suppose you have an input layer with n neurons and the first hidden layer has $m$ neurons, with typically $m &amp;lt; n$. Then you compute the actication $a_j$ of the $j$-th neuron in the hidden layer by &lt;/p&gt;

&lt;p&gt;$a_j =  f\left(\sum\limits_{i=1..n} w_{i,j} x_i+b_j\right)$, where $f$ is an activation function like $\tanh$ or $\text{sigmoid}$. &lt;/p&gt;

&lt;p&gt;To train the network, you compute the reconstruction of the input, denoted $z$, and minimize the error between $z$ and $x$. Now, the $i$-th element in $z$ is typically computed as:&lt;/p&gt;

&lt;p&gt;$
z_i = f\left ( \sum\limits_{j=1..m}  w_{j,i}'' a_j+b''_i \right)
$&lt;/p&gt;

&lt;p&gt;I am wondering why are the reconstructed $z$ are usually computed with the same activation function instead of using the inverse function, and why separate $w''$ and $b''$ are useful instead of using tied weights and biases? It seems much more intuitive to me to compute the reconstructed with the inverse activation function $f^{-1}$, e.g., $\text{arctanh}$, as follows:&lt;/p&gt;

&lt;p&gt;$$
z_i'' = \sum\limits_{j=1..m} \frac{f^{-1}(a_j)-b_j}{w_{j,i}^T}
$$&lt;/p&gt;

&lt;p&gt;Note, that here tied weights are used, i.e., $w'' = w^T$, and the biases $b_j$ of the hidden layer are used, instead of introducing an additional set of biases for the input layer. &lt;/p&gt;

&lt;p&gt;And a very related question: To visualize features, instead of computing the reconstruction, one would usually create an identity matrix with the dimension of the hidden layer. Then, one would use each column of the matrix as input to a reactivation function, which induces an output in the input neurons. For the reactivation function, would it be better to use the same activation function (resp. the $z_i$) or the inverse function (resp. the $z''_i$)?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9770, 2, '2016-01-13 00:10:30.31', 1, NULL, NULL, '&lt;p&gt;K-means clustering is known to give local minima, depending on your initial initialisation of the cluster centres.&lt;/p&gt;

&lt;p&gt;However, k-means segmentation can, I think, be solved globally, since we do not permute anything in finding the solution. &lt;/p&gt;

&lt;p&gt;I can see from your comments that you did manage eventually to reach a segmentation. Would you be able to give some feedback, please? Is your solution the best solution? Or did you settle for a good enough solution?&lt;/p&gt;
', NULL, NULL, '''/p'':21C,44C,85C ''abl'':64C ''anyth'':39C ''best'':74C ''centr'':20C ''cluster'':5C,19C ''comment'':51C ''depend'':12C ''enough'':83C ''eventu'':56C ''feedback'':68C ''find'':41C ''give'':9C,66C ''global'':33C ''good'':82C ''howev'':23C ''initi'':15C ''initialis'':16C ''k'':3C,25C ''k-mean'':2C,24C ''known'':7C ''local'':10C ''manag'':55C ''mean'':4C,26C ''minima'':11C ''p'':1C,22C,45C ''permut'':38C ''pleas'':69C ''reach'':58C ''see'':48C ''segment'':27C,60C ''settl'':79C ''sinc'':34C ''solut'':43C,72C,75C,84C ''solv'':32C ''think'':30C ''would'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9772, 2, '2016-01-13 01:19:43.893', 0, NULL, NULL, '&lt;p&gt;One way to classify the text is by calculating  the Term Frequency and Inverse Document Frequency. You can refer this paper: &lt;a href="http://www.oracle.com/technetwork/testcontent/feature-preparation-130942.pdf" rel="nofollow"&gt;http://www.oracle.com/technetwork/testcontent/feature-preparation-130942.pdf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/technetwork/testcontent/feature-preparation-130942.pdf'':27C ''/technetwork/testcontent/feature-preparation-130942.pdf&lt;/a&gt;&lt;/p&gt;'':32C ''calcul'':10C ''classifi'':5C ''document'':16C ''frequenc'':13C,17C ''href'':24C ''invers'':15C ''nofollow'':29C ''one'':2C ''p'':1C ''paper'':22C ''refer'':20C ''rel'':28C ''term'':12C ''text'':7C ''way'':3C ''www.oracle.com'':26C,31C ''www.oracle.com/technetwork/testcontent/feature-preparation-130942.pdf'':25C ''www.oracle.com/technetwork/testcontent/feature-preparation-130942.pdf&lt;/a&gt;&lt;/p&gt;'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9773, 2, '2016-01-13 03:28:17.657', 2, NULL, NULL, '&lt;p&gt;Did you try time series modelling?  If not, then you should.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;I tried linear regression with only date and sales, but this does not
  account for seasonality&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The &lt;a href="https://en.wikipedia.org/wiki/Moving-average_model" rel="nofollow"&gt;moving average model&lt;/a&gt; is something which would fit nicely to your dataset.&lt;/p&gt;

&lt;p&gt;However, as you say that your model is exhibiting seasonality, you need to adjust the moving averages so that it takes the seasonality into account. &lt;/p&gt;

&lt;p&gt;So, the best model for your dataset would be the SARIMA model. It is just the Auto-Regressive Integrated Moving Average (ARIMA) model but with seasonal adjustments.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stats.stackexchange.com/a/165169/84191"&gt;Here&lt;/a&gt; is one of the questions which I have answered which further helps you understand minor seasonality and trend adjustments, along with the R code.&lt;/p&gt;

&lt;p&gt;[&lt;a href="https://www.otexts.org/fpp/8" rel="nofollow"&gt;Further reading&lt;/a&gt;]&lt;/p&gt;
', NULL, NULL, '''/a'':46C,121C,157C ''/a/165169/84191'':119C ''/blockquote'':33C ''/fpp/8'':152C ''/p'':13C,32C,56C,83C,113C,146C,158C ''/wiki/moving-average_model'':40C ''account'':29C,82C ''adjust'':71C,112C,140C ''along'':141C ''answer'':130C ''arima'':107C ''auto'':102C ''auto-regress'':101C ''averag'':44C,74C,106C ''best'':87C ''blockquot'':14C ''code'':145C ''dataset'':55C,91C ''date'':22C ''en.wikipedia.org'':39C ''en.wikipedia.org/wiki/moving-average_model'':38C ''exhibit'':66C ''fit'':51C ''help'':133C ''howev'':58C ''href'':37C,116C,149C ''integr'':104C ''linear'':18C ''minor'':136C ''model'':7C,45C,64C,88C,96C,108C ''move'':43C,73C,105C ''need'':69C ''nice'':52C ''nofollow'':42C,154C ''one'':123C ''p'':1C,15C,34C,57C,84C,114C,147C ''question'':126C ''r'':144C ''read'':156C ''regress'':19C,103C ''rel'':41C,153C ''sale'':24C ''sarima'':95C ''say'':61C ''season'':31C,67C,80C,111C,137C ''seri'':6C ''someth'':48C ''stats.stackexchange.com'':118C ''stats.stackexchange.com/a/165169/84191'':117C ''take'':78C ''time'':5C ''trend'':139C ''tri'':4C,17C ''understand'':135C ''would'':50C,92C ''www.otexts.org'':151C ''www.otexts.org/fpp/8'':150C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9790, 1, '2016-01-14 13:07:54.567', 1, 23, 'When text mining what consideration should I give to picking a class label', '&lt;p&gt;If I create a dataset and there are attributes that could be used to label it how do I pick which or what tests should I use in pre processing to identify the most suitable?&lt;/p&gt;

&lt;p&gt;As example the attributes I''m considered are related to courses of study
1) a  descriptor of the subject  type of programme 
2)  a descriptor of the length and award of the programme&lt;/p&gt;
', 'classification  text-mining', NULL, '''/p'':54C,89C ''1'':69C ''2'':78C ''attribut'':27C,59C ''award'':85C ''class'':12A ''classif'':14B ''consid'':62C ''consider'':5A ''could'':29C ''cours'':66C ''creat'':21C ''dataset'':23C ''descriptor'':71C,80C ''exampl'':57C ''give'':8A ''identifi'':50C ''label'':13A,33C ''length'':83C ''m'':61C ''mine'':3A,17B ''p'':18C,55C ''pick'':10A,38C ''pre'':47C ''process'':48C ''programm'':77C,88C ''relat'':64C ''studi'':68C ''subject'':74C ''suitabl'':53C ''test'':42C ''text'':2A,16B ''text-min'':15B ''type'':75C ''use'':31C,45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9776, 2, '2016-01-13 13:00:10.607', 2, NULL, NULL, '&lt;p&gt;I don''t think that your assumption $w'' = w^T$ holds. Or rather is not necessary, and if it is done, it is not in order to somehow automatically reverse the calculation to create the hidden layer features. It is not possible to reverse the compression in general, going from n to smaller m, directly in this way. If that was the goal, then you would want a form of matrix inversion, not simple transpose.&lt;/p&gt;

&lt;p&gt;Instead we just want $w_{ij}$ for the compressed higher-level feature representation, and will discard $w''_{ij}$ after auto-encoder is finished. &lt;/p&gt;

&lt;p&gt;You &lt;em&gt;can&lt;/em&gt; set $w'' = w^T$ and tie the weights. This can help with regularisation - helping the autoencoder generalise. But it is not necessary.&lt;/p&gt;

&lt;p&gt;For the autoencoder to function it doesn''t actually matter what activation function you use after the layer that you are pre-training, provided the last layer of the autoencoder can express the range of possible inputs. However, you may get varying quality of results depending on what you use, as normal for a neural network. &lt;/p&gt;

&lt;p&gt;It is quite reasonable to use the same activation function that you are building the pre-trained layer for, as it is the simplest choice. &lt;/p&gt;

&lt;p&gt;Using an inverse function is possible too, but not advisable for sigmoid or tanh, because e.g. arctanh is not defined &amp;lt; -1 or &gt; 1, so likely would not be numerically stable.&lt;/p&gt;
', NULL, NULL, '''-1'':242C ''/em'':109C ''/p'':77C,104C,132C,191C,219C,252C ''1'':244C ''activ'':145C,201C ''actual'':142C ''advis'':230C ''arctanh'':237C ''assumpt'':8C ''auto'':100C ''auto-encod'':99C ''autoencod'':125C,136C,164C ''automat'':30C ''build'':206C ''calcul'':33C ''choic'':218C ''compress'':47C,87C ''creat'':35C ''defin'':240C ''depend'':180C ''direct'':56C ''discard'':95C ''doesn'':140C ''done'':22C ''e.g'':236C ''em'':107C ''encod'':101C ''express'':166C ''featur'':39C,91C ''finish'':103C ''form'':70C ''function'':138C,146C,202C,224C ''general'':49C ''generalis'':126C ''get'':175C ''go'':50C ''goal'':64C ''help'':120C,123C ''hidden'':37C ''higher'':89C ''higher-level'':88C ''hold'':12C ''howev'':172C ''ij'':84C,97C ''input'':171C ''instead'':79C ''invers'':73C,223C ''last'':160C ''layer'':38C,151C,161C,211C ''level'':90C ''like'':246C ''lt'':241C ''m'':55C ''matrix'':72C ''matter'':143C ''may'':174C ''n'':52C ''necessari'':17C,131C ''network'':190C ''neural'':189C ''normal'':186C ''numer'':250C ''order'':27C ''p'':1C,78C,105C,133C,192C,220C ''possibl'':43C,170C,226C ''pre'':156C,209C ''pre-train'':155C,208C ''provid'':158C ''qualiti'':177C ''quit'':195C ''rang'':168C ''rather'':14C ''reason'':196C ''regularis'':122C ''represent'':92C ''result'':179C ''revers'':31C,45C ''set'':110C ''sigmoid'':232C ''simpl'':75C ''simplest'':217C ''smaller'':54C ''somehow'':29C ''stabl'':251C ''tanh'':234C ''think'':5C ''tie'':115C ''train'':157C,210C ''transpos'':76C ''use'':148C,184C,198C,221C ''vari'':176C ''w'':9C,10C,83C,96C,111C,112C ''want'':68C,82C ''way'':59C ''weight'':117C ''would'':67C,247C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9777, 1, '2016-01-13 13:46:57.73', 1, 66, 'One-Hot Vector representation vs Label Encoding for Categorical Variables', '&lt;p&gt;I have one categorical variable of string type in my dataset. I need to convert it to numerical value for further processing. I know standard way to represent categorical data is to use one-hot encoding. But that will convert each entry of the variable to a vector. &lt;/p&gt;

&lt;p&gt;LabelEncoder of sklearn converts each entry to a scalar value. I realise this is a very naive and possibly stupid question but which representation is more commonly used and is there a reason for the bias?&lt;/p&gt;
', 'categorical-data', NULL, '''/p'':65C,103C ''bias'':102C ''categor'':10A,13B,19C,44C ''categorical-data'':12B ''common'':93C ''convert'':30C,56C,70C ''data'':14B,45C ''dataset'':26C ''encod'':8A,52C ''entri'':58C,72C ''hot'':3A,51C ''know'':39C ''label'':7A ''labelencod'':67C ''naiv'':83C ''need'':28C ''numer'':33C ''one'':2A,18C,50C ''one-hot'':1A,49C ''p'':15C,66C ''possibl'':85C ''process'':37C ''question'':87C ''realis'':78C ''reason'':99C ''repres'':43C ''represent'':5A,90C ''scalar'':75C ''sklearn'':69C ''standard'':40C ''string'':22C ''stupid'':86C ''type'':23C ''use'':48C,94C ''valu'':34C,76C ''variabl'':11A,20C,61C ''vector'':4A,64C ''vs'':6A ''way'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9778, 2, '2016-01-13 14:09:03.943', 2, NULL, NULL, '&lt;p&gt;The main difference I can think of is that using one-hot encoding will mean that all your strings will be at the same (hamming) distance from each other, while using a scalar value means that distances between the resulting features will be meaningless (it may encode "red" as 1, "blue" as 2 and "green" as 3, but there is no reason why red is more similar to blue than to green).&lt;/p&gt;
', NULL, NULL, '''/p'':75C ''1'':52C ''2'':55C ''3'':59C ''blue'':53C,71C ''differ'':4C ''distanc'':28C,39C ''encod'':15C,49C ''featur'':43C ''green'':57C,74C ''ham'':27C ''hot'':14C ''main'':3C ''may'':48C ''mean'':17C,37C ''meaningless'':46C ''one'':13C ''one-hot'':12C ''p'':1C ''reason'':64C ''red'':50C,66C ''result'':42C ''scalar'':35C ''similar'':69C ''string'':21C ''think'':7C ''use'':11C,33C ''valu'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9781, 2, '2016-01-13 16:28:38.463', 1, NULL, NULL, '&lt;p&gt;You should also do a grid search for the "alpha" hyperparameter for the SGDClassifier. It is explicitly mentioned in the sklearn documentation and from my experience has a big impact on accuracy. 
Second hyperparameter you should look at is "n_iter" - however I saw a smaller effect with my data.&lt;/p&gt;
', NULL, NULL, '''/p'':52C ''accuraci'':33C ''alpha'':11C ''also'':4C ''big'':30C ''data'':51C ''document'':23C ''effect'':48C ''experi'':27C ''explicit'':18C ''grid'':7C ''howev'':43C ''hyperparamet'':12C,35C ''impact'':31C ''iter'':42C ''look'':38C ''mention'':19C ''n'':41C ''p'':1C ''saw'':45C ''search'':8C ''second'':34C ''sgdclassifi'':15C ''sklearn'':22C ''smaller'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9782, 2, '2016-01-13 16:41:50.893', 0, NULL, NULL, '&lt;p&gt;Without labels, I guess you are looking for Unsupervised anomaly detection technique. You may want to do some research on this topic. &lt;/p&gt;

&lt;p&gt;Be very careful that you are not "classifying" since your data is not labeled. You are actually looking for outliers of the cluster of normal people. Your data is not labeled so any supervised technique is not applicable including K-NN. Clustering methods like K-means can be used, and you want to look for the outliers and label them as "abnormal". There are other techniques available like one class support vector machine.&lt;/p&gt;

&lt;p&gt;With labels, it become classification problem. Any classification methods can be applied. Feature selection can be done to remove irrelevant or correlated features.&lt;/p&gt;
', NULL, NULL, '''/p'':24C,101C,126C ''abnorm'':89C ''actual'':42C ''anomali'':11C ''appli'':114C ''applic'':63C ''avail'':94C ''becom'':106C ''care'':28C ''class'':97C ''classif'':107C,110C ''classifi'':33C ''cluster'':48C,68C ''correl'':124C ''data'':36C,53C ''detect'':12C ''done'':119C ''featur'':115C,125C ''guess'':5C ''includ'':64C ''irrelev'':122C ''k'':66C,72C ''k-mean'':71C ''k-nn'':65C ''label'':3C,39C,56C,86C,104C ''like'':70C,95C ''look'':8C,43C,81C ''machin'':100C ''may'':15C ''mean'':73C ''method'':69C,111C ''nn'':67C ''normal'':50C ''one'':96C ''outlier'':45C,84C ''p'':1C,25C,102C ''peopl'':51C ''problem'':108C ''remov'':121C ''research'':20C ''select'':116C ''sinc'':34C ''supervis'':59C ''support'':98C ''techniqu'':13C,60C,93C ''topic'':23C ''unsupervis'':10C ''use'':76C ''vector'':99C ''want'':16C,79C ''without'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9791, 1, '2016-01-14 13:48:53.553', 1, 83, 'How to define person''s gender from the fullname?', '&lt;p&gt;Given person''s name, e.g. ''Adjutor Ferguson''.&lt;/p&gt;

&lt;p&gt;How to define is it a male or female?&lt;/p&gt;

&lt;p&gt;One solution came to my mind:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;     I have found Person NLP training dataset here mbejda.github.io.

      And via a machine learning software like Apache Mahout, train it and provide real data.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But I am not sure about the accuracy of the results.&lt;/p&gt;

&lt;p&gt;May be another approach exist? (e.g. scikit-learn.org)&lt;/p&gt;
', 'classification  nlp  algorithms', NULL, '''/code'':67C ''/p'':21C,32C,40C,81C,90C ''/pre'':68C ''accuraci'':77C ''adjutor'':19C ''algorithm'':12B ''anoth'':85C ''apach'':59C ''approach'':86C ''came'':36C ''classif'':10B ''code'':42C ''data'':66C ''dataset'':49C ''defin'':3A,25C ''e.g'':18C,88C ''exist'':87C ''femal'':31C ''ferguson'':20C ''found'':45C ''fullnam'':9A ''gender'':6A ''given'':14C ''learn'':56C ''like'':58C ''machin'':55C ''mahout'':60C ''male'':29C ''may'':83C ''mbejda.github.io'':51C ''mind'':39C ''name'':17C ''nlp'':11B,47C ''one'':34C ''p'':13C,22C,33C,69C,82C ''person'':4A,15C,46C ''pre'':41C ''provid'':64C ''real'':65C ''result'':80C ''scikit-learn.org'':89C ''softwar'':57C ''solut'':35C ''sure'':74C ''train'':48C,61C ''via'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10156, 1, '2016-02-09 14:15:25.57', 0, 88, 'Logistic regression model and linearly separable dataset', '&lt;p&gt;Show that for a linearly separable dataset, the maximum likelihood solution for the logistic regression model is obtained by finding a vector $w$, whose decision boundary $
w^Tx=0
$
 separates the classes and then taking the magnitude of $w$ to infinity.&lt;/p&gt;

&lt;p&gt;Which commonly known technique can help to overcome this issue?&lt;/p&gt;
', 'dataset  logistic-regression  self-study', NULL, '''/p'':57C,69C ''0'':44C ''boundari'':41C ''class'':47C ''common'':60C ''dataset'':7A,8B,22C ''decis'':40C ''find'':35C ''help'':64C ''infin'':56C ''issu'':68C ''known'':61C ''likelihood'':25C ''linear'':5A,20C ''logist'':1A,10B,29C ''logistic-regress'':9B ''magnitud'':52C ''maximum'':24C ''model'':3A,31C ''obtain'':33C ''overcom'':66C ''p'':15C,58C ''regress'':2A,11B,30C ''self'':13B ''self-studi'':12B ''separ'':6A,21C,45C ''show'':16C ''solut'':26C ''studi'':14B ''take'':50C ''techniqu'':62C ''tx'':43C ''vector'':37C ''w'':38C,42C,54C ''whose'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9783, 1, '2016-01-14 04:12:54.237', 3, 65, 'Using machine learning specifically for feature analysis, not predictions', '&lt;p&gt;I''m new to machine learning and have spent the last couple months having a blast using Sci-Kit Learn to try to understand the basics of building feature sets and predictive models.&lt;/p&gt;

&lt;p&gt;Now I''m trying to use ML on a data set not to predict future values but to understand the importance and direction (positive or negative) of each feature.&lt;/p&gt;

&lt;p&gt;My features (X) are boolean and integer values that describe a product. My target (y) is the sales of the product. I have ~15,000 observations with 16 features a piece.&lt;/p&gt;

&lt;p&gt;With my limited ML knowledge to this point, I''m confident that I can predict (with some level of accuracy) a new y based on a new set of features X. However I''m struggling to coherently identify, report on and present &lt;em&gt;the importance and direction of each feature that makes up X&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Thus far, I''ve taken a two-step approach:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Use a linear regression to observe coefficients&lt;/li&gt;
&lt;li&gt;Use a random forest to observe feature importance&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;The code&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;First, I try to get the directional impact of each feature:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from sklearn import linear_model
linreg = linear_model.LinearRegression()
linreg.fit(X, y)
coef = linreg.coef_
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Second, I try to get the importance of each feature:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from sklearn import ensemble
forest = ensemble.RandomForestRegressor()
forest.fit(X, y)
importance = forest.feature_importances_
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then I multiply the two derived values together for each feature and end up with some value that maybe perhaps could be the information I''m looking for!&lt;/p&gt;

&lt;p&gt;I''d love to know if I''m on the right track with any of this. Is this a common use case for ML? Are there tools, ideas, packages I should focus on to help guide me?&lt;/p&gt;

&lt;p&gt;Thank you very much.&lt;/p&gt;
', 'machine-learning  scikit', NULL, '''/code'':237C,265C ''/em'':169C ''/li'':192C,202C ''/ol'':203C ''/p'':49C,80C,113C,170C,182C,209C,222C,250C,296C,335C,341C ''/pre'':238C,266C ''/strong'':208C ''000'':106C ''15'':105C ''16'':109C ''accuraci'':134C ''analysi'':7A ''approach'':181C ''base'':138C ''basic'':41C ''blast'':30C ''boolean'':86C ''build'':43C ''case'':319C ''code'':207C,224C,252C ''coef'':235C ''coeffici'':191C ''coher'':151C ''common'':317C ''confid'':125C ''could'':288C ''coupl'':26C ''d'':299C ''data'':60C ''deriv'':273C ''describ'':91C ''direct'':73C,161C,217C ''em'':157C ''end'':280C ''ensembl'':256C ''ensemble.randomforestregressor'':258C ''far'':173C ''featur'':6A,44C,79C,83C,110C,144C,164C,200C,221C,249C,278C ''first'':211C ''focus'':329C ''forest'':197C,257C ''forest.feature'':263C ''forest.fit'':259C ''futur'':65C ''get'':215C,244C ''guid'':333C ''help'':332C ''howev'':146C ''idea'':325C ''identifi'':152C ''impact'':218C ''import'':71C,159C,201C,227C,246C,255C,262C,264C ''inform'':291C ''integ'':88C ''kit'':34C ''know'':302C ''knowledg'':119C ''last'':25C ''learn'':3A,12B,20C,35C ''level'':132C ''li'':184C,193C ''limit'':117C ''linear'':187C,228C ''linear_model.linearregression'':231C ''linreg'':230C ''linreg.coef'':236C ''linreg.fit'':232C ''look'':294C ''love'':300C ''m'':16C,53C,124C,148C,293C,305C ''machin'':2A,11B,19C ''machine-learn'':10B ''make'':166C ''mayb'':286C ''ml'':57C,118C,321C ''model'':48C,229C ''month'':27C ''much'':340C ''multipli'':270C ''negat'':76C ''new'':17C,136C,141C ''observ'':107C,190C,199C ''ol'':183C ''p'':14C,50C,81C,114C,171C,204C,210C,239C,267C,297C,336C ''packag'':326C ''perhap'':287C ''piec'':112C ''point'':122C ''posit'':74C ''pre'':223C,251C ''predict'':9A,47C,64C,129C ''present'':156C ''product'':93C,102C ''random'':196C ''regress'':188C ''report'':153C ''right'':308C ''sale'':99C ''sci'':33C ''sci-kit'':32C ''scikit'':13B ''second'':240C ''set'':45C,61C,142C ''sklearn'':226C,254C ''specif'':4A ''spent'':23C ''step'':180C ''strong'':205C ''struggl'':149C ''taken'':176C ''target'':95C ''thank'':337C ''thus'':172C ''togeth'':275C ''tool'':324C ''track'':309C ''tri'':37C,54C,213C,242C ''two'':179C,272C ''two-step'':178C ''understand'':39C,69C ''use'':1A,31C,56C,185C,194C,318C ''valu'':66C,89C,274C,284C ''ve'':175C ''x'':84C,145C,168C,233C,260C ''y'':96C,137C,234C,261C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9785, 1, '2016-01-14 07:13:45.81', 3, 64, 'Predicting a word using Word2vec model', '&lt;p&gt;Given a sentence:
"When I open the &lt;strong&gt;??&lt;/strong&gt; door it starts heating automatically"&lt;/p&gt;

&lt;p&gt;I would like to get the list of possible words in ?? with a probability.&lt;/p&gt;

&lt;p&gt;The basic concept used in word2vec model is to "predict" a word given surrounding context.&lt;/p&gt;

&lt;p&gt;Once the model is build, what is the right context vectors operation to perform my prediction task on new sentences? &lt;/p&gt;

&lt;p&gt;Is it simply a linear sum?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;model.most_similar(positive=[''When'',''I'',''open'',''the'',''door'',''it'',''starts'' ,''heating'',''automatically''])
&lt;/code&gt;&lt;/pre&gt;
', 'nlp  predictive-modeling  word-embeddings', NULL, '''/code'':107C ''/p'':29C,45C,62C,84C,92C ''/pre'':108C ''/strong'':23C ''automat'':28C,106C ''basic'':48C ''build'':68C ''code'':94C ''concept'':49C ''context'':61C,73C ''door'':24C,102C ''embed'':13B ''get'':35C ''given'':15C,59C ''heat'':27C,105C ''like'':33C ''linear'':90C ''list'':37C ''model'':6A,10B,53C,66C ''model.most'':95C ''new'':82C ''nlp'':7B ''open'':20C,100C ''oper'':75C ''p'':14C,30C,46C,63C,85C ''perform'':77C ''posit'':97C ''possibl'':39C ''pre'':93C ''predict'':1A,9B,56C,79C ''predictive-model'':8B ''probabl'':44C ''right'':72C ''sentenc'':17C,83C ''similar'':96C ''simpli'':88C ''start'':26C,104C ''strong'':22C ''sum'':91C ''surround'':60C ''task'':80C ''use'':4A,50C ''vector'':74C ''word'':3A,12B,40C,58C ''word-embed'':11B ''word2vec'':5A,52C ''would'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9786, 1, '2016-01-14 08:59:38.307', 0, 10, 'Penalties on variables in scikit-learn GradientBoostingClassifier?', '&lt;p&gt;Is there a way to penalize a feature in so that it doesn''t dominate the model? (In Salford Predictive Modeller, there is a setting called "Penalties on Variables")&lt;/p&gt;

&lt;p&gt;The situation is that I have one categorical feature which I want to include in the model, but I don''t want to have as the most important feature, since then the model doesn''t properly capture the variance explained by the other predictors.&lt;/p&gt;
', 'machine-learning  scikit', NULL, '''/p'':43C,89C ''call'':39C ''captur'':81C ''categor'':52C ''doesn'':26C,78C ''domin'':28C ''explain'':84C ''featur'':21C,53C,73C ''gradientboostingclassifi'':8A ''import'':72C ''includ'':58C ''learn'':7A,11B ''machin'':10B ''machine-learn'':9B ''model'':30C,34C,61C,77C ''one'':51C ''p'':13C,44C ''penal'':19C ''penalti'':1A,40C ''predict'':33C ''predictor'':88C ''proper'':80C ''salford'':32C ''scikit'':6A,12B ''scikit-learn'':5A ''set'':38C ''sinc'':74C ''situat'':46C ''variabl'':3A,42C ''varianc'':83C ''want'':56C,66C ''way'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9787, 2, '2016-01-14 09:05:42.04', 1, NULL, NULL, '&lt;p&gt;You don''t need the linear regression to understand the effect of features in your random forest, you''re better off looking at the partial dependence plots directly, this what you get when you hold all the variables fixed, and you vary one at a time. You can plot these using &lt;code&gt;sklearn.ensemble.partial_depence.plot_partial_dependence&lt;/code&gt;. Take a look at the &lt;a href="http://scikit-learn.org/stable/modules/ensemble.html#partial-dependence" rel="nofollow"&gt;documentation&lt;/a&gt; for an example of how to use it.&lt;/p&gt;

&lt;p&gt;Another type of model that can be useful for exploratory data analysis is a &lt;code&gt;DecisionTreeClassifier&lt;/code&gt;, you can produce a graphical representation of this using &lt;code&gt;export_graphviz&lt;/code&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':71C ''/code'':57C,98C,111C ''/p'':80C,112C ''/stable/modules/ensemble.html#partial-dependence'':67C ''analysi'':93C ''anoth'':82C ''better'':21C ''code'':53C,96C,108C ''data'':92C ''decisiontreeclassifi'':97C ''depend'':27C,56C ''direct'':29C ''document'':70C ''effect'':12C ''exampl'':74C ''exploratori'':91C ''export'':109C ''featur'':14C ''fix'':40C ''forest'':18C ''get'':33C ''graphic'':103C ''graphviz'':110C ''hold'':36C ''href'':64C ''linear'':7C ''look'':23C,60C ''model'':85C ''need'':5C ''nofollow'':69C ''one'':44C ''p'':1C,81C ''partial'':26C,55C ''plot'':28C,50C ''produc'':101C ''random'':17C ''re'':20C ''regress'':8C ''rel'':68C ''represent'':104C ''scikit-learn.org'':66C ''scikit-learn.org/stable/modules/ensemble.html#partial-dependence'':65C ''sklearn.ensemble.partial_depence.plot'':54C ''take'':58C ''time'':47C ''type'':83C ''understand'':10C ''use'':52C,78C,89C,107C ''vari'':43C ''variabl'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9821, 2, '2016-01-17 16:44:48.993', 2, NULL, NULL, '&lt;p&gt;Bayesian networks are preferred for genome interpretation. See, for example, &lt;a href="https://jscholarship.library.jhu.edu/handle/1774.2/37140" rel="nofollow"&gt;this dissertation&lt;/a&gt; discussing computational methods for genome interpretation.&lt;/p&gt;
', NULL, NULL, '''/a'':21C ''/handle/1774.2/37140'':16C ''/p'':28C ''bayesian'':2C ''comput'':23C ''discuss'':22C ''dissert'':20C ''exampl'':11C ''genom'':7C,26C ''href'':13C ''interpret'':8C,27C ''jscholarship.library.jhu.edu'':15C ''jscholarship.library.jhu.edu/handle/1774.2/37140'':14C ''method'':24C ''network'':3C ''nofollow'':18C ''p'':1C ''prefer'':5C ''rel'':17C ''see'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9819, 1, '2016-01-17 13:14:44.827', 1, 56, 'Number of epochs in Gensim Word2Vec implementation', '&lt;p&gt;There''s an &lt;code&gt;iter&lt;/code&gt; parameter in the &lt;code&gt;gensim&lt;/code&gt; Word2Vec implementation &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;class gensim.models.word2vec.Word2Vec(sentences=None, size=100,
  alpha=0.025, window=5, min_count=5, max_vocab_size=None, sample=0,
  seed=1, workers=1, min_alpha=0.0001, sg=1, hs=1, negative=0,
  cbow_mean=0, hashfxn=, &lt;strong&gt;iter=1&lt;/strong&gt;, null_word=0,
  trim_rule=None, sorted_vocab=1)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;that specifies the number of epochs, i.e.:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;iter = number of iterations (epochs) over the corpus.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Does anyone know whether that helps in improving the model over the corpus?&lt;/strong&gt; &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is there any reason why the &lt;code&gt;iter&lt;/code&gt; is set to 1 by default? Is there not much effect in increasing the no. of epochs?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is there any scientific/empirical evaluation of how to set the no. of epochs?&lt;/strong&gt; &lt;/p&gt;

&lt;p&gt;Unlike classification/regression task, the grid search method wouldn''t really work since the vectors are generated in an unsupervised manner and the objective function is simply by either hierarchical softmax or negative sampling.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is there an early stopping mechanism to cut short the no. of epochs once vectors converges? And can the hierarchical softmax or negative sampling objective converge?&lt;/strong&gt;&lt;/p&gt;
', 'gensim  word2vec  embeddings  epochs  convergence', NULL, '''/blockquote'':82C,103C ''/code'':19C,25C,131C ''/p'':28C,81C,91C,102C,120C,150C,167C,202C,232C ''/strong'':71C,119C,149C,166C,231C ''0'':50C,63C,66C,74C ''0.0001'':57C ''0.025'':39C ''1'':52C,54C,59C,61C,70C,80C,135C ''100'':37C ''5'':41C,44C ''alpha'':38C,56C ''anyon'':107C ''blockquot'':29C,92C ''cbow'':64C ''class'':31C ''classification/regression'':170C ''code'':17C,23C,129C ''converg'':12B,220C,230C ''corpus'':101C,118C ''count'':43C ''cut'':212C ''default'':137C ''earli'':208C ''effect'':142C ''either'':196C ''embed'':10B ''epoch'':3A,11B,89C,98C,148C,165C,217C ''evalu'':157C ''function'':192C ''generat'':184C ''gensim'':5A,8B,24C ''gensim.models'':32C ''grid'':173C ''hashfxn'':67C ''help'':111C ''hierarch'':197C,224C ''hs'':60C ''i.e'':90C ''implement'':7A,27C ''improv'':113C ''increas'':144C ''iter'':18C,69C,94C,97C,130C ''know'':108C ''manner'':188C ''max'':45C ''mean'':65C ''mechan'':210C ''method'':175C ''min'':42C,55C ''model'':115C ''much'':141C ''negat'':62C,200C,227C ''none'':35C,48C,77C ''null'':72C ''number'':1A,87C,95C ''object'':191C,229C ''p'':13C,30C,83C,93C,104C,121C,151C,168C,203C ''paramet'':20C ''realli'':178C ''reason'':126C ''rule'':76C ''sampl'':49C,201C,228C ''scientific/empirical'':156C ''search'':174C ''seed'':51C ''sentenc'':34C ''set'':133C,161C ''sg'':58C ''short'':213C ''simpli'':194C ''sinc'':180C ''size'':36C,47C ''softmax'':198C,225C ''sort'':78C ''specifi'':85C ''stop'':209C ''strong'':68C,105C,122C,152C,204C ''task'':171C ''trim'':75C ''unlik'':169C ''unsupervis'':187C ''vector'':182C,219C ''vocab'':46C,79C ''whether'':109C ''window'':40C ''word'':73C ''word2vec'':6A,9B,26C ''word2vec.word2vec'':33C ''work'':179C ''worker'':53C ''wouldn'':176C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9792, 2, '2016-01-14 14:34:22.81', 2, NULL, NULL, '&lt;p&gt;That dataset looks like a good starting point. Keep in mind that when you make your own dataset from those datasets you''ll want to keep the male to female ratio balanced if you want it to predict both well. &lt;/p&gt;

&lt;p&gt;It should not matter what machine learning software you use (Apache Mahout, scikit-learn, weka, etc.). Pick one that fits your language of choice since speed will probably not be too much of a concern with the smallish dataset size. As for features, you''d generally use ngrams as your baseline for NLP classification tasks. If you use ngrams here you won''t end up with anything very interesting because the model won''t generalize to any unseen names. I''d suggest as a feature baseline that you try character ngrams, and maybe something like syllable ngrams for something slightly more advanced (for syllable tokenization see &lt;a href="http://stackoverflow.com/questions/405161/detecting-syllables-in-a-word"&gt;http://stackoverflow.com/questions/405161/detecting-syllables-in-a-word&lt;/a&gt;).&lt;/p&gt;
', NULL, NULL, '''/p'':42C ''/questions/405161/detecting-syllables-in-a-word'':155C ''/questions/405161/detecting-syllables-in-a-word&lt;/a&gt;).&lt;/p&gt;'':158C ''advanc'':146C ''anyth'':111C ''apach'':54C ''balanc'':33C ''baselin'':95C,130C ''charact'':134C ''choic'':68C ''classif'':98C ''concern'':79C ''d'':89C,125C ''dataset'':3C,19C,22C,83C ''end'':108C ''etc'':60C ''featur'':87C,129C ''femal'':31C ''fit'':64C ''general'':90C,119C ''good'':7C ''href'':152C ''interest'':113C ''keep'':10C,27C ''languag'':66C ''learn'':50C,58C ''like'':5C,139C ''ll'':24C ''look'':4C ''machin'':49C ''mahout'':55C ''make'':16C ''male'':29C ''matter'':47C ''mayb'':137C ''mind'':12C ''model'':116C ''much'':76C ''name'':123C ''ngram'':92C,103C,135C,141C ''nlp'':97C ''one'':62C ''p'':1C,43C ''pick'':61C ''point'':9C ''predict'':39C ''probabl'':72C ''ratio'':32C ''scikit'':57C ''scikit-learn'':56C ''see'':150C ''sinc'':69C ''size'':84C ''slight'':144C ''smallish'':82C ''softwar'':51C ''someth'':138C,143C ''speed'':70C ''stackoverflow.com'':154C,157C ''stackoverflow.com/questions/405161/detecting-syllables-in-a-word'':153C ''stackoverflow.com/questions/405161/detecting-syllables-in-a-word&lt;/a&gt;).&lt;/p&gt;'':156C ''start'':8C ''suggest'':126C ''syllabl'':140C,148C ''task'':99C ''token'':149C ''tri'':133C ''unseen'':122C ''use'':53C,91C,102C ''want'':25C,36C ''weka'':59C ''well'':41C ''won'':106C,117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9793, 1, '2016-01-14 16:37:46.247', 1, 69, 'Running examples from scikit-learn tutorials', '&lt;p&gt;I am new to scikit-learn. I went through the examples given in the docs and I downloaded the script for how to recognize images of hand-written digits. When I made to run the script on my laptop, I got the following errors:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Traceback (most recent call last):
   File "C:\Python34\plot_digits_classification.py", line 22, in &amp;lt;module&amp;gt;
    from sklearn import datasets, svm, metrics
    File "C:\Python34\lib\site-packages\sklearn\__init__.py", line 57, in&amp;lt;module&amp;gt;
    from .base import clone
   File "C:\Python34\lib\site-packages\sklearn\base.py", line 11, in &amp;lt;module&amp;gt;
  from .utils.fixes import signature
  File "C:\Python34\lib\site-packages\sklearn\utils\__init__.py", line 11, in &amp;lt;module&amp;gt;
   from .validation import (as_float_array,
   File "C:\Python34\lib\site-packages\sklearn\utils\validation.py", line 16, in &amp;lt;module&amp;gt;
   from ..utils.fixes import signature
  File "C:\Python34\lib\site-packages\sklearn\utils\fixes.py", line 324, in &amp;lt;module&amp;gt;
   from scipy.sparse.linalg import lsqr as sparse_lsqr
   File "C:\Python34\lib\site-packages\scipy\sparse\linalg\__init__.py", line 109, in &amp;lt;module&amp;gt;
   from .isolve import *
  File "C:\Python34\lib\site-packages\scipy\sparse\linalg\isolve\__init__.py", line 6, in &amp;lt;module&amp;gt;
  from .iterative import *
  File "C:\Python34\lib\site-packages\scipy\sparse\linalg\isolve\iterative.py", line 7, in &amp;lt;module&amp;gt;
  from . import _iterative
  ImportError: DLL load failed: The specified module could not be found.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Please help me . Also, I want to know if I want to load data for prediction ,how should I do that? For example , if I want to test a hand written digit that is stored somewhere on my disk, how to prepare that data for loading and passing into this model for prediction.&lt;/p&gt;
', 'machine-learning  python  scikit', NULL, '''/code'':264C ''/p'':60C,320C ''/pre'':265C ''109'':202C ''11'':114C,135C ''16'':157C ''22'':73C ''324'':177C ''57'':95C ''6'':224C ''7'':245C ''also'':270C ''array'':145C ''base'':101C ''base.py'':112C ''c'':69C,85C,105C,124C,147C,167C,190C,211C,233C ''call'':66C ''clone'':103C ''code'':62C ''could'':260C ''data'':280C,310C ''dataset'':81C ''digit'':44C,298C ''disk'':305C ''dll'':254C ''doc'':29C ''download'':32C ''error'':59C ''exampl'':2A,25C,289C ''fail'':256C ''file'':68C,84C,104C,123C,146C,166C,189C,210C,232C ''fixes.py'':175C ''float'':144C ''follow'':58C ''found'':263C ''given'':26C ''got'':56C ''gt'':77C,99C,118C,139C,161C,181C,206C,228C,249C ''hand'':42C,296C ''hand-written'':41C ''help'':268C ''imag'':39C ''import'':80C,102C,121C,142C,164C,184C,209C,231C,251C ''importerror'':253C ''init'':92C,132C,199C,221C ''isolv'':208C,220C,242C ''iter'':230C,252C ''iterative.py'':243C ''know'':274C ''laptop'':54C ''last'':67C ''learn'':6A,10B,20C ''lib'':87C,107C,126C,149C,169C,192C,213C,235C ''linalg'':198C,219C,241C ''line'':72C,94C,113C,134C,156C,176C,201C,223C,244C ''load'':255C,279C,312C ''lsqr'':185C,188C ''lt'':75C,97C,116C,137C,159C,179C,204C,226C,247C ''machin'':9B ''machine-learn'':8B ''made'':47C ''metric'':83C ''model'':317C ''modul'':76C,98C,117C,138C,160C,180C,205C,227C,248C,259C ''new'':16C ''p'':13C,266C ''packag'':90C,110C,129C,152C,172C,195C,216C,238C ''pass'':314C ''pleas'':267C ''plot_digits_classification.py'':71C ''pre'':61C ''predict'':282C,319C ''prepar'':308C ''py'':93C,133C,200C,222C ''python'':11B ''python34'':70C,86C,106C,125C,148C,168C,191C,212C,234C ''recent'':65C ''recogn'':38C ''run'':1A,49C ''scikit'':5A,12B,19C ''scikit-learn'':4A,18C ''scipi'':196C,217C,239C ''scipy.sparse.linalg'':183C ''script'':34C,51C ''signatur'':122C,165C ''site'':89C,109C,128C,151C,171C,194C,215C,237C ''site-packag'':88C,108C,127C,150C,170C,193C,214C,236C ''sklearn'':79C,91C,111C,130C,153C,173C ''somewher'':302C ''spars'':187C,197C,218C,240C ''specifi'':258C ''store'':301C ''svm'':82C ''test'':294C ''traceback'':63C ''tutori'':7A ''util'':131C,154C,174C ''utils.fixes'':120C,163C ''valid'':141C ''validation.py'':155C ''want'':272C,277C,292C ''went'':22C ''written'':43C,297C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9794, 2, '2016-01-14 16:39:58.943', 5, NULL, NULL, '&lt;p&gt;The comments about iteration number are spot on. The default &lt;code&gt;SGDClassifier&lt;/code&gt; &lt;code&gt;n_iter&lt;/code&gt; is &lt;code&gt;5&lt;/code&gt; meaning you do &lt;code&gt;5 * num_rows&lt;/code&gt; steps in weight space. The &lt;a href="http://scikit-learn.org/stable/modules/sgd.html#tips-on-practical-use"&gt;sklearn rule of thumb&lt;/a&gt; is ~ 1 million steps for typical data. For your example, just set it to 1000 and it might reach tolerance first. Your accuracy is lower with &lt;code&gt;SGDClassifier&lt;/code&gt; because it''s hitting iteration limit before tolerance so you are "early stopping"&lt;/p&gt;

&lt;p&gt;Modifying your code quick and dirty I get:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Added n_iter here
params = [{}, {"loss": "log", "penalty": "l2", ''n_iter'':1000}]

for param, Model in zip(params, Models):
    total = 0
    for train_indices, test_indices in kf:
        train_X = X[train_indices, :]; train_Y = Y[train_indices]
        test_X = X[test_indices, :]; test_Y = Y[test_indices]
        reg = Model(**param)
        reg.fit(train_X, train_Y)
        predictions = reg.predict(test_X)
        total += accuracy_score(test_Y, predictions)

    accuracy = total / numFolds
    print "Accuracy score of {0}: {1}".format(Model.__name__, accuracy)

Accuracy score of LogisticRegression: 0.96
Accuracy score of SGDClassifier: 0.96
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':45C ''/code'':14C,18C,22C,30C,74C,189C ''/p'':88C,98C ''/pre'':190C ''/stable/modules/sgd.html#tips-on-practical-use'':40C ''0'':121C,174C ''0.96'':183C,188C ''1'':47C,175C ''1000'':60C,112C ''5'':21C,27C ''accuraci'':68C,162C,167C,171C,178C,179C,184C ''ad'':101C ''code'':12C,15C,20C,26C,72C,92C,100C ''comment'':3C ''data'':52C ''default'':11C ''dirti'':95C ''earli'':86C ''exampl'':55C ''first'':66C ''format'':176C ''get'':97C ''hit'':78C ''href'':37C ''indic'':124C,126C,133C,138C,143C,148C ''iter'':5C,17C,79C,103C,111C ''kf'':128C ''l2'':109C ''limit'':80C ''log'':107C ''logisticregress'':182C ''loss'':106C ''lower'':70C ''mean'':23C ''might'':63C ''million'':48C ''model'':115C,119C,150C ''model.__name__'':177C ''modifi'':90C ''n'':16C,102C,110C ''num'':28C ''number'':6C ''numfold'':169C ''p'':1C,89C ''param'':105C,114C,118C,151C ''penalti'':108C ''pre'':99C ''predict'':157C,166C ''print'':170C ''quick'':93C ''reach'':64C ''reg'':149C ''reg.fit'':152C ''reg.predict'':158C ''row'':29C ''rule'':42C ''scikit-learn.org'':39C ''scikit-learn.org/stable/modules/sgd.html#tips-on-practical-use'':38C ''score'':163C,172C,180C,185C ''set'':57C ''sgdclassifi'':13C,73C,187C ''sklearn'':41C ''space'':34C ''spot'':8C ''step'':31C,49C ''stop'':87C ''test'':125C,139C,142C,144C,147C,159C,164C ''thumb'':44C ''toler'':65C,82C ''total'':120C,161C,168C ''train'':123C,129C,132C,134C,137C,153C,155C ''typic'':51C ''weight'':33C ''x'':130C,131C,140C,141C,154C,160C ''y'':135C,136C,145C,146C,156C,165C ''zip'':117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9795, 2, '2016-01-14 18:10:37.08', 0, NULL, NULL, '&lt;p&gt;Seems like you''ve got to install the &lt;a href="http://scikit-learn.org/stable/install.html" rel="nofollow"&gt;module,&lt;/a&gt; for example with the &lt;code&gt;pip&lt;/code&gt; package manager. Perhaps you could install anaconda, which is highly recommended&lt;/p&gt;
', NULL, NULL, '''/a'':18C ''/code'':25C ''/p'':37C ''/stable/install.html'':14C ''anaconda'':32C ''code'':23C ''could'':30C ''exampl'':20C ''got'':6C ''high'':35C ''href'':11C ''instal'':8C,31C ''like'':3C ''manag'':27C ''modul'':17C ''nofollow'':16C ''p'':1C ''packag'':26C ''perhap'':28C ''pip'':24C ''recommend'':36C ''rel'':15C ''scikit-learn.org'':13C ''scikit-learn.org/stable/install.html'':12C ''seem'':2C ''ve'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9796, 2, '2016-01-14 18:51:20.203', 0, NULL, NULL, '&lt;p&gt;I installed older version of numpy,that''s the problem. If you have installed scikit-learn using windows binaries, then you must first install numpy+mkl from the windows binaries site. It is a prerequisite for scikit-learn.&lt;/p&gt;
', NULL, NULL, '''/p'':42C ''binari'':21C,32C ''first'':25C ''instal'':3C,15C,26C ''learn'':18C,41C ''mkl'':28C ''must'':24C ''numpi'':7C,27C ''older'':4C ''p'':1C ''prerequisit'':37C ''problem'':11C ''scikit'':17C,40C ''scikit-learn'':16C,39C ''site'':33C ''use'':19C ''version'':5C ''window'':20C,31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9797, 1, '2016-01-14 20:18:07.897', 4, 58, 'Parallel Q-learning', '&lt;p&gt;I''m looking for academic papers or other credible sources focusing on the topic of parralelized reinforcement learning, specifically Q-learning.
I''m mostly interested in methods of sharing Q-table between processes (or joining/syncing them together if each process have it''s own).
I''d also appreciate a brief description of method used in linked/mentioned sources.&lt;/p&gt;

&lt;p&gt;I should mention that I use neural network (PyBrain) as approximation.&lt;/p&gt;
', 'neuralnetwork  parallel  reinforcement-learning  q-learning  pybrain', NULL, '''/p'':74C,87C ''academ'':19C ''also'':63C ''appreci'':64C ''approxim'':86C ''brief'':66C ''credibl'':23C ''d'':62C ''descript'':67C ''focus'':25C ''interest'':40C ''joining/syncing'':51C ''learn'':4A,9B,12B,32C,36C ''linked/mentioned'':72C ''look'':17C ''m'':16C,38C ''mention'':78C ''method'':42C,69C ''most'':39C ''network'':83C ''neural'':82C ''neuralnetwork'':5B ''p'':14C,75C ''paper'':20C ''parallel'':1A,6B ''parralel'':30C ''process'':49C,56C ''pybrain'':13B,84C ''q'':3A,11B,35C,46C ''q-learn'':2A,10B,34C ''q-tabl'':45C ''reinforc'':8B,31C ''reinforcement-learn'':7B ''share'':44C ''sourc'':24C,73C ''specif'':33C ''tabl'':47C ''togeth'':53C ''topic'':28C ''use'':70C,81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9822, 2, '2016-01-17 17:59:09.063', 2, NULL, NULL, '&lt;p&gt;You could use a hashing vectorizer on your documents. The result will be a list of vectors. Then vectorize your ngrams in the same way and calculate the projection of this new vector on the old ones. This is equivalent to the database join on an index, but may have less overhead.&lt;/p&gt;
', NULL, NULL, '''/p'':54C ''calcul'':28C ''could'':3C ''databas'':44C ''document'':10C ''equival'':41C ''hash'':6C ''index'':48C ''join'':45C ''less'':52C ''list'':16C ''may'':50C ''new'':33C ''ngram'':22C ''old'':37C ''one'':38C ''overhead'':53C ''p'':1C ''project'':30C ''result'':12C ''use'':4C ''vector'':7C,18C,20C,34C ''way'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9823, 1, '2016-01-17 18:26:54.32', 5, 265, 'Hyperparameter search for LSTM-RNN using Keras (Python)', '&lt;p&gt;From Keras RNN Tutorial: &lt;em&gt;"RNNs are tricky. Choice of batch size is important, choice of loss and optimizer is critical, etc. Some configurations won''t converge."&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;So this is more a general question about tuning the hyperparameters of a LSTM-RNN on Keras. I would like to know about an approach to finding the best parameters for your RNN. &lt;/p&gt;

&lt;p&gt;I began with the &lt;a href="https://github.com/fchollet/keras/blob/master/examples/imdb_lstm.py" rel="nofollow"&gt;IMDB example on Keras'' Github&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;the main model looks like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(X_train, y_train), (X_test, y_test) = imdb.load_data(nb_words=max_features,
                                                      test_split=0.2)

max_features = 20000
maxlen = 100  # cut texts after this number of words (among top max_features most common words)
batch_size = 32

model = Sequential()
model.add(Embedding(max_features, 128, input_length=maxlen))
model.add(LSTM(128))  
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation(''sigmoid''))

# try using different optimizers and different optimizer configs
model.compile(loss=''binary_crossentropy'',
          optimizer=''adam'',
          class_mode="binary")

print("Train...")
model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=3,
      validation_data=(X_test, y_test), show_accuracy=True)
score, acc = model.evaluate(X_test, y_test,
                        batch_size=batch_size,
                        show_accuracy=True)

print(''Test accuracy:'', acc)
Test accuracy:81.54321846
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;81.5 is a fair score and more importantly it means that the model, even though not fully optimized, it works. &lt;/p&gt;

&lt;p&gt;My data is Time Series and the task is binary prediction, the same as the example. And now my problem looks like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;#Training Data
train = genfromtxt(os.getcwd() + "/Data/trainMatrix.csv", delimiter='','', skip_header=1)
validation = genfromtxt(os.getcwd() + "/Data/validationMatrix.csv", delimiter='','', skip_header=1)

#Targets
miniTrainTargets = [int(x) for x in genfromtxt(os.getcwd() + "/Data/trainTarget.csv", delimiter='','', skip_header=1)]
validationTargets = [int(x) for x in genfromtxt(os.getcwd() + "/Data/validationTarget.csv", delimiter='','', skip_header=1)]

#LSTM
model = Sequential()
model.add(Embedding(train.shape[0], 64, input_length=train.shape[1]))
model.add(LSTM(64)) 
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation(''sigmoid''))

# try using different optimizers and different optimizer configs
model.compile(loss=''binary_crossentropy'',
          optimizer=''adam'',
          class_mode="binary")

model.fit(train, miniTrainTargets, batch_size=batch_size, nb_epoch=5,
      validation_data=(validation, validationTargets), show_accuracy=True)
valid_preds = model.predict_proba(validation, verbose=0)
roc = metrics.roc_auc_score(validationTargets, valid_preds)
print("ROC:", roc)
ROC:0.5006526
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The model is basically the same as the IMDB one. Though the result means it''s not learning anything. However, when I use a vanilla MLP-NN I don''t have the same problem, the model learns and the score increases. I tried increasing the number of epochs and increasing-decreasing the number of LTSM units but the score won''t increase.&lt;/p&gt;

&lt;p&gt;So I would like to know a standard approach to tuning the network because in theory the algorithm should perform better than a multilayer perceptron network specially for this time series data.&lt;/p&gt;
', 'python  neuralnetwork  deep-learning  cross-validation  hyperparameter', NULL, '''/a'':102C ''/code'':236C,409C ''/data/trainmatrix.csv'':292C ''/data/traintarget.csv'':314C ''/data/validationmatrix.csv'':300C ''/data/validationtarget.csv'':327C ''/em'':47C ''/fchollet/keras/blob/master/examples/imdb_lstm.py'':94C ''/p'':48C,84C,103C,111C,259C,284C,476C,510C ''/pre'':237C,410C ''0'':338C,396C ''0.2'':130C ''0.5'':168C,349C ''0.5006526'':408C ''1'':171C,296C,304C,318C,331C,343C,352C ''100'':135C ''128'':159C,165C ''20000'':133C ''3'':205C ''32'':152C ''5'':382C ''64'':339C,346C ''81.5'':239C ''81.54321846'':235C ''acc'':216C,232C ''accuraci'':213C,227C,231C,234C,388C ''activ'':173C,354C ''adam'':188C,369C ''algorithm'':495C ''among'':143C ''anyth'':430C ''approach'':75C,486C ''auc'':399C ''basic'':415C ''batch'':30C,150C,199C,201C,222C,224C,376C,378C ''began'':87C ''best'':79C ''better'':498C ''binari'':185C,191C,270C,366C,372C ''choic'':28C,34C ''class'':189C,370C ''code'':113C,286C ''common'':148C ''config'':182C,363C ''configur'':43C ''converg'':46C ''critic'':40C ''cross'':16B ''cross-valid'':15B ''crossentropi'':186C,367C ''cut'':136C ''data'':123C,207C,262C,288C,384C,509C ''decreas'':464C ''deep'':13B ''deep-learn'':12B ''delimit'':293C,301C,315C,328C ''dens'':170C,351C ''differ'':177C,180C,358C,361C ''dropout'':167C,348C ''em'':24C ''embed'':156C,336C ''epoch'':204C,381C,460C ''etc'':41C ''even'':252C ''exampl'':98C,276C ''fair'':242C ''featur'':127C,132C,146C,158C ''find'':77C ''fulli'':255C ''general'':55C ''genfromtxt'':290C,298C,312C,325C ''github'':101C ''github.com'':93C ''github.com/fchollet/keras/blob/master/examples/imdb_lstm.py'':92C ''header'':295C,303C,317C,330C ''howev'':431C ''href'':91C ''hyperparamet'':1A,18B,60C ''imdb'':97C,420C ''imdb.load'':122C ''import'':33C,246C ''increas'':453C,456C,463C,475C ''increasing-decreas'':462C ''input'':160C,340C ''int'':307C,320C ''kera'':8A,21C,67C,100C ''know'':72C,483C ''learn'':14B,429C,449C ''length'':161C,341C ''like'':70C,109C,282C,481C ''look'':108C,281C ''loss'':36C,184C,365C ''lstm'':5A,64C,164C,332C,345C ''lstm-rnn'':4A,63C ''ltsm'':468C ''main'':106C ''max'':126C,131C,145C,157C ''maxlen'':134C,162C ''mean'':248C,425C ''metrics.roc'':398C ''minitraintarget'':306C,375C ''mlp'':438C ''mlp-nn'':437C ''mode'':190C,371C ''model'':107C,153C,251C,333C,413C,448C ''model.add'':155C,163C,166C,169C,172C,335C,344C,347C,350C,353C ''model.compile'':183C,364C ''model.evaluate'':217C ''model.fit'':194C,373C ''model.predict'':392C ''multilay'':501C ''nb'':124C,203C,380C ''network'':490C,503C ''neuralnetwork'':11B ''nn'':439C ''nofollow'':96C ''number'':140C,458C,466C ''one'':421C ''optim'':38C,178C,181C,187C,256C,359C,362C,368C ''os.getcwd'':291C,299C,313C,326C ''p'':19C,49C,85C,104C,238C,260C,411C,477C ''paramet'':80C ''perceptron'':502C ''perform'':497C ''pre'':112C,285C ''pred'':391C,403C ''predict'':271C ''print'':192C,229C,404C ''proba'':393C ''problem'':280C,446C ''python'':9A,10B ''question'':56C ''rel'':95C ''result'':424C ''rnn'':6A,22C,65C,83C ''rnns'':25C ''roc'':397C,405C,406C,407C ''score'':215C,243C,400C,452C,472C ''search'':2A ''sequenti'':154C,334C ''seri'':265C,508C ''show'':212C,226C,387C ''sigmoid'':174C,355C ''size'':31C,151C,200C,202C,223C,225C,377C,379C ''skip'':294C,302C,316C,329C ''special'':504C ''split'':129C ''standard'':485C ''target'':305C ''task'':268C ''test'':119C,121C,128C,209C,211C,219C,221C,230C,233C ''text'':137C ''theori'':493C ''though'':253C,422C ''time'':264C,507C ''top'':144C ''train'':115C,117C,193C,196C,198C,287C,289C,374C ''train.shape'':337C,342C ''tri'':175C,356C,455C ''tricki'':27C ''true'':214C,228C,389C ''tune'':58C,488C ''tutori'':23C ''unit'':469C ''use'':7A,176C,357C,434C ''valid'':17B,206C,297C,383C,385C,390C,394C,402C ''validationtarget'':319C,386C,401C ''vanilla'':436C ''verbos'':395C ''won'':44C,473C ''word'':125C,142C,149C ''work'':258C ''would'':69C,480C ''x'':114C,118C,195C,208C,218C,308C,310C,321C,323C ''y'':116C,120C,197C,210C,220C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9799, 1, '2016-01-15 01:41:59.553', 2, 18, 'What knoweldge framework supports this type of query?', '&lt;p&gt;I have knowledge that is organized as in the example below, where items or nodes can belong to multiple hierarchies and can have arbitrary numbers of children and parents:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;sports
    golf
    baseball
    tennis
equipment
    golf
        clubs
            club1
    baseball
        bats
            bat1
    tennis
        racket
            racket1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I need to be able to run queries like "Show me all sports that use clubs or rackets, and their associated equipment."  The queries do not need to be posed in natural language, but the results must be displayed in a hierarchy:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;sports
    golf
        club1
    tennis
        racket1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m going crazy trying to figure out what I need.  It doesn''t seem to quite be a relational database, an ontology, or a taxonomy.  Or maybe I don''t quite understand how to use one or more of those to do what I need.  What standard approach is most similar to what I''m trying to do?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;As always with stackexchange questions, I''m utterly terrified of asking a question that doesn''t fit the exact requirements of a particular forum.  I tried to make it as specific and answerable as possible.  I BEG YOU PLEASE, do not delete or lock this question without at least explaining why it is unacceptable or offering a suggestion for making it acceptable.  Thank you!&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;
', 'knowledge-base', NULL, '''/code'':59C,112C ''/em'':242C ''/p'':42C,104C,174C,244C ''/pre'':60C,113C ''/strong'':243C ''abl'':66C ''accept'':239C ''alway'':179C ''answer'':210C ''approach'':163C ''arbitrari'':36C ''ask'':188C ''associ'':82C ''base'':11B ''basebal'':47C,53C ''bat'':54C ''bat1'':55C ''beg'':214C ''belong'':29C ''children'':39C ''club'':51C,77C ''club1'':52C,109C ''code'':44C,106C ''crazi'':118C ''databas'':135C ''delet'':219C ''display'':100C ''doesn'':127C,192C ''em'':177C ''equip'':49C,83C ''exact'':196C ''exampl'':22C ''explain'':227C ''figur'':121C ''fit'':194C ''forum'':201C ''framework'':3A ''go'':117C ''golf'':46C,50C,108C ''hierarchi'':32C,103C ''item'':25C ''knoweldg'':2A ''knowledg'':10B,15C ''knowledge-bas'':9B ''languag'':94C ''least'':226C ''like'':70C ''lock'':221C ''m'':116C,170C,184C ''make'':205C,237C ''mayb'':142C ''multipl'':31C ''must'':98C ''natur'':93C ''need'':63C,88C,125C,160C ''node'':27C ''number'':37C ''offer'':233C ''one'':151C ''ontolog'':137C ''organ'':18C ''p'':12C,61C,114C,175C ''parent'':41C ''particular'':200C ''pleas'':216C ''pose'':91C ''possibl'':212C ''pre'':43C,105C ''queri'':8A,69C,85C ''question'':182C,190C,223C ''quit'':131C,146C ''racket'':57C,79C ''racket1'':58C,111C ''relat'':134C ''requir'':197C ''result'':97C ''run'':68C ''seem'':129C ''show'':71C ''similar'':166C ''specif'':208C ''sport'':45C,74C,107C ''stackexchang'':181C ''standard'':162C ''strong'':176C ''suggest'':235C ''support'':4A ''taxonomi'':140C ''tenni'':48C,56C,110C ''terrifi'':186C ''thank'':240C ''tri'':119C,171C,203C ''type'':6A ''unaccept'':231C ''understand'':147C ''use'':76C,150C ''utter'':185C ''without'':224C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9800, 2, '2016-01-15 08:11:59.377', 0, NULL, NULL, '&lt;p&gt;Well, to answer my own question, it turns out the issues I was struggling with are almost exactly the issues that motivated the development of and that are addressed by &lt;a href="https://en.wikipedia.org/wiki/Graph_database" rel="nofollow"&gt;graph databases&lt;/a&gt;.  I would have been out of luck if this was 1999, but it turns out 2016 is a good time to be alive for databases that go beyond the traditional relational model!  I guess all this NoSQL stuff that the kids are always talking about is more than just hype.  :)&lt;/p&gt;
', NULL, NULL, '''/a'':41C ''/p'':92C ''/wiki/graph_database'':36C ''1999'':52C ''2016'':57C ''address'':30C ''aliv'':64C ''almost'':18C ''alway'':84C ''answer'':4C ''beyond'':69C ''databas'':40C,66C ''develop'':25C ''en.wikipedia.org'':35C ''en.wikipedia.org/wiki/graph_database'':34C ''exact'':19C ''go'':68C ''good'':60C ''graph'':39C ''guess'':75C ''href'':33C ''hype'':91C ''issu'':12C,21C ''kid'':82C ''luck'':48C ''model'':73C ''motiv'':23C ''nofollow'':38C ''nosql'':78C ''p'':1C ''question'':7C ''rel'':37C ''relat'':72C ''struggl'':15C ''stuff'':79C ''talk'':85C ''time'':61C ''tradit'':71C ''turn'':9C,55C ''well'':2C ''would'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9801, 1, '2016-01-15 09:03:47.237', 2, 37, 'How does the Gaussian Radial Basis Function Kernel make data linearly Separable?', '&lt;p&gt;How does the Gaussian Radial Basis Function Kernel make data linearly Separable?
How come it maps to feature spaces of infinite dimensions?&lt;/p&gt;

&lt;p&gt;A lot of the literature I''ve read states that the Gaussian RBF kernel is quite common, and gives the formula for the kernel but I''ve found none that explain &lt;strong&gt;why we use it? and why it is useful?&lt;/strong&gt; Are there papers out there that address these issues?&lt;/p&gt;
', 'machine-learning  svm  feature-extraction  reference-request  feature-construction', NULL, '''/p'':49C,102C ''/strong'':92C ''address'':99C ''basi'':6A,32C ''come'':40C ''common'':67C ''construct'':25B ''data'':10A,36C ''dimens'':48C ''explain'':81C ''extract'':19B ''featur'':18B,24B,44C ''feature-construct'':23B ''feature-extract'':17B ''formula'':71C ''found'':78C ''function'':7A,33C ''gaussian'':4A,30C,62C ''give'':69C ''infinit'':47C ''issu'':101C ''kernel'':8A,34C,64C,74C ''learn'':15B ''linear'':11A,37C ''literatur'':55C ''lot'':52C ''machin'':14B ''machine-learn'':13B ''make'':9A,35C ''map'':42C ''none'':79C ''p'':26C,50C ''paper'':95C ''quit'':66C ''radial'':5A,31C ''rbf'':63C ''read'':58C ''refer'':21B ''reference-request'':20B ''request'':22B ''separ'':12A,38C ''space'':45C ''state'':59C ''strong'':82C ''svm'':16B ''use'':85C,91C ''ve'':57C,77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9802, 1, '2016-01-15 11:00:41.637', 3, 80, 'What is the difference in xgboost binary:logistic and reg:logistic', '&lt;p&gt;What is the difference in R in xgboost between binary:logistic and reg:logistic? Is it only in evaluation metric? &lt;/p&gt;

&lt;p&gt;If yes, how does RMSE on binary classification compare to error rate? Is the relationship between the metrics more or less monotonic, output from tuning on one metric should not differ significantly between those two approaches?&lt;/p&gt;
', 'r  logistic-regression  xgboost', NULL, '''/p'':38C,76C ''approach'':75C ''binari'':7A,27C,46C ''classif'':47C ''compar'':48C ''differ'':4A,21C,70C ''error'':50C ''evalu'':36C ''less'':60C ''logist'':8A,11A,14B,28C,31C ''logistic-regress'':13B ''metric'':37C,57C,67C ''monoton'':61C ''one'':66C ''output'':62C ''p'':17C,39C ''r'':12B,23C ''rate'':51C ''reg'':10A,30C ''regress'':15B ''relationship'':54C ''rmse'':44C ''signific'':71C ''tune'':64C ''two'':74C ''xgboost'':6A,16B,25C ''yes'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9803, 1, '2016-01-15 15:08:43.7', 0, 20, '(Pretty) Print C5.0 ruleModel as a tree?', '&lt;p&gt;If I train a C5.0 rule model like&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ruleModel &amp;lt;- C5.0(churn ~ ., data = churnTrain, rules = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and I print this with &lt;code&gt;summary(ruleModel)&lt;/code&gt;, I get the rules. How can I print the underlaying tree &lt;em&gt;without&lt;/em&gt; re-training with&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;treeModel &amp;lt;- C5.0(churn ~ ., data = churnTrain, rules = FALSE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;(In other words, I do &lt;em&gt;not&lt;/em&gt; want to use &lt;code&gt;summary(treeModel)&lt;/code&gt; - it takes a rather long time to train a model in my case, and I already have a trained &lt;code&gt;ruleModel&lt;/code&gt;.)&lt;/p&gt;

&lt;p&gt;I noticed that &lt;code&gt;ruleModel&lt;/code&gt; and &lt;code&gt;treeModel&lt;/code&gt; are lists, but treeModel has the additional entries "Terms" and "xlevels" and also there are some differences (found out with &lt;code&gt;all.equal&lt;/code&gt;) in the list entries that have the same name. Can I transform those models into each other or is there another printing way? How do I print the ruleModel as a tree?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Bonus:&lt;/strong&gt; Pretty print the C5.0 object similar to what &lt;code&gt;rpart.plot&lt;/code&gt; does.&lt;/p&gt;
', 'machine-learning  r', NULL, '''/code'':32C,43C,73C,90C,112C,120C,124C,147C,195C ''/em'':57C,83C ''/p'':21C,62C,113C,180C,197C ''/pre'':33C,74C ''/strong'':184C ''addit'':131C ''all.equal'':146C ''alreadi'':106C ''also'':137C ''anoth'':168C ''bonus'':183C ''c5.0'':3A,17C,26C,67C,188C ''case'':103C ''churn'':27C,68C ''churntrain'':29C,70C ''code'':23C,40C,64C,87C,110C,118C,122C,145C,193C ''data'':28C,69C ''differ'':141C ''em'':55C,81C ''entri'':132C,151C ''fals'':72C ''found'':142C ''get'':45C ''learn'':10B ''like'':20C ''list'':126C,150C ''long'':95C ''lt'':25C,66C ''machin'':9B ''machine-learn'':8B ''model'':19C,100C,161C ''name'':156C ''notic'':116C ''object'':189C ''p'':12C,34C,75C,114C,181C ''pre'':22C,63C ''pretti'':1A,185C ''print'':2A,37C,51C,169C,174C,186C ''r'':11B ''rather'':94C ''re'':59C ''re-train'':58C ''rpart.plot'':194C ''rule'':18C,30C,47C,71C ''rulemodel'':4A,24C,42C,111C,119C,176C ''similar'':190C ''strong'':182C ''summari'':41C,88C ''take'':92C ''term'':133C ''time'':96C ''train'':15C,60C,98C,109C ''transform'':159C ''tree'':7A,54C,179C ''treemodel'':65C,89C,123C,128C ''true'':31C ''underlay'':53C ''use'':86C ''want'':84C ''way'':170C ''without'':56C ''word'':78C ''xlevel'':135C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9804, 2, '2016-01-15 17:19:25.27', 1, NULL, NULL, '&lt;p&gt;Regression trees, particularly gradient boosting (essentially many trees), tend to do very well on continuous predictions, often outperforming models that are truly continuous like linear regression when. This is especially true when there are variable interactions and when you have a sufficiently large dataset (over 10,000 records) so that overfitting is less likely. If your primary objective is simply predictive power, then whether the model is 100% continuous or pseudo continuous should be irrelevant. If making your regression trees more continuous enhances out of sample predictive power, then you can simply increase tree depth or add more trees.&lt;/p&gt;
', NULL, NULL, '''/p'':101C ''000'':48C ''10'':47C ''100'':69C ''add'':98C ''boost'':6C ''continu'':16C,24C,70C,73C,83C ''dataset'':45C ''depth'':96C ''enhanc'':84C ''especi'':31C ''essenti'':7C ''gradient'':5C ''increas'':94C ''interact'':37C ''irrelev'':76C ''larg'':44C ''less'':54C ''like'':25C,55C ''linear'':26C ''make'':78C ''mani'':8C ''model'':20C,67C ''object'':59C ''often'':18C ''outperform'':19C ''overfit'':52C ''p'':1C ''particular'':4C ''power'':63C,89C ''predict'':17C,62C,88C ''primari'':58C ''pseudo'':72C ''record'':49C ''regress'':2C,27C,80C ''sampl'':87C ''simpli'':61C,93C ''suffici'':43C ''tend'':10C ''tree'':3C,9C,81C,95C,100C ''true'':32C ''truli'':23C ''variabl'':36C ''well'':14C ''whether'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9820, 2, '2016-01-17 16:42:08.18', 16, NULL, NULL, '&lt;p&gt;One of the areas where Bayesian approaches are often used, is where one needs interpretability of the prediction system. You don''t want to give doctors a Neural net and say that it''s 95% accurate. You rather want to explain the assumptions your method makes, as well as the decision process the method uses. &lt;/p&gt;

&lt;p&gt;Similar area is when you have a strong prior domain knowledge and want to use it in the system. &lt;/p&gt;
', NULL, NULL, '''/p'':57C,78C ''95'':36C ''accur'':37C ''approach'':8C ''area'':5C,60C ''assumpt'':44C ''bayesian'':7C ''decis'':52C ''doctor'':27C ''domain'':68C ''explain'':42C ''give'':26C ''interpret'':16C ''knowledg'':69C ''make'':47C ''method'':46C,55C ''need'':15C ''net'':30C ''neural'':29C ''often'':10C ''one'':2C,14C ''p'':1C,58C ''predict'':19C ''prior'':67C ''process'':53C ''rather'':39C ''say'':32C ''similar'':59C ''strong'':66C ''system'':20C,77C ''use'':11C,56C,73C ''want'':24C,40C,71C ''well'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9806, 2, '2016-01-15 21:51:27.033', 0, NULL, NULL, '&lt;p&gt;1) Like 0xF suggested: 
Please check the distribution of the label you are predicting i.e. number of 0''s and 1''s.
If there''s a class imbalance problem and you have assigned equal weight to all instances, this problem is most likely to occur.&lt;/p&gt;

&lt;p&gt;2) Assuming, you took care of class imbalance, also, use the function predict_proba() along with predict().
Now, take the ration of prob(1) by prob(0). It doesn''t matter in case of divide by zero error as you can replace those numbers by some very large number. Now, adjust the margins of classification according to your convenience to minimize false positives and false negatives.&lt;/p&gt;
', NULL, NULL, '''/p'':47C,115C ''0'':19C,75C ''0xf'':4C ''1'':2C,22C,72C ''2'':49C ''accord'':104C ''adjust'':99C ''along'':63C ''also'':57C ''assign'':34C ''assum'':50C ''care'':53C ''case'':81C ''check'':7C ''class'':28C,55C ''classif'':103C ''conveni'':107C ''distribut'':9C ''divid'':83C ''doesn'':77C ''equal'':35C ''error'':86C ''fals'':110C,113C ''function'':60C ''i.e'':16C ''imbal'':29C,56C ''instanc'':39C ''label'':12C ''larg'':96C ''like'':3C,44C ''margin'':101C ''matter'':79C ''minim'':109C ''negat'':114C ''number'':17C,92C,97C ''occur'':46C ''p'':1C,48C ''pleas'':6C ''posit'':111C ''predict'':15C,61C,65C ''prob'':71C,74C ''proba'':62C ''problem'':30C,41C ''ration'':69C ''replac'':90C ''suggest'':5C ''take'':67C ''took'':52C ''use'':58C ''weight'':36C ''zero'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9807, 1, '2016-01-15 23:09:45.597', 0, 33, 'Overflow encountered in square, invalid value encountered in multiply', '&lt;p&gt;I create a neural network like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;n = FeedForwardNetwork()

inLayer = LinearLayer(43)
bias = BiasUnit()
hiddenLayer = SigmoidLayer(100)
outLayer = LinearLayer(1)

n.addInputModule(inLayer)
n.addModule(bias)
n.addModule(hiddenLayer)
n.addOutputModule(outLayer)

in_to_hidden = FullConnection(inLayer, hiddenLayer)
bias_to_hidden = FullConnection(bias, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)

n.addConnection(in_to_hidden)
n.addConnection(bias_to_hidden)
n.addConnection(hidden_to_out)

n.sortModules()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I train it the following way (I''m simplifying, it''s being trained in multiple iterations):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;self.trainer = BackpropTrainer(self.neural_net, learningrate=0.8)
(...)
ds = SupervisedDataSet(self.net_input_size, 1)
ds.addSample([...], np.float64(learned_value))
(...)
self.trainer.trainOnDataset(ds)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Sometimes I get the following warnings:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;(...)/lib/python3.5/site-packages/PyBrain-0.3.1-py3.5.egg/pybrain/supervised/trainers/backprop.py:99: RuntimeWarning: overflow encountered in square
    error += 0.5 * sum(outerr ** 2)&lt;/p&gt;
  
  &lt;p&gt;(...)/lib/python3.5/site-packages/PyBrain-0.3.1-py3.5.egg/pybrain/structure/modules/sigmoidlayer.py:14: RuntimeWarning: invalid value encountered in multiply
    inerr[:] = outbuf * (1 - outbuf) * outerr&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;And then when I check the saved net file I see that &lt;em&gt;all&lt;/em&gt; weights are &lt;code&gt;nan&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(...)
&amp;lt;FullConnection class="pybrain.structure.connections.full.FullConnection" name="FullConnection-8"&amp;gt;
    &amp;lt;inmod val="BiasUnit-5"/&amp;gt;
    &amp;lt;outmod val="SigmoidLayer-11"/&amp;gt;
    &amp;lt;Parameters&amp;gt;[nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan]&amp;lt;/Parameters&amp;gt;
&amp;lt;/FullConnection&amp;gt;
(...)
&lt;/code&gt;&lt;/pre&gt;
', 'python  pybrain', NULL, '''-11'':198C ''-5'':192C ''-8'':186C ''/blockquote'':155C ''/code'':75C,115C,176C,309C ''/em'':171C ''/fullconnection'':307C ''/lib/python3.5/site-packages/pybrain-0.3.1-py3.5.egg/pybrain/structure/modules/sigmoidlayer.py'':141C ''/lib/python3.5/site-packages/pybrain-0.3.1-py3.5.egg/pybrain/supervised/trainers/backprop.py'':127C ''/p'':20C,94C,124C,139C,154C,177C ''/parameters'':304C ''/pre'':76C,116C,310C ''0.5'':135C ''0.8'':102C ''1'':35C,108C,151C ''100'':32C ''14'':142C ''2'':138C ''43'':27C ''99'':128C ''backproptrain'':98C ''bias'':28C,39C,50C,54C,67C ''biasunit'':29C,191C ''blockquot'':125C ''check'':161C ''class'':182C ''code'':22C,96C,174C,179C ''creat'':14C ''ds'':103C,114C ''ds.addsample'':109C ''em'':169C ''encount'':2A,7A,131C,146C ''error'':134C ''feedforwardnetwork'':24C ''file'':165C ''follow'':82C,122C ''fullconnect'':47C,53C,59C,181C,185C ''get'':120C ''gt'':187C,193C,199C,202C,305C,308C ''hidden'':46C,52C,56C,65C,69C,71C ''hiddenlay'':30C,41C,49C,55C,60C ''inerr'':149C ''inlay'':25C,37C,48C ''inmod'':189C ''input'':106C ''invalid'':5A,144C ''iter'':93C ''learn'':111C ''learningr'':101C ''like'':18C ''linearlay'':26C,34C ''lt'':180C,188C,194C,200C,303C,306C ''m'':85C ''multipl'':92C ''multipli'':9A,148C ''n'':23C ''n.addconnection'':62C,66C,70C ''n.addinputmodule'':36C ''n.addmodule'':38C,40C ''n.addoutputmodule'':42C ''n.sortmodules'':74C ''name'':184C ''nan'':175C,203C,204C,205C,206C,207C,208C,209C,210C,211C,212C,213C,214C,215C,216C,217C,218C,219C,220C,221C,222C,223C,224C,225C,226C,227C,228C,229C,230C,231C,232C,233C,234C,235C,236C,237C,238C,239C,240C,241C,242C,243C,244C,245C,246C,247C,248C,249C,250C,251C,252C,253C,254C,255C,256C,257C,258C,259C,260C,261C,262C,263C,264C,265C,266C,267C,268C,269C,270C,271C,272C,273C,274C,275C,276C,277C,278C,279C,280C,281C,282C,283C,284C,285C,286C,287C,288C,289C,290C,291C,292C,293C,294C,295C,296C,297C,298C,299C,300C,301C,302C ''net'':100C,164C ''network'':17C ''neural'':16C ''np.float64'':110C ''outbuf'':150C,152C ''outerr'':137C,153C ''outlay'':33C,43C,61C ''outmod'':195C ''overflow'':1A,130C ''p'':12C,77C,117C,126C,140C,156C ''paramet'':201C ''pre'':21C,95C,178C ''pybrain'':11B ''pybrain.structure.connections.full.fullconnection'':183C ''python'':10B ''runtimewarn'':129C,143C ''save'':163C ''see'':167C ''self.net'':105C ''self.neural'':99C ''self.trainer'':97C ''self.trainer.trainondataset'':113C ''sigmoidlay'':31C,197C ''simplifi'':86C ''size'':107C ''sometim'':118C ''squar'':4A,133C ''sum'':136C ''superviseddataset'':104C ''train'':79C,90C ''val'':190C,196C ''valu'':6A,112C,145C ''warn'':123C ''way'':83C ''weight'':172C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9810, 1, '2016-01-16 16:50:36.893', 2, 60, 'Clustering weekly data to detect outbreaks by having some sort of continuity among the adjacent weeks', '&lt;p&gt;I have a dataset from some health institues. The data contains information about malaria cases on a 52 week range. The dataset has 52 columns, one for each week and about 16 rows one for each hospital reporting the number of cases in the particular week diagnosed in that hospital. Example of the dataset with 9 weeks entries :-&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;SAD Lakhwar 0   0   0   0   0   1   4   3   1
Rural Health Center 2   0   0   6   0   2   0   2   2
Herbertpur Christian Hospital   1   0   1   0   2   0   1   0   1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have used Hierarchical and K-Means clustering for both determining clusters of hospitals as well as clusters of weeks but my real goal is to cluster in such a manner that outbreaks can be detected using data from consecutive weeks and at the same time the cluster of hospitals in which the outbreak is noticed is also found out.&lt;/p&gt;

&lt;p&gt;The techniques I have used till now find me clusters where in some cases weeks are away from each other, for example the 7th and 37th week fall into the same cluster as can be seen in the output given below but I want to achieve continuity in weeks because outbreaks span over a few weeks I understand the reason of the results I am getting but I want continuity, if anyone could help.&lt;/p&gt;

&lt;p&gt;The result of trying to cluster the weeks into 4 clusters using k means&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Week No x
1   2
2   4
3   4
4   2
5   4
6   2
7   1
8   2
9   2
10  2
11  2
12  4
13  1
14  4
15  4
16  4
17  1
18  4
19  1
20  4
21  1
22  1
23  1
24  1
25  1
26  1
27  1
28  1
29  1
30  1
31  1
32  3
33  1
34  1
35  1
36  1
37  1
38  3
39  3
40  3
41  3
42  3
43  3
44  3
45  3
46  3
47  3
48  3
49  3
50  1
51  4
52  4
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Dput of the data&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;structure(list(V1 = structure(c(13L, 15L, 6L, 10L, 3L, 17L, 12L, 
1L, 2L, 11L, 4L, 14L, 8L, 9L, 7L, 5L), .Label = c("CHC Sahaspur", 
"Comb. Hosp. Premnagar", "Doon Hospital", "FRI Hospital", "Herbertpur                     Christian Hospital", 
"HIHT Jollygrant", "Kalindi Hospital", "MAX Hospital", "PHC Herbatpur ", 
"PHC Kalsi", "PHC Rajawala", "Rural Health Center", "SAD Lakhwar", 
"Shubharti Hospital", "SPS Rishikesh", "Total", "Vaish Nursing Home"
), class = "factor"), V2 = c(0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 
0, 0, 0, 0, 0), V3 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0), V4 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0), V5 = c(0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0), V6 = c(0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), V7 = c(1, 0, 0, 
0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0), V8 = c(4, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0), V9 = c(3, 0, 0, 0, 0, 0, 2, 
0, 0, 0, 0, 0, 1, 0, 0, 0), V10 = c(1, 0, 0, 0, 0, 1, 2, 0, 0, 
0, 0, 0, 0, 0, 0, 0), V11 = c(0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 
0, 0, 0, 0, 0), V12 = c(0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 
0, 0, 0), V13 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0), V14 = c(0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), 
V15 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V16 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V17 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V18 = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1), 
V19 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V20 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), 
V21 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V22 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2), 
V23 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0), 
V24 = c(2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), 
V25 = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0), 
V26 = c(0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1), 
V27 = c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V28 = c(0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0), 
V29 = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0), 
V30 = c(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), 
V31 = c(0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V32 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), 
V33 = c(0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), 
V34 = c(5, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0, 0), 
V35 = c(1, 0, 22, 0, 1, 1, 0, 0, 0, 0, 0, 38, 0, 2, 0, 0), 
V36 = c(0, 2, 4, 2, 1, 0, 0, 0, 0, 0, 0, 23, 0, 2, 0, 1), 
V37 = c(0, 0, 10, 0, 2, 0, 0, 0, 0, 0, 0, 10, 0, 2, 0, 7), 
V38 = c(1, 2, 2, 1, 2, 0, 0, 0, 0, 0, 0, 16, 2, 2, 0, 7), 
V39 = c(0, 1, 9, 0, 28, 2, 0, 0, 0, 0, 8, 12, 0, 1, 0, 2), 
V40 = c(1, 0, 2, 0, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V41 = c(0, 0, 3, 0, 10, 0, 0, 0, 1, 0, 0, 18, 0, 0, 0, 1), 
V42 = c(0, 0, 1, 0, 8, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0), 
V43 = c(0, 1, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V44 = c(1, 0, 1, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V45 = c(0, 0, 9, 0, 6, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1), 
V46 = c(0, 0, 4, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V47 = c(0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V48 = c(0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0), 
V49 = c(0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V50 = c(0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V51 = c(0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V52 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), 
V53 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), .Names =                 c("V1", 
"V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", 
"V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", 
"V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", 
"V30", "V31", "V32", "V33", "V34", "V35", "V36", "V37", "V38", 
"V39", "V40", "V41", "V42", "V43", "V44", "V45", "V46", "V47", 
"V48", "V49", "V50", "V51", "V52", "V53"), row.names = c(NA, 
16L), class = "data.frame")
&lt;/code&gt;&lt;/pre&gt;
', 'r  clustering', NULL, '''/code'':116C,382C,1451C ''/p'':78C,180C,256C,272C,389C ''/pre'':117C,383C,1452C 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''c'':396C,414C,455C,473C,491C,509C,527C,545C,563C,581C,599C,617C,635C,653C,671C,689C,707C,725C,743C,761C,779C,797C,815C,833C,851C,869C,887C,905C,923C,941C,959C,977C,995C,1013C,1031C,1049C,1067C,1085C,1103C,1121C,1139C,1157C,1175C,1193C,1211C,1229C,1247C,1265C,1283C,1301C,1319C,1337C,1355C,1373C,1391C,1446C ''case'':34C,61C,195C ''center'':94C,441C ''chc'':415C ''christian'':105C,425C ''class'':452C,1449C ''cluster'':1A,18B,127C,131C,137C,146C,167C,191C,213C,263C,268C ''code'':80C,274C,391C ''column'':44C ''comb'':417C ''consecut'':159C ''contain'':30C ''continu'':12A,228C,251C ''could'':254C ''data'':3A,29C,157C,388C ''data.frame'':1450C ''dataset'':23C,41C,73C ''detect'':5A,155C ''determin'':130C ''diagnos'':66C ''doon'':420C ''dput'':385C ''entri'':77C ''exampl'':70C,203C ''factor'':453C ''fall'':209C ''find'':189C ''found'':178C ''fri'':422C ''get'':247C ''given'':221C ''goal'':143C ''health'':26C,93C,440C ''help'':255C ''herbatpur'':434C ''herbertpur'':104C,424C ''hierarch'':122C 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''use'':121C,156C,186C,269C ''v1'':394C,1392C ''v10'':598C,1401C ''v11'':616C,1402C ''v12'':634C,1403C ''v13'':652C,1404C ''v14'':670C,1405C ''v15'':688C,1406C ''v16'':706C,1407C ''v17'':724C,1408C ''v18'':742C,1409C ''v19'':760C,1410C ''v2'':454C,1393C ''v20'':778C,1411C ''v21'':796C,1412C ''v22'':814C,1413C ''v23'':832C,1414C ''v24'':850C,1415C ''v25'':868C,1416C ''v26'':886C,1417C ''v27'':904C,1418C ''v28'':922C,1419C ''v29'':940C,1420C ''v3'':472C,1394C ''v30'':958C,1421C ''v31'':976C,1422C ''v32'':994C,1423C ''v33'':1012C,1424C ''v34'':1030C,1425C ''v35'':1048C,1426C ''v36'':1066C,1427C ''v37'':1084C,1428C ''v38'':1102C,1429C ''v39'':1120C,1430C ''v4'':490C,1395C ''v40'':1138C,1431C ''v41'':1156C,1432C ''v42'':1174C,1433C ''v43'':1192C,1434C ''v44'':1210C,1435C ''v45'':1228C,1436C ''v46'':1246C,1437C ''v47'':1264C,1438C ''v48'':1282C,1439C ''v49'':1300C,1440C ''v5'':508C,1396C ''v50'':1318C,1441C ''v51'':1336C,1442C ''v52'':1354C,1443C ''v53'':1372C,1444C ''v6'':526C,1397C ''v7'':544C,1398C ''v8'':562C,1399C ''v9'':580C,1400C ''vaish'':449C ''want'':225C,250C ''week'':2A,16A,38C,48C,65C,76C,139C,160C,196C,208C,230C,237C,265C,275C ''well'':135C ''x'':277C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9811, 1, '2016-01-16 18:10:40.52', 0, 17, 'Hadoop MapReduce2 Optimization in Heterogeneous Cluster', '&lt;p&gt;I have this configuration:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Hadoop: v2.7.1 (Yarn)&lt;/li&gt;
&lt;li&gt;An input file: Size = 100 GB.&lt;/li&gt;
&lt;li&gt;3 Slaves: each has 4 VCORES with Speed = 2 GHz and RAM = 8 GB&lt;/li&gt;
&lt;li&gt;5 Slaves: each has 2 VCORES with Speed = 1 GHz and RAM = 2 GB&lt;/li&gt;
&lt;li&gt;MapReduce program: WordCount&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;How can I minimize WordCount execution time by assigning small input splits to the 5 slower slaves and big input splits to the 3 fastest slaves?&lt;/p&gt;
', 'hadoop  map-reduce', NULL, '''/li'':22C,30C,46C,62C,67C ''/p'':16C,96C ''/ul'':68C ''1'':56C ''100'':28C ''2'':40C,52C,60C ''3'':32C,93C ''4'':36C ''5'':48C,84C ''8'':44C ''assign'':78C ''big'':88C ''cluster'':6A ''configur'':15C ''execut'':75C ''fastest'':94C ''file'':26C ''gb'':29C,45C,61C ''ghz'':41C,57C ''hadoop'':1A,7B,19C ''heterogen'':5A ''input'':25C,80C,89C ''li'':18C,23C,31C,47C,63C ''map'':9B ''map-reduc'':8B ''mapreduc'':64C ''mapreduce2'':2A ''minim'':73C ''optim'':3A ''p'':11C,69C ''program'':65C ''ram'':43C,59C ''reduc'':10B ''size'':27C ''slave'':33C,49C,86C,95C ''slower'':85C ''small'':79C ''speed'':39C,55C ''split'':81C,90C ''time'':76C ''ul'':17C ''v2.7.1'':20C ''vcore'':37C,53C ''wordcount'':66C,74C ''yarn'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9812, 1, '2016-01-16 18:54:01.93', 1, 19, 'Natural Language text categorization using RapidMiner', '&lt;p&gt;I''m new to data mining, so this might sound like a very simple task to some.&lt;/p&gt;

&lt;p&gt;So, I work in reliability engineering in aviation &amp;amp; I have a set of data that is generated on a daily basis regarding system failures &amp;amp; failure rectification. This data is categorized using numerical tags of maintenance manual tasks (reference data) by chapter, section and paragraph. However, since the data is entered manually by people, sometimes, the wrong chapter/section tags entered &amp;amp; would require to be manually checked to insure the data''s validity.&lt;/p&gt;

&lt;p&gt;The failure/resolution data is available in table format (CSV, Excel...) and I also have the maintenance manual data keywords including their chapters/sections in table format.&lt;/p&gt;

&lt;p&gt;My question is, is it possible using RapidMiner to take these tables, cross check some keywords in the text (failure/rectification) &amp;amp; compare it with the reference data, and output it with the proper reference tags (of the chapters, sections...), taking into account spelling errors, acronyms &amp;amp; abbreviations, or is there a program/application that is more specialized than RapidMiner to do these functions?&lt;/p&gt;

&lt;p&gt;Example: Failure on system X was rectified and logged on the database. System X is under chapter 4 section 33, however, when entering the data, the person put it under chapter 3 section 44. I have a document that has the reference for system X being under Chapter 4 Section 33. Is it possible for RapidMiner to check the text in the failure text &amp;amp; the rectification text &amp;amp; cross-check it with a predefined list where System x is under Chapter 4 Section 33 &amp;amp; give me the output with the right Chapter/Section, taking into account spelling mistakes &amp;amp; the fact that some people write abbreviations and acronyms differently (example, I.B.M/IBM/I.B.M.)&lt;/p&gt;
', 'data-mining  classification  data-cleaning', NULL, '''/p'':32C,108C,135C,199C,314C ''3'':232C ''33'':220C,251C,286C ''4'':218C,249C,284C ''44'':234C ''abbrevi'':183C,308C ''account'':178C,298C ''acronym'':181C,310C ''also'':122C ''amp'':42C,59C,95C,157C,182C,265C,269C,287C,301C ''avail'':114C ''aviat'':41C ''basi'':55C ''categor'':4A,65C ''chapter'':76C,174C,217C,231C,248C,283C ''chapter/section'':92C,295C ''chapters/sections'':131C ''check'':101C,150C,258C,272C ''classif'':10B ''clean'':13B ''compar'':158C ''cross'':149C,271C ''cross-check'':270C ''csv'':118C ''daili'':54C ''data'':8B,12B,19C,48C,63C,74C,83C,105C,112C,127C,163C,225C ''data-clean'':11B ''data-min'':7B ''databas'':212C ''differ'':311C ''document'':238C ''engin'':39C ''enter'':85C,94C,223C ''error'':180C ''exampl'':201C,312C ''excel'':119C ''fact'':303C ''failur'':58C,60C,202C,263C ''failure/rectification'':156C ''failure/resolution'':111C ''format'':117C,134C ''function'':198C ''generat'':51C ''give'':288C ''howev'':80C,221C ''i.b.m/ibm/i.b.m'':313C ''includ'':129C ''insur'':103C ''keyword'':128C,152C ''languag'':2A ''like'':25C ''list'':277C ''log'':209C ''m'':16C ''mainten'':70C,125C ''manual'':71C,86C,100C,126C ''might'':23C ''mine'':9B,20C ''mistak'':300C ''natur'':1A ''new'':17C ''numer'':67C ''output'':165C,291C ''p'':14C,33C,109C,136C,200C ''paragraph'':79C ''peopl'':88C,306C ''person'':227C ''possibl'':142C,254C ''predefin'':276C ''program/application'':188C ''proper'':169C ''put'':228C ''question'':138C ''rapidmin'':6A,144C,194C,256C ''rectif'':61C,267C ''rectifi'':207C ''refer'':73C,162C,170C,242C ''regard'':56C ''reliabl'':38C ''requir'':97C ''right'':294C ''section'':77C,175C,219C,233C,250C,285C ''set'':46C ''simpl'':28C ''sinc'':81C ''sometim'':89C ''sound'':24C ''special'':192C ''spell'':179C,299C ''system'':57C,204C,213C,244C,279C ''tabl'':116C,133C,148C ''tag'':68C,93C,171C ''take'':146C,176C,296C ''task'':29C,72C ''text'':3A,155C,260C,264C,268C ''use'':5A,66C,143C ''valid'':107C ''work'':36C ''would'':96C ''write'':307C ''wrong'':91C ''x'':205C,214C,245C,280C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9813, 1, '2016-01-17 04:36:02.373', 1, 54, 'Face Recognition using eigenfaces and SVM', '&lt;p&gt;I am new to machine learning. I want to develop a face recognition system using scikit-learn. The following is the example given in the tutorials of scikit-learn:
&lt;a href="http://scikit-learn.org/stable/auto_examples/applications/face_recognition.html#example-applications-face-recognition-py" rel="nofollow"&gt;http://scikit-learn.org/stable/auto_examples/applications/face_recognition.html#example-applications-face-recognition-py&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I am not getting how the input is being provided to the program. How should I load a particular image and makes my program run to predict the label for that?
Please help!!&lt;/p&gt;
', 'machine-learning  python  svm  scikit', NULL, '''/p'':89C ''/stable/auto_examples/applications/face_recognition.html#example-applications-face-recognition-py'':49C ''/stable/auto_examples/applications/face_recognition.html#example-applications-face-recognition-py&lt;/a&gt;&lt;/p&gt;'':54C ''develop'':23C ''eigenfac'':4A ''exampl'':36C ''face'':1A,25C ''follow'':33C ''get'':59C ''given'':37C ''help'':88C ''href'':46C ''imag'':75C ''input'':62C ''label'':84C ''learn'':9B,19C,31C,44C ''load'':72C ''machin'':8B,18C ''machine-learn'':7B ''make'':77C ''new'':16C ''nofollow'':51C ''p'':13C,55C ''particular'':74C ''pleas'':87C ''predict'':82C ''program'':68C,79C ''provid'':65C ''python'':10B ''recognit'':2A,26C ''rel'':50C ''run'':80C ''scikit'':12B,30C,43C ''scikit-learn'':29C,42C ''scikit-learn.org'':48C,53C ''scikit-learn.org/stable/auto_examples/applications/face_recognition.html#example-applications-face-recognition-py'':47C ''scikit-learn.org/stable/auto_examples/applications/face_recognition.html#example-applications-face-recognition-py&lt;/a&gt;&lt;/p&gt;'':52C ''svm'':6A,11B ''system'':27C ''tutori'':40C ''use'':3A,28C ''want'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9830, 4, '2016-01-18 12:18:28.227', 0, NULL, NULL, 'A probabilistic graphical model (PGM) is a probabilisic model for which conditional dependencies are expressed with a graph G = (X, E) where X are random variables.', NULL, NULL, '''condit'':12C ''depend'':13C ''e'':21C ''express'':15C ''g'':19C ''graph'':18C ''graphic'':3C ''model'':4C,9C ''pgm'':5C ''probabilis'':8C ''probabilist'':2C ''random'':25C ''variabl'':26C ''x'':20C,23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9814, 2, '2016-01-17 06:02:00.537', 1, NULL, NULL, '&lt;p&gt;Take a look at the code that you linked to:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# Download the data, if not already on disk and load it as numpy arrays

lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_lfw_people.html#sklearn.datasets.fetch_lfw_people" rel="nofollow"&gt;&lt;code&gt;fetch_lfw-poeple&lt;/code&gt;&lt;/a&gt; is a routine that loads the data and is detailed &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_lfw_people.html#sklearn.datasets.fetch_lfw_people" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/a'':57C,76C ''/code'':41C,56C ''/p'':12C,77C,82C ''/pre'':42C ''/stable/modules/generated/sklearn.datasets.fetch_lfw_people.html#sklearn.datasets.fetch_lfw_people'':48C,72C ''0.4'':40C ''70'':38C ''alreadi'':20C ''array'':28C ''code'':7C,14C,51C ''data'':17C,64C ''detail'':67C ''disk'':22C ''download'':15C ''face'':35C ''fetch'':31C,52C ''help'':81C ''hope'':79C ''href'':45C,69C ''lfw'':29C,32C,54C ''lfw-poepl'':53C ''link'':10C ''load'':24C,62C ''look'':4C ''min'':34C ''nofollow'':50C,74C ''numpi'':27C ''p'':1C,43C,78C ''peopl'':30C,33C ''per'':36C ''person'':37C ''poepl'':55C ''pre'':13C ''rel'':49C,73C ''resiz'':39C ''routin'':60C ''scikit-learn.org'':47C,71C ''scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_lfw_people.html#sklearn.datasets.fetch_lfw_people'':46C,70C ''take'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9843, 2, '2016-01-19 00:19:09.127', 2, NULL, NULL, '&lt;p&gt;If one likes cross-platform visual programming tools, &lt;a href="http://orange.biolab.si" rel="nofollow"&gt;Orange&lt;/a&gt; is an option. Having recently moved to Python 3, they haven''t yet got all the widgets ported.
It''s bringing the PyData stack (NumPy, SciPy, SciKit Learn, ...) to Python 3, PyQt, PyQtGraph, and it''s GPL''d &lt;a href="https://github.com/biolab/orange3" rel="nofollow"&gt;on GitHub&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/YnQh6.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/YnQh6.png" alt="Orange screenshow"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':17C,65C,83C ''/biolab/orange3'':60C ''/p'':66C,84C ''/ynqh6.png'':72C,79C ''3'':26C,48C ''alt'':80C ''bring'':38C ''cross'':6C ''cross-platform'':5C ''d'':55C ''github'':64C ''github.com'':59C ''github.com/biolab/orange3'':58C ''got'':31C ''gpl'':54C ''haven'':28C ''href'':12C,57C,69C ''i.stack.imgur.com'':71C,78C ''i.stack.imgur.com/ynqh6.png'':70C,77C ''img'':75C ''learn'':45C ''like'':4C ''move'':23C ''nofollow'':15C,62C,74C ''numpi'':42C ''one'':3C ''option'':20C ''orang'':16C,81C ''orange.biolab.si'':13C ''p'':1C,67C ''platform'':7C ''port'':35C ''program'':9C ''pydata'':40C ''pyqt'':49C ''pyqtgraph'':50C ''python'':25C,47C ''recent'':22C ''rel'':14C,61C,73C ''scikit'':44C ''scipi'':43C ''screenshow'':82C ''src'':76C ''stack'':41C ''tool'':10C ''visual'':8C ''widget'':34C ''yet'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9816, 2, '2016-01-17 10:45:18.583', 1, NULL, NULL, '&lt;p&gt;You can try the exact same heuristics you would use for k-means.&lt;/p&gt;

&lt;p&gt;But usually, you would do this:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;try some parameters by experience&lt;/li&gt;
&lt;li&gt;run the algorithm&lt;/li&gt;
&lt;li&gt;carefully inspect the result&lt;/li&gt;
&lt;li&gt;go back to 1 and try other parameters until you have found something interesting or are tired of trying&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;There is no "correct" solution in clustering.&lt;/p&gt;

&lt;p&gt;It''s about discovering some &lt;em&gt;new&lt;/em&gt; pattern and you cannot out the "new" into an optimization equation. The point is to try, and try again. It is not a drop-in replacement for classification.&lt;/p&gt;
', NULL, NULL, '''/em'':83C ''/li'':32C,37C,43C,64C ''/ol'':65C ''/p'':16C,24C,74C,113C ''1'':48C ''algorithm'':36C ''back'':46C ''cannot'':87C ''care'':39C ''classif'':112C ''cluster'':73C ''correct'':70C ''discov'':79C ''drop'':108C ''drop-in'':107C ''em'':81C ''equat'':94C ''exact'':6C ''experi'':31C ''found'':56C ''go'':45C ''heurist'':8C ''inspect'':40C ''interest'':58C ''k'':14C ''k-mean'':13C ''li'':26C,33C,38C,44C ''mean'':15C ''new'':82C,90C ''ol'':25C ''optim'':93C ''p'':1C,17C,66C,75C ''paramet'':29C,52C ''pattern'':84C ''point'':96C ''replac'':110C ''result'':42C ''run'':34C ''solut'':71C ''someth'':57C ''tire'':61C ''tri'':4C,27C,50C,63C,99C,101C ''use'':11C ''usual'':19C ''would'':10C,21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9817, 2, '2016-01-17 11:12:16.17', 0, NULL, NULL, '&lt;p&gt;Do you really want clustering here?&lt;/p&gt;

&lt;p&gt;Instead, I''d look at &lt;em&gt;segmentation&lt;/em&gt; and &lt;em&gt;change detection&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;For example, you could compute the absolute (or relative) change from one row to another, and segment at the largest change points.&lt;/p&gt;
', NULL, NULL, '''/em'':17C,22C ''/p'':8C,23C,47C ''absolut'':31C ''anoth'':39C ''chang'':20C,34C,45C ''cluster'':6C ''comput'':29C ''could'':28C ''d'':12C ''detect'':21C ''em'':15C,19C ''exampl'':26C ''instead'':10C ''largest'':44C ''look'':13C ''one'':36C ''p'':1C,9C,24C ''point'':46C ''realli'':4C ''relat'':33C ''row'':37C ''segment'':16C,41C ''want'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9818, 1, '2016-01-17 13:04:57.1', 23, 5249, 'Is there any domain where Bayesian Networks outperform neural networks?', '&lt;p&gt;Neural networks get top results in Computer Vision tasks (see &lt;a href="http://yann.lecun.com/exdb/mnist/"&gt;MNIST&lt;/a&gt;, &lt;a href="http://www.image-net.org/challenges/LSVRC/"&gt;ILSVRC&lt;/a&gt;, &lt;a href="http://blog.kaggle.com/2014/04/18/winning-the-galaxy-challenge-with-convnets/"&gt;Kaggle Galaxy Challenge&lt;/a&gt;).  They seem to outperform every other approach in Computer Vision. But there are also other tasks:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://blog.kaggle.com/2012/11/01/deep-learning-how-i-did-it-merck-1st-place-interview/"&gt;Kaggle Molecular Activity Challenge&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Regression: &lt;a href="http://blog.kaggle.com/2016/01/04/how-much-did-it-rain-ii-winners-interview-1st-place-pupa-aka-aaron-sim/"&gt;Kaggle Rain prediction&lt;/a&gt;, also the &lt;a href="http://blog.kaggle.com/2015/12/17/how-much-did-it-rain-ii-2nd-place-luis-andre-dutra-e-silva/"&gt;2nd place&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://blog.kaggle.com/2015/09/29/grasp-and-lift-eeg-detection-winners-interview-2nd-place-daheimao/"&gt;Grasp and Lift 2nd&lt;/a&gt; also &lt;a href="http://blog.kaggle.com/2015/10/05/grasp-and-lift-eeg-detection-winners-interview-3rd-place-team-hedj/"&gt;third place&lt;/a&gt; - Identify hand motions from EEG recordings&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I''m not too sure about ASR (automatic speech recognition) and machine translation, but I think I''ve also heard that (recurrent) neural networks (start to) outperform other approaches.&lt;/p&gt;

&lt;p&gt;I am currently learning about Bayesian Networks and I wonder in which cases those models are usually applied. So my question is:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is there any challenge / (Kaggle) competition, where the state of the art are Bayesian Networks or at least very similar models?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;(Side note: I''ve also seen &lt;a href="http://blog.kaggle.com/2015/12/21/rossmann-store-sales-winners-interview-1st-place-gert/"&gt;decision trees&lt;/a&gt;, &lt;a href="http://blog.kaggle.com/2015/11/09/profiling-top-kagglers-gilberto-titericz-new-1-in-the-world/"&gt;2&lt;/a&gt;, &lt;a href="http://blog.kaggle.com/2015/10/30/dato-winners-interview-2nd-place-mortehu/"&gt;3&lt;/a&gt;, &lt;a href="http://blog.kaggle.com/2015/10/21/recruit-coupon-purchase-winners-interview-2nd-place-halla-yang/"&gt;4&lt;/a&gt;, &lt;a href="http://blog.kaggle.com/2015/10/20/caterpillar-winners-interview-3rd-place-team-shift-workers/"&gt;5&lt;/a&gt;, &lt;a href="http://blog.kaggle.com/2015/09/28/liberty-mutual-property-inspection-winners-interview-qingchen-wang/"&gt;6&lt;/a&gt;, &lt;a href="http://blog.kaggle.com/2015/09/22/caterpillar-winners-interview-1st-place-gilberto-josef-leustagos-mario/"&gt;7&lt;/a&gt; win in several recent Kaggle challenges)&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9892, 1, '2016-01-21 20:15:04.757', 6, 114, 'How can I dynamically distinguish between categorical data and numerical data?', '&lt;p&gt;I know someone who is working on a project that involves ingesting files of data without regard to the columns or data types. The task is to take a file with any number of columns and various data types and output summary statistics on the numerical data.&lt;/p&gt;

&lt;p&gt;However, he is unsure of how to go about dynamically assigning data types for certain number-based data. For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    CITY
    Albuquerque
    Boston
    Chicago
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This is obviously not numerical data and will be stored as text. However,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    ZIP
    80221
    60653
    25525
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;are not clearly marked as categorical. His software would assign the ZIP code as numerical and output summary statistics for it, which does not make sense for that sort of data.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;A couple ideas we had were:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;If a column is all integers, label it as categorical. This clearly wouldn''t work, but it was an idea.&lt;/li&gt;
&lt;li&gt;If a column has fewer than &lt;em&gt;n&lt;/em&gt; unique values and is numeric, label it categorical. This might be closer, but there could still be issues with numerical data falling through.&lt;/li&gt;
&lt;li&gt;Maintain a list of common numeric data that should actually be categorical and compare the column headers to this list for matches. For example, anything with "ZIP" in it would be categorical.&lt;/li&gt;
&lt;/ol&gt;

&lt;hr&gt;

&lt;p&gt;My gut tells me that there is no way to accurately assign numeric data as categorical or numerical, but was hoping for a suggestion. Any insight you have is greatly appreciated.&lt;/p&gt;
', 'categorical-data  numerical', NULL, '''/code'':94C,117C ''/em'':193C ''/li'':183C,217C,251C ''/ol'':252C ''/p'':64C,87C,110C,151C,160C,286C ''/pre'':95C,118C ''25525'':116C ''60653'':115C ''80221'':114C ''accur'':265C ''actual'':228C ''albuquerqu'':91C ''anyth'':243C ''appreci'':285C ''assign'':76C,129C,266C ''base'':83C ''boston'':92C ''categor'':7A,13B,125C,172C,201C,230C,250C,270C ''categorical-data'':12B ''certain'':80C ''chicago'':93C ''citi'':90C ''clear'':122C,174C ''closer'':205C ''code'':89C,112C,132C ''column'':36C,51C,165C,187C,234C ''common'':223C ''compar'':232C ''could'':208C ''coupl'':155C ''data'':8A,11A,14B,31C,38C,54C,63C,77C,84C,102C,150C,214C,225C,268C ''distinguish'':5A ''dynam'':4A,75C ''em'':191C ''exampl'':86C,242C ''fall'':215C ''fewer'':189C ''file'':29C,46C ''go'':73C ''great'':284C ''gut'':256C ''header'':235C ''hope'':275C ''howev'':66C,109C ''hr'':152C,253C ''idea'':156C,182C ''ingest'':28C ''insight'':280C ''integ'':168C ''involv'':27C ''issu'':211C ''know'':18C ''label'':169C,199C ''li'':162C,184C,218C ''list'':221C,238C ''maintain'':219C ''make'':144C ''mark'':123C ''match'':240C ''might'':203C ''n'':192C ''number'':49C,82C ''number-bas'':81C ''numer'':10A,15B,62C,101C,134C,198C,213C,224C,267C,272C ''obvious'':99C ''ol'':161C ''output'':57C,136C ''p'':16C,65C,96C,119C,153C,254C ''pre'':88C,111C ''project'':25C ''regard'':33C ''sens'':145C ''softwar'':127C ''someon'':19C ''sort'':148C ''statist'':59C,138C ''still'':209C ''store'':106C ''suggest'':278C ''summari'':58C,137C ''take'':44C ''task'':41C ''tell'':257C ''text'':108C ''type'':39C,55C,78C ''uniqu'':194C ''unsur'':69C ''valu'':195C ''various'':53C ''way'':263C ''without'':32C ''work'':22C,177C ''would'':128C,248C ''wouldn'':175C ''zip'':113C,131C,245C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9824, 2, '2016-01-17 19:53:08.783', 14, NULL, NULL, '&lt;p&gt;Bayesian networks and neural networks are not exclusive of each other. In fact, Bayesian networks are just another term for "directed graphical model". They can be very useful in designing objective functions neural networks. Yann Lecun has pointed this out here: &lt;a href="https://plus.google.com/+YannLeCunPhD/posts/gWE7Jca3Zoq"&gt;https://plus.google.com/+YannLeCunPhD/posts/gWE7Jca3Zoq&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;One example.&lt;/p&gt;

&lt;p&gt;The variational auto encoder and derivatives are directed graphical models of the form $$p(x) = \int_z p(x|z)p(z) dz.$$ A neural networks is used to implemented $p(x|z)$ and an approximation to its inverse: $q(z|x) \approx p(z|x)$.&lt;/p&gt;
', NULL, NULL, '''/+yannlecunphd/posts/gwe7jca3zoq'':47C ''/+yannlecunphd/posts/gwe7jca3zoq&lt;/a&gt;.&lt;/p&gt;'':50C ''/p'':54C,102C ''anoth'':19C ''approx'':98C ''approxim'':91C ''auto'':58C ''bayesian'':2C,15C ''deriv'':61C ''design'':31C ''direct'':22C,63C ''dz'':78C ''encod'':59C ''exampl'':53C ''exclus'':9C ''fact'':14C ''form'':68C ''function'':33C ''graphic'':23C,64C ''href'':44C ''implement'':85C ''int'':71C ''invers'':94C ''lecun'':37C ''model'':24C,65C ''network'':3C,6C,16C,35C,81C ''neural'':5C,34C,80C ''object'':32C ''one'':52C ''p'':1C,51C,55C,69C,73C,76C,86C,99C ''plus.google.com'':46C,49C ''plus.google.com/+yannlecunphd/posts/gwe7jca3zoq'':45C ''plus.google.com/+yannlecunphd/posts/gwe7jca3zoq&lt;/a&gt;.&lt;/p&gt;'':48C ''point'':39C ''q'':95C ''term'':20C ''use'':29C,83C ''variat'':57C ''x'':70C,74C,87C,97C,101C ''yann'':36C ''z'':72C,75C,77C,88C,96C,100C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9844, 2, '2016-01-19 03:05:25.887', 1, NULL, NULL, '&lt;p&gt;I don''t know about RapidMiner, but for a beginner, &lt;a href="http://orange.biolab.si" rel="nofollow"&gt;Orange&lt;/a&gt; seems an awesome open-source tool for the job. &lt;/p&gt;

&lt;p&gt;Particularly if you want to do unsupervised learning, i.e. clustering. You''d probably want to preprocess your textual data in some &lt;em&gt;bag of words&lt;/em&gt; model and save it as CSV, I guess.&lt;/p&gt;

&lt;p&gt;I also hear Orange has a &lt;a href="https://pypi.python.org/pypi/Orange3-Text/" rel="nofollow"&gt;text mining add-on&lt;/a&gt;, but I haven''t tried it yet.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/YnQh6.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/YnQh6.png" alt="Orange screenshow"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':18C,85C,110C ''/em'':56C ''/p'':29C,65C,93C,111C ''/pypi/orange3-text/'':77C ''/ynqh6.png'':99C,106C ''add'':83C ''add-on'':82C ''also'':68C ''alt'':107C ''awesom'':21C ''bag'':53C ''beginn'':11C ''cluster'':40C ''csv'':62C ''d'':42C ''data'':49C ''em'':52C ''guess'':64C ''haven'':88C ''hear'':69C ''href'':13C,74C,96C ''i.e'':39C ''i.stack.imgur.com'':98C,105C ''i.stack.imgur.com/ynqh6.png'':97C,104C ''img'':102C ''job'':28C ''know'':5C ''learn'':38C ''mine'':81C ''model'':57C ''nofollow'':16C,79C,101C ''open'':23C ''open-sourc'':22C ''orang'':17C,70C,108C ''orange.biolab.si'':14C ''p'':1C,30C,66C,94C ''particular'':31C ''preprocess'':46C ''probabl'':43C ''pypi.python.org'':76C ''pypi.python.org/pypi/orange3-text/'':75C ''rapidmin'':7C ''rel'':15C,78C,100C ''save'':59C ''screenshow'':109C ''seem'':19C ''sourc'':24C ''src'':103C ''text'':80C ''textual'':48C ''tool'':25C ''tri'':90C ''unsupervis'':37C ''want'':34C,44C ''word'':55C ''yet'':92C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10033, 2, '2016-01-30 20:35:13.647', 1, NULL, NULL, '&lt;p&gt;Add a regularization term for synonymity to the objective function (to be maximized):&lt;/p&gt;

&lt;p&gt;$\log P(w_i|h) \rightarrow \log P(w_i|h) + \lambda \frac{1}{|S|} \sum_{(i,j) \in S} sim(w_i, w_j)$ where $S$ is the set of synonyms, and $sim$ is your word similarity function (e.g., cosine similarity).&lt;/p&gt;
', NULL, NULL, '''/p'':15C,59C ''1'':30C ''add'':2C ''cosin'':57C ''e.g'':56C ''frac'':29C ''function'':11C,55C ''h'':21C,27C ''j'':34C,41C ''lambda'':28C ''log'':17C,23C ''maxim'':14C ''object'':10C ''p'':1C,16C,18C,24C ''regular'':4C ''rightarrow'':22C ''set'':46C ''sim'':37C,50C ''similar'':54C,58C ''sum'':32C ''synonym'':7C,48C ''term'':5C ''w'':19C,25C,38C,40C ''word'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9825, 2, '2016-01-18 03:20:15.597', 11, NULL, NULL, '&lt;p&gt;Excellent answers already.&lt;/p&gt;

&lt;p&gt;One domain which I can think of, and is working extensively in, is the &lt;strong&gt;customer analytics&lt;/strong&gt; domain.&lt;/p&gt;

&lt;p&gt;I have to understand and predict the moves and motives of the customers in order to inform and warn both the customer support, the marketing and also the growth teams.&lt;/p&gt;

&lt;p&gt;So here, neural networks do a really good job in churn prediction, etc. But, I found and prefer the Bayesian networks style, and here are the reasons for preferring it:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Customers always have a pattern. They always have a &lt;em&gt;reason&lt;/em&gt; to act. And that reason would be something which my team has done for them, or they have learnt themselves. So, everything has a prior here, and in fact that reason is very important as it fuels most of the decision taken by the customer.&lt;/li&gt;
&lt;li&gt;Every move by the customer and the growth teams in the marketing/sales funnel is cause-effect. So, prior knowledge is vital when it comes to converting a prospective lead into a customer.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;So, the concept of &lt;em&gt;prior&lt;/em&gt; is very important when it comes to customer analytics, which makes the concept of Bayesian networks very important to this domain.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Suggested Learning: &lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.cs.cmu.edu/afs/cs/academic/class/15782-f06/slides/bayesian.pdf"&gt;Bayesian Methods for Neural Networks&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://fedcsis.org/proceedings/2012/pliks/190.pdf"&gt;Bayesian networks in business analytics&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':233C,246C ''/afs/cs/academic/class/15782-f06/slides/bayesian.pdf'':227C ''/em'':104C,194C ''/li'':150C,185C ''/ol'':186C ''/p'':5C,26C,58C,90C,216C,221C,234C,247C ''/proceedings/2012/pliks/190.pdf'':240C ''/strong'':24C ''act'':106C ''alreadi'':4C ''also'':54C ''alway'':94C,99C ''analyt'':23C,203C,245C ''answer'':3C ''bayesian'':79C,209C,228C,241C ''busi'':244C ''caus'':167C ''cause-effect'':166C ''churn'':70C ''come'':176C,200C ''concept'':190C,207C ''convert'':178C ''custom'':22C,40C,49C,93C,149C,156C,184C,202C ''decis'':145C ''domain'':8C,25C,215C ''done'':117C ''effect'':168C ''em'':102C,192C ''etc'':72C ''everi'':152C ''everyth'':126C ''excel'':2C ''extens'':17C ''fact'':133C ''fedcsis.org'':239C ''fedcsis.org/proceedings/2012/pliks/190.pdf'':238C ''found'':75C ''fuel'':141C ''funnel'':164C ''good'':67C ''growth'':56C,159C ''hr'':217C ''href'':224C,237C ''import'':138C,197C,212C ''inform'':44C ''job'':68C ''knowledg'':171C ''lead'':181C ''learn'':220C ''learnt'':123C ''li'':92C,151C ''make'':205C ''market'':52C ''marketing/sales'':163C ''method'':229C ''motiv'':37C ''move'':35C,153C ''network'':63C,80C,210C,232C,242C ''neural'':62C,231C ''ol'':91C ''one'':7C ''order'':42C ''p'':1C,6C,27C,59C,187C,218C,222C,235C ''pattern'':97C ''predict'':33C,71C ''prefer'':77C,88C ''prior'':129C,170C,193C ''prospect'':180C ''realli'':66C ''reason'':86C,103C,109C,135C ''someth'':112C ''strong'':21C ''style'':81C ''suggest'':219C ''support'':50C ''taken'':146C ''team'':57C,115C,160C ''think'':12C ''understand'':31C ''vital'':173C ''warn'':46C ''work'':16C ''would'':110C ''www.cs.cmu.edu'':226C ''www.cs.cmu.edu/afs/cs/academic/class/15782-f06/slides/bayesian.pdf'':225C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9827, 1, '2016-01-18 08:18:54.693', 2, 130, 'Is there an appropriate sequence of parameters to be considered when building a pipeline in Scikit learn?', '&lt;p&gt;&lt;strong&gt;DISCLAIMER:&lt;/strong&gt; &lt;em&gt;I understand this could be considered a subjective type of question, but I''m hoping it is specific enough to not be flagged. Please give it a chance.&lt;/em&gt;  &lt;/p&gt;

&lt;p&gt;I am doing a GridSearch in scikit-learn on a corpus of 50 books (all kinda average commercially published texts...each ~200 pages of text).  &lt;/p&gt;

&lt;p&gt;As I began playing with GridSearch, I got to thinking. Surely there must be some sort of ''best practices'' for building a pipeline?  &lt;/p&gt;

&lt;p&gt;Sure, I can include and remove parameters, but I''m really just sorta stabbing around in the dark for the best score.  &lt;/p&gt;

&lt;p&gt;Possibly, it could make sense to create a smaller, exploratory pipeline using the larger, more effective parameters first. After running this basic pipeline, more parameters that have a smaller effect (in general) could be run next.  &lt;/p&gt;

&lt;p&gt;For example, say I''m GridSearching a TfidfTransformer.  I am assuming that flipping the &lt;code&gt;use_idf&lt;/code&gt; switch on or off is going to make a real impact on the scoring, whereas perhaps specifying &lt;code&gt;alpha=0.000010&lt;/code&gt; versus &lt;code&gt;alpha=0.000015&lt;/code&gt; won''t.  &lt;/p&gt;

&lt;p&gt;In general, when using a pipeline, is there an alternative to "throw in the kitchen sink" that results in a more cognizant, informative (and possibly faster) experience?&lt;/p&gt;
', 'python  scikit', NULL, '''/code'':188C,209C,214C ''/em'':53C ''/p'':54C,82C,107C,131C,169C,217C,246C ''/strong'':23C ''0.000010'':208C ''0.000015'':213C ''200'':78C ''50'':69C ''alpha'':207C,212C ''altern'':228C ''appropri'':4A ''around'':123C ''assum'':181C ''averag'':73C ''basic'':154C ''began'':86C ''best'':101C,129C ''book'':70C ''build'':12A,104C ''chanc'':52C ''code'':185C,206C,211C ''cogniz'':240C ''commerci'':74C ''consid'':10A,30C ''corpus'':67C ''could'':28C,135C,165C ''creat'':139C ''dark'':126C ''disclaim'':22C ''effect'':148C,162C ''em'':24C ''enough'':43C ''exampl'':172C ''experi'':245C ''exploratori'':142C ''faster'':244C ''first'':150C ''flag'':47C ''flip'':183C ''general'':164C,220C ''give'':49C ''go'':194C ''got'':91C ''gridsearch'':60C,89C,176C ''hope'':39C ''idf'':187C ''impact'':199C ''includ'':112C ''inform'':241C ''kinda'':72C ''kitchen'':233C ''larger'':146C ''learn'':17A,64C ''m'':38C,118C,175C ''make'':136C,196C ''must'':96C ''next'':168C ''p'':20C,55C,83C,108C,132C,170C,218C ''page'':79C ''paramet'':7A,115C,149C,157C ''perhap'':204C ''pipelin'':14A,106C,143C,155C,224C ''play'':87C ''pleas'':48C ''possibl'':133C,243C ''practic'':102C ''publish'':75C ''python'':18B ''question'':35C ''real'':198C ''realli'':119C ''remov'':114C ''result'':236C ''run'':152C,167C ''say'':173C ''scikit'':16A,19B,63C ''scikit-learn'':62C ''score'':130C,202C ''sens'':137C ''sequenc'':5A ''sink'':234C ''smaller'':141C,161C ''sort'':99C ''sorta'':121C ''specif'':42C ''specifi'':205C ''stab'':122C ''strong'':21C ''subject'':32C ''sure'':94C,109C ''switch'':189C ''text'':76C,81C ''tfidftransform'':178C ''think'':93C ''throw'':230C ''type'':33C ''understand'':26C ''use'':144C,186C,222C ''versus'':210C ''wherea'':203C ''won'':215C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9828, 2, '2016-01-18 08:45:04.647', 0, NULL, NULL, '&lt;p&gt;If you are working with large dataset. Training and test set disribution may not be too different. In theory "law of large numbers" ensures that the distribution remains same. For smaller set of data probably this is a good point to take care of distribution. As said by Hoap Humanoid "Kullbak-Liebler" can be used to find the difference of distributions of two sets.&lt;/p&gt;
', NULL, NULL, '''/p'':67C ''care'':44C ''data'':35C ''dataset'':8C ''differ'':18C,61C ''disribut'':13C ''distribut'':28C,46C,63C ''ensur'':25C ''find'':59C ''good'':40C ''hoap'':50C ''humanoid'':51C ''kullbak'':53C ''kullbak-liebl'':52C ''larg'':7C,23C ''law'':21C ''liebler'':54C ''may'':14C ''number'':24C ''p'':1C ''point'':41C ''probabl'':36C ''remain'':29C ''said'':48C ''set'':12C,33C,66C ''smaller'':32C ''take'':43C ''test'':11C ''theori'':20C ''train'':9C ''two'':65C ''use'':57C ''work'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9829, 5, '2016-01-18 12:18:28.227', 0, NULL, NULL, '&lt;p&gt;Examples for Probabilistic Graphical Models (PGMs) are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Markov Random Fields (MRFs, undirected)

&lt;ul&gt;
&lt;li&gt;Conditional Random Fields (CRFs)&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;Bayesian networks (BNs, directed)&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':23C,25C,31C ''/p'':9C ''/ul'':24C,32C ''bayesian'':27C ''bns'':29C ''condit'':19C ''crfs'':22C ''direct'':30C ''exampl'':2C ''field'':14C,21C ''graphic'':5C ''li'':11C,18C,26C ''markov'':12C ''model'':6C ''mrfs'':15C ''network'':28C ''p'':1C ''pgms'':7C ''probabilist'':4C ''random'':13C,20C ''ul'':10C,17C ''undirect'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9831, 2, '2016-01-18 12:53:01.577', 1, NULL, NULL, '&lt;p&gt;Clustering allows you to find clusters of similar hospitals. In your case, hospitals in the same cluster will have&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;around the same number of patients&lt;/li&gt;
&lt;li&gt;around the same time&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;But depending on the number of clusters you specify and your data, you might not get results that feel satisfactory. Moreover, clustering gives you &lt;em&gt;an&lt;/em&gt; answer, but it might not be an answer to the question you want answered.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Exemple 1:&lt;/strong&gt; Hospital 1 at week 1 might be grouped with Hospital 2 at week 1, while having respectively 0 and 5 patients. This might make sense in the model, but might not in &lt;em&gt;real life&lt;/em&gt;, where 0 or 5 patients could be very different.&lt;/p&gt;

&lt;p&gt;To adress this problem, you might want to &lt;a href="https://en.wikipedia.org/wiki/Feature_scaling#Standardization" rel="nofollow"&gt;standardize&lt;/a&gt; your patient count. It will help the model to make sense of &lt;em&gt;how different&lt;/em&gt; 0 and 5 patients are.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Exemple 2:&lt;/strong&gt; Hospital 1 at week 1 might be grouped with hospital 3 at week 12, if they are the only one having 23 patients while every Hospital never gets more than 10. &lt;/p&gt;

&lt;p&gt;This is the other problem you are facing with clustering; it helps you find similarities, but similarities might not be helpful. K-means can not fully use the fact that the data at week 2 depends from the data at week 1. It only tries to find how &lt;em&gt;close&lt;/em&gt; two points are in the 2-dimensional space formed by the week number and patient count that week.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;So you want&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;To detect outbreaks&lt;/li&gt;
&lt;li&gt;To find the hospital in which the outbreak is happening&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Anony-Mousse proposed a very simple way of finding "outbreaks"; Compute the difference in patients from week to week. Here would be a way to elaborate on this:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Set week 1 at 0 and week $i$ at (number of patients at week $i$ $-$ the number of patients at week $i-1$)&lt;/li&gt;
&lt;li&gt;Compute the sum of the change over all hospitals with past week, and use a threshold to determine outbreaks. If there are 10 new patients or more that week, there is an "outbreak"&lt;/li&gt;
&lt;li&gt;Use the initial number of patient per hospital at the week the outbreak is detected to determine which hospital are touched. Use 2-means for (High/Low), 3-means for (High/Medium/Low), and so on.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;This is just a simple example, but should give you an hint on to where to search next.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Additional &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;What is an &lt;em&gt;outbreak&lt;/em&gt;? If you can define more clearly what you are searching for, you will have better results in the end. Clustering tries to find &lt;em&gt;structure&lt;/em&gt; in the data, and outbreaks &lt;em&gt;might&lt;/em&gt; be part of that structure, but you should focus on methods specifically for it.&lt;/li&gt;
&lt;li&gt;If you want to apply data science/machine learning techniques to a problem, try to formulate the problem in that language. It is a framework to reason about data, but a good portion of the job is to translate your problem in ways it can be handled.&lt;/li&gt;
&lt;li&gt;Learn about methods specifically designed to handle this type of problems. A good place to start would be &lt;a href="https://en.wikipedia.org/wiki/Change_detection" rel="nofollow"&gt;Change detection&lt;/a&gt;, as pointed out by Anony-Mousse.&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9855, 2, '2016-01-19 17:17:04.363', 3, NULL, NULL, '&lt;p&gt;We don''t have to do anything crazy to prove this, we can simply leverage the UAT. &lt;/p&gt;

&lt;p&gt;By the &lt;a href="https://en.wikipedia.org/wiki/Universal_approximation_theorem" rel="nofollow"&gt;universal approximation theorem&lt;/a&gt;, yes, you could concieve of a neural network that precisely matched your Bayesian network.&lt;/p&gt;
', NULL, NULL, '''/a'':33C ''/p'':19C,48C ''/wiki/universal_approximation_theorem'':27C ''anyth'':8C ''approxim'':31C ''bayesian'':46C ''conciev'':37C ''could'':36C ''crazi'':9C ''en.wikipedia.org'':26C ''en.wikipedia.org/wiki/universal_approximation_theorem'':25C ''href'':24C ''leverag'':16C ''match'':44C ''network'':41C,47C ''neural'':40C ''nofollow'':29C ''p'':1C,20C ''precis'':43C ''prove'':11C ''rel'':28C ''simpli'':15C ''theorem'':32C ''uat'':18C ''univers'':30C ''yes'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10045, 1, '2016-02-01 11:26:37.103', 1, 38, 'How to apply Multiarm bandit algorithm to facebook adset budget allocation?', '&lt;p&gt;The current multi-arm bandit algorithm has trials and successes while the adset has budget and goal metric data(transaction or revenue).The facebook advertising works in way where you allot a daily budget to different adsets which are a part of a single campaign.Just like MAB where we no prior knowledge about the slot machines , here we cannot predict which adset will succeed. How do I apply this on the multi-arm algorithm?&lt;/p&gt;
', 'machine-learning  statistics  predictive-modeling  reinforcement-learning', NULL, '''/p'':99C ''adset'':9A,36C,60C,85C ''advertis'':48C ''algorithm'':6A,29C,98C ''alloc'':11A ''allot'':54C ''appli'':3A,91C ''arm'':27C,97C ''bandit'':5A,28C ''budget'':10A,38C,57C ''campaign.just'':68C ''cannot'':82C ''current'':24C ''daili'':56C ''data'':42C ''differ'':59C ''facebook'':8A,47C ''goal'':40C ''knowledg'':75C ''learn'':14B,21B ''like'':69C ''mab'':70C ''machin'':13B,79C ''machine-learn'':12B ''metric'':41C ''model'':18B ''multi'':26C,96C ''multi-arm'':25C,95C ''multiarm'':4A ''p'':22C ''part'':64C ''predict'':17B,83C ''predictive-model'':16B ''prior'':74C ''reinforc'':20B ''reinforcement-learn'':19B ''revenu'':45C ''singl'':67C ''slot'':78C ''statist'':15B ''succeed'':87C ''success'':33C ''transact'':43C ''trial'':31C ''way'':51C ''work'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9832, 1, '2016-01-18 13:51:25.52', 3, 48, 'What is the Q function and what is the V function in reinforcement learning?', '&lt;p&gt;It seems to me that the $V$ function can be easily expressed by the $Q$ function and thus the $V$ function seems to be superfluous to me. However, I''m new to reinforcement learning so I guess I got something wrong.&lt;/p&gt;

&lt;h2&gt;Definitions&lt;/h2&gt;

&lt;p&gt;Q- and V-learning are in the context of &lt;a href="https://en.wikipedia.org/wiki/Markov_decision_process#Definition" rel="nofollow"&gt;Markov Decision Processes&lt;/a&gt;. A &lt;strong&gt;MDP&lt;/strong&gt; is a 5-tuple $(S, A, P, R, \gamma)$ with&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;$S$ is a set of states (typically finite)&lt;/li&gt;
&lt;li&gt;$A$ is a set of actions (typically finite)&lt;/li&gt;
&lt;li&gt;$P(s, s'', a) = P(s_{t+1} = s'' | s_t = s, a_t = a)$ is the probability to get from state $s$ to state $s''$ with action $a$.&lt;/li&gt;
&lt;li&gt;$R(s, s'', a) \in \mathbb{R}$ is the immediate reward after going from state $s$ to state $s''$ with action $a$. (It seems to me that usually only $s''$ matters).&lt;/li&gt;
&lt;li&gt;$\gamma \in [0, 1]$ is called discount factor and determines if one focuses on immediate rewards ($\gamma = 0$), the total reward ($\gamma = 1$) or some trade-off.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;A &lt;strong&gt;policy $\pi$&lt;/strong&gt;, according to &lt;a href="https://webdocs.cs.ualberta.ca/~sutton/book/ebook/node9.html" rel="nofollow"&gt;Reinforcement Learning: An Introduction&lt;/a&gt; by Sutton and Barto is a function $\pi: S \rightarrow A$ (this could be probabilistic).&lt;/p&gt;

&lt;p&gt;According to &lt;a href="http://www.cs.upc.edu/~mmartin/Ag4-4x.pdf" rel="nofollow"&gt;Mario Martins slides&lt;/a&gt;, the &lt;strong&gt;$V$ function&lt;/strong&gt; is
$$V^\pi(s) = E_\pi \{R_t | s_t = s\} = E_\pi \{\sum_{k=0}^\infty \gamma^k r_{t+k+1} | s_t = s\}$$
and the &lt;strong&gt;Q function&lt;/strong&gt; is
$$Q^\pi(s, a) = E_\pi \{R_t | s_t = s, a_t = a\} = E_\pi \{\sum_{k=0}^\infty \gamma^k r_{t+k+1} | s_t = s, a_t=a\}$$&lt;/p&gt;

&lt;h2&gt;My thoughts&lt;/h2&gt;

&lt;p&gt;The $V$ function states what the expected overall value (not reward!) of a state $s$ under the policy $\pi$ is.&lt;/p&gt;

&lt;p&gt;The $Q$ function states what the value of a state $s$ and an action $a$  under the policy $\pi$ is.&lt;/p&gt;

&lt;p&gt;This means,
$$Q^\pi(s, \pi(s)) = V^\pi(s)$$&lt;/p&gt;

&lt;p&gt;Right? So why do we have the value function at all? (I guess I mixed up something)&lt;/p&gt;
', 'machine-learning  reinforcement-learning', NULL, '''+1'':133C,297C,333C ''/a'':88C,239C,269C ''/h2'':66C,344C ''/li'':114C,124C,155C,188C,218C ''/p'':63C,103C,255C,340C,366C,388C,400C,419C ''/strong'':92C,225C,274C,306C ''/ul'':219C ''/wiki/markov_decision_process#definition'':82C ''/~mmartin/ag4-4x.pdf'':263C ''/~sutton/book/ebook/node9.html'':232C ''0'':192C,207C,290C,326C ''1'':193C,212C ''5'':95C ''accord'':226C,257C ''action'':121C,153C,177C,381C ''barto'':243C ''call'':195C ''context'':76C ''could'':252C ''decis'':86C ''definit'':65C ''determin'':199C ''discount'':196C ''e'':279C,286C,312C,322C ''easili'':32C ''en.wikipedia.org'':81C ''en.wikipedia.org/wiki/markov_decision_process#definition'':80C ''expect'':352C ''express'':33C ''factor'':197C ''finit'':113C,123C ''focus'':202C ''function'':5A,11A,29C,37C,42C,246C,273C,305C,348C,370C,410C ''gamma'':101C,190C,206C,211C,292C,328C ''get'':145C ''go'':169C ''got'':60C ''guess'':58C,414C ''h2'':64C,341C ''howev'':49C ''href'':79C,229C,260C ''immedi'':166C,204C ''infti'':291C,327C ''introduct'':238C ''k'':289C,293C,296C,325C,329C,332C ''learn'':14A,17B,20B,55C,72C,236C ''li'':105C,115C,125C,156C,189C ''m'':51C ''machin'':16B ''machine-learn'':15B ''mario'':266C ''markov'':85C ''martin'':267C ''mathbb'':162C ''matter'':187C ''mdp'':91C ''mean'':391C ''mix'':416C ''new'':52C ''nofollow'':84C,234C,265C ''one'':201C ''overal'':353C ''p'':21C,67C,99C,126C,130C,220C,256C,345C,367C,389C,401C ''pi'':224C,247C,277C,280C,287C,309C,313C,323C,364C,386C,393C,395C,398C ''polici'':223C,363C,385C ''probabilist'':254C ''probabl'':143C ''process'':87C ''q'':4A,36C,68C,304C,308C,369C,392C ''r'':100C,157C,163C,281C,294C,314C,330C ''reinforc'':13A,19B,54C,235C ''reinforcement-learn'':18B ''rel'':83C,233C,264C ''reward'':167C,205C,210C,356C ''right'':402C ''rightarrow'':249C ''seem'':23C,43C,180C ''set'':109C,119C ''slide'':268C ''someth'':61C,418C ''state'':111C,147C,150C,171C,174C,349C,359C,371C,377C ''strong'':90C,222C,271C,303C ''sum'':288C,324C ''superflu'':46C ''sutton'':241C ''thought'':343C ''thus'':39C ''total'':209C ''trade'':216C ''trade-off'':215C ''tupl'':96C ''typic'':112C,122C ''ul'':104C ''usual'':184C ''v'':10A,28C,41C,71C,272C,276C,347C,397C ''v-learn'':70C ''valu'':354C,374C,409C ''webdocs.cs.ualberta.ca'':231C ''webdocs.cs.ualberta.ca/~sutton/book/ebook/node9.html'':230C ''wrong'':62C ''www.cs.upc.edu'':262C ''www.cs.upc.edu/~mmartin/ag4-4x.pdf'':261C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9881, 2, '2016-01-21 11:29:32.98', 2, NULL, NULL, '&lt;p&gt;I don''t see a huge problem here. So, I would try to answer all of your questions from the production-level point of view: &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;How do I confirm that classification will not take much time?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Take a subset of the corpus data you have (you can randomly do it, no need of sampling), and test your algorithm on it, and they approximate/generalize it to the overall dataset.&lt;/p&gt;

&lt;p&gt;(SVM is comparatively faster. Nevertheless, do the above process just to be sure.)&lt;/p&gt;

&lt;p&gt;And do test it in the development environment before pushing to production.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Is using Python''s scikit library for websites/web applications
  suitable for this?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Yes&lt;/strong&gt;, it is. It is &lt;a href="http://scikit-learn.org/stable/testimonials/testimonials.html" rel="nofollow"&gt;already being used&lt;/a&gt; by a nice chunk of companies out there.&lt;/p&gt;

&lt;p&gt;The third question about Amazon and Flipkart cannot be answered by someone outside their teams.&lt;/p&gt;

&lt;p&gt;In addition, I would advise you to use the mapreduce techniques for training your models. And as already advised, pickle your models so that you don''t need to train them with every request.&lt;/p&gt;
', NULL, NULL, '''/a'':141C ''/blockquote'':43C,122C ''/p'':28C,42C,77C,92C,106C,121C,150C,167C,203C ''/stable/testimonials/testimonials.html'':135C ''/strong'':126C ''addit'':170C ''advis'':173C,187C ''algorithm'':66C ''alreadi'':138C,186C ''amazon'':156C ''answer'':15C,161C ''applic'':117C ''approximate/generalize'':71C ''blockquot'':29C,107C ''cannot'':159C ''chunk'':145C ''classif'':36C ''compani'':147C ''compar'':81C ''confirm'':34C ''corpus'':50C ''data'':51C ''dataset'':76C ''develop'':100C ''environ'':101C ''everi'':201C ''faster'':82C ''flipkart'':158C ''href'':132C ''huge'':7C ''level'':24C ''librari'':114C ''mapreduc'':178C ''model'':183C,190C ''much'':40C ''need'':60C,196C ''nevertheless'':83C ''nice'':144C ''nofollow'':137C ''outsid'':164C ''overal'':75C ''p'':1C,30C,44C,78C,93C,108C,123C,151C,168C ''pickl'':188C ''point'':25C ''problem'':8C ''process'':87C ''product'':23C,105C ''production-level'':22C ''push'':103C ''python'':111C ''question'':19C,154C ''random'':56C ''rel'':136C ''request'':202C ''sampl'':62C ''scikit'':113C ''scikit-learn.org'':134C ''scikit-learn.org/stable/testimonials/testimonials.html'':133C ''see'':5C ''someon'':163C ''strong'':124C ''subset'':47C ''suitabl'':118C ''sure'':91C ''svm'':79C ''take'':39C,45C ''team'':166C ''techniqu'':179C ''test'':64C,96C ''third'':153C ''time'':41C ''train'':181C,198C ''tri'':13C ''use'':110C,140C,176C ''view'':27C ''websites/web'':116C ''would'':12C,172C ''yes'':125C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9960, 2, '2016-01-26 02:02:48.413', 2, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;How best could I organize this data, in what type of database?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;A simple relational database should do, but you could also use a "fancy" graph database if you want. One table for the users, and one for the "interactions". Each interaction would have foreign key columns for the two participants, labeled winner and loser, and the number of the episode the interaction it occurred.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Also any ideas on the best way to visually represent this data?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;A graphical representation for &lt;a href="https://en.wikipedia.org/wiki/Social_network_analysis" rel="nofollow"&gt;social network analysis&lt;/a&gt; suggests itself. Here are &lt;a href="https://www.researchgate.net/publication/225765648_Social_network_analysis_in_a_movie_using_character-net" rel="nofollow"&gt;some&lt;/a&gt; &lt;a href="http://bi.snu.ac.kr/Publications/Conferences/International/ASONAM2015_CJNan.pdf" rel="nofollow"&gt;papers&lt;/a&gt; and a &lt;a href="https://www.reddit.com/r/sna/" rel="nofollow"&gt;subreddit&lt;/a&gt; for inspiration. In your case, there is a concept of competition with clear winners/losers, so you could make your graph directed. Have the characters be the nodes, and add &lt;em&gt;directed&lt;/em&gt; edges from the winning party to the losing party for each interaction. Collapse repeated interactions, etc. This approach would let you quickly identify overall winners and losers, as well as simply who interacts with whom. &lt;/p&gt;
', NULL, NULL, '''/a'':103C,116C,125C,136C ''/blockquote'':16C,87C ''/em'':168C ''/p'':15C,71C,86C,204C ''/publication/225765648_social_network_analysis_in_a_movie_using_character-net'':112C ''/publications/conferences/international/asonam2015_cjnan.pdf'':121C ''/r/sna/'':132C ''/wiki/social_network_analysis'':97C ''add'':165C ''also'':27C,74C ''analysi'':102C ''approach'':186C ''best'':4C,79C ''bi.snu.ac.kr'':120C ''bi.snu.ac.kr/publications/conferences/international/asonam2015_cjnan.pdf'':119C ''blockquot'':1C,72C ''case'':141C ''charact'':160C ''clear'':149C ''collaps'':181C ''column'':52C ''competit'':147C ''concept'':145C ''could'':5C,26C,153C ''data'':9C,85C ''databas'':14C,21C,32C ''direct'':157C,167C ''edg'':169C ''em'':166C ''en.wikipedia.org'':96C ''en.wikipedia.org/wiki/social_network_analysis'':95C ''episod'':66C ''etc'':184C ''fanci'':30C ''foreign'':50C ''graph'':31C,156C ''graphic'':90C ''href'':94C,109C,118C,129C ''idea'':76C ''identifi'':191C ''inspir'':138C ''interact'':45C,47C,68C,180C,183C,201C ''key'':51C ''label'':57C ''let'':188C ''lose'':176C ''loser'':60C,195C ''make'':154C ''network'':101C ''node'':163C ''nofollow'':99C,114C,123C,134C ''number'':63C ''occur'':70C ''one'':36C,42C ''organ'':7C ''overal'':192C ''p'':2C,17C,73C,88C ''paper'':124C ''parti'':173C,177C ''particip'':56C ''quick'':190C ''rel'':98C,113C,122C,133C ''relat'':20C ''repeat'':182C ''repres'':83C ''represent'':91C ''simpl'':19C ''simpli'':199C ''social'':100C ''subreddit'':135C ''suggest'':104C ''tabl'':37C ''two'':55C ''type'':12C ''use'':28C ''user'':40C ''visual'':82C ''want'':35C ''way'':80C ''well'':197C ''win'':172C ''winner'':58C,193C ''winners/losers'':150C ''would'':48C,187C ''www.reddit.com'':131C ''www.reddit.com/r/sna/'':130C ''www.researchgate.net'':111C ''www.researchgate.net/publication/225765648_social_network_analysis_in_a_movie_using_character-net'':110C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9834, 2, '2016-01-18 14:15:40.53', 6, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Sometimes you care as much about changing the outcome then predicting the outcome.&lt;/strong&gt;   &lt;/p&gt;

&lt;p&gt;A neural network given enough training data will tend to predict the outcome better, but once you can predict the outcome, you then may wish to predict the effect of making changes.  &lt;/p&gt;

&lt;p&gt;An example from real life, knowing that someone is likely to have a heart attack is useful, but being able to tell the person that if they stopped doing XX, the risk would reduce by 30% is of much greater benefit.&lt;/p&gt;

&lt;p&gt;Likewise for customer retention, knowing why customers stop shopping with you, is worth as much as predicting the customers that are likely to stop shopping with you.&lt;/p&gt;

&lt;p&gt;Also a simpler Bayesian Network that predicts less well but &lt;strong&gt;leads to more action being taken&lt;/strong&gt; may often be better than a more âcorrectâ Bayesian Network.&lt;/p&gt;
', NULL, NULL, '''/p'':17C,51C,94C,123C,154C ''/strong'':16C,142C ''30'':88C ''abl'':72C ''action'':139C ''also'':125C ''attack'':67C ''bayesian'':128C,152C ''benefit'':93C ''better'':32C,146C ''care'':5C ''chang'':9C,50C ''correctâ'':151C ''custom'':98C,102C,114C ''data'':25C ''effect'':47C ''enough'':23C ''exampl'':54C ''given'':22C ''greater'':92C ''heart'':66C ''know'':58C,100C ''lead'':136C ''less'':132C ''life'':57C ''like'':62C,117C ''likewis'':96C ''make'':49C ''may'':42C,143C ''much'':7C,91C,110C ''network'':21C,129C,153C ''neural'':20C ''often'':144C ''outcom'':11C,15C,31C,39C ''p'':1C,18C,52C,95C,124C ''person'':76C ''predict'':13C,29C,37C,45C,112C,131C ''real'':56C ''reduc'':86C ''retent'':99C ''risk'':84C ''shop'':104C,120C ''simpler'':127C ''someon'':60C ''sometim'':3C ''stop'':80C,103C,119C ''strong'':2C,135C ''taken'':141C ''tell'':74C ''tend'':27C ''train'':24C ''use'':69C ''well'':133C ''wish'':43C ''worth'':108C ''would'':85C ''xx'':82C ''â'':150C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9837, 2, '2016-01-18 15:53:57.513', -4, NULL, NULL, '&lt;p&gt;I strongly  do not agree   that  neural nets  do  well  then other learners.
In  fact  neural  nets  are doing  pretty bad  compared to  other   methods.
There is also  no  methodology   despite some  advices on  choosing   parameters this   beeing   done  very often by chance.
There are  some dudes also  that  talk  random on forums  about  how neural  nets  are  so  good, not because  they  have some evidence    regarding  that, but   because  they are  atracted  about  the  fancy  and  buzz word ,, neural''''.They  are also  very unstable ,  did you tryied  a  neural net  to   compare with  xgboost?I  will not  try  any neural   net  until  it will be  self concious .So  until  then  happy neural neting :)&lt;/p&gt;
', NULL, NULL, '''/p'':116C ''advic'':34C ''agre'':6C ''also'':29C,49C,84C ''atract'':74C ''bad'':22C ''bee'':39C ''buzz'':79C ''chanc'':44C ''choos'':36C ''compar'':23C,94C ''concious'':109C ''despit'':32C ''done'':40C ''dude'':48C ''evid'':67C ''fact'':16C ''fanci'':77C ''forum'':54C ''good'':61C ''happi'':113C ''learner'':14C ''method'':26C ''methodolog'':31C ''net'':9C,18C,58C,92C,103C ''nete'':115C ''neural'':8C,17C,57C,81C,91C,102C,114C ''often'':42C ''p'':1C ''paramet'':37C ''pretti'':21C ''random'':52C ''regard'':68C ''self'':108C ''strong'':3C ''talk'':51C ''tri'':100C ''tryi'':89C ''unstabl'':86C ''well'':11C ''word'':80C ''xgboost'':96C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9838, 2, '2016-01-18 17:09:03.993', 3, NULL, NULL, '&lt;p&gt;Bayesian networks might outperform Neural Networks in small data setting. If the prior information is properly managed via the network structure, priors and other hyperparameters, it might have an edge over Neural Networks. Neural Networks, especially the ones with more layers, are very well known to be data hungry. Almost by definition lots of data is necessary to properly train them.&lt;/p&gt;
', NULL, NULL, '''/p'':63C ''almost'':51C ''bayesian'':2C ''data'':10C,49C,56C ''definit'':53C ''edg'':31C ''especi'':37C ''hungri'':50C ''hyperparamet'':26C ''inform'':15C ''known'':46C ''layer'':42C ''lot'':54C ''manag'':18C ''might'':4C,28C ''necessari'':58C ''network'':3C,7C,21C,34C,36C ''neural'':6C,33C,35C ''one'':39C ''outperform'':5C ''p'':1C ''prior'':14C,23C ''proper'':17C,60C ''set'':11C ''small'':9C ''structur'':22C ''train'':61C ''via'':19C ''well'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9839, 1, '2016-01-18 19:48:29.68', 2, 45, 'Any case studies using Bayesian Networks for system design trades?', '&lt;p&gt;I am exploring using Bayesian Networks to identify the best parameters within a system design, to improve its performance. I''m trying to find any case studies where this has been used successfully or unsuccessfully.&lt;/p&gt;

&lt;p&gt;As an example consider an automatic assisted anti-collision system on a car.  It could use some combination of ultrasonic, radar &amp;amp; IR seekers to sense and measure the closing speed with the car in front.  These all have different costs / reliabilities and perform differently in different weather conditions.  I am interested in understanding how effective Bayesian Networks can be in evaluating the trade space and identifying the best solutions to these types of problems.&lt;/p&gt;
', 'data-mining  predictive-modeling  bayesian-networks', NULL, '''/p'':56C,134C ''amp'':80C ''anti'':66C ''anti-collis'':65C ''assist'':64C ''automat'':63C ''bayesian'':5A,18B,25C,115C ''bayesian-network'':17B ''best'':30C,127C ''car'':71C,92C ''case'':2A,46C ''close'':88C ''collis'':67C ''combin'':76C ''condit'':107C ''consid'':61C ''cost'':99C ''could'':73C ''data'':12B ''data-min'':11B ''design'':9A,35C ''differ'':98C,103C,105C ''effect'':114C ''evalu'':120C ''exampl'':60C ''explor'':23C ''find'':44C ''front'':94C ''identifi'':28C,125C ''improv'':37C ''interest'':110C ''ir'':81C ''m'':41C ''measur'':86C ''mine'':13B ''model'':16B ''network'':6A,19B,26C,116C ''p'':20C,57C ''paramet'':31C ''perform'':39C,102C ''predict'':15B ''predictive-model'':14B ''problem'':133C ''radar'':79C ''reliabl'':100C ''seeker'':82C ''sens'':84C ''solut'':128C ''space'':123C ''speed'':89C ''studi'':3A,47C ''success'':53C ''system'':8A,34C,68C ''trade'':10A,122C ''tri'':42C ''type'':131C ''ultrason'':78C ''understand'':112C ''unsuccess'':55C ''use'':4A,24C,52C,74C ''weather'':106C ''within'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9842, 2, '2016-01-19 00:01:42.273', 0, NULL, NULL, '&lt;p&gt;If Python 3 is an option, &lt;a href="http://orange.biolab.si" rel="nofollow"&gt;Orange 3&lt;/a&gt; features FP-growth in its &lt;a href="https://pypi.python.org/pypi/Orange3-Associate" rel="nofollow"&gt;Orange3-Associate add-on&lt;/a&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;pip3 install Orange3-Associate
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;It''s well documented: &lt;a href="http://orange3-associate.readthedocs.org/" rel="nofollow"&gt;http://orange3-associate.readthedocs.org/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Apriori due to its slowness and resource requirements, as you''d noticed, really isn''t an algorithm to consider anymore.&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':58C ''/a'':15C,35C ''/code'':44C ''/p'':36C,80C ''/pre'':45C ''/pypi/orange3-associate'':26C ''3'':4C,14C ''add'':33C ''add-on'':32C ''algorithm'':76C ''anymor'':79C ''apriori'':60C ''associ'':31C,43C ''code'':38C ''consid'':78C ''d'':70C ''document'':50C ''due'':61C ''featur'':16C ''fp'':18C ''fp-growth'':17C ''growth'':19C ''href'':9C,23C,52C ''instal'':40C ''isn'':73C ''nofollow'':12C,28C,55C ''notic'':71C ''option'':7C ''orang'':13C ''orange.biolab.si'':10C ''orange3'':30C,42C ''orange3-associate'':29C,41C ''orange3-associate.readthedocs.org'':53C,57C ''orange3-associate.readthedocs.org/&lt;/a&gt;&lt;/p&gt;'':56C ''p'':1C,46C,59C ''pip3'':39C ''pre'':37C ''pypi.python.org'':25C ''pypi.python.org/pypi/orange3-associate'':24C ''python'':3C ''realli'':72C ''rel'':11C,27C,54C ''requir'':67C ''resourc'':66C ''slow'':64C ''well'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9845, 2, '2016-01-19 06:11:24.24', 2, NULL, NULL, '&lt;p&gt;I''ve posted &lt;a href="https://www.reddit.com/r/MachineLearning/comments/41dij6/is_there_any_domain_where_bayesian_networks/" rel="nofollow"&gt;this link on Reddit&lt;/a&gt; and got a lot of feedback. Some have posted their answers here, others didn''t. This answer should sum the reddit post up. (I made it community wiki, so that I don''t get points for it)&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://arxiv.org/abs/1312.6114v10" rel="nofollow"&gt;Auto-Encoding Variational Bayes&lt;/a&gt; is a combination of a Bayes Network and a neural network. The paper &lt;a href="http://arxiv.org/abs/1506.02158v5" rel="nofollow"&gt;Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference&lt;/a&gt; seems to go in the same direction.&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf" rel="nofollow"&gt;Dropout: A Simple Way to Prevent Neural Networks from
Overfitting&lt;/a&gt; is an example where Bayesian neural networks outperform their dropout approach (see section 6.4 "Comparison with Bayesian Neural Networks")&lt;/li&gt;
&lt;li&gt;&lt;a href="http://web.mit.edu/cocosci/Papers/Science-2015-Lake-1332-8.pdf" rel="nofollow"&gt;Human-level concept learning through probabilistic program induction&lt;/a&gt; is a paper "on a Bayesian net network that does one-shot classification that way outperformed neural networks" (according to trashacount12345 - I didn''t check that by now).&lt;/li&gt;
&lt;li&gt;Yann LeCun wrote a &lt;a href="https://plus.google.com/+YannLeCunPhD/posts/gWE7Jca3Zoq" rel="nofollow"&gt;Google+ post&lt;/a&gt; in which he argues that neural networks and probabilisitc graphical models are not orthogonal concepts.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/+yannlecunphd/posts/gwe7jca3zoq'':204C ''/a'':16C,69C,99C,126C,164C,209C ''/abs/1312.6114v10'':61C ''/abs/1506.02158v5'':87C ''/cocosci/papers/science-2015-lake-1332-8.pdf'':152C ''/li'':107C,146C,194C,225C ''/p'':54C ''/r/machinelearning/comments/41dij6/is_there_any_domain_where_bayesian_networks/'':9C ''/ul'':226C ''/~hinton/absps/jmlrdropout.pdf'':113C ''6.4'':140C ''accord'':184C ''answer'':27C,33C ''approach'':137C ''approxim'':96C ''argu'':213C ''arxiv.org'':60C,86C ''arxiv.org/abs/1312.6114v10'':59C ''arxiv.org/abs/1506.02158v5'':85C ''auto'':65C ''auto-encod'':64C ''bay'':68C,75C ''bayesian'':90C,131C,143C,170C ''bernoulli'':95C ''check'':190C ''classif'':178C ''combin'':72C ''communiti'':43C ''comparison'':141C ''concept'':158C,224C ''convolut'':91C ''didn'':30C,188C ''direct'':106C ''dropout'':116C,136C ''encod'':66C ''exampl'':129C ''feedback'':22C ''get'':50C ''go'':102C ''googl'':207C ''got'':18C ''graphic'':219C ''href'':6C,58C,84C,110C,149C,201C ''human'':156C ''human-level'':155C ''induct'':163C ''infer'':98C ''learn'':159C ''lecun'':197C ''level'':157C ''li'':56C,108C,147C,195C ''link'':13C ''lot'':20C ''made'':41C ''model'':220C ''net'':171C ''network'':76C,80C,93C,123C,133C,145C,172C,183C,216C ''neural'':79C,92C,122C,132C,144C,182C,215C ''nofollow'':11C,63C,89C,115C,154C,206C ''one'':176C ''one-shot'':175C ''orthogon'':223C ''other'':29C ''outperform'':134C,181C ''overfit'':125C ''p'':1C ''paper'':82C,167C ''plus.google.com'':203C ''plus.google.com/+yannlecunphd/posts/gwe7jca3zoq'':202C ''point'':51C ''post'':4C,25C,38C,208C ''prevent'':121C ''probabilisitc'':218C ''probabilist'':161C ''program'':162C ''reddit'':15C,37C ''rel'':10C,62C,88C,114C,153C,205C ''section'':139C ''see'':138C ''seem'':100C ''shot'':177C ''simpl'':118C ''sum'':35C ''trashacount12345'':186C ''ul'':55C ''variat'':67C,97C ''ve'':3C ''way'':119C,180C ''web.mit.edu'':151C ''web.mit.edu/cocosci/papers/science-2015-lake-1332-8.pdf'':150C ''wiki'':44C ''wrote'':198C ''www.cs.toronto.edu'':112C ''www.cs.toronto.edu/~hinton/absps/jmlrdropout.pdf'':111C ''www.reddit.com'':8C ''www.reddit.com/r/machinelearning/comments/41dij6/is_there_any_domain_where_bayesian_networks/'':7C ''yann'':196C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9846, 1, '2016-01-19 06:26:00.08', 3, 47, 'What regressors are recommended with text modeling?', '&lt;p&gt;For the sake of my own exploration, I am working on a sales prediction project. I am using text extracted from a set of books to build a predictive model.  &lt;/p&gt;

&lt;p&gt;With scikit learn, I have created a Tfidf, and together with the numeric sales numbers, I created a SGDRegressor. But I''d like to practice with other models.  &lt;/p&gt;

&lt;p&gt;I am looking over the options available to me, and I''m wondering, what other algorithms might be useful in this kind of regression scenario? And/or, what other algorithms in scikit learn will take a tfidf as the dataset?   &lt;/p&gt;
', 'nlp  text-mining  regression  scikit', NULL, '''/p'':45C,75C,116C ''algorithm'':92C,105C ''and/or'':102C ''avail'':83C ''book'':39C ''build'':41C ''creat'':52C,63C ''d'':68C ''dataset'':115C ''explor'':21C ''extract'':34C ''kind'':98C ''learn'':49C,108C ''like'':69C ''look'':79C ''m'':88C ''might'':93C ''mine'':11B ''model'':7A,44C,74C ''nlp'':8B ''number'':61C ''numer'':59C ''option'':82C ''p'':14C,46C,76C ''practic'':71C ''predict'':28C,43C ''project'':29C ''recommend'':4A ''regress'':12B,100C ''regressor'':2A ''sake'':17C ''sale'':27C,60C ''scenario'':101C ''scikit'':13B,48C,107C ''set'':37C ''sgdregressor'':65C ''take'':110C ''text'':6A,10B,33C ''text-min'':9B ''tfidf'':54C,112C ''togeth'':56C ''use'':32C,95C ''wonder'':89C ''work'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9848, 1, '2016-01-19 09:03:57.597', 3, 61, 'How to combine PCA and MCA on mixed data?', '&lt;p&gt;Suppose I have mixed data and (python) code which is capable of doing &lt;a href="https://en.wikipedia.org/wiki/Principal_component_analysis" rel="nofollow"&gt;PCA&lt;/a&gt; (principal component analysis) on continuous predictors and &lt;a href="https://en.wikipedia.org/wiki/Multiple_correspondence_analysis" rel="nofollow"&gt;MCA&lt;/a&gt; (multiple correspondence analysis) on nominal predictors. Is it possible to combine results from PCA and MCA into one?&lt;/p&gt;
', 'python  categorical-data', NULL, '''/a'':36C,52C ''/p'':71C ''/wiki/multiple_correspondence_analysis'':48C ''/wiki/principal_component_analysis'':32C ''analysi'':39C,55C ''capabl'':25C ''categor'':12B ''categorical-data'':11B ''code'':22C ''combin'':3A,63C ''compon'':38C ''continu'':41C ''correspond'':54C ''data'':9A,13B,19C ''en.wikipedia.org'':31C,47C ''en.wikipedia.org/wiki/multiple_correspondence_analysis'':46C ''en.wikipedia.org/wiki/principal_component_analysis'':30C ''href'':29C,45C ''mca'':6A,51C,68C ''mix'':8A,18C ''multipl'':53C ''nofollow'':34C,50C ''nomin'':57C ''one'':70C ''p'':14C ''pca'':4A,35C,66C ''possibl'':61C ''predictor'':42C,58C ''princip'':37C ''python'':10B,21C ''rel'':33C,49C ''result'':64C ''suppos'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9849, 1, '2016-01-19 09:11:00.45', 1, 29, 'How to implement a BiLSTM to classify speech data', '&lt;p&gt;I would like to know how should I provide the inputs to a BiLSTM if I am going to classify speech files (.wav) files. What is the proper way to label the data? Do I have to label each individual frame with the target label (which I think is meaningless because content of a single frame doesn''t imply that the end label of the given file is what it is. Eg: First frame of utterance_0.wav doesn''t directly say output label is 0)&lt;/p&gt;

&lt;p&gt;Therefore what would be the correct way of labelling these data? And how should I maintain the .mfcc values calculated per each .wav files. (I have around 4500 files and trying to generate coefficients when we try to do the training would add extra overhead) Please correct me if I am wrong.&lt;/p&gt;

&lt;p&gt;Since I am new to this ML approaches and training networks and RNN, I would like to have some guidance on above questions.&lt;/p&gt;
', 'machine-learning  classification  deep-learning', NULL, '''/p'':102C,156C,181C ''0'':101C ''4500'':131C ''add'':146C ''approach'':165C ''around'':130C ''bilstm'':5A,31C ''calcul'':123C ''classif'':13B ''classifi'':7A,37C ''coeffici'':137C ''content'':69C ''correct'':109C,150C ''data'':9A,50C,114C ''deep'':15B ''deep-learn'':14B ''direct'':96C ''doesn'':74C,94C ''eg'':89C ''end'':79C ''extra'':147C ''file'':39C,41C,84C,127C,132C ''first'':90C ''frame'':58C,73C,91C ''generat'':136C ''given'':83C ''go'':35C ''guidanc'':177C ''implement'':3A ''impli'':76C ''individu'':57C ''input'':28C ''know'':22C ''label'':48C,55C,62C,80C,99C,112C ''learn'':12B,16B ''like'':20C,173C ''machin'':11B ''machine-learn'':10B ''maintain'':119C ''meaningless'':67C ''mfcc'':121C ''ml'':164C ''network'':168C ''new'':161C ''output'':98C ''overhead'':148C ''p'':17C,103C,157C ''per'':124C ''pleas'':149C ''proper'':45C ''provid'':26C ''question'':180C ''rnn'':170C ''say'':97C ''sinc'':158C ''singl'':72C ''speech'':8A,38C ''target'':61C ''therefor'':104C ''think'':65C ''train'':144C,167C ''tri'':134C,140C ''utterance_0.wav'':93C ''valu'':122C ''wav'':40C,126C ''way'':46C,110C ''would'':19C,106C,145C,172C ''wrong'':155C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9851, 2, '2016-01-19 13:55:58.777', 2, NULL, NULL, '&lt;p&gt;You may want to use &lt;a href="https://en.wikipedia.org/wiki/Factor_analysis_of_mixed_data" rel="nofollow"&gt;Factor analysis of mixed data&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;It allows you to do dimension reduction on a complete data set.&lt;/p&gt;

&lt;p&gt;A R implementation could be found in the FactoMineR package. But this function struggle when you have a high number of data/columns.&lt;/p&gt;

&lt;p&gt;I am not aware of the existence of the equivalent in python.&lt;/p&gt;
', NULL, NULL, '''/a'':19C ''/p'':20C,34C,58C,72C ''/wiki/factor_analysis_of_mixed_data'':11C ''allow'':23C ''analysi'':15C ''awar'':63C ''complet'':31C ''could'':39C ''data'':18C,32C ''data/columns'':57C ''dimens'':27C ''en.wikipedia.org'':10C ''en.wikipedia.org/wiki/factor_analysis_of_mixed_data'':9C ''equival'':69C ''exist'':66C ''factomin'':44C ''factor'':14C ''found'':41C ''function'':48C ''high'':54C ''href'':8C ''implement'':38C ''may'':3C ''mix'':17C ''nofollow'':13C ''number'':55C ''p'':1C,21C,35C,59C ''packag'':45C ''python'':71C ''r'':37C ''reduct'':28C ''rel'':12C ''set'':33C ''struggl'':49C ''use'':6C ''want'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9857, 1, '2016-01-19 19:02:45.53', 1, 16, 'Unbalanced data fit in gbm', '&lt;p&gt;I''m trying to build a model using GBM in r in order to get probability of two classes ( ''yes'',''no''). My data are unbalanced, and because of this I trained my model using a balanced data(undersampling the ''no'' class). I trained my model With the balanced sample and then I evaluate the performance on my test sample ( 30% of the balanaced sample).&lt;/p&gt;

&lt;p&gt;My question is:&lt;/p&gt;

&lt;p&gt;There is any problem to predict the probability with my fit in the unbalanced sample? When I performe this, the probability distribution are equal of the probability distribution of my balanced sample, and because of this maybe my score is meaningless&lt;/p&gt;
', 'machine-learning  r  gbm', NULL, '''/p'':76C,81C,124C ''30'':71C ''balanac'':74C ''balanc'':47C,59C,113C ''build'':16C ''class'':30C,52C ''data'':2A,34C,48C ''distribut'':104C,110C ''equal'':106C ''evalu'':64C ''fit'':3A,93C ''gbm'':5A,10B,20C ''get'':26C ''learn'':8B ''m'':13C ''machin'':7B ''machine-learn'':6B ''mayb'':119C ''meaningless'':123C ''model'':18C,44C,56C ''order'':24C ''p'':11C,77C,82C ''perform'':66C,100C ''predict'':88C ''probabl'':27C,90C,103C,109C ''problem'':86C ''question'':79C ''r'':9B,22C ''sampl'':60C,70C,75C,97C,114C ''score'':121C ''test'':69C ''train'':42C,54C ''tri'':14C ''two'':29C ''unbalanc'':1A,36C,96C ''undersampl'':49C ''use'':19C,45C ''yes'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9852, 1, '2016-01-19 14:00:09.217', 3, 75, 'For every Bayesian Network, is there a Neural Network that gives the same output?', '&lt;p&gt;After thinking about a question &lt;a href="http://datascience.stackexchange.com/questions/9818/is-there-any-domain-where-bayesian-networks-outperform-neural-networks"&gt;comparing Bayesian Network to Neural Network&lt;/a&gt;, I am now wondering if they may not be one and the same thing!&lt;/p&gt;

&lt;p&gt;At this point, my maths (unused for over 20 years fails me!).   But....&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;If infinite random test data was used&lt;/li&gt;
&lt;li&gt;The output of the Bayesian Network was found for each data item&lt;/li&gt;
&lt;li&gt;And the Neural Network was then trained on it&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Would the Neural Network not tent towards being the same as the Bayesian Network?&lt;/p&gt;
', 'machine-learning  bayesian-networks', NULL, '''/a'':38C ''/li'':78C,92C,103C ''/p'':53C,68C,120C ''/questions/9818/is-there-any-domain-where-bayesian-networks-outperform-neural-networks'':31C ''/ul'':104C ''20'':63C ''bayesian'':3A,19B,33C,84C,118C ''bayesian-network'':18B ''compar'':32C ''data'':75C,90C ''datascience.stackexchange.com'':30C ''datascience.stackexchange.com/questions/9818/is-there-any-domain-where-bayesian-networks-outperform-neural-networks'':29C ''everi'':2A ''fail'':65C ''found'':87C ''give'':11A ''href'':28C ''infinit'':72C ''item'':91C ''learn'':17B ''li'':70C,79C,93C ''machin'':16B ''machine-learn'':15B ''math'':59C ''may'':45C ''network'':4A,9A,20B,34C,37C,85C,97C,109C,119C ''neural'':8A,36C,96C,108C ''one'':48C ''output'':14A,81C ''p'':21C,54C,105C ''point'':57C ''question'':26C ''random'':73C ''tent'':111C ''test'':74C ''thing'':52C ''think'':23C ''toward'':112C ''train'':100C ''ul'':69C ''unus'':60C ''use'':77C ''wonder'':42C ''would'':106C ''year'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9860, 1, '2016-01-19 21:26:28.15', 0, 81, 'How can I read in a .csv file with special characters in it in pandas?', '&lt;p&gt;I am trying to read in a .csv file containing some data. I only need to read in specific chunks of rows from the file, such as line 15- line 20, line 45-line 50, and so on. However, the file contains text and copy write information like, such as &lt;code&gt;Â©1990-2016 AAR,All rights reserved&lt;/code&gt; in several places. Such lines seem to be producing the error &lt;code&gt;ValueError: No columns to parse from file&lt;/code&gt;, because when I just copy lines without such information using &lt;code&gt;pd.read_csv()&lt;/code&gt;, it works fine. My goal is to automate the process of downloading these files from the web and reading them into pandas to grab chunks of rows and then do some processing with it, so I can''t just manually specify the windows of text lacking such characters.&lt;/p&gt;

&lt;p&gt;Here is what I tried:&lt;code&gt;pd.read_csv("filename.csv",encoding=utf-8, skiprows = 14)&lt;/code&gt; and &lt;code&gt;pd.read_csv("filename.csv",encoding=utf-16, skiprows = 15)&lt;/code&gt;, after looking at similar answers in stack exchange, but this didn''t work. Can anyone give me some guidance on this?&lt;/p&gt;
', 'python  pandas  scraping  unicode', NULL, '''-16'':186C ''-2016'':74C ''-8'':175C ''/code'':79C,99C,113C,178C,189C ''/p'':162C,211C ''14'':177C ''15'':49C,188C ''1990'':73C ''20'':51C ''45'':53C ''50'':55C ''aar'':75C ''answer'':194C ''anyon'':204C ''autom'':121C ''charact'':11A,161C ''chunk'':40C,138C ''code'':71C,91C,110C,169C,180C ''column'':94C ''contain'':30C,62C ''copi'':65C,104C ''csv'':7A,28C,112C,171C,182C ''data'':32C ''didn'':200C ''download'':125C ''encod'':173C,184C ''error'':90C ''exchang'':197C ''file'':8A,29C,45C,61C,98C,127C ''filename.csv'':172C,183C ''fine'':116C ''give'':205C ''goal'':118C ''grab'':137C ''guidanc'':208C ''howev'':59C ''inform'':67C,108C ''lack'':159C ''like'':68C ''line'':48C,50C,52C,54C,84C,105C ''look'':191C ''manual'':153C ''need'':35C ''p'':20C,163C ''panda'':15A,17B,135C ''pars'':96C ''pd.read'':111C,170C,181C ''place'':82C ''process'':123C,145C ''produc'':88C ''python'':16B ''read'':4A,25C,37C,132C ''reserv'':78C ''right'':77C ''row'':42C,140C ''scrape'':18B ''seem'':85C ''sever'':81C ''similar'':193C ''skiprow'':176C,187C ''special'':10A ''specif'':39C ''specifi'':154C ''stack'':196C ''text'':63C,158C ''tri'':23C,168C ''unicod'':19B ''use'':109C ''utf'':174C,185C ''valueerror'':92C ''web'':130C ''window'':156C ''without'':106C ''work'':115C,202C ''write'':66C ''â'':72C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9853, 1, '2016-01-19 14:21:40.87', 2, 25, 'How to estimate the lambda e to reweighing translation probabilities of entries in a dictionary', '&lt;p&gt;In &lt;a href="http://ucrel.lancs.ac.uk/acl/H/H93/H93-1039.pdf" rel="nofollow"&gt;this paper&lt;/a&gt;, Brown et al. proposed the usage of dictionary entries in addition to the machine translation probabilities that we''re used to in the &lt;a href="http://www.aclweb.org/anthology/J93-2003" rel="nofollow"&gt;Math of Statistical Machine Translation&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;He describes:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;We formalize an intuitive account of lexicographic behavior as
  follows. We imagine that a lexicographer, when constructing an entry
  for the English word or phrase e, first chooses a random size s, and
  then selects at random a sample of s instances of the use of e, each
  with its French translation. We imagine, further, that he includes in
  his entry for e a list consisting of all of tile translations that
  occur at least once in his random sample. The probability that he
  will, in this way, obtain tile list fi, ..., f,,, is&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/drgci.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/drgci.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;That much is understandable, and the &lt;code&gt;Pr(f|e)&lt;/code&gt; in the formula is what we get from the normal machine translation probabilities. and then reweighted by the binomial recursion of finding the probability of the lexicographer choosing the sample sentence given the english word/phrase, i.e. &lt;code&gt;Pr(s|e)&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Then he went on to simply the formula and estimated a Poisson distribution instead of a binomial one and that will simplify the computation to:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/JYJ0U.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/JYJ0U.png" alt="enter image description here"&gt;&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;Everything still looks okay but there''s an additional variable &lt;code&gt;exp^lambda(e)&lt;/code&gt; and that he explains is the mean of the Possion distribution of selecting the sample examples given the English source word. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;So the question is how did he estimate that &lt;code&gt;exp^lambda(e)&lt;/code&gt; from the corpus?&lt;/strong&gt;&lt;/p&gt;
', 'algorithms  machine-translation  poisson-distribution  parameter-estimation  corpus', NULL, '''/a'':38C,74C,196C,297C ''/acl/h/h93/h93-1039.pdf'':33C ''/anthology/j93-2003'':66C ''/blockquote'':177C ''/code'':209C,250C,314C,352C ''/drgci.png'':183C,190C ''/jyj0u.png'':284C,291C ''/p'':75C,79C,176C,197C,251C,278C,298C,336C,357C ''/strong'':356C ''account'':86C ''addit'':49C,308C ''al'':41C ''algorithm'':16B ''alt'':191C,292C ''behavior'':89C ''binomi'':228C,269C ''blockquot'':80C ''brown'':39C ''choos'':109C,237C ''code'':205C,246C,310C,348C ''comput'':276C ''consist'':147C ''construct'':98C ''corpus'':26B,355C ''describ'':78C ''descript'':194C,295C ''dictionari'':15A,46C ''distribut'':22B,265C,325C ''e'':6A,107C,128C,144C,208C,249C,313C,351C ''english'':103C,243C,333C ''enter'':192C,293C ''entri'':12A,47C,100C,142C ''estim'':3A,25B,262C,346C ''et'':40C ''everyth'':300C ''exampl'':330C ''exp'':311C,349C ''explain'':318C ''f'':174C,207C ''fi'':173C ''find'':231C ''first'':108C ''follow'':91C ''formal'':83C ''formula'':212C,260C ''french'':132C ''get'':216C ''given'':241C,331C ''href'':30C,63C,180C,281C ''i.e'':245C ''i.stack.imgur.com'':182C,189C,283C,290C ''i.stack.imgur.com/drgci.png'':181C,188C ''i.stack.imgur.com/jyj0u.png'':282C,289C ''imag'':193C,294C ''imagin'':93C,135C ''img'':186C,287C ''includ'':139C ''instanc'':123C ''instead'':266C ''intuit'':85C ''lambda'':5A,312C,350C ''least'':156C ''lexicograph'':88C,96C,236C ''list'':146C,172C ''look'':302C ''machin'':18B,52C,72C,220C ''machine-transl'':17B ''math'':69C ''mean'':321C ''much'':200C ''nofollow'':35C,68C,185C,286C ''normal'':219C ''obtain'':170C ''occur'':154C ''okay'':303C ''one'':270C ''p'':27C,76C,81C,178C,198C,252C,279C,299C,337C ''paper'':37C ''paramet'':24B ''parameter-estim'':23B ''phrase'':106C ''poisson'':21B,264C ''poisson-distribut'':20B ''possion'':324C ''pr'':206C,247C ''probabl'':10A,54C,163C,222C,233C ''propos'':42C ''question'':341C ''random'':111C,118C,160C ''re'':57C ''recurs'':229C ''rel'':34C,67C,184C,285C ''reweigh'':8A ''reweight'':225C ''sampl'':120C,161C,239C,329C ''select'':116C,327C ''sentenc'':240C ''simpli'':258C ''simplifi'':274C ''size'':112C ''sourc'':334C ''src'':187C,288C ''statist'':71C ''still'':301C ''strong'':338C ''tile'':151C,171C ''translat'':9A,19B,53C,73C,133C,152C,221C ''ucrel.lancs.ac.uk'':32C ''ucrel.lancs.ac.uk/acl/h/h93/h93-1039.pdf'':31C ''understand'':202C ''usag'':44C ''use'':58C,126C ''variabl'':309C ''way'':169C ''went'':255C ''word'':104C,335C ''word/phrase'':244C ''www.aclweb.org'':65C ''www.aclweb.org/anthology/j93-2003'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9854, 1, '2016-01-19 15:14:38.403', 0, 41, 'sklearn.naive_bayes VS categorical variables', '&lt;p&gt;In a binary classification, how can I use sklearn.naive_bayes python module to predict the class of inputs with 5 categorical variables (not binary)?&lt;/p&gt;
', 'machine-learning  data-mining  python  scikit', NULL, '''/p'':39C ''5'':34C ''bay'':2A,24C ''binari'':17C,38C ''categor'':4A,35C ''class'':30C ''classif'':18C ''data'':10B ''data-min'':9B ''input'':32C ''learn'':8B ''machin'':7B ''machine-learn'':6B ''mine'':11B ''modul'':26C ''p'':14C ''predict'':28C ''python'':12B,25C ''scikit'':13B ''sklearn.naive'':1A,23C ''use'':22C ''variabl'':5A,36C ''vs'':3A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9856, 1, '2016-01-19 17:26:39.433', 2, 70, 'Contrastive divergence in RBM', '&lt;p&gt;I have the following code, where x in the input data, w is the weight matrix, bv and bh are the biases for the visible and hidden units.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import theano.tensor as T

x_states = x &amp;gt; numpy.random.rand(training_examples, feats)

hid =  T.nnet.sigmoid(T.dot(x_states, w) + bh)    # Activation function of hidden layer
hid_states = hid &amp;gt; numpy.random.rand(training_examples, nhidden)

# Construct Theano expression graph
vis = T.nnet.sigmoid(T.dot(hid_states, w.T) + by) 
vis_states = vis &amp;gt; numpy.random.rand(training_examples, feats)

hid2 = T.nnet.sigmoid(T.dot(vis_states, w) + bh)
hid2_states = hid2 &amp;gt; numpy.random.rand(training_examples, nhidden)

xent = T.sum((x - vis)**2)
parameters = [w, bh, by]  # this line defines all parameters for each layer
cost = xent.mean()

pos_associations = T.dot(x.T, hid)
neg_associations = T.dot(vis.T, hid2)  

wparameters = [w]
byparameters = [by]
bhparameters = [bh]

update = []
for wparam, byparam, bhparam in zip(wparameters, byparameters, bhparameters):
    update.append((wparam, wparam + tr_rate * (pos_associations - neg_associations)))
    update.append((byparam, byparam + tr_rate*(T.sum(x.T[:]) - T.sum(vis.T[:]))))      
    update.append((bhparam, bhparam + tr_rate*(T.sum(hid.T[:]) - T.sum(hid2.T[:]))))


train = theano.function(
          inputs=[x],
          outputs=[cost],
          updates=update)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What is the correct implementation for the update? In my example this is clearly not working. I tested this on the classic MNIST dataset.&lt;/p&gt;

&lt;p&gt;This is the result I get for the cost. As you can see it does not decreases. What is wrong?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Epoch: 0
cost =  [ 969572.73014003]
Epoch: 1
cost =  [ 258872.77507019]
Epoch: 2
cost =  [ 258872.77507019]
Epoch: 3
cost =  [ 258872.77507019]
Epoch: 4
cost =  [ 258872.77507019]
Epoch: 5
cost =  [ 2003326.79850769]
Epoch: 6
cost =  [ 258872.77507019]
Epoch: 7
cost =  [ 258872.77507019]
Epoch: 8
cost =  [ 258872.77507019]
Epoch: 9
cost =  [ 258872.77507019]
Epoch: 10
cost =  [ 258872.77507019]
Epoch: 11
cost =  [ 258872.77507019]
Epoch: 12
cost =  [ 2003326.79850769]
Epoch: 13
cost =  [ 258872.77507019]
Epoch: 14
cost =  [ 258872.77507019]
Epoch: 15
cost =  [ 258872.77507019]
Epoch: 16
cost =  [ 258872.77507019]
Epoch: 17
cost =  [ 258872.77507019]
Epoch: 18
cost =  [ 258872.77507019]
Epoch: 19
cost =  [ 258872.77507019]
Epoch: 20
cost =  [ 2003326.79850769]
Epoch: 21
cost =  [ 258872.77507019]
Epoch: 22
cost =  [ 258872.77507019]
Epoch: 23
cost =  [ 258872.77507019]
Epoch: 24
cost =  [ 258872.77507019]
Epoch: 25
cost =  [ 258872.77507019]
Epoch: 26
cost =  [ 258872.77507019]
Epoch: 27
cost =  [ 258872.77507019]
Epoch: 28
cost =  [ 2003326.79850769]
Epoch: 29
cost =  [ 258872.77507019]
Epoch: 30
cost =  [ 258872.77507019]
Epoch: 31
cost =  [ 258872.77507019]
Epoch: 32
cost =  [ 258872.77507019]
Epoch: 33
cost =  [ 258872.77507019]
Epoch: 34
cost =  [ 258872.77507019]
Epoch: 35
cost =  [ 258872.77507019]
Epoch: 36
cost =  [ 2003326.79850769]
Epoch: 37
cost =  [ 258872.77507019]
Epoch: 38
cost =  [ 258872.77507019]
Epoch: 39
cost =  [ 258872.77507019]
Epoch: 40
cost =  [ 258872.77507019]
Epoch: 41
cost =  [ 258872.77507019]
Epoch: 42
cost =  [ 258872.77507019]
Epoch: 43
cost =  [ 2003326.79850769]
Epoch: 44
cost =  [ 258872.77507019]
Epoch: 45
cost =  [ 258872.77507019]
Epoch: 46
cost =  [ 258872.77507019]
Epoch: 47
cost =  [ 258872.77507019]
Epoch: 48
cost =  [ 258872.77507019]
Epoch: 49
cost =  [ 258872.77507019]
Epoch: 50
cost =  [ 258872.77507019]
Epoch: 51
cost =  [ 2003326.79850769]
Epoch: 52
cost =  [ 258872.77507019]
Epoch: 53
cost =  [ 258872.77507019]
Epoch: 54
cost =  [ 258872.77507019]
Epoch: 55
cost =  [ 258872.77507019]
Epoch: 56
cost =  [ 258872.77507019]
Epoch: 57
cost =  [ 258872.77507019]
Epoch: 58
cost =  [ 258872.77507019]
Epoch: 59
cost =  [ 2003326.79850769]
Epoch: 60
cost =  [ 258872.77507019]
Epoch: 61
cost =  [ 258872.77507019]
Epoch: 62
cost =  [ 258872.77507019]
Epoch: 63
cost =  [ 258872.77507019]
Epoch: 64
cost =  [ 258872.77507019]
Epoch: 65
cost =  [ 258872.77507019]
Epoch: 66
cost =  [ 258872.77507019]
Epoch: 67
cost =  [ 2003326.79850769]
Epoch: 68
cost =  [ 258872.77507019]
Epoch: 69
cost =  [ 258872.77507019]
Epoch: 70
cost =  [ 258872.77507019]
Epoch: 71
cost =  [ 258872.77507019]
Epoch: 72
cost =  [ 258872.77507019]
Epoch: 73
cost =  [ 258872.77507019]
Epoch: 74
cost =  [ 2003326.79850769]
Epoch: 75
cost =  [ 258872.77507019]
Epoch: 76
cost =  [ 258872.77507019]
Epoch: 77
cost =  [ 258872.77507019]
Epoch: 78
cost =  [ 258872.77507019]
Epoch: 79
cost =  [ 258872.77507019]
Epoch: 80
cost =  [ 258872.77507019]
Epoch: 81
cost =  [ 258872.77507019]
Epoch: 82
cost =  [ 2003326.79850769]
Epoch: 83
cost =  [ 258872.77507019]
Epoch: 84
cost =  [ 258872.77507019]
Epoch: 85
cost =  [ 258872.77507019]
Epoch: 86
cost =  [ 258872.77507019]
Epoch: 87
cost =  [ 258872.77507019]
Epoch: 88
cost =  [ 258872.77507019]
Epoch: 89
cost =  [ 258872.77507019]
Epoch: 90
cost =  [ 2003326.79850769]
Epoch: 91
cost =  [ 258872.77507019]
Epoch: 92
cost =  [ 258872.77507019]
Epoch: 93
cost =  [ 258872.77507019]
Epoch: 94
cost =  [ 258872.77507019]
Epoch: 95
cost =  [ 258872.77507019]
Epoch: 96
cost =  [ 258872.77507019]
Epoch: 97
cost =  [ 258872.77507019]
Epoch: 98
cost =  [ 2003326.79850769]
Epoch: 99
cost =  [ 258872.77507019]
&lt;/code&gt;&lt;/pre&gt;
', 'neuralnetwork  deep-learning  theano  rbm', NULL, '''/code'':189C,641C ''/p'':40C,216C,238C ''/pre'':190C,642C ''0'':242C ''1'':246C ''10'':282C ''11'':286C ''12'':290C ''13'':294C ''14'':298C ''15'':302C ''16'':306C ''17'':310C ''18'':314C ''19'':318C ''2'':113C,250C ''20'':322C ''2003326.79850769'':264C,292C,324C,356C,388C,416C,448C,480C,512C,540C,572C,604C,636C ''21'':326C ''22'':330C ''23'':334C ''24'':338C ''25'':342C ''258872.77507019'':248C,252C,256C,260C,268C,272C,276C,280C,284C,288C,296C,300C,304C,308C,312C,316C,320C,328C,332C,336C,340C,344C,348C,352C,360C,364C,368C,372C,376C,380C,384C,392C,396C,400C,404C,408C,412C,420C,424C,428C,432C,436C,440C,444C,452C,456C,460C,464C,468C,472C,476C,484C,488C,492C,496C,500C,504C,508C,516C,520C,524C,528C,532C,536C,544C,548C,552C,556C,560C,564C,568C,576C,580C,584C,588C,592C,596C,600C,608C,612C,616C,620C,624C,628C,632C,640C ''26'':346C ''27'':350C ''28'':354C ''29'':358C ''3'':254C ''30'':362C ''31'':366C ''32'':370C ''33'':374C ''34'':378C ''35'':382C ''36'':386C ''37'':390C ''38'':394C ''39'':398C ''4'':258C ''40'':402C ''41'':406C ''42'':410C ''43'':414C ''44'':418C ''45'':422C ''46'':426C ''47'':430C ''48'':434C ''49'':438C ''5'':262C ''50'':442C ''51'':446C ''52'':450C ''53'':454C ''54'':458C ''55'':462C ''56'':466C ''57'':470C ''58'':474C ''59'':478C ''6'':266C ''60'':482C ''61'':486C ''62'':490C ''63'':494C ''64'':498C ''65'':502C ''66'':506C ''67'':510C ''68'':514C ''69'':518C ''7'':270C ''70'':522C ''71'':526C ''72'':530C ''73'':534C ''74'':538C ''75'':542C ''76'':546C ''77'':550C ''78'':554C ''79'':558C ''8'':274C ''80'':562C ''81'':566C ''82'':570C ''83'':574C ''84'':578C ''85'':582C ''86'':586C ''87'':590C ''88'':594C ''89'':598C ''9'':278C ''90'':602C ''91'':606C ''92'':610C ''93'':614C ''94'':618C ''95'':622C ''96'':626C ''969572.73014003'':244C ''97'':630C ''98'':634C ''99'':638C ''activ'':62C ''associ'':129C,134C,160C,162C ''bh'':30C,61C,100C,116C,143C ''bhparam'':148C,173C,174C ''bhparamet'':142C,153C ''bias'':33C ''bv'':28C ''byparam'':147C,164C,165C ''byparamet'':140C,152C ''classic'':213C ''clear'':205C ''code'':16C,42C,240C ''construct'':75C ''contrast'':1A ''correct'':195C ''cost'':126C,186C,226C,243C,247C,251C,255C,259C,263C,267C,271C,275C,279C,283C,287C,291C,295C,299C,303C,307C,311C,315C,319C,323C,327C,331C,335C,339C,343C,347C,351C,355C,359C,363C,367C,371C,375C,379C,383C,387C,391C,395C,399C,403C,407C,411C,415C,419C,423C,427C,431C,435C,439C,443C,447C,451C,455C,459C,463C,467C,471C,475C,479C,483C,487C,491C,495C,499C,503C,507C,511C,515C,519C,523C,527C,531C,535C,539C,543C,547C,551C,555C,559C,563C,567C,571C,575C,579C,583C,587C,591C,595C,599C,603C,607C,611C,615C,619C,623C,627C,631C,635C,639C ''data'':22C ''dataset'':215C ''decreas'':234C ''deep'':7B ''deep-learn'':6B ''defin'':120C ''diverg'':2A ''epoch'':241C,245C,249C,253C,257C,261C,265C,269C,273C,277C,281C,285C,289C,293C,297C,301C,305C,309C,313C,317C,321C,325C,329C,333C,337C,341C,345C,349C,353C,357C,361C,365C,369C,373C,377C,381C,385C,389C,393C,397C,401C,405C,409C,413C,417C,421C,425C,429C,433C,437C,441C,445C,449C,453C,457C,461C,465C,469C,473C,477C,481C,485C,489C,493C,497C,501C,505C,509C,513C,517C,521C,525C,529C,533C,537C,541C,545C,549C,553C,557C,561C,565C,569C,573C,577C,581C,585C,589C,593C,597C,601C,605C,609C,613C,617C,621C,625C,629C,633C,637C ''exampl'':53C,73C,92C,107C,202C ''express'':77C ''feat'':54C,93C ''follow'':15C ''function'':63C ''get'':223C ''graph'':78C ''gt'':50C,70C,89C,104C ''hid'':55C,67C,69C,82C,132C ''hid.t'':178C ''hid2'':94C,101C,103C,137C ''hid2.t'':180C ''hidden'':38C,65C ''implement'':196C ''import'':43C ''input'':21C,183C ''layer'':66C,125C ''learn'':8B ''line'':119C ''matrix'':27C ''mnist'':214C ''neg'':133C,161C ''neuralnetwork'':5B ''nhidden'':74C,108C ''numpy.random.rand'':51C,71C,90C,105C ''output'':185C ''p'':11C,191C,217C ''paramet'':114C,122C ''pos'':128C,159C ''pre'':41C,239C ''rate'':158C,167C,176C ''rbm'':4A,10B ''result'':221C ''see'':230C ''state'':48C,59C,68C,83C,87C,98C,102C ''t.dot'':57C,81C,96C,130C,135C ''t.nnet.sigmoid'':56C,80C,95C ''t.sum'':110C,168C,170C,177C,179C ''test'':209C ''theano'':9B,76C ''theano.function'':182C ''theano.tensor'':44C ''tr'':157C,166C,175C ''train'':52C,72C,91C,106C,181C ''unit'':39C ''updat'':144C,187C,188C,199C ''update.append'':154C,163C,172C ''vis'':79C,86C,88C,97C,112C ''vis.t'':136C,171C ''visibl'':36C ''w'':23C,60C,99C,115C,139C ''w.t'':84C ''weight'':26C ''work'':207C ''wparam'':146C,155C,156C ''wparamet'':138C,151C ''wrong'':237C ''x'':18C,47C,49C,58C,111C,184C ''x.t'':131C,169C ''xent'':109C ''xent.mean'':127C ''zip'':150C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9875, 1, '2016-01-20 19:03:33.203', 2, 47, 'ARIMAX with spark-timeseries', '&lt;p&gt;Cloudera recently added the &lt;a href="https://github.com/cloudera/spark-timeseries" rel="nofollow"&gt;spark-time series library to github&lt;/a&gt;. According to the &lt;a href="http://cloudera.github.io/spark-timeseries/0.2.0/scaladocs/index.html#com.cloudera.sparkts.models.ARIMA$" rel="nofollow"&gt;user docs&lt;/a&gt;, it definitely can fit autoregressive integrated moving average (&lt;a href="https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average" rel="nofollow"&gt;ARIMA&lt;/a&gt;) models, but I see no mention of ARIMAX, which takes into account explanatory variables. Does anyone with more experience with this library know whether ARIMAX is possible in the library''s current implementation? Mathematically, I understand the difference between ARIMA and ARIMAX, and I know extending it myself wouldn''t be terribly difficult, but I''m right now looking for an off-the-shelf solution in spark. Can anyone recommend an alternative spark implementation?&lt;/p&gt;
', 'time-series  apache-spark', NULL, '''/a'':31C,44C,61C ''/cloudera/spark-timeseries'':21C ''/p'':137C ''/spark-timeseries/0.2.0/scaladocs/index.html#com.cloudera.sparkts.models.arima$'':39C ''/wiki/autoregressive_integrated_moving_average'':57C ''accord'':32C ''account'':73C ''ad'':15C ''altern'':134C ''anyon'':77C,131C ''apach'':10B ''apache-spark'':9B ''arima'':60C,101C ''arimax'':1A,69C,86C,103C ''autoregress'':49C ''averag'':52C ''cloudera'':13C ''cloudera.github.io'':38C ''cloudera.github.io/spark-timeseries/0.2.0/scaladocs/index.html#com.cloudera.sparkts.models.arima$'':37C ''current'':93C ''definit'':46C ''differ'':99C ''difficult'':114C ''doc'':43C ''en.wikipedia.org'':56C ''en.wikipedia.org/wiki/autoregressive_integrated_moving_average'':55C ''experi'':80C ''explanatori'':74C ''extend'':107C ''fit'':48C ''github'':30C ''github.com'':20C ''github.com/cloudera/spark-timeseries'':19C ''href'':18C,36C,54C ''implement'':94C,136C ''integr'':50C ''know'':84C,106C ''librari'':28C,83C,91C ''look'':120C ''m'':117C ''mathemat'':95C ''mention'':67C ''model'':62C ''move'':51C ''nofollow'':23C,41C,59C ''off-the-shelf'':123C ''p'':12C ''possibl'':88C ''recent'':14C ''recommend'':132C ''rel'':22C,40C,58C ''right'':118C ''see'':65C ''seri'':8B,27C ''shelf'':126C ''solut'':127C ''spark'':4A,11B,25C,129C,135C ''spark-tim'':24C ''spark-timeseri'':3A ''take'':71C ''terribl'':113C ''time'':7B,26C ''time-seri'':6B ''timeseri'':5A ''understand'':97C ''user'':42C ''variabl'':75C ''whether'':85C ''wouldn'':110C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9858, 1, '2016-01-19 20:59:28.04', 3, 47, 'Convergence in Hartigan-Wong k-means method and other algorithms', '&lt;p&gt;I have been trying to understand the different k-means clustering algorithms mainly that are implemented in the &lt;code&gt;stats&lt;/code&gt; package of the &lt;code&gt;R&lt;/code&gt; language.&lt;/p&gt;

&lt;p&gt;I understand the Lloyd''s algorithm and MacQueen''s online algorithm. The way I understand them is as follows:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Lloyd''s Algorithm:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Initially âkâ random observations are chosen that will serve as the centroids of the âkâ clusters. Then the following steps occur in iteration till the centroids converge.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;The Euclidean distance between each observation and the chosen centroids is calculated. &lt;/li&gt;
&lt;li&gt;The observations that are closest to each centroids are tagged within âkâ buckets. &lt;/li&gt;
&lt;li&gt;The mean of all the observations in each bucket serves as new centroids. &lt;/li&gt;
&lt;li&gt;The new centroids replace the old centroids and the iteration goes back to step 1 if the old and new centroids have not converged.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The conditions to converge are the following: the old and the new centroids are exactly identical, the difference between the centroids is small (of the order of 10^-3) or the maximum number of iterations (10 or 100) are reached.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;MacQueen''s Algorithm:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This is an online version where the first ''k'' instances are chosen as centroids.&lt;/p&gt;

&lt;p&gt;Then each instance is placed in buckets depending on which centroid is closest to that instance. The respective centroid is recalculated.&lt;/p&gt;

&lt;p&gt;Repeat this step till each instance is placed in the appropriate bucket.&lt;/p&gt;

&lt;p&gt;This algorithm only has one iteration and the loop goes on for ''x'' instances&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Hartigan-Wong Algorithm:&lt;/strong&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Assign all the points/instances to random buckets and calculate the respective centroid.&lt;/li&gt;
&lt;li&gt;Starting from the first instance find the nearest centroid and assing that bucket. If the bucket changed then recalculate the new centroids i.e. the centroid of the newly assigned bucket and the centroid of the old bucket assignment as those are two centroids that are affected by the change&lt;/li&gt;
&lt;li&gt;Loop through all the points and get new centroids.&lt;/li&gt;
&lt;li&gt;Do a second iteration of points 2 and 3 which performs sort of a clean-up operation and reassigns stray points to correct buckets.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;So this algorithm performs 2 iterations before we see the convergence result.&lt;/p&gt;

&lt;p&gt;Now, I am unsure if what I think in point 4 in the Hartigan-Wong algorithm is the correct method of the algorithm. My question is, if the following method for Hartigan-Wong is the correct method to implement k-means? Are there only two iterations for this method? if not, what is the condition for convergence (when to stop)?&lt;/p&gt;

&lt;p&gt;Another possible implementation explanation what I understand is.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Assign all the points/instances to random buckets and calculate the respective centroid.&lt;/li&gt;
&lt;li&gt;Starting from the first instance find the nearest centroid and assign that bucket. If the bucket changed then recalculate the new centroids i.e. the centroid of the newly assigned bucket and the centroid of the old bucket assignment as those are two centroids that are affected by the change.&lt;/li&gt;
&lt;li&gt;Once there is a change in the bucket for any point, go back to the first instance and repeat the steps again.&lt;/li&gt;
&lt;li&gt;The iteration ends when all the instances are iterated and none of the points change buckets.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;This way there are a lot of iterations that start from the beginning of the dataset again and again every time when an instance changes buckets.&lt;/p&gt;

&lt;p&gt;Any explanations would be helpful and please let me know if I my understanding for any of these methods is wrong.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9861, 2, '2016-01-19 21:48:19.997', 4, NULL, NULL, '&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/One-hot" rel="nofollow"&gt;Hot encode&lt;/a&gt; the categorical variables and use Bernoulli naive Bayes. Hot encoding is usually the trick one uses in representing categorical variables.&lt;/p&gt;
', NULL, NULL, '''/a'':11C ''/p'':32C ''/wiki/one-hot'':6C ''bay'':19C ''bernoulli'':17C ''categor'':13C,30C ''en.wikipedia.org'':5C ''en.wikipedia.org/wiki/one-hot'':4C ''encod'':10C,21C ''hot'':9C,20C ''href'':3C ''naiv'':18C ''nofollow'':8C ''one'':26C ''p'':1C ''rel'':7C ''repres'':29C ''trick'':25C ''use'':16C,27C ''usual'':23C ''variabl'':14C,31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9862, 1, '2016-01-19 22:19:43.05', 1, 76, 'Classification using xgboost - predictions', '&lt;p&gt;I was trying to build a 0-1 classifier using xgboost R package. My question is how predictions are made? For example in random forests, trees "vote" against each option and the final prediction is based on majority. As regard xgboost, the regression case is simple since prediction on whole model is equal to sum of predcitions for weak learners (boosted trees), but what about classification? &lt;/p&gt;

&lt;p&gt;Does xgboost classifier works the same as in the random forest (I don''t think so, since it can return predictive probabilities, not class membership).&lt;/p&gt;
', 'classification  predictive-modeling  xgboost', NULL, '''-1'':18C ''/p'':77C,104C ''0'':17C ''base'':46C ''boost'':71C ''build'':15C ''case'':54C ''class'':102C ''classif'':1A,5B,76C ''classifi'':19C,81C ''equal'':63C ''exampl'':32C ''final'':43C ''forest'':35C,89C ''learner'':70C ''made'':30C ''major'':48C ''membership'':103C ''model'':8B,61C ''option'':40C ''p'':10C,78C ''packag'':23C ''predcit'':67C ''predict'':4A,7B,28C,44C,58C,99C ''predictive-model'':6B ''probabl'':100C ''question'':25C ''r'':22C ''random'':34C,88C ''regard'':50C ''regress'':53C ''return'':98C ''simpl'':56C ''sinc'':57C,95C ''sum'':65C ''think'':93C ''tree'':36C,72C ''tri'':13C ''use'':2A,20C ''vote'':37C ''weak'':69C ''whole'':60C ''work'':82C ''xgboost'':3A,9B,21C,51C,80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9886, 1, '2016-01-21 16:08:59.477', 4, 68, 'Classifying survey response text SVM', '&lt;p&gt;I have 800 responses to an open-ended survey question. Each response is categorized into 3 categories based on a list of 70 categories. These categories are things like "stronger leadership", "better customer service", "programs", and etc...&lt;/p&gt;

&lt;p&gt;My question is, can I use this as a training data set in order to develop a model that I can use in the future as we get more survey responses? We would like to be able to tag, label, or classify each survey response into (up to) 3 of the 70 categories.&lt;/p&gt;

&lt;p&gt;Is this even possible? Or do I have to use a NB with simple words? Can you please guide me to tutorials, examples, etc.?&lt;/p&gt;

&lt;p&gt;Using R in this exercise.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9863, 1, '2016-01-20 01:18:02.193', 1, 53, 'Tensorflow RNN: Batching data of different length', '&lt;p&gt;I''m starting to experiment with neural networks in Google''s tensorflow framework, and in particular am working on a model that has an LSTM layer. I am using the &lt;a href="https://www.tensorflow.org/versions/master/tutorials/recurrent/index.html" rel="nofollow"&gt;RNN tutorial&lt;/a&gt; to get me started.&lt;/p&gt;

&lt;p&gt;To train my model, I have sequences of data that are several thousand data points long. In the tutorial, the author is processing small sequences of data (short sentences of paragraphs) and concatenates those sequences together, separated by &lt;code&gt;&amp;lt;end of stream&amp;gt;&lt;/code&gt;, divides the entire data set equally into &lt;code&gt;batch_size&lt;/code&gt; number of chunks and processes those chunks in parallel. I would like to process each of my sequences of data separately from start to finish, but would like to be able to process several of them in parallel, as the &lt;code&gt;rnn_cell&lt;/code&gt; objects are set up to do (they process &lt;code&gt;batch_size&lt;/code&gt; sequences at onces).&lt;/p&gt;

&lt;p&gt;This would be straight forward to do if the sequences were of the same length, but they are not. An &lt;code&gt;rnn_cell.LSTMCell&lt;/code&gt; expects the data to come in with a fixed dimensionaltiy, &lt;code&gt;batch_size x input_size&lt;/code&gt;, so there''s a problem once some of the sequences reach their end and my &lt;code&gt;batch_size&lt;/code&gt; wants to decrease.&lt;/p&gt;

&lt;p&gt;How can I work around this? I don''t think I can ''left'' or ''right'' justify my data so that they line up without affecting the training.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9901, 2, '2016-01-22 08:12:36.267', 2, NULL, NULL, '&lt;p&gt;&lt;em&gt;k-means&lt;/em&gt; finds only a local optima. Thus a wrong number of cluster or simply some random state of equilibrium in the attracting forces could lead to empty clusters. Technically k-means does not provide a procedure for that, but you can enrich the algorithm with no problem. &lt;/p&gt;

&lt;p&gt;There are two approaches which I found that are useful:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;remove the stale cluster, choose a random instance from your data set and create a new cluster with centroid equal with the chosen random point&lt;/li&gt;
&lt;li&gt;remove the stale cluster, choose the farthest distant point from any other centroids, create a new cluster with centroid in that point&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Both procedures can lead to indefinite running time, but if the number of this kind of adjustments is finite (and usually it is) that it will converge with no problem. To guard yourself from infinite running time you can set an upper bound for the number of &lt;em&gt;adjustments&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;The procedure itself is not practical if you have a huge data set a a large number of clusters. The running time can became prohibitive.&lt;/p&gt;

&lt;p&gt;Another procedure to decrease the chances for that to happen is to use a better initialization procedure, like &lt;em&gt;k-means++&lt;/em&gt;. In fact the second suggestion is an idea from &lt;em&gt;k-means++&lt;/em&gt;. There are no guarantees, however.&lt;/p&gt;

&lt;p&gt;Finally a note regarding implementation. If you can''t change to code of the algorithm to make those improvements on the fly, your only option which comes to my mind is to start a new clustering procedure where you initialize the centroid positions for non-stale clusters, and follow procedures for stale clusters.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9864, 2, '2016-01-20 06:13:41.527', 2, NULL, NULL, '&lt;p&gt;I would say &lt;strong&gt;Yes.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;As Jacob neatly put, the Universal Approximation algo. can be leveraged for proving the fact.&lt;/p&gt;

&lt;p&gt;In fact, as neural networks are highly flexible, owing to their portable structure definitions and a huge set of activation functions one can choose from, I don''t see why a Bayesian network cannot be mimicked by a neural network. But, from my experience of both research and coding deep belief networks, I''d advise you &lt;strong&gt;not to try this at home.&lt;/strong&gt;  Cause, you would often end up with painfully complex networks. :D&lt;/p&gt;

&lt;p&gt;But, I am skeptical about the vice-versa process. (owing to my limited knowledge of Bayesian networks)&lt;/p&gt;
', NULL, NULL, '''/p'':8C,25C,102C,122C ''/strong'':7C,90C ''activ'':46C ''advis'':81C ''algo'':17C ''approxim'':16C ''bayesian'':58C,120C ''belief'':77C ''cannot'':60C ''caus'':91C ''choos'':50C ''code'':75C ''complex'':99C ''d'':80C,101C ''deep'':76C ''definit'':40C ''end'':95C ''experi'':70C ''fact'':24C,28C ''flexibl'':34C ''function'':47C ''high'':33C ''home'':89C ''huge'':43C ''jacob'':11C ''knowledg'':118C ''leverag'':20C ''limit'':117C ''mimick'':62C ''neat'':12C ''network'':31C,59C,66C,78C,100C,121C ''neural'':30C,65C ''often'':94C ''one'':48C ''owe'':35C,114C ''p'':1C,9C,26C,103C ''pain'':98C ''portabl'':38C ''process'':113C ''prove'':22C ''put'':13C ''research'':73C ''say'':4C ''see'':55C ''set'':44C ''skeptic'':107C ''strong'':5C,83C ''structur'':39C ''tri'':86C ''univers'':15C ''versa'':112C ''vice'':111C ''vice-versa'':110C ''would'':3C,93C ''yes'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9865, 1, '2016-01-20 06:33:54.923', 3, 323, 'What is the difference between NLP and text mining?', '&lt;p&gt;As discussed with Sean in &lt;a href="http://meta.datascience.stackexchange.com/q/2200/11097"&gt;this Meta post&lt;/a&gt;, I thought it would be nice to have a question which can help people who were confused like me, to know about the differences between text mining and NLP!&lt;/p&gt;

&lt;p&gt;So, what are the differences between &lt;a href="/questions/tagged/nlp" class="post-tag" title="show questions tagged &amp;#39;nlp&amp;#39;" rel="tag"&gt;nlp&lt;/a&gt; and &lt;a href="/questions/tagged/text-mining" class="post-tag" title="show questions tagged &amp;#39;text-mining&amp;#39;" rel="tag"&gt;text-mining&lt;/a&gt;?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;I have included my understanding as an answer. If possible, please explain your answer with a brief example!&lt;/p&gt;
', 'nlp  text-mining', NULL, '''/a'':28C,83C,106C ''/p'':58C,107C,128C ''/q/2200/11097'':24C ''/questions/tagged/nlp'':68C ''/questions/tagged/text-mining'':87C ''39'':77C,79C,96C,100C ''answer'':117C,123C ''brief'':126C ''class'':69C,88C ''confus'':45C ''differ'':4A,52C,64C ''discuss'':16C ''exampl'':127C ''explain'':121C ''help'':41C ''hr'':108C ''href'':21C,67C,86C ''includ'':112C ''know'':49C ''like'':46C ''meta'':26C ''meta.datascience.stackexchange.com'':23C ''meta.datascience.stackexchange.com/q/2200/11097'':22C ''mine'':9A,13B,55C,99C,105C ''nice'':34C ''nlp'':6A,10B,57C,78C,82C ''p'':14C,59C,109C ''peopl'':42C ''pleas'':120C ''possibl'':119C ''post'':27C,71C,90C ''post-tag'':70C,89C ''question'':38C,75C,94C ''rel'':80C,101C ''sean'':18C ''show'':74C,93C ''tag'':72C,76C,81C,91C,95C,102C ''text'':8A,12B,54C,98C,104C ''text-min'':11B,97C,103C ''thought'':30C ''titl'':73C,92C ''understand'':114C ''would'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9866, 2, '2016-01-20 06:39:32.65', 2, NULL, NULL, '&lt;p&gt;I have had this doubt since a long time. So, &lt;a href="http://discuss.analyticsvidhya.com/t/difference-between-nlp-and-text-mining/2977/2" rel="nofollow"&gt;this post&lt;/a&gt; here helped me figure the differences between the two.&lt;/p&gt;

&lt;p&gt;So, this is the difference between text mining and NLP:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Text Mining deals with the text itself, while NLP deals with the
  underlying/latent metadata.&lt;/p&gt;
  
  &lt;p&gt;Answering questions like - frequency counts of words, length of the
  sentence, presence/absence of certain words etc. is text mining.&lt;/p&gt;
  
  &lt;p&gt;NLP on the other hand allows you to answer questions like;
  - What is the sentiment?
  - What are the keywords? (using POS tagging &amp;amp; parsers)
  - What category of content it falls under?
  - Which are the entities in the sentence? And more&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/a'':21C ''/blockquote'':124C ''/p'':31C,43C,60C,81C,123C ''/t/difference-between-nlp-and-text-mining/2977/2'':16C ''allow'':88C ''amp'':105C ''answer'':62C,91C ''blockquot'':44C ''categori'':108C ''certain'':75C ''content'':110C ''count'':66C ''deal'':48C,55C ''differ'':27C,37C ''discuss.analyticsvidhya.com'':15C ''discuss.analyticsvidhya.com/t/difference-between-nlp-and-text-mining/2977/2'':14C ''doubt'':6C ''entiti'':117C ''etc'':77C ''fall'':112C ''figur'':25C ''frequenc'':65C ''hand'':87C ''help'':23C ''href'':13C ''keyword'':101C ''length'':69C ''like'':64C,93C ''long'':9C ''metadata'':59C ''mine'':40C,47C,80C ''nlp'':42C,54C,83C ''nofollow'':18C ''p'':1C,32C,45C,61C,82C ''parser'':106C ''pos'':103C ''post'':20C ''presence/absence'':73C ''question'':63C,92C ''rel'':17C ''sentenc'':72C,120C ''sentiment'':97C ''sinc'':7C ''tag'':104C ''text'':39C,46C,51C,79C ''time'':10C ''two'':30C ''underlying/latent'':58C ''use'':102C ''word'':68C,76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9876, 2, '2016-01-20 21:15:52.153', 1, NULL, NULL, '&lt;p&gt;Blast. It looks as though ARIMAX is &lt;a href="https://github.com/cloudera/spark-timeseries/issues/85" rel="nofollow"&gt;currently a work in progress&lt;/a&gt; for the cloudera/spark-timeseries community. Although &lt;a href="https://github.com/cloudera/spark-timeseries/pull/51" rel="nofollow"&gt;some work has been done&lt;/a&gt; on the model development side, it doesn''t appear to have been merged in. &lt;/p&gt;
', NULL, NULL, '''/a'':21C,39C ''/cloudera/spark-timeseries/issues/85'':13C ''/cloudera/spark-timeseries/pull/51'':31C ''/p'':54C ''although'':26C ''appear'':48C ''arimax'':7C ''blast'':2C ''cloudera/spark-timeseries'':24C ''communiti'':25C ''current'':16C ''develop'':43C ''doesn'':46C ''done'':38C ''github.com'':12C,30C ''github.com/cloudera/spark-timeseries/issues/85'':11C ''github.com/cloudera/spark-timeseries/pull/51'':29C ''href'':10C,28C ''look'':4C ''merg'':52C ''model'':42C ''nofollow'':15C,33C ''p'':1C ''progress'':20C ''rel'':14C,32C ''side'':44C ''though'':6C ''work'':18C,35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9877, 1, '2016-01-21 00:40:12.583', 2, 47, 'What is the best file format to store an uncompressed 2D matrix?', '&lt;p&gt;For what it''s worth my particular case is a symmetrical matrix, but this question should be answered more generally.&lt;/p&gt;
', 'data-formats', NULL, '''/p'':37C ''2d'':11A ''answer'':34C ''best'':4A ''case'':24C ''data'':14B ''data-format'':13B ''file'':5A ''format'':6A,15B ''general'':36C ''matrix'':12A,28C ''p'':16C ''particular'':23C ''question'':31C ''store'':8A ''symmetr'':27C ''uncompress'':10A ''worth'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9883, 1, '2016-01-21 12:14:59.203', 2, 42, 'Non-linear data preprocessing before mini-batch gradient descent', '&lt;p&gt;I found some realisation of pretty interesting ML algorithm. &lt;a href="https://github.com/EderSantana/DeepEEG" rel="nofollow"&gt;https://github.com/EderSantana/DeepEEG&lt;/a&gt; We have dataset X, matrix of spacial filter W, matrix of time filter V, and matrices U and B - parameters of classyfier. 
$Xfilt_i=ln(W*(X_i*v)^2))$&lt;/p&gt;

&lt;p&gt;$Z=(Xfilt)^T * U+B$&lt;/p&gt;

&lt;p&gt;$Y\_pred = argmax(softmax(Z))$&lt;/p&gt;

&lt;p&gt;So, quite simple logistic regression classification. But most inreresting thing was the way authors tune W,V,U and B  matrices. W was initialized by CSP algorith. V was initialized by ones, U and B by zeros. And then cross-entropy cost function optimised (mini-batch gradient descent) respect to this parameters. And accuracy, achived by this way pretty high. My quiestion is, why we need non-linearity here (squaring and log)&lt;/p&gt;

&lt;p&gt;$Xfilt_i=ln(W*(X_i*v)^2))$
?&lt;/p&gt;

&lt;p&gt;PS In experiments optimisation algorithm did not converge without this nonlineraity.  &lt;/p&gt;
', 'machine-learning  classification  gradient-descent', NULL, '''/edersantana/deepeeg'':33C ''/edersantana/deepeeg&lt;/a&gt;'':38C ''/p'':69C,76C,83C,160C,170C,183C ''2'':68C,169C ''accuraci'':140C ''achiv'':141C ''algorith'':111C ''algorithm'':28C,176C ''argmax'':80C ''author'':98C ''b'':57C,75C,104C,119C ''batch'':9A,132C ''classif'':15B,90C ''classyfi'':60C ''converg'':179C ''cost'':127C ''cross'':125C ''cross-entropi'':124C ''csp'':110C ''data'':4A ''dataset'':41C ''descent'':11A,18B,134C ''entropi'':126C ''experi'':174C ''filter'':46C,51C ''found'':21C ''function'':128C ''github.com'':32C,37C ''github.com/edersantana/deepeeg'':31C ''github.com/edersantana/deepeeg&lt;/a&gt;'':36C ''gradient'':10A,17B,133C ''gradient-desc'':16B ''high'':146C ''href'':30C ''initi'':108C,114C ''inrerest'':93C ''interest'':26C ''learn'':14B ''linear'':3A,155C ''ln'':63C,164C ''log'':159C ''logist'':88C ''machin'':13B ''machine-learn'':12B ''matric'':54C,105C ''matrix'':43C,48C ''mini'':8A,131C ''mini-batch'':7A,130C ''ml'':27C ''need'':152C ''nofollow'':35C ''non'':2A,154C ''non-linear'':1A,153C ''nonlinera'':182C ''one'':116C ''optimis'':129C,175C ''p'':19C,70C,77C,84C,161C,171C ''paramet'':58C,138C ''pred'':79C ''preprocess'':5A ''pretti'':25C,145C ''ps'':172C ''quiestion'':148C ''quit'':86C ''realis'':23C ''regress'':89C ''rel'':34C ''respect'':135C ''simpl'':87C ''softmax'':81C ''spacial'':45C ''squar'':157C ''thing'':94C ''time'':50C ''tune'':99C ''u'':55C,74C,102C,117C ''v'':52C,67C,101C,112C,168C ''w'':47C,64C,100C,106C,165C ''way'':97C,144C ''without'':180C ''x'':42C,65C,166C ''xfilt'':61C,72C,162C ''y'':78C ''z'':71C,82C ''zero'':121C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9888, 2, '2016-01-21 17:06:52.9', 3, NULL, NULL, '&lt;p&gt;Can you clarify what you''re trying to predict with these responses?&lt;/p&gt;

&lt;p&gt;My initial reaction is that with open-ended surveys you''ll have a tough time implementing classification algorithms. Open-ended-ness means you don''t have a finite feature space and thus you can''t do the usual transformation of responses into a feature matrix.&lt;/p&gt;

&lt;p&gt;However, there may be other ways to make this work. If you have 5 questions, for instance, you may be able to use sentiment analysis or other methods to come up with metadata about the open ended response that can help you design a classification scheme.&lt;/p&gt;
', NULL, NULL, '''/p'':14C,63C,111C ''5'':78C ''abl'':85C ''algorithm'':34C ''analysi'':89C ''clarifi'':4C ''classif'':33C,109C ''come'':94C ''design'':107C ''end'':24C,37C,101C ''featur'':46C,61C ''finit'':45C ''help'':105C ''howev'':65C ''implement'':32C ''initi'':17C ''instanc'':81C ''ll'':27C ''make'':72C ''matrix'':62C ''may'':67C,83C ''mean'':39C ''metadata'':97C ''method'':92C ''ness'':38C ''open'':23C,36C,100C ''open-end'':22C ''open-ended-'':35C ''p'':1C,15C,64C ''predict'':10C ''question'':79C ''re'':7C ''reaction'':18C ''respons'':13C,58C,102C ''scheme'':110C ''sentiment'':88C ''space'':47C ''survey'':25C ''thus'':49C ''time'':31C ''tough'':30C ''transform'':56C ''tri'':8C ''use'':87C ''usual'':55C ''way'':70C ''work'':74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9867, 2, '2016-01-20 06:56:14.43', 5, NULL, NULL, '&lt;p&gt;I agree with Sean''s answer.
&lt;a href="https://en.wikipedia.org/wiki/Natural_language_processing" rel="nofollow"&gt;NLP&lt;/a&gt; and &lt;a href="https://en.wikipedia.org/wiki/Text_mining" rel="nofollow"&gt;text mining&lt;/a&gt; are usually used for different goals.
Also, there is indeed an overlap and both definitions are vogue.&lt;/p&gt;

&lt;p&gt;Other than the difference in goal, there is a difference in methods.
Text mining techniques are usually shallow and do not consider the text structure.Usually, text mining will use bag of words, n-grams and possibly stemming over that.&lt;/p&gt;

&lt;p&gt;In NLP methods usually involve the test structure. You can find there sentence splitting, part of speech tagging and parse tree construction.  &lt;/p&gt;

&lt;p&gt;Typical text mining method will consider the following sentences to indicate happiness while typical NLP methods detects that they are not&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;I am not happy&lt;/li&gt;
&lt;li&gt;I will be happy when it will rain&lt;/li&gt;
&lt;li&gt;If it will rain, I''ll be happy.&lt;/li&gt;
&lt;li&gt;She asked whether I am happy&lt;/li&gt;
&lt;li&gt;Are you happy?&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':16C,27C ''/li'':141C,151C,161C,169C,174C ''/ol'':175C ''/p'':45C,87C,111C,134C ''/wiki/natural_language_processing'':12C ''/wiki/text_mining'':22C ''agre'':3C ''also'':34C ''answer'':7C ''ask'':164C ''bag'':76C ''consid'':68C,118C ''construct'':110C ''definit'':42C ''detect'':129C ''differ'':32C,50C,56C ''en.wikipedia.org'':11C,21C ''en.wikipedia.org/wiki/natural_language_processing'':10C ''en.wikipedia.org/wiki/text_mining'':20C ''find'':99C ''follow'':120C ''goal'':33C,52C ''gram'':81C ''happi'':124C,140C,146C,160C,168C,173C ''href'':9C,19C ''inde'':37C ''indic'':123C ''involv'':93C ''li'':136C,142C,152C,162C,170C ''ll'':158C ''method'':58C,91C,116C,128C ''mine'':26C,60C,73C,115C ''n'':80C ''n-gram'':79C ''nlp'':15C,90C,127C ''nofollow'':14C,24C ''ol'':135C ''overlap'':39C ''p'':1C,46C,88C,112C ''pars'':108C ''part'':103C ''possibl'':83C ''rain'':150C,156C ''rel'':13C,23C ''sean'':5C ''sentenc'':101C,121C ''shallow'':64C ''speech'':105C ''split'':102C ''stem'':84C ''structur'':96C ''structure.usually'':71C ''tag'':106C ''techniqu'':61C ''test'':95C ''text'':25C,59C,70C,72C,114C ''tree'':109C ''typic'':113C,126C ''use'':30C,75C ''usual'':29C,63C,92C ''vogu'':44C ''whether'':165C ''word'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9868, 2, '2016-01-20 09:53:43.583', 1, NULL, NULL, '&lt;p&gt;The gradient boost algorithm create a set of decision tree.&lt;/p&gt;

&lt;p&gt;The prediction process used &lt;a href="https://gist.github.com/shanebutler/5456942" rel="nofollow"&gt;here&lt;/a&gt; use these steps:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;for each tree, create a temporary "predicted variable", applying the tree to the new data set.&lt;/li&gt;
&lt;li&gt;use a formula to aggregate all these tree. Depending on the model:

&lt;ul&gt;
&lt;li&gt;bernoulli: 1/(1 + exp(-(intercept + SUM(temporary pred))))&lt;/li&gt;
&lt;li&gt;poisson, gamma: exp(intercept + SUM(temporary pred))&lt;/li&gt;
&lt;li&gt;adaboost: 1 /(1 + exp(-2*(intercept + SUM(temporary pred))))&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The temporary "predicted variable" is a probability, having no sense by its own.&lt;/p&gt;

&lt;p&gt;The more tree you have, the more smooth is your prediction.( as for each tree, only a finite set of value is spread across your observations)&lt;/p&gt;

&lt;p&gt;The R process is probably optimised, but it is enough to understand the concept.&lt;/p&gt;

&lt;p&gt;In the h2o implementation of the gradient boost, the output is a flag 0/1.
I think the &lt;a href="https://en.wikipedia.org/wiki/F1_score" rel="nofollow"&gt;F1 score&lt;/a&gt; is used by default to convert probability into flag. I''ll do some search/test to confirm that.&lt;/p&gt;

&lt;p&gt;In that same implementation, one of the default output for a binary outcome is a confusion matrix, which is a great way to assess your model ( and open a whole new bunch of interrogations).&lt;/p&gt;

&lt;p&gt;The intercept is "the initial predicted value to which trees make adjustments". Basically,just an initial adjustment.&lt;/p&gt;

&lt;p&gt;In addition: &lt;a href="http://h2o-release.s3.amazonaws.com/h2o/rel-tibshirani/8/docs-website/h2o-docs/booklets/GBM_Vignette.pdf" rel="nofollow"&gt;h2o.gbm documentation&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''-2'':88C ''/a'':26C,183C,269C ''/h2o/rel-tibshirani/8/docs-website/h2o-docs/booklets/gbm_vignette.pdf'':264C ''/li'':49C,73C,82C,93C,95C ''/p'':12C,30C,111C,139C,155C,201C,237C,256C,270C ''/shanebutler/5456942'':22C ''/ul'':94C,96C ''/wiki/f1_score'':178C ''0/1'':170C ''1'':66C,67C,85C,86C ''across'':136C ''adaboost'':84C ''addit'':259C ''adjust'':250C,255C ''aggreg'':55C ''algorithm'':5C ''appli'':41C ''assess'':226C ''basic'':251C ''bernoulli'':65C ''binari'':214C ''boost'':4C,164C ''bunch'':234C ''concept'':154C ''confirm'':199C ''confus'':218C ''convert'':189C ''creat'':6C,36C ''data'':47C ''decis'':10C ''default'':187C,210C ''depend'':59C ''document'':268C ''en.wikipedia.org'':177C ''en.wikipedia.org/wiki/f1_score'':176C ''enough'':150C ''exp'':68C,77C,87C ''f1'':181C ''finit'':130C ''flag'':169C,192C ''formula'':53C ''gamma'':76C ''gist.github.com'':21C ''gist.github.com/shanebutler/5456942'':20C ''gradient'':3C,163C ''great'':223C ''h2o'':159C ''h2o-release.s3.amazonaws.com'':263C ''h2o-release.s3.amazonaws.com/h2o/rel-tibshirani/8/docs-website/h2o-docs/booklets/gbm_vignette.pdf'':262C ''h2o.gbm'':267C ''href'':19C,175C,261C ''implement'':160C,206C ''initi'':243C,254C ''intercept'':69C,78C,89C,240C ''interrog'':236C ''li'':32C,50C,64C,74C,83C ''ll'':194C ''make'':249C ''matrix'':219C ''model'':62C,228C ''new'':46C,233C ''nofollow'':24C,180C,266C ''observ'':138C ''one'':207C ''open'':230C ''optimis'':146C ''outcom'':215C ''output'':166C,211C ''p'':1C,13C,97C,112C,140C,156C,202C,238C,257C ''poisson'':75C ''pred'':72C,81C,92C ''predict'':15C,39C,100C,123C,244C ''probabl'':104C,145C,190C ''process'':16C,143C ''r'':142C ''rel'':23C,179C,265C ''score'':182C ''search/test'':197C ''sens'':107C ''set'':8C,48C,131C ''smooth'':120C ''spread'':135C ''step'':29C ''sum'':70C,79C,90C ''temporari'':38C,71C,80C,91C,99C ''think'':172C ''tree'':11C,35C,43C,58C,115C,127C,248C ''ul'':31C,63C ''understand'':152C ''use'':17C,27C,51C,185C ''valu'':133C,245C ''variabl'':40C,101C ''way'':224C ''whole'':232C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9869, 1, '2016-01-20 12:53:31.883', 1, 25, 'What are prototypes in RBF networks?', '&lt;p&gt;I am currently reading &lt;em&gt;Boosting the Performance of RBF Networks with Dynamic Decay Adjustment&lt;/em&gt; by Michael R. Berthold and Jay Diamond (&lt;a href="http://kops.uni-konstanz.de/handle/123456789/5427" rel="nofollow"&gt;online&lt;/a&gt;) to understand how Dynamic Decay Adjustment (DDA; a constructive trainining algorithm for RBF networks). Doing so, I stumbled over the word &lt;strong&gt;prototype&lt;/strong&gt; a couple of times:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Unfortunately PRCE networks do not adjust the standard deviation of their prototypes individually, using only one global value for this parameter.&lt;/p&gt;
  
  &lt;p&gt;[...]&lt;/p&gt;
  
  &lt;p&gt;This paper introduces the Dynamic Decay Adjustment (DDA) algorithm which utilizes the constructive nature of the PRCE algorithm together with independent adaptation of each prototype''s decay factor.&lt;/p&gt;
  
  &lt;p&gt;[...]&lt;/p&gt;
  
  &lt;p&gt;PNNs are not suitable for large databases because they commit one new prototype for each training pattern they encounter, eeffectively becoming a referential memory scheme.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I''ve tried to find it in the only resource they referenced in this context (D.L. Reilly, L.N. Cooper, C. Elbaum: "A Neural Model for Category Learning"), but sadly I don''t have access to that one.&lt;/p&gt;

&lt;p&gt;I found an explanation on &lt;a href="https://chrisjmccormick.wordpress.com/2013/08/15/radial-basis-function-network-rbfn-tutorial/" rel="nofollow"&gt;https://chrisjmccormick.wordpress.com/2013/08/15/radial-basis-function-network-rbfn-tutorial/&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;An RBFN performs classification by measuring the inputâs similarity to examples from the training set. Each RBFN neuron stores a âprototypeâ, which is just one of the examples from the training set. When we want to classify a new input, each neuron computes the Euclidean distance between the input and its prototype. Roughly speaking, if the input more closely resembles the class A prototypes than the class B prototypes, it is classified as class A.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;So a prototype is just the parameters (center and radius, assuming Gaussians are used) of an RBFs neuron?&lt;/p&gt;

&lt;p&gt;Rephrasing the first quoted sentence, does it mean that the RBF networks usually only learn the center and the radius is fixed?&lt;/p&gt;

&lt;p&gt;My question is if I understood it correct. Please add a reference (which is not a random blog article) which makes it more clear.&lt;/p&gt;
', 'machine-learning  neuralnetwork  reference-request', NULL, '''/2013/08/15/radial-basis-function-network-rbfn-tutorial/'':211C ''/2013/08/15/radial-basis-function-network-rbfn-tutorial/&lt;/a&gt;:&lt;/p&gt;'':216C ''/a'':46C ''/blockquote'':161C,297C ''/em'':30C ''/handle/123456789/5427'':42C ''/p'':75C,99C,101C,131C,133C,160C,200C,296C,317C,341C,367C ''/strong'':70C ''access'':196C ''adapt'':124C ''add'':352C ''adjust'':29C,52C,83C,109C ''algorithm'':57C,111C,120C ''articl'':361C ''assum'':309C ''b'':288C ''becom'':155C ''berthold'':34C ''blockquot'':76C,217C ''blog'':360C ''boost'':20C ''c'':182C ''categori'':188C ''center'':306C,335C ''chrisjmccormick.wordpress.com'':210C,215C ''chrisjmccormick.wordpress.com/2013/08/15/radial-basis-function-network-rbfn-tutorial/'':209C ''chrisjmccormick.wordpress.com/2013/08/15/radial-basis-function-network-rbfn-tutorial/&lt;/a&gt;:&lt;/p&gt;'':214C ''class'':282C,287C,294C ''classif'':222C ''classifi'':257C,292C ''clear'':366C ''close'':279C ''commit'':144C ''comput'':263C ''construct'':55C,115C ''context'':177C ''cooper'':181C ''correct'':350C ''coupl'':72C ''current'':17C ''d.l'':178C ''databas'':141C ''dda'':53C,110C ''decay'':28C,51C,108C,129C ''deviat'':86C ''diamond'':37C ''distanc'':266C ''dynam'':27C,50C,107C ''eeffect'':154C ''elbaum'':183C ''em'':19C ''encount'':153C ''euclidean'':265C ''exampl'':230C,248C ''explan'':205C ''factor'':130C ''find'':167C ''first'':321C ''fix'':340C ''found'':203C ''gaussian'':310C ''global'':94C ''href'':39C,208C ''independ'':123C ''individu'':90C ''input'':260C,269C,277C ''inputâ'':226C ''introduc'':105C ''jay'':36C ''kops.uni-konstanz.de'':41C ''kops.uni-konstanz.de/handle/123456789/5427'':40C ''l.n'':180C ''larg'':140C ''learn'':9B,189C,333C ''machin'':8B ''machine-learn'':7B ''make'':363C ''mean'':326C ''measur'':224C ''memori'':158C ''michael'':32C ''model'':186C ''natur'':116C ''network'':6A,25C,60C,80C,330C ''neural'':185C ''neuralnetwork'':10B ''neuron'':237C,262C,316C ''new'':146C,259C ''nofollow'':44C,213C ''one'':93C,145C,199C,245C ''onlin'':45C ''p'':14C,77C,100C,102C,132C,134C,162C,201C,218C,298C,318C,342C ''paper'':104C ''paramet'':98C,305C ''pattern'':151C ''perform'':22C,221C ''pleas'':351C ''pnns'':135C ''prce'':79C,119C ''prototyp'':3A,69C,89C,127C,147C,272C,284C,289C,301C ''prototypeâ'':241C ''question'':344C ''quot'':322C ''r'':33C ''radius'':308C,338C ''random'':359C ''rbf'':5A,24C,59C,329C ''rbfn'':220C,236C ''rbfs'':315C ''read'':18C ''refer'':12B,354C ''referenc'':174C ''reference-request'':11B ''referenti'':157C ''reilli'':179C ''rel'':43C,212C ''rephras'':319C ''request'':13B ''resembl'':280C ''resourc'':172C ''rough'':273C ''sad'':191C ''scheme'':159C ''sentenc'':323C ''set'':234C,252C ''similar'':228C ''speak'':274C ''standard'':85C ''store'':238C ''strong'':68C ''stumbl'':64C ''suitabl'':138C ''time'':74C ''togeth'':121C ''train'':150C,233C,251C ''trainin'':56C ''tri'':165C ''understand'':48C ''understood'':348C ''unfortun'':78C ''use'':91C,312C ''usual'':331C ''util'':113C ''valu'':95C ''ve'':164C ''want'':255C ''word'':67C ''â'':240C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9880, 1, '2016-01-21 11:02:06.503', 3, 52, 'How should I convert Logistic Regression''s coefs into action strategy?', '&lt;p&gt;I am trying to analyze soccer''s data set:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;W_OVER_2_5  PREDICTED MATCH_DATE LEAGUE HOME AWAY MATCH_HOME MATCH_DRAW MATCH_AWAY  MATCH_U2_50 MATCH_O2_50
0            0        1105135200    5   260  289    2.05        3.00        4.50        1.65       2.30
0            1        1105308000    16  715  700    2.50        3.30        3.05        1.80       2.14
1            1        1105308000    11  445  479    1.36        5.25       12.00        2.15       1.78
0            1        1105308000    11  453  474    3.00        3.35        2.62        1.75       2.20....
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now, I selected ''the best estimator'' - &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class=''ovr'', n_jobs=1,
          penalty=''l1'', random_state=None, solver=''liblinear'', tol=0.0001,
          verbose=1, warm_start=False)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;with best coefs - &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1. -2.40477246e-10  
2. -5.57611571e-02  
3. -1.32010761e-04   
4.  1.51666398e-03
5.  7.54521399e-02   
6.  6.38889247e-02  
7. -2.25746953e-01  
8. -3.79313902e-01
9.  3.70514297e-02
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now, I have a question - how should I understand coefs in terms of real strategy? As example, &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;If `MATCH_HOME` is min among all [`MATCH_HOME`, `MATCH_DRAW`, `MATCH_WAY`] AND `MATCH_O2_50'' = 1 
THEN PREDICTED := 1
ELSE PREDICTED := 0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;PS. I would very appreciated for any science papers about that thematic :)&lt;/p&gt;
', 'machine-learning  classification  python  predictive-modeling  scikit', NULL, '''-1.32010761e-04'':163C ''-2.25746953e-01'':171C ''-2.40477246e-10'':159C ''-3.79313902e-01'':173C ''-5.57611571e-02'':161C ''/code'':100C,149C,176C,223C ''/p'':31C,109C,155C,196C,238C ''/pre'':101C,150C,177C,224C ''0'':56C,57C,67C,89C,222C ''0.0001'':143C ''1'':68C,78C,79C,90C,125C,134C,145C,158C,216C,219C ''1.0'':114C ''1.36'':84C ''1.51666398e-03'':165C ''1.65'':65C ''1.75'':98C ''1.78'':88C ''1.80'':76C ''100'':128C ''11'':81C,92C ''1105135200'':58C ''1105308000'':69C,80C,91C ''12.00'':86C ''16'':70C ''2'':36C,160C ''2.05'':62C ''2.14'':77C ''2.15'':87C ''2.20'':99C ''2.30'':66C ''2.50'':73C ''2.62'':97C ''260'':60C ''289'':61C ''3'':162C ''3.00'':63C,95C ''3.05'':75C ''3.30'':74C ''3.35'':96C ''3.70514297e-02'':175C ''4'':164C ''4.50'':64C ''445'':82C ''453'':93C ''474'':94C ''479'':83C ''5'':37C,59C,166C ''5.25'':85C ''50'':52C,55C,215C ''6'':168C ''6.38889247e-02'':169C ''7'':170C ''7.54521399e-02'':167C ''700'':72C ''715'':71C ''8'':172C ''9'':174C ''action'':10A ''among'':204C ''analyz'':26C ''appreci'':230C ''away'':43C,49C ''best'':107C,153C ''c'':113C ''class'':115C,130C ''classif'':15B ''code'':33C,111C,157C,198C ''coef'':8A,154C,188C ''convert'':4A ''data'':29C ''date'':40C ''draw'':47C,209C ''dual'':118C ''els'':220C ''estim'':108C ''exampl'':195C ''fals'':119C,148C ''fit'':120C ''home'':42C,45C,201C,207C ''intercept'':121C,123C ''iter'':127C ''job'':133C ''l1'':136C ''leagu'':41C ''learn'':14B ''liblinear'':141C ''logist'':5A ''logisticregress'':112C ''machin'':13B ''machine-learn'':12B ''match'':39C,44C,46C,48C,50C,53C,200C,206C,208C,210C,213C ''max'':126C ''min'':203C ''model'':19B ''multi'':129C ''n'':132C ''none'':117C,139C ''o2'':54C,214C ''ovr'':131C ''p'':21C,102C,151C,178C,225C ''paper'':234C ''penalti'':135C ''pre'':32C,110C,156C,197C ''predict'':18B,38C,218C,221C ''predictive-model'':17B ''ps'':226C ''python'':16B ''question'':183C ''random'':137C ''real'':192C ''regress'':6A ''scale'':124C ''scienc'':233C ''scikit'':20B ''select'':105C ''set'':30C ''soccer'':27C ''solver'':140C ''start'':147C ''state'':138C ''strategi'':11A,193C ''term'':190C ''themat'':237C ''tol'':142C ''tri'':24C ''true'':122C ''u2'':51C ''understand'':187C ''verbos'':144C ''w'':34C ''warm'':146C ''way'':211C ''weight'':116C ''would'':228C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9873, 2, '2016-01-20 14:40:27.38', 2, NULL, NULL, '&lt;p&gt;This does not answere your question directly, but might include some helpful pointers:&lt;/p&gt;

&lt;p&gt;In the recent &lt;a href="http://arxiv.org/abs/1512.03385" rel="nofollow" title="Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun Submitted on 10 Dec 2015"&gt;Deep Residual Learning for Image Recognition&lt;/a&gt; paper it is written:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;If one hypothesizes that multiple nonlinear layers can asymptotically
  approximate complicated functions[...]&lt;/p&gt;
  
  &lt;p&gt;This hypothesis, however, is still an open question. See [&lt;a href="http://arxiv.org/abs/1402.1869" rel="nofollow" title="G. Montufar, R. Pascanu, K. Cho, and Y. Bengio. On the number of linear regions of deep neural networks. In NIPS, 2014."&gt;28&lt;/a&gt;].&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/a'':47C,109C ''/abs/1402.1869'':82C ''/abs/1512.03385'':24C ''/blockquote'':111C ''/p'':15C,52C,67C,110C ''10'':38C ''2014'':107C ''2015'':40C ''28'':108C ''answer'':5C ''approxim'':64C ''arxiv.org'':23C,81C ''arxiv.org/abs/1402.1869'':80C ''arxiv.org/abs/1512.03385'':22C ''asymptot'':63C ''bengio'':94C ''blockquot'':53C ''cho'':91C ''complic'':65C ''dec'':39C ''deep'':41C,102C ''direct'':8C ''function'':66C ''g'':86C ''help'':13C ''howev'':71C ''href'':21C,79C ''hypothes'':57C ''hypothesi'':70C ''imag'':45C ''includ'':11C ''jian'':34C ''k'':90C ''kaim'':28C ''layer'':61C ''learn'':43C ''linear'':99C ''might'':10C ''montufar'':87C ''multipl'':59C ''network'':104C ''neural'':103C ''nip'':106C ''nofollow'':26C,84C ''nonlinear'':60C ''number'':97C ''one'':56C ''open'':75C ''p'':1C,16C,54C,68C ''paper'':48C ''pascanu'':89C ''pointer'':14C ''question'':7C,76C ''r'':88C ''recent'':19C ''recognit'':46C ''region'':100C ''rel'':25C,83C ''ren'':33C ''residu'':42C ''see'':77C ''shaoq'':32C ''still'':73C ''submit'':36C ''sun'':35C ''titl'':27C,85C ''written'':51C ''xiangyu'':30C ''y'':93C ''zhang'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9874, 2, '2016-01-20 18:55:57.01', 0, NULL, NULL, '&lt;p&gt;There is a paper that was published at Systems&amp;amp;Cybernetics journal. See how they use Bayesian Networks with sensor fusion. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1605392&amp;amp;tag=1" rel="nofollow"&gt;Here the paper&lt;/a&gt;. Hope it helps.&lt;/p&gt;
', NULL, NULL, '''/a'':35C ''/p'':23C,39C ''/xpls/abs_all.jsp?arnumber=1605392&amp;amp;tag=1'':29C ''amp'':11C ''bayesian'':18C ''cybernet'':12C ''fusion'':22C ''help'':38C ''hope'':36C ''href'':26C ''ieeexplore.ieee.org'':28C ''ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1605392&amp;amp;tag=1'':27C ''journal'':13C ''network'':19C ''nofollow'':31C ''p'':1C,24C ''paper'':5C,34C ''publish'':8C ''rel'':30C ''see'':14C ''sensor'':21C ''system'':10C ''use'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9878, 2, '2016-01-21 07:42:08.003', 1, NULL, NULL, '&lt;p&gt;There is &lt;code&gt;df.drop&lt;/code&gt; command that can be used as follows to remove certain rows (in this case, 15 &amp;amp; 16):&lt;/p&gt;

&lt;p&gt;&lt;code&gt;df.drop(df.index[[15,16]])&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;If the rows you don''t need are regular (e.g. you never need row 15) then this is a quick and dirty solution.&lt;/p&gt;

&lt;p&gt;If you only want to drop arbitrary rows containing some value, this should do the trick:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;df = df.drop([df.column_name == Â©1990-2016 AAR])&lt;/code&gt;&lt;/p&gt;
', NULL, NULL, '''-2016'':84C ''/code'':6C,31C,86C ''/p'':24C,32C,57C,75C,87C ''15'':21C,29C,48C ''16'':23C,30C ''1990'':83C ''aar'':85C ''amp'':22C ''arbitrari'':65C ''case'':20C ''certain'':16C ''code'':4C,26C,77C ''command'':7C ''contain'':67C ''df'':78C ''df.column'':80C ''df.drop'':5C,27C,79C ''df.index'':28C ''dirti'':55C ''drop'':64C ''e.g'':43C ''follow'':13C ''name'':81C ''need'':40C,46C ''never'':45C ''p'':1C,25C,33C,58C,76C ''quick'':53C ''regular'':42C ''remov'':15C ''row'':17C,36C,47C,66C ''solut'':56C ''trick'':74C ''use'':11C ''valu'':69C ''want'':62C ''â'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9879, 2, '2016-01-21 09:39:22.573', 1, NULL, NULL, '&lt;p&gt;I would go with &lt;em&gt;.csv&lt;/em&gt; as it''s universally accepted and can be read in different programming languages easily. Moreover you can simply open it with an office software. If you are using your matrix only in Python I also recommend &lt;a href="https://wiki.python.org/moin/UsingPickle" rel="nofollow"&gt;Pickle library&lt;/a&gt; which writes your matrix in a &lt;em&gt;.p&lt;/em&gt; format and can be easily read in Python with a simple load function.&lt;/p&gt;
', NULL, NULL, '''/a'':54C ''/em'':8C,63C ''/moin/usingpickle'':49C ''/p'':77C ''accept'':13C ''also'':43C ''csv'':7C ''differ'':19C ''easili'':22C,68C ''em'':6C,61C ''format'':64C ''function'':76C ''go'':4C ''href'':46C ''languag'':21C ''librari'':53C ''load'':75C ''matrix'':38C,58C ''moreov'':23C ''nofollow'':51C ''offic'':31C ''open'':27C ''p'':1C,62C ''pickl'':52C ''program'':20C ''python'':41C,71C ''read'':17C,69C ''recommend'':44C ''rel'':50C ''simpl'':74C ''simpli'':26C ''softwar'':32C ''univers'':12C ''use'':36C ''wiki.python.org'':48C ''wiki.python.org/moin/usingpickle'':47C ''would'':3C ''write'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9895, 2, '2016-01-21 23:50:16.067', 0, NULL, NULL, '&lt;p&gt;If you use SGNS or CBOW, &lt;a href="https://radimrehurek.com/gensim/" rel="nofollow"&gt;https://radimrehurek.com/gensim/&lt;/a&gt; will let you continue continue training a pre-trained model on a different corpus if that is what you mean by fine tuning.&lt;/p&gt;
', NULL, NULL, '''/gensim/'':12C ''/gensim/&lt;/a&gt;'':17C ''/p'':42C ''cbow'':7C ''continu'':21C,22C ''corpus'':32C ''differ'':31C ''fine'':40C ''href'':9C ''let'':19C ''mean'':38C ''model'':28C ''nofollow'':14C ''p'':1C ''pre'':26C ''pre-train'':25C ''radimrehurek.com'':11C,16C ''radimrehurek.com/gensim/'':10C ''radimrehurek.com/gensim/&lt;/a&gt;'':15C ''rel'':13C ''sgns'':5C ''train'':23C,27C ''tune'':41C ''use'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9889, 1, '2016-01-21 17:59:08.77', 2, 42, 'Analyzing customer response', '&lt;p&gt;I''m new to ML. I''m taking over a Classification project which involves analyzing data for customers which returned a product and determine return reason (10 categories). This data was captured at counter, and could be like: LGTM (Looks good to me) NFF (No fault found), etc.&lt;/p&gt;

&lt;p&gt;I have a training set of 1000 records and when using Google Prediction API I get a "classificationAccuracy" value of "0.82" and 10 labels.&lt;/p&gt;

&lt;p&gt;Questions:
1. Any recommended API to analyze this type of data?.
2. What is a good  "classificationAccuracy" value?&lt;/p&gt;

&lt;p&gt;Thank you&lt;/p&gt;
', 'machine-learning  data-mining  classification  google-prediction-api', NULL, '''/p'':64C,90C,110C,114C ''0.82'':86C ''1'':93C ''10'':42C,88C ''1000'':72C ''2'':103C ''analyz'':1A,30C,98C ''api'':14B,79C,96C ''captur'':47C ''categori'':43C ''classif'':10B,26C ''classificationaccuraci'':83C,108C ''could'':51C ''counter'':49C ''custom'':2A,33C ''data'':8B,31C,45C,102C ''data-min'':7B ''determin'':39C ''etc'':63C ''fault'':61C ''found'':62C ''get'':81C ''good'':56C,107C ''googl'':12B,77C ''google-prediction-api'':11B ''involv'':29C ''label'':89C ''learn'':6B ''lgtm'':54C ''like'':53C ''look'':55C ''m'':17C,22C ''machin'':5B ''machine-learn'':4B ''mine'':9B ''ml'':20C ''new'':18C ''nff'':59C ''p'':15C,65C,91C,111C ''predict'':13B,78C ''product'':37C ''project'':27C ''question'':92C ''reason'':41C ''recommend'':95C ''record'':73C ''respons'':3A ''return'':35C,40C ''set'':70C ''take'':23C ''thank'':112C ''train'':69C ''type'':100C ''use'':76C ''valu'':84C,109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9884, 2, '2016-01-21 13:30:26.147', 1, NULL, NULL, '&lt;p&gt;In sklearn Anything that will take sparse data can take output from TFIDF.&lt;/p&gt;

&lt;p&gt;In sklearn basically all data any model can take is either dense (normal array) or sparse (that only stores the location of values != 0). You can convert from sparse to dense, but chances are you''ll run out of memory if you try.&lt;/p&gt;

&lt;p&gt;I''m quite a big fan of using linear algorithms on text data (sgdregressor for example have a ton of different options you can play with). But other algorithms like randomforest that Sergey mentioned, and naive bayes models can also work with that kind of data. Basically what you are looking for is anything that can take sparse input data.&lt;/p&gt;

&lt;p&gt;(One thing I''ve done in the past when working with text+other data is taking output from an sgd''s analysis of the data and feeding that + the other data to another algorithm like randomforest. It''s a simple and quite powerful method)&lt;/p&gt;
', NULL, NULL, '''/p'':15C,60C,122C,168C ''0'':40C ''algorithm'':71C,90C,157C ''also'':101C ''analysi'':145C ''anoth'':156C ''anyth'':4C,115C ''array'':30C ''basic'':19C,108C ''bay'':98C ''big'':66C ''chanc'':49C ''convert'':43C ''data'':9C,21C,74C,107C,121C,137C,148C,154C ''dens'':28C,47C ''differ'':82C ''done'':128C ''either'':27C ''exampl'':77C ''fan'':67C ''feed'':150C ''input'':120C ''kind'':105C ''like'':91C,158C ''linear'':70C ''ll'':52C ''locat'':37C ''look'':112C ''m'':63C ''memori'':56C ''mention'':95C ''method'':167C ''model'':23C,99C ''naiv'':97C ''normal'':29C ''one'':124C ''option'':83C ''output'':12C,140C ''p'':1C,16C,61C,123C ''past'':131C ''play'':86C ''power'':166C ''quit'':64C,165C ''randomforest'':92C,159C ''run'':53C ''sergey'':94C ''sgd'':143C ''sgdregressor'':75C ''simpl'':163C ''sklearn'':3C,18C ''spars'':8C,32C,45C,119C ''store'':35C ''take'':7C,11C,25C,118C,139C ''text'':73C,135C ''tfidf'':14C ''thing'':125C ''ton'':80C ''tri'':59C ''use'':69C ''valu'':39C ''ve'':127C ''work'':102C,133C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9896, 2, '2016-01-21 23:58:17.55', 2, NULL, NULL, '&lt;p&gt;I would recommend Bayesian Optimization for hyper parameter search and had good results with Spearmint. &lt;a href="https://github.com/HIPS/Spearmint" rel="nofollow"&gt;https://github.com/HIPS/Spearmint&lt;/a&gt; You might have to use an older version for commercial use.&lt;/p&gt;
', NULL, NULL, '''/hips/spearmint'':21C ''/hips/spearmint&lt;/a&gt;'':26C ''/p'':38C ''bayesian'':5C ''commerci'':36C ''github.com'':20C,25C ''github.com/hips/spearmint'':19C ''github.com/hips/spearmint&lt;/a&gt;'':24C ''good'':13C ''href'':18C ''hyper'':8C ''might'':28C ''nofollow'':23C ''older'':33C ''optim'':6C ''p'':1C ''paramet'':9C ''recommend'':4C ''rel'':22C ''result'':14C ''search'':10C ''spearmint'':16C ''use'':31C,37C ''version'':34C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9897, 2, '2016-01-22 01:00:00.07', 1, NULL, NULL, '&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html" rel="nofollow"&gt;http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You can use the above tutorial to get acquaintance with text classification. Afterwards it should be easier to formulate nontrivial questions to move even further.&lt;/p&gt;
', NULL, NULL, '''/p'':38C ''/stable/tutorial/text_analytics/working_with_text_data.html'':6C ''/stable/tutorial/text_analytics/working_with_text_data.html&lt;/a&gt;&lt;/p&gt;'':11C ''acquaint'':21C ''afterward'':25C ''classif'':24C ''easier'':29C ''even'':36C ''formul'':31C ''get'':20C ''href'':3C ''move'':35C ''nofollow'':8C ''nontrivi'':32C ''p'':1C,12C ''question'':33C ''rel'':7C ''scikit-learn.org'':5C,10C ''scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html'':4C ''scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html&lt;/a&gt;&lt;/p&gt;'':9C ''text'':23C ''tutori'':18C ''use'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9898, 1, '2016-01-22 04:47:28.773', 2, 24, 'What to do with stale centroids in K-means', '&lt;p&gt;When I run Kmeans on my dataset, I notice that some centroids become stale in the they are no longer the closest centroid to any point after some iteration. Right now I am skipping these stale centroids in my next iteration because I think those centroids no longer represent any useful set of the data, however I wanted to know if there are other reasonable ways to deal with these centroids.&lt;/p&gt;
', 'clustering  k-means  unsupervised-learning', NULL, '''/p'':90C ''becom'':31C ''centroid'':6A,30C,41C,55C,64C,89C ''closest'':40C ''cluster'':11B ''data'':73C ''dataset'':25C ''deal'':86C ''howev'':74C ''iter'':47C,59C ''k'':9A,13B ''k-mean'':8A,12B ''kmean'':22C ''know'':78C ''learn'':17B ''longer'':38C,66C ''mean'':10A,14B ''next'':58C ''notic'':27C ''p'':18C ''point'':44C ''reason'':83C ''repres'':67C ''right'':48C ''run'':21C ''set'':70C ''skip'':52C ''stale'':5A,32C,54C ''think'':62C ''unsupervis'':16B ''unsupervised-learn'':15B ''use'':69C ''want'':76C ''way'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9885, 1, '2016-01-21 14:47:08.167', 3, 77, 'ggvis vs. ggplot2+Shiny; which one to choose for interactive visualization?', '&lt;p&gt;There is a similar question here in CrossValidated, and I have read the answers. My question is a bit different. I don''t want to merely visualize my data, and indeed what I want to visualize is not easy to visualize with either package. &lt;/p&gt;

&lt;p&gt;I have two sets of points ($x,y$ coordinates) on my plot. I want to add edges and make it similar to a graph. If I have $n$ points, then based on the nature of my problem, I need to add $(n/2)^2$ edges to the plot. &lt;/p&gt;

&lt;p&gt;Then I want to add some interactive features to my plot. For instance, by clicking on one point (vertex), all other vertices which are not connected to this clicked vertex must disappear to have a more lucid and focused plot. &lt;/p&gt;

&lt;p&gt;I tried to find a network visualization package suited to my project, but it seems all of them use the data about connection of edges or nodes and provide a network based on some specific layout algorithm. My data is different. I have the coordinates of nodes and I want to establish some edges between some of them. In my data the nodes have their own fixed locations and must not be moved around.&lt;/p&gt;

&lt;p&gt;Finally, I narrowed down the packages to &lt;code&gt;ggvis&lt;/code&gt; and &lt;code&gt;ggplot2&lt;/code&gt;. &lt;code&gt;ggvis&lt;/code&gt; is new and fresh in addition to having intentional design to generate interactive plots. But I found out that &lt;code&gt;ggplot2+Shiny&lt;/code&gt; can also produce interactive plots. (Is that correct?) Besides, I figured that &lt;code&gt;ggplot2&lt;/code&gt;''s capabilities are more varied and matured than &lt;code&gt;ggvis&lt;/code&gt;: for instance &lt;code&gt;ggplot2&lt;/code&gt; has a zooming feature while &lt;code&gt;ggvis&lt;/code&gt; does not have it. More important, as &lt;code&gt;ggvis&lt;/code&gt; is growing and changing, I don''t want to invest a lot of time on a work that becomes technically obsolete or buggy in a year or so. I thought this is the case if I code based on &lt;code&gt;ggvis&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Now may you please evaluate my choice (&lt;code&gt;ggplot2+shiny&lt;/code&gt;) and advise me about possibility to do what I want to do with it? &lt;/p&gt;

&lt;p&gt;As I did not know any of the mentioned packages, I have started learning &lt;code&gt;ggplot2&lt;/code&gt; from Wickham''s book and I love it! But I am afraid of spending a few weeks and finding out that this amazing package cannot do what I want to do even with the aid of &lt;code&gt;Shiny&lt;/code&gt;. &lt;/p&gt;

&lt;p&gt;P.S: I want to put the final code on github so probable users can download and run it. Therefore, I do not need to make a web application, there is no need to any website or putting the interactive map online. I just need to have an interactive plot which can be run from inside of Rstudio. &lt;/p&gt;
', 'r  visualization  ggplot2', NULL, '''/code'':238C,242C,245C,268C,283C,294C,299C,307C,317C,359C,372C,405C,444C ''/p'':60C,109C,151C,227C,360C,387C,445C,504C ''2'':104C ''add'':77C,102C,115C ''addit'':251C ''advis'':374C ''afraid'':417C ''aid'':440C ''algorithm'':189C ''also'':270C ''amaz'':428C ''answer'':29C ''applic'':474C ''around'':226C ''base'':92C,184C,355C ''becom'':336C ''besid'':277C ''bit'':34C ''book'':409C ''buggi'':340C ''cannot'':430C ''capabl'':285C ''case'':351C ''chang'':321C ''choic'':368C ''choos'':8A ''click'':125C,139C ''code'':236C,240C,243C,265C,281C,292C,297C,305C,315C,354C,357C,369C,403C,442C,454C ''connect'':136C,175C ''coordin'':70C,197C ''correct'':276C ''crossvalid'':23C ''data'':44C,173C,191C,213C ''design'':255C ''differ'':35C,193C ''disappear'':142C ''download'':461C ''easi'':54C ''edg'':78C,105C,177C,206C ''either'':58C ''establish'':204C ''evalu'':366C ''even'':437C ''featur'':118C,303C ''figur'':279C ''final'':229C,453C ''find'':156C,424C ''fix'':219C ''focus'':149C ''found'':262C ''fresh'':249C ''generat'':257C ''ggplot2'':3A,14B,241C,266C,282C,298C,370C,404C ''ggvis'':1A,237C,244C,293C,306C,316C,358C ''github'':456C ''graph'':85C ''grow'':319C ''import'':313C ''inde'':46C ''insid'':501C ''instanc'':123C,296C ''intent'':254C ''interact'':10A,117C,258C,272C,485C,494C ''invest'':327C ''know'':393C ''layout'':188C ''learn'':402C ''locat'':220C ''lot'':329C ''love'':412C ''lucid'':147C ''make'':80C,471C ''map'':486C ''matur'':290C ''may'':363C ''mention'':397C ''mere'':41C ''move'':225C ''must'':141C,222C ''n'':89C ''n/2'':103C ''narrow'':231C ''natur'':95C ''need'':100C,469C,478C,490C ''network'':158C,183C ''new'':247C ''node'':179C,199C,215C ''obsolet'':338C ''one'':6A,127C ''onlin'':487C ''p'':15C,61C,110C,152C,228C,361C,388C,446C ''p.s'':447C ''packag'':59C,160C,234C,398C,429C ''pleas'':365C ''plot'':73C,108C,121C,150C,259C,273C,495C ''point'':67C,90C,128C ''possibl'':377C ''probabl'':458C ''problem'':98C ''produc'':271C ''project'':164C ''provid'':181C ''put'':451C,483C ''question'':20C,31C ''r'':12B ''read'':27C ''rstudio'':503C ''run'':463C,499C ''seem'':167C ''set'':65C ''shini'':4A,267C,371C,443C ''similar'':19C,82C ''specif'':187C ''spend'':419C ''start'':401C ''suit'':161C ''technic'':337C ''therefor'':465C ''thought'':347C ''time'':331C ''tri'':154C ''two'':64C ''use'':171C ''user'':459C ''vari'':288C ''vertex'':129C,140C ''vertic'':132C ''visual'':11A,13B,42C,51C,56C,159C ''vs'':2A ''want'':39C,49C,75C,113C,202C,325C,382C,434C,449C ''web'':473C ''websit'':481C ''week'':422C ''wickham'':407C ''work'':334C ''x'':68C ''y'':69C ''year'':343C ''zoom'':302C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9890, 1, '2016-01-21 19:36:21.35', 0, 56, 'Problem encountered while updating a particular cell of a pandas data frame in Python', '&lt;p&gt;I am trying to read in an excel file into a pandas data frame using pandas.read_excel() function. The first column is &lt;code&gt;''Date''&lt;/code&gt;. The first entry into that column has asterisk mark which I want to remove. Here are the first 2 rows of the data frame &lt;code&gt;DF&lt;/code&gt;.    &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;0   1/4/1996*   11540   10841   3592    1827    26476   14510   6900    0   0   NaN NaN NaN NaN NaN NaN NaN NaN NaN 
1   1996-01-11 00:00:00 13881   11735   2532    4188    32064   21989   3819    2586    0   NaN NaN`
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In order to remove that &lt;code&gt;*&lt;/code&gt;, i am doing this:&lt;code&gt;DF[''Date''][0] = ''1/4/1996''&lt;/code&gt;. However, once I do that, I am getting a warning message &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  app.launch_new_instance()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and it changes the cell value, but, it also attaches another ''Date'' column at the end of the &lt;code&gt;df&lt;/code&gt; like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;0   1/4/1996   11540   10841   3592    1827    26476   14510   6900    0   0   NaN NaN NaN NaN NaN NaN NaN NaN NaN 1/4/1996
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I don''t understand why this is happening. &lt;/p&gt;

&lt;p&gt;I would greatly appreciate it if someone can explain this  or point out the correct way of changing the cell value. &lt;/p&gt;
', 'python  pandas', NULL, '''-01'':95C ''-11'':96C ''/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py'':145C ''/code'':42C,69C,111C,120C,130C,177C,200C,227C ''/p'':70C,142C,203C,238C,261C ''/pandas-docs/stable/indexing.html#indexing-view-versus-copy'':173C ''/pre'':112C,178C,228C ''0'':73C,82C,83C,108C,128C,206C,215C,216C ''00'':97C,98C,99C ''1'':93C ''1/4/1996'':74C,129C,207C,226C ''10841'':76C,209C ''11540'':75C,208C ''11735'':101C ''13881'':100C ''14510'':80C,213C ''1827'':78C,211C ''1996'':94C ''2'':61C ''21989'':105C ''2532'':102C ''2586'':107C ''26476'':79C,212C ''3'':146C ''32064'':104C ''3592'':77C,210C ''3819'':106C ''4188'':103C ''6900'':81C,214C ''also'':188C ''anoth'':190C ''app.launch'':174C ''appreci'':243C ''asterisk'':50C ''attach'':189C ''caveat'':167C ''cell'':7A,184C,259C ''chang'':182C,257C ''code'':40C,67C,72C,119C,125C,144C,198C,205C ''column'':38C,48C,192C ''copi'':157C ''correct'':254C ''data'':11A,30C,65C ''datafram'':163C ''date'':41C,127C,191C ''df'':68C,126C,199C ''document'':170C ''encount'':2A ''end'':195C ''entri'':45C ''excel'':25C,34C ''explain'':248C ''file'':26C ''first'':37C,44C,60C ''frame'':12A,31C,66C ''function'':35C ''get'':138C ''great'':242C ''happen'':237C ''howev'':131C ''instanc'':176C ''like'':201C ''mark'':51C ''messag'':141C ''nan'':84C,85C,86C,87C,88C,89C,90C,91C,92C,109C,110C,217C,218C,219C,220C,221C,222C,223C,224C,225C ''new'':175C ''order'':115C ''p'':17C,113C,179C,229C,239C ''panda'':10A,16B,29C ''pandas.pydata.org'':172C ''pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy'':171C ''pandas.read'':33C ''particular'':6A ''point'':251C ''pre'':71C,143C,204C ''problem'':1A ''python'':14A,15B ''read'':22C ''remov'':56C,117C ''row'':62C ''see'':164C ''set'':154C ''settingwithcopywarn'':147C ''slice'':160C ''someon'':246C ''tri'':20C,151C ''understand'':233C ''updat'':4A ''use'':32C ''valu'':149C,185C,260C ''want'':54C ''warn'':140C ''way'':255C ''would'':241C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9893, 2, '2016-01-21 22:25:51.053', 4, NULL, NULL, '&lt;p&gt;I''m not aware of a foolproof way to do this. Here''s one idea off the top of my head:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Treat values as categorical by default.&lt;/li&gt;
&lt;li&gt;Check for various attributes of the data that would imply it is actually continuous. Weight these attributes based on how likely they are to correlate with continuous data. Here are some possible examples:

&lt;ul&gt;
&lt;li&gt;Values are integers: +.7&lt;/li&gt;
&lt;li&gt;Values are floats: +.8&lt;/li&gt;
&lt;li&gt;Values are normally distributed: +.3&lt;/li&gt;
&lt;li&gt;Values contain a relatively small number of unique values: +.3&lt;/li&gt;
&lt;li&gt;Values aren''t all the same number of characters: +.1&lt;/li&gt;
&lt;li&gt;Values don''t contain leading zeros: +.1&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;Treat any columns that sum to greater than 1 as being numerical. Adjust the factors and weights based on testing against different data sets to suit your needs. You could even build and train a separate machine learning algorithm just to do this.&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':32C,73C,79C,86C,98C,110C,119C,121C,166C ''/ol'':167C ''/p'':23C ''/ul'':120C ''1'':109C,118C,131C ''3'':85C,97C ''7'':72C ''8'':78C ''actual'':46C ''adjust'':135C ''algorithm'':161C ''aren'':101C ''attribut'':37C,50C ''awar'':5C ''base'':51C,140C ''build'':154C ''categor'':29C ''charact'':108C ''check'':34C ''column'':125C ''contain'':89C,115C ''continu'':47C,60C ''correl'':58C ''could'':152C ''data'':40C,61C,145C ''default'':31C ''differ'':144C ''distribut'':84C ''even'':153C ''exampl'':66C ''factor'':137C ''float'':77C ''foolproof'':8C ''greater'':129C ''head'':22C ''idea'':16C ''impli'':43C ''integ'':71C ''lead'':116C ''learn'':160C ''li'':25C,33C,68C,74C,80C,87C,99C,111C,122C ''like'':54C ''m'':3C ''machin'':159C ''need'':150C ''normal'':83C ''number'':93C,106C ''numer'':134C ''ol'':24C ''one'':15C ''p'':1C ''possibl'':65C ''relat'':91C ''separ'':158C ''set'':146C ''small'':92C ''suit'':148C ''sum'':127C ''test'':142C ''top'':19C ''train'':156C ''treat'':26C,123C ''ul'':67C ''uniqu'':95C ''valu'':27C,69C,75C,81C,88C,96C,100C,112C ''various'':36C ''way'':9C ''weight'':48C,139C ''would'':42C ''zero'':117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9894, 1, '2016-01-21 22:51:03.573', 2, 44, 'How to decide the number of trees parameter for Random Forest algorithm in PySpark MLlib?', '&lt;p&gt;I am working on &lt;code&gt;Random Forest algorithm&lt;/code&gt; in &lt;code&gt;PySpark&lt;/code&gt; &lt;code&gt;MLlib&lt;/code&gt; and have a doubt regarding the number of trees parameter that we pass to the model. The standard format of &lt;code&gt;Random Forest modeling&lt;/code&gt; in &lt;code&gt;PySpark&lt;/code&gt; &lt;code&gt;MLlib&lt;/code&gt; is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;model = RandomForest.trainRegressor(trainingData, categoricalFeaturesInfo={},
                                    numTrees=3, featureSubsetStrategy="auto",
                                    impurity=''variance'', maxDepth=4, maxBins=32)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The doubt that I have is how to decide the optimum value of trees to pass to &lt;code&gt;numTrees&lt;/code&gt; parameter? I assume the more the number of trees better should be the performance but would it keep on improving with increase in number of trees? Is there a point after which it will start to have negative impact in performance? If yes then how can I find the optimum number of trees for my dataset?&lt;/p&gt;
', 'machine-learning  python  regression  random-forest  apache-spark', NULL, '''/code'':36C,40C,43C,68C,72C,75C,94C,116C ''/p'':77C,173C ''/pre'':95C ''3'':85C ''32'':93C ''4'':91C ''algorithm'':12A,35C ''apach'':25B ''apache-spark'':24B ''assum'':119C ''auto'':87C ''better'':126C ''categoricalfeaturesinfo'':83C ''code'':32C,38C,41C,64C,70C,73C,79C,114C ''dataset'':172C ''decid'':3A,105C ''doubt'':47C,98C ''featuresubsetstrategi'':86C ''find'':164C ''forest'':11A,23B,34C,66C ''format'':62C ''impact'':155C ''improv'':136C ''impur'':88C ''increas'':138C ''keep'':134C ''learn'':18B ''machin'':17B ''machine-learn'':16B ''maxbin'':92C ''maxdepth'':90C ''mllib'':15A,42C,74C ''model'':59C,67C,80C ''negat'':154C ''number'':5A,50C,123C,140C,167C ''numtre'':84C,115C ''optimum'':107C,166C ''p'':27C,96C ''paramet'':8A,53C,117C ''pass'':56C,112C ''perform'':130C,157C ''point'':146C ''pre'':78C ''pyspark'':14A,39C,71C ''python'':19B ''random'':10A,22B,33C,65C ''random-forest'':21B ''randomforest.trainregressor'':81C ''regard'':48C ''regress'':20B ''spark'':26B ''standard'':61C ''start'':151C ''trainingdata'':82C ''tree'':7A,52C,110C,125C,142C,169C ''valu'':108C ''varianc'':89C ''work'':30C ''would'':132C ''yes'':159C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9899, 1, '2016-01-22 05:10:26.04', 5, 136, 'Time series data: How I measure influence of new product sales on existing product sales (statistically)?', '&lt;p&gt;Here my goal isâ¦&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Find Product 5 (New Product) is really influencing other product sales (product 1 to 4) or not?&lt;/li&gt;
&lt;li&gt;If it is influencing other product sales, how much?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;New to R and tried several related posts but didnât find exact answer to my question. I love R and learning every day something new which helping us in taking data driven decisions.&lt;/p&gt;

&lt;p&gt;My sample Dataset is like below (Week and Product 1 to Product 5 Sales per each week)
Here my new product is Product 5 and launched on Week 5.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Week    Product-1   Product-2   Product-3   Product-4   Product-5
1   2   4   5   5   
2   4   4   6   4   
3   4   4   6   5   
4   4   4   6   6   4
5   4   6   5   3   5
6   2   7   6   4   3
7   3   8   7   5   6
8   2   9   9   3   6
&lt;/code&gt;&lt;/pre&gt;

&lt;h2&gt;Here my questions are&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;What is the best process or model to show the influence of product 5 (statistically)?&lt;/li&gt;
&lt;li&gt;Do I need to run co-integration tests before I run correlation?
Example some of these products are never be correlated with Product 5
(example: growth in cockle growth vs. growth in electricity demand)&lt;/li&gt;
&lt;li&gt;How I know correlation vs. causation in this mix?&lt;/li&gt;
&lt;li&gt;Since my new
product launched on week 5, where I can start my correlations? Is it
from week 5 or from earlier weeks?&lt;/li&gt;
&lt;li&gt;Do I need to test for stationarity first? and bring the data to stationary?&lt;/li&gt;
&lt;/ul&gt;
', 'machine-learning  r  time-series', NULL, '''-1'':133C ''-2'':135C ''-3'':137C ''-4'':139C ''-5'':141C ''/code'':187C ''/h2'':194C ''/li'':49C,60C,212C,249C,260C,285C,301C ''/p'':29C,97C,128C ''/pre'':188C ''/ul'':61C,302C ''1'':44C,108C,142C ''2'':143C,147C,170C,182C ''3'':152C,167C,174C,176C,185C ''4'':46C,144C,148C,149C,151C,153C,154C,157C,158C,159C,162C,164C,173C ''5'':34C,111C,122C,127C,145C,146C,156C,163C,166C,168C,179C,210C,238C,269C,280C ''6'':150C,155C,160C,161C,165C,169C,172C,180C,186C ''7'':171C,175C,178C ''8'':177C,181C ''9'':183C,184C ''answer'':76C ''best'':200C ''bring'':296C ''causat'':256C ''co'':220C ''co-integr'':219C ''cockl'':242C ''code'':130C ''correl'':226C,235C,254C,275C ''data'':3A,94C,298C ''dataset'':101C ''day'':86C ''decis'':96C ''demand'':248C ''didnâ'':72C ''driven'':95C ''earlier'':283C ''electr'':247C ''everi'':85C ''exact'':75C ''exampl'':227C,239C ''exist'':13A ''find'':32C,74C ''first'':294C ''goal'':27C ''growth'':240C,243C,245C ''h2'':189C ''help'':90C ''influenc'':7A,39C,54C,207C ''integr'':221C ''isâ'':28C ''know'':253C ''launch'':124C,266C ''learn'':19B,84C ''li'':31C,50C,196C,213C,250C,261C,286C ''like'':103C ''love'':81C ''machin'':18B ''machine-learn'':17B ''measur'':6A ''mix'':259C ''model'':203C ''much'':59C ''need'':216C,289C ''never'':233C ''new'':9A,35C,63C,88C,118C,264C ''p'':24C,62C,98C ''per'':113C ''post'':70C ''pre'':129C ''process'':201C ''product'':10A,14A,33C,36C,41C,43C,56C,107C,110C,119C,121C,132C,134C,136C,138C,140C,209C,231C,237C,265C ''question'':79C,192C ''r'':20B,65C,82C ''realli'':38C ''relat'':69C ''run'':218C,225C ''sale'':11A,15A,42C,57C,112C ''sampl'':100C ''seri'':2A,23B ''sever'':68C ''show'':205C ''sinc'':262C ''someth'':87C ''start'':273C ''stationar'':293C ''stationari'':300C ''statist'':16A,211C ''take'':93C ''test'':222C,291C ''time'':1A,22B ''time-seri'':21B ''tri'':67C ''ul'':30C,195C ''us'':91C ''vs'':244C,255C ''week'':105C,115C,126C,131C,268C,279C,284C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9900, 2, '2016-01-22 06:15:55.073', 3, NULL, NULL, '&lt;p&gt;Use a combination of string processing and type casting to process the whole column in one swoop:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;excelDF[datecol] = pandas.to_datetime(excelDF[datecol].str.replace(''*'',''''))&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I tried using &lt;code&gt;str.rstrip(''*'')&lt;/code&gt; but I could not get it to work.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':30C ''/code'':37C ''/p'':19C,29C,46C ''blockquot'':20C ''cast'':10C ''code'':35C ''column'':15C ''combin'':4C ''could'':40C ''datecol'':23C,27C ''datetim'':25C ''exceldf'':22C,26C ''get'':42C ''one'':17C ''p'':1C,21C,31C ''pandas.to'':24C ''process'':7C,12C ''str.replace'':28C ''str.rstrip'':36C ''string'':6C ''swoop'':18C ''tri'':33C ''type'':9C ''use'':2C,34C ''whole'':14C ''work'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9965, 2, '2016-01-26 06:01:48.653', 1, NULL, NULL, '&lt;p&gt;Since you say you are a beginner, I suggest using a simple binary classifier: logistic regression. It takes the input vector, and transforms it by taking its dot product with a weight parameter (estimating which is the goal) before passing it through the logistic function:&lt;/p&gt;

&lt;p&gt;$\hat y(x) = \dfrac{1}{1+\exp(-\left&amp;lt; w, x \right&amp;gt;)}$ and $y=\begin{cases}1, \text{user present} \\ 0, \text{user absent} \end{cases}$. &lt;/p&gt;

&lt;p&gt;For details read &lt;a href="https://en.wikipedia.org/wiki/Logistic_regression" rel="nofollow"&gt;the wikipedia article&lt;/a&gt;. For features, I would use the time of day encoded as $\left( \sin (2\pi t/T), \cos (2\pi t/T) \right)$, a hot-encoded categorical variable for the day of the week, a binary variable to indicate weekends, and so on. It''s a crude model that leaves a lot of data on table, such as correlations between users, but as a beginner''s exercise it is appropriate.&lt;/p&gt;
', NULL, NULL, '''/a'':91C ''/p'':47C,76C,161C ''/wiki/logistic_regression'':85C ''0'':70C ''1'':53C,54C,66C ''2'':105C,109C ''absent'':73C ''appropri'':160C ''articl'':90C ''begin'':64C ''beginn'':8C,155C ''binari'':14C,126C ''case'':65C,75C ''categor'':117C ''classifi'':15C ''correl'':149C ''cos'':108C ''crude'':137C ''data'':144C ''day'':100C,121C ''detail'':79C ''dfrac'':52C ''dot'':29C ''en.wikipedia.org'':84C ''en.wikipedia.org/wiki/logistic_regression'':83C ''encod'':101C,116C ''end'':74C ''estim'':35C ''exercis'':157C ''exp'':55C ''featur'':93C ''function'':46C ''goal'':39C ''gt'':61C ''hat'':49C ''hot'':115C ''hot-encod'':114C ''href'':82C ''indic'':129C ''input'':21C ''leav'':140C ''left'':56C,103C ''logist'':16C,45C ''lot'':142C ''lt'':57C ''model'':138C ''nofollow'':87C ''p'':1C,48C,77C ''paramet'':34C ''pass'':41C ''pi'':106C,110C ''present'':69C ''product'':30C ''read'':80C ''regress'':17C ''rel'':86C ''right'':60C,112C ''say'':4C ''simpl'':13C ''sin'':104C ''sinc'':2C ''suggest'':10C ''t/t'':107C,111C ''tabl'':146C ''take'':19C,27C ''text'':67C,71C ''time'':98C ''transform'':24C ''use'':11C,96C ''user'':68C,72C,151C ''variabl'':118C,127C ''vector'':22C ''w'':58C ''week'':124C ''weekend'':130C ''weight'':33C ''wikipedia'':89C ''would'':95C ''x'':51C,59C ''y'':50C,63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9902, 2, '2016-01-22 08:36:18.62', 3, NULL, NULL, '&lt;p&gt;Assigning ~3 of 70 categories means you would be performing &lt;a href="https://en.wikipedia.org/wiki/Multi-label_classification" rel="nofollow"&gt;multi-label classification&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In the end, it doesn''t make much difference if you use Naive Bayes or SVM; they are both families of algorithms that translate provided independent variables (your feature space) into hopefully correct dependent variables (target classes).&lt;/p&gt;

&lt;p&gt;The question is how to construct a good feature space. The state of the art approaches in text mining are (or were) first tokenizing words, stripping punctuation and &lt;a href="https://en.wikipedia.org/wiki/Stop_words" rel="nofollow"&gt;stop words&lt;/a&gt;, &lt;a href="http://stackoverflow.com/questions/16069406/text-mining-with-the-tm-package-word-stemming"&gt;stemming&lt;/a&gt; or &lt;a href="http://stackoverflow.com/questions/28214148/how-to-perform-lemmatization-in-r"&gt;lemmatizing&lt;/a&gt; them, creating a &lt;a href="https://en.wikipedia.org/wiki/Bag-of-words_model" rel="nofollow"&gt;bag-of-words model&lt;/a&gt; of those words'' &lt;a href="https://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;relative frequencies&lt;/a&gt; and perhaps the frequencies of those words'' &lt;a href="https://en.wikipedia.org/wiki/N-gram" rel="nofollow"&gt;bigrams or trigrams&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Then run your classification learners on that. Assume the resulting feature space table might get really wide (lots of words and combinations of words), so you might want to consider some form of &lt;a href="http://stats.stackexchange.com/questions/3048/how-to-do-dimensionality-reduction-in-r"&gt;dimensionality reduction&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Of course, you will have to repeat the same filtering process with exact same parameters for each new survey you want to classify.&lt;/p&gt;

&lt;p&gt;Here''s another good batch of answers on &lt;a href="http://stats.stackexchange.com/questions/12907/off-the-shelf-tool-for-multi-label-classification"&gt;multi-label text classification&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':23C,102C,109C,117C,133C,146C,164C,207C,253C ''/p'':24C,63C,165C,208C,233C,254C ''/questions/12907/off-the-shelf-tool-for-multi-label-classification'':247C ''/questions/16069406/text-mining-with-the-tm-package-word-stemming'':107C ''/questions/28214148/how-to-perform-lemmatization-in-r'':115C ''/questions/3048/how-to-do-dimensionality-reduction-in-r'':204C ''/wiki/bag-of-words_model'':125C ''/wiki/multi-label_classification'':16C ''/wiki/n-gram'':158C ''/wiki/stop_words'':97C ''/wiki/tf%e2%80%93idf'':141C ''3'':3C ''70'':5C ''algorithm'':47C ''anoth'':237C ''answer'':241C ''approach'':80C ''art'':79C ''assign'':2C ''assum'':174C ''bag'':129C ''bag-of-word'':128C ''batch'':239C ''bay'':39C ''bigram'':161C ''categori'':6C ''class'':62C ''classif'':22C,170C,252C ''classifi'':232C ''combin'':188C ''consid'':196C ''construct'':70C ''correct'':58C ''cours'':211C ''creat'':119C ''depend'':59C ''differ'':34C ''dimension'':205C ''doesn'':30C ''en.wikipedia.org'':15C,96C,124C,140C,157C ''en.wikipedia.org/wiki/bag-of-words_model'':123C ''en.wikipedia.org/wiki/multi-label_classification'':14C ''en.wikipedia.org/wiki/n-gram'':156C ''en.wikipedia.org/wiki/stop_words'':95C ''en.wikipedia.org/wiki/tf%e2%80%93idf'':139C ''end'':28C ''exact'':222C ''famili'':45C ''featur'':54C,73C,177C ''filter'':219C ''first'':87C ''form'':198C ''frequenc'':145C,150C ''get'':181C ''good'':72C,238C ''hope'':57C ''href'':13C,94C,104C,112C,122C,138C,155C,201C,244C ''independ'':51C ''label'':21C,250C ''learner'':171C ''lemmat'':116C ''lot'':184C ''make'':32C ''mean'':7C ''might'':180C,193C ''mine'':83C ''model'':132C ''much'':33C ''multi'':20C,249C ''multi-label'':19C,248C ''naiv'':38C ''new'':227C ''nofollow'':18C,99C,127C,143C,160C ''p'':1C,25C,64C,166C,209C,234C ''paramet'':224C ''perform'':11C ''perhap'':148C ''process'':220C ''provid'':50C ''punctuat'':91C ''question'':66C ''realli'':182C ''reduct'':206C ''rel'':17C,98C,126C,142C,159C ''relat'':144C ''repeat'':216C ''result'':176C ''run'':168C ''space'':55C,74C,178C ''stackoverflow.com'':106C,114C ''stackoverflow.com/questions/16069406/text-mining-with-the-tm-package-word-stemming'':105C ''stackoverflow.com/questions/28214148/how-to-perform-lemmatization-in-r'':113C ''state'':76C ''stats.stackexchange.com'':203C,246C ''stats.stackexchange.com/questions/12907/off-the-shelf-tool-for-multi-label-classification'':245C ''stats.stackexchange.com/questions/3048/how-to-do-dimensionality-reduction-in-r'':202C ''stem'':108C ''stop'':100C ''strip'':90C ''survey'':228C ''svm'':41C ''tabl'':179C ''target'':61C ''text'':82C,251C ''token'':88C ''translat'':49C ''trigram'':163C ''use'':37C ''variabl'':52C,60C ''want'':194C,230C ''wide'':183C ''word'':89C,101C,131C,136C,153C,186C,190C ''would'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9903, 2, '2016-01-22 08:48:23.16', 1, NULL, NULL, '&lt;p&gt;The most compatible format is surely CSV/TSV. It''s text and you can usually Gzip it on the fly with the software package you are using. There is no widely standardized format for storing matrix array data. Matlab has its *.mat files, NumPy has *.npz, Stata and SAS have their own, ... Best just use a clear-text file.&lt;/p&gt;

&lt;p&gt;If the matrix is symmetric, if it is very large or if there will be a lot of them, you could spare 50% in space requirement by storing only the lower (or upper) triangular part of it. If you chose to do so, there is, again, no widely accepted format. Just store the shape first and then the flattened, 1D data.&lt;/p&gt;
', NULL, NULL, '''/p'':61C,124C ''1d'':122C ''50'':85C ''accept'':111C ''array'':37C ''best'':53C ''chose'':102C ''clear'':58C ''clear-text'':57C ''compat'':4C ''could'':83C ''csv/tsv'':8C ''data'':38C,123C ''file'':43C,60C ''first'':117C ''flatten'':121C ''fli'':20C ''format'':5C,33C,112C ''gzip'':16C ''larg'':72C ''lot'':79C ''lower'':93C ''mat'':42C ''matlab'':39C ''matrix'':36C,65C ''npz'':46C ''numpi'':44C ''p'':1C,62C ''packag'':24C ''part'':97C ''requir'':88C ''sas'':49C ''shape'':116C ''softwar'':23C ''space'':87C ''spare'':84C ''standard'':32C ''stata'':47C ''store'':35C,90C,114C ''sure'':7C ''symmetr'':67C ''text'':11C,59C ''triangular'':96C ''upper'':95C ''use'':27C,55C ''usual'':15C ''wide'':31C,110C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9904, 2, '2016-01-22 11:07:40.893', 2, NULL, NULL, '&lt;p&gt;If you have, for example, number of children of a family (which could range, for example, between 0 and 5), is it a categorical or numerical variable? Actually it depends on your problem and how you intend to solve it. In this sense, you can do the following:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Compute the number of unique values of that column&lt;/li&gt;
&lt;li&gt;Divide this number by the total number of rows&lt;/li&gt;
&lt;li&gt;If this ratio is bellow some threshold (for example, 20%), you consider it categorical.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In case of discrete values, one additional test could be: use a regression model to estimate some of the parameters and check if the estimated values are contained in the original set of values. If this is not true, you are probably dealing with categorical data (as it is the case of ZIP).&lt;/p&gt;

&lt;p&gt;It worked relatively well for me in the past...&lt;/p&gt;
', NULL, NULL, '''/li'':62C,73C,89C ''/p'':50C,145C,156C ''/ul'':90C ''0'':19C ''20'':84C ''5'':21C ''actual'':29C ''addit'':98C ''bellow'':79C ''case'':93C,142C ''categor'':25C,88C,136C ''check'':113C ''children'':9C ''column'':61C ''comput'':53C ''consid'':86C ''contain'':119C ''could'':14C,100C ''data'':137C ''deal'':134C ''depend'':31C ''discret'':95C ''divid'':64C ''estim'':107C,116C ''exampl'':6C,17C,83C ''famili'':12C ''follow'':49C ''intend'':38C ''li'':52C,63C,74C ''model'':105C ''number'':7C,55C,66C,70C ''numer'':27C ''one'':97C ''origin'':122C ''p'':1C,91C,146C ''paramet'':111C ''past'':155C ''probabl'':133C ''problem'':34C ''rang'':15C ''ratio'':77C ''regress'':104C ''relat'':149C ''row'':72C ''sens'':44C ''set'':123C ''solv'':40C ''test'':99C ''threshold'':81C ''total'':69C ''true'':130C ''ul'':51C ''uniqu'':57C ''use'':102C ''valu'':58C,96C,117C,125C ''variabl'':28C ''well'':150C ''work'':148C ''zip'':144C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9905, 2, '2016-01-22 11:38:22.84', 1, NULL, NULL, '&lt;p&gt;The issue is in how Pandas indices / selectors work. When you only do &lt;code&gt;Dataframe[''Column'']&lt;/code&gt;, you are selecting a copy/view of the dataframe. If you do &lt;code&gt;Dataframe[''NewColumn'']&lt;/code&gt;, so the &lt;code&gt;''NewColumn''&lt;/code&gt; column doesn''t exist yet, it creates this new column. But if it does exist, it will raise this warning.&lt;/p&gt;

&lt;p&gt;What you should do is use &lt;code&gt;.iloc&lt;/code&gt;, &lt;code&gt;.ix&lt;/code&gt; or something similar to select data from the frame and be able to alter it.&lt;/p&gt;

&lt;p&gt;Or follow @Emre advice.&lt;/p&gt;
', NULL, NULL, '''/code'':18C,33C,38C,69C,72C ''/p'':59C,88C,94C ''abl'':84C ''advic'':93C ''alter'':86C ''code'':15C,30C,36C,67C,70C ''column'':17C,39C,48C ''copy/view'':23C ''creat'':45C ''data'':78C ''datafram'':16C,26C,31C ''doesn'':40C ''emr'':92C ''exist'':42C,53C ''follow'':91C ''frame'':81C ''iloc'':68C ''indic'':8C ''issu'':3C ''ix'':71C ''new'':47C ''newcolumn'':32C,37C ''p'':1C,60C,89C ''panda'':7C ''rais'':56C ''select'':21C,77C ''selector'':9C ''similar'':75C ''someth'':74C ''use'':66C ''warn'':58C ''work'':10C ''yet'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9906, 2, '2016-01-22 12:13:07.807', 1, NULL, NULL, '&lt;p&gt;Since this question has been cross-posted, the initial comments by @nickcox on Cross Validated are highly relevant and true. My views are slightly different. For instance, I would rephrase the question, decomposing it into two parts: first, there is the issue of &lt;em&gt;how&lt;/em&gt; one would go about classifying a stream of unknown information by data type and, second, what kind misclassification rate can be expected. Both questions are immediately answerable, if someone wants to take the time to do it.&lt;/p&gt;

&lt;p&gt;The first issue amounts to developing a rules-based expert system along the lines of what has been preliminarily proposed in the comments and answers on these threads using samples of real-world data that are readily available. &lt;/p&gt;

&lt;p&gt;Second, given that this real-world data contains known data types, benchmarking the classification error. &lt;/p&gt;

&lt;p&gt;A third step would involve iteratively updating the rules to more accurately reflect the information that is being passed through -- to the extent that this is possible.&lt;/p&gt;

&lt;p&gt;In my mind, while tedious, such a project wouldn''t involve a prohibitive amount of work.&lt;/p&gt;

&lt;p&gt;---- Additional Comment ----&lt;/p&gt;

&lt;p&gt;A good discussion of feature extraction as it relates to the data generating process popped up on CV here:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stats.stackexchange.com/questions/191935/what-does-the-process-that-generates-the-data-mean-and-how-does-feature-selec"&gt;http://stats.stackexchange.com/questions/191935/what-does-the-process-that-generates-the-data-mean-and-how-does-feature-selec&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Feature extraction, selection and the DGP is an excellent point of departure for developing an expert system.&lt;/p&gt;
', NULL, NULL, '''/em'':48C ''/p'':86C,128C,146C,175C,193C,197C,218C,246C ''/questions/191935/what-does-the-process-that-generates-the-data-mean-and-how-does-feature-selec'':224C ''/questions/191935/what-does-the-process-that-generates-the-data-mean-and-how-does-feature-selec&lt;/a&gt;&lt;/p&gt;'':227C ''accur'':159C ''addit'':195C ''along'':100C ''amount'':91C,190C ''answer'':75C,113C ''avail'':127C ''base'':97C ''benchmark'':142C ''classif'':144C ''classifi'':53C ''comment'':12C,111C,196C ''contain'':138C ''cross'':8C,16C ''cross-post'':7C ''cv'':216C ''data'':60C,123C,137C,140C,210C ''decompos'':35C ''departur'':240C ''develop'':93C,242C ''dgp'':234C ''differ'':27C ''discuss'':201C ''em'':46C ''error'':145C ''excel'':237C ''expect'':70C ''expert'':98C,244C ''extent'':170C ''extract'':204C,230C ''featur'':203C,229C ''first'':40C,89C ''generat'':211C ''given'':131C ''go'':51C ''good'':200C ''high'':19C ''href'':221C ''immedi'':74C ''inform'':58C,162C ''initi'':11C ''instanc'':29C ''involv'':152C,187C ''issu'':44C,90C ''iter'':153C ''kind'':65C ''known'':139C ''line'':102C ''mind'':179C ''misclassif'':66C ''nickcox'':14C ''one'':49C ''p'':1C,87C,129C,147C,176C,194C,198C,219C,228C ''part'':39C ''pass'':166C ''point'':238C ''pop'':213C ''possibl'':174C ''post'':9C ''preliminarili'':107C ''process'':212C ''prohibit'':189C ''project'':184C ''propos'':108C ''question'':4C,34C,72C ''rate'':67C ''readili'':126C ''real'':121C,135C ''real-world'':120C,134C ''reflect'':160C ''relat'':207C ''relev'':20C ''rephras'':32C ''rule'':96C,156C ''rules-bas'':95C ''sampl'':118C ''second'':63C,130C ''select'':231C ''sinc'':2C ''slight'':26C ''someon'':77C ''stats.stackexchange.com'':223C,226C ''stats.stackexchange.com/questions/191935/what-does-the-process-that-generates-the-data-mean-and-how-does-feature-selec'':222C ''stats.stackexchange.com/questions/191935/what-does-the-process-that-generates-the-data-mean-and-how-does-feature-selec&lt;/a&gt;&lt;/p&gt;'':225C ''step'':150C ''stream'':55C ''system'':99C,245C ''take'':80C ''tedious'':181C ''third'':149C ''thread'':116C ''time'':82C ''true'':22C ''two'':38C ''type'':61C,141C ''unknown'':57C ''updat'':154C ''use'':117C ''valid'':17C ''view'':24C ''want'':78C ''work'':192C ''world'':122C,136C ''would'':31C,50C,151C ''wouldn'':185C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9907, 1, '2016-01-22 15:14:05.493', 4, 58, 'Correct output labelling for RNNs/ANNs with sequential data and single label', '&lt;p&gt;I have a labelled time-series dataset; for example, a sequential input length nn from kk sensors corresponds to class ''1'' (see below for n=50,k=2n=50,k=2). How can I correctly classify it?&lt;/p&gt;

&lt;p&gt;If I use RNN with infinite input length, the input layer will have 2 units (2 channels), but what about the output? Label ''1'' applies to the full sequence, not just a single input.&lt;/p&gt;

&lt;p&gt;Therefore, there are two related questions: 1) how do I design an output unit? It has to reflect the input of the full sequence, 2) how do I compute an error? It also needs to account for the error of the whole sequence.&lt;/p&gt;

&lt;p&gt;If I use ANN with a sliding window, the problem remains virtually the same - different classes can have comparable values in the window, so the output must correspond to the full sequence.&lt;/p&gt;

&lt;p&gt;In other words, how do I match a time series to just 1 number?&lt;/p&gt;

&lt;p&gt;EDIT:&lt;/p&gt;

&lt;p&gt;For example, there is an $n \times k$ array labelled ''1'' (this can be for example readings off $n$ sensors, which match 1 class, like a gesture: ''snap''), and another $m \times j$ array labelled ''0'', where $n$ is the number of features/input neurons and $k \neq j$ is the length/size of the input data. If I use an RNN to feedforward the first array I do it $k$ times, obviously. But how do I label it? Do I use a unity vector length $k$, get an error each time and them average out to get the sample error and backprop? I don''t think so, but not too sure. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/i2Sh2.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/i2Sh2.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'neuralnetwork', NULL, '''/a'':317C ''/i2sh2.png'':304C,311C ''/p'':52C,88C,133C,167C,183C,186C,298C,318C ''0'':223C ''1'':35C,77C,96C,181C,198C,210C ''2'':45C,67C,69C,114C ''2n'':42C ''50'':40C,43C ''account'':125C ''also'':122C ''alt'':312C ''ann'':138C ''anoth'':217C ''appli'':78C ''array'':196C,221C,252C ''averag'':280C ''backprop'':288C ''channel'':70C ''class'':34C,150C,211C ''classifi'':50C ''compar'':153C ''comput'':118C ''correct'':1A,49C ''correspond'':32C,162C ''data'':8A,242C ''dataset'':21C ''descript'':315C ''design'':100C ''differ'':149C ''edit'':185C ''enter'':313C ''error'':120C,128C,275C,286C ''exampl'':23C,189C,203C ''features/input'':230C ''feedforward'':249C ''first'':251C ''full'':81C,112C,165C ''gestur'':214C ''get'':273C,283C ''href'':301C ''i.stack.imgur.com'':303C,310C ''i.stack.imgur.com/i2sh2.png'':302C,309C ''imag'':314C ''img'':307C ''infinit'':59C ''input'':26C,60C,63C,87C,109C,241C ''j'':220C,235C ''k'':41C,44C,195C,233C,256C,272C ''kk'':30C ''label'':3A,11A,17C,76C,197C,222C,263C ''layer'':64C ''length'':27C,61C,271C ''length/size'':238C ''like'':212C ''m'':218C ''match'':175C,209C ''must'':161C ''n'':39C,193C,206C,225C ''need'':123C ''neq'':234C ''neuralnetwork'':12B ''neuron'':231C ''nn'':28C ''nofollow'':306C ''number'':182C,228C ''obvious'':258C ''output'':2A,75C,102C,160C ''p'':13C,53C,89C,134C,168C,184C,187C,299C ''problem'':144C ''question'':95C ''read'':204C ''reflect'':107C ''rel'':305C ''relat'':94C ''remain'':145C ''rnn'':57C,247C ''rnns/anns'':5A ''sampl'':285C ''see'':36C ''sensor'':31C,207C ''sequenc'':82C,113C,132C,166C ''sequenti'':7A,25C ''seri'':20C,178C ''singl'':10A,86C ''slide'':141C ''snap'':215C ''src'':308C ''sure'':297C ''therefor'':90C ''think'':292C ''time'':19C,177C,194C,219C,257C,277C ''time-seri'':18C ''two'':93C ''unit'':68C,103C ''uniti'':269C ''use'':56C,137C,245C,267C ''valu'':154C ''vector'':270C ''virtual'':146C ''whole'':131C ''window'':142C,157C ''word'':171C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9908, 2, '2016-01-22 15:42:46.927', 2, NULL, NULL, '&lt;p&gt;Maybe you will start to have negative impact of performance by increasing the number of trees. But definitely, at some point, increasing the number of trees will not add much accuracy in your model. &lt;/p&gt;

&lt;p&gt;The usual way is to perform a k-fold cross-validation for different number of trees (and any other combination of model''s parameters) and choose the one with the best performance. If you have any question about cross-validation, check &lt;a href="https://en.wikipedia.org/wiki/Cross-validation_(statistics)" rel="nofollow"&gt;this page&lt;/a&gt;. Instead of using simple accuracy, try to use other metrics that take into account the complexity of the model, like &lt;a href="https://en.wikipedia.org/wiki/Akaike_information_criterion" rel="nofollow"&gt;AIC&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Obs.: the final accuracy of the algorithm should be calculated using a separate dataset, not used in the cross-validation (usually called test set).&lt;/p&gt;
', NULL, NULL, '''/a'':91C,120C ''/p'':36C,121C,148C ''/wiki/akaike_information_criterion'':116C ''/wiki/cross-validation_(statistics)'':86C ''account'':105C ''accuraci'':32C,96C,126C ''add'':30C ''aic'':119C ''algorithm'':129C ''best'':70C ''calcul'':132C ''call'':145C ''check'':81C ''choos'':65C ''combin'':59C ''complex'':107C ''cross'':49C,79C,142C ''cross-valid'':48C,78C,141C ''dataset'':136C ''definit'':19C ''differ'':52C ''en.wikipedia.org'':85C,115C ''en.wikipedia.org/wiki/akaike_information_criterion'':114C ''en.wikipedia.org/wiki/cross-validation_(statistics)'':84C ''final'':125C ''fold'':47C ''href'':83C,113C ''impact'':9C ''increas'':13C,23C ''instead'':92C ''k'':46C ''k-fold'':45C ''like'':111C ''mayb'':2C ''metric'':101C ''model'':35C,61C,110C ''much'':31C ''negat'':8C ''nofollow'':88C,118C ''number'':15C,25C,53C ''ob'':123C ''one'':67C ''p'':1C,37C,122C ''page'':90C ''paramet'':63C ''perform'':11C,43C,71C ''point'':22C ''question'':76C ''rel'':87C,117C ''separ'':135C ''set'':147C ''simpl'':95C ''start'':5C ''take'':103C ''test'':146C ''tree'':17C,27C,55C ''tri'':97C ''use'':94C,99C,133C,138C ''usual'':39C,144C ''valid'':50C,80C,143C ''way'':40C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9909, 1, '2016-01-22 17:56:30.493', 4, 59, 'What are the advantages of contrastive divergence vs the gradient of the quadratic difference between the original data and the reconstructed data?', '&lt;p&gt;In this example I have a RBM with a visible layer and a hidden layer. The original data is "data", the values of the hidden neurons is "hid", the values of the visible neurons calculated from "hid" is "vis", and then the value for the hidden neurons calculated from "vis"is "hid2".&lt;/p&gt;

&lt;p&gt;I calculate the positive_association and the negative_association by:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;pos_associations = T.dot(data.T, hid)
neg_associations = T.dot(vis.T, hid2)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where T.dot represents the usual matrix calculation. I then update the weights using contrastive divergence (CD)&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;w = w + (training_rate)*(pos_associations - neg_associations)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;like described in this &lt;a href="http://blog.echen.me/2011/07/18/introduction-to-restricted-boltzmann-machines/" rel="nofollow"&gt;post&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I do get a good reconstruction (vis looks like data) and I can see features representing the weights. However, it takes a while and many epochs.&lt;/p&gt;

&lt;p&gt;If instead of using CD I simply calculate the quadratic differnce between data and vis&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;cost = T.sum((data - vis)**2).mean()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and I update the weights using the gradient of cost, I get a faster approximation of the data. So my question is, what is the advantage of using CD?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9911, 2, '2016-01-22 19:17:40.53', 1, NULL, NULL, '&lt;p&gt;As &lt;a href="https://fr.wikipedia.org/wiki/Carriage_Return_Line_Feed" rel="nofollow"&gt;carriage return/line feed (CR/LF)&lt;/a&gt; might cause  issues depending on the system, I suggest to store the matrix "inline" or in &lt;a href="https://en.wikipedia.org/wiki/Raster_graphics" rel="nofollow"&gt;raster-style&lt;/a&gt; with a short header to specify your convention, a versioning number (you might change your mind later, and increase the version), etc. and &lt;strong&gt;at least&lt;/strong&gt; the matrix size, and the rest in some text format (CSV,TSV). This is similar to what is done in the &lt;a href="http://netpbm.sourceforge.net/doc/pgm.html" rel="nofollow"&gt;portable gray map&lt;/a&gt; or bitmap image format.&lt;/p&gt;

&lt;p&gt;I used that to store text &lt;a href="http://dx.doi.org/10.1109/TSP.2009.2023947" rel="nofollow"&gt;filter bank coefficients&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;A minimum example  could be:
&lt;code&gt;2,3,0.1,1.2,2.3,3.4,4.5,5.6&lt;/code&gt; for the $2\times 3$ matrix:
\begin{array}
0.1&amp;amp;1.2&amp;amp;2.3\\3.4&amp;amp;4.5&amp;amp;5.6
\end{array}
but you can use for instance &lt;code&gt;#2,#3,0.1,1.2,2.3,3.4,4.5,5.6&lt;/code&gt;
so that aliens (think about the &lt;a href="https://en.wikipedia.org/wiki/Pioneer_plaque" rel="nofollow"&gt;golden Pioneer plaque&lt;/a&gt;) understand that the  first two integers are "different" and  provide hints on how the following numbers should be read. With a square matrix (typical of symmetrical matrices), this is even more interesting, as you only need one header number &lt;code&gt;#n&lt;/code&gt; (the side) and the acute readers will see that the remaining numbers are in $n^2$ quantity.&lt;/p&gt;

&lt;p&gt;You can also have a look at other &lt;a href="http://www.netlib.org/lapack/lug/node121.html" rel="nofollow"&gt;Matrix Storage Schemes&lt;/a&gt;, and if your matrix is sparse, &lt;a href="http://netlib.org/linalg/html_templates/node91.html" rel="nofollow"&gt;Compressed Row Storage (CRS)&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/10.1109/tsp.2009.2023947'':117C ''/a'':14C,41C,100C,123C,193C,273C,291C ''/code'':140C,176C,235C ''/doc/pgm.html'':94C ''/lapack/lug/node121.html'':267C ''/linalg/html_templates/node91.html'':284C ''/p'':105C,124C,253C,292C ''/strong'':68C ''/wiki/carriage_return_line_feed'':7C ''/wiki/pioneer_plaque'':187C ''/wiki/raster_graphics'':35C ''0.1'':134C,149C,170C ''1.2'':135C,151C,171C ''2'':132C,143C,168C,251C ''2.3'':136C,153C,172C ''3'':133C,145C,169C ''3.4'':137C,154C,173C ''4.5'':138C,156C,174C ''5.6'':139C,158C,175C ''acut'':240C ''alien'':179C ''also'':257C ''amp'':150C,152C,155C,157C ''array'':148C,160C ''bank'':121C ''begin'':147C ''bitmap'':102C ''carriag'':10C ''caus'':16C ''chang'':55C ''code'':131C,167C,233C ''coeffici'':122C ''compress'':287C ''convent'':49C ''could'':129C ''cr/lf'':13C ''crs'':290C ''csv'':79C ''depend'':18C ''differ'':201C ''done'':87C ''dx.doi.org'':116C ''dx.doi.org/10.1109/tsp.2009.2023947'':115C ''en.wikipedia.org'':34C,186C ''en.wikipedia.org/wiki/pioneer_plaque'':185C ''en.wikipedia.org/wiki/raster_graphics'':33C ''end'':159C ''etc'':63C ''even'':223C ''exampl'':128C ''feed'':12C ''filter'':120C ''first'':197C ''follow'':208C ''format'':78C,104C ''fr.wikipedia.org'':6C ''fr.wikipedia.org/wiki/carriage_return_line_feed'':5C ''golden'':190C ''gray'':98C ''header'':45C,231C ''hint'':204C ''href'':4C,32C,91C,114C,184C,264C,281C ''imag'':103C ''increas'':60C ''inlin'':28C ''instanc'':166C ''integ'':199C ''interest'':225C ''issu'':17C ''later'':58C ''least'':67C ''look'':260C ''map'':99C ''matric'':220C ''matrix'':27C,70C,146C,216C,270C,277C ''might'':15C,54C ''mind'':57C ''minimum'':127C ''n'':234C,250C ''need'':229C ''netlib.org'':283C ''netlib.org/linalg/html_templates/node91.html'':282C ''netpbm.sourceforge.net'':93C ''netpbm.sourceforge.net/doc/pgm.html'':92C ''nofollow'':9C,37C,96C,119C,189C,269C,286C ''number'':52C,209C,232C,247C ''one'':230C ''p'':1C,106C,125C,254C ''pioneer'':191C ''plaqu'':192C ''portabl'':97C ''provid'':203C ''quantiti'':252C ''raster'':39C ''raster-styl'':38C ''read'':212C ''reader'':241C ''rel'':8C,36C,95C,118C,188C,268C,285C ''remain'':246C ''rest'':74C ''return/line'':11C ''row'':288C ''scheme'':272C ''see'':243C ''short'':44C ''side'':237C ''similar'':83C ''size'':71C ''spars'':279C ''specifi'':47C ''squar'':215C ''storag'':271C,289C ''store'':25C,111C ''strong'':65C ''style'':40C ''suggest'':23C ''symmetr'':219C ''system'':21C ''text'':77C,112C ''think'':180C ''time'':144C ''tsv'':80C ''two'':198C ''typic'':217C ''understand'':194C ''use'':108C,164C ''version'':51C,62C ''www.netlib.org'':266C ''www.netlib.org/lapack/lug/node121.html'':265C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9913, 1, '2016-01-23 05:44:51.323', 2, 70, 'Periodically executing a scraping script with Python', '&lt;p&gt;Here is my idea and my early work.    &lt;/p&gt;

&lt;h3&gt;&lt;em&gt;My target&lt;/em&gt;&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;Fetch 1-hour resolution air pollution data from China''s goverment continuously.      &lt;/li&gt;
&lt;li&gt;The website''s data which collected from the monitor sites over the country update per hour .      &lt;/li&gt;
&lt;/ul&gt;

&lt;h3&gt;&lt;em&gt;My Code&lt;/em&gt;&lt;/h3&gt;

&lt;p&gt;Now, I can grab the useful information for a single hour. Here is my code:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;Input the website links for different pollution(co,no2,pm10, etc)&lt;/em&gt;     &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;html_co = urllib.urlopen("http://www.pm25.in/api/querys/co.json?city=beijing&amp;amp;token=5j1znBVAsnSf5xQyNQyq").read().decode(''utf-8'')
html_no2 = urllib.urlopen("http://www.pm25.in/api/querys/no2.json?city=beijing&amp;amp;token=5j1znBVAsnSf5xQyNQyq").read().decode(''utf-8'')
html_pm10 = urllib.urlopen("http://www.pm25.in/api/querys/pm10.json?city=beijing&amp;amp;token=5j1znBVAsnSf5xQyNQyq").read().decode(''utf-8'')
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;Get the content of the html doc.&lt;/em&gt;      &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;soup_co = BeautifulSoup(html_co)
soup_no2 = BeautifulSoup(html_no2)
soup_pm10 = BeautifulSoup(html_pm10)
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;Extract the useful information from the whole content.&lt;/em&gt;        &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;l = soup_co.p.get_text() 
co= json.loads(l) 
l = soup_no2.p.get_text() 
no2= json.loads(l) 
l = soup_pm10.p.get_text() 
pm10= json.loads(l)       
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;em&gt;Tight the raw data into neat Pandas.Dataframe.&lt;/em&gt;          &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data = {"time":[],"station":[],"code":[],"co":[],"no2":[],"pm10":[]}
for i in range(0,len(pm10)-1,1):
    ## ''station'' is the monitor station''s name in Chinese
    data["station"].append(co[i]["position_name"])
    data["time"].append(co[i]["time_point"])
    data["co"].append(co[i]["co"])
    ## ''code'' is the monitor station''s index
    data["code"].append(co[i]["station_code"])
    data["no2"].append(no2[i]["no2"])
    data["pm10"].append(pm10[i]["pm10"])
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;h3&gt;&lt;em&gt;My result&lt;/em&gt;&lt;/h3&gt;

&lt;p&gt;&lt;em&gt;Some pre-explanation&lt;/em&gt;      &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Ignore the chinese character in the table. &lt;/li&gt;
&lt;li&gt;I only grab one city(Beijing here)''s data, and index from 0-11 notify that Beijing has 12 monitor site.         &lt;/li&gt;
&lt;li&gt;The columns ''co''/''NO2''/''PM10'' represent the concentration of these air pollutants.&lt;br&gt;
&lt;img src="http://i8.tietuku.com/cf59fbec6f89566d.png" alt=""&gt;         &lt;/li&gt;
&lt;/ul&gt;

&lt;h3&gt;&lt;em&gt;My problem&lt;/em&gt;&lt;/h3&gt;

&lt;p&gt;Now, I can grab the web data manually according my code above. But, I want to &lt;strong&gt;achieve the working flow below hourly  automatically&lt;/strong&gt;.    &lt;/p&gt;

&lt;p&gt;Hour i      &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Execute the code     &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;(1) Grab the data for Hour i''s air pollutants data from website;     &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;(2) Save the data into .csv based on the true date(like 20160101.csv)   &lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;After one hour.     &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Execute the code     &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;(1) Grab the data for Hour i+1''s air pollutants data from website;     &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;(2) Save the data into .csv based on the true date.&lt;br&gt;
  if it''s the same day liken to hour i --&gt; same .csv(like 2016-01-01.csv)&lt;br&gt;
  if the present day has past --&gt; creat a new .csv(like 2016-01-02.csv)      &lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I havn''t done these kind of stuff before. Can somebody offer me some advice?&lt;br&gt;
So, I can get an useful data scraping tool run in the background and I don''t have to worry about it.      &lt;/p&gt;
', 'python  dataset  scraping', NULL, '''+1'':464C ''-1'':232C ''-11'':331C ''-8'':112C,122C,132C ''/api/querys/co.json?city=beijing&amp;amp;token=5j1znbvasnsf5xqynqyq'':108C ''/api/querys/no2.json?city=beijing&amp;amp;token=5j1znbvasnsf5xqynqyq'':118C ''/api/querys/pm10.json?city=beijing&amp;amp;token=5j1znbvasnsf5xqynqyq'':128C ''/cf59fbec6f89566d.png'':358C ''/code'':133C,165C,201C,289C ''/em'':25C,65C,99C,146C,179C,214C,297C,305C,366C ''/h3'':26C,66C,298C,367C ''/li'':41C,59C,135C,167C,203C,291C,316C,339C,360C,406C,423C,440C,454C,472C,515C ''/ol'':292C ''/p'':20C,83C,100C,147C,180C,215C,306C,394C,398C,405C,422C,439C,446C,453C,471C,514C,556C ''/pre'':134C,166C,202C,290C ''/strong'':393C ''/ul'':60C,361C,441C,516C ''0'':229C,330C ''1'':30C,233C,409C,457C ''12'':336C ''2'':426C,475C ''2016-01-01.csv'':500C ''2016-01-02.csv'':513C ''20160101.csv'':438C ''accord'':377C ''achiev'':386C ''advic'':532C ''air'':33C,351C,417C,466C ''alt'':359C ''append'':245C,252C,259C,272C,279C,285C ''automat'':392C ''background'':546C ''base'':432C,481C ''beautifulsoup'':152C,157C,162C ''beij'':323C,334C ''br'':353C,486C,501C,533C ''charact'':312C ''china'':37C ''chines'':242C,311C ''citi'':322C ''co'':95C,104C,151C,154C,186C,222C,246C,253C,258C,260C,262C,273C,343C ''code'':64C,82C,102C,149C,182C,217C,221C,263C,271C,276C,379C,404C,452C ''collect'':48C ''column'':342C ''concentr'':348C ''content'':141C,178C ''continu'':40C ''countri'':55C ''creat'':508C ''csv'':431C,480C,498C,511C ''data'':35C,46C,210C,218C,243C,250C,257C,270C,277C,283C,326C,375C,412C,419C,429C,460C,468C,478C,540C ''dataset'':9B ''date'':436C,485C ''day'':492C,505C ''decod'':110C,120C,130C ''differ'':93C ''doc'':145C ''done'':521C ''earli'':18C ''em'':22C,62C,87C,138C,170C,206C,294C,300C,363C ''etc'':98C ''execut'':2A,402C,450C ''explan'':304C ''extract'':171C ''fetch'':29C ''flow'':389C ''get'':139C,537C ''gover'':39C ''grab'':71C,320C,372C,410C,458C ''h3'':21C,61C,293C,362C ''havn'':519C ''hour'':31C,58C,78C,391C,396C,414C,445C,462C,495C ''html'':103C,113C,123C,144C,153C,158C,163C ''i8.tietuku.com'':357C ''i8.tietuku.com/cf59fbec6f89566d.png'':356C ''idea'':15C ''ignor'':309C ''img'':354C ''index'':269C,328C ''inform'':74C,174C ''input'':88C ''json.loads'':187C,193C,199C ''kind'':523C ''l'':183C,188C,189C,194C,195C,200C ''len'':230C ''li'':28C,42C,85C,136C,168C,204C,308C,317C,340C,400C,407C,424C,448C,455C,473C ''like'':437C,499C,512C ''liken'':493C ''link'':91C ''manual'':376C ''monitor'':51C,237C,266C,337C ''name'':240C,249C ''neat'':212C ''new'':510C ''no2'':96C,114C,156C,159C,192C,223C,278C,280C,282C,344C ''notifi'':332C ''offer'':529C ''ol'':84C ''one'':321C,444C ''p'':11C,67C,86C,137C,169C,205C,299C,368C,395C,401C,408C,425C,442C,449C,456C,474C,517C ''pandas.dataframe'':213C ''past'':507C ''per'':57C ''period'':1A ''pm10'':97C,124C,161C,164C,198C,224C,231C,284C,286C,288C,345C ''point'':256C ''pollut'':34C,94C,352C,418C,467C ''posit'':248C ''pre'':101C,148C,181C,216C,303C ''pre-explan'':302C ''present'':504C ''problem'':365C ''python'':7A,8B ''rang'':228C ''raw'':209C ''read'':109C,119C,129C ''repres'':346C ''resolut'':32C ''result'':296C ''run'':543C ''save'':427C,476C ''scrape'':4A,10B,541C ''script'':5A ''singl'':77C ''site'':52C,338C ''somebodi'':528C ''soup'':150C,155C,160C ''soup_co.p.get'':184C ''soup_no2.p.get'':190C ''soup_pm10.p.get'':196C ''src'':355C ''station'':220C,234C,238C,244C,267C,275C ''strong'':385C ''stuff'':525C ''tabl'':315C ''target'':24C ''text'':185C,191C,197C ''tight'':207C ''time'':219C,251C,255C ''tool'':542C ''true'':435C,484C ''ul'':27C,307C,399C,447C ''updat'':56C ''urllib.urlopen'':105C,115C,125C ''use'':73C,173C,539C ''utf'':111C,121C,131C ''want'':383C ''web'':374C ''websit'':44C,90C,421C,470C ''whole'':177C ''work'':19C,388C ''worri'':553C ''www.pm25.in'':107C,117C,127C ''www.pm25.in/api/querys/co.json?city=beijing&amp;amp;token=5j1znbvasnsf5xqynqyq'':106C ''www.pm25.in/api/querys/no2.json?city=beijing&amp;amp;token=5j1znbvasnsf5xqynqyq'':116C ''www.pm25.in/api/querys/pm10.json?city=beijing&amp;amp;token=5j1znbvasnsf5xqynqyq'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9914, 2, '2016-01-23 06:08:11.307', 5, NULL, NULL, '&lt;p&gt;If you are on a Unix-based OS, use a scheduler &lt;a href="https://www.maketecheasier.com/cron-alternatives-linux/" rel="nofollow"&gt;like cron&lt;/a&gt; to run your script periodically. cron is the most common one, and you can find many tutorials for it. I guess this is the part you are unfamiliar with.&lt;/p&gt;

&lt;p&gt;When it executes your script, make it check the time like so: &lt;code&gt;fname=datetime.datetime.now().strftime(''%Y-%m-%d-%H.csv''&lt;/code&gt;). Open the appropriate file in append mode (&lt;code&gt;with open(fname, ''a'') as csv_file&lt;/code&gt;) ; if it does not exist it will be created. That''s all there''s to it!&lt;/p&gt;
', NULL, NULL, '''/a'':23C ''/code'':75C,91C ''/cron-alternatives-linux/'':18C ''/p'':53C,108C ''append'':81C ''appropri'':78C ''base'':9C ''check'':62C ''code'':67C,83C ''common'':33C ''creat'':100C ''cron'':22C,29C ''csv'':89C ''d'':73C ''datetime.datetime.now'':69C ''execut'':57C ''exist'':96C ''file'':79C,90C ''find'':38C ''fname'':68C,86C ''guess'':44C ''h.csv'':74C ''href'':15C ''like'':21C,65C ''m'':72C ''make'':60C ''mani'':39C ''mode'':82C ''nofollow'':20C ''one'':34C ''open'':76C,85C ''os'':10C ''p'':1C,54C ''part'':48C ''period'':28C ''rel'':19C ''run'':25C ''schedul'':13C ''script'':27C,59C ''strftime'':70C ''time'':64C ''tutori'':40C ''unfamiliar'':51C ''unix'':8C ''unix-bas'':7C ''use'':11C ''www.maketecheasier.com'':17C ''www.maketecheasier.com/cron-alternatives-linux/'':16C ''y'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9915, 1, '2016-01-23 06:20:17.123', 1, 12, 'Multiple linear regression, fMRI', '&lt;p&gt;Say I have an fMRI experiment where I have a task that is suppose to measure response time, and the corresponding "activation" in the brain. I then have participants complete this task. Then I measure some variables through tests or assessments, and questionaries such as IQ, working memory, age, and years of education. &lt;/p&gt;

&lt;p&gt;In fMRI experiments, as I am aware typically voxels (the name for the 3d fmri representation of a pixel) that remain "active" after statistical tests, while the participant completes a task, say something about how that brain region where those voxels are modulates that cognitive task. &lt;/p&gt;

&lt;p&gt;Now, what if I want to use the variables that I measured, IQ, age, working memory, and years of education in a multiple linear regression test, with the brain activation to this task as the response variable, or dependent variable. What does this tell me? &lt;/p&gt;
', 'linear-regression', NULL, '''/p'':62C,110C,157C ''3d'':77C ''activ'':30C,85C,141C ''age'':57C,125C ''assess'':49C ''awar'':70C ''brain'':33C,100C,140C ''cognit'':108C ''complet'':38C,92C ''correspond'':29C ''depend'':150C ''educ'':61C,131C ''experi'':14C,66C ''fmri'':4A,13C,65C,78C ''iq'':54C,124C ''linear'':2A,6B,135C ''linear-regress'':5B ''measur'':24C,43C,123C ''memori'':56C,127C ''modul'':106C ''multipl'':1A,134C ''name'':74C ''p'':8C,63C,111C ''particip'':37C,91C ''pixel'':82C ''questionari'':51C ''region'':101C ''regress'':3A,7B,136C ''remain'':84C ''represent'':79C ''respons'':25C,147C ''say'':9C,95C ''someth'':96C ''statist'':87C ''suppos'':22C ''task'':19C,40C,94C,109C,144C ''tell'':155C ''test'':47C,88C,137C ''time'':26C ''typic'':71C ''use'':118C ''variabl'':45C,120C,148C,151C ''voxel'':72C,104C ''want'':116C ''work'':55C,126C ''year'':59C,129C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9916, 1, '2016-01-23 08:04:13.13', 2, 52, 'Correlation as an evaluation metric for regression', '&lt;p&gt;I''m dealing with a regression prediction challenge where the evaluation metric is (pearson) correlation.
However, I have the impression that this metric is kind of arbitrary.
While I can keep the RMSE stable the correlation can have great variety.&lt;/p&gt;

&lt;p&gt;Could someone please explain this metric and how to optimise for it?&lt;/p&gt;
', 'machine-learning  predictive-modeling  correlation  evaluation', NULL, '''/p'':57C,71C ''arbitrari'':43C ''challeng'':24C ''correl'':1A,14B,31C,52C ''could'':59C ''deal'':19C ''evalu'':4A,15B,27C ''explain'':62C ''great'':55C ''howev'':32C ''impress'':36C ''keep'':47C ''kind'':41C ''learn'':10B ''m'':18C ''machin'':9B ''machine-learn'':8B ''metric'':5A,28C,39C,64C ''model'':13B ''optimis'':68C ''p'':16C,58C ''pearson'':30C ''pleas'':61C ''predict'':12B,23C ''predictive-model'':11B ''regress'':7A,22C ''rmse'':49C ''someon'':60C ''stabl'':50C ''varieti'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9917, 1, '2016-01-23 08:11:03.673', 2, 64, 'How to measure confidence in prediction?', '&lt;p&gt;I built a prediction model and predicted on new data. I now want to specify a value of my confidence in this predicted value, e.g. ranging from 0 to 1.
Three methods come to my mind&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Built 100 models on bootstrapped data, predict 100 times on each new observation, then calculate confidence intervals. Smaller intervals mean higher confidence. High computational effort.&lt;/li&gt;
&lt;li&gt;use oob predictions of a random forest from every tree&lt;/li&gt;
&lt;li&gt;Bayesian methods can give confidence intervals through the posterior&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Are there other/better ones?&lt;/p&gt;
', 'machine-learning  predictive-modeling', NULL, '''/li'':78C,90C,101C ''/ol'':102C ''/p'':50C,108C ''0'':41C ''1'':43C ''100'':54C,60C ''bayesian'':92C ''bootstrap'':57C ''built'':15C,53C ''calcul'':67C ''come'':46C ''comput'':76C ''confid'':4A,33C,68C,74C,96C ''data'':23C,58C ''e.g'':38C ''effort'':77C ''everi'':88C ''forest'':86C ''give'':95C ''high'':75C ''higher'':73C ''interv'':69C,71C,97C ''learn'':9B ''li'':52C,79C,91C ''machin'':8B ''machine-learn'':7B ''mean'':72C ''measur'':3A ''method'':45C,93C ''mind'':49C ''model'':12B,18C,55C ''new'':22C,64C ''observ'':65C ''ol'':51C ''one'':107C ''oob'':81C ''other/better'':106C ''p'':13C,103C ''posterior'':100C ''predict'':6A,11B,17C,20C,36C,59C,82C ''predictive-model'':10B ''random'':85C ''rang'':39C ''smaller'':70C ''specifi'':28C ''three'':44C ''time'':61C ''tree'':89C ''use'':80C ''valu'':30C,37C ''want'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9918, 1, '2016-01-23 13:04:17.457', 1, 62, 'Optimizing co-occurrence matrix computation', '&lt;p&gt;I am computing co-occurence matrix for a fixed windows size in python using scipy''s lil_matrix for storing the counts and computing the counts by sliding the context window over each word and then counting in the window. &lt;/p&gt;

&lt;p&gt;Now the code is taking too much time for relatively small corpus size also (100 MB Wikipedia dump). The code is :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def gen_coocur(window_size=5):
        ''''''
        Generates coocurrence matrix
        ''''''
        # vocab is precomputed. 
        coocur_matrix = lil_matrix((len(vocab)+1, len(vocab)+1), dtype=np.float64)
        for page in self.wiki_extract.get_page():
            # word_tokenize is tokenizer from nltk 
            doc_tokens = word_tokenize(page.decode(''utf-8''))
            N = len(doc_tokens)
            for token in self.vocab:
                for i in xrange(0,window_size):
                    if (token in doc_tokens[0:i] or token in doc_tokens[i:(i+window_size+1)]) and token != doc_tokens[i]:
                        coocur_matrix[self.vocab[doc_tokens[i]],self.vocab[token]] +=1
                for i in xrange(window_size, (N-window_size)):
                    if token in doc_tokens[(i-window_size):(i+window_size+1)] and token != doc_tokens[i]:
                        coocur_matrix[self.vocab[doc_tokens[i]],self.vocab[token]] +=1
                for i in xrange(N-window_size, N):
                    if (token in doc_tokens[i:N] or token in doc_tokens[i-window_size:N]) and token != doc_tokens[i]:
                        coocur_matrix[self.vocab[doc_tokens[i]],self.vocab[token]] +=1            
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;vocab is a dictionary which maps words -&gt; wordId. How can I optimize this code to run faster?&lt;/p&gt;
', 'python  text-mining', NULL, '''+1'':97C,100C,152C,189C ''-8'':120C ''/code'':244C ''/p'':53C,76C,264C ''/pre'':245C ''0'':133C,141C ''1'':166C,203C,243C ''100'':69C ''5'':84C ''also'':68C ''co'':3A,16C ''co-occur'':15C ''co-occurr'':2A ''code'':57C,74C,78C,260C ''comput'':6A,14C,36C ''context'':42C ''coocur'':81C,91C,158C,195C,235C ''coocurr'':86C ''corpus'':66C ''count'':34C,38C,49C ''def'':79C ''dictionari'':250C ''doc'':114C,123C,139C,146C,155C,161C,180C,192C,198C,216C,223C,232C,238C ''dtype'':101C ''dump'':72C ''faster'':263C ''fix'':21C ''gen'':80C ''generat'':85C ''i-window'':182C,225C ''len'':95C,98C,122C ''lil'':29C,93C ''map'':252C ''matrix'':5A,18C,30C,87C,92C,94C,159C,196C,236C ''mb'':70C ''mine'':10B ''much'':61C ''n'':121C,174C,209C,212C,219C,229C ''n-window'':173C,208C ''nltk'':113C ''np.float64'':102C ''occur'':17C ''occurr'':4A ''optim'':1A,258C ''p'':11C,54C,246C ''page'':104C,107C ''page.decode'':118C ''pre'':77C ''precomput'':90C ''python'':7B,25C ''relat'':64C ''run'':262C ''scipi'':27C ''self.vocab'':128C,160C,164C,197C,201C,237C,241C ''self.wiki_extract.get'':106C ''size'':23C,67C,83C,135C,151C,172C,176C,185C,188C,211C,228C ''slide'':40C ''small'':65C ''store'':32C ''take'':59C ''text'':9B ''text-min'':8B ''time'':62C ''token'':109C,111C,115C,117C,124C,126C,137C,140C,144C,147C,154C,156C,162C,165C,178C,181C,191C,193C,199C,202C,214C,217C,221C,224C,231C,233C,239C,242C ''use'':26C ''utf'':119C ''vocab'':88C,96C,99C,247C ''wikipedia'':71C ''window'':22C,43C,52C,82C,134C,150C,171C,175C,184C,187C,210C,227C ''word'':46C,108C,116C,253C ''wordid'':254C ''xrang'':132C,170C,207C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9939, 1, '2016-01-25 11:03:17.52', 1, 49, 'How to predict an approximate weekly/monthly number, when the Unique Daily Visitors for that week/month are already known', '&lt;p&gt;I am trying to come up with a formula or machine learning algorithm using which I can approximately predict the weekly or monthly users.&lt;/p&gt;

&lt;p&gt;What to keep in mind is that I already have counts for the &lt;strong&gt;unique visitors per day&lt;/strong&gt; for the week/months that I would like to make a near accurate prediction. Here, simply summing the daily unique users would not work, as they can be unique on one day but not on two days as they can have a session lasting over 2 days.&lt;/p&gt;

&lt;p&gt;This method is to serve as an alternative to running a Spark job on the whole week/month data in order to save time and resources - Is this possible?&lt;/p&gt;

&lt;p&gt;I have looked at Time Series and Linear Regression, but need more clarification on the possible approaches and also on any work-arounds?&lt;/p&gt;
', 'machine-learning  predictive-modeling  regression  time-series  apache-spark', NULL, '''/p'':57C,124C,154C,180C ''/strong'':77C ''2'':122C ''accur'':89C ''algorithm'':45C ''alreadi'':17A,67C ''also'':174C ''altern'':133C ''apach'':30B ''apache-spark'':29B ''approach'':172C ''approxim'':5A,50C ''around'':179C ''clarif'':168C ''come'':37C ''count'':69C ''daili'':11A,95C ''data'':143C ''day'':76C,108C,113C,123C ''formula'':41C ''job'':138C ''keep'':61C ''known'':18A ''last'':120C ''learn'':21B,44C ''like'':84C ''linear'':163C ''look'':158C ''machin'':20B,43C ''machine-learn'':19B ''make'':86C ''method'':127C ''mind'':63C ''model'':24B ''month'':55C ''near'':88C ''need'':166C ''number'':7A ''one'':107C ''order'':145C ''p'':32C,58C,125C,155C ''per'':75C ''possibl'':153C,171C ''predict'':3A,23B,51C,90C ''predictive-model'':22B ''regress'':25B,164C ''resourc'':150C ''run'':135C ''save'':147C ''seri'':28B,161C ''serv'':130C ''session'':119C ''simpli'':92C ''spark'':31B,137C ''strong'':72C ''sum'':93C ''time'':27B,148C,160C ''time-seri'':26B ''tri'':35C ''two'':112C ''uniqu'':10A,73C,96C,105C ''use'':46C ''user'':56C,97C ''visitor'':12A,74C ''week'':53C ''week/month'':15A,142C ''week/months'':80C ''weekly/monthly'':6A ''whole'':141C ''work'':100C,178C ''work-around'':177C ''would'':83C,98C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9926, 2, '2016-01-24 04:42:16.733', 1, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;Should I choose cluster based / density based approach?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Yes,&lt;/strong&gt; but depends on how good the algorithm is, and how effectively is the algorithm identifying important clusters. There is &lt;a href="http://www.cs.stir.ac.uk/courses/msc/projects/PastDissertations/2012-2013/Dissertations/Elangovan.pdf" rel="nofollow"&gt;a lot of literature&lt;/a&gt; where people have taken the clustering approach.&lt;/p&gt;

&lt;p&gt;If I were to go with the clustering approach, I would go with the &lt;a href="https://en.wikipedia.org/wiki/Hierarchical_clustering" rel="nofollow"&gt;hierarchical clustering technique&lt;/a&gt;, cause it would help me re-check my clusters and build hierarchies in them so that the seriousness of the fraud can also be explored.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;How do I find rare &amp;amp; suspicious events that parents should be notified
  for with the help of machine learning ?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I would point out &lt;a href="http://datascience.stackexchange.com/a/8100/11097"&gt;an answer&lt;/a&gt; which I have written for a very similar question, which also explores the aspect of fraud detection and anomaly analysis. And as I have said in the answer, the genetic algorithm still goes strong when it comes to fraud detection, owing to it''s neat method of fitness calculation of the data points.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Any suggestions on how to present the results visually ?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Generally such charts are presented through pie charts and network graphs. you can also use infographics if you have a nice idea about design and aesthetics.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Is a week of data enough for training ?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Generally, data science teams at Paypal, Palantir, etc churn years of data for sensible insights. As you say it''s a class project, data of several months should do good. &lt;strong&gt;You wouldn''t really get anything suspicious or sensible from a week of data.&lt;/strong&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Are there any good open sources in Java to detect anomaly ?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Yes, there are some:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="http://jgap.sourceforge.net/" rel="nofollow"&gt;Genetic algorithms library&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://jubat.us/en/index.html" rel="nofollow"&gt;A Distributed Online Machine Learning Framework in java&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9920, 5, '2016-01-23 17:39:45.1', 0, NULL, NULL, '&lt;p&gt;&lt;a href="http://orange.biolab.si/" rel="nofollow"&gt;Orange&lt;/a&gt; is a free and open-source component-based data mining and machine learning software suite, featuring a visual programming front-end for explorative data analysis and visualization. It includes a comprehensive set of components for data preprocessing, feature scoring and filtering, modeling, model evaluation, and various exploration techniques.&lt;/p&gt;

&lt;p&gt;It is maintained by the &lt;a href="http://www.biolab.si/en/" rel="nofollow"&gt;Bioinformatics Laboratory of the Faculty of Computer and Information Science&lt;/a&gt;, &lt;a href="https://www.uni-lj.si/eng/" rel="nofollow"&gt;University of Ljubljana&lt;/a&gt;, &lt;a href="http://www.slovenia.info/?lng=2" rel="nofollow"&gt;Slovenia&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/?lng=2'':100C ''/a'':8C,84C,95C,104C ''/en/'':71C ''/eng/'':89C ''/p'':60C,105C ''analysi'':36C ''base'':18C ''bioinformat'':74C ''compon'':17C,45C ''component-bas'':16C ''comprehens'':42C ''comput'':80C ''data'':19C,35C,47C ''end'':32C ''evalu'':55C ''explor'':34C,58C ''faculti'':78C ''featur'':26C,49C ''filter'':52C ''free'':11C ''front'':31C ''front-end'':30C ''href'':3C,68C,86C,97C ''includ'':40C ''inform'':82C ''laboratori'':75C ''learn'':23C ''ljubljana'':94C ''machin'':22C ''maintain'':64C ''mine'':20C ''model'':53C,54C ''nofollow'':6C,73C,91C,102C ''open'':14C ''open-sourc'':13C ''orang'':7C ''orange.biolab.si'':4C ''p'':1C,61C ''preprocess'':48C ''program'':29C ''rel'':5C,72C,90C,101C ''scienc'':83C ''score'':50C ''set'':43C ''slovenia'':103C ''softwar'':24C ''sourc'':15C ''suit'':25C ''techniqu'':59C ''univers'':92C ''various'':57C ''visual'':28C,38C ''www.biolab.si'':70C ''www.biolab.si/en/'':69C ''www.slovenia.info'':99C ''www.slovenia.info/?lng=2'':98C ''www.uni-lj.si'':88C ''www.uni-lj.si/eng/'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9922, 2, '2016-01-23 19:15:14.31', 0, NULL, NULL, '&lt;p&gt;From easiest to hardest:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Try running it in &lt;a href="http://pypy.org" rel="nofollow"&gt;pypy&lt;/a&gt; or &lt;a href="http://numba.pydata.org/" rel="nofollow"&gt;numba&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://github.com/search?q=co-occurrence&amp;amp;type=Repositories" rel="nofollow"&gt;Find a faster implementation&lt;/a&gt;. Unfortunately I can not recommend one.&lt;/li&gt;
&lt;li&gt;Parallelize the loop over the documents. Not so hard since your vocabulary is precomputed. (Even if it weren''t you could get away with it using the &lt;a href="https://en.wikipedia.org/wiki/Feature_hashing#Feature_vectorization_using_the_hashing_trick" rel="nofollow"&gt;hashing trick&lt;/a&gt;.) Combine this with the first bullet.&lt;/li&gt;
&lt;li&gt;Rewrite the &lt;a href="http://docs.scipy.org/doc/numpy/reference/arrays.nditer.html#putting-the-inner-loop-in-cython" rel="nofollow"&gt;inner loop&lt;/a&gt; in &lt;a href="http://docs.cython.org/src/userguide/numpy_tutorial.html" rel="nofollow"&gt;Cython&lt;/a&gt;.&lt;/li&gt;
&lt;li&gt;Rewrite the whole thing in a faster language like C++ or Scala.&lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9923, 1, '2016-01-23 23:07:16.237', 1, 7, 'Convert Out-of-bag (OOB) estimate to quad weighted kappa score', '&lt;p&gt;Is there a way to directly calculate an approximate quad weighted kappa measure from an OOB estimate, obtained from a gradient boosting model with subsampling without going through cross validation?&lt;/p&gt;
', 'cross-validation  gbm', NULL, '''/p'':48C ''approxim'':26C ''bag'':5A ''boost'':39C ''calcul'':24C ''convert'':1A ''cross'':14B,46C ''cross-valid'':13B ''direct'':23C ''estim'':7A,34C ''gbm'':16B ''go'':44C ''gradient'':38C ''kappa'':11A,29C ''measur'':30C ''model'':40C ''obtain'':35C ''oob'':6A,33C ''out-of-bag'':2A ''p'':17C ''quad'':9A,27C ''score'':12A ''subsampl'':42C ''valid'':15B,47C ''way'':21C ''weight'':10A,28C ''without'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9924, 1, '2016-01-24 01:12:43.523', 1, 42, 'anomaly detection alert system', '&lt;p&gt;I need to design and implement my 3rd year project which is a real-time alert system for parents to keep their kids safe with their smartphones.&lt;/p&gt;

&lt;p&gt;My data is time-based and unsupervised, which means I don''t have score for any events, it contains events from : &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;mobile browsing ( http requests )&lt;/li&gt;
&lt;li&gt;gps coordinates&lt;/li&gt;
&lt;li&gt;calls ( outgoing &amp;amp; incoming ) &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Should I choose cluster based / density based approach ? 
How do I find rare &amp;amp; suspicious events that parents should be notified for with the help of machine learning ? &lt;/p&gt;

&lt;p&gt;Any suggestions on how to present the results visually ?&lt;/p&gt;

&lt;p&gt;Is a week of data enough for training ? &lt;/p&gt;

&lt;p&gt;Are there any good open sources in Java to detect anomaly ?&lt;/p&gt;
', 'java  anomaly-detection', NULL, '''/li'':69C,73C,79C ''/p'':38C,62C,110C,121C,131C,144C ''/ul'':80C ''3rd'':17C ''alert'':3A,26C ''amp'':77C,95C ''anomali'':1A,7B,143C ''anomaly-detect'':6B ''approach'':89C ''base'':45C,86C,88C ''brows'':66C ''call'':75C ''choos'':84C ''cluster'':85C ''contain'':59C ''coordin'':72C ''data'':41C,127C ''densiti'':87C ''design'':13C ''detect'':2A,8B,142C ''enough'':128C ''event'':57C,60C,97C ''find'':93C ''good'':136C ''gps'':71C ''help'':106C ''http'':67C ''implement'':15C ''incom'':78C ''java'':5B,140C ''keep'':31C ''kid'':33C ''learn'':109C ''li'':64C,70C,74C ''machin'':108C ''mean'':49C ''mobil'':65C ''need'':11C ''notifi'':102C ''open'':137C ''outgo'':76C ''p'':9C,39C,81C,111C,122C,132C ''parent'':29C,99C ''present'':117C ''project'':19C ''rare'':94C ''real'':24C ''real-tim'':23C ''request'':68C ''result'':119C ''safe'':34C ''score'':54C ''smartphon'':37C ''sourc'':138C ''suggest'':113C ''suspici'':96C ''system'':4A,27C ''time'':25C,44C ''time-bas'':43C ''train'':130C ''ul'':63C ''unsupervis'':47C ''visual'':120C ''week'':125C ''year'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9925, 2, '2016-01-24 04:24:00.45', 2, NULL, NULL, '&lt;p&gt;As Emre already put it nicely, you can use cron for the job.&lt;/p&gt;

&lt;p&gt;Having said that, I would suggest you to use any one of these:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Luigi&lt;/li&gt;
&lt;li&gt;Airflow&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;These are dependency management, scheduler libraries for Python, which are very easy to use and much better than vanilla cron.&lt;/p&gt;

&lt;p&gt;And as I trigger hundreds of Airflow scripts everyday in production, I can as well vouch for it''s ease of use and usefulness.&lt;/p&gt;

&lt;p&gt;So, this is a screenshot from my computer, of a very basic Airflow script running:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;a href="http://i.stack.imgur.com/PZHP2.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/PZHP2.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;So, here the tasks are dependent on each other. For example, the second task is dependant on the first one. So, if the first task fails, the second automatically fails, so that you don''t need to fire-fight with the code/database everytime.&lt;/p&gt;

&lt;p&gt;So, if you need the analytics task &lt;strong&gt;depends&lt;/strong&gt; on the scripting task, it would make sure both occur as they are intended to.&lt;/p&gt;

&lt;p&gt;In addition, you can do this:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Set the Airflow script to run every hour automatically for you&lt;/li&gt;
&lt;li&gt;Trigger emails whenever an error occurs (which is very helpful when the task runs when you aren''t monitoring them)&lt;/li&gt;
&lt;li&gt;Neat visualization of the failed, running and completed tasks.&lt;/li&gt;
&lt;/ol&gt;

&lt;hr&gt;

&lt;p&gt;Helpful reading:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://softwarerecs.stackexchange.com/a/24885/18022"&gt;My answer for DAG based script execution scheduler/planner with GUI; similar to airbnb''s airflow or BODS or Informatica&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':123C,285C ''/a/24885/18022'':265C ''/blockquote'':125C ''/li'':34C,37C,220C,242C,253C ''/ol'':254C ''/p'':15C,30C,60C,86C,103C,124C,171C,198C,206C,259C,286C ''/pzhp2.png'':110C,117C ''/strong'':182C ''/ul'':38C ''addit'':201C ''airbnb'':278C ''airflow'':36C,68C,100C,211C,280C ''alreadi'':4C ''alt'':118C ''analyt'':178C ''answer'':267C ''aren'':238C ''automat'':155C,217C ''base'':270C ''basic'':99C ''better'':56C ''blockquot'':104C ''bod'':282C ''code/database'':169C ''complet'':251C ''comput'':95C ''cron'':11C,59C ''dag'':269C ''depend'':42C,132C,142C,181C ''descript'':121C ''eas'':81C ''easi'':51C ''email'':223C ''emr'':3C ''enter'':119C ''error'':226C ''everi'':215C ''everyday'':70C ''everytim'':170C ''exampl'':137C ''execut'':272C ''fail'':152C,156C,248C ''fight'':166C ''fire'':165C ''fire-fight'':164C ''first'':145C,150C ''gui'':275C ''help'':231C,257C ''hour'':216C ''hr'':255C ''href'':107C,262C ''hundr'':66C ''i.stack.imgur.com'':109C,116C ''i.stack.imgur.com/pzhp2.png'':108C,115C ''imag'':120C ''img'':113C ''informatica'':284C ''intend'':196C ''job'':14C ''li'':32C,35C,208C,221C,243C ''librari'':45C ''luigi'':33C ''make'':189C ''manag'':43C ''monitor'':240C ''much'':55C ''neat'':244C ''need'':162C,176C ''nice'':7C ''nofollow'':112C ''occur'':192C,227C ''ol'':207C ''one'':27C,146C ''p'':1C,16C,39C,61C,87C,105C,126C,172C,199C,256C,260C ''product'':72C ''put'':5C ''python'':47C ''read'':258C ''rel'':111C ''run'':102C,214C,235C,249C ''said'':18C ''schedul'':44C ''scheduler/planner'':273C ''screenshot'':92C ''script'':69C,101C,185C,212C,271C ''second'':139C,154C ''set'':209C ''similar'':276C ''softwarerecs.stackexchange.com'':264C ''softwarerecs.stackexchange.com/a/24885/18022'':263C ''src'':114C ''strong'':180C ''suggest'':22C ''sure'':190C ''task'':130C,140C,151C,179C,186C,234C,252C ''trigger'':65C,222C ''ul'':31C ''use'':10C,25C,53C,83C,85C ''vanilla'':58C ''visual'':245C ''vouch'':77C ''well'':76C ''whenev'':224C ''would'':21C,188C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9935, 2, '2016-01-24 19:36:38.207', 1, NULL, NULL, '&lt;p&gt;The organizers might deem the &lt;em&gt;direction&lt;/em&gt; of predicted change more important than the &lt;em&gt;magnitude&lt;/em&gt;, i.e., it is more important that your prediction is high when the known value is high (and vice versa) than to get as close to the known value as possible. The measurements might be noisy anyway.&lt;/p&gt;

&lt;p&gt;One, fairly robust way of optimizing for it would be by &lt;a href="https://en.wikipedia.org/wiki/Hyperparameter_optimization" rel="nofollow"&gt;grid-searching&lt;/a&gt; for the local optimum, like in &lt;a href="http://datascience.stackexchange.com/questions/8064/how-to-use-cohens-kappa-as-the-evaluation-metric-in-gridsearchcv-in-scikit-lear"&gt;this QA&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;However, you should also take note that algorithms tweak ther internal parameters when fitting according to some &lt;a href="https://en.wikipedia.org/wiki/Loss_function" rel="nofollow"&gt;loss function&lt;/a&gt;. Some algorithms &lt;a href="https://www.google.com/search?num=20&amp;amp;q=custom+(loss+OR+cost)+function" rel="nofollow"&gt;accept custom cost functions and derivatives&lt;/a&gt;, yet &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html" rel="nofollow"&gt;some implementations&lt;/a&gt; &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html" rel="nofollow"&gt;don''t&lt;/a&gt;. &lt;a href="https://en.wikipedia.org/wiki/Information_gain_in_decision_trees" rel="nofollow"&gt;Information-theoretic measureas&lt;/a&gt; are standard in classification while MSE in regression.&lt;/p&gt;

&lt;p&gt;Theoretically, you should be able to tell your Random Forest (&lt;a href="http://www.scipy-lectures.org/advanced/mathematical_optimization/" rel="nofollow"&gt;or another algorithm accordingly&lt;/a&gt;) what the optimal split is as a function of &lt;a href="https://scholar.google.com/scholar?hl=en&amp;amp;q=pearson+correlation+derivative" rel="nofollow"&gt;pearson correlation&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':79C,93C,122C,138C,149C,159C,171C,203C,222C ''/advanced/mathematical_optimization/'':196C ''/em'':9C,19C ''/p'':56C,94C,180C,223C ''/questions/8064/how-to-use-cohens-kappa-as-the-evaluation-metric-in-gridsearchcv-in-scikit-lear'':90C ''/scholar?hl=en&amp;amp;q=pearson+correlation+derivative'':217C ''/search?num=20&amp;amp;q=custom+(loss+or+cost)+function'':129C ''/stable/modules/generated/sklearn.ensemble.gradientboostingregressor.html'':144C ''/stable/modules/generated/sklearn.ensemble.randomforestregressor.html'':154C ''/wiki/hyperparameter_optimization'':73C ''/wiki/information_gain_in_decision_trees'':164C ''/wiki/loss_function'':117C ''abl'':186C ''accept'':132C ''accord'':110C,202C ''algorithm'':103C,124C,201C ''also'':99C ''anoth'':200C ''anyway'':55C ''chang'':12C ''classif'':175C ''close'':43C ''correl'':221C ''cost'':134C ''custom'':133C ''datascience.stackexchange.com'':89C ''datascience.stackexchange.com/questions/8064/how-to-use-cohens-kappa-as-the-evaluation-metric-in-gridsearchcv-in-scikit-lear'':88C ''deem'':5C ''deriv'':137C ''direct'':8C ''em'':7C,17C ''en.wikipedia.org'':72C,116C,163C ''en.wikipedia.org/wiki/hyperparameter_optimization'':71C ''en.wikipedia.org/wiki/information_gain_in_decision_trees'':162C ''en.wikipedia.org/wiki/loss_function'':115C ''fair'':59C ''fit'':109C ''forest'':191C ''function'':121C,135C,211C ''get'':41C ''grid'':77C ''grid-search'':76C ''high'':29C,35C ''howev'':96C ''href'':70C,87C,114C,126C,141C,151C,161C,193C,214C ''i.e'':20C ''implement'':148C ''import'':14C,24C ''inform'':168C ''information-theoret'':167C ''intern'':106C ''known'':32C,46C ''like'':84C ''local'':82C ''loss'':120C ''magnitud'':18C ''measur'':51C ''measurea'':170C ''might'':4C,52C ''mse'':177C ''nofollow'':75C,119C,131C,146C,156C,166C,198C,219C ''noisi'':54C ''note'':101C ''one'':58C ''optim'':63C,206C ''optimum'':83C ''organ'':3C ''p'':1C,57C,95C,181C ''paramet'':107C ''pearson'':220C ''possibl'':49C ''predict'':11C,27C ''qa'':92C ''random'':190C ''regress'':179C ''rel'':74C,118C,130C,145C,155C,165C,197C,218C ''robust'':60C ''scholar.google.com'':216C ''scholar.google.com/scholar?hl=en&amp;amp;q=pearson+correlation+derivative'':215C ''scikit-learn.org'':143C,153C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.gradientboostingregressor.html'':142C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.randomforestregressor.html'':152C ''search'':78C ''split'':207C ''standard'':173C ''take'':100C ''tell'':188C ''theoret'':169C,182C ''ther'':105C ''tweak'':104C ''valu'':33C,47C ''versa'':38C ''vice'':37C ''way'':61C ''would'':66C ''www.google.com'':128C ''www.google.com/search?num=20&amp;amp;q=custom+(loss+or+cost)+function'':127C ''www.scipy-lectures.org'':195C ''www.scipy-lectures.org/advanced/mathematical_optimization/'':194C ''yet'':139C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9927, 2, '2016-01-24 06:36:04.6', 0, NULL, NULL, '&lt;p&gt;We can look at the source for guidance.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;How does word2vec handle the input word being in the context?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;It is skipped; for both the &lt;a href="https://github.com/piskvorky/gensim/blob/f267abf94e84484047fb7569ebacba5626bc8391/gensim/models/word2vec.py#L129" rel="nofollow"&gt;skip-gram&lt;/a&gt; and &lt;a href="https://github.com/piskvorky/gensim/blob/f267abf94e84484047fb7569ebacba5626bc8391/gensim/models/word2vec.py#L154" rel="nofollow"&gt;CBOW&lt;/a&gt; models.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;If word2vec encounters the same word multiple times in the same window, what occurs?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="https://github.com/piskvorky/gensim/blob/f267abf94e84484047fb7569ebacba5626bc8391/gensim/models/word2vec.py#L131" rel="nofollow"&gt;The relationship is strengthened&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':43C,53C,86C ''/blockquote'':25C,73C ''/p'':10C,24C,55C,72C,87C ''/piskvorky/gensim/blob/f267abf94e84484047fb7569ebacba5626bc8391/gensim/models/word2vec.py#l129'':37C ''/piskvorky/gensim/blob/f267abf94e84484047fb7569ebacba5626bc8391/gensim/models/word2vec.py#l131'':79C ''/piskvorky/gensim/blob/f267abf94e84484047fb7569ebacba5626bc8391/gensim/models/word2vec.py#l154'':49C ''blockquot'':11C,56C ''cbow'':52C ''context'':23C ''encount'':60C ''github.com'':36C,48C,78C ''github.com/piskvorky/gensim/blob/f267abf94e84484047fb7569ebacba5626bc8391/gensim/models/word2vec.py#l129'':35C ''github.com/piskvorky/gensim/blob/f267abf94e84484047fb7569ebacba5626bc8391/gensim/models/word2vec.py#l131'':77C ''github.com/piskvorky/gensim/blob/f267abf94e84484047fb7569ebacba5626bc8391/gensim/models/word2vec.py#l154'':47C ''gram'':42C ''guidanc'':9C ''handl'':16C ''href'':34C,46C,76C ''input'':18C ''look'':4C ''model'':54C ''multipl'':64C ''nofollow'':39C,51C,81C ''occur'':71C ''p'':1C,12C,26C,57C,74C ''rel'':38C,50C,80C ''relationship'':83C ''skip'':29C,41C ''skip-gram'':40C ''sourc'':7C ''strengthen'':85C ''time'':65C ''window'':69C ''word'':19C,63C ''word2vec'':15C,59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9928, 1, '2016-01-24 11:17:01.783', 1, 11, 'Mining rules from ordinal attributes without binarization', '&lt;p&gt;I am looking for papers concerning mining rules from data that has ordinal attributes. My aim is to create an efficient way to incorporate this kind of data without binarization, which requires expert knowledge of topic or an assumption about distribution of examples. &lt;/p&gt;

&lt;p&gt;Ideally, I want to come up with a technique to include these ordinal data in OPUS algorithm, which offers efficient pruning.&lt;/p&gt;

&lt;p&gt;I am aware that this question might be considered ''too broad'', I am hoping for pointing out at least a direction in research.&lt;/p&gt;
', 'data-mining  reference-request', NULL, '''/p'':58C,81C,106C ''aim'':30C ''algorithm'':76C ''assumpt'':53C ''attribut'':5A,28C ''awar'':85C ''binar'':7A,44C ''broad'':93C ''come'':64C ''concern'':20C ''consid'':91C ''creat'':33C ''data'':9B,24C,42C,73C ''data-min'':8B ''direct'':103C ''distribut'':55C ''effici'':35C,79C ''exampl'':57C ''expert'':47C ''hope'':96C ''ideal'':60C ''includ'':70C ''incorpor'':38C ''kind'':40C ''knowledg'':48C ''least'':101C ''look'':17C ''might'':89C ''mine'':1A,10B,21C ''offer'':78C ''opus'':75C ''ordin'':4A,27C,72C ''p'':14C,59C,82C ''paper'':19C ''point'':98C ''prune'':80C ''question'':88C ''refer'':12B ''reference-request'':11B ''request'':13B ''requir'':46C ''research'':105C ''rule'':2A,22C ''techniqu'':68C ''topic'':50C ''want'':62C ''way'':36C ''without'':6A,43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9930, 1, '2016-01-24 16:49:11.727', 0, 37, 'What is a "residual mapping"?', '&lt;p&gt;A recent paper by He et al. (&lt;a href="http://arxiv.org/pdf/1512.03385v1.pdf" rel="nofollow"&gt;Deep Residual Learning for Image Recognition&lt;/a&gt;, Microsoft Research, 2015) claims that they use up to 4096 layers (not neurons!).&lt;/p&gt;

&lt;p&gt;I am trying to understand the paper, but I stumble about the word "residual".&lt;/p&gt;

&lt;p&gt;Could somebody please give me an explanation / definition what residual means in this case?&lt;/p&gt;

&lt;h2&gt;Examples&lt;/h2&gt;

&lt;blockquote&gt;
  &lt;p&gt;We explicitly reformulate the layers as learning
  residual functions with reference to the layer inputs, instead
  of learning unreferenced functions.&lt;/p&gt;
  
  &lt;p&gt;[...]&lt;/p&gt;
  
  &lt;p&gt;Instead
  of hoping each few stacked layers directly fit a
  desired underlying mapping, we explicitly let these layers
  fit a residual mapping. Formally, denoting the desired
  underlying mapping as $\mathcal{H}(x)$, we let the stacked nonlinear
  layers fit another mapping of $\mathcal{F}(x) := \mathcal{H}(x)âx$. The original
  mapping is recast into $\mathcal{F}(x)+x$. We hypothesize that it
  is easier to optimize the residual mapping than to optimize
  the original, unreferenced mapping&lt;/p&gt;
&lt;/blockquote&gt;
', 'machine-learning  neuralnetwork', NULL, '''/a'':31C ''/blockquote'':186C ''/h2'':80C ''/p'':45C,61C,77C,103C,105C,185C ''/pdf/1512.03385v1.pdf'':22C ''2015'':34C ''4096'':41C ''al'':17C ''anoth'':146C ''arxiv.org'':21C ''arxiv.org/pdf/1512.03385v1.pdf'':20C ''blockquot'':81C ''case'':76C ''claim'':35C ''could'':63C ''deep'':25C ''definit'':70C ''denot'':130C ''desir'':117C,132C ''direct'':114C ''easier'':172C ''et'':16C ''exampl'':79C ''explan'':69C ''explicit'':84C,121C ''f'':150C,164C ''fit'':115C,125C,145C ''formal'':129C ''function'':91C,102C ''give'':66C ''h'':137C,153C ''h2'':78C ''hope'':109C ''href'':19C ''hypothes'':168C ''imag'':29C ''input'':97C ''instead'':98C,107C ''layer'':42C,87C,96C,113C,124C,144C ''learn'':8B,27C,89C,100C ''let'':122C,140C ''machin'':7B ''machine-learn'':6B ''map'':5A,119C,128C,134C,147C,159C,177C,184C ''mathcal'':136C,149C,152C,163C ''mean'':73C ''microsoft'':32C ''neuralnetwork'':9B ''neuron'':44C ''nofollow'':24C ''nonlinear'':143C ''optim'':174C,180C ''origin'':158C,182C ''p'':10C,46C,62C,82C,104C,106C ''paper'':13C,53C ''pleas'':65C ''recast'':161C ''recent'':12C ''recognit'':30C ''refer'':93C ''reformul'':85C ''rel'':23C ''research'':33C ''residu'':4A,26C,60C,72C,90C,127C,176C ''somebodi'':64C ''stack'':112C,142C ''stumbl'':56C ''tri'':49C ''under'':118C,133C ''understand'':51C ''unreferenc'':101C,183C ''use'':38C ''word'':59C ''x'':138C,151C,154C,156C,165C,166C ''â'':155C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9932, 2, '2016-01-24 18:58:14.58', 1, NULL, NULL, '&lt;p&gt;It''s $F(x)$; the difference between the mapping $H(x)$ and its input $x$. It''s a &lt;a href="https://en.wikipedia.org/wiki/Residual_%28numerical_analysis%29" rel="nofollow"&gt;common term in mathematics&lt;/a&gt; (&lt;a href="https://de.wikipedia.org/wiki/Residuum_%28Numerische_Mathematik%29" rel="nofollow"&gt;DE&lt;/a&gt;).&lt;/p&gt;
', NULL, NULL, '''/a'':31C,40C ''/p'':41C ''/wiki/residual_%28numerical_analysis%29'':24C ''/wiki/residuum_%28numerische_mathematik%29'':36C ''common'':27C ''de'':39C ''de.wikipedia.org'':35C ''de.wikipedia.org/wiki/residuum_%28numerische_mathematik%29'':34C ''differ'':7C ''en.wikipedia.org'':23C ''en.wikipedia.org/wiki/residual_%28numerical_analysis%29'':22C ''f'':4C ''h'':11C ''href'':21C,33C ''input'':15C ''map'':10C ''mathemat'':30C ''nofollow'':26C,38C ''p'':1C ''rel'':25C,37C ''term'':28C ''x'':5C,12C,16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9933, 2, '2016-01-24 19:02:29.723', 2, NULL, NULL, '&lt;p&gt;Use the extra features for unsupervised learning. You might enjoy Vladimir Vapnik''s take on this in the context of SVMs, which he calls privileged learning: &lt;a href="http://research.microsoft.com/apps/video/default.aspx?id=259655&amp;amp;l=i" rel="nofollow"&gt;Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':44C ''/apps/video/default.aspx?id=259655&amp;amp;l=i'':32C ''/p'':45C ''call'':25C ''context'':20C ''control'':40C ''enjoy'':11C ''extra'':4C ''featur'':5C ''href'':29C ''intellig'':37C ''knowledg'':42C ''learn'':8C,27C,35C ''might'':10C ''nofollow'':34C ''p'':1C ''privileg'':26C ''rel'':33C ''research.microsoft.com'':31C ''research.microsoft.com/apps/video/default.aspx?id=259655&amp;amp;l=i'':30C ''similar'':39C ''svms'':22C ''take'':15C ''teacher'':38C ''transfer'':43C ''unsupervis'':7C ''use'':2C ''vapnik'':13C ''vladimir'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9940, 2, '2016-01-25 11:51:20.74', 2, NULL, NULL, '&lt;p&gt;The &lt;a href="https://en.wikipedia.org/wiki/Churn_rate" rel="nofollow"&gt;churn rate&lt;/a&gt; is users the ratio of active users that stop being active after a given period.&lt;/p&gt;

&lt;p&gt;The churn rate for application user is exactly the one used for other cases, like employees. 
However, when dealing with employees it is clear whether the employee is active,
The problem with application users is the identification that they are no longer active.&lt;/p&gt;

&lt;p&gt;What you can do is to identify a period of in activity after which, the vast majority of these user never return.&lt;/p&gt;

&lt;p&gt;For example, take the users that were active during January of the previous year and were not active for 7 days since then. If 99% of them were not active for the rest of the year than 7 days are a good indication of being in active.&lt;/p&gt;

&lt;p&gt;The suitable period is usually determined by the domain (e.g., some applications are used in periods longer than 7 days) and the required confidence level (99%, 90%).
Note that you don''t want to be too safe because if you''ll decide that a user is inactive only after 2 years, this definition will prevent you from identifying users that are practicality lost and take actions to reduce the churn rate.&lt;/p&gt;
', NULL, NULL, '''/a'':12C ''/p'':28C,73C,97C,146C,219C ''/wiki/churn_rate'':7C ''2'':197C ''7'':118C,136C,166C ''90'':174C ''99'':123C,173C ''action'':213C ''activ'':18C,23C,58C,72C,86C,106C,116C,128C,145C ''applic'':34C,62C,159C ''case'':43C ''churn'':10C,31C,217C ''clear'':53C ''confid'':171C ''day'':119C,137C,167C ''deal'':48C ''decid'':189C ''definit'':200C ''determin'':153C ''domain'':156C ''e.g'':157C ''employe'':45C,50C,56C ''en.wikipedia.org'':6C ''en.wikipedia.org/wiki/churn_rate'':5C ''exact'':37C ''exampl'':100C ''given'':26C ''good'':140C ''howev'':46C ''href'':4C ''identif'':66C ''identifi'':81C,205C ''inact'':194C ''indic'':141C ''januari'':108C ''level'':172C ''like'':44C ''ll'':188C ''longer'':71C,164C ''lost'':210C ''major'':91C ''never'':95C ''nofollow'':9C ''note'':175C ''one'':39C ''p'':1C,29C,74C,98C,147C ''period'':27C,83C,150C,163C ''practic'':209C ''prevent'':202C ''previous'':111C ''problem'':60C ''rate'':11C,32C,218C ''ratio'':16C ''reduc'':215C ''rel'':8C ''requir'':170C ''rest'':131C ''return'':96C ''safe'':184C ''sinc'':120C ''stop'':21C ''suitabl'':149C ''take'':101C,212C ''use'':40C,161C ''user'':14C,19C,35C,63C,94C,103C,192C,206C ''usual'':152C ''vast'':90C ''want'':180C ''whether'':54C ''year'':112C,134C,198C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9941, 1, '2016-01-25 12:18:00.553', 2, 55, 'Perform classification on market basket analysis', '&lt;p&gt;I have the following problem that I don''t know how to solve:&lt;/p&gt;

&lt;p&gt;I have the data for different market baskets with a corresponding class. So for example I know:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Student - {beer, milk, water}&lt;/li&gt;
&lt;li&gt;Professional -  {nuts, pizza, bananas}&lt;/li&gt;
&lt;li&gt;Student - {oranges, tomatoes, beer}&lt;/li&gt;
&lt;li&gt;...&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Is there a method to create a classification model so that I can use the content of the market basket in order to determine the corresponding class (Student, Professional, ...)? Thank you!&lt;/p&gt;
', 'machine-learning  classification  methods  market-basket-analysis  association-rules', NULL, '''/li'':59C,65C,71C,73C ''/p'':33C,52C,107C ''/ul'':74C ''analysi'':6A,15B ''associ'':17B ''association-rul'':16B ''banana'':64C ''basket'':5A,14B,42C,95C ''beer'':56C,70C ''class'':46C,102C ''classif'':2A,10B,83C ''content'':91C ''correspond'':45C,101C ''creat'':81C ''data'':38C ''determin'':99C ''differ'':40C ''exampl'':49C ''follow'':23C ''know'':29C,51C ''learn'':9B ''li'':54C,60C,66C,72C ''machin'':8B ''machine-learn'':7B ''market'':4A,13B,41C,94C ''market-basket-analysi'':12B ''method'':11B,79C ''milk'':57C ''model'':84C ''nut'':62C ''orang'':68C ''order'':97C ''p'':19C,34C,75C ''perform'':1A ''pizza'':63C ''problem'':24C ''profession'':61C,104C ''rule'':18B ''solv'':32C ''student'':55C,67C,103C ''thank'':105C ''tomato'':69C ''ul'':53C ''use'':89C ''water'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9942, 2, '2016-01-25 12:23:46.063', 0, NULL, NULL, '&lt;p&gt;Using &lt;a href="http://orgmode.org/worg/org-contrib/babel/" rel="nofollow"&gt;org-babel&lt;/a&gt; could be a solution. In a single file you can have executable code blocks containing different languages. The caveat is that you have to use &lt;a href="https://www.gnu.org/software/emacs/" rel="nofollow"&gt;emacs&lt;/a&gt; and &lt;a href="http://orgmode.org/" rel="nofollow"&gt;org-mode&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I don''t have an example of Python and R together, but I did use Python and Bash for instance in the same document. Note that, I assume you won''t be able to share results generated by Python inside R block and vice versa.&lt;/p&gt;
', NULL, NULL, '''/a'':13C,47C,57C ''/p'':58C,105C ''/software/emacs/'':43C ''/worg/org-contrib/babel/'':7C ''abl'':92C ''assum'':87C ''babel'':12C ''bash'':77C ''block'':27C,101C ''caveat'':32C ''code'':26C ''contain'':28C ''could'':14C ''differ'':29C ''document'':83C ''emac'':46C ''exampl'':65C ''execut'':25C ''file'':21C ''generat'':96C ''href'':4C,40C,50C ''insid'':99C ''instanc'':79C ''languag'':30C ''mode'':56C ''nofollow'':9C,45C,53C ''note'':84C ''org'':11C,55C ''org-babel'':10C ''org-mod'':54C ''orgmode.org'':6C,51C ''orgmode.org/worg/org-contrib/babel/'':5C ''p'':1C,59C ''python'':67C,75C,98C ''r'':69C,100C ''rel'':8C,44C,52C ''result'':95C ''share'':94C ''singl'':20C ''solut'':17C ''togeth'':70C ''use'':2C,38C,74C ''versa'':104C ''vice'':103C ''won'':89C ''www.gnu.org'':42C ''www.gnu.org/software/emacs/'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9943, 1, '2016-01-25 13:57:24.367', 1, 125, 'Use spark_csv inside Jupyter and using Python', '&lt;p&gt;My ultimate goal is to use Jupyter together with Python for data analysis using Spark. The current hurdle I face is loading the external &lt;code&gt;spark_csv&lt;/code&gt; library. I am using Mac OS and Anaconda as the Python distribution.&lt;/p&gt;

&lt;p&gt;In particular, the following:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from pyspark import SparkContext
sc = SparkContext(''local'', ''pyspark'')
sqlContext = SQLContext(sc)
df = sqlContext.read.format(''com.databricks.spark.csv'').options(header=''true'').load(''file.csv'')
df.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;when invoked from Jupyter yields: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Py4JJavaError: An error occurred while calling o22.load.
: java.lang.ClassNotFoundException: Failed to find data source: com.databricks.spark.csv. Please find packages at http://spark-packages.org
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here are more details:&lt;/p&gt;

&lt;h2&gt;Setting Spark together with Jupyter&lt;/h2&gt;

&lt;p&gt;I managed to set up Spark/PySpark in Jupyter/IPython (using Python 3.x). &lt;/p&gt;

&lt;h2&gt;System initial setting&lt;/h2&gt;

&lt;p&gt;On my OS X I installed Python using Anaconda. The default version of Python I have currently installed is 3.4.4 (Anaconda 2.4.0). Note, that I also have installed also 2.x version of Python using &lt;code&gt;conda create -n python2 python=2.7&lt;/code&gt;.&lt;/p&gt;

&lt;h2&gt;Installing Spark&lt;/h2&gt;

&lt;p&gt;This is actually the simplest step; download the latest binaries into &lt;code&gt;~/Applications&lt;/code&gt; or some other directory of your choice. Next, untar the archive &lt;code&gt;tar -xzf spark-X.Y.Z-bin-hadoopX.Y.tgz&lt;/code&gt;.
For easy access to Spark create a symbolic link to the Spark:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ln -s ~/Applications/spark-X.Y.Z-bin-hadoopX.Y ~/Applications/spark
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Lastly, add the Spark symbolic link to the PATH:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;export SPARK_HOME=~/Applications/spark
export PATH=$SPARK_HOME/bin:$PATH
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You can now run Spark/PySpark locally: simply invoke &lt;code&gt;spark-shell&lt;/code&gt; or &lt;code&gt;pyspark&lt;/code&gt;.&lt;/p&gt;

&lt;h2&gt;Setting Jupyter&lt;/h2&gt;

&lt;p&gt;In order to use Spark from within a Jupyter notebook, prepand the following to &lt;code&gt;PYTHONPATH&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;export PYTHONPATH=$SPARKHOME/python/lib/py4j-0.8.2.1-src.zip:$SPARKHOME/python/:$PYTHONPATH
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Further details can be found &lt;a href="https://github.com/drorata/Jupyter-Spark-Python-setup" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;
', 'python  apache-spark', NULL, '''/a'':337C ''/applications/spark'':245C,264C ''/applications/spark-x.y.z-bin-hadoopx.y'':244C ''/code'':41C,83C,113C,190C,210C,226C,246C,270C,285C,289C,312C,321C ''/drorata/jupyter-spark-python-setup'':333C ''/h2'':127C,146C,195C,294C ''/p'':54C,60C,91C,120C,141C,191C,239C,258C,290C,313C,338C ''/pre'':84C,114C,247C,271C,322C ''2'':177C ''2.4.0'':169C ''2.7'':189C ''3'':139C ''3.4.4'':167C ''access'':229C ''actual'':199C ''add'':250C ''also'':173C,176C ''anaconda'':49C,156C,168C ''analysi'':26C ''apach'':11B ''apache-spark'':10B ''archiv'':221C ''binari'':206C ''call'':99C ''choic'':217C ''code'':38C,62C,93C,183C,208C,222C,241C,260C,281C,287C,310C,315C ''com.databricks.spark.csv'':76C,107C ''conda'':184C ''creat'':185C,232C ''csv'':3A,40C ''current'':30C,164C ''data'':25C,105C ''default'':158C ''detail'':119C,325C ''df'':74C ''df.show'':82C ''directori'':214C ''distribut'':53C ''download'':203C ''easi'':228C ''error'':96C ''export'':261C,265C,316C ''extern'':37C ''face'':33C ''fail'':102C ''file.csv'':81C ''find'':104C,109C ''follow'':59C,308C ''found'':328C ''github.com'':332C ''github.com/drorata/jupyter-spark-python-setup'':331C ''goal'':16C ''h2'':121C,142C,192C,291C ''header'':78C ''home'':263C ''home/bin'':268C ''href'':330C ''hurdl'':31C ''import'':65C ''initi'':144C ''insid'':4A ''instal'':153C,165C,175C,193C ''invok'':87C,280C ''java.lang.classnotfoundexception'':101C ''jupyt'':5A,20C,89C,126C,293C,304C ''jupyter/ipython'':136C ''last'':249C ''latest'':205C ''librari'':42C ''link'':235C,254C ''ln'':242C ''load'':35C,80C ''local'':69C,278C ''mac'':46C ''manag'':130C ''n'':186C ''next'':218C ''nofollow'':335C ''note'':170C ''notebook'':305C ''o22.load'':100C ''occur'':97C ''option'':77C ''order'':297C ''os'':47C,150C ''p'':13C,55C,85C,115C,128C,147C,196C,248C,272C,295C,323C ''packag'':110C ''particular'':57C ''path'':257C,266C,269C ''pleas'':108C ''pre'':61C,92C,240C,259C,314C ''prepand'':306C ''py4jjavaerror'':94C ''pyspark'':64C,70C,288C ''python'':8A,9B,23C,52C,138C,154C,161C,181C,188C ''python2'':187C ''pythonpath'':311C,317C,320C ''rel'':334C ''run'':276C ''sc'':67C,73C ''set'':122C,132C,145C,292C ''shell'':284C ''simplest'':201C ''simpli'':279C ''sourc'':106C ''spark'':2A,12B,28C,39C,123C,194C,231C,238C,252C,262C,267C,283C,300C ''spark-packages.org'':112C ''spark-shel'':282C ''spark-x.y.z-bin-hadoopx.y.tgz'':225C ''spark/pyspark'':134C,277C ''sparkcontext'':66C,68C ''sparkhome/python'':319C ''sparkhome/python/lib/py4j-0.8.2.1-src.zip'':318C ''sqlcontext'':71C,72C ''sqlcontext.read.format'':75C ''step'':202C ''symbol'':234C,253C ''system'':143C ''tar'':223C ''togeth'':21C,124C ''true'':79C ''ultim'':15C ''untar'':219C ''use'':1A,7A,19C,27C,45C,137C,155C,182C,299C ''version'':159C,179C ''within'':302C ''x'':140C,151C,178C ''xzf'':224C ''yield'':90C ''~/applications'':209C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9944, 2, '2016-01-25 13:58:56.627', 1, NULL, NULL, '&lt;p&gt;To calculate the (estimated) value of the monthly/weekly unique visitors based on the per day count you may use the &lt;a href="https://en.wikipedia.org/wiki/HyperLogLog" rel="nofollow"&gt;HyperLogLog&lt;/a&gt; algorihm.&lt;/p&gt;

&lt;p&gt;This will give you the historical values and you can use any time series approach to predict future counts&lt;/p&gt;
', NULL, NULL, '''/a'':30C ''/p'':32C,53C ''/wiki/hyperloglog'':26C ''algorihm'':31C ''approach'':48C ''base'':12C ''calcul'':3C ''count'':17C,52C ''day'':16C ''en.wikipedia.org'':25C ''en.wikipedia.org/wiki/hyperloglog'':24C ''estim'':5C ''futur'':51C ''give'':36C ''histor'':39C ''href'':23C ''hyperloglog'':29C ''may'':19C ''monthly/weekly'':9C ''nofollow'':28C ''p'':1C,33C ''per'':15C ''predict'':50C ''rel'':27C ''seri'':47C ''time'':46C ''uniqu'':10C ''use'':20C,44C ''valu'':6C,40C ''visitor'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9947, 2, '2016-01-25 16:30:28.977', 1, NULL, NULL, '&lt;p&gt;You are looking into s classification task where text in the left (like Student etc.) is the target class, while list of shoppings is an almost ready-to-go list of features - variables on which you will be learning a classification model.
While you should looking into classification algorithm family in general, really most basic algorithms you may use are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Naive_Bayes_classifier" rel="nofollow"&gt;Naive Bayes classifier&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Support_vector_machine" rel="nofollow"&gt;Support Vector Machines classifier&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Implementation of the first is so damn simple, that you may code it in mostly any language straight away to just blow the dust of coding skills, while more sophisticated algorithms usually have a number of implementations, which you should be looking based on your language of choice.&lt;/p&gt;
', NULL, NULL, '''/a'':76C,90C ''/li'':77C,91C ''/p'':63C,142C ''/ul'':92C ''/wiki/naive_bayes_classifier'':70C ''/wiki/support_vector_machine'':83C ''algorithm'':51C,58C,124C ''almost'':27C ''away'':112C ''base'':136C ''basic'':57C ''bay'':74C ''blow'':115C ''choic'':141C ''class'':20C ''classif'':7C,43C,50C ''classifi'':75C,89C ''code'':105C,119C ''damn'':100C ''dust'':117C ''en.wikipedia.org'':69C,82C ''en.wikipedia.org/wiki/naive_bayes_classifier'':68C ''en.wikipedia.org/wiki/support_vector_machine'':81C ''etc'':16C ''famili'':52C ''featur'':34C ''first'':97C ''general'':54C ''go'':31C ''href'':67C,80C ''implement'':94C,130C ''languag'':110C,139C ''learn'':41C ''left'':13C ''li'':65C,78C ''like'':14C ''list'':22C,32C ''look'':4C,48C,135C ''machin'':88C ''may'':60C,104C ''model'':44C ''most'':108C ''naiv'':73C ''nofollow'':72C,85C ''number'':128C ''p'':1C,93C ''readi'':29C ''ready-to-go'':28C ''realli'':55C ''rel'':71C,84C ''shop'':24C ''simpl'':101C ''skill'':120C ''sophist'':123C ''straight'':111C ''student'':15C ''support'':86C ''target'':19C ''task'':8C ''text'':10C ''ul'':64C ''use'':61C ''usual'':125C ''variabl'':35C ''vector'':87C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9948, 1, '2016-01-25 17:33:24.343', 0, 37, 'Decompose annual time series in R', '&lt;p&gt;I have a time series. Data points are available for each year from 1966 to 2000. Using R, I want to decompose this time series into trend, seasonal and random components. When I run the decompose command, I get the error "time series has no or less than 2 periods". Since my data is annual I have specified a frequency of 1. What am I doing wrong? &lt;/p&gt;

&lt;p&gt;Here is the R code that I am using:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dat=c(37.2,37,37.4,37.5,37.7,37.7,37.4,37.2,37.3,37.2,36.9,36.7,36.7,36.5,
      36.3,35.9, 35.8,35.9,36,35.7,35.6, 35.2, 34.8, 35.3,35.6,35.6, 35.6,
      35.9,36,35.7, 35.7, 35.5, 35.6, 36.3, 36.5)

whts &amp;lt;- ts(dat, frequency = 1, start=1966, end=2000)  
is.ts(whts)  
plot.ts(whts)  
whtimeseriescomponents &amp;lt;- decompose(whts)  
&lt;/code&gt;&lt;/pre&gt;
', 'r  time-series', NULL, '''/code'':148C ''/p'':79C,90C ''/pre'':149C ''1'':73C,135C ''1966'':25C,137C ''2'':60C ''2000'':27C,139C ''34.8'':117C ''35.2'':116C ''35.3'':118C ''35.5'':126C ''35.6'':115C,119C,120C,121C,127C ''35.7'':114C,124C,125C ''35.8'':111C ''35.9'':110C,112C,122C ''36'':113C,123C ''36.3'':109C,128C ''36.5'':108C,129C ''36.7'':106C,107C ''36.9'':105C ''37'':96C ''37.2'':95C,102C,104C ''37.3'':103C ''37.4'':97C,101C ''37.5'':98C ''37.7'':99C,100C ''annual'':2A,66C ''avail'':20C ''c'':94C ''code'':85C,92C ''command'':48C ''compon'':42C ''dat'':93C,133C ''data'':17C,64C ''decompos'':1A,33C,47C,146C ''end'':138C ''error'':52C ''frequenc'':71C,134C ''get'':50C ''is.ts'':140C ''less'':58C ''lt'':131C,145C ''p'':11C,80C ''period'':61C ''plot.ts'':142C ''point'':18C ''pre'':91C ''r'':6A,7B,29C,84C ''random'':41C ''run'':45C ''season'':39C ''seri'':4A,10B,16C,36C,54C ''sinc'':62C ''specifi'':69C ''start'':136C ''time'':3A,9B,15C,35C,53C ''time-seri'':8B ''trend'':38C ''ts'':132C ''use'':28C,89C ''want'':31C ''whtimeseriescompon'':144C ''whts'':130C,141C,143C,147C ''wrong'':78C ''year'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9949, 1, '2016-01-25 18:41:09.973', 2, 62, 'What model should I use to find a common pattern for a specific user group based on the other dimensions?', '&lt;p&gt;I have a big .CSV database of 25k users with various attributes of the last user''s activity and events during the past 6 weeks&lt;/p&gt;

&lt;p&gt;This is an example of the data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;username        (B)      (C)             (D)        (E)  
nicole          524      329             203        787
asteria         197      186             286        120 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to create a common behavior pattern based on the values of the attributes of each user and run an algorithm to find a common pattern that defines this group''s behavior and to find out if there is any correlation in the dimensions values and which dimension define this list of users.
I am fully aware that correlation does not necessarily equal causation.&lt;/p&gt;

&lt;p&gt;Now I see several challenges in front of me and would greatly appreciate some input from others, or some good resources to find further information.&lt;/p&gt;

&lt;p&gt;What is the model for this problem ? 
What kind of Algorithm is the best to deal with this situation?
What is the tools that you recommend to use of the project ?&lt;/p&gt;

&lt;p&gt;Any ideas would be great.&lt;/p&gt;
', 'machine-learning  r  data-mining  bigdata  predictive-modeling', NULL, '''/code'':85C ''/p'':58C,67C,153C,180C,213C,220C ''/pre'':86C ''120'':84C ''186'':82C ''197'':81C ''203'':78C ''25k'':40C ''286'':83C ''329'':77C ''524'':76C ''6'':56C ''787'':79C ''activ'':50C ''algorithm'':109C,192C ''appreci'':167C ''asteria'':80C ''attribut'':44C,102C ''awar'':145C ''b'':71C ''base'':16A,96C ''behavior'':94C,120C ''best'':195C ''big'':36C ''bigdata'':28B ''c'':72C ''causat'':152C ''challeng'':159C ''code'':69C ''common'':9A,93C,113C ''correl'':129C,147C ''creat'':91C ''csv'':37C ''d'':73C ''data'':26B,66C ''data-min'':25B ''databas'':38C ''deal'':197C ''defin'':116C,137C ''dimens'':20A,132C,136C ''e'':74C ''equal'':151C ''event'':52C ''exampl'':63C ''find'':7A,111C,123C,177C ''front'':161C ''fulli'':144C ''good'':174C ''great'':166C,219C ''group'':15A,118C ''idea'':216C ''inform'':179C ''input'':169C ''kind'':190C ''last'':47C ''learn'':23B ''list'':139C ''machin'':22B ''machine-learn'':21B ''mine'':27B ''model'':2A,31B,185C ''necessarili'':150C ''nicol'':75C ''other'':171C ''p'':32C,59C,87C,154C,181C,214C ''past'':55C ''pattern'':10A,95C,114C ''pre'':68C ''predict'':30B ''predictive-model'':29B ''problem'':188C ''project'':212C ''r'':24B ''recommend'':207C ''resourc'':175C ''run'':107C ''see'':157C ''sever'':158C ''situat'':200C ''specif'':13A ''tool'':204C ''use'':5A,209C ''user'':14A,41C,48C,105C,141C ''usernam'':70C ''valu'':99C,133C ''various'':43C ''want'':89C ''week'':57C ''would'':165C,217C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9950, 1, '2016-01-25 18:41:24.083', 2, 82, 'NLP : Is Gazetteer a cheat', '&lt;p&gt;In NLP there is a concept of &lt;code&gt;Gazetteer&lt;/code&gt; which can be quite useful for creating annotations. As far as i understand, &lt;/p&gt;

&lt;p&gt;&lt;code&gt;A gazetteer consists of a set of lists containing names of entities such as cities, organisations, days of the week, etc. These lists are used to ï¬nd occurrences of these names in text, e.g. for the task of named entity recognition.&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;So it is essentially a lookup. Isn''t this kind of a cheat? If we use a &lt;code&gt;Gazetteer&lt;/code&gt; for detecting named entities, then there is not much &lt;code&gt;Natural Language Processing&lt;/code&gt; going on. Ideally, i would want to detect named entities using &lt;code&gt;NLP&lt;/code&gt; techniques. Otherwise how is it any better than a regex pattern matcher.&lt;/p&gt;

&lt;p&gt;Does that make sense?&lt;/p&gt;
', 'nlp  named-entity-recognition', NULL, '''/code'':21C,80C,102C,116C,130C ''/p'':35C,81C,143C,149C ''annot'':29C ''better'':137C ''cheat'':5A,95C ''citi'':52C ''code'':19C,37C,100C,112C,128C ''concept'':17C ''consist'':40C ''contain'':46C ''creat'':28C ''day'':54C ''detect'':104C,124C ''e.g'':72C ''entiti'':9B,49C,78C,106C,126C ''essenti'':86C ''etc'':58C ''far'':31C ''gazett'':3A,20C,39C,101C ''go'':117C ''ideal'':119C ''isn'':89C ''kind'':92C ''languag'':114C ''list'':45C,60C ''lookup'':88C ''make'':147C ''matcher'':142C ''much'':111C ''name'':8B,47C,69C,77C,105C,125C ''named-entity-recognit'':7B ''natur'':113C ''nd'':65C ''nlp'':1A,6B,13C,129C ''occurr'':66C ''organis'':53C ''otherwis'':132C ''p'':11C,36C,82C,144C ''pattern'':141C ''process'':115C ''quit'':25C ''recognit'':10B,79C ''regex'':140C ''sens'':148C ''set'':43C ''task'':75C ''techniqu'':131C ''text'':71C ''understand'':34C ''use'':26C,62C,98C,127C ''want'':122C ''week'':57C ''would'':121C ''ï'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9969, 1, '2016-01-26 12:34:54.873', 3, 59, 'Bayes Classification', '&lt;blockquote&gt;
  &lt;p&gt;In theory we would always like to predict qualitative responses using
  the Bayes classifier. But for real data, we do not know the
  conditional distribution of Y given X, and so computing the Bayes
  classifier is impossible. Therefore, the Bayes classifier serves as an
  unattainable gold standard against which to compare other methods.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;ul&gt;
&lt;li&gt;In a real life situation why we are not able to calculate the &lt;strong&gt;Bayes Classifier&lt;/strong&gt; ? &lt;/li&gt;
&lt;li&gt;Why can''t we calculate the conditional distribution of Y given X ?&lt;/li&gt;
&lt;li&gt;Can some one explanation with an example?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Any help is appreciated. &lt;/p&gt;
', 'classification  logistic-regression  supervised-learning  bayesian-networks', NULL, '''/blockquote'':69C ''/li'':89C,103C,112C ''/p'':68C,119C ''/strong'':88C ''/ul'':113C ''abl'':81C ''alway'':19C ''appreci'':118C ''bay'':1A,27C,48C,54C,86C ''bayesian'':11B ''bayesian-network'':10B ''blockquot'':13C ''calcul'':83C,95C ''classif'':2A,3B ''classifi'':28C,49C,55C,87C ''compar'':65C ''comput'':46C ''condit'':38C,97C ''data'':32C ''distribut'':39C,98C ''exampl'':111C ''explan'':108C ''given'':42C,101C ''gold'':60C ''help'':116C ''imposs'':51C ''know'':36C ''learn'':9B ''li'':71C,90C,104C ''life'':75C ''like'':20C ''logist'':5B ''logistic-regress'':4B ''method'':67C ''network'':12B ''one'':107C ''p'':14C,114C ''predict'':22C ''qualit'':23C ''real'':31C,74C ''regress'':6B ''respons'':24C ''serv'':56C ''situat'':76C ''standard'':61C ''strong'':85C ''supervis'':8B ''supervised-learn'':7B ''theori'':16C ''therefor'':52C ''ul'':70C ''unattain'':59C ''use'':25C ''would'':18C ''x'':43C,102C ''y'':41C,100C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9951, 2, '2016-01-25 18:43:14.507', 1, NULL, NULL, '&lt;p&gt;Seasonal decomposition doesn''t make sense in this situation. You''re sampling frequency needs to be greater than 1 for this to work!
I know this changes your model, but just for the sake of example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; dat=c(37.2,37,37.4,37.5,37.7,37.7,37.4,37.2,37.3,37.2,36.9,36.7,36.7,36.5, 36.3,35.9, 35.8,35.9,36,35.7,35.6, 35.2, 34.8, 35.3,35.6,35.6, 35.6, 35.9,36,35.7, 35.7, 35.5, 35.6, 36.3, 36.5)
&amp;gt; whts &amp;lt;- ts(dat, frequency=2, start=1966, end=2000)
&amp;gt; decompose(whts)
$x
Time Series:
Start = c(1966, 1) 
End = c(2000, 1) 
Frequency = 2 
 [1] 37.2 37.0 37.4 37.5 37.7 37.7 37.4 37.2 37.3 37.2 36.9 36.7 36.7 36.5 36.3 35.9 35.8 35.9 36.0 35.7 35.6
[22] 35.2 34.8 35.3 35.6 35.6 35.6 35.9 36.0 35.7 35.7 35.5 35.6 36.3 36.5 37.2 37.0 37.4 37.5 37.7 37.7 37.4
[43] 37.2 37.3 37.2 36.9 36.7 36.7 36.5 36.3 35.9 35.8 35.9 36.0 35.7 35.6 35.2 34.8 35.3 35.6 35.6 35.6 35.9
[64] 36.0 35.7 35.7 35.5 35.6 36.3

$seasonal
Time Series:
Start = c(1966, 1) 
End = c(2000, 1) 
Frequency = 2 
 [1] -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266
 [9] -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266
[17] -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266
[25] -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266
[33] -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266
[41] -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266
[49] -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266
[57] -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266  0.007141266
[65] -0.007141266  0.007141266 -0.007141266  0.007141266 -0.007141266

$trend
Time Series:
Start = c(1966, 1) 
End = c(2000, 1) 
Frequency = 2 
 [1]     NA 37.150 37.325 37.525 37.650 37.625 37.425 37.275 37.250 37.150 36.925 36.750 36.650 36.500 36.250
[16] 35.975 35.850 35.900 35.900 35.750 35.525 35.200 35.025 35.250 35.525 35.600 35.675 35.850 35.900 35.775
[31] 35.650 35.575 35.750 36.175 36.625 36.975 37.150 37.325 37.525 37.650 37.625 37.425 37.275 37.250 37.150
[46] 36.925 36.750 36.650 36.500 36.250 35.975 35.850 35.900 35.900 35.750 35.525 35.200 35.025 35.250 35.525
[61] 35.600 35.675 35.850 35.900 35.775 35.650 35.575 35.750     NA

$random
Time Series:
Start = c(1966, 1) 
End = c(2000, 1) 
Frequency = 2 
 [1]           NA -0.157141266  0.082141266 -0.032141266  0.057141266  0.067858734 -0.017858734 -0.082141266
 [9]  0.057141266  0.042858734 -0.017858734 -0.057141266  0.057141266 -0.007141266  0.057141266 -0.082141266
[17] -0.042858734 -0.007141266  0.107141266 -0.057141266  0.082141266 -0.007141266 -0.217858734  0.042858734
[25]  0.082141266 -0.007141266 -0.067858734  0.042858734  0.107141266 -0.082141266  0.057141266 -0.082141266
[33] -0.142858734  0.117858734 -0.117858734  0.217858734 -0.142858734  0.067858734 -0.017858734  0.042858734
[41]  0.082141266 -0.032141266 -0.067858734  0.042858734  0.057141266 -0.032141266 -0.042858734  0.042858734
[49]  0.007141266  0.042858734 -0.067858734 -0.057141266  0.007141266  0.092858734 -0.042858734  0.067858734
[57]  0.007141266 -0.232141266  0.057141266  0.067858734  0.007141266 -0.082141266  0.057141266  0.092858734
[65] -0.067858734  0.042858734 -0.067858734 -0.157141266           NA

$figure
[1] -0.007141266  0.007141266

$type
[1] "additive"

attr(,"class")
[1] "decomposed.ts"
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''-0.007141266'':193C,195C,197C,199C,202C,204C,206C,208C,211C,213C,215C,217C,220C,222C,224C,226C,229C,231C,233C,235C,238C,240C,242C,244C,247C,249C,251C,253C,256C,258C,260C,262C,265C,267C,269C,385C,390C,394C,399C,450C ''-0.017858734'':377C,382C,413C ''-0.032141266'':374C,417C,421C ''-0.042858734'':389C,422C,431C ''-0.057141266'':383C,392C,428C ''-0.067858734'':400C,418C,427C,443C,445C ''-0.082141266'':378C,387C,403C,405C,439C ''-0.117858734'':409C ''-0.142858734'':407C,411C ''-0.157141266'':372C,446C ''-0.217858734'':395C ''-0.232141266'':435C ''/code'':459C ''/p'':38C ''/pre'':460C ''0.007141266'':194C,196C,198C,200C,203C,205C,207C,209C,212C,214C,216C,218C,221C,223C,225C,227C,230C,232C,234C,236C,239C,241C,243C,245C,248C,250C,252C,254C,257C,259C,261C,263C,266C,268C,425C,429C,434C,438C,451C ''0.042858734'':381C,396C,401C,414C,419C,423C,426C,444C ''0.057141266'':375C,380C,384C,386C,404C,420C,436C,440C ''0.067858734'':376C,412C,432C,437C ''0.082141266'':373C,393C,398C,416C ''0.092858734'':430C,441C ''0.107141266'':391C,402C ''0.117858734'':408C ''0.217858734'':410C ''1'':20C,99C,103C,106C,185C,189C,192C,276C,280C,283C,363C,367C,370C,449C,453C,457C ''16'':299C ''17'':210C,388C ''1966'':87C,98C,184C,275C,362C ''2'':85C,105C,191C,282C,369C ''2000'':89C,102C,188C,279C,366C ''22'':128C ''25'':219C,397C ''31'':315C ''33'':228C,406C ''34.8'':66C,130C,166C ''35.025'':307C,344C ''35.2'':65C,129C,165C ''35.200'':306C,343C ''35.250'':308C,345C ''35.3'':67C,131C,167C ''35.5'':75C,139C,176C ''35.525'':305C,309C,342C,346C ''35.575'':317C,354C ''35.6'':64C,68C,69C,70C,76C,127C,132C,133C,134C,140C,164C,168C,169C,170C,177C ''35.600'':310C,348C ''35.650'':316C,353C ''35.675'':311C,349C ''35.7'':63C,73C,74C,126C,137C,138C,163C,174C,175C ''35.750'':304C,318C,341C,355C ''35.775'':314C,352C ''35.8'':60C,123C,160C ''35.850'':301C,312C,338C,350C ''35.9'':59C,61C,71C,122C,124C,135C,159C,161C,171C ''35.900'':302C,303C,313C,339C,340C,351C ''35.975'':300C,337C ''36'':62C,72C ''36.0'':125C,136C,162C,173C ''36.175'':319C ''36.250'':298C,336C ''36.3'':58C,77C,121C,141C,158C,178C ''36.5'':57C,78C,120C,142C,157C ''36.500'':297C,335C ''36.625'':320C ''36.650'':296C,334C ''36.7'':55C,56C,118C,119C,155C,156C ''36.750'':295C,333C ''36.9'':54C,117C,154C ''36.925'':294C,332C ''36.975'':321C ''37'':45C ''37.0'':108C,144C ''37.150'':285C,293C,322C,330C ''37.2'':44C,51C,53C,107C,114C,116C,143C,151C,153C ''37.250'':292C,329C ''37.275'':291C,328C ''37.3'':52C,115C,152C ''37.325'':286C,323C ''37.4'':46C,50C,109C,113C,145C,149C ''37.425'':290C,327C ''37.5'':47C,110C,146C ''37.525'':287C,324C ''37.625'':289C,326C ''37.650'':288C,325C ''37.7'':48C,49C,111C,112C,147C,148C ''41'':237C,415C ''43'':150C ''46'':331C ''49'':246C,424C ''57'':255C,433C ''61'':347C ''64'':172C ''65'':264C,442C ''9'':201C,379C ''addit'':454C ''attr'':455C ''c'':43C,97C,101C,183C,187C,274C,278C,361C,365C ''chang'':28C ''class'':456C ''code'':40C ''dat'':42C,83C ''decompos'':91C ''decomposed.ts'':458C ''decomposit'':3C ''doesn'':4C ''end'':88C,100C,186C,277C,364C ''exampl'':37C ''figur'':448C ''frequenc'':14C,84C,104C,190C,281C,368C ''greater'':18C ''gt'':41C,79C,90C ''know'':26C ''lt'':81C ''make'':6C ''model'':30C ''na'':284C,356C,371C,447C ''need'':15C ''p'':1C ''pre'':39C ''random'':357C ''re'':12C ''sake'':35C ''sampl'':13C ''season'':2C,179C ''sens'':7C ''seri'':95C,181C,272C,359C ''situat'':10C ''start'':86C,96C,182C,273C,360C ''time'':94C,180C,271C,358C ''trend'':270C ''ts'':82C ''type'':452C ''whts'':80C,92C ''work'':24C ''x'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9953, 2, '2016-01-25 20:11:41.21', 2, NULL, NULL, '&lt;p&gt;Two small amendments to the previous complete answer.&lt;/p&gt;

&lt;p&gt;You may classify the churn based on cause, this is typically &lt;em&gt;active&lt;/em&gt; and &lt;em&gt;passive&lt;/em&gt; churn. In your case this will be &lt;strong&gt;churn due to inactivity&lt;/strong&gt; and &lt;strong&gt;churn due to lock account by the administrator&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;To handle the uncertainty of the inactivity churn, you may introduce a measure called &lt;strong&gt;dechurn&lt;/strong&gt; (probably not a stable term), measuring the reactivation of a churned user.   &lt;/p&gt;
', NULL, NULL, '''/em'':25C,29C ''/p'':10C,54C,85C ''/strong'':42C,53C,72C ''account'':49C ''activ'':24C ''administr'':52C ''amend'':4C ''answer'':9C ''base'':17C ''call'':69C ''case'':33C ''caus'':19C ''churn'':16C,30C,38C,45C,63C,83C ''classifi'':14C ''complet'':8C ''dechurn'':71C ''due'':39C,46C ''em'':23C,27C ''handl'':57C ''inact'':41C,62C ''introduc'':66C ''lock'':48C ''may'':13C,65C ''measur'':68C,78C ''p'':1C,11C,55C ''passiv'':28C ''previous'':7C ''probabl'':73C ''reactiv'':80C ''small'':3C ''stabl'':76C ''strong'':37C,44C,70C ''term'':77C ''two'':2C ''typic'':22C ''uncertainti'':59C ''user'':84C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9954, 1, '2016-01-25 21:00:28.35', 1, 37, 'Visualising a big number of polynomials as atlas of graphs?', '&lt;p&gt;Suppose you have a big number of polynomials such as &lt;code&gt;xyz+xy+z&lt;/code&gt; where &lt;code&gt;xyz&lt;/code&gt;, &lt;code&gt;xy&lt;/code&gt; and &lt;code&gt;z&lt;/code&gt; are monomials. You wanna visualise the polynomials together something such as atlas below and show how they are related graphically.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/Vs1KS.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Vs1KS.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;where the example is from &lt;a href="https://networkx.github.io/documentation/latest/examples/drawing/atlas.html" rel="nofollow"&gt;here&lt;/a&gt; where the edges could correspond to sum while the vertices could be monomials. It is also possible to do it so that each variable in monomial has their own vertex while two different kinds of edges are drawn: edges inside monomials and distinct edges to connect monomials of the same polynomial. Other visualisations are possible as well.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How can you visualise a big number of polynomials graphically?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;P.s. Aspiring &lt;a href="http://chat.stackexchange.com/transcript/message/27092695#27092695"&gt;discussion in Mathematica SE&lt;/a&gt;.&lt;/em&gt;&lt;/p&gt;
', 'visualization  graphs  infographics', NULL, '''/a'':80C,96C,182C ''/code'':29C,33C,36C,40C ''/documentation/latest/examples/drawing/atlas.html'':92C ''/em'':183C ''/p'':61C,81C,154C,168C,184C ''/strong'':167C ''/transcript/message/27092695#27092695'':177C ''/vs1ks.png'':67C,74C ''also'':112C ''alt'':75C ''aspir'':172C ''atlas'':8A,52C ''big'':3A,19C,162C ''chat.stackexchange.com'':176C ''chat.stackexchange.com/transcript/message/27092695#27092695'':175C ''code'':25C,31C,34C,38C ''connect'':142C ''correspond'':101C ''could'':100C,107C ''descript'':78C ''differ'':129C ''discuss'':178C ''distinct'':139C ''drawn'':134C ''edg'':99C,132C,135C,140C ''em'':170C ''enter'':76C ''exampl'':85C ''graph'':10A,12B ''graphic'':60C,166C ''href'':64C,89C,174C ''i.stack.imgur.com'':66C,73C ''i.stack.imgur.com/vs1ks.png'':65C,72C ''imag'':77C ''img'':70C ''infograph'':13B ''insid'':136C ''kind'':130C ''mathematica'':180C ''monomi'':42C,109C,122C,137C,143C ''networkx.github.io'':91C ''networkx.github.io/documentation/latest/examples/drawing/atlas.html'':90C ''nofollow'':69C,94C ''number'':4A,20C,163C ''p'':14C,62C,82C,155C,169C ''p.s'':171C ''polynomi'':6A,22C,47C,147C,165C ''possibl'':113C,151C ''rel'':68C,93C ''relat'':59C ''se'':181C ''show'':55C ''someth'':49C ''src'':71C ''strong'':156C ''sum'':103C ''suppos'':15C ''togeth'':48C ''two'':128C ''variabl'':120C ''vertex'':126C ''vertic'':106C ''visual'':11B ''visualis'':1A,45C,149C,160C ''wanna'':44C ''well'':153C ''xy'':27C,35C ''xyz'':26C,32C ''z'':28C,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9955, 1, '2016-01-25 21:13:50.57', 1, 33, 'Which machine learning/AI method is this problem related to?', '&lt;p&gt;I am new to machine learning and AI, and I am working on a problem in which I need to clean a table (database) of text/words. For example I should delete words like and, the, etc. , or replace words like COMP with company or substitute other acronyms with the suitable words. I wanted to find some resources on the techniques proposed for this purpose and to find what else I can do to better clean the table. Thanks for your help.&lt;/p&gt;
', 'machine-learning  data-mining  text-mining', NULL, '''/p'':100C ''acronym'':65C ''ai'':26C ''better'':92C ''clean'':39C,93C ''comp'':59C ''compani'':61C ''data'':13B ''data-min'':12B ''databas'':42C ''delet'':49C ''els'':87C ''etc'':54C ''exampl'':46C ''find'':73C,85C ''help'':99C ''learn'':11B,24C ''learning/ai'':3A ''like'':51C,58C ''machin'':2A,10B,23C ''machine-learn'':9B ''method'':4A ''mine'':14B,17B ''need'':37C ''new'':21C ''p'':18C ''problem'':7A,33C ''propos'':79C ''purpos'':82C ''relat'':8A ''replac'':56C ''resourc'':75C ''substitut'':63C ''suitabl'':68C ''tabl'':41C,95C ''techniqu'':78C ''text'':16B ''text-min'':15B ''text/words'':44C ''thank'':96C ''want'':71C ''word'':50C,57C,69C ''work'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9956, 1, '2016-01-25 21:47:26.747', 0, 16, 'Support vector machines used for prime factorisation with polynomials?', '&lt;p&gt;Suppose a polynomial. Each monomial can be linked to other monomials in a number of ways. The distribution of degrees, centrality measures and order investigations with different bases like &lt;a href="http://stackoverflow.com/questions/31734234/how-to-analyse-a-sparse-adjacency-matrix"&gt;here&lt;/a&gt; may speed up the factorisation and selecting the most important term to factor out. &lt;/p&gt;

&lt;p&gt;Support vector machines are new to me and I would like to find out how they are used for prime factorisation, please mention reference requests. Thank you in advance.&lt;/p&gt;
', 'svm  reference-request', NULL, '''/a'':50C ''/p'':65C,96C ''/questions/31734234/how-to-analyse-a-sparse-adjacency-matrix'':48C ''advanc'':95C ''base'':42C ''central'':35C ''degre'':34C ''differ'':41C ''distribut'':32C ''factor'':63C ''factoris'':7A,55C,87C ''find'':79C ''href'':45C ''import'':60C ''investig'':39C ''like'':43C,77C ''link'':22C ''machin'':3A,69C ''may'':51C ''measur'':36C ''mention'':89C ''monomi'':19C,25C ''new'':71C ''number'':28C ''order'':38C ''p'':14C,66C ''pleas'':88C ''polynomi'':9A,17C ''prime'':6A,86C ''refer'':12B,90C ''reference-request'':11B ''request'':13B,91C ''select'':57C ''speed'':52C ''stackoverflow.com'':47C ''stackoverflow.com/questions/31734234/how-to-analyse-a-sparse-adjacency-matrix'':46C ''support'':1A,67C ''suppos'':15C ''svm'':10B ''term'':61C ''thank'':92C ''use'':4A,84C ''vector'':2A,68C ''way'':30C ''would'':76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9957, 2, '2016-01-25 22:28:44.19', 2, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;For example I should delete words like and, the, etc. ...&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;In natural language processing, this first task is called &lt;a href="https://en.wikipedia.org/wiki/Stop_words" rel="nofollow"&gt;stop word&lt;/a&gt; removal. You can identify them by looking at the &lt;a href="https://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;words'' frequency over the documents&lt;/a&gt;; uninformative words appear very often.&lt;/p&gt;
', NULL, NULL, '''/a'':34C,56C ''/blockquote'':14C ''/p'':13C,62C ''/wiki/stop_words'':29C ''/wiki/tf%e2%80%93idf'':48C ''appear'':59C ''blockquot'':1C ''call'':24C ''delet'':7C ''document'':55C ''en.wikipedia.org'':28C,47C ''en.wikipedia.org/wiki/stop_words'':27C ''en.wikipedia.org/wiki/tf%e2%80%93idf'':46C ''etc'':12C ''exampl'':4C ''first'':21C ''frequenc'':52C ''href'':26C,45C ''identifi'':38C ''languag'':18C ''like'':9C ''look'':41C ''natur'':17C ''nofollow'':31C,50C ''often'':61C ''p'':2C,15C ''process'':19C ''rel'':30C,49C ''remov'':35C ''stop'':32C ''task'':22C ''uninform'':57C ''word'':8C,33C,51C,58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10030, 1, '2016-01-30 11:18:52.89', 0, 80, 'Algorithm for classification of words into given categories', '&lt;p&gt;I''m working with textual data from medical field. I have a list of words and I want to build an algorithm that can classify each word into one or more given categories, like &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Medicine_Name&lt;/strong&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Disease_Name&lt;/strong&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Body_Part&lt;/strong&gt; &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;etc. &lt;/p&gt;

&lt;p&gt;Please give me some ideas to implement it, specifically how to prepare the training example and implement the training phase. &lt;/p&gt;
', 'machine-learning  nlp  text-mining  named-entity-recognition', NULL, '''/li'':62C,68C,74C ''/p'':55C,78C,100C ''/strong'':61C,67C,73C ''/ul'':75C ''algorithm'':1A,42C ''bodi'':71C ''build'':40C ''categori'':8A,53C ''classif'':3A ''classifi'':45C ''data'':26C ''diseas'':65C ''entiti'':18B ''etc'':77C ''exampl'':94C ''field'':29C ''give'':81C ''given'':7A,52C ''idea'':84C ''implement'':86C,96C ''learn'':11B ''li'':57C,63C,69C ''like'':54C ''list'':33C ''m'':22C ''machin'':10B ''machine-learn'':9B ''medic'':28C ''medicin'':59C ''mine'':15B ''name'':17B,60C,66C ''named-entity-recognit'':16B ''nlp'':12B ''one'':49C ''p'':20C,76C,79C ''part'':72C ''phase'':99C ''pleas'':80C ''prepar'':91C ''recognit'':19B ''specif'':88C ''strong'':58C,64C,70C ''text'':14B ''text-min'':13B ''textual'':25C ''train'':93C,98C ''ul'':56C ''want'':38C ''word'':5A,35C,47C ''work'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9963, 1, '2016-01-26 02:57:59.407', 2, 41, 'Predicting or patron find of a binary variable over time', '&lt;p&gt;I''m new to ML and trying to find some practical use to it I encountered with the chance of saving the connections and disconnections (the binary variable) of a bunch of users like this: &lt;/p&gt;

&lt;p&gt;"User A connected at 10:02:33&lt;/p&gt;

&lt;p&gt;User A discconected at 10:05:02"&lt;/p&gt;

&lt;p&gt;I was wondering how could I determine whenever the user is going to connect again, analyzing his pasts connections, disconnections and time online.&lt;/p&gt;

&lt;p&gt;My concers are the following:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Would this be posible?&lt;/li&gt;
&lt;li&gt;What''s the best method for this?&lt;/li&gt;
&lt;li&gt;How much samples per user would I need?&lt;/li&gt;
&lt;li&gt;What would be the best way to structure the data, and besides the user identifier, action(connect/disconnect) and time the action take place what other info would be useful?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;An just as an extra question: Would it be possible to add more data in real-time to improve the algorithm prediction? How?&lt;/p&gt;
', 'machine-learning  dataset  predictive-modeling  binary', NULL, '''/li'':112C,121C,131C,162C ''/p'':55C,64C,73C,98C,105C,189C ''/ul'':163C ''02'':62C,72C ''05'':71C ''10'':61C,70C ''33'':63C ''action'':148C,153C ''add'':176C ''algorithm'':186C ''analyz'':90C ''besid'':144C ''best'':117C,137C ''binari'':7A,18B,46C ''bunch'':50C ''chanc'':38C ''concer'':101C ''connect'':42C,59C,88C,93C ''connect/disconnect'':149C ''could'':79C ''data'':142C,178C ''dataset'':14B ''determin'':81C ''discconect'':68C ''disconnect'':44C,94C ''encount'':35C ''extra'':169C ''find'':4A,28C ''follow'':104C ''go'':86C ''identifi'':147C ''improv'':184C ''info'':158C ''learn'':13B ''li'':107C,113C,122C,132C ''like'':53C ''m'':21C ''machin'':12B ''machine-learn'':11B ''method'':118C ''ml'':24C ''model'':17B ''much'':124C ''need'':130C ''new'':22C ''onlin'':97C ''p'':19C,56C,65C,74C,99C,164C ''past'':92C ''patron'':3A ''per'':126C ''place'':155C ''posibl'':111C ''possibl'':174C ''practic'':30C ''predict'':1A,16B,187C ''predictive-model'':15B ''question'':170C ''real'':181C ''real-tim'':180C ''sampl'':125C ''save'':40C ''structur'':140C ''take'':154C ''time'':10A,96C,151C,182C ''tri'':26C ''ul'':106C ''use'':31C,161C ''user'':52C,57C,66C,84C,127C,146C ''variabl'':8A,47C ''way'':138C ''whenev'':82C ''wonder'':77C ''would'':108C,128C,134C,159C,171C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9981, 1, '2016-01-27 03:50:11.847', 2, 78, 'Machine learning algorithm for a dynamic set of attributes', '&lt;p&gt;I''m new to machine learning programming and am working on an application which will calculate weights of the attributes to calculate the ETAT (Estimated Turn Around Time) of some tasks in workflow. The set of attributes is not fixed. &lt;/p&gt;

&lt;p&gt;For example: ETAT will depend on-&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Language   &lt;/li&gt;
&lt;li&gt;No of properties   &lt;/li&gt;
&lt;li&gt;Experience level of resources and many
more.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;For each workflow entity there can by a set of these attributes, i.e. a single workflow may be like:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Language - English &lt;/li&gt;
&lt;li&gt;No. of properties - 1200 &lt;/li&gt;
&lt;li&gt;Experience level of
resources - Expert &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;and&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Language - Russian&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;No. of properties - 21000&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Experience level of resources - Basic&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Now for the above set of attributes, how can I design my machine learning algorithm? Any pointers will be highly useful.&lt;/p&gt;
', 'machine-learning  python', NULL, '''/li'':67C,72C,81C,108C,114C,121C,132C,140C,149C ''/ol'':82C ''/p'':55C,63C,103C,125C,131C,139C,148C,173C ''/ul'':122C,150C ''1200'':113C ''21000'':138C ''algorithm'':3A,166C ''applic'':27C ''around'':41C ''attribut'':9A,34C,51C,95C,158C ''basic'':147C ''calcul'':30C,36C ''depend'':61C ''design'':162C ''dynam'':6A ''english'':107C ''entiti'':87C ''estim'':39C ''etat'':38C,59C ''exampl'':58C ''experi'':74C,116C,143C ''expert'':120C ''fix'':54C ''high'':171C ''i.e'':96C ''languag'':66C,106C,129C ''learn'':2A,12B,20C,165C ''level'':75C,117C,144C ''li'':65C,68C,73C,105C,109C,115C,127C,133C,141C ''like'':102C ''m'':16C ''machin'':1A,11B,19C,164C ''machine-learn'':10B ''mani'':79C ''may'':100C ''new'':17C ''ol'':64C ''p'':14C,56C,83C,123C,128C,134C,142C,151C ''pointer'':168C ''program'':21C ''properti'':71C,112C,137C ''python'':13B ''resourc'':77C,119C,146C ''russian'':130C ''set'':7A,49C,92C,156C ''singl'':98C ''task'':45C ''time'':42C ''turn'':40C ''ul'':104C,126C ''use'':172C ''weight'':31C ''work'':24C ''workflow'':47C,86C,99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9973, 2, '2016-01-26 18:07:06.92', 3, NULL, NULL, '&lt;p&gt;As for a complete machine learning package on GPU''s, no such package exists.  However, there are actually a handful of R packages that can use GPU''s.  You can see these packages on the &lt;a href="https://cran.r-project.org/web/views/HighPerformanceComputing.html" rel="nofollow"&gt;CRAN High Performance Computing page&lt;/a&gt;.  You should note that most of these packages do require you to have a NVIDIA card.  Of the packages available, there are three packages you most likely would utilize unless you have a special case.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="https://cran.r-project.org/web/packages/gputools/index.html" rel="nofollow"&gt;gputools&lt;/a&gt; - if interested in distance computations (only NVIDIA).&lt;/li&gt;
&lt;li&gt;&lt;a href="https://cran.r-project.org/web/packages/gmatrix/index.html" rel="nofollow"&gt;gmatrix&lt;/a&gt; - general numeric computations (only NVIDIA).&lt;/li&gt;
&lt;li&gt;&lt;a href="https://cran.r-project.org/web/packages/gpuR/index.html" rel="nofollow"&gt;gpuR&lt;/a&gt; - general numeric computations (any GPU via OpenCL).*&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;* &lt;strong&gt;NOTE&lt;/strong&gt; - At the risk of self promotion I am the author of the gpuR package.&lt;/p&gt;

&lt;p&gt;You can likely use the latter two packages to reproduce existing machine learning algorithms.  I am actually using my gpuR package to create a GPU accelerated neuralnet package but this is in progress.&lt;/p&gt;

&lt;p&gt;So in summary, if you are determined, the basic resources are available in R.  But if you need something in the immediate future, you will need to explore other resources/approaches as pointed out by @YCR.&lt;/p&gt;
', NULL, NULL, '''/a'':49C,96C,114C,130C ''/li'':104C,120C,138C ''/ol'':139C ''/p'':85C,158C,193C,230C ''/strong'':143C ''/web/packages/gmatrix/index.html'':110C ''/web/packages/gpur/index.html'':126C ''/web/packages/gputools/index.html'':92C ''/web/views/highperformancecomputing.html'':41C ''acceler'':185C ''actual'':19C,176C ''algorithm'':173C ''author'':153C ''avail'':69C,206C ''basic'':203C ''card'':65C ''case'':84C ''complet'':5C ''comput'':47C,101C,117C,133C ''cran'':44C ''cran.r-project.org'':40C,91C,109C,125C ''cran.r-project.org/web/packages/gmatrix/index.html'':108C ''cran.r-project.org/web/packages/gpur/index.html'':124C ''cran.r-project.org/web/packages/gputools/index.html'':90C ''cran.r-project.org/web/views/highperformancecomputing.html'':39C ''creat'':182C ''determin'':201C ''distanc'':100C ''exist'':15C,170C ''explor'':222C ''futur'':217C ''general'':115C,131C ''gmatrix'':113C ''gpu'':10C,28C,135C,184C ''gpur'':129C,156C,179C ''gputool'':95C ''hand'':21C ''high'':45C ''howev'':16C ''href'':38C,89C,107C,123C ''immedi'':216C ''interest'':98C ''latter'':165C ''learn'':7C,172C ''li'':87C,105C,121C ''like'':76C,162C ''machin'':6C,171C ''need'':212C,220C ''neuralnet'':186C ''nofollow'':43C,94C,112C,128C ''note'':52C,142C ''numer'':116C,132C ''nvidia'':64C,103C,119C ''ol'':86C ''opencl'':137C ''p'':1C,140C,159C,194C ''packag'':8C,14C,24C,34C,57C,68C,73C,157C,167C,180C,187C ''page'':48C ''perform'':46C ''point'':226C ''progress'':192C ''promot'':149C ''r'':23C,208C ''rel'':42C,93C,111C,127C ''reproduc'':169C ''requir'':59C ''resourc'':204C ''resources/approaches'':224C ''risk'':146C ''see'':32C ''self'':148C ''someth'':213C ''special'':83C ''strong'':141C ''summari'':197C ''three'':72C ''two'':166C ''unless'':79C ''use'':27C,163C,177C ''util'':78C ''via'':136C ''would'':77C ''ycr'':229C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9967, 2, '2016-01-26 09:31:52.94', 3, NULL, NULL, '&lt;p&gt;Gazetteer or any other option of intentionally fixed size feature seems a very popular approach in &lt;em&gt;academic&lt;/em&gt; papers, when you have a problem of finite size, for example NER in a fixed corpora, or POS tagging or anything else. I would not consider it cheating unless the only feature you will be using is Gazetteer matching.&lt;/p&gt;

&lt;p&gt;However, when you train any kind of NLP model, which does rely on dictionary while training, you may get real world performance way lower than your initial testing would report, unless you can include all objects of interest into the gazetteer (and why then you need that model?) because  your trained model will rely on the feature at some point and, in a case when other features will be too weak or not descriptive, new objects of interest would not be recognized.&lt;/p&gt;

&lt;p&gt;If you do use a Gazetteer in your models, you should make sure, that that feature has a counter feature to let model balance itself, so that simple dictionary match won''t be the only feature of positive class (and more importantly, gazetteer should match not only positive examples, but also negative ones).&lt;/p&gt;

&lt;p&gt;For example, assume you do have a full set of infinite variations of all person names, which makes general person NER irrelevant, but now you try to decide whether the object mentioned in text is capable of singing. You will rely on features of inclusion into your Person gazetteer, which will give you a lot of false positives; then, you will add a verb-centric feature of "&lt;strong&gt;Is Subject of verb sing&lt;/strong&gt;", and that would probably give you false positives from all kind of objects like birds, your tummy when you''re hungry and a drunk fellow who &lt;em&gt;thinks&lt;/em&gt; he can sing (but let''s be honest, he can not) -- but that verb-centric feature will balance with your person gazetteer to assign positive class of ''Singer'' to persons and not animals or any other objects. Though, it doesn''t solve the case of drunk performer.&lt;/p&gt;
', NULL, NULL, '''/em'':20C,304C ''/p'':60C,144C,199C,353C ''/strong'':275C ''academ'':19C ''add'':262C ''also'':196C ''anim'':338C ''anyth'':41C ''approach'':16C ''assign'':329C ''assum'':203C ''balanc'':169C,323C ''bird'':290C ''capabl'':236C ''case'':125C,349C ''centric'':266C,320C ''cheat'':48C ''class'':184C,331C ''consid'':46C ''corpora'':36C ''counter'':164C ''decid'':228C ''descript'':135C ''dictionari'':75C,174C ''doesn'':345C ''drunk'':299C,351C ''els'':42C ''em'':18C,302C ''exampl'':31C,194C,202C ''fals'':257C,282C ''featur'':11C,52C,118C,128C,161C,165C,181C,243C,267C,321C ''fellow'':300C ''finit'':28C ''fix'':9C,35C ''full'':208C ''gazett'':2C,58C,102C,151C,188C,249C,327C ''general'':219C ''get'':80C ''give'':252C,280C ''honest'':312C ''howev'':62C ''hungri'':296C ''import'':187C ''includ'':95C ''inclus'':245C ''infinit'':211C ''initi'':88C ''intent'':8C ''interest'':99C,139C ''irrelev'':222C ''kind'':67C,286C ''let'':167C,309C ''like'':289C ''lot'':255C ''lower'':85C ''make'':157C,218C ''match'':59C,175C,190C ''may'':79C ''mention'':232C ''model'':70C,109C,113C,154C,168C ''name'':216C ''need'':107C ''negat'':197C ''ner'':32C,221C ''new'':136C ''nlp'':69C ''object'':97C,137C,231C,288C,342C ''one'':198C ''option'':6C ''p'':1C,61C,145C,200C ''paper'':21C ''perform'':83C,352C ''person'':215C,220C,248C,326C,335C ''point'':121C ''popular'':15C ''pos'':38C ''posit'':183C,193C,258C,283C,330C ''probabl'':279C ''problem'':26C ''re'':295C ''real'':81C ''recogn'':143C ''reli'':73C,115C,241C ''report'':91C ''seem'':12C ''set'':209C ''simpl'':173C ''sing'':238C,274C,307C ''singer'':333C ''size'':10C,29C ''solv'':347C ''strong'':269C ''subject'':271C ''sure'':158C ''tag'':39C ''test'':89C ''text'':234C ''think'':303C ''though'':343C ''train'':65C,77C,112C ''tri'':226C ''tummi'':292C ''unless'':49C,92C ''use'':56C,149C ''variat'':212C ''verb'':265C,273C,319C ''verb-centr'':264C,318C ''way'':84C ''weak'':132C ''whether'':229C ''won'':176C ''world'':82C ''would'':44C,90C,140C,278C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9968, 1, '2016-01-26 11:25:57.627', 2, 39, 'Jobs for gaining experience in preperation for data science', '&lt;p&gt;Data Scientist positions are not the most common entry level jobs. They require hard work, knowledge and most of all experience. The problem is, I am uncertain what entry level jobs can help you get experience in data science. I apologise for not asking a technical question, but I have been searching an answer and most importantly advice from experts and data science practitioners. &lt;/p&gt;

&lt;p&gt;Currently, I am studying to become a Data Scientist and I was wondering what are good entry level jobs that can help me build a good foundation for future success in the field?&lt;/p&gt;
', 'beginner  career', NULL, '''/p'':77C,112C ''advic'':70C ''answer'':66C ''apologis'':53C ''ask'':56C ''becom'':84C ''beginn'':10B ''build'':102C ''career'':11B ''common'':20C ''current'':79C ''data'':8A,13C,50C,74C,86C ''entri'':21C,41C,95C ''experi'':4A,33C,48C ''expert'':72C ''field'':111C ''foundat'':105C ''futur'':107C ''gain'':3A ''get'':47C ''good'':94C,104C ''hard'':26C ''help'':45C,100C ''import'':69C ''job'':1A,23C,43C,97C ''knowledg'':28C ''level'':22C,42C,96C ''p'':12C,78C ''posit'':15C ''practition'':76C ''preper'':6A ''problem'':35C ''question'':59C ''requir'':25C ''scienc'':9A,51C,75C ''scientist'':14C,87C ''search'':64C ''studi'':82C ''success'':108C ''technic'':58C ''uncertain'':39C ''wonder'':91C ''work'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10468, 1, '2016-03-01 21:25:29.73', 0, 21, 'What research can be done with searched words', '&lt;p&gt;I have a list of search words created by users. I don''t have any other data.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;word1,word2....wordn
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What insights can I get from this data ?&lt;/p&gt;

&lt;p&gt;I don''t know what user search for each word.&lt;/p&gt;

&lt;p&gt;A word cloud is possible but I don''t think this will have much value.&lt;/p&gt;

&lt;p&gt;If I could classify the words in categories that would be useful but as do not have training data then this is not possible ?&lt;/p&gt;
', 'machine-learning  classification  data  naive-bayes-classifier', NULL, '''/code'':42C ''/p'':36C,53C,65C,82C,108C ''/pre'':43C ''bay'':16B ''categori'':91C ''classif'':12B ''classifi'':17B,87C ''cloud'':69C ''code'':38C ''could'':86C ''creat'':26C ''data'':13B,35C,52C,102C ''done'':5A ''get'':49C ''insight'':46C ''know'':58C ''learn'':11B ''list'':22C ''machin'':10B ''machine-learn'':9B ''much'':80C ''naiv'':15B ''naive-bayes-classifi'':14B ''p'':18C,44C,54C,66C,83C ''possibl'':71C,107C ''pre'':37C ''research'':2A ''search'':7A,24C,61C ''think'':76C ''train'':101C ''use'':95C ''user'':28C,60C ''valu'':81C ''word'':8A,25C,64C,68C,89C ''word1'':39C ''word2'':40C ''wordn'':41C ''would'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9980, 1, '2016-01-27 01:40:51.273', 2, 53, 'Opposite of churn?', '&lt;p&gt;I''m trying to build a model in R that predicts when a customer will purchase a product again. &lt;/p&gt;

&lt;p&gt;However, I don''t know what kind of model can handle time and predict this kind of outcome. I''m wondering to use: buy till you die package, but this package output the frequency of future purchases, and not when this purchases will happening.&lt;/p&gt;
', 'machine-learning  r  predictive-modeling  churn', NULL, '''/p'':32C,78C ''build'':17C ''buy'':57C ''churn'':3A,11B ''custom'':26C ''die'':60C ''frequenc'':67C ''futur'':69C ''handl'':44C ''happen'':77C ''howev'':34C ''kind'':40C,49C ''know'':38C ''learn'':6B ''m'':14C,53C ''machin'':5B ''machine-learn'':4B ''model'':10B,19C,42C ''opposit'':1A ''outcom'':51C ''output'':65C ''p'':12C,33C ''packag'':61C,64C ''predict'':9B,23C,47C ''predictive-model'':8B ''product'':30C ''purchas'':28C,70C,75C ''r'':7B,21C ''till'':58C ''time'':45C ''tri'':15C ''use'':56C ''wonder'':54C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9970, 2, '2016-01-26 14:12:26.117', 2, NULL, NULL, '&lt;p&gt;When you want to use Auto-Encoders (AEs) for dimensionality reduction, you usally add a bottleneck layer. This means, for example, you have 1234-dimensional data. You feed this into your AE, and - as it is an AE - you have an output of dimension 1234. However, you might have many layers in that network and one of them has significantly less dimensions. Lets say you have the topology &lt;code&gt;1234:1024:784:1024:1234&lt;/code&gt;. You train it like this, but you only use the weights from the &lt;code&gt;1234:1024:784&lt;/code&gt; part.&lt;/p&gt;

&lt;p&gt;When you get new input, you just feed it into this network. You can see it as a kind of preprocessing. For the later stages, this is a black box.&lt;/p&gt;

&lt;p&gt;This is manly useful when you have a lot of unlabeled data. It is called &lt;em&gt;Semi Supervised Learning&lt;/em&gt; (SSL).&lt;/p&gt;
', NULL, NULL, '''/code'':77C,95C ''/em'':150C ''/p'':97C,129C,152C ''1024'':73C,75C,93C ''1234'':26C,47C,72C,76C,92C ''784'':74C,94C ''add'':16C ''ae'':10C,34C,40C ''auto'':8C ''auto-encod'':7C ''black'':127C ''bottleneck'':18C ''box'':128C ''call'':145C ''code'':71C,91C ''data'':28C,142C ''dimens'':46C,64C ''dimension'':12C,27C ''em'':146C ''encod'':9C ''exampl'':23C ''feed'':30C,106C ''get'':101C ''howev'':48C ''input'':103C ''kind'':117C ''later'':122C ''layer'':19C,53C ''learn'':149C ''less'':63C ''let'':65C ''like'':81C ''lot'':139C ''man'':133C ''mani'':52C ''mean'':21C ''might'':50C ''network'':56C,110C ''new'':102C ''one'':58C ''output'':44C ''p'':1C,98C,130C ''part'':96C ''preprocess'':119C ''reduct'':13C ''say'':66C ''see'':113C ''semi'':147C ''signific'':62C ''ssl'':151C ''stage'':123C ''supervis'':148C ''topolog'':70C ''train'':79C ''unlabel'':141C ''usal'':15C ''use'':6C,86C,134C ''want'':4C ''weight'':88C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9972, 1, '2016-01-26 16:15:33.01', 1, 35, 'NLP : What are some common verbs surrounding organization names in text', '&lt;p&gt;I am trying to come up with some rules to detect named entities, specifically company or organization names in text. I think it makes sense to focus on verbs. There are a lot of &lt;code&gt;POS Taggers&lt;/code&gt; that can easily detect proper nouns. I personally like &lt;code&gt;StanfordPOSTagger&lt;/code&gt;. Now, once i have the proper noun, i know that it is a named entity. However, to be certain that it is the name of a company, i need to come up with rules and possibly &lt;code&gt;Gazetteers&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;I was thinking of focusing on verbs. Is there a set of common verbs that occur frequently around company names?&lt;/p&gt;

&lt;p&gt;I could create an annotated corpus and explicitly train a Machine Learning classifier to predict such verbs, but that is a LOT of work. It would be great if someone has already done some research on this.&lt;/p&gt;

&lt;p&gt;Additionally, can some other &lt;code&gt;POS&lt;/code&gt; tags give clues? Not just verbs.&lt;/p&gt;
', 'python  nlp  named-entity-recognition  nltk', NULL, '''/code'':57C,69C,108C,178C ''/p'':109C,131C,170C,185C ''addit'':172C ''alreadi'':164C ''annot'':137C ''around'':128C ''certain'':88C ''classifi'':145C ''clue'':181C ''code'':54C,67C,106C,176C ''come'':24C,100C ''common'':5A,123C ''compani'':34C,96C,129C ''corpus'':138C ''could'':134C ''creat'':135C ''detect'':30C,61C ''done'':165C ''easili'':60C ''entiti'':16B,32C,84C ''explicit'':140C ''focus'':46C,115C ''frequent'':127C ''gazett'':107C ''give'':180C ''great'':160C ''howev'':85C ''know'':78C ''learn'':144C ''like'':66C ''lot'':52C,154C ''machin'':143C ''make'':43C ''name'':9A,15B,31C,37C,83C,93C,130C ''named-entity-recognit'':14B ''need'':98C ''nlp'':1A,13B ''nltk'':18B ''noun'':63C,76C ''occur'':126C ''organ'':8A,36C ''p'':19C,110C,132C,171C ''person'':65C ''pos'':55C,177C ''possibl'':105C ''predict'':147C ''proper'':62C,75C ''python'':12B ''recognit'':17B ''research'':167C ''rule'':28C,103C ''sens'':44C ''set'':121C ''someon'':162C ''specif'':33C ''stanfordpostagg'':68C ''surround'':7A ''tag'':179C ''tagger'':56C ''text'':11A,39C ''think'':41C,113C ''train'':141C ''tri'':22C ''verb'':6A,48C,117C,124C,149C,184C ''work'':156C ''would'':158C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9982, 2, '2016-01-27 06:19:25.23', 1, NULL, NULL, '&lt;p&gt;I would train a &lt;a href="https://en.wikipedia.org/wiki/Gamma_distribution" rel="nofollow"&gt;gamma-distributed&lt;/a&gt; &lt;a href="https://en.wikipedia.org/wiki/Generalized_linear_model" rel="nofollow"&gt;GLM&lt;/a&gt;; one for each language. Now, I assume the set of parameters is fixed within each language. You can incorporate ordinal variables (basic -&gt; intermediate -&gt; expert) using dummy coding (i.e., treat them as categorical variables). If there is no intermediate level one bit will do (0 = basic, 1 = expert). And so on for the other properties.&lt;/p&gt;

&lt;p&gt;You can find free software to implement GLMs in various language, including &lt;a href="http://statsmodels.sourceforge.net/devel/glm.html" rel="nofollow"&gt;python&lt;/a&gt; and &lt;a href="http://data.princeton.edu/R/glms.html" rel="nofollow"&gt;R&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':16C,25C,102C,112C ''/devel/glm.html'':98C ''/p'':80C,113C ''/r/glms.html'':108C ''/wiki/gamma_distribution'':10C ''/wiki/generalized_linear_model'':21C ''0'':69C ''1'':71C ''assum'':32C ''basic'':47C,70C ''bit'':66C ''categor'':57C ''code'':52C ''data.princeton.edu'':107C ''data.princeton.edu/r/glms.html'':106C ''distribut'':15C ''dummi'':51C ''en.wikipedia.org'':9C,20C ''en.wikipedia.org/wiki/gamma_distribution'':8C ''en.wikipedia.org/wiki/generalized_linear_model'':19C ''expert'':49C,72C ''find'':84C ''fix'':38C ''free'':85C ''gamma'':14C ''gamma-distribut'':13C ''glm'':24C ''glms'':89C ''href'':7C,18C,95C,105C ''i.e'':53C ''implement'':88C ''includ'':93C ''incorpor'':44C ''intermedi'':48C,63C ''languag'':29C,41C,92C ''level'':64C ''nofollow'':12C,23C,100C,110C ''one'':26C,65C ''ordin'':45C ''p'':1C,81C ''paramet'':36C ''properti'':79C ''python'':101C ''r'':111C ''rel'':11C,22C,99C,109C ''set'':34C ''softwar'':86C ''statsmodels.sourceforge.net'':97C ''statsmodels.sourceforge.net/devel/glm.html'':96C ''train'':4C ''treat'':54C ''use'':50C ''variabl'':46C,58C ''various'':91C ''within'':39C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9974, 2, '2016-01-26 18:15:29.43', 3, NULL, NULL, '&lt;p&gt;You can''t calculate the conditional distribution because that distribution is defined over the entire population and assuming a probabilistic model which you do not know. &lt;/p&gt;

&lt;p&gt;In real life you have only a sample of the population. More often than not, in real life you do not have the structure of the model also. You do not know if it is a linear model, a polynomial or another type. &lt;/p&gt;

&lt;p&gt;The probabilistic approach is to assume a model, consider some assumptions for that model and fit the model''s parameters using data. That is not &lt;em&gt;the Bayes classifier&lt;/em&gt;, it''s only an approximation. &lt;/p&gt;

&lt;p&gt;The trick is ''all models are wrong, some models are useful''. There are many approaches to learn this approximation. You can estimate the joint probability (generative classifiers), you can estimate directly the conditional probability (discriminative classifiers) or you can use a different approach (like SVM, for example) and twist it somehow to approximate the conditional probability. But all of them are approximations. The real one, &lt;em&gt;THE Bayes classifier&lt;/em&gt; will remain unknown.&lt;/p&gt;

&lt;p&gt;[Later edit]&lt;/p&gt;

&lt;p&gt;Looking at the &lt;a href="https://en.wikipedia.org/wiki/Bayes_classifier" rel="nofollow"&gt;Wikipedia page&lt;/a&gt; for Bayes classifier it states that: "The Bayes classifier is a useful benchmark in statistical classification.". My opinion is that this statement is confusing, if not dead wrong. Probably this is related with the wrong usage of expression "Bayes classifier" to designate the Naive Bayes classifier. If one search on google for "Bayes classifier", he will see a lot of materials related with Naive Bayes. Personally I do not agree with the statement from wikipedia. Bayes classifier is that state of nature that we do not know but we aim to approximate as well as possible.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9993, 2, '2016-01-27 16:11:58.653', 4, NULL, NULL, '&lt;p&gt;As David states in the comments if you want to interpret a model you likely want to explore something besides neural nets.  That said it you want to intuitively understand the network plot it is best to think of it with respect to images (something neural networks are very good at).&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;The left-most nodes (i.e. input nodes) are your raw data variables.&lt;/li&gt;
&lt;li&gt;The arrows in black (and associated numbers) are the &lt;strong&gt;weights&lt;/strong&gt; which you can think of as &lt;strong&gt;how much that variable contributes to the next node.&lt;/strong&gt;  The blue lines are the bias weights.  You can find the purpose of these weights in the excellent answer &lt;a href="http://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks"&gt;here&lt;/a&gt;.&lt;/li&gt;
&lt;li&gt;The middle nodes (i.e. anything between the input and output nodes) are your hidden nodes.  This is where the image analogy helps.  &lt;strong&gt;Each of these nodes constitute a component that the network is learning to recognize.&lt;/strong&gt;  For example a nose, mouth, or eye.  This is not easily determined and is far more abstract when you are dealing with non-image data.&lt;/li&gt;
&lt;li&gt;The far-right (output node(s)) node is the final output of your neural network.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Note that this all is omitting the activation function that would be applied at each layer of the network as well.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9975, 2, '2016-01-26 21:24:44.633', 3, NULL, NULL, '&lt;p&gt;One way to approach this would be to examine the deconvolution layer implementation in Caffe, see the following relevant bits of code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;DeconvolutionLayer&amp;lt;Dtype&amp;gt;::Forward_gpu
ConvolutionLayer&amp;lt;Dtype&amp;gt;::Backward_gpu
CuDNNConvolutionLayer&amp;lt;Dtype&amp;gt;::Backward_gpu
BaseConvolutionLayer&amp;lt;Dtype&amp;gt;::backward_cpu_gemm
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You can see that it''s implemented in Caffe exactly as backprop for a regular forward convolutional layer (to me it was more obvious after i compared the implementation of backprop in cuDNN conv layer vs ConvolutionLayer::Backward_gpu implemented using GEMM). So if you work through how backpropagation is done for regular convolution you will understand what happens on a mechanical computation level. However I couldn''t find a good detailed rationalization for using that specific implementation anywhere.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;However, I don''t know how the learning of convolutional layers works. (I understand how simple MLPs learn with gradient descent, if that helps).&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;To answer your other question inside your first question, there are two main differences between MLP backpropagation (fully connected layer) and convolutional nets:&lt;/p&gt;

&lt;p&gt;1) the influence of weights is localized, so first figure out how to do backprop for, say a 3x3 filter convolved with a small 3x3 area of an input image, mapping to a single point in the result image.&lt;/p&gt;

&lt;p&gt;2) the weights of convolutional filters are shared for spatial invariance. What this means in practice is that in the forward pass the same 3x3 filter with the same weights is dragged through the entire image with the same weights for forward computation to yield the output image (for that particular filter). What this means for backprop is that the backprop gradients for each point in the source image are summed over the entire range that we dragged that filter during the forward pass. Note that there are also different gradients of loss wrt x, w and bias since dLoss/dx needs to be backpropagated, and dLoss/dw is how we update the weights. w and bias are independent inputs in the computation DAG (there are no prior inputs), so there''s no need to do backpropagation on those.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;(my notation here assumes that convolution is y = x*w+b where ''*'' is the convolution operation)
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9976, 1, '2016-01-26 22:07:26.397', 2, 66, 'Perform clustering on individual data in categories', '&lt;p&gt;I am looking for ways to perform clustering on an aggregated category while still using individual data.&lt;/p&gt;

&lt;p&gt;For example, assume we have a multitude users that we have acquired through different channels. I would like to cluster groups of channels together, so that it is easier to deal with similar channels in similar ways. However, if I aggregate the user data into averages for each channel, then because these averages are heavily driven by outliers (high-spenders), it doesn''t give that much insight to the behavior of the majority of users from that channel.&lt;/p&gt;

&lt;p&gt;Is there a way to use the individual user data but still gain insight into and cluster based on the channel they come from?&lt;/p&gt;
', 'r  clustering', NULL, '''/p'':28C,109C,135C ''acquir'':41C ''aggreg'':21C,70C ''assum'':32C ''averag'':75C,82C ''base'':128C ''behavior'':100C ''categori'':7A,22C ''channel'':44C,52C,63C,78C,108C,131C ''cluster'':2A,9B,18C,49C,127C ''come'':133C ''data'':5A,27C,73C,120C ''deal'':60C ''differ'':43C ''doesn'':92C ''driven'':85C ''easier'':58C ''exampl'':31C ''gain'':123C ''give'':94C ''group'':50C ''heavili'':84C ''high'':89C ''high-spend'':88C ''howev'':67C ''individu'':4A,26C,118C ''insight'':97C,124C ''like'':47C ''look'':13C ''major'':103C ''much'':96C ''multitud'':36C ''outlier'':87C ''p'':10C,29C,110C ''perform'':1A,17C ''r'':8B ''similar'':62C,65C ''spender'':90C ''still'':24C,122C ''togeth'':53C ''use'':25C,116C ''user'':37C,72C,105C,119C ''way'':15C,66C,114C ''would'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9977, 2, '2016-01-26 23:45:55.99', 0, NULL, NULL, '&lt;p&gt;The simplest thing that comes to mind is cluster the users for each channel then compare the channels by their cluster parameters. If you use a non-probabilistic model such as k-means, you could compare the cluster parameters directly. With a large number of clusters this would not be a good idea, for somewhat technical reasons. If you use a &lt;a href="https://en.wikipedia.org/wiki/Mixture_model" rel="nofollow"&gt;probabilistic model&lt;/a&gt; you would look at something like the &lt;a href="https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence" rel="nofollow"&gt;KLD&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':74C,90C ''/p'':91C ''/wiki/kullback%e2%80%93leibler_divergence'':86C ''/wiki/mixture_model'':69C ''channel'':15C,19C ''cluster'':10C,22C,41C,49C ''come'':6C ''compar'':17C,39C ''could'':38C ''direct'':43C ''en.wikipedia.org'':68C,85C ''en.wikipedia.org/wiki/kullback%e2%80%93leibler_divergence'':84C ''en.wikipedia.org/wiki/mixture_model'':67C ''good'':55C ''href'':66C,83C ''idea'':56C ''k'':35C ''k-mean'':34C ''kld'':89C ''larg'':46C ''like'':80C ''look'':77C ''mean'':36C ''mind'':8C ''model'':31C,73C ''nofollow'':71C,88C ''non'':29C ''non-probabilist'':28C ''number'':47C ''p'':1C ''paramet'':23C,42C ''probabilist'':30C,72C ''reason'':60C ''rel'':70C,87C ''simplest'':3C ''someth'':79C ''somewhat'':58C ''technic'':59C ''thing'':4C ''use'':26C,63C ''user'':12C ''would'':51C,76C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10003, 2, '2016-01-27 21:01:09.243', 5, NULL, NULL, '&lt;p&gt;What you described is the more-or-less standard approach, with a couple of caveats.&lt;/p&gt;

&lt;h1&gt;Leakers&lt;/h1&gt;

&lt;p&gt;Remember that we live in a stochastic world, so a perfect model is immediately suspect of using &lt;em&gt;leakers&lt;/em&gt;, i.e., variables which have a causal connection with the target. In your case, e.g., it would be the presence of a service contract for the machine (which would indicated that the company actually does own one). This, building an interpretable model and examining the most powerful variable for such causal connections (and eliminating them) would be a good idea.&lt;/p&gt;

&lt;h1&gt;Better Approach&lt;/h1&gt;

&lt;p&gt;However, one could improve on this approach in a very powerful way by replacing the &lt;strong&gt;current&lt;/strong&gt; state of the company with its &lt;strong&gt;historical&lt;/strong&gt; state at the time of the machine purchase. This way one eliminates the leakers automatically and also gets a better model because the predictor values which are more relevant: after all, you are trying to predict which company is ready to &lt;strong&gt;buy&lt;/strong&gt; the machine &lt;strong&gt;now&lt;/strong&gt; rather that which company bought it in the past when its conditions were very different.&lt;/p&gt;

&lt;p&gt;In fact, your approach works precisely because it is an approximation of this approach (approximating the company state at the time it bought the machine with its current state) and the absurdity/uselessness of the perfect model that you mention is the artifact of your approach being &lt;em&gt;an approximation&lt;/em&gt; of The Right Thing.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9978, 1, '2016-01-27 01:05:15.363', 1, 38, 'Trajectory data mining and pattern recognition using ORB-SLAM and KNN-DTW', '&lt;p&gt;I''ve started working on a project about the trajectory data mining from videos - for example: snowboarding video from GoPro action camera.&lt;/p&gt;

&lt;p&gt;This is the continuation of my previous experiment (MotionML), which was based on the accelerometer datasets and KNN-DTW classification: &lt;a href="https://github.com/llvll/motionml" rel="nofollow"&gt;https://github.com/llvll/motionml&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Visual odometry and monocular SLAM topics have been researched with the following algorithms:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;PTAM&lt;/li&gt;
&lt;li&gt;DTAM&lt;/li&gt;
&lt;li&gt;LSD-SLAM&lt;/li&gt;
&lt;li&gt;ORB-SLAM&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;At this stage I''ve selected ORB-SLAM algorithm for the initial experiments: &lt;a href="https://github.com/raulmur/ORB_SLAM" rel="nofollow"&gt;https://github.com/raulmur/ORB_SLAM&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The implementation will be done in Python using Scikit-Learn and FastDTW for the model training purposes based on the trajectory data. The data mining itself, based on OpenCV and ORB-SLAM, will execute on Apache Spark for video file processing.&lt;/p&gt;

&lt;p&gt;The following results are expected:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Extracted trajectory data.&lt;/li&gt;
&lt;li&gt;Discovered and classified motion patterns - similar to MotionML project above.&lt;/li&gt;
&lt;li&gt;Video georeferencing or spatio-temporal referencing - GPS geotagging might be present for frame #1, trajectory data will extrapolate geotagging to the next frames. Clock synchronization should take place.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Two questions from my side:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;DTW for trajectory classification - I''m still doubting about DTW and reviewing HMM (Hidden Markov Models) or LSTM neural nets for the same purpose. Any practical advices are really appreciated.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;I''m also looking for any examples or advices about SLAM-based feature extraction for machine learning. Ideally for trajectory data mining, but other examples will work as well. &lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;If you would like to cooperate or to participate in this project, then please write me directly: oleg.v.puzanov@gmail.com&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9979, 2, '2016-01-27 01:38:22.92', 0, NULL, NULL, '&lt;p&gt;Can''t help if you''re fixed on KNIME, but if you can use &lt;a href="http://orange.biolab.si" rel="nofollow"&gt;Orange Data Mining&lt;/a&gt;, I''ve had success clustering (took a minute or so) and previewing a scatter plot of a dataset with 32k rows.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/IFDOA.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/IFDOA.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':24C,65C ''/ifdoa.png'':52C,59C ''/p'':46C,66C ''32k'':44C ''alt'':60C ''cluster'':29C ''data'':22C ''dataset'':42C ''descript'':63C ''enter'':61C ''fix'':8C ''help'':4C ''href'':17C,49C ''i.stack.imgur.com'':51C,58C ''i.stack.imgur.com/ifdoa.png'':50C,57C ''imag'':62C ''img'':55C ''knime'':10C ''mine'':23C ''minut'':32C ''nofollow'':20C,54C ''orang'':21C ''orange.biolab.si'':18C ''p'':1C,47C ''plot'':39C ''preview'':36C ''re'':7C ''rel'':19C,53C ''row'':45C ''scatter'':38C ''src'':56C ''success'':28C ''took'':30C ''use'':15C ''ve'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10206, 2, '2016-02-11 17:40:00.273', 2, NULL, NULL, '&lt;p&gt;Assuming the rest of your configuration is correct all you have to do is to make &lt;code&gt;spark-csv&lt;/code&gt; jar available to your program. There are a few ways you can achieve this:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;manually download required jars including  &lt;a href="http://central.maven.org/maven2/com/databricks/spark-csv_2.10/1.3.0/spark-csv_2.10-1.3.0.jar" rel="nofollow"&gt;&lt;code&gt;spark-csv&lt;/code&gt;&lt;/a&gt; and csv parser (for example &lt;a href="http://mvnrepository.com/artifact/org.apache.commons/commons-csv/1.2" rel="nofollow"&gt;&lt;code&gt;org.apache.commons.commons-csv&lt;/code&gt;&lt;/a&gt;) and put them somewhere on the &lt;code&gt;CLASSPATH&lt;/code&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;using &lt;code&gt;--packages&lt;/code&gt; option (use Scala version which has been used to build Spark. Pre-built versions use 2.10):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;using &lt;code&gt;PYSPARK_SUBMIT_ARGS&lt;/code&gt; environmental variable:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;export PACKAGES="com.databricks:spark-csv_2.11:1.3.0"
export PYSPARK_SUBMIT_ARGS="--packages ${PACKAGES} pyspark-shell"
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;adding Gradle string to &lt;code&gt;spark.jars.packages&lt;/code&gt; in &lt;code&gt;conf/spark-defaults.conf&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;spark.jars.packages    com.databricks:spark-csv_2.11:1.3.0
&lt;/code&gt;&lt;/pre&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':58C,75C ''/artifact/org.apache.commons/commons-csv/1.2'':68C ''/code'':22C,57C,74C,84C,92C,119C,142C,153C,157C,168C ''/li'':86C,144C,170C,172C ''/maven2/com/databricks/spark-csv_2.10/1.3.0/spark-csv_2.10-1.3.0.jar'':50C ''/p'':37C,85C,110C,122C,158C ''/pre'':143C,169C ''/ul'':171C,173C ''1.3.0'':132C,167C ''2.10'':109C ''2.11'':131C,166C ''achiev'':35C ''ad'':147C ''arg'':118C,136C ''assum'':2C ''avail'':24C ''build'':102C ''built'':106C ''central.maven.org'':49C ''central.maven.org/maven2/com/databricks/spark-csv_2.10/1.3.0/spark-csv_2.10-1.3.0.jar'':48C ''classpath'':83C ''code'':18C,53C,71C,82C,90C,115C,124C,151C,155C,160C ''com.databricks'':127C,162C ''conf/spark-defaults.conf'':156C ''configur'':7C ''correct'':9C ''csv'':21C,56C,60C,73C,130C,165C ''download'':42C ''environment'':120C ''exampl'':63C ''export'':125C,133C ''gradl'':148C ''href'':47C,65C ''includ'':45C ''jar'':23C,44C ''li'':39C,87C,112C,145C ''make'':17C ''manual'':41C ''mvnrepository.com'':67C ''mvnrepository.com/artifact/org.apache.commons/commons-csv/1.2'':66C ''nofollow'':52C,70C ''option'':93C ''org.apache.commons.commons'':72C ''p'':1C,40C,88C,113C,146C ''packag'':91C,126C,137C,138C ''parser'':61C ''pre'':105C,123C,159C ''pre-built'':104C ''program'':27C ''put'':77C ''pyspark'':116C,134C,140C ''pyspark-shel'':139C ''rel'':51C,69C ''requir'':43C ''rest'':4C ''scala'':95C ''shell'':141C ''somewher'':79C ''spark'':20C,55C,103C,129C,164C ''spark-csv'':19C,54C,128C,163C ''spark.jars.packages'':152C,161C ''string'':149C ''submit'':117C,135C ''ul'':38C,111C ''use'':89C,94C,100C,108C,114C ''variabl'':121C ''version'':96C,107C ''way'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9987, 2, '2016-01-27 08:33:33.873', 1, NULL, NULL, '&lt;p&gt;Using a list of entities has few disadvantages:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;The list is closed&lt;/li&gt;
&lt;li&gt;The list is not context sensitive. You need context in order to differ between "a white house" and "the white house".&lt;/li&gt;
&lt;li&gt;List building require a lot of labor&lt;/li&gt;
&lt;li&gt;List might also contain errors.&lt;/li&gt;
&lt;li&gt;It does feel like cheating (or at list no NLP insights are used).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You can cope with these disadvantages by going along the direction @emre suggested and use the list in order to learn a classifier.&lt;/p&gt;

&lt;p&gt;For example, you can use tokens near the entity and learn rule like that "I live at X" is an indicator of a place and "I talked with X" is an indicator of a person.
You can play this game few rounds by increasing your list by the hits of the rules and use the new list to learn more rule.&lt;/p&gt;

&lt;p&gt;Please not that in this learning you will introduce noisy into the data so in most cases the learning should be so straight forward. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9988, 2, '2016-01-27 10:45:48.673', 1, NULL, NULL, '&lt;p&gt;The most harrowing problem would be "interpolation". That is, computing $p(x_i | y)$ for $x_i$ and $y$ you have not yet observed, but fall within the convex hull of your observations. &lt;/p&gt;

&lt;p&gt;You have to bin events together, or come up with a wacky interpolation scheme. This &lt;em&gt;does&lt;/em&gt; work. However, If you assume a distribution, $p(x_i | y)$ becomes continuous, and you end up with a more statisically sound means of interpolation. &lt;/p&gt;
', NULL, NULL, '''/em'':54C ''/p'':35C,80C ''assum'':59C ''becom'':66C ''bin'':40C ''come'':44C ''comput'':11C ''continu'':67C ''convex'':30C ''distribut'':61C ''em'':52C ''end'':70C ''event'':41C ''fall'':27C ''harrow'':4C ''howev'':56C ''hull'':31C ''interpol'':8C,49C,79C ''mean'':77C ''observ'':25C,34C ''p'':1C,12C,36C,62C ''problem'':5C ''scheme'':50C ''sound'':76C ''statis'':75C ''togeth'':42C ''wacki'':48C ''within'':28C ''work'':55C ''would'':6C ''x'':13C,17C,63C ''y'':15C,20C,65C ''yet'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9989, 2, '2016-01-27 13:47:03.163', 3, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Short Answer:&lt;/strong&gt; You could just build a model to predict purchase (0/1) and call it a day. Create a feature that is count of past purchases to control for repeat customers. BUT if you want to specifically deal with the &lt;em&gt;time&lt;/em&gt; component (which is certainly more interesting)...&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;TTE Models:&lt;/strong&gt; You should look at Time-to-Event models (&lt;a href="https://en.wikipedia.org/wiki/Accelerated_failure_time_model" rel="nofollow"&gt;accelerated time failure model&lt;/a&gt;, etc.) Normally, the event (0/1) is usually something phrased as a negative outcome (like death or loan default), but this is just because of the research fields these models stem from. There is no reason you can''t re-pose your outcome to be a purchase event. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Watch Out For:&lt;/strong&gt; Dealing with Censoring in the data. &lt;a href="https://en.wikipedia.org/wiki/Censoring_(statistics)" rel="nofollow"&gt;See Here&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Get creative&lt;/strong&gt;, I can''t really comment on great detail here because you didn''t give a feel for your exact dataset but you might try and work your dependent variable into something more concrete, for example, time between first view of customer and first purchase, or time between most resent 2 purchases. &lt;/p&gt;

&lt;p&gt;Think about the &lt;strong&gt;purchase time&lt;/strong&gt; distribution. Essentially, start to think of ways of parameterizing the purchase-time distribution given some conditionals (customer tenure, total purchases, etc.) As you expand on this vein of thought you''ll come across methods that will help. For (a simple) example, using EM to estimate the parameters of various purchase time conditional distributions. &lt;/p&gt;

&lt;p&gt;Hope this helps! &lt;/p&gt;
', NULL, NULL, '''/a'':79C,150C ''/em'':46C ''/p'':53C,128C,151C,208C,271C,276C ''/strong'':5C,58C,134C,156C,216C ''/wiki/accelerated_failure_time_model'':72C ''/wiki/censoring_(statistics)'':145C ''0/1'':15C,84C ''2'':206C ''acceler'':75C ''across'':250C ''answer'':4C ''build'':9C ''call'':17C ''censor'':137C ''certain'':50C ''come'':249C ''comment'':161C ''compon'':47C ''concret'':189C ''condit'':233C,269C ''control'':31C ''could'':7C ''count'':26C ''creat'':21C ''creativ'':155C ''custom'':34C,197C,234C ''data'':140C ''dataset'':176C ''day'':20C ''deal'':41C,135C ''death'':94C ''default'':97C ''depend'':184C ''detail'':164C ''didn'':168C ''distribut'':217C,230C,270C ''em'':44C,260C ''en.wikipedia.org'':71C,144C ''en.wikipedia.org/wiki/accelerated_failure_time_model'':70C ''en.wikipedia.org/wiki/censoring_(statistics)'':143C ''essenti'':218C ''estim'':262C ''etc'':80C,238C ''event'':66C,83C,127C ''exact'':175C ''exampl'':191C,258C ''expand'':241C ''failur'':77C ''featur'':23C ''feel'':172C ''field'':106C ''first'':194C,199C ''get'':154C ''give'':170C ''given'':231C ''great'':163C ''help'':254C,275C ''hope'':273C ''href'':69C,142C ''interest'':52C ''like'':93C ''ll'':248C ''loan'':96C ''look'':61C ''method'':251C ''might'':179C ''model'':11C,57C,67C,78C,108C ''negat'':91C ''nofollow'':74C,147C ''normal'':81C ''outcom'':92C,122C ''p'':1C,54C,129C,152C,209C,272C ''paramet'':264C ''parameter'':225C ''past'':28C ''phrase'':88C ''pose'':120C ''predict'':13C ''purchas'':14C,29C,126C,200C,207C,214C,228C,237C,267C ''purchase-tim'':227C ''re'':119C ''re-pos'':118C ''realli'':160C ''reason'':114C ''rel'':73C,146C ''repeat'':33C ''research'':105C ''resent'':205C ''see'':148C ''short'':3C ''simpl'':257C ''someth'':87C,187C ''specif'':40C ''start'':219C ''stem'':109C ''strong'':2C,55C,130C,153C,213C ''tenur'':235C ''think'':210C,221C ''thought'':246C ''time'':45C,64C,76C,192C,202C,215C,229C,268C ''time-to-ev'':63C ''total'':236C ''tri'':180C ''tte'':56C ''use'':259C ''usual'':86C ''variabl'':185C ''various'':266C ''vein'':244C ''view'':195C ''want'':38C ''watch'':131C ''way'':223C ''work'':182C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9990, 1, '2016-01-27 14:02:57.43', 3, 38, 'Estimating the battery capacity using current power consumption and battery percentage', '&lt;p&gt;I want to estimate the current maximum capacity (in kWh) having the current power consumption (in kWh) and the state of charge of the battery (in %) available in a time series. &lt;/p&gt;

&lt;p&gt;I do not have a full battery charge circle recorded but only a snippet with the state of charge going from ~95% to ~35% in a 1 second data recording interval.&lt;/p&gt;

&lt;p&gt;At first, i cumulated the current power consumption and noticed that the progression between cumulated power consumption and state of charge was almost the same (see figure 1 and 2). &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;progression of state of charge vs. cumulated power consumption&lt;/strong&gt;
&lt;a href="http://i.stack.imgur.com/Y5tTp.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Y5tTp.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;So i tried to use a linear regression model to predict two values of cumulated power consumption at 0% and 100% state of charge. In my assumption the delta of these two leaves me with the current maximum capacity of the battery.&lt;/p&gt;

&lt;p&gt;Ideally i want to monitor the capacity during the data recording. Which means i only have data of a few percent of state of charge.
In figure 3 and 4 you can see my attempt of trying to create a linear regression model for every 2% state of charge. As you can see in the right plot, the estimated capacity has a very high variance.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;LEFT: regression lines (green) of every 2% state of charge; RIGHT: estimated capacity of every 2% state of charge&lt;/strong&gt;
&lt;a href="http://i.stack.imgur.com/qOmlY.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/qOmlY.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Since i am not an expert in neither the matter of regression nor batteries/physics, my questions are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Is there a much simple or more exact way (or both) to estimate the current maximum capacity of the battery?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;If yes, is there a good way to estimate an sufficiently exact capacity with an even smaller snippet in an ongoing process, lets say every 2% state of charge? (maybe using a characteristic curve of the discharge process)&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9991, 1, '2016-01-27 14:12:48.323', 6, 126, 'Is information lost when converting an image to a single vector for analysis?', '&lt;p&gt;On several tutorials on object and pattern recognition in images (such as the typical &lt;a href="http://yann.lecun.com/exdb/mnist/" rel="nofollow"&gt;mnist digit recognition&lt;/a&gt; problem), when working with a sample of $m$ images of $n \times n$ pixels each, the first step is to build a matrix of $m \times (n * n)$, where each image is converted into a single row-vector with all of its pixels, one next to the other. Different techniques are then applied to this matrix, such as PCA.&lt;/p&gt;

&lt;p&gt;My question is: when converting a two-dimensional image to a single-dimensional vector by placing each image row next to another, the fact of which pixels are neighbours (in the same column, different row) seems to be lost. For instance, in image:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;a b c
d e f
g h i
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Which is converted to:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;a b c d e f g h i
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The fact that &lt;code&gt;b&lt;/code&gt; and &lt;code&gt;e&lt;/code&gt; were next to each other seems to be lost.&lt;/p&gt;

&lt;p&gt;Is that conclusion correct? Does that fact even matter for analysis? Are there other ways to represent the image that do preserve that piece of information?&lt;/p&gt;

&lt;p&gt;Apologies in advance if the question is too vague or basic, I''ll be glad to reformulate if needed. Thank you.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9992, 2, '2016-01-27 14:56:58.933', 8, NULL, NULL, '&lt;p&gt;The short answer is it depends on two things: what definition of information you use and if you really use that information.&lt;/p&gt;

&lt;p&gt;From information theory point of view, if your transformation is reversible than the information is there. This happens because you can apply the inverse transformation to recover the original. So nothing is lost. This is similar with what happens in various lose-less compression algorithms. &lt;/p&gt;

&lt;p&gt;So, information is there, but is encoded in another place, specifically in the transformation function. As a consequence, to learn also the position you have to create those new features to express that information. Not also that when you have a classical learner this information is not understood by learner. Take for example a linear model. There is no difference between $f_1 = \beta_0 +\beta_1 x_1 + \beta_2 x_2$ and $f_2 = \beta_2 x_2 + \beta_0 + \beta_1 x_1$. So learning with a simple linear model is impossible to use directly this locality information. This happens with most learners. So, even in the original space the locality is an information in your head, and not in the data.&lt;/p&gt;

&lt;p&gt;On the other hand if your neural net is tailored for images and expects to receive patches of images where the order of the values is important than yes, your neural net perhaps understands those relations and use them. Even so, perhaps that custom neural net has a custom way to specify the location information, so what you will have to do would be to encode this information in a format specific to that custom neural net. &lt;/p&gt;

&lt;p&gt;On the other hand if we are talking about a standard back propagation neural net, the order is irrelevant. The model is simply not able to use the location.&lt;/p&gt;

&lt;p&gt;As a conclusion, if you have a standard generic learning algorithm your only your only chance would be to encode this locality information somehow in the features. For example (this is a made-up example, I do not even know if that would word) one can add a new set of input variable, one for each square of 4 adjacent pixels and use the average of pixels intensity as value. You do not made that explicitly enough, but you created a new set of features which is created using the locality information. &lt;/p&gt;
', NULL, NULL, '''/p'':24C,72C,199C,278C,309C,404C ''0'':138C,155C ''1'':136C,140C,142C,157C,159C ''2'':144C,146C,149C,151C,153C ''4'':370C ''abl'':304C ''add'':358C ''adjac'':371C ''algorithm'':71C,321C ''also'':94C,109C ''anoth'':82C ''answer'':4C ''appli'':47C ''averag'':376C ''back'':291C ''beta'':137C,139C,143C,150C,154C,156C ''chanc'':326C ''classic'':115C ''compress'':70C ''conclus'':313C ''consequ'':91C ''creat'':100C,391C,399C ''custom'':244C,249C,275C ''data'':198C ''definit'':12C ''depend'':7C ''differ'':133C ''direct'':171C ''encod'':80C,266C,330C ''enough'':388C ''even'':181C,240C,350C ''exampl'':126C,339C,346C ''expect'':214C ''explicit'':387C ''express'':105C ''f'':135C,148C ''featur'':103C,337C,396C ''format'':271C ''function'':88C ''generic'':319C ''hand'':204C,283C ''happen'':43C,64C,176C ''head'':193C ''imag'':212C,219C ''import'':227C ''imposs'':168C ''inform'':14C,23C,27C,39C,75C,107C,118C,174C,190C,255C,268C,333C,403C ''input'':363C ''intens'':379C ''invers'':49C ''irrelev'':298C ''know'':351C ''learn'':93C,161C,320C ''learner'':116C,123C,179C ''less'':69C ''linear'':128C,165C ''local'':173C,187C,332C,402C ''locat'':254C,308C ''lose'':68C ''lose-less'':67C ''lost'':58C ''made'':344C,385C ''made-up'':343C ''model'':129C,166C,300C ''net'':208C,232C,246C,277C,294C ''neural'':207C,231C,245C,276C,293C ''new'':102C,360C,393C ''noth'':56C ''one'':356C,365C ''order'':222C,296C ''origin'':54C,184C ''p'':1C,25C,73C,200C,279C,310C ''patch'':217C ''perhap'':233C,242C ''pixel'':372C,378C ''place'':83C ''point'':29C ''posit'':96C ''propag'':292C ''realli'':20C ''receiv'':216C ''recov'':52C ''relat'':236C ''revers'':36C ''set'':361C,394C ''short'':3C ''similar'':61C ''simpl'':164C ''simpli'':302C ''somehow'':334C ''space'':185C ''specif'':84C,272C ''specifi'':252C ''squar'':368C ''standard'':290C,318C ''tailor'':210C ''take'':124C ''talk'':287C ''theori'':28C ''thing'':10C ''transform'':34C,50C,87C ''two'':9C ''understand'':234C ''understood'':121C ''use'':16C,21C,170C,238C,306C,374C,400C ''valu'':225C,381C ''variabl'':364C ''various'':66C ''view'':31C ''way'':250C ''word'':355C ''would'':263C,327C,354C ''x'':141C,145C,152C,158C ''yes'':229C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9994, 2, '2016-01-27 16:52:11.45', 1, NULL, NULL, '&lt;p&gt;Well, I would say knowing the intricacies of 1 or 2 algorithms in detail( like the inner workings of their parameters) is definitely better than knowing how to run a bunch of them.&lt;/p&gt;

&lt;p&gt;I have been in the Analytics area for about 11 years and a Data Scientist for 2.5 years and I''m speaking from experience. On the other hand, you should definitely be aware of other things out there (more recent algorithms like deep learning, SVM, XGboost etc.) which might be more applicable to your problem at hand. &lt;/p&gt;

&lt;p&gt;I think Dr. Andrew Ng''s course goes into a quite a few details of some algorithms and it''s a good start. As others have pointed out, &lt;a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/" rel="nofollow"&gt;http://statweb.stanford.edu/~tibs/ElemStatLearn/&lt;/a&gt; is a good book and it has videos to go with it.&lt;/p&gt;

&lt;p&gt;This is my personal opinion, the algorithms which you shouldn''t miss out on are:
(Know these in detail):&lt;/p&gt;

&lt;p&gt;1) Multiple linear regression
2) Logistic regression
3) Common techniques of dimensionality reduction like PCA
4) K-means clustering
5) Non-linear regression
6) Optimization methods: gradient based search methods, linear programming and discrete optimization
7) Concepts and algorithms in feature engineering
8) Simple time-series forecasting methods&lt;/p&gt;

&lt;p&gt;More Esoteric algorithms:&lt;/p&gt;

&lt;p&gt;1) Random Forests
2) SVM
3) deep learning 
4) Other methods of dimensionality reduction like LDA
5) Other kernel based methods
6) Genetic algorithms
7) XgBoost
8) Dynamic regression
9) GARCH/ARCH methods
10) Structural equation modeling
11) Box Jenkins methods in time-series forecasting
12) Information theory: information gain, mutual gain etc.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9996, 2, '2016-01-27 17:11:39.913', 1, NULL, NULL, '&lt;p&gt;An ideal data scientist is 60-70% Statistician and 30-40% a computer scientist and so the old name of "Data scientist'' was somebody who was part statistician and part computer science guy.&lt;/p&gt;
', NULL, NULL, '''-40'':12C ''-70'':8C ''/p'':35C ''30'':11C ''60'':7C ''comput'':14C,32C ''data'':4C,22C ''guy'':34C ''ideal'':3C ''name'':20C ''old'':19C ''p'':1C ''part'':28C,31C ''scienc'':33C ''scientist'':5C,15C,23C ''somebodi'':25C ''statistician'':9C,29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9997, 1, '2016-01-27 18:53:31.557', 3, 84, 'Classification of potential customers', '&lt;p&gt;I have a table with many financial information of thousands of companies around the world (revenue, income, number of employees, total assets etc) and an extra information indicating whether that company owns a specific high value machine ("1" if the company has and "0" if the company hasn''t).&lt;/p&gt;

&lt;p&gt;My goal is to identify which companies don''t have this machine, but are potential customers. My first approach was to use a classification algorithm, such as SVM, random Forest etc and use those financial characteristics as predictors of the "has machine"/"hasn''t machine" column. The "potential customers" would be, actually, the individuals the model classified as "1" (has machine), but they are actually "0" (hasn''t machine). In other words, the false positives of this model. &lt;/p&gt;

&lt;p&gt;I am not comfortable with this approach (after all, a perfect model wouldn''t indicate a single potential customer!), but I don''t know any other way I could approach this problem. I was wondering if someone could give me some direction!&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9999, 1, '2016-01-27 19:56:50.257', 4, 133, 'What''s a good machine learning algorithm for low frequency trading?', '&lt;p&gt;I''m trying to train an algorithm to copy some of the top traders on various forex social trading sites. The problem is that the traders only trade around say 10 times per month so even if I only look at minute resolution numbers that''s .02% of the time [ 10/(60*24*30)*100 ].&lt;/p&gt;

&lt;p&gt;I''ve tried using random forest and it gives an error rate of around 2% which is unacceptable and from what I''ve read most machine learning algorithms have similar errors rates.&lt;/p&gt;

&lt;p&gt;Does anyone know of a better approach?&lt;/p&gt;
', 'machine-learning  classification  random-forest', NULL, '''/p'':75C,109C,118C ''02'':66C ''10'':50C,70C ''100'':74C ''2'':91C ''24'':72C ''30'':73C ''60'':71C ''algorithm'':7A,26C,104C ''anyon'':112C ''approach'':117C ''around'':48C,90C ''better'':116C ''classif'':15B ''copi'':28C ''error'':87C,107C ''even'':55C ''forest'':18B,82C ''forex'':36C ''frequenc'':10A ''give'':85C ''good'':4A ''know'':113C ''learn'':6A,14B,103C ''look'':59C ''low'':9A ''m'':21C ''machin'':5A,13B,102C ''machine-learn'':12B ''minut'':61C ''month'':53C ''number'':63C ''p'':19C,76C,110C ''per'':52C ''problem'':41C ''random'':17B,81C ''random-forest'':16B ''rate'':88C,108C ''read'':100C ''resolut'':62C ''say'':49C ''similar'':106C ''site'':39C ''social'':37C ''time'':51C,69C ''top'':32C ''trade'':11A,38C,47C ''trader'':33C,45C ''train'':24C ''tri'':22C,79C ''unaccept'':94C ''use'':80C ''various'':35C ''ve'':78C,99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10000, 1, '2016-01-27 19:58:00.083', 3, 61, 'What is the difference between a (dynamic) Bayes network and a HMM?', '&lt;p&gt;I have read that HMMs, Particle Filters and Kalman filters are special cases of dynamic Bayes networks. However, I only know HMMs and I don''t see the difference to dynamic Bayes networks.&lt;/p&gt;

&lt;p&gt;Could somebody please explain?&lt;/p&gt;

&lt;p&gt;It would be nice if your answer could be similar to the following, but for bayes Networks:&lt;/p&gt;

&lt;h2&gt;Hidden Markov Models&lt;/h2&gt;

&lt;p&gt;A Hidden Markov Model (HMM) is a 5-tuple $\lambda = (S, O, A, B, \Pi)$:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;$S \neq \emptyset$: A set of states (e.g. "beginning of phoneme", "middle of phoneme", "end of phoneme")&lt;/li&gt;
&lt;li&gt;$O \neq \emptyset$: A set of possible observations (audio signals)&lt;/li&gt;
&lt;li&gt;$A \in \mathbb{R}^{|S| \times |S|}$: A stochastic matrix which gives probabilites $(a_{ij})$ to get from state $i$ to state $j$.&lt;/li&gt;
&lt;li&gt;$B \in \mathbb{R}^{|S| \times |O|}$: A stochastic matrix which gives probabilites $(b_{kl})$ to get in state $k$ the observation $l$.&lt;/li&gt;
&lt;li&gt;$\Pi \in \mathbb{R}^{|S|}$: Initial distribution to start in one of the states.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It is usually displayed as a directed graph, where each node corresponds to one state $s \in S$ and the transition probabilities are denoted on the edges.&lt;/p&gt;

&lt;p&gt;Hidden Markov Models are called "hidden", because the current state is hidden. The algorithms have to guess it from the observations and the model itself. They are called "Markov", because for the next state only the current state matters.&lt;/p&gt;

&lt;p&gt;For HMMs, you give a fixed topology (number of states, possible edges). Then there are 3 possible tasks&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Evaluation&lt;/strong&gt;: given a HMM $\lambda$, how likely is it to get observations $o_1, \dots, o_t$ (Forward algorithm)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Decoding&lt;/strong&gt;: given a HMM $\lambda$ and a observations $o_1, \dots, o_t$, what is the most likely sequence of states $s_1, \dots, s_t$ (Viterbi algorithm)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Learning&lt;/strong&gt;: learn $A, B, \Pi$: &lt;a href="https://en.wikipedia.org/wiki/Baum%E2%80%93Welch_algorithm" rel="nofollow"&gt;Baum-Welch algorithm&lt;/a&gt;, which is a special case of Expectation maximization.&lt;/li&gt;
&lt;/ul&gt;

&lt;h2&gt;Bayes networks&lt;/h2&gt;

&lt;p&gt;Bayes networks are directed acyclical graphs (DAGs) $G = (\mathcal{X}, \mathcal{E})$. The nodes represent random variables $X \in \mathcal{X}$. For every $X$, there is a probability distribution which is conditioned on the parents of $X$:&lt;/p&gt;

&lt;p&gt;$$P(X|\text{parents}(X))$$&lt;/p&gt;

&lt;p&gt;There seem to be (please clarify) two tasks:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Inference&lt;/strong&gt;: Given some variables, get the most likely values of the others variables. Exact inference is NP-hard. Approximately, you can use MCMC.&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Learning&lt;/strong&gt;: How you learn those distributions depends on the exact problem (&lt;a href="http://www.eng.tau.ac.il/~bengal/BN.pdf" rel="nofollow"&gt;source&lt;/a&gt;):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;known structure, fully observable:  maximum likelihood estimation (MLE)&lt;/li&gt;
&lt;li&gt;known structure, partially observable: Expectation Maximization (EM) or Markov Chain Monte Carlo (MCMC)&lt;/li&gt;
&lt;li&gt;unknown structure, fully observable: search through model space&lt;/li&gt;
&lt;li&gt;unknown structure, partially observable: EM + search through model space&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;h2&gt;Dynamic Bayes networks&lt;/h2&gt;

&lt;p&gt;I guess dynamic Bayes networks (DBNs) are also directed probabilistic graphical models. The variability seems to come from the network changing over time. However, it seems to me that this is equivalent to only copying the same network and connecting every node at time $t$ with every the corresponding node at time $t+1$. Is that the case?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10006, 1, '2016-01-27 22:35:26.847', 2, 70, 'Data categorization', '&lt;p&gt;I''m using Google API to categorize and predict problems with laptops (Hardware, Software, Network, Customer satisfaction, No reason). I inserted my training data and categories are very well evaluated and predicted. 
Training data only uses 5 main categories as it comes from free text and being specific requires some manual work.
Now I want to go a level deeper and tag these categories: Example. Laptop was returned because Display was cracked:
Hardware category was defined already, but I have a dictionary where I can also look for the word "Display". Example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Hardware:
   Display:
      failure: True
   Info:
      Physical_damage: Display cracked
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Unable to install Skype:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Software:
   Applications:
      Skype:
       failure: True
   Info:
      Description: Unable to upgrade    
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to categorize first using main categories and after main category is chosen, look into my text for possible options from a bag of words (Example: &lt;code&gt;{"display": ["screen", "display", "monitor"] }, {"applications", "skype","office","adobe","spotify"}&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Should I run these in 2 stages? Any recommended pattern or solution?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10058, 1, '2016-02-01 22:11:56.673', 2, 43, 'Analysis of Real-Time Bidding', '&lt;p&gt;I''m totally new to the topic of real-time bidding in which I know Machine Learning algorithms are used pretty often.&lt;/p&gt;

&lt;p&gt;Can somebody explain me the system in a plain language i.e. a language for a non-technical person?&lt;/p&gt;

&lt;p&gt;What is the bidding? Who bids on what? Where does Machine Learning get involve?&lt;/p&gt;

&lt;p&gt;What is &lt;a href="https://developers.google.com/ad-exchange/rtb/cookie-guide?hl=en" rel="nofollow"&gt;cookie matching&lt;/a&gt; mainly about?&lt;/p&gt;
', 'data-stream-mining', NULL, '''/a'':85C ''/ad-exchange/rtb/cookie-guide?hl=en'':80C ''/p'':35C,56C,72C,88C ''algorithm'':30C ''analysi'':1A ''bid'':6A,23C,61C,63C ''cooki'':83C ''data'':8B ''data-stream-min'':7B ''developers.google.com'':79C ''developers.google.com/ad-exchange/rtb/cookie-guide?hl=en'':78C ''explain'':39C ''get'':70C ''href'':77C ''i.e'':47C ''involv'':71C ''know'':27C ''languag'':46C,49C ''learn'':29C,69C ''m'':13C ''machin'':28C,68C ''main'':86C ''match'':84C ''mine'':10B ''new'':15C ''nofollow'':82C ''non'':53C ''non-techn'':52C ''often'':34C ''p'':11C,36C,57C,73C ''person'':55C ''plain'':45C ''pretti'':33C ''real'':4A,21C ''real-tim'':3A,20C ''rel'':81C ''somebodi'':38C ''stream'':9B ''system'':42C ''technic'':54C ''time'':5A,22C ''topic'':18C ''total'':14C ''use'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10001, 2, '2016-01-27 20:02:29.903', 1, NULL, NULL, '&lt;p&gt;In several subfields, some were simply called &lt;a href="https://en.wikipedia.org/wiki/Analyst" rel="nofollow"&gt;&lt;strong&gt;analysts&lt;/strong&gt;&lt;/a&gt;. If you go back earlier in time, in a pre-science era, I tend to believe that people involved in &lt;a href="https://en.wikipedia.org/wiki/Divination" rel="nofollow"&gt;divination&lt;/a&gt; or astrology (several of them because they were paid for that, much more than for serious science) were precursors.&lt;/p&gt;

&lt;p&gt;On a lighter note, I like the neologism &lt;em&gt;dedomenology&lt;/em&gt; (in French, &lt;a href="http://laurent-duval.blogspot.fr/search/label/dedomenology" rel="nofollow"&gt;dÃ©domÃ©nologie&lt;/a&gt;), and I tend to consider that &lt;a href="http://laurent-duval.blogspot.fr/2015/08/steinhaus-k-means-clustering-french.html" rel="nofollow"&gt;Hugo Steinhaus (the inventor of $k$-means)&lt;/a&gt; was, due  to his interest in many applied fields, one of the first to spark the fire of data science.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10027, 1, '2016-01-29 17:56:53.437', 1, 35, 'ARIMAX v. ARX Time Series Modeling', '&lt;p&gt;I need to build a time series model with explanatory variables, and &lt;a href="https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average" rel="nofollow"&gt;ARIMAX&lt;/a&gt; seems to be the one that comes up most frequently in practice, based on my survey of related work. &lt;/p&gt;

&lt;p&gt;I know &lt;a href="https://en.wikipedia.org/wiki/Autoregressive_model" rel="nofollow"&gt;ARX&lt;/a&gt; solves a similar problem, but I''m having trouble wrapping my mind around the practical differences in what an ARX representation of my data would have compared with an ARIMAX approach. &lt;/p&gt;

&lt;p&gt;I know ARX lacks the moving average component, but I''m curious whether anyone can point me toward some best practice for choosing one approach over the other. &lt;/p&gt;

&lt;p&gt;Are there certain characteristics in my data I should look for to make an informed decision?&lt;/p&gt;
', 'time-series', NULL, '''/a'':31C,63C ''/p'':51C,95C,125C,143C ''/wiki/autoregressive_integrated_moving_average'':27C ''/wiki/autoregressive_model'':59C ''anyon'':110C ''approach'':94C,121C ''arimax'':1A,30C,93C ''around'':76C ''arx'':3A,62C,83C,99C ''averag'':103C ''base'':44C ''best'':116C ''build'':14C ''certain'':129C ''characterist'':130C ''choos'':119C ''come'':38C ''compar'':90C ''compon'':104C ''curious'':108C ''data'':87C,133C ''decis'':142C ''differ'':79C ''en.wikipedia.org'':26C,58C ''en.wikipedia.org/wiki/autoregressive_integrated_moving_average'':25C ''en.wikipedia.org/wiki/autoregressive_model'':57C ''explanatori'':20C ''frequent'':41C ''href'':24C,56C ''inform'':141C ''know'':54C,98C ''lack'':100C ''look'':136C ''m'':70C,107C ''make'':139C ''mind'':75C ''model'':6A,18C ''move'':102C ''need'':12C ''nofollow'':29C,61C ''one'':36C,120C ''p'':10C,52C,96C,126C ''point'':112C ''practic'':43C,78C,117C ''problem'':67C ''rel'':28C,60C ''relat'':49C ''represent'':84C ''seem'':32C ''seri'':5A,9B,17C ''similar'':66C ''solv'':64C ''survey'':47C ''time'':4A,8B,16C ''time-seri'':7B ''toward'':114C ''troubl'':72C ''v'':2A ''variabl'':21C ''whether'':109C ''work'':50C ''would'':88C ''wrap'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10042, 2, '2016-01-31 21:06:06.353', 1, NULL, NULL, '&lt;p&gt;Yes, but no-one can tell if they will work well for your problem, so just try it and see. Don''t give up if it does not work at first, because training neural networks requires some practice; there are lots of parameters, and not every configuration will work well. Even the optimization algorithm is a hyperparameter. &lt;/p&gt;
', NULL, NULL, '''/p'':60C ''algorithm'':56C ''configur'':49C ''even'':53C ''everi'':48C ''first'':33C ''give'':25C ''hyperparamet'':59C ''lot'':43C ''network'':37C ''neural'':36C ''no-on'':4C ''one'':6C ''optim'':55C ''p'':1C ''paramet'':45C ''practic'':40C ''problem'':16C ''requir'':38C ''see'':22C ''tell'':8C ''train'':35C ''tri'':19C ''well'':13C,52C ''work'':12C,31C,51C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10004, 2, '2016-01-27 21:10:51.063', 3, NULL, NULL, '&lt;p&gt;The reason NB is called "Naive" is that is makes the assumption that the predictive variables are all &lt;strong&gt;independent&lt;/strong&gt;.
This assumption usually skews the model scores (which, under the above naive assumption are unbiased probability estimates) towards 0 or 1.&lt;/p&gt;

&lt;p&gt;In your case, e.g., the presence of words &lt;code&gt;flower&lt;/code&gt; and &lt;code&gt;petal&lt;/code&gt; indicate &lt;code&gt;gardening&lt;/code&gt; category, but, because the presence of these words is not independent (if one is present, the other is likely to be present too), the model will over-value their appearance. Taking the extreme case, if words &lt;code&gt;A&lt;/code&gt; and &lt;code&gt;B&lt;/code&gt; appear only together, then &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;P(Category=X | A &amp;amp; B) = P(Category=X | B) = P(Category=X | A)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and thus one should multiply by the odds ratio of &lt;code&gt;A&amp;amp;B&lt;/code&gt; once, not twice, as the Naive Bayes algorithm requires.&lt;/p&gt;

&lt;p&gt;Your remedy is to use &lt;em&gt;calibration&lt;/em&gt; (a separate model which maps model scores to probabilities).&lt;/p&gt;
', NULL, NULL, '''/code'':56C,60C,64C,104C,108C,130C,147C ''/em'':166C ''/p'':44C,113C,157C,176C ''/pre'':131C ''/strong'':22C ''0'':41C ''1'':43C ''algorithm'':155C ''amp'':120C,145C ''appear'':95C,109C ''assumpt'':13C,24C,35C ''b'':107C,121C,125C,146C ''bay'':154C ''calibr'':165C ''call'':6C ''case'':48C,99C ''categori'':65C,117C,123C,127C ''code'':54C,58C,62C,102C,106C,115C,143C ''e.g'':49C ''em'':164C ''estim'':39C ''extrem'':98C ''flower'':55C ''garden'':63C ''independ'':21C,75C ''indic'':61C ''like'':83C ''make'':11C ''map'':171C ''model'':28C,89C,169C,172C ''multipli'':137C ''naiv'':7C,34C,153C ''nb'':4C ''odd'':140C ''one'':77C,135C ''over-valu'':91C ''p'':1C,45C,116C,122C,126C,132C,158C ''petal'':59C ''pre'':114C ''predict'':16C ''presenc'':51C,69C ''present'':79C,86C ''probabl'':38C,175C ''ratio'':141C ''reason'':3C ''remedi'':160C ''requir'':156C ''score'':29C,173C ''separ'':168C ''skew'':26C ''strong'':20C ''take'':96C ''thus'':134C ''togeth'':111C ''toward'':40C ''twice'':150C ''unbias'':37C ''use'':163C ''usual'':25C ''valu'':93C ''variabl'':17C ''word'':53C,72C,101C ''x'':118C,124C,128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10005, 1, '2016-01-27 22:05:51.333', 2, 35, 'Difference between rbfnn and svr with gaussian kernel', '&lt;p&gt;I try to understand the difference between radial basis neuron network and support vector regression with Gaussian Kernel. I watched Andrew Ng presentation about kernels in SVN, I have read also an introduction to RBFNN in Bishop''s book and Wikipedia''s article about SVR.&lt;/p&gt;

&lt;p&gt;As I understand it correctly in both cases I try to find a function from features space to R by computation of gaussians of a distances between data from training set to some centers distributed in feature space (activation of rbfs). Next I try to find the best set of linear combination of coefficients, to approximate my data as a linear combination of activated RBFs.&lt;/p&gt;

&lt;p&gt;However in some papers these methods are mentioned as two different, competitive methods.&lt;/p&gt;

&lt;p&gt;So my question is what exactly is the difference? &lt;/p&gt;
', 'machine-learning  neuralnetwork  svm', NULL, '''/p'':60C,127C,142C,153C ''activ'':100C,125C ''also'':45C ''andrew'':35C ''approxim'':117C ''articl'':57C ''basi'':23C ''best'':109C ''bishop'':51C ''book'':53C ''case'':69C ''center'':95C ''coeffici'':115C ''combin'':113C,123C ''competit'':140C ''comput'':82C ''correct'':66C ''data'':89C,119C ''differ'':1A,20C,139C,152C ''distanc'':87C ''distribut'':96C ''exact'':149C ''featur'':77C,98C ''find'':73C,107C ''function'':75C ''gaussian'':7A,31C,84C ''howev'':129C ''introduct'':47C ''kernel'':8A,32C,39C ''learn'':11B ''linear'':112C,122C ''machin'':10B ''machine-learn'':9B ''mention'':136C ''method'':134C,141C ''network'':25C ''neuralnetwork'':12B ''neuron'':24C ''next'':103C ''ng'':36C ''p'':14C,61C,128C,143C ''paper'':132C ''present'':37C ''question'':146C ''r'':80C ''radial'':22C ''rbfnn'':3A,49C ''rbfs'':102C,126C ''read'':44C ''regress'':29C ''set'':92C,110C ''space'':78C,99C ''support'':27C ''svm'':13B ''svn'':41C ''svr'':5A,59C ''train'':91C ''tri'':16C,71C,105C ''two'':138C ''understand'':18C,64C ''vector'':28C ''watch'':34C ''wikipedia'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10007, 2, '2016-01-28 03:15:22.817', 2, NULL, NULL, '&lt;p&gt;Some really nice answers already. However, I would break the entire process of breaking the work of a data scientist into who actually did those:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;strong&gt;Getting the data from databases and other sources:&lt;/strong&gt; Generally, it used to be the &lt;strong&gt;DBA&lt;/strong&gt; who gets the data from the DB''s and the people who collects data from other sources are called &lt;em&gt;data guys&lt;/em&gt;, they don''t really have a specific name (atleast in India). And the scraping and crawling scripts are written by &lt;strong&gt;software engineers&lt;/strong&gt; who are hired especially for that purpose.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Analytics and prediction:&lt;/strong&gt; Done by people called the &lt;strong&gt;statisticians&lt;/strong&gt; or the &lt;strong&gt;mathematicians&lt;/strong&gt;.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Visualizations and reporting:&lt;/strong&gt; Done by people called &lt;strong&gt;business analysts&lt;/strong&gt; or the MBA guys in the company.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Big Data and pipelining stuff:&lt;/strong&gt; Done by &lt;strong&gt;software engineers&lt;/strong&gt; hired especially for thar particular purpose.&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/em'':71C ''/li'':103C,123C,145C,166C ''/ol'':167C ''/p'':27C ''/strong'':39C,48C,95C,109C,117C,122C,129C,137C,153C,159C ''actual'':24C ''alreadi'':6C ''analyst'':136C ''analyt'':106C ''answer'':5C ''atleast'':80C ''big'':148C ''break'':10C,15C ''busi'':135C ''call'':67C,113C,133C ''collect'':61C ''compani'':144C ''crawl'':87C ''data'':20C,33C,52C,62C,69C,149C ''databas'':35C ''db'':55C ''dba'':47C ''done'':110C,130C,154C ''em'':68C ''engin'':94C,158C ''entir'':12C ''especi'':99C,161C ''general'':40C ''get'':31C,50C ''guy'':70C,141C ''hire'':98C,160C ''howev'':7C ''india'':82C ''li'':29C,104C,124C,146C ''mathematician'':121C ''mba'':140C ''name'':79C ''nice'':4C ''ol'':28C ''p'':1C ''particular'':164C ''peopl'':59C,112C,132C ''pipelin'':151C ''predict'':108C ''process'':13C ''purpos'':102C,165C ''realli'':3C,75C ''report'':128C ''scientist'':21C ''scrape'':85C ''script'':88C ''softwar'':93C,157C ''sourc'':38C,65C ''specif'':78C ''statistician'':116C ''strong'':30C,46C,92C,105C,115C,120C,125C,134C,147C,156C ''stuff'':152C ''thar'':163C ''use'':42C ''visual'':126C ''work'':17C ''would'':9C ''written'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10028, 1, '2016-01-29 19:39:59.317', 0, 15, 'NLP : Rules for chunking Verb Phrases', '&lt;p&gt;I have read a lot of documentation surrounding &lt;code&gt;NP&lt;/code&gt; chunking but what about Verb Phrases? Has there been a fixed set of rules for &lt;code&gt;VP&lt;/code&gt; chunking? &lt;/p&gt;
', 'nlp  nltk', NULL, '''/code'':20C,38C ''/p'':40C ''chunk'':4A,21C,39C ''code'':18C,36C ''document'':16C ''fix'':31C ''lot'':14C ''nlp'':1A,7B ''nltk'':8B ''np'':19C ''p'':9C ''phrase'':6A,26C ''read'':12C ''rule'':2A,34C ''set'':32C ''surround'':17C ''verb'':5A,25C ''vp'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10008, 2, '2016-01-28 06:22:11.38', 1, NULL, NULL, '&lt;p&gt;In case that the number of items is quite small, turning the problem into a classification problem will be the most convenient solution. Use each item as a feature, the class as a concept. Now you can apply the methods that @OvisAmmon recommended on or other classification methods.&lt;/p&gt;

&lt;p&gt;However, usually in market basket analysis there are many items and each user buy few of them.  That leads to a very sparse matrix and most classification methods have problems dealing with them.&lt;/p&gt;

&lt;p&gt;A common method to analyze market basket data is using 
&lt;a href="https://en.wikipedia.org/wiki/Association_rule_learning" rel="nofollow"&gt;Association rules&lt;/a&gt; which are implemented by first locating frequent item sets.&lt;/p&gt;

&lt;p&gt;You can use association rules on order to learn on market basket in the following way:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Add item for every class&lt;/li&gt;
&lt;li&gt;Add the owner class to its basket (e.g, transform {milk, beer} basket of a student in to the basket {mlik, beer, student}&lt;/li&gt;
&lt;li&gt;Use a frequent item set algorithm to find them&lt;/li&gt;
&lt;li&gt;Remove all frequent item sets that don''t have a class member in them. Such sets can''t help deduce the class.&lt;/li&gt;
&lt;li&gt;From the frequent items set find rules that imply the classes (with good enough support and confidence&lt;/li&gt;
&lt;li&gt;Use the these rules that match a basket in order to predict class&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You should note some points:&lt;/p&gt;

&lt;p&gt;Some association rules with different implication might apply to the same basket. You might have the rules milk -&gt; X and beer -Y and the basket {milk, beer}. If the set {milk, beer} is frequent enough you might have a rule specifically to it. Otherwise you''ll need a met rule in order to base by the rules. Such rules might be taking the rule with the highest confidence or the most likely class.&lt;/p&gt;

&lt;p&gt;Association rules are based only on the existence of item, not on the lack of them. If students never buy diapers, you will not find such association rule. You can add "no diapers" as an item to your data set but that will turn it from a sparse one to a full one and the storage might be unfeasible.&lt;/p&gt;

&lt;p&gt;The complexity of association rules is exponential. The is simply since the output it self is exponential , as can be noted when you have a &lt;a href="https://en.wikipedia.org/wiki/Clique_(graph_theory)" rel="nofollow"&gt;Clique&lt;/a&gt; in the data.
Usually people don''t try to find all association rules due to that problem, which is the practical thing to do. 
However, it means that there might be association rules in your data that you won''t find.&lt;/p&gt;
', NULL, NULL, '''/a'':106C,414C ''/li'':142C,166C,177C,201C,220C,235C ''/p'':50C,85C,116C,134C,243C,318C,379C,456C ''/ul'':236C ''/wiki/association_rule_learning'':101C ''/wiki/clique_(graph_theory)'':410C ''add'':137C,144C,350C ''algorithm'':173C ''analysi'':57C ''analyz'':91C ''appli'':39C,252C ''associ'':104C,121C,246C,320C,346C,384C,426C,446C ''base'':298C,323C ''basket'':56C,93C,129C,150C,155C,162C,229C,256C,269C ''beer'':154C,164C,265C,271C,276C ''buy'':65C,339C ''case'':3C ''class'':32C,141C,147C,189C,200C,213C,234C,317C ''classif'':17C,48C,78C ''cliqu'':413C ''common'':88C ''complex'':382C ''concept'':35C ''confid'':219C,312C ''conveni'':23C ''data'':94C,358C,417C,450C ''deal'':82C ''deduc'':198C ''diaper'':340C,352C ''differ'':249C ''due'':428C ''e.g'':151C ''en.wikipedia.org'':100C,409C ''en.wikipedia.org/wiki/association_rule_learning'':99C ''en.wikipedia.org/wiki/clique_(graph_theory)'':408C ''enough'':216C,279C ''everi'':140C ''exist'':327C ''exponenti'':387C,397C ''featur'':30C ''find'':175C,208C,344C,424C,455C ''first'':111C ''follow'':132C ''frequent'':113C,170C,181C,205C,278C ''full'':371C ''good'':215C ''help'':197C ''highest'':311C ''howev'':52C,439C ''href'':98C,407C ''implement'':109C ''impli'':211C ''implic'':250C ''item'':8C,27C,61C,114C,138C,171C,182C,206C,329C,355C ''lack'':333C ''lead'':70C ''learn'':126C ''li'':136C,143C,167C,178C,202C,221C ''like'':316C ''ll'':290C ''locat'':112C ''mani'':60C ''market'':55C,92C,128C ''match'':227C ''matrix'':75C ''mean'':441C ''member'':190C ''met'':293C ''method'':41C,49C,79C,89C ''might'':251C,258C,281C,304C,376C,444C ''milk'':153C,262C,270C,275C ''mlik'':163C ''need'':291C ''never'':338C ''nofollow'':103C,412C ''note'':240C,401C ''number'':6C ''one'':368C,372C ''order'':124C,231C,296C ''otherwis'':288C ''output'':393C ''ovisammon'':43C ''owner'':146C ''p'':1C,51C,86C,117C,237C,244C,319C,380C ''peopl'':419C ''point'':242C ''practic'':435C ''predict'':233C ''problem'':14C,18C,81C,431C ''quit'':10C ''recommend'':44C ''rel'':102C,411C ''remov'':179C ''rule'':105C,122C,209C,225C,247C,261C,284C,294C,301C,303C,308C,321C,347C,385C,427C,447C ''self'':395C ''set'':115C,172C,183C,194C,207C,274C,359C ''simpli'':390C ''sinc'':391C ''small'':11C ''solut'':24C ''spars'':74C,367C ''specif'':285C ''storag'':375C ''student'':158C,165C,337C ''support'':217C ''take'':306C ''thing'':436C ''transform'':152C ''tri'':422C ''turn'':12C,363C ''ul'':135C ''unfeas'':378C ''use'':25C,96C,120C,168C,222C ''user'':64C ''usual'':53C,418C ''way'':133C ''won'':453C ''x'':263C ''y'':266C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10009, 2, '2016-01-28 09:52:40.29', 2, NULL, NULL, '&lt;p&gt;You can download a free as in beer software Qlikview that allows you to do interactive data discovery via graphical interface similar to Excel but also featuring a powerful scripting language for data load and transformation. Huge flat files is no problem at all. It is an in-memory technology so you''d need a computer with a lot of RAM. 
The advantage though is that it can load billions of records in a star schema but still allows you to do discovery ad hoc (second or sub-second time) without writing and rewriting SQL. I am always using it to screen the data and run descriptive statistics + visual exploration on it. 
From Data Science standpoint this is a very advanced column based data engine integrated with a powerful dictionary of descriptive stat functions and interactive graphics UI. You will be surprised what is possible with your data.&lt;/p&gt;
', NULL, NULL, '''/p'':152C ''ad'':86C ''advanc'':124C ''advantag'':65C ''allow'':13C,81C ''also'':27C ''alway'':101C ''base'':126C ''beer'':9C ''billion'':72C ''column'':125C ''comput'':58C ''d'':55C ''data'':18C,34C,107C,117C,127C,151C ''descript'':110C,135C ''dictionari'':133C ''discoveri'':19C,85C ''download'':4C ''engin'':128C ''excel'':25C ''explor'':113C ''featur'':28C ''file'':40C ''flat'':39C ''free'':6C ''function'':137C ''graphic'':21C,140C ''hoc'':87C ''huge'':38C ''in-memori'':49C ''integr'':129C ''interact'':17C,139C ''interfac'':22C ''languag'':32C ''load'':35C,71C ''lot'':61C ''memori'':51C ''need'':56C ''p'':1C ''possibl'':148C ''power'':30C,132C ''problem'':43C ''qlikview'':11C ''ram'':63C ''record'':74C ''rewrit'':97C ''run'':109C ''schema'':78C ''scienc'':118C ''screen'':105C ''script'':31C ''second'':88C,92C ''similar'':23C ''softwar'':10C ''sql'':98C ''standpoint'':119C ''star'':77C ''stat'':136C ''statist'':111C ''still'':80C ''sub'':91C ''sub-second'':90C ''surpris'':145C ''technolog'':52C ''though'':66C ''time'':93C ''transform'':37C ''ui'':141C ''use'':102C ''via'':20C ''visual'':112C ''without'':94C ''write'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10296, 1, '2016-02-18 00:08:09.197', 0, 32, 'Why is there no end user Application, yet?', '&lt;p&gt;machine learning is being hyped since the deep neural networks.&lt;/p&gt;

&lt;p&gt;It seems to me, that you have to program in order to do machine learning.
But is the process of training data and labeling data is the same of every problem. Why isn''t there an Excel like application that enables thousands of non experts to do machine learning ?&lt;/p&gt;

&lt;p&gt;Disclaimer : I am not a data scientist .&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':23C,74C,83C ''applic'':7A,63C ''data'':46C,49C,81C ''deep'':20C ''disclaim'':76C ''enabl'':65C ''end'':5A ''everi'':54C ''excel'':61C ''expert'':69C ''hype'':17C ''isn'':57C ''label'':48C ''learn'':11B,14C,39C,73C ''like'':62C ''machin'':10B,13C,38C,72C ''machine-learn'':9B ''network'':22C ''neural'':21C ''non'':68C ''order'':35C ''p'':12C,24C,75C ''problem'':55C ''process'':43C ''program'':33C ''scientist'':82C ''seem'':26C ''sinc'':18C ''thousand'':66C ''train'':45C ''user'':6A ''yet'':8A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10048, 1, '2016-02-01 15:18:33.907', 1, 109, 'What is the best Keras model for multi-label classification?', '&lt;p&gt;I am working on research, where need to classify one of three event WINNER=(&lt;code&gt;win&lt;/code&gt;, &lt;code&gt;draw&lt;/code&gt;, &lt;code&gt;lose&lt;/code&gt;)&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;WINNER  LEAGUE  HOME    AWAY    MATCH_HOME  MATCH_DRAW  MATCH_AWAY  MATCH_U2_50 MATCH_O2_50
3         13    550      571          1.86        3.34        4.23       1.66     2.11
3         7     322     334           7.55         4.1         1.4       2.17     1.61
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;My current model is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def build_model(input_dim, output_classes):
    model = Sequential()
    model.add(Dense(input_dim=input_dim, output_dim=12, activation=relu))
    model.add(Dropout(0.5))
    model.add(Dense(output_dim=output_classes, activation=''softmax''))
    model.compile(loss=''categorical_crossentropy'', optimizer=''adadelta'')
    return model
&lt;/code&gt;&lt;/pre&gt;

&lt;ol&gt;
&lt;li&gt;I am not sure that is the correct one for multi-label classification&lt;/li&gt;
&lt;li&gt;What is the best setup for binary classification?&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;EDIT: #2 - Like that?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;model.add(Dense(input_dim=input_dim, output_dim=12, activation=''sigmoid''))
model.add(Dropout(0.5))
model.add(Dense(output_dim=output_classes, activation=''softmax''))
model.compile(loss=''binary_crossentropy'', optimizer=''adadelta'')
&lt;/code&gt;&lt;/pre&gt;
', 'classification  python  neuralnetwork  clustering', NULL, '''/code'':33C,36C,39C,77C,126C,192C ''/li'':144C,154C ''/ol'':155C ''/p'':40C,84C,161C ''/pre'':78C,127C,193C ''0.5'':109C,177C ''1.4'':74C ''1.61'':76C ''1.66'':66C ''1.86'':63C ''12'':104C,172C ''13'':60C ''2'':158C ''2.11'':67C ''2.17'':75C ''3'':59C,68C ''3.34'':64C ''322'':70C ''334'':71C ''4.1'':73C ''4.23'':65C ''50'':55C,58C ''550'':61C ''571'':62C ''7'':69C ''7.55'':72C ''activ'':105C,116C,173C,184C ''adadelta'':123C,191C ''away'':46C,52C ''best'':4A,149C ''binari'':152C,188C ''build'':88C ''categor'':120C ''class'':93C,115C,183C ''classif'':11A,12B,143C,153C ''classifi'':25C ''cluster'':15B ''code'':31C,34C,37C,42C,86C,163C ''correct'':137C ''crossentropi'':121C,189C ''current'':81C ''def'':87C ''dens'':97C,111C,165C,179C ''dim'':91C,99C,101C,103C,113C,167C,169C,171C,181C ''draw'':35C,50C ''dropout'':108C,176C ''edit'':157C ''event'':29C ''home'':45C,48C ''input'':90C,98C,100C,166C,168C ''kera'':5A ''label'':10A,142C ''leagu'':44C ''li'':129C,145C ''like'':159C ''lose'':38C ''loss'':119C,187C ''match'':47C,49C,51C,53C,56C ''model'':6A,82C,89C,94C,125C ''model.add'':96C,107C,110C,164C,175C,178C ''model.compile'':118C,186C ''multi'':9A,141C ''multi-label'':8A,140C ''need'':23C ''neuralnetwork'':14B ''o2'':57C ''ol'':128C ''one'':26C,138C ''optim'':122C,190C ''output'':92C,102C,112C,114C,170C,180C,182C ''p'':16C,79C,156C ''pre'':41C,85C,162C ''python'':13B ''relu'':106C ''research'':21C ''return'':124C ''sequenti'':95C ''setup'':150C ''sigmoid'':174C ''softmax'':117C,185C ''sure'':133C ''three'':28C ''u2'':54C ''win'':32C ''winner'':30C,43C ''work'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10010, 2, '2016-01-28 12:41:51.127', 2, NULL, NULL, '&lt;p&gt;Speaking as a Graph/Complex Networks guy I''d recommend &lt;a href="http://networkx.github.io/" rel="nofollow"&gt;Networkx&lt;/a&gt; package in Python. This is the main library I used for my master thesis and my research during last 2 years. As long as your graph is not gigantic (millions of nodes) you can handle it using Networkx.&lt;/p&gt;

&lt;p&gt;But what you need is not only the library but a philosophy to convert your data into a graph. What is your data exactly? If you have some addresses, what would be the node and what would define an edge? This is important actually. Graph is not constructed from your addresses unless you have a definition for vertices and edges.&lt;/p&gt;

&lt;p&gt;When you convert your data into a graph Networkx provides you all fancy algorithms for everything (shortest path, community detection, statistical analysis, etc).&lt;/p&gt;

&lt;p&gt;If you provide info in the comment here I''ll answer your question in details.&lt;/p&gt;
', NULL, NULL, '''/a'':17C ''/p'':56C,118C,143C,160C ''2'':37C ''actual'':101C ''address'':86C,108C ''algorithm'':133C ''analysi'':141C ''answer'':155C ''comment'':151C ''communiti'':138C ''construct'':105C ''convert'':71C,122C ''d'':9C ''data'':73C,80C,124C ''defin'':95C ''definit'':113C ''detail'':159C ''detect'':139C ''edg'':97C,117C ''etc'':142C ''everyth'':135C ''exact'':81C ''fanci'':132C ''gigant'':46C ''graph'':43C,76C,102C,127C ''graph/complex'':5C ''guy'':7C ''handl'':52C ''href'':12C ''import'':100C ''info'':148C ''last'':36C ''librari'':25C,66C ''ll'':154C ''long'':40C ''main'':24C ''master'':30C ''million'':47C ''need'':61C ''network'':6C ''networkx'':16C,55C,128C ''networkx.github.io'':13C ''node'':49C,91C ''nofollow'':15C ''p'':1C,57C,119C,144C ''packag'':18C ''path'':137C ''philosophi'':69C ''provid'':129C,147C ''python'':20C ''question'':157C ''recommend'':10C ''rel'':14C ''research'':34C ''shortest'':136C ''speak'':2C ''statist'':140C ''thesi'':31C ''unless'':109C ''use'':27C,54C ''vertic'':115C ''would'':88C,94C ''year'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10011, 1, '2016-01-28 13:13:52.68', 3, 29, 'Assessing significance / confidence of a crossvalidated performance measure', '&lt;p&gt;I have a prediction model $P$ and I use some performance measure $I$ to measure $P$''s accuracy. 
The distribution of $I$ is unknown (it''s a custom metric, which is somehow similar to the &lt;a href="https://en.wikipedia.org/wiki/Precision_and_recall" rel="nofollow"&gt;precision&lt;/a&gt; metric).&lt;/p&gt;

&lt;p&gt;My validation prediction is as following: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Randomly split the data to $k$ stratified folds&lt;/li&gt;
&lt;li&gt;Fit $k$ models &lt;/li&gt;
&lt;li&gt;Estimate each model according to $I$ (which results $k$-crossvalidated values of $I$)&lt;/li&gt;
&lt;li&gt;The final model prediction performance is calculated by the average of $k$-crossvalidated $I$ measures.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I would like to perform some &lt;strong&gt;significance testing&lt;/strong&gt; - to be able to say the &lt;em&gt;confidence&lt;/em&gt; for the model prediction performance - to what extent I am âsureâ in this $I$?&lt;/p&gt;
', 'statistics  cross-validation  evaluation', NULL, '''/a'':58C ''/em'':137C ''/li'':79C,84C,99C,116C ''/p'':60C,68C,153C ''/strong'':128C ''/ul'':117C ''/wiki/precision_and_recall'':54C ''abl'':131C ''accord'':89C ''accuraci'':32C ''assess'':1A ''averag'':110C ''calcul'':107C ''confid'':3A,136C ''cross'':11B ''cross-valid'':10B ''crossvalid'':6A,95C,113C ''custom'':42C ''data'':74C ''distribut'':34C ''em'':135C ''en.wikipedia.org'':53C ''en.wikipedia.org/wiki/precision_and_recall'':52C ''estim'':86C ''evalu'':13B ''extent'':145C ''final'':102C ''fit'':81C ''fold'':78C ''follow'':67C ''href'':51C ''k'':76C,82C,94C,112C ''li'':70C,80C,85C,100C ''like'':121C ''measur'':8A,26C,29C,115C ''metric'':43C,59C ''model'':19C,83C,88C,103C,140C ''nofollow'':56C ''p'':14C,20C,30C,61C,118C ''perform'':7A,25C,105C,123C,142C ''precis'':57C ''predict'':18C,64C,104C,141C ''random'':71C ''rel'':55C ''result'':93C ''say'':133C ''signific'':2A,126C ''similar'':47C ''somehow'':46C ''split'':72C ''statist'':9B ''stratifi'':77C ''strong'':125C ''sureâ'':149C ''test'':127C ''ul'':69C ''unknown'':38C ''use'':23C ''valid'':12B,63C ''valu'':96C ''would'':120C ''â'':148C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10012, 2, '2016-01-28 14:34:36.233', 2, NULL, NULL, '&lt;p&gt;Random forests, GBM or even the newer and fancier xgboost are not the best candidates for binary classification (predicting ups and down) of stocks predictions or forex trading or at least not as the main algorithm. The reason is that, for this particular problem, they require a huge amount of trees (and tree depth in case of GBM or xgboost) to obtain reasonable accuracy (Breiman suggested using at least 5000 trees and to "not be stingy" and in fact his main ML paper on RF he used 50,000 trees per run).&lt;/p&gt;

&lt;p&gt;However, some quants use random forests as feature selectors while others use it to generate new features. It all depends on the characteristics of the data.&lt;/p&gt;

&lt;p&gt;I would suggest you read this &lt;a href="https://quant.stackexchange.com/questions/9313/machine-learning-vs-regression-and-or-why-still-use-the-latter/9317#9317"&gt;question and answers on quant.stackexchange&lt;/a&gt; where people discuss what methods are the best and when to use them, among them ISOMAP, Laplacian eigenmaps, ANNs, swarm optimization. &lt;/p&gt;

&lt;p&gt;Check out the &lt;a href="https://quant.stackexchange.com/tags/machine-learning/hot"&gt;machine-learning tag on the same site&lt;/a&gt;, there you might find information related to your particular dataset.&lt;/p&gt;
', NULL, NULL, '''/a'':140C,180C ''/p'':94C,122C,162C,191C ''/questions/9313/machine-learning-vs-regression-and-or-why-still-use-the-latter/9317#9317'':134C ''/tags/machine-learning/hot'':171C ''000'':90C ''50'':89C ''5000'':71C ''accuraci'':65C ''algorithm'':37C ''among'':154C ''amount'':50C ''ann'':159C ''answer'':137C ''best'':15C,148C ''binari'':18C ''breiman'':66C ''candid'':16C ''case'':57C ''characterist'':118C ''check'':164C ''classif'':19C ''data'':121C ''dataset'':190C ''depend'':115C ''depth'':55C ''discuss'':143C ''eigenmap'':158C ''even'':6C ''fact'':80C ''fancier'':10C ''featur'':103C,112C ''find'':184C ''forest'':3C,101C ''forex'':28C ''gbm'':4C,59C ''generat'':110C ''howev'':96C ''href'':131C,168C ''huge'':49C ''inform'':185C ''isomap'':156C ''laplacian'':157C ''learn'':174C ''least'':32C,70C ''machin'':173C ''machine-learn'':172C ''main'':36C,82C ''method'':145C ''might'':183C ''ml'':83C ''new'':111C ''newer'':8C ''obtain'':63C ''optim'':161C ''other'':106C ''p'':1C,95C,123C,163C ''paper'':84C ''particular'':44C,189C ''peopl'':142C ''per'':92C ''predict'':20C,26C ''problem'':45C ''quant'':98C ''quant.stackexchange'':139C ''quant.stackexchange.com'':133C,170C ''quant.stackexchange.com/questions/9313/machine-learning-vs-regression-and-or-why-still-use-the-latter/9317#9317'':132C ''quant.stackexchange.com/tags/machine-learning/hot'':169C ''question'':135C ''random'':2C,100C ''read'':128C ''reason'':39C,64C ''relat'':186C ''requir'':47C ''rf'':86C ''run'':93C ''selector'':104C ''site'':179C ''stingi'':77C ''stock'':25C ''suggest'':67C,126C ''swarm'':160C ''tag'':175C ''trade'':29C ''tree'':52C,54C,72C,91C ''up'':21C ''use'':68C,88C,99C,107C,152C ''would'':125C ''xgboost'':11C,61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10013, 1, '2016-01-28 16:02:34.74', 2, 39, 'full name of each variable in Plotmo graph', '&lt;p&gt;R function plotmo &lt;a href="http://www.milbo.users.sonic.net/gallery/plotmo.html" rel="nofollow"&gt;http://www.milbo.users.sonic.net/gallery/plotmo.html&lt;/a&gt; is used for plotting dependence plot from e.g. decision tree model. &lt;/p&gt;

&lt;p&gt;In some cases, resulting graph does not print full names of features on each plot. What I am saying is, e.g. the "feature1" is automatically renamed "f1" or something similar.&lt;/p&gt;

&lt;p&gt;I want full name. But plotmo just adjust automatically to short name (I believe for memory convenience, but not useful for perception).  &lt;/p&gt;

&lt;p&gt;Maybe Plotmo has parameter forcing it to produce full name? I just see -main parameter. But what values to set as to produce full original feature names for each generated plot?&lt;/p&gt;
', 'machine-learning  r', NULL, '''/gallery/plotmo.html'':21C ''/gallery/plotmo.html&lt;/a&gt;'':26C ''/p'':38C,70C,94C,127C ''adjust'':79C ''automat'':64C,80C ''believ'':85C ''case'':42C ''conveni'':88C ''decis'':35C ''depend'':31C ''e.g'':34C,60C ''f1'':66C ''featur'':51C,121C ''feature1'':62C ''forc'':100C ''full'':1A,48C,74C,104C,119C ''function'':15C ''generat'':125C ''graph'':8A,44C ''href'':18C ''learn'':11B ''machin'':10B ''machine-learn'':9B ''main'':109C ''mayb'':96C ''memori'':87C ''model'':37C ''name'':2A,49C,75C,83C,105C,122C ''nofollow'':23C ''origin'':120C ''p'':13C,39C,71C,95C ''paramet'':99C,110C ''percept'':93C ''plot'':30C,32C,54C,126C ''plotmo'':7A,16C,77C,97C ''print'':47C ''produc'':103C,118C ''r'':12B,14C ''rel'':22C ''renam'':65C ''result'':43C ''say'':58C ''see'':108C ''set'':115C ''short'':82C ''similar'':69C ''someth'':68C ''tree'':36C ''use'':28C,91C ''valu'':113C ''variabl'':5A ''want'':73C ''www.milbo.users.sonic.net'':20C,25C ''www.milbo.users.sonic.net/gallery/plotmo.html'':19C ''www.milbo.users.sonic.net/gallery/plotmo.html&lt;/a&gt;'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10015, 1, '2016-01-29 03:10:22.99', 5, 133, 'Implement MLP in tensorflow', '&lt;p&gt;There are many resources online about how to implement MLP in tensorflow, and most of the samples do work :) But I am interested in a particular one, that I learned from &lt;a href="https://www.coursera.org/learn/machine-learning"&gt;https://www.coursera.org/learn/machine-learning&lt;/a&gt;. In which, it uses a &lt;em&gt;cost&lt;/em&gt; function defined as follow:&lt;/p&gt;

&lt;p&gt;$
J(\theta) = \frac{1}{m} \sum_{i=1}^{m} \sum_{k=1}^{K} \left[ -y_k^{(i)} \log((h_\theta(x^{(i)}))_k - (1 - y_k^{(i)}) \log(1 - (h_\theta(x^{(i)}))_k \right]
$&lt;/p&gt;

&lt;p&gt;$h_\theta$ is the &lt;em&gt;sigmoid&lt;/em&gt; function.&lt;/p&gt;

&lt;p&gt;And there''s my implementation:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# one hidden layer MLP

x = tf.placeholder(tf.float32, shape=[None, 784])
y = tf.placeholder(tf.float32, shape=[None, 10])

W_h1 = tf.Variable(tf.random_normal([784, 512]))
h1 = tf.nn.sigmoid(tf.matmul(x, W_h1))

W_out = tf.Variable(tf.random_normal([512, 10]))
y_ = tf.matmul(h1, W_out)

# cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(y_, y)
cross_entropy = tf.reduce_sum(- y * tf.log(y_) - (1 - y) * tf.log(1 - y_), 1)
loss = tf.reduce_mean(cross_entropy)
train_step = tf.train.GradientDescentOptimizer(0.05).minimize(loss)

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# train
with tf.Session() as s:
    s.run(tf.initialize_all_variables())

    for i in range(10000):
        batch_x, batch_y = mnist.train.next_batch(100)
        s.run(train_step, feed_dict={x: batch_x, y: batch_y})

        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={x: batch_x, y: batch_y})
            print(''step {0}, training accuracy {1}''.format(i, train_accuracy))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I think the definition for the layers are correct, but the problem is in the &lt;em&gt;cross_entropy&lt;/em&gt;. If I use the first one, &lt;em&gt;the one got commented out&lt;/em&gt;, the model converges quickly; &lt;strong&gt;but if I use the 2nd one, which I think/hope is the translation of the previous equation, the model won''t converge.&lt;/strong&gt;&lt;/p&gt;
', 'machine-learning  tensorflow', NULL, '''/code'':265C ''/em'':56C,106C,286C,299C ''/learn/machine-learning'':45C ''/learn/machine-learning&lt;/a&gt;.'':48C ''/p'':61C,98C,108C,115C,328C ''/pre'':266C ''/strong'':327C ''0'':243C,257C ''0.05'':189C ''1'':66C,70C,74C,86C,91C,175C,178C,180C,197C,200C,260C ''10'':133C,153C ''100'':228C,242C ''10000'':221C ''2nd'':310C ''512'':140C,152C ''784'':127C,139C ''accuraci'':201C,245C,259C,264C ''accuracy.eval'':246C ''batch'':222C,224C,227C,235C,238C,250C,253C ''code'':117C ''comment'':297C ''converg'':302C,326C ''correct'':192C,205C,276C ''cost'':55C ''cross'':159C,162C,168C,184C,284C ''defin'':58C ''definit'':271C ''dict'':233C,248C ''em'':54C,104C,283C,293C ''entropi'':160C,163C,169C,185C,285C ''equat'':321C ''feed'':232C,247C ''first'':291C ''follow'':60C ''format'':261C ''frac'':65C ''function'':57C,107C ''got'':296C ''h'':81C,92C,100C ''h1'':135C,141C,146C,156C ''hidden'':119C ''href'':42C ''implement'':1A,18C,114C ''interest'':32C ''j'':63C ''k'':73C,75C,78C,85C,88C,96C ''layer'':120C,274C ''learn'':7B,39C ''left'':76C ''log'':80C,90C ''logit'':165C ''loss'':181C,191C ''m'':67C,71C ''machin'':6B ''machine-learn'':5B ''mani'':12C ''mean'':183C,203C ''minim'':190C ''mlp'':2A,19C,121C ''mnist.train.next'':226C ''model'':301C,323C ''none'':126C,132C ''normal'':138C,151C ''one'':36C,118C,292C,295C,311C ''onlin'':14C ''p'':9C,62C,99C,109C,267C ''particular'':35C ''pre'':116C ''predict'':193C,206C ''previous'':320C ''print'':255C ''problem'':279C ''quick'':303C ''rang'':220C ''resourc'':13C ''right'':97C ''s.run'':213C,229C ''sampl'':26C ''shape'':125C,131C ''sigmoid'':105C ''step'':187C,231C,256C ''strong'':304C ''sum'':68C,72C,171C ''tensorflow'':4A,8B,21C ''tf.argmax'':195C,198C ''tf.cast'':204C ''tf.equal'':194C ''tf.float32'':124C,130C,207C ''tf.initialize'':214C ''tf.log'':173C,177C ''tf.matmul'':143C,155C ''tf.nn.sigmoid'':142C,161C ''tf.placeholder'':123C,129C ''tf.random'':137C,150C ''tf.reduce'':170C,182C,202C ''tf.session'':210C ''tf.train.gradientdescentoptimizer'':188C ''tf.variable'':136C,149C ''theta'':64C,82C,93C,101C ''think'':269C ''think/hope'':314C ''train'':186C,208C,230C,244C,258C,263C ''translat'':317C ''use'':52C,289C,308C ''variabl'':216C ''w'':134C,145C,147C,157C ''won'':324C ''work'':28C ''www.coursera.org'':44C,47C ''www.coursera.org/learn/machine-learning'':43C ''www.coursera.org/learn/machine-learning&lt;/a&gt;.'':46C ''x'':83C,94C,122C,144C,223C,234C,236C,249C,251C ''y'':77C,87C,128C,154C,166C,167C,172C,174C,176C,179C,196C,199C,225C,237C,239C,252C,254C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10017, 4, '2016-01-29 06:50:57.143', 0, NULL, NULL, 'A form of signal processing where the input is an image. Usually treating the digital image as a two-dimensional signal (or multidimensional). This processing may include image restoration and enhancement (in particular, pattern recognition and projection).', NULL, NULL, '''digit'':15C ''dimension'':21C ''enhanc'':32C ''form'':2C ''imag'':11C,16C,29C ''includ'':28C ''input'':8C ''may'':27C ''multidimension'':24C ''particular'':34C ''pattern'':35C ''process'':5C,26C ''project'':38C ''recognit'':36C ''restor'':30C ''signal'':4C,22C ''treat'':13C ''two'':20C ''two-dimension'':19C ''usual'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10019, 4, '2016-01-29 06:51:48.63', 0, NULL, NULL, 'Variables (used for prediction or explication) used in regression or regression-like models (like clustering, discrimination). Use this tag for questions about constructing such variables or selecting the best among them.', NULL, NULL, '''among'':31C ''best'':30C ''cluster'':16C ''construct'':24C ''discrimin'':17C ''explic'':6C ''like'':13C,15C ''model'':14C ''predict'':4C ''question'':22C ''regress'':9C,12C ''regression-lik'':11C ''select'':28C ''tag'':20C ''use'':2C,7C,18C ''variabl'':1C,26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10021, 4, '2016-01-29 06:52:59.09', 0, NULL, NULL, 'Java (not to be confused with JavaScript) is a general-purpose object-oriented programming language designed to be used in conjunction with the Java Virtual Machine (JVM). "Java platform" is the name for a computing system that has installed tools for developing and running Java programs. Use this tag for questions referring to Java programming language or Java platform tools.', NULL, NULL, '''comput'':37C ''confus'':5C ''conjunct'':23C ''design'':18C ''develop'':44C ''general'':11C ''general-purpos'':10C ''instal'':41C ''java'':1C,26C,30C,47C,56C,60C ''javascript'':7C ''jvm'':29C ''languag'':17C,58C ''machin'':28C ''name'':34C ''object'':14C ''object-ori'':13C ''orient'':15C ''platform'':31C,61C ''program'':16C,48C,57C ''purpos'':12C ''question'':53C ''refer'':54C ''run'':46C ''system'':38C ''tag'':51C ''tool'':42C,62C ''use'':21C,49C ''virtual'':27C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10060, 1, '2016-02-02 01:42:38.053', 5, 109, 'Improve k-means accuracy', '&lt;p&gt;&lt;strong&gt;Our weapons&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;I am experimenting with k-means and Hadoop, where I am chained to these options for various reasons (e.g. &lt;a href="http://askubuntu.com/questions/725444/help-me-win-this-war"&gt;Help me win this war!&lt;/a&gt;).&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;The battlefield&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;I have articles, which belong to &lt;em&gt;c&lt;/em&gt; categories, where &lt;em&gt;c&lt;/em&gt; is fixed. I am vectorizing the contents of the articles to &lt;strong&gt;TF-IDF&lt;/strong&gt; features. Now I am running a naive k-means algorithm, which takes &lt;code&gt;c&lt;/code&gt; centroids to begin with and starts, iteratively, grouping articles (i.e. rows of the TF-IDF matrix, where you can see &lt;a href="http://stackoverflow.com/questions/35109424/how-to-make-tf-idf-matrix-dense"&gt;here&lt;/a&gt; how I built it), until converenge occurs.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Special notes&lt;/strong&gt;:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Initial centroids: Tried with random from within each category or
with the mean of all the articles from each category.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Distance function: Euclidean.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Question(s)&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;The accuracy is poor, as expected, can I do any better, by making another choice for the initial centroids, or/and pick another distance function?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;sub&gt;print "Hello Data Science site!" :)&lt;/sub&gt;&lt;/p&gt;
', 'python  text-mining  hadoop  k-means  distance', NULL, '''/a'':52C,135C ''/code'':107C ''/em'':70C,75C ''/li'':175C,182C ''/ol'':183C ''/p'':20C,53C,60C,143C,150C,174C,181C,190C,216C,226C ''/questions/35109424/how-to-make-tf-idf-matrix-dense'':133C ''/questions/725444/help-me-win-this-war'':46C ''/strong'':19C,59C,91C,149C,189C ''/sub'':225C ''accuraci'':5A,193C ''algorithm'':102C ''anoth'':205C,213C ''articl'':64C,85C,116C,170C ''askubuntu.com'':45C ''askubuntu.com/questions/725444/help-me-win-this-war'':44C ''battlefield'':58C ''begin'':110C ''belong'':66C ''better'':202C ''built'':138C ''c'':69C,74C,106C ''categori'':71C,162C,173C ''centroid'':108C,155C,210C ''chain'':34C ''choic'':206C ''code'':105C ''content'':82C ''convereng'':141C ''data'':222C ''distanc'':14B,178C,214C ''e.g'':41C ''em'':68C,73C ''euclidean'':180C ''expect'':197C ''experi'':24C ''featur'':92C ''fix'':77C ''function'':179C,215C ''group'':115C ''hadoop'':10B,30C ''hello'':221C ''help'':47C ''hr'':54C,144C,184C,217C ''href'':43C,130C ''i.e'':117C ''idf'':90C,123C ''improv'':1A ''initi'':154C,209C ''iter'':114C ''k'':3A,12B,27C,100C ''k-mean'':2A,11B,26C,99C ''li'':152C,176C ''make'':204C ''matrix'':124C ''mean'':4A,13B,28C,101C,166C ''mine'':9B ''naiv'':98C ''note'':148C ''occur'':142C ''ol'':151C ''option'':37C ''or/and'':211C ''p'':15C,21C,55C,61C,145C,153C,177C,185C,191C,218C ''pick'':212C ''poor'':195C ''print'':220C ''python'':6B ''question'':187C ''random'':158C ''reason'':40C ''row'':118C ''run'':96C ''scienc'':223C ''see'':128C ''site'':224C ''special'':147C ''stackoverflow.com'':132C ''stackoverflow.com/questions/35109424/how-to-make-tf-idf-matrix-dense'':131C ''start'':113C ''strong'':16C,56C,87C,146C,186C ''sub'':219C ''take'':104C ''text'':8B ''text-min'':7B ''tf'':89C,122C ''tf-idf'':88C,121C ''tri'':156C ''various'':39C ''vector'':80C ''war'':51C ''weapon'':18C ''win'':49C ''within'':160C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10024, 2, '2016-01-29 14:22:55.907', 1, NULL, NULL, '&lt;p&gt;your grammar is correct!&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;grammar = """MEDIA: {&amp;lt;DT&amp;gt;?&amp;lt;JJ&amp;gt;*&amp;lt;NN.*&amp;gt;+}
           RELATION: {&amp;lt;V.*&amp;gt;}
                     {&amp;lt;DT&amp;gt;?&amp;lt;JJ&amp;gt;*&amp;lt;NN.*&amp;gt;+}
           ENTITY: {&amp;lt;NN.*&amp;gt;}"""
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;by specifying &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;RELATION: {&amp;lt;V.*&amp;gt;}
          {&amp;lt;DT&amp;gt;?&amp;lt;JJ&amp;gt;*&amp;lt;NN.*&amp;gt;+}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;you are indicating that there are two ways to generate the &lt;code&gt;RELATION&lt;/code&gt; chunk i.e. &lt;code&gt;{&amp;lt;V.*&amp;gt;}&lt;/code&gt; or &lt;code&gt;{&amp;lt;DT&amp;gt;?&amp;lt;JJ&amp;gt;*&amp;lt;NN.*&amp;gt;+}&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;so &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;grammar = """MEDIA: {&amp;lt;DT&amp;gt;?&amp;lt;JJ&amp;gt;*&amp;lt;NN.*&amp;gt;+}
               RELATION: {&amp;lt;V.*&amp;gt;}
                         {&amp;lt;DT&amp;gt;?&amp;lt;JJ&amp;gt;*&amp;lt;NN.*&amp;gt;+}
               ENTITY: {&amp;lt;NN.*&amp;gt;}"""
    chunkParser = nltk.RegexpParser(grammar)
    tagged = nltk.pos_tag(nltk.word_tokenize("adventure movies between 2000 and 2015 featuring performances by daniel craig"))

    tree = chunkParser.parse(tagged)

    for subtree in tree.subtrees():
        if subtree.label() == "RELATION": 
            print("RELATION: "+str(subtree.leaves()))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;gives&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;RELATION: [(''featuring'', ''VBG'')]
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':37C,58C,74C,81C,93C,161C,171C ''/p'':6C,42C,94C,97C,165C ''/pre'':38C,59C,162C,172C ''2000'':139C ''2015'':141C ''adventur'':136C ''chunk'':75C ''chunkpars'':128C ''chunkparser.parse'':148C ''code'':8C,44C,72C,77C,83C,99C,167C ''correct'':5C ''craig'':146C ''daniel'':145C ''dt'':12C,25C,50C,85C,103C,116C ''entiti'':33C,124C ''featur'':142C,169C ''generat'':70C ''give'':164C ''grammar'':3C,9C,100C,130C ''gt'':13C,16C,19C,23C,26C,29C,32C,36C,48C,51C,54C,57C,80C,86C,89C,92C,104C,107C,110C,114C,117C,120C,123C,127C ''i.e'':76C ''indic'':63C ''jj'':15C,28C,53C,88C,106C,119C ''lt'':11C,14C,17C,21C,24C,27C,30C,34C,46C,49C,52C,55C,78C,84C,87C,90C,102C,105C,108C,112C,115C,118C,121C,125C ''media'':10C,101C ''movi'':137C ''nltk.pos'':132C ''nltk.regexpparser'':129C ''nltk.word'':134C ''nn'':18C,31C,35C,56C,91C,109C,122C,126C ''p'':1C,39C,60C,95C,163C ''perform'':143C ''pre'':7C,43C,98C,166C ''print'':157C ''relat'':20C,45C,73C,111C,156C,158C,168C ''specifi'':41C ''str'':159C ''subtre'':151C ''subtree.label'':155C ''subtree.leaves'':160C ''tag'':131C,133C,149C ''token'':135C ''tree'':147C ''tree.subtrees'':153C ''two'':67C ''v'':22C,47C,79C,113C ''vbg'':170C ''way'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10038, 2, '2016-01-31 14:47:21.82', 2, NULL, NULL, '&lt;p&gt;The task you are asking about is &lt;a href="https://en.wikipedia.org/wiki/Named-entity_recognition" rel="nofollow"&gt;Named entity recognition&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;A good way to identify a word category is by using patterns for the context of the word.&lt;/p&gt;

&lt;p&gt;In your case you can use patterns like:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;"I took X" for a medicine name&lt;/li&gt;
&lt;li&gt;"I had pain in my X" for a body part&lt;/li&gt;
&lt;li&gt;"I suffered from X" for a disease &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The easiest and fastest way is to discuss the pattern with a domain expert and do a sanity check on the data you have.&lt;/p&gt;

&lt;p&gt;A more systematic approach is to deduce the pattern from your data.
I guess you can get a list of samples of the items.
The technical details depend on the patterns you use but for this example let consider tokenization. 
Take the context of k-words next to the items on the list. Compare it to the context of other words. Contexts that are related to your items are suitable as patterns. &lt;/p&gt;

&lt;p&gt;Bare in mind that these pattern will have very different precision and recall and they will lead to errors. It is important that you will have a labeled dataset so you will be able to estimate the patterns performance. &lt;/p&gt;

&lt;p&gt;It will be fairly easy to get pretty good classification. It might be very hard to get a very good classification.&lt;/p&gt;
', NULL, NULL, '''/a'':19C ''/li'':60C,72C,81C ''/p'':20C,40C,50C,108C,183C,224C,247C ''/ul'':82C ''/wiki/named-entity_recognition'':13C ''abl'':218C ''approach'':113C ''ask'':6C ''bare'':185C ''bodi'':70C ''case'':44C ''categori'':29C ''check'':102C ''classif'':235C,246C ''compar'':164C ''consid'':148C ''context'':36C,152C,168C,172C ''data'':105C,121C ''dataset'':213C ''deduc'':116C ''depend'':137C ''detail'':136C ''differ'':194C ''discuss'':91C ''diseas'':80C ''domain'':96C ''easi'':230C ''easiest'':85C ''en.wikipedia.org'':12C ''en.wikipedia.org/wiki/named-entity_recognition'':11C ''entiti'':17C ''error'':203C ''estim'':220C ''exampl'':146C ''expert'':97C ''fair'':229C ''fastest'':87C ''get'':126C,232C,242C ''good'':23C,234C,245C ''guess'':123C ''hard'':240C ''href'':10C ''identifi'':26C ''import'':206C ''item'':133C,160C,178C ''k'':155C ''k-word'':154C ''label'':212C ''lead'':201C ''let'':147C ''li'':52C,61C,73C ''like'':49C ''list'':128C,163C ''medicin'':58C ''might'':237C ''mind'':187C ''name'':16C,59C ''next'':157C ''nofollow'':15C ''p'':1C,21C,41C,83C,109C,184C,225C ''pain'':64C ''part'':71C ''pattern'':33C,48C,93C,118C,140C,182C,190C,222C ''perform'':223C ''precis'':195C ''pretti'':233C ''recal'':197C ''recognit'':18C ''rel'':14C ''relat'':175C ''sampl'':130C ''saniti'':101C ''suffer'':75C ''suitabl'':180C ''systemat'':112C ''take'':150C ''task'':3C ''technic'':135C ''token'':149C ''took'':54C ''ul'':51C ''use'':32C,47C,142C ''way'':24C,88C ''word'':28C,39C,156C,171C ''x'':55C,67C,77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10025, 1, '2016-01-29 15:39:26.277', 2, 44, 'Deep Learning with Spectograms for sound recognition', '&lt;p&gt;I was looking into the possibility to classify sound (for example sounds of animals) using spectrograms. The idea is to use a deep convolutional neural networks to recognize segments in the spectrogram and output one (or many) class labels.  This is not a new idea (see for example &lt;a href="http://danielnouri.org/notes/2014/01/10/using-deep-learning-to-listen-for-whales/" rel="nofollow" title="using it to classify whale sounds"&gt;whale sound classification&lt;/a&gt; or &lt;a href="http://papers.nips.cc/paper/3674-unsupervised-feature-learning-for-audio-classification-using-convolutional-deep-belief-networks.pdf" rel="nofollow"&gt;music style recognition&lt;/a&gt;). The problem that I''m facing is that I have sound files of different length and therefore spectrograms of different sizes. So far, every approach I have seen uses a fixed size sound sample but I can''t do that because my sound file might be 10 seconds or 2 minutes long. With, for example, a bird sound in the beginning and a frog sound at the end (output should be "Bird, Frog"). My current solution would be to add a temporal component to the neural network (creating more of a recurrent neural network) but I would like to keep it simple for now. Any ideas, links, tutorials, ...?&lt;/p&gt;
', 'deep-learning  multilabel-classification  audio-recognition', NULL, '''/a'':83C,95C ''/notes/2014/01/10/using-deep-learning-to-listen-for-whales/'':70C ''/p'':204C ''/paper/3674-unsupervised-feature-learning-for-audio-classification-using-convolutional-deep-belief-networks.pdf'':89C ''10'':142C ''2'':145C ''add'':175C ''anim'':31C ''approach'':120C ''audio'':15B ''audio-recognit'':14B ''begin'':156C ''bird'':152C,167C ''class'':55C ''classif'':13B,82C ''classifi'':25C,77C ''compon'':178C ''convolut'':41C ''creat'':183C ''current'':170C ''danielnouri.org'':69C ''danielnouri.org/notes/2014/01/10/using-deep-learning-to-listen-for-whales/'':68C ''deep'':1A,9B,40C ''deep-learn'':8B ''differ'':109C,115C ''end'':163C ''everi'':119C ''exampl'':28C,65C,150C ''face'':101C ''far'':118C ''file'':107C,139C ''fix'':126C ''frog'':159C,168C ''href'':67C,86C ''idea'':35C,62C,201C ''keep'':195C ''label'':56C ''learn'':2A,10B ''length'':110C ''like'':193C ''link'':202C ''long'':147C ''look'':20C ''m'':100C ''mani'':54C ''might'':140C ''minut'':146C ''multilabel'':12B ''multilabel-classif'':11B ''music'':92C ''network'':43C,182C,189C ''neural'':42C,181C,188C ''new'':61C ''nofollow'':72C,91C ''one'':52C ''output'':51C,164C ''p'':17C ''papers.nips.cc'':88C ''papers.nips.cc/paper/3674-unsupervised-feature-learning-for-audio-classification-using-convolutional-deep-belief-networks.pdf'':87C ''possibl'':23C ''problem'':97C ''recogn'':45C ''recognit'':7A,16B,94C ''recurr'':187C ''rel'':71C,90C ''sampl'':129C ''second'':143C ''see'':63C ''seen'':123C ''segment'':46C ''simpl'':197C ''size'':116C,127C ''solut'':171C ''sound'':6A,26C,29C,79C,81C,106C,128C,138C,153C,160C ''spectogram'':4A ''spectrogram'':33C,49C,113C ''style'':93C ''tempor'':177C ''therefor'':112C ''titl'':73C ''tutori'':203C ''use'':32C,38C,74C,124C ''whale'':78C,80C ''would'':172C,192C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10026, 2, '2016-01-29 17:22:56.89', 1, NULL, NULL, '&lt;p&gt;I would suggest "&lt;a href="http://www.iip.ist.i.kyoto-u.ac.jp/member/cuturi/Papers/cuturi11fast.pdf" rel="nofollow"&gt;Generalized Alignment Kernels&lt;/a&gt;" for time-series classification. The idea is to use the well known DTW distance in the SVM. The problem which was solved in the paper is that the DTW distance does not result in a valid SVM kernel; there are some tricks employed by the authors to enable that.&lt;/p&gt;

&lt;p&gt;I ran this algorithm on some popular time-series datasets and noticed:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;The algorithm is very fast&lt;/li&gt;
&lt;li&gt;The performances are very good on the respective datasets with respect to the published test sets (&lt;a href="http://www.cs.ucr.edu/~eamonn/time_series_data/" rel="nofollow"&gt;UCR Time Series&lt;/a&gt;)&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Finally, the source code is available.&lt;/p&gt;
', NULL, NULL, '''/a'':15C,117C ''/li'':89C,118C ''/member/cuturi/papers/cuturi11fast.pdf'':9C ''/ol'':119C ''/p'':66C,81C,127C ''/~eamonn/time_series_data/'':111C ''algorithm'':71C,85C ''align'':13C ''author'':62C ''avail'':126C ''classif'':20C ''code'':124C ''dataset'':78C,99C ''distanc'':30C,46C ''dtw'':29C,45C ''employ'':59C ''enabl'':64C ''fast'':88C ''final'':121C ''general'':12C ''good'':95C ''href'':6C,108C ''idea'':22C ''kernel'':14C,54C ''known'':28C ''li'':83C,90C ''nofollow'':11C,113C ''notic'':80C ''ol'':82C ''p'':1C,67C,120C ''paper'':41C ''perform'':92C ''popular'':74C ''problem'':35C ''publish'':104C ''ran'':69C ''rel'':10C,112C ''respect'':98C,101C ''result'':49C ''seri'':19C,77C,116C ''set'':106C ''solv'':38C ''sourc'':123C ''suggest'':4C ''svm'':33C,53C ''test'':105C ''time'':18C,76C,115C ''time-seri'':17C,75C ''trick'':58C ''ucr'':114C ''use'':25C ''valid'':52C ''well'':27C ''would'':3C ''www.cs.ucr.edu'':110C ''www.cs.ucr.edu/~eamonn/time_series_data/'':109C ''www.iip.ist.i.kyoto-u.ac.jp'':8C ''www.iip.ist.i.kyoto-u.ac.jp/member/cuturi/papers/cuturi11fast.pdf'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10031, 1, '2016-01-30 16:51:26.207', 2, 32, 'how to provide alternate for a word in corpus for word2vec model', '&lt;p&gt;I need to specify semantically close words in a corpus. For instance &lt;em&gt;newspaper/magazine&lt;/em&gt; in the following:&lt;/p&gt;

&lt;p&gt;I read [newspaper/magazine] yesterday.&lt;/p&gt;

&lt;p&gt;In this example both words are close in meaning. I need to find these kind of words.&lt;/p&gt;

&lt;p&gt;How can I find those words?&lt;/p&gt;
', 'word2vec', NULL, '''/em'':29C ''/p'':33C,39C,58C,66C ''altern'':4A ''close'':20C,47C ''corpus'':9A,24C ''em'':27C ''exampl'':43C ''find'':53C,63C ''follow'':32C ''instanc'':26C ''kind'':55C ''mean'':49C ''model'':12A ''need'':16C,51C ''newspaper/magazine'':28C,37C ''p'':14C,34C,40C,59C ''provid'':3A ''read'':36C ''semant'':19C ''specifi'':18C ''word'':7A,21C,45C,57C,65C ''word2vec'':11A,13B ''yesterday'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10034, 1, '2016-01-30 20:37:07.527', 3, 47, 'Making fake result in data mining using weka j48 algorithm', '&lt;p&gt;I have 130 records in one CSV dataset and I''m using j48 decision tree. I used the whole training set for testing, and the result was 79 correctly classified and 51 incorrectly classified records.
Now! I want a result between 95% - 100% and don''t have ability to add or remove any record to those 130. But I''m allowed to play a little with my dataset, for example use 10 out of 130 in such a way that all of those 10 must be classified correctly.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;The Quality of tree is not Important&lt;/li&gt;
&lt;li&gt;The Number of test samples is not so important (10-15 is good)&lt;/li&gt;
&lt;li&gt;The Only Important thing is Accuracy between 95-100 %  &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;FIRST, I tried to move 10 correct samples to bottom of dataset and use "split by percentage" (92.30%) but it wasn''t useful.&lt;/p&gt;

&lt;p&gt;At SECOND, I tried to choose them by "try and error" and chose randomly, and I was just testing the last sample (130th), the I found some correct samples and keep it there, then tried another below the last successful sample and tested the last two lines, the result should have been 50% or 100%, but surprisingly was 0% (Total instants:2 correct:0 Incorrect:2).&lt;/p&gt;

&lt;p&gt;Can anyone help me please?
I already thank a lot dear readers...&lt;/p&gt;
', 'data-mining  decision-trees  weka', NULL, '''-100'':142C ''-15'':129C ''/li'':117C,132C,143C ''/p'':107C,169C,237C,251C ''/ul'':144C ''0'':229C,234C ''10'':90C,102C,128C,151C ''100'':61C,225C ''130'':21C,75C,93C ''130th'':193C ''2'':232C,236C ''50'':223C ''51'':50C ''79'':46C ''92.30'':163C ''95'':60C,141C ''abil'':66C ''accuraci'':139C ''add'':68C ''algorithm'':10A ''allow'':79C ''alreadi'':245C ''anoth'':206C ''anyon'':240C ''bottom'':155C ''choos'':176C ''chose'':183C ''classifi'':48C,52C,105C ''correct'':47C,106C,152C,198C,233C ''csv'':25C ''data'':5A,12B ''data-min'':11B ''dataset'':26C,86C,157C ''dear'':249C ''decis'':15B,32C ''decision-tre'':14B ''error'':181C ''exampl'':88C ''fake'':2A ''first'':146C ''found'':196C ''good'':131C ''help'':241C ''import'':116C,127C,136C ''incorrect'':51C,235C ''instant'':231C ''j48'':9A,31C ''keep'':201C ''last'':191C,209C,215C ''li'':109C,118C,133C ''line'':217C ''littl'':83C ''lot'':248C ''m'':29C,78C ''make'':1A ''mine'':6A,13B ''move'':150C ''must'':103C ''number'':120C ''one'':24C ''p'':18C,145C,170C,238C ''percentag'':162C ''play'':81C ''pleas'':243C ''qualiti'':111C ''random'':184C ''reader'':250C ''record'':22C,53C,72C ''remov'':70C ''result'':3A,44C,58C,219C ''sampl'':123C,153C,192C,199C,211C ''second'':172C ''set'':39C ''split'':160C ''success'':210C ''surpris'':227C ''test'':41C,122C,189C,213C ''thank'':246C ''thing'':137C ''total'':230C ''train'':38C ''tree'':16B,33C,113C ''tri'':148C,174C,179C,205C ''two'':216C ''ul'':108C ''use'':7A,30C,35C,89C,159C,168C ''want'':56C ''wasn'':166C ''way'':97C ''weka'':8A,17B ''whole'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10036, 2, '2016-01-30 23:56:07.94', 4, NULL, NULL, '&lt;p&gt;The existing programs before AlphaGo were based on:&lt;/p&gt;

&lt;p&gt;1) Monte Carlo tree search&lt;/p&gt;

&lt;p&gt;or&lt;/p&gt;

&lt;p&gt;2) Convolutional neural networks (CNN), trained on databases of existing games.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Novelties in AlphaGo are:&lt;/p&gt;

&lt;p&gt;A) reinforcement learning: train networks by letting versions of CNN''s (see above) play against eachother.&lt;/p&gt;

&lt;p&gt;and&lt;/p&gt;

&lt;p&gt;B) perform  Monte Carlo tree search using moves generated by step A)&lt;/p&gt;

&lt;p&gt;And using distributed computing: it is not a novelty,
but having a certain amount of CPU''s and GPU''s increased
the result even further.&lt;/p&gt;
', NULL, NULL, '''/p'':10C,17C,20C,33C,40C,58C,61C,75C,102C ''1'':12C ''2'':22C ''alphago'':6C,38C ''amount'':90C ''b'':63C ''base'':8C ''carlo'':14C,66C ''certain'':89C ''cnn'':26C,51C ''comput'':80C ''convolut'':23C ''cpu'':92C ''databas'':29C ''distribut'':79C ''eachoth'':57C ''even'':100C ''exist'':3C,31C ''game'':32C ''generat'':71C ''gpu'':95C ''hr'':34C ''increas'':97C ''learn'':44C ''let'':48C ''mont'':13C,65C ''move'':70C ''network'':25C,46C ''neural'':24C ''novelti'':36C,85C ''p'':1C,11C,18C,21C,35C,41C,59C,62C,76C ''perform'':64C ''play'':55C ''program'':4C ''reinforc'':43C ''result'':99C ''search'':16C,68C ''see'':53C ''step'':73C ''train'':27C,45C ''tree'':15C,67C ''use'':69C,78C ''version'':49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10037, 1, '2016-01-31 10:05:49.3', 2, 44, 'Filter Data for clustering', '&lt;p&gt;I am trying to synthetize clients Data in order to do clustering. &lt;strong&gt;My problem is for 1 customer I have severals rows&lt;/strong&gt;. I would like to synthetize informations to get 1 row per customer. This clustering is about how customers use fidelity program. Here is a picture of my table : By Column (left to right) : &lt;/p&gt;

&lt;p&gt;&lt;em&gt;1) CustomerID 2)Date at which their use their points 3) Category number (Ex: 1 is gift card, 2 is a flight etc) 4) How many points they used 5) How many items they purchased with points&lt;/em&gt;
&lt;a href="http://i.stack.imgur.com/pCvIh.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/pCvIh.png" alt="Table"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;My question is how could I have 1 customer per row without loosing informations.
Maybe Pivot Table? But I don"t know how it work exactly. I am new to statistic btw.&lt;/p&gt;

&lt;p&gt;Thank you&lt;/p&gt;

&lt;p&gt;CÃ©dric &lt;/p&gt;
', 'clustering', NULL, '''/a'':119C ''/em'':104C ''/p'':64C,120C,154C,158C,162C ''/pcvih.png'':109C,116C ''/strong'':30C ''1'':24C,39C,67C,81C,129C ''2'':69C,85C ''3'':77C ''4'':90C ''5'':96C ''alt'':117C ''btw'':153C ''card'':84C ''categori'':78C ''client'':12C ''cluster'':4A,5B,18C,44C ''column'':60C ''could'':126C ''custom'':25C,42C,48C,130C ''customerid'':68C ''cã'':160C ''data'':2A,13C ''date'':70C ''dric'':161C ''em'':66C ''etc'':89C ''ex'':80C ''exact'':147C ''fidel'':50C ''filter'':1A ''flight'':88C ''get'':38C ''gift'':83C ''href'':106C ''i.stack.imgur.com'':108C,115C ''i.stack.imgur.com/pcvih.png'':107C,114C ''img'':112C ''inform'':36C,135C ''item'':99C ''know'':143C ''left'':61C ''like'':33C ''loos'':134C ''mani'':92C,98C ''mayb'':136C ''new'':150C ''nofollow'':111C ''number'':79C ''order'':15C ''p'':6C,65C,121C,155C,159C ''per'':41C,131C ''pictur'':55C ''pivot'':137C ''point'':76C,93C,103C ''problem'':21C ''program'':51C ''purchas'':101C ''question'':123C ''rel'':110C ''right'':63C ''row'':29C,40C,132C ''sever'':28C ''src'':113C ''statist'':152C ''strong'':19C ''synthet'':11C,35C ''tabl'':58C,118C,138C ''thank'':156C ''tri'':9C ''use'':49C,74C,95C ''without'':133C ''work'':146C ''would'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10047, 1, '2016-02-01 12:58:14.967', 2, 92, 'On coursera what exactly does Andrew Ng say in videos Lectures 60 &amp; 61 of machine learning?', '&lt;p&gt;&lt;a href="https://www.coursera.org/learn/machine-learning/lecture/QGKbr/model-selection-and-train-validation-test-sets" rel="nofollow"&gt;Model Selection and Train/Validation/Test Sets - Stanford University | Coursera:&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;At &lt;strong&gt;10:59&lt;/strong&gt;~&lt;strong&gt;11:10&lt;/strong&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;One final note: I should say that in the machine learning as of this practice today, there &lt;strong&gt;aren''t&lt;/strong&gt; many people that will do that early thing that I talked about, and said that, you know...â&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Is my comprehension correct?
Because English subtitles on coursera sometimes are not correct.
As I konw, here what Chinese subtitle means is opposite to what English one does.
So I am not sure whether Andrew Ng said "there &lt;strong&gt;aren''t&lt;/strong&gt;" or "there &lt;strong&gt;are&lt;/strong&gt;"&lt;/p&gt;

&lt;p&gt;Thanks for your reading.â&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;I would like to ask &lt;strong&gt;another one&lt;/strong&gt;. &lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.coursera.org/learn/machine-learning/lecture/yCAup/diagnosing-bias-vs-variance" rel="nofollow"&gt;Diagnosing Bias vs. Variance - Stanford University | Coursera:&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;At &lt;strong&gt;02:34&lt;/strong&gt;~&lt;strong&gt;02:36&lt;/strong&gt;, what Andrew Ng said is not quite clear as well as the English subtitle.&lt;/p&gt;

&lt;p&gt;My comprehension is as following&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;If d equals 1,.... to be high training error.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;It''s not that complete.&lt;/p&gt;

&lt;p&gt;Would anyone like to identify that?&lt;/p&gt;

&lt;p&gt;Thank you...&lt;/p&gt;
', 'machine-learning  cross-validation  model-selection', NULL, '''/a'':42C,181C ''/blockquote'':97C,227C ''/learn/machine-learning/lecture/qgkbr/model-selection-and-train-validation-test-sets'':31C ''/learn/machine-learning/lecture/ycaup/diagnosing-bias-vs-variance'':171C ''/p'':43C,54C,96C,146C,153C,165C,182C,207C,214C,226C,234C,242C,246C ''/strong'':49C,53C,77C,140C,145C,164C,188C,192C ''02'':186C,190C ''1'':220C ''10'':47C,52C ''11'':51C ''34'':187C ''36'':191C ''59'':48C ''60'':12A ''61'':13A ''andrew'':6A,133C,194C ''anoth'':162C ''anyon'':237C ''aren'':75C,138C ''ask'':160C ''bias'':175C ''blockquot'':55C,215C ''chines'':117C ''clear'':200C ''complet'':233C ''comprehens'':101C,210C ''correct'':102C,111C ''coursera'':2A,41C,107C,180C ''cross'':21B ''cross-valid'':20B ''d'':218C ''diagnos'':174C ''earli'':84C ''english'':104C,124C,205C ''equal'':219C ''error'':225C ''exact'':4A ''final'':58C ''follow'':213C ''high'':223C ''hr'':154C ''href'':28C,168C ''identifi'':240C ''know'':94C ''konw'':114C ''learn'':16A,19B,67C ''lectur'':11A ''like'':158C,238C ''machin'':15A,18B,66C ''machine-learn'':17B ''mani'':78C ''mean'':119C ''model'':24B,34C ''model-select'':23B ''ng'':7A,134C,195C ''nofollow'':33C,173C ''note'':59C ''one'':57C,125C,163C ''opposit'':121C ''p'':26C,44C,56C,98C,147C,155C,166C,183C,208C,216C,228C,235C,243C ''peopl'':79C ''practic'':71C ''quit'':199C ''read'':151C ''rel'':32C,172C ''said'':91C,135C,196C ''say'':8A,62C ''select'':25B,35C ''set'':38C ''sometim'':108C ''stanford'':39C,178C ''strong'':46C,50C,74C,137C,143C,161C,185C,189C ''subtitl'':105C,118C,206C ''sure'':131C ''talk'':88C ''thank'':148C,244C ''thing'':85C ''today'':72C ''train'':224C ''train/validation/test'':37C ''univers'':40C,179C ''valid'':22B ''varianc'':177C ''video'':10A ''vs'':176C ''well'':202C ''whether'':132C ''would'':157C,236C ''www.coursera.org'':30C,170C ''www.coursera.org/learn/machine-learning/lecture/qgkbr/model-selection-and-train-validation-test-sets'':29C ''www.coursera.org/learn/machine-learning/lecture/ycaup/diagnosing-bias-vs-variance'':169C ''â'':95C,152C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10039, 1, '2016-01-31 18:10:33.1', 2, 47, 'Amount of data needed and hypothesis for SVD', '&lt;p&gt;I was looking into the definition of SVD and trying to understand which are the conditions needed to be met in order to be able to use it. &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Is there any hypothesis concerning the distribution of the data that I want to apply SVD on ? &lt;/li&gt;
&lt;li&gt;Is the condition (number of data vectors &gt; the dimension of the vectors space) strict ? Or is there always a way to decompose a dataset using SVD ? In the definition of SVD, all websites that I could find start by the same condition without fearther explanitions :&lt;/li&gt;
&lt;/ol&gt;

&lt;blockquote&gt;
  &lt;p&gt;given an $m\times n$ real matrix A with $n&amp;gt;m$, A can be writter as ..  &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Sorry if the question is too simple. Thanks. &lt;/p&gt;
', 'data-mining', NULL, '''/blockquote'':130C ''/li'':61C,108C ''/ol'':109C ''/p'':41C,129C,140C ''abl'':37C ''alway'':80C ''amount'':1A ''appli'':58C ''blockquot'':110C ''concern'':48C ''condit'':28C,65C,104C ''could'':98C ''data'':3A,10B,53C,68C ''data-min'':9B ''dataset'':86C ''decompos'':84C ''definit'':18C,91C ''dimens'':71C ''distribut'':50C ''explanit'':107C ''fearther'':106C ''find'':99C ''given'':112C ''gt'':122C ''hypothesi'':6A,47C ''li'':43C,62C ''look'':15C ''m'':114C,123C ''matrix'':118C ''met'':32C ''mine'':11B ''n'':116C,121C ''need'':4A,29C ''number'':66C ''ol'':42C ''order'':34C ''p'':12C,111C,131C ''question'':135C ''real'':117C ''simpl'':138C ''sorri'':132C ''space'':75C ''start'':100C ''strict'':76C ''svd'':8A,20C,59C,88C,93C ''thank'':139C ''time'':115C ''tri'':22C ''understand'':24C ''use'':39C,87C ''vector'':69C,74C ''want'':56C ''way'':82C ''websit'':95C ''without'':105C ''writter'':127C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10040, 1, '2016-01-31 19:09:31.433', 1, 48, 'do autoencoders work well for non images?', '&lt;p&gt;I have a classification problem for which a feedforward, fully connected neural net works reasonably well (two classes, true positive and true negative rate close to 80%).&lt;/p&gt;

&lt;p&gt;I want to get these rates to 90%, and more features is one of the catalysts for improvements I can think of.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Do autoencoders to learn additional, interesting features work well for problems that do not involve images?&lt;/strong&gt;&lt;/p&gt;
', 'classification  neuralnetwork  autoencoder', NULL, '''/p'':39C,63C,83C ''/strong'':82C ''80'':38C ''90'':48C ''addit'':70C ''autoencod'':2A,10B,67C ''catalyst'':56C ''class'':29C ''classif'':8B,15C ''close'':36C ''connect'':22C ''featur'':51C,72C ''feedforward'':20C ''fulli'':21C ''get'':44C ''imag'':7A,81C ''improv'':58C ''interest'':71C ''involv'':80C ''learn'':69C ''negat'':34C ''net'':24C ''neural'':23C ''neuralnetwork'':9B ''non'':6A ''one'':53C ''p'':11C,40C,64C ''posit'':31C ''problem'':16C,76C ''rate'':35C,46C ''reason'':26C ''strong'':65C ''think'':61C ''true'':30C,33C ''two'':28C ''want'':42C ''well'':4A,27C,74C ''work'':3A,25C,73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10041, 2, '2016-01-31 21:03:18.233', 0, NULL, NULL, '&lt;p&gt;If you can afford to do the full join once, do it and learn which columns are useful through &lt;a href="https://en.wikipedia.org/wiki/Feature_selection" rel="nofollow"&gt;feature selection&lt;/a&gt;. Then you can only SELECT these columns for subsequent iterations, when the database is updated.&lt;/p&gt;

&lt;p&gt;Here''s a survey: &lt;a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.295.8115&amp;amp;rep=rep1&amp;amp;type=pdf" rel="nofollow"&gt;Feature Selection for Clustering: A Review&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':30C,65C ''/p'':46C,66C ''/viewdoc/download?doi=10.1.1.295.8115&amp;amp;rep=rep1&amp;amp;type=pdf'':56C ''/wiki/feature_selection'':25C ''afford'':5C ''citeseerx.ist.psu.edu'':55C ''citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.295.8115&amp;amp;rep=rep1&amp;amp;type=pdf'':54C ''cluster'':62C ''column'':17C,37C ''databas'':43C ''en.wikipedia.org'':24C ''en.wikipedia.org/wiki/feature_selection'':23C ''featur'':28C,59C ''full'':9C ''href'':22C,53C ''iter'':40C ''join'':10C ''learn'':15C ''nofollow'':27C,58C ''p'':1C,47C ''rel'':26C,57C ''review'':64C ''select'':29C,35C,60C ''subsequ'':39C ''survey'':51C ''updat'':45C ''use'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10046, 2, '2016-02-01 12:03:47.26', 2, NULL, NULL, '&lt;p&gt;I don''t understand the purpose when the quality of the tree is not important but you should be able to make a tree to have 100% accuracy on training data set easily. Just avoid any pruning and let the tree grow as maximum as possible. As far as I remember Weka set a pruning policy for J48 by default, you should disable that. Also check to see that the split nodes with minimum number of instances possible.&lt;/p&gt;

&lt;p&gt;Now that I an considering better, there are cases when full accuracy is impossible. Think for example a simple toy data set with 10 instances. Suppose that all instances are the same for input variables, but for target variable 5 are positive and 5 are negative. Either way you take a decision for prediction, the accuracy on training data would be 0.5. In this case you simply do not have enough data to discriminate on. &lt;/p&gt;
', NULL, NULL, '''/p'':80C,157C ''0.5'':143C ''10'':105C ''100'':28C ''5'':121C,125C ''abl'':21C ''accuraci'':29C,93C,137C ''also'':66C ''avoid'':36C ''better'':87C ''case'':90C,146C ''check'':67C ''consid'':86C ''data'':32C,102C,140C,153C ''decis'':133C ''default'':61C ''disabl'':64C ''discrimin'':155C ''easili'':34C ''either'':128C ''enough'':152C ''exampl'':98C ''far'':49C ''full'':92C ''grow'':43C ''import'':16C ''imposs'':95C ''input'':115C ''instanc'':78C,106C,110C ''j48'':59C ''let'':40C ''make'':23C ''maximum'':45C ''minimum'':75C ''negat'':127C ''node'':73C ''number'':76C ''p'':1C,81C ''polici'':57C ''posit'':123C ''possibl'':47C,79C ''predict'':135C ''prune'':38C,56C ''purpos'':7C ''qualiti'':10C ''rememb'':52C ''see'':69C ''set'':33C,54C,103C ''simpl'':100C ''simpli'':148C ''split'':72C ''suppos'':107C ''take'':131C ''target'':119C ''think'':96C ''toy'':101C ''train'':31C,139C ''tree'':13C,25C,42C ''understand'':5C ''variabl'':116C,120C ''way'':129C ''weka'':53C ''would'':141C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10049, 1, '2016-02-01 16:05:41.11', 2, 37, 'Inferring Relational Hierarchies of Words', '&lt;p&gt;I am new to natural language processing and I have not heard of a problem similar to mine yet. I was wondering if anyone could refer me to a method for solving my problem, or tell me how this problem is referred to in the academic literature, so that I can look for resources online.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Here is the problem :&lt;/strong&gt; 
From some text (wikipedia articles, for example), I would like to extract the hierarchy of different concepts that can be found in it. By hierarchy I mean a tree wherein A is a descendant of B if A or one of A''s parents (transitive) is defined by B. For instance, normal distribution would be a descendant of probability (since normal distribution is defined using probabilities) and probability would be a descendant (or child) of mathematics. Since it is transitive, normal distribution would also be a child of mathematics.&lt;/p&gt;

&lt;p&gt;One way I thought about solving this is by looking at the number of times a word A is used alone (called A), the words A and B are used together (called A AND B, ''together'' could be, for instance, in the same article or in the same paragraph, or in the same sentence), and the number of times the word B is used alone (called B). Let A be mathematics and B be probability. Then, if the ratios (A AND B)/A and (A AND B)/B are low, then it could imply that there is no direct link between A and B (but a link could exist through transitivity). Conversely, if A is bigger than B, A is a bigger concept than B. If A and B are almost the same then they are probably siblings (children of the same parent).&lt;/p&gt;

&lt;p&gt;Let''s take 3 examples:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Mathematics (A) and carrot (B). A AND B is really low compared to A and B, so there is no direct link between them (or only an indirect link by transitivity).&lt;/li&gt;
&lt;li&gt;Mathematics (A) and probabilities (B). A AND B is quite high compared to B, and A is much bigger than B, so B should be a child of A (probabilities is a child of mathematics).&lt;/li&gt;
&lt;li&gt;Topology (A) and Probabilities (B). A AND B is relativaly high (the texts that present the different areas of mathematics will likely speak about the 2), A and B are about the same order of magnitude, so A and B should be the children of a same parent. Indeed, Topology and Probabilities are the children of Mathematics.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;This way of solving the problem is far from perfect, for instance ''the'' (A) and ''probability'' (B) would probably end up saying probability is a child of the (because A AND B is huge and A is much bigger than B).&lt;/p&gt;

&lt;p&gt;If anyone knows some papers on this or has any ideas on how I might solve this problem, I would appreciate some direction. Also, does my solution seem viable? How could it be improved?&lt;/p&gt;
', 'nlp  unsupervised-learning  nltk', NULL, '''/a'':248C ''/b'':253C ''/li'':350C,387C,446C ''/p'':67C,164C,309C,316C,490C,526C ''/strong'':74C ''/ul'':447C ''2'':414C ''3'':314C ''academ'':57C ''almost'':296C ''alon'':186C,230C ''also'':158C,515C ''anyon'':35C,493C ''appreci'':512C ''area'':406C ''articl'':79C,209C ''b'':110C,123C,193C,200C,227C,232C,238C,247C,252C,269C,283C,290C,294C,323C,326C,334C,356C,359C,365C,372C,374C,393C,396C,417C,428C,465C,480C,489C ''bigger'':281C,287C,370C,487C ''call'':187C,197C,231C ''carrot'':322C ''child'':148C,161C,378C,384C,474C ''children'':304C,432C,443C ''compar'':330C,363C ''concept'':91C,288C ''convers'':277C ''could'':36C,202C,258C,273C,522C ''defin'':121C,138C ''descend'':108C,131C,146C ''differ'':90C,405C ''direct'':264C,339C,514C ''distribut'':127C,136C,156C ''end'':468C ''exampl'':81C,315C ''exist'':274C ''extract'':86C ''far'':456C ''found'':95C ''heard'':23C ''hierarchi'':3A,88C,99C ''high'':362C,399C ''huge'':482C ''idea'':502C ''impli'':259C ''improv'':525C ''inde'':437C ''indirect'':346C ''infer'':1A ''instanc'':125C,205C,460C ''know'':494C ''languag'':17C ''learn'':9B ''let'':233C,311C ''li'':318C,351C,388C ''like'':84C,410C ''link'':265C,272C,340C,347C ''literatur'':58C ''look'':63C,175C ''low'':255C,329C ''magnitud'':424C ''mathemat'':150C,163C,236C,319C,352C,386C,408C,445C ''mean'':101C ''method'':41C ''might'':506C ''mine'':29C ''much'':369C,486C ''natur'':16C ''new'':14C ''nlp'':6B ''nltk'':10B ''normal'':126C,135C,155C ''number'':178C,222C ''one'':114C,166C ''onlin'':66C ''order'':422C ''p'':11C,68C,165C,310C,448C,491C ''paper'':496C ''paragraph'':214C ''parent'':118C,308C,436C ''perfect'':458C ''present'':403C ''probabl'':133C,140C,142C,240C,302C,355C,381C,392C,440C,464C,467C,471C ''problem'':26C,45C,51C,73C,454C,509C ''process'':18C ''quit'':361C ''ratio'':244C ''realli'':328C ''refer'':37C,53C ''relat'':2A ''relativali'':398C ''resourc'':65C ''say'':470C ''seem'':519C ''sentenc'':219C ''sibl'':303C ''similar'':27C ''sinc'':134C,151C ''solut'':518C ''solv'':43C,171C,452C,507C ''speak'':411C ''strong'':69C ''take'':313C ''tell'':47C ''text'':77C,401C ''thought'':169C ''time'':180C,224C ''togeth'':196C,201C ''topolog'':389C,438C ''transit'':119C,154C,276C,349C ''tree'':103C ''ul'':317C ''unsupervis'':8B ''unsupervised-learn'':7B ''use'':139C,185C,195C,229C ''viabl'':520C ''way'':167C,450C ''wherein'':104C ''wikipedia'':78C ''wonder'':33C ''word'':5A,182C,190C,226C ''would'':83C,128C,143C,157C,466C,511C ''yet'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10050, 1, '2016-02-01 16:17:26.047', 2, 100, 'Simple ANN visualisation', '&lt;p&gt;&lt;strong&gt;TLDR:&lt;/strong&gt; Please help me understand the graph representation of the network in the image below.&lt;/p&gt;

&lt;p&gt;Hi, this is pretty stupid, but I''m just have trouble visualising what I''m actually doing with this neural network.&lt;/p&gt;

&lt;p&gt;I''ve read about neural networks and multilayer perceptrons for some time and I''m just getting started with actually using them. I started with a super simple example, just to get warmed up but now I''ve confused myself.&lt;/p&gt;

&lt;p&gt;I artificially generated some data and used nntools in matlab to attempt to "predict" the results. I built a neural network with the following parameters:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;feed forward backprop network.&lt;/li&gt;
&lt;li&gt;Gradient Descent training algorithm.&lt;/li&gt;
&lt;li&gt;Gradient Descent learniing algorithm.&lt;/li&gt;
&lt;li&gt;Performance/loss function of mean squared error.&lt;/li&gt;
&lt;li&gt;two layers:

&lt;ul&gt;
&lt;li&gt;first with three neurons and Tansig activation function.&lt;/li&gt;
&lt;li&gt;the second with one neuron and linear activation.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I end up with something looking like this:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/EPajk.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/EPajk.png" alt="My network"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;However, I don''t know what this actually represents, I''m all sorts of confused right now. Could someone please explain/upload an image/draw some ascii to represent the neurons and edges in the above network? It would really help clear my head.&lt;/p&gt;

&lt;p&gt;Currently I think it''s like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;           T  L
           o
          / \
         /   \
   IN &amp;gt; o--o--o--o &amp;gt; OUT
         \   /
          \ /
           o
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;With linear activations in columns L and Tanh activations in columns T. Is that right? Doesn''t make sense to me.&lt;/p&gt;
', 'neuralnetwork', NULL, '''/a'':196C ''/code'':265C ''/epajk.png'':185C,192C ''/li'':122C,128C,134C,142C,156C,166C,168C ''/p'':23C,46C,88C,115C,179C,197C,241C,250C,289C ''/pre'':266C ''/strong'':8C ''/ul'':167C,169C ''activ'':154C,165C,270C,276C ''actual'':40C,67C,206C ''algorithm'':127C,133C ''alt'':193C ''ann'':2A ''artifici'':91C ''ascii'':223C ''attempt'':101C ''backprop'':120C ''built'':107C ''clear'':238C ''code'':252C ''column'':272C,278C ''confus'':86C,213C ''could'':216C ''current'':243C ''data'':94C ''descent'':125C,131C ''doesn'':283C ''edg'':229C ''end'':172C ''error'':141C ''exampl'':76C ''explain/upload'':219C ''feed'':118C ''first'':148C ''follow'':113C ''forward'':119C ''function'':137C,155C ''generat'':92C ''get'':64C,79C ''gradient'':124C,130C ''graph'':14C ''gt'':257C,262C ''head'':240C ''help'':10C,237C ''hi'':25C ''howev'':199C ''href'':182C ''i.stack.imgur.com'':184C,191C ''i.stack.imgur.com/epajk.png'':183C,190C ''imag'':21C ''image/draw'':221C ''img'':188C ''know'':203C ''l'':254C,273C ''layer'':145C ''learni'':132C ''li'':117C,123C,129C,135C,143C,147C,157C ''like'':177C,248C ''linear'':164C,269C ''look'':176C ''m'':32C,39C,62C,209C ''make'':285C ''matlab'':99C ''mean'':139C ''multilay'':55C ''network'':18C,45C,53C,110C,121C,195C,233C ''neural'':44C,52C,109C ''neuralnetwork'':4B ''neuron'':151C,162C,227C ''nntool'':97C ''nofollow'':187C ''o'':255C,258C,259C,260C,261C,264C ''one'':161C ''p'':5C,24C,47C,89C,170C,180C,198C,242C,267C ''paramet'':114C ''perceptron'':56C ''performance/loss'':136C ''pleas'':9C,218C ''pre'':251C ''predict'':103C ''pretti'':28C ''read'':50C ''realli'':236C ''rel'':186C ''repres'':207C,225C ''represent'':15C ''result'':105C ''right'':214C,282C ''second'':159C ''sens'':286C ''simpl'':1A,75C ''someon'':217C ''someth'':175C ''sort'':211C ''squar'':140C ''src'':189C ''start'':65C,71C ''strong'':6C ''stupid'':29C ''super'':74C ''tanh'':275C ''tansig'':153C ''think'':245C ''three'':150C ''time'':59C ''tldr'':7C ''train'':126C ''troubl'':35C ''two'':144C ''ul'':116C,146C ''understand'':12C ''use'':68C,96C ''ve'':49C,85C ''visualis'':3A,36C ''warm'':80C ''would'':235C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10051, 2, '2016-02-01 16:19:04.107', 4, NULL, NULL, '&lt;p&gt;&lt;strong&gt;No&lt;/strong&gt;, he actually says the opposite:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;One final note: I should say that in the machine learning as of this practice today, there &lt;strong&gt;are&lt;/strong&gt; many people that will do that early thing that I talked about, and said that, you know...â&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Then he says (the "early thing" he talked about):&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;selecting your model as a test set and then using the same test set to report the error
  ...
  unfortunately many people do that&lt;/p&gt;
&lt;/blockquote&gt;

&lt;hr&gt;

&lt;p&gt;In this lesson he explains about separating the data set:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;em&gt;training set&lt;/em&gt; to train the model;&lt;/li&gt;
&lt;li&gt;&lt;em&gt;cross validation set&lt;/em&gt; to find the right parameters;&lt;/li&gt;
&lt;li&gt;&lt;em&gt;test set&lt;/em&gt; to find the final generalization error (of the function with the best parameter values found during using the &lt;em&gt;cross validation set&lt;/em&gt;).&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;So Andrew Ng is complaining that many people us the same data set to find the right parameters, and then report the error of that data set as final &lt;em&gt;generalization error&lt;/em&gt;.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':52C,90C ''/em'':109C,120C,131C,154C,190C ''/li'':114C,126C,155C ''/ol'':156C ''/p'':10C,51C,63C,89C,103C,191C ''/strong'':4C,32C ''actual'':6C ''andrew'':159C ''best'':143C ''blockquot'':11C,64C ''complain'':162C ''cross'':117C,151C ''data'':101C,169C,183C ''earli'':39C,58C ''em'':106C,116C,128C,150C,187C ''error'':83C,137C,180C,189C ''explain'':97C ''final'':14C,135C,186C ''find'':122C,133C,172C ''found'':146C ''function'':140C ''general'':136C,188C ''hr'':91C ''know'':49C ''learn'':23C ''lesson'':95C ''li'':105C,115C,127C ''machin'':22C ''mani'':33C,85C,164C ''model'':68C,113C ''ng'':160C ''note'':15C ''ol'':104C ''one'':13C ''opposit'':9C ''p'':1C,12C,53C,65C,92C,157C ''paramet'':125C,144C,175C ''peopl'':34C,86C,165C ''practic'':27C ''report'':81C,178C ''right'':124C,174C ''said'':46C ''say'':7C,18C,56C ''select'':66C ''separ'':99C ''set'':72C,79C,102C,108C,119C,130C,153C,170C,184C ''strong'':2C,30C ''talk'':43C,61C ''test'':71C,78C,129C ''thing'':40C,59C ''today'':28C ''train'':107C,111C ''unfortun'':84C ''us'':166C ''use'':75C,148C ''valid'':118C,152C ''valu'':145C ''â'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10069, 2, '2016-02-02 20:26:11.31', 2, NULL, NULL, '&lt;p&gt;This might be a bit unsatisfying, so feel free not to accept this answer, and apologies in advance. &lt;/p&gt;

&lt;p&gt;In a Bayes net, nodes represent random variables, and edges represent conditional dependences. When you interpret the nodes a certain way, conditioning flows a certain way naturally. Arbitrarily reversing them doesn''t really make sense in the context of modeling data. And a lot of time, the arrows do represent causality.&lt;/p&gt;
', NULL, NULL, '''/p'':20C,73C ''accept'':13C ''advanc'':19C ''answer'':15C ''apolog'':17C ''arbitrarili'':49C ''arrow'':69C ''bay'':24C ''bit'':6C ''causal'':72C ''certain'':41C,46C ''condit'':33C,43C ''context'':59C ''data'':62C ''depend'':34C ''doesn'':52C ''edg'':31C ''feel'':9C ''flow'':44C ''free'':10C ''interpret'':37C ''lot'':65C ''make'':55C ''might'':3C ''model'':61C ''natur'':48C ''net'':25C ''node'':26C,39C ''p'':1C,21C ''random'':28C ''realli'':54C ''repres'':27C,32C,71C ''revers'':50C ''sens'':56C ''time'':67C ''unsatisfi'':7C ''variabl'':29C ''way'':42C,47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10052, 2, '2016-02-01 16:41:58.29', 2, NULL, NULL, '&lt;p&gt;Your choices of &lt;code&gt;activation=''softmax''&lt;/code&gt; in the last layer and compile choice of &lt;code&gt;loss=''categorical_crossentropy''&lt;/code&gt; are good for a model to predict multiple mutually-exclusive classes.&lt;/p&gt;

&lt;p&gt;Regarding more general choices, there is rarely a "right" way to construct the architecture. Instead that should be something you test with different meta-params (such as layer sizes, number of layers, amount of drop-out), and should be results-driven (including any limits you might have on resource use for training time/memory use etc). &lt;/p&gt;

&lt;p&gt;Use a cross-validation set to help choose a suitable architecture. Once done, to get a more accurate measure of your model''s general performance, you should use a separate test set. Data held out from your training set separate to the CV set should be used for this. A reasonable split might be 60/20/20 train/cv/test, depending on how much data you have, and how much you need to report an accurate final figure.&lt;/p&gt;

&lt;p&gt;For Question #2, you can either just have two outputs with a softmax final similar to now, or you can have &lt;em&gt;final&lt;/em&gt; layer with one output, &lt;code&gt;activation=''sigmoid''&lt;/code&gt; and &lt;code&gt;loss=''binary_crossentropy''&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Purely from a gut feel from what might work with this data, I would suggest trying with &lt;code&gt;''tanh''&lt;/code&gt; or &lt;code&gt;''sigmoid''&lt;/code&gt; activations in the hidden layer, instead of &lt;code&gt;''relu''&lt;/code&gt;, and I would also suggest increasing the number of hidden neurons (e.g. 100) and reducing the amount of dropout (e.g. 0.2). Caveat: Gut feeling on neural network architecture is not scientific. Try it, and test it.&lt;/p&gt;
', NULL, NULL, '''/code'':8C,21C,204C,210C,232C,236C,246C ''/em'':196C ''/p'':34C,94C,171C,211C,283C ''0.2'':267C ''100'':259C ''2'':175C ''60/20/20'':151C ''accur'':114C,168C ''activ'':6C,202C,237C ''also'':250C ''amount'':69C,263C ''architectur'':49C,107C,274C ''binari'':208C ''categor'':19C ''caveat'':268C ''choic'':3C,15C,39C ''choos'':104C ''class'':33C ''code'':5C,17C,201C,206C,230C,234C,244C ''compil'':14C ''construct'':47C ''cross'':99C ''cross-valid'':98C ''crossentropi'':20C,209C ''cv'':139C ''data'':129C,157C,224C ''depend'':153C ''differ'':58C ''done'':109C ''driven'':79C ''drop'':72C ''drop-out'':71C ''dropout'':265C ''e.g'':258C,266C ''either'':178C ''em'':194C ''etc'':93C ''exclus'':32C ''feel'':217C,270C ''figur'':170C ''final'':169C,186C,195C ''general'':38C,120C ''get'':111C ''good'':23C ''gut'':216C,269C ''held'':130C ''help'':103C ''hidden'':240C,256C ''includ'':80C ''increas'':252C ''instead'':50C,242C ''last'':11C ''layer'':12C,64C,68C,197C,241C ''limit'':82C ''loss'':18C,207C ''measur'':115C ''meta'':60C ''meta-param'':59C ''might'':84C,149C,220C ''model'':26C,118C ''much'':156C,162C ''multipl'':29C ''mutual'':31C ''mutually-exclus'':30C ''need'':164C ''network'':273C ''neural'':272C ''neuron'':257C ''number'':66C,254C ''one'':199C ''output'':182C,200C ''p'':1C,35C,95C,172C,212C ''param'':61C ''perform'':121C ''predict'':28C ''pure'':213C ''question'':174C ''rare'':42C ''reason'':147C ''reduc'':261C ''regard'':36C ''relu'':245C ''report'':166C ''resourc'':87C ''result'':78C ''results-driven'':77C ''right'':44C ''scientif'':277C ''separ'':126C,136C ''set'':101C,128C,135C,140C ''sigmoid'':203C,235C ''similar'':187C ''size'':65C ''softmax'':7C,185C ''someth'':54C ''split'':148C ''suggest'':227C,251C ''suitabl'':106C ''tanh'':231C ''test'':56C,127C,281C ''time/memory'':91C ''train'':90C,134C ''train/cv/test'':152C ''tri'':228C,278C ''two'':181C ''use'':88C,92C,96C,124C,143C ''valid'':100C ''way'':45C ''work'':221C ''would'':226C,249C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10053, 1, '2016-02-01 17:36:16.72', 2, 57, 'Data structure design for supporting arbitrary number of columns in table or database', '&lt;p&gt;I''m currently working on a sort-of a meta-modeler to build a free web service so that people can input data and run several models on that data.&lt;/p&gt;

&lt;p&gt;The task I''m currently struggling is: user needs to enter data column by column, which would consist of a number n of ID''s , a number m of attributes and a number k of classes, with the conditions that n, m &gt; 0 and k &gt;= 0. Data is heterogeneous, so indexes can be both numeric or text, and the same goes for attributes and classes. I''m supposing there will be no null in the data for simplicity.&lt;/p&gt;

&lt;p&gt;I''m currently thinking on:&lt;/p&gt;

&lt;p&gt;1) Creating a table with more than enough columns (all with null values), so that I can work using only the non-null columns (which will be got from user input). However this would limit the size of the datasets people could input.&lt;/p&gt;

&lt;p&gt;2) Resorting to create an specialized data structure on a programming language, do all the work there and finally, create a table dynamically  to store the result data there. &lt;/p&gt;

&lt;p&gt;3) Using a database specialized for this kind of data (maybe a document-based DB).&lt;/p&gt;

&lt;p&gt;4) Create a data structure on the RDBMS itself (I''m using PostgreSQL), let''s say a variable size array, so that I can create the table directly from the user input, using only 3 variable arrays (one for indexes, one for attributes and one for classes). However, I keep in mind that attributes and indexes could be of different types, so the array would have to support heterogeneous data type and I don''t know if this is possible on a RDBMS or SQL.&lt;/p&gt;

&lt;p&gt;I''ve been looking for information on information but got no result until now. Any guidances to a package, language library, extension or paper, thesis, technical report with relevant information would be appreciated.
Also, personal experiences with doing something similar could be useful.&lt;/p&gt;
', 'dataset  recommendation  data  databases', NULL, '''/p'':51C,131C,138C,184C,215C,233C,320C,365C ''0'':95C,98C ''1'':140C ''2'':186C ''3'':217C,269C ''4'':235C ''also'':355C ''appreci'':354C ''arbitrari'':6A ''array'':254C,271C,298C ''attribut'':82C,115C,277C,288C ''base'':231C ''build'':33C ''class'':88C,117C,281C ''column'':9A,65C,67C,148C,164C ''condit'':91C ''consist'':70C ''could'':182C,291C,362C ''creat'':141C,189C,205C,236C,259C ''current'':21C,57C,135C ''data'':1A,16B,43C,50C,64C,99C,128C,192C,213C,226C,238C,304C ''databas'':13A,17B,220C ''dataset'':14B,180C ''db'':232C ''design'':3A ''differ'':294C ''direct'':262C ''document'':230C ''document-bas'':229C ''dynam'':208C ''enough'':147C ''enter'':63C ''experi'':357C ''extens'':343C ''final'':204C ''free'':35C ''goe'':113C ''got'':168C,331C ''guidanc'':337C ''heterogen'':101C,303C ''howev'':172C,282C ''id'':76C ''index'':103C,274C,290C ''inform'':327C,329C,351C ''input'':42C,171C,183C,266C ''k'':86C,97C ''keep'':284C ''kind'':224C ''know'':310C ''languag'':197C,341C ''let'':248C ''librari'':342C ''limit'':175C ''look'':325C ''m'':20C,56C,80C,94C,119C,134C,245C ''mayb'':227C ''meta'':30C ''meta-model'':29C ''mind'':286C ''model'':31C,47C ''n'':74C,93C ''need'':61C ''non'':162C ''non-nul'':161C ''null'':125C,151C,163C ''number'':7A,73C,79C,85C ''numer'':107C ''one'':272C,275C,279C ''p'':18C,52C,132C,139C,185C,216C,234C,321C ''packag'':340C ''paper'':345C ''peopl'':40C,181C ''person'':356C ''possibl'':314C ''postgresql'':247C ''program'':196C ''rdbms'':242C,317C ''recommend'':15B ''relev'':350C ''report'':348C ''resort'':187C ''result'':212C,333C ''run'':45C ''say'':250C ''servic'':37C ''sever'':46C ''similar'':361C ''simplic'':130C ''size'':177C,253C ''someth'':360C ''sort'':26C ''sort-of'':25C ''special'':191C,221C ''sql'':319C ''store'':210C ''structur'':2A,193C,239C ''struggl'':58C ''support'':5A,302C ''suppos'':120C ''tabl'':11A,143C,207C,261C ''task'':54C ''technic'':347C ''text'':109C ''thesi'':346C ''think'':136C ''type'':295C,305C ''use'':158C,218C,246C,267C,364C ''user'':60C,170C,265C ''valu'':152C ''variabl'':252C,270C ''ve'':323C ''web'':36C ''work'':22C,157C,201C ''would'':69C,174C,299C,352C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10054, 2, '2016-02-01 19:44:47.643', 1, NULL, NULL, '&lt;p&gt;I''ve done something like you''re describing using &lt;a href="https://www.mongodb.com/" rel="nofollow"&gt;mongoDB&lt;/a&gt;--I think you''ll best use your time using some sort of NoSQL approach, rather than creating a specialized one-off solution. If you''re using Python, I''ve had excellent experiences using &lt;a href="https://api.mongodb.org/python/current/" rel="nofollow"&gt;PyMongo&lt;/a&gt; to handle reads and writes from within my code. &lt;/p&gt;

&lt;p&gt;I would strongly caution you against adopting your approach #1. This could break very easily in the future, and there are databases designed to handle your exact problem!&lt;/p&gt;
', NULL, NULL, '''/a'':17C,60C ''/p'':70C,100C ''/python/current/'':56C ''1'':81C ''adopt'':78C ''api.mongodb.org'':55C ''api.mongodb.org/python/current/'':54C ''approach'':31C,80C ''best'':22C ''break'':84C ''caution'':75C ''code'':69C ''could'':83C ''creat'':34C ''databas'':93C ''describ'':9C ''design'':94C ''done'':4C ''easili'':86C ''exact'':98C ''excel'':49C ''experi'':50C ''futur'':89C ''handl'':62C,96C ''href'':12C,53C ''like'':6C ''ll'':21C ''mongodb'':16C ''nofollow'':15C,58C ''nosql'':30C ''one'':38C ''one-off'':37C ''p'':1C,71C ''problem'':99C ''pymongo'':59C ''python'':45C ''rather'':32C ''re'':8C,43C ''read'':63C ''rel'':14C,57C ''solut'':40C ''someth'':5C ''sort'':28C ''special'':36C ''strong'':74C ''think'':19C ''time'':25C ''use'':10C,23C,26C,44C,51C ''ve'':3C,47C ''within'':67C ''would'':73C ''write'':65C ''www.mongodb.com'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10055, 1, '2016-02-01 21:26:08.853', 0, 97, 'How to color a tif file in R?', '&lt;p&gt;I have a tif file that shows a map. On white background the border of ditricts are drawn with black, nothing else is on the picture. I want to color the districts according to certain values. Is it possible to do that in R? (since the picture doesn''t "know" the name of districts in advance I suppose I have to do some manual work but it''s ok for me)&lt;/p&gt;

&lt;p&gt;UPDATE: the picture is similar to this one: &lt;a href="http://www.geoindex.hu/wp-content/uploads/evk_106_20140101.png" rel="nofollow"&gt;http://www.geoindex.hu/wp-content/uploads/evk_106_20140101.png&lt;/a&gt;&lt;/p&gt;
', 'r', NULL, '''/p'':82C ''/wp-content/uploads/evk_106_20140101.png'':96C ''/wp-content/uploads/evk_106_20140101.png&lt;/a&gt;&lt;/p&gt;'':101C ''accord'':43C ''advanc'':66C ''background'':22C ''black'':30C ''border'':24C ''certain'':45C ''color'':3A,40C ''district'':42C,64C ''ditrict'':26C ''doesn'':58C ''drawn'':28C ''els'':32C ''file'':6A,15C ''href'':93C ''know'':60C ''manual'':74C ''map'':19C ''name'':62C ''nofollow'':98C ''noth'':31C ''ok'':79C ''one'':91C ''p'':10C,83C ''pictur'':36C,57C,86C ''possibl'':49C ''r'':8A,9B,54C ''rel'':97C ''show'':17C ''similar'':88C ''sinc'':55C ''suppos'':68C ''tif'':5A,14C ''updat'':84C ''valu'':46C ''want'':38C ''white'':21C ''work'':75C ''www.geoindex.hu'':95C,100C ''www.geoindex.hu/wp-content/uploads/evk_106_20140101.png'':94C ''www.geoindex.hu/wp-content/uploads/evk_106_20140101.png&lt;/a&gt;&lt;/p&gt;'':99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10056, 2, '2016-02-01 21:43:53.117', 2, NULL, NULL, '&lt;p&gt;Look up taxonomy/ontology construction/induction. Relevant papers:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Automatic Taxonomy Construction from Keywords via Scalable Bayesian Rose Trees&lt;/li&gt;
&lt;li&gt;Topic Models for Taxonomies&lt;/li&gt;
&lt;li&gt;OntoLearn Reloaded. A Graph-Based Algorithm for Taxonomy Induction&lt;/li&gt;
&lt;li&gt;Ontology Population and Enrichment: State of the Art&lt;/li&gt;
&lt;li&gt;Probabilistic Topic Models for Learning Terminological Ontologies&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':21C,27C,39C,49C,58C ''/p'':8C ''/ul'':59C ''algorithm'':35C ''art'':48C ''automat'':11C ''base'':34C ''bayesian'':18C ''construct'':13C ''construction/induction'':5C ''enrich'':44C ''graph'':33C ''graph-bas'':32C ''induct'':38C ''keyword'':15C ''learn'':55C ''li'':10C,22C,28C,40C,50C ''look'':2C ''model'':24C,53C ''ontolearn'':29C ''ontolog'':41C,57C ''p'':1C ''paper'':7C ''popul'':42C ''probabilist'':51C ''relev'':6C ''reload'':30C ''rose'':19C ''scalabl'':17C ''state'':45C ''taxonomi'':12C,26C,37C ''taxonomy/ontology'':4C ''terminolog'':56C ''topic'':23C,52C ''tree'':20C ''ul'':9C ''via'':16C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10057, 1, '2016-02-01 21:54:04.777', 2, 33, 'Best network structure for unsupervised learning', '&lt;p&gt;Currently using networkX to build a probability based network for unsupervised learning, it is a basic reader understanding relational connectivity through probabilities, network slows significantly after about 40 pages (8000 words) any suggestions for a better network platform would be much appreciated.&lt;/p&gt;
', 'nlp  unsupervised-learning', NULL, '''/p'':54C ''40'':39C ''8000'':41C ''appreci'':53C ''base'':19C ''basic'':27C ''best'':1A ''better'':47C ''build'':16C ''connect'':31C ''current'':12C ''learn'':6A,10B,23C ''much'':52C ''network'':2A,20C,34C,48C ''networkx'':14C ''nlp'':7B ''p'':11C ''page'':40C ''platform'':49C ''probabl'':18C,33C ''reader'':28C ''relat'':30C ''signific'':36C ''slow'':35C ''structur'':3A ''suggest'':44C ''understand'':29C ''unsupervis'':5A,9B,22C ''unsupervised-learn'':8B ''use'':13C ''word'':42C ''would'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10061, 2, '2016-02-02 04:34:30.6', 1, NULL, NULL, '&lt;p&gt;If you have a lot of data you could split your words into sentences of single letters and treat each sentence as a separate document. Then feed it into CountVectorizer from sklearn to create a sparse matrix containing vectors for ngrams of length from 2 to max length. Use binary=True to avoid double counting of a repeating pattern inside the word. Last step is to calculate which columns of the matrix have the highest number of ''ones'' and look up which ngrams that were from the vectorizer dictionary.&lt;/p&gt;
', NULL, NULL, '''/p'':91C ''2'':46C ''avoid'':54C ''binari'':51C ''calcul'':68C ''column'':70C ''contain'':39C ''could'':10C ''count'':56C ''countvector'':31C ''creat'':35C ''data'':8C ''dictionari'':90C ''document'':26C ''doubl'':55C ''feed'':28C ''highest'':76C ''insid'':61C ''last'':64C ''length'':44C,49C ''letter'':18C ''look'':81C ''lot'':6C ''matrix'':38C,73C ''max'':48C ''ngram'':42C,84C ''number'':77C ''one'':79C ''p'':1C ''pattern'':60C ''repeat'':59C ''sentenc'':15C,22C ''separ'':25C ''singl'':17C ''sklearn'':33C ''spars'':37C ''split'':11C ''step'':65C ''treat'':20C ''true'':52C ''use'':50C ''vector'':40C,89C ''word'':13C,63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10062, 1, '2016-02-02 07:36:26.677', 1, 46, 'Estimating precision &amp; recall', '&lt;p&gt;Given a very large population, is there an accepted recall estimation method to use when one is validating a trained model against the population? &lt;/p&gt;

&lt;p&gt;In my case, a trained model was tested on a disjoint data set, so it has test precision and recall values, but the test data is an extremely small fraction the population. &lt;/p&gt;

&lt;p&gt;When the model is run against ''live'' data, and when I look at randomly sampled results, I observe an overfit. I want to estimate how bad the situation is, so it makes sense to estimate precision and recall estimates in the population. &lt;/p&gt;

&lt;p&gt;I can think of a way to do this for precision calculations, as one can randomly select from the classifier''s positive outcomes at various times to calculate a sample mean and standard deviation for precision. However, it does not appear to be easy to estimate recall in the case of drastic class imbalance, and samples drawn from the population keep coming up with zero positive-class data points. &lt;/p&gt;

&lt;p&gt;Has anyone come across a situation like this? I would appreciate any pointers or reference materials on the subject.&lt;/p&gt;
', 'text-mining  evaluation', NULL, '''/p'':33C,67C,111C,183C,204C ''accept'':17C ''across'':188C ''anyon'':186C ''appear'':153C ''appreci'':195C ''bad'':94C ''calcul'':124C,140C ''case'':37C,162C ''class'':165C,180C ''classifi'':132C ''come'':174C,187C ''data'':46C,59C,76C,181C ''deviat'':146C ''disjoint'':45C ''drastic'':164C ''drawn'':169C ''easi'':156C ''estim'':1A,19C,92C,103C,107C,158C ''evalu'':7B ''extrem'':62C ''fraction'':64C ''given'':9C ''howev'':149C ''imbal'':166C ''keep'':173C ''larg'':12C ''like'':191C ''live'':75C ''look'':80C ''make'':100C ''materi'':200C ''mean'':143C ''method'':20C ''mine'':6B ''model'':29C,40C,71C ''observ'':86C ''one'':24C,126C ''outcom'':135C ''overfit'':88C ''p'':8C,34C,68C,112C,184C ''point'':182C ''pointer'':197C ''popul'':13C,32C,66C,110C,172C ''posit'':134C,179C ''positive-class'':178C ''precis'':2A,52C,104C,123C,148C ''random'':82C,128C ''recal'':3A,18C,54C,106C,159C ''refer'':199C ''result'':84C ''run'':73C ''sampl'':83C,142C,168C ''select'':129C ''sens'':101C ''set'':47C ''situat'':96C,190C ''small'':63C ''standard'':145C ''subject'':203C ''test'':42C,51C,58C ''text'':5B ''text-min'':4B ''think'':115C ''time'':138C ''train'':28C,39C ''use'':22C ''valid'':26C ''valu'':55C ''various'':137C ''want'':90C ''way'':118C ''would'':194C ''zero'':177C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10063, 1, '2016-02-02 08:36:55.023', 5, 87, 'For which real world data sets does DBSCAN surpass K-means.?', '&lt;p&gt;For clustering, DBSCAN surpass k-means in terms of handling arbitrary  shape data sets. In the most published papers about density based clustering, the experiments are performed with synthetic data sets that have special features (for example, the moon-shaped data sets). Can someone point me to some real world datasets where DBSCAN outperforms K-means. &lt;/p&gt;
', 'machine-learning  clustering  dataset  k-means  clusters', NULL, '''/p'':82C ''arbitrari'':34C ''base'':45C ''cluster'':16B,21B,24C,46C ''data'':5A,36C,53C,65C ''dataset'':17B,75C ''dbscan'':8A,25C,77C ''densiti'':44C ''exampl'':60C ''experi'':48C ''featur'':58C ''handl'':33C ''k'':11A,19B,28C,80C ''k-mean'':10A,18B,27C,79C ''learn'':15B ''machin'':14B ''machine-learn'':13B ''mean'':12A,20B,29C,81C ''moon'':63C ''moon-shap'':62C ''outperform'':78C ''p'':22C ''paper'':42C ''perform'':50C ''point'':69C ''publish'':41C ''real'':3A,73C ''set'':6A,37C,54C,66C ''shape'':35C,64C ''someon'':68C ''special'':57C ''surpass'':9A,26C ''synthet'':52C ''term'':31C ''world'':4A,74C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10064, 1, '2016-02-02 09:05:18.94', 1, 20, 'Is the direction of edges in a Bayes Network irrelevant?', '&lt;p&gt;Today, in a lecture it was claimed that the direction of edges in a Bayes network doesn''t really matter. They don''t have to represent causality.&lt;/p&gt;

&lt;p&gt;It is obvious that you cannot switch any single edge in a Bayes network. For example, let $G = (V, E)$ with $V = \{v_1, v_2, v_3\}$ and $E=\{(v_1, v_2), (v_1, v_3), (v_2, v_3)\}$. If you would switch $(v_1, v_3)$ to $(v_3, v_1)$, then $G$ would no longer be acyclical and hence not a Bayes network. This seems to be mainly a practical problem how to estimate the probabilities then. This case seems to be much more difficult to answer, so I will skip it.&lt;/p&gt;

&lt;p&gt;This made me ask the following questions for which I hope to get answers here:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Is it possible for any directed acyclical graph (DAG) to reverse all edges and still have a DAG?&lt;/li&gt;
&lt;li&gt;Assume a DAG $G$ and data is given. Now we construct the inverse DAG $G_\text{inv}$. For both DAGs, we fit the data to the corresponding Bayes networks. Now we have a set of data for which we want to use the Bayes network to predict the missing attributes. Could there be different results for both DAGs? (Bonus if you come up with an example)&lt;/li&gt;
&lt;li&gt;Similar to 2, but simpler: Assume a DAG $G$ and data is given. You may create a new graph $G''$ by inverting any set of edges, as long as $G''$ remains acyclical. Are the Bayes networks equivalent when it comes to their predictions?&lt;/li&gt;
&lt;li&gt;Do we get something if we have edges which do represent causality?&lt;/li&gt;
&lt;/ol&gt;
', 'bayesian-networks', NULL, '''/li'':179C,247C,292C,306C ''/ol'':307C ''/p'':42C,141C,158C ''1'':67C,75C,79C,91C,98C ''2'':69C,77C,83C,251C ''3'':71C,81C,85C,93C,96C ''acycl'':105C,167C,280C ''answer'':135C,156C ''ask'':146C ''assum'':181C,254C ''attribut'':230C ''bay'':8A,29C,56C,110C,208C,224C,283C ''bayesian'':12B ''bayesian-network'':11B ''bonus'':239C ''cannot'':49C ''case'':127C ''causal'':41C,305C ''claim'':21C ''come'':242C,288C ''construct'':191C ''correspond'':207C ''could'':231C ''creat'':264C ''dag'':169C,178C,183C,194C,200C,238C,256C ''data'':186C,204C,216C,259C ''differ'':234C ''difficult'':133C ''direct'':3A,24C,166C ''doesn'':31C ''e'':63C,73C ''edg'':5A,26C,53C,173C,274C,301C ''equival'':285C ''estim'':122C ''exampl'':59C,246C ''fit'':202C ''follow'':148C ''g'':61C,100C,184C,195C,257C,268C,278C ''get'':155C,296C ''given'':188C,261C ''graph'':168C,267C ''henc'':107C ''hope'':153C ''inv'':197C ''invers'':193C ''invert'':270C ''irrelev'':10A ''lectur'':18C ''let'':60C ''li'':160C,180C,248C,293C ''long'':276C ''longer'':103C ''made'':144C ''main'':116C ''matter'':34C ''may'':263C ''miss'':229C ''much'':131C ''network'':9A,13B,30C,57C,111C,209C,225C,284C ''new'':266C ''obvious'':46C ''ol'':159C ''p'':14C,43C,142C ''possibl'':163C ''practic'':118C ''predict'':227C,291C ''probabl'':124C ''problem'':119C ''question'':149C ''realli'':33C ''remain'':279C ''repres'':40C,304C ''result'':235C ''revers'':171C ''seem'':113C,128C ''set'':214C,272C ''similar'':249C ''simpler'':253C ''singl'':52C ''skip'':139C ''someth'':297C ''still'':175C ''switch'':50C,89C ''text'':196C ''today'':15C ''use'':222C ''v'':62C,65C,66C,68C,70C,74C,76C,78C,80C,82C,84C,90C,92C,95C,97C ''want'':220C ''would'':88C,101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10115, 1, '2016-02-07 04:36:41.013', 2, 11, 'Pruning tree using REP', '&lt;p&gt;i create a decision tree model using c4.5 algorithm. After create the model, i evaluate model using 10 fold cross validation and classify model using test data to get accuracy. And then I run prune tree with REP. &lt;/p&gt;

&lt;p&gt;My question is what data should i use to compare accuracy (after prune and before prune) to decide to remove the leaf? and from where i get the accuracy? using cross validation or classify test data?&lt;/p&gt;
', 'decision-trees', NULL, '''/p'':47C,85C ''10'':26C ''accuraci'':38C,59C,77C ''algorithm'':17C ''c4.5'':16C ''classifi'':31C,82C ''compar'':58C ''creat'':10C,19C ''cross'':28C,79C ''data'':35C,53C,84C ''decid'':66C ''decis'':6B,12C ''decision-tre'':5B ''evalu'':23C ''fold'':27C ''get'':37C,75C ''leaf'':70C ''model'':14C,21C,24C,32C ''p'':8C,48C ''prune'':1A,43C,61C,64C ''question'':50C ''remov'':68C ''rep'':4A,46C ''run'':42C ''test'':34C,83C ''tree'':2A,7B,13C,44C ''use'':3A,15C,25C,33C,56C,78C ''valid'':29C,80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10065, 1, '2016-02-02 09:38:57.447', 2, 17, 'How to apply K-Medoids in many CFG?', '&lt;p&gt;I am having around 1000 DAG(Directed Acyclic Graph) of different files showing java.io.BufferedReader usage. Following is representation of one of the graphs&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;digraph G {
  9 [ label="9 : ROOT:setup()#0" ];
  10 [ label="10 : START IF" ];
  12 [ label="12 : java.net.URL.openConnection()#1" ];
  11 [ label="11 : END IF" ];
  13 [ label="13 : java.net.URL.openConnection()#0" ];
  14 [ label="14 : START IF" ];
  16 [ label="16 : java.net.HttpURLConnection.setRequestProperty()#2" ];
  15 [ label="15 : END IF" ];
  17 [ label="17 : START IF" ];
  19 [ label="19 : java.net.HttpURLConnection.addRequestProperty()#2" ];
  18 [ label="18 : END IF" ];
  21 [ label="21 : java.net.HttpURLConnection.setReadTimeout()#1" ];
  22 [ label="22 : java.net.HttpURLConnection.setConnectTimeout()#1" ];
  23 [ label="23 : java.net.HttpURLConnection.setUseCaches()#1" ];
  24 [ label="24 : &amp;lt;static&amp;gt;java.net.HttpURLConnection.setFollowRedirects()#1" ];
  25 [ label="25 : START IF" ];
  27 [ label="27 : java.net.HttpURLConnection.setRequestMethod()#1" ];
  28 [ label="28 : java.net.HttpURLConnection.setDoOutput()#1" ];
  29 [ label="29 : java.net.HttpURLConnection.setDoInput()#1" ];
  30 [ label="30 : java.net.HttpURLConnection.getOutputStream()#0" ];
  31 [ label="31 : java.io.DataOutputStream.&amp;lt;init&amp;gt;()#1" ];
  32 [ label="32 : java.io.DataOutputStream.writeBytes()#1" ];
  33 [ label="33 : java.io.DataOutputStream.flush()#0" ];
  26 [ label="26 : END IF" ];
  34 [ label="34 : java.net.HttpURLConnection.getInputStream()#0" ];
  35 [ label="35 : java.io.InputStreamReader.&amp;lt;init&amp;gt;()#1" ];
  36 [ label="36 : java.io.BufferedReader.&amp;lt;init&amp;gt;()#1" ];
  9 -&amp;gt; 10;
  10 -&amp;gt; 12;
  12 -&amp;gt; 11;
  10 -&amp;gt; 13;
  12 -&amp;gt; 13;
  13 -&amp;gt; 11;
  11 -&amp;gt; 14;
  14 -&amp;gt; 16;
  12 -&amp;gt; 16;
  13 -&amp;gt; 16;
  16 -&amp;gt; 15;
  15 -&amp;gt; 17;
  17 -&amp;gt; 19;
  12 -&amp;gt; 19;
  13 -&amp;gt; 19;
  16 -&amp;gt; 19;
  19 -&amp;gt; 18;
  18 -&amp;gt; 20;
  16 -&amp;gt; 20;
  19 -&amp;gt; 20;
  20 -&amp;gt; 21;
  12 -&amp;gt; 21;
  13 -&amp;gt; 21;
  19 -&amp;gt; 21;
  21 -&amp;gt; 22;
  12 -&amp;gt; 22;
  13 -&amp;gt; 22;
  16 -&amp;gt; 22;
  19 -&amp;gt; 22;
  22 -&amp;gt; 23;
  12 -&amp;gt; 23;
  13 -&amp;gt; 23;
  16 -&amp;gt; 23;
  19 -&amp;gt; 23;
  21 -&amp;gt; 23;
  23 -&amp;gt; 24;
  24 -&amp;gt; 25;
  25 -&amp;gt; 27;
  12 -&amp;gt; 27;
  13 -&amp;gt; 27;
  16 -&amp;gt; 27;
  19 -&amp;gt; 27;
  21 -&amp;gt; 27;
  22 -&amp;gt; 27;
  23 -&amp;gt; 27;
  27 -&amp;gt; 28;
  12 -&amp;gt; 28;
  13 -&amp;gt; 28;
  16 -&amp;gt; 28;
  19 -&amp;gt; 28;
  21 -&amp;gt; 28;
  22 -&amp;gt; 28;
  23 -&amp;gt; 28;
  28 -&amp;gt; 29;
  12 -&amp;gt; 29;
  13 -&amp;gt; 29;
  16 -&amp;gt; 29;
  19 -&amp;gt; 29;
  21 -&amp;gt; 29;
  22 -&amp;gt; 29;
  23 -&amp;gt; 29;
  27 -&amp;gt; 29;
  29 -&amp;gt; 30;
  12 -&amp;gt; 30;
  13 -&amp;gt; 30;
  16 -&amp;gt; 30;
  19 -&amp;gt; 30;
  21 -&amp;gt; 30;
  22 -&amp;gt; 30;
  23 -&amp;gt; 30;
  27 -&amp;gt; 30;
  28 -&amp;gt; 30;
  30 -&amp;gt; 31;
  31 -&amp;gt; 32;
  32 -&amp;gt; 33;
  31 -&amp;gt; 33;
  33 -&amp;gt; 26;
  26 -&amp;gt; 34;
  12 -&amp;gt; 34;
  13 -&amp;gt; 34;
  16 -&amp;gt; 34;
  19 -&amp;gt; 34;
  21 -&amp;gt; 34;
  22 -&amp;gt; 34;
  23 -&amp;gt; 34;
  27 -&amp;gt; 34;
  28 -&amp;gt; 34;
  29 -&amp;gt; 34;
  30 -&amp;gt; 34;
  34 -&amp;gt; 35;
  35 -&amp;gt; 36;
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to cluster these graphs.I am trying to implement K-Medoids.&lt;/p&gt;

&lt;p&gt;How to start with K-Medoid implementation?
How can i find distance between two graphs?&lt;/p&gt;
', 'clustering  graphs', NULL, '''/code'':462C ''/p'':36C,478C,496C ''/pre'':463C ''0'':46C,66C,144C,162C,172C ''1'':56C,101C,106C,111C,119C,129C,134C,139C,152C,157C,180C,188C ''10'':47C,49C,191C,192C,198C ''1000'':17C ''11'':57C,59C,197C,206C,207C ''12'':52C,54C,194C,195C,201C,213C,228C,252C,264C,279C,303C,327C,351C,378C,423C ''13'':62C,64C,200C,203C,204C,216C,231C,255C,267C,282C,306C,330C,354C,381C,426C ''14'':67C,69C,209C,210C ''15'':77C,79C,221C,222C ''16'':72C,74C,212C,215C,218C,219C,234C,243C,270C,285C,309C,333C,357C,384C,429C ''17'':82C,84C,224C,225C ''18'':92C,94C,239C,240C ''19'':87C,89C,227C,230C,233C,236C,237C,246C,258C,273C,288C,312C,336C,360C,387C,432C ''2'':76C,91C ''20'':242C,245C,248C,249C ''21'':97C,99C,251C,254C,257C,260C,261C,291C,315C,339C,363C,390C,435C ''22'':102C,104C,263C,266C,269C,272C,275C,276C,318C,342C,366C,393C,438C ''23'':107C,109C,278C,281C,284C,287C,290C,293C,294C,321C,345C,369C,396C,441C ''24'':112C,114C,296C,297C ''25'':120C,122C,299C,300C ''26'':163C,165C,419C,420C ''27'':125C,127C,302C,305C,308C,311C,314C,317C,320C,323C,324C,372C,399C,444C ''28'':130C,132C,326C,329C,332C,335C,338C,341C,344C,347C,348C,402C,447C ''29'':135C,137C,350C,353C,356C,359C,362C,365C,368C,371C,374C,375C,450C ''30'':140C,142C,377C,380C,383C,386C,389C,392C,395C,398C,401C,404C,405C,453C ''31'':145C,147C,407C,408C,414C ''32'':153C,155C,410C,411C ''33'':158C,160C,413C,416C,417C ''34'':168C,170C,422C,425C,428C,431C,434C,437C,440C,443C,446C,449C,452C,455C,456C ''35'':173C,175C,458C,459C ''36'':181C,183C,461C ''9'':41C,43C,189C ''acycl'':20C ''appli'':3A ''around'':16C ''cfg'':9A ''cluster'':10B,468C ''code'':38C ''dag'':18C ''differ'':23C ''digraph'':39C ''direct'':19C ''distanc'':492C ''end'':60C,80C,95C,166C ''file'':24C ''find'':491C ''follow'':28C ''g'':40C ''graph'':11B,21C,35C,495C ''graphs.i'':470C ''gt'':117C,151C,179C,187C,190C,193C,196C,199C,202C,205C,208C,211C,214C,217C,220C,223C,226C,229C,232C,235C,238C,241C,244C,247C,250C,253C,256C,259C,262C,265C,268C,271C,274C,277C,280C,283C,286C,289C,292C,295C,298C,301C,304C,307C,310C,313C,316C,319C,322C,325C,328C,331C,334C,337C,340C,343C,346C,349C,352C,355C,358C,361C,364C,367C,370C,373C,376C,379C,382C,385C,388C,391C,394C,397C,400C,403C,406C,409C,412C,415C,418C,421C,424C,427C,430C,433C,436C,439C,442C,445C,448C,451C,454C,457C,460C ''implement'':474C,487C ''init'':150C,178C,186C ''java.io.bufferedreader'':26C,184C ''java.io.dataoutputstream'':148C ''java.io.dataoutputstream.flush'':161C ''java.io.dataoutputstream.writebytes'':156C ''java.io.inputstreamreader'':176C ''java.net.httpurlconnection.addrequestproperty'':90C ''java.net.httpurlconnection.getinputstream'':171C ''java.net.httpurlconnection.getoutputstream'':143C ''java.net.httpurlconnection.setconnecttimeout'':105C ''java.net.httpurlconnection.setdoinput'':138C ''java.net.httpurlconnection.setdooutput'':133C ''java.net.httpurlconnection.setfollowredirects'':118C ''java.net.httpurlconnection.setreadtimeout'':100C ''java.net.httpurlconnection.setrequestmethod'':128C ''java.net.httpurlconnection.setrequestproperty'':75C ''java.net.httpurlconnection.setusecaches'':110C ''java.net.url.openconnection'':55C,65C ''k'':5A,476C,485C ''k-medoid'':4A,475C,484C ''label'':42C,48C,53C,58C,63C,68C,73C,78C,83C,88C,93C,98C,103C,108C,113C,121C,126C,131C,136C,141C,146C,154C,159C,164C,169C,174C,182C ''lt'':115C,149C,177C,185C ''mani'':8A ''medoid'':6A,477C,486C ''one'':32C ''p'':12C,464C,479C ''pre'':37C ''represent'':30C ''root'':44C ''setup'':45C ''show'':25C ''start'':50C,70C,85C,123C,482C ''static'':116C ''tri'':472C ''two'':494C ''usag'':27C ''want'':466C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10076, 2, '2016-02-03 09:55:59.357', 4, NULL, NULL, '&lt;p&gt;There is no good evaluation data &lt;em&gt;at all&lt;/em&gt; for clustering that would allow such conclusions.&lt;/p&gt;

&lt;p&gt;There isn''t even good real data where you could say variant 1 of k-means is better than variant 2 of k-means.&lt;/p&gt;

&lt;p&gt;There is also no good evaluation &lt;em&gt;measure&lt;/em&gt; that would handle the notion of "noise" well either.&lt;/p&gt;

&lt;p&gt;So: &lt;strong&gt;don''t go by some number&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Clustering is about solving a data problem. You have data, you try algorithms and parameters and &lt;em&gt;study&lt;/em&gt; (important) the result. Until you find something &lt;em&gt;interesting&lt;/em&gt;. This cannot be automated, or measured (well, you could ask 100 scientists on what worked for them - i remember having seen e.g. some astronomy papers that used DBSCAN with success - but of course they can''t put a number 0 to 1 on this).&lt;/p&gt;

&lt;p&gt;You may want to look e.g. at this &lt;a href="https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Clustering/Density-Based_Clustering#Case_Study:_Identifying_Celestial_Entities" rel="nofollow"&gt;Case Study with DBSCAN&lt;/a&gt; - I doubt k-means is of much help on this data set. In particular, it will assign the noise points to &lt;em&gt;some&lt;/em&gt; cluster, when they should not be assigned at all.&lt;/p&gt;
', NULL, NULL, '''/a'':174C ''/em'':11C,57C,98C,108C,199C ''/p'':19C,47C,67C,78C,153C,209C ''/strong'':77C ''/wiki/data_mining_algorithms_in_r/clustering/density-based_clustering#case_study:_identifying_celestial_entities'':167C ''0'':148C ''1'':33C,150C ''100'':119C ''2'':42C ''algorithm'':92C ''allow'':16C ''also'':51C ''ask'':118C ''assign'':192C,206C ''astronomi'':132C ''autom'':112C ''better'':39C ''cannot'':110C ''case'':170C ''cluster'':13C,80C,200C ''conclus'':18C ''could'':30C,117C ''cours'':141C ''data'':7C,27C,85C,89C,186C ''dbscan'':136C,173C ''doubt'':176C ''e.g'':130C,160C ''either'':66C ''em'':8C,55C,96C,106C,197C ''en.wikibooks.org'':166C ''en.wikibooks.org/wiki/data_mining_algorithms_in_r/clustering/density-based_clustering#case_study:_identifying_celestial_entities'':165C ''evalu'':6C,54C ''even'':24C ''find'':104C ''go'':73C ''good'':5C,25C,53C ''handl'':60C ''help'':183C ''href'':164C ''import'':99C ''interest'':107C ''isn'':22C ''k'':36C,45C,178C ''k-mean'':35C,44C,177C ''look'':159C ''may'':156C ''mean'':37C,46C,179C ''measur'':56C,114C ''much'':182C ''nofollow'':169C ''nois'':64C,194C ''notion'':62C ''number'':76C,147C ''p'':1C,20C,48C,68C,79C,154C ''paper'':133C ''paramet'':94C ''particular'':189C ''point'':195C ''problem'':86C ''put'':145C ''real'':26C ''rel'':168C ''rememb'':127C ''result'':101C ''say'':31C ''scientist'':120C ''seen'':129C ''set'':187C ''solv'':83C ''someth'':105C ''strong'':70C ''studi'':97C,171C ''success'':138C ''tri'':91C ''use'':135C ''variant'':32C,41C ''want'':157C ''well'':65C,115C ''work'':123C ''would'':15C,59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10080, 1, '2016-02-03 20:25:08.253', 2, 13, 'Interpretation of level, trend and seasonal indices in holt winters exponential smoothing', '&lt;p&gt;I am trying to learn Holt Winters exponential smoothing. In the algorithm there are three indices involved (level, trend, seasonality) while forecasting.&lt;/p&gt;

&lt;p&gt;My questions:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;What is the interpretation of these 3 indices?
How do I differentiate between different values of these indices? For example, what is the difference between level 100 and level 200 or what is the difference between trend 2 and trend 5?
How do we initiate these values?
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Any help will be greatly appreciated.&lt;/p&gt;
', 'time-series', NULL, '''/code'':93C ''/p'':39C,43C,102C ''/pre'':94C ''100'':72C ''2'':83C ''200'':75C ''3'':52C ''5'':86C ''algorithm'':28C ''appreci'':101C ''code'':45C ''differ'':59C,69C,80C ''differenti'':57C ''exampl'':65C ''exponenti'':11A,24C ''forecast'':38C ''great'':100C ''help'':97C ''holt'':9A,22C ''indic'':7A,32C,53C,63C ''initi'':90C ''interpret'':1A,49C ''involv'':33C ''learn'':21C ''level'':3A,34C,71C,74C ''p'':16C,40C,95C ''pre'':44C ''question'':42C ''season'':6A,36C ''seri'':15B ''smooth'':12A,25C ''three'':31C ''time'':14B ''time-seri'':13B ''trend'':4A,35C,82C,85C ''tri'':19C ''valu'':60C,92C ''winter'':10A,23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10066, 2, '2016-02-02 10:02:30.633', 1, NULL, NULL, '&lt;p&gt;The question asks about the &lt;code&gt;Limitations on the number of items to use in apriori algorithm?&lt;/code&gt; and I would answer in terms of space and time complexity.&lt;/p&gt;

&lt;p&gt;Let us say that number of input transactions are N(=20) and the number of unique elements be R(approx 900). Assuming your threshold count is quite small (which means very few candidates are pruned), the time and space complexity for size i candidates would be &lt;code&gt;O(R^i)&lt;/code&gt;. So you see, if very few candidates are pruned, the space (and time) requirements become exponential. It might seem unintutive at first, given that you have only 20 transactions. But the bottleneck is the number of candidates which increases exponentially with number of items.&lt;/p&gt;
', NULL, NULL, '''/code'':19C,83C ''/p'':31C,128C ''20'':43C,111C ''900'':53C ''algorithm'':18C ''answer'':23C ''approx'':52C ''apriori'':17C ''ask'':4C ''assum'':54C ''becom'':98C ''bottleneck'':115C ''candid'':65C,76C,90C,120C ''code'':7C,79C ''complex'':30C,72C ''count'':57C ''element'':49C ''exponenti'':99C,123C ''first'':105C ''given'':106C ''increas'':122C ''input'':39C ''item'':13C,127C ''let'':33C ''limit'':8C ''mean'':62C ''might'':101C ''n'':42C ''number'':11C,37C,46C,118C,125C ''o'':80C ''p'':1C,32C ''prune'':67C,92C ''question'':3C ''quit'':59C ''r'':51C,81C ''requir'':97C ''say'':35C ''see'':86C ''seem'':102C ''size'':74C ''small'':60C ''space'':27C,71C,94C ''term'':25C ''threshold'':56C ''time'':29C,69C,96C ''transact'':40C,112C ''unintut'':103C ''uniqu'':48C ''us'':34C ''use'':15C ''would'':22C,77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10067, 2, '2016-02-02 17:25:00.407', 3, NULL, NULL, '&lt;p&gt;Great question, @gsamaras! The way you''ve set up this experiment makes a lot of sense to me, from a design point of view, but I think there are a couple aspects you can still examine. &lt;/p&gt;

&lt;p&gt;First, it''s possible that uninformative features are distracting your classifier, leading to poorer results. In text analytics, we often talk about &lt;a href="https://en.wikipedia.org/wiki/Stop_words" rel="nofollow"&gt;stop word&lt;/a&gt; filtering, which is just the process of removing such text (e.g., the, and, or, etc.). There are standard stop word lists you can easily find online (e.g., &lt;a href="http://www.ranks.nl/stopwords" rel="nofollow"&gt;this one&lt;/a&gt;), but they can sometimes be heavy-handed. The best approach is to build a table relating feature frequency to class, as this will get at domain-specific features that you won''t likely find in such look-up tables. There is varying evidence as to the efficacy of stop word removal in the literature, but I think these findings mostly have to do with classifier-specific (for example, support vector machines tend to be less affected by uninformative features than does a naive bayes classifier. I suspect k-means falls into the latter category).&lt;/p&gt;

&lt;p&gt;Second, you might consider a different feature modeling approach, rather than tf-idf. Nothing against tf-idf--it works fine for many problems--but I like to start with binary feature modeling, unless I have experimental evidence showing a more complex approach leads to better results. That said, it''s possible that k-means could respond strangely to the switch from a floating-point feature space to a binary one. It''s certainly an easily-testable hypothesis!&lt;/p&gt;

&lt;p&gt;Finally, you might look at the expected class distribution in your data set. Are all classes equally likely? If not, you may get better results from either a sampling approach, or using a different distance metric. &lt;a href="https://www.erpublication.org/admin/vol_issue1/upload%20Image/IJETR021251.pdf" rel="nofollow"&gt;k-means is known to respond poorly in skewed class situations&lt;/a&gt;, so this is something to consider as well! There is probably research available in your specific domain describing how others have handled this situation.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10068, 2, '2016-02-02 19:32:30.473', 4, NULL, NULL, '&lt;p&gt;A simplified example: suppose two companies: the webpage owner and a book store. And a customer named Jane, interested in reading.&lt;/p&gt;

&lt;p&gt;The web page owner has some free space on its page, which can be sold for advertisement. The book store wants to place an advertisement on the same web page, in order to increase sales. Both companies meet eachother at an auction, where the web space is sold to the highest bidder in real-time.&lt;/p&gt;

&lt;p&gt;So, the book store is bidding on the right to show an advertisement to Jane, who is visiting the page of the webpage owner.&lt;/p&gt;

&lt;p&gt;The machine learning is done on the part of the book store, who receives information about the web page visitor. This can be all sort of information that the web page owner wants to release, and that could be of any use to the bookstore. &lt;/p&gt;

&lt;p&gt;Based on this information, it is decided weather to make a bidding for the advertisement space or not, and the amount up to which the book store will want to make a bidding. &lt;/p&gt;

&lt;p&gt;Without cookie matching, the webpage visitor Jane will probably not be identifiable by the bookstore, so the store must decide on bidding, based on parameters like geographical location of the customer, browser version (just to name a few ).&lt;/p&gt;

&lt;p&gt;With cookie matching, each visitor/customer gets a unique identifier at the book store. Based on this identifier, the book store has more information of the visitor, like: what ads have been served before, and how long ago ? This visit to the web page can be linked to earlier visits and this information will likely ease the decision making process for doing the bidding on the advertisement space.&lt;/p&gt;

&lt;p&gt;( There is more to it as there can be two more intermediary companies: one that holds the auction and one that delivers the ad )&lt;/p&gt;
', NULL, NULL, '''/p'':23C,81C,107C,154C,189C,229C,298C,324C ''ad'':259C,323C ''advertis'':41C,49C,95C,170C,296C ''ago'':267C ''amount'':176C ''auction'':66C,317C ''base'':156C,213C,244C ''bid'':88C,167C,188C,212C,293C ''bidder'':76C ''book'':13C,43C,85C,119C,181C,242C,249C ''bookstor'':153C,205C ''browser'':222C ''compani'':7C,61C,312C ''cooki'':192C,232C ''could'':146C ''custom'':17C,221C ''decid'':162C,210C ''decis'':287C ''deliv'':321C ''done'':113C ''eachoth'':63C ''earlier'':278C ''eas'':285C ''exampl'':4C ''free'':31C ''geograph'':217C ''get'':236C ''highest'':75C ''hold'':315C ''identifi'':202C,239C,247C ''increas'':58C ''inform'':123C,135C,159C,253C,282C ''interest'':20C ''intermediari'':311C ''jane'':19C,97C,197C ''learn'':111C ''like'':216C,257C,284C ''link'':276C ''locat'':218C ''long'':266C ''machin'':110C ''make'':165C,186C,288C ''match'':193C,233C ''meet'':62C ''must'':209C ''name'':18C,226C ''one'':313C,319C ''order'':56C ''owner'':10C,28C,106C,140C ''p'':1C,24C,82C,108C,155C,190C,230C,299C ''page'':27C,35C,54C,102C,127C,139C,273C ''paramet'':215C ''part'':116C ''place'':47C ''probabl'':199C ''process'':289C ''read'':22C ''real'':79C ''real-tim'':78C ''receiv'':122C ''releas'':143C ''right'':91C ''sale'':59C ''serv'':262C ''show'':93C ''simplifi'':3C ''sold'':39C,72C ''sort'':133C ''space'':32C,70C,171C,297C ''store'':14C,44C,86C,120C,182C,208C,243C,250C ''suppos'':5C ''time'':80C ''two'':6C,309C ''uniqu'':238C ''use'':150C ''version'':223C ''visit'':100C,269C,279C ''visitor'':128C,196C,256C ''visitor/customer'':235C ''want'':45C,141C,184C ''weather'':163C ''web'':26C,53C,69C,126C,138C,272C ''webpag'':9C,105C,195C ''without'':191C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10070, 1, '2016-02-02 21:49:39.92', 2, 131, 'Sentiment Analysis: Creating dictionary from dataset', '&lt;p&gt;I''ve collected around half a million of unmarked comments from a newspage. The newspage has anti-foreign background. Because of that, a relativley  high number of comments contain hate. &lt;/p&gt;

&lt;p&gt;Do you have an idea how to start creating a dictionary out of the comments? The dictionary shall contain words, which are connected to hate. With the dictionary I want to perform a text analysis for hate, later.&lt;/p&gt;

&lt;p&gt;Because there is no such dictionary for German language I tried some things. For instance, I checked how often words, which are connected to emotions, appear in the comments. But it turned up, that they seem to be contained as often as any other word. I also thought about a Bag-of-Words and some other things, but I don''t know.&lt;/p&gt;

&lt;p&gt;I''m doing the analysis with Python 3 and R.&lt;/p&gt;

&lt;p&gt;Kind regards and thanks in advance!&lt;/p&gt;
', 'r  python  dataset  nlp', NULL, '''/p'':43C,83C,148C,160C,168C ''3'':157C ''advanc'':167C ''also'':131C ''analysi'':2A,79C,154C ''anti'':29C ''anti-foreign'':28C ''appear'':110C ''around'':15C ''background'':31C ''bag'':136C ''bag-of-word'':135C ''check'':101C ''collect'':14C ''comment'':21C,40C,59C,113C ''connect'':67C,107C ''contain'':41C,63C,123C ''creat'':3A,53C ''dataset'':6A,9B ''dictionari'':4A,55C,61C,72C,90C ''emot'':109C ''foreign'':30C ''german'':92C ''half'':16C ''hate'':42C,69C,81C ''high'':37C ''idea'':49C ''instanc'':99C ''kind'':162C ''know'':147C ''languag'':93C ''later'':82C ''m'':151C ''million'':18C ''newspag'':24C,26C ''nlp'':10B ''number'':38C ''often'':103C,125C ''p'':11C,44C,84C,149C,161C ''perform'':76C ''python'':8B,156C ''r'':7B,159C ''regard'':163C ''relativley'':36C ''seem'':120C ''sentiment'':1A ''shall'':62C ''start'':52C ''text'':78C ''thank'':165C ''thing'':97C,142C ''thought'':132C ''tri'':95C ''turn'':116C ''unmark'':20C ''ve'':13C ''want'':74C ''word'':64C,104C,129C,138C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10072, 2, '2016-02-03 06:14:48.873', 2, NULL, NULL, '&lt;p&gt;I am currently on same stage like you. I am finding best option for anomaly detection, doing some research.&lt;/p&gt;

&lt;p&gt;What I have found is I think best matches your need and is better compare to what you have seen. i.e., TwitterAnomalyDetection, SkyLine.&lt;/p&gt;

&lt;p&gt;I have found better is Numenta''s NAB (Numenta Anomaly Benchmark).
It also have a very good community support and for you plus point is its open source &amp;amp; developed in python. You can add your algorithm in it.&lt;/p&gt;

&lt;p&gt;In case of algorithm, I found LOF or CBLOF  are good option.&lt;/p&gt;

&lt;p&gt;so, check it out once. It may help you out.
&lt;a href="https://github.com/numenta/nab" rel="nofollow"&gt;https://github.com/numenta/nab&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you find better option. please, tell me. I am also on the same path.&lt;/p&gt;

&lt;p&gt;Best Luck!!&lt;/p&gt;
', NULL, NULL, '''/numenta/nab'':117C ''/numenta/nab&lt;/a&gt;&lt;/p&gt;'':122C ''/p'':21C,46C,87C,101C,139C,143C ''add'':82C ''algorithm'':84C,92C ''also'':60C,134C ''amp'':76C ''anomali'':16C,57C ''benchmark'':58C ''best'':13C,30C,141C ''better'':36C,51C,127C ''case'':90C ''cblof'':97C ''check'':104C ''communiti'':65C ''compar'':37C ''current'':4C ''detect'':17C ''develop'':77C ''find'':12C,126C ''found'':26C,50C,94C ''github.com'':116C,121C ''github.com/numenta/nab'':115C ''github.com/numenta/nab&lt;/a&gt;&lt;/p&gt;'':120C ''good'':64C,99C ''help'':110C ''href'':114C ''i.e'':43C ''like'':8C ''lof'':95C ''luck'':142C ''match'':31C ''may'':109C ''nab'':55C ''need'':33C ''nofollow'':119C ''numenta'':53C,56C ''open'':74C ''option'':14C,100C,128C ''p'':1C,22C,47C,88C,102C,123C,140C ''path'':138C ''pleas'':129C ''plus'':70C ''point'':71C ''python'':79C ''rel'':118C ''research'':20C ''seen'':42C ''skylin'':45C ''sourc'':75C ''stage'':7C ''support'':66C ''tell'':130C ''think'':29C ''twitteranomalydetect'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10098, 1, '2016-02-05 20:34:02.073', 1, 22, 'Creating Clusters in Excel office', '&lt;p&gt;I am trying to make group categories based on the similarity in attributes. For example; &lt;/p&gt;

&lt;p&gt;Car1: blue, big tires, slow, comfortable&lt;/p&gt;

&lt;p&gt;Car2: green, small tires, fast, comfortable&lt;/p&gt;

&lt;p&gt;Car3: blue, big tires, slow, comfortable&lt;/p&gt;

&lt;p&gt;My data set it much larger than that , but pretty much the same idea. What can I use in excel to find categories that are the most similar to each other? &lt;/p&gt;

&lt;p&gt;I tried few methods, but have not been able to group qualitative data. &lt;/p&gt;
', 'sql  clusters  excel', NULL, '''/p'':25C,33C,41C,49C,82C,97C ''abl'':92C ''attribut'':22C ''base'':17C ''big'':29C,45C ''blue'':28C,44C ''car1'':27C ''car2'':35C ''car3'':43C ''categori'':16C,73C ''cluster'':2A,7B ''comfort'':32C,40C,48C ''creat'':1A ''data'':52C,96C ''exampl'':24C ''excel'':4A,8B,70C ''fast'':39C ''find'':72C ''green'':36C ''group'':15C,94C ''idea'':64C ''larger'':56C ''make'':14C ''method'':87C ''much'':55C,61C ''offic'':5A ''p'':9C,26C,34C,42C,50C,83C ''pretti'':60C ''qualit'':95C ''set'':53C ''similar'':20C,78C ''slow'':31C,47C ''small'':37C ''sql'':6B ''tire'':30C,38C,46C ''tri'':12C,85C ''use'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10081, 1, '2016-02-03 20:36:41.05', 2, 28, 'RTextTools multi-label classification', '&lt;p&gt;In follow up to my &lt;a href="http://datascience.stackexchange.com/questions/9886/classifying-survey-response-text-svm"&gt;previous question&lt;/a&gt;, I was able to label survey responses based on 70 categories using RTextTools. Each survey gets one label while if manually coded some would have up to 8 labels.&lt;/p&gt;

&lt;p&gt;The good news is my training data set does have multi-labels. So my question is, would be possible to train on 8 labels all at once? And then on the test data, get probabilities associated with each label and sort based on probability to get the top 8 labels?&lt;/p&gt;

&lt;p&gt;Or can anyone suggest a better approach?&lt;/p&gt;
', 'machine-learning  r  text-mining  multilabel-classification', NULL, '''/a'':29C ''/p'':59C,112C,121C ''/questions/9886/classifying-survey-response-text-svm'':26C ''70'':39C ''8'':57C,84C,110C ''abl'':32C ''anyon'':116C ''approach'':120C ''associ'':97C ''base'':37C,103C ''better'':119C ''categori'':40C ''classif'':5A,15B ''code'':51C ''data'':67C,94C ''datascience.stackexchange.com'':25C ''datascience.stackexchange.com/questions/9886/classifying-survey-response-text-svm'':24C ''follow'':18C ''get'':45C,95C,107C ''good'':62C ''href'':23C ''label'':4A,34C,47C,58C,73C,85C,100C,111C ''learn'':8B ''machin'':7B ''machine-learn'':6B ''manual'':50C ''mine'':12B ''multi'':3A,72C ''multi-label'':2A,71C ''multilabel'':14B ''multilabel-classif'':13B ''news'':63C ''one'':46C ''p'':16C,60C,113C ''possibl'':80C ''previous'':27C ''probabl'':96C,105C ''question'':28C,76C ''r'':9B ''respons'':36C ''rtexttool'':1A,42C ''set'':68C ''sort'':102C ''suggest'':117C ''survey'':35C,44C ''test'':93C ''text'':11B ''text-min'':10B ''top'':109C ''train'':66C,82C ''use'':41C ''would'':53C,78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10073, 2, '2016-02-03 07:34:40.943', 3, NULL, NULL, '&lt;p&gt;When looking on the probability of word occurrence, you will get &lt;a href="https://en.wikipedia.org/wiki/Stop_words" rel="nofollow"&gt;stop words&lt;/a&gt; and other popular words. You are interested in words that appear more in the comments (assumed hate related) than in normal use.&lt;/p&gt;

&lt;p&gt;Get a neutral  resource (e.eg., a German newspaper, the German wikipedia, maybe &lt;a href="https://archive.org/details/google_ngrams-german" rel="nofollow"&gt;Google ngrams for German&lt;/a&gt;). Compute the probability on the neutral source $P_{neutral}(word)$, the probability of the comments $P_{comments}(word)$ and look for the words of lift  $\frac{P_{comments}(word)}{P_{neutral}(word)} &amp;gt; 1$. These are the words that are more popular at the comments.&lt;/p&gt;

&lt;p&gt;As @chi wrote, many repositories can both give you a head start and help you tune the needed lift threshold (you might want words that appear much more often in the comments).&lt;/p&gt;

&lt;p&gt;After this phase you might need do do a finer analysis. For example, I guess that there will be politicians names that will appear more often in the comments. See &lt;a href="http://datascience.stackexchange.com/questions/10030/algorithm-for-classification-of-words-into-given-categories/10038#10038"&gt;here&lt;/a&gt; for a possible approach.&lt;/p&gt;
', NULL, NULL, '''/a'':22C,70C,188C ''/details/google_ngrams-german'':63C ''/p'':45C,116C,150C,193C ''/questions/10030/algorithm-for-classification-of-words-into-given-categories/10038#10038'':186C ''/wiki/stop_words'':17C ''1'':104C ''analysi'':162C ''appear'':33C,143C,175C ''approach'':192C ''archive.org'':62C ''archive.org/details/google_ngrams-german'':61C ''assum'':38C ''chi'':119C ''comment'':37C,85C,87C,98C,115C,149C,180C ''comput'':71C ''datascience.stackexchange.com'':185C ''datascience.stackexchange.com/questions/10030/algorithm-for-classification-of-words-into-given-categories/10038#10038'':184C ''e.eg'':51C ''en.wikipedia.org'':16C ''en.wikipedia.org/wiki/stop_words'':15C ''exampl'':164C ''finer'':161C ''frac'':96C ''german'':53C,56C,69C ''get'':12C,47C ''give'':125C ''googl'':66C ''gt'':103C ''guess'':166C ''hate'':39C ''head'':128C ''help'':131C ''href'':14C,60C,183C ''interest'':29C ''lift'':95C,136C ''look'':3C,90C ''mani'':121C ''mayb'':58C ''might'':139C,156C ''much'':144C ''name'':172C ''need'':135C,157C ''neutral'':49C,76C,79C,101C ''newspap'':54C ''ngram'':67C ''nofollow'':19C,65C ''normal'':43C ''occurr'':9C ''often'':146C,177C ''p'':1C,46C,78C,86C,97C,100C,117C,151C ''phase'':154C ''politician'':171C ''popular'':25C,112C ''possibl'':191C ''probabl'':6C,73C,82C ''rel'':18C,64C ''relat'':40C ''repositori'':122C ''resourc'':50C ''see'':181C ''sourc'':77C ''start'':129C ''stop'':20C ''threshold'':137C ''tune'':133C ''use'':44C ''want'':140C ''wikipedia'':57C ''word'':8C,21C,26C,31C,80C,88C,93C,99C,102C,108C,141C ''wrote'':120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10074, 2, '2016-02-03 08:25:58.97', 1, NULL, NULL, '&lt;p&gt;You could restate your problem as a text classification "hate vs neutral or compassion". The standard text classification methods then apply. Get yourself a neutral or "compassion" corpus and label their elements as such. Then run a classification learner pipeline. It''s features dictionary for the "hate" category will be what you are looking for. &lt;/p&gt;

&lt;p&gt;If that does not work out of the box or you don''t have contrasting corpus, you could try to emulate the classifier and do the selection manually. Run the texts through a vectorizer with German stopwords, try both TfidfVectorizer and CountVectorizer. Then sort their resulting dictionary by the weight descending and just collect the words manually.&lt;/p&gt;
', NULL, NULL, '''/p'':57C,116C ''appli'':22C ''box'':67C ''categori'':49C ''classif'':10C,19C,39C ''classifi'':81C ''collect'':112C ''compass'':15C,28C ''contrast'':73C ''corpus'':29C,74C ''could'':3C,76C ''countvector'':100C ''descend'':109C ''dictionari'':45C,105C ''element'':33C ''emul'':79C ''featur'':44C ''german'':94C ''get'':23C ''hate'':11C,48C ''label'':31C ''learner'':40C ''look'':55C ''manual'':86C,115C ''method'':20C ''neutral'':13C,26C ''p'':1C,58C ''pipelin'':41C ''problem'':6C ''restat'':4C ''result'':104C ''run'':37C,87C ''select'':85C ''sort'':102C ''standard'':17C ''stopword'':95C ''text'':9C,18C,89C ''tfidfvector'':98C ''tri'':77C,96C ''vector'':92C ''vs'':12C ''weight'':108C ''word'':114C ''work'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10135, 4, '2016-02-08 05:45:45.807', 0, NULL, NULL, 'This is a special tag that is designed to be used only by the system for questions that have had all of their other tags removed.  Do not add this tag to existing questions, it is reserved for use by the system to identify posts with no valid tags.', NULL, NULL, '''add'':29C ''design'':8C ''exist'':33C ''identifi'':44C ''post'':45C ''question'':17C,34C ''remov'':26C ''reserv'':37C ''special'':4C ''system'':15C,42C ''tag'':5C,25C,31C,49C ''use'':11C,39C ''valid'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10077, 1, '2016-02-03 10:56:51.447', 1, 116, 'Keyword/phrase extraction from Text using Deep Learning libraries', '&lt;p&gt;Perhaps this is too broad, but I am looking for references on how to use deep learning in a text summarization task.&lt;/p&gt;

&lt;p&gt;I have already implemented text summarization using standard word-frequency approaches and sentence-ranking, but I''d like to explore the possibility of using deep learning techniques for this task. I have also gone through some implementations given on &lt;a href="http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/" rel="nofollow"&gt;wildml.com&lt;/a&gt; using Convolutional Neural Networks (CNN) for sentiment analysis; I''d like to know how one could use libraries such as TensorFlow or Theano for text summarization and keyword extraction. Its been about a week since I started experimenting with Neural nets, and I am really excited to see how the performance of these libraries compares to my previous approaches to this problem.&lt;/p&gt;

&lt;p&gt;I am particularly looking for some interesting papers and github projects related to text summarization using these frameworks. Can anyone provide me with some references?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10078, 2, '2016-02-03 13:27:54.427', 2, NULL, NULL, '&lt;p&gt;As I said, maybe there is already a public domain vector file that fits your needs, e.g.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(raster)
map &amp;lt;- getData("GADM", country="HUN", level=2) 
plot(map, col=rainbow(length(map)), border=NA)
&lt;/code&gt;&lt;/pre&gt;

&lt;hr&gt;

&lt;p&gt;Otherwise, try a dedicated tool to polygonize your raster image and save it as a shapefile. E.g. using QGIS (German): &lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/sA5xc.gif" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/sA5xc.gif" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;After saving the shapefile, you can load and process it in R:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(raster)
map &amp;lt;- shapefile("evk_106_20140101.shp")
plot(map, col=rainbow(length(map)), border=NA)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/gujpr.gif" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/gujpr.gif" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;(Side note: I assume it''s ok for any copyright owner oto modify and use the image for demonstrational purposes in the above way. If not, drop a comment and I''ll delete it.)&lt;/em&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10079, 2, '2016-02-03 15:26:18.303', 1, NULL, NULL, '&lt;p&gt;It is not completely clear whether your dataset has any kind of mark up (like ''comment'', ''neutral'', ''positive''), and, from my point of view and experience, to get a quite precise dictionary of any kind, you should take human insights as source and stick with supervised learning algorithms.&lt;/p&gt;

&lt;p&gt;If your dataset does contain such information, you may use Dan Levin''s approach which seems quite promising and probability-wise comprehensive.&lt;/p&gt;

&lt;p&gt;Alternatively to it, you may use advanced vector-space representations of words (word2vec) in a following manner:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;train a word2vec model on large German text bank. That text shouldn''t be of any specific character though you would benefit from presence of all your comment-words in that dataset.&lt;/li&gt;
&lt;li&gt;then, for particular words in your hate-comments, find &lt;strong&gt;similar&lt;/strong&gt; words  using word2vec model. In that way, given your word2vec text bank will be representative enough, you may get a dictionary even richer that the one people tend to use in comments.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Anyway, keep us posted on achieved results :)&lt;/p&gt;
', NULL, NULL, '''/li'':131C,178C ''/p'':50C,75C,95C,188C ''/strong'':145C ''/ul'':179C ''achiev'':186C ''advanc'':83C ''algorithm'':49C ''altern'':77C ''anyway'':181C ''approach'':65C ''bank'':106C,157C ''benefit'':119C ''charact'':115C ''clear'':6C ''comment'':17C,126C,141C,177C ''comment-word'':125C ''complet'':5C ''comprehens'':74C ''contain'':56C ''dan'':62C ''dataset'':9C,54C,130C ''dictionari'':33C,166C ''enough'':161C ''even'':167C ''experi'':27C ''find'':142C ''follow'':93C ''german'':104C ''get'':29C,164C ''given'':153C ''hate'':140C ''hate-com'':139C ''human'':40C ''inform'':58C ''insight'':41C ''keep'':182C ''kind'':12C,36C ''larg'':103C ''learn'':48C ''levin'':63C ''li'':97C,132C ''like'':16C ''manner'':94C ''mark'':14C ''may'':60C,81C,163C ''model'':101C,149C ''neutral'':18C ''one'':171C ''p'':1C,51C,76C,180C ''particular'':135C ''peopl'':172C ''point'':23C ''posit'':19C ''post'':184C ''precis'':32C ''presenc'':121C ''probability-wis'':71C ''probabl'':72C ''promis'':69C ''quit'':31C,68C ''repres'':160C ''represent'':87C ''result'':187C ''richer'':168C ''seem'':67C ''shouldn'':109C ''similar'':144C ''sourc'':43C ''space'':86C ''specif'':114C ''stick'':45C ''strong'':143C ''supervis'':47C ''take'':39C ''tend'':173C ''text'':105C,108C,156C ''though'':116C ''train'':98C ''ul'':96C ''us'':183C ''use'':61C,82C,147C,175C ''vector'':85C ''vector-spac'':84C ''view'':25C ''way'':152C ''whether'':7C ''wise'':73C ''word'':89C,127C,136C,146C ''word2vec'':90C,100C,148C,155C ''would'':118C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10082, 2, '2016-02-03 20:42:24', 1, NULL, NULL, '&lt;p&gt;You should be using a combination of 2) and 3) in your scenario above.&lt;/p&gt;

&lt;p&gt;For 2) use JSON as it allows you to construct an arbitrary structure on the fly with as many columns as you need without tying yourself to a fixed schema. JSON also has wide language adoption (Python, R, Java, Scala eg).&lt;/p&gt;

&lt;p&gt;For 3) I agree that MongoDB is the simplest as it is designed to be used for JSON.&lt;/p&gt;
', NULL, NULL, '''/p'':16C,59C,79C ''2'':9C,19C ''3'':11C,62C ''adopt'':53C ''agre'':64C ''allow'':24C ''also'':49C ''arbitrari'':29C ''column'':37C ''combin'':7C ''construct'':27C ''design'':73C ''eg'':58C ''fix'':46C ''fli'':33C ''java'':56C ''json'':21C,48C,78C ''languag'':52C ''mani'':36C ''mongodb'':66C ''need'':40C ''p'':1C,17C,60C ''python'':54C ''r'':55C ''scala'':57C ''scenario'':14C ''schema'':47C ''simplest'':69C ''structur'':30C ''tie'':42C ''use'':5C,20C,76C ''wide'':51C ''without'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10083, 1, '2016-02-04 01:46:37.107', 2, 47, 'Classification in noise', '&lt;p&gt;I''m new to machine learning but am interested in decoding applications like the Higgs Boson challenge on Kaggle. I just wondered, what are the best algorithms or approaches for classifying such a small signal in large amounts of noise? I think supervised and unsupervised are equally fine to use for my project, but hearing about your thoughts in general would help a lot! &lt;/p&gt;

&lt;p&gt;I wanted to start with the Higgs Boson challenge to explore machine learning methods for improving discovery significance of scientific data. The experiment and data are available at &lt;a href="https://www.kaggle.com/c/higgs-boson" rel="nofollow"&gt;https://www.kaggle.com/c/higgs-boson&lt;/a&gt; . The code used by the winner was released here: &lt;a href="https://www.kaggle.com/c/higgs-boson/forums/t/10425/code-release" rel="nofollow"&gt;https://www.kaggle.com/c/higgs-boson/forums/t/10425/code-release&lt;/a&gt; . &lt;/p&gt;

&lt;p&gt;I know the basic theory of supervised and unsupervised learning, and have studied and used Linear Regression and SVM. I''ve read about neural networks also but on a basic approach level. I''m just learning about deep learning coming from the natural sciences, to improve data analysis, and found SVM very useful but otherwise don''t know more advanced techniques. &lt;/p&gt;
', 'machine-learning', NULL, '''/c/higgs-boson'':106C ''/c/higgs-boson&lt;/a&gt;'':111C ''/c/higgs-boson/forums/t/10425/code-release'':125C ''/c/higgs-boson/forums/t/10425/code-release&lt;/a&gt;'':130C ''/p'':72C,131C,194C ''advanc'':192C ''algorithm'':34C ''also'':158C ''amount'':45C ''analysi'':180C ''applic'':19C ''approach'':36C,163C ''avail'':100C ''basic'':136C,162C ''best'':33C ''boson'':23C,81C ''challeng'':24C,82C ''classif'':1A ''classifi'':38C ''code'':113C ''come'':172C ''data'':94C,98C,179C ''decod'':18C ''deep'':170C ''discoveri'':90C ''equal'':54C ''experi'':96C ''explor'':84C ''fine'':55C ''found'':182C ''general'':67C ''hear'':62C ''help'':69C ''higg'':22C,80C ''href'':103C,122C ''improv'':89C,178C ''interest'':16C ''kaggl'':26C ''know'':134C,190C ''larg'':44C ''learn'':6B,13C,86C,142C,168C,171C ''level'':164C ''like'':20C ''linear'':148C ''lot'':71C ''m'':9C,166C ''machin'':5B,12C,85C ''machine-learn'':4B ''method'':87C ''natur'':175C ''network'':157C ''neural'':156C ''new'':10C ''nofollow'':108C,127C ''nois'':3A,47C ''otherwis'':187C ''p'':7C,73C,132C ''project'':60C ''read'':154C ''regress'':149C ''rel'':107C,126C ''releas'':119C ''scienc'':176C ''scientif'':93C ''signal'':42C ''signific'':91C ''small'':41C ''start'':77C ''studi'':145C ''supervis'':50C,139C ''svm'':151C,183C ''techniqu'':193C ''theori'':137C ''think'':49C ''thought'':65C ''unsupervis'':52C,141C ''use'':57C,114C,147C,185C ''ve'':153C ''want'':75C ''winner'':117C ''wonder'':29C ''would'':68C ''www.kaggle.com'':105C,110C,124C,129C ''www.kaggle.com/c/higgs-boson'':104C ''www.kaggle.com/c/higgs-boson&lt;/a&gt;'':109C ''www.kaggle.com/c/higgs-boson/forums/t/10425/code-release'':123C ''www.kaggle.com/c/higgs-boson/forums/t/10425/code-release&lt;/a&gt;'':128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10084, 1, '2016-02-04 05:39:51.25', 2, 65, 'Validate output', '&lt;p&gt;I am trying to find the top 10 useful item recommendation. Items are divided into categories and then top10 in each category is calculated.
There are six features based on which a score is assigned to each item and then they are sorted in decreasing order to get the top 10.&lt;/p&gt;

&lt;p&gt;I am not sure are these the best top items? How to validate the output? I need to see which feature is dominating the most in calculating the score?&lt;/p&gt;

&lt;p&gt;I have already calculated correlation, any other statistical measure?&lt;/p&gt;
', 'regression', NULL, '''/p'':56C,87C,98C ''10'':12C,55C ''alreadi'':91C ''assign'':39C ''base'':33C ''best'':65C ''calcul'':28C,84C,92C ''categori'':20C,26C ''correl'':93C ''decreas'':49C ''divid'':18C ''domin'':80C ''featur'':32C,78C ''find'':9C ''get'':52C ''item'':14C,16C,42C,67C ''measur'':97C ''need'':74C ''order'':50C ''output'':2A,72C ''p'':4C,57C,88C ''recommend'':15C ''regress'':3B ''score'':37C,86C ''see'':76C ''six'':31C ''sort'':47C ''statist'':96C ''sure'':61C ''top'':11C,54C,66C ''top10'':23C ''tri'':7C ''use'':13C ''valid'':1A,70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10116, 1, '2016-02-07 05:19:47.063', 1, 36, 'Do something with the output of reducers', '&lt;p&gt;I am executing the job as:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;hadoop/bin/./hadoop jar                                \
/home/hadoopuser/hadoop/share/hadoop/tools/lib/hadoop-streaming-2.6.0.jar                                                  \
-D mapred.reduce.tasks=2                               \
-file kmeans_mapper.py                                 \
-mapper kmeans_mapper.py -file kmeans_reducer.py       \
-reducer kmeans_reducer.py                             \
-input gutenberg/small_train.csv                       \
-output gutenberg/out
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;When the two reducers are done, I would like to do something with their output. The &lt;a href="https://hadoop.apache.org/docs/r1.2.1/mapred_tutorial.html" rel="nofollow"&gt;MapReduce tutorial&lt;/a&gt; states:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Users may need to chain MapReduce jobs to accomplish complex tasks which cannot be done via a single MapReduce job. This is fairly easy since the output of the job typically goes to distributed file-system, and the output, in turn, can be used as the input for the next job.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;but it does not give any example. Can I make this happen?&lt;/p&gt;

&lt;p&gt;Relevant &lt;a href="http://stackoverflow.com/questions/35249753/call-mapper-when-reducer-is-done"&gt;question&lt;/a&gt; of mine, but I decided to ask the experts too! :)&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;A &lt;em&gt;negative&lt;/em&gt; answer would still be &lt;em&gt;very&lt;/em&gt; useful!&lt;/p&gt;
', 'python  hadoop  map-reduce  java  distributed', NULL, '''/a'':71C,152C ''/blockquote'':129C ''/code'':43C ''/docs/r1.2.1/mapred_tutorial.html'':66C ''/em'':169C,176C ''/home/hadoopuser/hadoop/share/hadoop/tools/lib/hadoop-streaming-2.6.0.jar'':27C ''/p'':22C,73C,128C,143C,163C,178C ''/pre'':44C ''/questions/35249753/call-mapper-when-reducer-is-done'':150C ''2'':30C ''accomplish'':84C ''answer'':170C ''ask'':159C ''blockquot'':74C ''cannot'':88C ''chain'':80C ''code'':24C ''complex'':85C ''d'':28C ''decid'':157C ''distribut'':14B,109C ''done'':51C,90C ''easi'':99C ''em'':167C,174C ''exampl'':137C ''execut'':18C ''expert'':161C ''fair'':98C ''file'':31C,35C,111C ''file-system'':110C ''give'':135C ''goe'':107C ''gutenberg/out'':42C ''gutenberg/small_train.csv'':40C ''hadoop'':9B ''hadoop.apache.org'':65C ''hadoop.apache.org/docs/r1.2.1/mapred_tutorial.html'':64C ''hadoop/bin/./hadoop'':25C ''happen'':142C ''hr'':164C ''href'':63C,147C ''input'':39C,123C ''jar'':26C ''java'':13B ''job'':20C,82C,95C,105C,127C ''kmeans_mapper.py'':32C,34C ''kmeans_reducer.py'':36C,38C ''like'':54C ''make'':140C ''map'':11B ''map-reduc'':10B ''mapper'':33C ''mapred.reduce.tasks'':29C ''mapreduc'':69C,81C,94C ''may'':77C ''mine'':154C ''need'':78C ''negat'':168C ''next'':126C ''nofollow'':68C ''output'':5A,41C,60C,102C,115C ''p'':15C,45C,75C,130C,144C,165C ''pre'':23C ''python'':8B ''question'':151C ''reduc'':7A,12B,37C,49C ''rel'':67C ''relev'':145C ''sinc'':100C ''singl'':93C ''someth'':2A,57C ''stackoverflow.com'':149C ''stackoverflow.com/questions/35249753/call-mapper-when-reducer-is-done'':148C ''state'':72C ''still'':172C ''system'':112C ''task'':86C ''turn'':117C ''tutori'':70C ''two'':48C ''typic'':106C ''use'':120C,177C ''user'':76C ''via'':91C ''would'':53C,171C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10085, 1, '2016-02-04 08:43:12.847', 4, 259, 'Machine Learning Steps', '&lt;p&gt;Which of the below set of steps options is the correct one when creating a predictive model?&lt;/p&gt;

&lt;p&gt;Option 1:&lt;/p&gt;

&lt;p&gt;First eliminate the most obviously bad predictors, and preprocess the remaining if needed, then train various models with cross-validation, pick the few best ones, identify the top predictors each one has used, then retrain those models with those predictors only and evaluate accuracy again with cross-validation, then pick the best one and train it on the full training set using its key predictors and then use it to predict the test set.&lt;/p&gt;

&lt;p&gt;Option 2:&lt;/p&gt;

&lt;p&gt;First eliminate the most obviously bad predictors, then preprocess the remaining if needed, then use a feature selection technique like recursive feature selection (eg. RFE with rf ) with cross-validation for example to identify the ideal number of key predictors and what these predictors are, then train different model types with cross-validation and see which one gives the best accuracy with those top predictors identified earlier. Then train the best one of those models again with those predictors on the full training set and then use it to predict the test set.&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':25C,29C,107C,111C,208C ''1'':28C ''2'':110C ''accuraci'':75C,175C ''bad'':36C,118C ''best'':55C,84C,174C,185C ''correct'':18C ''creat'':21C ''cross'':50C,79C,142C,166C ''cross-valid'':49C,78C,141C,165C ''differ'':161C ''earlier'':181C ''eg'':136C ''elimin'':32C,114C ''evalu'':74C ''exampl'':145C ''featur'':129C,134C ''first'':31C,113C ''full'':91C,196C ''give'':172C ''ideal'':149C ''identifi'':57C,147C,180C ''key'':96C,152C ''learn'':2A,6B ''like'':132C ''machin'':1A,5B ''machine-learn'':4B ''model'':24C,47C,68C,162C,189C ''need'':43C,125C ''number'':150C ''obvious'':35C,117C ''one'':19C,56C,62C,85C,171C,186C ''option'':15C,27C,109C ''p'':7C,26C,30C,108C,112C ''pick'':52C,82C ''predict'':23C,103C,204C ''predictor'':37C,60C,71C,97C,119C,153C,157C,179C,193C ''preprocess'':39C,121C ''recurs'':133C ''remain'':41C,123C ''retrain'':66C ''rf'':139C ''rfe'':137C ''see'':169C ''select'':130C,135C ''set'':12C,93C,106C,198C,207C ''step'':3A,14C ''techniqu'':131C ''test'':105C,206C ''top'':59C,178C ''train'':45C,87C,92C,160C,183C,197C ''type'':163C ''use'':64C,94C,100C,127C,201C ''valid'':51C,80C,143C,167C ''various'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10086, 2, '2016-02-04 09:52:53.81', 3, NULL, NULL, '&lt;p&gt;I believe this is the representation you''re after, please excuse the rough sketch but I think it explains the structure appropriately.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Single input going to three hidden units, each with a bias and tansig activation.&lt;/li&gt;
&lt;li&gt;The outputs of the hidden layer are summed (via linear activation) with a bias to produce the output.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/CdMAf.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/CdMAf.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':81C ''/cdmaf.jpg'':68C,75C ''/li'':41C,61C ''/p'':24C,82C ''/ul'':62C ''activ'':40C,53C ''alt'':76C ''appropri'':23C ''believ'':3C ''bias'':37C,56C ''descript'':79C ''enter'':77C ''excus'':12C ''explain'':20C ''go'':29C ''hidden'':32C,47C ''href'':65C ''i.stack.imgur.com'':67C,74C ''i.stack.imgur.com/cdmaf.jpg'':66C,73C ''imag'':78C ''img'':71C ''input'':28C ''layer'':48C ''li'':26C,42C ''linear'':52C ''nofollow'':70C ''output'':44C,60C ''p'':1C,63C ''pleas'':11C ''produc'':58C ''re'':9C ''rel'':69C ''represent'':7C ''rough'':14C ''singl'':27C ''sketch'':15C ''src'':72C ''structur'':22C ''sum'':50C ''tansig'':39C ''think'':18C ''three'':31C ''ul'':25C ''unit'':33C ''via'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10087, 2, '2016-02-04 10:27:20.423', 10, NULL, NULL, '&lt;p&gt;I found both of your options slightly faulty. So, this is generally (very broadly) how a predictive modelling:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Data Cleaning&lt;/strong&gt;: Takes the most time, but every second spent here is worth it. The cleaner your data gets through this step, the lesser would your total time spent would be. &lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Splitting the data set&lt;/strong&gt;: The data set would be splitted into training and testing sets, which would be used for the modelling and prediction purposes respectively. In addition, an additional split as a cross-validation set would also need to be done.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Transformation and Reduction:&lt;/strong&gt; Involves processes like transformations, mean and median scaling, etc.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Feature Selection:&lt;/strong&gt; This can be done in a lot of ways like threshold selection, subset selection, etc.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Designing predictive model:&lt;/strong&gt; Design the predictive model on the training data depending on the features you have at hand.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Cross Validation:&lt;/strong&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Final Prediction, Validation&lt;/strong&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':56C,103C,119C,140C,163C,169C,176C ''/p'':20C ''/strong'':26C,63C,109C,124C,146C,168C,175C ''/ul'':177C ''addit'':87C,89C ''also'':98C ''broad'':15C ''clean'':25C ''cleaner'':40C ''cross'':94C,166C ''cross-valid'':93C ''data'':24C,42C,61C,65C,154C ''depend'':155C ''design'':143C,147C ''done'':102C,128C ''etc'':118C,139C ''everi'':32C ''faulti'':9C ''featur'':122C,158C ''final'':172C ''found'':3C ''general'':13C ''get'':43C ''hand'':162C ''involv'':110C ''lesser'':48C ''li'':22C,57C,104C,120C,141C,164C,170C ''like'':112C,134C ''lot'':131C ''mean'':114C ''median'':116C ''model'':19C,81C,145C,150C ''need'':99C ''option'':7C ''p'':1C ''predict'':18C,83C,144C,149C,173C ''process'':111C ''purpos'':84C ''reduct'':108C ''respect'':85C ''scale'':117C ''second'':33C ''select'':123C,136C,138C ''set'':62C,66C,74C,96C ''slight'':8C ''spent'':34C,53C ''split'':59C,69C,90C ''step'':46C ''strong'':23C,58C,105C,121C,142C,165C,171C ''subset'':137C ''take'':27C ''test'':73C ''threshold'':135C ''time'':30C,52C ''total'':51C ''train'':71C,153C ''transform'':106C,113C ''ul'':21C ''use'':78C ''valid'':95C,167C,174C ''way'':133C ''worth'':37C ''would'':49C,54C,67C,76C,97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10123, 1, '2016-02-07 17:59:19.587', 0, 40, 'Ontology learning in R or python', '&lt;p&gt;Lots of machine learning algorithms are already implemented in R and/or Python. Nevertheless, I could not find implementations of ontology learning. Do you know if such packages/modules exist ?&lt;/p&gt;

&lt;p&gt;Thanks a lot!&lt;/p&gt;
', 'machine-learning  r  python  nlp  open-source', NULL, '''/p'':45C,50C ''algorithm'':21C ''alreadi'':23C ''and/or'':27C ''could'':31C ''exist'':44C ''find'':33C ''implement'':24C,34C ''know'':40C ''learn'':2A,9B,20C,37C ''lot'':17C,49C ''machin'':8B,19C ''machine-learn'':7B ''nevertheless'':29C ''nlp'':12B ''ontolog'':1A,36C ''open'':14B ''open-sourc'':13B ''p'':16C,46C ''packages/modules'':43C ''python'':6A,11B,28C ''r'':4A,10B,26C ''sourc'':15B ''thank'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10090, 1, '2016-02-05 07:31:09.19', 2, 31, 'Modified Voting Algorithm to find the best recommendation', '&lt;p&gt;I have to find the best 10 items from the set of items for x number of given features. I don''t know what the best recommendation will be. User data is not available to validate it. After reading a number of papers I came to know about voting algorithm.&lt;/p&gt;

&lt;p&gt;Trying to implement something like this-&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Find x number of features for static variables( that doesn''t change over time), dynamic variables (that change very frequently).&lt;/li&gt;
&lt;li&gt;Assume some weights for the weights on the basis of requirement for dynamic variables.&lt;/li&gt;
&lt;li&gt;Try to come up with 3-4 regression models by combining the static and dynamic variables and find the best 10 items from each&lt;/li&gt;
&lt;li&gt;Take the common one out, and then calculate the error.&lt;/li&gt;
&lt;li&gt;Back-propagate the error to get the new weights.&lt;/li&gt;
&lt;li&gt;Keep on iterating till we get at-least common top 8 elements.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Is the approach right?&lt;/p&gt;

&lt;p&gt;Any guidance for what to take as error.&lt;/p&gt;

&lt;p&gt;My approach to error calculation was-&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Mean of common items feature values (X).&lt;/li&gt;
&lt;li&gt;For each model MSE where mean is X and Xi is feature values for uncommon items of that model.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I am new to it. Can anyone please guide me through it.&lt;/p&gt;
', 'regression  backpropagation  error-handling', NULL, '''-4'':120C ''/li'':96C,112C,138C,150C,162C,177C,212C,233C ''/ol'':178C,234C ''/p'':65C,73C,184C,194C,202C,248C ''10'':21C,134C ''3'':119C ''8'':175C ''algorithm'':3A,64C ''anyon'':242C ''approach'':182C,197C ''assum'':98C ''at-least'':170C ''avail'':48C ''back'':153C ''back-propag'':152C ''backpropag'':10B ''basi'':106C ''best'':7A,20C,40C,133C ''calcul'':147C,200C ''came'':59C ''chang'':87C,93C ''combin'':124C ''come'':116C ''common'':142C,173C,207C ''data'':45C ''doesn'':85C ''dynam'':90C,110C,128C ''element'':176C ''error'':12B,149C,156C,193C,199C ''error-handl'':11B ''featur'':33C,80C,209C,225C ''find'':5A,18C,76C,131C ''frequent'':95C ''get'':158C,169C ''given'':32C ''guid'':244C ''guidanc'':187C ''handl'':13B ''implement'':69C ''item'':22C,27C,135C,208C,229C ''iter'':166C ''keep'':164C ''know'':37C,61C ''least'':172C ''li'':75C,97C,113C,139C,151C,163C,204C,213C ''like'':71C ''mean'':205C,219C ''model'':122C,216C,232C ''modifi'':1A ''mse'':217C ''new'':160C,238C ''number'':30C,55C,78C ''ol'':74C,203C ''one'':143C ''p'':14C,66C,179C,185C,195C,235C ''paper'':57C ''pleas'':243C ''propag'':154C ''read'':53C ''recommend'':8A,41C ''regress'':9B,121C ''requir'':108C ''right'':183C ''set'':25C ''someth'':70C ''static'':82C,126C ''take'':140C,191C ''till'':167C ''time'':89C ''top'':174C ''tri'':67C,114C ''uncommon'':228C ''user'':44C ''valid'':50C ''valu'':210C,226C ''variabl'':83C,91C,111C,129C ''vote'':2A,63C ''weight'':100C,103C,161C ''x'':29C,77C,211C,221C ''xi'':223C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10091, 1, '2016-02-05 07:50:28.437', 2, 86, 'Image classification in python', '&lt;p&gt;I have a set of images that are considered as good quality image and other set that are considered as bad quality image. I have to train a classification model so that any new image can be said good/bad.
SVM seems to be the best approach to do it. I know how to do it in MATLAB.&lt;/p&gt;

&lt;p&gt;But,can anyone suggest how to do it in python? What are the libraries?
For SVM scikit is there, what about feature extraction of image and PCA?&lt;/p&gt;
', 'python  image-classification', NULL, '''/p'':67C,96C ''anyon'':71C ''approach'':55C ''bad'':30C ''best'':54C ''classif'':2A,8B,38C ''consid'':18C,28C ''extract'':91C ''featur'':90C ''good'':20C ''good/bad'':48C ''imag'':1A,7B,15C,22C,32C,44C,93C ''image-classif'':6B ''know'':60C ''librari'':82C ''matlab'':66C ''model'':39C ''new'':43C ''p'':9C,68C ''pca'':95C ''python'':4A,5B,78C ''qualiti'':21C,31C ''said'':47C ''scikit'':85C ''seem'':50C ''set'':13C,25C ''suggest'':72C ''svm'':49C,84C ''train'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10092, 2, '2016-02-05 08:17:26.803', 2, NULL, NULL, '&lt;p&gt;As this question highly overlaps with a similar question I have already answered, I would include that answer here (linked in the comments underneath the question):&lt;/p&gt;

&lt;p&gt;In images, some frequently used techniques for feature extraction are &lt;strong&gt;binarizing&lt;/strong&gt; and &lt;strong&gt;blurring&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Binarizing:&lt;/strong&gt; converts the image array into 1s and 0s. This is done while converting the image to a 2D image. Even gray-scaling can also be used. It gives you a numerical matrix of the image. Grayscale takes much lesser space when stored on Disc.&lt;/p&gt;

&lt;p&gt;This is how you do it in Python:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from PIL import Image

%matplotlib inline  

#Import an image
image = Image.open("xyz.jpg")

image
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Example Image: &lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/mkf97.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/mkf97.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Now, convert into gray-scale:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;im = image.convert(''L'')

im
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;will return you this image:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/AGxy6.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/AGxy6.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;And the matrix can be seen by running this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;array(im)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The array would look something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;array([[213, 213, 213, ..., 176, 176, 176],
       [213, 213, 213, ..., 176, 176, 176],
       [213, 213, 213, ..., 175, 175, 175],
       ..., 
       [173, 173, 173, ..., 204, 204, 204],
       [173, 173, 173, ..., 205, 205, 204],
       [173, 173, 173, ..., 205, 205, 205]], dtype=uint8)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now, use a histogram plot and/or a contour plot to have a look at the image features:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from pylab import *

# create a new figure
figure()
gray()
# show contours with origin upper left corner
contour(im, origin=''image'')
axis(''equal'')
axis(''off'')


figure()


hist(im_array.flatten(), 128)

show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This would return you a plot, which looks something like this:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/56A0K.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/56A0K.png" alt="enter image description here"&gt;&lt;/a&gt;
&lt;a href="http://i.stack.imgur.com/tWpNy.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/tWpNy.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Blurring:&lt;/strong&gt; Blurring algorithm takes weighted average of neighbouring pixels to incorporate surroundings color into every pixel. It enhances the contours better and helps in understanding the features and their importance better.&lt;/p&gt;

&lt;p&gt;And this is how you do it in Python:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from PIL import *


figure()
p = image.convert("L").filter(ImageFilter.GaussianBlur(radius = 2))
p.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And the blurred image is:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/0Dx8q.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/0Dx8q.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;So, these are some ways in which you can do feature engineering. And for advanced methods, you have to understand the basics of Computer Vision and neural networks, and also the different types of filters and their significance and the math behind them.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;The entire analytics is done with the &lt;a href="http://www.pythonware.com/products/pil/" rel="nofollow"&gt;PIL package&lt;/a&gt;. I wouldn''t claim that it''s a one-stop shop for Image analytics, but for a starter to novice level, it is pretty much it.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10093, 1, '2016-02-05 13:52:08.66', 1, 41, 'How to find a confidence level given the z value', '&lt;p&gt;I have a task from which I would like to find the confidence level given the z value. I have a sample population. From that population, given its distribution, I would like to find the confidence level of a given value of that population. In other words, given a a value of the population, I would like to know if it is within 95% (confidence level) of the whole population or 68% or 50% and so on. Usually, we can find the z value and confidence interval and given confidence level as explained here &lt;a href="http://www.wikihow.com/Calculate-Confidence-Interval" rel="nofollow"&gt;How to find the confidence Interval&lt;/a&gt;. But I would like to find the confidence level given the z value (which in this case is a given value from the population).&lt;/p&gt;

&lt;p&gt;How can I tackle this? If possible it should be in python or in R&lt;/p&gt;
', 'r  python  statistics', NULL, '''/a'':122C ''/calculate-confidence-interval'':113C ''/p'':147C,164C ''50'':88C ''68'':86C ''95'':78C ''case'':139C ''confid'':5A,27C,50C,79C,100C,104C,120C,130C ''distribut'':43C ''explain'':107C ''find'':3A,25C,48C,95C,118C,128C ''given'':7A,29C,41C,54C,62C,103C,132C,142C ''href'':110C ''interv'':101C,121C ''know'':73C ''level'':6A,28C,51C,80C,105C,131C ''like'':23C,46C,71C,126C ''nofollow'':115C ''p'':14C,148C ''popul'':37C,40C,58C,68C,84C,146C ''possibl'':155C ''python'':12B,160C ''r'':11B,163C ''rel'':114C ''sampl'':36C ''statist'':13B ''tackl'':152C ''task'':18C ''usual'':92C ''valu'':10A,32C,55C,65C,98C,135C,143C ''whole'':83C ''within'':77C ''word'':61C ''would'':22C,45C,70C,125C ''www.wikihow.com'':112C ''www.wikihow.com/calculate-confidence-interval'':111C ''z'':9A,31C,97C,134C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10095, 2, '2016-02-05 15:23:40.55', 0, NULL, NULL, '&lt;p&gt;To convert between z-scores and confidence values with python, use the &lt;code&gt;cdf&lt;/code&gt; and &lt;code&gt;ppf&lt;/code&gt; functions in &lt;a href="http://docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.stats.norm.html" rel="nofollow"&gt;scipy.stats.norm&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;There is a nice example of how to use them in the answer for &lt;a href="http://stackoverflow.com/questions/20864847/probability-to-z-score-and-vice-versa-in-python"&gt;this question&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':32C,56C ''/code'':17C,21C ''/doc/scipy-0.16.0/reference/generated/scipy.stats.norm.html'':28C ''/p'':33C,57C ''/questions/20864847/probability-to-z-score-and-vice-versa-in-python'':53C ''answer'':47C ''cdf'':16C ''code'':15C,19C ''confid'':9C ''convert'':3C ''docs.scipy.org'':27C ''docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.stats.norm.html'':26C ''exampl'':39C ''function'':22C ''href'':25C,50C ''nice'':38C ''nofollow'':30C ''p'':1C,34C ''ppf'':20C ''python'':12C ''question'':55C ''rel'':29C ''scipy.stats.norm'':31C ''score'':7C ''stackoverflow.com'':52C ''stackoverflow.com/questions/20864847/probability-to-z-score-and-vice-versa-in-python'':51C ''use'':13C,43C ''valu'':10C ''z'':6C ''z-score'':5C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10096, 2, '2016-02-05 15:35:00.86', 1, NULL, NULL, '&lt;p&gt;This is the typical value function of Reinforcement Learning. The discount factor evaluates the importance of the accumulated future events in your current value. The smaller the number, the less important are the future events in the current action.&lt;/p&gt;

&lt;p&gt;Usually this number is selected heuristically. I usually select 0.9. If I don''t want any discount then I would select 1.&lt;/p&gt;
', NULL, NULL, '''/p'':41C,65C ''0.9'':52C ''1'':64C ''accumul'':19C ''action'':40C ''current'':24C,39C ''discount'':12C,59C ''evalu'':14C ''event'':21C,36C ''factor'':13C ''function'':7C ''futur'':20C,35C ''heurist'':48C ''import'':16C,32C ''learn'':10C ''less'':31C ''number'':29C,45C ''p'':1C,42C ''reinforc'':9C ''select'':47C,51C,63C ''smaller'':27C ''typic'':5C ''usual'':43C,50C ''valu'':6C,25C ''want'':57C ''would'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10145, 2, '2016-02-09 01:32:29.22', 0, NULL, NULL, '&lt;p&gt;It appears the widget uses hierarchical clustering. I guess the metric is Euclidean distance by default and there doesn''t seem to be a way to specify another one (except by using &lt;em&gt;Distances&lt;/em&gt; widget and connecting it into the &lt;em&gt;Distance Map&lt;/em&gt; widget).&lt;/p&gt;

&lt;p&gt;I don''t think it is possible to export the widget''s workflow as pure code, but you can look at what the widget does in &lt;a href="https://github.com/biolab/orange3/blob/master/Orange/widgets/visualize/owheatmap.py" rel="nofollow"&gt;the source code&lt;/a&gt; (seems pretty low-level, though). What you &lt;em&gt;can&lt;/em&gt; do, however, is select subsets of data (can be saved with &lt;em&gt;Save Data&lt;/em&gt; widget) for further analysis if that''s of any help.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/YXUzC.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/YXUzC.png" alt="Orange Heatmap widget screenshot"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':86C,142C ''/biolab/orange3/blob/master/orange/widgets/visualize/owheatmap.py'':80C ''/em'':36C,46C,97C,112C ''/p'':48C,123C,143C ''/yxuzc.png'':129C,136C ''alt'':137C ''analysi'':116C ''anoth'':29C ''appear'':3C ''cluster'':8C ''code'':65C,85C ''connect'':39C ''data'':104C,111C ''default'':17C ''distanc'':15C,35C,44C ''doesn'':20C ''em'':34C,43C,95C,109C ''euclidean'':14C ''except'':31C ''export'':58C ''github.com'':79C ''github.com/biolab/orange3/blob/master/orange/widgets/visualize/owheatmap.py'':78C ''guess'':10C ''heatmap'':139C ''help'':122C ''hierarch'':7C ''howev'':99C ''href'':77C,126C ''i.stack.imgur.com'':128C,135C ''i.stack.imgur.com/yxuzc.png'':127C,134C ''img'':132C ''level'':91C ''look'':69C ''low'':90C ''low-level'':89C ''map'':45C ''metric'':12C ''nofollow'':82C,131C ''one'':30C ''orang'':138C ''p'':1C,49C,124C ''possibl'':56C ''pretti'':88C ''pure'':64C ''rel'':81C,130C ''save'':107C,110C ''screenshot'':141C ''seem'':22C,87C ''select'':101C ''sourc'':84C ''specifi'':28C ''src'':133C ''subset'':102C ''think'':53C ''though'':92C ''use'':6C,33C ''way'':26C ''widget'':5C,37C,47C,60C,73C,113C,140C ''workflow'':62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10100, 1, '2016-02-06 02:38:20.75', 2, 41, 'How to make k-means distributed?', '&lt;p&gt;After setting up a &lt;a href="https://gsamaras.wordpress.com/code/hadoop-cluster-with-pc-and-virtualbox/" rel="nofollow"&gt;2-noded Hadoop cluster&lt;/a&gt;, understanding &lt;a href="http://www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/" rel="nofollow"&gt;Hadoop and Python&lt;/a&gt; and based on this naive &lt;a href="http://codereview.stackexchange.com/questions/80050/k-means-clustering-algorithm-in-python"&gt;implementation&lt;/a&gt;, I ended up with this code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def kmeans(data, k, c=None):
    if c is not None:
        centroids = c
    else:
        centroids = []
        centroids = randomize_centroids(data, centroids, k)

    old_centroids = [[] for i in range(k)] 

    iterations = 0
    while not (has_converged(centroids, old_centroids, iterations)):
        iterations += 1

        clusters = [[] for i in range(k)]

        # assign data points to clusters
        clusters = euclidean_dist(data, centroids, clusters)

        # recalculate centroids
        index = 0
        for cluster in clusters:
            old_centroids[index] = centroids[index]
            centroids[index] = np.mean(cluster, axis=0).tolist()
            index += 1


    print("The total number of data instances is: " + str(len(data)))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have tested it for serial execution and it is OK. How to make it distributed in Hadoop? In other words, what should go to the reducer and what to the mapper?&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Please&lt;/em&gt; note that &lt;em&gt;if&lt;/em&gt; possible, I would like to follow the tutorial''s style, since it''s something I have understood.&lt;/p&gt;
', 'python  hadoop  k-means  map-reduce  distributed', NULL, '''/a'':33C,45C,57C ''/code'':157C ''/code/hadoop-cluster-with-pc-and-virtualbox/'':26C ''/em'':196C,201C ''/p'':64C,192C,219C ''/pre'':158C ''/questions/80050/k-means-clustering-algorithm-in-python'':55C ''/tutorials/writing-an-hadoop-mapreduce-program-in-python/'':39C ''0'':96C,127C,142C ''1'':106C,145C ''2'':29C ''assign'':113C ''axi'':141C ''base'':47C ''c'':71C,74C,79C ''centroid'':78C,81C,82C,84C,86C,89C,101C,103C,122C,125C,133C,135C,137C ''cluster'':32C,107C,117C,118C,123C,129C,131C,140C ''code'':63C,66C ''codereview.stackexchange.com'':54C ''codereview.stackexchange.com/questions/80050/k-means-clustering-algorithm-in-python'':53C ''converg'':100C ''data'':69C,85C,114C,121C,151C,156C ''def'':67C ''dist'':120C ''distribut'':7A,16B,175C ''els'':80C ''em'':194C,199C ''end'':59C ''euclidean'':119C ''execut'':166C ''follow'':207C ''go'':183C ''gsamaras.wordpress.com'':25C ''gsamaras.wordpress.com/code/hadoop-cluster-with-pc-and-virtualbox/'':24C ''hadoop'':9B,31C,42C,177C ''href'':23C,36C,52C ''implement'':56C ''index'':126C,134C,136C,138C,144C ''instanc'':152C ''iter'':95C,104C,105C ''k'':5A,11B,70C,87C,94C,112C ''k-mean'':4A,10B ''kmean'':68C ''len'':155C ''like'':205C ''make'':3A,173C ''map'':14B ''map-reduc'':13B ''mapper'':191C ''mean'':6A,12B ''naiv'':50C ''node'':30C ''nofollow'':28C,41C ''none'':72C,77C ''note'':197C ''np.mean'':139C ''number'':149C ''ok'':170C ''old'':88C,102C,132C ''p'':17C,159C,193C ''pleas'':195C ''point'':115C ''possibl'':202C ''pre'':65C ''print'':146C ''python'':8B,44C ''random'':83C ''rang'':93C,111C ''recalcul'':124C ''reduc'':15B,186C ''rel'':27C,40C ''serial'':165C ''set'':19C ''sinc'':212C ''someth'':215C ''str'':154C ''style'':211C ''test'':162C ''tolist'':143C ''total'':148C ''tutori'':209C ''understand'':34C ''understood'':218C ''word'':180C ''would'':204C ''www.michael-noll.com'':38C ''www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10102, 2, '2016-02-06 12:32:22.06', 1, NULL, NULL, '&lt;p&gt;From your question, I imagine you are interested in:&lt;/p&gt;

&lt;p&gt;$\alpha = P[\mu - k \, \sigma \le x \le \mu + k \, \sigma]$&lt;/p&gt;

&lt;p&gt;where, in your case, $k = 0.1$. In other words, by "confidence level" you mean alpha.&lt;/p&gt;

&lt;p&gt;In R, you can obtain that via function &lt;code&gt;pnorm&lt;/code&gt; (empirical cumulative distribution function for the standardized normal distribution):&lt;/p&gt;

&lt;p&gt;&lt;code&gt;alpha &amp;lt;- 2 * (pnorm(k) - 0.5)&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;which, for &lt;code&gt;k = 0.1&lt;/code&gt; produces 8%.&lt;/p&gt;
', NULL, NULL, '''/code'':53C,72C,80C ''/p'':11C,24C,41C,63C,73C,83C ''0.1'':31C,79C ''0.5'':71C ''2'':68C ''8'':82C ''alpha'':13C,40C,66C ''case'':29C ''code'':51C,65C,77C ''confid'':36C ''cumul'':55C ''distribut'':56C,62C ''empir'':54C ''function'':50C,57C ''imagin'':6C ''interest'':9C ''k'':16C,22C,30C,70C,78C ''le'':18C,20C ''level'':37C ''lt'':67C ''mean'':39C ''mu'':15C,21C ''normal'':61C ''obtain'':47C ''p'':1C,12C,14C,25C,42C,64C,74C ''pnorm'':52C,69C ''produc'':81C ''question'':4C ''r'':44C ''sigma'':17C,23C ''standard'':60C ''via'':49C ''word'':34C ''x'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10103, 1, '2016-02-06 14:19:10.243', 3, 66, 'Improve the speed of t-sne implementation in python for huge data', '&lt;p&gt;I would like to do dimensionality reduction on nearly 1 million vectors each with 200 dimensions(&lt;code&gt;doc2vec&lt;/code&gt;). 
I am using &lt;code&gt;TSNE&lt;/code&gt; implementation from &lt;code&gt;sklearn.manifold&lt;/code&gt; module for it and the major problem is time complexity. Even with &lt;code&gt;method = barnes_hut&lt;/code&gt;, the speed of computation is still low. Some time even it runs out of Memory.&lt;/p&gt;

&lt;p&gt;I am running it on a 48 core processor with 130G RAM. Is there a method to run it parallely or make use of the plentiful resource to speed up the process. &lt;/p&gt;
', 'python  bigdata  nlp  scikit  dimensionality-reduction', NULL, '''/code'':40C,46C,51C,68C ''/p'':84C,118C ''1'':31C ''130g'':96C ''200'':36C ''48'':92C ''barn'':66C ''bigdata'':15B ''code'':38C,44C,49C,64C ''complex'':61C ''comput'':72C ''core'':93C ''data'':13A ''dimens'':37C ''dimension'':19B,27C ''dimensionality-reduct'':18B ''doc2vec'':39C ''even'':62C,78C ''huge'':12A ''hut'':67C ''implement'':8A,47C ''improv'':1A ''like'':24C ''low'':75C ''major'':57C ''make'':107C ''memori'':83C ''method'':65C,101C ''million'':32C ''modul'':52C ''near'':30C ''nlp'':16B ''p'':21C,85C ''parall'':105C ''plenti'':111C ''problem'':58C ''process'':117C ''processor'':94C ''python'':10A,14B ''ram'':97C ''reduct'':20B,28C ''resourc'':112C ''run'':80C,88C,103C ''scikit'':17B ''sklearn.manifold'':50C ''sne'':7A ''speed'':3A,70C,114C ''still'':74C ''t-sne'':5A ''time'':60C,77C ''tsne'':45C ''use'':43C,108C ''vector'':33C ''would'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10106, 1, '2016-02-06 17:34:16.887', 2, 18, 'How to retrive the results saved in model of gensim?', '&lt;p&gt;I have a file that contains words. I have to calculate cosine similarity of each word with every other words. For that I used the following formula in gensim:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;model.most_similar([word])&lt;/li&gt;
&lt;li&gt;model.save(''text.txt'')&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I want to save the result of cosine-similarity of each words with respective to other words in such a way that I can used them further in classification and clustering. But don''t know how to retrieve the results which are saved using gensim model ?&lt;/p&gt;
', 'text-mining  word-embeddings  gensim  word2vec', NULL, '''/li'':55C,59C ''/p'':49C,109C ''/ul'':60C ''calcul'':30C ''classif'':91C ''cluster'':93C ''contain'':25C ''cosin'':31C,70C ''cosine-similar'':69C ''embed'':16B ''everi'':37C ''file'':23C ''follow'':45C ''formula'':46C ''gensim'':10A,17B,48C,107C ''know'':97C ''li'':51C,56C ''mine'':13B ''model'':8A,108C ''model.most'':52C ''model.save'':57C ''p'':19C,61C ''respect'':76C ''result'':5A,67C,102C ''retriev'':100C ''retriv'':3A ''save'':6A,65C,105C ''similar'':32C,53C,71C ''text'':12B ''text-min'':11B ''text.txt'':58C ''ul'':50C ''use'':43C,87C,106C ''want'':63C ''way'':83C ''word'':15B,26C,35C,39C,54C,74C,79C ''word-embed'':14B ''word2vec'':18B');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10107, 2, '2016-02-06 17:40:47.583', 3, NULL, NULL, '&lt;p&gt;There are a lot of ways to see how well a recommender works and I think it really depends on your final goal.  A list of &lt;a href="https://en.wikipedia.org/wiki/Learning_to_rank" rel="nofollow"&gt;evaluation metrics&lt;/a&gt; are here that might be useful to looks through. Although a measure like Accuracy/Precision/RMSE might suit your needs.&lt;/p&gt;

&lt;p&gt;For simplicity, lets say you have a year worth of historical sales.  You can build your system on the first 8 months of sales, then for each following month you can see what products would be recommended in that month and what products were actually purchased in that month.  So within that you can get an idea of how well it is performing (of the 5 products someone bought, we had all 5 in the  in the spots 1,2,6,8,9 of the top-10 list).  You would want the product purchases to be closer to the top, which is why ranking metrics could be useful for you.&lt;/p&gt;

&lt;p&gt;Of course there is a lot more to consider, but I think this is a good place to start. Are you trying to increase the pure number of sales or the revenue? Do certain products sell more depending on seasons/holidays?  Are you using ratings or just associated sales (how do you recommend new products?).  Factors like these can change your recommender and how you would want to evaluate it.&lt;/p&gt;
', NULL, NULL, '''-10'':144C ''/a'':37C ''/p'':56C,168C,239C ''/wiki/learning_to_rank'':32C ''1'':136C ''2'':137C ''5'':123C,130C ''6'':138C ''8'':78C,139C ''9'':140C ''accuracy/precision/rmse'':51C ''actual'':102C ''although'':47C ''associ'':216C ''bought'':126C ''build'':72C ''certain'':203C ''chang'':228C ''closer'':154C ''consid'':178C ''could'':163C ''cours'':171C ''depend'':20C,207C ''en.wikipedia.org'':31C ''en.wikipedia.org/wiki/learning_to_rank'':30C ''evalu'':35C,237C ''factor'':224C ''final'':23C ''first'':77C ''follow'':85C ''get'':112C ''goal'':24C ''good'':185C ''histor'':68C ''href'':29C ''idea'':114C ''increas'':193C ''let'':60C ''like'':50C,225C ''list'':26C,145C ''look'':45C ''lot'':5C,175C ''measur'':49C ''metric'':36C,162C ''might'':41C,52C ''month'':79C,86C,97C,106C ''need'':55C ''new'':222C ''nofollow'':34C ''number'':196C ''p'':1C,57C,169C ''perform'':120C ''place'':186C ''product'':91C,100C,124C,150C,204C,223C ''purchas'':103C,151C ''pure'':195C ''rank'':161C ''rate'':213C ''realli'':19C ''recommend'':13C,94C,221C,230C ''rel'':33C ''revenu'':201C ''sale'':69C,81C,198C,217C ''say'':61C ''seasons/holidays'':209C ''see'':9C,89C ''sell'':205C ''simplic'':59C ''someon'':125C ''spot'':135C ''start'':188C ''suit'':53C ''system'':74C ''think'':17C,181C ''top'':143C,157C ''tri'':191C ''use'':43C,165C,212C ''want'':148C,235C ''way'':7C ''well'':11C,117C ''within'':108C ''work'':14C ''worth'':66C ''would'':92C,147C,234C ''year'':65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10108, 1, '2016-02-06 18:29:08.427', 2, 46, 'Is parsing files an application of machine learning?', '&lt;p&gt;I presently receive files from a device in a semi-csv format. I have a written a simple recursive descent parser for getting information out of these files. Every time the device updates firmware, I have a new version of the parser for the changes the update brings.&lt;/p&gt;

&lt;p&gt;Down the road, we will be taking data from other devices, which means another parser and more updates to firmware. I''m wondering if I could define a basic structure of "this is the data I need" and use a neural network to get the parsed data without having to write a parser for each new file type that comes in.&lt;/p&gt;

&lt;p&gt;Is this a pipe dream or is it a valid application of machine learning? I''m much more of a software engineer than I am a data scientist, but I''m starting to dip my toes into the machine learning realm. &lt;/p&gt;

&lt;p&gt;Thanks in advance.&lt;/p&gt;
', 'machine-learning  parsing', NULL, '''/p'':63C,126C,169C,174C ''advanc'':173C ''anoth'':78C ''applic'':5A,138C ''basic'':93C ''bring'':62C ''chang'':59C ''come'':124C ''could'':90C ''csv'':25C ''data'':72C,99C,111C,154C ''defin'':91C ''descent'':34C ''devic'':20C,46C,75C ''dip'':161C ''dream'':132C ''engin'':149C ''everi'':43C ''file'':3A,17C,42C,121C ''firmwar'':48C,84C ''format'':26C ''get'':37C,108C ''inform'':38C ''learn'':8A,11B,141C,167C ''m'':86C,143C,158C ''machin'':7A,10B,140C,166C ''machine-learn'':9B ''mean'':77C ''much'':144C ''need'':101C ''network'':106C ''neural'':105C ''new'':52C,120C ''p'':13C,64C,127C,170C ''pars'':2A,12B,110C ''parser'':35C,56C,79C,117C ''pipe'':131C ''present'':15C ''realm'':168C ''receiv'':16C ''recurs'':33C ''road'':67C ''scientist'':155C ''semi'':24C ''semi-csv'':23C ''simpl'':32C ''softwar'':148C ''start'':159C ''structur'':94C ''take'':71C ''thank'':171C ''time'':44C ''toe'':163C ''type'':122C ''updat'':47C,61C,82C ''use'':103C ''valid'':137C ''version'':53C ''without'':112C ''wonder'':87C ''write'':115C ''written'':30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10124, 2, '2016-02-07 18:47:21.247', 2, NULL, NULL, '&lt;p&gt;This is a good question but it''s rather complicated. I can suggest two approaches:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Graphical_model" rel="nofollow"&gt;Graphical models&lt;/a&gt;; specifically &lt;a href="https://en.wikipedia.org/wiki/Bayesian_network" rel="nofollow"&gt;Bayesian networks&lt;/a&gt; since your graph is directed.&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Recurrent_neural_network" rel="nofollow"&gt;Recurrent neural networks&lt;/a&gt;. Here''s a talk on popular recent model: &lt;a href="http://research.microsoft.com/apps/video/?id=239083" rel="nofollow"&gt;Sequence to Sequence Learning with Neural Networks&lt;/a&gt;.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':29C,40C,58C,81C ''/apps/video/?id=239083'':71C ''/li'':46C,82C ''/p'':17C ''/ul'':83C ''/wiki/bayesian_network'':35C ''/wiki/graphical_model'':24C ''/wiki/recurrent_neural_network'':52C ''approach'':16C ''bayesian'':38C ''complic'':11C ''direct'':45C ''en.wikipedia.org'':23C,34C,51C ''en.wikipedia.org/wiki/bayesian_network'':33C ''en.wikipedia.org/wiki/graphical_model'':22C ''en.wikipedia.org/wiki/recurrent_neural_network'':50C ''good'':5C ''graph'':43C ''graphic'':27C ''href'':21C,32C,49C,68C ''learn'':77C ''li'':19C,47C ''model'':28C,66C ''network'':39C,57C,80C ''neural'':56C,79C ''nofollow'':26C,37C,54C,73C ''p'':1C ''popular'':64C ''question'':6C ''rather'':10C ''recent'':65C ''recurr'':55C ''rel'':25C,36C,53C,72C ''research.microsoft.com'':70C ''research.microsoft.com/apps/video/?id=239083'':69C ''sequenc'':74C,76C ''sinc'':41C ''specif'':30C ''suggest'':14C ''talk'':62C ''two'':15C ''ul'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10109, 1, '2016-02-06 19:47:41.373', 1, 12, 'Processing frequent large volume data feeds and storing results in aggregate tables', '&lt;p&gt;My background is one of statistics (SAS, R) this world of big high frequency data is fairly new to me so excuse my ignorance, I am hoping you can help.
Basically this is the problem description:
1. On a frequent (hourly) basis I will be receiving a batch of data in flat files (biggest table being max of 20K-50K rows, the other tables just 2/3 K max)
2. I then need to take these files, process them for some calculations including some complex SQL manipulations and use the results to update some aggregate tables which are in turn stored in a relational (MySQL) database
3. I need to repeat this process at least once per hour, each time updating the relational database tables with the results of my calculations&lt;/p&gt;

&lt;p&gt;Now, I started looking into tools such as Google BigQuery but the more I search online the more confused I become, as I have seen other options such as Kafka, Storm, Spark Streaming etc...&lt;/p&gt;

&lt;p&gt;Could you please provide your advice on this, I am particularly interested in which technology and architecture you''d choose?&lt;/p&gt;

&lt;p&gt;Thanks so much in advance!&lt;/p&gt;
', 'python  bigdata  hadoop  apache-spark  apache-kafka', NULL, '''/p'':154C,190C,212C,219C ''1'':59C ''2'':92C ''2/3'':89C ''20k'':82C ''20k-50k'':81C ''3'':129C ''50k'':83C ''advanc'':218C ''advic'':197C ''aggreg'':11A,117C ''apach'':17B,20B ''apache-kafka'':19B ''apache-spark'':16B ''architectur'':208C ''background'':24C ''basi'':64C ''basic'':53C ''batch'':70C ''becom'':176C ''big'':34C ''bigdata'':14B ''biggest'':76C ''bigqueri'':165C ''calcul'':104C,153C ''choos'':211C ''complex'':107C ''confus'':174C ''could'':192C ''d'':210C ''data'':5A,37C,72C ''databas'':128C,146C ''descript'':58C ''etc'':189C ''excus'':44C ''fair'':39C ''feed'':6A ''file'':75C,99C ''flat'':74C ''frequenc'':36C ''frequent'':2A,62C ''googl'':164C ''hadoop'':15B ''help'':52C ''high'':35C ''hope'':49C ''hour'':63C,140C ''ignor'':46C ''includ'':105C ''interest'':203C ''k'':90C ''kafka'':21B,185C ''larg'':3A ''least'':137C ''look'':159C ''manipul'':109C ''max'':79C,91C ''much'':216C ''mysql'':127C ''need'':95C,131C ''new'':40C ''one'':26C ''onlin'':171C ''option'':182C ''p'':22C,155C,191C,213C ''particular'':202C ''per'':139C ''pleas'':194C ''problem'':57C ''process'':1A,100C,135C ''provid'':195C ''python'':13B ''r'':30C ''receiv'':68C ''relat'':126C,145C ''repeat'':133C ''result'':9A,113C,150C ''row'':84C ''sas'':29C ''search'':170C ''seen'':180C ''spark'':18B,187C ''sql'':108C ''start'':158C ''statist'':28C ''store'':8A,123C ''storm'':186C ''stream'':188C ''tabl'':12A,77C,87C,118C,147C ''take'':97C ''technolog'':206C ''thank'':214C ''time'':142C ''tool'':161C ''turn'':122C ''updat'':115C,143C ''use'':111C ''volum'':4A ''world'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10110, 2, '2016-02-06 20:52:06.76', 0, NULL, NULL, '&lt;p&gt;Can you tell us which criteria do you use for considering a picture "good quality image" or "bad quality image"?&lt;/p&gt;

&lt;p&gt;An idea of how to handle it could be for example choosing unfocused pictures as bad ones. One way to discriminate it could be compute the &lt;a href="https://en.wikipedia.org/wiki/Sobel_operator" rel="nofollow"&gt;Sobel operator&lt;/a&gt; over the image in order to obtain the edges. A blurry image will contain less edges than a good one, but also it will depend on the type of image (it''s not the same a landscape with just the sea and the sand than a picture of a table full of stuff), so you will need to normalize your image, but no idea yet how to handle this normalization.&lt;/p&gt;

&lt;p&gt;My suggestion on libraries for python is &lt;a href="https://opencv-python-tutroals.readthedocs.org/en/latest/" rel="nofollow"&gt;OpenCv&lt;/a&gt;. It''s documentation for Python language was awful when I tried to use some years ago, but surely it has improved a lot since then.&lt;/p&gt;
', NULL, NULL, '''/a'':59C,147C ''/en/latest/'':143C ''/p'':22C,130C,173C ''/wiki/sobel_operator'':54C ''ago'':163C ''also'':81C ''aw'':155C ''bad'':19C,39C ''blurri'':70C ''choos'':35C ''comput'':48C ''consid'':12C ''contain'':73C ''could'':31C,46C ''criteria'':7C ''depend'':84C ''discrimin'':44C ''document'':150C ''edg'':68C,75C ''en.wikipedia.org'':53C ''en.wikipedia.org/wiki/sobel_operator'':52C ''exampl'':34C ''full'':110C ''good'':15C,78C ''handl'':29C,127C ''href'':51C,140C ''idea'':25C,123C ''imag'':17C,21C,62C,71C,89C,120C ''improv'':168C ''landscap'':96C ''languag'':153C ''less'':74C ''librari'':135C ''lot'':170C ''need'':116C ''nofollow'':56C,145C ''normal'':118C,129C ''obtain'':66C ''one'':40C,41C,79C ''opencv'':146C ''opencv-python-tutroals.readthedocs.org'':142C ''opencv-python-tutroals.readthedocs.org/en/latest/'':141C ''oper'':58C ''order'':64C ''p'':1C,23C,131C ''pictur'':14C,37C,106C ''python'':137C,152C ''qualiti'':16C,20C ''rel'':55C,144C ''sand'':103C ''sea'':100C ''sinc'':171C ''sobel'':57C ''stuff'':112C ''suggest'':133C ''sure'':165C ''tabl'':109C ''tell'':4C ''tri'':158C ''type'':87C ''unfocus'':36C ''us'':5C ''use'':10C,160C ''way'':42C ''year'':162C ''yet'':124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10112, 1, '2016-02-06 22:17:04.323', 3, 72, 'Which machine learning approach/algorithm do I choose for path validation?', '&lt;p&gt;I apologize for lack of terminology, I''m no computer scientist.
I have a problem of validating paths in a directed graph with complex nodes.&lt;/p&gt;

&lt;p&gt;The full description is the following:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;I have a decent set (about 1K) of directed graphs;&lt;/li&gt;
&lt;li&gt;Each node contains a complex data structure (it is a hierarchical data structure, not a picture or sound);&lt;/li&gt;
&lt;li&gt;I have some of paths in those graphs known as "correct" paths (based
mostly on data in nodes);&lt;/li&gt;
&lt;li&gt;And I have some of paths in those graphs known as "incorrect" paths
(with classification why it is incorrect).&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I''d like to predict given a graph with those complex nodes and a path, is this path "correct".&lt;/p&gt;

&lt;p&gt;Which machine learning algorithm will suit me best?
In general, what approach I should use?&lt;/p&gt;

&lt;p&gt;Edit:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Each full graph is either have app paths processed
(correct/incorrect) or completely blank (no path is processed);&lt;/li&gt;
&lt;li&gt;Correctness depends on both position of node in a graph AND data in
the node;&lt;/li&gt;
&lt;li&gt;Humans would need heuristics to decide or guess which paths are
correct;&lt;/li&gt;
&lt;li&gt;Most of the paths are "correct";&lt;/li&gt;
&lt;li&gt;I hope to convert human heuristics to some kind of "correctness"
recognition.&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning', NULL, '''/li'':61C,81C,101C,122C,184C,201C,215C,223C,237C ''/ol'':123C,238C ''/p'':40C,48C,144C,161C,164C ''1k'':57C ''algorithm'':149C ''apolog'':16C ''app'':173C ''approach'':157C ''approach/algorithm'':4A ''base'':95C ''best'':153C ''blank'':179C ''choos'':7A ''classif'':117C ''complet'':178C ''complex'':38C,67C,135C ''comput'':24C ''contain'':65C ''convert'':228C ''correct'':93C,143C,186C,214C,222C,235C ''correct/incorrect'':176C ''d'':126C ''data'':68C,74C,98C,197C ''decent'':54C ''decid'':208C ''depend'':187C ''descript'':44C ''direct'':35C,59C ''edit'':163C ''either'':171C ''follow'':47C ''full'':43C,168C ''general'':155C ''given'':130C ''graph'':36C,60C,90C,111C,132C,169C,195C ''guess'':210C ''heurist'':206C,230C ''hierarch'':73C ''hope'':226C ''human'':203C,229C ''incorrect'':114C,121C ''kind'':233C ''known'':91C,112C ''lack'':18C ''learn'':3A,13B,148C ''li'':50C,62C,82C,102C,166C,185C,202C,216C,224C ''like'':127C ''m'':22C ''machin'':2A,12B,147C ''machine-learn'':11B ''most'':96C ''need'':205C ''node'':39C,64C,100C,136C,192C,200C ''ol'':49C,165C ''p'':14C,41C,124C,145C,162C ''path'':9A,32C,87C,94C,108C,115C,139C,142C,174C,181C,212C,220C ''pictur'':78C ''posit'':190C ''predict'':129C ''problem'':29C ''process'':175C,183C ''recognit'':236C ''scientist'':25C ''set'':55C ''sound'':80C ''structur'':69C,75C ''suit'':151C ''terminolog'':20C ''use'':160C ''valid'':10A,31C ''would'':204C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10113, 1, '2016-02-07 02:55:46.907', 1, 21, 'class imbalance - applied SMOTE - next steps', '&lt;p&gt;I am new to ML and learnt a lot from your valuable posts. I need your advise with the following situation and guidance on if the steps make sense.
I have a binary classification problem, my dataset has a severe imbalance approximately 2% positive cases (4,000 cases) out of a total of 200,000 cases. I separated my dataset into a train and a test (80/20 stratified split). My train now has total of 160,000 cases (3,200 positive cases) and test has total of 40,000 (800 positive cases).&lt;/p&gt;

&lt;p&gt;Next, from Train I created a (50-50) SMOTE sample which has ~9,000 positive cases (original 3,200 and SMOTE create synthetic samples of ~ 5,800 positives) and ~ 9,000 negative cases (so a total of 18,000 cases in the "new train"). &lt;/p&gt;

&lt;p&gt;next, I have developed bagged/ boosted tree based classifiers on the "new train" with ROC training criteria in R caret (I get high ROC around 0.9 which is usual). I then applied the model/s on the test set which has the original class imbalance and got the predicted probabilities (the AUC is now 0.65). I identified the optimal threshold for classification based on the pROC package for creation of the final class predictions on test dataset.&lt;/p&gt;

&lt;p&gt;I need your advise on if I am transitioning from the trained SMOTE model with a 50/50 imbalance to the test model with the original imbalance of 50/1 correctly. is there anything fundamentally wrong in what I am doing and any suggestions on how to improve the process will be very helpful. are any corrections needed to the SMOTE based probabilities on test or is thresholding the best I can do?&lt;/p&gt;
', 'unbalanced-classes', NULL, '''-50'':112C ''/p'':103C,149C,227C,300C ''0.65'':204C ''0.9'':176C ''000'':57C,65C,87C,99C,118C,135C,143C ''160'':86C ''18'':142C ''2'':53C ''200'':64C,90C,123C ''3'':89C,122C ''4'':56C ''40'':98C ''5'':130C ''50'':111C ''50/1'':256C ''50/50'':245C ''80/20'':77C ''800'':100C,131C ''9'':117C,134C ''advis'':27C,232C ''anyth'':260C ''appli'':3A,182C ''approxim'':52C ''around'':175C ''auc'':201C ''bag'':155C ''base'':158C,212C,288C ''best'':296C ''binari'':43C ''boost'':156C ''caret'':170C ''case'':55C,58C,66C,88C,92C,102C,120C,137C,144C ''class'':1A,9B,193C,222C ''classif'':44C,211C ''classifi'':159C ''correct'':257C,283C ''creat'':109C,126C ''creation'':218C ''criteria'':167C ''dataset'':47C,70C,226C ''develop'':154C ''final'':221C ''follow'':30C ''fundament'':261C ''get'':172C ''got'':196C ''guidanc'':33C ''help'':280C ''high'':173C ''identifi'':206C ''imbal'':2A,51C,194C,246C,254C ''improv'':274C ''learnt'':17C ''lot'':19C ''make'':38C ''ml'':15C ''model'':242C,250C ''model/s'':184C ''need'':25C,230C,284C ''negat'':136C ''new'':13C,147C,162C ''next'':5A,105C,151C ''optim'':208C ''origin'':121C,192C,253C ''p'':10C,104C,150C,228C ''packag'':216C ''posit'':54C,91C,101C,119C,132C ''post'':23C ''predict'':198C,223C ''probabl'':199C,289C ''problem'':45C ''proc'':215C ''process'':276C ''r'':169C ''roc'':165C,174C ''sampl'':114C,128C ''sens'':39C ''separ'':68C ''set'':188C ''sever'':50C ''situat'':31C ''smote'':4A,113C,125C,241C,287C ''split'':79C ''step'':6A,37C ''stratifi'':78C ''suggest'':270C ''synthet'':127C ''test'':76C,94C,187C,225C,249C,291C ''threshold'':209C,294C ''total'':62C,84C,96C,140C ''train'':73C,81C,107C,148C,163C,166C,240C ''transit'':237C ''tree'':157C ''unbalanc'':8B ''unbalanced-class'':7B ''usual'':179C ''valuabl'':22C ''wrong'':262C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10114, 1, '2016-02-07 04:25:04.64', -1, 18, 'using movielens dataset build recommendation engine', '&lt;p&gt;Where I can get the complete guide (step by step )on building a recommender system for example using movielens datsets building content based, collaborative or may be hybrid system.&lt;/p&gt;
', 'recommendation', NULL, '''/p'':38C ''base'':31C ''build'':4A,20C,29C ''collabor'':32C ''complet'':14C ''content'':30C ''dataset'':3A ''datset'':28C ''engin'':6A ''exampl'':25C ''get'':12C ''guid'':15C ''hybrid'':36C ''may'':34C ''movielen'':2A,27C ''p'':8C ''recommend'':5A,7B,22C ''step'':16C,18C ''system'':23C,37C ''use'':1A,26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10117, 2, '2016-02-07 09:31:46.453', 3, NULL, NULL, '&lt;p&gt;I would answer the question at two levels. The first level is "&lt;strong&gt;can it be done using machine learning&lt;/strong&gt;?" I would say that machine learning is essentially about learning. So given that you prepare sufficient examples of sample documents and the output to expect from those documents, you can train a network to learn the structure of documents and extract the relevant information. The more general form of extracting information from documents is a well-researched problem and is more commonly known as &lt;a href="https://en.wikipedia.org/wiki/Information_retrieval" rel="nofollow"&gt;Information Retrieval&lt;/a&gt;. And it is not limited to just machine learning techniques, you can use Natural Language Processing tools as well. So, in its general form, it is actually being done in practice. Coming to the second level, "&lt;strong&gt;should you be doing it using machine learning&lt;/strong&gt;?". I would agree to what @NeilSlater said. The better and more feasible approach would be to use good programming practices so that you can reuse parts of your parser as your dataset evolves.&lt;/p&gt;
', NULL, NULL, '''/a'':97C ''/p'':177C ''/strong'':22C,143C ''/wiki/information_retrieval'':92C ''actual'':124C ''agre'':146C ''answer'':4C ''approach'':156C ''better'':152C ''come'':129C ''common'':85C ''dataset'':175C ''document'':42C,50C,61C,75C ''done'':18C,126C ''en.wikipedia.org'':91C ''en.wikipedia.org/wiki/information_retrieval'':90C ''essenti'':30C ''evolv'':176C ''exampl'':39C ''expect'':47C ''extract'':63C,72C ''feasibl'':155C ''first'':11C ''form'':70C,121C ''general'':69C,120C ''given'':34C ''good'':161C ''href'':89C ''inform'':66C,73C,95C ''known'':86C ''languag'':112C ''learn'':21C,28C,32C,57C,106C,142C ''level'':9C,12C,133C ''limit'':102C ''machin'':20C,27C,105C,141C ''natur'':111C ''neilslat'':149C ''network'':55C ''nofollow'':94C ''output'':45C ''p'':1C ''parser'':172C ''part'':169C ''practic'':128C,163C ''prepar'':37C ''problem'':81C ''process'':113C ''program'':162C ''question'':6C ''rel'':93C ''relev'':65C ''research'':80C ''retriev'':96C ''reus'':168C ''said'':150C ''sampl'':41C ''say'':25C ''second'':132C ''strong'':14C,134C ''structur'':59C ''suffici'':38C ''techniqu'':107C ''tool'':114C ''train'':53C ''two'':8C ''use'':19C,110C,140C,160C ''well'':79C,116C ''well-research'':78C ''would'':3C,24C,145C,157C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10333, 1, '2016-02-21 00:45:37.42', 1, 80, 'ML packages in R: caret v e1071', '&lt;p&gt;I''ve looked and surprisingly have not found too much discussion on the relative strengths of the &lt;code&gt;caret&lt;/code&gt; and &lt;code&gt;e1071&lt;/code&gt; package.  From my understanding, these packages perform many of the same ML algorithms.  With that in mind, I''m interested in what those practitioners who have experience with both think these packages relative strengths and weaknesses are.  What do you think about readability of code, ease of coding, methods, speed, etc...&lt;/p&gt;

&lt;p&gt;In particular I''m interested in multi-class SVM&lt;/p&gt;
', 'r', NULL, '''/code'':29C,33C ''/p'':85C,97C ''algorithm'':46C ''caret'':5A,28C ''class'':95C ''code'':27C,31C,78C,81C ''discuss'':20C ''e1071'':7A,32C ''eas'':79C ''etc'':84C ''experi'':60C ''found'':17C ''interest'':53C,91C ''look'':12C ''m'':52C,90C ''mani'':41C ''method'':82C ''mind'':50C ''ml'':1A,45C ''much'':19C ''multi'':94C ''multi-class'':93C ''p'':9C,86C ''packag'':2A,34C,39C,65C ''particular'':88C ''perform'':40C ''practition'':57C ''r'':4A,8B ''readabl'':76C ''relat'':23C,66C ''speed'':83C ''strength'':24C,67C ''surpris'':14C ''svm'':96C ''think'':63C,74C ''understand'':37C ''v'':6A ''ve'':11C ''weak'':69C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10118, 2, '2016-02-07 11:51:18.74', 1, NULL, NULL, '&lt;p&gt;There are plenty of ways to realize this. Originally everybodies approach to the Netflix Prize (quite similar to the movielense dataset) were nearest neighbor methods. Their intuition should be fairly clear. Matrix Factorization became the next big thing and was nicely described by Simon Funk in his journal: &lt;a href="http://sifter.org/~simon/journal/20061211.html" rel="nofollow"&gt;http://sifter.org/~simon/journal/20061211.html&lt;/a&gt; . If you are more interested in these two topics and don''t mind a bit of bad english you may consider this thesis: &lt;a href="http://patrick-ott.de/dl/diploma_ott.pdf" rel="nofollow"&gt;http://patrick-ott.de/dl/diploma_ott.pdf&lt;/a&gt; .&lt;/p&gt;

&lt;p&gt;The crowd then went on to Restricted Boltzmann Machines for the task, e.g. (cs.utoronto.ca/~hinton/absps/netflixICML.pdf) . I think it is beyond this answer to explain these for this particular task -- maybe worth another question,&lt;/p&gt;

&lt;p&gt;All three approaches though (Nearest Neighbor, Matrix Factorization and RBMs) never we are able to reach to goal set by Netflix, which let to the development of more or less clever methods to combine their predictions, which finally was enough to get to the point Netflix set out. &lt;/p&gt;

&lt;p&gt;These are all somewhat old approaches to the problem, their might be newer ones. However, the most recent machine learning hype, i.e. Deep Learning has so far not given many possible solutions although dominating in other fieds.&lt;/p&gt;
', NULL, NULL, '''/dl/diploma_ott.pdf'':87C ''/dl/diploma_ott.pdf&lt;/a&gt;'':92C ''/p'':93C,129C,179C,218C ''/~hinton/absps/netflixicml.pdf)'':110C ''/~simon/journal/20061211.html'':54C ''/~simon/journal/20061211.html&lt;/a&gt;'':59C ''abl'':144C ''although'':213C ''anoth'':127C ''answer'':117C ''approach'':12C,133C,186C ''bad'':76C ''becam'':35C ''beyond'':115C ''big'':38C ''bit'':74C ''boltzmann'':102C ''clear'':32C ''clever'':161C ''combin'':164C ''consid'':80C ''crowd'':96C ''cs.utoronto.ca'':109C ''cs.utoronto.ca/~hinton/absps/netflixicml.pdf)'':108C ''dataset'':22C ''deep'':203C ''describ'':43C ''develop'':156C ''domin'':214C ''e.g'':107C ''english'':77C ''enough'':170C ''everybodi'':11C ''explain'':119C ''factor'':34C,138C ''fair'':31C ''far'':207C ''fi'':217C ''final'':168C ''funk'':46C ''get'':172C ''given'':209C ''goal'':148C ''howev'':195C ''href'':51C,84C ''hype'':201C ''i.e'':202C ''interest'':64C ''intuit'':28C ''journal'':49C ''learn'':200C,204C ''less'':160C ''let'':153C ''machin'':103C,199C ''mani'':210C ''matrix'':33C,137C ''may'':79C ''mayb'':125C ''method'':26C,162C ''might'':191C ''mind'':72C ''movielens'':21C ''nearest'':24C,135C ''neighbor'':25C,136C ''netflix'':15C,151C,176C ''never'':141C ''newer'':193C ''next'':37C ''nice'':42C ''nofollow'':56C,89C ''old'':185C ''one'':194C ''origin'':10C ''p'':1C,94C,130C,180C ''particular'':123C ''patrick-ott.de'':86C,91C ''patrick-ott.de/dl/diploma_ott.pdf'':85C ''patrick-ott.de/dl/diploma_ott.pdf&lt;/a&gt;'':90C ''plenti'':4C ''point'':175C ''possibl'':211C ''predict'':166C ''prize'':16C ''problem'':189C ''question'':128C ''quit'':17C ''rbms'':140C ''reach'':146C ''realiz'':8C ''recent'':198C ''rel'':55C,88C ''restrict'':101C ''set'':149C,177C ''sifter.org'':53C,58C ''sifter.org/~simon/journal/20061211.html'':52C ''sifter.org/~simon/journal/20061211.html&lt;/a&gt;'':57C ''similar'':18C ''simon'':45C ''solut'':212C ''somewhat'':184C ''task'':106C,124C ''thesi'':82C ''thing'':39C ''think'':112C ''though'':134C ''three'':132C ''topic'':68C ''two'':67C ''way'':6C ''went'':98C ''worth'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10140, 1, '2016-02-08 11:36:29.633', 2, 29, 'Orange 3 Heatmap clustering under the hood', '&lt;p&gt;I have recently used the heatmap widget in Orange 3. All the documentation says is "Clustering (clusters data by similarity)". Is this using hierarchical or k-means or some other type of clustering? &lt;/p&gt;

&lt;p&gt;On that note, is there a way to look at the code being run by all the widgets to see whats going on under the hood? It would be nice if after you finish the workflow you would get a file with the script run to perform the analysis.  &lt;/p&gt;
', 'orange', NULL, '''/p'':44C,95C ''3'':2A,19C ''analysi'':94C ''cluster'':4A,25C,26C,43C ''code'':57C ''data'':27C ''document'':22C ''file'':86C ''finish'':79C ''get'':84C ''go'':67C ''heatmap'':3A,15C ''hierarch'':33C ''hood'':7A,71C ''k'':36C ''k-mean'':35C ''look'':54C ''mean'':37C ''nice'':75C ''note'':48C ''orang'':1A,8B,18C ''p'':9C,45C ''perform'':92C ''recent'':12C ''run'':59C,90C ''say'':23C ''script'':89C ''see'':65C ''similar'':29C ''type'':41C ''use'':13C,32C ''way'':52C ''what'':66C ''widget'':16C,63C ''workflow'':81C ''would'':73C,83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10119, 1, '2016-02-07 12:42:49.03', 2, 18, 'differences between LSQR and FTRL when working with very sparse data', '&lt;p&gt;I have a 2M instances dataset with millions of very very sparse dummy variables created using the &lt;code&gt;hashing trick&lt;/code&gt; = &lt;code&gt;hash(orig_feature_name + orig_feature_value)=1&lt;/code&gt;. Note that the data is sparse both on rows (every instance has only a limited &amp;lt;100 features=1) and on columns (most features are relevant only to very few instances &amp;lt; 1%) &lt;/p&gt;

&lt;p&gt;I discovered that in such sparse scenarios the &lt;code&gt;follow-the-regularized-leader FTRL proximal gradient descent&lt;/code&gt; is very popular:
&lt;a href="http://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf" rel="nofollow"&gt;paper&lt;/a&gt;, &lt;a href="https://github.com/mlandry22/kaggle/blob/master/modern_logistic_regression/fast_solution_v3.py" rel="nofollow"&gt;reference implementation&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;But I''m not sure why shouldn''t I prefer a &lt;code&gt;batch&lt;/code&gt; gradient descent algorithm? FTRL for all its merits is still an &lt;code&gt;online-learning&lt;/code&gt; algorithm that sees one instance at a time. &lt;/p&gt;

&lt;p&gt;So what are the advantages and disadvantages of using FTRL vs. a well known sparse least squares algorithm such as &lt;code&gt;LSQR&lt;/code&gt; (&lt;a href="https://web.stanford.edu/class/cme324/paige-saunders2.pdf" rel="nofollow"&gt;paper&lt;/a&gt;, &lt;a href="https://github.com/scipy/scipy/blob/master/scipy/sparse/linalg/isolve/lsqr.py" rel="nofollow"&gt;reference implementation&lt;/a&gt;)?&lt;/p&gt;

&lt;p&gt;My intuition is that if possible to use all the data for each iteration, we should do it, but I''m not sure... &lt;/p&gt;
', 'linear-regression  gradient-descent  online-learning', NULL, '''/a'':118C,128C,202C,212C ''/class/cme324/paige-saunders2.pdf'':198C ''/code'':42C,52C,106C,144C,160C,193C ''/mlandry22/kaggle/blob/master/modern_logistic_regression/fast_solution_v3.py'':123C ''/p'':86C,129C,169C,213C,238C ''/scipy/scipy/blob/master/scipy/sparse/linalg/isolve/lsqr.py'':207C ''/~dsculley/papers/ad-click-prediction.pdf'':114C ''1'':51C,71C,85C ''100'':69C ''2m'':25C ''advantag'':175C ''algorithm'':147C,161C,188C ''batch'':143C ''code'':39C,43C,96C,142C,156C,191C ''column'':74C ''creat'':36C ''data'':11A,56C,225C ''dataset'':27C ''descent'':17B,105C,146C ''differ'':1A ''disadvantag'':177C ''discov'':89C ''dummi'':34C ''everi'':62C ''featur'':46C,49C,70C,76C ''follow'':98C ''follow-the-regularized-lead'':97C ''ftrl'':5A,102C,148C,180C ''github.com'':122C,206C ''github.com/mlandry22/kaggle/blob/master/modern_logistic_regression/fast_solution_v3.py'':121C ''github.com/scipy/scipy/blob/master/scipy/sparse/linalg/isolve/lsqr.py'':205C ''gradient'':16B,104C,145C ''gradient-desc'':15B ''hash'':40C,44C ''href'':111C,120C,195C,204C ''implement'':127C,211C ''instanc'':26C,63C,83C,165C ''intuit'':216C ''iter'':228C ''known'':184C ''leader'':101C ''learn'':20B,159C ''least'':186C ''limit'':67C ''linear'':13B ''linear-regress'':12B ''lsqr'':3A,192C ''lt'':68C,84C ''m'':133C,235C ''merit'':152C ''million'':29C ''name'':47C ''nofollow'':116C,125C,200C,209C ''note'':53C ''one'':164C ''onlin'':19B,158C ''online-learn'':18B,157C ''orig'':45C,48C ''p'':21C,87C,130C,170C,214C ''paper'':117C,201C ''popular'':109C ''possibl'':220C ''prefer'':140C ''proxim'':103C ''refer'':126C,210C ''regress'':14B ''regular'':100C ''rel'':115C,124C,199C,208C ''relev'':78C ''row'':61C ''scenario'':94C ''see'':163C ''shouldn'':137C ''spars'':10A,33C,58C,93C,185C ''squar'':187C ''still'':154C ''sure'':135C,237C ''time'':168C ''trick'':41C ''use'':37C,179C,222C ''valu'':50C ''variabl'':35C ''vs'':181C ''web.stanford.edu'':197C ''web.stanford.edu/class/cme324/paige-saunders2.pdf'':196C ''well'':183C ''work'':7A ''www.eecs.tufts.edu'':113C ''www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf'':112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10121, 1, '2016-02-07 16:25:39.26', 1, 29, 'features'' range in logistic regression', '&lt;p&gt;I use logistic regression. We know that it is a supervised method and needs calculated feature values both in training and test data. There are six features. Although the functions produce these featuresâ values are different and their maximum value can be 1, there are four features (both in training and test data) that have very low values. e.g. they range between 0 and 0.1 and are never 1, even more than 0.1!!!. Thus these featuresâ values are very close to each other. Other features are distributed normally (they range between 0 and 0.9). So the difference between these two kinds of features is high, I think this causes trouble in learning process for logistic regression. Am I right?! Does it need any transforming/normalizing these features? Any help would be highly appreciated.&lt;/p&gt;
', 'machine-learning  classification  logistic-regression  feature-selection  normalization', NULL, '''/p'':150C ''0'':80C,109C ''0.1'':82C,90C ''0.9'':111C ''1'':60C,86C ''although'':45C ''appreci'':149C ''calcul'':32C ''caus'':126C ''classif'':9B ''close'':97C ''data'':40C,70C ''differ'':53C,114C ''distribut'':104C ''e.g'':76C ''even'':87C ''featur'':1A,14B,33C,44C,64C,102C,120C,143C ''feature-select'':13B ''featuresâ'':50C,93C ''four'':63C ''function'':47C ''help'':145C ''high'':122C,148C ''kind'':118C ''know'':23C ''learn'':8B,129C ''logist'':4A,11B,20C,132C ''logistic-regress'':10B ''low'':74C ''machin'':7B ''machine-learn'':6B ''maximum'':56C ''method'':29C ''need'':31C,139C ''never'':85C ''normal'':16B,105C ''p'':17C ''process'':130C ''produc'':48C ''rang'':2A,78C,107C ''regress'':5A,12B,21C,133C ''right'':136C ''select'':15B ''six'':43C ''supervis'':28C ''test'':39C,69C ''think'':124C ''thus'':91C ''train'':37C,67C ''transforming/normalizing'':141C ''troubl'':127C ''two'':117C ''use'':19C ''valu'':34C,51C,57C,75C,94C ''would'':146C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10122, 2, '2016-02-07 17:38:05.697', 1, NULL, NULL, '&lt;p&gt;You do not &lt;em&gt;have&lt;/em&gt; to normalise features used in logistic regression, but sometimes it can help.&lt;/p&gt;

&lt;p&gt;You should normalise features used in logistic regression, if you are using a gradient-based optimiser (e.g. SGD) to find the optimum weights. That is because the optimiser will perform better when partial derivatives of the cost function are of similar magnitude in each direction. When the derivatives vary too much, you will need a lower learning rate to compensate (making learning slower, and more likely to get stuck) or the optimiser will not converge - it may oscillate or start to diverge instead.&lt;/p&gt;
', NULL, NULL, '''/em'':7C ''/p'':20C,106C ''base'':37C ''better'':53C ''compens'':82C ''converg'':97C ''cost'':59C ''deriv'':56C,70C ''direct'':67C ''diverg'':104C ''e.g'':39C ''em'':5C ''featur'':10C,25C ''find'':42C ''function'':60C ''get'':90C ''gradient'':36C ''gradient-bas'':35C ''help'':19C ''instead'':105C ''learn'':79C,84C ''like'':88C ''logist'':13C,28C ''lower'':78C ''magnitud'':64C ''make'':83C ''may'':99C ''much'':73C ''need'':76C ''normalis'':9C,24C ''optimis'':38C,50C,94C ''optimum'':44C ''oscil'':100C ''p'':1C,21C ''partial'':55C ''perform'':52C ''rate'':80C ''regress'':14C,29C ''sgd'':40C ''similar'':63C ''slower'':85C ''sometim'':16C ''start'':102C ''stuck'':91C ''use'':11C,26C,33C ''vari'':71C ''weight'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10126, 2, '2016-02-07 20:48:02.95', 4, NULL, NULL, '&lt;p&gt;You might be interested in reading the following paper by researchers of Microsoft Research:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: &lt;a href="http://arxiv.org/abs/1512.03385" rel="nofollow"&gt;Deep Residual Learning for Image Recognition&lt;/a&gt; on arxiv, 2015.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;They had similar problems as you had:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;When deeper networks are able to start converging, a
  degradation problem has been exposed: with the network
  depth increasing, accuracy gets saturated (which might be
  unsurprising) and then degrades rapidly. &lt;strong&gt;Unexpectedly,
  such degradation is not caused by overfitting, and adding
  more layers to a suitably deep model leads to higher training
  error&lt;/strong&gt;, as reported in [11, 42] and thoroughly verified by
  our experiments.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;To solve the problem, they have made use of a skip architecture. With that, they trained very deep networks (1202 layers) and achieved the best result in the ILSVRC 2015 challenge.&lt;/p&gt;
', NULL, NULL, '''/a'':40C ''/abs/1512.03385'':31C ''/blockquote'':45C,123C ''/p'':16C,44C,54C,122C,156C ''/strong'':110C ''11'':114C ''1202'':144C ''2015'':43C,154C ''42'':115C ''abl'':61C ''accuraci'':76C ''achiev'':147C ''ad'':97C ''architectur'':136C ''arxiv'':42C ''arxiv.org'':30C ''arxiv.org/abs/1512.03385'':29C ''best'':149C ''blockquot'':17C,55C ''caus'':93C ''challeng'':155C ''converg'':64C ''deep'':34C,103C,142C ''deeper'':58C ''degrad'':66C,85C,90C ''depth'':74C ''error'':109C ''experi'':121C ''expos'':70C ''follow'':9C ''get'':77C ''higher'':107C ''href'':28C ''ilsvrc'':153C ''imag'':38C ''increas'':75C ''interest'':5C ''jian'':25C ''kaim'':19C ''layer'':99C,145C ''lead'':105C ''learn'':36C ''made'':131C ''microsoft'':14C ''might'':3C,80C ''model'':104C ''network'':59C,73C,143C ''nofollow'':33C ''overfit'':95C ''p'':1C,18C,46C,56C,124C ''paper'':10C ''problem'':50C,67C,128C ''rapid'':86C ''read'':7C ''recognit'':39C ''rel'':32C ''ren'':24C ''report'':112C ''research'':12C,15C ''residu'':35C ''result'':150C ''satur'':78C ''shaoq'':23C ''similar'':49C ''skip'':135C ''solv'':126C ''start'':63C ''strong'':87C ''suitabl'':102C ''sun'':26C ''thorough'':117C ''train'':108C,140C ''unexpect'':88C ''unsurpris'':82C ''use'':132C ''verifi'':118C ''xiangyu'':21C ''zhang'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10127, 1, '2016-02-07 20:55:57.947', 3, 38, 'How are non-restricted Boltzmann machines trained?', '&lt;p&gt;Restricted Boltzmann machines are stochastic neural networks. The neurons form a complete bipartite graph of visible units and hidden units. The "restricted" is exactly the bipartite property: There may not be a connection between any two visible units and there may not be a connection between two hidden units.&lt;/p&gt;

&lt;p&gt;Restricted Boltzmann machines are trained with Contrastive Divergence (CD-k, see &lt;a href="https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf" rel="nofollow"&gt;A Practical Guide to Training
Restricted Boltzmann Machines&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;Now I wonder: How are non-restricted Boltzmann Machines trained?&lt;/p&gt;

&lt;p&gt;When I google for "Boltzmann Machine", I only finde RBMs.&lt;/p&gt;
', 'machine-learning  rbm', NULL, '''/a'':92C ''/p'':63C,93C,106C,118C ''/~hinton/absps/guidetr.pdf'':81C ''bipartit'':26C,39C ''boltzmann'':6A,15C,66C,90C,103C,112C ''cd'':74C ''cd-k'':73C ''complet'':25C ''connect'':46C,58C ''contrast'':71C ''diverg'':72C ''exact'':37C ''find'':116C ''form'':23C ''googl'':110C ''graph'':27C ''guid'':86C ''hidden'':32C,61C ''href'':78C ''k'':75C ''learn'':11B ''machin'':7A,10B,16C,67C,91C,104C,113C ''machine-learn'':9B ''may'':42C,54C ''network'':20C ''neural'':19C ''neuron'':22C ''nofollow'':83C ''non'':4A,101C ''non-restrict'':3A,100C ''p'':13C,64C,94C,107C ''practic'':85C ''properti'':40C ''rbm'':12B ''rbms'':117C ''rel'':82C ''restrict'':5A,14C,35C,65C,89C,102C ''see'':76C ''stochast'':18C ''train'':8A,69C,88C,105C ''two'':49C,60C ''unit'':30C,33C,51C,62C ''visibl'':29C,50C ''wonder'':97C ''www.cs.toronto.edu'':80C ''www.cs.toronto.edu/~hinton/absps/guidetr.pdf'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10128, 5, '2016-02-07 20:58:14.257', 0, NULL, NULL, '&lt;p&gt;A restricted Boltzmann maschine (RBM) is a stochastic neural network. RBMs can be stacked to deep networks.&lt;/p&gt;

&lt;h2&gt;See also&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Restricted_Boltzmann_machine" rel="nofollow"&gt;Wikipedia&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.scholarpedia.org/article/Boltzmann_machine" rel="nofollow"&gt;Scholarpedia&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':34C,45C ''/article/boltzmann_machine'':41C ''/h2'':23C ''/li'':35C,46C ''/p'':19C ''/ul'':47C ''/wiki/restricted_boltzmann_machine'':30C ''also'':22C ''boltzmann'':4C ''deep'':17C ''en.wikipedia.org'':29C ''en.wikipedia.org/wiki/restricted_boltzmann_machine'':28C ''h2'':20C ''href'':27C,38C ''li'':25C,36C ''maschin'':5C ''network'':11C,18C ''neural'':10C ''nofollow'':32C,43C ''p'':1C ''rbm'':6C ''rbms'':12C ''rel'':31C,42C ''restrict'':3C ''scholarpedia'':44C ''see'':21C ''stack'':15C ''stochast'':9C ''ul'':24C ''wikipedia'':33C ''www.scholarpedia.org'':40C ''www.scholarpedia.org/article/boltzmann_machine'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10131, 2, '2016-02-07 22:28:31.13', 1, NULL, NULL, '&lt;p&gt;Selecting the discount factor $\gamma$ depends on the problem. As explained by &lt;a href="http://webdocs.cs.ualberta.ca/~sutton/book/ebook/node30.html" rel="nofollow"&gt;Sutton &amp;amp; Barto&lt;/a&gt; the value is always between 0 and 1: $0&amp;lt;=\gamma&amp;lt;=1.0$. If $\gamma=0$ the policy will be greedy, i.e. it will choose the best action only for the current state. And if $\gamma&amp;gt;0$ then (possible) future rewards will be taken into account.
When ï¿¼$\gamma&amp;lt;1$ then the infinite sum is finite as long as the reward sequenceï¿¼ is bounded.&lt;/p&gt;

&lt;p&gt;As also commented in &lt;a href="http://stats.stackexchange.com/a/137595/90930"&gt;this related answers&lt;/a&gt;, with a higher $\gamma$ the policy is optimized for gains further in time, but will take more time to converge.&lt;/p&gt;
', NULL, NULL, '''/a'':24C,105C ''/a/137595/90930'':101C ''/p'':91C,126C ''/~sutton/book/ebook/node30.html'':18C ''0'':30C,33C,40C,62C ''1'':32C,76C ''1.0'':37C ''account'':71C ''action'':52C ''also'':94C ''alway'':28C ''amp'':22C ''answer'':104C ''barto'':23C ''best'':51C ''bound'':90C ''choos'':49C ''comment'':95C ''converg'':125C ''current'':56C ''depend'':7C ''discount'':4C ''explain'':12C ''factor'':5C ''finit'':82C ''futur'':65C ''gain'':115C ''gamma'':6C,35C,39C,60C,74C,109C ''greedi'':45C ''gt'':61C ''higher'':108C ''href'':15C,98C ''i.e'':46C ''infinit'':79C ''long'':84C ''lt'':34C,36C,75C ''nofollow'':20C ''optim'':113C ''p'':1C,92C ''polici'':42C,111C ''possibl'':64C ''problem'':10C ''rel'':19C ''relat'':103C ''reward'':66C,87C ''select'':2C ''sequenceï'':88C ''state'':57C ''stats.stackexchange.com'':100C ''stats.stackexchange.com/a/137595/90930'':99C ''sum'':80C ''sutton'':21C ''take'':121C ''taken'':69C ''time'':118C,123C ''valu'':26C ''webdocs.cs.ualberta.ca'':17C ''webdocs.cs.ualberta.ca/~sutton/book/ebook/node30.html'':16C ''ï'':73C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10133, 2, '2016-02-08 04:53:55.723', 2, NULL, NULL, '&lt;p&gt;&lt;strong&gt;PCA doesn''t really make sense for a time series if it is left as a time series:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;There are times when its useful to &lt;a href="http://faculty.washington.edu/ezivot/econ424/Working%20with%20Time%20Series%20Data%20in%20R.pdf" rel="nofollow"&gt;aggregate time series data into a rectangular representation&lt;/a&gt; in order to gain insight into interesting aspects of the data.  This is a feature engineering where the time series provides lots of the features.  For instance, you can create features for ''minute of hour'', ''hour of day'', ''morning'', ''afternoon'',''evening'', ''day of week'', ''day of month'', ''week of month'', ''week of year'', ''season'', ...  You can even join this data with other data, like historical weather data or economic index, to see how daily temperature or economics affect your phenomenon. In these cases you could do all of this feature engineeting and then use PCA to find the most important features (radar plot) or simply to select the most important orthogonal features from the post PCA mashup (linear transformation). &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Time Series Smothing:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;There are a bunch of different methods to smooth and down-sample time series data. In this case, you are turning the quantized stochastic events and either turning them into regularly spaced data, or taking your already regularly spaced data and downsampling and smoothing it. Smoothing algorithms include Exponentially weighted moving average &lt;a href="https://en.wikipedia.org/wiki/Moving_average" rel="nofollow"&gt;(EWMA)&lt;/a&gt; or Holt-Winters smoothing.  Connor Johnson has a &lt;a href="http://connor-johnson.com/2014/02/01/smoothing-with-exponentially-weighted-moving-averages/" rel="nofollow"&gt;nice blog write-up&lt;/a&gt; on the these.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average" rel="nofollow"&gt;ARIMA&lt;/a&gt; is a more complete algorithm that includes smoothing, de-seasoning, and forecasting. It is very powerful, but &lt;a href="http://people.duke.edu/~rnau/411arim.htm" rel="nofollow"&gt;takes some time to master.&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/2014/02/01/smoothing-with-exponentially-weighted-moving-averages/'':250C ''/a'':46C,236C,258C,272C,303C ''/ezivot/econ424/working%20with%20time%20series%20data%20in%20r.pdf'':35C ''/p'':22C,166C,173C,262C,304C,309C ''/strong'':21C,172C ''/wiki/autoregressive_integrated_moving_average'':268C ''/wiki/moving_average'':232C ''/~rnau/411arim.htm'':295C ''affect'':124C ''afternoon'':86C ''aggreg'':38C ''algorithm'':222C,277C ''alreadi'':212C ''arima'':271C ''aspect'':54C ''averag'':227C ''blog'':254C ''bunch'':178C ''case'':129C,193C ''complet'':276C ''connor'':242C ''connor-johnson.com'':249C ''connor-johnson.com/2014/02/01/smoothing-with-exponentially-weighted-moving-averages/'':248C ''could'':131C ''creat'':76C ''daili'':120C ''data'':41C,57C,106C,109C,113C,190C,208C,215C ''day'':84C,88C,91C ''de'':282C ''de-season'':281C ''differ'':180C ''doesn'':4C ''down-sampl'':185C ''downsampl'':217C ''econom'':115C,123C ''either'':202C ''en.wikipedia.org'':231C,267C ''en.wikipedia.org/wiki/autoregressive_integrated_moving_average'':266C ''en.wikipedia.org/wiki/moving_average'':230C ''engin'':62C ''engineet'':137C ''even'':87C,103C ''event'':200C ''ewma'':235C ''exponenti'':224C ''faculty.washington.edu'':34C ''faculty.washington.edu/ezivot/econ424/working%20with%20time%20series%20data%20in%20r.pdf'':33C ''featur'':61C,71C,77C,136C,147C,158C ''find'':143C ''forecast'':285C ''gain'':50C ''help'':308C ''histor'':111C ''holt'':239C ''holt-wint'':238C ''hope'':306C ''hour'':81C,82C ''href'':32C,229C,247C,265C,292C ''import'':146C,156C ''includ'':223C,279C ''index'':116C ''insight'':51C ''instanc'':73C ''interest'':53C ''johnson'':243C ''join'':104C ''left'':16C ''like'':110C ''linear'':164C ''lot'':68C ''make'':7C ''mashup'':163C ''master'':302C ''method'':181C ''minut'':79C ''month'':93C,96C ''morn'':85C ''move'':226C ''nice'':253C ''nofollow'':37C,234C,252C,270C,297C ''order'':48C ''orthogon'':157C ''p'':1C,23C,167C,174C,263C,305C ''pca'':3C,141C,162C ''people.duke.edu'':294C ''people.duke.edu/~rnau/411arim.htm'':293C ''phenomenon'':126C ''plot'':149C ''post'':161C ''power'':289C ''provid'':67C ''quantiz'':198C ''radar'':148C ''realli'':6C ''rectangular'':44C ''regular'':206C,213C ''rel'':36C,233C,251C,269C,296C ''represent'':45C ''sampl'':187C ''season'':100C,283C ''see'':118C ''select'':153C ''sens'':8C ''seri'':12C,20C,40C,66C,170C,189C ''simpli'':151C ''smooth'':183C,219C,221C,241C,280C ''smoth'':171C ''space'':207C,214C ''stochast'':199C ''strong'':2C,168C ''take'':210C,298C ''temperatur'':121C ''time'':11C,19C,26C,39C,65C,169C,188C,300C ''transform'':165C ''turn'':196C,203C ''use'':29C,140C ''weather'':112C ''week'':90C,94C,97C ''weight'':225C ''winter'':240C ''write'':256C ''write-up'':255C ''year'':99C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10138, 1, '2016-02-08 06:03:16.453', 5, 129, 'Represent time-series data in much compact form', '&lt;p&gt;I have time series data of one month plotted day-wise as
&lt;a href="http://i.stack.imgur.com/tazsC.png"&gt;&lt;img src="http://i.stack.imgur.com/tazsC.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Notice &lt;strong&gt;every day follows a different pattern&lt;/strong&gt;. Now, I want to show this "diversity in pattern" of each day in much compact form in a research paper. What are the different ways/options of representing this in compact form using R.&lt;/p&gt;
', 'r  visualization  ggplot2', NULL, '''/a'':42C ''/p'':43C,87C ''/strong'':53C ''/tazsc.png'':31C,36C ''alt'':37C ''compact'':8A,68C,83C ''data'':5A,18C ''day'':24C,48C,65C ''day-wis'':23C ''descript'':40C ''differ'':51C,77C ''divers'':60C ''enter'':38C ''everi'':47C ''follow'':49C ''form'':9A,69C,84C ''ggplot2'':12B ''href'':28C ''i.stack.imgur.com'':30C,35C ''i.stack.imgur.com/tazsc.png'':29C,34C ''imag'':39C ''img'':32C ''month'':21C ''much'':7A,67C ''notic'':45C ''one'':20C ''p'':13C,44C ''paper'':73C ''pattern'':52C,62C ''plot'':22C ''r'':10B,86C ''repres'':1A,80C ''research'':72C ''seri'':4A,17C ''show'':58C ''src'':33C ''strong'':46C ''time'':3A,16C ''time-seri'':2A ''use'':85C ''visual'':11B ''want'':56C ''ways/options'':78C ''wise'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10139, 1, '2016-02-08 06:38:59.6', 3, 58, 'How exactly dependent variable is expressed in terms of independent variables using Partial Least Square Regression Method?', '&lt;p&gt;I understand the working of NIPALS algorithm but while doing the regression using PLS how exactly the relation between known and unknown is established using Principle Component Analysis.
The idea is clear up to the points both dependent and independent are expressed in form of their principle components. After I am not able to understand. &lt;/p&gt;

&lt;p&gt;PS: I have read text and some papers about it but they are very difficult to understand. This Question may sound a trivial one, but please bear with me. Correct me if I am wrong.&lt;/p&gt;
', 'regression  linear-regression  dimensionality-reduction  supervised-learning', NULL, '''/p'':84C,121C ''abl'':81C ''algorithm'':35C ''analysi'':56C ''bear'':112C ''clear'':60C ''compon'':55C,76C ''correct'':115C ''depend'':3A,66C ''difficult'':100C ''dimension'':23B ''dimensionality-reduct'':22B ''establish'':52C ''exact'':2A,44C ''express'':6A,70C ''form'':72C ''idea'':58C ''independ'':10A,68C ''known'':48C ''learn'':27B ''least'':14A ''linear'':20B ''linear-regress'':19B ''may'':105C ''method'':17A ''nipal'':34C ''one'':109C ''p'':28C,85C ''paper'':93C ''partial'':13A ''pleas'':111C ''pls'':42C ''point'':64C ''principl'':54C,75C ''ps'':86C ''question'':104C ''read'':89C ''reduct'':24B ''regress'':16A,18B,21B,40C ''relat'':46C ''sound'':106C ''squar'':15A ''supervis'':26B ''supervised-learn'':25B ''term'':8A ''text'':90C ''trivial'':108C ''understand'':30C,83C,102C ''unknown'':50C ''use'':12A,41C,53C ''variabl'':4A,11A ''work'':32C ''wrong'':120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10141, 2, '2016-02-08 12:07:51.693', 6, NULL, NULL, '&lt;p&gt;Simulate some data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(ggplot2)
library(purrr)
library(ggthemes)

days &amp;lt;- seq(as.Date("2015-08-01"), as.Date("2015-08-31"), by="1 day")
hours &amp;lt;- sprintf("%02d", 0:23)

map_df(days, function(x) {
  map_df(hours, function(y) {
    data.frame(day=x, hour=y, val=sample(2500, 1), stringsAsFactors=FALSE)
  })
}) -&amp;gt; df
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Check it:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ggplot(df, aes(x=hour, y=val, group=day)) +
  geom_line() +
  facet_wrap(~day) +
  theme_tufte(base_family="Helvetica") +
  labs(x=NULL, y=NULL)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/p6Oj2.png"&gt;&lt;img src="http://i.stack.imgur.com/p6Oj2.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Since you''re only trying to convey the scope of the variation, perhaps use a boxplot of the values of hours across days?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ggplot(df, aes(x=hour, y=val)) +
  geom_boxplot(fill="#2b2b2b", alpha=0.25, width=0.75, size=0.25) +
  scale_x_discrete(expand=c(0,0)) +
  scale_y_continuous(expand=c(0,0)) +
  coord_flip() +
  theme_tufte(base_family="Helvetica") +
  theme(axis.ticks=element_blank()) +
  labs(x=NULL, y=NULL)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/B1EZD.png"&gt;&lt;img src="http://i.stack.imgur.com/B1EZD.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;That can be tweaked to fit into most publication graphics slots and the boxplot shows just how varied each day''s readings are.&lt;/p&gt;

&lt;p&gt;You could also use &lt;code&gt;boxplot.stats&lt;/code&gt; to get the summary data and plot it on a line chart:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(dplyr)
library(tidyr)

bps &amp;lt;- function(x) {
  cnf &amp;lt;- boxplot.stats(x)$conf
  data.frame(as.list(set_names(cnf, c("lwr", "upr"))), mean=mean(x))
}

group_by(df, hour) %&amp;gt;% 
  do(bps(.$val)) %&amp;gt;% 
  ggplot(aes(x=hour, y=mean, ymin=lwr, ymax=upr, group=1)) +
  geom_ribbon(fill="#2b2b2b", alpha=0.25) +
  geom_line(size=0.25) +
  theme_tufte(base_family="Helvetica") +
  theme(axis.ticks=element_blank()) +
  labs(x=NULL, y=NULL)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/RMQRa.png"&gt;&lt;img src="http://i.stack.imgur.com/RMQRa.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''-01'':20C ''-08'':19C,23C ''-31'':24C ''/a'':107C,201C,338C ''/b1ezd.png'':190C,195C ''/code'':57C,89C,183C,235C,320C ''/p'':5C,62C,108C,133C,202C,227C,248C,339C ''/p6oj2.png'':96C,101C ''/pre'':58C,90C,184C,321C ''/rmqra.png'':327C,332C ''0'':32C,158C,159C,165C,166C ''0.25'':148C,152C,301C,305C ''0.75'':150C ''02d'':31C ''1'':26C,52C,295C ''2015'':18C,22C ''23'':33C ''2500'':51C ''2b2b2b'':146C,299C ''across'':131C ''ae'':67C,138C,285C ''alpha'':147C,300C ''also'':231C ''alt'':102C,196C,333C ''as.date'':17C,21C ''as.list'':265C ''axis.ticks'':175C,312C ''base'':81C,171C,308C ''blank'':177C,314C ''boxplot'':125C,144C,217C ''boxplot.stats'':234C,261C ''bps'':255C,281C ''c'':157C,164C,269C ''chart'':247C ''check'':60C ''cnf'':259C,268C ''code'':7C,64C,135C,233C,250C ''conf'':263C ''continu'':162C ''convey'':116C ''coord'':167C ''could'':230C ''data'':4C,240C ''data.frame'':44C,264C ''day'':14C,27C,36C,45C,73C,78C,132C,223C ''descript'':105C,199C,336C ''df'':35C,40C,56C,66C,137C,277C ''discret'':155C ''dplyr'':252C ''element'':176C,313C ''enter'':103C,197C,334C ''expand'':156C,163C ''facet'':76C ''fals'':54C ''famili'':82C,172C,309C ''fill'':145C,298C ''fit'':209C ''flip'':168C ''function'':37C,42C,257C ''geom'':74C,143C,296C,302C ''get'':237C ''ggplot'':65C,136C,284C ''ggplot2'':9C ''ggtheme'':13C ''graphic'':213C ''group'':72C,275C,294C ''gt'':55C,279C,283C ''helvetica'':83C,173C,310C ''hour'':28C,41C,47C,69C,130C,140C,278C,287C ''href'':93C,187C,324C ''i.stack.imgur.com'':95C,100C,189C,194C,326C,331C ''i.stack.imgur.com/b1ezd.png'':188C,193C ''i.stack.imgur.com/p6oj2.png'':94C,99C ''i.stack.imgur.com/rmqra.png'':325C,330C ''imag'':104C,198C,335C ''img'':97C,191C,328C ''lab'':84C,178C,315C ''librari'':8C,10C,12C,251C,253C ''line'':75C,246C,303C ''lt'':15C,29C,256C,260C ''lwr'':270C,291C ''map'':34C,39C ''mean'':272C,273C,289C ''name'':267C ''null'':86C,88C,180C,182C,317C,319C ''p'':1C,59C,91C,109C,185C,203C,228C,322C ''perhap'':122C ''plot'':242C ''pre'':6C,63C,134C,249C ''public'':212C ''purrr'':11C ''re'':112C ''read'':225C ''ribbon'':297C ''sampl'':50C ''scale'':153C,160C ''scope'':118C ''seq'':16C ''set'':266C ''show'':218C ''simul'':2C ''sinc'':110C ''size'':151C,304C ''slot'':214C ''sprintf'':30C ''src'':98C,192C,329C ''stringsasfactor'':53C ''summari'':239C ''theme'':79C,169C,174C,306C,311C ''tidyr'':254C ''tri'':114C ''tuft'':80C,170C,307C ''tweak'':207C ''upr'':271C,293C ''use'':123C,232C ''val'':49C,71C,142C,282C ''valu'':128C ''vari'':221C ''variat'':121C ''width'':149C ''wrap'':77C ''x'':38C,46C,68C,85C,139C,154C,179C,258C,262C,274C,286C,316C ''y'':43C,48C,70C,87C,141C,161C,181C,288C,318C ''ymax'':292C ''ymin'':290C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10142, 1, '2016-02-08 16:49:53.303', 2, 34, 'Hyperparameters in Gaussian Process', '&lt;p&gt;My academical background is in physics and analysis (PDE''s), but now I''am reading about data science. &lt;/p&gt;

&lt;p&gt;I''m reading about &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcess.html#sklearn.gaussian_process.GaussianProcess" rel="nofollow"&gt;Gaussian Process implementation in Sci-Kit Learn&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I could not find a simple explanation about those hyper-parameters theta0, thetaL and thetaU. Can someone explain to me what those parameters are, and how to choose then?&lt;/p&gt;
', 'python  statistics  regression  hyperparameter', NULL, '''/a'':49C ''/p'':28C,50C,82C ''/stable/modules/generated/sklearn.gaussian_process.gaussianprocess.html#sklearn.gaussian_process.gaussianprocess'':38C ''academ'':11C ''analysi'':17C ''background'':12C ''choos'':80C ''could'':53C ''data'':26C ''explain'':70C ''explan'':58C ''find'':55C ''gaussian'':3A,41C ''href'':35C ''hyper'':62C ''hyper-paramet'':61C ''hyperparamet'':1A,8B ''implement'':43C ''kit'':47C ''learn'':48C ''m'':31C ''nofollow'':40C ''p'':9C,29C,51C ''paramet'':63C,75C ''pde'':18C ''physic'':15C ''process'':4A,42C ''python'':5B ''read'':24C,32C ''regress'':7B ''rel'':39C ''sci'':46C ''sci-kit'':45C ''scienc'':27C ''scikit-learn.org'':37C ''scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.gaussianprocess.html#sklearn.gaussian_process.gaussianprocess'':36C ''simpl'':57C ''someon'':69C ''statist'':6B ''theta0'':64C ''thetal'':65C ''thetau'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10143, 1, '2016-02-08 16:58:47.657', 4, 119, 'Python Machine Learning/Data Science Project Structure', '&lt;p&gt;I''m looking for information on how should a Python Machine Learning project be organized. For Python usual projects there is &lt;a href="http://cookiecutter.readthedocs.org/en/latest/" rel="nofollow"&gt;Cookiecutter&lt;/a&gt; and for R &lt;a href="http://projecttemplate.net/getting_started.html" rel="nofollow"&gt;ProjectTemplate&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;This is my current folder structure, but I''m mixing Jupyter Notebooks with actual Python code and it does not seems very clear.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;.
âââ cache
âââ data
âââ my_module
âââ logs
âââ notebooks
âââ scripts
âââ snippets
âââ tools
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I work in the scripts folder and currently adding all the functions in files under my_module, but that leads to errors loading data(relative/absolute paths) and other problems.&lt;/p&gt;

&lt;p&gt;I could not find proper &lt;em&gt;best practices&lt;/em&gt; or good examples on this topic besides some kaggle competition solutions and some Notebooks that have all the functions condensed at the start of such Notebook.&lt;/p&gt;
', 'python', NULL, '''/a'':38C,50C ''/code'':112C ''/em'':154C ''/en/latest/'':34C ''/getting_started.html'':46C ''/p'':51C,76C,144C,181C ''/pre'':113C ''actual'':66C ''ad'':123C ''besid'':161C ''best'':152C ''cach'':82C ''clear'':75C ''code'':68C,78C ''competit'':164C ''condens'':174C ''cookiecutt'':37C ''cookiecutter.readthedocs.org'':33C ''cookiecutter.readthedocs.org/en/latest/'':32C ''could'':147C ''current'':56C,122C ''data'':86C,138C ''em'':151C ''error'':136C ''exampl'':157C ''file'':128C ''find'':149C ''folder'':57C,120C ''function'':126C,173C ''good'':156C ''href'':31C,43C ''inform'':13C ''jupyt'':63C ''kaggl'':163C ''lead'':134C ''learn'':20C ''learning/data'':3A ''load'':137C ''log'':95C ''look'':11C ''m'':10C,61C ''machin'':2A,19C ''mix'':62C ''modul'':91C,131C ''nofollow'':36C,48C ''notebook'':64C,99C,168C,180C ''organ'':23C ''p'':8C,52C,114C,145C ''path'':140C ''practic'':153C ''pre'':77C ''problem'':143C ''project'':5A,21C,27C ''projecttempl'':49C ''projecttemplate.net'':45C ''projecttemplate.net/getting_started.html'':44C ''proper'':150C ''python'':1A,7B,18C,25C,67C ''r'':41C ''rel'':35C,47C ''relative/absolute'':139C ''scienc'':4A ''script'':103C,119C ''seem'':73C ''snippet'':107C ''solut'':165C ''start'':177C ''structur'':6A,58C ''tool'':111C ''topic'':160C ''usual'':26C ''work'':116C ''â'':79C,80C,81C,83C,84C,85C,87C,88C,89C,92C,93C,94C,96C,97C,98C,100C,101C,102C,104C,105C,106C,108C,109C,110C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10146, 2, '2016-02-09 05:23:06.453', 0, NULL, NULL, '&lt;p&gt;Firstly, for understanding the Markov switching models, a nice knowledge of Markov models and the way they work. Most importantly, an idea of time series models and how they work, is very important.&lt;/p&gt;

&lt;p&gt;I found &lt;a href="http://homepage.ntu.edu.tw/~ckuan/pdf/Lec-Markov_note_spring%202010.pdf" rel="nofollow"&gt;this tutorial&lt;/a&gt; good enough for getting up to speed with the concept.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://econweb.ucsd.edu/~jhamilto/palgrav1.pdf" rel="nofollow"&gt;This&lt;/a&gt; is another tutorial on a similar application of the switching model, which is the regime switching model.&lt;/p&gt;

&lt;p&gt;The &lt;a href="http://statsmodels.sourceforge.net/" rel="nofollow"&gt;statsmodels&lt;/a&gt; library has a nice support for building the Morkov switching models.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://pages.uoregon.edu/cfulton/posts/mar_hamilton.html" rel="nofollow"&gt;Here&lt;/a&gt; is one simple and quick Python tutorial which uses the statsmodels library.&lt;/p&gt;
', NULL, NULL, '''/a'':48C,69C,96C,118C ''/cfulton/posts/mar_hamilton.html'':114C ''/p'':35C,59C,87C,108C,131C ''/~ckuan/pdf/lec-markov_note_spring%202010.pdf'':43C ''/~jhamilto/palgrav1.pdf'':65C ''anoth'':71C ''applic'':76C ''build'':103C ''concept'':58C ''econweb.ucsd.edu'':64C ''econweb.ucsd.edu/~jhamilto/palgrav1.pdf'':63C ''enough'':50C ''first'':2C ''found'':38C ''get'':52C ''good'':49C ''homepage.ntu.edu.tw'':42C ''homepage.ntu.edu.tw/~ckuan/pdf/lec-markov_note_spring%202010.pdf'':41C ''href'':40C,62C,91C,111C ''idea'':23C ''import'':21C,34C ''knowledg'':11C ''librari'':97C,130C ''markov'':6C,13C ''model'':8C,14C,27C,80C,86C,107C ''morkov'':105C ''nice'':10C,100C ''nofollow'':45C,67C,94C,116C ''one'':120C ''p'':1C,36C,60C,88C,109C ''pages.uoregon.edu'':113C ''pages.uoregon.edu/cfulton/posts/mar_hamilton.html'':112C ''python'':124C ''quick'':123C ''regim'':84C ''rel'':44C,66C,93C,115C ''seri'':26C ''similar'':75C ''simpl'':121C ''speed'':55C ''statsmodel'':95C,129C ''statsmodels.sourceforge.net'':92C ''support'':101C ''switch'':7C,79C,85C,106C ''time'':25C ''tutori'':47C,72C,125C ''understand'':4C ''use'':127C ''way'':17C ''work'':19C,31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10147, 1, '2016-02-09 08:10:30.34', 1, 53, 'How do MS-TDNNs work?', '&lt;p&gt;Multi-State Time Delay Neural Networks (MS-TDNNs) were introduced in&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Haffner, Patrick and Waibel, Alex: &lt;a href="http://isl.anthropomatik.kit.edu/downloads/0135_Kopie_.pdf" rel="nofollow"&gt;Multi-state time delay networks for continuous speech recognition&lt;/a&gt;. In Advances in neural information processing systems, 1992.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;They are an extension of TDNNs. TDNNs are convolutional neural networks for automatic speech recognition (ASR), where the convolution happens over the time.&lt;/p&gt;

&lt;p&gt;The aim of MS-TDNNs seems to be to get rid of the hybrid approach in ASR, where you need dynamic programming / HMMs to chunk the audio stream and then neural networks to recognize the phonemes. Somehow MS-TDNNs seem to do the segmentation as well.&lt;/p&gt;

&lt;p&gt;I don''t understand how. Could somebody please explain it to me?&lt;/p&gt;

&lt;p&gt;(Related side questions: Are MS-TDNNs recurrent networks? Where exactly does the name "multi-state" come from?)&lt;/p&gt;
', 'neuralnetwork', NULL, '''/a'':47C ''/blockquote'':57C ''/downloads/0135_kopie_.pdf'':34C ''/p'':22C,56C,82C,132C,146C,167C ''1992'':55C ''advanc'':49C ''aim'':85C ''alex'':29C ''approach'':99C ''asr'':74C,101C ''audio'':111C ''automat'':71C ''blockquot'':23C ''chunk'':109C ''come'':165C ''continu'':44C ''convolut'':67C,77C ''could'':139C ''delay'':13C,41C ''dynam'':105C ''exact'':158C ''explain'':142C ''extens'':62C ''get'':94C ''haffner'':25C ''happen'':78C ''hmms'':107C ''href'':31C ''hybrid'':98C ''inform'':52C ''introduc'':20C ''isl.anthropomatik.kit.edu'':33C ''isl.anthropomatik.kit.edu/downloads/0135_kopie_.pdf'':32C ''ms'':4A,17C,88C,123C,153C ''ms-tdnns'':3A,16C,87C,122C,152C ''multi'':10C,38C,163C ''multi-st'':9C,37C,162C ''name'':161C ''need'':104C ''network'':15C,42C,69C,116C,156C ''neural'':14C,51C,68C,115C ''neuralnetwork'':7B ''nofollow'':36C ''p'':8C,24C,58C,83C,133C,147C ''patrick'':26C ''phonem'':120C ''pleas'':141C ''process'':53C ''program'':106C ''question'':150C ''recogn'':118C ''recognit'':46C,73C ''recurr'':155C ''rel'':35C ''relat'':148C ''rid'':95C ''seem'':90C,125C ''segment'':129C ''side'':149C ''somebodi'':140C ''somehow'':121C ''speech'':45C,72C ''state'':11C,39C,164C ''stream'':112C ''system'':54C ''tdnns'':5A,18C,64C,65C,89C,124C,154C ''time'':12C,40C,81C ''understand'':137C ''waibel'':28C ''well'':131C ''work'':6A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10149, 2, '2016-02-09 08:53:31.053', 0, NULL, NULL, '&lt;p&gt;You can do &lt;a href="https://en.wikipedia.org/wiki/Topological_sorting" rel="nofollow"&gt;topological sorting&lt;/a&gt;. For example, here is some pythonic pseudocode&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def order(current_neuron, level):
    current_neuron.level = max(level, current_neuron.level)
    for child in sorted(current_neuron.children, key=children level, reverse=True):
        order(child, level + 1)

for input_neuron in input_neurons:
    order(input_neuron, level=0)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;(Take this pseudo code with a grain of salt. This is just what came directly to my mind; it is not tested.)&lt;/p&gt;

&lt;p&gt;Then you can calculate the activations in order of &lt;code&gt;node.level&lt;/code&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':14C ''/code'':59C,97C ''/p'':22C,84C,98C ''/pre'':60C ''/wiki/topological_sorting'':9C ''0'':58C ''1'':47C ''activ'':91C ''calcul'':89C ''came'':75C ''child'':35C,45C ''children'':40C ''code'':24C,65C,95C ''current'':27C ''current_neuron.children'':38C ''current_neuron.level'':30C,33C ''def'':25C ''direct'':76C ''en.wikipedia.org'':8C ''en.wikipedia.org/wiki/topological_sorting'':7C ''exampl'':16C ''grain'':68C ''href'':6C ''input'':49C,52C,55C ''key'':39C ''level'':29C,32C,41C,46C,57C ''max'':31C ''mind'':79C ''neuron'':28C,50C,53C,56C ''node.level'':96C ''nofollow'':11C ''order'':26C,44C,54C,93C ''p'':1C,61C,85C ''pre'':23C ''pseudo'':64C ''pseudocod'':21C ''python'':20C ''rel'':10C ''revers'':42C ''salt'':70C ''sort'':13C,37C ''take'':62C ''test'':83C ''topolog'':12C ''true'':43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10151, 1, '2016-02-09 09:32:54.87', 1, 52, 'What is a Dichotomy?', '&lt;p&gt;I am currently reading:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Stephen Jose Hanson: &lt;a href="http://papers.nips.cc/paper/227-meiosis-networks.pdf" rel="nofollow"&gt;Meiosis Networks&lt;/a&gt;, 1990.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;and I stumbled about this:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;It is possible to precisely characterize the search problem in terms of the resources or degress of freedom in the learning model. If the task the learning system is to perform is classification then the system can be analyzed in terms of its ability to dichotomize stimulus points in feature space.&lt;/p&gt;
  
  &lt;p&gt;&lt;em&gt;Dichotomization Capability: Network Capacity&lt;/em&gt; Using a linear fan-in or hyperplane type neuron we can characterize the degrees of freedom inherent in a network of units with thresholded output. For example, with linear boundaries, consider 4 points, well distributed in a 2-dimensional feature space. There are exactly 14 linearly separable dichotomies that can be formed with the 4 target points. However, there are actually 16 ($2^4$) possible dichotomies of 4 points in 2 dimensions consequently, the number of possible dichotomies or arbitrary categories that are linearly implementable can be thought of as a capacity of the linear network in $k$ dimensions with $n$ examples. &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;What is a "dichonomy" in this case?&lt;/p&gt;

&lt;p&gt;(Side questions: what is a fan-in type neuron?)&lt;/p&gt;
', 'machine-learning  neuralnetwork', NULL, '''/a'':29C ''/blockquote'':32C,205C ''/em'':100C ''/p'':14C,31C,39C,93C,204C,214C,226C ''/paper/227-meiosis-networks.pdf'':24C ''14'':146C ''16'':163C ''1990'':30C ''2'':139C,164C,172C ''4'':133C,156C,165C,169C ''abil'':85C ''actual'':162C ''analyz'':80C ''arbitrari'':181C ''blockquot'':15C,40C ''boundari'':131C ''capabl'':97C ''capac'':99C,193C ''case'':213C ''categori'':182C ''character'':47C,113C ''classif'':74C ''consequ'':174C ''consid'':132C ''current'':12C ''degre'':115C ''degress'':57C ''dichonomi'':210C ''dichotom'':87C,96C ''dichotomi'':4A,149C,167C,179C ''dimens'':173C,200C ''dimension'':140C ''distribut'':136C ''em'':95C ''exact'':145C ''exampl'':128C,203C ''fan'':105C,222C ''fan-in'':104C,221C ''featur'':91C,141C ''form'':153C ''freedom'':59C,117C ''hanson'':19C ''howev'':159C ''href'':21C ''hyperplan'':108C ''implement'':186C ''inher'':118C ''jose'':18C ''k'':199C ''learn'':7B,62C,68C ''linear'':103C,130C,147C,185C,196C ''machin'':6B ''machine-learn'':5B ''meiosi'':27C ''model'':63C ''n'':202C ''network'':28C,98C,121C,197C ''neuralnetwork'':8B ''neuron'':110C,225C ''nofollow'':26C ''number'':176C ''output'':126C ''p'':9C,16C,33C,41C,94C,206C,215C ''papers.nips.cc'':23C ''papers.nips.cc/paper/227-meiosis-networks.pdf'':22C ''perform'':72C ''point'':89C,134C,158C,170C ''possibl'':44C,166C,178C ''precis'':46C ''problem'':50C ''question'':217C ''read'':13C ''rel'':25C ''resourc'':55C ''search'':49C ''separ'':148C ''side'':216C ''space'':92C,142C ''stephen'':17C ''stimulus'':88C ''stumbl'':36C ''system'':69C,77C ''target'':157C ''task'':66C ''term'':52C,82C ''thought'':189C ''threshold'':125C ''type'':109C,224C ''unit'':123C ''use'':101C ''well'':135C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10159, 1, '2016-02-09 15:12:59.843', 2, 53, 'An analysis of analysis?', '&lt;p&gt;This is perhaps not an ideal question by StackExchange rules, but I don''t know where to find a more informed community, so I''ll attempt to make it as specific as possible.&lt;/p&gt;

&lt;p&gt;I''m trying to wrap my brain around ''data analysis'', and I''m looking for attempts to analyze the field, to perhaps create a taxonomy of the analytic process, or in some other way to impose an order on ''analysis'', where analysis might include both descriptive and predictive modeling.&lt;/p&gt;

&lt;p&gt;In the field of data graphics, there are several well known examples: One could look at &lt;em&gt;Visualizing Data&lt;/em&gt; by Cleveland, in which he organizes graphical techniques by dimensionality, or more broadly, at Bertin''s &lt;em&gt;Semiology of Graphics&lt;/em&gt;, or Wilkinson''s &lt;em&gt;The Grammar of Graphics&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;In a related domain (a sub-domain of analysis, actually), Codd''s relational algebra attempts to organize the filtering and summarization of tabular data. (I''m very much focused on tabular data.)&lt;/p&gt;

&lt;p&gt;What I''m asking for are pointers to a proposed "grammar" of analytics.   &lt;/p&gt;
', 'statistics', NULL, '''/em'':112C,133C,142C ''/p'':40C,91C,143C,178C,193C ''actual'':155C ''algebra'':159C ''analysi'':2A,4A,51C,81C,83C,154C ''analyt'':69C,192C ''analyz'':59C ''around'':49C ''ask'':183C ''attempt'':32C,57C,160C ''bertin'':127C ''brain'':48C ''broad'':125C ''cleveland'':114C ''codd'':156C ''communiti'':28C ''could'':106C ''creat'':64C ''data'':50C,97C,111C,169C,177C ''descript'':87C ''dimension'':122C ''domain'':148C,152C ''em'':109C,129C,137C ''exampl'':104C ''field'':61C,95C ''filter'':164C ''find'':24C ''focus'':174C ''grammar'':139C,190C ''graphic'':98C,119C,132C,141C ''ideal'':12C ''impos'':77C ''includ'':85C ''inform'':27C ''know'':21C ''known'':103C ''ll'':31C ''look'':55C,107C ''m'':43C,54C,171C,182C ''make'':34C ''might'':84C ''model'':90C ''much'':173C ''one'':105C ''order'':79C ''organ'':118C,162C ''p'':6C,41C,92C,144C,179C ''perhap'':9C,63C ''pointer'':186C ''possibl'':39C ''predict'':89C ''process'':70C ''propos'':189C ''question'':13C ''relat'':147C,158C ''rule'':16C ''semiolog'':130C ''sever'':101C ''specif'':37C ''stackexchang'':15C ''statist'':5B ''sub'':151C ''sub-domain'':150C ''summar'':166C ''tabular'':168C,176C ''taxonomi'':66C ''techniqu'':120C ''tri'':44C ''visual'':110C ''way'':75C ''well'':102C ''wilkinson'':135C ''wrap'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10152, 2, '2016-02-09 09:32:54.87', 0, NULL, NULL, '&lt;p&gt;It seems to be about classification with 2 classes. To understand where the 14 comes from, just try all cases of 4 points being in one of two classes:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;#!/usr/bin/env python

"""Print all cases of 4 points being in one of two classes in 2D."""


def print_pattern(pattern):
    """Print four points in 2D."""
    print("%s\t%s" % (pattern[0], pattern[1]))
    print("%s\t%s" % (pattern[2], pattern[3]))


get_bin = lambda x, n: format(x, ''b'').zfill(n)

# 16 possible cases how 4 points can belong to 2 classes
for i in range(16):
    bin_ = get_bin(i, 4)
    print_pattern(bin_)
    print("-"*60)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;which gives:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;0    0
0    0
------------------------------------------------------------
0    0
0    1
------------------------------------------------------------
0    0
1    0
------------------------------------------------------------
0    0
1    1
------------------------------------------------------------
0    1
0    0
------------------------------------------------------------
0    1
0    1
------------------------------------------------------------
0    1        --- Not linearly seperable
1    0
------------------------------------------------------------
0    1
1    1
------------------------------------------------------------
1    0
0    0
------------------------------------------------------------
1    0        --- Not linearly seperable
0    1
------------------------------------------------------------
1    0
1    0
------------------------------------------------------------
1    0
1    1
------------------------------------------------------------
1    1
0    0
------------------------------------------------------------
1    1
0    1
------------------------------------------------------------
1    1
1    0
------------------------------------------------------------
1    1
1    1
------------------------------------------------------------
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m still not quite sure what a dichtonomy is, but this seems to be part of the answer.&lt;/p&gt;
', NULL, NULL, '''/code'':111C,189C ''/p'':31C,116C,211C ''/pre'':112C,190C ''/usr/bin/env'':34C ''0'':64C,119C,120C,121C,122C,123C,124C,125C,127C,128C,130C,131C,132C,135C,137C,138C,139C,141C,143C,149C,150C,155C,156C,157C,159C,163C,166C,168C,170C,175C,176C,179C,184C ''1'':66C,126C,129C,133C,134C,136C,140C,142C,144C,148C,151C,152C,153C,154C,158C,164C,165C,167C,169C,171C,172C,173C,174C,177C,178C,180C,181C,182C,183C,185C,186C,187C,188C ''14'':15C ''16'':85C,100C ''2'':9C,72C,94C ''2d'':49C,58C ''3'':74C ''4'':23C,40C,89C,105C ''60'':110C ''answer'':210C ''b'':82C ''belong'':92C ''bin'':76C,101C,103C,108C ''case'':21C,38C,87C ''class'':10C,30C,47C,95C ''classif'':7C ''code'':33C,118C ''come'':16C ''def'':50C ''dichtonomi'':200C ''format'':80C ''four'':55C ''get'':75C,102C ''give'':115C ''lambda'':77C ''linear'':146C,161C ''m'':193C ''n'':79C,84C ''one'':27C,44C ''p'':1C,113C,191C ''part'':207C ''pattern'':52C,53C,63C,65C,71C,73C,107C ''point'':24C,41C,56C,90C ''possibl'':86C ''pre'':32C,117C ''print'':36C,51C,54C,59C,67C,106C,109C ''python'':35C ''quit'':196C ''rang'':99C ''seem'':3C,204C ''seper'':147C,162C ''still'':194C ''sure'':197C ''tri'':19C ''two'':29C,46C ''understand'':12C ''x'':78C,81C ''zfill'':83C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10153, 1, '2016-02-09 11:16:43.31', 1, 15, 'Generating a set of Rules that defines a Group of users ( Multivariate Analysis )', '&lt;p&gt;I am working on a Multivariate Analysis on my dataset, I have a large dataset of 50k users let''s call it (Loyal Users) with various numerical attributes +13 tracking the user''s activity and events within a timeframe.
I want to come up with Set of rules to define this group of users so that i can come up with a common pattern.
This is an example of the data that i am working on&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;username        Like     Share           Select     Send  Verify  Register

Alex            524      329             203        787    1       1
Kristina        197      186             286        120    0       1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here is an example of rules that i want to make : 
If Share &gt; 23 and Verify =1 and Like &gt;120  then Loyal User&lt;/p&gt;

&lt;p&gt;would that be possible after doing a principal components analysis (PCA) and linear discriminant analysis (LDA) ? And how can i do that ? &lt;/p&gt;

&lt;p&gt;Any ideas would be great.&lt;/p&gt;
', 'machine-learning  r  data-mining  predictive-modeling', NULL, '''+13'':53C ''/code'':125C ''/p'':101C,151C,175C,182C ''/pre'':126C ''0'':123C ''1'':116C,117C,124C,144C ''120'':122C,147C ''186'':120C ''197'':119C ''203'':114C ''23'':141C ''286'':121C ''329'':113C ''50k'':41C ''524'':112C ''787'':115C ''activ'':58C ''alex'':111C ''analysi'':13A,31C,162C,167C ''attribut'':52C ''call'':45C ''code'':103C ''come'':67C,83C ''common'':87C ''compon'':161C ''data'':19B,95C ''data-min'':18B ''dataset'':34C,39C ''defin'':7A,74C ''discrimin'':166C ''event'':60C ''exampl'':92C,131C ''generat'':1A ''great'':181C ''group'':9A,76C ''idea'':178C ''kristina'':118C ''larg'':38C ''lda'':168C ''learn'':16B ''let'':43C ''like'':105C,146C ''linear'':165C ''loyal'':47C,149C ''machin'':15B ''machine-learn'':14B ''make'':138C ''mine'':20B ''model'':23B ''multivari'':12A,30C ''numer'':51C ''p'':24C,127C,152C,176C ''pattern'':88C ''pca'':163C ''possibl'':156C ''pre'':102C ''predict'':22B ''predictive-model'':21B ''princip'':160C ''r'':17B ''regist'':110C ''rule'':5A,72C,133C ''select'':107C ''send'':108C ''set'':3A,70C ''share'':106C,140C ''timefram'':63C ''track'':54C ''user'':11A,42C,48C,56C,78C,150C ''usernam'':104C ''various'':50C ''verifi'':109C,143C ''want'':65C,136C ''within'':61C ''work'':27C,99C ''would'':153C,179C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10154, 1, '2016-02-09 11:22:31.957', 1, 28, 'How to predict Estimated Time for Arrival given only trajectory data and time?', '&lt;p&gt;I have data of latitude, longitude and timestamp. I am trying to build a graph based on pincodes (in India) (equivalent to zipcode). Based on this graph and trajectory data that I have, I want to predict ETA given source, destination and time for dispatch. &lt;/p&gt;

&lt;p&gt;I am very new to this field, So I dont know how to handle and make structures using this data. &lt;/p&gt;

&lt;p&gt;I have already gone through this question
&lt;a href="http://datascience.stackexchange.com/questions/5802/learning-time-of-arrival-eta-from-historical-location-data-of-vehicle"&gt;Learning time of arrival (ETA) from historical location data of vehicle&lt;/a&gt; but it is not very clear to me. Any kind of help/suggestions is appreciated. &lt;/p&gt;

&lt;p&gt;Thanks in advance.&lt;/p&gt;

&lt;p&gt;Edit : Pincodes are equivalent to zipcodes.&lt;/p&gt;

&lt;p&gt;Graph : Nodes of the graph will be pincode boundaries and if these pincodes share boundary then there will be edge between them.&lt;/p&gt;

&lt;p&gt;Trajectory : It is the data collected from GPS when vehicle is travelling on the road. Data is in format (Latitude, Longitude, TimeStamp, vehicleId)&lt;/p&gt;
', 'predictive-modeling  graphs  linear-regression', NULL, '''/a'':114C ''/p'':67C,89C,129C,134C,142C,166C,191C ''/questions/5802/learning-time-of-arrival-eta-from-historical-location-data-of-vehicle'':102C ''advanc'':133C ''alreadi'':93C ''appreci'':128C ''arriv'':7A,106C ''base'':37C,45C ''boundari'':152C,158C ''build'':34C ''clear'':120C ''collect'':173C ''data'':11A,24C,51C,88C,111C,172C,183C ''datascience.stackexchange.com'':101C ''datascience.stackexchange.com/questions/5802/learning-time-of-arrival-eta-from-historical-location-data-of-vehicle'':100C ''destin'':62C ''dispatch'':66C ''dont'':78C ''edg'':163C ''edit'':136C ''equival'':42C,139C ''estim'':4A ''eta'':59C,107C ''field'':75C ''format'':186C ''given'':8A,60C ''gone'':94C ''gps'':175C ''graph'':17B,36C,48C,144C,148C ''handl'':82C ''help/suggestions'':126C ''histor'':109C ''href'':99C ''india'':41C ''kind'':124C ''know'':79C ''latitud'':26C,187C ''learn'':103C ''linear'':19B ''linear-regress'':18B ''locat'':110C ''longitud'':27C,188C ''make'':84C ''model'':16B ''new'':72C ''node'':145C ''p'':21C,68C,90C,130C,135C,143C,167C ''pincod'':39C,137C,151C,156C ''predict'':3A,15B,58C ''predictive-model'':14B ''question'':97C ''regress'':20B ''road'':182C ''share'':157C ''sourc'':61C ''structur'':85C ''thank'':131C ''time'':5A,13A,64C,104C ''timestamp'':29C,189C ''trajectori'':10A,50C,168C ''travel'':179C ''tri'':32C ''use'':86C ''vehicl'':113C,177C ''vehicleid'':190C ''want'':56C ''zipcod'':44C,141C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10157, 2, '2016-02-09 14:22:41.033', 1, NULL, NULL, '&lt;p&gt;So I think you understood that a MS-TDNN has &lt;strong&gt;two parts&lt;/strong&gt;: a conventional &lt;strong&gt;TDNN&lt;/strong&gt; that calculates state probabilities for each frame and kind of a &lt;strong&gt;perceptron&lt;/strong&gt; on top of it linking the states of several frames to a word. The later part is doing the segmentation. &lt;strong&gt;Its connections aren''t trained&lt;/strong&gt;, but just used for running BP and train the TDNN.&lt;/p&gt;

&lt;p&gt;The segmentation is obtained by some other algorithm (I don''t remember exactly, but something like dynamic time warping). In normal DNN-HMM hybrid systems the DNNs are trained separately to predict the correct state for each frame. The &lt;strong&gt;error function is calculated on frame level!&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This is not optimal as we do not care so much about each state being correct, but the final text. &lt;strong&gt;WER is calculated on world level&lt;/strong&gt;. MS-TDNNs try to solve this by having this extra layer that allows passing a word level based error to BP. This is very similar to todays methods called sequence training.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Neither TDNN nor MS-TDNN are recurrent networks.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;If you are interested in RNNs and ASR systems that do not require HMMs I would recommend you to look at CTC objective that does indeed learn the alignment automatically.&lt;/em&gt;&lt;/p&gt;
', NULL, NULL, '''/em'':232C ''/p'':73C,124C,186C,199C,233C ''/strong'':16C,21C,35C,62C,123C,153C,198C ''algorithm'':82C ''align'':230C ''allow'':167C ''aren'':59C ''asr'':209C ''automat'':231C ''base'':172C ''bp'':68C,175C ''calcul'':23C,119C,149C ''call'':183C ''care'':134C ''connect'':58C ''convent'':18C ''correct'':109C,141C ''ctc'':223C ''dnn'':97C ''dnn-hmm'':96C ''dnns'':102C ''dynam'':91C ''em'':201C ''error'':116C,173C ''exact'':87C ''extra'':164C ''final'':144C ''frame'':28C,45C,113C,121C ''function'':117C ''hmm'':98C ''hmms'':215C ''hybrid'':99C ''inde'':227C ''interest'':205C ''kind'':30C ''later'':50C ''layer'':165C ''learn'':228C ''level'':122C,152C,171C ''like'':90C ''link'':40C ''look'':221C ''method'':182C ''ms'':10C,155C,193C ''ms-tdnn'':9C,192C ''ms-tdnns'':154C ''much'':136C ''neither'':189C ''network'':197C ''normal'':95C ''object'':224C ''obtain'':78C ''optim'':129C ''p'':1C,74C,125C,187C,200C ''part'':15C,51C ''pass'':168C ''perceptron'':34C ''predict'':107C ''probabl'':25C ''recommend'':218C ''recurr'':196C ''rememb'':86C ''requir'':214C ''rnns'':207C ''run'':67C ''segment'':55C,76C ''separ'':105C ''sequenc'':184C ''sever'':44C ''similar'':179C ''solv'':159C ''someth'':89C ''state'':24C,42C,110C,139C ''strong'':13C,19C,33C,56C,115C,146C,188C ''system'':100C,210C ''tdnn'':11C,20C,72C,190C,194C ''tdnns'':156C ''text'':145C ''think'':4C ''time'':92C ''today'':181C ''top'':37C ''train'':61C,70C,104C,185C ''tri'':157C ''two'':14C ''understood'':6C ''use'':65C ''warp'':93C ''wer'':147C ''word'':48C,170C ''world'':151C ''would'':217C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10162, 1, '2016-02-09 16:29:52.363', 3, 33, 'Knn distance plot for determining eps of DBSCAN', '&lt;p&gt;I would like to use the knn distance plot to be able to figure out which eps value should I choose for the DBSCAN algorithm.
Based on &lt;a href="http://www.sthda.com/english/wiki/dbscan-density-based-clustering-for-discovering-clusters-in-large-datasets-with-noise-unsupervised-machine-learning" rel="nofollow"&gt;this&lt;/a&gt; page:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The idea is to calculate, the average of the distances of every point
  to its k nearest neighbors.  The value of k will be specified by the
  user and corresponds to MinPts. Next, these k-distances are plotted in
  an ascending order. The aim is to determine the âkneeâ, which
  corresponds to the optimal eps parameter.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Using python with numpy/sklearn, I have the following points, with the following distance for 6-knn:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
nbrs = NearestNeighbors(n_neighbors=len(X)).fit(X)
distances, indices = nbrs.kneighbors(X)

# Indices

[[0 1 2 3 4 5]
 [1 0 2 3 4 5]
 [2 1 0 3 4 5]
 [3 4 5 0 1 2]
 [4 3 5 0 1 2]
 [5 4 3 0 1 2]]

# Distances
[[ 0.          1.          2.23606798  2.82842712  3.60555128  5.        ]
[ 0.          1.          1.41421356  3.60555128  4.47213595  5.83095189]
[ 0.          1.41421356  2.23606798  5.          5.83095189  7.21110255]
[ 0.          1.          2.23606798  2.82842712  3.60555128  5.        ]
[ 0.          1.          1.41421356  3.60555128  4.47213595  5.83095189]
[ 0.          1.41421356  2.23606798  5.          5.83095189  7.21110255]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;then I computed the average distance:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;distances.mean()
2.9269575028354495
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The problem is I don''t understand how exactly could I represent the same plot as them with distances in y-axis and number of points according to the distances on the x-axis using python.&lt;/p&gt;

&lt;p&gt;Thank for your help.&lt;/p&gt;
', 'python  clustering  parameter-estimation  dbscan', NULL, '''-1'':138C,139C,141C ''-2'':140C,143C ''-3'':142C ''/a'':51C ''/blockquote'':115C ''/code'':236C,250C ''/english/wiki/dbscan-density-based-clustering-for-discovering-clusters-in-large-datasets-with-noise-unsupervised-machine-learning'':47C ''/p'':53C,114C,133C,245C,291C,297C ''/pre'':237C,251C ''0'':163C,170C,177C,184C,190C,196C,200C,206C,212C,218C,224C,230C ''1'':144C,145C,147C,164C,169C,176C,185C,191C,197C,201C,207C,219C,225C ''1.41421356'':208C,213C,226C,231C ''2'':146C,149C,165C,171C,175C,186C,192C,198C ''2.23606798'':202C,214C,220C,232C ''2.82842712'':203C,221C ''2.9269575028354495'':249C ''3'':148C,166C,172C,178C,181C,188C,195C ''3.60555128'':204C,209C,222C,227C ''4'':167C,173C,179C,182C,187C,194C ''4.47213595'':210C,228C ''5'':168C,174C,180C,183C,189C,193C,205C,215C,223C,233C ''5.83095189'':211C,216C,229C,234C ''6'':131C ''7.21110255'':217C,235C ''abl'':27C ''accord'':280C ''aim'':100C ''algorithm'':40C ''ascend'':97C ''averag'':62C,243C ''axi'':275C,288C ''base'':41C ''blockquot'':54C ''calcul'':60C ''choos'':36C ''cluster'':10B ''code'':135C,247C ''comput'':241C ''correspond'':85C,108C ''could'':262C ''dbscan'':8A,14B,39C ''determin'':5A,103C ''distanc'':2A,23C,65C,92C,129C,158C,199C,244C,271C,283C ''distances.mean'':248C ''ep'':6A,32C,112C ''estim'':13B ''everi'':67C ''exact'':261C ''figur'':29C ''fit'':156C ''follow'':124C,128C ''help'':296C ''href'':44C ''idea'':57C ''indic'':159C,162C ''k'':71C,77C,91C ''k-distanc'':90C ''kneeâ'':106C ''knn'':1A,22C,132C ''len'':154C ''like'':18C ''minpt'':87C ''n'':152C ''nbrs'':150C ''nbrs.kneighbors'':160C ''nearest'':72C ''nearestneighbor'':151C ''neighbor'':73C,153C ''next'':88C ''nofollow'':49C ''np.array'':137C ''number'':277C ''numpy/sklearn'':120C ''optim'':111C ''order'':98C ''p'':15C,55C,116C,238C,252C,292C ''page'':52C ''paramet'':12B,113C ''parameter-estim'':11B ''plot'':3A,24C,94C,267C ''point'':68C,125C,279C ''pre'':134C,246C ''problem'':254C ''python'':9B,118C,290C ''rel'':48C ''repres'':264C ''specifi'':80C ''thank'':293C ''understand'':259C ''use'':20C,117C,289C ''user'':83C ''valu'':33C,75C ''would'':17C ''www.sthda.com'':46C ''www.sthda.com/english/wiki/dbscan-density-based-clustering-for-discovering-clusters-in-large-datasets-with-noise-unsupervised-machine-learning'':45C ''x'':136C,155C,157C,161C,287C ''x-axi'':286C ''y'':274C ''y-axi'':273C ''â'':105C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10163, 1, '2016-02-09 16:54:06.513', 0, 9, 'Discrepancy in the final probability calculation in paper Multi-digit Number Recognition from Street View Imagery using DConvNet', '&lt;p&gt;In the paper, ''Goodfellow, I., et al. Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks. ICLR, 2014'', on page 10 there is a table which calculate log(P(S|H)) given log(P(s1,s2,...,sl) and log(P(l))&lt;/p&gt;

&lt;p&gt;From the equation given in page 4 it should be&lt;/p&gt;

&lt;p&gt;log(P(S|H)) = log(P(s1,s2,...,sl) + log(P(l))&lt;/p&gt;

&lt;p&gt;but if you see the first 2 lines in the lower table it is&lt;/p&gt;

&lt;p&gt;log(P(S|H)) = 10*log(P(s1,s2,...,sl) + log(P(l))&lt;/p&gt;

&lt;p&gt;and on third line it is correctly specified as 
log(P(S|H)) = log(P(s1,s2,...,sl) + log(P(l))&lt;/p&gt;

&lt;p&gt;Did I miss anything??
&lt;a href="http://i.stack.imgur.com/8vuym.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/8vuym.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Here is the table of probability (idealized) predicted by the model.
&lt;a href="http://i.stack.imgur.com/V9hoY.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/V9hoY.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'deep-learning  convnet', NULL, '''/8vuym.png'':161C,168C ''/a'':174C,205C ''/p'':71C,83C,97C,113C,128C,151C,175C,206C ''/v9hoy.png'':192C,199C ''10'':50C,119C ''2'':105C ''2014'':47C ''4'':79C ''al'':31C ''alt'':169C,200C ''anyth'':156C ''calcul'':6A,56C ''convnet'':23B ''convolut'':43C ''correct'':136C ''dconvnet'':19A ''deep'':21B,42C ''deep-learn'':20B ''descript'':172C,203C ''digit'':11A,34C ''discrep'':1A ''enter'':170C,201C ''equat'':75C ''et'':30C ''final'':4A ''first'':104C ''given'':61C,76C ''goodfellow'':28C ''h'':60C,88C,118C,142C ''href'':158C,189C ''i.stack.imgur.com'':160C,167C,191C,198C ''i.stack.imgur.com/8vuym.png'':159C,166C ''i.stack.imgur.com/v9hoy.png'':190C,197C ''iclr'':46C ''ideal'':183C ''imag'':171C,202C ''imageri'':17A,40C ''img'':164C,195C ''l'':70C,96C,127C,150C ''learn'':22B ''line'':106C,133C ''log'':57C,62C,68C,85C,89C,94C,115C,120C,125C,139C,143C,148C ''lower'':109C ''miss'':155C ''model'':187C ''multi'':10A,33C ''multi-digit'':9A,32C ''network'':45C ''neural'':44C ''nofollow'':163C,194C ''number'':12A,35C ''p'':24C,58C,63C,69C,72C,84C,86C,90C,95C,98C,114C,116C,121C,126C,129C,140C,144C,149C,152C,176C ''page'':49C,78C ''paper'':8A,27C ''predict'':184C ''probabl'':5A,182C ''recognit'':13A,36C ''rel'':162C,193C ''s1'':64C,91C,122C,145C ''s2'':65C,92C,123C,146C ''see'':102C ''sl'':66C,93C,124C,147C ''specifi'':137C ''src'':165C,196C ''street'':15A,38C ''tabl'':54C,110C,180C ''third'':132C ''use'':18A,41C ''view'':16A,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10164, 1, '2016-02-09 18:19:50.867', 0, 21, 'Support Vector Machine is unable to recognize the digits properly', '&lt;p&gt;I am training a SVM on MNIST Handwritten digits database to make a basic digit recognizer. It is able to recognize digits easily on some standard images I took from a source. I then captured an image using camera of Laptop and took any random image of handwritten digits that the user showd in front of the camera.After some processing of the image, I fed it to SVM,but it couldn''t recognize the digits.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/MM6Xg.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/MM6Xg.png" alt="Correctly recognized digits"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/CUbY6.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/CUbY6.png" alt="Incorrectly recognized digits"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/QkuO4.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/QkuO4.jpg" alt="Test image for incorrectly recognized digits"&gt;&lt;/a&gt;&lt;/p&gt;
', 'machine-learning  classification  image-classification  computer-vision  image-recognition', NULL, '''/a'':118C,137C,159C ''/cuby6.png'':125C,132C ''/mm6xg.png'':106C,113C ''/p'':100C,119C,138C,160C ''/qkuo4.jpg'':144C,151C ''abl'':43C ''alt'':114C,133C,152C ''basic'':38C ''camera'':63C ''camera.after'':82C ''captur'':59C ''classif'':14B,17B ''comput'':19B ''computer-vis'':18B ''correct'':115C ''couldn'':95C ''databas'':34C ''digit'':9A,33C,39C,46C,73C,99C,117C,136C,158C ''easili'':47C ''fed'':89C ''front'':79C ''handwritten'':32C,72C ''href'':103C,122C,141C ''i.stack.imgur.com'':105C,112C,124C,131C,143C,150C ''i.stack.imgur.com/cuby6.png'':123C,130C ''i.stack.imgur.com/mm6xg.png'':104C,111C ''i.stack.imgur.com/qkuo4.jpg'':142C,149C ''imag'':16B,22B,51C,61C,70C,87C,154C ''image-classif'':15B ''image-recognit'':21B ''img'':109C,128C,147C ''incorrect'':134C,156C ''laptop'':65C ''learn'':13B ''machin'':3A,12B ''machine-learn'':11B ''make'':36C ''mnist'':31C ''nofollow'':108C,127C,146C ''p'':24C,101C,120C,139C ''process'':84C ''proper'':10A ''random'':69C ''recogn'':7A,40C,45C,97C,116C,135C,157C ''recognit'':23B ''rel'':107C,126C,145C ''showd'':77C ''sourc'':56C ''src'':110C,129C,148C ''standard'':50C ''support'':1A ''svm'':29C,92C ''test'':153C ''took'':53C,67C ''train'':27C ''unabl'':5A ''use'':62C ''user'':76C ''vector'':2A ''vision'':20B');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10166, 2, '2016-02-09 19:29:40.513', 0, NULL, NULL, '&lt;p&gt;If you are interested in performance comparisons, Soumith Chintala maintains a set of convnet benchmarks that cover several of the already mentioned python frameworks (&lt;a href="https://github.com/tensorflow/tensorflow" rel="nofollow"&gt;TensorFlow&lt;/a&gt;, &lt;a href="https://github.com/pfnet/chainer" rel="nofollow"&gt;chainer&lt;/a&gt;, &lt;a href="https://github.com/NervanaSystems/neon" rel="nofollow"&gt;neon&lt;/a&gt;, &lt;a href="https://github.com/Theano/Theano" rel="nofollow"&gt;Theano&lt;/a&gt;):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://github.com/soumith/convnet-benchmarks" rel="nofollow"&gt;https://github.com/soumith/convnet-benchmarks&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':34C,43C,52C,61C ''/nervanasystems/neon'':48C ''/p'':62C ''/pfnet/chainer'':39C ''/soumith/convnet-benchmarks'':69C ''/soumith/convnet-benchmarks&lt;/a&gt;&lt;/li&gt;'':74C ''/tensorflow/tensorflow'':30C ''/theano/theano'':57C ''/ul'':75C ''alreadi'':22C ''benchmark'':16C ''chainer'':42C ''chintala'':10C ''comparison'':8C ''convnet'':15C ''cover'':18C ''framework'':25C ''github.com'':29C,38C,47C,56C,68C,73C ''github.com/nervanasystems/neon'':46C ''github.com/pfnet/chainer'':37C ''github.com/soumith/convnet-benchmarks'':67C ''github.com/soumith/convnet-benchmarks&lt;/a&gt;&lt;/li&gt;'':72C ''github.com/tensorflow/tensorflow'':28C ''github.com/theano/theano'':55C ''href'':27C,36C,45C,54C,66C ''interest'':5C ''li'':64C ''maintain'':11C ''mention'':23C ''neon'':51C ''nofollow'':32C,41C,50C,59C,71C ''p'':1C ''perform'':7C ''python'':24C ''rel'':31C,40C,49C,58C,70C ''set'':13C ''sever'':19C ''soumith'':9C ''tensorflow'':33C ''theano'':60C ''ul'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10167, 2, '2016-02-09 19:34:15.977', 2, NULL, NULL, '&lt;p&gt;You &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;take the last column of that matrix&lt;/li&gt;
&lt;li&gt;sort descending&lt;/li&gt;
&lt;li&gt;plot index, distance&lt;/li&gt;
&lt;li&gt;hope to see a knee (if the distance does not work well. there might be none)&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':13C,17C,22C,40C ''/ol'':41C ''/p'':3C ''column'':9C ''descend'':16C ''distanc'':21C,31C ''hope'':24C ''index'':20C ''knee'':28C ''last'':8C ''li'':5C,14C,18C,23C ''matrix'':12C ''might'':37C ''none'':39C ''ol'':4C ''p'':1C ''plot'':19C ''see'':26C ''sort'':15C ''take'':6C ''well'':35C ''work'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10174, 2, '2016-02-10 12:06:23.797', 1, NULL, NULL, '&lt;p&gt;Talking about SQL world: from your requirements point of view parallel column based DB Vertica seems to fit well. It is not free of charge though. 
Even more powerful solution if you have good budget is Teradata. &lt;/p&gt;

&lt;p&gt;You''d need to have at least 70 spindles to store your data on HDDs (5TB) on site +/- compression and redundancy. May be add more RAM or SDD if you can cache. That could fit in 3-10 boxes, depending on the processing power you require.&lt;/p&gt;

&lt;p&gt;The rest is a software question on a trade-off between data management, feature set, optimization, your own time spent and the price.
Teradata and Vertica will surely reduce your time spent writing queries and optimizing them, managing data. In case you''d need joins that advantage will be much bigger yet.&lt;/p&gt;
', NULL, NULL, '''-10'':78C ''/p'':39C,87C,142C ''3'':77C ''5tb'':56C ''70'':48C ''add'':64C ''advantag'':136C ''base'':14C ''bigger'':140C ''box'':79C ''budget'':36C ''cach'':72C ''case'':130C ''charg'':26C ''column'':13C ''compress'':59C ''could'':74C ''d'':42C,132C ''data'':53C,101C,128C ''db'':15C ''depend'':80C ''even'':28C ''featur'':103C ''fit'':19C,75C ''free'':24C ''good'':35C ''hdds'':55C ''join'':134C ''least'':47C ''manag'':102C,127C ''may'':62C ''much'':139C ''need'':43C,133C ''optim'':105C,125C ''p'':1C,40C,88C ''parallel'':12C ''point'':9C ''power'':30C,84C ''price'':112C ''process'':83C ''queri'':123C ''question'':94C ''ram'':66C ''reduc'':118C ''redund'':61C ''requir'':8C,86C ''rest'':90C ''sdd'':68C ''seem'':17C ''set'':104C ''site'':58C ''softwar'':93C ''solut'':31C ''spent'':109C,121C ''spindl'':49C ''sql'':4C ''store'':51C ''sure'':117C ''talk'':2C ''teradata'':38C,113C ''though'':27C ''time'':108C,120C ''trade'':98C ''trade-off'':97C ''vertica'':16C,115C ''view'':11C ''well'':20C ''world'':5C ''write'':122C ''yet'':141C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10175, 2, '2016-02-10 12:40:19.98', 1, NULL, NULL, '&lt;p&gt;Where the feature selection finds a place in your pipeline depends on the problem. If you know your data well, you can select features based on this knowledge manually. If you don''t - the experimentation with the models using cross validation may be best. Reducing number of features a priory with some additional technique like chi2 or PCA may actually reduce model accuracy. &lt;/p&gt;

&lt;p&gt;In my experience with text classification with SGD classifier for example leaving all hundred thousands words encoded as  binary features brought better results compared to reducing to a few thousands or hundreds. Training time is actually faster with all features as feature selection is rather slow with my toolset (sklearn) because it is not stochastic like SGD.&lt;/p&gt;

&lt;p&gt;Multicollinearity is something to watch out for, but the feature interpretability might equally be important.&lt;/p&gt;

&lt;p&gt;Then people report getting best result with ensembles of models. Each model capturing a particular part of information space better than the others. That would also preclude you from selecting the features before fitting all models you''d include into your ensemble.&lt;/p&gt;
', NULL, NULL, '''/p'':65C,124C,141C,185C ''accuraci'':64C ''actual'':61C,102C ''addit'':54C ''also'':168C ''base'':26C ''best'':45C,147C ''better'':88C,162C ''binari'':85C ''brought'':87C ''captur'':155C ''chi2'':57C ''classif'':72C ''classifi'':75C ''compar'':90C ''cross'':41C ''d'':180C ''data'':20C ''depend'':12C ''encod'':83C ''ensembl'':150C,184C ''equal'':138C ''exampl'':77C ''experi'':69C ''experiment'':36C ''faster'':103C ''featur'':4C,25C,49C,86C,106C,108C,135C,174C ''find'':6C ''fit'':176C ''get'':146C ''hundr'':80C,98C ''import'':140C ''includ'':181C ''inform'':160C ''interpret'':136C ''know'':18C ''knowledg'':29C ''leav'':78C ''like'':56C,122C ''manual'':30C ''may'':43C,60C ''might'':137C ''model'':39C,63C,152C,154C,178C ''multicollinear'':126C ''number'':47C ''other'':165C ''p'':1C,66C,125C,142C ''part'':158C ''particular'':157C ''pca'':59C ''peopl'':144C ''pipelin'':11C ''place'':8C ''preclud'':169C ''priori'':51C ''problem'':15C ''rather'':111C ''reduc'':46C,62C,92C ''report'':145C ''result'':89C,148C ''select'':5C,24C,109C,172C ''sgd'':74C,123C ''sklearn'':116C ''slow'':112C ''someth'':128C ''space'':161C ''stochast'':121C ''techniqu'':55C ''text'':71C ''thousand'':81C,96C ''time'':100C ''toolset'':115C ''train'':99C ''use'':40C ''valid'':42C ''watch'':130C ''well'':21C ''word'':82C ''would'':167C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10191, 2, '2016-02-10 23:28:08.403', 1, NULL, NULL, '&lt;p&gt;The 1/2 coefficient is merely for convenience; it makes the derivative, which is the function actually being optimized, look nicer. The 1/m is more fundamental; it suggests that we are interested in the &lt;em&gt;mean&lt;/em&gt; squared error. This allows you to make fair comparisons when changing the sample size, and prevents overflow. So called "stochastic" optimizers use a subset of the data set (m'' &amp;lt; m). When you introduce a regularizer (an additive term to the objective function), using the 1/m factor allows you to use the same coefficient for the regularizer regardless of the sample size.&lt;/p&gt;

&lt;p&gt;As for the question of why the square and not simply the difference: don''t you want underestimates to be penalized similarly to overestimates? Squaring eliminates the effect of the sign of the error. Taking the absolute value (L1 norm) does too, but its derivative is undefined at the origin, so it requires more sophistication to use. The L1 norm has its uses, so keep it in mind, and perhaps ask the teacher if (s)he''s going to cover it.&lt;/p&gt;
', NULL, NULL, '''/em'':37C ''/p'':100C,183C ''1/2'':3C ''1/m'':23C,83C ''absolut'':138C ''actual'':17C ''addit'':75C ''allow'':41C,85C ''ask'':172C ''call'':56C ''chang'':48C ''coeffici'':4C,91C ''comparison'':46C ''conveni'':8C ''cover'':181C ''data'':64C ''deriv'':12C,146C ''differ'':114C ''effect'':129C ''elimin'':127C ''em'':35C ''error'':39C,135C ''factor'':84C ''fair'':45C ''function'':16C,80C ''fundament'':26C ''go'':179C ''interest'':32C ''introduc'':71C ''keep'':166C ''l1'':140C,160C ''look'':20C ''lt'':67C ''m'':66C,68C ''make'':10C,44C ''mean'':36C ''mere'':6C ''mind'':169C ''nicer'':21C ''norm'':141C,161C ''object'':79C ''optim'':19C,58C ''origin'':151C ''overestim'':125C ''overflow'':54C ''p'':1C,101C ''penal'':122C ''perhap'':171C ''prevent'':53C ''question'':105C ''regardless'':95C ''regular'':73C,94C ''requir'':154C ''sampl'':50C,98C ''set'':65C ''sign'':132C ''similar'':123C ''simpli'':112C ''size'':51C,99C ''sophist'':156C ''squar'':38C,109C,126C ''stochast'':57C ''subset'':61C ''suggest'':28C ''take'':136C ''teacher'':174C ''term'':76C ''undefin'':148C ''underestim'':119C ''use'':59C,81C,88C,158C,164C ''valu'':139C ''want'':118C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10176, 1, '2016-02-10 14:28:34.993', 0, 12, 'How do you properly connect to Cassandra using RJDBC &amp; the DataStax Cassandra JDBC?', '&lt;p&gt;I''m trying to use RJDBC in conjunction with the DataStax JDBC driver for Cassandra to connect to a Cassandra server. There are some examples of doing this online, but none seem to work or offer little to no explanation.&lt;/p&gt;

&lt;p&gt;I have read through the RJDBC documentation and do have &lt;code&gt;cassandra-driver-jdbc-2.1.6.jar&lt;/code&gt; and its dependencies in a sub-directory of my user home directory. However, I only receive a vague error in the console of RStudio Server when trying to simply load the JDBC driver using RJDBC.&lt;/p&gt;

&lt;p&gt;This is the very simple code snippet I''m trying:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(RJDBC)

#Load in the Cassandra-JDBC diver
cassandra_driver &amp;lt;- JDBC("com.datastax.driver.jdbc.CassandraDriver", "/home/hsatterwhite/lib/cassandra-driver-jdbc-2.1.6.jar")

# Build a connection to a Cassdandra instance
cassandra_connection &amp;lt;- dbConnect(cassandra_driver, "jdbc:cassandra://mycassandraserver/myKeyspace")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The error I receive is &lt;code&gt;Error in .jfindClass(as.character(driverClass)[1]) : class not found&lt;/code&gt;, but as far as I know I am declaring the correct classPath in the first parameter of the RJDBC object.&lt;/p&gt;

&lt;p&gt;Any help or insight would be much appreciated.&lt;/p&gt;
', 'r  dataset  databases  java', NULL, '''/code'':73C,155C,173C ''/home/hsatterwhite/lib/cassandra-driver-jdbc-2.1.6.jar'':139C ''/p'':59C,110C,122C,194C,204C ''/pre'':156C ''1'':169C ''appreci'':203C ''as.character'':167C ''build'':140C ''cassandra'':7A,12A,33C,38C,131C,134C,147C,151C ''cassandra-driver-jdbc-2.1.6.jar'':72C ''cassandra-jdbc'':130C ''cassdandra'':145C ''class'':170C ''classpath'':185C ''code'':71C,117C,124C,163C ''com.datastax.driver.jdbc.cassandradriver'':138C ''conjunct'':26C ''connect'':5A,35C,142C,148C ''consol'':96C ''correct'':184C ''databas'':16B ''dataset'':15B ''datastax'':11A,29C ''dbconnect'':150C ''declar'':182C ''depend'':76C ''directori'':81C,86C ''diver'':133C ''document'':67C ''driver'':31C,107C,135C,152C ''driverclass'':168C ''error'':93C,159C,164C ''exampl'':43C ''explan'':58C ''far'':176C ''first'':188C ''found'':172C ''help'':197C ''home'':85C ''howev'':87C ''insight'':199C ''instanc'':146C ''java'':17B ''jdbc'':13A,30C,106C,132C,137C,153C ''jfindclass'':166C ''know'':179C ''librari'':125C ''littl'':55C ''load'':104C,127C ''lt'':136C,149C ''m'':20C,120C ''much'':202C ''mycassandraserver/mykeyspace'':154C ''none'':49C ''object'':193C ''offer'':54C ''onlin'':47C ''p'':18C,60C,111C,157C,195C ''paramet'':189C ''pre'':123C ''proper'':4A ''r'':14B ''read'':63C ''receiv'':90C,161C ''rjdbc'':9A,24C,66C,109C,126C,192C ''rstudio'':98C ''seem'':50C ''server'':39C,99C ''simpl'':116C ''simpli'':103C ''snippet'':118C ''sub'':80C ''sub-directori'':79C ''tri'':21C,101C,121C ''use'':8A,23C,108C ''user'':84C ''vagu'':92C ''work'':52C ''would'':200C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10177, 1, '2016-02-10 14:50:49.61', 1, 18, 'Spark ALS-WR giving the same recommended items for all users', '&lt;p&gt;We are trying to build a recommendation system for a supermarket with diverse item types (ranging from fast-moving grocery to low-moving electronic items). Some items are purchased more frequently in high volume and some items are purchased only once.&lt;/p&gt;

&lt;p&gt;We have purchase history data of 4 months from 25K+ customers across 30K+ SKU''s from 100+ departments. We ran ALS-WR in Spark to generate recommendations. To our surprise, we are receiving top 15 recommendations for each customer quite generic without much variation.&lt;/p&gt;

&lt;p&gt;We have tried several means to diversify the recommendations as below- 
- computed "rating" = normalized # of purchase 
- computed "rating" = log of # of purchase 
- computed "rating" = 1 (if purchase # &gt; 1) 
- We have used following combination of parameters - lambda = 0.01 to 300, alpha = 5 to 50, rank = 10, 20, 30 and # of iterations = 10, 20 
- preference considered is explicit. &lt;/p&gt;

&lt;p&gt;Do you think that ALS can be used for such heterogeneous data? If yes, what modifications will make the recommendations diverse &amp;amp; personalized?&lt;/p&gt;
', 'machine-learning  recommendation  apache-spark', NULL, '''/p'':64C,111C,169C,194C ''0.01'':149C ''1'':137C,140C ''10'':157C,163C ''100'':82C ''15'':101C ''20'':158C,164C ''25k'':75C ''30'':159C ''300'':151C ''30k'':78C ''4'':72C ''5'':153C ''50'':155C ''across'':77C ''al'':3A,87C,175C ''alpha'':152C ''als-wr'':2A,86C ''amp'':192C ''apach'':18B ''apache-spark'':17B ''build'':25C ''combin'':145C ''comput'':124C,129C,135C ''consid'':166C ''custom'':76C,105C ''data'':70C,182C ''depart'':83C ''divers'':33C,191C ''diversifi'':119C ''electron'':46C ''explicit'':168C ''fast'':39C ''fast-mov'':38C ''follow'':144C ''frequent'':53C ''generat'':92C ''generic'':107C ''give'':5A ''groceri'':41C ''heterogen'':181C ''high'':55C ''histori'':69C ''item'':9A,34C,47C,49C,59C ''iter'':162C ''lambda'':148C ''learn'':15B ''log'':131C ''low'':44C ''low-mov'':43C ''machin'':14B ''machine-learn'':13B ''make'':188C ''mean'':117C ''modif'':186C ''month'':73C ''move'':40C,45C ''much'':109C ''normal'':126C ''p'':20C,65C,112C,170C ''paramet'':147C ''person'':193C ''prefer'':165C ''purchas'':51C,61C,68C,128C,134C,139C ''quit'':106C ''ran'':85C ''rang'':36C ''rank'':156C ''rate'':125C,130C,136C ''receiv'':99C ''recommend'':8A,16B,27C,93C,102C,121C,190C ''sever'':116C ''sku'':79C ''spark'':1A,19B,90C ''supermarket'':31C ''surpris'':96C ''system'':28C ''think'':173C ''top'':100C ''tri'':23C,115C ''type'':35C ''use'':143C,178C ''user'':12A ''variat'':110C ''volum'':56C ''without'':108C ''wr'':4A,88C ''yes'':184C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10178, 1, '2016-02-10 16:37:43.837', 0, 26, 't test or anova', '&lt;p&gt;I have a pandas data frame of the form:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;r1    r2    r3    r4    r5

0    1    12    0    4
1    1    2    9    2
32   5    0    0    0
12   14   3    1    23
0    2    43    5    2
9    3    5    1    1
0    0    0    0    1
1    0    0    0    0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And I want to check if any column: &lt;code&gt;r1, r2, r3, r4, r5&lt;/code&gt; significantly differs from any of the other. Should I do a t test or an anova? And how would I set it up for the computation? &lt;/p&gt;
', 'python  statistics  pandas', NULL, '''/code'':66C,83C ''/p'':18C,110C ''/pre'':67C ''0'':26C,29C,38C,39C,40C,46C,56C,57C,58C,59C,62C,63C,64C,65C ''1'':27C,31C,32C,44C,54C,55C,60C,61C ''12'':28C,41C ''14'':42C ''2'':33C,35C,47C,50C ''23'':45C ''3'':43C,52C ''32'':36C ''4'':30C ''43'':48C ''5'':37C,49C,53C ''9'':34C,51C ''anova'':4A,99C ''check'':73C ''code'':20C,77C ''column'':76C ''comput'':109C ''data'':13C ''differ'':85C ''form'':17C ''frame'':14C ''p'':8C,68C ''panda'':7B,12C ''pre'':19C ''python'':5B ''r1'':21C,78C ''r2'':22C,79C ''r3'':23C,80C ''r4'':24C,81C ''r5'':25C,82C ''set'':104C ''signific'':84C ''statist'':6B ''test'':2A,96C ''want'':71C ''would'':102C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10179, 1, '2016-02-10 16:40:34.743', 1, 33, 'How fit pairwise ranking models in xgBoost?', '&lt;p&gt;As far as I know, to train learning to rank models, you need to have three things in the dataset:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;label or relevance&lt;/li&gt;
&lt;li&gt;group or query id&lt;/li&gt;
&lt;li&gt;feature vector&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;For example, the &lt;a href="http://research.microsoft.com/en-us/um/beijing/projects/letor/letor4dataset.aspx" rel="nofollow"&gt;Microsoft Learning to Rank dataset&lt;/a&gt; uses this format (label, group id and features).&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1 qid:10 1:0.031310 2:0.666667 ...
0 qid:10 1:0.078682 2:0.166667 ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am trying out xgBoost that utilizes GBMs to do pairwise ranking. They have an &lt;a href="https://github.com/dmlc/xgboost/tree/master/demo/rank" rel="nofollow"&gt;example for a ranking task&lt;/a&gt; that uses the C++ program to learn on the Microsoft dataset like above.&lt;/p&gt;

&lt;p&gt;However, I am using their Python wrapper and cannot seem to find where I can input the group id (&lt;code&gt;qid&lt;/code&gt; above). I can train the model using just the features and relevance scores but I feel like I am missing something. &lt;/p&gt;

&lt;p&gt;Here is a sample script.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;gbm = XGBRegressor(objective="rank:pairwise")

X =  np.random.normal(0, 1, 1000).reshape(100, 10)
y = np.random.randint(0, 5, 100)

gbm.fit(X, y) ### --- no group id needed???

print gbm.predict(X)

# should be in reverse order of relevance score
print y[gbm.predict_proba(X)[:, 1].argsort()][::-1]
&lt;/code&gt;&lt;/pre&gt;
', 'search  ranking  xgboost  gbm', NULL, '''-1'':235C ''/a'':67C,123C ''/code'':93C,160C,236C ''/dmlc/xgboost/tree/master/demo/rank'':115C ''/en-us/um/beijing/projects/letor/letor4dataset.aspx'':59C ''/li'':39C,45C,49C ''/p'':33C,76C,137C,182C,189C ''/pre'':94C,237C ''/ul'':50C ''0'':86C,199C,207C ''0.031310'':83C ''0.078682'':90C ''0.166667'':92C ''0.666667'':85C ''1'':79C,82C,89C,200C,233C ''10'':81C,88C,204C ''100'':203C,209C ''1000'':201C ''2'':84C,91C ''5'':208C ''argsort'':234C ''c'':127C ''cannot'':147C ''code'':78C,158C,191C ''dataset'':32C,66C,134C ''exampl'':53C,118C ''far'':14C ''featur'':47C,75C,170C ''feel'':176C ''find'':150C ''fit'':2A ''format'':70C ''gbm'':11B,192C ''gbm.fit'':210C ''gbm.predict'':218C,230C ''gbms'':103C ''github.com'':114C ''github.com/dmlc/xgboost/tree/master/demo/rank'':113C ''group'':41C,72C,156C,214C ''howev'':139C ''href'':56C,112C ''id'':44C,73C,157C,215C ''input'':154C ''know'':17C ''label'':36C,71C ''learn'':20C,63C,130C ''li'':35C,40C,46C ''like'':135C,177C ''microsoft'':62C,133C ''miss'':180C ''model'':5A,23C,166C ''need'':25C,216C ''nofollow'':61C,117C ''np.random.normal'':198C ''np.random.randint'':206C ''object'':194C ''order'':224C ''p'':12C,51C,95C,138C,183C ''pairwis'':3A,106C,196C ''pre'':77C,190C ''print'':217C,228C ''proba'':231C ''program'':128C ''python'':144C ''qid'':80C,87C,159C ''queri'':43C ''rank'':4A,9B,22C,65C,107C,121C,195C ''rel'':60C,116C ''relev'':38C,172C,226C ''research.microsoft.com'':58C ''research.microsoft.com/en-us/um/beijing/projects/letor/letor4dataset.aspx'':57C ''reshap'':202C ''revers'':223C ''sampl'':187C ''score'':173C,227C ''script'':188C ''search'':8B ''seem'':148C ''someth'':181C ''task'':122C ''thing'':29C ''three'':28C ''train'':19C,164C ''tri'':98C ''ul'':34C ''use'':68C,125C,142C,167C ''util'':102C ''vector'':48C ''wrapper'':145C ''x'':197C,211C,219C,232C ''xgboost'':7A,10B,100C ''xgbregressor'':193C ''y'':205C,212C,229C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10180, 2, '2016-02-10 16:46:04.273', 2, NULL, NULL, '&lt;p&gt;This is typical statistics problem.  When you have multiple ''classes'' that you assume are normally distributed you first run an ANOVA.  Then, IFF (if-and-only-if) the ANOVA is significant, then run post-hoc pairwise t-tests with an appropriate correction (e.g. Bonferroni).&lt;/p&gt;
', NULL, NULL, '''/p'':49C ''anova'':22C,31C ''appropri'':45C ''assum'':14C ''bonferroni'':48C ''class'':11C ''correct'':46C ''distribut'':17C ''e.g'':47C ''first'':19C ''hoc'':38C ''if-and-only-if'':25C ''iff'':24C ''multipl'':10C ''normal'':16C ''p'':1C ''pairwis'':39C ''post'':37C ''post-hoc'':36C ''problem'':6C ''run'':20C,35C ''signific'':33C ''statist'':5C ''t-test'':40C ''test'':42C ''typic'':4C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10181, 2, '2016-02-10 17:19:47.57', 0, NULL, NULL, '&lt;p&gt;I will apologize in advance for linking to the Wikipedia article, but I''m not sure I can do a better job explaining it.  &lt;a href="https://en.wikipedia.org/wiki/Boltzmann_machine" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Boltzmann_machine&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;(Will extract selected passages to prevent link rot shortly.)&lt;/p&gt;

&lt;p&gt;One of the reasons we see so much talk about training RBMs and not much talk about the other is, by my recollection of a talk by Hinton, "Intractable, except for very small problems or limited cases."  The activation of a hidden node depends not only on the state of visible nodes, but on its compatriots. This means our clever contrastive divergence and gradient decent methods don''t work!&lt;/p&gt;

&lt;p&gt;But you didn''t ask why it''s difficult, you asked how to do it.&lt;/p&gt;

&lt;p&gt;The short, short answer is stimulated annealing.  &lt;/p&gt;

&lt;p&gt;The long short answer, we select an initial state for the network, then perform updates until the network reaches a global thermal equilibrium, then try and adjust the weights while reducing the temperature until our activations approximate a global minimum.  The process repeats until we''re happy with the outcome.&lt;/p&gt;
', NULL, NULL, '''/p'':46C,117C,134C,143C,195C ''/wiki/boltzmann_machine'':30C ''/wiki/boltzmann_machine&lt;/a&gt;&lt;/p&gt;'':35C ''activ'':86C,180C ''adjust'':171C ''advanc'':6C ''anneal'':142C ''answer'':139C,148C ''apolog'':4C ''approxim'':181C ''articl'':12C ''ask'':123C,129C ''better'':22C ''case'':84C ''clever'':107C ''compatriot'':103C ''contrast'':108C ''decent'':112C ''depend'':91C ''didn'':121C ''difficult'':127C ''diverg'':109C ''en.wikipedia.org'':29C,34C ''en.wikipedia.org/wiki/boltzmann_machine'':28C ''en.wikipedia.org/wiki/boltzmann_machine&lt;/a&gt;&lt;/p&gt;'':33C ''equilibrium'':167C ''except'':77C ''explain'':24C ''extract'':38C ''global'':165C,183C ''gradient'':111C ''happi'':191C ''hidden'':89C ''hinton'':75C ''href'':27C ''initi'':152C ''intract'':76C ''job'':23C ''limit'':83C ''link'':8C,43C ''long'':146C ''m'':15C ''mean'':105C ''method'':113C ''minimum'':184C ''much'':55C,62C ''network'':156C,162C ''node'':90C,99C ''nofollow'':32C ''one'':48C ''outcom'':194C ''p'':1C,36C,47C,118C,135C,144C ''passag'':40C ''perform'':158C ''prevent'':42C ''problem'':81C ''process'':186C ''rbms'':59C ''re'':190C ''reach'':163C ''reason'':51C ''recollect'':70C ''reduc'':175C ''rel'':31C ''repeat'':187C ''rot'':44C ''see'':53C ''select'':39C,150C ''short'':45C,137C,138C,147C ''small'':80C ''state'':96C,153C ''stimul'':141C ''sure'':17C ''talk'':56C,63C,73C ''temperatur'':177C ''thermal'':166C ''train'':58C ''tri'':169C ''updat'':159C ''visibl'':98C ''weight'':173C ''wikipedia'':11C ''work'':116C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10189, 1, '2016-02-10 22:53:49.62', 3, 149, 'Distributed k-means in Spark', '&lt;p&gt;I want to implement K-means algorithm in Spark. I am looking for a starting point and I found &lt;a href="http://www.cs.berkeley.edu/~rxin/ampcamp-ecnu/machine-learning-with-spark.html" rel="nofollow"&gt;Berkeley''s naive implementation&lt;/a&gt;. However, is that distributed?&lt;/p&gt;

&lt;p&gt;I mean I see no mapreduce operations. Or maybe, when submitted in Spark, the framework actually makes the needed tricks under the hood to distribute the algorithm?&lt;/p&gt;

&lt;p&gt;&lt;sub&gt;I also found that &lt;a href="http://www.infoworld.com/article/2982475/big-data/spark-15-shows-mapreduce-the-exit.html" rel="nofollow"&gt;Spark shows mapreduce the exit&lt;/a&gt; and I am using Spark 1.6.&lt;/sub&gt;&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;EDIT: This code produces a runtime error, check &lt;a href="http://stackoverflow.com/questions/35373478/mapping-of-elements-gone-bad"&gt;here&lt;/a&gt;.&lt;/p&gt;
', 'clustering  k-means  apache-spark  distributed  scala', NULL, '''/a'':48C,101C,126C ''/article/2982475/big-data/spark-15-shows-mapreduce-the-exit.html'':93C ''/p'':53C,82C,109C,127C ''/questions/35373478/mapping-of-elements-gone-bad'':124C ''/sub'':108C ''/~rxin/ampcamp-ecnu/machine-learning-with-spark.html'':41C ''1.6'':107C ''actual'':70C ''algorithm'':24C,81C ''also'':86C ''apach'':12B ''apache-spark'':11B ''berkeley'':44C ''check'':119C ''cluster'':7B ''code'':114C ''distribut'':1A,14B,52C,79C ''edit'':112C ''error'':118C ''exit'':100C ''found'':36C,87C ''framework'':69C ''hood'':77C ''howev'':49C ''hr'':110C ''href'':38C,90C,121C ''implement'':20C,47C ''k'':3A,9B,22C ''k-mean'':2A,8B,21C ''look'':29C ''make'':71C ''mapreduc'':60C,98C ''mayb'':63C ''mean'':4A,10B,23C,56C ''naiv'':46C ''need'':73C ''nofollow'':43C,95C ''oper'':61C ''p'':16C,54C,83C,111C ''point'':33C ''produc'':115C ''rel'':42C,94C ''runtim'':117C ''scala'':15B ''see'':58C ''show'':97C ''spark'':6A,13B,26C,67C,96C,106C ''stackoverflow.com'':123C ''stackoverflow.com/questions/35373478/mapping-of-elements-gone-bad'':122C ''start'':32C ''sub'':84C ''submit'':65C ''trick'':74C ''use'':105C ''want'':18C ''www.cs.berkeley.edu'':40C ''www.cs.berkeley.edu/~rxin/ampcamp-ecnu/machine-learning-with-spark.html'':39C ''www.infoworld.com'':92C ''www.infoworld.com/article/2982475/big-data/spark-15-shows-mapreduce-the-exit.html'':91C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10202, 2, '2016-02-11 15:00:59.437', 1, NULL, NULL, '&lt;p&gt;Since, there are no answers in SO, I have asked myself in github page and the issue has been closed by stating the following reply by GaelVaroquaux..&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;If you only want to parallelise vector operation, then you should use a
  build of numpy compiled with MKL (don''t attempt to do it yourself, it''s
  challenging).&lt;/p&gt;
  
  &lt;p&gt;There could be approaches to high-level parallelism in the algorithm
  itself, which would probably lead to larger gains. However, after a quick
  look at the code, I didn''t see any clear way of doing that.&lt;/p&gt;
  
  &lt;p&gt;I am going to ahead and close this issue, as it is more of a blue-sky
  whish list. I completely agree, I would like TSNE to go faster, and it
  would be great is parallelism was easy. But in the current state of
  affairs, more work is required to be in a state where we can tackle such
  wish list.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/blockquote'':166C ''/p'':29C,61C,101C,165C ''affair'':148C ''agre'':125C ''ahead'':107C ''algorithm'':74C ''answer'':6C ''approach'':66C ''ask'':11C ''attempt'':53C ''blockquot'':30C ''blue'':119C ''blue-ski'':118C ''build'':45C ''challeng'':60C ''clear'':96C ''close'':21C,109C ''code'':90C ''compil'':48C ''complet'':124C ''could'':64C ''current'':145C ''didn'':92C ''easi'':141C ''faster'':132C ''follow'':25C ''gaelvaroquaux'':28C ''gain'':82C ''github'':14C ''go'':105C,131C ''great'':137C ''high'':69C ''high-level'':68C ''howev'':83C ''issu'':18C,111C ''larger'':81C ''lead'':79C ''level'':70C ''like'':128C ''list'':122C,164C ''look'':87C ''mkl'':50C ''numpi'':47C ''oper'':39C ''p'':1C,31C,62C,102C ''page'':15C ''parallel'':71C,139C ''parallelis'':37C ''probabl'':78C ''quick'':86C ''repli'':26C ''requir'':152C ''see'':94C ''sinc'':2C ''sky'':120C ''state'':23C,146C,157C ''tackl'':161C ''tsne'':129C ''use'':43C ''vector'':38C ''want'':35C ''way'':97C ''whish'':121C ''wish'':163C ''work'':150C ''would'':77C,127C,135C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10183, 2, '2016-02-10 18:56:11.913', 3, NULL, NULL, '&lt;p&gt;Two alternative way are a &lt;em&gt;density plot&lt;/em&gt; for each hour and &lt;em&gt;level plot&lt;/em&gt; of hour and factorized value.&lt;/p&gt;

&lt;p&gt;I''m showing ganarated data with two different unified distribution (for day and night hours)&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Densityplot&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(lattice)
xy &amp;lt;- densityplot(~ value,  groups = hour ,   
plot.points=FALSE, 
data =  df,   
scales=list(x=list(rot=90, cex= .9 ),y=list(cex=.9)),par.strip.text=list(cex=.8),   
ylab="density", xlab="value", main=paste(  "DensityPlot"  ) )
print (xy)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/rJAPX.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/rJAPX.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Levelplot&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(RColorBrewer)
xy &amp;lt;- levelplot(cnt ~    hour + value,   
 col.regions=colorRampPalette(brewer.pal(9,"YlOrRd"))(16) ,
data =  df,   
scales=list(x=list(rot=90, cex= .9 ),y=list(cex=.9)),par.strip.text=list(cex=.8),   
ylab="value", xlab="hour", main=paste( "LevelPlot"  ) )
print (xy)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/V0sPJ.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/V0sPJ.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10184, 1, '2016-02-10 19:32:57.467', 0, 15, 'Are ICD codes forward compatible?', '&lt;p&gt;The ICD code scheme has gone through many revisions: &lt;a href="https://en.wikipedia.org/wiki/International_Statistical_Classification_of_Diseases_and_Related_Health_Problems" rel="nofollow"&gt;https://en.wikipedia.org/wiki/International_Statistical_Classification_of_Diseases_and_Related_Health_Problems&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Is there any compatibility between the different versions? (e.g. can ICD-9 code be directly used where ICD-10 codes are expected?)&lt;/p&gt;
', 'data-formats', NULL, '''-10'':48C ''-9'':41C ''/p'':52C ''/wiki/international_statistical_classification_of_diseases_and_related_health_problems'':23C ''/wiki/international_statistical_classification_of_diseases_and_related_health_problems&lt;/a&gt;&lt;/p&gt;'':28C ''code'':3A,12C,42C,49C ''compat'':5A,33C ''data'':7B ''data-format'':6B ''differ'':36C ''direct'':44C ''e.g'':38C ''en.wikipedia.org'':22C,27C ''en.wikipedia.org/wiki/international_statistical_classification_of_diseases_and_related_health_problems'':21C ''en.wikipedia.org/wiki/international_statistical_classification_of_diseases_and_related_health_problems&lt;/a&gt;&lt;/p&gt;'':26C ''expect'':51C ''format'':8B ''forward'':4A ''gone'':15C ''href'':20C ''icd'':2A,11C,40C,47C ''mani'':17C ''nofollow'':25C ''p'':9C,29C ''rel'':24C ''revis'':18C ''scheme'':13C ''use'':45C ''version'':37C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10185, 2, '2016-02-10 20:51:16.73', 2, NULL, NULL, '&lt;p&gt;I''ve been reading up on cascade correlation quite a bit recently and made a python implementation &lt;a href="https://github.com/DanielSlater/CascadeCorrelation" rel="nofollow"&gt;https://github.com/DanielSlater/CascadeCorrelation&lt;/a&gt; (though it still needs a bit of cleaning up/extra work and has a bunch of me mucking around with using Particle Swarm Optimization for selecting candidates, definitely not production ready).&lt;/p&gt;

&lt;p&gt;To try and explain step 3.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Start by creating a number of candidate hidden nodes with random weights. These have incoming connection from all existing hidden nodes and input nodes.&lt;/li&gt;
&lt;li&gt;We then use that equation $$S=\sum_o \left | \sum_p(V_p - \overline{V})(E_{p,o} - \overline{E_o}) \right |$$ to train the candidate nodes. &lt;/li&gt;
&lt;li&gt;The residual output error is the difference between the output of the network and the target value(think sum of squared error without the square).&lt;/li&gt;
&lt;li&gt;$S$ is the correlation between the activation of our candidate node and the the residual error.&lt;/li&gt;
&lt;li&gt;$V_p$ is the activation of the candidate node given input $p$.&lt;/li&gt;
&lt;li&gt;After a bit of backprop training against $S$ we choose our best candidate. This becomes a new hidden node.&lt;/li&gt;
&lt;li&gt;This is when it''s weights are frozen. That is to say after random initialization then back-prop training, then selecting the best one.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Hope this helps :)&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10186, 2, '2016-02-10 21:11:01.363', 1, NULL, NULL, '&lt;p&gt;Instead of using a random forest classifier, you could instead use a random forrest regression analysis.  Use the mean value of the bin as the value (which will take into account the relative values as you suggest).  This has the added benefit that if you get a dataset that has actual ages, you don''t need to change the training, and you''ll be used to translating the scores (ages) provided by the analytic to your bins.&lt;/p&gt;
', NULL, NULL, '''/p'':79C ''account'':32C ''actual'':52C ''ad'':42C ''age'':53C,71C ''analysi'':17C ''analyt'':75C ''benefit'':43C ''bin'':24C,78C ''chang'':59C ''classifi'':8C ''could'':10C ''dataset'':49C ''forest'':7C ''forrest'':15C ''get'':47C ''instead'':2C,11C ''ll'':64C ''mean'':20C ''need'':57C ''p'':1C ''provid'':72C ''random'':6C,14C ''regress'':16C ''relat'':34C ''score'':70C ''suggest'':38C ''take'':30C ''train'':61C ''translat'':68C ''use'':4C,12C,18C,66C ''valu'':21C,27C,35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10188, 1, '2016-02-10 21:52:30.73', 1, 64, 'Why do cost functions use the square error?', '&lt;p&gt;I''m just getting started with some machine learning, and until now I have been dealing with linear regression over one variable.&lt;/p&gt;

&lt;p&gt;I have learnt that there is a hypothesis, which is:&lt;/p&gt;

&lt;p&gt;$h_\theta(x)=\theta_0+\theta_1x$&lt;/p&gt;

&lt;p&gt;To find out good values for the parameters $\theta_0$ and $\theta_1$ we want to minimize the difference between the calculated result and the actual result of our test data. So we subtract&lt;/p&gt;

&lt;p&gt;$h_\theta(x^{(i)})-y^{(i)}$&lt;/p&gt;

&lt;p&gt;for all $i$ from $1$ to $m$. Hence we calculate the sum over this difference and then calculate the average by multiplying the sum by $\frac{1}{m}$. So far, so good. This would result in:&lt;/p&gt;

&lt;p&gt;$\frac{1}{m}\sum_{i=1}^mh_\theta(x^{(i)})-y^{(i)}$&lt;/p&gt;

&lt;p&gt;But this is not what has been suggested. Instead the course suggests to take the square value of the difference, and to multiply by $\frac{1}{2m}$. So the formula is:&lt;/p&gt;

&lt;p&gt;$\frac{1}{2m}\sum_{i=1}^m(h_\theta(x^{(i)})-y^{(i)})^2$&lt;/p&gt;

&lt;p&gt;Why is that? Why do we use the square function here, and why do we multiply by $\frac{1}{2m}$ instead of $\frac{1}{m}$?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10203, 2, '2016-02-11 15:32:05.06', 1, NULL, NULL, '&lt;p&gt;In that great &lt;a href="http://www.joelonsoftware.com/items/2007/10/26.html" rel="nofollow"&gt;post&lt;/a&gt;, Joel Spolsky use Monte-Carlo simulation to give an estimation of the end of a project.&lt;/p&gt;

&lt;p&gt;Thinking about a similar concept whould be to replace the notion of velocity by efficiency.&lt;/p&gt;

&lt;p&gt;$$efficiency = \frac{quote}{hours} $$ &lt;/p&gt;

&lt;p&gt;The more discrete the scales of the quotes, the better.&lt;/p&gt;

&lt;p&gt;It should work even with a few weeks of data.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10204, 1, '2016-02-11 16:35:19.59', 3, 45, 'Should I take random elements for mini-batch gradient descent?', '&lt;p&gt;When implementing mini-batch gradient descent for neural networks, is it important to take random elements in each mini-batch? Or is it enough to shuffle the elements at the beginning of the training once?&lt;/p&gt;

&lt;p&gt;(I''m also interested in sources which definitely say what they do.)&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10192, 2, '2016-02-11 01:18:54.24', 2, NULL, NULL, '&lt;p&gt;Your loss function here would not work because it incentivizes putting both $\theta_0$ and $\theta_1$ to $-\infty$.&lt;/p&gt;

&lt;p&gt;Lets call $r(x,y)=h_\theta(x) -y$ the residual (as is often done). You''re asking to make it as small as possible, i.e. $h_\theta$ as negative as possible since $y$, the ground truth, is just a constant. The squared error sidesteps this issue because it forces $h(x)$ and $y$ to match, i.e. $(u-v)^2$ is minimized when $u=v$, if possible, and is always $&amp;gt;0$ otherwise, because it''s a square.&lt;/p&gt;

&lt;p&gt;One justification for using the squared error is that it relates to Gaussian Noise. Suppose the residual, which measures the error is the sum of many small independent noise terms. By the &lt;a href="https://en.wikipedia.org/wiki/Central_limit_theorem" rel="nofollow"&gt;Central Limit Theorem&lt;/a&gt;, it should be distributed Normally. So, we want to pick $\theta$ where this noise distribution - the things your model cannot explain - has the smallest variance. This corresponds to the least squares loss.&lt;/p&gt;

&lt;p&gt;Let $R$ be a random variable that follows a Gaussian distribution $\mathcal{N}(\mu,\sigma)$. Then, the variance of $R$ is $E[R^2] = \sigma^2$. See &lt;a href="http://math.stackexchange.com/questions/518281/how-to-derive-the-mean-and-variance-of-a-gaussian-random-variable"&gt;here&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Regarding your second question, the 1/2 does not matter and actually, the $m$ doesn''t matter either :) . The optimal value of $\theta$ would remain the same in both cases, it is put in so that when you take the derivative, the expression is prettier, because the 2 cancels out the 2 from the square term. The $m$ is useful if you solve this problem with gradient descent. Then your gradient is the sum of $m$ terms &lt;em&gt;divided&lt;/em&gt; by $m$, so it is like an average over your points. This lets you handle all sizes of datasets, so your gradients won''t overflow the integers if you scale up the dataset. &lt;/p&gt;

&lt;p&gt;It''s also used to maintain consistency with future equations where you''ll add &lt;a href="http://www.mit.edu/~9.520/spring07/Classes/rlsslides.pdf" rel="nofollow"&gt;regularization&lt;/a&gt; terms. The slides I linked here don''t do it, but you can see that if you do, the regularization parameter $\lambda$ will not depend on the dataset size $m$ so it''ll be more interpretable.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10193, 1, '2016-02-11 01:47:47.487', 2, 57, 'How to interpret a decision tree correctly?', '&lt;p&gt;I''m trying to work out if I''m correctly interpreting a decision tree found online.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;The dependent variable of this decision tree is Credit Rating which
has two classes, Bad or Good. The root of this tree contains all 2464
observations in this dataset.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;The most influential attribute to determine how to classify a good or bad credit rating is the Income Level attribute.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;The majority of the people (454 out of 553) in our sample that had a less than low income also had a bad credit rating. If I was to launch a premium credit card without a limit I should ignore these people.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;If I were to use this decision tree for predictions to classify new observations, are the largest number of class in a leaf used as the prediction? E.g. Observation x has medium income, 7 credit cards and 34 years old. Would the predicted classification for credit rating = "Good"&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Another new observation could be Observation Y, which has less than low income so their credit rating = "Bad"&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Is this the correct way to interpret a decision tree or have I got this completely wrong?&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/QI0QU.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/QI0QU.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'predictive-modeling  decision-trees', NULL, '''/a'':248C ''/li'':65C,89C,135C,187C,209C ''/p'':31C,64C,88C,134C,186C,208C,229C,249C ''/qi0qu.jpg'':235C,242C ''/ul'':210C ''2464'':59C ''34'':175C ''454'':97C ''553'':100C ''7'':171C ''also'':111C ''alt'':243C ''anoth'':190C ''attribut'':71C,87C ''bad'':49C,80C,114C,207C ''card'':125C,173C ''class'':48C,157C ''classif'':181C ''classifi'':76C,149C ''complet'':227C ''contain'':57C ''correct'':7A,24C,215C ''could'':193C ''credit'':43C,81C,115C,124C,172C,183C,205C ''dataset'':63C ''decis'':5A,12B,27C,40C,144C,220C ''decision-tre'':11B ''depend'':36C ''descript'':246C ''determin'':73C ''e.g'':165C ''enter'':244C ''found'':29C ''good'':51C,78C,185C ''got'':225C ''href'':232C ''i.stack.imgur.com'':234C,241C ''i.stack.imgur.com/qi0qu.jpg'':233C,240C ''ignor'':131C ''imag'':245C ''img'':238C ''incom'':85C,110C,170C,202C ''influenti'':70C ''interpret'':3A,25C,218C ''largest'':154C ''launch'':121C ''leaf'':160C ''less'':107C,199C ''level'':86C ''li'':33C,66C,90C,136C,188C ''limit'':128C ''low'':109C,201C ''m'':16C,23C ''major'':93C ''medium'':169C ''model'':10B ''new'':150C,191C ''nofollow'':237C ''number'':155C ''observ'':60C,151C,166C,192C,195C ''old'':177C ''onlin'':30C ''p'':14C,34C,67C,91C,137C,189C,211C,230C ''peopl'':96C,133C ''predict'':9B,147C,164C,180C ''predictive-model'':8B ''premium'':123C ''rate'':44C,82C,116C,184C,206C ''rel'':236C ''root'':53C ''sampl'':103C ''src'':239C ''tree'':6A,13B,28C,41C,56C,145C,221C ''tri'':17C ''two'':47C ''ul'':32C ''use'':142C,161C ''variabl'':37C ''way'':216C ''without'':126C ''work'':19C ''would'':178C ''wrong'':228C ''x'':167C ''y'':196C ''year'':176C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10295, 2, '2016-02-17 23:51:18.347', 2, NULL, NULL, '&lt;p&gt;I have been using torch for a few months now but I will give it a go (apologies if incorrect).&lt;/p&gt;

&lt;p&gt;Yes a weight $w$ is updated as follows;&lt;/p&gt;

&lt;p&gt;$$ w_{new} = w_{old} - \gamma \partial E/ \partial w_{old}  $$&lt;/p&gt;

&lt;p&gt;where $ \gamma $ is your learning rate and $E$ is the error calculated using something like &lt;code&gt;criterion:forward(output,target)&lt;/code&gt;. The criterion could be, for example, &lt;code&gt;nn.MSECriterion()&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;To calculate $\partial E/\partial W$  you need $\partial E/\partial y$ &lt;code&gt;gradOutput = criterion:backward(output,target)&lt;/code&gt;(gradient respect to output) as well as the input &lt;code&gt;input&lt;/code&gt; to the net i.e. your $X$ (e.g. image data) to generate the recursive set of equations which multiply with &lt;code&gt;gradOutput&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;model:backward(input, gradOutput)&lt;/code&gt; therefore serves to update the weights so that they are ready for the next &lt;code&gt;model:forward(input)&lt;/code&gt; as it generates a big derivative tensor $dE/dW_{old}$. &lt;/p&gt;

&lt;p&gt;This is then combined with a optimiser such as &lt;code&gt;optim.sgd&lt;/code&gt; using &lt;code&gt;optimMethod&lt;/code&gt; and the old weights $W_{old}$ to generate the new weights in the first equation. Of course you can just update the weights without an optimiser with &lt;code&gt;model:updateParameters(learningRate)&lt;/code&gt; but you miss useful stuff like momentum, weight decay etc.&lt;/p&gt;

&lt;p&gt;Got a bit side tracked there but hope this helps.&lt;/p&gt;
', NULL, NULL, '''/code'':66C,75C,96C,108C,130C,138C,157C,180C,184C,216C ''/p'':22C,32C,44C,76C,131C,167C,227C,239C ''apolog'':19C ''backward'':93C,135C ''big'':162C ''bit'':231C ''calcul'':57C,79C ''code'':61C,73C,90C,106C,128C,133C,153C,178C,182C,212C ''combin'':172C ''could'':69C ''cours'':201C ''criterion'':62C,68C,92C ''data'':117C ''de/dw_'':165C ''decay'':225C ''deriv'':163C ''e'':40C,53C,81C,87C ''e.g'':115C ''equat'':124C,199C ''error'':56C ''etc'':226C ''exampl'':72C ''first'':198C ''follow'':31C ''forward'':63C,155C ''gamma'':38C,47C ''generat'':119C,160C,192C ''give'':15C ''go'':18C ''got'':229C ''gradient'':97C ''gradoutput'':91C,129C,137C ''help'':238C ''hope'':236C ''i.e'':112C ''imag'':116C ''incorrect'':21C ''input'':105C,107C,136C,156C ''learn'':50C ''learningr'':215C ''like'':60C,222C ''miss'':219C ''model'':134C,154C,213C ''momentum'':223C ''month'':10C ''multipli'':126C ''need'':85C ''net'':111C ''new'':35C,194C ''next'':152C ''nn.msecriterion'':74C ''old'':37C,43C,166C,187C,190C ''optim.sgd'':179C ''optimis'':175C,210C ''optimmethod'':183C ''output'':64C,94C,100C ''p'':1C,23C,33C,45C,77C,132C,168C,228C ''partial'':39C,41C,80C,82C,86C,88C ''rate'':51C ''readi'':149C ''recurs'':121C ''respect'':98C ''serv'':140C ''set'':122C ''side'':232C ''someth'':59C ''stuff'':221C ''target'':65C,95C ''tensor'':164C ''therefor'':139C ''torch'':6C ''track'':233C ''updat'':29C,142C,205C ''updateparamet'':214C ''use'':5C,58C,181C,220C ''w'':27C,34C,36C,42C,83C,189C ''weight'':26C,144C,188C,195C,207C,224C ''well'':102C ''without'':208C ''x'':114C ''y'':89C ''yes'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10194, 2, '2016-02-11 01:55:10.15', 4, NULL, NULL, '&lt;p&gt;In that link you posted, you can look at the python full solution &lt;a href="http://www.cs.berkeley.edu/~rxin/ampcamp-ecnu/machine-learning-with-spark.html#solution_11" rel="nofollow"&gt;here&lt;/a&gt; at the end and go through it to see what all is distributed. 
In short, some parts are distributed, like reading data from the file, but the very important parts like the distance computation are not.&lt;/p&gt;

&lt;p&gt;Running down, we see:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;code&gt;sc = SparkContext("local[6]", "PythonKMeans")&lt;/code&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This instantiates the context and creates a local cluster which the jobs will be submitted to&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;code&gt;lines = sc.textFile(..)&lt;/code&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This is still setting up. No operations have taken place yet. You can verify this by putting timing statements in the code&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;code&gt;data = lines.map(lambda x: (x.split("#")[0], parseVector(x.split("#")[1])))&lt;/code&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The lambda here will be applied to lines, so this operation will split the file in parallel. Note that the actual line also has a &lt;code&gt;cache()&lt;/code&gt; at the end (see &lt;a href="http://spark.apache.org/docs/latest/quick-start.html#caching" rel="nofollow"&gt;cache&lt;/a&gt;]). &lt;code&gt;data&lt;/code&gt; is just a reference to the spark object in memory. (I may be wrong here, but I think the operation still doesn''t happen yet)&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;count = data.count()&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This forces the parallel computation to start, and the count to be stored. At the end, the reference data is still valid, and we''ll use it for further computations. I''ll stop with detailed explanations here, but wherever &lt;code&gt;data&lt;/code&gt; is being used is a possible parallel computation. The python code itself is single threaded, and interfaces with the Spark cluster.&lt;/p&gt;

&lt;p&gt;An interesting line is:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;tempDist = sum(np.sum((centroids[x] - y) ** 2) for (x, y) in newCentroids.iteritems())&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;code&gt;centroids&lt;/code&gt; is an object in python memory, as is &lt;code&gt;newCentroids&lt;/code&gt;. So, at this point, all computations are being done in memory (and &lt;strong&gt;on the client&lt;/strong&gt;, typically clients are slim, i.e. have limited capabilities, or the client is an SSH shell, so the computers resources are shared. You should ideally &lt;em&gt;never&lt;/em&gt; do any computation here), so no parallelization is being used. You could optimize this method further by doing this computation in parallel. Ideally you want the python program to never directly handle individual points'' $x$ and $y$ values.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10210, 2, '2016-02-11 19:51:36.483', 0, NULL, NULL, '&lt;p&gt;In a machine learning context, a &lt;a href="https://en.wikipedia.org/wiki/Dichotomy" rel="nofollow"&gt;dichotomy&lt;/a&gt; is simply a split of a set into two mutually exclusive subsets whose union is the original set. The point being made in your quoted text is that for four points, a linear boundary can not form all possible dichotomies (i.e., it does not &lt;a href="https://en.wikipedia.org/wiki/Shattered_set" rel="nofollow"&gt;shatter&lt;/a&gt; the set). For example, if the four points are arranged on the corners of a square, a linear boundary can be used to create all possible dichotomies &lt;em&gt;except&lt;/em&gt; it cannot produce a boundary that splits the two points lying along one diagonal from the other two points (and vice versa), as you indicated in your own answer.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10195, 2, '2016-02-11 02:12:34.163', 0, NULL, NULL, '&lt;p&gt;Yes, your interpretation is correct. Each level in your tree is related to one of the variables (this is not always the case for decision trees, you can imagine them being more general). &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;X has medium income, so you go to Node 2, and more than 7 cards, so you go to Node 5. Now, you''ve reached a leaf node. you see that in your dataset, you had 54 people like X, who you determined has a Bad rating (a human presumably did this rating based on other factors. And you had 336 people like X who had a Good rating. So, based on only this information, you can say X probably has a Good rating. So, the decision tree has given you a quick, though approximate answer.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Side point: The 54 vs 336 here gives you a measure of confidence. For example, you could treat it like a probability. You could say $P(Good)=336/(54+336) \approx 0.86$, and also calculate a confidence interval in various ways.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Y has Low income, so you can immediately look at the tree and go to Node 1, and say he probably has a Bad rating, with $P(Bad)=454/(454+99) \approx 0.82$.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Regarding the comment about "most influential" attribute, this really depends on the way the tree is constructed, and what definition of "influential" you use. So, you would have to ask the person/software/algorithm that made the tree. It is certainly an important attribute, as you can see from the table itself. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10205, 2, '2016-02-11 17:22:39.867', 1, NULL, NULL, '&lt;p&gt;Given&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(igraph)
set.seed(1)
g &amp;lt;- sample_pa(5)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;you got&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ends(g, E(g))
#      [,1] [,2]
# [1,]    2    1
# [2,]    3    1
# [3,]    4    1
# [4,]    5    2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;head_of(g, E(g))
# + 4/5 vertices:
# [1] 2 3 4 5

tail_of(g, E(g))
# + 4/5 vertices:
# [1] 1 1 1 2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;code&gt;source()&lt;/code&gt; is an important base function. &lt;/p&gt;
', NULL, NULL, '''/code'':15C,41C,72C,77C ''/p'':3C,20C,45C,83C ''/pre'':16C,42C,73C ''1'':9C,27C,29C,31C,34C,37C,55C,67C,68C,69C,70C ''2'':28C,30C,32C,40C,56C,71C ''3'':33C,35C,57C ''4'':36C,38C,58C ''4/5'':53C,65C ''5'':14C,39C,59C ''base'':81C ''code'':5C,22C,47C,75C ''e'':25C,51C,63C ''end'':23C ''function'':82C ''g'':10C,24C,26C,50C,52C,62C,64C ''given'':2C ''got'':19C ''head'':48C ''igraph'':7C ''import'':80C ''librari'':6C ''lt'':11C ''p'':1C,17C,43C,74C ''pa'':13C ''pre'':4C,21C,46C ''sampl'':12C ''set.seed'':8C ''sourc'':76C ''tail'':60C ''vertic'':54C,66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10196, 2, '2016-02-11 02:22:10.373', 1, NULL, NULL, '&lt;p&gt;Let me evaluate each of your observations one by one, so that it would be more clear:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The dependent variable of this decision tree is Credit Rating which
  has two classes, Bad or Good. The root of this tree contains all 2464
  observations in this dataset.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;If &lt;code&gt;Good, Bad&lt;/code&gt; is what you mean by credit rating, then &lt;strong&gt;Yes&lt;/strong&gt;. And you are right with the conclusion that all the 2464 observations are contained in the root of the tree.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The most influential attribute to determine how to classify a good or
  bad credit rating is the Income Level attribute.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Debatable&lt;/strong&gt; Depends on how you consider something to be &lt;em&gt;influential&lt;/em&gt;. Some might argue that the number of cards might be the most influential, and some might agree with your point. So, you are both right and wrong here.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The majority of the people (454 out of 553) in our sample that had a
  less than low income also had a bad credit rating. If I was to launch
  a premium credit card without a limit I should ignore these people.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Yes&lt;/strong&gt;, but it would also be better if you consider the probability of getting a bad credit from these people. But, even that would turn out to be NO for this class, which makes your observation correct again.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;If I were to use this decision tree for predictions to classify new
  observations, are the largest number of class in a leaf used as the
  prediction? E.g. Observation x has medium income, 7 credit cards and
  34 years old. Would the predicted classification for credit rating =
  "Good"&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Depends on the probability&lt;/strong&gt;. So, &lt;a href="https://social.msdn.microsoft.com/Forums/sqlserver/en-US/97c9ce39-024f-450f-8b21-a2d2961d8be7/decision-trees-how-is-prediction-probability-calculated?forum=sqldatamining" rel="nofollow"&gt;calculate the probability&lt;/a&gt; from the leaves and then make a decision depending on that. Or much simpler, use a library like the Sklearn''s decision tree classifier to do that for you.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Another new observation could be Observation Y, which has less than
  low income so their credit rating = "Bad"&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Again, same as the explanation above.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Is this the correct way to interpret a decision tree or have I got
  this completely wrong?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Yes&lt;/strong&gt;, this is a correct way of interpreting decision trees. You might be tempted to sway when it comes to selection of &lt;em&gt;influential&lt;/em&gt; variables, but that is dependant on a lot of factors, including the problem statement, construction of the tree, analyst''s judgement, etc.&lt;/p&gt;
', NULL, NULL, '''/a'':317C ''/blockquote'':52C,114C,204C,298C,369C,398C ''/code'':58C ''/em'':129C,426C ''/forums/sqlserver/en-us/97c9ce39-024f-450f-8b21-a2d2961d8be7/decision-trees-how-is-prediction-probability-calculated?forum=sqldatamining'':311C ''/p'':19C,51C,90C,113C,158C,203C,246C,297C,347C,368C,377C,397C,449C ''/strong'':69C,118C,208C,305C,402C ''2464'':46C,80C ''34'':286C ''454'':166C ''553'':169C ''7'':282C ''agre'':146C ''also'':180C,212C ''analyst'':445C ''anoth'':350C ''argu'':132C ''attribut'':96C,112C ''bad'':36C,57C,105C,183C,223C,367C ''better'':214C ''blockquot'':20C,91C,159C,247C,348C,378C ''calcul'':314C ''card'':137C,194C,284C ''class'':35C,239C,268C ''classif'':292C ''classifi'':101C,260C,341C ''clear'':18C ''code'':55C ''come'':420C ''complet'':395C ''conclus'':76C ''consid'':123C,217C ''construct'':441C ''contain'':44C,83C ''correct'':244C,383C,406C ''could'':353C ''credit'':30C,64C,106C,184C,193C,224C,283C,294C,365C ''dataset'':50C ''debat'':117C ''decis'':27C,255C,325C,339C,388C,410C ''depend'':23C,119C,301C,326C,431C ''determin'':98C ''e.g'':276C ''em'':127C,424C ''etc'':448C ''evalu'':4C ''even'':229C ''explan'':375C ''factor'':436C ''get'':221C ''good'':38C,56C,103C,296C ''got'':393C ''href'':308C ''ignor'':200C ''includ'':437C ''incom'':110C,179C,281C,362C ''influenti'':95C,128C,142C,425C ''interpret'':386C,409C ''judgement'':447C ''largest'':265C ''launch'':190C ''leaf'':271C ''leav'':320C ''less'':176C,359C ''let'':2C ''level'':111C ''librari'':334C ''like'':335C ''limit'':197C ''lot'':434C ''low'':178C,361C ''major'':162C ''make'':241C,323C ''mean'':62C ''medium'':280C ''might'':131C,138C,145C,413C ''much'':330C ''new'':261C,351C ''nofollow'':313C ''number'':135C,266C ''observ'':8C,47C,81C,243C,262C,277C,352C,355C ''old'':288C ''one'':9C,11C ''p'':1C,21C,53C,92C,115C,160C,205C,248C,299C,349C,370C,379C,399C ''peopl'':165C,202C,227C ''point'':149C ''predict'':258C,275C,291C ''premium'':192C ''probabl'':219C,304C,316C ''problem'':439C ''rate'':31C,65C,107C,185C,295C,366C ''rel'':312C ''right'':73C,154C ''root'':40C,86C ''sampl'':172C ''select'':422C ''simpler'':331C ''sklearn'':337C ''social.msdn.microsoft.com'':310C ''social.msdn.microsoft.com/forums/sqlserver/en-us/97c9ce39-024f-450f-8b21-a2d2961d8be7/decision-trees-how-is-prediction-probability-calculated?forum=sqldatamining'':309C ''someth'':124C ''statement'':440C ''strong'':67C,116C,206C,300C,400C ''sway'':417C ''tempt'':415C ''tree'':28C,43C,89C,256C,340C,389C,411C,444C ''turn'':232C ''two'':34C ''use'':253C,272C,332C ''variabl'':24C,427C ''way'':384C,407C ''without'':195C ''would'':15C,211C,231C,289C ''wrong'':156C,396C ''x'':278C ''y'':356C ''year'':287C ''yes'':68C,207C,401C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10197, 1, '2016-02-11 09:47:09.2', 2, 39, 'Soccer Field Segmentation', '&lt;p&gt;I would like to develop a soccer field segmentation method. For this purpose, I prepared a training image data set and annotated field and non-field pixels. Following is a gr-chromacity plot of all training samples, colored with respect to their labels. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/QObI3.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/QObI3.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I want to train a classifier for inferring the label of a new sample. The first approach comes to my mind is using Gaussian mixture models to model both distributions. Would you recommend another method for this purpose?&lt;/p&gt;
', 'machine-learning  classification  statistics  svm  naive-bayes-classifier', NULL, '''/a'':79C ''/p'':60C,80C,120C ''/qobi3.png'':66C,73C ''alt'':74C ''annot'':36C ''anoth'':115C ''approach'':98C ''bay'':12B ''chromac'':48C ''classif'':7B ''classifi'':13B,87C ''color'':54C ''come'':99C ''data'':33C ''descript'':77C ''develop'':19C ''distribut'':111C ''enter'':75C ''field'':2A,22C,37C,41C ''first'':97C ''follow'':43C ''gaussian'':105C ''gr'':47C ''gr-chromac'':46C ''href'':63C ''i.stack.imgur.com'':65C,72C ''i.stack.imgur.com/qobi3.png'':64C,71C ''imag'':32C,76C ''img'':69C ''infer'':89C ''label'':59C,91C ''learn'':6B ''like'':17C ''machin'':5B ''machine-learn'':4B ''method'':24C,116C ''mind'':102C ''mixtur'':106C ''model'':107C,109C ''naiv'':11B ''naive-bayes-classifi'':10B ''new'':94C ''nofollow'':68C ''non'':40C ''non-field'':39C ''p'':14C,61C,81C ''pixel'':42C ''plot'':49C ''prepar'':29C ''purpos'':27C,119C ''recommend'':114C ''rel'':67C ''respect'':56C ''sampl'':53C,95C ''segment'':3A,23C ''set'':34C ''soccer'':1A,21C ''src'':70C ''statist'':8B ''svm'':9B ''train'':31C,52C,85C ''use'':104C ''want'':83C ''would'':16C,112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10198, 1, '2016-02-11 10:02:59.413', 0, 24, 'source and target functions in igraph package in R', '&lt;p&gt;What are the source and target functions in igraph package in R?  so that source(s-&gt;t) = s and target(s-&gt;t) = t&lt;/p&gt;

&lt;p&gt;I am working on this directed graph example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;require(igraph, quietly=TRUE)
bsk &amp;lt;-read.table("http://www.dimiter.eu/Data_files/edgesdata3.txt", sep=''\t'', dec='','', header=T)

bsk.network &amp;lt;- graph.data.frame(bsk, directed=T) 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;edit: I am trying to write a function like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;f_source &amp;lt;- function(str,network){
 result &amp;lt;- head_of(network, E(network)[str]); 
}

f_source(AA%-&amp;gt;%DD,bsk.network)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The problem is the string. It includes quotations while E(network)[str] does not work with quotations.&lt;/p&gt;
', 'r  graphs', NULL, '''/code'':70C,106C ''/data_files/edgesdata3.txt'':58C ''/p'':36C,46C,83C,126C ''/pre'':71C,107C ''aa'':102C ''bsk'':53C,67C ''bsk.network'':64C,105C ''code'':48C,85C ''dd'':104C ''dec'':61C ''direct'':43C,68C ''e'':97C,118C ''edit'':73C ''exampl'':45C ''f'':86C,100C ''function'':4A,19C,80C,89C ''graph'':11B,44C ''graph.data.frame'':66C ''gt'':103C ''head'':94C ''header'':62C ''igraph'':6A,21C,50C ''includ'':115C ''like'':81C ''lt'':54C,65C,88C,93C ''network'':91C,96C,98C,119C ''p'':12C,37C,72C,108C ''packag'':7A,22C ''pre'':47C,84C ''problem'':110C ''quiet'':51C ''quotat'':116C,125C ''r'':9A,10B,24C ''read.table'':55C ''requir'':49C ''result'':92C ''sep'':59C ''sourc'':1A,16C,27C,87C,101C ''str'':90C,99C,120C ''string'':113C ''target'':3A,18C,32C ''tri'':76C ''true'':52C ''work'':40C,123C ''write'':78C ''www.dimiter.eu'':57C ''www.dimiter.eu/data_files/edgesdata3.txt'':56C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10199, 1, '2016-02-11 11:08:06.757', 2, 44, 'Ideas on cost function', '&lt;p&gt;[If this is the wrong place to ask this question, please let me know where I can receive better answers:)]&lt;/p&gt;

&lt;p&gt;So I''m building an internal tool for an digital media organization. The tool aims to track the number of hours (or any other measure of effort) spent on each brand by different people-roles (Eg: Designers, Content Writers, etc). Using the data collected for the first few months, the organization aims to achieve a better estimate for future quotes given to new clients.&lt;/p&gt;

&lt;p&gt;I have thought about the following roles and their metrics:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Designers: Hours&lt;/li&gt;
&lt;li&gt;Content Writers: Hours&lt;/li&gt;
&lt;li&gt;SEO Guys: Hours&lt;/li&gt;
&lt;li&gt;Client-Facing people. (Who actually talk to all the clients): Effort on a scale of 1-10&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Whenever somebody starts work on a client (brand), they will start their timer and when done, they''ll stop the timer. The tool will log the amount of time spent. Also, the current quotes to the different brands will be entered into the system initially.&lt;/p&gt;

&lt;p&gt;I can think about this as a regression problem with the hours put in by different roles as the features and the ''quote'' as the output. But I don''t think there will be enough data collected in the first 2 months to give a realistic estimate for quotes to new clients.&lt;/p&gt;

&lt;p&gt;What can my cost function be?&lt;/p&gt;

&lt;p&gt;This will be my first project of this kind. Please give me some ideas. Even new approaches are welcome.&lt;/p&gt;

&lt;p&gt;Thanks :)&lt;/p&gt;
', 'regression', NULL, '''-10'':140C ''/li'':111C,116C,121C,141C ''/p'':27C,94C,106C,189C,243C,251C,272C,275C ''/ul'':142C ''1'':139C ''2'':231C ''achiev'':83C ''actual'':128C ''aim'':43C,81C ''also'':174C ''amount'':170C ''answer'':26C ''approach'':269C ''ask'':14C ''better'':25C,85C ''brand'':59C,151C,181C ''build'':32C ''client'':93C,124C,133C,150C,242C ''client-fac'':123C ''collect'':73C,227C ''content'':67C,113C ''cost'':3A,248C ''current'':176C ''data'':72C,226C ''design'':66C,109C ''differ'':61C,180C,206C ''digit'':38C ''done'':159C ''effort'':55C,134C ''eg'':65C ''enough'':225C ''enter'':184C ''estim'':86C,237C ''etc'':69C ''even'':267C ''face'':125C ''featur'':210C ''first'':76C,230C,257C ''follow'':101C ''function'':4A,249C ''futur'':88C ''give'':234C,263C ''given'':90C ''guy'':119C ''hour'':49C,110C,115C,120C,202C ''idea'':1A,266C ''initi'':188C ''intern'':34C ''kind'':261C ''know'':20C ''let'':18C ''li'':108C,112C,117C,122C ''ll'':161C ''log'':168C ''m'':31C ''measur'':53C ''media'':39C ''metric'':105C ''month'':78C,232C ''new'':92C,241C,268C ''number'':47C ''organ'':40C,80C ''output'':216C ''p'':6C,28C,95C,143C,190C,244C,252C,273C ''peopl'':63C,126C ''people-rol'':62C ''place'':12C ''pleas'':17C,262C ''problem'':199C ''project'':258C ''put'':203C ''question'':16C ''quot'':89C,177C,213C,239C ''realist'':236C ''receiv'':24C ''regress'':5B,198C ''role'':64C,102C,207C ''scale'':137C ''seo'':118C ''somebodi'':145C ''spent'':56C,173C ''start'':146C,154C ''stop'':162C ''system'':187C ''talk'':129C ''thank'':274C ''think'':193C,221C ''thought'':98C ''time'':172C ''timer'':156C,164C ''tool'':35C,42C,166C ''track'':45C ''ul'':107C ''use'':70C ''welcom'':271C ''whenev'':144C ''work'':147C ''writer'':68C,114C ''wrong'':11C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10211, 1, '2016-02-11 21:52:50.03', 0, 22, 'Sentiment retriving from text (Russian)', '&lt;p&gt;Does anybody knows python library to retrieve sentiment from Russian text. The dictionary with sentiment parameterization will be ok to. The idea of library something like in GPOMS in &lt;a href="http://arxiv.org/abs/1010.3003" rel="nofollow"&gt;article&lt;/a&gt;.&lt;/p&gt;
', 'python  nlp', NULL, '''/a'':46C ''/abs/1010.3003'':42C ''/p'':47C ''anybodi'':10C ''articl'':45C ''arxiv.org'':41C ''arxiv.org/abs/1010.3003'':40C ''dictionari'':21C ''gpom'':36C ''href'':39C ''idea'':30C ''know'':11C ''librari'':13C,32C ''like'':34C ''nlp'':7B ''nofollow'':44C ''ok'':27C ''p'':8C ''parameter'':24C ''python'':6B,12C ''rel'':43C ''retriev'':15C ''retriv'':2A ''russian'':5A,18C ''sentiment'':1A,16C,23C ''someth'':33C ''text'':4A,19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10200, 1, '2016-02-11 13:36:17.973', 1, 7, 'freebcp getting stalled for huge data', '&lt;p&gt;I am working on a project which processes big data (size ~3TB) every day. The first stage of our data pipeline copies the data from our MS-SQL Server to a host machine(linux server) using a tool called &lt;code&gt;freebcp&lt;/code&gt;. More info on freebcp is &lt;a href="http://linux.die.net/man/1/freebcp" rel="nofollow"&gt;here&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;Using this tool from the linux server, we run a set of stored procedures on SQL Server and export the data and transfer it in bulk. Recently I am observing that if the data is huge (~200GB), the data transfer is getting stalled after sometime. I ran a couple of commands (&lt;code&gt;sp_who2&lt;/code&gt;, and &lt;code&gt;dbcc inputbuffer(spid)&lt;/code&gt;) to monitor the execution of stored procedure on SQL server. We observe the CPU time and Disk IO utilized by this procedure. If this is not changing for a few minutes, we assume the job is stalled, and manually kill these stored procedures to continue our data processing tasks. &lt;/p&gt;

&lt;p&gt;What are the probable reasons for this stalling of data copy?&lt;/p&gt;

&lt;p&gt;Is there any better way to copy the data in bulk from SQL server to linux host? May be an alternative to freebcp. After this, we load this data into hadoop file system run our map reduce tasks.&lt;/p&gt;
', 'bigdata  hadoop', NULL, '''/a'':66C ''/code'':52C,123C,129C ''/man/1/freebcp'':62C ''/p'':67C,179C,192C,232C ''200gb'':105C ''3tb'':21C ''altern'':214C ''assum'':162C ''better'':197C ''big'':18C ''bigdata'':7B ''bulk'':94C,204C ''call'':49C ''chang'':156C ''code'':50C,120C,125C ''command'':119C ''continu'':174C ''copi'':31C,191C,200C ''coupl'':117C ''cpu'':143C ''data'':6A,19C,29C,33C,89C,102C,107C,176C,190C,202C,222C ''day'':23C ''dbcc'':126C ''disk'':146C ''everi'':22C ''execut'':133C ''export'':87C ''file'':225C ''first'':25C ''freebcp'':1A,51C,56C,216C ''get'':2A,110C ''hadoop'':8B,224C ''host'':42C,210C ''href'':59C ''huge'':5A,104C ''info'':54C ''inputbuff'':127C ''io'':147C ''job'':164C ''kill'':169C ''linux'':44C,74C,209C ''linux.die.net'':61C ''linux.die.net/man/1/freebcp'':60C ''load'':220C ''machin'':43C ''manual'':168C ''map'':229C ''may'':211C ''minut'':160C ''monitor'':131C ''ms'':37C ''ms-sql'':36C ''nofollow'':64C ''observ'':98C,141C ''p'':9C,68C,180C,193C ''pipelin'':30C ''probabl'':184C ''procedur'':82C,136C,151C,172C ''process'':17C,177C ''project'':15C ''ran'':115C ''reason'':185C ''recent'':95C ''reduc'':230C ''rel'':63C ''run'':77C,227C ''server'':39C,45C,75C,85C,139C,207C ''set'':79C ''size'':20C ''sometim'':113C ''sp'':121C ''spid'':128C ''sql'':38C,84C,138C,206C ''stage'':26C ''stall'':3A,111C,166C,188C ''store'':81C,135C,171C ''system'':226C ''task'':178C,231C ''time'':144C ''tool'':48C,71C ''transfer'':91C,108C ''use'':46C,69C ''util'':148C ''way'':198C ''who2'':122C ''work'':12C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10201, 2, '2016-02-11 14:03:27.103', 0, NULL, NULL, '&lt;p&gt;9 and 10 have different schema. For example, a displaced transverse fracture of the right femur would be&lt;/p&gt;

&lt;p&gt;821.01RT in 9
S72.321 in 10&lt;/p&gt;

&lt;p&gt;10 is also more specific. In the example, 9 doesn''t tell you that the fracture is transverse and displaced. &lt;/p&gt;

&lt;p&gt;Since ICD10 is basically a better organized and more precise superset of the data represented by ICD9, someone has probably created a mapping. However, I haven''t looked into that. &lt;/p&gt;
', NULL, NULL, '''/p'':20C,29C,51C,83C ''10'':4C,28C,31C ''821.01'':22C ''9'':2C,25C,39C ''also'':33C ''basic'':56C ''better'':58C ''creat'':73C ''data'':66C ''differ'':6C ''displac'':11C,50C ''doesn'':40C ''exampl'':9C,38C ''femur'':17C ''fractur'':13C,46C ''haven'':78C ''howev'':76C ''icd10'':54C ''icd9'':69C ''look'':80C ''map'':75C ''organ'':59C ''p'':1C,21C,30C,52C ''precis'':62C ''probabl'':72C ''repres'':67C ''right'':16C ''rt'':23C ''s72.321'':26C ''schema'':7C ''sinc'':53C ''someon'':70C ''specif'':35C ''superset'':63C ''tell'':42C ''transvers'':12C,48C ''would'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10207, 2, '2016-02-11 17:42:35.96', 3, NULL, NULL, '&lt;p&gt;It should be enough to shuffle the elements at the beginning of the training and then to read them sequentially. This really achieves the same objective as taking random elements every time, which is to break any sort of predefined structure that may exist in your original dataset (e.g. all positives in the beginning, sequential images, etc).&lt;/p&gt;

&lt;p&gt;While it would work to fetch random elements every time, this operation is typically not optimal performance-wise. Datasets are usually large and are not saved in your memory with fast random access, but rather in your slow HDD. This means sequential reads are pretty much the only option you have for good performance.&lt;/p&gt;

&lt;p&gt;Caffe for example uses LevelDB, which does not support efficient random seeking. See &lt;a href="https://github.com/BVLC/caffe/issues/1087" rel="nofollow"&gt;https://github.com/BVLC/caffe/issues/1087&lt;/a&gt;, which confirms that the dataset is trained with images always in the same order.&lt;/p&gt;
', NULL, NULL, '''/bvlc/caffe/issues/1087'':135C ''/bvlc/caffe/issues/1087&lt;/a&gt;,'':140C ''/p'':59C,116C,155C ''access'':94C ''achiev'':24C ''alway'':150C ''begin'':12C,55C ''break'':37C ''caff'':118C ''confirm'':142C ''dataset'':49C,80C,145C ''e.g'':50C ''effici'':127C ''element'':9C,31C,68C ''enough'':5C ''etc'':58C ''everi'':32C,69C ''exampl'':120C ''exist'':45C ''fast'':92C ''fetch'':66C ''github.com'':134C,139C ''github.com/bvlc/caffe/issues/1087'':133C ''github.com/bvlc/caffe/issues/1087&lt;/a&gt;,'':138C ''good'':114C ''hdd'':100C ''href'':132C ''imag'':57C,149C ''larg'':83C ''leveldb'':122C ''may'':44C ''mean'':102C ''memori'':90C ''much'':107C ''nofollow'':137C ''object'':27C ''oper'':72C ''optim'':76C ''option'':110C ''order'':154C ''origin'':48C ''p'':1C,60C,117C ''perform'':78C,115C ''performance-wis'':77C ''posit'':52C ''predefin'':41C ''pretti'':106C ''random'':30C,67C,93C,128C ''rather'':96C ''read'':19C,104C ''realli'':23C ''rel'':136C ''save'':87C ''see'':130C ''seek'':129C ''sequenti'':21C,56C,103C ''shuffl'':7C ''slow'':99C ''sort'':39C ''structur'':42C ''support'':126C ''take'':29C ''time'':33C,70C ''train'':15C,147C ''typic'':74C ''use'':121C ''usual'':82C ''wise'':79C ''work'':64C ''would'':63C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10208, 1, '2016-02-11 18:39:11.837', 0, 13, 'Theil Sen esitmator that yields non linear line', '&lt;p&gt;I have a data set from which I run the theil sen regression. My data points are the following&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  column
   nan
   nan
   nan
  -8.1
  -8.1
  -8.1
   nan
  -7.2
  -7.2
   nan
   nan
  -7.2
  -7.2
  -7.2
  -7.2
  -7.2
  -7.1
  -7.2
  -7.1
  -7.2
  -7.1
  -7.1
  -7.2
  -6.5
  -6.5
  -6.5
  -6.5
  -6.5
   nan
  -5.8
   nan
  -5.8
  -5.8
  -8.1
  -8.1
  -8.1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I wrote the following to do some linear regression on this data&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;res = stats.theilslopes(ym,x,  0.90).
line=res[1]+res[0]*x
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where ym are the values from the column and x are the values from 0 to 35.  The problem is that the resulting line is not linear as i get the following points.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;line=[-7.83,-7.783333333,-7.736666667,-7.69,-7.643333333,-7.596666667,-7.55,-7.503333333,-7.456666667,-7.41,-7.363333333,-7.316666667,-7.27,-7.223333333,-7.176666667-7.13,-7.083333333,-7.036666667,-6.99,-6.943333333,-6.896666667,-6.85,-6.803333333,-6.756666667,-6.71,-6.663333333,-6.616666667,-6.57]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The line are points are actually less than the number of rows. What can I do to make sure that my line has the exact same number points as the number of rows, so that my regression line is linear?&lt;/p&gt;
', 'python  predictive-modeling  regression', NULL, '''-5.8'':67C,69C,70C ''-6.5'':61C,62C,63C,64C,65C ''-6.57'':170C ''-6.616666667'':169C ''-6.663333333'':168C ''-6.71'':167C ''-6.756666667'':166C ''-6.803333333'':165C ''-6.85'':164C ''-6.896666667'':163C ''-6.943333333'':162C ''-6.99'':161C ''-7.036666667'':160C ''-7.083333333'':159C ''-7.1'':54C,56C,58C,59C ''-7.13'':158C ''-7.176666667'':157C ''-7.2'':45C,46C,49C,50C,51C,52C,53C,55C,57C,60C ''-7.223333333'':156C ''-7.27'':155C ''-7.316666667'':154C ''-7.363333333'':153C ''-7.41'':152C ''-7.456666667'':151C ''-7.503333333'':150C ''-7.55'':149C ''-7.596666667'':148C ''-7.643333333'':147C ''-7.69'':146C ''-7.736666667'':145C ''-7.783333333'':144C ''-7.83'':143C ''-8.1'':41C,42C,43C,71C,72C,73C ''/code'':74C,103C,171C ''/p'':34C,89C,139C,214C ''/pre'':75C,104C,172C ''0'':101C,120C ''0.90'':96C ''1'':99C ''35'':122C ''actual'':179C ''code'':36C,91C,141C ''column'':37C,113C ''data'':18C,29C,88C ''esitm'':3A ''exact'':198C ''follow'':33C,80C,137C ''get'':135C ''less'':180C ''line'':8A,97C,129C,142C,175C,195C,211C ''linear'':7A,84C,132C,213C ''make'':191C ''model'':12B ''nan'':38C,39C,40C,44C,47C,48C,66C,68C ''non'':6A ''number'':183C,200C,204C ''p'':14C,76C,105C,173C ''point'':30C,138C,177C,201C ''pre'':35C,90C,140C ''predict'':11B ''predictive-model'':10B ''problem'':124C ''python'':9B ''regress'':13B,27C,85C,210C ''res'':92C,98C,100C ''result'':128C ''row'':185C,206C ''run'':23C ''sen'':2A,26C ''set'':19C ''stats.theilslopes'':93C ''sure'':192C ''theil'':1A,25C ''valu'':110C,118C ''wrote'':78C ''x'':95C,102C,115C ''yield'':5A ''ym'':94C,107C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10209, 1, '2016-02-11 19:21:28.653', 1, 24, 'Tag categorizer', '&lt;p&gt;I''m using Google prediction API and I need to perform tag categorization for free text. So when I receive a text like "Cant update application" is tagged with #Apps #General #CantUpdate.&lt;/p&gt;

&lt;p&gt;My categories exist in 3 different levels.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Level1  Level2  Level3      Description

Apps    General CantUpdate   "Cannot update applications"
Network Wifi    CantConnect  "Cant connect to internet"
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I need to perform multiple tag categorization.
I was thinking in have multiple lines for this.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Tag     Description

Apps   "Cannot update applications"
General "Cannot update applications"
CantUpdate "Cannot update applications"
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Any better idea?&lt;/p&gt;
', 'data  categorical-data  google  google-prediction-api', NULL, '''/code'':74C,110C ''/p'':45C,54C,93C,116C ''/pre'':75C,111C ''3'':51C ''api'':11B,18C ''app'':42C,61C,98C ''applic'':38C,66C,101C,105C,109C ''better'':114C ''cannot'':64C,99C,103C,107C ''cant'':36C,70C ''cantconnect'':69C ''cantupd'':44C,63C,106C ''categor'':2A,5B,25C,83C ''categori'':48C ''categorical-data'':4B ''code'':56C,95C ''connect'':71C ''data'':3B,6B ''descript'':60C,97C ''differ'':52C ''exist'':49C ''free'':27C ''general'':43C,62C,102C ''googl'':7B,9B,16C ''google-prediction-api'':8B ''idea'':115C ''internet'':73C ''level'':53C ''level1'':57C ''level2'':58C ''level3'':59C ''like'':35C ''line'':90C ''m'':14C ''multipl'':81C,89C ''need'':21C,78C ''network'':67C ''p'':12C,46C,76C,112C ''perform'':23C,80C ''pre'':55C,94C ''predict'':10B,17C ''receiv'':32C ''tag'':1A,24C,40C,82C,96C ''text'':28C,34C ''think'':86C ''updat'':37C,65C,100C,104C,108C ''use'':15C ''wifi'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10212, 1, '2016-02-11 22:21:42.73', 1, 33, 'Data Scientists | Sectors , Departments and Profiles', '&lt;p&gt;I''m searching a lot about what people call Data Scientist. From my brief search, I''ve came with the conclusion that Data Scientists'' are probably what you may call the future evolution of a Business Intelligence Professional, by focusing important topics such has machine learning, big data, etc.&lt;/p&gt;

&lt;p&gt;In this search It''s being difficult to define what may be the most important sectors, departments (inside companies) and main profiles to this subject.&lt;/p&gt;

&lt;p&gt;Can someone help me on understanding what is happening to the business world around this subject?&lt;/p&gt;

&lt;p&gt;Summary:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Most important sectors? (for example, is it the retail and bank sector? There are more?)&lt;/li&gt;
&lt;li&gt;Most important departments? (are the majority of Data Scientists working on IT departments? Risk departments? Marketing? I have no idea.)&lt;/li&gt;
&lt;li&gt;Most important topics about the profile of a Data Scientist (what are the imperative skills to have?)&lt;/li&gt;
&lt;/ul&gt;
', 'machine-learning  data-mining  bigdata  data', NULL, '''/li'':131C,153C,172C ''/p'':65C,92C,110C,113C ''/ul'':173C ''around'':107C ''bank'':126C ''big'':62C ''bigdata'':13B ''brief'':29C ''busi'':51C,105C ''call'':24C,45C ''came'':33C ''compani'':85C ''conclus'':36C ''data'':1A,11B,14B,25C,38C,63C,140C,163C ''data-min'':10B ''defin'':75C ''depart'':4A,83C,135C,145C,147C ''difficult'':73C ''etc'':64C ''evolut'':48C ''exampl'':120C ''focus'':55C ''futur'':47C ''happen'':102C ''help'':96C ''idea'':152C ''imper'':168C ''import'':56C,81C,117C,134C,156C ''insid'':84C ''intellig'':52C ''learn'':9B,61C ''li'':115C,132C,154C ''lot'':20C ''m'':17C ''machin'':8B,60C ''machine-learn'':7B ''main'':87C ''major'':138C ''market'':148C ''may'':44C,77C ''mine'':12B ''p'':15C,66C,93C,111C ''peopl'':23C ''probabl'':41C ''profession'':53C ''profil'':6A,88C,160C ''retail'':124C ''risk'':146C ''scientist'':2A,26C,39C,141C,164C ''search'':18C,30C,69C ''sector'':3A,82C,118C,127C ''skill'':169C ''someon'':95C ''subject'':91C,109C ''summari'':112C ''topic'':57C,157C ''ul'':114C ''understand'':99C ''ve'':32C ''work'':142C ''world'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10213, 1, '2016-02-11 22:58:56.057', 1, 49, 'How can I ensure about my R^2 score?', '&lt;p&gt;I have a dataset with 10 columns and 158 rows. I try to predict my test dataset which is 1 columns with 158 rows.&lt;/p&gt;

&lt;p&gt;I made cross-validations, grid-search and use ElasticNet algorithm. &lt;/p&gt;

&lt;p&gt;Also before the evaluate the model I check the pearson correlation between 10 columns which I used for train with other 1 column which I try to predict. The correlation is not good but when I evaluate model the R^2 score is near 0.98 . &lt;/p&gt;

&lt;p&gt;How can I be ensure that this score is confidental ? Because I didn''t expect a R^2 like this. This is too high that I expect.&lt;/p&gt;

&lt;p&gt;Thanks in advance.&lt;/p&gt;
', 'machine-learning  data-mining  scikit', NULL, '''/p'':42C,56C,103C,132C,137C ''0.98'':102C ''1'':37C,79C ''10'':23C,70C ''158'':26C,40C ''2'':8A,98C,122C ''advanc'':136C ''algorithm'':55C ''also'':58C ''check'':65C ''column'':24C,38C,71C,80C ''confident'':114C ''correl'':68C,87C ''cross'':47C ''cross-valid'':46C ''data'':14B ''data-min'':13B ''dataset'':21C,34C ''didn'':117C ''elasticnet'':54C ''ensur'':4A,109C ''evalu'':61C,94C ''expect'':119C,131C ''good'':90C ''grid'':50C ''grid-search'':49C ''high'':128C ''learn'':12B ''like'':123C ''machin'':11B ''machine-learn'':10B ''made'':45C ''mine'':15B ''model'':63C,95C ''near'':101C ''p'':17C,43C,57C,104C,133C ''pearson'':67C ''predict'':31C,85C ''r'':7A,97C,121C ''row'':27C,41C ''scikit'':16B ''score'':9A,99C,112C ''search'':51C ''test'':33C ''thank'':134C ''train'':76C ''tri'':29C,83C ''use'':53C,74C ''valid'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10214, 2, '2016-02-12 01:24:49.527', 0, NULL, NULL, '&lt;p&gt;The question is not really clear. &lt;/p&gt;

&lt;p&gt;If the question is how can you save and retrieve the cosine-similarity of each word with every other word, then you can save them in an array and use &lt;a href="https://wiki.python.org/moin/UsingPickle" rel="nofollow"&gt;pickle&lt;/a&gt; for saving and loading them again.&lt;/p&gt;

&lt;p&gt;Or, If the question is how you can retrieve the &lt;code&gt;gensim model&lt;/code&gt;, then the function is &lt;code&gt;gensim.models.word2vec.Word2Vec.load()&lt;/code&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':49C ''/code'':71C,78C ''/moin/usingpickle'':45C ''/p'':8C,56C,79C ''array'':38C ''clear'':7C ''code'':68C,76C ''cosin'':22C ''cosine-similar'':21C ''everi'':28C ''function'':74C ''gensim'':69C ''gensim.models.word2vec.word2vec.load'':77C ''href'':42C ''load'':53C ''model'':70C ''nofollow'':47C ''p'':1C,9C,57C ''pickl'':48C ''question'':3C,12C,61C ''realli'':6C ''rel'':46C ''retriev'':19C,66C ''save'':17C,34C,51C ''similar'':23C ''use'':40C ''wiki.python.org'':44C ''wiki.python.org/moin/usingpickle'':43C ''word'':26C,30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10216, 1, '2016-02-12 02:22:01.94', 1, 95, 'Doc2Vec - How to label the paragraphs (gensim)', '&lt;p&gt;I am wondering how to label (tag) sentences / paragraphs / documents with doc2vec in gensim - from a practical standpoint. &lt;/p&gt;

&lt;p&gt;Do you need to have each sentence / paragraph / document with its own unique label (e.g. "Sent_123")? This seems useful if you want to say "what words or sentences are most similar to a single specific sentence labeled "Sent_123".&lt;/p&gt;

&lt;p&gt;Can you have the labels be repeated based on content? For example if each sentence / paragraph / document is about a certain product item (and there are multiple sentence / paragraph / document for a given product item) can you label the sentences based on the item and then compute the similarity between a word or a sentence and this label (which I guess would be like an average of all those sentences that had to do with the product item)?&lt;/p&gt;
', 'machine-learning  text-mining  word-embeddings  word2vec', NULL, '''/p'':37C,79C,159C ''123'':55C,78C ''averag'':146C ''base'':88C,121C ''certain'':101C ''comput'':127C ''content'':90C ''doc2vec'':1A,30C ''document'':28C,47C,97C,110C ''e.g'':53C ''embed'':16B ''exampl'':92C ''gensim'':7A,32C ''given'':113C ''guess'':141C ''item'':103C,115C,124C,158C ''label'':4A,24C,52C,76C,85C,118C,138C ''learn'':10B ''like'':144C ''machin'':9B ''machine-learn'':8B ''mine'':13B ''multipl'':107C ''need'':41C ''p'':18C,38C,80C ''paragraph'':6A,27C,46C,96C,109C ''practic'':35C ''product'':102C,114C,157C ''repeat'':87C ''say'':63C ''seem'':57C ''sent'':54C,77C ''sentenc'':26C,45C,67C,75C,95C,108C,120C,135C,150C ''similar'':70C,129C ''singl'':73C ''specif'':74C ''standpoint'':36C ''tag'':25C ''text'':12B ''text-min'':11B ''uniqu'':51C ''use'':58C ''want'':61C ''wonder'':21C ''word'':15B,65C,132C ''word-embed'':14B ''word2vec'':17B ''would'':142C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10217, 2, '2016-02-12 03:19:49.117', 1, NULL, NULL, '&lt;p&gt;I''m fairly new to Spark, and have figured out how to integrate with with IPython on Windows 10 and 7. First, check your environment variables for Python and Spark. Here are mine: 
SPARK_HOME: C:\spark-1.6.0-bin-hadoop2.6\
I use Enthought Canopy, so Python is already integrated in my system path. Next, launch Python or IPython and use the following code. If you get an error, check what you get for ''spark_home''. Otherwise, it should run just fine.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import os

import sys

spark_home = os.environ.get(''SPARK_HOME'', None)

if not spark_home:

raise ValueError(''SPARK_HOME environment variable is not set'')

sys.path.insert(0, os.path.join(spark_home, ''python''))

sys.path.insert(0, os.path.join(spark_home, ''C:/spark-1.6.0-bin-hadoop2.6/python/lib/py4j-0.9-src.zip'')) ## may need to adjust on your system depending on which Spark version you''re using and where you installed it.

execfile(os.path.join(spark_home, ''python/pyspark/shell.py''))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/Ou0CE.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Ou0CE.png" alt="pySpark on IPython"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':168C ''/code'':149C ''/ou0ce.png'':156C,163C ''/p'':85C,169C ''/pre'':150C ''/spark-1.6.0-bin-hadoop2.6/python/lib/py4j-0.9-src.zip'':123C ''0'':112C,118C ''1.6.0'':39C ''10'':20C ''6'':43C ''7'':22C ''adjust'':127C ''alreadi'':51C ''alt'':164C ''bin'':41C ''bin-hadoop2'':40C ''c'':37C,122C ''canopi'':47C ''check'':24C,72C ''code'':66C,87C ''depend'':131C ''enthought'':46C ''environ'':26C,106C ''error'':71C ''execfil'':144C ''fair'':4C ''figur'':10C ''fine'':84C ''first'':23C ''follow'':65C ''get'':69C,75C ''hadoop2'':42C ''home'':36C,78C,93C,96C,101C,105C,115C,121C,147C ''href'':153C ''i.stack.imgur.com'':155C,162C ''i.stack.imgur.com/ou0ce.png'':154C,161C ''img'':159C ''import'':88C,90C ''instal'':142C ''integr'':14C,52C ''ipython'':17C,61C,167C ''launch'':58C ''m'':3C ''may'':124C ''mine'':34C ''need'':125C ''new'':5C ''next'':57C ''nofollow'':158C ''none'':97C ''os'':89C ''os.environ.get'':94C ''os.path.join'':113C,119C,145C ''otherwis'':79C ''p'':1C,151C ''path'':56C ''pre'':86C ''pyspark'':165C ''python'':29C,49C,59C,116C ''python/pyspark/shell.py'':148C ''rais'':102C ''re'':137C ''rel'':157C ''run'':82C ''set'':110C ''spark'':7C,31C,35C,38C,77C,92C,95C,100C,104C,114C,120C,134C,146C ''src'':160C ''sys'':91C ''sys.path.insert'':111C,117C ''system'':55C,130C ''use'':45C,63C,138C ''valueerror'':103C ''variabl'':27C,107C ''version'':135C ''window'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10218, 2, '2016-02-12 06:33:58.687', 3, NULL, NULL, '&lt;p&gt;I play around quite a bit with location data and have found examples both where k-means works fine and where k-means is a poor representation and DBSCAN is a great fit.&lt;/p&gt;

&lt;p&gt;If you''ve ever gone hiking or mountain climbing on a day with fog or a low cloud cover, there are times where you get to the top of the peak and can only see the surrounding peaks poking up through the clouds.  I like to use this analogy when I think of DBSCAN.  The density filtering allows one to select a threshold where data is kept, and all of the remaining data is filtered out.&lt;/p&gt;

&lt;p&gt;Take a look at this Seattle crime incident data.  Suppose I want to cluster the data by location to form pseudo neighborhoods i.e. neighborhoods that are roughly defined the the geography of where criminal events tend to occur. This is an example of k-means working just fine in location analytics:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Seattle crime data superimposing a primitive map with k-means clustering&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/3DhWo.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/3DhWo.png" alt="Seattle Crime data clustered with k-means"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you know Seattle at all, you can see that the clusters tend to pick out neighborhoods and this works quite well in breaking up neighborhoods.  Now suppose I want to pick out the high crime areas of Seattle in order to identify the hot spots. No matter how I adjust k, the k-means clustering doesn''t really provide any additional insight.  But the density filtering in DBSCAN does a wonderful job of identifying high crime areas:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Seattle crime data superimposing a primitive map with DBSCAN clustering&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/J2WSO.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/J2WSO.png" alt="Seattle Crime data clustered with DBSCAN"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I think this gives the jest of some strengths that can be exploited using the two algorithms.  There''s nothing special about the crime data.  For what its worth, I have a &lt;a href="http://angusmacnab.com/Location_SKLearn_Tut_MSData_0.2.html" rel="nofollow"&gt;tutorial that goes through the analytics of identifying a user''s home and work locations from their cell phone GPS pings&lt;/a&gt;. This is another case where DBSCAN was very useful, but the DBSCAN specific parts a buried a ways down in the tutorial.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/3dhwo.png'':191C,198C ''/a'':208C,325C,388C ''/j2wso.png'':310C,317C ''/location_sklearn_tut_msdata_0.2.html'':364C ''/p'':37C,115C,169C,185C,209C,290C,304C,326C,411C,416C ''/strong'':184C,303C ''addit'':273C ''adjust'':261C ''algorithm'':344C ''allow'':96C ''alt'':199C,318C ''analog'':87C ''analyt'':168C,372C ''angusmacnab.com'':363C ''angusmacnab.com/location_sklearn_tut_msdata_0.2.html'':362C ''anoth'':391C ''area'':247C,289C ''around'':4C ''bit'':7C ''break'':234C ''buri'':404C ''case'':392C ''cell'':384C ''climb'':47C ''cloud'':56C,81C ''cluster'':130C,183C,203C,222C,267C,302C,322C ''cover'':57C ''crime'':123C,173C,201C,246C,288C,294C,320C,351C ''crimin'':150C ''data'':10C,103C,111C,125C,132C,174C,202C,295C,321C,352C ''day'':50C ''dbscan'':32C,92C,280C,301C,324C,394C,400C ''defin'':144C ''densiti'':94C,277C ''doesn'':268C ''event'':151C ''ever'':42C ''exampl'':14C,158C ''exploit'':340C ''filter'':95C,113C,278C ''fine'':21C,165C ''fit'':36C ''fog'':52C ''form'':136C ''found'':13C ''geographi'':147C ''get'':63C ''give'':331C ''goe'':369C ''gone'':43C ''gps'':386C ''great'':35C ''help'':415C ''high'':245C,287C ''hike'':44C ''home'':378C ''hope'':413C ''hot'':255C ''href'':188C,307C,361C ''i.e'':139C ''i.stack.imgur.com'':190C,197C,309C,316C ''i.stack.imgur.com/3dhwo.png'':189C,196C ''i.stack.imgur.com/j2wso.png'':308C,315C ''identifi'':253C,286C,374C ''img'':194C,313C ''incid'':124C ''insight'':274C ''jest'':333C ''job'':284C ''k'':18C,25C,161C,181C,206C,262C,265C ''k-mean'':17C,24C,160C,180C,205C,264C ''kept'':105C ''know'':213C ''like'':83C ''locat'':9C,134C,167C,381C ''look'':119C ''low'':55C ''map'':178C,299C ''matter'':258C ''mean'':19C,26C,162C,182C,207C,266C ''mountain'':46C ''neighborhood'':138C,140C,227C,236C ''nofollow'':193C,312C,366C ''noth'':347C ''occur'':154C ''one'':97C ''order'':251C ''p'':1C,38C,116C,170C,186C,210C,291C,305C,327C,412C ''part'':402C ''peak'':69C,76C ''phone'':385C ''pick'':225C,242C ''ping'':387C ''play'':3C ''poke'':77C ''poor'':29C ''primit'':177C,298C ''provid'':271C ''pseudo'':137C ''quit'':5C,231C ''realli'':270C ''rel'':192C,311C,365C ''remain'':110C ''represent'':30C ''rough'':143C ''seattl'':122C,172C,200C,214C,249C,293C,319C ''see'':73C,219C ''select'':99C ''special'':348C ''specif'':401C ''spot'':256C ''src'':195C,314C ''strength'':336C ''strong'':171C,292C ''superimpos'':175C,296C ''suppos'':126C,238C ''surround'':75C ''take'':117C ''tend'':152C,223C ''think'':90C,329C ''threshold'':101C ''time'':60C ''top'':66C ''tutori'':367C,410C ''two'':343C ''use'':85C,341C,397C ''user'':376C ''ve'':41C ''want'':128C,240C ''way'':406C ''well'':232C ''wonder'':283C ''work'':20C,163C,230C,380C ''worth'':356C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10219, 1, '2016-02-12 07:27:22.183', 1, 56, 'What model should I choose for Jobs recommender system?', '&lt;p&gt;Now, I have enormous number of resumes of someone who search for next jobs.
There are infomation about how old they are, what schools they graduated,and 
what jobs they experienced.etc...&lt;/p&gt;

&lt;p&gt;I want to make a recommender system using those data.
In the system, if I get new resume, I want to recommend certain jobs for him.&lt;/p&gt;

&lt;p&gt;I have some idea what models will I use, for example,
collaborative filtering, recurrent neural network, word2vec etc...&lt;/p&gt;

&lt;p&gt;But I couldn''t find best method for this problem.
If someone Knows good idea, please tell me that.&lt;/p&gt;

&lt;p&gt;This is an example of one data, &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[age,education,skills,past_job1,job1''s occupational category, past_job2,job2''s occupational category,
...,]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;All data is stored in string.&lt;/p&gt;
', 'python  nlp  deep-learning', NULL, '''/code'':143C ''/p'':47C,75C,95C,116C,125C,152C ''/pre'':144C ''age'':128C ''best'':102C ''categori'':136C,142C ''certain'':71C ''choos'':5A ''code'':127C ''collabor'':88C ''couldn'':99C ''data'':58C,124C,147C ''deep'':13B ''deep-learn'':12B ''educ'':129C ''enorm'':19C ''etc'':94C ''exampl'':87C,121C ''experienced.etc'':46C ''filter'':89C ''find'':101C ''get'':64C ''good'':110C ''graduat'':41C ''idea'':80C,111C ''infom'':32C ''job'':7A,29C,44C,72C ''job1'':132C,133C ''job2'':138C,139C ''know'':109C ''learn'':14B ''make'':52C ''method'':103C ''model'':2A,82C ''network'':92C ''neural'':91C ''new'':65C ''next'':28C ''nlp'':11B ''number'':20C ''occup'':135C,141C ''old'':35C ''one'':123C ''p'':15C,48C,76C,96C,117C,145C ''past'':131C,137C ''pleas'':112C ''pre'':126C ''problem'':106C ''python'':10B ''recommend'':8A,54C,70C ''recurr'':90C ''resum'':22C,66C ''school'':39C ''search'':26C ''skill'':130C ''someon'':24C,108C ''store'':149C ''string'':151C ''system'':9A,55C,61C ''tell'':113C ''use'':56C,85C ''want'':50C,68C ''word2vec'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10223, 1, '2016-02-12 15:25:52.077', 0, 51, 'How to retrieve the clustering results of rpart', '&lt;p&gt;I am using rpart package in order to create a segmentation of my data using decision tree. As final result I want to obtain a classification of my data. For exemple, if the rpart devide data into 3 classes, I want to divide my data onto this three subsets such that I know that row nÂ°1 is in subset 1, row nÂ°2 in subset 3, etc. I can''t find how could I get this information from the results of rpart object?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;tree.res &amp;lt;- rpart(x ~ ., data, method="class", parms=list(split = "gini"))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I know that I can retrieve the results of this classification as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;plot(tree)    #plot the tree
text(tree)
printcp(tree) #Displays CP table for Fitted Rpart Object
predict(tree) #displays prediction results of x variable
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;How can I get the information on the segmentation of my data set? As first attempt I used : &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;predict(tree, type = "vector")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is this a correct approach?&lt;/p&gt;
', 'machine-learning  r  clustering  decision-trees', NULL, '''/code'':116C,159C,187C ''/p'':102C,132C,180C,195C ''/pre'':117C,160C,188C ''1'':74C,78C ''2'':81C ''3'':55C,84C ''approach'':194C ''attempt'':177C ''class'':56C,111C ''classif'':43C,129C ''cluster'':5A,13B ''code'':104C,134C,182C ''correct'':193C ''could'':91C ''cp'':145C ''creat'':26C ''data'':31C,46C,53C,62C,109C,173C ''decis'':15B,33C ''decision-tre'':14B ''devid'':52C ''display'':144C,153C ''divid'':60C ''etc'':85C ''exempl'':48C ''final'':36C ''find'':89C ''first'':176C ''fit'':148C ''follow'':131C ''get'':93C,165C ''gini'':115C ''inform'':95C,167C ''know'':70C,120C ''learn'':11B ''list'':113C ''lt'':106C ''machin'':10B ''machine-learn'':9B ''method'':110C ''nâ'':73C,80C ''object'':101C,150C ''obtain'':41C ''onto'':63C ''order'':24C ''p'':17C,118C,161C,189C ''packag'':22C ''parm'':112C ''plot'':135C,137C ''pre'':103C,133C,181C ''predict'':151C,154C,183C ''printcp'':142C ''r'':12B ''result'':6A,37C,98C,126C,155C ''retriev'':3A,124C ''row'':72C,79C ''rpart'':8A,21C,51C,100C,107C,149C ''segment'':28C,170C ''set'':174C ''split'':114C ''subset'':66C,77C,83C ''tabl'':146C ''text'':140C ''three'':65C ''tree'':16B,34C,136C,139C,141C,143C,152C,184C ''tree.res'':105C ''type'':185C ''use'':20C,32C,179C ''variabl'':158C ''vector'':186C ''want'':39C,58C ''x'':108C,157C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10229, 1, '2016-02-12 22:17:56.217', 1, 13, 'dealing with dropped event data', '&lt;p&gt;I have a set of game play event data. One of our goals is to provide a picture of how much time the person has spent playing. This sounds simple but for the fact that events are often dropped and result in vast amounts of time between events. &lt;/p&gt;

&lt;p&gt;We are in field trials right now and don''t have a huge pool of data to use as reference. &lt;/p&gt;

&lt;p&gt;I don''t have much experience in statics, though I''m a competent programmer. My question is: What might I do to account for the missing data? How do I ensure that these missing events don''t skew my data?&lt;/p&gt;
', 'missing-data', NULL, '''/p'':58C,80C,122C ''account'':104C ''amount'':53C ''compet'':94C ''data'':5A,8B,18C,75C,108C,121C ''deal'':1A ''drop'':3A,48C ''ensur'':112C ''event'':4A,17C,45C,57C,116C ''experi'':87C ''fact'':43C ''field'':63C ''game'':15C ''goal'':22C ''huge'':72C ''m'':92C ''might'':100C ''miss'':7B,107C,115C ''missing-data'':6B ''much'':30C,86C ''often'':47C ''one'':19C ''p'':9C,59C,81C ''person'':33C ''pictur'':27C ''play'':16C,36C ''pool'':73C ''programm'':95C ''provid'':25C ''question'':97C ''refer'':79C ''result'':50C ''right'':65C ''set'':13C ''simpl'':39C ''skew'':119C ''sound'':38C ''spent'':35C ''static'':89C ''though'':90C ''time'':31C,55C ''trial'':64C ''use'':77C ''vast'':52C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10224, 1, '2016-02-12 17:00:51.213', 1, 15, 'What are standard ways to normalize lengths for multiple time series?', '&lt;p&gt;An example of some time series input I have in mind. Suppose I have multiple users working on a 20-minute search task, using a commercial search engine like Google, Yahoo, or Bing. It''s the same task, but users may finish after 10 minutes if they feel they have completed the task to the best of their ability. The y-axis is a "performance metric" (like normalized discounted cumulative gain), and measures are taken either:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;At each query&lt;/li&gt;
&lt;li&gt;At each minute&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Both the # of queries and time are obviously not constant across users.  The goal here is for visualization/visual comparison, but if there''s methods that are more suited to recommendation/prediction, I''m happy to hear those solutions, too!&lt;/p&gt;
', 'time-series', NULL, '''/li'':99C,104C ''/p'':93C,145C ''/ul'':105C ''10'':59C ''20'':35C ''abil'':74C ''across'':117C ''axi'':78C ''best'':71C ''bing'':48C ''commerci'':41C ''comparison'':125C ''complet'':66C ''constant'':116C ''cumul'':86C ''discount'':85C ''either'':92C ''engin'':43C ''exampl'':17C ''feel'':63C ''finish'':57C ''gain'':87C ''goal'':120C ''googl'':45C ''happi'':139C ''hear'':141C ''input'':22C ''length'':7A ''li'':95C,100C ''like'':44C,83C ''m'':138C ''may'':56C ''measur'':89C ''method'':130C ''metric'':82C ''mind'':26C ''minut'':36C,60C,103C ''multipl'':9A,30C ''normal'':6A,84C ''obvious'':114C ''p'':15C,106C ''perform'':81C ''queri'':98C,110C ''recommendation/prediction'':136C ''search'':37C,42C ''seri'':11A,14B,21C ''solut'':143C ''standard'':3A ''suit'':134C ''suppos'':27C ''taken'':91C ''task'':38C,53C,68C ''time'':10A,13B,20C,112C ''time-seri'':12B ''ul'':94C ''use'':39C ''user'':31C,55C,118C ''visualization/visual'':124C ''way'':4A ''work'':32C ''y'':77C ''y-axi'':76C ''yahoo'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10234, 1, '2016-02-13 18:35:09.057', -2, 259, 'Finding databases with same columns and different content/ model', '&lt;p&gt;I am looking for datasets that have the same columns and their content (rows) are different. When I run a decision tree (classification) I''ll get different models (trees) for each of them. It can be data of customers of different bank or insurance companies. &lt;/p&gt;

&lt;p&gt;Just for clarification the datasets should have the following criteria:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1. Have same columns
2. Categorized target column (i.e. I can build decision tree)
3. Each dataset can have enough data (over 1000 tuples)
4. The decision trees that I create (2) from each dataset is different
&lt;/code&gt;&lt;/pre&gt;
', 'dataset', NULL, '''/code'':109C ''/p'':57C,69C ''/pre'':110C ''1'':72C ''1000'':94C ''2'':76C,103C ''3'':86C ''4'':96C ''bank'':53C ''build'':83C ''categor'':77C ''clarif'':61C ''classif'':34C ''code'':71C ''column'':5A,21C,75C,79C ''compani'':56C ''content'':8A,24C ''creat'':102C ''criteria'':68C ''custom'':50C ''data'':48C,92C ''databas'':2A ''dataset'':10B,16C,63C,88C,106C ''decis'':32C,84C,98C ''differ'':7A,27C,38C,52C,108C ''enough'':91C ''find'':1A ''follow'':67C ''get'':37C ''i.e'':80C ''insur'':55C ''ll'':36C ''look'':14C ''model'':9A,39C ''p'':11C,58C ''pre'':70C ''row'':25C ''run'':30C ''target'':78C ''tree'':33C,40C,85C,99C ''tupl'':95C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10403, 1, '2016-02-26 06:01:19.07', 0, 28, 'Recovering from a bad selection of experiment/control group', '&lt;p&gt;Recently I wanted to test out the effectiveness of a marketing campaign using A/B testing, I selected 100 people from California as my control group and 100 people from Texas as my experiment group. (I know, I should have randomized first my bad) The result showed that the people in Texas has a 15% higher response rate.&lt;/p&gt;

&lt;p&gt;My question here is: How do I know that the 15% higher response rate is cause by my experiment and not because Texan people tend to respond more? Assuming I have no additional data on this, just the ones I have in my experiment.&lt;/p&gt;
', 'data-mining  ab-test', NULL, '''/p'':73C,119C ''100'':33C,42C ''15'':69C,85C ''a/b'':29C ''ab'':13B ''ab-test'':12B ''addit'':107C ''assum'':103C ''bad'':4A,58C ''california'':36C ''campaign'':27C ''caus'':90C ''control'':39C ''data'':10B,108C ''data-min'':9B ''effect'':23C ''experi'':48C,93C,118C ''experiment/control'':7A ''first'':56C ''group'':8A,40C,49C ''higher'':70C,86C ''know'':51C,82C ''market'':26C ''mine'':11B ''one'':113C ''p'':15C,74C ''peopl'':34C,43C,64C,98C ''question'':76C ''random'':55C ''rate'':72C,88C ''recent'':16C ''recov'':1A ''respond'':101C ''respons'':71C,87C ''result'':60C ''select'':5A,32C ''show'':61C ''tend'':99C ''test'':14B,20C,30C ''texa'':45C,66C ''texan'':97C ''use'':28C ''want'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10225, 1, '2016-02-12 17:52:48.12', 3, 39, 'Tools for automatic anomaly detection on a SQL table?', '&lt;p&gt;I have a large SQL table that is essentially a log. The data is pretty complex and I''m trying to find some way to identify anomalies without me understanding all the data. I''ve found lots of tools for Anomaly Detection but most of them require a "middle-man" of sorts, ie Elastic Search, Splunk, etc.&lt;/p&gt;

&lt;p&gt;Does anyone know of a tool that can run against a SQL table which builds a baseline and alerts of anomalies automagically?&lt;/p&gt;

&lt;p&gt;This may sound lazy but I''ve spent dozens of hours writing individual reporting scripts as I learn what each event type means and which other fields go with each event and I don''t feel any closer to being able to alert on real problems in a meaningful way. The table has 41 columns and just hit 500 million rows (3 years of data).&lt;/p&gt;
', 'sql  anomaly-detection  outlier', NULL, '''/p'':74C,98C,165C ''3'':161C ''41'':153C ''500'':158C ''abl'':140C ''alert'':94C,142C ''anomali'':4A,12B,42C,56C,96C ''anomaly-detect'':11B ''anyon'':77C ''automag'':97C ''automat'':3A ''baselin'':92C ''build'':90C ''closer'':137C ''column'':154C ''complex'':31C ''data'':28C,48C,164C ''detect'':5A,13B,57C ''dozen'':108C ''elast'':70C ''essenti'':24C ''etc'':73C ''event'':120C,130C ''feel'':135C ''field'':126C ''find'':37C ''found'':51C ''go'':127C ''hit'':157C ''hour'':110C ''identifi'':41C ''ie'':69C ''individu'':112C ''know'':78C ''larg'':19C ''lazi'':103C ''learn'':117C ''log'':26C ''lot'':52C ''m'':34C ''man'':66C ''may'':101C ''mean'':122C ''meaning'':148C ''middl'':65C ''middle-man'':64C ''million'':159C ''outlier'':14B ''p'':15C,75C,99C ''pretti'':30C ''problem'':145C ''real'':144C ''report'':113C ''requir'':62C ''row'':160C ''run'':84C ''script'':114C ''search'':71C ''sort'':68C ''sound'':102C ''spent'':107C ''splunk'':72C ''sql'':8A,10B,20C,87C ''tabl'':9A,21C,88C,151C ''tool'':1A,54C,81C ''tri'':35C ''type'':121C ''understand'':45C ''ve'':50C,106C ''way'':39C,149C ''without'':43C ''write'':111C ''year'':162C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10227, 2, '2016-02-12 21:12:01.79', 2, NULL, NULL, '&lt;p&gt;&lt;code&gt;doc2vec&lt;/code&gt; model gets its algorithm from &lt;code&gt;word2vec&lt;/code&gt;. &lt;/p&gt;

&lt;p&gt;In &lt;code&gt;word2vec&lt;/code&gt; there is no need to label the words, because every word has their own semantic meaning in the vocabulary.
But in case of &lt;code&gt;doc2vec&lt;/code&gt;, there is a need to specify that how many number of words or sentences convey a semantic meaning, so that the algorithm could identify it as a single entity. For this reason, we are specifying &lt;code&gt;labels&lt;/code&gt; or &lt;code&gt;tags&lt;/code&gt; to sentence or paragraph depending on the level of semantic meaning conveyed.&lt;/p&gt;

&lt;p&gt;If we specify a single label to multiple sentences in a paragraph, it means that all the sentences in the paragraph are required to convey the meaning. On the other hand, if we specify variable labels to all the sentences in a paragraph, it means that each conveys a semantic meaning and they may or may not have similarity among them.&lt;/p&gt;

&lt;p&gt;In simple terms, a &lt;code&gt;label&lt;/code&gt; means semantic meaning of something.&lt;/p&gt;
', NULL, NULL, '''/code'':4C,12C,18C,44C,82C,86C,170C ''/p'':13C,99C,162C,176C ''algorithm'':8C,66C ''among'':160C ''case'':40C ''code'':2C,10C,16C,42C,80C,84C,168C ''convey'':59C,98C,125C,148C ''could'':67C ''depend'':91C ''doc2vec'':3C,43C ''entiti'':73C ''everi'':28C ''get'':6C ''hand'':131C ''identifi'':68C ''label'':24C,81C,106C,136C,169C ''level'':94C ''mani'':53C ''may'':154C,156C ''mean'':34C,62C,97C,114C,127C,145C,151C,171C,173C ''model'':5C ''multipl'':108C ''need'':22C,48C ''number'':54C ''p'':1C,14C,100C,163C ''paragraph'':90C,112C,121C,143C ''reason'':76C ''requir'':123C ''semant'':33C,61C,96C,150C,172C ''sentenc'':58C,88C,109C,118C,140C ''similar'':159C ''simpl'':165C ''singl'':72C,105C ''someth'':175C ''specifi'':50C,79C,103C,134C ''tag'':85C ''term'':166C ''variabl'':135C ''vocabulari'':37C ''word'':26C,29C,56C ''word2vec'':11C,17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10228, 1, '2016-02-12 22:05:41.193', 4, 280, 'Gini Impurity vs Entropy', '&lt;p&gt;Can someone practically explain the rationale behind &lt;a href="https://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity" rel="nofollow"&gt;Gini impurity&lt;/a&gt; vs &lt;a href="https://en.wikipedia.org/wiki/Decision_tree_learning#Information_gain" rel="nofollow"&gt;Information gain&lt;/a&gt; (based on Entropy)?&lt;/p&gt;

&lt;p&gt;Which &lt;a href="https://en.wikipedia.org/wiki/Decision_tree_learning#Metrics" rel="nofollow"&gt;metric&lt;/a&gt; is better to use in different scenarios while using decision trees?&lt;/p&gt;
', 'machine-learning  decision-trees', NULL, '''/a'':28C,39C,54C ''/p'':43C,66C ''/wiki/decision_tree_learning#gini_impurity'':23C ''/wiki/decision_tree_learning#information_gain'':34C ''/wiki/decision_tree_learning#metrics'':50C ''base'':40C ''behind'':18C ''better'':56C ''decis'':9B,64C ''decision-tre'':8B ''differ'':60C ''en.wikipedia.org'':22C,33C,49C ''en.wikipedia.org/wiki/decision_tree_learning#gini_impurity'':21C ''en.wikipedia.org/wiki/decision_tree_learning#information_gain'':32C ''en.wikipedia.org/wiki/decision_tree_learning#metrics'':48C ''entropi'':4A,42C ''explain'':15C ''gain'':38C ''gini'':1A,26C ''href'':20C,31C,47C ''impur'':2A,27C ''inform'':37C ''learn'':7B ''machin'':6B ''machine-learn'':5B ''metric'':53C ''nofollow'':25C,36C,52C ''p'':11C,44C ''practic'':14C ''rational'':17C ''rel'':24C,35C,51C ''scenario'':61C ''someon'':13C ''tree'':10B,65C ''use'':58C,63C ''vs'':3A,29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10230, 2, '2016-02-12 22:27:54.073', 2, NULL, NULL, '&lt;p&gt;Both are possible. You can give every document a unique ID (such as a sequential serial number) as a doctag, or a shared string doctag representing something else about it, or both at the same time. &lt;/p&gt;

&lt;p&gt;The TaggedDocument constructor takes a list of tags. (If you happen to limit yourself to to plain ints ascending from 0, the Doc2Vec model will use those as direct indexes into its backing array, and you''ll save a lot of memory that would otherwise be devoted to a string -&gt; index lookup, which could be important for large datasets. But you can use string doctags or even a mixture of int and string doctags.)&lt;/p&gt;

&lt;p&gt;You''ll have to experiment with what works best for your needs. &lt;/p&gt;

&lt;p&gt;For some classification tasks, an approach that''s sometimes worked better than I would have expected is skipping per-text IDs entirely, and just training the Doc2Vec model with known-class examples, with the desired classes as the doctags. You then get ''doc vectors'' just for the class doctags â not every document â a potentially much smaller model. Later inferring vectors for new texts results in vectors meaningfully close to related class doc vectors.&lt;/p&gt;
', NULL, NULL, '''/p'':38C,114C,128C,207C ''0'':60C ''approach'':135C ''array'':73C ''ascend'':58C ''back'':72C ''best'':124C ''better'':140C ''class'':162C,167C,179C,204C ''classif'':132C ''close'':201C ''constructor'':42C ''could'':93C ''dataset'':98C ''desir'':166C ''devot'':86C ''direct'':68C ''doc'':174C,205C ''doc2vec'':62C,157C ''doctag'':21C,26C,104C,113C,170C,180C ''document'':9C,184C ''els'':29C ''entir'':152C ''even'':106C ''everi'':8C,183C ''exampl'':163C ''expect'':145C ''experi'':120C ''get'':173C ''give'':7C ''happen'':50C ''id'':12C,151C ''import'':95C ''index'':69C,90C ''infer'':192C ''int'':57C,110C ''known'':161C ''known-class'':160C ''larg'':97C ''later'':191C ''limit'':52C ''list'':45C ''ll'':76C,117C ''lookup'':91C ''lot'':79C ''meaning'':200C ''memori'':81C ''mixtur'':108C ''model'':63C,158C,190C ''much'':188C ''need'':127C ''new'':195C ''number'':18C ''otherwis'':84C ''p'':1C,39C,115C,129C ''per'':149C ''per-text'':148C ''plain'':56C ''possibl'':4C ''potenti'':187C ''relat'':203C ''repres'':27C ''result'':197C ''save'':77C ''sequenti'':16C ''serial'':17C ''share'':24C ''skip'':147C ''smaller'':189C ''someth'':28C ''sometim'':138C ''string'':25C,89C,103C,112C ''tag'':47C ''taggeddocu'':41C ''take'':43C ''task'':133C ''text'':150C,196C ''time'':37C ''train'':155C ''uniqu'':11C ''use'':65C,102C ''vector'':175C,193C,199C,206C ''work'':123C,139C ''would'':83C,143C ''â'':181C,185C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10233, 2, '2016-02-13 17:32:20.82', 0, NULL, NULL, '&lt;p&gt;Try regression diagnostics. There are a couple of methods to help you make sense of the data and the model. Try &lt;a href="https://us.sagepub.com/en-us/nam/an-r-companion-to-applied-regression/book233899" rel="nofollow"&gt;this link&lt;/a&gt;, go to resources tab and download Chapter 6 - Diagnosing Problems in Linear and Generalized Linear Models. The code is in R. However you can find the python equivalent of the code.&lt;/p&gt;
', NULL, NULL, '''/a'':32C ''/en-us/nam/an-r-companion-to-applied-regression/book233899'':27C ''/p'':64C ''6'':40C ''chapter'':39C ''code'':50C,63C ''coupl'':8C ''data'':18C ''diagnos'':41C ''diagnost'':4C ''download'':38C ''equival'':60C ''find'':57C ''general'':46C ''go'':33C ''help'':12C ''howev'':54C ''href'':24C ''linear'':44C,47C ''link'':31C ''make'':14C ''method'':10C ''model'':21C,48C ''nofollow'':29C ''p'':1C ''problem'':42C ''python'':59C ''r'':53C ''regress'':3C ''rel'':28C ''resourc'':35C ''sens'':15C ''tab'':36C ''tri'':2C,22C ''us.sagepub.com'':26C ''us.sagepub.com/en-us/nam/an-r-companion-to-applied-regression/book233899'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10235, 1, '2016-02-13 20:46:02.713', 1, 18, 'knn won''t return anything in R', '&lt;p&gt;I trying to create a matrix with three columns and x rows. 
eveytime i try to append a row to the matrix i get this error.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Error in sort.list(y) : ''x'' must be atomic for ''sort.list'' 
Have you called ''sort'' on a list?
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here is the code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;knn_tester &amp;lt;-function(max_smooth, maxK){
  output =  data.frame(x=numeric(maxK*max_smooth), y =numeric(maxK*max_smooth), z=numeric(maxK*max_smooth), stringsAsFactors = FALSE)
  for(smooth in 1:max_smooth){
    if(smooth%%2 != 0){
      message("Smoothing level: " , smooth)
      fullDataList[[1]] = loadSinglePersonsData(DPI,2,1,smooth); gc();
      fullDataList[[2]] = loadSinglePersonsData(DPI,2,2,smooth); gc(); 
      data&amp;lt;- dataset_extracter(fullDataList)
      # data[1] = training
      # data[2] = trainClass
      # data[3] = testing
      # data[4] = testClass
      for(k in 1:maxK){
        message("iteration: " , k)
        output$z[k] = smooth 
            output$x[k] = k
        predicted = knn(data[1],data[3],data[2],k)# &amp;lt;- Here is where the error occurs.. 
        output$y[k] = mean(predicted == data[4])
      }
    }
  }
  return(output)
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and when i set &lt;code&gt;traceback()&lt;/code&gt; on i get this. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;stop("''x'' must be atomic for ''sort.list'' \nHave you called ''sort'' in a list?")
sort.list(y)
factor(x)
as.factor(cl)
knn(data[1],data[3],data[2],k) at #19
knn_tester(12,10)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am not sure why i can''t assign the value of &lt;code&gt;predicted&lt;/code&gt;?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10254, 1, '2016-02-15 12:19:11.687', 0, 6, 'Publicly Available UK Datasets', '&lt;p&gt;With no disrespect to our fellow data scientists over "the pond", one major problem from the rest of the world''s point of view with questions like &lt;a href="http://datascience.stackexchange.com/questions/155/publicly-available-datasets"&gt;Publicly Available Datasets&lt;/a&gt; is the American focus of the answers. They are great answers, and great sources of data, but lack a lot relevance to others not in the US due to geographic, demographic, social or other factors.&lt;/p&gt;

&lt;p&gt;This question I have is particularly focused on &lt;strong&gt;public data&lt;/strong&gt; sites for the &lt;strong&gt;UK&lt;/strong&gt;. &lt;/p&gt;

&lt;p&gt;Are there sites that have a large variety of data collected? I suspect at the moment the UK isn''t as progressive as the USA in terms of meta-data repositories, so I would be happy for sites that are quite rich in content and interesting in terms of their data science or analytical possibilities!&lt;/p&gt;
', 'dataset  open-source', NULL, '''/a'':45C ''/p'':81C,101C,159C ''/questions/155/publicly-available-datasets'':41C ''/strong'':94C,100C ''american'':48C ''analyt'':157C ''answer'':52C,56C ''avail'':2A,43C ''collect'':113C ''content'':147C ''data'':16C,61C,93C,112C,133C,154C ''datascience.stackexchange.com'':40C ''datascience.stackexchange.com/questions/155/publicly-available-datasets'':39C ''dataset'':4A,5B,44C ''demograph'':76C ''disrespect'':12C ''due'':73C ''factor'':80C ''fellow'':15C ''focus'':49C,89C ''geograph'':75C ''great'':55C,58C ''happi'':139C ''href'':38C ''interest'':149C ''isn'':121C ''lack'':63C ''larg'':109C ''like'':36C ''lot'':65C ''major'':22C ''meta'':132C ''meta-data'':131C ''moment'':118C ''one'':21C ''open'':7B ''open-sourc'':6B ''other'':68C ''p'':9C,82C,102C ''particular'':88C ''point'':31C ''pond'':20C ''possibl'':158C ''problem'':23C ''progress'':124C ''public'':1A,42C,92C ''question'':35C,84C ''quit'':144C ''relev'':66C ''repositori'':134C ''rest'':26C ''rich'':145C ''scienc'':155C ''scientist'':17C ''site'':95C,105C,141C ''social'':77C ''sourc'':8B,59C ''strong'':91C,98C ''suspect'':115C ''term'':129C,151C ''uk'':3A,99C,120C ''us'':72C ''usa'':127C ''varieti'':110C ''view'':33C ''world'':29C ''would'':137C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10238, 1, '2016-02-14 15:08:20.813', 0, 24, 'Contour plot from data frame?', '&lt;p&gt;I am having some troubles plotting a contour plot based on this data.frame &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  output =  data.frame(k=numeric((maxK*max_smooth/2)-1), error =numeric((maxK*max_smooth/2)-1), kernel_size=numeric((maxK*max_smooth/2)-1), stringsAsFactors = FALSE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This is basically derived from this function&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;knn_tester &amp;lt;-function(max_smooth, maxK){
  output =  data.frame(k=numeric((maxK*max_smooth/2)-1), error =numeric((maxK*max_smooth/2)-1), kernel_size=numeric((maxK*max_smooth/2)-1), stringsAsFactors = FALSE)
  #output = as.data.frame(matrix(ncol= 3))
  #output = output[-1,]
  number = 0
  for(smooth in 1:max_smooth){
     if(smooth%%2 != 0){
      message("Smoothing level: " , smooth)
      fullDataList[[1]] = loadSinglePersonsData(DPI,2,1,smooth); gc(); #Kiddi
      fullDataList[[2]] = loadSinglePersonsData(DPI,2,2,smooth); gc(); #Should be Mikael
      data&amp;lt;- dataset_extracter(fullDataList)
      # data[1] = training
      # data[2] = trainClass
      # data[3] = testing
      # data[4] = testClass
      for(i in 1:maxK){
        #print(data)
        message("iteration: " , i)
        output$kernel_size[number]= smooth
            #z = smooth
            output$k[number]=i
        #x = i
        predicted = knn(data$training,data[3]$testing,data[2]$trainClass,k = 1)
            #message("done")
            Error = mean(predicted == data[4]$testClass)
        output$error[number] = Error
        #y = Error
        #print(z)
        #print(x)
        #print(y)
        #rbind(output,data.frame(z,x,y))
        #print(output)
        number = number + 1
      }
    }
  }
  return(output)
}

dai = knn_tester(12,10)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I tried do this filled.contour3(dai), which just give me an error message stating. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;error in plot.window(xlim = c(0,1), ylim = range(levels), xaxs ="i", :
need finite ''ylim'' values:
in addition: warning messages
1: in min(x,na.rm = na.rm):  no non-missing arguments to min; returning Inf
2: in max(x,na.rm = na.rm):  no non-missing argument to max;  returning -Inf
3: in min(x): no non-missing arguments to min; returning Inf
4: in max(x): no non-missing arguments to max; returning -Inf
&lt;/code&gt;&lt;/pre&gt;
', 'r  error-handling', NULL, '''-1'':34C,40C,47C,77C,83C,90C,100C ''/code'':50C,227C,323C ''/p'':24C,60C,244C ''/pre'':51C,228C,324C ''0'':102C,112C,252C ''1'':106C,118C,122C,143C,157C,188C,219C,253C,267C ''10'':226C ''12'':225C ''2'':111C,121C,127C,130C,131C,146C,185C,282C ''3'':97C,149C,182C,297C ''4'':152C,195C,310C ''addit'':264C ''argument'':277C,292C,305C,318C ''as.data.frame'':94C ''base'':20C ''basic'':55C ''c'':251C ''code'':26C,62C,246C ''contour'':1A,18C ''dai'':222C,235C ''data'':4A,137C,142C,145C,148C,151C,160C,179C,181C,184C,194C ''data.frame'':23C,28C,71C,211C ''dataset'':139C ''deriv'':56C ''done'':190C ''dpi'':120C,129C ''error'':8B,35C,78C,191C,198C,200C,202C,241C,247C ''error-handl'':7B ''extract'':140C ''fals'':49C,92C ''filled.contour3'':234C ''finit'':260C ''frame'':5A ''fulldatalist'':117C,126C,141C ''function'':59C,66C ''gc'':124C,133C ''give'':238C ''handl'':9B ''inf'':281C,296C,309C,322C ''iter'':162C ''k'':29C,72C,172C,187C ''kernel'':41C,84C,165C ''kiddi'':125C ''knn'':63C,178C,223C ''level'':115C,256C ''loadsinglepersonsdata'':119C,128C ''lt'':65C,138C ''matrix'':95C ''max'':32C,38C,45C,67C,75C,81C,88C,107C,284C,294C,312C,320C ''maxk'':31C,37C,44C,69C,74C,80C,87C,158C ''mean'':192C ''messag'':113C,161C,189C,242C,266C ''mikael'':136C ''min'':269C,279C,299C,307C ''miss'':276C,291C,304C,317C ''na.rm'':271C,272C,286C,287C ''ncol'':96C ''need'':259C ''non'':275C,290C,303C,316C ''non-miss'':274C,289C,302C,315C ''number'':101C,167C,173C,199C,217C,218C ''numer'':30C,36C,43C,73C,79C,86C ''output'':27C,70C,93C,98C,99C,164C,171C,197C,210C,216C,221C ''p'':10C,52C,229C ''plot'':2A,16C,19C ''plot.window'':249C ''pre'':25C,61C,245C ''predict'':177C,193C ''print'':159C,203C,205C,207C,215C ''r'':6B ''rang'':255C ''rbind'':209C ''return'':220C,280C,295C,308C,321C ''size'':42C,85C,166C ''smooth'':68C,104C,108C,110C,114C,116C,123C,132C,168C,170C ''smooth/2'':33C,39C,46C,76C,82C,89C ''state'':243C ''stringsasfactor'':48C,91C ''test'':150C,183C ''testclass'':153C,196C ''tester'':64C,224C ''train'':144C,180C ''trainclass'':147C,186C ''tri'':231C ''troubl'':15C ''valu'':262C ''warn'':265C ''x'':175C,206C,213C,270C,285C,300C,313C ''xax'':257C ''xlim'':250C ''y'':201C,208C,214C ''ylim'':254C,261C ''z'':169C,204C,212C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10239, 1, '2016-02-14 16:21:21.51', 1, 24, 'Combining parameters for Douglas-Peucker Simplification', '&lt;p&gt;&lt;em&gt;NOTE : I''m not sure if this is the right forum for this question. if not, please advice.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Context&lt;/strong&gt; : I am collecting a huge amount of data using an android app that is placed on a vehicle. I collect the data at ~1second intervals for about 2 hours, which gives almost 7200 data sets. These are the parameters :&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Timestamp (milliseconds)&lt;/li&gt;
&lt;li&gt;Latitude&lt;/li&gt;
&lt;li&gt;Longitude&lt;/li&gt;
&lt;li&gt;Speed&lt;/li&gt;
&lt;li&gt;Acceleration&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Now I was looking at ways to simplify this data, as processing and rendering these many date points, especially on mobile devices is not a good idea.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT :&lt;/strong&gt; as answer to &lt;em&gt;Spacedman&lt;/em&gt;''s comment : I want to simplify the data because most of it is redundant. The data is collected every 1 second regardless of whether the values are constant or changing. Accuracy is not crucial as its only used for displaying visual graphs on a website, drawing a polyline on a map. etc. So I want to keep only the minimum necessary data points required to reproduce the graph/line.&lt;/p&gt;

&lt;p&gt;While searching, I came across the &lt;a href="https://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm" rel="nofollow"&gt;RamerâDouglasâPeucker algorithm&lt;/a&gt; and also found a &lt;a href="http://mourner.github.io/simplify-js/" rel="nofollow"&gt;library&lt;/a&gt; that implements this.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;The issue&lt;/strong&gt; : I am a bit confused as to how to simplify this data. I could :&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Somehow simply the entire thing by considering each data as a 5D point
, &lt;strong&gt;or&lt;/strong&gt;&lt;/li&gt;
&lt;li&gt;Generate three sets of arrays, namely : &lt;code&gt;[Lat, Long, time]&lt;/code&gt;, &lt;code&gt;[Speed, time]&lt;/code&gt; and &lt;code&gt;[acceleration, time]&lt;/code&gt;.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;strong&gt;So my question is :&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Are there any advantages/disadvantages of using either of the two approaches?&lt;/p&gt;

&lt;p&gt;I was thinking that - as each metric will have different patterns of variations, will combining them reduce the efficiency of the whole simplification process?&lt;/p&gt;

&lt;p&gt;Or is it best to keep the metrics separate, so that each will be simplified to its maximum efficiency?&lt;/p&gt;

&lt;p&gt;I am a Java/Obj-C developer, normally active on &lt;a href="http://stackoverflow.com/users/3894781/shahim"&gt;SO&lt;/a&gt;, and I am &lt;strong&gt;not&lt;/strong&gt; an expert on these things, So I would like to know what you guys think.&lt;/p&gt;

&lt;p&gt;Thanks in advance&lt;/p&gt;
', 'bigdata  dataset  data-cleaning', NULL, '''/a'':223C,236C,386C ''/code'':291C,295C,300C ''/em'':33C,135C ''/li'':83C,86C,89C,92C,95C,279C,301C ''/ol'':96C,302C ''/p'':34C,78C,125C,204C,240C,260C,310C,323C,349C,370C,408C,413C ''/simplify-js/'':232C ''/strong'':38C,129C,245C,278C,309C,392C ''/users/3894781/shahim'':384C ''/wiki/ramer%e2%80%93douglas%e2%80%93peucker_algorithm'':216C ''1'':155C ''1second'':62C ''2'':66C ''5d'':274C ''7200'':71C ''acceler'':94C,298C ''accuraci'':166C ''across'':210C ''activ'':378C ''advanc'':412C ''advantages/disadvantages'':315C ''advic'':32C ''algorithm'':222C ''almost'':70C ''also'':225C ''amount'':44C ''android'':49C ''answer'':131C ''app'':50C ''approach'':322C ''array'':285C ''best'':354C ''bigdata'':8B ''bit'':249C ''came'':209C ''chang'':165C ''clean'':12B ''code'':287C,292C,297C ''collect'':41C,58C,153C ''combin'':1A,339C ''comment'':137C ''confus'':250C ''consid'':269C ''constant'':163C ''context'':37C ''could'':259C ''crucial'':169C ''data'':11B,46C,60C,72C,107C,143C,151C,197C,257C,271C ''data-clean'':10B ''dataset'':9B ''date'':114C ''develop'':376C ''devic'':119C ''differ'':334C ''display'':175C ''dougla'':5A ''douglas-peuck'':4A ''douglasâ'':220C ''draw'':181C ''edit'':128C ''effici'':343C,369C ''either'':318C ''em'':14C,133C ''en.wikipedia.org'':215C ''en.wikipedia.org/wiki/ramer%e2%80%93douglas%e2%80%93peucker_algorithm'':214C ''entir'':266C ''especi'':116C ''etc'':187C ''everi'':154C ''expert'':394C ''forum'':25C ''found'':226C ''generat'':281C ''give'':69C ''good'':123C ''graph'':177C ''graph/line'':203C ''guy'':406C ''hour'':67C ''href'':213C,229C,381C ''huge'':43C ''idea'':124C ''implement'':238C ''interv'':63C ''issu'':244C ''java/obj-c'':375C ''keep'':192C,356C ''know'':403C ''lat'':288C ''latitud'':85C ''li'':80C,84C,87C,90C,93C,262C,280C ''librari'':235C ''like'':401C ''long'':289C ''longitud'':88C ''look'':101C ''m'':17C ''mani'':113C ''map'':186C ''maximum'':368C ''metric'':331C,358C ''millisecond'':82C ''minimum'':195C ''mobil'':118C ''mourner.github.io'':231C ''mourner.github.io/simplify-js/'':230C ''name'':286C ''necessari'':196C ''nofollow'':218C,234C ''normal'':377C ''note'':15C ''ol'':79C,261C ''p'':13C,35C,97C,126C,205C,241C,303C,311C,324C,350C,371C,409C ''paramet'':2A,77C ''pattern'':335C ''peucker'':6A,221C ''place'':53C ''pleas'':31C ''point'':115C,198C,275C ''polylin'':183C ''process'':109C,348C ''question'':28C,307C ''ramerâ'':219C ''reduc'':341C ''redund'':149C ''regardless'':157C ''rel'':217C,233C ''render'':111C ''reproduc'':201C ''requir'':199C ''right'':24C ''search'':207C ''second'':156C ''separ'':359C ''set'':73C,283C ''simpli'':264C ''simplif'':7A,347C ''simplifi'':105C,141C,255C,365C ''somehow'':263C ''spacedman'':134C ''speed'':91C,293C ''stackoverflow.com'':383C ''stackoverflow.com/users/3894781/shahim'':382C ''strong'':36C,127C,242C,276C,304C,390C ''sure'':19C ''thank'':410C ''thing'':267C,397C ''think'':327C,407C ''three'':282C ''time'':290C,294C,299C ''timestamp'':81C ''two'':321C ''use'':47C,173C,317C ''valu'':161C ''variat'':337C ''vehicl'':56C ''visual'':176C ''want'':139C,190C ''way'':103C ''websit'':180C ''whether'':159C ''whole'':346C ''would'':400C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10242, 1, '2016-02-14 23:53:59.437', 0, 12, 'How to setup RCurl headers in R for Tinder API', '&lt;p&gt;This is probably a stupid question, but for some reason I can''t get my head around the RCurl headers in R in order to access an API. I''m trying to authenticate with the Tinder API, and have already authenticated to Facebook (using Rfacebook), but when authenticating to Tinder, all I get is &lt;code&gt;Error: Forbidden&lt;/code&gt;. Here''s what I have so far (the last two lines is where I think the problem is):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(Rfacebook)
library(devtools)
library(RCurl)

appID&amp;lt;-''MYAPPID''
appSecret&amp;lt;-''MYAPPSECRET''

fb_oauth &amp;lt;- fbOAuth(app_id=appID, app_secret=appSecret)
me &amp;lt;- getUsers("me",token=fb_oauth)
fbID &amp;lt;- me$id

save(fb_oauth,file="fb_oauth")
load("fb_oauth")

my_Token &amp;lt;- toString(fb_oauth$credentials)
my_httpheader &amp;lt;- c("Content-type=''application/json'', User-agent=''User-Agent: Tinder/3.0.4''")

myTest &amp;lt;- postForm("https://api.gotinder.com/auth", facebook_id=fbID, facebook_token=my_Token, .opts=list(httpheader=my_httpheader))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m assuming my problem is the headers, and &lt;a href="https://gist.github.com/rtt/10403467" rel="nofollow"&gt;here''s what Tinder''s API says about the headers&lt;/a&gt;. But it just returns &lt;code&gt;Error: Forbidden&lt;/code&gt; which isn''t really helpful for troubleshooting.&lt;/p&gt;

&lt;p&gt;Any suggestions about what I''m doing wrong here?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10259, 2, '2016-02-16 03:53:46.017', 1, NULL, NULL, '&lt;p&gt;You could build an ARIMAX model. This would permit to include autoregressive (AR) terms as well as well as the sales in product 5 as an Exogenous Input (X). This would give you a potential model where the sales for a product $i$ at time $t$ is given by $s^i_t$ then,&lt;/p&gt;

&lt;p&gt;$s^1_t=\alpha_1 s^1_{t-1} + \alpha_2 s^1_{t-2} + \ldots + \beta_0 s^5_t + \beta_1 s^5_{t-1} + \ldots $&lt;/p&gt;

&lt;p&gt;Note that you may need to make the series stationary first, but see more on that below. You could estimate this model with the &lt;a href="https://cran.r-project.org/web/packages/seasonal/" rel="nofollow"&gt;seasonal&lt;/a&gt; R package that relies on the &lt;a href="https://www.census.gov/srd/www/x13as/" rel="nofollow"&gt;X-13ARIMA-SEATS software developed by the US Census Bureau&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I would recommend to ensure that your time series are all stationary, see for example &lt;a href="http://stats.stackexchange.com/questions/27332/how-to-know-if-a-time-series-is-stationary-or-non-stationary"&gt;this post&lt;/a&gt; before you use X13. I would also run cointegration tests. See for more explanation this &lt;a href="http://www.econ.uiuc.edu/~econ508/R/e-ta8_R.html" rel="nofollow"&gt;excellent post&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Since you only have data on week 5 I would start modeling in week 5 but you could include autoregressive (AR) terms related to the sales of product 1 prior to week 5.&lt;/p&gt;
', NULL, NULL, '''-1'':65C,83C ''-2'':71C ''/a'':119C,144C,169C,194C ''/p'':55C,85C,145C,195C,230C ''/questions/27332/how-to-know-if-a-time-series-is-stationary-or-non-stationary'':166C ''/srd/www/x13as/'':130C ''/web/packages/seasonal/'':115C ''/~econ508/r/e-ta8_r.html'':189C ''0'':74C ''1'':58C,61C,63C,69C,79C,225C ''13arima'':135C ''2'':67C ''5'':25C,76C,81C,204C,211C,229C ''alpha'':60C,66C ''also'':176C ''ar'':14C,217C ''arimax'':6C ''autoregress'':13C,216C ''beta'':73C,78C ''build'':4C ''bureau'':143C ''census'':142C ''cointegr'':178C ''could'':3C,105C,214C ''cran.r-project.org'':114C ''cran.r-project.org/web/packages/seasonal/'':113C ''data'':201C ''develop'':138C ''ensur'':151C ''estim'':106C ''exampl'':161C ''excel'':192C ''exogen'':28C ''explan'':183C ''first'':97C ''give'':33C ''given'':49C ''href'':112C,127C,163C,186C ''includ'':12C,215C ''input'':29C ''ldot'':72C,84C ''make'':93C ''may'':90C ''model'':7C,37C,108C,208C ''need'':91C ''nofollow'':117C,132C,191C ''note'':87C ''p'':1C,56C,86C,146C,196C ''packag'':121C ''permit'':10C ''post'':168C,193C ''potenti'':36C ''prior'':226C ''product'':24C,43C,224C ''r'':120C ''recommend'':149C ''rel'':116C,131C,190C ''relat'':219C ''reli'':123C ''run'':177C ''sale'':22C,40C,222C ''season'':118C ''seat'':136C ''see'':99C,159C,180C ''seri'':95C,155C ''sinc'':197C ''softwar'':137C ''start'':207C ''stationari'':96C,158C ''stats.stackexchange.com'':165C ''stats.stackexchange.com/questions/27332/how-to-know-if-a-time-series-is-stationary-or-non-stationary'':164C ''term'':15C,218C ''test'':179C ''time'':46C,154C ''us'':141C ''use'':172C ''week'':203C,210C,228C ''well'':17C,19C ''would'':9C,32C,148C,175C,206C ''www.census.gov'':129C ''www.census.gov/srd/www/x13as/'':128C ''www.econ.uiuc.edu'':188C ''www.econ.uiuc.edu/~econ508/r/e-ta8_r.html'':187C ''x'':30C,134C ''x-13arima-seats'':133C ''x13'':173C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10246, 2, '2016-02-15 03:54:53.16', 4, NULL, NULL, '&lt;p&gt;Here is an R version:&lt;/p&gt;

&lt;p&gt;The codes here seem outdated for ggplot2: 2.0.0&lt;/p&gt;

&lt;p&gt;Try my package zmisc: &lt;code&gt;devtools:install_github("jerryzhujian9/zmisc")&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;After you install it, you will be able to run:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;df = mtcars
df$model = rownames(mtcars)

z.radarmap(df, "model", stats="mean", lwd=1, angle=0, fontsize=0.6, facet=T, facetfontsize=1, color=id, linetype=NULL)
z.radarmap(df, "model", stats="none", lwd=1, angle=0, fontsize=1.5, facet=F, facetfontsize=1, color=id, linetype=NULL)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The main codes were adapted from &lt;a href="http://www.cmap.polytechnique.fr/~lepennec/R/Radar/RadarAndParallelPlots.html" rel="nofollow"&gt;http://www.cmap.polytechnique.fr/~lepennec/R/Radar/RadarAndParallelPlots.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/5PL78.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/5PL78.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/5pl78.jpg'':112C,119C ''/a'':125C ''/code'':28C,88C ''/p'':7C,17C,29C,41C,126C ''/pre'':89C ''/~lepennec/r/radar/radarandparallelplots.html'':101C ''/~lepennec/r/radar/radarandparallelplots.html&lt;/a&gt;&lt;/p&gt;'':106C ''0'':58C,77C ''0.6'':60C ''1'':56C,64C,75C,83C ''1.5'':79C ''2.0.0'':16C ''abl'':38C ''adapt'':95C ''alt'':120C ''angl'':57C,76C ''code'':10C,23C,43C,93C ''color'':65C,84C ''descript'':123C ''devtool'':24C ''df'':44C,46C,51C,70C ''enter'':121C ''f'':81C ''facet'':61C,80C ''facetfonts'':63C,82C ''fontsiz'':59C,78C ''ggplot2'':15C ''github'':26C ''href'':98C,109C ''i.stack.imgur.com'':111C,118C ''i.stack.imgur.com/5pl78.jpg'':110C,117C ''id'':66C,85C ''imag'':122C ''img'':115C ''instal'':25C,33C ''jerryzhujian9/zmisc'':27C ''linetyp'':67C,86C ''lwd'':55C,74C ''main'':92C ''mean'':54C ''model'':47C,52C,71C ''mtcar'':45C,49C ''nofollow'':103C,114C ''none'':73C ''null'':68C,87C ''outdat'':13C ''p'':1C,8C,18C,30C,90C,107C ''packag'':21C ''pre'':42C ''r'':5C ''rel'':102C,113C ''rownam'':48C ''run'':40C ''seem'':12C ''src'':116C ''stat'':53C,72C ''tri'':19C ''version'':6C ''www.cmap.polytechnique.fr'':100C,105C ''www.cmap.polytechnique.fr/~lepennec/r/radar/radarandparallelplots.html'':99C ''www.cmap.polytechnique.fr/~lepennec/r/radar/radarandparallelplots.html&lt;/a&gt;&lt;/p&gt;'':104C ''z.radarmap'':50C,69C ''zmisc'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10248, 1, '2016-02-15 08:30:39.1', 0, 32, 'How to make data predictions', '&lt;p&gt;As a total beginner I am trying to apply some "predictions" on top of a bunch of csv files which contains house transactions for the last 20 years divided per area. What I would like to predict is the trend of the transactions for lets say the next year for a specific area.&lt;/p&gt;

&lt;p&gt;What general steps would you follow, to analyse those data and then predict?&lt;/p&gt;

&lt;p&gt;I read different articles but what I am looking for it is a sort of "best general practice" for this sort of problem.&lt;/p&gt;
', 'data-mining  predictive-modeling', NULL, '''/p'':66C,81C,106C ''20'':39C ''analys'':75C ''appli'':21C ''area'':43C,65C ''articl'':86C ''beginn'':16C ''best'':98C ''bunch'':28C ''contain'':33C ''csv'':30C ''data'':4A,7B,77C ''data-min'':6B ''differ'':85C ''divid'':41C ''file'':31C ''follow'':73C ''general'':69C,99C ''hous'':34C ''last'':38C ''let'':57C ''like'':47C ''look'':91C ''make'':3A ''mine'':8B ''model'':11B ''next'':60C ''p'':12C,67C,82C ''per'':42C ''practic'':100C ''predict'':5A,10B,23C,49C,80C ''predictive-model'':9B ''problem'':105C ''read'':84C ''say'':58C ''sort'':96C,103C ''specif'':64C ''step'':70C ''top'':25C ''total'':15C ''transact'':35C,55C ''trend'':52C ''tri'':19C ''would'':46C,71C ''year'':40C,61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10250, 1, '2016-02-15 09:34:13.66', 1, 29, 'What is the difference between (objective / error / criterion / cost / loss) function in the context of neural networks?', '&lt;p&gt;The title says it all: I have seen three terms for functions so far, that seem to be the same / similar:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;error function&lt;/li&gt;
&lt;li&gt;criterion function&lt;/li&gt;
&lt;li&gt;cost function&lt;/li&gt;
&lt;li&gt;objective function&lt;/li&gt;
&lt;li&gt;loss function&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I was working on classification problems&lt;/p&gt;

&lt;p&gt;$$E(W) = \frac{1}{2} \sum_{x \in E}(t_x-o(x))^2$$&lt;/p&gt;

&lt;p&gt;where $W$ are the weights, $E$ is the evaluation set, $t_x$ is the desired output (the class) of $x$ and $o(x)$ is the given output. This function
seems to be commonly called "error function".&lt;/p&gt;

&lt;p&gt;But while reading about this topic, I''ve also seen the terms "criterion function" and "objective function". Do they all mean  the same for neural nets?&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://class.coursera.org/neuralnets-2012-001/lecture/47" rel="nofollow"&gt;Geoffrey Hinton&lt;/a&gt; called cross-entropy for softmax-neurons and $E(W) = \frac{1}{2} \sum_{x \in E}(t_x-o(x))^2$ a &lt;em&gt;cost function&lt;/em&gt;.&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10272, 1, '2016-02-17 06:12:09.65', 3, 90, 'Understanding regularization', '&lt;p&gt;I''m currently trying to understand regularization for logistic regression.&lt;/p&gt;

&lt;p&gt;So far, I''m not quite sure whether I &lt;em&gt;really&lt;/em&gt; got it. Basically, the problem is that when we add an additional features to a model we might overfit the training set. This leads to an algorithm which perfectly matches the training set, but fails for future values (as it too exactly fits what has been there in the past).&lt;/p&gt;

&lt;p&gt;So, this is why we introduce regularization. With this we decrease the impact of the features, so the model may produce an algorithm which does a (seemingly) worse job on the training set, but overall works better.&lt;/p&gt;

&lt;p&gt;Is this correct?&lt;/p&gt;
', 'logistic-regression  regularization', NULL, '''/em'':31C ''/p'':18C,82C,121C,126C ''add'':41C ''addit'':43C ''algorithm'':58C,106C ''basic'':34C ''better'':120C ''correct'':125C ''current'':10C ''decreas'':94C ''em'':29C ''exact'':73C ''fail'':66C ''far'':21C ''featur'':44C,99C ''fit'':74C ''futur'':68C ''got'':32C ''impact'':96C ''introduc'':89C ''job'':112C ''lead'':55C ''logist'':4B,16C ''logistic-regress'':3B ''m'':9C,23C ''match'':61C ''may'':103C ''might'':49C ''model'':47C,102C ''overal'':118C ''overfit'':50C ''p'':7C,19C,83C,122C ''past'':81C ''perfect'':60C ''problem'':36C ''produc'':104C ''quit'':25C ''realli'':30C ''regress'':5B,17C ''regular'':2A,6B,14C,90C ''seem'':110C ''set'':53C,64C,116C ''sure'':26C ''train'':52C,63C,115C ''tri'':11C ''understand'':1A,13C ''valu'':69C ''whether'':27C ''work'':119C ''wors'':111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10251, 2, '2016-02-15 09:42:51.11', 2, NULL, NULL, '&lt;p&gt;Regression will work well if your data set is large, but only for predicting current house prices (say, for example, estimating the value of your house). That''s what people generally mean when they talk about predicting house prices from current house sales data.&lt;/p&gt;

&lt;p&gt;The question of how house prices will behave in the next year is much, but much more complicated, and would not depend simply on the data you currently have. You would need to involve other information and a much more complex model, which would need to involve things like current level of household debt, inflation rate, economic outlook, etc. Daunting.&lt;/p&gt;

&lt;p&gt;Generally, speculative prices follow some stochastic process. They depend on the current value, but diverge more and more the farther you go in the future.&lt;/p&gt;
', NULL, NULL, '''/p'':46C,108C,135C ''behav'':55C ''complex'':88C ''complic'':65C ''current'':16C,42C,75C,97C,121C ''data'':8C,45C,73C ''daunt'':107C ''debt'':101C ''depend'':69C,118C ''diverg'':124C ''econom'':104C ''estim'':22C ''etc'':106C ''exampl'':21C ''farther'':129C ''follow'':113C ''futur'':134C ''general'':32C,110C ''go'':131C ''hous'':17C,27C,39C,43C,52C ''household'':100C ''inflat'':102C ''inform'':83C ''involv'':81C,94C ''larg'':11C ''level'':98C ''like'':96C ''mean'':33C ''model'':89C ''much'':61C,63C,86C ''need'':79C,92C ''next'':58C ''outlook'':105C ''p'':1C,47C,109C ''peopl'':31C ''predict'':15C,38C ''price'':18C,40C,53C,112C ''process'':116C ''question'':49C ''rate'':103C ''regress'':2C ''sale'':44C ''say'':19C ''set'':9C ''simpli'':70C ''specul'':111C ''stochast'':115C ''talk'':36C ''thing'':95C ''valu'':24C,122C ''well'':5C ''work'':4C ''would'':67C,78C,91C ''year'':59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10253, 2, '2016-02-15 12:17:55.757', 3, NULL, NULL, '&lt;p&gt;Generally, your performance will not change whether you use Gini impurity or Entropy. &lt;/p&gt;

&lt;p&gt;A short blog with some unofficial references can be found &lt;a href="https://www.garysieling.com/blog/sklearn-gini-vs-entropy-criteria" rel="nofollow"&gt;here&lt;/a&gt;, of which I think the most important remarks are: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;It only matters in 2% of the cases whether you use gini impurity or entropy. &lt;/li&gt;
&lt;li&gt;Entropy might be a little slower to compute (because it makes use of the logarithm). &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I was once told that both metrics exist because they emerged in different disciplines of science. &lt;/p&gt;
', NULL, NULL, '''/a'':35C ''/blog/sklearn-gini-vs-entropy-criteria'':31C ''/li'':63C,80C ''/p'':15C,45C,99C ''/ul'':81C ''2'':52C ''blog'':19C ''case'':55C ''chang'':7C ''comput'':72C ''differ'':95C ''disciplin'':96C ''emerg'':93C ''entropi'':14C,62C,65C ''exist'':90C ''found'':26C ''general'':2C ''gini'':11C,59C ''href'':28C ''import'':42C ''impur'':12C,60C ''li'':47C,64C ''littl'':69C ''logarithm'':79C ''make'':75C ''matter'':50C ''metric'':89C ''might'':66C ''nofollow'':33C ''p'':1C,16C,82C ''perform'':4C ''refer'':23C ''rel'':32C ''remark'':43C ''scienc'':98C ''short'':18C ''slower'':70C ''think'':39C ''told'':86C ''ul'':46C ''unoffici'':22C ''use'':10C,58C,76C ''whether'':8C,56C ''www.garysieling.com'':30C ''www.garysieling.com/blog/sklearn-gini-vs-entropy-criteria'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10255, 1, '2016-02-15 15:15:49.033', 1, 44, 'Forecast on data with irregular timestamp using pandas', '&lt;p&gt;I am newbie to time series analysis ,and i have a dataset in the following &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;clear_date  Work_Item_Type
7/7/2014    1
7/10/2014   1
7/11/2014   5
7/17/2014   2
7/22/2014   1
7/24/2014   1
7/29/2014   3
7/30/2014   4
8/13/2014   1
8/15/2014   1
8/20/2014   12
8/21/2014   1
8/22/2014   2
8/26/2014   5
8/27/2014   17
8/28/2014   30
8/29/2014   12
8/30/2014   4
9/1/2014    1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;My aim is to &lt;code&gt;forecast the Work_Item_Type&lt;/code&gt; for the next 7 days.The timestamp is irregular here.If any one can provide me with a plan/flow/guide .It would be really helpful.Also any steps to be followed for pre-processing,if required.&lt;/p&gt;

&lt;p&gt;I am not sure ,if i have have provided a complete picture,if not please do comment.&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'python  time-series  pandas', NULL, '''/code'':76C,89C ''/p'':30C,123C,142C,145C ''/pre'':77C ''1'':39C,41C,47C,49C,55C,57C,61C,75C ''12'':59C,71C ''17'':67C ''2'':45C,63C ''3'':51C ''30'':69C ''4'':53C,73C ''5'':43C,65C ''7'':93C ''7/10/2014'':40C ''7/11/2014'':42C ''7/17/2014'':44C ''7/22/2014'':46C ''7/24/2014'':48C ''7/29/2014'':50C ''7/30/2014'':52C ''7/7/2014'':38C ''8/13/2014'':54C ''8/15/2014'':56C ''8/20/2014'':58C ''8/21/2014'':60C ''8/22/2014'':62C ''8/26/2014'':64C ''8/27/2014'':66C ''8/28/2014'':68C ''8/29/2014'':70C ''8/30/2014'':72C ''9/1/2014'':74C ''aim'':80C ''analysi'':21C ''clear'':33C ''code'':32C,83C ''comment'':141C ''complet'':135C ''data'':3A ''dataset'':26C ''date'':34C ''days.the'':94C ''follow'':29C,116C ''forecast'':1A,84C ''helpful.also'':111C ''here.if'':98C ''irregular'':5A,97C ''item'':36C,87C ''newbi'':17C ''next'':92C ''one'':100C ''p'':14C,78C,124C,143C ''panda'':8A,13B ''pictur'':136C ''plan/flow/guide'':106C ''pleas'':139C ''pre'':31C,119C ''pre-process'':118C ''process'':120C ''provid'':102C,133C ''python'':9B ''realli'':110C ''requir'':122C ''seri'':12B,20C ''step'':113C ''sure'':128C ''thank'':144C ''time'':11B,19C ''time-seri'':10B ''timestamp'':6A,95C ''type'':37C,88C ''use'':7A ''work'':35C,86C ''would'':108C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10256, 1, '2016-02-15 15:52:00.317', 0, 22, 'Gathering the number of Google results from a large amount of searches.', '&lt;p&gt;I am trying to build a simple dataset using Google, mainly because it seems like the best option for what I want. &lt;/p&gt;

&lt;p&gt;I want to measure fame for a large group of scientists. The quick method is to measure the amount of Google results when searching their name. I do not care about the results, only the number of them. That method has it''s flaws I know, so I am not opposed to an alternative. &lt;/p&gt;

&lt;p&gt;My scientist data is composed of thousands of entries. Which is causing issues. I tried to programmatically search Google, but less then 1000 searches later they blocked the program. I also looked into their Search API, but that is limited to 100 searches a day unless I pay for more, but since I am a poor college student that isn''t an option. &lt;/p&gt;

&lt;p&gt;I was hoping someone here may be able to offer suggestions on build a dataset with some way of measuring fame. &lt;/p&gt;
', 'dataset  search  google', NULL, '''/p'':39C,95C,160C,183C ''100'':138C ''1000'':119C ''abl'':169C ''also'':127C ''altern'':94C ''amount'':10A,59C ''api'':132C ''best'':33C ''block'':123C ''build'':21C,174C ''care'':70C ''caus'':108C ''colleg'':153C ''compos'':101C ''data'':99C ''dataset'':13B,24C,176C ''day'':141C ''entri'':105C ''fame'':45C,182C ''flaw'':84C ''gather'':1A ''googl'':5A,15B,26C,61C,115C ''group'':49C ''hope'':164C ''isn'':156C ''issu'':109C ''know'':86C ''larg'':9A,48C ''later'':121C ''less'':117C ''like'':31C ''limit'':136C ''look'':128C ''main'':27C ''may'':167C ''measur'':44C,57C,181C ''method'':54C,80C ''name'':66C ''number'':3A,76C ''offer'':171C ''oppos'':91C ''option'':34C,159C ''p'':16C,40C,96C,161C ''pay'':144C ''poor'':152C ''program'':125C ''programmat'':113C ''quick'':53C ''result'':6A,62C,73C ''scientist'':51C,98C ''search'':12A,14B,64C,114C,120C,131C,139C ''seem'':30C ''simpl'':23C ''sinc'':148C ''someon'':165C ''student'':154C ''suggest'':172C ''thousand'':103C ''tri'':19C,111C ''unless'':142C ''use'':25C ''want'':38C,42C ''way'':179C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10273, 2, '2016-02-17 06:13:06.997', 2, NULL, NULL, '&lt;p&gt;Gini impurity and Information Gain Entropy are pretty much the same. And people do use the values interchangeably. Below are the formulae of both:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;$\textit{Gini}: \mathit{Gini}(E) = 1 - \sum_{j=1}^{c}p_j^2$&lt;/li&gt;
&lt;li&gt;$\textit{Entropy}: H(E) = -\sum_{j=1}^{c}p_j\log p_j$&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Given a choice, I would use the Gini impurity, as it doesn''t require me to compute logarithmic functions, which are computationally intensive. The closed form of it''s solution can also be found.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Which metric is better to use in different scenarios while using
  decision trees ?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The Gini impurity, for reasons stated above.&lt;/p&gt;

&lt;p&gt;So, &lt;strong&gt;they are pretty much same when it comes to CART analytics.&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/rasbt/python-machine-learning-book/blob/master/faq/decision-tree-binary.md" rel="nofollow"&gt;Helpful reference for computational comparison of the two methods&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':154C ''/blockquote'':111C ''/li'':42C,57C ''/ol'':58C ''/p'':26C,94C,110C,120C,136C,155C ''/rasbt/python-machine-learning-book/blob/master/faq/decision-tree-binary.md'':142C ''/strong'':135C ''1'':34C,37C,50C ''2'':41C ''also'':91C ''analyt'':134C ''better'':100C ''blockquot'':95C ''c'':38C,51C ''cart'':133C ''choic'':62C ''close'':84C ''come'':131C ''comparison'':149C ''comput'':76C,81C,148C ''decis'':108C ''differ'':104C ''doesn'':71C ''e'':33C,47C ''entropi'':7C,45C ''form'':85C ''formula'':23C ''found'':93C ''function'':78C ''gain'':6C ''gini'':2C,30C,32C,67C,114C ''github.com'':141C ''github.com/rasbt/python-machine-learning-book/blob/master/faq/decision-tree-binary.md'':140C ''given'':60C ''h'':46C ''help'':145C ''href'':139C ''impur'':3C,68C,115C ''inform'':5C ''intens'':82C ''interchang'':19C ''j'':36C,40C,49C,53C,56C ''li'':28C,43C ''log'':54C ''logarithm'':77C ''mathit'':31C ''method'':153C ''metric'':98C ''much'':10C,127C ''nofollow'':144C ''ol'':27C ''p'':1C,39C,52C,55C,59C,96C,112C,121C,137C ''peopl'':14C ''pretti'':9C,126C ''reason'':117C ''refer'':146C ''rel'':143C ''requir'':73C ''scenario'':105C ''solut'':89C ''state'':118C ''strong'':123C ''sum'':35C,48C ''textit'':29C,44C ''tree'':109C ''two'':152C ''use'':16C,65C,102C,107C ''valu'':18C ''would'':64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10257, 2, '2016-02-15 16:52:04.893', 1, NULL, NULL, '&lt;p&gt;With any search engine you will be limited by number of requests and any way of outcoming those limits will be a gray zone of violation of end user agreement (and, eventually, you will get banned for some time, of course). You should be looking into Search APIs of known search engines, for example, Bing gives you 5000 searches per month for free which - for a proof of concept research - might be enough. Also, 5k/month will give you some 20-30k until summer, so your data set will become bigger while you will be polishing your idea.&lt;/p&gt;

&lt;p&gt;Also, Google''s free tier search is limited to 100 requests per day. Which gives you completely legal 3k per month as well. Combined (given, you treat Google and Bing results as equal) you get 8k per month.&lt;/p&gt;
', NULL, NULL, '''-30'':82C ''/p'':100C,140C ''100'':111C ''20'':81C ''3k'':120C ''5000'':59C ''5k/month'':76C ''8k'':137C ''agreement'':31C ''also'':75C,102C ''api'':49C ''ban'':37C ''becom'':91C ''bigger'':92C ''bing'':56C,131C ''combin'':125C ''complet'':118C ''concept'':70C ''cours'':42C ''data'':88C ''day'':114C ''end'':29C ''engin'':5C,53C ''enough'':74C ''equal'':134C ''eventu'':33C ''exampl'':55C ''free'':64C,105C ''get'':36C,136C ''give'':57C,78C,116C ''given'':126C ''googl'':103C,129C ''gray'':24C ''idea'':99C ''k'':83C ''known'':51C ''legal'':119C ''limit'':9C,20C,109C ''look'':46C ''might'':72C ''month'':62C,122C,139C ''number'':11C ''outcom'':18C ''p'':1C,101C ''per'':61C,113C,121C,138C ''polish'':97C ''proof'':68C ''request'':13C,112C ''research'':71C ''result'':132C ''search'':4C,48C,52C,60C,107C ''set'':89C ''summer'':85C ''tier'':106C ''time'':40C ''treat'':128C ''user'':30C ''violat'':27C ''way'':16C ''well'':124C ''zone'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10258, 1, '2016-02-15 17:41:46.373', 1, 33, 'Is there a tool/ library/ algorithm that can learn from the steps I perform in cleaning text and apply them to similar data?', '&lt;p&gt;I have a dataset of organization names that is quite messy. I used all the popular NER tools on it without much success(I assume it''s because they lack context). I resolved to using OpenRefine but I reached a dead end with it''s filters not picking up a lot of similar strings. I would like to use the data that I cleaned so far with OpenRefine for a (preferably supervised) machine learning algorithm that can afterwards continue the cleaning.&lt;br&gt;
Are there any resources that could help with this? &lt;/p&gt;
', 'machine-learning  data-mining  data-cleaning', NULL, '''/p'':125C ''afterward'':111C ''algorithm'':6A,108C ''appli'':19A ''assum'':58C ''br'':115C ''clean'':16A,32B,97C,114C ''context'':64C ''continu'':112C ''could'':121C ''data'':23A,28B,31B,94C ''data-clean'':30B ''data-min'':27B ''dataset'':37C ''dead'':74C ''end'':75C ''far'':99C ''filter'':79C ''help'':122C ''lack'':63C ''learn'':9A,26B,107C ''librari'':5A ''like'':90C ''lot'':84C ''machin'':25B,106C ''machine-learn'':24B ''messi'':44C ''mine'':29B ''much'':55C ''name'':40C ''ner'':50C ''openrefin'':69C,101C ''organ'':39C ''p'':33C ''perform'':14A ''pick'':81C ''popular'':49C ''prefer'':104C ''quit'':43C ''reach'':72C ''resolv'':66C ''resourc'':119C ''similar'':22A,86C ''step'':12A ''string'':87C ''success'':56C ''supervis'':105C ''text'':17A ''tool'':4A,51C ''use'':46C,68C,92C ''without'':54C ''would'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10260, 2, '2016-02-16 04:04:00.967', 1, NULL, NULL, '&lt;p&gt;I have faced similar challenges. You could try to use the &lt;a href="http://www.revolutionanalytics.com/sites/default/files/revoscalerdecisiontrees.pdf" rel="nofollow"&gt;RevolutionR rxDTree function&lt;/a&gt; which performs reasonably well on large data sets and can be converted to a rpart object.&lt;/p&gt;

&lt;p&gt;I found to achieve a reasonable performance boost by specifying the minimum bin size with &lt;code&gt;rpart.control(minbucket=2)&lt;/code&gt;. You could also tune the complexity parameter. Note that both may lead to poor performance. &lt;/p&gt;
', NULL, NULL, '''/a'':23C ''/code'':61C ''/p'':40C,77C ''/sites/default/files/revoscalerdecisiontrees.pdf'':17C ''2'':60C ''achiev'':45C ''also'':64C ''bin'':54C ''boost'':49C ''challeng'':6C ''code'':57C ''complex'':67C ''convert'':35C ''could'':8C,63C ''data'':30C ''face'':4C ''found'':43C ''function'':22C ''href'':14C ''larg'':29C ''lead'':73C ''may'':72C ''minbucket'':59C ''minimum'':53C ''nofollow'':19C ''note'':69C ''object'':39C ''p'':1C,41C ''paramet'':68C ''perform'':25C,48C,76C ''poor'':75C ''reason'':26C,47C ''rel'':18C ''revolutionr'':20C ''rpart'':38C ''rpart.control'':58C ''rxdtree'':21C ''set'':31C ''similar'':5C ''size'':55C ''specifi'':51C ''tri'':9C ''tune'':65C ''use'':11C ''well'':27C ''www.revolutionanalytics.com'':16C ''www.revolutionanalytics.com/sites/default/files/revoscalerdecisiontrees.pdf'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10261, 1, '2016-02-16 08:50:44.807', 2, 30, 'Which is the most appropiate algorithm to use with Mlib for predicting prices', '&lt;p&gt;I am reading the docs of MLlib, but I am new with it and I don''t know which would be the best algorithm to predict prices.&lt;/p&gt;

&lt;p&gt;I have seen that with RandomRDDs I can use a generator for random numbers that follow a normal distribution.&lt;/p&gt;

&lt;p&gt;In my case, this is not good enought, since the prices I want to simulate doesn''t follow a normal distribution. The demand changes depending on if its daytime or night, so the prices also change. The seasonality also affects the demand and influence the resulting prices.&lt;/p&gt;

&lt;p&gt;Because of that, I would need a model that I can feed with historical data in order to generate new prices for simulations that have "sense" depending on the day of the year or the hour (day/night) I want to simulate.&lt;/p&gt;

&lt;p&gt;As I said, I''m looking for a solution with MLlib since I''m working with Spark. I''m blocked in finding the appropiate algorithm when reading the docs. I only would need a name or a few names in order to continue investigating (I, myself, can enrich later the answer in order to help other people that reach this post)&lt;/p&gt;
', 'apache-spark  beginner', NULL, '''/p'':46C,67C,116C,159C,222C ''affect'':108C ''algorithm'':6A,42C,185C ''also'':103C,107C ''answer'':211C ''apach'':15B ''apache-spark'':14B ''appropi'':5A,184C ''beginn'':17B ''best'':41C ''block'':180C ''case'':71C ''chang'':92C,104C ''continu'':203C ''data'':132C ''day'':147C ''day/night'':154C ''daytim'':97C ''demand'':91C,110C ''depend'':93C,144C ''distribut'':66C,89C ''doc'':23C,189C ''doesn'':84C ''enought'':76C ''enrich'':208C ''feed'':129C ''find'':182C ''follow'':63C,86C ''generat'':58C,136C ''good'':75C ''help'':215C ''histor'':131C ''hour'':153C ''influenc'':112C ''investig'':204C ''know'':36C ''later'':209C ''look'':166C ''m'':165C,174C,179C ''mlib'':10A ''mllib'':25C,171C ''model'':125C ''name'':195C,199C ''need'':123C,193C ''new'':29C,137C ''night'':99C ''normal'':65C,88C ''number'':61C ''order'':134C,201C,213C ''p'':18C,47C,68C,117C,160C ''peopl'':217C ''post'':221C ''predict'':12A,44C ''price'':13A,45C,79C,102C,115C,138C ''random'':60C ''randomrdd'':53C ''reach'':219C ''read'':21C,187C ''result'':114C ''said'':163C ''season'':106C ''seen'':50C ''sens'':143C ''simul'':83C,140C,158C ''sinc'':77C,172C ''solut'':169C ''spark'':16B,177C ''use'':8A,56C ''want'':81C,156C ''work'':175C ''would'':38C,122C,192C ''year'':150C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10262, 1, '2016-02-16 10:26:52.147', 2, 49, 'Why is the correlation coefficient of a constant function with function input is not defined?', '&lt;p&gt;Mathematically, in case of a constant function (&lt;code&gt;f(x) = c&lt;/code&gt;) the correlation coefficient of a constant function with function input is not defined. Neverthless, from the function plot, we see that there is no correlation between the function input and output, so it must be returning 0.&lt;/p&gt;
', 'statistics  correlation', NULL, '''/code'':30C ''/p'':68C ''0'':67C ''c'':29C ''case'':21C ''code'':26C ''coeffici'':5A,33C ''constant'':8A,24C,36C ''correl'':4A,17B,32C,55C ''defin'':15A,43C ''f'':27C ''function'':9A,11A,25C,37C,39C,47C,58C ''input'':12A,40C,59C ''mathemat'':19C ''must'':64C ''neverthless'':44C ''output'':61C ''p'':18C ''plot'':48C ''return'':66C ''see'':50C ''statist'':16B ''x'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10263, 2, '2016-02-16 11:13:06.967', 2, NULL, NULL, '&lt;p&gt;The &lt;strong&gt;error function&lt;/strong&gt; is the function representing the difference between the values computed by your model and the real values. In the optimization field often they speak about two phases: a training phase in which the model is set, and a test phase in which the model tests its behaviour against the real values of output. In the training phase the error is necessary to improve the model, while in the test phase the error is useful to check if the model works properly.&lt;/p&gt;

&lt;p&gt;The &lt;strong&gt;objective function&lt;/strong&gt; is the function you want to maximize or minimize. When they call it "&lt;strong&gt;cost function&lt;/strong&gt;" (again, it''s the objective function) it''s because they want to only minimize it. I see the cost function and the objective function as the same thing seen from slightly different perspectives. &lt;/p&gt;

&lt;p&gt;The "&lt;strong&gt;criterion&lt;/strong&gt;" is usually the rule for stopping the algorithm you''re using. Suppose you want that your model find the minimum of an objective function, in real experiences it is often hard to find the exact minimum and the algorithm could continuing to work for a very long time. In that case you could accept to stop it "near" to the optimum with a particular stopping criterion.&lt;/p&gt;

&lt;p&gt;I hope I gave you a correct idea of these topics.&lt;/p&gt;
', NULL, NULL, '''/p'':88C,145C,217C,230C ''/strong'':6C,94C,111C,150C ''accept'':204C ''algorithm'':158C,189C ''behaviour'':53C ''call'':106C ''case'':201C ''check'':82C ''comput'':16C ''continu'':191C ''correct'':225C ''cost'':109C,130C ''could'':190C,203C ''criterion'':149C,216C ''differ'':12C,143C ''error'':4C,65C,78C ''exact'':185C ''experi'':177C ''field'':27C ''find'':168C,183C ''function'':5C,9C,93C,97C,110C,117C,131C,135C,174C ''gave'':222C ''hard'':181C ''hope'':220C ''idea'':226C ''improv'':69C ''long'':197C ''maxim'':101C ''minim'':103C,125C ''minimum'':170C,186C ''model'':19C,40C,50C,71C,85C,167C ''near'':208C ''necessari'':67C ''object'':92C,116C,134C,173C ''often'':28C,180C ''optim'':26C ''optimum'':211C ''output'':59C ''p'':1C,89C,146C,218C ''particular'':214C ''perspect'':144C ''phase'':33C,36C,46C,63C,76C ''proper'':87C ''re'':160C ''real'':22C,56C,176C ''repres'':10C ''rule'':154C ''see'':128C ''seen'':140C ''set'':42C ''slight'':142C ''speak'':30C ''stop'':156C,206C,215C ''strong'':3C,91C,108C,148C ''suppos'':162C ''test'':45C,51C,75C ''thing'':139C ''time'':198C ''topic'':229C ''train'':35C,62C ''two'':32C ''use'':80C,161C ''usual'':152C ''valu'':15C,23C,57C ''want'':99C,122C,164C ''work'':86C,193C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10275, 2, '2016-02-17 07:41:17.917', 1, NULL, NULL, '&lt;p&gt;If I understand your goal correctly, you want to build classifier on similar entities taken from different distributions and than compare the classifier/source distributions. Am I right?&lt;/p&gt;

&lt;p&gt;By the way, if it is indeed so you might find the area of &lt;a href="https://en.wikipedia.org/wiki/Domain_adaptation" rel="nofollow"&gt;domain adaptation&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In this case I would go in your direction of splitting a database (since you need the same entities and features). In order the splitting will make sense I recommend doing the splitting by a feature that should change the entities behavior (e.g., country, far periods in time). That dataset you should use must be quite large in order to have large enough partitioned datasets.&lt;/p&gt;

&lt;p&gt;Specific such dataset that you might find useful are &lt;a href="http://www.census.gov/main/www/cen2000.html" rel="nofollow"&gt;US census 2000&lt;/a&gt; (plenty of options for concept and splitting features).&lt;/p&gt;
', NULL, NULL, '''/a'':54C,143C ''/main/www/cen2000.html'':137C ''/p'':29C,55C,122C,152C ''/wiki/domain_adaptation'':49C ''2000'':142C ''adapt'':53C ''area'':43C ''behavior'':98C ''build'':11C ''case'':59C ''census'':141C ''chang'':95C ''classifi'':12C ''classifier/source'':24C ''compar'':22C ''concept'':148C ''correct'':7C ''countri'':100C ''databas'':69C ''dataset'':106C,121C,126C ''differ'':18C ''direct'':65C ''distribut'':19C,25C ''domain'':52C ''e.g'':99C ''en.wikipedia.org'':48C ''en.wikipedia.org/wiki/domain_adaptation'':47C ''enough'':119C ''entiti'':15C,75C,97C ''far'':101C ''featur'':77C,92C,151C ''find'':41C,130C ''go'':62C ''goal'':6C ''href'':46C,134C ''inde'':37C ''larg'':113C,118C ''make'':83C ''might'':40C,129C ''must'':110C ''need'':72C ''nofollow'':51C,139C ''option'':146C ''order'':79C,115C ''p'':1C,30C,56C,123C ''partit'':120C ''period'':102C ''plenti'':144C ''quit'':112C ''recommend'':86C ''rel'':50C,138C ''right'':28C ''sens'':84C ''similar'':14C ''sinc'':70C ''specif'':124C ''split'':67C,81C,89C,150C ''taken'':16C ''time'':104C ''understand'':4C ''us'':140C ''use'':109C,131C ''want'':9C ''way'':33C ''would'':61C ''www.census.gov'':136C ''www.census.gov/main/www/cen2000.html'':135C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10305, 2, '2016-02-18 13:42:13.21', 1, NULL, NULL, '&lt;p&gt;RapidMiner is actually pretty good at multivariate analysis. The difficult part is getting the data into an appropriate format.&lt;/p&gt;

&lt;p&gt;In order to perform clustering, the data should be converted to a format so that two jobs can be &lt;em&gt;compared&lt;/em&gt; based on skills. For example, each job can be represented as a 12-dimensional boolean vector $$[x_1,\ldots,x_{12}]$$ where each $x_i$ represents a skill which is $true$ if the job covers the skill and $false$ otherwise. A distance measure between jobs is then for example &lt;a href="https://en.wikipedia.org/wiki/Hamming_distance" rel="nofollow"&gt;Hamming-distance&lt;/a&gt;. RapidMiner offers a wide range of distance measures between different kinds of feature vectors (Hamming distance is called &lt;code&gt;NominalDistance&lt;/code&gt; in RapidMiner, but others might be better).&lt;/p&gt;
', NULL, NULL, '''/a'':104C ''/code'':125C ''/em'':44C ''/p'':21C,133C ''/wiki/hamming_distance'':98C ''1'':62C ''12'':57C,65C ''actual'':4C ''analysi'':9C ''appropri'':19C ''base'':45C ''better'':132C ''boolean'':59C ''call'':122C ''cluster'':27C ''code'':123C ''compar'':43C ''convert'':32C ''cover'':79C ''data'':16C,29C ''differ'':114C ''difficult'':11C ''dimension'':58C ''distanc'':86C,103C,111C,120C ''em'':42C ''en.wikipedia.org'':97C ''en.wikipedia.org/wiki/hamming_distance'':96C ''exampl'':49C,93C ''fals'':83C ''featur'':117C ''format'':20C,35C ''get'':14C ''good'':6C ''ham'':102C,119C ''hamming-dist'':101C ''href'':95C ''job'':39C,51C,78C,89C ''kind'':115C ''ldot'':63C ''measur'':87C,112C ''might'':130C ''multivari'':8C ''nofollow'':100C ''nominaldist'':124C ''offer'':106C ''order'':24C ''other'':129C ''otherwis'':84C ''p'':1C,22C ''part'':12C ''perform'':26C ''pretti'':5C ''rang'':109C ''rapidmin'':2C,105C,127C ''rel'':99C ''repres'':54C,70C ''skill'':47C,72C,81C ''true'':75C ''two'':38C ''vector'':60C,118C ''wide'':108C ''x'':61C,64C,68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10265, 1, '2016-02-16 13:04:42.263', 1, 15, 'local regression with streaming data', '&lt;p&gt;From a data stream i''m receiving a pair of measurements consisting of a current consumption and a current percentage every second. By accumulating the consumption over time it will represent eventually the maximum capacity when the percentage reaches from 100% to 0%.&lt;/p&gt;

&lt;p&gt;I want to predict the maximum capacity in (almost) real time using linear regression with a small sample size window of two percent. However, when i compare the models of these local regressions of every two percent with the model of the whole data regression, i get very different results due to perhaps local fluctuation. (see figure)&lt;/p&gt;

&lt;p&gt;Is there a way to bring the local regression models closer to the whole data model? (in a way that i can see the differences due to fluctuation but overall closer predictions to the whole data model)&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/e1c8w.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/e1c8w.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'r  predictive-modeling  time-series  linear-regression  data-stream-mining', NULL, '''/a'':181C ''/e1c8w.png'':168C,175C ''/p'':64C,123C,162C,182C ''0'':63C ''100'':61C ''accumul'':44C ''almost'':74C ''alt'':176C ''bring'':130C ''capac'':55C,72C ''closer'':135C,155C ''compar'':92C ''consist'':32C ''consumpt'':36C,46C ''current'':35C,39C ''data'':5A,17B,23C,109C,139C,160C ''data-stream-min'':16B ''descript'':179C ''differ'':114C,149C ''due'':116C,150C ''enter'':177C ''eventu'':52C ''everi'':41C,100C ''figur'':122C ''fluctuat'':120C,152C ''get'':112C ''howev'':89C ''href'':165C ''i.stack.imgur.com'':167C,174C ''i.stack.imgur.com/e1c8w.png'':166C,173C ''imag'':178C ''img'':171C ''linear'':14B,78C ''linear-regress'':13B ''local'':1A,97C,119C,132C ''m'':26C ''maximum'':54C,71C ''measur'':31C ''mine'':19B ''model'':9B,94C,105C,134C,140C,161C ''nofollow'':170C ''overal'':154C ''p'':20C,65C,124C,163C ''pair'':29C ''percent'':88C,102C ''percentag'':40C,58C ''perhap'':118C ''predict'':8B,69C,156C ''predictive-model'':7B ''r'':6B ''reach'':59C ''real'':75C ''receiv'':27C ''regress'':2A,15B,79C,98C,110C,133C ''rel'':169C ''repres'':51C ''result'':115C ''sampl'':83C ''second'':42C ''see'':121C,147C ''seri'':12B ''size'':84C ''small'':82C ''src'':172C ''stream'':4A,18B,24C ''time'':11B,48C,76C ''time-seri'':10B ''two'':87C,101C ''use'':77C ''want'':67C ''way'':128C,143C ''whole'':108C,138C,159C ''window'':85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10270, 2, '2016-02-16 22:15:52.36', 0, NULL, NULL, '&lt;ul&gt;
&lt;li&gt;How I know correlation vs. causation in this mix?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Finding the causal affect of one variable on another is a difficult one because there are probably a few hidden variables that are the driving factors behind product 5 and all others. For example the true causal effect could be that the weather improved causing sales to increase for product 5 and others, making them correlated but not having any causal relationship.&lt;/p&gt;

&lt;p&gt;One way of trying to remove bias in determining causality is through the use of the following &lt;a href="https://en.wikipedia.org/wiki/Instrumental_variable" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Instrumental_variable&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/li'':12C ''/p'':77C ''/ul'':13C ''/wiki/instrumental_variable'':100C ''/wiki/instrumental_variable&lt;/a&gt;.&lt;/p&gt;'':105C ''5'':43C,65C ''affect'':18C ''anoth'':23C ''behind'':41C ''bias'':85C ''caus'':59C ''causal'':17C,51C,75C,88C ''causat'':8C ''correl'':6C,70C ''could'':53C ''determin'':87C ''difficult'':26C ''drive'':39C ''effect'':52C ''en.wikipedia.org'':99C,104C ''en.wikipedia.org/wiki/instrumental_variable'':98C ''en.wikipedia.org/wiki/instrumental_variable&lt;/a&gt;.&lt;/p&gt;'':103C ''exampl'':48C ''factor'':40C ''find'':15C ''follow'':95C ''hidden'':34C ''href'':97C ''improv'':58C ''increas'':62C ''know'':5C ''li'':2C ''make'':68C ''mix'':11C ''nofollow'':102C ''one'':20C,27C,79C ''other'':46C,67C ''p'':14C,78C ''probabl'':31C ''product'':42C,64C ''rel'':101C ''relationship'':76C ''remov'':84C ''sale'':60C ''tri'':82C ''true'':50C ''ul'':1C ''use'':92C ''variabl'':21C,35C ''vs'':7C ''way'':80C ''weather'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10271, 1, '2016-02-17 00:45:23.133', 0, 25, 'Content based recommender with apache mahout', '&lt;p&gt;I know mahout have no supported content-based recommender, but I want to 
recommend with item''s specific attributes. &lt;/p&gt;

&lt;p&gt;The book &lt;code&gt;mahout in action&lt;/code&gt; mentions that it can be implemented based on item-based recommender, but I don''t know how to do it specifically. &lt;/p&gt;
', 'apache-mahout  recommender-system', NULL, '''/code'':42C ''/p'':34C,65C ''action'':41C ''apach'':5A,8B ''apache-mahout'':7B ''attribut'':33C ''base'':2A,22C,49C,53C ''book'':37C ''code'':38C ''content'':1A,21C ''content-bas'':20C ''implement'':48C ''item'':30C,52C ''item-bas'':51C ''know'':15C,59C ''mahout'':6A,9B,16C,39C ''mention'':43C ''p'':13C,35C ''recommend'':3A,11B,23C,28C,54C ''recommender-system'':10B ''specif'':32C,64C ''support'':19C ''system'':12B ''want'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10274, 1, '2016-02-17 06:52:49.147', 2, 22, 'Finding dominating attributes with in the clusters generated', '&lt;p&gt;I am having a dataset of customers where each customer is represented as some feature vector and I am applying K-means algorithm to this dataset. On the basis of those features, I can abstract and give names to these clusters. But I want to map demographic features on these clusters to validate my clusters whether they make sense or not.&lt;/p&gt;

&lt;p&gt;Now for example I have original data set of 100 customers with 90 females and 10 males.&lt;/p&gt;

&lt;p&gt;I suppose find 4 clusters and each is of size 25. In Cluster No: 1,2 and 3, I have all males and no females. In Cluster No: 4, I have 15 males and 10 females.&lt;/p&gt;

&lt;p&gt;How to map genders to these clusters to describe clusters as male or female dominating ?&lt;/p&gt;

&lt;p&gt;Am I making sense here ?&lt;/p&gt;
', 'machine-learning  data-mining  clustering  k-means  unsupervised-learning', NULL, '''/p'':85C,104C,142C,159C,166C ''1'':120C ''10'':102C,140C ''100'':96C ''15'':137C ''2'':121C ''25'':116C ''3'':123C ''4'':109C,134C ''90'':99C ''abstract'':58C ''algorithm'':46C ''appli'':42C ''attribut'':3A ''basi'':52C ''cluster'':7A,15B,64C,74C,78C,110C,118C,132C,150C,153C ''custom'':29C,32C,97C ''data'':13B,93C ''data-min'':12B ''dataset'':27C,49C ''demograph'':70C ''describ'':152C ''domin'':2A,158C ''exampl'':89C ''featur'':37C,55C,71C ''femal'':100C,130C,141C,157C ''find'':1A,108C ''gender'':147C ''generat'':8A ''give'':60C ''k'':17B,44C ''k-mean'':16B,43C ''learn'':11B,21B ''machin'':10B ''machine-learn'':9B ''make'':81C,163C ''male'':103C,127C,138C,155C ''map'':69C,146C ''mean'':18B,45C ''mine'':14B ''name'':61C ''origin'':92C ''p'':22C,86C,105C,143C,160C ''repres'':34C ''sens'':82C,164C ''set'':94C ''size'':115C ''suppos'':107C ''unsupervis'':20B ''unsupervised-learn'':19B ''valid'':76C ''vector'':38C ''want'':67C ''whether'':79C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10278, 1, '2016-02-17 09:53:10.923', 1, 22, 'Recommendation for boolean dataset with apache mahout', '&lt;p&gt;I was trying to implement Item based Recommender System with the boolean dataset, Dataset example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;      User-id | movie-id | Action | Comedy | Drama
        1              200    0         1        1
        2              210    1         1        0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And I tried implementing it with item-based similarity algorithm as follows:&lt;/p&gt;

&lt;p&gt;package prediction.contentrecommender;&lt;/p&gt;

&lt;p&gt;import java.io.File;
import java.io.IOException;
import java.util.List;&lt;/p&gt;

&lt;p&gt;import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
import org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;
import org.apache.mahout.cf.taste.impl.recommender.GenericBooleanPrefItemBasedRecommender;&lt;/p&gt;

&lt;p&gt;public class ContentRecommender {&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;/**
 * @param args
 */
public static void main(String[] args) {
    int userid=10;
       int noOfRecommendations=1;


    try {

        DataModel dm = new FileDataModel(new File("data/dataset.csv"));
        ItemSimilarity sim = new TanimotoCoefficientSimilarity(dm);
        GenericItemBasedRecommender recommender =
            new GenericBooleanPrefItemBasedRecommender(dm,sim);

     List&amp;lt;RecommendedItem&amp;gt; recommendations =
         recommender.recommend(userid, noOfRecommendations);
            for(RecommendedItem recommendation : recommendations)
             {
                System.out.println(recommendation.getItemID());
            }


    } 
    catch (IOException e) 
    {
        System.out.println("there is an error");
        e.printStackTrace();
    } 
   catch (TasteException e) 
    {
        System.out.println("error");
        e.printStackTrace();
    }


}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;}&lt;/p&gt;

&lt;p&gt;When I am running this code, I am not getting any output, although there is no error.I don''t know what can be the problem. Can anyone figure out how to implement item-based similarity algorithm with Boolean data-set. &lt;/p&gt;
', 'machine-learning  apache-mahout  recommender-system', NULL, '''/code'':55C,171C ''/p'':33C,71C,75C,83C,101C,106C,174C,218C ''/pre'':56C,172C ''0'':47C,54C ''1'':45C,48C,49C,52C,53C,122C ''10'':119C ''2'':50C ''200'':46C ''210'':51C ''action'':42C ''algorithm'':68C,212C ''although'':188C ''anyon'':202C ''apach'':6A,12B ''apache-mahout'':11B ''arg'':110C,116C ''base'':24C,66C,210C ''boolean'':3A,29C,214C ''catch'':156C,165C ''class'':104C ''code'':35C,108C,181C ''comedi'':43C ''contentrecommend'':105C ''data'':216C ''data-set'':215C ''data/dataset.csv'':130C ''datamodel'':124C ''dataset'':4A,30C,31C ''dm'':125C,135C,140C ''drama'':44C ''e'':158C,167C ''e.printstacktrace'':164C,170C ''error'':163C,169C ''error.i'':192C ''exampl'':32C ''figur'':203C ''file'':129C ''filedatamodel'':127C ''follow'':70C ''genericbooleanprefitembasedrecommend'':139C ''genericitembasedrecommend'':136C ''get'':185C ''gt'':145C ''id'':38C,41C ''implement'':22C,61C,207C ''import'':77C,79C,81C,85C,87C,89C,91C,93C,95C,97C,99C ''int'':117C,120C ''ioexcept'':157C ''item'':23C,65C,209C ''item-bas'':64C,208C ''itemsimilar'':131C ''java.io.file'':78C ''java.io.ioexception'':80C ''java.util.list'':82C ''know'':195C ''learn'':10B ''list'':142C ''lt'':143C ''machin'':9B ''machine-learn'':8B ''mahout'':7A,13B ''main'':114C ''movi'':40C ''movie-id'':39C ''new'':126C,128C,133C,138C ''noofrecommend'':121C,149C ''org.apache.mahout.cf.taste.common.tasteexception'':86C ''org.apache.mahout.cf.taste.impl.model.file.filedatamodel'':88C ''org.apache.mahout.cf.taste.impl.recommender.genericbooleanprefitembasedrecommender'':100C ''org.apache.mahout.cf.taste.impl.recommender.genericitembasedrecommender'':90C ''org.apache.mahout.cf.taste.impl.similarity.tanimotocoefficientsimilarity'':92C ''org.apache.mahout.cf.taste.model.datamodel'':94C ''org.apache.mahout.cf.taste.recommender.recommendeditem'':96C ''org.apache.mahout.cf.taste.similarity.itemsimilarity'':98C ''output'':187C ''p'':17C,57C,72C,76C,84C,102C,173C,175C ''packag'':73C ''param'':109C ''pre'':34C,107C ''prediction.contentrecommender'':74C ''problem'':200C ''public'':103C,111C ''recommend'':1A,15B,25C,137C,146C,152C,153C ''recommendation.getitemid'':155C ''recommendeditem'':144C,151C ''recommender-system'':14B ''recommender.recommend'':147C ''run'':179C ''set'':217C ''sim'':132C,141C ''similar'':67C,211C ''static'':112C ''string'':115C ''system'':16B,26C ''system.out.println'':154C,159C,168C ''tanimotocoefficientsimilar'':134C ''tasteexcept'':166C ''tri'':20C,60C,123C ''user'':37C ''user-id'':36C ''userid'':118C,148C ''void'':113C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10279, 2, '2016-02-17 11:12:45.037', 4, NULL, NULL, '&lt;p&gt;They are both discriminative models, yes. The logistic regression loss function is conceptually a function of all points. Correctly classified points add very little to the loss function, adding more if they are close to the boundary. The points near the boundary are therefore more important to the loss and therefore deciding how good the boundary is.&lt;/p&gt;

&lt;p&gt;SVM uses a hinge loss, which conceptually puts the emphasis on the boundary points. Anything farther than the closest points contributes nothing to the loss because of the "hinge" (the max) in the function. Those closest points are the support vectors, simply. Therefore it actually reduces to picking a boundary that creates the largest margin -- distance to closest point. The theory is that the boundary case is all that really matters to generalization.&lt;/p&gt;

&lt;p&gt;The downside is that hinge loss is not differentiable, but that just means it takes more math to discover how to optimize it via Lagrange multipliers. It doesn''t really handle the case where data isn''t linearly separable. Slack variables are a trick that lets this possibility be incorporated cleanly into the optimization problem.&lt;/p&gt;

&lt;p&gt;You can use hinge loss with "deep learning", e.g. &lt;a href="http://arxiv.org/pdf/1306.0239.pdf" rel="nofollow"&gt;http://arxiv.org/pdf/1306.0239.pdf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':59C,134C,191C ''/pdf/1306.0239.pdf'':206C ''/pdf/1306.0239.pdf&lt;/a&gt;&lt;/p&gt;'':211C ''actual'':105C ''ad'':30C ''add'':23C ''anyth'':75C ''arxiv.org'':205C,210C ''arxiv.org/pdf/1306.0239.pdf'':204C ''arxiv.org/pdf/1306.0239.pdf&lt;/a&gt;&lt;/p&gt;'':209C ''boundari'':38C,43C,57C,73C,110C,125C ''case'':126C,168C ''classifi'':21C ''clean'':186C ''close'':35C ''closest'':79C,96C,118C ''conceptu'':14C,67C ''contribut'':81C ''correct'':20C ''creat'':112C ''data'':170C ''decid'':53C ''deep'':199C ''differenti'':144C ''discov'':154C ''discrimin'':5C ''distanc'':116C ''doesn'':163C ''downsid'':137C ''e.g'':201C ''emphasi'':70C ''farther'':76C ''function'':12C,16C,29C,94C ''general'':133C ''good'':55C ''handl'':166C ''hing'':64C,89C,140C,196C ''href'':203C ''import'':47C ''incorpor'':185C ''isn'':171C ''lagrang'':160C ''largest'':114C ''learn'':200C ''let'':181C ''linear'':173C ''littl'':25C ''logist'':9C ''loss'':11C,28C,50C,65C,85C,141C,197C ''margin'':115C ''math'':152C ''matter'':131C ''max'':91C ''mean'':148C ''model'':6C ''multipli'':161C ''near'':41C ''nofollow'':208C ''noth'':82C ''optim'':157C,189C ''p'':1C,60C,135C,192C ''pick'':108C ''point'':19C,22C,40C,74C,80C,97C,119C ''possibl'':183C ''problem'':190C ''put'':68C ''realli'':130C,165C ''reduc'':106C ''regress'':10C ''rel'':207C ''separ'':174C ''simpli'':102C ''slack'':175C ''support'':100C ''svm'':61C ''take'':150C ''theori'':121C ''therefor'':45C,52C,103C ''trick'':179C ''use'':62C,195C ''variabl'':176C ''vector'':101C ''via'':159C ''yes'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10280, 1, '2016-02-17 11:32:54.833', 2, 36, 'What features most contribute for label y?', '&lt;p&gt;On a neural network, how can I find out what features most contribute for some label (the dependent variable y)?&lt;/p&gt;
', 'machine-learning  neuralnetwork  feature-selection', NULL, '''/p'':36C ''contribut'':4A,28C ''depend'':33C ''featur'':2A,13B,26C ''feature-select'':12B ''find'':23C ''label'':6A,31C ''learn'':10B ''machin'':9B ''machine-learn'':8B ''network'':19C ''neural'':18C ''neuralnetwork'':11B ''p'':15C ''select'':14B ''variabl'':34C ''y'':7A,35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10281, 2, '2016-02-17 14:39:40.497', 2, NULL, NULL, '&lt;p&gt;That''s correct. Without regularization your model would fit to an irrelevant noise present in your dataset. It means that training set will fit better but the overall predictive power will decrease. &lt;a href="http://neuralnetworksanddeeplearning.com/chap3.html#overfitting_and_regularization" rel="nofollow"&gt;This&lt;/a&gt; is a good article about overfitting and regularization.&lt;/p&gt;

&lt;p&gt;Also you can get some intuition by watching these images:
&lt;a href="http://i.imgur.com/1UjpHRt.png" rel="nofollow"&gt;Without regularization&lt;/a&gt;
&lt;a href="http://i.imgur.com/kiNG4wk.png" rel="nofollow"&gt;With regularization&lt;/a&gt;
You can clearly see that the shape at the 1st image has problems with prediction at the upper left corner but it perfectly fits to the training points.&lt;/p&gt;
', NULL, NULL, '''/1ujphrt.png'':67C ''/a'':42C,72C,82C ''/chap3.html#overfitting_and_regularization'':38C ''/king4wk.png'':77C ''/p'':51C,111C ''1st'':92C ''also'':53C ''articl'':46C ''better'':26C ''clear'':85C ''corner'':102C ''correct'':4C ''dataset'':18C ''decreas'':33C ''fit'':10C,25C,106C ''get'':56C ''good'':45C ''href'':35C,64C,74C ''i.imgur.com'':66C,76C ''i.imgur.com/1ujphrt.png'':65C ''i.imgur.com/king4wk.png'':75C ''imag'':62C,93C ''intuit'':58C ''irrelev'':13C ''left'':101C ''mean'':20C ''model'':8C ''neuralnetworksanddeeplearning.com'':37C ''neuralnetworksanddeeplearning.com/chap3.html#overfitting_and_regularization'':36C ''nofollow'':40C,69C,79C ''nois'':14C ''overal'':29C ''overfit'':48C ''p'':1C,52C ''perfect'':105C ''point'':110C ''power'':31C ''predict'':30C,97C ''present'':15C ''problem'':95C ''regular'':6C,50C,71C,81C ''rel'':39C,68C,78C ''see'':86C ''set'':23C ''shape'':89C ''train'':22C,109C ''upper'':100C ''watch'':60C ''without'':5C,70C ''would'':9C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10282, 2, '2016-02-17 15:15:28.297', 2, NULL, NULL, '&lt;p&gt;Yes, that is correct.&lt;/p&gt;

&lt;p&gt;For example, think of a polynomial $a_n x^n + a_{n-1} x^{n-1} + \dots + a_2 x^2 + a_1 x^1 + a_0 x^0$ which should fit 100 data points $(x_, y_i)$ where all $y_i$ were generated by one polynomial with some noise. Of course, you could always perfectly fit the model to the data making the MSE error&lt;/p&gt;

&lt;p&gt;$$MSE = \sum_{(x_i, y_i)} (y_i - \text{predict}_{\text{Model}}(x_i))^2$$&lt;/p&gt;

&lt;p&gt;be 0. But considering that there is noise, you might actually prefer a "simpler" model. One way to think about simplicity is having weights $a_i$ which are smaller. This is done by adding a regularization term to the error. One common regularization is $L_1$ regularization ($+ \sum_{1}^{100} | a_i |$), another one is $L_2$ regulariazion ($+ \sum_{1}^{100} a_i^2$).&lt;/p&gt;
', NULL, NULL, '''-1'':20C,23C ''/p'':6C,74C,91C,157C ''0'':34C,36C,94C ''1'':30C,32C,138C,141C,152C ''100'':40C,142C,153C ''2'':26C,28C,90C,149C,156C ''actual'':103C ''ad'':126C ''alway'':62C ''anoth'':145C ''common'':134C ''consid'':96C ''correct'':5C ''could'':61C ''cours'':59C ''data'':41C,69C ''done'':124C ''dot'':24C ''error'':73C,132C ''exampl'':9C ''fit'':39C,64C ''generat'':51C ''l'':137C,148C ''make'':70C ''might'':102C ''model'':66C,87C,107C ''mse'':72C,76C ''n'':15C,17C,19C,22C ''nois'':57C,100C ''one'':53C,108C,133C,146C ''p'':1C,7C,75C,92C ''perfect'':63C ''point'':42C ''polynomi'':13C,54C ''predict'':85C ''prefer'':104C ''regular'':128C,135C,139C ''regulariazion'':150C ''simpler'':106C ''simplic'':113C ''smaller'':121C ''sum'':77C,140C,151C ''term'':129C ''text'':84C,86C ''think'':10C,111C ''way'':109C ''weight'':116C ''x'':16C,21C,27C,31C,35C,43C,78C,88C ''y'':44C,48C,80C,82C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10283, 2, '2016-02-17 16:10:14.62', 1, NULL, NULL, '&lt;p&gt;You should use one of the regression methods for predicting a quantitative variable. For the qualitative features, you can binarize them to pass to the learning algorithm. If you use Python, scikit-learn provide classes for doing that in the preprocessing module.&lt;/p&gt;
', NULL, NULL, '''/p'':45C ''algorithm'':28C ''binar'':21C ''class'':37C ''featur'':18C ''learn'':27C,35C ''method'':9C ''modul'':44C ''one'':5C ''p'':1C ''pass'':24C ''predict'':11C ''preprocess'':43C ''provid'':36C ''python'':32C ''qualit'':17C ''quantit'':13C ''regress'':8C ''scikit'':34C ''scikit-learn'':33C ''use'':4C,31C ''variabl'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10284, 1, '2016-02-17 16:50:36.51', 1, 26, 'Classification problem: estimate the number of people in a household through account transactions', '&lt;p&gt;I want to estimate how many people are in a household by looking at account transactions. It would be also interesting to understand if in the household there are children.&lt;/p&gt;

&lt;p&gt;I thought that a possible way to proceed could be to compare the living expenses with that of the average individual according to the national statistics. Moreover I could also perform text analytics to retrieve informations about what products people bought (in order to understand if they spent money to buy children products).&lt;/p&gt;

&lt;p&gt;As model I could maybe use a random forest for classification (but please correct me if I''m wrong).&lt;/p&gt;

&lt;p&gt;Are there better way to solve this problem?&lt;/p&gt;
', 'machine-learning  classification  random-forest', NULL, '''/p'':52C,107C,128C,138C ''accord'':75C ''account'':12A,36C ''also'':41C,83C ''analyt'':86C ''averag'':73C ''better'':132C ''bought'':94C ''buy'':104C ''children'':51C,105C ''classif'':1A,17B,119C ''compar'':65C ''correct'':122C ''could'':62C,82C,112C ''estim'':3A,25C ''expens'':68C ''forest'':20B,117C ''household'':10A,32C,48C ''individu'':74C ''inform'':89C ''interest'':42C ''learn'':16B ''live'':67C ''look'':34C ''m'':126C ''machin'':15B ''machine-learn'':14B ''mani'':27C ''mayb'':113C ''model'':110C ''money'':102C ''moreov'':80C ''nation'':78C ''number'':5A ''order'':96C ''p'':21C,53C,108C,129C ''peopl'':7A,28C,93C ''perform'':84C ''pleas'':121C ''possibl'':58C ''problem'':2A,137C ''proceed'':61C ''product'':92C,106C ''random'':19B,116C ''random-forest'':18B ''retriev'':88C ''solv'':135C ''spent'':101C ''statist'':79C ''text'':85C ''thought'':55C ''transact'':13A,37C ''understand'':44C,98C ''use'':114C ''want'':23C ''way'':59C,133C ''would'':39C ''wrong'':127C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10285, 2, '2016-02-17 17:37:15.28', 0, NULL, NULL, '&lt;p&gt;I would not suggest GMM at this point as the distribution of points in the space is not well-shaped enough. Even if you want to use it it''s better to look at your data in PC space (i.e. using PCA). My suggestion would be:&lt;/p&gt;

&lt;p&gt;1) Think of your features. What are they? Are you going to use these gr-chromacity as features? If yes you should know that kernel methods work better on this as the features are highly nonlinear. The image show that you need a feature mapping anyway.&lt;/p&gt;

&lt;p&gt;2) It seems you have already thought of kernel methods as you put SVM as a tag. you can use it for classification. Might work better than GMM here. Also think of probabilistic graphical models as they have been used intensively for image segmentation and your images are structured enough (a football field has its fixed position in the image anyway).&lt;/p&gt;

&lt;p&gt;3) If you have raw labeled dataset, I''d recommend to think of smarter features for segmentation. in gr-chromacity you already loose some information about colors which is the most important thing for you here. I would recommend taking the position of pixels into account as well. Then a PCA on the new data may reveal some more linearly separated classes.&lt;/p&gt;
', NULL, NULL, '''/p'':49C,98C,161C,226C ''1'':51C ''2'':100C ''3'':163C ''account'':209C ''alreadi'':105C,185C ''also'':129C ''anyway'':97C,160C ''better'':33C,79C,125C ''chromac'':67C,183C ''class'':225C ''classif'':122C ''color'':190C ''d'':171C ''data'':38C,218C ''dataset'':169C ''distribut'':12C ''enough'':23C,149C ''even'':24C ''featur'':55C,69C,84C,95C,177C ''field'':152C ''fix'':155C ''footbal'':151C ''gmm'':6C,127C ''go'':61C ''gr'':66C,182C ''gr-chromac'':65C,181C ''graphic'':133C ''high'':86C ''i.e'':42C ''imag'':89C,142C,146C,159C ''import'':195C ''inform'':188C ''intens'':140C ''kernel'':76C,108C ''know'':74C ''label'':168C ''linear'':223C ''look'':35C ''loos'':186C ''map'':96C ''may'':219C ''method'':77C,109C ''might'':123C ''model'':134C ''need'':93C ''new'':217C ''nonlinear'':87C ''p'':1C,50C,99C,162C ''pc'':40C ''pca'':44C,214C ''pixel'':207C ''point'':9C,14C ''posit'':156C,205C ''probabilist'':132C ''put'':112C ''raw'':167C ''recommend'':172C,202C ''reveal'':220C ''seem'':102C ''segment'':143C,179C ''separ'':224C ''shape'':22C ''show'':90C ''smarter'':176C ''space'':17C,41C ''structur'':148C ''suggest'':5C,46C ''svm'':113C ''tag'':116C ''take'':203C ''thing'':196C ''think'':52C,130C,174C ''thought'':106C ''use'':29C,43C,63C,119C,139C ''want'':27C ''well'':21C,211C ''well-shap'':20C ''work'':78C,124C ''would'':3C,47C,201C ''yes'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10324, 2, '2016-02-19 18:44:15.09', 1, NULL, NULL, '&lt;p&gt;Just pandas, no bokeh (copy the data to the clipboard before running):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas, seaborn
DF = pandas.read_clipboard()
DF.plot.bar(x=''year'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/Dyw3p.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Dyw3p.png" alt="pandas plot"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':44C ''/code'':26C ''/dyw3p.png'':33C,40C ''/p'':14C,45C ''/pre'':27C ''alt'':41C ''bokeh'':5C ''clipboard'':11C,22C ''code'':16C ''copi'':6C ''data'':8C ''df'':20C ''df.plot.bar'':23C ''href'':30C ''i.stack.imgur.com'':32C,39C ''i.stack.imgur.com/dyw3p.png'':31C,38C ''img'':36C ''import'':17C ''nofollow'':35C ''p'':1C,28C ''panda'':3C,18C,42C ''pandas.read'':21C ''plot'':43C ''pre'':15C ''rel'':34C ''run'':13C ''seaborn'':19C ''src'':37C ''x'':24C ''year'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10286, 1, '2016-02-17 18:59:27.833', 3, 44, 'When being in a perfect "Long Valley" situation, does momentum help?', '&lt;p&gt;I am thinking of the following image where we have two weights and an error (so we can make a 3D visualization). In this case a "long valley" looks like $x^2$ in the plane of the gradient, but in a perpendicular plane the function can still be minimized:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/5JyJB.gif" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/5JyJB.gif" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;According to my understanding, all optimization algorithms which are based on the gradient should be "trapped" at the saddle point like SGD is. Why does having a momentum term help in this case?&lt;/p&gt;

&lt;p&gt;(I''m sorry, I don''t know who made those images. &lt;a href="http://imgur.com/a/Hqolp" rel="nofollow"&gt;More of them&lt;/a&gt; are available.)&lt;/p&gt;
', 'neuralnetwork  optimization  gradient-descent', NULL, '''/5jyjb.gif'':73C,80C ''/a'':86C,145C ''/a/hqolp'':139C ''/p'':67C,87C,122C,148C ''2'':49C ''3d'':38C ''accord'':89C ''algorithm'':95C ''alt'':81C ''avail'':147C ''base'':98C ''case'':42C,121C ''descent'':16B ''descript'':84C ''enter'':82C ''error'':32C ''follow'':23C ''function'':62C ''gradient'':15B,55C,101C ''gradient-desc'':14B ''help'':11A,118C ''href'':70C,136C ''i.stack.imgur.com'':72C,79C ''i.stack.imgur.com/5jyjb.gif'':71C,78C ''imag'':24C,83C,134C ''img'':76C ''imgur.com'':138C ''imgur.com/a/hqolp'':137C ''know'':130C ''like'':47C,109C ''long'':6A,44C ''look'':46C ''m'':125C ''made'':132C ''make'':36C ''minim'':66C ''momentum'':10A,116C ''neuralnetwork'':12B ''nofollow'':75C,141C ''optim'':13B,94C ''p'':17C,68C,88C,123C ''perfect'':5A ''perpendicular'':59C ''plane'':52C,60C ''point'':108C ''rel'':74C,140C ''saddl'':107C ''sgd'':110C ''situat'':8A ''sorri'':126C ''src'':77C ''still'':64C ''term'':117C ''think'':20C ''trap'':104C ''two'':28C ''understand'':92C ''valley'':7A,45C ''visual'':39C ''weight'':29C ''x'':48C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10287, 2, '2016-02-17 20:37:03.703', 1, NULL, NULL, '&lt;p&gt;I have good news and bad news.&lt;/p&gt;

&lt;p&gt;The bad news is that due to the nonlinear nature of neural networks, the same feature will contribute differently depending on the other features.  &lt;/p&gt;

&lt;p&gt;Here''s a simple nonlinear function: f(a,b,c) = y = ba^3 + bc + c.  One could argue that ''a'' contributes most to this function, but when b is negative, a more positive a might make things worse.  When b is zero, c contributes most.  When b is negative, a very negative a will contribute most.&lt;/p&gt;

&lt;p&gt;The good news is that you may be able to visualize some simple single-layer networks.  There''s a fun trick for this used in deep learning.  &lt;/p&gt;

&lt;p&gt;Assuming you''ve got x for your examples, W for your weights, and y for your outcomes, set the feature you want to show in y to 1 and the rest to zero, then multiply by W-transpose.  You should get some output values for x which show you how connected everything is.  It''s not a perfect solution, but if you get some huge values coming out after the multiplication, you can say those values are highly-connected for y.  (Usually.)&lt;/p&gt;
', NULL, NULL, '''/p'':9C,35C,93C,123C,209C ''1'':152C ''3'':49C ''abl'':103C ''argu'':54C ''assum'':125C ''b'':45C,64C,76C,83C ''ba'':48C ''bad'':7C,12C ''bc'':50C ''c'':46C,51C,79C ''come'':192C ''connect'':176C,205C ''contribut'':28C,57C,80C,91C ''could'':53C ''deep'':121C ''depend'':30C ''differ'':29C ''due'':16C ''everyth'':177C ''exampl'':132C ''f'':43C ''featur'':26C,34C,144C ''fun'':115C ''function'':42C,61C ''get'':166C,188C ''good'':4C,96C ''got'':128C ''high'':204C ''highly-connect'':203C ''huge'':190C ''layer'':110C ''learn'':122C ''make'':72C ''may'':101C ''might'':71C ''multipl'':196C ''multipli'':159C ''natur'':20C ''negat'':66C,85C,88C ''network'':23C,111C ''neural'':22C ''news'':5C,8C,13C,97C ''nonlinear'':19C,41C ''one'':52C ''outcom'':141C ''output'':168C ''p'':1C,10C,36C,94C,124C ''perfect'':183C ''posit'':69C ''rest'':155C ''say'':199C ''set'':142C ''show'':148C,173C ''simpl'':40C,107C ''singl'':109C ''single-lay'':108C ''solut'':184C ''thing'':73C ''transpos'':163C ''trick'':116C ''use'':119C ''usual'':208C ''valu'':169C,191C,201C ''ve'':127C ''visual'':105C ''w'':133C,162C ''w-transpos'':161C ''want'':146C ''weight'':136C ''wors'':74C ''x'':129C,171C ''y'':47C,138C,150C,207C ''zero'':78C,157C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10288, 1, '2016-02-17 20:43:08.74', 2, 70, 'Sort by average votes/ratings', '&lt;p&gt;I have a data set that''s a dictionary of tuples. Each key represents an ID number and each tuple is (yesvotes, totalvotes). Example:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;{17: (6, 10), 18: (1, 1), 21: (0, 2), 26: (1, 1), 27: (3, 4), 13: (2, 2)}&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;I need to find the max key of the set. I want to assign weights so, for instance, key 17 would be ranked higher than key 18 because even though the ratio is much smaller, it has ten times the total votes.&lt;/p&gt;

&lt;p&gt;Is there an optimal way to do this? My best guess is simply calculate new ratios by &lt;code&gt;(yesvotes/totalvotes)*(totalvotes+1)&lt;/code&gt; but that doesn''t seem right... Is there some kind of standardized field of study concerning fair-voting?&lt;/p&gt;
', 'python  data-cleaning  weighted-data  data-wrangling', NULL, '''+1'':128C ''/code'':61C,129C ''/p'':40C,62C,106C,149C ''0'':50C ''1'':47C,48C,53C,54C ''10'':45C ''13'':58C ''17'':43C,83C ''18'':46C,90C ''2'':51C,59C,60C ''21'':49C ''26'':52C ''27'':55C ''3'':56C ''4'':57C ''6'':44C ''assign'':77C ''averag'':3A ''best'':117C ''calcul'':121C ''clean'':8B ''code'':42C,125C ''concern'':145C ''data'':7B,11B,13B,19C ''data-clean'':6B ''data-wrangl'':12B ''dictionari'':24C ''doesn'':132C ''even'':92C ''exampl'':39C ''fair'':147C ''fair-vot'':146C ''field'':142C ''find'':67C ''guess'':118C ''higher'':87C ''id'':31C ''instanc'':81C ''key'':28C,70C,82C,89C ''kind'':139C ''max'':69C ''much'':97C ''need'':65C ''new'':122C ''number'':32C ''optim'':111C ''p'':15C,41C,63C,107C ''python'':5B ''rank'':86C ''ratio'':95C,123C ''repres'':29C ''right'':135C ''seem'':134C ''set'':20C,73C ''simpli'':120C ''smaller'':98C ''sort'':1A ''standard'':141C ''studi'':144C ''ten'':101C ''though'':93C ''time'':102C ''total'':104C ''totalvot'':38C,127C ''tupl'':26C,35C ''vote'':105C,148C ''votes/ratings'':4A ''want'':75C ''way'':112C ''weight'':10B,78C ''weighted-data'':9B ''would'':84C ''wrangl'':14B ''yesvot'':37C ''yesvotes/totalvotes'':126C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10289, 2, '2016-02-17 20:48:33.153', 0, NULL, NULL, '&lt;p&gt;The animation is by &lt;a href="https://www.reddit.com/r/MachineLearning/comments/2gopfa/visualizing_gradient_optimization_techniques/cklhott" rel="nofollow"&gt;Alec Radford&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Momentum helps by "pushing through" the local minimum or saddle point. Of course, it does not push in the optimal direction, which is why it exhibits oscillation, as you can see above, but it eventually recovers.&lt;/p&gt;

&lt;p&gt;For further study, I suggest reading about &lt;a href="https://en.wikipedia.org/wiki/Preconditioner#Preconditioning_in_optimization" rel="nofollow"&gt;preconditioning&lt;/a&gt; and the &lt;a href="https://en.wikipedia.org/wiki/Condition_number" rel="nofollow"&gt;condition number&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':15C,71C,83C ''/p'':16C,54C,84C ''/r/machinelearning/comments/2gopfa/visualizing_gradient_optimization_techniques/cklhott'':10C ''/wiki/condition_number'':78C ''/wiki/preconditioner#preconditioning_in_optimization'':67C ''alec'':13C ''anim'':3C ''condit'':81C ''cours'':30C ''direct'':38C ''en.wikipedia.org'':66C,77C ''en.wikipedia.org/wiki/condition_number'':76C ''en.wikipedia.org/wiki/preconditioner#preconditioning_in_optimization'':65C ''eventu'':52C ''exhibit'':43C ''help'':19C ''href'':7C,64C,75C ''local'':24C ''minimum'':25C ''momentum'':18C ''nofollow'':12C,69C,80C ''number'':82C ''optim'':37C ''oscil'':44C ''p'':1C,17C,55C ''point'':28C ''precondit'':70C ''push'':21C,34C ''radford'':14C ''read'':61C ''recov'':53C ''rel'':11C,68C,79C ''saddl'':27C ''see'':48C ''studi'':58C ''suggest'':60C ''www.reddit.com'':9C ''www.reddit.com/r/machinelearning/comments/2gopfa/visualizing_gradient_optimization_techniques/cklhott'':8C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10290, 2, '2016-02-17 20:56:27.763', 1, NULL, NULL, '&lt;p&gt;I think regression makes more sense here than classification; you are trying to predict a number. In particular I would recommend &lt;a href="https://en.wikipedia.org/wiki/Poisson_regression" rel="nofollow"&gt;Poisson regression&lt;/a&gt; since you are modeling count data. Software is readily available for various languages.&lt;/p&gt;
', NULL, NULL, '''/a'':32C ''/p'':46C ''/wiki/poisson_regression'':27C ''avail'':42C ''classif'':10C ''count'':37C ''data'':38C ''en.wikipedia.org'':26C ''en.wikipedia.org/wiki/poisson_regression'':25C ''href'':24C ''languag'':45C ''make'':5C ''model'':36C ''nofollow'':29C ''number'':17C ''p'':1C ''particular'':19C ''poisson'':30C ''predict'':15C ''readili'':41C ''recommend'':22C ''regress'':4C,31C ''rel'':28C ''sens'':7C ''sinc'':33C ''softwar'':39C ''think'':3C ''tri'':13C ''various'':44C ''would'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10291, 2, '2016-02-17 21:19:57.337', 0, NULL, NULL, '&lt;p&gt;The &lt;a href="https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient" rel="nofollow"&gt;correlation coefficient&lt;/a&gt; between two random variables is a rigorously defined mathematical parameter. It is undefined when either of the random variables have zero variance. Therefore "NaN" is a very appropriate value to return in this case.&lt;/p&gt;

&lt;p&gt;You are welcome to define it to be zero when one of the random variables is constant, if you find this more practical. But this does not agree with my mathematical intuition using limits. For example, if $X$ is $Uniform[-1,1]$, then the correlation coefficient between $X$ and $Y = c + \epsilon X$ is 1 for all $\epsilon &amp;gt; 0$, -1 for all $\epsilon &amp;lt; 0$. You can modify this example to get functions that get closer and closer to a constant function as $\epsilon$ goes to zero, while the correlation between X and its image is any number from the interval [-1,1].&lt;/p&gt;

&lt;p&gt;I also like &lt;a href="http://stats.stackexchange.com/a/18427"&gt;this explanation&lt;/a&gt; by David Epstein: the cosine of an angle between a non-zero and a zero vector is fundamentally undefined.&lt;/p&gt;
', NULL, NULL, '''-1'':89C,109C,151C ''/a'':12C,165C ''/a/18427'':162C ''/p'':47C,153C,186C ''/wiki/pearson_product-moment_correlation_coefficient'':7C ''0'':108C,114C ''1'':90C,103C,152C ''agre'':76C ''also'':156C ''angl'':173C ''appropri'':40C ''c'':99C ''case'':46C ''closer'':125C,127C ''coeffici'':11C,94C ''constant'':65C,130C ''correl'':10C,93C,139C ''cosin'':170C ''david'':167C ''defin'':20C,53C ''either'':27C ''en.wikipedia.org'':6C ''en.wikipedia.org/wiki/pearson_product-moment_correlation_coefficient'':5C ''epsilon'':100C,106C,112C,133C ''epstein'':168C ''exampl'':84C,119C ''explan'':164C ''find'':68C ''function'':122C,131C ''fundament'':184C ''get'':121C,124C ''goe'':134C ''gt'':107C ''href'':4C,159C ''imag'':144C ''interv'':150C ''intuit'':80C ''like'':157C ''limit'':82C ''lt'':113C ''mathemat'':21C,79C ''modifi'':117C ''nan'':36C ''nofollow'':9C ''non'':177C ''non-zero'':176C ''number'':147C ''one'':59C ''p'':1C,48C,154C ''paramet'':22C ''practic'':71C ''random'':15C,30C,62C ''rel'':8C ''return'':43C ''rigor'':19C ''stats.stackexchange.com'':161C ''stats.stackexchange.com/a/18427'':160C ''therefor'':35C ''two'':14C ''undefin'':25C,185C ''uniform'':88C ''use'':81C ''valu'':41C ''variabl'':16C,31C,63C ''varianc'':34C ''vector'':182C ''welcom'':51C ''x'':86C,96C,101C,141C ''y'':98C ''zero'':33C,57C,136C,178C,181C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10293, 2, '2016-02-17 22:16:03.997', 1, NULL, NULL, '&lt;p&gt;I think most people would simply average a statistic like accuracy or kappa over the several runs, rather than compute one overall statistic.&lt;/p&gt;

&lt;p&gt;While it''s not necessarily true that an average (weighted by the test set size in each case) of these statistics equals the statistic computed over all of the runs at once, it happens to be true for accuracy and kappa. Hence it shouldn''t matter.&lt;/p&gt;

&lt;p&gt;I think the reason you may be getting a difference is that you''re normalizing away the test set size. I don''t think you should.&lt;/p&gt;
', NULL, NULL, '''/p'':25C,73C,101C ''accuraci'':12C,65C ''averag'':8C,35C ''away'':90C ''case'':44C ''comput'':21C,51C ''differ'':84C ''equal'':48C ''get'':82C ''happen'':60C ''henc'':68C ''kappa'':14C,67C ''like'':11C ''matter'':72C ''may'':80C ''necessarili'':31C ''normal'':89C ''one'':22C ''overal'':23C ''p'':1C,26C,74C ''peopl'':5C ''rather'':19C ''re'':88C ''reason'':78C ''run'':18C,56C ''set'':40C,93C ''sever'':17C ''shouldn'':70C ''simpli'':7C ''size'':41C,94C ''statist'':10C,24C,47C,50C ''test'':39C,92C ''think'':3C,76C,98C ''true'':32C,63C ''weight'':36C ''would'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10294, 2, '2016-02-17 22:25:38.42', 3, NULL, NULL, '&lt;p&gt;Yes, this is a well-studied problem: rank aggregation. &lt;a href="http://www.evanmiller.org/how-not-to-sort-by-average-rating.html" rel="nofollow"&gt;Here&lt;/a&gt; is a solution with code.&lt;/p&gt;

&lt;p&gt;The problem is that the quantity you are trying to estimate, the "score" of the item, is subject to noise. The fewer votes you have the greater the noise. Therefore you want to consider the variance of your estimates when ranking them.&lt;/p&gt;
', NULL, NULL, '''/a'':20C ''/how-not-to-sort-by-average-rating.html'':16C ''/p'':26C,70C ''aggreg'':11C ''code'':25C ''consid'':61C ''estim'':38C,66C ''fewer'':49C ''greater'':54C ''href'':13C ''item'':43C ''nofollow'':18C ''nois'':47C,56C ''p'':1C,27C ''problem'':9C,29C ''quantiti'':33C ''rank'':10C,68C ''rel'':17C ''score'':40C ''solut'':23C ''studi'':8C ''subject'':45C ''therefor'':57C ''tri'':36C ''varianc'':63C ''vote'':50C ''want'':59C ''well'':7C ''well-studi'':6C ''www.evanmiller.org'':15C ''www.evanmiller.org/how-not-to-sort-by-average-rating.html'':14C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10297, 2, '2016-02-18 01:22:14.33', 0, NULL, NULL, '&lt;p&gt;The answer is simple: in a perfect situation never. These optimizers are 1st order methods (unlike for example Hessian-Free optimization). The reason for they escaping from the stationary point above is that there is some small gradient (too small for ordinary SGD) in the "correct" direction. Momentum and Nesterov gradient can build up the velocity over time in that direction and Adagrad/Adadelta/RMS can immediately scale the gradient (increase it in the direction for which the past gradients were small).&lt;/p&gt;
', NULL, NULL, '''/p'':83C ''1st'':14C ''adagrad/adadelta/rms'':65C ''answer'':3C ''build'':55C ''correct'':48C ''direct'':49C,63C,75C ''escap'':28C ''exampl'':19C ''free'':22C ''gradient'':40C,53C,70C,80C ''hessian'':21C ''hessian-fre'':20C ''immedi'':67C ''increas'':71C ''method'':16C ''momentum'':50C ''nesterov'':52C ''never'':10C ''optim'':12C,23C ''order'':15C ''ordinari'':44C ''p'':1C ''past'':79C ''perfect'':8C ''point'':32C ''reason'':25C ''scale'':68C ''sgd'':45C ''simpl'':5C ''situat'':9C ''small'':39C,42C,82C ''stationari'':31C ''time'':60C ''unlik'':17C ''veloc'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10339, 1, '2016-02-21 14:28:49.49', 1, 29, 'In Neural Networks and deep neural networks what does label-dropout mean', '&lt;p&gt;If you take the following sentence from an article on deep neural networks &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;to regularize the classifier layer by estimating the marginalized effect of &lt;strong&gt;label-dropout&lt;/strong&gt; during training.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;What does label-dropout mean&lt;/p&gt;
', 'classification  neuralnetwork  deep-learning', NULL, '''/blockquote'':55C ''/p'':33C,54C,63C ''/strong'':51C ''articl'':28C ''blockquot'':34C ''classif'':14B ''classifi'':39C ''deep'':5A,17B,30C ''deep-learn'':16B ''dropout'':12A,50C,61C ''effect'':45C ''estim'':42C ''follow'':24C ''label'':11A,49C,60C ''label-dropout'':10A,48C,59C ''layer'':40C ''learn'':18B ''margin'':44C ''mean'':13A,62C ''network'':3A,7A,32C ''neural'':2A,6A,31C ''neuralnetwork'':15B ''p'':19C,35C,56C ''regular'':37C ''sentenc'':25C ''strong'':47C ''take'':22C ''train'':53C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10298, 2, '2016-02-18 02:35:39.41', 1, NULL, NULL, '&lt;p&gt;Listing 2 examples:
&lt;a href="https://www.ibm.com/marketplace/cloud/watson-analytics/us/en-us" rel="nofollow"&gt;IBM Watson Analytics&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.ibm.com/marketplace/cloud/watson-analytics/us/en-us" rel="nofollow"&gt;Amazon ML use case&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Preparing the data for supervised learning is require skills. Not all data came labeled and in form to be used as in need to solving problems. &lt;/p&gt;

&lt;p&gt;Also many more platforms/Api are in market now but for sure you can''t solve a problem only with 1 algorithm, is needed much more ... Hope it help. &lt;/p&gt;
', NULL, NULL, '''/a'':15C,29C ''/marketplace/cloud/watson-analytics/us/en-us'':9C,22C ''/p'':16C,30C,58C,88C ''1'':79C ''2'':3C ''algorithm'':80C ''also'':60C ''amazon'':25C ''analyt'':14C ''came'':44C ''case'':28C ''data'':34C,43C ''exampl'':4C ''form'':48C ''help'':87C ''hope'':85C ''href'':6C,19C ''ibm'':12C ''label'':45C ''learn'':37C ''list'':2C ''mani'':61C ''market'':66C ''ml'':26C ''much'':83C ''need'':54C,82C ''nofollow'':11C,24C ''p'':1C,17C,31C,59C ''platforms/api'':63C ''prepar'':32C ''problem'':57C,76C ''rel'':10C,23C ''requir'':39C ''skill'':40C ''solv'':56C,74C ''supervis'':36C ''sure'':70C ''use'':27C,51C ''watson'':13C ''www.ibm.com'':8C,21C ''www.ibm.com/marketplace/cloud/watson-analytics/us/en-us'':7C,20C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10299, 1, '2016-02-18 04:25:38.627', 0, 22, 'What is the best way to understand Non Negative Matrix Factorization?', '&lt;p&gt;I am trying to find a resource to understand non-negative matrix factorization. Apart from Wikipedia I couldn''t find something useful.&lt;/p&gt;
', 'dimensionality-reduction  reference-request', NULL, '''/p'':42C ''apart'':33C ''best'':4A ''couldn'':37C ''dimension'':13B ''dimensionality-reduct'':12B ''factor'':11A,32C ''find'':23C,39C ''matrix'':10A,31C ''negat'':9A,30C ''non'':8A,29C ''non-neg'':28C ''p'':18C ''reduct'':14B ''refer'':16B ''reference-request'':15B ''request'':17B ''resourc'':25C ''someth'':40C ''tri'':21C ''understand'':7A,27C ''use'':41C ''way'':5A ''wikipedia'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10316, 2, '2016-02-19 11:54:02.797', 0, NULL, NULL, '&lt;p&gt;&lt;a href="http://auto-sklearn.readthedocs.org/en/master/" rel="nofollow"&gt;auto-sklearn&lt;/a&gt; team is working on finding a way to fully automate the pipeline. It has delivered good results for me so far. Better as what I have got manually with the GridsearchCV.
This can be an answer to your question if you ''re happy to use such tool or read their source code.&lt;/p&gt;

&lt;p&gt;And at &lt;a href="http://www.ml4aad.org/algorithm-configuration/" rel="nofollow"&gt;ML4AAD&lt;/a&gt; you can find further links to the attempts to solve this problem.&lt;/p&gt;
', NULL, NULL, '''/a'':12C,77C ''/algorithm-configuration/'':73C ''/en/master/'':6C ''/p'':65C,90C ''answer'':48C ''attempt'':85C ''auto'':10C ''auto-sklearn'':9C ''auto-sklearn.readthedocs.org'':5C ''auto-sklearn.readthedocs.org/en/master/'':4C ''autom'':22C ''better'':34C ''code'':64C ''deliv'':27C ''far'':33C ''find'':17C,80C ''fulli'':21C ''good'':28C ''got'':39C ''gridsearchcv'':43C ''happi'':55C ''href'':3C,70C ''link'':82C ''manual'':40C ''ml4aad'':76C ''nofollow'':8C,75C ''p'':1C,66C ''pipelin'':24C ''problem'':89C ''question'':51C ''re'':54C ''read'':61C ''rel'':7C,74C ''result'':29C ''sklearn'':11C ''solv'':87C ''sourc'':63C ''team'':13C ''tool'':59C ''use'':57C ''way'':19C ''work'':15C ''www.ml4aad.org'':72C ''www.ml4aad.org/algorithm-configuration/'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10300, 1, '2016-02-18 09:45:12.703', 1, 15, 'How do I measure the execution time of a query running on Spark?', '&lt;p&gt;I am running a spark application that dynamically fetches the coordinates (lat and long) given a set of addresses as input. I want to know the amount of time it takes for the spark to process each address and return results once the query completes its execution. 
I tried logging the events by setting the followings in the spark-defaults.conf file:&lt;/p&gt;

&lt;p&gt;spark.eventLog.enabled             true&lt;/p&gt;

&lt;p&gt;spark.eventLog.dir                 file:///D:/eventLogging&lt;/p&gt;

&lt;p&gt;The event log file has got generated after running the application. However, I couldn''t get what I wanted.
Please someone help on how can I accomplish this.
Thanks in advance.&lt;/p&gt;
', 'apache-spark', NULL, '''/d'':85C ''/eventlogging'':86C ''/p'':78C,82C,87C,120C ''accomplish'':115C ''address'':36C,55C ''advanc'':119C ''amount'':44C ''apach'':15B ''apache-spark'':14B ''applic'':23C,99C ''complet'':62C ''coordin'':28C ''couldn'':102C ''dynam'':25C ''event'':69C,90C ''execut'':6A,64C ''fetch'':26C ''file'':77C,92C ''follow'':73C ''generat'':95C ''get'':104C ''given'':32C ''got'':94C ''help'':110C ''howev'':100C ''input'':38C ''know'':42C ''lat'':29C ''log'':67C,91C ''long'':31C ''measur'':4A ''p'':17C,79C,83C,88C ''pleas'':108C ''process'':53C ''queri'':10A,61C ''result'':58C ''return'':57C ''run'':11A,20C,97C ''set'':34C,71C ''someon'':109C ''spark'':13A,16B,22C,51C ''spark-defaults.conf'':76C ''spark.eventlog.dir'':84C ''spark.eventlog.enabled'':80C ''take'':48C ''thank'':117C ''time'':7A,46C ''tri'':66C ''true'':81C ''want'':40C,107C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10301, 1, '2016-02-18 09:59:43.063', 2, 135, 'How to predict ETA using Regression?', '&lt;p&gt;I have a data from GPS in the form &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1.(''latitude'', ''longitude'',''Timestamp'').
2.(''latitude'', ''longitude'',''Timestamp'').
3.(''latitude'', ''longitude'',''Timestamp'').
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am changing this data into the below form &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;''latitude_1'', ''longitude_1'', ''Timestamp_1'', ''latitude_2'', ''longitude_2'', ''Timestamp_2, Timestamp_2-Timestamp_1.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;With this format I am training a &lt;code&gt;LinearRegressionWithSGD&lt;/code&gt; model of spark where label is &lt;code&gt;Timestamp_2-Timestamp_1&lt;/code&gt; and features are &lt;code&gt;latitude_1, longitude_1, latitude_2, longitude_2&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;But when I am giving Origin ( latitude and longitude ) and destination ( latitude and longitude ) the results are very bad.&lt;/p&gt;

&lt;p&gt;Kindly guide me whether this approach is the right approach ? and if not then how to build a prediction model from given data to predict &lt;code&gt;Estimated Time of Arrival&lt;/code&gt;.&lt;/p&gt;
', 'machine-learning  linear-regression', NULL, '''/code'':38C,69C,81C,93C,106C,160C ''/p'':23C,50C,107C,128C,161C ''/pre'':39C,70C ''1'':26C,54C,56C,58C,68C,92C,99C,101C ''2'':30C,60C,62C,64C,66C,90C,103C,105C ''3'':34C ''approach'':135C,139C ''arriv'':159C ''bad'':127C ''build'':146C ''chang'':43C ''code'':25C,52C,79C,88C,97C,155C ''data'':17C,45C,152C ''destin'':119C ''estim'':156C ''eta'':4A ''featur'':95C ''form'':22C,49C ''format'':74C ''give'':113C ''given'':151C ''gps'':19C ''guid'':131C ''kind'':130C ''label'':86C ''latitud'':27C,31C,35C,53C,59C,98C,102C,115C,120C ''learn'':9B ''linear'':11B ''linear-regress'':10B ''linearregressionwithsgd'':80C ''longitud'':28C,32C,36C,55C,61C,100C,104C,117C,122C ''machin'':8B ''machine-learn'':7B ''model'':82C,149C ''origin'':114C ''p'':13C,40C,71C,108C,129C ''pre'':24C,51C ''predict'':3A,148C,154C ''regress'':6A,12B ''result'':124C ''right'':138C ''spark'':84C ''time'':157C ''timestamp'':29C,33C,37C,57C,63C,65C,67C,89C,91C ''train'':77C ''use'':5A ''whether'':133C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10309, 2, '2016-02-18 15:39:32.197', 2, NULL, NULL, '&lt;p&gt;What you are asking for is a gradient based hyperparameter optimization. To enable such a grid search one would have to define a metric on the space of all hyper parameter combinations for the current pipeline. &lt;/p&gt;

&lt;p&gt;I don''t think this makes sense in your example because of the binary nature of the &lt;code&gt;use_idf&lt;/code&gt; flag. Lets say your parameter combinations are &lt;code&gt;(use_idf, alpha)&lt;/code&gt;. Then you would have to do decide what the distance between &lt;code&gt;(True, 0.00015)&lt;/code&gt; and &lt;code&gt;(True, 0.00010)&lt;/code&gt; is. In your case it should be smaller than the distance between &lt;code&gt;(True, 0.00015)&lt;/code&gt;and &lt;code&gt;(False, 0.00015)&lt;/code&gt;. So&lt;/p&gt;

&lt;p&gt;&lt;code&gt;d((True, 0.00015), (True, 0.00010)) &amp;lt; d((True, 0.00015), (False, 0.00015))&lt;/code&gt; &lt;/p&gt;

&lt;p&gt;But what about the distance between &lt;code&gt;(True, 0.00015)&lt;/code&gt; and &lt;code&gt;(True, 0.05)&lt;/code&gt;? Will it be still smaller than &lt;code&gt;d((True, 0.00015), (False, 0.00015))&lt;/code&gt;? I think you get the point.&lt;/p&gt;

&lt;p&gt;By choosing the metric you will subjectively influence the search for the best hyper parameters. Probably a random search would be more suitable as suggested in the following paper&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;@article{bergstra2012random,
  title={Random search for hyper-parameter optimization},
  author={Bergstra, James and Bengio, Yoshua},
  journal={The Journal of Machine Learning Research},
  volume={13},
  number={1},
  pages={281--305},
  year={2012},
  publisher={JMLR. org}
}
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''-305'':229C ''/code'':60C,72C,87C,92C,108C,113C,129C,141C,146C,159C,235C ''/p'':38C,115C,130C,166C,197C ''/pre'':236C ''0.00010'':91C,122C ''0.00015'':86C,107C,112C,120C,126C,128C,140C,156C,158C ''0.05'':145C ''1'':226C ''13'':224C ''2012'':231C ''281'':228C ''alpha'':71C ''articl'':200C ''ask'':5C ''author'':210C ''base'':10C ''bengio'':214C ''bergstra'':211C ''bergstra2012random'':201C ''best'':180C ''binari'':53C ''case'':96C ''choos'':169C ''code'':57C,68C,84C,89C,105C,110C,117C,138C,143C,153C,199C ''combin'':33C,66C ''current'':36C ''d'':118C,124C,154C ''decid'':79C ''defin'':23C ''distanc'':82C,103C,136C ''enabl'':14C ''exampl'':49C ''fals'':111C,127C,157C ''flag'':61C ''follow'':195C ''get'':163C ''gradient'':9C ''grid'':17C ''hyper'':31C,181C,207C ''hyper-paramet'':206C ''hyperparamet'':11C ''idf'':59C,70C ''influenc'':175C ''jame'':212C ''jmlr'':233C ''journal'':216C,218C ''learn'':221C ''let'':62C ''lt'':123C ''machin'':220C ''make'':45C ''metric'':25C,171C ''natur'':54C ''number'':225C ''one'':19C ''optim'':12C,209C ''org'':234C ''p'':1C,39C,116C,131C,167C ''page'':227C ''paper'':196C ''paramet'':32C,65C,182C,208C ''pipelin'':37C ''point'':165C ''pre'':198C ''probabl'':183C ''publish'':232C ''random'':185C,203C ''research'':222C ''say'':63C ''search'':18C,177C,186C,204C ''sens'':46C ''smaller'':100C,151C ''space'':28C ''still'':150C ''subject'':174C ''suggest'':192C ''suitabl'':190C ''think'':43C,161C ''titl'':202C ''true'':85C,90C,106C,119C,121C,125C,139C,144C,155C ''use'':58C,69C ''volum'':223C ''would'':20C,75C,187C ''year'':230C ''yoshua'':215C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10302, 1, '2016-02-18 10:11:23.997', 6, 96, 'Understanding Reinforcement Learning with Neural Net (Q-learning)', '&lt;p&gt;I am trying to understand reinforcement learning and markov decision processes (MDP) in the case where a neural net is being used as the function approximator.&lt;/p&gt;

&lt;p&gt;I''m having difficulty with the relationship between the MDP where the environment is explored in a probabilistic manner, how this maps back to learning parameters and how the final solution/policies are found.&lt;/p&gt;

&lt;p&gt;Am I correct to assume that in the case of Q-learning, the neural-network essentially acts as a function approximator for q-value itself so many steps in the future? How does this map to updating parameters via backpropagation or other methods?&lt;/p&gt;

&lt;p&gt;Also, once the network has learned how to predict the future reward, how does this fit in with the system in terms of actually making decisions? I am assuming that the final system would not probabilistically make state transitions.&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'machine-learning  neuralnetwork  q-learning', NULL, '''/p'':44C,79C,127C,168C,171C ''act'':99C ''actual'':152C ''also'':129C ''approxim'':43C,103C ''assum'':85C,157C ''back'':68C ''backpropag'':123C ''case'':32C,89C ''correct'':83C ''decis'':27C,154C ''difficulti'':49C ''environ'':58C ''essenti'':98C ''explor'':60C ''final'':75C,160C ''fit'':144C ''found'':78C ''function'':42C,102C ''futur'':114C,139C ''learn'':3A,9A,12B,16B,24C,70C,93C,134C ''m'':47C ''machin'':11B ''machine-learn'':10B ''make'':153C,165C ''mani'':110C ''manner'':64C ''map'':67C,118C ''markov'':26C ''mdp'':29C,55C ''method'':126C ''net'':6A,36C ''network'':97C,132C ''neural'':5A,35C,96C ''neural-network'':95C ''neuralnetwork'':13B ''p'':17C,45C,80C,128C,169C ''paramet'':71C,121C ''predict'':137C ''probabilist'':63C,164C ''process'':28C ''q'':8A,15B,92C,106C ''q-learn'':7A,14B,91C ''q-valu'':105C ''reinforc'':2A,23C ''relationship'':52C ''reward'':140C ''solution/policies'':76C ''state'':166C ''step'':111C ''system'':148C,161C ''term'':150C ''thank'':170C ''transit'':167C ''tri'':20C ''understand'':1A,22C ''updat'':120C ''use'':39C ''valu'':107C ''via'':122C ''would'':162C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10304, 2, '2016-02-18 12:26:20.643', 1, NULL, NULL, '&lt;p&gt;To predict timestamps from two predictor variables longitude and latitude, you want to train a multiple linear regression model of the form&lt;/p&gt;

&lt;p&gt;$$Timestamp = \alpha + \beta_0 \cdot Longitude + \beta_1 \cdot Latitude.$$&lt;/p&gt;

&lt;p&gt;Given a new latitude-longitude pair of you destination, you can then compute the ETA.&lt;/p&gt;

&lt;p&gt;Spark''s &lt;code&gt;LinearRegressionWithSGD&lt;/code&gt; model should be able to perform multiple linear regression out of the box, using &lt;code&gt;Timestamp&lt;/code&gt; as label and &lt;code&gt;latitude&lt;/code&gt; and &lt;code&gt;longitude&lt;/code&gt; as features. There''s no need to transform the data beforehand.&lt;/p&gt;
', NULL, NULL, '''/code'':60C,77C,83C,87C ''/p'':24C,36C,54C,99C ''0'':29C ''1'':33C ''abl'':64C ''alpha'':27C ''beforehand'':98C ''beta'':28C,32C ''box'':73C ''cdot'':30C,34C ''code'':58C,75C,81C,85C ''comput'':51C ''data'':97C ''destin'':47C ''eta'':53C ''featur'':89C ''form'':23C ''given'':38C ''label'':79C ''latitud'':11C,35C,42C,82C ''latitude-longitud'':41C ''linear'':18C,68C ''linearregressionwithsgd'':59C ''longitud'':9C,31C,43C,86C ''model'':20C,61C ''multipl'':17C,67C ''need'':93C ''new'':40C ''p'':1C,25C,37C,55C ''pair'':44C ''perform'':66C ''predict'':3C ''predictor'':7C ''regress'':19C,69C ''spark'':56C ''timestamp'':4C,26C,76C ''train'':15C ''transform'':95C ''two'':6C ''use'':74C ''variabl'':8C ''want'':13C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10306, 2, '2016-02-18 14:25:11.43', 3, NULL, NULL, '&lt;p&gt;To understand the coefficients you just need to understand how the logistic regression model that you fit uses the coefficients to make predictions. No, it does not work like a decision tree. It''s a linear model.&lt;/p&gt;

&lt;p&gt;Really, predictions are based on the dot product of the coefficients and the values from some new instance to predict. This is just the sum of their products. The higher the dot product, the more positive the prediction.&lt;/p&gt;

&lt;p&gt;So you can understand it as computing something like &lt;code&gt;-2.40477246e-10 * MATCH_HOME + -5.57611571e-02 * MATCH_AWAY + ...&lt;/code&gt; (I don''t know what coefficients go with what feature in your model.)&lt;/p&gt;

&lt;p&gt;That generally means that inputs with bigger coefficients matter more, and inputs with positive coefficients correlate positively with a positive prediction. That''s most of what you can interpret here.&lt;/p&gt;

&lt;p&gt;The first of those conclusions is only really valid if inputs have been normalized to be on the same scale though. I''m not clear that you''ve done that here. You should also in general use L1 regularization if you intend to interpret the coefficients this way.&lt;/p&gt;
', NULL, NULL, '''-2.40477246e-10'':91C ''-5.57611571e-02'':94C ''/code'':97C ''/p'':39C,79C,111C,143C,193C ''also'':178C ''away'':96C ''base'':44C ''bigger'':119C ''clear'':169C ''code'':90C ''coeffici'':5C,21C,51C,103C,120C,127C,190C ''comput'':87C ''conclus'':149C ''correl'':128C ''decis'':32C ''done'':173C ''dot'':47C,72C ''featur'':107C ''first'':146C ''fit'':18C ''general'':114C,180C ''go'':104C ''higher'':70C ''home'':93C ''input'':117C,124C,155C ''instanc'':58C ''intend'':186C ''interpret'':141C,188C ''know'':101C ''l1'':182C ''like'':30C,89C ''linear'':37C ''logist'':13C ''m'':167C ''make'':23C ''match'':92C,95C ''matter'':121C ''mean'':115C ''model'':15C,38C,110C ''need'':8C ''new'':57C ''normal'':158C ''p'':1C,40C,80C,112C,144C ''posit'':76C,126C,129C,132C ''predict'':24C,42C,60C,78C,133C ''product'':48C,68C,73C ''realli'':41C,152C ''regress'':14C ''regular'':183C ''scale'':164C ''someth'':88C ''sum'':65C ''though'':165C ''tree'':33C ''understand'':3C,10C,84C ''use'':19C,181C ''valid'':153C ''valu'':54C ''ve'':172C ''way'':192C ''work'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10307, 1, '2016-02-18 14:38:46.413', 4, 42, '2 stage ensemble -- CV MSE valid in 1st stage but not in 2nd', '&lt;p&gt;I''m trying out a &lt;a href="http://www.kaggle.com" rel="nofollow"&gt;Kaggle&lt;/a&gt; competition, which puts me in the unusual position of being able to get feedback on my models'' "true" performance (you can submit several predictions per day and they give you your score on a performance metric -- in this case MSE -- each time).&lt;/p&gt;

&lt;p&gt;As such, I''ve been observing a situation I can''t explain -- my cross-validation results for my first stage Random Forest model, constructed with &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html" rel="nofollow"&gt;sklearn in Python&lt;/a&gt;, are consistently accurate within 1 percentage point of the true MSE.&lt;/p&gt;

&lt;p&gt;For example, my first  stage model will give a CV MSE of about .22 and the "ground truth" (actual) MSE will turn out to be about .23. &lt;/p&gt;

&lt;p&gt;OTOH, my 2nd stage Random Forest, which is a different type of RF constructed in R, gives way "better" (smaller) training and cross validation MSE errors, like 0.05, but consistently turns out to have a much higher true MSE, like .23. In the 2nd stage Random Forest I have the predictions from the first stage and a few additional engineered features as regressors.&lt;/p&gt;

&lt;p&gt;Is there a statistical explanation for this? &lt;/p&gt;

&lt;p&gt;For example is it the result of some sort of recursion from stacking the models? If so, given that most winners of these Machine Learning contests are stacking many models, what''s the appropriate way to do so that would avoid this statistical problem?&lt;/p&gt;
', 'machine-learning  random-forest  ensemble-modeling', NULL, '''/a'':35C,116C ''/p'':78C,128C,157C,222C,231C,277C ''/stable/modules/generated/sklearn.ensemble.randomforestregressor.html'':110C ''0.05'':186C ''1'':121C ''1st'':8A ''2'':1A ''22'':143C ''23'':156C,199C ''2nd'':13A,161C,202C ''abl'':46C ''accur'':119C ''actual'':148C ''addit'':217C ''appropri'':266C ''avoid'':273C ''better'':177C ''case'':74C ''competit'':36C ''consist'':118C,188C ''construct'':104C,172C ''contest'':258C ''cross'':94C,181C ''cross-valid'':93C ''cv'':4A,139C ''day'':61C ''differ'':168C ''engin'':218C ''ensembl'':3A,21B ''ensemble-model'':20B ''error'':184C ''exampl'':131C,234C ''explain'':91C ''explan'':228C ''featur'':219C ''feedback'':49C ''first'':99C,133C,212C ''forest'':19B,102C,164C,205C ''get'':48C ''give'':64C,137C,175C ''given'':250C ''ground'':146C ''higher'':195C ''href'':30C,107C ''kaggl'':34C ''learn'':16B,257C ''like'':185C,198C ''m'':25C ''machin'':15B,256C ''machine-learn'':14B ''mani'':261C ''metric'':71C ''model'':22B,52C,103C,135C,247C,262C ''mse'':5A,75C,127C,140C,149C,183C,197C ''much'':194C ''nofollow'':33C,112C ''observ'':85C ''otoh'':159C ''p'':23C,79C,129C,158C,223C,232C ''per'':60C ''percentag'':122C ''perform'':54C,70C ''point'':123C ''posit'':43C ''predict'':59C,209C ''problem'':276C ''put'':38C ''python'':115C ''r'':174C ''random'':18B,101C,163C,204C ''random-forest'':17B ''recurs'':243C ''regressor'':221C ''rel'':32C,111C ''result'':96C,238C ''rf'':171C ''scikit-learn.org'':109C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.randomforestregressor.html'':108C ''score'':67C ''sever'':58C ''situat'':87C ''sklearn'':113C ''smaller'':178C ''sort'':241C ''stack'':245C,260C ''stage'':2A,9A,100C,134C,162C,203C,213C ''statist'':227C,275C ''submit'':57C ''time'':77C ''train'':179C ''tri'':26C ''true'':53C,126C,196C ''truth'':147C ''turn'':151C,189C ''type'':169C ''unusu'':42C ''valid'':6A,95C,182C ''ve'':83C ''way'':176C,267C ''winner'':253C ''within'':120C ''would'':272C ''www.kaggle.com'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10308, 2, '2016-02-18 15:21:31.547', 1, NULL, NULL, '&lt;p&gt;According to the &lt;a href="http://xgboost.readthedocs.org/en/latest/input_format.html" rel="nofollow"&gt;XGBoost documentation&lt;/a&gt;, XGboost expects:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;the examples of a same group to be consecutive examples,&lt;/li&gt;
&lt;li&gt;a list with the size of each group (which you can set with &lt;code&gt;set_group&lt;/code&gt; method of &lt;code&gt;DMatrix&lt;/code&gt; in Python).&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':14C ''/code'':48C,53C ''/en/latest/input_format.html'':9C ''/li'':30C,56C ''/p'':17C ''/ul'':57C ''accord'':2C ''code'':45C,51C ''consecut'':28C ''dmatrix'':52C ''document'':13C ''exampl'':21C,29C ''expect'':16C ''group'':25C,39C,47C ''href'':6C ''li'':19C,31C ''list'':33C ''method'':49C ''nofollow'':11C ''p'':1C ''python'':55C ''rel'':10C ''set'':43C,46C ''size'':36C ''ul'':18C ''xgboost'':12C,15C ''xgboost.readthedocs.org'':8C ''xgboost.readthedocs.org/en/latest/input_format.html'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10311, 2, '2016-02-19 03:26:46.01', 1, NULL, NULL, '&lt;p&gt;I agree with the existing answer that &lt;em&gt;feature scaling&lt;/em&gt; is a superset into which techniques like mean normalization, residual normalization, etc falls under.&lt;/p&gt;

&lt;p&gt;So, assuming that by &lt;em&gt;feature scaling&lt;/em&gt;, you mean the techniques other than mean normalization, I would attempt to answer your questions:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;1) Can I mix and match these two approaches? e.g. Feature Scale x1 and
  Mean Normalize x2?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;In most cases &lt;strong&gt;No&lt;/strong&gt;. Generally, only one normalization technique is used and it pretty much suffices the need. In addition to that argument, it should also be noted that any normalization technique introduces duplication in the data records (not necessarily redundant duplication).&lt;/p&gt;

&lt;p&gt;So, pretty much a single normalization technique would suffice most of the times.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;2) How do you determine which of these options to apply? It seems that
  either could accomplish the task of increasing your convergence
  rates... I suppose you just need to know your data set to understand
  which will reliably reduce your values while leaving as few outliers
  as possible?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Yes&lt;/strong&gt;, you are right. The selection of the technique depends on the data. And the feature scaling (and normalization) process comes under the process of data cleaning. So, it is done immediately after the selection of the relevant data for the analytics process.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':73C,187C ''/em'':12C,36C ''/p'':27C,52C,72C,119C,134C,186C,234C ''/strong'':80C,191C ''1'':55C ''2'':137C ''accomplish'':153C ''addit'':96C ''agre'':3C ''also'':102C ''analyt'':232C ''answer'':7C,49C ''appli'':147C ''approach'':63C ''argument'':99C ''assum'':30C ''attempt'':47C ''blockquot'':53C,135C ''case'':77C ''clean'':217C ''come'':211C ''converg'':159C ''could'':152C ''data'':113C,169C,203C,216C,229C ''depend'':200C ''determin'':141C ''done'':221C ''duplic'':110C,118C ''e.g'':64C ''either'':151C ''em'':9C,33C ''etc'':24C ''exist'':6C ''fall'':25C ''featur'':10C,34C,65C,206C ''general'':81C ''immedi'':222C ''increas'':157C ''introduc'':109C ''know'':167C ''leav'':180C ''like'':19C ''match'':60C ''mean'':20C,38C,43C,69C ''mix'':58C ''much'':91C,123C ''necessarili'':116C ''need'':94C,165C ''normal'':21C,23C,44C,70C,84C,107C,126C,209C ''note'':104C ''one'':83C ''option'':145C ''outlier'':183C ''p'':1C,28C,54C,74C,120C,136C,188C ''possibl'':185C ''pretti'':90C,122C ''process'':210C,214C,233C ''question'':51C ''rate'':160C ''record'':114C ''reduc'':176C ''redund'':117C ''relev'':228C ''reliabl'':175C ''residu'':22C ''right'':194C ''scale'':11C,35C,66C,207C ''seem'':149C ''select'':196C,225C ''set'':170C ''singl'':125C ''strong'':78C,189C ''suffic'':92C,129C ''superset'':15C ''suppos'':162C ''task'':155C ''techniqu'':18C,40C,85C,108C,127C,199C ''time'':133C ''two'':62C ''understand'':172C ''use'':87C ''valu'':178C ''would'':46C,128C ''x1'':67C ''x2'':71C ''yes'':190C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10312, 2, '2016-02-19 05:48:30.247', 0, NULL, NULL, '&lt;p&gt;I think the effect of the certification from coursera is dependent on the individual as well as the classes. The requirement says min 3-5 hours a week, if you put more, and the material do open up for a lot more than the 3-5 hours, then these classes and certifications can be equivalent to strong knowledge base and experience in the field. Science comes to those who request it.&lt;/p&gt;
', NULL, NULL, '''-5'':26C,47C ''/p'':73C ''3'':25C,46C ''base'':60C ''certif'':8C,53C ''class'':20C,51C ''come'':67C ''coursera'':10C ''depend'':12C ''effect'':5C ''equival'':56C ''experi'':62C ''field'':65C ''hour'':27C,48C ''individu'':15C ''knowledg'':59C ''lot'':42C ''materi'':36C ''min'':24C ''open'':38C ''p'':1C ''put'':32C ''request'':71C ''requir'':22C ''say'':23C ''scienc'':66C ''strong'':58C ''think'':3C ''week'':29C ''well'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10314, 1, '2016-02-19 10:05:44.533', 1, 23, 'Regarding Class Balancing in Deep Neural Network', '&lt;p&gt;I was trying to do class balancing on the image semantic segmentation problem for some classes in the images are in the minority. The weight for each class is calculated as mentioned in this paper: &lt;a href="http://arxiv.org/pdf/1511.00561v2.pdf" rel="nofollow"&gt;http://arxiv.org/pdf/1511.00561v2.pdf&lt;/a&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;we weight each pixel by Î±c = median_freq / freq(c) where freq(c) is the number of pixels of class c divided by the total number of pixels in images where c is present, and median_freq is the median of these frequencies.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Then I weighted the cross entropy loss as follows, the size of the label is (img_col, img_row, num_class) for the labels are one-hot-labels:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def weighted_cce(coding_dist, true_dist, weights):
    # calculate weighted cross entropy loss
    # true_dist: ground truth, coding_dist: predicted
    coding_dist = T.clip(coding_dist, 10e-8, 1.0-10e-8)
    return -T.sum(weights * true_dist * T.log(coding_dist), axis=coding_dist.ndim-1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What''s strange is that instead of producing a more balanced output, the result is even more biased than without class balancing, namely the network now can only recognize the most dominant classes in the images.&lt;/p&gt;

&lt;p&gt;Could anyone share some thoughts on this? Thanks in advance!&lt;/p&gt;
', 'python  deep-learning  image-classification', NULL, '''-1'':180C ''-10e-8'':169C ''/blockquote'':108C ''/code'':181C ''/p'':107C,139C,220C,232C ''/pdf/1511.00561v2.pdf'':55C ''/pdf/1511.00561v2.pdf&lt;/a&gt;&lt;/p&gt;'':60C ''/pre'':182C ''1.0'':168C ''10e-8'':167C ''advanc'':231C ''anyon'':223C ''arxiv.org'':54C,59C ''arxiv.org/pdf/1511.00561v2.pdf'':53C ''arxiv.org/pdf/1511.00561v2.pdf&lt;/a&gt;&lt;/p&gt;'':58C ''axi'':178C ''balanc'':3A,22C,194C,205C ''bias'':201C ''blockquot'':61C ''c'':69C,73C,76C,84C,95C ''calcul'':45C,150C ''cce'':144C ''class'':2A,21C,31C,43C,83C,130C,204C,216C ''classif'':14B ''code'':141C,145C,159C,162C,165C,176C ''coding_dist.ndim'':179C ''col'':126C ''could'':222C ''cross'':114C,152C ''deep'':5A,10B ''deep-learn'':9B ''def'':142C ''dist'':146C,148C,156C,160C,163C,166C,174C,177C ''divid'':85C ''domin'':215C ''entropi'':115C,153C ''even'':199C ''follow'':118C ''freq'':71C,72C,75C,100C ''frequenc'':106C ''ground'':157C ''hot'':137C ''href'':52C ''imag'':13B,25C,34C,93C,219C ''image-classif'':12B ''img'':125C,127C ''instead'':189C ''label'':123C,133C,138C ''learn'':11B ''loss'':116C,154C ''median'':70C,99C,103C ''mention'':47C ''minor'':38C ''name'':206C ''network'':7A,208C ''neural'':6A ''nofollow'':57C ''num'':129C ''number'':79C,89C ''one'':136C ''one-hot-label'':135C ''output'':195C ''p'':15C,62C,109C,183C,221C ''paper'':50C ''pixel'':66C,81C,91C ''pre'':140C ''predict'':161C ''present'':97C ''problem'':28C ''produc'':191C ''python'':8B ''recogn'':212C ''regard'':1A ''rel'':56C ''result'':197C ''return'':170C ''row'':128C ''segment'':27C ''semant'':26C ''share'':224C ''size'':120C ''strang'':186C ''t.clip'':164C ''t.log'':175C ''t.sum'':171C ''thank'':229C ''thought'':226C ''total'':88C ''tri'':18C ''true'':147C,155C,173C ''truth'':158C ''weight'':40C,64C,112C,143C,149C,151C,172C ''without'':203C ''î'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10317, 1, '2016-02-19 12:13:59.707', 1, 24, 'MATLAB code crashing for higher number of CSV records', '&lt;p&gt;I am completely new to MATLAB; learning it now.&lt;/p&gt;

&lt;p&gt;I am trying to run this MATLAB code: &lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/BingZhangHu/random-clustering-forest" rel="nofollow"&gt;https://github.com/BingZhangHu/random-clustering-forest&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;It works fine. The .mat file contains 600 records and 30 columns. However, when I try to run this code with a CSV file I have which contains 3498 records an 17 columns, it crashes; computer hangs completely. The same code works for the same CSV file when I reduce the number of records to 600 and 17 columns. I have been looking through the code where the parameters are set but it did not help me. I also tried to look to convert it to MAT file but still no use. &lt;/p&gt;

&lt;p&gt;My CSV File: 
&lt;a href="https://github.com/dresenhista/Advanced_Methods/blob/master/assignment%202/pendigits.tes.csv" rel="nofollow"&gt;https://github.com/dresenhista/Advanced_Methods/blob/master/assignment%202/pendigits.tes.csv&lt;/a&gt;&lt;/p&gt;
', 'matlab', NULL, '''/bingzhanghu/random-clustering-forest'':37C ''/bingzhanghu/random-clustering-forest&lt;/a&gt;&lt;/p&gt;'':42C ''/dresenhista/advanced_methods/blob/master/assignment%202/pendigits.tes.csv'':145C ''/dresenhista/advanced_methods/blob/master/assignment%202/pendigits.tes.csv&lt;/a&gt;&lt;/p&gt;'':150C ''/p'':21C,31C,136C ''17'':75C,101C ''30'':54C ''3498'':72C ''600'':51C,99C ''also'':122C ''code'':2A,30C,63C,84C,109C ''column'':55C,76C,102C ''complet'':14C,81C ''comput'':79C ''contain'':50C,71C ''convert'':127C ''crash'':3A,78C ''csv'':8A,66C,89C,139C ''file'':49C,67C,90C,131C,140C ''fine'':46C ''github.com'':36C,41C,144C,149C ''github.com/bingzhanghu/random-clustering-forest'':35C ''github.com/bingzhanghu/random-clustering-forest&lt;/a&gt;&lt;/p&gt;'':40C ''github.com/dresenhista/advanced_methods/blob/master/assignment%202/pendigits.tes.csv'':143C ''github.com/dresenhista/advanced_methods/blob/master/assignment%202/pendigits.tes.csv&lt;/a&gt;&lt;/p&gt;'':148C ''hang'':80C ''help'':119C ''higher'':5A ''howev'':56C ''href'':34C,142C ''learn'':18C ''look'':106C,125C ''mat'':48C,130C ''matlab'':1A,10B,17C,29C ''new'':15C ''nofollow'':39C,147C ''number'':6A,95C ''p'':11C,22C,32C,43C,137C ''paramet'':112C ''record'':9A,52C,73C,97C ''reduc'':93C ''rel'':38C,146C ''run'':27C,61C ''set'':114C ''still'':133C ''tri'':25C,59C,123C ''use'':135C ''work'':45C,85C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10318, 1, '2016-02-19 15:14:30.483', 1, 47, 'PCA and maintaining relationship with target variable', '&lt;p&gt;I''m rather new to PCA and was hoping to have some confusion cleared up. Lets say for example we have a feature matrix that''s nx100 and I want to get it down to something a bit smaller, p-dimensions, without losing too much variance. &lt;/p&gt;

&lt;p&gt;After applying PCA and receiving and new feature matrix nxp, I would use x_reduced to predict some target variable y.&lt;/p&gt;

&lt;p&gt;My question is, after the transformation, the new reduced feature matrix has been rotated by the eigenvectors and is sitting on a new basis. Yet, our y has not changed relative to X_reduced. &lt;/p&gt;

&lt;p&gt;I''m unsure about how y_original and x_reduced can be used for training since y has not changed with respect to x_reduced. &lt;/p&gt;

&lt;p&gt;Is there a way to correct for this or am I not thinking about it correctly?&lt;/p&gt;
', 'statistics  feature-scaling', NULL, '''/p'':60C,83C,119C,146C,164C ''appli'':63C ''basi'':108C ''bit'':50C ''chang'':114C,140C ''clear'':26C ''confus'':25C ''correct'':153C,163C ''dimens'':54C ''eigenvector'':101C ''exampl'':31C ''featur'':10B,35C,69C,94C ''feature-sc'':9B ''get'':44C ''hope'':21C ''let'':28C ''lose'':56C ''m'':14C,122C ''maintain'':3A ''matrix'':36C,70C,95C ''much'':58C ''new'':16C,68C,92C,107C ''nx100'':39C ''nxp'':71C ''origin'':127C ''p'':12C,53C,61C,84C,120C,147C ''p-dimens'':52C ''pca'':1A,18C,64C ''predict'':78C ''question'':86C ''rather'':15C ''receiv'':66C ''reduc'':76C,93C,118C,130C,145C ''relat'':115C ''relationship'':4A ''respect'':142C ''rotat'':98C ''say'':29C ''scale'':11B ''sinc'':136C ''sit'':104C ''smaller'':51C ''someth'':48C ''statist'':8B ''target'':6A,80C ''think'':160C ''train'':135C ''transform'':90C ''unsur'':123C ''use'':74C,133C ''variabl'':7A,81C ''varianc'':59C ''want'':42C ''way'':151C ''without'':55C ''would'':73C ''x'':75C,117C,129C,144C ''y'':82C,111C,126C,137C ''yet'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10319, 2, '2016-02-19 15:29:30.033', 3, NULL, NULL, '&lt;p&gt;The short answer is that the y_original and x_reduced are still connected to each other, so it is safe to train your data using y_original and x_reduced.  While x_reduced is on a different scale, as you mentioned via eigenvectors, it still is representative of the data that was attached to that observation, just in a different format.  You lose a lot of interpretability as far as what the actual numbers mean which is why it may seem confusing, but it''s just a transformed representation of the x_original that (hopefully) contains enough of the x_original variability to make it useful.&lt;/p&gt;
', NULL, NULL, '''/p'':109C ''actual'':75C ''answer'':4C ''attach'':55C ''confus'':84C ''connect'':15C ''contain'':98C ''data'':26C,52C ''differ'':39C,62C ''eigenvector'':45C ''enough'':99C ''far'':71C ''format'':63C ''hope'':97C ''interpret'':69C ''lose'':65C ''lot'':67C ''make'':106C ''may'':82C ''mean'':77C ''mention'':43C ''number'':76C ''observ'':58C ''origin'':9C,29C,95C,103C ''p'':1C ''reduc'':12C,32C,35C ''repres'':49C ''represent'':91C ''safe'':22C ''scale'':40C ''seem'':83C ''short'':3C ''still'':14C,47C ''train'':24C ''transform'':90C ''use'':27C,108C ''variabl'':104C ''via'':44C ''x'':11C,31C,34C,94C,102C ''y'':8C,28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10320, 1, '2016-02-19 16:08:12.4', 1, 29, 'Colouring points based on cluster on matplotlib', '&lt;p&gt;I have a set of points where I performed a KMeans classification. How make a plot where the color of the point is based on the cluster they belong?&lt;/p&gt;

&lt;p&gt;EDIT: for clarification, having the set of points, I want to use the values of the array generated from &lt;code&gt;KMeans.predict()&lt;/code&gt; ( from sklearn) to choose the color of each point.&lt;/p&gt;
', 'python  visualization', NULL, '''/code'':63C ''/p'':40C,73C ''array'':58C ''base'':3A,34C ''belong'':39C ''choos'':67C ''clarif'':44C ''classif'':22C ''cluster'':5A,37C ''code'':61C ''color'':29C,69C ''colour'':1A ''edit'':42C ''generat'':59C ''kmean'':21C ''kmeans.predict'':62C ''make'':24C ''matplotlib'':7A ''p'':10C,41C ''perform'':19C ''plot'':26C ''point'':2A,16C,32C,49C,72C ''python'':8B ''set'':14C,47C ''sklearn'':65C ''use'':53C ''valu'':55C ''visual'':9B ''want'':51C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10321, 2, '2016-02-19 16:37:04.99', 1, NULL, NULL, '&lt;p&gt;The &lt;a href="http://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html" rel="nofollow"&gt;sklearn documentation&lt;/a&gt; shows you how:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;colors = np.array([x for x in ''bgrcmykbgrcmykbgrcmykbgrcmyk''])
colors = np.hstack([colors] * 20)
...
if hasattr(algorithm, ''cluster_centers_''):
        centers = algorithm.cluster_centers_
        center_colors = colors[:len(centers)]
        plt.scatter(centers[:, 0], centers[:, 1], s=100, c=center_colors)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/quQKq.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/quQKq.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':12C,73C ''/code'':53C ''/p'':16C,74C ''/pre'':54C ''/quqkq.png'':60C,67C ''/stable/auto_examples/cluster/plot_cluster_comparison.html'':7C ''0'':45C ''1'':47C ''100'':49C ''20'':29C ''algorithm'':32C ''algorithm.cluster'':36C ''alt'':68C ''bgrcmykbgrcmykbgrcmykbgrcmyk'':25C ''c'':50C ''center'':34C,35C,37C,38C,42C,44C,46C,51C ''cluster'':33C ''code'':18C ''color'':19C,26C,28C,39C,40C,52C ''descript'':71C ''document'':11C ''enter'':69C ''hasattr'':31C ''href'':4C,57C ''i.stack.imgur.com'':59C,66C ''i.stack.imgur.com/quqkq.png'':58C,65C ''imag'':70C ''img'':63C ''len'':41C ''nofollow'':9C,62C ''np.array'':20C ''np.hstack'':27C ''p'':1C,55C ''plt.scatter'':43C ''pre'':17C ''rel'':8C,61C ''scikit-learn.org'':6C ''scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html'':5C ''show'':13C ''sklearn'':10C ''src'':64C ''x'':21C,23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10323, 2, '2016-02-19 18:21:54.06', 1, NULL, NULL, '&lt;p&gt;As you probably know, the Gaussian distribution is fully specified by its mean and covariance. theta0, thetaL, and thetaU are, respectively, initial values, lower bounds, and upper bounds on the covariance. You can set them based on your knowledge of the particulars of the problem. To learn more about Gaussian processes, I suggest visiting this great Web site: &lt;a href="http://www.gaussianprocess.org" rel="nofollow"&gt;http://www.gaussianprocess.org&lt;/a&gt; It even offers a free ebook.&lt;/p&gt;
', NULL, NULL, '''/a'':66C ''/p'':73C ''base'':37C ''bound'':26C,29C ''covari'':16C,32C ''distribut'':8C ''ebook'':72C ''even'':68C ''free'':71C ''fulli'':10C ''gaussian'':7C,51C ''great'':57C ''href'':61C ''initi'':23C ''know'':5C ''knowledg'':40C ''learn'':48C ''lower'':25C ''mean'':14C ''nofollow'':64C ''offer'':69C ''p'':1C ''particular'':43C ''probabl'':4C ''problem'':46C ''process'':52C ''rel'':63C ''respect'':22C ''set'':35C ''site'':59C ''specifi'':11C ''suggest'':54C ''theta0'':17C ''thetal'':18C ''thetau'':20C ''upper'':28C ''valu'':24C ''visit'':55C ''web'':58C ''www.gaussianprocess.org'':62C,65C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10383, 2, '2016-02-25 00:39:31.59', 1, NULL, NULL, '&lt;p&gt;I''m not sure what you mean by "performance", but if what you mean is fit the answer is clear. You need to be using the log-likelihood to differentiate between different models. Basically, when you are fitting the model you are trying to maximize the log-likelihood. Thus the log-likelihood is giving you some sense of how well the parameters of your model are doing at fitting the data.&lt;/p&gt;

&lt;p&gt;In your case, you want to get log-likelihood to be as close to zero as possible. Now this is kind of terrible advice, because if you were clever enough to come up with a feature for every observation, you could get a perfect fit. That''s bad because your model would be completely useless. There are functions that take your log-likelihood as an input and transform it to penalize you for adding more variables, etc. We won''t worry about those right now. Just keep in mind not to mindlessly lower the log-likelihood.&lt;/p&gt;

&lt;p&gt;Once you have a model that you can live with you should run some sort of cross-validation, and/or hold out set. Then you can use any number of metrics to validate the predictive performance of your model. I think that is the more important of the two issues. You could calculate the mean square error on your hold out set. $$MSE=\sum_{i=1}^n(\hat y_i - y_i)^2$$
This would give you a really basic metric to assess how well your model is predictive of the output.&lt;/p&gt;
', NULL, NULL, '''/p'':76C,177C,271C ''1'':244C ''2'':251C ''ad'':153C ''advic'':102C ''and/or'':198C ''answer'':19C ''assess'':261C ''bad'':126C ''basic'':36C,258C ''calcul'':231C ''case'':80C ''clear'':21C ''clever'':107C ''close'':91C ''come'':110C ''complet'':132C ''could'':119C,230C ''cross'':196C ''cross-valid'':195C ''data'':75C ''differ'':34C ''differenti'':32C ''enough'':108C ''error'':235C ''etc'':156C ''everi'':116C ''featur'':114C ''fit'':17C,40C,73C,123C ''function'':136C ''get'':84C,120C ''give'':58C,254C ''hat'':246C ''hold'':199C,238C ''import'':224C ''input'':145C ''issu'':228C ''keep'':166C ''kind'':99C ''likelihood'':30C,51C,56C,87C,142C,176C ''live'':187C ''log'':29C,50C,55C,86C,141C,175C ''log-likelihood'':28C,49C,54C,85C,140C,174C ''lower'':172C ''m'':3C ''maxim'':47C ''mean'':8C,15C,233C ''metric'':209C,259C ''mind'':168C ''mindless'':171C ''model'':35C,42C,69C,129C,183C,217C,265C ''mse'':241C ''n'':245C ''need'':23C ''number'':207C ''observ'':117C ''output'':270C ''p'':1C,77C,178C ''paramet'':66C ''penal'':150C ''perfect'':122C ''perform'':10C,214C ''possibl'':95C ''predict'':213C,267C ''realli'':257C ''right'':163C ''run'':191C ''sens'':61C ''set'':201C,240C ''sort'':193C ''squar'':234C ''sum'':242C ''sure'':5C ''take'':138C ''terribl'':101C ''think'':219C ''thus'':52C ''transform'':147C ''tri'':45C ''two'':227C ''use'':26C,205C ''useless'':133C ''valid'':197C,211C ''variabl'':155C ''want'':82C ''well'':64C,263C ''won'':158C ''worri'':160C ''would'':130C,253C ''y'':247C,249C ''zero'':93C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10327, 2, '2016-02-20 05:39:30.017', 2, NULL, NULL, '&lt;p&gt;The details are just a &lt;code&gt;help(summary.lm)&lt;/code&gt; in R. The meaning of the codes and the terms are as follows:&lt;/p&gt;

&lt;p&gt;Considering R = Correlation Coefficient&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;The &lt;strong&gt;residual standard error&lt;/strong&gt; is nothing more than the positive square root of the mean square error. &lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Regression degrees of freedom&lt;/strong&gt; = number of independent variables (factors) in $y$ = $a_1x_1 + a_2x_2 + a_3x3 + ...$   &lt;a href="https://en.wikipedia.org/wiki/Degrees_of_freedom_%28statistics%29" rel="nofollow"&gt;Wikipedia link&lt;/a&gt; for a better in-depth explanation&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;R Square&lt;/strong&gt; = $(Multiple R)^2$ = $R^2$ =  $1 - (Residual SS / Total SS) = (Regress SS / Total SS)$&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Adjusted R Square&lt;/strong&gt; = $1 - (Total df / Residual df)(Residual SS / Total SS)$&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;A &lt;a href="http://stats.stackexchange.com/a/17148/84191"&gt;wonderful post&lt;/a&gt; on how to understand degrees of freedom.&lt;/p&gt;
', NULL, NULL, '''/a'':84C,139C ''/a/17148/84191'':136C ''/code'':10C ''/li'':52C,92C,112C,128C ''/ol'':129C ''/p'':24C,30C,147C ''/strong'':38C,59C,97C,118C ''/wiki/degrees_of_freedom_%28statistics%29'':79C ''1'':69C,103C,119C ''1x'':68C ''2'':72C,100C,102C ''2x'':71C ''3x3'':74C ''adjust'':115C ''better'':87C ''code'':7C,17C ''coeffici'':29C ''consid'':26C ''correl'':28C ''degre'':56C,144C ''depth'':90C ''detail'':3C ''df'':121C,123C ''en.wikipedia.org'':78C ''en.wikipedia.org/wiki/degrees_of_freedom_%28statistics%29'':77C ''error'':37C,51C ''explan'':91C ''factor'':64C ''follow'':23C ''freedom'':58C,146C ''help'':8C ''href'':76C,133C ''in-depth'':88C ''independ'':62C ''li'':32C,53C,93C,113C ''link'':83C ''mean'':14C,49C ''multipl'':98C ''nofollow'':81C ''noth'':40C ''number'':60C ''ol'':31C ''p'':1C,25C,130C ''posit'':44C ''post'':138C ''r'':12C,27C,95C,99C,101C,116C ''regress'':55C,108C ''rel'':80C ''residu'':35C,104C,122C,124C ''root'':46C ''squar'':45C,50C,96C,117C ''ss'':105C,107C,109C,111C,125C,127C ''standard'':36C ''stats.stackexchange.com'':135C ''stats.stackexchange.com/a/17148/84191'':134C ''strong'':34C,54C,94C,114C ''summary.lm'':9C ''term'':20C ''total'':106C,110C,120C,126C ''understand'':143C ''variabl'':63C ''wikipedia'':82C ''wonder'':137C ''y'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10328, 1, '2016-02-20 06:18:40.88', 1, 23, 'Why does image segmentation benefit from fuzzy clustering?', '&lt;p&gt;In many image processing papers, I''ve seen that they used fuzzy logic for segmentation
I wonder how fuzzification impact the result that made Fuzzy-C-Means better than ordinary K-Means.&lt;/p&gt;

&lt;p&gt;PS. If possible could you provide me the sample data sets for case study&lt;/p&gt;
', 'machine-learning  clustering  k-means  image-classification  fuzzy-logic', NULL, '''/p'':57C,73C ''benefit'':5A ''better'':51C ''c'':49C ''case'':71C ''classif'':18B ''cluster'':8A,12B ''could'':62C ''data'':68C ''fuzzi'':7A,20B,34C,48C ''fuzzif'':41C ''fuzzy-c-mean'':47C ''fuzzy-log'':19B ''imag'':3A,17B,25C ''image-classif'':16B ''impact'':42C ''k'':14B,55C ''k-mean'':13B,54C ''learn'':11B ''logic'':21B,35C ''machin'':10B ''machine-learn'':9B ''made'':46C ''mani'':24C ''mean'':15B,50C,56C ''ordinari'':53C ''p'':22C,58C ''paper'':27C ''possibl'':61C ''process'':26C ''provid'':64C ''ps'':59C ''result'':44C ''sampl'':67C ''seen'':30C ''segment'':4A,37C ''set'':69C ''studi'':72C ''use'':33C ''ve'':29C ''wonder'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10340, 1, '2016-02-21 15:48:03.543', 1, 18, 'Bayesian network for classification using PyMc or PyMc3', '&lt;p&gt;I am seraching for a  while  an example  on how to use  PyMc/PyMc3 to do  classification task, but have not  found  an concludent   example  regarding  on how to do   the  predicton on a  new data point.
I am currious if some   could   give  me some references.&lt;/p&gt;
', 'bayesian-networks', NULL, '''/p'':59C ''bayesian'':1A,10B ''bayesian-network'':9B ''classif'':4A,28C ''conclud'':35C ''could'':54C ''currious'':51C ''data'':47C ''exampl'':20C,36C ''found'':33C ''give'':55C ''network'':2A,11B ''new'':46C ''p'':12C ''point'':48C ''predicton'':43C ''pymc'':6A ''pymc/pymc3'':25C ''pymc3'':8A ''refer'':58C ''regard'':37C ''serach'':15C ''task'':29C ''use'':5A,24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10343, 1, '2016-02-21 20:45:40.75', 2, 45, 'Feature scaling data with missing values', '&lt;p&gt;am interested in doing some feature scaling to try and tease out something from my data (box plots by outcome show that the 25/50/75 quantiles are very similar; certain variables have more "outliers" than other by class. The issue I have however is that there is a lot of missing data. I would like to replace these NAs (they are numerical) with something like -9999. Should I first scale my data and then assign -9999 to NAs or first assign then scale?&lt;/p&gt;
', 'feature-scaling  missing-data', NULL, '''-9999'':78C,88C ''/p'':96C ''25/50/75'':37C ''assign'':87C,93C ''box'':30C ''certain'':42C ''class'':50C ''data'':3A,12B,29C,64C,84C ''featur'':1A,8B,19C ''feature-sc'':7B ''first'':81C,92C ''howev'':55C ''interest'':15C ''issu'':52C ''like'':67C,77C ''lot'':61C ''miss'':5A,11B,63C ''missing-data'':10B ''nas'':71C,90C ''numer'':74C ''outcom'':33C ''outlier'':46C ''p'':13C ''plot'':31C ''quantil'':38C ''replac'':69C ''scale'':2A,9B,20C,82C,95C ''show'':34C ''similar'':41C ''someth'':26C,76C ''teas'':24C ''tri'':22C ''valu'':6A ''variabl'':43C ''would'':66C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10329, 1, '2016-02-20 07:24:56.14', 3, 82, 'Training Deep Nets on an Ordinary Laptop', '&lt;p&gt;Would it be possible for a an amateur who is interested in getting  some "hands-on" experience in desining and training deep neural networks, to use an ordinary laptop for that purpose (no GPU), or is it hopeless to get good results in reasonable time without a powerful computer/cluster/GPU?&lt;/p&gt;

&lt;p&gt;To be more specific, the laptop''s CPU is an Intel Core i7 5500U fith generation, with 8GB RAM.&lt;/p&gt;

&lt;p&gt;Now, since I haven''t specified what problems I would like to work on, I''ll frame my questions in a different way: which deep architectures would you recommend that I try to implement with my hardware, such that the following goal is achieved: Acquiring intuition and knowledge about how and when to use techniques that were introduced in the past 10 years and were essential to the uprising of deep nets (such as understanding of initialisations, drop-out, rmsprop, just to name a few).&lt;/p&gt;

&lt;p&gt;I read about these techniques, but of course without trying them out myself I wouldn''t know exactly how and when to implement these in an effective way. On the other hand, I''m afraid that if I try using a PC which isn''t strong enough, then my own learning rate will be so slow that it would be meaningless to say that I''ve acquired any better understanding. And if I try using these techniques on shallow nets, maybe I wouldn''t be building the right intuition.&lt;/p&gt;

&lt;p&gt;I imagine the process of (my) learning as follows: I implement a neural net, let it practice for up to several hours, see what I''ve got, and repeat the process. If I do this once or twice a day, I would be happy if after, say, 6 months I will have gained practical knowledge which is comparable to what a professional in the field should know.&lt;/p&gt;
', 'machine-learning  deep-learning', NULL, '''/p'':65C,86C,174C,265C,334C ''10'':149C ''5500u'':80C ''6'':314C ''8gb'':84C ''achiev'':131C ''acquir'':132C,242C ''afraid'':210C ''amateur'':22C ''architectur'':113C ''better'':244C ''build'':261C ''compar'':324C ''computer/cluster/gpu'':64C ''core'':78C ''cours'':183C ''cpu'':74C ''day'':306C ''deep'':2A,12B,37C,112C,158C ''deep-learn'':11B ''desin'':34C ''differ'':109C ''drop'':166C ''drop-out'':165C ''effect'':202C ''enough'':222C ''essenti'':153C ''exact'':193C ''experi'':32C ''field'':331C ''fith'':81C ''follow'':128C,275C ''frame'':104C ''gain'':319C ''generat'':82C ''get'':27C,55C ''goal'':129C ''good'':56C ''got'':293C ''gpu'':49C ''hand'':30C,207C ''hands-on'':29C ''happi'':310C ''hardwar'':124C ''haven'':91C ''hopeless'':53C ''hour'':288C ''i7'':79C ''imagin'':268C ''implement'':121C,198C,277C ''initialis'':164C ''intel'':77C ''interest'':25C ''introduc'':145C ''intuit'':133C,264C ''isn'':219C ''know'':192C,333C ''knowledg'':135C,321C ''laptop'':7A,44C,72C ''learn'':10B,13B,226C,273C ''let'':281C ''like'':98C ''ll'':103C ''m'':209C ''machin'':9B ''machine-learn'':8B ''mayb'':256C ''meaningless'':236C ''month'':315C ''name'':171C ''net'':3A,159C,255C,280C ''network'':39C ''neural'':38C,279C ''ordinari'':6A,43C ''p'':14C,66C,87C,175C,266C ''past'':148C ''pc'':217C ''possibl'':18C ''power'':63C ''practic'':283C,320C ''problem'':95C ''process'':270C,297C ''profession'':328C ''purpos'':47C ''question'':106C ''ram'':85C ''rate'':227C ''read'':177C ''reason'':59C ''recommend'':116C ''repeat'':295C ''result'':57C ''right'':263C ''rmsprop'':168C ''say'':238C,313C ''see'':289C ''sever'':287C ''shallow'':254C ''sinc'':89C ''slow'':231C ''specif'':70C ''specifi'':93C ''strong'':221C ''techniqu'':142C,180C,252C ''time'':60C ''train'':1A,36C ''tri'':119C,185C,214C,249C ''twice'':304C ''understand'':162C,245C ''upris'':156C ''use'':41C,141C,215C,250C ''ve'':241C,292C ''way'':110C,203C ''without'':61C,184C ''work'':100C ''would'':15C,97C,114C,234C,308C ''wouldn'':190C,258C ''year'':150C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10350, 2, '2016-02-22 18:15:10.043', 1, NULL, NULL, '&lt;p&gt;I suggest to calculate the &lt;a href="https://en.wikipedia.org/wiki/Haversine_formula" rel="nofollow"&gt;Haversine&lt;/a&gt; distance between two points, and fit a linear regression to find the relation between the Haversine distance and the trip duration.
So your regression will be&lt;/p&gt;

&lt;p&gt;$duration_t = timestamp_t - timestamp_{t-1} = \alpha + \beta*d(point_t,point_{t-1})$&lt;/p&gt;

&lt;p&gt;Where $d$ is the Haversine distance. $point_t$ is a lat/long pair at time $t$.&lt;/p&gt;

&lt;p&gt;Note however that there''s an assumptions that the user drives at the same speed. If half of your data was gathered while walking and half while driving, then your relation between time and ETA is possibly not linear.&lt;/p&gt;
', NULL, NULL, '''-1'':50C,58C ''/a'':15C ''/p'':42C,59C,76C,117C ''/wiki/haversine_formula'':11C ''alpha'':51C ''assumpt'':84C ''beta'':52C ''calcul'':5C ''d'':53C,62C ''data'':97C ''distanc'':16C,32C,66C ''drive'':88C,105C ''durat'':36C,44C ''en.wikipedia.org'':10C ''en.wikipedia.org/wiki/haversine_formula'':9C ''eta'':112C ''find'':26C ''fit'':21C ''gather'':99C ''half'':94C,103C ''haversin'':14C,31C,65C ''howev'':79C ''href'':8C ''lat/long'':71C ''linear'':23C,116C ''nofollow'':13C ''note'':78C ''p'':1C,43C,60C,77C ''pair'':72C ''point'':19C,54C,56C,67C ''possibl'':114C ''regress'':24C,39C ''rel'':12C ''relat'':28C,108C ''speed'':92C ''suggest'':3C ''time'':74C,110C ''timestamp'':46C,48C ''trip'':35C ''two'':18C ''user'':87C ''walk'':101C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10330, 1, '2016-02-20 10:11:17.397', 1, 45, 'What metrics must i use in my data(unstructured) preprocessing research?', '&lt;p&gt;i am currently working on preprocessing unstructured data (emails,logs,bug reports and irc chats).&lt;/p&gt;

&lt;p&gt;i wish to prove preprocessing improves the content quality.&lt;/p&gt;

&lt;p&gt;are there metrics available to prove this? that is to compare the raw data and the preprocessed data and show some quality improvement in numbers.   &lt;/p&gt;

&lt;p&gt;content quality - i mean to say that the preprocessed corpus is more suitable for mining with topic models than the raw one.  &lt;/p&gt;

&lt;p&gt;I am aware of perplexity. i dont want to use this metric as it only evaluates the topic model performance and not the corpus.Also i wish to evaluate the corpus quality even before the topic model is applied.  &lt;/p&gt;

&lt;p&gt;Can topic coherence be used as a metric to evaluate the quality of the corpus?
can more coherent topics be generated from preprocessed documents than the raw one?&lt;/p&gt;
', 'text-mining  data-cleaning  information-retrieval  topic-model  preprocessing', NULL, '''/p'':41C,52C,79C,103C,143C,173C ''appli'':142C ''avail'':57C ''awar'':107C ''bug'':36C ''chat'':40C ''clean'':17B ''coher'':147C,162C ''compar'':64C ''content'':50C,81C ''corpus'':90C,134C,159C ''corpus.also'':128C ''current'':28C ''data'':8A,16B,33C,67C,71C ''data-clean'':15B ''document'':168C ''dont'':111C ''email'':34C ''evalu'':120C,132C,154C ''even'':136C ''generat'':165C ''improv'':48C,76C ''inform'':19B ''information-retriev'':18B ''irc'':39C ''log'':35C ''mean'':84C ''metric'':2A,56C,116C,152C ''mine'':14B,95C ''model'':23B,98C,123C,140C ''must'':3A ''number'':78C ''one'':102C,172C ''p'':25C,42C,53C,80C,104C,144C ''perform'':124C ''perplex'':109C ''preprocess'':10A,24B,31C,47C,70C,89C,167C ''prove'':46C,59C ''qualiti'':51C,75C,82C,135C,156C ''raw'':66C,101C,171C ''report'':37C ''research'':11A ''retriev'':20B ''say'':86C ''show'':73C ''suitabl'':93C ''text'':13B ''text-min'':12B ''topic'':22B,97C,122C,139C,146C,163C ''topic-model'':21B ''unstructur'':9A,32C ''use'':5A,114C,149C ''want'':112C ''wish'':44C,130C ''work'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10331, 1, '2016-02-20 14:13:24.257', 0, 24, 'Normalization of vectors with data spanning 52 weeks for each vector', '&lt;p&gt;I was going through the Spherical kMeans algorithm and it states that the vectors have to be normalized to fit into a unit sphere as it uses cosine similarity measures, I''ve used the function predefined in R and also read the algorithm but if I have weekly data pertaining to 50 regions for 52 weeks and if every region is considered a vector giving me 50 vectors in totality, how do i normalize the vectors, I tried going through various texts and videos but did not find any of them explaining this issue.&lt;/p&gt;
', 'r  k-means  cosine-distance', NULL, '''/p'':114C ''50'':71C,86C ''52'':7A,74C ''algorithm'':27C,62C ''also'':59C ''consid'':81C ''cosin'':17B,47C ''cosine-dist'':16B ''data'':5A,68C ''distanc'':18B ''everi'':78C ''explain'':111C ''find'':107C ''fit'':39C ''function'':54C ''give'':84C ''go'':22C,98C ''issu'':113C ''k'':14B ''k-mean'':13B ''kmean'':26C ''mean'':15B ''measur'':49C ''normal'':1A,37C,93C ''p'':19C ''pertain'':69C ''predefin'':55C ''r'':12B,57C ''read'':60C ''region'':72C,79C ''similar'':48C ''span'':6A ''sphere'':43C ''spheric'':25C ''state'':30C ''text'':101C ''total'':89C ''tri'':97C ''unit'':42C ''use'':46C,52C ''various'':100C ''ve'':51C ''vector'':3A,11A,33C,83C,87C,95C ''video'':103C ''week'':8A,67C,75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10334, 5, '2016-02-21 05:39:00.67', 0, NULL, NULL, '&lt;p&gt;Some good beginner resources for getting started in Data Science are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="https://www.quora.com/topic/Data-Science" rel="nofollow"&gt;Data Science wiki of Quora&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Some good beginner resources for getting started in Machine Learning are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="https://www.coursera.org/learn/machine-learning" rel="nofollow"&gt;Professor Andrew Ng''s course on Machine Learning&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':28C,61C ''/learn/machine-learning'':50C ''/li'':29C,62C ''/ol'':30C,63C ''/p'':13C,43C ''/topic/data-science'':20C ''andrew'':54C ''beginn'':4C,34C ''cours'':57C ''data'':10C,23C ''get'':7C,37C ''good'':3C,33C ''href'':17C,47C ''learn'':41C,60C ''li'':15C,45C ''machin'':40C,59C ''ng'':55C ''nofollow'':22C,52C ''ol'':14C,44C ''p'':1C,31C ''professor'':53C ''quora'':27C ''rel'':21C,51C ''resourc'':5C,35C ''scienc'':11C,24C ''start'':8C,38C ''wiki'':25C ''www.coursera.org'':49C ''www.coursera.org/learn/machine-learning'':48C ''www.quora.com'':19C ''www.quora.com/topic/data-science'':18C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10336, 1, '2016-02-21 05:41:22.113', 2, 35, 'Why is each successive tree in GBM fit on the negative gradient of the loss function?', '&lt;p&gt;Page 359 of &lt;a href="http://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLII_print4.pdf" rel="nofollow"&gt;Elements Of Statistical Learning 2nd edition&lt;/a&gt; says the below.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/PgHFz.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/PgHFz.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Can someone explain the intuition &amp;amp; simplify it in layman terms?&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Questions&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;What is the reason/intuition &amp;amp; math behind fitting each successive tree in GBM on the negative gradient of the loss function?&lt;/li&gt;
&lt;li&gt;Is it done to make GBM more generalization on unseen test dataset? If so how does fitting on negative gradient achieve this generalization on test data?&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  optimization  gradient-descent  gbm  loss-function', NULL, '''/a'':45C,68C ''/em'':84C,91C ''/li'':117C,145C ''/ol'':146C ''/p'':49C,69C,86C,93C ''/pghfz.jpg'':55C,62C ''/strong'':85C,92C ''/~hastie/local.ftp/springer/old/eslii_print4.pdf'':36C ''2nd'':43C ''359'':30C ''achiev'':139C ''alt'':63C ''amp'':78C,100C ''behind'':102C ''data'':144C ''dataset'':130C ''descent'':23B ''descript'':66C ''done'':121C ''edit'':44C ''element'':39C ''em'':72C,89C ''enter'':64C ''explain'':75C ''fit'':8A,103C,135C ''function'':16A,27B,116C ''gbm'':7A,24B,108C,124C ''general'':126C,141C ''gradient'':12A,22B,112C,138C ''gradient-desc'':21B ''href'':33C,52C ''i.stack.imgur.com'':54C,61C ''i.stack.imgur.com/pghfz.jpg'':53C,60C ''imag'':65C ''img'':58C ''intuit'':77C ''layman'':82C ''learn'':19B,42C ''li'':95C,118C ''loss'':15A,26B,115C ''loss-funct'':25B ''machin'':18B ''machine-learn'':17B ''make'':123C ''math'':101C ''negat'':11A,111C,137C ''nofollow'':38C,57C ''ol'':94C ''optim'':20B ''p'':28C,50C,70C,87C ''page'':29C ''question'':90C ''reason/intuition'':99C ''rel'':37C,56C ''say'':46C ''simplifi'':79C ''someon'':74C ''src'':59C ''statist'':41C ''strong'':71C,88C ''success'':4A,105C ''term'':83C ''test'':129C,143C ''tree'':5A,106C ''unseen'':128C ''web.stanford.edu'':35C ''web.stanford.edu/~hastie/local.ftp/springer/old/eslii_print4.pdf'':34C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10337, 1, '2016-02-21 10:54:24.973', 0, 10, 'Medical text corpus', '&lt;p&gt;This should be a simple question to many in the area, but for the life of me I can''t find a solution by myself.&lt;/p&gt;

&lt;p&gt;I need a corpus of medical text for the purposes of training a spell checker which will be used on... medical text. For this I need a large amount of text that has few (or ideally no) errors in it. The abstracts from medline articles would be a good bet.&lt;/p&gt;

&lt;p&gt;However, while I can many solutions that are grossly more complicated than I require (such as &lt;a href="http://www.geniaproject.org/genia-corpus/treebank" rel="nofollow"&gt;treebank&lt;/a&gt;) designed for tree-building or POS tasks, and would require significant work to remove annotation and compile into a single .txt, surely such a resource already exists? &lt;/p&gt;

&lt;p&gt;Alternatively I could just visit a hundred or so medline articles and start copy-pasting their abstract details I suppose... &lt;/p&gt;
', 'machine-learning  text-mining  corpus', NULL, '''/a'':115C ''/genia-corpus/treebank'':111C ''/p'':37C,89C,144C,167C ''abstract'':80C,163C ''alreadi'':142C ''altern'':146C ''amount'':67C ''annot'':131C ''area'':22C ''articl'':83C,156C ''bet'':88C ''build'':120C ''checker'':53C ''compil'':133C ''complic'':101C ''copi'':160C ''copy-past'':159C ''corpus'':3A,10B,42C ''could'':148C ''design'':116C ''detail'':164C ''error'':76C ''exist'':143C ''find'':32C ''good'':87C ''grossli'':99C ''howev'':91C ''href'':108C ''hundr'':152C ''ideal'':74C ''larg'':66C ''learn'':6B ''life'':26C ''machin'':5B ''machine-learn'':4B ''mani'':19C,95C ''medic'':1A,44C,59C ''medlin'':82C,155C ''mine'':9B ''need'':40C,64C ''nofollow'':113C ''p'':11C,38C,90C,145C ''past'':161C ''pos'':122C ''purpos'':48C ''question'':17C ''rel'':112C ''remov'':130C ''requir'':104C,126C ''resourc'':141C ''signific'':127C ''simpl'':16C ''singl'':136C ''solut'':34C,96C ''spell'':52C ''start'':158C ''suppos'':166C ''sure'':138C ''task'':123C ''text'':2A,8B,45C,60C,69C ''text-min'':7B ''train'':50C ''tree'':119C ''tree-build'':118C ''treebank'':114C ''txt'':137C ''use'':57C ''visit'':150C ''work'':128C ''would'':84C,125C ''www.geniaproject.org'':110C ''www.geniaproject.org/genia-corpus/treebank'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10338, 1, '2016-02-21 12:58:57.12', 1, 38, 'What is the current state-of-the-art within aspect-based sentiment analysis?', '&lt;p&gt;I''ve been reading Bing Liu''s book on Sentiment Analysis. He mentions all of these slightly different approaches seen in research since 2004, but doesn''t talk much about efficacy at all.&lt;/p&gt;

&lt;p&gt;That leaves me - someone who has not done any sentiment analysis before - wondering what approaches are seeing the best results currently. And it specifically needs to be an approach that can extract the sentiments of individual topics found in the text, not just if a document/sentence is positive or negative.&lt;/p&gt;

&lt;p&gt;In case it makes a huge difference: the object of analysis will be reddit comments, not tweets or reviews which seem to be the most common source of data.&lt;/p&gt;
', 'machine-learning  nlp', NULL, '''/p'':54C,106C,137C ''2004'':44C ''analysi'':15A,31C,66C,118C ''approach'':39C,70C,84C ''art'':9A ''aspect'':12A ''aspect-bas'':11A ''base'':13A ''best'':74C ''bing'':25C ''book'':28C ''case'':109C ''comment'':122C ''common'':133C ''current'':4A,76C ''data'':136C ''differ'':38C,114C ''document/sentence'':101C ''doesn'':46C ''done'':63C ''efficaci'':51C ''extract'':87C ''found'':93C ''huge'':113C ''individu'':91C ''learn'':18B ''leav'':57C ''liu'':26C ''machin'':17B ''machine-learn'':16B ''make'':111C ''mention'':33C ''much'':49C ''need'':80C ''negat'':105C ''nlp'':19B ''object'':116C ''p'':20C,55C,107C ''posit'':103C ''read'':24C ''reddit'':121C ''research'':42C ''result'':75C ''review'':126C ''see'':72C ''seem'':128C ''seen'':40C ''sentiment'':14A,30C,65C,89C ''sinc'':43C ''slight'':37C ''someon'':59C ''sourc'':134C ''specif'':79C ''state'':6A ''state-of-the-art'':5A ''talk'':48C ''text'':96C ''topic'':92C ''tweet'':124C ''ve'':22C ''within'':10A ''wonder'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10344, 2, '2016-02-22 01:42:39.323', 1, NULL, NULL, '&lt;p&gt;Well -9999 can matter more or less depending on the variability within your data (is -9999 an extreme outlier value, or a moderate value somewhat close to the mean/median&gt;)&lt;/p&gt;

&lt;p&gt;Depends a lot on your data.  A value like that can skew it a lot, and can affect modeling quite a bit depending on the technique you use.  Are the NA''s biased for a class?  Consider decision trees that classify on optimal splits.  They would detect a bias (e.g. if 90% of your NA''s a certain class) and split at -9999 value to manifest that bias.&lt;/p&gt;

&lt;p&gt;also check out Pareto scaling, which emphasizes small to medium changes in your data.  Might be along the lines you are looking for.&lt;/p&gt;
', NULL, NULL, '''-9999'':3C,17C,95C ''/p'':31C,101C,126C ''90'':84C ''affect'':50C ''along'':119C ''also'':103C ''bias'':65C,81C,100C ''bit'':54C ''certain'':90C ''chang'':113C ''check'':104C ''class'':68C,91C ''classifi'':73C ''close'':27C ''consid'':69C ''data'':15C,38C,116C ''decis'':70C ''depend'':9C,33C,55C ''detect'':79C ''e.g'':82C ''emphas'':109C ''extrem'':19C ''less'':8C ''like'':41C ''line'':121C ''look'':124C ''lot'':35C,47C ''manifest'':98C ''matter'':5C ''mean/median'':30C ''medium'':112C ''might'':117C ''model'':51C ''moder'':24C ''na'':63C,87C ''optim'':75C ''outlier'':20C ''p'':1C,32C,102C ''pareto'':106C ''quit'':52C ''scale'':107C ''skew'':44C ''small'':110C ''somewhat'':26C ''split'':76C,93C ''techniqu'':58C ''tree'':71C ''use'':60C ''valu'':21C,25C,40C,96C ''variabl'':12C ''well'':2C ''within'':13C ''would'':78C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10347, 1, '2016-02-22 12:05:42.773', 2, 64, 'Machine learning on data with only time stamps', '&lt;p&gt;I''m working on trying to predict physical traffic volume over a network of intersections.&lt;/p&gt;

&lt;p&gt;The data I have is sorted per intersection and consists of the time (in sec after epoch) they passed and the direction someone came from. A mock sample here (I have months worth of data for 60 intersections):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;   Intersection 1:{
     (1456140051, 1),
     (1456140066, 2),
     (1456140070, 2),
     (1456140073, 3),
     (1456140078, 1),
     (1456140091, 1)}
   Intersection 2:{
     (1456140051, 1),
     (1456140058, 4),
     (1456140060, 3),
     (1456140063, 2),
     (1456140067, 2),
     (1456140071, 4)}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;(Note: the data is not for the complete network, there are many smaller intersections for which no data is gathered)&lt;/p&gt;

&lt;p&gt;So far I''ve done some fourier analysis which gives me strong weekly and daily periodicity (obviously) and thus calculated daily/weekly averages/noise etc. for each intersection, and I''ve looked for some correlations between some neighbouring intersections. The next step for me is to use the data from all the intersections to estimate the traffic volume at one intersection, and then do the same for the traffic volume 10 minutes in the future.&lt;/p&gt;

&lt;p&gt;I have found various machine learning methods that should work well with time series with constant time intervals and differing values (e.g. 1 data point of varying value every minute), and I can aggregate my data so I get a value of traffic volume each minute or so (typical traffic is between 5 and 100 /minute /intersection). However, I feel I would lose a lot of information that way.&lt;/p&gt;

&lt;p&gt;So what I''m looking for it a method that can work with the data I have (or some derivative of that without much information loss) as input. Can anyone recommend some articles or point me in the right direction?&lt;/p&gt;
', 'machine-learning  time-series', NULL, '''/code'':102C ''/intersection'':258C ''/minute'':257C ''/p'':31C,71C,125C,200C,271C,313C ''/pre'':103C ''1'':75C,77C,85C,87C,91C,224C ''10'':195C ''100'':256C ''1456140051'':76C,90C ''1456140058'':92C ''1456140060'':94C ''1456140063'':96C ''1456140066'':78C ''1456140067'':98C ''1456140070'':80C ''1456140071'':100C ''1456140073'':82C ''1456140078'':84C ''1456140091'':86C ''2'':79C,81C,89C,97C,99C ''3'':83C,95C ''4'':93C,101C ''5'':254C ''60'':69C ''aggreg'':235C ''analysi'':134C ''anyon'':302C ''articl'':305C ''averages/noise'':148C ''calcul'':146C ''came'':56C ''code'':73C ''complet'':112C ''consist'':42C ''constant'':217C ''correl'':159C ''daili'':141C ''daily/weekly'':147C ''data'':4A,34C,67C,107C,122C,173C,225C,237C,287C ''deriv'':292C ''differ'':221C ''direct'':54C,312C ''done'':131C ''e.g'':223C ''epoch'':49C ''estim'':179C ''etc'':149C ''everi'':230C ''far'':128C ''feel'':261C ''found'':204C ''fourier'':133C ''futur'':199C ''gather'':124C ''get'':240C ''give'':136C ''howev'':259C ''inform'':268C,297C ''input'':300C ''intersect'':30C,40C,70C,74C,88C,118C,152C,163C,177C,185C ''interv'':219C ''learn'':2A,11B,207C ''look'':156C,277C ''lose'':264C ''loss'':298C ''lot'':266C ''m'':17C,276C ''machin'':1A,10B,206C ''machine-learn'':9B ''mani'':116C ''method'':208C,281C ''minut'':196C,231C,247C ''mock'':59C ''month'':64C ''much'':296C ''neighbour'':162C ''network'':28C,113C ''next'':165C ''note'':105C ''obvious'':143C ''one'':184C ''p'':15C,32C,104C,126C,201C,272C ''pass'':51C ''per'':39C ''period'':142C ''physic'':23C ''point'':226C,307C ''pre'':72C ''predict'':22C ''recommend'':303C ''right'':311C ''sampl'':60C ''sec'':47C ''seri'':14B,215C ''smaller'':117C ''someon'':55C ''sort'':38C ''stamp'':8A ''step'':166C ''strong'':138C ''thus'':145C ''time'':7A,13B,45C,214C,218C ''time-seri'':12B ''traffic'':24C,181C,193C,244C,251C ''tri'':20C ''typic'':250C ''use'':171C ''valu'':222C,229C,242C ''vari'':228C ''various'':205C ''ve'':130C,155C ''volum'':25C,182C,194C,245C ''way'':270C ''week'':139C ''well'':212C ''without'':295C ''work'':18C,211C,284C ''worth'':65C ''would'':263C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10348, 2, '2016-02-22 15:44:49.743', 0, NULL, NULL, '&lt;p&gt;If you can''t use -inf, NaN or just remove the data, you should try something a little better than using a fixed value. For example, -1000*min(data)&lt;/p&gt;
', NULL, NULL, '''-1000'':28C ''/p'':31C ''better'':20C ''data'':13C,30C ''exampl'':27C ''fix'':24C ''inf'':7C ''littl'':19C ''min'':29C ''nan'':8C ''p'':1C ''remov'':11C ''someth'':17C ''tri'':16C ''use'':6C,22C ''valu'':25C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10404, 1, '2016-02-26 06:04:11.28', 4, 61, 'Why use bootstrapping?', '&lt;p&gt;The wiki page for bootstrapping says that you use it in the case where the underlying distribution is unknown. Why is bootstrapping, or sampling with replacement, better than just calculating the variance and other properties from the data directly?&lt;/p&gt;
', 'statistics', NULL, '''/p'':45C ''better'':32C ''bootstrap'':3A,10C,27C ''calcul'':35C ''case'':18C ''data'':43C ''direct'':44C ''distribut'':22C ''p'':5C ''page'':8C ''properti'':40C ''replac'':31C ''sampl'':29C ''say'':11C ''statist'':4B ''under'':21C ''unknown'':24C ''use'':2A,14C ''varianc'':37C ''wiki'':7C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10349, 1, '2016-02-22 17:04:58.207', 1, 42, 'Analyze performance Poisson regression model on a time series(count forecasting)', '&lt;p&gt;I have tried to build a model to forecast the count of a particular variable.The model that was used for the purpose was poisson .Unfortunately ,i don''t have enough stat knowledge to analyze the model performance .If somebody can provide some insights as of how the model is performing,as well as some tweaks to improve the model performance will be greatly helpful. I am also willing to try out other models if it performs better.&lt;/p&gt;

&lt;p&gt;I am using python with the statmodels package to build the model.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Attaching a graph which shows the fitted and the actual
  values(&lt;strong&gt;Green&lt;/strong&gt; shows the actual values and &lt;strong&gt;Blue&lt;/strong&gt; shows the fitted
  values)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/YtRYb.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/YtRYb.png" alt="enter image description here"&gt;&lt;/a&gt;
Also,providing the summary() output of the model&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;                 Generalized Linear Model Regression Results                  
==============================================================================
Dep. Variable:         Work_Item_Type   No. Observations:                  581
Model:                            GLM   Df Residuals:                      574
Model Family:                 Poisson   Df Model:                            6
Link Function:                    log   Scale:                             1.0
Method:                          IRLS   Log-Likelihood:                -16752.
Date:                Mon, 22 Feb 2016   Deviance:                       31268.
Time:                        21:59:12   Pearson chi2:                 1.05e+05
No. Iterations:                     9                                         
===============================================================================
                  coef    std err          z      P&amp;gt;|z|      [95.0% Conf. Int.]
-------------------------------------------------------------------------------
Intercept       2.8492      0.051     55.426      0.000         2.748     2.950
Weekday        -0.2066      0.032     -6.446      0.000        -0.269    -0.144
day_of_week    -0.0926      0.007    -13.367      0.000        -0.106    -0.079
wom             0.1122      0.007     16.996      0.000         0.099     0.125
week           -0.0411      0.001    -53.597      0.000        -0.043    -0.040
TimeDelta       0.0001    5.1e-05      2.933      0.003      4.96e-05     0.000
month_of_yr     0.2192      0.004     60.981      0.000         0.212     0.226
===============================================================================


Also attaching a sample of the dataset used 

clear_date  Count_Work_Item_Type
7/7/2014    1
7/10/2014   1
7/11/2014   5
7/17/2014   2
7/22/2014   1
7/24/2014   1
7/29/2014   3
7/30/2014   4
8/13/2014   1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Since i had only the date and the variable to be forecast i created a bunch of other variables like &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Weekday(binomial)?
Day of week
Week of Month
Week
Time Delta (Starts from 0 increment by one until end)
Month of Year
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Also,i haven''t done any kind of transformation on the variables.
Please do comment if you need additional information:
Thhanks&lt;/p&gt;
', 'python  time-series  forecast', NULL, '''-0.040'':268C ''-0.0411'':263C ''-0.043'':267C ''-0.079'':254C ''-0.0926'':249C ''-0.106'':253C ''-0.144'':245C ''-0.2066'':240C ''-0.269'':244C ''-13.367'':251C ''-16752'':204C ''-53.597'':265C ''-6.446'':242C ''/a'':158C ''/blockquote'':139C ''/code'':317C,365C ''/p'':95C,109C,138C,167C,340C,389C ''/pre'':318C,366C ''/strong'':125C,133C ''/ytryb.png'':145C,152C ''0'':356C ''0.000'':236C,243C,252C,259C,266C,275C,282C ''0.0001'':270C ''0.001'':264C ''0.003'':273C ''0.004'':280C ''0.007'':250C,257C ''0.032'':241C ''0.051'':234C ''0.099'':260C ''0.1122'':256C ''0.125'':261C ''0.212'':283C ''0.2192'':279C ''0.226'':284C ''1'':300C,302C,308C,310C,316C ''1.0'':198C ''1.05e+05'':218C ''12'':215C ''16.996'':258C ''2'':306C ''2.748'':237C ''2.8492'':233C ''2.933'':272C ''2.950'':238C ''2016'':209C ''21'':213C ''22'':207C ''3'':312C ''31268'':211C ''4'':314C ''4.96e-05'':274C ''5'':304C ''5.1e-05'':271C ''55.426'':235C ''574'':187C ''581'':182C ''59'':214C ''6'':193C ''60.981'':281C ''7/10/2014'':301C ''7/11/2014'':303C ''7/17/2014'':305C ''7/22/2014'':307C ''7/24/2014'':309C ''7/29/2014'':311C ''7/30/2014'':313C ''7/7/2014'':299C ''8/13/2014'':315C ''9'':221C ''95.0'':229C ''actual'':121C,128C ''addit'':386C ''also'':84C,159C,285C,368C ''alt'':153C ''analyz'':1A,51C ''attach'':112C,286C ''better'':94C ''binomi'':344C ''blockquot'':110C ''blue'':132C ''build'':22C,106C ''bunch'':335C ''chi2'':217C ''clear'':293C ''code'':169C,342C ''coef'':222C ''comment'':382C ''conf'':230C ''count'':10A,28C,295C ''creat'':333C ''dataset'':291C ''date'':205C,294C,325C ''day'':246C,345C ''delta'':353C ''dep'':175C ''descript'':156C ''devianc'':210C ''df'':185C,191C ''done'':372C ''end'':361C ''enough'':47C ''enter'':154C ''err'':224C ''famili'':189C ''feb'':208C ''fit'':118C,136C ''forecast'':11A,16B,26C,331C ''function'':195C ''general'':170C ''glm'':184C ''graph'':114C ''great'':80C ''green'':124C ''gt'':227C ''haven'':370C ''help'':81C ''href'':142C ''i.stack.imgur.com'':144C,151C ''i.stack.imgur.com/ytryb.png'':143C,150C ''imag'':155C ''img'':148C ''improv'':74C ''increment'':357C ''inform'':387C ''insight'':60C ''int'':231C ''intercept'':232C ''irl'':200C ''item'':178C,297C ''iter'':220C ''kind'':374C ''knowledg'':49C ''like'':339C ''likelihood'':203C ''linear'':171C ''link'':194C ''log'':196C,202C ''log-likelihood'':201C ''method'':199C ''model'':5A,24C,33C,53C,65C,76C,90C,108C,166C,172C,183C,188C,192C ''mon'':206C ''month'':276C,350C,362C ''need'':385C ''nofollow'':147C ''observ'':181C ''one'':359C ''output'':163C ''p'':17C,96C,111C,140C,226C,319C,367C ''packag'':104C ''particular'':31C ''pearson'':216C ''perform'':2A,54C,67C,77C,93C ''pleas'':380C ''poisson'':3A,41C,190C ''pre'':168C,341C ''provid'':58C,160C ''purpos'':39C ''python'':12B,100C ''regress'':4A,173C ''rel'':146C ''residu'':186C ''result'':174C ''sampl'':288C ''scale'':197C ''seri'':9A,15B ''show'':116C,126C,134C ''sinc'':320C ''somebodi'':56C ''src'':149C ''start'':354C ''stat'':48C ''statmodel'':103C ''std'':223C ''strong'':123C,131C ''summari'':162C ''thhank'':388C ''time'':8A,14B,212C,352C ''time-seri'':13B ''timedelta'':269C ''transform'':376C ''tri'':20C,87C ''tweak'':72C ''type'':179C,298C ''unfortun'':42C ''use'':36C,99C,292C ''valu'':122C,129C,137C ''variabl'':176C,328C,338C,379C ''variable.the'':32C ''week'':248C,262C,347C,348C,351C ''weekday'':239C,343C ''well'':69C ''will'':85C ''wom'':255C ''work'':177C,296C ''year'':364C ''yr'':278C ''z'':225C,228C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10353, 1, '2016-02-23 02:17:59.543', 1, 25, 'generalisation of k-handed bandit to n-handed player', '&lt;p&gt;Is there a generalisation of $k$-handed bandit to $n$-handed player in a sense that the sample is taken not from just one distribution (one bandit from $k$ bandits) but from n distributons ($n$ bandits) where $n&amp;lt;k$? &lt;/p&gt;

&lt;p&gt;In other words instead of predicting best ad banner in terms of click though rate I''d need to predict a set of ad banners that will be shown simultaneously on the page (but only one clicked or not and it does not matter which). &lt;/p&gt;

&lt;p&gt;Or yet another way to formulate is to say that the generalized $n$-arm is a vector of bandits arms instead of one banidts arm. The pool of bandits is fixed.&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':56C,103C,136C ''ad'':65C,81C ''anoth'':107C ''arm'':118C,124C,129C ''bandit'':6A,23C,42C,45C,51C,123C,133C ''banidt'':128C ''banner'':66C,82C ''best'':64C ''click'':70C,94C ''d'':74C ''distribut'':40C ''distributon'':49C ''fix'':135C ''formul'':110C ''general'':116C ''generalis'':1A,19C ''hand'':5A,10A,22C,26C ''instead'':61C,125C ''k'':4A,21C,44C,55C ''k-hand'':3A ''learn'':14B ''lt'':54C ''machin'':13B ''machine-learn'':12B ''matter'':101C ''n'':9A,25C,48C,50C,53C,117C ''n-hand'':8A ''need'':75C ''one'':39C,41C,93C,127C ''p'':15C,57C,104C ''page'':90C ''player'':11A,27C ''pool'':131C ''predict'':63C,77C ''rate'':72C ''sampl'':33C ''say'':113C ''sens'':30C ''set'':79C ''shown'':86C ''simultan'':87C ''taken'':35C ''term'':68C ''though'':71C ''vector'':121C ''way'':108C ''word'':60C ''yet'':106C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10354, 2, '2016-02-23 02:45:38.143', 2, NULL, NULL, '&lt;p&gt;Basically, there are two types of sentiment analysis.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Symbolic approach - the sentiment of the sentence is classified based on the kind of words used in the sentence. Involves a lexical database such as wordnet and some rules based on the grammatical structure of the language as knowledge representation.&lt;/li&gt;
&lt;li&gt;Vector models - rather than detecting the sentiment in a sentence, the query sentence is classified into one of the category based on the training dataset and the features are represented as word vectors. Distributed and Distributional representations are types of it.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Whether it is coarse-classification (positive - negative) or fine-classification (types of emotions in it), a training dataset is needed if you are going to approach it via vector models as it is much more easier to play with.&lt;/p&gt;

&lt;p&gt;The skip-gram model and continuous bag of words model developed in Google based on Recurrent neural network language model made a breakthrough in Natural language processing seems to be the state-of-art model in Distributional representation.&lt;/p&gt;

&lt;p&gt;Since the code has been open-sourced, the c-code is available in &lt;a href="https://code.google.com/archive/p/word2vec" rel="nofollow"&gt;https://code.google.com/archive/p/word2vec&lt;/a&gt; and the python library is maintained in &lt;a href="https://radimrehurek.com/gensim/models/word2vec.html" rel="nofollow"&gt;https://radimrehurek.com/gensim/models/word2vec.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;It doesn''t matter if you are going to use reddit comments, tweets or reviews. What matters is how well the sentences are classified for the training data. Because garbage in is garbage out.&lt;/p&gt;
', NULL, NULL, '''/archive/p/word2vec'':203C ''/archive/p/word2vec&lt;/a&gt;'':208C ''/gensim/models/word2vec.html'':220C ''/gensim/models/word2vec.html&lt;/a&gt;&lt;/p&gt;'':225C ''/li'':53C,96C ''/ol'':97C ''/p'':10C,140C,182C,261C ''analysi'':9C ''approach'':14C,126C ''art'':177C ''avail'':197C ''bag'':149C ''base'':22C,42C,75C,156C ''basic'':2C ''breakthrough'':165C ''c'':194C ''c-code'':193C ''categori'':74C ''classif'':104C,110C ''classifi'':21C,69C,250C ''coars'':103C ''coarse-classif'':102C ''code'':186C,195C ''code.google.com'':202C,207C ''code.google.com/archive/p/word2vec'':201C ''code.google.com/archive/p/word2vec&lt;/a&gt;'':206C ''comment'':238C ''continu'':148C ''data'':254C ''databas'':35C ''dataset'':79C,118C ''detect'':59C ''develop'':153C ''distribut'':88C,90C,180C ''doesn'':228C ''easier'':136C ''emot'':113C ''featur'':82C ''fine'':109C ''fine-classif'':108C ''garbag'':256C,259C ''go'':124C,234C ''googl'':155C ''gram'':145C ''grammat'':45C ''href'':200C,217C ''involv'':32C ''kind'':25C ''knowledg'':51C ''languag'':49C,161C,168C ''lexic'':34C ''li'':12C,54C ''librari'':212C ''made'':163C ''maintain'':214C ''matter'':230C,243C ''model'':56C,130C,146C,152C,162C,178C ''much'':134C ''natur'':167C ''need'':120C ''negat'':106C ''network'':160C ''neural'':159C ''nofollow'':205C,222C ''ol'':11C ''one'':71C ''open'':190C ''open-sourc'':189C ''p'':1C,98C,141C,183C,226C ''play'':138C ''posit'':105C ''process'':169C ''python'':211C ''queri'':66C ''radimrehurek.com'':219C,224C ''radimrehurek.com/gensim/models/word2vec.html'':218C ''radimrehurek.com/gensim/models/word2vec.html&lt;/a&gt;&lt;/p&gt;'':223C ''rather'':57C ''recurr'':158C ''reddit'':237C ''rel'':204C,221C ''repres'':84C ''represent'':52C,91C,181C ''review'':241C ''rule'':41C ''seem'':170C ''sentenc'':19C,31C,64C,67C,248C ''sentiment'':8C,16C,61C ''sinc'':184C ''skip'':144C ''skip-gram'':143C ''sourc'':191C ''state'':175C ''state-of-art'':174C ''structur'':46C ''symbol'':13C ''train'':78C,117C,253C ''tweet'':239C ''two'':5C ''type'':6C,93C,111C ''use'':28C,236C ''vector'':55C,87C,129C ''via'':128C ''well'':246C ''whether'':99C ''word'':27C,86C,151C ''wordnet'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10356, 2, '2016-02-23 14:05:49.773', 1, NULL, NULL, '&lt;p&gt;Although not required, it is extremely common to use some approximation scheme once you start working with larger problems.&lt;/p&gt;

&lt;p&gt;If you are thinking of value-based model-free RL, a typical problem is that the state-space if huge. Using some approximation scheme is necessary not only to store but also useful if you can generalise and take advantage some structure in your data. It is then any function approximation method (regression, neural networks, etc.) can be beneficial.&lt;/p&gt;

&lt;p&gt;For model-based approaches something different happens. You need to build up a model of the environment based on data. Using sampled data, you try to approximate your transition and reward functions so you can later use planning methods. Again, supervised learning is applicable.&lt;/p&gt;
', NULL, NULL, '''/p'':21C,85C,132C ''advantag'':65C ''also'':57C ''although'':2C ''applic'':131C ''approach'':91C ''approxim'':12C,48C,76C,114C ''base'':30C,90C,105C ''benefici'':84C ''build'':98C ''common'':8C ''data'':70C,107C,110C ''differ'':93C ''environ'':104C ''etc'':81C ''extrem'':7C ''free'':33C ''function'':75C,119C ''generalis'':62C ''happen'':94C ''huge'':45C ''larger'':19C ''later'':123C ''learn'':129C ''method'':77C,126C ''model'':32C,89C,101C ''model-bas'':88C ''model-fre'':31C ''necessari'':51C ''need'':96C ''network'':80C ''neural'':79C ''p'':1C,22C,86C ''plan'':125C ''problem'':20C,37C ''regress'':78C ''requir'':4C ''reward'':118C ''rl'':34C ''sampl'':109C ''scheme'':13C,49C ''someth'':92C ''space'':43C ''start'':16C ''state'':42C ''state-spac'':41C ''store'':55C ''structur'':67C ''supervis'':128C ''take'':64C ''think'':26C ''transit'':116C ''tri'':112C ''typic'':36C ''use'':10C,46C,58C,108C,124C ''valu'':29C ''value-bas'':28C ''work'':17C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10358, 2, '2016-02-23 14:51:06.107', 1, NULL, NULL, '&lt;p&gt;Q-values are a great way to the make actions explicit so  you can deal with problems where the transition function is not available (model-free). However, when your action-space is large, things are not so nice and Q-values are not so convenient. Think of a huge number of action or even continuous action-spaces.&lt;/p&gt;

&lt;p&gt;From a sampling perspective, the dimensionality of Q(s,a) is higher than V(s) so it might get harder to get enough (s,a) samples in comparison with (s). If you have access to the transition function sometimes V is good. &lt;/p&gt;

&lt;p&gt;There are also other uses where both are combined. For instance, the advantage function where A(s,a)=Q(s,a)-V(s). If you are interested, you can find a recent example using advantage functions here:&lt;/p&gt;

&lt;p&gt;Wang Z, de Freitas N, Lanctot M. Dueling Network Architectures for Deep Reinforcement Learning. arXiv:151106581&lt;/p&gt;

&lt;p&gt;Available from: &lt;a href="http://arxiv.org/abs/1511.06581" rel="nofollow"&gt;http://arxiv.org/abs/1511.06581&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/abs/1511.06581'':173C ''/abs/1511.06581&lt;/a&gt;&lt;/p&gt;'':178C ''/p'':64C,108C,147C,165C ''151106581'':164C ''access'':99C ''action'':12C,34C,57C,62C ''action-spac'':33C,61C ''advantag'':122C,144C ''also'':112C ''architectur'':158C ''arxiv'':163C ''arxiv.org'':172C,177C ''arxiv.org/abs/1511.06581'':171C ''arxiv.org/abs/1511.06581&lt;/a&gt;&lt;/p&gt;'':176C ''avail'':26C,167C ''combin'':118C ''comparison'':93C ''continu'':60C ''conveni'':50C ''de'':151C ''deal'':17C ''deep'':160C ''dimension'':71C ''duel'':156C ''enough'':88C ''even'':59C ''exampl'':142C ''explicit'':13C ''find'':139C ''free'':29C ''freita'':152C ''function'':23C,103C,123C,145C ''get'':84C,87C ''good'':107C ''great'':7C ''harder'':85C ''higher'':77C ''howev'':30C ''href'':170C ''huge'':54C ''instanc'':120C ''interest'':136C ''lanctot'':154C ''larg'':37C ''learn'':162C ''m'':155C ''make'':11C ''might'':83C ''model'':28C ''model-fre'':27C ''n'':153C ''network'':157C ''nice'':42C ''nofollow'':175C ''number'':55C ''p'':1C,65C,109C,148C,166C ''perspect'':69C ''problem'':19C ''q'':3C,45C,73C,128C ''q-valu'':2C,44C ''recent'':141C ''reinforc'':161C ''rel'':174C ''sampl'':68C,91C ''sometim'':104C ''space'':35C,63C ''thing'':38C ''think'':51C ''transit'':22C,102C ''use'':114C,143C ''v'':79C,105C,131C ''valu'':4C,46C ''wang'':149C ''way'':8C ''z'':150C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10359, 2, '2016-02-23 15:10:46.217', 2, NULL, NULL, '&lt;p&gt;I think you will like the following two papers:&lt;/p&gt;

&lt;p&gt;Available from: &lt;a href="http://arxiv.org/abs/1507.04296" rel="nofollow"&gt;http://arxiv.org/abs/1507.04296&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Nair A, Srinivasan P, Blackwell S, Alcicek C, Fearon R, De Maria A, et al. Massively Parallel Methods for Deep Reinforcement Learning. arXiv preprint arXiv:150704296&lt;/p&gt;

&lt;p&gt;Available from: &lt;a href="http://arxiv.org/abs/1602.01783" rel="nofollow"&gt;http://arxiv.org/abs/1602.01783&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Mnih V, Badia AP, Mirza M, Graves A, Lillicrap TP, Harley T, et al. Asynchronous Methods for Deep Reinforcement Learning. arXiv:160201783&lt;/p&gt;
', NULL, NULL, '''/abs/1507.04296'':19C ''/abs/1507.04296&lt;/a&gt;&lt;/p&gt;'':24C ''/abs/1602.01783'':60C ''/abs/1602.01783&lt;/a&gt;&lt;/p&gt;'':65C ''/p'':11C,52C,89C ''150704296'':51C ''160201783'':88C ''al'':40C,80C ''alcicek'':32C ''ap'':70C ''arxiv'':48C,50C,87C ''arxiv.org'':18C,23C,59C,64C ''arxiv.org/abs/1507.04296'':17C ''arxiv.org/abs/1507.04296&lt;/a&gt;&lt;/p&gt;'':22C ''arxiv.org/abs/1602.01783'':58C ''arxiv.org/abs/1602.01783&lt;/a&gt;&lt;/p&gt;'':63C ''asynchron'':81C ''avail'':13C,54C ''badia'':69C ''blackwel'':30C ''c'':33C ''de'':36C ''deep'':45C,84C ''et'':39C,79C ''fearon'':34C ''follow'':8C ''grave'':73C ''harley'':77C ''href'':16C,57C ''learn'':47C,86C ''like'':6C ''lillicrap'':75C ''m'':72C ''maria'':37C ''massiv'':41C ''method'':43C,82C ''mirza'':71C ''mnih'':67C ''nair'':26C ''nofollow'':21C,62C ''p'':1C,12C,25C,29C,53C,66C ''paper'':10C ''parallel'':42C ''preprint'':49C ''r'':35C ''reinforc'':46C,85C ''rel'':20C,61C ''srinivasan'':28C ''think'':3C ''tp'':76C ''two'':9C ''v'':68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10361, 2, '2016-02-23 17:08:03.497', 6, NULL, NULL, '&lt;p&gt;Linear, binary classifiers can choose either class (but consistently) when the datapoint which is to classify is on the hyperplane. It just depends on how you programmed it.&lt;/p&gt;

&lt;p&gt;Also, it doesn''t really matter. This is very unlikely to happen. In fact, if we had arbitrary precision computing and normal distributed features, there would be a probability of 0 (exactly, not rounded) that this would happen. We have IEEE 754 floats, so the probability is not 0, but still so small that there are much more important factors to worry about.&lt;/p&gt;
', NULL, NULL, '''/p'':30C,95C ''0'':62C,80C ''754'':73C ''also'':32C ''arbitrari'':49C ''binari'':3C ''choos'':6C ''class'':8C ''classifi'':4C,17C ''comput'':51C ''consist'':10C ''datapoint'':13C ''depend'':24C ''distribut'':54C ''doesn'':34C ''either'':7C ''exact'':63C ''fact'':45C ''factor'':91C ''featur'':55C ''float'':74C ''happen'':43C,69C ''hyperplan'':21C ''ieee'':72C ''import'':90C ''linear'':2C ''matter'':37C ''much'':88C ''normal'':53C ''p'':1C,31C ''precis'':50C ''probabl'':60C,77C ''program'':28C ''realli'':36C ''round'':65C ''small'':84C ''still'':82C ''unlik'':41C ''worri'':93C ''would'':57C,68C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10362, 2, '2016-02-23 18:24:58.23', 2, NULL, NULL, '&lt;p&gt;In Q-Learning, on every step you will use observations and rewards to update your Q-value function:&lt;/p&gt;

&lt;p&gt;$$ 
Q_{t+1}(s_t,a_t) = Q_t(s_t,a_t) +  \alpha [R_{t+1}+ \gamma  \underset{a''}{\max} Q_t(s_{t+1},a'') - Q_t(s_t, a_t)]
$$&lt;/p&gt;

&lt;p&gt;You are correct in saying that the neural network is just a function approximation for the q-value function. &lt;/p&gt;

&lt;p&gt;In general, the approximation part is just a standard supervised learning problem. Your network uses (s,a) as input and the output is the q-value. As q-values are adjusted, you need to train these new samples to the network. Still, you will find some issues as you as using correlated samples and SGD will suffer.&lt;/p&gt;

&lt;p&gt;If you are looking at the DQN paper, things are slightly different. In that case, what they are doing is putting samples in a vector (experience replay). To teach the network, they sample tuples from the vector, bootstrap using this information to obtain a new q-value that is taught to the network. When I say teaching, I mean adjusting the network parameters using stochastic gradient descent or your favourite optimisation approach. By not teaching the samples in the order that are being collected by the policy the decorrelate them and that helps in the training.&lt;/p&gt;

&lt;p&gt;Lastly, in order to make a decision on state $ s $, you choose the action that provides the highest q-value:&lt;/p&gt;

&lt;p&gt;$$
a^*(s)= \underset{a}{argmax} \space Q(s,a)
$$&lt;/p&gt;

&lt;p&gt;If your Q-value function has been learnt completely and the environment is stationary, it is fine to be greedy at this point. However, while learning, you are expected to explore. There are several approaches being $\varepsilon$-greedy one of the easiest and most common ways.&lt;/p&gt;
', NULL, NULL, '''+1'':26C,40C,49C ''/p'':22C,57C,79C,140C,239C,262C,273C,322C ''action'':254C ''adjust'':113C,202C ''alpha'':37C ''approach'':214C,310C ''approxim'':72C,84C ''argmax'':268C ''bootstrap'':179C ''case'':156C ''choos'':252C ''collect'':226C ''common'':320C ''complet'':284C ''correct'':61C ''correl'':134C ''decis'':247C ''decorrel'':231C ''descent'':209C ''differ'':153C ''dqn'':148C ''easiest'':317C ''environ'':287C ''everi'':7C ''expect'':304C ''experi'':167C ''explor'':306C ''favourit'':212C ''find'':127C ''fine'':292C ''function'':21C,71C,78C,280C ''gamma'':41C ''general'':82C ''gradient'':208C ''greedi'':295C,313C ''help'':235C ''highest'':258C ''howev'':299C ''inform'':182C ''input'':99C ''issu'':129C ''last'':241C ''learn'':5C,91C,301C ''learnt'':283C ''look'':145C ''make'':245C ''max'':44C ''mean'':201C ''need'':115C ''network'':67C,94C,123C,172C,195C,204C ''neural'':66C ''new'':119C,186C ''observ'':12C ''obtain'':184C ''one'':314C ''optimis'':213C ''order'':222C,243C ''output'':102C ''p'':1C,23C,58C,80C,141C,240C,263C,274C ''paper'':149C ''paramet'':205C ''part'':85C ''point'':298C ''polici'':229C ''problem'':92C ''provid'':256C ''put'':162C ''q'':4C,19C,24C,31C,45C,51C,76C,106C,110C,188C,260C,270C,278C ''q-learn'':3C ''q-valu'':18C,75C,105C,109C,187C,259C,277C ''r'':38C ''replay'':168C ''reward'':14C ''sampl'':120C,135C,163C,174C,219C ''say'':63C,198C ''sever'':309C ''sgd'':137C ''slight'':152C ''space'':269C ''standard'':89C ''state'':249C ''stationari'':289C ''step'':8C ''still'':124C ''stochast'':207C ''suffer'':139C ''supervis'':90C ''taught'':192C ''teach'':170C,199C,217C ''thing'':150C ''train'':117C,238C ''tupl'':175C ''underset'':42C,266C ''updat'':16C ''use'':11C,95C,133C,180C,206C ''valu'':20C,77C,107C,111C,189C,261C,279C ''varepsilon'':312C ''vector'':166C,178C ''way'':321C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10363, 1, '2016-02-23 18:25:24.797', 1, 29, 'Ignoring symbols and select only numerical values with pandas', '&lt;p&gt;In one field I have entries like ''U$ 192,0''. Working on pandas, how I ignore non numerical data and get only the numerical part?&lt;/p&gt;
', 'python  data-cleaning  pandas', NULL, '''/p'':41C ''0'':25C ''192'':24C ''clean'':13B ''data'':12B,34C ''data-clean'':11B ''entri'':21C ''field'':18C ''get'':36C ''ignor'':1A,31C ''like'':22C ''non'':32C ''numer'':6A,33C,39C ''one'':17C ''p'':15C ''panda'':9A,14B,28C ''part'':40C ''python'':10B ''select'':4A ''symbol'':2A ''u'':23C ''valu'':7A ''work'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10364, 2, '2016-02-23 18:59:11.567', 1, NULL, NULL, '&lt;p&gt;Use &lt;code&gt;str.strip&lt;/code&gt; if the prefix is fixed or &lt;code&gt;str.replace&lt;/code&gt; if not:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data = pandas.Series(["U$ 192.0"])
data.str.replace(''^[^\d]*'', '''').astype(float)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This removes all the non-numeric characters to the left of the number, and casts to float.&lt;/p&gt;
', NULL, NULL, '''/code'':5C,14C,28C ''/p'':17C,49C ''/pre'':29C ''192.0'':23C ''astyp'':26C ''cast'':46C ''charact'':38C ''code'':3C,12C,19C ''d'':25C ''data'':20C ''data.str.replace'':24C ''fix'':10C ''float'':27C,48C ''left'':41C ''non'':36C ''non-numer'':35C ''number'':44C ''numer'':37C ''p'':1C,30C ''pandas.series'':21C ''pre'':18C ''prefix'':8C ''remov'':32C ''str.replace'':13C ''str.strip'':4C ''u'':22C ''use'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10365, 2, '2016-02-23 19:40:37.38', 2, NULL, NULL, '&lt;p&gt;If I understand correctly:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Table1 &amp;lt;- data.frame(key = seq(1,100),a.data = rnorm(100))
Table2 &amp;lt;- data.frame(key = c(seq(1,30),rep(NA,30)), b.data = seq(1,60))


##Assuming this is what you want
library(sqldf)
sql.ans &amp;lt;- sqldf("select a.*, b.key from Table1 a LEFT OUTER JOIN Table2 b on a.key = b.key where b.key is null")

## dplyr version
library(dplyr)

dplyr.ans &amp;lt;- Table1 %&amp;gt;% filter(!key %in% Table2$key)

## Regular R version
R.ans &amp;lt;- Table1[which(!Table1$key %in% Table2$key),]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;EDIT after dummy data and expected output&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dplyr.ans2 &amp;lt;- left_join(df1,df2, by = c("Key1" = "Key2"))
 Key1 BV1  BV2
 1   A1 100  150
 2   A2 200  250
 3   A3 300  350
 4   A4 400 &amp;lt;NA&amp;gt;
 5   A5 500 &amp;lt;NA&amp;gt;
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':88C,138C ''/p'':6C,98C ''/pre'':89C,139C ''1'':14C,25C,32C,114C ''100'':15C,18C,116C ''150'':117C ''2'':118C ''200'':120C ''250'':121C ''3'':122C ''30'':26C,29C ''300'':124C ''350'':125C ''4'':126C ''400'':128C ''5'':132C ''500'':134C ''60'':33C ''a.data'':16C ''a.key'':57C ''a1'':115C ''a2'':119C ''a3'':123C ''a4'':127C ''a5'':133C ''assum'':34C ''b'':55C ''b.data'':30C ''b.key'':47C,58C,60C ''bv1'':112C ''bv2'':113C ''c'':23C,108C ''code'':8C,100C ''correct'':5C ''data'':94C ''data.frame'':11C,21C ''df1'':105C ''df2'':106C ''dplyr'':63C,66C ''dplyr.ans'':67C ''dplyr.ans2'':101C ''dummi'':93C ''edit'':91C ''expect'':96C ''filter'':71C ''gt'':70C,131C,137C ''join'':53C,104C ''key'':12C,22C,72C,75C,84C,87C ''key1'':109C,111C ''key2'':110C ''left'':51C,103C ''librari'':40C,65C ''lt'':10C,20C,43C,68C,80C,102C,129C,135C ''na'':28C,130C,136C ''null'':62C ''outer'':52C ''output'':97C ''p'':1C,90C ''pre'':7C,99C ''r'':77C ''r.ans'':79C ''regular'':76C ''rep'':27C ''rnorm'':17C ''select'':45C ''seq'':13C,24C,31C ''sql.ans'':42C ''sqldf'':41C,44C ''table1'':9C,49C,69C,81C,83C ''table2'':19C,54C,74C,86C ''understand'':4C ''version'':64C,78C ''want'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10366, 1, '2016-02-24 02:18:18.447', 1, 22, 'Ploting img with matplotlib', '&lt;p&gt;I have some points I''d like to plot for a presentation, but, instead of plotting the characteristic ball-points, is there any way to plot a png image, something like&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;plt.scatter(X,Y, img=''figure.png'')
&lt;/code&gt;&lt;/pre&gt;
', 'python  visualization', NULL, '''/code'':48C ''/p'':40C ''/pre'':49C ''ball'':27C ''ball-point'':26C ''characterist'':25C ''code'':42C ''d'':13C ''figure.png'':47C ''imag'':37C ''img'':2A,46C ''instead'':21C ''like'':14C,39C ''matplotlib'':4A ''p'':7C ''plot'':16C,23C,34C ''plote'':1A ''plt.scatter'':43C ''png'':36C ''point'':11C,28C ''pre'':41C ''present'':19C ''python'':5B ''someth'':38C ''visual'':6B ''way'':32C ''x'':44C ''y'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10384, 2, '2016-02-25 00:42:47.98', 1, NULL, NULL, '&lt;p&gt;Yes, there are characteristics. You can use a correlogram to inform you as to the error structure in your data. That will tell you whether or not your data needs to account for AR and/or MA terms. Also check for unit roots to tell you whether you need to difference the time series.&lt;/p&gt;

&lt;p&gt;This &lt;a href="https://onlinecourses.science.psu.edu/stat510/node/60" rel="nofollow"&gt;link&lt;/a&gt; has a good introduction to analyzing the correlogram that shows the pattern for AR(1) process. It is charecterized by a declining or oscillating but declining values. Depending on whether or not you have positive or negative autocorrelation. &lt;/p&gt;

&lt;p&gt;The ACF for an MA(1) process is below. It is characterized by one significant value and then non-significant oscillating values thereafter.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/YJrrw.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/YJrrw.jpg" alt="MA(1) Process"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;To tell if you need to do differencing you should use a test like the augmented dickey-fuller test to check for the unit roots (i.e. your data is integrated).&lt;/p&gt;
', NULL, NULL, '''/a'':66C,149C ''/p'':55C,105C,131C,150C,183C ''/stat510/node/60'':62C ''/yjrrw.jpg'':137C,144C ''1'':81C,112C,147C ''account'':33C ''acf'':108C ''also'':39C ''alt'':145C ''analyz'':72C ''and/or'':36C ''ar'':35C,80C ''augment'':167C ''autocorrel'':104C ''character'':118C ''characterist'':5C ''charecter'':85C ''check'':40C,173C ''correlogram'':10C,74C ''data'':21C,30C,180C ''declin'':88C,92C ''depend'':94C ''dickey'':169C ''dickey-ful'':168C ''differ'':51C ''differenc'':159C ''error'':17C ''fuller'':170C ''good'':69C ''href'':59C,134C ''i.e'':178C ''i.stack.imgur.com'':136C,143C ''i.stack.imgur.com/yjrrw.jpg'':135C,142C ''img'':140C ''inform'':12C ''integr'':182C ''introduct'':70C ''like'':165C ''link'':65C ''ma'':37C,111C,146C ''need'':31C,49C,156C ''negat'':103C ''nofollow'':64C,139C ''non'':126C ''non-signific'':125C ''one'':120C ''onlinecourses.science.psu.edu'':61C ''onlinecourses.science.psu.edu/stat510/node/60'':60C ''oscil'':90C,128C ''p'':1C,56C,106C,132C,151C ''pattern'':78C ''posit'':101C ''process'':82C,113C,148C ''rel'':63C,138C ''root'':43C,177C ''seri'':54C ''show'':76C ''signific'':121C,127C ''src'':141C ''structur'':18C ''tell'':24C,45C,153C ''term'':38C ''test'':164C,171C ''thereaft'':130C ''time'':53C ''unit'':42C,176C ''use'':8C,162C ''valu'':93C,122C,129C ''whether'':26C,47C,96C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10368, 1, '2016-02-24 04:17:12.343', 3, 23, 'Image Captioning in Keras', '&lt;p&gt;I am trying to implement demo of Image Captioning system from Keras documentation (link: &lt;a href="http://keras.io/examples/" rel="nofollow"&gt;http://keras.io/examples/&lt;/a&gt;). From the documentation I could understand training part. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;max_caption_len = 16
vocab_size = 10000

# first, let''s define an image model that
# will encode pictures into 128-dimensional vectors.
# it should be initialized with pre-trained weights.
image_model = VGG-16 CNN definition
image_model.load_weights(''weight_file.h5'')

# next, let''s define a RNN model that encodes sequences of words
# into sequences of 128-dimensional word vectors.
language_model = Sequential()
language_model.add(Embedding(vocab_size, 256, input_length=max_caption_len))
language_model.add(GRU(output_dim=128, return_sequences=True))
language_model.add(TimeDistributedDense(128))

# let''s repeat the image vector to turn it into a sequence.
image_model.add(RepeatVector(max_caption_len))

# the output of both models will be tensors of shape (samples, max_caption_len, 128).
# let''s concatenate these 2 vector sequences.
model = Merge([image_model, language_model], mode=''concat'', concat_axis=-1)
# let''s encode this vector sequence into a single vector
model.add(GRU(256, 256, return_sequences=False))
# which will be used to compute a probability
# distribution over what the next word in the caption should be!
model.add(Dense(vocab_size))
model.add(Activation(''softmax''))

model.compile(loss=''categorical_crossentropy'', optimizer=''rmsprop'')

model.fit([images, partial_captions], next_words, batch_size=16, nb_epoch=100)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But now I am confused in how to generate caption for test image. Input here is [image, partial_caption] pair, now for test image how to input partial caption ? &lt;/p&gt;

&lt;p&gt;EDIT: link to documentation included&lt;/p&gt;
', 'neuralnetwork  deep-learning  image-classification  keras', NULL, '''-1'':182C ''-16'':83C ''/code'':244C ''/examples/'':32C ''/examples/&lt;/a&gt;).'':37C ''/p'':46C,276C,283C ''/pre'':245C ''100'':243C ''10000'':55C ''128'':68C,105C,126C,132C,164C ''16'':52C,240C ''2'':169C ''256'':116C,195C,196C ''activ'':224C ''axi'':181C ''batch'':238C ''caption'':2A,22C,50C,120C,148C,162C,216C,235C,256C,265C,275C ''categor'':228C ''classif'':11B ''cnn'':84C ''code'':48C ''comput'':205C ''concat'':179C,180C ''concaten'':167C ''confus'':251C ''could'':42C ''crossentropi'':229C ''deep'':7B ''deep-learn'':6B ''defin'':59C,93C ''definit'':85C ''demo'':19C ''dens'':220C ''dim'':125C ''dimension'':69C,106C ''distribut'':208C ''document'':26C,40C,281C ''edit'':278C ''embed'':113C ''encod'':65C,98C,185C ''epoch'':242C ''fals'':199C ''file.h5'':89C ''first'':56C ''generat'':255C ''gru'':123C,194C ''href'':29C ''imag'':1A,10B,21C,61C,80C,137C,174C,233C,259C,263C,270C ''image-classif'':9B ''image_model.add'':145C ''image_model.load'':86C ''implement'':18C ''includ'':282C ''initi'':74C ''input'':117C,260C,273C ''kera'':4A,12B,25C ''keras.io'':31C,36C ''keras.io/examples/'':30C ''keras.io/examples/&lt;/a&gt;).'':35C ''languag'':109C,176C ''language_model.add'':112C,122C,130C ''learn'':8B ''len'':51C,121C,149C,163C ''length'':118C ''let'':57C,91C,133C,165C,183C ''link'':27C,279C ''loss'':227C ''max'':49C,119C,147C,161C ''merg'':173C ''mode'':178C ''model'':62C,81C,96C,110C,154C,172C,175C,177C ''model.add'':193C,219C,223C ''model.compile'':226C ''model.fit'':232C ''nb'':241C ''neuralnetwork'':5B ''next'':90C,212C,236C ''nofollow'':34C ''optim'':230C ''output'':124C,151C ''p'':13C,246C,277C ''pair'':266C ''part'':45C ''partial'':234C,264C,274C ''pictur'':66C ''pre'':47C,77C ''pre-train'':76C ''probabl'':207C ''rel'':33C ''repeat'':135C ''repeatvector'':146C ''return'':127C,197C ''rmsprop'':231C ''rnn'':95C ''sampl'':160C ''sequenc'':99C,103C,128C,144C,171C,188C,198C ''sequenti'':111C ''shape'':159C ''singl'':191C ''size'':54C,115C,222C,239C ''softmax'':225C ''system'':23C ''tensor'':157C ''test'':258C,269C ''timedistributeddens'':131C ''train'':44C,78C ''tri'':16C ''true'':129C ''turn'':140C ''understand'':43C ''use'':203C ''vector'':70C,108C,138C,170C,187C,192C ''vgg'':82C ''vocab'':53C,114C,221C ''weight'':79C,87C,88C ''word'':101C,107C,213C,237C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10369, 1, '2016-02-24 06:07:13.507', 5, 213, 'General way to reduce features', '&lt;p&gt;Let''s say I have a giant dataset (600+ columns) and I have no idea what insights I might get from it or what model I want to run. &lt;/p&gt;

&lt;p&gt;What are some of the best ways to find the most influential columns/features? I know it''s important to know what models to run first, but is there a general way to find columns that''s more important than others? &lt;/p&gt;

&lt;p&gt;So far I''ve already removed the columns that are 80%+ empty, but I still have over 600 columns and I have no idea where to start. Please let me know your thoughts! I''m kind of new so any pointers will help.&lt;/p&gt;
', 'data-mining  feature-selection  data', NULL, '''/p'':43C,85C,130C ''600'':22C,104C ''80'':97C ''alreadi'':91C ''best'':50C ''column'':23C,78C,94C,105C ''columns/features'':57C ''data'':7B,12B ''data-min'':6B ''dataset'':21C ''empti'':98C ''far'':88C ''featur'':5A,10B ''feature-select'':9B ''find'':53C,77C ''first'':69C ''general'':1A,74C ''get'':33C ''giant'':20C ''help'':129C ''idea'':28C,110C ''import'':62C,82C ''influenti'':56C ''insight'':30C ''kind'':122C ''know'':59C,64C,117C ''let'':14C,115C ''m'':121C ''might'':32C ''mine'':8B ''model'':38C,66C ''new'':124C ''other'':84C ''p'':13C,44C,86C ''pleas'':114C ''pointer'':127C ''reduc'':4A ''remov'':92C ''run'':42C,68C ''say'':16C ''select'':11B ''start'':113C ''still'':101C ''thought'':119C ''ve'':90C ''want'':40C ''way'':2A,51C,75C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10370, 2, '2016-02-24 06:49:50.923', 5, NULL, NULL, '&lt;p&gt;Another way of looking at 600 columns is having a vector with 600 dimensions. So, the answer to reduce the 600 dimensional vectors to fewer dimensions without losing lot of information will be dimensionality reduction.&lt;/p&gt;

&lt;p&gt;Principal component analysis(PCA) is a commonly used one. T-distributed stochastic neighbour embedding (TSNE) has been the state of the art for reducing higher dimensional vectors. &lt;a href="https://indico.io/blog/visualizing-with-t-sne/" rel="nofollow" title="data visualisaiton using TSNE"&gt;Data visualisation using TSNE&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;To get the quick intuition about the vectors, the best thing is to reduce it to 2 or 3 dimensional vectors and plot them in a graph. Data visualisation always helps to get an overview of the data you are dealing with.&lt;/p&gt;
', NULL, NULL, '''/a'':83C ''/blog/visualizing-with-t-sne/'':71C ''/p'':37C,84C,128C ''2'':102C ''3'':104C ''600'':7C,14C,22C ''alway'':115C ''analysi'':41C ''anoth'':2C ''answer'':18C ''art'':61C ''best'':95C ''column'':8C ''common'':45C ''compon'':40C ''data'':75C,79C,113C,123C ''deal'':126C ''dimens'':15C,27C ''dimension'':23C,35C,65C,105C ''distribut'':50C ''embed'':53C ''fewer'':26C ''get'':87C,118C ''graph'':112C ''help'':116C ''higher'':64C ''href'':68C ''indico.io'':70C ''indico.io/blog/visualizing-with-t-sne/'':69C ''inform'':32C ''intuit'':90C ''look'':5C ''lose'':29C ''lot'':30C ''neighbour'':52C ''nofollow'':73C ''one'':47C ''overview'':120C ''p'':1C,38C,85C ''pca'':42C ''plot'':108C ''princip'':39C ''quick'':89C ''reduc'':20C,63C,99C ''reduct'':36C ''rel'':72C ''state'':58C ''stochast'':51C ''t-distribut'':48C ''thing'':96C ''titl'':74C ''tsne'':54C,78C,82C ''use'':46C,77C,81C ''vector'':12C,24C,66C,93C,106C ''visualis'':80C,114C ''visualisaiton'':76C ''way'':3C ''without'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10371, 2, '2016-02-24 07:17:11.247', 5, NULL, NULL, '&lt;p&gt;Since you didn''t mention labels of any kind, I presume you are not doing supervised learning. Note, if you were, there would be no need to throw out features. You could use something like lasso regression to build a model that sparsely selects the influential features while fitting a predictive model. &lt;/p&gt;

&lt;p&gt;Unsupervised methods exist to reduce your feature set size but could potentially lose their original feature interpretability (if that''s important to you), like PCA or auto encoders. But there are alternatives to these as well, so depends what you are looking for. &lt;/p&gt;
', NULL, NULL, '''/p'':54C,99C ''altern'':87C ''auto'':82C ''build'':40C ''could'':33C,66C ''depend'':93C ''didn'':4C ''encod'':83C ''exist'':58C ''featur'':31C,48C,62C,71C ''fit'':50C ''import'':76C ''influenti'':47C ''interpret'':72C ''kind'':10C ''label'':7C ''lasso'':37C ''learn'':18C ''like'':36C,79C ''look'':97C ''lose'':68C ''mention'':6C ''method'':57C ''model'':42C,53C ''need'':27C ''note'':19C ''origin'':70C ''p'':1C,55C ''pca'':80C ''potenti'':67C ''predict'':52C ''presum'':12C ''reduc'':60C ''regress'':38C ''select'':45C ''set'':63C ''sinc'':2C ''size'':64C ''someth'':35C ''spars'':44C ''supervis'':17C ''throw'':29C ''unsupervis'':56C ''use'':34C ''well'':91C ''would'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10372, 1, '2016-02-24 10:35:30.387', 0, 36, 'Merging Two Tables', '&lt;p&gt;I have  FFT frequency feed  from a particular Node . To make the time difference linear , I have a day into four group(6 hours each) and now i would like to insert the missing rows . For example see below.&lt;/p&gt;

&lt;p&gt;I have tried generating time series like&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Date and time      Group

2012-02-24             0

2012-02-24            1

2012-02-24            2

2012-02-24            3

2012-02-25            0

2012-02-25            1

2012-02-25            2

2012-02-25            3
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And I have a time series like&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Date and time Group

24.2.2012            1

24.2.2012            2

24.2.2012            3

25.2.2012           0

25.2.2012           1

25.2.2012            2

25.2.2012           3
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;May I know how to merge them in knime to achieve the below.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Date and time Group

2012-02-24      Null

2012-02-24         1

2012-02-24         2

2012-02-24         3

2012-02-25         0

2012-02-25         1

2012-02-25         2

2012-02-25         3
&lt;/code&gt;&lt;/pre&gt;
', 'data-cleaning  databases  sql', NULL, '''-02'':66C,70C,74C,78C,82C,86C,90C,94C,152C,156C,160C,164C,168C,172C,176C,180C ''-24'':67C,71C,75C,79C,153C,157C,161C,165C ''-25'':83C,87C,91C,95C,169C,173C,177C,181C ''/code'':97C,128C,183C ''/p'':49C,58C,107C,144C ''/pre'':98C,129C,184C ''0'':68C,84C,121C,170C ''1'':72C,88C,115C,123C,158C,174C ''2'':76C,92C,117C,125C,162C,178C ''2012'':65C,69C,73C,77C,81C,85C,89C,93C,151C,155C,159C,163C,167C,171C,175C,179C ''24.2.2012'':114C,116C,118C ''25.2.2012'':120C,122C,124C,126C ''3'':80C,96C,119C,127C,166C,182C ''6'':32C ''achiev'':141C ''clean'':6B ''code'':60C,109C,146C ''data'':5B ''data-clean'':4B ''databas'':7B ''date'':61C,110C,147C ''day'':28C ''differ'':23C ''exampl'':46C ''feed'':14C ''fft'':12C ''four'':30C ''frequenc'':13C ''generat'':54C ''group'':31C,64C,113C,150C ''hour'':33C ''insert'':41C ''knime'':139C ''know'':133C ''like'':39C,57C,106C ''linear'':24C ''make'':20C ''may'':131C ''merg'':1A,136C ''miss'':43C ''node'':18C ''null'':154C ''p'':9C,50C,99C,130C ''particular'':17C ''pre'':59C,108C,145C ''row'':44C ''see'':47C ''seri'':56C,105C ''sql'':8B ''tabl'':3A ''time'':22C,55C,63C,104C,112C,149C ''tri'':53C ''two'':2A ''would'':38C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10373, 2, '2016-02-24 10:59:20.057', 1, NULL, NULL, '&lt;p&gt;&lt;a href="http://danielweitzenfeld.github.io/passtheroc/blog/2014/10/28/bayes-premier-league/" rel="nofollow"&gt;http://danielweitzenfeld.github.io/passtheroc/blog/2014/10/28/bayes-premier-league/&lt;/a&gt; uses a bayesian approach on data taken from wikipedia (You can use any source in fact.)&lt;/p&gt;

&lt;p&gt;It uses the following paper: &lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.statistica.it/gianluca/Research/BaioBlangiardo.pdf" rel="nofollow"&gt;http://www.statistica.it/gianluca/Research/BaioBlangiardo.pdf&lt;/a&gt;
Bayesian hierarchical model for the prediction of football/soccer results&lt;/p&gt;
', NULL, NULL, '''/gianluca/research/baioblangiardo.pdf'':41C ''/gianluca/research/baioblangiardo.pdf&lt;/a&gt;'':46C ''/p'':28C,35C,56C ''/passtheroc/blog/2014/10/28/bayes-premier-league/'':6C ''/passtheroc/blog/2014/10/28/bayes-premier-league/&lt;/a&gt;'':11C ''approach'':15C ''bayesian'':14C,47C ''danielweitzenfeld.github.io'':5C,10C ''danielweitzenfeld.github.io/passtheroc/blog/2014/10/28/bayes-premier-league/'':4C ''danielweitzenfeld.github.io/passtheroc/blog/2014/10/28/bayes-premier-league/&lt;/a&gt;'':9C ''data'':17C ''fact'':27C ''follow'':33C ''football/soccer'':54C ''hierarch'':48C ''href'':3C,38C ''model'':49C ''nofollow'':8C,43C ''p'':1C,29C,36C ''paper'':34C ''predict'':52C ''rel'':7C,42C ''result'':55C ''sourc'':25C ''taken'':18C ''use'':12C,23C,31C ''wikipedia'':20C ''www.statistica.it'':40C,45C ''www.statistica.it/gianluca/research/baioblangiardo.pdf'':39C ''www.statistica.it/gianluca/research/baioblangiardo.pdf&lt;/a&gt;'':44C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10374, 2, '2016-02-24 12:19:09.397', 2, NULL, NULL, '&lt;p&gt;There are a lot of feature selection techniques, but depends if you have the "labels" of the samples. One simple technique involves the variance "which removes all features whose variance doesnât meet some threshold. By default, it removes all zero-variance features"&lt;/p&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/modules/feature_selection.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/feature_selection.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':46C ''/stable/modules/feature_selection.html'':52C ''/stable/modules/feature_selection.html&lt;/a&gt;&lt;/p&gt;'':57C ''default'':38C ''depend'':11C ''doesnâ'':32C ''featur'':7C,29C,45C ''href'':49C ''involv'':23C ''label'':16C ''lot'':5C ''meet'':34C ''nofollow'':54C ''one'':20C ''p'':1C,47C ''rel'':53C ''remov'':27C,40C ''sampl'':19C ''scikit-learn.org'':51C,56C ''scikit-learn.org/stable/modules/feature_selection.html'':50C ''scikit-learn.org/stable/modules/feature_selection.html&lt;/a&gt;&lt;/p&gt;'':55C ''select'':8C ''simpl'':21C ''techniqu'':9C,22C ''threshold'':36C ''varianc'':25C,31C,44C ''whose'':30C ''zero'':43C ''zero-vari'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10375, 2, '2016-02-24 15:33:03.407', 0, NULL, NULL, '&lt;p&gt;As far as I know there are no parallel implementations of SVM available directly in R. The benefit of running on RevolutionR is that it will parallelize the matrix computations through the use of the Intel MKL library. Since SVM relies on linear algebra you will achieve probably some performance gain.&lt;/p&gt;

&lt;p&gt;Another option that you could try and which may lead to better results is using Spark and &lt;a href="https://amplab-extras.github.io/SparkR-pkg/" rel="nofollow"&gt;SparkR&lt;/a&gt; which could expose the &lt;a href="http://spark.apache.org/docs/latest/mllib-linear-methods.html#linear-support-vector-machines-svms" rel="nofollow"&gt;SVM in MLib&lt;/a&gt; that is part of Spark. I have not tried this myself yet but if you are stuck this might help.&lt;/p&gt;
', NULL, NULL, '''/a'':80C,95C ''/docs/latest/mllib-linear-methods.html#linear-support-vector-machines-svms'':89C ''/p'':53C,116C ''/sparkr-pkg/'':76C ''achiev'':48C ''algebra'':45C ''amplab-extras.github.io'':75C ''amplab-extras.github.io/sparkr-pkg/'':74C ''anoth'':55C ''avail'':14C ''benefit'':19C ''better'':66C ''comput'':31C ''could'':59C,82C ''direct'':15C ''expos'':83C ''far'':3C ''gain'':52C ''help'':115C ''href'':73C,86C ''implement'':11C ''intel'':37C ''know'':6C ''lead'':64C ''librari'':39C ''linear'':44C ''matrix'':30C ''may'':63C ''might'':114C ''mkl'':38C ''mlib'':94C ''nofollow'':78C,91C ''option'':56C ''p'':1C,54C ''parallel'':10C,28C ''part'':98C ''perform'':51C ''probabl'':49C ''r'':17C ''rel'':77C,90C ''reli'':42C ''result'':67C ''revolutionr'':23C ''run'':21C ''sinc'':40C ''spark'':70C,100C ''spark.apache.org'':88C ''spark.apache.org/docs/latest/mllib-linear-methods.html#linear-support-vector-machines-svms'':87C ''sparkr'':79C ''stuck'':112C ''svm'':13C,41C,92C ''tri'':60C,104C ''use'':34C,69C ''yet'':107C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10377, 1, '2016-02-24 17:54:04.143', 1, 48, 'Comparing training and validation data set Root MSE for a best subset regression?', '&lt;p&gt;Iâve a model with 14 dependent variables (all of them are significant) and 678 observations. I used best subset regression and validation set (33% of data for the validation) to find which statistical model has the lowest MSE (for my curiosity). I got the following graph which surprisingly the MSE for validation data set is always lower than training data set for all the models (from 1 to 14 dependent variables).
Here is the code that I used,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(MASS)
set.seed(1)
train=sample(seq(678),452,replace=FALSE)
train

regfit.exh=regsubsets(HPV~. -Model.Types..code.-Year..code.,data=Mydata, nvmax=NULL,force.in = NULL, force.out = NULL, method="exhaustive")

val.errors=rep(NA,14)

x.test=model.matrix(HPV~.-Model.Types..code.-Year..code.,data=Mydata[-train,])     
for(i in 1:14){
  coefi=coef(regfit.exh,id=i)
  pred=x.test[,names(coefi)]%*%coefi
  val.errors[i]=mean((Mydata$HPV[-train]-pred)^2)
}

plot(sqrt(val.errors),ylab="Root MSE",ylim=c(3,12), pch=11, type="b")
points(sqrt(regfit.exh$rss[-1]/452),col="blue",pch=11,type="b")
legend("topright",legend=c("Training","Validation"),col=c("blue","black"),pch=11)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/R4OwJ.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/R4OwJ.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;How come the validation root MSE could always beat the training?. Any feedback would be appreciated.&lt;/p&gt;
', 'r  regression', NULL, '''-1'':183C ''/452'':184C ''/a'':223C ''/code'':203C ''/p'':96C,224C,242C ''/pre'':204C ''/r4owj.png'':210C,217C ''1'':84C,102C,145C ''11'':176C,188C,202C ''12'':174C ''14'':22C,86C,131C,146C ''2'':164C ''3'':173C ''33'':41C ''452'':107C ''678'':31C,106C ''alt'':218C ''alway'':73C,233C ''appreci'':241C ''b'':178C,190C ''beat'':234C ''best'':11A,35C ''black'':200C ''blue'':186C,199C ''c'':172C,194C,198C ''code'':92C,98C,115C,117C,136C,138C ''coef'':148C ''coefi'':147C,155C,156C ''col'':185C,197C ''come'':227C ''compar'':1A ''could'':232C ''curios'':58C ''data'':5A,43C,70C,77C,118C,139C ''depend'':23C,87C ''descript'':221C ''enter'':219C ''exhaust'':127C ''fals'':109C ''feedback'':238C ''find'':48C ''follow'':62C ''force.in'':122C ''force.out'':124C ''got'':60C ''graph'':63C ''hpv'':113C,134C,161C ''href'':207C ''i.stack.imgur.com'':209C,216C ''i.stack.imgur.com/r4owj.png'':208C,215C ''id'':150C ''imag'':220C ''img'':213C ''iâ'':17C ''legend'':191C,193C ''librari'':99C ''lower'':74C ''lowest'':54C ''mass'':100C ''mean'':159C ''method'':126C ''model'':20C,51C,82C ''model.matrix'':133C ''model.types'':114C,135C ''mse'':8A,55C,67C,170C,231C ''mydata'':119C,140C,160C ''na'':130C ''name'':154C ''nofollow'':212C ''null'':121C,123C,125C ''nvmax'':120C ''observ'':32C ''p'':16C,205C,225C ''pch'':175C,187C,201C ''plot'':165C ''point'':179C ''pre'':97C ''pred'':152C,163C ''r'':14B ''regfit.exh'':111C,149C,181C ''regress'':13A,15B,37C ''regsubset'':112C ''rel'':211C ''rep'':129C ''replac'':108C ''root'':7A,169C,230C ''rss'':182C ''sampl'':104C ''seq'':105C ''set'':6A,40C,71C,78C ''set.seed'':101C ''signific'':29C ''sqrt'':166C,180C ''src'':214C ''statist'':50C ''subset'':12A,36C ''surpris'':65C ''topright'':192C ''train'':2A,76C,103C,110C,141C,162C,195C,236C ''type'':177C,189C ''use'':34C,95C ''val.errors'':128C,157C,167C ''valid'':4A,39C,46C,69C,196C,229C ''variabl'':24C,88C ''ve'':18C ''would'':239C ''x.test'':132C,153C ''year'':116C,137C ''ylab'':168C ''ylim'':171C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10378, 2, '2016-02-24 17:58:35.893', 1, NULL, NULL, '&lt;p&gt;It is mostly a performance benefit thing. You can run a neural network on a computer (slow or not). A TCAM is the only real hardware suited to the problem of seeing the cause and supplying the effect, TCAM can perform hopfield net operations very natively in pure hardware.&lt;/p&gt;

&lt;p&gt;It puts the network in a hardware spreadsheet formfactor,  its really simple, and can be bewilderingly so, you have a linear amount of patterns, (which I like to call bullets, because its like a machine gun ready to fire off and enter in patterns really damn fast, faster than a minigun.)    and requires alot of miniaturization to have a large amount, say a billion bullets,  more so than just ordinary ram.&lt;/p&gt;

&lt;p&gt;If you ever wanted to see a "real" robot in action,  its going to use hardware TCAM.&lt;/p&gt;
', NULL, NULL, '''/p'':51C,124C,143C ''action'':136C ''alot'':106C ''amount'':74C,113C ''benefit'':7C ''bewild'':68C ''billion'':116C ''bullet'':82C,117C ''call'':81C ''caus'':35C ''comput'':17C ''damn'':98C ''effect'':39C ''enter'':94C ''ever'':128C ''fast'':99C ''faster'':100C ''fire'':91C ''formfactor'':61C ''go'':138C ''gun'':88C ''hardwar'':27C,50C,59C,141C ''hopfield'':43C ''larg'':112C ''like'':79C,85C ''linear'':73C ''machin'':87C ''miniatur'':108C ''minigun'':103C ''most'':4C ''nativ'':47C ''net'':44C ''network'':14C,56C ''neural'':13C ''oper'':45C ''ordinari'':122C ''p'':1C,52C,125C ''pattern'':76C,96C ''perform'':6C,42C ''problem'':31C ''pure'':49C ''put'':54C ''ram'':123C ''readi'':89C ''real'':26C,133C ''realli'':63C,97C ''requir'':105C ''robot'':134C ''run'':11C ''say'':114C ''see'':33C,131C ''simpl'':64C ''slow'':18C ''spreadsheet'':60C ''suit'':28C ''suppli'':37C ''tcam'':22C,40C,142C ''thing'':8C ''use'':140C ''want'':129C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10379, 2, '2016-02-24 19:04:11.023', 1, NULL, NULL, '&lt;p&gt;I don''t think you need prior knowledge like SVM and MLP. In fact, reinforcement is another type of machine learning beside supervised learning, which including SVM, and unsupervised learning, which includes MLP. Reinforcement learning is actually very different from the latter two, as it is learning for interaction (agent-environment interaction). The trade-off between exploration and exploit is the key point. &lt;/p&gt;

&lt;p&gt;Markov decision process is the basic framework for reinforcement learning, which is very different from the other two types of learning. I highly recommend the textbook "&lt;a href="https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html" rel="nofollow"&gt;Reinforcement Learning: An Introduction&lt;/a&gt;" by Richard S. Sutton and Andrew G. Barto". That''s the book I am reading now. The language is very easy to follow and the content is comprehensive.&lt;/p&gt;
', NULL, NULL, '''/a'':106C ''/p'':67C,135C ''/~sutton/book/the-book.html'':99C ''actual'':38C ''agent'':52C ''agent-environ'':51C ''andrew'':112C ''anoth'':18C ''barto'':114C ''basic'':74C ''besid'':23C ''book'':118C ''comprehens'':134C ''content'':132C ''decis'':70C ''differ'':40C,82C ''easi'':127C ''environ'':53C ''exploit'':62C ''explor'':60C ''fact'':15C ''follow'':129C ''framework'':75C ''g'':113C ''high'':91C ''href'':96C ''includ'':27C,33C ''interact'':50C,54C ''introduct'':105C ''key'':65C ''knowledg'':9C ''languag'':124C ''latter'':43C ''learn'':22C,25C,31C,36C,48C,78C,89C,103C ''like'':10C ''machin'':21C ''markov'':69C ''mlp'':13C,34C ''need'':7C ''nofollow'':101C ''p'':1C,68C ''point'':66C ''prior'':8C ''process'':71C ''read'':121C ''recommend'':92C ''reinforc'':16C,35C,77C,102C ''rel'':100C ''richard'':108C ''supervis'':24C ''sutton'':110C ''svm'':11C,28C ''textbook'':94C ''think'':5C ''trade'':57C ''trade-off'':56C ''two'':44C,86C ''type'':19C,87C ''unsupervis'':30C ''webdocs.cs.ualberta.ca'':98C ''webdocs.cs.ualberta.ca/~sutton/book/the-book.html'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10386, 1, '2016-02-25 09:53:29.63', 4, 58, 'Role and need of Mathematics in Data Science', '&lt;p&gt;I am Bachelors of Engineering in Software Engineering field and having 3 years professional work experience in Oracle ERP/Database related technologies. Now I''m planning to pursue MSc in Data Science. What I need to know that what sort of Mathematics skills should a Data Scientist have? As we had learned numerous subjects like Numerical Analysis, Statistics, Linear Algebra etc.&lt;/p&gt;
', 'career', NULL, '''/p'':71C ''3'':22C ''algebra'':69C ''analysi'':66C ''bachelor'':13C ''career'':9B ''data'':7A,40C,55C ''engin'':15C,18C ''erp/database'':29C ''etc'':70C ''experi'':26C ''field'':19C ''know'':46C ''learn'':61C ''like'':64C ''linear'':68C ''m'':34C ''mathemat'':5A,51C ''msc'':38C ''need'':3A,44C ''numer'':62C,65C ''oracl'':28C ''p'':10C ''plan'':35C ''profession'':24C ''pursu'':37C ''relat'':30C ''role'':1A ''scienc'':8A,41C ''scientist'':56C ''skill'':52C ''softwar'':17C ''sort'':49C ''statist'':67C ''subject'':63C ''technolog'':31C ''work'':25C ''year'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10380, 1, '2016-02-24 22:00:24.553', 1, 40, 'Replacing the softmax activation function with SVM in lasagne', '&lt;p&gt;Newbie here,
I''ve been trying to replace my lasagne CNN''s softmax function with an Sklearn SVM, in order to increase its accuracy (per Charlie Tang: &lt;a href="http://arxiv.org/pdf/1306.0239.pdf" rel="nofollow"&gt;http://arxiv.org/pdf/1306.0239.pdf&lt;/a&gt;) on the MNIST dataset. Here is the output layer:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  # And, finally, the 10-unit output layer with 50% dropout on its inputs:
network = lasagne.layers.DenseLayer(
        lasagne.layers.dropout(network, p=.5),
        num_units=10,
        nonlinearity=lasagne.nonlinearities.softmax)
       #What I want to replace ^^^: the softmax function with an sklearn SVM


           #Below are my efforts at replacing the function.
        #nonlinearity=SVC.fit(network))
        #nonlinearity=rvm.predict_proba(network))
        #rvm.fit(y_train,network))

return network
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I would really appreciate any assistance and I can give more info if you need it.  Cheers&lt;/p&gt;
', 'machine-learning  python  neuralnetwork  deep-learning  theano', NULL, '''/code'':126C ''/p'':66C,146C ''/pdf/1306.0239.pdf'':51C ''/pdf/1306.0239.pdf&lt;/a&gt;)'':56C ''/pre'':127C ''10'':72C,90C ''5'':87C ''50'':77C ''accuraci'':43C ''activ'':4A ''appreci'':132C ''arxiv.org'':50C,55C ''arxiv.org/pdf/1306.0239.pdf'':49C ''arxiv.org/pdf/1306.0239.pdf&lt;/a&gt;)'':54C ''assist'':134C ''charli'':45C ''cheer'':145C ''cnn'':30C ''code'':68C ''dataset'':60C ''deep'':16B ''deep-learn'':15B ''dropout'':78C ''effort'':108C ''final'':70C ''function'':5A,33C,100C,112C ''give'':138C ''href'':48C ''increas'':41C ''info'':140C ''input'':81C ''lasagn'':9A,29C ''lasagne.layers.denselayer'':83C ''lasagne.layers.dropout'':84C ''lasagne.nonlinearities.softmax'':92C ''layer'':65C,75C ''learn'':12B,17B ''machin'':11B ''machine-learn'':10B ''mnist'':59C ''need'':143C ''network'':82C,85C,115C,119C,123C,125C ''neuralnetwork'':14B ''newbi'':20C ''nofollow'':53C ''nonlinear'':91C,113C,116C ''num'':88C ''order'':39C ''output'':64C,74C ''p'':19C,86C,128C ''per'':44C ''pre'':67C ''proba'':118C ''python'':13B ''realli'':131C ''rel'':52C ''replac'':1A,27C,97C,110C ''return'':124C ''rvm.fit'':120C ''rvm.predict'':117C ''sklearn'':36C,103C ''softmax'':3A,32C,99C ''svc.fit'':114C ''svm'':7A,37C,104C ''tang'':46C ''theano'':18B ''train'':122C ''tri'':25C ''unit'':73C,89C ''ve'':23C ''want'':95C ''would'':130C ''y'':121C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10381, 2, '2016-02-24 23:58:00.033', 0, NULL, NULL, '&lt;p&gt;The file you want to edit is right &lt;a href="https://github.com/Lasagne/Lasagne/blob/master/lasagne/nonlinearities.py" rel="nofollow"&gt;hereâ&lt;/a&gt;â, and &lt;a href="https://github.com/Lasagne/Lasagne/blob/master/lasagne/nonlinearities.py#L27" rel="nofollow"&gt;here&lt;/a&gt; is the definition of the softmax function. And if you need to brush up on your theano: &lt;a href="http://deeplearning.net/software/theano/library/" rel="nofollow"&gt;http://deeplearning.net/software/theano/library/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':18C,29C ''/lasagne/lasagne/blob/master/lasagne/nonlinearities.py'':14C ''/lasagne/lasagne/blob/master/lasagne/nonlinearities.py#l27'':25C ''/software/theano/library/'':51C ''/software/theano/library/&lt;/a&gt;&lt;/p&gt;'':56C ''brush'':42C ''deeplearning.net'':50C,55C ''deeplearning.net/software/theano/library/'':49C ''deeplearning.net/software/theano/library/&lt;/a&gt;&lt;/p&gt;'':54C ''definit'':32C ''edit'':7C ''file'':3C ''function'':36C ''github.com'':13C,24C ''github.com/lasagne/lasagne/blob/master/lasagne/nonlinearities.py'':12C ''github.com/lasagne/lasagne/blob/master/lasagne/nonlinearities.py#l27'':23C ''hereâ'':17C ''href'':11C,22C,48C ''need'':40C ''nofollow'':16C,27C,53C ''p'':1C ''rel'':15C,26C,52C ''right'':9C ''softmax'':35C ''theano'':46C ''want'':5C ''â'':19C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10382, 2, '2016-02-25 00:23:39.297', 3, NULL, NULL, '&lt;p&gt;Purging columns with &gt;80% zeros might have been a mistake. Those non-zero values may be the most significant bits in your data. There''s a whole field of study in text mining dedicated to discovering knowledge from &lt;a href="https://en.wikipedia.org/wiki/Bag-of-words_model" rel="nofollow"&gt;bag of words&lt;/a&gt; models which are tables like yours except can be hundreds of thousands of features wide and &gt;99% empty (i.e. sparse).&lt;/p&gt;

&lt;p&gt;That said, an idiots approach to feature selection is to fit a linear curve (with linear or logistic regression; or another simple model) to the data, then extract the individual feature coefficients from the model. Those are your influence weights.&lt;/p&gt;

&lt;p&gt;A simple tool to visualize feature importances is &lt;a href="http://orange.biolab.si/" rel="nofollow"&gt;Orange&lt;/a&gt;. I find it''s Rank widget works for me most of the time.
&lt;a href="http://i.stack.imgur.com/q4AF0.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/q4AF0.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':51C,130C,161C ''/p'':72C,114C,162C ''/q4af0.png'':148C,155C ''/wiki/bag-of-words_model'':45C ''80'':5C ''99'':68C ''alt'':156C ''anoth'':94C ''approach'':78C ''bag'':48C ''bit'':22C ''coeffici'':105C ''column'':3C ''curv'':87C ''data'':25C,99C ''dedic'':36C ''descript'':159C ''discov'':38C ''empti'':69C ''en.wikipedia.org'':44C ''en.wikipedia.org/wiki/bag-of-words_model'':43C ''enter'':157C ''except'':58C ''extract'':101C ''featur'':65C,80C,104C,121C ''field'':30C ''find'':132C ''fit'':84C ''href'':42C,125C,145C ''hundr'':61C ''i.e'':70C ''i.stack.imgur.com'':147C,154C ''i.stack.imgur.com/q4af0.png'':146C,153C ''idiot'':77C ''imag'':158C ''img'':151C ''import'':122C ''individu'':103C ''influenc'':112C ''knowledg'':39C ''like'':56C ''linear'':86C,89C ''logist'':91C ''may'':17C ''might'':7C ''mine'':35C ''mistak'':11C ''model'':52C,96C,108C ''nofollow'':47C,128C,150C ''non'':14C ''non-zero'':13C ''orang'':129C ''orange.biolab.si'':126C ''p'':1C,73C,115C ''purg'':2C ''rank'':135C ''regress'':92C ''rel'':46C,127C,149C ''said'':75C ''select'':81C ''signific'':21C ''simpl'':95C,117C ''spars'':71C ''src'':152C ''studi'':32C ''tabl'':55C ''text'':34C ''thousand'':63C ''time'':143C ''tool'':118C ''valu'':16C ''visual'':120C ''weight'':113C ''whole'':29C ''wide'':66C ''widget'':136C ''word'':50C ''work'':137C ''zero'':6C,15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10412, 1, '2016-02-26 18:28:57.417', 0, 23, 'Stanford NER - Increase probability for a certain class', '&lt;p&gt;I''m new to machine learning so I apologize if this question is silly. I''m using Stanford NER''s english 4class classifier with good results. However, since my dataset is mostly focused on organizations, I think the results could be improved if I could boost the probability for an entity to be an organization to the detriment of other classes.(Ex: I would prefer "Carl Zeis" to be identified as an organization rather than a person). Is my supposition correct? If so, can it be achieved in an easier way than retraining the model?&lt;/p&gt;
', 'machine-learning  nlp', NULL, '''/p'':109C ''4class'':35C ''achiev'':100C ''apolog'':22C ''boost'':59C ''carl'':79C ''certain'':7A ''class'':8A,74C ''classifi'':36C ''correct'':94C ''could'':53C,58C ''dataset'':43C ''detriment'':71C ''easier'':103C ''english'':34C ''entiti'':64C ''ex'':75C ''focus'':46C ''good'':38C ''howev'':40C ''identifi'':83C ''improv'':55C ''increas'':3A ''learn'':11B,19C ''m'':15C,29C ''machin'':10B,18C ''machine-learn'':9B ''model'':108C ''most'':45C ''ner'':2A,32C ''new'':16C ''nlp'':12B ''organ'':48C,68C,86C ''p'':13C ''person'':90C ''prefer'':78C ''probabl'':4A,61C ''question'':25C ''rather'':87C ''result'':39C,52C ''retrain'':106C ''silli'':27C ''sinc'':41C ''stanford'':1A,31C ''supposit'':93C ''think'':50C ''use'':30C ''way'':104C ''would'':77C ''zei'':80C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10385, 1, '2016-02-25 05:42:07.063', 1, 55, 'Can i find similar players using a clustering method like the k-mean algorithm?', '&lt;p&gt;I am working on a data mining project on NBA data. I want to make a recommendation system similar to the google one, where you search for players and you get recommendation for similar players. I intend to make such system based on their stats. My data looks like &lt;a href="https://github.com/joed7/Data-Mining-Project/blob/master/data/active.csv" rel="nofollow"&gt;this&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;14  Tony Allen  709     357     15386   2178    4580    0.476   102     363     0.281   2076    4217    0.492   0.487   1220    1701    0.717   804     1567    2371    942     995     299     964     1675    5678

15  Al-Farouq Aminu     431     228     9988    1119    2592    0.432   188     607     0.31    931     1985    0.469   0.468   530     731     0.725   618     1724    2342    488     413     245     540     813     2956
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;One approach I have in mind is using k-means clustering, but I am not sure how I will use the clusters to find similar players. Each cluster will have hundreds of players, I will need further filtering. &lt;/p&gt;

&lt;p&gt;Does anybody have any suggestions/tips for handling this problem?&lt;/p&gt;
', 'clustering  recommender-system', NULL, '''/a'':78C ''/code'':140C ''/joed7/data-mining-project/blob/master/data/active.csv'':74C ''/p'':79C,182C,193C ''/pre'':141C ''0.281'':93C ''0.31'':123C ''0.432'':120C ''0.468'':127C ''0.469'':126C ''0.476'':90C ''0.487'':97C ''0.492'':96C ''0.717'':100C ''0.725'':130C ''102'':91C ''1119'':118C ''1220'':98C ''14'':82C ''15'':110C ''15386'':87C ''1567'':102C ''1675'':108C ''1701'':99C ''1724'':132C ''188'':121C ''1985'':125C ''2076'':94C ''2178'':88C ''228'':116C ''2342'':133C ''2371'':103C ''245'':136C ''2592'':119C ''2956'':139C ''299'':106C ''357'':86C ''363'':92C ''413'':135C ''4217'':95C ''431'':115C ''4580'':89C ''488'':134C ''530'':128C ''540'':137C ''5678'':109C ''607'':122C ''618'':131C ''709'':85C ''731'':129C ''804'':101C ''813'':138C ''931'':124C ''942'':104C ''964'':107C ''995'':105C ''9988'':117C ''al'':112C ''al-farouq'':111C ''algorithm'':15A ''allen'':84C ''aminu'':114C ''anybodi'':185C ''approach'':144C ''base'':62C ''cluster'':8A,16B,154C,165C,171C ''code'':81C ''data'':26C,31C,67C ''farouq'':113C ''filter'':181C ''find'':3A,167C ''get'':51C ''github.com'':73C ''github.com/joed7/data-mining-project/blob/master/data/active.csv'':72C ''googl'':42C ''handl'':190C ''href'':71C ''hundr'':174C ''intend'':57C ''k'':13A,152C ''k-mean'':12A,151C ''like'':10A,69C ''look'':68C ''make'':35C,59C ''mean'':14A,153C ''method'':9A ''mind'':148C ''mine'':27C ''nba'':30C ''need'':179C ''nofollow'':76C ''one'':43C,143C ''p'':20C,142C,183C ''player'':5A,48C,55C,169C,176C ''pre'':80C ''problem'':192C ''project'':28C ''recommend'':18B,37C,52C ''recommender-system'':17B ''rel'':75C ''search'':46C ''similar'':4A,39C,54C,168C ''stat'':65C ''suggestions/tips'':188C ''sure'':159C ''system'':19B,38C,61C ''toni'':83C ''use'':6A,150C,163C ''want'':33C ''work'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10411, 2, '2016-02-26 16:10:44.31', 2, NULL, NULL, '&lt;p&gt;In some cases it is useful to do stratified sampling. For example, imagine you have 4 different groups in your population each with differing n (e.g., A = 10k, B = 20k, C = 30k D = 40k). In creating your train/test you may want to ensure that each subset maintains the same proportions of subgroup. This can ensure better equivalence of train/test than pure random assignment (which could result in lopsided train/test).&lt;/p&gt;
', NULL, NULL, '''/p'':71C ''10k'':29C ''20k'':31C ''30k'':33C ''4'':17C ''40k'':35C ''assign'':64C ''b'':30C ''better'':57C ''c'':32C ''case'':4C ''could'':66C ''creat'':37C ''d'':34C ''differ'':18C,25C ''e.g'':27C ''ensur'':44C,56C ''equival'':58C ''exampl'':13C ''group'':19C ''imagin'':14C ''lopsid'':69C ''maintain'':48C ''may'':41C ''n'':26C ''p'':1C ''popul'':22C ''proport'':51C ''pure'':62C ''random'':63C ''result'':67C ''sampl'':11C ''stratifi'':10C ''subgroup'':53C ''subset'':47C ''train/test'':39C,60C,70C ''use'':7C ''want'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10405, 2, '2016-02-26 07:34:23.683', 1, NULL, NULL, '&lt;p&gt;In general when you are evaluating statistics there are two main approaches: either you use some distributional assumptions (a generating model for your data) or not. Whenever you can you would probably go with a statistical model. But often the distributional assumptions can be wrong or they are not available to you. &lt;/p&gt;

&lt;p&gt;If you are doing inferences about the mean of the population, than you can rely on central limit theorem and build a statistic for that. The same for variance. However what about estimating something which does not rely on general assumptions? Bootstrap works for any kind of statistic, this is where it lies its power. It''s simple, and does require only minimal assumptions. &lt;/p&gt;

&lt;p&gt;And there is another problem which appears in practice. Estimating mean rely on central limit theorem. It is true that your set of assumptions required only independent and identical distributed data. But it''s something more. CLT works on large samples, it works in the limit. For small samples it can go wrong more often. And what about if your distribution is skewed. While it''s true that CLT relies only on i.i.d. the needed sample size for estimating a mean on a skewed distribution is in general greater that for a symmetric distribution. &lt;/p&gt;

&lt;p&gt;I found bootstrapping very useful in two main situations: when the sample is fairly small (but not tiny) and when the distribution is not clean (suppose it''s a mixture of two distributions). And is very useful because you can build not only estimates of the population parameters, but also estimations for confidence intervals of p-values. And because it''s easy to apply I also found sometimes useful even when you are assuming some model. Doing in parallel can help you to see if there is something really spooky in your sample or assumptions if inferences produces too different results, it makes you think or re-evaluate your setup.&lt;/p&gt;
', NULL, NULL, '''/p'':54C,120C,216C,330C ''also'':267C,284C ''anoth'':125C ''appear'':128C ''appli'':282C ''approach'':13C ''assum'':292C ''assumpt'':19C,43C,96C,119C,145C,313C ''avail'':51C ''bootstrap'':97C,220C ''build'':76C,258C ''central'':72C,135C ''clean'':242C ''clt'':158C,190C ''confid'':270C ''data'':25C,152C ''differ'':318C ''distribut'':18C,42C,151C,182C,206C,215C,239C,250C ''easi'':280C ''either'':14C ''estim'':88C,131C,200C,261C,268C ''evalu'':7C,327C ''even'':288C ''fair'':231C ''found'':219C,285C ''general'':3C,95C,209C ''generat'':21C ''go'':34C,173C ''greater'':210C ''help'':299C ''howev'':85C ''i.i.d'':194C ''ident'':150C ''independ'':148C ''infer'':60C,315C ''interv'':271C ''kind'':101C ''larg'':161C ''lie'':108C ''limit'':73C,136C,167C ''main'':12C,225C ''make'':321C ''mean'':63C,132C,202C ''minim'':118C ''mixtur'':247C ''model'':22C,38C,294C ''need'':196C ''often'':40C,176C ''p'':1C,55C,121C,217C,274C ''p-valu'':273C ''parallel'':297C ''paramet'':265C ''popul'':66C,264C ''power'':110C ''practic'':130C ''probabl'':33C ''problem'':126C ''produc'':316C ''re'':326C ''re-evalu'':325C ''realli'':307C ''reli'':70C,93C,133C,191C ''requir'':116C,146C ''result'':319C ''sampl'':162C,170C,197C,229C,311C ''see'':302C ''set'':143C ''setup'':329C ''simpl'':113C ''situat'':226C ''size'':198C ''skew'':184C,205C ''small'':169C,232C ''someth'':89C,156C,306C ''sometim'':286C ''spooki'':308C ''statist'':8C,37C,78C,103C ''suppos'':243C ''symmetr'':214C ''theorem'':74C,137C ''think'':323C ''tini'':235C ''true'':140C,188C ''two'':11C,224C,249C ''use'':16C,222C,254C,287C ''valu'':275C ''varianc'':84C ''whenev'':28C ''work'':98C,159C,164C ''would'':32C ''wrong'':46C,174C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10387, 2, '2016-02-25 10:08:44.567', 2, NULL, NULL, '&lt;p&gt;You can go as further or specific you want with this kind of data.
I would suggest first to analyze the data thoroughly before deciding which algorithms to apply. Evaluate the mean, quartiles, max and min of each parameter (such as in a box plot), verify if you have missing values and in that case decide a way of dealing with that (removing data, imputating - predicting missing value from data - or average, as common techniques). Also verify if is possible to create a new representative feature from the information you have, or even mix a set of features to reduce (a priori feature selection; just ok if it makes a lot of sense from your knowledge on the context - NBA). For example, a player position can be considerably representative, in the sense of giving context to data (e.g. it will come up that defensive players steal more balls than attacking players but score less).&lt;/p&gt;

&lt;p&gt;Afterwards I would suggest some simple algorithms such as:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Hierarchical clustering&lt;/strong&gt;:  can help you to build a genealogy of players, so you see which ones cluster together directly and which others influence this cluster (so they are similar in certain more general aspects but not so much so to be clustered together).&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;K-Means&lt;/strong&gt;: Well, its always good to try it; it won''t be that expensive in computational terms. The thing is that determining K will be rather complicated, but it can give you hints from a Visualization point of view, while not actually helping directly the task of information retrieval.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;K-Nearest-Neighbors&lt;/strong&gt;: Also determining K is complex (but not that much) it can give a first hint for retrieving similar players based on their distance in the space. This is a form of case-based reasoning, but actually is quite practical for your purposes. The key thing here is determining the right distance measure. Start with Euclidean as a typical approach. Then you can try some other distance measures such as the Malahanobis Distance, which takes into account the covariance of features.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You can have a first glance and even a functional solution following these approaches. Once observing the results you can get deeper (if necessary).&lt;/p&gt;

&lt;p&gt;Remember to normalize or standarize the data previously. You could also visualize the first principal components after applying PCA or LDA (this last if you have a target variable) so to observe how well the data distributes.&lt;/p&gt;

&lt;p&gt;The same methods can be applied after refining the data through feature selection and/or extraction techniques; which in some cases produces very interesting results.&lt;/p&gt;
', NULL, NULL, '''/li'':215C,270C,359C ''/p'':156C,167C,386C,425C,451C ''/strong'':173C,221C,277C ''/ul'':360C ''account'':354C ''actual'':262C,314C ''afterward'':158C ''algorithm'':28C,164C ''also'':77C,278C,398C ''alway'':224C ''analyz'':21C ''and/or'':440C ''appli'':30C,405C,432C ''approach'':337C,375C ''aspect'':205C ''attack'':151C ''averag'':73C ''ball'':149C ''base'':297C,311C ''box'':45C ''build'':178C ''case'':56C,310C,446C ''case-bas'':309C ''certain'':202C ''cluster'':172C,188C,196C,213C ''come'':142C ''common'':75C ''complex'':282C ''complic'':247C ''compon'':403C ''comput'':236C ''consider'':129C ''context'':120C,136C ''could'':397C ''covari'':356C ''creat'':83C ''data'':15C,23C,65C,71C,138C,394C,423C,436C ''deal'':61C ''decid'':26C,57C ''deeper'':383C ''defens'':145C ''determin'':242C,279C,326C ''direct'':190C,264C ''distanc'':300C,329C,344C,350C ''distribut'':424C ''e.g'':139C ''euclidean'':333C ''evalu'':31C ''even'':94C,369C ''exampl'':123C ''expens'':234C ''extract'':441C ''featur'':87C,99C,104C,358C,438C ''first'':19C,291C,366C,401C ''follow'':373C ''form'':307C ''function'':371C ''genealog'':180C ''general'':204C ''get'':382C ''give'':135C,251C,289C ''glanc'':367C ''go'':4C ''good'':225C ''help'':175C,263C ''hierarch'':171C ''hint'':253C,292C ''imput'':66C ''influenc'':194C ''inform'':90C,268C ''interest'':449C ''k'':219C,243C,274C,280C ''k-mean'':218C ''k-nearest-neighbor'':273C ''key'':322C ''kind'':13C ''knowledg'':117C ''last'':410C ''lda'':408C ''less'':155C ''li'':169C,216C,271C ''lot'':112C ''make'':110C ''malahanobi'':349C ''max'':35C ''mean'':33C,220C ''measur'':330C,345C ''method'':429C ''min'':37C ''miss'':51C,68C ''mix'':95C ''much'':209C,286C ''nba'':121C ''nearest'':275C ''necessari'':385C ''neighbor'':276C ''new'':85C ''normal'':390C ''observ'':377C,419C ''ok'':107C ''one'':187C ''other'':193C ''p'':1C,157C,361C,387C,426C ''paramet'':40C ''pca'':406C ''player'':125C,146C,152C,182C,296C ''plot'':46C ''point'':257C ''posit'':126C ''possibl'':81C ''practic'':317C ''predict'':67C ''previous'':395C ''princip'':402C ''priori'':103C ''produc'':447C ''purpos'':320C ''quartil'':34C ''quit'':316C ''rather'':246C ''reason'':312C ''reduc'':101C ''refin'':434C ''rememb'':388C ''remov'':64C ''repres'':86C,130C ''result'':379C,450C ''retriev'':269C,294C ''right'':328C ''score'':154C ''see'':185C ''select'':105C,439C ''sens'':114C,133C ''set'':97C ''similar'':200C,295C ''simpl'':163C ''solut'':372C ''space'':303C ''specif'':8C ''standar'':392C ''start'':331C ''steal'':147C ''strong'':170C,217C,272C ''suggest'':18C,161C ''take'':352C ''target'':415C ''task'':266C ''techniqu'':76C,442C ''term'':237C ''thing'':239C,323C ''thorough'':24C ''togeth'':189C,214C ''tri'':227C,341C ''typic'':336C ''ul'':168C ''valu'':52C,69C ''variabl'':416C ''verifi'':47C,78C ''view'':259C ''visual'':256C,399C ''want'':10C ''way'':59C ''well'':222C,421C ''won'':230C ''would'':17C,160C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10388, 2, '2016-02-25 10:14:56', 3, NULL, NULL, '&lt;p&gt;For &lt;strong&gt;finding similar&lt;/strong&gt; objects, k-means does not make much sense.&lt;/p&gt;

&lt;p&gt;k-means is based on similar objects. If squared deviations don''t work for your data then k-means will not work either.&lt;/p&gt;

&lt;p&gt;Thus, instead of using k-means, just use a &lt;strong&gt;nearest neighbor search&lt;/strong&gt; without k-means.&lt;/p&gt;
', NULL, NULL, '''/p'':16C,43C,64C ''/strong'':6C,59C ''base'':22C ''data'':34C ''deviat'':28C ''either'':42C ''find'':4C ''instead'':46C ''k'':9C,19C,37C,50C,62C ''k-mean'':8C,18C,36C,49C,61C ''make'':13C ''mean'':10C,20C,38C,51C,63C ''much'':14C ''nearest'':56C ''neighbor'':57C ''object'':7C,25C ''p'':1C,17C,44C ''search'':58C ''sens'':15C ''similar'':5C,24C ''squar'':27C ''strong'':3C,55C ''thus'':45C ''use'':48C,53C ''without'':60C ''work'':31C,41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10389, 1, '2016-02-25 10:30:42.69', 3, 42, 'How do we know Kernels are successful in making data linearly Separable?', '&lt;p&gt;When we have linearly inseparable datasets and we are using machine learning algorithms such as SVMs, we use kernels to &lt;em&gt;implicitly&lt;/em&gt; map datapoints into a feature space that makes them linearly separable. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;But how do we know if a kernel has indeed, &lt;em&gt;implicitly&lt;/em&gt;, been successful in making the datapoints linearly separable in the new feature space? What is the guarantee?&lt;/strong&gt; &lt;/p&gt;
', 'machine-learning  classification  svm  supervised-learning', NULL, '''/em'':44C,71C ''/p'':56C,90C ''/strong'':89C ''algorithm'':34C ''classif'':16B ''data'':10A ''datapoint'':46C,77C ''dataset'':27C ''em'':42C,69C ''featur'':49C,83C ''guarante'':88C ''implicit'':43C,70C ''inde'':68C ''insepar'':26C ''kernel'':5A,40C,66C ''know'':4A,63C ''learn'':15B,20B,33C ''linear'':11A,25C,54C,78C ''machin'':14B,32C ''machine-learn'':13B ''make'':9A,52C,75C ''map'':45C ''new'':82C ''p'':21C,57C ''separ'':12A,55C,79C ''space'':50C,84C ''strong'':58C ''success'':7A,73C ''supervis'':19B ''supervised-learn'':18B ''svm'':17B ''svms'':37C ''use'':31C,39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10390, 2, '2016-02-25 13:25:42.463', 3, NULL, NULL, '&lt;p&gt;Firstly, I think this question is both broad and opinion-based, however most people don''t think so. I''ll explain why:&lt;/p&gt;

&lt;p&gt;Data Science is a domain which various very much with the industry it is applied in, so very much application-based.&lt;/p&gt;

&lt;p&gt;For example: Someone like me in the growth and customer support segment, I use markov models and reinforcement learning, as customers moves can most likely be modeled as a (stochastic) graph. So, when I look out for hiring someone into my team, I look for graph theory skills, linear algebra (obviously) and statistics. &lt;/p&gt;

&lt;p&gt;However, for someone working in the segment where NLP is used, the math needed is different, and so does with every other application of data science.&lt;/p&gt;

&lt;p&gt;However, some really very important courses which help someone in the path of a data scientist according to me are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Linear Algebra&lt;/li&gt;
&lt;li&gt;Statistics&lt;/li&gt;
&lt;li&gt;Probability theory&lt;/li&gt;
&lt;li&gt;Discrete Math&lt;/li&gt;
&lt;li&gt;Analytical methodology (Numerical Analysis would be a &lt;em&gt;close to precise&lt;/em&gt; name for that)&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Some would argue that &lt;em&gt;Information Theory&lt;/em&gt; is also very important, however I haven''t really used it heavily in my day job, so I don''t think it''s absolutely necessary.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/a/816/11097"&gt;Helpful Post&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':232C ''/a/816/11097'':229C ''/em'':183C,197C ''/li'':159C,162C,166C,170C,187C ''/ol'':188C ''/p'':25C,49C,104C,132C,154C,223C,233C ''absolut'':221C ''accord'':150C ''algebra'':100C,158C ''also'':199C ''analysi'':175C ''analyt'':172C ''appli'':41C ''applic'':47C,128C ''application-bas'':46C ''argu'':192C ''base'':13C,48C ''broad'':9C ''close'':180C ''cours'':139C ''custom'':60C,71C ''data'':27C,130C,148C ''datascience.stackexchange.com'':228C ''datascience.stackexchange.com/a/816/11097'':227C ''day'':212C ''differ'':121C ''discret'':168C ''domain'':31C ''em'':179C,194C ''everi'':126C ''exampl'':52C ''explain'':23C ''first'':2C ''graph'':81C,96C ''growth'':58C ''haven'':204C ''heavili'':209C ''help'':141C,230C ''hire'':88C ''howev'':14C,106C,134C,202C ''href'':226C ''import'':138C,201C ''industri'':38C ''inform'':195C ''job'':213C ''learn'':69C ''li'':156C,160C,163C,167C,171C ''like'':54C,75C ''linear'':99C,157C ''ll'':22C ''look'':85C,94C ''markov'':65C ''math'':118C,169C ''methodolog'':173C ''model'':66C,77C ''move'':72C ''much'':35C,45C ''name'':184C ''necessari'':222C ''need'':119C ''nlp'':114C ''numer'':174C ''obvious'':101C ''ol'':155C ''opinion'':12C ''opinion-bas'':11C ''p'':1C,26C,50C,105C,133C,189C,224C ''path'':145C ''peopl'':16C ''post'':231C ''precis'':182C ''probabl'':164C ''question'':6C ''realli'':136C,206C ''reinforc'':68C ''scienc'':28C,131C ''scientist'':149C ''segment'':62C,112C ''skill'':98C ''someon'':53C,89C,108C,142C ''statist'':103C,161C ''stochast'':80C ''support'':61C ''team'':92C ''theori'':97C,165C,196C ''think'':4C,19C,218C ''use'':64C,116C,207C ''various'':33C ''work'':109C ''would'':176C,191C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10406, 1, '2016-02-26 13:27:48.553', 4, 78, 'What are the methods to ensure that the population split for A/B test is random?', '&lt;p&gt;Before launching an A/B test, what are the methods to ensure that the population split in control and target group is random for a particular label say, purchase rate.&lt;/p&gt;
', 'data-mining  statistics  data  experiments  ab-test', NULL, '''/p'':55C ''a/b'':12A,29C ''ab'':23B ''ab-test'':22B ''control'':42C ''data'':17B,20B ''data-min'':16B ''ensur'':6A,36C ''experi'':21B ''group'':45C ''label'':51C ''launch'':27C ''method'':4A,34C ''mine'':18B ''p'':25C ''particular'':50C ''popul'':9A,39C ''purchas'':53C ''random'':15A,47C ''rate'':54C ''say'':52C ''split'':10A,40C ''statist'':19B ''target'':44C ''test'':13A,24B,30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10421, 2, '2016-02-27 06:34:40.823', 0, NULL, NULL, '&lt;p&gt;That''s a general comment since I''m not sure what you have done but I do agree that looks odd.It could happpen for some data partitions. Repeat it changing the seed several times and check those results. In case it remains I would review the entire code. Good luck!  &lt;/p&gt;
', NULL, NULL, '''/p'':52C ''agre'':19C ''case'':41C ''chang'':31C ''check'':37C ''code'':49C ''comment'':6C ''could'':23C ''data'':27C ''done'':15C ''entir'':48C ''general'':5C ''good'':50C ''happpen'':24C ''look'':21C ''luck'':51C ''m'':9C ''odd.it'':22C ''p'':1C ''partit'':28C ''remain'':43C ''repeat'':29C ''result'':39C ''review'':46C ''seed'':33C ''sever'':34C ''sinc'':7C ''sure'':11C ''time'':35C ''would'':45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10407, 1, '2016-02-26 13:54:54.9', 4, 46, 'How to choose best classifier for Low positive to negative class ratio in data (training, validation and real time)?', '&lt;p&gt;Positive class is ~4%. The class weight methods will not work as even if I balance the data while training by scaling positive class samples, in real time (or in test data), the distribution is expected to be low. How to choose a good decision boundary for such cases. &lt;/p&gt;
', 'data-mining  classification  unbalanced-classes  preprocessing', NULL, '''/p'':78C ''4'':32C ''balanc'':44C ''best'':4A ''boundari'':74C ''case'':77C ''choos'':3A,70C ''class'':11A,26B,30C,34C,52C ''classif'':23B ''classifi'':5A ''data'':14A,21B,46C,60C ''data-min'':20B ''decis'':73C ''distribut'':62C ''even'':41C ''expect'':64C ''good'':72C ''low'':7A,67C ''method'':36C ''mine'':22B ''negat'':10A ''p'':28C ''posit'':8A,29C,51C ''preprocess'':27B ''ratio'':12A ''real'':18A,55C ''sampl'':53C ''scale'':50C ''test'':59C ''time'':19A,56C ''train'':15A,48C ''unbalanc'':25B ''unbalanced-class'':24B ''valid'':16A ''weight'':35C ''work'':39C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10391, 1, '2016-02-25 14:12:30.2', 2, 15, 'Selecting the number of hashes for minhash? Working with extremely sparse data and want more collisions', '&lt;p&gt;I''m attempting to use minhash to generate clusters and similarities, and I am primarily using ideas from these resources.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www2007.org/papers/paper570.pdf" rel="nofollow"&gt;http://www2007.org/papers/paper570.pdf&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://chrisjmccormick.wordpress.com/2015/06/12/minhash-tutorial-with-python-code/" rel="nofollow"&gt;https://chrisjmccormick.wordpress.com/2015/06/12/minhash-tutorial-with-python-code/&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The data that I am working with consists of interactions between users and items. There are 2.2M distinct users and 440M distinct items. Across all of the data, there are only 905M records, so it is very sparse.&lt;/p&gt;

&lt;p&gt;In my approach, I am calculating H minimum hash values for each user by reordering the items (of which there are 440M). Users have a wide range of item interactions. The user with the most interactions as 2.5M interactions, the lowest is 1 interaction, the average is 403, and the median is only 26.&lt;/p&gt;

&lt;p&gt;In google''s doc about Google News, they recommend concatenating 2-4 keys (LSH) and doing this 10-20 times. I imagine this works well when a user has interacted with a smaller amount of items like news articles, but it is woefully low for what I am doing. When I test this number of keys for users that have 1,000+ interactions, many do not have any concatenated min has matches with another user. This is a problem because I can manually calculate cosine or jaccard similarity for some of these users and see an acceptable amount of similarity for my needs. I have found better results by not concatenating hash keys and using as many as 200.&lt;/p&gt;

&lt;p&gt;For most of my hash key groups, there are around 2M distinct hash keys for the 2.24M users. As such, there is a fairly low amount of collision.&lt;/p&gt;

&lt;p&gt;Do you guys have any tips for increasing the amount of clustering? I am considering using 1,000 hash keys and pairing users if they match on more than one. Thanks in advance.&lt;/p&gt;
', 'data-mining  bigdata  clustering  recommendation  similarity', NULL, '''-20'':187C ''-4'':180C ''/2015/06/12/minhash-tutorial-with-python-code/'':63C ''/2015/06/12/minhash-tutorial-with-python-code/&lt;/a&gt;&lt;/li&gt;'':68C ''/p'':45C,110C,167C,288C,319C,354C ''/papers/paper570.pdf'':52C ''/papers/paper570.pdf&lt;/a&gt;&lt;/li&gt;'':57C ''/ul'':69C ''000'':230C,338C ''1'':155C,229C,337C ''10'':186C ''2'':179C ''2.2'':87C ''2.24'':306C ''2.5'':149C ''200'':287C ''26'':166C ''2m'':300C ''403'':160C ''440m'':92C,133C ''905m'':103C ''accept'':265C ''across'':95C ''advanc'':353C ''amount'':202C,266C,316C,330C ''anoth'':242C ''approach'':114C ''around'':299C ''articl'':207C ''attempt'':27C ''averag'':158C ''better'':275C ''bigdata'':20B ''calcul'':117C,252C ''chrisjmccormick.wordpress.com'':62C,67C ''chrisjmccormick.wordpress.com/2015/06/12/minhash-tutorial-with-python-code/'':61C ''chrisjmccormick.wordpress.com/2015/06/12/minhash-tutorial-with-python-code/&lt;/a&gt;&lt;/li&gt;'':66C ''cluster'':21B,33C,332C ''collis'':16A,318C ''concaten'':178C,237C,279C ''consid'':335C ''consist'':78C ''cosin'':253C ''data'':12A,18B,72C,99C ''data-min'':17B ''distinct'':89C,93C,301C ''doc'':172C ''extrem'':10A ''fair'':314C ''found'':274C ''generat'':32C ''googl'':170C,174C ''group'':296C ''guy'':323C ''h'':118C ''hash'':5A,120C,280C,294C,302C,339C ''href'':49C,60C ''idea'':41C ''imagin'':190C ''increas'':328C ''interact'':80C,141C,147C,151C,156C,198C,231C ''item'':84C,94C,128C,140C,204C ''jaccard'':255C ''key'':181C,224C,281C,295C,303C,340C ''li'':47C,58C ''like'':205C ''low'':212C,315C ''lowest'':153C ''lsh'':182C ''m'':26C,88C,150C,307C ''mani'':232C,285C ''manual'':251C ''match'':240C,346C ''median'':163C ''min'':238C ''mine'':19B ''minhash'':7A,30C ''minimum'':119C ''need'':271C ''news'':175C,206C ''nofollow'':54C,65C ''number'':3A,222C ''one'':350C ''p'':24C,70C,111C,168C,289C,320C ''pair'':342C ''primarili'':39C ''problem'':247C ''rang'':138C ''recommend'':22B,177C ''record'':104C ''rel'':53C,64C ''reorder'':126C ''resourc'':44C ''result'':276C ''see'':263C ''select'':1A ''similar'':23B,35C,256C,268C ''smaller'':201C ''spars'':11A,109C ''test'':220C ''thank'':351C ''time'':188C ''tip'':326C ''ul'':46C ''use'':29C,40C,283C,336C ''user'':82C,90C,124C,134C,143C,196C,226C,243C,261C,308C,343C ''valu'':121C ''want'':14A ''well'':193C ''wide'':137C ''woefulli'':211C ''work'':8A,76C,192C ''www2007.org'':51C,56C ''www2007.org/papers/paper570.pdf'':50C ''www2007.org/papers/paper570.pdf&lt;/a&gt;&lt;/li&gt;'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10392, 1, '2016-02-25 16:13:17.88', 1, 28, 'Determining completeness of dataset', '&lt;p&gt;I''m hoping you have some research or experience with determining the completeness of a data set. I''m trying to use a twitter dataset I scraped myself and want to have an indication on the completeness. 
Obviously, I will miss some data but I am wondering if there is a formula or method to calculate a probability on the completeness.&lt;/p&gt;
', 'dataset  statistics  research', NULL, '''/p'':70C ''calcul'':64C ''complet'':2A,21C,45C,69C ''data'':24C,51C ''dataset'':4A,5B,33C ''determin'':1A,19C ''experi'':17C ''formula'':60C ''hope'':11C ''indic'':42C ''m'':10C,27C ''method'':62C ''miss'':49C ''obvious'':46C ''p'':8C ''probabl'':66C ''research'':7B,15C ''scrape'':35C ''set'':25C ''statist'':6B ''tri'':28C ''twitter'':32C ''use'':30C ''want'':38C ''wonder'':55C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10393, 2, '2016-02-25 17:47:01.463', 1, NULL, NULL, '&lt;p&gt;You cannot guarantee this. Some data &lt;em&gt;is&lt;/em&gt; not separable by any kernel because of duplicates.&lt;/p&gt;

&lt;p&gt;By trying too hard, you will &lt;strong&gt;cause overfitting&lt;/strong&gt;. Essentially, you force the implicit mapping to be so complex it contains a copy of your training data (which is exactly what happens if you choose a too small bandwidth with RBF).&lt;/p&gt;

&lt;p&gt;If you want a good generalization performance, you will have to tolerate some errors, and use e.g. soft-margin and such techniques.&lt;/p&gt;

&lt;p&gt;Perfect separation is not something to aim for. Such a guarantee is just a guarantee of being able to overfit! Use cross-validation to reduce the risk of overfitting and find the right balance between being optimal on training data and actual performance.&lt;/p&gt;
', NULL, NULL, '''/em'':10C ''/p'':19C,63C,88C,134C ''/strong'':30C ''abl'':107C ''actual'':132C ''aim'':96C ''balanc'':124C ''bandwidth'':60C ''cannot'':3C ''caus'':28C ''choos'':56C ''complex'':40C ''contain'':42C ''copi'':44C ''cross'':112C ''cross-valid'':111C ''data'':7C,48C,130C ''duplic'':18C ''e.g'':81C ''em'':8C ''error'':78C ''essenti'':31C ''exact'':51C ''find'':121C ''forc'':33C ''general'':70C ''good'':69C ''guarante'':4C,100C,104C ''happen'':53C ''hard'':24C ''implicit'':35C ''kernel'':15C ''map'':36C ''margin'':84C ''optim'':127C ''overfit'':29C,109C,119C ''p'':1C,20C,64C,89C ''perfect'':90C ''perform'':71C,133C ''rbf'':62C ''reduc'':115C ''right'':123C ''risk'':117C ''separ'':12C,91C ''small'':59C ''soft'':83C ''soft-margin'':82C ''someth'':94C ''strong'':27C ''techniqu'':87C ''toler'':76C ''train'':47C,129C ''tri'':22C ''use'':80C,110C ''valid'':113C ''want'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10431, 2, '2016-02-27 18:42:38.753', 0, NULL, NULL, '&lt;p&gt;I would use a parallelized Gaussian process regression model. The next points to query in this framework are given by the posterior extrema or those of &lt;a href="http://people.orie.cornell.edu/pfrazier/Presentations/2011.11.INFORMS.Tutorial.pdf" rel="nofollow"&gt;maximum expected improvement&lt;/a&gt;. &lt;a href="https://github.com/arikcj/pgpr" rel="nofollow"&gt;Here''s an implementation&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':38C,50C ''/arikcj/pgpr'':43C ''/p'':51C ''/pfrazier/presentations/2011.11.informs.tutorial.pdf'':32C ''expect'':36C ''extrema'':24C ''framework'':18C ''gaussian'':7C ''github.com'':42C ''github.com/arikcj/pgpr'':41C ''given'':20C ''href'':29C,40C ''implement'':49C ''improv'':37C ''maximum'':35C ''model'':10C ''next'':12C ''nofollow'':34C,45C ''p'':1C ''parallel'':6C ''people.orie.cornell.edu'':31C ''people.orie.cornell.edu/pfrazier/presentations/2011.11.informs.tutorial.pdf'':30C ''point'':13C ''posterior'':23C ''process'':8C ''queri'':15C ''regress'':9C ''rel'':33C,44C ''use'':4C ''would'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10413, 2, '2016-02-26 19:00:49.5', 2, NULL, NULL, '&lt;p&gt;You don''t necessarily have to train any differently if you are happy with the result. If you aren''t, undersample the majority class and/or adjust the class weights in your loss function. The thing you really have to be more careful about is the evaluation of the result because a naive metric like the accuracy would give you false hope since the imbalance allows you to achieve a high accuracy by simply always predicting the majority class or randomly predicting each class according to its probability of occurrence. It is this baseline against which you have to judge your success; like the &lt;a href="https://en.wikipedia.org/wiki/Coefficient_of_determination" rel="nofollow"&gt;coefficient of determination&lt;/a&gt; in regression, where the baseline is the sample mean.&lt;/p&gt;

&lt;p&gt;You could use a graphical approach (ROC or precision/recall curves), or report a size-weighted average of the class positive rate instead of the accuracy. Here are some other ideas: &lt;a href="http://ipl.ece.ubc.ca/bci_files/C8.pdf" rel="nofollow"&gt;Comparison of Evaluation Metrics in Classification Applications with Imbalanced Datasets&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':115C,175C ''/bci_files/c8.pdf'':162C ''/p'':125C,176C ''/wiki/coefficient_of_determination'':109C ''accord'':85C ''accuraci'':57C,72C,152C ''achiev'':69C ''adjust'':27C ''allow'':66C ''alway'':75C ''and/or'':26C ''applic'':171C ''approach'':132C ''aren'':20C ''averag'':143C ''baselin'':94C,120C ''care'':43C ''class'':25C,29C,79C,84C,146C ''classif'':170C ''coeffici'':112C ''comparison'':165C ''could'':128C ''curv'':136C ''dataset'':174C ''determin'':114C ''differ'':10C ''en.wikipedia.org'':108C ''en.wikipedia.org/wiki/coefficient_of_determination'':107C ''evalu'':47C,167C ''fals'':61C ''function'':34C ''give'':59C ''graphic'':131C ''happi'':14C ''high'':71C ''hope'':62C ''href'':106C,159C ''idea'':157C ''imbal'':65C ''imbalanc'':173C ''instead'':149C ''ipl.ece.ubc.ca'':161C ''ipl.ece.ubc.ca/bci_files/c8.pdf'':160C ''judg'':100C ''like'':55C,103C ''loss'':33C ''major'':24C,78C ''mean'':124C ''metric'':54C,168C ''naiv'':53C ''necessarili'':5C ''nofollow'':111C,164C ''occurr'':90C ''p'':1C,126C ''posit'':147C ''precision/recall'':135C ''predict'':76C,82C ''probabl'':88C ''random'':81C ''rate'':148C ''realli'':38C ''regress'':117C ''rel'':110C,163C ''report'':138C ''result'':17C,50C ''roc'':133C ''sampl'':123C ''simpli'':74C ''sinc'':63C ''size'':141C ''size-weight'':140C ''success'':102C ''thing'':36C ''train'':8C ''undersampl'':22C ''use'':129C ''weight'':30C,142C ''would'':58C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10414, 2, '2016-02-26 19:33:43.4', 0, NULL, NULL, '&lt;p&gt;For your prototype: for each time segment t1-t300, for each feature f1-f20, compute the mean feature value across subjects s1-sn. This is your prototype subject about which you can compute a confindence interval set to your target threshold (e.g, 95%) or set a threshold to +/- Nsd depending upon your definition of an outlier. &lt;/p&gt;
', NULL, NULL, '''/p'':61C ''95'':47C ''across'':23C ''comput'':18C,37C ''confind'':39C ''definit'':57C ''depend'':54C ''e.g'':46C ''f1'':16C ''f1-f20'':15C ''f20'':17C ''featur'':14C,21C ''interv'':40C ''mean'':20C ''nsd'':53C ''outlier'':60C ''p'':1C ''prototyp'':4C,31C ''s1'':26C ''s1-sn'':25C ''segment'':8C ''set'':41C,49C ''sn'':27C ''subject'':24C,32C ''t1'':10C ''t1-t300'':9C ''t300'':11C ''target'':44C ''threshold'':45C,51C ''time'':7C ''upon'':55C ''valu'':22C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10408, 2, '2016-02-26 14:51:42.707', 2, NULL, NULL, '&lt;p&gt;Here you have some good references on Reinforcement Learning:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Classic&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Sutton RS, Barto AG. Reinforcement Learning: An Introduction. Cambridge, Mass: A Bradford Book; 1998. 322 p.&lt;/em&gt; &lt;/p&gt;

&lt;p&gt;The draft for the second edition is available for free: &lt;a href="https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html" rel="nofollow"&gt;https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Russell/Norvig Chapter 21:&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Russell SJ, Norvig P, Davis E. Artificial intelligence: a modern approach. Upper Saddle River, NJ: Prentice Hall; 2010.&lt;/em&gt; &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;More technical&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;SzepesvÃ¡ri C. Algorithms for reinforcement learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. 2010;4(1):1â103.&lt;/em&gt;  &lt;a href="http://www.ualberta.ca/~szepesva/RLBook.html" rel="nofollow"&gt;http://www.ualberta.ca/~szepesva/RLBook.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Bertsekas DP. Dynamic Programming and Optimal Control. 4th edition. Belmont, Mass.: Athena Scientific; 2007. 1270 p.&lt;/em&gt; 
Chapter 6, vol 2 is available for free: &lt;a href="http://web.mit.edu/dimitrib/www/dpchapter.pdf" rel="nofollow"&gt;http://web.mit.edu/dimitrib/www/dpchapter.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;For more recent developments&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Wiering M, van Otterlo M, editors. Reinforcement Learning. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012&lt;/em&gt; Available from: &lt;a href="http://link.springer.com/10.1007/978-3-642-27645-3" rel="nofollow"&gt;http://link.springer.com/10.1007/978-3-642-27645-3&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Kochenderfer MJ, Amato C, Chowdhary G, How JP, Reynolds HJD, Thornton JR, et al. Decision Making Under Uncertainty: Theory and Application. 1 edition. Cambridge, Massachusetts: The MIT Press; 2015. 352 p.&lt;/em&gt; &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Multi-agent reinforcement learning&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;BuÅoniu L, BabuÅ¡ka R, Schutter BD. Multi-agent Reinforcement Learning: An Overview. In: Srinivasan D, Jain LC, editors. Innovations in Multi-Agent Systems and Applications - 1 . Springer Berlin Heidelberg; 2010 p. 183â221.&lt;/em&gt; Available from: &lt;a href="http://link.springer.com/chapter/10.1007/978-3-642-14435-6_7" rel="nofollow"&gt;http://link.springer.com/chapter/10.1007/978-3-642-14435-6_7&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Schwartz HM. Multi-agent machine learningâ¯: a reinforcement approach. Hoboken, New Jersey: Wiley; 2014.&lt;/em&gt; &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Videos / Courses&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I would also suggest David Silver course in YouTube: &lt;a href="https://www.youtube.com/playlist?list=PL5X3mDkKaJrL42i_jhE4N-p6E2Ol62Ofa" rel="nofollow"&gt;https://www.youtube.com/playlist?list=PL5X3mDkKaJrL42i_jhE4N-p6E2Ol62Ofa&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10396, 2, '2016-02-25 19:30:55.66', 1, NULL, NULL, '&lt;p&gt;In my experience, there are a few things to consider here:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;What field you''re going into&lt;/li&gt;
&lt;li&gt;What kinds of technology you believe you''ll be working with&lt;/li&gt;
&lt;li&gt;What kinds of teams you believe you''ll be working with&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Field&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This is a huge determiner. To my understanding, SAS is standard in finance, banking, some biostats, and other industries. R, on the other hand, is open source, free, and is receiving a lot of attention currently from Microsoft after their acquisition of Revolution Analytics.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Technology&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Are you thinking you''ll be at a large corporation or a small shop? Do you think you''ll need to work with creating production ready algorithms or simply producing insights and analysis?&lt;/p&gt;

&lt;p&gt;The reason this matters is that open source technology can often times be a bit easier to convert to production ready use. The cost of software can also be a barrier at smaller companies and may dictate what you are capable of using.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Collaboration with Other Teams&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;If you sit firmly on the business side, it''s possible that everyone you work with may use SAS or may be comfortable with the outputs. In this case, you may not need to collaborate across any additional technology.&lt;/p&gt;

&lt;p&gt;If you work with technology, however, it may be difficult to integrate SAS or proprietary solutions into other workflows. An example would be creating a real-time user scoring system. If you were able to program this into R or Python, you may be able to pass the code directly to a developer for implementation. This would be more difficult if a proprietary solution was involved.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Other Considerations&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The analytics space is evolving rapidly. On top of the above, Python is coming out as a very popular technology for use in machine learning and data mining and pairs well with Spark as well as some other large data technology. A specific example of a library here would be scikit-learn.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Closing Thoughts/My Experience&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I''m an R/Python user by experience, though I have some experience with SAS in school as well as in work. Generally, I''ve found the level of support with R and Python to be great - especially since they''ve started to rise in popularity. SAS has its uses and has definitely carved out piece of the industry for itself.&lt;/p&gt;

&lt;p&gt;All in all, however - getting a solid base in statistical theory, programming (scripting), and understanding the application and value that analysis can provide will go a long way. The syntax between these tools is, generally, not worlds apart. SAS can be a bit strange compared to R and Python, but all of these tools generally have syntax which is fairly readable and is not difficult to adapt to another tool.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10409, 1, '2016-02-26 15:26:10.943', 4, 39, 'How to build a mean prototype from data', '&lt;p&gt;I have a dataset with physiological measures of subjects along time. I would like to create (or select) a mean prototype example in order to be able to identify in new examples how far are they from the mean prototype. A second issue will be to select a threshold to determine what is considered near or far.
Each example has 20 numeric features and I have around 300 examples per subject.&lt;/p&gt;

&lt;p&gt;First ideas (disregarding outliers):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;To iterate through all the examples of a subject and find the one who has the minimum mean distance to all the other examples. This will select a specific example from the dataset.&lt;/li&gt;
&lt;li&gt;To use an evolutionary algorithm to find a prototypical example which has the lowest mean distance to all the other examples. This will create a new example that can be used as prototype.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Now I would like to determine when a new example is close, far or very far from the prototype (mean). A possible approach is to set two thresholds distance to determine to which class or case corresponds the new example (close, far or very far). How could I determine these thresholds? Possibly using number of standard deviations? What other approaches can be followed to perform all this?&lt;/p&gt;

&lt;p&gt;Let''s assume the distance metric is already selected.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10397, 1, '2016-02-25 20:02:44.52', 0, 12, 'Controlling regions for mutation and crossover in R-based genetic algorithm'' chromosomes', '&lt;p&gt;This is my first time so please, be patient with me...&lt;/p&gt;

&lt;p&gt;Recently I get used to optimize models'' parameters and input variables simultaneously with genetic algorithms. 
One part of the chromosome encoded modelsâ parameters and the other part the input variables and the fitness function was based on the mean performance through cross-validation.&lt;/p&gt;

&lt;p&gt;I used integer chromosomes and those parameters that needed real values were got dividing the corresponding gene by 100 or 1000. However the crossover, mutation etc. of such chromosomes may be not optimal since they could mix genes with completely different meaning.&lt;/p&gt;

&lt;p&gt;I would like then to split the chromosome in two parts allowing these parts to interact only with the corresponding parts of the other chromosomes. It would be nice to allow one part to be composed by real values and the other one by bit strings. Further I would like to get the first part ordered because in the problem Iâm actually involved; they encode the vertices of triangular fuzzy logic membership functions.&lt;/p&gt;

&lt;p&gt;Finally, I only work with R so,&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Does exist any GA able to deal with that?&lt;/li&gt;
&lt;li&gt;Is there at least one GA implementation in R?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It will be a good start to tune the actual code (if possible). Iâm working with rgenoud thus perhaps a much simpler GA may avoid (partially) such deficiencies, since they consider a smaller amount of operatorsâ¦&lt;/p&gt;

&lt;p&gt;Ideas?
Thanks!&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10398, 2, '2016-02-25 22:30:45.597', 0, NULL, NULL, '&lt;p&gt;If you are talking about exploring your data for patterns of missing data you can try using Self-Organizing Maps [&lt;a href="https://en.wikipedia.org/wiki/Self-organizing_map]" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Self-organizing_map]&lt;/a&gt;, which are a special flavor of neural networks. Here is a small &lt;a href="http://users.ics.aalto.fi/sami/thesis/node18.html#SECTION00074000000000000000" rel="nofollow"&gt;research paper&lt;/a&gt; explaining the concept a bit. Here is another [link][3] with historical info and links to some original papers about the topic, specifically by Kohonen &lt;em&gt;et al.&lt;/em&gt; &lt;/p&gt;

&lt;p&gt;From Wikipedia: &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Self-organizing maps are different from other artificial neural
  networks as they apply competitive learning as opposed to
  error-correction learning (such as backpropagation with gradient
  descent), and in the sense that they use a neighborhood function to
  preserve the topological properties of the input space. This makes
  SOMs useful for visualizing low-dimensional views of high-dimensional
  data...&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Essentially this method will allow you to look for patterns in your data. From there you can determine what level of completeness you are dealing with in your data set.&lt;/p&gt;

&lt;p&gt;Specifically for your example for Twitter data, I would imagine there are many fields in the JSON data with missing values. Maybe some users choose not to fill in their gender or age etc. Visualizing your data as well calculating summary statistics will help you to paint an overall picture of your data. And when you have high dimensional data using tools to visualize it in a lower dimensional space can always be handy. Hope this helps you!&lt;/p&gt;
', NULL, NULL, '''/a'':54C ''/blockquote'':155C ''/em'':83C ''/p'':84C,88C,154C,187C,267C ''/sami/thesis/node18.html#section00074000000000000000'':49C ''/wiki/self-organizing_map]'':27C ''/wiki/self-organizing_map]&lt;/a&gt;,'':32C ''3'':64C ''age'':221C ''al'':82C ''allow'':161C ''alway'':260C ''anoth'':62C ''appli'':104C ''artifici'':99C ''backpropag'':116C ''bit'':59C ''blockquot'':89C ''calcul'':228C ''choos'':213C ''competit'':105C ''complet'':178C ''concept'':57C ''correct'':112C ''data'':9C,14C,153C,169C,185C,195C,206C,225C,241C,248C ''deal'':181C ''descent'':119C ''determin'':174C ''differ'':96C ''dimension'':147C,152C,247C,257C ''em'':80C ''en.wikipedia.org'':26C,31C ''en.wikipedia.org/wiki/self-organizing_map]'':25C ''en.wikipedia.org/wiki/self-organizing_map]&lt;/a&gt;,'':30C ''error'':111C ''error-correct'':110C ''essenti'':157C ''et'':81C ''etc'':222C ''exampl'':192C ''explain'':55C ''explor'':7C ''field'':202C ''fill'':216C ''flavor'':37C ''function'':129C ''gender'':219C ''gradient'':118C ''handi'':262C ''help'':232C,265C ''high'':151C,246C ''high-dimension'':150C ''histor'':66C ''hope'':263C ''href'':24C,46C ''imagin'':198C ''info'':67C ''input'':137C ''json'':205C ''kohonen'':79C ''learn'':106C,113C ''level'':176C ''link'':63C,69C ''look'':164C ''low'':146C ''low-dimension'':145C ''lower'':256C ''make'':140C ''mani'':201C ''map'':22C,94C ''mayb'':210C ''method'':159C ''miss'':13C,208C ''neighborhood'':128C ''network'':40C,101C ''neural'':39C,100C ''nofollow'':29C,51C ''oppos'':108C ''organ'':21C,93C ''origin'':72C ''overal'':237C ''p'':1C,85C,90C,156C,188C ''paint'':235C ''paper'':53C,73C ''pattern'':11C,166C ''pictur'':238C ''preserv'':131C ''properti'':134C ''rel'':28C,50C ''research'':52C ''self'':20C,92C ''self-organ'':19C,91C ''sens'':123C ''set'':186C ''small'':44C ''som'':141C ''space'':138C,258C ''special'':36C ''specif'':77C,189C ''statist'':230C ''summari'':229C ''talk'':5C ''tool'':250C ''topic'':76C ''topolog'':133C ''tri'':17C ''twitter'':194C ''use'':18C,126C,142C,249C ''user'':212C ''users.ics.aalto.fi'':48C ''users.ics.aalto.fi/sami/thesis/node18.html#section00074000000000000000'':47C ''valu'':209C ''view'':148C ''visual'':144C,223C,252C ''well'':227C ''wikipedia'':87C ''would'':197C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10399, 1, '2016-02-25 23:16:02.823', 2, 42, 'How to adapt the softmax layer for multiple labels?', '&lt;p&gt;Question: A image has multiple labels. Given a set of image with labels, how to adapt the softmax layers?&lt;/p&gt;

&lt;p&gt;My idea:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Encode multiple labels to 0-1 variables. Use logistics regression as the output layer.&lt;/li&gt;
&lt;li&gt;Choose the top X labels in softmax output. But I do not know 

&lt;ol&gt;
&lt;li&gt;how to determine the threshold.&lt;/li&gt;
&lt;li&gt;how to modify the code in a pretrained net. Let''s say &lt;/li&gt;
&lt;/ol&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;blockquote&gt;
&lt;pre&gt;&lt;code&gt;{
      "op": "null",
      "param": {},
      "name": "softmax_label",
      "inputs": [],
      "backward_source_id": -1
    },
    {
      "op": "Softmax",
      "param": {"grad_scale": "1"},
      "name": "softmax",
      "inputs": [[510, 0], [511, 0]],
      "backward_source_id": -1
    }
&lt;/code&gt;&lt;/pre&gt;
&lt;/blockquote&gt;

&lt;p&gt;Any recommendations on tutorials are appreciated. I have some theory background from the book ESL.&lt;/p&gt;
', 'neuralnetwork  recommendation  image-classification', NULL, '''-1'':47C,109C,126C ''/blockquote'':129C ''/code'':127C ''/li'':56C,78C,92C,94C ''/ol'':93C ''/p'':35C,39C,146C ''/pre'':128C ''/ul'':95C ''0'':46C,120C,122C ''1'':115C ''510'':119C ''511'':121C ''adapt'':3A,31C ''appreci'':136C ''background'':141C ''backward'':106C,123C ''blockquot'':96C ''book'':144C ''choos'':58C ''classif'':14B ''code'':84C,98C ''determin'':75C ''encod'':42C ''esl'':145C ''given'':22C ''grad'':113C ''id'':108C,125C ''idea'':38C ''imag'':13B,18C,26C ''image-classif'':12B ''input'':105C,118C ''know'':70C ''label'':9A,21C,28C,44C,62C,104C ''layer'':6A,34C,55C ''let'':89C ''li'':41C,57C,72C,79C ''logist'':50C ''modifi'':82C ''multipl'':8A,20C,43C ''name'':102C,116C ''net'':88C ''neuralnetwork'':10B ''null'':100C ''ol'':71C ''op'':99C,110C ''output'':54C,65C ''p'':15C,36C,130C ''param'':101C,112C ''pre'':97C ''pretrain'':87C ''question'':16C ''recommend'':11B,132C ''regress'':51C ''say'':91C ''scale'':114C ''set'':24C ''softmax'':5A,33C,64C,103C,111C,117C ''sourc'':107C,124C ''theori'':140C ''threshold'':77C ''top'':60C ''tutori'':134C ''ul'':40C ''use'':49C ''variabl'':48C ''x'':61C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10400, 2, '2016-02-25 23:56:11.483', 1, NULL, NULL, '&lt;p&gt;I don''t know what''s going on in your code, but you seem to be close: to get multiple labels, simply replace the softmax output layer with a logistic layer (or something else that maps a real number to a probability), then optimize the cross entropy. That way you will have a probability associated with each label such that their sum across labels no longer need to add to unity.&lt;/p&gt;
', NULL, NULL, '''/p'':73C ''across'':64C ''add'':70C ''associ'':56C ''close'':18C ''code'':12C ''cross'':47C ''els'':35C ''entropi'':48C ''get'':20C ''go'':8C ''know'':5C ''label'':22C,59C,65C ''layer'':28C,32C ''logist'':31C ''longer'':67C ''map'':37C ''multipl'':21C ''need'':68C ''number'':40C ''optim'':45C ''output'':27C ''p'':1C ''probabl'':43C,55C ''real'':39C ''replac'':24C ''seem'':15C ''simpli'':23C ''softmax'':26C ''someth'':34C ''sum'':63C ''uniti'':72C ''way'':50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10401, 2, '2016-02-26 01:07:23.793', 1, NULL, NULL, '&lt;p&gt;It depends.&lt;/p&gt;

&lt;p&gt;All most all the professors (in my univ) would show students examples in &lt;code&gt;e1071&lt;/code&gt;. The somewhat weird name &lt;code&gt;e1071&lt;/code&gt; is from a course name.&lt;/p&gt;

&lt;p&gt;However, if you have hand-on experience on industry or kaggle, you would find the preprocessing functions of &lt;code&gt;caret&lt;/code&gt; is quiet useful. Specify what you want by several arguments. You get normalization, fill NAs, cross validation etc. Extremely easy. No headache at all.&lt;/p&gt;

&lt;p&gt;If time permits, play both.&lt;/p&gt;
', NULL, NULL, '''/code'':21C,28C,57C ''/p'':4C,34C,82C,89C ''argument'':67C ''caret'':56C ''code'':19C,26C,55C ''cours'':32C ''cross'':73C ''depend'':3C ''e1071'':20C,27C ''easi'':77C ''etc'':75C ''exampl'':17C ''experi'':43C ''extrem'':76C ''fill'':71C ''find'':50C ''function'':53C ''get'':69C ''hand'':41C ''hand-on'':40C ''headach'':79C ''howev'':36C ''industri'':45C ''kaggl'':47C ''name'':25C,33C ''nas'':72C ''normal'':70C ''p'':1C,5C,35C,83C ''permit'':86C ''play'':87C ''preprocess'':52C ''professor'':10C ''quiet'':59C ''sever'':66C ''show'':15C ''somewhat'':23C ''specifi'':61C ''student'':16C ''time'':85C ''univ'':13C ''use'':60C ''valid'':74C ''want'':64C ''weird'':24C ''would'':14C,49C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10415, 2, '2016-02-26 21:45:25.57', 0, NULL, NULL, '&lt;p&gt;Your data set will influence the labeling results. If it is focused on organizations, the NER should favor them simply by virtue of the data it''s fed. So you might need to do anything. But if you do observe undesirable behavior in the resulting NER, you can adjust the weights.&lt;/p&gt;

&lt;p&gt;It''s the same with any machine learning algorithm. A humorous demonstration was provided by Google''s Deep Dream: &lt;a href="http://rhizome.org/editorial/2015/jul/10/deep-dream-doggy-monster/?ref=tags_dogs_post_readbtn" rel="nofollow"&gt;it kept seeing dogs every where&lt;/a&gt;. Why? Because the data set they used for training had an abundance of dogs.&lt;/p&gt;

&lt;p&gt;(And Carl Zeis should be labeled as a person. The company is Carl Zeiss.)&lt;/p&gt;
', NULL, NULL, '''/a'':87C ''/editorial/2015/jul/10/deep-dream-doggy-monster/?ref=tags_dogs_post_readbtn'':78C ''/p'':53C,102C,118C ''abund'':99C ''adjust'':50C ''algorithm'':63C ''anyth'':36C ''behavior'':43C ''carl'':105C,116C ''compani'':114C ''data'':3C,26C,91C ''deep'':72C ''demonstr'':66C ''dog'':84C,101C ''dream'':73C ''everi'':85C ''favor'':19C ''fed'':29C ''focus'':13C ''googl'':70C ''href'':75C ''humor'':65C ''influenc'':6C ''kept'':82C ''label'':8C,109C ''learn'':62C ''machin'':61C ''might'':32C ''need'':33C ''ner'':17C,47C ''nofollow'':80C ''observ'':41C ''organ'':15C ''p'':1C,54C,103C ''person'':112C ''provid'':68C ''rel'':79C ''result'':9C,46C ''rhizome.org'':77C ''rhizome.org/editorial/2015/jul/10/deep-dream-doggy-monster/?ref=tags_dogs_post_readbtn'':76C ''see'':83C ''set'':4C,92C ''simpli'':21C ''train'':96C ''undesir'':42C ''use'':94C ''virtu'':23C ''weight'':52C ''zei'':106C ''zeiss'':117C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10416, 1, '2016-02-26 22:26:52.777', -1, 10, 'need advice to choose project related to big data', '&lt;p&gt;I am looking for a small project that is related to big data or data mining. Could you give me some suggestions to pick up one which is small and easy?&lt;/p&gt;
', 'data-mining  bigdata', NULL, '''/p'':46C ''advic'':2A ''big'':8A,26C ''bigdata'':13B ''choos'':4A ''could'':31C ''data'':9A,11B,27C,29C ''data-min'':10B ''easi'':45C ''give'':33C ''look'':17C ''mine'':12B,30C ''need'':1A ''one'':40C ''p'':14C ''pick'':38C ''project'':5A,21C ''relat'':6A,24C ''small'':20C,43C ''suggest'':36C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10417, 2, '2016-02-27 00:29:41.117', 0, NULL, NULL, '&lt;p&gt;Word2vec works in two models CBOW and skip-gram. Let''s take CBOW model, as your question goes in the same way that predict the target word, given the surrounding words.&lt;/p&gt;

&lt;p&gt;Fundamentally, the model develops input and output weight matrices, which depends upon the input context words and output target word with the help of a hidden layer. Thus back-propagation is used to update the weights when the error difference between predicted output vector and the current output matrix.&lt;/p&gt;

&lt;p&gt;Basically speaking, predicting the target word from given context words is used as an equation to obtain the optimal weight matrix for the given data.&lt;/p&gt;

&lt;p&gt;To answer the second part, it seems a bit complex than just a linear sum.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Obtain all the word vectors of context words&lt;/li&gt;
&lt;li&gt;Average them to find out the hidden layer vector &lt;code&gt;h&lt;/code&gt; of size &lt;code&gt;Nx1&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Obtain the output matrix &lt;code&gt;syn1&lt;/code&gt;(&lt;code&gt;word2vec.c&lt;/code&gt; or &lt;code&gt;gensim&lt;/code&gt;) which is of size &lt;code&gt;VxN&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Multiply &lt;code&gt;syn1&lt;/code&gt; by &lt;code&gt;h&lt;/code&gt;, the resulting vector will be &lt;code&gt;z&lt;/code&gt; with size &lt;code&gt;Vx1&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Compute the probability vector &lt;code&gt;y = softmax(z)&lt;/code&gt; with size &lt;code&gt;Vx1&lt;/code&gt;, where the highest probability denotes the one-hot representation of the target word in vocabulary.
&lt;code&gt;V&lt;/code&gt; denotes size of vocabulary and &lt;code&gt;N&lt;/code&gt; denotes size of embedding vector.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Source : &lt;a href="http://cs224d.stanford.edu/lecture_notes/LectureNotes1.pdf" rel="nofollow"&gt;http://cs224d.stanford.edu/lecture_notes/LectureNotes1.pdf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/code'':154C,159C,168C,171C,175C,182C,188C,192C,200C,205C,216C,221C,240C,248C ''/lecture_notes/lecturenotes1.pdf'':262C ''/lecture_notes/lecturenotes1.pdf&lt;/a&gt;&lt;/p&gt;'':267C ''/li'':141C,160C,183C,206C,254C ''/ol'':255C ''/p'':34C,86C,113C,130C ''answer'':116C ''averag'':143C ''back'':65C ''back-propag'':64C ''basic'':88C ''bit'':123C ''cbow'':7C,15C ''code'':152C,157C,166C,169C,173C,180C,186C,190C,198C,203C,212C,219C,238C,246C ''complex'':124C ''comput'':208C ''context'':50C,96C,139C ''cs224d.stanford.edu'':261C,266C ''cs224d.stanford.edu/lecture_notes/lecturenotes1.pdf'':260C ''cs224d.stanford.edu/lecture_notes/lecturenotes1.pdf&lt;/a&gt;&lt;/p&gt;'':265C ''current'':83C ''data'':112C ''denot'':226C,241C,249C ''depend'':46C ''develop'':39C ''differ'':76C ''embed'':252C ''equat'':102C ''error'':75C ''find'':146C ''fundament'':36C ''gensim'':174C ''given'':30C,95C,111C ''goe'':20C ''gram'':11C ''h'':153C,191C ''help'':58C ''hidden'':61C,149C ''highest'':224C ''hot'':230C ''href'':259C ''input'':40C,49C ''layer'':62C,150C ''let'':12C ''li'':132C,142C,161C,184C,207C ''linear'':128C ''matric'':44C ''matrix'':85C,108C,165C ''model'':6C,16C,38C ''multipli'':185C ''n'':247C ''nofollow'':264C ''nx1'':158C ''obtain'':104C,133C,162C ''ol'':131C ''one'':229C ''one-hot'':228C ''optim'':106C ''output'':42C,53C,79C,84C,164C ''p'':1C,35C,87C,114C,256C ''part'':119C ''predict'':26C,78C,90C ''probabl'':210C,225C ''propag'':66C ''question'':19C ''rel'':263C ''represent'':231C ''result'':194C ''second'':118C ''seem'':121C ''size'':156C,179C,202C,218C,242C,250C ''skip'':10C ''skip-gram'':9C ''softmax'':214C ''sourc'':257C ''speak'':89C ''sum'':129C ''surround'':32C ''syn1'':167C,187C ''take'':14C ''target'':28C,54C,92C,234C ''thus'':63C ''two'':5C ''updat'':70C ''upon'':47C ''use'':68C,99C ''v'':239C ''vector'':80C,137C,151C,195C,211C,253C ''vocabulari'':237C,244C ''vx1'':204C,220C ''vxn'':181C ''way'':24C ''weight'':43C,72C,107C ''word'':29C,33C,51C,55C,93C,97C,136C,140C,235C ''word2vec'':2C ''word2vec.c'':170C ''work'':3C ''y'':213C ''z'':199C,215C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10418, 2, '2016-02-27 00:30:13.717', 0, NULL, NULL, '&lt;p&gt;Check whether the classifier offers class_weight=''balanced'' or ''auto'' option . I had better results as when down or up-sampled. Best classifier for me turned out to be SGD from sklearn. Auto-sklearn shows even better results.&lt;/p&gt;
', NULL, NULL, '''/p'':42C ''auto'':11C,36C ''auto-sklearn'':35C ''balanc'':9C ''best'':24C ''better'':15C,40C ''check'':2C ''class'':7C ''classifi'':5C,25C ''even'':39C ''offer'':6C ''option'':12C ''p'':1C ''result'':16C,41C ''sampl'':23C ''sgd'':32C ''show'':38C ''sklearn'':34C,37C ''turn'':28C ''up-sampl'':21C ''weight'':8C ''whether'':3C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10419, 2, '2016-02-27 00:41:11.417', 2, NULL, NULL, '&lt;p&gt;Go for R. SAS is a monster and is not fun. Having said that your market value will be determined more by what you can and want to do and the general education level than just by this or that technology.&lt;/p&gt;
', NULL, NULL, '''/p'':43C ''determin'':21C ''educ'':34C ''fun'':12C ''general'':33C ''go'':2C ''level'':35C ''market'':17C ''monster'':8C ''p'':1C ''r'':4C ''said'':14C ''sas'':5C ''technolog'':42C ''valu'':18C ''want'':28C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10432, 1, '2016-02-27 19:14:08.533', 5, 81, 'How are Hyperplane Heatmaps created and how should they be interpreted?', '&lt;p&gt;For nonlinear data, when we are using Support Vector Machines, we can use kernels such as Gaussian RBF, Polynomial, etc to achieve linearity in a different (potentially unknown to us) feature space and &lt;em&gt;the algorithm learns a maximal separating hyperplane in that feature space&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;My question is how do we create heatmaps such as the one seen in the image below to show this max. separating hyperplane in our original space and how should it be interpreted?&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/tZYnK.png"&gt;&lt;img src="http://i.stack.imgur.com/tZYnK.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'machine-learning  python  svm  supervised-learning  discriminant-analysis', NULL, '''/a'':122C ''/em'':69C ''/p'':70C,105C,123C ''/tzynk.png'':111C,116C ''achiev'':45C ''algorithm'':59C ''alt'':117C ''analysi'':22B ''creat'':5A,78C ''data'':26C ''descript'':120C ''differ'':49C ''discrimin'':21B ''discriminant-analysi'':20B ''em'':57C ''enter'':118C ''etc'':43C ''featur'':54C,67C ''gaussian'':40C ''heatmap'':4A,79C ''href'':108C ''hyperplan'':3A,64C,94C ''i.stack.imgur.com'':110C,115C ''i.stack.imgur.com/tzynk.png'':109C,114C ''imag'':87C,119C ''img'':112C ''interpret'':11A,104C ''kernel'':37C ''learn'':14B,19B,60C ''linear'':46C ''machin'':13B,33C ''machine-learn'':12B ''max'':92C ''maxim'':62C ''nonlinear'':25C ''one'':83C ''origin'':97C ''p'':23C,71C,106C ''polynomi'':42C ''potenti'':50C ''python'':15B ''question'':73C ''rbf'':41C ''seen'':84C ''separ'':63C,93C ''show'':90C ''space'':55C,68C,98C ''src'':113C ''supervis'':18B ''supervised-learn'':17B ''support'':31C ''svm'':16B ''unknown'':51C ''us'':53C ''use'':30C,36C ''vector'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10420, 2, '2016-02-27 03:16:34.32', 0, NULL, NULL, '&lt;p&gt;This is my answer just using matplotlib and numpy.     &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;My code seem to be very lengthy comparing with the accepted answer.
  Hence, if someone can help me to improve it, that will be great!&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Code here: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;## Reading the data
year = np.arange(1994,2015,1)
type = ["Recalled", "Recovered"]
value_1 = [11.472, 11.81, 10.632, 13.857, 13.861, 13.375, 11.278, 12.827, 12.687, 10.859, np.nan, 11.782, 12.089,\
       14.351, 14.921, 11.759, 12.987, 13.262, 9.98, 10.626, 12.199]
value_2 = [10.207, 10.326, 10.094, 12.944, 12.588, 11.951, np.nan, np.nan, np.nan, np.nan, np.nan, 11.047, 10.194,\
       13.401, 13.886, 10.815, 11.482, 10.73, 9.52, 9.591, 10.27] 


## Changing data into ndarray format
dpoints = np.array([type[0], year[0], value_1[0]])
for i in range(1,len(year),1):
    dpoints = np.vstack([dpoints,np.array([type[0],year[i],value_1[i]])])
for i in range(0,len(year),1):
    dpoints = np.vstack([dpoints,np.array([type[1],year[i],value_2[i]])])

# Plot it!
fig = plt.figure(figsize=(16,5))
ax = plt.subplot()
#the space between each set of bars
space = 0.2
n = len(conditions)
width = (1 - space) / (len(conditions))

# Create a set of bars at each position
for i,cond in enumerate(conditions):
    indeces = range(1, len(categories)+1)
    vals = dpoints[dpoints[:,0] == cond][:,2].astype(np.float)
    pos = [j - (1 - space) / 2. + i * width for j in indeces]

    ax.bar(pos, vals, width=width, label =cond,lw = 0,color=  ["blue","r"][i],alpha = 0.6)


    # Set the x-axis tick labels to be equal to the categories
    ax.set_xticks(indeces)
    ax.set_xticklabels(categories)
    ax.set_xlim(0,22)
    plt.setp(plt.xticks()[1], rotation=0,fontsize = 12)
    ax.set_ylabel(''Variables'',fontsize =15)
    ax.set_xlabel("Year",fontsize =15)


    # Add a legend
    handles, labels = ax.get_legend_handles_labels()
    ax.legend(handles[::-1], labels[::-1], loc=''upper left'',frameon =False,fontsize =12)    
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Figure here    &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i13.tietuku.com/ce9f48c529559f79.png" alt=""&gt;&lt;/p&gt;
', NULL, NULL, '''+1'':201C ''-1'':287C,289C ''/blockquote'':40C ''/ce9f48c529559f79.png'':308C ''/code'':297C ''/p'':11C,39C,44C,302C,310C ''/pre'':298C ''0'':112C,114C,117C,131C,141C,205C,229C,257C,263C ''0.2'':173C ''0.6'':235C ''1'':54C,59C,116C,122C,125C,135C,144C,150C,178C,198C,212C,261C ''10.094'':85C ''10.194'':95C ''10.207'':83C ''10.27'':103C ''10.326'':84C ''10.626'':79C ''10.632'':62C ''10.73'':100C ''10.815'':98C ''10.859'':69C ''11.047'':94C ''11.278'':66C ''11.472'':60C ''11.482'':99C ''11.759'':75C ''11.782'':71C ''11.81'':61C ''11.951'':88C ''12'':265C,296C ''12.089'':72C ''12.199'':80C ''12.588'':87C ''12.687'':68C ''12.827'':67C ''12.944'':86C ''12.987'':76C ''13.262'':77C ''13.375'':65C ''13.401'':96C ''13.857'':63C ''13.861'':64C ''13.886'':97C ''14.351'':73C ''14.921'':74C ''15'':270C,275C ''16'':161C ''1994'':52C ''2'':82C,154C,207C,214C ''2015'':53C ''22'':258C ''5'':162C ''9.52'':101C ''9.591'':102C ''9.98'':78C ''accept'':24C ''add'':276C ''alpha'':234C ''alt'':309C ''answer'':5C,25C ''astyp'':208C ''ax'':163C ''ax.bar'':221C ''ax.get'':281C ''ax.legend'':285C ''ax.set'':249C,252C,255C,266C,271C ''axi'':240C ''bar'':171C,186C ''blockquot'':12C ''blue'':231C ''categori'':200C,248C,254C ''chang'':104C ''code'':15C,42C,46C ''color'':230C ''compar'':21C ''cond'':192C,206C,227C ''condit'':176C,181C,195C ''creat'':182C ''data'':49C,105C ''dpoint'':109C,126C,128C,145C,147C,203C,204C ''enumer'':194C ''equal'':245C ''fals'':294C ''fig'':158C ''figsiz'':160C ''figur'':300C ''fontsiz'':264C,269C,274C,295C ''format'':108C ''frameon'':293C ''great'':38C ''handl'':279C,283C,286C ''help'':30C ''henc'':26C ''i13.tietuku.com'':307C ''i13.tietuku.com/ce9f48c529559f79.png'':306C ''img'':304C ''improv'':33C ''indec'':196C,220C,251C ''j'':211C,218C ''label'':226C,242C,280C,284C,288C ''left'':292C ''legend'':278C,282C ''len'':123C,142C,175C,180C,199C ''lengthi'':20C ''loc'':290C ''lw'':228C ''matplotlib'':8C ''n'':174C ''ndarray'':107C ''np.arange'':51C ''np.array'':110C,129C,148C ''np.float'':209C ''np.nan'':70C,89C,90C,91C,92C,93C ''np.vstack'':127C,146C ''numpi'':10C ''p'':1C,13C,41C,299C,303C ''plot'':156C ''plt.figure'':159C ''plt.setp'':259C ''plt.subplot'':164C ''plt.xticks'':260C ''pos'':210C,222C ''posit'':189C ''pre'':45C ''r'':232C ''rang'':121C,140C,197C ''read'':47C ''recal'':56C ''recov'':57C ''rotat'':262C ''seem'':16C ''set'':169C,184C,236C ''someon'':28C ''space'':166C,172C,179C,213C ''src'':305C ''tick'':241C ''type'':55C,111C,130C,149C ''upper'':291C ''use'':7C ''val'':202C,223C ''valu'':58C,81C,115C,134C,153C ''variabl'':268C ''width'':177C,216C,224C,225C ''x'':239C ''x-axi'':238C ''xlabel'':272C ''xlim'':256C ''xtick'':250C ''xticklabel'':253C ''year'':50C,113C,124C,132C,143C,151C,273C ''ylabel'':267C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10422, 2, '2016-02-27 06:52:09.12', 0, NULL, NULL, '&lt;p&gt;I would say that it strongly depends on the nature of your labels. Why they overlap? I''m use to work with fish and we are use to get several false-negative data. It''s simple, there are not enocuh fish to occupy every suitable place. In that case we usually favour presence data. However, the input variables/features could be not good for an adequate discrimination and that''s why they appear overlapped ... (We would need additional info.). You can favour one or more labels penalizing others however that''s always at the expense of accuracy. In that case SVMs with case weights may work. Likewise others will if they allow the inclusion of case weights. Good luck!&lt;/p&gt;
', NULL, NULL, '''/p'':121C ''accuraci'':98C ''addit'':79C ''adequ'':67C ''allow'':113C ''alway'':93C ''appear'':74C ''case'':51C,101C,104C,117C ''could'':61C ''data'':35C,56C ''depend'':8C ''discrimin'':68C ''enocuh'':42C ''everi'':46C ''expens'':96C ''fals'':33C ''false-neg'':32C ''favour'':54C,83C ''fish'':24C,43C ''get'':30C ''good'':64C,119C ''howev'':57C,90C ''inclus'':115C ''info'':80C ''input'':59C ''label'':14C,87C ''likewis'':108C ''luck'':120C ''m'':19C ''may'':106C ''natur'':11C ''need'':78C ''negat'':34C ''occupi'':45C ''one'':84C ''other'':89C,109C ''overlap'':17C,75C ''p'':1C ''penal'':88C ''place'':48C ''presenc'':55C ''say'':4C ''sever'':31C ''simpl'':38C ''strong'':7C ''suitabl'':47C ''svms'':102C ''use'':20C,28C ''usual'':53C ''variables/features'':60C ''weight'':105C,118C ''work'':22C,107C ''would'':3C,77C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10424, 1, '2016-02-27 10:01:28.513', 1, 27, 'Tokenize sentence based on a dictionary', '&lt;p&gt;I have few key words say - RAM, Speaker, Brand, Display etc. I have made a dictionary of all possible values these keys can have. Like - RAM can have 12, 12gb, 12GB; Display can have 12inch, 12", 12 inch.&lt;/p&gt;

&lt;p&gt;I am given a title say - "Apple iPhone 5s (Space Grey, 16GB)"&lt;/p&gt;

&lt;p&gt;I have to extract tokens from these titles and map to the dictionary values and assign appropriate keyword to each token.&lt;/p&gt;

&lt;p&gt;Like for this - Brand - Apple, Color - Grey, Storage - 16GB&lt;/p&gt;

&lt;p&gt;How should i tokenize these sentences? Doing it just by space will not be enough- like in this title - "Samsung Guru Music 2 SM-B310E (White)" 2 will be one token and can map to RAM,Display anything.&lt;/p&gt;

&lt;p&gt;Will any NLP library help with this? I am using python to code and new to NLP.&lt;/p&gt;
', 'python  nlp', NULL, '''/p'':48C,62C,85C,96C,137C,156C ''12'':38C,45C,46C ''12gb'':39C,40C ''12inch'':44C ''16gb'':61C,95C ''2'':120C,125C ''5s'':58C ''anyth'':136C ''appl'':56C,91C ''appropri'':80C ''assign'':79C ''b310e'':123C ''base'':3A ''brand'':18C,90C ''code'':151C ''color'':92C ''dictionari'':6A,25C,76C ''display'':19C,41C,135C ''enough'':112C ''etc'':20C ''extract'':67C ''given'':52C ''grey'':60C,93C ''guru'':118C ''help'':143C ''inch'':47C ''iphon'':57C ''key'':13C,31C ''keyword'':81C ''librari'':142C ''like'':34C,87C,113C ''made'':23C ''map'':73C,132C ''music'':119C ''new'':153C ''nlp'':8B,141C,155C ''one'':128C ''p'':9C,49C,63C,86C,97C,138C ''possibl'':28C ''python'':7B,149C ''ram'':16C,35C,134C ''samsung'':117C ''say'':15C,55C ''sentenc'':2A,103C ''sm'':122C ''sm-b310e'':121C ''space'':59C,108C ''speaker'':17C ''storag'':94C ''titl'':54C,71C,116C ''token'':1A,68C,84C,101C,129C ''use'':148C ''valu'':29C,77C ''white'':124C ''word'':14C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10428, 2, '2016-02-27 12:50:57.053', 0, NULL, NULL, '&lt;p&gt;This seems like it might a good fit to regular expressions but lets first talk about how to restrict the search space so you don''t have to use regex. Before you even start though you need to make sure your requirements are possible - it looks like you want to match 16 to storage and 12 to screen size without requiring GB or inches at the end, which means you need to find some way to distinguish those numbers. Here''s a few ideas of how to make these restrictions: &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Storage&lt;/strong&gt;: this is very likely to be powers of 2, (8, 16, 32, 64...) so you only really need to check if the string has a power of two in it:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[storage in "iPhone 5s 16 GB" for storage in ["8", "16", "32", "64"]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;will return you &lt;code&gt;[False, True, False, False]&lt;/code&gt;, telling you that there''s a 16 in there.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Display&lt;/strong&gt;: again, these are likely to be quite restricted - as you say, 12, 13, etc. You could probably get away with something similar to my storage trick above and ignore trying to find inches.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Brand, Model&lt;/strong&gt;: These should be really simple - again, make a list of the brands you''re searching for, and then do the list comprehension above.&lt;/p&gt;

&lt;p&gt;If this doesn''t work, you can try regexes. These are more complex and usually more trouble than they''re worth - they''re hard to read, maintain and I''ve often got bitten by them. However:&lt;/p&gt;

&lt;p&gt;The regex &lt;code&gt;(?i)[0-9]+(\s|)GB&lt;/code&gt; in python will do a case insensitive (&lt;code&gt;(?i)&lt;/code&gt;) search for strings containing one of more numbers 0-9 (&lt;code&gt;[0-9]+&lt;/code&gt;), zero or more white space (&lt;code&gt;\s*&lt;/code&gt;) and then the letters GB (&lt;code&gt;GB&lt;/code&gt;). GB will be matched in upper or lower case because of the &lt;code&gt;(?i)&lt;/code&gt; option are the start. Calling&lt;/p&gt;

&lt;p&gt;&lt;code&gt;re.search(''(?i)[0-9]+\s*GB'', "Apple iPhone 5s (Space Grey, 16GB)")&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;will return a match object giving you the matched string.&lt;/p&gt;

&lt;p&gt;I find regexs quite complicated so general use a &lt;a href="http://regexr.com" rel="nofollow"&gt;helpful website&lt;/a&gt; to at least try catch bugs before I code them.&lt;/p&gt;
', NULL, NULL, '''-9'':276C,299C,302C,346C ''/a'':386C ''/code'':143C,154C,279C,289C,303C,311C,319C,334C,355C ''/p'':92C,127C,164C,202C,231C,269C,340C,356C,368C,397C ''/pre'':144C ''/strong'':96C,168C,207C ''0'':275C,298C,301C,345C ''12'':57C,180C ''13'':181C ''16'':53C,107C,134C,140C,161C ''16gb'':354C ''2'':105C ''32'':108C,141C ''5s'':133C,351C ''64'':109C,142C ''8'':106C,139C ''appl'':349C ''away'':187C ''bitten'':265C ''brand'':205C,219C ''bug'':392C ''call'':339C ''case'':285C,328C ''catch'':391C ''check'':116C ''code'':129C,149C,273C,287C,300C,309C,317C,332C,342C,395C ''complex'':245C ''complic'':374C ''comprehens'':229C ''contain'':293C ''could'':184C ''display'':167C ''distinguish'':78C ''doesn'':235C ''end'':68C ''etc'':182C ''even'':34C ''express'':12C ''fals'':150C,152C,153C ''find'':74C,200C,371C ''first'':15C ''fit'':9C ''gb'':63C,135C,278C,316C,318C,320C,348C ''general'':376C ''get'':186C ''give'':363C ''good'':8C ''got'':264C ''grey'':353C ''hard'':256C ''help'':384C ''howev'':268C ''href'':380C ''idea'':85C ''ignor'':197C ''inch'':65C,201C ''insensit'':286C ''iphon'':132C,350C ''least'':389C ''let'':14C ''letter'':315C ''like'':4C,48C,100C,172C ''list'':216C,228C ''look'':47C ''lower'':327C ''maintain'':259C ''make'':40C,89C,214C ''match'':52C,323C,361C,366C ''mean'':70C ''might'':6C ''model'':206C ''need'':38C,72C,114C ''nofollow'':383C ''number'':80C,297C ''object'':362C ''often'':263C ''one'':294C ''option'':335C ''p'':1C,93C,145C,165C,203C,232C,270C,341C,357C,369C ''possibl'':45C ''power'':103C,122C ''pre'':128C ''probabl'':185C ''python'':281C ''quit'':175C,373C ''re'':221C,252C,255C ''re.search'':343C ''read'':258C ''realli'':113C,211C ''regex'':31C,241C,272C,372C ''regexr.com'':381C ''regular'':11C ''rel'':382C ''requir'':43C,62C ''restrict'':20C,91C,176C ''return'':147C,359C ''say'':179C ''screen'':59C ''search'':22C,222C,290C ''seem'':3C ''similar'':190C ''simpl'':212C ''size'':60C ''someth'':189C ''space'':23C,308C,352C ''start'':35C,338C ''storag'':55C,95C,130C,137C,193C ''string'':119C,292C,367C ''strong'':94C,166C,204C ''sure'':41C ''talk'':16C ''tell'':155C ''though'':36C ''tri'':198C,240C,390C ''trick'':194C ''troubl'':249C ''true'':151C ''two'':124C ''upper'':325C ''use'':30C,377C ''usual'':247C ''ve'':262C ''want'':50C ''way'':76C ''websit'':385C ''white'':307C ''without'':61C ''work'':237C ''worth'':253C ''zero'':304C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10429, 1, '2016-02-27 15:34:34.027', 3, 45, 'Can xgboost (or any other algorithm) give bad results with some bad features?', '&lt;p&gt;till now I was under the impression that machine learning algorithms (gbm, random forest, xgboost etc) can handle bad features (variable) present in the data.&lt;/p&gt;

&lt;p&gt;In one of my problems, there are around 150 features and with xgboost I am getting a logloss of around 1 if I use all features. But if I remove around 10 bad features (found using some technique) I am observing a logloss of .45. That is huge improvement.&lt;/p&gt;

&lt;p&gt;My question is, can bad features really make such big differences?&lt;/p&gt;
', 'machine-learning  random-forest  gbm  xgboost', NULL, '''/p'':48C,99C,112C ''1'':70C ''10'':81C ''150'':58C ''45'':94C ''algorithm'':6A,33C ''around'':57C,69C,80C ''bad'':8A,12A,41C,82C,105C ''big'':110C ''data'':47C ''differ'':111C ''etc'':38C ''featur'':13A,42C,59C,75C,83C,106C ''forest'':19B,36C ''found'':84C ''gbm'':20B,34C ''get'':65C ''give'':7A ''handl'':40C ''huge'':97C ''impress'':29C ''improv'':98C ''learn'':16B,32C ''logloss'':67C,92C ''machin'':15B,31C ''machine-learn'':14B ''make'':108C ''observ'':90C ''one'':51C ''p'':22C,49C,100C ''present'':44C ''problem'':54C ''question'':102C ''random'':18B,35C ''random-forest'':17B ''realli'':107C ''remov'':79C ''result'':9A ''techniqu'':87C ''till'':23C ''use'':73C,85C ''variabl'':43C ''xgboost'':2A,21B,37C,62C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10430, 1, '2016-02-27 15:59:49.043', 0, 25, 'Parallel active optimization', '&lt;p&gt;I''m trying to optimize an expensive function for which I can choose sample points. The difficulty is that many function evaluations may be computed in parallel, taking varying amounts of time. I don''t know which keywords to search for to find existing literature(/implementations).&lt;/p&gt;

&lt;p&gt;So at a time, I might have already computed function values at 18 points, with 15 still being computed, and I want to start evaluating the function another point. Without the running jobs, I could make a model and find which next point might provide the most information. But now, I need to somehow tell the model that there are also 15 points for which I don''t have values, but near which I don''t want to evaluate the function.&lt;/p&gt;

&lt;p&gt;Specifically, I''m looking for something with minimal assumptions and no gradient information. But I''ll be happy if I just know the keyword(s) to search for. (I can come up with several hacks to that kind of work, but I wonder if there is any ''real'' solution).&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;em&gt;For illustration, say every column is a function evaluation point and every row is a timestep in the below ascii-figure. An &lt;code&gt;X&lt;/code&gt; means the evaluation is still being computed and a &lt;code&gt;*&lt;/code&gt; in the bottom row means it''s done. A new evaluation starts as soon as an old one terminates, with others still busy.&lt;/em&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;XXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXX
 XXXXXXXXXXX XXXXX
 XXXXXXXXXXX XXXXX
 XXXXXXXXXXX XXXXX
 XXXXXXXXXXX X  XXX X
 XXXX XXXXXX X  XXX XX
 XXXX XXXXXX X  XXX  XX
 XXXX XXXXXX X  XXX  XX
 XXXX XXXXXX X  XXX  XX
 XXXX XXXXXX X  XXX  XX
 XXXX X XXXX X  XXX  XXX
 XXXX X XXXX X  X X  XXXX
 XXXX X XXXX X  X X  XXXX
  XXX X XXXX X  X X  XXXXX
  XXX X XXXX X  X X  XXXXX
  XXX X XXXX X  X X  XXXXX
  XXX X XXXX X  X X  XXXXX
  XXX X XX X X  X X  XXXXXX
  XXX   XX X X  X X  XXXXXXX
  XXX   XX X X  X X  XXXXXXX
  XXX   XX X X  X X  XXXXXXX
  XXX   XX   X  X    X XXXXXXXX
  XXX   XX   X  X    X XXXXXXXX
  XXX   XX   X  X    X XXXXXX XX
  XXX   XX   X  X    X XXXXXX XX
  XXX   XX   X       X XXXXXX XXX
  XX    XX   X       X XXXXXX XXXX
**  ****  *** ******* *      *
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;em&gt;Each time an evaluation completes, how to choose the next point to evaluate, given several complete and several incomplete evaluations?&lt;/em&gt;&lt;/p&gt;
', 'optimization  sampling  parallel', NULL, '''/code'':217C,228C,408C ''/em'':252C,432C ''/implementations'':53C ''/p'':54C,137C,188C,253C,433C ''/pre'':409C ''15'':71C,117C ''18'':68C ''activ'':2A ''alreadi'':63C ''also'':116C ''amount'':37C ''anoth'':83C ''ascii'':212C ''ascii-figur'':211C ''assumpt'':147C ''bottom'':231C ''busi'':251C ''choos'':20C,419C ''code'':215C,227C,255C ''column'':196C ''come'':169C ''complet'':416C,427C ''comput'':32C,64C,74C,224C ''could'':90C ''difficulti'':24C ''done'':236C ''em'':191C,411C ''evalu'':29C,80C,134C,200C,220C,239C,415C,424C,431C ''everi'':195C,203C ''exist'':51C ''expens'':14C ''figur'':213C ''find'':50C,95C ''function'':15C,28C,65C,82C,136C,199C ''given'':425C ''gradient'':150C ''hack'':173C ''happi'':156C ''hr'':189C ''illustr'':193C ''incomplet'':430C ''inform'':103C,151C ''job'':88C ''keyword'':45C,162C ''kind'':176C ''know'':43C,160C ''literatur'':52C ''ll'':154C ''look'':142C ''m'':9C,141C ''make'':91C ''mani'':27C ''may'':30C ''mean'':218C,233C ''might'':61C,99C ''minim'':146C ''model'':93C,112C ''near'':127C ''need'':107C ''new'':238C ''next'':97C,421C ''old'':245C ''one'':246C ''optim'':3A,4B,12C ''other'':249C ''p'':7C,55C,138C,190C,410C ''parallel'':1A,6B,34C ''point'':22C,69C,84C,98C,118C,201C,422C ''pre'':254C ''provid'':100C ''real'':186C ''row'':204C,232C ''run'':87C ''sampl'':5B,21C ''say'':194C ''search'':47C,165C ''sever'':172C,426C,429C ''solut'':187C ''somehow'':109C ''someth'':144C ''soon'':242C ''specif'':139C ''start'':79C,240C ''still'':72C,222C,250C ''take'':35C ''tell'':110C ''termin'':247C ''time'':39C,59C,413C ''timestep'':207C ''tri'':10C ''valu'':66C,125C ''vari'':36C ''want'':77C,132C ''without'':85C ''wonder'':181C ''work'':178C ''x'':216C,265C,267C,270C,275C,280C,285C,290C,294C,296C,300C,302C,303C,304C,307C,309C,310C,311C,314C,316C,317C,318C,321C,323C,324C,325C,328C,330C,331C,332C,335C,337C,338C,339C,342C,344C,345C,346C,347C,351C,352C,353C,354C,358C,359C,360C,361C,365C,366C,367C,368C,372C,373C,374C,378C,379C,380C,384C,385C,386C,391C,392C,393C,398C,399C,404C,405C ''xx'':272C,277C,282C,287C,292C,343C,350C,357C,364C,371C,377C,383C,388C,390C,395C,397C,402C,403C ''xxx'':266C,271C,276C,281C,286C,291C,297C,298C,313C,320C,327C,334C,341C,349C,356C,363C,370C,376C,382C,389C,396C,401C ''xxxx'':268C,273C,278C,283C,288C,293C,295C,299C,301C,305C,306C,308C,312C,315C,322C,329C,336C,407C ''xxxxx'':259C,261C,263C,319C,326C,333C,340C ''xxxxxx'':269C,274C,279C,284C,289C,348C,387C,394C,400C,406C ''xxxxxxx'':355C,362C,369C ''xxxxxxxx'':375C,381C ''xxxxxxxxxxx'':258C,260C,262C,264C ''xxxxxxxxxxxxxxxx'':256C,257C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10433, 1, '2016-02-28 07:05:01.517', 0, 20, 'Problem in constructing co-occurence matrix', '&lt;p&gt;I have a basic doubt in construction of co-occurence matrix from text. Coocurence matrix X is defined as :&lt;/p&gt;

&lt;p&gt;Xij : number of times word i occurs in context of word j in a window of size w.&lt;/p&gt;

&lt;p&gt;Now is this context limited to sentence i.e do we context word of a main word in a sentence only or across the whole document?&lt;/p&gt;

&lt;p&gt;For ex:&lt;/p&gt;

&lt;p&gt;Consider the corpus :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;human interface computer
survey user computer system response time
eps user interface system
system human system eps
user response time
trees
graph trees
graph minors trees
graph minors survey
I like graph and stuff
I like trees and stuff
Sometimes I build a graph
Sometimes I build trees
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now for word graph and context window 10 do we look for only 4th last line or do we search for words across line also considering whole doc as one line?&lt;/p&gt;
', 'text-mining  preprocessing', NULL, '''/code'':140C ''/p'':33C,53C,80C,84C,89C,174C ''/pre'':141C ''10'':150C ''4th'':156C ''across'':76C,165C ''also'':167C ''basic'':16C ''build'':133C,138C ''co'':5A,22C ''co-occur'':4A,21C ''code'':91C ''comput'':94C,97C ''consid'':86C,168C ''construct'':3A,19C ''context'':43C,58C,65C,148C ''coocur'':27C ''corpus'':88C ''defin'':31C ''doc'':170C ''document'':79C ''doubt'':17C ''ep'':101C,108C ''ex'':83C ''graph'':113C,115C,118C,123C,135C,146C ''human'':92C,106C ''i.e'':62C ''interfac'':93C,103C ''j'':46C ''last'':157C ''like'':122C,127C ''limit'':59C ''line'':158C,166C,173C ''look'':153C ''main'':69C ''matrix'':7A,24C,28C ''mine'':10B ''minor'':116C,119C ''number'':36C ''occur'':6A,23C,41C ''one'':172C ''p'':12C,34C,54C,81C,85C,142C ''pre'':90C ''preprocess'':11B ''problem'':1A ''respons'':99C,110C ''search'':162C ''sentenc'':61C,73C ''size'':51C ''sometim'':131C,136C ''stuff'':125C,130C ''survey'':95C,120C ''system'':98C,104C,105C,107C ''text'':9B,26C ''text-min'':8B ''time'':38C,100C,111C ''tree'':112C,114C,117C,128C,139C ''user'':96C,102C,109C ''w'':52C ''whole'':78C,169C ''window'':49C,149C ''word'':39C,45C,66C,70C,145C,164C ''x'':29C ''xij'':35C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10434, 2, '2016-02-28 11:21:19.167', 2, NULL, NULL, '&lt;p&gt;Whether to window only over sentences or over the whole corpus depends on the context of your problem and on the structure of your corpus. In this corpus the order of your sentences does not matter, if they were switched around it would still be the same corpus. This means that you should stick to only the lines and not see it as one line. If your corpus would be a book for example, spilling over sentences makes a lot more sense. If you are unsure, try both, it is just a hyperparameter of your model.&lt;/p&gt;
', NULL, NULL, '''/p'':98C ''around'':42C ''book'':73C ''context'':16C ''corpus'':12C,26C,29C,49C,69C ''depend'':13C ''exampl'':75C ''hyperparamet'':94C ''line'':59C,66C ''lot'':81C ''make'':79C ''matter'':37C ''mean'':51C ''model'':97C ''one'':65C ''order'':31C ''p'':1C ''problem'':19C ''see'':62C ''sens'':83C ''sentenc'':7C,34C,78C ''spill'':76C ''stick'':55C ''still'':45C ''structur'':23C ''switch'':41C ''tri'':88C ''unsur'':87C ''whether'':2C ''whole'':11C ''window'':4C ''would'':44C,70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10435, 2, '2016-02-28 11:25:05.73', 4, NULL, NULL, '&lt;p&gt;Yes it can for sure, some algorithms are more robust to this than others but doing proper feature selection is adviced. This is due to the curse of dimensionality. It''s not only the algorithm but also has a lot to do with the amount of data points you have compared to the number of features. If you have 10,000,000 data points 150 features is not an issue, if you have 400 data points 150 features is way too much. How can you learn something when there is so little to learn from? Overfitting is a real issue for almost any algorithm, and noisy features make it more easy to learn things that are not actually there.&lt;/p&gt;
', NULL, NULL, '''/p'':121C ''000'':62C,63C ''10'':61C ''150'':66C,78C ''400'':75C ''actual'':119C ''advic'':22C ''algorithm'':8C,36C,105C ''almost'':103C ''also'':38C ''amount'':46C ''compar'':52C ''curs'':28C ''data'':48C,64C,76C ''dimension'':30C ''due'':25C ''easi'':112C ''featur'':19C,57C,67C,79C,108C ''issu'':71C,101C ''learn'':87C,95C,114C ''littl'':93C ''lot'':41C ''make'':109C ''much'':83C ''noisi'':107C ''number'':55C ''other'':15C ''overfit'':97C ''p'':1C ''point'':49C,65C,77C ''proper'':18C ''real'':100C ''robust'':11C ''select'':20C ''someth'':88C ''sure'':6C ''thing'':115C ''way'':81C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10436, 1, '2016-02-28 12:26:09.147', 0, 38, 'Bug in Gradient Descent code', '&lt;p&gt;I am performing Stochastic Gradient Descent on a cost function defined as below:&lt;/p&gt;

&lt;p&gt;cost_ij = f(X_ij) * (W[i,:].dot(W1[j,:] + b[i] + b1[j] - logX_ij. X_ij) **2, X is the given matrix which is coocurence matrix of words.&lt;/p&gt;

&lt;p&gt;I have initialized W and W1 as (np.random.randn(N, M) * 0.01, and b and b1 are initialized as np.random.randn(N) * 0.01.&lt;/p&gt;

&lt;p&gt;I am updating paramters in online fashion to minimize this cost (with AdaGrad) with number of updates equal to number fo non_zeros entries in X. The code for the same is:&lt;/p&gt;

&lt;p&gt;nnz[0], nnz[1] are indices of non-zero entries in X.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for epoch in range(epochs):
    tot_cost = 0.0
    for i, j in zip(nnz[0], nnz[1]):
            if coocur_mat[i, j] &amp;gt; min_freq and i!=j:
                x = coocur_mat[i,j]
                if x &amp;lt; 100:
                    fx = (x/100) ** 0.75
                else:
                    fx = 1
                cost = np.dot(W[i, :], W1[j, :]) + b[i] + b1[j] - np.log(x)
                cost = (fx) * ((cost) ** 2)
                grad_W1j = (2 * fx * cost) * W[i,:]
                grad_Wi = (2 * fx * cost) * W1[j,:]
                grad_bi = 2 * fx * cost
                grad_b1j = 2 * fx * cost
                W[i,:] -= lr * (grad_Wi / np.sqrt(gW[i,:]))
                W1[j,:] -= lr * (grad_W1j / np.sqrt(gW1[j,:]))
                b[i] -= lr * (grad_bi / np.sqrt(gB[i]))
                b1[j] -= lr * (grad_b1j / np.sqrt(gB1[j]))
                gW[i,:] += (grad_Wi)**2
                gW1[j,:] += (grad_W1j)**2
                gB[i] += grad_bi ** 2
                gB1[j] += grad_b1j ** 2
                tot_cost += cost
    print "Cost is %f " % tot_cost
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;When I run this, the cost increases monotonically inspite of the fact that I am doing gradient descent. It even explodes sometimes to Inf. I have tried different initialization technique to paramters (different variance and all) but none seems to work. &lt;/p&gt;

&lt;p&gt;Can anyone point out where the bug is in this code? I am not able to figure it out.&lt;/p&gt;
', 'machine-learning  gradient-descent', NULL, '''/code'':277C ''/p'':26C,58C,81C,115C,130C,321C,342C ''/pre'':278C ''0'':118C,147C ''0.0'':140C ''0.01'':70C,80C ''0.75'':172C ''1'':120C,149C,175C ''100'':169C ''2'':46C,191C,194C,201C,208C,213C,252C,257C,262C,267C ''abl'':337C ''adagrad'':95C ''anyon'':324C ''b'':38C,72C,182C,232C ''b1'':40C,74C,184C,240C ''b1j'':212C,244C,266C ''bi'':207C,236C,261C ''bug'':1A,329C ''code'':5A,110C,132C,333C ''coocur'':54C,151C,162C ''cost'':21C,28C,93C,139C,176C,188C,190C,196C,203C,210C,215C,269C,270C,272C,276C,285C ''defin'':23C ''descent'':4A,11B,18C,297C ''differ'':307C,312C ''dot'':35C ''els'':173C ''entri'':106C,127C ''epoch'':134C,137C ''equal'':100C ''even'':299C ''explod'':300C ''f'':30C,274C ''fact'':291C ''fashion'':89C ''figur'':339C ''fo'':103C ''freq'':157C ''function'':22C ''fx'':170C,174C,189C,195C,202C,209C,214C ''gb'':238C,258C ''gb1'':246C,263C ''given'':50C ''grad'':192C,199C,206C,211C,219C,227C,235C,243C,250C,255C,260C,265C ''gradient'':3A,10B,17C,296C ''gradient-desc'':9B ''gt'':155C ''gw'':222C,248C ''gw1'':230C,253C ''ij'':29C,32C,43C,45C ''increas'':286C ''indic'':122C ''inf'':303C ''initi'':62C,76C,308C ''inspit'':288C ''j'':37C,41C,143C,154C,160C,165C,181C,185C,205C,225C,231C,241C,247C,254C,264C ''learn'':8B ''logx'':42C ''lr'':218C,226C,234C,242C ''lt'':168C ''m'':69C ''machin'':7B ''machine-learn'':6B ''mat'':152C,163C ''matrix'':51C,55C ''min'':156C ''minim'':91C ''monoton'':287C ''n'':68C,79C ''nnz'':117C,119C,146C,148C ''non'':104C,125C ''non-zero'':124C ''none'':317C ''np.dot'':177C ''np.log'':186C ''np.random.randn'':67C,78C ''np.sqrt'':221C,229C,237C,245C ''number'':97C,102C ''onlin'':88C ''p'':12C,27C,59C,82C,116C,279C,322C ''paramt'':86C,311C ''perform'':15C ''point'':325C ''pre'':131C ''print'':271C ''rang'':136C ''run'':282C ''seem'':318C ''sometim'':301C ''stochast'':16C ''techniqu'':309C ''tot'':138C,268C,275C ''tri'':306C ''updat'':85C,99C ''varianc'':313C ''w'':33C,63C,178C,197C,216C ''w1'':36C,65C,180C,204C,224C ''w1j'':193C,228C,256C ''wi'':200C,220C,251C ''word'':57C ''work'':320C ''x'':31C,44C,47C,108C,129C,161C,167C,187C ''x/100'':171C ''zero'':105C,126C ''zip'':145C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10437, 1, '2016-02-28 17:25:28.147', 3, 50, 'Why is Spark''s LinearRegressionWithSGD very slow locally?', '&lt;p&gt;I have been trying to run linear regression with SGD that is found in Spark mllib for some time and I am experiencing huge performance problems. All examples that I was looking have number of iterations set to 100 and say that Spark mllib can run very fast for big data.&lt;/p&gt;

&lt;p&gt;This the code I am having problems with:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    def train(data: RDD[TrainFeature], scalerModel: StandardScalerModel): LinearRegressionModel = {
    val labeledData = data map { trainFeature =&amp;gt;
      LabeledPoint(trainFeature.relevance.value, toVector(trainFeature.feature, scalerModel))
    }
    labeledData.cache()

    val algorithm = new LinearRegressionWithSGD()
    algorithm.optimizer
      .setNumIterations(10)
      .setRegParam(0.01)
      .setStepSize(0.1)

    algorithm run labeledData
  }

private def toVector(feature: Feature, scalerModel: StandardScalerModel): Vector = scalerModel transform toVector(feature)

private def toVector(feature: Feature): Vector = Vectors dense feature.coordinates.toArray
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have scaled the data first and then run the algorithm to train the model. Even when I use 10 iterations it takes around 10 minutes to train model for 70,000 entries with feature vector of size 2. And the results I get are not good at all. I start getting decent results after numberOfIterations = 1000, but that would take ages.&lt;/p&gt;

&lt;p&gt;Is it normal for linear regression with SGD to be this slow for 70,000 vectors of size 2?&lt;/p&gt;

&lt;p&gt;My JVM min and initial memory is set to 4g.
I have tried setting the following as well (devastated try): &lt;code&gt;System.setProperty("spark.executor.memory", "3g")&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;I am running this locally and since normal LinearRegression written in MatLab would finish the job very fast, I am wondering what am I doing wrong?&lt;/p&gt;

&lt;p&gt;Edit: When I look at the spark UI in jobs section, I see that it is creating way too many jobs for gradient descent. Is there a way that I could tell Spark to create very little jobs - i.e. don''t split data, run everything in a single thread? Maybe that can help me debug the problem further.&lt;/p&gt;
', 'predictive-modeling  regression  apache-spark', NULL, '''/code'':136C,248C ''/p'':68C,78C,201C,222C,249C,277C,337C ''/pre'':137C ''0.01'':109C ''0.1'':111C ''000'':170C,217C ''10'':107C,158C,163C ''100'':55C ''1000'':195C ''2'':177C,221C ''3g'':247C ''4g'':233C ''70'':169C,216C ''age'':200C ''algorithm'':102C,112C,149C ''algorithm.optimizer'':105C ''apach'':14B ''apache-spark'':13B ''around'':162C ''big'':66C ''code'':72C,80C,244C ''could'':309C ''creat'':295C,313C ''data'':67C,83C,91C,143C,321C ''debug'':333C ''decent'':191C ''def'':81C,116C,128C ''dens'':134C ''descent'':302C ''devast'':242C ''edit'':279C ''entri'':171C ''even'':154C ''everyth'':323C ''exampl'':44C ''experienc'':39C ''fast'':64C,268C ''featur'':118C,119C,126C,130C,131C,173C ''feature.coordinates.toarray'':135C ''finish'':264C ''first'':144C ''follow'':239C ''found'':29C ''get'':182C,190C ''good'':185C ''gradient'':301C ''gt'':94C ''help'':331C ''huge'':40C ''i.e'':317C ''initi'':228C ''iter'':52C,159C ''job'':266C,288C,299C,316C ''jvm'':225C ''labeleddata'':90C,114C ''labeleddata.cache'':100C ''labeledpoint'':95C ''linear'':23C,207C ''linearregress'':259C ''linearregressionmodel'':88C ''linearregressionwithsgd'':5A,104C ''littl'':315C ''local'':8A,255C ''look'':48C,282C ''mani'':298C ''map'':92C ''matlab'':262C ''mayb'':328C ''memori'':229C ''min'':226C ''minut'':164C ''mllib'':32C,60C ''model'':11B,153C,167C ''new'':103C ''normal'':205C,258C ''number'':50C ''numberofiter'':194C ''p'':16C,69C,138C,202C,223C,250C,278C ''perform'':41C ''pre'':79C ''predict'':10B ''predictive-model'':9B ''privat'':115C,127C ''problem'':42C,76C,335C ''rdd'':84C ''regress'':12B,24C,208C ''result'':180C,192C ''run'':22C,62C,113C,147C,253C,322C ''say'':57C ''scale'':141C ''scalermodel'':86C,99C,120C,123C ''section'':289C ''see'':291C ''set'':53C,231C,237C ''setnumiter'':106C ''setregparam'':108C ''setsteps'':110C ''sgd'':26C,210C ''sinc'':257C ''singl'':326C ''size'':176C,220C ''slow'':7A,214C ''spark'':3A,15B,31C,59C,285C,311C ''spark.executor.memory'':246C ''split'':320C ''standardscalermodel'':87C,121C ''start'':189C ''system.setproperty'':245C ''take'':161C,199C ''tell'':310C ''thread'':327C ''time'':35C ''tovector'':97C,117C,125C,129C ''train'':82C,151C,166C ''trainfeatur'':85C,93C ''trainfeature.feature'':98C ''trainfeature.relevance.value'':96C ''transform'':124C ''tri'':20C,236C,243C ''ui'':286C ''use'':157C ''val'':89C,101C ''vector'':122C,132C,133C,174C,218C ''way'':296C,306C ''well'':241C ''wonder'':271C ''would'':198C,263C ''written'':260C ''wrong'':276C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10497, 1, '2016-03-03 03:09:33.047', 1, 43, 'Machine Learning with fixed and variable inputs and multiple outputs', '&lt;p&gt;I''ve been doing machine learning and neural networks for a couple months, and finished a 5 months online course of it last year, but I''m struggling in an specific scenario:&lt;/p&gt;

&lt;p&gt;Let''s say we''ve got this data: &lt;strong&gt;X1&lt;/strong&gt;, &lt;strong&gt;X2&lt;/strong&gt;, &lt;strong&gt;F3&lt;/strong&gt;, &lt;strong&gt;F4&lt;/strong&gt; and &lt;strong&gt;Y1&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;X1 and X2 are possible to change.
F3 and F4 change only with time and arbitrary.
Y1 only changes as a result of X1, X2, F3 and F4 changing.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''ve collected thousands of samples ready to train, but I don''t know what type of algorithm should I use.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;This is how I want to use it:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I want to "reach" a desired target value of &lt;strong&gt;Y1&lt;/strong&gt;, setting the combination of values of &lt;strong&gt;X1&lt;/strong&gt; and &lt;strong&gt;X2&lt;/strong&gt;, respecting the existing and unchangeable values of &lt;strong&gt;F3&lt;/strong&gt; and &lt;strong&gt;F4&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;E.g.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[X1, X2] = run_algorithm(Y1, F3, F4);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And what if I also decide to include these &lt;strong&gt;X1&lt;/strong&gt; and &lt;strong&gt;X2&lt;/strong&gt; in the algorithm too, so it finds the most efficient way of doing it knowing the already existing values of &lt;strong&gt;X1&lt;/strong&gt; and &lt;strong&gt;X2&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;E.g.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[X1, X2] = run_algorithm(Y1, F3, F4, X1, X2);
&lt;/code&gt;&lt;/pre&gt;
', 'machine-learning  algorithms  regression  random-forest  linear-regression', NULL, '''/code'':113C,203C,264C ''/p'':55C,81C,137C,149C,190C,193C,249C,252C ''/pre'':114C,204C,265C ''/strong'':67C,70C,73C,76C,80C,148C,162C,171C,175C,185C,189C,217C,221C,244C,248C ''5'':39C ''algorithm'':14B,133C,199C,224C,258C ''alreadi'':238C ''also'':210C ''arbitrari'':99C ''chang'':90C,94C,102C,112C ''code'':83C,195C,254C ''collect'':118C ''combin'':165C ''coupl'':34C ''cours'':42C ''data'':64C ''decid'':211C ''desir'':156C ''e.g'':192C,251C ''effici'':231C ''exist'':178C,239C ''f3'':72C,91C,109C,184C,201C,260C ''f4'':75C,93C,111C,188C,202C,261C ''find'':228C ''finish'':37C ''fix'':4A ''forest'':18B ''got'':62C ''includ'':213C ''input'':7A ''know'':129C,236C ''last'':45C ''learn'':2A,13B,28C ''let'':57C ''linear'':20B ''linear-regress'':19B ''m'':49C ''machin'':1A,12B,27C ''machine-learn'':11B ''month'':35C,40C ''multipl'':9A ''network'':31C ''neural'':30C ''onlin'':41C ''output'':10A ''p'':22C,56C,115C,138C,150C,191C,205C,250C ''possibl'':88C ''pre'':82C,194C,253C ''random'':17B ''random-forest'':16B ''reach'':154C ''readi'':122C ''regress'':15B,21B ''respect'':176C ''result'':105C ''run'':198C,257C ''sampl'':121C ''say'':59C ''scenario'':54C ''set'':163C ''specif'':53C ''strong'':65C,68C,71C,74C,78C,139C,160C,169C,173C,183C,187C,215C,219C,242C,246C ''struggl'':50C ''target'':157C ''thousand'':119C ''time'':97C ''train'':124C ''type'':131C ''unchang'':180C ''use'':136C,146C ''valu'':158C,167C,181C,240C ''variabl'':6A ''ve'':24C,61C,117C ''want'':144C,152C ''way'':232C ''x1'':66C,84C,107C,170C,196C,216C,243C,255C,262C ''x2'':69C,86C,108C,174C,197C,220C,247C,256C,263C ''y1'':79C,100C,161C,200C,259C ''year'':46C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10438, 2, '2016-02-28 19:39:41.407', 1, NULL, NULL, '&lt;p&gt;I have read related tutorials over the past several days.&lt;/p&gt;

&lt;p&gt;There are two possible solutions&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;transfer the problem into several multi-classication problem.&lt;/li&gt;
&lt;li&gt;use algorithms that can handle multi-label problem.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;On the software side:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;extract network parameters except the last layer.&lt;/li&gt;
&lt;li&gt;use a SVM(or other methods) to handle the last layer.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Reference:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;stanford computer vision online course&lt;/li&gt;
&lt;li&gt;caffe tutorial&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':31C,42C,59C,72C,84C,88C ''/ol'':43C,73C,89C ''/p'':12C,19C,49C,76C ''algorithm'':34C ''caff'':86C ''classic'':29C ''comput'':80C ''cours'':83C ''day'':11C ''except'':55C ''extract'':52C ''handl'':37C,68C ''label'':40C ''last'':57C,70C ''layer'':58C,71C ''li'':21C,32C,51C,60C,78C,85C ''method'':66C ''multi'':28C,39C ''multi-class'':27C ''multi-label'':38C ''network'':53C ''ol'':20C,50C,77C ''onlin'':82C ''p'':1C,13C,44C,74C ''paramet'':54C ''past'':9C ''possibl'':17C ''problem'':24C,30C,41C ''read'':4C ''refer'':75C ''relat'':5C ''sever'':10C,26C ''side'':48C ''softwar'':47C ''solut'':18C ''stanford'':79C ''svm'':63C ''transfer'':22C ''tutori'':6C,87C ''two'':16C ''use'':33C,61C ''vision'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10439, 2, '2016-02-28 21:51:28.71', 1, NULL, NULL, '&lt;p&gt;You''ll need both or even more for your "career in analytics".  Start with SAS, SAS studio is intuitive and easy, once you are comfortable with the "stats" and looking for next level than is coming R in place.&lt;/p&gt;

&lt;p&gt;SAS also have free courses for intro in stat and their platform.
&lt;a href="http://support.sas.com/training/tutorial/index.html" rel="nofollow"&gt;SAS free courses&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;For long term believe me that is not about the tools/platform. You can check the free book listed bellow and most important modeling and prediction techniques listed in the book. Theoretical and practical understanding of many important methods is essential and after that you will be able to compute in any language.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www-bcf.usc.edu/~gareth/ISL/" rel="nofollow"&gt;An Introduction to Statistical Learning&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':65C,134C ''/p'':41C,66C,120C,135C ''/training/tutorial/index.html'':59C ''/~gareth/isl/'':126C ''abl'':114C ''also'':44C ''analyt'':13C ''believ'':71C ''bellow'':86C ''book'':84C,97C ''career'':11C ''check'':81C ''come'':37C ''comfort'':26C ''comput'':116C ''cours'':47C,64C ''easi'':22C ''essenti'':107C ''even'':7C ''free'':46C,63C,83C ''href'':56C,123C ''import'':89C,104C ''intro'':49C ''introduct'':130C ''intuit'':20C ''languag'':119C ''learn'':133C ''level'':34C ''list'':85C,94C ''ll'':3C ''long'':69C ''look'':31C ''mani'':103C ''method'':105C ''model'':90C ''need'':4C ''next'':33C ''nofollow'':61C,128C ''p'':1C,42C,67C,121C ''place'':40C ''platform'':54C ''practic'':100C ''predict'':92C ''r'':38C ''rel'':60C,127C ''sas'':16C,17C,43C,62C ''start'':14C ''stat'':29C,51C ''statist'':132C ''studio'':18C ''support.sas.com'':58C ''support.sas.com/training/tutorial/index.html'':57C ''techniqu'':93C ''term'':70C ''theoret'':98C ''tools/platform'':78C ''understand'':101C ''www-bcf.usc.edu'':125C ''www-bcf.usc.edu/~gareth/isl/'':124C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10440, 2, '2016-02-29 00:53:07.89', 0, NULL, NULL, '&lt;p&gt;As far as I know, Orange supports advanced annotation with a &lt;a href="http://orange-visual-programming.readthedocs.org/loading-your-data/index.html" rel="nofollow"&gt;three-line header&lt;/a&gt; in both CSV and Excel files. So if you have a discrete attribute that gets marked as string, you should annote it as &lt;code&gt;discrete&lt;/code&gt; on the second header line, or prefix the attribute name on the first line with &lt;code&gt;D#&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;When no advanced annotation is provided, it seems Orange''s heuristic treats attributes containing general strings (as opposed to numbers) as discrete only if they contain fewer than approximately $\sqrt{|data|}$ unique values.&lt;/p&gt;
', NULL, NULL, '''/a'':24C ''/code'':50C,68C ''/loading-your-data/index.html'':17C ''/p'':69C,104C ''advanc'':9C,73C ''annot'':10C,45C,74C ''approxim'':99C ''attribut'':37C,59C,83C ''code'':48C,66C ''contain'':84C,96C ''csv'':27C ''d'':67C ''data'':101C ''discret'':36C,49C,92C ''excel'':29C ''far'':3C ''fewer'':97C ''file'':30C ''first'':63C ''general'':85C ''get'':39C ''header'':23C,54C ''heurist'':81C ''href'':14C ''know'':6C ''line'':22C,55C,64C ''mark'':40C ''name'':60C ''nofollow'':19C ''number'':90C ''oppos'':88C ''orang'':7C,79C ''orange-visual-programming.readthedocs.org'':16C ''orange-visual-programming.readthedocs.org/loading-your-data/index.html'':15C ''p'':1C,70C ''prefix'':57C ''provid'':76C ''rel'':18C ''second'':53C ''seem'':78C ''sqrt'':100C ''string'':42C,86C ''support'':8C ''three'':21C ''three-lin'':20C ''treat'':82C ''uniqu'':102C ''valu'':103C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10441, 2, '2016-02-29 04:25:23.52', 1, NULL, NULL, '&lt;p&gt;@ImperativelyAblative has many good points. &lt;/p&gt;

&lt;p&gt;Adding to that, many companies, especially banks, that rooted in SAS have started to experiment with R. This is because of the cost saving and maturing enterprise support, think of RStudio Server and Cloudera. SAS is great, but it comes with a premium price tag (some may argue it''s even overpriced). &lt;/p&gt;

&lt;p&gt;I am a management consultant working at a top tier firm with specialty in data science. We serve most of the global companies and work with their executives and operating teams. Moving to open source tools, such as R, has gained the blessing of many top executives and building tractions among core analytics teams (Marketing, Modelling, etc.). The point being - the market is moving towards open source tools&lt;/p&gt;

&lt;p&gt;The other consideration, in my opinion, is the role you aspire to play in an organization. Here are some archetypes based on my experience in working with the analytics teams: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;General Business Analyst&lt;/strong&gt;: R, Interactive visualization tools (Tableau / QlikView), some SQL, and Excel / Powerpoint (of course) &lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Modeller&lt;/strong&gt;: SAS is must, R (my clients are all trying to pick this up), deep in SQL / HQL (querying on Hadoop stack), and strong domain knowledge (risk model, pricing, operation optimization, etc.)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Application Developer&lt;/strong&gt; (people who put things into production): SAS is must, Python, and automation language &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Of course, this is not an exhaustive list, but hope it provides some industry trend for your reference. There is always the superstar in any organization who knows all these stuff, could be you :) &lt;/p&gt;
', NULL, NULL, '''/li'':184C,220C,239C ''/p'':7C,61C,131C,163C,276C ''/strong'':170C,188C,225C ''/ul'':240C ''ad'':9C ''alway'':262C ''among'':113C ''analyst'':169C ''analyt'':115C,161C ''applic'':223C ''archetyp'':152C ''argu'':56C ''aspir'':143C ''autom'':237C ''bank'':15C ''base'':153C ''bless'':105C ''build'':111C ''busi'':168C ''client'':194C ''cloudera'':42C ''come'':48C ''compani'':13C,85C ''consider'':135C ''consult'':67C ''core'':114C ''cost'':31C ''could'':273C ''cours'':183C,243C ''data'':77C ''deep'':202C ''develop'':224C ''domain'':212C ''enterpris'':35C ''especi'':14C ''etc'':119C,219C ''even'':59C ''excel'':180C ''execut'':90C,109C ''exhaust'':248C ''experi'':23C,156C ''firm'':73C ''gain'':103C ''general'':167C ''global'':84C ''good'':5C ''great'':45C ''hadoop'':208C ''hope'':251C ''hql'':205C ''imperativelyabl'':2C ''industri'':255C ''interact'':172C ''know'':269C ''knowledg'':213C ''languag'':238C ''li'':165C,185C,221C ''list'':249C ''manag'':66C ''mani'':4C,12C,107C ''market'':117C,124C ''matur'':34C ''may'':55C ''model'':118C,187C,215C ''move'':94C,126C ''must'':191C,234C ''open'':96C,128C ''oper'':92C,217C ''opinion'':138C ''optim'':218C ''organ'':148C,267C ''overpr'':60C ''p'':1C,8C,62C,132C,241C ''peopl'':226C ''pick'':199C ''play'':145C ''point'':6C,121C ''powerpoint'':181C ''premium'':51C ''price'':52C,216C ''product'':231C ''provid'':253C ''put'':228C ''python'':235C ''qlikview'':176C ''queri'':206C ''r'':25C,101C,171C,192C ''refer'':259C ''risk'':214C ''role'':141C ''root'':17C ''rstudio'':39C ''sas'':19C,43C,189C,232C ''save'':32C ''scienc'':78C ''serv'':80C ''server'':40C ''sourc'':97C,129C ''specialti'':75C ''sql'':178C,204C ''stack'':209C ''start'':21C ''strong'':166C,186C,211C,222C ''stuff'':272C ''superstar'':264C ''support'':36C ''tableau'':175C ''tag'':53C ''team'':93C,116C,162C ''thing'':229C ''think'':37C ''tier'':72C ''tool'':98C,130C,174C ''top'':71C,108C ''toward'':127C ''traction'':112C ''trend'':256C ''tri'':197C ''ul'':164C ''visual'':173C ''work'':68C,87C,158C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10451, 2, '2016-02-29 18:18:58.38', 4, NULL, NULL, '&lt;p&gt;This is a tough question to answer without more information. I''m going to assume that this is for model building, but without more detail it''s hard to recommend something.&lt;/p&gt;

&lt;p&gt;However, there are some things which should &lt;strong&gt;generally&lt;/strong&gt; be known:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Population size&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;How large is the population? Does your 2TB of data comprise the total population, or is this a sample of a given timeframe? What frame of data are you looking at - is this 2 days worth of data that is only representative for a given subset of the population, or is this everything? You''ll need to know this to know what conclusions you can draw from this dataset.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Variance&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;What''s the variance of the sample? If it''s categorical data, how many unique values are there? Having a metric around this will help determine the number of samples you''ll need. If this is a low variance set, you may only need a few hundred/thousand observations.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Stratification/grouping&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Is your data grouped in a meaningful way? If so, you''ll need to factor this into your sample. Depending on what you''re doing, you''ll want a meaningful representation of the population. If the data is not grouped, but has distinct groups within it that you care about, you may need to stratify or pre-process your data.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Model and goals&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;All of this ends up coming down to what you''re trying to do. If you''re trying to classify or parse a set of unique entities, you may be better off streaming a large set of your data rather than trying to sample it. If you''re trying to classify images or customers based on behavior, you may only need a small subset depending on how these groups differ.&lt;/p&gt;
', NULL, NULL, '''/p'':33C,47C,53C,124C,129C,179C,184C,247C,254C,326C ''/strong'':44C,52C,128C,183C,253C ''2'':88C ''2tb'':62C ''answer'':8C ''around'':152C ''assum'':16C ''base'':310C ''behavior'':312C ''better'':286C ''build'':22C ''care'':234C ''categor'':141C ''classifi'':275C,306C ''come'':261C ''compris'':65C ''conclus'':117C ''custom'':309C ''data'':64C,81C,92C,142C,188C,222C,246C,294C ''dataset'':123C ''day'':89C ''depend'':205C,320C ''detail'':26C ''determin'':156C ''differ'':325C ''distinct'':228C ''draw'':120C ''end'':259C ''entiti'':282C ''everyth'':107C ''factor'':200C ''frame'':79C ''general'':43C ''given'':76C,99C ''go'':14C ''goal'':252C ''group'':189C,225C,229C,324C ''hard'':29C ''help'':155C ''howev'':35C ''hundred/thousand'':177C ''imag'':307C ''inform'':11C ''know'':112C,115C ''known'':46C ''larg'':56C,290C ''ll'':109C,162C,197C,212C ''look'':84C ''low'':168C ''m'':13C ''mani'':144C ''may'':172C,237C,284C,314C ''meaning'':192C,215C ''metric'':151C ''model'':21C,250C ''need'':110C,163C,174C,198C,238C,316C ''number'':158C ''observ'':178C ''p'':1C,34C,48C,54C,125C,130C,180C,185C,248C,255C ''pars'':277C ''popul'':50C,59C,68C,103C,219C ''pre'':243C ''pre-process'':242C ''process'':244C ''question'':6C ''rather'':295C ''re'':209C,266C,272C,303C ''recommend'':31C ''repres'':96C ''represent'':216C ''sampl'':73C,137C,160C,204C,299C ''set'':170C,279C,291C ''size'':51C ''small'':318C ''someth'':32C ''stratifi'':240C ''stratification/grouping'':182C ''stream'':288C ''strong'':42C,49C,126C,181C,249C ''subset'':100C,319C ''thing'':39C ''timefram'':77C ''total'':67C ''tough'':5C ''tri'':267C,273C,297C,304C ''uniqu'':145C,281C ''valu'':146C ''varianc'':127C,134C,169C ''want'':213C ''way'':193C ''within'':230C ''without'':9C,24C ''worth'':90C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10443, 1, '2016-02-29 10:46:16.47', -1, 10, 'unable to classify using Weka from command line', '&lt;p&gt;i am using weka 3.6.13  and trying to use a model to classify data&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;java -cp weka-stable-3.6.13.jar weka.classifiers.Evaluation weka.classifiers.trees.RandomForest -l Parking.model -t Data_features_class_ques-2.arff  &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;java.lang.Exception: training and test set are not compatible&lt;/p&gt;

&lt;p&gt;though the model works when we use the GUI through Explorer-&gt;Claasify -&gt;Supplied test set and load the arff file-&gt;right click on result list and load model-&gt; again right click -&gt; re-evaluate model on current data set...&lt;/p&gt;

&lt;p&gt;any pointers please help&lt;/p&gt;
', 'machine-learning  classification  weka', NULL, '''/blockquote'':42C ''/p'':29C,41C,52C,93C,99C ''3.6.13'':19C ''arff'':72C ''blockquot'':30C ''claasifi'':65C ''classif'':12B ''classifi'':3A,27C ''click'':75C,84C ''command'':7A ''compat'':51C ''cp'':33C ''current'':90C ''data'':28C,91C ''data_features_class_ques-2.arff'':40C ''evalu'':87C ''explor'':64C ''file'':73C ''gui'':62C ''help'':98C ''java'':32C ''java.lang.exception'':44C ''l'':37C ''learn'':11B ''line'':8A ''list'':78C ''load'':70C,80C ''machin'':10B ''machine-learn'':9B ''model'':25C,56C,81C,88C ''p'':14C,31C,43C,53C,94C ''parking.model'':38C ''pleas'':97C ''pointer'':96C ''re'':86C ''re-evalu'':85C ''result'':77C ''right'':74C,83C ''set'':48C,68C,92C ''suppli'':66C ''test'':47C,67C ''though'':54C ''train'':45C ''tri'':21C ''unabl'':1A ''use'':4A,17C,23C,60C ''weka'':5A,13B,18C ''weka-stable-3.6.13.jar'':34C ''weka.classifiers.evaluation'':35C ''weka.classifiers.trees.randomforest'':36C ''work'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10445, 2, '2016-02-29 12:28:00.573', 0, NULL, NULL, '&lt;p&gt;If a couple of words appears together many times in your corpus, e.g., ''new'', ''york'', you can use &lt;a href="https://radimrehurek.com/gensim/models/phrases.html" rel="nofollow"&gt;https://radimrehurek.com/gensim/models/phrases.html&lt;/a&gt; It works with more than 2 tokens. This way you have one token called "new_york"&lt;/p&gt;

&lt;p&gt;Also for your dict, you can use &lt;a href="https://radimrehurek.com/gensim/corpora/dictionary.html" rel="nofollow"&gt;https://radimrehurek.com/gensim/corpora/dictionary.html&lt;/a&gt; it does exactly what you are looking for.&lt;/p&gt;
', NULL, NULL, '''/gensim/corpora/dictionary.html'':59C ''/gensim/corpora/dictionary.html&lt;/a&gt;'':64C ''/gensim/models/phrases.html'':24C ''/gensim/models/phrases.html&lt;/a&gt;'':29C ''/p'':46C,73C ''2'':35C ''also'':48C ''appear'':7C ''call'':43C ''corpus'':13C ''coupl'':4C ''dict'':51C ''e.g'':14C ''exact'':67C ''href'':21C,56C ''look'':71C ''mani'':9C ''new'':15C,44C ''nofollow'':26C,61C ''one'':41C ''p'':1C,47C ''radimrehurek.com'':23C,28C,58C,63C ''radimrehurek.com/gensim/corpora/dictionary.html'':57C ''radimrehurek.com/gensim/corpora/dictionary.html&lt;/a&gt;'':62C ''radimrehurek.com/gensim/models/phrases.html'':22C ''radimrehurek.com/gensim/models/phrases.html&lt;/a&gt;'':27C ''rel'':25C,60C ''time'':10C ''togeth'':8C ''token'':36C,42C ''use'':19C,54C ''way'':38C ''word'':6C ''work'':31C ''york'':16C,45C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10447, 1, '2016-02-29 13:26:54.4', -1, 7, 'File widget not working on Mac', '&lt;p&gt;I have a simple and stupid question, but somehow my orange File widget does not really take in the data. Even I work with a data sample provided, I do not have any advance setting option, it reads the dataset but nothing else really Like even thew table data cannot connect to it case there is not really any data ingested - I keep seeing line links between the objects but i cannot make one.... It looks like it is missing a button at File?. 
Any idea? I can provide a screenshot if needed, see here: &lt;a href="https://www.dropbox.com/s/lh6lcx2xoygghx6/Screen%20Shot%202016-02-29%20at%2014.25.48.png?dl=0" rel="nofollow"&gt;https://www.dropbox.com/s/lh6lcx2xoygghx6/Screen%20Shot%202016-02-29%20at%2014.25.48.png?dl=0&lt;/a&gt;&lt;/p&gt;
', 'orange', NULL, '''/s/lh6lcx2xoygghx6/screen%20shot%202016-02-29%20at%2014.25.48.png?dl=0'':108C ''/s/lh6lcx2xoygghx6/screen%20shot%202016-02-29%20at%2014.25.48.png?dl=0&lt;/a&gt;&lt;/p&gt;'':113C ''advanc'':42C ''button'':90C ''cannot'':58C,80C ''case'':62C ''connect'':59C ''data'':28C,34C,57C,68C ''dataset'':48C ''els'':51C ''even'':29C,54C ''file'':1A,20C,92C ''href'':105C ''idea'':94C ''ingest'':69C ''keep'':71C ''like'':53C,85C ''line'':73C ''link'':74C ''look'':84C ''mac'':6A ''make'':81C ''miss'':88C ''need'':101C ''nofollow'':110C ''noth'':50C ''object'':77C ''one'':82C ''option'':44C ''orang'':7B,19C ''p'':8C ''provid'':36C,97C ''question'':15C ''read'':46C ''realli'':24C,52C,66C ''rel'':109C ''sampl'':35C ''screenshot'':99C ''see'':72C,102C ''set'':43C ''simpl'':12C ''somehow'':17C ''stupid'':14C ''tabl'':56C ''take'':25C ''thew'':55C ''widget'':2A,21C ''work'':4A,31C ''www.dropbox.com'':107C,112C ''www.dropbox.com/s/lh6lcx2xoygghx6/screen%20shot%202016-02-29%20at%2014.25.48.png?dl=0'':106C ''www.dropbox.com/s/lh6lcx2xoygghx6/screen%20shot%202016-02-29%20at%2014.25.48.png?dl=0&lt;/a&gt;&lt;/p&gt;'':111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10448, 1, '2016-02-29 14:26:42.98', 1, 15, 'Trend Decline Graph Normalization', '&lt;p&gt;I am working on a project that should predict Alarm based on the input data. I am trying to use a supervised learning algorithm. But I do not have the exact target value based on the input for feeding the learning algorithm. I am trying to generate the target value manually based on the input data (Current Target = previous input[frequency] +constant). But the plot of the target is not a  smooth curve. A sample is given below.&lt;br&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/waKuf.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/waKuf.png" alt="Sample"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In the graph given below the yellow line plots the current alarm (manually set), pink is the generated target alarm value and red is the input (frequency).&lt;/p&gt;

&lt;p&gt;As the pink plot isn''t a smooth curve, it cannot be used as a efficient target output for learning. I have also tried normalization of the graph which gives a really undesired output. &lt;br&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Question&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;What method should be used to generate the target value (There are no fixed rule for the alarm value)?&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;&lt;em&gt;How can the curve be smoothed desirably?&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Project background&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Working on Knime with a dataset of 6000 rows. &lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10449, 1, '2016-02-29 16:48:33.507', 1, 35, 'What is a "good" sample size', '&lt;p&gt;Let''s say if I have 2 TB of data, what is the best sample size to pick? I understand that there is a limit on how much RAM/processing power I have and hence I should make my sampling decision around that. But let''s say if the processing power is not a concern for me right now. What would be a good way to approach my sample size?&lt;/p&gt;
', 'data-mining  sampling', NULL, '''/p'':81C ''2'':18C ''approach'':77C ''around'':52C ''best'':25C ''concern'':65C ''data'':8B,21C ''data-min'':7B ''decis'':51C ''good'':4A,74C ''henc'':45C ''let'':12C,55C ''limit'':36C ''make'':48C ''mine'':9B ''much'':39C ''p'':11C ''pick'':29C ''power'':41C,61C ''process'':60C ''ram/processing'':40C ''right'':68C ''sampl'':5A,10B,26C,50C,79C ''say'':14C,57C ''size'':6A,27C,80C ''tb'':19C ''understand'':31C ''way'':75C ''would'':71C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10470, 2, '2016-03-02 00:18:37.36', 1, NULL, NULL, '&lt;p&gt;If you can find a way to convert python code into JavaScript, Highcharts JS might support a large-enough heatmap:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.highcharts.com/demo/heatmap-canvas" rel="nofollow"&gt;http://www.highcharts.com/demo/heatmap-canvas&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/demo/heatmap-canvas'':29C ''/demo/heatmap-canvas&lt;/a&gt;'':34C ''/p'':23C,35C ''code'':11C ''convert'':9C ''enough'':21C ''find'':5C ''heatmap'':22C ''highchart'':14C ''href'':26C ''javascript'':13C ''js'':15C ''larg'':20C ''large-enough'':19C ''might'':16C ''nofollow'':31C ''p'':1C,24C ''python'':10C ''rel'':30C ''support'':17C ''way'':7C ''www.highcharts.com'':28C,33C ''www.highcharts.com/demo/heatmap-canvas'':27C ''www.highcharts.com/demo/heatmap-canvas&lt;/a&gt;'':32C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10457, 1, '2016-03-01 02:46:31.693', 6, 45, 'Geometric Interpretation of Whether SVMs are performing well or not', '&lt;p&gt;I came across this &lt;a href="http://media.cs.tsinghua.edu.cn/~taopin/ML2005/intro2Kernel-basedLearn-TNN-2001.pdf"&gt;research paper&lt;/a&gt; which contained this figure &lt;a href="http://i.stack.imgur.com/CycSG.png"&gt;&lt;img src="http://i.stack.imgur.com/CycSG.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;which talks about the center of mass (presumably, of the training dataset''s datapoints?) and represents the solution of an SVM as polygon (or is it a point?).
I''m having trouble understanding this figure, and since it seems to provide a geometric interpretation of when an SVM performs poorly or not, I''m interested in it. Any thoughts?&lt;/p&gt;
', 'machine-learning  svm  visualization  performance  supervised-learning', NULL, '''/a'':30C,50C ''/cycsg.png'':39C,44C ''/p'':51C,112C ''/~taopin/ml2005/intro2kernel-basedlearn-tnn-2001.pdf'':27C ''across'':21C ''alt'':45C ''came'':20C ''center'':57C ''contain'':32C ''datapoint'':66C ''dataset'':64C ''descript'':48C ''enter'':46C ''figur'':34C,87C ''geometr'':1A,95C ''href'':24C,36C ''i.stack.imgur.com'':38C,43C ''i.stack.imgur.com/cycsg.png'':37C,42C ''imag'':47C ''img'':40C ''interest'':107C ''interpret'':2A,96C ''learn'':11B,17B ''m'':82C,106C ''machin'':10B ''machine-learn'':9B ''mass'':59C ''media.cs.tsinghua.edu.cn'':26C ''media.cs.tsinghua.edu.cn/~taopin/ml2005/intro2kernel-basedlearn-tnn-2001.pdf'':25C ''p'':18C,52C ''paper'':29C ''perform'':7A,14B,101C ''point'':80C ''polygon'':75C ''poor'':102C ''presum'':60C ''provid'':93C ''repres'':68C ''research'':28C ''seem'':91C ''sinc'':89C ''solut'':70C ''src'':41C ''supervis'':16B ''supervised-learn'':15B ''svm'':12B,73C,100C ''svms'':5A ''talk'':54C ''thought'':111C ''train'':63C ''troubl'':84C ''understand'':85C ''visual'':13B ''well'':8A ''whether'':4A');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10452, 2, '2016-02-29 18:21:35.043', 2, NULL, NULL, '&lt;p&gt;You need to use some function approximation scheme. In addition, experience replay would be useful for two reasons: (1) you want to keep past memories (2) you need to decorrelate the way to teach your network.&lt;/p&gt;

&lt;p&gt;Have a look at Deepmind''s DQN on ATARI games. What you are describing is basically what they have solved. The paper is in their website: &lt;a href="http://deepmind.com/dqn.html" rel="nofollow"&gt;http://deepmind.com/dqn.html&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;&lt;em&gt;Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, et al. Human-level control through deep reinforcement learning. Nature. 2015 Feb 25;518(7540):529â33.&lt;/em&gt; &lt;/p&gt;

&lt;p&gt;With respect to the network architecture, it will definitely require some experimentation. For an alternative, you can also have a look at HyperNEAT. They evolve the network topology:&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Hausknecht M, Khandelwal P, Miikkulainen R, Stone P. HyperNEAT-GGP: A HyperNEAT-based Atari general game player. In: Proceedings of the 14th annual conference on Genetic and evolutionary computation p. 217â24. Available from: &lt;a href="http://dl.acm.org/citation.cfm?id=2330195" rel="nofollow"&gt;http://dl.acm.org/citation.cfm?id=2330195&lt;/a&gt;&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;For more strategic games. Maybe you can have a look at "Giraffe: Using Deep Reinforcement Learning to Play Chess" &lt;a href="http://arxiv.org/abs/1509.01549" rel="nofollow"&gt;http://arxiv.org/abs/1509.01549&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10453, 1, '2016-02-29 18:52:54.237', 0, 22, 'Similarity of learning in doc2vec and word2vec', '&lt;p&gt;I have understood &lt;code&gt;word2vec&lt;/code&gt; algorithm from Richard Socher''s &lt;a href="http://cs224d.stanford.edu/lecture_notes/LectureNotes1.pdf" rel="nofollow"&gt;Lecture notes&lt;/a&gt;.
Basically he uses one input matrix with one-hot vectors of words from vocabulary &lt;code&gt;X1&lt;/code&gt;, one hidden layer &lt;code&gt;h1&lt;/code&gt;, Two weight matrices - both for learning the input &lt;code&gt;Wi&lt;/code&gt; and output &lt;code&gt;Wo&lt;/code&gt; of the model.&lt;/p&gt;

&lt;p&gt;For understanding &lt;code&gt;doc2vec&lt;/code&gt;, I went with the main &lt;a href="https://cs.stanford.edu/~quocle/paragraph_vector.pdf" rel="nofollow"&gt;paper&lt;/a&gt; by Thomas Mikolov. For distributed memory model, one input matrix with one-hot vectors of words from vocabulary &lt;code&gt;X2&lt;/code&gt;, one weight matrix for paragraph vectors &lt;code&gt;Wp&lt;/code&gt; and one weight matrix for word embeddings &lt;code&gt;Wi&lt;/code&gt; are used. And it has been said that stochastic gradient descent method is used via back propagation for learning. &lt;/p&gt;

&lt;p&gt;Does that mean that the hidden layer and output weight matrix are also used by not mentioned ?.&lt;/p&gt;

&lt;p&gt;In &lt;code&gt;word2vec&lt;/code&gt;, the columns from the &lt;code&gt;X1&lt;/code&gt; matrix is multiplied with &lt;code&gt;Wi&lt;/code&gt; matrix in order to determine &lt;code&gt;h1&lt;/code&gt;, If the similar condition is checked for &lt;code&gt;doc2vec&lt;/code&gt; as being said in the paper, only the weight matrix &lt;code&gt;Wp&lt;/code&gt; is mentioned but not any one-hot represented document vectors.&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10454, 2, '2016-02-29 21:09:03.92', 1, NULL, NULL, '&lt;p&gt;You might want to provide some more information here. This is either very straightforward or incredibly difficult, depending on your needs.&lt;/p&gt;

&lt;p&gt;@Brandon Loudermilk is dead on for recommending stratification, especially for something like purchase rate, prior product purchased, etc. This will get you a long ways, but there are a few places this is heavily dependent, and a lot of this boils down to experimental design:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;What are you hoping to test&lt;/li&gt;
&lt;li&gt;What defines success? A 10% improvement? A 0.001% improvement?&lt;/li&gt;
&lt;li&gt;How many observations do you expect in each group?&lt;/li&gt;
&lt;li&gt;Are you assigning users to groups on the fly, or are you designing this prior?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If you''re doing this on the fly, for instance, if your tool can split over a given variable/quartile of that variable, it should place users into A or B depending on the strata they belong to. That would cause every using coming in to have a 50/50 chance of landing in the right bucket - you should never end up with only high spenders in one bucket.&lt;/p&gt;

&lt;p&gt;That being said - if you can design this before the test, you should be able to ensure this split fairly easily by doing this prior to the test. However, you might want to look up blocking/experimental design if you have a complicated experimental setup. As a final note, remember that you cannot test for randomness - only test for signs against randomness.&lt;/p&gt;
', NULL, NULL, '''/li'':79C,90C,101C,118C ''/p'':23C,70C,188C,251C ''/ul'':119C ''0.001'':88C ''10'':85C ''50/50'':168C ''abl'':204C ''assign'':105C ''b'':150C ''belong'':156C ''blocking/experimental'':225C ''boil'':65C ''brandon'':25C ''bucket'':175C,187C ''cannot'':241C ''caus'':160C ''chanc'':169C ''come'':163C ''complic'':231C ''dead'':28C ''defin'':82C ''depend'':19C,59C,151C ''design'':69C,115C,196C,226C ''difficult'':18C ''easili'':210C ''either'':13C ''end'':179C ''ensur'':206C ''especi'':33C ''etc'':42C ''everi'':161C ''expect'':97C ''experiment'':68C,232C ''fair'':209C ''final'':236C ''fli'':111C,128C ''get'':45C ''given'':138C ''group'':100C,108C ''heavili'':58C ''high'':183C ''hope'':76C ''howev'':218C ''improv'':86C,89C ''incred'':17C ''inform'':9C ''instanc'':130C ''land'':171C ''li'':72C,80C,91C,102C ''like'':36C ''long'':48C ''look'':223C ''lot'':62C ''loudermilk'':26C ''mani'':93C ''might'':3C,220C ''need'':22C ''never'':178C ''note'':237C ''observ'':94C ''one'':186C ''p'':1C,24C,120C,189C ''place'':55C,145C ''prior'':39C,117C,214C ''product'':40C ''provid'':6C ''purchas'':37C,41C ''random'':244C,250C ''rate'':38C ''re'':123C ''recommend'':31C ''rememb'':238C ''right'':174C ''said'':192C ''setup'':233C ''sign'':248C ''someth'':35C ''spender'':184C ''split'':135C,208C ''straightforward'':15C ''strata'':154C ''stratif'':32C ''success'':83C ''test'':78C,200C,217C,242C,246C ''tool'':133C ''ul'':71C ''use'':162C ''user'':106C,146C ''variabl'':142C ''variable/quartile'':139C ''want'':4C,221C ''way'':49C ''would'':159C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10455, 1, '2016-02-29 23:32:53.733', 1, 23, 'What tools are available for semi-automated matching of dirty columnar data', '&lt;p&gt;Are there any automated or semi-automated tools for finding matching "similar" or data in two columnar data sets?  The data I''m working with was collected (and handled) by different organizations.  Some rows describe the same events and even carry the same unique identifier (about 70% of the smaller data set can be matched using the unique ids).  For the unmatched cases, there are slight differences such as typos in the unique id, date differences (+/- 1-2 days), and slight differences in value variables... 997.5 v. 1000, etc.  &lt;/p&gt;

&lt;p&gt;If, by inspection, the value variables, date, and location are close, I call it a match.  Sometimes it is even more obvious such when a one or more digits in the unique ID digit have been transposed or the wrong one has been duplicated.&lt;/p&gt;

&lt;p&gt;Thus far, I''ve been doing this by hand.  I''ve been using R for data cleaning and Excel for comparison and matching (I export comparable subsets, make a crosswalk of old-new unique IDs by hand, and re-import the crosswalk).  &lt;/p&gt;

&lt;p&gt;What I''d really like is a tool that automates this process by presenting me with information such as: "record 1 in data set A is really similar (for the variables that matter) to records 10, 20, and 25 in data set B."  Then I could quickly examine the differences, make a choice (say record 20), and then move on.  &lt;/p&gt;

&lt;p&gt;I''d like it to feel very similar to the way Ancestry.com helps you match up birth, death, marriage, and census records.  But, anything that simplifies and automates this process would be welcome. &lt;/p&gt;

&lt;p&gt;Thoughts? &lt;/p&gt;
', 'data-cleaning', NULL, '''-2'':96C ''/p'':108C,154C,200C,262C,297C,300C ''1'':95C,222C ''10'':237C ''1000'':106C ''20'':238C,257C ''25'':240C ''70'':65C ''997.5'':104C ''ancestry.com'':275C ''anyth'':287C ''autom'':8A,21C,25C,211C,291C ''avail'':4A ''b'':244C ''birth'':280C ''call'':122C ''carri'':59C ''case'':81C ''census'':284C ''choic'':254C ''clean'':16B,172C ''close'':120C ''collect'':45C ''columnar'':12A,35C ''compar'':181C ''comparison'':176C ''could'':247C ''crosswalk'':185C,199C ''d'':204C,265C ''data'':13A,15B,32C,36C,39C,69C,171C,224C,242C ''data-clean'':14B ''date'':93C,116C ''day'':97C ''death'':281C ''describ'':53C ''differ'':49C,85C,94C,100C,251C ''digit'':138C,143C ''dirti'':11A ''duplic'':153C ''etc'':107C ''even'':58C,129C ''event'':56C ''examin'':249C ''excel'':174C ''export'':180C ''far'':157C ''feel'':269C ''find'':28C ''hand'':164C,193C ''handl'':47C ''help'':276C ''id'':77C,92C,142C,191C ''identifi'':63C ''import'':197C ''inform'':218C ''inspect'':112C ''like'':206C,266C ''locat'':118C ''m'':41C ''make'':183C,252C ''marriag'':282C ''match'':9A,29C,73C,125C,178C,278C ''matter'':234C ''move'':260C ''new'':189C ''obvious'':131C ''old'':188C ''old-new'':187C ''one'':135C,150C ''organ'':50C ''p'':17C,109C,155C,201C,263C,298C ''present'':215C ''process'':213C,293C ''quick'':248C ''r'':169C ''re'':196C ''re-import'':195C ''realli'':205C,228C ''record'':221C,236C,256C,285C ''row'':52C ''say'':255C ''semi'':7A,24C ''semi-autom'':6A,23C ''set'':37C,70C,225C,243C ''similar'':30C,229C,271C ''simplifi'':289C ''slight'':84C,99C ''smaller'':68C ''sometim'':126C ''subset'':182C ''thought'':299C ''thus'':156C ''tool'':2A,26C,209C ''transpos'':146C ''two'':34C ''typo'':88C ''uniqu'':62C,76C,91C,141C,190C ''unmatch'':80C ''use'':74C,168C ''v'':105C ''valu'':102C,114C ''variabl'':103C,115C,232C ''ve'':159C,166C ''way'':274C ''welcom'':296C ''work'':42C ''would'':294C ''wrong'':149C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10543, 1, '2016-03-06 01:16:44.603', 0, 2, 'What data science tools does Khan Academy use to track student performance?', '&lt;p&gt;I''m curious as to how they track their performance in relation to a student that is not using Khan Academy and with common core standards.  Or if they attempt to predict student performance? Or how they might see if schools do better after employing their product?  (Within the scope of data science)&lt;/p&gt;
', 'machine-learning  data-mining  predictive-modeling', NULL, '''/p'':76C ''academi'':7A,43C ''attempt'':52C ''better'':65C ''common'':46C ''core'':47C ''curious'':25C ''data'':2A,17B,74C ''data-min'':16B ''employ'':67C ''khan'':6A,42C ''learn'':15B ''m'':24C ''machin'':14B ''machine-learn'':13B ''might'':60C ''mine'':18B ''model'':21B ''p'':22C ''perform'':12A,32C,56C ''predict'':20B,54C ''predictive-model'':19B ''product'':69C ''relat'':34C ''school'':63C ''scienc'':3A,75C ''scope'':72C ''see'':61C ''standard'':48C ''student'':11A,37C,55C ''tool'':4A ''track'':10A,30C ''use'':8A,41C ''within'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10456, 1, '2016-03-01 01:56:31.823', -5, 50, 'merging two video camera into one screen?', '&lt;p&gt;Hello there community im looking for a way to merge 2 live video feeds onto one screen but still keeping 2 different perspectives from individual cameras in their own windows while in right side of the left camera and the left side of the right camera join both of their feed to create one single frame so that at the end your left with 3 video feeds, far left from left cam middle feed from both cameras and the right cam having its far right side.&lt;/p&gt;

&lt;p&gt;i know it can sound confusing so here is a example 
&lt;a href="http://i.stack.imgur.com/truV6.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/truV6.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'visualization  computer-vision', NULL, '''/a'':129C ''/p'':99C,130C ''/truv6.jpg'':116C,123C ''2'':23C,33C ''3'':77C ''alt'':124C ''cam'':84C,93C ''camera'':4A,38C,50C,58C,89C ''communiti'':15C ''comput'':10B ''computer-vis'':9B ''confus'':106C ''creat'':65C ''descript'':127C ''differ'':34C ''end'':73C ''enter'':125C ''exampl'':111C ''far'':80C,96C ''feed'':26C,63C,79C,86C ''frame'':68C ''hello'':13C ''href'':113C ''i.stack.imgur.com'':115C,122C ''i.stack.imgur.com/truv6.jpg'':114C,121C ''im'':16C ''imag'':126C ''img'':119C ''individu'':37C ''join'':59C ''keep'':32C ''know'':102C ''left'':49C,53C,75C,81C,83C ''live'':24C ''look'':17C ''merg'':1A,22C ''middl'':85C ''nofollow'':118C ''one'':6A,28C,66C ''onto'':27C ''p'':12C,100C ''perspect'':35C ''rel'':117C ''right'':45C,57C,92C,97C ''screen'':7A,29C ''side'':46C,54C,98C ''singl'':67C ''sound'':105C ''src'':120C ''still'':31C ''two'':2A ''video'':3A,25C,78C ''vision'':11B ''visual'':8B ''way'':20C ''window'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10466, 1, '2016-03-01 16:44:40.45', 2, 24, 'How does Seaborn calculate error bars when using estimators other than the arithmetic mean?', '&lt;p&gt;If I create a barplot using Seaborn and specify the geometric mean or the median as the estimator, does Seaborn know to use the appropriate standard error formula to create error bars?&lt;/p&gt;
', 'python  pandas  descriptive-statistics', NULL, '''/p'':53C ''appropri'':45C ''arithmet'':13A ''bar'':6A,52C ''barplot'':25C ''calcul'':4A ''creat'':23C,50C ''descript'':18B ''descriptive-statist'':17B ''error'':5A,47C,51C ''estim'':9A,38C ''formula'':48C ''geometr'':31C ''know'':41C ''mean'':14A,32C ''median'':35C ''p'':20C ''panda'':16B ''python'':15B ''seaborn'':3A,27C,40C ''specifi'':29C ''standard'':46C ''statist'':19B ''use'':8A,26C,43C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10458, 2, '2016-03-01 05:11:56.833', 2, NULL, NULL, '&lt;p&gt;This problem is called &lt;em&gt;real-time video stitching&lt;/em&gt;. It''s pretty hard to do yourself if you''re not familiar with computer vision, so &lt;a href="http://www.mytechlogy.com/IT-blogs/9702/5-real-time-video-stitching-tools" rel="nofollow"&gt;here&lt;/a&gt; are some off-the-shelf tools.&lt;/p&gt;

&lt;p&gt;edit: If you are looking for a paper: &lt;a href="http://research.microsoft.com/apps/pubs/default.aspx?id=112596" rel="nofollow"&gt;Stitching videos streamed by mobile phones in real-time&lt;/a&gt;, or &lt;a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5326097" rel="nofollow"&gt;Real-time photogrammetric stitching of high resolution video on COTS hardware&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':37C,72C,93C ''/apps/pubs/default.aspx?id=112596'':59C ''/em'':12C ''/it-blogs/9702/5-real-time-video-stitching-tools'':33C ''/p'':45C,94C ''/xpls/abs_all.jsp?arnumber=5326097'':78C ''call'':5C ''comput'':26C ''cot'':91C ''edit'':47C ''em'':6C ''familiar'':24C ''hard'':16C ''hardwar'':92C ''high'':87C ''href'':30C,56C,75C ''ieeexplore.ieee.org'':77C ''ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5326097'':76C ''look'':51C ''mobil'':66C ''nofollow'':35C,61C,80C ''off-the-shelf'':40C ''p'':1C,46C ''paper'':54C ''phone'':67C ''photogrammetr'':84C ''pretti'':15C ''problem'':3C ''re'':22C ''real'':8C,70C,82C ''real-tim'':7C,69C,81C ''rel'':34C,60C,79C ''research.microsoft.com'':58C ''research.microsoft.com/apps/pubs/default.aspx?id=112596'':57C ''resolut'':88C ''shelf'':43C ''stitch'':11C,62C,85C ''stream'':64C ''time'':9C,71C,83C ''tool'':44C ''video'':10C,63C,89C ''vision'':27C ''www.mytechlogy.com'':32C ''www.mytechlogy.com/it-blogs/9702/5-real-time-video-stitching-tools'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10459, 1, '2016-03-01 05:56:37.497', 4, 44, 'Calculation and Visualization of Correlation Matrix with Pandas', '&lt;p&gt;I have a pandas data frame with several entries, and I want to calculate the correlation between the income of some type of stores. There are a number of stores with income data, classification of area of activity (theater, cloth stores, food ...) and other data. &lt;/p&gt;

&lt;p&gt;I tried to create a new data frame and insert a column with the income of all kinds of stores that belong to the same category, and the returning data frame has only the first column filled and the rest is full of NaN''s.
The code that I tired: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;corr = pd.DataFrame()
for at in activity:
    stores.loc[stores[''Activity'']==at][''income'']
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to do so, so I can use &lt;code&gt;.corr()&lt;/code&gt; to gave the correlation matrix between the category of stores.&lt;/p&gt;

&lt;p&gt;After that, I would like to know how I can plot the matrix values (-1 to 1, since I want to use Pearson''s correlation) with matplolib.&lt;/p&gt;
', 'python  statistics  visualization  pandas', NULL, '''-1'':166C ''/code'':125C,139C ''/p'':59C,111C,150C,179C ''/pre'':126C ''1'':168C ''activ'':51C,119C,122C ''area'':49C ''belong'':82C ''calcul'':1A,27C ''categori'':86C,147C ''classif'':47C ''cloth'':53C ''code'':107C,113C,137C ''column'':72C,96C ''corr'':114C,138C ''correl'':5A,29C,143C,176C ''creat'':64C ''data'':18C,46C,58C,67C,90C ''entri'':22C ''fill'':97C ''first'':95C ''food'':55C ''frame'':19C,68C,91C ''full'':102C ''gave'':141C ''incom'':32C,45C,75C,124C ''insert'':70C ''kind'':78C ''know'':158C ''like'':156C ''matplolib'':178C ''matrix'':6A,144C,164C ''nan'':104C ''new'':66C ''number'':41C ''p'':13C,60C,127C,151C ''panda'':8A,12B,17C ''pd.dataframe'':115C ''pearson'':174C ''plot'':162C ''pre'':112C ''python'':9B ''rest'':100C ''return'':89C ''sever'':21C ''sinc'':169C ''statist'':10B ''store'':37C,43C,54C,80C,121C,149C ''stores.loc'':120C ''theater'':52C ''tire'':110C ''tri'':62C ''type'':35C ''use'':136C,173C ''valu'':165C ''visual'':3A,11B ''want'':25C,129C,171C ''would'':155C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10460, 2, '2016-03-01 08:01:09.95', 1, NULL, NULL, '&lt;p&gt;Firstly, cohorts are used mostly for descriptive and inferential analytics, rather than for predictive analytics.&lt;/p&gt;

&lt;p&gt;Upon a close look at your dataset, I see a clear seasonal periodicity and there might be trends which can be observed. So, my advise would be to go with the time series analysis (Start with the basic seasonal naive (&lt;code&gt;snaive&lt;/code&gt; in R) algorithm). The &lt;code&gt;snaive&lt;/code&gt; line would give you a nice idea whether to go forward with time series or whether to use a simpler estimation like the regression approach.&lt;/p&gt;

&lt;p&gt;If there is seasonality and trend, then the &lt;code&gt;snaive&lt;/code&gt; fit would be more accurate than simple regression. Of course, the confidence interval would get wider and wider as you move from &lt;/p&gt;
', NULL, NULL, '''/code'':61C,68C,106C ''/p'':17C,94C,129C ''accur'':111C ''advis'':43C ''algorithm'':64C ''analysi'':52C ''analyt'':11C,16C ''approach'':93C ''basic'':56C ''clear'':29C ''close'':21C ''code'':59C,66C,104C ''cohort'':3C ''confid'':118C ''cours'':116C ''dataset'':25C ''descript'':8C ''estim'':89C ''first'':2C ''fit'':107C ''forward'':79C ''get'':121C ''give'':71C ''go'':47C,78C ''idea'':75C ''inferenti'':10C ''interv'':119C ''like'':90C ''line'':69C ''look'':22C ''might'':34C ''most'':6C ''move'':127C ''naiv'':58C ''nice'':74C ''observ'':40C ''p'':1C,18C,95C ''period'':31C ''predict'':15C ''r'':63C ''rather'':12C ''regress'':92C,114C ''season'':30C,57C,99C ''see'':27C ''seri'':51C,82C ''simpl'':113C ''simpler'':88C ''snaiv'':60C,67C,105C ''start'':53C ''time'':50C,81C ''trend'':36C,101C ''upon'':19C ''use'':5C,86C ''whether'':76C,84C ''wider'':122C,124C ''would'':44C,70C,108C,120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10461, 2, '2016-03-01 08:49:41.437', 4, NULL, NULL, '&lt;p&gt;&lt;strong&gt;I suggest some sort of play on the following:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Using the UCI Abalone data for this example...&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import matplotlib
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

# Read file into a Pandas dataframe
from pandas import DataFrame, read_csv
f = ''https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data''
df = read_csv(f)
df=df[0:10]
df
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/efITk.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/efITk.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Correlation matrix plotting function:&lt;/strong&gt; &lt;/p&gt;

&lt;p&gt;# Correlation matric plotting function &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def correlation_matrix(df):
    import numpy as np
    from matplotlib import pyplot as plt
    from matplotlib import cm as cm

    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    cmap = cm.get_cmap(''jet'', 30)
    cax = ax1.imshow(df.corr(), interpolation="nearest", cmap=cmap)
    ax1.grid(True)
    plt.title(''Abalone Feature Correlation'')
    labels=[''Sex'',''Length'',''Diam'',''Height'',''Whole'',''Shucked'',''Viscera'',''Shell'',''Rings'',]
    ax1.set_xticklabels(labels,fontsize=6)
    ax1.set_yticklabels(labels,fontsize=6)
    # Add colorbar, make sure to specify tick locations to match desired ticklabels
    cbar = fig.colorbar(cax, ticks=[.75,.8,.85,.90,.95,1])
    plt.show()

correlation_matrix(df)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/YZsF8.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/YZsF8.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/a'':83C,211C ''/code'':63C,191C ''/efitk.png'':70C,77C ''/ml/machine-learning-databases/abalone/abalone.data'''''':53C ''/p'':13C,23C,84C,92C,98C,212C,217C ''/pre'':64C,192C ''/strong'':12C,91C ''/yzsf8.png'':198C,205C ''0'':60C ''1'':186C ''10'':61C ''111'':126C ''30'':131C ''6'':159C,164C ''75'':181C ''8'':182C ''85'':183C ''90'':184C ''95'':185C ''abalon'':18C,142C ''add'':165C ''alt'':78C,206C ''archive.ics.uci.edu'':52C ''archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data'''''':51C ''ax1'':123C ''ax1.grid'':139C ''ax1.imshow'':133C ''ax1.set'':155C,160C ''cax'':132C,179C ''cbar'':177C ''cm'':118C,120C ''cm.get'':128C ''cmap'':127C,129C,137C,138C ''code'':25C,100C ''colorbar'':166C ''correl'':87C,94C,102C,144C,188C ''csv'':49C,56C ''data'':19C ''datafram'':43C,47C ''def'':101C ''descript'':81C,209C ''desir'':175C ''df'':54C,58C,59C,62C,104C,190C ''df.corr'':134C ''diam'':148C ''enter'':79C,207C ''exampl'':22C ''f'':50C,57C ''featur'':143C ''fig'':121C ''fig.add'':124C ''fig.colorbar'':178C ''file'':39C ''follow'':11C ''fontsiz'':158C,163C ''function'':90C,97C ''height'':149C ''help'':216C ''hope'':214C ''href'':67C,195C ''i.stack.imgur.com'':69C,76C,197C,204C ''i.stack.imgur.com/efitk.png'':68C,75C ''i.stack.imgur.com/yzsf8.png'':196C,203C ''imag'':80C,208C ''img'':73C,201C ''import'':26C,28C,32C,46C,105C,111C,117C ''inlin'':37C ''interpol'':135C ''jet'':130C ''label'':145C,157C,162C ''length'':147C ''locat'':172C ''make'':167C ''match'':174C ''matplotlib'':27C,36C,110C,116C ''matplotlib.pyplot'':33C ''matric'':95C ''matrix'':88C,103C,189C ''nearest'':136C ''nofollow'':72C,200C ''np'':31C,108C ''numpi'':29C,106C ''p'':1C,14C,65C,85C,93C,193C,213C ''panda'':42C,45C ''play'':8C ''plot'':89C,96C ''plt'':35C,114C ''plt.figure'':122C ''plt.show'':187C ''plt.title'':141C ''pre'':24C,99C ''pyplot'':112C ''read'':38C,48C,55C ''rel'':71C,199C ''ring'':154C ''sex'':146C ''shell'':153C ''shuck'':151C ''sort'':6C ''specifi'':170C ''src'':74C,202C ''strong'':2C,86C ''subplot'':125C ''suggest'':4C ''sure'':168C ''tick'':171C,180C ''ticklabel'':176C ''true'':140C ''uci'':17C ''use'':15C ''viscera'':152C ''whole'':150C ''xticklabel'':156C ''yticklabel'':161C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10462, 1, '2016-03-01 15:15:25.953', 1, 36, 'What is a visual bag of words and how is it implemented?', '&lt;p&gt;I''m currently working on implementing a bag of visual words in Python. I get the general gist of how it works but I can''t seem to find any sources that explain it in more detail to a level where I can implement it. I''m guessing scikit learn and scikit image would come in but I can''t seem to point myself in the right direction. Any help?&lt;/p&gt;
', 'python  classification  scikit', NULL, '''/p'':87C ''bag'':5A,24C ''classif'':14B ''come'':71C ''current'':19C ''detail'':53C ''direct'':84C ''explain'':49C ''find'':45C ''general'':33C ''get'':31C ''gist'':34C ''guess'':64C ''help'':86C ''imag'':69C ''implement'':12A,22C,60C ''learn'':66C ''level'':56C ''m'':18C,63C ''p'':16C ''point'':79C ''python'':13B,29C ''right'':83C ''scikit'':15B,65C,68C ''seem'':43C,77C ''sourc'':47C ''visual'':4A,26C ''word'':7A,27C ''work'':20C,38C ''would'':70C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10463, 1, '2016-03-01 15:59:41.713', 1, 39, 'Skewed binomial data for small p', '&lt;p&gt;Say you have a binomial distribution with $p$ very small ($\approx 0.001$). &lt;/p&gt;

&lt;p&gt;You are asked to predict the conditional success rate $SR=S/T$ with $S$ successes out of $T$ trials given a set of conditions $X$.&lt;/p&gt;

&lt;p&gt;One would expect (correct me if I''m wrong, though I ran simulations and am quite confident) $SR$ to have a downward with increasing $T$ when $T$ is still small, and to approach $p$ when $T$ is large.&lt;/p&gt;

&lt;p&gt;The distribution of trials and successes is not uniform across $X$, so the train set tends to give higher $SR$ to conditions with smaller $T$ values.&lt;/p&gt;

&lt;p&gt;How would you treat this data skew when constructing a model?&lt;/p&gt;
', 'predictive-modeling  feature-engineering', NULL, '''/p'':26C,52C,92C,120C,133C ''0.001'':25C ''across'':103C ''approach'':86C ''approx'':24C ''ask'':30C ''binomi'':2A,18C ''condit'':34C,50C,115C ''confid'':70C ''construct'':130C ''correct'':57C ''data'':3A,127C ''distribut'':19C,95C ''downward'':75C ''engin'':12B ''expect'':56C ''featur'':11B ''feature-engin'':10B ''give'':111C ''given'':46C ''higher'':112C ''increas'':77C ''larg'':91C ''m'':61C ''model'':9B,132C ''one'':54C ''p'':6A,13C,21C,27C,53C,87C,93C,121C ''predict'':8B,32C ''predictive-model'':7B ''quit'':69C ''ran'':65C ''rate'':36C ''s/t'':38C ''say'':14C ''set'':48C,108C ''simul'':66C ''skew'':1A,128C ''small'':5A,23C,83C ''smaller'':117C ''sr'':37C,71C,113C ''still'':82C ''success'':35C,41C,99C ''tend'':109C ''though'':63C ''train'':107C ''treat'':125C ''trial'':45C,97C ''uniform'':102C ''valu'':119C ''would'':55C,123C ''wrong'':62C ''x'':51C,104C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10464, 2, '2016-03-01 16:15:42.543', 0, NULL, NULL, '&lt;p&gt;When fitting a GLM (at least in R), I know there is a optional weight vector that you can include.  This weight is not to give more importance to an observation, but to rather weight observations based on $T$ for example.&lt;/p&gt;

&lt;p&gt;The R documentation says:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; For a binomial GLM prior weights are used to give the number of trials
 when the response is the proportion of successes
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So I believe this would help adjust for your non-uniformity.  How to choose those weights can be tricky I suppose and can vary a lot with your data, but its worth looking into if it looks like it will help your problem.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/D7TPT.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/D7TPT.jpg" alt="Simulation of problem"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Do you have enough data where you can play with dropping data, or maybe if you simulate how much the $SR$ is inflated at low $T$ you can try to adjust for it somehow.  It looks like after about 1000 trials the high success rate issue begins to mellow out, and after 3000 or so you start getting more reliable measures.&lt;/p&gt;
', NULL, NULL, '''/a'':139C ''/code'':74C ''/d7tpt.jpg'':127C,134C ''/p'':43C,49C,121C,140C,203C ''/pre'':75C ''1000'':181C ''3000'':194C ''adjust'':83C,172C ''alt'':135C ''base'':38C ''begin'':188C ''believ'':79C ''binomi'':54C ''choos'':91C ''code'':51C ''data'':106C,146C,153C ''document'':47C ''drop'':152C ''enough'':145C ''exampl'':42C ''fit'':3C ''get'':199C ''give'':27C,61C ''glm'':5C,55C ''help'':82C,118C ''high'':184C ''href'':124C ''i.stack.imgur.com'':126C,133C ''i.stack.imgur.com/d7tpt.jpg'':125C,132C ''img'':130C ''import'':29C ''includ'':21C ''inflat'':164C ''issu'':187C ''know'':11C ''least'':7C ''like'':115C,178C ''look'':110C,114C,177C ''lot'':103C ''low'':166C ''mayb'':155C ''measur'':202C ''mellow'':190C ''much'':160C ''nofollow'':129C ''non'':87C ''non-uniform'':86C ''number'':63C ''observ'':32C,37C ''option'':15C ''p'':1C,44C,76C,122C,141C ''play'':150C ''pre'':50C ''prior'':56C ''problem'':120C,138C ''proport'':71C ''r'':9C,46C ''rate'':186C ''rather'':35C ''rel'':128C ''reliabl'':201C ''respons'':68C ''say'':48C ''simul'':136C,158C ''somehow'':175C ''sr'':162C ''src'':131C ''start'':198C ''success'':73C,185C ''suppos'':98C ''tri'':170C ''trial'':65C,182C ''tricki'':96C ''uniform'':88C ''use'':59C ''vari'':101C ''vector'':17C ''weight'':16C,23C,36C,57C,93C ''worth'':109C ''would'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10465, 1, '2016-03-01 16:29:54.667', 0, 29, 'How can I access dataset from Nasa website', '&lt;p&gt;So Now I was working on my project ("Failure prediction models in FoF") and the Tool I will be using is RapidMinder but I need to have a dataset to run the tool on, I found a nice description of dataset that could be helpful on Nasa website &lt;a href="https://data.nasa.gov/external-dataset?datasetId=dc5d-zf5v" rel="nofollow"&gt;https://data.nasa.gov/external-dataset?datasetId=dc5d-zf5v&lt;/a&gt; , However I do not know how to access the data itself in form of CSV file.&lt;/p&gt;
', 'dataset', NULL, '''/external-dataset?datasetid=dc5d-zf5v'':63C ''/external-dataset?datasetid=dc5d-zf5v&lt;/a&gt;'':68C ''/p'':85C ''access'':4A,76C ''could'':53C ''csv'':83C ''data'':78C ''data.nasa.gov'':62C,67C ''data.nasa.gov/external-dataset?datasetid=dc5d-zf5v'':61C ''data.nasa.gov/external-dataset?datasetid=dc5d-zf5v&lt;/a&gt;'':66C ''dataset'':5A,9B,39C,51C ''descript'':49C ''failur'':19C ''file'':84C ''fof'':23C ''form'':81C ''found'':46C ''help'':55C ''howev'':69C ''href'':60C ''know'':73C ''model'':21C ''nasa'':7A,57C ''need'':35C ''nice'':48C ''nofollow'':65C ''p'':10C ''predict'':20C ''project'':18C ''rapidmind'':32C ''rel'':64C ''run'':41C ''tool'':26C,43C ''use'':30C ''websit'':8A,58C ''work'':15C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10469, 1, '2016-03-01 21:26:04.46', 4, 42, 'How to plot large web-based heatmaps?', '&lt;p&gt;I want to plot large heatmaps (say a matrix $500 \times 500$). I can do it in Python/matplotlib.pyplot with pcolor, but it is not interactive (and I need an interactive heatmap). I have tried with D3.js but what I found is aiming at displaying small heatmaps: &lt;a href="http://bl.ocks.org/tjdecke/5558084" rel="nofollow"&gt;http://bl.ocks.org/tjdecke/5558084&lt;/a&gt;
Naively extending this example with a bigger matrix (e.g. $500 \times 500$) can crash the web-browser.&lt;/p&gt;

&lt;p&gt;So, can anyone point me toward a good way of displaying and interacting with large heatmaps with a web-based technology: I want to be able to interact on a web-page or a ipython notebook.&lt;/p&gt;
', 'visualization  javascript  ipython', NULL, '''/p'':87C,127C ''/tjdecke/5558084'':63C ''/tjdecke/5558084&lt;/a&gt;'':68C ''500'':22C,24C,78C,80C ''abl'':115C ''aim'':54C ''anyon'':91C ''base'':7A,109C ''bigger'':75C ''bl.ocks.org'':62C,67C ''bl.ocks.org/tjdecke/5558084'':61C ''bl.ocks.org/tjdecke/5558084&lt;/a&gt;'':66C ''browser'':86C ''crash'':82C ''d3.js'':48C ''display'':56C,99C ''e.g'':77C ''exampl'':72C ''extend'':70C ''found'':52C ''good'':96C ''heatmap'':8A,18C,43C,58C,104C ''href'':60C ''interact'':37C,42C,101C,117C ''ipython'':11B,125C ''javascript'':10B ''larg'':4A,17C,103C ''matrix'':21C,76C ''naiv'':69C ''need'':40C ''nofollow'':65C ''notebook'':126C ''p'':12C,88C ''page'':122C ''pcolor'':32C ''plot'':3A,16C ''point'':92C ''python/matplotlib.pyplot'':30C ''rel'':64C ''say'':19C ''small'':57C ''technolog'':110C ''time'':23C,79C ''toward'':94C ''tri'':46C ''visual'':9B ''want'':14C,112C ''way'':97C ''web'':6A,85C,108C,121C ''web-bas'':5A,107C ''web-brows'':84C ''web-pag'':120C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10471, 1, '2016-03-02 01:09:39.42', 4, 102, 'Linear regression with non-symmetric cost function?', '&lt;p&gt;I want to predict some value $Y(x)$ and I am trying to get some prediction $\hat Y(x)$ that optimizes between being as low as possible, but still being larger than $Y(x)$. In other words:
$$\text{cost}\left\{ Y(x) \gtrsim \hat Y(x) \right\}  &amp;gt;&amp;gt; \text{cost}\left\{ \hat Y(x) \gtrsim Y(x) \right\}  $$&lt;/p&gt;

&lt;p&gt;I think a simple linear regression should do totally fine.
So I somewhat know how to implement this manually, but I guess I''m not the first one with this kind of problem. Are there any packages/libraries (preferably python) out there doing what I want to do?
What''s the keyword I need to look for?&lt;/p&gt;

&lt;p&gt;What if I knew a function $Y_0(x) &amp;gt; 0$ where $Y(x) &amp;gt; Y_0(x)$. What''s the best way to implement these restrictions?&lt;/p&gt;
', 'machine-learning  logistic-regression', NULL, '''/p'':75C,133C,162C ''0'':142C,145C,151C ''best'':156C ''cost'':7A,54C,66C ''fine'':86C ''first'':103C ''function'':8A,140C ''get'':29C ''gt'':63C,64C,144C,149C ''gtrsim'':58C,71C ''guess'':98C ''hat'':32C,59C,68C ''implement'':93C,159C ''keyword'':127C ''kind'':107C ''knew'':138C ''know'':90C ''larger'':46C ''learn'':11B ''left'':55C,67C ''linear'':1A,81C ''logist'':13B ''logistic-regress'':12B ''look'':131C ''low'':40C ''m'':100C ''machin'':10B ''machine-learn'':9B ''manual'':95C ''need'':129C ''non'':5A ''non-symmetr'':4A ''one'':104C ''optim'':36C ''p'':15C,76C,134C ''packages/libraries'':113C ''possibl'':42C ''predict'':19C,31C ''prefer'':114C ''problem'':109C ''python'':115C ''regress'':2A,14B,82C ''restrict'':161C ''right'':62C,74C ''simpl'':80C ''somewhat'':89C ''still'':44C ''symmetr'':6A ''text'':53C,65C ''think'':78C ''total'':85C ''tri'':27C ''valu'':21C ''want'':17C,121C ''way'':157C ''word'':52C ''x'':23C,34C,49C,57C,61C,70C,73C,143C,148C,152C ''y'':22C,33C,48C,56C,60C,69C,72C,141C,147C,150C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10472, 2, '2016-03-02 01:48:44.923', 3, NULL, NULL, '&lt;p&gt;&lt;a href="https://plot.ly/api/" rel="nofollow"&gt;Plotly&lt;/a&gt; and &lt;a href="http://lightning-viz.org/" rel="nofollow"&gt;Lightning&lt;/a&gt; are [supposedly] able to visualize extremely large data sets.&lt;/p&gt;
', NULL, NULL, '''/a'':10C,18C ''/api/'':6C ''/p'':28C ''abl'':21C ''data'':26C ''extrem'':24C ''href'':3C,13C ''larg'':25C ''lightn'':17C ''lightning-viz.org'':14C ''nofollow'':8C,16C ''p'':1C ''plot'':9C ''plot.ly'':5C ''plot.ly/api/'':4C ''rel'':7C,15C ''set'':27C ''suppos'':20C ''visual'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10474, 2, '2016-03-02 03:13:43.577', 3, NULL, NULL, '&lt;p&gt;If I understand you correctly, you want to err on the side of overestimating. If so, you need an appropriate, asymmetric cost function. One simple candidate is to tweak the squared loss:&lt;/p&gt;

&lt;p&gt;$\mathcal L: (x,\alpha) \to x^2 \left( \mathrm{sgn} x + \alpha \right)^2$&lt;/p&gt;

&lt;p&gt;where $-1 &amp;lt; \alpha &amp;lt; 1$ is a parameter you can use to trade off the penalty of underestimation against overestimation. Positive values of $\alpha$ penalize overestimation, so you will want to set $\alpha$ negative. In python this looks like &lt;code&gt;def loss(x, a): return x**2 * (numpy.sign(x) + a)**2&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/zCSNb.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/zCSNb.png" alt="Loss functions for two values of a"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Next let''s generate some data:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy
x = numpy.arange(-10, 10, 0.1)
y = -0.1*x**2 + x + numpy.sin(x) + 0.1*numpy.random.randn(len(x))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/XNhIG.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/XNhIG.png" alt="Arbitrary function"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Finally, we will do our regression in &lt;code&gt;tensorflow&lt;/code&gt;, a machine learning library from Google that supports automated differentiation (making gradient-based optimization of such problems simpler). I will use &lt;a href="https://github.com/nlintz/TensorFlow-Tutorials/blob/master/1_linear_regression.py" rel="nofollow"&gt;this example&lt;/a&gt; as a starting point.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import tensorflow as tf

X = tf.placeholder("float") # create symbolic variables
Y = tf.placeholder("float") 

w = tf.Variable(0.0, name="coeff")
b = tf.Variable(0.0, name="offset")
y_model = tf.mul(X, w) + b

cost = tf.pow(y_model-Y, 2) # use sqr error for cost function
def acost(a): return tf.pow(y_model-Y, 2) * tf.pow(tf.sign(y_model-Y) + a, 2)

train_op = tf.train.AdamOptimizer().minimize(cost)
train_op2 = tf.train.AdamOptimizer().minimize(acost(-0.5))

sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)

for i in range(100):
    for (xi, yi) in zip(x, y): 
#         sess.run(train_op, feed_dict={X: xi, Y: yi})
        sess.run(train_op2, feed_dict={X: xi, Y: yi})

print(sess.run(w), sess.run(b))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;code&gt;cost&lt;/code&gt; is the regular squared error, while &lt;code&gt;acost&lt;/code&gt; is the aforementioned asymmetric loss function. &lt;/p&gt;

&lt;p&gt;If you use &lt;code&gt;cost&lt;/code&gt; you get&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;1.00764 -3.32445&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/5pW89.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/5pW89.png" alt="cost"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you use &lt;code&gt;acost&lt;/code&gt; you get&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;1.02604 -1.07742&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/s6IDA.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/s6IDA.png" alt="acost"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;code&gt;acost&lt;/code&gt; clearly tries not to underestimate. I did not check for convergence, but you get the idea.&lt;/p&gt;
', NULL, NULL, '''-0.1'':147C ''-0.5'':297C ''-1'':53C ''-1.07742'':409C ''-10'':143C ''-3.32445'':376C ''/5pw89.png'':384C,391C ''/a'':127C,175C,219C,394C,427C ''/blockquote'':378C,411C ''/code'':104C,157C,187C,341C,346C,355C,369C,402C,432C ''/nlintz/tensorflow-tutorials/blob/master/1_linear_regression.py'':214C ''/p'':34C,50C,105C,128C,136C,176C,224C,362C,372C,377C,395C,405C,410C,428C,449C ''/pre'':158C,342C ''/s6ida.png'':417C,424C ''/xnhig.png'':164C,171C ''/zcsnb.png'':111C,118C ''0.0'':242C,247C ''0.1'':145C,153C ''1'':57C ''1.00764'':375C ''1.02604'':408C ''10'':144C ''100'':310C ''2'':42C,49C,99C,103C,149C,262C,278C,286C ''acost'':270C,296C,354C,401C,426C,431C ''aforement'':358C ''alpha'':39C,47C,55C,76C,85C ''alt'':119C,172C,392C,425C ''appropri'':21C ''arbitrari'':173C ''asymmetr'':22C,359C ''autom'':196C ''b'':245C,255C,340C ''base'':201C ''blockquot'':373C,406C ''candid'':27C ''check'':441C ''clear'':433C ''code'':92C,138C,185C,226C,344C,353C,367C,400C,430C ''coeff'':244C ''converg'':443C ''correct'':6C ''cost'':23C,256C,267C,291C,345C,368C,393C ''creat'':234C ''data'':135C ''def'':93C,269C ''dict'':322C,331C ''differenti'':197C ''err'':10C ''error'':265C,351C ''exampl'':218C ''feed'':321C,330C ''final'':178C ''float'':233C,239C ''function'':24C,121C,174C,268C,361C ''generat'':133C ''get'':371C,404C,446C ''github.com'':213C ''github.com/nlintz/tensorflow-tutorials/blob/master/1_linear_regression.py'':212C ''googl'':193C ''gradient'':200C ''gradient-bas'':199C ''href'':108C,161C,211C,381C,414C ''i.stack.imgur.com'':110C,117C,163C,170C,383C,390C,416C,423C ''i.stack.imgur.com/5pw89.png'':382C,389C ''i.stack.imgur.com/s6ida.png'':415C,422C ''i.stack.imgur.com/xnhig.png'':162C,169C ''i.stack.imgur.com/zcsnb.png'':109C,116C ''idea'':448C ''img'':114C,167C,387C,420C ''import'':139C,227C ''init'':300C,305C ''l'':37C ''learn'':190C ''left'':43C ''len'':155C ''let'':131C ''librari'':191C ''like'':91C ''look'':90C ''loss'':33C,94C,120C,360C ''lt'':54C,56C ''machin'':189C ''make'':198C ''mathcal'':36C ''mathrm'':44C ''minim'':290C,295C ''model'':251C,260C,276C,283C ''model-i'':259C,275C,282C ''name'':243C,248C ''need'':19C ''negat'':86C ''next'':130C ''nofollow'':113C,166C,216C,386C,419C ''numpi'':140C ''numpy.arange'':142C ''numpy.random.randn'':154C ''numpy.sign'':100C ''numpy.sin'':151C ''offset'':249C ''one'':25C ''op'':288C,320C ''op2'':293C,329C ''optim'':202C ''overestim'':15C,72C,78C ''p'':1C,35C,51C,106C,129C,159C,177C,343C,363C,374C,379C,396C,407C,412C,429C ''paramet'':60C ''penal'':77C ''penalti'':68C ''point'':223C ''posit'':73C ''pre'':137C,225C ''print'':336C ''problem'':205C ''python'':88C ''rang'':309C ''regress'':183C ''regular'':349C ''rel'':112C,165C,215C,385C,418C ''return'':97C,272C ''right'':48C ''sess'':298C ''sess.run'':304C,318C,327C,337C,339C ''set'':84C ''sgn'':45C ''side'':13C ''simpl'':26C ''simpler'':206C ''sqr'':264C ''squar'':32C,350C ''src'':115C,168C,388C,421C ''start'':222C ''support'':195C ''symbol'':235C ''tensorflow'':186C,228C ''tf'':230C ''tf.initialize'':301C ''tf.mul'':252C ''tf.placeholder'':232C,238C ''tf.pow'':257C,273C,279C ''tf.session'':299C ''tf.sign'':280C ''tf.train.adamoptimizer'':289C,294C ''tf.variable'':241C,246C ''trade'':65C ''train'':287C,292C,319C,328C ''tri'':434C ''tweak'':30C ''two'':123C ''underestim'':70C,437C ''understand'':4C ''use'':63C,209C,263C,366C,399C ''valu'':74C,124C ''variabl'':236C,303C ''w'':240C,254C,338C ''want'':8C,82C ''x'':38C,41C,46C,95C,98C,101C,141C,148C,150C,152C,156C,231C,253C,316C,323C,332C ''xi'':312C,324C,333C ''y'':146C,237C,250C,258C,261C,274C,277C,281C,284C,317C,325C,334C ''yi'':313C,326C,335C ''zip'':315C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10475, 1, '2016-03-02 04:36:11.83', 1, 14, 'Fuzzy Rules with more than two variable in python', '&lt;p&gt;I am trying to build a fuzzy inference system in python using skfuzzy library. I have 4 variables depending on which output class is decided.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def fuzzInferenceself():

### Input ###
    hf_very_poor = fuzz.trimf(hotel_facility, [0, 0.15, 0.3])
    hf_poor = fuzz.trimf(hotel_facility, [2.5,0.3,0.45])
    hf_average = fuzz.trimf(hotel_facility, [0.4, 0.5, 0.75])
    hf_good = fuzz.trimf(hotel_facility, [0.7, 0.85, 0.9])
    hf_very_good = fuzz.trimf(hotel_facility, [0.875, 0.92, 1.0])
    vc_less = fuzz.trimf(visited_count, [0, 0.05, 0.1])
    vc_average = fuzz.trimf(visited_count, [0.05, 0.2, 0.35])
    vc_many = fuzz.trapmf(visited_count, [0.3,0.45,0.55,0.7])
    vc_a_lot = fuzz.trapmf(visited_count, [0.65,0.8,0.9,1.0])
    rm_very_poor = fuzz.trimf(hotel_facility, [0, 0.15, 0.3])
    rm_poor = fuzz.trimf(hotel_facility, [2.5,0.3,0.45])
    rm_average = fuzz.trimf(hotel_facility, [0.4, 0.5, 0.75])
    rm_good = fuzz.trimf(hotel_facility, [0.7, 0.8, 0.9])
    rm_very_good = fuzz.trimf(hotel_facility, [0.85, 0.9,1.0])

## output ####
    class_very_poor = fuzz.gaussmf(class_score,1,0.5)
    class_poor = fuzz.gaussmf(class_score,1.75,0.65)
    class_average = fuzz.gaussmf(class_score,2.25,0.75)
    class_good = fuzz.gaussmf(class_score,3,0.25)
    class_very_good = fuzz.gaussmf(class_score, 3.5, 0.5)


def hotelFaclilityClassification(self,A):
    hf_vp= fuzz.interp_membership(hotel_facility, hf_very_poor, A)
    hf_p= fuzz.interp_membership(hotel_facility, hf_poor, A)
    hf_av= fuzz.interp_membership(hotel_facility, hf_average, A)
    hf_gd= fuzz.interp_membership(hotel_facility, hf_good, A)
    hf_vg= fuzz.interp_membership(hotel_facility, hf_very_good, A) 
    return dict(hfVP = hf_vp, hfP = hf_p, hfAV = hf_av,hGD = hf_gd, hVG = hf_vg)

def visitCountClassification(B):
    vc_l = fuzz.interp_membership(visited_count,vc_less)
    vc_av = fuzz.interp_membership(visited_count,vc_average)
    vc_mn = fuzz.interp_membership(visited_count,vc_many)
    vc_al = fuzz.interp_membership(visited_count,vc_a_lot)
    return dict(vcL = vc_l, vcAV=vc_av, vcMN = vc_mn, vcAL = vc_al )

def roomFacilityClassification(C):
    rm_vp = fuzz.interp_membership(room_facility,rm_very_poor)
    rm_p =  fuzz.interp_membership(room_facility,rm_poor)
    rm_av = fuzz.interp_membership(room_facility,rm_average)
    rm_gd = fuzz.interp_membership(room_facility,rm_good)
    rm_vg = fuzz.interp_membership(room_facility,rm_very_good)
    return dict(rmVP = rm_vp, rmP = rm_p, rmAV = rm_av, rmGD = rm_gd, rmVG = rm_vg)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;A similar function for price is: def priceClassification(D).&lt;/p&gt;

&lt;p&gt;The rules are as follows:&lt;/p&gt;

&lt;p&gt;"If Hotel facility score is Very good, visited count is alot, room facility is very good,prices is less then class is very good."&lt;/p&gt;

&lt;p&gt;I do not understand how to code the rules. All sources I have seen takes one input and one output variable. But this is not the case in my code.&lt;/p&gt;

&lt;p&gt;Can anyone give me a good resource or idea about how to code this rule?&lt;/p&gt;
', 'python  scikit  fuzzy-logic', NULL, '''/code'':384C ''/p'':41C,396C,403C,429C,461C,478C ''/pre'':385C ''0'':53C,94C,130C ''0.05'':95C,102C ''0.1'':96C ''0.15'':54C,131C ''0.2'':103C ''0.25'':195C ''0.3'':55C,62C,110C,132C,139C ''0.35'':104C ''0.4'':69C,146C ''0.45'':63C,111C,140C ''0.5'':70C,147C,174C,203C ''0.55'':112C ''0.65'':120C,181C ''0.7'':77C,113C,154C ''0.75'':71C,148C,188C ''0.8'':121C,155C ''0.85'':78C,163C ''0.875'':86C ''0.9'':79C,122C,156C,164C ''0.92'':87C ''1'':173C ''1.0'':88C,123C,165C ''1.75'':180C ''2.25'':187C ''2.5'':61C,138C ''3'':194C ''3.5'':202C ''4'':32C ''al'':300C,321C ''alot'':415C ''anyon'':464C ''av'':228C,265C,284C,315C,343C,377C ''averag'':65C,98C,142C,183C,234C,290C,349C ''b'':274C ''build'':20C ''c'':324C ''case'':457C ''class'':38C,167C,171C,175C,178C,182C,185C,189C,192C,196C,200C,425C ''code'':43C,437C,460C,475C ''count'':93C,101C,109C,119C,280C,288C,296C,304C,413C ''d'':395C ''decid'':40C ''def'':44C,204C,272C,322C,393C ''depend'':34C ''dict'':256C,309C,368C ''facil'':52C,60C,68C,76C,85C,129C,137C,145C,153C,162C,213C,223C,232C,241C,250C,330C,339C,347C,355C,363C,407C,417C ''follow'':402C ''function'':389C ''fuzz.gaussmf'':170C,177C,184C,191C,199C ''fuzz.interp'':210C,220C,229C,238C,247C,277C,285C,293C,301C,327C,336C,344C,352C,360C ''fuzz.trapmf'':107C,117C ''fuzz.trimf'':50C,58C,66C,74C,83C,91C,99C,127C,135C,143C,151C,160C ''fuzzi'':1A,13B,22C ''fuzzinferenceself'':45C ''fuzzy-log'':12B ''gd'':237C,268C,351C,380C ''give'':465C ''good'':73C,82C,150C,159C,190C,198C,243C,253C,357C,366C,411C,420C,428C,468C ''hf'':47C,56C,64C,72C,80C,208C,214C,218C,224C,227C,233C,236C,242C,245C,251C,258C,261C,264C,267C,270C ''hfav'':263C ''hfp'':260C ''hfvp'':257C ''hgd'':266C ''hotel'':51C,59C,67C,75C,84C,128C,136C,144C,152C,161C,212C,222C,231C,240C,249C,406C ''hotelfaclilityclassif'':205C ''hvg'':269C ''idea'':471C ''infer'':23C ''input'':46C,447C ''l'':276C,312C ''less'':90C,282C,423C ''librari'':29C ''logic'':14B ''lot'':116C,307C ''mani'':106C,298C ''membership'':211C,221C,230C,239C,248C,278C,286C,294C,302C,328C,337C,345C,353C,361C ''mn'':292C,318C ''one'':446C,449C ''output'':37C,166C,450C ''p'':15C,219C,262C,335C,374C,386C,397C,404C,430C,462C ''poor'':49C,57C,126C,134C,169C,176C,216C,225C,333C,341C ''pre'':42C ''price'':391C,421C ''priceclassif'':394C ''python'':9A,10B,26C ''resourc'':469C ''return'':255C,308C,367C ''rm'':124C,133C,141C,149C,157C,325C,331C,334C,340C,342C,348C,350C,356C,358C,364C,370C,373C,376C,379C,382C ''rmav'':375C ''rmgd'':378C ''rmp'':372C ''rmvg'':381C ''rmvp'':369C ''room'':329C,338C,346C,354C,362C,416C ''roomfacilityclassif'':323C ''rule'':2A,399C,439C,477C ''scikit'':11B ''score'':172C,179C,186C,193C,201C,408C ''seen'':444C ''self'':206C ''similar'':388C ''skfuzzi'':28C ''sourc'':441C ''system'':24C ''take'':445C ''tri'':18C ''two'':6A ''understand'':434C ''use'':27C ''variabl'':7A,33C,451C ''vc'':89C,97C,105C,114C,275C,281C,283C,289C,291C,297C,299C,305C,311C,314C,317C,320C ''vcal'':319C ''vcav'':313C ''vcl'':310C ''vcmn'':316C ''vg'':246C,271C,359C,383C ''visit'':92C,100C,108C,118C,279C,287C,295C,303C,412C ''visitcountclassif'':273C ''vp'':209C,259C,326C,371C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10476, 1, '2016-03-02 06:14:13.313', 3, 32, 'how to make sklearn pipeline using custom model?', '&lt;p&gt;I want to make a sklearn pipeline using the custom Artificial Neural Network I already have. I want to make pipeline in which input goes to ANN and its output goes to the sklearn.svm.SVC model and final prediction is made.
So, how can I do this using sklearn pipeline?&lt;/p&gt;
', 'neuralnetwork  svm  sklearn', NULL, '''/p'':62C ''alreadi'':27C ''ann'':39C ''artifici'':23C ''custom'':7A,22C ''final'':49C ''goe'':37C,43C ''input'':36C ''made'':52C ''make'':3A,16C,32C ''model'':8A,47C ''network'':25C ''neural'':24C ''neuralnetwork'':9B ''output'':42C ''p'':12C ''pipelin'':5A,19C,33C,61C ''predict'':50C ''sklearn'':4A,11B,18C,60C ''sklearn.svm.svc'':46C ''svm'':10B ''use'':6A,20C,59C ''want'':14C,30C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10495, 2, '2016-03-03 01:23:15.633', 1, NULL, NULL, '&lt;p&gt;Assume your table can be put into a pandas DataFrame object &lt;b&gt;data&lt;/b&gt; with 4 columns as above. One way to achieve what you want is to do a GROUPBY using ID and Location. Then gradually assign values to each row of new table:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    newdata = pd.DataFrame(columns=[''ID'', ''Location'', ''Feat1'', ''Feat2'', ''Feat3'', ''Feat4'', ''Feat5'', ''Feat6''])
    grouped = data.groupby([''ID'', ''Location''])

    for index, (group_name, d) in enumerate(grouped):
        newdata.loc[index, ''ID''] = group_name[0]
        newdata.loc[index, ''Location''] = group_name[1]
        for feature, amount in zip(d[''Feature''], d[''amount'']):
            newdata.loc[index, feature] = amount
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/b'':15C ''/code'':98C ''/p'':47C ''/pre'':99C ''0'':78C ''1'':84C ''4'':17C ''achiev'':24C ''amount'':87C,93C,97C ''assign'':39C ''assum'':2C ''b'':13C ''code'':49C ''column'':18C,52C ''d'':69C,90C,92C ''data'':14C ''data.groupby'':62C ''datafram'':11C ''enumer'':71C ''feat1'':55C ''feat2'':56C ''feat3'':57C ''feat4'':58C ''feat5'':59C ''feat6'':60C ''featur'':86C,91C,96C ''gradual'':38C ''group'':61C,67C,72C,76C,82C ''groupbi'':32C ''id'':34C,53C,63C,75C ''index'':66C,74C,80C,95C ''locat'':36C,54C,64C,81C ''name'':68C,77C,83C ''new'':45C ''newdata'':50C ''newdata.loc'':73C,79C,94C ''object'':12C ''one'':21C ''p'':1C ''panda'':10C ''pd.dataframe'':51C ''pre'':48C ''put'':7C ''row'':43C ''tabl'':4C,46C ''use'':33C ''valu'':40C ''want'':27C ''way'':22C ''zip'':89C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10477, 2, '2016-03-02 07:59:43.067', 3, NULL, NULL, '&lt;p&gt;Implementing a custom transformer is simple. You have to implement the fit and transform methods like below. Since your ANN is already trained (right?) the fit method has to do nothing, just return self. And the transform method has to pass the incoming data to the ANN and return its output.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from sklearn.base import BaseEstimator, TransformerMixin

class MyANNTransformer(BaseEstimator, TransformerMixin):
    def __init__(self, ann):
        self.ann = ann

    def fit(self, X, y):
        return self

    def transform(self, X)
        return self.ann.predict(X)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now you can include that in pipelines:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from sklearn.pipeline import make_pipeline
from sklearn.svm import SVC

pipe = make_pipeline(MyANNTransformer(ann),
                     SVC())

pipe.fit(Xtrain, ytrain)
pipe.predict(Xtest)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':85C,118C ''/p'':53C,95C ''/pre'':86C,119C ''alreadi'':23C ''ann'':21C,48C,68C,70C,111C ''baseestim'':59C,63C ''class'':61C ''code'':55C,97C ''custom'':4C ''data'':45C ''def'':65C,71C,78C ''fit'':13C,27C,72C ''implement'':2C,11C ''import'':58C,100C,105C ''includ'':91C ''incom'':44C ''init'':66C ''like'':17C ''make'':101C,108C ''method'':16C,28C,39C ''myanntransform'':62C,110C ''noth'':32C ''output'':52C ''p'':1C,87C ''pass'':42C ''pipe'':107C ''pipe.fit'':113C ''pipe.predict'':116C ''pipelin'':94C,102C,109C ''pre'':54C,96C ''return'':34C,50C,76C,82C ''right'':25C ''self'':35C,67C,73C,77C,80C ''self.ann'':69C ''self.ann.predict'':83C ''simpl'':7C ''sinc'':19C ''sklearn.base'':57C ''sklearn.pipeline'':99C ''sklearn.svm'':104C ''svc'':106C,112C ''train'':24C ''transform'':5C,15C,38C,79C ''transformermixin'':60C,64C ''x'':74C,81C,84C ''xtest'':117C ''xtrain'':114C ''y'':75C ''ytrain'':115C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10478, 1, '2016-03-02 08:54:10.503', 1, 26, 'is there any data tidying tool for python/pandas similar to R tidyr tool?', '&lt;p&gt;I''m working on a Kaggle challenge where some variables are represented by rows instead of columns (Telstra Network Disruption). I am currently searching for the equivalent of gather(), separate() and spread(), which can be found in R tidyr tool.&lt;/p&gt;
', 'r  python  dataset  data-cleaning  pandas', NULL, '''/p'':62C ''challeng'':28C ''clean'':19B ''column'':38C ''current'':44C ''data'':4A,18B ''data-clean'':17B ''dataset'':16B ''disrupt'':41C ''equival'':48C ''found'':57C ''gather'':50C ''instead'':36C ''kaggl'':27C ''m'':23C ''network'':40C ''p'':21C ''panda'':20B ''python'':15B ''python/pandas'':8A ''r'':11A,14B,59C ''repres'':33C ''row'':35C ''search'':45C ''separ'':51C ''similar'':9A ''spread'':53C ''telstra'':39C ''tidi'':5A ''tidyr'':12A,60C ''tool'':6A,13A,61C ''variabl'':31C ''work'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10479, 1, '2016-03-02 09:27:41.087', 2, 28, 'On the properties of Hyperbolic Tangent Kernel', '&lt;p&gt;How do Hyperbolic Tangent Kernels work? That is what is the intuition behind them? Can you provide proofs and examples for illustration?&lt;/p&gt;

&lt;p&gt;Hyperbolic Tangent Kernels are defined as:
$$ K(x, x^\prime) = tanh\bigg(\alpha (x\cdot x^\prime) + c\bigg)$$&lt;/p&gt;

&lt;p&gt;For example, for Gaussian RBF kernel, the intuition is that the support vectors affect the decision surface based on the locality of influence. What is the analog for Hyperbolic Tangent (Sigmoid Kernels)?&lt;/p&gt;

&lt;p&gt;Some references on the Hyperbolic Tangent Kernels are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf" rel="nofollow"&gt;Hsuan-Tien Lin and Chih-Jen Lin&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://perso.lcpc.fr/tarel.jean-philippe/publis/jpt-icme05.pdf" rel="nofollow"&gt;Sabri Boughorbel, Jean-Philippe Tarel, Nozha Boujemaa&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  svm  supervised-learning  reference-request  definitions', NULL, '''/a'':126C,144C ''/li'':127C,145C ''/ol'':146C ''/p'':42C,63C,97C,107C ''/tarel.jean-philippe/publis/jpt-icme05.pdf'':133C ''/~cjlin/papers/tanh.pdf'':114C ''affect'':78C ''alpha'':56C ''analog'':91C ''base'':82C ''behind'':32C ''bigg'':55C,62C ''boughorbel'':137C ''boujemaa'':143C ''c'':61C ''cdot'':58C ''chih'':123C ''chih-jen'':122C ''decis'':80C ''defin'':48C ''definit'':18B ''exampl'':39C,66C ''gaussian'':68C ''href'':111C,130C ''hsuan'':118C ''hsuan-tien'':117C ''hyperbol'':5A,22C,44C,93C,103C ''illustr'':41C ''influenc'':87C ''intuit'':31C,72C ''jean'':139C ''jean-philipp'':138C ''jen'':124C ''k'':50C ''kernel'':7A,24C,46C,70C,96C,105C ''learn'':10B,14B ''li'':109C,128C ''lin'':120C,125C ''local'':85C ''machin'':9B ''machine-learn'':8B ''nofollow'':116C,135C ''nozha'':142C ''ol'':108C ''p'':19C,43C,64C,98C ''perso.lcpc.fr'':132C ''perso.lcpc.fr/tarel.jean-philippe/publis/jpt-icme05.pdf'':131C ''philipp'':140C ''prime'':53C,60C ''proof'':37C ''properti'':3A ''provid'':36C ''rbf'':69C ''refer'':16B,100C ''reference-request'':15B ''rel'':115C,134C ''request'':17B ''sabri'':136C ''sigmoid'':95C ''supervis'':13B ''supervised-learn'':12B ''support'':76C ''surfac'':81C ''svm'':11B ''tangent'':6A,23C,45C,94C,104C ''tanh'':54C ''tarel'':141C ''tien'':119C ''vector'':77C ''work'':25C ''www.csie.ntu.edu.tw'':113C ''www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf'':112C ''x'':51C,52C,57C,59C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10512, 2, '2016-03-03 21:39:07.36', 0, NULL, NULL, '&lt;p&gt;It''s quite hard to understand what your actual goal is.&lt;/p&gt;

&lt;p&gt;I understood you want to calculate optimal fruit bundles from a daily given set of fruits which may not have any fruit in common.&lt;/p&gt;

&lt;p&gt;The easiest way to go about would be to just accumulate data how often each fruit is bought and take this as a measure of value for the customer.
Then you could assume the bundling does not matter. From here you can just split up the fruit baskets by ranking the fruits according to their value and assign every odd element to basket one and every even element to basket two.&lt;/p&gt;

&lt;p&gt;If you are really sure the composition of the basket matters because there are interactions between fruits you have to do a different strategy.
If you can accumulate vast amounts of data, you could just sell every possible combination of baskets and again take the sales as a value measure.&lt;/p&gt;
', NULL, NULL, '''/p'':13C,39C,112C,164C ''accord'':93C ''accumul'':51C,141C ''actual'':10C ''amount'':143C ''assign'':98C ''assum'':73C ''basket'':88C,103C,110C,123C,154C ''bought'':58C ''bundl'':23C,75C ''calcul'':20C ''combin'':152C ''common'':38C ''composit'':120C ''could'':72C,147C ''custom'':69C ''daili'':26C ''data'':52C,145C ''differ'':136C ''easiest'':42C ''element'':101C,108C ''even'':107C ''everi'':99C,106C,150C ''fruit'':22C,30C,36C,56C,87C,92C,130C ''given'':27C ''go'':45C ''goal'':11C ''hard'':5C ''interact'':128C ''matter'':78C,124C ''may'':32C ''measur'':64C,163C ''odd'':100C ''often'':54C ''one'':104C ''optim'':21C ''p'':1C,14C,40C,113C ''possibl'':151C ''quit'':4C ''rank'':90C ''realli'':117C ''sale'':159C ''sell'':149C ''set'':28C ''split'':84C ''strategi'':137C ''sure'':118C ''take'':60C,157C ''two'':111C ''understand'':7C ''understood'':16C ''valu'':66C,96C,162C ''vast'':142C ''want'':18C ''way'':43C ''would'':47C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10480, 1, '2016-03-02 09:34:14.203', 1, 6, 'Beginner: how to model net change given an incomplete set of input/output figures', '&lt;p&gt;this is my first post on the Stack Exchange site, and I''m looking for some guidance.&lt;/p&gt;

&lt;p&gt;I have a set of 10x input numbers (positive), and a set of 10x output numbers (negative), as well as the real net change.&lt;/p&gt;

&lt;p&gt;In a perfect world, adding those twenty numbers up would be equal to the ''real net change''. Unfortunately, the set in incomplete, and every week there is an error. If it''s of any help, the error is always in the same direction (modelled change too big) - it appears that the set of output numbers is incomplete. Sometimes though, the error is non-existent.&lt;/p&gt;

&lt;p&gt;What approach would you recommend to model the real change?&lt;/p&gt;

&lt;p&gt;I used Weka for regression analysis, but it appears to me that weka is much more powerful when used with categorising problems rather than continuous data. &lt;/p&gt;

&lt;p&gt;I appreciate all comments and apologise if the problem stated is too general.&lt;/p&gt;
', 'regression', NULL, '''/p'':33C,59C,126C,138C,166C,181C ''10x'':40C,48C ''ad'':65C ''alway'':99C ''analysi'':145C ''apologis'':173C ''appear'':109C,148C ''appreci'':169C ''approach'':129C ''beginn'':1A ''big'':107C ''categoris'':160C ''chang'':6A,58C,77C,105C,137C ''comment'':171C ''continu'':164C ''data'':165C ''direct'':103C ''equal'':72C ''error'':89C,97C,121C ''everi'':84C ''exchang'':24C ''exist'':125C ''figur'':13A ''first'':19C ''general'':180C ''given'':7A ''guidanc'':32C ''help'':95C ''incomplet'':9A,82C,117C ''input'':41C ''input/output'':12A ''look'':29C ''m'':28C ''model'':4A,104C,134C ''much'':154C ''negat'':51C ''net'':5A,57C,76C ''non'':124C ''non-exist'':123C ''number'':42C,50C,68C,115C ''output'':49C,114C ''p'':15C,34C,60C,127C,139C,167C ''perfect'':63C ''posit'':43C ''post'':20C ''power'':156C ''problem'':161C,176C ''rather'':162C ''real'':56C,75C,136C ''recommend'':132C ''regress'':14B,144C ''set'':10A,38C,46C,80C,112C ''site'':25C ''sometim'':118C ''stack'':23C ''state'':177C ''though'':119C ''twenti'':67C ''unfortun'':78C ''use'':141C,158C ''week'':85C ''weka'':142C,152C ''well'':53C ''world'':64C ''would'':70C,130C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10481, 1, '2016-03-02 10:09:15.65', 2, 22, 'Adjusting predicted values based on average offsets from past predictions', '&lt;p&gt;This question is more about inference and decision making based on statistical data, so I hope I''m in the right place. If not, let me know if I should migrate this question elsewhere.&lt;/p&gt;

&lt;p&gt;I have a data set specifying the predicted shoe size of different people for various shoe models, and also the manually measured correct shoe size. &lt;/p&gt;

&lt;p&gt;For instance person #1 was predicted to have a shoe size of 8 for shoe model A, and the actual size she wears is 8.5. Each record includes the person, shoe model, predicted size and actual size. I already calculated the delta between these two values for each record, then summarized the average offset and standard deviation for all shoe models. Here''s an example of the offset data (&lt;em&gt;n&lt;/em&gt; is the sample size from the average offset and standard deviation was calculated for each model):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;+-------+----------------+-----+----+
| model | Average Offset | SD  | n  |
+-------+----------------+-----+----+
|     1 | 0.4            | 1.0 | 16 |
|     2 | -0.8           | 0.8 |  5 |
|     3 | 0.8            | 0.7 | 10 |
|     4 | 0.5            | 0.9 | 12 |
|     5 | 0.7            | 0.8 |  6 |
|     6 | 0.1            | 0.9 | 28 |
|     7 | 0.5            | 0.8 | 16 |
|     8 | 0.1            | 0.7 | 18 |
|     9 | 0.3            | 0.5 |  6 |
|    10 | 2.7            | 0.3 |  5 |
|    11 | -0.2           | 0.6 | 33 |
|    12 | 1.0            | 0.5 |  6 |
|    13 | 0.0            | 0.0 |  5 |
|    14 | -0.1           | 0.6 | 13 |
|    15 | 0.0            | 0.4 |  4 |
|    16 | -0.9           | 0.5 |  7 |
|    17 | 0.2            | 0.8 |  9 |
|    18 | -0.2           | 0.8 | 20 |
|    19 | -1.1           | 0.7 |  9 |
|    20 | -0.1           | 0.6 | 14 |
|    21 | -1.1           | 0.8 | 55 |
|    22 | -1.2           | 0.8 | 12 |
|    23 | -0.3           | 0.5 | 12 |
|    24 | -0.1           | 0.4 | 10 |
|    25 | 0.6            | 0.9 | 29 |
+-------+----------------+-----+----+
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And as a chart:
&lt;a href="http://i.stack.imgur.com/w2irU.jpg" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/w2irU.jpg" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you''re wondering why analyze this per shoe model at all, then the answer is that we know from shop experience that different shoe models have particular structures and materials that have of consistent effect on shoe size preference.&lt;/p&gt;

&lt;p&gt;Now to my actual question:
My goal is to plug average offsets back into the prediction calculation to adjust it based on previously recorded offsets.&lt;/p&gt;

&lt;p&gt;The question is - when does it make sense to use an offset value, and when that value is unusable.&lt;/p&gt;

&lt;p&gt;I tend to disregard average offsets where the SD is close to the size of the average, but mathematically, plugging in such an average to adjust all predictions for this model is likely to do more good than harm. Or does it?&lt;/p&gt;

&lt;p&gt;What would be a good approach to decide which of these averages is useful?&lt;/p&gt;

&lt;p&gt;Suggestion for additional insights or analysis techniques are welcome.&lt;/p&gt;
', 'statistics  predictive-modeling  descriptive-statistics', NULL, '''-0.1'':231C,255C,271C ''-0.2'':219C,247C ''-0.3'':267C ''-0.8'':183C ''-0.9'':239C ''-1.1'':251C,259C ''-1.2'':263C ''/a'':302C ''/code'':278C ''/em'':153C ''/p'':53C,80C,170C,303C,345C,372C,393C,437C,453C,464C ''/pre'':279C ''/w2iru.jpg'':289C,296C ''0.0'':227C,228C,235C ''0.1'':199C,207C ''0.2'':243C ''0.3'':211C,216C ''0.4'':179C,236C,272C ''0.5'':191C,203C,212C,224C,240C,268C ''0.6'':220C,232C,256C,275C ''0.7'':188C,195C,208C,252C ''0.8'':184C,187C,196C,204C,244C,248C,260C,264C ''0.9'':192C,200C,276C ''1'':85C,178C ''1.0'':180C,223C ''10'':189C,214C,273C ''11'':218C ''12'':193C,222C,265C,269C ''13'':226C,233C ''14'':230C,257C ''15'':234C ''16'':181C,205C,238C ''17'':242C ''18'':209C,246C ''19'':250C ''2'':182C ''2.7'':215C ''20'':249C,254C ''21'':258C ''22'':262C ''23'':266C ''24'':270C ''25'':274C ''28'':201C ''29'':277C ''3'':186C ''33'':221C ''4'':190C,237C ''5'':185C,194C,217C,229C ''55'':261C ''6'':197C,198C,213C,225C ''7'':202C,241C ''8'':94C,206C ''8.5'':106C ''9'':210C,245C,253C ''actual'':101C,117C,350C ''addit'':457C ''adjust'':1A,365C,420C ''alreadi'':120C ''also'':73C ''alt'':297C ''analysi'':460C ''analyz'':310C ''answer'':319C ''approach'':444C ''averag'':6A,134C,160C,174C,357C,399C,411C,418C,450C ''back'':359C ''base'':4A,28C,367C ''calcul'':121C,166C,363C ''chart'':284C ''close'':405C ''code'':172C ''consist'':339C ''correct'':77C ''data'':31C,58C,150C ''decid'':446C ''decis'':26C ''delta'':123C ''descript'':16B,300C ''descriptive-statist'':15B ''deviat'':138C,164C ''differ'':66C,328C ''disregard'':398C ''effect'':340C ''elsewher'':52C ''em'':151C ''enter'':298C ''exampl'':146C ''experi'':326C ''goal'':353C ''good'':431C,443C ''harm'':433C ''hope'':34C ''href'':286C ''i.stack.imgur.com'':288C,295C ''i.stack.imgur.com/w2iru.jpg'':287C,294C ''imag'':299C ''img'':292C ''includ'':109C ''infer'':24C ''insight'':458C ''instanc'':83C ''know'':45C,323C ''let'':43C ''like'':427C ''m'':36C ''make'':27C,380C ''manual'':75C ''materi'':335C ''mathemat'':413C ''measur'':76C ''migrat'':49C ''model'':14B,71C,97C,113C,142C,169C,173C,314C,330C,425C ''n'':152C,177C ''nofollow'':291C ''offset'':7A,135C,149C,161C,175C,358C,371C,385C,400C ''p'':18C,54C,81C,280C,304C,346C,373C,394C,438C,454C ''particular'':332C ''past'':9A ''peopl'':67C ''per'':312C ''person'':84C,111C ''place'':40C ''plug'':356C,414C ''pre'':171C ''predict'':2A,10A,13B,62C,87C,114C,362C,422C ''predictive-model'':12B ''prefer'':344C ''previous'':369C ''question'':20C,51C,351C,375C ''re'':307C ''record'':108C,130C,370C ''rel'':290C ''right'':39C ''sampl'':156C ''sd'':176C,403C ''sens'':381C ''set'':59C ''shoe'':63C,70C,78C,91C,96C,112C,141C,313C,329C,342C ''shop'':325C ''size'':64C,79C,92C,102C,115C,118C,157C,343C,408C ''specifi'':60C ''src'':293C ''standard'':137C,163C ''statist'':11B,17B,30C ''structur'':333C ''suggest'':455C ''summar'':132C ''techniqu'':461C ''tend'':396C ''two'':126C ''unus'':392C ''use'':383C,452C ''valu'':3A,127C,386C,390C ''various'':69C ''wear'':104C ''welcom'':463C ''wonder'':308C ''would'':440C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10483, 2, '2016-03-02 12:00:50.99', 2, NULL, NULL, '&lt;p&gt;Jaccard similarity is used for two types of binary cases:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Symmetric, where 1 and 0 has equal importance (gender, marital status,etc)&lt;/li&gt;
&lt;li&gt;Asymmetric, where 1 and 0 have different levels of importance (testing positive for a disease)&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Cosine similarity is usually used in the context of text mining for comparing documents or emails. If the cosine similarity between two document term vectors is higher, then both the documents have more number of words in common&lt;/p&gt;

&lt;p&gt;Another difference is 1 - Jaccard Coefficient can be used as a dissimilarity or distance measure, whereas the cosine similarity has no such constructs. A similar thing is the Tonimoto distance, which is used in taxonomy.&lt;/p&gt;
', NULL, NULL, '''/li'':27C,44C ''/ol'':45C ''/p'':12C,85C,122C ''0'':19C,33C ''1'':17C,31C,90C ''anoth'':87C ''asymmetr'':29C ''binari'':10C ''case'':11C ''coeffici'':92C ''common'':84C ''compar'':59C ''construct'':109C ''context'':54C ''cosin'':47C,65C,104C ''differ'':35C,88C ''diseas'':43C ''dissimilar'':98C ''distanc'':100C,116C ''document'':60C,69C,77C ''email'':62C ''equal'':21C ''etc'':26C ''gender'':23C ''higher'':73C ''import'':22C,38C ''jaccard'':2C,91C ''level'':36C ''li'':14C,28C ''marit'':24C ''measur'':101C ''mine'':57C ''number'':80C ''ol'':13C ''p'':1C,46C,86C ''posit'':40C ''similar'':3C,48C,66C,105C,111C ''status'':25C ''symmetr'':15C ''taxonomi'':121C ''term'':70C ''test'':39C ''text'':56C ''thing'':112C ''tonimoto'':115C ''two'':7C,68C ''type'':8C ''use'':5C,51C,95C,119C ''usual'':50C ''vector'':71C ''wherea'':102C ''word'':82C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10484, 1, '2016-03-02 12:49:00.37', 1, 33, 'heatmap color and d3.js', '&lt;p&gt;This follows a recent post where I got my answer concerning possible web-based techno for plotting big heatmaps ($600 \times 600$ dissimilarity matrix).&lt;/p&gt;

&lt;p&gt;Now, that I have achieved that, I''m struggling with setting the color scale. 
I would like some fine continuous color scale. For now I have plugged a discretization into 17 colors... not enough. With Python, pretty much is done with pyplot.pcolor.&lt;/p&gt;

&lt;p&gt;How can I obtain such a ''continuous'' color scale?&lt;/p&gt;

&lt;p&gt;Here the difference between the two heatmaps parameterized by the same matrix:&lt;/p&gt;

&lt;p&gt;Using matplotlib.pyplot in Python
&lt;a href="http://i.stack.imgur.com/qNbgX.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/qNbgX.png" alt="using matplotlib.pyplot in python"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Using D3.js and the code below
&lt;a href="http://i.stack.imgur.com/1W7Lr.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/1W7Lr.png" alt="using d3.js and the code below"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Here is the code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;script src="http://d3js.org/d3.v3.js"&amp;gt;&amp;lt;/script&amp;gt;
&amp;lt;canvas id="heatmap"&amp;gt;&amp;lt;/canvas&amp;gt;
&amp;lt;script&amp;gt;
var tsvName = ''wasserstein_dists.tsv'';
var canvas = document.getElementById(''heatmap'');
var ctx = canvas.getContext("2d");

var scale = 1;
var colors = ["#6363FF", "#6373FF", "#63A3FF", "#63E3FF", "#63FFFB", "#63FFCB",
           "#63FF9B", "#63FF6B", "#7BFF63", "#BBFF63", "#DBFF63", "#FBFF63", 
           "#FFD363", "#FFB363", "#FF8363", "#FF7363", "#FF6364"];
var heatmapColor = d3.scale.linear()
    .domain(d3.range(0, 1, 1.0 / (colors.length - 1)))
    .range(colors);


// On charge le tsv :
d3.tsv(tsvName, function (d) {
  return {
    x: +d.x,
    y: +d.y,
    v: +d.v,
  };
}, function (err, data) {
  if (err != null) { return alert(''No tsv file''); }


  var size = Math.sqrt(data.length);
  canvas.width = size * scale;
  canvas.height = size * scale;

  var c = d3.scale.linear()
    .domain([d3.min(data, function (d) { return d.v; }), d3.max(data, function (d) { return d.v; })])
    .range([0,1]);

  data.forEach(function (d) {
    ctx.fillStyle = heatmapColor(c(d.v));
    ctx.fillRect(d.x * scale, d.y * scale, (d.x + 1) * scale, (d.y + 1) * scale);
  });
});
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;/p&gt;
', 'visualization  javascript', NULL, '''/1w7lr.png'':138C,145C ''/a'':125C,153C ''/canvas'':179C ''/code'':303C ''/d3.v3.js'':168C ''/p'':33C,77C,88C,102C,126C,154C,160C,306C ''/pre'':304C ''/qnbgx.png'':112C,119C ''/script'':171C ''0'':223C,283C ''1'':197C,224C,227C,284C,298C,301C ''1.0'':225C ''17'':65C ''2d'':194C ''600'':28C,30C ''6363ff'':200C ''6373ff'':201C ''63a3ff'':202C ''63e3'':203C ''63ff6b'':208C ''63ff9b'':207C ''63ffcb'':206C ''63fffb'':205C ''7bff63'':209C ''achiev'':39C ''alert'':252C ''alt'':120C,146C ''answer'':17C ''base'':22C ''bbff63'':210C ''big'':26C ''c'':267C,290C ''canva'':174C,188C ''canvas.getcontext'':193C ''canvas.height'':263C ''canvas.width'':260C ''charg'':231C ''code'':132C,151C,159C,162C ''color'':2A,47C,55C,66C,86C,199C,229C ''colors.length'':226C ''concern'':18C ''continu'':54C,85C ''ctx'':192C ''ctx.fillrect'':292C ''ctx.fillstyle'':288C ''d'':237C,273C,279C,287C ''d.v'':244C,275C,281C,291C ''d.x'':240C,293C,297C ''d.y'':242C,295C,300C ''d3.js'':4A,129C,148C ''d3.max'':276C ''d3.min'':270C ''d3.range'':222C ''d3.scale.linear'':220C,268C ''d3.tsv'':234C ''d3js.org'':167C ''d3js.org/d3.v3.js'':166C ''data'':247C,271C,277C ''data.foreach'':285C ''data.length'':259C ''dbff63'':211C ''differ'':92C ''discret'':63C ''dissimilar'':31C ''document.getelementbyid'':189C ''domain'':221C,269C ''done'':74C ''enough'':68C ''err'':246C,249C ''fbff63'':212C ''ff'':204C ''ff6364'':217C ''ff7363'':216C ''ff8363'':215C ''ffb363'':214C ''ffd363'':213C ''file'':255C ''fine'':53C ''follow'':9C ''function'':236C,245C,272C,278C,286C ''got'':15C ''gt'':169C,172C,177C,180C,183C ''heatmap'':1A,27C,96C,176C,190C ''heatmapcolor'':219C,289C ''href'':109C,135C ''i.stack.imgur.com'':111C,118C,137C,144C ''i.stack.imgur.com/1w7lr.png'':136C,143C ''i.stack.imgur.com/qnbgx.png'':110C,117C ''id'':175C ''img'':115C,141C ''javascript'':6B ''le'':232C ''like'':51C ''lt'':163C,170C,173C,178C,181C ''m'':42C ''math.sqrt'':258C ''matplotlib.pyplot'':105C,122C ''matrix'':32C,101C ''much'':72C ''nofollow'':114C,140C ''null'':250C ''obtain'':82C ''p'':7C,34C,78C,89C,103C,127C,155C,305C ''parameter'':97C ''plot'':25C ''plug'':61C ''possibl'':19C ''post'':12C ''pre'':161C ''pretti'':71C ''pyplot.pcolor'':76C ''python'':70C,107C,124C ''rang'':228C,282C ''recent'':11C ''rel'':113C,139C ''return'':238C,251C,274C,280C ''scale'':48C,56C,87C,196C,262C,265C,294C,296C,299C,302C ''script'':164C,182C ''set'':45C ''size'':257C,261C,264C ''src'':116C,142C,165C ''struggl'':43C ''techno'':23C ''time'':29C ''tsv'':233C,254C ''tsvname'':185C,235C ''two'':95C ''use'':104C,121C,128C,147C ''v'':243C ''var'':184C,187C,191C,195C,198C,218C,256C,266C ''visual'':5B ''wasserstein_dists.tsv'':186C ''web'':21C ''web-bas'':20C ''would'':50C ''x'':239C ''y'':241C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10486, 2, '2016-03-02 13:44:39.057', 2, NULL, NULL, '&lt;p&gt;Actually you are almost there. Just use the min/max function directly in the  color scale instead of using the c var. Use the max/min function in the domain and the start/stop color in the range. The d3.scale is already a gradient function.&lt;/p&gt;

&lt;p&gt;Something like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;script src="http://d3js.org/d3.v3.js"&amp;gt;&amp;lt;/script&amp;gt;
&amp;lt;canvas id="heatmap"&amp;gt;&amp;lt;/canvas&amp;gt;
&amp;lt;script&amp;gt;
var tsvName = ''wasserstein_dists.tsv'';
var canvas = document.getElementById(''heatmap'');
var ctx = canvas.getContext("2d");

var scale = 1;

// On charge le tsv :
d3.tsv(tsvName, function (d) {
  return {
    x: +d.x,
    y: +d.y,
    v: +d.v,
  };
}, function (err, data) {
  if (err != null) { return alert(''No tsv file''); }

  var size = Math.sqrt(data.length);
  var heatmapColor = d3.scale.linear()
    .domain([d3.min(data, function(d) { return d.v; }), d3.max(data, function(d) { return d.v; })])
    .range(["#6363FF",  "#FF6364"]);
  canvas.width = size * scale;
  canvas.height = size * scale;

  data.forEach(function (d) {
    ctx.fillStyle = heatmapColor(d.v);
    ctx.fillRect(d.x * scale, d.y * scale, (d.x + 1) * scale, (d.y + 1) * scale);
  });
});
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Of cause you have to first define data and then the color gradient with the min/max function.&lt;/p&gt;
', NULL, NULL, '''/canvas'':67C ''/code'':158C ''/d3.v3.js'':56C ''/p'':44C,48C,178C ''/pre'':159C ''/script'':59C ''1'':85C,153C,156C ''2d'':82C ''6363ff'':133C ''actual'':2C ''alert'':108C ''almost'':5C ''alreadi'':40C ''c'':21C ''canva'':62C,76C ''canvas.getcontext'':81C ''canvas.height'':138C ''canvas.width'':135C ''caus'':162C ''charg'':87C ''code'':50C ''color'':15C,33C,172C ''ctx'':80C ''ctx.fillrect'':147C ''ctx.fillstyle'':144C ''d'':93C,123C,129C,143C ''d.v'':100C,125C,131C,146C ''d.x'':96C,148C,152C ''d.y'':98C,150C,155C ''d3.max'':126C ''d3.min'':120C ''d3.scale'':38C ''d3.scale.linear'':118C ''d3.tsv'':90C ''d3js.org'':55C ''d3js.org/d3.v3.js'':54C ''data'':103C,121C,127C,168C ''data.foreach'':141C ''data.length'':115C ''defin'':167C ''direct'':12C ''document.getelementbyid'':77C ''domain'':29C,119C ''err'':102C,105C ''ff6364'':134C ''file'':111C ''first'':166C ''function'':11C,26C,43C,92C,101C,122C,128C,142C,177C ''gradient'':42C,173C ''gt'':57C,60C,65C,68C,71C ''heatmap'':64C,78C ''heatmapcolor'':117C,145C ''id'':63C ''instead'':17C ''le'':88C ''like'':47C ''lt'':51C,58C,61C,66C,69C ''math.sqrt'':114C ''max/min'':25C ''min/max'':10C,176C ''null'':106C ''p'':1C,45C,160C ''pre'':49C ''rang'':36C,132C ''return'':94C,107C,124C,130C ''scale'':16C,84C,137C,140C,149C,151C,154C,157C ''script'':52C,70C ''size'':113C,136C,139C ''someth'':46C ''src'':53C ''start/stop'':32C ''tsv'':89C,110C ''tsvname'':73C,91C ''use'':8C,19C,23C ''v'':99C ''var'':22C,72C,75C,79C,83C,112C,116C ''wasserstein_dists.tsv'':74C ''x'':95C ''y'':97C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10487, 1, '2016-03-02 17:07:17.373', 0, 10, 'Is it appropriate to use the Jaccard Index for multiple sets simultaneously?', '&lt;p&gt;I''m wondering if it''s sensible to use the Jaccard Index to measure similarity among sets A, B, C, and D &lt;em&gt;simultaneously&lt;/em&gt;, i.e., not simply comparing A to B, A to C, A to D, etc and then averaging across all those pairs.&lt;/p&gt;

&lt;p&gt;I think this should work, since computing intersection and union (which the Jaccard index is a function of) for 4 sets is a perfectly sensible notion, but I wanted to make sure there wasn''t something else that I was overlooking.&lt;/p&gt;

&lt;p&gt;I''m asking because I have longitudinal data of groups of humans'' gestural descriptions basic objects, and I''d like to figure out how ''conventionalization'' of the productions, as a result of gestural conversation between the participants (in a procedure kind of like charades), proceeds.&lt;/p&gt;
', 'similarity', NULL, '''/em'':39C ''/p'':61C,104C,151C ''4'':82C ''across'':57C ''among'':30C ''appropri'':3A ''ask'':108C ''averag'':56C ''b'':33C,46C ''basic'':120C ''c'':34C,49C ''charad'':149C ''compar'':43C ''comput'':69C ''convention'':130C ''convers'':139C ''d'':36C,52C,124C ''data'':113C ''descript'':119C ''els'':99C ''em'':37C ''etc'':53C ''figur'':127C ''function'':79C ''gestur'':118C,138C ''group'':115C ''human'':117C ''i.e'':40C ''index'':8A,26C,76C ''intersect'':70C ''jaccard'':7A,25C,75C ''kind'':146C ''like'':125C,148C ''longitudin'':112C ''m'':16C,107C ''make'':93C ''measur'':28C ''multipl'':10A ''notion'':88C ''object'':121C ''overlook'':103C ''p'':14C,62C,105C ''pair'':60C ''particip'':142C ''perfect'':86C ''procedur'':145C ''proceed'':150C ''product'':133C ''result'':136C ''sensibl'':21C,87C ''set'':11A,31C,83C ''similar'':13B,29C ''simpli'':42C ''simultan'':12A,38C ''sinc'':68C ''someth'':98C ''sure'':94C ''think'':64C ''union'':72C ''use'':5A,23C ''want'':91C ''wasn'':96C ''wonder'':17C ''work'':67C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10489, 1, '2016-03-02 22:10:32.293', 1, 25, 'Expand or compact features?', '&lt;p&gt;I have a classification task for people with 3 categories. I want to apply machine learning for that. I have 10 sources of data, which have the same fields (say 4: age, job title, a number of organizations, a number of followers). Data is incomplete, some fields can be missing in some profiles. The training set is limited (say, 300 examples).&lt;/p&gt;

&lt;p&gt;I have two strategies for feature engineering, and I don''t know which one to use.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Expand features: take 40 features (Profile 1 age, Profile 1 job title, ..., Profile 10 age, Profile 10 job title).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Compact features: take 4 features, and apply some heuristics to merge the values from different profiles. Say, take age and job title which occur most frequently, take a maximum number of organizations, take a sum of numbers of followers.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;What strategy is generally used to give best results and why?&lt;/p&gt;
', 'machine-learning  classification  feature-selection', NULL, '''/li'':116C,159C ''/ol'':160C ''/p'':74C,92C,115C,158C,173C ''1'':102C,105C ''10'':33C,109C,112C ''3'':21C ''300'':72C ''4'':43C,122C ''40'':99C ''age'':44C,103C,110C,137C ''appli'':26C,125C ''best'':169C ''categori'':22C ''classif'':8B,16C ''compact'':3A,119C ''data'':36C,55C ''differ'':133C ''engin'':82C ''exampl'':73C ''expand'':1A,96C ''featur'':4A,10B,81C,97C,100C,120C,123C ''feature-select'':9B ''field'':41C,59C ''follow'':54C,157C ''frequent'':144C ''general'':165C ''give'':168C ''heurist'':127C ''incomplet'':57C ''job'':45C,106C,113C,139C ''know'':87C ''learn'':7B,28C ''li'':94C,117C ''limit'':70C ''machin'':6B,27C ''machine-learn'':5B ''maximum'':147C ''merg'':129C ''miss'':62C ''number'':48C,52C,148C,155C ''occur'':142C ''ol'':93C ''one'':89C ''organ'':50C,150C ''p'':12C,75C,95C,118C,161C ''peopl'':19C ''profil'':65C,101C,104C,108C,111C,134C ''result'':170C ''say'':42C,71C,135C ''select'':11B ''set'':68C ''sourc'':34C ''strategi'':79C,163C ''sum'':153C ''take'':98C,121C,136C,145C,151C ''task'':17C ''titl'':46C,107C,114C,140C ''train'':67C ''two'':78C ''use'':91C,166C ''valu'':131C ''want'':24C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10490, 1, '2016-03-02 22:16:43.58', 3, 64, 'How to store and analyze classification results with Python?', '&lt;p&gt;I''m applying ML for classification task in Python using sklearn/pandas. I''m going to try various things to get the best results, and I wonder how do I effectively store and analyze all the parameters and results of the classification? Parameters include:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Number of training examples (which can be extended as I get more labeled data).&lt;/li&gt;
&lt;li&gt;Set of features.&lt;/li&gt;
&lt;li&gt;Classification algorithm.&lt;/li&gt;
&lt;li&gt;Algorithm hyperparameters.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Results include:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Precision/recall for each of the classes.&lt;/li&gt;
&lt;li&gt;Overall precision/recall.&lt;/li&gt;
&lt;li&gt;Support for each class, etc.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Of course, I can manually copy the parameters and results to an Excel spreadsheet every time, but it''s not an optimal solution. Are there any Python libraries (or modules of sklearn/pandas) which allow to easily store and display the parameters and results for later analysis? How do you solve this task?&lt;/p&gt;
', 'machine-learning  python  classification', NULL, '''/li'':76C,81C,85C,89C,103C,107C,114C ''/ol'':90C,115C ''/p'':59C,94C,169C ''algorithm'':84C,87C ''allow'':150C ''analysi'':162C ''analyz'':5A,48C ''appli'':18C ''best'':37C ''class'':102C,112C ''classif'':6A,14B,21C,56C,83C ''copi'':122C ''cours'':118C ''data'':75C ''display'':155C ''easili'':152C ''effect'':45C ''etc'':113C ''everi'':131C ''exampl'':65C ''excel'':129C ''extend'':69C ''featur'':80C ''get'':35C,72C ''go'':29C ''hyperparamet'':88C ''includ'':58C,93C ''label'':74C ''later'':161C ''learn'':12B ''li'':61C,77C,82C,86C,96C,104C,108C ''librari'':144C ''m'':17C,28C ''machin'':11B ''machine-learn'':10B ''manual'':121C ''ml'':19C ''modul'':146C ''number'':62C ''ol'':60C,95C ''optim'':138C ''overal'':105C ''p'':15C,91C,116C ''paramet'':51C,57C,124C,157C ''precision/recall'':97C,106C ''python'':9A,13B,24C,143C ''result'':7A,38C,53C,92C,126C,159C ''set'':78C ''sklearn/pandas'':26C,148C ''solut'':139C ''solv'':166C ''spreadsheet'':130C ''store'':3A,46C,153C ''support'':109C ''task'':22C,168C ''thing'':33C ''time'':132C ''train'':64C ''tri'':31C ''use'':25C ''various'':32C ''wonder'':41C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10491, 2, '2016-03-02 22:51:19.1', 3, NULL, NULL, '&lt;p&gt;&lt;a href="http://sacred.readthedocs.org/" rel="nofollow"&gt;Sacred&lt;/a&gt; is a &lt;a href="https://github.com/IDSIA/sacred" rel="nofollow"&gt;python library&lt;/a&gt; developed by the &lt;a href="http://www.idsia.ch/" rel="nofollow"&gt;IDSIA lab&lt;/a&gt; that "facilitates automated and reproducible experimental research". It is available through pip as &lt;code&gt;sacred&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;For a related discussion see &lt;a href="https://www.reddit.com/r/MachineLearning/comments/3npg0d" rel="nofollow"&gt;reddit&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':8C,20C,31C,63C ''/code'':47C ''/idsia/sacred'':15C ''/p'':48C,64C ''/r/machinelearning/comments/3npg0d'':59C ''autom'':34C ''avail'':41C ''code'':45C ''develop'':21C ''discuss'':53C ''experiment'':37C ''facilit'':33C ''github.com'':14C ''github.com/idsia/sacred'':13C ''href'':3C,12C,25C,56C ''idsia'':29C ''lab'':30C ''librari'':19C ''nofollow'':6C,17C,28C,61C ''p'':1C,49C ''pip'':43C ''python'':18C ''reddit'':62C ''rel'':5C,16C,27C,60C ''relat'':52C ''reproduc'':36C ''research'':38C ''sacr'':7C,46C ''sacred.readthedocs.org'':4C ''see'':54C ''www.idsia.ch'':26C ''www.reddit.com'':58C ''www.reddit.com/r/machinelearning/comments/3npg0d'':57C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10492, 2, '2016-03-03 00:05:55.693', 3, NULL, NULL, '&lt;p&gt;The way I see it is that your 10 sources of data, they all refer to the same set of people. Depending on the attributes, some can be expanded, some can be merged ...&lt;/p&gt;

&lt;p&gt;Attributes such as age should be unique, so it doesn''t make sense to expand it to Profile 1 age, profile 2 age ... One simple way is merge them is by using the average or use max. Expanding age only add redundant data to your feature matrix, and increase its dimensionality, in most cases, this doesn''t help generalization performance of your model. &lt;/p&gt;

&lt;p&gt;On the other hand, number of followers can be expanded. Depending on the data source, a guy has 10 followers on Twitter but 1000 followers on Google+ might simply mean that he barely uses Twitter. &lt;/p&gt;

&lt;p&gt;That being said, the way you pick your features or engineer new features should increase your model performance, so if expanding number of followers actually decrease Cross Validation or Test performance, compared to the one using sum of followers then you can simply use sum of followers. &lt;/p&gt;
', NULL, NULL, '''/p'':35C,100C,137C,186C ''1'':55C ''10'':10C,120C ''1000'':125C ''2'':58C ''actual'':163C ''add'':77C ''age'':40C,56C,59C,75C ''attribut'':26C,37C ''averag'':70C ''bare'':134C ''case'':90C ''compar'':170C ''cross'':165C ''data'':13C,79C,115C ''decreas'':164C ''depend'':23C,112C ''dimension'':87C ''doesn'':46C,92C ''engin'':149C ''expand'':30C,51C,74C,111C,159C ''featur'':82C,147C,151C ''follow'':108C,121C,126C,162C,177C,185C ''general'':95C ''googl'':128C ''guy'':118C ''hand'':105C ''help'':94C ''increas'':85C,153C ''make'':48C ''matrix'':83C ''max'':73C ''mean'':131C ''merg'':34C,64C ''might'':129C ''model'':99C,155C ''new'':150C ''number'':106C,160C ''one'':60C,173C ''p'':1C,36C,101C,138C ''peopl'':22C ''perform'':96C,156C,169C ''pick'':145C ''profil'':54C,57C ''redund'':78C ''refer'':16C ''said'':141C ''see'':5C ''sens'':49C ''set'':20C ''simpl'':61C ''simpli'':130C,181C ''sourc'':11C,116C ''sum'':175C,183C ''test'':168C ''twitter'':123C,136C ''uniqu'':43C ''use'':68C,72C,135C,174C,182C ''valid'':166C ''way'':3C,62C,143C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10502, 1, '2016-03-03 16:25:23.677', 1, 15, 'Does traning the Word2Vec model multiple times affect `min_count` parameter?', '&lt;p&gt;In &lt;code&gt;Word2Vec&lt;/code&gt;, If I train a set of sentences multiple times with change in order (as it increases the vector representations), will the frequency of a word get changed due to it.?&lt;/p&gt;

&lt;p&gt;For example, if I have the word "deer" in my corpus 4 times and If I set the &lt;code&gt;min_count&lt;/code&gt; to be 5, does training the model 3 times repeatedly count "deer" with frequency 12 and will be included in the model ?&lt;/p&gt;

&lt;p&gt;If it knows it is the same corpus then how it is possible to differentiate, if I retrain the model with a new corpus.&lt;/p&gt;
', 'predictive-modeling  word-embeddings  word2vec  gensim', NULL, '''/code'':24C,78C ''/p'':55C,101C,127C ''12'':93C ''3'':86C ''4'':68C ''5'':81C ''affect'':8A ''chang'':35C,51C ''code'':22C,75C ''corpus'':67C,110C,126C ''count'':10A,77C,89C ''deer'':64C,90C ''differenti'':117C ''due'':52C ''embed'':17B ''exampl'':58C ''frequenc'':46C,92C ''gensim'':19B ''get'':50C ''includ'':97C ''increas'':40C ''know'':105C ''min'':9A,76C ''model'':5A,14B,85C,100C,122C ''multipl'':6A,32C ''new'':125C ''order'':37C ''p'':20C,56C,102C ''paramet'':11A ''possibl'':115C ''predict'':13B ''predictive-model'':12B ''repeat'':88C ''represent'':43C ''retrain'':120C ''sentenc'':31C ''set'':29C,73C ''time'':7A,33C,69C,87C ''train'':27C,83C ''trane'':2A ''vector'':42C ''word'':16B,49C,63C ''word-embed'':15B ''word2vec'':4A,18B,23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10493, 1, '2016-03-03 00:09:55.31', 1, 21, 'convert some observations into variables', '&lt;p&gt;I have a table formatted like the following table:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Feature amount  ID  Location
Feat1      2    1   US
Feat2      0    1   US
Feat3      0    1   US
Feat4      1    1   US
Feat2      2    2   US
Feat4      0    2   US
Feat3      0    2   US
Feat6      1    2   US
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Let''s say I have 200 different IDs.
I want to convert all different features into variables and amount into observations, so I combine rows with same ID into one row. For example,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Feat1 Feat2 Feat3 Feat4 Feat5 Feat6 ID Location
  2     0     0     1    NA    NA   1    US
 NA     2     0     0    NA    1    2    US
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is there a good way to do it either in Python (pandas) or R?&lt;/p&gt;

&lt;p&gt;Thanks in advance!&lt;/p&gt;
', 'data-cleaning', NULL, '''/code'':58C,121C ''/p'':19C,94C,138C,143C ''/pre'':59C,122C ''0'':31C,35C,47C,51C,106C,107C,115C,116C ''1'':28C,32C,36C,39C,40C,55C,108C,111C,118C ''2'':27C,43C,44C,48C,52C,56C,105C,114C,119C ''200'':66C ''advanc'':142C ''amount'':23C,79C ''clean'':8B ''code'':21C,96C ''combin'':84C ''convert'':1A,72C ''data'':7B ''data-clean'':6B ''differ'':67C,74C ''either'':132C ''exampl'':93C ''feat1'':26C,97C ''feat2'':30C,42C,98C ''feat3'':34C,50C,99C ''feat4'':38C,46C,100C ''feat5'':101C ''feat6'':54C,102C ''featur'':22C,75C ''follow'':17C ''format'':14C ''good'':127C ''id'':24C,68C,88C,103C ''let'':61C ''like'':15C ''locat'':25C,104C ''na'':109C,110C,113C,117C ''observ'':3A,81C ''one'':90C ''p'':9C,60C,123C,139C ''panda'':135C ''pre'':20C,95C ''python'':134C ''r'':137C ''row'':85C,91C ''say'':63C ''tabl'':13C,18C ''thank'':140C ''us'':29C,33C,37C,41C,45C,49C,53C,57C,112C,120C ''variabl'':5A,77C ''want'':70C ''way'':128C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10494, 1, '2016-03-03 00:21:26.643', 3, 54, 'Is this cluster analysis / prediction?', '&lt;p&gt;I have a series of seemingly random data dripping in one value at a time through time. Although it appears to be random, the data forms clusters when certain attributes are analysed which the charts show. I''m trying to avoid the fallacy of seeing patterns that are not there, but across several datasets the data does indeed appear to form clusters through time. I''m interested in trying to:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;detect the clusters of either the blue squares and / or the white squares in the data I have&lt;/li&gt;
&lt;li&gt;ignoring data which are outliers&lt;/li&gt;
&lt;li&gt;predicting the future shape of the clusters as data is dripped in one column at a time. (There appears to be a certain momentum through time of the cluster shapes where the current cluster areas of the new data is in nearby region of the preceding clusters)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;They appear to take on regular semi-predictable shapes which (to my eyes at least) follow a momentum from the data before. How could I detect this?&lt;/p&gt;

&lt;p&gt;My questions are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;is this cluster analysis, and&lt;/li&gt;
&lt;li&gt;what would be the best way to detect the white squares and / or the blue areas. I''m guessing the blue areas would be the easiest to cluster?&lt;/li&gt;
&lt;li&gt;the white squares appear fairly close to the previous y-values and may continue at a certain angle up or down in value. How is the best way to predict this?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/MjTMN.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/MjTMN.png" alt="enter image description here"&gt;&lt;/a&gt;
&lt;a href="http://i.stack.imgur.com/zMEjM.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/zMEjM.png" alt="enter image description here"&gt;&lt;/a&gt;
&lt;a href="http://i.stack.imgur.com/0LVAh.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/0LVAh.png" alt="enter image description here"&gt;&lt;/a&gt;
&lt;a href="http://i.stack.imgur.com/Tro3y.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Tro3y.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'clustering', NULL, '''/0lvah.png'':306C,313C ''/a'':283C,301C,319C,337C ''/li'':99C,106C,155C,199C,229C,263C ''/mjtmn.png'':270C,277C ''/p'':78C,186C,191C,338C ''/tro3y.png'':324C,331C ''/ul'':156C,264C ''/zmejm.png'':288C,295C ''across'':59C ''alt'':278C,296C,314C,332C ''although'':25C ''analys'':39C ''analysi'':4A,197C ''angl'':249C ''appear'':27C,66C,126C,159C,234C ''area'':142C,216C,222C ''attribut'':37C ''avoid'':48C ''best'':205C,258C ''blue'':87C,215C,221C ''certain'':36C,130C,248C ''chart'':42C ''close'':236C ''cluster'':3A,6B,34C,69C,83C,114C,136C,141C,154C,196C,228C ''column'':121C ''continu'':245C ''could'':182C ''current'':140C ''data'':15C,32C,63C,96C,102C,116C,146C,179C ''dataset'':61C ''descript'':281C,299C,317C,335C ''detect'':81C,184C,208C ''drip'':16C,118C ''easiest'':226C ''either'':85C ''enter'':279C,297C,315C,333C ''eye'':171C ''fair'':235C ''fallaci'':50C ''follow'':174C ''form'':33C,68C ''futur'':110C ''guess'':219C ''href'':267C,285C,303C,321C ''i.stack.imgur.com'':269C,276C,287C,294C,305C,312C,323C,330C ''i.stack.imgur.com/0lvah.png'':304C,311C ''i.stack.imgur.com/mjtmn.png'':268C,275C ''i.stack.imgur.com/tro3y.png'':322C,329C ''i.stack.imgur.com/zmejm.png'':286C,293C ''ignor'':101C ''imag'':280C,298C,316C,334C ''img'':273C,291C,309C,327C ''inde'':65C ''interest'':74C ''least'':173C ''li'':80C,100C,107C,193C,200C,230C ''m'':45C,73C,218C ''may'':244C ''momentum'':131C,176C ''nearbi'':149C ''new'':145C ''nofollow'':272C,290C,308C,326C ''one'':18C,120C ''outlier'':105C ''p'':7C,157C,187C,265C ''pattern'':53C ''preced'':153C ''predict'':5A,108C,166C,261C ''previous'':239C ''question'':189C ''random'':14C,30C ''region'':150C ''regular'':163C ''rel'':271C,289C,307C,325C ''see'':52C ''seem'':13C ''semi'':165C ''semi-predict'':164C ''seri'':11C ''sever'':60C ''shape'':111C,137C,167C ''show'':43C ''squar'':88C,93C,211C,233C ''src'':274C,292C,310C,328C ''take'':161C ''time'':22C,24C,71C,124C,133C ''tri'':46C,76C ''ul'':79C,192C ''valu'':19C,242C,254C ''way'':206C,259C ''white'':92C,210C,232C ''would'':202C,223C ''y'':241C ''y-valu'':240C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10498, 2, '2016-03-03 03:11:39.9', 2, NULL, NULL, '&lt;p&gt;You made three mistakes:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;You omitted the offset terms before the nonlinear transformations (variables b_1 and b_out). This increases the representative power of the neural network.&lt;/li&gt;
&lt;li&gt;You omitted the softmax transformation at the top layer. This makes the output a probability distributions, so you can calculate the cross-entropy, which is the usual cost function for classification.&lt;/li&gt;
&lt;li&gt;You used the binary form of the cross-entropy when you should have used the multi-class form.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;When I run this I get accuracies over 90%:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets(''/tmp/MNIST_data'', one_hot=True)

x = tf.placeholder(tf.float32, shape=[None, 784])
y = tf.placeholder(tf.float32, shape=[None, 10])

W_h1 = tf.Variable(tf.random_normal([784, 512]))
b_1 = tf.Variable(tf.random_normal([512]))
h1 = tf.nn.sigmoid(tf.matmul(x, W_h1) + b_1)

W_out = tf.Variable(tf.random_normal([512, 10]))
b_out = tf.Variable(tf.random_normal([10]))
y_ = tf.nn.softmax(tf.matmul(h1, W_out) + b_out)

# cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(y_, y)
cross_entropy = tf.reduce_sum(- y * tf.log(y_), 1)
loss = tf.reduce_mean(cross_entropy)
train_step = tf.train.GradientDescentOptimizer(0.05).minimize(loss)

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# train
with tf.Session() as s:
    s.run(tf.initialize_all_variables())

    for i in range(10000):
        batch_x, batch_y = mnist.train.next_batch(100)
        s.run(train_step, feed_dict={x: batch_x, y: batch_y})

        if i % 1000 == 0:
            train_accuracy = accuracy.eval(feed_dict={x: batch_x, y: batch_y})
            print(''step {0}, training accuracy {1}''.format(i, train_accuracy))
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':276C ''/li'':33C,67C,89C ''/ol'':90C ''/p'':6C,101C ''/pre'':277C ''/tmp/mnist_data'':117C ''0'':254C,268C ''0.05'':200C ''1'':20C,141C,153C,191C,208C,211C,271C ''10'':132C,160C,166C ''100'':239C ''1000'':253C ''10000'':232C ''512'':139C,145C,159C ''784'':126C,138C ''90'':100C ''accuraci'':98C,212C,256C,270C,275C ''accuracy.eval'':257C ''b'':19C,22C,140C,152C,161C,173C ''batch'':233C,235C,238C,246C,249C,261C,264C ''binari'':72C ''calcul'':54C ''class'':87C ''classif'':66C ''code'':103C ''correct'':203C,216C ''cost'':63C ''cross'':57C,77C,175C,178C,184C,195C ''cross-entropi'':56C,76C ''data'':112C,115C ''dict'':244C,259C ''distribut'':50C ''entropi'':58C,78C,176C,179C,185C,196C ''feed'':243C,258C ''form'':73C,88C ''format'':272C ''function'':64C ''get'':97C ''h1'':134C,146C,151C,170C ''hot'':119C ''import'':104C,110C ''increas'':25C ''input'':111C ''input_data.read'':114C ''layer'':43C ''li'':8C,34C,68C ''logit'':181C ''loss'':192C,202C ''made'':3C ''make'':45C ''mean'':194C,214C ''minim'':201C ''mistak'':5C ''mnist'':113C ''mnist.train.next'':237C ''multi'':86C ''multi-class'':85C ''network'':32C ''neural'':31C ''none'':125C,131C ''nonlinear'':16C ''normal'':137C,144C,158C,165C ''offset'':12C ''ol'':7C ''omit'':10C,36C ''one'':118C ''output'':47C ''p'':1C,91C ''power'':28C ''pre'':102C ''predict'':204C,217C ''print'':266C ''probabl'':49C ''rang'':231C ''repres'':27C ''run'':94C ''s.run'':224C,240C ''set'':116C ''shape'':124C,130C ''softmax'':38C ''step'':198C,242C,267C ''sum'':187C ''tensorflow'':105C ''tensorflow.examples.tutorials.mnist'':109C ''term'':13C ''tf'':107C ''tf.argmax'':206C,209C ''tf.cast'':215C ''tf.equal'':205C ''tf.float32'':123C,129C,218C ''tf.initialize'':225C ''tf.log'':189C ''tf.matmul'':148C,169C ''tf.nn.sigmoid'':147C,177C ''tf.nn.softmax'':168C ''tf.placeholder'':122C,128C ''tf.random'':136C,143C,157C,164C ''tf.reduce'':186C,193C,213C ''tf.session'':221C ''tf.train.gradientdescentoptimizer'':199C ''tf.variable'':135C,142C,156C,163C ''three'':4C ''top'':42C ''train'':197C,219C,241C,255C,269C,274C ''transform'':17C,39C ''true'':120C ''use'':70C,83C ''usual'':62C ''variabl'':18C,227C ''w'':133C,150C,154C,171C ''x'':121C,149C,234C,245C,247C,260C,262C ''y'':127C,167C,182C,183C,188C,190C,207C,210C,236C,248C,250C,263C,265C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10499, 1, '2016-03-03 09:36:36.193', 1, 37, 'Collaborative filtering when multiple items are rated multiple times by same user', '&lt;p&gt;When trying to model as a recommendation problem the selection of an item that can be selected (and rated) by the same user many times, I can''t find references of previous work. &lt;/p&gt;

&lt;p&gt;For example User1 can select item A (and rate it) then item A again (and give it a different rate) and then again item A can be selected n times.. then the same for item B, but item A could be again selected at any time point, and that could happen for any of the A, B, C... Z items. Could this be modelled somehow as a recommender system?&lt;/p&gt;

&lt;p&gt;When asked in SO I was told that can''t be modelled even as a multi-modal recommneder, then I think my question should be extended to how could I model this for prediction of next rate?&lt;/p&gt;

&lt;p&gt;&lt;a href="http://stackoverflow.com/questions/35767636/collaborative-filtering-when-multiple-items-are-rated-multiple-times-by-same-use?noredirect=1#comment59207724_35767636"&gt;SO question&lt;/a&gt;&lt;/p&gt;
', 'recommender-system', NULL, '''..'':82C ''/a'':171C ''/p'':50C,122C,162C,172C ''/questions/35767636/collaborative-filtering-when-multiple-items-are-rated-multiple-times-by-same-use?noredirect=1#comment59207724_35767636'':168C ''ask'':125C ''b'':88C,109C ''c'':110C ''collabor'':1A ''could'':92C,102C,113C,153C ''differ'':70C ''even'':136C ''exampl'':53C ''extend'':150C ''filter'':2A ''find'':45C ''give'':67C ''happen'':103C ''href'':165C ''item'':5A,29C,57C,63C,75C,87C,90C,112C ''mani'':40C ''modal'':141C ''model'':20C,116C,135C,155C ''multi'':140C ''multi-mod'':139C ''multipl'':4A,8A ''n'':80C ''next'':160C ''p'':16C,51C,123C,163C ''point'':99C ''predict'':158C ''previous'':48C ''problem'':24C ''question'':147C,170C ''rate'':7A,35C,60C,71C,161C ''recommend'':14B,23C,120C ''recommender-system'':13B ''recommned'':142C ''refer'':46C ''select'':26C,33C,56C,79C,95C ''somehow'':117C ''stackoverflow.com'':167C ''stackoverflow.com/questions/35767636/collaborative-filtering-when-multiple-items-are-rated-multiple-times-by-same-use?noredirect=1#comment59207724_35767636'':166C ''system'':15B,121C ''think'':145C ''time'':9A,41C,81C,98C ''told'':130C ''tri'':18C ''user'':12A,39C ''user1'':54C ''work'':49C ''z'':111C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10501, 1, '2016-03-03 14:47:36.427', 0, 5, 'Merge Data widget settings lost after loading', '&lt;p&gt;I have a workflow in which I need to merge different datasets. In the merge data widget, I need to select which attributes to match. When I save my workflow and load it at a later stage, my attribute matching is lost. I need to reapply it on all my merge data widgets in order to get everything working again. Any idea on how to solve this?&lt;/p&gt;

&lt;p&gt;thanks,
Steven&lt;/p&gt;
', 'data-mining  orange', NULL, '''/p'':80C,84C ''attribut'':35C,51C ''data'':2A,9B,28C,64C ''data-min'':8B ''dataset'':24C ''differ'':23C ''everyth'':70C ''get'':69C ''idea'':74C ''later'':48C ''load'':7A,44C ''lost'':5A,54C ''match'':37C,52C ''merg'':1A,22C,27C,63C ''mine'':10B ''need'':20C,31C,56C ''orang'':11B ''order'':67C ''p'':12C,81C ''reappli'':58C ''save'':40C ''select'':33C ''set'':4A ''solv'':78C ''stage'':49C ''steven'':83C ''thank'':82C ''widget'':3A,29C,65C ''work'':71C ''workflow'':16C,42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10503, 2, '2016-03-03 16:31:55.73', 0, NULL, NULL, '&lt;p&gt;I don''t think there is any academic work on the subject, at least that I know of. &lt;/p&gt;

&lt;p&gt;One simple way of using that data would be to use the &lt;strong&gt;mean&lt;/strong&gt; of the ratings or other average like measures such as a &lt;strong&gt;moving average&lt;/strong&gt;, a &lt;strong&gt;time weighted average&lt;/strong&gt;, the &lt;strong&gt;median&lt;/strong&gt;, etc. &lt;/p&gt;

&lt;p&gt;But this approach is probably not exactly what you''re looking for.&lt;/p&gt;

&lt;p&gt;Try to look at collaborative filtering approaches with temporal dynamics, there might be something interesting for you.&lt;/p&gt;
', NULL, NULL, '''/p'':20C,63C,77C,96C ''/strong'':36C,51C,57C,61C ''academ'':9C ''approach'':67C,85C ''averag'':42C,50C,56C ''collabor'':83C ''data'':28C ''dynam'':88C ''etc'':62C ''exact'':71C ''filter'':84C ''interest'':93C ''know'':18C ''least'':15C ''like'':43C ''look'':75C,81C ''mean'':35C ''measur'':44C ''median'':60C ''might'':90C ''move'':49C ''one'':22C ''p'':1C,21C,64C,78C ''probabl'':69C ''rate'':39C ''re'':74C ''simpl'':23C ''someth'':92C ''strong'':34C,48C,53C,59C ''subject'':13C ''tempor'':87C ''think'':5C ''time'':54C ''tri'':79C ''use'':26C,32C ''way'':24C ''weight'':55C ''work'':10C ''would'':29C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10504, 2, '2016-03-03 16:57:57.617', 0, NULL, NULL, '&lt;p&gt;As to predicting the next rate:&lt;/p&gt;

&lt;p&gt;Very simple (kind of brute force)
Would be a Dataming approach: Treat it as a timeseries or a lagged model. All or a certain number of past rates are used as inputs to explain the current rate. Input the data into your favorite classifier algorithm (should be something able to handle nominal target variables). Of cause you need a rather large number of observations.&lt;/p&gt;

&lt;p&gt;If you prefere a graph approach you might want to take a look into random fields or markov models for sequences.&lt;/p&gt;

&lt;p&gt;If this is going to be an online recommendation problem, a graph database would offer the structure and capabilities to efficiently compute the most likely next outcome.&lt;/p&gt;
', NULL, NULL, '''/p'':8C,74C,97C,126C ''abl'':58C ''algorithm'':54C ''approach'':20C,81C ''brute'':14C ''capabl'':117C ''caus'':65C ''certain'':33C ''classifi'':53C ''comput'':120C ''current'':45C ''data'':49C ''databas'':111C ''datam'':19C ''effici'':119C ''explain'':43C ''favorit'':52C ''field'':91C ''forc'':15C ''go'':102C ''graph'':80C,110C ''handl'':60C ''input'':41C,47C ''kind'':12C ''lag'':28C ''larg'':70C ''like'':123C ''look'':88C ''markov'':93C ''might'':83C ''model'':29C,94C ''need'':67C ''next'':6C,124C ''nomin'':61C ''number'':34C,71C ''observ'':73C ''offer'':113C ''onlin'':106C ''outcom'':125C ''p'':1C,9C,75C,98C ''past'':36C ''predict'':4C ''prefer'':78C ''problem'':108C ''random'':90C ''rate'':7C,37C,46C ''rather'':69C ''recommend'':107C ''sequenc'':96C ''simpl'':11C ''someth'':57C ''structur'':115C ''take'':86C ''target'':62C ''timeseri'':25C ''treat'':21C ''use'':39C ''variabl'':63C ''want'':84C ''would'':16C,112C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10506, 2, '2016-03-03 18:21:17.377', 0, NULL, NULL, '&lt;p&gt;I ended up using &lt;a href="https://github.com/machinalis/featureforge/" rel="nofollow"&gt;Feature Forge&lt;/a&gt;, which is a python library that "provides a set of tools that can be useful in many machine learning applications (classification, clustering, regression, etc.), and particularly helpful if you use scikit-learn (although this can work if you have a different algorithm)".&lt;/p&gt;

&lt;p&gt;I chose it because it seems to be a more specific tool for machine learning than Sacred, which appears to be a more generic tool. Feature Forge also provides a convenient framework for describing features in a scalable and reusable class-based way. It also has functions to store experimental results in a Mongo database.&lt;/p&gt;

&lt;p&gt;Thanks to @Emre for pointing to &lt;a href="https://www.reddit.com/r/MachineLearning/comments/3npg0d" rel="nofollow"&gt;reddit discussion&lt;/a&gt;, where featureforge was mentioned.&lt;/p&gt;
', NULL, NULL, '''/a'':15C,134C ''/machinalis/featureforge/'':10C ''/p'':59C,117C,139C ''/r/machinelearning/comments/3npg0d'':129C ''algorithm'':58C ''also'':88C,106C ''although'':49C ''appear'':79C ''applic'':35C ''base'':103C ''chose'':62C ''class'':102C ''class-bas'':101C ''classif'':36C ''cluster'':37C ''conveni'':91C ''databas'':116C ''describ'':94C ''differ'':57C ''discuss'':133C ''emr'':121C ''end'':3C ''etc'':39C ''experiment'':111C ''featur'':13C,86C,95C ''featureforg'':136C ''forg'':14C,87C ''framework'':92C ''function'':108C ''generic'':84C ''github.com'':9C ''github.com/machinalis/featureforge/'':8C ''help'':42C ''href'':7C,126C ''learn'':34C,48C,75C ''librari'':20C ''machin'':33C,74C ''mani'':32C ''mention'':138C ''mongo'':115C ''nofollow'':12C,131C ''p'':1C,60C,118C ''particular'':41C ''point'':123C ''provid'':22C,89C ''python'':19C ''reddit'':132C ''regress'':38C ''rel'':11C,130C ''result'':112C ''reusabl'':100C ''sacr'':77C ''scalabl'':98C ''scikit'':47C ''scikit-learn'':46C ''seem'':66C ''set'':24C ''specif'':71C ''store'':110C ''thank'':119C ''tool'':26C,72C,85C ''use'':5C,30C,45C ''way'':104C ''work'':52C ''www.reddit.com'':128C ''www.reddit.com/r/machinelearning/comments/3npg0d'':127C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10507, 1, '2016-03-03 19:17:03.607', 0, 13, 'Examining Data Pollution', '&lt;p&gt;I have been having a difficult time quantifying the amount of data pollution (redundant or unusable data) on the internet. Are there any tools that could help me analyze the amount of data pollution on the internet? I have tried searching for studies done on data pollution, but I haven''t found much luck in finding any.&lt;/p&gt;
', 'data', NULL, '''/p'':63C ''amount'':15C,36C ''analyz'':34C ''could'':31C ''data'':2A,4B,17C,22C,38C,51C ''difficult'':11C ''done'':49C ''examin'':1A ''find'':61C ''found'':57C ''haven'':55C ''help'':32C ''internet'':25C,42C ''luck'':59C ''much'':58C ''p'':5C ''pollut'':3A,18C,39C,52C ''quantifi'':13C ''redund'':19C ''search'':46C ''studi'':48C ''time'':12C ''tool'':29C ''tri'':45C ''unus'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10508, 1, '2016-03-03 19:46:52.493', 2, 29, 'Is it possible to predict groups vs groups in machine learning?', '&lt;p&gt;I have a problem that I think machine learning can solve. I''m new to it however and don''t know where to start so any pointers to appropriate solving methods/guides etc for the problem are appreciated. 
I''ve searched a bit and couldn''t find any questions similar to this, but most likely because I dont know exactly what to search for:&lt;/p&gt;

&lt;p&gt;Lets say we have a bunch of fruits: &lt;code&gt;a,b,c,d,e,f&lt;/code&gt; which all have two factors associated with it, a price and a quality. Every day in a store the customers can choose between two bundles&lt;code&gt;(g1,g2)&lt;/code&gt;, each consisting of two fruits with varying price and quality&lt;code&gt;(p,q)&lt;/code&gt;. The 4 fruits every day must be unique, so g1 cannot have a fruit in g2.
The customers then buy one bundle each. They value the price and quality, but are probably also influenced by outside factors such as what fruit it is and what it is combined with.&lt;/p&gt;

&lt;p&gt;Is it possible to create a training set from this?&lt;br&gt; I came up with two ways to represent this:&lt;br&gt;
The simplest way i could think of: let a fruit x be represented as &lt;code&gt;x:{x_p,x_q}&lt;/code&gt;, a group as &lt;code&gt;g1(a,b)&lt;/code&gt;, and a training element would then be defined as &lt;code&gt;day(g1,g2)&lt;/code&gt; which we will use to train our machine.&lt;br&gt;
The second way was to let the fruits contain a present flag to mark if they are in a group or not, &lt;code&gt;x:{x_p,x_q,present}&lt;/code&gt; and a group would then become &lt;code&gt;g1(a,b,c,d,e,f)&lt;/code&gt; and a day still &lt;code&gt;day(g1,g2)&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Is it possible to do it the first way without data for several fruits, or is it just implementation specific?&lt;br&gt;
How would one train for the mirroring case of &lt;code&gt;day(g1(a,b),g2(c,d))&lt;/code&gt; vs &lt;code&gt;day(g1(c,d),g2(a,b))&lt;/code&gt;, by just creating the mirrored training element or is something that the machine could figure out on its own?&lt;br&gt;
While this is a minimum example, what would be different if we were to cover more fruits(5/bundle) from a larger fruit pool(20)?&lt;br&gt;
Can we use it to predict which bundle is more likely to be picked even if that group/fruit combo hasn''t been evaluated in the set?&lt;/p&gt;

&lt;p&gt;Another way I thought of was training on the groups, but I think that would result in a flat score for the groups and most likely miss relationships between the groups, such as g1 is preferred over g2 which is preferred over g3 which is preferred over g1? Is this true or would a machine be able to understand that too?&lt;/p&gt;

&lt;p&gt;I was looking a bit at the javascript libraries brain and mind and thought that it might be possible with one of them, but I dont know what type of algorithm/solving method I''m looking for. Any suggestions for libraries to solve this in, preferrably python/php/java/javascript are appreciated!&lt;/p&gt;

&lt;p&gt;Also if someone could add suitable tags to the question would be great!&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT&lt;/strong&gt;: same as with the group mirroring, how do we distinguish the groups, do we need to sort the fruits alphabetically or can a machine handle it too? (apple, banana) should be identical to (banana,apple)&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10509, 1, '2016-03-03 20:29:22.797', 1, 42, 'How to deal with categorical feature of very high cardinality?', '&lt;p&gt;I would like to train a binary classifier on feature vectors.
One of the features is categorical feature with string, it is the zip codes of a country.&lt;/p&gt;

&lt;p&gt;Typically, there is thousands of zip codes, and in my case they are strings.
How can convert this feature into numerical?&lt;/p&gt;

&lt;p&gt;I do not think that using one-hot-encoding is good as a solution for my case.
Am I right by saying that? If yes, what would be a suitable solution?&lt;/p&gt;
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INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10510, 2, '2016-03-03 20:36:09.343', 1, NULL, NULL, '&lt;p&gt;One-hot-encoded ZIP codes shouldn''t present a problem with modern tools, where features can be &lt;em&gt;much&lt;/em&gt; wider (millions, billions even), but if you really want you could aggregate area codes into regions, such as states. Of course, you should not use strings, but bit vectors. Two other dimensionality reduction options are &lt;a href="https://github.com/esafak/mca" rel="nofollow"&gt;MCA&lt;/a&gt; (PCA for categorical variables) and &lt;a href="https://en.wikipedia.org/wiki/Random_projection" rel="nofollow"&gt;random projection&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':66C,81C ''/em'':22C ''/esafak/mca'':62C ''/p'':82C ''/wiki/random_projection'':76C ''aggreg'':34C ''area'':35C ''billion'':25C ''bit'':50C ''categor'':69C ''code'':7C,36C ''could'':33C ''cours'':43C ''dimension'':54C ''em'':20C ''en.wikipedia.org'':75C ''en.wikipedia.org/wiki/random_projection'':74C ''encod'':5C ''even'':26C ''featur'':17C ''github.com'':61C ''github.com/esafak/mca'':60C ''hot'':4C ''href'':59C,73C ''mca'':65C ''million'':24C ''modern'':14C ''much'':21C ''nofollow'':64C,78C ''one'':3C ''one-hot-encod'':2C ''option'':56C ''p'':1C ''pca'':67C ''present'':10C ''problem'':12C ''project'':80C ''random'':79C ''realli'':30C ''reduct'':55C ''region'':38C ''rel'':63C,77C ''shouldn'':8C ''state'':41C ''string'':48C ''tool'':15C ''two'':52C ''use'':47C ''variabl'':70C ''vector'':51C ''want'':31C ''wider'':23C ''zip'':6C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10514, 2, '2016-03-03 23:19:01.583', 0, NULL, NULL, '&lt;p&gt;The necessary information can be found on wikipedia:
&lt;a href="https://en.wikipedia.org/wiki/Bag-of-words_model_in_computer_vision" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Bag-of-words_model_in_computer_vision&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;"when we use more technical features such as colour histograms" 
Judging from this sentence I guess you need to understand the "Codebook" generation.&lt;/p&gt;

&lt;p&gt;First step is to extract features of patches in the image. For efficiency you only want to take patches which are interesting and calculate discriminative features on them. SIFT is one Method which performs both steps for you. It takes care of finding good spots and it calculates features on this spot.&lt;/p&gt;

&lt;p&gt;Now you can generate your codebook. A codebook will map every possible featurevector ( after all they''re just numeric vectors ) to a certain output codeword. One possibility to do this, is to use k-means for codebook generation. After you built your codebook, a vector is mapped to a code by finding the minimal distance to all the entries ( since you used k-means you can use euclidean distance ).&lt;/p&gt;

&lt;p&gt;Now you have a complete realization of the bag of words model. You can now dive into using it for classification.
The required algorithms can be implemented using the libraries you mentioned.&lt;/p&gt;
', NULL, NULL, '''/p'':44C,98C,171C,205C ''/wiki/bag-of-words_model_in_computer_vision'':14C ''/wiki/bag-of-words_model_in_computer_vision&lt;/a&gt;&lt;/p&gt;'':19C ''algorithm'':196C ''bag'':181C ''built'':141C ''calcul'':69C,93C ''care'':86C ''certain'':122C ''classif'':193C ''code'':150C ''codebook'':42C,105C,107C,137C,143C ''codeword'':124C ''colour'':29C ''complet'':177C ''discrimin'':70C ''distanc'':155C,170C ''dive'':188C ''effici'':58C ''en.wikipedia.org'':13C,18C ''en.wikipedia.org/wiki/bag-of-words_model_in_computer_vision'':12C ''en.wikipedia.org/wiki/bag-of-words_model_in_computer_vision&lt;/a&gt;&lt;/p&gt;'':17C ''entri'':159C ''euclidean'':169C ''everi'':110C ''extract'':50C ''featur'':26C,51C,71C,94C ''featurevector'':112C ''find'':88C,152C ''first'':46C ''found'':7C ''generat'':43C,103C,138C ''good'':89C ''guess'':36C ''histogram'':30C ''href'':11C ''imag'':56C ''implement'':199C ''inform'':4C ''interest'':67C ''judg'':31C ''k'':134C,164C ''k-mean'':133C,163C ''librari'':202C ''map'':109C,147C ''mean'':135C,165C ''mention'':204C ''method'':77C ''minim'':154C ''model'':184C ''necessari'':3C ''need'':38C ''nofollow'':16C ''numer'':118C ''one'':76C,125C ''output'':123C ''p'':1C,20C,45C,99C,172C ''patch'':53C,64C ''perform'':79C ''possibl'':111C,126C ''re'':116C ''realiz'':178C ''rel'':15C ''requir'':195C ''sentenc'':34C ''sift'':74C ''sinc'':160C ''spot'':90C,97C ''step'':47C,81C ''take'':63C,85C ''technic'':25C ''understand'':40C ''use'':23C,132C,162C,168C,190C,200C ''vector'':119C,145C ''want'':61C ''wikipedia'':9C ''word'':183C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10515, 2, '2016-03-03 23:34:21.943', 2, NULL, NULL, '&lt;p&gt;In my opinion you''re looking into the wrong methods to solve your problem.&lt;/p&gt;

&lt;p&gt;You have strictly no numeric data.&lt;/p&gt;

&lt;p&gt;Statistics based machine learning has a very hard time with such problems.
Your problem sounds more like one of the problems which should be solved with rule based systems. My first instinct would be to try to understand the rules and put them into code leading to a classification.&lt;/p&gt;

&lt;p&gt;There are however methods for learning such rule based systems based on logic. They are only quite unfashionable for machine learning today. &lt;a href="https://en.wikipedia.org/wiki/Rule-based_system" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Rule-based_system&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/p'':16C,24C,75C,109C ''/wiki/rule-based_system'':103C ''/wiki/rule-based_system&lt;/a&gt;'':108C ''base'':27C,53C,85C,87C ''classif'':74C ''code'':70C ''data'':23C ''en.wikipedia.org'':102C,107C ''en.wikipedia.org/wiki/rule-based_system'':101C ''en.wikipedia.org/wiki/rule-based_system&lt;/a&gt;'':106C ''first'':56C ''hard'':33C ''howev'':79C ''href'':100C ''instinct'':57C ''lead'':71C ''learn'':29C,82C,97C ''like'':42C ''logic'':89C ''look'':7C ''machin'':28C,96C ''method'':11C,80C ''nofollow'':105C ''numer'':22C ''one'':43C ''opinion'':4C ''p'':1C,17C,25C,76C ''problem'':15C,37C,39C,46C ''put'':67C ''quit'':93C ''re'':6C ''rel'':104C ''rule'':52C,65C,84C ''solv'':13C,50C ''sound'':40C ''statist'':26C ''strict'':20C ''system'':54C,86C ''time'':34C ''today'':98C ''tri'':61C ''understand'':63C ''unfashion'':94C ''would'':58C ''wrong'':10C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10516, 2, '2016-03-03 23:39:18.74', 0, NULL, NULL, '&lt;p&gt;Every Blob in caffe can be assigned a nonzero loss weight.&lt;/p&gt;

&lt;p&gt;And you can have an arbitrary number of outputs.&lt;/p&gt;

&lt;p&gt;This means you can just learn n different networks on the same data with different targets and assign every loss function it''s own weight. Caffe takes care of adding up all the loss.&lt;/p&gt;
', NULL, NULL, '''/p'':13C,24C,60C ''ad'':55C ''arbitrari'':20C ''assign'':8C,43C ''blob'':3C ''caff'':5C,51C ''care'':53C ''data'':38C ''differ'':33C,40C ''everi'':2C,44C ''function'':46C ''learn'':31C ''loss'':11C,45C,59C ''mean'':27C ''n'':32C ''network'':34C ''nonzero'':10C ''number'':21C ''output'':23C ''p'':1C,14C,25C ''take'':52C ''target'':41C ''weight'':12C,50C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10517, 1, '2016-03-03 23:41:57.497', 2, 78, 'If my data looks like this, is linear regression just never gonna be useful?', '&lt;p&gt;&lt;a href="http://i.stack.imgur.com/Nurw0.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/Nurw0.png" alt="These are 7 features in scatter plots across my output variable "&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Clearly you can see that there is no linear relationship. If there was one it would be a vertical line which is not that useful. I need some type of regression rather than classification as the output is continuous. The only options I can think of are neural networks and SVR (support vector regressors). Is linear regression a waste of time here? What would you use?&lt;/p&gt;

&lt;p&gt;The use case is - I am trying to predict the rgb color of a polygon based on its area, length, index, complexity, #of lines, # of curves, and intensity (black and white). &lt;/p&gt;

&lt;p&gt;In this first step I am merely trying to predict the amount of red from 0 to 1. (scaled down from 255).&lt;/p&gt;

&lt;p&gt;Edit: &lt;/p&gt;

&lt;p&gt;here are the labels - rgb values: &lt;a href="http://www.heypasteit.com/clip/2HZX" rel="nofollow"&gt;http://www.heypasteit.com/clip/2HZX&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;here is the data with the 7 features: &lt;a href="http://www.heypasteit.com/clip/2HZY" rel="nofollow"&gt;http://www.heypasteit.com/clip/2HZY&lt;/a&gt;&lt;/p&gt;
', 'neuralnetwork  regression  linear-regression', NULL, '''/a'':45C ''/clip/2hzx'':186C ''/clip/2hzx&lt;/a&gt;&lt;/p&gt;'':191C ''/clip/2hzy'':205C ''/clip/2hzy&lt;/a&gt;&lt;/p&gt;'':210C ''/nurw0.png'':25C,32C ''/p'':46C,114C,147C,171C,174C ''0'':164C ''1'':166C ''255'':170C ''7'':36C,199C ''across'':41C ''alt'':33C ''amount'':160C ''area'':134C ''base'':131C ''black'':144C ''case'':118C ''classif'':81C ''clear'':48C ''color'':127C ''complex'':137C ''continu'':86C ''curv'':141C ''data'':3A,196C ''edit'':173C ''featur'':37C,200C ''first'':151C ''gonna'':12A ''href'':22C,183C,202C ''i.stack.imgur.com'':24C,31C ''i.stack.imgur.com/nurw0.png'':23C,30C ''img'':28C ''index'':136C ''intens'':143C ''label'':179C ''length'':135C ''like'':5A ''line'':67C,139C ''linear'':8A,18B,56C,103C ''linear-regress'':17B ''look'':4A ''mere'':155C ''need'':74C ''network'':96C ''neural'':95C ''neuralnetwork'':15B ''never'':11A ''nofollow'':27C,188C,207C ''one'':61C ''option'':89C ''output'':43C,84C ''p'':20C,47C,115C,148C,172C,175C,192C ''plot'':40C ''polygon'':130C ''predict'':124C,158C ''rather'':79C ''red'':162C ''regress'':9A,16B,19B,78C,104C ''regressor'':101C ''rel'':26C,187C,206C ''relationship'':57C ''rgb'':126C,180C ''scale'':167C ''scatter'':39C ''see'':51C ''src'':29C ''step'':152C ''support'':99C ''svr'':98C ''think'':92C ''time'':108C ''tri'':122C,156C ''type'':76C ''use'':14A,72C,113C,117C ''valu'':181C ''variabl'':44C ''vector'':100C ''vertic'':66C ''wast'':106C ''white'':146C ''would'':63C,111C ''www.heypasteit.com'':185C,190C,204C,209C ''www.heypasteit.com/clip/2hzx'':184C ''www.heypasteit.com/clip/2hzx&lt;/a&gt;&lt;/p&gt;'':189C ''www.heypasteit.com/clip/2hzy'':203C ''www.heypasteit.com/clip/2hzy&lt;/a&gt;&lt;/p&gt;'':208C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10523, 1, '2016-03-04 09:32:17.287', 4, 47, 'Guidelines for selecting an optimizer for training neural networks', '&lt;p&gt;I''ve been using neural networks for a while now. However, one thing that I constantly struggle with is the selection of an optimizer for training the network (using backprop). What I usually do is just start with one (e.g. standard SGD) and then try other others pretty much randomly. I was wondering if there''s a better (and less random) approach to finding a good optimizer, e.g. from this list:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;SGD (with or without momentum)&lt;/li&gt;
&lt;li&gt;AdaDelta&lt;/li&gt;
&lt;li&gt;AdaGrad&lt;/li&gt;
&lt;li&gt;RMSProp&lt;/li&gt;
&lt;li&gt;Adam&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In particular, I''m interested if there''s some theoretical justification for picking one over another given the training data has some property, e.g. it being sparse. I would also imagine that some optimizers work better than others in specific domains, e.g. when training convolutional networks vs. feed-forward networks or classification vs. regression.&lt;/p&gt;

&lt;p&gt;If any of you have developed some strategy and/or intuition on how you pick optimizers, I''d be greatly interested in hearing it. Furthermore, if there''s some work that provides theoretical justification for picking one over another, that''d be even better.&lt;/p&gt;

&lt;p&gt;Thanks in advance!&lt;/p&gt;
', 'neuralnetwork  optimization  backpropagation', NULL, '''/li'':93C,96C,99C,102C,105C ''/p'':85C,163C,208C,213C ''/ul'':106C ''adadelta'':95C ''adagrad'':98C ''adam'':104C ''advanc'':212C ''also'':137C ''and/or'':173C ''anoth'':123C,202C ''approach'':75C ''backprop'':43C ''backpropag'':12B ''better'':71C,143C,207C ''classif'':160C ''constant'':29C ''convolut'':152C ''d'':181C,204C ''data'':127C ''develop'':170C ''domain'':148C ''e.g'':53C,81C,131C,149C ''even'':206C ''feed'':156C ''feed-forward'':155C ''find'':77C ''forward'':157C ''furthermor'':188C ''given'':124C ''good'':79C ''great'':183C ''guidelin'':1A ''hear'':186C ''howev'':24C ''imagin'':138C ''interest'':112C,184C ''intuit'':174C ''justif'':118C,197C ''less'':73C ''li'':87C,94C,97C,100C,103C ''list'':84C ''m'':111C ''momentum'':92C ''much'':62C ''network'':9A,19C,41C,153C,158C ''neural'':8A,18C ''neuralnetwork'':10B ''one'':25C,52C,121C,200C ''optim'':5A,11B,37C,80C,141C,179C ''other'':60C,145C ''p'':13C,107C,164C,209C ''particular'':109C ''pick'':120C,178C,199C ''pretti'':61C ''properti'':130C ''provid'':195C ''random'':63C,74C ''regress'':162C ''rmsprop'':101C ''select'':3A,34C ''sgd'':55C,88C ''spars'':134C ''specif'':147C ''standard'':54C ''start'':50C ''strategi'':172C ''struggl'':30C ''thank'':210C ''theoret'':117C,196C ''thing'':26C ''train'':7A,39C,126C,151C ''tri'':58C ''ul'':86C ''use'':17C,42C ''usual'':46C ''ve'':15C ''vs'':154C,161C ''without'':91C ''wonder'':66C ''work'':142C,193C ''would'':136C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10518, 1, '2016-03-04 02:31:04.177', -4, 10, 'Anomaly detection and security analytic', '&lt;p&gt;I am looking for a technical assistants to solve these &lt;strong&gt;problems(In today business environment; the face of doing or ruining trading activities has significantly change due to technological advancement and its full inclusion to be the driving pivot that can survive the business.Financial institution like the today received millions of multiple data emanating from customers in-person, social media, mobile phones, tablets etc. Managing and extracting information from said data is not only time consuming; but posse a lot of technical challenges, hence fraudsters now take advantage of perpetrating various forms of attack hard to combat.They away million of cash in every micro seconds due to "the 3V''s-volume,velocity and variety" which is new field of study as Big Data. How due we determine anomaly in these situation?&lt;/strong&gt;: anomaly detection using big data techniques and applications in Cyber-security warfare. ***The purpose of this research is to:
 (1)develop a more secure algorithm for the implementation of Anomaly Detection using Big Data and Cyber Security warfare techniques and &lt;/p&gt;

&lt;p&gt;(2)applications in the E-commerce/E-business environment***  &lt;em&gt;T&lt;strong&gt;he main goal of  is to obtain unlawful intelligence with the development of machine learning models in real time whenever cyber criminals use complex methods and unknown methods in fraudulent activities&lt;/em&gt;&lt;/strong&gt;. &lt;/p&gt;

&lt;p&gt;I am also looking for;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Big Data and Cyber Security warfare techniques and applications can be employed in the E-commerce/E-business sectors to analyze financial transactions, log files, and network traffic to identify anomalies and suspicious activities, and to correlate multiple sources of information into a clear view.&lt;/em&gt;&lt;/strong&gt; 
I also need data set to test whatever tool,application or techniques/framework, i will be help to develop.&lt;/p&gt;

&lt;p&gt;Is there any sophisticated architecture/framework for this work?
What algorithm is appropriate?
What algorithm can read malicious code hiding in a data in real time? how can one intercept these data in transition, how do you clean the the data? how do you filter this data? how do classified them; assuming they emanating from multiple sources?
What machine technique is suitable
What tool is appropriate?&lt;/p&gt;
', 'machine-learning  data-mining  classification  visualization  apache-spark', NULL, '''/e-business'':203C,269C ''/em'':239C,297C ''/p'':194C,241C,248C,317C,384C ''/strong'':152C,240C,298C ''1'':173C ''2'':196C ''3v'':128C ''activ'':41C,238C,285C ''advanc'':48C ''advantag'':107C ''algorithm'':178C,328C,332C ''also'':245C,300C ''analyt'':5A ''analyz'':272C ''anomali'':1A,148C,153C,183C,282C ''apach'':15B ''apache-spark'':14B ''applic'':160C,197C,260C,308C ''appropri'':330C,383C ''architecture/framework'':323C ''assist'':24C ''assum'':369C ''attack'':113C ''away'':117C ''big'':142C,156C,186C,252C ''busi'':32C ''business.financial'':62C ''cash'':120C ''challeng'':102C ''chang'':44C ''classif'':12B ''classifi'':367C ''clean'':355C ''clear'':295C ''code'':336C ''combat.they'':116C ''commerc'':202C,268C ''complex'':231C ''consum'':95C ''correl'':288C ''crimin'':229C ''custom'':74C ''cyber'':163C,189C,228C,255C ''cyber-secur'':162C ''data'':10B,71C,90C,143C,157C,187C,253C,302C,340C,349C,358C,364C ''data-min'':9B ''detect'':2A,154C,184C ''determin'':147C ''develop'':174C,219C,316C ''drive'':56C ''due'':45C,125C,145C ''e'':201C,267C ''e-commerc'':200C,266C ''em'':205C,251C ''eman'':72C,371C ''employ'':263C ''environ'':33C,204C ''etc'':83C ''everi'':122C ''extract'':86C ''face'':35C ''field'':138C ''file'':276C ''filter'':362C ''financi'':273C ''form'':111C ''fraudster'':104C ''fraudul'':237C ''full'':51C ''goal'':210C ''hard'':114C ''help'':314C ''henc'':103C ''hide'':337C ''identifi'':281C ''implement'':181C ''in-person'':75C ''inclus'':52C ''inform'':87C,292C ''institut'':63C ''intellig'':216C ''intercept'':347C ''learn'':8B,222C ''like'':64C ''log'':275C ''look'':20C,246C ''lot'':99C ''machin'':7B,221C,376C ''machine-learn'':6B ''main'':209C ''malici'':335C ''manag'':84C ''media'':79C ''method'':232C,235C ''micro'':123C ''million'':68C,118C ''mine'':11B ''mobil'':80C ''model'':223C ''multipl'':70C,289C,373C ''need'':301C ''network'':278C ''new'':137C ''obtain'':214C ''one'':346C ''p'':17C,195C,242C,249C,318C ''perpetr'':109C ''person'':77C ''phone'':81C ''pivot'':57C ''poss'':97C ''problem'':29C ''purpos'':167C ''read'':334C ''real'':225C,342C ''receiv'':67C ''research'':170C ''ruin'':39C ''s-volum'':129C ''said'':89C ''second'':124C ''sector'':270C ''secur'':4A,164C,177C,190C,256C ''set'':303C ''signific'':43C ''situat'':151C ''social'':78C ''solv'':26C ''sophist'':322C ''sourc'':290C,374C ''spark'':16B ''strong'':28C,207C,250C ''studi'':140C ''suitabl'':379C ''surviv'':60C ''suspici'':284C ''tablet'':82C ''take'':106C ''technic'':23C,101C ''techniqu'':158C,192C,258C,377C ''techniques/framework'':310C ''technolog'':47C ''test'':305C ''time'':94C,226C,343C ''today'':31C,66C ''tool'':307C,381C ''trade'':40C ''traffic'':279C ''transact'':274C ''transit'':351C ''unknown'':234C ''unlaw'':215C ''use'':155C,185C,230C ''varieti'':134C ''various'':110C ''veloc'':132C ''view'':296C ''visual'':13B ''volum'':131C ''warfar'':165C,191C,257C ''whatev'':306C ''whenev'':227C ''work'':326C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10520, 2, '2016-03-04 07:23:58.337', 1, NULL, NULL, '&lt;p&gt;In line with the above comment, I suggest you try a rule-based approach. For each server you have, query its services. If all of the services are down on a server, then you have a server problem. For each service, if no server reports that the service is running, then you have a problem with the service. In the case that it''s both, you''ll get notices for each and then be able to go in an inspect what''s going on with any of the constituents.&lt;/p&gt;

&lt;p&gt;The cost, maintenance, and accuracy of an ANN model all exceed the simple, rule-based solution and your boss will probably pat you on the back for just doing what makes sense here. &lt;/p&gt;

&lt;p&gt;If you''re really serious about keeping your servers and processes functional, I suggest you invest in a APM service that gives you reliable, real-time notifications on what''s going on in your production environment.&lt;/p&gt;

&lt;p&gt;In the case that you''re just trying to learn how ANN work - try a different problem. Any well-known dataset for classification or anomaly detection will provide you with a lot more insight into how ANN work than a custom dataset, which can be horribly hard to coerce into an effective learning scheme.&lt;/p&gt;
', NULL, NULL, '''/p'':92C,128C,167C,224C ''abl'':77C ''accuraci'':98C ''ann'':101C,180C,206C ''anomali'':194C ''apm'':148C ''approach'':16C ''back'':120C ''base'':15C,109C ''boss'':113C ''case'':63C,171C ''classif'':192C ''coerc'':218C ''comment'':7C ''constitu'':91C ''cost'':95C ''custom'':210C ''dataset'':190C,211C ''detect'':195C ''differ'':184C ''effect'':221C ''environ'':166C ''exceed'':104C ''function'':141C ''get'':70C ''give'':151C ''go'':79C,85C,161C ''hard'':216C ''horribl'':215C ''insight'':203C ''inspect'':82C ''invest'':145C ''keep'':136C ''known'':189C ''learn'':178C,222C ''line'':3C ''ll'':69C ''lot'':201C ''mainten'':96C ''make'':125C ''model'':102C ''notic'':71C ''notif'':157C ''p'':1C,93C,129C,168C ''pat'':116C ''probabl'':115C ''problem'':40C,57C,185C ''process'':140C ''product'':165C ''provid'':197C ''queri'':22C ''re'':132C,174C ''real'':155C ''real-tim'':154C ''realli'':133C ''reliabl'':153C ''report'':47C ''rule'':14C,108C ''rule-bas'':13C,107C ''run'':52C ''scheme'':223C ''sens'':126C ''serious'':134C ''server'':19C,34C,39C,46C,138C ''servic'':24C,29C,43C,50C,60C,149C ''simpl'':106C ''solut'':110C ''suggest'':9C,143C ''time'':156C ''tri'':11C,176C,182C ''well'':188C ''well-known'':187C ''work'':181C,207C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10521, 1, '2016-03-04 08:00:39.95', 3, 24, 'Output prediction from a sequence of data', '&lt;p&gt;Given the machine learning libraries available for many different languages, it''s possible to utilise algorithms where you don''t need to have a detailed understanding of their application or workings or datascience in general.&lt;/p&gt;

&lt;p&gt;I have tried to find a list of algorithms that are suitable for sequence prediction from a window of previous input values, which I could then apply in such a library e.g. Accord for C# but have failed.&lt;/p&gt;

&lt;p&gt;Each observation I have is an array of 2-dimensonal data in the following form: {MODE1/2/3, int 1-98}. That is, multiple inputs produce multiple outputs. I''d like to examine preceding sets of values from either &lt;code&gt;t-1&lt;/code&gt; or &lt;code&gt;t-x&lt;/code&gt; where x could be a variable amount of preceding value sets.&lt;/p&gt;

&lt;p&gt;So, I''d like to ask what algorithms excel at prediction from a window of sequence data, and what are their strengths / weaknesses.&lt;/p&gt;
', 'predictive-modeling  algorithms', NULL, '''-1'':129C ''-98'':108C ''/code'':130C,136C ''/p'':48C,88C,148C,173C ''1'':107C ''2'':98C ''accord'':82C ''algorithm'':11B,28C,58C,157C ''amount'':143C ''appli'':76C ''applic'':41C ''array'':96C ''ask'':155C ''avail'':18C ''c'':84C ''code'':127C,132C ''could'':74C,139C ''d'':117C,152C ''data'':7A,100C,166C ''datasci'':45C ''detail'':37C ''differ'':21C ''dimenson'':99C ''e.g'':81C ''either'':126C ''examin'':120C ''excel'':158C ''fail'':87C ''find'':54C ''follow'':103C ''form'':104C ''general'':47C ''given'':13C ''input'':70C,112C ''int'':106C ''languag'':22C ''learn'':16C ''librari'':17C,80C ''like'':118C,153C ''list'':56C ''machin'':15C ''mani'':20C ''mode1/2/3'':105C ''model'':10B ''multipl'':111C,114C ''need'':33C ''observ'':91C ''output'':1A,115C ''p'':12C,49C,89C,149C ''possibl'':25C ''preced'':121C,145C ''predict'':2A,9B,64C,160C ''predictive-model'':8B ''previous'':69C ''produc'':113C ''sequenc'':5A,63C,165C ''set'':122C,147C ''strength'':171C ''suitabl'':61C ''t-x'':133C ''tri'':52C ''understand'':38C ''utilis'':27C ''valu'':71C,124C,146C ''variabl'':142C ''weak'':172C ''window'':67C,163C ''work'':43C ''x'':135C,138C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10522, 1, '2016-03-04 08:33:36.833', 3, 46, 'Visualize graph network with more than 30k edges', '&lt;p&gt;I have a dataset with users connection and I want to create a directed network graph.&lt;/p&gt;

&lt;p&gt;Nodes: ~20.000
Edges: ~33.000&lt;/p&gt;

&lt;p&gt;I used to work before on NodeXL and Gephi, but now I am on Mac and I don''t have access on NodeXL.&lt;/p&gt;

&lt;p&gt;I tried Gephi and another tool called Cytoscape. Both of them were lagging and I could barely do some statistics. Visualizing the graph was a dream. &lt;/p&gt;

&lt;p&gt;Is there any software (or maybe Python library) that can handle such a big network Graph?&lt;/p&gt;
', 'graphs  social-network-analysis', NULL, '''/p'':31C,37C,62C,90C,108C ''20.000'':34C ''30k'':7A ''33.000'':36C ''access'':59C ''analysi'':13B ''anoth'':68C ''bare'':80C ''big'':105C ''call'':70C ''connect'':21C ''could'':79C ''creat'':26C ''cytoscap'':71C ''dataset'':18C ''direct'':28C ''dream'':89C ''edg'':8A,35C ''gephi'':47C,66C ''graph'':2A,9B,30C,86C,107C ''handl'':102C ''lag'':76C ''librari'':99C ''mac'':53C ''mayb'':97C ''network'':3A,12B,29C,106C ''node'':33C ''nodexl'':45C,61C ''p'':14C,32C,38C,63C,91C ''python'':98C ''social'':11B ''social-network-analysi'':10B ''softwar'':95C ''statist'':83C ''tool'':69C ''tri'':65C ''use'':40C ''user'':20C ''visual'':1A,84C ''want'':24C ''work'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10524, 2, '2016-03-04 09:34:33.68', 2, NULL, NULL, '&lt;p&gt;The question has been answered in google groups by Gordon mohr.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Normally there''s one read of the corpus to build the vocabulary (which includes initializing the model based on the learned vocabulary size), then any number of extra passes for training. It''s only after the one vocabulary-learning scan that word counts are looked at (and compared to &lt;code&gt;min_count&lt;/code&gt; for trimming). &lt;/p&gt;
  
  &lt;p&gt;If you supply a corpus (as a restartable iterator) as one of the arguments to the initial creation of the Word2Vec model, all these steps are done automatically: one read of the corpus (through the &lt;code&gt;build_vocab()&lt;/code&gt; method) to collect words/counts, then one or more passes (as controlled by the ''iter'' parameter and done through the &lt;code&gt;train()&lt;/code&gt; method) for training. Still, only the count for the single pass over the supplied corpus matters for frequency decisions. &lt;/p&gt;
  
  &lt;p&gt;If you don''t supply a corpus at model-initialization, you can then call &lt;code&gt;build_vocab(â¦)&lt;/code&gt; and &lt;code&gt;train(â¦)&lt;/code&gt; yourself. It''s only what''s passed to &lt;code&gt;build_vocab()&lt;/code&gt; that matters for retained frequency counts (and the estimate of corpus size). You can then call &lt;code&gt;train(â¦)&lt;/code&gt; in other ways, or repeatedly â it just keeps using the vocabulary from the one earlier &lt;code&gt;build_vocab(â¦)&lt;/code&gt; call. &lt;/p&gt;
  
  &lt;p&gt;(Note that &lt;code&gt;train(â¦)&lt;/code&gt; does try to reuse the single-pass corpus size, remembered from the vocab-scanning pass, to give accurate progress-estimates and schedule the decay of the training-rate &lt;code&gt;alpha&lt;/code&gt;. So if you give a different-sized corpus to &lt;code&gt;train(â¦)&lt;/code&gt;, you should also use its other optional parameters to give it a hint of the size.)&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/blockquote'':309C ''/code'':69C,112C,134C,175C,180C,192C,212C,233C,242C,277C,291C ''/p'':13C,72C,154C,235C,308C ''accur'':262C ''alpha'':276C ''also'':294C ''answer'':6C ''argument'':87C ''automat'':101C ''base'':33C ''blockquot'':14C ''build'':25C,110C,172C,190C,230C ''call'':170C,208C,234C ''code'':66C,109C,132C,171C,177C,189C,209C,229C,239C,275C,288C ''collect'':115C ''compar'':64C ''control'':123C ''corpus'':23C,78C,106C,149C,162C,203C,251C,286C ''count'':59C,68C,141C,198C ''creation'':91C ''decay'':269C ''decis'':153C ''differ'':284C ''different-s'':283C ''done'':100C,129C ''earlier'':228C ''estim'':201C,265C ''extra'':43C ''frequenc'':152C,197C ''give'':261C,281C,301C ''googl'':8C ''gordon'':11C ''group'':9C ''hint'':304C ''includ'':29C ''initi'':30C,90C,166C ''iter'':82C,126C ''keep'':221C ''learn'':36C,55C ''look'':61C ''matter'':150C,194C ''method'':113C,135C ''min'':67C ''model'':32C,95C,165C ''model-initi'':164C ''mohr'':12C ''normal'':16C ''note'':237C ''number'':41C ''one'':19C,52C,84C,102C,118C,227C ''option'':298C ''p'':1C,15C,73C,155C,236C ''paramet'':127C,299C ''pass'':44C,121C,145C,187C,250C,259C ''progress'':264C ''progress-estim'':263C ''question'':3C ''rate'':274C ''read'':20C,103C ''rememb'':253C ''repeat'':217C ''restart'':81C ''retain'':196C ''reus'':246C ''scan'':56C,258C ''schedul'':267C ''singl'':144C,249C ''single-pass'':248C ''size'':38C,204C,252C,285C,307C ''step'':98C ''still'':138C ''suppli'':76C,148C,160C ''train'':46C,133C,137C,178C,210C,240C,273C,289C ''training-r'':272C ''tri'':244C ''trim'':71C ''use'':222C,295C ''vocab'':111C,173C,191C,231C,257C ''vocab-scan'':256C ''vocabulari'':27C,37C,54C,224C ''vocabulary-learn'':53C ''way'':215C ''word'':58C ''word2vec'':94C ''words/counts'':116C ''â'':174C,179C,211C,218C,232C,241C,290C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10525, 1, '2016-03-04 10:03:04.807', 1, 12, 'What models are used to get the Next Best Action to convert a physician from a non writer to a write?', '&lt;p&gt;Assume that a sales rep has actions A, B, C, D.... X that he can perform to influence a physician to write his drug&lt;/p&gt;

&lt;p&gt;I want to predict what the next best action a sales rep should take to have the highest impact on a physician&lt;/p&gt;

&lt;p&gt;For example, if a sales rep has already performed A, D, F then i want to predict the next best action for the sales rep to perform to convert the physician to a writer&lt;/p&gt;

&lt;p&gt;What models/algorithms are suitable for this type of a problem?&lt;/p&gt;
', 'bigdata  statistics  predictive-modeling  model-selection', NULL, '''/p'':55C,79C,115C,127C ''action'':10A,37C,65C,101C ''alreadi'':88C ''assum'':31C ''b'':39C ''best'':9A,64C,100C ''bigdata'':22B ''c'':40C ''convert'':12A,109C ''d'':41C,91C ''drug'':54C ''exampl'':82C ''f'':92C ''get'':6A ''highest'':74C ''impact'':75C ''influenc'':48C ''model'':2A,26B,28B ''model-select'':27B ''models/algorithms'':118C ''next'':8A,63C,99C ''non'':17A ''p'':30C,56C,80C,116C ''perform'':46C,89C,107C ''physician'':14A,50C,78C,111C ''predict'':25B,60C,97C ''predictive-model'':24B ''problem'':126C ''rep'':35C,68C,86C,105C ''sale'':34C,67C,85C,104C ''select'':29B ''statist'':23B ''suitabl'':120C ''take'':70C ''type'':123C ''use'':4A ''want'':58C,95C ''write'':21A,52C ''writer'':18A,114C ''x'':42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10526, 2, '2016-03-04 11:42:27.55', 1, NULL, NULL, '&lt;p&gt;Expanding on my comment, the simplest predictive model would be to just compute the conversion rates Pr(conversion | action) for each action {A,B,C,D, ... }.&lt;/p&gt;

&lt;p&gt;A solution in R:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library(dplyr)
library(tidyr)

set.seed(100) # for reproducibility
action &amp;lt;- sample(letters[1:5], 100, replace = TRUE) # dummy up sales actions (independent variable)
converted &amp;lt;- as.logical(rbinom(100, 1, prob = .5))  # dummy up physician conversion (dependent variable)

(df &amp;lt;- data.frame(action = action, converted = converted))

# Compute conversion rates for each action
conv_df &amp;lt;- df %&amp;gt;% 
  group_by(action, converted) %&amp;gt;% # unique combinations of action by converted
  summarize(freq = n()) %&amp;gt;% # compute freq of unique combinations
  spread(converted, freq) %&amp;gt;% # spread rows to cols
  mutate(rate = `TRUE`/sum(`TRUE`,`FALSE`)) # compute conversion rate

conv_df[order(-conv_df$rate),] #reverse sort

##Source: local data frame [5 x 4]
##Groups: action [5]
##
##  action FALSE  TRUE      rate
##  (fctr) (int) (int)     (dbl)
##1      d     8    13 0.6190476
##2      e     7    11 0.6111111
##3      a     5     6 0.5454545
##4      c    13    10 0.4347826
##5      b    17    10 0.3703704
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The solution above assumes the application is to be some sort of guide for sales reps... if you are wanting to model the distribution of actions in sales contexts, you would need to compute the prevalence of each action as well.&lt;/p&gt;
', NULL, NULL, '''/code'':180C ''/p'':28C,34C,224C ''/pre'':181C ''/sum'':123C ''0.3703704'':179C ''0.4347826'':174C ''0.5454545'':169C ''0.6111111'':164C ''0.6190476'':159C ''1'':49C,65C,155C ''10'':173C,178C ''100'':42C,51C,64C ''11'':163C ''13'':158C,172C ''17'':177C ''2'':160C ''3'':165C ''4'':143C,170C ''5'':50C,67C,141C,146C,167C,175C ''6'':168C ''7'':162C ''8'':157C ''action'':20C,23C,45C,57C,77C,78C,86C,94C,100C,145C,147C,208C,221C ''applic'':188C ''as.logical'':62C ''assum'':186C ''b'':25C,176C ''c'':26C,171C ''code'':36C ''col'':119C ''combin'':98C,111C ''comment'':5C ''comput'':14C,81C,107C,126C,216C ''context'':211C ''conv'':87C,129C,132C ''convers'':16C,19C,71C,82C,127C ''convert'':60C,79C,80C,95C,102C,113C ''d'':27C,156C ''data'':139C ''data.frame'':76C ''dbl'':154C ''depend'':72C ''df'':74C,88C,90C,130C,133C ''distribut'':206C ''dplyr'':38C ''dummi'':54C,68C ''e'':161C ''expand'':2C ''fals'':125C,148C ''fctr'':151C ''frame'':140C ''freq'':104C,108C,114C ''group'':92C,144C ''gt'':91C,96C,106C,115C ''guid'':195C ''independ'':58C ''int'':152C,153C ''letter'':48C ''librari'':37C,39C ''local'':138C ''lt'':46C,61C,75C,89C ''model'':9C,204C ''mutat'':120C ''n'':105C ''need'':214C ''order'':131C ''p'':1C,29C,182C ''physician'':70C ''pr'':18C ''pre'':35C ''predict'':8C ''preval'':218C ''prob'':66C ''r'':33C ''rate'':17C,83C,121C,128C,134C,150C ''rbinom'':63C ''rep'':198C ''replac'':52C ''reproduc'':44C ''revers'':135C ''row'':117C ''sale'':56C,197C,210C ''sampl'':47C ''set.seed'':41C ''simplest'':7C ''solut'':31C,184C ''sort'':136C,193C ''sourc'':137C ''spread'':112C,116C ''summar'':103C ''tidyr'':40C ''true'':53C,122C,124C,149C ''uniqu'':97C,110C ''variabl'':59C,73C ''want'':202C ''well'':223C ''would'':10C,213C ''x'':142C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10528, 1, '2016-03-04 13:00:54.08', 2, 32, 'How to interpret a dataset with a pseudo-logarithmic trend?', '&lt;p&gt;I am dealing with a scatterplot where I am trying to figure out the relationship between two variables, but I have so far failed to identify what the best fit could be. The presence of zero values prevents me from using exponential and power law regression lines.&lt;/p&gt;

&lt;p&gt;Zero values are not shown as the Y axis starts from 1, but they are present. My bad.&lt;/p&gt;

&lt;p&gt;This is the scatterplot. To me it looks like this is some sort of logarithmic trend but I am not sure about it.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;What model will best interpret this trend?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/J2WVX.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/J2WVX.png" alt="enter image description here"&gt;&lt;/a&gt;&lt;/p&gt;
', 'dataset  regression', NULL, '''/a'':137C ''/j2wvx.png'':124C,131C ''/p'':62C,82C,107C,118C,138C ''/strong'':117C ''1'':75C ''alt'':132C ''axi'':72C ''bad'':81C ''best'':43C,113C ''could'':45C ''dataset'':5A,12B ''deal'':17C ''descript'':135C ''enter'':133C ''exponenti'':56C ''fail'':38C ''far'':37C ''figur'':26C ''fit'':44C ''href'':121C ''i.stack.imgur.com'':123C,130C ''i.stack.imgur.com/j2wvx.png'':122C,129C ''identifi'':40C ''imag'':134C ''img'':127C ''interpret'':3A,114C ''law'':59C ''like'':92C ''line'':61C ''logarithm'':10A,98C ''look'':91C ''model'':111C ''nofollow'':126C ''p'':14C,63C,83C,108C,119C ''power'':58C ''presenc'':48C ''present'':79C ''prevent'':52C ''pseudo'':9A ''pseudo-logarithm'':8A ''regress'':13B,60C ''rel'':125C ''relationship'':29C ''scatterplot'':20C,87C ''shown'':68C ''sort'':96C ''src'':128C ''start'':73C ''strong'':109C ''sure'':104C ''trend'':11A,99C,116C ''tri'':24C ''two'':31C ''use'':55C ''valu'':51C,65C ''variabl'':32C ''y'':71C ''zero'':50C,64C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10529, 1, '2016-03-04 16:49:25.55', 2, 18, 'LSTM neural network for music generation', '&lt;p&gt;I am doing a project where I have to train a LSTM neural network to generate music. The first step is of course training it. I am using Keras on top of Theano for this task and music21 for feature extraction from MIDI files.
So far the data I extract from MIDI files has the following format:&lt;/p&gt;

&lt;p&gt;[[69, -7, 1, 0.75],
 [62, -1, 0, 0.25] ... ]&lt;/p&gt;

&lt;p&gt;These are the 4 features that I take from the files. They are then placed in a 3D numpy array with the shape - (number of notes in sequence, 1 (time steps per sample), 4 (features) ).
I create another array with the expected output for each time step being the data in the next time step. I''ve built the following model: &lt;a href="http://pastebin.com/AHCJj1wa" rel="nofollow"&gt;http://pastebin.com/AHCJj1wa&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The problem is that when it starts training the model I get some nonsensical output. First the epochs'' output looks like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Epoch 1/100
0s - loss: 15.2986 - acc: 0.9738
Epoch 2/100
0s - loss: -3.3534e+02 - acc: 0.8265
Epoch 3/100
0s - loss: -3.8183e+02 - acc: 0.8265
Epoch 4/100
0s - loss: -3.8218e+02 - acc: 0.8265
Epoch 5/100
0s - loss: -3.8256e+02 - acc: 0.8265
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;It seems to be taking 0s per epoch and the loss just becomes larger and larger or stops changing at all (differs between runs).
Also at the end of the training cycle when I call .predict it prints something like that:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[[ 0.11846249 -0.13457553  0.08614471 -0.1007532 ]
 [ 0.14596225 -0.17533173  0.10007301 -0.08799653]
 [ 0.15234099 -0.19707087  0.10395571 -0.08419057]
 [ 0.14596225 -0.17533173  0.10007301 -0.08799653]
 [ 0.14340727 -0.1664145   0.0987886  -0.09037785]
 [ 0.14596225 -0.17533173  0.10007301 -0.08799653]
 [ 0.14340727 -0.1664145   0.0987886  -0.09037785]
 [ 0.14802463 -0.17770432  0.10296948 -0.08740482]
 ..... ]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I think that there is something fundamentally wrong with my model and/or representation of the data. Any ideas on what I can do or at least some pointers to where I can read about this?&lt;/p&gt;
', 'machine-learning  python  neuralnetwork  keras', NULL, '''-0.08419057'':273C ''-0.08740482'':293C ''-0.08799653'':269C,277C,285C ''-0.09037785'':281C,289C ''-0.1007532'':265C ''-0.13457553'':263C ''-0.1664145'':279C,287C ''-0.17533173'':267C,275C,283C ''-0.17770432'':291C ''-0.19707087'':271C ''-1'':78C ''-3.3534e+02'':191C ''-3.8183e+02'':198C ''-3.8218e+02'':205C ''-3.8256e+02'':212C ''-7'':74C ''/ahcjj1wa'':148C ''/ahcjj1wa&lt;/a&gt;&lt;/p&gt;'':153C ''/code'':215C,294C ''/p'':71C,81C,177C,259C,332C ''/pre'':216C,295C ''0'':79C ''0.08614471'':264C ''0.0987886'':280C,288C ''0.10007301'':268C,276C,284C ''0.10296948'':292C ''0.10395571'':272C ''0.11846249'':262C ''0.14340727'':278C,286C ''0.14596225'':266C,274C,282C ''0.14802463'':290C ''0.15234099'':270C ''0.25'':80C ''0.75'':76C ''0.8265'':193C,200C,207C,214C ''0.9738'':186C ''0s'':182C,189C,196C,203C,210C,223C ''1'':75C,111C ''1/100'':181C ''15.2986'':184C ''2/100'':188C ''3/100'':195C ''3d'':100C ''4'':86C,116C ''4/100'':202C ''5/100'':209C ''62'':77C ''69'':73C ''acc'':185C,192C,199C,206C,213C ''also'':242C ''and/or'':308C ''anoth'':120C ''array'':102C,121C ''becom'':230C ''built'':140C ''call'':252C ''chang'':236C ''code'':179C,261C ''cours'':36C ''creat'':119C ''cycl'':249C ''data'':61C,132C,312C ''differ'':239C ''end'':245C ''epoch'':172C,180C,187C,194C,201C,208C,225C ''expect'':124C ''extract'':54C,63C ''far'':59C ''featur'':53C,87C,117C ''file'':57C,66C,93C ''first'':32C,170C ''follow'':69C,142C ''format'':70C ''fundament'':303C ''generat'':6A,29C ''get'':166C ''href'':145C ''idea'':314C ''kera'':12B,42C ''larger'':231C,233C ''learn'':9B ''least'':322C ''like'':175C,257C ''look'':174C ''loss'':183C,190C,197C,204C,211C,228C ''lstm'':1A,25C ''machin'':8B ''machine-learn'':7B ''midi'':56C,65C ''model'':143C,164C,307C ''music'':5A,30C ''music21'':51C ''network'':3A,27C ''neural'':2A,26C ''neuralnetwork'':11B ''next'':135C ''nofollow'':150C ''nonsens'':168C ''note'':108C ''number'':106C ''numpi'':101C ''output'':125C,169C,173C ''p'':13C,72C,82C,154C,217C,296C ''pastebin.com'':147C,152C ''pastebin.com/ahcjj1wa'':146C ''pastebin.com/ahcjj1wa&lt;/a&gt;&lt;/p&gt;'':151C ''per'':114C,224C ''place'':97C ''pointer'':324C ''pre'':178C,260C ''predict'':253C ''print'':255C ''problem'':156C ''project'':18C ''python'':10B ''read'':329C ''rel'':149C ''represent'':309C ''run'':241C ''sampl'':115C ''seem'':219C ''sequenc'':110C ''shape'':105C ''someth'':256C,302C ''start'':161C ''step'':33C,113C,129C,137C ''stop'':235C ''take'':90C,222C ''task'':49C ''theano'':46C ''think'':298C ''time'':112C,128C,136C ''top'':44C ''train'':23C,37C,162C,248C ''use'':41C ''ve'':139C ''wrong'':304C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10531, 1, '2016-03-04 18:31:17.653', 2, 37, 'Create training data', '&lt;p&gt;My task is to classify free text originated from customer complaints about our product. &lt;/p&gt;

&lt;p&gt;I have created a Taxonomy and I have around 10 different categories.  I have realized that these categories include keywords.
Example: &lt;/p&gt;

&lt;p&gt;"&lt;em&gt;Customer doesn''t understand how to use the product"&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;Keywords: understand, knowledge, know, aware.&lt;/p&gt;

&lt;p&gt;Record:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Training, Customer doesn''t understand how to use the product
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''m using &lt;a href="https://cloud.google.com/prediction/docs" rel="nofollow"&gt;Google Prediction API&lt;/a&gt;. When training the model, I would categorize previous text as: "Customer doesn''t understand how to use the product" - Training. &lt;/p&gt;

&lt;p&gt;How can I add keywords to free text/training data to help the model perform better and provide a better confidence level.&lt;/p&gt;

&lt;p&gt;Data in training set:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Training, understand knowledge know aware
Training, Customer doesn''t understand how to use the product
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Right now, I''m adding Keywords into same training data, but looking for a better suggestion.&lt;/p&gt;
', 'nlp  data-cleaning  nltk  google-prediction-api', NULL, '''/a'':103C ''/code'':87C,171C ''/em'':63C ''/p'':28C,51C,64C,71C,74C,124C,147C,153C,190C ''/pre'':88C,172C ''/prediction/docs'':97C ''10'':39C ''ad'':178C ''add'':129C ''api'':12B,102C ''around'':38C ''awar'':70C,160C ''better'':140C,144C,188C ''categor'':110C ''categori'':41C,47C ''classifi'':18C ''clean'':7B ''cloud.google.com'':96C ''cloud.google.com/prediction/docs'':95C ''code'':76C,155C ''complaint'':24C ''confid'':145C ''creat'':1A,32C ''custom'':23C,54C,78C,114C,162C ''data'':3A,6B,134C,149C,183C ''data-clean'':5B ''differ'':40C ''doesn'':55C,79C,115C,163C ''em'':53C ''exampl'':50C ''free'':19C,132C ''googl'':10B,100C ''google-prediction-api'':9B ''help'':136C ''href'':94C ''includ'':48C ''keyword'':49C,66C,130C,179C ''know'':69C,159C ''knowledg'':68C,158C ''level'':146C ''look'':185C ''m'':91C,177C ''model'':107C,138C ''nlp'':4B ''nltk'':8B ''nofollow'':99C ''origin'':21C ''p'':13C,29C,52C,65C,72C,89C,125C,148C,173C ''perform'':139C ''pre'':75C,154C ''predict'':11B,101C ''previous'':111C ''product'':27C,62C,86C,122C,170C ''provid'':142C ''realiz'':44C ''record'':73C ''rel'':98C ''right'':174C ''set'':152C ''suggest'':189C ''task'':15C ''taxonomi'':34C ''text'':20C,112C ''text/training'':133C ''train'':2A,77C,105C,123C,151C,156C,161C,182C ''understand'':57C,67C,81C,117C,157C,165C ''use'':60C,84C,92C,120C,168C ''would'':109C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10533, 2, '2016-03-05 03:27:53.87', 1, NULL, NULL, '&lt;p&gt;I tried running a &lt;a href="http://scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html" rel="nofollow"&gt;RANSAC model&lt;/a&gt; on your data, but got worse results than a straight linear regressor. The ten-fold cross-validated mean absolute error over all three response variables (r,g,b) for the linear model was about 0.37. I also ran a random forest model for comparison, and got about the same score. This suggests that the linear model is not shabby, but it is up to you to decide if either is good enough.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import sklearn.linear_model, sklearn.cross_validation, sklearn.ensemble, pandas

labels = pandas.read_csv(''labels.csv'', header=None, names=[''r'', ''g'', ''b''])
features = pandas.read_csv(''features.csv'', header=None, names=[''area'', ''length'', ''index'', ''complexity'', ''lines'', ''curves'', ''intensity''])

sklearn.cross_validation.cross_val_score(sklearn.linear_model.LinearRegression(), features, labels[:len(features)], ''mean_absolute_error'', 10, -1).mean()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I also tried visualizing the data with the outliers clipped, but did not find it very enlightening, so I did not include it here.&lt;/p&gt;
', NULL, NULL, '''-1'':136C ''/a'':15C ''/code'':138C ''/p'':90C,165C ''/pre'':139C ''/stable/auto_examples/linear_model/plot_ransac.html'':10C ''0.37'':52C ''10'':135C ''absolut'':36C,133C ''also'':54C,142C ''area'':117C ''b'':45C,109C ''clip'':150C ''code'':92C ''comparison'':61C ''complex'':120C ''cross'':33C ''cross-valid'':32C ''csv'':102C,112C ''curv'':122C ''data'':18C,146C ''decid'':84C ''either'':86C ''enlighten'':157C ''enough'':89C ''error'':37C,134C ''featur'':110C,128C,131C ''features.csv'':113C ''find'':154C ''fold'':31C ''forest'':58C ''g'':44C,108C ''good'':88C ''got'':20C,63C ''header'':104C,114C ''href'':7C ''import'':93C ''includ'':162C ''index'':119C ''intens'':123C ''label'':100C,129C ''labels.csv'':103C ''len'':130C ''length'':118C ''line'':121C ''linear'':26C,48C,72C ''mean'':35C,132C,137C ''model'':14C,49C,59C,73C,95C ''name'':106C,116C ''nofollow'':12C ''none'':105C,115C ''outlier'':149C ''p'':1C,140C ''panda'':99C ''pandas.read'':101C,111C ''pre'':91C ''r'':43C,107C ''ran'':55C ''random'':57C ''ransac'':13C ''regressor'':27C ''rel'':11C ''respons'':41C ''result'':22C ''run'':4C ''scikit-learn.org'':9C ''scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html'':8C ''score'':67C,126C ''shabbi'':76C ''sklearn.cross'':96C ''sklearn.cross_validation.cross'':124C ''sklearn.ensemble'':98C ''sklearn.linear'':94C ''sklearn.linear_model.linearregression'':127C ''straight'':25C ''suggest'':69C ''ten'':30C ''ten-fold'':29C ''three'':40C ''tri'':3C,143C ''val'':125C ''valid'':34C,97C ''variabl'':42C ''visual'':144C ''wors'':21C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10534, 1, '2016-03-05 08:09:29.46', 2, 8, 'Deriving Feature Map formula for Inhomogeneous Polynomial Kernel', '&lt;p&gt;How do we derive/extrapolate the feature map for inhomogeneous polynomial kernels for degree $d$, define as $$K(x, x^\prime) = \bigg( (x \cdot x^\prime) + 1\bigg)^d$$&lt;/p&gt;

&lt;p&gt;I know we don''t need to know a valid feature map to use the inhomogeneous polynomial kernels, I''m simply curious as to how one would derive/extrapolate it.&lt;/p&gt;
', 'machine-learning  classification  neuralnetwork  svm  feature-extraction', NULL, '''/p'':47C,78C ''1'':44C ''bigg'':39C,45C ''cdot'':41C ''classif'':12B ''curious'':70C ''d'':32C,46C ''defin'':33C ''degre'':31C ''deriv'':1A ''derive/extrapolate'':22C,76C ''extract'':17B ''featur'':2A,16B,24C,59C ''feature-extract'':15B ''formula'':4A ''inhomogen'':6A,27C,64C ''k'':35C ''kernel'':8A,29C,66C ''know'':50C,56C ''learn'':11B ''m'':68C ''machin'':10B ''machine-learn'':9B ''map'':3A,25C,60C ''need'':54C ''neuralnetwork'':13B ''one'':74C ''p'':18C,48C ''polynomi'':7A,28C,65C ''prime'':38C,43C ''simpli'':69C ''svm'':14B ''use'':62C ''valid'':58C ''would'':75C ''x'':36C,37C,40C,42C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10535, 1, '2016-03-05 08:36:11.283', 2, 19, 'Create training data from correlation matrix to find odd one out', '&lt;p&gt;I have N time varying feature vectors obtained by recording different parameters over time.This results in N*N similarity matrix which contains one to one correlations value for each feature. We need to consider only the upper triangle matrix since it''s symmetric, so the correlation value for one feature is low corresponding to all other feature. Is there any learning method that can identify that feature autonomously.
For Example the matrix looks something like this&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;             Feature 1   Feature2 Feature 3 Feature 4
   Feature1  1           0.91      0.81       0.44
   Feature2  0.91          1       0.98       0.31
   Feature3  0.81       0.98       1          0.32
   Feature4  0.44       0.31       0.32        1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So we know Feature 4 is behaving differently . Any learning method that can learn this difference and identify the corresponding feature.Sorry, if this is a very trivial question, I''m new to data analysis.&lt;/p&gt;
', 'neuralnetwork  clustering  random-forest  anomaly-detection', NULL, '''/code'':126C ''/p'':96C,163C ''/pre'':127C ''0.31'':115C,123C ''0.32'':120C,124C ''0.44'':110C,122C ''0.81'':109C,117C ''0.91'':108C,112C ''0.98'':114C,118C ''1'':100C,107C,113C,119C,125C ''3'':103C ''4'':105C,133C ''analysi'':162C ''anomali'':17B ''anomaly-detect'':16B ''autonom'':87C ''behav'':135C ''cluster'':12B ''code'':98C ''consid'':53C ''contain'':41C ''correl'':5A,45C,65C ''correspond'':72C,148C ''creat'':1A ''data'':3A,161C ''detect'':18B ''differ'':30C,136C,144C ''exampl'':89C ''featur'':25C,49C,69C,76C,86C,99C,102C,104C,132C ''feature.sorry'':149C ''feature1'':106C ''feature2'':101C,111C ''feature3'':116C ''feature4'':121C ''find'':8A ''forest'':15B ''identifi'':84C,146C ''know'':131C ''learn'':80C,138C,142C ''like'':94C ''look'':92C ''low'':71C ''m'':158C ''matrix'':6A,39C,58C,91C ''method'':81C,139C ''n'':22C,36C,37C ''need'':51C ''neuralnetwork'':11B ''new'':159C ''obtain'':27C ''odd'':9A ''one'':10A,42C,44C,68C ''p'':19C,128C ''paramet'':31C ''pre'':97C ''question'':156C ''random'':14B ''random-forest'':13B ''record'':29C ''result'':34C ''similar'':38C ''sinc'':59C ''someth'':93C ''symmetr'':62C ''time'':23C ''time.this'':33C ''train'':2A ''triangl'':57C ''trivial'':155C ''upper'':56C ''valu'':46C,66C ''vari'':24C ''vector'':26C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10541, 1, '2016-03-05 20:56:59.99', 0, 7, 'Multiclass classification with large number of classes but for each user the set of target classes is known', '&lt;p&gt;Hi Data Science Stack Exchange! I''m new here but I''m familiar with some machine learning theory (took some courses in school) and my question is more about how to apply ML in a practical setting.&lt;/p&gt;

&lt;p&gt;I have this project where I''m trying to design a system to predict which "store" a user is going to buy a given item from. However, the set of stores a user can potentially buy from is already known for each user (because this promotion only works at limited stores they signed up for). On average, the set of stores the average user can buy from is around 3 but the # of distinct stores across all users is about 10000.&lt;/p&gt;

&lt;p&gt;In theory, for a single user this seems like a simple classification problem. We have historical information such as the time/day/month the user bought item Y from store X along with other features such as location (postal code) where the user lives and features related to the type of item they are buying (cost, weight, brand).&lt;/p&gt;

&lt;p&gt;However, the issue is that there''s currently around 6000 users so going with this approach seems like I would need a separate model for each user but that doesn''t seem like an efficient solution to me or at least not how I''ve generally seen ML algorithms used. Unfortunately, I don''t see any other way I could take into account the fact that the set of stores a user can already buy from is known for each user already. I suppose I could have a categorical variable for each buy-able store as a feature but then that would be equivalent to adding 10000 features and I''m not sure if this would scale as the number of distinct stores increases.&lt;/p&gt;

&lt;p&gt;It would be really helpful if anyone has any insight on how to apply machine learning techniques to this kind of problem in general as this is sort my first time working on a "real" problem. Thanks!&lt;/p&gt;
', 'machine-learning  classification  dataset  multiclass-classification', NULL, '''/p'':65C,148C,206C,333C,372C ''10000'':147C,315C ''3'':136C ''6000'':217C ''abl'':302C ''account'':270C ''across'':142C ''ad'':314C ''algorithm'':256C ''along'':179C ''alreadi'':105C,281C,289C ''anyon'':341C ''appli'':59C,348C ''approach'':223C ''around'':135C,216C ''averag'':123C,129C ''bought'':173C ''brand'':205C ''buy'':88C,102C,132C,202C,282C,301C ''buy-abl'':300C ''categor'':296C ''class'':7A,16A ''classif'':2A,22B,26B,161C ''code'':187C ''cost'':203C ''could'':267C,293C ''cours'':48C ''current'':215C ''data'':29C ''dataset'':23B ''design'':76C ''distinct'':140C,330C ''doesn'':237C ''effici'':242C ''equival'':312C ''exchang'':32C ''fact'':272C ''familiar'':40C ''featur'':182C,193C,306C,316C ''first'':364C ''general'':253C,358C ''given'':90C ''go'':86C,220C ''help'':339C ''hi'':28C ''histor'':165C ''howev'':93C,208C ''increas'':332C ''inform'':166C ''insight'':344C ''issu'':210C ''item'':91C,174C,199C ''kind'':354C ''known'':18A,106C,285C ''larg'':4A ''learn'':21B,44C,350C ''least'':248C ''like'':158C,225C,240C ''limit'':116C ''live'':191C ''locat'':185C ''m'':34C,39C,73C,319C ''machin'':20B,43C,349C ''machine-learn'':19B ''ml'':60C,255C ''model'':231C ''multiclass'':1A,25B ''multiclass-classif'':24B ''need'':228C ''new'':35C ''number'':5A,328C ''p'':27C,66C,149C,207C,334C ''postal'':186C ''potenti'':101C ''practic'':63C ''predict'':80C ''problem'':162C,356C,370C ''project'':70C ''promot'':112C ''question'':53C ''real'':369C ''realli'':338C ''relat'':194C ''scale'':325C ''school'':50C ''scienc'':30C ''see'':262C ''seem'':157C,224C,239C ''seen'':254C ''separ'':230C ''set'':13A,64C,95C,125C,275C ''sign'':119C ''simpl'':160C ''singl'':154C ''solut'':243C ''sort'':362C ''stack'':31C ''store'':82C,97C,117C,127C,141C,177C,277C,303C,331C ''suppos'':291C ''sure'':321C ''system'':78C ''take'':268C ''target'':15A ''techniqu'':351C ''thank'':371C ''theori'':45C,151C ''time'':365C ''time/day/month'':170C ''took'':46C ''tri'':74C ''type'':197C ''unfortun'':258C ''use'':257C ''user'':11A,84C,99C,109C,130C,144C,155C,172C,190C,218C,234C,279C,288C ''variabl'':297C ''ve'':252C ''way'':265C ''weight'':204C ''work'':114C,366C ''would'':227C,310C,324C,336C ''x'':178C ''y'':175C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10536, 1, '2016-03-05 11:18:28.513', 4, 30, 'What are the most concrete and easiest to understand applications of deep learning in the industry?', '&lt;p&gt;I am a researcher in deep learning, and have given courses on the theory. Recently I was approached to give the same course adding sections using some very concrete examples of deep learning for companies. &lt;/p&gt;

&lt;p&gt;My audience will be industry managers never exposed to deep learning before. What are the most easily performed examples I could provide without having to develop millions of lines of code that can be of general interest to managers never exposed to deep learning before? These examples need to be quite concrete and easily applicable.&lt;/p&gt;

&lt;p&gt;In particular, they look for examples that can have the label "big data" slapped on them. &lt;/p&gt;

&lt;p&gt;I know about using ML for detecting digits, or all the other applications on kaggle, but they do not seem to care for those.&lt;/p&gt;
', 'bigdata  deep-learning', NULL, '''/p'':57C,114C,132C,158C ''ad'':45C ''applic'':10A,113C,146C ''approach'':39C ''audienc'':60C ''big'':127C ''bigdata'':17B ''care'':155C ''code'':89C ''compani'':56C ''concret'':5A,50C,110C ''could'':79C ''cours'':32C,44C ''data'':128C ''deep'':12A,19B,27C,53C,68C,101C ''deep-learn'':18B ''detect'':140C ''develop'':84C ''digit'':141C ''easiest'':7A ''easili'':75C,112C ''exampl'':51C,77C,105C,121C ''expos'':66C,99C ''general'':94C ''give'':41C ''given'':31C ''industri'':16A,63C ''interest'':95C ''kaggl'':148C ''know'':135C ''label'':126C ''learn'':13A,20B,28C,54C,69C,102C ''line'':87C ''look'':119C ''manag'':64C,97C ''million'':85C ''ml'':138C ''need'':106C ''never'':65C,98C ''p'':21C,58C,115C,133C ''particular'':117C ''perform'':76C ''provid'':80C ''quit'':109C ''recent'':36C ''research'':25C ''section'':46C ''seem'':153C ''slap'':129C ''theori'':35C ''understand'':9A ''use'':47C,137C ''without'':81C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10537, 2, '2016-03-05 14:18:09.057', 1, NULL, NULL, '&lt;p&gt;Yes There are!&lt;/p&gt;

&lt;h2&gt;&lt;a href="https://networkx.github.io/" rel="nofollow"&gt;Networkx&lt;/a&gt;&lt;/h2&gt;

&lt;p&gt;I think 20k-30k node-edge would be OK on Networkx, IF YOU HAVE A GOOD MACHINE! Networkx is a great library in Python particularly for Graph Analysis so you have access to great analysis tools beside visualizing but visualizing 20k vertices needs much RAM and takes long.&lt;/p&gt;

&lt;h2&gt;&lt;a href="http://igraph.org/redirect.html" rel="nofollow"&gt;Igraph&lt;/a&gt;&lt;/h2&gt;

&lt;p&gt;Igraph is another great tool for Graph Analysis with APIs in Python, R and C. You can do a lot with Igraph library including nice plotting facilities.&lt;/p&gt;

&lt;h2&gt;&lt;a href="http://snap.stanford.edu/" rel="nofollow"&gt;SNAP&lt;/a&gt;&lt;/h2&gt;

&lt;p&gt;SNAP was developed by Jure Leskovec and his colleagues at Stanford. A large scale graph processing library natively written in C with API in Python. This library can handle huge graphs on a single personal machine.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;PS.1&lt;/strong&gt; Both of these guys have interfaces with different graph softwares like Gephi, etc.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;PS.2&lt;/strong&gt; For large graphs I strongly recommend to work in C++ if you can. Speaking as a Network/Graph guy, C++ solves many problems out there!&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':13C,76C,114C ''/h2'':14C,77C,115C ''/li'':172C,201C ''/p'':5C,66C,106C,153C ''/redirect.html'':72C ''/strong'':158C,176C ''/ul'':202C ''20k'':19C,58C ''20k-30k'':18C ''30k'':20C ''access'':49C ''analysi'':45C,52C,86C ''anoth'':81C ''api'':88C,139C ''besid'':54C ''c'':93C,137C,186C,195C ''colleagu'':125C ''develop'':119C ''differ'':166C ''edg'':23C ''etc'':171C ''facil'':105C ''gephi'':170C ''good'':33C ''graph'':44C,85C,131C,147C,167C,179C ''great'':38C,51C,82C ''guy'':162C,194C ''h2'':6C,67C,107C ''handl'':145C ''href'':8C,69C,109C ''huge'':146C ''igraph'':75C,79C,100C ''igraph.org'':71C ''igraph.org/redirect.html'':70C ''includ'':102C ''interfac'':164C ''jure'':121C ''larg'':129C,178C ''leskovec'':122C ''li'':155C,173C ''librari'':39C,101C,133C,143C ''like'':169C ''long'':65C ''lot'':98C ''machin'':34C,152C ''mani'':197C ''much'':61C ''nativ'':134C ''need'':60C ''network/graph'':193C ''networkx'':12C,28C,35C ''networkx.github.io'':9C ''nice'':103C ''node'':22C ''node-edg'':21C ''nofollow'':11C,74C,112C ''ok'':26C ''p'':1C,15C,78C,116C ''particular'':42C ''person'':151C ''plot'':104C ''problem'':198C ''process'':132C ''ps.1'':157C ''ps.2'':175C ''python'':41C,90C,141C ''r'':91C ''ram'':62C ''recommend'':182C ''rel'':10C,73C,111C ''scale'':130C ''singl'':150C ''snap'':113C,117C ''snap.stanford.edu'':110C ''softwar'':168C ''solv'':196C ''speak'':190C ''stanford'':127C ''strong'':156C,174C,181C ''take'':64C ''think'':17C ''tool'':53C,83C ''ul'':154C ''vertic'':59C ''visual'':55C,57C ''work'':184C ''would'':24C ''written'':135C ''yes'':2C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10538, 1, '2016-03-05 17:43:06.277', 1, 12, 'What''s the best way to rank aggregate imdb rating data?', '&lt;p&gt;I have an &lt;a href="http://www.imdb.com/title/tt0472954/epdate" rel="nofollow"&gt;average rating of all votes&lt;/a&gt; as well as the total number of votes for all episodes of the TV show Always Sunny. Is it mathematically sound to?: &lt;/p&gt;

&lt;p&gt;1) multiply each average rating by the total votes cast for that episode&lt;/p&gt;

&lt;p&gt;2) take the array of all those values and normalize it so the lowest is 0 and the highest is 10&lt;/p&gt;

&lt;p&gt;3) use that new value as the actual rating&lt;/p&gt;
', 'dataset  data-cleaning  ranking', NULL, '''/a'':33C ''/p'':56C,71C,94C,105C ''/title/tt0472954/epdate'':25C ''0'':88C ''1'':58C ''10'':93C ''2'':73C ''3'':96C ''actual'':103C ''aggreg'':8A ''alway'':49C ''array'':76C ''averag'':28C,61C ''best'':4A ''cast'':67C ''clean'':15B ''data'':11A,14B ''data-clean'':13B ''dataset'':12B ''episod'':44C,70C ''highest'':91C ''href'':22C ''imdb'':9A ''lowest'':86C ''mathemat'':53C ''multipli'':59C ''new'':99C ''nofollow'':27C ''normal'':82C ''number'':39C ''p'':17C,57C,72C,95C ''rank'':7A,16B ''rate'':10A,29C,62C,104C ''rel'':26C ''show'':48C ''sound'':54C ''sunni'':50C ''take'':74C ''total'':38C,65C ''tv'':47C ''use'':97C ''valu'':80C,100C ''vote'':32C,41C,66C ''way'':5A ''well'':35C ''www.imdb.com'':24C ''www.imdb.com/title/tt0472954/epdate'':23C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10539, 2, '2016-03-05 18:37:21.273', 1, NULL, NULL, '&lt;p&gt;This seems like a matter of opinion to me, but here are some applications of machine learning that you seen &lt;em&gt;very&lt;/em&gt; often in Silicon Valley:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Advertising&lt;/strong&gt;: what to show whom and when? The data comes fast and furious so the engineering part is quite sophisticated.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Recommendation&lt;/strong&gt;: similar to the advertising problem, except there are no advertisers, and the volume is slower, so you can afford to use more sophisticated machine algorithms.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Security&lt;/strong&gt;: did somebody outside penetrate your system? did somebody inside abuse your system?&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Text/audio/video processing&lt;/strong&gt;: information retrieval on media. Requires finding good representations of the items using machine learning; e.g., fetch all pictures similar to the input, where the similarity can be construed in various ways, such as similarity of texture, style, color, content, etc.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;These are not necessarily simple, but they''re representative. If you want to give a demo, I suggest the last one. You could show them how to find similar images.&lt;/p&gt;
', NULL, NULL, '''/em'':24C ''/li'':54C,84C,101C,147C ''/p'':29C,180C ''/strong'':34C,58C,88C,106C ''/ul'':148C ''abus'':98C ''advertis'':33C,62C,68C ''afford'':77C ''algorithm'':83C ''applic'':15C ''color'':144C ''come'':43C ''constru'':134C ''content'':145C ''could'':172C ''data'':42C ''demo'':165C ''e.g'':121C ''em'':22C ''engin'':49C ''etc'':146C ''except'':64C ''fast'':44C ''fetch'':122C ''find'':112C,177C ''furious'':46C ''give'':163C ''good'':113C ''imag'':179C ''inform'':107C ''input'':128C ''insid'':97C ''item'':117C ''last'':169C ''learn'':18C,120C ''li'':31C,55C,85C,102C ''like'':4C ''machin'':17C,82C,119C ''matter'':6C ''media'':110C ''necessarili'':153C ''often'':25C ''one'':170C ''opinion'':8C ''outsid'':91C ''p'':1C,149C ''part'':50C ''penetr'':92C ''pictur'':124C ''problem'':63C ''process'':105C ''quit'':52C ''re'':157C ''recommend'':57C ''repres'':158C ''represent'':114C ''requir'':111C ''retriev'':108C ''secur'':87C ''seem'':3C ''seen'':21C ''show'':37C,173C ''silicon'':27C ''similar'':59C,125C,131C,140C,178C ''simpl'':154C ''slower'':73C ''somebodi'':90C,96C ''sophist'':53C,81C ''strong'':32C,56C,86C,103C ''style'':143C ''suggest'':167C ''system'':94C,100C ''text/audio/video'':104C ''textur'':142C ''ul'':30C ''use'':79C,118C ''valley'':28C ''various'':136C ''volum'':71C ''want'':161C ''way'':137C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10540, 1, '2016-03-05 19:07:07.393', 1, 5, 'Interpreting the results of randomized PCA in scikit-learn', '&lt;p&gt;I''m using scikit-learn to do a genome-wide association study with a feature vector of about 100K SNPs. My goal is to tell the biologists which SNPs are "interesting". &lt;/p&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.RandomizedPCA.html" rel="nofollow"&gt;RandomizedPCA&lt;/a&gt; really improved my models, but I''m having trouble interpreting the results. Can scikit-learn tell me which features are used in each component? &lt;/p&gt;
', 'feature-selection  sklearn  pca  randomized-algorithms', NULL, '''/a'':63C ''/p'':53C,89C ''/stable/modules/generated/sklearn.decomposition.randomizedpca.html'':59C ''100k'':40C ''algorithm'':18B ''associ'':32C ''biologist'':48C ''compon'':88C ''featur'':12B,36C,83C ''feature-select'':11B ''genom'':30C ''genome-wid'':29C ''goal'':43C ''href'':56C ''improv'':65C ''interest'':52C ''interpret'':1A,73C ''learn'':10A,25C,79C ''m'':21C,70C ''model'':67C ''nofollow'':61C ''p'':19C,54C ''pca'':6A,15B ''random'':5A,17B ''randomized-algorithm'':16B ''randomizedpca'':62C ''realli'':64C ''rel'':60C ''result'':3A,75C ''scikit'':9A,24C,78C ''scikit-learn'':8A,23C,77C ''scikit-learn.org'':58C ''scikit-learn.org/stable/modules/generated/sklearn.decomposition.randomizedpca.html'':57C ''select'':13B ''sklearn'':14B ''snps'':41C,50C ''studi'':33C ''tell'':46C,80C ''troubl'':72C ''use'':22C,85C ''vector'':37C ''wide'':31C');
INSERT INTO "public"."posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10542, 1, '2016-03-05 21:29:48.683', 0, 7, 'How to keep track of assumptions/design choices and avoid errors in complex projects', '&lt;p&gt;I am part of a small team data analysts that has been trying to optimise the process we use to keep track of the assumptions and design choices that are made over the lifetime of a complex data science project (several months) - ultimately we want to (i) catch (conceptual) errors when they are made (ii) ensure that it is at all times clear what assumptions underlie the results being produced. &lt;/p&gt;

&lt;p&gt;Our current process relies on code-generated and manually maintained excel spreadsheets - the process is (i) time consuming/results in too many files, (ii) introduces a new source of error (the copying of results into the spreadsheet) and (iii) is not dynamic, i.e. cannot easily be adapted as a project evolves. &lt;/p&gt;

&lt;p&gt;While there are a large number of questions/answers focused on version control, reproducible research and project organisation more broadly we are not looking for a new suite of tools but a light-weight process that addresses a very specific problem. &lt;/p&gt;

&lt;p&gt;The ideal process: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Is easy to  maintain &amp;amp; as much as possible automated to reduce
errors&lt;/li&gt;
&lt;li&gt;Captures all design choices and assumptions made&lt;/li&gt;
&lt;li&gt;At a glance reflects the impact of any changes, i.e. reflects (changes
in) the ''key metrics'' that resulted from the change&lt;/li&gt;
&lt;li&gt;Links all changes with the related code file versions&lt;/li&gt;
&lt;li&gt;Can easily be shared with other analysts &amp;amp; Principal Investigators&lt;/li&gt;
&lt;/ul&gt;
', 'r', NULL, '''/li'':203C,212C,235C,246C,258C ''/p'':86C,139C,182C,187C ''/ul'':259C ''adapt'':134C ''address'':177C ''amp'':194C,255C ''analyst'':24C,254C ''assumpt'':40C,80C,210C ''assumptions/design'':6A ''autom'':199C ''avoid'':9A ''broad'':159C ''cannot'':131C ''captur'':205C ''catch'':63C ''chang'':222C,225C,234C,239C ''choic'':7A,43C,208C ''clear'':78C ''code'':94C,243C ''code-gener'':93C ''complex'':12A,52C ''conceptu'':64C ''consuming/results'':106C ''control'':152C ''copi'':119C ''current'':89C ''data'':23C,53C ''design'':42C,207C ''dynam'':129C ''easi'':191C ''easili'':132C,249C ''ensur'':71C ''error'':10A,65C,117C,202C ''evolv'':138C ''excel'':99C ''file'':110C,244C ''focus'':149C ''generat'':95C ''glanc'':216C ''i.e'':130C,223C ''ideal'':185C ''ii'':70C,111C ''iii'':126C ''impact'':219C ''introduc'':112C ''investig'':257C ''keep'':3A,36C ''key'':228C ''larg'':145C ''li'':189C,204C,213C,236C,247C ''lifetim'':49C ''light'':173C ''light-weight'':172C ''link'':237C ''look'':163C ''made'':46C,69C,211C ''maintain'':98C,193C ''mani'':109C ''manual'':97C ''metric'':229C ''month'':57C ''much'':196C ''new'':114C,166C ''number'':146C ''optimis'':30C ''organis'':157C ''p'':15C,87C,140C,183C ''part'':18C ''possibl'':198C ''princip'':256C ''problem'':181C ''process'':32C,90C,102C,175C,186C ''produc'':85C ''project'':13A,55C,137C,156C ''questions/answers'':148C ''r'':14B ''reduc'':201C ''reflect'':217C,224C ''relat'':242C ''reli'':91C ''reproduc'':153C ''research'':154C ''result'':83C,121C,231C ''scienc'':54C ''sever'':56C ''share'':251C ''small'':21C ''sourc'':115C ''specif'':180C ''spreadsheet'':100C,124C ''suit'':167C ''team'':22C ''time'':77C,105C ''tool'':169C ''track'':4A,37C ''tri'':28C ''ul'':188C ''ultim'':58C ''underli'':81C ''use'':34C ''version'':151C,245C ''want'':60C ''weight'':174C');


--
-- TOC entry 3609 (class 0 OID 1846297)
-- Dependencies: 242
-- Data for Name: roonga_posts; Type: TABLE DATA; Schema: public; Owner: postgres
--

INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (7, 1, '2014-05-14 00:11:06.457', 2, 266, 'What open-source books (or other materials) provide a relatively thorough overview of data science?', '&lt;p&gt;As a researcher and instructor, I''m looking for open-source books (or similar materials) that provide a relatively thorough overview of data science from an applied perspective. To be clear, I''m especially interested in a thorough overview that provides material suitable for a college-level course, not particular pieces or papers.&lt;/p&gt;
', 'education  open-source', NULL, '''/p'':77C ''appli'':49C ''book'':5A,34C ''clear'':53C ''colleg'':69C ''college-level'':68C ''cours'':71C ''data'':15A,45C ''educ'':17B ''especi'':56C ''instructor'':26C ''interest'':57C ''level'':70C ''look'':29C ''m'':28C,55C ''materi'':8A,37C,64C ''open'':3A,19B,32C ''open-sourc'':2A,18B,31C ''overview'':13A,43C,61C ''p'':21C ''paper'':76C ''particular'':73C ''perspect'':50C ''piec'':74C ''provid'':9A,39C,63C ''relat'':11A,41C ''research'':24C ''scienc'':16A,46C ''similar'':36C ''sourc'':4A,20B,33C ''suitabl'':65C ''thorough'':12A,42C,60C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10532, 2, '2016-03-04 19:17:18.713', 3, NULL, NULL, '&lt;p&gt;Assuming you are doing supervized learning to train a model that when deployed will take text as input and output a label (e.g., topic) or class probability, then what you probably want to do is balanced, &lt;a href="https://en.wikipedia.org/wiki/Stratified_sampling" rel="nofollow"&gt;stratified sampling&lt;/a&gt;. Assuming sufficient labelled data, ensure that your final training set has a balanced number of text examples for each class/label. Depending on your situation, you may need to over/under sample or somehow deal with the problem of highly imbalanced classes (see &lt;a href="http://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/" rel="nofollow"&gt;8 tactics to combat imbalanced classes&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;The simplest NLP approach to use a &lt;a href="https://en.wikipedia.org/wiki/Bag-of-words_model" rel="nofollow"&gt;bag of words&lt;/a&gt; technique, simply indicating the presence/absence of a word in the sentence. Thus each sentence becomes represented as vector of length n, where n = the number of unique words in your data set. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data_set &amp;lt;- c("the big dog", "the small cat", "the big and fat cow")
words &amp;lt;- strsplit(data_set, split = " ") #tokenize sentences
words
##[[1]]
##[1] "the" "big" "dog"
##
##[[2]]
##[1] "the"   "small" "cat"  
##
##[[3]]
##[1] "the" "big" "and" "fat" "cow"


vec &amp;lt;- unique(unlist(words)) #vector representation of sentences
##[1] "the"   "big"   "dog"   "small" "cat"   "and"  
##[7] "fat"   "cow" 

m &amp;lt;- matrix(nrow = length(data_set), ncol = length(vec))

for (i in 1:length(words)) { #iterate the index of tokenized sentences
  vec_rep &amp;lt;- vec %in% words[[i]] #create binary word-feature vector
  m[i,] &amp;lt;- vec_rep #update matrix
}

df &amp;lt;- data.frame(m, row.names = NULL)
names(df) &amp;lt;- vec
df
##   the   big   dog small   cat   and   fat   cow
##1 TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE
##2 TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
##3 TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Sometimes you can increase performance by adding &lt;a href="https://en.wikipedia.org/wiki/Bigram" rel="nofollow"&gt;bi-gram&lt;/a&gt; and tri-gram features. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;##given: "the big dog"
##unigrams &amp;lt;- {the, big, dog}
##bigrams &amp;lt;- {the big, big dog}
##trigrams &amp;lt;- {the big dog}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Sometimes weighting words by their frequency improves performance or computing the &lt;a href="http://blog.christianperone.com/2011/09/machine-learning-text-feature-extraction-tf-idf-part-i/" rel="nofollow"&gt;tf-idf&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;Another way to increase performance, in my experience, has been custom language feature engineering, especially if the data is from social media sources replete with spelling errors, acronyms, slang, and other word variants. Standard NLP approaches will typically remove &lt;a href="http://datascience.stackexchange.com/questions/5893/how-to-create-a-good-list-of-stopwords?rq=1"&gt;stop words&lt;/a&gt; (e.g., the, a/an, this/that, etc.) from vector representations (because closed class, high frequency words often don''t help discriminate among class/label boundaries). Because vector representations are typically highly dimensional (approximately num of unique words in the corpus/data set), dimensionality reductions techniques can increase performance. For example, one can compute chi-sq, info gain, etc. on a word feature''s distribution across classes -- only keep those features/words above some threshold or below some pre-established p value). &lt;/p&gt;
', NULL, NULL, '''/2011/09/machine-learning-text-feature-extraction-tf-idf-part-i/'':373C ''/a'':47C,102C,122C,326C,379C,428C ''/code'':306C,355C ''/p'':103C,155C,332C,380C,507C ''/pre'':307C,356C ''/questions/5893/how-to-create-a-good-list-of-stopwords?rq=1'':425C ''/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/'':93C ''/wiki/bag-of-words_model'':116C ''/wiki/bigram'':320C ''/wiki/stratified_sampling'':42C ''1'':182C,183C,188C,193C,208C,231C,279C ''2'':187C,288C ''3'':192C,297C ''7'':215C ''8'':96C ''a/an'':431C ''acronym'':409C ''across'':490C ''ad'':315C ''among'':448C ''anoth'':382C ''approach'':108C,417C ''approxim'':458C ''assum'':2C,48C ''bag'':119C ''balanc'':37C,60C ''becom'':137C ''bi'':324C ''bi-gram'':323C ''big'':163C,169C,185C,195C,210C,272C,337C,342C,347C,348C,353C ''bigram'':344C ''binari'':248C ''blog.christianperone.com'':372C ''blog.christianperone.com/2011/09/machine-learning-text-feature-extraction-tf-idf-part-i/'':371C ''boundari'':450C ''c'':161C ''cat'':167C,191C,213C,275C ''chi'':479C ''chi-sq'':478C ''class'':27C,87C,101C,439C,491C ''class/label'':67C,449C ''close'':438C ''code'':157C,334C ''combat'':99C ''comput'':367C,477C ''corpus/data'':465C ''cow'':172C,198C,217C,278C ''creat'':247C ''custom'':392C ''data'':51C,153C,158C,176C,223C,399C ''data.frame'':262C ''datascience.stackexchange.com'':424C ''datascience.stackexchange.com/questions/5893/how-to-create-a-good-list-of-stopwords?rq=1'':423C ''deal'':80C ''depend'':68C ''deploy'':14C ''df'':260C,267C,270C ''dimension'':457C,467C ''discrimin'':447C ''distribut'':489C ''dog'':164C,186C,211C,273C,338C,343C,349C,354C ''e.g'':24C,429C ''en.wikipedia.org'':41C,115C,319C ''en.wikipedia.org/wiki/bag-of-words_model'':114C ''en.wikipedia.org/wiki/bigram'':318C ''en.wikipedia.org/wiki/stratified_sampling'':40C ''engin'':395C ''ensur'':52C ''error'':408C ''especi'':396C ''establish'':504C ''etc'':433C,483C ''exampl'':64C,474C ''experi'':389C ''fals'':283C,284C,285C,286C,287C,290C,291C,294C,295C,296C,300C,301C,302C ''fat'':171C,197C,216C,277C ''featur'':251C,331C,394C,487C ''features/words'':495C ''final'':55C ''frequenc'':363C,441C ''gain'':482C ''given'':335C ''gram'':325C,330C ''help'':446C ''high'':85C,440C,456C ''href'':39C,90C,113C,317C,370C,422C ''idf'':378C ''imbalanc'':86C,100C ''improv'':364C ''increas'':312C,385C,471C ''index'':236C ''indic'':125C ''info'':481C ''input'':19C ''iter'':234C ''keep'':493C ''label'':23C,50C ''languag'':393C ''learn'':7C ''length'':142C,222C,226C,232C ''lt'':160C,174C,200C,219C,242C,255C,261C,268C,340C,345C,351C ''m'':218C,253C,263C ''machinelearningmastery.com'':92C ''machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/'':91C ''matrix'':220C,259C ''may'':73C ''media'':403C ''model'':11C ''n'':143C,145C ''name'':266C ''ncol'':225C ''need'':74C ''nlp'':107C,416C ''nofollow'':44C,95C,118C,322C,375C ''nrow'':221C ''null'':265C ''num'':459C ''number'':61C,147C ''often'':443C ''one'':475C ''output'':21C ''over/under'':76C ''p'':1C,104C,308C,357C,381C,505C ''perform'':313C,365C,386C,472C ''pre'':156C,333C,503C ''pre-establish'':502C ''presence/absence'':127C ''probabl'':28C,32C ''problem'':83C ''reduct'':468C ''rel'':43C,94C,117C,321C,374C ''remov'':420C ''rep'':241C,257C ''replet'':405C ''repres'':138C ''represent'':205C,436C,453C ''row.names'':264C ''sampl'':46C,77C ''see'':88C ''sentenc'':133C,136C,180C,207C,239C ''set'':57C,154C,159C,177C,224C,466C ''simplest'':106C ''simpli'':124C ''situat'':71C ''slang'':410C ''small'':166C,190C,212C,274C ''social'':402C ''somehow'':79C ''sometim'':309C,358C ''sourc'':404C ''spell'':407C ''split'':178C ''sq'':480C ''standard'':415C ''stop'':426C ''stratifi'':45C ''strsplit'':175C ''suffici'':49C ''superv'':6C ''tactic'':97C ''take'':16C ''techniqu'':123C,469C ''text'':17C,63C ''tf'':377C ''tf-idf'':376C ''this/that'':432C ''threshold'':498C ''thus'':134C ''token'':179C,238C ''topic'':25C ''train'':9C,56C ''tri'':329C ''tri-gram'':328C ''trigram'':350C ''true'':280C,281C,282C,289C,292C,293C,298C,299C,303C,304C,305C ''typic'':419C,455C ''unigram'':339C ''uniqu'':149C,201C,461C ''unlist'':202C ''updat'':258C ''use'':110C ''valu'':506C ''variant'':414C ''vec'':199C,227C,240C,243C,256C,269C ''vector'':140C,204C,252C,435C,452C ''want'':33C ''way'':383C ''weight'':359C ''word'':121C,130C,150C,173C,181C,203C,233C,245C,250C,360C,413C,427C,442C,462C,486C ''word-featur'':249C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5, 1, '2014-05-13 23:58:30.457', 7, 286, 'How can I do simple machine learning without hard-coding behavior?', '&lt;p&gt;I''ve always been interested in machine learning, but I can''t figure out one thing about starting out with a simple "Hello World" example - how can I avoid hard-coding behavior?&lt;/p&gt;

&lt;p&gt;For example, if I wanted to "teach" a bot how to avoid randomly placed obstacles, I couldn''t just use relative motion, because the obstacles move around, but I don''t want to hard code, say, distance, because that ruins the whole point of machine learning.&lt;/p&gt;

&lt;p&gt;Obviously, randomly generating code would be impractical, so how could I do this?&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':50C,98C,113C ''alway'':19C ''around'':78C ''avoid'':45C,63C ''behavior'':12A,49C ''bot'':60C ''code'':11A,48C,86C,103C ''could'':109C ''couldn'':68C ''distanc'':88C ''exampl'':41C,53C ''figur'':29C ''generat'':102C ''hard'':10A,47C,85C ''hard-cod'':9A,46C ''hello'':39C ''impract'':106C ''interest'':21C ''learn'':7A,15B,24C,97C ''machin'':6A,14B,23C,96C ''machine-learn'':13B ''motion'':73C ''move'':77C ''obstacl'':66C,76C ''obvious'':100C ''one'':31C ''p'':16C,51C,99C ''place'':65C ''point'':94C ''random'':64C,101C ''relat'':72C ''ruin'':91C ''say'':87C ''simpl'':5A,38C ''start'':34C ''teach'':58C ''thing'':32C ''use'':71C ''ve'':18C ''want'':56C,83C ''whole'':93C ''without'':8A ''world'':40C ''would'':104C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (53, 2, '2014-05-14 15:42:02.393', 10, NULL, NULL, '&lt;p&gt;For windows, use the task scheduler to set the task to run for example daily at 4:00 AM&lt;/p&gt;

&lt;p&gt;It gives you many other options regarding frequency etc.
&lt;a href="http://en.wikipedia.org/wiki/Windows_Task_Scheduler"&gt;http://en.wikipedia.org/wiki/Windows_Task_Scheduler&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':21C ''/wiki/windows_task_scheduler'':36C ''/wiki/windows_task_scheduler&lt;/a&gt;&lt;/p&gt;'':39C ''00'':19C ''4'':18C ''daili'':16C ''en.wikipedia.org'':35C,38C ''en.wikipedia.org/wiki/windows_task_scheduler'':34C ''en.wikipedia.org/wiki/windows_task_scheduler&lt;/a&gt;&lt;/p&gt;'':37C ''etc'':31C ''exampl'':15C ''frequenc'':30C ''give'':24C ''href'':33C ''mani'':26C ''option'':28C ''p'':1C,22C ''regard'':29C ''run'':13C ''schedul'':7C ''set'':9C ''task'':6C,11C ''use'':4C ''window'':3C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9, 2, '2014-05-14 00:36:31.077', 4, NULL, NULL, '&lt;p&gt;Not sure if this fits the scope of this SE, but here''s a stab at an answer anyway.&lt;/p&gt;

&lt;p&gt;With all AI approaches you have to decide what it is you''re modelling and what kind of uncertainty there is. Once you pick a framework that allows modelling of your situation, you then see which elements are "fixed" and which are flexible. For example, the model may allow you to define your own network structure (or even learn it) with certain constraints. You have to decide whether this flexibility is sufficient for your purposes. Then within a particular network structure, you can learn parameters given a specific training dataset.&lt;/p&gt;

&lt;p&gt;You rarely hard-code behavior in AI/ML solutions. It''s all about modelling the underlying situation and accommodating different situations by tweaking elements of the model.&lt;/p&gt;

&lt;p&gt;In your example, perhaps you might have the robot learn how to detect obstacles (by analyzing elements in the environment), or you might have it keep track of where the obstacles were and which way they were moving.&lt;/p&gt;
', NULL, NULL, '''/p'':21C,113C,142C,182C ''accommod'':133C ''ai'':25C ''ai/ml'':122C ''allow'':50C,71C ''analyz'':159C ''answer'':19C ''anyway'':20C ''approach'':26C ''behavior'':120C ''certain'':84C ''code'':119C ''constraint'':85C ''dataset'':112C ''decid'':30C,89C ''defin'':74C ''detect'':156C ''differ'':134C ''element'':59C,138C,160C ''environ'':163C ''even'':80C ''exampl'':67C,146C ''fit'':6C ''fix'':61C ''flexibl'':65C,92C ''framework'':48C ''given'':108C ''hard'':118C ''hard-cod'':117C ''keep'':169C ''kind'':39C ''learn'':81C,106C,153C ''may'':70C ''might'':149C,166C ''model'':36C,51C,69C,128C,141C ''move'':181C ''network'':77C,102C ''obstacl'':157C,174C ''p'':1C,22C,114C,143C ''paramet'':107C ''particular'':101C ''perhap'':147C ''pick'':46C ''purpos'':97C ''rare'':116C ''re'':35C ''robot'':152C ''scope'':8C ''se'':11C ''see'':57C ''situat'':54C,131C,135C ''solut'':123C ''specif'':110C ''stab'':16C ''structur'':78C,103C ''suffici'':94C ''sure'':3C ''track'':170C ''train'':111C ''tweak'':137C ''uncertainti'':41C ''under'':130C ''way'':178C ''whether'':90C ''within'':99C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10, 2, '2014-05-14 00:53:43.273', 9, NULL, NULL, '&lt;p&gt;One book that''s freely available is "The Elements of Statistical Learning" by Hastie, Tibshirani, and Friedman (published by Springer): &lt;a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/"&gt;see Tibshirani''s website&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Another fantastic source, although it isn''t a book, is Andrew Ng''s Machine Learning course on Coursera. This has a much more applied-focus than the above book, and Prof. Ng does a great job of explaining the thinking behind several different machine learning algorithms/situations.&lt;/p&gt;
', NULL, NULL, '''/a'':31C ''/p'':32C,81C ''/~tibs/elemstatlearn/'':26C ''algorithms/situations'':80C ''although'':37C ''andrew'':44C ''anoth'':34C ''appli'':58C ''applied-focus'':57C ''avail'':7C ''behind'':75C ''book'':3C,42C,63C ''cours'':49C ''coursera'':51C ''differ'':77C ''element'':10C ''explain'':72C ''fantast'':35C ''focus'':59C ''freeli'':6C ''friedman'':18C ''great'':69C ''hasti'':15C ''href'':23C ''isn'':39C ''job'':70C ''learn'':13C,48C,79C ''machin'':47C,78C ''much'':55C ''ng'':45C,66C ''one'':2C ''p'':1C,33C ''prof'':65C ''publish'':19C ''see'':27C ''sever'':76C ''sourc'':36C ''springer'':21C ''statist'':12C ''statweb.stanford.edu'':25C ''statweb.stanford.edu/~tibs/elemstatlearn/'':24C ''think'':74C ''tibshirani'':16C,28C ''websit'':30C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (14, 1, '2014-05-14 01:25:59.677', 14, 686, 'Is Data Science the Same as Data Mining?', '&lt;p&gt;I am sure data science as will be discussed in this forum has several synonyms or at least related fields where large data is analyzed.&lt;/p&gt;

&lt;p&gt;My particular question is in regards to Data Mining.  I took a graduate class in Data Mining a few years back.  What are the differences between Data Science and Data Mining and in particular what more would I need to look at to become proficient in Data Mining?&lt;/p&gt;
', 'data-mining  definitions', NULL, '''/p'':39C,89C ''analyz'':38C ''back'':61C ''becom'':84C ''class'':54C ''data'':2A,7A,10B,17C,36C,48C,56C,67C,70C,87C ''data-min'':9B ''definit'':12B ''differ'':65C ''discuss'':22C ''field'':33C ''forum'':25C ''graduat'':53C ''larg'':35C ''least'':31C ''look'':81C ''mine'':8A,11B,49C,57C,71C,88C ''need'':79C ''p'':13C,40C ''particular'':42C,74C ''profici'':85C ''question'':43C ''regard'':46C ''relat'':32C ''scienc'':3A,18C,68C ''sever'':27C ''sure'':16C ''synonym'':28C ''took'':51C ''would'':77C ''year'':60C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6982, 5, '2015-09-02 09:01:54.15', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (178, 4, '2014-05-21 07:05:44.78', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (180, 5, '2014-05-21 07:10:45.827', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (181, 4, '2014-05-21 07:10:45.827', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8888, 5, '2015-11-18 11:23:07.643', 0, NULL, NULL, '&lt;p&gt;Datasets are structured data files in any format, collected together with the documentation that explains their production or use.&lt;/p&gt;
', NULL, NULL, '''/p'':21C ''collect'':10C ''data'':5C ''dataset'':2C ''document'':14C ''explain'':16C ''file'':6C ''format'':9C ''p'':1C ''product'':18C ''structur'':4C ''togeth'':11C ''use'':20C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (16, 1, '2014-05-14 01:57:56.88', 15, 213, 'Use liblinear on big data for semantic analysis', '&lt;p&gt;I use &lt;a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm/"&gt;Libsvm&lt;/a&gt; to train data and predict classification on &lt;strong&gt;semantic analysis&lt;/strong&gt; problem. But it has a &lt;strong&gt;performance&lt;/strong&gt; issue on large-scale data, because semantic analysis concerns &lt;strong&gt;&lt;em&gt;n-dimension&lt;/em&gt;&lt;/strong&gt; problem.&lt;/p&gt;

&lt;p&gt;Last year, &lt;a href="http://www.csie.ntu.edu.tw/~cjlin/liblinear/"&gt;Liblinear&lt;/a&gt; was release, and it can solve performance bottleneck.
But it cost too much &lt;strong&gt;memory&lt;/strong&gt;. Is &lt;strong&gt;MapReduce&lt;/strong&gt; the only way to solve semantic analysis problem on big data? Or are there any other methods that can improve memory bottleneck on &lt;strong&gt;Liblinear&lt;/strong&gt;?&lt;/p&gt;
', 'machine-learning  bigdata  libsvm', NULL, '''/a'':23C,71C ''/em'':58C ''/p'':61C,118C ''/strong'':34C,42C,59C,87C,91C,117C ''/~cjlin/liblinear/'':69C ''/~cjlin/libsvm/'':21C ''analysi'':8A,33C,51C,98C ''big'':4A,101C ''bigdata'':12B ''bottleneck'':79C,113C ''classif'':29C ''concern'':52C ''cost'':82C ''data'':5A,26C,48C,102C ''dimens'':57C ''em'':54C ''href'':18C,66C ''improv'':111C ''issu'':43C ''larg'':46C ''large-scal'':45C ''last'':63C ''learn'':11B ''liblinear'':2A,70C,116C ''libsvm'':13B,22C ''machin'':10B ''machine-learn'':9B ''mapreduc'':90C ''memori'':86C,112C ''method'':108C ''much'':84C ''n'':56C ''n-dimens'':55C ''p'':14C,62C ''perform'':41C,78C ''predict'':28C ''problem'':35C,60C,99C ''releas'':73C ''scale'':47C ''semant'':7A,32C,50C,97C ''solv'':77C,96C ''strong'':31C,40C,53C,85C,89C,115C ''train'':25C ''use'':1A,16C ''way'':94C ''www.csie.ntu.edu.tw'':20C,68C ''www.csie.ntu.edu.tw/~cjlin/liblinear/'':67C ''www.csie.ntu.edu.tw/~cjlin/libsvm/'':19C ''year'':64C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (17, 5, '2014-05-14 02:49:14.58', 0, NULL, NULL, '&lt;p&gt;&lt;a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm/" rel="nofollow"&gt;LIBSVM&lt;/a&gt; is a library for support vector classification (SVM) and regression.
It was created by Chih-Chung Chang and Chih-Jen Lin in 2001.&lt;/p&gt;
', NULL, NULL, '''/a'':10C ''/p'':36C ''/~cjlin/libsvm/'':6C ''2001'':35C ''chang'':28C ''chih'':26C,31C ''chih-chung'':25C ''chih-jen'':30C ''chung'':27C ''classif'':17C ''creat'':23C ''href'':3C ''jen'':32C ''librari'':13C ''libsvm'':9C ''lin'':33C ''nofollow'':8C ''p'':1C ''regress'':20C ''rel'':7C ''support'':15C ''svm'':18C ''vector'':16C ''www.csie.ntu.edu.tw'':5C ''www.csie.ntu.edu.tw/~cjlin/libsvm/'':4C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8342, 2, '2015-10-07 04:38:27.75', 1, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;LinkedIn&lt;/li&gt;
&lt;li&gt;Github&lt;/li&gt;
&lt;li&gt;Personal Website (of course)&lt;/li&gt;
&lt;li&gt;Coderbits (not popular, but you can display your profile link on the resume/LinkedIn)&lt;/li&gt;
&lt;li&gt;Kaggle&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':4C,7C,13C,28C,31C ''/ol'':32C ''coderbit'':15C ''cours'':12C ''display'':21C ''github'':6C ''kaggl'':30C ''li'':2C,5C,8C,14C,29C ''link'':24C ''linkedin'':3C ''ol'':1C ''person'':9C ''popular'':17C ''profil'':23C ''resume/linkedin'':27C ''websit'':10C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (19, 1, '2014-05-14 03:56:20.963', 45, 1710, 'How big is big data?', '&lt;p&gt;Lots of people use the term &lt;em&gt;big data&lt;/em&gt; in a rather &lt;em&gt;commercial&lt;/em&gt; way, as a means of indicating that large datasets are involved in the computation, and therefore potential solutions must have good performance. Of course, &lt;em&gt;big data&lt;/em&gt; always carry associated terms, like scalability and efficiency, but what exactly defines a problem as a &lt;em&gt;big data&lt;/em&gt; problem?&lt;/p&gt;

&lt;p&gt;Does the computation have to be related to some set of specific purposes, like data mining/information retrieval, or could an algorithm for general graph problems be labeled &lt;em&gt;big data&lt;/em&gt; if the dataset was &lt;em&gt;big enough&lt;/em&gt;? Also, how &lt;em&gt;big&lt;/em&gt; is &lt;em&gt;big enough&lt;/em&gt; (if this is possible to define)?&lt;/p&gt;
', 'bigdata  scalability  efficiency  performance', NULL, '''/em'':20C,26C,54C,74C,108C,116C,121C,126C ''/p'':76C,133C ''algorithm'':98C ''also'':117C ''alway'':55C ''associ'':57C ''big'':2A,4A,18C,52C,72C,106C,114C,120C,124C ''bigdata'':6B ''carri'':56C ''commerci'':25C ''comput'':40C,80C ''could'':96C ''cours'':50C ''data'':5A,19C,53C,73C,92C,107C ''dataset'':35C,111C ''defin'':66C,132C ''effici'':8B,62C ''em'':17C,24C,51C,71C,105C,113C,119C,123C ''enough'':115C,125C ''exact'':65C ''general'':100C ''good'':47C ''graph'':101C ''indic'':32C ''involv'':37C ''label'':104C ''larg'':34C ''like'':59C,91C ''lot'':11C ''mean'':30C ''mining/information'':93C ''must'':45C ''p'':10C,77C ''peopl'':13C ''perform'':9B,48C ''possibl'':130C ''potenti'':43C ''problem'':68C,75C,102C ''purpos'':90C ''rather'':23C ''relat'':84C ''retriev'':94C ''scalabl'':7B,60C ''set'':87C ''solut'':44C ''specif'':89C ''term'':16C,58C ''therefor'':42C ''use'':14C ''way'':27C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (20, 1, '2014-05-14 05:37:46.78', 14, 205, 'the data on our relational DBMS is getting big, is it the time to move to NoSQL?', '&lt;p&gt;we created this social network application for eLearning purposes, it''s an experimental thing we are researching on in our lab. it has been used by some case studies for a while and the data on our relational DBMS (SQL Server 2008) is getting big, it''s a few gigabytes now and the tables are highly connected to each other. the performance is still fine, but when should we consider other options? is it the matter of performance?  &lt;/p&gt;
', 'nosql  relational-dbms', NULL, '''/p'':101C ''2008'':64C ''applic'':28C ''big'':9A,67C ''case'':50C ''connect'':79C ''consid'':92C ''creat'':24C ''data'':2A,57C ''dbms'':6A,21B,61C ''elearn'':30C ''experiment'':35C ''fine'':87C ''get'':8A,66C ''gigabyt'':72C ''high'':78C ''lab'':43C ''matter'':98C ''move'':15A ''network'':27C ''nosql'':17A,18B ''option'':94C ''p'':22C ''perform'':84C,100C ''purpos'':31C ''relat'':5A,20B,60C ''relational-dbm'':19B ''research'':39C ''server'':63C ''social'':26C ''sql'':62C ''still'':86C ''studi'':51C ''tabl'':76C ''thing'':36C ''time'':13A ''use'':47C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (27, 2, '2014-05-14 07:53:02.56', 8, NULL, NULL, '&lt;p&gt;To answer this question you have to answer which kind of compromise you can afford. RDBMs implements &lt;a href="http://en.wikipedia.org/wiki/ACID"&gt;ACID&lt;/a&gt;. This is expensive in terms of resources. There are no NoSQL solutions which are ACID. See &lt;a href="http://en.wikipedia.org/wiki/CAP_theorem"&gt;CAP theorem&lt;/a&gt; to dive deep into these ideas. &lt;/p&gt;

&lt;p&gt;So you have to understand each compromise given by each solution and choose the one which is the most appropriate for your problem.&lt;/p&gt;
', NULL, NULL, '''/a'':25C,49C ''/p'':56C,81C ''/wiki/acid'':23C ''/wiki/cap_theorem'':46C ''acid'':24C,40C ''afford'':16C ''answer'':3C,9C ''appropri'':77C ''cap'':47C ''choos'':70C ''compromis'':13C,64C ''deep'':52C ''dive'':51C ''en.wikipedia.org'':22C,45C ''en.wikipedia.org/wiki/acid'':21C ''en.wikipedia.org/wiki/cap_theorem'':44C ''expens'':28C ''given'':65C ''href'':20C,43C ''idea'':55C ''implement'':18C ''kind'':11C ''nosql'':36C ''one'':72C ''p'':1C,57C ''problem'':80C ''question'':5C ''rdbms'':17C ''resourc'':32C ''see'':41C ''solut'':37C,68C ''term'':30C ''theorem'':48C ''understand'':62C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (28, 2, '2014-05-14 07:55:40.133', 4, NULL, NULL, '&lt;p&gt;There is free ebook "&lt;a href="http://jsresearch.net/" rel="nofollow"&gt;Introduction to Data Science&lt;/a&gt;" based on &lt;a href="/questions/tagged/r" class="post-tag" title="show questions tagged &amp;#39;r&amp;#39;" rel="tag"&gt;r&lt;/a&gt; language&lt;/p&gt;
', NULL, NULL, '''/a'':15C,35C ''/p'':37C ''/questions/tagged/r'':20C ''39'':29C,31C ''base'':16C ''class'':21C ''data'':13C ''ebook'':5C ''free'':4C ''href'':7C,19C ''introduct'':11C ''jsresearch.net'':8C ''languag'':36C ''nofollow'':10C ''p'':1C ''post'':23C ''post-tag'':22C ''question'':27C ''r'':30C,34C ''rel'':9C,32C ''scienc'':14C ''show'':26C ''tag'':24C,28C,33C ''titl'':25C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (21, 2, '2014-05-14 05:44:29.34', 22, NULL, NULL, '&lt;p&gt;As you rightly note, these days "big data" is something everyone wants to say they''ve got, which entails a certain looseness in how people define the term.  Generally, though, I''d say you''re certainly dealing with big data if the scale is such that it''s no longer feasible to manage with more traditional technologies such as RDBMS, at least without complementing them with big data technologies such as Hadoop.&lt;/p&gt;

&lt;p&gt;How big your data has to actually be for that to be the case is debatable.  Here''s a (somewhat provocative) &lt;a href="http://www.chrisstucchio.com/blog/2013/hadoop_hatred.html"&gt;blog post&lt;/a&gt; that claims that it''s not really the case for less than 5 TB of data.  (To be clear, it doesn''t claim "Less than 5 TB isn''t big data", but just "Less than 5 TB isn''t big enough that you need Hadoop".)&lt;/p&gt;

&lt;p&gt;But even on smaller datasets, big data technologies like Hadoop can have other advantages, including being well suited to batch operations, playing well with unstructured data (as well as data whose structure isn''t known in advance or could change), horizontal scalability (scaling by adding more nodes instead of beefing up your existing servers), and (as one of the commenters on the above-linked post notes) the ability to integrate your data processing with external data sets (think of a map-reduce where the mapper makes a call to another server).  Other technologies associated with big data, like NoSql databases, emphasize fast performance and consistent availability while dealing with large sets of data, as well also being able to handle semi-unstructured data and to scale horizontally.&lt;/p&gt;

&lt;p&gt;Of course, traditional RDBMS have their own advantages including ACID guarantees (Atomicity, Consistency, Isolation, Durability) and better performance for certain operations, as well as being more standardized, more mature, and (for many users) more familiar.  So even for indisputably "big" data, it may make sense to load at least a portion of your data into a traditional SQL database and use that in conjunction with big data technologies.&lt;/p&gt;

&lt;p&gt;So, a more generous definition would be that you have big data so long as it''s big enough that big data technologies provide some added value for you.  But as you can see, that can depend not just on the size of your data but on how you want to work with it and what sort of requirements you have in terms of flexibility, consistency, and performance.  &lt;em&gt;How&lt;/em&gt; you''re using your data is more relevant to the question than what you''re using it &lt;em&gt;for&lt;/em&gt; (e.g. data mining).  That said, uses like data mining and machine learning are more likely to yield useful results if you have a big enough data set to work with.&lt;/p&gt;
', NULL, NULL, '''/a'':104C ''/blog/2013/hadoop_hatred.html'':101C ''/em'':424C,444C ''/p'':74C,150C,282C,352C,475C ''5'':117C,130C,140C ''abil'':220C ''abl'':271C ''above-link'':214C ''acid'':293C ''actual'':82C ''ad'':196C,379C ''advanc'':188C ''advantag'':165C,291C ''also'':269C ''anoth'':243C ''associ'':247C ''atom'':295C ''avail'':259C ''batch'':171C ''beef'':201C ''better'':300C ''big'':8C,40C,68C,77C,134C,144C,157C,249C,323C,349C,364C,371C,374C,468C ''blog'':102C ''call'':241C ''case'':89C,113C ''certain'':22C,37C,303C ''chang'':191C ''claim'':106C,127C ''clear'':123C ''comment'':211C ''complement'':65C ''conjunct'':347C ''consist'':258C,296C,419C ''could'':190C ''cours'':285C ''d'':33C ''data'':9C,41C,69C,79C,120C,135C,158C,177C,181C,224C,228C,250C,266C,277C,324C,337C,350C,365C,375C,398C,429C,446C,452C,470C ''databas'':253C,342C ''dataset'':156C ''day'':7C ''deal'':38C,261C ''debat'':91C ''defin'':27C ''definit'':358C ''depend'':390C ''doesn'':125C ''durabl'':298C ''e.g'':445C ''em'':422C,442C ''emphas'':254C ''enough'':145C,372C,469C ''entail'':20C ''even'':153C,320C ''everyon'':12C ''exist'':204C ''extern'':227C ''familiar'':318C ''fast'':255C ''feasibl'':52C ''flexibl'':418C ''general'':30C ''generous'':357C ''got'':18C ''guarante'':294C ''hadoop'':73C,149C,161C ''handl'':273C ''horizont'':192C,281C ''href'':98C ''includ'':166C,292C ''indisput'':322C ''instead'':199C ''integr'':222C ''isn'':132C,142C,184C ''isol'':297C ''known'':186C ''larg'':263C ''learn'':456C ''least'':63C,332C ''less'':115C,128C,138C ''like'':160C,251C,451C,459C ''link'':216C ''load'':330C ''long'':367C ''longer'':51C ''loos'':23C ''machin'':455C ''make'':239C,327C ''manag'':54C ''mani'':315C ''map'':234C ''map-reduc'':233C ''mapper'':238C ''matur'':312C ''may'':326C ''mine'':447C,453C ''need'':148C ''node'':198C ''nosql'':252C ''note'':5C,218C ''one'':208C ''oper'':172C,304C ''p'':1C,75C,151C,283C,353C ''peopl'':26C ''perform'':256C,301C,421C ''play'':173C ''portion'':334C ''post'':103C,217C ''process'':225C ''provid'':377C ''provoc'':96C ''question'':435C ''rdbms'':61C,287C ''re'':36C,426C,439C ''realli'':111C ''reduc'':235C ''relev'':432C ''requir'':412C ''result'':463C ''right'':4C ''said'':449C ''say'':15C,34C ''scalabl'':193C ''scale'':44C,194C,280C ''see'':387C ''semi'':275C ''semi-unstructur'':274C ''sens'':328C ''server'':205C,244C ''set'':229C,264C,471C ''size'':395C ''smaller'':155C ''someth'':11C ''somewhat'':95C ''sort'':410C ''sql'':341C ''standard'':310C ''structur'':183C ''suit'':169C ''tb'':118C,131C,141C ''technolog'':58C,70C,159C,246C,351C,376C ''term'':29C,416C ''think'':230C ''though'':31C ''tradit'':57C,286C,340C ''unstructur'':176C,276C ''use'':344C,427C,440C,450C,462C ''user'':316C ''valu'':380C ''ve'':17C ''want'':13C,403C ''well'':168C,174C,179C,268C,306C ''whose'':182C ''without'':64C ''work'':405C,473C ''would'':359C ''www.chrisstucchio.com'':100C ''www.chrisstucchio.com/blog/2013/hadoop_hatred.html'':99C ''yield'':461C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (29, 2, '2014-05-14 07:56:34.437', 17, NULL, NULL, '&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/users/36/statsrus"&gt;@statsRus&lt;/a&gt; starts to lay the groundwork for your answer in another question &lt;a href="http://datascience.stackexchange.com/questions/1/what-characterises-the-difference-between-data-science-and-statistics"&gt;What characterises the difference between data science and statistics?&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;ul&gt;
  &lt;li&gt;&lt;strong&gt;Data collection&lt;/strong&gt;: web scraping and online surveys&lt;/li&gt;
  &lt;li&gt;&lt;strong&gt;Data manipulation&lt;/strong&gt;: recoding messy data and extracting meaning from linguistic and social network data&lt;/li&gt;
  &lt;li&gt;&lt;strong&gt;Data scale&lt;/strong&gt;: working with extremely large data sets&lt;/li&gt;
  &lt;li&gt;&lt;strong&gt;Data mining&lt;/strong&gt;: finding patterns in large, complex data sets, with an emphasis on algorithmic techniques&lt;/li&gt;
  &lt;li&gt;&lt;strong&gt;Data communication&lt;/strong&gt;: helping turn "machine-readable" data into "human-readable" information via visualization&lt;/li&gt;
  &lt;/ul&gt;
&lt;/blockquote&gt;

&lt;h2&gt;Definition&lt;/h2&gt;

&lt;p&gt;&lt;a href="/questions/tagged/data-mining" class="post-tag" title="show questions tagged &amp;#39;data-mining&amp;#39;" rel="tag"&gt;data-mining&lt;/a&gt; can be seen as one item (or set of skills and applications) in the toolkit of the data scientist.  I like how he separates the definition of mining from collection in a sort of trade-specific jargon.&lt;/p&gt;

&lt;p&gt;However, I think that &lt;em&gt;data-mining&lt;/em&gt; would be synonymous with &lt;em&gt;data-collection&lt;/em&gt; in a US-English colloquial definition.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;As to where to go to become proficient?&lt;/em&gt;  I think that question is too broad as it is currently stated and would receive answers that are primarily opinion based.  Perhaps if you could refine your question, it might be easier to see what you are asking.&lt;/p&gt;
', NULL, NULL, '''/a'':8C,34C,144C ''/blockquote'':118C ''/em'':193C,202C,221C ''/h2'':121C ''/li'':48C,66C,78C,97C,116C ''/p'':35C,183C,210C,260C ''/questions/1/what-characterises-the-difference-between-data-science-and-statistics'':24C ''/questions/tagged/data-mining'':125C ''/strong'':42C,53C,71C,83C,102C ''/ul'':117C ''/users/36/statsrus'':6C ''39'':134C,138C ''algorithm'':95C ''anoth'':18C ''answer'':16C,237C ''applic'':156C ''ask'':259C ''base'':242C ''becom'':219C ''blockquot'':36C ''broad'':228C ''characteris'':26C ''class'':126C ''collect'':41C,174C,201C ''colloqui'':208C ''communic'':101C ''complex'':88C ''could'':246C ''current'':232C ''data'':30C,40C,51C,56C,65C,69C,76C,81C,89C,100C,108C,136C,142C,162C,191C,200C ''data-collect'':199C ''data-min'':135C,141C,190C ''datascience.stackexchange.com'':5C,23C ''datascience.stackexchange.com/questions/1/what-characterises-the-difference-between-data-science-and-statistics'':22C ''datascience.stackexchange.com/users/36/statsrus'':4C ''definit'':120C,170C,209C ''differ'':28C ''easier'':253C ''em'':189C,198C,212C ''emphasi'':93C ''english'':207C ''extract'':58C ''extrem'':74C ''find'':84C ''go'':217C ''groundwork'':13C ''h2'':119C ''help'':103C ''howev'':185C ''href'':3C,21C,124C ''human'':111C ''human-read'':110C ''inform'':113C ''item'':150C ''jargon'':182C ''larg'':75C,87C ''lay'':11C ''li'':38C,49C,67C,79C,98C ''like'':165C ''linguist'':61C ''machin'':106C ''machine-read'':105C ''manipul'':52C ''mean'':59C ''messi'':55C ''might'':251C ''mine'':82C,137C,143C,172C,192C ''network'':64C ''one'':149C ''onlin'':46C ''opinion'':241C ''p'':1C,122C,184C,211C ''pattern'':85C ''perhap'':243C ''post'':128C ''post-tag'':127C ''primarili'':240C ''profici'':220C ''question'':19C,132C,225C,249C ''readabl'':107C,112C ''receiv'':236C ''recod'':54C ''refin'':247C ''rel'':139C ''scale'':70C ''scienc'':31C ''scientist'':163C ''scrape'':44C ''see'':255C ''seen'':147C ''separ'':168C ''set'':77C,90C,152C ''show'':131C ''skill'':154C ''social'':63C ''sort'':177C ''specif'':181C ''start'':9C ''state'':233C ''statist'':33C ''statsrus'':7C ''strong'':39C,50C,68C,80C,99C ''survey'':47C ''synonym'':196C ''tag'':129C,133C,140C ''techniqu'':96C ''think'':187C,223C ''titl'':130C ''toolkit'':159C ''trade'':180C ''trade-specif'':179C ''turn'':104C ''ul'':37C ''us'':206C ''us-english'':205C ''via'':114C ''visual'':115C ''web'':43C ''work'':72C ''would'':194C,235C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2341, 2, '2014-10-23 21:12:27.35', 1, NULL, NULL, '&lt;p&gt;I would think about starting with a power analysis: i.e. how many data points do you need to measure the effect (or parameter) that you are interested to a specified level of confidence, &lt;em&gt;ceteris paribus&lt;/em&gt;? Then, you estimate a cost. &lt;/p&gt;
', NULL, NULL, '''/em'':38C ''/p'':44C ''analysi'':10C ''ceteri'':36C ''confid'':34C ''cost'':43C ''data'':14C ''effect'':22C ''em'':35C ''estim'':41C ''i.e'':11C ''interest'':28C ''level'':32C ''mani'':13C ''measur'':20C ''need'':18C ''p'':1C ''paramet'':24C ''paribus'':37C ''point'':15C ''power'':9C ''specifi'':31C ''start'':6C ''think'':4C ''would'':3C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (30, 2, '2014-05-14 08:03:28.117', 19, NULL, NULL, '&lt;p&gt;Total amount of data in the world: 2.8 zetabytes in 2012, estimated to reach 8 zetabytes by 2015 (&lt;a href="http://siliconangle.com/blog/2012/05/21/when-will-the-world-reach-8-zetabytes-of-stored-data-infographic/"&gt;source&lt;/a&gt;) and with a doubling time of 40 months. Can''t get bigger than that :)&lt;/p&gt;

&lt;p&gt;As an example of a single large organization, Facebook pulls in 500 terabytes per day, into a 100 petabyte warehouse, and runs 70k queries per day on it as of 2012 (&lt;a href="http://gigaom.com/2012/08/22/facebook-is-collecting-your-data-500-terabytes-a-day/"&gt;source&lt;/a&gt;)  Their current warehouse is &gt;300 petabytes.&lt;/p&gt;

&lt;p&gt;Big data is probably something that is a good fraction of the Facebook numbers (1/100 probably yes, 1/10000 probably not: it''s a spectrum not a single number).&lt;/p&gt;

&lt;p&gt;In addition to size, some of the features that make it "big" are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;it is actively analyzed, not just stored  (quote "If you arenât taking advantage of big data, then you donât have big data, you have just a pile of data" Jay Parikh @ Facebook)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;building and running a data warehouse is a major infrastructure project&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;it is growing at a significant rate&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;it is unstructured or has irregular structure&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Gartner definition: "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing" (The 3Vs)  So they also think "bigness" isn''t entirely about the size of the dataset, but also about the velocity and structure and the kind of tools needed.&lt;/p&gt;
', NULL, NULL, '''/2012/08/22/facebook-is-collecting-your-data-500-terabytes-a-day/'':78C ''/a'':26C,80C ''/blog/2012/05/21/when-will-the-world-reach-8-zetabytes-of-stored-data-infographic/'':24C ''/li'':171C,186C,197C,208C ''/p'':41C,87C,117C,132C,170C,185C,196C,207C,260C ''/ul'':209C ''1/100'':103C ''1/10000'':106C ''100'':60C ''2.8'':9C ''2012'':12C,73C ''2015'':19C ''300'':85C ''3vs'':232C ''40'':33C ''500'':54C ''70k'':65C ''8'':16C ''activ'':138C ''addit'':120C ''advantag'':149C ''also'':235C,248C ''amount'':3C ''analyz'':139C ''and/or'':220C ''arenâ'':146C ''asset'':224C ''big'':89C,130C,151C,158C,213C,237C ''bigger'':38C ''build'':174C ''current'':82C ''data'':5C,90C,152C,159C,166C,178C,214C ''dataset'':246C ''day'':57C,68C ''definit'':212C ''donâ'':155C ''doubl'':30C ''entir'':240C ''estim'':13C ''exampl'':45C ''facebook'':51C,101C,169C ''featur'':126C ''form'':228C ''fraction'':98C ''gartner'':211C ''get'':37C ''gigaom.com'':77C ''gigaom.com/2012/08/22/facebook-is-collecting-your-data-500-terabytes-a-day/'':76C ''good'':97C ''grow'':191C ''high'':216C,218C,221C ''href'':21C,75C ''inform'':223C ''infrastructur'':183C ''irregular'':205C ''isn'':238C ''jay'':167C ''kind'':256C ''larg'':49C ''li'':134C,172C,187C,198C ''major'':182C ''make'':128C ''month'':34C ''need'':259C ''new'':227C ''number'':102C,116C ''organ'':50C ''p'':1C,42C,88C,118C,135C,173C,188C,199C,210C ''parikh'':168C ''per'':56C,67C ''petabyt'':61C,86C ''pile'':164C ''probabl'':92C,104C,107C ''process'':230C ''project'':184C ''pull'':52C ''queri'':66C ''quot'':143C ''rate'':195C ''reach'':15C ''requir'':226C ''run'':64C,176C ''signific'':194C ''siliconangle.com'':23C ''siliconangle.com/blog/2012/05/21/when-will-the-world-reach-8-zetabytes-of-stored-data-infographic/'':22C ''singl'':48C,115C ''size'':122C,243C ''someth'':93C ''sourc'':25C,79C ''spectrum'':112C ''store'':142C ''structur'':206C,253C ''take'':148C ''terabyt'':55C ''think'':236C ''time'':31C ''tool'':258C ''total'':2C ''ul'':133C ''unstructur'':202C ''varieti'':222C ''veloc'':219C,251C ''volum'':217C ''warehous'':62C,83C,179C ''world'':8C ''yes'':105C ''zetabyt'':10C,17C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (22, 1, '2014-05-14 05:58:21.927', 36, 16558, 'K-Means clustering for mixed numeric and categorical data', '&lt;p&gt;My data set contains a number of numeric attributes and one categorical.&lt;/p&gt;

&lt;p&gt;Say, &lt;code&gt;NumericAttr1, NumericAttr2, ..., NumericAttrN, CategoricalAttr&lt;/code&gt;, &lt;/p&gt;

&lt;p&gt;where &lt;code&gt;CategoricalAttr&lt;/code&gt; takes one of three possible values: &lt;code&gt;CategoricalAttrValue1&lt;/code&gt;, &lt;code&gt;CategoricalAttrValue2&lt;/code&gt; or &lt;code&gt;CategoricalAttrValue3&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;I''m using default k-means clustering algorithm implementation for Octave &lt;a href="https://blog.west.uni-koblenz.de/2012-07-14/a-working-k-means-code-for-octave/"&gt;https://blog.west.uni-koblenz.de/2012-07-14/a-working-k-means-code-for-octave/&lt;/a&gt;.
It works with numeric data only.&lt;/p&gt;

&lt;p&gt;So my question: is it correct to split the categorical attribute &lt;code&gt;CategoricalAttr&lt;/code&gt; into three numeric (binary) variables, like &lt;code&gt;IsCategoricalAttrValue1, IsCategoricalAttrValue2, IsCategoricalAttrValue3&lt;/code&gt; ?&lt;/p&gt;
', 'data-mining  clustering  octave  k-means  categorical-data', NULL, '''/2012-07-14/a-working-k-means-code-for-octave/'':84C ''/2012-07-14/a-working-k-means-code-for-octave/&lt;/a&gt;.'':87C ''/code'':43C,49C,58C,61C,65C,109C,120C ''/p'':35C,44C,66C,94C,121C ''algorithm'':76C ''attribut'':31C,106C ''binari'':113C ''blog.west.uni-koblenz.de'':83C,86C ''blog.west.uni-koblenz.de/2012-07-14/a-working-k-means-code-for-octave/'':82C ''blog.west.uni-koblenz.de/2012-07-14/a-working-k-means-code-for-octave/&lt;/a&gt;.'':85C ''categor'':9A,20B,34C,105C ''categorical-data'':19B ''categoricalattr'':42C,48C,108C ''categoricalattrvalue1'':57C ''categoricalattrvalue2'':60C ''categoricalattrvalue3'':64C ''cluster'':4A,14B,75C ''code'':38C,47C,56C,59C,63C,107C,116C ''contain'':26C ''correct'':101C ''data'':10A,12B,21B,24C,92C ''data-min'':11B ''default'':71C ''href'':81C ''implement'':77C ''iscategoricalattrvalue1'':117C ''iscategoricalattrvalue2'':118C ''iscategoricalattrvalue3'':119C ''k'':2A,17B,73C ''k-mean'':1A,16B,72C ''like'':115C ''m'':69C ''mean'':3A,18B,74C ''mine'':13B ''mix'':6A ''number'':28C ''numer'':7A,30C,91C,112C ''numericattr1'':39C ''numericattr2'':40C ''numericattrn'':41C ''octav'':15B,79C ''one'':33C,51C ''p'':22C,36C,45C,67C,95C ''possibl'':54C ''question'':98C ''say'':37C ''set'':25C ''split'':103C ''take'':50C ''three'':53C,111C ''use'':70C ''valu'':55C ''variabl'':114C ''work'':89C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (23, 2, '2014-05-14 06:06:13.603', 6, NULL, NULL, '&lt;p&gt;Data Science specialization from Johns Hopkins University at Coursera would be a great start.
&lt;a href="https://www.coursera.org/specialization/jhudatascience/1"&gt;https://www.coursera.org/specialization/jhudatascience/1&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/specialization/jhudatascience/1'':20C ''/specialization/jhudatascience/1&lt;/a&gt;&lt;/p&gt;'':23C ''coursera'':10C ''data'':2C ''great'':14C ''hopkin'':7C ''href'':17C ''john'':6C ''p'':1C ''scienc'':3C ''special'':4C ''start'':15C ''univers'':8C ''would'':11C ''www.coursera.org'':19C,22C ''www.coursera.org/specialization/jhudatascience/1'':18C ''www.coursera.org/specialization/jhudatascience/1&lt;/a&gt;&lt;/p&gt;'':21C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (24, 2, '2014-05-14 06:26:27.163', 34, NULL, NULL, '&lt;p&gt;The standard k-means algorithm isn''t directly applicable to categorical data, for various reasons.  The sample space for categorical data is discrete, and doesn''t have a natural origin.  A Euclidean distance function on such a space isn''t really meaningful.  As someone put it, "The fact a snake possesses neither wheels nor legs allows us to say nothing about the relative value of wheels and legs." (from &lt;a href="http://www.daylight.com/meetings/mug04/Bradshaw/why_k-modes.html"&gt;here&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;There''s a variation of k-means known as k-modes, introduced in &lt;a href="http://www.cs.ust.hk/~qyang/Teaching/537/Papers/huang98extensions.pdf"&gt;this paper&lt;/a&gt; by Zhexue Huang, which is suitable for categorical data.   Note that the solutions you get are sensitive to initial conditions, as discussed &lt;a href="http://arxiv.org/ftp/cs/papers/0603/0603120.pdf"&gt;here&lt;/a&gt; (PDF), for instance.&lt;/p&gt;

&lt;p&gt;Huang''s paper (linked above) also has a section on "k-protoypes" which applies to data with a mix of categorical and numeric features.  It uses a distance measure which mixes the Hamming distance for categorical features and the Euclidean distance for numeric features.&lt;/p&gt;

&lt;p&gt;A Google search for "k-means mix of categorical data" turns up quite a few more recent papers on various algorithms for k-means-like clustering with a mix of categorical and numeric data.  (I haven''t yet read them, so I can''t comment on their merits.)  &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Actually, what you suggest (converting categorical attributes to binary values, and then doing k-means as if these were numeric values) is another approach that has been tried before (predating k-modes).  (See Ralambondrainy, H. 1995. A conceptual version of the k-means algorithm. Pattern Recognition Letters, 16:1147â1157.)  But I believe the k-modes approach is preferred for the reasons I indicated above.&lt;/p&gt;
', NULL, NULL, '''/a'':78C,103C,132C ''/ftp/cs/papers/0603/0603120.pdf'':130C ''/meetings/mug04/bradshaw/why_k-modes.html'':76C ''/p'':79C,136C,183C,235C,307C ''/~qyang/teaching/537/papers/huang98extensions.pdf'':100C ''1147â'':289C ''1157'':290C ''16'':288C ''1995'':275C ''actual'':238C ''algorithm'':7C,206C,284C ''allow'':58C ''also'':143C ''anoth'':261C ''appli'':152C ''applic'':11C ''approach'':262C,298C ''arxiv.org'':129C ''arxiv.org/ftp/cs/papers/0603/0603120.pdf'':128C ''attribut'':244C ''believ'':293C ''binari'':246C ''categor'':13C,22C,111C,159C,174C,194C,217C,243C ''cluster'':212C ''comment'':231C ''conceptu'':277C ''condit'':123C ''convert'':242C ''data'':14C,23C,112C,154C,195C,220C ''direct'':10C ''discret'':25C ''discuss'':125C ''distanc'':35C,166C,172C,179C ''doesn'':27C ''euclidean'':34C,178C ''fact'':50C ''featur'':162C,175C,182C ''function'':36C ''get'':118C ''googl'':186C ''h'':274C ''ham'':171C ''haven'':222C ''hr'':236C ''href'':73C,97C,127C ''huang'':106C,138C ''indic'':305C ''initi'':122C ''instanc'':135C ''introduc'':94C ''isn'':8C,41C ''k'':5C,87C,92C,149C,190C,209C,252C,270C,282C,296C ''k-mean'':4C,86C,189C,251C,281C ''k-means-lik'':208C ''k-mode'':91C,269C,295C ''k-protoyp'':148C ''known'':89C ''leg'':57C,70C ''letter'':287C ''like'':211C ''link'':141C ''mean'':6C,88C,191C,210C,253C,283C ''meaning'':44C ''measur'':167C ''merit'':234C ''mix'':157C,169C,192C,215C ''mode'':93C,271C,297C ''natur'':31C ''neither'':54C ''note'':113C ''noth'':62C ''numer'':161C,181C,219C,258C ''origin'':32C ''p'':1C,80C,137C,184C,237C ''paper'':102C,140C,203C ''pattern'':285C ''pdf'':133C ''possess'':53C ''predat'':268C ''prefer'':300C ''protoyp'':150C ''put'':47C ''quit'':198C ''ralambondraini'':273C ''read'':225C ''realli'':43C ''reason'':17C,303C ''recent'':202C ''recognit'':286C ''relat'':65C ''sampl'':19C ''say'':61C ''search'':187C ''section'':146C ''see'':272C ''sensit'':120C ''snake'':52C ''solut'':116C ''someon'':46C ''space'':20C,40C ''standard'':3C ''suggest'':241C ''suitabl'':109C ''tri'':266C ''turn'':196C ''us'':59C ''use'':164C ''valu'':66C,247C,259C ''variat'':84C ''various'':16C,205C ''version'':278C ''wheel'':55C,68C ''www.cs.ust.hk'':99C ''www.cs.ust.hk/~qyang/teaching/537/papers/huang98extensions.pdf'':98C ''www.daylight.com'':75C ''www.daylight.com/meetings/mug04/bradshaw/why_k-modes.html'':74C ''yet'':224C ''zhexu'':105C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (25, 2, '2014-05-14 07:26:04.39', 5, NULL, NULL, '&lt;p&gt;Big Data is defined by the volume of data, that''s right, but not only. The particularity of big data is that you need to store a &lt;strong&gt;lots&lt;/strong&gt; of &lt;strong&gt;various&lt;/strong&gt; and sometimes &lt;strong&gt;unstructured&lt;/strong&gt; stuffs &lt;strong&gt;all the times&lt;/strong&gt; and from a &lt;strong&gt;tons of sensors&lt;/strong&gt;, usually &lt;strong&gt;for years or decade&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Furthermore you need something scalable, so that it doesn''t take you half a year to find a data back.&lt;/p&gt;

&lt;p&gt;So here''s come Big Data, where traditional method won''t work anymore. SQL is not scalable. And SQL works with very structured and linked data (with all those Primary and foreign key mess, innerjoin, imbricated request...). &lt;/p&gt;

&lt;p&gt;Basically, because storage becomes cheaper and cheaper and data becomes more and more valuable, big manager ask engineer to records everything. Add to this tons of new sensors with all those mobile, social network, embeded stuff ...etc. So as classic methods won''t work, they have to find new technologies (storing everything in files, in json format, with big index, what we call noSQL). &lt;/p&gt;

&lt;p&gt;So Big Data may be very big but can be not so big but complexe unstructured or various data which has to be store quickly and on-the-run in a raw format. We focus and storing at first, and then we look at how to link everything together.&lt;/p&gt;
', NULL, NULL, '''/p'':62C,84C,123C,189C,241C ''/strong'':31C,35C,40C,46C,54C,61C ''add'':146C ''anymor'':98C ''ask'':141C ''back'':83C ''basic'':125C ''becom'':128C,134C ''big'':2C,20C,90C,139C,183C,192C,197C,203C ''call'':187C ''cheaper'':129C,131C ''classic'':164C ''come'':89C ''complex'':205C ''data'':3C,10C,21C,82C,91C,111C,133C,193C,209C ''decad'':60C ''defin'':5C ''doesn'':72C ''embed'':159C ''engin'':142C ''etc'':161C ''everyth'':145C,176C,239C ''file'':178C ''find'':80C,172C ''first'':230C ''focus'':226C ''foreign'':117C ''format'':181C,224C ''furthermor'':64C ''half'':76C ''imbric'':121C ''index'':184C ''innerjoin'':120C ''json'':180C ''key'':118C ''link'':110C,238C ''look'':234C ''lot'':30C ''manag'':140C ''may'':194C ''mess'':119C ''method'':94C,165C ''mobil'':156C ''need'':25C,66C ''network'':158C ''new'':151C,173C ''nosql'':188C ''on-the-run'':217C ''p'':1C,63C,85C,124C,190C ''particular'':18C ''primari'':115C ''quick'':215C ''raw'':223C ''record'':144C ''request'':122C ''right'':13C ''run'':220C ''scalabl'':68C,102C ''sensor'':53C,152C ''social'':157C ''someth'':67C ''sometim'':37C ''sql'':99C,104C ''storag'':127C ''store'':27C,175C,214C,228C ''strong'':29C,33C,38C,42C,50C,56C ''structur'':108C ''stuff'':41C,160C ''take'':74C ''technolog'':174C ''time'':45C ''togeth'':240C ''ton'':51C,149C ''tradit'':93C ''unstructur'':39C,206C ''usual'':55C ''valuabl'':138C ''various'':34C,208C ''volum'':8C ''won'':95C,166C ''work'':97C,105C,168C ''year'':58C,78C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (26, 2, '2014-05-14 07:38:31.103', 12, NULL, NULL, '&lt;p&gt;A few gigabytes is not very "&lt;strong&gt;big&lt;/strong&gt;". It''s more like the normal size of an enterprise DB. As long as you go over PK when joining tables it should work out really well, even in the future (as long as you don''t get TB''s of data a day).&lt;/p&gt;

&lt;p&gt;Most professionals working in a big data environment consider &lt;strong&gt;&gt; ~5TB&lt;/strong&gt; as the &lt;em&gt;beginning&lt;/em&gt; of the term big data. But even then it''s not always the best way to just install the next best nosql database. You should always think about the task that you want to archive with the data (aggregate,read,search,mine,..) to find the best tools for you problem.&lt;/p&gt;

&lt;p&gt;i.e. if you do alot of searches in you database it would probably be better to run a solr instance/cluster and denormalize your data from a DBMS like Postgres or your SQL Server from time to time and put it into solr instead of just moving the data from sql to nosql in term of persistence and performance.&lt;/p&gt;
', NULL, NULL, '''/em'':73C ''/p'':55C,124C,184C ''/strong'':10C,68C ''5tb'':67C ''aggreg'':112C ''alot'':130C ''alway'':85C,99C ''archiv'':108C ''begin'':72C ''best'':87C,94C,119C ''better'':140C ''big'':9C,62C,77C ''consid'':65C ''data'':52C,63C,78C,111C,149C,173C ''databas'':96C,135C ''day'':54C ''db'':21C ''dbms'':152C ''denorm'':147C ''em'':71C ''enterpris'':20C ''environ'':64C ''even'':38C,80C ''find'':117C ''futur'':41C ''get'':48C ''gigabyt'':4C ''go'':26C ''i.e'':126C ''instal'':91C ''instance/cluster'':145C ''instead'':168C ''join'':30C ''like'':14C,153C ''long'':23C,43C ''mine'':115C ''move'':171C ''next'':93C ''normal'':16C ''nosql'':95C,177C ''p'':1C,56C,125C ''perform'':183C ''persist'':181C ''pk'':28C ''postgr'':154C ''probabl'':138C ''problem'':123C ''profession'':58C ''put'':164C ''read'':113C ''realli'':36C ''run'':142C ''search'':114C,132C ''server'':158C ''size'':17C ''solr'':144C,167C ''sql'':157C,175C ''strong'':8C,66C ''tabl'':31C ''task'':103C ''tb'':49C ''term'':76C,179C ''think'':100C ''time'':160C,162C ''tool'':120C ''want'':106C ''way'':88C ''well'':37C ''work'':34C,59C ''would'':137C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (192, 1, '2014-05-22 15:15:41.133', 6, 185, 'What are the most popular data science application use cases for consumer web companies', '&lt;p&gt;The most popular use case seem to be recommender systems of different kinds (such as recommending shopping items, users in social networks etc.).&lt;/p&gt;

&lt;p&gt;But what are other typical data science applications, which may be used in a different verticals?&lt;/p&gt;

&lt;p&gt;For example: customer churn prediction with machine learning, evaluating customer lifetime value, sales forecasting.&lt;/p&gt;
', 'usecase  consumerweb', NULL, '''/p'':41C,59C,75C ''applic'':8A,50C ''case'':10A,22C ''churn'':64C ''compani'':14A ''consum'':12A ''consumerweb'':16B ''custom'':63C,70C ''data'':6A,48C ''differ'':29C,57C ''etc'':40C ''evalu'':69C ''exampl'':62C ''forecast'':74C ''item'':35C ''kind'':30C ''learn'':68C ''lifetim'':71C ''machin'':67C ''may'':52C ''network'':39C ''p'':17C,42C,60C ''popular'':5A,20C ''predict'':65C ''recommend'':26C,33C ''sale'':73C ''scienc'':7A,49C ''seem'':23C ''shop'':34C ''social'':38C ''system'':27C ''typic'':47C ''use'':9A,21C,54C ''usecas'':15B ''user'':36C ''valu'':72C ''vertic'':58C ''web'':13A');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (31, 1, '2014-05-14 08:38:07.007', 6, 467, 'Clustering customer data stored in ElasticSearch', '&lt;p&gt;I have a bunch of customer profiles stored in a &lt;a href="/questions/tagged/elasticsearch" class="post-tag" title="show questions tagged &amp;#39;elasticsearch&amp;#39;" rel="tag"&gt;elasticsearch&lt;/a&gt; cluster. These profiles are now used for creation of target groups for our email subscriptions. &lt;/p&gt;

&lt;p&gt;Target groups are now formed manually using elasticsearch faceted search capabilities (like  get all male customers of age 23 with one car and 3 children).&lt;/p&gt;

&lt;p&gt;How could I search for interesting groups &lt;strong&gt;automatically&lt;/strong&gt; - using data science, machine learning, clustering or something else?&lt;/p&gt;

&lt;p&gt;&lt;a href="/questions/tagged/r" class="post-tag" title="show questions tagged &amp;#39;r&amp;#39;" rel="tag"&gt;r&lt;/a&gt; programming language seems to be a good tool for this task, but I can''t form a methodology of such group search. One solution is to somehow find the largest clusters of customers and use them as target groups, so the question is:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How can I automatically choose largest clusters of similar customers (similar by parameters that I don''t know at this moment)?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;For example: my program will connect to elasticsearch, offload customer data to CSV and using R language script will find that large portion of customers are male with no children and another large portion of customers have a car and their eye color is brown.&lt;/p&gt;
', 'data-mining  clustering', NULL, '''/a'':39C,122C ''/p'':55C,82C,103C,166C,191C,238C ''/questions/tagged/elasticsearch'':24C ''/questions/tagged/r'':107C ''/strong'':93C,190C ''23'':75C ''3'':80C ''39'':33C,35C,116C,118C ''age'':74C ''anoth'':224C ''automat'':92C,172C ''brown'':237C ''bunch'':15C ''capabl'':67C ''car'':78C,231C ''children'':81C,222C ''choos'':173C ''class'':25C,108C ''cluster'':1A,10B,40C,99C,153C,175C ''color'':235C ''connect'':198C ''could'':85C ''creation'':47C ''csv'':205C ''custom'':2A,17C,72C,155C,178C,202C,217C,228C ''data'':3A,8B,95C,203C ''data-min'':7B ''elasticsearch'':6A,34C,38C,64C,200C ''els'':102C ''email'':53C ''exampl'':194C ''eye'':234C ''facet'':65C ''find'':150C,212C ''form'':61C,138C ''get'':69C ''good'':129C ''group'':50C,58C,90C,143C,161C ''href'':23C,106C ''interest'':89C ''know'':186C ''languag'':124C,209C ''larg'':214C,225C ''largest'':152C,174C ''learn'':98C ''like'':68C ''machin'':97C ''male'':71C,219C ''manual'':62C ''methodolog'':140C ''mine'':9B ''moment'':189C ''offload'':201C ''one'':77C,145C ''p'':11C,56C,83C,104C,167C,192C ''paramet'':181C ''portion'':215C,226C ''post'':27C,110C ''post-tag'':26C,109C ''profil'':18C,42C ''program'':123C,196C ''question'':31C,114C,164C ''r'':117C,121C,208C ''rel'':36C,119C ''scienc'':96C ''script'':210C ''search'':66C,87C,144C ''seem'':125C ''show'':30C,113C ''similar'':177C,179C ''solut'':146C ''somehow'':149C ''someth'':101C ''store'':4A,19C ''strong'':91C,168C ''subscript'':54C ''tag'':28C,32C,37C,111C,115C,120C ''target'':49C,57C,160C ''task'':133C ''titl'':29C,112C ''tool'':130C ''use'':45C,63C,94C,157C,207C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (33, 2, '2014-05-14 09:34:15.477', 4, NULL, NULL, '&lt;p&gt;Depends on 2 things: the nature/structure of your data; and your performance.&lt;/p&gt;

&lt;p&gt;SQL databases excel when your data is well structured (e.g. when it can be modeled as a table or Excel spreadsheet). A set of rows with a fixed # of columns. Also, when you need to do a lot of table joins (which it sounds like you do).&lt;/p&gt;

&lt;p&gt;NoSQL databases excel when the data is UN-structured beyond key-value pairs.&lt;/p&gt;

&lt;p&gt;Performance wise, you gotta ask yourself one question: is your current SQL solution slow? 
If it isn''t, go with "IIABDFI" principle. &lt;/p&gt;
', NULL, NULL, '''/p'':14C,63C,80C,104C ''2'':4C ''also'':46C ''ask'':86C ''beyond'':75C ''column'':45C ''current'':92C ''data'':10C,21C,70C ''databas'':17C,66C ''depend'':2C ''e.g'':25C ''excel'':18C,35C,67C ''fix'':43C ''go'':100C ''gotta'':85C ''iiabdfi'':102C ''isn'':98C ''join'':56C ''key'':77C ''key-valu'':76C ''like'':60C ''lot'':53C ''model'':30C ''nature/structure'':7C ''need'':49C ''nosql'':65C ''one'':88C ''p'':1C,15C,64C,81C ''pair'':79C ''perform'':13C,82C ''principl'':103C ''question'':89C ''row'':40C ''set'':38C ''slow'':95C ''solut'':94C ''sound'':59C ''spreadsheet'':36C ''sql'':16C,93C ''structur'':24C,74C ''tabl'':33C,55C ''thing'':5C ''un'':73C ''un-structur'':72C ''valu'':78C ''well'':23C ''wise'':83C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2650, 2, '2014-12-08 22:32:05.817', 1, NULL, NULL, '&lt;p&gt;Neil is correct. There are partial derivatives evwrywhere in gradient computation for machine learning models training.&lt;/p&gt;

&lt;p&gt;For instance you can look at the gradient descent method used in the backpropagation method for a neural network. The course from AndrewNg on coursera describes it very well.&lt;/p&gt;
', NULL, NULL, '''/p'':18C,49C ''andrewng'':42C ''backpropag'':33C ''comput'':12C ''correct'':4C ''cours'':40C ''coursera'':44C ''deriv'':8C ''descent'':28C ''describ'':45C ''evwrywher'':9C ''gradient'':11C,27C ''instanc'':21C ''learn'':15C ''look'':24C ''machin'':14C ''method'':29C,34C ''model'':16C ''neil'':2C ''network'':38C ''neural'':37C ''p'':1C,19C ''partial'':7C ''train'':17C ''use'':30C ''well'':48C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (35, 1, '2014-05-14 09:51:54.753', 16, 210, 'How to scale up algorithm development?', '&lt;p&gt;In working on exploratory data analysis, and developing algorithms, I find that most of my time is spent in a cycle of visualize, write some code, run on small dataset, repeat.   The data I have tends to be computer vision/sensor fusion type stuff, and algorithms are vision-heavy (for example object detection and tracking, etc), and the off the shelf algorithms don''t work in this context.  I find that this takes a lot of iterations (for example, to dial in the type of algorithm or tune the parameters in the algorithm, or to get a visualization right) and also the run times even on a small dataset are quite long, so all together it takes a while. &lt;/p&gt;

&lt;p&gt;How can the algorithm development itself be sped up and made more scalable?&lt;/p&gt;

&lt;p&gt;Some specific challenges: &lt;/p&gt;

&lt;p&gt;How can the number of iterations be reduced?  (Esp. when what kind of algorithm, let alone the specifics of it, does not seem to be easily foreseeable without trying different versions and examining their behavior)&lt;/p&gt;

&lt;p&gt;How to run on bigger datasets during development?  (Often going from small to large dataset is when a bunch of new behavior and new issues is seen)&lt;/p&gt;

&lt;p&gt;How can algorithm parameters be tuned faster?&lt;/p&gt;

&lt;p&gt;How to apply machine learning type tools to algorithm development itself?  (For example, instead of writing the algorithm by hand, write some simple building blocks and combine them in a way learned from the problem, etc)&lt;/p&gt;
', 'algorithms', NULL, '''/p'':128C,143C,148C,185C,214C,223C,261C ''algorithm'':5A,7B,17C,53C,70C,94C,101C,133C,163C,218C,233C,242C ''alon'':165C ''also'':109C ''analysi'':14C ''appli'':227C ''behavior'':184C,208C ''bigger'':191C ''block'':249C ''build'':248C ''bunch'':205C ''challeng'':147C ''code'':34C ''combin'':251C ''comput'':47C ''context'':76C ''cycl'':29C ''data'':13C,41C ''dataset'':38C,117C,192C,201C ''detect'':61C ''develop'':6A,16C,134C,194C,234C ''dial'':89C ''differ'':179C ''easili'':175C ''esp'':158C ''etc'':64C,260C ''even'':113C ''examin'':182C ''exampl'':59C,87C,237C ''exploratori'':12C ''faster'':222C ''find'':19C,78C ''forese'':176C ''fusion'':49C ''get'':104C ''go'':196C ''hand'':244C ''heavi'':57C ''instead'':238C ''issu'':211C ''iter'':85C,155C ''kind'':161C ''larg'':200C ''learn'':229C,256C ''let'':164C ''long'':120C ''lot'':83C ''machin'':228C ''made'':140C ''new'':207C,210C ''number'':153C ''object'':60C ''often'':195C ''p'':8C,129C,144C,149C,186C,215C,224C ''paramet'':98C,219C ''problem'':259C ''quit'':119C ''reduc'':157C ''repeat'':39C ''right'':107C ''run'':35C,111C,189C ''scalabl'':142C ''scale'':3A ''seem'':172C ''seen'':213C ''shelf'':69C ''simpl'':247C ''small'':37C,116C,198C ''specif'':146C,167C ''sped'':137C ''spent'':26C ''stuff'':51C ''take'':81C,125C ''tend'':44C ''time'':24C,112C ''togeth'':123C ''tool'':231C ''track'':63C ''tri'':178C ''tune'':96C,221C ''type'':50C,92C,230C ''version'':180C ''vision'':56C ''vision-heavi'':55C ''vision/sensor'':48C ''visual'':31C,106C ''way'':255C ''without'':177C ''work'':10C,73C ''write'':32C,240C,245C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (66, 4, '2014-05-14 18:45:23.917', 0, NULL, NULL, 'Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization.', NULL, NULL, '''analysi'':43C ''applic'':33C ''becom'':18C ''big'':1C ''captur'':37C ''challeng'':35C ''collect'':8C ''complex'':15C ''curat'':38C ''data'':2C,10C,31C ''databas'':26C ''difficult'':19C ''hand'':25C ''includ'':36C ''larg'':13C ''manag'':27C ''on-hand'':23C ''process'':21C,32C ''search'':40C ''set'':11C ''share'':41C ''storag'':39C ''term'':5C ''tool'':28C ''tradit'':30C ''transfer'':42C ''use'':22C ''visual'':45C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (540, 1, '2014-06-23 15:38:10.92', 1, 220, 'First steps on a new cleaned dataset', '&lt;p&gt;What is the very first thing you do when you get your hands on a new data set (assuming it is cleaned and well structured)? Please share sample code snippets as I am sure this would be extremely helpful for both beginners and experienced. &lt;/p&gt;
', 'knowledge-base  dataset', NULL, '''/p'':57C ''assum'':31C ''base'':10B ''beginn'':54C ''clean'':6A,34C ''code'':41C ''data'':29C ''dataset'':7A,11B ''experienc'':56C ''extrem'':50C ''first'':1A,17C ''get'':23C ''hand'':25C ''help'':51C ''knowledg'':9B ''knowledge-bas'':8B ''new'':5A,28C ''p'':12C ''pleas'':38C ''sampl'':40C ''set'':30C ''share'':39C ''snippet'':42C ''step'':2A ''structur'':37C ''sure'':46C ''thing'':18C ''well'':36C ''would'':48C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (37, 2, '2014-05-14 10:41:23.823', 55, NULL, NULL, '&lt;p&gt;To me (coming from a relational database background), "Big Data" is not primarily about the data size (which is the bulk of what the other answers are so far).&lt;/p&gt;

&lt;p&gt;"Big Data" and "Bad Data" are closely related. Relational Databases require ''pristine data''. If the data is in the database, it is accurate, clean, and 100% reliable. Relational Databases require "Great Data"  and a huge amount of time, money, and accountability is put on to making sure the data is well prepared before loading it in to the database. If the data is in the database, it is ''gospel'', and it defines the system understanding of reality.&lt;/p&gt;

&lt;p&gt;"Big Data" tackles this problem from the other direction. The data is poorly defined, much of it may be inaccurate, and much of it may in fact be missing. The structure and layout of the data is linear as opposed to relational.&lt;/p&gt;

&lt;p&gt;Big Data has to have enough volume so that the amount of bad data, or missing data becomes statistically insignificant. When the errors in your data are common enough to cancel each other out, and when the missing data is proportionally small enough to be negligible. When your data access requirements and algorithms are functional even with incomplete and inaccurate data, then you have "Big Data".&lt;/p&gt;

&lt;p&gt;"Big Data" is not really about the volume, it is about the characteristics of the data.&lt;/p&gt;
', NULL, NULL, '''/p'':31C,110C,154C,222C,240C ''100'':58C ''access'':205C ''account'':73C ''accur'':55C ''algorithm'':208C ''amount'':68C,166C ''answer'':27C ''background'':9C ''bad'':36C,168C ''becom'':173C ''big'':10C,33C,112C,156C,220C,224C ''bulk'':22C ''cancel'':186C ''characterist'':236C ''clean'':56C ''close'':39C ''come'':4C ''common'':183C ''data'':11C,17C,34C,37C,45C,48C,64C,81C,94C,113C,122C,147C,157C,169C,172C,181C,194C,204C,216C,221C,225C,239C ''databas'':8C,42C,52C,61C,91C,98C ''defin'':104C,125C ''direct'':120C ''enough'':161C,184C,198C ''error'':178C ''even'':211C ''fact'':138C ''far'':30C ''function'':210C ''gospel'':101C ''great'':63C ''huge'':67C ''inaccur'':131C,215C ''incomplet'':213C ''insignific'':175C ''layout'':144C ''linear'':149C ''load'':86C ''make'':78C ''may'':129C,136C ''miss'':140C,171C,193C ''money'':71C ''much'':126C,133C ''neglig'':201C ''oppos'':151C ''p'':1C,32C,111C,155C,223C ''poor'':124C ''prepar'':84C ''primarili'':14C ''pristin'':44C ''problem'':116C ''proport'':196C ''put'':75C ''realiti'':109C ''realli'':228C ''relat'':7C,40C,41C,60C,153C ''reliabl'':59C ''requir'':43C,62C,206C ''size'':18C ''small'':197C ''statist'':174C ''structur'':142C ''sure'':79C ''system'':106C ''tackl'':114C ''time'':70C ''understand'':107C ''volum'':162C,231C ''well'':83C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (38, 1, '2014-05-14 10:44:58.933', 12, 1569, 'What is the difference between Hadoop and noSQL', '&lt;p&gt;I heard about many tools / frameworks for helping people to process their data (big data environment). &lt;/p&gt;

&lt;p&gt;One is called Hadoop and the other is the noSQL concept. What is the difference in point of processing? &lt;/p&gt;

&lt;p&gt;Are they complementary?&lt;/p&gt;
', 'nosql  tools  processing  hadoop', NULL, '''/p'':30C,51C,56C ''big'':27C ''call'':34C ''complementari'':55C ''concept'':42C ''data'':26C,28C ''differ'':4A,46C ''environ'':29C ''framework'':19C ''hadoop'':6A,12B,35C ''heard'':15C ''help'':21C ''mani'':17C ''nosql'':8A,9B,41C ''one'':32C ''p'':13C,31C,52C ''peopl'':22C ''point'':48C ''process'':11B,24C,50C ''tool'':10B,18C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (52, 1, '2014-05-14 15:25:21.7', 24, 1044, 'Organized processes to clean data', '&lt;p&gt;From my limited dabbling with data science using R, I realized that cleaning bad data is a very important part of preparing data for analysis. &lt;/p&gt;

&lt;p&gt;Are there any best practices or processes for cleaning data before processing it? If so, are there any automated or semi-automated tools which implement some of these best practices?&lt;/p&gt;
', 'r  data-cleaning', NULL, '''/p'':36C,69C ''analysi'':35C ''autom'':56C,60C ''bad'':24C ''best'':41C,67C ''clean'':4A,9B,23C,46C ''dabbl'':14C ''data'':5A,8B,16C,25C,33C,47C ''data-clean'':7B ''implement'':63C ''import'':29C ''limit'':13C ''organ'':1A ''p'':10C,37C ''part'':30C ''practic'':42C,68C ''prepar'':32C ''process'':2A,44C,49C ''r'':6B,19C ''realiz'':21C ''scienc'':17C ''semi'':59C ''semi-autom'':58C ''tool'':61C ''use'':18C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1045, 1, '2014-08-26 21:37:12.107', 2, 70, 'Using Shark with Apache Spark', '&lt;p&gt;Have anyone used Shark as repository from resulting datasets from Apache Spark?&lt;/p&gt;

&lt;p&gt;I''m starting some tests with Spark and read about this database tecnology. Have anyone been using it?&lt;/p&gt;
', 'hadoop', NULL, '''/p'':20C,40C ''anyon'':9C,36C ''apach'':4A,18C ''databas'':33C ''dataset'':16C ''hadoop'':6B ''m'':23C ''p'':7C,21C ''read'':30C ''repositori'':13C ''result'':15C ''shark'':2A,11C ''spark'':5A,19C,28C ''start'':24C ''tecnolog'':34C ''test'':26C ''use'':1A,10C,38C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6455, 2, '2015-07-14 10:02:18.477', 1, NULL, NULL, '&lt;p&gt;You can also modify your cost function so that instances in the smaller class have more weight. This makes training computationally less demanding, when compared to your option 1.&lt;/p&gt;
', NULL, NULL, '''/p'':31C ''1'':30C ''also'':4C ''class'':15C ''compar'':26C ''comput'':22C ''cost'':7C ''demand'':24C ''function'':8C ''instanc'':11C ''less'':23C ''make'':20C ''modifi'':5C ''option'':29C ''p'':1C ''smaller'':14C ''train'':21C ''weight'':18C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8986, 4, '2015-11-23 10:57:43.013', 0, NULL, NULL, 'A Markov process is a stochastic process for which the Markov property holds: If you know the current state, then the next state is independent of all past states.', NULL, NULL, '''current'':18C ''hold'':13C ''independ'':25C ''know'':16C ''markov'':2C,11C ''next'':22C ''past'':28C ''process'':3C,7C ''properti'':12C ''state'':19C,23C,29C ''stochast'':6C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (65, 5, '2014-05-14 18:45:23.917', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (40, 2, '2014-05-14 11:12:03.88', 6, NULL, NULL, '&lt;p&gt;Big Data is actually not so about the "how big it is". &lt;/p&gt;

&lt;p&gt;First, few gigabytes is not big at all, it''s almost nothing. So don''t bother yourself, your system will continu to work efficiently for some time I think.&lt;/p&gt;

&lt;p&gt;Then you have to think of how do you use your data. &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;SQL approach: Every data is precious, well collected and selected, and the focus is put on storing high valuable and well structured data. This can be costly, everything is interlink, and it''s good for well stuctured system and functionnal data.&lt;/li&gt;
&lt;li&gt;Big Data approach: In big data you basically store almost everything, regardless of the value it has, and then do a active analytics process. Things are not linked, they are copied. For example let''s say I have a blog entry. In Big Data there will not be a link to its author, but the author will be embedded inside the blog entry. Way more scalable, but require a different and more complex approach.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If your storing "functionnal" data use by your application, I will suggest you to remain on SQL. If your storing data in order to search on them later or to do reporting, and if this amount of data may increase quickly, I will suggest big data.
In my opinion, big data is useful when you are dealing with real data that have to be collect and analyzed continuously.&lt;/p&gt;
', NULL, NULL, '''/li'':103C,179C ''/p'':14C,45C,59C,250C ''/ul'':180C ''activ'':126C ''actual'':5C ''almost'':26C,114C ''amount'':217C ''analyt'':127C ''analyz'':248C ''applic'':190C ''approach'':63C,107C,178C ''author'':157C,160C ''basic'':112C ''big'':2C,11C,21C,105C,109C,147C,226C,231C ''blog'':144C,166C ''bother'':31C ''collect'':69C,246C ''complex'':177C ''continu'':36C,249C ''copi'':135C ''cost'':88C ''data'':3C,58C,65C,84C,102C,106C,110C,148C,186C,202C,219C,227C,232C,241C ''deal'':238C ''differ'':174C ''effici'':39C ''embed'':163C ''entri'':145C,167C ''everi'':64C ''everyth'':89C,115C ''exampl'':137C ''first'':16C ''focus'':74C ''functionn'':101C,185C ''gigabyt'':18C ''good'':95C ''high'':79C ''increas'':221C ''insid'':164C ''interlink'':91C ''later'':209C ''let'':138C ''li'':61C,104C ''link'':132C,154C ''may'':220C ''noth'':27C ''opinion'':230C ''order'':204C ''p'':1C,15C,46C,181C ''precious'':67C ''process'':128C ''put'':76C ''quick'':222C ''real'':240C ''regardless'':116C ''remain'':196C ''report'':213C ''requir'':172C ''say'':140C ''scalabl'':170C ''search'':206C ''select'':71C ''sql'':62C,198C ''store'':78C,113C,184C,201C ''structur'':83C ''stuctur'':98C ''suggest'':193C,225C ''system'':34C,99C ''thing'':129C ''think'':44C,51C ''time'':42C ''ul'':60C ''use'':56C,187C,234C ''valu'':119C ''valuabl'':80C ''way'':168C ''well'':68C,82C,97C ''work'':38C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (41, 1, '2014-05-14 11:15:40.907', 28, 1897, 'Is the R language suitable for Big Data', '&lt;p&gt;R has many libraries which are aimed at Data Analysis (e.g. JAGS, BUGS, ARULES etc..), and is mentioned in popular textbooks such as: J.Krusche, Doing Bayesian Data Analysis; B.Lantz, "Machine Learning with R".&lt;/p&gt;

&lt;p&gt;I''ve seen a guideline of 5TB for a dataset to be considered as Big Data.&lt;/p&gt;

&lt;p&gt;My question is: Is R suitable for the amount of Data typically seen in Big Data problems? 
Are there strategies to be employed when using R with this size of dataset?&lt;/p&gt;
', 'bigdata  r', NULL, '''/p'':45C,63C,96C ''5tb'':53C ''aim'':18C ''amount'':73C ''analysi'':21C,39C ''arul'':25C ''b.lantz'':40C ''bayesian'':37C ''big'':7A,61C,79C ''bigdata'':9B ''bug'':24C ''consid'':59C ''data'':8A,20C,38C,62C,75C,80C ''dataset'':56C,95C ''e.g'':22C ''employ'':87C ''etc'':26C ''guidelin'':51C ''j.krusche'':35C ''jag'':23C ''languag'':4A ''learn'':42C ''librari'':15C ''machin'':41C ''mani'':14C ''mention'':29C ''p'':11C,46C,64C ''popular'':31C ''problem'':81C ''question'':66C ''r'':3A,10B,12C,44C,69C,90C ''seen'':49C,77C ''size'':93C ''strategi'':84C ''suitabl'':5A,70C ''textbook'':32C ''typic'':76C ''use'':89C ''ve'':48C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (42, 2, '2014-05-14 11:21:31.5', 4, NULL, NULL, '&lt;p&gt;NoSQL is a way to store data that does not require there to be some sort of relation. The simplicity of its design and horizontal scale-ability, one way they store data is the &lt;code&gt;key : value&lt;/code&gt; pair design. This lends itself to processing that is similar to Hadoop. The use of a NoSQL db really depends on the type of problem that one is after.&lt;/p&gt;

&lt;p&gt;Here is a good wikipedia link &lt;a href="https://en.wikipedia.org/wiki/NoSQL" rel="nofollow"&gt;NoSQL&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Hadoop is a system that is meant to store and process huge chunks of data. It is a distributed file system dfs. The reason it does this is that central to its design it makes the assumption that hardware failures are common, thus making multiple copies of the same piece of information and spreading it across multiple machines and racks, so if one goes down, no problem, we have two more copies. Here is a great link for Hadoop from wikipedia as well, you will see that it is, in my opinion more than just storage, but also processing:
 &lt;a href="https://en.wikipedia.org/wiki/Apache_Hadoop" rel="nofollow"&gt;Hadoop&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':86C,196C ''/code'':40C ''/p'':70C,87C,197C ''/wiki/apache_hadoop'':192C ''/wiki/nosql'':82C ''abil'':29C ''across'':144C ''also'':186C ''assumpt'':125C ''central'':118C ''chunk'':101C ''code'':37C ''common'':130C ''copi'':134C,160C ''data'':8C,34C,103C ''db'':58C ''depend'':60C ''design'':24C,42C,121C ''dfs'':110C ''distribut'':107C ''en.wikipedia.org'':81C,191C ''en.wikipedia.org/wiki/apache_hadoop'':190C ''en.wikipedia.org/wiki/nosql'':80C ''failur'':128C ''file'':108C ''goe'':152C ''good'':75C ''great'':164C ''hadoop'':52C,89C,167C,195C ''hardwar'':127C ''horizont'':26C ''href'':79C,189C ''huge'':100C ''inform'':140C ''key'':38C ''lend'':44C ''link'':77C,165C ''machin'':146C ''make'':123C,132C ''meant'':95C ''multipl'':133C,145C ''nofollow'':84C,194C ''nosql'':2C,57C,85C ''one'':30C,67C,151C ''opinion'':180C ''p'':1C,71C,88C ''pair'':41C ''piec'':138C ''problem'':65C,155C ''process'':47C,99C,187C ''rack'':148C ''realli'':59C ''reason'':112C ''rel'':83C,193C ''relat'':19C ''requir'':12C ''scale'':28C ''scale-'':27C ''see'':174C ''similar'':50C ''simplic'':21C ''sort'':17C ''spread'':142C ''storag'':184C ''store'':7C,33C,97C ''system'':92C,109C ''thus'':131C ''two'':158C ''type'':63C ''use'':54C ''valu'':39C ''way'':5C,31C ''well'':171C ''wikipedia'':76C,169C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (43, 2, '2014-05-14 11:23:25.913', 14, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Hadoop is not a database&lt;/strong&gt;, hadoop is an entire ecosystem.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/oOYp7.png" alt="the hadoop ecosystem"&gt;&lt;/p&gt;

&lt;p&gt;Most people will refer to &lt;a href="http://de.wikipedia.org/wiki/MapReduce" rel="nofollow"&gt;mapreduce&lt;/a&gt; jobs while talking about hadoop. A mapreduce job splits big datasets in some little chunks of data and spread them over a cluster of nodes to get proceed. In the end the result from each node will be put together again as one dataset.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Let''s assume you load into hadoop a set of &lt;code&gt;&amp;lt;String, Integer&amp;gt;&lt;/code&gt; with the population of some neighborhoods within a city and you want to get the average population over the whole neighborhoods of each city(figure 1).&lt;/p&gt;

&lt;p&gt;&lt;em&gt;figure 1&lt;/em&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    [new york, 40394]
    [new york, 134]
    [la, 44]
    [la, 647]
    ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now hadoop will first map each value by using the keys (figure 2)&lt;/p&gt;

&lt;p&gt;&lt;em&gt;figure 2&lt;/em&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[new york, [40394,134]]
[la, [44,647]]
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;After the mapping it will reduce the values of each key to a new value (in this example the average over the value set of each key)(figure 3)&lt;/p&gt;

&lt;p&gt;&lt;em&gt;figure 3&lt;/em&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[new york, [20264]]
[la, [346]]
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;now hadoop would be done with everything. You can now load the result into the HDFS (hadoop distributed file system) or into any DBMS or file.&lt;/p&gt;

&lt;p&gt;Thats just one &lt;strong&gt;very basic&lt;/strong&gt; and &lt;strong&gt;simple&lt;/strong&gt; example of what hadoop can do. You can run much more complicated tasks in hadoop.&lt;/p&gt;

&lt;p&gt;As you already mentioned in your question, hadoop and noSQL are complementary. I know a few setups where i.e. billions of datasets from sensors are stored in HBase and get then through hadoop to finally be stored in a DBMS.&lt;/p&gt;
', NULL, NULL, '''/a'':40C ''/code'':103C,149C,181C,227C ''/em'':135C,170C,218C ''/ooyp7.png'':20C ''/p'':14C,25C,85C,130C,136C,165C,171C,213C,219C,256C,284C,326C ''/pre'':150C,182C,228C ''/strong'':8C,264C,268C ''/wiki/mapreduce'':36C ''1'':129C,134C ''134'':144C,177C ''2'':164C,169C ''20264'':224C ''3'':212C,217C ''346'':226C ''40394'':141C,176C ''44'':146C,179C ''647'':148C,180C ''alreadi'':288C ''alt'':21C ''assum'':90C ''averag'':119C,203C ''basic'':263C ''big'':50C ''billion'':305C ''chunk'':55C ''citi'':112C,127C ''cluster'':63C ''code'':98C,138C,173C,221C ''complementari'':297C ''complic'':280C ''data'':57C ''databas'':7C ''dataset'':51C,84C,307C ''dbms'':253C,325C ''de.wikipedia.org'':35C ''de.wikipedia.org/wiki/mapreduce'':34C ''distribut'':247C ''done'':234C ''ecosystem'':13C,24C ''em'':132C,167C,215C ''end'':71C ''entir'':12C ''everyth'':236C ''exampl'':201C,269C ''figur'':128C,133C,163C,168C,211C,216C ''file'':248C,255C ''final'':320C ''first'':155C ''get'':67C,117C,315C ''gt'':102C ''hadoop'':3C,9C,23C,45C,94C,153C,231C,246C,272C,283C,293C,318C ''hbase'':313C ''hdfs'':245C ''hr'':86C ''href'':33C ''i.e'':304C ''i.stack.imgur.com'':19C ''i.stack.imgur.com/ooyp7.png'':18C ''img'':16C ''integ'':101C ''job'':41C,48C ''key'':162C,194C,210C ''know'':299C ''la'':145C,147C,178C,225C ''let'':88C ''littl'':54C ''load'':92C,240C ''lt'':99C ''map'':156C,186C ''mapreduc'':39C,47C ''mention'':289C ''much'':278C ''neighborhood'':109C,124C ''new'':139C,142C,174C,197C,222C ''node'':65C,76C ''nofollow'':38C ''nosql'':295C ''one'':83C,260C ''p'':1C,15C,26C,87C,131C,151C,166C,183C,214C,229C,257C,285C ''peopl'':28C ''popul'':106C,120C ''pre'':137C,172C,220C ''proceed'':68C ''put'':79C ''question'':292C ''reduc'':189C ''refer'':30C ''rel'':37C ''result'':73C,242C ''run'':277C ''sensor'':309C ''set'':96C,207C ''setup'':302C ''simpl'':267C ''split'':49C ''spread'':59C ''src'':17C ''store'':311C,322C ''string'':100C ''strong'':2C,261C,266C ''system'':249C ''talk'':43C ''task'':281C ''that'':258C ''togeth'':80C ''use'':160C ''valu'':158C,191C,198C,206C ''want'':115C ''whole'':123C ''within'':110C ''would'':232C ''york'':140C,143C,175C,223C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (44, 2, '2014-05-14 11:24:39.53', 35, NULL, NULL, '&lt;p&gt;Actually this is coming around. In the book R in a Nutshell there is even a section on using R with Hadoop for big data processing. There are some work arounds that need to be done because R does all it''s work in memory, so you are basically limited to the amount of RAM you have available to you.&lt;/p&gt;

&lt;p&gt;A mature project for R and Hadoop is &lt;a href="https://github.com/RevolutionAnalytics/RHadoop"&gt;RHadoop&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;RHadoop has been divided into several sub-projects, rhdfs, rhbase, rmr2, plyrmr, and quickcheck (&lt;a href="https://github.com/RevolutionAnalytics/RHadoop/wiki"&gt;wiki&lt;/a&gt;).&lt;/p&gt;
', NULL, NULL, '''/a'':78C,102C ''/p'':62C,79C,103C ''/revolutionanalytics/rhadoop'':76C ''/revolutionanalytics/rhadoop/wiki'':100C ''actual'':2C ''amount'':54C ''around'':6C,32C ''avail'':59C ''basic'':50C ''big'':25C ''book'':9C ''come'':5C ''data'':26C ''divid'':84C ''done'':37C ''even'':16C ''github.com'':75C,99C ''github.com/revolutionanalytics/rhadoop'':74C ''github.com/revolutionanalytics/rhadoop/wiki'':98C ''hadoop'':23C,70C ''href'':73C,97C ''limit'':51C ''matur'':65C ''memori'':46C ''need'':34C ''nutshel'':13C ''p'':1C,63C,80C ''plyrmr'':93C ''process'':27C ''project'':66C,89C ''quickcheck'':95C ''r'':10C,21C,39C,68C ''ram'':56C ''rhadoop'':77C,81C ''rhbase'':91C ''rhdfs'':90C ''rmr2'':92C ''section'':18C ''sever'':86C ''sub'':88C ''sub-project'':87C ''use'':20C ''wiki'':101C ''work'':31C,44C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (80, 4, '2014-05-15 03:19:40.36', 0, NULL, NULL, 'An activity that seeks patterns in large, complex data sets. It usually emphasizes algorithmic techniques, but may also involve any set of related skills, applications, or methodologies with that goal.', NULL, NULL, '''activ'':2C ''algorithm'':14C ''also'':18C ''applic'':25C ''complex'':8C ''data'':9C ''emphas'':13C ''goal'':30C ''involv'':19C ''larg'':7C ''may'':17C ''methodolog'':27C ''pattern'':5C ''relat'':23C ''seek'':4C ''set'':10C,21C ''skill'':24C ''techniqu'':15C ''usual'':12C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (272, 2, '2014-06-10 11:53:07.737', 12, NULL, NULL, '&lt;p&gt;I would recommend to start with some MOOC on machine learning. For example Andrew Ng''s &lt;a href="https://www.coursera.org/course/ml"&gt;course&lt;/a&gt; at coursera.&lt;/p&gt;

&lt;p&gt;You should also take a look at &lt;a href="http://orange.biolab.si/"&gt;Orange&lt;/a&gt; application. It has a graphical interface and probably it is easier to understand some ML techniques using it. &lt;/p&gt;
', NULL, NULL, '''/a'':24C,40C ''/course/ml'':22C ''/p'':27C,59C ''also'':31C ''andrew'':15C ''applic'':41C ''cours'':23C ''coursera'':26C ''easier'':51C ''exampl'':14C ''graphic'':45C ''href'':19C,37C ''interfac'':46C ''learn'':12C ''look'':34C ''machin'':11C ''ml'':55C ''mooc'':9C ''ng'':16C ''orang'':39C ''orange.biolab.si'':38C ''p'':1C,28C ''probabl'':48C ''recommend'':4C ''start'':6C ''take'':32C ''techniqu'':56C ''understand'':53C ''use'':57C ''would'':3C ''www.coursera.org'':21C ''www.coursera.org/course/ml'':20C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (45, 2, '2014-05-14 11:26:40.58', 6, NULL, NULL, '&lt;p&gt;First off, if your data has as many variations (in function of time, context, and others) as to make it hard to apply a single strategy to cope with it, you may be interested in doing a prior temporal/contextual/... characterization of the dataset. Characterizing data, i.e., extracting information about how the volume or specifics of the content varies according to some criteria, usually provides with a better understanding (more consise and precise) than simply inferring algorithms on a brute-force fashion.&lt;/p&gt;

&lt;p&gt;So, answering each question:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;characterization is definitely a means of reducing the number of &lt;em&gt;iterations&lt;/em&gt; while trying to select proper algorithms for specific data;&lt;/li&gt;
&lt;li&gt;if you have a discrete set of criterias on which your data varies, it becomes much easier to &lt;em&gt;scale up&lt;/em&gt; solutions, as will know what information you''d gain/lose if simpler/specific solutions were applied;&lt;/li&gt;
&lt;li&gt;after a characterization, you should be also easier to select parameters, since you''d know what kind of &lt;em&gt;specific data&lt;/em&gt; you''d be dealing with;&lt;/li&gt;
&lt;li&gt;finally, you may use data mining/machine learning algorithms to support this characterization. This includes using:
&lt;ul&gt;
&lt;li&gt;clustering algorithms, to reduce the dimensionality of data;&lt;/li&gt;
&lt;li&gt;classification algorithms, to help deciding on specific properties the data in function of time/context/... may present;&lt;/li&gt;
&lt;li&gt;association rules, to predict particular knowledge from the dataset, while also improving/fine-graining the data used for later analysis;&lt;/li&gt;
&lt;li&gt;and other possible strategies and analyses.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;And &lt;a href="http://www.cs.cmu.edu/Groups/sage/sagedc.html"&gt;here&lt;/a&gt; is a list of some criterias on which to analyse data, which you may find helpful.&lt;/p&gt;
', NULL, NULL, '''/a'':267C ''/em'':105C,138C,176C ''/groups/sage/sagedc.html'':265C ''/li'':115C,153C,182C,209C,227C,247C,255C,257C ''/ol'':258C ''/p'':84C,90C,284C ''/ul'':256C ''accord'':60C ''algorithm'':77C,111C,191C,202C,212C ''also'':161C,239C ''analys'':254C,277C ''analysi'':246C ''answer'':87C ''appli'':24C,152C ''associ'':229C ''becom'':131C ''better'':68C ''brute'':81C ''brute-forc'':80C ''character'':41C,45C,93C,157C,195C ''classif'':211C ''cluster'':201C ''consis'':71C ''content'':58C ''context'':15C ''cope'':29C ''criteria'':63C,124C,273C ''d'':146C,168C,178C ''data'':6C,46C,114C,128C,175C,188C,208C,220C,242C,278C ''dataset'':44C,237C ''deal'':180C ''decid'':215C ''definit'':95C ''dimension'':206C ''discret'':121C ''easier'':133C,162C ''em'':103C,135C,173C ''extract'':48C ''fashion'':83C ''final'':184C ''find'':282C ''first'':2C ''forc'':82C ''function'':12C,222C ''gain/lose'':147C ''hard'':22C ''help'':214C,283C ''href'':262C ''i.e'':47C ''improving/fine-graining'':240C ''includ'':197C ''infer'':76C ''inform'':49C,144C ''interest'':35C ''iter'':104C ''kind'':171C ''know'':142C,169C ''knowledg'':234C ''later'':245C ''learn'':190C ''li'':92C,116C,154C,183C,200C,210C,228C,248C ''list'':270C ''make'':20C ''mani'':9C ''may'':33C,186C,225C,281C ''mean'':97C ''mining/machine'':189C ''much'':132C ''number'':101C ''ol'':91C ''other'':17C ''p'':1C,85C,259C ''paramet'':165C ''particular'':233C ''possibl'':251C ''precis'':73C ''predict'':232C ''present'':226C ''prior'':39C ''proper'':110C ''properti'':218C ''provid'':65C ''question'':89C ''reduc'':99C,204C ''rule'':230C ''scale'':136C ''select'':109C,164C ''set'':122C ''simpler/specific'':149C ''simpli'':75C ''sinc'':166C ''singl'':26C ''solut'':139C,150C ''specif'':55C,113C,174C,217C ''strategi'':27C,252C ''support'':193C ''temporal/contextual'':40C ''time'':14C ''time/context'':224C ''tri'':107C ''ul'':199C ''understand'':69C ''use'':187C,198C,243C ''usual'':64C ''vari'':59C,129C ''variat'':10C ''volum'':53C ''www.cs.cmu.edu'':264C ''www.cs.cmu.edu/groups/sage/sagedc.html'':263C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (46, 2, '2014-05-14 12:32:29.503', 10, NULL, NULL, '&lt;p&gt;Note that there is an early version of LIBLINEAR ported to &lt;a href="http://spark.apache.org"&gt;Apache Spark&lt;/a&gt;. See &lt;a href="http://apache-spark-user-list.1001560.n3.nabble.com/Spark-LIBLINEAR-td5546.html"&gt;mailing list comments&lt;/a&gt; for some early details, and the &lt;a href="http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/distributed-liblinear/"&gt;project site&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':18C,28C,42C ''/p'':43C ''/spark-liblinear-td5546.html'':24C ''/~cjlin/libsvmtools/distributed-liblinear/'':39C ''apach'':16C ''apache-spark-user-list.1001560.n3.nabble.com'':23C ''apache-spark-user-list.1001560.n3.nabble.com/spark-liblinear-td5546.html'':22C ''comment'':27C ''detail'':32C ''earli'':7C,31C ''href'':14C,21C,36C ''liblinear'':10C ''list'':26C ''mail'':25C ''note'':2C ''p'':1C ''port'':11C ''project'':40C ''see'':19C ''site'':41C ''spark'':17C ''spark.apache.org'':15C ''version'':8C ''www.csie.ntu.edu.tw'':38C ''www.csie.ntu.edu.tw/~cjlin/libsvmtools/distributed-liblinear/'':37C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (47, 2, '2014-05-14 12:39:41.197', 25, NULL, NULL, '&lt;p&gt;The main problem with using R for large data sets is the RAM constraint. The reason behind keeping all the data in RAM is that it provides much faster access and data manipulations than would storing on HDDs. If you are willing to take a hit on performance, then yes, it is quite practical to work with &lt;a href="http://statistics.org.il/wp-content/uploads/2010/04/Big_Memory%20V0.pdf"&gt;large datasets in R&lt;/a&gt;.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;RODBC Package: Allows connecting to external DB from R to retrieve and handle data. Hence, the data being &lt;em&gt;manipulated&lt;/em&gt; is restricted to your RAM. The overall data set can go much larger.&lt;/li&gt;
&lt;li&gt;The ff package allows using larger than RAM data sets by utilising memory-mapped pages.&lt;/li&gt;
&lt;li&gt;BigLM: It builds generalized linear models on big data. It loads data into memory in chunks.&lt;/li&gt;
&lt;li&gt;bigmemory : An R package which allows powerful and memory-efficient parallel 
analyses and data mining of massive data sets. It permits storing large objects (matrices etc.) in memory (on the RAM) using external pointer objects to refer to them. &lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':68C ''/em'':92C ''/li'':106C,124C,142C,184C ''/p'':69C ''/ul'':185C ''/wp-content/uploads/2010/04/big_memory%20v0.pdf'':63C ''access'':31C ''allow'':74C,111C,149C ''analys'':156C ''behind'':18C ''big'':133C ''biglm'':126C ''bigmemori'':144C ''build'':128C ''chunk'':141C ''connect'':75C ''constraint'':15C ''data'':10C,22C,33C,85C,88C,100C,116C,134C,137C,158C,162C ''dataset'':65C ''db'':78C ''effici'':154C ''em'':90C ''etc'':170C ''extern'':77C,177C ''faster'':30C ''ff'':109C ''general'':129C ''go'':103C ''handl'':84C ''hdds'':39C ''henc'':86C ''hit'':47C ''href'':60C ''keep'':19C ''larg'':9C,64C,167C ''larger'':105C,113C ''li'':71C,107C,125C,143C ''linear'':130C ''load'':136C ''main'':3C ''manipul'':34C,91C ''map'':122C ''massiv'':161C ''matric'':169C ''memori'':121C,139C,153C,172C ''memory-effici'':152C ''memory-map'':120C ''mine'':159C ''model'':131C ''much'':29C,104C ''object'':168C,179C ''overal'':99C ''p'':1C ''packag'':73C,110C,147C ''page'':123C ''parallel'':155C ''perform'':49C ''permit'':165C ''pointer'':178C ''power'':150C ''practic'':55C ''problem'':4C ''provid'':28C ''quit'':54C ''r'':7C,67C,80C,146C ''ram'':14C,24C,97C,115C,175C ''reason'':17C ''refer'':181C ''restrict'':94C ''retriev'':82C ''rodbc'':72C ''set'':11C,101C,117C,163C ''statistics.org.il'':62C ''statistics.org.il/wp-content/uploads/2010/04/big_memory%20v0.pdf'':61C ''store'':37C,166C ''take'':45C ''ul'':70C ''use'':6C,112C,176C ''utilis'':119C ''will'':43C ''work'':57C ''would'':36C ''yes'':51C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (48, 5, '2014-05-14 13:08:26.647', 0, NULL, NULL, '&lt;p&gt;&lt;a href="http://www.r-project.org" rel="nofollow"&gt;R&lt;/a&gt; is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&amp;amp;T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.&lt;/p&gt;

&lt;p&gt;R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.&lt;/p&gt;

&lt;p&gt;One of R''s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.&lt;/p&gt;

&lt;p&gt;R was created by &lt;a href="http://www.stat.auckland.ac.nz/~ihaka/" rel="nofollow"&gt;Ross Ihaka&lt;/a&gt; and &lt;a href="http://www.gene.com/scientists/our-scientists/robert-gentleman" rel="nofollow"&gt;Robert Gentleman&lt;/a&gt; and is now developed by the &lt;a href="http://www.r-project.org/contributors.html" rel="nofollow"&gt;R Development Core Team&lt;/a&gt;. The R environment is easily extended through a packaging system on &lt;a href="http://cran.r-project.org" rel="nofollow" title="CRAN The Comprehensive R Archive Network"&gt;CRAN&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;R is available as Free Software under the terms of the Free Software Foundation''s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and Mac OS.&lt;/p&gt;
', NULL, NULL, '''/a'':8C,196C,207C,225C,250C ''/contributors.html'':218C ''/p'':76C,131C,181C,251C,298C ''/scientists/our-scientists/robert-gentleman'':202C ''/~ihaka/'':191C ''activ'':130C ''amp'':41C ''analysi'':95C ''archiv'':247C ''avail'':255C ''bell'':37C ''care'':161C ''chamber'':48C ''choic'':113C,172C ''classic'':89C ''classif'':96C ''cluster'':97C ''code'':68C,274C ''colleagu'':50C ''compil'':277C ''comprehens'':245C ''comput'':16C ''consid'':54C ''control'':180C ''core'':223C ''cran'':243C,249C ''cran.r-project.org'':239C ''creat'':185C ''default'':167C ''design'':145C,171C ''develop'':35C,211C,222C ''differ'':57C,65C ''eas'':140C ''easili'':230C ''environ'':13C,32C,228C ''extend'':231C ''extens'':104C ''form'':275C ''former'':39C ''formula'':157C ''foundat'':266C ''free'':257C,264C ''freebsd'':291C ''full'':179C ''general'':269C ''gentleman'':206C ''gnu'':22C,268C ''graphic'':18C,99C,174C ''great'':160C ''high'':103C ''href'':3C,188C,199C,215C,238C ''ihaka'':195C ''implement'':58C ''import'':64C ''includ'':153C,290C ''john'':47C ''laboratori'':38C ''languag'':11C,30C,107C ''licens'':271C ''linear'':85C ''linux'':293C ''lucent'':44C ''mac'':296C ''mathemat'':154C ''methodolog'':118C ''minor'':170C ''model'':88C ''much'':67C ''need'':159C ''network'':248C ''nofollow'':6C,193C,204C,220C,241C ''nonlinear'':87C ''often'':109C ''one'':133C ''open'':123C ''os'':297C ''p'':1C,77C,132C,182C,252C ''packag'':234C ''particip'':127C ''platform'':286C ''plot'':149C ''produc'':152C ''project'':23C ''provid'':79C,121C ''public'':147C,270C ''publication-qu'':146C ''qualiti'':148C ''r'':7C,51C,75C,78C,120C,135C,183C,221C,227C,246C,253C ''rel'':5C,192C,203C,219C,240C ''research'':115C ''retain'':178C ''robert'':205C ''ross'':194C ''rout'':125C ''run'':72C,279C ''seri'':94C ''similar'':26C,288C ''softwar'':258C,265C ''sourc'':124C,273C ''statist'':15C,84C,90C,117C ''strength'':137C ''symbol'':155C ''system'':235C,289C ''taken'':164C ''team'':224C ''techniqu'':100C ''technolog'':45C ''term'':261C ''test'':91C ''time'':93C ''time-seri'':92C ''titl'':242C ''unalt'':73C ''unix'':285C ''user'':177C ''varieti'':82C,283C ''vehicl'':111C ''well'':144C ''well-design'':143C ''wide'':81C,282C ''window'':294C ''written'':69C ''www.gene.com'':201C ''www.gene.com/scientists/our-scientists/robert-gentleman'':200C ''www.r-project.org'':4C,217C ''www.r-project.org/contributors.html'':216C ''www.stat.auckland.ac.nz'':190C ''www.stat.auckland.ac.nz/~ihaka/'':189C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (49, 4, '2014-05-14 13:08:26.647', 0, NULL, NULL, 'R is a free, open-source programming language and software environment for statistical computing, bioinformatics, and graphics.', NULL, NULL, '''bioinformat'':16C ''comput'':15C ''environ'':12C ''free'':4C ''graphic'':18C ''languag'':9C ''open'':6C ''open-sourc'':5C ''program'':8C ''r'':1C ''softwar'':11C ''sourc'':7C ''statist'':14C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (50, 1, '2014-05-14 14:26:54.313', 5, 159, 'Running an R script programmatically', '&lt;p&gt;I have an R script that generates a report based on the current contents of a database. This database is constantly in flux with records being added/deleted many times each day. How can I ask my computer to run this every night at 4 am so that I have an up to date report waiting for me in the morning? Or perhaps I want it to re-run once a certain number of new records have been added to the database. How might I go about automating this? I should mention I''m on Windows, but I could easily put this script on my Linux machine if that would simplify the process. &lt;/p&gt;
', 'r  databases  efficiency  tools', NULL, '''/p'':124C ''4'':54C ''ad'':89C ''added/deleted'':37C ''ask'':45C ''autom'':98C ''base'':20C ''certain'':82C ''comput'':47C ''constant'':31C ''content'':24C ''could'':109C ''current'':23C ''databas'':7B,27C,29C,92C ''date'':63C ''day'':41C ''easili'':110C ''effici'':8B ''everi'':51C ''flux'':33C ''generat'':17C ''go'':96C ''linux'':116C ''m'':104C ''machin'':117C ''mani'':38C ''mention'':102C ''might'':94C ''morn'':70C ''new'':85C ''night'':52C ''number'':83C ''p'':10C ''perhap'':72C ''process'':123C ''programmat'':5A ''put'':111C ''r'':3A,6B,14C ''re'':78C ''re-run'':77C ''record'':35C,86C ''report'':19C,64C ''run'':1A,49C,79C ''script'':4A,15C,113C ''simplifi'':121C ''time'':39C ''tool'':9B ''wait'':65C ''want'':74C ''window'':106C ''would'':120C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (51, 2, '2014-05-14 14:48:32.18', 9, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;How can I ask my computer to run this every night at 4 am so that I have an up to date report waiting for me in the morning?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;You can set up a cronjob on a Linux system. These are run at the set time, if the computer is on. To do so, open a terminal and type:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;crontab -e
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And add:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;00 4 * * * r source(/home/FilePath/.../myRscript.R)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Source: &lt;a href="http://stackoverflow.com/a/14710307/1488917"&gt;Stack Overflow&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':95C ''/a/14710307/1488917'':92C ''/blockquote'':33C ''/code'':70C,84C ''/home/filepath'':82C ''/myrscript.r'':83C ''/p'':32C,65C,75C,96C ''/pre'':71C,85C ''00'':78C ''4'':15C,79C ''add'':74C ''ask'':6C ''blockquot'':1C ''code'':67C,77C ''comput'':8C,54C ''cronjob'':40C ''crontab'':68C ''date'':24C ''e'':69C ''everi'':12C ''href'':89C ''linux'':43C ''morn'':31C ''night'':13C ''open'':60C ''overflow'':94C ''p'':2C,34C,72C,86C ''pre'':66C,76C ''r'':80C ''report'':25C ''run'':10C,47C ''set'':37C,50C ''sourc'':81C,87C ''stack'':93C ''stackoverflow.com'':91C ''stackoverflow.com/a/14710307/1488917'':90C ''system'':44C ''termin'':62C ''time'':51C ''type'':64C ''wait'':26C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5429, 2, '2015-04-01 22:37:39.3', 9, NULL, NULL, '&lt;p&gt;The general approach is to do traditional statistical analysis on your data set to define a multidimensional random process that will generate data with the same statistical characteristics.  The virtue of this approach is that your synthetic data is independent of your ML model, but statistically "close" to your data. (see below for discussion of your alternative)&lt;/p&gt;

&lt;p&gt;In essence, you are estimating the multivariate probability distribution associated with the process.  Once you have estimated the distribution, you can generate synthetic data through the Monte Carlo method or similar repeated sampling methods.  If your data resembles some parametric distribution (e.g. lognormal) then this approach is straightforward and reliable.  The tricky part is to estimate the dependence between variables. See: &lt;a href="https://www.encyclopediaofmath.org/index.php/Multi-dimensional_statistical_analysis" rel="nofollow"&gt;https://www.encyclopediaofmath.org/index.php/Multi-dimensional_statistical_analysis&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;If your data is irregular, then non-parametric methods are easier and probably more robust.  &lt;a href="https://en.wikipedia.org/wiki/Multivariate_kernel_density_estimation" rel="nofollow"&gt;Multivariate kernal density estimation&lt;/a&gt; is a method that is accessible and appealing to people with ML background. For a general introduction and links to specific methods, see: &lt;a href="https://en.wikipedia.org/wiki/Nonparametric_statistics" rel="nofollow"&gt;https://en.wikipedia.org/wiki/Nonparametric_statistics&lt;/a&gt; .&lt;/p&gt;

&lt;p&gt;To validate that this process worked for you, you go through the machine learning process again with the synthesized data, and you should end up with a model that is fairly close to your original.  Likewise, if you put the synthesized data into your ML model, you should get outputs that have similar distribution as your original outputs.&lt;/p&gt;

&lt;p&gt;In contrast, you are proposing this:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;[original data --&gt; build machine learning model --&gt; use ml model to generate synthetic data....!!!]&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This accomplishes something different that the method I just described.  This would solve the &lt;em&gt;&lt;a href="https://en.wikipedia.org/wiki/Inverse_problem#Conceptual_understanding" rel="nofollow"&gt;inverse problem&lt;/a&gt;&lt;/em&gt;: "what inputs could generate any given set of model outputs". Unless your ML model is over-fitted to your original data, this synthesized data &lt;em&gt;will not&lt;/em&gt; look like your original data in every respect, or even most.&lt;/p&gt;

&lt;p&gt;Consider a linear regression model. The same linear regression model can have identical fit to data that have very different characteristics. A famous demonstration of this is through &lt;a href="http://en.wikipedia.org/wiki/Anscombe%27s_quartet" rel="nofollow"&gt;Anscombe''s quartet&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/P9s4w.png" alt="All four sets are identical when examined using simple summary statistics, but vary considerably when graphed"&gt;&lt;/p&gt;

&lt;p&gt;Thought I don''t have references, I believe this problem can also arise in logistic regression, generalized linear models, SVM, and K-means clustering.&lt;/p&gt;

&lt;p&gt;There are some ML model types (e.g. decision tree) where it''s possible to inverse them to generate synthetic data, though it takes some work.  See: &lt;a href="http://csce.uark.edu/~cwt/DOCS/2008-06--IEEE-Internet-Computing--Reverse-Data-Mining--Eno-Thompson.pdf" rel="nofollow"&gt;Generating Synthetic Data to Match Data Mining Patterns&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5474, 2, '2015-04-08 00:00:33.73', 2, NULL, NULL, '&lt;p&gt;this might take a few iterations, but perhaps it can be moved to chat if it gets too long.  are you wondering about a specific application or just the general concept?  as far as i know, the canonical form of something is the unique/simplest arrangement/representation of that something.  as for isomorphism (one to one relationship, a function gets you from one set to the other), you can oversimplify it and think about factors of numbers.&lt;/p&gt;

&lt;p&gt;for a moment consider an analogy - instead of graphs, using sets of numbers.  this is randomly off the top of my head so hopefully there aren''t too many holes in it.  we have&lt;/p&gt;

&lt;p&gt;$A = 6, 10, 14$&lt;/p&gt;

&lt;p&gt;and&lt;/p&gt;

&lt;p&gt;$B = 18, 30, 42$&lt;/p&gt;

&lt;p&gt;let''s call $A$ and $B$ isomorphic because to get between any value in $A$ to the corresponding value in $B$, you multiply or divide by 3.  now let''s introduce another isomorphic set:&lt;/p&gt;

&lt;p&gt;$C = 3, 5, 7$&lt;/p&gt;

&lt;p&gt;it looks like $A$, $B$, and $C$ are all isomorphic because there is a one to one relationship between any of their initial values.  the same can be said for any of their middle values, or any of their ending values.  while it''s great that they''re all isomorphic, we''ll consider something additional about $C$.  i''m going to call $C$ canonical because if you remove the greatest common factor from $A$, or you remove the greatest common factor from $B$, you get $C$.  there is no common factor we can remove to reduce $C$ itself.&lt;/p&gt;

&lt;p&gt;extending this, you can see that a canonical representation of a graph &lt;em&gt;is&lt;/em&gt; an isomorphic version of that graph, but not necessarily the other way around.  all isomorphic versions of said graph will reduce to the same canonical form.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;that being said, the definition of canonical is more complicated for graphs.  first, for graph isomorphism, &lt;a href="http://www.tcs.hut.fi/Software/bliss/index.html" rel="nofollow"&gt;this page about a code named bliss&lt;/a&gt; presents some small graphs as an example.  they define $G_{1}$ as&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/xWHFw.png" alt="g1"&gt;
and $G_{2}$ as
&lt;img src="http://i.stack.imgur.com/gKwj1.png" alt="g2"&gt;&lt;/p&gt;

&lt;p&gt;and offer two transformations for $G_{1}$ that will make $G_{1}$ and $G_{2}$ isomorphic.  first, they consider $\{\langle 1\to 2, 2\to 4, 3\to 1, 4\to 3\rangle\}$, and then $\{\langle 1\to 2, 2\to 4, 3\to 3, 4\to 1\rangle\}$ - both of these transformations swap vertices.  the transformations result in the following isomorphic graphs:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Ovd06.png" alt="transformationResults"&gt;&lt;/p&gt;

&lt;p&gt;each pair of connected vertices has a corresponding pair in $G_{2}$ ($2\to 1,3,4; 1\to 4; 3\to 4$).&lt;/p&gt;

&lt;p&gt;now, to discuss canonical graphs, we must identify a cost function with which we can measure what is the ''most canonical'' form.  &lt;a href="http://www.csie.ntu.edu.tw/~r94054/paper/draw_plane_graph.pdf" rel="nofollow"&gt;this paper&lt;/a&gt; mentions metrics such as area, minimum angle, and total number of bends.  for simplicity, i''ll try to minimize bends and favor angles that are closer to $90^{\circ}$.&lt;/p&gt;

&lt;p&gt;if you use a coordinate transformation, you can see that this simple graph&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Atqpw.png" alt="firstIsomorphic"&gt;&lt;/p&gt;

&lt;p&gt;and this graph&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/kG7mg.png" alt="otherIsomorphic"&gt;&lt;/p&gt;

&lt;p&gt;are isomorphic.  the same edges connect the same pairs of vertices, and if you move vertices from one around, you can make the other.  this graph&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/CtUNC.png" alt="canonical"&gt;&lt;/p&gt;

&lt;p&gt;is also isomorphic with the first two, but since it has the optimal amount of bent edges (zero) and optimal angles (four right angles), it is also the canonical representation for these graphs.&lt;/p&gt;

&lt;p&gt;for further reading, you might consider &lt;a href="http://www.borgelt.net/papers/mgts_05.pdf" rel="nofollow"&gt;this paper&lt;/a&gt;, which discusses several algorithms used to find canonical representations for web mining, or &lt;a href="http://www.ecs.umass.edu/ece/labs/vlsicad/papers/dd-chapter.pdf" rel="nofollow"&gt;this paper&lt;/a&gt;, which discusses canonical representations of binary decision diagrams, word-level decision diagrams, binary moment diagrams, taylor expansion diagrams, and finite field decision diagrams.  &lt;/p&gt;

&lt;p&gt;tl;dr:&lt;/p&gt;

&lt;p&gt;just have a look at the &lt;strong&gt;minion with bananas&lt;/strong&gt; &lt;a href="http://webhome.cs.uvic.ca/~wendym/courses/422/14/notes/422_14_group_theory.pdf" rel="nofollow"&gt;on slide 10 of this presentation&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5512, 2, '2015-04-12 21:54:11.407', 2, NULL, NULL, '&lt;p&gt;Here''s a solution using the caret package for R. A Random Forest is first trained on the data. All observations for which the probability (from the voting) is less than 99% are then passed to model 2, linear discriminant analysis. Only the probabilities from unseen resampling observations are used, since the Random Forest will otherwise fit the training data perfectly. That is what caret is needed for.&lt;/p&gt;

&lt;p&gt;The accuracy is a little higher for the uncertain cases, but this is probably overfitting since I have tried several different models and the data set is small.&lt;/p&gt;

&lt;p&gt;I''d like to know if this really improves the performance in your application for the out of sample test data. Are there any papers that recommend this approach? This approach seems to resemble boosting. I tried it on some of my data but could not improve the out of sample performance I got from model 1 (a Random Forest).&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data(iris)
library(caret)
myTrainControl &amp;lt;- trainControl(method = "cv", number = 10,
                               savePredictions = T,
                               classProbs = TRUE)
set.seed(4213) # To get the same resamples every time
M1 &amp;lt;- train(y = iris$Species,
            x = iris[, !(names(iris) == "Species")],
            method = "rf",
            trControl = myTrainControl)

M1pred &amp;lt;- M1$pred[M1$pred$mtry == 2, ]
confusionMatrix(M1pred$pred, M1pred$obs)
# Accuracy 96%

# Inspect the predicted class probabilities:
probs &amp;lt;- cbind(M1pred$setosa,
                   M1pred$versicolor,
               M1pred$virginica)
colnames(probs) &amp;lt;- c("setosa", "versicolor", "virginica")
maxCol &amp;lt;- max.col(probs)
probs &amp;lt;- cbind(probs, maxCol)
maxProbs &amp;lt;- apply(probs, 1, function(x) x[x["maxCol"]])
summary(maxProbs)
# Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
# 0.5120  0.9805  0.9980  0.9645  1.0000  1.0000

# Let''s define anything below 99% as uncertain:
uncertain &amp;lt;- which(maxProbs &amp;lt; 0.99)
length(uncertain) # 45
# Find rows in iris data that belong to ''uncertain'' cases:
uncertainIndex &amp;lt;- M1pred$rowIndex[uncertain]
    confusionMatrix(M1pred$pred[uncertain],
                reference = M1pred$obs[uncertain])
# M1 Performance in uncertain cases:
#              Reference
# Prediction   setosa versicolor virginica
# setosa          5          0         0
# versicolor      0         17         3
# virginica       0          3        17
#
# Overall Statistics
# Accuracy : 0.8667

# Train new model on uncertain data only:
irisUncertain &amp;lt;- iris[uncertainIndex, ]
set.seed(4213) # To get the same resamples every time
M2 &amp;lt;- train(y = irisUncertain$Species,
            x = irisUncertain[, !(names(irisUncertain) == "Species")],
            method = "lda",
            trControl = myTrainControl)

M2pred &amp;lt;- M2$pred

confusionMatrix(M2pred$pred, reference = M2pred$obs)
#              Reference
# Prediction   setosa versicolor virginica
# setosa          4          0         0
# versicolor      1         16         0
# virginica       0          4        20
# 
# Overall Statistics
# Accuracy : 0.8889 
# (Small discrepancy: Why does caret report an accuracy of 88,5% for M2?)

# For new data predictions can be made as follows:
# (just as an example from the original data again)
# Some ''uncertain'' cases are ''certain'' now using the full M2 model
newdat &amp;lt;- irisUncertain[30:34, -5]
M1maxProbs &amp;lt;- apply(predict(M1, newdat, type = "prob"), 1, max)
ifelse(M1maxProbs &amp;lt; 0.99,
       paste("M2:", predict(M2, newdat)),
       paste("M1:", predict(M1, newdat)))
# 85               94              107              126 
# "M2: versicolor" "M1: versicolor"  "M2: virginica"  "M1: virginica" 
# 71 
# "M2: virginica"  
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (62, 2, '2014-05-14 18:27:56.043', 34, NULL, NULL, '&lt;p&gt;Overfitting is &lt;em&gt;empirically&lt;/em&gt; bad.  Suppose you have a data set which you split in two, test and training.  An overfitted model is one that performs much worse on the test dataset than on training dataset.  It is often observed that models like that also in general perform worse on additional (new) test datasets than models which are not overfitted.  &lt;/p&gt;

&lt;p&gt;One way to understand that intuitively is that a model may use some relevant parts of the data (signal) and some irrelevant parts (noise).  An overfitted model uses more of the noise, which increases its performance in the case of known noise (training data) and decreases its performance in the case of novel noise (test data).  The difference in performance between training and test data indicates how much noise the model picks up; and picking up noise directly translates into worse performance on test data (including future data).&lt;/p&gt;

&lt;p&gt;Summary: overfitting is bad by definition, this has not much to do with either complexity or ability to generalize, but rather has to do with mistaking noise for signal.&lt;/p&gt;

&lt;p&gt;P.S. On the "ability to generalize" part of the question, it is very possible to have a model which has inherently limited ability to generalize due to the structure of the model (for example linear SVM, ...) but is still prone to overfitting.  In a sense overfitting is just one way that generalization may fail.&lt;/p&gt;
', NULL, NULL, '''/em'':6C ''/p'':63C,153C,184C,240C ''abil'':171C,189C,208C ''addit'':53C ''also'':47C ''bad'':7C,158C ''case'':103C,115C ''complex'':169C ''data'':12C,82C,108C,120C,129C,149C,152C ''dataset'':34C,38C,56C ''decreas'':110C ''definit'':160C ''differ'':122C ''direct'':142C ''due'':211C ''either'':168C ''em'':4C ''empir'':5C ''exampl'':219C ''fail'':239C ''futur'':151C ''general'':49C,173C,191C,210C,237C ''includ'':150C ''increas'':98C ''indic'':130C ''inher'':206C ''intuit'':70C ''irrelev'':86C ''known'':105C ''like'':45C ''limit'':207C ''linear'':220C ''may'':75C,238C ''mistak'':180C ''model'':24C,44C,58C,74C,91C,135C,203C,217C ''much'':29C,132C,164C ''new'':54C ''nois'':88C,96C,106C,118C,133C,141C,181C ''novel'':117C ''observ'':42C ''often'':41C ''one'':26C,65C,234C ''overfit'':2C,23C,62C,90C,156C,227C,231C ''p'':1C,64C,154C,185C ''p.s'':186C ''part'':79C,87C,192C ''perform'':28C,50C,100C,112C,124C,146C ''pick'':136C,139C ''possibl'':199C ''prone'':225C ''question'':195C ''rather'':175C ''relev'':78C ''sens'':230C ''set'':13C ''signal'':83C,183C ''split'':16C ''still'':224C ''structur'':214C ''summari'':155C ''suppos'':8C ''svm'':221C ''test'':19C,33C,55C,119C,128C,148C ''train'':21C,37C,107C,126C ''translat'':143C ''two'':18C ''understand'':68C ''use'':76C,92C ''way'':66C,235C ''wors'':30C,51C,145C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (81, 1, '2014-05-15 04:59:54.317', 12, 169, 'Parallel and distributed computing', '&lt;p&gt;What is(are) the difference(s) between parallel and distributed computing? When it comes to scalability and efficiency, it is very common to see solutions dealing with computations in clusters of machines, and sometimes it is referred to as a parallel processing, or as distributed processing.&lt;/p&gt;

&lt;p&gt;In a certain way, the computation seems to be always parallel, since there are things running concurrently. But is the distributed computation simply related to the use of more than one machine, or are there any further specificities that distinguishes these two kinds of processing? Wouldn''t it be redundant to say, for example, that a computation is &lt;em&gt;parallel AND distributed&lt;/em&gt;?&lt;/p&gt;
', 'definitions  parallel  distributed', NULL, '''/em'':119C ''/p'':55C,120C ''alway'':66C ''certain'':59C ''cluster'':38C ''come'':22C ''common'':30C ''comput'':4A,19C,36C,62C,78C,113C ''concurr'':73C ''deal'':34C ''definit'':5B ''differ'':13C ''distinguish'':96C ''distribut'':3A,7B,18C,53C,77C,118C ''effici'':26C ''em'':115C ''exampl'':110C ''kind'':99C ''machin'':40C,88C ''one'':87C ''p'':8C,56C ''parallel'':1A,6B,16C,49C,67C,116C ''process'':50C,54C,101C ''redund'':106C ''refer'':45C ''relat'':80C ''run'':72C ''say'':108C ''scalabl'':24C ''see'':32C ''seem'':63C ''simpli'':79C ''sinc'':68C ''solut'':33C ''sometim'':42C ''specif'':94C ''thing'':71C ''two'':98C ''use'':83C ''way'':60C ''wouldn'':102C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (57, 2, '2014-05-14 16:29:39.927', 15, NULL, NULL, '&lt;p&gt;From my point of view, this question is suitable for a two-step answer. The first part, let us call it &lt;em&gt;soft preprocessing&lt;/em&gt;, could be taken as the usage of different data mining algorithms to preprocess data in such a way that makes it suitable for further analyses. Notice that this could be the analysis itself, in case the goal is simple enough to be tackled in a single shot.&lt;/p&gt;

&lt;p&gt;The second part, the &lt;em&gt;hard preprocessing&lt;/em&gt;, actually comes prior to any other process, and is may be taken as the usage of simple tools or scripts to clean up data, selecting specific contents to be processed. To this problem, POSIX provides us with a wonderous set of magic tools, which can be used to compose concise -- and very powerful -- preprocessing scripts.&lt;/p&gt;

&lt;p&gt;For example, for people who deal with data coming from social websites (twitter, facebook, ...), the &lt;em&gt;data retrieval&lt;/em&gt; usually yields files with very specific format -- although not always nicely structure, as they may contain missing fields, and so. For these cases, a simple &lt;code&gt;awk&lt;/code&gt; script could clean up the data, producing a &lt;em&gt;valid&lt;/em&gt; input file for later processing. From the magic set, one may also point out &lt;code&gt;grep&lt;/code&gt;, &lt;code&gt;sed&lt;/code&gt;, &lt;code&gt;cut&lt;/code&gt;, &lt;code&gt;join&lt;/code&gt;, &lt;code&gt;paste&lt;/code&gt;, &lt;code&gt;sort&lt;/code&gt;, and a whole multitude of other tools.&lt;/p&gt;

&lt;p&gt;In case simple the source file has too many nitty-gritties, it may also be necessary to produce a bundle of methods to clean up data. In such cases, it is usually better to use scripting languages (other than shell ones), such as Python, Ruby, and Perl. This allows for building up &lt;em&gt;API&lt;/em&gt;''s to select specific data in a very straightforward and reusable way. Such &lt;em&gt;API&lt;/em&gt;''s are sometimes made public by their writers, such as &lt;a href="http://imdbpy.sourceforge.net/"&gt;IMDbPY&lt;/a&gt;, &lt;a href="https://api.stackexchange.com/docs"&gt;Stack Exchange API&lt;/a&gt;, and many others.&lt;/p&gt;

&lt;p&gt;So, answering the question: are there any best practices? It usually depends on your task. If you will always deal with the same data format, it''s commonly best to write an &lt;em&gt;organized&lt;/em&gt; script to preprocess it; whereas, if you just need a simple and fast clean up on some dataset, count on POSIX tools for concise shell scripts that will do the whole job &lt;strong&gt;much&lt;/strong&gt; faster than a Python script, or so. Since the &lt;em&gt;clean up&lt;/em&gt; depends both on the dataset and on your purposes, it''s hard to have everything already done. Yet, there are lots of API''s that puts you halfway through with the problem.&lt;/p&gt;
', NULL, NULL, '''/a'':327C,336C ''/code'':188C,216C,219C,222C,225C,228C,231C ''/docs'':332C ''/em'':27C,84C,160C,199C,296C,312C,376C,424C ''/p'':75C,140C,239C,340C,457C ''/strong'':411C ''actual'':85C ''algorithm'':38C ''allow'':290C ''alreadi'':440C ''also'':211C,255C ''although'':168C ''alway'':170C,360C ''analys'':52C ''analysi'':59C ''answer'':16C,343C ''api'':295C,311C,335C,447C ''api.stackexchange.com'':331C ''api.stackexchange.com/docs'':330C ''awk'':187C ''best'':349C,370C ''better'':274C ''build'':292C ''bundl'':261C ''call'':22C ''case'':62C,183C,242C,270C ''clean'':106C,191C,265C,390C,422C ''code'':186C,214C,217C,220C,223C,226C,229C ''come'':86C,150C ''common'':369C ''compos'':133C ''concis'':134C,400C ''contain'':176C ''content'':111C ''could'':28C,56C,190C ''count'':395C ''cut'':221C ''data'':36C,41C,108C,149C,158C,194C,267C,301C,365C ''dataset'':394C,429C ''deal'':147C,361C ''depend'':353C,425C ''differ'':35C ''done'':441C ''em'':24C,81C,157C,197C,294C,310C,374C,421C ''enough'':67C ''everyth'':439C ''exampl'':143C ''exchang'':334C ''facebook'':155C ''fast'':389C ''faster'':412C ''field'':178C ''file'':163C,201C,246C ''first'':18C ''format'':167C,366C ''goal'':64C ''grep'':215C ''gritti'':252C ''halfway'':452C ''hard'':82C,436C ''href'':324C,329C ''imdbpi'':326C ''imdbpy.sourceforge.net'':325C ''input'':200C ''job'':408C ''join'':224C ''languag'':278C ''later'':203C ''let'':20C ''lot'':445C ''made'':316C ''magic'':126C,207C ''make'':47C ''mani'':249C,338C ''may'':94C,175C,210C,254C ''method'':263C ''mine'':37C ''miss'':177C ''much'':410C ''multitud'':235C ''necessari'':257C ''need'':385C ''nice'':171C ''nitti'':251C ''nitty-gritti'':250C ''notic'':53C ''one'':209C,282C ''organ'':375C ''other'':339C ''p'':1C,76C,141C,240C,341C ''part'':19C,79C ''past'':227C ''peopl'':145C ''perl'':288C ''point'':4C,212C ''posix'':118C,397C ''power'':137C ''practic'':350C ''preprocess'':26C,40C,83C,138C,379C ''prior'':87C ''problem'':117C,456C ''process'':91C,114C,204C ''produc'':195C,259C ''provid'':119C ''public'':317C ''purpos'':433C ''put'':450C ''python'':285C,415C ''question'':8C,345C ''retriev'':159C ''reusabl'':307C ''rubi'':286C ''script'':104C,139C,189C,277C,377C,402C,416C ''second'':78C ''sed'':218C ''select'':109C,299C ''set'':124C,208C ''shell'':281C,401C ''shot'':74C ''simpl'':66C,101C,185C,243C,387C ''sinc'':419C ''singl'':73C ''social'':152C ''soft'':25C ''sometim'':315C ''sort'':230C ''sourc'':245C ''specif'':110C,166C,300C ''stack'':333C ''step'':15C ''straightforward'':305C ''strong'':409C ''structur'':172C ''suitabl'':10C,49C ''tackl'':70C ''taken'':30C,96C ''task'':356C ''tool'':102C,127C,238C,398C ''twitter'':154C ''two'':14C ''two-step'':13C ''us'':21C,120C ''usag'':33C,99C ''use'':131C,276C ''usual'':161C,273C,352C ''valid'':198C ''view'':6C ''way'':45C,308C ''websit'':153C ''wherea'':381C ''whole'':234C,407C ''wonder'':123C ''write'':372C ''writer'':320C ''yet'':442C ''yield'':162C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (58, 2, '2014-05-14 17:06:33.337', 9, NULL, NULL, '&lt;p&gt;You can check out &lt;a href="http://hunch.net/~vw/"&gt;vowpal wabbit&lt;/a&gt;. It is quite popular for large-scale learning and includes parallel provisions.&lt;/p&gt;

&lt;p&gt;From their website:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;VW is the essence of speed in machine learning, able to learn from terafeature datasets with ease. Via parallel learning, it can exceed the throughput of any single machine network interface when doing linear learning, a first amongst learning algorithms.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/a'':13C ''/blockquote'':76C ''/p'':27C,32C,75C ''/~vw/'':10C ''abl'':44C ''algorithm'':74C ''amongst'':72C ''blockquot'':33C ''check'':4C ''dataset'':49C ''eas'':51C ''essenc'':38C ''exceed'':57C ''first'':71C ''href'':7C ''hunch.net'':9C ''hunch.net/~vw/'':8C ''includ'':24C ''interfac'':65C ''larg'':20C ''large-scal'':19C ''learn'':22C,43C,46C,54C,69C,73C ''linear'':68C ''machin'':42C,63C ''network'':64C ''p'':1C,28C,34C ''parallel'':25C,53C ''popular'':17C ''provis'':26C ''quit'':16C ''scale'':21C ''singl'':62C ''speed'':40C ''terafeatur'':48C ''throughput'':59C ''via'':52C ''vowpal'':11C ''vw'':35C ''wabbit'':12C ''websit'':31C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (59, 1, '2014-05-14 17:48:21.24', 9, 625, 'What are R''s memory constraints?', '&lt;p&gt;In reviewing â&lt;a href="http://rads.stackoverflow.com/amzn/click/1461468485" rel="nofollow"&gt;Applied Predictive Modeling&lt;/a&gt;" a &lt;a href="http://www.information-management.com/blogs/applied-predictive-modeling-10024771-1.html" rel="nofollow"&gt;reviewer states&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;One critique I have of statistical learning (SL) pedagogy is the
  absence of computation performance considerations in the evaluation of
  different modeling techniques. With its emphases on bootstrapping and
  cross-validation to tune/test models, SL is quite compute-intensive.
  Add to that the re-sampling that''s embedded in techniques like bagging
  and boosting, and you have the specter of computation hell for
  supervised learning of large data sets. &lt;strong&gt;In fact, R''s memory
  constraints impose pretty severe limits on the size of models that can
  be fit by top-performing methods like random forests.&lt;/strong&gt; Though SL does a
  good job calibrating model performance against small data sets, it''d
  sure be nice to understand performance versus computational cost for
  larger data.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;What are R''s memory constraints, and do they impose severe limits on the size of models that can be fit by top-performing methods like &lt;a href="http://en.wikipedia.org/wiki/Random_forest" rel="nofollow"&gt;random forests&lt;/a&gt;?&lt;/p&gt;
', 'hadoop  r', NULL, '''/a'':23C,34C,205C ''/amzn/click/1461468485'':17C ''/blockquote'':167C ''/blogs/applied-predictive-modeling-10024771-1.html'':29C ''/p'':35C,166C,206C ''/strong'':138C ''/wiki/random_forest'':200C ''absenc'':49C ''add'':79C ''appli'':20C ''bag'':92C ''blockquot'':36C ''boost'':94C ''bootstrap'':65C ''calibr'':145C ''comput'':51C,77C,101C,161C ''compute-intens'':76C ''consider'':53C ''constraint'':6A,116C,174C ''cost'':162C ''critiqu'':39C ''cross'':68C ''cross-valid'':67C ''d'':153C ''data'':108C,150C,165C ''differ'':58C ''embed'':88C ''emphas'':63C ''en.wikipedia.org'':199C ''en.wikipedia.org/wiki/random_forest'':198C ''evalu'':56C ''fact'':112C ''fit'':129C,189C ''forest'':137C,204C ''good'':143C ''hadoop'':7B ''hell'':102C ''href'':14C,26C,197C ''impos'':117C,178C ''intens'':78C ''job'':144C ''larg'':107C ''larger'':164C ''learn'':44C,105C ''like'':91C,135C,195C ''limit'':120C,180C ''memori'':5A,115C,173C ''method'':134C,194C ''model'':22C,59C,72C,125C,146C,185C ''nice'':156C ''nofollow'':19C,31C,202C ''one'':38C ''p'':9C,37C,168C ''pedagogi'':46C ''perform'':52C,133C,147C,159C,193C ''predict'':21C ''pretti'':118C ''quit'':75C ''r'':3A,8B,113C,171C ''rads.stackoverflow.com'':16C ''rads.stackoverflow.com/amzn/click/1461468485'':15C ''random'':136C,203C ''re'':84C ''re-sampl'':83C ''rel'':18C,30C,201C ''review'':11C,32C ''sampl'':85C ''set'':109C,151C ''sever'':119C,179C ''size'':123C,183C ''sl'':45C,73C,140C ''small'':149C ''specter'':99C ''state'':33C ''statist'':43C ''strong'':110C ''supervis'':104C ''sure'':154C ''techniqu'':60C,90C ''though'':139C ''top'':132C,192C ''top-perform'':131C,191C ''tune/test'':71C ''understand'':158C ''valid'':69C ''versus'':160C ''www.information-management.com'':28C ''www.information-management.com/blogs/applied-predictive-modeling-10024771-1.html'':27C ''â'':12C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (60, 2, '2014-05-14 17:58:48.297', 6, NULL, NULL, '&lt;p&gt;R performs all computation in-memory so you can''t perform operation on a dataset that is larger than available RAM amount. However there are some libraries that allow bigdata processing using R and one of popular libraries for bigdata processing like Hadoop.&lt;/p&gt;
', NULL, NULL, '''/p'':46C ''allow'':31C ''amount'':24C ''avail'':22C ''bigdata'':32C,42C ''comput'':5C ''dataset'':17C ''hadoop'':45C ''howev'':25C ''in-memori'':6C ''larger'':20C ''librari'':29C,40C ''like'':44C ''memori'':8C ''one'':37C ''oper'':14C ''p'':1C ''perform'':3C,13C ''popular'':39C ''process'':33C,43C ''r'':2C,35C ''ram'':23C ''use'':34C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (61, 1, '2014-05-14 18:09:01.94', 25, 2258, 'Why Is Overfitting Bad in Machine Learning?', '&lt;p&gt;Logic often states that by overfitting a model, its capacity to generalize is limited, though this might only mean that overfitting stops a model from improving after a certain complexity. Does overfitting cause models to become worse regardless of the complexity of data, and if so, why is this the case?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Related:&lt;/strong&gt; Followup to the question above, "&lt;a href="http://datascience.stackexchange.com/questions/361/when-is-a-model-underfitted"&gt;When is a Model Underfitted?&lt;/a&gt;"&lt;/p&gt;
', 'machine-learning  predictive-modeling', NULL, '''/a'':87C ''/p'':66C,88C ''/questions/361/when-is-a-model-underfitted'':81C ''/strong'':71C ''bad'':4A ''becom'':50C ''capac'':24C ''case'':65C ''caus'':47C ''certain'':43C ''complex'':44C,55C ''data'':57C ''datascience.stackexchange.com'':80C ''datascience.stackexchange.com/questions/361/when-is-a-model-underfitted'':79C ''followup'':72C ''general'':26C ''hr'':67C ''href'':78C ''improv'':40C ''learn'':7A,10B ''limit'':28C ''logic'':15C ''machin'':6A,9B ''machine-learn'':8B ''mean'':33C ''might'':31C ''model'':13B,22C,38C,48C,85C ''often'':16C ''overfit'':3A,20C,35C,46C ''p'':14C,68C ''predict'':12B ''predictive-model'':11B ''question'':75C ''regardless'':52C ''relat'':70C ''state'':17C ''stop'':36C ''strong'':69C ''though'':29C ''underfit'':86C ''wors'':51C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (64, 2, '2014-05-14 18:37:52.333', 12, NULL, NULL, '&lt;p&gt;Overfitting, in a nutshell, means take into account &lt;strong&gt;too much&lt;/strong&gt; information from your data and/or prior knowledge, and use it in a model. To make it more straightforward, consider the following example: you''re hired by some scientists to provide them with a model to predict the growth of some kind of plants. The scientists have given you information collected from their work 
with such plants throughout a whole year, and they shall continuously give you information on the future development of their plantation.&lt;/p&gt;

&lt;p&gt;So, you run through the data received, and build up a model out of it. Now suppose that, in your model, you considered just as many characteristics as possible to always find out the exact behavior of the plants you saw in the initial dataset. Now, as the production continues, you''ll always take into account those characteristics, and will produce very &lt;em&gt;fine-grained&lt;/em&gt; results. However, if the plantation eventually suffer from some seasonal change, the results you will receive may fit your model in such a way that your predictions will begin to fail (either saying that the growth will slow down, while it shall actually speed up, or the opposite).&lt;/p&gt;

&lt;p&gt;Apart from being unable to detect such small variations, and to usually classify your entries incorrectly, the &lt;em&gt;fine-grain&lt;/em&gt; on the model, i.e., the great amount of variables, may cause the processing to be too costly. Now, imagine that your data is already complex. Overfitting your model to the data not only will make the classification/evaluation very complex, but will most probably make you error the prediction over the slightest variation you may have on the input.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit&lt;/strong&gt;: &lt;a href="https://www.youtube.com/watch?v=DQWI1kvmwRg"&gt;This&lt;/a&gt; might as well be of some use, perhaps adding dynamicity to the above explanation :D&lt;/p&gt;
', NULL, NULL, '''/a'':298C ''/em'':156C,228C ''/p'':88C,205C,287C,314C ''/strong'':13C,291C ''/watch?v=dqwi1kvmwrg'':296C ''account'':9C,145C ''actual'':199C ''ad'':307C ''alreadi'':252C ''alway'':120C,142C ''amount'':235C ''and/or'':18C ''apart'':207C ''begin'':185C ''behavior'':125C ''build'':98C ''caus'':239C ''chang'':167C ''characterist'':116C,147C ''classifi'':219C ''classification/evaluation'':265C ''collect'':63C ''complex'':253C,267C ''consid'':32C,112C ''continu'':77C,139C ''cost'':245C ''d'':313C ''data'':17C,95C,250C,259C ''dataset'':134C ''detect'':212C ''develop'':84C ''dynam'':308C ''edit'':290C ''either'':188C ''em'':152C,224C ''entri'':221C ''error'':274C ''eventu'':162C ''exact'':124C ''exampl'':35C ''explan'':312C ''fail'':187C ''find'':121C ''fine'':154C,226C ''fine-grain'':153C,225C ''fit'':174C ''follow'':34C ''futur'':83C ''give'':78C ''given'':60C ''grain'':155C,227C ''great'':234C ''growth'':51C,192C ''hire'':38C ''howev'':158C ''href'':293C ''i.e'':232C ''imagin'':247C ''incorrect'':222C ''inform'':14C,62C,80C ''initi'':133C ''input'':286C ''kind'':54C ''knowledg'':20C ''ll'':141C ''make'':28C,263C,272C ''mani'':115C ''may'':173C,238C,282C ''mean'':6C ''might'':299C ''model'':26C,47C,101C,110C,176C,231C,256C ''much'':12C ''nutshel'':5C ''opposit'':204C ''overfit'':2C,254C ''p'':1C,89C,206C,288C ''perhap'':306C ''plant'':56C,69C,128C ''plantat'':87C,161C ''possibl'':118C ''predict'':49C,183C,276C ''prior'':19C ''probabl'':271C ''process'':241C ''produc'':150C ''product'':138C ''provid'':43C ''re'':37C ''receiv'':96C,172C ''result'':157C,169C ''run'':92C ''saw'':130C ''say'':189C ''scientist'':41C,58C ''season'':166C ''shall'':76C,198C ''slightest'':279C ''slow'':194C ''small'':214C ''speed'':200C ''straightforward'':31C ''strong'':10C,289C ''suffer'':163C ''suppos'':106C ''take'':7C,143C ''throughout'':70C ''unabl'':210C ''use'':22C,305C ''usual'':218C ''variabl'':237C ''variat'':215C,280C ''way'':180C ''well'':301C ''whole'':72C ''work'':66C ''www.youtube.com'':295C ''www.youtube.com/watch?v=dqwi1kvmwrg'':294C ''year'':73C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (69, 1, '2014-05-14 20:03:15.233', 2, 59, 'Is it possible to automate generating reproducibility documentation?', '&lt;p&gt;First, think it''s worth me stating what I mean by replication &amp;amp; reproducibility:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Replication of analysis A results in an exact copy of all inputs and processes that are supply and result in incidental outputs in analysis B.&lt;/li&gt;
&lt;li&gt;Reproducibility of analysis A results in inputs, processes, and outputs that are semantically incidental to analysis A, without access to the exact inputs and processes.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Putting aside how easy it might be to replicate a given build, especially an ad-hoc one, to me replication always possible if it''s planned for and worth doing. That said, it is unclear to me is how to execute a data science workflow that allows for reproducibility.&lt;/p&gt;

&lt;p&gt;The closet comparison I''m able to think of is &lt;a href="http://en.wikipedia.org/wiki/Documentation_generator" rel="nofollow"&gt;documentation generators&lt;/a&gt; that generates software documentation intended for programmers - though the main difference I see is that in theory, if two sets of analysis ran the "reproducibility documentation generators" the documentation should match.&lt;/p&gt;

&lt;p&gt;Another issue, is that while I get the concept of reproducibility documentation, I am having a hard time imagining what it would look like in usable form without just being a guide to replicating the analysis.&lt;/p&gt;

&lt;p&gt;Lastly, whole intent of this is to understand if it''s possible to "bake-in" reproducibility documentation as you build out a stack, not after the stack is built.&lt;/p&gt;

&lt;p&gt;So, Is it possible to automate generating reproducibility documentation, and if so how, and what would it look like?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;UPDATE:&lt;/strong&gt; Please note that this is the second draft of this question and that &lt;a href="http://datascience.stackexchange.com/users/178/christopher-louden"&gt;Christopher Louden&lt;/a&gt; was kind enough to let me edit the question after I realized it was likely the first draft was unclear. Thanks!&lt;/em&gt;&lt;/p&gt;
', 'processing', NULL, '''/a'':154C,304C ''/em'':326C ''/li'':53C,80C ''/p'':25C,133C,186C,224C,256C,277C,327C ''/strong'':283C ''/ul'':81C ''/users/178/christopher-louden'':301C ''/wiki/documentation_generator'':149C ''abl'':140C ''access'':73C ''ad'':98C ''ad-hoc'':97C ''allow'':130C ''alway'':104C ''amp'':23C ''analysi'':30C,51C,57C,70C,176C,223C ''anoth'':188C ''asid'':84C ''autom'':5A,263C ''b'':52C ''bake'':240C ''bake-in'':239C ''build'':94C,246C ''built'':255C ''christoph'':302C ''closet'':136C ''comparison'':137C ''concept'':196C ''copi'':36C ''data'':126C ''datascience.stackexchange.com'':300C ''datascience.stackexchange.com/users/178/christopher-louden'':299C ''differ'':165C ''document'':8A,152C,158C,180C,183C,199C,243C,266C ''draft'':291C,322C ''easi'':86C ''edit'':311C ''em'':281C ''en.wikipedia.org'':148C ''en.wikipedia.org/wiki/documentation_generator'':147C ''enough'':307C ''especi'':95C ''exact'':35C,76C ''execut'':124C ''first'':11C,321C ''form'':214C ''generat'':6A,153C,156C,181C,264C ''get'':194C ''given'':93C ''guid'':219C ''hard'':204C ''hoc'':99C ''hr'':278C ''href'':146C,298C ''imagin'':206C ''incident'':48C,68C ''input'':39C,61C,77C ''intend'':159C ''intent'':228C ''issu'':189C ''kind'':306C ''last'':226C ''let'':309C ''li'':27C,54C ''like'':211C,276C,319C ''look'':210C,275C ''louden'':303C ''m'':139C ''main'':164C ''match'':185C ''mean'':20C ''might'':88C ''nofollow'':151C ''note'':285C ''one'':100C ''output'':49C,64C ''p'':10C,82C,134C,187C,225C,257C,279C ''plan'':109C ''pleas'':284C ''possibl'':3A,105C,237C,261C ''process'':9B,41C,62C,79C ''programm'':161C ''put'':83C ''question'':294C,313C ''ran'':177C ''realiz'':316C ''rel'':150C ''replic'':22C,28C,91C,103C,221C ''reproduc'':7A,24C,55C,132C,179C,198C,242C,265C ''result'':32C,46C,59C ''said'':115C ''scienc'':127C ''second'':290C ''see'':167C ''semant'':67C ''set'':174C ''softwar'':157C ''stack'':249C,253C ''state'':17C ''strong'':280C ''suppli'':44C ''thank'':325C ''theori'':171C ''think'':12C,142C ''though'':162C ''time'':205C ''two'':173C ''ul'':26C ''unclear'':118C,324C ''understand'':233C ''updat'':282C ''usabl'':213C ''whole'':227C ''without'':72C,215C ''workflow'':128C ''worth'':15C,112C ''would'':209C,273C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (70, 2, '2014-05-14 22:03:50.597', 2, NULL, NULL, '&lt;p&gt;To be reproducible without being just a replication, you would need to redo the experiment with new data, following the same technique as before.  The work flow is not as important as the techniques used.  Sample data in the same way, use the same type of models.  It doesn''t matter if you switch from one language to another, so long as the models and the data manipulations are the same.&lt;/p&gt;

&lt;p&gt;This type of replication will show that the results you got in the first experiment are less likely to be a fluke than they were earlier.&lt;/p&gt;
', NULL, NULL, '''/p'':73C,101C ''anoth'':60C ''data'':19C,38C,68C ''doesn'':50C ''earlier'':100C ''experi'':16C,89C ''first'':88C ''flow'':28C ''fluke'':96C ''follow'':20C ''got'':85C ''import'':32C ''languag'':58C ''less'':91C ''like'':92C ''long'':62C ''manipul'':69C ''matter'':52C ''model'':48C,65C ''need'':12C ''new'':18C ''one'':57C ''p'':1C,74C ''redo'':14C ''replic'':9C,78C ''reproduc'':4C ''result'':83C ''sampl'':37C ''show'':80C ''switch'':55C ''techniqu'':23C,35C ''type'':46C,76C ''use'':36C,43C ''way'':42C ''without'':5C ''work'':27C ''would'':11C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (71, 1, '2014-05-14 22:12:37.203', 10, 280, 'When are p-values deceptive?', '&lt;p&gt;What are the data conditions that we should watch out for, where p-values may not be the best way of deciding statistical significance?  Are there specific problem types that fall into this category?&lt;/p&gt;
', 'bigdata  statistics', NULL, '''/p'':45C ''best'':29C ''bigdata'':7B ''categori'':44C ''condit'':14C ''data'':13C ''decept'':6A ''decid'':32C ''fall'':41C ''may'':25C ''p'':4A,9C,23C ''p-valu'':3A,22C ''problem'':38C ''signific'':34C ''specif'':37C ''statist'':8B,33C ''type'':39C ''valu'':5A,24C ''watch'':18C ''way'':30C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (72, 2, '2014-05-14 22:40:40.363', 3, NULL, NULL, '&lt;p&gt;One algorithm that can be used for this is the &lt;a href="http://en.wikipedia.org/wiki/K-means_clustering" rel="nofollow"&gt;k-means clustering algorithm&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Basically:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Randomly choose k datapoints from your set, m_1, ..., m_k.&lt;/li&gt;
&lt;li&gt;&lt;p&gt;"Until convergence":&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Assign your data points to k clusters, where cluster i is the set of points for which m_i is the closest of your current means&lt;/li&gt;
&lt;li&gt;Replace each m_i by the mean of all points assigned to cluster i.&lt;/li&gt;
&lt;/ol&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;It is good practice to repeat this algorithm several times, then choose the outcome that minimizes distances between the points of each cluster i and the center m_i.&lt;/p&gt;

&lt;p&gt;Of course, you have to know k to start here; you can use cross-validation to choose this parameter, though.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (146, 5, '2014-05-18 15:01:24.08', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (85, 2, '2014-05-15 08:44:47.327', 3, NULL, NULL, '&lt;p&gt;See also &lt;a href="http://datascience.stackexchange.com/questions/71/when-are-p-values-deceptive/84#84"&gt;When are p-values deceptive?&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;When there are a lot of variables that can be tested for pair-wise correlation (for example), the replacement is to use any of the corrections for &lt;a href="http://en.wikipedia.org/wiki/False_discovery_rate" rel="nofollow"&gt;False discovery rate&lt;/a&gt; (to limit probability that any given discovery is false) or &lt;a href="http://en.wikipedia.org/wiki/Familywise_error_rate" rel="nofollow"&gt;Familywise error rate&lt;/a&gt; (to limit probability of one or more false discoveries).  For example, you might use the HolmâBonferroni method.&lt;/p&gt;

&lt;p&gt;In the case of a large sample rather than a lot of variables, something else is needed.  As Christopher said, magnitude of effect a way to treat this.  Combining these two ideas, you might use a confidence interval around your magnitude of effect, and apply a false discovery rate correction to the p-value of the confidence interval.  The effects for which even the lowest bound of the corrected confidence interval is high are likely to be strong effects, regardless of huge data set size.  I am not aware of any published paper that combines confidence intervals with false discovery rate correction in this way, but it seems like a straightforward and intuitively understandable approach.&lt;/p&gt;

&lt;p&gt;To make this even better, use a non-parametric way to estimate confidence intervals.  Assuming a distribution is likely to give very optimistic estimates here, and even fitting a distribution to the data is likely to be inaccurate.  Since the information about the shape of the distribution past the edges of the confidence interval comes from a relatively small subsample of the data, this is where it really pays to be careful.  You can use bootstrapping to get a non-parametric confidence interval.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (73, 2, '2014-05-14 22:43:23.587', 2, NULL, NULL, '&lt;p&gt;You shouldn''t consider the p-value out of context.&lt;/p&gt;

&lt;p&gt;One rather basic point (as illustrated by &lt;a href="http://xkcd.com/882/" rel="nofollow"&gt;xkcd&lt;/a&gt;) is that you need to consider how many tests you''re actually doing.  Obviously, you shouldn''t be shocked to see p &amp;lt; 0.05 for one out of 20 tests, even if the null hypothesis is true every time.  &lt;/p&gt;

&lt;p&gt;A more subtle example of this occurs in high-energy physics, and is known as the &lt;a href="https://en.wikipedia.org/wiki/Look-elsewhere_effect" rel="nofollow"&gt;look-elsewhere effect&lt;/a&gt;.  The larger the parameter space you search for a signal that might represent a new particle, the more likely you are to see an apparent signal that''s really just due to random fluctuations. &lt;/p&gt;
', NULL, NULL, '''/882/'':26C ''/a'':30C,100C ''/p'':13C,70C,135C ''/wiki/look-elsewhere_effect'':93C ''0.05'':54C ''20'':59C ''actual'':42C ''appar'':125C ''basic'':17C ''consid'':5C,36C ''context'':12C ''due'':131C ''effect'':99C ''elsewher'':98C ''en.wikipedia.org'':92C ''en.wikipedia.org/wiki/look-elsewhere_effect'':91C ''energi'':82C ''even'':61C ''everi'':68C ''exampl'':75C ''fluctuat'':134C ''high'':81C ''high-energi'':80C ''href'':23C,90C ''hypothesi'':65C ''illustr'':20C ''known'':86C ''larger'':102C ''like'':119C ''look'':97C ''look-elsewher'':96C ''lt'':53C ''mani'':38C ''might'':112C ''need'':34C ''new'':115C ''nofollow'':28C,95C ''null'':64C ''obvious'':44C ''occur'':78C ''one'':15C,56C ''p'':1C,8C,14C,52C,71C ''p-valu'':7C ''paramet'':104C ''particl'':116C ''physic'':83C ''point'':18C ''random'':133C ''rather'':16C ''re'':41C ''realli'':129C ''rel'':27C,94C ''repres'':113C ''search'':107C ''see'':51C,123C ''shock'':49C ''shouldn'':3C,46C ''signal'':110C,126C ''space'':105C ''subtl'':74C ''test'':39C,60C ''time'':69C ''true'':67C ''valu'':9C ''xkcd'':29C ''xkcd.com'':25C ''xkcd.com/882/'':24C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (74, 2, '2014-05-14 22:58:11.583', 2, NULL, NULL, '&lt;p&gt;One thing you should be aware of is the sample size you are using. Very large samples, such as economists using census data, will lead to deflated p-values. This paper &lt;a href="http://galitshmueli.com/system/files/Print%20Version.pdf" rel="nofollow" title="Too Big to Fail: Large Samples and the p-Value Problem"&gt;"Too Big to Fail: Large Samples and the p-Value Problem"&lt;/a&gt; covers some of the issues. &lt;/p&gt;
', NULL, NULL, '''/a'':66C ''/p'':72C ''/system/files/print%20version.pdf'':38C ''awar'':7C ''big'':43C,55C ''census'':23C ''cover'':67C ''data'':24C ''deflat'':28C ''economist'':21C ''fail'':45C,57C ''galitshmueli.com'':37C ''galitshmueli.com/system/files/print%20version.pdf'':36C ''href'':35C ''issu'':71C ''larg'':17C,46C,58C ''lead'':26C ''nofollow'':40C ''one'':2C ''p'':1C,30C,51C,63C ''p-valu'':29C,50C,62C ''paper'':33C ''problem'':53C,65C ''rel'':39C ''sampl'':11C,18C,47C,59C ''size'':12C ''thing'':3C ''titl'':41C ''use'':15C,22C ''valu'':31C,52C,64C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (75, 1, '2014-05-15 00:26:11.387', 4, 101, 'Is there a replacement for small p-values in big data?', '&lt;p&gt;If small p-values are plentiful in big data, what is a comparable replacement for p-values in data with million of samples?&lt;/p&gt;
', 'statistics', NULL, '''/p'':40C ''big'':11A,23C ''compar'':28C ''data'':12A,24C,35C ''million'':37C ''p'':8A,14C,18C,32C ''p-valu'':7A,17C,31C ''plenti'':21C ''replac'':4A,29C ''sampl'':39C ''small'':6A,16C ''statist'':13B ''valu'':9A,19C,33C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (76, 1, '2014-05-15 00:39:33.433', 6, 114, 'Which Big Data technology stack is most suitable for processing tweets, extracting/expanding URLs and pushing (only) new links into 3rd party system?', '&lt;p&gt;&lt;em&gt;(Note: Pulled this question from the &lt;a href="http://area51.stackexchange.com/proposals/55053/data-science/57398#57398"&gt;list of questions in Area51&lt;/a&gt;, but believe the question is self explanatory. That said, believe I get the general intent of the question, and as a result likely able to field any questions on the question that might pop-up.)&lt;/em&gt; &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Which Big Data technology stack is most suitable for processing tweets, extracting/expanding URLs and pushing (only) new links into 3rd party system?&lt;/strong&gt;&lt;/p&gt;
', 'bigdata  tools  data-stream-mining', NULL, '''/a'':47C ''/em'':84C ''/p'':85C,111C ''/proposals/55053/data-science/57398#57398'':41C ''/strong'':110C ''3rd'':20A,107C ''abl'':71C ''area51'':46C ''area51.stackexchange.com'':40C ''area51.stackexchange.com/proposals/55053/data-science/57398#57398'':39C ''believ'':49C,57C ''big'':2A,89C ''bigdata'':23B ''data'':3A,26B,90C ''data-stream-min'':25B ''em'':30C ''explanatori'':54C ''extracting/expanding'':12A,99C ''field'':73C ''general'':61C ''get'':59C ''href'':38C ''intent'':62C ''like'':70C ''link'':18A,105C ''list'':42C ''might'':80C ''mine'':28B ''new'':17A,104C ''note'':31C ''p'':29C,86C ''parti'':21A,108C ''pop'':82C ''pop-up'':81C ''process'':10A,97C ''pull'':32C ''push'':15A,102C ''question'':34C,44C,51C,65C,75C,78C ''result'':69C ''said'':56C ''self'':53C ''stack'':5A,92C ''stream'':27B ''strong'':87C ''suitabl'':8A,95C ''system'':22A,109C ''technolog'':4A,91C ''tool'':24B ''tweet'':11A,98C ''url'':13A,100C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (77, 1, '2014-05-15 01:22:35.167', 7, 249, 'Is this Neo4j comparison to RDBMS execution time correct?', '&lt;p&gt;&lt;strong&gt;Background:&lt;/strong&gt; Following is from the book &lt;a href="http://rads.stackoverflow.com/amzn/click/1449356265"&gt;Graph Databases&lt;/a&gt;, which covers a performance test mentioned in the book &lt;a href="http://rads.stackoverflow.com/amzn/click/1617290769"&gt;Neo4j in Action&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Relationships in a graph naturally form paths. Querying, or
  traversing, the graph involves following paths. Because of the
  fundamentally path-oriented nature of the datamodel, the majority of
  path-based graph database operations are highly aligned with the way
  in which the data is laid out, making them extremely efficient. In
  their book Neo4j in Action, Partner and Vukotic perform an experiment
  using a relational store and Neo4j.&lt;/p&gt;
  
  &lt;p&gt;The comparison shows that the graph database is substantially quicker
  for connected data than a relational store.Partner and Vukoticâs
  experiment seeks to find friends-of-friends in a social network, to a
  maximum depth of five. Given any two persons chosen at random, is
  there a path that connects them which is at most five relationships
  long? For a social network containing 1,000,000 people, each with
  approximately 50 friends, the results strongly suggest that graph
  databases are the best choice for connected data, as we see in Table
  2-1.&lt;/p&gt;
  
  &lt;p&gt;Table 2-1. Finding extended friends in a relational database versus efficient finding in Neo4j&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Depth   RDBMS Execution time (s)    Neo4j Execution time (s)     Records returned
2       0.016                       0.01                         ~2500    
3       30.267                      0.168                        ~110,000 
4       1543.505                    1.359                        ~600,000 
5       Unfinished                  2.132                        ~800,000
&lt;/code&gt;&lt;/pre&gt;
  
  &lt;p&gt;At depth two (friends-of-friends) both the relational database and the graph database perform well enough for us to consider using them in an online system. While the Neo4j query runs in two-thirds the time of the relational one, an end-user would barely notice the the difference in milliseconds between the two. By the time we reach depth three (friend-of-friend-of-friend), however, itâs clear that the relational database can no longer deal with the query in a reasonable timeframe: the thirty seconds it takes to complete would be completely unacceptable for an online system. In contrast, Neo4jâs response time remains relatively flat: just a fraction of a second to perform the queryâdefinitely quick enough for an online system.&lt;/p&gt;
  
  &lt;p&gt;At depth four the relational database exhibits crippling latency,
  making it practically useless for an online system. Neo4jâs timings
  have deteriorated a little too, but the latency here is at the
  periphery of being acceptable for a responsive online system. Finally,
  at depth five, the relational database simply takes too long to
  complete the query. Neo4j, in contrast, returns a result in around two
  seconds. At depth five, it transpires almost the entire network is our
  friend: for many real-world use cases, weâd likely trim the results,
  and the timings.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;Questions are:&lt;/strong&gt; &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Is this a reasonable test to emulate what one might except to find in a social network? &lt;em&gt;(Meaning do real social networks normally have nodes with approximately 50 friends for example; seems like the "&lt;a href="http://en.wikipedia.org/wiki/The_rich_get_richer_%28statistics%29"&gt;rich get richer&lt;/a&gt;" model would be more natural for social networks, though might be wrong.)&lt;/em&gt;&lt;/li&gt;
&lt;li&gt;Regardless of the naturalness of the emulation, is there any reason to believe the results are off, or unreproducible? &lt;/li&gt;
&lt;/ul&gt;
', 'databases  nosql  neo4j', NULL, '''-1'':216C,221C ''/a'':29C,47C,550C ''/amzn/click/1449356265'':26C ''/amzn/click/1617290769'':43C ''/blockquote'':498C ''/code'':267C ''/em'':563C ''/li'':564C,585C ''/p'':48C,121C,217C,234C,401C,497C,504C ''/pre'':268C ''/strong'':16C,503C ''/ul'':586C ''/wiki/the_rich_get_richer_%28statistics%29'':546C ''0.01'':250C ''0.016'':249C ''0.168'':254C ''000'':188C,189C,256C,261C,266C ''1'':187C ''1.359'':259C ''110'':255C ''1543.505'':258C ''2'':215C,220C,248C ''2.132'':264C ''2500'':251C ''3'':252C ''30.267'':253C ''4'':257C ''5'':262C ''50'':194C,535C ''600'':260C ''800'':265C ''accept'':438C ''action'':46C,108C ''align'':88C ''almost'':474C ''approxim'':193C,534C ''around'':466C ''background'':15C ''bare'':318C ''base'':82C ''believ'':578C ''best'':205C ''blockquot'':49C ''book'':21C,38C,105C ''case'':487C ''choic'':206C ''chosen'':165C ''clear'':344C ''code'':236C ''comparison'':4A,124C ''complet'':366C,369C,456C ''connect'':134C,173C,208C ''consid'':291C ''contain'':186C ''contrast'':376C,461C ''correct'':9A ''cover'':31C ''crippl'':410C ''d'':489C ''data'':95C,135C,209C ''databas'':10B,28C,84C,129C,202C,228C,280C,284C,348C,408C,450C ''datamodel'':76C ''deal'':352C ''definit'':394C ''depth'':158C,237C,271C,333C,404C,446C,470C ''deterior'':424C ''differ'':322C ''effici'':102C,230C ''em'':524C ''emul'':513C,572C ''en.wikipedia.org'':545C ''en.wikipedia.org/wiki/the_rich_get_richer_%28statistics%29'':544C ''end'':315C ''end-us'':314C ''enough'':287C,396C ''entir'':476C ''exampl'':538C ''except'':517C ''execut'':7A,239C,243C ''exhibit'':409C ''experi'':114C,143C ''extend'':223C ''extrem'':101C ''final'':444C ''find'':146C,222C,231C,519C ''five'':160C,179C,447C,471C ''flat'':383C ''follow'':17C,64C ''form'':56C ''four'':405C ''fraction'':386C ''friend'':148C,150C,195C,224C,274C,276C,336C,338C,340C,480C,536C ''friend-of-friend-of-friend'':335C ''friends-of-friend'':147C,273C ''fundament'':69C ''get'':548C ''given'':161C ''graph'':27C,54C,62C,83C,128C,201C,283C ''high'':87C ''howev'':341C ''href'':23C,40C,543C ''involv'':63C ''itâ'':342C ''laid'':97C ''latenc'':411C,430C ''li'':506C,565C ''like'':490C,540C ''littl'':426C ''long'':181C,454C ''longer'':351C ''major'':78C ''make'':99C,412C ''mani'':482C ''maximum'':157C ''mean'':525C ''mention'':35C ''might'':516C,560C ''millisecond'':324C ''model'':551C ''natur'':55C,73C,555C,569C ''neo4j'':3A,12B,44C,106C,120C,233C,242C,300C,459C ''neo4jâ'':377C,420C ''network'':154C,185C,477C,523C,529C,558C ''node'':532C ''normal'':530C ''nosql'':11B ''notic'':319C ''one'':312C,515C ''onlin'':296C,373C,399C,418C,442C ''oper'':85C ''orient'':72C ''p'':13C,50C,122C,218C,269C,402C,499C ''partner'':109C ''path'':57C,65C,71C,81C,171C ''path-bas'':80C ''path-ori'':70C ''peopl'':190C ''perform'':33C,112C,285C,391C ''peripheri'':435C ''person'':164C ''practic'':414C ''pre'':235C ''queri'':58C,301C,355C,458C ''queryâ'':393C ''question'':501C ''quick'':395C ''quicker'':132C ''rads.stackoverflow.com'':25C,42C ''rads.stackoverflow.com/amzn/click/1449356265'':24C ''rads.stackoverflow.com/amzn/click/1617290769'':41C ''random'':167C ''rdbms'':6A,238C ''reach'':332C ''real'':484C,527C ''real-world'':483C ''reason'':358C,510C,576C ''record'':246C ''regardless'':566C ''relat'':117C,138C,227C,279C,311C,347C,382C,407C,449C ''relationship'':51C,180C ''remain'':381C ''respons'':379C,441C ''result'':197C,464C,493C,580C ''return'':247C,462C ''rich'':547C ''richer'':549C ''run'':302C ''second'':362C,389C,468C ''see'':212C ''seek'':144C ''seem'':539C ''show'':125C ''simpli'':451C ''social'':153C,184C,522C,528C,557C ''store'':118C ''store.partner'':139C ''strong'':14C,198C,500C ''substanti'':131C ''suggest'':199C ''system'':297C,374C,400C,419C,443C ''tabl'':214C,219C ''take'':364C,452C ''test'':34C,511C ''third'':306C ''thirti'':361C ''though'':559C ''three'':334C ''time'':8A,240C,244C,308C,330C,380C,422C,496C ''timefram'':359C ''transpir'':473C ''travers'':60C ''trim'':491C ''two'':163C,272C,305C,327C,467C ''two-third'':304C ''ul'':505C ''unaccept'':370C ''unfinish'':263C ''unreproduc'':584C ''us'':289C ''use'':115C,292C,486C ''useless'':415C ''user'':316C ''versus'':229C ''vukot'':111C ''vukoticâ'':141C ''way'':91C ''well'':286C ''weâ'':488C ''world'':485C ''would'':317C,367C,552C ''wrong'':562C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8943, 1, '2015-11-20 14:15:37.753', 1, 36, 'What''s the most robust way to predict the value of some noisy time series data?', '&lt;p&gt;I have some time series data (time, value) such as:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;281,97.0
284,98.0
287,98.0
290,98.0
293,98.0
297,99.0
309,100.0
320,101.0
333,101.0
346,103.0
359,104.0
372,105.0
385,107.0
396,108.0
405,108.0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am trying to predict when the value will be 160.&lt;/p&gt;

&lt;p&gt;The data is somewhat noisy. The values go mostly up, but could go down sharply every now and then, or stay the same for a short time. When these distortions occur, the prediction is far off for a period of time.&lt;/p&gt;

&lt;p&gt;What algorithms or heuristics would work the best to deliver a robust prediction that doesn''t vary to much?&lt;/p&gt;

&lt;p&gt;I am currently using a linear fit over a time window of 60 data points. The results are not stable enough.&lt;/p&gt;
', 'predictive-modeling  time-series', NULL, '''/code'':67C ''/p'':34C,81C,124C,145C,168C ''/pre'':68C ''100.0'':50C ''101.0'':52C,54C ''103.0'':56C ''104.0'':58C ''105.0'':60C ''107.0'':62C ''108.0'':64C,66C ''160'':80C ''281'':37C ''284'':39C ''287'':41C ''290'':43C ''293'':45C ''297'':47C ''309'':49C ''320'':51C ''333'':53C ''346'':55C ''359'':57C ''372'':59C ''385'':61C ''396'':63C ''405'':65C ''60'':159C ''97.0'':38C ''98.0'':40C,42C,44C,46C ''99.0'':48C ''algorithm'':127C ''best'':133C ''code'':36C ''could'':94C ''current'':149C ''data'':16A,29C,84C,160C ''deliv'':135C ''distort'':112C ''doesn'':140C ''enough'':167C ''everi'':98C ''far'':117C ''fit'':153C ''go'':90C,95C ''heurist'':129C ''linear'':152C ''model'':19B ''most'':91C ''much'':144C ''noisi'':13A,87C ''occur'':113C ''p'':23C,69C,82C,125C,146C ''period'':121C ''point'':161C ''pre'':35C ''predict'':8A,18B,74C,115C,138C ''predictive-model'':17B ''result'':163C ''robust'':5A,137C ''seri'':15A,22B,28C ''sharpli'':97C ''short'':108C ''somewhat'':86C ''stabl'':166C ''stay'':103C ''time'':14A,21B,27C,30C,109C,123C,156C ''time-seri'':20B ''tri'':72C ''use'':150C ''valu'':10A,31C,77C,89C ''vari'':142C ''way'':6A ''window'':157C ''work'':131C ''would'':130C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5676, 2, '2015-05-03 04:22:43.507', 3, NULL, NULL, '&lt;p&gt;I have worked on building a fraud detection solution using text mining, so I understand the scenario that has led to this question. I''ll talk about the approach/techniques that is to be followed to build the fraud detection solution.I''ll divide it into 4 sections&lt;/p&gt;

&lt;hr&gt;

&lt;ul&gt;
&lt;li&gt;Build line of business fraud dictionary&lt;/li&gt;
&lt;li&gt;Fraud detection &amp;amp; scoring&lt;/li&gt;
&lt;li&gt;Tools&lt;/li&gt;
&lt;li&gt;Additional notes&lt;/li&gt;
&lt;/ul&gt;

&lt;hr&gt;

&lt;h3&gt;Building Fraud Dictionary:&lt;/h3&gt;

&lt;p&gt;As a first step you''ll have to identify fraud concepts with help from subject matter expert for the line of business in concern. Fraud concept is a person, characteristic, entity, or event that represents a suspicious scenario and similar concepts can be grouped together to form a concept category. Each concept is further represented by words, phrases, entities etc. The presence of these words/phrases in the document implies occurrence of fraud concept in that transaction. The end result of this exercise will be a fraud dictionary that is a repository of concepts and suspicious key words.&lt;/p&gt;

&lt;p&gt;For example considering the data set mentioned in the question, the &lt;code&gt;numeric patterns&lt;/code&gt; become the concept category, and &lt;code&gt;fictitious invoice numbers&lt;/code&gt; and &lt;code&gt;fictitiously-generated transaction amounts&lt;/code&gt; are 2 different concepts. There will be a keywords/phrases that is associated to this concept which should be captured onto the fraud dictionary.&lt;/p&gt;

&lt;p&gt;You can use NLP technique to build the dictionary. &lt;/p&gt;

&lt;p&gt;Steps:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1) Convert the sentence to lowercase

2) Remove stopwords, these are common words found in a language. Words like for, very, and, of, are, etc, are common stop words

3) Extract n-gram i.e., a contiguous sequence of n items from a given sequence of text. simply increasing n, model can be used to store more context

4) Assign a syntactic label (noun, verb etc.)

5) Knowledge extraction from text through semantic/syntactic analysis approach i.e., try to retain words that hold higher weight in a sentence like Noun/Verb
&lt;/code&gt;&lt;/pre&gt;

&lt;h3&gt;Fraud detection &amp;amp; scoring:&lt;/h3&gt;

&lt;p&gt;Phrases/Keywords to be semantically matched with Phrases/Keywords from Fraud Dictionary and Fraud concepts in transactions can be identified. At this point, care needs to be taken to maintain the context (negation, positive sense) in which a keyword/phrase has been used to avoid false positives.&lt;/p&gt;

&lt;p&gt;For each identified occurrence of Fraud concept in the transaction, a weight of 1 can be assigned. This is done for calculating a suspicious score for the transaction. Higher the score high suspecious.&lt;/p&gt;

&lt;p&gt;Note that using a combination of larger data set with high textual content, and extensive fraud dictionary would result in higher number potentially identified.&lt;/p&gt;

&lt;h3&gt;Tools:&lt;/h3&gt;

&lt;p&gt;You can consider using OpenNLP / StanfordNLP for Part of Speech tagging. Most of the programming language have supporting library for OpenNLP/StanfordNLP. You can choose the language based on your comfort. &lt;/p&gt;

&lt;h3&gt;Additional notes:&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;You can refer &lt;a href="http://datascience.stackexchange.com/a/5388/8465"&gt;here&lt;/a&gt; to get an idea of extracting concepts from sentences&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Fruther read my blog on &lt;a href="https://manoharswamynathan.wordpress.com/category/text-mining/" rel="nofollow"&gt;Text mining 101&lt;/a&gt; to learn more about &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;TM process overview&lt;/li&gt;
&lt;li&gt;Calculate term weight (TF-IDF)&lt;/li&gt;
&lt;li&gt;Similarity distance measure (Cosine)&lt;/li&gt;
&lt;li&gt;Overview of key text mining techniques&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Hope this helps.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (78, 2, '2014-05-15 01:46:28.467', 5, NULL, NULL, '&lt;p&gt;There is no replacement in the strict sense of the word.  Instead you should look at other measures.&lt;/p&gt;

&lt;p&gt;The other measures you look at depend on what you type of problem you are solving.  In general, if you have a small p-value, also consider the magnitude of the effect size.  It may be highly statistically significant but in practice meaningless.  It is also helpful to report the confidence interval of the effect size.&lt;/p&gt;

&lt;p&gt;I would consider &lt;a href="http://galitshmueli.com/system/files/Print%20Version.pdf"&gt;this paper&lt;/a&gt; as mentoned in DanC''s answer to &lt;a href="http://datascience.stackexchange.com/questions/71/when-are-p-values-deceptive"&gt;this question&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':91C,106C ''/p'':20C,79C,107C ''/questions/71/when-are-p-values-deceptive'':103C ''/system/files/print%20version.pdf'':88C ''also'':48C,68C ''answer'':97C ''confid'':73C ''consid'':49C,83C ''danc'':95C ''datascience.stackexchange.com'':102C ''datascience.stackexchange.com/questions/71/when-are-p-values-deceptive'':101C ''depend'':28C ''effect'':54C,77C ''galitshmueli.com'':87C ''galitshmueli.com/system/files/print%20version.pdf'':86C ''general'':39C ''help'':69C ''high'':59C ''href'':85C,100C ''instead'':13C ''interv'':74C ''look'':16C,26C ''magnitud'':51C ''may'':57C ''meaningless'':65C ''measur'':19C,24C ''menton'':93C ''p'':1C,21C,46C,80C ''p-valu'':45C ''paper'':90C ''practic'':64C ''problem'':34C ''question'':105C ''replac'':5C ''report'':71C ''sens'':9C ''signific'':61C ''size'':55C,78C ''small'':44C ''solv'':37C ''statist'':60C ''strict'':8C ''type'':32C ''valu'':47C ''word'':12C ''would'':82C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (145, 4, '2014-05-18 14:58:34.853', 0, NULL, NULL, 'Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval etc.', NULL, NULL, '''analysi'':2C,61C,72C ''anoth'':34C ''call'':24C ''cluster'':1C,4C,26C,44C ''common'':56C ''data'':52C,60C ''etc'':75C ''exploratori'':51C ''field'':65C ''group'':9C,23C,43C ''imag'':71C ''includ'':66C ''inform'':73C ''learn'':68C ''machin'':67C ''main'':48C ''mani'':64C ''mine'':53C ''object'':13C,19C ''pattern'':69C ''recognit'':70C ''retriev'':74C ''sens'':32C ''set'':11C ''similar'':29C ''statist'':59C ''task'':7C,49C ''techniqu'':57C ''use'':62C ''way'':17C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (79, 5, '2014-05-15 03:19:40.36', 0, NULL, NULL, '&lt;p&gt;Conceptually speaking, &lt;em&gt;data-mining&lt;/em&gt; can be thought of as one item (or set of skills and applications) in the toolkit of the data scientist.&lt;/p&gt;

&lt;p&gt;More specifically, data-mining is an activity that seeks patterns in large, complex data sets. It usually emphasizes algorithmic techniques, but may also involve any set of related skills, applications, or methodologies with that goal.&lt;/p&gt;

&lt;p&gt;In US-English colloquial speech, data-mining and data-collection are often used interchangeably.&lt;/p&gt;

&lt;p&gt;However, a main difference between these two related activities is &lt;em&gt;intentionality&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;&lt;em&gt;Definition inspired mostly by the contributions of &lt;a href="http://datascience.stackexchange.com/users/36/statsrus"&gt;@statsRus&lt;/a&gt; to Data Science.SE&lt;/em&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':117C ''/em'':8C,100C,121C ''/p'':29C,67C,86C,101C,122C ''/users/36/statsrus'':115C ''activ'':38C,96C ''algorithm'':50C ''also'':54C ''applic'':21C,61C ''collect'':81C ''colloqui'':73C ''complex'':44C ''conceptu'':2C ''contribut'':109C ''data'':6C,27C,34C,45C,76C,80C,119C ''data-collect'':79C ''data-min'':5C,33C,75C ''datascience.stackexchange.com'':114C ''datascience.stackexchange.com/users/36/statsrus'':113C ''definit'':104C ''differ'':91C ''em'':4C,98C,103C ''emphas'':49C ''english'':72C ''goal'':66C ''howev'':88C ''href'':112C ''inspir'':105C ''intent'':99C ''interchang'':85C ''involv'':55C ''item'':15C ''larg'':43C ''main'':90C ''may'':53C ''methodolog'':63C ''mine'':7C,35C,77C ''most'':106C ''often'':83C ''one'':14C ''p'':1C,30C,68C,87C,102C ''pattern'':41C ''relat'':59C,95C ''science.se'':120C ''scientist'':28C ''seek'':40C ''set'':17C,46C,57C ''skill'':19C,60C ''speak'':3C ''specif'':32C ''speech'':74C ''statsrus'':116C ''techniqu'':51C ''thought'':11C ''toolkit'':24C ''two'':94C ''us'':71C ''us-english'':70C ''use'':84C ''usual'':48C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (82, 2, '2014-05-15 05:19:34.757', 13, NULL, NULL, '&lt;p&gt;Simply set, ''parallel'' means running concurrently on distinct resources (CPUs), while ''distributed'' means running across distinct computers, involving issues related to networks.&lt;/p&gt;

&lt;p&gt;Parallel computing using for instance &lt;a href="http://en.wikipedia.org/wiki/OpenMP"&gt;OpenMP&lt;/a&gt; is not distributed, while parallel computing with &lt;a href="http://en.wikipedia.org/wiki/Message_Passing_Interface"&gt;Message Passing&lt;/a&gt; is often distributed.&lt;/p&gt;

&lt;p&gt;Being in a ''distributed but not parallel'' setting would mean under-using resources so it is seldom encountered but it is conceptually possible.&lt;/p&gt;
', NULL, NULL, '''/a'':37C,52C ''/p'':24C,56C,82C ''/wiki/message_passing_interface'':49C ''/wiki/openmp'':35C ''across'':16C ''comput'':18C,27C,43C ''conceptu'':80C ''concurr'':7C ''cpus'':11C ''distinct'':9C,17C ''distribut'':13C,40C,55C,61C ''en.wikipedia.org'':34C,48C ''en.wikipedia.org/wiki/message_passing_interface'':47C ''en.wikipedia.org/wiki/openmp'':33C ''encount'':76C ''href'':32C,46C ''instanc'':30C ''involv'':19C ''issu'':20C ''mean'':5C,14C,67C ''messag'':50C ''network'':23C ''often'':54C ''openmp'':36C ''p'':1C,25C,57C ''parallel'':4C,26C,42C,64C ''pass'':51C ''possibl'':81C ''relat'':21C ''resourc'':10C,71C ''run'':6C,15C ''seldom'':75C ''set'':3C,65C ''simpli'':2C ''under-us'':68C ''use'':28C,70C ''would'':66C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8656, 1, '2015-10-30 02:49:08.59', 3, 43, 'How to predict the duration of burst given several series', '&lt;p&gt;I''ve got sets of time series data collected from Weibo. It contains the number of posts under certain topics over a year. It can be found that from time to time there''re bursts of discussion on certain topic. What''s interesting is some burst die down quickly while others sustain on the agenda for quite a long time and die down gradually. How to predict and model the decays given a time series data. Also, I''ve got data of some exogenous data including time series data of links, pictures, and retweeting and original data in the community. Can I predict the duration of decay from these variables?&lt;/p&gt;
', 'bigdata  predictive-modeling  time-series', NULL, '''/p'':129C ''agenda'':73C ''also'':95C ''bigdata'':11B ''burst'':7A,53C,64C ''certain'':37C,57C ''collect'':27C ''communiti'':118C ''contain'':31C ''data'':26C,94C,99C,103C,107C,115C ''decay'':89C,125C ''die'':65C,80C ''discuss'':55C ''durat'':5A,123C ''exogen'':102C ''found'':45C ''given'':8A,90C ''got'':21C,98C ''gradual'':82C ''includ'':104C ''interest'':61C ''link'':109C ''long'':77C ''model'':14B,87C ''number'':33C ''origin'':114C ''other'':69C ''p'':18C ''pictur'':110C ''post'':35C ''predict'':3A,13B,85C,121C ''predictive-model'':12B ''quick'':67C ''quit'':75C ''re'':52C ''retweet'':112C ''seri'':10A,17B,25C,93C,106C ''set'':22C ''sever'':9A ''sustain'':70C ''time'':16B,24C,48C,50C,78C,92C,105C ''time-seri'':15B ''topic'':38C,58C ''variabl'':128C ''ve'':20C,97C ''weibo'':29C ''year'':41C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5716, 2, '2015-05-07 20:36:54.033', 3, NULL, NULL, '&lt;p&gt;&lt;em&gt;(While I think that your question is usually not regarded as a good question for this site, I think that it should be preserved because almost everyone new to the field is similarly confused at first and it''s hard to find straight, sober, and balanced information about it.)&lt;/em&gt;&lt;/p&gt;

&lt;h2&gt;Is the Semantic Web dead? Are ontologies dead?&lt;/h2&gt;

&lt;p&gt;Many people moved away from the Semantic Web. On the other hand, there are still many using it. There''s always been a lot of confusion about where its value lies. There are use cases that genuinely benefit from the semantic web technologies but they are quite few and far between.&lt;/p&gt;

&lt;h2&gt;Linked Open Data&lt;/h2&gt;

&lt;p&gt;That''s partially why the subfield of Linked Open Data was created. You can see Linked Data as a more pragmatic stripped down version of the Semantic Web (or as a necessary enabler of the grander semantic web vision).&lt;/p&gt;

&lt;p&gt;Linked Data, while more pragmatic, still uses ontologies. It is just not so uptight about using OWL and designing your ontologies first and in a very formal way.&lt;/p&gt;

&lt;p&gt;A knowledge base such as Freebase (now being replaced by &lt;a href="http://www.wikidata.org/wiki/Wikidata:Main_Page" rel="nofollow"&gt;Wikidata&lt;/a&gt;) doesn''t even build on Semantic Web technologies (it''s always been available also in the form of RDF dumps though). On the other hand, even Freebase builds on concepts similar to those in the Semantic Web and it does have a form of ontologies.&lt;/p&gt;

&lt;h2&gt;Software to generate ontologies&lt;/h2&gt;

&lt;p&gt;There is no software that would create high-quality ontologies automatically for you. At least not in the sense of OWL/first-order logic ontologies. On the other hand, many kinds of software, including Cratilo mentioned by you, can help you to build a lower step in the &lt;a href="http://en.wikipedia.org/wiki/Semantic_spectrum" rel="nofollow"&gt;semantic spectrum&lt;/a&gt; such as a glossary or maybe even a folksonomy which can later be transformed into more of a taxonomy or ontology by other algorithms. There might be even software to create fullblown OWL ontologies but I think that''s still rather an active area of research.&lt;/p&gt;

&lt;h2&gt;Ontologies and methodologies&lt;/h2&gt;

&lt;p&gt;As always, it depends on what exactly your needs and goals are. In fields such as biomedicine and life sciences in general, they create complex formal ontologies because they have data with highly varied structure and ontologies help them keep things organized and usable. In contrast, an eshop might be better off sticking with normal relational database modelling and adding only global identifiers (URIs) in the spirit of Linked Data if they want to build a proper knowledge graph later. &lt;/p&gt;

&lt;p&gt;Even if you need ontologies you might want to skip the methodologies first. Especially if you are really new to the field and have little idea what your needs are.&lt;/p&gt;

&lt;h2&gt;Jena, Sesame, ...&lt;/h2&gt;

&lt;p&gt;Jena, Sesame, Virtuoso, etc. are &lt;a href="http://en.wikipedia.org/wiki/Triplestore" rel="nofollow"&gt;triplestores&lt;/a&gt; - they are used to store and query RDF. Most ontologies can be represented in RDF. Even those written in OWL. RDF has the semantic part, RDF/S, which allows you to formulate some basic ontologies. That might well be enough for start. Even RDF/S can get confusing and convoluted when you start to think about blank nodes, named graphs (are they fixed? are they dynamic?), etc.&lt;/p&gt;

&lt;p&gt;While I think that your question is usually not regarded as a good question for this site, I think that it should be preserved because almost everyone new to the field is similarly confused at first and it''s hard to find straight, sober, and balanced information about it.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (91, 2, '2014-05-15 11:56:43.607', 11, NULL, NULL, '&lt;p&gt;Well, I''m not sure if it is MapReduce that solves the problem, but it surely wouldn''t be MapReduce alone to solve all these questions you raised. But here are important things to take into account, and that make it &lt;em&gt;feasible&lt;/em&gt; to have such low latency on queries from all these TBs of data in different machines:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;distributed computing: by being distributed does not mean that the indexes are simply distributed in different machines, they are actually replicated along different clusters, which allows for lots of users performing different queries with low retrieval time (yes, huge companies can afford for that much of machines);&lt;/li&gt;
&lt;li&gt;caching: caches tremendously reduce execution time, be it for the crawling step, for the retrieval of pages, or for the ranking and exihibition of results;&lt;/li&gt;
&lt;li&gt;lots of tweaking: all the above and very efficient algorithms/solutions can only be effective if the implementation is also efficient. There are tons of (hard coded) optimizations, such as locality of reference, compression, caching; all of them usually appliable to different parts of the processing.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Considering that, lets try to address your questions:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;but I imagine it infeasible for the results of every single possible query to be indexed&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Yes, it would be, and actually is infeasible to have results for &lt;em&gt;every single possible query&lt;/em&gt;. There is an infinite number of terms in the world (even if you assume that only terms properly spelled will be entered), and there is an exponential number of queries from these &lt;code&gt;n -&amp;gt; inf&lt;/code&gt; terms (&lt;code&gt;2^n&lt;/code&gt;). So what is done? Caching. But if there are so many queries/results, which ones to cache? Caching policies. The most frequent/popular/relevant-for-the-user queries are the ones cached.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;wouldn''t the hardware latency in Google''s hardware be huge? Even if the data in Google were all stored in TB/s SSDs&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Nowdays, with such highly developed processors, people tend to think that every possible task that must finish within a second (or less), and that deals with so much data, must be processed by extremely powerful processors with multiple cores and lots of memory. However, the one thing &lt;em&gt;ruling&lt;/em&gt; market is money, and the investors are not interested in wasting it. So what is done?&lt;/p&gt;

&lt;p&gt;The preference is actually for having lots of machines, each using simple/accessible (in terms of cost) processors, which lowers the price of building up the multitude of clusters there are. And yes, it does work. The main bottleneck always boils down to disk, if you consider simple measurements of &lt;a href="http://i.stack.imgur.com/Uf6al.gif"&gt;performance&lt;/a&gt;. But once there are so many machines, one can afford to load things up to main memory, instead of working on hard disks.&lt;/p&gt;

&lt;p&gt;Memory cards are &lt;em&gt;expensive&lt;/em&gt; for us, mere human beings, but they are very cheap for enterprises that buy lots of such cards at once. Since it''s not costly, having much memory as needed to load indexes and keep caches at hand is not a problem. And since there are so many machines, there is no need for super fast processors, as you can direct queries to different places, and have clusters of machines responsible for attending &lt;em&gt;specific geographical regions&lt;/em&gt;, which allows for more &lt;em&gt;specialized&lt;/em&gt; data caching, and even better response times.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Does MapReduce help solve this problem?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Although I don''t think that using or not MapReduce is restricted information inside Google, I''m not conversant about this point. However, Google''s implementation of MapReduce (which is surely &lt;em&gt;not&lt;/em&gt; Hadoop) must have lots of optimizations, many involving the aspects discussed above. So, the architecture of MapReduce probably helps guiding how the computations are physically distributed, but there are many other points to be considered to justify such speed in querying time.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Okay, so I understand that popular searches can be cached in memory. But what about unpopular searches?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The graph below presents a curve of how the &lt;em&gt;kinds&lt;/em&gt; of queries occur. You can see that there are three main kinds of searches, each of them holding approximately 1/3 of the volume of queries (area below curve). The plot shows power law, and reinforces the fact that smaller queries are the most popular. The second third of queries are still feasible to process, since they hold few words. But the set of so-called &lt;em&gt;obscure queries&lt;/em&gt;, which usually consist of non-experienced users'' queries, are not a negligible part of the queries.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/CpcNf.jpg" alt="Heavy-tailed distribution"&gt;&lt;/p&gt;

&lt;p&gt;And there lies space for novel solutions. Since it''s not just one or two queries (but one third of them), they must have &lt;em&gt;relevant&lt;/em&gt; results. If you type in something &lt;em&gt;much too obscure&lt;/em&gt; in a Google search, it shan''t take longer to return a list of results, but will most probably show you something it &lt;em&gt;inferred&lt;/em&gt; you''d like to say. Or it may simply state that there was no document with such terms -- or even cut down your search to 32 words (which just happened to me in a random test here).&lt;/p&gt;

&lt;p&gt;There are dozens of appliable heuristics, which may be either to ignore some words, or to try to break the query into smaller ones, and gather the most &lt;em&gt;popular&lt;/em&gt; results. And all these solutions can be tailored and tweaked to respect &lt;em&gt;feasible waiting times&lt;/em&gt; of, say, less then a second? :D&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (96, 2, '2014-05-15 15:10:30.243', 6, NULL, NULL, '&lt;p&gt;For my part I can say that I use click frequency on i.e. eCommerce products. When you combine it with the days of the year it can even bring you great suggestions.&lt;/p&gt;

&lt;p&gt;i.e.: We have historical data from 1 year over 2 products (Snowboots[], Sandalettes[])&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Snowboots[1024,1253,652,123,50,12,8,4,50,148,345,896]
Sandalettes[23,50,73,100,534,701,1053,1503,1125,453,213,25]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where [0] = January&lt;/p&gt;

&lt;p&gt;As you can see, snowboots are much more searched in January than sandalettes, so you should suggest snowboots to someone searching shoes on your site or /we on january.&lt;/p&gt;

&lt;p&gt;You can also see if something is "fresh" at this time, like when people often click a unknown product it could be an insight for a new comming trend or something.&lt;/p&gt;

&lt;p&gt;That are just some examples where you could use click frequency as an insight. I think there are &lt;strong&gt;no rules&lt;/strong&gt; for what you can use or not in recommendations, &lt;strong&gt;as long as it makes sense&lt;/strong&gt;.&lt;/p&gt;
', NULL, NULL, '''/code'':78C ''/p'':34C,49C,84C,115C,148C,189C ''/pre'':79C ''/strong'':171C,188C ''/we'':112C ''0'':82C ''1'':42C ''100'':69C ''1024'':53C ''1053'':72C ''1125'':74C ''12'':58C ''123'':56C ''1253'':54C ''148'':62C ''1503'':73C ''2'':45C ''213'':76C ''23'':66C ''25'':77C ''345'':63C ''4'':60C ''453'':75C ''50'':57C,61C,67C ''534'':70C ''652'':55C ''701'':71C ''73'':68C ''8'':59C ''896'':64C ''also'':119C ''bring'':30C ''click'':11C,132C,159C ''code'':51C ''com'':144C ''combin'':19C ''could'':137C,157C ''data'':40C ''day'':23C ''ecommerc'':15C ''even'':29C ''exampl'':154C ''frequenc'':12C,160C ''fresh'':124C ''great'':32C ''histor'':39C ''i.e'':14C,36C ''insight'':140C,163C ''januari'':83C,96C,114C ''like'':128C ''long'':183C ''make'':186C ''much'':92C ''new'':143C ''often'':131C ''p'':1C,35C,80C,85C,116C,149C ''part'':4C ''peopl'':130C ''pre'':50C ''product'':16C,46C,135C ''recommend'':180C ''rule'':170C ''sandalett'':48C,65C,98C ''say'':7C ''search'':94C,106C ''see'':89C,120C ''sens'':187C ''shoe'':107C ''site'':110C ''snowboot'':47C,52C,90C,103C ''someon'':105C ''someth'':122C,147C ''strong'':168C,181C ''suggest'':33C,102C ''think'':165C ''time'':127C ''trend'':145C ''unknown'':134C ''use'':10C,158C,176C ''year'':26C,43C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (83, 2, '2014-05-15 07:47:44.71', 7, NULL, NULL, '&lt;p&gt;I posted a pretty detailed answer on stackoverflow about when it is appropriate to use relational vs document (or NoSQL) database, here:  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;a href="http://stackoverflow.com/questions/13528216/motivations-for-using-relational-database-orm-or-document-database-odm/13599767#13599767"&gt;Motivations for using relational database / ORM or document database / ODM&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Summary:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;for small stuff, go with whatever tools you are familiar with&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;a few gigabytes is definitely small stuff: it doesn''t get big until it is too big to fit in a single &lt;a href="http://www.mysql.com/products/cluster/scalability.html"&gt;MySQL Cluster&lt;/a&gt; with a reasonable number of nodes (16-32), which means maybe 8-16TB data and a few million transactions per second (or a more conventional hard-drive-based database with up to 100''s of TB data and a few thousand transactions per second).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;if you''re stuck with another database (not MySQL Cluster), get more mileage out of it by throwing in FusionIO hardware.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;once you have data larger than a few TB &lt;em&gt;and&lt;/em&gt; faster than thousands of transactions per second, it is a good time to look at moving to logical sharding in the application code first and then to NoSQL.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://cassandra.apache.org/"&gt;Cassandra&lt;/a&gt; :)&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (84, 2, '2014-05-15 08:19:40.577', 7, NULL, NULL, '&lt;p&gt;You are asking about &lt;a href="http://en.wikipedia.org/wiki/Data_dredging"&gt;Data Dredging&lt;/a&gt;, which is what happens when testing a very large number of hypotheses against a data set, or testing hypotheses against a data set that were suggested by the same data.  &lt;/p&gt;

&lt;p&gt;In particular, check out &lt;a href="http://en.wikipedia.org/wiki/Multiple_comparisons"&gt;Multiple hypothesis hazard&lt;/a&gt;, and &lt;a href="http://en.wikipedia.org/wiki/Testing_hypotheses_suggested_by_the_data"&gt;Testing hypotheses suggested by the data&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The solution is to use some kind of correction for &lt;a href="http://en.wikipedia.org/wiki/False_discovery_rate"&gt;False discovery rate&lt;/a&gt; or &lt;a href="http://en.wikipedia.org/wiki/Familywise_error_rate"&gt;Familywise error rate&lt;/a&gt;, such as &lt;a href="http://en.wikipedia.org/wiki/Scheff%C3%A9%27s_method"&gt;ScheffÃ©''s method&lt;/a&gt; or the (very old-school) &lt;a href="http://en.wikipedia.org/wiki/Bonferroni_correction"&gt;Bonferroni correction&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In a somewhat less rigorous way, it may help to filter your discoveries by the confidence interval for the odds ratio (OR) for each statistical result.  If the 99% confidence interval for the odds ratio is 10-12, then the OR is &amp;lt;= 1 with some &lt;em&gt;extremely&lt;/em&gt; small probability, especially if the sample size is also large.  If you find something like this, it is probably a strong effect even if it came out of a test of millions of hypotheses.  &lt;/p&gt;
', NULL, NULL, '''-12'':167C ''/a'':13C,58C,71C,92C,102C,113C,127C ''/em'':178C ''/p'':44C,72C,128C,213C ''/wiki/bonferroni_correction'':124C ''/wiki/data_dredging'':10C ''/wiki/false_discovery_rate'':88C ''/wiki/familywise_error_rate'':98C ''/wiki/multiple_comparisons'':54C ''/wiki/scheff%c3%a9%27s_method'':109C ''/wiki/testing_hypotheses_suggested_by_the_data'':64C ''1'':173C ''10'':166C ''99'':158C ''also'':187C ''ask'':4C ''bonferroni'':125C ''came'':204C ''check'':48C ''confid'':145C,159C ''correct'':82C,126C ''data'':11C,28C,35C,43C,70C ''discoveri'':90C,142C ''dredg'':12C ''effect'':200C ''em'':176C ''en.wikipedia.org'':9C,53C,63C,87C,97C,108C,123C ''en.wikipedia.org/wiki/bonferroni_correction'':122C ''en.wikipedia.org/wiki/data_dredging'':8C ''en.wikipedia.org/wiki/false_discovery_rate'':86C ''en.wikipedia.org/wiki/familywise_error_rate'':96C ''en.wikipedia.org/wiki/multiple_comparisons'':52C ''en.wikipedia.org/wiki/scheff%c3%a9%27s_method'':107C ''en.wikipedia.org/wiki/testing_hypotheses_suggested_by_the_data'':62C ''error'':100C ''especi'':181C ''even'':201C ''extrem'':177C ''fals'':89C ''familywis'':99C ''filter'':140C ''find'':191C ''happen'':17C ''hazard'':57C ''help'':138C ''href'':7C,51C,61C,85C,95C,106C,121C ''hypothes'':25C,32C,66C,212C ''hypothesi'':56C ''interv'':146C,160C ''kind'':80C ''larg'':22C,188C ''less'':133C ''like'':193C ''lt'':172C ''may'':137C ''method'':112C ''million'':210C ''multipl'':55C ''number'':23C ''odd'':149C,163C ''old'':118C ''old-school'':117C ''p'':1C,45C,73C,129C ''particular'':47C ''probabl'':180C,197C ''rate'':91C,101C ''ratio'':150C,164C ''result'':155C ''rigor'':134C ''sampl'':184C ''scheffã'':110C ''school'':119C ''set'':29C,36C ''size'':185C ''small'':179C ''solut'':75C ''someth'':192C ''somewhat'':132C ''statist'':154C ''strong'':199C ''suggest'':39C,67C ''test'':19C,31C,65C,208C ''use'':78C ''way'':135C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (375, 1, '2014-06-14 23:52:10.49', 3, 1292, 'Amazon S3 vs Google Drive', '&lt;p&gt;The majority of people use S3. However, Google Drive seems a promising alternative solution for storing large amounts of data. Are there specific reasons why one is better than the other?&lt;/p&gt;
', 'bigdata', NULL, '''/p'':39C ''altern'':20C ''amazon'':1A ''amount'':25C ''better'':35C ''bigdata'':6B ''data'':27C ''drive'':5A,16C ''googl'':4A,15C ''howev'':14C ''larg'':24C ''major'':9C ''one'':33C ''p'':7C ''peopl'':11C ''promis'':19C ''reason'':31C ''s3'':2A,13C ''seem'':17C ''solut'':21C ''specif'':30C ''store'':23C ''use'':12C ''vs'':3A');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (92, 2, '2014-05-15 13:18:38.693', 8, NULL, NULL, '&lt;p&gt;MapReduce has nothing to do with real-time anything. It is a batch-oriented processing framework suitable for some offline tasks, like ETL and index building. Google has moved off of MapReduce for most jobs now, and even the Hadoop ecosystem is doing the same.&lt;/p&gt;

&lt;p&gt;The answer to low latency is generally to keep precomputed indices in memory. Anything that touches disk is hard to make fast and scale. This is how newer-generation Hadoop-based SQL engines like &lt;a href="http://www.cloudera.com/content/cloudera/en/products-and-services/cdh/impala.html"&gt;Impala&lt;/a&gt; get so much speed compared to MapReduce-based infrastructure like &lt;a href="http://hive.apache.org/"&gt;Hive&lt;/a&gt;, for example.&lt;/p&gt;

&lt;p&gt;Search infrastructure can''t cache the results of every single query. But it sure can cache intermediate results, or, more complete results for top queries. With a little caching you can serve results for a significant minority of all queries.&lt;/p&gt;

&lt;p&gt;Search is also split across servers. So one machine can delegate to 100 to each get a part of the result and then combine them.&lt;/p&gt;

&lt;p&gt;You can also get away with some degree of approximation. Google does not literally form a thousand pages of search results; it just has to get the first page about right.&lt;/p&gt;

&lt;p&gt;Keep in mind that Google has &lt;em&gt;millions&lt;/em&gt; of computers around the globe. Your queries are going to a data center geographically near to you and that is only serving your geography. This cuts out most of the latency, which is network and not processing time in the data center.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (86, 1, '2014-05-15 09:04:09.71', 9, 418, 'Clustering unique visitors by useragent, ip, session_id', '&lt;p&gt;Given website access data in the form &lt;code&gt;session_id, ip, user_agent&lt;/code&gt;, and optionally timestamp, following the conditions below, how would you best cluster the sessions into unique visitors?&lt;/p&gt;

&lt;p&gt;&lt;code&gt;session_id&lt;/code&gt;: is an id given to every new visitor. It does not expire, however if the user doesn''t accept cookies/clears cookies/changes browser/changes device, he will not be recognised anymore&lt;/p&gt;

&lt;p&gt;&lt;code&gt;IP&lt;/code&gt; can be shared between different users (Imagine a free wi-fi cafe, or your ISP reassigning IPs), and they will often have at least 2, home and work.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;User_agent&lt;/code&gt; is the browser+OS version, allowing to distinguish between devices. For example a user is likely to use both phone and laptop, but is unlikely to use windows+apple laptops. It is unlikely that the same session id has multiple useragents.&lt;/p&gt;

&lt;p&gt;Data might look as the fiddle here:
&lt;a href="http://sqlfiddle.com/#!2/c4de40/1"&gt;http://sqlfiddle.com/#!2/c4de40/1&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Of course, we are talking about assumptions, but it''s about getting as close to reality as possible. For example, if we encounter the same ip and useragent in a limited time frame with a different session_id, it would be a fair assumption that it''s the same user, with some edge case exceptions.&lt;/p&gt;

&lt;p&gt;Edit: Language in which the problem is solved is irellevant, it''s mostly about logic and not implementation. Pseudocode is fine.&lt;/p&gt;

&lt;p&gt;Edit: due to the slow nature of the fiddle, you can alternatively read/run the mysql:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;select session_id, floor(rand()*256*256*256*256) as ip_num , floor(rand()*1000) as user_agent_id
from 
    (select 1+a.nr+10*b.nr as session_id, ceil(rand()*3) as nr
    from
        (select 1 as nr union all select 2 union all select 3   union all select 4 union all select 5
        union all select 6 union all select 7 union all select 8 union all select 9 union all select 0)a
    join
        (select 1 as nr union all select 2 union all select 3   union all select 4 union all select 5
        union all select 6 union all select 7 union all select 8 union all select 9 union all select 0)b
        order by 1
    )d
inner join
    (select 1 as nr union all select 2 union all select 3   union all select 4 union all select 5
    union all select 6 union all select 7 union all select 8 union all select 9 )e
    on d.nr&amp;gt;=e.nr
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (87, 2, '2014-05-15 09:30:36.46', 6, NULL, NULL, '&lt;p&gt;Looking at this document called &lt;a href="https://www.facebook.com/notes/facebook-data-team/anatomy-of-facebook/10150388519243859"&gt;Anatomy of Facebook&lt;/a&gt; I note that the median is 100. Looking at the cumulative function plot I can bet that the average is higher, near 200. So 50 seems to not be the best number here. However I think that this is not the main issue here. &lt;/p&gt;

&lt;p&gt;The main issue is the lack of information on how the database was used.&lt;/p&gt;

&lt;p&gt;It seems reasonable that a data storage designed specially for graph structures to be more efficient than traditional RDBMs. However, even if the RDBMs are not in the latest trends as a data storage of choice, these systems evolved continuously in a race with the data set dimensions. There are various types of possible designs, various ways of indexing data, improvements related with concurrency and so on. &lt;/p&gt;

&lt;p&gt;To conclude I think that regarding reproducibility, the study lack a proper description of how the database schema was designed. I do not expect that a database to dominate on such king of interrogations, however I would expect that with a well-tuned design the differences to not be such massive.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (89, 1, '2014-05-15 11:22:27.293', 8, 211, 'How does a query into a huge database return with negligible latency?', '&lt;p&gt;For example, when searching something in Google, results return nigh-instantly.&lt;/p&gt;

&lt;p&gt;I understand that Google sorts and indexes pages with algorithms etc., but I imagine it infeasible for the results of every single possible query to be indexed (and results are personalized, which renders this even more infeasible)?&lt;/p&gt;

&lt;p&gt;Moreover, wouldn''t the hardware latency in Google''s hardware be huge? Even if the data in Google were all stored in TB/s SSDs, I imagine the hardware latency to be huge, given the sheer amount of data to process.&lt;/p&gt;

&lt;p&gt;Does MapReduce help solve this problem?&lt;/p&gt;

&lt;p&gt;EDIT: Okay, so I understand that popular searches can be cached in memory. But what about unpopular searches? Even for the most obscure search I have conducted, I don''t think the search has ever been reported to be larger than 5 seconds. How is this possible?&lt;/p&gt;
', 'bigdata  google  search', NULL, '''/p'':29C,68C,110C,118C,167C ''5'':161C ''algorithm'':40C ''amount'':105C ''bigdata'':13B ''cach'':130C ''conduct'':146C ''data'':85C,107C ''databas'':8A ''edit'':120C ''etc'':41C ''even'':65C,82C,138C ''ever'':154C ''everi'':51C ''exampl'':18C ''given'':102C ''googl'':14B,23C,34C,77C,87C ''hardwar'':74C,79C,97C ''help'':114C ''huge'':7A,81C,101C ''imagin'':44C,95C ''index'':37C,57C ''infeas'':46C,67C ''instant'':28C ''larger'':159C ''latenc'':12A,75C,98C ''mapreduc'':113C ''memori'':132C ''moreov'':70C ''neglig'':11A ''nigh'':27C ''nigh-inst'':26C ''obscur'':142C ''okay'':121C ''p'':16C,30C,69C,111C,119C ''page'':38C ''person'':61C ''popular'':126C ''possibl'':53C,166C ''problem'':117C ''process'':109C ''queri'':4A,54C ''render'':63C ''report'':156C ''result'':24C,49C,59C ''return'':9A,25C ''search'':15B,20C,127C,137C,143C,152C ''second'':162C ''sheer'':104C ''singl'':52C ''solv'':115C ''someth'':21C ''sort'':35C ''ssds'':93C ''store'':90C ''tb/s'':92C ''think'':150C ''understand'':32C,124C ''unpopular'':136C ''wouldn'':71C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (90, 2, '2014-05-15 11:46:38.17', 4, NULL, NULL, '&lt;p&gt;The terms "parallel computing" and "distributed computing" certainly have a large overlap, but can be differentiated further. Actually, you already did this in your question, by later asking about "parallel processing" and "distributed processing". &lt;/p&gt;

&lt;p&gt;One could consider "distributed computing" as the more general term that involves "distributed processing" as well as, for example, "distributed storage". The common term, "distributed", usually refers to some sort of &lt;a href="http://en.wikipedia.org/wiki/Message_Passing_Interface" rel="nofollow"&gt;Message Passing&lt;/a&gt; over a network, between machines that are physically separated.&lt;/p&gt;

&lt;p&gt;The term "parallel computing" is also in the process of being further defined, e.g. by explicitly differentiating between the terms "parallel" and "concurrent", where - roughly - the first one refers &lt;a href="http://en.wikipedia.org/wiki/Data_parallelism" rel="nofollow"&gt;data parallelism&lt;/a&gt; and the latter to &lt;a href="http://en.wikipedia.org/wiki/Task_parallelism" rel="nofollow"&gt;task parallelism&lt;/a&gt;, although there are hardly really strict and binding defintions. &lt;/p&gt;

&lt;p&gt;So one could say that &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;"distributed processing" usually (although not necessarily) means that it also is "parallel processing"&lt;/li&gt;
&lt;li&gt;"distributed computing" is more general, and also covers aspects that are not related to parallelism&lt;/li&gt;
&lt;li&gt;and obviously, "parallel computing"/"parallel processing" does not imply that it is "distributed"&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':78C,128C,142C ''/li'':175C,192C,207C ''/p'':36C,88C,152C,159C ''/ul'':208C ''/wiki/data_parallelism'':123C ''/wiki/message_passing_interface'':73C ''/wiki/task_parallelism'':137C ''actual'':19C ''alreadi'':21C ''also'':95C,171C,183C ''although'':143C,165C ''ask'':29C ''aspect'':185C ''bind'':150C ''certain'':9C ''common'':60C ''comput'':5C,8C,42C,93C,178C,197C ''concurr'':112C ''consid'':40C ''could'':39C,156C ''cover'':184C ''data'':126C ''defin'':102C ''defint'':151C ''differenti'':17C,106C ''distribut'':7C,34C,41C,50C,57C,62C,162C,177C,206C ''e.g'':103C ''en.wikipedia.org'':72C,122C,136C ''en.wikipedia.org/wiki/data_parallelism'':121C ''en.wikipedia.org/wiki/message_passing_interface'':71C ''en.wikipedia.org/wiki/task_parallelism'':135C ''exampl'':56C ''explicit'':105C ''first'':116C ''general'':46C,181C ''hard'':146C ''href'':70C,120C,134C ''impli'':202C ''involv'':49C ''larg'':12C ''later'':28C ''latter'':131C ''li'':161C,176C,193C ''machin'':83C ''mean'':168C ''messag'':76C ''necessarili'':167C ''network'':81C ''nofollow'':75C,125C,139C ''obvious'':195C ''one'':38C,117C,155C ''overlap'':13C ''p'':1C,37C,89C,153C ''parallel'':4C,31C,92C,110C,127C,141C,173C,191C,196C,198C ''pass'':77C ''physic'':86C ''process'':32C,35C,51C,98C,163C,174C,199C ''question'':26C ''realli'':147C ''refer'':64C,118C ''rel'':74C,124C,138C ''relat'':189C ''rough'':114C ''say'':157C ''separ'':87C ''sort'':67C ''storag'':58C ''strict'':148C ''task'':140C ''term'':3C,47C,61C,91C,109C ''ul'':160C ''usual'':63C,164C ''well'':53C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (231, 1, '2014-06-05 09:00:27.95', 6, 332, 'How to get an aggregate confusion matrix from n different classifications', '&lt;p&gt;I want to test the accuracy of a methodology. I ran it ~400 times, and I got a different classification for each run. I also have the ground truth, i.e., the real classification to test against.&lt;/p&gt;

&lt;p&gt;For each classification I computed a confusion matrix. Now I want to aggregate these results in order to get the overall confusion matrix. How can I achieve it?&lt;/p&gt;

&lt;p&gt;May I sum all confusion matrices in order to obtain the overall one?&lt;/p&gt;
', 'classification  confusion-matrix  accuracy', NULL, '''/p'':54C,84C,99C ''400'':30C ''accuraci'':16B,23C ''achiev'':82C ''aggreg'':5A,68C ''also'':42C ''classif'':11A,12B,37C,50C,58C ''comput'':60C ''confus'':6A,14B,62C,77C,90C ''confusion-matrix'':13B ''differ'':10A,36C ''get'':3A,74C ''got'':34C ''ground'':45C ''i.e'':47C ''matric'':91C ''matrix'':7A,15B,63C,78C ''may'':86C ''methodolog'':26C ''n'':9A ''obtain'':95C ''one'':98C ''order'':72C,93C ''overal'':76C,97C ''p'':17C,55C,85C ''ran'':28C ''real'':49C ''result'':70C ''run'':40C ''sum'':88C ''test'':21C,52C ''time'':31C ''truth'':46C ''want'':19C,66C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (93, 2, '2014-05-15 13:30:04.27', 5, NULL, NULL, '&lt;p&gt;There''s not much you can do with just this data, but what little you can do does not rely on machine learning. &lt;/p&gt;

&lt;p&gt;Yes, sessions from the same IP but different User-Agents are almost certainly distinct users. Sessions with the same IP and User-Agent are usually the same user, except in the case of proxies / wi-fi access points. Those you might identify by looking at the distribution of session count per IP to identify likely ''aggregate'' IPs. Sessions from the same IP / User-Agent that overlap in time are almost surely distinct.&lt;/p&gt;

&lt;p&gt;To further distinguish users you would need more info. For example, the sites or IP addresses that the user is connecting to would be a very strong basis for differentiating sessions. Then you could get into more sophisticated learning to figure out when sessions are the same or different users.&lt;/p&gt;
', NULL, NULL, '''/p'':25C,103C,155C ''access'':66C ''address'':120C ''agent'':37C,51C,94C ''aggreg'':85C ''almost'':39C,100C ''basi'':132C ''case'':60C ''certain'':40C ''connect'':125C ''could'':138C ''count'':79C ''data'':12C ''differ'':34C,153C ''differenti'':134C ''distinct'':41C,102C ''distinguish'':107C ''distribut'':76C ''exampl'':115C ''except'':57C ''fi'':65C ''figur'':145C ''get'':139C ''identifi'':71C,83C ''info'':113C ''ip'':32C,47C,81C,86C,91C,119C ''learn'':24C,143C ''like'':84C ''littl'':15C ''look'':73C ''machin'':23C ''might'':70C ''much'':5C ''need'':111C ''overlap'':96C ''p'':1C,26C,104C ''per'':80C ''point'':67C ''proxi'':62C ''reli'':21C ''session'':28C,43C,78C,87C,135C,148C ''site'':117C ''sophist'':142C ''strong'':131C ''sure'':101C ''time'':98C ''user'':36C,42C,50C,56C,93C,108C,123C,154C ''user-ag'':35C,49C,92C ''usual'':53C ''wi'':64C ''wi-fi'':63C ''would'':110C,127C ''yes'':27C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (94, 1, '2014-05-15 14:41:24.02', 15, 125, 'Does click frequency account for relevance?', '&lt;p&gt;While building a rank, say for a search engine, or a recommendation system, is it valid to rely on click frequency to determine the relevance of an entry?&lt;/p&gt;
', 'recommendation  information-retrieval', NULL, '''/p'':40C ''account'':4A ''build'':13C ''click'':2A,31C ''determin'':34C ''engin'':20C ''entri'':39C ''frequenc'':3A,32C ''inform'':9B ''information-retriev'':8B ''p'':11C ''rank'':15C ''recommend'':7B,23C ''relev'':6A,36C ''reli'':29C ''retriev'':10B ''say'':16C ''search'':19C ''system'':24C ''valid'':27C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10450, 1, '2016-02-29 17:16:57.917', 0, 10, 'sklearn VoteClassifier integrate self-defined classifiers', '&lt;p&gt;I''m trying to integrate several user defined classifiers in sklearn.ensemble.VotingClassifier. More specifically, I followed a Kaggle example to write an XgboostClassifier wrapper class for xgboost with predict(), predict_proba(), fit() etc, passed it to VotingClassifer and called predict_proba() on VC.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;logreg = LogisticRegression(C=0.75,penalty=''l1'')
logreg.fit(X_train, y_train)
xc = XGBoostClassifier(
            eval_metric = ''logloss'',
            num_class = 3,
            nthread = 4,
            eta = 0.1,
            max_depth = 7,
            subsample = 0.9,
            colsample_bytree = 0.35,
            silent = 1,
            early_stopping_rounds=10,
        )
xc.fit(X_train,y_train,num_boost_round=20)
# Create voting classifier
eclf3 = VotingClassifier(estimators=[(''lr'', logreg),(''xc'',xc)],voting=''soft'', weights=[1,2])
eclf3.fit(X_train, y_train)
print(''Ensemble LogLoss {score}''.format(score=log_loss(y_test, eclf3.predict_proba(X_test))))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The Xgboost wrapper class is like below. Credit to &lt;a href="https://www.kaggle.com/tanitter/introducing-kaggle-scripts/grid-search-xgboost-with-scikit-learn/run/23363" rel="nofollow"&gt;this link&lt;/a&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;class XGBoostClassifier():
    def __init__(self, num_boost_round=10, **params):
        self.clf = None
        self.num_boost_round = num_boost_round
        self.params = params
        self.params.update({''objective'': ''multi:softprob''})
    def fit(self, X, y, num_boost_round=None):
        num_boost_round = num_boost_round or self.num_boost_round
        self.label2num = dict((label, i) for i, label in enumerate(sorted(set(y))))
        dtrain = xgb.DMatrix(X, label=[self.label2num[label] for label in y])
        self.clf = xgb.train(params=self.params, dtrain=dtrain, num_boost_round=num_boost_round)
    def predict(self, X):
        num2label = dict((i, label)for label, i in self.label2num.items())
        Y = self.predict_proba(X)
        y = np.argmax(Y, axis=1)
        return np.array([num2label[i] for i in y])
    def predict_proba(self, X):
        dtest = xgb.DMatrix(X)
        return self.clf.predict(dtest)
    def score(self, X, y):
        Y = self.predict_proba(X)
        return 1 / logloss(y, Y)
    def get_params(self, deep=True):
        return self.params
    def set_params(self, **params):
        if ''num_boost_round'' in params:
            self.num_boost_round = params.pop(''num_boost_round'')
        if ''objective'' in params:
            del params[''objective'']
        self.params.update(params)
        return self
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;But it seems to me that a user-written class that implements several functions that predict() and predict_proba() cannot be directly used by VoteClassifier. The ensemble returns the following error:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Traceback (most recent call last):
  File "/home/bitnami/PycharmProjects/xgboostTest/ensemble.py", line 231, in &amp;lt;module&amp;gt;
ec.fit(trainSample,trainLabel,testSample,testLabel)
  File "/home/bitnami/PycharmProjects/xgboostTest/ensemble.py", line 155, in fit
print(''Ensemble LogLoss {score}''.format(score=log_loss(y_test, eclf3.predict_proba(X_test))))
  File "/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/voting_classifier.py", line 166, in _predict_proba
avg = np.average(self._collect_probas(X), axis=0, weights=self.weights)
  File "/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/voting_classifier.py", line 162, in _collect_probas
return np.asarray([clf.predict_proba(X) for clf in self.estimators_])
AttributeError: ''NoneType'' object has no attribute ''predict_proba''
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;After some checking I found the XgboostClassifer was lost after the eclf3.fit(). The eclf3.estimators_ is &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[LogisticRegression(C=0.75, class_weight=None, dual=False, fit_intercept=True,
      intercept_scaling=1, max_iter=100, multi_class=''ovr'', n_jobs=1,
      penalty=''l1'', random_state=None, solver=''liblinear'', tol=0.0001,
      verbose=0, warm_start=False), None]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Please kindly advise whether it is possible to integrate user defined classes in the VotingClassifier. I''m not good with Python. I checked the code of VC but I see no requirement on class inheritance etc. &lt;/p&gt;

&lt;p&gt;Thanks in advance!&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (119, 4, '2014-05-17 13:41:20.283', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (95, 2, '2014-05-15 15:06:24.6', 4, NULL, NULL, '&lt;p&gt;Is it valid to &lt;em&gt;use&lt;/em&gt; click frequency, then &lt;strong&gt;yes&lt;/strong&gt;. Is it valid to use &lt;strong&gt;only&lt;/strong&gt; the click frequency, then probably &lt;strong&gt;no&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Search relevance is much more complicated than just one metric. &lt;a href="http://www.amazon.ca/s/ref=nb_sb_noss?url=search-alias=aps&amp;amp;field-keywords=search%20ranking" rel="nofollow"&gt;There are entire books on the subject&lt;/a&gt;. Extending this answer beyond a simple yes/no would likely make the answer far too broad (and opinionated)&lt;/p&gt;
', NULL, NULL, '''/a'':57C ''/em'':8C ''/p'':31C,75C ''/s/ref=nb_sb_noss?url=search-alias=aps&amp;amp;field-keywords=search%20ranking'':47C ''/strong'':14C,22C,30C ''answer'':60C,69C ''beyond'':61C ''book'':53C ''broad'':72C ''click'':9C,24C ''complic'':38C ''em'':6C ''entir'':52C ''extend'':58C ''far'':70C ''frequenc'':10C,25C ''href'':44C ''like'':66C ''make'':67C ''metric'':42C ''much'':36C ''nofollow'':49C ''one'':41C ''opinion'':74C ''p'':1C,32C ''probabl'':27C ''rel'':48C ''relev'':34C ''search'':33C ''simpl'':63C ''strong'':12C,20C,28C ''subject'':56C ''use'':7C,19C ''valid'':4C,17C ''would'':65C ''www.amazon.ca'':46C ''www.amazon.ca/s/ref=nb_sb_noss?url=search-alias=aps&amp;amp;field-keywords=search%20ranking'':45C ''yes'':13C ''yes/no'':64C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (273, 2, '2014-06-10 12:10:28.713', 1, NULL, NULL, '&lt;p&gt;You can apply data science techniques to data on one machine so the answer to the question as the OP phrased it, is no.&lt;/p&gt;
', NULL, NULL, '''/p'':26C ''answer'':15C ''appli'':4C ''data'':5C,9C ''machin'':12C ''one'':11C ''op'':21C ''p'':1C ''phrase'':22C ''question'':18C ''scienc'':6C ''techniqu'':7C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (97, 2, '2014-05-15 17:14:36.817', 13, NULL, NULL, '&lt;p&gt;&lt;a href="http://research.microsoft.com/pubs/169639/cikm-clickpatterns.pdf"&gt;Depends on the user''s intent&lt;/a&gt;, for starters. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.seoresearcher.com/distribution-of-clicks-on-googles-serps-and-eye-tracking-analysis.htm"&gt;Users normally only view the first set of links&lt;/a&gt;, which means that unless the link is viewable, it''s not getting clicks; meaning you''d have to be positive those are the best links, otherwise the clicks are most likely going to reflect placement, not relevance. For example, here''s a &lt;a href="http://www.seoresearcher.com/distribution-of-clicks-on-googles-serps-and-eye-tracking-analysis.htm"&gt;click and attention distribution heat-map&lt;/a&gt; for  Google search results:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/8kO5S.jpg" alt="Google SEPR Click and Attention distribution âheat-mapâ"&gt;&lt;/p&gt;

&lt;p&gt;Further, using click frequency to account for relevance is not a direct measure of the resource''s relevance. Also, using clicks is problematic, since issues like click-inflation, click-fraud, etc. will pop-up and are hard to counter. &lt;/p&gt;

&lt;p&gt;That said, if you''re interested in using user interaction to model relevance, I would suggest you attempt to measure post-click engagement, not how users respond to search results; see "&lt;a href="http://www.youtube.com/watch?v=BsCeNCVb-d8"&gt;YouTube''s head of engineering speaking about clicks vs engagement&lt;/a&gt;" for more information, though note that the &lt;a href="http://www.orbitmedia.com/blog/ideal-blog-post-length/"&gt;size itself of the content is a factor&lt;/a&gt; too.&lt;/p&gt;

&lt;p&gt;Might be worth noting that historically Google was known for &lt;a href="http://en.wikipedia.org/wiki/PageRank"&gt;PageRank algorithm&lt;/a&gt; though it''s possible your intent is only to review click-streams, so I won''t delve &lt;a href="https://www.google.com/search?q=google%20ranking%20factors"&gt;Google ranking factors&lt;/a&gt;; if you are interested in the Google''s approach, you might find a review of &lt;a href="http://static.googleusercontent.com/media/www.google.com/en/us/insidesearch/howsearchworks/assets/searchqualityevaluatorguidelines.pdf"&gt;Google''s Search Quality Rating Guidelines&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/8ko5s.jpg'':98C ''/a'':13C,32C,87C,203C,224C,245C,272C,299C ''/blog/ideal-blog-post-length/'':215C ''/distribution-of-clicks-on-googles-serps-and-eye-tracking-analysis.htm'':22C,79C ''/media/www.google.com/en/us/insidesearch/howsearchworks/assets/searchqualityevaluatorguidelines.pdf'':292C ''/p'':16C,92C,110C,154C,226C,300C ''/pubs/169639/cikm-clickpatterns.pdf'':6C ''/search?q=google%20ranking%20factors'':268C ''/watch?v=bscencvb-d8'':192C ''/wiki/pagerank'':242C ''account'':117C ''algorithm'':244C ''also'':130C ''alt'':99C ''approach'':281C ''attempt'':173C ''attent'':82C,104C ''best'':56C ''click'':45C,60C,80C,102C,114C,132C,139C,142C,178C,200C,257C ''click-fraud'':141C ''click-infl'':138C ''click-stream'':256C ''content'':220C ''counter'':153C ''d'':48C ''delv'':263C ''depend'':7C ''direct'':123C ''distribut'':83C,105C ''en.wikipedia.org'':241C ''en.wikipedia.org/wiki/pagerank'':240C ''engag'':179C,202C ''engin'':197C ''etc'':144C ''exampl'':71C ''factor'':223C,271C ''find'':284C ''first'':28C ''fraud'':143C ''frequenc'':115C ''get'':44C ''go'':64C ''googl'':89C,100C,234C,269C,279C,293C ''guidelin'':298C ''hard'':151C ''head'':195C ''heat'':85C,108C ''heat-map'':84C ''heat-mapâ'':107C ''histor'':233C ''href'':3C,19C,76C,189C,212C,239C,265C,289C ''i.stack.imgur.com'':97C ''i.stack.imgur.com/8ko5s.jpg'':96C ''img'':94C ''inflat'':140C ''inform'':206C ''intent'':12C,251C ''interact'':165C ''interest'':161C,276C ''issu'':136C ''known'':236C ''like'':63C,137C ''link'':31C,38C,57C ''map'':86C ''mapâ'':109C ''mean'':34C,46C ''measur'':124C,175C ''might'':228C,283C ''model'':167C ''normal'':24C ''note'':208C,231C ''otherwis'':58C ''p'':1C,17C,93C,111C,155C,227C ''pagerank'':243C ''placement'':67C ''pop'':147C ''pop-up'':146C ''posit'':52C ''possibl'':249C ''post'':177C ''post-click'':176C ''problemat'':134C ''qualiti'':296C ''rank'':270C ''rate'':297C ''re'':160C ''reflect'':66C ''relev'':69C,119C,129C,168C ''research.microsoft.com'':5C ''research.microsoft.com/pubs/169639/cikm-clickpatterns.pdf'':4C ''resourc'':127C ''respond'':183C ''result'':91C,186C ''review'':255C,286C ''said'':157C ''search'':90C,185C,295C ''see'':187C ''sepr'':101C ''set'':29C ''sinc'':135C ''size'':216C ''speak'':198C ''src'':95C ''starter'':15C ''static.googleusercontent.com'':291C ''static.googleusercontent.com/media/www.google.com/en/us/insidesearch/howsearchworks/assets/searchqualityevaluatorguidelines.pdf'':290C ''stream'':258C ''suggest'':171C ''though'':207C,246C ''unless'':36C ''use'':113C,131C,163C ''user'':10C,23C,164C,182C ''view'':26C ''viewabl'':40C ''vs'':201C ''won'':261C ''worth'':230C ''would'':170C ''www.google.com'':267C ''www.google.com/search?q=google%20ranking%20factors'':266C ''www.orbitmedia.com'':214C ''www.orbitmedia.com/blog/ideal-blog-post-length/'':213C ''www.seoresearcher.com'':21C,78C ''www.seoresearcher.com/distribution-of-clicks-on-googles-serps-and-eye-tracking-analysis.htm'':20C,77C ''www.youtube.com'':191C ''www.youtube.com/watch?v=bscencvb-d8'':190C ''youtub'':193C ''â'':106C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (101, 2, '2014-05-15 21:41:22.703', 7, NULL, NULL, '&lt;p&gt;One possibility here (and this is really an extension of what Sean Owen posted) is to define a "stable user."&lt;/p&gt;

&lt;p&gt;For the given info you have you can imagine making a user_id that is a hash of ip and some user agent info (pseudo code):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;uid = MD5Hash(ip + UA.device + UA.model)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then you flag these ids with "stable" or "unstable" based on usage heuristics you observe for your users.  This can be a threshold of # of visits in a given time window, length of time their cookies persist, some end action on your site (I realize this wasn''t stated in your original log), etc...&lt;/p&gt;

&lt;p&gt;The idea here is to separate the users that don''t drop cookies from those that do.&lt;/p&gt;

&lt;p&gt;From here you can attribute session_ids to stable uids from your logs.  You will then have "left over" session_ids for unstable users that you are relatively unsure about.  You may be over or under counting sessions, attributing behavior to multiple people when there is only one, etc...  But this is at least limited to the users you are now "less certain" about.&lt;/p&gt;

&lt;p&gt;You then perform analytics on your stable group and project that to the unstable group.  Take a user count for example, you know the total # of sessions, but you are unsure of how many users generated those sessions.  You can find the # sessions / unique stable user and use this to project the "estimated" number of unique users in the unstable group since you know the number of sessions attributed to that group.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;projected_num_unstable_users = num_sess_unstable / num_sess_per_stable_uid
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This doesn''t help with per user level investigation on unstable users but you can at least get some mileage out of a cohort of stable users that persist for some time.  You can, by various methods, project behavior and counts into the unstable group.  The above is a simple example of something you might want to know.  The general idea is again to define a set of users you are confident persist, measure what you want to measure, and use certain ground truths (num searches, visits, clicks, etc...) to project into the unknown user space and estimate counts for them.&lt;/p&gt;

&lt;p&gt;This is a longstanding problem in unique user counting, logging, etc... for services that don''t require log in.&lt;/p&gt;
', NULL, NULL, '''/code'':58C,289C ''/p'':22C,50C,115C,134C,200C,274C,393C,414C ''/pre'':59C,290C ''action'':100C ''agent'':46C ''analyt'':205C ''attribut'':140C,174C,270C ''base'':70C ''behavior'':175C,330C ''certain'':198C,373C ''click'':379C ''code'':49C,52C,276C ''cohort'':315C ''confid'':363C ''cooki'':96C,129C ''count'':172C,220C,332C,390C,403C ''defin'':18C,356C ''doesn'':293C ''drop'':128C ''end'':99C ''estim'':254C,389C ''etc'':114C,184C,380C,405C ''exampl'':222C,342C ''extens'':10C ''find'':242C ''flag'':63C ''general'':351C ''generat'':237C ''get'':309C ''given'':26C,89C ''ground'':374C ''group'':209C,216C,262C,273C,336C ''hash'':40C ''help'':295C ''heurist'':73C ''id'':36C,65C,142C,156C ''idea'':118C,352C ''imagin'':32C ''info'':27C,47C ''investig'':300C ''ip'':42C,55C ''know'':224C,265C,349C ''least'':189C,308C ''left'':153C ''length'':92C ''less'':197C ''level'':299C ''limit'':190C ''log'':113C,148C,404C,412C ''longstand'':398C ''make'':33C ''mani'':235C ''may'':167C ''md5hash'':54C ''measur'':365C,370C ''method'':328C ''might'':346C ''mileag'':311C ''multipl'':177C ''num'':278C,281C,284C,376C ''number'':255C,267C ''observ'':75C ''one'':2C,183C ''origin'':112C ''owen'':14C ''p'':1C,23C,60C,116C,135C,201C,291C,394C ''peopl'':178C ''per'':286C,297C ''perform'':204C ''persist'':97C,320C,364C ''possibl'':3C ''post'':15C ''pre'':51C,275C ''problem'':399C ''project'':211C,252C,277C,329C,382C ''pseudo'':48C ''realiz'':105C ''realli'':8C ''relat'':163C ''requir'':411C ''sean'':13C ''search'':377C ''separ'':122C ''servic'':407C ''sess'':282C,285C ''session'':141C,155C,173C,228C,239C,244C,269C ''set'':358C ''simpl'':341C ''sinc'':263C ''site'':103C ''someth'':344C ''space'':387C ''stabl'':20C,67C,144C,208C,246C,287C,317C ''state'':109C ''take'':217C ''threshold'':83C ''time'':90C,94C,323C ''total'':226C ''truth'':375C ''ua.device'':56C ''ua.model'':57C ''uid'':53C,145C,288C ''uniqu'':245C,257C,401C ''unknown'':385C ''unstabl'':69C,158C,215C,261C,279C,283C,302C,335C ''unsur'':164C,232C ''usag'':72C ''use'':249C,372C ''user'':21C,35C,45C,78C,124C,159C,193C,219C,236C,247C,258C,280C,298C,303C,318C,360C,386C,402C ''various'':327C ''visit'':86C,378C ''want'':347C,368C ''wasn'':107C ''window'':91C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (102, 1, '2014-05-16 05:09:33.557', 6, 214, 'What is the Best NoSQL backend for a mobile game', '&lt;p&gt;What is the best noSQL backend to use for a mobile game? Users can make a lot of servers requests, it needs also to retrieve users'' historical records (like app purchasing) and analytics of usage behavior.&lt;/p&gt;
', 'nosql  performance', NULL, '''/p'':50C ''also'':36C ''analyt'':46C ''app'':43C ''backend'':6A,19C ''behavior'':49C ''best'':4A,17C ''game'':10A,25C ''histor'':40C ''like'':42C ''lot'':30C ''make'':28C ''mobil'':9A,24C ''need'':35C ''nosql'':5A,11B,18C ''p'':13C ''perform'':12B ''purchas'':44C ''record'':41C ''request'':33C ''retriev'':38C ''server'':32C ''usag'':48C ''use'':21C ''user'':26C,39C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (103, 1, '2014-05-16 14:26:12.27', 12, 473, 'Clustering based on similarity scores', '&lt;p&gt;Assume that we have a set of elements &lt;em&gt;E&lt;/em&gt; and a similarity (&lt;strong&gt;not distance&lt;/strong&gt;) function &lt;em&gt;sim(ei, ej)&lt;/em&gt; between two elements &lt;em&gt;ei,ej â E&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;How could we (efficiently) cluster the elements of &lt;em&gt;E&lt;/em&gt;, using &lt;em&gt;sim&lt;/em&gt;?&lt;/p&gt;

&lt;p&gt;&lt;em&gt;k&lt;/em&gt;-means, for example, requires a given &lt;em&gt;k&lt;/em&gt;, Canopy Clustering requires two threshold values. What if we don''t want such predefined parameters?&lt;/p&gt;

&lt;p&gt;Note, that &lt;em&gt;sim&lt;/em&gt; is not neccessarily a metric (i.e. the triangle inequality may, or may not hold). Moreover, it doesn''t matter if the clusters are disjoint (partitions of &lt;em&gt;E&lt;/em&gt;).&lt;/p&gt;
', 'clustering  algorithms  similarity', NULL, '''/em'':20C,33C,42C,55C,59C,64C,73C,95C,124C ''/p'':43C,60C,89C,125C ''/strong'':27C ''algorithm'':7B ''assum'':10C ''base'':2A ''canopi'':74C ''cluster'':1A,6B,49C,75C,117C ''could'':46C ''disjoint'':119C ''distanc'':26C ''doesn'':112C ''e'':19C,41C,54C,123C ''effici'':48C ''ei'':31C,38C ''ej'':32C,39C ''element'':17C,36C,51C ''em'':18C,29C,37C,53C,57C,62C,71C,93C,122C ''exampl'':67C ''function'':28C ''given'':70C ''hold'':109C ''i.e'':101C ''inequ'':104C ''k'':63C,72C ''matter'':114C ''may'':105C,107C ''mean'':65C ''metric'':100C ''moreov'':110C ''neccessarili'':98C ''note'':91C ''p'':9C,44C,61C,90C ''paramet'':88C ''partit'':120C ''predefin'':87C ''requir'':68C,76C ''score'':5A ''set'':15C ''sim'':30C,58C,94C ''similar'':4A,8B,23C ''strong'':24C ''threshold'':78C ''triangl'':103C ''two'':35C,77C ''use'':56C ''valu'':79C ''want'':85C ''â'':40C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (104, 5, '2014-05-16 15:35:51.42', 0, NULL, NULL, '&lt;h2&gt;Use the &lt;a href="/questions/tagged/definitions" class="post-tag" title="show questions tagged &amp;#39;definitions&amp;#39;" rel="tag"&gt;definitions&lt;/a&gt; tag when:&lt;/h2&gt;

&lt;p&gt;You think we should create an official definition.&lt;/p&gt;

&lt;p&gt;An existing Tag Wiki needs a more precise definition to avoid confusion and we need to create consensus before an edit.&lt;/p&gt;

&lt;p&gt;(rough draft - needs filling out)&lt;/p&gt;
', NULL, NULL, '''/a'':21C ''/h2'':24C ''/p'':34C,57C,64C ''/questions/tagged/definitions'':6C ''39'':15C,17C ''avoid'':46C ''class'':7C ''confus'':47C ''consensus'':53C ''creat'':30C,52C ''definit'':16C,20C,33C,44C ''draft'':60C ''edit'':56C ''exist'':37C ''fill'':62C ''h2'':1C ''href'':5C ''need'':40C,50C,61C ''offici'':32C ''p'':25C,35C,58C ''post'':9C ''post-tag'':8C ''precis'':43C ''question'':13C ''rel'':18C ''rough'':59C ''show'':12C ''tag'':10C,14C,19C,22C,38C ''think'':27C ''titl'':11C ''use'':2C ''wiki'':39C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (105, 4, '2014-05-16 15:35:51.42', 0, NULL, NULL, 'a discussion (meta) tag used when there exists *disagreement* or *confusion* about the everyday meaning of a term or phrase.', NULL, NULL, '''confus'':11C ''disagr'':9C ''discuss'':2C ''everyday'':14C ''exist'':8C ''mean'':15C ''meta'':3C ''phrase'':20C ''tag'':4C ''term'':18C ''use'':5C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (106, 2, '2014-05-16 16:25:58.25', 2, NULL, NULL, '&lt;p&gt;There are many overlaps between data mining and datascience. I would say that people with the role of datamining are concerned with data collection and the extraction of features from unfiltered, unorganised and mostly raw/wild datasets. Some very important data may be difficult to extract, not do to the implementation issues but because it may have foreign artifacts. &lt;/p&gt;

&lt;p&gt;Eg. if I needed someone to look at financial data from written tax returns in the 70s which were scanned and machine read to find out if people saved more on car insurance; a dataminer would be the person to get. &lt;/p&gt;

&lt;p&gt;If I needed someone to examine the influence Nike''s Twitter profile in the tweets of Brazil and identify key positive features from the profile, I would look for a datascientist.&lt;/p&gt;
', NULL, NULL, '''/p'':60C,103C,136C ''70s'':78C ''artifact'':59C ''brazil'':121C ''car'':93C ''collect'':25C ''concern'':22C ''data'':7C,24C,41C,71C ''datamin'':20C,96C ''datasci'':10C ''datascientist'':135C ''dataset'':37C ''difficult'':44C ''eg'':62C ''examin'':110C ''extract'':28C,46C ''featur'':30C,126C ''financi'':70C ''find'':86C ''foreign'':58C ''get'':102C ''identifi'':123C ''implement'':51C ''import'':40C ''influenc'':112C ''insur'':94C ''issu'':52C ''key'':124C ''look'':68C,132C ''machin'':83C ''mani'':4C ''may'':42C,56C ''mine'':8C ''most'':35C ''need'':65C,107C ''nike'':113C ''overlap'':5C ''p'':1C,61C,104C ''peopl'':15C,89C ''person'':100C ''posit'':125C ''profil'':116C,129C ''raw/wild'':36C ''read'':84C ''return'':75C ''role'':18C ''save'':90C ''say'':13C ''scan'':81C ''someon'':66C,108C ''tax'':74C ''tweet'':119C ''twitter'':115C ''unfilt'':32C ''unorganis'':33C ''would'':12C,97C,131C ''written'':73C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (107, 1, '2014-05-16 20:07:50.983', 9, 69, 'Opensource tools for help in mining stream of leader board scores', '&lt;p&gt;Consider a stream containing &lt;a href="http://en.m.wikipedia.org/wiki/Tuple" rel="nofollow"&gt;tuples&lt;/a&gt; &lt;code&gt;(user, new_score)&lt;/code&gt; representing users'' scores in an online game. The stream could have 100-1,000 new elements per second. The game has 200K to 300K unique players. &lt;/p&gt;

&lt;p&gt;I would like to have some standing queries like: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Which players posted more than x scores in a sliding window of one hour&lt;/li&gt;
&lt;li&gt;Which players gained x% score in a sliding window of one hour&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;My question is which open source tools can I employ to jumpstart this project? I am considering &lt;a href="http://esper.codehaus.org/" rel="nofollow"&gt;Esper&lt;/a&gt; at the moment. &lt;/p&gt;

&lt;p&gt;Note: I have just completed reading "Mining Data Streams" (chapter 4 of &lt;a href="http://infolab.stanford.edu/~ullman/mmds.html" rel="nofollow"&gt;Mining of Massive Datasets&lt;/a&gt;) and I am quite new to mining data streams.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (109, 4, '2014-05-16 20:24:38.98', 0, NULL, NULL, 'An activity that seeks patterns in a continuous stream of data elements, usually involving summarizing the stream in some way.', NULL, NULL, '''activ'':2C ''continu'':8C ''data'':11C ''element'':12C ''involv'':14C ''pattern'':5C ''seek'':4C ''stream'':9C,17C ''summar'':15C ''usual'':13C ''way'':20C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (270, 2, '2014-06-10 11:39:19.603', 3, NULL, NULL, '&lt;p&gt;Machine learning and statistical techniques can improve the forecast, but nobody can predict the real result.&lt;/p&gt;

&lt;p&gt;There was a kaggle competition a few month ago about &lt;a href="https://www.kaggle.com/c/march-machine-learning-mania" rel="nofollow"&gt;predicting the 2014 NCAA Tournament&lt;/a&gt;. You can read the Competition Forum to get a better idea on what people did and what results did they achieve.&lt;/p&gt;
', NULL, NULL, '''/a'':42C ''/c/march-machine-learning-mania'':34C ''/p'':18C,64C ''2014'':39C ''achiev'':63C ''ago'':28C ''better'':52C ''competit'':24C,47C ''forecast'':10C ''forum'':48C ''get'':50C ''href'':31C ''idea'':53C ''improv'':8C ''kaggl'':23C ''learn'':3C ''machin'':2C ''month'':27C ''ncaa'':40C ''nobodi'':12C ''nofollow'':36C ''p'':1C,19C ''peopl'':56C ''predict'':14C,37C ''read'':45C ''real'':16C ''rel'':35C ''result'':17C,60C ''statist'':5C ''techniqu'':6C ''tournament'':41C ''www.kaggle.com'':33C ''www.kaggle.com/c/march-machine-learning-mania'':32C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (147, 4, '2014-05-18 15:01:24.08', 0, NULL, NULL, 'Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of humanâcomputer interaction. Many challenges in NLP involve natural language understanding, that is, enabling computers to derive meaning from human or natural language input, and others involve natural language generation.', NULL, NULL, '''area'':32C ''artifici'':11C ''challeng'':38C ''comput'':9C,20C,35C,48C ''concern'':15C ''deriv'':50C ''enabl'':47C ''field'':7C ''generat'':63C ''human'':22C,53C ''humanâ'':34C ''input'':57C ''intellig'':12C ''interact'':18C,36C ''involv'':41C,60C ''languag'':2C,24C,43C,56C,62C ''linguist'':14C ''mani'':37C ''mean'':51C ''natur'':1C,23C,42C,55C,61C ''nlp'':4C,27C,40C ''other'':59C ''process'':3C ''relat'':29C ''scienc'':10C ''understand'':44C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8664, 1, '2015-10-30 23:10:31.113', 1, 44, 'Is this a correct way improving a statistical model?', '&lt;p&gt;I am building a fair amount of statistical models - text classifiers and sequence taggers. The statistical models are linear in features - Logistic Regression and linear chain first order CRF.&lt;/p&gt;

&lt;p&gt;The models undergo several stages starting from a rough, initial version all the way to a mature production model. Given a specification and some dataset (often with incomplete annotation, noisy labels and features), I build the first model from a small, hand labeled dataset. This dataset is then grown iteratively by adding more labeled examples (e.g. using crowdsourcing).&lt;/p&gt;

&lt;p&gt;Then comes the test phase. The statistical model is actively evaluated within a larger system by testers. I, as a developer, start getting the failure reports on their input. I am supposed to fix the model, update it in the system and deploy it.&lt;/p&gt;

&lt;p&gt;Every individual error report is fairly small to manually go through and fix every misclassification. Obviously it is incorrect just to simply add the failure cases with correct labels to the training set. You never know if the model learned that new pattern.
I do this by comparing the failure cases with a vast unlabeled dataset of examples (real usage data but no labels) and extract similar examples. I label those similar examples, add to the training set and re-train the model. Then I check if the new model fixes the originally reported issues.&lt;/p&gt;

&lt;p&gt;My question is: Is this a correct approach to build and continuously improve the statistical models? May be there are some hidden caveats that will bias my model in some sense?&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (111, 2, '2014-05-17 03:07:59.707', 7, NULL, NULL, '&lt;p&gt;Some factors you might consider:&lt;/p&gt;

&lt;p&gt;Developer familiarity: go with whatever you or your developers are familiar with.  Mongo, Couch, Riak, DynamoDB etc all have their strengths but all should do ok here, so rather than going for an unfamiliar solution that might be slightly better go for familiar and save a bunch of development time.&lt;/p&gt;

&lt;p&gt;Ease of cloud deployment:  for example, if you are using Amazon AWS, then DynamoDB is likely an excellent choice.  Sure, you could use Mongo on AWS, but why bother?  Other cloud providers have their own preferred db, for example if you are using Google AppEngine, it makes sense to use BigTable or Cloud Datastore. &lt;/p&gt;

&lt;p&gt;Your use case seems both well suited to NoSQL and not very challenging since your data has a natural partition by user.  I think you''d be technically ok with anything, which is why I''m mainly covering other factors.&lt;/p&gt;
', NULL, NULL, '''/p'':7C,59C,115C,157C ''amazon'':71C ''anyth'':147C ''appengin'':105C ''aw'':72C,86C ''better'':48C ''bigtabl'':111C ''bother'':89C ''bunch'':55C ''case'':119C ''challeng'':129C ''choic'':79C ''cloud'':63C,91C,113C ''consid'':6C ''couch'':22C ''could'':82C ''cover'':154C ''d'':142C ''data'':132C ''datastor'':114C ''db'':97C ''deploy'':64C ''develop'':9C,17C,57C ''dynamodb'':24C,74C ''eas'':61C ''etc'':25C ''exampl'':66C,99C ''excel'':78C ''factor'':3C,156C ''familiar'':10C,19C,51C ''go'':11C,39C,49C ''googl'':104C ''like'':76C ''m'':152C ''main'':153C ''make'':107C ''might'':5C,45C ''mongo'':21C,84C ''natur'':135C ''nosql'':125C ''ok'':34C,145C ''p'':1C,8C,60C,116C ''partit'':136C ''prefer'':96C ''provid'':92C ''rather'':37C ''riak'':23C ''save'':53C ''seem'':120C ''sens'':108C ''sinc'':130C ''slight'':47C ''solut'':43C ''strength'':29C ''suit'':123C ''sure'':80C ''technic'':144C ''think'':140C ''time'':58C ''unfamiliar'':42C ''use'':70C,83C,103C,110C,118C ''user'':138C ''well'':122C ''whatev'':13C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (112, 2, '2014-05-17 04:18:10.02', 7, NULL, NULL, '&lt;p&gt;This isn''t a full solution, but you may want to look into &lt;a href="http://www.orientechnologies.com/"&gt;OrientDB&lt;/a&gt; as part of your stack. Orient is a Graph-Document database server written entirely in Java. &lt;/p&gt;

&lt;p&gt;In graph databases, relationships are considered first class citizens and therefore traversing those relationships can be done pretty quickly. Orient is also a document database which would allow you the kind of schema-free architecture it sounds like you would need. The real reason I suggest Orient, however, is because of its extensiblity. It supports streaming via sockets, and the entire database can be embedded into another application. Finally, it can be scaled efficiently and/or can work entirely through memory. So, with some Java expertise, you can actually run your preset queries against the database in memory.&lt;/p&gt;

&lt;p&gt;We are doing something similar. In creating an app/site for social science research collaboration, we found ourselves with immensely complex data models. We ended up writing several of the queries using the Gremlin Traversal Language (a subset of Groovy, which is, of course, Java at its heart), and then exposing those queries through the binary connection server of the OrientDB. So, the client opens a TCP socket, sends a short binary message, and the query is executing in Java directly against the in-memory database.&lt;/p&gt;

&lt;p&gt;OrientDB also supports writing function queries in Javascript, and you can use Node.js to interact directly with an Orient instance.&lt;/p&gt;

&lt;p&gt;For something of this size, I would want to use Orient in conjunction with Hadoop or something like that. You can also use Orient in conjunction with esper.&lt;/p&gt;

&lt;p&gt;Consider:
An introduction to orient: &lt;a href="http://www.sitepoint.com/a-look-at-orientdb-the-graph-document-nosql/"&gt;http://www.sitepoint.com/a-look-at-orientdb-the-graph-document-nosql/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Complex, real-time queries: &lt;a href="http://www.gft-blog.com/business-trends/leveraging-real-time-scoring-through-bigdata-to-detect-insurance-fraud/"&gt;http://www.gft-blog.com/business-trends/leveraging-real-time-scoring-through-bigdata-to-detect-insurance-fraud/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;A discussion about streaming options with java and orient: &lt;a href="https://github.com/orientechnologies/orientdb/issues/1227"&gt;https://github.com/orientechnologies/orientdb/issues/1227&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (113, 1, '2014-05-17 04:53:03.913', 10, 113, 'When a relational database has better performance than a no relational', '&lt;p&gt;When a relational database like mySQL has better performance than a no relational, like mongo?&lt;/p&gt;

&lt;p&gt;I saw a question on Quora other day, about why Quora still uses mySQL as their backend. And how their performance is still good.&lt;/p&gt;
', 'bigdata  nosql  databases', NULL, '''/p'':31C,57C ''backend'':49C ''better'':6A,23C ''bigdata'':12B ''databas'':4A,14B,19C ''day'':40C ''good'':56C ''like'':20C,29C ''mongo'':30C ''mysql'':21C,46C ''nosql'':13B ''p'':15C,32C ''perform'':7A,24C,53C ''question'':36C ''quora'':38C,43C ''relat'':3A,11A,18C,28C ''saw'':34C ''still'':44C,55C ''use'':45C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (115, 1, '2014-05-17 08:45:08.42', 6, 174, 'Is there any APIs for crawling abstract of paper?', '&lt;p&gt;If I have a very long list of paper names, how could I get abstract of these papers from internet or any database?&lt;/p&gt;

&lt;p&gt;The paper names are like "Assessment of Utility in Web Mining for the Domain of Public Health".&lt;/p&gt;

&lt;p&gt;Does any one know any API that can give me a solution? I tried to crawl google scholar, however, google blocked my crawler.&lt;/p&gt;
', 'data-mining  machine-learning', NULL, '''/p'':40C,59C,84C ''abstract'':7A,31C ''api'':4A,66C ''assess'':47C ''block'':81C ''could'':28C ''crawl'':6A,76C ''crawler'':83C ''data'':11B ''data-min'':10B ''databas'':39C ''domain'':55C ''get'':30C ''give'':69C ''googl'':77C,80C ''health'':58C ''howev'':79C ''internet'':36C ''know'':64C ''learn'':15B ''like'':46C ''list'':23C ''long'':22C ''machin'':14B ''machine-learn'':13B ''mine'':12B,52C ''name'':26C,44C ''one'':63C ''p'':16C,41C,60C ''paper'':9A,25C,34C,43C ''public'':57C ''scholar'':78C ''solut'':72C ''tri'':74C ''util'':49C ''web'':51C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (116, 1, '2014-05-17 09:16:18.823', 23, 1294, 'Machine learning techniques for estimating users'' age based on Facebook sites they like', '&lt;p&gt;I have a database from my Facebook application and I am trying to use machine learning to estimate users'' age based on what Facebook sites they like.&lt;/p&gt;

&lt;p&gt;There are three crucial characteristics of my database:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;the age distribution in my training set (12k of users in sum) is skewed towards younger users (i.e. I have 1157 users aged 27, and 23 users aged 65);&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;many sites have no more than 5 likers (I filtered out the FB sites with less than 5 likers).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;there''s many more features than samples.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So, my questions are: what strategy would you suggest to prepare the data for further analysis? Should I perform some sort of dimensionality reduction? Which ML method would be most appropriate to use in this case?&lt;/p&gt;

&lt;p&gt;I mainly use Python, so Python-specific hints would be greatly appreciated.&lt;/p&gt;
', 'machine-learning  dimensionality-reduction  python', NULL, '''/li'':93C,116C,127C ''/p'':49C,59C,92C,115C,126C,166C,181C ''/ul'':128C ''1157'':83C ''12k'':70C ''23'':88C ''27'':86C ''5'':102C,113C ''65'':91C ''age'':7A,41C,64C,85C,90C ''analysi'':145C ''applic'':29C ''appreci'':180C ''appropri'':160C ''base'':8A,42C ''case'':165C ''characterist'':55C ''crucial'':54C ''data'':142C ''databas'':25C,58C ''dimension'':18B,152C ''dimensionality-reduct'':17B ''distribut'':65C ''estim'':5A,39C ''facebook'':10A,28C,45C ''fb'':108C ''featur'':123C ''filter'':105C ''great'':179C ''hint'':176C ''i.e'':80C ''learn'':2A,16B,37C ''less'':111C ''li'':61C,94C,117C ''like'':13A,48C ''liker'':103C,114C ''machin'':1A,15B,36C ''machine-learn'':14B ''main'':169C ''mani'':96C,121C ''method'':156C ''ml'':155C ''p'':21C,50C,62C,95C,118C,129C,167C ''perform'':148C ''prepar'':140C ''python'':20B,171C,174C ''python-specif'':173C ''question'':132C ''reduct'':19B,153C ''sampl'':125C ''set'':69C ''site'':11A,46C,97C,109C ''skew'':76C ''sort'':150C ''specif'':175C ''strategi'':135C ''suggest'':138C ''sum'':74C ''techniqu'':3A ''three'':53C ''toward'':77C ''train'':68C ''tri'':33C ''ul'':60C ''use'':35C,162C,170C ''user'':6A,40C,72C,79C,84C,89C ''would'':136C,157C,177C ''younger'':78C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (148, 5, '2014-05-18 15:08:08.913', 0, NULL, NULL, '&lt;p&gt;In Computer science and Technologies The Data is the most important part.
Since to work with data in an efficient manner to store the data in mediums and reuse, we use some techniques which named in general indexing.&lt;/p&gt;

&lt;p&gt;This tag interests with these techniques, efficiency, and anything about using stored data with indexing&lt;/p&gt;
', NULL, NULL, '''/p'':40C,57C ''anyth'':50C ''comput'':3C ''data'':8C,18C,26C,54C ''effici'':21C,48C ''general'':38C ''import'':12C ''index'':39C,56C ''interest'':44C ''manner'':22C ''medium'':28C ''name'':36C ''p'':1C,41C ''part'':13C ''reus'':30C ''scienc'':4C ''sinc'':14C ''store'':24C,53C ''tag'':43C ''techniqu'':34C,47C ''technolog'':6C ''use'':32C,52C ''work'':16C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (118, 5, '2014-05-17 13:41:20.283', 0, NULL, NULL, '&lt;p&gt;&lt;strong&gt;NoSQL&lt;/strong&gt; (sometimes expanded to "not only &lt;a href="/questions/tagged/sql" class="post-tag" title="show questions tagged &amp;#39;sql&amp;#39;" rel="tag"&gt;sql&lt;/a&gt;") is a broad class of database management systems that differ from the classic model of the relational database management system (&lt;a href="/questions/tagged/rdbms" class="post-tag" title="show questions tagged &amp;#39;rdbms&amp;#39;" rel="tag"&gt;rdbms&lt;/a&gt;) in some significant ways.&lt;/p&gt;

&lt;h3&gt;NoSQL systems:&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;Specifically designed for high load&lt;/li&gt;
&lt;li&gt;Natively support horizontal scalability&lt;/li&gt;
&lt;li&gt;Fault tolerant&lt;/li&gt;
&lt;li&gt;Store data in denormalised manner&lt;/li&gt;
&lt;li&gt;Do not usually enforce strict database schema&lt;/li&gt;
&lt;li&gt;Do not usually store data in a table&lt;/li&gt;
&lt;li&gt;Sometimes provide eventual consistency instead of ACID transactions&lt;/li&gt;
&lt;/ul&gt;

&lt;h3&gt;In contrast to RDBMS, NoSQL systems:&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;Do not guarantee data consistency&lt;/li&gt;
&lt;li&gt;Usually support a limited query language (subset of SQL or another custom query language)&lt;/li&gt;
&lt;li&gt;May not provide support for transactions/distributed transactions&lt;/li&gt;
&lt;li&gt;Do not usually use some advanced concepts of RDBMS, such as triggers, views, stored procedures&lt;/li&gt;
&lt;/ul&gt;

&lt;h3&gt;NoSQL implementations can be categorised by their manner of implementation:&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Column-oriented_DBMS" rel="nofollow"&gt;Column-oriented&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Document-oriented_database" rel="nofollow"&gt;Document store&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Graph_database" rel="nofollow"&gt;Graph&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://dba.stackexchange.com/questions/607/what-is-a-key-value-store-database"&gt;Key-value store&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/MultiValue" rel="nofollow"&gt;Multivalue databases&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Object_database" rel="nofollow"&gt;Object databases&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Triplestore" rel="nofollow"&gt;Tripplestore&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/NoSQL#Tuple_store" rel="nofollow"&gt;Tuple store&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;h3&gt;Free NoSQL Books&lt;/h3&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://books.couchdb.org/relax/" rel="nofollow"&gt;CouchDB: The Definitive Guide&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://openmymind.net/2011/3/28/The-Little-MongoDB-Book" rel="nofollow"&gt;The Little MongoDB Book&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://openmymind.net/2012/1/23/The-Little-Redis-Book/" rel="nofollow"&gt;The Little Redis Book&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/2011/3/28/the-little-mongodb-book'':323C ''/2012/1/23/the-little-redis-book/'':337C ''/a'':27C,65C,213C,225C,236C,248C,260C,272C,283C,295C,316C,330C,344C ''/h3'':74C,137C,200C,302C ''/li'':82C,88C,92C,99C,108C,118C,128C,145C,161C,170C,187C,214C,226C,237C,249C,261C,273C,284C,296C,317C,331C,345C ''/p'':70C ''/questions/607/what-is-a-key-value-store-database'':243C ''/questions/tagged/rdbms'':50C ''/questions/tagged/sql'':12C ''/relax/'':309C ''/strong'':4C ''/ul'':129C,188C,297C,346C ''/wiki/column-oriented_dbms'':207C ''/wiki/document-oriented_database'':220C ''/wiki/graph_database'':232C ''/wiki/multivalue'':255C ''/wiki/nosql#tuple_store'':290C ''/wiki/object_database'':267C ''/wiki/triplestore'':279C ''39'':21C,23C,59C,61C ''acid'':126C ''advanc'':177C ''anoth'':157C ''book'':301C,329C,343C ''books.couchdb.org'':308C ''books.couchdb.org/relax/'':307C ''broad'':30C ''categoris'':194C ''class'':13C,31C,51C ''classic'':40C ''column'':211C ''column-ori'':210C ''concept'':178C ''consist'':123C,144C ''contrast'':132C ''couchdb'':312C ''custom'':158C ''data'':95C,114C,143C ''databas'':33C,45C,106C,259C,271C ''dba.stackexchange.com'':242C ''dba.stackexchange.com/questions/607/what-is-a-key-value-store-database'':241C ''definit'':314C ''denormalis'':97C ''design'':78C ''differ'':37C ''document'':223C ''en.wikipedia.org'':206C,219C,231C,254C,266C,278C,289C ''en.wikipedia.org/wiki/column-oriented_dbms'':205C ''en.wikipedia.org/wiki/document-oriented_database'':218C ''en.wikipedia.org/wiki/graph_database'':230C ''en.wikipedia.org/wiki/multivalue'':253C ''en.wikipedia.org/wiki/nosql#tuple_store'':288C ''en.wikipedia.org/wiki/object_database'':265C ''en.wikipedia.org/wiki/triplestore'':277C ''enforc'':104C ''eventu'':122C ''expand'':6C ''fault'':90C ''free'':299C ''graph'':235C ''guarante'':142C ''guid'':315C ''h3'':71C,130C,189C,298C ''high'':80C ''horizont'':86C ''href'':11C,49C,204C,217C,229C,240C,252C,264C,276C,287C,306C,320C,334C ''implement'':191C,199C ''instead'':124C ''key'':245C ''key-valu'':244C ''languag'':152C,160C ''li'':76C,83C,89C,93C,100C,109C,119C,139C,146C,162C,171C,202C,215C,227C,238C,250C,262C,274C,285C,304C,318C,332C ''limit'':150C ''littl'':327C,341C ''load'':81C ''manag'':34C,46C ''manner'':98C,197C ''may'':163C ''model'':41C ''mongodb'':328C ''multivalu'':258C ''nativ'':84C ''nofollow'':209C,222C,234C,257C,269C,281C,292C,311C,325C,339C ''nosql'':3C,72C,135C,190C,300C ''object'':270C ''openmymind.net'':322C,336C ''openmymind.net/2011/3/28/the-little-mongodb-book'':321C ''openmymind.net/2012/1/23/the-little-redis-book/'':335C ''orient'':212C ''p'':1C ''post'':15C,53C ''post-tag'':14C,52C ''procedur'':186C ''provid'':121C,165C ''queri'':151C,159C ''question'':19C,57C ''rdbms'':60C,64C,134C,180C ''redi'':342C ''rel'':24C,62C,208C,221C,233C,256C,268C,280C,291C,310C,324C,338C ''relat'':44C ''scalabl'':87C ''schema'':107C ''show'':18C,56C ''signific'':68C ''sometim'':5C,120C ''specif'':77C ''sql'':22C,26C,155C ''store'':94C,113C,185C,224C,247C,294C ''strict'':105C ''strong'':2C ''subset'':153C ''support'':85C,148C,166C ''system'':35C,47C,73C,136C ''tabl'':117C ''tag'':16C,20C,25C,54C,58C,63C ''titl'':17C,55C ''toler'':91C ''transact'':127C,169C ''transactions/distributed'':168C ''trigger'':183C ''tripplestor'':282C ''tupl'':293C ''ul'':75C,138C,201C,303C ''use'':175C ''usual'':103C,112C,147C,174C ''valu'':246C ''view'':184C ''way'':69C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (120, 2, '2014-05-17 18:15:11.937', 2, NULL, NULL, '&lt;p&gt;arXiv has an &lt;a href="http://arxiv.org/help/bulk_data" rel="nofollow"&gt;API and bulk download&lt;/a&gt; but if you want something for paid journals it will be hard to come by without paying an indexer like pubmed or elsevier or the like.&lt;/p&gt;
', NULL, NULL, '''/a'':16C ''/help/bulk_data'':9C ''/p'':43C ''api'':12C ''arxiv'':2C ''arxiv.org'':8C ''arxiv.org/help/bulk_data'':7C ''bulk'':14C ''come'':30C ''download'':15C ''elsevi'':39C ''hard'':28C ''href'':6C ''index'':35C ''journal'':24C ''like'':36C,42C ''nofollow'':11C ''p'':1C ''paid'':23C ''pay'':33C ''pubm'':37C ''rel'':10C ''someth'':21C ''want'':20C ''without'':32C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (130, 1, '2014-05-18 06:26:15.673', 25, 3812, 'What is dimensionality reduction? What is the difference between feature selection and extraction?', '&lt;p&gt;From wikipedia, &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;dimensionality reduction or dimension reduction is the process of
  reducing the number of random variables under consideration, and
  can be divided into feature selection and feature extraction.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;&lt;strong&gt;What is the difference between feature selection and feature extraction?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;What is an example of dimensionality reduction in a Natural Language Processing task?&lt;/strong&gt;&lt;/p&gt;
', 'feature-selection  feature-extraction  dimensionality-reduction', NULL, '''/blockquote'':57C ''/p'':26C,56C,71C,88C ''/strong'':70C,87C ''blockquot'':27C ''consider'':45C ''differ'':8A,63C ''dimens'':32C ''dimension'':3A,21B,29C,79C ''dimensionality-reduct'':20B ''divid'':49C ''exampl'':77C ''extract'':13A,19B,55C,69C ''featur'':10A,15B,18B,51C,54C,65C,68C ''feature-extract'':17B ''feature-select'':14B ''languag'':84C ''natur'':83C ''number'':40C ''p'':23C,28C,58C,72C ''process'':36C,85C ''random'':42C ''reduc'':38C ''reduct'':4A,22B,30C,33C,80C ''select'':11A,16B,52C,66C ''strong'':59C,73C ''task'':86C ''variabl'':43C ''wikipedia'':25C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (230, 2, '2014-06-02 15:03:35.94', 4, NULL, NULL, '&lt;p&gt;In general, you don''t want to use XML tags to tag documents in this way because tags may overlap.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://uima.apache.org/" rel="nofollow"&gt;UIMA&lt;/a&gt;, &lt;a href="http://gate.ac.uk/" rel="nofollow"&gt;GATE&lt;/a&gt; and similar NLP frameworks denote the tags separate from the text.  Each tag, such as &lt;code&gt;Person&lt;/code&gt;, &lt;code&gt;ACME&lt;/code&gt;, &lt;code&gt;John&lt;/code&gt; etc. is stored as the position that the tag begins and the position that it ends.  So, for the tag &lt;code&gt;ACME&lt;/code&gt;, it would be stored as starting a position 11 and ending at position 17. &lt;/p&gt;
', NULL, NULL, '''/a'':30C,37C ''/code'':55C,58C,61C,84C ''/p'':22C,99C ''11'':93C ''17'':98C ''acm'':57C,83C ''begin'':71C ''code'':53C,56C,59C,82C ''denot'':42C ''document'':14C ''end'':77C,95C ''etc'':62C ''framework'':41C ''gate'':36C ''gate.ac.uk'':33C ''general'':3C ''href'':25C,32C ''john'':60C ''may'':20C ''nlp'':40C ''nofollow'':28C,35C ''overlap'':21C ''p'':1C,23C ''person'':54C ''posit'':67C,74C,92C,97C ''rel'':27C,34C ''separ'':45C ''similar'':39C ''start'':90C ''store'':64C,88C ''tag'':11C,13C,19C,44C,50C,70C,81C ''text'':48C ''uima'':29C ''uima.apache.org'':26C ''use'':9C ''want'':7C ''way'':17C ''would'':86C ''xml'':10C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (154, 2, '2014-05-18 17:53:37.75', 3, NULL, NULL, '&lt;p&gt;Check &lt;a href="http://www.martinfowler.com" rel="nofollow"&gt;Martin Fowler''s Personal website&lt;/a&gt; He write good, and specially answer of your question One of his book : "NoSQL Distilled" &lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/p'':29C ''answer'':19C ''book'':26C ''check'':2C ''distil'':28C ''fowler'':9C ''good'':16C ''href'':4C ''martin'':8C ''nofollow'':7C ''nosql'':27C ''one'':23C ''p'':1C ''person'':11C ''question'':22C ''rel'':6C ''special'':18C ''websit'':12C ''write'':15C ''www.martinfowler.com'':5C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (121, 2, '2014-05-17 18:53:30.123', 14, NULL, NULL, '&lt;p&gt;One thing to start off with would be k-NN.  The idea here is that you have a user/item matrix and for some of the users you have a reported age.  The age for a person in the user item matrix might be well determined by something like the mean or median age of some nearest neighbors in the item space.&lt;/p&gt;

&lt;p&gt;So you have each user expressed as a vector in item space, find the k nearest neighbors and assign the vector in question some summary stat of the nearest neighbor ages.  You can choose k on a distance cutoff or more realistically by iteratively assigning ages to a train hold out and choosing the k that minimizes the error in that assignment.&lt;/p&gt;

&lt;p&gt;If the dimensionality is a problem you can easily perform reduction in this setup by single value decomposition choosing the m vectors that capture the most variance across the group.&lt;/p&gt;

&lt;p&gt;In all cases since each feature is binary it seems that cosine similarity would be your go to distance metric.&lt;/p&gt;

&lt;p&gt;I need to think a bit more about other approaches (regression, rf, etc...) given the narrow focus of your feature space (all variants of the same action, liking) I think the user/item approach might be the best.&lt;/p&gt;

&lt;p&gt;One note of caution, if the ages you have for train are self reported you might need to correct some of them.  People on facebook tend to report ages in the decade they were born.  Plot a histogram of the birth dates (derived from ages) and see if you have spikes at decades like 70s, 80s, 90s.&lt;/p&gt;
', NULL, NULL, '''/p'':64C,128C,160C,182C,221C,280C ''70s'':277C ''80s'':278C ''90s'':279C ''across'':157C ''action'':210C ''age'':33C,35C,55C,96C,111C,229C,251C,267C ''approach'':193C,216C ''assign'':84C,110C,127C ''best'':220C ''binari'':169C ''birth'':263C ''bit'':189C ''born'':257C ''captur'':153C ''case'':164C ''caution'':226C ''choos'':99C,118C,148C ''correct'':241C ''cosin'':173C ''cutoff'':104C ''date'':264C ''decad'':254C,275C ''decomposit'':147C ''deriv'':265C ''determin'':47C ''dimension'':132C ''distanc'':103C,180C ''easili'':138C ''error'':124C ''etc'':196C ''express'':71C ''facebook'':247C ''featur'':167C,203C ''find'':78C ''focus'':200C ''given'':197C ''go'':178C ''group'':159C ''histogram'':260C ''hold'':115C ''idea'':14C ''item'':42C,62C,76C ''iter'':109C ''k'':11C,80C,100C,120C ''k-nn'':10C ''like'':50C,211C,276C ''m'':150C ''matrix'':22C,43C ''mean'':52C ''median'':54C ''metric'':181C ''might'':44C,217C,238C ''minim'':122C ''narrow'':199C ''nearest'':58C,81C,94C ''need'':185C,239C ''neighbor'':59C,82C,95C ''nn'':12C ''note'':224C ''one'':2C,223C ''p'':1C,65C,129C,161C,183C,222C ''peopl'':245C ''perform'':139C ''person'':38C ''plot'':258C ''problem'':135C ''question'':88C ''realist'':107C ''reduct'':140C ''regress'':194C ''report'':32C,236C,250C ''rf'':195C ''see'':269C ''seem'':171C ''self'':235C ''setup'':143C ''similar'':174C ''sinc'':165C ''singl'':145C ''someth'':49C ''space'':63C,77C,204C ''spike'':273C ''start'':5C ''stat'':91C ''summari'':90C ''tend'':248C ''thing'':3C ''think'':187C,213C ''train'':114C,233C ''user'':28C,41C,70C ''user/item'':21C,215C ''valu'':146C ''varianc'':156C ''variant'':206C ''vector'':74C,86C,151C ''well'':46C ''would'':8C,175C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (122, 2, '2014-05-17 20:56:15.577', 6, NULL, NULL, '&lt;p&gt;It depends on your data and what you''re doing with it. For example, if the processing you have to do requires transactions to synchronize across nodes, it will likely be faster to use transactions implemented in an RDBMS rather than implementing it yourself on top of NoSQL databases which don''t support it natively. &lt;/p&gt;
', NULL, NULL, '''/p'':57C ''across'':27C ''data'':6C ''databas'':50C ''depend'':3C ''exampl'':15C ''faster'':33C ''implement'':37C,43C ''like'':31C ''nativ'':56C ''node'':28C ''nosql'':49C ''p'':1C ''process'':18C ''rather'':41C ''rdbms'':40C ''re'':10C ''requir'':23C ''support'':54C ''synchron'':26C ''top'':47C ''transact'':24C,36C ''use'':35C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2574, 2, '2014-12-01 12:23:03.083', 1, NULL, NULL, '&lt;p&gt;I suggest using machine learning libraries with already functional linear regression
&lt;a href="https://spark.apache.org/docs/1.1.0/mllib-guide.html" rel="nofollow"&gt;Spark MLlib&lt;/a&gt; or &lt;a href="https://github.com/myui/hivemall" rel="nofollow"&gt;hivemall&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':22C,32C ''/docs/1.1.0/mllib-guide.html'':17C ''/myui/hivemall'':28C ''/p'':33C ''alreadi'':9C ''function'':10C ''github.com'':27C ''github.com/myui/hivemall'':26C ''hivemal'':31C ''href'':14C,25C ''learn'':6C ''librari'':7C ''linear'':11C ''machin'':5C ''mllib'':21C ''nofollow'':19C,30C ''p'':1C ''regress'':12C ''rel'':18C,29C ''spark'':20C ''spark.apache.org'':16C ''spark.apache.org/docs/1.1.0/mllib-guide.html'':15C ''suggest'':3C ''use'':4C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (123, 5, '2014-05-17 21:10:41.99', 0, NULL, NULL, '&lt;p&gt;The most basic relationship to describe is a &lt;strong&gt;linear relationship&lt;/strong&gt; between variables, &lt;em&gt;x&lt;/em&gt; and &lt;em&gt;y&lt;/em&gt;, such that they can be said to be highly-correlated when every increase in &lt;em&gt;x&lt;/em&gt; results in a proportional increase in &lt;em&gt;y&lt;/em&gt;. They can also be said to be &lt;em&gt;inversely proportional&lt;/em&gt; so that when &lt;em&gt;x&lt;/em&gt; increases, &lt;em&gt;y&lt;/em&gt; decreases. And finally, the two variables can be said to be &lt;a href="http://en.wikipedia.org/wiki/Independence_%28probability_theory%29" rel="nofollow"&gt;independent&lt;/a&gt; in the event that there is no linear relationship between the two (they are uncorrelated, or have a &lt;strong&gt;Pearson correlation coefficient&lt;/strong&gt; of 0. [LaTeX support would be highly desirable at this point.]&lt;/p&gt;

&lt;h2&gt;Different correlation coefficients and their uses:&lt;/h2&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient" rel="nofollow"&gt;Pearson correlation coefficient&lt;/a&gt; is useful.....
[draft]&lt;/p&gt;
', NULL, NULL, '''/a'':90C,145C ''/em'':18C,22C,40C,49C,60C,66C,70C ''/h2'':133C ''/p'':125C,149C ''/strong'':13C,113C ''/wiki/independence_%28probability_theory%29'':86C ''/wiki/pearson_product-moment_correlation_coefficient'':139C ''0'':115C ''also'':52C ''basic'':4C ''coeffici'':112C,129C,144C ''correl'':33C,111C,128C,143C ''decreas'':71C ''describ'':7C ''desir'':121C ''differ'':127C ''draft'':148C ''em'':16C,20C,38C,47C,57C,64C,68C ''en.wikipedia.org'':85C,138C ''en.wikipedia.org/wiki/independence_%28probability_theory%29'':84C ''en.wikipedia.org/wiki/pearson_product-moment_correlation_coefficient'':137C ''event'':93C ''everi'':35C ''final'':73C ''h2'':126C ''high'':32C,120C ''highly-correl'':31C ''href'':83C,136C ''increas'':36C,45C,67C ''independ'':89C ''invers'':58C ''latex'':116C ''linear'':11C,98C ''nofollow'':88C,141C ''p'':1C,134C ''pearson'':110C,142C ''point'':124C ''proport'':44C,59C ''rel'':87C,140C ''relationship'':5C,12C,99C ''result'':41C ''said'':28C,54C,79C ''strong'':10C,109C ''support'':117C ''two'':75C,102C ''uncorrel'':105C ''use'':132C,147C ''variabl'':15C,76C ''would'':118C ''x'':17C,39C,65C ''y'':21C,48C,69C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (124, 4, '2014-05-17 21:10:41.99', 0, NULL, NULL, 'A statistics term used to describe a type of dependence between variables (or data sets). Correlations are often used as an indicator of predictability. However, correlation does NOT imply causation. Different methods of calculating correlation exist to capture more complicated relationships between the variables being studied.', NULL, NULL, '''calcul'':34C ''captur'':38C ''causat'':30C ''complic'':40C ''correl'':16C,26C,35C ''data'':14C ''depend'':10C ''describ'':6C ''differ'':31C ''exist'':36C ''howev'':25C ''impli'':29C ''indic'':22C ''method'':32C ''often'':18C ''predict'':24C ''relationship'':41C ''set'':15C ''statist'':2C ''studi'':46C ''term'':3C ''type'':8C ''use'':4C,19C ''variabl'':12C,44C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (125, 1, '2014-05-17 21:52:34.563', -1, 90, 'How to learn noSQL databases and how to know when SQL or noSQL is better', '&lt;p&gt;I want learn about NoSQL and when is better to use SQL or NoSQL. I know that this question depends on the case, but I''m asking for a good documentation on NoSQL, and some explanation of when is better to use SQL or NoSQL (use cases, etc). Also, your opinions on NoSQL databases, and any recommendations for learning about this topic are welcome.&lt;/p&gt;
', 'nosql', NULL, '''/p'':82C ''also'':66C ''ask'':44C ''better'':15A,26C,57C ''case'':40C,64C ''databas'':5A,71C ''depend'':37C ''document'':48C ''etc'':65C ''explan'':53C ''good'':47C ''know'':9A,33C ''learn'':3A,20C,76C ''m'':43C ''nosql'':4A,13A,16B,22C,31C,50C,62C,70C ''opinion'':68C ''p'':17C ''question'':36C ''recommend'':74C ''sql'':11A,29C,60C ''topic'':79C ''use'':28C,59C,63C ''want'':19C ''welcom'':81C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (126, 2, '2014-05-17 23:53:42.7', 2, NULL, NULL, '&lt;p&gt;Please have a look at my answer here: &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;a href="http://stackoverflow.com/questions/13528216/motivations-for-using-relational-database-orm-or-document-database-odm/13599767#13599767"&gt;Motivations for using relational database / ORM or document database / ODM&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Short version:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Use NoSQL is data size and number of transactions per second forces it, which typically happens above a few tens of TB and millions of transactions per second (db in memory, running on cluster), &lt;em&gt;or&lt;/em&gt; at hundreds of TB and thousands of transactions per second (traditional db on disk, transactions per second is &lt;em&gt;highly&lt;/em&gt; dependent on the usage pattern). Traditional SQL scales up to that point just fine.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;NoSQL is well suited for some problems (data has a natural sharding, schema is flexible, eventual consistency is ok).  You can use there even if scaling doesn''t force you to.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Developer familiarity with tools and ops ease of deployment are &lt;em&gt;major&lt;/em&gt; factors, don''t overlook those.  A solution may be technically better but you may have a hard time using it, make sure you need it and make sure you budget for the learning curve.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;As to how to learn it: fire up a MongoDB image on AWS, or DynamoDB, and have fun! 
&lt;a href="http://docs.mongodb.org/ecosystem/platforms/amazon-ec2/" rel="nofollow"&gt;http://docs.mongodb.org/ecosystem/platforms/amazon-ec2/&lt;/a&gt;
&lt;a href="http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GettingStartedDynamoDB.html" rel="nofollow"&gt;http://docs.aws.amazon.com/amazondynamodb/latest/developerguide/GettingStartedDynamoDB.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':28C ''/amazondynamodb/latest/developerguide/gettingstarteddynamodb.html'':232C ''/amazondynamodb/latest/developerguide/gettingstarteddynamodb.html&lt;/a&gt;&lt;/p&gt;'':237C ''/ecosystem/platforms/amazon-ec2/'':222C ''/ecosystem/platforms/amazon-ec2/&lt;/a&gt;'':227C ''/em'':74C,95C,161C ''/li'':111C,146C,197C ''/p'':10C,30C,34C,110C,145C,196C ''/questions/13528216/motivations-for-using-relational-database-orm-or-document-database-odm/13599767#13599767'':17C ''/strong'':29C ''/ul'':198C ''answer'':8C ''aw'':212C ''better'':172C ''budget'':191C ''cluster'':71C ''consist'':130C ''curv'':195C ''data'':41C,121C ''databas'':22C,26C ''db'':66C,86C ''depend'':96C ''deploy'':157C ''develop'':149C ''disk'':88C ''docs.aws.amazon.com'':231C,236C ''docs.aws.amazon.com/amazondynamodb/latest/developerguide/gettingstarteddynamodb.html'':230C ''docs.aws.amazon.com/amazondynamodb/latest/developerguide/gettingstarteddynamodb.html&lt;/a&gt;&lt;/p&gt;'':235C ''docs.mongodb.org'':221C,226C ''docs.mongodb.org/ecosystem/platforms/amazon-ec2/'':220C ''docs.mongodb.org/ecosystem/platforms/amazon-ec2/&lt;/a&gt;'':225C ''document'':25C ''doesn'':140C ''dynamodb'':214C ''eas'':155C ''em'':72C,93C,159C ''even'':137C ''eventu'':129C ''factor'':162C ''familiar'':150C ''fine'':109C ''fire'':206C ''flexibl'':128C ''forc'':49C,142C ''fun'':217C ''happen'':53C ''hard'':178C ''high'':94C ''href'':14C,219C,229C ''hundr'':76C ''imag'':210C ''learn'':194C,204C ''li'':36C,112C,147C ''look'':5C ''major'':160C ''make'':182C,188C ''may'':169C,175C ''memori'':68C ''million'':61C ''mongodb'':209C ''motiv'':18C ''natur'':124C ''need'':185C ''nofollow'':224C,234C ''nosql'':39C,114C ''number'':44C ''odm'':27C ''ok'':132C ''op'':154C ''orm'':23C ''overlook'':165C ''p'':1C,11C,31C,37C,113C,148C,199C ''pattern'':100C ''per'':47C,64C,83C,90C ''pleas'':2C ''point'':107C ''problem'':120C ''rel'':223C,233C ''relat'':21C ''run'':69C ''scale'':103C,139C ''schema'':126C ''second'':48C,65C,84C,91C ''shard'':125C ''short'':32C ''size'':42C ''solut'':168C ''sql'':102C ''stackoverflow.com'':16C ''stackoverflow.com/questions/13528216/motivations-for-using-relational-database-orm-or-document-database-odm/13599767#13599767'':15C ''strong'':12C ''suit'':117C ''sure'':183C,189C ''tb'':59C,78C ''technic'':171C ''ten'':57C ''thousand'':80C ''time'':179C ''tool'':152C ''tradit'':85C,101C ''transact'':46C,63C,82C,89C ''typic'':52C ''ul'':35C ''usag'':99C ''use'':20C,38C,135C,180C ''version'':33C ''well'':116C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (128, 1, '2014-05-18 06:10:52.543', 25, 2376, 'Latent Dirichlet Allocation vs Hierarchical Dirichlet Process', '&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation"&gt;Latent Dirichlet Allocation (LDA)&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/Hierarchical_Dirichlet_process"&gt;Hierarchical Dirichlet Process (HDP)&lt;/a&gt; are both topic modeling processes. The major difference is LDA requires the specification of the number of topics, and HDP doesn''t. Why is that so? And what are the differences, pros, and cons of both topic modelling methods?&lt;/p&gt;
', 'nlp  topic-model  lda', NULL, '''/a'':23C,34C ''/p'':74C ''/wiki/hierarchical_dirichlet_process'':29C ''/wiki/latent_dirichlet_allocation'':18C ''alloc'':3A,21C ''con'':68C ''differ'':42C,65C ''dirichlet'':2A,6A,20C,31C ''doesn'':55C ''en.wikipedia.org'':17C,28C ''en.wikipedia.org/wiki/hierarchical_dirichlet_process'':27C ''en.wikipedia.org/wiki/latent_dirichlet_allocation'':16C ''hdp'':33C,54C ''hierarch'':5A,30C ''href'':15C,26C ''latent'':1A,19C ''lda'':12B,22C,44C ''major'':41C ''method'':73C ''model'':11B,38C,72C ''nlp'':8B ''number'':50C ''p'':13C ''process'':7A,32C,39C ''pros'':66C ''requir'':45C ''specif'':47C ''topic'':10B,37C,52C,71C ''topic-model'':9B ''vs'':4A');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (129, 1, '2014-05-18 06:17:37.587', 4, 90, 'What is generative and discriminative model? How are they used in Natural Language Processing?', '&lt;p&gt;&lt;a href="http://stackoverflow.com/questions/879432/what-is-the-difference-between-a-generative-and-discriminative-algorithm"&gt;This question&lt;/a&gt; asks about generative vs. discriminative algorithm, but can someone give an example of the difference between these forms when applied to Natural Language Processing? &lt;strong&gt;How are generative and discriminative models used in NLP?&lt;/strong&gt;&lt;/p&gt;
', 'nlp  language-model', NULL, '''/a'':27C ''/p'':63C ''/questions/879432/what-is-the-difference-between-a-generative-and-discriminative-algorithm'':24C ''/strong'':62C ''algorithm'':33C ''appli'':47C ''ask'':28C ''differ'':42C ''discrimin'':5A,32C,57C ''exampl'':39C ''form'':45C ''generat'':3A,30C,55C ''give'':37C ''href'':21C ''languag'':13A,17B,50C ''language-model'':16B ''model'':6A,18B,58C ''natur'':12A,49C ''nlp'':15B,61C ''p'':19C ''process'':14A,51C ''question'':26C ''someon'':36C ''stackoverflow.com'':23C ''stackoverflow.com/questions/879432/what-is-the-difference-between-a-generative-and-discriminative-algorithm'':22C ''strong'':52C ''use'':10A,59C ''vs'':31C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (131, 2, '2014-05-18 06:54:08.56', 4, NULL, NULL, '&lt;p&gt;Look it up on:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Google Scholar &lt;a href="http://scholar.google.com/scholar?hl=en&amp;amp;q=Assessment%20of%20Utility%20in%20Web%20Mining%20for%20the%20Domain%20of%20Public%20Health&amp;amp;btnG=&amp;amp;as_sdt=1,5&amp;amp;as_sdtp=" rel="nofollow"&gt;link&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Citeseer &lt;a href="http://citeseerx.ist.psu.edu/search?q=Assessment%20of%20Utility%20in%20Web%20Mining%20for%20the%20Domain%20of%20Public%20Health&amp;amp;submit=Search&amp;amp;sort=rlv&amp;amp;t=doc" rel="nofollow"&gt;link&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If you get a single exact title match then you have probably found the right article, and can fill in the rest of the info from there.  Both give you download links and bibtex-style output.  What you would likely want to do though to get perfect metadata is download and parse the pdf (if any) and look for DOI-style identifier.&lt;/p&gt;

&lt;p&gt;Please be nice and rate-limit your requests if you do this.&lt;/p&gt;
', NULL, NULL, '''/a'':19C,31C ''/li'':20C,32C ''/p'':6C,99C,114C ''/scholar?hl=en&amp;amp;q=assessment%20of%20utility%20in%20web%20mining%20for%20the%20domain%20of%20public%20health&amp;amp;btng=&amp;amp;as_sdt=1,5&amp;amp;as_sdtp='':15C ''/search?q=assessment%20of%20utility%20in%20web%20mining%20for%20the%20domain%20of%20public%20health&amp;amp;submit=search&amp;amp;sort=rlv&amp;amp;t=doc'':27C ''/ul'':33C ''articl'':50C ''bibtex'':69C ''bibtex-styl'':68C ''cites'':22C ''citeseerx.ist.psu.edu'':26C ''citeseerx.ist.psu.edu/search?q=assessment%20of%20utility%20in%20web%20mining%20for%20the%20domain%20of%20public%20health&amp;amp;submit=search&amp;amp;sort=rlv&amp;amp;t=doc'':25C ''doi'':96C ''doi-styl'':95C ''download'':65C,85C ''exact'':40C ''fill'':53C ''found'':47C ''get'':37C,81C ''give'':63C ''googl'':9C ''href'':12C,24C ''identifi'':98C ''info'':59C ''li'':8C,21C ''like'':75C ''limit'':107C ''link'':18C,30C,66C ''look'':2C,93C ''match'':42C ''metadata'':83C ''nice'':103C ''nofollow'':17C,29C ''output'':71C ''p'':1C,34C,100C ''pars'':87C ''pdf'':89C ''perfect'':82C ''pleas'':101C ''probabl'':46C ''rate'':106C ''rate-limit'':105C ''rel'':16C,28C ''request'':109C ''rest'':56C ''right'':49C ''scholar'':10C ''scholar.google.com'':14C ''scholar.google.com/scholar?hl=en&amp;amp;q=assessment%20of%20utility%20in%20web%20mining%20for%20the%20domain%20of%20public%20health&amp;amp;btng=&amp;amp;as_sdt=1,5&amp;amp;as_sdtp='':13C ''singl'':39C ''style'':70C,97C ''though'':79C ''titl'':41C ''ul'':7C ''want'':76C ''would'':74C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (132, 2, '2014-05-18 07:53:58.203', 22, NULL, NULL, '&lt;p&gt;Simply put: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;feature selection: you select a subset of the original feature set; while&lt;/li&gt;
&lt;li&gt;feature extraction: you build a new set of features from the original feature set. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Examples of feature extraction: extraction of contours in images, extraction of digrams from a text, extraction of phonemes from recording of spoken text, etc.&lt;/p&gt;

&lt;p&gt;Feature extraction involves a transformation of the features, which often is not reversible because some information is lost in the process of dimensionality reduction.&lt;/p&gt;
', NULL, NULL, '''/li'':19C,35C ''/p'':4C,62C,88C ''/ul'':36C ''build'':24C ''contour'':44C ''digram'':49C ''dimension'':86C ''etc'':61C ''exampl'':38C ''extract'':22C,41C,42C,47C,53C,65C ''featur'':7C,16C,21C,29C,33C,40C,64C,71C ''imag'':46C ''inform'':79C ''involv'':66C ''li'':6C,20C ''lost'':81C ''new'':26C ''often'':73C ''origin'':15C,32C ''p'':1C,37C,63C ''phonem'':55C ''process'':84C ''put'':3C ''record'':57C ''reduct'':87C ''revers'':76C ''select'':8C,10C ''set'':17C,27C,34C ''simpli'':2C ''spoken'':59C ''subset'':12C ''text'':52C,60C ''transform'':68C ''ul'':5C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (133, 2, '2014-05-18 09:09:47.78', 6, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;&lt;p&gt;I think a number of clustering algorithms that normally use a metric, do not actually rely on the metric properties (other than commutativity, but I think you''d have that here).  For example, DBSCAN uses epsilon-neighborhoods around a point; there is nothing in there that specifically says the triangle inequality matters.  So you can probably use DBSCAN, although you may have to do some kind of nonstandard spatial index to do efficient lookups in your case.  Your version of epsilon-neighborhood will likely be sim &gt; 1/epsilon rather than the other way around.  Same story with k-means and related algorithms.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Can you construct a metric from your similarity?  One possibility: dist(ei, ej) = min( sim(ei, ek) + sim(ek, ej) ) for all k ...  Alternately, can you provide an upper  bound such that sim(ei, ej) &amp;lt; sim(ei, ek) + sim(ek, ej) + d, for all k and some positive constant d?  Intuitively, large sim values means closer together: is 1/sim metric-like?  What about 1/(sim + constant)?  What about min( 1/sim(ei, ek) + 1/sim(ek, ej) ) for all k? (that last is guaranteed to be a metric, btw)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;An alternate construction of a metric is to do an embedding.  As a first step, you can try to map your points ei -&gt; xi, such that xi minimize sum( abs( sim(ei, ej) - f( dist(xi, xj) ) ), for some suitable function f and metric dist.  The function f converts distance in the embedding to a similarity-like value; you''d have to experiment a bit, but 1/dist or exp^-dist are good starting points.  You''d also have to experiment on the best dimension for xi.  From there, you can use conventional clustering on xi.  The idea here is that you can almost (in a best fit sense) convert your distances in the embedding to similarity values, so they would cluster correctly.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;On the use of predefined parameters, all algorithms have some tuning.  DBSCAN can find the number of clusters, but you still need to give it some parameters.  In general, tuning requires multiple runs of the algorithm with different values for the tunable parameters, together with some function that evaluates goodness-of-clustering (either calculated separately, provided by the clustering algorithm itself, or just eyeballed :)  If the character of your data doesn''t change, you can tune once and then use those fixed parameters; if it changes then you have to tune for each run.  You can find that out by tuning for each run and then comparing how well the parameters from one run work on another, compared to the parameters specifically tuned for that.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':109C,202C,329C,459C ''/ol'':460C ''/p'':108C,201C,328C,458C ''1'':177C ''1/dist'':272C ''1/epsilon'':92C ''1/sim'':171C,183C,186C ''ab'':234C ''actual'':18C ''algorithm'':10C,107C,339C,367C,392C ''almost'':308C ''also'':282C ''altern'':135C,206C ''although'':63C ''anoth'':449C ''around'':42C,98C ''best'':288C,311C ''bit'':270C ''bound'':141C ''btw'':200C ''calcul'':386C ''case'':81C ''chang'':405C,418C ''charact'':399C ''closer'':168C ''cluster'':9C,298C,326C,349C,384C,391C ''commut'':26C ''compar'':439C,450C ''constant'':161C,179C ''construct'':114C,207C ''convent'':297C ''convert'':253C,314C ''correct'':327C ''d'':31C,154C,162C,265C,281C ''data'':402C ''dbscan'':37C,62C,343C ''differ'':369C ''dimens'':289C ''dist'':122C,239C,249C,275C ''distanc'':254C,316C ''doesn'':403C ''effici'':77C ''ei'':123C,127C,145C,149C,184C,227C,236C ''either'':385C ''ej'':124C,131C,146C,153C,188C,237C ''ek'':128C,130C,150C,152C,185C,187C ''embed'':215C,257C,319C ''epsilon'':40C,86C ''epsilon-neighborhood'':39C,85C ''evalu'':380C ''exampl'':36C ''exp'':274C ''experi'':268C,285C ''eyebal'':396C ''f'':238C,246C,252C ''find'':345C,429C ''first'':218C ''fit'':312C ''fix'':414C ''function'':245C,251C,378C ''general'':360C ''give'':355C ''good'':277C,382C ''goodness-of-clust'':381C ''guarante'':195C ''idea'':302C ''index'':74C ''inequ'':55C ''intuit'':163C ''k'':103C,134C,157C,191C ''k-mean'':102C ''kind'':70C ''larg'':164C ''last'':193C ''li'':2C,110C,203C,330C ''like'':89C,174C,262C ''lookup'':78C ''lt'':147C ''map'':224C ''matter'':56C ''may'':65C ''mean'':104C,167C ''metric'':15C,22C,116C,173C,199C,210C,248C ''metric-lik'':172C ''min'':125C,182C ''minim'':232C ''multipl'':363C ''need'':353C ''neighborhood'':41C,87C ''nonstandard'':72C ''normal'':12C ''noth'':47C ''number'':7C,347C ''ol'':1C ''one'':120C,445C ''p'':3C,111C,204C,331C ''paramet'':337C,358C,374C,415C,443C,453C ''point'':44C,226C,279C ''posit'':160C ''possibl'':121C ''predefin'':336C ''probabl'':60C ''properti'':23C ''provid'':138C,388C ''rather'':93C ''relat'':106C ''reli'':19C ''requir'':362C ''run'':364C,426C,436C,446C ''say'':52C ''sens'':313C ''separ'':387C ''sim'':91C,126C,129C,144C,148C,151C,165C,178C,235C ''similar'':119C,261C,321C ''similarity-lik'':260C ''spatial'':73C ''specif'':51C,454C ''start'':278C ''step'':219C ''still'':352C ''stori'':100C ''suitabl'':244C ''sum'':233C ''think'':5C,29C ''togeth'':169C,375C ''tri'':222C ''triangl'':54C ''tunabl'':373C ''tune'':342C,361C,408C,423C,433C,455C ''upper'':140C ''use'':13C,38C,61C,296C,334C,412C ''valu'':166C,263C,322C,370C ''version'':83C ''way'':97C ''well'':441C ''work'':447C ''would'':325C ''xi'':228C,231C,240C,291C,300C ''xj'':241C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (134, 1, '2014-05-18 12:03:21.65', 10, 405, 'Can map-reduce algorithms written for MongoDB be ported to Hadoop later?', '&lt;p&gt;In our company, we have a MongoDB database containing a lot of unstructured data, on which we need to run map-reduce algorithms to generate reports and other analyses. We have two approaches to select from for implementing the required analyses:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;One approach is to extract the data from MongoDB to a Hadoop cluster and do the analysis completely in Hadoop platform. However, this requires considerable investment on preparing the platform (software and hardware) and educating the team to work with Hadoop and write map-reduce tasks for it.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Another approach is to just put our effort on designing the map-reduce algorithms, and run the algorithms on MongoDB map-reduce functionalities. This way, we can create an initial prototype of final system that can generate the reports. I know that the MongoDB''s map-reduce functionalities are much slower compared to Hadoop, but currently the data is not that big that makes this a bottleneck yet, at least not for the next six months.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The question is, using the second approach and writing the algorithms for MongoDB, can them be later ported to Hadoop with little needed modification and algorithm redesign? MongoDB just supports JavaScript but programming language differences are easy to handle. However, is there any fundamental differences in the map-reduce model of MongoDB and Hadoop that may force us to redesign algorithms substantially for porting to Hadoop?&lt;/p&gt;
', 'scalability  hadoop  map-reduce  mongodb', NULL, '''/li'':117C,200C ''/ol'':201C ''/p'':63C,116C,199C,270C ''algorithm'':5A,44C,134C,138C,213C,228C,264C ''analys'':50C,62C ''analysi'':83C ''anoth'':120C ''approach'':54C,68C,121C,209C ''big'':184C ''bottleneck'':189C ''cluster'':79C ''compani'':23C ''compar'':174C ''complet'':84C ''consider'':91C ''contain'':29C ''creat'':149C ''current'':178C ''data'':34C,73C,180C ''databas'':28C ''design'':129C ''differ'':237C,247C ''easi'':239C ''educ'':101C ''effort'':127C ''extract'':71C ''final'':154C ''forc'':260C ''function'':144C,170C ''fundament'':246C ''generat'':46C,158C ''hadoop'':12A,15B,78C,86C,107C,176C,222C,257C,269C ''handl'':241C ''hardwar'':99C ''howev'':88C,242C ''implement'':59C ''initi'':151C ''invest'':92C ''javascript'':233C ''know'':162C ''languag'':236C ''later'':13A,219C ''least'':192C ''li'':65C,118C ''littl'':224C ''lot'':31C ''make'':186C ''map'':3A,17B,42C,111C,132C,142C,168C,251C ''map-reduc'':2A,16B,41C,110C,131C,141C,167C,250C ''may'':259C ''model'':253C ''modif'':226C ''mongodb'':8A,19B,27C,75C,140C,165C,215C,230C,255C ''month'':198C ''much'':172C ''need'':38C,225C ''next'':196C ''ol'':64C ''one'':67C ''p'':20C,66C,119C,202C ''platform'':87C,96C ''port'':10A,220C,267C ''prepar'':94C ''program'':235C ''prototyp'':152C ''put'':125C ''question'':204C ''redesign'':229C,263C ''reduc'':4A,18B,43C,112C,133C,143C,169C,252C ''report'':47C,160C ''requir'':61C,90C ''run'':40C,136C ''scalabl'':14B ''second'':208C ''select'':56C ''six'':197C ''slower'':173C ''softwar'':97C ''substanti'':265C ''support'':232C ''system'':155C ''task'':113C ''team'':103C ''two'':53C ''unstructur'':33C ''us'':261C ''use'':206C ''way'':146C ''work'':105C ''write'':109C,211C ''written'':6A ''yet'':190C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (135, 2, '2014-05-18 12:30:06.853', 8, NULL, NULL, '&lt;p&gt;Considering another criteria, I think that in some cases using Python may be much superior to R for Big Data. I know the wide-spread use of R in data science educational materials and the good data analysis libraries available for it, but sometimes it just depend on the team.&lt;/p&gt;

&lt;p&gt;In my experience, for people already familiar with programming, using Python provides much more flexibility and productivity boost compared to a language like R, which is not as well-designed and powerful compared to Python in terms of a programming language. As an evidence, in a data mining course in my university, the best final project was written in Python, although the others has access to R''s rich data analysis library. That is, sometimes the overall productivity (considering learning materials, documentation, etc.) for Python may be better than R even in the lack of special-purpose data analysis libraries for Python. Also, there are some good articles explaining the fast pace of Python in data science: &lt;a href="http://readwrite.com/2013/11/25/python-displacing-r-as-the-programming-language-for-data-science"&gt;Python Displacing R&lt;/a&gt; and &lt;a href="http://wesmckinney.com/blog/?p=77"&gt;Rich Scientific Data Structures in Python&lt;/a&gt; that may soon fill the gap of available libraries for R.&lt;/p&gt;

&lt;p&gt;Another important reason for not using R is when working with real world Big Data problems, contrary to academical only problems, there is much need for other tools and techniques, like data parsing, cleaning, visualization, web scrapping, and a lot of others that are much easier using a general purpose programming language. This may be why the default language used in many Hadoop courses (including the Udacity''s &lt;a href="https://www.udacity.com/course/ud617"&gt;online course&lt;/a&gt;) is Python.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Recently DARPA has also invested $3 million to help fund Python''s data processing and visualization capabilities for big data jobs, which is clearly a sign of Python''s future in Big Data. (&lt;a href="http://www.computerworld.com/s/article/9236558/Python_gets_a_big_data_boost_from_DARPA"&gt;details&lt;/a&gt;)&lt;/p&gt;
', NULL, NULL, '''/2013/11/25/python-displacing-r-as-the-programming-language-for-data-science'':178C ''/a'':182C,195C,284C,333C ''/blog/?p=77'':188C ''/course/ud617'':281C ''/p'':53C,207C,287C,292C,334C ''/s/article/9236558/python_gets_a_big_data_boost_from_darpa'':331C ''/strong'':291C ''3'':299C ''academ'':227C ''access'':120C ''alreadi'':60C ''also'':159C,297C ''although'':116C ''analysi'':40C,126C,155C ''anoth'':3C,209C ''articl'':164C ''avail'':42C,203C ''best'':109C ''better'':143C ''big'':20C,222C,312C,325C ''boost'':72C ''capabl'':310C ''case'':10C ''clean'':242C ''clear'':317C ''compar'':73C,88C ''consid'':2C,134C ''contrari'':225C ''cours'':104C,272C,283C ''criteria'':4C ''darpa'':295C ''data'':21C,32C,39C,102C,125C,154C,172C,191C,223C,240C,306C,313C,326C ''default'':266C ''depend'':49C ''design'':85C ''detail'':332C ''displac'':180C ''document'':137C ''easier'':254C ''edit'':290C ''educ'':34C ''etc'':138C ''even'':146C ''evid'':99C ''experi'':57C ''explain'':165C ''familiar'':61C ''fast'':167C ''fill'':199C ''final'':110C ''flexibl'':69C ''fund'':303C ''futur'':323C ''gap'':201C ''general'':257C ''good'':38C,163C ''hadoop'':271C ''help'':302C ''href'':175C,185C,278C,328C ''import'':210C ''includ'':273C ''invest'':298C ''job'':314C ''know'':23C ''lack'':149C ''languag'':76C,96C,260C,267C ''learn'':135C ''librari'':41C,127C,156C,204C ''like'':77C,239C ''lot'':248C ''mani'':270C ''materi'':35C,136C ''may'':13C,141C,197C,262C ''million'':300C ''mine'':103C ''much'':15C,67C,232C,253C ''need'':233C ''onlin'':282C ''other'':118C,250C ''overal'':132C ''p'':1C,54C,208C,288C,293C ''pace'':168C ''pars'':241C ''peopl'':59C ''power'':87C ''problem'':224C,229C ''process'':307C ''product'':71C,133C ''program'':63C,95C,259C ''project'':111C ''provid'':66C ''purpos'':153C,258C ''python'':12C,65C,90C,115C,140C,158C,170C,179C,194C,286C,304C,321C ''r'':18C,30C,78C,122C,145C,181C,206C,215C ''readwrite.com'':177C ''readwrite.com/2013/11/25/python-displacing-r-as-the-programming-language-for-data-science'':176C ''real'':220C ''reason'':211C ''recent'':294C ''rich'':124C,189C ''scienc'':33C,173C ''scientif'':190C ''scrap'':245C ''sign'':319C ''sometim'':46C,130C ''soon'':198C ''special'':152C ''special-purpos'':151C ''spread'':27C ''strong'':289C ''structur'':192C ''superior'':16C ''team'':52C ''techniqu'':238C ''term'':92C ''think'':6C ''tool'':236C ''udac'':275C ''univers'':107C ''use'':11C,28C,64C,214C,255C,268C ''visual'':243C,309C ''web'':244C ''well'':84C ''well-design'':83C ''wesmckinney.com'':187C ''wesmckinney.com/blog/?p=77'':186C ''wide'':26C ''wide-spread'':25C ''work'':218C ''world'':221C ''written'':113C ''www.computerworld.com'':330C ''www.computerworld.com/s/article/9236558/python_gets_a_big_data_boost_from_darpa'':329C ''www.udacity.com'':280C ''www.udacity.com/course/ud617'':279C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (136, 5, '2014-05-18 12:55:39.657', 0, NULL, NULL, '&lt;p&gt;MapReduce is a framework for processing parallelizable problems across huge datasets using a large number of computers (nodes), collectively referred to as a cluster (if all nodes are on the same local network and use similar hardware) or a grid (if the nodes are shared across geographically and administratively distributed systems, and use more heterogenous hardware). Computational processing can occur on data stored either in a filesystem (unstructured) or in a database (structured). MapReduce can take advantage of locality of data, processing it on or near the storage assets in order to reduce the distance over which it must be transmitted.&lt;/p&gt;
', NULL, NULL, '''/p'':103C ''across'':10C,47C ''administr'':50C ''advantag'':78C ''asset'':90C ''cluster'':25C ''collect'':20C ''comput'':18C,58C ''data'':63C,82C ''databas'':73C ''dataset'':12C ''distanc'':96C ''distribut'':51C ''either'':65C ''filesystem'':68C ''framework'':5C ''geograph'':48C ''grid'':41C ''hardwar'':38C,57C ''heterogen'':56C ''huge'':11C ''larg'':15C ''local'':33C,80C ''mapreduc'':2C,75C ''must'':100C ''near'':87C ''network'':34C ''node'':19C,28C,44C ''number'':16C ''occur'':61C ''order'':92C ''p'':1C ''paralleliz'':8C ''problem'':9C ''process'':7C,59C,83C ''reduc'':94C ''refer'':21C ''share'':46C ''similar'':37C ''storag'':89C ''store'':64C ''structur'':74C ''system'':52C ''take'':77C ''transmit'':102C ''unstructur'':69C ''use'':13C,36C,54C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (137, 4, '2014-05-18 12:55:39.657', 0, NULL, NULL, 'MapReduce is a programming model for processing large data sets with a parallel, distributed algorithm on a cluster.', NULL, NULL, '''algorithm'':15C ''cluster'':18C ''data'':9C ''distribut'':14C ''larg'':8C ''mapreduc'':1C ''model'':5C ''parallel'':13C ''process'':7C ''program'':4C ''set'':10C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9081, 2, '2015-11-27 14:55:43.017', 0, NULL, NULL, '&lt;p&gt;I recommend calculating 1-gram, 2-grams and ... frequencies, sorting based on frequencies and observing what pops up. Sometimes you see patterns that need extra data cleansing. &lt;/p&gt;
', NULL, NULL, '''/p'':29C ''1'':5C ''2'':7C ''base'':12C ''calcul'':4C ''cleans'':28C ''data'':27C ''extra'':26C ''frequenc'':10C,14C ''gram'':6C,8C ''need'':25C ''observ'':16C ''p'':1C ''pattern'':23C ''pop'':18C ''recommend'':3C ''see'':22C ''sometim'':20C ''sort'':11C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (138, 1, '2014-05-18 14:02:51.35', 8, 159, 'Why is it hard to grant efficiency while using libraries?', '&lt;p&gt;Any small database processing can be easily tackled by Python/Perl/... scripts, that uses libraries and/or even utilities from the language itself. However, when it comes to performance, people tend to reach out for C/C++/low-level languages. The possibility of tailoring the code to the needs seems to be what makes these languages so appealing for BigData -- be it concerning memory management, parallelism, disk access, or even low-level optimizations (via assembly constructs at C/C++ level).&lt;/p&gt;

&lt;p&gt;Of course such set of benefits would not come without a cost: writing the code, and sometimes even &lt;em&gt;reinventing the wheel&lt;/em&gt;, can be quite expensive/tiresome. Although there are lots of libraries available, people are inclined to write the code by themselves whenever they need to &lt;em&gt;grant&lt;/em&gt; performance. What &lt;em&gt;disables&lt;/em&gt; performance assertions from using libraries while processing large databases?&lt;/p&gt;

&lt;p&gt;For example, consider an entreprise that continuously crawls webpages and parses the data collected. For each sliding-window, different data mining algorithms are run upon the data extracted. Why would the developers ditch off using available libraries/frameworks (be it for crawling, text processing, and data mining)? Using stuff already implemented would not only ease the burden of coding the whole process, but also would save a lot of time.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;In a single shot&lt;/strong&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;what makes writing the code by oneself a &lt;em&gt;guarantee&lt;/em&gt; of performance?&lt;/li&gt;
&lt;li&gt;why is it &lt;em&gt;risky&lt;/em&gt; to rely on a frameworks/libraries when you must &lt;strong&gt;assure&lt;/strong&gt; high performance?&lt;/li&gt;
&lt;/ul&gt;
', 'bigdata  efficiency  performance', NULL, '''/em'':115C,142C,147C,250C,260C ''/li'':253C,274C ''/low-level'':49C ''/p'':91C,157C,229C,237C ''/strong'':236C,271C ''/ul'':275C ''access'':78C ''algorithm'':181C ''alreadi'':208C ''also'':222C ''although'':120C ''and/or'':29C ''appeal'':68C ''assembl'':86C ''assert'':149C ''assur'':270C ''avail'':126C,195C ''benefit'':98C ''bigdata'':11B,70C ''burden'':215C ''c/c'':48C,89C ''code'':56C,107C,133C,217C,244C ''collect'':172C ''come'':39C,101C ''concern'':73C ''consid'':161C ''construct'':87C ''continu'':165C ''cost'':104C ''cours'':94C ''crawl'':166C,200C ''data'':171C,179C,186C,204C ''databas'':17C,156C ''develop'':191C ''differ'':178C ''disabl'':146C ''disk'':77C ''ditch'':192C ''eas'':213C ''easili'':21C ''effici'':7A,12B ''em'':111C,140C,145C,248C,258C ''entrepris'':163C ''even'':30C,80C,110C ''exampl'':160C ''expensive/tiresome'':119C ''extract'':187C ''frameworks/libraries'':265C ''grant'':6A,141C ''guarante'':249C ''hard'':4A ''high'':272C ''howev'':36C ''implement'':209C ''inclin'':129C ''languag'':34C,50C,66C ''larg'':155C ''level'':83C,90C ''li'':239C,254C ''librari'':10A,28C,125C,152C ''libraries/frameworks'':196C ''lot'':123C,226C ''low'':82C ''low-level'':81C ''make'':64C,241C ''manag'':75C ''memori'':74C ''mine'':180C,205C ''must'':268C ''need'':59C,138C ''oneself'':246C ''optim'':84C ''p'':14C,92C,158C,230C ''parallel'':76C ''pars'':169C ''peopl'':42C,127C ''perform'':13B,41C,143C,148C,252C,273C ''possibl'':52C ''process'':18C,154C,202C,220C ''python/perl'':24C ''quit'':118C ''reach'':45C ''reinvent'':112C ''reli'':262C ''riski'':259C ''run'':183C ''save'':224C ''script'':25C ''seem'':60C ''set'':96C ''shot'':235C ''singl'':234C ''slide'':176C ''sliding-window'':175C ''small'':16C ''sometim'':109C ''strong'':231C,269C ''stuff'':207C ''tackl'':22C ''tailor'':54C ''tend'':43C ''text'':201C ''time'':228C ''ul'':238C ''upon'':184C ''use'':9A,27C,151C,194C,206C ''util'':31C ''via'':85C ''webpag'':167C ''wheel'':114C ''whenev'':136C ''whole'':219C ''window'':177C ''without'':102C ''would'':99C,189C,210C,223C ''write'':105C,131C,242C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (139, 2, '2014-05-18 14:04:37.87', 3, NULL, NULL, '&lt;p&gt;I''d suggest &lt;a href="http://kafka.apache.org" rel="nofollow"&gt;Apache Kafka&lt;/a&gt; as message store and any stream processing solution of your choice like &lt;a href="https://camel.apache.org" rel="nofollow"&gt;Apache Camel&lt;/a&gt; or &lt;a href="https://github.com/apache/incubator-storm" rel="nofollow"&gt;Twitter Storm&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':12C,32C,43C ''/apache/incubator-storm'':38C ''/p'':44C ''apach'':10C,30C ''camel'':31C ''camel.apache.org'':27C ''choic'':23C ''d'':3C ''github.com'':37C ''github.com/apache/incubator-storm'':36C ''href'':6C,26C,35C ''kafka'':11C ''kafka.apache.org'':7C ''like'':24C ''messag'':14C ''nofollow'':9C,29C,40C ''p'':1C ''process'':19C ''rel'':8C,28C,39C ''solut'':20C ''store'':15C ''storm'':42C ''stream'':18C ''suggest'':4C ''twitter'':41C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (140, 2, '2014-05-18 14:30:10.553', 2, NULL, NULL, '&lt;p&gt;I''ve read very good &lt;a href="http://www.michael-noll.com/blog/2013/01/18/implementing-real-time-trending-topics-in-storm/" rel="nofollow"&gt;article&lt;/a&gt; recently that suggests using &lt;a href="https://github.com/nathanmarz/storm" rel="nofollow"&gt;Twitter storm&lt;/a&gt; for a task that looks pretty similar to yours.&lt;/p&gt;
', NULL, NULL, '''/a'':15C,29C ''/blog/2013/01/18/implementing-real-time-trending-topics-in-storm/'':11C ''/nathanmarz/storm'':24C ''/p'':39C ''articl'':14C ''github.com'':23C ''github.com/nathanmarz/storm'':22C ''good'':6C ''href'':8C,21C ''look'':34C ''nofollow'':13C,26C ''p'':1C ''pretti'':35C ''read'':4C ''recent'':16C ''rel'':12C,25C ''similar'':36C ''storm'':28C ''suggest'':18C ''task'':32C ''twitter'':27C ''use'':19C ''ve'':3C ''www.michael-noll.com'':10C ''www.michael-noll.com/blog/2013/01/18/implementing-real-time-trending-topics-in-storm/'':9C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (142, 4, '2014-05-18 14:36:16.35', 0, NULL, NULL, 'Efficiency, in algorithmic processing, is usually associated to resource usage. The metrics to evaluate the efficiency of a process are commonly account for execution time, memory/disk or storage requirements, network usage and power consumption.', NULL, NULL, '''account'':22C ''algorithm'':3C ''associ'':7C ''common'':21C ''consumpt'':34C ''effici'':1C,16C ''evalu'':14C ''execut'':24C ''memory/disk'':26C ''metric'':12C ''network'':30C ''power'':33C ''process'':4C,19C ''requir'':29C ''resourc'':9C ''storag'':28C ''time'':25C ''usag'':10C,31C ''usual'':6C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (143, 1, '2014-05-18 14:37:20.477', 7, 212, 'What is the most efficient data indexing technique', '&lt;p&gt;As we all know, there are some data indexing techniques, using by well-known indexing apps, like Lucene (for java) or Lucene.NET (for .NET), MurMurHash, B+Tree etc. For a No-Sql / Object Oriented Database (which I try to write/play a little around with C#), which technique you suggest?&lt;/p&gt;

&lt;p&gt;I read about MurMurhash-2 and specially v3 comments say Murmur is very fast. Also Lucene.Net has good comments on it. But what about their memory footprints in general? Is there any efficient solution which uses less footprint (and of course if faster is preferable) than Lucene or Murmur? Or should I write a special index structure to get the best results?&lt;/p&gt;

&lt;p&gt;If I try to write my own, then is there any accepted scale for a good indexing, something like 1% of data-node, or 5% of data-node? Any useful hint will be appreciated.&lt;/p&gt;
', 'nosql  efficiency  indexing  data-indexing-techniques  .net', NULL, '''-2'':75C ''/p'':69C,133C,171C ''1'':154C ''5'':160C ''accept'':146C ''also'':85C ''app'':34C ''appreci'':170C ''around'':62C ''b'':44C ''best'':131C ''c'':64C ''comment'':79C,89C ''cours'':111C ''data'':6A,13B,25C,157C,163C ''data-indexing-techniqu'':12B ''data-nod'':156C,162C ''databas'':54C ''effici'':5A,10B,103C ''etc'':46C ''fast'':84C ''faster'':113C ''footprint'':97C,108C ''general'':99C ''get'':129C ''good'':88C,150C ''hint'':167C ''index'':7A,11B,14B,26C,33C,126C,151C ''java'':38C ''know'':21C ''known'':32C ''less'':107C ''like'':35C,153C ''littl'':61C ''lucen'':36C,117C ''lucene.net'':40C,86C ''memori'':96C ''murmur'':81C,119C ''murmurhash'':43C,74C ''net'':16B,42C ''no-sql'':49C ''node'':158C,164C ''nosql'':9B ''object'':52C ''orient'':53C ''p'':17C,70C,134C ''prefer'':115C ''read'':72C ''result'':132C ''say'':80C ''scale'':147C ''solut'':104C ''someth'':152C ''special'':77C,125C ''sql'':51C ''structur'':127C ''suggest'':68C ''techniqu'':8A,15B,27C,66C ''tree'':45C ''tri'':57C,137C ''use'':28C,106C,166C ''v3'':78C ''well'':31C ''well-known'':30C ''write'':123C,139C ''write/play'':59C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (144, 5, '2014-05-18 14:58:34.853', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (149, 4, '2014-05-18 15:08:08.913', 0, NULL, NULL, 'In Computer science and Technologies The Data is the most important part.&#x0d;
Since to work with data in an efficient manner to store the data in mediums and reuse, we use some techniques which named in general indexing.&#x0d;
&#x0d;
This tag interests with these techniques, efficiency, and anything about using stored data with indexing', NULL, NULL, '''anyth'':47C ''comput'':2C ''data'':7C,17C,25C,51C ''effici'':20C,45C ''general'':37C ''import'':11C ''index'':38C,53C ''interest'':41C ''manner'':21C ''medium'':27C ''name'':35C ''part'':12C ''reus'':29C ''scienc'':3C ''sinc'':13C ''store'':23C,50C ''tag'':40C ''techniqu'':33C,44C ''technolog'':5C ''use'':31C,49C ''work'':15C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (151, 5, '2014-05-18 15:34:16.437', 0, NULL, NULL, '&lt;p&gt;Indexing is the almost most important part of data to get an efficient, properly storing and retrieval data from mediums&lt;/p&gt;

&lt;p&gt;In different Programming Languages, there are different indexing algorithms and structures can be found.&lt;/p&gt;

&lt;p&gt;As an Example, in Java Language the Apache Foundation''s Lucene is very popular.&lt;/p&gt;

&lt;p&gt;In years there are very efficient, fast, scalable Indexing Algorithms projectioned to the computer-world such as MurMurHashing Algorithm (using by some NoSQL Databases), B+Tree Algorithm (using by versions of Windows OS itself), or Lucene (very popular in web technologies) and its variations.&lt;/p&gt;

&lt;p&gt;Also there can be found problem-specific Indexing within Chemicals or Medical Data Representations in Digital World&lt;/p&gt;
', NULL, NULL, '''/p'':22C,38C,54C,101C,121C ''algorithm'':32C,65C,75C,83C ''almost'':5C ''also'':103C ''apach'':47C ''b'':81C ''chemic'':113C ''comput'':70C ''computer-world'':69C ''data'':10C,19C,116C ''databas'':80C ''differ'':25C,30C ''digit'':119C ''effici'':14C,61C ''exampl'':42C ''fast'':62C ''found'':37C,107C ''foundat'':48C ''get'':12C ''import'':7C ''index'':2C,31C,64C,111C ''java'':44C ''languag'':27C,45C ''lucen'':50C,92C ''medic'':115C ''medium'':21C ''murmurhash'':74C ''nosql'':79C ''os'':89C ''p'':1C,23C,39C,55C,102C ''part'':8C ''popular'':53C,94C ''problem'':109C ''problem-specif'':108C ''program'':26C ''project'':66C ''proper'':15C ''represent'':117C ''retriev'':18C ''scalabl'':63C ''specif'':110C ''store'':16C ''structur'':34C ''technolog'':97C ''tree'':82C ''use'':76C,84C ''variat'':100C ''version'':86C ''web'':96C ''window'':88C ''within'':112C ''world'':71C,120C ''year'':57C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (152, 4, '2014-05-18 15:34:16.437', 0, NULL, NULL, 'Indexing is the almost most important part of data to get an efficient, properly storing and retrieval data from mediums', NULL, NULL, '''almost'':4C ''data'':9C,18C ''effici'':13C ''get'':11C ''import'':6C ''index'':1C ''medium'':20C ''part'':7C ''proper'':14C ''retriev'':17C ''store'':15C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (266, 1, '2014-06-10 11:05:47.273', 9, 1941, 'What are some easy to learn machine-learning applications?', '&lt;p&gt;Being new to machine-learning in general, I''d like to start playing around and see what the possibilities are.&lt;/p&gt;

&lt;p&gt;I''m curious as to what applications you might recommend that would offer the fastest time from installation to producing a meaningful result.&lt;/p&gt;

&lt;p&gt;Also, any recommendations for good getting-started materials on the subject of machine-learning in general would be appreciated.&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':36C,61C,84C ''also'':63C ''applic'':10A,44C ''appreci'':83C ''around'':29C ''curious'':40C ''d'':24C ''easi'':4A ''fastest'':52C ''general'':22C,80C ''get'':69C ''getting-start'':68C ''good'':67C ''instal'':55C ''learn'':6A,9A,13B,20C,78C ''like'':25C ''m'':39C ''machin'':8A,12B,19C,77C ''machine-learn'':7A,11B,18C,76C ''materi'':71C ''meaning'':59C ''might'':46C ''new'':16C ''offer'':50C ''p'':14C,37C,62C ''play'':28C ''possibl'':34C ''produc'':57C ''recommend'':47C,65C ''result'':60C ''see'':31C ''start'':27C,70C ''subject'':74C ''time'':53C ''would'':49C,81C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (153, 2, '2014-05-18 17:38:01.383', 3, NULL, NULL, '&lt;p&gt;The answers presented so far are very nice, but I was also expecting an emphasis on a particular difference between parallel and distributed processing: the code executed. Considering parallel processes, the code executed is the same, regardless of the level of parallelism (instruction, data, task). You write a &lt;em&gt;single code&lt;/em&gt;, and it will be executed by different threads/processors, e.g., while computing matrices products, or generating permutations.&lt;/p&gt;

&lt;p&gt;On the other hand, distributed computing involves the execution of different algorithms/programs at the same time in different processors (from one or more machines). Such computations are later merged into a intermediate/final results by using the available means of data communication/synchronization (shared memory, network). Further, distributed computing is very appealing for BigData processing, as it allows for exploiting disk parallelism (usually the bottleneck for large databases).&lt;/p&gt;

&lt;p&gt;Finally, for the level of parallelism, it may be taken rather as a constraint on the synchronization. For example, in GPGPU, which is single-instruction multiple-data (SIMD), the parallelism occurs by having different inputs for a single instruction, each pair &lt;em&gt;(data_i, instruction)&lt;/em&gt; being executed by a different thread. Such is the restraint that, in case of divergent branches, it is necessary to discard lots of unnecessary computations, until the threads reconverge. For CPU threads, though, they commonly diverge; yet, one may use synchronization structures to grant concurrent execution of specific sections of the code.&lt;/p&gt;
', NULL, NULL, '''/em'':53C,187C ''/p'':70C,138C,240C ''algorithms/programs'':83C ''allow'':127C ''also'':13C ''answer'':3C ''appeal'':121C ''avail'':108C ''bigdata'':123C ''bottleneck'':134C ''branch'':203C ''case'':200C ''code'':27C,33C,52C,239C ''common'':222C ''communication/synchronization'':112C ''comput'':64C,77C,97C,118C,212C ''concurr'':232C ''consid'':29C ''constraint'':153C ''cpu'':218C ''data'':45C,111C,168C,184C ''databas'':137C ''differ'':20C,60C,82C,89C,175C,192C ''discard'':208C ''disk'':130C ''distribut'':24C,76C,117C ''diverg'':202C,223C ''e.g'':62C ''em'':50C,183C ''emphasi'':16C ''exampl'':158C ''execut'':28C,34C,58C,80C,189C,233C ''expect'':14C ''exploit'':129C ''far'':6C ''final'':140C ''generat'':68C ''gpgpu'':160C ''grant'':231C ''hand'':75C ''input'':176C ''instruct'':44C,165C,180C,186C ''intermediate/final'':103C ''involv'':78C ''larg'':136C ''later'':99C ''level'':41C,143C ''lot'':209C ''machin'':95C ''matric'':65C ''may'':147C,226C ''mean'':109C ''memori'':114C ''merg'':100C ''multipl'':167C ''multiple-data'':166C ''necessari'':206C ''network'':115C ''nice'':9C ''occur'':172C ''one'':92C,225C ''p'':1C,71C,139C ''pair'':182C ''parallel'':22C,30C,43C,131C,145C,171C ''particular'':19C ''permut'':69C ''present'':4C ''process'':25C,31C,124C ''processor'':90C ''product'':66C ''rather'':150C ''reconverg'':216C ''regardless'':38C ''restraint'':197C ''result'':104C ''section'':236C ''share'':113C ''simd'':169C ''singl'':51C,164C,179C ''single-instruct'':163C ''specif'':235C ''structur'':229C ''synchron'':156C,228C ''taken'':149C ''task'':46C ''though'':220C ''thread'':193C,215C,219C ''threads/processors'':61C ''time'':87C ''unnecessari'':211C ''use'':106C,227C ''usual'':132C ''write'':48C ''yet'':224C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (155, 1, '2014-05-18 18:45:38.957', 76, 5667, 'Publicly Available Datasets', '&lt;p&gt;One of the common problems in data science is gathering data from various sources in a somehow cleaned (semi-structured) format and combining metrics from various sources for making a higher level analysis. Looking at the other people''s effort, especially other questions on this site, it appears that many people in this field are doing somewhat repetitive work. For example analyzing tweets, facebook posts, Wikipedia articles etc. is a part of a lot of big data problems.&lt;/p&gt;

&lt;p&gt;Some of these data sets are accessible using public APIs provided by the provider site, but usually some valuable information or metrics are missing from these APIs and everyone has to do the same analyses again and again. For example, although clustering users may depend on different use cases and selection of features, but having a base clustering of Twitter/Facebook users can be useful in many Big Data applications, which is neither provided by the API, nor available publicly in independent data sets.&lt;/p&gt;

&lt;p&gt;Is there any index or publicly available data set hosting site containing valuable data sets that can be reused in solving other big data problems? I mean something like GitHub (or a group of sites/public data sets or at least a comprehensive listing) for the data science. If not, what are the reasons of not having such a platform for data science? Commercial value of data, need to frequently update data sets, ...? Can we not have an open-source model for sharing data sets devised for data scientists?&lt;/p&gt;
', 'open-source  dataset', NULL, '''/p'':88C,173C,264C ''access'':96C ''although'':130C ''analys'':124C ''analysi'':42C ''analyz'':71C ''api'':99C,116C,165C ''appear'':57C ''applic'':158C ''articl'':76C ''avail'':2A,167C,181C ''base'':146C ''big'':85C,156C,197C ''case'':138C ''clean'':26C ''cluster'':131C,147C ''combin'':32C ''commerci'':237C ''common'':12C ''comprehens'':216C ''contain'':186C ''data'':15C,19C,86C,93C,157C,171C,182C,188C,198C,210C,220C,235C,240C,245C,258C,262C ''dataset'':3A,7B ''depend'':134C ''devis'':260C ''differ'':136C ''effort'':49C ''especi'':50C ''etc'':77C ''everyon'':118C ''exampl'':70C,129C ''facebook'':73C ''featur'':142C ''field'':63C ''format'':30C ''frequent'':243C ''gather'':18C ''github'':204C ''group'':207C ''higher'':40C ''host'':184C ''independ'':170C ''index'':178C ''inform'':109C ''least'':214C ''level'':41C ''like'':203C ''list'':217C ''look'':43C ''lot'':83C ''make'':38C ''mani'':59C,155C ''may'':133C ''mean'':201C ''metric'':33C,111C ''miss'':113C ''model'':255C ''need'':241C ''neither'':161C ''one'':9C ''open'':5B,253C ''open-sourc'':4B,252C ''p'':8C,89C,174C ''part'':80C ''peopl'':47C,60C ''platform'':233C ''post'':74C ''problem'':13C,87C,199C ''provid'':100C,103C,162C ''public'':1A,98C,168C,180C ''question'':52C ''reason'':227C ''repetit'':67C ''reus'':193C ''scienc'':16C,221C,236C ''scientist'':263C ''select'':140C ''semi'':28C ''semi-structur'':27C ''set'':94C,172C,183C,189C,211C,246C,259C ''share'':257C ''site'':55C,104C,185C ''sites/public'':209C ''solv'':195C ''somehow'':25C ''someth'':202C ''somewhat'':66C ''sourc'':6B,22C,36C,254C ''structur'':29C ''tweet'':72C ''twitter/facebook'':149C ''updat'':244C ''use'':97C,137C,153C ''user'':132C,150C ''usual'':106C ''valu'':238C ''valuabl'':108C,187C ''various'':21C,35C ''wikipedia'':75C ''work'':68C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (156, 2, '2014-05-18 19:19:44.24', 20, NULL, NULL, '&lt;p&gt;&lt;a href="https://www.freebase.com"&gt;Freebase&lt;/a&gt; is a free community driven database that spans many interesting topics and contains about 2,5 billion facts in machine readable format. It is also have good API to perform data queries.&lt;/p&gt;
', NULL, NULL, '''/a'':6C ''/p'':39C ''2'':21C ''5'':22C ''also'':31C ''api'':34C ''billion'':23C ''communiti'':10C ''contain'':19C ''data'':37C ''databas'':12C ''driven'':11C ''fact'':24C ''format'':28C ''free'':9C ''freebas'':5C ''good'':33C ''href'':3C ''interest'':16C ''machin'':26C ''mani'':15C ''p'':1C ''perform'':36C ''queri'':38C ''readabl'':27C ''span'':14C ''topic'':17C ''www.freebase.com'':4C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1007, 1, '2014-08-20 14:12:03.87', -3, 422, 'Looking for Web scraping tool for unstructured data', '&lt;p&gt;I want to scrape some data from a website. 
I have used import.io but still not much satisfied.. can any of you suggest about it.. whats the best tool to get the unstructured data from web&lt;/p&gt;
', 'tools  crawling', NULL, '''..'':30C,38C ''/p'':50C ''best'':41C ''crawl'':10B ''data'':8A,17C,47C ''get'':44C ''import.io'':24C ''look'':1A ''much'':28C ''p'':11C ''satisfi'':29C ''scrape'':4A,15C ''still'':26C ''suggest'':35C ''tool'':5A,9B,42C ''unstructur'':7A,46C ''use'':23C ''want'':13C ''web'':3A,49C ''websit'':20C ''what'':39C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (291, 2, '2014-06-10 18:55:39.01', 7, NULL, NULL, '&lt;p&gt;If I were you I would take a MOOC or two (e.g., &lt;a href="https://www.coursera.org/course/algs4partI"&gt;Algorithms, Part I&lt;/a&gt;, &lt;a href="https://www.coursera.org/course/algs4partII"&gt;Algorithms, Part II&lt;/a&gt;, &lt;a href="https://www.coursera.org/course/progfun"&gt;Functional Programming Principles in Scala&lt;/a&gt;), a good book on data structures and algorithms, then just code as much as possible. You could implement some statistics or ML algorithms, for example; that would be good practice for you and useful to the community.&lt;/p&gt;

&lt;p&gt;For a PhD program, however, I would also make sure I were familiar with the type of maths they use. If you want to see what it''s like at the deep end, browse the papers at the &lt;a href="http://jmlr.org/papers/"&gt;JMLR&lt;/a&gt;. That will let you calibrate yourself in regards to theory; can you sort of follow the maths?&lt;/p&gt;

&lt;p&gt;Oh, and you don''t need a PhD to work at top companies, unless you want to join research departments like his. But then you''ll spend more time doing development, and you''ll need good coding skills...&lt;/p&gt;
', NULL, NULL, '''/a'':22C,31C,42C,126C ''/course/algs4parti'':18C ''/course/algs4partii'':27C ''/course/progfun'':36C ''/p'':80C,144C,184C ''/papers/'':124C ''algorithm'':19C,28C,50C,65C ''also'':89C ''book'':45C ''brows'':115C ''calibr'':131C ''code'':53C,182C ''communiti'':79C ''compani'':158C ''could'':59C ''data'':47C ''deep'':113C ''depart'':165C ''develop'':176C ''e.g'':13C ''end'':114C ''exampl'':67C ''familiar'':94C ''follow'':141C ''function'':37C ''good'':44C,71C,181C ''howev'':86C ''href'':15C,24C,33C,121C ''ii'':30C ''implement'':60C ''jmlr'':125C ''jmlr.org'':123C ''jmlr.org/papers/'':122C ''join'':163C ''let'':129C ''like'':110C,166C ''ll'':171C,179C ''make'':90C ''math'':99C,143C ''ml'':64C ''mooc'':10C ''much'':55C ''need'':151C,180C ''oh'':146C ''p'':1C,81C,145C ''paper'':117C ''part'':20C,29C ''phd'':84C,153C ''possibl'':57C ''practic'':72C ''principl'':39C ''program'':38C,85C ''regard'':134C ''research'':164C ''scala'':41C ''see'':106C ''skill'':183C ''sort'':139C ''spend'':172C ''statist'':62C ''structur'':48C ''sure'':91C ''take'':8C ''theori'':136C ''time'':174C ''top'':157C ''two'':12C ''type'':97C ''unless'':159C ''use'':76C,101C ''want'':104C,161C ''work'':155C ''would'':7C,69C,88C ''www.coursera.org'':17C,26C,35C ''www.coursera.org/course/algs4parti'':16C ''www.coursera.org/course/algs4partii'':25C ''www.coursera.org/course/progfun'':34C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (157, 2, '2014-05-18 19:22:05.16', 10, NULL, NULL, '&lt;p&gt;R is great for "big data"! However, you need a workflow since R is limited (with some simplification) by the amount of RAM in the operating system. The approach I take is to interact with a relational database (see the &lt;code&gt;RSQLite&lt;/code&gt; package for creating and interacting with a SQLite databse), run SQL-style queries to understand the structure of the data, and then extract particular subsets of the data for computationally-intensive statistical analysis.&lt;/p&gt;

&lt;p&gt;This just one approach, however: there are packages that allow you to interact with other databases (e.g., Monet) or run analyses in R with fewer memory limitations (e.g., see &lt;code&gt;pbdR&lt;/code&gt;).&lt;/p&gt;
', NULL, NULL, '''/code'':44C,113C ''/p'':80C,114C ''allow'':91C ''amount'':22C ''analys'':102C ''analysi'':79C ''approach'':30C,85C ''big'':6C ''code'':42C,111C ''comput'':76C ''computationally-intens'':75C ''creat'':47C ''data'':7C,65C,73C ''databas'':39C,97C ''databs'':53C ''e.g'':98C,109C ''extract'':68C ''fewer'':106C ''great'':4C ''howev'':8C,86C ''intens'':77C ''interact'':35C,49C,94C ''limit'':16C,108C ''memori'':107C ''monet'':99C ''need'':10C ''one'':84C ''oper'':27C ''p'':1C,81C ''packag'':45C,89C ''particular'':69C ''pbdr'':112C ''queri'':58C ''r'':2C,14C,104C ''ram'':24C ''relat'':38C ''rsqlite'':43C ''run'':54C,101C ''see'':40C,110C ''simplif'':19C ''sinc'':13C ''sql'':56C ''sql-style'':55C ''sqlite'':52C ''statist'':78C ''structur'':62C ''style'':57C ''subset'':70C ''system'':28C ''take'':32C ''understand'':60C ''workflow'':12C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (158, 2, '2014-05-18 19:29:53.53', 42, NULL, NULL, '&lt;p&gt;There is, in fact, a very reasonable list of publicly-available datasets, supported by different enterprises/sources. Here are some of them:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://aws.amazon.com/publicdatasets/"&gt;Public Datasets on Amazon WebServices&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://fimi.ua.ac.be/data/"&gt;Frequent Itemset Mining Implementation Repository&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://archive.ics.uci.edu/ml/datasets.html"&gt;UCI Machine Learning Repository&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.kdnuggets.com/datasets/index.html"&gt;KDnuggets&lt;/a&gt; -- big list of lots of public repositories.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Now, two considerations on your question. First one, regarding policies of database sharing. From personal experience, there are some databases that can''t be made publicly available, either for involving privacy restraints (as for some social network informations), or for concerning government information (like health system databases).&lt;/p&gt;

&lt;p&gt;Another point concerns the usage/application of the dataset. Although some bases can be reprocessed to suit the needs of the application, it would be great to have some &lt;em&gt;nice organization&lt;/em&gt; of the datasets by purpose. The &lt;em&gt;taxonomy&lt;/em&gt; should involve social graph analysis, itemset mining, classification, and lots of other reasearch areas there may be.&lt;/p&gt;
', NULL, NULL, '''/a'':37C,50C,62C,71C ''/data/'':44C ''/datasets/index.html'':69C ''/em'':162C,171C ''/li'':38C,51C,63C,79C ''/ml/datasets.html'':57C ''/p'':24C,129C,189C ''/publicdatasets/'':31C ''/ul'':80C ''although'':139C ''amazon'':35C ''analysi'':176C ''anoth'':131C ''applic'':151C ''archive.ics.uci.edu'':56C ''archive.ics.uci.edu/ml/datasets.html'':55C ''area'':185C ''avail'':13C,108C ''aws.amazon.com'':30C ''aws.amazon.com/publicdatasets/'':29C ''base'':141C ''big'':72C ''classif'':179C ''concern'':122C,133C ''consider'':84C ''databas'':93C,101C,128C ''dataset'':14C,33C,138C,165C ''differ'':17C ''either'':109C ''em'':159C,169C ''enterprises/sources'':18C ''experi'':97C ''fact'':5C ''fimi.ua.ac.be'':43C ''fimi.ua.ac.be/data/'':42C ''first'':88C ''frequent'':45C ''govern'':123C ''graph'':175C ''great'':155C ''health'':126C ''href'':28C,41C,54C,66C ''implement'':48C ''inform'':119C,124C ''involv'':111C,173C ''itemset'':46C,177C ''kdnugget'':70C ''learn'':60C ''li'':26C,39C,52C,64C ''like'':125C ''list'':9C,73C ''lot'':75C,181C ''machin'':59C ''made'':106C ''may'':187C ''mine'':47C,178C ''need'':148C ''network'':118C ''nice'':160C ''one'':89C ''organ'':161C ''p'':1C,81C,130C ''person'':96C ''point'':132C ''polici'':91C ''privaci'':112C ''public'':12C,32C,77C,107C ''publicly-avail'':11C ''purpos'':167C ''question'':87C ''reasearch'':184C ''reason'':8C ''regard'':90C ''repositori'':49C,61C,78C ''reprocess'':144C ''restraint'':113C ''share'':94C ''social'':117C,174C ''suit'':146C ''support'':15C ''system'':127C ''taxonomi'':170C ''two'':83C ''uci'':58C ''ul'':25C ''usage/application'':135C ''webservic'':36C ''would'':153C ''www.kdnuggets.com'':68C ''www.kdnuggets.com/datasets/index.html'':67C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (159, 1, '2014-05-18 19:46:44.653', 4, 335, 'Is Data Science just a trend or is a long term concept?', '&lt;p&gt;I see a lot of courses in Data Science emerging in the last 2 years. Even big universities like Stanford and Columbia offers MS specifically in Data Science. But as long as I see, it looks like data science is just a mix of computer science and statistics techniques.
So I always think about this. If it is just a trend and if in 10 years from now, someone will still mention Data Science as an entire field or just a subject/topic inside CS or stats.
What do you think?&lt;/p&gt;
', 'bigdata  machine-learning  databases  statistics  education', NULL, '''/p'':111C ''10'':85C ''2'':34C ''alway'':72C ''big'':37C ''bigdata'':13B ''columbia'':42C ''comput'':65C ''concept'':12A ''cours'':26C ''cs'':104C ''data'':2A,28C,47C,58C,93C ''databas'':17B ''educ'':19B ''emerg'':30C ''entir'':97C ''even'':36C ''field'':98C ''insid'':103C ''last'':33C ''learn'':16B ''like'':39C,57C ''long'':10A,51C ''look'':56C ''lot'':24C ''machin'':15B ''machine-learn'':14B ''mention'':92C ''mix'':63C ''ms'':44C ''offer'':43C ''p'':20C ''scienc'':3A,29C,48C,59C,66C,94C ''see'':22C,54C ''someon'':89C ''specif'':45C ''stanford'':40C ''stat'':106C ''statist'':18B,68C ''still'':91C ''subject/topic'':102C ''techniqu'':69C ''term'':11A ''think'':73C,110C ''trend'':6A,81C ''univers'':38C ''year'':35C,86C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (182, 2, '2014-05-21 07:19:32.297', 3, NULL, NULL, '&lt;p&gt;So, a few of your rows will have too many columns by one or more as a result. That''s easy to detect, but harder to infer where the error was -- which two columns are actually one? which delimiter is not a delimiter?&lt;/p&gt;

&lt;p&gt;In some cases, you can use the metadata, because it helps you know when an interpretation of the columns can''t be right. For example, if just the one column can have a text value, and all the others must be numeric, it''s unambiguous where the error is. Any additional columns created by this error occur right after the text column.&lt;/p&gt;

&lt;p&gt;If they''re all text, this doesn''t work of course.&lt;/p&gt;

&lt;p&gt;You might be able to leverage more than the metadata''s column type. For example you may know that some fields are from an enumerated set of values, and use that to determine when a column assignment is wrong.&lt;/p&gt;
', NULL, NULL, '''/p'':45C,109C,122C,163C ''abl'':127C ''actual'':37C ''addit'':97C ''assign'':160C ''case'':49C ''column'':12C,35C,65C,76C,98C,108C,135C,159C ''cours'':121C ''creat'':99C ''delimit'':40C,44C ''detect'':24C ''determin'':156C ''doesn'':117C ''easi'':22C ''enumer'':148C ''error'':31C,94C,102C ''exampl'':71C,138C ''field'':144C ''harder'':26C ''help'':57C ''infer'':28C ''interpret'':62C ''know'':59C,141C ''leverag'':129C ''mani'':11C ''may'':140C ''metadata'':54C,133C ''might'':125C ''must'':86C ''numer'':88C ''occur'':103C ''one'':14C,38C,75C ''other'':85C ''p'':1C,46C,110C,123C ''re'':113C ''result'':19C ''right'':69C,104C ''row'':7C ''set'':149C ''text'':80C,107C,115C ''two'':34C ''type'':136C ''unambigu'':91C ''use'':52C,153C ''valu'':81C,151C ''work'':119C ''wrong'':162C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8812, 2, '2015-11-12 07:18:48.46', 4, NULL, NULL, '&lt;p&gt;Know I''m a bit late, but to get probabilities from &lt;code&gt;xgboost&lt;/code&gt; you should specify &lt;code&gt;multi:softmax&lt;/code&gt; objective like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;xgboost(param, data = x_mat, label = y_mat,nround = 3000, objective=''multi:softprob'')
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;From the &lt;code&gt;?xgb.train&lt;/code&gt;: &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;multi:softprob same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/blockquote'':88C ''/code'':15C,22C,42C,49C ''/p'':26C,50C,87C ''/pre'':43C ''3000'':38C ''belong'':83C ''bit'':6C ''blockquot'':51C ''class'':86C ''code'':13C,19C,28C,47C ''contain'':76C ''data'':31C,81C ''get'':10C ''know'':2C ''label'':34C ''late'':7C ''like'':24C ''m'':4C ''mat'':33C,36C ''matrix'':73C ''multi'':20C,40C,53C ''nclass'':64C,72C ''ndata'':63C,71C ''nround'':37C ''object'':23C,39C ''output'':59C ''p'':1C,44C,52C ''param'':30C ''point'':82C ''pre'':27C ''predict'':77C ''probabl'':11C,78C ''reshap'':69C ''result'':75C ''softmax'':21C,57C ''softprob'':41C,54C ''specifi'':18C ''vector'':61C ''x'':32C ''xgb.train'':48C ''xgboost'':14C,29C ''y'':35C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2406, 2, '2014-11-05 17:03:40.513', 9, NULL, NULL, '&lt;p&gt;Drew Conway published the &lt;a href="http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram" rel="nofollow"&gt;Data Science Venn Diagram&lt;/a&gt;, with which I heartily agree:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/7W0VM.png" alt="Data Science Venn Diagram"&gt;&lt;/p&gt;

&lt;p&gt;On the one hand, you should really read his post. On the other hand, I can offer my own experience: my subject matter expertise (which I like better as a term than "Substantive Expertise", because you should really also have "Substantive Expertise" in math/stats and hacking) is in the retail business, my math/stats are forecasting and inferential statistics, and my hacking skills lie in R.&lt;/p&gt;

&lt;p&gt;From this vantage point, I can talk to and understand retailers, and someone who doesn''t have at least a passing knowledge of this field will have to face a &lt;em&gt;steep&lt;/em&gt; learning curve in a project with retailers. As a side gig, I do statistics in psychology, and it''s exactly the same there. And even with quite some knowledge of the hacking/math/stats part of the diagram, I would have a hard time getting up to speed in, say, credit scoring or some other new subject field.&lt;/p&gt;

&lt;p&gt;Once you have a certain amount of math/stats and hacking skills, it is &lt;em&gt;much&lt;/em&gt; better to acquire a grounding in one or more subjects than in adding &lt;em&gt;yet&lt;/em&gt; another programming language to your hacking skills, or &lt;em&gt;yet&lt;/em&gt; another machine learning algorithm to your math/stats portfolio. After all, once you have a solid math/stats/hacking grounding, you could if need be learn such new tools from the web or from textbooks in a relative short time period. But the subject matter expertise, on the other hand, you will likely not be able to learn from scratch if you start from zero. And clients will rather work with some data scientist A who understands their specific field than with another data scientist B who first needs to learn the basics - even if B is better in math/stats/hacking.&lt;/p&gt;

&lt;p&gt;Of course, all this will also mean that you will never become an expert in &lt;em&gt;either&lt;/em&gt; of the three fields. But that''s fine, because you are a data scientist, not a programmer or a statistician or a subject matter expert. There will always be people in the three separate circles who you can learn from. Which is part of what I like about data science.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;EDIT: A little while and a few thoughts later, I''d like to update this post with a new version of the diagram. I still think that Hacking Skills, Math &amp;amp; Statistics Knowledge and Substantive Expertise (shortened to "Programming", "Statistics" and "Business" for legibility) are important... but I think that the role of &lt;em&gt;Communication&lt;/em&gt; is important, too. All the insights you derive by leveraging your hacking, stats and business expertise won''t make a bit of a difference unless you can communicate them to people who may &lt;em&gt;not&lt;/em&gt; have that unique blend of knowledge. You may need to explain your statistical insights to a business manager who needs to be convinced to spend money or change processes. Or to a programmer who doesn''t think statistically.&lt;/p&gt;

&lt;p&gt;So here is the new data science Venn diagram, which also includes communication as one indispensable ingredient. I have labeled the areas in ways that should guarantee maximum flaming, while being easy to remember.&lt;/p&gt;

&lt;p&gt;Comment away.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://i.stack.imgur.com/aiQeT.png" rel="nofollow"&gt;&lt;img src="http://i.stack.imgur.com/aiQeT.png" alt="new data science Venn diagram"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;R code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;draw.ellipse &amp;lt;- function(center,angle,semimajor,semiminor,radius,h,s,v,...) {
    shape &amp;lt;- rbind(c(cos(angle),-sin(angle)),c(sin(angle),cos(angle))) %*% diag(c(semimajor,semiminor))
    tt &amp;lt;- seq(0,2*pi,length.out=1000)
    foo &amp;lt;- matrix(center,nrow=2,ncol=length(tt),byrow=FALSE) + shape%*%(radius*rbind(cos(tt),sin(tt)))
    polygon(foo[1,],foo[2,],col=hsv(h,s,v,alpha=0.5),border="black",...)
}
name &amp;lt;- function(x,y,label,cex=1.2,...) text(x,y,label,cex=cex,...)

png("Venn.png",width=600,height=600)
    opar &amp;lt;- par(mai=c(0,0,0,0),lwd=3,font=2)
        plot(c(0,100),c(0,90),type="n",bty="n",xaxt="n",yaxt="n",xlab="",ylab="")
        draw.ellipse(center=c(30,30),angle=0.75*pi,semimajor=2,semiminor=1,radius=20,h=60/360,s=.068,v=.976)
        draw.ellipse(center=c(70,30),angle=0.25*pi,semimajor=2,semiminor=1,radius=20,h=83/360,s=.482,v=.894)
        draw.ellipse(center=c(48,40),angle=0.7*pi,semimajor=2,semiminor=1,radius=20,h=174/360,s=.397,v=.8)
        draw.ellipse(center=c(52,40),angle=0.3*pi,semimajor=2,semiminor=1,radius=20,h=200/360,s=.774,v=.745)

        name(50,90,"The Data Scientist Venn Diagram",pos=1,cex=2)
        name(8,62,"Communi-\ncation",cex=1.5,pos=3)
        name(30,78,"Statistics",cex=1.5)
        name(70,78,"Programming",cex=1.5)
        name(92,62,"Business",cex=1.5,pos=3)

        name(10,45,"Hot\nAir")
        name(90,45,"The\nAccountant")
        name(33,65,"The\nData\nNerd")
        name(67,65,"The\nHacker")
        name(27,50,"The\nStats\nProf")
        name(73,50,"The\nIT\nGuy")
        name(50,55,"R\nCore\nTeam")
        name(38,38,"The\nGood\nConsultant")
        name(62,38,"Drew\nConway''s\nData\nScientist")
        name(50,24,"The\nperfect\nData\nScientist!")
        name(31,18,"Comp\nSci\nProf")
        name(69,18,"The\nNumber\nCruncher")
        name(42,11,"Head\nof IT")
        name(58,11,"Ana-\nlyst")
        name(50,5,"The\nSalesperson")
    par(opar)
dev.off()
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (160, 2, '2014-05-18 21:05:28.99', 10, NULL, NULL, '&lt;p&gt;The one thing that you can say for sure is: Nobody can say this for sure. And it might indeed be opinion-based to some extent. The introduction of terms like "Big Data" that some people consider as "hypes" or "buzzwords" don''t make it easier to flesh out an appropriate answer here. But I''ll try. &lt;/p&gt;

&lt;p&gt;In general, interdisciplinary fields often seem to have the problem of not being taken serious by either of the fields they are spanning. However, the more research is invested into a particular field, the greater is the urge to split this field into several sub-topics. And these sub-topics sonner of later have to be re-combined in new ways, in order to prevent an overspecialization, and to increase and broaden the applicability of techniques that are developed by the (over?)specialized experts in the different fields. &lt;/p&gt;

&lt;p&gt;And I consider "Data Science" as such an approach to combine the expertise and findings from different fields. You described it as&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;...a mix of computer science and statistics techniques&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;And indeed, several questions here aim at the differentiation between data science and statistics. But a pure statistician will most likely not be able to set up a Hadoop cluster and show the results of his analysis in an interactive HTML5 dashboard. And someone who can implement a nice HTML5 dashboard might not be so familiar with the mathematical background of a Chi-Squared-Test. &lt;/p&gt;

&lt;p&gt;It is reasonable to assume that giving students enough knowledge to &lt;em&gt;apply&lt;/em&gt; the most important techniques from the different fields that are covered by data science will lead to new applications of these techniques, and be beneficial - also for the "purists" in these fields. The combination of these techniques is not straightforward in many cases, and can justify an own branch of research. &lt;/p&gt;

&lt;p&gt;You also asked whether in 10 years, data science will be considered as "just a topic inside computer science". Again: Nobody can say for sure. But I wonder at which point people stopped asking the question whether "Computer Science" will one day only be considered only as a mix of (or a subject of) Electrical Engineering and Mathematics...&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (161, 2, '2014-05-18 21:25:29.04', 6, NULL, NULL, '&lt;p&gt;I don''t think that everyone reaches for C/C++ when performance is an issue.&lt;/p&gt;

&lt;p&gt;The advantage to writing low-level code is using fewer CPU cycles, or sometimes, less memory. But I''d note that higher-level languages can call down to lower-level languages, and do, to get some of this value. Python and JVM languages can do this.&lt;/p&gt;

&lt;p&gt;The data scientist using, for example, scikit-learn on her desktop is already calling heavily optimized native routines to do the number crunching. There is no point in writing new code for speed.&lt;/p&gt;

&lt;p&gt;In the distributed "big data" context, you are more typically bottleneck on data movement: network transfer and I/O. Native code does not help. What helps is not writing the same code to run faster, but writing smarter code.&lt;/p&gt;

&lt;p&gt;Higher-level languages are going to let you implement more sophisticated distributed algorithms in a given amount of developer time than C/C++. At scale, the smarter algorithm with better data movement will beat dumb native code.&lt;/p&gt;

&lt;p&gt;It''s also usually true that developer time, and bugs, cost loads more than new hardware. A year of a senior developer''s time might be $200K fully loaded; over a year that also rents hundreds of servers worth of computation time. It may just not make sense in most cases to bother optimizing over throwing more hardware at it.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;I don''t understand the follow up about "grant" and "disable" and "assert"?&lt;/em&gt;&lt;/p&gt;
', NULL, NULL, '''/em'':260C ''/p'':16C,67C,103C,143C,182C,244C,261C ''200k'':210C ''advantag'':19C ''algorithm'':158C,172C ''alreadi'':82C ''also'':186C,217C ''amount'':162C ''assert'':259C ''beat'':178C ''better'':174C ''big'':108C ''bother'':236C ''bottleneck'':115C ''bug'':193C ''c/c'':10C,167C ''call'':45C,83C ''case'':234C ''code'':25C,100C,124C,135C,142C,181C ''comput'':224C ''context'':110C ''cost'':194C ''cpu'':29C ''crunch'':92C ''cycl'':30C ''d'':37C ''data'':70C,109C,117C,175C ''desktop'':80C ''develop'':164C,190C,205C ''disabl'':257C ''distribut'':107C,157C ''dumb'':179C ''em'':246C ''everyon'':7C ''exampl'':74C ''faster'':138C ''fewer'':28C ''follow'':252C ''fulli'':211C ''get'':55C ''given'':161C ''go'':150C ''grant'':255C ''hardwar'':199C,241C ''heavili'':84C ''help'':127C,129C ''higher'':41C,146C ''higher-level'':40C,145C ''hundr'':219C ''i/o'':122C ''implement'':154C ''issu'':15C ''jvm'':62C ''languag'':43C,51C,63C,148C ''learn'':77C ''less'':33C ''let'':152C ''level'':24C,42C,50C,147C ''load'':195C,212C ''low'':23C ''low-level'':22C ''lower'':49C ''lower-level'':48C ''make'':230C ''may'':227C ''memori'':34C ''might'':208C ''movement'':118C,176C ''nativ'':86C,123C,180C ''network'':119C ''new'':99C,198C ''note'':38C ''number'':91C ''optim'':85C,237C ''p'':1C,17C,68C,104C,144C,183C,245C ''perform'':12C ''point'':96C ''python'':60C ''reach'':8C ''rent'':218C ''routin'':87C ''run'':137C ''scale'':169C ''scientist'':71C ''scikit'':76C ''scikit-learn'':75C ''senior'':204C ''sens'':231C ''server'':221C ''smarter'':141C,171C ''sometim'':32C ''sophist'':156C ''speed'':102C ''think'':5C ''throw'':239C ''time'':165C,191C,207C,225C ''transfer'':120C ''true'':188C ''typic'':114C ''understand'':250C ''use'':27C,72C ''usual'':187C ''valu'':59C ''worth'':222C ''write'':21C,98C,132C,140C ''year'':201C,215C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (162, 2, '2014-05-18 22:16:19.3', 21, NULL, NULL, '&lt;p&gt;There are many openly available data sets, one many people often overlook is &lt;a href="http://www.data.gov/"&gt;data.gov&lt;/a&gt;. As mentioned previously Freebase is great, so are all the examples posted by @Rubens&lt;/p&gt;
', NULL, NULL, '''/a'':19C ''/p'':34C ''avail'':6C ''data'':7C ''data.gov'':18C ''exampl'':30C ''freebas'':23C ''great'':25C ''href'':16C ''mani'':4C,10C ''mention'':21C ''often'':12C ''one'':9C ''open'':5C ''overlook'':13C ''p'':1C ''peopl'':11C ''post'':31C ''previous'':22C ''ruben'':33C ''set'':8C ''www.data.gov'':17C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (163, 2, '2014-05-18 23:21:07.22', 3, NULL, NULL, '&lt;p&gt;As all we know, in Digital world there are many ways to do the same work / get expected results..&lt;/p&gt;

&lt;p&gt;And responsibilities / risks which comes from the code are on developers'' shoulders..&lt;/p&gt;

&lt;p&gt;This is small but i guess a very useful example from .NET world..&lt;/p&gt;

&lt;p&gt;So Many .NET developers use the built-in BinaryReader - BinaryWriter on their data serialization for performance / get control over the process..&lt;/p&gt;

&lt;p&gt;This is CSharp source code of the FrameWork''s built in BinaryWriter class'' one of the overloaded Write Methods :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;// Writes a boolean to this stream. A single byte is written to the stream
// with the value 0 representing false or the value 1 representing true.
// 
public virtual void Write(bool value) 
{
     //_buffer is a byte array which declared in ctor / init codes of the class
    _buffer = ((byte) (value? 1:0));

    //OutStream is the stream instance which BinaryWriter Writes the value(s) into it.
    OutStream.WriteByte(_buffer[0]);
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As you see, this method could written without the extra assigning to _buffer variable:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;public virtual void Write(bool value) 
{
    OutStream.WriteByte((byte) (value ? 1 : 0));
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Without assigning we could gain few milliseconds..This few milliseconds can accept as "almost nothing" but what if there are multi-thousands of writing (i.e. in a server process)?&lt;/p&gt;

&lt;p&gt;Lets suppose that "few" is 2 (milliseconds) and multi-Thousands instances are only 2.000..
This means 4 seconds more process time..4 seconds later returning..&lt;/p&gt;

&lt;p&gt;If we continue to subject from .NET and if you can check the source codes of BCL - .NET Base Class Library- from MSDN you can see a lot of performance losts from the developer decides..&lt;/p&gt;

&lt;p&gt;Any of the point from BCL source It''s normal that you see developer decided to use while() or foreach() loops which could implement a faster for() loop in their code.&lt;/p&gt;

&lt;p&gt;This small gains give us the total performance..&lt;/p&gt;

&lt;p&gt;And if we return to the BinaryWriter.Write() Method..&lt;/p&gt;

&lt;p&gt;Actually extra assigning to a _buffer implementation is not a developer fault..This is exactly decide to "stay in safe" !&lt;/p&gt;

&lt;p&gt;Suppose that we decide to not use _buffer and decided to implement the second method..If we try to send multi-thousands bytes over a wire (i.e. upload / download a BLOB or CLOB data) with the second method, it can fail commonly because of connection lost..Cause we try to send all data without any checks and controlling mechanism.When connection lost, Both the server and Client never know the sent data completed or not.&lt;/p&gt;

&lt;p&gt;If the developer decides "stay in safe" then normally it means performance costs depends to implemented "stay in safe" mechanism(s).&lt;/p&gt;

&lt;p&gt;But if the developer decides "get risky, gain performance" this is not a fault also..Till there are some discussions about "risky" coding.&lt;/p&gt;

&lt;p&gt;And as a small note : Commercial library developers always try to stay in safe because they can''t know where their code will use.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (164, 2, '2014-05-19 00:09:17.9', 6, NULL, NULL, '&lt;p&gt;What @Clayton posted seems about right to me, for those terms, and for "data mining" being one tool of the data scientist. However, I haven''t really used the term "data collection," and it doesn''t strike me as synonymous with "data mining."&lt;/p&gt;

&lt;p&gt;My own answer to your question: &lt;strong&gt;no&lt;/strong&gt;, the terms aren''t the same. Definitions may be loose in this field, but I haven''t seen those terms used interchangeably. In my work, we sometimes use them to differentiate between goals, or methodologies. For us, &lt;a href="/questions/tagged/data-science" class="post-tag" title="show questions tagged &amp;#39;data-science&amp;#39;" rel="tag"&gt;data-science&lt;/a&gt; is more about testing a hypothesis, and typically the data have been collected just for that purpose. &lt;a href="/questions/tagged/data-mining" class="post-tag" title="show questions tagged &amp;#39;data-mining&amp;#39;" rel="tag"&gt;data-mining&lt;/a&gt; is more about sifting through existing data, looking for structure, and perhaps generating hypotheses. Data mining can start with a hypothesis, but it''s often very weak or general, and can be difficult to resolve with confidence. (Dig long enough and you''ll find &lt;em&gt;something&lt;/em&gt;, though it may turn out to be pyrite.)&lt;/p&gt;

&lt;p&gt;However, we also have used "data science" as a wider term, to include "data mining." We also talk about "data modeling," which for us is about finding a model for a system of interest, based on data as well as other knowledge and objectives. Sometimes that means trying to find the math that explains the real system, and sometimes it means finding a predictive model that is good enough for a purpose.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8828, 1, '2015-11-13 07:21:29.24', 1, 33, 'Learning Data Science and Analytic at Middle School age?', '&lt;p&gt;My son who is in middle school, has just been offered by his school to learn Data Science and analytic for the next 4 years. According to son''s teacher, the boys will be the first batch of guinea pigs. I am uncertain if it''s advisable to learn data science at this young age. Will it be too challenging for the young mind? Are there any benefit learning Data Science at this age? Thank you.&lt;/p&gt;
', 'beginner  education', NULL, '''/p'':89C ''4'':36C ''accord'':38C ''advis'':59C ''age'':9A,67C,86C ''analyt'':5A,32C ''batch'':49C ''beginn'':10B ''benefit'':80C ''boy'':44C ''challeng'':72C ''data'':2A,29C,62C,82C ''educ'':11B ''first'':48C ''guinea'':51C ''learn'':1A,28C,61C,81C ''middl'':7A,18C ''mind'':76C ''next'':35C ''offer'':23C ''p'':12C ''pig'':52C ''school'':8A,19C,26C ''scienc'':3A,30C,63C,83C ''son'':14C,40C ''teacher'':42C ''thank'':87C ''uncertain'':55C ''year'':37C ''young'':66C,75C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (193, 2, '2014-05-22 15:43:57.16', 3, NULL, NULL, '&lt;p&gt;It depends, of course, on the focus of the company: commerce, service, etc.  In adition to the use cases you suggested, some other use cases would be:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Funnel analysis: Analyzing the way in which consumers use a website and complete a sale may include data science techniques, especially if the company operates at a large scale.&lt;/li&gt;
&lt;li&gt;Advertising: Companies that place ads use &lt;em&gt;a lot&lt;/em&gt; of machine learning techniques to analyze and predict which ads would be most effective or most remunerative give the user''s demographics that would view them.&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (165, 2, '2014-05-19 08:46:47.683', 9, NULL, NULL, '&lt;p&gt;I think you messed up some things in your question. Lucene (I know nothing about Lucene,NET, but I suppose is the same) is a library used to analyze, split in tokens, and store documents in order to be able to query and retrieve them later. Lucene has a pretty old but effective model, it uses inverted trees to find and retrieve documents. Without further details, all documents are split in tokens (terms), and for each term is maintained a data structure, which stores all the documents which contains the given term. As a data structure could be used a BTree, a hash table and in the latest major revisions you can even plug in your own data structures.&lt;/p&gt;

&lt;p&gt;A BTree (see &lt;a href="http://en.wikipedia.org/wiki/B-tree"&gt;Wikipedia page&lt;/a&gt; for further details), is a kind of a tree data structure, which is appropriate for working with big chunks of data and is often used for storing tree-like ordered structures on disk. For in-memory other trees performs better.&lt;/p&gt;

&lt;p&gt;Murmur hash (see &lt;a href="http://en.wikipedia.org/wiki/MurmurHash"&gt;Wikipedia page&lt;/a&gt; for further details), is a family of hash functions used in hash table. The implementation of the hash table is not important, it could be a standard chained implementation or more advanced open hash addressing scheme. The idea is that the hash tables allows one to get fast a key, from an unordered set of keys, and can answer to tasks like: is this key part of this set of keys? which is the value associated with this key? &lt;/p&gt;

&lt;p&gt;Now back to your main problem. You have one library (Lucene) and to data structures, both data structures are used in Lucene. Now you see that it is not possible to answer your question in these terms since they are not comparable.&lt;/p&gt;

&lt;p&gt;However, regarding you footprint and performance part of the question. First of all you have to know which kind of operations you need to implement. &lt;/p&gt;

&lt;p&gt;&lt;em&gt;Do you need only get value for key, or do you need to find all elements in a range? In other words do you need order or not?&lt;/em&gt; If you do, than a tree can help. If you do not, than a hash table, which is faster could be used instead. &lt;/p&gt;

&lt;p&gt;&lt;em&gt;Do you have a lot of data which does not fit the memory?&lt;/em&gt; If yes than a disk-based solution would help (like BTree). If your data fit the memory, than use the fastest in-memory solution and use disk only as a storage (with a different structure, much simpler).&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (166, 2, '2014-05-19 11:13:48.067', 4, NULL, NULL, '&lt;p&gt;Let''s say you are predicting the topic of a document given its words.&lt;/p&gt;

&lt;p&gt;A generative model describes how likely each topic is, and how likely words are given the topic. This is how it says documents are actually "generated" by the world -- a topic arises according to some distribution, words arise because of the topic, you have a document. Classifying documents of words W into topic T is a matter of maximizing the joint likelihood: P(T,W) = P(W|T)P(T)&lt;/p&gt;

&lt;p&gt;A discriminative model operates by only describing how likely a topic is given the words. It says nothing about how likely the words or topic are by themselves. The task is to model P(T|W) directly and find the T that maximizes this. These approaches do not care about P(T) or P(W) directly.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (167, 5, '2014-05-19 12:17:45.96', 0, NULL, NULL, '&lt;p&gt;&lt;strong&gt;.NET&lt;/strong&gt; is a very popular Object Oriented Programming Language Family.This Family includes members such as &lt;strong&gt;C#&lt;/strong&gt; (pronounced &lt;strong&gt;CSharp&lt;/strong&gt;), &lt;strong&gt;VB.NET&lt;/strong&gt;, &lt;strong&gt;F#&lt;/strong&gt; (pronounced &lt;strong&gt;Fsharp&lt;/strong&gt;), &lt;strong&gt;J#&lt;/strong&gt; (pronounced &lt;strong&gt;JSharp&lt;/strong&gt;) and much more. The &lt;strong&gt;.NET&lt;/strong&gt; Family offers programming with small effort with well-known high speed of compiled languages such as &lt;strong&gt;C&lt;/strong&gt; and &lt;strong&gt;C++&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This Tag &lt;strong&gt;aims&lt;/strong&gt; to group &lt;strong&gt;Data Science Questions and Answers&lt;/strong&gt; which users want to operate their processes &lt;strong&gt;under .NET&lt;/strong&gt; Programming Language Family&lt;/p&gt;
', NULL, NULL, '''/p'':74C,104C ''/strong'':4C,21C,25C,28C,31C,35C,38C,42C,49C,69C,73C,80C,89C,100C ''aim'':79C ''answer'':88C ''c'':20C,68C,72C ''compil'':63C ''csharp'':24C ''data'':84C ''effort'':55C ''f'':30C ''famili'':14C,50C,103C ''family.this'':13C ''fsharp'':34C ''group'':82C ''high'':60C ''includ'':15C ''j'':37C ''jsharp'':41C ''known'':59C ''languag'':12C,64C,102C ''member'':16C ''much'':44C ''net'':3C,48C,99C ''object'':9C ''offer'':51C ''oper'':94C ''orient'':10C ''p'':1C,75C ''popular'':8C ''process'':96C ''program'':11C,52C,101C ''pronounc'':22C,32C,39C ''question'':86C ''scienc'':85C ''small'':54C ''speed'':61C ''strong'':2C,19C,23C,26C,29C,33C,36C,40C,47C,67C,71C,78C,83C,97C ''tag'':77C ''user'':91C ''vb.net'':27C ''want'':92C ''well'':58C ''well-known'':57C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (168, 4, '2014-05-19 12:17:45.96', 0, NULL, NULL, '.NET is a very popular Object Oriented Programming Language Family which includes members such as C# (pronounced CSharp), VB.NET, F# (pronounced FSharp), J# (pronounced JSharp) and much more. The .NET Family offers programming with small effort with well-known high speed of compiled languages such as C and C++&#x0d;
&#x0d;
This Tag aims to group Data Science Questions and Answers which users want to operate their processes under .NET Programming Language Family', NULL, NULL, '''aim'':53C ''answer'':60C ''c'':16C,48C,50C ''compil'':44C ''csharp'':18C ''data'':56C ''effort'':36C ''f'':20C ''famili'':10C,31C,72C ''fsharp'':22C ''group'':55C ''high'':41C ''includ'':12C ''j'':23C ''jsharp'':25C ''known'':40C ''languag'':9C,45C,71C ''member'':13C ''much'':27C ''net'':1C,30C,69C ''object'':6C ''offer'':32C ''oper'':65C ''orient'':7C ''popular'':5C ''process'':67C ''program'':8C,33C,70C ''pronounc'':17C,21C,24C ''question'':58C ''scienc'':57C ''small'':35C ''speed'':42C ''tag'':52C ''user'':62C ''vb.net'':19C ''want'':63C ''well'':39C ''well-known'':38C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9337, 2, '2015-12-11 10:13:12.463', 2, NULL, NULL, '&lt;p&gt;Yes, the parameter &lt;code&gt;nonnegative&lt;/code&gt; is for constraining values for both matrices: user features and item features to not have values below zeros while optimizing using ALS algorithm. It drive which solver to use: CholeskySolver or NNLS(conjugated gradient). Both are implemented in Spark mllib. &lt;/p&gt;

&lt;p&gt;The flag is of course connected with the input. If You have ratings below zeros and You directly pump all of Your data to model, the prediction of negatives values while You have &lt;code&gt;nonnegative&lt;/code&gt; constrain check will be somehow hard :-)&lt;/p&gt;

&lt;p&gt;In Your case, what''s important is that You have implicit feedback. So, You should set appropriate flag or use &lt;code&gt;trainImplicit&lt;/code&gt; method. The easiest way to find out what works for Your case best with the &lt;code&gt;nonnegative&lt;/code&gt; flag is to threat it as another hyper-parameter which needs cross-validation check. And for me, the measurement of the model while validating is a more crucial aspect. If You are trying to do top-N recommendations, go for ranking measures like Recall@N, MRR@N or AUC.&lt;/p&gt;
', NULL, NULL, '''/code'':7C,85C,116C,134C ''/p'':48C,92C,187C ''al'':29C ''algorithm'':30C ''anoth'':141C ''appropri'':110C ''aspect'':165C ''auc'':186C ''best'':129C ''case'':96C,128C ''check'':87C,150C ''choleskysolv'':37C ''code'':5C,83C,114C,132C ''conjug'':40C ''connect'':55C ''constrain'':10C,86C ''cours'':54C ''cross'':148C ''cross-valid'':147C ''crucial'':164C ''data'':72C ''direct'':67C ''drive'':32C ''easiest'':119C ''featur'':16C,19C ''feedback'':105C ''find'':122C ''flag'':51C,111C,135C ''go'':176C ''gradient'':41C ''hard'':91C ''hyper'':143C ''hyper-paramet'':142C ''implement'':44C ''implicit'':104C ''import'':99C ''input'':58C ''item'':18C ''like'':180C ''matric'':14C ''measur'':155C,179C ''method'':117C ''mllib'':47C ''model'':74C,158C ''mrr'':183C ''n'':174C,182C,184C ''need'':146C ''negat'':78C ''nnls'':39C ''nonneg'':6C,84C,133C ''optim'':27C ''p'':1C,49C,93C ''paramet'':4C,144C ''predict'':76C ''pump'':68C ''rank'':178C ''rate'':62C ''recal'':181C ''recommend'':175C ''set'':109C ''solver'':34C ''somehow'':90C ''spark'':46C ''threat'':138C ''top'':173C ''top-n'':172C ''trainimplicit'':115C ''tri'':169C ''use'':28C,36C,113C ''user'':15C ''valid'':149C,160C ''valu'':11C,23C,79C ''way'':120C ''work'':125C ''yes'':2C ''zero'':25C,64C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (169, 1, '2014-05-19 15:55:24.983', 11, 295, 'How to specify important attributes?', '&lt;p&gt;Assume a set of loosely structured data (e.g. Web tables/Linked Open Data), composed of many data sources. There is no common schema followed by the data and each source can use synonym attributes to describe the values (e.g. "nationality" vs "bornIn"). &lt;/p&gt;

&lt;p&gt;My goal is to find some "important" attributes that somehow "define" the entities that they describe. So, when I find the same value for such an attribute, I will know that the two descriptions are most likely about the same entity (e.g. the same person).&lt;/p&gt;

&lt;p&gt;For example, the attribute "lastName" is more discriminative than the attribute "nationality". &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;How could I (statistically) find such attributes that are more important than others?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;A naive solution would be to take the average IDF of the values of each attribute and make this the "importance" factor of the attribute. A similar approach would be to count how many distinct values appear for each attribute.&lt;/p&gt;

&lt;p&gt;I have seen the term feature, or attribute selection in machine learning, but I don''t want to discard the remaining attributes, I just want to put higher weights to the most important ones.&lt;/p&gt;
', 'machine-learning  statistics  feature-selection', NULL, '''/p'':55C,102C,116C,133C,175C,211C ''/strong'':132C ''appear'':171C ''approach'':162C ''assum'':14C ''attribut'':5A,46C,64C,83C,107C,114C,125C,150C,159C,174C,184C,198C ''averag'':143C ''bornin'':54C ''common'':34C ''compos'':26C ''could'':120C ''count'':166C ''data'':20C,25C,29C,39C ''defin'':67C ''describ'':48C,72C ''descript'':90C ''discard'':195C ''discrimin'':111C ''distinct'':169C ''e.g'':21C,51C,98C ''entiti'':69C,97C ''exampl'':105C ''factor'':156C ''featur'':11B,182C ''feature-select'':10B ''find'':61C,76C,123C ''follow'':36C ''goal'':58C ''higher'':204C ''idf'':144C ''import'':4A,63C,129C,155C,209C ''know'':86C ''lastnam'':108C ''learn'':8B,188C ''like'':93C ''loos'':18C ''machin'':7B,187C ''machine-learn'':6B ''make'':152C ''mani'':28C,168C ''naiv'':136C ''nation'':52C,115C ''one'':210C ''open'':24C ''other'':131C ''p'':13C,56C,103C,117C,134C,176C ''person'':101C ''put'':203C ''remain'':197C ''schema'':35C ''seen'':179C ''select'':12B,185C ''set'':16C ''similar'':161C ''solut'':137C ''somehow'':66C ''sourc'':30C,42C ''specifi'':3A ''statist'':9B,122C ''strong'':118C ''structur'':19C ''synonym'':45C ''tables/linked'':23C ''take'':141C ''term'':181C ''two'':89C ''use'':44C ''valu'':50C,79C,147C,170C ''vs'':53C ''want'':193C,201C ''web'':22C ''weight'':205C ''would'':138C,163C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (170, 2, '2014-05-19 18:08:32.327', 13, NULL, NULL, '&lt;p&gt;A possible solution is to calculate the &lt;a href="http://en.wikipedia.org/wiki/Decision_tree_learning#Information_gain"&gt;information gain&lt;/a&gt; associated to each attribute:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/sUheW.png" alt="Information Gain"&gt;&lt;/p&gt;

&lt;p&gt;Initially you have the whole dataset, and compute the information gain of each item. The item with the best information gain is the one you should use to partition the dataset (considering the item''s values). Then, perform the same computations for each item (but the ones selected), and always choose the one which best &lt;em&gt;describes/differentiates&lt;/em&gt; the entries from your dataset.&lt;/p&gt;

&lt;p&gt;There are implementations available for such computations. &lt;a href="http://en.wikipedia.org/wiki/Decision_tree_learning"&gt;Decision trees&lt;/a&gt; usually base their feature selection on the features with best information gain. You may use the resulting tree structure to find these &lt;em&gt;important&lt;/em&gt; items.&lt;/p&gt;
', NULL, NULL, '''/a'':16C,112C ''/em'':90C,137C ''/p'':21C,31C,96C,139C ''/suhew.png'':27C ''/wiki/decision_tree_learning'':109C ''/wiki/decision_tree_learning#information_gain'':13C ''alt'':28C ''alway'':82C ''associ'':17C ''attribut'':20C ''avail'':101C ''base'':114C ''best'':51C,87C,122C ''calcul'':7C ''choos'':83C ''comput'':40C,73C,104C ''consid'':64C ''dataset'':38C,63C,95C ''decis'':110C ''describes/differentiates'':89C ''em'':88C,135C ''en.wikipedia.org'':12C,108C ''en.wikipedia.org/wiki/decision_tree_learning'':107C ''en.wikipedia.org/wiki/decision_tree_learning#information_gain'':11C ''entri'':92C ''featur'':116C,120C ''find'':133C ''gain'':15C,30C,43C,53C,124C ''href'':10C,106C ''i.stack.imgur.com'':26C ''i.stack.imgur.com/suhew.png'':25C ''img'':23C ''implement'':100C ''import'':136C ''inform'':14C,29C,42C,52C,123C ''initi'':33C ''item'':46C,48C,66C,76C,138C ''may'':126C ''one'':56C,79C,85C ''p'':1C,22C,32C,97C ''partit'':61C ''perform'':70C ''possibl'':3C ''result'':129C ''select'':80C,117C ''solut'':4C ''src'':24C ''structur'':131C ''tree'':111C,130C ''use'':59C,127C ''usual'':113C ''valu'':68C ''whole'':37C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (171, 2, '2014-05-19 19:44:48.5', 4, NULL, NULL, '&lt;p&gt;Two things you might find useful:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Meta_learning_%28computer_science%29" rel="nofollow"&gt;meta-learning&lt;/a&gt; to speedup the search for the right model and the optimal parameters.
Meta learning consists in applying machine learning tools to the problem of finding the right machine learning tool/parameters for the problem at hand. This for instance &lt;a href="http://link.springer.com/chapter/10.1007/978-3-642-14464-6_11" rel="nofollow"&gt;this paper&lt;/a&gt; for a practical example;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/General-purpose_computing_on_graphics_processing_units" rel="nofollow"&gt;gpucomputing&lt;/a&gt; to speedup the algorithm on larger datasets. For instance, &lt;a href="http://docs.opencv.org/modules/gpu/doc/introduction.html" rel="nofollow"&gt;OpenCV can use GPUs&lt;/a&gt;, which are very effective at processing images/videos and can bring 10 to 100 speedups with respect to CPUs. As your computer most probably has a gpucomputing-able GPU, you could gain lots of time using it.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':22C,70C,87C,108C ''/chapter/10.1007/978-3-642-14464-6_11'':65C ''/li'':76C,147C ''/modules/gpu/doc/introduction.html'':101C ''/ol'':148C ''/p'':8C,75C,146C ''/wiki/general-purpose_computing_on_graphics_processing_units'':83C ''/wiki/meta_learning_%28computer_science%29'':16C ''10'':119C ''100'':121C ''abl'':136C ''algorithm'':91C ''appli'':39C ''bring'':118C ''comput'':129C ''consist'':37C ''could'':139C ''cpus'':126C ''dataset'':94C ''docs.opencv.org'':100C ''docs.opencv.org/modules/gpu/doc/introduction.html'':99C ''effect'':112C ''en.wikipedia.org'':15C,82C ''en.wikipedia.org/wiki/general-purpose_computing_on_graphics_processing_units'':81C ''en.wikipedia.org/wiki/meta_learning_%28computer_science%29'':14C ''exampl'':74C ''find'':6C,47C ''gain'':140C ''gpu'':137C ''gpucomput'':86C,135C ''gpucomputing-'':134C ''gpus'':107C ''hand'':57C ''href'':13C,62C,80C,98C ''images/videos'':115C ''instanc'':60C,96C ''larger'':93C ''learn'':21C,36C,41C,51C ''li'':10C,77C ''link.springer.com'':64C ''link.springer.com/chapter/10.1007/978-3-642-14464-6_11'':63C ''lot'':141C ''machin'':40C,50C ''meta'':20C,35C ''meta-learn'':19C ''might'':5C ''model'':30C ''nofollow'':18C,67C,85C,103C ''ol'':9C ''opencv'':104C ''optim'':33C ''p'':1C,11C,78C ''paper'':69C ''paramet'':34C ''practic'':73C ''probabl'':131C ''problem'':45C,55C ''process'':114C ''rel'':17C,66C,84C,102C ''respect'':124C ''right'':29C,49C ''search'':26C ''speedup'':24C,89C,122C ''thing'':3C ''time'':143C ''tool'':42C ''tool/parameters'':52C ''two'':2C ''use'':7C,106C,144C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (172, 1, '2014-05-19 23:59:58.07', 16, 2986, 'Is there a straightforward way to run pandas.DataFrame.isin in parallel?', '&lt;p&gt;I have a modeling and scoring program that makes heavy use of the &lt;code&gt;DataFrame.isin&lt;/code&gt; function of pandas, searching through lists of facebook "like" records of individual users for each of a few thousand specific pages. This is the most time-consuming part of the program, more so than the modeling or scoring pieces, simply because it only runs on one core while the rest runs on a few dozen simultaneously.&lt;/p&gt;

&lt;p&gt;Though I know I could manually break up the dataframe into chunks and run the operation in parallel, is there any straightforward way to do that automatically? In other words, is there any kind of package out there that will recognize I''m running an easily-delegated operation and automatically distribute it? Perhaps that''s asking for too much, but I''ve been surprised enough in the past by what''s already available in Python, so I figure it''s worth asking.&lt;/p&gt;

&lt;p&gt;Any other suggestions about how this might be accomplished (even if not by some magic unicorn package!) would also be appreciated. Mainly, just trying to find a way to shave off 15-20 minutes per run without spending an equal amount of time coding the solution.&lt;/p&gt;
', 'performance  python  pandas  parallel', NULL, '''-20'':208C ''/code'':31C ''/p'':89C,174C,222C ''15'':207C ''accomplish'':184C ''alreadi'':163C ''also'':194C ''amount'':216C ''appreci'':196C ''ask'':147C,173C ''automat'':117C,141C ''avail'':164C ''break'':97C ''chunk'':102C ''code'':29C,219C ''consum'':59C ''core'':79C ''could'':95C ''datafram'':100C ''dataframe.isin'':30C ''deleg'':138C ''distribut'':142C ''dozen'':87C ''easili'':137C ''easily-deleg'':136C ''enough'':156C ''equal'':215C ''even'':185C ''facebook'':39C ''figur'':169C ''find'':201C ''function'':32C ''heavi'':25C ''individu'':43C ''kind'':124C ''know'':93C ''like'':40C ''list'':37C ''m'':133C ''magic'':190C ''main'':197C ''make'':24C ''manual'':96C ''might'':182C ''minut'':209C ''model'':19C,68C ''much'':150C ''one'':78C ''oper'':106C,139C ''p'':15C,90C,175C ''packag'':126C,192C ''page'':52C ''panda'':13B,34C ''pandas.dataframe.isin'':8A ''parallel'':10A,14B,108C ''part'':60C ''past'':159C ''per'':210C ''perform'':11B ''perhap'':144C ''piec'':71C ''program'':22C,63C ''python'':12B,166C ''recogn'':131C ''record'':41C ''rest'':82C ''run'':7A,76C,83C,104C,134C,211C ''score'':21C,70C ''search'':35C ''shave'':205C ''simpli'':72C ''simultan'':88C ''solut'':221C ''specif'':51C ''spend'':213C ''straightforward'':4A,112C ''suggest'':178C ''surpris'':155C ''though'':91C ''thousand'':50C ''time'':58C,218C ''time-consum'':57C ''tri'':199C ''unicorn'':191C ''use'':26C ''user'':44C ''ve'':153C ''way'':5A,113C,203C ''without'':212C ''word'':120C ''worth'':172C ''would'':193C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (568, 2, '2014-06-24 20:34:18.473', 3, NULL, NULL, '&lt;p&gt;I suggest the use of Hidden Markov Models, with two possible states: (1) high levels and (0) low levels. &lt;/p&gt;

&lt;p&gt;This technique might be helpful to decode your signal. Probably you would need a specific HMM for each codification.&lt;/p&gt;

&lt;p&gt;If noise is an issue an FIR filter with a Blackman-Harris window function would allow you to isolate the frequency you''re concerned with. &lt;/p&gt;
', NULL, NULL, '''/p'':21C,42C,70C ''0'':18C ''1'':14C ''allow'':60C ''blackman'':55C ''blackman-harri'':54C ''codif'':41C ''concern'':68C ''decod'':29C ''filter'':51C ''fir'':50C ''frequenc'':65C ''function'':58C ''harri'':56C ''help'':27C ''hidden'':7C ''high'':15C ''hmm'':38C ''isol'':63C ''issu'':48C ''level'':16C,20C ''low'':19C ''markov'':8C ''might'':25C ''model'':9C ''need'':35C ''nois'':45C ''p'':1C,22C,43C ''possibl'':12C ''probabl'':32C ''re'':67C ''signal'':31C ''specif'':37C ''state'':13C ''suggest'':3C ''techniqu'':24C ''two'':11C ''use'':5C ''window'':57C ''would'':34C,59C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (173, 2, '2014-05-20 03:56:43.147', 3, NULL, NULL, '&lt;p&gt;Guessing it''s likely you''ve seen this &lt;a href="http://www.youtube.com/watch?v=1GhNXHCQGsM" rel="nofollow"&gt;YouTube demo&lt;/a&gt; and the related &lt;a href="http://www.youtube.com/watch?v=lmG_FjG4Dy8" rel="nofollow"&gt;Google Tech Talk&lt;/a&gt;, which is related to these papers: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://eprints.pascal-network.org/archive/00006951/01/cvpr2010.pdf" rel="nofollow"&gt;P-N Learning: Bootstrapping Binary Classifiers by Structural Constraints&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://epubs.surrey.ac.uk/713800/1/Kalal-PAMI-2011%281%29.pdf" rel="nofollow"&gt;Tracking-Learning-Detection&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;And this set of code on GitHub for &lt;a href="https://github.com/zk00006/OpenTLD/" rel="nofollow"&gt;OpenTLD&lt;/a&gt;. If you check the "read me" on GitHub here, you''ll notice that &lt;a href="https://github.com/zk00006/OpenTLD/blob/master/README" rel="nofollow"&gt;author''s email (Zdenek Kalal)&lt;/a&gt; is listed, so it might be worth sending him an email about your questions, or even inviting him to reply to this question too.&lt;/p&gt;
', NULL, NULL, '''/713800/1/kalal-pami-2011%281%29.pdf'':67C ''/a'':19C,33C,60C,74C,94C,120C ''/archive/00006951/01/cvpr2010.pdf'':47C ''/li'':61C,75C ''/p'':40C,145C ''/ul'':76C ''/watch?v=1ghnxhcqgsm'':14C ''/watch?v=lmg_fjg4dy8'':27C ''/zk00006/opentld/'':90C ''/zk00006/opentld/blob/master/readme'':112C ''author'':115C ''binari'':55C ''bootstrap'':54C ''check'':97C ''classifi'':56C ''code'':82C ''constraint'':59C ''demo'':18C ''detect'':73C ''email'':117C,131C ''eprints.pascal-network.org'':46C ''eprints.pascal-network.org/archive/00006951/01/cvpr2010.pdf'':45C ''epubs.surrey.ac.uk'':66C ''epubs.surrey.ac.uk/713800/1/kalal-pami-2011%281%29.pdf'':65C ''even'':136C ''github'':84C,102C ''github.com'':89C,111C ''github.com/zk00006/opentld/'':88C ''github.com/zk00006/opentld/blob/master/readme'':110C ''googl'':30C ''guess'':2C ''href'':11C,24C,44C,64C,87C,109C ''invit'':137C ''kalal'':119C ''learn'':53C,72C ''li'':42C,62C ''like'':5C ''list'':122C ''ll'':105C ''might'':125C ''n'':52C ''nofollow'':16C,29C,49C,69C,92C,114C ''notic'':106C ''opentld'':93C ''p'':1C,51C,77C ''p-n'':50C ''paper'':39C ''question'':134C,143C ''read'':99C ''rel'':15C,28C,48C,68C,91C,113C ''relat'':22C,36C ''repli'':140C ''seen'':8C ''send'':128C ''set'':80C ''structur'':58C ''talk'':32C ''tech'':31C ''track'':71C ''tracking-learning-detect'':70C ''ul'':41C ''ve'':7C ''worth'':127C ''www.youtube.com'':13C,26C ''www.youtube.com/watch?v=1ghnxhcqgsm'':12C ''www.youtube.com/watch?v=lmg_fjg4dy8'':25C ''youtub'':17C ''zdenek'':118C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9952, 2, '2016-01-25 19:37:47.28', 1, NULL, NULL, '&lt;p&gt;The most common approach is to create business rules handmade, based on the univariate and multivariate analysis of the variable.&lt;/p&gt;

&lt;p&gt;Basically, do some frequency count, see if you could isolate some subset of your data just looking at one or two variables.&lt;/p&gt;

&lt;p&gt;Then when you have your labels, create a linear or so model with this new variable as output. For exemple, a &lt;a href="https://en.wikipedia.org/wiki/Linear_discriminant_analysis" rel="nofollow"&gt;linear discriminant analysis&lt;/a&gt;. The analysis will supply you with new insights on your group.&lt;/p&gt;

&lt;p&gt;If you want to rely on an algorithm, two solutions:&lt;/p&gt;

&lt;p&gt;As you don''t seems to have a lot of variables, a non-supervised segmentation could do the job. For exemple, a k-Nearest Neighbor or a decision tree are basic and good aproachs.&lt;/p&gt;

&lt;p&gt;With a few more variables, I like is to do a &lt;a href="https://en.wikipedia.org/wiki/Principal_component_analysis" rel="nofollow"&gt;principal component analysis&lt;/a&gt; then a non-supervised classification to define your group on the result of the PCA.
Note that a PCA + handmade rules based on the analysis of your PCA result may be enough.&lt;/p&gt;

&lt;p&gt;Each time, in the end, a discriminant analysis and a profile of your groups to asses the quality of your results.&lt;/p&gt;
', NULL, NULL, '''/a'':79C,164C ''/p'':22C,46C,91C,103C,141C,198C,221C ''/wiki/linear_discriminant_analysis'':73C ''/wiki/principal_component_analysis'':158C ''algorithm'':100C ''analysi'':18C,78C,81C,163C,190C,207C ''approach'':5C ''aproach'':140C ''ass'':215C ''base'':12C,187C ''basic'':24C,137C ''busi'':9C ''classif'':170C ''common'':4C ''compon'':162C ''could'':32C,121C ''count'':28C ''creat'':8C,54C ''data'':38C ''decis'':134C ''defin'':172C ''discrimin'':77C,206C ''en.wikipedia.org'':72C,157C ''en.wikipedia.org/wiki/linear_discriminant_analysis'':71C ''en.wikipedia.org/wiki/principal_component_analysis'':156C ''end'':204C ''enough'':197C ''exempl'':67C,126C ''frequenc'':27C ''good'':139C ''group'':90C,174C,213C ''handmad'':11C,185C ''href'':70C,155C ''insight'':87C ''isol'':33C ''job'':124C ''k'':129C ''k-nearest'':128C ''label'':53C ''like'':149C ''linear'':56C,76C ''look'':40C ''lot'':113C ''may'':195C ''model'':59C ''multivari'':17C ''nearest'':130C ''neighbor'':131C ''new'':62C,86C ''nofollow'':75C,160C ''non'':118C,168C ''non-supervis'':117C,167C ''note'':181C ''one'':42C ''output'':65C ''p'':1C,23C,47C,92C,104C,142C,199C ''pca'':180C,184C,193C ''princip'':161C ''profil'':210C ''qualiti'':217C ''rel'':74C,159C ''reli'':97C ''result'':177C,194C,220C ''rule'':10C,186C ''see'':29C ''seem'':109C ''segment'':120C ''solut'':102C ''subset'':35C ''supervis'':119C,169C ''suppli'':83C ''time'':201C ''tree'':135C ''two'':44C,101C ''univari'':15C ''variabl'':21C,45C,63C,115C,147C ''want'':95C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (174, 2, '2014-05-20 09:24:30.697', 4, NULL, NULL, '&lt;p&gt;Another suggestion is to test the &lt;a href="http://en.wikipedia.org/wiki/Logistic_regression" rel="nofollow"&gt;logistic regression&lt;/a&gt;. As an added bonus, the  weights (coefficients) of the model will give you an idea of which sites are age-distriminant.  &lt;/p&gt;

&lt;p&gt;Sklearn offers the &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html" rel="nofollow"&gt;sklearn.linear_model.LogisticRegression&lt;/a&gt; package that is designed to handle sparse data as well.&lt;/p&gt;

&lt;p&gt;As mentionned in the comments, in the present case, with more input variables than samples, you need to regularize the model (with &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html" rel="nofollow"&gt;sklearn.linear_model.LogisticRegression&lt;/a&gt; use the &lt;code&gt;penalty=''l1''&lt;/code&gt; argument).&lt;/p&gt;
', NULL, NULL, '''/a'':17C,53C,96C ''/code'':102C ''/p'':40C,64C,104C ''/stable/modules/generated/sklearn.linear_model.logisticregression.html'':49C,92C ''/wiki/logistic_regression'':12C ''ad'':20C ''age'':38C ''age-distrimin'':37C ''anoth'':2C ''argument'':103C ''bonus'':21C ''case'':74C ''code'':99C ''coeffici'':24C ''comment'':70C ''data'':61C ''design'':57C ''distrimin'':39C ''en.wikipedia.org'':11C ''en.wikipedia.org/wiki/logistic_regression'':10C ''give'':29C ''handl'':59C ''href'':9C,46C,89C ''idea'':32C ''input'':77C ''l1'':101C ''logist'':15C ''mention'':67C ''model'':27C,86C ''need'':82C ''nofollow'':14C,51C,94C ''offer'':43C ''p'':1C,41C,65C ''packag'':54C ''penalti'':100C ''present'':73C ''regress'':16C ''regular'':84C ''rel'':13C,50C,93C ''sampl'':80C ''scikit-learn.org'':48C,91C ''scikit-learn.org/stable/modules/generated/sklearn.linear_model.logisticregression.html'':47C,90C ''site'':35C ''sklearn'':42C ''sklearn.linear_model.logisticregression'':52C,95C ''spars'':60C ''suggest'':3C ''test'':6C ''use'':97C ''variabl'':78C ''weight'':23C ''well'':63C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (175, 1, '2014-05-20 22:14:02.927', 2, 35, 'Can metadata be used to adapt parsing for an unescaped in field use of the delimiter?', '&lt;p&gt;I have data coming from a source system that is pipe delimited. Pipe was selected over comma since it was believed no pipes appeared in field, while it was known that commas do occur. After ingesting this data into Hive however it has been discovered that rarely a field does in fact contain a pipe character.&lt;/p&gt;

&lt;p&gt;Due to a constraint we are unable to regenerate from source to escape the delimiter or change delimiters in the usual way. However we have the metadata used to create the Hive table. Could we use knowledge of the fields around the problem field to reprocess the file on our side to escape it or to change the file delimiter prior to reloading the data into Hive?&lt;/p&gt;
', 'metadata  parsing', NULL, '''/p'':76C,145C ''adapt'':6A ''appear'':43C ''around'':118C ''believ'':40C ''chang'':94C,134C ''charact'':75C ''come'':23C ''comma'':36C,51C ''constraint'':81C ''contain'':72C ''could'':111C ''creat'':107C ''data'':22C,57C,142C ''delimit'':16A,31C,92C,95C,137C ''discov'':64C ''due'':78C ''escap'':90C,130C ''fact'':71C ''field'':12A,45C,68C,117C,121C ''file'':125C,136C ''hive'':59C,109C,144C ''howev'':60C,100C ''ingest'':55C ''knowledg'':114C ''known'':49C ''metadata'':2A,17B,104C ''occur'':53C ''p'':19C,77C ''pars'':7A,18B ''pipe'':30C,32C,42C,74C ''prior'':138C ''problem'':120C ''rare'':66C ''regener'':86C ''reload'':140C ''reprocess'':123C ''select'':34C ''side'':128C ''sinc'':37C ''sourc'':26C,88C ''system'':27C ''tabl'':110C ''unabl'':84C ''unescap'':10A ''use'':4A,13A,105C,113C ''usual'':98C ''way'':99C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (316, 2, '2014-06-11 16:25:34.747', 8, NULL, NULL, '&lt;p&gt;As Konstantin has pointed, R performs all its computation in the system''s memory i.e. RAM. Hence, RAM capacity is a very important constraint for computation intensive operations in R. Overcoming this constraint, data is being stored these days in HDFS systems, where data isn''t loaded onto memory and program is run instead, program goes to the data and performs the operations, thus overcoming the memory constraints.  RHadoop (&lt;a href="https://github.com/RevolutionAnalytics/RHadoop/wiki"&gt;https://github.com/RevolutionAnalytics/RHadoop/wiki&lt;/a&gt;) is the connector you are looking for.&lt;/p&gt;

&lt;p&gt;Coming to the impact on algorithms which are computation intensive, Random Forests/Decision Trees/Ensemble methods on a considerable amount of data (minimum 50,000 observations in my experience) take up a lot of memory and are considerably slow. To speed up the process, parallelization is the way to go and parallelization is inherently available in Hadoop! That''s where, Hadoop is really efficient.&lt;/p&gt;

&lt;p&gt;So, if you are going for ensemble methods which are compute intensive and are slow, you would want to try out on the HDFS system which gives a considerable performance improvement.&lt;/p&gt;
', NULL, NULL, '''/p'':86C,150C,183C ''/revolutionanalytics/rhadoop/wiki'':75C ''/revolutionanalytics/rhadoop/wiki&lt;/a&gt;)'':78C ''000'':110C ''50'':109C ''algorithm'':93C ''amount'':105C ''avail'':140C ''capac'':20C ''come'':88C ''comput'':10C,27C,96C,162C ''connector'':81C ''consider'':104C,123C,180C ''constraint'':25C,34C,69C ''data'':35C,45C,60C,107C ''day'':40C ''effici'':149C ''ensembl'':158C ''experi'':114C ''forests/decision'':99C ''github.com'':74C,77C ''github.com/revolutionanalytics/rhadoop/wiki'':73C ''github.com/revolutionanalytics/rhadoop/wiki&lt;/a&gt;)'':76C ''give'':178C ''go'':135C,156C ''goe'':57C ''hadoop'':142C,146C ''hdfs'':42C,175C ''henc'':18C ''href'':72C ''i.e'':16C ''impact'':91C ''import'':24C ''improv'':182C ''inher'':139C ''instead'':55C ''intens'':28C,97C,163C ''isn'':46C ''konstantin'':3C ''load'':48C ''look'':84C ''lot'':118C ''memori'':15C,50C,68C,120C ''method'':101C,159C ''minimum'':108C ''observ'':111C ''onto'':49C ''oper'':29C,64C ''overcom'':32C,66C ''p'':1C,87C,151C ''parallel'':130C,137C ''perform'':7C,62C,181C ''point'':5C ''process'':129C ''program'':52C,56C ''r'':6C,31C ''ram'':17C,19C ''random'':98C ''realli'':148C ''rhadoop'':70C ''run'':54C ''slow'':124C,166C ''speed'':126C ''store'':38C ''system'':13C,43C,176C ''take'':115C ''thus'':65C ''trees/ensemble'':100C ''tri'':171C ''want'':169C ''way'':133C ''would'':168C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (176, 1, '2014-05-21 05:29:36.787', 9, 836, 'How to animate growth of a social network?', '&lt;p&gt;I am seeking for a library/tool to visualize how social network changes when new nodes/edges are added to it.&lt;/p&gt;

&lt;p&gt;One of the existing solutions is &lt;a href="http://www.stanford.edu/group/sonia/"&gt;SoNIA: Social Network Image Animator&lt;/a&gt;. It let''s you make movies like &lt;a href="https://www.youtube.com/watch?v=yGSNCED6mDc"&gt;this one&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;SoNIA''s documentation says that it''s broken at the moment, and besides this I would prefer JavaScript-based solution instead. So, my question is: are you familiar with any tools or are you able to point me to some libraries which would make this task as easy as possible?&lt;/p&gt;

&lt;p&gt;Right after posting this question I''ll dig into &lt;a href="http://sigmajs.org/"&gt;sigma.js&lt;/a&gt;, so please consider this library covered.&lt;/p&gt;

&lt;p&gt;In general, my input data would be something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;time_elapsed; node1; node2
1; A; B
2; A; C
3; B; C
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So, here we have three points in time (1, 2, 3), three nodes (A, B, C), and three edges, which represent a triadic closure between the three considered nodes.&lt;/p&gt;

&lt;p&gt;Moreover, every node will have two attributes (age and gender), so I would like to be able to change the shape/colour of the nodes.&lt;/p&gt;

&lt;p&gt;Also, after adding a new node, it would be perfect to have some ForceAtlas2 or similar algorithm to adjust the layout of the graph.&lt;/p&gt;
', 'social-network-analysis  time-series  javascript  visualization', NULL, '''/a'':56C,71C,140C ''/code'':175C ''/group/sonia/'':50C ''/p'':38C,72C,125C,147C,159C,207C,233C,259C ''/pre'':176C ''/watch?v=ygsnced6mdc'':68C ''1'':166C,186C ''2'':169C,187C ''3'':172C,188C ''abl'':109C,225C ''ad'':35C,237C ''adjust'':253C ''age'':216C ''algorithm'':251C ''also'':235C ''analysi'':12B ''anim'':3A,55C ''attribut'':215C ''b'':168C,173C,192C ''base'':93C ''besid'':86C ''broken'':81C ''c'':171C,174C,193C ''chang'':30C,227C ''closur'':201C ''code'':161C ''consid'':143C,205C ''cover'':146C ''data'':153C ''dig'':134C ''document'':76C ''easi'':122C ''edg'':196C ''elaps'':163C ''everi'':210C ''exist'':43C ''familiar'':102C ''forceatlas2'':248C ''gender'':218C ''general'':150C ''graph'':258C ''growth'':4A ''href'':47C,65C,137C ''imag'':54C ''input'':152C ''instead'':95C ''javascript'':16B,92C ''javascript-bas'':91C ''layout'':255C ''let'':58C ''librari'':115C,145C ''library/tool'':24C ''like'':63C,157C,222C ''ll'':133C ''make'':61C,118C ''moment'':84C ''moreov'':209C ''movi'':62C ''network'':8A,11B,29C,53C ''new'':32C,239C ''node'':190C,206C,211C,232C,240C ''node1'':164C ''node2'':165C ''nodes/edges'':33C ''one'':40C,70C ''p'':18C,39C,73C,126C,148C,177C,208C,234C ''perfect'':244C ''pleas'':142C ''point'':111C,183C ''possibl'':124C ''post'':129C ''pre'':160C ''prefer'':90C ''question'':98C,131C ''repres'':198C ''right'':127C ''say'':77C ''seek'':21C ''seri'':15B ''shape/colour'':229C ''sigma.js'':139C ''sigmajs.org'':138C ''similar'':250C ''social'':7A,10B,28C,52C ''social-network-analysi'':9B ''solut'':44C,94C ''someth'':156C ''sonia'':51C,74C ''task'':120C ''three'':182C,189C,195C,204C ''time'':14B,162C,185C ''time-seri'':13B ''tool'':105C ''triadic'':200C ''two'':214C ''visual'':17B,26C ''would'':89C,117C,154C,221C,242C ''www.stanford.edu'':49C ''www.stanford.edu/group/sonia/'':48C ''www.youtube.com'':67C ''www.youtube.com/watch?v=ygsnced6mdc'':66C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (448, 2, '2014-06-18 05:35:35.7', 1, NULL, NULL, '&lt;p&gt;Not sure about the YARN, but I think that Spark makes a real difference compared to Hadoop (advertised as 100 times faster) if data can fit nicely in the memory of the computational nodes. Simply because it avoids hard disk access. If data doesn''t fit memory there''s still some gain because of buffering.&lt;/p&gt;
', NULL, NULL, '''/p'':57C ''100'':21C ''access'':42C ''advertis'':19C ''avoid'':39C ''buffer'':56C ''compar'':16C ''comput'':34C ''data'':25C,44C ''differ'':15C ''disk'':41C ''doesn'':45C ''faster'':23C ''fit'':27C,47C ''gain'':53C ''hadoop'':18C ''hard'':40C ''make'':12C ''memori'':31C,48C ''nice'':28C ''node'':35C ''p'':1C ''real'':14C ''simpli'':36C ''spark'':11C ''still'':51C ''sure'':3C ''think'':9C ''time'':22C ''yarn'':6C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8944, 2, '2015-11-20 14:30:49.877', 1, NULL, NULL, '&lt;p&gt;How much help this community can be depends on where in the process you are.  If you have a dataset and are looking on ways to &lt;a href="https://en.wikipedia.org/wiki/Feature_engineering" rel="nofollow"&gt;determine the most predictive features&lt;/a&gt;, then you could include some information as to what kind of data you''ve collected/have access to.  &lt;/p&gt;

&lt;p&gt;If instead your question is specifically "what data should I collect to predict purchases from banks, and what models should I use," you are unlikely to get a very specific answer.  &lt;/p&gt;
', NULL, NULL, '''/a'':40C ''/p'':58C,91C ''/wiki/feature_engineering'':32C ''access'':56C ''answer'':90C ''bank'':75C ''collect'':70C ''collected/have'':55C ''communiti'':6C ''could'':43C ''data'':52C,67C ''dataset'':21C ''depend'':9C ''determin'':35C ''en.wikipedia.org'':31C ''en.wikipedia.org/wiki/feature_engineering'':30C ''featur'':39C ''get'':86C ''help'':4C ''href'':29C ''includ'':44C ''inform'':46C ''instead'':61C ''kind'':50C ''look'':24C ''model'':78C ''much'':3C ''nofollow'':34C ''p'':1C,59C ''predict'':38C,72C ''process'':14C ''purchas'':73C ''question'':63C ''rel'':33C ''specif'':65C,89C ''unlik'':84C ''use'':81C ''ve'':54C ''way'':26C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (179, 2, '2014-05-21 07:09:20.093', 4, NULL, NULL, '&lt;p&gt;My first guess is to &lt;a href="https://www.google.com/search?q=visualize%20social%20network%20in%20tableau" rel="nofollow"&gt;visualize social network in Tableau&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;And particularly: &lt;a href="http://www.clearlyandsimply.com/clearly_and_simply/2012/12/build-network-graphs-in-tableau.html" rel="nofollow"&gt;building network graphs in Tableau&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;What you need is to add time dimension to the "Pages" section to be able to see network change dynamics.&lt;/p&gt;

&lt;p&gt;This is screen from the link above.
&lt;img src="http://i.stack.imgur.com/Quq8G.png" alt="enter image description here"&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':19C,36C ''/clearly_and_simply/2012/12/build-network-graphs-in-tableau.html'':28C ''/p'':20C,37C,59C,78C ''/quq8g.png'':72C ''/search?q=visualize%20social%20network%20in%20tableau'':11C ''abl'':53C ''add'':44C ''alt'':73C ''build'':31C ''chang'':57C ''descript'':76C ''dimens'':46C ''dynam'':58C ''enter'':74C ''first'':3C ''graph'':33C ''guess'':4C ''href'':8C,25C ''i.stack.imgur.com'':71C ''i.stack.imgur.com/quq8g.png'':70C ''imag'':75C ''img'':68C ''link'':66C ''need'':41C ''network'':16C,32C,56C ''nofollow'':13C,30C ''p'':1C,21C,38C,60C ''page'':49C ''particular'':23C ''rel'':12C,29C ''screen'':63C ''section'':50C ''see'':55C ''social'':15C ''src'':69C ''tableau'':18C,35C ''time'':45C ''visual'':14C ''www.clearlyandsimply.com'':27C ''www.clearlyandsimply.com/clearly_and_simply/2012/12/build-network-graphs-in-tableau.html'':26C ''www.google.com'':10C ''www.google.com/search?q=visualize%20social%20network%20in%20tableau'':9C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (229, 2, '2014-06-01 19:48:41.693', 8, NULL, NULL, '&lt;p&gt;This is kind of like asking about the tradeoffs between frying pan and your drawer of silverware. They are not two things you compare, really. You might use them together as part of a larger project. &lt;/p&gt;

&lt;p&gt;Hadoop itself is not one thing, but a name for a federation of services, like HDFS, Hive, HBase, MapReduce, etc. Storm is something you use with some of these services, like HDFS or HBase. It is a stream-processing framework. There are others within the extended Hadoop ecosystem, like Spark Streaming.&lt;/p&gt;

&lt;p&gt;When would you choose a stream-processing framework? when you need to react to new data in near-real-time. If you need this kind of tool, you deploy this kind of tool, too.&lt;/p&gt;
', NULL, NULL, '''/p'':38C,92C,130C ''ask'':7C ''choos'':97C ''compar'':25C ''data'':110C ''deploy'':124C ''drawer'':16C ''ecosystem'':88C ''etc'':59C ''extend'':86C ''feder'':51C ''framework'':80C,102C ''fri'':12C ''hadoop'':40C,87C ''hbase'':57C,73C ''hdfs'':55C,71C ''hive'':56C ''kind'':4C,120C,126C ''larger'':36C ''like'':6C,54C,70C,89C ''mapreduc'':58C ''might'':28C ''name'':48C ''near'':113C ''near-real-tim'':112C ''need'':105C,118C ''new'':109C ''one'':44C ''other'':83C ''p'':1C,39C,93C ''pan'':13C ''part'':33C ''process'':79C,101C ''project'':37C ''react'':107C ''real'':114C ''realli'':26C ''servic'':53C,69C ''silverwar'':18C ''someth'':62C ''spark'':90C ''storm'':60C ''stream'':78C,91C,100C ''stream-process'':77C,99C ''thing'':23C,45C ''time'':115C ''togeth'':31C ''tool'':122C,128C ''tradeoff'':10C ''two'':22C ''use'':29C,64C ''within'':84C ''would'':95C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (218, 1, '2014-05-29 13:08:09.06', 2, 468, 'built-in cov in pandas DataFrame results ValueError array is too big', '&lt;p&gt;So, I have a dataset with 39.949 variables and 180 rows. dataset is successfully 
saved in DataFrame but when I try to find cov() it result an error.
here is the code&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  import pandas as pd
  cov_data=pd.DataFrame(dataset).cov()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here is the error&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;File "/home/syahdeini/Desktop/FP/pca_2.py", line 44, in find_eagen
cov_data=pd.DataFrame(data_mat).cov()
File "/usr/lib/python2.7/dist-packages/pandas/core/frame.py", line 3716, in cov
baseCov = np.cov(mat.T)
File "/usr/lib/python2.7/dist-packages/numpy/lib/function_base.py", line 1766, in cov
return (dot(X, X.T.conj()) / fact).squeeze()
ValueError: array is too big.
&lt;/code&gt;&lt;/pre&gt;
', 'python  pandas', NULL, '''/code'':61C,110C ''/home/syahdeini/desktop/fp/pca_2.py'':72C ''/p'':49C,68C ''/pre'':62C,111C ''/usr/lib/python2.7/dist-packages/numpy/lib/function_base.py'':94C ''/usr/lib/python2.7/dist-packages/pandas/core/frame.py'':85C ''1766'':96C ''180'':26C ''3716'':87C ''39.949'':23C ''44'':74C ''array'':10A,106C ''basecov'':90C ''big'':13A,109C ''built'':2A ''built-in'':1A ''code'':48C,51C,70C ''cov'':4A,40C,56C,60C,78C,83C,89C,98C ''data'':57C,79C,81C ''datafram'':7A,33C ''dataset'':21C,28C,59C ''dot'':100C ''eagen'':77C ''error'':44C,67C ''fact'':103C ''file'':71C,84C,93C ''find'':39C,76C ''import'':52C ''line'':73C,86C,95C ''mat'':82C ''mat.t'':92C ''np.cov'':91C ''p'':16C,63C ''panda'':6A,15B,53C ''pd'':55C ''pd.dataframe'':58C,80C ''pre'':50C,69C ''python'':14B ''result'':8A,42C ''return'':99C ''row'':27C ''save'':31C ''squeez'':104C ''success'':30C ''tri'':37C ''valueerror'':9A,105C ''variabl'':24C ''x'':101C ''x.t.conj'':102C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (488, 1, '2014-06-19 18:02:24.65', 6, 1366, 'Is GLM a statistical or machine learning model?', '&lt;p&gt;I thought that generalized linear model (GLM) would be considered a statistical model, but a friend told me that some papers classify it as a machine learning technique. Which one is true (or more precise)? Any explanation would be appreciated.&lt;/p&gt;
', 'machine-learning  statistics  glm', NULL, '''/p'':55C ''appreci'':54C ''classifi'':36C ''consid'':24C ''explan'':51C ''friend'':30C ''general'':18C ''glm'':2A,13B,21C ''learn'':7A,11B,41C ''linear'':19C ''machin'':6A,10B,40C ''machine-learn'':9B ''model'':8A,20C,27C ''one'':44C ''p'':14C ''paper'':35C ''precis'':49C ''statist'':4A,12B,26C ''techniqu'':42C ''thought'':16C ''told'':31C ''true'':46C ''would'':22C,52C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (183, 2, '2014-05-21 10:53:26.87', 9, NULL, NULL, '&lt;h3&gt;Fancy animations are cool&lt;/h3&gt;

&lt;p&gt;I was very impressed when I saw &lt;a href="http://youtu.be/T7Ncmq6scck"&gt;this animation&lt;/a&gt; of the &lt;a href="http://www.discourse.org"&gt;discourse&lt;/a&gt; git repository. They used &lt;a href="https://code.google.com/p/gource/"&gt;Gourse&lt;/a&gt; which is specifically for git. But it may give ideas about how to represent the dynamics of growth.&lt;/p&gt;

&lt;h3&gt;You can create animations with matplotlib&lt;/h3&gt;

&lt;p&gt;&lt;a href="http://stackoverflow.com/a/13571425/1083707"&gt;This stackoverflow answer&lt;/a&gt; seems to point at a python/networkx/matplotlib solution.&lt;/p&gt;

&lt;h3&gt;But D3.js provides interaction&lt;/h3&gt;

&lt;p&gt;If you''re looking for a web-based solution then d3.js is excellent. See &lt;a href="http://bl.ocks.org/mbostock/4062045"&gt;this&lt;/a&gt;, &lt;a href="http://mbostock.github.io/d3/talk/20111116/force-collapsible.html"&gt;this&lt;/a&gt; and &lt;a href="http://bl.ocks.org/mbostock/929623"&gt;this&lt;/a&gt; for example.
See also &lt;a href="http://stackoverflow.com/questions/7416659/interactive-graph-visualisation"&gt;this stackoverflow question&lt;/a&gt;, the accepted answer points to D3.js again.&lt;/p&gt;

&lt;h2&gt;Conclusion&lt;/h2&gt;

&lt;p&gt;I would be drawn towards the python/networkx options for network analysis (possibly to add attributes to your raw data file for example). Then, for visualisation and dissemination D3.js is perfect. You might be surprised how easy it can be to write d3.js once you get into it. I believe &lt;a href="https://www.youtube.com/watch?v=8UtoIR2IEkI"&gt;it even works within an ipython notebook!&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':22C,29C,40C,77C,114C,121C,129C,142C,216C ''/a/13571425/1083707'':73C ''/d3/talk/20111116/force-collapsible.html'':119C ''/h2'':153C ''/h3'':6C,67C,91C ''/mbostock/4062045'':112C ''/mbostock/929623'':127C ''/p'':59C,85C,150C,217C ''/p/gource/'':38C ''/questions/7416659/interactive-graph-visualisation'':138C ''/t7ncmq6scck'':19C ''/watch?v=8utoir2ieki'':208C ''accept'':144C ''add'':168C ''also'':133C ''analysi'':165C ''anim'':3C,21C,64C ''answer'':76C,145C ''attribut'':169C ''base'':101C ''believ'':203C ''bl.ocks.org'':111C,126C ''bl.ocks.org/mbostock/4062045'':110C ''bl.ocks.org/mbostock/929623'':125C ''code.google.com'':37C ''code.google.com/p/gource/'':36C ''conclus'':152C ''cool'':5C ''creat'':63C ''d3.js'':88C,104C,148C,182C,196C ''data'':173C ''discours'':28C ''dissemin'':181C ''drawn'':158C ''dynam'':56C ''easi'':190C ''even'':210C ''exampl'':131C,176C ''excel'':106C ''fanci'':2C ''file'':174C ''get'':199C ''git'':30C,45C ''give'':49C ''gours'':39C ''growth'':58C ''h2'':151C ''h3'':1C,60C,86C ''href'':16C,26C,35C,70C,109C,116C,124C,135C,205C ''idea'':50C ''impress'':11C ''interact'':90C ''ipython'':214C ''look'':96C ''matplotlib'':66C ''may'':48C ''mbostock.github.io'':118C ''mbostock.github.io/d3/talk/20111116/force-collapsible.html'':117C ''might'':186C ''network'':164C ''notebook'':215C ''option'':162C ''p'':7C,68C,92C,154C ''perfect'':184C ''point'':80C,146C ''possibl'':166C ''provid'':89C ''python/networkx'':161C ''python/networkx/matplotlib'':83C ''question'':141C ''raw'':172C ''re'':95C ''repositori'':31C ''repres'':54C ''saw'':14C ''see'':107C,132C ''seem'':78C ''solut'':84C,102C ''specif'':43C ''stackoverflow'':75C,140C ''stackoverflow.com'':72C,137C ''stackoverflow.com/a/13571425/1083707'':71C ''stackoverflow.com/questions/7416659/interactive-graph-visualisation'':136C ''surpris'':188C ''toward'':159C ''use'':33C ''visualis'':179C ''web'':100C ''web-bas'':99C ''within'':212C ''work'':211C ''would'':156C ''write'':195C ''www.discourse.org'':27C ''www.youtube.com'':207C ''www.youtube.com/watch?v=8utoir2ieki'':206C ''youtu.be'':18C ''youtu.be/t7ncmq6scck'':17C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (184, 1, '2014-05-21 11:22:34.657', 6, 360, 'Google prediction API: What training/prediction methods Google Prediction API employs?', '&lt;p&gt;The details of the Google Prediction API are on this &lt;a href="https://developers.google.com/prediction/"&gt;page&lt;/a&gt;, but I am not able to find any details about the prediction algorithms running behind the API. &lt;/p&gt;

&lt;p&gt;So far I have gathered that they let you provide your preprocessing steps in PMML format.&lt;/p&gt;
', 'tools', NULL, '''/a'':29C ''/p'':47C,65C ''/prediction/'':27C ''abl'':34C ''algorithm'':42C ''api'':3A,9A,19C,46C ''behind'':44C ''detail'':14C,38C ''developers.google.com'':26C ''developers.google.com/prediction/'':25C ''employ'':10A ''far'':50C ''find'':36C ''format'':64C ''gather'':53C ''googl'':1A,7A,17C ''href'':24C ''let'':56C ''method'':6A ''p'':12C,48C ''page'':28C ''pmml'':63C ''predict'':2A,8A,18C,41C ''preprocess'':60C ''provid'':58C ''run'':43C ''step'':61C ''tool'':11B ''training/prediction'':5A');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (185, 2, '2014-05-21 14:14:38.797', 4, NULL, NULL, '&lt;p&gt;If you take a look over the specifications of PMML which you can find &lt;a href="http://www.dmg.org/v3-0/GeneralStructure.html" rel="nofollow"&gt;here&lt;/a&gt; you can see on the left menu what options you have (like ModelTree, NaiveBayes, Neural Nets and so on).&lt;/p&gt;
', NULL, NULL, '''/a'':24C ''/p'':44C ''/v3-0/generalstructure.html'':20C ''find'':15C ''href'':17C ''left'':30C ''like'':36C ''look'':6C ''menu'':31C ''modeltre'':37C ''naivebay'':38C ''net'':40C ''neural'':39C ''nofollow'':22C ''option'':33C ''p'':1C ''pmml'':11C ''rel'':21C ''see'':27C ''specif'':9C ''take'':4C ''www.dmg.org'':19C ''www.dmg.org/v3-0/generalstructure.html'':18C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (186, 1, '2014-05-21 15:12:18.98', 5, 59, 'Using SVM as a binary classifier, is the label for a data point chosen by consensus?', '&lt;p&gt;I''m learning &lt;a href="http://en.wikipedia.org/wiki/Support_vector_machine"&gt;Support Vector Machines&lt;/a&gt;, and I''m unable to understand how a class label is chosen for a data point in a binary classifier. Is it chosen by consensus with respect to the classification in each dimension of the separating hyperplane?&lt;/p&gt;
', 'svm  classification  binary', NULL, '''/a'':32C ''/p'':70C ''/wiki/support_vector_machine'':28C ''binari'':5A,19B,51C ''chosen'':14A,44C,55C ''class'':41C ''classif'':18B,62C ''classifi'':6A,52C ''consensus'':16A,57C ''data'':12A,47C ''dimens'':65C ''en.wikipedia.org'':27C ''en.wikipedia.org/wiki/support_vector_machine'':26C ''href'':25C ''hyperplan'':69C ''label'':9A,42C ''learn'':23C ''m'':22C,35C ''machin'':31C ''p'':20C ''point'':13A,48C ''respect'':59C ''separ'':68C ''support'':29C ''svm'':2A,17B ''unabl'':36C ''understand'':38C ''use'':1A ''vector'':30C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2405, 2, '2014-11-05 15:16:36.867', -1, NULL, NULL, '&lt;p&gt;Try out AirXcell : &lt;a href="http://airxcell.com" rel="nofollow"&gt;AirXcell calculation software&lt;/a&gt;.
See documentation &lt;a href="http://www.airxcell.com/doc/howTo/useConsole.html" rel="nofollow"&gt;Use AirXCell as an r Console&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':13C,29C ''/doc/howto/useconsole.html'':20C ''/p'':30C ''airxcel'':4C,10C,24C ''airxcell.com'':7C ''calcul'':11C ''consol'':28C ''document'':15C ''href'':6C,17C ''nofollow'':9C,22C ''p'':1C ''r'':27C ''rel'':8C,21C ''see'':14C ''softwar'':12C ''tri'':2C ''use'':23C ''www.airxcell.com'':19C ''www.airxcell.com/doc/howto/useconsole.html'':18C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (187, 2, '2014-05-21 15:39:54.83', 6, NULL, NULL, '&lt;p&gt;The term &lt;em&gt;consensus&lt;/em&gt;, as far as I''m concerned, is used rather for cases when you have more a than one source of metric/measure/choice from which to make a decision. And, in order to choose a possible result, you perform some &lt;em&gt;average evaluation/consensus&lt;/em&gt; over the values available.&lt;/p&gt;

&lt;p&gt;This is not the case for SVM. The algorithm is based on a &lt;a href="http://upload.wikimedia.org/wikipedia/commons/2/2a/Svm_max_sep_hyperplane_with_margin.png"&gt;quadratic optimization&lt;/a&gt;, that maximizes the distance from the closest documents of two different classes, using a hyperplane to make the split.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/CCO7Z.png" alt="Hyperplane separating two different classes"&gt;&lt;/p&gt;

&lt;p&gt;So, the only &lt;em&gt;consensus&lt;/em&gt; here is the resulting hyperplane, computed from the closest documents of each class. In other words, the classes are attributed to each point by calculating the distance from the point to the hyperplane derived. If the distance is positive, it belongs to a certain class, otherwise, it belongs to the other one.&lt;/p&gt;
', NULL, NULL, '''/a'':75C ''/cco7z.png'':101C ''/em'':6C,48C,115C ''/p'':53C,95C,108C,168C ''/wikipedia/commons/2/2a/svm_max_sep_hyperplane_with_margin.png'':72C ''algorithm'':63C ''alt'':102C ''attribut'':135C ''avail'':52C ''averag'':46C ''base'':65C ''belong'':156C,163C ''calcul'':140C ''case'':17C,59C ''certain'':159C ''choos'':38C ''class'':87C,107C,128C,133C,160C ''closest'':82C,124C ''comput'':121C ''concern'':12C ''consensus'':5C,114C ''decis'':33C ''deriv'':149C ''differ'':86C,106C ''distanc'':79C,142C,152C ''document'':83C,125C ''em'':4C,45C,113C ''evaluation/consensus'':47C ''far'':8C ''href'':69C ''hyperplan'':90C,103C,120C,148C ''i.stack.imgur.com'':100C ''i.stack.imgur.com/cco7z.png'':99C ''img'':97C ''m'':11C ''make'':31C,92C ''maxim'':77C ''metric/measure/choice'':27C ''one'':24C,167C ''optim'':74C ''order'':36C ''otherwis'':161C ''p'':1C,54C,96C,109C ''perform'':43C ''point'':138C,145C ''posit'':154C ''possibl'':40C ''quadrat'':73C ''rather'':15C ''result'':41C,119C ''separ'':104C ''sourc'':25C ''split'':94C ''src'':98C ''svm'':61C ''term'':3C ''two'':85C,105C ''upload.wikimedia.org'':71C ''upload.wikimedia.org/wikipedia/commons/2/2a/svm_max_sep_hyperplane_with_margin.png'':70C ''use'':14C,88C ''valu'':51C ''word'':131C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (188, 2, '2014-05-21 16:13:25.59', 4, NULL, NULL, '&lt;p&gt;You can use map reduce algorithms in Hadoop without programming them in Java. It is called streaming and works like Linux piping. If you believe that you can port your functions to read and write to terminal, it should work nicely. &lt;a href="http://www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/" rel="nofollow"&gt;Here&lt;/a&gt; is example blog post which shows how to use map reduce functions written in Python in Hadoop.&lt;/p&gt;
', NULL, NULL, '''/a'':51C ''/p'':69C ''/tutorials/writing-an-hadoop-mapreduce-program-in-python/'':47C ''algorithm'':7C ''believ'':26C ''blog'':54C ''call'':17C ''exampl'':53C ''function'':32C,63C ''hadoop'':9C,68C ''href'':44C ''java'':14C ''like'':21C ''linux'':22C ''map'':5C,61C ''nice'':42C ''nofollow'':49C ''p'':1C ''pipe'':23C ''port'':30C ''post'':55C ''program'':11C ''python'':66C ''read'':34C ''reduc'':6C,62C ''rel'':48C ''show'':57C ''stream'':18C ''termin'':38C ''use'':4C,60C ''without'':10C ''work'':20C,41C ''write'':36C ''written'':64C ''www.michael-noll.com'':46C ''www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/'':45C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (196, 1, '2014-05-22 21:47:26.98', 6, 627, 'Algorithm for generating classification rules', '&lt;p&gt;So we have potential for a machine learning application that fits fairly neatly into the traditional problem domain solved by classifiers, i.e., we have a set of attributes describing an item and a "bucket" that they end up in. However, rather than create models of probabilities like in Naive Bayes or similar classifiers, we want our output to be a set of roughly human-readable rules that can be reviewed and modified by an end user.&lt;/p&gt;

&lt;p&gt;Association rule learning looks like the family of algorithms that solves this type of problem, but these algorithms seem to focus on identifying common combinations of features and don''t include the concept of a final bucket that those features might point to. For example, our data set looks something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Item A { 4-door, small, steel } =&amp;gt; { sedan }
Item B { 2-door, big,   steel } =&amp;gt; { truck }
Item C { 2-door, small, steel } =&amp;gt; { coupe }
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I just want the rules that say "if it''s big and a 2-door, it''s a truck," not the rules that say "if it''s a 4-door it''s also small." &lt;/p&gt;

&lt;p&gt;One workaround I can think of is to simply use association rule learning algorithms and ignore the rules that don''t involve an end bucket, but that seems a bit hacky. Have I missed some family of algorithms out there? Or perhaps I''m approaching the problem incorrectly to begin with?&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/code'':169C ''/p'':88C,142C,206C,259C ''/pre'':170C ''2'':155C,163C,185C ''4'':147C,200C ''algorithm'':1A,98C,107C,221C,245C ''also'':204C ''applic'':19C ''approach'':252C ''associ'':90C,218C ''attribut'':38C ''b'':154C ''bay'':60C ''begin'':257C ''big'':157C,182C ''bit'':237C ''bucket'':44C,126C,232C ''c'':162C ''classif'':4A,9B ''classifi'':31C,63C ''code'':144C ''combin'':114C ''common'':113C ''concept'':122C ''coup'':168C ''creat'':53C ''data'':136C ''describ'':39C ''domain'':28C ''door'':148C,156C,164C,186C,201C ''end'':47C,86C,231C ''exampl'':134C ''fair'':22C ''famili'':96C,243C ''featur'':116C,129C ''final'':125C ''fit'':21C ''focus'':110C ''generat'':3A ''gt'':151C,159C,167C ''hacki'':238C ''howev'':50C ''human'':75C ''human-read'':74C ''i.e'':32C ''identifi'':112C ''ignor'':223C ''includ'':120C ''incorrect'':255C ''involv'':229C ''item'':41C,145C,153C,161C ''learn'':8B,18C,92C,220C ''like'':57C,94C,140C ''look'':93C,138C ''m'':251C ''machin'':7B,17C ''machine-learn'':6B ''might'':130C ''miss'':241C ''model'':54C ''modifi'':83C ''naiv'':59C ''neat'':23C ''one'':208C ''output'':67C ''p'':10C,89C,171C,207C ''perhap'':249C ''point'':131C ''potenti'':14C ''pre'':143C ''probabl'':56C ''problem'':27C,104C,254C ''rather'':51C ''readabl'':76C ''review'':81C ''rough'':73C ''rule'':5A,77C,91C,176C,193C,219C,225C ''say'':178C,195C ''sedan'':152C ''seem'':108C,235C ''set'':36C,71C,137C ''similar'':62C ''simpli'':216C ''small'':149C,165C,205C ''solv'':29C,100C ''someth'':139C ''steel'':150C,158C,166C ''think'':212C ''tradit'':26C ''truck'':160C,190C ''type'':102C ''use'':217C ''user'':87C ''want'':65C,174C ''workaround'':209C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (189, 1, '2014-05-21 19:41:19.857', 2, 251, 'Open source solver for large mixed integer programming task?', '&lt;p&gt;I''m currently using &lt;a href="http://en.wikipedia.org/wiki/General_Algebraic_Modeling_System" rel="nofollow"&gt;General Algebraic Modeling System&lt;/a&gt; (GAMS), and more specifically CPLEX within GAMS, to solve a very large mixed integer programming problem. This allows me to parallelize the process over 4 cores (although I have more, CPLEX utilizes a maximum of 4 cores), and it finds an optimal solution in a relatively short amount of time.&lt;/p&gt;

&lt;p&gt;Is there an open source mixed integer programming tool that I could use as an alternative to GAMS and CPLEX? It must be comparable in speed or faster for me to consider it. I have a preference for R based solutions, but I''m open to suggestions of all kinds, and other users may be interested in different solutions.&lt;/p&gt;
', 'r  open-source  parallel  optimization', NULL, '''/a'':32C ''/p'':83C,144C ''/wiki/general_algebraic_modeling_system'':25C ''4'':57C,68C ''algebra'':29C ''allow'':50C ''altern'':100C ''although'':59C ''amount'':80C ''base'':124C ''compar'':108C ''consid'':116C ''core'':58C,69C ''could'':96C ''cplex'':37C,63C,104C ''current'':19C ''differ'':142C ''en.wikipedia.org'':24C ''en.wikipedia.org/wiki/general_algebraic_modeling_system'':23C ''faster'':112C ''find'':72C ''gam'':33C,39C,102C ''general'':28C ''href'':22C ''integ'':7A,46C,91C ''interest'':140C ''kind'':134C ''larg'':5A,44C ''m'':18C,128C ''maximum'':66C ''may'':138C ''mix'':6A,45C,90C ''model'':30C ''must'':106C ''nofollow'':27C ''open'':1A,12B,88C,129C ''open-sourc'':11B ''optim'':15B,74C ''p'':16C,84C ''parallel'':14B,53C ''prefer'':121C ''problem'':48C ''process'':55C ''program'':8A,47C,92C ''r'':10B,123C ''rel'':26C ''relat'':78C ''short'':79C ''solut'':75C,125C,143C ''solv'':41C ''solver'':3A ''sourc'':2A,13B,89C ''specif'':36C ''speed'':110C ''suggest'':131C ''system'':31C ''task'':9A ''time'':82C ''tool'':93C ''use'':20C,97C ''user'':137C ''util'':64C ''within'':38C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (190, 2, '2014-05-22 12:14:49.727', 3, NULL, NULL, '&lt;p&gt;It turned out that this task was quite easy to accomplish using &lt;a href="http://visjs.org/" rel="nofollow"&gt;vis.js&lt;/a&gt;. &lt;a href="http://visjs.org/examples/graph/20_navigation.html" rel="nofollow"&gt;This&lt;/a&gt; was the best example code which I have found.&lt;/p&gt;

&lt;p&gt;The example of what I have built upon this is &lt;a href="http://laboratoriumdanych.pl/jak-powstaje-siec/" rel="nofollow"&gt;here&lt;/a&gt; (scroll to the bottom of this post). This graph represents the growth of a subnetwork of Facebook friends. Green dots are females, blue ones are males. The darker the colour, the older the user. By clicking "Dodaj wÄzÅy" you can add more nodes and edges to the graph.&lt;/p&gt;

&lt;p&gt;Anyway, I am still interested in other ways to accomplish this task, so I won''t accept any answer as for now.&lt;/p&gt;

&lt;p&gt;Thanks for your contributions!&lt;/p&gt;
', NULL, NULL, '''/a'':20C,29C,59C ''/examples/graph/20_navigation.html'':25C ''/jak-powstaje-siec/'':55C ''/p'':39C,110C,134C,140C ''accept'':128C ''accomplish'':12C,121C ''add'':102C ''answer'':130C ''anyway'':112C ''best'':32C ''blue'':82C ''bottom'':63C ''built'':47C ''click'':95C ''code'':34C ''colour'':89C ''contribut'':139C ''darker'':87C ''dodaj'':96C ''dot'':79C ''easi'':10C ''edg'':106C ''exampl'':33C,42C ''facebook'':76C ''femal'':81C ''found'':38C ''friend'':77C ''graph'':68C,109C ''green'':78C ''growth'':71C ''href'':15C,22C,52C ''interest'':116C ''laboratoriumdanych.pl'':54C ''laboratoriumdanych.pl/jak-powstaje-siec/'':53C ''male'':85C ''node'':104C ''nofollow'':18C,27C,57C ''older'':91C ''one'':83C ''p'':1C,40C,111C,135C ''post'':66C ''quit'':9C ''rel'':17C,26C,56C ''repres'':69C ''scroll'':60C ''still'':115C ''subnetwork'':74C ''task'':7C,123C ''thank'':136C ''turn'':3C ''upon'':48C ''use'':13C ''user'':93C ''vis.js'':19C ''visjs.org'':16C,24C ''visjs.org/examples/graph/20_navigation.html'':23C ''way'':119C ''won'':126C ''wä'':97C ''y'':99C ''zå'':98C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (191, 1, '2014-05-22 13:36:24.12', 5, 196, 'Multi layer back propagation Neural network for classification', '&lt;p&gt;Can someone explain me, how to classify a data like MNIST with MLBP-Neural network if I make more than one output (e.g 8), I mean if I just use one output I can easily classify the data, but if I use more than one, which output should I choose ?&lt;/p&gt;
', 'neuralnetwork', NULL, '''/p'':62C ''8'':35C ''back'':3A ''choos'':61C ''classif'':8A ''classifi'':17C,47C ''data'':19C,49C ''e.g'':34C ''easili'':46C ''explain'':13C ''layer'':2A ''like'':20C ''make'':29C ''mean'':37C ''mlbp'':24C ''mlbp-neural'':23C ''mnist'':21C ''multi'':1A ''network'':6A,26C ''neural'':5A,25C ''neuralnetwork'':9B ''one'':32C,42C,56C ''output'':33C,43C,58C ''p'':10C ''propag'':4A ''someon'':12C ''use'':41C,53C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (194, 2, '2014-05-22 19:20:14.13', 4, NULL, NULL, '&lt;p&gt;Suppose that you need to classify something in K classes, where K &gt; 2. In this case the most often setup I use is one hot encoding. You will have K output columns, and in the training set you will set all values to 0, except the one which has the category index, which could have value 1. Thus, for each training data set instance you will have all outputs with values 0 or 1, all outputs sum to 1 for each instance.&lt;/p&gt;

&lt;p&gt;This looks like a probability, which reminds me of a technique used often to connect some outputs which are modeled as probability. This is called softmax function, more details &lt;a href="http://en.wikipedia.org/wiki/Softmax_activation_function" rel="nofollow"&gt;on Wikipedia&lt;/a&gt;. This will allow you to put some constraints on the output values (it is basically a logistic function generalization) so that the output values will be modeled as probabilities. &lt;/p&gt;

&lt;p&gt;Finally, with or without softmax you can use the output as a discriminant function to select the proper category.&lt;/p&gt;

&lt;p&gt;Another final thought would be to avoid to encode you variables in a connected way. For example you can have the binary representation of the category index. This would induce to the learner an artificial connection between some outputs which are arbitrary. The one hot encoding has the advantage that is neutral to how labels are indexed.&lt;/p&gt;
', NULL, NULL, '''/a'':124C ''/p'':84C,154C,175C,234C ''/wiki/softmax_activation_function'':119C ''0'':45C,73C ''1'':58C,75C,80C ''2'':14C ''advantag'':225C ''allow'':127C ''anoth'':177C ''arbitrari'':218C ''artifici'':211C ''avoid'':183C ''basic'':139C ''binari'':198C ''call'':110C ''case'':17C ''categori'':52C,174C,202C ''class'':11C ''classifi'':7C ''column'':33C ''connect'':100C,190C,212C ''constraint'':132C ''could'':55C ''data'':63C ''detail'':114C ''discrimin'':168C ''en.wikipedia.org'':118C ''en.wikipedia.org/wiki/softmax_activation_function'':117C ''encod'':27C,185C,222C ''exampl'':193C ''except'':46C ''final'':156C,178C ''function'':112C,142C,169C ''general'':143C ''hot'':26C,221C ''href'':116C ''index'':53C,203C,233C ''induc'':206C ''instanc'':65C,83C ''k'':10C,13C,31C ''label'':231C ''learner'':209C ''like'':88C ''logist'':141C ''look'':87C ''model'':105C,151C ''need'':5C ''neutral'':228C ''nofollow'':121C ''often'':20C,98C ''one'':25C,48C,220C ''output'':32C,70C,77C,102C,135C,147C,165C,215C ''p'':1C,85C,155C,176C ''probabl'':90C,107C,153C ''proper'':173C ''put'':130C ''rel'':120C ''remind'':92C ''represent'':199C ''select'':171C ''set'':38C,41C,64C ''setup'':21C ''softmax'':111C,160C ''someth'':8C ''sum'':78C ''suppos'':2C ''techniqu'':96C ''thought'':179C ''thus'':59C ''train'':37C,62C ''use'':23C,97C,163C ''valu'':43C,57C,72C,136C,148C ''variabl'':187C ''way'':191C ''wikipedia'':123C ''without'':159C ''would'':180C,205C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (195, 2, '2014-05-22 20:48:55.297', 4, NULL, NULL, '&lt;p&gt;Satisfaction is a huge one that I run into a lot.  Huge referring to importance/difficulty/complexity.&lt;/p&gt;

&lt;p&gt;The bottom line is that for very large services (search engines, facebook, linkedin, etc...) your users are simply a collection of log lines.  You have little ability to solicit feed back from them (not a hard and fast rule necessarily).  So you have to infer their positive or negative feedback most of the time.&lt;/p&gt;

&lt;p&gt;This means finding ways, even outside of predictive modelling, to truly tell, from a collection of log lines, whether or not someone actually liked something they experienced.  This simple act is even more fundamental (in my biased opinion) than a/b testing since you''re talking about metrics you will eventually track on a test scorecard.&lt;/p&gt;

&lt;p&gt;Once you have a handle on good SAT metrics then you can start making predictive models and experimenting.  But even deciding what piece of log instrumentation can tell you about SAT is non-trivial (and often changes).&lt;/p&gt;
', NULL, NULL, '''/p'':17C,73C,130C,170C ''a/b'':114C ''abil'':45C ''act'':104C ''actual'':97C ''back'':49C ''bias'':111C ''bottom'':20C ''chang'':169C ''collect'':38C,89C ''decid'':152C ''engin'':29C ''etc'':32C ''even'':79C,106C,151C ''eventu'':124C ''experi'':149C ''experienc'':101C ''facebook'':30C ''fast'':56C ''feed'':48C ''feedback'':68C ''find'':77C ''fundament'':108C ''good'':138C ''handl'':136C ''hard'':54C ''huge'':5C,13C ''importance/difficulty/complexity'':16C ''infer'':63C ''instrument'':157C ''larg'':26C ''like'':98C ''line'':21C,41C,92C ''linkedin'':31C ''littl'':44C ''log'':40C,91C,156C ''lot'':12C ''make'':145C ''mean'':76C ''metric'':121C,140C ''model'':83C,147C ''necessarili'':58C ''negat'':67C ''non'':165C ''non-trivi'':164C ''often'':168C ''one'':6C ''opinion'':112C ''outsid'':80C ''p'':1C,18C,74C,131C ''piec'':154C ''posit'':65C ''predict'':82C,146C ''re'':118C ''refer'':14C ''rule'':57C ''run'':9C ''sat'':139C,162C ''satisfact'':2C ''scorecard'':129C ''search'':28C ''servic'':27C ''simpl'':103C ''simpli'':36C ''sinc'':116C ''solicit'':47C ''someon'':96C ''someth'':99C ''start'':144C ''talk'':119C ''tell'':86C,159C ''test'':115C,128C ''time'':72C ''track'':125C ''trivial'':166C ''truli'':85C ''user'':34C ''way'':78C ''whether'':93C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (197, 2, '2014-05-22 21:54:05.66', 7, NULL, NULL, '&lt;p&gt;C45 made by Quinlan is able to produce rule for prediction. Check this &lt;a href="http://en.wikipedia.org/wiki/C4.5_algorithm"&gt;Wikipedia&lt;/a&gt; page. I know that in &lt;a href="http://www.cs.waikato.ac.nz/~ml/weka/"&gt;Weka&lt;/a&gt; its name is J48. I have no idea which are implementations in R or Python. Anyway, from this kind of decision tree  you should be able to infer rules for prediction.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Later edit&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;Also you might be interested in algorithms for directly inferring rules for classification. RIPPER is one, which again in Weka it received a different name JRip. See the original paper for RIPPER: &lt;a href="http://www.google.ro/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;ved=0CCYQFjAA&amp;amp;url=http://www.cs.utsa.edu/~bylander/cs6243/cohen95ripper.pdf&amp;amp;ei=-XJ-U-7pGoqtyAOej4Ag&amp;amp;usg=AFQjCNFqLnuJWi3gGXVCrugmv3NTRhHHLA&amp;amp;bvm=bv.67229260,d.bGQ&amp;amp;cad=rja"&gt;Fast Effective Rule Induction, W.W. Cohen 1995&lt;/a&gt; &lt;/p&gt;
', NULL, NULL, '''/a'':21C,33C,117C ''/em'':70C ''/p'':65C,71C,118C ''/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;ved=0ccyqfjaa&amp;amp;url=http://www.cs.utsa.edu/~bylander/cs6243/cohen95ripper.pdf&amp;amp;ei=-xj-u-7pgoqtyaoej4ag&amp;amp;usg=afqjcnfqlnujwi3ggxvcrugmv3ntrhhhla&amp;amp;bvm=bv.67229260,d.bgq&amp;amp;cad=rja'':109C ''/wiki/c4.5_algorithm'':19C ''/~ml/weka/'':31C ''1995'':116C ''abl'':7C,59C ''algorithm'':79C ''also'':73C ''anyway'':49C ''c45'':2C ''check'':13C ''classif'':85C ''cohen'':115C ''decis'':54C ''differ'':96C ''direct'':81C ''edit'':69C ''effect'':111C ''em'':67C ''en.wikipedia.org'':18C ''en.wikipedia.org/wiki/c4.5_algorithm'':17C ''fast'':110C ''href'':16C,28C,106C ''idea'':41C ''implement'':44C ''induct'':113C ''infer'':61C,82C ''interest'':77C ''j48'':37C ''jrip'':98C ''kind'':52C ''know'':24C ''later'':68C ''made'':3C ''might'':75C ''name'':35C,97C ''one'':88C ''origin'':101C ''p'':1C,66C,72C ''page'':22C ''paper'':102C ''predict'':12C,64C ''produc'':9C ''python'':48C ''quinlan'':5C ''r'':46C ''receiv'':94C ''ripper'':86C,104C ''rule'':10C,62C,83C,112C ''see'':99C ''tree'':55C ''w.w'':114C ''weka'':32C,92C ''wikipedia'':20C ''www.cs.waikato.ac.nz'':30C ''www.cs.waikato.ac.nz/~ml/weka/'':29C ''www.google.ro'':108C ''www.google.ro/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;ved=0ccyqfjaa&amp;amp;url=http://www.cs.utsa.edu/~bylander/cs6243/cohen95ripper.pdf&amp;amp;ei=-xj-u-7pgoqtyaoej4ag&amp;amp;usg=afqjcnfqlnujwi3ggxvcrugmv3ntrhhhla&amp;amp;bvm=bv.67229260,d.bgq&amp;amp;cad=rja'':107C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (198, 2, '2014-05-23 03:05:57.99', 4, NULL, NULL, '&lt;p&gt;Also, there seem to be a very comprehensive list of data science use cases by function and by vertical on Kaggle - &lt;a href="http://www.kaggle.com/wiki/DataScienceUseCases" rel="nofollow"&gt;"Data Science Use Cases"&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':34C ''/p'':35C ''/wiki/datascienceusecases'':27C ''also'':2C ''case'':15C,33C ''comprehens'':9C ''data'':12C,30C ''function'':17C ''href'':24C ''kaggl'':22C ''list'':10C ''nofollow'':29C ''p'':1C ''rel'':28C ''scienc'':13C,31C ''seem'':4C ''use'':14C,32C ''vertic'':20C ''www.kaggle.com'':26C ''www.kaggle.com/wiki/datascienceusecases'':25C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (199, 1, '2014-05-23 06:25:50.48', 8, 1218, 'What does the alpha and beta hyperparameters contribute to in Latent Dirichlet allocation?', '&lt;p&gt;LDA has two hyperparameters, tuning them changes the induced topics. &lt;/p&gt;

&lt;p&gt;What does the alpha and beta hyperparameters contribute to LDA? &lt;/p&gt;

&lt;p&gt;How does the topic change if one or the other hyperparameters increase or decrease? &lt;/p&gt;

&lt;p&gt;Why are they hyperparamters and not just parameters?&lt;/p&gt;
', 'topic-model  lda  parameter', NULL, '''/p'':30C,42C,58C,68C ''alloc'':13A ''alpha'':4A,35C ''beta'':6A,37C ''chang'':26C,48C ''contribut'':8A,39C ''decreas'':57C ''dirichlet'':12A ''hyperparamet'':7A,23C,38C,54C ''hyperparamt'':63C ''increas'':55C ''induc'':28C ''latent'':11A ''lda'':17B,20C,41C ''model'':16B ''one'':50C ''p'':19C,31C,43C,59C ''paramet'':18B,67C ''topic'':15B,29C,47C ''topic-model'':14B ''tune'':24C ''two'':22C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (200, 2, '2014-05-23 08:34:38.9', 3, NULL, NULL, '&lt;p&gt;You also can create a MongoDB-Hadoop &lt;a href="http://docs.mongodb.org/ecosystem/tutorial/getting-started-with-hadoop/" rel="nofollow"&gt;connection&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':18C ''/ecosystem/tutorial/getting-started-with-hadoop/'':14C ''/p'':19C ''also'':3C ''connect'':17C ''creat'':5C ''docs.mongodb.org'':13C ''docs.mongodb.org/ecosystem/tutorial/getting-started-with-hadoop/'':12C ''hadoop'':9C ''href'':11C ''mongodb'':8C ''mongodb-hadoop'':7C ''nofollow'':16C ''p'':1C ''rel'':15C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (206, 2, '2014-05-23 21:01:18.63', 4, NULL, NULL, '&lt;p&gt;All things considered equal (cost, CPU perf, etc.) you could choose the smallest instance that can hold all of my dataset in memory and scale out. That way &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;you make sure not to induce unnecessary latencies due to network communications, and&lt;/li&gt;
&lt;li&gt;you tend to maximize the overall available memory bandwidth for your processes.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Assuming you are running some sort of &lt;a href="http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29" rel="nofollow"&gt;cross-validation scheme&lt;/a&gt; to optimize some &lt;a href="http://en.wikipedia.org/wiki/Meta-optimization" rel="nofollow"&gt;meta parameter&lt;/a&gt; of your model, assign each core a value to test and choose an many instances as needed to cover all the parameter space in as few rounds as you see fit.&lt;/p&gt;

&lt;p&gt;If your data does not fit in the memory of one system, of course you''ll need to distribute across instances. Then it is a matter of balancing memory latency (better with many instances) with network latency (better with fewer instances) but given the nature of EC2 I''d bet you''ll often prefer to work with few fat instances.&lt;/p&gt;
', NULL, NULL, '''/a'':81C,94C ''/li'':46C,60C ''/p'':30C,126C,188C ''/ul'':61C ''/wiki/cross-validation_%28statistics%29'':74C ''/wiki/meta-optimization'':89C ''across'':147C ''assign'':98C ''assum'':63C ''avail'':54C ''balanc'':155C ''bandwidth'':56C ''bet'':177C ''better'':158C,165C ''choos'':12C,106C ''communic'':44C ''consid'':4C ''core'':100C ''cost'':6C ''could'':11C ''cours'':141C ''cover'':113C ''cpu'':7C ''cross'':78C ''cross-valid'':77C ''d'':176C ''data'':130C ''dataset'':22C ''distribut'':146C ''due'':41C ''ec2'':174C ''en.wikipedia.org'':73C,88C ''en.wikipedia.org/wiki/cross-validation_%28statistics%29'':72C ''en.wikipedia.org/wiki/meta-optimization'':87C ''equal'':5C ''etc'':9C ''fat'':186C ''fewer'':167C ''fit'':125C,133C ''given'':170C ''hold'':18C ''href'':71C,86C ''induc'':38C ''instanc'':15C,109C,148C,161C,168C,187C ''latenc'':40C,157C,164C ''li'':32C,47C ''ll'':143C,179C ''make'':34C ''mani'':108C,160C ''matter'':153C ''maxim'':51C ''memori'':24C,55C,136C,156C ''meta'':92C ''model'':97C ''natur'':172C ''need'':111C,144C ''network'':43C,163C ''nofollow'':76C,91C ''often'':180C ''one'':138C ''optim'':83C ''overal'':53C ''p'':1C,62C,127C ''paramet'':93C,116C ''perf'':8C ''prefer'':181C ''process'':59C ''rel'':75C,90C ''round'':121C ''run'':66C ''scale'':26C ''scheme'':80C ''see'':124C ''smallest'':14C ''sort'':68C ''space'':117C ''sure'':35C ''system'':139C ''tend'':49C ''test'':104C ''thing'':3C ''ul'':31C ''unnecessari'':39C ''valid'':79C ''valu'':102C ''way'':29C ''work'':183C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (201, 2, '2014-05-23 09:09:44.49', 29, NULL, NULL, '&lt;p&gt;Update:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Kaggle.com&lt;/strong&gt;, a home of modern data science &amp;amp; machine learning enthusiasts:), opened &lt;strong&gt;&lt;a href="https://www.kaggle.com/datasets" rel="nofollow"&gt;it''s own repository of the data sets&lt;/a&gt;&lt;/strong&gt;.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;In addition to the listed sources.&lt;/p&gt;

&lt;p&gt;Some social network data sets:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://snap.stanford.edu/data/" rel="nofollow"&gt;Stanford University large network dataset collection (SNAP)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://blog.infochimps.com/2008/12/29/massive-scrape-of-twitters-friend-graph/" rel="nofollow"&gt;A huge twitter dataset that includes followers&lt;/a&gt; + &lt;a href="http://www.infochimps.com/collections/twitter-census" rel="nofollow"&gt;large collection of twitter datasets here&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://mtg.upf.edu/node/1671" rel="nofollow"&gt;LastFM data set&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;There are plenty of sources listed at Stats SE:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/questions/7/locating-freely-available-data-samples/"&gt;Locating freely available data samples&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/questions/12670/data-apis-feeds-available-as-packages-in-r"&gt;Data APIs/feeds available as packages in R&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/questions/973/free-data-set-for-very-high-dimensional-classification"&gt;Free data set for very high dimensional classification&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/2008/12/29/massive-scrape-of-twitters-friend-graph/'':77C ''/a'':35C,70C,87C,101C,114C,140C,155C,171C ''/collections/twitter-census'':92C ''/data/'':60C ''/datasets'':24C ''/li'':71C,102C,115C,141C,156C,172C ''/node/1671'':108C ''/p'':3C,37C,46C,53C,127C ''/questions/12670/data-apis-feeds-available-as-packages-in-r'':147C ''/questions/7/locating-freely-available-data-samples/'':134C ''/questions/973/free-data-set-for-very-high-dimensional-classification'':162C ''/strong'':7C,36C ''/ul'':116C,173C ''addit'':41C ''amp'':14C ''apis/feeds'':149C ''avail'':137C,150C ''blog.infochimps.com'':76C ''blog.infochimps.com/2008/12/29/massive-scrape-of-twitters-friend-graph/'':75C ''classif'':170C ''collect'':68C,96C ''data'':12C,33C,51C,112C,138C,148C,164C ''dataset'':67C,83C,99C ''dimension'':169C ''enthusiast'':17C ''follow'':86C ''free'':163C ''freeli'':136C ''high'':168C ''home'':9C ''hr'':38C ''href'':21C,57C,74C,89C,105C,131C,144C,159C ''huge'':81C ''includ'':85C ''kaggle.com'':6C ''larg'':65C,95C ''lastfm'':111C ''learn'':16C ''li'':55C,72C,103C,129C,142C,157C ''list'':44C,123C ''locat'':135C ''machin'':15C ''modern'':11C ''mtg.upf.edu'':107C ''mtg.upf.edu/node/1671'':106C ''network'':50C,66C ''nofollow'':26C,62C,79C,94C,110C ''open'':18C ''p'':1C,4C,39C,47C,117C ''packag'':152C ''plenti'':120C ''r'':154C ''rel'':25C,61C,78C,93C,109C ''repositori'':30C ''sampl'':139C ''scienc'':13C ''se'':126C ''set'':34C,52C,113C,165C ''snap'':69C ''snap.stanford.edu'':59C ''snap.stanford.edu/data/'':58C ''social'':49C ''sourc'':45C,122C ''stanford'':63C ''stat'':125C ''stats.stackexchange.com'':133C,146C,161C ''stats.stackexchange.com/questions/12670/data-apis-feeds-available-as-packages-in-r'':145C ''stats.stackexchange.com/questions/7/locating-freely-available-data-samples/'':132C ''stats.stackexchange.com/questions/973/free-data-set-for-very-high-dimensional-classification'':160C ''strong'':5C,19C ''twitter'':82C,98C ''ul'':54C,128C ''univers'':64C ''updat'':2C ''www.infochimps.com'':91C ''www.infochimps.com/collections/twitter-census'':90C ''www.kaggle.com'':23C ''www.kaggle.com/datasets'':22C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (203, 2, '2014-05-23 14:28:41.563', 1, NULL, NULL, '&lt;p&gt;Never done stuff &lt;strong&gt;on that scale&lt;/strong&gt;, but as no-one else has jumped in yet have you seen these two papers that discuss non-commercial solutions?  Symphony and COIN-OR seem to be the dominant suggestions.&lt;/p&gt;

&lt;p&gt;Linderoth, Jeffrey T., and Andrea Lodi. "MILP software." Wiley encyclopedia of operations research and management science (2010). &lt;a href="http://homepages.cae.wisc.edu/~Linderot/papers/Linderoth-Lodi-10.pdf" rel="nofollow"&gt;PDF version&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Linderoth, Jeffrey T., and Ted K. Ralphs. "Noncommercial software for mixed-integer linear programming." Integer programming: theory and practice 3 (2005): 253-303. &lt;a href="http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.5872" rel="nofollow"&gt;Compares performance&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''-303'':97C ''/a'':71C,107C ''/p'':43C,72C,108C ''/strong'':9C ''/viewdoc/summary?doi=10.1.1.147.5872'':102C ''/~linderot/papers/linderoth-lodi-10.pdf'':66C ''2005'':95C ''2010'':61C ''253'':96C ''3'':94C ''andrea'':49C ''citeseerx.ist.psu.edu'':101C ''citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.5872'':100C ''coin'':35C ''coin-or'':34C ''commerci'':30C ''compar'':105C ''discuss'':27C ''domin'':41C ''done'':3C ''els'':15C ''encyclopedia'':54C ''homepages.cae.wisc.edu'':65C ''homepages.cae.wisc.edu/~linderot/papers/linderoth-lodi-10.pdf'':64C ''href'':63C,99C ''integ'':86C,89C ''jeffrey'':46C,75C ''jump'':17C ''k'':79C ''linderoth'':45C,74C ''linear'':87C ''lodi'':50C ''manag'':59C ''milp'':51C ''mix'':85C ''mixed-integ'':84C ''never'':2C ''no-on'':12C ''nofollow'':68C,104C ''non'':29C ''non-commerci'':28C ''noncommerci'':81C ''one'':14C ''oper'':56C ''p'':1C,44C,73C ''paper'':25C ''pdf'':69C ''perform'':106C ''practic'':93C ''program'':88C,90C ''ralph'':80C ''rel'':67C,103C ''research'':57C ''scale'':8C ''scienc'':60C ''seem'':37C ''seen'':22C ''softwar'':52C,82C ''solut'':31C ''strong'':5C ''stuff'':4C ''suggest'':42C ''symphoni'':32C ''ted'':78C ''theori'':91C ''two'':24C ''version'':70C ''wiley'':53C ''yet'':19C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (208, 2, '2014-05-24 11:18:26.497', 8, NULL, NULL, '&lt;p&gt;When using IPython, you very nearly don''t have to worry about it (at the expense of some loss of efficiency/greater communication overhead).  The parallel IPython plugin in StarCluster will by default start one engine per physical core on each node (I believe this is configurable but not sure where).  You just run whatever you want across all engines by using the DirectView api (map_sync, apply_sync, ...) or the %px magic commands.   If you are already using IPython in parallel on one machine, using it on a cluster is no different.&lt;/p&gt;

&lt;p&gt;Addressing some of your specific questions:&lt;/p&gt;

&lt;p&gt;"how to reconcile distributing work across cores on an instance vs. instances on a cluster" - You get one engine per core (at least); work is automatically distributed across all cores and across all instances.&lt;/p&gt;

&lt;p&gt;"Is it even practical to parallelize across instances as well as across cores on each instance?" - Yes :)  If the code you are running is embarrassingly parallel (exact same algo on multiple data sets) then you can mostly ignore where a particular engine is running.  If the core requires a lot of communication between engines, then of course you need to structure it so that engines primarily communicate with other engines on the same physical machine; but that kind of problem is not ideally suited for IPython, I think.&lt;/p&gt;

&lt;p&gt;"If so, can anyone give a quick rundown of the pros + cons of running many instances with few cores each vs. a few instances with many cores? Is there a rule of thumb for choosing the right ratio of instances to cores per instance?" - Use the largest c3 instances for compute-bound, and the smallest for memory-bandwidth-bound problems (or small enough that the problem almost stops being memory-bandwidth-bound); for message-passing-bound problems, also use the largest instances but try to partition the problem so that each partition runs on one physical machine and most message passing is within the same partition.  Problems which run significantly slower on N quadruple c3 than on 2N double c3 are rare (an artificial example may be running multiple simple filters on a large number of images, where you go through all images for each filter rather than all filters for the same image).  Using largest instances is a good rule of thumb.&lt;/p&gt;
', NULL, NULL, '''/p'':94C,102C,138C,228C,397C ''2n'':351C ''across'':58C,109C,131C,135C,146C,151C ''address'':96C ''algo'':168C ''almost'':298C ''alreadi'':78C ''also'':311C ''anyon'':233C ''api'':65C ''appli'':68C ''artifici'':357C ''automat'':129C ''bandwidth'':289C,303C ''believ'':44C ''bound'':282C,290C,304C,309C ''c3'':277C,348C,353C ''choos'':264C ''cluster'':90C,118C ''code'':159C ''command'':74C ''communic'':23C,191C,206C ''comput'':281C ''compute-bound'':280C ''con'':241C ''configur'':47C ''core'':39C,110C,124C,133C,152C,186C,248C,256C,271C ''cours'':196C ''data'':171C ''default'':33C ''differ'':93C ''directview'':64C ''distribut'':107C,130C ''doubl'':352C ''efficiency/greater'':22C ''embarrass'':164C ''engin'':36C,60C,122C,181C,193C,204C,209C ''enough'':294C ''even'':142C ''exact'':166C ''exampl'':358C ''expens'':17C ''filter'':364C,379C,383C ''get'':120C ''give'':234C ''go'':373C ''good'':393C ''ideal'':222C ''ignor'':177C ''imag'':370C,376C,387C ''instanc'':113C,115C,137C,147C,155C,245C,253C,269C,273C,278C,315C,390C ''ipython'':4C,27C,80C,225C ''kind'':217C ''larg'':367C ''largest'':276C,314C,389C ''least'':126C ''loss'':20C ''lot'':189C ''machin'':85C,214C,330C ''magic'':73C ''mani'':244C,255C ''map'':66C ''may'':359C ''memori'':288C,302C ''memory-bandwidth-bound'':287C,301C ''messag'':307C,333C ''message-passing-bound'':306C ''most'':176C ''multipl'':170C,362C ''n'':346C ''near'':7C ''need'':198C ''node'':42C ''number'':368C ''one'':35C,84C,121C,328C ''overhead'':24C ''p'':1C,95C,103C,139C,229C ''parallel'':26C,82C,145C,165C ''particular'':180C ''partit'':319C,325C,339C ''pass'':308C,334C ''per'':37C,123C,272C ''physic'':38C,213C,329C ''plugin'':28C ''practic'':143C ''primarili'':205C ''problem'':219C,291C,297C,310C,321C,340C ''pros'':240C ''px'':72C ''quadrupl'':347C ''question'':101C ''quick'':236C ''rare'':355C ''rather'':380C ''ratio'':267C ''reconcil'':106C ''requir'':187C ''right'':266C ''rule'':260C,394C ''run'':54C,162C,183C,243C,326C,342C,361C ''rundown'':237C ''set'':172C ''signific'':343C ''simpl'':363C ''slower'':344C ''small'':293C ''smallest'':285C ''specif'':100C ''starclust'':30C ''start'':34C ''stop'':299C ''structur'':200C ''suit'':223C ''sure'':50C ''sync'':67C,69C ''think'':227C ''thumb'':262C,396C ''tri'':317C ''use'':3C,62C,79C,86C,274C,312C,388C ''vs'':114C,250C ''want'':57C ''well'':149C ''whatev'':55C ''within'':336C ''work'':108C,127C ''worri'':12C ''yes'':156C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (204, 2, '2014-05-23 19:28:01.903', 6, NULL, NULL, '&lt;p&gt;I recently did a similar project in Python (predicting opinions using FB like data), and had good results with the following basic process:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Read in the training set (n = N) by iterating over comma-delimited like records line-by-line and use a counter to identify the most popular pages&lt;/li&gt;
&lt;li&gt;For each of the K most popular pages (I used about 5000, but you can play around with different values), use pandas.DataFrame.isin to test whether each individual in the training set likes each page, then make a N x K dataframe of the results (I''ll call it xdata_train)&lt;/li&gt;
&lt;li&gt;Create a series (I''ll call it ydata_train) containing all of the outcome variables (in my case opinions, in yours age) with the same index as xdata_train&lt;/li&gt;
&lt;li&gt;Set up a random forest classifier through scikit-learn to predict
ydata_train based on xdata_train&lt;/li&gt;
&lt;li&gt;Use scikit-learn''s cross-validation testing to tweak parameters and
refine accuracy (tweaking number of popular pages, number of trees,
min leaf size, etc.)&lt;/li&gt;
&lt;li&gt;Output random forest classifier and list of most popular pages with pickle (or keep in memory if you are doing everything at once)&lt;/li&gt;
&lt;li&gt;Load in the rest of your data, load the list of popular pages (if necessary), and repeat step 2 to produce xdata_new&lt;/li&gt;
&lt;li&gt;Load the random forest classifier (if necessary) and use it to predict values for the xdata_new data&lt;/li&gt;
&lt;li&gt;Output the predicted scores to a new CSV or other output format of your choosing&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;In your case, you''d need to swap out the classifier for a regressor (so see here: &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html"&gt;http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html&lt;/a&gt;) but otherwise the same process should work without much trouble. &lt;/p&gt;

&lt;p&gt;Also, you should be aware of the most amazing feature of random forests in Python: instant parallelization! Those of us who started out doing this in R and then moved over are always amazed, especially when you get to work on a machine with a few dozen cores (see here: &lt;a href="http://blog.yhathq.com/posts/comparing-random-forests-in-python-and-r.html"&gt;http://blog.yhathq.com/posts/comparing-random-forests-in-python-and-r.html&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;Finally, note that this would be a perfect application for network analysis if you have the data on friends as well as the individuals themselves. If you can analyze the ages of a user''s friends, the age of the user will almost certainly be within a year or two of the median among his or her friends, particularly if the users are young enough to have built their friend networks while still in school (since most will be classmates). That prediction would likely trump any you would get from modeling---this is a textbook example of a problem where the right data &gt; the right model every time. &lt;/p&gt;

&lt;p&gt;Good luck!&lt;/p&gt;
', NULL, NULL, '''/li'':57C,109C,140C,160C,189C,214C,239C,259C,276C ''/ol'':277C ''/p'':25C,314C,483C,487C ''/posts/comparing-random-forests-in-python-and-r.html'':370C ''/posts/comparing-random-forests-in-python-and-r.html&lt;/a&gt;).&lt;/p&gt;'':373C ''/stable/modules/generated/sklearn.ensemble.randomforestregressor.html'':300C ''/stable/modules/generated/sklearn.ensemble.randomforestregressor.html&lt;/a&gt;)'':303C ''2'':234C ''5000'':70C ''accuraci'':176C ''age'':132C,405C,412C ''almost'':417C ''also'':316C ''alway'':348C ''amaz'':324C,349C ''among'':428C ''analysi'':386C ''analyz'':403C ''applic'':383C ''around'':75C ''awar'':320C ''base'':156C ''basic'':23C ''blog.yhathq.com'':369C,372C ''blog.yhathq.com/posts/comparing-random-forests-in-python-and-r.html'':368C ''blog.yhathq.com/posts/comparing-random-forests-in-python-and-r.html&lt;/a&gt;).&lt;/p&gt;'':371C ''built'':442C ''call'':105C,116C ''case'':128C,281C ''certain'':418C ''choos'':275C ''classifi'':147C,194C,245C,289C ''classmat'':454C ''comma'':39C ''comma-delimit'':38C ''contain'':120C ''core'':363C ''counter'':50C ''creat'':111C ''cross'':168C ''cross-valid'':167C ''csv'':268C ''d'':283C ''data'':15C,222C,258C,391C,477C ''datafram'':99C ''delimit'':40C ''differ'':77C ''dozen'':362C ''enough'':439C ''especi'':350C ''etc'':188C ''everi'':481C ''everyth'':211C ''exampl'':470C ''fb'':13C ''featur'':325C ''final'':375C ''follow'':22C ''forest'':146C,193C,244C,328C ''format'':272C ''friend'':393C,410C,432C,444C ''get'':353C,463C ''good'':18C,485C ''href'':297C,367C ''identifi'':52C ''index'':136C ''individu'':85C,398C ''instant'':331C ''iter'':36C ''k'':63C,98C ''keep'':204C ''leaf'':186C ''learn'':151C,165C ''li'':27C,58C,110C,141C,161C,190C,215C,240C,260C ''like'':14C,41C,90C,458C ''line'':44C,46C ''line-by-lin'':43C ''list'':196C,225C ''ll'':104C,115C ''load'':216C,223C,241C ''luck'':486C ''machin'':358C ''make'':94C ''median'':427C ''memori'':206C ''min'':185C ''model'':465C,480C ''move'':345C ''much'':312C ''n'':33C,34C,96C ''necessari'':230C,247C ''need'':284C ''network'':385C,445C ''new'':238C,257C,267C ''note'':376C ''number'':178C,182C ''ol'':26C ''opinion'':11C,129C ''otherwis'':305C ''outcom'':124C ''output'':191C,261C,271C ''p'':1C,278C,315C,374C,484C ''page'':56C,66C,92C,181C,200C,228C ''pandas.dataframe.isin'':80C ''parallel'':332C ''paramet'':173C ''particular'':433C ''perfect'':382C ''pickl'':202C ''play'':74C ''popular'':55C,65C,180C,199C,227C ''predict'':10C,153C,252C,263C,456C ''problem'':473C ''process'':24C,308C ''produc'':236C ''project'':7C ''python'':9C,330C ''r'':342C ''random'':145C,192C,243C,327C ''read'':28C ''recent'':3C ''record'':42C ''refin'':175C ''regressor'':292C ''repeat'':232C ''rest'':219C ''result'':19C,102C ''right'':476C,479C ''school'':449C ''scikit'':150C,164C ''scikit-learn'':149C,163C ''scikit-learn.org'':299C,302C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.randomforestregressor.html'':298C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.randomforestregressor.html&lt;/a&gt;)'':301C ''score'':264C ''see'':294C,364C ''seri'':113C ''set'':32C,89C,142C ''similar'':6C ''sinc'':450C ''size'':187C ''start'':337C ''step'':233C ''still'':447C ''swap'':286C ''test'':82C,170C ''textbook'':469C ''time'':482C ''train'':31C,88C,108C,119C,139C,155C,159C ''tree'':184C ''troubl'':313C ''trump'':459C ''tweak'':172C,177C ''two'':424C ''us'':335C ''use'':12C,48C,68C,79C,162C,249C ''user'':408C,415C,436C ''valid'':169C ''valu'':78C,253C ''variabl'':125C ''well'':395C ''whether'':83C ''within'':420C ''without'':311C ''work'':310C,355C ''would'':379C,457C,462C ''x'':97C ''xdata'':107C,138C,158C,237C,256C ''ydata'':118C,154C ''year'':422C ''young'':438C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9001, 2, '2015-11-24 10:45:09.197', 4, NULL, NULL, '&lt;p&gt;In &lt;a href="http://dataskeptic.com/" rel="nofollow"&gt;&lt;strong&gt;Data Skeptic&lt;/strong&gt;&lt;/a&gt; they talk about different aspects of data science:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Data Skeptic is a podcast that alternates between short mini episodes with the host explaining concepts from data science to his non-data scientist wife, and longer interviews featuring practitioners and experts on interesting topics related to data, all through the eye of scientific skepticism.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This is a good intro to Data Science which also explains the basic concepts in a not too technical way.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;A bit more technical is &lt;a href="http://www.partiallyderivative.com/" rel="nofollow"&gt;&lt;strong&gt;Partially Derivative&lt;/strong&gt;&lt;/a&gt;:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Partially Derivative is a podcast about the data of everything. Each week we look at a new way that data has changed how we understand and experience the world. From ancient warfare to modern love and everything in between.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;hr&gt;

&lt;p&gt;A related Quora question: &lt;a href="https://www.quora.com/What-are-the-best-data-science-podcasts" rel="nofollow"&gt;What are the best data science podcasts?&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':12C,110C,175C ''/blockquote'':71C,154C ''/p'':21C,70C,93C,111C,153C,176C ''/strong'':11C,109C ''/what-are-the-best-data-science-podcasts'':165C ''also'':82C ''altern'':30C ''ancient'':144C ''aspect'':17C ''basic'':85C ''best'':171C ''bit'':97C ''blockquot'':22C,112C ''chang'':135C ''concept'':39C,86C ''data'':9C,19C,24C,41C,47C,62C,79C,121C,133C,172C ''dataskeptic.com'':5C ''deriv'':108C,115C ''differ'':16C ''episod'':34C ''everyth'':123C,150C ''experi'':140C ''expert'':56C ''explain'':38C,83C ''eye'':66C ''featur'':53C ''good'':76C ''host'':37C ''hr'':94C,155C ''href'':4C,102C,162C ''interest'':58C ''interview'':52C ''intro'':77C ''longer'':51C ''look'':127C ''love'':148C ''mini'':33C ''modern'':147C ''new'':130C ''nofollow'':7C,105C,167C ''non'':46C ''non-data'':45C ''p'':1C,23C,72C,95C,113C,156C ''partial'':107C,114C ''podcast'':28C,118C,174C ''practition'':54C ''question'':160C ''quora'':159C ''rel'':6C,104C,166C ''relat'':60C,158C ''scienc'':20C,42C,80C,173C ''scientif'':68C ''scientist'':48C ''short'':32C ''skeptic'':10C,25C,69C ''strong'':8C,106C ''talk'':14C ''technic'':91C,99C ''topic'':59C ''understand'':138C ''warfar'':145C ''way'':92C,131C ''week'':125C ''wife'':49C ''world'':142C ''www.partiallyderivative.com'':103C ''www.quora.com'':164C ''www.quora.com/what-are-the-best-data-science-podcasts'':163C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (205, 1, '2014-05-23 19:45:54.283', 8, 303, 'Instances vs. cores when using EC2', '&lt;p&gt;Working on what could often be called "medium data" projects, I''ve been able to parallelize my code (mostly for modeling and prediction in Python) on a single system across anywhere from 4 to 32 cores. Now I''m looking at scaling up to clusters on EC2 (probably with StarCluster/IPython, but open to other suggestions as well), and have been puzzled by how to reconcile distributing work across cores on an instance vs. instances on a cluster.&lt;/p&gt;

&lt;p&gt;Is it even practical to parallelize across instances as well as across cores on each instance? If so, can anyone give a quick rundown of the pros + cons of running many instances with few cores each vs. a few instances with many cores? Is there a rule of thumb for choosing the right ratio of instances to cores per instance? &lt;/p&gt;

&lt;p&gt;Bandwidth and RAM are non-trivial concerns in my projects, but it''s easy to spot when those are the bottlenecks and readjust. It''s much harder, I''d imagine, to benchmark the right mix of cores to instances without repeated testing, and my projects vary too much for any single test to apply to all circumstances. Thanks in advance, and if I''ve just failed to google this one properly, feel free to point me to the right answer somewhere else!&lt;/p&gt;
', 'parallel  clusters  aws', NULL, '''/p'':88C,150C,235C ''32'':45C ''4'':43C ''abl'':24C ''across'':40C,78C,96C,101C ''advanc'':212C ''answer'':232C ''anyon'':109C ''anywher'':41C ''appli'':206C ''aw'':9B ''bandwidth'':152C ''benchmark'':184C ''bottleneck'':173C ''call'':17C ''choos'':140C ''circumst'':209C ''cluster'':8B,55C,87C ''code'':28C ''con'':117C ''concern'':159C ''core'':3A,46C,79C,102C,124C,132C,147C,189C ''could'':14C ''d'':181C ''data'':19C ''distribut'':76C ''easi'':166C ''ec2'':6A,57C ''els'':234C ''even'':92C ''fail'':218C ''feel'':224C ''free'':225C ''give'':110C ''googl'':220C ''harder'':179C ''imagin'':182C ''instanc'':1A,82C,84C,97C,105C,121C,129C,145C,149C,191C ''look'':50C ''m'':49C ''mani'':120C,131C ''medium'':18C ''mix'':187C ''model'':31C ''most'':29C ''much'':178C,200C ''non'':157C ''non-trivi'':156C ''often'':15C ''one'':222C ''open'':62C ''p'':10C,89C,151C ''parallel'':7B,26C,95C ''per'':148C ''point'':227C ''practic'':93C ''predict'':33C ''probabl'':58C ''project'':20C,162C,197C ''proper'':223C ''pros'':116C ''puzzl'':71C ''python'':35C ''quick'':112C ''ram'':154C ''ratio'':143C ''readjust'':175C ''reconcil'':75C ''repeat'':193C ''right'':142C,186C,231C ''rule'':136C ''run'':119C ''rundown'':113C ''scale'':52C ''singl'':38C,203C ''somewher'':233C ''spot'':168C ''starcluster/ipython'':60C ''suggest'':65C ''system'':39C ''test'':194C,204C ''thank'':210C ''thumb'':138C ''trivial'':158C ''use'':5A ''vari'':198C ''ve'':22C,216C ''vs'':2A,83C,126C ''well'':67C,99C ''without'':192C ''work'':11C,77C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (268, 2, '2014-06-10 11:36:19.287', 4, NULL, NULL, '&lt;p&gt;I think &lt;a href="http://www.cs.waikato.ac.nz/ml/weka/" rel="nofollow"&gt;Weka&lt;/a&gt; is a good starting point. You can do a bunch of stuff like supervised learning or clustering and easily compare a large set of algorithms na methodologies.&lt;/p&gt;

&lt;p&gt;Weka''s manual is actually a book on machine learning and data mining that can be used as introductory material. &lt;/p&gt;
', NULL, NULL, '''/a'':12C ''/ml/weka/'':8C ''/p'':40C,62C ''actual'':46C ''algorithm'':37C ''book'':48C ''bunch'':22C ''cluster'':29C ''compar'':32C ''data'':53C ''easili'':31C ''good'':15C ''href'':5C ''introductori'':60C ''larg'':34C ''learn'':27C,51C ''like'':25C ''machin'':50C ''manual'':44C ''materi'':61C ''methodolog'':39C ''mine'':54C ''na'':38C ''nofollow'':10C ''p'':1C,41C ''point'':17C ''rel'':9C ''set'':35C ''start'':16C ''stuff'':24C ''supervis'':26C ''think'':3C ''use'':58C ''weka'':11C,42C ''www.cs.waikato.ac.nz'':7C ''www.cs.waikato.ac.nz/ml/weka/'':6C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (212, 2, '2014-05-27 10:58:00.62', 4, NULL, NULL, '&lt;p&gt;Your system isn''t just trained on items that are recommended right? if so you have a big feedback loop here. You want to learn from all clicks/views, I hope.&lt;/p&gt;

&lt;p&gt;You suggest that not-looking at an item is a negative signal. I strongly suggest you do not treat it that way. Not interacting with something is almost always best treated as no information. If you have an explicit signal that indicates a dislike, like a down vote (or, maybe watched 10 seconds of a video and stopped), maybe that''s valid.&lt;/p&gt;

&lt;p&gt;I would not construe this input as rating-like data. (Although in your case, you may get away with it.) Instead think of them as weights, which is exactly the treatment in the Hu Koren Volinsky paper on ALS that @Trey mentions in a comment. This lets you record relative strength of positive/negative interactions.&lt;/p&gt;

&lt;p&gt;Finally I would note that this paper, while is very likely to be what you''re looking for, does not provide for negative weights. It is simple to extend in this way. If you get that far I can point you to the easy extension, which exists already in two implementations that I know of, in &lt;a href="https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala" rel="nofollow"&gt;Spark&lt;/a&gt; and &lt;a href="https://github.com/cloudera/oryx/blob/master/als-common/src/main/java/com/cloudera/oryx/als/common/factorizer/als/AlternatingLeastSquares.java" rel="nofollow"&gt;Oryx&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':220C,230C ''/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/recommendation/als.scala'':216C ''/cloudera/oryx/blob/master/als-common/src/main/java/com/cloudera/oryx/als/common/factorizer/als/alternatingleastsquares.java'':226C ''/p'':32C,97C,154C,231C ''10'':86C ''al'':138C ''almost'':62C ''alreadi'':203C ''although'':110C ''alway'':63C ''away'':117C ''best'':64C ''big'':19C ''case'':113C ''clicks/views'':29C ''comment'':144C ''constru'':102C ''data'':109C ''dislik'':78C ''easi'':199C ''exact'':128C ''exist'':202C ''explicit'':73C ''extend'':184C ''extens'':200C ''far'':192C ''feedback'':20C ''final'':156C ''get'':116C,190C ''github.com'':215C,225C ''github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/recommendation/als.scala'':214C ''github.com/cloudera/oryx/blob/master/als-common/src/main/java/com/cloudera/oryx/als/common/factorizer/als/alternatingleastsquares.java'':224C ''hope'':31C ''href'':213C,223C ''hu'':133C ''implement'':206C ''indic'':76C ''inform'':68C ''input'':104C ''instead'':120C ''interact'':58C,153C ''isn'':4C ''item'':9C,42C ''know'':209C ''koren'':134C ''learn'':26C ''let'':146C ''like'':79C,108C,166C ''look'':39C,172C ''loop'':21C ''may'':115C ''mayb'':84C,93C ''mention'':141C ''negat'':45C,178C ''nofollow'':218C,228C ''not-look'':37C ''note'':159C ''oryx'':229C ''p'':1C,33C,98C,155C ''paper'':136C,162C ''point'':195C ''positive/negative'':152C ''provid'':176C ''rate'':107C ''rating-lik'':106C ''re'':171C ''recommend'':12C ''record'':148C ''rel'':217C,227C ''relat'':149C ''right'':13C ''second'':87C ''signal'':46C,74C ''simpl'':182C ''someth'':60C ''spark'':219C ''stop'':92C ''strength'':150C ''strong'':48C ''suggest'':35C,49C ''system'':3C ''think'':121C ''train'':7C ''treat'':53C,65C ''treatment'':130C ''trey'':140C ''two'':205C ''valid'':96C ''video'':90C ''volinski'':135C ''vote'':82C ''want'':24C ''watch'':85C ''way'':56C,187C ''weight'':125C,179C ''would'':100C,158C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (207, 2, '2014-05-24 10:36:58.987', 8, NULL, NULL, '&lt;p&gt;A general rule of thumb is to not distribute until you have to. It''s usually more efficient to have N servers of a certain capacity than 2N servers of half that capacity. More of the data access will be local, and therefore fast in memory versus slow across the network.&lt;/p&gt;

&lt;p&gt;At a certain point, scaling up one machine becomes uneconomical because the cost of additional resource scales more than linearly. However this point is still amazingly high.&lt;/p&gt;

&lt;p&gt;On Amazon in particular though, the economics of each instance type can vary a lot if you are using spot market instances. The default pricing more or less means that the same amount of resource costs about the same regardless of the instance type, that can vary a lot; large instances can be cheaper than small ones, or N small instances can be much cheaper than one large machine with equivalent resources.&lt;/p&gt;

&lt;p&gt;One massive consideration here is that the computation paradigm can change quite a lot when you move from one machine to multiple machines. The tradeoffs that the communication overhead induce may force you to, for example, adopt a data-parallel paradigm to scale. That means a different choice of tools and algorithm. For example, SGD looks quite different in-memory and in Python than on MapReduce. So you would have to consider this before parallelizing.&lt;/p&gt;

&lt;p&gt;You may choose to distribute work across a cluster, even if a single node and non-distributed paradigms work for you, for reliability. If a single node fails, you lose all of the computation; a distributed computation can potentially recover and complete just the part of the computation that was lost.&lt;/p&gt;
', NULL, NULL, '''/p'':53C,82C,156C,235C,289C ''2n'':29C ''access'':39C ''across'':50C,243C ''addit'':69C ''adopt'':194C ''algorithm'':210C ''amaz'':80C ''amazon'':85C ''amount'':116C ''becom'':63C ''capac'':27C,34C ''certain'':26C,57C ''chang'':168C ''cheaper'':137C,148C ''choic'':206C ''choos'':239C ''cluster'':245C ''communic'':185C ''complet'':279C ''comput'':165C,271C,274C,285C ''consid'':231C ''consider'':160C ''cost'':67C,119C ''data'':38C,197C ''data-parallel'':196C ''default'':107C ''differ'':205C,216C ''distribut'':10C,241C,254C,273C ''econom'':90C ''effici'':19C ''equival'':154C ''even'':246C ''exampl'':193C,212C ''fail'':265C ''fast'':45C ''forc'':189C ''general'':3C ''half'':32C ''high'':81C ''howev'':75C ''in-memori'':217C ''induc'':187C ''instanc'':93C,105C,126C,134C,144C ''larg'':133C,151C ''less'':111C ''linear'':74C ''local'':42C ''look'':214C ''lose'':267C ''lost'':288C ''lot'':98C,132C,171C ''machin'':62C,152C,177C,180C ''mapreduc'':225C ''market'':104C ''massiv'':159C ''may'':188C,238C ''mean'':112C,203C ''memori'':47C,219C ''move'':174C ''much'':147C ''multipl'':179C ''n'':22C,142C ''network'':52C ''node'':250C,264C ''non'':253C ''non-distribut'':252C ''one'':61C,140C,150C,158C,176C ''overhead'':186C ''p'':1C,54C,83C,157C,236C ''paradigm'':166C,199C,255C ''parallel'':198C,234C ''part'':282C ''particular'':87C ''point'':58C,77C ''potenti'':276C ''price'':108C ''python'':222C ''quit'':169C,215C ''recov'':277C ''regardless'':123C ''reliabl'':260C ''resourc'':70C,118C,155C ''rule'':4C ''scale'':59C,71C,201C ''server'':23C,30C ''sgd'':213C ''singl'':249C,263C ''slow'':49C ''small'':139C,143C ''spot'':103C ''still'':79C ''therefor'':44C ''though'':88C ''thumb'':6C ''tool'':208C ''tradeoff'':182C ''type'':94C,127C ''uneconom'':64C ''use'':102C ''usual'':17C ''vari'':96C,130C ''versus'':48C ''work'':242C,256C ''would'':228C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (324, 1, '2014-06-12 03:11:00.033', -3, 210, 'How can I extract news about a particular company from various websites using RODBC package in R? And perform sentiment analysis on the data?', '&lt;p&gt;I want to extract news about a company from online news by using RODBC package in R. And I want to use the extracted data for sentiment analysis. I want to accomplish this in such a way that the positive news is assigned a value of +1, the negative news is assigned a value of -1 and the neutral news is assigned a value of 0.&lt;/p&gt;
', 'r  text-mining', NULL, '''+1'':76C ''-1'':85C ''/p'':96C ''0'':95C ''accomplish'':61C ''analysi'':21A,57C ''assign'':72C,81C,91C ''compani'':9A,37C ''data'':24A,54C ''extract'':4A,33C,53C ''mine'':28B ''negat'':78C ''neutral'':88C ''news'':5A,34C,40C,70C,79C,89C ''onlin'':39C ''p'':29C ''packag'':15A,44C ''particular'':8A ''perform'':19A ''posit'':69C ''r'':17A,25B,46C ''rodbc'':14A,43C ''sentiment'':20A,56C ''text'':27B ''text-min'':26B ''use'':13A,42C,51C ''valu'':74C,83C,93C ''various'':11A ''want'':31C,49C,59C ''way'':66C ''websit'':12A');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (209, 1, '2014-05-25 13:57:52.657', 5, 155, 'How should one deal with implicit data in recommendation', '&lt;p&gt;A recommendation system keeps a log of what recommendations have been made to a particular user and whether that user accepts the recommendation. It''s like&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;user_id item_id result
1       4       1
1       7       -1
5       19      1
5       80      1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where 1 means the user accepted the recommendation while -1 means the user did not respond to the recommendation. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Question:&lt;/strong&gt; If I am going to make recommendations to a bunch of users based on the kind of log described above, and I want to maximize MAP@3 scores, how should I deal with the implicit data (1 or -1)?&lt;/p&gt;

&lt;p&gt;My idea is to treat 1 and -1 as ratings, and predict the rating using factorization machines-type algorithms. But this does not seem right, given the asymmetry of the implicit data (-1 does not mean the user does not like the recommendation).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit 1&lt;/strong&gt;
Let us think about it in the context of a matrix factorization approach. If we treat -1 and 1 as ratings, there will be some problem. For example, user 1 likes movie A which scores high in one factor (e.g. having glorious background music) in the latent factor space. The system recommends movie B which also scores high in "glorious background music", but for some reason user 1 is too busy to look into the recommendation, and we have a -1 rating movie B. If we just treat 1 or -1 equally, then the system might be discouraged to recommend movie with glorious BGM to user 1 while user 1 still loves movie with glorious BGM. I think this situation is to be avoided.&lt;/p&gt;
', 'recommendation', NULL, '''-1'':51C,70C,123C,133C,159C,192C,256C,266C ''/code'':58C ''/p'':38C,80C,124C,170C,300C ''/pre'':59C ''/strong'':84C,175C ''1'':46C,48C,49C,54C,57C,62C,121C,131C,174C,194C,205C,243C,264C,282C,285C ''19'':53C ''3'':111C ''4'':47C ''5'':52C,55C ''7'':50C ''80'':56C ''accept'':32C,66C ''algorithm'':145C ''also'':231C ''approach'':188C ''asymmetri'':154C ''avoid'':299C ''b'':229C,259C ''background'':218C,236C ''base'':97C ''bgm'':279C,291C ''bunch'':94C ''busi'':246C ''code'':40C ''context'':183C ''data'':7A,120C,158C ''deal'':4A,116C ''describ'':103C ''discourag'':273C ''e.g'':215C ''edit'':173C ''equal'':267C ''exampl'':203C ''factor'':141C,187C,214C,223C ''given'':152C ''glorious'':217C,235C,278C,290C ''go'':88C ''high'':211C,233C ''id'':42C,44C ''idea'':127C ''implicit'':6A,119C,157C ''item'':43C ''keep'':15C ''kind'':100C ''latent'':222C ''let'':176C ''like'':37C,167C,206C ''log'':17C,102C ''look'':248C ''love'':287C ''machin'':143C ''machines-typ'':142C ''made'':23C ''make'':90C ''map'':110C ''matrix'':186C ''maxim'':109C ''mean'':63C,71C,162C ''might'':271C ''movi'':207C,228C,258C,276C,288C ''music'':219C,237C ''one'':3A,213C ''p'':11C,60C,81C,125C,171C ''particular'':26C ''pre'':39C ''predict'':137C ''problem'':201C ''question'':83C ''rate'':135C,139C,196C,257C ''reason'':241C ''recommend'':9A,10B,13C,20C,34C,68C,79C,91C,169C,227C,251C,275C ''respond'':76C ''result'':45C ''right'':151C ''score'':112C,210C,232C ''seem'':150C ''situat'':295C ''space'':224C ''still'':286C ''strong'':82C,172C ''system'':14C,226C,270C ''think'':178C,293C ''treat'':130C,191C,263C ''type'':144C ''us'':177C ''use'':140C ''user'':27C,31C,41C,65C,73C,96C,164C,204C,242C,281C,284C ''want'':107C ''whether'':29C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (210, 2, '2014-05-26 04:07:32.39', 1, NULL, NULL, '&lt;p&gt;Assuming symmetric Dirichlet distributions (for simplicity), a low alpha value places more weight on having each document composed of only a few dominant topics (whereas a high value will return many more relatively dominant topics). Similarly, a low beta value places more weight on having each topic composed of only a few dominant words.&lt;/p&gt;
', NULL, NULL, '''/p'':56C ''alpha'':10C ''assum'':2C ''beta'':40C ''compos'':19C,49C ''dirichlet'':4C ''distribut'':5C ''document'':18C ''domin'':24C,35C,54C ''high'':28C ''low'':9C,39C ''mani'':32C ''p'':1C ''place'':12C,42C ''relat'':34C ''return'':31C ''similar'':37C ''simplic'':7C ''symmetr'':3C ''topic'':25C,36C,48C ''valu'':11C,29C,41C ''weight'':14C,44C ''wherea'':26C ''word'':55C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (211, 1, '2014-05-27 10:41:33.22', 5, 1155, 'Human activity recognition using smartphone data set problem', '&lt;p&gt;I''m new to this community and hopefully my question will well fit in here.
As part of my undergraduate data analytics course I have choose to do the project on human activity recognition using smartphone data sets. As far as I''m concern this topic relates to Machine Learning and Support Vector Machines. I''m not well familiar with this technologies yet so I will need some help. &lt;/p&gt;

&lt;p&gt;I have decided to follow this project idea &lt;a href="http://www.inf.ed.ac.uk/teaching/courses/dme/2014/datasets.html"&gt;http://www.inf.ed.ac.uk/teaching/courses/dme/2014/datasets.html&lt;/a&gt; (first project on the top)
The project goal is determine what activity a person is engaging in (e.g., WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) from data recorded by a smartphone (Samsung Galaxy S II) on the subject''s waist. Using its embedded accelerometer and gyroscope, the data includes 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz.&lt;/p&gt;

&lt;p&gt;All the data set is given in one folder with some description and feature labels. The data is divided for ''test'' and ''train'' files in which data is represented in this format:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  2.5717778e-001 -2.3285230e-002 -1.4653762e-002 -9.3840400e-001 -9.2009078e-001 -6.6768331e-001 -9.5250112e-001 -9.2524867e-001 -6.7430222e-001 -8.9408755e-001 -5.5457721e-001 -4.6622295e-001  7.1720847e-001  6.3550240e-001  7.8949666e-001 -8.7776423e-001 -9.9776606e-001 -9.9841381e-001 -9.3434525e-001 -9.7566897e-001 -9.4982365e-001 -8.3047780e-001 -1.6808416e-001 -3.7899553e-001  2.4621698e-001  5.2120364e-001 -4.8779311e-001  4.8228047e-001 -4.5462113e-002  2.1195505e-001 -1.3489443e-001  1.3085848e-001 -1.4176313e-002 -1.0597085e-001  7.3544013e-002 -1.7151642e-001  4.0062978e-002  7.6988933e-002 -4.9054573e-001 -7.0900265e-001
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And that''s only a very small sample of what the file contain. &lt;/p&gt;

&lt;p&gt;I don''t really know what this data represents and how can be interpreted. Also for analyzing, classification and clustering of the data, what tools will I need to use? 
Is there any way I can put this data into excel with labels included and for example use R or python to extract sample data and work on this?&lt;/p&gt;

&lt;p&gt;Any hints/tips would be much appreciated.&lt;/p&gt;
', 'bigdata  machine-learning  databases  clustering  data-mining', NULL, '''-1.0597085e-001'':241C ''-1.3489443e-001'':238C ''-1.4176313e-002'':240C ''-1.4653762e-002'':210C ''-1.6808416e-001'':230C ''-1.7151642e-001'':243C ''-2.3285230e-002'':209C ''-3.7899553e-001'':231C ''-4.5462113e-002'':236C ''-4.6622295e-001'':219C ''-4.8779311e-001'':234C ''-4.9054573e-001'':246C ''-5.5457721e-001'':218C ''-6.6768331e-001'':213C ''-6.7430222e-001'':216C ''-7.0900265e-001'':247C ''-8.3047780e-001'':229C ''-8.7776423e-001'':223C ''-8.9408755e-001'':217C ''-9.2009078e-001'':212C ''-9.2524867e-001'':215C ''-9.3434525e-001'':226C ''-9.3840400e-001'':211C ''-9.4982365e-001'':228C ''-9.5250112e-001'':214C ''-9.7566897e-001'':227C ''-9.9776606e-001'':224C ''-9.9841381e-001'':225C ''/code'':248C ''/p'':88C,171C,205C,264C,325C,333C ''/pre'':249C ''/teaching/courses/dme/2014/datasets.html'':102C ''/teaching/courses/dme/2014/datasets.html&lt;/a&gt;'':105C ''1.3085848e-001'':239C ''2.1195505e-001'':237C ''2.4621698e-001'':232C ''2.5717778e-001'':208C ''3'':156C,161C ''4.0062978e-002'':244C ''4.8228047e-001'':235C ''5.2120364e-001'':233C ''50hz'':170C ''6.3550240e-001'':221C ''7.1720847e-001'':220C ''7.3544013e-002'':242C ''7.6988933e-002'':245C ''7.8949666e-001'':222C ''acceler'':159C ''acceleromet'':150C ''activ'':2A,51C,117C ''also'':280C ''analyt'':40C ''analyz'':282C ''angular'':163C ''appreci'':332C ''axial'':157C,162C ''bigdata'':9B ''choos'':44C ''classif'':283C ''cluster'':14B,285C ''code'':207C ''communiti'':24C ''concern'':62C ''constant'':167C ''contain'':263C ''cours'':41C ''data'':6A,16B,39C,55C,133C,154C,175C,189C,199C,273C,288C,304C,320C ''data-min'':15B ''databas'':13B ''decid'':92C ''descript'':184C ''determin'':115C ''divid'':191C ''downstair'':128C ''e.g'':123C ''embed'':149C ''engag'':121C ''exampl'':312C ''excel'':306C ''extract'':318C ''familiar'':77C ''far'':58C ''featur'':186C ''file'':196C,262C ''first'':106C ''fit'':31C ''folder'':181C ''follow'':94C ''format'':204C ''galaxi'':139C ''given'':178C ''goal'':113C ''gyroscop'':152C ''help'':87C ''hints/tips'':328C ''hope'':26C ''href'':99C ''human'':1A,50C ''idea'':97C ''ii'':141C ''includ'':155C,309C ''interpret'':279C ''know'':270C ''label'':187C,308C ''lay'':131C ''learn'':12B,68C ''linear'':158C ''m'':20C,61C,74C ''machin'':11B,67C,72C ''machine-learn'':10B ''mine'':17B ''much'':331C ''need'':85C,293C ''new'':21C ''one'':180C ''p'':18C,89C,172C,250C,265C,326C ''part'':35C ''person'':119C ''pre'':206C ''problem'':8A ''project'':48C,96C,107C,112C ''put'':302C ''python'':316C ''question'':28C ''r'':314C ''rate'':168C ''realli'':269C ''recognit'':3A,52C ''record'':134C ''relat'':65C ''repres'':201C,274C ''sampl'':258C,319C ''samsung'':138C ''set'':7A,56C,176C ''sit'':129C ''small'':257C ''smartphon'':5A,54C,137C ''stand'':130C ''subject'':144C ''support'':70C ''technolog'':80C ''test'':193C ''tool'':290C ''top'':110C ''topic'':64C ''train'':195C ''undergradu'':38C ''upstair'':126C ''use'':4A,53C,147C,295C,313C ''vector'':71C ''veloc'':164C ''waist'':146C ''walk'':124C,125C,127C ''way'':299C ''well'':30C,76C ''work'':322C ''would'':329C ''www.inf.ed.ac.uk'':101C,104C ''www.inf.ed.ac.uk/teaching/courses/dme/2014/datasets.html'':100C ''www.inf.ed.ac.uk/teaching/courses/dme/2014/datasets.html&lt;/a&gt;'':103C ''yet'':81C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (275, 2, '2014-06-10 13:17:48.433', 14, NULL, NULL, '&lt;p&gt;For time series data in particular, &lt;a href="http://www.quandl.com/"&gt;Quandl&lt;/a&gt; is an excellent resource -- an easily browsable directory of (mostly) clean time series.&lt;/p&gt;

&lt;p&gt;One of their coolest features is &lt;a href="http://blog.quandl.com/blog/quandl-open-data/"&gt;open-data stock prices&lt;/a&gt; -- i.e. financial data that can be edited wiki-style, and isn''t encumbered by licensing.&lt;/p&gt;
', NULL, NULL, '''/a'':12C,44C ''/blog/quandl-open-data/'':38C ''/p'':26C,61C ''blog.quandl.com'':37C ''blog.quandl.com/blog/quandl-open-data/'':36C ''browsabl'':19C ''clean'':23C ''coolest'':31C ''data'':5C,41C,47C ''directori'':20C ''easili'':18C ''edit'':51C ''encumb'':58C ''excel'':15C ''featur'':32C ''financi'':46C ''href'':9C,35C ''i.e'':45C ''isn'':56C ''licens'':60C ''most'':22C ''one'':28C ''open'':40C ''open-data'':39C ''p'':1C,27C ''particular'':7C ''price'':43C ''quandl'':11C ''resourc'':16C ''seri'':4C,25C ''stock'':42C ''style'':54C ''time'':3C,24C ''wiki'':53C ''wiki-styl'':52C ''www.quandl.com'':10C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (213, 2, '2014-05-27 12:07:45.92', 6, NULL, NULL, '&lt;p&gt;The data set definitions are on the page here:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones#"&gt;Attribute Information at the bottom&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;or you can see inside the ZIP folder the file named activity_labels, that has your column headings inside of it, make sure you read the README carefully, it has some good info in it. You can easily bring in a &lt;code&gt;.csv&lt;/code&gt; file in R using the &lt;code&gt;read.csv&lt;/code&gt; command.&lt;/p&gt;

&lt;p&gt;For example if you name you file &lt;code&gt;samsungdata&lt;/code&gt; you can open R and run this command:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data &amp;lt;- read.csv("directory/where/file/is/located/samsungdata.csv", header = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Or if you are already inside of the working directory in R you can just run the following&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data &amp;lt;- read.csv("samsungdata.csv", header = TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Where the name &lt;code&gt;data&lt;/code&gt; can be changed to whatever you want to call your data set.&lt;/p&gt;
', NULL, NULL, '''/a'':23C ''/code'':69C,77C,90C,108C,138C,146C ''/ml/datasets/human+activity+recognition+using+smartphones#'':17C ''/p'':11C,24C,79C,99C,129C,159C ''/pre'':109C,139C ''activ'':37C ''alreadi'':115C ''archive.ics.uci.edu'':16C ''archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones#'':15C ''attribut'':18C ''bottom'':22C ''bring'':64C ''call'':155C ''care'':53C ''chang'':149C ''code'':67C,75C,88C,101C,131C,144C ''column'':42C ''command'':78C,98C ''csv'':68C ''data'':3C,102C,132C,145C,157C ''definit'':5C ''directori'':120C ''directory/where/file/is/located/samsungdata.csv'':105C ''easili'':63C ''exampl'':82C ''file'':35C,70C,87C ''folder'':33C ''follow'':128C ''good'':57C ''head'':43C ''header'':106C,136C ''href'':14C ''info'':58C ''inform'':19C ''insid'':30C,44C,116C ''label'':38C ''lt'':103C,133C ''make'':47C ''name'':36C,85C,143C ''open'':93C ''p'':1C,12C,25C,80C,110C,140C ''page'':9C ''pre'':100C,130C ''r'':72C,94C,122C ''read'':50C ''read.csv'':76C,104C,134C ''readm'':52C ''run'':96C,126C ''samsungdata'':89C ''samsungdata.csv'':135C ''see'':29C ''set'':4C,158C ''sure'':48C ''true'':107C,137C ''use'':73C ''want'':153C ''whatev'':151C ''work'':119C ''zip'':32C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (325, 2, '2014-06-12 05:17:46.453', 2, NULL, NULL, '&lt;p&gt;This isn''t a question with a simple answer, so all I can really do is point you in the right direction.&lt;/p&gt;

&lt;p&gt;The RODBC package isn''t meant to extract data online, it''s meant to pull data from a database.  If you will be leveraging that package, it will be after you pull data down from the web.&lt;/p&gt;

&lt;p&gt;Jeffrey Bean put together a &lt;a href="http://jeffreybreen.wordpress.com/2011/07/04/twitter-text-mining-r-slides/" rel="nofollow"&gt;slideshow tutorial&lt;/a&gt; for doing sentiment analysis with Twitter data a few years back.  He used the Twitter stream as well as some data pulled in from web scraping.  It''s a good starting point.&lt;/p&gt;

&lt;p&gt;There''s also this site that discusses a few different approaches to this problem in detail, including Bean''s, the sentiment package, and ViralHeat (which is a commercial sentiment analysis service who''s data you can pull into R).  Sentiment has since been removed (&lt;a href="http://cran.us.r-project.org/src/contrib/Archive/sentiment/" rel="nofollow"&gt;archived versions here&lt;/a&gt;), but the &lt;a href="http://cran.us.r-project.org/web/packages/qdap/index.html" rel="nofollow"&gt;qdap package&lt;/a&gt; is available and is designed for use in transcript analysis.&lt;/p&gt;
', NULL, NULL, '''/2011/07/04/twitter-text-mining-r-slides/'':74C ''/a'':79C,168C,180C ''/p'':24C,63C,112C,191C ''/src/contrib/archive/sentiment/'':162C ''/web/packages/qdap/index.html'':175C ''also'':116C ''analysi'':83C,143C,190C ''answer'':10C ''approach'':124C ''archiv'':165C ''avail'':182C ''back'':90C ''bean'':66C,131C ''commerci'':141C ''cran.us.r-project.org'':161C,174C ''cran.us.r-project.org/src/contrib/archive/sentiment/'':160C ''cran.us.r-project.org/web/packages/qdap/index.html'':173C ''data'':34C,41C,58C,86C,100C,147C ''databas'':44C ''design'':185C ''detail'':129C ''differ'':123C ''direct'':23C ''discuss'':120C ''extract'':33C ''good'':109C ''href'':71C,159C,172C ''includ'':130C ''isn'':3C,29C ''jeffrey'':65C ''jeffreybreen.wordpress.com'':73C ''jeffreybreen.wordpress.com/2011/07/04/twitter-text-mining-r-slides/'':72C ''leverag'':49C ''meant'':31C,38C ''nofollow'':76C,164C,177C ''onlin'':35C ''p'':1C,25C,64C,113C ''packag'':28C,51C,135C,179C ''point'':18C,111C ''problem'':127C ''pull'':40C,57C,101C,150C ''put'':67C ''qdap'':178C ''question'':6C ''r'':152C ''realli'':15C ''rel'':75C,163C,176C ''remov'':157C ''right'':22C ''rodbc'':27C ''scrape'':105C ''sentiment'':82C,134C,142C,153C ''servic'':144C ''simpl'':9C ''sinc'':155C ''site'':118C ''slideshow'':77C ''start'':110C ''stream'':95C ''togeth'':68C ''transcript'':189C ''tutori'':78C ''twitter'':85C,94C ''use'':92C,187C ''version'':166C ''viralheat'':137C ''web'':62C,104C ''well'':97C ''year'':89C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (214, 2, '2014-05-27 16:05:02.883', 17, NULL, NULL, '&lt;p&gt;Public Data Sets&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://www.opensciencedatacloud.org/publicdata/"&gt;https://www.opensciencedatacloud.org/publicdata/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Google Public Data Sets&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;
&lt;a href="http://www.google.com/publicdata/directory"&gt;http://www.google.com/publicdata/directory&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Amazon Web Services&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://aws.amazon.com/publicdatasets/"&gt;https://aws.amazon.com/publicdatasets/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Finding Data on the Internet&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.inside-r.org/howto/finding-data-internet"&gt;http://www.inside-r.org/howto/finding-data-internet&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/howto/finding-data-internet'':66C ''/howto/finding-data-internet&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':69C ''/p'':5C,23C,39C,58C ''/publicdata/'':13C ''/publicdata/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':16C ''/publicdata/directory'':30C ''/publicdata/directory&lt;/a&gt;&lt;/li&gt;'':33C ''/publicdatasets/'':47C ''/publicdatasets/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':50C ''/ul'':17C,34C,51C,70C ''amazon'':36C ''aws.amazon.com'':46C,49C ''aws.amazon.com/publicdatasets/'':45C ''aws.amazon.com/publicdatasets/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':48C ''data'':3C,21C,54C ''find'':53C ''googl'':19C ''href'':10C,27C,44C,63C ''internet'':57C ''li'':7C,25C,41C,60C ''p'':1C,8C,18C,35C,42C,52C,61C ''public'':2C,20C ''servic'':38C ''set'':4C,22C ''ul'':6C,24C,40C,59C ''web'':37C ''www.google.com'':29C,32C ''www.google.com/publicdata/directory'':28C ''www.google.com/publicdata/directory&lt;/a&gt;&lt;/li&gt;'':31C ''www.inside-r.org'':65C,68C ''www.inside-r.org/howto/finding-data-internet'':64C ''www.inside-r.org/howto/finding-data-internet&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':67C ''www.opensciencedatacloud.org'':12C,15C ''www.opensciencedatacloud.org/publicdata/'':11C ''www.opensciencedatacloud.org/publicdata/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':14C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (215, 1, '2014-05-27 21:07:48.973', 13, 476, 'Where in the workflow should we deal with missing data?', '&lt;p&gt;I''m building a workflow for creating machine learning models (in my case, using Python''s &lt;code&gt;pandas&lt;/code&gt; and &lt;code&gt;sklearn&lt;/code&gt; packages) from data pulled from a very large database (here, Vertica by way of SQL and &lt;code&gt;pyodbc&lt;/code&gt;), and a critical step in that process involves imputing missing values of the predictors. This is straightforward within a single analytics or stats platform---be it Python, R, Stata, etc.---but I''m curious where best to locate this step in a multi-platform workflow.&lt;/p&gt;

&lt;p&gt;It''s simple enough to do this in Python, either with the &lt;a href="http://scikit-learn.org/stable/modules/preprocessing.html#imputation-of-missing-values"&gt;&lt;code&gt;sklearn.preprocessing.Imputer&lt;/code&gt;&lt;/a&gt; class, using the &lt;a href="http://pandas.pydata.org/pandas-docs/version/0.13.1/generated/pandas.DataFrame.fillna.html"&gt;&lt;code&gt;pandas.DataFrame.fillna&lt;/code&gt;&lt;/a&gt; method, or by hand (depending upon the complexity of the imputation method used). But since I''m going to be using this for dozens or hundreds of columns across hundreds of millions of records, I wonder if there''s a more efficient way to do this directly through SQL ahead of time. Aside from the potential efficiencies of doing this in a distributed platform like Vertica, this would have the added benefit of allowing us to create an automated pipeline for building "complete" versions of tables, so we don''t need to fill in a new set of missing values from scratch every time we want to run a model.&lt;/p&gt;

&lt;p&gt;I haven''t been able to find much guidance about this, but I imagine that we could:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;create a table of substitute values (e.g., mean/median/mode, either overall or by group) for each incomplete column&lt;/li&gt;
&lt;li&gt;join the substitute value table with the original table to assign a substitute value for each row and incomplete column&lt;/li&gt;
&lt;li&gt;use a series of case statements to take the original value if available and the substitute value otherwise&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Is this a reasonable thing to do in Vertica/SQL, or is there a good reason not to bother and just handle it in Python instead? And if the latter, is there a strong case for doing this in pandas rather than sklearn or vice-versa? Thanks!&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (216, 2, '2014-05-28 07:08:05.393', 11, NULL, NULL, '&lt;p&gt;My strong opinion regarding automated tasks like imputation (but, here I can include also scaling, centering, feature selection, etc) is to avoid in any way do such things without carefully inspecting your data. &lt;/p&gt;

&lt;p&gt;Of course, after deciding what kind of imputation to apply it can be automated (under the assumption that the new data has the same shape/problems).&lt;/p&gt;

&lt;p&gt;So, before anything, take a wise decision. I often wasted time trying to automate this things, destroying my data. I will give you some examples:
- a marketplace encoded as N/A, which I missed and considered to be North/America
- numbers like -999.0, because the data producer could not find a better replacement for missing data
- number like 0 for blood pressure or body temperature, instead of missing data (it is hard to imagine a living human with 0 blood pressure)
- multiple placeholders for missing data, due to the fact that the data was collected from various sources&lt;/p&gt;

&lt;p&gt;After that you need to understand what kind of imputation would resemble better the information from your data for a given task. This is often much harder to do it right than it seems.&lt;/p&gt;

&lt;p&gt;After all those things, my advice is to delay your imputation task to an upper layer where you have tools to reproduce on new data and to inspect if the assumptions for the new data are not violated (if it is possible).&lt;/p&gt;
', NULL, NULL, '''-999.0'':104C ''/p'':35C,62C,160C,196C,240C ''0'':120C,140C ''advic'':203C ''also'':15C ''anyth'':66C ''appli'':46C ''assumpt'':53C,228C ''autom'':6C,50C,77C ''avoid'':23C ''better'':113C,174C ''blood'':122C,141C ''bodi'':125C ''care'':31C ''center'':17C ''collect'':156C ''consid'':98C ''could'':109C ''cours'':38C ''data'':34C,57C,82C,107C,117C,130C,147C,154C,179C,222C,232C ''decid'':40C ''decis'':70C ''delay'':206C ''destroy'':80C ''due'':148C ''encod'':91C ''etc'':20C ''exampl'':88C ''fact'':151C ''featur'':18C ''find'':111C ''give'':85C ''given'':182C ''hard'':133C ''harder'':188C ''human'':138C ''imagin'':135C ''imput'':9C,44C,171C,208C ''includ'':14C ''inform'':176C ''inspect'':32C,225C ''instead'':127C ''kind'':42C,169C ''layer'':213C ''like'':8C,103C,119C ''live'':137C ''marketplac'':90C ''miss'':96C,116C,129C,146C ''much'':187C ''multipl'':143C ''n/a'':93C ''need'':165C ''new'':56C,221C,231C ''north/america'':101C ''number'':102C,118C ''often'':72C,186C ''opinion'':4C ''p'':1C,36C,63C,161C,197C ''placehold'':144C ''possibl'':239C ''pressur'':123C,142C ''produc'':108C ''regard'':5C ''replac'':114C ''reproduc'':219C ''resembl'':173C ''right'':192C ''scale'':16C ''seem'':195C ''select'':19C ''shape/problems'':61C ''sourc'':159C ''strong'':3C ''take'':67C ''task'':7C,183C,209C ''temperatur'':126C ''thing'':29C,79C,201C ''time'':74C ''tool'':217C ''tri'':75C ''understand'':167C ''upper'':212C ''various'':158C ''violat'':235C ''wast'':73C ''way'':26C ''wise'':69C ''without'':30C ''would'':172C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (217, 2, '2014-05-28 09:43:54.197', 4, NULL, NULL, '&lt;p&gt;It looks like this (or very similar data set) is used for Coursera courses. Cleaning this dataset is task for &lt;a href="https://www.coursera.org/course/getdata" rel="nofollow"&gt;Getting and Cleaning Data&lt;/a&gt;, but it is also used for case study for &lt;a href="https://class.coursera.org/exdata-002" rel="nofollow"&gt;Exploratory Data analysis&lt;/a&gt;. Video from this case study is available in videos for week 4 of EDA course-ware. It might help you with starting with this data.&lt;/p&gt;
', NULL, NULL, '''/a'':33C,53C ''/course/getdata'':26C ''/exdata-002'':47C ''/p'':80C ''4'':65C ''also'':37C ''analysi'':52C ''avail'':60C ''case'':40C,57C ''class.coursera.org'':46C ''class.coursera.org/exdata-002'':45C ''clean'':16C,31C ''cours'':15C,69C ''course-war'':68C ''coursera'':14C ''data'':9C,32C,51C,79C ''dataset'':18C ''eda'':67C ''exploratori'':50C ''get'':29C ''help'':73C ''href'':23C,44C ''like'':4C ''look'':3C ''might'':72C ''nofollow'':28C,49C ''p'':1C ''rel'':27C,48C ''set'':10C ''similar'':8C ''start'':76C ''studi'':41C,58C ''task'':20C ''use'':12C,38C ''video'':54C,62C ''ware'':70C ''week'':64C ''www.coursera.org'':25C ''www.coursera.org/course/getdata'':24C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (276, 2, '2014-06-10 13:38:31.207', 8, NULL, NULL, '&lt;p&gt;Not all government data is listed on data.gov - &lt;a href="http://sunlightfoundation.com/blog/2014/02/21/open-data-inventories-ready-for-human-consumption/"&gt;Sunlight Foundation&lt;/a&gt; put together a &lt;a href="https://drive.google.com/folderview?id=0B4QuErjcV2a0WXVDOURwbzh6S2s&amp;amp;usp=sharing"&gt;set of spreadsheets&lt;/a&gt; back in February describing sets of available data.&lt;/p&gt;
', NULL, NULL, '''/a'':17C,29C ''/blog/2014/02/21/open-data-inventories-ready-for-human-consumption/'':14C ''/folderview?id=0b4querjcv2a0wxvdourwbzh6s2s&amp;amp;usp=sharing'':25C ''/p'':38C ''avail'':36C ''back'':30C ''data'':5C,37C ''data.gov'':9C ''describ'':33C ''drive.google.com'':24C ''drive.google.com/folderview?id=0b4querjcv2a0wxvdourwbzh6s2s&amp;amp;usp=sharing'':23C ''februari'':32C ''foundat'':16C ''govern'':4C ''href'':11C,22C ''list'':7C ''p'':1C ''put'':18C ''set'':26C,34C ''spreadsheet'':28C ''sunlight'':15C ''sunlightfoundation.com'':13C ''sunlightfoundation.com/blog/2014/02/21/open-data-inventories-ready-for-human-consumption/'':12C ''togeth'':19C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (327, 2, '2014-06-12 07:05:05.653', 9, NULL, NULL, '&lt;p&gt;There is nothing like "python is better" or "R is much better than x". &lt;/p&gt;

&lt;p&gt;The only fact I know is that in the industry allots of people stick to python because that is what they learned at the university. The python community is really active and have a few great frameworks for ML and data mining etc. &lt;/p&gt;

&lt;p&gt;But to be honest, if you get a good c programmer he can do the same as people do in python or r, if you got a good java programmer he can also do (near to) everything in java. &lt;/p&gt;

&lt;p&gt;So just stick with the language you are comfortable with.&lt;/p&gt;
', NULL, NULL, '''/p'':16C,61C,102C,114C ''activ'':48C ''allot'':28C ''also'':95C ''better'':8C,13C ''c'':72C ''comfort'':112C ''communiti'':45C ''data'':58C ''etc'':60C ''everyth'':99C ''fact'':20C ''framework'':54C ''get'':69C ''good'':71C,90C ''got'':88C ''great'':53C ''honest'':66C ''industri'':27C ''java'':91C,101C ''know'':22C ''languag'':109C ''learn'':39C ''like'':5C ''mine'':59C ''ml'':56C ''much'':12C ''near'':97C ''noth'':4C ''p'':1C,17C,62C,103C ''peopl'':30C,80C ''programm'':73C,92C ''python'':6C,33C,44C,83C ''r'':10C,85C ''realli'':47C ''stick'':31C,106C ''univers'':42C ''x'':15C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (220, 2, '2014-05-29 14:13:00.317', 4, NULL, NULL, '&lt;p&gt;Christopher is right about the size of the array. To be simplistic about it, if this translates to 1.6B floats, at 16 bytes per float (32-bit version; 64-bit is bigger), then you''re trying to create an array of about 26 GB. Even if you have the RAM for that, I''d imagine that it''s probably going to overload something else along the way. &lt;/p&gt;

&lt;p&gt;(Maybe not, but generally speaking, any operations that are that computationally intensive should first raise the question of whether you are doing the right calculation in the first place. And if you do need to do something of that magnitude, you should then try to break it down into more manageable chunks that can be run in parallel or distributed across machines.)&lt;/p&gt;

&lt;p&gt;But given that you are describing a very, very wide dataset (~40k columns x 180 rows), I wonder whether you really want to take the covariance matrix of the transposed dataset (so 180x180 = 32,400 covariances)? That would be a far more tractable problem, and it''s easier to see how it might be useful.&lt;/p&gt;

&lt;p&gt;In any case, you''re probably far better off calculating each pairwise covariance (or at least, the vector of cov(x_i,x_k) for all x_k != x_i) at the point where you''ll actually use it, rather than calculating a giant matrix initially then referring back to it later. Memory issues aside, it''ll make your life much easier if you start running things in parallel, and will help ensure you don''t waste resources on unnecessary calculations.&lt;/p&gt;
', NULL, NULL, '''/p'':70C,134C,191C,274C ''1.6'':20C ''16'':24C ''180'':150C ''180x180'':168C ''26'':45C ''32'':28C,169C ''400'':170C ''40k'':147C ''64'':31C ''across'':132C ''actual'':229C ''along'':67C ''array'':10C,42C ''asid'':247C ''b'':21C ''back'':241C ''better'':200C ''bigger'':34C ''bit'':29C,32C ''break'':117C ''byte'':25C ''calcul'':96C,202C,234C,273C ''case'':195C ''christoph'':2C ''chunk'':123C ''column'':148C ''comput'':82C ''cov'':212C ''covari'':161C,171C,205C ''creat'':40C ''d'':56C ''dataset'':146C,166C ''describ'':141C ''distribut'':131C ''easier'':183C,254C ''els'':66C ''ensur'':265C ''even'':47C ''far'':176C,199C ''first'':85C,99C ''float'':22C,27C ''gb'':46C ''general'':75C ''giant'':236C ''given'':137C ''go'':62C ''help'':264C ''imagin'':57C ''initi'':238C ''intens'':83C ''issu'':246C ''k'':216C,220C ''later'':244C ''least'':208C ''life'':252C ''ll'':228C,249C ''machin'':133C ''magnitud'':111C ''make'':250C ''manag'':122C ''matrix'':162C,237C ''mayb'':72C ''memori'':245C ''might'':188C ''much'':253C ''need'':105C ''oper'':78C ''overload'':64C ''p'':1C,71C,135C,192C ''pairwis'':204C ''parallel'':129C,261C ''per'':26C ''place'':100C ''point'':225C ''probabl'':61C,198C ''problem'':179C ''question'':88C ''rais'':86C ''ram'':52C ''rather'':232C ''re'':37C,197C ''realli'':156C ''refer'':240C ''resourc'':270C ''right'':4C,95C ''row'':151C ''run'':127C,258C ''see'':185C ''simplist'':13C ''size'':7C ''someth'':65C,108C ''speak'':76C ''start'':257C ''take'':159C ''thing'':259C ''tractabl'':178C ''translat'':18C ''transpos'':165C ''tri'':38C,115C ''unnecessari'':272C ''use'':190C,230C ''vector'':210C ''version'':30C ''want'':157C ''wast'':269C ''way'':69C ''whether'':90C,154C ''wide'':145C ''wonder'':153C ''would'':173C ''x'':149C,213C,215C,219C,221C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (221, 2, '2014-05-29 14:30:21.357', 4, NULL, NULL, '&lt;p&gt;It''s actually even simpler than that, from what you describe---you''re just looking for a basic classification tree algorithm (so no need for slightly more complex variants like C4.5 which are optimized for prediction accuracy). The canonical text is:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0412048418" rel="nofollow"&gt;http://www.amazon.com/Classification-Regression-Wadsworth-Statistics-Probability/dp/0412048418&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This is readily implemented in R: &lt;/p&gt;

&lt;p&gt;&lt;a href="http://cran.r-project.org/web/packages/tree/tree.pdf" rel="nofollow"&gt;http://cran.r-project.org/web/packages/tree/tree.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;and Python:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/modules/tree.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/tree.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/amzn/click/0412048418'':49C ''/classification-regression-wadsworth-statistics-probability/dp/0412048418&lt;/a&gt;&lt;/p&gt;'':54C ''/p'':43C,62C,77C ''/stable/modules/tree.html'':83C ''/stable/modules/tree.html&lt;/a&gt;&lt;/p&gt;'':88C ''/web/packages/tree/tree.pdf'':68C ''/web/packages/tree/tree.pdf&lt;/a&gt;&lt;/p&gt;'':73C ''accuraci'':38C ''actual'':4C ''algorithm'':22C ''basic'':19C ''c4.5'':32C ''canon'':40C ''classif'':20C ''complex'':29C ''cran.r-project.org'':67C,72C ''cran.r-project.org/web/packages/tree/tree.pdf'':66C ''cran.r-project.org/web/packages/tree/tree.pdf&lt;/a&gt;&lt;/p&gt;'':71C ''describ'':12C ''even'':5C ''href'':46C,65C,80C ''implement'':59C ''like'':31C ''look'':16C ''need'':25C ''nofollow'':51C,70C,85C ''optim'':35C ''p'':1C,44C,55C,63C,74C,78C ''predict'':37C ''python'':76C ''r'':61C ''rads.stackoverflow.com'':48C ''rads.stackoverflow.com/amzn/click/0412048418'':47C ''re'':14C ''readili'':58C ''rel'':50C,69C,84C ''scikit-learn.org'':82C,87C ''scikit-learn.org/stable/modules/tree.html'':81C ''scikit-learn.org/stable/modules/tree.html&lt;/a&gt;&lt;/p&gt;'':86C ''simpler'':6C ''slight'':27C ''text'':41C ''tree'':21C ''variant'':30C ''www.amazon.com'':53C ''www.amazon.com/classification-regression-wadsworth-statistics-probability/dp/0412048418&lt;/a&gt;&lt;/p&gt;'':52C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (241, 2, '2014-06-09 21:51:53.793', 2, NULL, NULL, '&lt;p&gt;It seems to me that the premise of a PhD is to expand knowledge in some little slice of the world. Since a "data scientist" is by nature is somewhat of a jack-of-all-trades it does seem a little odd to me. A masters program seems much more appropriate.&lt;/p&gt;

&lt;p&gt;What do you hope to gain from a PhD? If the rigor scares (or bores) you, then what about a more applied area? Signal processing, robotics, applied physics, operations research, etc.&lt;/p&gt;
', NULL, NULL, '''/p'':54C,87C ''appli'':77C,82C ''appropri'':53C ''area'':78C ''bore'':70C ''data'':25C ''etc'':86C ''expand'':14C ''gain'':61C ''hope'':59C ''jack'':35C ''jack-of-all-trad'':34C ''knowledg'':15C ''littl'':18C,43C ''master'':48C ''much'':51C ''natur'':29C ''odd'':44C ''oper'':84C ''p'':1C,55C ''phd'':11C,64C ''physic'':83C ''premis'':8C ''process'':80C ''program'':49C ''research'':85C ''rigor'':67C ''robot'':81C ''scare'':68C ''scientist'':26C ''seem'':3C,41C,50C ''signal'':79C ''sinc'':23C ''slice'':19C ''somewhat'':31C ''trade'':38C ''world'':22C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (222, 2, '2014-05-29 19:02:13.21', 11, NULL, NULL, '&lt;p&gt;&lt;a href="http://enigma.io"&gt;Enigma&lt;/a&gt; is a repository of public available datasets. Its free plan offers public data search, with 10k API calls per month. Not all public databases are listed, but the list is enough for common cases.&lt;/p&gt;

&lt;p&gt;I used it for academic research and it saved me a lot of time.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Another interesting source of data is the &lt;a href="http://theunitedstates.io/"&gt;@unitedstates project&lt;/a&gt;, containing data and tools to collect them, about the United States (members of Congress, geographic shapesâ¦).&lt;/p&gt;
', NULL, NULL, '''/a'':6C,72C ''/p'':41C,57C,89C ''10k'':22C ''academ'':47C ''anoth'':60C ''api'':23C ''avail'':12C ''call'':24C ''case'':40C ''collect'':78C ''common'':39C ''congress'':86C ''contain'':73C ''data'':19C,64C,74C ''databas'':30C ''dataset'':13C ''enigma'':5C ''enigma.io'':4C ''enough'':37C ''free'':15C ''geograph'':87C ''hr'':58C ''href'':3C,68C ''interest'':61C ''list'':32C,35C ''lot'':54C ''member'':84C ''month'':26C ''offer'':17C ''p'':1C,42C,59C ''per'':25C ''plan'':16C ''project'':71C ''public'':11C,18C,29C ''repositori'':9C ''research'':48C ''save'':51C ''search'':20C ''shapesâ'':88C ''sourc'':62C ''state'':83C ''theunitedstates.io'':69C ''time'':56C ''tool'':76C ''unit'':82C ''unitedst'':70C ''use'':44C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (223, 1, '2014-05-29 20:11:16.327', 9, 302, 'How to annotate text documents with meta-data?', '&lt;p&gt;Having a lot of text documents (in natural language, unstructured), what are the possible ways of annotating them with some semantic meta-data? For example, consider a short document:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;I saw the company''s manager last day.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;To be able to extract information from it, it must be annotated with additional data to be less ambiguous. The process of finding such meta-data is not in question, so assume it is done manually. The question is how to store these data in a way that further analysis on it can be done more conveniently/efficiently?&lt;/p&gt;

&lt;p&gt;A possible approach is to use XML tags (see below), but it seems too verbose, and maybe there are better approaches/guidelines for storing such meta-data on text documents.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;Person name="John"&amp;gt;I&amp;lt;/Person&amp;gt; saw the &amp;lt;Organization name="ACME"&amp;gt;company&amp;lt;/Organization&amp;gt;''s
manager &amp;lt;Time value="2014-5-29"&amp;gt;last day&amp;lt;/Time&amp;gt;.
&lt;/code&gt;&lt;/pre&gt;
', 'nlp  metadata  data-cleaning  text-mining', NULL, '''-29'':183C ''-5'':182C ''/code'':60C,190C ''/organization'':174C ''/p'':49C,121C,153C ''/person'':163C ''/pre'':61C,191C ''/time'':188C ''2014'':181C ''abl'':65C ''acm'':170C ''addit'':76C ''ambigu'':81C ''analysi'':113C ''annot'':3A,35C,74C ''approach'':125C ''approaches/guidelines'':143C ''assum'':95C ''better'':142C ''clean'':14B ''code'':51C,155C ''compani'':55C,172C ''consid'':45C ''conveniently/efficiently'':120C ''data'':9A,13B,42C,77C,89C,107C,149C ''data-clean'':12B ''day'':59C,186C ''document'':5A,24C,48C,152C ''done'':98C,118C ''exampl'':44C ''extract'':67C ''find'':85C ''gt'':160C,164C,171C,175C,184C,189C ''inform'':68C ''john'':159C ''languag'':27C ''last'':58C,185C ''less'':80C ''lot'':21C ''lt'':156C,162C,167C,173C,178C,187C ''manag'':57C,177C ''manual'':99C ''mayb'':139C ''meta'':8A,41C,88C,148C ''meta-data'':7A,40C,87C,147C ''metadata'':11B ''mine'':17B ''must'':72C ''name'':158C,169C ''natur'':26C ''nlp'':10B ''organ'':168C ''p'':18C,62C,122C ''person'':157C ''possibl'':32C,124C ''pre'':50C,154C ''process'':83C ''question'':93C,101C ''saw'':53C,165C ''see'':131C ''seem'':135C ''semant'':39C ''short'':47C ''store'':105C,145C ''tag'':130C ''text'':4A,16B,23C,151C ''text-min'':15B ''time'':179C ''unstructur'':28C ''use'':128C ''valu'':180C ''verbos'':137C ''way'':33C,110C ''xml'':129C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (250, 2, '2014-06-10 04:47:13.04', 2, NULL, NULL, '&lt;p&gt;Google does not publish the models they use, but they specifically do not support models from the PMML specification.&lt;/p&gt;

&lt;p&gt;If you look closely at the documentation on &lt;a href="https://developers.google.com/prediction/docs/pmml-schema" rel="nofollow"&gt;this page&lt;/a&gt;, you will notice that the model selection within the schema is greyed out indicating that it is an unsupported feature of the schema.&lt;/p&gt;

&lt;p&gt;The &lt;a href="https://developers.google.com/prediction/docs/developer-guide#whatisprediction" rel="nofollow"&gt;documentation does spell out&lt;/a&gt; that by default it will use a regression model for training data that has numeric answers, and an unspecified categorization model for training data that results in text based answers.&lt;/p&gt;

&lt;p&gt;The Google Prediction API also supports hosted models (although only a few demo models are currently available), and models specified with a PMML transform.  The documentation does contain an &lt;a href="https://developers.google.com/prediction/docs/pmml-schema" rel="nofollow"&gt;example of a model defined by a PMML transform&lt;/a&gt;.  (There is also a note on that page stating that PMML ...Model elements are not supported).  &lt;/p&gt;

&lt;p&gt;The PMML standard that google partially supports is &lt;a href="http://www.dmg.org//pmml-v4-0-1.html" rel="nofollow"&gt;version 4.0.1&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''//pmml-v4-0-1.html'':187C ''/a'':40C,78C,156C,192C ''/p'':21C,64C,109C,173C,193C ''/prediction/docs/developer-guide#whatisprediction'':71C ''/prediction/docs/pmml-schema'':35C,144C ''4.0.1'':191C ''also'':115C,159C ''although'':119C ''answer'':94C,108C ''api'':114C ''avail'':127C ''base'':107C ''categor'':98C ''close'':26C ''contain'':138C ''current'':126C ''data'':90C,102C ''default'':81C ''defin'':151C ''demo'':123C ''developers.google.com'':34C,70C,143C ''developers.google.com/prediction/docs/developer-guide#whatisprediction'':69C ''developers.google.com/prediction/docs/pmml-schema'':33C,142C ''document'':29C,74C,136C ''element'':169C ''exampl'':147C ''featur'':60C ''googl'':2C,112C,179C ''grey'':52C ''host'':117C ''href'':32C,68C,141C,184C ''indic'':54C ''look'':25C ''model'':7C,16C,46C,87C,99C,118C,124C,129C,150C,168C ''nofollow'':37C,73C,146C,189C ''note'':161C ''notic'':43C ''numer'':93C ''p'':1C,22C,65C,110C,174C ''page'':39C,164C ''partial'':180C ''pmml'':19C,133C,154C,167C,176C ''predict'':113C ''publish'':5C ''regress'':86C ''rel'':36C,72C,145C,188C ''result'':104C ''schema'':50C,63C ''select'':47C ''specif'':12C,20C ''specifi'':130C ''spell'':76C ''standard'':177C ''state'':165C ''support'':15C,116C,172C,181C ''text'':106C ''train'':89C,101C ''transform'':134C,155C ''unspecifi'':97C ''unsupport'':59C ''use'':9C,84C ''version'':190C ''within'':48C ''www.dmg.org'':186C ''www.dmg.org//pmml-v4-0-1.html'':185C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (224, 1, '2014-05-31 14:28:42.317', 2, 200, 'How to get phrase tables from word alignments?', '&lt;p&gt;The output of my word alignment file looks as such:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;I wish to say with regard to the initiative of the Portuguese Presidency that we support the spirit and the political intention behind it . In bezug auf die Initiative der portugiesischen PrÃ¤sidentschaft mÃ¶chte ich zum Ausdruck bringen , daÃ wir den Geist und die politische Absicht , die dahinter stehen , unterstÃ¼tzen .   0-0 5-1 5-2 2-3 8-4 7-5 11-6 12-7 1-8 0-9 9-10 3-11 10-12 13-13 13-14 14-15 16-16 17-17 18-18 16-19 20-20 21-21 19-22 19-23 22-24 22-25 23-26 15-27 24-28
It may not be an ideal initiative in terms of its structure but we accept Mr President-in-Office , that it is rooted in idealism and for that reason we are inclined to support it .    Von der Struktur her ist es vielleicht keine ideale Initiative , aber , Herr amtierender RatsprÃ¤sident , wir akzeptieren , daÃ sie auf Idealismus fuÃt , und sind deshalb geneigt , sie mitzutragen .   0-0 11-2 8-3 0-4 3-5 1-6 2-7 5-8 6-9 12-11 17-12 15-13 16-14 16-15 17-16 13-17 14-18 17-19 18-20 19-21 21-22 23-23 21-24 26-25 24-26 29-27 27-28 30-29 31-30 33-31 32-32 34-33
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;How can I produce the phrase tables that are used by MOSES from this output?&lt;/p&gt;
', 'untagged', NULL, '''-0'':77C,200C ''-1'':79C ''-10'':97C ''-11'':99C,218C ''-12'':101C,220C ''-13'':103C,222C ''-14'':105C,224C ''-15'':107C,226C ''-16'':109C,228C ''-17'':111C,230C ''-18'':113C,232C ''-19'':115C,234C ''-2'':81C,202C ''-20'':117C,236C ''-21'':119C,238C ''-22'':121C,240C ''-23'':123C,242C ''-24'':125C,244C ''-25'':127C,246C ''-26'':129C,248C ''-27'':131C,250C ''-28'':133C,252C ''-29'':254C ''-3'':83C,204C ''-30'':256C ''-31'':258C ''-32'':260C ''-33'':262C ''-4'':85C,206C ''-5'':87C,208C ''-6'':89C,210C ''-7'':91C,212C ''-8'':93C,214C ''-9'':95C,216C ''/code'':263C ''/p'':21C,281C ''/pre'':264C ''0'':76C,94C,199C,205C ''1'':92C,209C ''10'':100C ''11'':88C,201C ''12'':90C,217C ''13'':102C,104C,229C ''14'':106C,231C ''15'':130C,221C ''16'':108C,114C,223C,225C ''17'':110C,219C,227C,233C ''18'':112C,235C ''19'':120C,122C,237C ''2'':82C,211C ''20'':116C ''21'':118C,239C,243C ''22'':124C,126C ''23'':128C,241C ''24'':132C,247C ''26'':245C ''27'':251C ''29'':249C ''3'':98C,207C ''30'':253C ''31'':255C ''32'':259C ''33'':257C ''34'':261C ''5'':78C,80C,213C ''6'':215C ''7'':86C ''8'':84C,203C ''9'':96C ''aber'':180C ''absicht'':70C ''accept'':148C ''akzeptieren'':186C ''align'':8A,16C ''amtierend'':182C ''auf'':50C,189C ''ausdruck'':61C ''behind'':46C ''bezug'':49C ''bringen'':62C ''chte'':58C ''code'':23C ''dahint'':72C ''daã'':63C,187C ''den'':65C ''der'':53C,171C ''deshalb'':195C ''die'':51C,68C,71C ''es'':175C ''file'':17C ''fuã'':191C ''geist'':66C ''geneigt'':196C ''get'':3A ''herr'':181C ''ich'':59C ''ideal'':139C,159C,178C ''idealismus'':190C ''inclin'':166C ''initi'':32C,52C,140C,179C ''intent'':45C ''ist'':174C ''kein'':177C ''look'':18C ''may'':135C ''mitzutragen'':198C ''mose'':277C ''mr'':149C ''mã'':57C ''offic'':153C ''output'':12C,280C ''p'':10C,265C ''phrase'':4A,271C ''polit'':44C ''politisch'':69C ''portugiesischen'':54C ''portugues'':35C ''pre'':22C ''presid'':36C,151C ''president-in-offic'':150C ''produc'':269C ''prã'':55C ''ratsprã'':183C ''reason'':163C ''regard'':29C ''root'':157C ''say'':27C ''sident'':184C ''sidentschaft'':56C ''sie'':188C,197C ''sind'':194C ''spirit'':41C ''stehen'':73C ''structur'':145C ''struktur'':172C ''support'':39C,168C ''tabl'':5A,272C ''term'':142C ''tzen'':75C ''und'':67C,193C ''untag'':9B ''unterstã'':74C ''use'':275C ''vielleicht'':176C ''von'':170C ''wir'':64C,185C ''wish'':25C ''word'':7A,15C ''zum'':60C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10488, 1, '2016-03-02 18:20:21.793', 0, 2, 'Hadoop Pseudo Distribution Set-up Exception in thread âmainâ java.lang.RuntimeException: java.net.ConnectException', '&lt;p&gt;I am a beginner in Hadoop and I am trying to execute some program (jar file) in pseudo-distribution mode on windows using cygwin. I have done the preliminary steps such as changed all the xml files under conf folder and have ssh configured as well for pseudo mode. I am able to start all the nodes using the command bin/start-all.sh and I am able to bring up &lt;a href="http://localhost:50030/jobtracker.jsp" rel="nofollow"&gt;http://localhost:50030/jobtracker.jsp&lt;/a&gt; locally. But when i tried to execute the jar files which I am testing for pseudo-mode I am getting the below error.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;16/03/02 01:50:14 INFO ipc.Client: Retrying connect to server:    
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 0 time(s).
16/03/02 01:50:16 INFO ipc.Client: Retrying connect to server:   
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 1 time(s).
16/03/02 01:50:18 INFO ipc.Client: Retrying connect to server:  
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 2 time(s).
16/03/02 01:50:20 INFO ipc.Client: Retrying connect to server: 
localhost/127.0.0                                                                                                                   
.1:9000. Already tried 3 time(s).
16/03/02 01:50:22 INFO ipc.Client: Retrying connect to server:  
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 4 time(s).
16/03/02 01:50:24 INFO ipc.Client: Retrying connect to server:  
localhost/127.0.0                                                                                                                 
.1:9000. Already tried 5 time(s).
16/03/02 01:50:26 INFO ipc.Client: Retrying connect to server: 
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 6 time(s).
16/03/02 01:50:28 INFO ipc.Client: Retrying connect to server:  
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 7 time(s).
16/03/02 01:50:30 INFO ipc.Client: Retrying connect to server:  
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 8 time(s).
16/03/02 01:50:32 INFO ipc.Client: Retrying connect to server:  
localhost/127.0.0                                                                                                                  
.1:9000. Already tried 9 time(s).
Exception in thread "main" java.lang.RuntimeException:  
java.net.ConnectException                                                                                                                 
: Call to localhost/127.0.0.1:9000 failed on connection exception:  
java.net.Conn                                                                                                                   
ectException: Connection refused: no further information
at org.apache.hadoop.mapred.JobConf.getWorkingDirectory(JobConf.java:371                                                                                                                  
)
at org.apache.hadoop.mapred.FileInputFormat.setInputPaths(FileInputForma                                                                                                                
t.java:309)
at SpeciesGraphBuilder.main(SpeciesGraphBuilder.java:56)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.                                                                                                                
java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAcces                                                                                                                
sorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.util.RunJar.main(RunJar.java:156)
Caused by: java.net.ConnectException: Call to localhost/127.0.0.1:9000   
failed on                                                                                                                  
connection exception: java.net.ConnectException: Connection refused: no   
further                                                                                                               
information
at org.apache.hadoop.ipc.Client.wrapException(Client.java:767)
at org.apache.hadoop.ipc.Client.call(Client.java:743)
at org.apache.hadoop.ipc.RPC$Invoker.invoke(RPC.java:220)
    at com.sun.proxy.$Proxy0.getProtocolVersion(Unknown Source)
at org.apache.hadoop.ipc.RPC.getProxy(RPC.java:359)
at org.apache.hadoop.hdfs.DFSClient.createRPCNamenode(DFSClient.java:106                                                                                                                 
)
at org.apache.hadoop.hdfs.DFSClient.&amp;lt;init&amp;gt;(DFSClient.java:207)
at org.apache.hadoop.hdfs.DFSClient.&amp;lt;init&amp;gt;(DFSClient.java:170)
at org.apache.hadoop.hdfs.DistributedFileSystem.initialize(DistributedFi                                                                                                                 
leSystem.java:82)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:1378                                                                                                                 
)
at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:66)
    at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:1390)
at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:196)
at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:95)
at org.apache.hadoop.mapred.JobConf.getWorkingDirectory(JobConf.java:367                                                                                                                 
)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have done all the initial testing by refreshing the name nodes, starting the name node, data node, job trackers etc and all went fine. But I am unable to tackle this strange error. I am using hadoop 0.20.2 version and trying to run it on windows. I am providing the changes that I made in xml files:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;core-site.xml&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;configuration&amp;gt;
 &amp;lt;property&amp;gt;
  &amp;lt;name&amp;gt;hadoop.tmp.dir&amp;lt;/name&amp;gt;
  &amp;lt;value&amp;gt;/tmp/hadoop-Sriram&amp;lt;/value&amp;gt;
 &amp;lt;/property&amp;gt;

 &amp;lt;property&amp;gt;
   &amp;lt;name&amp;gt;fs.default.name&amp;lt;/name&amp;gt;
   &amp;lt;value&amp;gt;hdfs://localhost:9000&amp;lt;/value&amp;gt;
 &amp;lt;/property&amp;gt;    
&amp;lt;/configuration&amp;gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;HDFS-Site.xml&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;configuration&amp;gt;
 &amp;lt;property&amp;gt;
  &amp;lt;name&amp;gt;dfs.replication&amp;lt;/name&amp;gt;
  &amp;lt;value&amp;gt;1&amp;lt;/value&amp;gt;
 &amp;lt;/property&amp;gt;

 &amp;lt;property&amp;gt;
  &amp;lt;name&amp;gt;dfs.name.dir&amp;lt;/name&amp;gt;
  &amp;lt;value&amp;gt;/home/Sriram/hdfs/name&amp;lt;/value&amp;gt;
 &amp;lt;/property&amp;gt;

 &amp;lt;property&amp;gt;
  &amp;lt;name&amp;gt;dfs.data.dir&amp;lt;/name&amp;gt;
  &amp;lt;value&amp;gt;/home/Sriram/hdfs/data&amp;lt;/value&amp;gt;
 &amp;lt;/property&amp;gt;
&amp;lt;/configuration&amp;gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Mapred-site.xml&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;lt;configuration&amp;gt;
 &amp;lt;property&amp;gt;
  &amp;lt;name&amp;gt;mapred.job.tracker&amp;lt;/name&amp;gt;
  &amp;lt;value&amp;gt;localhost:9001&amp;lt;/value&amp;gt;
 &amp;lt;/property&amp;gt;

 &amp;lt;property&amp;gt;
  &amp;lt;name&amp;gt;mapred.child.java.opts&amp;lt;/name&amp;gt;
  &amp;lt;value&amp;gt;-Xmx2g&amp;lt;/value&amp;gt;
 &amp;lt;/property&amp;gt;
&amp;lt;/configuration&amp;gt;
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I have referred previous posts for this issue, but could not find one for windows. I need to track the jobs locally once the execution starts (task tracker/job tracker). Any help would be greatly appreciated as I am blocked at this state.&lt;/p&gt;
', 'bigdata  dataset  hadoop  java', NULL, '''/a'':96C ''/code'':429C,545C,620C,676C ''/configuration'':543C,618C,674C ''/home/sriram/hdfs/data'':610C ''/home/sriram/hdfs/name'':590C ''/name'':509C,529C,565C,585C,605C,640C,661C ''/p'':121C,490C,495C,551C,626C,721C ''/pre'':430C,546C,621C,677C ''/property'':519C,540C,575C,595C,615C,651C,671C ''/strong'':494C,550C,625C ''/tmp/hadoop-sriram'':514C ''/value'':516C,537C,572C,592C,612C,648C,668C ''0'':139C ''0.20.2'':470C ''01'':125C,143C,161C,179C,197C,215C,233C,251C,269C,287C ''1'':135C,153C,157C,171C,189C,207C,225C,243C,261C,279C,297C,570C ''14'':127C ''16'':145C ''16/03/02'':124C,142C,160C,178C,196C,214C,232C,250C,268C,286C ''18'':163C ''2'':175C ''20'':181C ''200'':414C ''22'':199C ''24'':217C ''26'':235C ''28'':253C ''3'':193C ''30'':271C ''32'':289C ''4'':211C ''5'':229C ''50'':126C,144C,162C,180C,198C,216C,234C,252C,270C,288C ''50030/jobtracker.jsp'':91C,95C ''6'':247C ''62'':344C ''7'':265C ''8'':283C ''9'':301C ''9000'':136C,154C,172C,190C,208C,226C,244C,262C,280C,298C,313C,361C,535C ''9001'':646C ''abl'':71C,84C ''alreadi'':137C,155C,173C,191C,209C,227C,245C,263C,281C,299C ''appreci'':713C ''beginn'':22C ''bigdata'':14B ''bin/start-all.sh'':80C ''block'':717C ''bring'':86C ''cache.get'':418C ''call'':310C,358C ''caus'':355C ''chang'':52C,483C ''client.java:743'':377C ''client.java:767'':374C ''code'':123C,497C,553C,628C ''com.sun.proxy'':383C ''command'':79C ''conf'':58C ''configur'':63C,499C,555C,630C ''connect'':131C,149C,167C,185C,203C,221C,239C,257C,275C,293C,316C,320C,364C,367C ''core-site.xml'':493C ''could'':688C ''cygwin'':43C ''data'':448C ''dataset'':15B ''delegatingmethodacc'':347C ''dfs.data.dir'':603C ''dfs.name.dir'':583C ''dfs.replication'':563C ''dfsclient.java:106'':392C ''dfsclient.java:170'':404C ''dfsclient.java:207'':398C ''distribut'':3A,38C ''distributedfi'':407C ''done'':46C,434C ''ectexcept'':319C ''error'':120C,465C ''etc'':452C ''except'':7A,304C,317C,365C ''execut'':30C,103C,703C ''fail'':314C,362C ''file'':34C,56C,106C,489C ''fileinputforma'':330C ''filesystem.java:1378'':411C ''filesystem.java:1390'':419C ''filesystem.java:196'':422C ''filesystem.java:66'':415C ''filesystem.java:95'':425C ''find'':690C ''fine'':456C ''folder'':59C ''fs.default.name'':527C ''get'':117C ''great'':712C ''gt'':397C,403C,500C,503C,506C,510C,513C,517C,520C,523C,526C,530C,533C,538C,541C,544C,556C,559C,562C,566C,569C,573C,576C,579C,582C,586C,589C,593C,596C,599C,602C,606C,609C,613C,616C,619C,631C,634C,637C,641C,644C,649C,652C,655C,658C,662C,665C,669C,672C,675C ''hadoop'':1A,16B,24C,469C ''hadoop.tmp.dir'':507C ''hdfs-site.xml'':549C ''help'':709C ''href'':89C ''info'':128C,146C,164C,182C,200C,218C,236C,254C,272C,290C ''inform'':324C,371C ''init'':396C,402C ''initi'':437C ''invoke0'':337C ''invoker.invoke'':380C ''ipc.client'':129C,147C,165C,183C,201C,219C,237C,255C,273C,291C ''issu'':686C ''jar'':33C,105C ''java'':17B,343C ''java.lang.reflect.method.invoke'':350C ''java.lang.runtimeexception'':12A,308C ''java.net.conn'':318C ''java.net.connectexception'':13A,309C,357C,366C ''job'':450C,699C ''jobconf.java:367'':428C ''jobconf.java:371'':327C ''lesystem.java:82'':408C ''local'':97C,700C ''localhost'':90C,94C,534C,645C ''localhost/127.0.0'':134C,152C,170C,188C,206C,224C,242C,260C,278C,296C ''localhost/127.0.0.1'':312C,360C ''lt'':395C,401C,498C,501C,504C,508C,511C,515C,518C,521C,524C,528C,531C,536C,539C,542C,554C,557C,560C,564C,567C,571C,574C,577C,580C,584C,587C,591C,594C,597C,600C,604C,607C,611C,614C,617C,629C,632C,635C,639C,642C,647C,650C,653C,656C,660C,663C,667C,670C,673C ''made'':486C ''main'':307C ''mainâ'':11A ''mapred-site.xml'':624C ''mapred.child.java.opts'':659C ''mapred.job.tracker'':638C ''method'':339C ''method.java:498'':351C ''mode'':39C,68C,114C ''name'':442C,446C,505C,525C,561C,581C,601C,636C,657C ''nativ'':338C ''nativemethodaccessorimpl'':342C ''need'':695C ''node'':76C,443C,447C,449C ''nofollow'':93C ''one'':691C ''org.apache.hadoop.fs.filesystem'':417C ''org.apache.hadoop.fs.filesystem.access'':413C ''org.apache.hadoop.fs.filesystem.createfilesystem'':410C ''org.apache.hadoop.fs.filesystem.get'':421C,424C ''org.apache.hadoop.hdfs.dfsclient'':394C,400C ''org.apache.hadoop.hdfs.dfsclient.createrpcnamenode'':391C ''org.apache.hadoop.hdfs.distributedfilesystem.initialize'':406C ''org.apache.hadoop.ipc.client.call'':376C ''org.apache.hadoop.ipc.client.wrapexception'':373C ''org.apache.hadoop.ipc.rpc'':379C ''org.apache.hadoop.ipc.rpc.getproxy'':388C ''org.apache.hadoop.mapred.fileinputformat.setinputpaths'':329C ''org.apache.hadoop.mapred.jobconf.getworkingdirectory'':326C,427C ''org.apache.hadoop.util.runjar.main'':353C ''p'':18C,431C,491C,547C,622C,678C ''post'':683C ''pre'':122C,496C,552C,627C ''preliminari'':48C ''previous'':682C ''program'':32C ''properti'':502C,522C,558C,578C,598C,633C,654C ''provid'':481C ''proxy0.getprotocolversion'':384C ''pseudo'':2A,37C,67C,113C ''pseudo-distribut'':36C ''pseudo-mod'':112C ''refer'':681C ''refresh'':440C ''refus'':321C,368C ''rel'':92C ''retri'':130C,148C,166C,184C,202C,220C,238C,256C,274C,292C ''rpc.java:220'':381C ''rpc.java:359'':389C ''run'':475C ''runjar.java:156'':354C ''server'':133C,151C,169C,187C,205C,223C,241C,259C,277C,295C ''set'':5A ''set-up'':4A ''sorimpl.java:43'':348C ''sourc'':386C ''speciesgraphbuilder.java:56'':334C ''speciesgraphbuilder.main'':333C ''ssh'':62C ''start'':73C,444C,704C ''state'':720C ''step'':49C ''strang'':464C ''strong'':492C,548C,623C ''sun.reflect.delegatingmethodaccessorimpl.invoke'':346C ''sun.reflect.nativemethodaccessorimpl'':336C ''sun.reflect.nativemethodaccessorimpl.invoke'':341C ''t.java:309'':331C ''tackl'':462C ''task'':705C ''test'':110C,438C ''thread'':9A,306C ''time'':140C,158C,176C,194C,212C,230C,248C,266C,284C,302C ''track'':697C ''tracker'':451C,707C ''tracker/job'':706C ''tri'':28C,101C,138C,156C,174C,192C,210C,228C,246C,264C,282C,300C,473C ''unabl'':460C ''unknown'':385C ''use'':42C,77C,468C ''valu'':512C,532C,568C,588C,608C,643C,664C ''version'':471C ''well'':65C ''went'':455C ''window'':41C,478C,693C ''would'':710C ''xml'':55C,488C ''xmx2g'':666C ''â'':10A');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (226, 2, '2014-05-31 19:59:15.563', 11, NULL, NULL, '&lt;p&gt;I''ve found this link in Data Science Central with a list of free datasets: &lt;a href="http://www.datasciencecentral.com/profiles/blogs/big-data-sets-available-for-free"&gt;Big data sets available for free&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':28C ''/p'':29C ''/profiles/blogs/big-data-sets-available-for-free'':21C ''avail'':25C ''big'':22C ''central'':10C ''data'':8C,23C ''dataset'':16C ''found'':4C ''free'':15C,27C ''href'':18C ''link'':6C ''list'':13C ''p'':1C ''scienc'':9C ''set'':24C ''ve'':3C ''www.datasciencecentral.com'':20C ''www.datasciencecentral.com/profiles/blogs/big-data-sets-available-for-free'':19C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (227, 1, '2014-06-01 10:25:51.163', 7, 3756, 'Tradeoffs between Storm and Hadoop (MapReduce)', '&lt;p&gt;Can someone kindly tell me about the trade-offs involved when choosing between Storm and MapReduce in Hadoop Cluster for data processing? Of course, aside from the obvious one, that Hadoop (processing via MapReduce in a Hadoop Cluster) is a batch processing system, and Storm is a real-time processing system.&lt;/p&gt;

&lt;p&gt;I have worked a bit with Hadoop Eco System, but I haven''t worked with Storm. After looking through a lot of presentations and articles, I still haven''t been able to find a satisfactory and comprehensive answer.&lt;/p&gt;

&lt;p&gt;Note: The term tradeoff here is not meant to compare to similar things. It is meant to represent the consequences of getting results real-time that are absent from a batch processing system. &lt;/p&gt;
', 'bigdata  efficiency  hadoop  distributed', NULL, '''/p'':65C,105C,141C ''abl'':97C ''absent'':135C ''answer'':104C ''articl'':91C ''asid'':37C ''batch'':53C,138C ''bigdata'':7B ''bit'':71C ''choos'':24C ''cluster'':31C,50C ''compar'':116C ''comprehens'':103C ''consequ'':126C ''cours'':36C ''data'':33C ''distribut'':10B ''eco'':74C ''effici'':8B ''find'':99C ''get'':128C ''hadoop'':5A,9B,30C,43C,49C,73C ''haven'':78C,94C ''involv'':22C ''kind'':14C ''look'':84C ''lot'':87C ''mapreduc'':6A,28C,46C ''meant'':114C,122C ''note'':107C ''obvious'':40C ''off'':21C ''one'':41C ''p'':11C,66C,106C ''present'':89C ''process'':34C,44C,54C,63C,139C ''real'':61C,131C ''real-tim'':60C,130C ''repres'':124C ''result'':129C ''satisfactori'':101C ''similar'':118C ''someon'':13C ''still'':93C ''storm'':3A,26C,57C,82C ''system'':55C,64C,75C,140C ''tell'':15C ''term'':109C ''thing'':119C ''time'':62C,132C ''trade'':20C ''trade-off'':19C ''tradeoff'':1A,110C ''via'':45C ''work'':69C,80C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (228, 1, '2014-06-01 12:51:25.04', 6, 50, 'Cascaded Error in Apache Storm', '&lt;p&gt;Going through the presentation and material of Summingbird by Twitter, one of the reasons that is mentioned for using Storm and Hadoop clusters together in Summingbird is that processing through Storm results in cascading of error. In order to avoid this cascading of error and accumulation of it, Hadoop cluster is used to batch process the data and discard the Storm results after the same data is processed by Hadoop. &lt;/p&gt;

&lt;p&gt;What is the reasons for generation of this accumulation of error? and why is it not present in Hadoop? Since I have not worked with Storm, I do not know the reasons for it. Is it because Storm uses some approximate algorithm to process the data in order to process them in real time? or is the cause something else?&lt;/p&gt;
', 'bigdata  hadoop', NULL, '''/p'':79C,141C ''accumul'':54C,89C ''algorithm'':122C ''apach'':4A ''approxim'':121C ''avoid'':48C ''batch'':62C ''bigdata'':6B ''cascad'':1A,42C,50C ''caus'':138C ''cluster'':31C,58C ''data'':65C,74C,126C ''discard'':67C ''els'':140C ''error'':2A,44C,52C,91C ''generat'':86C ''go'':9C ''hadoop'':7B,30C,57C,78C,99C ''know'':110C ''materi'':14C ''mention'':25C ''one'':19C ''order'':46C,128C ''p'':8C,80C ''present'':12C,97C ''process'':37C,63C,76C,124C,130C ''real'':133C ''reason'':22C,84C,112C ''result'':40C,70C ''sinc'':100C ''someth'':139C ''storm'':5A,28C,39C,69C,106C,118C ''summingbird'':16C,34C ''time'':134C ''togeth'':32C ''twitter'':18C ''use'':27C,60C,119C ''work'':104C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (243, 2, '2014-06-10 01:36:27.52', 2, NULL, NULL, '&lt;p&gt;A variety of methods are available to the user.   The support documentation gives walkthroughs and tips for when one or another model is most appropriate. &lt;/p&gt;

&lt;p&gt;&lt;a href="https://developers.google.com/prediction/docs/pmml-schema" rel="nofollow"&gt;This page&lt;/a&gt; shows the following learning methods:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;"AssociationModel"&lt;/li&gt;
&lt;li&gt;"ClusteringModel" &lt;/li&gt;
&lt;li&gt;"GeneralRegressionModel"&lt;/li&gt;
&lt;li&gt;"MiningModel" &lt;/li&gt;
&lt;li&gt;"NaiveBayesModel" &lt;/li&gt;
&lt;li&gt;"NeuralNetwork" &lt;/li&gt;
&lt;li&gt;"RegressionModel"&lt;/li&gt;
&lt;li&gt;"RuleSetModel" &lt;/li&gt;
&lt;li&gt;"SequenceModel" &lt;/li&gt;
&lt;li&gt;"SupportVectorMachineModel"&lt;/li&gt;
&lt;li&gt;"TextModel" &lt;/li&gt;
&lt;li&gt;"TimeSeriesModel" &lt;/li&gt;
&lt;li&gt;"TreeModel"&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;EDIT: I don''t see any specific information about the algorithms, though.  For example, does the tree model use information gain or gini index for splits? &lt;/p&gt;
', NULL, NULL, '''/a'':38C ''/li'':48C,51C,54C,57C,60C,63C,66C,69C,72C,75C,78C,81C,84C ''/p'':27C,44C,113C ''/prediction/docs/pmml-schema'':33C ''/ul'':85C ''algorithm'':97C ''anoth'':22C ''appropri'':26C ''associationmodel'':47C ''avail'':7C ''clusteringmodel'':50C ''developers.google.com'':32C ''developers.google.com/prediction/docs/pmml-schema'':31C ''document'':13C ''edit'':87C ''exampl'':100C ''follow'':41C ''gain'':107C ''generalregressionmodel'':53C ''gini'':109C ''give'':14C ''href'':30C ''index'':110C ''inform'':94C,106C ''learn'':42C ''li'':46C,49C,52C,55C,58C,61C,64C,67C,70C,73C,76C,79C,82C ''method'':5C,43C ''miningmodel'':56C ''model'':23C,104C ''naivebayesmodel'':59C ''neuralnetwork'':62C ''nofollow'':35C ''one'':20C ''p'':1C,28C,86C ''page'':37C ''regressionmodel'':65C ''rel'':34C ''rulesetmodel'':68C ''see'':91C ''sequencemodel'':71C ''show'':39C ''specif'':93C ''split'':112C ''support'':12C ''supportvectormachinemodel'':74C ''textmodel'':77C ''though'':98C ''timeseriesmodel'':80C ''tip'':17C ''tree'':103C ''treemodel'':83C ''ul'':45C ''use'':105C ''user'':10C ''varieti'':3C ''walkthrough'':15C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (262, 1, '2014-06-10 09:26:06.593', 13, 402, 'Hierarchical Data Format. What are the advantages compared to alternative formats?', '&lt;p&gt;What are the main benefits from storing data in HDF? And what are the main data science tasks where HDF is really suitable and useful?&lt;/p&gt;
', 'data-formats  hierarchical-data-format', NULL, '''/p'':45C ''advantag'':7A ''altern'':10A ''benefit'':24C ''compar'':8A ''data'':2A,13B,17B,27C,35C ''data-format'':12B ''format'':3A,11A,14B,18B ''hdf'':29C,39C ''hierarch'':1A,16B ''hierarchical-data-format'':15B ''main'':23C,34C ''p'':19C ''realli'':41C ''scienc'':36C ''store'':26C ''suitabl'':42C ''task'':37C ''use'':44C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8945, 2, '2015-11-20 19:30:24.2', 6, NULL, NULL, '&lt;p&gt;PyData - talks about Python Data tools&lt;br&gt;
Link: &lt;a href="http://pydata.org/events/" rel="nofollow"&gt;http://pydata.org/events/&lt;/a&gt;  &lt;/p&gt;

&lt;p&gt;There is one PyData conference on the east coast and one on the west coast each year.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;NIPS - Neural Information Processing Systems (NIPS)&lt;br&gt;
Link: &lt;a href="https://nips.cc/" rel="nofollow"&gt;https://nips.cc/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This is one of the hardest / most prestigious academic Machine Learning conferences to get an abstract / poster accepted.    &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;The 5th International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (IEEE IPDPS 2016)&lt;br&gt;
Link: &lt;a href="http://parlearning.ecs.fullerton.edu/" rel="nofollow"&gt;http://parlearning.ecs.fullerton.edu/&lt;/a&gt;  &lt;/p&gt;

&lt;p&gt;This one is also an academic conference with paper submission.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Note:
I am not sure if you want academic or nor academic conferences (have conference proceedings / papers associated with the conference).
Some conferences are not about new data science methodologies  but the tools and libraries (e.g. PyData) that implement existing methodologies.
Also, data science is very broad and includes Stat, Machine Learning and data warehousing / mining etc.&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':110C ''/&lt;/a&gt;&lt;/p&gt;'':57C ''/events/'':14C ''/events/&lt;/a&gt;'':19C ''/p'':20C,39C,77C,111C,123C,183C ''2016'':100C ''5th'':81C ''abstract'':74C ''academ'':67C,118C,134C,137C ''accept'':76C ''also'':116C,167C ''analyt'':97C ''associ'':143C ''big'':95C ''br'':8C,48C,101C ''broad'':172C ''coast'':30C,36C ''comput'':88C ''confer'':26C,70C,119C,138C,140C,146C,148C ''data'':6C,96C,153C,168C,179C ''distribut'':87C ''e.g'':161C ''east'':29C ''etc'':182C ''exist'':165C ''get'':72C ''hardest'':64C ''hr'':40C,78C,124C ''href'':11C,51C,104C ''ieee'':98C ''implement'':164C ''includ'':174C ''inform'':44C ''intern'':82C ''ipdp'':99C ''larg'':90C ''learn'':69C,93C,177C ''librari'':160C ''link'':9C,49C,102C ''machin'':68C,92C,176C ''methodolog'':155C,166C ''mine'':181C ''neural'':43C ''new'':152C ''nip'':42C,47C ''nips.cc'':52C,56C ''nips.cc/&lt;/a&gt;&lt;/p&gt;'':55C ''nofollow'':16C,54C,107C ''note'':126C ''one'':24C,32C,61C,114C ''p'':1C,21C,41C,58C,79C,112C,125C ''paper'':121C,142C ''parallel'':85C ''parlearning.ecs.fullerton.edu'':105C,109C ''parlearning.ecs.fullerton.edu/&lt;/a&gt;'':108C ''poster'':75C ''prestigi'':66C ''proceed'':141C ''process'':45C ''pydata'':2C,25C,162C ''pydata.org'':13C,18C ''pydata.org/events/'':12C ''pydata.org/events/&lt;/a&gt;'':17C ''python'':5C ''rel'':15C,53C,106C ''scale'':91C ''scienc'':154C,169C ''stat'':175C ''submiss'':122C ''sure'':130C ''system'':46C ''talk'':3C ''tool'':7C,158C ''want'':133C ''wareh'':180C ''west'':35C ''workshop'':83C ''year'':38C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (232, 2, '2014-06-06 14:55:18.867', 3, NULL, NULL, '&lt;p&gt;There are a few ways to achieve your "master confusion matrix".&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Sum all the confusion matrices together:  Like you suggested, summing this results in a confusion matrix.  The problem with this is you can not interpret totals.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Average the entries.  This method is the same as number one, but you divide each entry by the number of trials (~400 in your case).  This would be my preferred method because then you can actually translate each category to a (mean) +- (an error measurement) and actually see which categories are the most volatile or stable.  Careful with interpreting this ''error measurement'' though.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Report a problem specific measurement of the confusion numbers.  For example, if your numbers have outliers, medians would preferred over means.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;There are other statistics that are possible to report as well.  You can redo the method to keep track of individual classifications.  Then we can say other important stats like ''% of classifications that stay the same and are accurate'', etc...&lt;/p&gt;
', NULL, NULL, '''/li'':44C,111C,136C ''/ol'':137C ''/p'':13C,43C,110C,135C,179C ''400'':68C ''accur'':177C ''achiev'':8C ''actual'':82C,93C ''averag'':47C ''care'':103C ''case'':71C ''categori'':85C,96C ''classif'':160C,170C ''confus'':11C,20C,31C,121C ''divid'':60C ''entri'':49C,62C ''error'':90C,107C ''etc'':178C ''exampl'':124C ''import'':166C ''individu'':159C ''interpret'':41C,105C ''keep'':156C ''li'':15C,45C,112C ''like'':23C,168C ''master'':10C ''matric'':21C ''matrix'':12C,32C ''mean'':88C,134C ''measur'':91C,108C,118C ''median'':130C ''method'':51C,77C,154C ''number'':56C,65C,122C,127C ''ol'':14C ''one'':57C ''outlier'':129C ''p'':1C,16C,46C,113C,138C ''possibl'':145C ''prefer'':76C,132C ''problem'':34C,116C ''redo'':152C ''report'':114C,147C ''result'':28C ''say'':164C ''see'':94C ''specif'':117C ''stabl'':102C ''stat'':167C ''statist'':142C ''stay'':172C ''suggest'':25C ''sum'':17C,26C ''though'':109C ''togeth'':22C ''total'':42C ''track'':157C ''translat'':83C ''trial'':67C ''volatil'':100C ''way'':6C ''well'':149C ''would'':73C,131C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (233, 2, '2014-06-08 07:03:43.71', 2, NULL, NULL, '&lt;p&gt;To complete Damien''s answer, an example of dimensionality reduction in NLP is a &lt;a href="http://en.wikipedia.org/wiki/Topic_model" rel="nofollow"&gt;topic model&lt;/a&gt;, where you represent the document by a vector indicating the weights of its constituent topics.&lt;/p&gt;
', NULL, NULL, '''/a'':25C ''/p'':41C ''/wiki/topic_model'':20C ''answer'':6C ''complet'':3C ''constitu'':39C ''damien'':4C ''dimension'':10C ''document'':30C ''en.wikipedia.org'':19C ''en.wikipedia.org/wiki/topic_model'':18C ''exampl'':8C ''href'':17C ''indic'':34C ''model'':24C ''nlp'':13C ''nofollow'':22C ''p'':1C ''reduct'':11C ''rel'':21C ''repres'':28C ''topic'':23C,40C ''vector'':33C ''weight'':36C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (234, 1, '2014-06-09 04:43:03.497', 4, 285, 'Data science Ph.D. program, what do you think?', '&lt;p&gt;As Yann LeCun &lt;a href="http://www.reddit.com/r/MachineLearning/comments/25lnbt/ama_yann_lecun/chisdw1" rel="nofollow"&gt;mentioned&lt;/a&gt;, a number of PhD programs in data science will be popping up in the next few years.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://datascience.nyu.edu/academics/programs/" rel="nofollow"&gt;NYU&lt;/a&gt; already have one, where Prof.LeCun is at right now.&lt;/p&gt;

&lt;p&gt;A statistics or cs PhD in machine learning is probably more rigorous than a data science one.  Is data science PhD for the less mathy people like myself? &lt;/p&gt;

&lt;p&gt;Are these cash cow programs?&lt;/p&gt;

&lt;p&gt;There is a huge industry demand for big data, but what is the academic value of these programs, as you probably can''t be a professor or publish any paper.&lt;/p&gt;
', 'knowledge-base', NULL, '''/a'':24C,52C ''/academics/programs/'':48C ''/p'':42C,62C,92C,99C,131C ''/r/machinelearning/comments/25lnbt/ama_yann_lecun/chisdw1'':20C ''academ'':114C ''alreadi'':53C ''base'':11B ''big'':108C ''cash'':96C ''cow'':97C ''cs'':67C ''data'':1A,31C,78C,82C,109C ''datascience.nyu.edu'':47C ''datascience.nyu.edu/academics/programs/'':46C ''demand'':106C ''href'':17C,45C ''huge'':104C ''industri'':105C ''knowledg'':10B ''knowledge-bas'':9B ''learn'':71C ''lecun'':15C ''less'':87C ''like'':90C ''machin'':70C ''mathi'':88C ''mention'':23C ''next'':39C ''nofollow'':22C,50C ''number'':26C ''nyu'':51C ''one'':55C,80C ''p'':12C,43C,63C,93C,100C ''paper'':130C ''peopl'':89C ''ph.d'':3A ''phd'':28C,68C,84C ''pop'':35C ''probabl'':73C,121C ''prof.lecun'':57C ''professor'':126C ''program'':4A,29C,98C,118C ''publish'':128C ''rel'':21C,49C ''right'':60C ''rigor'':75C ''scienc'':2A,32C,79C,83C ''statist'':65C ''think'':8A ''valu'':115C ''www.reddit.com'':19C ''www.reddit.com/r/machinelearning/comments/25lnbt/ama_yann_lecun/chisdw1'':18C ''yann'':14C ''year'':41C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (235, 1, '2014-06-09 08:34:29.337', 0, 573, 'Are there any python based data visualization toolkit?', '&lt;p&gt;Data visualization is an important sub-field in data science and python programmers would need to have available toolkits for them.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is there a Python API to Tableau?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Are there any Python-based data visualization toolkits?&lt;/strong&gt;&lt;/p&gt;
', 'python  visualization', NULL, '''/p'':34C,45C,58C ''/strong'':44C,57C ''api'':41C ''avail'':30C ''base'':5A,53C ''data'':6A,12C,21C,54C ''field'':19C ''import'':16C ''need'':27C ''p'':11C,35C,46C ''programm'':25C ''python'':4A,9B,24C,40C,52C ''python-bas'':51C ''scienc'':22C ''strong'':36C,47C ''sub'':18C ''sub-field'':17C ''tableau'':43C ''toolkit'':8A,31C,56C ''visual'':7A,10B,13C,55C ''would'':26C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (236, 2, '2014-06-09 18:02:00.613', 1, NULL, NULL, '&lt;p&gt;No-one knows since no-one''s completed one of these PhD programs yet! However, I would look at the syllabus and the teachers to base my decision. It all depends on what you want to do; industry or academia?&lt;/p&gt;
', NULL, NULL, '''/p'':44C ''academia'':43C ''base'':29C ''complet'':11C ''decis'':31C ''depend'':34C ''howev'':18C ''industri'':41C ''know'':5C ''look'':21C ''no-on'':2C,7C ''one'':4C,9C,12C ''p'':1C ''phd'':15C ''program'':16C ''sinc'':6C ''syllabus'':24C ''teacher'':27C ''want'':38C ''would'':20C ''yet'':17C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (237, 2, '2014-06-09 19:52:41.847', 7, NULL, NULL, '&lt;p&gt;There is a Tablaeu API and you can use Python to use it, but maybe not in the sense that you think. There is a Data Extract API that you could use to import your data into Python and do your visualizations there, so I do not know if this is going to answer your question entirely.&lt;/p&gt;

&lt;p&gt;As in the first comment you can use Matplotlib from &lt;a href="http://www.matplotlib.org"&gt;Matplotlib website&lt;/a&gt;, or you could install Canopy from Enthought which has it available, there is also Pandas, which you could also use for data analysis and some visualizations. There is also a package called &lt;code&gt;ggplot&lt;/code&gt; which is used in &lt;code&gt;R&lt;/code&gt; alot, but is also made for Python, which you can find here &lt;a href="https://pypi.python.org/pypi/ggplot"&gt;ggplot for python&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The Tableau data extract API and some information about it can be found &lt;a href="http://www.tableausoftware.com/new-features/data-engine-api-0"&gt;at this link&lt;/a&gt;. There are a few web sources that I found concerning it using duckduckgo &lt;a href="https://duckduckgo.com/?q=tableau%20PYTHON%20API&amp;amp;kp=1&amp;amp;kd=-1"&gt;at this link&lt;/a&gt;.
Here are some samples:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.interworks.com/blogs/bbickell/2012/12/06/introducing-python-tableau-data-extract-api-csv-extract-example"&gt;Link 1&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://ryrobes.com/python/building-tableau-data-extract-files-with-python-in-tableau-8-sample-usage/"&gt;Link 2&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://nbviewer.ipython.org/github/Btibert3/tableau-r/blob/master/Python-R-Tableau-Predictive-Modeling.ipynb"&gt;Link 3&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;As far as an API like matplotlib, I cannot say for certain that one exists. Hopefully this gives some sort of reference to help answer your question.&lt;/p&gt;

&lt;p&gt;Also to help avoid closure flags and downvotes you should try and show some of what you have tried to do or find, this makes for a better question and helps to illicit responses.&lt;/p&gt;
', NULL, NULL, '''/?q=tableau%20python%20api&amp;amp;kp=1&amp;amp;kd=-1'':181C ''/a'':76C,139C,163C,185C,199C,209C,219C ''/blogs/bbickell/2012/12/06/introducing-python-tableau-data-extract-api-csv-extract-example'':196C ''/code'':111C,118C ''/github/btibert3/tableau-r/blob/master/python-r-tableau-predictive-modeling.ipynb'':216C ''/new-features/data-engine-api-0'':159C ''/p'':59C,140C,190C,200C,210C,220C,249C,285C ''/pypi/ggplot'':135C ''/python/building-tableau-data-extract-files-with-python-in-tableau-8-sample-usage/'':206C ''1'':198C ''2'':208C ''3'':218C ''alot'':119C ''also'':90C,95C,105C,122C,251C ''analysi'':99C ''answer'':55C,246C ''api'':6C,29C,146C,226C ''avail'':87C ''avoid'':254C ''better'':278C ''call'':108C ''cannot'':230C ''canopi'':81C ''certain'':233C ''closur'':255C ''code'':109C,116C ''comment'':65C ''concern'':173C ''could'':32C,79C,94C ''data'':27C,37C,98C,144C ''downvot'':258C ''duckduckgo'':176C ''duckduckgo.com'':180C ''duckduckgo.com/?q=tableau%20python%20api&amp;amp;kp=1&amp;amp;kd=-1'':179C ''enthought'':83C ''entir'':58C ''exist'':236C ''extract'':28C,145C ''far'':223C ''find'':129C,273C ''first'':64C ''flag'':256C ''found'':154C,172C ''ggplot'':110C,136C ''give'':239C ''go'':53C ''help'':245C,253C,281C ''hope'':237C ''href'':72C,132C,156C,178C,193C,203C,213C ''illicit'':283C ''import'':35C ''inform'':149C ''instal'':80C ''know'':49C ''like'':227C ''link'':162C,184C,197C,207C,217C ''made'':123C ''make'':275C ''matplotlib'':69C,74C,228C ''mayb'':16C ''nbviewer.ipython.org'':215C ''nbviewer.ipython.org/github/btibert3/tableau-r/blob/master/python-r-tableau-predictive-modeling.ipynb'':214C ''one'':235C ''p'':1C,60C,141C,191C,201C,211C,221C,250C ''packag'':107C ''panda'':91C ''pypi.python.org'':134C ''pypi.python.org/pypi/ggplot'':133C ''python'':11C,39C,125C,138C ''question'':57C,248C,279C ''r'':117C ''refer'':243C ''respons'':284C ''ryrobes.com'':205C ''ryrobes.com/python/building-tableau-data-extract-files-with-python-in-tableau-8-sample-usage/'':204C ''sampl'':189C ''say'':231C ''sens'':20C ''show'':263C ''sort'':241C ''sourc'':169C ''tablaeu'':5C ''tableau'':143C ''think'':23C ''tri'':261C,269C ''use'':10C,13C,33C,68C,96C,114C,175C ''visual'':43C,102C ''web'':168C ''websit'':75C ''www.interworks.com'':195C ''www.interworks.com/blogs/bbickell/2012/12/06/introducing-python-tableau-data-extract-api-csv-extract-example'':194C ''www.matplotlib.org'':73C ''www.tableausoftware.com'':158C ''www.tableausoftware.com/new-features/data-engine-api-0'':157C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (238, 2, '2014-06-09 21:36:44.297', 1, NULL, NULL, '&lt;p&gt;I think this question assumes a false premise. As a student at NYU, I only know of a Masters in Data Science. You linked to a page that confirms this.&lt;/p&gt;

&lt;p&gt;It''s hard to gauge the benefit of a program that doesn''t exist yet.&lt;/p&gt;
', NULL, NULL, '''/p'':32C,49C ''assum'':6C ''benefit'':40C ''confirm'':30C ''data'':22C ''doesn'':45C ''exist'':47C ''fals'':8C ''gaug'':38C ''hard'':36C ''know'':17C ''link'':25C ''master'':20C ''nyu'':14C ''p'':1C,33C ''page'':28C ''premis'':9C ''program'':43C ''question'':5C ''scienc'':23C ''student'':12C ''think'':3C ''yet'':48C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (242, 2, '2014-06-09 21:57:30.24', 10, NULL, NULL, '&lt;p&gt;&lt;strong&gt;MapReduce&lt;/strong&gt;: A fault tolerant distributed computational framework. MapReduce allows you to operate over huge amounts of data- with a lot of work put in to prevent failure due to hardware. MapReduce is a poor choice for computing results on the fly because it is slow. (A typical MapReduce job takes on the order of minutes or hours, not microseconds)&lt;/p&gt;

&lt;p&gt;A MapReduce job takes a file (or some data store) as an input and writes a file of results. If you want these results available to an application, it is your responsibility to put this data in a place that is accessible. This is likely slow, and there will be a lag between the values you can display, and the values that represent your system in its current state.&lt;/p&gt;

&lt;p&gt;An important distinction to make when considering using MapReduce in building realtime systems is that of training your model, and applying your model. If you think your model parameters do not change quickly, you can fit them with MapReduce, and then have a mechanism for accessing these pre-fit parameters when you want to apply your model.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Storm&lt;/strong&gt;: A real-time, streaming computational system. Storm is online framework, meaning, in this sense, a service that interacts with a running application. In contrast to MapReduce, it receives small pieces of data (not a whole file) as they are processed in your application. You define a DAG of operations to perform on the data. A common and simple use case for Storm is tracking counters, and using that information to populate a real-time dashboard.&lt;/p&gt;

&lt;p&gt;Storm doesn''t have anything (necessarily) to do with persisting your data. Here, streaming is another way to say keeping the information you care about and throwing the rest away. In reality, you probably have a persistence layer in your application that has already recorded the data, and so this a good and justified separation of concerns.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;If you want to know more...&lt;/strong&gt;
If you would like to learn more about real-time systems that that fit parameters with MR and apply the models a different way &lt;a href="http://www.slideshare.net/cloudera/hbasecon-2013-24063525"&gt;here are slides for a talk I gave on building real-time recommendation engines on HBase.&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;An excellent paper that marries real-time counting and persistence in an interesting way is &lt;a href="http://dl.acm.org/citation.cfm?id=1242610"&gt;Google News Personalization: Scalable Online Collaborative Filtering&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;Another interesting marriage of MR and Storm is &lt;a href="https://github.com/twitter/summingbird/wiki"&gt;SummingBird&lt;/a&gt;. Summingbird allows you to define data analysis operations that can be applied via Storm or MR.&lt;/p&gt;
', NULL, NULL, '''/a'':392C,423C,440C ''/citation.cfm?id=1242610'':415C ''/cloudera/hbasecon-2013-24063525'':374C ''/p'':63C,134C,194C,276C,335C,393C,424C,457C ''/strong'':4C,198C,344C ''/twitter/summingbird/wiki'':438C ''access'':106C,181C ''allow'':12C,442C ''alreadi'':321C ''amount'':18C ''analysi'':447C ''anoth'':293C,426C ''anyth'':282C ''appli'':156C,191C,364C,452C ''applic'':92C,221C,242C,318C ''avail'':89C ''away'':307C ''build'':146C,384C ''care'':301C ''case'':259C ''chang'':167C ''choic'':38C ''collabor'':421C ''common'':255C ''comput'':9C,40C,204C ''concern'':334C ''consid'':142C ''contrast'':223C ''count'':403C ''counter'':264C ''current'':132C ''dag'':246C ''dashboard'':275C ''data'':20C,73C,100C,231C,253C,289C,324C,446C ''defin'':244C,445C ''differ'':368C ''display'':122C ''distinct'':138C ''distribut'':8C ''dl.acm.org'':414C ''dl.acm.org/citation.cfm?id=1242610'':413C ''doesn'':279C ''due'':31C ''engin'':389C ''excel'':396C ''failur'':30C ''fault'':6C ''file'':70C,81C,235C ''filter'':422C ''fit'':171C,185C,359C ''fli'':44C ''framework'':10C,209C ''gave'':382C ''github.com'':437C ''github.com/twitter/summingbird/wiki'':436C ''good'':329C ''googl'':416C ''hardwar'':33C ''hbase'':391C ''hour'':60C ''href'':371C,412C,435C ''huge'':17C ''import'':137C ''inform'':268C,299C ''input'':77C ''interact'':217C ''interest'':408C,427C ''job'':52C,67C ''justifi'':331C ''keep'':297C ''know'':342C ''lag'':116C ''layer'':315C ''learn'':350C ''like'':109C,348C ''lot'':23C ''make'':140C ''mapreduc'':3C,11C,34C,51C,66C,144C,174C,225C ''marri'':399C ''marriag'':428C ''mean'':210C ''mechan'':179C ''microsecond'':62C ''minut'':58C ''model'':154C,158C,163C,193C,366C ''mr'':362C,430C,456C ''necessarili'':283C ''news'':417C ''onlin'':208C,420C ''oper'':15C,248C,448C ''order'':56C ''p'':1C,64C,135C,195C,277C,336C,394C,425C ''paper'':397C ''paramet'':164C,186C,360C ''perform'':250C ''persist'':287C,314C,405C ''person'':418C ''piec'':229C ''place'':103C ''poor'':37C ''popul'':270C ''pre'':184C ''pre-fit'':183C ''prevent'':29C ''probabl'':311C ''process'':239C ''put'':26C,98C ''quick'':168C ''real'':201C,273C,354C,386C,401C ''real-tim'':200C,272C,353C,385C,400C ''realiti'':309C ''realtim'':147C ''receiv'':227C ''recommend'':388C ''record'':322C ''repres'':127C ''respons'':96C ''rest'':306C ''result'':41C,83C,88C ''run'':220C ''say'':296C ''scalabl'':419C ''sens'':213C ''separ'':332C ''servic'':215C ''simpl'':257C ''slide'':377C ''slow'':48C,110C ''small'':228C ''state'':133C ''store'':74C ''storm'':197C,206C,261C,278C,432C,454C ''stream'':203C,291C ''strong'':2C,196C,337C ''summingbird'':439C,441C ''system'':129C,148C,205C,356C ''take'':53C,68C ''talk'':380C ''think'':161C ''throw'':304C ''time'':202C,274C,355C,387C,402C ''toler'':7C ''track'':263C ''train'':152C ''typic'':50C ''use'':143C,258C,266C ''valu'':119C,125C ''via'':453C ''want'':86C,189C,340C ''way'':294C,369C,409C ''whole'':234C ''work'':25C ''would'':347C ''write'':79C ''www.slideshare.net'':373C ''www.slideshare.net/cloudera/hbasecon-2013-24063525'':372C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (274, 2, '2014-06-10 12:57:04.307', 7, NULL, NULL, '&lt;p&gt;One benefit is wide support - C, Java, Perl, Python, and R all have HDF5 bindings.&lt;/p&gt;

&lt;p&gt;Another benefit is speed.  I haven''t ever seen it benchmarked, but HDF is supposed to be faster than SQL databases.&lt;/p&gt;

&lt;p&gt;I understand that it is very good when used with both large sets of scientific data and time series data - network monitoring, usage tracking, etc.&lt;/p&gt;

&lt;p&gt;I don''t believe there is a size limitation for HDF files (although OS limits would still apply.&lt;/p&gt;
', NULL, NULL, '''/p'':17C,40C,67C,87C ''although'':81C ''anoth'':19C ''appli'':86C ''believ'':72C ''benchmark'':29C ''benefit'':3C,20C ''bind'':16C ''c'':7C ''data'':57C,61C ''databas'':39C ''etc'':66C ''ever'':26C ''faster'':36C ''file'':80C ''good'':48C ''haven'':24C ''hdf'':31C,79C ''hdf5'':15C ''java'':8C ''larg'':53C ''limit'':77C,83C ''monitor'':63C ''network'':62C ''one'':2C ''os'':82C ''p'':1C,18C,41C,68C ''perl'':9C ''python'':10C ''r'':12C ''scientif'':56C ''seen'':27C ''seri'':60C ''set'':54C ''size'':76C ''speed'':22C ''sql'':38C ''still'':85C ''support'':6C ''suppos'':33C ''time'':59C ''track'':65C ''understand'':43C ''usag'':64C ''use'':50C ''wide'':5C ''would'':84C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1046, 2, '2014-08-26 21:51:07.247', 1, NULL, NULL, '&lt;p&gt;Try &lt;a href="http://deeplearning4j.org/word2vec.html" rel="nofollow"&gt;http://deeplearning4j.org/word2vec.html&lt;/a&gt;. This has an implementation of Word2Vec used instead of Bag of Words for NER and other NLP tasks.&lt;/p&gt;
', NULL, NULL, '''/p'':31C ''/word2vec.html'':7C ''/word2vec.html&lt;/a&gt;.'':12C ''bag'':22C ''deeplearning4j.org'':6C,11C ''deeplearning4j.org/word2vec.html'':5C ''deeplearning4j.org/word2vec.html&lt;/a&gt;.'':10C ''href'':4C ''implement'':16C ''instead'':20C ''ner'':26C ''nlp'':29C ''nofollow'':9C ''p'':1C ''rel'':8C ''task'':30C ''tri'':2C ''use'':19C ''word'':24C ''word2vec'':18C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (244, 2, '2014-06-10 01:40:23.263', 3, NULL, NULL, '&lt;p&gt;Coming from a programmers perspective, frameworks rarely target performance as the highest priority.  If your library is going to be widely leveraged the things people are likely to value most are ease of use, flexibility, and reliability.&lt;/p&gt;

&lt;p&gt;Performance is generally valued in secondary competitive libraries.  "X library is better because it''s faster."  Even then very frequently those libraries will trade off the most optimal solution for one that can be widely leveraged.  &lt;/p&gt;

&lt;p&gt;By using any framework you are inherently taking a risk that a faster solution exists.  I might go so far as to say that a faster solution almost always exists.&lt;/p&gt;

&lt;p&gt;Writing something yourself is not a guarantee of performance, but if you know what you are doing and have a fairly limited set of requirements it can help.&lt;/p&gt;

&lt;p&gt;An example might be JSON parsing.  There are a hundred libraries out there for a variety of languages that will turn JSON into a referable object and vice versa.  I know of one implementation that does it all in CPU registers.  It''s measurably faster than all other parsers, but it is also very limited and that limitation will vary based on what CPU you are working with.&lt;/p&gt;

&lt;p&gt;Is the task of building a high-performant environment specific JSON parser a good idea?  I would leverage a respected library 99 times out of 100.  In that one separate instance a few extra CPU cycles multiplied by a million iterations would make the development time worth it.&lt;/p&gt;
', NULL, NULL, '''/p'':39C,77C,109C,139C,209C,260C ''100'':237C ''99'':233C ''almost'':106C ''also'':193C ''alway'':107C ''base'':201C ''better'':52C ''build'':215C ''come'':2C ''competit'':47C ''cpu'':180C,204C,246C ''cycl'':247C ''develop'':256C ''eas'':33C ''environ'':220C ''even'':57C ''exampl'':142C ''exist'':93C,108C ''extra'':245C ''fair'':131C ''far'':98C ''faster'':56C,91C,104C,185C ''flexibl'':36C ''framework'':7C,82C ''frequent'':60C ''general'':43C ''go'':19C,96C ''good'':225C ''guarante'':117C ''help'':138C ''high'':218C ''high-perform'':217C ''highest'':13C ''hundr'':150C ''idea'':226C ''implement'':174C ''inher'':85C ''instanc'':242C ''iter'':252C ''json'':145C,162C,222C ''know'':123C,171C ''languag'':158C ''leverag'':23C,76C,229C ''librari'':17C,48C,50C,62C,151C,232C ''like'':28C ''limit'':132C,195C,198C ''make'':254C ''measur'':184C ''might'':95C,143C ''million'':251C ''multipli'':248C ''object'':166C ''one'':71C,173C,240C ''optim'':68C ''p'':1C,40C,78C,110C,140C,210C ''pars'':146C ''parser'':189C,223C ''peopl'':26C ''perform'':10C,41C,119C,219C ''perspect'':6C ''prioriti'':14C ''programm'':5C ''rare'':8C ''refer'':165C ''regist'':181C ''reliabl'':38C ''requir'':135C ''respect'':231C ''risk'':88C ''say'':101C ''secondari'':46C ''separ'':241C ''set'':133C ''solut'':69C,92C,105C ''someth'':112C ''specif'':221C ''take'':86C ''target'':9C ''task'':213C ''thing'':25C ''time'':234C,257C ''trade'':64C ''turn'':161C ''use'':35C,80C ''valu'':30C,44C ''vari'':200C ''varieti'':156C ''versa'':169C ''vice'':168C ''wide'':22C,75C ''work'':207C ''worth'':258C ''would'':228C,253C ''write'':111C ''x'':49C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (245, 2, '2014-06-10 01:54:40.647', 2, NULL, NULL, '&lt;p&gt;Computer Science is itself a multi-disciplinary field which has varying requirements among universities.  For example, Stockholm University does not require any math above algebra for its CS programs (some courses may have higher requirements, but not often).  &lt;/p&gt;

&lt;p&gt;I am not sure what you mean by a machine learning program being more rigorous.  They are just two different programs.  Data Science would likely take a broader view and focus on application and management (business courses are maybe on offer?).  The research could be rigorous in its own right, but it definitely won''t be tailored to someone who wants to optimize new algorithms or solve the low-level problems of machine learning.  &lt;/p&gt;

&lt;p&gt;I don''t see the Ph.D program listed yet in the link you provided.  Will you please follow up here if you get more specific information?&lt;/p&gt;
', NULL, NULL, '''/p'':41C,118C,146C ''algebra'':27C ''algorithm'':107C ''among'':15C ''applic'':75C ''broader'':70C ''busi'':78C ''comput'':2C ''could'':86C ''cours'':33C,79C ''cs'':30C ''data'':64C ''definit'':95C ''differ'':62C ''disciplinari'':9C ''exampl'':18C ''field'':10C ''focus'':73C ''follow'':137C ''get'':142C ''higher'':36C ''inform'':145C ''learn'':53C,117C ''level'':113C ''like'':67C ''link'':131C ''list'':127C ''low'':112C ''low-level'':111C ''machin'':52C,116C ''manag'':77C ''math'':25C ''may'':34C ''mayb'':81C ''mean'':49C ''multi'':8C ''multi-disciplinari'':7C ''new'':106C ''offer'':83C ''often'':40C ''optim'':105C ''p'':1C,42C,119C ''ph.d'':125C ''pleas'':136C ''problem'':114C ''program'':31C,54C,63C,126C ''provid'':133C ''requir'':14C,23C,37C ''research'':85C ''right'':92C ''rigor'':57C,88C ''scienc'':3C,65C ''see'':123C ''solv'':109C ''someon'':101C ''specif'':144C ''stockholm'':19C ''sure'':46C ''tailor'':99C ''take'':68C ''two'':61C ''univers'':16C,20C ''vari'':13C ''view'':71C ''want'':103C ''won'':96C ''would'':66C ''yet'':128C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2509, 2, '2014-11-20 16:28:48.343', 2, NULL, NULL, '&lt;p&gt;How about considering each string as a process trace and applying alpha-algorithm?
That would give you a graph and nodes with a big number out-edges will most likely point to values.&lt;/p&gt;

&lt;p&gt;You can mark these nodes and for every new string parse/traverse the graph until you reach those areas.&lt;/p&gt;
', NULL, NULL, '''/p'':37C,57C ''algorithm'':15C ''alpha'':14C ''alpha-algorithm'':13C ''appli'':12C ''area'':56C ''big'':26C ''consid'':4C ''edg'':30C ''everi'':46C ''give'':18C ''graph'':21C,51C ''like'':33C ''mark'':41C ''new'':47C ''node'':23C,43C ''number'':27C ''out-edg'':28C ''p'':1C,38C ''parse/traverse'':49C ''point'':34C ''process'':9C ''reach'':54C ''string'':6C,48C ''trace'':10C ''valu'':36C ''would'':17C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (246, 2, '2014-06-10 02:42:02.05', 2, NULL, NULL, '&lt;p&gt;There will definitely be a translation task at the end if you prototype using just mongo.&lt;/p&gt;

&lt;p&gt;When you run a MapReduce task on mongodb, it has the data source and structure built in.  When you eventually convert to hadoop, your data structures might not look the same.  You could leverage the mongodb-hadoop connector to access mongo data directly from within hadoop, but that won''t be quite as straightforward as you might think.  The time to figure out how exactly to do the conversion most optimally will be easier to justify once you have a prototype in place, IMO.&lt;/p&gt;

&lt;p&gt;While you will need to translate mapreduce functions, the basic pseudocode should apply well to both systems.  You won''t find anything that can be done in MongoDB that can''t be done using Java or that is significantly more complex to do with Java.&lt;/p&gt;
', NULL, NULL, '''/p'':18C,105C,152C ''access'':60C ''anyth'':128C ''appli'':119C ''basic'':116C ''built'':35C ''complex'':147C ''connector'':58C ''convers'':89C ''convert'':40C ''could'':52C ''data'':31C,44C,62C ''definit'':4C ''direct'':63C ''done'':132C,139C ''easier'':94C ''end'':11C ''eventu'':39C ''exact'':85C ''figur'':82C ''find'':127C ''function'':114C ''hadoop'':42C,57C,66C ''imo'':104C ''java'':141C,151C ''justifi'':96C ''leverag'':53C ''look'':48C ''mapreduc'':24C,113C ''might'':46C,77C ''mongo'':17C,61C ''mongodb'':27C,56C,134C ''mongodb-hadoop'':55C ''need'':110C ''optim'':91C ''p'':1C,19C,106C ''place'':103C ''prototyp'':14C,101C ''pseudocod'':117C ''quit'':72C ''run'':22C ''signific'':145C ''sourc'':32C ''straightforward'':74C ''structur'':34C,45C ''system'':123C ''task'':8C,25C ''think'':78C ''time'':80C ''translat'':7C,112C ''use'':15C,140C ''well'':120C ''within'':65C ''won'':69C,125C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (247, 2, '2014-06-10 02:50:51.153', 7, NULL, NULL, '&lt;p&gt;&lt;a href="http://bokeh.pydata.org/"&gt;Bokeh&lt;/a&gt; is an excellent data visualization library for python.
&lt;a href="http://www.cityinabottle.org/nodebox/"&gt;NodeBox&lt;/a&gt; is another that comes to mind.&lt;/p&gt;
', NULL, NULL, '''/a'':6C,21C ''/nodebox/'':19C ''/p'':28C ''anoth'':23C ''bokeh'':5C ''bokeh.pydata.org'':4C ''come'':25C ''data'':10C ''excel'':9C ''href'':3C,16C ''librari'':12C ''mind'':27C ''nodebox'':20C ''p'':1C ''python'':14C ''visual'':11C ''www.cityinabottle.org'':18C ''www.cityinabottle.org/nodebox/'':17C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (248, 2, '2014-06-10 03:21:56.473', 2, NULL, NULL, '&lt;p&gt;A cash cow program?  No.  PhD programs are never cash cows.&lt;/p&gt;

&lt;p&gt;I don''t know why you couldn''t be a professor with a PhD in data science.  Rarely does a professor of a given course have to have a specific degree in order to teach it.&lt;/p&gt;

&lt;p&gt;As far as publishing goes, there are any number of related journals that would accept papers from somebody on topics that would be covered by the topic of Data Science.&lt;/p&gt;

&lt;p&gt;When I went to college, MIS, Computer Engineering, and Computer Science were new subjects.  Most of the people in my graduating class for Computer Science couldn''t program anything significant at graduation.  Within a few years, CS programs around the country matured significantly.&lt;/p&gt;

&lt;p&gt;When you are part of a new program, sometimes it''s possible to help define what it is that''s required for graduation.  Being a part of that puts you in rare company for that field.&lt;/p&gt;

&lt;p&gt;As far as mathematical rigor is concerned, I would expect Data Science to leverage a heavy dose of mathematically based material.  I wouldn''t expect anything particularly new - statistics, calculus, etc. should have been covered in undergrad.  Masters and PhD programs should be more about applying that knowledge and not so much about learning it.&lt;/p&gt;
', NULL, NULL, '''/p'':13C,51C,83C,128C,166C,223C ''accept'':67C ''anyth'':113C,193C ''appli'':213C ''around'':123C ''base'':187C ''calculus'':197C ''cash'':3C,11C ''class'':106C ''colleg'':89C ''compani'':162C ''comput'':91C,94C,108C ''concern'':174C ''couldn'':21C,110C ''countri'':125C ''cours'':39C ''cover'':76C,202C ''cow'':4C,12C ''cs'':121C ''data'':30C,81C,178C ''defin'':144C ''degre'':45C ''dose'':184C ''engin'':92C ''etc'':198C ''expect'':177C,192C ''far'':54C,169C ''field'':165C ''given'':38C ''goe'':57C ''graduat'':105C,116C,152C ''heavi'':183C ''help'':143C ''journal'':64C ''know'':18C ''knowledg'':215C ''learn'':221C ''leverag'':181C ''master'':205C ''materi'':188C ''mathemat'':171C,186C ''matur'':126C ''mis'':90C ''much'':219C ''never'':10C ''new'':97C,136C,195C ''number'':61C ''order'':47C ''p'':1C,14C,52C,84C,129C,167C ''paper'':68C ''part'':133C,155C ''particular'':194C ''peopl'':102C ''phd'':7C,28C,207C ''possibl'':141C ''professor'':25C,35C ''program'':5C,8C,112C,122C,137C,208C ''publish'':56C ''put'':158C ''rare'':32C,161C ''relat'':63C ''requir'':150C ''rigor'':172C ''scienc'':31C,82C,95C,109C,179C ''signific'':114C,127C ''somebodi'':70C ''sometim'':138C ''specif'':44C ''statist'':196C ''subject'':98C ''teach'':49C ''topic'':72C,79C ''undergrad'':204C ''went'':87C ''within'':117C ''would'':66C,74C,176C ''wouldn'':190C ''year'':120C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (249, 2, '2014-06-10 03:42:51.637', 3, NULL, NULL, '&lt;p&gt;Twitter uses Storm for real-time processing of data.  Problems can happen with real-time data.  Systems might go down.  Data might be inadvertently processed twice.  Network connections can be lost.  A lot can happen in a real-time system.  &lt;/p&gt;

&lt;p&gt;They use hadoop to reliably process historical data.  I don''t know specifics, but for instance, getting solid information from aggregated logs is probably more reliable than attaching to the stream.&lt;/p&gt;

&lt;p&gt;If they simply relied on Storm for everything - Storm would have problems due to the nature of providing real-time information at scale.  If they relied on hadoop for everything, there''s a good deal of latency involved.  Combining the two with Summingbird is the next logical step.&lt;/p&gt;
', NULL, NULL, '''/p'':45C,78C,129C ''aggreg'':67C ''attach'':74C ''combin'':119C ''connect'':31C ''data'':11C,19C,24C,54C ''deal'':115C ''due'':92C ''everyth'':87C,110C ''get'':63C ''go'':22C ''good'':114C ''hadoop'':49C,108C ''happen'':14C,38C ''histor'':53C ''inadvert'':27C ''inform'':65C,101C ''instanc'':62C ''involv'':118C ''know'':58C ''latenc'':117C ''log'':68C ''logic'':127C ''lost'':34C ''lot'':36C ''might'':21C,25C ''natur'':95C ''network'':30C ''next'':126C ''p'':1C,46C,79C ''probabl'':70C ''problem'':12C,91C ''process'':9C,28C,52C ''provid'':97C ''real'':7C,17C,42C,99C ''real-tim'':6C,16C,41C,98C ''reli'':83C,106C ''reliabl'':51C,72C ''scale'':103C ''simpli'':82C ''solid'':64C ''specif'':59C ''step'':128C ''storm'':4C,85C,88C ''stream'':77C ''summingbird'':123C ''system'':20C,44C ''time'':8C,18C,43C,100C ''twice'':29C ''twitter'':2C ''two'':121C ''use'':3C,48C ''would'':89C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (271, 2, '2014-06-10 11:49:23.777', 3, NULL, NULL, '&lt;p&gt;It has been shown before that machine learning techniques can be applied for predicting sport results. Simple google search should give you a bunch of results.&lt;/p&gt;

&lt;p&gt;However, it has also been showed (for NFL btw) that very complex predictive models, simple predictive models, questioning people, or crowd knowledge by utilising betting info, they all perform more or less the same. Source: "&lt;a href="http://everythingisobvious.com/" rel="nofollow"&gt;Everything is obvious once you know the answer - How common sense Fails&lt;/a&gt;", Chapter 7, by Duncan Watts. &lt;/p&gt;
', NULL, NULL, '''/a'':82C ''/p'':28C,88C ''7'':84C ''also'':33C ''answer'':77C ''appli'':13C ''bet'':54C ''btw'':38C ''bunch'':25C ''chapter'':83C ''common'':79C ''complex'':41C ''crowd'':50C ''duncan'':86C ''everyth'':70C ''everythingisobvious.com'':67C ''fail'':81C ''give'':22C ''googl'':19C ''howev'':30C ''href'':66C ''info'':55C ''know'':75C ''knowledg'':51C ''learn'':9C ''less'':61C ''machin'':8C ''model'':43C,46C ''nfl'':37C ''nofollow'':69C ''obvious'':72C ''p'':1C,29C ''peopl'':48C ''perform'':58C ''predict'':15C,42C,45C ''question'':47C ''rel'':68C ''result'':17C,27C ''search'':20C ''sens'':80C ''show'':35C ''shown'':5C ''simpl'':18C,44C ''sourc'':64C ''sport'':16C ''techniqu'':10C ''utilis'':53C ''watt'':87C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (300, 2, '2014-06-10 23:20:16.67', 2, NULL, NULL, '&lt;p&gt;Topological Data Analysis is a method explicitly designed for the setting you describe. Rather than a global distance metric, it relies only on a local metric of proximity or neighborhood. See: &lt;a href="http://www.ams.org/bull/2009-46-02/S0273-0979-09-01249-X/S0273-0979-09-01249-X.pdf" rel="nofollow"&gt;Topology and data&lt;/a&gt; and &lt;a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3566620/" rel="nofollow"&gt;Extracting insights from the shape of complex data using topology&lt;/a&gt;. You can find additional resources at the website for Ayasdi.&lt;/p&gt;
', NULL, NULL, '''/a'':43C,62C ''/bull/2009-46-02/s0273-0979-09-01249-x/s0273-0979-09-01249-x.pdf'':37C ''/p'':73C ''/pmc/articles/pmc3566620/'':49C ''addit'':66C ''analysi'':4C ''ayasdi'':72C ''complex'':58C ''data'':3C,42C,59C ''describ'':14C ''design'':9C ''distanc'':19C ''explicit'':8C ''extract'':52C ''find'':65C ''global'':18C ''href'':34C,46C ''insight'':53C ''local'':26C ''method'':7C ''metric'':20C,27C ''neighborhood'':31C ''nofollow'':39C,51C ''p'':1C ''proxim'':29C ''rather'':15C ''rel'':38C,50C ''reli'':22C ''resourc'':67C ''see'':32C ''set'':12C ''shape'':56C ''topolog'':2C,40C,61C ''use'':60C ''websit'':70C ''www.ams.org'':36C ''www.ams.org/bull/2009-46-02/s0273-0979-09-01249-x/s0273-0979-09-01249-x.pdf'':35C ''www.ncbi.nlm.nih.gov'':48C ''www.ncbi.nlm.nih.gov/pmc/articles/pmc3566620/'':47C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (251, 2, '2014-06-10 05:57:13.897', 3, NULL, NULL, '&lt;p&gt;Having done the rewriting game over and over myself (and still doing it), my immediate reaction was &lt;em&gt;adaptability&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;While frameworks and libraries have a huge arsenal of (possibly intertwinable) routines for standard tasks, their framework property often (always?) disallows shortcuts.  In fact, most frameworks have some sort of core infrastructure around which a core layer of basic functionality is implemented.  More specific functionality makes use of the basic layer and is placed in a second layer around the core.&lt;/p&gt;

&lt;p&gt;Now by shortcuts I mean going straight from a second layer routine to another second layer routine without using the core.  Typical example (from my domain) would be timestamps: You have a timestamped data source of some kind.  Thus far the job is simply to read the data off the wire and pass it to the core so your other code can feast on it.&lt;/p&gt;

&lt;p&gt;Now your industry changes the default timestamp format for a very good reason (in my case they went from unix time to GPS time).  Unless your framework is industry-specific it is very unlikely that they''re willing to change the core representation of time, so you end up using a framework that &lt;em&gt;almost&lt;/em&gt; does what you want.  Every time you access your data you have to convert it to industry-time-format first, and every time you want it modified you have to convert it back to whatever the core deems appropriate.  There is no way that you can hand over data straight from the source to a sink without double conversion.&lt;/p&gt;

&lt;p&gt;This is where your hand-crafted frameworks will shine, it''s just a minor change and you''re back modelling the real world whereas all other (non-industry-specific) frameworks will now have a performance disadvantage.&lt;/p&gt;

&lt;p&gt;Over time, the discrepancy between the real world and the model will add up.  With an off-the-shelf framework you''d soon be facing questions like: How can I represent &lt;code&gt;this&lt;/code&gt; in &lt;code&gt;that&lt;/code&gt; or how do make routine &lt;code&gt;X&lt;/code&gt; accept/produce &lt;code&gt;Y&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;So far this wasn''t about C/C++.  But if, for some reason, you can''t change the framework, i.e. you do have to put up with double conversion of data to go from one end to another, then you''d typically employ something that minimises the additional overhead.  In my case, a TAI-&gt;UTC or UTC-&gt;TAI converter is best left to raw C (or an FPGA).  There is no elegance possible, no profound smart data structure that makes the problem trivial.  It''s just a boring switch statement, and why not use a language whose compilers are good at optimising exactly that?&lt;/p&gt;
', NULL, NULL, '''/code'':347C,351C,359C,363C ''/em'':21C,210C ''/p'':22C,85C,152C,271C,311C,364C,469C ''accept/produce'':360C ''access'':218C ''adapt'':20C ''add'':325C ''addit'':412C ''almost'':209C ''alway'':43C ''anoth'':100C,402C ''appropri'':250C ''around'':56C,82C ''arsenal'':31C ''back'':244C,292C ''basic'':62C,73C ''best'':425C ''bore'':452C ''c'':429C ''c/c'':372C ''case'':169C,416C ''chang'':157C,194C,288C,381C ''code'':147C,345C,349C,357C,361C ''compil'':462C ''convers'':270C,393C ''convert'':224C,242C,423C ''core'':54C,59C,84C,107C,143C,196C,248C ''craft'':279C ''d'':335C,405C ''data'':120C,134C,220C,260C,395C,441C ''deem'':249C ''default'':159C ''disadvantag'':310C ''disallow'':44C ''discrep'':316C ''domain'':112C ''done'':3C ''doubl'':269C,392C ''eleg'':436C ''em'':19C,208C ''employ'':407C ''end'':202C,400C ''everi'':215C,233C ''exact'':467C ''exampl'':109C ''face'':338C ''fact'':47C ''far'':126C,367C ''feast'':149C ''first'':231C ''format'':161C,230C ''fpga'':432C ''framework'':25C,40C,49C,180C,206C,280C,304C,333C,383C ''function'':63C,68C ''game'':6C ''go'':92C,397C ''good'':165C,464C ''gps'':176C ''hand'':258C,278C ''hand-craft'':277C ''huge'':30C ''i.e'':384C ''immedi'':16C ''implement'':65C ''industri'':156C,183C,228C,302C ''industry-specif'':182C ''industry-time-format'':227C ''infrastructur'':55C ''intertwin'':34C ''job'':128C ''kind'':124C ''languag'':460C ''layer'':60C,74C,81C,97C,102C ''left'':426C ''librari'':27C ''like'':340C ''make'':69C,355C,444C ''mean'':91C ''minimis'':410C ''minor'':287C ''model'':293C,323C ''modifi'':238C ''non'':301C ''non-industry-specif'':300C ''off-the-shelf'':329C ''often'':42C ''one'':399C ''optimis'':466C ''overhead'':413C ''p'':1C,23C,86C,153C,272C,312C,365C ''pass'':139C ''perform'':309C ''place'':77C ''possibl'':33C,437C ''problem'':446C ''profound'':439C ''properti'':41C ''put'':389C ''question'':339C ''raw'':428C ''re'':191C,291C ''reaction'':17C ''read'':132C ''real'':295C,319C ''reason'':166C,377C ''repres'':344C ''represent'':197C ''rewrit'':5C ''routin'':35C,98C,103C,356C ''second'':80C,96C,101C ''shelf'':332C ''shine'':282C ''shortcut'':45C,89C ''simpli'':130C ''sink'':267C ''smart'':440C ''someth'':408C ''soon'':336C ''sort'':52C ''sourc'':121C,264C ''specif'':67C,184C,303C ''standard'':37C ''statement'':454C ''still'':12C ''straight'':93C,261C ''structur'':442C ''switch'':453C ''tai'':418C,422C ''task'':38C ''thus'':125C ''time'':174C,177C,199C,216C,229C,234C,314C ''timestamp'':115C,119C,160C ''trivial'':447C ''typic'':108C,406C ''unix'':173C ''unless'':178C ''unlik'':188C ''use'':70C,105C,204C,458C ''utc'':419C,421C ''want'':214C,236C ''wasn'':369C ''way'':254C ''went'':171C ''whatev'':246C ''wherea'':297C ''whose'':461C ''will'':192C ''wire'':137C ''without'':104C,268C ''world'':296C,320C ''would'':113C ''x'':358C ''y'':362C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (252, 1, '2014-06-10 06:19:46.51', 4, 326, 'HBase connector - Thrift or REST', '&lt;p&gt;There seem to be at least 2 ways to connect to HBase from external application, with language other then Java (i.e. Python):&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;HBase Thrift API&lt;/li&gt;
&lt;li&gt;HBase Stargate (REST API) &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Does anyone know which one should be used in which circumstances?
I.e. what are their main differences, and pros/cons?&lt;/p&gt;
', 'bigdata  hadoop', NULL, '''/li'':37C,43C ''/ol'':44C ''/p'':31C,65C ''2'':15C ''anyon'':47C ''api'':36C,42C ''applic'':23C ''bigdata'':6B ''circumst'':56C ''connect'':18C ''connector'':2A ''differ'':62C ''extern'':22C ''hadoop'':7B ''hbase'':1A,20C,34C,39C ''i.e'':29C,57C ''java'':28C ''know'':48C ''languag'':25C ''least'':14C ''li'':33C,38C ''main'':61C ''ol'':32C ''one'':50C ''p'':8C,45C ''pros/cons'':64C ''python'':30C ''rest'':5A,41C ''seem'':10C ''stargat'':40C ''thrift'':3A,35C ''use'':53C ''way'':16C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (253, 1, '2014-06-10 06:20:20.817', 24, 4977, 'Do I need to learn Hadoop to be a Data Scientist?', '&lt;p&gt;An aspiring data scientist here. I don''t know anything about Hadoop, but as I have been reading about Data Science and Big Data, I see a lot of talk about Hadoop. Is it absolutely necessary to learn Hadoop to be a Data Scientist? &lt;/p&gt;
', 'bigdata  hadoop', NULL, '''/p'':59C ''absolut'':49C ''anyth'':24C ''aspir'':16C ''big'':37C ''bigdata'':12B ''data'':10A,17C,34C,38C,57C ''hadoop'':6A,13B,26C,46C,53C ''know'':23C ''learn'':5A,52C ''lot'':42C ''necessari'':50C ''need'':3A ''p'':14C ''read'':32C ''scienc'':35C ''scientist'':11A,18C,58C ''see'':40C ''talk'':44C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (254, 2, '2014-06-10 07:10:45.057', 6, NULL, NULL, '&lt;p&gt;Actually there are more than one question to answer here:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;How to work on schemaless/loose/missing data&lt;/li&gt;
&lt;li&gt;How to label a person (from what I understand unsupervised) and create an identifier&lt;/li&gt;
&lt;li&gt;How to train your system so that it can tell you which attributes you should use in order to identify the person &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;As Rubens mentioned, you can use &lt;strong&gt;decision tree&lt;/strong&gt; methods, specifically &lt;a href="http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm"&gt;Random Forests&lt;/a&gt; for calculating the most important attributes based on information gain if you have already found a way to identify how to label a person.&lt;/p&gt;

&lt;p&gt;However, if you do not have any label information maybe you can use some expert view for preliminary attribute selection. After that you make &lt;strong&gt;unsupervised classification&lt;/strong&gt; in order to retrieve your labels. Lastly, you can select the most important fields using &lt;strong&gt;Random Forest&lt;/strong&gt; or other methods like &lt;strong&gt;Bayesian Belief Networks&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;In order to achieve all that, you also need complete data set. If your data set is loose you have to manually or heuristically find a way to couple attributes indicating same thing with different names. What is more, you can use &lt;em&gt;imputation&lt;/em&gt; techniques such as &lt;a href="http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm"&gt;Expectation Maximization&lt;/a&gt; method and complete your data set. Or you can also work with Bayesian Networks and can leave missing fields as they are.&lt;/p&gt;
', NULL, NULL, '''/a'':83C,223C ''/em'':212C ''/li'':21C,37C,61C ''/ol'':62C ''/p'':12C,108C,166C,246C ''/strong'':73C,137C,156C,165C ''/wiki/expectation%e2%80%93maximization_algorithm'':220C ''/~breiman/randomforests/cc_home.htm'':80C ''achiev'':171C ''actual'':2C ''alreadi'':97C ''also'':175C,233C ''answer'':10C ''attribut'':51C,89C,128C,197C ''base'':90C ''bayesian'':162C,236C ''belief'':163C ''calcul'':85C ''classif'':136C ''complet'':177C,226C ''coupl'':196C ''creat'':34C ''data'':20C,178C,182C,228C ''decis'':71C ''differ'':202C ''em'':210C ''en.wikipedia.org'':219C ''en.wikipedia.org/wiki/expectation%e2%80%93maximization_algorithm'':218C ''expect'':221C ''expert'':124C ''field'':151C,242C ''find'':192C ''forest'':82C,155C ''found'':98C ''gain'':93C ''heurist'':191C ''howev'':110C ''href'':77C,217C ''identifi'':36C,58C,102C ''import'':88C,150C ''imput'':211C ''indic'':198C ''inform'':92C,118C ''label'':25C,105C,117C,143C ''last'':144C ''leav'':240C ''li'':14C,22C,38C ''like'':160C ''loos'':185C ''make'':133C ''manual'':189C ''maxim'':222C ''mayb'':119C ''mention'':66C ''method'':74C,159C,224C ''miss'':241C ''name'':203C ''need'':176C ''network'':164C,237C ''ol'':13C ''one'':7C ''order'':56C,139C,169C ''p'':1C,63C,109C,167C ''person'':27C,60C,107C ''preliminari'':127C ''question'':8C ''random'':81C,154C ''retriev'':141C ''ruben'':65C ''schemaless/loose/missing'':19C ''select'':129C,147C ''set'':179C,183C,229C ''specif'':75C ''strong'':70C,134C,153C,161C ''system'':43C ''techniqu'':213C ''tell'':48C ''thing'':200C ''train'':41C ''tree'':72C ''understand'':31C ''unsupervis'':32C,135C ''use'':54C,69C,122C,152C,209C ''view'':125C ''way'':100C,194C ''work'':17C,234C ''www.stat.berkeley.edu'':79C ''www.stat.berkeley.edu/~breiman/randomforests/cc_home.htm'':78C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (255, 2, '2014-06-10 07:28:49.127', 2, NULL, NULL, '&lt;p&gt;You should learn Hadoop if you want to be work as data scientist, but maybe before starting with Hadoop you should read something about ETL or Big Data... this book could be a good starting point: &lt;a href="http://rads.stackoverflow.com/amzn/click/1617290343" rel="nofollow"&gt;http://www.amazon.com/Big-Data-Principles-practices-scalable/dp/1617290343&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Hope it helps and good luck!&lt;/p&gt;
', NULL, NULL, '''/amzn/click/1617290343'':42C ''/big-data-principles-practices-scalable/dp/1617290343&lt;/a&gt;&lt;/p&gt;'':47C ''/p'':55C ''big'':28C ''book'':31C ''could'':32C ''data'':13C,29C ''etl'':26C ''good'':35C,53C ''hadoop'':5C,20C ''help'':51C ''hope'':49C ''href'':39C ''learn'':4C ''luck'':54C ''mayb'':16C ''nofollow'':44C ''p'':1C,48C ''point'':37C ''rads.stackoverflow.com'':41C ''rads.stackoverflow.com/amzn/click/1617290343'':40C ''read'':23C ''rel'':43C ''scientist'':14C ''someth'':24C ''start'':18C,36C ''want'':8C ''work'':11C ''www.amazon.com'':46C ''www.amazon.com/big-data-principles-practices-scalable/dp/1617290343&lt;/a&gt;&lt;/p&gt;'':45C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (269, 2, '2014-06-10 11:37:28.293', 4, NULL, NULL, '&lt;p&gt;Definitely they can.
I can target you to a &lt;strong&gt;&lt;a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.7448&amp;amp;rep=rep1&amp;amp;type=pdf" rel="nofollow"&gt;nice paper&lt;/a&gt;&lt;/strong&gt;. Once I used it for soccer league results prediction algorithm implementation, primarily aiming at having some value against bookmakers.&lt;/p&gt;

&lt;p&gt;From paper''s abstract:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;a Bayesian dynamic generalized model to estimate the time dependent skills of all teams in a league, and to predict next weekend''s soccer matches.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Keywords:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Dynamic Models, Generalized Linear Models, Graphical Models, Markov
  Chain Monte Carlo Methods, Prediction of Soccer Matches&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Citation:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Rue, Havard, and Oyvind Salvesen. "Prediction and retrospective analysis of soccer matches in a league." Journal of the Royal Statistical Society: Series D (The Statistician) 49.3 (2000): 399-418.&lt;/p&gt;
&lt;/blockquote&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (256, 2, '2014-06-10 07:30:53.51', 29, NULL, NULL, '&lt;p&gt;Different people use different tools for different things.  Terms like Data Science are generic for a reason.  A data scientist could spend an entire career without having to learn a particular tool like hadoop.  Hadoop is widely used, but it is not the only platform that is capable of managing and manipulating data, even large scale data.&lt;/p&gt;

&lt;p&gt;I would say that a data scientist should be familiar with concepts like MapReduce, distributed systems, distributed file systems, and the like, but I wouldn''t judge someone for not knowing about such things.  &lt;/p&gt;

&lt;p&gt;It''s a big field.  There is a sea of knowledge and most people are capable of learning and being an expert in a single drop.  The key to being a scientist is having the desire to learn and the motivation to know that which you don''t already know.&lt;/p&gt;

&lt;p&gt;As an example:  I could hand the right person a hundred structured CSV files containing information about classroom performance in one particular class over a decade.  A data scientist would be able to spend a year gleaning insights from the data without ever needing to spread computation across multiple machines.  You could apply machine learning algorithms, analyze it using visualizations, combine it with external data about the region, ethnic makeup, changes to environment over time, political information, weather patterns, etc.  All of that would be "data science" in my opinion.  It might take something like hadoop to test and apply anything you learned to data comprising an entire country of students rather than just a classroom, but that final step doesn''t necessarily make someone a data scientist.  And not taking that final step doesn''t necessarily disqualify someone from being a data scientist.&lt;/p&gt;
', NULL, NULL, '''/p'':59C,95C,147C,293C ''abl'':180C ''across'':196C ''algorithm'':204C ''alreadi'':145C ''analyz'':205C ''anyth'':249C ''appli'':201C,248C ''big'':100C ''capabl'':49C,112C ''career'':26C ''chang'':219C ''class'':171C ''classroom'':166C,264C ''combin'':209C ''compris'':254C ''comput'':195C ''concept'':72C ''contain'':163C ''could'':22C,153C,200C ''countri'':257C ''csv'':161C ''data'':12C,20C,54C,58C,66C,176C,189C,213C,234C,253C,275C,291C ''decad'':174C ''desir'':132C ''differ'':2C,5C,8C ''disqualifi'':286C ''distribut'':75C,77C ''doesn'':269C,283C ''drop'':122C ''entir'':25C,256C ''environ'':221C ''etc'':228C ''ethnic'':217C ''even'':55C ''ever'':191C ''exampl'':151C ''expert'':118C ''extern'':212C ''familiar'':70C ''field'':101C ''file'':78C,162C ''final'':267C,281C ''generic'':15C ''glean'':185C ''hadoop'':35C,36C,244C ''hand'':154C ''hundr'':159C ''inform'':164C,225C ''insight'':186C ''judg'':87C ''key'':124C ''know'':91C,139C,146C ''knowledg'':107C ''larg'':56C ''learn'':30C,114C,134C,203C,251C ''like'':11C,34C,73C,82C,243C ''machin'':198C,202C ''make'':272C ''makeup'':218C ''manag'':51C ''manipul'':53C ''mapreduc'':74C ''might'':240C ''motiv'':137C ''multipl'':197C ''necessarili'':271C,285C ''need'':192C ''one'':169C ''opinion'':238C ''p'':1C,60C,96C,148C ''particular'':32C,170C ''pattern'':227C ''peopl'':3C,110C ''perform'':167C ''person'':157C ''platform'':46C ''polit'':224C ''rather'':260C ''reason'':18C ''region'':216C ''right'':156C ''say'':63C ''scale'':57C ''scienc'':13C,235C ''scientist'':21C,67C,128C,177C,276C,292C ''sea'':105C ''singl'':121C ''someon'':88C,273C,287C ''someth'':242C ''spend'':23C,182C ''spread'':194C ''step'':268C,282C ''structur'':160C ''student'':259C ''system'':76C,79C ''take'':241C,279C ''term'':10C ''test'':246C ''thing'':9C,94C ''time'':223C ''tool'':6C,33C ''use'':4C,39C,207C ''visual'':208C ''weather'':226C ''wide'':38C ''without'':27C,190C ''would'':62C,178C,232C ''wouldn'':85C ''year'':184C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (257, 2, '2014-06-10 07:42:57.47', 4, NULL, NULL, '&lt;p&gt;Yes, you should learn a platform that is capable of dissecting your problem as a data parallel problem. Hadoop is one. For your simple needs (design patterns like counting, aggregation, filtering etc.) you need Hadoop and for more complex Machine Learning stuff like doing some Bayesian, SVM you need Mahout which in turn needs Hadoop (Now Apache Spark) to solve your problem using a data-parallel approach.&lt;/p&gt;

&lt;p&gt;So Hadoop is a good platform to learn and really important for your batch processing needs. Not only Hadoop but you also need to know Spark (Mahout runs it''s algorithms utilizing Spark) &amp;amp; Twitter Storm (for your real time analytics needs). This list will continue and evolve so if you are good with the building blocks (Distributed Computing, Data-Parallel Problems and so on) and know how one such platform (say Hadoop) operates you will fairly quickly be up to speed on others.&lt;/p&gt;
', NULL, NULL, '''/p'':70C,157C ''aggreg'':31C ''algorithm'':102C ''also'':93C ''amp'':105C ''analyt'':112C ''apach'':58C ''approach'':69C ''batch'':85C ''bayesian'':47C ''block'':128C ''build'':127C ''capabl'':10C ''complex'':40C ''comput'':130C ''continu'':117C ''count'':30C ''data'':17C,67C,132C ''data-parallel'':66C,131C ''design'':27C ''dissect'':12C ''distribut'':129C ''etc'':33C ''evolv'':119C ''fair'':149C ''filter'':32C ''good'':76C,124C ''hadoop'':20C,36C,56C,73C,90C,145C ''import'':82C ''know'':96C,139C ''learn'':5C,42C,79C ''like'':29C,44C ''list'':115C ''machin'':41C ''mahout'':51C,98C ''need'':26C,35C,50C,55C,87C,94C,113C ''one'':22C,141C ''oper'':146C ''other'':156C ''p'':1C,71C ''parallel'':18C,68C,133C ''pattern'':28C ''platform'':7C,77C,143C ''problem'':14C,19C,63C,134C ''process'':86C ''quick'':150C ''real'':110C ''realli'':81C ''run'':99C ''say'':144C ''simpl'':25C ''solv'':61C ''spark'':59C,97C,104C ''speed'':154C ''storm'':107C ''stuff'':43C ''svm'':48C ''time'':111C ''turn'':54C ''twitter'':106C ''use'':64C ''util'':103C ''yes'':2C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (258, 2, '2014-06-10 07:56:45.587', 2, NULL, NULL, '&lt;p&gt;Thrift is generally faster because the data being exchanged is smaller.  Stargate offers a web service which is an integration method that is widely supported, which is a concern when you are working with commercial products with limited integration possibilities.&lt;/p&gt;

&lt;p&gt;In a closed environment where everything is controlled, I would prefer Thrift.  If I''m exposing data to external teams or systems I would prefer Stargate.&lt;/p&gt;
', NULL, NULL, '''/p'':42C,70C ''close'':46C ''commerci'':36C ''concern'':30C ''control'':51C ''data'':8C,60C ''environ'':47C ''everyth'':49C ''exchang'':10C ''expos'':59C ''extern'':62C ''faster'':5C ''general'':4C ''integr'':21C,40C ''limit'':39C ''m'':58C ''method'':22C ''offer'':14C ''p'':1C,43C ''possibl'':41C ''prefer'':54C,68C ''product'':37C ''servic'':17C ''smaller'':12C ''stargat'':13C,69C ''support'':26C ''system'':65C ''team'':63C ''thrift'':2C,55C ''web'':16C ''wide'':25C ''work'':34C ''would'':53C,67C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (259, 2, '2014-06-10 08:04:20.4', 11, NULL, NULL, '&lt;p&gt;I would like to point to &lt;a href="http://national.census.okfn.org/"&gt;The Open Data Census&lt;/a&gt;. It is an initiative of the Open Knowledge Foundation based on contributions from open data advocates and experts around the world. &lt;/p&gt;

&lt;p&gt;The value of Open data Census is open, community driven, and systematic effort to collect and update the database of open datasets globally on country and, in some cases, &lt;a href="http://us-city.census.okfn.org/"&gt;like U.S., on city level&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;Also, it presents an opportunity to compare different countries and cities on in selected areas of interest.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (260, 2, '2014-06-10 08:38:27.093', 1, NULL, NULL, '&lt;p&gt;The algorithm that is used in this case is called &lt;a href="https://class.coursera.org/ml-003/lecture/38" rel="nofollow"&gt;one-vs-all classifier&lt;/a&gt; or multiclass classifier. &lt;/p&gt;

&lt;p&gt;In your case you have to take one class, e. g. number 1 , mark it as positive and combine the rest seven classes in one negative class. The neural network will output the probability of this case being class number 1 vs the rest of the classes. &lt;/p&gt;

&lt;p&gt;Afterwords, you have to assign as positive another class, e.g. number 2, assign all other classes as one big negative class and get the predicted probability from the network again.&lt;/p&gt;

&lt;p&gt;After repeating this procedure for all eight classes, assign each case to the the class that had the maximum probability from all the classes outputted from the neural network. &lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (261, 2, '2014-06-10 09:21:14.013', 2, NULL, NULL, '&lt;p&gt;You could take a look at CN2 rule learner in Orange 2 &lt;a href="http://orange.biolab.si/orange2/" rel="nofollow"&gt;http://orange.biolab.si/orange2/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/orange2/'':18C ''/orange2/&lt;/a&gt;&lt;/p&gt;'':23C ''2'':13C ''cn2'':8C ''could'':3C ''href'':15C ''learner'':10C ''look'':6C ''nofollow'':20C ''orang'':12C ''orange.biolab.si'':17C,22C ''orange.biolab.si/orange2/'':16C ''orange.biolab.si/orange2/&lt;/a&gt;&lt;/p&gt;'':21C ''p'':1C ''rel'':19C ''rule'':9C ''take'':4C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1200, 2, '2014-10-03 11:39:11.24', 2, NULL, NULL, '&lt;p&gt;I think I''d try to model the &lt;em&gt;rate&lt;/em&gt; at which the ambulances are released instead of the time it takes. That would let me use Poisson regression, which is the canonical type of GLM for rates (in R''s GLM, set family = "poisson".) However, in order to use PR, the data needs to have its variance equal to its mean, or at least close to it. &lt;/p&gt;
', NULL, NULL, '''/em'':12C ''/p'':71C ''ambul'':16C ''canon'':35C ''close'':68C ''d'':5C ''data'':55C ''em'':10C ''equal'':61C ''famili'':46C ''glm'':38C,44C ''howev'':48C ''instead'':19C ''least'':67C ''let'':27C ''mean'':64C ''model'':8C ''need'':56C ''order'':50C ''p'':1C ''poisson'':30C,47C ''pr'':53C ''r'':42C ''rate'':11C,40C ''regress'':31C ''releas'':18C ''set'':45C ''take'':24C ''think'':3C ''time'':22C ''tri'':6C ''type'':36C ''use'':29C,52C ''varianc'':60C ''would'':26C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (263, 2, '2014-06-10 09:41:34.697', 6, NULL, NULL, '&lt;p&gt;Unfortunately, parallelization is not yet implemented in pandas. You can join &lt;a href="http://github.com/pydata/pandas/issues/5751"&gt;this github issue&lt;/a&gt; if you want to participate in the development of this feature.&lt;/p&gt;

&lt;p&gt;I don''t know any "magic unicorn package" for this purposes, so the best thing will be write your own solution. But if you still don''t want to spend time on that and want to learn something new â you can try the two methods built into MongoDB (map reduce and agg framework).  See &lt;a href="http://docs.mongodb.org/manual/core/aggregation/"&gt;mongodb_agg_framework&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':21C,98C ''/manual/core/aggregation/'':94C ''/p'':33C,99C ''/pydata/pandas/issues/5751'':17C ''agg'':87C,96C ''best'':48C ''built'':81C ''develop'':29C ''docs.mongodb.org'':93C ''docs.mongodb.org/manual/core/aggregation/'':92C ''featur'':32C ''framework'':88C,97C ''github'':19C ''github.com'':16C ''github.com/pydata/pandas/issues/5751'':15C ''href'':14C,91C ''implement'':7C ''issu'':20C ''join'':12C ''know'':38C ''learn'':71C ''magic'':40C ''map'':84C ''method'':80C ''mongodb'':83C,95C ''new'':73C ''p'':1C,34C ''packag'':42C ''panda'':9C ''parallel'':3C ''particip'':26C ''purpos'':45C ''reduc'':85C ''see'':89C ''solut'':55C ''someth'':72C ''spend'':64C ''still'':59C ''thing'':49C ''time'':65C ''tri'':77C ''two'':79C ''unfortun'':2C ''unicorn'':41C ''want'':24C,62C,69C ''write'':52C ''yet'':6C ''â'':74C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (264, 2, '2014-06-10 10:48:58.457', 0, NULL, NULL, '&lt;p&gt;You can also give the Expectation Maximization clustering algorithm a try.  It can work on categorical data and will give you a statistical likelihood of which categorical value (or values) a cluster is most likely to take on.&lt;/p&gt;
', NULL, NULL, '''/p'':40C ''algorithm'':10C ''also'':4C ''categor'':17C,28C ''cluster'':9C,33C ''data'':18C ''expect'':7C ''give'':5C,21C ''like'':36C ''likelihood'':25C ''maxim'':8C ''p'':1C ''statist'':24C ''take'':38C ''tri'':12C ''valu'':29C,31C ''work'':15C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (265, 1, '2014-06-10 10:58:58.447', 19, 11227, 'Can machine learning algorithms predict sports scores or plays?', '&lt;p&gt;I have a variety of NFL datasets that I think might make a good side-project, but I haven''t done anything with them just yet.&lt;/p&gt;

&lt;p&gt;Coming to this site made me think of machine learning algorithms and I wondering how good they might be at either predicting the outcome of football games or even the next play.&lt;/p&gt;

&lt;p&gt;It seems to me that there would be some trends that could be identified - on 3rd down and 1, a team with a strong running back &lt;em&gt;theoretically should&lt;/em&gt; have a tendency to run the ball in that situation.&lt;/p&gt;

&lt;p&gt;Scoring might be more difficult to predict, but the winning team might be.&lt;/p&gt;

&lt;p&gt;My question is whether these are good questions to throw at a machine learning algorithm.  It could be that a thousand people have tried it before, but the nature of sports makes it an unreliable topic.&lt;/p&gt;
', 'machine-learning  sports', NULL, '''/em'':107C ''/p'':42C,76C,118C,133C,171C ''1'':96C ''3rd'':93C ''algorithm'':4A,54C,149C ''anyth'':37C ''back'':103C ''ball'':114C ''come'':44C ''could'':89C,151C ''dataset'':21C ''difficult'':124C ''done'':36C ''either'':64C ''em'':104C ''even'':72C ''footbal'':69C ''game'':70C ''good'':28C,59C,141C ''haven'':34C ''identifi'':91C ''learn'':3A,12B,53C,148C ''machin'':2A,11B,52C,147C ''machine-learn'':10B ''made'':48C ''make'':26C,166C ''might'':25C,61C,121C,131C ''natur'':163C ''next'':74C ''nfl'':20C ''outcom'':67C ''p'':14C,43C,77C,119C,134C ''peopl'':156C ''play'':9A,75C ''predict'':5A,65C,126C ''project'':31C ''question'':136C,142C ''run'':102C,112C ''score'':7A,120C ''seem'':79C ''side'':30C ''side-project'':29C ''site'':47C ''situat'':117C ''sport'':6A,13B,165C ''strong'':101C ''team'':98C,130C ''tendenc'':110C ''theoret'':105C ''think'':24C,50C ''thousand'':155C ''throw'':144C ''topic'':170C ''trend'':87C ''tri'':158C ''unreli'':169C ''varieti'':18C ''whether'':138C ''win'':129C ''wonder'':57C ''would'':84C ''yet'':41C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1076, 2, '2014-09-05 06:17:28.533', 3, NULL, NULL, '&lt;p&gt;You could look at scikit-learn or Orange module in Python.
Scikit-learn has a SGD classifier and regressor that could do a partial fit data in case of online learning.
In R, take a look at caret package &lt;/p&gt;
', NULL, NULL, '''/p'':43C ''caret'':41C ''case'':31C ''classifi'':20C ''could'':3C,24C ''data'':29C ''fit'':28C ''learn'':8C,16C,34C ''look'':4C,39C ''modul'':11C ''onlin'':33C ''orang'':10C ''p'':1C ''packag'':42C ''partial'':27C ''python'':13C ''r'':36C ''regressor'':22C ''scikit'':7C,15C ''scikit-learn'':6C,14C ''sgd'':19C ''take'':37C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (393, 2, '2014-06-15 22:33:17.67', 4, NULL, NULL, '&lt;p&gt;Linear SVM should be a good starting point. Take a look at &lt;a href="http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html" rel="nofollow"&gt;this&lt;/a&gt; guide to choose the right estimator.&lt;/p&gt;
', NULL, NULL, '''/a'':22C ''/p'':29C ''/stable/tutorial/machine_learning_map/index.html'':18C ''choos'':25C ''estim'':28C ''good'':7C ''guid'':23C ''href'':15C ''linear'':2C ''look'':12C ''nofollow'':20C ''p'':1C ''point'':9C ''rel'':19C ''right'':27C ''scikit-learn.org'':17C ''scikit-learn.org/stable/tutorial/machine_learning_map/index.html'':16C ''start'':8C ''svm'':3C ''take'':10C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (277, 2, '2014-06-10 14:25:41.903', 10, NULL, NULL, '&lt;p&gt;To be honest, I think that doing some projects will teach you much more than doing a full course. One reason is that doing a project is more motivating and open-ended than doing assignments.&lt;/p&gt;

&lt;p&gt;A course, if you have the time AND motivation (real motivation), is better than doing a project. The other commentators have made good platform recommendations on tech. &lt;/p&gt;

&lt;p&gt;I think, from a fun project standpoint, you should ask a question and get a computer to learn to answer it. &lt;/p&gt;

&lt;p&gt;Some good classic questions that have good examples are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Neural Networks for recognizing hand written digits&lt;/li&gt;
&lt;li&gt;Spam email classification using logistic regression&lt;/li&gt;
&lt;li&gt;Classification of objects using Gaussian Mixture models&lt;/li&gt;
&lt;li&gt;Some use of linear regression, perhaps forecasting of grocery prices given neighborhoods&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;These projects have the math done, code done, and can be found with Google readily.&lt;/p&gt;

&lt;p&gt;Other cool subjects can be done by you!&lt;/p&gt;

&lt;p&gt;Lastly, I research robotics, so for me the most FUN applications are behavioral.
Examples can include (if you can play with an arduino)&lt;/p&gt;

&lt;p&gt;Create a application, that uses logistic regression perhaps, that learns when to turn the fan off and on given the inner temperature, and the status of the light in the room.&lt;/p&gt;

&lt;p&gt;Create an application that teaches a robot to move an actuator, perhaps a wheel, based on sensor input (perhaps a button press), using Gaussian Mixture Models (learning from demonstration).&lt;/p&gt;

&lt;p&gt;Anyway, those are pretty advanced. The point I''m making is that if you pick a project that you (really really) like, and spend a few week on it, you will learn a massive amount, and understand so much more than you will get doing a few assignments.&lt;/p&gt;
', NULL, NULL, '''/li'':111C,119C,128C,142C ''/p'':38C,67C,90C,101C,160C,170C,195C,228C,259C,309C ''/ul'':143C ''actuat'':240C ''advanc'':265C ''amount'':295C ''answer'':88C ''anyway'':261C ''applic'':182C,199C,232C ''arduino'':194C ''ask'':78C ''assign'':37C,308C ''base'':244C ''behavior'':184C ''better'':52C ''button'':250C ''classic'':94C ''classif'':115C,121C ''code'':151C ''comment'':59C ''comput'':84C ''cool'':163C ''cours'':20C,41C ''creat'':197C,230C ''demonstr'':258C ''digit'':110C ''done'':150C,152C,167C ''email'':114C ''end'':34C ''exampl'':99C,185C ''fan'':211C ''forecast'':136C ''found'':156C ''full'':19C ''fun'':73C,181C ''gaussian'':125C,253C ''get'':82C,304C ''given'':140C,215C ''good'':62C,93C,98C ''googl'':158C ''groceri'':138C ''hand'':108C ''honest'':4C ''includ'':187C ''inner'':217C ''input'':247C ''last'':172C ''learn'':86C,206C,256C,292C ''li'':103C,112C,120C,129C ''light'':224C ''like'':282C ''linear'':133C ''logist'':117C,202C ''m'':269C ''made'':61C ''make'':270C ''massiv'':294C ''math'':149C ''mixtur'':126C,254C ''model'':127C,255C ''motiv'':30C,48C,50C ''move'':238C ''much'':14C,299C ''neighborhood'':141C ''network'':105C ''neural'':104C ''object'':123C ''one'':21C ''open'':33C ''open-end'':32C ''p'':1C,39C,68C,91C,144C,161C,171C,196C,229C,260C ''perhap'':135C,204C,241C,248C ''pick'':275C ''platform'':63C ''play'':191C ''point'':267C ''press'':251C ''pretti'':264C ''price'':139C ''project'':10C,27C,56C,74C,146C,277C ''question'':80C,95C ''readili'':159C ''real'':49C ''realli'':280C,281C ''reason'':22C ''recogn'':107C ''recommend'':64C ''regress'':118C,134C,203C ''research'':174C ''robot'':175C,236C ''room'':227C ''sensor'':246C ''spam'':113C ''spend'':284C ''standpoint'':75C ''status'':221C ''subject'':164C ''teach'':12C,234C ''tech'':66C ''temperatur'':218C ''think'':6C,70C ''time'':46C ''turn'':209C ''ul'':102C ''understand'':297C ''use'':116C,124C,131C,201C,252C ''week'':287C ''wheel'':243C ''written'':109C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (278, 2, '2014-06-10 14:30:33.667', 1, NULL, NULL, '&lt;p&gt;Assuming you''re familiar with programming I would recommend looking at &lt;a href="http://scikit-learn.org/stable/" rel="nofollow"&gt;scikit-learn&lt;/a&gt;. It has especially nice help pages that can serve as mini-tutorials/a quick tour through machine learning. Pick an area you find interesting and work through the examples. &lt;/p&gt;
', NULL, NULL, '''/a'':23C,37C ''/p'':54C ''/stable/'':17C ''area'':45C ''assum'':2C ''especi'':26C ''exampl'':53C ''familiar'':5C ''find'':47C ''help'':28C ''href'':14C ''interest'':48C ''learn'':22C,42C ''look'':11C ''machin'':41C ''mini'':35C ''mini-tutori'':34C ''nice'':27C ''nofollow'':19C ''p'':1C ''page'':29C ''pick'':43C ''program'':7C ''quick'':38C ''re'':4C ''recommend'':10C ''rel'':18C ''scikit'':21C ''scikit-learn'':20C ''scikit-learn.org'':16C ''scikit-learn.org/stable/'':15C ''serv'':32C ''tour'':39C ''tutori'':36C ''work'':50C ''would'':9C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (279, 2, '2014-06-10 14:57:47.81', 11, NULL, NULL, '&lt;p&gt;There is also another resource provided by The Guardian, the British Daily on their website. The datasets published by the Guardian Datablog are all hosted. Datasets related to Football Premier League Clubs'' accounts, Inflation and GDP details of UK, Grammy awards data etc.
The datasets are available at &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.theguardian.com/news/datablog/interactive/2013/jan/14/all-our-datasets-index"&gt;http://www.theguardian.com/news/datablog/interactive/2013/jan/14/all-our-datasets-index&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Some more resources. Some of the datasets are in R format or R commads exist for directly importing data to R.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.inside-r.org/howto/finding-data-internet"&gt;http://www.inside-r.org/howto/finding-data-internet&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/howto/finding-data-internet'':91C ''/howto/finding-data-internet&lt;/a&gt;&lt;/li&gt;'':94C ''/news/datablog/interactive/2013/jan/14/all-our-datasets-index'':57C ''/news/datablog/interactive/2013/jan/14/all-our-datasets-index&lt;/a&gt;&lt;/li&gt;'':60C ''/p'':50C,84C ''/ul'':61C,95C ''account'':34C ''also'':4C ''anoth'':5C ''avail'':48C ''award'':42C ''british'':12C ''club'':33C ''commad'':76C ''daili'':13C ''data'':43C,81C ''datablog'':23C ''dataset'':18C,27C,46C,69C ''detail'':38C ''direct'':79C ''etc'':44C ''exist'':77C ''footbal'':30C ''format'':73C ''gdp'':37C ''grammi'':41C ''guardian'':10C,22C ''host'':26C ''href'':54C,88C ''import'':80C ''inflat'':35C ''leagu'':32C ''li'':52C,86C ''p'':1C,62C ''premier'':31C ''provid'':7C ''publish'':19C ''r'':72C,75C,83C ''relat'':28C ''resourc'':6C,65C ''uk'':40C ''ul'':51C,85C ''websit'':16C ''www.inside-r.org'':90C,93C ''www.inside-r.org/howto/finding-data-internet'':89C ''www.inside-r.org/howto/finding-data-internet&lt;/a&gt;&lt;/li&gt;'':92C ''www.theguardian.com'':56C,59C ''www.theguardian.com/news/datablog/interactive/2013/jan/14/all-our-datasets-index'':55C ''www.theguardian.com/news/datablog/interactive/2013/jan/14/all-our-datasets-index&lt;/a&gt;&lt;/li&gt;'':58C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (280, 1, '2014-06-10 15:08:54.073', 6, 106, 'Feature selection for tracking user activity within an application', '&lt;p&gt;I am developing a system that is intended to capture the "context" of user activity within an application; it is a framework that web applications can use to tag user activity based on requests made to the system.  It is hoped that this data can then power ML features such as context aware information retrieval.&lt;/p&gt;

&lt;p&gt;I''m having trouble deciding on what features to select in addition to these user tags - the URL being requested, approximate time spent with any given resource, estimating the current "activity" within the system.&lt;/p&gt;

&lt;p&gt;I am interested to know if there are good examples of this kind of technology or any prior research on the subject - a cursory search of the ACM DL revealed some related papers but nothing really spot-on.&lt;/p&gt;
', 'feature-selection', NULL, '''/p'':69C,105C,146C ''acm'':134C ''activ'':6A,28C,44C,101C ''addit'':82C ''applic'':9A,31C,38C ''approxim'':91C ''awar'':66C ''base'':45C ''captur'':23C ''context'':25C,65C ''current'':100C ''cursori'':130C ''data'':57C ''decid'':75C ''develop'':16C ''dl'':135C ''estim'':98C ''exampl'':116C ''featur'':1A,11B,62C,78C ''feature-select'':10B ''framework'':35C ''given'':96C ''good'':115C ''hope'':54C ''inform'':67C ''intend'':21C ''interest'':109C ''kind'':119C ''know'':111C ''m'':72C ''made'':48C ''ml'':61C ''noth'':141C ''p'':13C,70C,106C ''paper'':139C ''power'':60C ''prior'':124C ''realli'':142C ''relat'':138C ''request'':47C,90C ''research'':125C ''resourc'':97C ''retriev'':68C ''reveal'':136C ''search'':131C ''select'':2A,12B,80C ''spent'':93C ''spot'':144C ''spot-on'':143C ''subject'':128C ''system'':18C,51C,104C ''tag'':42C,86C ''technolog'':121C ''time'':92C ''track'':4A ''troubl'':74C ''url'':88C ''use'':40C ''user'':5A,27C,43C,85C ''web'':37C ''within'':7A,29C,102C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (282, 2, '2014-06-10 16:25:24.223', 6, NULL, NULL, '&lt;p&gt;Yes. Why not?!
With so much of data being recorded in each sport in each game, smart use of data could lead us in obtaining important insights regarding player performance.&lt;/p&gt;

&lt;p&gt;Some examples:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Baseball&lt;/strong&gt;: In the movie Moneyball (which is an adaptation of the MoneyBall book), Brad Pitt plays a character who analyses player statistics to come up with a team that performs tremendously well! It was a depiction of the real life story of Oakland Athletics baseball team. For more info, &lt;a href="http://www.theatlantic.com/entertainment/archive/2013/09/forget-2002-this-years-oakland-as-are-the-real-em-moneyball-em-team/279927/"&gt;http://www.theatlantic.com/entertainment/archive/2013/09/forget-2002-this-years-oakland-as-are-the-real-em-moneyball-em-team/279927/&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Cricket&lt;/strong&gt;: SAP Labs has come up with an auction analytics tool that has given insights about impact players to buy in the 2014 Indian Premier League auction for the Kolkata Knight Riders team, which eventually went on to win the 2014 IPL &lt;strong&gt;Championship&lt;/strong&gt;. For more info, &lt;a href="http://scn.sap.com/community/hana-in-memory/blog/2014/06/10/sap-hana-academy-cricket-demo--how-sap-hana-powered-the-kolkata-knight-riders-to-ipl-championship"&gt;http://scn.sap.com/community/hana-in-memory/blog/2014/06/10/sap-hana-academy-cricket-demo--how-sap-hana-powered-the-kolkata-knight-riders-to-ipl-championship&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So, yes, statistical analysis of the player records can give us insights about &lt;strong&gt;which players are more likely to perform but not which players will perform&lt;/strong&gt;. So, machine learning, a close cousin of statistical analysis will be proving to be a game changer.&lt;/p&gt;
', NULL, NULL, '''/community/hana-in-memory/blog/2014/06/10/sap-hana-academy-cricket-demo--how-sap-hana-powered-the-kolkata-knight-riders-to-ipl-championship'':153C ''/community/hana-in-memory/blog/2014/06/10/sap-hana-academy-cricket-demo--how-sap-hana-powered-the-kolkata-knight-riders-to-ipl-championship&lt;/a&gt;&lt;/li&gt;'':156C ''/entertainment/archive/2013/09/forget-2002-this-years-oakland-as-are-the-real-em-moneyball-em-team/279927/'':94C ''/entertainment/archive/2013/09/forget-2002-this-years-oakland-as-are-the-real-em-moneyball-em-team/279927/&lt;/a&gt;&lt;/li&gt;'':97C ''/p'':32C,36C,204C ''/strong'':41C,101C,145C,186C ''/ul'':157C ''2014'':123C,141C ''adapt'':49C ''analys'':60C ''analysi'':162C,195C ''analyt'':110C ''athlet'':84C ''auction'':109C,127C ''basebal'':40C,85C ''book'':53C ''brad'':54C ''buy'':120C ''championship'':144C ''changer'':203C ''charact'':58C ''close'':191C ''come'':64C,105C ''could'':22C ''cousin'':192C ''cricket'':100C ''data'':9C,21C ''depict'':76C ''eventu'':135C ''exampl'':35C ''game'':17C,202C ''give'':168C ''given'':114C ''href'':91C,150C ''impact'':117C ''import'':27C ''indian'':124C ''info'':89C,148C ''insight'':28C,115C,170C ''ipl'':142C ''knight'':131C ''kolkata'':130C ''lab'':103C ''lead'':23C ''leagu'':126C ''learn'':189C ''li'':38C,98C ''life'':80C ''like'':177C ''machin'':188C ''moneybal'':45C,52C ''movi'':44C ''much'':7C ''oakland'':83C ''obtain'':26C ''p'':1C,33C,158C ''perform'':31C,70C,179C,185C ''pitt'':55C ''play'':56C ''player'':30C,61C,118C,165C,174C,183C ''premier'':125C ''prove'':198C ''real'':79C ''record'':11C,166C ''regard'':29C ''rider'':132C ''sap'':102C ''scn.sap.com'':152C,155C ''scn.sap.com/community/hana-in-memory/blog/2014/06/10/sap-hana-academy-cricket-demo--how-sap-hana-powered-the-kolkata-knight-riders-to-ipl-championship'':151C ''scn.sap.com/community/hana-in-memory/blog/2014/06/10/sap-hana-academy-cricket-demo--how-sap-hana-powered-the-kolkata-knight-riders-to-ipl-championship&lt;/a&gt;&lt;/li&gt;'':154C ''smart'':18C ''sport'':14C ''statist'':62C,161C,194C ''stori'':81C ''strong'':39C,99C,143C,172C ''team'':68C,86C,133C ''tool'':111C ''tremend'':71C ''ul'':37C ''us'':24C,169C ''use'':19C ''well'':72C ''went'':136C ''win'':139C ''www.theatlantic.com'':93C,96C ''www.theatlantic.com/entertainment/archive/2013/09/forget-2002-this-years-oakland-as-are-the-real-em-moneyball-em-team/279927/'':92C ''www.theatlantic.com/entertainment/archive/2013/09/forget-2002-this-years-oakland-as-are-the-real-em-moneyball-em-team/279927/&lt;/a&gt;&lt;/li&gt;'':95C ''yes'':2C,160C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (284, 2, '2014-06-10 17:06:54.95', 4, NULL, NULL, '&lt;p&gt;Well, this may not answer the question thoroughly, but since you''re dealing with information retrieval, it may be of some use. &lt;a href="http://moz.com/search-ranking-factors" rel="nofollow"&gt;This page&lt;/a&gt; mantains a set of features and associated &lt;em&gt;correlations&lt;/em&gt; with page-ranking methods of search engines. As a disclaimer from the webpage itself:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Note that these factors are not "proof" of what search engines use to rank websites, but simply show the characteristics of web pages that tend to rank higher.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The list pointed may give you some insights on which features would be nice to select. For example, considering the second most correlated feature, # of google +1''s, it may be possible to add some probability of a user making use of such service if he/she accesses many pages with high # of google +1 (infer "user context"). Thus, you could try to "guess" some other relations that may shed light on interesting features for your tracking app.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (285, 2, '2014-06-10 17:15:52.953', 5, NULL, NULL, '&lt;p&gt;There are a lot of good questions about Football (and sports, in general) that would be awesome to throw to an algorithm and see what comes out. The tricky part is to know &lt;em&gt;what&lt;/em&gt; to throw to the algorithm.&lt;/p&gt;

&lt;p&gt;A team with a good RB could just pass on 3rd-and-short just because the opponents would probably expect run, for instance. So, in order to actually produce some worthy results, I''d break the problem in smaller pieces and analyse them statistically while throwing them to the machines!&lt;/p&gt;

&lt;p&gt;There are a few (good) websites that try to do the same, you should check''em out and use whatever they found to help you out:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.footballoutsiders.com/"&gt;Football Outsiders&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.advancedfootballanalytics.com/"&gt;Advanced Football Analytics&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;And if you truly want to explore Sports Data Analysis, you should definitely check the &lt;a href="http://www.sloansportsconference.com/"&gt;Sloan Sports Conference&lt;/a&gt; videos. There''s a lot of them spread on Youtube.&lt;/p&gt;
', NULL, NULL, '''/a'':132C,141C,166C ''/em'':37C ''/li'':133C,142C ''/p'':43C,96C,124C,177C ''/ul'':143C ''3rd'':56C ''3rd-and-short'':55C ''actual'':73C ''advanc'':138C ''algorithm'':23C,42C ''analys'':87C ''analysi'':154C ''analyt'':140C ''awesom'':18C ''break'':80C ''check'':112C,158C ''come'':27C ''confer'':165C ''could'':51C ''d'':79C ''data'':153C ''definit'':157C ''em'':35C,113C ''expect'':65C ''explor'':151C ''footbal'':10C,130C,139C ''found'':119C ''general'':14C ''good'':7C,49C,102C ''help'':121C ''href'':128C,136C,161C ''instanc'':68C ''know'':34C ''li'':126C,134C ''lot'':5C,171C ''machin'':95C ''oppon'':62C ''order'':71C ''outsid'':131C ''p'':1C,44C,97C,144C ''part'':31C ''pass'':53C ''piec'':85C ''probabl'':64C ''problem'':82C ''produc'':74C ''question'':8C ''rb'':50C ''result'':77C ''run'':66C ''see'':25C ''short'':58C ''sloan'':163C ''smaller'':84C ''sport'':12C,152C,164C ''spread'':174C ''statist'':89C ''team'':46C ''throw'':20C,39C,91C ''tri'':105C ''tricki'':30C ''truli'':148C ''ul'':125C ''use'':116C ''video'':167C ''want'':149C ''websit'':103C ''whatev'':117C ''worthi'':76C ''would'':16C,63C ''www.advancedfootballanalytics.com'':137C ''www.footballoutsiders.com'':129C ''www.sloansportsconference.com'':162C ''youtub'':176C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (287, 2, '2014-06-10 17:32:19.12', 3, NULL, NULL, '&lt;p&gt;I do not know a standard answer to this, but I thought about it some times ago and I have some ideas to share. &lt;/p&gt;

&lt;p&gt;When you have one confusion matrix, you have more or less a picture of how you classification model confuse (mis-classify) classes. When you repeat classification tests you will end up having multiple confusion matrices. The question is how to get a meaningful aggregate confusion matrix. The answer depends on what is the meaning of meaningful (pun intended). I think there is not a single version of meaningful.&lt;/p&gt;

&lt;p&gt;One way is to follow the rough idea of multiple testing. In general, you test something multiple times in order to get more accurate results. As a general principle one can reason that averaging on the results of the multiple tests reduces the variance of the estimates, so as a consequence, it increases the precision of the estimates. You can proceed in this way, of course, by summing position by position and then dividing by the number of tests. You can go further and instead of estimating only a value for each cell of the confusion matrix, you can also compute some confidence intervals, t-values and so on. This is OK from my point of view. But it tell only one side of the story.&lt;/p&gt;

&lt;p&gt;The other side of the story which might be investigated is how stable are the results for the same instances. To exemplify that I will take an extreme example. Suppose you have a classification model for 3 classes. Suppose that these classes are in the same proportion. If your model is able to predict one class perfectly and the other 2 classes with random like performance, you will end up having 0.33 + 0.166 + 0.166 = 0.66 misclassification ratio. This might seem good, but even if you take a look on a single confusion matrix you will not know that your performance on the last 2 classes varies wildly. Multiple tests can help. But averaging the confusion matrices would reveal this? My belief is not. The averaging will give the same result more or less, and doing multiple tests will only decrease the variance of the estimation. However it says nothing about the wild instability of prediction.&lt;/p&gt;

&lt;p&gt;So another way to do compose the confusion matrices would better involve a prediction density for each instance. One can build this density by counting for each instance, the number of times it was predicted a given class. After normalization, you will have for each instance a prediction density rather a single prediction label. You can see that a single prediction label is similar with a degenerated density where you have probability of 1 for the predicted class and 0 for the other classes for each separate instance. Now having this densities one can build a confusion matrix by adding the probabilities from each instance and predicted class to the corresponding cell of the aggregated confusion matrix. &lt;/p&gt;

&lt;p&gt;One can argue that this would give similar results like the previous method. However I think that this might be the case sometimes, often when the model has low variance, the second method is less affected by how the samples from the tests are drawn, and thus more stable and closer to the reality. &lt;/p&gt;

&lt;p&gt;Also the second method might be altered in order to obtain a third method, where one can assign as prediction the label with highest density from the prediction of a given instance. &lt;/p&gt;

&lt;p&gt;I do not implemented those things but I plan to study further because I believe might worth spending some time. &lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (288, 2, '2014-06-10 17:36:11.58', 4, NULL, NULL, '&lt;p&gt;The goal determines the features, so I would initially take as many as possible, then use cross validation to select the optimal subset.&lt;/p&gt;

&lt;p&gt;My educated guess is that a Markov model would work. If you discretize the action space (e.g., select this menu item, press that button, etc.), you can predict the next action based on the past ones. It''s a &lt;em&gt;sequence&lt;/em&gt; or &lt;a href="http://en.wikipedia.org/wiki/Structured_prediction" rel="nofollow"&gt;structured prediction&lt;/a&gt; problem.&lt;/p&gt;

&lt;p&gt;For commercial offerings, search &lt;strong&gt;app analytics&lt;/strong&gt;.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (289, 1, '2014-06-10 17:56:34.847', 7, 286, 'Qualifications for PhD Programs', '&lt;p&gt;Yann LeCun mentioned in his &lt;a href="http://www.reddit.com/r/MachineLearning/comments/25lnbt/ama_yann_lecun/" rel="nofollow"&gt;AMA&lt;/a&gt; that he considers having a PhD very important in order to get a job at a top company.&lt;/p&gt;

&lt;p&gt;I have a masters in statistics and my undergrad was in economics and applied math, but I am now looking into ML PhD programs. Most programs say there are no absolutely necessary CS courses; however I tend to think most accepted students have at least a very strong CS background. I am currently working as a data scientist/statistician but my company will pay for courses. Should I take some intro software engineering courses at my local University to make myself a stronger candidate? What other advice you have for someone applying to PhD programs from outside the CS field?  &lt;/p&gt;

&lt;p&gt;edit: I have taken a few MOOCs (Machine Learning, Recommender Systems, NLP) and code R/python on a daily basis. I have a lot of coding experience with statistical languages and implement ML algorithms daily. I am more concerned with things that I can put on applications.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (301, 2, '2014-06-11 00:32:54.887', 9, NULL, NULL, '&lt;p&gt;One reason that data cleaning is rarely fully automated is that there is so much judgment required to define what "clean" means given your particular problem, methods, and goals.&lt;/p&gt;

&lt;p&gt;It may be as simple as imputing values for any missing data, or it might be as complex as diagnosing data entry errors or data transformation errors from previous automated processes (e.g. coding, censoring, transforming).  In these last two cases, the data &lt;em&gt;looks good&lt;/em&gt; by outward appearance but it''s really erroneous.  Such diagnosis often requires manual analysis and inspection, and also out-of-band information such as information about the data sources and methods they used.&lt;/p&gt;

&lt;p&gt;Also, some data analysis methods work better when erroneous or missing data is left blank (or N/A) rather than imputed or given a default value.  This is true when there is explicit representations of uncertainty and ignorance, such as Dempster-Shafer Belief functions.&lt;/p&gt;

&lt;p&gt;Finally, it''s useful to have specific diagnostics and metrics for the cleaning process.  Are missing or erroneous values randomly distributed or are they concentrated in any way that might affect the outcome of the analysis.  It''s useful to test the effects of alternative cleaning strategies or algorithms to see if they affect the final results.&lt;/p&gt;

&lt;p&gt;Given these concerns, I''m very suspicious of any method or process that treats data cleaning in a superficial, cavalier or full-automated fashion.  There are many devils hiding in those details and it pays to give them serious attention.&lt;/p&gt;
', NULL, NULL, '''/em'':78C ''/p'':31C,113C,159C,218C,261C ''affect'':191C,214C ''algorithm'':209C ''also'':96C,115C ''altern'':205C ''analysi'':92C,118C,196C ''appear'':81C ''attent'':260C ''autom'':10C,62C,243C ''band'':100C ''belief'':157C ''better'':121C ''blank'':129C ''case'':72C ''cavali'':239C ''censor'':66C ''clean'':6C,22C,173C,206C,235C ''code'':65C ''complex'':50C ''concentr'':185C ''concern'':222C ''data'':5C,44C,53C,57C,74C,107C,117C,126C,234C ''default'':138C ''defin'':20C ''dempster'':155C ''dempster-shaf'':154C ''detail'':252C ''devil'':248C ''diagnos'':52C ''diagnosi'':88C ''diagnost'':168C ''distribut'':181C ''e.g'':64C ''effect'':203C ''em'':75C ''entri'':54C ''erron'':86C,123C,178C ''error'':55C,59C ''explicit'':146C ''fashion'':244C ''final'':161C,216C ''full'':242C ''full-autom'':241C ''fulli'':9C ''function'':158C ''give'':257C ''given'':24C,136C,220C ''goal'':30C ''good'':77C ''hide'':249C ''ignor'':151C ''imput'':39C,134C ''inform'':101C,104C ''inspect'':94C ''judgment'':17C ''last'':70C ''left'':128C ''look'':76C ''m'':224C ''mani'':247C ''manual'':91C ''may'':34C ''mean'':23C ''method'':28C,110C,119C,229C ''metric'':170C ''might'':47C,190C ''miss'':43C,125C,176C ''much'':16C ''n/a'':131C ''often'':89C ''one'':2C ''out-of-band'':97C ''outcom'':193C ''outward'':80C ''p'':1C,32C,114C,160C,219C ''particular'':26C ''pay'':255C ''previous'':61C ''problem'':27C ''process'':63C,174C,231C ''random'':180C ''rare'':8C ''rather'':132C ''realli'':85C ''reason'':3C ''represent'':147C ''requir'':18C,90C ''result'':217C ''see'':211C ''serious'':259C ''shafer'':156C ''simpl'':37C ''sourc'':108C ''specif'':167C ''strategi'':207C ''superfici'':238C ''suspici'':226C ''test'':201C ''transform'':58C,67C ''treat'':233C ''true'':142C ''two'':71C ''uncertainti'':149C ''use'':112C,164C,199C ''valu'':40C,139C,179C ''way'':188C ''work'':120C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (290, 2, '2014-06-10 18:22:32.61', 1, NULL, NULL, '&lt;p&gt;I found the pluralsight course &lt;a href="http://pluralsight.com/training/courses/TableOfContents?courseName=introduction-to-machine-learning-encog&amp;amp;highlight=abhishek-kumar_introduction-to-machine-learning-encog-m2-applications!abhishek-kumar_introduction-to-machine-learning-encog-m3-tasks!abhishek-kumar_introduction-to-machine-learning-encog-m1-intro%2a1#introduction-to-machine-learning-encog-m2-applications" rel="nofollow"&gt;Introduction to machine learning encog&lt;/a&gt; a great resource so start with. It uses the &lt;a href="http://www.heatonresearch.com/encog" rel="nofollow"&gt;Encog library&lt;/a&gt; to quickly explore different ml techniques. &lt;/p&gt;
', NULL, NULL, '''/a'':19C,38C ''/encog'':33C ''/p'':45C ''/training/courses/tableofcontents?coursename=introduction-to-machine-learning-encog&amp;amp;highlight=abhishek-kumar_introduction-to-machine-learning-encog-m2-applications!abhishek-kumar_introduction-to-machine-learning-encog-m3-tasks!abhishek-kumar_introduction-to-machine-learning-encog-m1-intro%2a1#introduction-to-machine-learning-encog-m2-applications'':11C ''cours'':6C ''differ'':42C ''encog'':18C,36C ''explor'':41C ''found'':3C ''great'':21C ''href'':8C,30C ''introduct'':14C ''learn'':17C ''librari'':37C ''machin'':16C ''ml'':43C ''nofollow'':13C,35C ''p'':1C ''pluralsight'':5C ''pluralsight.com'':10C ''pluralsight.com/training/courses/tableofcontents?coursename=introduction-to-machine-learning-encog&amp;amp;highlight=abhishek-kumar_introduction-to-machine-learning-encog-m2-applications!abhishek-kumar_introduction-to-machine-learning-encog-m3-tasks!abhishek-kumar_introduction-to-machine-learning-encog-m1-intro%2a1#introduction-to-machine-learning-encog-m2-applications'':9C ''quick'':40C ''rel'':12C,34C ''resourc'':22C ''start'':24C ''techniqu'':44C ''use'':27C ''www.heatonresearch.com'':32C ''www.heatonresearch.com/encog'':31C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (292, 2, '2014-06-10 19:43:11.86', 6, NULL, NULL, '&lt;p&gt;Your time would probably be better spent on Kaggle than in a PhD program. When you read the stories by winners (&lt;a href="http://blog.kaggle.com/"&gt;Kaggle blog&lt;/a&gt;) you''ll see that it takes a large amount of practice and the winners are not just experts of one single method.&lt;/p&gt;

&lt;p&gt;On the other hand, being active and having a plan in a PhD program can get you connections that you otherwise would probably not get.&lt;/p&gt;

&lt;p&gt;I guess the real question is for you - what are the reasons for wanting a job at a top company?&lt;/p&gt;
', NULL, NULL, '''/a'':28C ''/p'':51C,78C,100C ''activ'':58C ''amount'':37C ''better'':7C ''blog'':27C ''blog.kaggle.com'':25C ''compani'':99C ''connect'':70C ''expert'':46C ''get'':68C,77C ''guess'':81C ''hand'':56C ''href'':24C ''job'':95C ''kaggl'':10C,26C ''larg'':36C ''ll'':30C ''method'':50C ''one'':48C ''otherwis'':73C ''p'':1C,52C,79C ''phd'':14C,65C ''plan'':62C ''practic'':39C ''probabl'':5C,75C ''program'':15C,66C ''question'':84C ''read'':18C ''real'':83C ''reason'':91C ''see'':31C ''singl'':49C ''spent'':8C ''stori'':20C ''take'':34C ''time'':3C ''top'':98C ''want'':93C ''winner'':22C,42C ''would'':4C,74C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9082, 1, '2015-11-27 16:11:32.24', 1, 27, 'result of correlation function in r', '&lt;p&gt;I would like to ask if the matrix that is returned from the R cor() function applied on a dataset, is considered a confusion matrix. Thank you&lt;/p&gt;
', 'r  correlation  confusion-matrix', NULL, '''/p'':40C ''appli'':29C ''ask'':17C ''confus'':10B,36C ''confusion-matrix'':9B ''consid'':34C ''cor'':27C ''correl'':3A,8B ''dataset'':32C ''function'':4A,28C ''like'':15C ''matrix'':11B,20C,37C ''p'':12C ''r'':6A,7B,26C ''result'':1A ''return'':23C ''thank'':38C ''would'':14C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (293, 2, '2014-06-10 20:28:54.613', 10, NULL, NULL, '&lt;p&gt;Perhaps a good way to paraphrase the question is, what are the advantages compared to alternative formats?  &lt;/p&gt;

&lt;p&gt;The main alternatives are, I think: a database, text files, or another packed/binary format.&lt;/p&gt;

&lt;p&gt;The database options to consider are probably a columnar store or NoSQL, or for small self-contained datasets SQLite.  The main advantage of the database is the ability to work with data much larger than memory, to have random or indexed access, and to add/append/modify data quickly.  The main *dis*advantage is that it is much slower than HDF, for problems in which the entire dataset needs to be read in and processed.  Another disadvantage is that, with the exception of embedded-style databases like SQLite, a database is a system (requiring admnistration, setup, maintenance, etc) rather than a simple self-contained data store.  &lt;/p&gt;

&lt;p&gt;The text file format options are XML/JSON/CSV.  They are cross-platform/language/toolkit, and are a good archival format due to the ability to be self-describing (or obvious :).  If uncompressed, they are huge (10x-100x HDF), but if compressed, they can be fairly space-efficient (compressed XML is about the same as HDF).  The main disadvantage here is again speed: parsing text is much, much slower than HDF.&lt;/p&gt;

&lt;p&gt;The other binary formats (npy/npz numpy files, blz blaze files, protocol buffers, Avro, ...) have very similar properties to HDF, except they are less widely supported (may be limited to just one platform: numpy) and may have specific other limitations.  They typically do not offer a compelling advantage.&lt;/p&gt;

&lt;p&gt;HDF is a good complement to databases, it may make sense to run a query to produce a roughly memory-sized dataset and then cache it in HDF if the same data would be used more than once.  If you have a dataset which is fixed, and usually processed as a whole, storing it as a collection of appropriately sized HDF files is not a bad option.  If you have a dataset which is updated often, staging some of it as HDF files periodically might still be helpful.&lt;/p&gt;

&lt;p&gt;To summarize, HDF is a good format for data which is read (or written) typically as a whole; it is the lingua franca or common/preferred interchange format for many applications due to wide support and compatibility, decent as an archival format, and very fast.&lt;/p&gt;

&lt;p&gt;P.S. To give this some practical context, my most recent experience comparing HDF to alternatives, a certain small (much less than memory-sized) dataset took 2 seconds to read as HDF (and most of this is probably overhead from Pandas); ~1 minute to read from JSON; and 1 &lt;em&gt;hour&lt;/em&gt; to write to database.  Certainly the database write could be sped up, but you''d better have a good DBA!  This is how it works out of the box.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (302, 2, '2014-06-11 00:44:20.517', 2, NULL, NULL, '&lt;p&gt;I''ve seen a few similar systems over the years.  I remember a company called ClickTrax which if I''m not mistaken got bought by Google and some of their features are now part of Google Analytics.&lt;/p&gt;

&lt;p&gt;Their purpose was marketing, but the same concept can be applied to user experience analytics.  The beauty of their system was that what was tracked was defined by the webmaster - in your case the application developer.&lt;/p&gt;

&lt;p&gt;I can imagine as an application developer I would want to be able to see statistical data on two things - task accomplishment, and general feature usage.&lt;/p&gt;

&lt;p&gt;As an example of task accomplishment, I might have 3 ways to print a page - Ctrl+P, File-&gt;Print, and a toolbar button.  I would want to be able to compare usage to see if the screenspace utilized by the toolbar button was actually worth it.&lt;/p&gt;

&lt;p&gt;As an example of general feature usage, I would want to define a set of features within my application and focus my development efforts on expanding the features used most by my end users.  Some features that take maybe 5 clicks and are popular, I might want to provide a hotkey for, or slim down the number of clicks to activate that feature.  There is also event timing.  Depending on the application, I might want to know the average amount of time spent on a particular feature.  &lt;/p&gt;

&lt;p&gt;Another thing I would want to look at are click streams.  How are people getting from point A to point B in my application?  What are the most popular point B''s?  What are the most popular starting points?  &lt;/p&gt;
', NULL, NULL, '''/p'':39C,77C,105C,153C,242C,283C ''3'':116C ''5'':194C ''abl'':91C,135C ''accomplish'':100C,112C ''activ'':215C ''actual'':150C ''also'':220C ''amount'':234C ''analyt'':38C,55C ''anoth'':244C ''appli'':51C ''applic'':75C,84C,173C,226C,267C ''averag'':233C ''b'':264C,274C ''beauti'':57C ''bought'':25C ''button'':129C,148C ''call'':16C ''case'':73C ''click'':195C,213C,253C ''clicktrax'':17C ''compani'':15C ''compar'':137C ''concept'':48C ''ctrl'':122C ''data'':95C ''defin'':67C,166C ''depend'':223C ''develop'':76C,85C,177C ''effort'':178C ''end'':187C ''event'':221C ''exampl'':109C,157C ''expand'':180C ''experi'':54C ''featur'':32C,103C,160C,170C,182C,190C,217C,241C ''file'':124C ''focus'':175C ''general'':102C,159C ''get'':258C ''googl'':27C,37C ''got'':24C ''hotkey'':205C ''imagin'':81C ''know'':231C ''look'':250C ''m'':21C ''market'':44C ''mayb'':193C ''might'':114C,200C,228C ''mistaken'':23C ''number'':211C ''p'':1C,40C,78C,106C,123C,154C,243C ''page'':121C ''part'':35C ''particular'':240C ''peopl'':257C ''point'':260C,263C,273C,282C ''popular'':198C,272C,280C ''print'':119C,125C ''provid'':203C ''purpos'':42C ''rememb'':13C ''screenspac'':143C ''see'':93C,140C ''seen'':4C ''set'':168C ''similar'':7C ''slim'':208C ''spent'':237C ''start'':281C ''statist'':94C ''stream'':254C ''system'':8C,60C ''take'':192C ''task'':99C,111C ''thing'':98C,245C ''time'':222C,236C ''toolbar'':128C,147C ''track'':65C ''two'':97C ''usag'':104C,138C,161C ''use'':183C ''user'':53C,188C ''util'':144C ''ve'':3C ''want'':88C,132C,164C,201C,229C,248C ''way'':117C ''webmast'':70C ''within'':171C ''worth'':151C ''would'':87C,131C,163C,247C ''year'':11C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (294, 2, '2014-06-10 20:40:25.623', 8, NULL, NULL, '&lt;p&gt;As a former Hadoop engineer, it is not needed but it helps. Hadoop is just one system - the most common system, based on Java, and a ecosystem of products, which apply a particular technique "Map/Reduce" to obtain results in a timely manner. Hadoop is not used at Google, though I assure you they use big data analytics. Google uses their own systems, developed in C++. In fact, Hadoop was created as a result of Google publishing their Map/Reduce and BigTable (HBase in Hadoop) white papers.&lt;/p&gt;

&lt;p&gt;Data scientists will interface with hadoop engineers, though at smaller places you may be required to wear both hats. If you are strictly a data scientist, then whatever you use for your analytics, R, Excel, Tableau, etc, will operate only on a small subset, then will need to be converted to run against the full data set involving hadoop. &lt;/p&gt;
', NULL, NULL, '''/p'':87C,148C ''analyt'':58C,121C ''appli'':32C ''assur'':52C ''base'':23C ''big'':56C ''bigtabl'':81C ''c'':66C ''common'':21C ''convert'':138C ''creat'':71C ''data'':57C,89C,113C,144C ''develop'':64C ''ecosystem'':28C ''engin'':6C,95C ''etc'':125C ''excel'':123C ''fact'':68C ''former'':4C ''full'':143C ''googl'':49C,59C,76C ''hadoop'':5C,14C,44C,69C,84C,94C,147C ''hat'':107C ''hbase'':82C ''help'':13C ''interfac'':92C ''involv'':146C ''java'':25C ''manner'':43C ''map/reduce'':36C,79C ''may'':101C ''need'':10C,135C ''obtain'':38C ''one'':17C ''oper'':127C ''p'':1C,88C ''paper'':86C ''particular'':34C ''place'':99C ''product'':30C ''publish'':77C ''r'':122C ''requir'':103C ''result'':39C,74C ''run'':140C ''scientist'':90C,114C ''set'':145C ''small'':131C ''smaller'':98C ''strict'':111C ''subset'':132C ''system'':18C,22C,63C ''tableau'':124C ''techniqu'':35C ''though'':50C,96C ''time'':42C ''use'':47C,55C,60C,118C ''wear'':105C ''whatev'':116C ''white'':85C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (326, 1, '2014-06-12 06:04:48.243', 28, 18307, 'Python vs R for machine learning', '&lt;p&gt;I''m just starting to develop a &lt;a href="https://en.wikipedia.org/wiki/Machine_learning"&gt;machine learning&lt;/a&gt; application for academic purposes. I''m currently using &lt;strong&gt;R&lt;/strong&gt; and training myself in it. However, in a lot of places, I saw people using &lt;strong&gt;Python&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;What are people using in academia and industry, and what is the recommendation?&lt;/p&gt;
', 'machine-learning  r  python', NULL, '''/a'':27C ''/p'':57C,72C ''/strong'':38C,56C ''/wiki/machine_learning'':24C ''academ'':30C ''academia'':64C ''applic'':28C ''current'':34C ''develop'':18C ''en.wikipedia.org'':23C ''en.wikipedia.org/wiki/machine_learning'':22C ''howev'':44C ''href'':21C ''industri'':66C ''learn'':6A,9B,26C ''lot'':47C ''m'':14C,33C ''machin'':5A,8B,25C ''machine-learn'':7B ''p'':12C,58C ''peopl'':52C,61C ''place'':49C ''purpos'':31C ''python'':1A,11B,55C ''r'':3A,10B,37C ''recommend'':71C ''saw'':51C ''start'':16C ''strong'':36C,54C ''train'':40C ''use'':35C,53C,62C ''vs'':2A');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (295, 2, '2014-06-10 20:43:28.533', 3, NULL, NULL, '&lt;p&gt;I am glad you also found Yann LeCun''s AMA page, it''s very useful.&lt;/p&gt;

&lt;p&gt;Here are my opinions&lt;br&gt;
Q: Should I take some intro software engineering courses at my local University to make myself a stronger candidate?&lt;br&gt;
A: No, you need to take more math courses.  It''s not the applied stuff that''s hard, it''s the theory stuff.  I don''t know what your school offers.  Take theoretical math courses, along with some computer science courses.  &lt;/p&gt;

&lt;p&gt;Q:What other advice you have for someone applying to PhD programs from outside the CS field?&lt;br&gt;
A:  How closely related are you looking for.  Without a specific question, it''s hard to give a specific answer.  &lt;/p&gt;
', NULL, NULL, '''/p'':17C,85C,125C ''advic'':90C ''along'':79C ''also'':6C ''ama'':11C ''answer'':124C ''appli'':57C,95C ''br'':23C,43C,104C ''candid'':42C ''close'':107C ''comput'':82C ''cours'':32C,52C,78C,84C ''cs'':102C ''engin'':31C ''field'':103C ''found'':7C ''give'':121C ''glad'':4C ''hard'':61C,119C ''intro'':29C ''know'':70C ''lecun'':9C ''local'':35C ''look'':111C ''make'':38C ''math'':51C,77C ''need'':47C ''offer'':74C ''opinion'':22C ''outsid'':100C ''p'':1C,18C,86C ''page'':12C ''phd'':97C ''program'':98C ''q'':24C,87C ''question'':116C ''relat'':108C ''school'':73C ''scienc'':83C ''softwar'':30C ''someon'':94C ''specif'':115C,123C ''stronger'':41C ''stuff'':58C,66C ''take'':27C,49C,75C ''theoret'':76C ''theori'':65C ''univers'':36C ''use'':16C ''without'':113C ''yann'':8C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (744, 1, '2014-07-15 21:30:11.6', 11, 3483, 'Cosine similarity versus dot product as distance metrics', '&lt;p&gt;It looks like the cosine similarity of two features is just their dot product scaled by the product of their magnitudes. When does cosine similarity make a better distance metric than the dot product? I.e. do the dot product and cosine similarity have different strengths or weaknesses in different situations?&lt;/p&gt;
', 'classification', NULL, '''/p'':61C ''better'':38C ''classif'':9B ''cosin'':1A,15C,34C,51C ''differ'':54C,59C ''distanc'':7A,39C ''dot'':4A,23C,43C,48C ''featur'':19C ''i.e'':45C ''like'':13C ''look'':12C ''magnitud'':31C ''make'':36C ''metric'':8A,40C ''p'':10C ''product'':5A,24C,28C,44C,49C ''scale'':25C ''similar'':2A,16C,35C,52C ''situat'':60C ''strength'':55C ''two'':18C ''versus'':3A ''weak'':57C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (296, 2, '2014-06-10 21:50:51.347', 15, NULL, NULL, '&lt;p&gt;HDP is an extension of LDA, designed to address the case where the number of mixture components (the number of "topics" in document-modeling terms) is not known a priori.  So that''s the reason why there''s a difference.&lt;/p&gt;

&lt;p&gt;Using LDA for document modeling, one treats each "topic" as a distribution of words in some known vocabulary.  For each document a mixture of topics is drawn from a Dirichlet distribution, and then each word in the document is an independent draw from that mixture (that is, selecting a topic and then using it to generate a word).&lt;/p&gt;

&lt;p&gt;For HDP (applied to document modeling), one also uses a Dirichlet process to capture the uncertainty in the number of topics.  So a common base distribution is selected which represents the countably-infinite set of possible topics for the corpus, and then the finite distribution of topics for each document is sampled from this base distribution.&lt;/p&gt;

&lt;p&gt;As far as pros and cons, HDP has the advantage that the maximum number of topics can be unbounded and learned from the data rather than specified in advance.  I suppose though it is more complicated to implement, and unnecessary in the case where a bounded number of topics is acceptable.&lt;/p&gt;
', NULL, NULL, '''/p'':43C,103C,162C,215C ''accept'':214C ''address'':10C ''advanc'':192C ''advantag'':173C ''also'':112C ''appli'':107C ''base'':129C,160C ''bound'':209C ''captur'':118C ''case'':12C,206C ''common'':128C ''complic'':199C ''compon'':18C ''con'':169C ''corpus'':145C ''countabl'':137C ''countably-infinit'':136C ''data'':187C ''design'':8C ''differ'':42C ''dirichlet'':74C,115C ''distribut'':56C,75C,130C,150C,161C ''document'':25C,48C,65C,82C,109C,155C ''document-model'':24C ''draw'':86C ''drawn'':71C ''extens'':5C ''far'':165C ''finit'':149C ''generat'':100C ''hdp'':2C,106C,170C ''implement'':201C ''independ'':85C ''infinit'':138C ''known'':30C,61C ''lda'':7C,46C ''learn'':184C ''maximum'':176C ''mixtur'':17C,67C,89C ''model'':26C,49C,110C ''number'':15C,20C,123C,177C,210C ''one'':50C,111C ''p'':1C,44C,104C,163C ''possibl'':141C ''priori'':32C ''process'':116C ''pros'':167C ''rather'':188C ''reason'':37C ''repres'':134C ''sampl'':157C ''select'':92C,132C ''set'':139C ''specifi'':190C ''suppos'':194C ''term'':27C ''though'':195C ''topic'':22C,53C,69C,94C,125C,142C,152C,179C,212C ''treat'':51C ''unbound'':182C ''uncertainti'':120C ''unnecessari'':203C ''use'':45C,97C,113C ''vocabulari'':62C ''word'':58C,79C,102C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (297, 2, '2014-06-10 22:26:53.623', 3, NULL, NULL, '&lt;p&gt;As in @damienfrancois answer feature selection is about selecting a subset of features. So in NLP it would be selecting a set of specific words (the typical in NLP is that each word represents a feature with value equal to the frequency of the word or some other weight based on TF/IDF or similar).&lt;/p&gt;

&lt;p&gt;Dimensionality reduction is the introduction of new feature space where the original features are represented. The new space is of lower dimension that the original space. In case of text an example would be the &lt;a href="http://en.wikipedia.org/wiki/Feature_hashing" rel="nofollow"&gt;hashing trick&lt;/a&gt; where a piece of text is reduced to a vector of few bits (say 16 or 32) or bytes. The amazing thing is that the geometry of the space is preserved (given enough bits), so relative distances between documents remain the same as in the original space, so you can deploy standard machine learning techniques without having to deal with unbound (and huge number of) dimensions found in text.&lt;/p&gt;
', NULL, NULL, '''/a'':102C ''/p'':56C,172C ''/wiki/feature_hashing'':97C ''16'':117C ''32'':119C ''amaz'':123C ''answer'':5C ''base'':51C ''bit'':115C,136C ''byte'':121C ''case'':85C ''damienfrancoi'':4C ''deal'':161C ''deploy'':153C ''dimens'':79C,168C ''dimension'':58C ''distanc'':139C ''document'':141C ''en.wikipedia.org'':96C ''en.wikipedia.org/wiki/feature_hashing'':95C ''enough'':135C ''equal'':40C ''exampl'':89C ''featur'':6C,14C,37C,65C,70C ''found'':169C ''frequenc'':43C ''geometri'':128C ''given'':134C ''hash'':100C ''href'':94C ''huge'':165C ''introduct'':62C ''learn'':156C ''lower'':78C ''machin'':155C ''new'':64C,74C ''nlp'':17C,30C ''nofollow'':99C ''number'':166C ''origin'':69C,82C,148C ''p'':1C,57C ''piec'':105C ''preserv'':133C ''reduc'':109C ''reduct'':59C ''rel'':98C ''relat'':138C ''remain'':142C ''repres'':35C,72C ''say'':116C ''select'':7C,10C,21C ''set'':23C ''similar'':55C ''space'':66C,75C,83C,131C,149C ''specif'':25C ''standard'':154C ''subset'':12C ''techniqu'':157C ''text'':87C,107C,171C ''tf/idf'':53C ''thing'':124C ''trick'':101C ''typic'':28C ''unbound'':163C ''valu'':39C ''vector'':112C ''weight'':50C ''without'':158C ''word'':26C,34C,46C ''would'':19C,90C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (298, 2, '2014-06-10 22:29:52.873', 6, NULL, NULL, '&lt;p&gt;You already have a Masters in Statistics, which is great! In general, I''d suggest to people to take as much statistics as they can, especially Bayesian Data Analysis. &lt;/p&gt;

&lt;p&gt;Depending on what you want to do with your PhD, you would benefit from foundational courses in the discipline(s) in your application area.  You already have Economics but if you want to do Data Science on social behavior, then courses in Sociology would be valuable.  If you want to work in fraud prevention, then a courses in banking and financial transactions would be good.  If you want to work in information security, then taking a few security courses would be good.&lt;/p&gt;

&lt;p&gt;There are people who argue that it''s not valuable for Data Scientists to spend time on courses in sociology or other disciplines.  But consider the recent case of the Google Flu Trends project.  In &lt;a href="http://www.uvm.edu/~cdanfort/csc-reading-group/lazer-flu-science-2014.pdf"&gt;this article&lt;/a&gt; their methods were strongly criticized for making avoidable mistakes.  The critics call it "Big Data hubris".&lt;/p&gt;

&lt;p&gt;There''s another reason for building strength in social science disciplines: personal competitive advantage.  With the rush of academic degree programs, certificate programs, and MOOCs, there is a mad rush of students into the Data Science field.  Most will come out with capabilities for core Machine Learning methods and tools.  PhD graduates will have more depth and more theoretical knowledge, but they are all competing for the same sorts of jobs, delivering the same sorts of value.  With this flood of graduates, I expect that they won''t be able to command premium salaries.&lt;/p&gt;

&lt;p&gt;But if you can differentiate yourself with a combination of formal education and practical experience in a particular domain and application area, then you should be able to set yourself apart from the crowd.&lt;/p&gt;

&lt;p&gt;(Context: I''m in a PhD program in Computational Social Science, which has a heavy focus on modeling, evolutionary computation, and social science disciplines, and less emphasis on ML and other empirical data analysis topics).&lt;/p&gt;
', NULL, NULL, '''/a'':159C ''/p'':31C,115C,176C,272C,308C,345C ''/~cdanfort/csc-reading-group/lazer-flu-science-2014.pdf'':156C ''abl'':267C,300C ''academ'':196C ''advantag'':191C ''alreadi'':3C,58C ''analysi'':30C,343C ''anoth'':180C ''apart'':304C ''applic'':55C,294C ''area'':56C,295C ''argu'':121C ''articl'':158C ''avoid'':167C ''bank'':91C ''bayesian'':28C ''behavior'':71C ''benefit'':45C ''big'':173C ''build'':183C ''call'':171C ''capabl'':220C ''case'':144C ''certif'':199C ''combin'':282C ''come'':217C ''command'':269C ''compet'':242C ''competit'':190C ''comput'':318C,329C ''consid'':141C ''context'':310C ''core'':222C ''cours'':48C,73C,89C,111C,134C ''critic'':164C,170C ''crowd'':307C ''d'':15C ''data'':29C,67C,128C,174C,212C,342C ''degre'':197C ''deliv'':249C ''depend'':33C ''depth'':233C ''differenti'':278C ''disciplin'':51C,139C,188C,333C ''domain'':292C ''econom'':60C ''educ'':285C ''emphasi'':336C ''empir'':341C ''especi'':27C ''evolutionari'':328C ''expect'':261C ''experi'':288C ''field'':214C ''financi'':93C ''flood'':257C ''flu'':148C ''focus'':325C ''formal'':284C ''foundat'':47C ''fraud'':85C ''general'':13C ''good'':97C,114C ''googl'':147C ''graduat'':229C,259C ''great'':11C ''heavi'':324C ''href'':153C ''hubri'':175C ''inform'':104C ''job'':248C ''knowledg'':237C ''learn'':224C ''less'':335C ''m'':312C ''machin'':223C ''mad'':206C ''make'':166C ''master'':6C ''method'':161C,225C ''mistak'':168C ''ml'':338C ''model'':327C ''mooc'':202C ''much'':22C ''p'':1C,32C,116C,177C,273C,309C ''particular'':291C ''peopl'':18C,119C ''person'':189C ''phd'':42C,228C,315C ''practic'':287C ''premium'':270C ''prevent'':86C ''program'':198C,200C,316C ''project'':150C ''reason'':181C ''recent'':143C ''rush'':194C,207C ''salari'':271C ''scienc'':68C,187C,213C,320C,332C ''scientist'':129C ''secur'':105C,110C ''set'':302C ''social'':70C,186C,319C,331C ''sociolog'':75C,136C ''sort'':246C,252C ''spend'':131C ''statist'':8C,23C ''strength'':184C ''strong'':163C ''student'':209C ''suggest'':16C ''take'':20C,107C ''theoret'':236C ''time'':132C ''tool'':227C ''topic'':344C ''transact'':94C ''trend'':149C ''valu'':254C ''valuabl'':78C,126C ''want'':37C,64C,81C,100C ''won'':264C ''work'':83C,102C ''would'':44C,76C,95C,112C ''www.uvm.edu'':155C ''www.uvm.edu/~cdanfort/csc-reading-group/lazer-flu-science-2014.pdf'':154C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1202, 2, '2014-10-03 16:07:30.327', 3, NULL, NULL, '&lt;p&gt;Try out some generative models like HMM. 
Just check the following link:
&lt;a href="http://stats.stackexchange.com/questions/91290/how-do-i-train-hmms-for-classification"&gt;http://stats.stackexchange.com/questions/91290/how-do-i-train-hmms-for-classification&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/questions/91290/how-do-i-train-hmms-for-classification'':18C ''/questions/91290/how-do-i-train-hmms-for-classification&lt;/a&gt;&lt;/p&gt;'':21C ''check'':10C ''follow'':12C ''generat'':5C ''hmm'':8C ''href'':15C ''like'':7C ''link'':13C ''model'':6C ''p'':1C ''stats.stackexchange.com'':17C,20C ''stats.stackexchange.com/questions/91290/how-do-i-train-hmms-for-classification'':16C ''stats.stackexchange.com/questions/91290/how-do-i-train-hmms-for-classification&lt;/a&gt;&lt;/p&gt;'':19C ''tri'':2C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (303, 2, '2014-06-11 01:09:06.1', 1, NULL, NULL, '&lt;p&gt;This isn''t my area of specialty and I''m not familiar with Moses, but I found this after some searching.  &lt;/p&gt;

&lt;p&gt;I think you are looking for GIZA++.  You''ll see GIZA++ listed in the "Training" section (left menu) on the Moses home page, as the second step. GIZA++ is briefly described in tutorial fashion &lt;a href="http://stackoverflow.com/questions/5752043/is-there-a-tutorial-about-giza"&gt;here&lt;/a&gt;. Here are a few tutorial PowerPoint slides: &lt;a href="http://www.tc.umn.edu/~bthomson/wordalignment/GIZA.ppt" rel="nofollow"&gt;http://www.tc.umn.edu/~bthomson/wordalignment/GIZA.ppt&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':65C ''/p'':23C ''/questions/5752043/is-there-a-tutorial-about-giza'':63C ''/~bthomson/wordalignment/giza.ppt'':77C ''/~bthomson/wordalignment/giza.ppt&lt;/a&gt;&lt;/p&gt;'':82C ''area'':6C ''briefli'':54C ''describ'':55C ''familiar'':13C ''fashion'':58C ''found'':18C ''giza'':31C,35C,52C ''home'':46C ''href'':60C,74C ''isn'':3C ''left'':41C ''list'':36C ''ll'':33C ''look'':29C ''m'':11C ''menu'':42C ''mose'':15C,45C ''nofollow'':79C ''p'':1C,24C ''page'':47C ''powerpoint'':71C ''rel'':78C ''search'':22C ''second'':50C ''section'':40C ''see'':34C ''slide'':72C ''specialti'':8C ''stackoverflow.com'':62C ''stackoverflow.com/questions/5752043/is-there-a-tutorial-about-giza'':61C ''step'':51C ''think'':26C ''train'':39C ''tutori'':57C,70C ''www.tc.umn.edu'':76C,81C ''www.tc.umn.edu/~bthomson/wordalignment/giza.ppt'':75C ''www.tc.umn.edu/~bthomson/wordalignment/giza.ppt&lt;/a&gt;&lt;/p&gt;'':80C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (305, 1, '2014-06-11 04:24:04.183', 9, 2008, 'Does Amazon RedShift replace Hadoop for ~1XTB data?', '&lt;p&gt;There is plenty of hype surrounding Hadoop and its eco-system.  However, in practice, where many data sets are in the terabyte range, is it not more reasonable to use &lt;a href="http://aws.amazon.com/redshift/"&gt;Amazon RedShift&lt;/a&gt; for querying large data sets, rather than spending time and effort building a Hadoop cluster? &lt;/p&gt;

&lt;p&gt;Also, how does Amazon Redshift compare with Hadoop with respect to setup complexity, cost, and performance?&lt;/p&gt;
', 'hadoop  map-reduce  aws', NULL, '''/a'':53C ''/p'':69C,87C ''/redshift/'':50C ''1xtb'':7A ''also'':71C ''amazon'':2A,51C,74C ''aw'':13B ''aws.amazon.com'':49C ''aws.amazon.com/redshift/'':48C ''build'':65C ''cluster'':68C ''compar'':76C ''complex'':83C ''cost'':84C ''data'':8A,32C,57C ''eco'':25C ''eco-system'':24C ''effort'':64C ''hadoop'':5A,9B,21C,67C,78C ''howev'':27C ''href'':47C ''hype'':19C ''larg'':56C ''mani'':31C ''map'':11B ''map-reduc'':10B ''p'':14C,70C ''perform'':86C ''plenti'':17C ''practic'':29C ''queri'':55C ''rang'':38C ''rather'':59C ''reason'':43C ''redshift'':3A,52C,75C ''reduc'':12B ''replac'':4A ''respect'':80C ''set'':33C,58C ''setup'':82C ''spend'':61C ''surround'':20C ''system'':26C ''terabyt'':37C ''time'':62C ''use'':45C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (306, 2, '2014-06-11 05:17:12.253', 2, NULL, NULL, '&lt;p&gt;Personally, I don''t think it''s all that difficult to set up a hadoop cluster, but I know that it is sometimes painful when you are getting started.&lt;/p&gt;

&lt;p&gt;HDFS size limitations well exceed a TB (or did you mean exabyte?).  If I''m not mistaken it scales to yottabytes or some other measurement that I don''t even know the word for.  Whatever it is, it''s really big.&lt;/p&gt;

&lt;p&gt;Tools like Redshift have their place, but I always worry about vendor specific solutions.  My main concern is always "what do I do when I am dissatisfied with their service?" - I can go to google and shift my analysis work into their paradigm or I can go to hadoop and shift that same work into that system.  Either way, I''m going to have to learn something new and do a lot of work translating things.&lt;/p&gt;

&lt;p&gt;That being said, it''s nice to be able to upload a dataset and get to work quickly - especially if what I''m doing has a short lifecycle.  Amazon has done a good job of answering the data security problem.&lt;/p&gt;

&lt;p&gt;If you want to avoid hadoop, there will always be an alternative.  But it''s not all that difficult to work with once you get going with it.&lt;/p&gt;
', NULL, NULL, '''/p'':31C,74C,152C,194C,224C ''abl'':162C ''altern'':207C ''alway'':84C,94C,204C ''amazon'':182C ''analysi'':114C ''answer'':189C ''avoid'':200C ''big'':73C ''cluster'':17C ''concern'':92C ''data'':191C ''dataset'':166C ''difficult'':11C,214C ''dissatisfi'':102C ''done'':184C ''either'':133C ''especi'':172C ''even'':62C ''exabyt'':44C ''exceed'':37C ''get'':29C,168C,220C ''go'':108C,122C,137C,221C ''good'':186C ''googl'':110C ''hadoop'':16C,124C,201C ''hdfs'':33C ''job'':187C ''know'':20C,63C ''learn'':141C ''lifecycl'':181C ''like'':77C ''limit'':35C ''lot'':147C ''m'':47C,136C,176C ''main'':91C ''mean'':43C ''measur'':57C ''mistaken'':49C ''new'':143C ''nice'':159C ''p'':1C,32C,75C,153C,195C ''pain'':25C ''paradigm'':118C ''person'':2C ''place'':81C ''problem'':193C ''quick'':171C ''realli'':72C ''redshift'':78C ''said'':156C ''scale'':51C ''secur'':192C ''servic'':105C ''set'':13C ''shift'':112C,126C ''short'':180C ''size'':34C ''solut'':89C ''someth'':142C ''sometim'':24C ''specif'':88C ''start'':30C ''system'':132C ''tb'':39C ''thing'':151C ''think'':6C ''tool'':76C ''translat'':150C ''upload'':164C ''vendor'':87C ''want'':198C ''way'':134C ''well'':36C ''whatev'':67C ''word'':65C ''work'':115C,129C,149C,170C,216C ''worri'':85C ''yottabyt'':53C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (307, 1, '2014-06-11 06:07:45.767', 11, 1356, 'Big data case study or use case example', '&lt;p&gt;I have read lot of blogs\article on how different type of industries are using Big Data Analytic. But most of these article fails to mention&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;What kinda data these companies used. What was the size of the data&lt;/li&gt;
&lt;li&gt;What kinda of tools technologies they used to process the data&lt;/li&gt;
&lt;li&gt;What was the problem they were facing and how the insight they got the data helped them to resolve the issue.&lt;/li&gt;
&lt;li&gt;How they selected the tool\technology to suit their need.&lt;/li&gt;
&lt;li&gt;What kinda pattern they identified from the data &amp;amp; what kind of patterns they were looking from the data.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I wonder if someone can provide me answer to all these questions or a link which at-least answer some of the the questions. I am looking for real world example. &lt;/p&gt;

&lt;p&gt;It would be great if someone share how finance industry is making use of Big Data Analytic.&lt;/p&gt;
', 'bigdata  hadoop  data-mining  social-network-analysis  usecase', NULL, '''/li'':62C,75C,98C,110C,131C ''/ol'':132C ''/p'':46C,166C,185C ''amp'':120C ''analysi'':17B ''analyt'':37C,184C ''answer'':141C,153C ''articl'':26C,42C ''at-least'':150C ''big'':1A,35C,182C ''bigdata'':9B ''blog'':25C ''case'':3A,7A ''compani'':53C ''data'':2A,12B,36C,51C,61C,74C,91C,119C,130C,183C ''data-min'':11B ''differ'':29C ''exampl'':8A,165C ''face'':83C ''fail'':43C ''financ'':176C ''got'':89C ''great'':171C ''hadoop'':10B ''help'':92C ''identifi'':116C ''industri'':32C,177C ''insight'':87C ''issu'':97C ''kind'':122C ''kinda'':50C,65C,113C ''least'':152C ''li'':48C,63C,76C,99C,111C ''link'':148C ''look'':127C,161C ''lot'':23C ''make'':179C ''mention'':45C ''mine'':13B ''need'':109C ''network'':16B ''ol'':47C ''p'':19C,133C,167C ''pattern'':114C,124C ''problem'':80C ''process'':72C ''provid'':139C ''question'':145C,158C ''read'':22C ''real'':163C ''resolv'':95C ''select'':102C ''share'':174C ''size'':58C ''social'':15B ''social-network-analysi'':14B ''someon'':137C,173C ''studi'':4A ''suit'':107C ''technolog'':68C,105C ''tool'':67C,104C ''type'':30C ''use'':6A,34C,54C,70C,180C ''usecas'':18B ''wonder'':135C ''world'':164C ''would'':169C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (309, 2, '2014-06-11 06:51:19.143', 11, NULL, NULL, '&lt;p&gt;&lt;strong&gt;tl;dr:&lt;/strong&gt; They markedly differ in many aspects and I can''t think Redshift will replace Hadoop.  &lt;/p&gt;

&lt;p&gt;-Function&lt;br&gt;
You can''t run anything other than SQL on Redshift. Perhaps most importantly, you can''t run any type of custom functions on Redshift. In Hadoop you can, using many languages (Java, Python, Ruby.. you name it). For example, NLP in Hadoop is easy, while it''s more or less impossible in Redshift. I.e. there are lots of things you can do in Hadoop but not on Redshift. This is probably the most important difference.&lt;/p&gt;

&lt;p&gt;-Performance Profile&lt;br&gt;
Query execution on Redshift is in most cases significantly more efficient than on Hadoop. However, this efficiency comes from the indexing that is done when the data is loaded into Redshift (I''m using the term &lt;code&gt;indexing&lt;/code&gt; very loose here). Therefore, it''s great if you load your data once and execute multiple queries, but if you want to execute only one query for example, you might actually lose out in performance overall.&lt;/p&gt;

&lt;p&gt;-Cost Profile&lt;br&gt;
Which solution wins out in cost depends on the situation (like performance), but you probably need quite a lot of queries in order to make it cheaper than Hadoop (more specifically Amazon''s Elastic Map Reduce). For example, if you are doing OLAP, it''s very likely that Redshift comes out cheaper. If you do daily batch ETLs, Hadoop is more likely to come out cheaper.  &lt;/p&gt;

&lt;p&gt;Having said that, we''ve replaced part of our ETL that was done in Hive to Redshift, and it was a pretty great experience; mostly for the ease of development. Redshift''s Query Engine is based on PostgreSQL and is very mature, compared to Hive''s. Its ACID characteristics make it easier to reason about it, and the quicker response time allows more testing to be done. It''s a great tool to have, but it won''t replace Hadoop.  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT&lt;/strong&gt;:  As for setup complexity, I''d even say it''s easier with Hadoop if you use AWS''s EMR. Their tools are so mature that it''s ridiculously easy to have your Hadoop job running. Tools and mechanisms surrounding Redshift''s operation aren''t that mature yet. For example, Redshift can''t handle trickle loading and thus you have to come up with something that turns that into a batched load, which can add some complexity to your ETL. &lt;/p&gt;
', NULL, NULL, '''..'':59C ''/code'':144C ''/p'':21C,101C,181C,252C,334C,418C ''/strong'':5C,338C ''acid'':301C ''actual'':175C ''add'':412C ''allow'':315C ''amazon'':217C ''anyth'':29C ''aren'':381C ''aspect'':11C ''aw'':355C ''base'':289C ''batch'':242C,408C ''br'':24C,105C,185C ''case'':113C ''characterist'':302C ''cheaper'':212C,237C,251C ''code'':142C ''come'':123C,235C,249C,399C ''compar'':296C ''complex'':342C,414C ''cost'':183C,191C ''custom'':45C ''d'':344C ''daili'':241C ''data'':132C,156C ''depend'':192C ''develop'':283C ''differ'':8C,100C ''done'':129C,266C,320C ''dr'':4C ''eas'':281C ''easi'':69C,367C ''easier'':305C,349C ''edit'':337C ''effici'':116C,122C ''elast'':219C ''emr'':357C ''engin'':287C ''etl'':243C,263C,417C ''even'':345C ''exampl'':64C,172C,223C,387C ''execut'':107C,159C,167C ''experi'':277C ''function'':23C,46C ''great'':151C,276C,324C ''hadoop'':20C,50C,67C,89C,119C,214C,244C,333C,351C,371C ''handl'':391C ''hive'':268C,298C ''howev'':120C ''i.e'':79C ''import'':37C,99C ''imposs'':76C ''index'':126C,143C ''java'':56C ''job'':372C ''languag'':55C ''less'':75C ''like'':196C,232C,247C ''load'':134C,154C,393C,409C ''loos'':146C ''lose'':176C ''lot'':82C,204C ''m'':138C ''make'':210C,303C ''mani'':10C,54C ''map'':220C ''mark'':7C ''matur'':295C,362C,384C ''mechan'':376C ''might'':174C ''most'':278C ''multipl'':160C ''name'':61C ''need'':201C ''nlp'':65C ''olap'':228C ''one'':169C ''oper'':380C ''order'':208C ''overal'':180C ''p'':1C,22C,102C,182C,253C,335C ''part'':260C ''perform'':103C,179C,197C ''perhap'':35C ''postgresql'':291C ''pretti'':275C ''probabl'':96C,200C ''profil'':104C,184C ''python'':57C ''queri'':106C,161C,170C,206C,286C ''quicker'':312C ''quit'':202C ''reason'':307C ''redshift'':17C,34C,48C,78C,93C,109C,136C,234C,270C,284C,378C,388C ''reduc'':221C ''replac'':19C,259C,332C ''respons'':313C ''ridicul'':366C ''rubi'':58C ''run'':28C,41C,373C ''said'':255C ''say'':346C ''setup'':341C ''signific'':114C ''situat'':195C ''solut'':187C ''someth'':402C ''specif'':216C ''sql'':32C ''strong'':2C,336C ''surround'':377C ''term'':141C ''test'':317C ''therefor'':148C ''thing'':84C ''think'':16C ''thus'':395C ''time'':314C ''tl'':3C ''tool'':325C,359C,374C ''trickl'':392C ''turn'':404C ''type'':43C ''use'':53C,139C,354C ''ve'':258C ''want'':165C ''win'':188C ''won'':330C ''yet'':385C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (310, 1, '2014-06-11 10:11:59.397', 8, 467, 'One-Class discriminatory classification with imbalanced, heterogenous Negative background?', '&lt;p&gt;I''m working on improving an existing supervised classifier, for classifying {protein} sequences as belonging to a specific class (Neuropeptide hormone precursors), or not.&lt;/p&gt;

&lt;p&gt;There are about 1,150 known "positives", against a background of about 13 million protein sequences ("Unknown/poorly annotated background"), or about 100,000 reviewed, relevant proteins, annotated with a variety of properties (but very few annotated in an explicitly "negative" way). &lt;/p&gt;

&lt;p&gt;My previous implementation looked at this as a binary classification problem: 
Positive set = Proteins marked as Neuropeptides.
Negative set: Random sampling of 1,300 samples (total) from among the remaining proteins of a roughly similar length-wise distribution. &lt;/p&gt;

&lt;p&gt;That worked, but I want to greatly improve the machines discriminatory abilities (Currently, it''s at about 83-86% in terms of accuracy, AUC, F1, measured by CV, on multiple randomly sampled negative sets).&lt;/p&gt;

&lt;p&gt;My thoughts were to:
1) Make this a multiclass problem, choosing 2-3 different classes of protein that will definetly be negatives, by their properties/functional class, along with (maybe) another randomly sampled set.
 (Priority here would be negative sets that are similar in their characteristics/features to the positive set, while still having defining characteristics) . 
2) One class learning - Would be nice, but as I understand it, it''s meant just for anomaly detection, and has poorer performance than discriminatory approaches.&lt;/p&gt;

&lt;p&gt;*) I''ve heard of P-U learning, which sounds neat, but I''m a programming N00b, and I don''t know of any existing implementations for it. (In Python/sci-kit learn).&lt;/p&gt;

&lt;p&gt;So, does approach 1 make sense in a theoretical POV? Is there a best way to make multiple negative sets? (I could also simply use a massive [50K] pick of the "negative" proteins, but they''re all very very different from each other, so I don''t know how well the classifier would handle them as one big , unbalanced mix).
Thanks!&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (311, 2, '2014-06-11 11:24:19.963', 4, NULL, NULL, '&lt;p&gt;The way I would attack the problem, in general, is to leverage statistical analysis like Principal Component Analysis or Ordinary Least Squares to help determine what attributes within these protein sequences are best suited to classify proteins as Neuropeptide hormone precursors.  &lt;/p&gt;

&lt;p&gt;In order to do that, you''ll have to convert the protein sequences into numeric data, but I believe some work has already been done in that regard using formulas leveraged in Amino Acid PCA.&lt;/p&gt;

&lt;p&gt;See these two links:
&lt;a href="http://www.ncbi.nlm.nih.gov/pubmed/24496727" rel="nofollow"&gt;http://www.ncbi.nlm.nih.gov/pubmed/24496727&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.ncbi.nlm.nih.gov/pubmed/16615809" rel="nofollow"&gt;http://www.ncbi.nlm.nih.gov/pubmed/16615809&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Once that work has been done, I would attempt to classify using the entire dataset and a reinforcement learning algorithm, like &lt;a href="http://findingscience.com/ankusa/hbase/hadoop/ruby/2010/12/02/naive-bayes-classification-in-ruby-using-hadoop-and-hbase.html" rel="nofollow"&gt;Naive Bayes&lt;/a&gt; while slimming down the data into that which PCA has identified as important.  &lt;/p&gt;

&lt;p&gt;The reason I would try to use Bayes is because it has proven to be one of the best methods for determining spam vs. regular email, which has a similarly skewed dataset.&lt;/p&gt;

&lt;p&gt;Having said all of that...&lt;/p&gt;

&lt;p&gt;Slimming down the number or type of negative classifications might skew your results a few points one way or the other, but I don''t think you''ll see the long term effectiveness change substantially until you do the leg work of determining how to best remove the fuzziness from your training data.  That will either require a field expert or statistical analysis.&lt;/p&gt;

&lt;p&gt;I could be completely off base.  I am interested in seeing some other answers, but that is my 2 cents.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (312, 2, '2014-06-11 11:32:13.433', 3, NULL, NULL, '&lt;p&gt;Therriault, really happy to hear you are using Vertica! Full disclosure, I am the chief data scientist there :) . The workflow you describe is exactly what I encounter quite frequently and I am a true believer in preprocessing those very large datasets in the database prior to any pyODBC and pandas work. I''d suggest creating a view or table via a file based query just to ensure reproducible work. Good Luck&lt;/p&gt;
', NULL, NULL, '''/p'':73C ''base'':64C ''believ'':36C ''chief'':16C ''creat'':56C ''d'':54C ''data'':17C ''databas'':45C ''dataset'':42C ''describ'':23C ''disclosur'':12C ''encount'':28C ''ensur'':68C ''exact'':25C ''file'':63C ''frequent'':30C ''full'':11C ''good'':71C ''happi'':4C ''hear'':6C ''larg'':41C ''luck'':72C ''p'':1C ''panda'':51C ''preprocess'':38C ''prior'':46C ''pyodbc'':49C ''queri'':65C ''quit'':29C ''realli'':3C ''reproduc'':69C ''scientist'':18C ''suggest'':55C ''tabl'':60C ''therriault'':2C ''true'':35C ''use'':9C ''vertica'':10C ''via'':61C ''view'':58C ''work'':52C,70C ''workflow'':21C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (313, 1, '2014-06-11 13:28:35.98', 19, 1116, 'Books about the "Science" in Data Science?', '&lt;p&gt;What are the books about the science and mathematics behind data science? It feels like so many "data science" books are programming tutorials and don''t touch things like data generating processes and statistical inference. I can already code, what I am weak on is the math/stats/theory behind what I am doing.&lt;/p&gt;

&lt;p&gt;If I am ready to burn $1000 on books (so around 10 books... sigh), what could I buy?&lt;/p&gt;

&lt;p&gt;Examples: Agresti''s &lt;a href="http://rads.stackoverflow.com/amzn/click/0470463635"&gt;Categorical Data Analysis&lt;/a&gt;, &lt;a href="http://rads.stackoverflow.com/amzn/click/1441902996"&gt;Linear Mixed Models for Longitudinal Data&lt;/a&gt;, etc... etc...&lt;/p&gt;
', 'statistics  reference-request', NULL, '''/a'':98C,110C ''/amzn/click/0470463635'':94C ''/amzn/click/1441902996'':103C ''/p'':65C,85C,113C ''10'':78C ''1000'':73C ''agresti'':88C ''alreadi'':50C ''analysi'':97C ''around'':77C ''behind'':22C,60C ''book'':1A,16C,32C,75C,79C ''burn'':72C ''buy'':84C ''categor'':95C ''code'':51C ''could'':82C ''data'':6A,23C,30C,42C,96C,109C ''etc'':111C,112C ''exampl'':87C ''feel'':26C ''generat'':43C ''href'':91C,100C ''infer'':47C ''like'':27C,41C ''linear'':104C ''longitudin'':108C ''mani'':29C ''math/stats/theory'':59C ''mathemat'':21C ''mix'':105C ''model'':106C ''p'':12C,66C,86C ''process'':44C ''program'':34C ''rads.stackoverflow.com'':93C,102C ''rads.stackoverflow.com/amzn/click/0470463635'':92C ''rads.stackoverflow.com/amzn/click/1441902996'':101C ''readi'':70C ''refer'':10B ''reference-request'':9B ''request'':11B ''scienc'':4A,7A,19C,24C,31C ''sigh'':80C ''statist'':8B,46C ''thing'':40C ''touch'':39C ''tutori'':35C ''weak'':55C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (314, 2, '2014-06-11 13:49:08.97', 10, NULL, NULL, '&lt;p&gt;If I could only recomend one to you, it would be: &lt;a href="http://rads.stackoverflow.com/amzn/click/0387848576"&gt;The Elements of Statistical Learning and Prediction&lt;/a&gt; by Hastie, Tibshirani and Friedman.  It provides the math/statistics behind a lot of commonly used techniques in data science.&lt;/p&gt;

&lt;p&gt;For Bayesian Techniques, &lt;a href="http://rads.stackoverflow.com/amzn/click/1439840954"&gt;Bayesian Data Analysis&lt;/a&gt; by Gelman, Carlin, Stern, Dunson, Vehtari and Rubin is excellent.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0534243126"&gt;Statistical Inference&lt;/a&gt; by Casella and Berger is a good graduate-level textbook on the theoretical foundation of statistics.  This book does require a pretty high level of comfort with math (probability theory is based on measure theory, which is not trivial to understand).&lt;/p&gt;

&lt;p&gt;With respect to data generating processes, I don''t have a recommendation for a book.  What I can say is that a good understanding of the assumptions of the techniques used and ensuring that the data was collected or generated in a manner that does not violate those assumptions goes a long way towards a good analysis.&lt;/p&gt;
', NULL, NULL, '''/a'':25C,58C,78C ''/amzn/click/0387848576'':17C ''/amzn/click/0534243126'':75C ''/amzn/click/1439840954'':54C ''/p'':45C,69C,121C,180C ''analysi'':57C,179C ''assumpt'':149C,171C ''base'':111C ''bayesian'':48C,55C ''behind'':35C ''berger'':82C ''book'':97C,137C ''carlin'':61C ''casella'':80C ''collect'':160C ''comfort'':105C ''common'':39C ''could'':4C ''data'':43C,56C,126C,158C ''dunson'':63C ''element'':19C ''ensur'':155C ''excel'':68C ''foundat'':93C ''friedman'':30C ''gelman'':60C ''generat'':127C,162C ''goe'':172C ''good'':85C,145C,178C ''graduat'':87C ''graduate-level'':86C ''hasti'':27C ''high'':102C ''href'':14C,51C,72C ''infer'':77C ''learn'':22C ''level'':88C,103C ''long'':174C ''lot'':37C ''manner'':165C ''math'':107C ''math/statistics'':34C ''measur'':113C ''one'':7C ''p'':1C,46C,70C,122C ''predict'':24C ''pretti'':101C ''probabl'':108C ''process'':128C ''provid'':32C ''rads.stackoverflow.com'':16C,53C,74C ''rads.stackoverflow.com/amzn/click/0387848576'':15C ''rads.stackoverflow.com/amzn/click/0534243126'':73C ''rads.stackoverflow.com/amzn/click/1439840954'':52C ''recomend'':6C ''recommend'':134C ''requir'':99C ''respect'':124C ''rubin'':66C ''say'':141C ''scienc'':44C ''statist'':21C,76C,95C ''stern'':62C ''techniqu'':41C,49C,152C ''textbook'':89C ''theoret'':92C ''theori'':109C,114C ''tibshirani'':28C ''toward'':176C ''trivial'':118C ''understand'':120C,146C ''use'':40C,153C ''vehtari'':64C ''violat'':169C ''way'':175C ''would'':11C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (328, 2, '2014-06-12 08:30:49.757', 5, NULL, NULL, '&lt;p&gt;There is no "better" language. I have tried both of them and I am comfortable with Python so I work with Python only. Though I am still learning stuff, but I haven''t encounter any roadblock with Python till now. The good thing about Python is community is too good and you can get a lot of help on the Internet easily. Other than that, I would say go with the language you like not the one people recommend. &lt;/p&gt;
', NULL, NULL, '''/p'':81C ''better'':5C ''comfort'':16C ''communiti'':48C ''easili'':63C ''encount'':35C ''get'':55C ''go'':70C ''good'':43C,51C ''haven'':33C ''help'':59C ''internet'':62C ''languag'':6C,73C ''learn'':29C ''like'':75C ''lot'':57C ''one'':78C ''p'':1C ''peopl'':79C ''python'':18C,23C,39C,46C ''recommend'':80C ''roadblock'':37C ''say'':69C ''still'':28C ''stuff'':30C ''thing'':44C ''though'':25C ''till'':40C ''tri'':9C ''work'':21C ''would'':68C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (317, 2, '2014-06-11 16:56:59.66', 1, NULL, NULL, '&lt;p&gt;If you already know R Studio, then the caret package is a good place to start. Here are some tutorials: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="https://class.coursera.org/predmachlearn-002" rel="nofollow"&gt;https://class.coursera.org/predmachlearn-002&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://caret.r-forge.r-project.org/index.html" rel="nofollow"&gt;http://caret.r-forge.r-project.org/index.html&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;With R and caret you can easily load and splice data sets, feature reduction, principal component analysis, and train and predict using various algorithms.&lt;/p&gt;
', NULL, NULL, '''/index.html'':40C ''/index.html&lt;/a&gt;&lt;/li&gt;'':45C ''/ol'':46C ''/p'':22C,72C ''/predmachlearn-002'':29C ''/predmachlearn-002&lt;/a&gt;&lt;/li&gt;'':34C ''algorithm'':71C ''alreadi'':4C ''analysi'':64C ''caret'':10C,51C ''caret.r-forge.r-project.org'':39C,44C ''caret.r-forge.r-project.org/index.html'':38C ''caret.r-forge.r-project.org/index.html&lt;/a&gt;&lt;/li&gt;'':43C ''class.coursera.org'':28C,33C ''class.coursera.org/predmachlearn-002'':27C ''class.coursera.org/predmachlearn-002&lt;/a&gt;&lt;/li&gt;'':32C ''compon'':63C ''data'':58C ''easili'':54C ''featur'':60C ''good'':14C ''href'':26C,37C ''know'':5C ''li'':24C,35C ''load'':55C ''nofollow'':31C,42C ''ol'':23C ''p'':1C,47C ''packag'':11C ''place'':15C ''predict'':68C ''princip'':62C ''r'':6C,49C ''reduct'':61C ''rel'':30C,41C ''set'':59C ''splice'':57C ''start'':17C ''studio'':7C ''train'':66C ''tutori'':21C ''use'':69C ''various'':70C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (318, 2, '2014-06-11 17:24:53.61', 1, NULL, NULL, '&lt;p&gt;If you can reproduce the 6x3 grid of graphs from the banner of the &lt;a href="http://scikit-learn.org/" rel="nofollow"&gt;http://scikit-learn.org/&lt;/a&gt; page then you will have learnt some ML and some Python. You didn''t mention a language. Python is easy enough to learn very quickly, and scikit-learn has a wide range of algorithms implemented.&lt;/p&gt;

&lt;p&gt;Then try on your own data!&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':23C ''/p'':60C,68C ''6x3'':7C ''algorithm'':58C ''banner'':13C ''data'':67C ''didn'':36C ''easi'':43C ''enough'':44C ''graph'':10C ''grid'':8C ''href'':17C ''implement'':59C ''languag'':40C ''learn'':46C,52C ''learnt'':29C ''mention'':38C ''ml'':31C ''nofollow'':20C ''p'':1C,61C ''page'':24C ''python'':34C,41C ''quick'':48C ''rang'':56C ''rel'':19C ''reproduc'':5C ''scikit'':51C ''scikit-learn'':50C ''scikit-learn.org'':18C,22C ''scikit-learn.org/&lt;/a&gt;'':21C ''tri'':63C ''wide'':55C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (319, 1, '2014-06-11 18:22:36.267', 6, 739, 'Debugging Neural Networks', '&lt;p&gt;I''ve built an artificial neural network in python using the scipy.optimize.minimize (Conjugate gradient) optimization function.&lt;/p&gt;

&lt;p&gt;I''ve implemented gradient checking, double checked everything etc and I''m pretty certain it''s working correctly.&lt;/p&gt;

&lt;p&gt;I''ve run it a few times and it reaches ''Optimization terminated successfully'' however when I increase the number of hidden layers, the cost of the hypothesis increases (everything else is kept the same) after it has successfully terminated.&lt;/p&gt;

&lt;p&gt;Intuitively it feels as if the cost should decrease when the number of hidden layers is increased, as it is able to generate a more complex hypothesis which can fit the data better, however this appears not to be the case.&lt;/p&gt;

&lt;p&gt;I''d be interested to understand what''s going on here, or if I''ve implemented neural net incorrectly?&lt;/p&gt;
', 'machine-learning  python  neuralnetwork', NULL, '''/p'':26C,46C,87C,130C,151C ''abl'':109C ''appear'':124C ''artifici'':14C ''better'':121C ''built'':12C ''case'':129C ''certain'':41C ''check'':32C,34C ''complex'':114C ''conjug'':22C ''correct'':45C ''cost'':71C,95C ''d'':133C ''data'':120C ''debug'':1A ''decreas'':97C ''doubl'':33C ''els'':77C ''etc'':36C ''everyth'':35C,76C ''feel'':91C ''fit'':118C ''function'':25C ''generat'':111C ''go'':140C ''gradient'':23C,31C ''hidden'':68C,102C ''howev'':61C,122C ''hypothesi'':74C,115C ''implement'':30C,147C ''incorrect'':150C ''increas'':64C,75C,105C ''interest'':135C ''intuit'':89C ''kept'':79C ''layer'':69C,103C ''learn'':6B ''m'':39C ''machin'':5B ''machine-learn'':4B ''net'':149C ''network'':3A,16C ''neural'':2A,15C,148C ''neuralnetwork'':8B ''number'':66C,100C ''optim'':24C,58C ''p'':9C,27C,47C,88C,131C ''pretti'':40C ''python'':7B,18C ''reach'':57C ''run'':50C ''scipy.optimize.minimize'':21C ''success'':60C,85C ''termin'':59C,86C ''time'':54C ''understand'':137C ''use'':19C ''ve'':11C,29C,49C,146C ''work'':44C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (320, 2, '2014-06-11 20:34:51.873', 7, NULL, NULL, '&lt;p&gt;There are so many ways to go wrong with a neural net that it''s going to be difficult to debug. Also, to address your intuition, each additional hidden layer makes learning much harder. With that said, here are some possibilities:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;You have added weight decay. Adding more layers adds more weights which increases your regularization cost.&lt;/li&gt;
&lt;li&gt;The problem is simple enough that a model with a single hidden layer is sufficient. Adding more hidden layers makes it harder for the network to learn (harder optimization problem).&lt;/li&gt;
&lt;li&gt;The optimization method is not doing a great job (I prefer climin to scipy.optimize).&lt;/li&gt;
&lt;li&gt;You are using the sigmoid/tanh activation function. The sigmoid function causes the vanishing gradient problem which makes learning hard with more layers. Try using the ReLu function.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Training neural nets takes a lot of practice, luck, and patience. Good luck.&lt;/p&gt;
', NULL, NULL, '''/li'':62C,94C,110C,139C ''/ol'':140C ''/p'':43C,155C ''activ'':117C ''ad'':48C,51C,79C ''add'':54C ''addit'':29C ''address'':25C ''also'':23C ''caus'':122C ''climin'':107C ''cost'':61C ''debug'':22C ''decay'':50C ''difficult'':20C ''enough'':68C ''function'':118C,121C,138C ''go'':8C,17C ''good'':153C ''gradient'':125C ''great'':103C ''hard'':130C ''harder'':35C,85C,91C ''hidden'':30C,75C,81C ''increas'':58C ''intuit'':27C ''job'':104C ''layer'':31C,53C,76C,82C,133C ''learn'':33C,90C,129C ''li'':45C,63C,95C,111C ''lot'':147C ''luck'':150C,154C ''make'':32C,83C,128C ''mani'':5C ''method'':98C ''model'':71C ''much'':34C ''net'':13C,144C ''network'':88C ''neural'':12C,143C ''ol'':44C ''optim'':92C,97C ''p'':1C,141C ''patienc'':152C ''possibl'':42C ''practic'':149C ''prefer'':106C ''problem'':65C,93C,126C ''regular'':60C ''relu'':137C ''said'':38C ''scipy.optimize'':109C ''sigmoid'':120C ''sigmoid/tanh'':116C ''simpl'':67C ''singl'':74C ''suffici'':78C ''take'':145C ''train'':142C ''tri'':134C ''use'':114C,135C ''vanish'':124C ''way'':6C ''weight'':49C,56C ''wrong'':9C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (323, 1, '2014-06-11 23:52:37.823', 4, 796, 'How can we calculate AUC for a simple decision tree?', '&lt;p&gt;The setup is simple: binary classification using a simple decision tree, each node of the tree has a single threshold applied on a single feature. In general, building a ROC curve requires moving a decision threshold over different values and computing the effect of that change on the true positive rate and the false positives rate of predictions. What''s that decision threshold in the case of a simple fixed decision tree? &lt;/p&gt;
', 'machine-learning', NULL, '''/p'':87C ''appli'':35C ''auc'':5A ''binari'':19C ''build'':42C ''calcul'':4A ''case'':80C ''chang'':60C ''classif'':20C ''comput'':55C ''curv'':45C ''decis'':9A,24C,49C,76C,85C ''differ'':52C ''effect'':57C ''fals'':68C ''featur'':39C ''fix'':84C ''general'':41C ''learn'':13B ''machin'':12B ''machine-learn'':11B ''move'':47C ''node'':27C ''p'':14C ''posit'':64C,69C ''predict'':72C ''rate'':65C,70C ''requir'':46C ''roc'':44C ''setup'':16C ''simpl'':8A,18C,23C,83C ''singl'':33C,38C ''threshold'':34C,50C,77C ''tree'':10A,25C,30C,86C ''true'':63C ''use'':21C ''valu'':53C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (489, 2, '2014-06-19 18:05:51.07', 14, NULL, NULL, '&lt;p&gt;A GLM is absolutely a statistical model, but statistical models and machine learning techniques are not mutually exclusive. In general, statistics is more concerned with inferring parameters, whereas in machine learning, prediction is the ultimate goal.&lt;/p&gt;
', NULL, NULL, '''/p'':38C ''absolut'':5C ''concern'':25C ''exclus'':19C ''general'':21C ''glm'':3C ''goal'':37C ''infer'':27C ''learn'':14C,32C ''machin'':13C,31C ''model'':8C,11C ''mutual'':18C ''p'':1C ''paramet'':28C ''predict'':33C ''statist'':7C,10C,22C ''techniqu'':15C ''ultim'':36C ''wherea'':29C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (332, 2, '2014-06-12 10:27:14.48', 1, NULL, NULL, '&lt;p&gt;In order to build the ROC curve and AUC (Area under curve) you have to have a binary classifier which provides you at classification time, the distribution (or at least a score), not the classification label. To give you an example, suppose you have a binary classification model, with classes c1 and c2. For a given instance, your classifier would have to return a score for c1 and another for c2. If this score is a probability-like (preferrable), than something like p(c1), p(c2) would work. In plain English is translated like "I (the model) classify this instance as c1 with probability p(c1), and as c2 with probability  p(c2)=1-p(c1)". &lt;/p&gt;

&lt;p&gt;This applies for all type of classifiers, not only for decision trees. Having these scores you can than compute ROC or AUC by varying a threshold on p(c1) values, from the smallest to the greatest value.&lt;/p&gt;

&lt;p&gt;Now, if you have an implementation of a decision tree and you want to change that implementation to return scores instead of labels you have to compute those values. The most used way for decision trees is to use the proportion of classes from the leaf nodes. So, for example you have built a decision tree and when you predict the class for an instance you arrive at a leaf node which have (stored from the learning phase) 10 instances of class c1 and 15 instances of class c2, you can use the ratios as the scores. So, in our example, you would return p(c1) = 10 / (10+15) = 0.4 probability of class c1 and p(c2) = 15/(10+15)=0.6 probability of being class c2.&lt;/p&gt;

&lt;p&gt;For further reading on the ROC curves, the best and inspiring source of information I found to be the Tom Fawcett paper called &lt;a href="https://www.google.ie/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;ved=0CCsQFjAA&amp;amp;url=https://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf&amp;amp;ei=DYCZU96QGPOV7AaAx4CgBA&amp;amp;usg=AFQjCNECLoecin6ieT-0ymLQ--FoMjkMZw&amp;amp;sig2=3bGE2y1N3I2Re_YWn0EDFA" rel="nofollow"&gt;An Introduction to ROC Analysis&lt;/a&gt;, it''s solid gold on this topic.&lt;/p&gt;
', NULL, NULL, '''+15'':268C,279C ''/a'':323C ''/p'':119C,158C,286C,331C ''/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;ved=0ccsqfjaa&amp;amp;url=https://ccrma.stanford.edu/workshops/mir2009/references/rocintro.pdf&amp;amp;ei=dyczu96qgpov7aaax4cgba&amp;amp;usg=afqjcnecloecin6iet-0ymlq--fomjkmzw&amp;amp;sig2=3bge2y1n3i2re_ywn0edfa'':315C ''0.4'':269C ''0.6'':280C ''1'':116C ''10'':238C,266C,267C,278C ''15'':244C,277C ''analysi'':322C ''anoth'':70C ''appli'':122C ''area'':11C ''arriv'':226C ''auc'':10C,142C ''best'':296C ''binari'':19C,47C ''build'':5C ''built'':212C ''c1'':52C,68C,86C,104C,108C,118C,149C,242C,265C,273C ''c2'':54C,72C,88C,111C,115C,248C,276C,285C ''call'':310C ''chang'':174C ''class'':51C,202C,221C,241C,247C,272C,284C ''classif'':25C,36C,48C ''classifi'':20C,60C,100C,127C ''comput'':139C,186C ''curv'':8C,13C,294C ''decis'':131C,168C,194C,214C ''distribut'':28C ''english'':93C ''exampl'':42C,209C,260C ''fawcett'':308C ''found'':303C ''give'':39C ''given'':57C ''gold'':327C ''greatest'':156C ''href'':312C ''implement'':165C,176C ''inform'':301C ''inspir'':298C ''instanc'':58C,102C,224C,239C,245C ''instead'':180C ''introduct'':319C ''label'':37C,182C ''leaf'':205C,229C ''learn'':236C ''least'':31C ''like'':80C,84C,96C ''model'':49C,99C ''node'':206C,230C ''nofollow'':317C ''order'':3C ''p'':1C,85C,87C,107C,114C,117C,120C,148C,159C,264C,275C,287C ''paper'':309C ''phase'':237C ''plain'':92C ''predict'':219C ''preferr'':81C ''probability-lik'':78C ''probabl'':79C,106C,113C,270C,281C ''proport'':200C ''provid'':22C ''ratio'':253C ''read'':290C ''rel'':316C ''return'':64C,178C,263C ''roc'':7C,140C,293C,321C ''score'':33C,66C,75C,135C,179C,256C ''smallest'':153C ''solid'':326C ''someth'':83C ''sourc'':299C ''store'':233C ''suppos'':43C ''threshold'':146C ''time'':26C ''tom'':307C ''topic'':330C ''translat'':95C ''tree'':132C,169C,195C,215C ''type'':125C ''use'':191C,198C,251C ''valu'':150C,157C,188C ''vari'':144C ''want'':172C ''way'':192C ''work'':90C ''would'':61C,89C,262C ''www.google.ie'':314C ''www.google.ie/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;ved=0ccsqfjaa&amp;amp;url=https://ccrma.stanford.edu/workshops/mir2009/references/rocintro.pdf&amp;amp;ei=dyczu96qgpov7aaax4cgba&amp;amp;usg=afqjcnecloecin6iet-0ymlq--fomjkmzw&amp;amp;sig2=3bge2y1n3i2re_ywn0edfa'':313C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (334, 1, '2014-06-12 10:52:03.41', 23, 4782, 'What do you think of Data Science certifications?', '&lt;p&gt;I''ve now seen two data science certification programs - the &lt;a href="https://www.coursera.org/specialization/jhudatascience/1?utm_medium=listingPage"&gt;John Hopkins one available at Coursera&lt;/a&gt; and the &lt;a href="http://cloudera.com/content/cloudera/en/training/certification/ccp-ds.html"&gt;Cloudera one&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I''m sure there are others out there.&lt;/p&gt;

&lt;p&gt;The John Hopkins set of classes is focused on R as a toolset, but covers a range of topics:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;R Programming&lt;/li&gt;
&lt;li&gt;cleaning and obtaining data&lt;/li&gt;
&lt;li&gt;Data Analysis&lt;/li&gt;
&lt;li&gt;Reproducible Research&lt;/li&gt;
&lt;li&gt;Statistical Inference&lt;/li&gt;
&lt;li&gt;Regression Models&lt;/li&gt;
&lt;li&gt;Machine Learning&lt;/li&gt;
&lt;li&gt;Developing Data Products&lt;/li&gt;
&lt;li&gt;And what looks to be a Project based completion task similar to Cloudera''s Data Science Challenge&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The Cloudera program looks thin on the surface, but looks to answer the two important questions - "Do you know the tools", "Can you apply the tools in the real world".  Their program consists of:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Introduction to Data Science&lt;/li&gt;
&lt;li&gt;Data Science Essentials Exam&lt;/li&gt;
&lt;li&gt;Data Science Challenge (a real world data science project scenario)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I am not looking for a recommendation on a program or a quality comparison.&lt;/p&gt;

&lt;p&gt;I am curious about other certifications out there, the topics they cover, and how seriously DS certifications are viewed at this point by the community.&lt;/p&gt;

&lt;p&gt;EDIT: These are all great answers.  I''m choosing the correct answer by votes.&lt;/p&gt;
', 'education', NULL, '''/a'':32C,42C ''/content/cloudera/en/training/certification/ccp-ds.html'':39C ''/li'':79C,85C,89C,93C,97C,101C,105C,110C,129C,173C,179C,191C ''/p'':43C,53C,74C,166C,208C,235C,251C ''/specialization/jhudatascience/1?utm_medium=listingpage'':25C ''/ul'':130C,192C ''analysi'':88C ''answer'':143C,242C,248C ''appli'':155C ''avail'':29C ''base'':119C ''certif'':8A,18C,215C,226C ''challeng'':128C,183C ''choos'':245C ''class'':60C ''clean'':81C ''cloudera'':40C,124C,133C ''cloudera.com'':38C ''cloudera.com/content/cloudera/en/training/certification/ccp-ds.html'':37C ''communiti'':234C ''comparison'':207C ''complet'':120C ''consist'':164C ''correct'':247C ''coursera'':31C ''cover'':69C,221C ''curious'':212C ''data'':6A,16C,84C,87C,108C,126C,171C,175C,181C,187C ''develop'':107C ''ds'':225C ''edit'':237C ''educ'':9B ''essenti'':177C ''exam'':178C ''focus'':62C ''great'':241C ''hopkin'':27C,57C ''href'':22C,36C ''import'':146C ''infer'':96C ''introduct'':169C ''john'':26C,56C ''know'':150C ''learn'':104C ''li'':76C,80C,86C,90C,94C,98C,102C,106C,111C,168C,174C,180C ''look'':114C,135C,141C,197C ''m'':46C,244C ''machin'':103C ''model'':100C ''obtain'':83C ''one'':28C,41C ''other'':50C ''p'':10C,44C,54C,131C,193C,209C,236C ''point'':231C ''product'':109C ''program'':19C,78C,134C,163C,203C ''project'':118C,189C ''qualiti'':206C ''question'':147C ''r'':64C,77C ''rang'':71C ''real'':160C,185C ''recommend'':200C ''regress'':99C ''reproduc'':91C ''research'':92C ''scenario'':190C ''scienc'':7A,17C,127C,172C,176C,182C,188C ''seen'':14C ''serious'':224C ''set'':58C ''similar'':122C ''statist'':95C ''sure'':47C ''surfac'':139C ''task'':121C ''thin'':136C ''think'':4A ''tool'':152C,157C ''toolset'':67C ''topic'':73C,219C ''two'':15C,145C ''ul'':75C,167C ''ve'':12C ''view'':228C ''vote'':250C ''world'':161C,186C ''www.coursera.org'':24C ''www.coursera.org/specialization/jhudatascience/1?utm_medium=listingpage'':23C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (335, 2, '2014-06-12 11:11:35.6', 8, NULL, NULL, '&lt;p&gt;The certification programs you mentioned are really entry level courses. Personally, I think these certificates show only person''s persistence and they can be only useful to those who is applying for internships, not the real data science jobs.&lt;/p&gt;
', NULL, NULL, '''/p'':41C ''appli'':32C ''certif'':3C,16C ''cours'':11C ''data'':38C ''entri'':9C ''internship'':34C ''job'':40C ''level'':10C ''mention'':6C ''p'':1C ''persist'':21C ''person'':12C,19C ''program'':4C ''real'':37C ''realli'':8C ''scienc'':39C ''show'':17C ''think'':14C ''use'':27C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (336, 2, '2014-06-12 11:30:20.943', 7, NULL, NULL, '&lt;p&gt;There isn''t a silver bullet language that can be used to solve each and every data related problem. The language choice depends on the context of the problem, size of data and if you are working at a workplace you have to stick to what they use.&lt;/p&gt;

&lt;p&gt;Personally I use R more often than Python due to its visualization libraries and interactive style. But if I need more performance or structured code I definitely use Python since it has some of the best libraries as SciKit-Learn, numpy, scipy etc. I use both R and Python in my projects interchangeably. &lt;/p&gt;

&lt;p&gt;So if you are starting on data science work I suggest you to learn both and it''s not difficult since Python also provides a similar interface to R with &lt;a href="http://pandas.pydata.org/"&gt;Pandas&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;If you have to deal with much larger datasets, you can''t escape eco-systems built with Java(Hadoop, Pig, Hbase etc).&lt;/p&gt;
', NULL, NULL, '''/a'':142C ''/p'':50C,106C,143C,168C ''also'':130C ''best'':87C ''built'':161C ''bullet'':7C ''choic'':23C ''code'':76C ''context'':27C ''data'':18C,33C,114C ''dataset'':153C ''deal'':149C ''definit'':78C ''depend'':24C ''difficult'':127C ''due'':60C ''eco'':159C ''eco-system'':158C ''escap'':157C ''etc'':95C,167C ''everi'':17C ''hadoop'':164C ''hbase'':166C ''href'':139C ''interact'':66C ''interchang'':105C ''interfac'':134C ''isn'':3C ''java'':163C ''languag'':8C,22C ''larger'':152C ''learn'':92C,121C ''librari'':64C,88C ''much'':151C ''need'':71C ''numpi'':93C ''often'':57C ''p'':1C,51C,107C,144C ''panda'':141C ''pandas.pydata.org'':140C ''perform'':73C ''person'':52C ''pig'':165C ''problem'':20C,30C ''project'':104C ''provid'':131C ''python'':59C,80C,101C,129C ''r'':55C,99C,136C ''relat'':19C ''scienc'':115C ''scikit'':91C ''scikit-learn'':90C ''scipi'':94C ''silver'':6C ''similar'':133C ''sinc'':81C,128C ''size'':31C ''solv'':14C ''start'':112C ''stick'':45C ''structur'':75C ''style'':67C ''suggest'':118C ''system'':160C ''use'':12C,49C,54C,79C,97C ''visual'':63C ''work'':38C,116C ''workplac'':41C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (337, 2, '2014-06-12 11:54:59.14', 9, NULL, NULL, '&lt;p&gt;I would add to what others have said till now. There is no single answer that one language is better than other.&lt;/p&gt;

&lt;p&gt;Having said that, R has a better community for data exploration and learning. It has extensive visualization capabilities. Python, on the other hand, has become better at data handling since introduction of pandas. Learning and development time is very less in Python, as compared to R (R being a low level language).&lt;/p&gt;

&lt;p&gt;I think it ultimately boils down to the eco-system you are in and personal preferences. For more details, you can look at this comparison &lt;a href="http://www.analyticsvidhya.com/blog/2014/03/sas-vs-vs-python-tool-learn/"&gt;here&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':112C ''/blog/2014/03/sas-vs-vs-python-tool-learn/'':110C ''/p'':24C,78C,113C ''add'':4C ''answer'':16C ''becom'':50C ''better'':21C,32C,51C ''boil'':84C ''capabl'':43C ''communiti'':33C ''compar'':69C ''comparison'':105C ''data'':35C,53C ''detail'':99C ''develop'':61C ''eco'':89C ''eco-system'':88C ''explor'':36C ''extens'':41C ''hand'':48C ''handl'':54C ''href'':107C ''introduct'':56C ''languag'':19C,77C ''learn'':38C,59C ''less'':65C ''level'':76C ''look'':102C ''low'':75C ''one'':18C ''other'':7C ''p'':1C,25C,79C ''panda'':58C ''person'':95C ''prefer'':96C ''python'':44C,67C ''r'':29C,71C,72C ''said'':9C,27C ''sinc'':55C ''singl'':15C ''system'':90C ''think'':81C ''till'':10C ''time'':62C ''ultim'':83C ''visual'':42C ''would'':3C ''www.analyticsvidhya.com'':109C ''www.analyticsvidhya.com/blog/2014/03/sas-vs-vs-python-tool-learn/'':108C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (338, 2, '2014-06-12 12:13:26.94', 9, NULL, NULL, '&lt;p&gt;I did the first 2 courses and I''m planning to do all the others too.  If you don''t know R, it''s a really good program. There are assignments and quizzes every week. Many people find some courses very difficult. You are going to have hard time if you don''t have any programming experience (even if they say it''s not required). &lt;/p&gt;

&lt;p&gt;Just remember.. it''s not because you can drive a car that you are a F1 pilot ;) &lt;/p&gt;
', NULL, NULL, '''..'':71C ''/p'':67C,87C ''2'':6C ''assign'':32C ''car'':80C ''cours'':7C,41C ''difficult'':43C ''drive'':78C ''even'':59C ''everi'':35C ''experi'':58C ''f1'':85C ''find'':39C ''first'':5C ''go'':46C ''good'':28C ''hard'':49C ''know'':22C ''m'':10C ''mani'':37C ''other'':16C ''p'':1C,68C ''peopl'':38C ''pilot'':86C ''plan'':11C ''program'':29C,57C ''quizz'':34C ''r'':23C ''realli'':27C ''rememb'':70C ''requir'':66C ''say'':62C ''time'':50C ''week'':36C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (339, 2, '2014-06-12 12:59:00.663', 34, NULL, NULL, '&lt;p&gt;Some real important differences to consider when you are choosing &lt;strong&gt;R&lt;/strong&gt; or &lt;strong&gt;Python&lt;/strong&gt; over one another:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Machine Learning&lt;/strong&gt; has 2 phases. Model Building and Prediction phase. Typically, model building is performed as a batch process and &lt;strong&gt;predictions are done realtime&lt;/strong&gt;. The model building process is a compute intensive process while the prediction happens in a jiffy. Therefore, performance of an algorithm in Python or R doesn''t really affect the turn-around time of the user. Python 1, R 1.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Production:&lt;/strong&gt; The real difference between Python and R comes in being production ready. Python, as such is a full fledged programming language and many organisations use it in their production systems. R is a statistical programming software favoured by many academia and due to the rise in data science and availability of libraries and being open source, the industry has started using R. Many of these organisations have their production systems either in Java, C++, C#, Python etc. So, ideally they would like to have the &lt;strong&gt;prediction system&lt;/strong&gt; in the same language to reduce the latency and maintenance issues.
Python 2, R 1.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Libraries:&lt;/strong&gt; Both the languages have enormous and reliable libraries. R has over 5000 libraries catering to many domains while Python has some incredible packages like &lt;strong&gt;Pandas, NumPy, SciPy, Scikit Learn, Matplotlib&lt;/strong&gt;. Python 3, R 2.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Development:&lt;/strong&gt; Both the language are interpreted languages. Many say that python has a good learning curve, it''s almost like reading english (to put it on a lighter note) but R has a reputation of having a steeper learning curve. Also, both of them have good IDEs (Spyder etc for Python and RStudio for R). Python 4, R 2.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Speed:&lt;/strong&gt; R software initially had problems with large computations (say, like nxn matrix multiplications). But, this issue is addressed with the introduction of R by Revolution Analytics. They have re-written computation intensive operations in C which is blazingly fast. Python being a high level language is relatively slow. Python 4, R 3.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Visualizations:&lt;/strong&gt; In data science, we frequently tend to plot data to showcase patterns to users. Therefore, visualisations become an important criteria in choosing a software and R completely kills Python in this regard. Thanks to Hadley Wickham for an incredible ggplot2 package. R wins hands down. Python 4, R 4.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Dealing with Big Data:&lt;/strong&gt; One of the constraints of R is it stores the data in system memory (RAM). So, RAM capacity becomes a constraint when you are handling Big Data. Python does well, but I would say, as both R and Python have HDFS connectors, leveraging Hadoop infrastructure would give substantial performance improvement. So, Python 5, R 5.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So, both the languages are equally good. Therefore, depending upon your domain and the place you work, you have to smartly choose the right language. The technology world usually prefers using a single language. Business users (marketing analytics, retail analytics) usually go with statistical programming languages like R, since they frequently do quick prototyping and build visualisations (which is faster done in R than Python).&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (355, 2, '2014-06-13 10:08:14.087', 11, NULL, NULL, '&lt;p&gt;The downvotes are because of the topic, but I''ll attempt to answer your question as best I can since it''s here.&lt;/p&gt;

&lt;p&gt;Data science is a term that is thrown around as loosely as Big Data.  Everyone has a rough idea of what they mean by the term, but when you look at the actual work tasks, a data scientist''s responsibilities will vary greatly from company to company.&lt;/p&gt;

&lt;p&gt;Statistical analysis could encompass the entirety of the workload in one job, and not even be a consideration for another.&lt;/p&gt;

&lt;p&gt;I wouldn''t chase after a job title per se.  If you are interested in the field, network (like you are doing now) and find a good fit.  If you are perusing job ads, just look for the ones that stress statistical and informatics backgrounds.  Hadoop and SQL are both easy to become familiar with given the time and motivation, but I would stick with the areas you are strongest in and go from there.&lt;/p&gt;
', NULL, NULL, '''/p'':25C,74C,96C,173C ''actual'':59C ''ad'':131C ''analysi'':77C ''anoth'':95C ''answer'':14C ''area'':164C ''around'':35C ''attempt'':12C ''background'':142C ''becom'':150C ''best'':18C ''big'':39C ''chase'':101C ''compani'':71C,73C ''consider'':93C ''could'':78C ''data'':27C,40C,63C ''downvot'':3C ''easi'':148C ''encompass'':79C ''entireti'':81C ''even'':90C ''everyon'':41C ''familiar'':151C ''field'':114C ''find'':122C ''fit'':125C ''given'':153C ''go'':170C ''good'':124C ''great'':69C ''hadoop'':143C ''idea'':45C ''informat'':141C ''interest'':111C ''job'':87C,104C,130C ''like'':116C ''ll'':11C ''look'':56C,133C ''loos'':37C ''mean'':49C ''motiv'':157C ''network'':115C ''one'':86C,136C ''p'':1C,26C,75C,97C ''per'':106C ''perus'':129C ''question'':16C ''respons'':66C ''rough'':44C ''scienc'':28C ''scientist'':64C ''se'':107C ''sinc'':21C ''sql'':145C ''statist'':76C,139C ''stick'':161C ''stress'':138C ''strongest'':167C ''task'':61C ''term'':31C,52C ''thrown'':34C ''time'':155C ''titl'':105C ''topic'':8C ''vari'':68C ''work'':60C ''workload'':84C ''would'':160C ''wouldn'':99C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (446, 2, '2014-06-17 23:38:20.133', 9, NULL, NULL, '&lt;p&gt;One of the references I mentioned in the OP led me to a potential solution that seems quite powerful, described in "Privacy-preserving record linkage using Bloom filters" (&lt;a href="http://www.biomedcentral.com/1472-6947/9/41"&gt;doi:10.1186/1472-6947-9-41&lt;/a&gt;):&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;A new protocol for privacy-preserving record linkage with encrypted identifiers allowing for errors in identifiers has been developed. The protocol is based on Bloom filters on q-grams of identifiers.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The article goes into detail about the method, which I will summarize here to the best of my ability.&lt;/p&gt;

&lt;p&gt;A Bloom filter is a fixed-length series of bits storing the results of a fixed set of independent hash functions, each computed on the same input value. The output of each hash function should be an index value from among the possible indexes in the filter; i.e., if you have a 0-indexed series of 10 bits, hash functions should return (or be mapped to) values from 0 to 9. &lt;/p&gt;

&lt;p&gt;The filter starts with each bit set to 0. After hashing the input value with each function from the set of hash functions, each bit corresponding to an index value returned by any hash function is set to 1. If the same index is returned by more than one hash function, the bit at that index is only set once. You could consider the Bloom filter to be a superposition of the set of hashes onto the fixed range of bits.&lt;/p&gt;

&lt;p&gt;The protocol described in the above-linked article divides strings into n-grams, which are in this case sets of characters. As an example, &lt;code&gt;"hello"&lt;/code&gt; might yield the following set of 2-grams:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;["_h", "he", "el", "ll", "lo", "o_"]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Padding the front and back with spaces seems to be generally optional when constructing n-grams; the examples given in the paper that proposes this method use such padding.&lt;/p&gt;

&lt;p&gt;Each n-gram can be hashed to produce a Bloom filter, and this set of Bloom filters can be superimposed on itself (bitwise OR operation) to produce the Bloom filter for the string.&lt;/p&gt;

&lt;p&gt;If the filter contains many more bits than there are hash functions or n-grams, arbitrary strings are relatively unlikely to produce exactly the same filter. However, the more n-grams two strings have in common, the more bits their filters will ultimately share. You can then compare any two filters &lt;code&gt;A, B&lt;/code&gt; by means of their Dice coefficient:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;D&lt;sub&gt;A, B&lt;/sub&gt; = 2h / (a + b)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Where &lt;code&gt;h&lt;/code&gt; is the number of bits that are set to 1 in both filters, &lt;code&gt;a&lt;/code&gt; is the number of bits set to 1 in &lt;em&gt;only&lt;/em&gt; filter A, and &lt;code&gt;b&lt;/code&gt; is the number of bits set to 1 in &lt;em&gt;only&lt;/em&gt; filter B. If the strings are exactly the same, the Dice coefficient will be 1; the more they differ, the closer the coefficient will be to &lt;code&gt;0&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Because the hash functions are mapping an indeterminate number of unique inputs to a small number of possible bit indexes, different inputs may produce the same filter, so the coefficient indicates only a &lt;em&gt;probability&lt;/em&gt; that the strings are the same or similar. The number of different hash functions and the number of bits in the filter are important parameters for determining the likelihood of false positives - pairs of inputs that are much less similar than the Dice coefficient produced by this method predicts.&lt;/p&gt;

&lt;p&gt;I found &lt;a href="http://billmill.org/bloomfilter-tutorial/"&gt;this tutorial&lt;/a&gt; to be very helpful for understanding the Bloom filter.&lt;/p&gt;

&lt;p&gt;There is some flexibility in the implementation of this method; see also &lt;a href="https://www.uni-due.de/~hq0215/documents/2010/Bachteler_2010_An_Empirical_Comparison_Of_Approaches_To_Approximate_String_Matching_In_Private_Record_Linkage.pdf"&gt;this 2010 paper&lt;/a&gt; (also linked at the end of the question) for some indications of how performant it is in relation to other methods, and with various parameters.&lt;/p&gt;
', NULL, NULL, '''/1472-6947-9-41'':38C ''/1472-6947/9/41'':35C ''/a'':39C,628C,660C ''/blockquote'':77C,449C ''/bloomfilter-tutorial/'':625C ''/code'':285C,303C,430C,454C,470C,488C,529C ''/em'':482C,500C,567C ''/p'':40C,76C,98C,172C,255C,294C,336C,372C,437C,448C,530C,617C,638C,686C ''/pre'':304C ''/sub'':444C ''/~hq0215/documents/2010/bachteler_2010_an_empirical_comparison_of_approaches_to_approximate_string_matching_in_private_record_linkage.pdf'':656C ''0'':153C,169C,182C,528C ''1'':212C,464C,478C,496C,515C ''10'':157C ''10.1186'':37C ''2'':292C ''2010'':658C ''2h'':445C ''9'':171C ''abil'':97C ''above-link'':262C ''allow'':55C ''also'':651C,661C ''among'':141C ''arbitrari'':390C ''articl'':80C,265C ''b'':429C,443C,447C,487C,502C ''back'':310C ''base'':66C ''best'':94C ''billmill.org'':624C ''billmill.org/bloomfilter-tutorial/'':623C ''bit'':110C,158C,179C,198C,226C,254C,380C,414C,459C,475C,493C,550C,586C ''bitwis'':361C ''blockquot'':41C,438C ''bloom'':29C,68C,101C,238C,348C,354C,367C,636C ''case'':276C ''charact'':279C ''closer'':521C ''code'':283C,296C,427C,452C,468C,486C,527C ''coeffici'':436C,512C,523C,561C,611C ''common'':411C ''compar'':423C ''comput'':123C ''consid'':236C ''construct'':319C ''contain'':377C ''correspond'':199C ''could'':235C ''d'':440C ''describ'':21C,259C ''detail'':83C ''determin'':594C ''develop'':62C ''dice'':435C,511C,610C ''differ'':519C,552C,579C ''divid'':266C ''doi'':36C ''el'':299C ''em'':480C,498C,565C ''encrypt'':53C ''end'':665C ''error'':57C ''exact'':397C,507C ''exampl'':282C,324C ''fals'':598C ''filter'':30C,69C,102C,147C,175C,239C,349C,355C,368C,376C,400C,416C,426C,467C,483C,501C,558C,589C,637C ''fix'':106C,116C,251C ''fixed-length'':105C ''flexibl'':643C ''follow'':289C ''found'':620C ''front'':308C ''function'':121C,134C,160C,190C,196C,208C,224C,385C,535C,581C ''general'':316C ''given'':325C ''goe'':81C ''gram'':73C,271C,293C,322C,341C,389C,406C ''h'':297C,453C ''hash'':120C,133C,159C,184C,195C,207C,223C,248C,344C,384C,534C,580C ''hello'':284C ''help'':632C ''howev'':401C ''href'':32C,622C,653C ''i.e'':148C ''identifi'':54C,59C,75C ''implement'':646C ''import'':591C ''independ'':119C ''indetermin'':539C ''index'':138C,144C,154C,202C,216C,229C,551C ''indic'':562C,671C ''input'':127C,186C,543C,553C,602C ''led'':11C ''length'':107C ''less'':606C ''likelihood'':596C ''link'':264C,662C ''linkag'':27C,51C ''ll'':300C ''lo'':301C ''mani'':378C ''map'':165C,537C ''may'':554C ''mean'':432C ''mention'':7C ''method'':86C,332C,615C,649C,681C ''might'':286C ''much'':605C ''n'':270C,321C,340C,388C,405C ''n-gram'':269C,320C,339C,387C,404C ''new'':44C ''number'':457C,473C,491C,540C,547C,577C,584C ''o'':302C ''one'':2C,222C ''onto'':249C ''op'':10C ''oper'':363C ''option'':317C ''output'':130C ''p'':1C,42C,78C,99C,173C,256C,305C,337C,373C,439C,450C,531C,618C,639C ''pad'':306C,335C ''pair'':600C ''paper'':328C,659C ''paramet'':592C,685C ''perform'':674C ''posit'':599C ''possibl'':143C,549C ''potenti'':15C ''power'':20C ''pre'':295C ''predict'':616C ''preserv'':25C,49C ''privaci'':24C,48C ''privacy-preserv'':23C,47C ''probabl'':566C ''produc'':346C,365C,396C,555C,612C ''propos'':330C ''protocol'':45C,64C,258C ''q'':72C ''q-gram'':71C ''question'':668C ''quit'':19C ''rang'':252C ''record'':26C,50C ''refer'':5C ''relat'':393C,678C ''result'':113C ''return'':162C,204C,218C ''see'':650C ''seem'':18C,313C ''seri'':108C,155C ''set'':117C,180C,193C,210C,232C,246C,277C,290C,352C,462C,476C,494C ''share'':419C ''similar'':575C,607C ''small'':546C ''solut'':16C ''space'':312C ''start'':176C ''store'':111C ''string'':267C,371C,391C,408C,505C,570C ''sub'':441C ''summar'':90C ''superimpos'':358C ''superposit'':243C ''tutori'':627C ''two'':407C,425C ''ultim'':418C ''understand'':634C ''uniqu'':542C ''unlik'':394C ''use'':28C,333C ''valu'':128C,139C,167C,187C,203C ''various'':684C ''www.biomedcentral.com'':34C ''www.biomedcentral.com/1472-6947/9/41'':33C ''www.uni-due.de'':655C ''www.uni-due.de/~hq0215/documents/2010/bachteler_2010_an_empirical_comparison_of_approaches_to_approximate_string_matching_in_private_record_linkage.pdf'':654C ''yield'':287C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (340, 2, '2014-06-12 13:38:10.877', 4, NULL, NULL, '&lt;p&gt;There''s plenty. If you''ve ever used ggplot2 in R and want to do that in python:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://pypi.python.org/pypi/ggplot/0.5.9" rel="nofollow"&gt;https://pypi.python.org/pypi/ggplot/0.5.9&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you want to use a similar visualisation grammar (Vega) and go via D3 then:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/wrobstory/vincent" rel="nofollow"&gt;https://github.com/wrobstory/vincent&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Or if you want the full-on 3d shizzle:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://docs.enthought.com/mayavi/mayavi/" rel="nofollow"&gt;http://docs.enthought.com/mayavi/mayavi/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/mayavi/mayavi/'':77C ''/mayavi/mayavi/&lt;/a&gt;&lt;/p&gt;'':82C ''/p'':20C,48C,71C ''/pypi/ggplot/0.5.9'':26C ''/pypi/ggplot/0.5.9&lt;/a&gt;&lt;/p&gt;'':31C ''/wrobstory/vincent'':54C ''/wrobstory/vincent&lt;/a&gt;&lt;/p&gt;'':59C ''3d'':69C ''d3'':46C ''docs.enthought.com'':76C,81C ''docs.enthought.com/mayavi/mayavi/'':75C ''docs.enthought.com/mayavi/mayavi/&lt;/a&gt;&lt;/p&gt;'':80C ''ever'':8C ''full'':67C ''full-on'':66C ''ggplot2'':10C ''github.com'':53C,58C ''github.com/wrobstory/vincent'':52C ''github.com/wrobstory/vincent&lt;/a&gt;&lt;/p&gt;'':57C ''go'':44C ''grammar'':41C ''href'':23C,51C,74C ''nofollow'':28C,56C,79C ''p'':1C,21C,32C,49C,60C,72C ''plenti'':4C ''pypi.python.org'':25C,30C ''pypi.python.org/pypi/ggplot/0.5.9'':24C ''pypi.python.org/pypi/ggplot/0.5.9&lt;/a&gt;&lt;/p&gt;'':29C ''python'':19C ''r'':12C ''rel'':27C,55C,78C ''shizzl'':70C ''similar'':39C ''use'':9C,37C ''ve'':7C ''vega'':42C ''via'':45C ''visualis'':40C ''want'':14C,35C,64C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (341, 2, '2014-06-12 13:42:05.383', 6, NULL, NULL, '&lt;p&gt;You have to first make it clear what do you mean by "learn Hadoop". If you mean using Hadoop, such as learning to program in MapReduce, then most probably it is a good idea. But fundamental knowledge (database, machine learning, statistics) may play a bigger role as time goes on.&lt;/p&gt;
', NULL, NULL, '''/p'':52C ''bigger'':46C ''clear'':8C ''databas'':39C ''first'':5C ''fundament'':37C ''goe'':50C ''good'':34C ''hadoop'':15C,20C ''idea'':35C ''knowledg'':38C ''learn'':14C,23C,41C ''machin'':40C ''make'':6C ''mapreduc'':27C ''may'':43C ''mean'':12C,18C ''p'':1C ''play'':44C ''probabl'':30C ''program'':25C ''role'':47C ''statist'':42C ''time'':49C ''use'':19C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (343, 2, '2014-06-12 15:22:16.247', 2, NULL, NULL, '&lt;p&gt;Increasing the number of hidden layers for a standard neural network actually won''t improve results in a majority of cases. Changing the &lt;em&gt;size&lt;/em&gt; of the hidden layer will.&lt;/p&gt;

&lt;p&gt;This fact (that the number of hidden layers does very little) has actually was noted historically and is the motivation behind the field of deep learning. Deep learning is effectively clever ways of training multilayer neural networks by, for example, isolating subsets of features when training different layers.&lt;/p&gt;

&lt;p&gt;Good introductory video on this topic on &lt;a href="https://www.youtube.com/watch?v=vXMpKYRhpmI&amp;amp;index=52&amp;amp;list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH" rel="nofollow"&gt;YouTube&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':100C ''/em'':27C ''/p'':33C,83C,101C ''/watch?v=vxmpkyrhpmi&amp;amp;index=52&amp;amp;list=pl6xpj9i5qxyecohn7tqghaj6naprnmubh'':96C ''actual'':13C,47C ''behind'':55C ''case'':22C ''chang'':23C ''clever'':65C ''deep'':59C,61C ''differ'':81C ''effect'':64C ''em'':25C ''exampl'':74C ''fact'':36C ''featur'':78C ''field'':57C ''good'':85C ''hidden'':6C,30C,41C ''histor'':50C ''href'':93C ''improv'':16C ''increas'':2C ''introductori'':86C ''isol'':75C ''layer'':7C,31C,42C,82C ''learn'':60C,62C ''littl'':45C ''major'':20C ''motiv'':54C ''multilay'':69C ''network'':12C,71C ''neural'':11C,70C ''nofollow'':98C ''note'':49C ''number'':4C,39C ''p'':1C,34C,84C ''rel'':97C ''result'':17C ''size'':26C ''standard'':10C ''subset'':76C ''topic'':90C ''train'':68C,80C ''video'':87C ''way'':66C ''won'':14C ''www.youtube.com'':95C ''www.youtube.com/watch?v=vxmpkyrhpmi&amp;amp;index=52&amp;amp;list=pl6xpj9i5qxyecohn7tqghaj6naprnmubh'':94C ''youtub'':99C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (345, 2, '2014-06-12 16:52:46.557', 17, NULL, NULL, '&lt;p&gt;Introductory:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1107422221"&gt;Machine Learning: The Art and Science of Algorithms that Make Sense of Data (Flach)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1600490069"&gt;Learning From Data (Abu-Mostafa et al.)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1461471370"&gt;Introduction to Statistical Learning (James et al.)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Digging deeper:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0387848576"&gt;Elements of Statistical Learning (Hastie et al.)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0387310738"&gt;Pattern Recognition and Machine Learning (Bishop)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Some special interest examples:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0521833787"&gt;Convex Optimization (Boyd)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.cs.ucl.ac.uk/staff/d.barber/brml/"&gt;Bayesian Reasoning and Machine Learning (Barber)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0262013193"&gt;Probabilistic Graphical Models (Koller)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0198538642"&gt;Neural Networks for Pattern Recognition (Bishop)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Broader reference works on machine learning (not really what you asked for, but for completeness):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0262018020"&gt;Machine Learning: A Probabilistic Perspective (Murphy)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0136042597"&gt;Artificial Intelligence: A Modern Approach (Russell &amp;amp; Norvig)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Bonus paper:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://projecteuclid.org/euclid.ss/1009213726"&gt;Statistical Modeling: The Two Cultures (Breiman)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':25C,41C,56C,77C,91C,110C,124C,136C,150C,183C,199C,219C ''/amzn/click/0136042597'':190C ''/amzn/click/0198538642'':143C ''/amzn/click/0262013193'':131C ''/amzn/click/0262018020'':176C ''/amzn/click/0387310738'':84C ''/amzn/click/0387848576'':69C ''/amzn/click/0521833787'':106C ''/amzn/click/1107422221'':10C ''/amzn/click/1461471370'':48C ''/amzn/click/1600490069'':32C ''/euclid.ss/1009213726'':212C ''/li'':26C,42C,57C,78C,92C,111C,125C,137C,151C,184C,200C,220C ''/p'':3C,62C,99C,169C,205C ''/staff/d.barber/brml/'':117C ''/ul'':58C,93C,152C,201C,221C ''abu'':37C ''abu-mostafa'':36C ''al'':40C,55C,76C ''algorithm'':18C ''amp'':197C ''approach'':195C ''art'':14C ''artifici'':191C ''ask'':164C ''barber'':123C ''bayesian'':118C ''bishop'':90C,149C ''bonus'':203C ''boyd'':109C ''breiman'':218C ''broader'':154C ''complet'':168C ''convex'':107C ''cultur'':217C ''data'':23C,35C ''deeper'':61C ''dig'':60C ''element'':70C ''et'':39C,54C,75C ''exampl'':98C ''flach'':24C ''graphic'':133C ''hasti'':74C ''href'':7C,29C,45C,66C,81C,103C,114C,128C,140C,173C,187C,209C ''intellig'':192C ''interest'':97C ''introduct'':49C ''introductori'':2C ''jame'':53C ''koller'':135C ''learn'':12C,33C,52C,73C,89C,122C,159C,178C ''li'':5C,27C,43C,64C,79C,101C,112C,126C,138C,171C,185C,207C ''machin'':11C,88C,121C,158C,177C ''make'':20C ''model'':134C,214C ''modern'':194C ''mostafa'':38C ''murphi'':182C ''network'':145C ''neural'':144C ''norvig'':198C ''optim'':108C ''p'':1C,59C,94C,153C,202C ''paper'':204C ''pattern'':85C,147C ''perspect'':181C ''probabilist'':132C,180C ''projecteuclid.org'':211C ''projecteuclid.org/euclid.ss/1009213726'':210C ''rads.stackoverflow.com'':9C,31C,47C,68C,83C,105C,130C,142C,175C,189C ''rads.stackoverflow.com/amzn/click/0136042597'':188C ''rads.stackoverflow.com/amzn/click/0198538642'':141C ''rads.stackoverflow.com/amzn/click/0262013193'':129C ''rads.stackoverflow.com/amzn/click/0262018020'':174C ''rads.stackoverflow.com/amzn/click/0387310738'':82C ''rads.stackoverflow.com/amzn/click/0387848576'':67C ''rads.stackoverflow.com/amzn/click/0521833787'':104C ''rads.stackoverflow.com/amzn/click/1107422221'':8C ''rads.stackoverflow.com/amzn/click/1461471370'':46C ''rads.stackoverflow.com/amzn/click/1600490069'':30C ''realli'':161C ''reason'':119C ''recognit'':86C,148C ''refer'':155C ''russel'':196C ''scienc'':16C ''sens'':21C ''special'':96C ''statist'':51C,72C,213C ''two'':216C ''ul'':4C,63C,100C,170C,206C ''work'':156C ''www.cs.ucl.ac.uk'':116C ''www.cs.ucl.ac.uk/staff/d.barber/brml/'':115C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (346, 2, '2014-06-12 17:03:15.733', 0, NULL, NULL, '&lt;p&gt;In addition to the courses and tutorials posted, I would suggest something a bit more ''hands on'': &lt;a href="http://www.kaggle.com/competitions" rel="nofollow"&gt;Kaggle&lt;/a&gt; has some introductory competitions that might pique your interest (most people start with the Titanic competition). And there''s a large variety of subjects to explore and compete in when you want to get more experience.&lt;/p&gt;
', NULL, NULL, '''/a'':27C ''/competitions'':23C ''/p'':64C ''addit'':3C ''bit'':15C ''compet'':55C ''competit'':31C,43C ''cours'':6C ''experi'':63C ''explor'':53C ''get'':61C ''hand'':17C ''href'':20C ''interest'':36C ''introductori'':30C ''kaggl'':26C ''larg'':48C ''might'':33C ''nofollow'':25C ''p'':1C ''peopl'':38C ''piqu'':34C ''post'':9C ''rel'':24C ''someth'':13C ''start'':39C ''subject'':51C ''suggest'':12C ''titan'':42C ''tutori'':8C ''varieti'':49C ''want'':59C ''would'':11C ''www.kaggle.com'':22C ''www.kaggle.com/competitions'':21C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8666, 2, '2015-10-31 01:57:54.07', 0, NULL, NULL, '&lt;p&gt;As everyone here knows, data science as a field is broad, and similarly the experience required for a particular job is broad. &lt;/p&gt;

&lt;p&gt;Depending on where you want to end up, either degree could suit you better. If you would like more of a traditional statistician''s job which has been rebranded to data scientist in order to gain more attention, then perhaps the MSc Stats would be more applicable. However, if your field requires more complex computer programming or database management, you may want to look into the masters in data science or masters in computer science, depending on your background and interest. Going with a "hot" degree like data science means that you will likely learn a lot of common analyses in superficial detail in order to be employable quickly, but with a more traditionally academic MSc Stats or MSc Com sci, you will learn the theoretical basis with the potential loss of applicability. My personal bias is towards to academic side (MSc/PhD Stats or applied math), but I think this is a decision you should think hard about. Regardless of your decision, in general data science / analysis / programming / whatever this field is requires quite a lot of self directed learning, so whatever program you choose &lt;strong&gt;must&lt;/strong&gt; provide you with the skills and tools to be able to teach yourself whatever you need to know.  &lt;/p&gt;

&lt;p&gt;I would personally take a hard look at what specific kinds of jobs appeal to you, and which programs may prepare you better. Talk to the admissions officers and look up the career paths of past students. Where did they end up? Is that where you want to be? Keep in mind the kind of life you want to live and the kind of work you want to do. I personally find anything financially or profit driven absolutely soul-sucking, but enjoying doing almost the same work in the medical field. Different skill sets, different programming languages, and different vocabulary are required, and the more you know before you enter the field the better you can prepare yourself for the the journey to come.&lt;/p&gt;

&lt;p&gt;Regardless, have fun, and don''t worry too much about making mistakes. No one knows how things will work out in the long run. You can prepare yourself with as much knowledge as possible, but you''ll never know which was the truly right choice, just which one was your choice. &lt;/p&gt;
', NULL, NULL, '''/p'':24C,232C,359C,412C ''/strong'':213C ''abl'':223C ''absolut'':311C ''academ'':140C,165C ''admiss'':260C ''almost'':318C ''analys'':125C ''analysi'':192C ''anyth'':306C ''appeal'':247C ''appli'':170C ''applic'':72C,158C ''attent'':63C ''background'':104C ''basi'':152C ''better'':39C,256C,348C ''bias'':161C ''broad'':12C,23C ''career'':266C ''choic'':405C,411C ''choos'':210C ''com'':145C ''come'':358C ''common'':124C ''complex'':79C ''comput'':80C,99C ''could'':36C ''data'':6C,56C,94C,113C,190C ''databas'':83C ''decis'':178C,187C ''degre'':35C,111C ''depend'':26C,101C ''detail'':128C ''differ'':326C,329C,333C ''direct'':204C ''driven'':310C ''either'':34C ''employ'':133C ''end'':32C,274C ''enjoy'':316C ''enter'':344C ''everyon'':3C ''experi'':16C ''field'':10C,76C,196C,325C,346C ''financi'':307C ''find'':305C ''fun'':363C ''gain'':61C ''general'':189C ''go'':107C ''hard'':182C,239C ''hot'':110C ''howev'':73C ''interest'':106C ''job'':21C,50C,246C ''journey'':356C ''keep'':283C ''kind'':244C,287C,296C ''know'':5C,231C,341C,375C,399C ''knowledg'':392C ''languag'':331C ''learn'':120C,149C,205C ''life'':289C ''like'':43C,112C,119C ''live'':293C ''ll'':397C ''long'':383C ''look'':89C,240C,263C ''loss'':156C ''lot'':122C,201C ''make'':371C ''manag'':84C ''master'':92C,97C ''math'':171C ''may'':86C,253C ''mean'':115C ''medic'':324C ''mind'':285C ''mistak'':372C ''msc'':67C,141C,144C ''msc/phd'':167C ''much'':369C,391C ''must'':212C ''need'':229C ''never'':398C ''offic'':261C ''one'':374C,408C ''order'':59C,130C ''p'':1C,25C,233C,360C ''particular'':20C ''past'':269C ''path'':267C ''perhap'':65C ''person'':160C,236C,304C ''possibl'':394C ''potenti'':155C ''prepar'':254C,351C,387C ''profit'':309C ''program'':81C,193C,208C,252C,330C ''provid'':214C ''quick'':134C ''quit'':199C ''rebrand'':54C ''regardless'':184C,361C ''requir'':17C,77C,198C,336C ''right'':404C ''run'':384C ''sci'':146C ''scienc'':7C,95C,100C,114C,191C ''scientist'':57C ''self'':203C ''set'':328C ''side'':166C ''similar'':14C ''skill'':218C,327C ''soul'':313C ''soul-suck'':312C ''specif'':243C ''stat'':68C,142C,168C ''statistician'':48C ''strong'':211C ''student'':270C ''suck'':314C ''suit'':37C ''superfici'':127C ''take'':237C ''talk'':257C ''teach'':225C ''theoret'':151C ''thing'':377C ''think'':174C,181C ''tool'':220C ''toward'':163C ''tradit'':47C,139C ''truli'':403C ''vocabulari'':334C ''want'':30C,87C,280C,291C,300C ''whatev'':194C,207C,227C ''work'':298C,321C,379C ''worri'':367C ''would'':42C,69C,235C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (354, 1, '2014-06-13 07:28:37.763', 4, 160, 'Data Science as a Social Scientist?', '&lt;p&gt;as I am very interested in programming and statistics, Data Science seems like a great career path to me - I like both fields and would like to combine them. Unfortunately, I have studied political science with a non-statistical sounding Master. I focused on statistics in this Master, visiting optional courses and writing a statistical thesis on a rather large dataset.&lt;/p&gt;

&lt;p&gt;Since almost all job adds are requiring  a degree in informatics, physics or some other techy-field, I am wondering if there is a chance to become a data scientist or if I should drop that idea.&lt;/p&gt;

&lt;p&gt;I am lacking knowledge in machine learning, sql and hadoop, while having a rather strong informatics and statistics background. &lt;/p&gt;

&lt;p&gt;So can somebody tell me how feasible my goal of becoming a data scientist is? &lt;/p&gt;
', 'statistics', NULL, '''/p'':71C,111C,132C,149C ''add'':77C ''almost'':74C ''background'':131C ''becom'':100C,144C ''career'':24C ''chanc'':98C ''combin'':36C ''cours'':60C ''data'':1A,18C,102C,146C ''dataset'':70C ''degre'':81C ''drop'':108C ''feasibl'':140C ''field'':31C,90C ''focus'':52C ''goal'':142C ''great'':23C ''hadoop'':122C ''idea'':110C ''informat'':83C,128C ''interest'':13C ''job'':76C ''knowledg'':116C ''lack'':115C ''larg'':69C ''learn'':119C ''like'':21C,29C,34C ''machin'':118C ''master'':50C,57C ''non'':47C ''non-statist'':46C ''option'':59C ''p'':8C,72C,112C,133C ''path'':25C ''physic'':84C ''polit'':42C ''program'':15C ''rather'':68C,126C ''requir'':79C ''scienc'':2A,19C,43C ''scientist'':6A,103C,147C ''seem'':20C ''sinc'':73C ''social'':5A ''somebodi'':136C ''sound'':49C ''sql'':120C ''statist'':7B,17C,48C,54C,64C,130C ''strong'':127C ''studi'':41C ''techi'':89C ''techy-field'':88C ''tell'':137C ''thesi'':65C ''unfortun'':38C ''visit'':58C ''wonder'':93C ''would'':33C ''write'':62C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (347, 2, '2014-06-12 17:58:21.467', 0, NULL, NULL, '&lt;p&gt;As mentioned in above answers grasp the basics of ML by following MOOCs by Prof.Andrew Ng and &lt;a href="http://work.caltech.edu/telecourse.html" rel="nofollow"&gt;''Learning From Data''&lt;/a&gt; by Prof. Yaser Abu-Mostafa.  &lt;/p&gt;

&lt;p&gt;R is the &lt;a href="http://www.kaggle.com/wiki/Software" rel="nofollow"&gt;clear winner&lt;/a&gt; as the most used tool in Kaggle competitions. (Don''t forget to check the resources on Kaggle wiki and forums)&lt;/p&gt;

&lt;p&gt;Learn basic R and Python. Coursera ''Data Science'' track has an &lt;a href="https://www.coursera.org/course/rprog" rel="nofollow"&gt;introductory R course&lt;/a&gt;. Almost all the algorithms can be found in Python and R libraries. Feel free to use the algorithms you learned in few kaggle competitions. As a starting point compare the performance of several algorithms on Titanic dataset and Digit recognizer dataset on &lt;a href="http://www.kaggle.com/" rel="nofollow"&gt;kaggle&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;And do continue practising on various datasets!&lt;/p&gt;
', NULL, NULL, '''/a'':29C,50C,94C,143C ''/course/rprog'':88C ''/p'':36C,71C,144C,153C ''/telecourse.html'':23C ''/wiki/software'':45C ''abu'':34C ''abu-mostafa'':33C ''algorithm'':98C,112C,128C ''almost'':95C ''answer'':6C ''basic'':9C,74C ''check'':63C ''clear'':48C ''compar'':123C ''competit'':58C,118C ''continu'':148C ''cours'':93C ''coursera'':78C ''data'':28C,79C ''dataset'':131C,135C,152C ''digit'':133C ''feel'':107C ''follow'':13C ''forget'':61C ''forum'':70C ''found'':101C ''free'':108C ''grasp'':7C ''href'':20C,42C,85C,138C ''introductori'':91C ''kaggl'':57C,67C,117C,142C ''learn'':26C,73C,114C ''librari'':106C ''mention'':3C ''ml'':11C ''mooc'':14C ''mostafa'':35C ''ng'':17C ''nofollow'':25C,47C,90C,141C ''p'':1C,37C,72C,145C ''perform'':125C ''point'':122C ''practis'':149C ''prof'':31C ''prof.andrew'':16C ''python'':77C,103C ''r'':38C,75C,92C,105C ''recogn'':134C ''rel'':24C,46C,89C,140C ''resourc'':65C ''scienc'':80C ''sever'':127C ''start'':121C ''titan'':130C ''tool'':55C ''track'':81C ''use'':54C,110C ''various'':151C ''wiki'':68C ''winner'':49C ''work.caltech.edu'':22C ''work.caltech.edu/telecourse.html'':21C ''www.coursera.org'':87C ''www.coursera.org/course/rprog'':86C ''www.kaggle.com'':44C,139C ''www.kaggle.com/wiki/software'':43C ''yaser'':32C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (348, 2, '2014-06-12 18:33:21.54', 5, NULL, NULL, '&lt;p&gt;Not sure about the cloud era one, but one of my friends joined the John Hopkins one and in his words it''s "brilliant to get you started". It has also been recommended by a lot of people. I am planning to join it in few weeks. As far as seriousness is concerned, I don''t think these certifications are gonna help you land a job, but they sure will help you learn.  &lt;/p&gt;
', NULL, NULL, '''/p'':75C ''also'':32C ''brilliant'':25C ''certif'':60C ''cloud'':6C ''concern'':54C ''era'':7C ''far'':50C ''friend'':13C ''get'':27C ''gonna'':62C ''help'':63C,72C ''hopkin'':17C ''job'':67C ''john'':16C ''join'':14C,44C ''land'':65C ''learn'':74C ''lot'':37C ''one'':8C,10C,18C ''p'':1C ''peopl'':39C ''plan'':42C ''recommend'':34C ''serious'':52C ''start'':29C ''sure'':3C,70C ''think'':58C ''week'':48C ''word'':22C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (349, 2, '2014-06-12 20:51:59.93', 24, NULL, NULL, '&lt;p&gt;As a former analytics manager and a current lead data scientist, I am very leery of the need for data science certificates.  The term data scientist is pretty vague and the field of data science is in it''s infancy.  A certificates implies some sort of uniform standard which is just lacking in data science, it is still very much the wild west. &lt;/p&gt;

&lt;p&gt;While a certificate is probably not going to hurt you, I think your time would be better spent developing the experience to know when to use a certain approach, and depth of understanding to be able to explain that approach to a non-technical audience.&lt;/p&gt;
', NULL, NULL, '''/p'':65C,113C ''abl'':102C ''analyt'':5C ''approach'':95C,106C ''audienc'':112C ''better'':83C ''certain'':94C ''certif'':23C,43C,69C ''current'':9C ''data'':11C,21C,26C,35C,55C ''depth'':97C ''develop'':85C ''experi'':87C ''explain'':104C ''field'':33C ''former'':4C ''go'':73C ''hurt'':75C ''impli'':44C ''infanc'':41C ''know'':89C ''lack'':53C ''lead'':10C ''leeri'':16C ''manag'':6C ''much'':61C ''need'':19C ''non'':110C ''non-techn'':109C ''p'':1C,66C ''pretti'':29C ''probabl'':71C ''scienc'':22C,36C,56C ''scientist'':12C,27C ''sort'':46C ''spent'':84C ''standard'':49C ''still'':59C ''technic'':111C ''term'':25C ''think'':78C ''time'':80C ''understand'':99C ''uniform'':48C ''use'':92C ''vagu'':30C ''west'':64C ''wild'':63C ''would'':81C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (350, 1, '2014-06-12 22:11:46.607', -3, 554, 'Example tasks of a data scientist and the necessary knowledge', '&lt;p&gt;Could you give some examples of typical tasks that a data scientist does in his daily job, and the must-know minimum for each of the levels (like junior, senior, etc. if there are any)? If possible, something like a &lt;a href="http://www.starling-software.com/employment/programmer-competency-matrix.html" rel="nofollow"&gt;Programmer competency matrix&lt;/a&gt;.&lt;/p&gt;
', 'knowledge-base', NULL, '''/a'':66C ''/employment/programmer-competency-matrix.html'':60C ''/p'':67C ''base'':13B ''compet'':64C ''could'':15C ''daili'':30C ''data'':5A,25C ''etc'':46C ''exampl'':1A,19C ''give'':17C ''href'':57C ''job'':31C ''junior'':44C ''know'':36C ''knowledg'':10A,12B ''knowledge-bas'':11B ''level'':42C ''like'':43C,54C ''matrix'':65C ''minimum'':37C ''must'':35C ''must-know'':34C ''necessari'':9A ''nofollow'':62C ''p'':14C ''possibl'':52C ''programm'':63C ''rel'':61C ''scientist'':6A,26C ''senior'':45C ''someth'':53C ''task'':2A,22C ''typic'':21C ''www.starling-software.com'':59C ''www.starling-software.com/employment/programmer-competency-matrix.html'':58C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9962, 1, '2016-01-26 02:50:56.333', 0, 15, 'How to generate combination shapes dataset using sklearn dataset generator?', '&lt;p&gt;I need to generate a dataset with many shapes ( like  an outer ring with two blobs overlapped in the center ). Is it possible using sklearn ? If not which package in python can I use instead ?&lt;/p&gt;
', 'machine-learning  python  clustering  sklearn', NULL, '''/p'':53C ''blob'':33C ''center'':37C ''cluster'':15B ''combin'':4A ''dataset'':6A,9A,23C ''generat'':3A,10A,21C ''instead'':52C ''learn'':13B ''like'':27C ''machin'':12B ''machine-learn'':11B ''mani'':25C ''need'':19C ''outer'':29C ''overlap'':34C ''p'':17C ''packag'':46C ''possibl'':40C ''python'':14B,48C ''ring'':30C ''shape'':5A,26C ''sklearn'':8A,16B,42C ''two'':32C ''use'':7A,41C,51C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (351, 2, '2014-06-13 03:50:24.61', 7, NULL, NULL, '&lt;p&gt;There are multiple certifications going on, but they have different focus area and style of teaching.&lt;/p&gt;

&lt;p&gt;I prefer The Analytics Edge on eDX lot more over John Hopkins specialization, as it is more intensive and hands on. The expectation in John Hopkins specialization is to put in 3 - 4 hours a week vs. 11 - 12 hours a week on Analytics Edge.&lt;/p&gt;

&lt;p&gt;From an industry perspective, I take these certifications as a sign of interest and not level of knowledge a person possesses. There are too many dropouts in these MOOCs. I value other experience (like participating in Kaggle competitions) lot more than undergoing XYZ certification on MOOC.&lt;/p&gt;
', NULL, NULL, '''/p'':18C,65C,113C ''11'':57C ''12'':58C ''3'':51C ''4'':52C ''analyt'':23C,63C ''area'':13C ''certif'':5C,74C,110C ''competit'':104C ''differ'':11C ''dropout'':92C ''edg'':24C,64C ''edx'':26C ''expect'':42C ''experi'':99C ''focus'':12C ''go'':6C ''hand'':39C ''hopkin'':31C,45C ''hour'':53C,59C ''industri'':69C ''intens'':37C ''interest'':79C ''john'':30C,44C ''kaggl'':103C ''knowledg'':84C ''level'':82C ''like'':100C ''lot'':27C,105C ''mani'':91C ''mooc'':95C,112C ''multipl'':4C ''p'':1C,19C,66C ''particip'':101C ''person'':86C ''perspect'':70C ''possess'':87C ''prefer'':21C ''put'':49C ''sign'':77C ''special'':32C,46C ''style'':15C ''take'':72C ''teach'':17C ''undergo'':108C ''valu'':97C ''vs'':56C ''week'':55C,61C ''xyz'':109C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (352, 1, '2014-06-13 05:40:39.36', 6, 110, 'How do I figure out if subsets can be plotted in a normal Euler diagram?', '&lt;p&gt;In some cases, &lt;a href="http://www.ncbi.nlm.nih.gov/pubmed/20975147"&gt;it may be impossible&lt;/a&gt; to draw Euler diagrams with overlapping circles to represent all the overlapping subsets in the correct proportions. This type of data then requires using polygons or other figures to represent each set. When dealing with data that describes overlapping subsets, how can I figure out whether a simple Euler diagram is possible?&lt;/p&gt;
', 'visualization', NULL, '''/a'':30C ''/p'':83C ''/pubmed/20975147'':25C ''case'':20C ''circl'':37C ''correct'':46C ''data'':51C,66C ''deal'':64C ''describ'':68C ''diagram'':15A,34C,80C ''draw'':32C ''euler'':14A,33C,79C ''figur'':4A,58C,74C ''href'':22C ''imposs'':29C ''may'':27C ''normal'':13A ''overlap'':36C,42C,69C ''p'':17C ''plot'':10A ''polygon'':55C ''possibl'':82C ''proport'':47C ''repres'':39C,60C ''requir'':53C ''set'':62C ''simpl'':78C ''subset'':7A,43C,70C ''type'':49C ''use'':54C ''visual'':16B ''whether'':76C ''www.ncbi.nlm.nih.gov'':24C ''www.ncbi.nlm.nih.gov/pubmed/20975147'':23C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (356, 1, '2014-06-13 10:57:10.623', 5, 99, 'How to best accomplish high speed comparison of like data?', '&lt;p&gt;I attack this problem frequently with inefficiency because it''s always pretty low on the priority list and my clients are resistant to change until things break.  I would like some input on how to speed things up.  &lt;/p&gt;

&lt;p&gt;I have multiple datasets of information in a SQL database.  The database is vendor-designed, so I have little control over the structure.  It''s a sql representation of a class-based structure.  It looks a little bit like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Main-class table
 -sub-class table 1
 -sub-class table 2
  -sub-sub-class table
 ...
 -sub-class table n
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Each table contains fields for each attribute of the class.  A join exists which contains all of the fields for each of the sub-classes which contains all of the fields in the class table and all of the fields in each parent class'' table, joined by a unique identifier.&lt;/p&gt;

&lt;p&gt;There are hundreds of classes. which means thousands of views and tens of thousands of columns.&lt;/p&gt;

&lt;p&gt;Beyond that, there are multiple datasets, indicated by a field value in the Main-class table.  There is the production dataset, visible to all end users, and there are several other datasets comprised of the most current version of the same data from various integration sources.  &lt;/p&gt;

&lt;p&gt;Daily, we run jobs that compare the production dataset to the live datasets and based on a set of rules we merge the data, purge the live datasets, then start all over again.  The rules are in place because we might trust one source of data more than another for a particular value of a particular class.&lt;/p&gt;

&lt;p&gt;The jobs are essentially a series of SQL statements that go row-by-row through each dataset, and field by field within each row.  The common changes are limited to a handful of fields in each row, but since anything can change we compare each value.&lt;/p&gt;

&lt;p&gt;There are 10s of millions of rows of data and in some environments the merge jobs can take longer than 24 hours.  We resolve that problem generally, by throwing more hardware at it, but this isn''t a hadoop environment currently so there''s a pretty finite limit to what can be done in that regard.&lt;/p&gt;

&lt;p&gt;How would you go about scaling a solution to this problem such that there were no limitations?  And how would you go about accomplishing the most efficient data-merge?  (currently it is field by field comparisons... painfully slow).&lt;/p&gt;
', 'efficiency  scalability  sql', NULL, '''/code'':124C ''/p'':53C,97C,178C,196C,245C,304C,353C,411C,452C ''/pre'':125C ''1'':108C ''10s'':357C ''2'':113C ''24'':375C ''accomplish'':4A,436C ''alway'':25C ''anoth'':295C ''anyth'':346C ''attack'':16C ''attribut'':133C ''base'':88C,261C ''best'':3A ''beyond'':198C ''bit'':94C ''break'':41C ''chang'':38C,333C,348C ''class'':87C,102C,106C,111C,117C,121C,136C,152C,161C,171C,184C,213C,303C ''class-bas'':86C ''client'':34C ''code'':99C ''column'':195C ''common'':332C ''compar'':252C,350C ''comparison'':7A,449C ''compris'':231C ''contain'':129C,141C,154C ''control'':75C ''current'':235C,395C,443C ''daili'':247C ''data'':10A,240C,270C,292C,363C,441C ''data-merg'':440C ''databas'':64C,66C ''dataset'':58C,203C,219C,230C,255C,259C,274C,323C ''design'':70C ''done'':407C ''effici'':11B,439C ''end'':223C ''environ'':367C,394C ''essenti'':309C ''exist'':139C ''field'':130C,145C,158C,167C,207C,325C,327C,340C,446C,448C ''finit'':401C ''frequent'':19C ''general'':381C ''go'':316C,416C,434C ''hadoop'':393C ''hand'':338C ''hardwar'':385C ''high'':5A ''hour'':376C ''hundr'':182C ''identifi'':177C ''indic'':204C ''ineffici'':21C ''inform'':60C ''input'':46C ''integr'':243C ''isn'':390C ''job'':250C,307C,370C ''join'':138C,173C ''like'':9A,44C,95C ''limit'':335C,402C,429C ''list'':31C ''littl'':74C,93C ''live'':258C,273C ''longer'':373C ''look'':91C ''low'':27C ''main'':101C,212C ''main-class'':100C,211C ''mean'':186C ''merg'':268C,369C,442C ''might'':287C ''million'':359C ''multipl'':57C,202C ''n'':123C ''one'':289C ''p'':14C,54C,126C,179C,197C,246C,305C,354C,412C ''pain'':450C ''parent'':170C ''particular'':298C,302C ''place'':284C ''pre'':98C ''pretti'':26C,400C ''prioriti'':30C ''problem'':18C,380C,423C ''product'':218C,254C ''purg'':271C ''regard'':410C ''represent'':83C ''resist'':36C ''resolv'':378C ''row'':318C,320C,330C,343C,361C ''row-by-row'':317C ''rule'':266C,281C ''run'':249C ''scalabl'':12B ''scale'':418C ''seri'':311C ''set'':264C ''sever'':228C ''sinc'':345C ''slow'':451C ''solut'':420C ''sourc'':244C,290C ''speed'':6A,50C ''sql'':13B,63C,82C,313C ''start'':276C ''statement'':314C ''structur'':78C,89C ''sub'':105C,110C,115C,116C,120C,151C ''sub-class'':104C,109C,119C,150C ''sub-sub-class'':114C ''tabl'':103C,107C,112C,118C,122C,128C,162C,172C,214C ''take'':372C ''ten'':191C ''thing'':40C,51C ''thousand'':187C,193C ''throw'':383C ''trust'':288C ''uniqu'':176C ''user'':224C ''valu'':208C,299C,352C ''various'':242C ''vendor'':69C ''vendor-design'':68C ''version'':236C ''view'':189C ''visibl'':220C ''within'':328C ''would'':43C,414C,432C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (357, 2, '2014-06-13 12:18:53.83', 7, NULL, NULL, '&lt;p&gt;I lead data science teams for a major Internet company and I have screened hundreds of profiles and interviewed dozens for our teams around the world. Many candidates have passed the aforementioned courses and programs or bring similar credentials. Personally, I have also taken the courses, some are good, others are disappointing but none of them makes you a "data scientist".&lt;/p&gt;

&lt;p&gt;In general, I agree with the others here. A certificate from Coursera or Cloudera just signalizes an interest but it does not move the needle.  There is a lot more to consider and you can have a bigger impact by providing a comprehensive repository of your work (github profile for example) and by networking with other data scientists. Anyone hiring for a data science profile will always prefer to see your previous work and coding style/abilities.&lt;/p&gt;
', NULL, NULL, '''/p'':63C,141C ''aforement'':33C ''agre'':68C ''also'':44C ''alway'':131C ''anyon'':123C ''around'':25C ''bigger'':102C ''bring'':38C ''candid'':29C ''certif'':74C ''cloudera'':78C ''code'':139C ''compani'':11C ''comprehens'':107C ''consid'':96C ''cours'':34C,47C ''coursera'':76C ''credenti'':40C ''data'':4C,61C,121C,127C ''disappoint'':53C ''dozen'':21C ''exampl'':115C ''general'':66C ''github'':112C ''good'':50C ''hire'':124C ''hundr'':16C ''impact'':103C ''interest'':82C ''internet'':10C ''interview'':20C ''lead'':3C ''lot'':93C ''major'':9C ''make'':58C ''mani'':28C ''move'':87C ''needl'':89C ''network'':118C ''none'':55C ''other'':51C,71C ''p'':1C,64C ''pass'':31C ''person'':41C ''prefer'':132C ''previous'':136C ''profil'':18C,113C,129C ''program'':36C ''provid'':105C ''repositori'':108C ''scienc'':5C,128C ''scientist'':62C,122C ''screen'':15C ''see'':134C ''signal'':80C ''similar'':39C ''style/abilities'':140C ''taken'':45C ''team'':6C,24C ''work'':111C,137C ''world'':27C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (358, 2, '2014-06-13 12:30:45.29', 3, NULL, NULL, '&lt;p&gt;Sounds interesting. Could the solution be to dump the data out, build a fast custom processing thingie to run it through and then import it back to the database? I''ve seen some blazing fast Java-based text processing tools for topic modeling that handle millions of lines of text per second.&lt;/p&gt;

&lt;p&gt;If it''s an option then you can build a shell script to first dump the data in as good as format as possible. Then some pre-processing to separate the datasets, then real processing of comparison and changes. Lastly something that writes it back to a good format for input into database.&lt;/p&gt;

&lt;p&gt;Definately not a one-afternoon project, but you could probably get it to work in a couple of weeks.&lt;/p&gt;
', NULL, NULL, '''/p'':55C,111C,133C ''afternoon'':118C ''back'':27C,102C ''base'':39C ''blaze'':35C ''build'':13C,65C ''chang'':96C ''comparison'':94C ''could'':4C,122C ''coupl'':130C ''custom'':16C ''data'':11C,73C ''databas'':30C,110C ''dataset'':89C ''defin'':113C ''dump'':9C,71C ''fast'':15C,36C ''first'':70C ''format'':78C,106C ''get'':124C ''good'':76C,105C ''handl'':47C ''import'':25C ''input'':108C ''interest'':3C ''java'':38C ''java-bas'':37C ''last'':97C ''line'':50C ''million'':48C ''model'':45C ''one'':117C ''one-afternoon'':116C ''option'':61C ''p'':1C,56C,112C ''per'':53C ''possibl'':80C ''pre'':84C ''pre-process'':83C ''probabl'':123C ''process'':17C,41C,85C,92C ''project'':119C ''real'':91C ''run'':20C ''script'':68C ''second'':54C ''seen'':33C ''separ'':87C ''shell'':67C ''solut'':6C ''someth'':98C ''sound'':2C ''text'':40C,52C ''thingi'':18C ''tool'':42C ''topic'':44C ''ve'':32C ''week'':132C ''work'':127C ''write'':100C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (359, 2, '2014-06-13 13:49:35.777', 2, NULL, NULL, '&lt;p&gt;&lt;a href="http://nirvacana.com/thoughts/becoming-a-data-scientist/" rel="nofollow"&gt;Becoming a Data Scientist â Curriculum via Metromap&lt;/a&gt; is a popular reference for this kind of question.&lt;/p&gt;
', NULL, NULL, '''/a'':17C ''/p'':27C ''/thoughts/becoming-a-data-scientist/'':6C ''becom'':9C ''curriculum'':14C ''data'':11C ''href'':3C ''kind'':24C ''metromap'':16C ''nirvacana.com'':5C ''nirvacana.com/thoughts/becoming-a-data-scientist/'':4C ''nofollow'':8C ''p'':1C ''popular'':20C ''question'':26C ''refer'':21C ''rel'':7C ''scientist'':12C ''via'':15C ''â'':13C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (360, 2, '2014-06-13 14:46:30.393', 3, NULL, NULL, '&lt;p&gt;That''s because something called &lt;a href="http://en.wikipedia.org/wiki/Bias%E2%80%93variance_dilemma" rel="nofollow"&gt;bias-variance dilema&lt;/a&gt;. The overfitted model means that we will have more complex decision boundary if we give more variance on model. The thing is, not only too simple models but also complex models are likely to have dis-classified result on unseen data. Consequently, over-fitted model is not good as under-fitted model. That''s why overfitting is bad and we need to fit the model somewhere in the middle.&lt;/p&gt;
', NULL, NULL, '''/a'':18C ''/p'':91C ''/wiki/bias%e2%80%93variance_dilemma'':11C ''also'':47C ''bad'':79C ''bias'':15C ''bias-vari'':14C ''boundari'':30C ''call'':6C ''classifi'':56C ''complex'':28C,48C ''consequ'':61C ''data'':60C ''decis'':29C ''dilema'':17C ''dis'':55C ''dis-classifi'':54C ''en.wikipedia.org'':10C ''en.wikipedia.org/wiki/bias%e2%80%93variance_dilemma'':9C ''fit'':64C,72C,84C ''give'':33C ''good'':68C ''href'':8C ''like'':51C ''mean'':22C ''middl'':90C ''model'':21C,37C,45C,49C,65C,73C,86C ''need'':82C ''nofollow'':13C ''over-fit'':62C ''overfit'':20C,77C ''p'':1C ''rel'':12C ''result'':57C ''simpl'':44C ''someth'':5C ''somewher'':87C ''thing'':39C ''under-fit'':70C ''unseen'':59C ''varianc'':16C,35C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1060, 2, '2014-08-29 10:42:14.957', 2, NULL, NULL, '&lt;p&gt;Checkout Breeze and apache commons math for the maths, and ScalaLab for some nice examples of how to plot things in Scala.&lt;/p&gt;

&lt;p&gt;I''ve managed to get an environment setup where this would just be a couple of lines. I dont actually use ScalaLab, rather borrow some of its code, I use Intellij worksheets instead.&lt;/p&gt;
', NULL, NULL, '''/p'':24C,59C ''actual'':45C ''apach'':5C ''borrow'':49C ''breez'':3C ''checkout'':2C ''code'':53C ''common'':6C ''coupl'':40C ''dont'':44C ''environ'':32C ''exampl'':16C ''get'':30C ''instead'':58C ''intellij'':56C ''line'':42C ''manag'':28C ''math'':7C,10C ''nice'':15C ''p'':1C,25C ''plot'':20C ''rather'':48C ''scala'':23C ''scalalab'':12C,47C ''setup'':33C ''thing'':21C ''use'':46C,55C ''ve'':27C ''worksheet'':57C ''would'':36C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (361, 1, '2014-06-13 16:44:29.323', 21, 5424, 'When is a Model Underfitted?', '&lt;p&gt;Logic often states that by underfitting a model, it''s capacity to generalize is increased. That said, clearly at some point underfitting a model cause models to become worse regardless of the complexity of data.&lt;/p&gt;

&lt;p&gt;How do you know when your model has struck the right balance and is not underfitting the data it seeks to model?&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;strong&gt;Note:&lt;/strong&gt; This is a followup to my question, "&lt;a href="http://datascience.stackexchange.com/questions/61/why-is-overfitting-bad/"&gt;Why Is Overfitting Bad?&lt;/a&gt;"&lt;/p&gt;
', 'efficiency  algorithms  parameter', NULL, '''/a'':91C ''/p'':45C,69C,92C ''/questions/61/why-is-overfitting-bad/'':86C ''/strong'':74C ''algorithm'':7B ''bad'':90C ''balanc'':58C ''becom'':37C ''capac'':20C ''caus'':34C ''clear'':27C ''complex'':42C ''data'':44C,64C ''datascience.stackexchange.com'':85C ''datascience.stackexchange.com/questions/61/why-is-overfitting-bad/'':84C ''effici'':6B ''followup'':78C ''general'':22C ''hr'':70C ''href'':83C ''increas'':24C ''know'':50C ''logic'':10C ''model'':4A,17C,33C,35C,53C,68C ''note'':73C ''often'':11C ''overfit'':89C ''p'':9C,46C,71C ''paramet'':8B ''point'':30C ''question'':81C ''regardless'':39C ''right'':57C ''said'':26C ''seek'':66C ''state'':12C ''strong'':72C ''struck'':55C ''underfit'':5A,15C,31C,62C ''wors'':38C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (362, 2, '2014-06-13 17:14:26.26', 4, NULL, NULL, '&lt;p&gt;Can''t you create a hash for each classes, and then merge rows by rows, field by field only the classes where the hash changed ? It should be faster if most of the classes don''t change..&lt;/p&gt;

&lt;p&gt;Or a hash of each rows or maybe columns.. depending on how the data normally change.. &lt;/p&gt;
', NULL, NULL, '''..'':50C,58C ''/p'':39C,59C ''chang'':26C,38C,57C ''class'':10C,22C,35C ''column'':49C ''creat'':5C ''data'':55C ''depend'':51C ''faster'':30C ''field'':17C,19C ''hash'':7C,25C,43C ''mayb'':48C ''merg'':13C ''normal'':56C ''p'':1C,40C ''row'':14C,16C,46C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (363, 2, '2014-06-13 17:14:57.517', 6, NULL, NULL, '&lt;p&gt;Models are but abstractions of what is seen in real life. They are designed in order to abstract-away nitty-gritties of the real system in observation, while keeping sufficient information to support desired analysis.&lt;/p&gt;

&lt;p&gt;If a model is overfit, it takes into account too many details about what is being observed, and small changes on such object may cause the model to lose precision. On the other hand, if a model is underfit, it evaluates so few attributes that noteworthy changes on the object may be ignored.&lt;/p&gt;

&lt;p&gt;Note also that underfit may be seen as an &lt;em&gt;overfit&lt;/em&gt;, depending on the dataset. If your input can be 99%-correctly-classified with a single attribute, you &lt;em&gt;overfit&lt;/em&gt; the model to the data by simplifying the abstraction to a single characteristic. And, in this case, you''d be generalizing too much the 1% of the base into the 99%-class -- or also specifying the model so much that it can only &lt;em&gt;see&lt;/em&gt; one class.&lt;/p&gt;

&lt;p&gt;A reasonable way to say that a model is neither over nor underfit is by performing cross-validations. You split your dataset into &lt;em&gt;k&lt;/em&gt; parts, and say, pick one of them to perform your analysis, while using the other &lt;em&gt;k - 1&lt;/em&gt; parts to train your model. Considering that the input itself is not biased, you should be able to have as much variance of data to train and evaluate as you''d have while using the model in real life processing.&lt;/p&gt;
', NULL, NULL, '''/em'':107C,128C,174C,205C,224C ''/p'':39C,94C,177C,265C ''1'':153C,223C ''99'':117C,159C ''abl'':241C ''abstract'':5C,20C,137C ''abstract-away'':19C ''account'':49C ''also'':97C,162C ''analysi'':38C,216C ''attribut'':84C,124C ''away'':21C ''base'':156C ''bias'':237C ''case'':145C ''caus'':65C ''chang'':60C,87C ''characterist'':141C ''class'':160C,176C ''classifi'':120C ''consid'':230C ''correct'':119C ''correctly-classifi'':118C ''cross'':196C ''cross-valid'':195C ''d'':147C,255C ''data'':133C,248C ''dataset'':111C,201C ''depend'':108C ''design'':15C ''desir'':37C ''detail'':52C ''em'':105C,126C,172C,203C,221C ''evalu'':81C,252C ''general'':149C ''gritti'':24C ''hand'':74C ''ignor'':93C ''inform'':34C ''input'':114C,233C ''k'':204C,222C ''keep'':32C ''life'':12C,263C ''lose'':69C ''mani'':51C ''may'':64C,91C,100C ''model'':2C,43C,67C,77C,130C,165C,186C,229C,260C ''much'':151C,167C,245C ''neither'':188C ''nitti'':23C ''nitty-gritti'':22C ''note'':96C ''noteworthi'':86C ''object'':63C,90C ''observ'':30C,57C ''one'':175C,210C ''order'':17C ''overfit'':45C,106C,127C ''p'':1C,40C,95C,178C ''part'':206C,225C ''perform'':194C,214C ''pick'':209C ''precis'':70C ''process'':264C ''real'':11C,27C,262C ''reason'':180C ''say'':183C,208C ''see'':173C ''seen'':9C,102C ''simplifi'':135C ''singl'':123C,140C ''small'':59C ''specifi'':163C ''split'':199C ''suffici'':33C ''support'':36C ''system'':28C ''take'':47C ''train'':227C,250C ''underfit'':79C,99C,191C ''use'':218C,258C ''valid'':197C ''varianc'':246C ''way'':181C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (865, 1, '2014-07-30 10:24:54.18', 1, 440, 'How to build parse tree with BNF', '&lt;p&gt;I need to build parse tree for some source code (on Python or any program language that describe by CFG).&lt;/p&gt;

&lt;p&gt;So, I have source code on some programming language and BNF this language.&lt;/p&gt;

&lt;p&gt;Can anybody give some advice how can I build parse tree in this case?
Preferably, with tools for Python.&lt;/p&gt;
', 'python  parsing', NULL, '''/p'':31C,46C,67C ''advic'':52C ''anybodi'':49C ''bnf'':7A,43C ''build'':3A,14C,56C ''case'':61C ''cfg'':30C ''code'':20C,37C ''describ'':28C ''give'':50C ''languag'':26C,41C,45C ''need'':12C ''p'':10C,32C,47C ''pars'':4A,9B,15C,57C ''prefer'':62C ''program'':25C,40C ''python'':8B,22C,66C ''sourc'':19C,36C ''tool'':64C ''tree'':5A,16C,58C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (364, 2, '2014-06-13 17:36:21.937', 0, NULL, NULL, '&lt;p&gt;The &lt;a href="http://sijinjoseph.com/programmer-competency-matrix/" rel="nofollow"&gt;Programmer Competency Matrix&lt;/a&gt; is just a set of skills, which are more likely to occur when being a real programmer than other skills, they are not a checklist to being a programmer, or for that matter, required to be a programmer; most common way to know someone is a programmer is that they''re paid to be a programmer, which honestly has nothing to do with programming skills.&lt;/p&gt;

&lt;p&gt;To be a data scientist, do data science.&lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/p'':79C,89C ''/programmer-competency-matrix/'':7C ''checklist'':38C ''common'':53C ''compet'':11C ''data'':84C,87C ''honest'':71C ''href'':4C ''know'':56C ''like'':23C ''matrix'':12C ''matter'':46C ''nofollow'':9C ''noth'':73C ''occur'':25C ''p'':1C,80C ''paid'':65C ''program'':77C ''programm'':10C,30C,42C,51C,60C,69C ''re'':64C ''real'':29C ''rel'':8C ''requir'':47C ''scienc'':88C ''scientist'':85C ''set'':17C ''sijinjoseph.com'':6C ''sijinjoseph.com/programmer-competency-matrix/'':5C ''skill'':19C,33C,78C ''someon'':57C ''way'':54C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (403, 1, '2014-06-16 13:30:01.32', 1, 165, 'Why is there such a mismatch between the Model''s predicted probability and theoretical probability in logistic regression?', '&lt;p&gt;&lt;img src="http://i.stack.imgur.com/4Ih6o.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;I am trying to do Logistic Regression using SAS Enterprise Miner. 
My Independent variables are &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;CPR/Inc (Categorical 1 to 7)
OD/Inc (Categorical 1 to 4)
Insurance (Binary 0 or 1)
Income Loss (Binary 0 or 1)
Living Arrangement (Categorical 1 to 7)
Employment Status (categorical 1 to 8)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;My Dependent Variable is Default (Binary 0 or 1)&lt;/p&gt;

&lt;p&gt;The following is the output from running Regression Model.&lt;/p&gt;

&lt;p&gt;Analysis of Maximum Likelihood Estimates&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;                                  Standard          Wald
Parameter       DF    Estimate       Error    Chi-Square    Pr &amp;gt; ChiSq    Exp(Est)

Intercept        1     -0.4148      0.0645         41.30        &amp;lt;.0001       0.660
CPR___Inc  1     1     -0.8022      0.1051         58.26        &amp;lt;.0001       0.448
CPR___Inc  2     1     -0.4380      0.0966         20.57        &amp;lt;.0001       0.645
CPR___Inc  3     1      0.3100      0.0871         12.68        0.0004       1.363
CPR___Inc  4     1    -0.00304      0.0898          0.00        0.9730       0.997
CPR___Inc  5     1      0.1331      0.0885          2.26        0.1324       1.142
CPR___Inc  6     1      0.1694      0.0881          3.70        0.0546       1.185
Emp_Status 1     1     -0.2289      0.1006          5.18        0.0229       0.795
Emp_Status 2     1      0.4061      0.0940         18.66        &amp;lt;.0001       1.501
Emp_Status 3     1     -0.2119      0.1004          4.46        0.0347       0.809
Emp_Status 4     1      0.1100      0.0963          1.30        0.2534       1.116
Emp_Status 5     1     -0.2280      0.1007          5.12        0.0236       0.796
Emp_Status 6     1      0.3761      0.0943         15.91        &amp;lt;.0001       1.457
Emp_Status 7     1     -0.3337      0.1026         10.59        0.0011       0.716
Inc_Loss   0     1     -0.1996      0.0449         19.76        &amp;lt;.0001       0.819
Insurance  0     1      0.1256      0.0559          5.05        0.0246       1.134
Liv_Arran  1     1     -0.1128      0.0916          1.52        0.2178       0.893
Liv_Arran  2     1      0.2576      0.0880          8.57        0.0034       1.294
Liv_Arran  3     1      0.0235      0.0904          0.07        0.7950       1.024
Liv_Arran  4     1      0.0953      0.0887          1.16        0.2825       1.100
Liv_Arran  5     1     -0.0493      0.0907          0.29        0.5871       0.952
Liv_Arran  6     1     -0.3732      0.0966         14.93        0.0001       0.689
OD___Inc   1     1     -0.2136      0.0557         14.72        0.0001       0.808
OD___Inc   2     1     -0.0279      0.0792          0.12        0.7248       0.973
OD___Inc   3     1     -0.0249      0.0793          0.10        0.7534       0.975
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now I used this Model to Score a new set of data. An example row of my new data is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;CPR - 7
OD - 4
Living Arrangement - 4
Employment Status - 4
Insurance - 0
Income Loss - 1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For this sample row, the model predicted output (Probability of default = 1) as 0.7335 
To check this manually, I added the estimates&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Intercept + Emp Status 4 + Liv Arran 4 + Insurance 0
-0.4148   + 0.1100  +   0.0953   +   0.1256    =   -0.0839
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Odds ratio = Exponential(-0.0839) = 0.9195&lt;/p&gt;

&lt;p&gt;Hence probability = 0.9195 / (1 + 0.9195)  =   0.4790&lt;/p&gt;

&lt;p&gt;I am unable to understand why there is such a mismatch between the Model''s predicted probability and theoretical probability. &lt;/p&gt;

&lt;p&gt;Any help would be much appreciated .
Thanks&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (365, 2, '2014-06-13 18:59:11.493', 3, NULL, NULL, '&lt;p&gt;@OP: Choosing answers by votes is the WORST idea.  &lt;/p&gt;

&lt;p&gt;Your question becomes a popularity contest.  You should seek the right answer, I doubt you know what you are asking, know what you are looking for.  &lt;/p&gt;

&lt;p&gt;To answer your question:&lt;br&gt;
Q: how seriously DS certifications are viewed at this point by the community.  &lt;/p&gt;

&lt;p&gt;A: What is your goal from taking these courses?  For work, for school, for self-improvement, etc?  Coursera classes are very applied, you will not learn much theory, they are intentionally reserved for classroom setting.  &lt;/p&gt;

&lt;p&gt;Nonetheless, Coursera classes are very useful.  I''d say it is equivalent to one year of stat grad class, out of a two year Master program.  &lt;/p&gt;

&lt;p&gt;I am not sure of its industry recognition yet, because the problem of how did you actually take the course?  How much time did you spend?  It''s a lot easier to get A''s in these courses than a classroom paper-pencil exam.  So, there is be a huge quality variation from person to person.&lt;/p&gt;
', NULL, NULL, '''/p'':11C,39C,59C,97C,125C,184C ''actual'':143C ''answer'':4C,24C,42C ''appli'':83C ''ask'':32C ''becom'':15C ''br'':45C ''certif'':50C ''choos'':3C ''class'':80C,101C,117C ''classroom'':95C,167C ''communiti'':58C ''contest'':18C ''cours'':69C,146C,164C ''coursera'':79C,100C ''d'':106C ''doubt'':26C ''ds'':49C ''easier'':157C ''equival'':110C ''etc'':78C ''exam'':171C ''get'':159C ''goal'':65C ''grad'':116C ''huge'':177C ''idea'':10C ''improv'':77C ''industri'':133C ''intent'':92C ''know'':28C,33C ''learn'':87C ''look'':37C ''lot'':156C ''master'':123C ''much'':88C,148C ''nonetheless'':99C ''one'':112C ''op'':2C ''p'':1C,12C,40C,60C,98C,126C ''paper'':169C ''paper-pencil'':168C ''pencil'':170C ''person'':181C,183C ''point'':55C ''popular'':17C ''problem'':138C ''program'':124C ''q'':46C ''qualiti'':178C ''question'':14C,44C ''recognit'':134C ''reserv'':93C ''right'':23C ''say'':107C ''school'':73C ''seek'':21C ''self'':76C ''self-improv'':75C ''serious'':48C ''set'':96C ''spend'':152C ''stat'':115C ''sure'':130C ''take'':67C,144C ''theori'':89C ''time'':149C ''two'':121C ''use'':104C ''variat'':179C ''view'':52C ''vote'':6C ''work'':71C ''worst'':9C ''year'':113C,122C ''yet'':135C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (366, 2, '2014-06-13 20:13:01.913', 7, NULL, NULL, '&lt;p&gt;To answer your question it is important to understand the frame of reference you are looking for, if you are looking for what philosophically you are trying to achieve in model fitting, check out Rubens answer he does a good job of explaining that context.&lt;/p&gt;

&lt;p&gt;However, in practice your question is almost entirely defined by business objectives.  &lt;/p&gt;

&lt;p&gt;To give a concrete example, lets say that you are a loan officer, you issued loans that are \$3,000 and when people pay you back you make \$50.  Naturally you are trying to build a model that predicts how if a person defaults on their loan.  Lets keep this simple and say that the outcomes are either full payment, or default.&lt;/p&gt;

&lt;p&gt;From a business perspective you can sum up a models performance with a contingency matrix:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/RgWr9.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;When the model predicts someone is going to default, do they?  To determining the downsides of over and under fitting I find it helpful to think of it as an optimization problem, because in each cross section of predicted verses actual model performance there is either a cost or profit to be made:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/78kH2.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;In this example predicting a default that is a default means avoiding any risk, and predicted a non-default which doesn''t default will make \$50 per loan issued.  Where things get dicey is when you are wrong, if you default when you predicted non-default you lose the entire loan principal and if you predict default when a customer actually would not have you suffer \$50 of missed opportunity.  The numbers here are not important, just the approach.&lt;/p&gt;

&lt;p&gt;With this framework we can now begin to understand the difficulties associated with over and under fitting.&lt;/p&gt;

&lt;p&gt;Over fitting in this case would mean that your model works far better on you development/test data then it does in production.  Or to put it another way, your model in production will far underperform what you saw in development, this false confidence will probably cause you to take on far more risky loans then you otherwise would and leaves you very vulnerable to losing money.&lt;/p&gt;

&lt;p&gt;On the other hand, under fitting in this context will leave you with a model that just does a poor job of matching reality.  While the results of this can be wildly unpredictable, (the opposite word you want to describe your predictive models), commonly what happens is standards are tightened up to compensate for this, leading to less overall customers leading to lost good customers.  &lt;/p&gt;

&lt;p&gt;Under fitting suffers a kind of opposite difficulty that over fitting does, which is under fitting gives you lower confidence.  Insidiously, the lack of predictability still leads you to take on unexpected risk, all of which is bad news.&lt;/p&gt;

&lt;p&gt;In my experience the best way to avoid both of these situations is validating your model on data that is completely outside the scope of your training data, so you can have some confidence that you have a representative sample of what you will see ''in the wild''.  &lt;/p&gt;

&lt;p&gt;Additionally, it is always a good practice to revalidate your models periodically, to determine how quickly your model is degrading, and if it is still accomplishing your objectives.&lt;/p&gt;

&lt;p&gt;Just to some things up, your model is under fitted when it does a poor job of predicting both the development and production data.&lt;/p&gt;
', NULL, NULL, '''/78kh2.png'':215C ''/p'':47C,61C,125C,142C,154C,209C,221C,304C,323C,391C,458C,499C,549C,579C,605C ''/rgwr9.png'':148C ''000'':82C ''3'':81C ''50'':91C,249C,291C ''accomplish'':576C ''achiev'':30C ''actual'':196C,285C ''addit'':551C ''almost'':55C ''alt'':149C,216C ''alway'':554C ''anoth'':351C ''answer'':3C,37C ''approach'':303C ''associ'':317C ''avoid'':234C,508C ''back'':88C ''bad'':497C ''begin'':312C ''best'':505C ''better'':337C ''build'':97C ''busi'':59C,129C ''case'':329C ''caus'':370C ''check'':34C ''common'':436C ''compens'':445C ''complet'':521C ''concret'':66C ''confid'':367C,479C,534C ''context'':46C,401C ''conting'':140C ''cost'':203C ''cross'':191C ''custom'':284C,452C,457C ''data'':341C,518C,528C,604C ''default'':106C,124C,164C,228C,232C,242C,246C,264C,270C,281C ''defin'':57C ''degrad'':570C ''describ'':432C ''descript'':152C,219C ''determin'':168C,564C ''develop'':364C,601C ''development/test'':340C ''dicey'':256C ''difficulti'':316C,467C ''doesn'':244C ''downsid'':170C ''either'':120C,201C ''enter'':150C,217C ''entir'':56C,274C ''exampl'':67C,225C ''experi'':503C ''explain'':44C ''fals'':366C ''far'':336C,358C,375C ''find'':177C ''fit'':33C,175C,322C,326C,398C,461C,470C,475C,590C ''frame'':12C ''framework'':308C ''full'':121C ''get'':255C ''give'':64C,476C ''go'':162C ''good'':41C,456C,556C ''hand'':396C ''happen'':438C ''help'':179C ''howev'':49C ''i.stack.imgur.com'':147C,214C ''i.stack.imgur.com/78kh2.png'':213C ''i.stack.imgur.com/rgwr9.png'':146C ''imag'':151C,218C ''img'':144C,211C ''import'':8C,300C ''insidi'':480C ''issu'':77C,252C ''job'':42C,413C,596C ''keep'':111C ''kind'':464C ''lack'':482C ''lead'':448C,453C,486C ''leav'':384C,403C ''less'':450C ''let'':68C,110C ''loan'':74C,78C,109C,251C,275C,378C ''look'':17C,22C ''lose'':272C,389C ''lost'':455C ''lower'':478C ''made'':208C ''make'':90C,248C ''match'':415C ''matrix'':141C ''mean'':233C,331C ''miss'':293C ''model'':32C,99C,136C,158C,197C,334C,354C,407C,435C,516C,561C,568C,587C ''money'':390C ''natur'':92C ''news'':498C ''non'':241C,269C ''non-default'':240C,268C ''number'':296C ''object'':60C,578C ''offic'':75C ''opportun'':294C ''opposit'':427C,466C ''optim'':186C ''otherwis'':381C ''outcom'':118C ''outsid'':522C ''overal'':451C ''p'':1C,48C,62C,126C,143C,155C,210C,222C,305C,324C,392C,459C,500C,550C,580C ''pay'':86C ''payment'':122C ''peopl'':85C ''per'':250C ''perform'':137C,198C ''period'':562C ''person'':105C ''perspect'':130C ''philosoph'':25C ''poor'':412C,595C ''practic'':51C,557C ''predict'':101C,159C,194C,226C,238C,267C,280C,434C,484C,598C ''princip'':276C ''probabl'':369C ''problem'':187C ''product'':346C,356C,603C ''profit'':205C ''put'':349C ''question'':5C,53C ''quick'':566C ''realiti'':416C ''refer'':14C ''repres'':539C ''result'':419C ''revalid'':559C ''risk'':236C,492C ''riski'':377C ''ruben'':36C ''sampl'':540C ''saw'':362C ''say'':69C,115C ''scope'':524C ''section'':192C ''see'':545C ''simpl'':113C ''situat'':512C ''someon'':160C ''src'':145C,212C ''standard'':440C ''still'':485C,575C ''suffer'':290C,462C ''sum'':133C ''take'':373C,489C ''thing'':254C,584C ''think'':181C ''tighten'':442C ''train'':527C ''tri'':28C,95C ''underperform'':359C ''understand'':10C,314C ''unexpect'':491C ''unpredict'':425C ''valid'':514C ''vers'':195C ''vulner'':387C ''want'':430C ''way'':352C,506C ''wild'':424C,548C ''word'':428C ''work'':335C ''would'':286C,330C,382C ''wrong'':261C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (368, 2, '2014-06-14 07:34:37.643', 2, NULL, NULL, '&lt;p&gt;If you know R and it''s ggplot library, you could try ggplot for python:&lt;/p&gt;

&lt;p&gt;I like it, because I do work in R and python, and both are virtually identical.&lt;/p&gt;

&lt;p&gt;But if you are not familiar you have to deal with a very "unpythonic" syntax. But I think it''s an easy library overall.&lt;/p&gt;
', NULL, NULL, '''/p'':17C,35C,61C ''could'':12C ''deal'':46C ''easi'':58C ''familiar'':42C ''ggplot'':9C,14C ''ident'':34C ''know'':4C ''librari'':10C,59C ''like'':20C ''overal'':60C ''p'':1C,18C,36C ''python'':16C,29C ''r'':5C,27C ''syntax'':51C ''think'':54C ''tri'':13C ''unpython'':50C ''virtual'':33C ''work'':25C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (369, 1, '2014-06-14 18:53:32.243', 5, 479, 'Difference between using RMSE and nDCG to evaluate Recommender Systems', '&lt;p&gt;What kind of error measures do RMSE and nDCG give while evaluating a recommender system, and how do I know when to use one over the other? If you could give an example of when to use each, that would be great as well!&lt;/p&gt;
', 'machine-learning  recommendation  evaluation', NULL, '''/p'':61C ''could'':46C ''differ'':1A ''error'':20C ''evalu'':8A,15B,28C ''exampl'':49C ''give'':26C,47C ''great'':58C ''kind'':18C ''know'':36C ''learn'':13B ''machin'':12B ''machine-learn'':11B ''measur'':21C ''ndcg'':6A,25C ''one'':40C ''p'':16C ''recommend'':9A,14B,30C ''rmse'':4A,23C ''system'':10A,31C ''use'':3A,39C,53C ''well'':60C ''would'':56C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (370, 1, '2014-06-14 19:54:53.193', 8, 467, 'Data Science oriented dataset/research question for Statistics MSc thesis', '&lt;p&gt;I''d like to explore ''data science''. The term seems a little vague to me, but I expect it to require:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;machine learning (rather than traditional statistics);&lt;/li&gt;
&lt;li&gt;a large enough dataset that you have to run analyses on clusters.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;What are some good datasets and problems, accessible to a statistician with some programming background, that I can use to explore the field of data science?&lt;/p&gt;

&lt;p&gt;To keep this as narrow as possible, I''d ideally like links to open, well used datasets and example problems.&lt;/p&gt;
', 'statistics  education  knowledge-base  definitions', NULL, '''/li'':47C,61C ''/ol'':62C ''/p'':38C,90C,112C ''access'':71C ''analys'':58C ''background'':78C ''base'':14B ''cluster'':60C ''d'':18C,100C ''data'':1A,22C,88C ''dataset'':52C,68C,108C ''dataset/research'':4A ''definit'':15B ''educ'':11B ''enough'':51C ''exampl'':110C ''expect'':34C ''explor'':21C,84C ''field'':86C ''good'':67C ''ideal'':101C ''keep'':93C ''knowledg'':13B ''knowledge-bas'':12B ''larg'':50C ''learn'':42C ''li'':40C,48C ''like'':19C,102C ''link'':103C ''littl'':28C ''machin'':41C ''msc'':8A ''narrow'':96C ''ol'':39C ''open'':105C ''orient'':3A ''p'':16C,63C,91C ''possibl'':98C ''problem'':70C,111C ''program'':77C ''question'':5A ''rather'':43C ''requir'':37C ''run'':57C ''scienc'':2A,23C,89C ''seem'':26C ''statist'':7A,10B,46C ''statistician'':74C ''term'':25C ''thesi'':9A ''tradit'':45C ''use'':82C,107C ''vagu'':29C ''well'':106C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (371, 1, '2014-06-14 20:32:06.143', 5, 120, 'How to process natural language queries?', '&lt;p&gt;I''m curious about natural language querying.  Stanford has what looks to be a strong set of &lt;a href="http://nlp.stanford.edu/software/index.shtml"&gt;software for processing natural language&lt;/a&gt;.  I''ve also seen the &lt;a href="http://opennlp.apache.org/documentation/1.5.3/manual/opennlp.html"&gt;Apache OpenNLP library&lt;/a&gt;, and the &lt;a href="http://gate.ac.uk/science.html"&gt;General Architecture for Text Engineering&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;There are an incredible amount of uses for natural language processing and that makes the documentation of these projects difficult to quickly absorb.&lt;/p&gt;

&lt;p&gt;Can you simplify things for me a bit and at a high level outline the tasks necessary for performing a basic translation of simple questions into SQL?&lt;/p&gt;

&lt;p&gt;The first rectangle on my flow chart is a bit of a mystery.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/wJPx9.png" alt="enter image description here"&gt;  &lt;/p&gt;

&lt;p&gt;For example, I might want to know:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;How many books were sold last month?
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And I''d want that translated into&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Select count(*) 
  from sales 
  where 
   item_type=''book'' and 
   sales_date &amp;gt;= ''5/1/2014'' and 
   sales_date &amp;lt;= ''5/31/2014''
&lt;/code&gt;&lt;/pre&gt;
', 'nlp', NULL, '''/a'':36C,50C,63C ''/code'':164C,195C ''/documentation/1.5.3/manual/opennlp.html'':46C ''/p'':64C,89C,118C,133C,145C,154C,174C ''/pre'':165C,196C ''/science.html'':57C ''/software/index.shtml'':30C ''/wjpx9.png'':139C ''5/1/2014'':189C ''5/31/2014'':194C ''absorb'':88C ''also'':39C ''alt'':140C ''amount'':70C ''apach'':47C ''architectur'':59C ''basic'':111C ''bit'':98C,129C ''book'':159C,184C ''chart'':126C ''code'':156C,176C ''count'':178C ''curious'':11C ''d'':169C ''date'':187C,192C ''descript'':143C ''difficult'':85C ''document'':81C ''engin'':62C ''enter'':141C ''exampl'':148C ''first'':121C ''flow'':125C ''gate.ac.uk'':56C ''gate.ac.uk/science.html'':55C ''general'':58C ''gt'':188C ''high'':102C ''href'':27C,43C,54C ''i.stack.imgur.com'':138C ''i.stack.imgur.com/wjpx9.png'':137C ''imag'':142C ''img'':135C ''incred'':69C ''item'':182C ''know'':153C ''languag'':5A,14C,35C,75C ''last'':162C ''level'':103C ''librari'':49C ''look'':19C ''lt'':193C ''m'':10C ''make'':79C ''mani'':158C ''might'':150C ''month'':163C ''mysteri'':132C ''natur'':4A,13C,34C,74C ''necessari'':107C ''nlp'':7B ''nlp.stanford.edu'':29C ''nlp.stanford.edu/software/index.shtml'':28C ''opennlp'':48C ''opennlp.apache.org'':45C ''opennlp.apache.org/documentation/1.5.3/manual/opennlp.html'':44C ''outlin'':104C ''p'':8C,65C,90C,119C,134C,146C,166C ''perform'':109C ''pre'':155C,175C ''process'':3A,33C,76C ''project'':84C ''queri'':6A,15C ''question'':115C ''quick'':87C ''rectangl'':122C ''sale'':180C,186C,191C ''seen'':40C ''select'':177C ''set'':24C ''simpl'':114C ''simplifi'':93C ''softwar'':31C ''sold'':161C ''sql'':117C ''src'':136C ''stanford'':16C ''strong'':23C ''task'':106C ''text'':61C ''thing'':94C ''translat'':112C,172C ''type'':183C ''use'':72C ''ve'':38C ''want'':151C,170C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (372, 2, '2014-06-14 20:52:00.873', 8, NULL, NULL, '&lt;p&gt;Just head to kaggle.com; it''ll keep you busy for a long time. For open data there''s the &lt;a href="http://archive.ics.uci.edu/ml/"&gt;UC Irvine Machine Learning Repository&lt;/a&gt;. In fact, there''s a whole &lt;a href="http://opendata.stackexchange.com/"&gt;Stackexchange site&lt;/a&gt; devoted to this; look there.&lt;/p&gt;
', NULL, NULL, '''/a'':31C,43C ''/ml/'':25C ''/p'':49C ''archive.ics.uci.edu'':24C ''archive.ics.uci.edu/ml/'':23C ''busi'':10C ''data'':17C ''devot'':44C ''fact'':33C ''head'':3C ''href'':22C,39C ''irvin'':27C ''kaggle.com'':5C ''keep'':8C ''learn'':29C ''ll'':7C ''long'':13C ''look'':47C ''machin'':28C ''open'':16C ''opendata.stackexchange.com'':40C ''p'':1C ''repositori'':30C ''site'':42C ''stackexchang'':41C ''time'':14C ''uc'':26C ''whole'':37C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8829, 1, '2015-11-13 09:29:35.127', 1, 25, 'Examining a DocumentTermMatrix in RTextTools', '&lt;p&gt;I created a DocumentTermMatrix  for text mining using RTextTools. The rows for this DocumentTermMatrix  correspond to dataframe rows and matix columns correspond to words. My question is : How can I get the words (labels vector) for examining the DocumentTermMatrix  ? In other words, How can I get the vector of these 904 words?&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;require(RTextTools,quietly=TRUE)
data(USCongress)

doc_matrix &amp;lt;- create_matrix(USCongress$text, language="english",   removeNumbers=TRUE, stemWords=TRUE, removeSparseTerms=.998)

dim(USCongress)
[1] 4449    6

dim(doc_matrix)
[1] 4449  904
&lt;/code&gt;&lt;/pre&gt;
', 'r  text-mining', NULL, '''/code'':98C ''/p'':63C ''/pre'':99C ''1'':89C,95C ''4449'':90C,96C ''6'':91C ''904'':61C,97C ''998'':86C ''code'':65C ''column'':31C ''correspond'':25C,32C ''creat'':12C,75C ''data'':70C ''datafram'':27C ''dim'':87C,92C ''doc'':72C,93C ''documenttermmatrix'':3A,14C,24C,49C ''english'':80C ''examin'':1A,47C ''get'':41C,56C ''label'':44C ''languag'':79C ''lt'':74C ''matix'':30C ''matrix'':73C,76C,94C ''mine'':9B,17C ''p'':10C ''pre'':64C ''question'':36C ''quiet'':68C ''r'':6B ''removenumb'':81C ''removesparseterm'':85C ''requir'':66C ''row'':21C,28C ''rtexttool'':5A,19C,67C ''stemword'':83C ''text'':8B,16C,78C ''text-min'':7B ''true'':69C,82C,84C ''uscongress'':71C,77C,88C ''use'':18C ''vector'':45C,58C ''word'':34C,43C,52C,62C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (373, 2, '2014-06-14 21:13:20.11', 5, NULL, NULL, '&lt;p&gt;The &lt;a href="http://sunlightfoundation.com/"&gt;Sunlight Foundation&lt;/a&gt; is an organization that is focused on opening up and encouraging non-partisan analysis of government data.&lt;/p&gt;

&lt;p&gt;There is a ton of analysis out there in the wild that can be used for comparison, and a wide variety of topics.&lt;/p&gt;

&lt;p&gt;They provide &lt;a href="http://sunlightfoundation.com/tools/"&gt;tools&lt;/a&gt; and &lt;a href="http://sunlightfoundation.com/api/"&gt;apis&lt;/a&gt; for accessing data, and have helped push to make data available in places like &lt;a href="http://www.data.gov/"&gt;data.gov&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;One interesting project is &lt;a href="http://influenceexplorer.com/"&gt;Influence Explorer&lt;/a&gt;.  You can get &lt;a href="http://data.influenceexplorer.com/"&gt;source data here&lt;/a&gt; as well as access to real time data.&lt;/p&gt;

&lt;p&gt;You might also want to take a look at one of our more popular questions:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/questions/155/publicly-available-datasets"&gt;Publicly available datasets&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':8C,62C,70C,89C,101C,111C,147C ''/api/'':68C ''/p'':27C,52C,90C,120C,137C,148C ''/questions/155/publicly-available-datasets'':143C ''/tools/'':60C ''access'':72C,115C ''also'':124C ''analysi'':23C,34C ''api'':69C ''avail'':81C,145C ''comparison'':45C ''data'':26C,73C,80C,109C,119C ''data.gov'':88C ''data.influenceexplorer.com'':107C ''datascience.stackexchange.com'':142C ''datascience.stackexchange.com/questions/155/publicly-available-datasets'':141C ''dataset'':146C ''encourag'':19C ''explor'':100C ''focus'':14C ''foundat'':7C ''get'':104C ''govern'':25C ''help'':76C ''href'':4C,57C,65C,86C,97C,106C,140C ''influenc'':99C ''influenceexplorer.com'':98C ''interest'':93C ''like'':84C ''look'':129C ''make'':79C ''might'':123C ''non'':21C ''non-partisan'':20C ''one'':92C,131C ''open'':16C ''organ'':11C ''p'':1C,28C,53C,91C,121C,138C ''partisan'':22C ''place'':83C ''popular'':135C ''project'':94C ''provid'':55C ''public'':144C ''push'':77C ''question'':136C ''real'':117C ''sourc'':108C ''sunlight'':6C ''sunlightfoundation.com'':5C,59C,67C ''sunlightfoundation.com/api/'':66C ''sunlightfoundation.com/tools/'':58C ''take'':127C ''time'':118C ''ton'':32C ''tool'':61C ''topic'':51C ''use'':43C ''varieti'':49C ''want'':125C ''well'':113C ''wide'':48C ''wild'':39C ''www.data.gov'':87C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (569, 2, '2014-06-24 20:35:44.713', 1, NULL, NULL, '&lt;p&gt;Is it a bird? Is it a cat? We have black-and-white cat-sized! magpies here. so that would fail.&lt;/p&gt;

&lt;p&gt;First thing would be to exclude all areas that are green, cats are seldom green.&lt;/p&gt;

&lt;p&gt;Then compare the rest to a reference image to remove static things like stones and stairs.&lt;/p&gt;

&lt;p&gt;Detecting objects of a minimum size should be possible, but for a classification the resolution is too low. Could be also your neighbor testing his new remote controlled drone.&lt;/p&gt;

&lt;p&gt;With two cameras you could do a 3d mapping of the objects and eliminate flying objects.&lt;/p&gt;
', NULL, NULL, '''/p'':25C,42C,60C,91C,109C ''3d'':100C ''also'':82C ''area'':34C ''bird'':5C ''black'':13C ''black-and-whit'':12C ''camera'':95C ''cat'':9C,17C,38C ''cat-siz'':16C ''classif'':74C ''compar'':45C ''control'':89C ''could'':80C,97C ''detect'':62C ''drone'':90C ''elimin'':106C ''exclud'':32C ''fail'':24C ''first'':27C ''fli'':107C ''green'':37C,41C ''imag'':51C ''like'':56C ''low'':79C ''magpi'':19C ''map'':101C ''minimum'':66C ''neighbor'':84C ''new'':87C ''object'':63C,104C,108C ''p'':1C,26C,43C,61C,92C ''possibl'':70C ''refer'':50C ''remot'':88C ''remov'':53C ''resolut'':76C ''rest'':47C ''seldom'':40C ''size'':18C,67C ''stair'':59C ''static'':54C ''stone'':57C ''test'':85C ''thing'':28C,55C ''two'':94C ''white'':15C ''would'':23C,29C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (374, 2, '2014-06-14 21:39:15.833', 3, NULL, NULL, '&lt;p&gt;Natural language querying poses very many intricacies which can be very difficult to generalize.  From a high level, I would start with trying to think of things in terms of nouns and verbs.&lt;/p&gt;

&lt;p&gt;So for the sentence: How many books were sold last month?&lt;/p&gt;

&lt;p&gt;You would start by breaking the sentence down with a parser which will return a tree format similar to this:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ogoiY.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;You can see that there is a subject books, a compound verbal phrase indicating the past action of sell, and then a noun phrase where you have the time focus of a month.&lt;/p&gt;

&lt;p&gt;We can further break down the subject for modifiers: "how many" for books, and "last" for month.&lt;/p&gt;

&lt;p&gt;Once you have broken the sentence down you need to map those elements to sql language e.g.: how many =&gt; count, books =&gt; book, sold =&gt; sales, month =&gt; sales_date (interval), and so on.  &lt;/p&gt;

&lt;p&gt;Finally, once you have the elements of the language you just need to come up with a set of rules for how different entities interact with each other, which leaves you with:&lt;/p&gt;

&lt;p&gt;Select count(*) 
  from sales 
  where 
   item_type=''book'' and 
   sales_date &gt;= ''5/1/2014'' and 
   sales_date &amp;lt;= ''5/31/2014''&lt;/p&gt;

&lt;p&gt;This is at a high level how I would begin, while almost every step I have mentioned is non-trivial and really the rabbit hole can be endless, this should give you many of the dots to connect.  &lt;/p&gt;
', NULL, NULL, '''/ogoiy.png'':76C ''/p'':35C,48C,70C,82C,117C,136C,169C,203C,222C,263C ''5/1/2014'':216C ''5/31/2014'':221C ''action'':100C ''almost'':235C ''alt'':77C ''begin'':233C ''book'':43C,92C,131C,158C,159C,212C ''break'':54C,122C ''broken'':141C ''come'':184C ''compound'':94C ''connect'':262C ''count'':157C,206C ''date'':164C,215C,219C ''descript'':80C ''differ'':193C ''difficult'':13C ''dot'':260C ''e.g'':154C ''element'':150C,176C ''endless'':252C ''enter'':78C ''entiti'':194C ''everi'':236C ''final'':171C ''focus'':113C ''format'':66C ''general'':15C ''give'':255C ''high'':18C,228C ''hole'':249C ''i.stack.imgur.com'':75C ''i.stack.imgur.com/ogoiy.png'':74C ''imag'':79C ''img'':72C ''indic'':97C ''interact'':195C ''interv'':165C ''intricaci'':8C ''item'':210C ''languag'':3C,153C,179C ''last'':46C,133C ''leav'':200C ''level'':19C,229C ''lt'':220C ''mani'':7C,42C,129C,156C,257C ''map'':148C ''mention'':240C ''modifi'':127C ''month'':47C,116C,135C,162C ''natur'':2C ''need'':146C,182C ''non'':243C ''non-trivi'':242C ''noun'':32C,106C ''p'':1C,36C,49C,71C,83C,118C,137C,170C,204C,223C ''parser'':60C ''past'':99C ''phrase'':96C,107C ''pose'':5C ''queri'':4C ''rabbit'':248C ''realli'':246C ''return'':63C ''rule'':190C ''sale'':161C,163C,208C,214C,218C ''see'':86C ''select'':205C ''sell'':102C ''sentenc'':40C,56C,143C ''set'':188C ''similar'':67C ''sold'':45C,160C ''sql'':152C ''src'':73C ''start'':22C,52C ''step'':237C ''subject'':91C,125C ''term'':30C ''thing'':28C ''think'':26C ''time'':112C ''tree'':65C ''tri'':24C ''trivial'':244C ''type'':211C ''verb'':34C ''verbal'':95C ''would'':21C,51C,232C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (376, 1, '2014-06-15 00:00:51.657', 6, 77, 'How to compare experiments run over different infrastructures', '&lt;p&gt;I''m developing a distributed algorithm, and to improve efficiency, it relies both on the number of disks (one per machine), and on an efficient load balance strategy. With more disks, we''re able to reduce the time spent with I/O; and with an efficient load balance policy, we can distribute tasks without much data replication overhead.&lt;/p&gt;

&lt;p&gt;There are many studies on the literature that deal with the same problem, and each of them runs different experiments to evaluate their proposal. Some experiments are specific of the strategy presented, and some others, like weak scaling (scalability) and strong scaling (speedup), are common to all of the works.&lt;/p&gt;

&lt;p&gt;The problem is the experiments are usually executed over entirely different infrastructures (disks, processors, # machines, network), and depending on what is being evaluated, it may raise &lt;em&gt;false/unfair&lt;/em&gt; comparisons. For example, I may get 100% of speedup in my application running on 10 machines with Infiniband connection, whereas I could get the same or even worse results if my connection was Ethernet.&lt;/p&gt;

&lt;p&gt;So, how can one honestly compare different experiments to point out efficiency gains? &lt;/p&gt;
', 'bigdata  efficiency  performance  scalability  distributed', NULL, '''/em'':154C ''/p'':72C,124C,189C,204C ''10'':169C ''100'':161C ''abl'':48C ''algorithm'':20C ''applic'':166C ''balanc'':41C,61C ''bigdata'':9B ''common'':118C ''compar'':3A,196C ''comparison'':155C ''connect'':173C,186C ''could'':176C ''data'':69C ''deal'':82C ''depend'':143C ''develop'':17C ''differ'':7A,92C,136C,197C ''disk'':32C,45C,138C ''distribut'':13B,19C,65C ''effici'':10B,24C,39C,59C,202C ''em'':152C ''entir'':135C ''ethernet'':188C ''evalu'':95C,148C ''even'':181C ''exampl'':157C ''execut'':133C ''experi'':4A,93C,99C,130C,198C ''false/unfair'':153C ''gain'':203C ''get'':160C,177C ''honest'':195C ''i/o'':55C ''improv'':23C ''infiniband'':172C ''infrastructur'':8A,137C ''like'':109C ''literatur'':80C ''load'':40C,60C ''m'':16C ''machin'':35C,140C,170C ''mani'':76C ''may'':150C,159C ''much'':68C ''network'':141C ''number'':30C ''one'':33C,194C ''other'':108C ''overhead'':71C ''p'':14C,73C,125C,190C ''per'':34C ''perform'':11B ''point'':200C ''polici'':62C ''present'':105C ''problem'':86C,127C ''processor'':139C ''propos'':97C ''rais'':151C ''re'':47C ''reduc'':50C ''reli'':26C ''replic'':70C ''result'':183C ''run'':5A,91C,167C ''scalabl'':12B,112C ''scale'':111C,115C ''specif'':101C ''speedup'':116C,163C ''spent'':53C ''strategi'':42C,104C ''strong'':114C ''studi'':77C ''task'':66C ''time'':52C ''usual'':132C ''weak'':110C ''wherea'':174C ''without'':67C ''work'':123C ''wors'':182C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (377, 2, '2014-06-15 00:11:54.95', 4, NULL, NULL, '&lt;p&gt;From our perspective on here, the big benefit of S3 is the ease of accessing the data from within EC2.&lt;/p&gt;

&lt;p&gt;Google Drive is directly accessible from the Google Cloud platform.&lt;/p&gt;

&lt;p&gt;There are a host of other differences that might matter depending on your usage requirements, but that''s the one that would matter most around here.&lt;/p&gt;

&lt;p&gt;The only other difference I can think of that would matter to the DS community is that when you are sharing something, you have no control of the address of a given file on google drive.&lt;/p&gt;
', NULL, NULL, '''/p'':22C,34C,62C,100C ''access'':16C,28C ''address'':92C ''around'':60C ''benefit'':9C ''big'':8C ''cloud'':32C ''communiti'':78C ''control'':89C ''data'':18C ''depend'':46C ''differ'':42C,67C ''direct'':27C ''drive'':25C,99C ''ds'':77C ''eas'':14C ''ec2'':21C ''file'':96C ''given'':95C ''googl'':24C,31C,98C ''host'':39C ''matter'':45C,58C,74C ''might'':44C ''one'':55C ''p'':1C,23C,35C,63C ''perspect'':4C ''platform'':33C ''requir'':50C ''s3'':11C ''share'':84C ''someth'':85C ''think'':70C ''usag'':49C ''within'':20C ''would'':57C,73C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8830, 2, '2015-11-13 10:00:41.03', 2, NULL, NULL, '&lt;p&gt;a documenttermmatrix is a simple_triplet_matrix. You can turn this into a simple matrix with the as.matrix command and then use all matrix functions.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;# turn into simple matrix
mat &amp;lt;- as.matrix(doc_matrix)

# vector of the words 
word_vector &amp;lt;- colnames(mat)

# Dataframe containing words and their frequency
df_words &amp;lt;- data.frame(words = colnames(mat), frequency = colSums(mat), row.names = NULL)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':66C ''/p'':27C ''/pre'':67C ''as.matrix'':19C,36C ''code'':29C ''colnam'':46C,59C ''colsum'':62C ''command'':20C ''contain'':49C ''data.frame'':57C ''datafram'':48C ''df'':54C ''doc'':37C ''documenttermmatrix'':3C ''frequenc'':53C,61C ''function'':26C ''lt'':35C,45C,56C ''mat'':34C,47C,60C,63C ''matrix'':8C,16C,25C,33C,38C ''null'':65C ''p'':1C ''pre'':28C ''row.names'':64C ''simpl'':6C,15C,32C ''triplet'':7C ''turn'':11C,30C ''use'':23C ''vector'':39C,44C ''word'':42C,43C,50C,55C,58C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (379, 2, '2014-06-15 01:25:48.563', 5, NULL, NULL, '&lt;p&gt;Financial Services is a big user of Big Data, and innovator too.  One example is mortgage bond trading.  To answer your questions for it:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;What kinda data these companies used. What was the size of the data?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;ul&gt;
&lt;li&gt;Long histories of each mortgage issued for the past many years, and payments by month against them.  (Billions of rows)&lt;/li&gt;
&lt;li&gt;Long histories of credit histories.  (Billions of rows)&lt;/li&gt;
&lt;li&gt;Home price indices.  (Not as big)&lt;/li&gt;
&lt;/ul&gt;

&lt;blockquote&gt;
  &lt;p&gt;What kinda of tools technologies they used to process the data?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;It varies.  Some use in-house solutions built on databases like Netezza or Teradata.  Others access the data via systems provided by the data providers.  (Corelogic, Experian, etc)  Some banks use columnal database technologies like KDB, or 1010data.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;What was the problem they were facing and how the insight they got the
  data helped them to resolve the issue.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The key issue is determining when mortgage bonds (mortgage backed-securities) will prepay or default.  This is especially important for bonds that lack the government guarantee.  By digging into payment histories, credit files, and understanding the current value of the house, it''s possible to predict the likelihood of a default.  Adding an interest rate model and prepayment model also helps predict the likelihood of a prepayment.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;How they selected the tool\technology to suit their need.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;If the project is driven by internal IT, usually it''s based off of a large database vendor like Oracle, Teradata or Netezza.  If it''s driven by the quants, then they are more likely to go straight to the data vendor, or a 3rd party "All in" system.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;What kinda pattern they identified from the data &amp;amp; what kind of
  patterns they were looking from the data.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Linking the data gives great insights into who is likely to default on their loans, and prepay them.  When you aggregated the loans into bonds, it can be the difference between a bond issued at $100,000,000 being worth that amount, or as little as $20,000,000.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':43C,100C,166C,250C,324C ''/li'':66C,76C,84C ''/p'':26C,42C,99C,141C,165C,236C,249C,301C,323C,375C ''/ul'':85C ''000'':362C,363C,373C,374C ''100'':361C ''1010data'':140C ''20'':372C ''3rd'':296C ''access'':118C ''ad'':220C ''aggreg'':346C ''also'':228C ''amount'':367C ''amp'':312C ''answer'':21C ''back'':178C ''backed-secur'':177C ''bank'':132C ''base'':263C ''big'':6C,9C,83C ''billion'':63C,73C ''blockquot'':27C,86C,142C,237C,302C ''bond'':18C,175C,189C,350C,358C ''built'':110C ''column'':134C ''compani'':33C ''corelog'':128C ''credit'':71C,200C ''current'':205C ''data'':10C,31C,41C,98C,120C,126C,158C,292C,311C,322C,328C ''databas'':112C,135C,268C ''default'':183C,219C,337C ''determin'':172C ''differ'':355C ''dig'':196C ''driven'':256C,278C ''especi'':186C ''etc'':130C ''exampl'':15C ''experian'':129C ''face'':150C ''file'':201C ''financi'':2C ''give'':329C ''go'':288C ''got'':156C ''govern'':193C ''great'':330C ''guarante'':194C ''help'':159C,229C ''histori'':47C,69C,72C,199C ''home'':78C ''hous'':108C,209C ''identifi'':308C ''import'':187C ''in-hous'':106C ''indic'':80C ''innov'':12C ''insight'':154C,331C ''interest'':222C ''intern'':258C ''issu'':51C,164C,170C,359C ''kdb'':138C ''key'':169C ''kind'':314C ''kinda'':30C,89C,305C ''lack'':191C ''larg'':267C ''li'':45C,67C,77C ''like'':113C,137C,270C,286C,335C ''likelihood'':216C,232C ''link'':326C ''littl'':370C ''loan'':340C,348C ''long'':46C,68C ''look'':319C ''mani'':55C ''model'':224C,227C ''month'':60C ''mortgag'':17C,50C,174C,176C ''need'':248C ''netezza'':114C,274C ''one'':14C ''oracl'':271C ''other'':117C ''p'':1C,28C,87C,101C,143C,167C,238C,251C,303C,325C ''parti'':297C ''past'':54C ''pattern'':306C,316C ''payment'':58C,198C ''possibl'':212C ''predict'':214C,230C ''prepay'':181C,226C,235C,342C ''price'':79C ''problem'':147C ''process'':96C ''project'':254C ''provid'':123C,127C ''quant'':281C ''question'':23C ''rate'':223C ''resolv'':162C ''row'':65C,75C ''secur'':179C ''select'':241C ''servic'':3C ''size'':38C ''solut'':109C ''straight'':289C ''suit'':246C ''system'':122C,300C ''technolog'':92C,136C,244C ''teradata'':116C,272C ''tool'':91C,243C ''trade'':19C ''ul'':44C ''understand'':203C ''use'':34C,94C,105C,133C ''user'':7C ''usual'':260C ''valu'':206C ''vari'':103C ''vendor'':269C,293C ''via'':121C ''worth'':365C ''year'':56C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (380, 2, '2014-06-15 01:29:15.24', 4, NULL, NULL, '&lt;p&gt;I suspect this will get closed since it is very narrow, but my 2 cents...&lt;/p&gt;

&lt;p&gt;Data Science requires 3 skills:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Math/Stats&lt;/li&gt;
&lt;li&gt;Programming&lt;/li&gt;
&lt;li&gt;Domain Knowledge&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It can be very hard to show all three.  #1 and #2 can be signaled via degrees, but a hiring manager who may not have them doesn''t want to trust a liberal arts degree.  If you''re looking to get into Data Science, position yourself as a domain expert first.  Publish election predictions.  If you''re correct, cite them.  That will get you noticed.&lt;/p&gt;

&lt;p&gt;If you''re Domain knowledge is A+ level, you don''t need A+ level programming skills, but learn programming enough so that you don''t need someone else to fetch data for you.&lt;/p&gt;
', NULL, NULL, '''/li'':28C,31C,35C ''/p'':17C,24C,103C,138C ''/ul'':36C ''1'':47C ''2'':15C,49C ''3'':22C ''art'':71C ''cent'':16C ''cite'':96C ''close'':7C ''correct'':95C ''data'':19C,80C,135C ''degre'':54C,72C ''doesn'':64C ''domain'':33C,86C,108C ''elect'':90C ''els'':132C ''enough'':124C ''expert'':87C ''fetch'':134C ''first'':88C ''get'':6C,78C,100C ''hard'':42C ''hire'':57C ''knowledg'':34C,109C ''learn'':122C ''level'':112C,118C ''li'':26C,29C,32C ''liber'':70C ''look'':76C ''manag'':58C ''math/stats'':27C ''may'':60C ''narrow'':12C ''need'':116C,130C ''notic'':102C ''p'':1C,18C,37C,104C ''posit'':82C ''predict'':91C ''program'':30C,119C,123C ''publish'':89C ''re'':75C,94C,107C ''requir'':21C ''scienc'':20C,81C ''show'':44C ''signal'':52C ''sinc'':8C ''skill'':23C,120C ''someon'':131C ''suspect'':3C ''three'':46C ''trust'':68C ''ul'':25C ''via'':53C ''want'':66C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (400, 2, '2014-06-16 11:45:05.8', 5, NULL, NULL, '&lt;p&gt;From my experience, the points to keep in mind when considering a data analysis platform are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Can it handle the size of the data that I need? If your data sets fit in memory, there''s usually no big trouble, although AFAIK Python is somewhat more memory-efficient than R. If you need to handle larger-than-memory data sets, the platform need to handle it conveniently. In this case, SQL would cover for basic statistics, Python + Apache Spark is another option.&lt;/li&gt;
&lt;li&gt;Does the platform covers all of my analysis needs? The greatest annoyance I''ve encountered in data mining projects is having to juggle between several tools, because tool A handles web connections well, tool B does the statistics and tool C renders nice pictures. You want your weapon-of-choice to cover as many aspects of your projects as possible. When considering this issue, Python is very comprehensive, but R has a lot of build-in statistical tests ready-to-use, if that''s what you need. &lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':89C,181C ''/ol'':182C ''/p'':18C ''afaik'':46C ''although'':45C ''analysi'':15C,98C ''annoy'':102C ''anoth'':87C ''apach'':84C ''aspect'':146C ''b'':125C ''basic'':81C ''big'':43C ''build'':167C ''build-in'':166C ''c'':131C ''case'':76C ''choic'':141C ''comprehens'':159C ''connect'':122C ''consid'':12C,153C ''conveni'':73C ''cover'':79C,94C,143C ''data'':14C,28C,34C,65C,107C ''effici'':53C ''encount'':105C ''experi'':4C ''fit'':36C ''greatest'':101C ''handl'':23C,60C,71C,120C ''issu'':155C ''juggl'':113C ''keep'':8C ''larger'':62C ''larger-than-memori'':61C ''li'':20C,90C ''lot'':164C ''mani'':145C ''memori'':38C,52C,64C ''memory-effici'':51C ''mind'':10C ''mine'':108C ''need'':31C,58C,69C,99C,180C ''nice'':133C ''ol'':19C ''option'':88C ''p'':1C ''pictur'':134C ''platform'':16C,68C,93C ''point'':6C ''possibl'':151C ''project'':109C,149C ''python'':47C,83C,156C ''r'':55C,161C ''readi'':172C ''ready-to-us'':171C ''render'':132C ''set'':35C,66C ''sever'':115C ''size'':25C ''somewhat'':49C ''spark'':85C ''sql'':77C ''statist'':82C,128C,169C ''test'':170C ''tool'':116C,118C,124C,130C ''troubl'':44C ''use'':174C ''usual'':41C ''ve'':104C ''want'':136C ''weapon'':139C ''weapon-of-choic'':138C ''web'':121C ''well'':123C ''would'':78C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (381, 2, '2014-06-15 01:36:51.693', 3, NULL, NULL, '&lt;p&gt;CAPM (Capital Asset Pricing Model) in Finance is a classic example of an underfit model.  It was built on the beautiful theory that "Investors only pay for risk they can''t diversify away" so expected excess returns are equal to correlation to market returns.&lt;/p&gt;

&lt;p&gt;As a formula [0] Ra = Rf + B (Rm - Rf)
where Ra is the expected return of the asset, Rf is the risk free rate, Rm is the market rate of return, and Beta is the correlation to the Equity premium (Rm - Rf)&lt;/p&gt;

&lt;p&gt;This is beautiful, elegant, and wrong.  Investors seem to require a higher rate of small stocks and value (defined by book to market, or dividend yield) stocks.  &lt;/p&gt;

&lt;p&gt;Fama and French [1] presented an update to the model, which adds additional Betas for Size and Value.  &lt;/p&gt;

&lt;p&gt;So how do you know in a general sense?  When the predictions you are making are wrong, and another variable with a logical explanation increases the prediction quality.  It''s easy to understand why someone might think small stocks are risky, independent of non-diversifiable risk.  It''s a good story, backed by the data.&lt;/p&gt;

&lt;p&gt;[0]  &lt;a href="http://www.investopedia.com/terms/c/capm.asp" rel="nofollow"&gt;http://www.investopedia.com/terms/c/capm.asp&lt;/a&gt;
[1]  &lt;a href="http://en.wikipedia.org/wiki/Fama%E2%80%93French_three-factor_model" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Fama%E2%80%93French_three-factor_model&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':46C,90C,119C,139C,197C ''/terms/c/capm.asp'':204C ''/terms/c/capm.asp&lt;/a&gt;'':209C ''/wiki/fama%e2%80%93french_three-factor_model'':215C ''/wiki/fama%e2%80%93french_three-factor_model&lt;/a&gt;&lt;/p&gt;'':220C ''0'':51C,199C ''1'':124C,210C ''add'':132C ''addit'':133C ''anoth'':159C ''asset'':4C,65C ''away'':34C ''b'':54C ''back'':193C ''beauti'':22C,94C ''beta'':80C,134C ''book'':112C ''built'':19C ''capit'':3C ''capm'':2C ''classic'':11C ''correl'':42C,83C ''data'':196C ''defin'':110C ''diversifi'':33C,186C ''dividend'':116C ''easi'':171C ''eleg'':95C ''en.wikipedia.org'':214C,219C ''en.wikipedia.org/wiki/fama%e2%80%93french_three-factor_model'':213C ''en.wikipedia.org/wiki/fama%e2%80%93french_three-factor_model&lt;/a&gt;&lt;/p&gt;'':218C ''equal'':40C ''equiti'':86C ''exampl'':12C ''excess'':37C ''expect'':36C,61C ''explan'':164C ''fama'':121C ''financ'':8C ''formula'':50C ''free'':70C ''french'':123C ''general'':148C ''good'':191C ''higher'':103C ''href'':201C,212C ''increas'':165C ''independ'':182C ''investor'':25C,98C ''know'':145C ''logic'':163C ''make'':155C ''market'':44C,75C,114C ''might'':176C ''model'':6C,16C,130C ''nofollow'':206C,217C ''non'':185C ''non-diversifi'':184C ''p'':1C,47C,91C,120C,140C,198C ''pay'':27C ''predict'':152C,167C ''premium'':87C ''present'':125C ''price'':5C ''qualiti'':168C ''ra'':52C,58C ''rate'':71C,76C,104C ''rel'':205C,216C ''requir'':101C ''return'':38C,45C,62C,78C ''rf'':53C,56C,66C,89C ''risk'':29C,69C,187C ''riski'':181C ''rm'':55C,72C,88C ''seem'':99C ''sens'':149C ''size'':136C ''small'':106C,178C ''someon'':175C ''stock'':107C,118C,179C ''stori'':192C ''theori'':23C ''think'':177C ''underfit'':15C ''understand'':173C ''updat'':127C ''valu'':109C,138C ''variabl'':160C ''wrong'':97C,157C ''www.investopedia.com'':203C,208C ''www.investopedia.com/terms/c/capm.asp'':202C ''www.investopedia.com/terms/c/capm.asp&lt;/a&gt;'':207C ''yield'':117C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (382, 1, '2014-06-15 12:26:50.06', 8, 140, 'How to debug data analysis?', '&lt;p&gt;I''ve came across the following problem, that I recon is rather typical.&lt;/p&gt;

&lt;p&gt;I have some large data, say, a few million rows. I run some non-trivial analysis on it, e.g. an SQL query consisting of several sub-queries. I get some result, stating, for example, that property X is increasing over time.&lt;/p&gt;

&lt;p&gt;Now, there are two possible things that could lead to that:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;X is indeed increasing over time&lt;/li&gt;
&lt;li&gt;I have a bug in my analysis&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;How can I test that the first happened, rather than the second? A step-wise debugger, even if one exists, won''t help, since intermediate results can still consist of millions of lines.&lt;/p&gt;

&lt;p&gt;The only thing I could think of was to somehow generate a small, synthetic data set with the property that I want to test and run the analysis on it as a unit test. Are there tools to do this? Particularly, but not limited to, SQL.&lt;/p&gt;
', 'data-mining  sql  experiments', NULL, '''/li'':92C,101C ''/ol'':102C ''/p'':25C,70C,83C,138C,186C ''across'':15C ''analysi'':5A,43C,100C,167C ''bug'':97C ''came'':14C ''consist'':50C,133C ''could'':79C,144C ''data'':4A,7B,31C,154C ''data-min'':6B ''debug'':3A ''debugg'':120C ''e.g'':46C ''even'':121C ''exampl'':62C ''exist'':124C ''experi'':10B ''first'':110C ''follow'':17C ''generat'':150C ''get'':57C ''happen'':111C ''help'':127C ''increas'':67C,89C ''inde'':88C ''intermedi'':129C ''larg'':30C ''lead'':80C ''li'':85C,93C ''limit'':183C ''line'':137C ''million'':35C,135C ''mine'':8B ''non'':41C ''non-trivi'':40C ''ol'':84C ''one'':123C ''p'':11C,26C,71C,103C,139C ''particular'':180C ''possibl'':76C ''problem'':18C ''properti'':64C,158C ''queri'':49C,55C ''rather'':23C,112C ''recon'':21C ''result'':59C,130C ''row'':36C ''run'':38C,165C ''say'':32C ''second'':115C ''set'':155C ''sever'':52C ''sinc'':128C ''small'':152C ''somehow'':149C ''sql'':9B,48C,185C ''state'':60C ''step'':118C ''step-wis'':117C ''still'':132C ''sub'':54C ''sub-queri'':53C ''synthet'':153C ''test'':107C,163C,173C ''thing'':77C,142C ''think'':145C ''time'':69C,91C ''tool'':176C ''trivial'':42C ''two'':75C ''typic'':24C ''unit'':172C ''ve'':13C ''want'':161C ''wise'':119C ''won'':125C ''x'':65C,86C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (384, 1, '2014-06-15 14:01:38.233', 8, 1944, 'Choose binary classification algorithm', '&lt;p&gt;I have a binary classification problem:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Approximately 1000 samples in training set&lt;/li&gt;
&lt;li&gt;10 attributes, including binary, numeric and categorical&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Which algorithm is the best choice for this type of problem?&lt;/p&gt;

&lt;p&gt;By default I''m going to start with SVM (preliminary having nominal attributes values converted to binary features), as it is considered the best for relatively clean and  not noisy data. &lt;/p&gt;
', 'classification  binary  svm  random-forest  logistic-regression', NULL, '''/li'':30C,39C ''/p'':21C,53C,86C ''/ul'':40C ''10'':32C ''1000'':25C ''algorithm'':4A,43C ''approxim'':24C ''attribut'':33C,67C ''best'':46C,78C ''binari'':2A,6B,18C,35C,71C ''categor'':38C ''choic'':47C ''choos'':1A ''classif'':3A,5B,19C ''clean'':81C ''consid'':76C ''convert'':69C ''data'':85C ''default'':56C ''featur'':72C ''forest'':10B ''go'':59C ''includ'':34C ''li'':23C,31C ''logist'':12B ''logistic-regress'':11B ''m'':58C ''noisi'':84C ''nomin'':66C ''numer'':36C ''p'':14C,41C,54C ''preliminari'':64C ''problem'':20C,52C ''random'':9B ''random-forest'':8B ''regress'':13B ''relat'':80C ''sampl'':26C ''set'':29C ''start'':61C ''svm'':7B,63C ''train'':28C ''type'':50C ''ul'':22C ''valu'':68C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (387, 1, '2014-06-15 15:11:29.97', 6, 153, 'Filtering spam from retrieved data', '&lt;p&gt;I once heard that filtering spam by using blacklists is not a good approach, since some user searching for entries in your dataset may be looking for particular information from the sources blocked. Also it''d become a burden to continuously validate the &lt;em&gt;current state&lt;/em&gt; of each spammer blocked, checking if the site/domain still disseminate spam data.&lt;/p&gt;

&lt;p&gt;Considering that any approach must be efficient and scalable, so as to support filtering on very large datasets, what are the strategies available to get rid of spam in a non-biased manner?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit&lt;/strong&gt;: if possible, any example of strategy, even if just the intuition behind it, would be very welcome along with the answer.&lt;/p&gt;
', 'bigdata  efficiency', NULL, '''/em'':55C ''/p'':68C,104C,130C ''/strong'':108C ''along'':126C ''also'':42C ''answer'':129C ''approach'':22C,73C ''avail'':92C ''becom'':45C ''behind'':120C ''bias'':102C ''bigdata'':6B ''blacklist'':17C ''block'':41C,59C ''burden'':47C ''check'':60C ''consid'':70C ''continu'':49C ''current'':53C ''d'':44C ''data'':5A,67C ''dataset'':31C,87C ''dissemin'':65C ''edit'':107C ''effici'':7B,76C ''em'':52C ''entri'':28C ''even'':115C ''exampl'':112C ''filter'':1A,13C,83C ''get'':94C ''good'':21C ''heard'':11C ''inform'':37C ''intuit'':119C ''larg'':86C ''look'':34C ''manner'':103C ''may'':32C ''must'':74C ''non'':101C ''non-bias'':100C ''p'':8C,69C,105C ''particular'':36C ''possibl'':110C ''retriev'':4A ''rid'':95C ''scalabl'':78C ''search'':26C ''sinc'':23C ''site/domain'':63C ''sourc'':40C ''spam'':2A,14C,66C,97C ''spammer'':58C ''state'':54C ''still'':64C ''strategi'':91C,114C ''strong'':106C ''support'':82C ''use'':16C ''user'':25C ''valid'':50C ''welcom'':125C ''would'':122C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (388, 2, '2014-06-15 15:20:31.3', 5, NULL, NULL, '&lt;p&gt;Spam filtering, especially in email, has been revolutionized by neural networks, here are a couple papers that provide good reading on the subject:&lt;/p&gt;

&lt;p&gt;On Neural Networks And The Future Of Spam 
A. C. Cosoi, M. S. Vlad, V. Sgarciu 
&lt;a href="http://ceai.srait.ro/index.php/ceai/article/viewFile/18/8" rel="nofollow"&gt;http://ceai.srait.ro/index.php/ceai/article/viewFile/18/8&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Intelligent Word-Based Spam Filter Detection Using 
Multi-Neural Networks 
Ann Nosseir, Khaled Nagati and Islam Taj-Eddin
&lt;a href="http://www.ijcsi.org/papers/IJCSI-10-2-1-17-21.pdf" rel="nofollow"&gt;http://www.ijcsi.org/papers/IJCSI-10-2-1-17-21.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Spam Detection using Adaptive Neural Networks: Adaptive Resonance Theory 
David Ndumiyana, Richard Gotora, and Tarisai Mupamombe
&lt;a href="http://onlineresearchjournals.org/JPESR/pdf/2013/apr/Ndumiyana%20et%20al.pdf" rel="nofollow"&gt;http://onlineresearchjournals.org/JPESR/pdf/2013/apr/Ndumiyana%20et%20al.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;EDIT:
The basic intuition behind using a neural network to help with spam filtering is by providing a weight to terms based on how often they are associated with spam.&lt;/p&gt;

&lt;p&gt;Neural networks can be trained most quickly in a supervised -- you explicitly provide the classification of the sentence in the training set -- environment.  Without going into the nitty gritty the basic idea can be illustrated with these sentences:&lt;/p&gt;

&lt;p&gt;Text = "How is the loss of the Viagra patent going to affect Pfizer", Spam = false
Text = "Cheap Viagra Buy Now", Spam = true
Text = "Online pharmacy Viagra Cialis Lipitor", Spam = true&lt;/p&gt;

&lt;p&gt;For a two stage neural network, the first stage will calculate the likelihood of spam based off of if the word exists in the sentence.  So from our example:&lt;/p&gt;

&lt;p&gt;viagra =&gt; 66%
buy =&gt; 100%
Pfizer =&gt; 0%
etc..&lt;/p&gt;

&lt;p&gt;Then for the second stage the results in the first stage are used as variables in the second stage:&lt;/p&gt;

&lt;p&gt;viagra &amp;amp; buy =&gt; 100%
Pfizer &amp;amp; viagra=&gt; 0%&lt;/p&gt;

&lt;p&gt;This basic idea is run for many of the permutations of the all the words in your training data.  The end results once trained is basically just an equation that based of the context of the words in the sentence can assign a probability of being spam.  Set spamminess threshold, and filter out any data higher then said threshold.&lt;/p&gt;
', NULL, NULL, '''/index.php/ceai/article/viewfile/18/8'':47C ''/index.php/ceai/article/viewfile/18/8&lt;/a&gt;&lt;/p&gt;'':52C ''/jpesr/pdf/2013/apr/ndumiyana%20et%20al.pdf'':106C ''/jpesr/pdf/2013/apr/ndumiyana%20et%20al.pdf&lt;/a&gt;&lt;/p&gt;'':111C ''/p'':25C,143C,183C,215C,246C,255C,276C,286C,347C ''/papers/ijcsi-10-2-1-17-21.pdf'':79C ''/papers/ijcsi-10-2-1-17-21.pdf&lt;/a&gt;&lt;/p&gt;'':84C ''0'':253C,285C ''100'':251C,281C ''66'':249C ''adapt'':89C,92C ''affect'':196C ''amp'':279C,283C ''ann'':66C ''assign'':329C ''associ'':140C ''base'':57C,134C,232C,318C ''basic'':115C,175C,289C,313C ''behind'':117C ''buy'':203C,250C,280C ''c'':36C ''calcul'':227C ''ceai.srait.ro'':46C,51C ''ceai.srait.ro/index.php/ceai/article/viewfile/18/8'':45C ''ceai.srait.ro/index.php/ceai/article/viewfile/18/8&lt;/a&gt;&lt;/p&gt;'':50C ''cheap'':201C ''ciali'':211C ''classif'':159C ''context'':321C ''cosoi'':37C ''coupl'':16C ''data'':306C,342C ''david'':95C ''detect'':60C,87C ''eddin'':74C ''edit'':113C ''email'':6C ''end'':308C ''environ'':167C ''equat'':316C ''especi'':4C ''etc'':254C ''exampl'':245C ''exist'':238C ''explicit'':156C ''fals'':199C ''filter'':3C,59C,126C,339C ''first'':224C,266C ''futur'':32C ''go'':169C,194C ''good'':20C ''gotora'':98C ''gritti'':173C ''help'':123C ''higher'':343C ''href'':44C,76C,103C ''idea'':176C,290C ''illustr'':179C ''intellig'':54C ''intuit'':116C ''islam'':71C ''khale'':68C ''likelihood'':229C ''lipitor'':212C ''loss'':189C ''m'':38C ''mani'':294C ''multi'':63C ''multi-neur'':62C ''mupamomb'':101C ''nagati'':69C ''ndumiyana'':96C ''network'':12C,29C,65C,91C,121C,146C,222C ''neural'':11C,28C,64C,90C,120C,145C,221C ''nitti'':172C ''nofollow'':49C,81C,108C ''nosseir'':67C ''often'':137C ''onlin'':208C ''onlineresearchjournals.org'':105C,110C ''onlineresearchjournals.org/jpesr/pdf/2013/apr/ndumiyana%20et%20al.pdf'':104C ''onlineresearchjournals.org/jpesr/pdf/2013/apr/ndumiyana%20et%20al.pdf&lt;/a&gt;&lt;/p&gt;'':109C ''p'':1C,26C,53C,85C,112C,144C,184C,216C,247C,256C,277C,287C ''paper'':17C ''patent'':193C ''permut'':297C ''pfizer'':197C,252C,282C ''pharmaci'':209C ''probabl'':331C ''provid'':19C,129C,157C ''quick'':151C ''read'':21C ''rel'':48C,80C,107C ''reson'':93C ''result'':263C,309C ''revolution'':9C ''richard'':97C ''run'':292C ''said'':345C ''second'':260C,274C ''sentenc'':162C,182C,241C,327C ''set'':166C,335C ''sgarciu'':42C ''spam'':2C,34C,58C,86C,125C,142C,198C,205C,213C,231C,334C ''spammi'':336C ''stage'':220C,225C,261C,267C,275C ''subject'':24C ''supervis'':154C ''taj'':73C ''taj-eddin'':72C ''tarisai'':100C ''term'':133C ''text'':185C,200C,207C ''theori'':94C ''threshold'':337C,346C ''train'':149C,165C,305C,311C ''true'':206C,214C ''two'':219C ''use'':61C,88C,118C,269C ''v'':41C ''variabl'':271C ''viagra'':192C,202C,210C,248C,278C,284C ''vlad'':40C ''weight'':131C ''without'':168C ''word'':56C,237C,302C,324C ''word-bas'':55C ''www.ijcsi.org'':78C,83C ''www.ijcsi.org/papers/ijcsi-10-2-1-17-21.pdf'':77C ''www.ijcsi.org/papers/ijcsi-10-2-1-17-21.pdf&lt;/a&gt;&lt;/p&gt;'':82C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (389, 2, '2014-06-15 15:49:12.907', 4, NULL, NULL, '&lt;p&gt;Here is a suggestion:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Code your analysis in such a way that it can be run on sub-samples.&lt;/li&gt;
&lt;li&gt;Code a complementary routine which can sample, either randomly, or by time, or by region, or ...  This may be domain-specific. This is where your knowledge enters.&lt;/li&gt;
&lt;li&gt;Combine the two and see if the results are stable across subsamples.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':25C,55C,69C ''/p'':6C ''/ul'':70C ''across'':67C ''analysi'':11C ''code'':9C,27C ''combin'':57C ''complementari'':29C ''domain'':47C ''domain-specif'':46C ''either'':34C ''enter'':54C ''knowledg'':53C ''li'':8C,26C,56C ''may'':44C ''p'':1C ''random'':35C ''region'':41C ''result'':64C ''routin'':30C ''run'':20C ''sampl'':24C,33C ''see'':61C ''specif'':48C ''stabl'':66C ''sub'':23C ''sub-sampl'':22C ''subsampl'':68C ''suggest'':5C ''time'':38C ''two'':59C ''ul'':7C ''way'':15C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (391, 2, '2014-06-15 16:59:50.19', 1, NULL, NULL, '&lt;p&gt;This is what I normally do - take up the most important variables (basis your business understanding and hypothesis - you can always revise it later), group by on these attributes to reduce the number of rows, which can then be imported into a Pivot. You should include the sum and count of the relevant metrics on each row.&lt;/p&gt;

&lt;p&gt;Make sure that you don''t put any filters in the previous step. Once you have entire data at a summarized level, you can play around in Pivot tables and see what things are changing / increasing or decreasing.&lt;/p&gt;

&lt;p&gt;If the data is too big to be summarized even on important parameters, you need to partition it in 3 - 4 subsets and then do this again.&lt;/p&gt;

&lt;p&gt;Hope it helps.&lt;/p&gt;
', NULL, NULL, '''/p'':59C,99C,128C,133C ''3'':120C ''4'':121C ''alway'':22C ''around'':86C ''attribut'':30C ''basi'':14C ''big'':106C ''busi'':16C ''chang'':95C ''count'':51C ''data'':78C,103C ''decreas'':98C ''entir'':77C ''even'':110C ''filter'':69C ''group'':26C ''help'':132C ''hope'':130C ''hypothesi'':19C ''import'':12C,41C,112C ''includ'':47C ''increas'':96C ''later'':25C ''level'':82C ''make'':61C ''metric'':55C ''need'':115C ''normal'':6C ''number'':34C ''p'':1C,60C,100C,129C ''paramet'':113C ''partit'':117C ''pivot'':44C,88C ''play'':85C ''previous'':72C ''put'':67C ''reduc'':32C ''relev'':54C ''revis'':23C ''row'':36C,58C ''see'':91C ''step'':73C ''subset'':122C ''sum'':49C ''summar'':81C,109C ''sure'':62C ''tabl'':89C ''take'':8C ''thing'':93C ''understand'':17C ''variabl'':13C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (570, 2, '2014-06-25 00:13:50.803', 2, NULL, NULL, '&lt;p&gt;Personally, I think Netflix got it right.  Break it down into a confidence rating from 1-5 and show your recommendations based on the number of yellow stars.  &lt;/p&gt;

&lt;p&gt;It doesn''t have to be stars, but those icon based graphs are very easy to interpret and get the point across clearly.&lt;/p&gt;
', NULL, NULL, '''-5'':18C ''/p'':30C,55C ''1'':17C ''across'':53C ''base'':23C,42C ''break'':9C ''clear'':54C ''confid'':14C ''doesn'':33C ''easi'':46C ''get'':50C ''got'':6C ''graph'':43C ''icon'':41C ''interpret'':48C ''netflix'':5C ''number'':26C ''p'':1C,31C ''person'':2C ''point'':52C ''rate'':15C ''recommend'':22C ''right'':8C ''show'':20C ''star'':29C,38C ''think'':4C ''yellow'':28C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (394, 2, '2014-06-16 04:21:36.34', 5, NULL, NULL, '&lt;p&gt;Personally, we use S3 on top of GCE and really love it. Depending on how much data you''re dealing with, Google Drive just doesn''t quite match the 5 TB max that S3 gives you. Also, if you''re using python, &lt;code&gt;boto&lt;/code&gt; does a pretty fantastic job of making most aws services pretty accessible regardless of what stack you''re dealing with. Even if you''re not using python, they''ve got a pretty straightforward API that generally is more accessible than Google Drive.&lt;/p&gt;

&lt;p&gt;Instead of google drive, though google did recently release a cloud storage service, apart from drive, that lets you more closely integrate your storage with any gce instance you''ve got, &lt;a href="https://cloud.google.com/products/cloud-storage/"&gt;https://cloud.google.com/products/cloud-storage/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;They''ve got an API which seems to be pretty comparable to S3''s, but I can''t profess to having really played around with it much. Pricing-wise the two are identical, but I think that the large community and experience with aws in general still puts S3 squarely above both google''s cloud storage and google drive.&lt;/p&gt;
', NULL, NULL, '''/code'':46C ''/p'':89C,191C ''/products/cloud-storage/'':126C ''/products/cloud-storage/&lt;/a&gt;&lt;/p&gt;'':129C ''5'':31C ''access'':58C,85C ''also'':38C ''apart'':104C ''api'':80C,135C ''around'':154C ''aw'':55C,175C ''boto'':45C ''close'':111C ''cloud'':101C,186C ''cloud.google.com'':125C,128C ''cloud.google.com/products/cloud-storage/'':124C ''cloud.google.com/products/cloud-storage/&lt;/a&gt;&lt;/p&gt;'':127C ''code'':44C ''communiti'':171C ''compar'':141C ''data'':18C ''deal'':21C,65C ''depend'':14C ''doesn'':26C ''drive'':24C,88C,94C,106C,190C ''even'':67C ''experi'':173C ''fantast'':50C ''gce'':9C,117C ''general'':82C,177C ''give'':36C ''googl'':23C,87C,93C,96C,184C,189C ''got'':76C,121C,133C ''href'':123C ''ident'':164C ''instanc'':118C ''instead'':91C ''integr'':112C ''job'':51C ''larg'':170C ''let'':108C ''love'':12C ''make'':53C ''match'':29C ''max'':33C ''much'':17C,157C ''p'':1C,90C,130C ''person'':2C ''play'':153C ''pretti'':49C,57C,78C,140C ''price'':159C ''pricing-wis'':158C ''profess'':149C ''put'':179C ''python'':43C,73C ''quit'':28C ''re'':20C,41C,64C,70C ''realli'':11C,152C ''recent'':98C ''regardless'':59C ''releas'':99C ''s3'':5C,35C,143C,180C ''seem'':137C ''servic'':56C,103C ''squar'':181C ''stack'':62C ''still'':178C ''storag'':102C,114C,187C ''straightforward'':79C ''tb'':32C ''think'':167C ''though'':95C ''top'':7C ''two'':162C ''use'':4C,42C,72C ''ve'':75C,120C,132C ''wise'':160C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (502, 2, '2014-06-20 10:01:03.363', 12, NULL, NULL, '&lt;p&gt;Regarding prediction, statistics and machine learning sciences started to solve mostly the same problem from different perspectives. &lt;/p&gt;

&lt;p&gt;Basically statistics assumes that the data were produced by a given stochastic model. So, from a statistical perspective, a model is assumed and given various assumptions the errors are treated and the model parameters and other questions are inferred. &lt;/p&gt;

&lt;p&gt;Machine learning comes from a computer science perspective. The models are algorithmic and usually very few assumptions are required regarding the data. We work with hypothesis space and learning bias. The best exposition of machine learning I found is contained in Tom Mitchell''s book called &lt;a href="http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html"&gt;Machine Learning&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;For a more exhaustive and complete idea regarding the two cultures you can read the Leo Broiman paper called &lt;a href="http://www.google.ie/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=1&amp;amp;cad=rja&amp;amp;uact=8&amp;amp;ved=0CDIQFjAA&amp;amp;url=http://strimmerlab.org/courses/ss09/current-topics/download/breiman2001.pdf&amp;amp;ei=RQGkU_DgH8Le7AaRmID4DA&amp;amp;usg=AFQjCNGNnrlqadmBT2fZMT_NfoUQ1rEuow&amp;amp;sig2=g5qovUTsuuJUGGE64g0nrQ"&gt;Statistical Modeling: The Two Cultures&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;However what must be added is that even if the two sciences started with different perspectives, both of them now now share a fair amount of common knowledge and techniques. Why, because the problems were the same, but the tools were different. So now machine learning is mostly treated from a statistical perspective (check the Hastie,Tibshirani, Friedman book &lt;a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/"&gt;The Elements of Statistical Learning&lt;/a&gt; from a machine learning point of view with a statistical treatement, and perhaps Kevin P. Murphy ''s book &lt;a href="http://mitpress.mit.edu/books/machine-learning-2"&gt;Machine Learning: A probabilistic perspective&lt;/a&gt;, to name just a few of the best books available today). &lt;/p&gt;

&lt;p&gt;Even the history of the development of this field show the benefits of this merge of perspectives. I will describe two events. &lt;/p&gt;

&lt;p&gt;The first is the creation of CART trees, which was created by Breiman with a solid statistical background. At approximately the same time, Quinlan developed ID3,C45,See5, and so on, decision tree suite with a more computer science background. Now both this families of trees and the ensemble methods like bagging and forests become quite similar. &lt;/p&gt;

&lt;p&gt;The second story is about boosting. Initially they were developed by Freund and Shapire when they discovered AdaBoost. The choices for designing AdaBoost were done mostly from a computational perspective. Even the authors did not understood well why it works. Only 5 years later Breiman (again!) described the adaboost model from a statistical perspective and gave an explanation for why that works. Since then, various eminent scientists, with both type of backgrounds, developed further those ideas leading to a Pleiads of boosting algorithms, like logistic boosting, gradient boosting, gentle boosting ans so on. It is hard now to think at boosting without a solid statistical background.&lt;/p&gt;

&lt;p&gt;GLM is a statistical development. However new Bayesian treatments puts this algorithm also in machine learning playground. So I believe both claims could be right, since the interpretation and treatment of how it works could be different. &lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (395, 2, '2014-06-16 04:37:58.817', 10, NULL, NULL, '&lt;p&gt;It''s hard to say without knowing a little more about your dataset, and how separable your dataset is based on your feature vector, but I would probably suggest using extreme random forest over standard random forests because of your relatively small sample set.&lt;/p&gt;

&lt;p&gt;Extreme random forests are pretty similar to standard random forests with the one exception that instead of optimizing splits on trees, extreme random forest makes splits at random. Initially this would seem like a negative, but it generally means that you have significantly better generalization and speed, though the AUC on your training set is likely to be a little worse.&lt;/p&gt;

&lt;p&gt;Logistic regression is also a pretty solid bet for these kinds of tasks, though with your relatively low dimensionality and small sample size I would be worried about overfitting. You might want to check out using K-Nearest Neighbors since it often performs very will with low dimensionalities, but it doesn''t usually handle categorical variables very well.&lt;/p&gt;

&lt;p&gt;If I had to pick one without knowing more about the problem I would certainly place my bets on extreme random forest, as it''s very likely to give you good generalization on this kind of dataset, and it also handles a mix of numerical and categorical data better than most other methods.&lt;/p&gt;
', NULL, NULL, '''/p'':46C,109C,170C,225C ''also'':114C,211C ''auc'':97C ''base'':21C ''bet'':118C,189C ''better'':91C,220C ''categor'':166C,218C ''certain'':186C ''check'':144C ''data'':219C ''dataset'':14C,19C,208C ''dimension'':129C,159C ''doesn'':162C ''except'':61C ''extrem'':32C,48C,69C,191C ''featur'':24C ''forest'':34C,38C,50C,57C,71C,193C ''general'':85C,92C,203C ''give'':200C ''good'':202C ''handl'':165C,212C ''hard'':4C ''initi'':76C ''instead'':63C ''k'':148C ''k-nearest'':147C ''kind'':121C,206C ''know'':8C,179C ''like'':80C,103C,198C ''littl'':10C,107C ''logist'':111C ''low'':128C,158C ''make'':72C ''mean'':86C ''method'':224C ''might'':141C ''mix'':214C ''nearest'':149C ''negat'':82C ''neighbor'':150C ''numer'':216C ''often'':153C ''one'':60C,177C ''optim'':65C ''overfit'':139C ''p'':1C,47C,110C,171C ''perform'':154C ''pick'':176C ''place'':187C ''pretti'':52C,116C ''probabl'':29C ''problem'':183C ''random'':33C,37C,49C,56C,70C,75C,192C ''regress'':112C ''relat'':42C,127C ''sampl'':44C,132C ''say'':6C ''seem'':79C ''separ'':17C ''set'':45C,101C ''signific'':90C ''similar'':53C ''sinc'':151C ''size'':133C ''small'':43C,131C ''solid'':117C ''speed'':94C ''split'':66C,73C ''standard'':36C,55C ''suggest'':30C ''task'':123C ''though'':95C,124C ''train'':100C ''tree'':68C ''use'':31C,146C ''usual'':164C ''variabl'':167C ''vector'':25C ''want'':142C ''well'':169C ''without'':7C,178C ''worri'':137C ''wors'':108C ''would'':28C,78C,135C,185C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (442, 2, '2014-06-17 21:17:31.21', 8, NULL, NULL, '&lt;p&gt;Christopher''s answers seem very reasonable. In particular tree based methods do well with this sort of data because they branch on discriminative features. It''s a little hard to say without knowing your specific application, but in general if you think that some of your features might be significantly more discriminative than others, you could try some dimensionality reduction techniques to clean this up a bit.&lt;/p&gt;

&lt;p&gt;Also if you use a dimensionality reduction technique you end up getting a slightly more robust format for your feature vector (they generally end up being straight numerical vectors instead of mixed data types), which might let you leverage different methods. You could also look into hand engineering features. With properly hand engineered features &lt;code&gt;Random Forest&lt;/code&gt; will get you very close to state of the art on most tasks.&lt;/p&gt;
', NULL, NULL, '''/code'':128C ''/p'':69C,142C ''also'':71C,114C ''answer'':4C ''applic'':37C ''art'':138C ''base'':11C ''bit'':68C ''branch'':22C ''christoph'':2C ''clean'':64C ''close'':133C ''code'':125C ''could'':57C,113C ''data'':19C,103C ''differ'':110C ''dimension'':60C,76C ''discrimin'':24C,53C ''end'':80C,94C ''engin'':118C,123C ''featur'':25C,48C,90C,119C,124C ''forest'':127C ''format'':87C ''general'':40C,93C ''get'':82C,130C ''hand'':117C,122C ''hard'':30C ''instead'':100C ''know'':34C ''let'':107C ''leverag'':109C ''littl'':29C ''look'':115C ''method'':12C,111C ''might'':49C,106C ''mix'':102C ''numer'':98C ''other'':55C ''p'':1C,70C ''particular'':9C ''proper'':121C ''random'':126C ''reason'':7C ''reduct'':61C,77C ''robust'':86C ''say'':32C ''seem'':5C ''signific'':51C ''slight'':84C ''sort'':17C ''specif'':36C ''state'':135C ''straight'':97C ''task'':141C ''techniqu'':62C,78C ''think'':43C ''tree'':10C ''tri'':58C ''type'':104C ''use'':74C ''vector'':91C,99C ''well'':14C ''without'':33C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (402, 2, '2014-06-16 13:25:48.453', 3, NULL, NULL, '&lt;p&gt;The &lt;a href="http://brat.nlplab.org/" rel="nofollow"&gt;brat annotation tool&lt;/a&gt; might be useful for you as per my comment.  I have tried many of them and this is the best I have found.  It has a nice user interface and can support a number of different types of annotations.  The annotations are stored in a separate .annot file which contain each annotation as well as its location within the original document.  A word of warning though, if you ultimately want to feed the annotations into a classifier like the Stanford NER tool then you will have to do some manipulation to get the data into a format that it will accept.&lt;/p&gt;
', NULL, NULL, '''/a'':11C ''/p'':113C ''accept'':112C ''annot'':9C,50C,52C,58C,63C,85C ''best'':31C ''brat'':8C ''brat.nlplab.org'':5C ''classifi'':88C ''comment'':20C ''contain'':61C ''data'':105C ''differ'':47C ''document'':72C ''feed'':83C ''file'':59C ''format'':108C ''found'':34C ''get'':103C ''href'':4C ''interfac'':40C ''like'':89C ''locat'':68C ''mani'':24C ''manipul'':101C ''might'':12C ''ner'':92C ''nice'':38C ''nofollow'':7C ''number'':45C ''origin'':71C ''p'':1C ''per'':18C ''rel'':6C ''separ'':57C ''stanford'':91C ''store'':54C ''support'':43C ''though'':77C ''tool'':10C,93C ''tri'':23C ''type'':48C ''ultim'':80C ''use'':14C ''user'':39C ''want'':81C ''warn'':76C ''well'':65C ''within'':69C ''word'':74C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (396, 2, '2014-06-16 04:51:47.847', 6, NULL, NULL, '&lt;p&gt;Alex made a number of good points, though I might have to push back a bit on his implication that DBSCAN is the best clustering algorithm to use here. Depending on your implementation, and whether or not you''re using accelerated indices (many implementations do not), your time and space complexity will both be &lt;code&gt;O(n2)&lt;/code&gt;, which is far from ideal.&lt;/p&gt;

&lt;p&gt;Personally, my go-to clustering algorithms are OpenOrd for winner-takes-all clustering and FLAME for fuzzy clustering. Both methods are indifferent to whether the metrics used are similarity or distance (FLAME in particular is nearly identical in both constructions). The implementation of OpenOrd in Gephi is &lt;code&gt;O(nlogn)&lt;/code&gt; and is known to be more scalable than any of the other clustering algorithms present in the Gephi package.&lt;/p&gt;

&lt;p&gt;FLAME on the other hand is great if you''re looking for a fuzzy clustering method. While the complexity of FLAME is a little harder to determine since it''s an iterative process, it has been shown to be sub-quadratic, and similar in run-speed to knn.&lt;/p&gt;
', NULL, NULL, '''/code'':59C,119C ''/p'':65C,139C,191C ''acceler'':42C ''alex'':2C ''algorithm'':27C,73C,133C ''back'':15C ''best'':25C ''bit'':17C ''cluster'':26C,72C,81C,86C,132C,155C ''code'':56C,116C ''complex'':52C,159C ''construct'':108C ''dbscan'':22C ''depend'':31C ''determin'':167C ''distanc'':99C ''far'':62C ''flame'':83C,100C,141C,161C ''fuzzi'':85C,154C ''gephi'':114C,137C ''go'':70C ''go-to'':69C ''good'':7C ''great'':147C ''hand'':145C ''harder'':165C ''ideal'':64C ''ident'':105C ''implement'':34C,45C,110C ''implic'':20C ''indic'':43C ''indiffer'':90C ''iter'':172C ''knn'':190C ''known'':122C ''littl'':164C ''look'':151C ''made'':3C ''mani'':44C ''method'':88C,156C ''metric'':94C ''might'':11C ''n2'':58C ''near'':104C ''nlogn'':118C ''number'':5C ''o'':57C,117C ''openord'':75C,112C ''p'':1C,66C,140C ''packag'':138C ''particular'':102C ''person'':67C ''point'':8C ''present'':134C ''process'':173C ''push'':14C ''quadrat'':182C ''re'':40C,150C ''run'':187C ''run-spe'':186C ''scalabl'':126C ''shown'':177C ''similar'':97C,184C ''sinc'':168C ''space'':51C ''speed'':188C ''sub'':181C ''sub-quadrat'':180C ''take'':79C ''though'':9C ''time'':49C ''use'':29C,41C,95C ''whether'':36C,92C ''winner'':78C ''winner-takes-al'':77C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8950, 2, '2015-11-21 00:34:08.703', 2, NULL, NULL, '&lt;p&gt;My favorites ones are Wrangle, Spark Summit and ampcamp.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;ol&gt;
  &lt;li&gt;&lt;p&gt;Wrangle is a new, single-day, single-track industry event about the principles, practice, and application of Data Science, across multiple data-rich industries. It includes talks from data scientists from companies like Salesforce, Pinterest, Facebook, and Uber about the hardest problems they''ve faced, and the solutions they found for them.
  If you''re a practicing Data Scientist, Wrangle is for you!&lt;/p&gt;&lt;/li&gt;
  &lt;li&gt;&lt;p&gt;spark-summit. DATA SCIENCE AND ENGINEERING AT SCALE &lt;/p&gt;&lt;/li&gt;
  &lt;li&gt;&lt;p&gt;AMP Camps are Big Data training events organized by the UC Berkeley AMPLab about big data analytics, machine learning, and popular open-source software projects produced by the AMPLab. All AMP Camp curricula, and whenever possible videos of instructional talks presented at AMP Camps, are published here and accessible for free.&lt;/p&gt;&lt;/li&gt;
  &lt;/ol&gt;
&lt;/blockquote&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://www.wrangleconf.com/" rel="nofollow"&gt;http://www.wrangleconf.com/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="https://spark-summit.org/" rel="nofollow"&gt;https://spark-summit.org/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://ampcamp.berkeley.edu/" rel="nofollow"&gt;http://ampcamp.berkeley.edu/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':165C,175C,185C ''/blockquote'':154C ''/li'':83C,96C,152C ''/ol'':153C,186C ''/p'':11C,82C,95C,151C ''access'':148C ''across'':37C ''amp'':99C,130C,142C ''ampcamp'':10C ''ampcamp.berkeley.edu'':180C,184C ''ampcamp.berkeley.edu/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':183C ''amplab'':111C,128C ''analyt'':115C ''applic'':33C ''berkeley'':110C ''big'':102C,113C ''blockquot'':12C ''camp'':100C,131C,143C ''compani'':50C ''curricula'':132C ''data'':35C,40C,47C,76C,89C,103C,114C ''data-rich'':39C ''day'':22C ''engin'':92C ''event'':27C,105C ''face'':63C ''facebook'':54C ''favorit'':3C ''found'':68C ''free'':150C ''hardest'':59C ''href'':159C,169C,179C ''includ'':44C ''industri'':26C,42C ''instruct'':138C ''learn'':117C ''li'':14C,84C,97C,156C,166C,176C ''like'':51C ''machin'':116C ''multipl'':38C ''new'':19C ''nofollow'':162C,172C,182C ''ol'':13C,155C ''one'':4C ''open'':121C ''open-sourc'':120C ''organ'':106C ''p'':1C,15C,85C,98C,157C,167C,177C ''pinterest'':53C ''popular'':119C ''possibl'':135C ''practic'':31C,75C ''present'':140C ''principl'':30C ''problem'':60C ''produc'':125C ''project'':124C ''publish'':145C ''re'':73C ''rel'':161C,171C,181C ''rich'':41C ''salesforc'':52C ''scale'':94C ''scienc'':36C,90C ''scientist'':48C,77C ''singl'':21C,24C ''single-day'':20C ''single-track'':23C ''softwar'':123C ''solut'':66C ''sourc'':122C ''spark'':7C,87C ''spark-summit'':86C ''spark-summit.org'':170C,174C ''spark-summit.org/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':173C ''summit'':8C,88C ''talk'':45C,139C ''track'':25C ''train'':104C ''uber'':56C ''uc'':109C ''ve'':62C ''video'':136C ''whenev'':134C ''wrangl'':6C,16C,78C ''www.wrangleconf.com'':160C,164C ''www.wrangleconf.com/&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;'':163C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (397, 2, '2014-06-16 06:34:53.683', 1, NULL, NULL, '&lt;p&gt;First you need to verify that your implementation of the algorithm is accurate. For that use a small sample of data and check whether the result is correct. At this stage the sample doesn''t need to be representative of the population. &lt;/p&gt;

&lt;p&gt;Once the implementation is verified, you need to verify that there is a significant relationship among the variables that you try to predict. To do that define null hypothesis and try to reject the null hypothesis with a significant confidence level. (&lt;a href="http://stattrek.com/regression/slope-test.aspx" rel="nofollow"&gt;hypothesis testing for linear regression&lt;/a&gt;) &lt;/p&gt;

&lt;p&gt;There might be unit test frameworks for your SQL distribution. But using a programming language like R will be more easier to implement.&lt;/p&gt;
', NULL, NULL, '''/a'':99C ''/p'':44C,100C,125C ''/regression/slope-test.aspx'':91C ''accur'':14C ''algorithm'':12C ''among'':61C ''check'':24C ''confid'':85C ''correct'':29C ''data'':22C ''defin'':72C ''distribut'':111C ''doesn'':35C ''easier'':122C ''first'':2C ''framework'':107C ''href'':88C ''hypothesi'':74C,81C,94C ''implement'':9C,48C,124C ''languag'':116C ''level'':86C ''like'':117C ''linear'':97C ''might'':103C ''need'':4C,37C,52C ''nofollow'':93C ''null'':73C,80C ''p'':1C,45C,101C ''popul'':43C ''predict'':68C ''program'':115C ''r'':118C ''regress'':98C ''reject'':78C ''rel'':92C ''relationship'':60C ''repres'':40C ''result'':27C ''sampl'':20C,34C ''signific'':59C,84C ''small'':19C ''sql'':110C ''stage'':32C ''stattrek.com'':90C ''stattrek.com/regression/slope-test.aspx'':89C ''test'':95C,106C ''tri'':66C,76C ''unit'':105C ''use'':17C,113C ''variabl'':63C ''verifi'':6C,50C,54C ''whether'':25C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (398, 1, '2014-06-16 07:32:29.137', 6, 646, 'What to consider before learning a new language for data analysis', '&lt;p&gt;I''m currently in the very early stages of preparing a new research-project (still at the funding-application stage), and expect that data-analysis and especially visualisation tools will play a role in this project.&lt;/p&gt;

&lt;p&gt;In view of this I face the following dilemma: Should I learn Python to be able to use its extensive scientific libraries (Pandas, Numpy, Scipy, ...), or should I just dive into similar packages of a language I''m already acquainted with (Racket, or to a lesser extent Scala)?&lt;/p&gt;

&lt;p&gt;(Ideally I would learn Python in parallel with using statistical libraries in Racket, but I''m not sure I''ll have time for both)&lt;/p&gt;

&lt;p&gt;I''m not looking for an answer to this dilemma, but rather for feedback on my different considerations:&lt;/p&gt;

&lt;p&gt;My current position is as follows:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;In favour of Python:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Extensively used libraries&lt;/li&gt;
&lt;li&gt;Widely used (may be decisive in case of collaboration with others)&lt;/li&gt;
&lt;li&gt;A lot of online material to start learning it&lt;/li&gt;
&lt;li&gt;Conferences that are specifically dedicated to Scientific Computing with Python&lt;/li&gt;
&lt;li&gt;Learning Python won''t be a waste of time anyway&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;In favour of a language I already know:&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;It''s a way to deepen my knowledge of one language rather than getting superficial knowledge of one more language (under the motto: you should at least know one language really well)&lt;/li&gt;
&lt;li&gt;It is feasible. Both Racket and Scala have good mathematics and statistics libraries&lt;/li&gt;
&lt;li&gt;I can start right away with learning what I need to know rather than first having to learn the basics&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Two concrete questions:&lt;/strong&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;What am I forgetting?&lt;/li&gt;
&lt;li&gt;How big of a nuisance could the Python 2 vs 3 issue be?&lt;/li&gt;
&lt;/ol&gt;
', 'python  visualization', NULL, '''/li'':172C,185C,196C,208C,220C,268C,283C,305C,320C,335C ''/ol'':336C ''/p'':54C,104C,130C,150C,158C,166C,233C,313C ''/strong'':165C,232C,312C ''/ul'':221C,306C ''2'':330C ''3'':332C ''abl'':71C ''acquaint'':95C ''alreadi'':94C,230C ''analysi'':11A,42C ''answer'':138C ''anyway'':219C ''applic'':35C ''away'':289C ''basic'':304C ''big'':323C ''case'':180C ''collabor'':182C ''comput'':205C ''concret'':310C ''confer'':198C ''consid'':3A ''consider'':149C ''could'':327C ''current'':17C,153C ''data'':10A,41C ''data-analysi'':40C ''decis'':178C ''dedic'':202C ''deepen'':241C ''differ'':148C ''dilemma'':64C,141C ''dive'':85C ''earli'':21C ''especi'':44C ''expect'':38C ''extens'':75C,169C ''extent'':102C ''face'':61C ''favour'':162C,225C ''feasibl'':272C ''feedback'':145C ''first'':299C ''follow'':63C,157C ''forget'':319C ''fund'':34C ''funding-appl'':33C ''get'':249C ''good'':278C ''ideal'':106C ''issu'':333C ''know'':231C,263C,296C ''knowledg'':243C,251C ''languag'':8A,91C,228C,246C,255C,265C ''learn'':5A,67C,109C,194C,210C,291C,302C ''least'':262C ''lesser'':101C ''li'':168C,173C,186C,197C,209C,235C,269C,284C,315C,321C ''librari'':77C,116C,171C,282C ''ll'':125C ''look'':135C ''lot'':188C ''m'':16C,93C,121C,133C ''materi'':191C ''mathemat'':279C ''may'':176C ''motto'':258C ''need'':294C ''new'':7A,26C ''nuisanc'':326C ''numpi'':79C ''ol'':314C ''one'':245C,253C,264C ''onlin'':190C ''other'':184C ''p'':14C,55C,105C,131C,151C,159C,222C,307C ''packag'':88C ''panda'':78C ''parallel'':112C ''play'':48C ''posit'':154C ''prepar'':24C ''project'':29C,53C ''python'':12B,68C,110C,164C,207C,211C,329C ''question'':311C ''racket'':97C,118C,274C ''rather'':143C,247C,297C ''realli'':266C ''research'':28C ''research-project'':27C ''right'':288C ''role'':50C ''scala'':103C,276C ''scientif'':76C,204C ''scipi'':80C ''similar'':87C ''specif'':201C ''stage'':22C,36C ''start'':193C,287C ''statist'':115C,281C ''still'':30C ''strong'':160C,223C,308C ''superfici'':250C ''sure'':123C ''time'':127C,218C ''tool'':46C ''two'':309C ''ul'':167C,234C ''use'':73C,114C,170C,175C ''view'':57C ''visual'':13B ''visualis'':45C ''vs'':331C ''wast'':216C ''way'':239C ''well'':267C ''wide'':174C ''won'':212C ''would'':108C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (399, 2, '2014-06-16 09:52:24.913', 0, NULL, NULL, '&lt;p&gt;According to me, all the factors, you have mentioned are superficial in nature. You have not considered the core of tool selection. In this case, there are 2 aspects, you mentioned:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Data analysis - What kind of analysis are you working on? There might be some analysis which are easier in some languages and more difficult in other.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Visualization - R provides similar community and learning material (as Python) and has the best visualizations compared to other languages here.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;At this stage, you can be flexible with what language to learn, since you are starting from scratch.&lt;/p&gt;

&lt;p&gt;Hope this helps.&lt;/p&gt;
', NULL, NULL, '''/li'':64C,88C ''/ol'':89C ''/p'':33C,63C,87C,109C,114C ''2'':29C ''accord'':2C ''analysi'':38C,42C,51C ''aspect'':30C ''best'':80C ''case'':26C ''communiti'':71C ''compar'':82C ''consid'':18C ''core'':20C ''data'':37C ''difficult'':60C ''easier'':54C ''factor'':7C ''flexibl'':97C ''help'':113C ''hope'':111C ''kind'':40C ''languag'':57C,85C,100C ''learn'':73C,102C ''li'':35C,65C ''materi'':74C ''mention'':10C,32C ''might'':48C ''natur'':14C ''ol'':34C ''p'':1C,36C,66C,90C,110C ''provid'':69C ''python'':76C ''r'':68C ''scratch'':108C ''select'':23C ''similar'':70C ''sinc'':103C ''stage'':93C ''start'':106C ''superfici'':12C ''tool'':22C ''visual'':67C,81C ''work'':45C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (401, 2, '2014-06-16 13:21:58.777', 1, NULL, NULL, '&lt;p&gt;I like a multiple step strategy:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Write clean easy to understand code, as opposed to short-tricky code. I know statisticians like tricky code, but spotting problems in tricky code is dangerous. 
( I am mentioning this because a supervisor of mine was fond of undocumented 500 lines python scrips - have fun debugging that mess and I have seen that pattern a lot, especially from people who are not from an IT background)&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Break down your code in smaller functions, which can be tested and evaluated in smaller stes.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Look for connected elements, e.g. the number of cases with condition X is Y - so this query MUST return Y. Most often this is more complex, but doable.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;When you are running your script the first time, test it with a small subsample and carefully check if everything is in order. While I like unit tests in IT, bugs in statistics scripts are often so pronounced that they are easily visible doing a carefully check. Or they are methodical errors, which are probably never caught by unit tests.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;That should suffice to ensure a clean "one - off " job. But for a time series as you seem to have, I would add that you should check for values out of range, impossible combinations etc. For me, most scripts that have reached step 4 are probably bug free - and they will stay that way unless something changes. And most often, the data are changing - and that is something which should be checked for every run. Writing code for that can be time consuming and annoying, but it beats subtle errors due to data entry errors.&lt;/p&gt;
', NULL, NULL, '''/li'':80C,100C,132C,196C ''/ol'':197C ''/p'':8C,79C,99C,131C,195C,294C ''4'':242C ''500'':52C ''add'':221C ''annoy'':283C ''background'':78C ''beat'':286C ''break'':83C ''bug'':165C,245C ''care'':151C,180C ''case'':111C ''caught'':191C ''chang'':255C,262C ''check'':152C,181C,225C,270C ''clean'':13C,205C ''code'':17C,24C,30C,36C,86C,275C ''combin'':232C ''complex'':128C ''condit'':113C ''connect'':105C ''consum'':281C ''danger'':38C ''data'':260C,291C ''debug'':58C ''doabl'':130C ''due'':289C ''e.g'':107C ''easi'':14C ''easili'':176C ''element'':106C ''ensur'':203C ''entri'':292C ''error'':186C,288C,293C ''especi'':69C ''etc'':233C ''evalu'':95C ''everi'':272C ''everyth'':154C ''first'':142C ''fond'':49C ''free'':246C ''fun'':57C ''function'':89C ''imposs'':231C ''job'':208C ''know'':26C ''li'':10C,81C,101C,133C ''like'':3C,28C,160C ''line'':53C ''look'':103C ''lot'':68C ''mention'':41C ''mess'':60C ''method'':185C ''mine'':47C ''multipl'':5C ''must'':120C ''never'':190C ''number'':109C ''often'':124C,170C,258C ''ol'':9C ''one'':206C ''oppos'':19C ''order'':157C ''p'':1C,11C,82C,102C,134C,198C ''pattern'':66C ''peopl'':71C ''probabl'':189C,244C ''problem'':33C ''pronounc'':172C ''python'':54C ''queri'':119C ''rang'':230C ''reach'':240C ''return'':121C ''run'':138C,273C ''scrip'':55C ''script'':140C,168C,237C ''seem'':216C ''seen'':64C ''seri'':213C ''short'':22C ''short-tricki'':21C ''small'':148C ''smaller'':88C,97C ''someth'':254C,266C ''spot'':32C ''statist'':167C ''statistician'':27C ''stay'':250C ''step'':6C,241C ''stes'':98C ''strategi'':7C ''subsampl'':149C ''subtl'':287C ''suffic'':201C ''supervisor'':45C ''test'':93C,144C,162C,194C ''time'':143C,212C,280C ''tricki'':23C,29C,35C ''understand'':16C ''undocu'':51C ''unit'':161C,193C ''unless'':253C ''valu'':227C ''visibl'':177C ''way'':252C ''would'':220C ''write'':12C,274C ''x'':114C ''y'':116C,122C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (404, 2, '2014-06-16 14:35:20.98', 10, NULL, NULL, '&lt;p&gt;Personally I would advocate using something that is both not-specific to the NLP field, and something that is sufficiently general that it can still be used as a tool even when you''ve started moving beyond this level of metadata. I would especially pick a format that can be used regardless of development environment and one that can keep some basic structure if that becomes relevant (like tokenization)&lt;/p&gt;

&lt;p&gt;It might seem strange, but I would honestly suggest &lt;code&gt;JSON&lt;/code&gt;. It''s extremely well supported, supports a lot of structure, and is flexible enough that you shouldn''t have to move from it for not being powerful enough. For your example, something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;{''text'': ''I saw the company''s manager last day.", {''Person'': [{''name'': ''John''}, {''indices'': [0:1]}, etc...]}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The one big advantage you''ve got over any NLP-specific formats here is that &lt;code&gt;JSON&lt;/code&gt; can be parsed in any environment, and since you''ll probably have to edit your format anyway, JSON lends itself to very simple edits that give you a short distance to other formats.&lt;/p&gt;

&lt;p&gt;You can also implicitly store tokenization information if you want:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;{"text": ["I", "saw", "the", "company''s", "manager", "last", "day."]}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;EDIT: To clarify the mapping of metadata is pretty open, but here''s an example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;{''body'': ''&amp;lt;some_text&amp;gt;'',
 ''metadata'': 
  {''&amp;lt;entity&amp;gt;'':
    {''&amp;lt;attribute&amp;gt;'': ''&amp;lt;value&amp;gt;'',
     ''location'': [&amp;lt;start_index&amp;gt;, &amp;lt;end_index&amp;gt;]
    }
  }
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Hope that helps, let me know if you''ve got any more questions.&lt;/p&gt;
', NULL, NULL, '''/code'':85C,139C,160C,218C,263C ''/p'':72C,120C,194C,206C,236C,279C ''/pre'':140C,219C,264C ''0'':136C ''1'':137C ''advantag'':145C ''advoc'':5C ''also'':198C ''anyway'':177C ''attribut'':249C ''basic'':64C ''becom'':68C ''beyond'':39C ''big'':144C ''bodi'':239C ''clarifi'':223C ''code'':83C,122C,158C,208C,238C ''compani'':127C,213C ''day'':131C,217C ''develop'':56C ''distanc'':190C ''edit'':174C,184C,221C ''end'':260C ''enough'':99C,113C ''entiti'':246C ''environ'':57C,166C ''especi'':46C ''etc'':138C ''even'':33C ''exampl'':116C,235C ''extrem'':88C ''field'':17C ''flexibl'':98C ''format'':49C,154C,176C,193C ''general'':23C ''give'':186C ''got'':148C,275C ''gt'':243C,247C,250C,253C,258C,262C ''help'':268C ''honest'':81C ''hope'':266C ''implicit'':199C ''index'':257C,261C ''indic'':135C ''inform'':202C ''john'':134C ''json'':84C,159C,178C ''keep'':62C ''know'':271C ''last'':130C,216C ''lend'':179C ''let'':269C ''level'':41C ''like'':70C,118C ''ll'':170C ''locat'':254C ''lot'':93C ''lt'':240C,245C,248C,251C,255C,259C ''manag'':129C,215C ''map'':225C ''metadata'':43C,227C,244C ''might'':75C ''move'':38C,106C ''name'':133C ''nlp'':16C,152C ''nlp-specif'':151C ''not-specif'':11C ''one'':59C,143C ''open'':230C ''p'':1C,73C,141C,195C,220C,265C ''pars'':163C ''person'':2C,132C ''pick'':47C ''power'':112C ''pre'':121C,207C,237C ''pretti'':229C ''probabl'':171C ''question'':278C ''regardless'':54C ''relev'':69C ''saw'':125C,211C ''seem'':76C ''short'':189C ''shouldn'':102C ''simpl'':183C ''sinc'':168C ''someth'':7C,19C,117C ''specif'':13C,153C ''start'':37C,256C ''still'':27C ''store'':200C ''strang'':77C ''structur'':65C,95C ''suffici'':22C ''suggest'':82C ''support'':90C,91C ''text'':123C,209C,242C ''token'':71C,201C ''tool'':32C ''use'':6C,29C,53C ''valu'':252C ''ve'':36C,147C,274C ''want'':205C ''well'':89C ''would'':4C,45C,80C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10101, 2, '2016-02-06 03:14:11.303', 2, NULL, NULL, '&lt;p&gt;Unless you are trying to do this as a learning exercise, just use Spark which has ML libraries made for distributed computing.  See &lt;a href="http://spark.apache.org/docs/latest/mllib-clustering.html" rel="nofollow"&gt;http://spark.apache.org/docs/latest/mllib-clustering.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/docs/latest/mllib-clustering.html'':29C ''/docs/latest/mllib-clustering.html&lt;/a&gt;&lt;/p&gt;'':34C ''comput'':23C ''distribut'':22C ''exercis'':12C ''href'':26C ''learn'':11C ''librari'':19C ''made'':20C ''ml'':18C ''nofollow'':31C ''p'':1C ''rel'':30C ''see'':24C ''spark'':15C ''spark.apache.org'':28C,33C ''spark.apache.org/docs/latest/mllib-clustering.html'':27C ''spark.apache.org/docs/latest/mllib-clustering.html&lt;/a&gt;&lt;/p&gt;'':32C ''tri'':5C ''unless'':2C ''use'':14C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (405, 2, '2014-06-16 15:00:04.577', 13, NULL, NULL, '&lt;p&gt;Personally going to make a strong argument in favor of Python here. There are a large number of reasons for this, but I''m going to build on some of the points that other people have mentioned here:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;strong&gt;Picking a single language:&lt;/strong&gt; It''s definitely possible to mix and match languages, picking &lt;code&gt;d3&lt;/code&gt; for your visualization needs, &lt;code&gt;FORTRAN&lt;/code&gt; for your fast matrix multiplies, and &lt;code&gt;python&lt;/code&gt; for all of your networking and scripting. You can do this down the line, but keeping your stack as simple as possible is a good move, especially early on.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Picking something bigger than you:&lt;/strong&gt; You never want to be pushing up against the barriers of the language you want to use. This is a huge issue when it comes to languages like &lt;code&gt;Julia&lt;/code&gt; and &lt;code&gt;FORTRAN&lt;/code&gt;, which simply don''t offer the full functionality of languages like &lt;code&gt;python&lt;/code&gt; or &lt;code&gt;R&lt;/code&gt;.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Pick Community&lt;/strong&gt;: The one most difficult thing to find in any language is community. &lt;code&gt;Python&lt;/code&gt; is the clear winner here. If you get stuck, you ask something on SO, and someone will answer in a matter of minutes, which is simply not the case for most other languages. If you''re learning something in a vacuum you will simply learn much slower.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;In terms of the minus points, I might actually push back on them.&lt;/p&gt;

&lt;p&gt;Deepening your knowledge of one language is a decent idea, but knowing &lt;em&gt;only&lt;/em&gt; one language, without having practice generalizing that knowledge to other languages is a good way to shoot yourself in the foot. I have changed my entire favored development stack three time over as many years, moving from &lt;code&gt;MATLAB&lt;/code&gt; to &lt;code&gt;Java&lt;/code&gt; to &lt;code&gt;haskell&lt;/code&gt; to &lt;code&gt;python&lt;/code&gt;. Learning to transfer your knowledge to another language is far more valuable than just knowing one.&lt;/p&gt;

&lt;p&gt;As far as feasibility, this is something you''re going to see again and again in any programming career. Turing completeness means you could technically do everything with &lt;code&gt;HTML4&lt;/code&gt; and &lt;code&gt;CSS3&lt;/code&gt;, but you want to pick the right tool for the job. If you see the ideal tool and decide to leave it by the roadside you''re going to find yourself slowed down wishing you had some of the tools you left behind.&lt;/p&gt;

&lt;p&gt;A great example of that last point is trying to deploy &lt;code&gt;R&lt;/code&gt; code. ''R''''s networking capabilities are hugely lacking compared to &lt;code&gt;python&lt;/code&gt;, and if you want to deploy a service, or use slightly off-the-beaten path packages, the fact that &lt;code&gt;pip&lt;/code&gt; has an order of magnitude more packages than &lt;code&gt;CRAN&lt;/code&gt; is a huge help.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (406, 1, '2014-06-16 15:49:55.673', 11, 1817, 'How can I predict traffic based on previous time series data?', '&lt;p&gt;If I have a retail store and have a way to measure how many people enter my store every minute, and timestamp that data, how can I predict future foot traffic?  &lt;/p&gt;

&lt;p&gt;I have looked into machine learning algorithms, but I''m not sure which one to use. In my test data, a year over year trend is more accurate compared to other things I''ve tried, like KNN (with what I think are sensible parameters and distance function).&lt;/p&gt;

&lt;p&gt;It almost seems like this could be similar to financial modeling, where you deal with time series data. Any ideas?&lt;/p&gt;
', 'machine-learning  time-series', NULL, '''/p'':50C,99C,121C ''accur'':79C ''algorithm'':58C ''almost'':102C ''base'':6A ''compar'':80C ''could'':106C ''data'':11A,42C,71C,118C ''deal'':114C ''distanc'':97C ''enter'':34C ''everi'':37C ''financi'':110C ''foot'':48C ''function'':98C ''futur'':47C ''idea'':120C ''knn'':88C ''learn'':14B,57C ''like'':87C,104C ''look'':54C ''m'':61C ''machin'':13B,56C ''machine-learn'':12B ''mani'':32C ''measur'':30C ''minut'':38C ''model'':111C ''one'':65C ''p'':18C,51C,100C ''paramet'':95C ''peopl'':33C ''predict'':4A,46C ''previous'':8A ''retail'':23C ''seem'':103C ''sensibl'':94C ''seri'':10A,17B,117C ''similar'':108C ''store'':24C,36C ''sure'':63C ''test'':70C ''thing'':83C ''think'':92C ''time'':9A,16B,116C ''time-seri'':15B ''timestamp'':40C ''traffic'':5A,49C ''trend'':76C ''tri'':86C ''use'':67C ''ve'':85C ''way'':28C ''year'':73C,75C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (429, 2, '2014-06-17 09:56:49.18', 7, NULL, NULL, '&lt;p&gt;It''s not a social network per se, but Stackexchange publish their entire database dump periodically:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://archive.org/details/stackexchange"&gt;Stackexchange data dump hosted on the archive.org&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://meta.stackoverflow.com/questions/2677/database-schema-documentation-for-the-public-data-dump-and-sede"&gt;Post describing the database dump schema&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;You can extract some social information by analyzing which users ask and answer to each other. One nice thing is that since posts are tagged, you can analyze sub-communities easily.&lt;/p&gt;
', NULL, NULL, '''/a'':33C,47C ''/details/stackexchange'':25C ''/li'':34C,48C ''/p'':18C,83C ''/questions/2677/database-schema-documentation-for-the-public-data-dump-and-sede'':40C ''/ul'':49C ''analyz'':58C,78C ''answer'':63C ''archive.org'':24C,32C ''archive.org/details/stackexchange'':23C ''ask'':61C ''communiti'':81C ''data'':27C ''databas'':15C,44C ''describ'':42C ''dump'':16C,28C,45C ''easili'':82C ''entir'':14C ''extract'':53C ''host'':29C ''href'':22C,37C ''inform'':56C ''li'':20C,35C ''meta.stackoverflow.com'':39C ''meta.stackoverflow.com/questions/2677/database-schema-documentation-for-the-public-data-dump-and-sede'':38C ''network'':7C ''nice'':68C ''one'':67C ''p'':1C,50C ''per'':8C ''period'':17C ''post'':41C,73C ''publish'':12C ''schema'':46C ''se'':9C ''sinc'':72C ''social'':6C,55C ''stackexchang'':11C,26C ''sub'':80C ''sub-commun'':79C ''tag'':75C ''thing'':69C ''ul'':19C ''user'':60C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (407, 2, '2014-06-16 16:10:34.423', 6, NULL, NULL, '&lt;p&gt;Other answers recommended a good set of books about the mathematics behind data science. But as you mentioned, its not just mathematics and activities like data collection and inference from data has their own rules and theories, even if not being as rigorous as mathematical backgrounds (yet).&lt;/p&gt;

&lt;p&gt;For theses parts, I suggest the book &lt;a href="http://rads.stackoverflow.com/amzn/click/0596157118"&gt;Beautiful Data: The Stories Behind Elegant Data Solutions&lt;/a&gt; which contains twenty case-study like chapters written by people really engaged with real world data analysis problems. It does not contain any mathematics, but explores areas like collecting data, finding practical ways of using data in analyses, scaling and selecting the best solutions very well.&lt;/p&gt;

&lt;p&gt;Another really interesting book is &lt;a href="http://rads.stackoverflow.com/amzn/click/1449362931"&gt;Thinking with Data: How to Turn Information into Insights&lt;/a&gt;, which is not technical (=programming tutorial) either, but covers important topics on how to really use the data science power in decision making and real world problems.&lt;/p&gt;
', NULL, NULL, '''/a'':71C,140C ''/amzn/click/0596157118'':62C ''/amzn/click/1449362931'':130C ''/p'':49C,119C,168C ''activ'':25C ''analys'':110C ''analysi'':89C ''anoth'':121C ''answer'':3C ''area'':99C ''background'':47C ''beauti'':63C ''behind'':13C,67C ''best'':115C ''book'':9C,57C,124C ''case'':76C ''case-studi'':75C ''chapter'':79C ''collect'':28C,101C ''contain'':73C,94C ''cover'':149C ''data'':14C,27C,32C,64C,69C,88C,102C,108C,133C,158C ''decis'':162C ''either'':147C ''eleg'':68C ''engag'':84C ''even'':39C ''explor'':98C ''find'':103C ''good'':6C ''href'':59C,127C ''import'':150C ''infer'':30C ''inform'':137C ''insight'':139C ''interest'':123C ''like'':26C,78C,100C ''make'':163C ''mathemat'':12C,23C,46C,96C ''mention'':19C ''p'':1C,50C,120C ''part'':53C ''peopl'':82C ''power'':160C ''practic'':104C ''problem'':90C,167C ''program'':145C ''rads.stackoverflow.com'':61C,129C ''rads.stackoverflow.com/amzn/click/0596157118'':60C ''rads.stackoverflow.com/amzn/click/1449362931'':128C ''real'':86C,165C ''realli'':83C,122C,155C ''recommend'':4C ''rigor'':44C ''rule'':36C ''scale'':111C ''scienc'':15C,159C ''select'':113C ''set'':7C ''solut'':70C,116C ''stori'':66C ''studi'':77C ''suggest'':55C ''technic'':144C ''theori'':38C ''these'':52C ''think'':131C ''topic'':151C ''turn'':136C ''tutori'':146C ''twenti'':74C ''use'':107C,156C ''way'':105C ''well'':118C ''world'':87C,166C ''written'':80C ''yet'':48C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (408, 2, '2014-06-16 16:34:50.317', 11, NULL, NULL, '&lt;p&gt;The problem with models like KNN is that they do not take into account seasonality (time-dependent variations in trend).  To take those into account, you should use Time Series analysis.&lt;/p&gt;

&lt;p&gt;For count data, such as yours, you can use generalized linear auto-regressive moving average models (GLARMA).  Fortunately, there is an R package that implements them (&lt;a href="http://cran.r-project.org/web/packages/glarma/index.html"&gt;glarma&lt;/a&gt;).  &lt;/p&gt;

&lt;p&gt;The &lt;a href="http://cran.r-project.org/web/packages/glarma/vignettes/glarma.pdf"&gt;vignette&lt;/a&gt; is a good resource for the theory behind the tool.&lt;/p&gt;
', NULL, NULL, '''/a'':69C,79C ''/p'':34C,70C,90C ''/web/packages/glarma/index.html'':67C ''/web/packages/glarma/vignettes/glarma.pdf'':77C ''account'':15C,27C ''analysi'':33C ''auto'':48C ''auto-regress'':47C ''averag'':51C ''behind'':87C ''count'':37C ''cran.r-project.org'':66C,76C ''cran.r-project.org/web/packages/glarma/index.html'':65C ''cran.r-project.org/web/packages/glarma/vignettes/glarma.pdf'':75C ''data'':38C ''depend'':19C ''fortun'':54C ''general'':45C ''glarma'':53C,68C ''good'':82C ''href'':64C,74C ''implement'':61C ''knn'':7C ''like'':6C ''linear'':46C ''model'':5C,52C ''move'':50C ''p'':1C,35C,71C ''packag'':59C ''problem'':3C ''r'':58C ''regress'':49C ''resourc'':83C ''season'':16C ''seri'':32C ''take'':13C,24C ''theori'':86C ''time'':18C,31C ''time-depend'':17C ''tool'':89C ''trend'':22C ''use'':30C,44C ''variat'':20C ''vignett'':78C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (409, 2, '2014-06-16 17:26:17.697', 7, NULL, NULL, '&lt;p&gt;I think Christopher''s answers above are entirely sensible.  As an alternate approach (or perhaps just in addition to the advise he''s given), I might start by just visualizing the data a bit to try get a rough sense of what''s going on.&lt;/p&gt;

&lt;p&gt;If you haven''t already done this, you might try adding a date''s month and day of week as features -- if you end up sticking with KNN, this will help the model pick up seasonality. &lt;/p&gt;

&lt;p&gt;As a different way of taking this on, you might consider starting with a really, really basic model (like OLS).. these often go a long way in generating reasonable predictions.  &lt;/p&gt;

&lt;p&gt;Finally, the more we know about your data, the easier it will be for us to help generate suggestions -- What time frame are you observing?  What are the features you''re currently using?  etc.&lt;/p&gt;

&lt;p&gt;Hope this helps --&lt;/p&gt;
', NULL, NULL, '''/p'':47C,85C,117C,153C,158C ''ad'':59C ''addit'':19C ''advis'':22C ''alreadi'':53C ''altern'':13C ''answer'':6C ''approach'':14C ''basic'':103C ''bit'':35C ''christoph'':4C ''consid'':97C ''current'':150C ''data'':33C,126C ''date'':61C ''day'':65C ''differ'':89C ''done'':54C ''easier'':128C ''end'':72C ''entir'':9C ''etc'':152C ''featur'':69C,147C ''final'':119C ''frame'':140C ''generat'':114C,136C ''get'':38C ''given'':25C ''go'':45C,109C ''haven'':51C ''help'':79C,135C,157C ''hope'':155C ''knn'':76C ''know'':123C ''like'':105C ''long'':111C ''might'':27C,57C,96C ''model'':81C,104C ''month'':63C ''observ'':143C ''often'':108C ''ol'':106C ''p'':1C,48C,86C,118C,154C ''perhap'':16C ''pick'':82C ''predict'':116C ''re'':149C ''realli'':101C,102C ''reason'':115C ''rough'':40C ''season'':84C ''sens'':41C ''sensibl'':10C ''start'':28C,98C ''stick'':74C ''suggest'':137C ''take'':92C ''think'':3C ''time'':139C ''tri'':37C,58C ''us'':133C ''use'':151C ''visual'':31C ''way'':90C,112C ''week'':67C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (473, 1, '2014-06-19 08:56:46.847', 4, 557, 'Is logistic regression actually a regression algorithm?', '&lt;p&gt;The usual definition of regression (as far as I am aware) is &lt;em&gt;predicting a continuous output variable from a given set of input variables&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;Logistic regression is a binary classification algorithm, so it produces a categorical output.&lt;/p&gt;

&lt;p&gt;Is it really a regression algorithm? If so, why?&lt;/p&gt;
', 'algorithms  logistic-regression', NULL, '''/em'':38C ''/p'':39C,54C,65C ''actual'':4A ''algorithm'':7A,8B,47C,61C ''awar'':23C ''binari'':45C ''categor'':52C ''classif'':46C ''continu'':28C ''definit'':15C ''em'':25C ''far'':19C ''given'':33C ''input'':36C ''logist'':2A,10B,41C ''logistic-regress'':9B ''output'':29C,53C ''p'':12C,40C,55C ''predict'':26C ''produc'':50C ''realli'':58C ''regress'':3A,6A,11B,17C,42C,60C ''set'':34C ''usual'':14C ''variabl'':30C,37C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (410, 1, '2014-06-16 18:08:38.623', 27, 4171, 'Choosing a learning rate', '&lt;p&gt;I''m currently working on implementing Stochastic Gradient Descent (SGD) for neural nets using backpropagation, and while I understand its purpose I have some questions about how to choose values for the learning rate.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Is the learning rate related to the shape of the error gradient, as it dictates the rate of descent?&lt;/li&gt;
&lt;li&gt;If so, how do you use this information to inform your decision about a value?&lt;/li&gt;
&lt;li&gt;If it''s not what sort of values should I choose, and how should I choose them?&lt;/li&gt;
&lt;li&gt;It seems like you would want small values to avoid overshooting, but how do you choose one such that you don''t get stuck in local minima or take to long to descend?&lt;/li&gt;
&lt;li&gt;Does it make sense to have a constant learning rate, or should I use some metric to alter its value as I get nearer a minimum in the gradient?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In short: How do I choose the learning rate for SGD?&lt;/p&gt;
', 'machine-learning  neuralnetwork  algorithms  optimization  hyperparameter', NULL, '''/li'':69C,86C,105C,140C,171C ''/p'':47C,185C ''/ul'':172C ''algorithm'':9B ''alter'':159C ''avoid'':116C ''backpropag'':27C ''choos'':1A,41C,98C,103C,122C,179C ''constant'':149C ''current'':15C ''decis'':82C ''descend'':139C ''descent'':21C,68C ''dictat'':64C ''error'':60C ''get'':129C,164C ''gradient'':20C,61C,170C ''hyperparamet'':11B ''implement'':18C ''inform'':78C,80C ''learn'':3A,7B,45C,52C,150C,181C ''li'':49C,70C,87C,106C,141C ''like'':109C ''local'':132C ''long'':137C ''m'':14C ''machin'':6B ''machine-learn'':5B ''make'':144C ''metric'':157C ''minima'':133C ''minimum'':167C ''nearer'':165C ''net'':25C ''neural'':24C ''neuralnetwork'':8B ''one'':123C ''optim'':10B ''overshoot'':117C ''p'':12C,173C ''purpos'':33C ''question'':37C ''rate'':4A,46C,53C,66C,151C,182C ''relat'':54C ''seem'':108C ''sens'':145C ''sgd'':22C,184C ''shape'':57C ''short'':175C ''small'':113C ''sort'':93C ''stochast'':19C ''stuck'':130C ''take'':135C ''ul'':48C ''understand'':31C ''use'':26C,76C,155C ''valu'':42C,85C,95C,114C,161C ''want'':112C ''work'':16C ''would'':111C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (411, 1, '2014-06-16 19:14:38.553', 6, 985, 'Best languages for scientific computing', '&lt;p&gt;It seems as though most languages have some number of scientific computing libraries available. &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Python has &lt;code&gt;Scipy&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;Rust&lt;/code&gt; has &lt;code&gt;SciRust&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;C++&lt;/code&gt; has several including &lt;code&gt;ViennaCL&lt;/code&gt; and &lt;code&gt;Armadillo&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;&lt;code&gt;Java&lt;/code&gt; has &lt;code&gt;Java Numerics&lt;/code&gt; and &lt;code&gt;Colt&lt;/code&gt; as well as several other&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Not to mention languages like &lt;code&gt;R&lt;/code&gt; and &lt;code&gt;Julia&lt;/code&gt; designed explicitly for scientific computing.&lt;/p&gt;

&lt;p&gt;With so many options how do you choose the best language for a task? Additionally which languages will be the most performant? &lt;code&gt;Python&lt;/code&gt; and &lt;code&gt;R&lt;/code&gt; seem to have the most traction in the space, but logically a compiled language seems like it would be a better choice. And will anything ever outperform &lt;code&gt;Fortran&lt;/code&gt;? Additionally compiled languages tend to have GPU acceleration, while interpreted languages like &lt;code&gt;R&lt;/code&gt; and &lt;code&gt;Python&lt;/code&gt; don''t. What should I take into account when choosing a language, and which languages provide the best balance of utility and performance? Also are there any languages with significant scientific computing resources that I''ve missed?&lt;/p&gt;
', 'efficiency  statistics  tools  knowledge-base', NULL, '''/code'':34C,39C,43C,48C,54C,58C,63C,68C,72C,88C,92C,124C,128C,158C,173C,177C ''/li'':35C,44C,59C,78C ''/p'':27C,98C,215C ''/ul'':79C ''acceler'':166C ''account'':185C ''addit'':114C,159C ''also'':201C ''anyth'':153C ''armadillo'':57C ''avail'':26C ''balanc'':196C ''base'':11B ''best'':1A,109C,195C ''better'':149C ''c'':47C ''choic'':150C ''choos'':107C,187C ''code'':32C,37C,41C,46C,52C,56C,61C,65C,70C,86C,90C,122C,126C,156C,171C,175C ''colt'':71C ''compil'':141C,160C ''comput'':5A,24C,97C,209C ''design'':93C ''effici'':6B ''ever'':154C ''explicit'':94C ''fortran'':157C ''gpu'':165C ''includ'':51C ''interpret'':168C ''java'':62C,66C ''julia'':91C ''knowledg'':10B ''knowledge-bas'':9B ''languag'':2A,18C,84C,110C,116C,142C,161C,169C,189C,192C,205C ''li'':29C,36C,45C,60C ''librari'':25C ''like'':85C,144C,170C ''logic'':139C ''mani'':102C ''mention'':83C ''miss'':214C ''number'':21C ''numer'':67C ''option'':103C ''outperform'':155C ''p'':12C,80C,99C ''perform'':121C,200C ''provid'':193C ''python'':30C,123C,176C ''r'':87C,127C,172C ''resourc'':210C ''rust'':38C ''scientif'':4A,23C,96C,208C ''scipi'':33C ''scirust'':42C ''seem'':14C,129C,143C ''sever'':50C,76C ''signific'':207C ''space'':137C ''statist'':7B ''take'':183C ''task'':113C ''tend'':162C ''though'':16C ''tool'':8B ''traction'':134C ''ul'':28C ''util'':198C ''ve'':213C ''viennacl'':53C ''well'':74C ''would'':146C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (412, 1, '2014-06-16 19:48:31.797', 26, 525, 'How can I transform names in a confidential data set to make it anonymous, but preserve some of the characteristics of the names?', '&lt;h1&gt;Motivation&lt;/h1&gt;

&lt;p&gt;I work with datasets that contain personally identifiable information (PII) and sometimes need to share part of a dataset with third parties, in a way that doesn''t expose PII and subject my employer to liability. Our usual approach here is to withhold data entirely, or in some cases to reduce its resolution; e.g., replacing an exact street address with the corresponding county or census tract. &lt;/p&gt;

&lt;p&gt;This means that certain types of analysis and processing must be done in-house, even when a third party has resources and expertise more suited to the task. Since the source data is not disclosed, the way we go about this analysis and processing lacks transparency. As a result, any third party''s ability to perform QA/QC, adjust parameters or make refinements may be very limited.&lt;/p&gt;

&lt;h1&gt;Anonymizing Confidential Data&lt;/h1&gt;

&lt;p&gt;One task involves identifying individuals by their names, in user-submitted data, while taking into account errors and inconsistencies. A private individual might be recorded in one place as "Dave" and in another as "David," commercial entities can have many different abbreviations, and there are always some typos. I''ve developed scripts based on a number of criteria that determine when two records with non-identical names represent the same individual, and assign them a common ID. &lt;/p&gt;

&lt;p&gt;At this point we can make the dataset anonymous by withholding the names and replacing them with this personal ID number. But this means the recipient has almost no information about e.g. the strength of the match. We would prefer to be able to pass along as much information as possible without divulging identity.&lt;/p&gt;

&lt;h1&gt;What Doesn''t Work&lt;/h1&gt;

&lt;p&gt;For instance, it would be great to be able to encrypt strings while preserving edit distance. This way, third parties could do some of their own QA/QC, or choose to do further processing on their own, without ever accessing (or being able to potentially reverse-engineer) PII. Perhaps we match strings in-house with edit distance &amp;lt;= 2, and the recipient wants to look at the implications of tightening that tolerance to edit distance &amp;lt;= 1.&lt;/p&gt;

&lt;p&gt;But the only method I am familiar with that does this is &lt;a href="http://www.techrepublic.com/blog/it-security/cryptographys-running-gag-rot13/" rel="nofollow"&gt;ROT13&lt;/a&gt; (more generally, any &lt;a href="https://en.wikipedia.org/wiki/Caesar_cipher" rel="nofollow"&gt;shift cipher&lt;/a&gt;), which hardly even counts as encryption; it''s like writing the names upside down and saying, "Promise you won''t flip the paper over?"&lt;/p&gt;

&lt;p&gt;Another &lt;strong&gt;bad&lt;/strong&gt; solution would be to abbreviate everything. "Ellen Roberts" becomes "ER" and so forth. This is a poor solution because in some cases the initials, in association with public data, will reveal a person''s identity, and in other cases it''s too ambiguous; "Benjamin Othello Ames" and "Bank of America" will have the same initials, but their names are otherwise dissimilar. So it doesn''t do either of the things we want.&lt;/p&gt;

&lt;p&gt;An inelegant alternative is to introduce additional fields to track certain attributes of the name, e.g.:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;+-----+----+-------------------+-----------+--------+
| Row | ID | Name              | WordChars | Origin |
+-----+----+-------------------+-----------+--------+
| 1   | 17 | "AMELIA BEDELIA"  | (6, 7)    | Eng    |
+-----+----+-------------------+-----------+--------+
| 2   | 18 | "CHRISTOPH BAUER" | (9, 5)    | Ger    |
+-----+----+-------------------+-----------+--------+
| 3   | 18 | "C J BAUER"       | (1, 1, 5) | Ger    |
+-----+----+-------------------+-----------+--------+
| 4   | 19 | "FRANZ HELLER"    | (5, 6)    | Ger    |
+-----+----+-------------------+-----------+--------+
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I call this "inelegant" because it requires anticipating which qualities might be interesting and it''s relatively coarse. If the names are removed, there''s not much you can reasonably conclude about the strength of the match between rows 2 &amp;amp; 3, or about the distance between rows 2 &amp;amp; 4 (i.e., how close they are to matching).&lt;/p&gt;

&lt;h1&gt;Conclusion&lt;/h1&gt;

&lt;p&gt;The goal is to transform strings in such a way that as many useful qualities of the original string are preserved as possible while obscuring the original string. Decryption should be impossible, or so impractical as to be effectively impossible, no matter the size of the data set. In particular, a method that preserves the edit distance between arbitrary strings would be very useful.&lt;/p&gt;

&lt;p&gt;I''ve found a couple papers that might be relevant, but they''re a bit over my head:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.merl.com/publications/docs/TR2010-109.pdf" rel="nofollow"&gt;Privacy Preserving String Comparisons Based on Levenshtein Distance&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.uni-due.de/~hq0215/documents/2010/Bachteler_2010_An_Empirical_Comparison_Of_Approaches_To_Approximate_String_Matching_In_Private_Record_Linkage.pdf" rel="nofollow"&gt;An Empirical Comparison of Approaches to Approximate String 
Matching in Private Record Linkage&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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''pii'':41C,61C,364C ''place'':202C ''point'':257C ''poor'':477C ''possibl'':305C,677C ''potenti'':360C ''pre'':552C ''prefer'':294C ''preserv'':16A,330C,675C,708C,750C ''privaci'':749C ''privat'':195C,777C ''process'':108C,144C,349C ''promis'':447C ''public'':488C ''qa/qc'':157C,343C ''qualiti'':601C,669C ''re'':733C ''reason'':621C ''recipi'':280C,379C ''record'':199C,237C,778C ''reduc'':82C ''refin'':162C ''rel'':414C,426C,747C,765C ''relat'':608C ''relev'':730C ''remov'':614C ''replac'':86C,269C ''repres'':243C ''requir'':598C ''resolut'':84C ''resourc'':121C ''result'':149C ''reveal'':491C ''revers'':362C ''reverse-engin'':361C ''robert'':468C ''rot13'':416C ''row'':554C,630C,639C ''say'':446C ''script'':226C ''set'':10A,702C ''share'':46C ''shift'':428C ''sinc'':129C ''size'':698C ''solut'':461C,478C ''sometim'':43C ''sourc'':131C ''street'':89C ''strength'':288C,625C ''string'':328C,368C,660C,673C,682C,714C,751C,774C ''strong'':458C ''subject'':63C ''submit'':185C ''suit'':125C ''take'':188C ''task'':128C,175C ''thing'':530C ''third'':52C,118C,151C,335C ''tighten'':387C ''toler'':389C ''track'':544C ''tract'':97C ''transform'':4A,659C ''transpar'':146C ''two'':236C ''type'':104C ''typo'':222C ''ul'':740C ''upsid'':443C ''use'':668C,718C ''user'':184C ''user-submit'':183C ''usual'':69C ''ve'':224C,722C ''want'':380C,532C ''way'':56C,137C,334C,664C ''withhold'':74C,265C ''without'':306C,353C ''won'':449C ''wordchar'':557C ''work'':33C,314C ''would'':293C,320C,462C,715C ''write'':440C ''www.merl.com'':745C ''www.merl.com/publications/docs/tr2010-109.pdf'':744C ''www.techrepublic.com'':412C ''www.techrepublic.com/blog/it-security/cryptographys-running-gag-rot13/'':411C ''www.uni-due.de'':763C ''www.uni-due.de/~hq0215/documents/2010/bachteler_2010_an_empirical_comparison_of_approaches_to_approximate_string_matching_in_private_record_linkage.pdf'':762C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5276, 2, '2015-03-04 06:05:05.32', 5, NULL, NULL, '&lt;p&gt;Terms that covered more or less the same topics that Data Science covers today:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Pattern Recognition&lt;/li&gt;
&lt;li&gt;Machine Learning &lt;/li&gt;
&lt;li&gt;Data Mining &lt;/li&gt;
&lt;li&gt;Quantitative methods&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':21C,25C,29C,33C ''/p'':16C ''/ul'':34C ''cover'':4C,14C ''data'':12C,27C ''learn'':24C ''less'':7C ''li'':18C,22C,26C,30C ''machin'':23C ''method'':32C ''mine'':28C ''p'':1C ''pattern'':19C ''quantit'':31C ''recognit'':20C ''scienc'':13C ''term'':2C ''today'':15C ''topic'':10C ''ul'':17C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (413, 2, '2014-06-16 19:49:24.377', 8, NULL, NULL, '&lt;p&gt;Dimensionality reduction is typically choosing a basis or mathematical representation within which you can describe most but not all of the variance within your data, thereby retaining the relevant information, while reducing the amount of information necessary to represent it. There are a variety of techniques for doing this including but not limited to &lt;code&gt;PCA&lt;/code&gt;, &lt;code&gt;ICA&lt;/code&gt;, and &lt;code&gt;Matrix Feature Factorization&lt;/code&gt;. These will take existing data and reduce it to the most discriminative components.These all allow you to represent most of the information in your dataset with fewer, more discriminative features.&lt;/p&gt;

&lt;p&gt;Feature Selection is hand selecting features which are highly discriminative. This has a lot more to do with feature engineering than analysis, and requires significantly more work on the part of the data scientist. It requires an understanding of what aspects of your dataset are important in whatever predictions you''re making, and which aren''t. Feature extraction usually involves generating new features which are composites of existing features. Both of these techniques fall into the category of feature engineering. Generally feature engineering is important if you want to obtain the best results, as it involves creating information that may not exist in your dataset, and increasing your signal to noise ratio.&lt;/p&gt;
', NULL, NULL, '''/code'':58C,61C,67C ''/p'':98C,212C ''allow'':82C ''amount'':35C ''analysi'':121C ''aren'':154C ''aspect'':140C ''basi'':8C ''best'':191C ''categori'':176C ''choos'':6C ''code'':56C,59C,63C ''components.these'':80C ''composit'':165C ''creat'':196C ''data'':26C,72C,132C ''dataset'':92C,143C,204C ''describ'':16C ''dimension'':2C ''discrimin'':79C,96C,109C ''engin'':119C,179C,182C ''exist'':71C,167C,201C ''extract'':157C ''factor'':66C ''fall'':173C ''featur'':65C,97C,100C,105C,118C,156C,162C,168C,178C,181C ''fewer'':94C ''general'':180C ''generat'':160C ''hand'':103C ''high'':108C ''ica'':60C ''import'':145C,184C ''includ'':51C ''increas'':206C ''inform'':31C,37C,89C,197C ''involv'':159C,195C ''limit'':54C ''lot'':113C ''make'':151C ''mathemat'':10C ''matrix'':64C ''may'':199C ''necessari'':38C ''new'':161C ''nois'':210C ''obtain'':189C ''p'':1C,99C ''part'':129C ''pca'':57C ''predict'':148C ''ratio'':211C ''re'':150C ''reduc'':33C,74C ''reduct'':3C ''relev'':30C ''repres'':40C,85C ''represent'':11C ''requir'':123C,135C ''result'':192C ''retain'':28C ''scientist'':133C ''select'':101C,104C ''signal'':208C ''signific'':124C ''take'':70C ''techniqu'':47C,172C ''therebi'':27C ''typic'':5C ''understand'':137C ''usual'':158C ''varianc'':23C ''varieti'':45C ''want'':187C ''whatev'':147C ''within'':12C,24C ''work'':126C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (414, 2, '2014-06-16 19:53:09.957', 27, NULL, NULL, '&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Is the learning rate related to the shape of the error gradient, as
it dictates the rate of descent?&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;In plain SGD, no. A global learning rate is used which is indifferent to the error gradient. However the intuition you are getting at has inspired various modifications of the SGD update rule.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;If so, how do you use this information to inform your decision about a value?&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Adagrad is the most widely known of these and scales a global learning rate &lt;em&gt;Î·&lt;/em&gt; on each dimension based on l2 norm of the history of the error gradient &lt;em&gt;gt&lt;/em&gt; on each dimension:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/aP96K.png" alt="enter image description here"&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;a href="http://arxiv.org/pdf/1212.5701v1.pdf"&gt;Adadelta&lt;/a&gt; is another such training algorithm which uses both the error gradient history like adagrad and the weight update history and has the advantage of &lt;em&gt;not having to set a learning rate at all&lt;/em&gt;.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;If it''s not what sort of values should I choose, and how should I choose them?&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Setting learning rates for plain SGD in neural nets is usually a
process of starting with a sane value such as 0.01 and then doing cross validation
to find an optimal value. Typical values range over a few orders of
magnitude from 0.0001 up to 1.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;It seems like you would want small values to avoid overshooting, but
how do you choose one such that you don''t get stuck in local minima
or take to long to descend? Does it make sense to have a constant learning rate, or should I use some metric to alter its value as I get nearer a minimum in the gradient?&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Usually the value that''s best is near the highest stable learning
rate and learning rate decay/annealing (either linear or
exponentially) is used over the course of training. The reason behind this is that early on there is a clear learning signal so aggressive updates encourage exploration while later on the smaller learning rates allow for more delicate exploitation of local error surface.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':146C ''/ap96k.png'':130C ''/em'':103C,120C,181C ''/li'':61C,63C,137C,183C,185C,256C,258C,391C,393C ''/p'':25C,83C,124C,136C,182C,207C,325C ''/pdf/1212.5701v1.pdf'':144C ''/strong'':24C,82C,206C,324C ''/ul'':62C,184C,257C,392C,394C ''0.0001'':252C ''0.01'':231C ''1'':255C ''adadelta'':145C ''adagrad'':87C,160C ''advantag'':169C ''aggress'':371C ''algorithm'':151C ''allow'':382C ''alt'':131C ''alter'':312C ''anoth'':148C ''arxiv.org'':143C ''arxiv.org/pdf/1212.5701v1.pdf'':142C ''avoid'':271C ''base'':107C ''behind'':358C ''best'':333C ''choos'':199C,204C,277C ''clear'':367C ''constant'':302C ''cours'':353C ''cross'':235C ''decay/annealing'':344C ''decis'':78C ''delic'':385C ''descend'':294C ''descent'':23C ''descript'':134C ''dictat'':19C ''dimens'':106C,123C ''earli'':362C ''either'':345C ''em'':101C,118C,171C ''encourag'':373C ''enter'':132C ''error'':15C,43C,116C,156C,389C ''exploit'':386C ''explor'':374C ''exponenti'':348C ''find'':238C ''get'':50C,284C,317C ''global'':33C,98C ''gradient'':16C,44C,117C,157C,323C ''gt'':119C ''highest'':337C ''histori'':113C,158C,165C ''howev'':45C ''href'':141C ''i.stack.imgur.com'':129C ''i.stack.imgur.com/ap96k.png'':128C ''imag'':133C ''img'':126C ''indiffer'':40C ''inform'':74C,76C ''inspir'':53C ''intuit'':47C ''known'':92C ''l2'':109C ''later'':376C ''learn'':7C,34C,99C,177C,211C,303C,339C,342C,368C,380C ''li'':2C,27C,64C,85C,138C,186C,209C,259C,327C ''like'':159C,264C ''linear'':346C ''local'':287C,388C ''long'':292C ''magnitud'':250C ''make'':297C ''metric'':310C ''minima'':288C ''minimum'':320C ''modif'':55C ''near'':335C ''nearer'':318C ''net'':218C ''neural'':217C ''norm'':110C ''one'':278C ''optim'':240C ''order'':248C ''overshoot'':272C ''p'':3C,65C,86C,125C,139C,187C,260C ''plain'':29C,214C ''process'':222C ''rang'':244C ''rate'':8C,21C,35C,100C,178C,212C,304C,340C,343C,381C ''reason'':357C ''relat'':9C ''rule'':60C ''sane'':227C ''scale'':96C ''seem'':263C ''sens'':298C ''set'':175C,210C ''sgd'':30C,58C,215C ''shape'':12C ''signal'':369C ''small'':268C ''smaller'':379C ''sort'':194C ''src'':127C ''stabl'':338C ''start'':224C ''strong'':4C,66C,188C,261C ''stuck'':285C ''surfac'':390C ''take'':290C ''train'':150C,355C ''typic'':242C ''ul'':1C,26C,84C,208C,326C ''updat'':59C,164C,372C ''use'':37C,72C,153C,308C,350C ''usual'':220C,328C ''valid'':236C ''valu'':81C,196C,228C,241C,243C,269C,314C,330C ''various'':54C ''want'':267C ''weight'':163C ''wide'':91C ''would'':266C ''î'':102C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (421, 1, '2014-06-17 04:31:34.067', 7, 539, 'Online machine learning tutorial', '&lt;p&gt;Does anyone know some good tutorials on online machine learning technics?
I.e. how it can be used in real-time environments, what are key differences compared to normal machine learning methods etc.&lt;/p&gt;

&lt;p&gt;UPD: Thank you everyone for answers, by "online" I mean methods which can be trained in a real-time mode, based on a new inputs one by one.&lt;/p&gt;
', 'machine-learning  education  beginner', NULL, '''/p'':44C,75C ''answer'':51C ''anyon'':12C ''base'':67C ''beginn'':9B ''compar'':37C ''differ'':36C ''educ'':8B ''environ'':32C ''etc'':43C ''everyon'':49C ''good'':15C ''i.e'':22C ''input'':71C ''key'':35C ''know'':13C ''learn'':3A,7B,20C,41C ''machin'':2A,6B,19C,40C ''machine-learn'':5B ''mean'':55C ''method'':42C,56C ''mode'':66C ''new'':70C ''normal'':39C ''one'':72C,74C ''onlin'':1A,18C,53C ''p'':10C,45C ''real'':30C,64C ''real-tim'':29C,63C ''technic'':21C ''thank'':47C ''time'':31C,65C ''train'':60C ''tutori'':4A,16C ''upd'':46C ''use'':27C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (415, 2, '2014-06-16 20:10:12.167', 11, NULL, NULL, '&lt;p&gt;This is a pretty massive question, so this is not intended to be a full answer, but hopefully this can help to inform general practice around determining the best tool for the job when it comes to data science. Generally, I have a relatively short list of qualifications I look for when it comes to any tool in this space. In no particular order they are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Performance&lt;/strong&gt;: Basically boils down to how quickly the language does matrix multiplication, as that is more or less the most important task in data science.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Scalability&lt;/strong&gt;: At least for me personally, this comes down to ease of building a distributed system. This is somewhere where languages like &lt;code&gt;Julia&lt;/code&gt; really shine.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Community&lt;/strong&gt;: With any language, you''re really looking for an active community that can help you when you get stuck using whichever tool you''re using. This is where &lt;code&gt;python&lt;/code&gt; pulls very far ahead of most other languages. &lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Flexibility&lt;/strong&gt;: Nothing is worse than being limited by the language that you use. It doesn''t happen very often, but trying to represent graph structures in &lt;code&gt;haskell&lt;/code&gt; is a notorious pain, and &lt;code&gt;Julia&lt;/code&gt; is filled with a lot of code architectures pains as a result of being such a young language.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Ease of Use&lt;/strong&gt;: If you want to use something in a larger environment, you want to make sure that setup is a straightforward and it can be automated. Nothing is worse than having to set up a finnicky build on half a dozen machines.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;There are a ton of articles out there about performance and scalability, but in general you''re going to be looking at a performance differential of maybe 5-10x between languages, which may or may not matter depending on your specific application. As far as GPU acceleration goes, &lt;code&gt;cudamat&lt;/code&gt; is a really seamless way of getting it working with &lt;code&gt;python&lt;/code&gt;, and the &lt;code&gt;cuda&lt;/code&gt; library in general has made GPU acceleration far more accessible than it used to be.&lt;/p&gt;

&lt;p&gt;The two primary metrics I use for both community and flexibility are to look at the language''s package manager, and the language questions on a site like SO. If there are a large number of high-quality questions and answers, it''s a good sign that the community is active. Number of packages and the general activity on those packages can also be a good proxy for this metric.&lt;/p&gt;

&lt;p&gt;As far as ease of use goes, I am a firm believer that the only way to actually know is to actually set it up yourself. There''s a lot of superstition around a lot of Data Science tools, specifically things like databases and distributed computing architecture, but there''s no way to really know if something is easy or hard to setup up and deploy without just building it yourself.&lt;/p&gt;
', NULL, NULL, '''-10'':311C ''/code'':126C,164C,205C,213C,334C,347C,352C ''/li'':98C,129C,173C,232C,280C ''/p'':68C,368C,441C,514C ''/strong'':73C,102C,133C,177C,238C ''/ul'':281C ''5'':310C ''acceler'':330C,359C ''access'':362C ''activ'':143C,421C,428C ''actual'':460C,464C ''ahead'':168C ''also'':433C ''answer'':17C,411C ''applic'':325C ''architectur'':221C,489C ''around'':27C,475C ''articl'':288C ''autom'':263C ''basic'':74C ''believ'':454C ''best'':30C ''boil'':75C ''build'':114C,274C,511C ''code'':124C,162C,203C,211C,220C,332C,345C,350C ''come'':37C,55C,109C ''communiti'':132C,144C,378C,419C ''comput'':488C ''cuda'':351C ''cudamat'':333C ''data'':39C,96C,479C ''databas'':485C ''depend'':321C ''deploy'':508C ''determin'':28C ''differenti'':307C ''distribut'':116C,487C ''doesn'':191C ''dozen'':278C ''eas'':112C,235C,446C ''easi'':501C ''environ'':248C ''far'':167C,327C,360C,444C ''fill'':215C ''finnicki'':273C ''firm'':453C ''flexibl'':176C,380C ''full'':16C ''general'':25C,41C,297C,355C,427C ''get'':151C,341C ''go'':300C ''goe'':331C,449C ''good'':415C,436C ''gpu'':329C,358C ''graph'':200C ''half'':276C ''happen'':193C ''hard'':503C ''haskel'':204C ''help'':22C,147C ''high'':407C ''high-qual'':406C ''hope'':19C ''import'':93C ''inform'':24C ''intend'':12C ''job'':34C ''julia'':125C,212C ''know'':461C,497C ''languag'':81C,122C,136C,172C,186C,231C,314C,386C,392C ''larg'':403C ''larger'':247C ''least'':104C ''less'':90C ''li'':70C,99C,130C,174C,233C ''librari'':353C ''like'':123C,397C,484C ''limit'':183C ''list'':47C ''look'':51C,140C,303C,383C ''lot'':218C,472C,477C ''machin'':279C ''made'':357C ''make'':252C ''manag'':389C ''massiv'':6C ''matrix'':83C ''matter'':320C ''may'':316C,318C ''mayb'':309C ''metric'':373C,440C ''multipl'':84C ''noth'':178C,264C ''notori'':208C ''number'':404C,422C ''often'':195C ''order'':65C ''p'':1C,282C,369C,442C ''packag'':388C,424C,431C ''pain'':209C,222C ''particular'':64C ''perform'':72C,292C,306C ''person'':107C ''practic'':26C ''pretti'':5C ''primari'':372C ''proxi'':437C ''pull'':165C ''python'':163C,346C ''qualif'':49C ''qualiti'':408C ''question'':7C,393C,409C ''quick'':79C ''re'':138C,157C,299C ''realli'':127C,139C,337C,496C ''relat'':45C ''repres'':199C ''result'':225C ''scalabl'':101C,294C ''scienc'':40C,97C,480C ''seamless'':338C ''set'':270C,465C ''setup'':255C,505C ''shine'':128C ''short'':46C ''sign'':416C ''site'':396C ''someth'':244C,499C ''somewher'':120C ''space'':61C ''specif'':324C,482C ''straightforward'':258C ''strong'':71C,100C,131C,175C,234C ''structur'':201C ''stuck'':152C ''superstit'':474C ''sure'':253C ''system'':117C ''task'':94C ''thing'':483C ''ton'':286C ''tool'':31C,58C,155C,481C ''tri'':197C ''two'':371C ''ul'':69C ''use'':153C,158C,189C,237C,243C,365C,375C,448C ''want'':241C,250C ''way'':339C,458C,494C ''whichev'':154C ''without'':509C ''work'':343C ''wors'':180C,266C ''x'':312C ''young'':230C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (416, 2, '2014-06-16 20:12:39.13', 6, NULL, NULL, '&lt;p&gt;If feasible I would link related records (e.g., Dave, David, etc.) and replace them with a sequence number (1,2,3, etc.) or a &lt;a href="http://en.wikipedia.org/wiki/Salt_%28cryptography%29" rel="nofollow"&gt;salted&lt;/a&gt; &lt;a href="http://www.cse.yorku.ca/~oz/hash.html" rel="nofollow"&gt;hash of the string&lt;/a&gt; that is used to represent all related records (e.g., David instead of Dave).&lt;/p&gt;

&lt;p&gt;I assume that third parties need not have any idea what the real name is, otherwise you might as well give it to them.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;edit&lt;/strong&gt;: You need to define and justify what kind of operations the third party needs to be able to do. For example, what is wrong with using initials followed by a number (e.g., BOA-1, BOA-2, etc.) to disambiguate Bank of America from Benjamin Othello Ames? If that''s too revealing, you could bin some of the letters or names; e.g., [A-E] -&gt; 1, [F-J] -&gt; 2, etc. so BOA would become 1OA, or ["Bank", "Barry", "Bruce", etc.] -&gt; 1 so Bank of America is again 1OA. &lt;/p&gt;

&lt;p&gt;For more information see &lt;a href="http://en.wikipedia.org/wiki/K-anonymity" rel="nofollow"&gt;k-anonymity&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''-1'':124C ''-2'':126C ''/a'':34C,46C,195C ''/p'':60C,86C,179C,196C ''/strong'':90C ''/wiki/k-anonymity'':189C ''/wiki/salt_%28cryptography%29'':30C ''/~oz/hash.html'':39C ''1'':20C,155C,171C ''1oa'':165C,178C ''2'':21C,159C ''3'':22C ''a-e'':152C ''abl'':107C ''ame'':136C ''america'':132C,175C ''anonym'':194C ''assum'':63C ''bank'':130C,167C,173C ''barri'':168C ''becom'':164C ''benjamin'':134C ''bin'':144C ''boa'':123C,125C,162C ''bruce'':169C ''could'':143C ''dave'':10C,59C ''david'':11C,56C ''defin'':94C ''disambigu'':129C ''e'':154C ''e.g'':9C,55C,122C,151C ''edit'':89C ''en.wikipedia.org'':29C,188C ''en.wikipedia.org/wiki/k-anonymity'':187C ''en.wikipedia.org/wiki/salt_%28cryptography%29'':28C ''etc'':12C,23C,127C,160C,170C ''exampl'':111C ''f'':157C ''f-j'':156C ''feasibl'':3C ''follow'':118C ''give'':82C ''hash'':42C ''href'':27C,36C,186C ''idea'':71C ''inform'':183C ''initi'':117C ''instead'':57C ''j'':158C ''justifi'':96C ''k'':193C ''k-anonym'':192C ''kind'':98C ''letter'':148C ''link'':6C ''might'':79C ''name'':75C,150C ''need'':67C,92C,104C ''nofollow'':32C,41C,191C ''number'':19C,121C ''oper'':100C ''othello'':135C ''otherwis'':77C ''p'':1C,61C,87C,180C ''parti'':66C,103C ''real'':74C ''record'':8C,54C ''rel'':31C,40C,190C ''relat'':7C,53C ''replac'':14C ''repres'':51C ''reveal'':141C ''salt'':33C ''see'':184C ''sequenc'':18C ''string'':45C ''strong'':88C ''third'':65C,102C ''use'':49C,116C ''well'':81C ''would'':5C,163C ''wrong'':114C ''www.cse.yorku.ca'':38C ''www.cse.yorku.ca/~oz/hash.html'':37C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (417, 2, '2014-06-16 20:18:50.123', 1, NULL, NULL, '&lt;p&gt;Blacklists aren''t  have value for a number of reasons:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;They''re easy to set up and scale - it''s just a key/value store, and you can probably just re-use some of your caching logic for the most basic implementation.&lt;/li&gt;
&lt;li&gt;Depending on the size and type of the spam attack, there will probably be some very specific terms or URLs being used. It''s much faster to throw that term into a blacklist than wait for your model to adapt.&lt;/li&gt;
&lt;li&gt;You can remove items just as quickly as you added them.&lt;/li&gt;
&lt;li&gt;Everybody understands how they work and any admin can use them.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The key to fighting spam is &lt;em&gt;monitoring&lt;/em&gt;. Make sure you have some sort of interface showing which items are on your blacklist, how often they''ve been hit in the last 10 minutes / hour / day / month, and the ability to easily add and remove items.&lt;/p&gt;

&lt;p&gt;You''ll want to combine a number of different spam detection models and tactics. Neural nets seem to be a good suggestion, and I''d recommend looking at &lt;em&gt;user behavior patterns&lt;/em&gt; in addition to just content. Normal humans don''t do things like send batches of 1,000 emails every 30 seconds for 12 consecutive hours.&lt;/p&gt;
', NULL, NULL, '''/em'':126C,199C ''/li'':47C,89C,102C,115C ''/ol'':116C ''/p'':12C,165C,225C ''000'':216C ''1'':215C ''10'':151C ''12'':222C ''30'':219C ''abil'':158C ''ad'':100C ''adapt'':88C ''add'':161C ''addit'':201C ''admin'':111C ''aren'':3C ''attack'':58C ''basic'':45C ''batch'':213C ''behavior'':197C ''blacklist'':2C,81C,141C ''cach'':40C ''combin'':171C ''consecut'':223C ''content'':204C ''d'':191C ''day'':154C ''depend'':49C ''detect'':177C ''differ'':175C ''easi'':17C ''easili'':160C ''em'':124C,195C ''email'':217C ''everi'':218C ''everybodi'':104C ''faster'':74C ''fight'':121C ''good'':187C ''hit'':147C ''hour'':153C,224C ''human'':206C ''implement'':46C ''interfac'':134C ''item'':94C,137C,164C ''key'':119C ''key/value'':27C ''last'':150C ''li'':14C,48C,90C,103C ''like'':211C ''ll'':168C ''logic'':41C ''look'':193C ''make'':127C ''minut'':152C ''model'':86C,178C ''monitor'':125C ''month'':155C ''much'':73C ''net'':182C ''neural'':181C ''normal'':205C ''number'':9C,173C ''often'':143C ''ol'':13C ''p'':1C,117C,166C ''pattern'':198C ''probabl'':32C,61C ''quick'':97C ''re'':16C,35C ''re-us'':34C ''reason'':11C ''recommend'':192C ''remov'':93C,163C ''scale'':22C ''second'':220C ''seem'':183C ''send'':212C ''set'':19C ''show'':135C ''size'':52C ''sort'':132C ''spam'':57C,122C,176C ''specif'':65C ''store'':28C ''suggest'':188C ''sure'':128C ''tactic'':180C ''term'':66C,78C ''thing'':210C ''throw'':76C ''type'':54C ''understand'':105C ''url'':68C ''use'':36C,70C,113C ''user'':196C ''valu'':6C ''ve'':145C ''wait'':83C ''want'':169C ''work'':108C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (418, 1, '2014-06-17 00:16:24.287', 5, 277, 'Best way to classify datasets with mixed types of attributes', '&lt;p&gt;I would like to know what is the best way to classify a data set composed of mixed types of attributes, for example, textual and numerical. I know I can convert textual to boolean, but the vocabulary is diverse and data become too sparse. I also tried to classify the types of attributes separately and combine the results through meta-learning techniques, but it did not work well.&lt;/p&gt;
', 'machine-learning  classification', NULL, '''/p'':85C ''also'':61C ''attribut'':10A,36C,68C ''becom'':57C ''best'':1A,24C ''boolean'':49C ''classif'':14B ''classifi'':4A,27C,64C ''combin'':71C ''compos'':31C ''convert'':46C ''data'':29C,56C ''dataset'':5A ''divers'':54C ''exampl'':38C ''know'':20C,43C ''learn'':13B,77C ''like'':18C ''machin'':12B ''machine-learn'':11B ''meta'':76C ''meta-learn'':75C ''mix'':7A,33C ''numer'':41C ''p'':15C ''result'':73C ''separ'':69C ''set'':30C ''spars'':59C ''techniqu'':78C ''textual'':39C,47C ''tri'':62C ''type'':8A,34C,66C ''vocabulari'':52C ''way'':2A,25C ''well'':84C ''work'':83C ''would'':17C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (437, 1, '2014-06-17 18:13:46.23', 5, 113, 'What are good sources to learn about Bootstrap?', '&lt;p&gt;I think that Bootstrap can be useful in my work, where we have a lot a variables that we don''t know the distribution of it. So, simulations could help.
What are good sources to learn about Bootstrap/other useful simulation methods?&lt;/p&gt;
', 'data-mining  statistics  education', NULL, '''/p'':56C ''bootstrap'':8A,18C ''bootstrap/other'':52C ''could'':43C ''data'':10B ''data-min'':9B ''distribut'':38C ''educ'':13B ''good'':3A,47C ''help'':44C ''know'':36C ''learn'':6A,50C ''lot'':29C ''method'':55C ''mine'':11B ''p'':14C ''simul'':42C,54C ''sourc'':4A,48C ''statist'':12B ''think'':16C ''use'':21C,53C ''variabl'':31C ''work'':24C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5334, 2, '2015-03-17 07:30:40.047', 1, NULL, NULL, '&lt;p&gt;Only reason for such a long subset time is that you''re running out of memory and the OS starts to swap. AFAIK base data.frame will create a copy of the data in memory before writing it to file.&lt;/p&gt;

&lt;p&gt;What you can do is use either dplyr or data.table packages as they manage some of these steps without copying the data.&lt;/p&gt;
', NULL, NULL, '''/p'':41C,65C ''afaik'':24C ''base'':25C ''copi'':30C,62C ''creat'':28C ''data'':33C,64C ''data.frame'':26C ''data.table'':52C ''dplyr'':50C ''either'':49C ''file'':40C ''long'':7C ''manag'':56C ''memori'':17C,35C ''os'':20C ''p'':1C,42C ''packag'':53C ''re'':13C ''reason'':3C ''run'':14C ''start'':21C ''step'':60C ''subset'':8C ''swap'':23C ''time'':9C ''use'':48C ''without'':61C ''write'':37C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (419, 2, '2014-06-17 00:19:09.773', 4, NULL, NULL, '&lt;p&gt;First you need to decide what you want to do, then look for the right tool for that task.&lt;/p&gt;

&lt;p&gt;A very general approach is to use R for first versions and to see if your approach is correct. It lacks a little in speed, but has very powerful commands and addon libraries, that you can try almost anything with it:
&lt;a href="http://www.r-project.org/" rel="nofollow"&gt;http://www.r-project.org/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The second idea is if you want to understand the algorithms behind the libraries, you might wanna take a look at the Numerical Recipies. They are available for different languages and free to use for learning. If you want to use them in commercial products, you need to ourchase a licence:
&lt;a href="http://en.wikipedia.org/wiki/Numerical_Recipes" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Numerical_Recipes&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Most of the time performance will not be the issue but finding the right algorithms and parameters for them, so it is important to have a fast scripting language instead of a monster program that first needs to compile 10 mins before calculating two numbers and putting out the result.&lt;/p&gt;

&lt;p&gt;And a big plus in using R is that it has built-in functions or libraries for almost any kind of diagram you might wanna need to visualize your data.&lt;/p&gt;

&lt;p&gt;If you then have a working version, it is almost easy to port it to any other language you think is more performant.&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':71C ''/p'':21C,185C,218C,243C ''/wiki/numerical_recipes'':128C ''/wiki/numerical_recipes&lt;/a&gt;&lt;/p&gt;'':133C ''10'':174C ''addon'':54C ''algorithm'':83C,149C ''almost'':60C,205C,229C ''anyth'':61C ''approach'':26C,39C ''avail'':99C ''behind'':84C ''big'':189C ''built'':199C ''built-in'':198C ''calcul'':177C ''command'':52C ''commerci'':116C ''compil'':173C ''correct'':41C ''data'':217C ''decid'':6C ''diagram'':209C ''differ'':101C ''easi'':230C ''en.wikipedia.org'':127C,132C ''en.wikipedia.org/wiki/numerical_recipes'':126C ''en.wikipedia.org/wiki/numerical_recipes&lt;/a&gt;&lt;/p&gt;'':131C ''fast'':161C ''find'':146C ''first'':2C,32C,170C ''free'':104C ''function'':201C ''general'':25C ''href'':65C,125C ''idea'':75C ''import'':157C ''instead'':164C ''issu'':144C ''kind'':207C ''lack'':43C ''languag'':102C,163C,237C ''learn'':108C ''librari'':55C,86C,203C ''licenc'':123C ''littl'':45C ''look'':13C,92C ''might'':88C,211C ''min'':175C ''monster'':167C ''need'':4C,119C,171C,213C ''nofollow'':68C,130C ''number'':179C ''numer'':95C ''ourchas'':121C ''p'':1C,22C,72C,134C,186C,219C ''paramet'':151C ''perform'':139C,242C ''plus'':190C ''port'':232C ''power'':51C ''product'':117C ''program'':168C ''put'':181C ''r'':30C,193C ''recipi'':96C ''rel'':67C,129C ''result'':184C ''right'':16C,148C ''script'':162C ''second'':74C ''see'':36C ''speed'':47C ''take'':90C ''task'':20C ''think'':239C ''time'':138C ''tool'':17C ''tri'':59C ''two'':178C ''understand'':81C ''use'':29C,106C,113C,192C ''version'':33C,226C ''visual'':215C ''wanna'':89C,212C ''want'':9C,79C,111C ''work'':225C ''www.r-project.org'':66C,70C ''www.r-project.org/&lt;/a&gt;&lt;/p&gt;'':69C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (420, 2, '2014-06-17 00:39:15.99', 4, NULL, NULL, '&lt;p&gt;It is hard to answer this question without knowing more about the data.  That said, I would offer the following advice:&lt;/p&gt;

&lt;p&gt;Most machine learning techniques can handle mixed-type data.  Tree based methods (such as AdaBoost and Random Forests) do well with this type of data.  The more important issue is actually the dimensionality, about which you are correct to be concerned.&lt;/p&gt;

&lt;p&gt;I would suggest that you do something to reduce that dimensionality.  For example, look for the words or phrases that separate the data the best and discard the other words (note: tree based methods do this automatically).&lt;/p&gt;
', NULL, NULL, '''/p'':23C,67C,106C ''actual'':56C ''adaboost'':40C ''advic'':22C ''answer'':6C ''automat'':105C ''base'':36C,101C ''best'':93C ''concern'':66C ''correct'':63C ''data'':14C,34C,50C,91C ''dimension'':58C,79C ''discard'':95C ''exampl'':81C ''follow'':21C ''forest'':43C ''handl'':30C ''hard'':4C ''import'':53C ''issu'':54C ''know'':10C ''learn'':27C ''look'':82C ''machin'':26C ''method'':37C,102C ''mix'':32C ''mixed-typ'':31C ''note'':99C ''offer'':19C ''p'':1C,24C,68C ''phrase'':87C ''question'':8C ''random'':42C ''reduc'':77C ''said'':16C ''separ'':89C ''someth'':75C ''suggest'':71C ''techniqu'':28C ''tree'':35C,100C ''type'':33C,48C ''well'':45C ''without'':9C ''word'':85C,98C ''would'':18C,70C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (422, 1, '2014-06-17 05:29:11.83', 13, 4285, 'Publicly available social network datasets/APIs', '&lt;p&gt;As an extension to our great list of &lt;a href="http://datascience.stackexchange.com/questions/155/publicly-available-datasets"&gt;publicly available datasets&lt;/a&gt;, I''d like to know if there is any list of publicly available social network datasets/crawling APIs. It would be very nice if alongside with a link to the dataset/API, characteristics of the data available were added. Such information should be, and is not limited to:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;the name of the social network;&lt;/li&gt;
&lt;li&gt;what kind of user information it provides (posts, profile, friendship network, ...);&lt;/li&gt;
&lt;li&gt;whether it allows for crawling its contents via an API (and rate: 10/min, 1k/month, ...);&lt;/li&gt;
&lt;li&gt;whether it simply provides a snapshot of the whole dataset.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Any suggestions and further characteristics to be added are very welcome.&lt;/p&gt;
', 'open-source  dataset  crawling', NULL, '''/a'':28C ''/li'':84C,97C,113C,125C ''/p'':75C,139C ''/questions/155/publicly-available-datasets'':24C ''/ul'':126C ''10/min'':111C ''1k/month'':112C ''ad'':65C,135C ''allow'':101C ''alongsid'':52C ''api'':45C,108C ''avail'':2A,26C,41C,63C ''characterist'':59C,132C ''content'':105C ''crawl'':10B,103C ''d'':30C ''data'':62C ''datascience.stackexchange.com'':23C ''datascience.stackexchange.com/questions/155/publicly-available-datasets'':22C ''dataset'':9B,27C,124C ''dataset/api'':58C ''datasets/apis'':5A ''datasets/crawling'':44C ''extens'':14C ''friendship'':95C ''great'':17C ''href'':21C ''inform'':67C,90C ''kind'':87C ''know'':33C ''li'':77C,85C,98C,114C ''like'':31C ''limit'':73C ''link'':55C ''list'':18C,38C ''name'':79C ''network'':4A,43C,83C,96C ''nice'':50C ''open'':7B ''open-sourc'':6B ''p'':11C,127C ''post'':93C ''profil'':94C ''provid'':92C,118C ''public'':1A,25C,40C ''rate'':110C ''simpli'':117C ''snapshot'':120C ''social'':3A,42C,82C ''sourc'':8B ''suggest'':129C ''ul'':76C ''user'':89C ''via'':106C ''welcom'':138C ''whether'':99C,115C ''whole'':123C ''would'':47C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (469, 2, '2014-06-19 05:52:57.527', 5, NULL, NULL, '&lt;p&gt;I suggest this tutorial on ordered logit: &lt;a href="http://www.ats.ucla.edu/stat/r/dae/ologit.htm"&gt;http://www.ats.ucla.edu/stat/r/dae/ologit.htm&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;It showcases the use of &lt;code&gt;polr&lt;/code&gt; in the &lt;code&gt;MASS&lt;/code&gt; package, and also explains the assumptions and how to interpret the results.&lt;/p&gt;
', NULL, NULL, '''/code'':25C,30C ''/p'':43C ''/stat/r/dae/ologit.htm'':13C ''/stat/r/dae/ologit.htm&lt;/a&gt;&lt;/p&gt;'':16C ''also'':33C ''assumpt'':36C ''code'':23C,28C ''explain'':34C ''href'':10C ''interpret'':40C ''logit'':8C ''mass'':29C ''order'':7C ''p'':1C,17C ''packag'':31C ''polr'':24C ''result'':42C ''showcas'':19C ''suggest'':3C ''tutori'':5C ''use'':21C ''www.ats.ucla.edu'':12C,15C ''www.ats.ucla.edu/stat/r/dae/ologit.htm'':11C ''www.ats.ucla.edu/stat/r/dae/ologit.htm&lt;/a&gt;&lt;/p&gt;'':14C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9124, 2, '2015-11-30 05:28:55.88', 2, NULL, NULL, '&lt;p&gt;Depends on how you define "hubs". In Network Science, a hub is simply a node with high degree i.e. those node who contribute to the power-law nature of the degree distribution the most. But you can also find other definitions for instance according to the information flow in the network where hubs are defined as those node that are critical in the process information flow (also called central nodes). &lt;/p&gt;

&lt;h2&gt;My Suggestions&lt;/h2&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Degree Distribution:&lt;/strong&gt; The simplest approach would be to choose high degree nodes as hubs. To ensure your results a bit more I recommend to calculate the summation of all correlations correspond to each node and have a look at these numbers as well. In this case you are looking for nodes which have high degrees and contain a larger value of correlation.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Centrality Measure:&lt;/strong&gt; or &lt;em&gt;Betweenness&lt;/em&gt; introduced by Linton Freeman which again somehow measures the influence of a node in the process of information flow over the network. Calculating the centrality for a vertex $v$ in a graph has basically 3 steps:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;For each pair of vertices $(s,t)$, compute the shortest paths between them.&lt;/li&gt;
&lt;li&gt;For each pair of vertices $(s,t)$, determine the fraction of shortest paths that pass through the vertex in question (here, vertex $v$).&lt;/li&gt;
&lt;li&gt;Sum this fraction over all pairs of vertices $(s,t)$.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;For more information read &lt;a href="https://en.wikipedia.org/wiki/Centrality" rel="nofollow"&gt;this&lt;/a&gt; carefully and in case you need any help (specially for implementation) just drop me a line in the comments :)&lt;/p&gt;

&lt;p&gt;Good Luck!&lt;/p&gt;
', NULL, NULL, '''/a'':263C ''/em'':156C ''/h2'':77C ''/li'':146C,209C,234C,246C,248C ''/ol'':249C ''/p'':73C,145C,193C,283C,287C ''/strong'':84C,152C ''/ul'':247C ''/wiki/centrality'':259C ''3'':191C ''accord'':46C ''also'':40C,69C ''approach'':87C ''basic'':190C ''between'':155C ''bit'':102C ''calcul'':107C,179C ''call'':70C ''care'':264C ''case'':128C,267C ''central'':71C,150C,181C ''choos'':91C ''comment'':282C ''comput'':203C ''contain'':139C ''contribut'':24C ''correl'':112C,144C ''correspond'':113C ''critic'':63C ''defin'':6C,57C ''definit'':43C ''degre'':19C,33C,82C,93C,137C ''depend'':2C ''determin'':218C ''distribut'':34C,83C ''drop'':276C ''em'':154C ''en.wikipedia.org'':258C ''en.wikipedia.org/wiki/centrality'':257C ''ensur'':98C ''find'':41C ''flow'':50C,68C,175C ''fraction'':220C,238C ''freeman'':160C ''good'':285C ''graph'':188C ''h2'':74C ''help'':271C ''high'':18C,92C,136C ''href'':256C ''hub'':7C,12C,55C,96C ''i.e'':20C ''implement'':274C ''influenc'':166C ''inform'':49C,67C,174C,253C ''instanc'':45C ''introduc'':157C ''larger'':141C ''law'':29C ''li'':79C,147C,195C,210C,235C ''line'':279C ''linton'':159C ''look'':120C,131C ''luck'':286C ''measur'':151C,164C ''natur'':30C ''need'':269C ''network'':9C,53C,178C ''node'':16C,22C,60C,72C,94C,116C,133C,169C ''nofollow'':261C ''number'':123C ''ol'':78C ''p'':1C,80C,148C,250C,284C ''pair'':198C,213C,241C ''pass'':225C ''path'':206C,223C ''power'':28C ''power-law'':27C ''process'':66C,172C ''question'':230C ''read'':254C ''recommend'':105C ''rel'':260C ''result'':100C ''scienc'':10C ''shortest'':205C,222C ''simplest'':86C ''simpli'':14C ''somehow'':163C ''special'':272C ''step'':192C ''strong'':81C,149C ''suggest'':76C ''sum'':236C ''summat'':109C ''ul'':194C ''v'':185C,233C ''valu'':142C ''vertex'':184C,228C,232C ''vertic'':200C,215C,243C ''well'':125C ''would'':88C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (423, 1, '2014-06-17 05:55:04.71', 5, 180, 'How to measure execution time on distributed system', '&lt;p&gt;I''m planning to run experiments with large datasets on distributed system in order to evaluate efficiency gains in comparison with previous proposals.&lt;/p&gt;

&lt;p&gt;I have limited number of machines nearly ten machines having 200 GB of free space on hard disk on each. On the contrary, I wished to perform experiments on more than available nodes in order to measure scalability, &lt;strong&gt;more precisely&lt;/strong&gt;. Since I don''t have any, I thought about using a commodity cluster. However, I''m not sure about the policies of usage, and I need to reliably measure execution times. &lt;/p&gt;

&lt;p&gt;Are there commodity services which will grant me that only my application would be running at a given time?&lt;/p&gt;
', 'bigdata  scalability  distributed', NULL, '''/p'':36C,111C,132C ''/strong'':79C ''200'':48C ''applic'':124C ''avail'':69C ''bigdata'':9B ''cluster'':92C ''commod'':91C,115C ''comparison'':32C ''contrari'':60C ''dataset'':21C ''disk'':55C ''distribut'':7A,11B,23C ''effici'':29C ''evalu'':28C ''execut'':4A,109C ''experi'':18C,65C ''free'':51C ''gain'':30C ''gb'':49C ''given'':130C ''grant'':119C ''hard'':54C ''howev'':93C ''larg'':20C ''limit'':40C ''m'':14C,95C ''machin'':43C,46C ''measur'':3A,74C,108C ''near'':44C ''need'':105C ''node'':70C ''number'':41C ''order'':26C,72C ''p'':12C,37C,112C ''perform'':64C ''plan'':15C ''polici'':100C ''precis'':78C ''previous'':34C ''propos'':35C ''reliabl'':107C ''run'':17C,127C ''scalabl'':10B,75C ''servic'':116C ''sinc'':80C ''space'':52C ''strong'':76C ''sure'':97C ''system'':8A,24C ''ten'':45C ''thought'':87C ''time'':5A,110C,131C ''usag'':102C ''use'':89C ''wish'':62C ''would'':125C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (424, 1, '2014-06-17 06:05:39.653', 13, 271, 'How to grow a list of related words based on initial keywords?', '&lt;p&gt;I recently saw a cool feature that &lt;a href="https://support.google.com/docs/answer/3543688?hl=en"&gt;was once available&lt;/a&gt; in Google Sheets: you start by writing a few related keywords in consecutive cells, say: "blue", "green", "yellow", and it automatically generates similar keywords (in this case, other colors). See more examples in &lt;a href="http://youtu.be/dlslNhfrQmw"&gt;this YouTube video&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I would like to reproduce this in my own program. I''m thinking of using Freebase, and it would work like this intuitively: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Retrieve the list of given words in Freebase;&lt;/li&gt;
&lt;li&gt;Find their "common denominator(s)" and construct a distance metric based on this;&lt;/li&gt;
&lt;li&gt;Rank other concepts based on their "distance" to the original keywords;&lt;/li&gt;
&lt;li&gt;Display the next closest concepts.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;As I''m not familiar with this area, my questions are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Is there a better way to do this?&lt;/li&gt;
&lt;li&gt;What tools are available for each step?&lt;/li&gt;
&lt;/ul&gt;
', 'nlp  text-mining  freebase', NULL, '''/a'':34C,76C ''/dlslnhfrqmw'':72C ''/docs/answer/3543688?hl=en'':30C ''/li'':113C,128C,141C,148C,173C,182C ''/ol'':149C ''/p'':77C,102C,162C ''/ul'':183C ''area'':158C ''automat'':55C ''avail'':33C,178C ''base'':9A,125C,133C ''better'':168C ''blue'':50C ''case'':61C ''cell'':48C ''closest'':146C ''color'':63C ''common'':117C ''concept'':132C,147C ''consecut'':47C ''construct'':121C ''cool'':23C ''denomin'':118C ''display'':143C ''distanc'':123C,136C ''exampl'':66C ''familiar'':155C ''featur'':24C ''find'':115C ''freebas'':17B,94C,112C ''generat'':56C ''given'':109C ''googl'':36C ''green'':51C ''grow'':3A ''href'':27C,69C ''initi'':11A ''intuit'':101C ''keyword'':12A,45C,58C,140C ''li'':104C,114C,129C,142C,164C,174C ''like'':81C,99C ''list'':5A,107C ''m'':90C,153C ''metric'':124C ''mine'':16B ''next'':145C ''nlp'':13B ''ol'':103C ''origin'':139C ''p'':18C,78C,150C ''program'':88C ''question'':160C ''rank'':130C ''recent'':20C ''relat'':7A,44C ''reproduc'':83C ''retriev'':105C ''saw'':21C ''say'':49C ''see'':64C ''sheet'':37C ''similar'':57C ''start'':39C ''step'':181C ''support.google.com'':29C ''support.google.com/docs/answer/3543688?hl=en'':28C ''text'':15B ''text-min'':14B ''think'':91C ''tool'':176C ''ul'':163C ''use'':93C ''video'':75C ''way'':169C ''word'':8A,110C ''work'':98C ''would'':80C,97C ''write'':41C ''yellow'':52C ''youtu.be'':71C ''youtu.be/dlslnhfrqmw'':70C ''youtub'':74C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (425, 2, '2014-06-17 06:32:22.883', 5, NULL, NULL, '&lt;p&gt;An example from germany: Xing a site similar to linkedin but limited to german speaking countries.&lt;/p&gt;

&lt;p&gt;Link to it''s developer central: &lt;a href="https://dev.xing.com/overview"&gt;https://dev.xing.com/overview&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Provides access to: User profiles, Conversations between users (limited to the user itself), Job advertisings, Contacts and Contacts of Contacts, news from the network and some geolocation api.&lt;/p&gt;

&lt;p&gt;Yes it has an api, but I did not find information about the rate. But it seems to me, that some information is limited to the consent of the user.&lt;/p&gt;
', NULL, NULL, '''/overview'':30C ''/overview&lt;/a&gt;&lt;/p&gt;'':33C ''/p'':18C,63C,95C ''access'':36C ''advertis'':49C ''api'':62C,69C ''central'':25C ''consent'':91C ''contact'':50C,52C,54C ''convers'':40C ''countri'':17C ''dev.xing.com'':29C,32C ''dev.xing.com/overview'':28C ''dev.xing.com/overview&lt;/a&gt;&lt;/p&gt;'':31C ''develop'':24C ''exampl'':3C ''find'':74C ''geoloc'':61C ''german'':15C ''germani'':5C ''href'':27C ''inform'':75C,86C ''job'':48C ''limit'':13C,43C,88C ''link'':20C ''linkedin'':11C ''network'':58C ''news'':55C ''p'':1C,19C,34C,64C ''profil'':39C ''provid'':35C ''rate'':78C ''seem'':81C ''similar'':9C ''site'':8C ''speak'':16C ''user'':38C,42C,46C,94C ''xing'':6C ''yes'':65C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (426, 2, '2014-06-17 06:53:44.007', 0, NULL, NULL, '&lt;p&gt;There are plenty on youtube and here''s a famous one by Andrew Ng from coursea: &lt;a href="https://www.coursera.org/course/ml" rel="nofollow"&gt;https://www.coursera.org/course/ml&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/course/ml'':22C ''/course/ml&lt;/a&gt;&lt;/p&gt;'':27C ''andrew'':14C ''coursea'':17C ''famous'':11C ''href'':19C ''ng'':15C ''nofollow'':24C ''one'':12C ''p'':1C ''plenti'':4C ''rel'':23C ''www.coursera.org'':21C,26C ''www.coursera.org/course/ml'':20C ''www.coursera.org/course/ml&lt;/a&gt;&lt;/p&gt;'':25C ''youtub'':6C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (427, 1, '2014-06-17 07:46:39.783', 3, 430, 'does anyone use Julia programming language?', '&lt;p&gt;Are anyone using &lt;code&gt;Julia&lt;/code&gt; (&lt;a href="http://julialang.org/" rel="nofollow"&gt;http://julialang.org/&lt;/a&gt;) for professional jobs?&lt;/p&gt;

&lt;p&gt;Or use it instead of R , Matlab or Mathematica? &lt;/p&gt;

&lt;p&gt;Is it a good language?&lt;/p&gt;

&lt;p&gt;If you have to predict next 5-10 years: Do you think it grow up enough to became such a standard in datascience like R or similar?&lt;/p&gt;
', 'tools', NULL, '''-10'':53C ''/&lt;/a&gt;)'':22C ''/code'':14C ''/p'':26C,37C,44C,73C ''5'':52C ''anyon'':2A,10C ''becam'':63C ''code'':12C ''datasci'':68C ''enough'':61C ''good'':42C ''grow'':59C ''href'':16C ''instead'':31C ''job'':25C ''julia'':4A,13C ''julialang.org'':17C,21C ''julialang.org/&lt;/a&gt;)'':20C ''languag'':6A,43C ''like'':69C ''mathematica'':36C ''matlab'':34C ''next'':51C ''nofollow'':19C ''p'':8C,27C,38C,45C ''predict'':50C ''profession'':24C ''program'':5A ''r'':33C,70C ''rel'':18C ''similar'':72C ''standard'':66C ''think'':57C ''tool'':7B ''use'':3A,11C,29C ''year'':54C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (428, 2, '2014-06-17 09:33:37.23', 1, NULL, NULL, '&lt;p&gt;This critique is no longer justified:&lt;/p&gt;

&lt;p&gt;While it is true that most of the standard and most respected R libraries were restricted to in-memory computations, there is a growing number of specialized libraries to deal with data that doesn''t fit into memory.&lt;br&gt;
For instance, for random forests on large datasets, you have the library &lt;code&gt;bigrf&lt;/code&gt;. More info here: &lt;a href="http://cran.r-project.org/web/packages/bigrf/" rel="nofollow"&gt;http://cran.r-project.org/web/packages/bigrf/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Another area of growth is R''s connectedness to big data environments like hadoop, which opens another world of possibilities.&lt;/p&gt;
', NULL, NULL, '''/code'':64C ''/p'':8C,99C ''/web/packages/bigrf/'':72C ''/web/packages/bigrf/&lt;/a&gt;&lt;/p&gt;'':77C ''anoth'':79C,95C ''area'':80C ''big'':88C ''bigrf'':63C ''br'':49C ''code'':62C ''comput'':30C ''connected'':86C ''cran.r-project.org'':71C,76C ''cran.r-project.org/web/packages/bigrf/'':70C ''cran.r-project.org/web/packages/bigrf/&lt;/a&gt;&lt;/p&gt;'':75C ''critiqu'':3C ''data'':42C,89C ''dataset'':57C ''deal'':40C ''doesn'':44C ''environ'':90C ''fit'':46C ''forest'':54C ''grow'':34C ''growth'':82C ''hadoop'':92C ''href'':69C ''in-memori'':27C ''info'':66C ''instanc'':51C ''justifi'':7C ''larg'':56C ''librari'':23C,38C,61C ''like'':91C ''longer'':6C ''memori'':29C,48C ''nofollow'':74C ''number'':35C ''open'':94C ''p'':1C,9C,78C ''possibl'':98C ''r'':22C,84C ''random'':53C ''rel'':73C ''respect'':21C ''restrict'':25C ''special'':37C ''standard'':18C ''true'':13C ''world'':96C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5521, 2, '2015-04-13 23:54:08.857', 0, NULL, NULL, '&lt;p&gt;Since you have your clusters set up already you should be able to calculate cluster centroids and then determine the distance between a each cluster centroid and the new data point. You could follow the same process with random samples of each cluster. &lt;/p&gt;
', NULL, NULL, '''/p'':45C ''abl'':13C ''alreadi'':9C ''calcul'':15C ''centroid'':17C,27C ''cluster'':6C,16C,26C,44C ''could'':34C ''data'':31C ''determin'':20C ''distanc'':22C ''follow'':35C ''new'':30C ''p'':1C ''point'':32C ''process'':38C ''random'':40C ''sampl'':41C ''set'':7C ''sinc'':2C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (430, 1, '2014-06-17 10:37:22.987', 12, 742, 'Looking for example infrastructure stacks/workflows/pipelines', '&lt;p&gt;I''m trying to understand how all the "big data" components play together in a real world use case, e.g. hadoop, monogodb/nosql, storm, kafka, ... I know that this is quite a wide range of tools used for different types, but I''d like to get to know more about their interaction in applications, e.g. thinking machine learning for an app, webapp, online shop.&lt;/p&gt;

&lt;p&gt;I have vistors/session, transaction data etc and store that; but if I want to make recommendations on the fly, I can''t run slow map/reduce jobs for that on some big database of logs I have. Where can I learn more about the infrastructure aspects? I think I can use most of the tools on their own, but plugging them into each other seems to be an art of its own.&lt;/p&gt;

&lt;p&gt;Are there any public examples/use cases etc available? I understand that the individual pipelines strongly depend on the use case and the user, but just examples will probably be very useful to me.&lt;/p&gt;
', 'machine-learning  bigdata  efficiency  scalability  distributed', NULL, '''/p'':77C,150C,185C ''app'':73C ''applic'':66C ''art'':146C ''aspect'':123C ''avail'':159C ''big'':22C,109C ''bigdata'':9B ''case'':32C,157C,171C ''compon'':24C ''d'':55C ''data'':23C,83C ''databas'':110C ''depend'':167C ''differ'':51C ''distribut'':12B ''e.g'':33C,67C ''effici'':10B ''etc'':84C,158C ''exampl'':3A,177C ''examples/use'':156C ''fli'':97C ''get'':58C ''hadoop'':34C ''individu'':164C ''infrastructur'':4A,122C ''interact'':64C ''job'':104C ''kafka'':37C ''know'':39C,60C ''learn'':8B,70C,118C ''like'':56C ''log'':112C ''look'':1A ''m'':15C ''machin'':7B,69C ''machine-learn'':6B ''make'':93C ''map/reduce'':103C ''monogodb/nosql'':35C ''onlin'':75C ''p'':13C,78C,151C ''pipelin'':165C ''play'':25C ''plug'':137C ''probabl'':179C ''public'':155C ''quit'':43C ''rang'':46C ''real'':29C ''recommend'':94C ''run'':101C ''scalabl'':11B ''seem'':142C ''shop'':76C ''slow'':102C ''stacks/workflows/pipelines'':5A ''store'':86C ''storm'':36C ''strong'':166C ''think'':68C,125C ''togeth'':26C ''tool'':48C,132C ''transact'':82C ''tri'':16C ''type'':52C ''understand'':18C,161C ''use'':31C,49C,128C,170C,182C ''user'':174C ''vistors/session'':81C ''want'':91C ''webapp'':74C ''wide'':45C ''world'':30C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (431, 2, '2014-06-17 12:37:17.15', 1, NULL, NULL, '&lt;p&gt;A small collection of such links can be found at &lt;a href="http://lgylym.github.io/big-graph/dataset.html" rel="nofollow"&gt;here&lt;/a&gt;. Many of them are social graphs.&lt;/p&gt;
', NULL, NULL, '''/a'':20C ''/big-graph/dataset.html'':16C ''/p'':27C ''collect'':4C ''found'':10C ''graph'':26C ''href'':13C ''lgylym.github.io'':15C ''lgylym.github.io/big-graph/dataset.html'':14C ''link'':7C ''mani'':21C ''nofollow'':18C ''p'':1C ''rel'':17C ''small'':3C ''social'':25C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (432, 2, '2014-06-17 12:41:58.9', 5, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;There is a very nice library of online machine learning algorithms from a group at NTU, called &lt;a href="http://libol.stevenhoi.org/" rel="nofollow"&gt;&lt;code&gt;LIBOL&lt;/code&gt;&lt;/a&gt;. This would be a very good place to start experimenting with the algorithms.&lt;br&gt;
The &lt;a href="http://libol.stevenhoi.org/LIBOL_manual.pdf" rel="nofollow"&gt;accompanying user guide&lt;/a&gt;, and &lt;a href="http://jmlr.org/papers/v15/hoi14a.html" rel="nofollow"&gt;associated JMLR publication&lt;/a&gt; are very nice introductions to the basic algorithms in this field.  &lt;/li&gt;
&lt;li&gt;Avrim Blum has an older and more technical &lt;a href="http://www.cs.cmu.edu/~avrim/Papers/survey.pdf" rel="nofollow"&gt;survey paper&lt;/a&gt; on online learning algorithms. &lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':28C,54C,66C,97C ''/code'':27C ''/li'':78C,102C ''/libol_manual.pdf'':48C ''/ol'':103C ''/papers/v15/hoi14a.html'':60C ''/~avrim/papers/survey.pdf'':92C ''accompani'':51C ''algorithm'':13C,41C,74C,101C ''associ'':63C ''avrim'':80C ''basic'':73C ''blum'':81C ''br'':42C ''call'':19C ''code'':25C ''experi'':38C ''field'':77C ''good'':34C ''group'':16C ''guid'':53C ''href'':21C,45C,57C,89C ''introduct'':70C ''jmlr'':64C ''jmlr.org'':59C ''jmlr.org/papers/v15/hoi14a.html'':58C ''learn'':12C,100C ''li'':2C,79C ''libol'':26C ''libol.stevenhoi.org'':22C,47C ''libol.stevenhoi.org/libol_manual.pdf'':46C ''librari'':8C ''machin'':11C ''nice'':7C,69C ''nofollow'':24C,50C,62C,94C ''ntu'':18C ''ol'':1C ''older'':84C ''onlin'':10C,99C ''paper'':96C ''place'':35C ''public'':65C ''rel'':23C,49C,61C,93C ''start'':37C ''survey'':95C ''technic'':87C ''user'':52C ''would'':30C ''www.cs.cmu.edu'':91C ''www.cs.cmu.edu/~avrim/papers/survey.pdf'':90C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (440, 2, '2014-06-17 18:59:14.947', 11, NULL, NULL, '&lt;p&gt;The &lt;a href="https://code.google.com/p/word2vec/"&gt;word2vec algorithm&lt;/a&gt; may be a good way to retrieve more elements for a list of similar words. It is an unsupervised "deep learning" algorithm that has previously been demonstrated with Wikipedia-based training data (helper scripts are provided on the Google code page).&lt;/p&gt;

&lt;p&gt;There are currently &lt;a href="https://code.google.com/p/word2vec/"&gt;C&lt;/a&gt; and &lt;a href="http://radimrehurek.com/gensim/models/word2vec.html"&gt;Python&lt;/a&gt; implementations. This &lt;a href="http://radimrehurek.com/2014/02/word2vec-tutorial"&gt;tutorial&lt;/a&gt; by &lt;a href="http://radimrehurek.com/"&gt;Radim ÅehÅ¯Åek&lt;/a&gt;, the author of the &lt;a href="http://radimrehurek.com/gensim/"&gt;Gensim topic modelling library&lt;/a&gt;, is an excellent place to start.&lt;/p&gt;

&lt;p&gt;The &lt;a href="http://radimrehurek.com/2014/02/word2vec-tutorial#single"&gt;"single topic"&lt;/a&gt; demonstration on the tutorial is a good example of retreiving similar words to a single term (try searching on ''red'' or ''yellow''). It should be possible to extend this technique to find the words that have the greatest overall similarity to a set of input words.&lt;/p&gt;
', NULL, NULL, '''/2014/02/word2vec-tutorial'':79C ''/2014/02/word2vec-tutorial#single'':119C ''/a'':10C,64C,72C,81C,91C,105C,122C ''/gensim/'':100C ''/gensim/models/word2vec.html'':70C ''/p'':53C,112C,169C ''/p/word2vec/'':7C,62C ''algorithm'':9C,32C ''author'':93C ''base'':41C ''c'':63C ''code'':51C ''code.google.com'':6C,61C ''code.google.com/p/word2vec/'':5C,60C ''current'':57C ''data'':43C ''deep'':30C ''demonstr'':37C,123C ''ehå'':88C ''ek'':90C ''element'':19C ''exampl'':130C ''excel'':108C ''extend'':150C ''find'':154C ''gensim'':101C ''good'':14C,129C ''googl'':50C ''greatest'':160C ''helper'':44C ''href'':4C,59C,67C,76C,84C,97C,116C ''implement'':73C ''input'':167C ''learn'':31C ''librari'':104C ''list'':22C ''may'':11C ''model'':103C ''overal'':161C ''p'':1C,54C,113C ''page'':52C ''place'':109C ''possibl'':148C ''previous'':35C ''provid'':47C ''python'':71C ''radim'':86C ''radimrehurek.com'':69C,78C,85C,99C,118C ''radimrehurek.com/2014/02/word2vec-tutorial'':77C ''radimrehurek.com/2014/02/word2vec-tutorial#single'':117C ''radimrehurek.com/gensim/'':98C ''radimrehurek.com/gensim/models/word2vec.html'':68C ''red'':142C ''retreiv'':132C ''retriev'':17C ''script'':45C ''search'':140C ''set'':165C ''similar'':24C,133C,162C ''singl'':120C,137C ''start'':111C ''techniqu'':152C ''term'':138C ''topic'':102C,121C ''train'':42C ''tri'':139C ''tutori'':80C,126C ''unsupervis'':29C ''way'':15C ''wikipedia'':40C ''wikipedia-bas'':39C ''word'':25C,134C,156C,168C ''word2vec'':8C ''yellow'':144C ''å'':87C,89C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (433, 2, '2014-06-17 13:31:30.503', 7, NULL, NULL, '&lt;p&gt;One of the most detailed and clear explanations of setting up a complex analytics pipeline is from the folks over at &lt;a href="http://www.twitch.tv/"&gt;Twitch&lt;/a&gt;.&lt;br&gt;
They give detailed motivations of each of the architecture choices for collection, transportation, coordination, processing, storage, and querying their data.&lt;br&gt;
Compelling reading! Find it &lt;a href="http://blog.twitch.tv/2014/04/twitch-data-analysis-part-1-the-twitch-statistics-pipeline/"&gt;here&lt;/a&gt; and &lt;a href="http://blog.twitch.tv/2014/04/twitch-data-analysis-part-2-architectural-decisions/"&gt;here&lt;/a&gt;. &lt;/p&gt;
', NULL, NULL, '''/2014/04/twitch-data-analysis-part-1-the-twitch-statistics-pipeline/'':58C ''/2014/04/twitch-data-analysis-part-2-architectural-decisions/'':66C ''/a'':27C,60C,68C ''/p'':69C ''analyt'':15C ''architectur'':37C ''blog.twitch.tv'':57C,65C ''blog.twitch.tv/2014/04/twitch-data-analysis-part-1-the-twitch-statistics-pipeline/'':56C ''blog.twitch.tv/2014/04/twitch-data-analysis-part-2-architectural-decisions/'':64C ''br'':28C,49C ''choic'':38C ''clear'':8C ''collect'':40C ''compel'':50C ''complex'':14C ''coordin'':42C ''data'':48C ''detail'':6C,31C ''explan'':9C ''find'':52C ''folk'':20C ''give'':30C ''href'':24C,55C,63C ''motiv'':32C ''one'':2C ''p'':1C ''pipelin'':16C ''process'':43C ''queri'':46C ''read'':51C ''set'':11C ''storag'':44C ''transport'':41C ''twitch'':26C ''www.twitch.tv'':25C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (434, 2, '2014-06-17 13:44:43.807', 6, NULL, NULL, '&lt;p&gt;There is really no question here as you ask for pure conjectures but consider at least that &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;this week has &lt;a href="http://juliacon.org/"&gt;Julia Con&lt;/a&gt;, the first Julia conference&lt;/li&gt;
&lt;li&gt;you could search GitHub and/or the registered Julia modules&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':30C ''/li'':35C,46C ''/p'':19C ''/ul'':47C ''and/or'':41C ''ask'':10C ''con'':29C ''confer'':34C ''conjectur'':13C ''consid'':15C ''could'':38C ''first'':32C ''github'':40C ''href'':26C ''julia'':28C,33C,44C ''juliacon.org'':27C ''least'':17C ''li'':21C,36C ''modul'':45C ''p'':1C ''pure'':12C ''question'':6C ''realli'':4C ''regist'':43C ''search'':39C ''ul'':20C ''week'':23C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (544, 2, '2014-06-23 17:56:32.917', 3, NULL, NULL, '&lt;p&gt;You could also try:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.quora.com" rel="nofollow"&gt;Quora&lt;/a&gt; &lt;/li&gt;
&lt;li&gt;&lt;a href="http://paper.li/MachineCoding/1370791453" rel="nofollow"&gt;Machine Learning Daily&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':15C,28C ''/li'':16C,29C ''/machinecoding/1370791453'':22C ''/p'':6C ''/ul'':30C ''also'':4C ''could'':3C ''daili'':27C ''href'':10C,19C ''learn'':26C ''li'':8C,17C ''machin'':25C ''nofollow'':13C,24C ''p'':1C ''paper.li'':21C ''paper.li/machinecoding/1370791453'':20C ''quora'':14C ''rel'':12C,23C ''tri'':5C ''ul'':7C ''www.quora.com'':11C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6894, 1, '2015-08-25 12:37:54.423', 0, 39, 'For logistic regression, Predict.glm() outputs $p$ or $ln(p/1-p)$?', '&lt;p&gt;I''m performing a logistic regression in R.&lt;br&gt;
I wanted to know if the function &lt;em&gt;predict.glm&lt;/em&gt; outputs $p$ (probability of event occurring) or log odds i.e. $log(p/1-p)$?&lt;/p&gt;
', 'r  logistic-regression', NULL, '''/em'':33C ''/p'':46C ''br'':23C ''em'':31C ''event'':38C ''function'':30C ''i.e'':43C ''know'':27C ''ln'':8A ''log'':41C,44C ''logist'':2A,12B,19C ''logistic-regress'':11B ''m'':16C ''occur'':39C ''odd'':42C ''output'':5A,34C ''p'':6A,14C,35C ''p/1-p'':9A,45C ''perform'':17C ''predict.glm'':4A,32C ''probabl'':36C ''r'':10B,22C ''regress'':3A,13B,20C ''want'':25C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (435, 1, '2014-06-17 13:46:06.367', 9, 165, 'Relational Data Mining without ILP', '&lt;p&gt;I have a huge dataset from a relational database which I need to create a classification model for. Normally for this situation I would use &lt;a href="http://en.wikipedia.org/wiki/Inductive_logic_programming"&gt;Inductive Logic Programming&lt;/a&gt; (ILP), but due to special circumstances I can''t do that.&lt;/p&gt;

&lt;p&gt;The other way to tackle this would be just to try to aggregate the values when I have a foreign relation. However, I have thousands of important and distinct rows for some nominal attributes (e.g.: A patient with a relation to several distinct drug prescriptions). So, I just can''t do that without creating a new attribute for each distinct row of that nominal attribute, and furthermore most of the new columns would have NULL values if I do that.&lt;/p&gt;

&lt;p&gt;Is there any non-ILP algorithm that allows me to data mine relational databases without resorting to techniques like pivoting, which would create thousands of new columns?&lt;/p&gt;
', 'data-mining  classification  relational-dbms', NULL, '''/a'':47C ''/p'':59C,141C,171C ''/wiki/inductive_logic_programming'':43C ''aggreg'':73C ''algorithm'':149C ''allow'':151C ''attribut'':94C,117C,125C ''circumst'':53C ''classif'':9B,29C ''column'':132C,170C ''creat'':27C,114C,166C ''data'':2A,7B,154C ''data-min'':6B ''databas'':22C,157C ''dataset'':18C ''dbms'':12B ''distinct'':89C,103C,120C ''drug'':104C ''due'':50C ''e.g'':95C ''en.wikipedia.org'':42C ''en.wikipedia.org/wiki/inductive_logic_programming'':41C ''foreign'':80C ''furthermor'':127C ''howev'':82C ''href'':40C ''huge'':17C ''ilp'':5A,48C,148C ''import'':87C ''induct'':44C ''like'':162C ''logic'':45C ''mine'':3A,8B,155C ''model'':30C ''need'':25C ''new'':116C,131C,169C ''nomin'':93C,124C ''non'':147C ''non-ilp'':146C ''normal'':32C ''null'':135C ''p'':13C,60C,142C ''patient'':97C ''pivot'':163C ''prescript'':105C ''program'':46C ''relat'':1A,11B,21C,81C,100C,156C ''relational-dbm'':10B ''resort'':159C ''row'':90C,121C ''sever'':102C ''situat'':35C ''special'':52C ''tackl'':65C ''techniqu'':161C ''thousand'':85C,167C ''tri'':71C ''use'':38C ''valu'':75C,136C ''way'':63C ''without'':4A,113C,158C ''would'':37C,67C,133C,165C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (436, 2, '2014-06-17 14:55:47.103', 9, NULL, NULL, '&lt;p&gt;I personally have used &lt;code&gt;Julia&lt;/code&gt; for a good number of professional projects, and while, as Dirk mentioned, this is purely conjecture, I can give some insights on where Julia really stands out. The question of whether or not these reasons will prove enough to have &lt;code&gt;Julia&lt;/code&gt; succeed as a language is anyone''s guess.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Distributed Systems&lt;/strong&gt;: Julia is the easiest language I''ve ever dealt with in terms of building distributed systems. This is becoming more and more relevant in computing, and will potentially become a deciding factor, but the question of whether or not &lt;code&gt;Julia&lt;/code&gt;''a relative ease decides this is up for debate&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;JIT Performance&lt;/strong&gt;: Julia''s JIT compiler is extremely fast, and while there is a lot of debate as to how accurate these benchmark numbers are, the &lt;a href="http://julialang.org/"&gt;Julia Website&lt;/a&gt; shows a series of relevant benchmarks&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Community&lt;/strong&gt;: This is an area where &lt;code&gt;Julia&lt;/code&gt; just isn''t quite there. The community that is there is generally supportive, but not quite as knowledgable as the &lt;code&gt;R&lt;/code&gt; or &lt;code&gt;python&lt;/code&gt; communities, which is a definite minus.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Extensibility&lt;/strong&gt;: This is another place where &lt;code&gt;Julia&lt;/code&gt; is currently lacking, there is a large disconnect between the implies code patterns that &lt;code&gt;Julia&lt;/code&gt; steers you toward and what it can actually support. The type system is currently overly bulky and difficult to use effectively.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Again, can''t say what this means for the future, but these are just a couple of relevant points when it comes to evaluating &lt;code&gt;Julia&lt;/code&gt; in my opinion.&lt;/p&gt;
', NULL, NULL, '''/a'':153C ''/code'':8C,51C,108C,172C,195C,199C,218C,235C,286C ''/li'':118C,160C,206C,257C ''/p'':60C,290C ''/strong'':66C,123C,164C,210C ''/ul'':258C ''accur'':142C ''actual'':243C ''anoth'':213C ''anyon'':57C ''area'':168C ''becom'':85C,95C ''benchmark'':144C,159C ''build'':80C ''bulki'':251C ''code'':6C,49C,106C,170C,193C,197C,216C,230C,233C,284C ''come'':281C ''communiti'':163C,179C,200C ''compil'':127C ''comput'':91C ''conjectur'':24C ''coupl'':275C ''current'':220C,249C ''dealt'':75C ''debat'':117C,138C ''decid'':97C,112C ''definit'':204C ''difficult'':253C ''dirk'':19C ''disconnect'':226C ''distribut'':64C,81C ''eas'':111C ''easiest'':70C ''effect'':256C ''enough'':46C ''evalu'':283C ''ever'':74C ''extens'':209C ''extrem'':129C ''factor'':98C ''fast'':130C ''futur'':269C ''general'':184C ''give'':27C ''good'':11C ''guess'':59C ''href'':149C ''impli'':229C ''insight'':29C ''isn'':174C ''jit'':121C,126C ''julia'':7C,32C,50C,67C,107C,124C,151C,171C,217C,234C,285C ''julialang.org'':150C ''knowledg'':190C ''lack'':221C ''languag'':55C,71C ''larg'':225C ''li'':62C,119C,161C,207C ''lot'':136C ''mean'':266C ''mention'':20C ''minus'':205C ''number'':12C,145C ''opinion'':289C ''over'':250C ''p'':1C,259C ''pattern'':231C ''perform'':122C ''person'':3C ''place'':214C ''point'':278C ''potenti'':94C ''profession'':14C ''project'':15C ''prove'':45C ''pure'':23C ''python'':198C ''question'':37C,101C ''quit'':176C,188C ''r'':194C ''realli'':33C ''reason'':43C ''relat'':110C ''relev'':89C,158C,277C ''say'':263C ''seri'':156C ''show'':154C ''stand'':34C ''steer'':236C ''strong'':63C,120C,162C,208C ''succeed'':52C ''support'':185C,244C ''system'':65C,82C,247C ''term'':78C ''toward'':238C ''type'':246C ''ul'':61C ''use'':5C,255C ''ve'':73C ''websit'':152C ''whether'':39C,103C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (439, 2, '2014-06-17 18:42:55.423', 6, NULL, NULL, '&lt;p&gt;Halfway through reading your question, I realized Levenshtein Distance could be a nice solution to your problem.  Its good to see that you have a link to a paper on the topic, let me see if I can shed some light into what a Levenshtein solution would look like.&lt;/p&gt;

&lt;p&gt;Levenshtein distance is used across many industries for entity resolution, what makes it useful is that it is a measure of the difference between two sequences.  In the case of string comparison it is just sequences characters.&lt;/p&gt;

&lt;p&gt;This could help solve your problem by allowing you to provide one number that gives a measure of how similar the text of another field is.  &lt;/p&gt;

&lt;p&gt;Here is an example of a basic way of using Levenshtein with the data you gave:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/vpaAH.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;This provides an ok solution, the distance of 8 provides some indication of a relationship, and it is very PII compliant.  However, it is still not super useful, let see what happens if we do some text magic to take only the first initial of the first name and the full last name dropping anything in the middle:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/JlPu9.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;As you can see the Levenshtein distance of 0 is pretty indicative of a relationship.  Commonly data providers will combine a bunch of Levenshtein permutations of the first and last names with 1, 2, or all of the characters just to give some dimensionality as to how entities are related while still maintaining anonymity within the data.&lt;/p&gt;
', NULL, NULL, '''/jlpu9.png'':214C ''/p'':51C,90C,118C,136C,148C,208C,220C,279C ''/vpaah.png'':142C ''0'':230C ''1'':254C ''2'':255C ''8'':158C ''across'':57C ''allow'':99C ''alt'':143C,215C ''anonym'':275C ''anoth'':115C ''anyth'':204C ''basic'':126C ''bunch'':243C ''case'':81C ''charact'':89C,260C ''combin'':241C ''common'':237C ''comparison'':84C ''compliant'':170C ''could'':11C,93C ''data'':133C,238C,278C ''descript'':146C,218C ''differ'':75C ''dimension'':265C ''distanc'':10C,54C,156C,228C ''drop'':203C ''enter'':144C,216C ''entiti'':61C,269C ''exampl'':123C ''field'':116C ''first'':192C,196C,249C ''full'':200C ''gave'':135C ''give'':106C,263C ''good'':20C ''halfway'':2C ''happen'':181C ''help'':94C ''howev'':171C ''i.stack.imgur.com'':141C,213C ''i.stack.imgur.com/jlpu9.png'':212C ''i.stack.imgur.com/vpaah.png'':140C ''imag'':145C,217C ''img'':138C,210C ''indic'':161C,233C ''industri'':59C ''initi'':193C ''last'':201C,251C ''let'':34C,178C ''levenshtein'':9C,46C,53C,130C,227C,245C ''light'':42C ''like'':50C ''link'':27C ''look'':49C ''magic'':187C ''maintain'':274C ''make'':64C ''mani'':58C ''measur'':72C,108C ''middl'':207C ''name'':197C,202C,252C ''nice'':14C ''number'':104C ''ok'':153C ''one'':103C ''p'':1C,52C,91C,119C,137C,149C,209C,221C ''paper'':30C ''permut'':246C ''pii'':169C ''pretti'':232C ''problem'':18C,97C ''provid'':102C,151C,159C,239C ''question'':6C ''read'':4C ''realiz'':8C ''relat'':271C ''relationship'':164C,236C ''resolut'':62C ''see'':22C,36C,179C,225C ''sequenc'':78C,88C ''shed'':40C ''similar'':111C ''solut'':15C,47C,154C ''solv'':95C ''src'':139C,211C ''still'':174C,273C ''string'':83C ''super'':176C ''take'':189C ''text'':113C,186C ''topic'':33C ''two'':77C ''use'':56C,66C,129C,177C ''way'':127C ''within'':276C ''would'':48C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5623, 2, '2015-04-27 07:33:24.683', 0, NULL, NULL, '&lt;p&gt;&lt;a href="http://www.kdnuggets.com/datasets/index.html" rel="nofollow"&gt;http://www.kdnuggets.com/datasets/index.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This should get you maximum datasets for your classification exercise.&lt;/p&gt;
', NULL, NULL, '''/datasets/index.html'':6C ''/datasets/index.html&lt;/a&gt;&lt;/p&gt;'':11C ''/p'':23C ''classif'':21C ''dataset'':18C ''exercis'':22C ''get'':15C ''href'':3C ''maximum'':17C ''nofollow'':8C ''p'':1C,12C ''rel'':7C ''www.kdnuggets.com'':5C,10C ''www.kdnuggets.com/datasets/index.html'':4C ''www.kdnuggets.com/datasets/index.html&lt;/a&gt;&lt;/p&gt;'':9C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (441, 1, '2014-06-17 20:48:35.267', 23, 8265, 'What are the use cases for Apache Spark vs Hadoop', '&lt;p&gt;With Hadoop 2.0 and YARN Hadoop is supposedly no longer tied only map-reduce solutions. With that advancement, what are the use cases for Apache Spark vs Hadoop considering both sit atop of HDFS? I''ve read through the introduction documentation for Spark, but I''m curious if anyone has encountered a problem that was more efficient and easier to solve with Spark compared to Hadoop.&lt;/p&gt;
', 'hadoop  distributed  knowledge-base', NULL, '''/p'':84C ''2.0'':19C ''advanc'':35C ''anyon'':66C ''apach'':7A,42C ''atop'':49C ''base'':15B ''case'':5A,40C ''compar'':81C ''consid'':46C ''curious'':64C ''distribut'':12B ''document'':58C ''easier'':76C ''effici'':74C ''encount'':68C ''hadoop'':10A,11B,18C,22C,45C,83C ''hdfs'':51C ''introduct'':57C ''knowledg'':14B ''knowledge-bas'':13B ''longer'':26C ''m'':63C ''map'':30C ''map-reduc'':29C ''p'':16C ''problem'':70C ''read'':54C ''reduc'':31C ''sit'':48C ''solut'':32C ''solv'':78C ''spark'':8A,43C,60C,80C ''suppos'':24C ''tie'':27C ''use'':4A,39C ''ve'':53C ''vs'':9A,44C ''yarn'':21C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (474, 1, '2014-06-19 09:42:28.16', 4, 93, 'Network structure: k-cliques vs. p-cliques', '&lt;p&gt;In network structure, what is the difference between &lt;strong&gt;k-cliques&lt;/strong&gt; and &lt;strong&gt;p-cliques&lt;/strong&gt;, can anyone give a brief explaination with examples? Thanks in advanced!&lt;/p&gt;

&lt;p&gt;============================
&lt;br&gt;EDIT:
I found an online &lt;a href="http://open.umich.edu/sites/default/files/SI508-F08-Week7-Lab6.ppt" rel="nofollow"&gt;ppt&lt;/a&gt; while I am googling, please take a look on &lt;strong&gt;p.37&lt;/strong&gt; and &lt;strong&gt;p.39&lt;/strong&gt;, can you comment on them?&lt;/p&gt;
', 'definitions  social-network-analysis  graphs', NULL, '''/a'':63C ''/p'':47C,85C ''/sites/default/files/si508-f08-week7-lab6.ppt'':59C ''/strong'':29C,35C,75C,79C ''advanc'':46C ''analysi'':14B ''anyon'':37C ''br'':49C ''brief'':40C ''cliqu'':5A,9A,28C,34C ''comment'':82C ''definit'':10B ''differ'':23C ''edit'':50C ''exampl'':43C ''explain'':41C ''found'':52C ''give'':38C ''googl'':67C ''graph'':15B ''href'':56C ''k'':4A,27C ''k-cliqu'':3A,26C ''look'':71C ''network'':1A,13B,18C ''nofollow'':61C ''onlin'':54C ''open.umich.edu'':58C ''open.umich.edu/sites/default/files/si508-f08-week7-lab6.ppt'':57C ''p'':8A,16C,33C,48C ''p-cliqu'':7A,32C ''p.37'':74C ''p.39'':78C ''pleas'':68C ''ppt'':62C ''rel'':60C ''social'':12B ''social-network-analysi'':11B ''strong'':25C,31C,73C,77C ''structur'':2A,19C ''take'':69C ''thank'':44C ''vs'':6A');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (443, 2, '2014-06-17 22:28:36.07', 5, NULL, NULL, '&lt;p&gt;One option (depending on your dataset size) is to just provide edit distances (or other measures of similarity you''re using) as an additional dataset.&lt;/p&gt;

&lt;p&gt;E.g.:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Generate a set of unique names in the dataset&lt;/li&gt;
&lt;li&gt;For each name, calculate edit distance to each other name&lt;/li&gt;
&lt;li&gt;Generate an ID or irreversable hash for each name&lt;/li&gt;
&lt;li&gt;Replace names in the original dataset with this ID&lt;/li&gt;
&lt;li&gt;Provide matrix of edit distances between ID numbers as new dataset&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Though there''s still a lot that could be done to deanonymise the data from these even. &lt;/p&gt;

&lt;p&gt;E.g. if "Tim" is known to be the most popular name for a boy, frequency counting of IDs that closely match the known percentage of Tims across the population might give that away.  From there you could then look for names with an edit distance of 1, and conclude that those IDs might refer to "Tom" or "Jim" (when combined with other info).&lt;/p&gt;
', NULL, NULL, '''/li'':42C,54C,65C,76C,89C ''/ol'':90C ''/p'':27C,30C,109C,174C ''1'':157C ''across'':137C ''addit'':25C ''away'':143C ''boy'':124C ''calcul'':47C ''close'':130C ''combin'':170C ''conclud'':159C ''could'':99C,147C ''count'':126C ''data'':105C ''dataset'':7C,26C,41C,72C,88C ''deanonymis'':103C ''depend'':4C ''distanc'':14C,49C,82C,155C ''done'':101C ''e.g'':29C,111C ''edit'':13C,48C,81C,154C ''even'':108C ''frequenc'':125C ''generat'':33C,56C ''give'':141C ''hash'':61C ''id'':58C,75C,84C,128C,162C ''info'':173C ''irrevers'':60C ''jim'':168C ''known'':115C,133C ''li'':32C,43C,55C,66C,77C ''look'':149C ''lot'':97C ''match'':131C ''matrix'':79C ''measur'':17C ''might'':140C,163C ''name'':38C,46C,53C,64C,68C,121C,151C ''new'':87C ''number'':85C ''ol'':31C ''one'':2C ''option'':3C ''origin'':71C ''p'':1C,28C,91C,110C ''percentag'':134C ''popul'':139C ''popular'':120C ''provid'':12C,78C ''re'':21C ''refer'':164C ''replac'':67C ''set'':35C ''similar'':19C ''size'':8C ''still'':95C ''though'':92C ''tim'':113C,136C ''tom'':166C ''uniqu'':37C ''use'':22C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (444, 2, '2014-06-17 22:29:36.72', 3, NULL, NULL, '&lt;p&gt;A classic book is by B. Efron who created the technique:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Bradley Efron; Robert Tibshirani (1994). An Introduction to the Bootstrap. Chapman &amp;amp; Hall/CRC. ISBN 978-0-412-04231-7.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''-0'':31C ''-04231'':33C ''-412'':32C ''-7'':34C ''/li'':35C ''/p'':13C ''/ul'':36C ''1994'':20C ''978'':30C ''amp'':27C ''b'':7C ''book'':4C ''bootstrap'':25C ''bradley'':16C ''chapman'':26C ''classic'':3C ''creat'':10C ''efron'':8C,17C ''hall/crc'':28C ''introduct'':22C ''isbn'':29C ''li'':15C ''p'':1C ''robert'':18C ''techniqu'':12C ''tibshirani'':19C ''ul'':14C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (445, 2, '2014-06-17 23:30:45.897', 5, NULL, NULL, '&lt;p&gt;Is your Masters in Computer Science?  Statistics?&lt;/p&gt;

&lt;p&gt;Is ''data science'' going to be at the center of your thesis?  Or a side topic?&lt;/p&gt;

&lt;p&gt;I''ll assume your in Statistics and that you want to focus your thesis on a ''data science'' problem.  If so, then I''m going to go against the grain and suggest that you &lt;em&gt;should not&lt;/em&gt; start with a data set or an ML method.  Instead, you should seek an interesting research problem that''s poorly understood or where ML methods have not yet been proven successful, or where there are many competing ML methods but none seem better than others.&lt;/p&gt;

&lt;p&gt;Consider this data source: &lt;a href="http://snap.stanford.edu/data/"&gt;Stanford Large Network Dataset Collection&lt;/a&gt;.  While you &lt;em&gt;could&lt;/em&gt; pick one of these data sets, make up a problem statement, and then run some list of ML methods, that approach really doesn''t tell you very much about what &lt;em&gt;data science&lt;/em&gt; is all about, and in my opinion doesn''t lead to a very good Masters thesis.&lt;/p&gt;

&lt;p&gt;Instead, you might do this: look for all the research papers that use ML on some specific category -- e.g. Collaboration networks (a.k.a. co-authorship). As you read each paper, try to find out what they &lt;em&gt;were&lt;/em&gt; able to accomplish with each ML method and what they weren''t able to address.  Especially look for their suggestions for "future research".&lt;/p&gt;

&lt;p&gt;Maybe they all use the same method, but never tried competing ML methods.  Or maybe they don''t adequately validate their results, or maybe there data sets are small, or maybe their research questions and hypothesis were simplistic or limited.&lt;/p&gt;

&lt;p&gt;Most important: try to find out where this line of research is going.  Why are they even bothering to do this?  What is significant about it?  Where and why are they encountering difficulties?&lt;/p&gt;
', NULL, NULL, '''/a'':128C ''/data/'':122C ''/em'':66C,133C,167C,224C ''/p'':9C,27C,112C,184C,248C,290C,325C ''a.k.a'':207C ''abl'':225C,237C ''accomplish'':227C ''address'':239C ''adequ'':268C ''approach'':154C ''assum'':31C ''authorship'':210C ''better'':109C ''bother'':309C ''categori'':203C ''center'':19C ''co'':209C ''co-authorship'':208C ''collabor'':205C ''collect'':127C ''compet'':103C,260C ''comput'':6C ''consid'':114C ''could'':132C ''data'':12C,45C,70C,116C,138C,165C,275C ''dataset'':126C ''difficulti'':324C ''doesn'':156C,175C ''e.g'':204C ''em'':63C,131C,164C,222C ''encount'':323C ''especi'':240C ''even'':308C ''find'':218C,296C ''focus'':40C ''futur'':246C ''go'':14C,53C,55C,304C ''good'':181C ''grain'':58C ''href'':119C ''hypothesi'':285C ''import'':293C ''instead'':76C,186C ''interest'':81C ''larg'':124C ''lead'':177C ''limit'':289C ''line'':300C ''list'':149C ''ll'':30C ''look'':191C,241C ''m'':52C ''make'':140C ''mani'':102C ''master'':4C,182C ''mayb'':250C,264C,273C,280C ''method'':75C,91C,105C,152C,231C,256C,262C ''might'':188C ''ml'':74C,90C,104C,151C,199C,230C,261C ''much'':161C ''network'':125C,206C ''never'':258C ''none'':107C ''one'':135C ''opinion'':174C ''other'':111C ''p'':1C,10C,28C,113C,185C,249C,291C ''paper'':196C,215C ''pick'':134C ''poor'':86C ''problem'':47C,83C,143C ''proven'':96C ''question'':283C ''read'':213C ''realli'':155C ''research'':82C,195C,247C,282C,302C ''result'':271C ''run'':147C ''scienc'':7C,13C,46C,166C ''seek'':79C ''seem'':108C ''set'':71C,139C,276C ''side'':25C ''signific'':315C ''simplist'':287C ''small'':278C ''snap.stanford.edu'':121C ''snap.stanford.edu/data/'':120C ''sourc'':117C ''specif'':202C ''stanford'':123C ''start'':67C ''statement'':144C ''statist'':8C,34C ''success'':97C ''suggest'':60C,244C ''tell'':158C ''thesi'':22C,42C,183C ''topic'':26C ''tri'':216C,259C,294C ''understood'':87C ''use'':198C,253C ''valid'':269C ''want'':38C ''weren'':235C ''yet'':94C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (580, 2, '2014-06-25 14:30:10.597', 1, NULL, NULL, '&lt;p&gt;I have read some about it and I had the following blog in mind:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://fellgernon.tumblr.com/post/46117939292/predicting-who-will-win-a-nfl-match-at-half-time#.UtehM7TWtQg" rel="nofollow"&gt;http://fellgernon.tumblr.com/post/46117939292/predicting-who-will-win-a-nfl-match-at-half-time#.UtehM7TWtQg&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This blog deals with the prediction of a NFL match after the half time is already over. The prediction is 80% accurate with simple GLM model.&lt;/p&gt;

&lt;p&gt;I do not know if that is suitable for soccer.&lt;/p&gt;
', NULL, NULL, '''/p'':16C,55C,67C ''/post/46117939292/predicting-who-will-win-a-nfl-match-at-half-time#.utehm7twtqg'':22C ''/post/46117939292/predicting-who-will-win-a-nfl-match-at-half-time#.utehm7twtqg&lt;/a&gt;&lt;/p&gt;'':27C ''80'':49C ''accur'':50C ''alreadi'':44C ''blog'':13C,30C ''deal'':31C ''fellgernon.tumblr.com'':21C,26C ''fellgernon.tumblr.com/post/46117939292/predicting-who-will-win-a-nfl-match-at-half-time#.utehm7twtqg'':20C ''fellgernon.tumblr.com/post/46117939292/predicting-who-will-win-a-nfl-match-at-half-time#.utehm7twtqg&lt;/a&gt;&lt;/p&gt;'':25C ''follow'':12C ''glm'':53C ''half'':41C ''href'':19C ''know'':60C ''match'':38C ''mind'':15C ''model'':54C ''nfl'':37C ''nofollow'':24C ''p'':1C,17C,28C,56C ''predict'':34C,47C ''read'':4C ''rel'':23C ''simpl'':52C ''soccer'':66C ''suitabl'':64C ''time'':42C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (447, 1, '2014-06-18 03:19:07.557', 8, 129, 'Learning signal encoding', '&lt;p&gt;I have a large number of samples which represent Manchester encoded bit streams as audio signals. The frequency at which they are encoded is the primary frequency component when it is high, and there is a consistent amount of white noise in the background.&lt;/p&gt;

&lt;p&gt;I have manually decoded these streams, but I was wondering if I could use some sort of machine learning technique to learn the encoding schemes. This would save a great deal of time manually recognizing these schemes. The difficulty is that different signals are encoded differently.&lt;/p&gt;

&lt;p&gt;Is it possible to build a model which can learn to decode more than one encoding scheme? How robust would such a model be, and what sort of techniques would I want to employ? &lt;a href="http://en.wikipedia.org/wiki/Independent_component_analysis" rel="nofollow"&gt;Independent Component Analysis&lt;/a&gt; (ICA) seems like could be useful for isolating the frequency I care about, but how would I learn the encoding scheme?&lt;/p&gt;
', 'machine-learning  data-mining  scalability  algorithms  feature-selection', NULL, '''/a'':154C ''/p'':60C,108C,176C ''/wiki/independent_component_analysis'':148C ''algorithm'':11B ''amount'':53C ''analysi'':153C ''audio'':30C ''background'':59C ''bit'':27C ''build'':114C ''care'':166C ''compon'':43C,152C ''consist'':52C ''could'':74C,158C ''data'':8B ''data-min'':7B ''deal'':92C ''decod'':65C,121C ''differ'':103C,107C ''difficulti'':100C ''employ'':143C ''en.wikipedia.org'':147C ''en.wikipedia.org/wiki/independent_component_analysis'':146C ''encod'':3A,26C,38C,85C,106C,125C,174C ''featur'':13B ''feature-select'':12B ''frequenc'':33C,42C,164C ''great'':91C ''high'':47C ''href'':145C ''ica'':155C ''independ'':151C ''isol'':162C ''larg'':19C ''learn'':1A,6B,80C,83C,119C,172C ''like'':157C ''machin'':5B,79C ''machine-learn'':4B ''manchest'':25C ''manual'':64C,95C ''mine'':9B ''model'':116C,132C ''nofollow'':150C ''nois'':56C ''number'':20C ''one'':124C ''p'':15C,61C,109C ''possibl'':112C ''primari'':41C ''recogn'':96C ''rel'':149C ''repres'':24C ''robust'':128C ''sampl'':22C ''save'':89C ''scalabl'':10B ''scheme'':86C,98C,126C,175C ''seem'':156C ''select'':14B ''signal'':2A,31C,104C ''sort'':77C,136C ''stream'':28C,67C ''techniqu'':81C,138C ''time'':94C ''use'':75C,160C ''want'':141C ''white'':55C ''wonder'':71C ''would'':88C,129C,139C,170C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10277, 1, '2016-02-17 09:49:45.097', 1, 17, 'Predict quantitative response variable from quantitative and qualitative variables', '&lt;p&gt;I have to predict a quantitative response variable Y from p quantitative and q qualitative variables. There''s around 100,000 rows in the data frame. What predictive methods can I use for that purpose? &lt;/p&gt;
', 'data-mining', NULL, '''/p'':49C ''000'':34C ''100'':33C ''around'':32C ''data'':11B,38C ''data-min'':10B ''frame'':39C ''method'':42C ''mine'':12B ''p'':13C,24C ''predict'':1A,17C,41C ''purpos'':48C ''q'':27C ''qualit'':8A,28C ''quantit'':2A,6A,19C,25C ''respons'':3A,20C ''row'':35C ''use'':45C ''variabl'':4A,9A,21C,29C ''y'':22C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9134, 1, '2015-11-30 16:08:12.507', 3, 131, 'Gradient boosting algorithm example', '&lt;p&gt;I''m trying to fully understand the gradient boosting (GB) method. I''ve read some wiki pages and papers about it, but it would really help me to see a full simple example carried out step-by-step. Can anyone provide one for me, or give me a link to such an example? Straightforward source code without tricky optimizations will also meet my needs.&lt;/p&gt;
', 'machine-learning  classification  algorithms  xgboost', NULL, '''/p'':77C ''algorithm'':3A,9B ''also'':73C ''anyon'':52C ''boost'':2A,20C ''carri'':45C ''classif'':8B ''code'':68C ''exampl'':4A,44C,65C ''full'':42C ''fulli'':16C ''gb'':21C ''give'':58C ''gradient'':1A,19C ''help'':37C ''learn'':7B ''link'':61C ''m'':13C ''machin'':6B ''machine-learn'':5B ''meet'':74C ''method'':22C ''need'':76C ''one'':54C ''optim'':71C ''p'':11C ''page'':28C ''paper'':30C ''provid'':53C ''read'':25C ''realli'':36C ''see'':40C ''simpl'':43C ''sourc'':67C ''step'':48C,50C ''step-by-step'':47C ''straightforward'':66C ''tri'':14C ''tricki'':70C ''understand'':17C ''ve'':24C ''wiki'':27C ''without'':69C ''would'':35C ''xgboost'':10B');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (449, 2, '2014-06-18 07:27:22.677', 32, NULL, NULL, '&lt;p&gt;Hadoop means HDFS, YARN, MapReduce, and a lot of other things. Do you mean Spark vs &lt;em&gt;MapReduce&lt;/em&gt;? Because Spark runs on/with Hadoop, which is rather the point.&lt;/p&gt;

&lt;p&gt;The primary reason to use Spark is for speed, and this comes from the fact that its execution can keep data in memory between stages rather than always persist back to HDFS after a Map or Reduce. This advantage is very pronounced for iterative computations, which have tens of stages each of which is touching the same data. This is where things might be "100x" faster. For simple, one-pass ETL-like jobs for which MapReduce was designed, it''s not in general faster.&lt;/p&gt;

&lt;p&gt;Another reason to use Spark is its nicer high-level language compared to MapReduce. It provides a functional programming-like view that mimics Scala, which is far nicer than writing MapReduce code. (Although you have to either use Scala, or adopt the slightly-less-developed Java or Python APIs for Spark). &lt;a href="http://crunch.apache.org"&gt;Crunch&lt;/a&gt; and &lt;a href="http://cascading.org"&gt;Cascading&lt;/a&gt; already provide a similar abstraction on top of MapReduce, but this is still an area where Spark is nice.&lt;/p&gt;

&lt;p&gt;Finally Spark has as-yet-young but promising subprojects for ML, graph analysis, and streaming, which expose a similar, coherent API. With MapReduce, you would have to turn to several different other projects for this (Mahout, Giraph, Storm). It''s nice to have it in one package, albeit not yet ''baked''.&lt;/p&gt;

&lt;p&gt;Why would you not use Spark? &lt;a href="https://www.quora.com/Apache-Spark/Assuming-you-have-a-system-with-both-Hadoop-and-Spark-installed-say-under-Yarn-is-there-any-reason-to-use-Hadoop-map-reduce-instead-of-the-equivalent-Spark-commands"&gt;paraphrasing&lt;/a&gt; myself:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Spark is primarily Scala, with ported Java APIs; MapReduce might be friendlier and more native for Java-based developers&lt;/li&gt;
&lt;li&gt;There is more MapReduce expertise out there now than Spark&lt;/li&gt;
&lt;li&gt;For the data-parallel, one-pass, ETL-like jobs MapReduce was designed for, MapReduce is lighter-weight compared to the Spark equivalent&lt;/li&gt;
&lt;li&gt;Spark is fairly mature, and so is YARN now, but Spark-on-YARN is still pretty new. The two may not be optimally integrated yet. For example until recently I don''t think Spark could ask YARN for allocations based on number of cores? That is: MapReduce might be easier to understand, manage and tune&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':179C,185C,273C ''/apache-spark/assuming-you-have-a-system-with-both-hadoop-and-spark-installed-say-under-yarn-is-there-any-reason-to-use-hadoop-map-reduce-instead-of-the-equivalent-spark-commands'':271C ''/em'':20C ''/li'':298C,310C,338C,396C ''/p'':31C,119C,205C,259C,275C ''/ul'':397C ''100x'':97C ''abstract'':190C ''adopt'':163C ''advantag'':71C ''albeit'':255C ''alloc'':379C ''alreadi'':186C ''although'':155C ''alway'':60C ''analysi'':220C ''anoth'':121C ''api'':172C,228C,285C ''area'':200C ''as-yet-young'':210C ''ask'':376C ''back'':62C ''bake'':258C ''base'':296C,380C ''cascad'':184C ''cascading.org'':183C ''code'':154C ''coher'':227C ''come'':44C ''compar'':133C,333C ''comput'':77C ''core'':384C ''could'':375C ''crunch'':178C ''crunch.apache.org'':177C ''data'':53C,90C,315C ''data-parallel'':314C ''design'':112C,326C ''develop'':168C,297C ''differ'':238C ''easier'':390C ''either'':159C ''em'':18C ''equival'':337C ''etl'':105C,321C ''etl-lik'':104C,320C ''exampl'':367C ''execut'':50C ''expertis'':304C ''expos'':224C ''fact'':47C ''fair'':342C ''far'':149C ''faster'':98C,118C ''final'':207C ''friendlier'':289C ''function'':139C ''general'':117C ''giraph'':244C ''graph'':219C ''hadoop'':2C,25C ''hdfs'':4C,64C ''high'':130C ''high-level'':129C ''href'':176C,182C,268C ''integr'':364C ''iter'':76C ''java'':169C,284C,295C ''java-bas'':294C ''job'':107C,323C ''keep'':52C ''languag'':132C ''less'':167C ''level'':131C ''li'':277C,299C,311C,339C ''lighter'':331C ''lighter-weight'':330C ''like'':106C,142C,322C ''lot'':9C ''mahout'':243C ''manag'':393C ''map'':67C ''mapreduc'':6C,19C,110C,135C,153C,194C,230C,286C,303C,324C,328C,387C ''matur'':343C ''may'':360C ''mean'':3C,15C ''memori'':55C ''might'':95C,287C,388C ''mimic'':145C ''ml'':218C ''nativ'':292C ''new'':357C ''nice'':204C,248C ''nicer'':128C,150C ''number'':382C ''on/with'':24C ''one'':102C,253C,318C ''one-pass'':101C,317C ''optim'':363C ''p'':1C,32C,120C,206C,260C ''packag'':254C ''parallel'':316C ''paraphras'':272C ''pass'':103C,319C ''persist'':61C ''point'':30C ''port'':283C ''pretti'':356C ''primari'':34C ''primarili'':280C ''program'':141C ''programming-lik'':140C ''project'':240C ''promis'':215C ''pronounc'':74C ''provid'':137C,187C ''python'':171C ''rather'':28C,58C ''reason'':35C,122C ''recent'':369C ''reduc'':69C ''run'':23C ''scala'':146C,161C,281C ''sever'':237C ''similar'':189C,226C ''simpl'':100C ''slight'':166C ''slightly-less-develop'':165C ''spark'':16C,22C,38C,125C,174C,202C,208C,266C,278C,309C,336C,340C,351C,374C ''spark-on-yarn'':350C ''speed'':41C ''stage'':57C,82C ''still'':198C,355C ''storm'':245C ''stream'':222C ''subproject'':216C ''ten'':80C ''thing'':12C,94C ''think'':373C ''top'':192C ''touch'':87C ''tune'':395C ''turn'':235C ''two'':359C ''ul'':276C ''understand'':392C ''use'':37C,124C,160C,265C ''view'':143C ''vs'':17C ''weight'':332C ''would'':232C,262C ''write'':152C ''www.quora.com'':270C ''www.quora.com/apache-spark/assuming-you-have-a-system-with-both-hadoop-and-spark-installed-say-under-yarn-is-there-any-reason-to-use-hadoop-map-reduce-instead-of-the-equivalent-spark-commands'':269C ''yarn'':5C,347C,353C,377C ''yet'':212C,257C,365C ''young'':213C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (475, 2, '2014-06-19 09:50:53.657', 1, NULL, NULL, '&lt;p&gt;As you discuss the definition of regression is predicting a continuous variable. &lt;a href="http://en.wikipedia.org/wiki/Logistic_regression" rel="nofollow"&gt;Logistic regression&lt;/a&gt; is a binary classifier.  Logistic regression is the application of a logit function on the output of a usual regression approach. Logit function turns (-inf,+inf) to [0,1]. I think it is just for historical reasons that keeps that name. &lt;/p&gt;

&lt;p&gt;Saying something like "I did some regression to classify images. In particular I used logistic regression." is wrong.&lt;/p&gt;
', NULL, NULL, '''/a'':23C ''/p'':65C,85C ''/wiki/logistic_regression'':18C ''0'':51C ''1'':52C ''applic'':32C ''approach'':44C ''binari'':26C ''classifi'':27C,75C ''continu'':12C ''definit'':6C ''discuss'':4C ''en.wikipedia.org'':17C ''en.wikipedia.org/wiki/logistic_regression'':16C ''function'':36C,46C ''histor'':59C ''href'':15C ''imag'':76C ''inf'':48C,49C ''keep'':62C ''like'':69C ''logist'':21C,28C,81C ''logit'':35C,45C ''name'':64C ''nofollow'':20C ''output'':39C ''p'':1C,66C ''particular'':78C ''predict'':10C ''reason'':60C ''regress'':8C,22C,29C,43C,73C,82C ''rel'':19C ''say'':67C ''someth'':68C ''think'':54C ''turn'':47C ''use'':80C ''usual'':42C ''variabl'':13C ''wrong'':84C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2354, 2, '2014-10-27 04:15:00.747', 5, NULL, NULL, '&lt;p&gt;I think your best bet would be &lt;strong&gt;&lt;a href="http://pythonhosted.org/rosetta/"&gt;rosetta&lt;/a&gt;&lt;/strong&gt;. I''m finding it extremely useful and easy. Check its &lt;a href="http://pythonhosted.org/rosetta/#module-rosetta.parallel.pandas_easy"&gt;pandas methods&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;You can get it by &lt;strong&gt;pip&lt;/strong&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':16C,35C ''/p'':36C,46C ''/rosetta/'':14C ''/rosetta/#module-rosetta.parallel.pandas_easy'':32C ''/strong'':17C,45C ''best'':5C ''bet'':6C ''check'':26C ''easi'':25C ''extrem'':22C ''find'':20C ''get'':40C ''href'':11C,29C ''m'':19C ''method'':34C ''p'':1C,37C ''panda'':33C ''pip'':44C ''pythonhosted.org'':13C,31C ''pythonhosted.org/rosetta/'':12C ''pythonhosted.org/rosetta/#module-rosetta.parallel.pandas_easy'':30C ''rosetta'':15C ''strong'':9C,43C ''think'':3C ''use'':23C ''would'':7C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (450, 2, '2014-06-18 14:15:37.957', 5, NULL, NULL, '&lt;p&gt;I''m not quite sure, but maybe  locality-sensitive hashing is a good solution. It does hashing of input data (in your case - names), so original strings would be preserved. On the other side, the main idea of LSH is to maximize hashes likelihood for similar items. There are a lot of different LSH-implementations. I tried &lt;a href="http://spdp.di.unimi.it/papers/pdcs04.pdf"&gt;Nilsimsa-hash&lt;/a&gt; for comparing tweet texts, and it worked quite well. But I''m not sure, how well it will work in case of short strings (names) - this issue require testing. I tried your examples, and here is the result (name A, name B, "distance" - maximum is 120):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1. AMELIA BEDELIA  - CHRISTOPH BAUER - 107
2. AMELIA BEDELIA  - C J BAUER       - 82
3. AMELIA BEDELIA  - FRANZ HELLER    - 91
4. CHRISTOPH BAUER - C J BAUER       - 81
5. CHRISTOPH BAUER - FRANZ HELLER    - 98
6. C J BAUER       - FRANZ HELLER    - 83
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;As you see, CHRISTOPH BAUER and C J BAUER turned up to be the closest pair. But difference is not significant.
And just for example - hash representation of these names:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;AMELIA BEDELIA  6b208299602b5000c3005a048122a43a828020889042240005011c1880864502
CHRISTOPH BAUER 22226448000ab10102e2860b52062487ff0000928e0822ee106028016cc01237
C J BAUER       2282204100961060048050004400240006032400148000802000a80130402002
FRANZ HELLER    58002002400880080b49172044020008030002442631e004009195020ad01158
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':69C ''/code'':158C,207C ''/p'':116C,191C ''/papers/pdcs04.pdf'':65C ''/pre'':159C,208C ''1'':119C ''107'':124C ''120'':115C ''2'':125C ''22226448000ab10102e2860b52062487ff0000928e0822ee106028016cc01237'':199C ''2282204100961060048050004400240006032400148000802000a80130402002'':203C ''3'':132C ''4'':138C ''5'':145C ''58002002400880080b49172044020008030002442631e004009195020ad01158'':206C ''6'':151C ''6b208299602b5000c3005a048122a43a828020889042240005011c1880864502'':196C ''81'':144C ''82'':131C ''83'':157C ''91'':137C ''98'':150C ''amelia'':120C,126C,133C,194C ''b'':111C ''bauer'':123C,130C,140C,143C,147C,154C,165C,169C,198C,202C ''bedelia'':121C,127C,134C,195C ''c'':128C,141C,152C,167C,200C ''case'':25C,90C ''christoph'':122C,139C,146C,164C,197C ''closest'':175C ''code'':118C,193C ''compar'':71C ''data'':22C ''differ'':55C,178C ''distanc'':112C ''exampl'':102C,185C ''franz'':135C,148C,155C,204C ''good'':15C ''hash'':12C,19C,45C,68C,186C ''heller'':136C,149C,156C,205C ''href'':62C ''idea'':39C ''implement'':58C ''input'':21C ''issu'':96C ''item'':49C ''j'':129C,142C,153C,168C,201C ''likelihood'':46C ''local'':10C ''locality-sensit'':9C ''lot'':53C ''lsh'':41C,57C ''lsh-implement'':56C ''m'':3C,81C ''main'':38C ''maxim'':44C ''maximum'':113C ''mayb'':8C ''name'':26C,94C,108C,110C,190C ''nilsimsa'':67C ''nilsimsa-hash'':66C ''origin'':28C ''p'':1C,160C ''pair'':176C ''pre'':117C,192C ''preserv'':32C ''quit'':5C,77C ''represent'':187C ''requir'':97C ''result'':107C ''see'':163C ''sensit'':11C ''short'':92C ''side'':36C ''signific'':181C ''similar'':48C ''solut'':16C ''spdp.di.unimi.it'':64C ''spdp.di.unimi.it/papers/pdcs04.pdf'':63C ''string'':29C,93C ''sure'':6C,83C ''test'':98C ''text'':73C ''tri'':60C,100C ''turn'':170C ''tweet'':72C ''well'':78C,85C ''work'':76C,88C ''would'':30C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (451, 1, '2014-06-18 14:36:07.98', 5, 371, 'How to speedup message passing between computing nodes', '&lt;p&gt;I''m developing a distributed application, and as it''s been designed, there''ll be a great load of communication during the processing. Since the communication is already as much &lt;em&gt;spread&lt;/em&gt; along the entire process as possible, I''m wondering if there any standard solutions to improve the performance of the message passing layer of my application.&lt;/p&gt;

&lt;p&gt;What changes/improvements could I apply to my code to reduce the time spent sending messages? For what it''s worth, I''m communicating up to 10GB between 9 computing nodes, and the framework I''m using is implemented with OpenMPI.&lt;/p&gt;
', 'efficiency  distributed  performance', NULL, '''/em'':45C ''/p'':72C,114C ''10gb'':99C ''9'':101C ''along'':46C ''alreadi'':40C ''appli'':78C ''applic'':18C,71C ''changes/improvements'':75C ''code'':81C ''communic'':32C,38C,96C ''comput'':7A,102C ''could'':76C ''design'':24C ''develop'':15C ''distribut'':10B,17C ''effici'':9B ''em'':43C ''entir'':48C ''framework'':106C ''great'':29C ''implement'':111C ''improv'':61C ''layer'':68C ''ll'':26C ''load'':30C ''m'':14C,53C,95C,108C ''messag'':4A,66C,88C ''much'':42C ''node'':8A,103C ''openmpi'':113C ''p'':12C,73C ''pass'':5A,67C ''perform'':11B,63C ''possibl'':51C ''process'':35C,49C ''reduc'':83C ''send'':87C ''sinc'':36C ''solut'':59C ''speedup'':3A ''spent'':86C ''spread'':44C ''standard'':58C ''time'':85C ''use'':109C ''wonder'':54C ''worth'':93C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (470, 2, '2014-06-19 06:09:43.963', 8, NULL, NULL, '&lt;p&gt;Just an idea - your data is highly seasonal: daily and weekly cycles are quite perceptible. So first of all, try to decompose your variables (gas and electricity consumption, temperature, and solar radiation). &lt;a href="http://www.r-bloggers.com/time-series-decomposition/"&gt;Here is&lt;/a&gt; a nice tutorial on time series decomposition for R. &lt;/p&gt;

&lt;p&gt;After obtaining trend and seasonal components, the most interesting part begins. It''s just an assumption, but I think, gas and electricity consumption variables would be quite predictable by means of time series analysis (e.g., &lt;a href="http://statsmodels.sourceforge.net/devel/examples/notebooks/generated/tsa_arma_0.html"&gt;ARIMA model&lt;/a&gt;). From my point of view, the most exiting part here is to try to predict residuals after decomposition, using available data (temperature anomalies, solar radiation, wind speed). I suppose, these residuals would be outliers, you are looking for. Hope, you will find this useful.&lt;/p&gt;
', NULL, NULL, '''/a'':41C,95C ''/devel/examples/notebooks/generated/tsa_arma_0.html'':92C ''/p'':51C,140C ''/time-series-decomposition/'':38C ''analysi'':86C ''anomali'':118C ''arima'':93C ''assumpt'':68C ''avail'':115C ''begin'':63C ''compon'':58C ''consumpt'':29C,75C ''cycl'':13C ''daili'':10C ''data'':6C,116C ''decompos'':23C ''decomposit'':48C,113C ''e.g'':87C ''electr'':28C,74C ''exit'':103C ''find'':137C ''first'':18C ''gas'':26C,72C ''high'':8C ''hope'':134C ''href'':35C,89C ''idea'':4C ''interest'':61C ''look'':132C ''mean'':82C ''model'':94C ''nice'':43C ''obtain'':54C ''outlier'':129C ''p'':1C,52C ''part'':62C,104C ''percept'':16C ''point'':98C ''predict'':80C,110C ''quit'':15C,79C ''r'':50C ''radiat'':33C,120C ''residu'':111C,126C ''season'':9C,57C ''seri'':47C,85C ''solar'':32C,119C ''speed'':122C ''statsmodels.sourceforge.net'':91C ''statsmodels.sourceforge.net/devel/examples/notebooks/generated/tsa_arma_0.html'':90C ''suppos'':124C ''temperatur'':30C,117C ''think'':71C ''time'':46C,84C ''trend'':55C ''tri'':21C,108C ''tutori'':44C ''use'':114C,139C ''variabl'':25C,76C ''view'':100C ''week'':12C ''wind'':121C ''would'':77C,127C ''www.r-bloggers.com'':37C ''www.r-bloggers.com/time-series-decomposition/'':36C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (494, 1, '2014-06-19 19:33:40.62', 6, 129, 'Techniques for trend extraction from unbalanced panel data', '&lt;p&gt;My data set is formatted like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;User-id | Threat_score
aaa       45
bbb       32
ccc       20
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The list contains the top 100 users with the highest threat scores. I generate such a list monthly and store each month''s list in its own file.&lt;/p&gt;

&lt;p&gt;There are three things I would like to get from this data:&lt;/p&gt;

&lt;ul&gt;1. Users who are consistently showing up in this list&lt;/ul&gt;

&lt;ul&gt;2. Users who are consistently showing up in this list with high threat scores&lt;/ul&gt;

&lt;ul&gt;3. Users whose threat scores are increasing very quickly&lt;/ul&gt;

&lt;p&gt;I am thinking a visual summary would be nice; each month (somehow) decide which users I want to plot on a graph of historic threat scores.&lt;/p&gt;

&lt;p&gt;Are there any known visualization techniques that deal with similar requirements?&lt;/p&gt;

&lt;p&gt;How should I be transforming my current data to achieve what I am looking for?&lt;/p&gt;
', 'visualization  dataset  graphs', NULL, '''/code'':34C ''/p'':20C,65C,79C,146C,159C,176C ''/pre'':35C ''/ul'':91C,107C,118C ''1'':81C ''100'':42C ''2'':93C ''20'':33C ''3'':109C ''32'':31C ''45'':29C ''aaa'':28C ''achiev'':170C ''bbb'':30C ''ccc'':32C ''code'':22C ''consist'':85C,97C ''contain'':39C ''current'':167C ''data'':8A,14C,78C,168C ''dataset'':10B ''deal'':155C ''decid'':132C ''extract'':4A ''file'':64C ''format'':17C ''generat'':50C ''get'':75C ''graph'':11B,141C ''high'':104C ''highest'':46C ''histor'':143C ''id'':25C ''increas'':115C ''known'':151C ''like'':18C,73C ''list'':38C,53C,60C,90C,102C ''look'':174C ''month'':54C,58C,130C ''nice'':128C ''p'':12C,36C,66C,119C,147C,160C ''panel'':7A ''plot'':138C ''pre'':21C ''quick'':117C ''requir'':158C ''score'':27C,48C,106C,113C,145C ''set'':15C ''show'':86C,98C ''similar'':157C ''somehow'':131C ''store'':56C ''summari'':125C ''techniqu'':1A,153C ''thing'':70C ''think'':122C ''threat'':26C,47C,105C,112C,144C ''three'':69C ''top'':41C ''transform'':165C ''trend'':3A ''ul'':80C,92C,108C ''unbalanc'':6A ''user'':24C,43C,82C,94C,110C,134C ''user-id'':23C ''visual'':9B,124C,152C ''want'':136C ''whose'':111C ''would'':72C,126C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (452, 2, '2014-06-18 15:10:22.637', 2, NULL, NULL, '&lt;p&gt;As @Christopher Lauden mentioned above, time-series analysis is most appropriate for this sort of thing. If, however, you wished to do a more traditional "machine learning approach", something that I have done in the past is to block up your data into overlapping windows of time as features, then use it to predict the next days (or weeks) traffic.&lt;/p&gt;

&lt;p&gt;Your feature matrix would be something like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;t1 | t2 | ... | tN
t2 | t3 | ... | tN+1
t3 | t4 | ... | tN+2
...
tW | tW+1 | ... |tN+W
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where &lt;code&gt;tI&lt;/code&gt; is the traffic on day &lt;code&gt;I&lt;/code&gt;. The feature you''ll be predicting is the traffic on the day after the last column. In essence, use a window of traffic to predict the next day''s traffic. &lt;/p&gt;

&lt;p&gt;Any sort of ML model would work for this. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;In response to the question, "can you elaborate on how you use this feature matrix":&lt;/p&gt;

&lt;p&gt;The feature matrix has values indicating past traffic over a period of time (for instance, hourly traffic over 1 week), and we use this to predict traffic for some specified time period in the future. We take our historic data and build a feature matrix of historic traffic and label this with the traffic at some period in the future (e.g. 2 days after the window in the feature). Using some sort of regression machine learning model, we can take historic traffic data, and try and build a model that can predict how traffic moved in our historic data set. The presumption is that future traffic will resemble past traffic.&lt;/p&gt;
', NULL, NULL, '''+1'':81C,88C ''+2'':85C ''/code'':91C,97C,105C ''/p'':63C,72C,136C,147C,152C,169C,281C ''/pre'':92C ''/strong'':151C ''1'':189C ''2'':232C ''analysi'':10C ''approach'':30C ''appropri'':13C ''block'':41C ''build'':212C,257C ''christoph'':3C ''code'':74C,95C,103C ''column'':121C ''data'':44C,210C,253C,269C ''day'':59C,102C,117C,133C,233C ''done'':35C ''e.g'':231C ''edit'':150C ''elabor'':161C ''essenc'':123C ''featur'':51C,66C,107C,167C,172C,214C,239C ''futur'':205C,230C,275C ''histor'':209C,217C,251C,268C ''hour'':186C ''howev'':20C ''indic'':176C ''instanc'':185C ''label'':220C ''last'':120C ''lauden'':4C ''learn'':29C,246C ''like'':71C ''ll'':109C ''machin'':28C,245C ''matrix'':67C,168C,173C,215C ''mention'':5C ''ml'':141C ''model'':142C,247C,259C ''move'':265C ''next'':58C,132C ''overlap'':46C ''p'':1C,64C,93C,137C,148C,153C,170C ''past'':38C,177C,279C ''period'':181C,202C,227C ''pre'':73C ''predict'':56C,111C,130C,196C,262C ''presumpt'':272C ''question'':158C ''regress'':244C ''resembl'':278C ''respons'':155C ''seri'':9C ''set'':270C ''someth'':31C,70C ''sort'':16C,139C,242C ''specifi'':200C ''strong'':149C ''t1'':75C ''t2'':76C,78C ''t3'':79C,82C ''t4'':83C ''take'':207C,250C ''thing'':18C ''ti'':96C ''time'':8C,49C,183C,201C ''time-seri'':7C ''tn'':77C,80C,84C,89C ''tradit'':27C ''traffic'':62C,100C,114C,128C,135C,178C,187C,197C,218C,224C,252C,264C,276C,280C ''tri'':255C ''tw'':86C,87C ''use'':53C,124C,165C,193C,240C ''valu'':175C ''w'':90C ''week'':61C,190C ''window'':47C,126C,236C ''wish'':22C ''work'':144C ''would'':68C,143C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (453, 1, '2014-06-18 15:27:23.313', 6, 79, 'What is the difference between global and universal compression methods?', '&lt;p&gt;I understand that compression methods may be split into two main sets: global and local. The first set works regardless of the data being processed, i.e., they do not rely on any characteristic of the data, and thus need not to perform any preprocessing over any part of the dataset (before the compression itself). On the other hand, local methods analyze the data, extracting informations that usually improve the compression rate.&lt;/p&gt;

&lt;p&gt;While reading about some of these methods, I noticed that &lt;a href="http://en.wikipedia.org/wiki/Universal_code_%28data_compression%29#Universal_and_non-universal_codes"&gt;the unary method is not universal&lt;/a&gt;, which surprised me since I thought "globality" and "universality" referred to the same thing. The unary method does not rely on characteristics of the data to yield its encoding (i.e., it is a global method), and therefore it should be global/universal, shouldn''t it?&lt;/p&gt;

&lt;p&gt;What is the difference between universal and global methods? Aren''t these classifications synonyms?&lt;/p&gt;
', 'algorithms', NULL, '''/a'':107C ''/p'':84C,152C,168C ''/wiki/universal_code_%28data_compression%29#universal_and_non-universal_codes'':100C ''algorithm'':11B ''analyz'':73C ''aren'':163C ''characterist'':45C,129C ''classif'':166C ''compress'':9A,16C,65C,82C ''data'':35C,48C,75C,132C ''dataset'':62C ''differ'':4A,157C ''en.wikipedia.org'':99C ''en.wikipedia.org/wiki/universal_code_%28data_compression%29#universal_and_non-universal_codes'':98C ''encod'':136C ''extract'':76C ''first'':29C ''global'':6A,25C,114C,141C,161C ''global/universal'':148C ''hand'':70C ''href'':97C ''i.e'':38C,137C ''improv'':80C ''inform'':77C ''local'':27C,71C ''main'':23C ''may'':18C ''method'':10A,17C,72C,92C,103C,124C,142C,162C ''need'':51C ''notic'':94C ''p'':12C,85C,153C ''part'':59C ''perform'':54C ''preprocess'':56C ''process'':37C ''rate'':83C ''read'':87C ''refer'':117C ''regardless'':32C ''reli'':42C,127C ''set'':24C,30C ''shouldn'':149C ''sinc'':111C ''split'':20C ''surpris'':109C ''synonym'':167C ''therefor'':144C ''thing'':121C ''thought'':113C ''thus'':50C ''two'':22C ''unari'':102C,123C ''understand'':14C ''univers'':8A,106C,116C,159C ''usual'':79C ''work'':31C ''yield'':134C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (454, 1, '2014-06-18 15:48:19.497', 8, 419, 'What are the implications for training a Tree Ensemble with highly biased datasets?', '&lt;p&gt;I have a highly biased binary dataset - I have 1000x more examples of the negative class than the positive class. I would like to train a Tree Ensemble (like Extra Random Trees or a Random Forest) on this data but it''s difficult to create training datasets that contain enough examples of the positive class.&lt;/p&gt;

&lt;p&gt;What would be the implications of doing a stratified sampling approach to normalize the number of positive and negative examples? In other words, is it a bad idea to, for instance, artificially inflate (by resampling) the number of positive class examples in the training set?&lt;/p&gt;
', 'machine-learning  feature-selection  unbalanced-classes', NULL, '''/p'':79C,126C ''1000x'':33C ''approach'':91C ''artifici'':112C ''bad'':107C ''bias'':12A,28C ''binari'':29C ''class'':22B,39C,43C,78C,120C ''contain'':72C ''creat'':68C ''data'':62C ''dataset'':13A,30C,70C ''difficult'':66C ''enough'':73C ''ensembl'':9A,51C ''exampl'':35C,74C,100C,121C ''extra'':53C ''featur'':18B ''feature-select'':17B ''forest'':59C ''high'':11A,27C ''idea'':108C ''implic'':4A,85C ''inflat'':113C ''instanc'':111C ''learn'':16B ''like'':46C,52C ''machin'':15B ''machine-learn'':14B ''negat'':38C,99C ''normal'':93C ''number'':95C,117C ''p'':23C,80C ''posit'':42C,77C,97C,119C ''random'':54C,58C ''resampl'':115C ''sampl'':90C ''select'':19B ''set'':125C ''stratifi'':89C ''train'':6A,48C,69C,124C ''tree'':8A,50C,55C ''unbalanc'':21B ''unbalanced-class'':20B ''word'':103C ''would'':45C,82C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (471, 2, '2014-06-19 07:22:38.987', 5, NULL, NULL, '&lt;p&gt;One of the most widely used test collection for text categorization research (link below). I''ve used many times. Enjoy your exploration :)&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.daviddlewis.com/resources/testcollections/reuters21578/"&gt;http://www.daviddlewis.com/resources/testcollections/reuters21578/&lt;/a&gt;
or
&lt;a href="http://archive.ics.uci.edu/ml/datasets/Reuters-21578+Text+Categorization+Collection"&gt;http://archive.ics.uci.edu/ml/datasets/Reuters-21578+Text+Categorization+Collection&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/ml/datasets/reuters-21578+text+categorization+collection'':39C ''/ml/datasets/reuters-21578+text+categorization+collection&lt;/a&gt;&lt;/p&gt;'':42C ''/p'':24C ''/resources/testcollections/reuters21578/'':30C ''/resources/testcollections/reuters21578/&lt;/a&gt;'':33C ''archive.ics.uci.edu'':38C,41C ''archive.ics.uci.edu/ml/datasets/reuters-21578+text+categorization+collection'':37C ''archive.ics.uci.edu/ml/datasets/reuters-21578+text+categorization+collection&lt;/a&gt;&lt;/p&gt;'':40C ''categor'':12C ''collect'':9C ''enjoy'':21C ''explor'':23C ''href'':27C,36C ''link'':14C ''mani'':19C ''one'':2C ''p'':1C,25C ''research'':13C ''test'':8C ''text'':11C ''time'':20C ''use'':7C,18C ''ve'':17C ''wide'':6C ''www.daviddlewis.com'':29C,32C ''www.daviddlewis.com/resources/testcollections/reuters21578/'':28C ''www.daviddlewis.com/resources/testcollections/reuters21578/&lt;/a&gt;'':31C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (455, 1, '2014-06-18 16:21:12.203', 5, 331, 'Suggest text classifier training datasets', '&lt;p&gt;Which freely available datasets can I use to train a text classifier?&lt;/p&gt;

&lt;p&gt;We are trying to enhance our users engagement by recommending the most related content for him, so we thought If we classified our content based on a predefined bag of words we can recommend to him engaging content by getting his feedback on random number of posts already classified before.&lt;/p&gt;

&lt;p&gt;We can use this info to recommend for him pulses labeled with those classes. But we found If we used a predefined bag of words not related to our content the feature vector will be full of zeros, also categories may be not relevant to our content. so for those reasons we tried another solution that will be clustering our content not classifying it.&lt;/p&gt;

&lt;p&gt;Thanks :)&lt;/p&gt;
', 'machine-learning  classification  dataset  clustering  text-mining', NULL, '''/p'':28C,80C,146C,149C ''alreadi'':77C ''also'':120C ''anoth'':135C ''avail'':18C ''bag'':58C,104C ''base'':54C ''categori'':121C ''class'':95C ''classif'':9B ''classifi'':3A,27C,51C,78C,144C ''cluster'':11B,140C ''content'':43C,53C,67C,111C,128C,142C ''dataset'':5A,10B,19C ''engag'':37C,66C ''enhanc'':34C ''featur'':113C ''feedback'':71C ''found'':98C ''freeli'':17C ''full'':117C ''get'':69C ''info'':86C ''label'':92C ''learn'':8B ''machin'':7B ''machine-learn'':6B ''may'':122C ''mine'':14B ''number'':74C ''p'':15C,29C,81C,147C ''post'':76C ''predefin'':57C,103C ''puls'':91C ''random'':73C ''reason'':132C ''recommend'':39C,63C,88C ''relat'':42C,108C ''relev'':125C ''solut'':136C ''suggest'':1A ''text'':2A,13B,26C ''text-min'':12B ''thank'':148C ''thought'':48C ''train'':4A,24C ''tri'':32C,134C ''use'':22C,84C,101C ''user'':36C ''vector'':114C ''word'':60C,106C ''zero'':119C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (511, 1, '2014-06-20 17:57:46.363', 6, 1367, 'Cross-validation: K-fold vs Repeated random sub-sampling', '&lt;p&gt;I wonder which type of model cross-validation to choose for classification problem: K-fold or random sub-sampling (bootstrap sampling)?&lt;/p&gt;

&lt;p&gt;My best guess is to use 2/3 of the data set (which is ~1000 items) for training and 1/3 for validation.&lt;/p&gt;

&lt;p&gt;In this case K-fold gives only three iterations(folds), which is not enough to see stable average error.&lt;/p&gt;

&lt;p&gt;On the other hand I don''t like random sub-sampling feature: that some items won''t be ever selected for training/validation, and some will be used more than once.&lt;/p&gt;

&lt;p&gt;Classification algorithms used: random forest &amp;amp; logistic regression.&lt;/p&gt;
', 'cross-validation  sampling', NULL, '''/p'':42C,65C,87C,120C,130C ''1/3'':62C ''1000'':57C ''2/3'':50C ''algorithm'':123C ''amp'':127C ''averag'':85C ''best'':45C ''bootstrap'':40C ''case'':69C ''choos'':28C ''classif'':30C,122C ''cross'':2A,14B,25C ''cross-valid'':1A,13B,24C ''data'':53C ''enough'':81C ''error'':86C ''ever'':108C ''featur'':101C ''fold'':6A,34C,72C,77C ''forest'':126C ''give'':73C ''guess'':46C ''hand'':92C ''item'':58C,104C ''iter'':76C ''k'':5A,33C,71C ''k-fold'':4A,32C,70C ''like'':96C ''logist'':128C ''model'':23C ''p'':17C,43C,66C,88C,121C ''problem'':31C ''random'':9A,36C,97C,125C ''regress'':129C ''repeat'':8A ''sampl'':12A,16B,39C,41C,100C ''see'':83C ''select'':109C ''set'':54C ''stabl'':84C ''sub'':11A,38C,99C ''sub-sampl'':10A,37C,98C ''three'':75C ''train'':60C ''training/validation'':111C ''type'':21C ''use'':49C,116C,124C ''valid'':3A,15B,26C,64C ''vs'':7A ''won'':105C ''wonder'':19C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (462, 2, '2014-06-18 22:27:06.503', 10, NULL, NULL, '&lt;p&gt;I would recommend training on more balanced subsets of your data. Training random forest on sets of randomly selected positive example with a similar number of negative samples. In particular if the discriminative features exhibit a lot of variance this will be fairly effective and avoid over-fitting. However in stratification it is important to find balance as over-fitting can become a problem regardless. I would suggest seeing how the model does with the whole data set then progressively increasing the ratio of positive to negative samples approaching an even ratio, and selecting for the one that maximizes your performance metric on some representative hold out data. &lt;/p&gt;

&lt;p&gt;This paper seems fairly relevant &lt;a href="http://statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf"&gt;http://statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf&lt;/a&gt; it talks about a &lt;code&gt;weighted Random Forest&lt;/code&gt; which more heavily penalizes misclassification of the minority class.  &lt;/p&gt;
', NULL, NULL, '''/code'':135C ''/p'':112C,145C ''/sites/default/files/tech-reports/666.pdf'':123C ''/sites/default/files/tech-reports/666.pdf&lt;/a&gt;'':126C ''approach'':92C ''avoid'':47C ''balanc'':8C,59C ''becom'':65C ''class'':144C ''code'':131C ''data'':12C,80C,111C ''discrimin'':34C ''effect'':45C ''even'':94C ''exampl'':22C ''exhibit'':36C ''fair'':44C,117C ''featur'':35C ''find'':58C ''fit'':50C,63C ''forest'':15C,134C ''heavili'':138C ''hold'':109C ''howev'':51C ''href'':120C ''import'':56C ''increas'':84C ''lot'':38C ''maxim'':102C ''metric'':105C ''minor'':143C ''misclassif'':140C ''model'':75C ''negat'':28C,90C ''number'':26C ''one'':100C ''over-fit'':48C,61C ''p'':1C,113C ''paper'':115C ''particular'':31C ''penal'':139C ''perform'':104C ''posit'':21C,88C ''problem'':67C ''progress'':83C ''random'':14C,19C,133C ''ratio'':86C,95C ''recommend'':4C ''regardless'':68C ''relev'':118C ''repres'':108C ''sampl'':29C,91C ''see'':72C ''seem'':116C ''select'':20C,97C ''set'':17C,81C ''similar'':25C ''statistics.berkeley.edu'':122C,125C ''statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf'':121C ''statistics.berkeley.edu/sites/default/files/tech-reports/666.pdf&lt;/a&gt;'':124C ''stratif'':53C ''subset'':9C ''suggest'':71C ''talk'':128C ''train'':5C,13C ''varianc'':40C ''weight'':132C ''whole'':79C ''would'':3C,70C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (581, 2, '2014-06-25 14:36:29.163', 5, NULL, NULL, '&lt;p&gt;In my opinion, there are solutions to deal with categorical data in clustering. R comes with a specific distance for categorical data. This distance is called Gower (&lt;a href="http://www.inside-r.org/packages/cran/StatMatch/docs/gower.dist"&gt;http://www.inside-r.org/packages/cran/StatMatch/docs/gower.dist&lt;/a&gt;) and it works pretty well.&lt;/p&gt;
', NULL, NULL, '''/p'':42C ''/packages/cran/statmatch/docs/gower.dist'':33C ''/packages/cran/statmatch/docs/gower.dist&lt;/a&gt;)'':36C ''call'':27C ''categor'':11C,22C ''cluster'':14C ''come'':16C ''data'':12C,23C ''deal'':9C ''distanc'':20C,25C ''gower'':28C ''href'':30C ''opinion'':4C ''p'':1C ''pretti'':40C ''r'':15C ''solut'':7C ''specif'':19C ''well'':41C ''work'':39C ''www.inside-r.org'':32C,35C ''www.inside-r.org/packages/cran/statmatch/docs/gower.dist'':31C ''www.inside-r.org/packages/cran/statmatch/docs/gower.dist&lt;/a&gt;)'':34C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (586, 1, '2014-06-25 17:37:23.38', 3, 253, 'What are the main types of NLP annotators?', '&lt;p&gt;I''m new to the world of text mining and have been reading up on annotators at places like the &lt;a href="http://uima.apache.org/" rel="nofollow"&gt;UIMA website&lt;/a&gt;. I''m encountering many new terms like named entity recognition, tokenizer, lemmatizer, gazetteer, etc. Coming from a layman background, this is all very confusing so can anyone tell me or link to resources that can explain what the main categories of annotators are and what they do?&lt;/p&gt;
', 'nlp  text-mining', NULL, '''/a'':41C ''/p'':89C ''annot'':8A,29C,83C ''anyon'':68C ''background'':60C ''categori'':81C ''come'':56C ''confus'':65C ''encount'':44C ''entiti'':50C ''etc'':55C ''explain'':77C ''gazett'':54C ''href'':35C ''layman'':59C ''lemmat'':53C ''like'':32C,48C ''link'':72C ''m'':15C,43C ''main'':4A,80C ''mani'':45C ''mine'':12B,22C ''name'':49C ''new'':16C,46C ''nlp'':7A,9B ''nofollow'':38C ''p'':13C ''place'':31C ''read'':26C ''recognit'':51C ''rel'':37C ''resourc'':74C ''tell'':69C ''term'':47C ''text'':11B,21C ''text-min'':10B ''token'':52C ''type'':5A ''uima'':39C ''uima.apache.org'':36C ''websit'':40C ''world'':19C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (456, 2, '2014-06-18 17:22:46.207', 4, NULL, NULL, '&lt;p&gt;If you expect (or find) that nodes are requesting the same data more than once, perhaps you could benefit from a caching strategy? Especially where some data is used much more often than others, so you can target only the most frequently-used information.&lt;/p&gt;

&lt;p&gt;If the data is mutable, you also need a way to confirm that it hasn''t changed since the last request that''s less expensive than repeating the request.&lt;/p&gt;

&lt;p&gt;This is further complicated if each node has its own separate cache. Depending on the nature of your system and task(s), you could consider adding a node dedicated to serving information between the processing nodes, and building a single cache on that node.&lt;/p&gt;

&lt;p&gt;For an example of when that &lt;em&gt;might&lt;/em&gt; be a good idea, let''s suppose I retrieve some data from a remote data store over a low-bandwidth connection, and I have some task(s) requiring that data, which are distributed exclusively among local nodes. I definitely wouldn''t want each node requesting information separately over that low-bandwidth connection, which another node might have previously requested. Since my local I/O is much less expensive than my I/O over the low-bandwidth connection, I might add a node between the processing nodes and the remote source that acts as an intermediate server. This node would take requests from the processing nodes, communicate with the remote data store, and cache frequently-requested data to minimize the use of that low-bandwidth connection.&lt;/p&gt;

&lt;p&gt;The core concepts here that &lt;em&gt;may&lt;/em&gt; be applicable to your specific case are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Eliminate or reduce redundant I/O;&lt;/li&gt;
&lt;li&gt;Take advantage of trade-offs between memory use and computation time;&lt;/li&gt;
&lt;li&gt;Not all I/O is created equal.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/em'':134C,272C ''/li'':288C,302C,310C ''/p'':47C,78C,124C,263C,280C ''/ul'':311C ''act'':227C ''ad'':105C ''add'':215C ''advantag'':291C ''also'':55C ''among'':170C ''anoth'':190C ''applic'':274C ''bandwidth'':155C,187C,211C,261C ''benefit'':20C ''build'':117C ''cach'':23C,91C,120C,248C ''case'':278C ''chang'':65C ''communic'':241C ''complic'':83C ''comput'':300C ''concept'':267C ''confirm'':60C ''connect'':156C,188C,212C,262C ''consid'':104C ''core'':266C ''could'':19C,103C ''creat'':308C ''data'':13C,28C,51C,145C,149C,165C,245C,252C ''dedic'':108C ''definit'':174C ''depend'':92C ''distribut'':168C ''elimin'':283C ''em'':132C,270C ''equal'':309C ''especi'':25C ''exampl'':128C ''exclus'':169C ''expect'':4C ''expens'':73C,203C ''find'':6C ''frequent'':44C,250C ''frequently-request'':249C ''frequently-us'':43C ''good'':137C ''hasn'':63C ''i/o'':199C,206C,287C,306C ''idea'':138C ''inform'':46C,111C,181C ''intermedi'':230C ''last'':68C ''less'':72C,202C ''let'':139C ''li'':282C,289C,303C ''local'':171C,198C ''low'':154C,186C,210C,260C ''low-bandwidth'':153C,185C,209C,259C ''may'':271C ''memori'':297C ''might'':133C,192C,214C ''minim'':254C ''much'':31C,201C ''mutabl'':53C ''natur'':95C ''need'':56C ''node'':8C,86C,107C,115C,123C,172C,179C,191C,217C,221C,233C,240C ''off'':295C ''often'':33C ''other'':35C ''p'':1C,48C,79C,125C,264C ''perhap'':17C ''previous'':194C ''process'':114C,220C,239C ''reduc'':285C ''redund'':286C ''remot'':148C,224C,244C ''repeat'':75C ''request'':10C,69C,77C,180C,195C,236C,251C ''requir'':163C ''retriev'':143C ''separ'':90C,182C ''serv'':110C ''server'':231C ''sinc'':66C,196C ''singl'':119C ''sourc'':225C ''specif'':277C ''store'':150C,246C ''strategi'':24C ''suppos'':141C ''system'':98C ''take'':235C,290C ''target'':39C ''task'':100C,161C ''time'':301C ''trade'':294C ''trade-off'':293C ''ul'':281C ''use'':30C,45C,256C,298C ''want'':177C ''way'':58C ''would'':234C ''wouldn'':175C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (457, 2, '2014-06-18 18:22:11.68', 6, NULL, NULL, '&lt;p&gt;Firstly, I would generally agree with everything that AirThomas suggested. Caching things is generally good if you can, but I find it slightly brittle since that''s very dependent on exactly what your application is. Data compression is another very solid suggestion, but my impression on both of these is that the speedups you''re looking at are going to be relatively marginal. Maybe as high as 2-5x, but I would be very surprised if they were any faster than that.&lt;/p&gt;

&lt;p&gt;Under the assumption that pure I/O (writing to/reading from memory) is &lt;em&gt;not&lt;/em&gt; your limiting factor (if it is, you''re probably not going to get a lot faster), I would make a strong plug for &lt;a href="http://zeromq.org/"&gt;zeromq&lt;/a&gt;. In the words of the creators:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;We took a normal TCP socket, injected it with a mix of radioactive
  isotopes stolen from a secret Soviet atomic research project,
  bombarded it with 1950-era cosmic rays, and put it into the hands of a
  drug-addled comic book author with a badly-disguised fetish for
  bulging muscles clad in spandex. Yes, ÃMQ sockets are the world-saving
  superheroes of the networking world.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;While that may be a little dramatic, &lt;code&gt;zeromq&lt;/code&gt; sockets in my opinion are one of the most amazing pieces of software that the world of computer networks has put together in several years. I''m not sure what you''re using for your message-passing layer right now, but if you''re using something traditional like &lt;code&gt;rabbitmq&lt;/code&gt;, you''re liable to see speedups of multiple orders of magnitude (personally noticed about 500x, but depends a lot of architecture)&lt;/p&gt;

&lt;p&gt;Check out some basic benchmarks &lt;a href="http://blog.x-aeon.com/2013/04/10/a-quick-message-queue-benchmark-activemq-rabbitmq-hornetq-qpid-apollo/"&gt;here.&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''-5'':70C ''/2013/04/10/a-quick-message-queue-benchmark-activemq-rabbitmq-hornetq-qpid-apollo/'':304C ''/a'':128C,306C ''/blockquote'':208C ''/code'':219C,271C ''/em'':100C ''/p'':85C,135C,207C,293C,307C ''1950'':163C ''2'':69C ''500x'':286C ''addl'':177C ''agre'':6C ''airthoma'':10C ''amaz'':229C ''anoth'':40C ''applic'':35C ''architectur'':292C ''assumpt'':89C ''atom'':157C ''author'':180C ''bad'':184C ''badly-disguis'':183C ''basic'':298C ''benchmark'':299C ''blockquot'':136C ''blog.x-aeon.com'':303C ''blog.x-aeon.com/2013/04/10/a-quick-message-queue-benchmark-activemq-rabbitmq-hornetq-qpid-apollo/'':302C ''bombard'':160C ''book'':179C ''brittl'':25C ''bulg'':188C ''cach'':12C ''check'':295C ''clad'':190C ''code'':217C,269C ''comic'':178C ''compress'':38C ''comput'':237C ''cosmic'':165C ''creator'':134C ''data'':37C ''depend'':30C,288C ''disguis'':185C ''dramat'':216C ''drug'':176C ''drug-addl'':175C ''em'':98C ''era'':164C ''everyth'':8C ''exact'':32C ''factor'':103C ''faster'':82C,116C ''fetish'':186C ''find'':22C ''first'':2C ''general'':5C,15C ''get'':113C ''go'':60C,111C ''good'':16C ''hand'':172C ''high'':67C ''href'':125C,301C ''i/o'':92C ''impress'':46C ''inject'':144C ''isotop'':151C ''layer'':258C ''liabl'':274C ''like'':268C ''limit'':102C ''littl'':215C ''look'':57C ''lot'':115C,290C ''m'':246C ''magnitud'':282C ''make'':119C ''margin'':64C ''may'':212C ''mayb'':65C ''memori'':96C ''messag'':256C ''message-pass'':255C ''mix'':148C ''mq'':195C ''multipl'':279C ''muscl'':189C ''network'':205C,238C ''normal'':141C ''notic'':284C ''one'':225C ''opinion'':223C ''order'':280C ''p'':1C,86C,137C,209C,294C ''pass'':257C ''person'':283C ''piec'':230C ''plug'':122C ''probabl'':109C ''project'':159C ''pure'':91C ''put'':168C,240C ''rabbitmq'':270C ''radioact'':150C ''ray'':166C ''re'':56C,108C,251C,264C,273C ''relat'':63C ''research'':158C ''right'':259C ''save'':201C ''secret'':155C ''see'':276C ''sever'':243C ''sinc'':26C ''slight'':24C ''socket'':143C,196C,220C ''softwar'':232C ''solid'':42C ''someth'':266C ''soviet'':156C ''spandex'':192C ''speedup'':54C,277C ''stolen'':152C ''strong'':121C ''suggest'':11C,43C ''superhero'':202C ''sure'':248C ''surpris'':77C ''tcp'':142C ''thing'':13C ''to/reading'':94C ''togeth'':241C ''took'':139C ''tradit'':267C ''use'':252C,265C ''word'':131C ''world'':200C,206C,235C ''world-sav'':199C ''would'':4C,74C,118C ''write'':93C ''x'':71C ''year'':244C ''yes'':193C ''zeromq'':127C,218C ''zeromq.org'':126C ''ã'':194C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (458, 1, '2014-06-18 19:48:54.883', 8, 413, 'K-means vs. online K-means', '&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/K-means_clustering"&gt;K-means&lt;/a&gt; is a well known algorithm for clustering, but there is also an online variation of such algorithm (online K-means). What are the pros and cons of these approaches, and when should each be preferred?&lt;/p&gt;
', 'algorithms  clustering', NULL, '''/a'':20C ''/p'':57C ''/wiki/k-means_clustering'':16C ''algorithm'':9B,25C,37C ''also'':31C ''approach'':50C ''cluster'':10B,27C ''con'':47C ''en.wikipedia.org'':15C ''en.wikipedia.org/wiki/k-means_clustering'':14C ''href'':13C ''k'':2A,7A,18C,40C ''k-mean'':1A,6A,17C,39C ''known'':24C ''mean'':3A,8A,19C,41C ''onlin'':5A,33C,38C ''p'':11C ''prefer'':56C ''pros'':45C ''variat'':34C ''vs'':4A ''well'':23C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (459, 2, '2014-06-18 20:07:05.017', 8, NULL, NULL, '&lt;p&gt;Online k-means (more commonly known as &lt;a href="http://stackoverflow.com/questions/3698532/online-k-means-clustering"&gt;sequential k-means&lt;/a&gt;) and traditional k-means are very similar.  The difference is that online k-means allows you to update the model as new data is received.&lt;/p&gt;

&lt;p&gt;Online k-means should be used when you expect the data to be received one by one (or maybe in chunks).  This allows you to update your model as you get more information about it.  The drawback of this method is that it is dependent on the order in which the data is received (&lt;a href="http://www.cs.princeton.edu/courses/archive/fall08/cos436/Duda/C/sk_means.htm"&gt;ref&lt;/a&gt;).  &lt;/p&gt;
', NULL, NULL, '''/a'':19C,110C ''/courses/archive/fall08/cos436/duda/c/sk_means.htm'':108C ''/p'':47C,111C ''/questions/3698532/online-k-means-clustering'':14C ''allow'':36C,72C ''chunk'':70C ''common'':7C ''data'':44C,60C,101C ''depend'':94C ''differ'':29C ''drawback'':86C ''expect'':58C ''get'':80C ''href'':11C,105C ''inform'':82C ''k'':4C,17C,23C,34C,51C ''k-mean'':3C,16C,22C,33C,50C ''known'':8C ''mayb'':68C ''mean'':5C,18C,24C,35C,52C ''method'':89C ''model'':41C,77C ''new'':43C ''one'':64C,66C ''onlin'':2C,32C,49C ''order'':97C ''p'':1C,48C ''receiv'':46C,63C,103C ''ref'':109C ''sequenti'':15C ''similar'':27C ''stackoverflow.com'':13C ''stackoverflow.com/questions/3698532/online-k-means-clustering'':12C ''tradit'':21C ''updat'':39C,75C ''use'':55C ''www.cs.princeton.edu'':107C ''www.cs.princeton.edu/courses/archive/fall08/cos436/duda/c/sk_means.htm'':106C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (460, 2, '2014-06-18 21:56:29.147', 4, NULL, NULL, '&lt;p&gt;A fast, easy an often effective way to approach this imbalance would be to randomly subsample the bigger class (which in your case is the negative class), run the classification N number of times with members from the two classes (one full and the other subsampled) and report the average metric values, the average being computed over N (say 1000) iterations.&lt;/p&gt;

&lt;p&gt;A more methodical approach would be to execute the Mapping Convergence (MC) algorithm, which involves identifying a subset of strong negative samples with the help of a one-class classifier, such as OSVM or SVDD, and then iteratively execute binary classification on the set of strong negative and positive samples. More details of the MC algorithm can be found in this &lt;a href="http://link.springer.com/article/10.1007/s10994-005-1122-7#page-1" rel="nofollow"&gt;paper&lt;/a&gt;. &lt;/p&gt;
', NULL, NULL, '''/a'':135C ''/article/10.1007/s10994-005-1122-7#page-1'':131C ''/p'':63C,136C ''1000'':61C ''algorithm'':77C,121C ''approach'':10C,68C ''averag'':51C,55C ''bigger'':19C ''binari'':105C ''case'':24C ''class'':20C,28C,41C,94C ''classif'':31C,106C ''classifi'':95C ''comput'':57C ''converg'':75C ''detail'':117C ''easi'':4C ''effect'':7C ''execut'':72C,104C ''fast'':3C ''found'':124C ''full'':43C ''help'':89C ''href'':128C ''identifi'':80C ''imbal'':12C ''involv'':79C ''iter'':62C,103C ''link.springer.com'':130C ''link.springer.com/article/10.1007/s10994-005-1122-7#page-1'':129C ''map'':74C ''mc'':76C,120C ''member'':37C ''method'':67C ''metric'':52C ''n'':32C,59C ''negat'':27C,85C,112C ''nofollow'':133C ''number'':33C ''often'':6C ''one'':42C,93C ''one-class'':92C ''osvm'':98C ''p'':1C,64C ''paper'':134C ''posit'':114C ''random'':16C ''rel'':132C ''report'':49C ''run'':29C ''sampl'':86C,115C ''say'':60C ''set'':109C ''strong'':84C,111C ''subsampl'':17C,47C ''subset'':82C ''svdd'':100C ''time'':35C ''two'':40C ''valu'':53C ''way'':8C ''would'':13C,69C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (571, 1, '2014-06-25 02:33:22.673', 5, 111, 'Standardize numbers for ranking ratios', '&lt;p&gt;I''m trying to rank some percentages. I have numerators and denominators for each ratio. To give a concrete example, consider ratio as &lt;code&gt;total graduates / total students&lt;/code&gt; in a school.&lt;/p&gt;

&lt;p&gt;But the issue is that &lt;code&gt;total students&lt;/code&gt; vary over a long range (1000-20000). Smaller schools seem to have higher percentage of students graduating, but I want to standardize it, and not let the size of the school affect the ranking. Is there a way to do it?&lt;/p&gt;
', 'statistics', NULL, '''-20000'':57C ''/code'':36C,50C ''/p'':40C,92C ''1000'':56C ''affect'':82C ''code'':31C,47C ''concret'':26C ''consid'':28C ''denomin'':19C ''exampl'':27C ''give'':24C ''graduat'':33C,67C ''higher'':63C ''issu'':44C ''let'':76C ''long'':54C ''m'':9C ''number'':2A ''numer'':17C ''p'':7C,41C ''percentag'':14C,64C ''rang'':55C ''rank'':4A,12C,84C ''ratio'':5A,22C,29C ''school'':39C,59C,81C ''seem'':60C ''size'':78C ''smaller'':58C ''standard'':1A,72C ''statist'':6B ''student'':35C,49C,66C ''total'':32C,34C,48C ''tri'':10C ''vari'':51C ''want'':70C ''way'':88C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (629, 2, '2014-06-30 01:51:11.027', 2, NULL, NULL, '&lt;h1&gt;Do you need a VM?&lt;/h1&gt;

&lt;p&gt;You need to keep in mind that a virtual machine is a software emulation of your own or another machine hardware configuration that can run an operating systems. In most basic terms, it acts as a layer interfacing between the virtual OS, and your own OS which then communicates with the lower level hardware to provide support to the virtual OS. What this means for you is:&lt;/p&gt;

&lt;h1&gt;Cons&lt;/h1&gt;

&lt;h3&gt;Hardware Support&lt;/h3&gt;

&lt;p&gt;A drawback of virtual machine technology is that it supports only the hardware that both the virtual machine hypervisor and the guest operating system support. Even if the guest operating system supports the physical hardware, it sees only the virtual hardware presented by the virtual machine. 
The second aspect of virtual machine hardware support is the hardware presented to the guest operating system. No matter the hardware in the host, the hardware presented to the guest environment is usually the same (with the exception of the CPU, which shows through). For example, VMware GSX Server presents an AMD PCnet32 Fast Ethernet card or an optimized VMware-proprietary network card, depending on which you choose. The network card in the host machine does not matter. VMware GSX Server performs the translation between the guest environment''s network card and the host environment''s network card. This is great for standardization, but it also means that host hardware that VMware does not understand will not be present in the guest environment.&lt;/p&gt;

&lt;h3&gt;Performance Penalty&lt;/h3&gt;

&lt;p&gt;Virtual machine technology imposes a performance penalty from running an additional layer above the physical hardware but beneath the guest operating system. The performance penalty varies based on the virtualization software used and the guest software being run. This is significant.&lt;/p&gt;

&lt;h1&gt;Pros&lt;/h1&gt;

&lt;h3&gt;Isolation&lt;/h3&gt;

&lt;blockquote&gt;
  &lt;p&gt;One of the key reasons to employ virtualization is to isolate applications from each other. Running everything on one machine would be great if it all worked, but many times it results in undesirable interactions or even outright conflicts. The cause often is software problems or business requirements, such as the need for isolated security. Virtual machines allow you to isolate each application (or group of applications) in its own sandbox environment. The virtual machines can run on the same physical machine (simplifying IT hardware management), yet appear as independent machines to the software you are running. For all intents and purposesâexcept performance, the virtual machines are independent machines. If one virtual machine goes down due to application or operating system error, the others continue running, providing services your business needs to function smoothly.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;h3&gt;Standardization&lt;/h3&gt;

&lt;blockquote&gt;
  &lt;p&gt;Another key benefit virtual machines provide is standardization. The hardware that is presented to the guest operating system is uniform for the most part, usually with the CPU being the only component that is "pass-through" in the sense that the guest sees what is on the host. A standardized hardware platform reduces support costs and increases the share of IT resources that you can devote to accomplishing goals that give your business a competitive advantage. The host machines can be different (as indeed they often are when hardware is acquired at different times), but the virtual machines will appear to be the same across all of them.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;h3&gt;Ease of Testing&lt;/h3&gt;

&lt;blockquote&gt;
  &lt;p&gt;Virtual machines let you test scenarios easily. Most virtual machine software today provides snapshot and rollback capabilities. This means you can stop a virtual machine, create a snapshot, perform more operations in the virtual machine, and then roll back again and again until you have finished your testing. This is very handy for software development, but it is also useful for system administration. Admins can snapshot a system and install some software or make some configuration changes that they suspect may destabilize the system. If the software installs or changes work, then the admin can commit the updates. If the updates damage or destroy the system, the admin can roll them back.
  Virtual machines also facilitate scenario testing by enabling virtual networks. In VMware Workstation, for example, you can set up multiple virtual machines on a virtual network with configurable parameters, such as packet loss from congestion and latency. You can thus test timing-sensitive or load-sensitive applications to see how they perform under the stress of a simulated heavy workload.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;h3&gt;Mobility&lt;/h3&gt;

&lt;blockquote&gt;
  &lt;p&gt;Virtual machines are easy to move between physical machines. Most of the virtual machine software on the market today stores a whole disk in the guest environment as a single file in the host environment. Snapshot and rollback capabilities are implemented by storing the change in state in a separate file in the host information. Having a single file represent an entire guest environment disk promotes the mobility of virtual machines. Transferring the virtual machine to another physical machine is as easy as moving the virtual disk file and some configuration files to the other physical machine. Deploying another copy of a virtual machine is the same as transferring a virtual machine, except that instead of moving the files, you copy them.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;h1&gt;Which VM should I use if I am starting out?&lt;/h1&gt;

&lt;p&gt;The Data Science Box or the Data Science Toolbox are your best bets if you just getting into data science. They have the basic software that you will need, with the primary difference being the virtual environment in which each of these can run. The DSB can run on AWS while the DST can run on Virtual Box (which is the most common tool used for VMs).&lt;/p&gt;

&lt;h1&gt;Sources&lt;/h1&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.devx.com/vmspecialreport/Article/30383" rel="nofollow"&gt;http://www.devx.com/vmspecialreport/Article/30383&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://jeroenjanssens.com/2013/12/07/lean-mean-data-science-machine.html" rel="nofollow"&gt;http://jeroenjanssens.com/2013/12/07/lean-mean-data-science-machine.html&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (522, 2, '2014-06-22 13:18:38.76', 2, NULL, NULL, '&lt;p&gt;I have been able to optimize very strange functions with simulated annealing, and it does not require a gradient. Instead, it uses random numbers in a way very similar to Markov Chain Monte Carlo, which helps it avoid getting stuck in local optima. A decent explanation that gives the intuition behind it can be found in this lecture: &lt;a href="http://iacs-courses.seas.harvard.edu/courses/am207/blog/lecture-13.html" rel="nofollow"&gt;Simulated Annealing&lt;/a&gt;. scipy 0.14 includes this algorithm in its optimization module: &lt;a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.anneal.html" rel="nofollow"&gt;scipy.optimize.anneal&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':69C,87C ''/courses/am207/blog/lecture-13.html'':64C ''/doc/scipy/reference/generated/scipy.optimize.anneal.html'':83C ''/p'':88C ''0.14'':71C ''abl'':5C ''algorithm'':74C ''anneal'':13C,68C ''avoid'':39C ''behind'':52C ''carlo'':35C ''chain'':33C ''decent'':46C ''docs.scipy.org'':82C ''docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.anneal.html'':81C ''explan'':47C ''found'':56C ''function'':10C ''get'':40C ''give'':49C ''gradient'':20C ''help'':37C ''href'':61C,80C ''iacs-courses.seas.harvard.edu'':63C ''iacs-courses.seas.harvard.edu/courses/am207/blog/lecture-13.html'':62C ''includ'':72C ''instead'':21C ''intuit'':51C ''lectur'':59C ''local'':43C ''markov'':32C ''modul'':78C ''mont'':34C ''nofollow'':66C,85C ''number'':25C ''optim'':7C,77C ''optima'':44C ''p'':1C ''random'':24C ''rel'':65C,84C ''requir'':18C ''scipi'':70C ''scipy.optimize.anneal'':86C ''similar'':30C ''simul'':12C,67C ''strang'':9C ''stuck'':41C ''use'':23C ''way'':28C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5298, 2, '2015-03-09 21:54:19.297', 4, NULL, NULL, '&lt;p&gt;They are not related in any meaningful sense. Sure, you can use them both to extract features, or do any number of things, but the same can be said about a many techniques. I would have asked "what kind of neural network?" to see if the interviewer had something specific in mind.&lt;/p&gt;
', NULL, NULL, '''/p'':54C ''ask'':38C ''extract'':17C ''featur'':18C ''interview'':48C ''kind'':40C ''mani'':33C ''meaning'':8C ''mind'':53C ''network'':43C ''neural'':42C ''number'':22C ''p'':1C ''relat'':5C ''said'':30C ''see'':45C ''sens'':9C ''someth'':50C ''specif'':51C ''sure'':10C ''techniqu'':34C ''thing'':24C ''use'':13C ''would'':36C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (463, 2, '2014-06-18 22:45:25.677', 8, NULL, NULL, '&lt;p&gt;My first suggestion would be to somehow map the non-quantifiable attributes to quantities with the help of suitable mapping functions. Otherwise, simply leave them out.&lt;/p&gt;

&lt;p&gt;Secondly, I don''t think that you need to assume that the list of attributes is not finite. A standard and intuitive approach is to represent each attribute as an individual dimension in a vector space. Each product is then simply a point in this space. In that case, if you want to dynamically add more attributes you simply have to remap the product vectors into the new feature space (with additional dimensions).    &lt;/p&gt;

&lt;p&gt;With this representation, a seller is a point in the feature space with product attributes and a buyer is a point in the same feature space with the preference attributes. The task is then to find out the most similar buyer point for a given seller point.&lt;/p&gt;

&lt;p&gt;If your dataset (i.e. the number of buyers/sellers) is not very large, you can solve this with a nearest neighbour approach implemented with the help of k-d trees.&lt;/p&gt;

&lt;p&gt;For very large sized data, you can take an IR approach. Index the set of sellers (i.e. the product attributes) by treating each attribute as a separate term with the term-weight being set to the attribute value. A query in this case is a buyer which is also encoded in the term space as a query vector with appropriate term weights. The retrieval step would return you a list of top K most similar matches.&lt;/p&gt;
', NULL, NULL, '''/p'':29C,104C,153C,185C,264C ''add'':85C ''addit'':102C ''also'':236C ''approach'':53C,175C,197C ''appropri'':247C ''assum'':40C ''attribut'':14C,45C,58C,87C,120C,135C,206C,210C,224C ''buyer'':123C,146C,233C ''buyers/sellers'':162C ''case'':79C,230C ''d'':183C ''data'':191C ''dataset'':157C ''dimens'':62C,103C ''dynam'':84C ''encod'':237C ''featur'':99C,116C,130C ''find'':141C ''finit'':48C ''first'':3C ''function'':23C ''given'':150C ''help'':19C,179C ''i.e'':158C,203C ''implement'':176C ''index'':198C ''individu'':61C ''intuit'':52C ''ir'':196C ''k'':182C,260C ''k-d'':181C ''larg'':166C,189C ''leav'':26C ''list'':43C,257C ''map'':9C,22C ''match'':263C ''nearest'':173C ''need'':38C ''neighbour'':174C ''new'':98C ''non'':12C ''non-quantifi'':11C ''number'':160C ''otherwis'':24C ''p'':1C,30C,105C,154C,186C ''point'':73C,113C,126C,147C,152C ''prefer'':134C ''product'':68C,94C,119C,205C ''quantifi'':13C ''quantiti'':16C ''queri'':227C,244C ''remap'':92C ''repres'':56C ''represent'':108C ''retriev'':251C ''return'':254C ''second'':31C ''seller'':110C,151C,202C ''separ'':213C ''set'':200C,221C ''similar'':145C,262C ''simpli'':25C,71C,89C ''size'':190C ''solv'':169C ''somehow'':8C ''space'':66C,76C,100C,117C,131C,241C ''standard'':50C ''step'':252C ''suggest'':4C ''suitabl'':21C ''take'':194C ''task'':137C ''term'':214C,218C,240C,248C ''term-weight'':217C ''think'':35C ''top'':259C ''treat'':208C ''tree'':184C ''valu'':225C ''vector'':65C,95C,245C ''want'':82C ''weight'':219C,249C ''would'':5C,253C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (464, 2, '2014-06-18 22:48:53.35', 10, NULL, NULL, '&lt;p&gt;Some standard datasets for text classification are the 20-News group, Reuters (with 8 and 52 classes) and WebKb. You can find all of them &lt;a href="http://web.ist.utl.pt/~acardoso/datasets/"&gt;here&lt;/a&gt;. &lt;/p&gt;
', NULL, NULL, '''/a'':33C ''/p'':34C ''/~acardoso/datasets/'':31C ''20'':10C ''52'':17C ''8'':15C ''class'':18C ''classif'':7C ''dataset'':4C ''find'':23C ''group'':12C ''href'':28C ''news'':11C ''p'':1C ''reuter'':13C ''standard'':3C ''text'':6C ''web.ist.utl.pt'':30C ''web.ist.utl.pt/~acardoso/datasets/'':29C ''webkb'':20C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (465, 2, '2014-06-18 22:58:35.26', 5, NULL, NULL, '&lt;p&gt;nDCG is used to evaluate a golden ranked list (typically human judged) against your output ranked list. The more is the correlation between the two ranked lists, i.e. the more similar are the ranks of the relevant items in the two lists, the closer is the value of nDCG to 1.&lt;/p&gt;

&lt;p&gt;RMSE (Root Mean Squared Error) is typically used to evaluate regression problems where the output (a predicted scalar value) is compared with the true scalar value output for a given data point.&lt;/p&gt;

&lt;p&gt;So, if you are simply recommending a score (such as recommending a movie rating), then use RMSE. Whereas, if you are recommending a list of items (such as a list of related movies), then use nDCG.  &lt;/p&gt;
', NULL, NULL, '''/p'':53C,87C,125C ''1'':52C ''closer'':45C ''compar'':75C ''correl'':23C ''data'':85C ''error'':59C ''evalu'':6C,64C ''given'':84C ''golden'':8C ''human'':12C ''i.e'':29C ''item'':39C,114C ''judg'':13C ''list'':10C,18C,28C,43C,112C,118C ''mean'':57C ''movi'':101C,121C ''ndcg'':2C,50C,124C ''output'':16C,69C,81C ''p'':1C,54C,88C ''point'':86C ''predict'':71C ''problem'':66C ''rank'':9C,17C,27C,35C ''rate'':102C ''recommend'':94C,99C,110C ''regress'':65C ''relat'':120C ''relev'':38C ''rmse'':55C,105C ''root'':56C ''scalar'':72C,79C ''score'':96C ''similar'':32C ''simpli'':93C ''squar'':58C ''true'':78C ''two'':26C,42C ''typic'':11C,61C ''use'':4C,62C,104C,123C ''valu'':48C,73C,80C ''wherea'':106C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (466, 1, '2014-06-18 23:02:54.2', 9, 279, 'Gas consumption outliers detection - Neural network project. Bad results', '&lt;p&gt;I tried to detect outliers in the energy gas consumption of some dutch buildings, building a neural network model. I have very bad results, but I don''t find the reason. &lt;/p&gt;

&lt;p&gt;I am not an expert so I would like to ask you what I can improve and what I''m doing wrong. This is the complete description: &lt;a href="https://github.com/denadai2/Gas-consumption-outliers"&gt;https://github.com/denadai2/Gas-consumption-outliers&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The neural network is a FeedFoward Network with Back Propagation. As described &lt;a href="http://nbviewer.ipython.org/github/denadai2/Gas-consumption-outliers/blob/master/3-%20Regression_NN.ipynb"&gt;here&lt;/a&gt; I splitted the dataset in a "small" dataset of 41''000 rows, 9 features and I tried to add more features. &lt;/p&gt;

&lt;p&gt;I trained the networks but the results have 14.14 RMSE, so it can''t predict so well the gas consumptions, consecutevely I can''t run a good outlier detection mechanism. I see that in some papers that even if they predict daily or hourly consumption in the electric power, they have errors like MSE = 0.01.&lt;/p&gt;

&lt;p&gt;What can I improve? What am I doing wrong? Can you have a look of my description?&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (468, 1, '2014-06-19 03:43:23.853', 8, 261, 'Learning ordinal regression in R?', '&lt;p&gt;I''m working on a project and need resources to get me up to speed.&lt;/p&gt;

&lt;p&gt;The dataset is around 35000 observations on 30 or so variables.  About half the variables are categorical with some having many different possible values, i.e. if you split the categorical variables into dummy variables you would have a lot more than 30 variables.  But still probably on the order of a couple of hundred max.  (n&gt;p).  &lt;/p&gt;

&lt;p&gt;The response we want to predict is ordinal with 5 levels (1,2,3,4,5).  Predictors are a mix of continuous and categorical, about half of each.  These are my thoughts/plans so far:
1.  Treat the response as continuous and run vanilla linear regression.
2.  Run nominal and ordinal logistic and probit regression
3.  Use MARS and/or another flavor of non-linear regression&lt;/p&gt;

&lt;p&gt;I''m familiar with linear regression.  MARS is well enough described by Hastie and Tibshirani.  But I''m at a loss when it comes to ordinal logit/probit, especially with so many variables and a big data set.  &lt;/p&gt;

&lt;p&gt;The r package &lt;a href="http://cran.r-project.org/web/packages/glmnetcr/index.html"&gt;glmnetcr&lt;/a&gt; seems to be my best bet so far, but the documentation hardly suffices to get me where I need to be.&lt;/p&gt;

&lt;p&gt;Where can I go to learn more?&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (477, 1, '2014-06-18 22:15:43.82', 2, 64, 'Preference Matching Algorithm', '&lt;p&gt;There''s this side project I''m working on where I need to structure a solution to the following problem.&lt;/p&gt;

&lt;p&gt;I have two groups of people (clients). Group "A" intends to buy and group "B" intends to sell a determined product "X". &lt;/p&gt;

&lt;p&gt;The product has a series of attributes x_i and my objective is to facilitate the transaction between "A" e "B" by matching their preferences. The main idea is to point out to each member of "A" a corresponding in "B" whoâs product better suits his needs, and vice versa. &lt;/p&gt;

&lt;p&gt;Some complicating aspects of the problem:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;The list of attributes is not finite. The buyer might be interested in a very particular characteristic or some kind of design which is rare among the population and I canât predict. Canât previously list all the attributes;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Attributes might be continuous, binary or non-quantifiable (ex: price, functionality, design).&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Any suggestion on how to approach this problem and solve it in an automated way?&lt;br&gt;
The idea is to really think out of the box here so feel free to "go wild" on your suggestions. &lt;/p&gt;

&lt;p&gt;I would also appreciate some references to other similar problems if possible. &lt;/p&gt;
', 'algorithms', NULL, '''/li'':156C,173C ''/ol'':174C ''/p'':26C,50C,103C,111C,155C,172C,212C,226C ''algorithm'':3A,4B ''also'':216C ''among'':140C ''appreci'':217C ''approach'':181C ''aspect'':107C ''attribut'':58C,118C,154C,159C ''autom'':189C ''b'':42C,72C,92C ''better'':96C ''binari'':163C ''box'':201C ''br'':191C ''buy'':39C ''buyer'':123C ''canâ'':145C,148C ''characterist'':131C ''client'':34C ''complic'':106C ''continu'':162C ''correspond'':90C ''design'':136C,171C ''determin'':47C ''e'':71C ''ex'':168C ''facilit'':66C ''feel'':204C ''finit'':121C ''follow'':24C ''free'':205C ''function'':170C ''go'':207C ''group'':31C,35C,41C ''idea'':79C,193C ''intend'':37C,43C ''interest'':126C ''kind'':134C ''li'':113C,157C ''list'':116C,151C ''m'':12C ''main'':78C ''match'':2A,74C ''member'':86C ''might'':124C,160C ''need'':17C,99C ''non'':166C ''non-quantifi'':165C ''object'':63C ''ol'':112C ''p'':5C,27C,51C,104C,114C,158C,175C,213C ''particular'':130C ''peopl'':33C ''point'':82C ''popul'':142C ''possibl'':225C ''predict'':147C ''prefer'':1A,76C ''previous'':150C ''price'':169C ''problem'':25C,110C,183C,223C ''product'':48C,53C,95C ''project'':10C ''quantifi'':167C ''rare'':139C ''realli'':196C ''refer'':219C ''sell'':45C ''seri'':56C ''side'':9C ''similar'':222C ''solut'':21C ''solv'':185C ''structur'':19C ''suggest'':177C,211C ''suit'':97C ''think'':197C ''transact'':68C ''two'':30C ''versa'':102C ''vice'':101C ''way'':190C ''whoâ'':93C ''wild'':208C ''work'':13C ''would'':215C ''x'':49C,59C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (478, 2, '2014-06-19 03:20:00.847', 1, NULL, NULL, '&lt;p&gt;This can be a cross between machine learning and simple matching exercise.&lt;/p&gt;

&lt;p&gt;I think X_i tend to be rather defined and finite, while A_i can be vague and not finite. From a pure algorithm perspective I would search for instances where X_i = A_i and store the results into a container of sort. The more hits for certain X''es where X_i_n = A_i_k the more points X scores. X''es are then presented to A in the order of points from best match to lowest match.&lt;/p&gt;

&lt;p&gt;Onto the machine learning mechanism, as the algorithm serves a lot of As (by that mean thousands and thousands, even millions) patterns will start to develop and certain combination of A_i''s will be more prevalent, or in other words, worth more to other A_i''s for a certain category of A. Using these patterns, the weighting of points will be re-balanced for higher chance of hitting the correct offers.&lt;/p&gt;

&lt;p&gt;Kind of like how a search engine works.&lt;/p&gt;
', NULL, NULL, '''/p'':14C,97C,173C,183C ''algorithm'':39C,106C ''balanc'':164C ''best'':92C ''categori'':150C ''certain'':64C,126C,149C ''chanc'':167C ''combin'':127C ''contain'':57C ''correct'':171C ''cross'':6C ''defin'':24C ''develop'':124C ''engin'':181C ''es'':66C,80C ''even'':118C ''exercis'':13C ''finit'':26C,35C ''higher'':166C ''hit'':62C,169C ''instanc'':45C ''k'':73C ''kind'':175C ''learn'':9C,102C ''like'':177C ''lot'':109C ''lowest'':95C ''machin'':8C,101C ''match'':12C,93C,96C ''mean'':114C ''mechan'':103C ''million'':119C ''n'':70C ''offer'':172C ''onto'':99C ''order'':88C ''p'':1C,15C,98C,174C ''pattern'':120C,155C ''perspect'':40C ''point'':76C,90C,159C ''present'':83C ''preval'':135C ''pure'':38C ''rather'':23C ''re'':163C ''re-balanc'':162C ''result'':54C ''score'':78C ''search'':43C,180C ''serv'':107C ''simpl'':11C ''sort'':59C ''start'':122C ''store'':52C ''tend'':20C ''think'':17C ''thousand'':115C,117C ''use'':153C ''vagu'':32C ''weight'':157C ''word'':139C ''work'':182C ''worth'':140C ''would'':42C ''x'':18C,47C,65C,68C,77C,79C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (480, 2, '2014-06-19 10:35:37.19', 4, NULL, NULL, '&lt;p&gt;One fairly powerful R package for regression with an ordinal categorical response is VGAM, on the CRAN. The vignette contains some examples of ordinal regression, but admittedly I have never tried it on such a large dataset, so I cannot estimate how long it may take. You may find some additional material about VGAM on the author''s &lt;a href="https://www.stat.auckland.ac.nz/~yee/VGAM/" rel="nofollow"&gt;page&lt;/a&gt;. Alternatively you could take a look at Laura Thompson''s &lt;a href="https://www.google.com/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=3&amp;amp;cad=rja&amp;amp;uact=8&amp;amp;ved=0CDUQFjAC&amp;amp;url=http://home.comcast.net/~lthompson221/Splusdiscrete2.pdf&amp;amp;ei=8bmiU4HSFcjA7Abk4YHgDg&amp;amp;usg=AFQjCNHuBp2_nRpaPOFgdkcQWJGuSO9V6A&amp;amp;sig2=hAy4d3mu9WCJZulqxCzraw" rel="nofollow"&gt;companion&lt;/a&gt; to Agresti''s book "Categorical Data Analysis". Chapter 7 of Thompson''s book describes cumulative logit models, which are frequently used with ordinal responses.&lt;/p&gt;

&lt;p&gt;Hope this helps!&lt;/p&gt;
', NULL, NULL, '''/a'':68C,87C ''/p'':112C,117C ''/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=3&amp;amp;cad=rja&amp;amp;uact=8&amp;amp;ved=0cduqfjac&amp;amp;url=http://home.comcast.net/~lthompson221/splusdiscrete2.pdf&amp;amp;ei=8bmiu4hsfcja7abk4yhgdg&amp;amp;usg=afqjcnhubp2_nrpapofgdkcqwjguso9v6a&amp;amp;sig2=hay4d3mu9wcjzulqxczraw'':83C ''/~yee/vgam/'':64C ''7'':96C ''addit'':52C ''admit'':28C ''agresti'':89C ''altern'':69C ''analysi'':94C ''author'':58C ''book'':91C,100C ''cannot'':41C ''categor'':12C,92C ''chapter'':95C ''companion'':86C ''contain'':21C ''could'':71C ''cran'':18C ''cumul'':102C ''data'':93C ''dataset'':38C ''describ'':101C ''estim'':42C ''exampl'':23C ''fair'':3C ''find'':50C ''frequent'':107C ''help'':116C ''hope'':114C ''href'':61C,80C ''larg'':37C ''laura'':76C ''logit'':103C ''long'':44C ''look'':74C ''materi'':53C ''may'':46C,49C ''model'':104C ''never'':31C ''nofollow'':66C,85C ''one'':2C ''ordin'':11C,25C,110C ''p'':1C,113C ''packag'':6C ''page'':67C ''power'':4C ''r'':5C ''regress'':8C,26C ''rel'':65C,84C ''respons'':13C,111C ''take'':47C,72C ''thompson'':77C,98C ''tri'':32C ''use'':108C ''vgam'':15C,55C ''vignett'':20C ''www.google.com'':82C ''www.google.com/url?sa=t&amp;amp;rct=j&amp;amp;q=&amp;amp;esrc=s&amp;amp;source=web&amp;amp;cd=3&amp;amp;cad=rja&amp;amp;uact=8&amp;amp;ved=0cduqfjac&amp;amp;url=http://home.comcast.net/~lthompson221/splusdiscrete2.pdf&amp;amp;ei=8bmiu4hsfcja7abk4yhgdg&amp;amp;usg=afqjcnhubp2_nrpapofgdkcqwjguso9v6a&amp;amp;sig2=hay4d3mu9wcjzulqxczraw'':81C ''www.stat.auckland.ac.nz'':63C ''www.stat.auckland.ac.nz/~yee/vgam/'':62C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (481, 2, '2014-06-19 12:22:42.017', 1, NULL, NULL, '&lt;p&gt;In graph theory a clique indicates a fully connected set of nodes: &lt;a href="http://books.google.com/books?id=E3-OSVSPbU0C&amp;amp;pg=PA40&amp;amp;lpg=PA40&amp;amp;dq=%22graph%20theory%22,%20%22p-clique%22&amp;amp;source=bl&amp;amp;ots=smbhcK-9AC&amp;amp;sig=X0v_EbqSqB4WBbudgPbqo_j1pvk&amp;amp;hl=en&amp;amp;sa=X&amp;amp;ei=VNWiU8KaA9WxsQSQhYGIAg&amp;amp;ved=0CDEQ6AEwBA#v=onepage&amp;amp;q=%22graph%20theory%22,%20%22p-clique%22&amp;amp;f=false" rel="nofollow"&gt;as noted here&lt;/a&gt;, a p-clique simply indicates a clique comoprised of p nodes. A k-clique is an undirected graph and a number k, and the output is a clique of size k if one exists.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikipedia.org/wiki/Clique_problem" rel="nofollow"&gt;Clique Problem&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (482, 2, '2014-06-19 13:23:53.387', 7, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Short Answer&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Yes, logistic regression is a regression algorithm and it does predict a continuous outcome: the probability of an event.  That we use it as a binary classifier is due to the interpretation of the outcome.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Detail&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Logistic regression is a type of generalize linear regression model.&lt;/p&gt;

&lt;p&gt;In an ordinary linear regression model, a continuous outcome, &lt;code&gt;y&lt;/code&gt;, is modeled as the sum of the product of predictors and their effect:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;y = b_0 + b_1 * x_1 + b_2 * x_2 + ... b_n * x_n + e
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where &lt;code&gt;e&lt;/code&gt; is the error.&lt;/p&gt;

&lt;p&gt;Generalized linear models do not model &lt;code&gt;y&lt;/code&gt; directly.  Instead, they use transformations to expand the domain of &lt;code&gt;y&lt;/code&gt; to all real numbers.  This transformation is called the link function.  For logistic regression the link function is the logit function (usually, see note below).&lt;/p&gt;

&lt;p&gt;The logit function is defined as &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ln(y/(1 + y))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Thus the form of logistic regression is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ln(y/(1 + y)) = b_0 + b_1 * x_1 + b_2 * x_2 + ... b_n * x_n + e
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where &lt;code&gt;y&lt;/code&gt; is the probability of an event.&lt;/p&gt;

&lt;p&gt;The fact that we use it as a binary classifier is due to the interpretation of the outcome.&lt;/p&gt;

&lt;p&gt;Note: probit is another link function used for logistic regression but logit is the most widely used.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (483, 2, '2014-06-19 14:15:07.433', 2, NULL, NULL, '&lt;p&gt;You could try Neural Network. You can find 2 great explanations on how to apply NN on time series &lt;a href="http://stats.stackexchange.com/questions/10162/how-to-apply-neural-network-to-time-series-forecasting"&gt;here&lt;/a&gt; and &lt;a href="http://stackoverflow.com/questions/18670558/prediction-using-recurrent-neural-network-on-time-series-dataset"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Note that it is best practice to :&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Deseasonalize/detrend the input data (so that the NN will not learn the seasonality).&lt;/li&gt;
&lt;li&gt;Rescale/Normalize the input data.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Because what you are looking for is a regression problem, the activation functions should be &lt;code&gt;linear&lt;/code&gt; and not &lt;code&gt;sigmoid&lt;/code&gt; or &lt;code&gt;tanh&lt;/code&gt; and you aim to minimize the &lt;code&gt;sum-of-squares error&lt;/code&gt; (as opposition to the maximization of the &lt;code&gt;negative log-likelihood&lt;/code&gt; in a classification problem).&lt;/p&gt;
', NULL, NULL, '''/a'':27C,35C ''/code'':87C,92C,96C,109C,122C ''/li'':61C,67C ''/p'':36C,45C,127C ''/questions/10162/how-to-apply-neural-network-to-time-series-forecasting'':25C ''/questions/18670558/prediction-using-recurrent-neural-network-on-time-series-dataset'':33C ''/ul'':68C ''2'':10C ''activ'':81C ''aim'':99C ''appli'':16C ''best'':42C ''classif'':125C ''code'':85C,90C,94C,103C,117C ''could'':3C ''data'':51C,66C ''deseasonalize/detrend'':48C ''error'':108C ''explan'':12C ''find'':9C ''function'':82C ''great'':11C ''href'':22C,30C ''input'':50C,65C ''learn'':58C ''li'':47C,62C ''likelihood'':121C ''linear'':86C ''log'':120C ''log-likelihood'':119C ''look'':74C ''maxim'':114C ''minim'':101C ''negat'':118C ''network'':6C ''neural'':5C ''nn'':17C,55C ''note'':38C ''opposit'':111C ''p'':1C,37C,69C ''practic'':43C ''problem'':79C,126C ''regress'':78C ''rescale/normalize'':63C ''season'':60C ''seri'':20C ''sigmoid'':91C ''squar'':107C ''stackoverflow.com'':32C ''stackoverflow.com/questions/18670558/prediction-using-recurrent-neural-network-on-time-series-dataset'':31C ''stats.stackexchange.com'':24C ''stats.stackexchange.com/questions/10162/how-to-apply-neural-network-to-time-series-forecasting'':23C ''sum'':105C ''sum-of-squar'':104C ''tanh'':95C ''time'':19C ''tri'':4C ''ul'':46C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (727, 2, '2014-07-11 21:35:22.677', 3, NULL, NULL, '&lt;p&gt;Your problem is that the resets aren''t part of your linear model. You either have to cut your data into different fragments at the resets, so that no reset occurs within each fragment, and you can fit a linear model to each fragment. Or you can build a more complicated model that allows for resets. In this case, either the time of occurrence of the resets has to be put into the model manually, or the time of resets has to be a free parameter in the model that is determined by fitting the model to the data.&lt;/p&gt;
', NULL, NULL, '''/p'':101C ''allow'':55C ''aren'':8C ''build'':49C ''case'':60C ''complic'':52C ''cut'':19C ''data'':21C,100C ''determin'':93C ''differ'':23C ''either'':16C,61C ''fit'':39C,95C ''fragment'':24C,35C,45C ''free'':86C ''linear'':13C,41C ''manual'':76C ''model'':14C,42C,53C,75C,90C,97C ''occur'':32C ''occurr'':65C ''p'':1C ''paramet'':87C ''part'':10C ''problem'':3C ''put'':72C ''reset'':7C,27C,31C,57C,68C,81C ''time'':63C,79C ''within'':33C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (484, 2, '2014-06-19 14:52:59.877', 13, NULL, NULL, '&lt;p&gt;Logistic regression is regression, first and foremost. It becomes a classifier by adding a decision rule. I will give an example that goes backwards. That is, instead of taking data and fitting a model, I''m going to start with the model in order to show how this is truly a regression problem.&lt;/p&gt;

&lt;p&gt;In logistic regression, we are modeling the log odds, or logit, that an event occurs, which is a continuous quantity. If the probability that event A occurs is P(A), the odds are:&lt;/p&gt;

&lt;p&gt;P(A) / (1 - P(A)) &lt;/p&gt;

&lt;p&gt;The log odds, then, are:&lt;/p&gt;

&lt;p&gt;log { (P(A) / (1 - P(A))) }&lt;/p&gt;

&lt;p&gt;As in linear regression, we model this with a linear combination of coefficients and predictors:&lt;/p&gt;

&lt;p&gt;logit = b0 + b1 * x1 + b2 * x2 ...&lt;/p&gt;

&lt;p&gt;Imagine we are given a model of whether a person has gray hair. Our model uses age as the only predictor. Here, our event A = a person has gray hair:&lt;/p&gt;

&lt;p&gt;log odds of gray hair = -10 + 0.25 * age&lt;/p&gt;

&lt;p&gt;...Regression! Here is some Python code and a plot:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

x = np.linspace(0, 100, 100)

def log_odds(x):
    return -10 + .25 * x

plt.plot(x, log_odds(x))
plt.xlabel("age")
plt.ylabel("log odds of gray hair")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/xR0OT.png" alt="plot of the log odds for our toy example"&gt;&lt;/p&gt;

&lt;p&gt;Now, let''s make it a classifier. First, we need to transform the log odds to get out our probability P(A). We can use the sigmoid function:&lt;/p&gt;

&lt;p&gt;P(A) = 1 / (1 + exp(-log odds))&lt;/p&gt;

&lt;p&gt;Here''s the code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;plt.plot(x, 1 / (1 + np.exp(-log_odds(x))))
plt.xlabel("age")
plt.ylabel("probability of gray hair")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/hSpCa.png" alt="plot of the probability of gray hair for our toy example"&gt;&lt;/p&gt;

&lt;p&gt;The last thing we need to make this a classifier is to add a decision rule. One very common rule is to classify a success whenever P(A) &gt; 0.5. We will adopt that rule, which implies that our classifier will predict gray hair whenever a person is older than 40 and will predict non-gray hair whenever a person is under 40.&lt;/p&gt;

&lt;p&gt;Logistic regression works great as a classifier in more realistic examples too, but before it can be a classifier, it must be a regression technique!&lt;/p&gt;
', NULL, NULL, '''-10'':176C,217C ''/code'':233C,314C ''/hspca.png'':321C ''/p'':55C,90C,97C,104C,112C,129C,137C,169C,179C,190C,251C,281C,290C,296C,334C,399C,426C ''/pre'':234C,315C ''/xr0ot.png'':240C ''0'':209C ''0.25'':177C ''0.5'':364C ''1'':94C,109C,285C,286C,301C,302C ''100'':210C,211C ''25'':218C ''40'':385C,398C ''ad'':14C ''add'':348C ''adopt'':367C ''age'':155C,178C,226C,308C ''alt'':241C,322C ''b0'':132C ''b1'':133C ''b2'':135C ''backward'':25C ''becom'':10C ''classifi'':12C,259C,345C,358C,374C,407C,419C ''code'':186C,192C,295C,298C ''coeffici'':126C ''combin'':124C ''common'':354C ''continu'':75C ''data'':31C ''decis'':16C,350C ''def'':212C ''event'':70C,81C,162C ''exampl'':22C,250C,333C,411C ''exp'':287C ''first'':6C,260C ''fit'':33C ''foremost'':8C ''function'':280C ''get'':269C ''give'':20C ''given'':142C ''go'':38C ''goe'':24C ''gray'':150C,167C,174C,231C,312C,328C,377C,391C ''great'':404C ''hair'':151C,168C,175C,232C,313C,329C,378C,392C ''i.stack.imgur.com'':239C,320C ''i.stack.imgur.com/hspca.png'':319C ''i.stack.imgur.com/xr0ot.png'':238C ''imagin'':139C ''img'':236C,317C ''impli'':371C ''import'':195C,199C,203C ''inlin'':194C ''instead'':28C ''last'':337C ''let'':254C ''linear'':116C,123C ''log'':64C,100C,106C,171C,213C,222C,228C,245C,266C,288C,304C ''logist'':2C,58C,401C ''logit'':67C,131C ''m'':37C ''make'':256C,342C ''matplotlib'':193C ''matplotlib.pyplot'':196C ''model'':35C,43C,62C,119C,144C,153C ''must'':421C ''need'':262C,340C ''non'':390C ''non-gray'':389C ''np'':202C ''np.exp'':303C ''np.linspace'':208C ''numpi'':200C ''occur'':71C,83C ''odd'':65C,88C,101C,172C,214C,223C,229C,246C,267C,289C,305C ''older'':383C ''one'':352C ''order'':45C ''p'':1C,56C,85C,91C,92C,95C,98C,105C,107C,110C,113C,130C,138C,170C,180C,235C,252C,273C,282C,283C,291C,316C,335C,362C,400C ''person'':148C,165C,381C,395C ''plot'':189C,242C,323C ''plt'':198C ''plt.plot'':220C,299C ''plt.xlabel'':225C,307C ''plt.ylabel'':227C,309C ''pre'':191C,297C ''predict'':376C,388C ''predictor'':128C,159C ''probabl'':79C,272C,310C,326C ''problem'':54C ''python'':185C ''quantiti'':76C ''realist'':410C ''regress'':3C,5C,53C,59C,117C,181C,402C,424C ''return'':216C ''rule'':17C,351C,355C,369C ''seaborn'':204C ''show'':47C ''sigmoid'':279C ''sns'':206C ''src'':237C,318C ''start'':40C ''success'':360C ''take'':30C ''techniqu'':425C ''thing'':338C ''toy'':249C,332C ''transform'':264C ''truli'':51C ''use'':154C,277C ''whenev'':361C,379C,393C ''whether'':146C ''work'':403C ''x'':207C,215C,219C,221C,224C,300C,306C ''x1'':134C ''x2'':136C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (485, 2, '2014-06-19 16:07:29.907', 3, NULL, NULL, '&lt;p&gt;My answer would be no. I consider Data mining to be one of the miscellaneous fields in Data science. Data Mining is mostly considered on yielding questions rather than answering them. It is often termed as "detecting something new", when compared to Data science, where the data scientist try to solve complex problems to be able to reach their end results. However both terms have many commonalities between them.
For example..if u have an agricultural land where u aim to find the affected plants..Here spatial data mining plays a key role in doing this job.There are good chances that you may end  up with not only finding out the affected plants in the land but also the extent to which they are affected.......this is something that is not possible with data science.&lt;/p&gt;
', NULL, NULL, '''/p'':137C ''abl'':57C ''affect'':85C,113C,126C ''agricultur'':77C ''aim'':81C ''also'':119C ''answer'':3C,31C ''chanc'':101C ''common'':68C ''compar'':42C ''complex'':53C ''consid'':8C,25C ''data'':9C,19C,21C,44C,48C,89C,135C ''detect'':38C ''end'':61C,105C ''exampl'':72C ''extent'':121C ''field'':17C ''find'':83C,110C ''good'':100C ''howev'':63C ''job.there'':98C ''key'':93C ''land'':78C,117C ''mani'':67C ''may'':104C ''mine'':10C,22C,90C ''miscellan'':16C ''most'':24C ''new'':40C ''often'':35C ''one'':13C ''p'':1C ''plant'':86C,114C ''play'':91C ''possibl'':133C ''problem'':54C ''question'':28C ''rather'':29C ''reach'':59C ''result'':62C ''role'':94C ''scienc'':20C,45C,136C ''scientist'':49C ''solv'':52C ''someth'':39C,129C ''spatial'':88C ''term'':36C,65C ''tri'':50C ''u'':74C,80C ''would'':4C ''yield'':27C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (487, 2, '2014-06-19 18:01:36.12', 5, NULL, NULL, '&lt;p&gt;A good list of publicly available social network datasets can be found on the Stanford Network Analysis Project website: &lt;/p&gt;

&lt;p&gt;&lt;a href="https://snap.stanford.edu/data/"&gt;SNAP datasets&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The site contains internet social network data (Facebook, Twitter, Google Plus), Citation networks for academic journals, co-purchasing networks from Amazon and several others kinds of networks. They have directed, undirected, and bipartite graphs and all datasets are snapshots that can be downloaded in compressed form.&lt;/p&gt;
', NULL, NULL, '''/a'':30C ''/data/'':27C ''/p'':21C,31C,80C ''academ'':47C ''amazon'':54C ''analysi'':18C ''avail'':7C ''bipartit'':66C ''citat'':44C ''co'':50C ''co-purchas'':49C ''compress'':78C ''contain'':35C ''data'':39C ''dataset'':10C,29C,70C ''direct'':63C ''download'':76C ''facebook'':40C ''form'':79C ''found'':13C ''good'':3C ''googl'':42C ''graph'':67C ''href'':24C ''internet'':36C ''journal'':48C ''kind'':58C ''list'':4C ''network'':9C,17C,38C,45C,52C,60C ''other'':57C ''p'':1C,22C,32C ''plus'':43C ''project'':19C ''public'':6C ''purchas'':51C ''sever'':56C ''site'':34C ''snap'':28C ''snap.stanford.edu'':26C ''snap.stanford.edu/data/'':25C ''snapshot'':72C ''social'':8C,37C ''stanford'':16C ''twitter'':41C ''undirect'':64C ''websit'':20C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (491, 2, '2014-06-19 18:24:32.023', 6, NULL, NULL, '&lt;p&gt;As suggested, &lt;strong&gt;going wild&lt;/strong&gt;. First of all, correct me if Iâm wrong:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Just a few features exist for each unique product;&lt;/li&gt;
&lt;li&gt;There is no ultimate features list, and clients are able to add new features to their products.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If so, constructing full product-feature table could be computational expensive. And final data table would be extremely sparse.&lt;/p&gt;

&lt;p&gt;The first step is narrowing customers (products) list for matching. Letâs build a bipartite graph, where sellers would be type-1 nodes, and buyers would be type-2 nodes. Create an edge between any seller and buyer every time they reference a similar product feature, as in the following sketch:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/q1JTN.png" alt="graph"&gt;&lt;/p&gt;

&lt;p&gt;Using the above graph, for every unique sellerâs product you can select only buyers who are interested in features that match the product (itâs possible to filter &lt;strong&gt;at least one&lt;/strong&gt; common feature, match the full set of features, or set a threshold level). But certainly, thatâs not enough. The next step is to compare feature values, as described by the seller and buyer. There are a lot of variants (e.g., k-Nearest-Neighbors). But why not try to solve this question using the existing graph? Letâs add weights to the edges:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;for continuous features (e.g., price): &lt;/p&gt;

&lt;p&gt;&lt;img src="https://chart.googleapis.com/chart?cht=tx&amp;amp;chl=weight%20%3D%20%5Cfrac%7B%7Cprice_%7Bsell%7D%20-%20price_%7Bbuy%7D%7C%7D%7B%5Cmax%28price%29%20-%20%5Cmin%28price%29%7D" alt="price_weight"&gt;&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;for binary and non-quantifiable features - just logical biconditional:&lt;/p&gt;

&lt;p&gt;&lt;img src="https://chart.googleapis.com/chart?cht=tx&amp;amp;chl=weight%20%3D%20feature_%7Bsell%7D%20%5Cleftrightarrow%20feature_%7Bbuy%7D" alt="feature_weight"&gt;&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The main idea here is to &lt;strong&gt;scale&lt;/strong&gt; every feature to the interval &lt;code&gt;[0, 1]&lt;/code&gt;. Additionally, we can use feature coefficients to determine most important features. E.g., assuming price is twice as important as availability of some rare function: &lt;/p&gt;

&lt;p&gt;&lt;img src="https://chart.googleapis.com/chart?cht=tx&amp;amp;chl=adj.price.weight%20%3D%202%20%5Ctimes%20price.weight" alt="adj_w_1"&gt;&lt;/p&gt;

&lt;p&gt;&lt;img src="https://chart.googleapis.com/chart?cht=tx&amp;amp;chl=adj.feature.weight%20%3D%201%20%5Ctimes%20feature.weight" alt="adj_w_2"&gt;&lt;/p&gt;

&lt;p&gt;One of the final steps is simplifying the graph structure and reducing many edges to one edge with weight equal to the sum of the previously calculated weights of each feature. With such a reduced structure every pair of customers/products could have only one edge (no parallel edges). So, to find the best deal for exact seller you just need to select connected buyers with max weighted edges.&lt;/p&gt;

&lt;p&gt;Future challenge: introduce a cheap method for weighting edges on first step :)&lt;/p&gt;
', NULL, NULL, '''-1'':94C ''-2'':101C ''/chart?cht=tx&amp;amp;chl=adj.feature.weight%20%3d%201%20%5ctimes%20feature.weight'':339C ''/chart?cht=tx&amp;amp;chl=adj.price.weight%20%3d%202%20%5ctimes%20price.weight'':328C ''/chart?cht=tx&amp;amp;chl=weight%20%3d%20%5cfrac%7b%7cprice_%7bsell%7d%20-%20price_%7bbuy%7d%7c%7d%7b%5cmax%28price%29%20-%20%5cmin%28price%29%7d'':248C ''/chart?cht=tx&amp;amp;chl=weight%20%3d%20feature_%7bsell%7d%20%5cleftrightarrow%20feature_%7bbuy%7d'':272C ''/code'':297C ''/li'':29C,48C,253C,277C ''/p'':17C,71C,124C,133C,233C,242C,252C,266C,276C,322C,333C,344C,414C,428C ''/q1jtn.png'':130C ''/strong'':7C,168C,288C ''/ul'':49C,278C ''0'':295C ''1'':296C,332C ''2'':343C ''abl'':40C ''add'':42C,228C ''addit'':298C ''adj'':330C,341C ''alt'':131C,249C,273C,329C,340C ''assum'':310C ''avail'':317C ''best'':398C ''bicondit'':265C ''binari'':257C ''bipartit'':87C ''build'':85C ''buyer'':97C,110C,149C,202C,409C ''calcul'':372C ''certain'':183C ''challeng'':417C ''chart.googleapis.com'':247C,271C,327C,338C ''chart.googleapis.com/chart?cht=tx&amp;amp;chl=adj.feature.weight%20%3d%201%20%5ctimes%20feature.weight'':337C ''chart.googleapis.com/chart?cht=tx&amp;amp;chl=adj.price.weight%20%3d%202%20%5ctimes%20price.weight'':326C ''chart.googleapis.com/chart?cht=tx&amp;amp;chl=weight%20%3d%20%5cfrac%7b%7cprice_%7bsell%7d%20-%20price_%7bbuy%7d%7c%7d%7b%5cmax%28price%29%20-%20%5cmin%28price%29%7d'':246C ''chart.googleapis.com/chart?cht=tx&amp;amp;chl=weight%20%3d%20feature_%7bsell%7d%20%5cleftrightarrow%20feature_%7bbuy%7d'':270C ''cheap'':420C ''client'':38C ''code'':294C ''coeffici'':303C ''common'':169C ''compar'':193C ''comput'':61C ''connect'':408C ''construct'':53C ''continu'':238C ''correct'':11C ''could'':59C,386C ''creat'':103C ''custom'':78C ''customers/products'':385C ''data'':65C ''deal'':399C ''describ'':197C ''determin'':305C ''e.g'':209C,240C,309C ''edg'':105C,232C,359C,362C,390C,393C,413C,424C ''enough'':187C ''equal'':365C ''everi'':111C,140C,289C,382C ''exact'':401C ''exist'':24C,224C ''expens'':62C ''extrem'':69C ''featur'':23C,35C,44C,57C,118C,154C,170C,176C,194C,239C,262C,274C,290C,302C,308C,376C ''filter'':163C ''final'':64C,349C ''find'':396C ''first'':8C,74C,426C ''follow'':122C ''full'':54C,173C ''function'':321C ''futur'':416C ''go'':5C ''graph'':88C,132C,138C,225C,354C ''i.stack.imgur.com'':129C ''i.stack.imgur.com/q1jtn.png'':128C ''idea'':282C ''img'':126C,244C,268C,324C,335C ''import'':307C,315C ''interest'':152C ''interv'':293C ''introduc'':418C ''itâ'':159C ''iâ'':14C ''k'':211C ''k-nearest-neighbor'':210C ''least'':166C ''letâ'':83C,226C ''level'':181C ''li'':19C,30C,235C,254C ''list'':36C,80C ''logic'':264C ''lot'':206C ''m'':15C ''main'':281C ''mani'':358C ''match'':82C,156C,171C ''max'':411C ''method'':421C ''narrow'':77C ''nearest'':212C ''need'':405C ''neighbor'':213C ''new'':43C ''next'':189C ''node'':95C,102C ''non'':260C ''non-quantifi'':259C ''one'':167C,346C,361C,389C ''p'':1C,50C,72C,125C,134C,236C,243C,255C,267C,279C,323C,334C,345C,415C ''pair'':383C ''parallel'':392C ''possibl'':161C ''previous'':371C ''price'':241C,250C,311C ''product'':28C,47C,56C,79C,117C,144C,158C ''product-featur'':55C ''quantifi'':261C ''question'':221C ''rare'':320C ''reduc'':357C,380C ''refer'':114C ''scale'':287C ''select'':147C,407C ''seller'':90C,108C,200C,402C ''sellerâ'':142C ''set'':174C,178C ''similar'':116C ''simplifi'':352C ''sketch'':123C ''solv'':219C ''spars'':70C ''src'':127C,245C,269C,325C,336C ''step'':75C,190C,350C,427C ''strong'':4C,164C,286C ''structur'':355C,381C ''suggest'':3C ''sum'':368C ''tabl'':58C,66C ''thatâ'':184C ''threshold'':180C ''time'':112C ''tri'':217C ''twice'':313C ''type'':93C,100C ''ul'':18C,234C ''ultim'':34C ''uniqu'':27C,141C ''use'':135C,222C,301C ''valu'':195C ''variant'':208C ''w'':331C,342C ''weight'':229C,251C,275C,364C,373C,412C,423C ''wild'':6C ''would'':67C,91C,98C ''wrong'':16C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (492, 1, '2014-06-19 19:29:57.797', 11, 1755, 'Word2Vec for Named Entity Recognition', '&lt;p&gt;I''m looking to use google''s word2vec implementation to build a named entity recognition system.  I''ve heard that recursive neural nets with back propagation through structure are well suited for named entity recognition tasks, but I''ve been unable to find a decent implementation or a decent tutorial for that type of model. Because I''m working with an atypical corpus, standard NER tools in NLTK and similar have performed very poorly, and it looks like I''ll have to train my own system.   &lt;/p&gt;

&lt;p&gt;In short, what resources are available for this kind of problem?  Is there a standard recursive neural net implementation available?&lt;/p&gt;
', 'machine-learning  python  nlp  neuralnetwork  recommendation', NULL, '''/p'':100C,122C ''atyp'':75C ''avail'':107C,121C ''back'':38C ''build'':24C ''corpus'':76C ''decent'':58C,62C ''entiti'':4A,27C,47C ''find'':56C ''googl'':19C ''heard'':32C ''implement'':22C,59C,120C ''kind'':110C ''learn'':8B ''like'':91C ''ll'':93C ''look'':16C,90C ''m'':15C,71C ''machin'':7B ''machine-learn'':6B ''model'':68C ''name'':3A,26C,46C ''ner'':78C ''net'':36C,119C ''neural'':35C,118C ''neuralnetwork'':11B ''nlp'':10B ''nltk'':81C ''p'':13C,101C ''perform'':85C ''poor'':87C ''problem'':112C ''propag'':39C ''python'':9B ''recognit'':5A,28C,48C ''recommend'':12B ''recurs'':34C,117C ''resourc'':105C ''short'':103C ''similar'':83C ''standard'':77C,116C ''structur'':41C ''suit'':44C ''system'':29C,99C ''task'':49C ''tool'':79C ''train'':96C ''tutori'':63C ''type'':66C ''unabl'':54C ''use'':18C ''ve'':31C,52C ''well'':43C ''word2vec'':1A,21C ''work'':72C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (493, 2, '2014-06-19 19:33:12.067', 1, NULL, NULL, '&lt;p&gt;Well, firstly, I would not even use things like Machine learning without having in depth knowledge. Simplistic things I would do if I had this time series is:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Write sql queries to understand which of the times you have the busiest, average and low foot traffic.&lt;/li&gt;
&lt;li&gt;Then try to visualize the whole time series, and you could use basic pattern matching algorithms to pick up patterns.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;This two things will help you understand what your data set is telling you. Then, with that in hand, you will probably be in a better state to use machine learning algorithms.&lt;/p&gt;

&lt;p&gt;Also, I''m currently working in building something on time series, and using time series analysis will help you much more than machine learning. For example, there are pattern recognition algorithms that you can use that uses every day data to show patterns, and ones which use up to as much as 3 to 6 months of data to catch a pattern.&lt;/p&gt;
', NULL, NULL, '''/li'':51C,73C ''/ol'':74C ''/p'':30C,108C,172C ''3'':162C ''6'':164C ''algorithm'':68C,107C,140C ''also'':110C ''analysi'':125C ''averag'':46C ''basic'':65C ''better'':101C ''build'':116C ''busiest'':45C ''catch'':169C ''could'':63C ''current'':113C ''data'':85C,149C,167C ''day'':148C ''depth'':16C ''even'':7C ''everi'':147C ''exampl'':135C ''first'':3C ''foot'':49C ''hand'':94C ''help'':80C,127C ''knowledg'':17C ''learn'':12C,106C,133C ''li'':32C,52C ''like'':10C ''low'':48C ''m'':112C ''machin'':11C,105C,132C ''match'':67C ''month'':165C ''much'':129C,160C ''ol'':31C ''one'':154C ''p'':1C,75C,109C ''pattern'':66C,72C,138C,152C,171C ''pick'':70C ''probabl'':97C ''queri'':35C ''recognit'':139C ''seri'':28C,60C,120C,124C ''set'':86C ''show'':151C ''simplist'':18C ''someth'':117C ''sql'':34C ''state'':102C ''tell'':88C ''thing'':9C,19C,78C ''time'':27C,41C,59C,119C,123C ''traffic'':50C ''tri'':54C ''two'':77C ''understand'':37C,82C ''use'':8C,64C,104C,122C,144C,146C,156C ''visual'':56C ''well'':2C ''whole'':58C ''without'':13C ''work'':114C ''would'':5C,21C ''write'':33C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (749, 1, '2014-07-16 09:24:51.78', 7, 372, 'Meaning of latent features?', '&lt;p&gt;I am learning about matrix factorization for recommender systems and I am seeing the term &lt;code&gt;latent features&lt;/code&gt; occurring too frequently but I am unable to understand what it means. I know what a feature is but I don''t understand the idea of latent features. Could please explain it? Or at least point me to a paper/place where I can read about it?&lt;/p&gt;
', 'machine-learning  data-mining  recommendation', NULL, '''/code'':31C ''/p'':78C ''code'':28C ''could'':60C ''data'':9B ''data-min'':8B ''explain'':62C ''factor'':18C ''featur'':4A,30C,48C,59C ''frequent'':34C ''idea'':56C ''know'':45C ''latent'':3A,29C,58C ''learn'':7B,15C ''least'':66C ''machin'':6B ''machine-learn'':5B ''matrix'':17C ''mean'':1A,43C ''mine'':10B ''occur'':32C ''p'':12C ''paper/place'':71C ''pleas'':61C ''point'':67C ''read'':75C ''recommend'':11B,20C ''see'':25C ''system'':21C ''term'':27C ''unabl'':38C ''understand'':40C,54C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (495, 2, '2014-06-19 22:02:44.05', 6, NULL, NULL, '&lt;p&gt;I would add a third column called &lt;code&gt;month&lt;/code&gt; and then concatenate each list. So if you have a top 100 list for 5 months you will create one big table with 500 entries:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;User-id | Threat_score | month
aaa       45             1
bbb       32             1
ccc       20             1
...       ...            ...
bbb       64             2
ccc       29             2
...       ...            ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Then, to answer your first question, you could simply count the occurrences of each user-id. For example, if user &lt;code&gt;bbb&lt;/code&gt; is in your concatenated table five times, then you know that person made your list all five months.&lt;/p&gt;

&lt;p&gt;To answer you second question, you could do a &lt;code&gt;group by&lt;/code&gt; operation to compute some aggregate function of the users. A &lt;code&gt;group by&lt;/code&gt; operation with an average function is a little crude and sensitive to outliers, but it would probably get you close to what you are looking for.&lt;/p&gt;

&lt;p&gt;One possibility for the third question is to compute the difference in threat score between month &lt;code&gt;n-1&lt;/code&gt; and month &lt;code&gt;n&lt;/code&gt;. That is, for each month (not including the first month) you subtract the user''s previous threat score from the current threat score. You can make this a new column so your table would now look like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;User-id | Threat_score | month | difference
aaa       45             1       null
bbb       32             1       null
ccc       20             1       null
...       ...            ...     ...
bbb       64             2       32
ccc       29             2       9
...       ...            ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;With this table, you could again do a &lt;code&gt;group by&lt;/code&gt; operation to find people who consistently have a higher threat score than the previous month or you could simply find people with a large difference between the current month and the previous month.&lt;/p&gt;

&lt;p&gt;As you suggest, visualizing this data is a really good idea. If you care about these threat scores over time (which I think you do), I strongly recommend a simple line chart, with month on the x-axis and threat score on the y-axis. It''s not fancy, but it''s extremely easy to interpret and should give you useful information about the trends.&lt;/p&gt;

&lt;p&gt;Most of this stuff (not the visualization) can be done in SQL and all of it can be done in R or Python (and many other languages). Good luck!&lt;/p&gt;
', NULL, NULL, '''-1'':181C ''/code'':11C,61C,87C,120C,134C,182C,187C,255C,269C ''/p'':37C,106C,161C,225C,303C,372C,403C ''/pre'':62C,256C ''1'':48C,51C,54C,237C,241C,245C ''100'':23C ''2'':57C,60C,249C,253C ''20'':53C,244C ''29'':59C,252C ''32'':50C,240C,250C ''45'':47C,236C ''5'':26C ''500'':35C ''64'':56C,248C ''9'':254C ''aaa'':46C,235C ''add'':4C ''aggreg'':125C ''answer'':66C,109C ''averag'':138C ''axi'':343C,351C ''bbb'':49C,55C,86C,239C,247C ''big'':32C ''call'':8C ''care'':318C ''ccc'':52C,58C,243C,251C ''chart'':336C ''close'':154C ''code'':9C,39C,85C,117C,131C,179C,185C,227C,266C ''column'':7C,217C ''comput'':123C,171C ''concaten'':14C,91C ''consist'':275C ''could'':71C,114C,262C,287C ''count'':73C ''creat'':30C ''crude'':143C ''current'':208C,297C ''data'':310C ''differ'':173C,234C,294C ''done'':383C,392C ''easi'':360C ''entri'':36C ''exampl'':82C ''extrem'':359C ''fanci'':355C ''find'':272C,289C ''first'':68C,196C ''five'':93C,104C ''function'':126C,139C ''get'':152C ''give'':365C ''good'':314C,401C ''group'':118C,132C,267C ''higher'':278C ''id'':42C,80C,230C ''idea'':315C ''includ'':194C ''inform'':368C ''interpret'':362C ''know'':97C ''languag'':400C ''larg'':293C ''like'':224C ''line'':335C ''list'':16C,24C,102C ''littl'':142C ''look'':159C,223C ''luck'':402C ''made'':100C ''make'':213C ''mani'':398C ''month'':10C,27C,45C,105C,178C,184C,192C,197C,233C,284C,298C,302C,338C ''n'':180C,186C ''new'':216C ''null'':238C,242C,246C ''occurr'':75C ''one'':31C,163C ''oper'':121C,135C,270C ''outlier'':147C ''p'':1C,63C,107C,162C,257C,304C,373C ''peopl'':273C,290C ''person'':99C ''possibl'':164C ''pre'':38C,226C ''previous'':203C,283C,301C ''probabl'':151C ''python'':396C ''question'':69C,112C,168C ''r'':394C ''realli'':313C ''recommend'':332C ''score'':44C,176C,205C,210C,232C,280C,322C,346C ''second'':111C ''sensit'':145C ''simpl'':334C ''simpli'':72C,288C ''sql'':385C ''strong'':331C ''stuff'':377C ''subtract'':199C ''suggest'':307C ''tabl'':33C,92C,220C,260C ''think'':327C ''third'':6C,167C ''threat'':43C,175C,204C,209C,231C,279C,321C,345C ''time'':94C,324C ''top'':22C ''trend'':371C ''use'':367C ''user'':41C,79C,84C,129C,201C,229C ''user-id'':40C,78C,228C ''visual'':308C,380C ''would'':3C,150C,221C ''x'':342C ''x-axi'':341C ''y'':350C ''y-axi'':349C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (496, 2, '2014-06-20 03:18:07.653', 3, NULL, NULL, '&lt;p&gt;You have three questions to answer and 100 records per month to analyze.&lt;/p&gt;

&lt;p&gt;Based on this size, I''d recommend doing analysis in a simple SQL database or a spreadsheet to start off with.  The first two questions are fairly easy to figure out.  The third is a little more difficult.&lt;/p&gt;

&lt;p&gt;I''d definitely add a column for month and group all of that data together into a spreadsheet or database table given the questions you want to answer.&lt;/p&gt;

&lt;p&gt;question 1. Users who are consistently showing up in this list&lt;/p&gt;

&lt;p&gt;In excel, this answer should help you out:  &lt;a href="http://superuser.com/questions/442653/ms-excel-how-count-occurence-of-item-in-a-list"&gt;http://superuser.com/questions/442653/ms-excel-how-count-occurence-of-item-in-a-list&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;For a SQL database:  &lt;a href="http://stackoverflow.com/questions/2516546/select-count-duplicates"&gt;http://stackoverflow.com/questions/2516546/select-count-duplicates&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;question 2. Users who are consistently showing up in this list with high risk score &lt;/p&gt;

&lt;p&gt;This is just adding a little complexity to the above.  For SQL, you would further qualify your query based on a minimum risk score value.&lt;/p&gt;

&lt;p&gt;In excel, a straight pivot isn''t going to work, you''ll have to copy the unique values in one column to another, then drag a CountIf function adjacent to each unique value, qualifying the countif function with a minimum risk score.&lt;/p&gt;

&lt;p&gt;question 3. Users who have/reaching the high risk level very fast.&lt;/p&gt;

&lt;p&gt;A fast rise in risk level could be defined as the difference between two months being larger than a given value.&lt;/p&gt;

&lt;p&gt;For each user record you want to know the previous month''s threat value, or assume zero as the previous threat value.&lt;/p&gt;

&lt;p&gt;If that difference is greater than your risk threshold, you want to include it in your report.  If not, they can be filtered from the list.  &lt;/p&gt;

&lt;p&gt;If I had to do this month after month, I would spend the two hours it might take to automate a report after the first couple of months.  I''d throw all the data in a SQL database and write a quick script in perl or java to iterate through the 100 records, do the calculation, and output the users who crossed the threshold.&lt;/p&gt;

&lt;p&gt;If I needed it to look pretty, I''d use a reporting tool.  I''m not particularly partial to any of them.&lt;/p&gt;

&lt;p&gt;If I needed to trend threshold values over time, I''d output the results for all people into a second table add records to that table each month.&lt;/p&gt;

&lt;p&gt;If I just needed to do it once or twice, figuring out how to do it in excel by adding a new column using VLookUp and some basic math and a filter would probably be the fastest and easiest way to get it done.  I tend to avoid using excel for things I''ll need to use with consistency because there are limits that you run into when your data gets sizeable.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (498, 2, '2014-06-20 04:09:04.23', 4, NULL, NULL, '&lt;p&gt;Have you considered a frequency-based approach exploiting simple word co-occurence in corpora? At least, that''s what I''ve seen most folks use for this. I think it might be covered briefly in Manning and SchÃ¼tze''s book, and I seem to remember something like this as a homework assignment back in grad school...&lt;/p&gt;

&lt;p&gt;More background here: &lt;a href="http://nlp.stanford.edu/IR-book/html/htmledition/automatic-thesaurus-generation-1.html" rel="nofollow"&gt;http://nlp.stanford.edu/IR-book/html/htmledition/automatic-thesaurus-generation-1.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;For this step:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Rank other concepts based on their "distance" to the original keywords;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;There are several semantic similarity metrics you could look into. Here''s a link to some slides I put together for a class project using a few of these similarity metrics in WordNet: &lt;a href="http://www.eecis.udel.edu/~trnka/CISC889-11S/lectures/greenbacker-WordNet-Similarity.pdf" rel="nofollow"&gt;http://www.eecis.udel.edu/~trnka/CISC889-11S/lectures/greenbacker-WordNet-Similarity.pdf&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (499, 2, '2014-06-20 04:27:02.467', 8, NULL, NULL, '&lt;p&gt;Anecdotally, I''ve never been impressed with the output from hierarchical LDA. It just doesn''t seem to find an optimal level of granularity for choosing the number of topics. I''ve gotten much better results by running a few iterations of regular LDA, manually inspecting the topics it produced, deciding whether to increase or decrease the number of topics, and continue iterating until I get the granularity I''m looking for.&lt;/p&gt;

&lt;p&gt;Remember: hierarchical LDA can''t read your mind... it doesn''t know what you actually intend to use the topic modeling for. Just like with k-means clustering, you should choose the k that makes the most sense for your use case.&lt;/p&gt;
', NULL, NULL, '''/p'':74C,119C ''actual'':90C ''anecdot'':2C ''better'':36C ''case'':118C ''choos'':27C,107C ''cluster'':104C ''continu'':63C ''decid'':52C ''decreas'':57C ''doesn'':16C,85C ''find'':20C ''get'':67C ''gotten'':34C ''granular'':25C,69C ''hierarch'':12C,77C ''impress'':7C ''increas'':55C ''inspect'':47C ''intend'':91C ''iter'':42C,64C ''k'':102C,109C ''k-mean'':101C ''know'':87C ''lda'':13C,45C,78C ''level'':23C ''like'':99C ''look'':72C ''m'':71C ''make'':111C ''manual'':46C ''mean'':103C ''mind'':83C ''model'':96C ''much'':35C ''never'':5C ''number'':29C,59C ''optim'':22C ''output'':10C ''p'':1C,75C ''produc'':51C ''read'':81C ''regular'':44C ''rememb'':76C ''result'':37C ''run'':39C ''seem'':18C ''sens'':114C ''topic'':31C,49C,61C,95C ''use'':93C,117C ''ve'':4C,33C ''whether'':53C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (512, 2, '2014-06-20 18:56:52.11', 2, NULL, NULL, '&lt;p&gt;Here''s an approach I didn''t see mentioned: separate the process into two steps: the first step focused on encoding names so that alternative versions of the same name are encoded the same (or nearly the same), and the second step focused on making them anonymous.&lt;/p&gt;

&lt;p&gt;For the first step, you could use one of the &lt;a href="https://en.wikipedia.org/wiki/Phonetic_encoding" rel="nofollow"&gt;Phonetic Algorithms (Soundex and variants)&lt;/a&gt;, applied to first name, last name, and initials in various orders.  (See &lt;a href="http://www.stevemorse.org/phonetics/bmpm2.htm" rel="nofollow"&gt;this article&lt;/a&gt;, also).  It''s in this step where you resolve similarities vs. differences in names to balance false positives from false negatives.&lt;/p&gt;

&lt;p&gt;For the second step, you can pick any hashing or cryptographic method you like, without concern for how that method affects name matching.  This gives you freedom to use a method that has the best characteristics for both performance, robustness, and anonymity.&lt;/p&gt;
', NULL, NULL, '''/a'':73C,95C ''/p'':49C,117C,161C ''/phonetics/bmpm2.htm'':90C ''/wiki/phonetic_encoding'':65C ''affect'':139C ''algorithm'':69C ''also'':96C ''altern'':26C ''anonym'':48C,160C ''appli'':74C ''approach'':5C ''articl'':94C ''balanc'':111C ''best'':153C ''characterist'':154C ''concern'':134C ''could'':56C ''cryptograph'':129C ''didn'':7C ''differ'':107C ''en.wikipedia.org'':64C ''en.wikipedia.org/wiki/phonetic_encoding'':63C ''encod'':22C,33C ''fals'':112C,115C ''first'':18C,53C,76C ''focus'':20C,44C ''freedom'':145C ''give'':143C ''hash'':127C ''href'':62C,87C ''initi'':81C ''last'':78C ''like'':132C ''make'':46C ''match'':141C ''mention'':10C ''method'':130C,138C,149C ''name'':23C,31C,77C,79C,109C,140C ''near'':37C ''negat'':116C ''nofollow'':67C,92C ''one'':58C ''order'':84C ''p'':1C,50C,118C ''perform'':157C ''phonet'':68C ''pick'':125C ''posit'':113C ''process'':13C ''rel'':66C,91C ''resolv'':104C ''robust'':158C ''second'':42C,121C ''see'':9C,85C ''separ'':11C ''similar'':105C ''soundex'':70C ''step'':16C,19C,43C,54C,101C,122C ''two'':15C ''use'':57C,147C ''variant'':72C ''various'':83C ''version'':27C ''vs'':106C ''without'':133C ''www.stevemorse.org'':89C ''www.stevemorse.org/phonetics/bmpm2.htm'':88C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (500, 2, '2014-06-20 06:49:25.237', 4, NULL, NULL, '&lt;p&gt;In addition to Ben''s answer, the subtle distinction between statistical models and machine learning models is that, in statistical models, you explicitly decide the output equation structure prior to building the model. The model is built to compute the parameters/coefficients.&lt;/p&gt;

&lt;p&gt;Take linear model or GLM for example,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;y = a1x1 + a2x2 + a3x3
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Your independent variables are x1, x2, x3 and the coefficients to be determined are a1,a2,a3. You define your equation structure this way prior to building the model and compute a1,a2,a3. If you believe that y is somehow correlated to x2 in a non-linear way, you could try something like this.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;y = a1x1 + a2(x2)^2 + a3x3.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Thus, you put a restriction in terms of the output structure. Inherently statistical models are linear models unless you explicitly apply transformations like sigmoid or kernel to make them nonlinear (GLM and SVM).&lt;/p&gt;

&lt;p&gt;In case of machine learning models, you rarely specify output structure and algorithms like decision trees are inherently non-linear and work efficiently.&lt;/p&gt;

&lt;p&gt;Contrary to what Ben pointed out, machine learning models aren''t just about prediction, they do classification, regression etc which can be used to make predictions which are also done by various statistical models.&lt;/p&gt;
', NULL, NULL, '''/code'':59C,127C ''/p'':43C,52C,118C,163C,189C,225C ''/pre'':60C,128C ''2'':125C ''a1'':76C,93C ''a1x1'':56C,122C ''a2'':77C,94C,123C ''a2x2'':57C ''a3'':78C,95C ''a3x3'':58C,126C ''addit'':3C ''algorithm'':177C ''also'':219C ''answer'':7C ''appli'':150C ''aren'':200C ''believ'':98C ''ben'':5C,194C ''build'':32C,88C ''built'':38C ''case'':166C ''classif'':207C ''code'':54C,120C ''coeffici'':71C ''comput'':40C,92C ''contrari'':191C ''correl'':103C ''could'':113C ''decid'':25C ''decis'':179C ''defin'':80C ''determin'':74C ''distinct'':10C ''done'':220C ''effici'':188C ''equat'':28C,82C ''etc'':209C ''exampl'':51C ''explicit'':24C,149C ''glm'':49C,160C ''independ'':63C ''inher'':141C,182C ''kernel'':155C ''learn'':16C,169C,198C ''like'':116C,152C,178C ''linear'':46C,110C,145C,185C ''machin'':15C,168C,197C ''make'':157C,215C ''model'':13C,17C,22C,34C,36C,47C,90C,143C,146C,170C,199C,224C ''non'':109C,184C ''non-linear'':108C,183C ''nonlinear'':159C ''output'':27C,139C,174C ''p'':1C,44C,61C,129C,164C,190C ''parameters/coefficients'':42C ''point'':195C ''pre'':53C,119C ''predict'':204C,216C ''prior'':30C,86C ''put'':132C ''rare'':172C ''regress'':208C ''restrict'':134C ''sigmoid'':153C ''somehow'':102C ''someth'':115C ''specifi'':173C ''statist'':12C,21C,142C,223C ''structur'':29C,83C,140C,175C ''subtl'':9C ''svm'':162C ''take'':45C ''term'':136C ''thus'':130C ''transform'':151C ''tree'':180C ''tri'':114C ''unless'':147C ''use'':213C ''variabl'':64C ''various'':222C ''way'':85C,111C ''work'':187C ''x1'':66C ''x2'':67C,105C,124C ''x3'':68C ''y'':55C,100C,121C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (501, 2, '2014-06-20 07:11:40.053', 5, NULL, NULL, '&lt;p&gt;The best language depends on what you want to do. First remark: don''t limit yourself to one language. Learning a new language is always a good thing, but at some point you will need to choose. Facilities offered by the language itself are an obvious thing to keep into account &lt;em&gt;but&lt;/em&gt; in my opinion the following are more important:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;available libraries&lt;/strong&gt;: do you have to implement everything from scratch or can you reuse existing stuff? Note that this these libraries need not be in whatever language you are considering, as long as you can interface easily. Working in a language without library access won''t help you get things done.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;number of experts&lt;/strong&gt;: if you want external developers or start working in a team, you have to consider how many people actually know the language. As an extreme example: if you decide to work in Brainfuck because you happen to like it, know that you will likely work alone. Many surveys exists that can help assess the popularity of languages, including the number of questions per language on SO.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;toolchain&lt;/strong&gt;: do you have access to &lt;em&gt;good&lt;/em&gt; debuggers, profilers, documentation tools and (if you''re into that) IDEs?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I am aware that most of my points favor established languages. This is from a ''get-things-done'' perspective.&lt;/p&gt;

&lt;p&gt;That said, I personally believe it is far better to become proficient in a low level language and a high level language:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;low level: C++, C, Fortran, ... using which you can implement certain profiling hot spots &lt;em&gt;only if you need to&lt;/em&gt; because developing in these languages is typically slower (though this is subject to debate). These languages remain king of the hill in terms of critical performance and are likely to stay on top for a long time.&lt;/li&gt;
&lt;li&gt;high level: Python, R, Clojure, ... to ''glue'' stuff together and do non-performance critical stuff (preprocessing, data handling, ...). I find this to be important simply because it is much easier to do rapid development and prototyping in these languages.&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (503, 2, '2014-06-20 14:17:27.97', 2, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Edit: Warning, i leave my message but my answer seems wrong, please check out the comment below!&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I''m not an expert but I guess the main problem is to answer this question:&lt;/p&gt;

&lt;p&gt;Has an/any event affected the number of hits on a certain day?&lt;/p&gt;

&lt;p&gt;But I don''t know how to treat multiple events, so I would try to answer this question:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Does event X affected the number of hits on a certain day?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Which can be answered using hypothesis testing with p-values (what scientist do to evaluate for instance if a medicine affects a disease or not).&lt;/p&gt;

&lt;p&gt;By using p-values, you could determinate if the number of hits in a certain day were mere random and acceptable under normal circumstances or that they must correspond to a change in your model.&lt;/p&gt;

&lt;p&gt;You can read more about p-values in &lt;a href="http://www.openintro.org/stat/" rel="nofollow"&gt;Open Intro to Statistics Book&lt;/a&gt;, I''ve actually learn about them from there.&lt;/p&gt;

&lt;p&gt;Then, the other parts of the problem are how to identify your events and calculate the necessary parameters to answer your question (average/median, variance, etc.) and also how to keep that up-to-date and working.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6292, 1, '2015-07-01 06:13:41.82', 2, 247, 'Is there any working alternatives to Netvizz?', '&lt;p&gt;I''d like to download my friends data as a graph/network from Facebook. In Feb 2015, Facebook banned the awesome application Netvizz. Do you know any alternative application for my purpose?&lt;/p&gt;
', 'graphs  social-network-analysis', NULL, '''/p'':45C ''2015'':29C ''altern'':5A,40C ''analysi'':12B ''applic'':34C,41C ''awesom'':33C ''ban'':31C ''d'':15C ''data'':21C ''download'':18C ''facebook'':26C,30C ''feb'':28C ''friend'':20C ''graph'':8B ''graph/network'':24C ''know'':38C ''like'':16C ''netvizz'':7A,35C ''network'':11B ''p'':13C ''purpos'':44C ''social'':10B ''social-network-analysi'':9B ''work'':4A');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (504, 1, '2014-06-20 14:58:09.32', 6, 92, 'Dealing with diverse text data', '&lt;p&gt;I''m currently working with a dataset with a wide range of document lengths -- anywhere from a single word to a full page of text.  In addition, the grammatical structure and use of punctuation varies wildly from document to document.  The goal is to classify those documents into one of about 10-15 categories.  I''m currently using ridge regression and logistic regression for the task, and CV for the alpha values of ridge.  The feature vectors are tf-idf ngrams.&lt;/p&gt;

&lt;p&gt;Recently I''ve noticed that longer documents are much less likely to be categorized. Why might this be the case, and how can one "normalize" for this kind of variation?  As a more general question, how does one typically deal with diverse data sets?  Should documents be grouped based off of metrics like document length, use of punctuation, grammatical rigor, etc. and then fed through different classifiers?  &lt;/p&gt;
', 'classification  nlp', NULL, '''-15'':61C ''/p'':91C,160C ''10'':60C ''addit'':35C ''alpha'':79C ''anywher'':23C ''base'':141C ''case'':112C ''categor'':106C ''categori'':62C ''classif'':6B ''classifi'':53C,159C ''current'':11C,65C ''cv'':76C ''data'':5A,135C ''dataset'':15C ''deal'':1A,132C ''differ'':158C ''divers'':3A,134C ''document'':21C,46C,48C,55C,99C,138C,146C ''etc'':153C ''featur'':84C ''fed'':156C ''full'':30C ''general'':126C ''goal'':50C ''grammat'':37C,151C ''group'':140C ''idf'':89C ''kind'':120C ''length'':22C,147C ''less'':102C ''like'':103C,145C ''logist'':70C ''longer'':98C ''m'':10C,64C ''metric'':144C ''might'':108C ''much'':101C ''ngram'':90C ''nlp'':7B ''normal'':117C ''notic'':96C ''one'':57C,116C,130C ''p'':8C,92C ''page'':31C ''punctuat'':42C,150C ''question'':127C ''rang'':19C ''recent'':93C ''regress'':68C,71C ''ridg'':67C,82C ''rigor'':152C ''set'':136C ''singl'':26C ''structur'':38C ''task'':74C ''text'':4A,33C ''tf'':88C ''tf-idf'':87C ''typic'':131C ''use'':40C,66C,148C ''valu'':80C ''vari'':43C ''variat'':122C ''ve'':95C ''vector'':85C ''wide'':18C ''wild'':44C ''word'':27C ''work'':12C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (508, 2, '2014-06-20 16:56:41.16', 4, NULL, NULL, '&lt;p&gt;I''m not sure how you are applying a regression framework for document classification. The way I''d approach the problem is to apply a standard discriminative classification approach such as SVM.&lt;/p&gt;

&lt;p&gt;In a discriminative classification approach the notion of similarity or inverse distance between data points (documents in this case) is pivotal. Fortunately for documents, there is a standard way of defining pairwise similarity. This is the standard &lt;a href="http://en.wikipedia.org/wiki/Cosine_similarity" rel="nofollow"&gt;cosine similarity&lt;/a&gt; measure which makes use of document length normalization to take different document lengths into account.&lt;/p&gt;

&lt;p&gt;Thus, practically speaking, in cosine similarity you would work with relative term weights normalized by document lengths and hence document length diversity should not be a major issue in the similarity computation.&lt;/p&gt;

&lt;p&gt;One also has to be careful when applying idf in term weights. If the number of documents is not significantly large the idf measure may be statistically imprecise thus adding noise to the term weights. It''s also a standard practice to ignore stopwords and punctuations.&lt;/p&gt;
', NULL, NULL, '''/a'':82C ''/p'':34C,98C,132C,180C ''/wiki/cosine_similarity'':77C ''account'':97C ''ad'':163C ''also'':135C,171C ''appli'':9C,25C,141C ''approach'':20C,30C,40C ''care'':139C ''case'':54C ''classif'':15C,29C,39C ''comput'':131C ''cosin'':80C,104C ''d'':19C ''data'':49C ''defin'':66C ''differ'':93C ''discrimin'':28C,38C ''distanc'':47C ''divers'':121C ''document'':14C,51C,59C,88C,94C,115C,119C,150C ''en.wikipedia.org'':76C ''en.wikipedia.org/wiki/cosine_similarity'':75C ''fortun'':57C ''framework'':12C ''henc'':118C ''href'':74C ''idf'':142C,156C ''ignor'':176C ''imprecis'':161C ''invers'':46C ''issu'':127C ''larg'':154C ''length'':89C,95C,116C,120C ''m'':3C ''major'':126C ''make'':85C ''may'':158C ''measur'':83C,157C ''nofollow'':79C ''nois'':164C ''normal'':90C,113C ''notion'':42C ''number'':148C ''one'':134C ''p'':1C,35C,99C,133C ''pairwis'':67C ''pivot'':56C ''point'':50C ''practic'':101C,174C ''problem'':22C ''punctuat'':179C ''regress'':11C ''rel'':78C ''relat'':110C ''signific'':153C ''similar'':44C,68C,81C,105C,130C ''speak'':102C ''standard'':27C,63C,72C,173C ''statist'':160C ''stopword'':177C ''sure'':5C ''svm'':33C ''take'':92C ''term'':111C,144C,167C ''thus'':100C,162C ''use'':86C ''way'':17C,64C ''weight'':112C,145C,168C ''work'':108C ''would'':107C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (509, 2, '2014-06-20 17:30:39.097', -2, NULL, NULL, '&lt;p&gt;For example...if u have an agricultural land then selecting one particular area of that land would be feature selection.If u aim to find the affected plants in that area den u need to observe each plant based on a particular feature that is common in each plant so as to find the abnormalities...for this u would be considering feature extraction.In this example the original agricultural land corresponds to Dimensionality reduction.&lt;/p&gt;
', NULL, NULL, '''/p'':74C ''abnorm'':55C ''affect'':27C ''agricultur'':8C,68C ''aim'':23C ''area'':14C,31C ''base'':39C ''common'':46C ''consid'':61C ''correspond'':70C ''den'':32C ''dimension'':72C ''exampl'':3C,65C ''extraction.in'':63C ''featur'':20C,43C,62C ''find'':25C,53C ''land'':9C,17C,69C ''need'':34C ''observ'':36C ''one'':12C ''origin'':67C ''p'':1C ''particular'':13C,42C ''plant'':28C,38C,49C ''reduct'':73C ''select'':11C ''selection.if'':21C ''u'':5C,22C,33C,58C ''would'':18C,59C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (510, 2, '2014-06-20 17:34:51.633', 9, NULL, NULL, '&lt;p&gt;A couple of words about social networks APIs. About a year ago I wrote a review of popular social networksâ APIs for researchers. Unfortunately, it is in Russian. Here is a summary:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Twitter&lt;/strong&gt; (&lt;a href="https://dev.twitter.com/docs/api/1.1"&gt;https://dev.twitter.com/docs/api/1.1&lt;/a&gt;)&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;almost all data about tweets/texts and users is available;&lt;/li&gt;
&lt;li&gt;lack of sociodemographic data;&lt;/li&gt;
&lt;li&gt;great streaming API: useful for real time text processing;&lt;/li&gt;
&lt;li&gt;a lot of wrappers for programing languages;&lt;/li&gt;
&lt;li&gt;getting network structure (connections) is possible, but time-expensive (1 request per 1 minute).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Facebook&lt;/strong&gt; (&lt;a href="https://developers.facebook.com/docs/reference/api/"&gt;https://developers.facebook.com/docs/reference/api/&lt;/a&gt;)&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;rate limits: about 1 request per second;&lt;/li&gt;
&lt;li&gt;well documented, sandbox present;&lt;/li&gt;
&lt;li&gt;FQL (SQL-like) and Â«regular RestÂ» Graph API;&lt;/li&gt;
&lt;li&gt;friendship data and sociodemographic features present;&lt;/li&gt;
&lt;li&gt;a lot of data is beyond &lt;em&gt;event horizon&lt;/em&gt;: only friends'' and friends'' of friends data is more or less complete, almost nothing could be investigated about random user;&lt;/li&gt;
&lt;li&gt;some strange API bugs, and looks like nobody cares about it (e.g., some features available through FQL, but not through Graph API synonym).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Instagram&lt;/strong&gt; (&lt;a href="http://instagram.com/developer/"&gt;http://instagram.com/developer/&lt;/a&gt;)&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;rate limits: 5000 requests per hour;&lt;/li&gt;
&lt;li&gt;real-time API (like Streaming API for Twitter, but with photos) - connection to it is a little bit tricky: callbacks are used;&lt;/li&gt;
&lt;li&gt;lack of sociodemographic data;&lt;/li&gt;
&lt;li&gt;photos, filters data available;&lt;/li&gt;
&lt;li&gt;unexpected imperfections (e.g., itâs possible to collect only 150 comments to post/photo).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Foursquare&lt;/strong&gt; (&lt;a href="https://developer.foursquare.com/overview/"&gt;https://developer.foursquare.com/overview/&lt;/a&gt;)&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;rate limits: 5000 requests per hour;&lt;/li&gt;
&lt;li&gt;kingdom of geosocial data :)&lt;/li&gt;
&lt;li&gt;quite closed from researches because of privacy issues. To collect checkins data one need to build composite parser working with 4sq, bit.ly, and twitter APIs at once;&lt;/li&gt;
&lt;li&gt;again: lack of sociodemographic data.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Google+&lt;/strong&gt; (&lt;a href="https://developers.google.com/+/api/latest/"&gt;https://developers.google.com/+/api/latest/&lt;/a&gt;)&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;about 5 requests per second (try to verify);&lt;/li&gt;
&lt;li&gt;main methods: activities and people;&lt;/li&gt;
&lt;li&gt;like on Facebook, a lot of personal data for random user is hidden;&lt;/li&gt;
&lt;li&gt;lack of user connections data.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;And out-of-competition: I reviewed social networks for Russian readers, and #1 network here is &lt;a href="http://en.wikipedia.org/wiki/VK_%28social_network%29"&gt;&lt;strong&gt;vk.com&lt;/strong&gt;&lt;/a&gt;. Itâs translated to many languages, but popular only in Russia and other CIS countries. API docs link: &lt;a href="http://vk.com/dev/"&gt;http://vk.com/dev/&lt;/a&gt;. And from my point of view, itâs the best choice for homebrew social media research. At least, in Russia. Thatâs why:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;rate limits: 3 requests per second;&lt;/li&gt;
&lt;li&gt;public text and media data available;&lt;/li&gt;
&lt;li&gt;sociodemographic data available: for random user availability level is about 60-70%;&lt;/li&gt;
&lt;li&gt;connections between users are also available: almost all friendships data for random user is available;&lt;/li&gt;
&lt;li&gt;some special methods: e.g., there is a method to get online/offline status for exact user in realtime, and one could build schedule for his audience.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''-70'':506C ''/+/api/latest/'':355C ''/+/api/latest/&lt;/a&gt;)&lt;/p&gt;'':358C ''/a'':426C ''/dev/'':449C ''/dev/&lt;/a&gt;.'':452C ''/developer/'':217C ''/developer/&lt;/a&gt;)&lt;/p&gt;'':220C ''/docs/api/1.1'':43C ''/docs/api/1.1&lt;/a&gt;)&lt;/p&gt;'':46C ''/docs/reference/api/'':111C ''/docs/reference/api/&lt;/a&gt;)&lt;/p&gt;'':114C ''/em'':161C ''/li'':58C,64C,75C,84C,101C,124C,130C,142C,150C,182C,207C,229C,254C,260C,266C,281C,303C,309C,338C,345C,369C,376C,391C,398C,485C,493C,507C,524C,551C ''/overview/'':291C ''/overview/&lt;/a&gt;)&lt;/p&gt;'':294C ''/p'':34C,476C ''/strong'':38C,106C,212C,286C,350C,425C ''/ul'':102C,208C,282C,346C,399C,552C ''/wiki/vk_%28social_network%29'':422C ''1'':96C,99C,120C,414C ''150'':277C ''3'':481C ''4sq'':331C ''5'':362C ''5000'':225C,299C ''60'':505C ''activ'':373C ''ago'':13C ''almost'':49C,174C,515C ''also'':513C ''api'':9C,22C,68C,141C,186C,205C,234C,237C,335C,442C ''audienc'':550C ''avail'':57C,198C,265C,492C,497C,501C,514C,523C ''best'':462C ''beyond'':157C ''bit'':249C ''bit.ly'':332C ''bug'':187C ''build'':326C,546C ''callback'':251C ''care'':192C ''checkin'':321C ''choic'':463C ''cis'':440C ''close'':312C ''collect'':275C,320C ''comment'':278C ''competit'':405C ''complet'':173C ''composit'':327C ''connect'':89C,243C,396C,509C ''could'':176C,545C ''countri'':441C ''coupl'':3C ''data'':51C,63C,145C,155C,168C,259C,264C,308C,322C,344C,385C,397C,491C,496C,518C ''dev.twitter.com'':42C,45C ''dev.twitter.com/docs/api/1.1'':41C ''dev.twitter.com/docs/api/1.1&lt;/a&gt;)&lt;/p&gt;'':44C ''developer.foursquare.com'':290C,293C ''developer.foursquare.com/overview/'':289C ''developer.foursquare.com/overview/&lt;/a&gt;)&lt;/p&gt;'':292C ''developers.facebook.com'':110C,113C ''developers.facebook.com/docs/reference/api/'':109C ''developers.facebook.com/docs/reference/api/&lt;/a&gt;)&lt;/p&gt;'':112C ''developers.google.com'':354C,357C ''developers.google.com/+/api/latest/'':353C ''developers.google.com/+/api/latest/&lt;/a&gt;)&lt;/p&gt;'':356C ''doc'':443C ''document'':127C ''e.g'':195C,270C,529C ''em'':158C ''en.wikipedia.org'':421C ''en.wikipedia.org/wiki/vk_%28social_network%29'':420C ''event'':159C ''exact'':539C ''expens'':95C ''facebook'':105C,380C ''featur'':148C,197C ''filter'':263C ''foursquar'':285C ''fql'':132C,200C ''friend'':163C,165C,167C ''friendship'':144C,517C ''geosoci'':307C ''get'':86C,535C ''googl'':349C ''graph'':140C,204C ''great'':66C ''hidden'':390C ''homebrew'':465C ''horizon'':160C ''hour'':228C,302C ''href'':40C,108C,214C,288C,352C,419C,446C ''imperfect'':269C ''instagram'':211C ''instagram.com'':216C,219C ''instagram.com/developer/'':215C ''instagram.com/developer/&lt;/a&gt;)&lt;/p&gt;'':218C ''investig'':178C ''issu'':318C ''itâ'':271C,427C,459C ''kingdom'':305C ''lack'':60C,256C,341C,393C ''languag'':83C,432C ''least'':470C ''less'':172C ''level'':502C ''li'':48C,59C,65C,76C,85C,116C,125C,131C,143C,151C,183C,222C,230C,255C,261C,267C,296C,304C,310C,339C,360C,370C,377C,392C,478C,486C,494C,508C,525C ''like'':135C,190C,235C,378C ''limit'':118C,224C,298C,480C ''link'':444C ''littl'':248C ''look'':189C ''lot'':78C,153C,382C ''main'':371C ''mani'':431C ''media'':467C,490C ''method'':372C,528C,533C ''minut'':100C ''need'':324C ''network'':8C,87C,409C,415C ''networksâ'':21C ''nobodi'':191C ''noth'':175C ''one'':323C,544C ''online/offline'':536C ''out-of-competit'':402C ''p'':1C,35C,103C,209C,283C,347C,400C ''parser'':328C ''peopl'':375C ''per'':98C,122C,227C,301C,364C,483C ''person'':384C ''photo'':242C,262C ''point'':456C ''popular'':19C,434C ''possibl'':91C,273C ''post/photo'':280C ''present'':129C,149C ''privaci'':317C ''process'':74C ''program'':82C ''public'':487C ''quit'':311C ''random'':180C,387C,499C,520C ''rate'':117C,223C,297C,479C ''reader'':412C ''real'':71C,232C ''real-tim'':231C ''realtim'':542C ''regular'':138C ''request'':97C,121C,226C,300C,363C,482C ''research'':24C,314C,468C ''restâ'':139C ''review'':17C,407C ''russia'':437C,472C ''russian'':29C,411C ''sandbox'':128C ''schedul'':547C ''second'':123C,365C,484C ''social'':7C,20C,408C,466C ''sociodemograph'':62C,147C,258C,343C,495C ''special'':527C ''sql'':134C ''sql-like'':133C ''status'':537C ''strang'':185C ''stream'':67C,236C ''strong'':36C,104C,210C,284C,348C,423C ''structur'':88C ''summari'':33C ''synonym'':206C ''text'':73C,488C ''thatâ'':473C ''time'':72C,94C,233C ''time-expens'':93C ''translat'':429C ''tri'':366C ''tricki'':250C ''tweets/texts'':53C ''twitter'':37C,239C,334C ''ul'':47C,115C,221C,295C,359C,477C ''unexpect'':268C ''unfortun'':25C ''use'':69C,253C ''user'':55C,181C,388C,395C,500C,511C,521C,540C ''verifi'':368C ''view'':458C ''vk.com'':424C,448C,451C ''vk.com/dev/'':447C ''vk.com/dev/&lt;/a&gt;.'':450C ''well'':126C ''word'':5C ''work'':329C ''wrapper'':80C ''wrote'':15C ''year'':12C ''â'':137C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5926, 2, '2015-05-27 13:27:44.253', 0, NULL, NULL, '&lt;p&gt;If you want to have some data science programming experience in R try &lt;a href="https://www.datacamp.com/" rel="nofollow"&gt;https://www.datacamp.com/&lt;/a&gt; Is'' an interactive online coding environment for learning R programming. &lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':22C ''/p'':33C ''code'':27C ''data'':8C ''environ'':28C ''experi'':11C ''href'':16C ''interact'':25C ''learn'':30C ''nofollow'':19C ''onlin'':26C ''p'':1C ''program'':10C,32C ''r'':13C,31C ''rel'':18C ''scienc'':9C ''tri'':14C ''want'':4C ''www.datacamp.com'':17C,21C ''www.datacamp.com/&lt;/a&gt;'':20C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (526, 2, '2014-06-22 18:50:07.193', 1, NULL, NULL, '&lt;p&gt;Chapter 1 of Practical Data Science with R (&lt;a href="http://www.manning.com/zumel/" rel="nofollow"&gt;http://www.manning.com/zumel/&lt;/a&gt;) has a great breakdown of the data science process, including team roles and how they relate to specific tasks. The book follows the models laid out in the chapter by referencing which stages/personnel this or that particular task would be performed by.  &lt;/p&gt;
', NULL, NULL, '''/p'':62C ''/zumel/'':14C ''/zumel/&lt;/a&gt;)'':19C ''1'':3C ''book'':40C ''breakdown'':23C ''chapter'':2C,48C ''data'':6C,26C ''follow'':41C ''great'':22C ''href'':11C ''includ'':29C ''laid'':44C ''model'':43C ''nofollow'':16C ''p'':1C ''particular'':56C ''perform'':60C ''practic'':5C ''process'':28C ''r'':9C ''referenc'':50C ''rel'':15C ''relat'':35C ''role'':31C ''scienc'':7C,27C ''specif'':37C ''stages/personnel'':52C ''task'':38C,57C ''team'':30C ''would'':58C ''www.manning.com'':13C,18C ''www.manning.com/zumel/'':12C ''www.manning.com/zumel/&lt;/a&gt;)'':17C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (513, 2, '2014-06-20 19:15:52.093', 4, NULL, NULL, '&lt;p&gt;If you have an adequate number of samples and want to use all the data, then k-fold cross-validation is the way to go. Having ~1,500 seems like a lot but whether it is adequate for k-fold cross-validation also depends on the dimensionality of the data (number of attributes and number of attribute values). For example, if each observation has 100 attributes, then 1,500 observations is low.&lt;/p&gt;

&lt;p&gt;Another potential downside to k-fold cross-validation is the possibility of a single, extreme outlier skewing the results. For example, if you have one extreme outlier that can heavily bias your classifier, then in a 10-fold cross-validation, 9 of the 10 partitions will be affected (though for random forests, I don''t think you would have that problem).&lt;/p&gt;

&lt;p&gt;Random subsampling (e.g., bootstrap sampling) is preferable when you are either undersampled or when you have the situation above, where you don''t want each observation to appear in k-1 folds.&lt;/p&gt;
', NULL, NULL, '''-1'':176C ''/p'':78C,144C,178C ''1'':30C,73C ''10'':118C,126C ''100'':70C ''500'':31C,74C ''9'':123C ''adequ'':6C,40C ''affect'':130C ''also'':48C ''anoth'':80C ''appear'':173C ''attribut'':58C,62C,71C ''bias'':112C ''bootstrap'':149C ''classifi'':114C ''cross'':22C,46C,88C,121C ''cross-valid'':21C,45C,87C,120C ''data'':16C,55C ''depend'':49C ''dimension'':52C ''downsid'':82C ''e.g'':148C ''either'':156C ''exampl'':65C,102C ''extrem'':96C,107C ''fold'':20C,44C,86C,119C,177C ''forest'':134C ''go'':28C ''heavili'':111C ''k'':19C,43C,85C,175C ''k-fold'':18C,42C,84C ''like'':33C ''lot'':35C ''low'':77C ''number'':7C,56C,60C ''observ'':68C,75C,171C ''one'':106C ''outlier'':97C,108C ''p'':1C,79C,145C ''partit'':127C ''possibl'':92C ''potenti'':81C ''prefer'':152C ''problem'':143C ''random'':133C,146C ''result'':100C ''sampl'':9C,150C ''seem'':32C ''singl'':95C ''situat'':163C ''skew'':98C ''subsampl'':147C ''think'':138C ''though'':131C ''undersampl'':157C ''use'':13C ''valid'':23C,47C,89C,122C ''valu'':63C ''want'':11C,169C ''way'':26C ''whether'':37C ''would'':140C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (514, 2, '2014-06-20 20:03:51.247', 6, NULL, NULL, '&lt;p&gt;I like Amir Ali Akbari''s suggestions, and I''ll add a few of my own, focusing on topics and skills that are not adequately covered in most machine learning and data analysis books that focus on math and/or programming.&lt;/p&gt;

&lt;p&gt;Data Cleaning:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1412988012"&gt;Osborne 2012, &lt;em&gt;Best Practices in Data Cleaning&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1449321887"&gt;McCallom 2012, &lt;em&gt;Bad Data Handbook: Cleaning Up The Data So You Can Get Back To Work&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Bayesian Data Analysis (alternative to Fisher-style Null Hypothesis Significance Testing):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0123814855"&gt;Kruschke 2011, &lt;em&gt;Doing Bayesian Data Analysis&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Inference in the face of uncertainty, incompleteness, contradictions, ambiguity, imprecision, ignorance, etc.:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0810118211"&gt;Schum &amp;amp; Starace, 2001, &lt;em&gt;The Evidential Foundations of Probabilistic Reasoning&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1844074749"&gt;Bammer &amp;amp; Smithson 2008, &lt;em&gt;Uncertainty and Risk: Multidisciplinary Perspectives&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0387969454"&gt;Smithson 1989, &lt;em&gt;Ignorance and Uncertainty&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/Tradecraft%20Primer-apr09.pdf"&gt;CIA 2008, &lt;em&gt;A Tradecraft Primer: Structured Analytic Techniques for Improving Intelligence Analysis&lt;/em&gt;&lt;/a&gt; (FREE! as PDF)&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0521671930"&gt;Morgan &amp;amp; Winship 2007, &lt;em&gt;Counterfactuals and Causal Inference: Methods and Principles for Social Research&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Experiments:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0691159270"&gt;Glennerster &amp;amp; Takavarasha 2013, &lt;em&gt;Running Randomized Evaluations: A Practical Guide&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1107698006"&gt;Dunning 2012, &lt;em&gt;Natural Experiments in the Social Sciences&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Simulation:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0691125473"&gt;Epstein 2006, &lt;em&gt;Generative Social Science: Studies in Agent-Based Computational Modeling&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0486477703"&gt;Nelson 2010, &lt;em&gt;Stochastic Modeling: Analysis and Simulation&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Expert elicitation, probabilistic estimation:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1118539273"&gt;Hubbard 2014, &lt;em&gt;How to Measure Anything: Finding the Value of Intangibles in Business&lt;/em&gt;&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':63C,89C,121C,157C,176C,191C,213C,240C,265C,283C,310C,327C,358C ''/amzn/click/0123814855'':112C ''/amzn/click/0387969454'':183C ''/amzn/click/0486477703'':317C ''/amzn/click/0521671930'':223C ''/amzn/click/0691125473'':295C ''/amzn/click/0691159270'':252C ''/amzn/click/0810118211'':144C ''/amzn/click/1107698006'':272C ''/amzn/click/1118539273'':342C ''/amzn/click/1412988012'':53C ''/amzn/click/1449321887'':70C ''/amzn/click/1844074749'':164C ''/em'':62C,88C,120C,156C,175C,190C,212C,239C,264C,282C,309C,326C,357C ''/li'':64C,90C,122C,158C,177C,192C,217C,241C,266C,284C,311C,328C,359C ''/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/tradecraft%20primer-apr09.pdf'':198C ''/p'':42C,46C,105C,137C,245C,288C,335C ''/ul'':91C,123C,242C,285C,329C,360C ''1989'':185C ''2001'':148C ''2006'':297C ''2007'':227C ''2008'':168C,200C ''2010'':319C ''2011'':114C ''2012'':55C,72C,274C ''2013'':256C ''2014'':344C ''add'':12C ''adequ'':26C ''agent'':305C ''agent-bas'':304C ''akbari'':6C ''ali'':5C ''altern'':96C ''ambigu'':133C ''amir'':4C ''amp'':146C,166C,225C,254C ''analysi'':34C,95C,119C,211C,323C ''analyt'':206C ''and/or'':40C ''anyth'':349C ''back'':85C ''bad'':74C ''bammer'':165C ''base'':306C ''bayesian'':93C,117C ''best'':57C ''book'':35C ''busi'':356C ''causal'':231C ''cia'':199C ''clean'':45C,61C,77C ''comput'':307C ''contradict'':132C ''counterfactu'':229C ''cover'':27C ''data'':33C,44C,60C,75C,80C,94C,118C ''dun'':273C ''elicit'':332C ''em'':56C,73C,115C,149C,169C,186C,201C,228C,257C,275C,298C,320C,345C ''epstein'':296C ''estim'':334C ''etc'':136C ''evalu'':260C ''evidenti'':151C ''experi'':244C,277C ''expert'':331C ''face'':128C ''find'':350C ''fisher'':99C ''fisher-styl'':98C ''focus'':18C,37C ''foundat'':152C ''free'':214C ''generat'':299C ''get'':84C ''glennerst'':253C ''guid'':263C ''handbook'':76C ''href'':50C,67C,109C,141C,161C,180C,195C,220C,249C,269C,292C,314C,339C ''hubbard'':343C ''hypothesi'':102C ''ignor'':135C,187C ''imprecis'':134C ''improv'':209C ''incomplet'':131C ''infer'':125C,232C ''intang'':354C ''intellig'':210C ''kruschk'':113C ''learn'':31C ''li'':48C,65C,107C,139C,159C,178C,193C,218C,247C,267C,290C,312C,337C ''like'':3C ''ll'':11C ''machin'':30C ''math'':39C ''mccallom'':71C ''measur'':348C ''method'':233C ''model'':308C,322C ''morgan'':224C ''multidisciplinari'':173C ''natur'':276C ''nelson'':318C ''null'':101C ''osborn'':54C ''p'':1C,43C,92C,124C,243C,286C,330C ''pdf'':216C ''perspect'':174C ''practic'':58C,262C ''primer'':204C ''principl'':235C ''probabilist'':154C,333C ''program'':41C ''rads.stackoverflow.com'':52C,69C,111C,143C,163C,182C,222C,251C,271C,294C,316C,341C ''rads.stackoverflow.com/amzn/click/0123814855'':110C ''rads.stackoverflow.com/amzn/click/0387969454'':181C ''rads.stackoverflow.com/amzn/click/0486477703'':315C ''rads.stackoverflow.com/amzn/click/0521671930'':221C ''rads.stackoverflow.com/amzn/click/0691125473'':293C ''rads.stackoverflow.com/amzn/click/0691159270'':250C ''rads.stackoverflow.com/amzn/click/0810118211'':142C ''rads.stackoverflow.com/amzn/click/1107698006'':270C ''rads.stackoverflow.com/amzn/click/1118539273'':340C ''rads.stackoverflow.com/amzn/click/1412988012'':51C ''rads.stackoverflow.com/amzn/click/1449321887'':68C ''rads.stackoverflow.com/amzn/click/1844074749'':162C ''random'':259C ''reason'':155C ''research'':238C ''risk'':172C ''run'':258C ''schum'':145C ''scienc'':281C,301C ''signific'':103C ''simul'':287C,325C ''skill'':22C ''smithson'':167C,184C ''social'':237C,280C,300C ''starac'':147C ''stochast'':321C ''structur'':205C ''studi'':302C ''style'':100C ''suggest'':8C ''takavarasha'':255C ''techniqu'':207C ''test'':104C ''topic'':20C ''tradecraft'':203C ''ul'':47C,106C,138C,246C,289C,336C ''uncertainti'':130C,170C,189C ''valu'':352C ''winship'':226C ''work'':87C ''www.cia.gov'':197C ''www.cia.gov/library/center-for-the-study-of-intelligence/csi-publications/books-and-monographs/tradecraft%20primer-apr09.pdf'':196C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (516, 1, '2014-06-21 04:59:06.62', 5, 116, 'Efficient solution of fmincg without providing gradient?', '&lt;p&gt;I''m working on multiclass logistic regression model with a large number of features (numFeatures &gt; 100). Using a Maximum Likelihood Estimation based on the cost function and gradient, the fmincg algorithm solves the problem quickly. However, I''m also experimenting with a different cost function and do not have a gradient.&lt;/p&gt;

&lt;p&gt;Is there a good way to speed up the calculation process? E.g., is there a different algorithm or fmincg setting that I can use?&lt;/p&gt;
', 'bigdata  data-mining', NULL, '''/p'':64C,90C ''100'':28C ''algorithm'':43C,82C ''also'':51C ''base'':34C ''bigdata'':8B ''calcul'':75C ''cost'':37C,56C ''data'':10B ''data-min'':9B ''differ'':55C,81C ''e.g'':77C ''effici'':1A ''estim'':33C ''experi'':52C ''featur'':26C ''fmincg'':4A,42C,84C ''function'':38C,57C ''good'':69C ''gradient'':7A,40C,63C ''howev'':48C ''larg'':23C ''likelihood'':32C ''logist'':18C ''m'':14C,50C ''maximum'':31C ''mine'':11B ''model'':20C ''multiclass'':17C ''number'':24C ''numfeatur'':27C ''p'':12C,65C ''problem'':46C ''process'':76C ''provid'':6A ''quick'':47C ''regress'':19C ''set'':85C ''solut'':2A ''solv'':44C ''speed'':72C ''use'':29C,89C ''way'':70C ''without'':5A ''work'':15C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (517, 2, '2014-06-21 07:02:10.687', 3, NULL, NULL, '&lt;p&gt;If you do not have a gradient available, but the problem is convex, you can use the &lt;a href="http://en.wikipedia.org/wiki/Nelder%E2%80%93Mead_method" rel="nofollow"&gt;Nelder-Mead simplex method&lt;/a&gt;. It is available in most optimization packages, for example in &lt;a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize" rel="nofollow"&gt;scipy.optimize&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':31C,50C ''/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize'':46C ''/p'':51C ''/wiki/nelder%e2%80%93mead_method'':23C ''avail'':9C,34C ''convex'':14C ''docs.scipy.org'':45C ''docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html#scipy.optimize.minimize'':44C ''en.wikipedia.org'':22C ''en.wikipedia.org/wiki/nelder%e2%80%93mead_method'':21C ''exampl'':40C ''gradient'':8C ''href'':20C,43C ''mead'':28C ''method'':30C ''nelder'':27C ''nelder-mead'':26C ''nofollow'':25C,48C ''optim'':37C ''p'':1C ''packag'':38C ''problem'':12C ''rel'':24C,47C ''scipy.optimize'':49C ''simplex'':29C ''use'':17C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (518, 1, '2014-06-21 10:55:41.7', -2, 84, 'Online k-means explanation', '&lt;p&gt;Please, could someone recommend a paper or blog post that describes the online k-means algorithm.&lt;/p&gt;

&lt;p&gt;Thanks :)&lt;/p&gt;
', 'machine-learning  clustering', NULL, '''/p'':28C,31C ''algorithm'':27C ''blog'':18C ''cluster'':9B ''could'':12C ''describ'':21C ''explan'':5A ''k'':3A,25C ''k-mean'':2A,24C ''learn'':8B ''machin'':7B ''machine-learn'':6B ''mean'':4A,26C ''onlin'':1A,23C ''p'':10C,29C ''paper'':16C ''pleas'':11C ''post'':19C ''recommend'':14C ''someon'':13C ''thank'':30C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (519, 2, '2014-06-21 12:59:15.373', 2, NULL, NULL, '&lt;p&gt;The main problem here is that even before attempting to apply anomaly detection algorithms, you are not getting good enough predictions of gas consumption using neural networks. &lt;/p&gt;

&lt;p&gt;If the main goal here is to reach the stage when anomaly detection algorithms could be used and you state that you have access to examples of successful application of linear regression for this problem, this approach could be more productive. One of the principles of successful machine learning application is that several different algorithms can be tried out before final selection based on results.&lt;/p&gt;

&lt;p&gt;It you choose to tune your neural network performance, &lt;a href="http://stackoverflow.com/questions/4617365/what-is-a-learning-curve-in-machine-learning"&gt;learning curve&lt;/a&gt; plotting the effect of change in different hyperparameters on the error rate can be used. Hyperparameters that can be modified are: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;number of features&lt;/li&gt;
&lt;li&gt;order of the polynomial&lt;/li&gt;
&lt;li&gt;regularization parameter &lt;/li&gt;
&lt;li&gt;number of layers in the network&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Best settings can be selected by the performance on cross validation set.&lt;/p&gt;
', NULL, NULL, '''/a'':114C ''/li'':142C,148C,152C,160C ''/p'':29C,96C,136C,175C ''/questions/4617365/what-is-a-learning-curve-in-machine-learning'':111C ''/ul'':161C ''access'':54C ''algorithm'':15C,44C,85C ''anomali'':13C,42C ''appli'':12C ''applic'':59C,80C ''approach'':67C ''attempt'':10C ''base'':93C ''best'':163C ''chang'':119C ''choos'':100C ''consumpt'':25C ''could'':45C,68C ''cross'':172C ''curv'':113C ''detect'':14C,43C ''differ'':84C,121C ''effect'':117C ''enough'':21C ''error'':125C ''even'':8C ''exampl'':56C ''featur'':141C ''final'':91C ''gas'':24C ''get'':19C ''goal'':34C ''good'':20C ''href'':108C ''hyperparamet'':122C,130C ''layer'':156C ''learn'':79C,112C ''li'':138C,143C,149C,153C ''linear'':61C ''machin'':78C ''main'':3C,33C ''modifi'':134C ''network'':28C,105C,159C ''neural'':27C,104C ''number'':139C,154C ''one'':72C ''order'':144C ''p'':1C,30C,97C,162C ''paramet'':151C ''perform'':106C,170C ''plot'':115C ''polynomi'':147C ''predict'':22C ''principl'':75C ''problem'':4C,65C ''product'':71C ''rate'':126C ''reach'':38C ''regress'':62C ''regular'':150C ''result'':95C ''select'':92C,167C ''set'':164C,174C ''sever'':83C ''stackoverflow.com'':110C ''stackoverflow.com/questions/4617365/what-is-a-learning-curve-in-machine-learning'':109C ''stage'':40C ''state'':50C ''success'':58C,77C ''tri'':88C ''tune'':102C ''ul'':137C ''use'':26C,47C,129C ''valid'':173C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (520, 2, '2014-06-21 19:13:47.757', 3, NULL, NULL, '&lt;p&gt;In your training notebook you present results for training with 20 epochs. Have you tried varying that parameter, to see if it affects your performance? This is an important parameter for back-propagation. &lt;/p&gt;

&lt;p&gt;For estimating your model parameters, as user tomaskazemekas pointed out, plotting Learning Curves is a very good approach. In addition to that, you could also create a plot using a model parameter (e.g. training epochs or hidden layer size) vs. Training and Validation error. This will allow you to understand the bias/variance tradeoff, and help you pick a good value for your parameters. &lt;a href="http://www.astroml.org/sklearn_tutorial/practical.html#cross-validation-and-testing" rel="nofollow"&gt;Some info can be found here&lt;/a&gt;. Naturally, it is a good idea to keep a small percentage of your data for a (third) Test set.&lt;/p&gt;

&lt;p&gt;As a side note, it seems that increasing the number of neurons in your model show no significant improvement for your RMSE. This suggests that you could also try with a simpler model, i.e. with less neurons and see how your model behaves.&lt;/p&gt;

&lt;p&gt;In fact, I would suggest (if you haven''t done so already) trying a simple model with few or no parameters first e.g. Linear Regression, and compare your results with the literature, just as a sanity check. &lt;/p&gt;
', NULL, NULL, '''/a'':114C ''/p'':36C,134C,179C,218C ''/sklearn_tutorial/practical.html#cross-validation-and-testing'':105C ''20'':12C ''addit'':57C ''affect'':24C ''allow'':84C ''alreadi'':192C ''also'':62C,163C ''approach'':55C ''back'':34C ''back-propag'':33C ''behav'':178C ''bias/variance'':89C ''check'':217C ''compar'':207C ''could'':61C,162C ''creat'':63C ''curv'':50C ''data'':128C ''done'':190C ''e.g'':70C,203C ''epoch'':13C,72C ''error'':81C ''estim'':39C ''fact'':182C ''first'':202C ''found'':112C ''good'':54C,96C,119C ''haven'':188C ''help'':92C ''hidden'':74C ''href'':102C ''i.e'':169C ''idea'':120C ''import'':30C ''improv'':154C ''increas'':143C ''info'':109C ''keep'':122C ''layer'':75C ''learn'':49C ''less'':171C ''linear'':204C ''literatur'':212C ''model'':41C,68C,150C,168C,177C,196C ''natur'':115C ''neuron'':147C,172C ''nofollow'':107C ''note'':139C ''notebook'':5C ''number'':145C ''p'':1C,37C,135C,180C ''paramet'':19C,31C,42C,69C,100C,201C ''percentag'':125C ''perform'':26C ''pick'':94C ''plot'':48C,65C ''point'':46C ''present'':7C ''propag'':35C ''regress'':205C ''rel'':106C ''result'':8C,209C ''rmse'':157C ''saniti'':216C ''see'':21C,174C ''seem'':141C ''set'':133C ''show'':151C ''side'':138C ''signific'':153C ''simpl'':195C ''simpler'':167C ''size'':76C ''small'':124C ''suggest'':159C,185C ''test'':132C ''third'':131C ''tomaskazemeka'':45C ''tradeoff'':90C ''train'':4C,10C,71C,78C ''tri'':16C,164C,193C ''understand'':87C ''use'':66C ''user'':44C ''valid'':80C ''valu'':97C ''vari'':17C ''vs'':77C ''would'':184C ''www.astroml.org'':104C ''www.astroml.org/sklearn_tutorial/practical.html#cross-validation-and-testing'':103C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (582, 2, '2014-06-25 14:43:53.68', 3, NULL, NULL, '&lt;p&gt;Some research from &lt;a href="http://www.dongnguyen.nl/publications.html" rel="nofollow"&gt;D. Nguyen et al.&lt;/a&gt; try to predict twitter user''s age based on their tweets. Maybe you find them useful. They use logistic and linear regression.&lt;/p&gt;
', NULL, NULL, '''/a'':16C ''/p'':39C ''/publications.html'':9C ''age'':23C ''al'':15C ''base'':24C ''d'':12C ''et'':14C ''find'':30C ''href'':6C ''linear'':37C ''logist'':35C ''mayb'':28C ''nguyen'':13C ''nofollow'':11C ''p'':1C ''predict'':19C ''regress'':38C ''rel'':10C ''research'':3C ''tri'':17C ''tweet'':27C ''twitter'':20C ''use'':32C,34C ''user'':21C ''www.dongnguyen.nl'':8C ''www.dongnguyen.nl/publications.html'':7C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (521, 2, '2014-06-22 12:02:52.773', 2, NULL, NULL, '&lt;p&gt;Conjugate Gradient -- the cg in Function MINimization (nonlinear) Conjugate Gradiant -- requires you to have a gradient function (or approximation) since that is a critical part of the algorithm itself: it needs to find the steepest descent direction quickly.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;fminsearch&lt;/code&gt; implements Nelder-Mead, a nonlinear gradient-free method. Its convergence properties are not anywhere near as good.&lt;/p&gt;

&lt;p&gt;What is your cost function? Are there approximations that are differentiable (pref. twice so you can use the very powerful quasi-Newton methods)?&lt;/p&gt;
', NULL, NULL, '''/code'':44C ''/p'':40C,64C,90C ''algorithm'':29C ''anywher'':60C ''approxim'':20C,73C ''cg'':5C ''code'':42C ''conjug'':2C,10C ''converg'':56C ''cost'':69C ''critic'':25C ''descent'':37C ''differenti'':76C ''direct'':38C ''find'':34C ''fminsearch'':43C ''free'':53C ''function'':7C,18C,70C ''good'':63C ''gradiant'':11C ''gradient'':3C,17C,52C ''gradient-fre'':51C ''implement'':45C ''mead'':48C ''method'':54C,89C ''minim'':8C ''near'':61C ''need'':32C ''nelder'':47C ''nelder-mead'':46C ''newton'':88C ''nonlinear'':9C,50C ''p'':1C,41C,65C ''part'':26C ''power'':85C ''pref'':77C ''properti'':57C ''quasi'':87C ''quasi-newton'':86C ''quick'':39C ''requir'':12C ''sinc'':21C ''steepest'':36C ''twice'':78C ''use'':82C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5333, 2, '2015-03-17 07:21:54.11', 1, NULL, NULL, '&lt;p&gt;If you filter something out by choosing one branch over another branch in the tree, the observations you did not choose are forever lost.&lt;/p&gt;

&lt;p&gt;But to directly answer your question - no, it does not always make it more general. If you construct a tree where all the decision are exactly the same, then pruning does not make it more general.&lt;/p&gt;
', NULL, NULL, '''/p'':26C,64C ''alway'':38C ''anoth'':12C ''answer'':31C ''branch'':10C,13C ''choos'':8C,22C ''construct'':45C ''decis'':51C ''direct'':30C ''exact'':53C ''filter'':4C ''forev'':24C ''general'':42C,63C ''lost'':25C ''make'':39C,60C ''observ'':18C ''one'':9C ''p'':1C,27C ''prune'':57C ''question'':33C ''someth'':5C ''tree'':16C,47C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (710, 1, '2014-07-09 11:05:40.813', 1, 214, 'Commercial Text Summarization Tools', '&lt;p&gt;I''m looking for commercial text summarization tools (APIs, Libraries,...) which are able to perform any of the following tasks:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Extractive Multi-Document Summarization (Generic or query-based)&lt;/li&gt;
&lt;li&gt;Extractive Single-Document Summarization (Generic or query-based)&lt;/li&gt;
&lt;li&gt;Generative Single-Document Summarization (Generic or query-based)&lt;/li&gt;
&lt;li&gt;Generative Multi-Document Summarization (Generic or query-based)&lt;/li&gt;
&lt;/ol&gt;
', 'text-mining', NULL, '''/li'':42C,54C,66C,78C ''/ol'':79C ''/p'':29C ''abl'':21C ''api'':17C ''base'':41C,53C,65C,77C ''commerci'':1A,13C ''document'':35C,47C,59C,71C ''extract'':32C,44C ''follow'':27C ''generat'':56C,68C ''generic'':37C,49C,61C,73C ''li'':31C,43C,55C,67C ''librari'':18C ''look'':11C ''m'':10C ''mine'':7B ''multi'':34C,70C ''multi-docu'':33C,69C ''ol'':30C ''p'':8C ''perform'':23C ''queri'':40C,52C,64C,76C ''query-bas'':39C,51C,63C,75C ''singl'':46C,58C ''single-docu'':45C,57C ''summar'':3A,15C,36C,48C,60C,72C ''task'':28C ''text'':2A,6B,14C ''text-min'':5B ''tool'':4A,16C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (523, 2, '2014-06-22 16:03:39.047', 2, NULL, NULL, '&lt;p&gt;In your notebooks, I did not see your neural network model, can you point which library is using, how many layers you have and what type of neural network are you using?&lt;/p&gt;

&lt;p&gt;In your notebooks, it seems you are using the noisy and outlier dataset to train the neural network, I think you should train the neural network on the dataset that you do not have any outliers so that you could see the observation distance from the prediction of the neural network to label the observation either outlier or not. &lt;/p&gt;

&lt;p&gt;I wrote &lt;a href="http://bugra.github.io/work/notes/2014-03-31/outlier-detection-in-time-series-signals-fft-median-filtering/" rel="nofollow"&gt;couple&lt;/a&gt; of &lt;a href="http://bugra.github.io/work/notes/2014-05-11/robust-regression-and-outlier-detection-via-gaussian-processes/" rel="nofollow"&gt;things&lt;/a&gt; on outlier detection in time-series signals, your data is highly seasonal as sobach mentioned and you could use FFT(first link above) to get the overall trend in the signal. After you get the frequency component in the gas consumption, you could look at the high frequency components to get the outliers. &lt;/p&gt;

&lt;p&gt;Also if you want to insist on using neural network for seasonal data, you may want to check recurrent neural networks out as they could incorporate the past observations better than a vanilla neural network, and supposedly may provide a better result for the data that you have. &lt;/p&gt;
', NULL, NULL, '''/a'':107C,117C ''/p'':34C,95C,172C,222C ''/work/notes/2014-03-31/outlier-detection-in-time-series-signals-fft-median-filtering/'':103C ''/work/notes/2014-05-11/robust-regression-and-outlier-detection-via-gaussian-processes/'':113C ''also'':174C ''better'':203C,214C ''bugra.github.io'':102C,112C ''bugra.github.io/work/notes/2014-03-31/outlier-detection-in-time-series-signals-fft-median-filtering/'':101C ''bugra.github.io/work/notes/2014-05-11/robust-regression-and-outlier-detection-via-gaussian-processes/'':111C ''check'':191C ''compon'':155C,167C ''consumpt'':159C ''could'':75C,136C,161C,198C ''coupl'':106C ''data'':127C,186C,218C ''dataset'':48C,64C ''detect'':120C ''distanc'':79C ''either'':91C ''fft'':138C ''first'':139C ''frequenc'':154C,166C ''gas'':158C ''get'':143C,152C,169C ''high'':129C,165C ''href'':100C,110C ''incorpor'':199C ''insist'':179C ''label'':88C ''layer'':22C ''librari'':17C ''link'':140C ''look'':162C ''mani'':21C ''may'':188C,211C ''mention'':133C ''model'':12C ''network'':11C,30C,53C,61C,86C,183C,194C,208C ''neural'':10C,29C,52C,60C,85C,182C,193C,207C ''nofollow'':105C,115C ''noisi'':45C ''notebook'':4C,38C ''observ'':78C,90C,202C ''outlier'':47C,71C,92C,119C,171C ''overal'':145C ''p'':1C,35C,96C,173C ''past'':201C ''point'':15C ''predict'':82C ''provid'':212C ''recurr'':192C ''rel'':104C,114C ''result'':215C ''season'':130C,185C ''see'':8C,76C ''seem'':40C ''seri'':124C ''signal'':125C,149C ''sobach'':132C ''suppos'':210C ''thing'':116C ''think'':55C ''time'':123C ''time-seri'':122C ''train'':50C,58C ''trend'':146C ''type'':27C ''use'':19C,33C,43C,137C,181C ''vanilla'':206C ''want'':177C,189C ''wrote'':98C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (525, 2, '2014-06-22 18:35:22.207', 10, NULL, NULL, '&lt;p&gt;In order to understand the variety of ways machine learning can be integrated into production applications, I think it is useful to look at open source projects and papers/blog posts from companies describing their infrastructure.&lt;/p&gt;

&lt;p&gt;The common theme that these systems have is the separation of model training from model application. In production systems, model application needs to be fast, on the order of 100s of ms, but there is more freedom in how frequently fitted model parameters (or equivalent) need to be updated.&lt;/p&gt;

&lt;p&gt;People use a wide range of solutions for model training and deployment:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;Build a model, then export and deploy it with PMML&lt;/p&gt;

&lt;blockquote&gt;
  &lt;ul&gt;
  &lt;li&gt;&lt;p&gt;&lt;a href="http://nerds.airbnb.com/architecting-machine-learning-system-risk/"&gt;AirBnB describes their model training&lt;/a&gt; in R/Python and deployment of PMML models via OpenScoring.&lt;/p&gt;&lt;/li&gt;
  &lt;li&gt;&lt;p&gt;&lt;a href="http://www.cascading.org/projects/pattern/"&gt;Pattern&lt;/a&gt; is project related to &lt;a href="http://www.cascading.org/"&gt;Cascading&lt;/a&gt; that can consume PMML and deploy predictive models.&lt;/p&gt;&lt;/li&gt;
  &lt;/ul&gt;
&lt;/blockquote&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Build a model in MapReduce and access values in a custom system&lt;/p&gt;

&lt;blockquote&gt;
  &lt;ul&gt;
  &lt;li&gt;&lt;p&gt;&lt;a href="http://codeascraft.com/2014/06/18/conjecture-scalable-machine-learning-in-hadoop-with-scalding/"&gt;Conjecture is an open source project from Etsy&lt;/a&gt; that allows for model training with &lt;a href="https://github.com/twitter/scalding"&gt;Scalding&lt;/a&gt;, an easier to use scala wrapper around MapReduce, and  deployment via Php.&lt;/p&gt;&lt;/li&gt;
  &lt;li&gt;&lt;p&gt;&lt;a href="http://www.kiji.org/"&gt;Kiji is an open source project from WibiData&lt;/a&gt; that allows for real-time model scoring (application) as well as functioanlity for persisting user data and training models on that data via &lt;a href="https://github.com/twitter/scalding"&gt;Scalding.&lt;/a&gt;&lt;/p&gt;&lt;/li&gt;
  &lt;/ul&gt;
&lt;/blockquote&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Use an online system that allows for continuously updating model parameters.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;ul&gt;
  &lt;li&gt;&lt;a href="http://dl.acm.org/citation.cfm?id=1242610"&gt;Google released a great paper about an online collaborative filtering&lt;/a&gt; they implemented to deal with recommendations in Google News.&lt;/li&gt;
  &lt;/ul&gt;
&lt;/blockquote&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/2014/06/18/conjecture-scalable-machine-learning-in-hadoop-with-scalding/'':197C ''/a'':131C,151C,160C,206C,219C,247C,278C,316C ''/architecting-machine-learning-system-risk/'':125C ''/blockquote'':172C,282C,328C ''/citation.cfm?id=1242610'':305C ''/li'':142C,170C,173C,233C,280C,283C,326C,329C ''/p'':37C,88C,102C,116C,141C,169C,188C,232C,279C,297C ''/projects/pattern/'':149C ''/twitter/scalding'':217C,276C ''/ul'':171C,281C,327C,330C ''100s'':68C ''access'':182C ''airbnb'':126C ''allow'':208C,249C,291C ''applic'':17C,54C,59C,256C ''around'':226C ''blockquot'':117C,189C,298C ''build'':106C,176C ''cascad'':159C ''codeascraft.com'':196C ''codeascraft.com/2014/06/18/conjecture-scalable-machine-learning-in-hadoop-with-scalding/'':195C ''collabor'':314C ''common'':40C ''compani'':33C ''conjectur'':198C ''consum'':163C ''continu'':293C ''custom'':186C ''data'':264C,270C ''deal'':320C ''deploy'':101C,112C,135C,166C,229C ''describ'':34C,127C ''dl.acm.org'':304C ''dl.acm.org/citation.cfm?id=1242610'':303C ''easier'':221C ''equival'':83C ''etsi'':205C ''export'':110C ''fast'':63C ''filter'':315C ''fit'':79C ''freedom'':75C ''frequent'':78C ''functioanl'':260C ''github.com'':216C,275C ''github.com/twitter/scalding'':215C,274C ''googl'':306C,324C ''great'':309C ''href'':122C,146C,157C,194C,214C,237C,273C,302C ''implement'':318C ''infrastructur'':36C ''integr'':14C ''kiji'':239C ''learn'':11C ''li'':104C,119C,143C,174C,191C,234C,284C,300C ''look'':24C ''machin'':10C ''mapreduc'':180C,227C ''model'':50C,53C,58C,80C,98C,108C,129C,138C,168C,178C,210C,254C,267C,295C ''ms'':70C ''need'':60C,84C ''nerds.airbnb.com'':124C ''nerds.airbnb.com/architecting-machine-learning-system-risk/'':123C ''news'':325C ''onlin'':288C,313C ''open'':26C,201C,242C ''openscor'':140C ''order'':3C,66C ''p'':1C,38C,89C,105C,120C,144C,175C,192C,235C,285C ''paper'':310C ''papers/blog'':30C ''paramet'':81C,296C ''pattern'':150C ''peopl'':90C ''persist'':262C ''php'':231C ''pmml'':115C,137C,164C ''post'':31C ''predict'':167C ''product'':16C,56C ''project'':28C,153C,203C,244C ''r/python'':133C ''rang'':94C ''real'':252C ''real-tim'':251C ''recommend'':322C ''relat'':154C ''releas'':307C ''scala'':224C ''scald'':218C,277C ''score'':255C ''separ'':48C ''solut'':96C ''sourc'':27C,202C,243C ''system'':44C,57C,187C,289C ''theme'':41C ''think'':19C ''time'':253C ''train'':51C,99C,130C,211C,266C ''ul'':103C,118C,190C,299C ''understand'':5C ''updat'':87C,294C ''use'':22C,91C,223C,286C ''user'':263C ''valu'':183C ''varieti'':7C ''via'':139C,230C,271C ''way'':9C ''well'':258C ''wibidata'':246C ''wide'':93C ''wrapper'':225C ''www.cascading.org'':148C,158C ''www.cascading.org/projects/pattern/'':147C ''www.kiji.org'':238C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (527, 1, '2014-06-22 21:45:35.8', 6, 380, 'Score matrix string similarity', '&lt;p&gt;I have a load of documents, which have a load of key value pairs in them. The key might not be unique so there might be multiple keys of the same type with different values.&lt;/p&gt;

&lt;p&gt;I want to compare the similarity of the keys between 2 documents. More specifically the string similarity of these values. I am thinking of using something like the &lt;a href="http://en.wikipedia.org/wiki/Smith%E2%80%93Waterman_algorithm"&gt;Smith-Waterman Algorithm&lt;/a&gt; to compare the similarity.&lt;/p&gt;

&lt;p&gt;So I''ve drawn a picture of how I''m thinking about representing the data - &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/VC4em.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;The values in the cells are the result of the smith-waterman algorithm (or some other string similarity metric).&lt;/p&gt;

&lt;p&gt;Image that this matrix represents a key type of "things" I then need to add the "things" similarity score into a vector of 0 or 1. Thats ok.&lt;/p&gt;

&lt;p&gt;What I can''t figure out is how I determine if the matrix is similar or not similar - ideally I want to convert the matrix to an number between 0 and 1 and then I''ll just set a threshold to score it as either 0 or 1.&lt;/p&gt;

&lt;p&gt;Any ideas how I can create a score of the matrix? Does anyone know any algorithms that do this type of thing (obviously things like how smith waterman works is kind of applicable).&lt;/p&gt;
', 'algorithms  similarity', NULL, '''/a'':82C ''/p'':43C,87C,104C,116C,138C,168C,218C,253C ''/vc4em.png'':110C ''/wiki/smith%e2%80%93waterman_algorithm'':77C ''0'':163C,199C,215C ''1'':165C,201C,217C ''2'':55C ''add'':154C ''algorithm'':5B,81C,131C,235C ''alt'':111C ''anyon'':232C ''applic'':252C ''cell'':122C ''compar'':48C,84C ''convert'':192C ''creat'':225C ''data'':103C ''descript'':114C ''determin'':179C ''differ'':41C ''document'':13C,56C ''drawn'':92C ''either'':214C ''en.wikipedia.org'':76C ''en.wikipedia.org/wiki/smith%e2%80%93waterman_algorithm'':75C ''enter'':112C ''figur'':174C ''href'':74C ''i.stack.imgur.com'':109C ''i.stack.imgur.com/vc4em.png'':108C ''idea'':221C ''ideal'':188C ''imag'':113C,140C ''img'':106C ''key'':19C,25C,35C,53C,146C ''kind'':250C ''know'':233C ''like'':71C,244C ''ll'':205C ''load'':11C,17C ''m'':98C ''matrix'':2A,143C,182C,194C,230C ''metric'':137C ''might'':26C,32C ''multipl'':34C ''need'':152C ''number'':197C ''obvious'':242C ''ok'':167C ''p'':7C,44C,88C,105C,117C,139C,169C,219C ''pair'':21C ''pictur'':94C ''repres'':101C,144C ''result'':125C ''score'':1A,158C,211C,227C ''set'':207C ''similar'':4A,6B,50C,61C,86C,136C,157C,184C,187C ''smith'':79C,129C,246C ''smith-waterman'':78C,128C ''someth'':70C ''specif'':58C ''src'':107C ''string'':3A,60C,135C ''that'':166C ''thing'':149C,156C,241C,243C ''think'':67C,99C ''threshold'':209C ''type'':39C,147C,239C ''uniqu'':29C ''use'':69C ''valu'':20C,42C,64C,119C ''ve'':91C ''vector'':161C ''want'':46C,190C ''waterman'':80C,130C,247C ''work'':248C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (529, 2, '2014-06-23 03:44:57.643', 3, NULL, NULL, '&lt;p&gt;&lt;a href="http://nerds.airbnb.com/architecting-machine-learning-system-risk/" rel="nofollow"&gt;Airbnb&lt;/a&gt; and &lt;a href="http://codeascraft.com/2014/06/18/conjecture-scalable-machine-learning-in-hadoop-with-scalding/" rel="nofollow"&gt;Etsy&lt;/a&gt; both recently posted detailed information about their workflows.&lt;/p&gt;
', NULL, NULL, '''/2014/06/18/conjecture-scalable-machine-learning-in-hadoop-with-scalding/'':16C ''/a'':10C,20C ''/architecting-machine-learning-system-risk/'':6C ''/p'':29C ''airbnb'':9C ''codeascraft.com'':15C ''codeascraft.com/2014/06/18/conjecture-scalable-machine-learning-in-hadoop-with-scalding/'':14C ''detail'':24C ''etsi'':19C ''href'':3C,13C ''inform'':25C ''nerds.airbnb.com'':5C ''nerds.airbnb.com/architecting-machine-learning-system-risk/'':4C ''nofollow'':8C,18C ''p'':1C ''post'':23C ''recent'':22C ''rel'':7C,17C ''workflow'':28C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (530, 1, '2014-06-23 04:39:26.623', 4, 269, 'How to select algorithms for ensemble methods?', '&lt;p&gt;There is a general recommendation that algorithms in ensemble learning combinations should be different in nature. Is there a classification table, a scale or some rules that allow to evaluate how far away are the algorithms from each other? What are the best combinations? &lt;/p&gt;
', 'machine-learning', NULL, '''/p'':56C ''algorithm'':4A,18C,47C ''allow'':39C ''away'':44C ''best'':54C ''classif'':31C ''combin'':22C,55C ''differ'':25C ''ensembl'':6A,20C ''evalu'':41C ''far'':43C ''general'':15C ''learn'':10B,21C ''machin'':9B ''machine-learn'':8B ''method'':7A ''natur'':27C ''p'':11C ''recommend'':16C ''rule'':37C ''scale'':34C ''select'':3A ''tabl'':32C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (538, 1, '2014-06-23 13:31:41.443', 2, 241, 'What are your favorite sources for news about Machine Learning and Data Science?', '&lt;p&gt;Data Science and Machine Learning include a lot of different topics and itÂ´s hard to stay up-to-date about all the news about papers, researches or new tutorials and tools.&lt;/p&gt;

&lt;p&gt;What sources do you use to get all the information?&lt;/p&gt;

&lt;p&gt;I use mostly Reddit as my first source and the subreddits Machine Learning and R
&lt;a href="http://www.reddit.com/r/MachineLearning" rel="nofollow"&gt;http://www.reddit.com/r/MachineLearning&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.reddit.com/r/rstats" rel="nofollow"&gt;http://www.reddit.com/r/rstats&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;But also datatau.com and of course the great KDNuggets page &lt;/p&gt;
', 'machine-learning', NULL, '''/p'':51C,63C,112C ''/r/machinelearning'':84C ''/r/machinelearning&lt;/a&gt;&lt;/p&gt;'':89C ''/r/rstats'':95C ''/r/rstats&lt;/a&gt;&lt;/p&gt;'':100C ''also'':103C ''cours'':107C ''data'':12A,18C ''datatau.com'':104C ''date'':38C ''differ'':27C ''favorit'':4A ''first'':71C ''get'':59C ''great'':109C ''hard'':32C ''href'':81C,92C ''includ'':23C ''inform'':62C ''itâ'':30C ''kdnugget'':110C ''learn'':10A,16B,22C,77C ''lot'':25C ''machin'':9A,15B,21C,76C ''machine-learn'':14B ''most'':67C ''new'':47C ''news'':7A,42C ''nofollow'':86C,97C ''p'':17C,52C,64C,90C,101C ''page'':111C ''paper'':44C ''r'':79C ''reddit'':68C ''rel'':85C,96C ''research'':45C ''scienc'':13A,19C ''sourc'':5A,54C,72C ''stay'':34C ''subreddit'':75C ''tool'':50C ''topic'':28C ''tutori'':48C ''up-to-d'':35C ''use'':57C,66C ''www.reddit.com'':83C,88C,94C,99C ''www.reddit.com/r/machinelearning'':82C ''www.reddit.com/r/machinelearning&lt;/a&gt;&lt;/p&gt;'':87C ''www.reddit.com/r/rstats'':93C ''www.reddit.com/r/rstats&lt;/a&gt;&lt;/p&gt;'':98C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (539, 2, '2014-06-23 13:36:18.273', 3, NULL, NULL, '&lt;p&gt;I like following certain tags on stats.stackexchange.com (a.k.a. CrossValidated), as well as the &lt;code&gt;r&lt;/code&gt; tag on StackOverflow.&lt;/p&gt;

&lt;p&gt;Arxiv.org is also pretty cool, it has several subsections devoted to stats and ML. &lt;/p&gt;
', NULL, NULL, '''/code'':17C ''/p'':21C,37C ''a.k.a'':9C ''also'':25C ''arxiv.org'':23C ''certain'':5C ''code'':15C ''cool'':27C ''crossvalid'':10C ''devot'':32C ''follow'':4C ''like'':3C ''ml'':36C ''p'':1C,22C ''pretti'':26C ''r'':16C ''sever'':30C ''stackoverflow'':20C ''stat'':34C ''stats.stackexchange.com'':8C ''subsect'':31C ''tag'':6C,18C ''well'':12C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (877, 1, '2014-07-31 18:52:56.307', 7, 151, 'What is the use of user data collection besides serving ads?', '&lt;p&gt;Well this looks like the most suited place for this question.   &lt;/p&gt;

&lt;p&gt;Every website collect data of the user, some just for usability and personalization, but the majority like social networks track every move on the web, some free apps on your phone scan text messages, call history and so on.&lt;/p&gt;

&lt;p&gt;All this data siphoning is just for selling your profile for advertisers?&lt;/p&gt;
', 'data-mining', NULL, '''/p'':27C,68C,82C ''ad'':11A ''advertis'':81C ''app'':56C ''besid'':9A ''call'':63C ''collect'':8A,31C ''data'':7A,13B,32C,72C ''data-min'':12B ''everi'':29C,49C ''free'':55C ''histori'':64C ''like'':19C,45C ''look'':18C ''major'':44C ''messag'':62C ''mine'':14B ''move'':50C ''network'':47C ''p'':15C,28C,69C ''person'':41C ''phone'':59C ''place'':23C ''profil'':79C ''question'':26C ''scan'':60C ''sell'':77C ''serv'':10A ''siphon'':73C ''social'':46C ''suit'':22C ''text'':61C ''track'':48C ''usabl'':39C ''use'':4A ''user'':6A,35C ''web'':53C ''websit'':30C ''well'':16C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (531, 1, '2014-06-23 07:03:15.643', 7, 452, 'Binary classification model for sparse / biased data', '&lt;p&gt;I have a dataset with following specifications:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Training dataset with 193176 samples with 2821 positives&lt;/li&gt;
&lt;li&gt;Test Dataset with 82887 samples with 673 positives&lt;/li&gt;
&lt;li&gt;There are 10 features.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I want to perform a binary classification ( say, 0/1 ). The issue I am facing is that the data is very biased or rather sparse. After normalization and scaling the data along with some feature engineering and using a couple of different algorithms, these are the best results I could achieve:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;mean square error : 0.00804710026904
Confusion matrix : [[82214   667]
                   [    0     6]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;i.e only 6 correct positive hits. This is using logistic regression. Here are the various things I tried with this:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Different algorithms like RandomForest, DecisionTree, SVM&lt;/li&gt;
&lt;li&gt;Changing parameters value to call the function&lt;/li&gt;
&lt;li&gt;Some intuition based feature engineering to include compounded features&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Now, my questions are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;What can I do to improve the number of positive hits ? &lt;/li&gt;
&lt;li&gt;How can one determine if there is an overfit in such a case ? ( I have tried plotting etc. )&lt;/li&gt;
&lt;li&gt;At what point could one conclude if maybe this is the best possible fit I could have? ( which seems sad considering only 6 hits out of 673 )&lt;/li&gt;
&lt;li&gt;Is there a way I could make the positive sample instances weigh more so the pattern recognition improves leading to more hits ?&lt;/li&gt;
&lt;li&gt;Which graphical plots could help detect outliers or some intuition about which pattern would fit the best?&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I am using the scikit-learn library with Python and all implementations are library functions.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;edit:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Here are the results with a few other algorithms:&lt;/p&gt;

&lt;p&gt;Random Forest Classifier(n_estimators=100)&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[[82211   667]
[    3     6]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Decision Trees:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[[78611   635]
[ 3603    38]]
&lt;/code&gt;&lt;/pre&gt;
', 'machine-learning  python  classification  logistic-regression  topic-model', NULL, '''/code'':120C,335C,347C ''/li'':38C,48C,54C,152C,161C,172C,193C,213C,242C,266C,285C ''/ol'':286C ''/p'':27C,107C,143C,179C,304C,309C,320C,328C,340C ''/pre'':121C,336C,348C ''/strong'':308C ''/ul'':55C,173C ''0'':118C ''0.00804710026904'':113C ''0/1'':65C ''10'':52C ''100'':327C ''193176'':33C ''2821'':36C ''3'':333C ''3603'':345C ''38'':346C ''6'':119C,125C,237C,334C ''635'':344C ''667'':117C,332C ''673'':46C,241C ''78611'':343C ''82211'':331C ''82214'':116C ''82887'':43C ''achiev'':106C ''algorithm'':98C,147C,319C ''along'':87C ''base'':165C ''best'':102C,226C,284C ''bias'':6A,77C ''binari'':1A,62C ''call'':158C ''case'':207C ''chang'':154C ''classif'':2A,12B,63C ''classifi'':324C ''code'':109C,330C,342C ''compound'':170C ''conclud'':220C ''confus'':114C ''consid'':235C ''correct'':126C ''could'':105C,218C,230C,249C,271C ''coupl'':95C ''data'':7A,74C,86C ''dataset'':23C,31C,41C ''decis'':338C ''decisiontre'':150C ''detect'':273C ''determin'':198C ''differ'':97C,146C ''edit'':307C ''engin'':91C,167C ''error'':112C ''estim'':326C ''etc'':212C ''face'':70C ''featur'':53C,90C,166C,171C ''fit'':228C,282C ''follow'':25C ''forest'':323C ''function'':160C,303C ''graphic'':269C ''help'':272C ''hit'':128C,192C,238C,265C ''i.e'':123C ''implement'':300C ''improv'':187C,261C ''includ'':169C ''instanc'':254C ''intuit'':164C,277C ''issu'':67C ''lead'':262C ''learn'':10B,294C ''li'':29C,39C,49C,145C,153C,162C,181C,194C,214C,243C,267C ''librari'':295C,302C ''like'':148C ''logist'':14B,132C ''logistic-regress'':13B ''machin'':9B ''machine-learn'':8B ''make'':250C ''matrix'':115C ''mayb'':222C ''mean'':110C ''model'':3A,18B ''n'':325C ''normal'':82C ''number'':189C ''ol'':180C ''one'':197C,219C ''outlier'':274C ''overfit'':203C ''p'':19C,56C,122C,174C,287C,305C,310C,321C,337C ''paramet'':155C ''pattern'':259C,280C ''perform'':60C ''plot'':211C,270C ''point'':217C ''posit'':37C,47C,127C,191C,252C ''possibl'':227C ''pre'':108C,329C,341C ''python'':11B,297C ''question'':177C ''random'':322C ''randomforest'':149C ''rather'':79C ''recognit'':260C ''regress'':15B,133C ''result'':103C,314C ''sad'':234C ''sampl'':34C,44C,253C ''say'':64C ''scale'':84C ''scikit'':293C ''scikit-learn'':292C ''seem'':233C ''spars'':5A,80C ''specif'':26C ''squar'':111C ''strong'':306C ''svm'':151C ''test'':40C ''thing'':138C ''topic'':17B ''topic-model'':16B ''train'':30C ''tree'':339C ''tri'':140C,210C ''ul'':28C,144C ''use'':93C,131C,290C ''valu'':156C ''various'':137C ''want'':58C ''way'':247C ''weigh'':255C ''would'':281C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (583, 2, '2014-06-25 15:42:19.287', 3, NULL, NULL, '&lt;p&gt;The original MacQueen k-means publication (the first to use the name "kmeans") is an online algorithm.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;MacQueen, J. B. (1967). "Some Methods for classification and Analysis of Multivariate Observations". Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability 1. University of California Press. pp. 281â297&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;After assigning each point, the mean is incrementally updated.&lt;/p&gt;

&lt;p&gt;As far as I can tell, it was also meant to be a single pass over the data only, although it can be trivially repeated multiple times to reassign points until convergence.&lt;/p&gt;

&lt;p&gt;MacQueen usually takes fewer iterations than Lloyds to converge if your data is shuffled. On ordered data, it can have problems. On the downside, it requires more computation for each object, so each iteration takes slightly longer.&lt;/p&gt;

&lt;p&gt;When you implement a parallel version of k-means, make sure to study the update formulas in MacQueens publication. They''re useful.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':55C ''/p'':20C,54C,66C,100C,139C,164C ''1'':46C ''1967'':26C ''281â'':52C ''297'':53C ''5th'':38C ''algorithm'':19C ''also'':76C ''although'':87C ''analysi'':32C ''assign'':58C ''b'':25C ''berkeley'':39C ''blockquot'':21C ''california'':49C ''classif'':30C ''comput'':129C ''converg'':99C,110C ''data'':85C,113C,118C ''downsid'':125C ''far'':69C ''fewer'':105C ''first'':10C ''formula'':157C ''implement'':143C ''increment'':64C ''iter'':106C,135C ''j'':24C ''k'':6C,149C ''k-mean'':5C,148C ''kmean'':15C ''lloyd'':108C ''longer'':138C ''macqueen'':4C,23C,102C,159C ''make'':151C ''mathemat'':42C ''mean'':7C,62C,150C ''meant'':77C ''method'':28C ''multipl'':93C ''multivari'':34C ''name'':14C ''object'':132C ''observ'':35C ''onlin'':18C ''order'':117C ''origin'':3C ''p'':1C,22C,56C,67C,101C,140C ''parallel'':145C ''pass'':82C ''point'':60C,97C ''pp'':51C ''press'':50C ''probabl'':45C ''problem'':122C ''proceed'':36C ''public'':8C,160C ''re'':162C ''reassign'':96C ''repeat'':92C ''requir'':127C ''shuffl'':115C ''singl'':81C ''slight'':137C ''statist'':43C ''studi'':154C ''sure'':152C ''symposium'':40C ''take'':104C,136C ''tell'':73C ''time'':94C ''trivial'':91C ''univers'':47C ''updat'':65C,156C ''use'':12C,163C ''usual'':103C ''version'':146C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (532, 2, '2014-06-23 08:03:50.327', 7, NULL, NULL, '&lt;p&gt;In general in an ensemble you try to combine the opinions of multiple classifiers. The idea is like asking a bunch of experts on the same thing. You get multiple opinions and you later have to combine their answers (e.g. by a voting scheme). For this trick to work you want the classifiers to be different from each other, that is you don''t want to ask the same "expert" twice for the same thing.&lt;/p&gt;

&lt;p&gt;In practice, the classifiers do not have to be different in the sense of a different algorithm. What you can do is train the same algorithm with different subset of the data or a different subset of features (or both). If you use different training sets you end up with different models and different "independent" classifiers. &lt;/p&gt;

&lt;p&gt;There is no golden rule on what works best in general. You have to try to see if there is an improvement for your specific problem. &lt;/p&gt;
', NULL, NULL, '''/p'':77C,135C,163C ''algorithm'':95C,104C ''answer'':40C ''ask'':20C,68C ''best'':145C ''bunch'':22C ''classifi'':15C,54C,82C,134C ''combin'':10C,38C ''data'':110C ''differ'':57C,88C,94C,106C,113C,122C,129C,132C ''e.g'':41C ''end'':126C ''ensembl'':6C ''expert'':24C,71C ''featur'':116C ''general'':3C,147C ''get'':30C ''golden'':140C ''idea'':17C ''improv'':158C ''independ'':133C ''later'':35C ''like'':19C ''model'':130C ''multipl'':14C,31C ''opinion'':12C,32C ''p'':1C,78C,136C ''practic'':80C ''problem'':162C ''rule'':141C ''scheme'':45C ''see'':153C ''sens'':91C ''set'':124C ''specif'':161C ''subset'':107C,114C ''thing'':28C,76C ''train'':101C,123C ''tri'':8C,151C ''trick'':48C ''twice'':72C ''use'':121C ''vote'':44C ''want'':52C,66C ''work'':50C,144C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (533, 2, '2014-06-23 08:13:22.837', 6, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Since you are doing binary classification, have you tried adjusting the classification threshold? Since your algorithm seems rather insensitive, I would try lowering it and check if there is an improvement.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;You can always use &lt;a href="http://stackoverflow.com/questions/4617365/what-is-a-learning-curve-in-machine-learning"&gt;Learning Curves&lt;/a&gt;, or a plot of one model parameter vs. Training and Validation error to determine whether your model is overfitting. It seems it is under fitting in your case, but that''s just intuition.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Well, ultimately it depends on your dataset, and the different models you have tried. At this point, and without further testing, there can not be a definite answer.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Without claiming to be an expert on the topic, there are a number of different techniques you may follow (hint: &lt;a href="http://florianhartl.com/thoughts-on-machine-learning-dealing-with-skewed-classes.html"&gt;first link on google&lt;/a&gt;), but in my opinion you should first make sure you choose your cost function carefully, so that it represents what you are actually looking for. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Not sure what you mean by pattern intuition, can you elaborate?&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;By the way, what were your results with the different algorithms you tried? Were they any different?&lt;/p&gt;
', NULL, NULL, '''/a'':50C,149C ''/li'':36C,85C,117C,176C,191C ''/ol'':192C ''/p'':35C,84C,116C,175C,190C,211C ''/questions/4617365/what-is-a-learning-curve-in-machine-learning'':47C ''/thoughts-on-machine-learning-dealing-with-skewed-classes.html'':144C ''actual'':172C ''adjust'':13C ''algorithm'':19C,204C ''alway'':41C ''answer'':115C ''binari'':8C ''care'':164C ''case'':78C ''check'':29C ''choos'':160C ''claim'':121C ''classif'':9C,15C ''cost'':162C ''curv'':49C ''dataset'':94C ''definit'':114C ''depend'':91C ''determin'':64C ''differ'':97C,134C,203C,210C ''elabor'':189C ''error'':62C ''expert'':125C ''first'':145C,156C ''fit'':75C ''florianhartl.com'':143C ''florianhartl.com/thoughts-on-machine-learning-dealing-with-skewed-classes.html'':142C ''follow'':138C ''function'':163C ''googl'':148C ''hint'':139C ''href'':44C,141C ''improv'':34C ''insensit'':22C ''intuit'':83C,186C ''learn'':48C ''li'':2C,37C,86C,118C,177C ''link'':146C ''look'':173C ''lower'':26C ''make'':157C ''may'':137C ''mean'':183C ''model'':56C,67C,98C ''number'':132C ''ol'':1C ''one'':55C ''opinion'':153C ''overfit'':69C ''p'':3C,38C,87C,119C,178C,193C ''paramet'':57C ''pattern'':185C ''plot'':53C ''point'':104C ''rather'':21C ''repres'':168C ''result'':200C ''seem'':20C,71C ''sinc'':4C,17C ''stackoverflow.com'':46C ''stackoverflow.com/questions/4617365/what-is-a-learning-curve-in-machine-learning'':45C ''sure'':158C,180C ''techniqu'':135C ''test'':108C ''threshold'':16C ''topic'':128C ''train'':59C ''tri'':12C,25C,101C,206C ''ultim'':89C ''use'':42C ''valid'':61C ''vs'':58C ''way'':196C ''well'':88C ''whether'':65C ''without'':106C,120C ''would'':24C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (534, 2, '2014-06-23 09:23:27.617', 2, NULL, NULL, '&lt;p&gt;If your goal is to transform your matrix into a number (your similarity measure), you may want to use a &lt;a href="http://en.wikipedia.org/wiki/Matrix_norm" rel="nofollow"&gt;matrix norm&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;For instance, using the &lt;a href="http://en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm" rel="nofollow"&gt;Frobenius norm&lt;/a&gt; on your example would return 1.488086.&lt;/p&gt;
', NULL, NULL, '''/a'':31C,47C ''/p'':32C,54C ''/wiki/matrix_norm'':26C ''/wiki/matrix_norm#frobenius_norm'':42C ''1.488086'':53C ''en.wikipedia.org'':25C,41C ''en.wikipedia.org/wiki/matrix_norm'':24C ''en.wikipedia.org/wiki/matrix_norm#frobenius_norm'':40C ''exampl'':50C ''frobenius'':45C ''goal'':4C ''href'':23C,39C ''instanc'':35C ''matrix'':9C,29C ''may'':17C ''measur'':15C ''nofollow'':28C,44C ''norm'':30C,46C ''number'':12C ''p'':1C,33C ''rel'':27C,43C ''return'':52C ''similar'':14C ''transform'':7C ''use'':20C,36C ''want'':18C ''would'':51C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (535, 2, '2014-06-23 09:42:46.45', 2, NULL, NULL, '&lt;p&gt;As I understood, Document 1 and Document 2 may have different number of keys. And you wand to get final similarity evaluation between 0 and 1. If so, I would propose following algorithm:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Sum of max. vals is equal to 0.&lt;/li&gt;
&lt;li&gt;Select maximum value from doc-doc matrix and add it to Sum of max. vals.&lt;/li&gt;
&lt;li&gt;Remove row and column with maximum value from the matrix.&lt;/li&gt;
&lt;li&gt;Repeat steps 2-3 until rows or columns are ended.&lt;/li&gt;
&lt;li&gt;Denominate Sum of max. vals by average number of key words in two texts.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Final estimation would be equal to 1, if both documents have identical length, and every word from Doc 1 has equivalent in Doc 2.&lt;/p&gt;

&lt;p&gt;You haven''t mentioned software, you are using, but here is &lt;strong&gt;R&lt;/strong&gt; example of function, computing such similarity (it takes object of class matrix as input):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;eval.sim &amp;lt;- function(sim.matrix){
  similarity &amp;lt;- 0
  denominator &amp;lt;- sum(dim(sim.matrix)) / 2
  for(i in 1:(min(c(nrow(sim.matrix), ncol(sim.matrix))) - 1)){
    extract &amp;lt;- which(sim.matrix == max(sim.matrix), arr.ind=T)[1, ]
    similarity &amp;lt;- similarity + sim.matrix[extract[1], extract[2]]
    sim.matrix &amp;lt;- sim.matrix[-extract[1], -extract[2]]
  }
  similarity &amp;lt;- similarity + max(sm.copy)
  similarity &amp;lt;- similarity / denominator
}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In python - &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np

def score_matrix(sim_matrix):
    similarity = 0
    denominator = sum(sim_matrix.shape) / 2
    for i in range(min(sim_matrix.shape)):
        x, y = np.where(sim_matrix == np.max(sim_matrix))[0][0], np.where(sim_matrix == np.max(sim_matrix))[1][0]
        similarity += sim_matrix[x, y]
        sim_matrix = np.delete(sim_matrix,(x),axis=0)
        sim_matrix = np.delete(sim_matrix,(y),axis=1)
    return similarity / denominator
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (550, 2, '2014-06-24 03:50:41.41', 2, NULL, NULL, '&lt;p&gt;Feature extraction is always a challenge and the less addressed topic in literature, since it''s widely application dependant.&lt;/p&gt;

&lt;p&gt;Some ideas you can try:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Raw data, measured day-by-day. That''s kind of obvious with some implications and extra preprocessing (normalisation) in order to make timelines of different length comparable.&lt;/li&gt;
&lt;li&gt;Higher moments: skewness, kurtosis, etc&lt;/li&gt;
&lt;li&gt;Derivative(s): speed of evolution&lt;/li&gt;
&lt;li&gt;Time span is not that large but maybe it is worth trying some time series analysis features like for example autocorrelation. &lt;/li&gt;
&lt;li&gt;Some customised features like breaking timeline in weeks and measure the quantities you already measure in each week separately. Then a non-linear classifier would be able to combine e.g first-week features with last-week features in order to get insight of evolution in time.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':59C,66C,73C,96C,147C ''/p'':21C,28C ''/ul'':148C ''abl'':125C ''address'':11C ''alreadi'':111C ''alway'':5C ''analysi'':90C ''applic'':19C ''autocorrel'':95C ''break'':102C ''challeng'':7C ''classifi'':122C ''combin'':127C ''compar'':58C ''customis'':99C ''data'':32C ''day'':35C,37C ''day-by-day'':34C ''depend'':20C ''deriv'':68C ''differ'':56C ''e.g'':128C ''etc'':65C ''evolut'':72C,144C ''exampl'':94C ''extra'':47C ''extract'':3C ''featur'':2C,91C,100C,132C,137C ''first'':130C ''first-week'':129C ''get'':141C ''higher'':61C ''idea'':24C ''implic'':45C ''insight'':142C ''kind'':40C ''kurtosi'':64C ''larg'':80C ''last'':135C ''last-week'':134C ''length'':57C ''less'':10C ''li'':30C,60C,67C,74C,97C ''like'':92C,101C ''linear'':121C ''literatur'':14C ''make'':53C ''mayb'':82C ''measur'':33C,107C,112C ''moment'':62C ''non'':120C ''non-linear'':119C ''normalis'':49C ''obvious'':42C ''order'':51C,139C ''p'':1C,22C ''preprocess'':48C ''quantiti'':109C ''raw'':31C ''separ'':116C ''seri'':89C ''sinc'':15C ''skew'':63C ''span'':76C ''speed'':70C ''time'':75C,88C,146C ''timelin'':54C,103C ''topic'':12C ''tri'':27C,86C ''ul'':29C ''week'':105C,115C,131C,136C ''wide'':18C ''worth'':85C ''would'':123C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (549, 2, '2014-06-24 00:54:13.11', 2, NULL, NULL, '&lt;p&gt;What you''re trying to do here is reduce the dimensionality of your features. You can search for dimensionality reduction to get several options, but one very popular technique is principal components analysis (PCA). Principal components are not interpretable like the options you''ve mentioned, but they do a good job of summarizing all of the information.&lt;/p&gt;
', NULL, NULL, '''/p'':59C ''analysi'':34C ''compon'':33C,37C ''dimension'':12C,20C ''featur'':15C ''get'':23C ''good'':51C ''inform'':58C ''interpret'':40C ''job'':52C ''like'':41C ''mention'':46C ''one'':27C ''option'':25C,43C ''p'':1C ''pca'':35C ''popular'':29C ''princip'':32C,36C ''re'':4C ''reduc'':10C ''reduct'':21C ''search'':18C ''sever'':24C ''summar'':54C ''techniqu'':30C ''tri'':5C ''ve'':45C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (536, 1, '2014-06-23 11:03:09.767', 5, 203, 'Good practices for manual modifications of data', '&lt;p&gt;More often than not, data I am working with is not 100% clean. Even if it is reasonably clean, still there are portions that need to be fixed. &lt;/p&gt;

&lt;p&gt;When a fraction of data needs it, I write a script and incorporate it in data processing.&lt;/p&gt;

&lt;p&gt;But what to do if only a few entries needs to be fixed (e.g. misspelled city or zip code)? Let''s focus on "small data",  such as that in CSV files or a relational database.&lt;/p&gt;

&lt;p&gt;The practical problems I encountered:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Writing a general script trying solve all similar errors may give unintended consequences (e.g. matching cities that are different but happen to have similar names).&lt;/li&gt;
&lt;li&gt;Copying and modifying data may make a mess, as:
&lt;ul&gt;
&lt;li&gt;Generating it again will destroy all fixes.&lt;/li&gt;
&lt;li&gt;When there are more errors of different kinds, too many copies of the same file result, and it is hard to keep track of them all.&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;Writing a script to modify particular entries seems the best, but there is overhead in comparison to opening CSV and fixing it (but still, &lt;em&gt;seems&lt;/em&gt; to be the best); and either we need to create more copies of data (as in the previous point) or run the script every time we load data.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;What are the best practices in such a case as this?&lt;/p&gt;

&lt;p&gt;EDIT: The question is on the workflow, not whether to use it or not.&lt;/p&gt;

&lt;p&gt;(In my particular case I don''t want the end-user to see misspelled cities and, even worse, see two points of data, for the same city but with different spelling; the data is small, ~500 different cities, so manual corrections do make sense.)&lt;/p&gt;
', 'data-cleaning', NULL, '''/em'':209C ''/li'':131C,151C,179C,181C,238C ''/p'':40C,59C,96C,103C,252C,268C,315C ''/ul'':180C,239C ''100'':23C ''500'':306C ''best'':192C,213C,244C ''case'':249C,273C ''citi'':76C,121C,285C,297C,308C ''clean'':10B,24C,30C ''code'':79C ''comparison'':198C ''consequ'':118C ''copi'':133C,163C,221C ''correct'':311C ''creat'':219C ''csv'':90C,201C ''data'':7A,9B,16C,46C,57C,85C,136C,223C,237C,293C,303C ''data-clean'':8B ''databas'':95C ''destroy'':148C ''differ'':124C,159C,300C,307C ''e.g'':74C,119C ''edit'':254C ''either'':215C ''em'':207C ''encount'':102C ''end'':280C ''end-us'':279C ''entri'':69C,189C ''error'':114C,157C ''even'':25C,287C ''everi'':233C ''file'':91C,167C ''fix'':39C,73C,150C,203C ''focus'':82C ''fraction'':44C ''general'':108C ''generat'':144C ''give'':116C ''good'':1A ''happen'':126C ''hard'':172C ''incorpor'':54C ''keep'':174C ''kind'':160C ''let'':80C ''li'':105C,132C,143C,152C,182C ''load'':236C ''make'':138C,313C ''mani'':162C ''manual'':4A,310C ''match'':120C ''may'':115C,137C ''mess'':140C ''misspel'':75C,284C ''modif'':5A ''modifi'':135C,187C ''name'':130C ''need'':36C,47C,70C,217C ''often'':13C ''open'':200C ''overhead'':196C ''p'':11C,41C,60C,97C,240C,253C,269C ''particular'':188C,272C ''point'':228C,291C ''portion'':34C ''practic'':2A,99C,245C ''previous'':227C ''problem'':100C ''process'':58C ''question'':256C ''reason'':29C ''relat'':94C ''result'':168C ''run'':230C ''script'':52C,109C,185C,232C ''see'':283C,289C ''seem'':190C,208C ''sens'':314C ''similar'':113C,129C ''small'':84C,305C ''solv'':111C ''spell'':301C ''still'':31C,206C ''time'':234C ''track'':175C ''tri'':110C ''two'':290C ''ul'':104C,142C ''unintend'':117C ''use'':264C ''user'':281C ''want'':277C ''whether'':262C ''work'':19C ''workflow'':260C ''wors'':288C ''write'':50C,106C,183C ''zip'':78C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (543, 2, '2014-06-23 17:50:51.32', 1, NULL, NULL, '&lt;p&gt;Well, you mention in your question of the data being ''clean and well structured''. In practice, close to 70% of the time is spent in doing these two steps. Of course the first thing you do is to separate the training and test data. Considering the plethora of libraries and tools available irrespective of which technology/language you prefer to use, the next step would be to understand the data via graph plots and drawing useful intuitions specific to your target goal. This would then be followed by various other problem specific methods. As pointed out, the question is very broad and citing code snippets is simply not feasible.&lt;/p&gt;
', NULL, NULL, '''/p'':110C ''70'':20C ''avail'':53C ''broad'':101C ''cite'':103C ''clean'':12C ''close'':18C ''code'':104C ''consid'':46C ''cours'':32C ''data'':10C,45C,70C ''draw'':75C ''feasibl'':109C ''first'':34C ''follow'':87C ''goal'':82C ''graph'':72C ''intuit'':77C ''irrespect'':54C ''librari'':50C ''mention'':4C ''method'':93C ''next'':63C ''p'':1C ''plethora'':48C ''plot'':73C ''point'':95C ''practic'':17C ''prefer'':59C ''problem'':91C ''question'':7C,98C ''separ'':40C ''simpli'':107C ''snippet'':105C ''specif'':78C,92C ''spent'':25C ''step'':30C,64C ''structur'':15C ''target'':81C ''technology/language'':57C ''test'':44C ''thing'':35C ''time'':23C ''tool'':52C ''train'':42C ''two'':29C ''understand'':68C ''use'':61C,76C ''various'':89C ''via'':71C ''well'':2C,14C ''would'':65C,84C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (545, 2, '2014-06-23 18:38:34.26', 2, NULL, NULL, '&lt;p&gt;I think this is a reasonable question. Here is what I do:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Peak at the first few rows&lt;/li&gt;
&lt;li&gt;Visualize the distribution of the features I care about (histograms)&lt;/li&gt;
&lt;li&gt;Visualize the relationship between pairs of features (scatterplots)&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I downloaded the abalone dataset from the UCI Machine Learning repository &lt;a href="https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data" rel="nofollow"&gt;here&lt;/a&gt;. Let''s say I care about how height and diameter can be used to predict whole weight. For completeness, I''ve included the step of reading the data from file.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

data = pd.read_csv("abalone.data", header=False)
data.columns = ["sex", "length", "diameter", "height", 
                "whole_weight", "shucked_weight",
                "viscera_weight", "shell_weight", "rings"]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now we can take a peak at the first few rows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data.head()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ZEFH6.png" alt="Head of dataset"&gt;&lt;/p&gt;

&lt;p&gt;Now, I know that the variables I care about are floating point values and they can be treated as continuous. I want to take a look to see how these three variables are distributed:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;fig = plt.figure(figsize=(20,5))
plt.subplot(1, 3, 1)
plt.hist(data[''diameter''], normed=True)
plt.title("Diameter")
plt.subplot(1, 3, 2)
plt.hist(data[''height''], normed=True)
plt.title("Height")
plt.subplot(1, 3, 3)
plt.hist(data[''whole_weight''], normed=True)
plt.title("Whole Weight")
plt.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Rmq4n.png" alt="histograms"&gt;&lt;/p&gt;

&lt;p&gt;Great! Now, I know that diameter and whole weight are skewed left and right (respectively). I also know that there are some outliers in terms of height (which is why matplotlib gives me extra room to the right of the distribution). Finally, I''d like to see if I can find any visual patterns between my predictors and outcome variable. I use a scatter plot for this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;plt.figure(figsize=(15,5))
plt.subplot(1, 2, 1)
plt.plot(data[''diameter''], data[''whole_weight''], ''o'')
plt.title("Diameter vs. Whole Weight")
plt.ylabel("Whole Weight")
plt.xlabel("Diameter")
plt.subplot(1, 2, 2)
plt.plot(data[''height''], data[''whole_weight''], ''o'')
plt.title("Height vs. Whole Weight")
plt.ylabel("Whole Weight")
plt.xlabel("Height")
plt.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/FMmN1.png" alt="scatterplots"&gt;&lt;/p&gt;

&lt;p&gt;Here, I see there is a non-linear relationship between diameter and whole weight and I''m going to have to deal with my height outliers. Now, I''m ready to do some analysis!&lt;/p&gt;
', NULL, NULL, '''/a'':67C ''/code'':133C,151C,243C,372C ''/fmmn1.png'':379C ''/li'':23C,35C,45C ''/ml/machine-learning-databases/abalone/abalone.data'':63C ''/ol'':46C ''/p'':14C,98C,147C,163C,199C,253C,322C,382C,419C ''/pre'':134C,152C,244C,373C ''/rmq4n.png'':250C ''/zefh6.png'':158C ''1'':208C,210C,219C,230C,330C,332C,351C ''15'':327C ''2'':221C,331C,352C,353C ''20'':205C ''3'':209C,220C,231C,232C ''5'':206C,328C ''abalon'':51C ''abalone.data'':116C ''also'':271C ''alt'':159C,251C,380C ''analysi'':418C ''archive.ics.uci.edu'':62C ''archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data'':61C ''care'':32C,72C,172C ''code'':100C,149C,201C,324C ''complet'':86C ''continu'':184C ''csv'':115C ''d'':298C ''data'':95C,113C,212C,223C,234C,334C,336C,355C,357C ''data.columns'':119C ''data.head'':150C ''dataset'':52C,162C ''deal'':406C ''diamet'':77C,122C,213C,217C,260C,335C,341C,349C,395C ''distribut'':27C,198C,295C ''download'':49C ''extra'':288C ''fals'':118C ''featur'':30C,43C ''fig'':202C ''figsiz'':204C,326C ''file'':97C ''final'':296C ''find'':305C ''first'':20C,144C ''float'':175C ''give'':286C ''go'':402C ''great'':255C ''head'':160C ''header'':117C ''height'':75C,123C,224C,228C,281C,356C,362C,370C,409C ''histogram'':34C,252C ''href'':60C ''i.stack.imgur.com'':157C,249C,378C ''i.stack.imgur.com/fmmn1.png'':377C ''i.stack.imgur.com/rmq4n.png'':248C ''i.stack.imgur.com/zefh6.png'':156C ''img'':154C,246C,375C ''import'':101C,105C,109C ''includ'':89C ''know'':167C,258C,272C ''learn'':57C ''left'':266C ''length'':121C ''let'':68C ''li'':16C,24C,36C ''like'':299C ''linear'':392C ''look'':190C ''m'':401C,413C ''machin'':56C ''matplotlib'':285C ''matplotlib.pyplot'':106C ''nofollow'':65C ''non'':391C ''non-linear'':390C ''norm'':214C,225C,237C ''o'':339C,360C ''ol'':15C ''outcom'':313C ''outlier'':277C,410C ''p'':1C,47C,135C,153C,164C,245C,254C,374C,383C ''pair'':41C ''panda'':102C ''pattern'':308C ''pd'':104C ''pd.read'':114C ''peak'':17C,141C ''plot'':319C ''plt'':108C ''plt.figure'':203C,325C ''plt.hist'':211C,222C,233C ''plt.plot'':333C,354C ''plt.show'':242C,371C ''plt.subplot'':207C,218C,229C,329C,350C ''plt.title'':216C,227C,239C,340C,361C ''plt.xlabel'':348C,369C ''plt.ylabel'':345C,366C ''point'':176C ''pre'':99C,148C,200C,323C ''predict'':82C ''predictor'':311C ''question'':8C ''read'':93C ''readi'':414C ''reason'':7C ''rel'':64C ''relationship'':39C,393C ''repositori'':58C ''respect'':269C ''right'':268C,292C ''ring'':132C ''room'':289C ''row'':22C,146C ''say'':70C ''scatter'':318C ''scatterplot'':44C,381C ''seaborn'':110C ''see'':192C,301C,386C ''sex'':120C ''shell'':130C ''shuck'':126C ''skew'':265C ''sns'':112C ''src'':155C,247C,376C ''step'':91C ''take'':139C,188C ''term'':279C ''think'':3C ''three'':195C ''treat'':182C ''true'':215C,226C,238C ''uci'':55C ''use'':80C,316C ''valu'':177C ''variabl'':170C,196C,314C ''ve'':88C ''viscera'':128C ''visual'':25C,37C,307C ''vs'':342C,363C ''want'':186C ''weight'':84C,125C,127C,129C,131C,236C,241C,263C,338C,344C,347C,359C,365C,368C,398C ''whole'':83C,124C,235C,240C,262C,337C,343C,346C,358C,364C,367C,397C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (546, 2, '2014-06-23 18:56:11.577', 11, NULL, NULL, '&lt;p&gt;Even if you are effectively modifying certain records by hand, as in the city name example you give, I would recommend doing it in code. The reason to strongly prefer code over hand-tweaking records is that the code makes a result reproducible. You want to make sure that you can always go from raw data to final result without any human intervention.&lt;/p&gt;

&lt;p&gt;Here''s a quick example. Let''s say I have a list of city names in a pandas dataframe and I am certain they should all be "omaha" (you need to be absolutely certain, because changing values by hand is fraught with danger). But instead I have the following strings:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;pd.unique(data.city)
array([''omaha'', ''omahd'', ''imaha'', ''omaka''], dtype=object)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You could make the change like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;data.city.values[data.city.values == ''omahd''] = ''omaha''
data.city.values[data.city.values == ''imaha''] = ''omaha''
data.city.values[data.city.values == ''omaka''] = ''omaha''
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;That code is ugly, but if you run it on the same raw dataset, you will &lt;em&gt;always&lt;/em&gt; get the same result.&lt;/p&gt;
', NULL, NULL, '''/code'':130C,155C ''/em'':176C ''/p'':66C,118C,140C,181C ''/pre'':131C,156C ''absolut'':100C ''alway'':54C,175C ''array'':123C ''certain'':8C,90C,101C ''chang'':103C,137C ''citi'':15C,81C ''code'':26C,32C,41C,120C,142C,159C ''could'':134C ''danger'':110C ''data'':58C ''data.city'':122C ''data.city.values'':143C,144C,147C,148C,151C,152C ''datafram'':86C ''dataset'':171C ''dtype'':128C ''effect'':6C ''em'':174C ''even'':2C ''exampl'':17C,72C ''final'':60C ''follow'':116C ''fraught'':108C ''get'':177C ''give'':19C ''go'':55C ''hand'':11C,35C,106C ''hand-tweak'':34C ''human'':64C ''imaha'':126C,149C ''instead'':112C ''intervent'':65C ''let'':73C ''like'':138C ''list'':79C ''make'':42C,49C,135C ''modifi'':7C ''name'':16C,82C ''need'':97C ''object'':129C ''omaha'':95C,124C,146C,150C,154C ''omahd'':125C,145C ''omaka'':127C,153C ''p'':1C,67C,132C,157C ''panda'':85C ''pd.unique'':121C ''pre'':119C,141C ''prefer'':31C ''quick'':71C ''raw'':57C,170C ''reason'':28C ''recommend'':22C ''record'':9C,37C ''reproduc'':45C ''result'':44C,61C,180C ''run'':165C ''say'':75C ''string'':117C ''strong'':30C ''sure'':50C ''tweak'':36C ''ugli'':161C ''valu'':104C ''want'':47C ''without'':62C ''would'':21C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (548, 1, '2014-06-23 23:20:36.18', 4, 794, 'Feature Extraction Technique - Summarizing a Sequence of Data', '&lt;p&gt;I often am building a model (classification or regression) where I have some predictor variables that are sequences and I have been trying to find technique recommendations for summarizing them in the best way possible for inclusion as predictors in the model.&lt;/p&gt;

&lt;p&gt;As a concrete example, say a model is being built to predict if a customer will leave the company in the next 90 days (anytime between t and t+90; thus a binary outcome). One of the predictors available is the level of the customers financial balance for periods t_0 to t-1. Maybe this represents monthly observations for the prior 12 months (i.e. 12 measurements). &lt;/p&gt;

&lt;p&gt;I am looking for ways to construct features from this series. I use descriptives of each customers series such as the mean, high, low, std dev., fit a OLS regression to get the trend. Are their other methods of calculating features? Other measures of change or volatility? &lt;/p&gt;

&lt;p&gt;ADD:&lt;/p&gt;

&lt;p&gt;As mentioned in a response below, I also considered (but forgot to add here) using Dynamic Time Warping (DTW) and then hierarchical clustering on the resulting distance matrix - creating some number of clusters and then using the cluster membership as a feature. Scoring test data would likely have to follow a process where the DTW was done on new cases and the cluster centroids - matching the new data series to their closest centroids... &lt;/p&gt;
', 'machine-learning  feature-selection  time-series', NULL, '''+90'':92C ''-1'':116C ''/p'':61C,130C,179C,182C,257C ''0'':113C ''12'':125C,128C ''90'':85C ''add'':181C,196C ''also'':191C ''anytim'':87C ''avail'':101C ''balanc'':109C ''best'':51C ''binari'':95C ''build'':22C ''built'':72C ''calcul'':171C ''case'':243C ''centroid'':247C,256C ''chang'':176C ''classif'':25C ''closest'':255C ''cluster'':206C,216C,221C,246C ''compani'':81C ''concret'':65C ''consid'':192C ''construct'':138C ''creat'':212C ''custom'':77C,107C,148C ''data'':8A,228C,251C ''day'':86C ''descript'':145C ''dev'':157C ''distanc'':210C ''done'':240C ''dtw'':202C,238C ''dynam'':199C ''exampl'':66C ''extract'':2A ''featur'':1A,13B,139C,172C,225C ''feature-select'':12B ''financi'':108C ''find'':43C ''fit'':158C ''follow'':233C ''forgot'':194C ''get'':163C ''hierarch'':205C ''high'':154C ''i.e'':127C ''inclus'':55C ''learn'':11B ''leav'':79C ''level'':104C ''like'':230C ''look'':134C ''low'':155C ''machin'':10B ''machine-learn'':9B ''match'':248C ''matrix'':211C ''mayb'':117C ''mean'':153C ''measur'':129C,174C ''membership'':222C ''mention'':185C ''method'':169C ''model'':24C,60C,69C ''month'':120C,126C ''new'':242C,250C ''next'':84C ''number'':214C ''observ'':121C ''often'':20C ''ol'':160C ''one'':97C ''outcom'':96C ''p'':18C,62C,131C,180C,183C ''period'':111C ''possibl'':53C ''predict'':74C ''predictor'':32C,57C,100C ''prior'':124C ''process'':235C ''recommend'':45C ''regress'':27C,161C ''repres'':119C ''respons'':188C ''result'':209C ''say'':67C ''score'':226C ''select'':14B ''sequenc'':6A,36C ''seri'':17B,142C,149C,252C ''std'':156C ''summar'':4A,47C ''techniqu'':3A,44C ''test'':227C ''thus'':93C ''time'':16B,200C ''time-seri'':15B ''trend'':165C ''tri'':41C ''use'':144C,198C,219C ''variabl'':33C ''volatil'':178C ''warp'':201C ''way'':52C,136C ''would'':229C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8482, 2, '2015-10-17 21:24:01.29', 2, NULL, NULL, '&lt;p&gt;Boosting, together with bagging, falls into the realm of so cold ensemble models: you randomly draw a sample from the data, fit a model, adjust your predictions, sample once again. Unless your samples are fixed, every time you run the algo you''ll get slightly different results.&lt;/p&gt;
', NULL, NULL, '''/p'':49C ''adjust'':26C ''algo'':42C ''bag'':5C ''boost'':2C ''cold'':12C ''data'':22C ''differ'':47C ''draw'':17C ''ensembl'':13C ''everi'':37C ''fall'':6C ''fit'':23C ''fix'':36C ''get'':45C ''ll'':44C ''model'':14C,25C ''p'':1C ''predict'':28C ''random'':16C ''realm'':9C ''result'':48C ''run'':40C ''sampl'':19C,29C,34C ''slight'':46C ''time'':38C ''togeth'':3C ''unless'':32C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (745, 1, '2014-07-16 00:06:02.16', 3, 865, 'ARMA/ARIMA on energy forecasts timeseries: strange prediction', '&lt;p&gt;I''m trying to use ARMA/ARIMA with the &lt;a href="http://statsmodels.sourceforge.net/devel/tsa.html#descriptive-statistics-and-tests" rel="nofollow"&gt;statsmodel Python package&lt;/a&gt;, in order to predict the gas consumption. I tried with &lt;a href="https://github.com/denadai2/Gas-consumption-outliers/blob/master/exportWeb.csv" rel="nofollow"&gt;a dataset&lt;/a&gt; of this format:&lt;/p&gt;

&lt;p&gt;&lt;img src="https://i.imgur.com/ZUvBlUP.png" alt="with this format"&gt;&lt;/p&gt;

&lt;p&gt;Using only the gas column.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from pandas.tseries.offsets import *

arma_mod20 = sm.tsa.ARMA(januaryFeb[[''gas [m3]'']], (5,3)).fit()
predict_sunspots = arma_mod20.predict(''2012-01-13'', ''2012-01-14'', dynamic=True)
ax = januaryFeb.ix[''2012-01-13 00:00:00'':''2012-01-15 22:00:00''][''gas [m3]''].plot(figsize=(12,8))
ax = predict_sunspots.plot(ax=ax, style=''r--'', label=''Dynamic Prediction'');
ax.legend();
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="https://i.imgur.com/oCPonu7.png" alt="result"&gt;&lt;/p&gt;

&lt;p&gt;Why is the prediction so bad?&lt;/p&gt;
', 'machine-learning  python', NULL, '''-01'':92C,95C,102C,108C ''-13'':93C,103C ''-14'':96C ''-15'':109C ''/a'':31C,51C ''/code'':129C ''/denadai2/gas-consumption-outliers/blob/master/exportweb.csv'':46C ''/devel/tsa.html#descriptive-statistics-and-tests'':25C ''/ocponu7.png'':136C ''/p'':55C,66C,73C,139C,147C ''/pre'':130C ''/zuvblup.png'':61C ''00'':104C,105C,106C,111C,112C ''12'':117C ''2012'':91C,94C,101C,107C ''22'':110C ''3'':86C ''5'':85C ''8'':118C ''alt'':62C,137C ''arma'':79C ''arma/arima'':1A,18C ''arma_mod20.predict'':90C ''ax'':99C,119C,121C,122C ''ax.legend'':128C ''bad'':146C ''code'':75C ''column'':72C ''consumpt'':38C ''dataset'':50C ''dynam'':97C,126C ''energi'':3A ''figsiz'':116C ''fit'':87C ''forecast'':4A ''format'':54C,65C ''gas'':37C,71C,83C,113C ''github.com'':45C ''github.com/denadai2/gas-consumption-outliers/blob/master/exportweb.csv'':44C ''href'':22C,43C ''i.imgur.com'':60C,135C ''i.imgur.com/ocponu7.png'':134C ''i.imgur.com/zuvblup.png'':59C ''img'':57C,132C ''import'':78C ''januaryfeb'':82C ''januaryfeb.ix'':100C ''label'':125C ''learn'':10B ''m'':14C ''m3'':84C,114C ''machin'':9B ''machine-learn'':8B ''mod20'':80C ''nofollow'':27C,48C ''order'':33C ''p'':12C,56C,67C,131C,140C ''packag'':30C ''pandas.tseries.offsets'':77C ''plot'':115C ''pre'':74C ''predict'':7A,35C,88C,127C,144C ''predict_sunspots.plot'':120C ''python'':11B,29C ''r'':124C ''rel'':26C,47C ''result'':138C ''sm.tsa.arma'':81C ''src'':58C,133C ''statsmodel'':28C ''statsmodels.sourceforge.net'':24C ''statsmodels.sourceforge.net/devel/tsa.html#descriptive-statistics-and-tests'':23C ''strang'':6A ''style'':123C ''sunspot'':89C ''timeseri'':5A ''tri'':15C,40C ''true'':98C ''use'':17C,68C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (552, 2, '2014-06-24 07:03:35.567', 2, NULL, NULL, '&lt;p&gt;At first glance, you need to extract features from your time series (x - 12) - x. One possible approach is to compute summary metrics: average, dispersion, etc. But doing so, you will loose all time-series related information. But data, extracted from curve shape may be quite useful. I recommend you to look through &lt;a href="http://cs.stanford.edu/people/jure/pubs/memeshapes-wsdm11.pdf" rel="nofollow"&gt;this&lt;/a&gt; article, where authors propose algorithm for time series clustering. Hope, it will be useful. Additionally to such clustering you can add summary statistics to your feature list. &lt;/p&gt;
', NULL, NULL, '''/a'':64C ''/p'':92C ''/people/jure/pubs/memeshapes-wsdm11.pdf'':60C ''12'':15C ''add'':85C ''addit'':79C ''algorithm'':69C ''approach'':19C ''articl'':65C ''author'':67C ''averag'':25C ''cluster'':73C,82C ''comput'':22C ''cs.stanford.edu'':59C ''cs.stanford.edu/people/jure/pubs/memeshapes-wsdm11.pdf'':58C ''curv'':44C ''data'':41C ''dispers'':26C ''etc'':27C ''extract'':8C,42C ''featur'':9C,90C ''first'':3C ''glanc'':4C ''hope'':74C ''href'':57C ''inform'':39C ''list'':91C ''look'':54C ''loos'':33C ''may'':46C ''metric'':24C ''need'':6C ''nofollow'':62C ''one'':17C ''p'':1C ''possibl'':18C ''propos'':68C ''quit'':48C ''recommend'':51C ''rel'':61C ''relat'':38C ''seri'':13C,37C,72C ''shape'':45C ''statist'':87C ''summari'':23C,86C ''time'':12C,36C,71C ''time-seri'':35C ''use'':49C,78C ''x'':14C,16C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (554, 1, '2014-06-24 07:29:57.787', 10, 276, 'Datasets understanding best practices', '&lt;p&gt;I am a CS master student in data mining. My supervisor once told me that before I run any classifier or do anything with a dataset I must fully understand the data and make sure that the data is clean and correct.&lt;/p&gt;

&lt;p&gt;My questions:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;What are the best practices to understand a dataset (high dimensional with numerical and nominal attributes)?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Practices to make sure the dataset is clean?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Practices to make sure the dataset doesn''t have wrong values or so?&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
', 'statistics  dataset', NULL, '''/li'':75C,87C,104C ''/p'':50C,54C,74C,86C,103C ''/ul'':105C ''anyth'':30C ''attribut'':73C ''best'':3A,61C ''classifi'':27C ''clean'':47C,85C ''correct'':49C ''cs'':11C ''data'':15C,39C,45C ''dataset'':1A,6B,33C,66C,83C,95C ''dimension'':68C ''doesn'':96C ''fulli'':36C ''high'':67C ''li'':56C,76C,88C ''make'':41C,80C,92C ''master'':12C ''mine'':16C ''must'':35C ''nomin'':72C ''numer'':70C ''p'':7C,51C,57C,77C,89C ''practic'':4A,62C,78C,90C ''question'':53C ''run'':25C ''statist'':5B ''student'':13C ''supervisor'':18C ''sure'':42C,81C,93C ''told'':20C ''ul'':55C ''understand'':2A,37C,64C ''valu'':100C ''wrong'':99C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (555, 2, '2014-06-24 07:35:16.93', 12, NULL, NULL, '&lt;p&gt;&lt;strong&gt;1.  Do not modify the original data&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Having the original data source intact is important.  You may find that updates you make to the data are not valid.  You may also find a more efficient way to make updates and you will want to regression test those updates.&lt;/p&gt;

&lt;p&gt;Always work with a copy of the data, and add columns/properties/metadata that includes any processed corrections.&lt;/p&gt;

&lt;p&gt;Example, if your data is a .csv file that includes a city name that contains several misspellings:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1. Copy the .csv to a new file
2. Add a column for containing "adjusted_city_name"
3. Copy data from the city_name column to the 
   adjusted_city_name column and make corrections in that column.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;2. Document proposed changes&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Any changes you want to make to data should be documented so that they can be replicated moving forward.&lt;/p&gt;

&lt;p&gt;Version control and timestamp the document every time you change it.  That will help in troubleshooting at a later date.&lt;/p&gt;

&lt;p&gt;Be explicit.  Do not simply state "correct capitalization problems", state "ensure that the first letter of each city name begins with a capital letter and the remaining letters are lower-case."&lt;/p&gt;

&lt;p&gt;Update the document with references to any automation routines that have been built to manage data cleansing.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;3. Decide on a standard data cleansing technology&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Whether you use perl, python, java, a particular utility, a manual process or something else is not the issue.  The issue is that in the future you want to hand the data cleansing process to somebody else.  If they have to know 12 different data cleansing technologies, delegating the cleansing procedure will be very difficult.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;4. Standardize the workflow&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;There should be a standard way to handle new data.  Ideally, it will be as simple as dropping a file in a specific location and a predictable automated process cleanses it and hands off a cleansed set of data to the next processing step.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;5. Make as few changes as is absolutely necessary&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;It''s always better to have a fault tolerant analysis than one that makes assumptions about the data.  &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;6. Avoid manual updates&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;It''s always tempting, but people are error-prone and again it makes delegation difficult.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Notes on manual processing&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;To more completely address the original question as to whether there''s a "good" way to do manual processing, I would say no, there is not.  My answer is based on experience and is not one that I make arbitrarily.&lt;/p&gt;

&lt;p&gt;I have had more than one project lose days of time due to a client insisting that a manual data cleansing process was just fine and could be handled internally.  You do not want your projects to be dependent on a single individual accomplishing a judgement based task of varying scale.&lt;/p&gt;

&lt;p&gt;It''s much better to have that individual build and document a rule set based on what they would do than to have them manually cleanse data.  (And then automating that rule set)&lt;/p&gt;

&lt;p&gt;If automation fails you in the end or is simply not possible, the ability to delegate that rule set to others without domain specific knowledge is vital.&lt;/p&gt;

&lt;p&gt;In the end, routines to do something like correct city names can be applied to other data sets.&lt;/p&gt;
', NULL, NULL, '''/code'':131C ''/p'':11C,54C,72C,91C,140C,161C,183C,217C,236C,248C,305C,313C,359C,372C,392C,400C,418C,426C,468C,521C,556C,585C,605C ''/pre'':132C ''/strong'':10C,139C,247C,312C,371C,399C,425C ''1'':3C,94C ''12'':292C ''2'':102C,135C ''3'':111C,239C ''4'':308C ''5'':362C ''6'':395C ''abil'':571C ''absolut'':369C ''accomplish'':513C ''add'':65C,103C ''address'':431C ''adjust'':108C,121C ''also'':36C ''alway'':56C,376C,404C ''analysi'':383C ''answer'':455C ''appli'':600C ''arbitrarili'':467C ''assumpt'':388C ''autom'':226C,342C,552C,559C ''avoid'':396C ''base'':457C,516C,537C ''begin'':204C ''better'':377C,526C ''build'':531C ''built'':231C ''capit'':192C,207C ''case'':216C ''chang'':138C,143C,172C,366C ''citi'':85C,109C,116C,122C,202C,596C ''cleans'':235C,245C,282C,295C,299C,344C,350C,490C,548C ''client'':484C ''code'':93C ''column'':105C,118C,124C,130C ''columns/properties/metadata'':66C ''complet'':430C ''contain'':88C,107C ''control'':164C ''copi'':60C,95C,112C ''correct'':71C,127C,191C,595C ''could'':496C ''csv'':80C,97C ''data'':9C,16C,30C,63C,77C,113C,149C,234C,244C,281C,294C,324C,353C,391C,489C,549C,603C ''date'':182C ''day'':478C ''decid'':240C ''deleg'':297C,416C,573C ''depend'':508C ''differ'':293C ''difficult'':304C,417C ''document'':136C,152C,168C,221C,533C ''domain'':580C ''drop'':332C ''due'':481C ''effici'':40C ''els'':264C,286C ''end'':564C,589C ''ensur'':195C ''error'':410C ''error-pron'':409C ''everi'':169C ''exampl'':74C ''experi'':459C ''explicit'':186C ''fail'':560C ''fault'':381C ''file'':81C,101C,334C ''find'':23C,37C ''fine'':494C ''first'':198C ''forward'':160C ''futur'':275C ''good'':441C ''hand'':279C,347C ''handl'':322C,498C ''help'':176C ''ideal'':325C ''import'':20C ''includ'':68C,83C ''individu'':512C,530C ''insist'':485C ''intact'':18C ''intern'':499C ''issu'':268C,270C ''java'':255C ''judgement'':515C ''know'':291C ''knowledg'':582C ''later'':181C ''letter'':199C,208C,212C ''like'':594C ''locat'':338C ''lose'':477C ''lower'':215C ''lower-cas'':214C ''make'':27C,43C,126C,147C,363C,387C,415C,466C ''manag'':233C ''manual'':260C,397C,423C,445C,488C,547C ''may'':22C,35C ''misspel'':90C ''modifi'':6C ''move'':159C ''much'':525C ''name'':86C,110C,117C,123C,203C,597C ''necessari'':370C ''new'':100C,323C ''next'':356C ''note'':421C ''one'':385C,463C,475C ''origin'':8C,15C,433C ''other'':578C ''p'':1C,12C,55C,73C,133C,141C,162C,184C,218C,237C,249C,306C,314C,360C,373C,393C,401C,419C,427C,469C,522C,557C,586C ''particular'':257C ''peopl'':407C ''perl'':253C ''possibl'':569C ''pre'':92C ''predict'':341C ''problem'':193C ''procedur'':300C ''process'':70C,261C,283C,343C,357C,424C,446C,491C ''project'':476C,505C ''prone'':411C ''propos'':137C ''python'':254C ''question'':434C ''refer'':223C ''regress'':50C ''remain'':211C ''replic'':158C ''routin'':227C,590C ''rule'':535C,554C,575C ''say'':449C ''scale'':520C ''set'':351C,536C,555C,576C,604C ''sever'':89C ''simpl'':330C ''simpli'':189C,567C ''singl'':511C ''somebodi'':285C ''someth'':263C,593C ''sourc'':17C ''specif'':337C,581C ''standard'':243C,309C,319C ''state'':190C,194C ''step'':358C ''strong'':2C,134C,238C,307C,361C,394C,420C ''task'':517C ''technolog'':246C,296C ''tempt'':405C ''test'':51C ''time'':170C,480C ''timestamp'':166C ''toler'':382C ''troubleshoot'':178C ''updat'':25C,44C,53C,219C,398C ''use'':252C ''util'':258C ''valid'':33C ''vari'':519C ''version'':163C ''vital'':584C ''want'':48C,145C,277C,503C ''way'':41C,320C,442C ''whether'':250C,437C ''without'':579C ''work'':57C ''workflow'':311C ''would'':448C,541C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (556, 2, '2014-06-24 08:09:49.3', 8, NULL, NULL, '&lt;p&gt;There are basic things you can do with any set of data:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Validate values (String length tolerance, data type, formatting masks, required field presence, etc.)&lt;/li&gt;
&lt;li&gt;Range correctness (Does this seemingly correct data fall within expected ranges of values)&lt;/li&gt;
&lt;li&gt;Preliminary processing (If I attempt to analyze this data, can I perform the basics without running into errors)&lt;/li&gt;
&lt;li&gt;Preliminary reporting (run a report against a data set and ensure that it passes a sanity test)&lt;/li&gt;
&lt;li&gt;Defining null vs. empty vs. zero vs. False for any given column of data&lt;/li&gt;
&lt;li&gt;Identifying data that is out of place (numeric values dramatically different than other values in a data set, string values that look like they might be misspelled, etc.)&lt;/li&gt;
&lt;li&gt;Eliminating or correcting obviously errant data&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Understanding data to identify errors is a whole different ball game, and it is very important.&lt;/p&gt;

&lt;p&gt;For instance, you can have a rule that says a serial number must be present in a given data set and that serial number must be alphanumeric with a maximum string length of 255 and a minimum string length of 5.&lt;/p&gt;

&lt;p&gt;Looking at the data, you may find one particular serial number value reads &lt;code&gt;"PLEASE ENTER SERIAL"&lt;/code&gt;  It''s perfectly valid, but wrong.&lt;/p&gt;

&lt;p&gt;That''s kind of an obvious one, but say you''re processing stock data and you had a price range for 1000 stocks that was under a dollar.  A lot of people would not know that a stock price so low is invalid on certain exchanges and perfectly valid on others.  You need knowledge about your data to understand if what you are seeing is problematic or not.  &lt;/p&gt;

&lt;p&gt;In the real world, you don''t always have the luxury of understanding your data intimately.&lt;/p&gt;

&lt;p&gt;The way I avoid problems is by leveraging the people around me.  For small data sets, I can ask someone to review the data in it''s entirety.  For large ones, pulling a set of random samples and asking someone to do a sanity check on the data is more appropriate.&lt;/p&gt;

&lt;p&gt;Further, questioning the source of the data and how well that data source can be trusted is imperative.  I often have multiple conflicting sources of data and we create rules to determine the "source of truth".  Sometimes one data set has great data in a given aspect, but other data sets are stronger in other areas.&lt;/p&gt;

&lt;p&gt;Manually entered data is usually what I''m most skeptical about, but in some cases it is stronger than anything that can be acquired through automation.&lt;/p&gt;
', NULL, NULL, '''/code'':219C ''/li'':30C,45C,65C,84C,100C,130C,138C ''/ol'':139C ''/p'':14C,157C,200C,226C,296C,314C,367C,425C,453C ''1000'':249C ''255'':192C ''5'':199C ''acquir'':450C ''alphanumer'':185C ''alway'':305C ''analyz'':53C ''anyth'':446C ''appropri'':366C ''area'':424C ''around'':326C ''ask'':334C,354C ''aspect'':415C ''attempt'':51C ''autom'':452C ''avoid'':319C ''ball'':150C ''basic'':4C,60C ''case'':441C ''certain'':272C ''check'':360C ''code'':215C ''column'':97C ''conflict'':391C ''correct'':33C,37C,134C ''creat'':397C ''data'':13C,22C,38C,55C,74C,99C,103C,118C,137C,142C,177C,205C,241C,284C,312C,330C,339C,363C,375C,380C,394C,407C,411C,418C,429C ''defin'':86C ''determin'':400C ''differ'':112C,149C ''dollar'':255C ''dramat'':111C ''elimin'':132C ''empti'':89C ''ensur'':77C ''enter'':217C,428C ''entireti'':343C ''errant'':136C ''error'':64C,145C ''etc'':29C,129C ''exchang'':273C ''expect'':41C ''fall'':39C ''fals'':93C ''field'':27C ''find'':208C ''format'':24C ''game'':151C ''given'':96C,176C,414C ''great'':410C ''identifi'':102C,144C ''imper'':386C ''import'':156C ''instanc'':160C ''intim'':313C ''invalid'':270C ''kind'':230C ''know'':262C ''knowledg'':281C ''larg'':345C ''length'':20C,190C,197C ''leverag'':323C ''li'':16C,31C,46C,66C,85C,101C,131C ''like'':124C ''look'':123C,202C ''lot'':257C ''low'':268C ''luxuri'':308C ''m'':434C ''manual'':427C ''mask'':25C ''maximum'':188C ''may'':207C ''might'':126C ''minimum'':195C ''misspel'':128C ''multipl'':390C ''must'':171C,183C ''need'':280C ''null'':87C ''number'':170C,182C,212C ''numer'':109C ''obvious'':135C,233C ''often'':388C ''ol'':15C ''one'':209C,234C,346C,406C ''other'':278C ''p'':1C,140C,158C,201C,227C,297C,315C,368C,426C ''particular'':210C ''pass'':80C ''peopl'':259C,325C ''perfect'':222C,275C ''perform'':58C ''place'':108C ''pleas'':216C ''preliminari'':47C,67C ''presenc'':28C ''present'':173C ''price'':246C,266C ''problem'':320C ''problemat'':293C ''process'':48C,239C ''pull'':347C ''question'':370C ''random'':351C ''rang'':32C,42C,247C ''re'':238C ''read'':214C ''real'':300C ''report'':68C,71C ''requir'':26C ''review'':337C ''rule'':165C,398C ''run'':62C,69C ''sampl'':352C ''saniti'':82C,359C ''say'':167C,236C ''see'':291C ''seem'':36C ''serial'':169C,181C,211C,218C ''set'':11C,75C,119C,178C,331C,349C,408C,419C ''skeptic'':436C ''small'':329C ''someon'':335C,355C ''sometim'':405C ''sourc'':372C,381C,392C,402C ''stock'':240C,250C,265C ''string'':19C,120C,189C,196C ''stronger'':421C,444C ''test'':83C ''thing'':5C ''toler'':21C ''trust'':384C ''truth'':404C ''type'':23C ''understand'':141C,286C,310C ''usual'':431C ''valid'':17C,223C,276C ''valu'':18C,44C,110C,115C,121C,213C ''vs'':88C,90C,92C ''way'':317C ''well'':378C ''whole'':148C ''within'':40C ''without'':61C ''world'':301C ''would'':260C ''wrong'':225C ''zero'':91C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (559, 1, '2014-06-24 12:28:10.99', 15, 1790, 'Detecting cats visually by means of anomaly detection', '&lt;p&gt;I have a hobby project which I am contemplating committing to as a way of increasing my so far limited experience of machine learning. I have taken and completed the Coursera MOOC on the topic. My question is with regards to the feasibility of the project.&lt;/p&gt;

&lt;p&gt;The task is the following:&lt;/p&gt;

&lt;p&gt;Neighboring cats are from time to time visiting my garden, which I dislike since they tend to defecate on my lawn. I would like to have a warning system that alerts me when there''s a cat present so that I may go chase it off using my super soaker. For simplicity''s sake, say that I only care about a cat with black and white coloring.&lt;/p&gt;

&lt;p&gt;I have setup a raspberry pi with camera module that can capture video and/or pictures of a part of the garden. &lt;/p&gt;

&lt;p&gt;Sample image:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/cNqus.jpg" alt="Sample garden image"&gt;&lt;/p&gt;

&lt;p&gt;My first idea was to train a classifier to identify cat or cat-like objects, but after realizing that I will be unable to obtain a large enough number of positive samples, I have abandoned that in favor of anomaly detection.&lt;/p&gt;

&lt;p&gt;I estimate that if I captured a photo every second of the day, I would end up with maybe five photos containing cats (out of about 60,000 with sunlight) per day. &lt;/p&gt;

&lt;p&gt;Is this feasible using anomaly detection? If so, what features would you suggest? My ideas so far would be to simply count the number of pixels with that has certain colors; do some kind of blob detection/image segmenting (which I do not know how do to, and would thus like to avoid) and perform the same color analysis on them.&lt;/p&gt;
', 'machine-learning', NULL, '''/cnqus.jpg'':168C ''/p'':59C,66C,135C,158C,162C,173C,217C,251C,313C ''000'':246C ''60'':245C ''abandon'':210C ''alert'':98C ''alt'':169C ''analysi'':310C ''and/or'':150C ''anomali'':7A,215C,257C ''avoid'':304C ''black'':131C ''blob'':288C ''camera'':144C ''captur'':148C,224C ''care'':126C ''cat'':2A,69C,104C,129C,185C,188C,241C ''cat-lik'':187C ''certain'':282C ''chase'':111C ''classifi'':182C ''color'':134C,283C,309C ''commit'':22C ''complet'':41C ''contain'':240C ''contempl'':21C ''count'':274C ''coursera'':43C ''day'':231C,250C ''defec'':85C ''detect'':1A,8A,216C,258C ''detection/image'':289C ''dislik'':80C ''end'':234C ''enough'':203C ''estim'':220C ''everi'':227C ''experi'':33C ''far'':31C,269C ''favor'':213C ''feasibl'':55C,255C ''featur'':262C ''first'':176C ''five'':238C ''follow'':65C ''garden'':77C,157C,171C ''go'':110C ''hobbi'':16C ''i.stack.imgur.com'':167C ''i.stack.imgur.com/cnqus.jpg'':166C ''idea'':177C,267C ''identifi'':184C ''imag'':161C,172C ''img'':164C ''increas'':28C ''kind'':286C ''know'':295C ''larg'':202C ''lawn'':88C ''learn'':11B,36C ''like'':91C,189C,302C ''limit'':32C ''machin'':10B,35C ''machine-learn'':9B ''may'':109C ''mayb'':237C ''mean'':5A ''modul'':145C ''mooc'':44C ''neighbor'':68C ''number'':204C,276C ''object'':190C ''obtain'':200C ''p'':12C,60C,67C,136C,159C,163C,174C,218C,252C ''part'':154C ''per'':249C ''perform'':306C ''photo'':226C,239C ''pi'':142C ''pictur'':151C ''pixel'':278C ''posit'':206C ''present'':105C ''project'':17C,58C ''question'':49C ''raspberri'':141C ''realiz'':193C ''regard'':52C ''sake'':121C ''sampl'':160C,170C,207C ''say'':122C ''second'':228C ''segment'':290C ''setup'':139C ''simpli'':273C ''simplic'':119C ''sinc'':81C ''soaker'':117C ''src'':165C ''suggest'':265C ''sunlight'':248C ''super'':116C ''system'':96C ''taken'':39C ''task'':62C ''tend'':83C ''thus'':301C ''time'':72C,74C ''topic'':47C ''train'':180C ''unabl'':198C ''use'':114C,256C ''video'':149C ''visit'':75C ''visual'':3A ''warn'':95C ''way'':26C ''white'':133C ''would'':90C,233C,263C,270C,300C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5300, 2, '2015-03-10 09:29:06.893', 2, NULL, NULL, '&lt;p&gt;Also: "Business Intelligence developer"&lt;/p&gt;
', NULL, NULL, '''/p'':6C ''also'':2C ''busi'':3C ''develop'':5C ''intellig'':4C ''p'':1C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (560, 2, '2014-06-24 12:55:26.457', 4, NULL, NULL, '&lt;p&gt;This is an interesting and also quite ambitious project :)&lt;/p&gt;

&lt;p&gt;I am not sure anomaly detection (at least in the sense described in the course you followed) would be a very fitting algorithm in this case. &lt;/p&gt;

&lt;p&gt;I would consider a more viable approach to be what has been discussed at the end of that course where a Photo OCR workflow was demonstrated.&lt;/p&gt;

&lt;p&gt;The approach would consist of segmenting your image in smaller "blocks", and going through them one-by-one using a supervised learning algorithm and try to classify each block according to whether it contains a cat or not. If one block contains a cat, the alarm goes off. As a bonus, you get the position of the cat as well, so that you may think of incorporating some "automatic" response as a future step to your project. &lt;/p&gt;

&lt;p&gt;The benefit here is that you will not have to train your algorithm using a dataset specific to your garden (which, as you mention is difficult to create), but you can use images of cats taken off the net (e.g. perhaps you can search for "cat on grass" or something), and perhaps patches of photos from your (or other) gardens. Therefore you don;t have to spend your time collecting photos from your camera, and you avoid the risk of having a very small (comparable) sample of positives (i.e. cats).&lt;/p&gt;

&lt;p&gt;Now, of course how easy it is to build an accurate cat detector is another topic..&lt;/p&gt;
', NULL, NULL, '''/p'':11C,39C,67C,147C,239C,257C ''accord'':99C ''accur'':251C ''alarm'':115C ''algorithm'':35C,92C,161C ''also'':7C ''ambiti'':9C ''anomali'':17C ''anoth'':255C ''approach'':47C,70C ''automat'':138C ''avoid'':225C ''benefit'':150C ''block'':79C,98C,110C ''bonus'':120C ''build'':249C ''camera'':222C ''case'':38C ''cat'':105C,113C,127C,183C,194C,238C,252C ''classifi'':96C ''collect'':218C ''compar'':233C ''consid'':43C ''consist'':72C ''contain'':103C,111C ''cours'':27C,59C,243C ''creat'':176C ''dataset'':164C ''demonstr'':66C ''describ'':24C ''detect'':18C ''detector'':253C ''difficult'':174C ''discuss'':53C ''e.g'':188C ''easi'':245C ''end'':56C ''fit'':34C ''follow'':29C ''futur'':142C ''garden'':168C,208C ''get'':122C ''go'':81C ''goe'':116C ''grass'':196C ''i.e'':237C ''imag'':76C,181C ''incorpor'':136C ''interest'':5C ''learn'':91C ''least'':20C ''may'':133C ''mention'':172C ''net'':187C ''ocr'':63C ''one'':85C,87C,109C ''one-by-on'':84C ''p'':1C,12C,40C,68C,148C,240C ''patch'':201C ''perhap'':189C,200C ''photo'':62C,203C,219C ''posit'':124C,236C ''project'':10C,146C ''quit'':8C ''respons'':139C ''risk'':227C ''sampl'':234C ''search'':192C ''segment'':74C ''sens'':23C ''small'':232C ''smaller'':78C ''someth'':198C ''specif'':165C ''spend'':215C ''step'':143C ''supervis'':90C ''sure'':16C ''taken'':184C ''therefor'':209C ''think'':134C ''time'':217C ''topic'':256C ''train'':159C ''tri'':94C ''use'':88C,162C,180C ''viabl'':46C ''well'':129C ''whether'':101C ''workflow'':64C ''would'':30C,42C,71C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (561, 2, '2014-06-24 13:34:12.107', 7, NULL, NULL, '&lt;p&gt;You could simplify your problem significantly by using a motion/change detection approach. For example, you could compare each image/frame with one from an early time (e.g., a minute earlier), then only consider pixels that have changed since the earlier time. You could then extract the rectangular region of change and use that as the basis for your classification or anomaly detection.&lt;/p&gt;

&lt;p&gt;Taking this type of approach can significantly simplify your classifier and reduce your false target rate because you can ignore anything that is not roughly the size of a cat (e.g., a person or bird). You would then use the extracted change regions that were not filtered out to form the training set for your classifier (or anomaly detector).&lt;/p&gt;

&lt;p&gt;Just be sure to get your false target rate sufficiently low before mounting a laser turret to your feline intrusion detection system.&lt;/p&gt;
', NULL, NULL, '''/p'':63C,124C,148C ''anomali'':61C,122C ''anyth'':85C ''approach'':13C,69C ''basi'':56C ''bird'':99C ''cat'':94C ''chang'':37C,50C,106C ''classif'':59C ''classifi'':74C,120C ''compar'':18C ''consid'':33C ''could'':3C,17C,43C ''detect'':12C,62C,146C ''detector'':123C ''e.g'':27C,95C ''earli'':25C ''earlier'':30C,40C ''exampl'':15C ''extract'':45C,105C ''fals'':78C,132C ''felin'':144C ''filter'':111C ''form'':114C ''get'':130C ''ignor'':84C ''image/frame'':20C ''intrus'':145C ''laser'':140C ''low'':136C ''minut'':29C ''motion/change'':11C ''mount'':138C ''one'':22C ''p'':1C,64C,125C ''person'':97C ''pixel'':34C ''problem'':6C ''rate'':80C,134C ''rectangular'':47C ''reduc'':76C ''region'':48C,107C ''rough'':89C ''set'':117C ''signific'':7C,71C ''simplifi'':4C,72C ''sinc'':38C ''size'':91C ''suffici'':135C ''sure'':128C ''system'':147C ''take'':65C ''target'':79C,133C ''time'':26C,41C ''train'':116C ''turret'':141C ''type'':67C ''use'':9C,52C,103C ''would'':101C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (562, 2, '2014-06-24 15:44:52.54', 3, NULL, NULL, '&lt;p&gt;As a rule of thumb I always propose three different options:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Use a bagging learning technique, similar to that one followed by Random Forest. This technique allows the training of ''small'' classifiers which see a small portion of the whole data. Afterwards, a simple voting scheme (as in Random Forest) will lead you to a very interesting and robust classification.&lt;/li&gt;
&lt;li&gt;Use any technique related to fusioning information or probabilistic fusion. This is a very suitable solution in order to combine different likelihoods from different classifiers.&lt;/li&gt;
&lt;li&gt;My last suggestion is the use of fuzzy logic, a very adequate tool in order to combine information properly from a probabilistic (belonging) perspective.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The selection of specific methods or strategies will depend enormously on the data.&lt;/p&gt;
', NULL, NULL, '''/li'':65C,92C,118C ''/p'':13C,134C ''/ul'':119C ''adequ'':105C ''afterward'':46C ''allow'':31C ''alway'':8C ''bag'':18C ''belong'':116C ''classif'':64C ''classifi'':36C,91C ''combin'':86C,110C ''data'':45C,133C ''depend'':129C ''differ'':11C,87C,90C ''enorm'':130C ''follow'':25C ''forest'':28C,54C ''fusion'':72C,76C ''fuzzi'':101C ''inform'':73C,111C ''interest'':61C ''last'':95C ''lead'':56C ''learn'':19C ''li'':15C,66C,93C ''likelihood'':88C ''logic'':102C ''method'':125C ''one'':24C ''option'':12C ''order'':84C,108C ''p'':1C,120C ''perspect'':117C ''portion'':41C ''probabilist'':75C,115C ''proper'':112C ''propos'':9C ''random'':27C,53C ''relat'':70C ''robust'':63C ''rule'':4C ''scheme'':50C ''see'':38C ''select'':122C ''similar'':21C ''simpl'':48C ''small'':35C,40C ''solut'':82C ''specif'':124C ''strategi'':127C ''suggest'':96C ''suitabl'':81C ''techniqu'':20C,30C,69C ''three'':10C ''thumb'':6C ''tool'':106C ''train'':33C ''ul'':14C ''use'':16C,67C,99C ''vote'':49C ''whole'':44C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (563, 2, '2014-06-24 15:55:37.11', 3, NULL, NULL, '&lt;p&gt;The strategy of motion/change detection is certainly adequate, but I would add an extra operation. I would detect those regions that are more likely to be changed, for instance, the ladder seems a place where humans can be (also cats) and grass where dogs, cats or humans can be.&lt;/p&gt;

&lt;p&gt;I would capture a map with size of the object and trajectory and with this I would create a cluster with the aim of detecting an object (with specific size within the image in terms of pixels) that moves with a certain speed and trajectory.&lt;/p&gt;

&lt;p&gt;You can achieve this by using R or I would suggest OpenCV in order to detect movement and follow different objects.&lt;/p&gt;
', NULL, NULL, '''/p'':51C,98C,121C ''achiev'':102C ''add'':13C ''adequ'':9C ''aim'':75C ''also'':40C ''captur'':55C ''cat'':41C,46C ''certain'':8C,94C ''chang'':28C ''cluster'':72C ''creat'':70C ''detect'':6C,19C,77C,115C ''differ'':119C ''dog'':45C ''extra'':15C ''follow'':118C ''grass'':43C ''human'':37C,48C ''imag'':85C ''instanc'':30C ''ladder'':32C ''like'':25C ''map'':57C ''motion/change'':5C ''move'':91C ''movement'':116C ''object'':62C,79C,120C ''opencv'':111C ''oper'':16C ''order'':113C ''p'':1C,52C,99C ''pixel'':89C ''place'':35C ''r'':106C ''region'':21C ''seem'':33C ''size'':59C,82C ''specif'':81C ''speed'':95C ''strategi'':3C ''suggest'':110C ''term'':87C ''trajectori'':64C,97C ''use'':105C ''within'':83C ''would'':12C,18C,54C,69C,109C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (564, 1, '2014-06-24 18:59:30.56', 5, 113, 'What visualization technique to best describe a recommendation dataset?', '&lt;p&gt;I''ve written a simple recommender which generates recommendations for users based on what they have clicked. The recommender generates a data file with the following format:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;userid,userid,simmilarity (between 0 and 1 - closer to 0 the more similar the users)
a,b,.2
a,c,.3
a,d,.4
a,e,.1
e,b,.3
e,c,.5
e,d,.8
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I''ve looked at &lt;a href="https://github.com/mbostock/d3/wiki/Gallery" rel="nofollow"&gt;some graphs&lt;/a&gt;, but I''m not sure which one to use, or if are there other ones that will better display the user similarities from the dataset above. Any suggestions?&lt;/p&gt;

&lt;p&gt;I''m aiming this visualization at business users who are not at all technical. I would just like to show them an easy to understand visual that details how similar some users are and so convince the business that for these users the recommendation system is useful.&lt;/p&gt;

&lt;p&gt;@Steve Kallestad do you mean something like this : &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/4zyQR.png" alt="enter image description here"&gt;&lt;/p&gt;
', 'visualization  javascript', NULL, '''/4zyqr.png'':189C ''/a'':95C ''/code'':79C ''/mbostock/d3/wiki/gallery'':90C ''/p'':40C,124C,173C,183C,195C ''/pre'':80C ''0'':47C,52C ''1'':49C,69C ''2'':60C ''3'':63C,72C ''4'':66C ''5'':75C ''8'':78C ''aim'':128C ''alt'':190C ''b'':59C,71C ''base'':24C ''best'':5A ''better'':113C ''busi'':132C,163C ''c'':62C,74C ''click'':29C ''closer'':50C ''code'':42C ''convinc'':161C ''d'':65C,77C ''data'':34C ''dataset'':9A,120C ''describ'':6A ''descript'':193C ''detail'':153C ''display'':114C ''e'':68C,70C,73C,76C ''easi'':148C ''enter'':191C ''file'':35C ''follow'':38C ''format'':39C ''generat'':20C,32C ''github.com'':89C ''github.com/mbostock/d3/wiki/gallery'':88C ''graph'':94C ''href'':87C ''i.stack.imgur.com'':188C ''i.stack.imgur.com/4zyqr.png'':187C ''imag'':192C ''img'':185C ''javascript'':11B ''kallestad'':176C ''like'':143C,181C ''look'':84C ''m'':98C,127C ''mean'':179C ''nofollow'':92C ''one'':102C,110C ''p'':12C,81C,125C,174C,184C ''pre'':41C ''recommend'':8A,18C,21C,31C,169C ''rel'':91C ''show'':145C ''similar'':55C,117C,155C ''simmilar'':45C ''simpl'':17C ''someth'':180C ''src'':186C ''steve'':175C ''suggest'':123C ''sure'':100C ''system'':170C ''technic'':139C ''techniqu'':3A ''understand'':150C ''use'':104C,172C ''user'':23C,57C,116C,133C,157C,167C ''userid'':43C,44C ''ve'':14C,83C ''visual'':2A,10B,130C,151C ''would'':141C ''written'':15C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (565, 1, '2014-06-24 19:43:24.643', 6, 709, 'Why does Gradient Boosting regression predict negative values when there are no negative y-values in my training set?', '&lt;p&gt;As I increase the number of trees in &lt;a href="http://scikit-learn.org/stable/" rel="nofollow"&gt;scikit learn&lt;/a&gt;''s &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html" rel="nofollow"&gt;&lt;code&gt;GradientBoostingRegressor&lt;/code&gt;&lt;/a&gt;, I get more negative predictions, even though there are no negative values in my training or testing set. I have about 10 features, most of which are binary.&lt;/p&gt;

&lt;p&gt;Some of the parameters that I was tuning were:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;the number of trees/iterations;&lt;/li&gt;
&lt;li&gt;learning depth;&lt;/li&gt;
&lt;li&gt;and learning rate.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The percentage of negative values seemed to max at ~2%. The learning depth of 1(stumps) seemed to have the largest % of negative values. This percentage also seemed to increase with more trees and a smaller learning rate. The dataset is from one of the kaggle playground competitions.&lt;/p&gt;

&lt;p&gt;My code is something like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;from sklearn.ensemble import GradientBoostingRegressor

X_train, X_test, y_train, y_test = train_test_split(X, y)

reg = GradientBoostingRegressor(n_estimators=8000, max_depth=1, loss = ''ls'', learning_rate = .01)

reg.fit(X_train, y_train)

ypred = reg.predict(X_test)
&lt;/code&gt;&lt;/pre&gt;
', 'machine-learning  python  algorithms  scikit  kaggle', NULL, '''/a'':46C,58C ''/code'':57C,214C ''/li'':105C,109C,114C ''/p'':87C,98C,165C,172C ''/pre'':215C ''/stable/'':41C ''/stable/modules/generated/sklearn.ensemble.gradientboostingregressor.html'':52C ''/ul'':115C ''01'':204C ''1'':131C,199C ''10'':80C ''2'':126C ''8000'':196C ''algorithm'':25B ''also'':143C ''binari'':86C ''boost'':4A ''code'':55C,168C,174C ''competit'':164C ''dataset'':156C ''depth'':108C,129C,198C ''estim'':195C ''even'':64C ''featur'':81C ''get'':60C ''gradient'':3A ''gradientboostingregressor'':56C,178C,193C ''href'':38C,49C ''import'':177C ''increas'':31C,146C ''kaggl'':27B,162C ''largest'':137C ''learn'':23B,45C,107C,112C,128C,153C,202C ''li'':100C,106C,110C ''like'':171C ''loss'':200C ''ls'':201C ''machin'':22B ''machine-learn'':21B ''max'':124C,197C ''n'':194C ''negat'':7A,13A,62C,69C,120C,139C ''nofollow'':43C,54C ''number'':33C,102C ''one'':159C ''p'':28C,88C,116C,166C ''paramet'':92C ''percentag'':118C,142C ''playground'':163C ''pre'':173C ''predict'':6A,63C ''python'':24B ''rate'':113C,154C,203C ''reg'':192C ''reg.fit'':205C ''reg.predict'':211C ''regress'':5A ''rel'':42C,53C ''scikit'':26B,44C ''scikit-learn.org'':40C,51C ''scikit-learn.org/stable/'':39C ''scikit-learn.org/stable/modules/generated/sklearn.ensemble.gradientboostingregressor.html'':50C ''seem'':122C,133C,144C ''set'':20A,76C ''sklearn.ensemble'':176C ''smaller'':152C ''someth'':170C ''split'':189C ''stump'':132C ''test'':75C,182C,186C,188C,213C ''though'':65C ''train'':19A,73C,180C,184C,187C,207C,209C ''tree'':35C,149C ''trees/iterations'':104C ''tune'':96C ''ul'':99C ''valu'':8A,16A,70C,121C,140C ''x'':179C,181C,190C,206C,212C ''y'':15A,183C,185C,191C,208C ''y-valu'':14A ''ypred'':210C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (566, 1, '2014-06-24 17:06:10.31', 5, 405, 'Named Entity Recognition: NLTK using Regular Expression', '&lt;p&gt;Many times &lt;a href="http://en.wikipedia.org/wiki/Named-entity_recognition" rel="nofollow"&gt;Named Entity Recognition&lt;/a&gt; (NER) doesn''t tag consecutive NNPs as one NE. I think editing the NER to use RegexpTagger also can improve the NER.&lt;/p&gt;

&lt;p&gt;For example, consider the following input:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;"Barack Obama is a great person."&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;And the output:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Tree(''S'', [Tree(''PERSON'', [(''Barack'', ''NNP'')]), Tree(''ORGANIZATION'', [(''Obama'', ''NNP'')]),
    (''is'', ''VBZ''), (''a'', ''DT''), (''great'', ''JJ''), (''person'', ''NN''), (''.'', ''.'')])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where as for the input: &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;''Former Vice President Dick Cheney told conservative radio host Laura Ingraham that he "was honored" to be compared to Darth Vader while in office.''&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;the output is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Tree(''S'', [(''Former'', ''JJ''), (''Vice'', ''NNP''), (''President'', ''NNP''),
    Tree(''NE'', [(''Dick'', ''NNP''), (''Cheney'', ''NNP'')]), (''told'', ''VBD''), (''conservative'', ''JJ''),
    (''radio'', ''NN''), (''host'', ''NN''), Tree(''NE'', [(''Laura'', ''NNP''), (''Ingraham'', ''NNP'')]),
    (''that'', ''IN''), (''he'', ''PRP''), (''``'', ''``''), (''was'', ''VBD''), (''honored'', ''VBN''),
    ("''''", "''''"), (''to'', ''TO''), (''be'', ''VB''), (''compared'', ''VBN''), (''to'', ''TO''),
    Tree(''NE'', [(''Darth'', ''NNP''), (''Vader'', ''NNP'')]), (''while'', ''IN''), (''in'', ''IN''),
    (''office'', ''NN''), (''.'', ''.'')])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here &lt;code&gt;Vice/NNP, President/NNP, (Dick/NNP, Cheney/NNP)&lt;/code&gt; is correctly extracted. So, I think if &lt;code&gt;nltk.ne_chunk&lt;/code&gt; is used first, and then if two consecutive trees are NNP, there are higher chances that both refer to one entity. &lt;/p&gt;

&lt;p&gt;I have been playing with NLTK toolkit, and I came across this problem a lot, but couldn''t find a satisfying answer. Any suggestion will be really appreciated. I''m looking for flaws in my approach.&lt;/p&gt;
', 'nlp', NULL, '''/a'':22C ''/blockquote'':63C,125C ''/code'':89C,189C,198C,209C ''/p'':45C,53C,62C,68C,97C,124C,130C,231C,269C ''/pre'':90C,190C ''/wiki/named-entity_recognition'':16C ''across'':243C ''also'':40C ''answer'':254C ''appreci'':260C ''approach'':268C ''barack'':56C,75C ''blockquot'':54C,98C ''came'':242C ''chanc'':224C ''cheney'':104C,145C ''cheney/nnp'':197C ''chunk'':208C ''code'':70C,132C,193C,206C ''compar'':117C,173C ''consecut'':27C,217C ''conserv'':106C,149C ''consid'':49C ''correct'':200C ''couldn'':249C ''darth'':119C,179C ''dick'':103C,143C ''dick/nnp'':196C ''doesn'':24C ''dt'':84C ''edit'':34C ''en.wikipedia.org'':15C ''en.wikipedia.org/wiki/named-entity_recognition'':14C ''entiti'':2A,20C,230C ''exampl'':48C ''express'':7A ''extract'':201C ''find'':251C ''first'':212C ''flaw'':265C ''follow'':51C ''former'':100C,135C ''great'':60C,85C ''higher'':223C ''honor'':114C,167C ''host'':108C,153C ''href'':13C ''improv'':42C ''ingraham'':110C,159C ''input'':52C,96C ''jj'':86C,136C,150C ''laura'':109C,157C ''look'':263C ''lot'':247C ''m'':262C ''mani'':10C ''name'':1A,19C ''ne'':31C,142C,156C,178C ''ner'':23C,36C,44C ''nlp'':8B ''nltk'':4A,238C ''nltk.ne'':207C ''nn'':88C,152C,154C,188C ''nnp'':76C,80C,138C,140C,144C,146C,158C,160C,180C,182C,220C ''nnps'':28C ''nofollow'':18C ''obama'':57C,79C ''offic'':123C,187C ''one'':30C,229C ''organ'':78C ''output'':67C,128C ''p'':9C,46C,55C,64C,91C,99C,126C,191C,232C ''person'':61C,74C,87C ''play'':236C ''pre'':69C,131C ''presid'':102C,139C ''president/nnp'':195C ''problem'':245C ''prp'':164C ''radio'':107C,151C ''realli'':259C ''recognit'':3A,21C ''refer'':227C ''regexptagg'':39C ''regular'':6A ''rel'':17C ''satisfi'':253C ''suggest'':256C ''tag'':26C ''think'':33C,204C ''time'':11C ''told'':105C,147C ''toolkit'':239C ''tree'':71C,73C,77C,133C,141C,155C,177C,218C ''two'':216C ''use'':5A,38C,211C ''vader'':120C,181C ''vb'':172C ''vbd'':148C,166C ''vbn'':168C,174C ''vbz'':82C ''vice'':101C,137C ''vice/nnp'':194C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (567, 2, '2014-06-24 20:28:33.693', 5, NULL, NULL, '&lt;p&gt;I think you''re looking for a &lt;a href="http://cgi.mtc.sri.com/Cluster-Lab/"&gt;similarity matrix&lt;/a&gt; (see bottom of the page). If you don''t have data on similarity between certain pairs, you can always leave them as grey or white. Also, this will only work for data sets small enough to actually make out what''s going on. I''d say 25 rows / columns maximum.&lt;/p&gt;

&lt;p&gt;In a similarity matrix, x, and y coordinates correspond to the two things you''re comparing, while a &lt;a href="http://en.wikipedia.org/wiki/False_color"&gt;colormap&lt;/a&gt; magnitude represents similarity &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;EDIT&lt;/strong&gt;:
One thing you could do to replace the colormap is the insert, say, circles of different sizes according to the similarity metric. Or you could insert the numbers themselves, again, varying the size of the number as the magnitude of that number varies. Size usually works best is business visualizations.&lt;/p&gt;
', NULL, NULL, '''/a'':16C,93C ''/cluster-lab/'':13C ''/p'':67C,97C,152C ''/strong'':101C ''/wiki/false_color'':91C ''25'':63C ''accord'':119C ''actual'':53C ''also'':42C ''alway'':35C ''best'':148C ''bottom'':18C ''busi'':150C ''certain'':31C ''cgi.mtc.sri.com'':12C ''cgi.mtc.sri.com/cluster-lab/'':11C ''circl'':115C ''colormap'':92C,110C ''column'':65C ''compar'':84C ''coordin'':76C ''correspond'':77C ''could'':105C,126C ''d'':61C ''data'':27C,48C ''differ'':117C ''edit'':100C ''en.wikipedia.org'':90C ''en.wikipedia.org/wiki/false_color'':89C ''enough'':51C ''go'':58C ''grey'':39C ''href'':10C,88C ''insert'':113C,127C ''leav'':36C ''look'':6C ''magnitud'':94C,140C ''make'':54C ''matrix'':15C,72C ''maximum'':66C ''metric'':123C ''number'':129C,137C,143C ''one'':102C ''p'':1C,68C,98C ''page'':21C ''pair'':32C ''re'':5C,83C ''replac'':108C ''repres'':95C ''row'':64C ''say'':62C,114C ''see'':17C ''set'':49C ''similar'':14C,29C,71C,96C,122C ''size'':118C,134C,145C ''small'':50C ''strong'':99C ''thing'':81C,103C ''think'':3C ''two'':80C ''usual'':146C ''vari'':132C,144C ''visual'':151C ''white'':41C ''work'':46C,147C ''x'':73C ''y'':75C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (572, 2, '2014-06-25 03:11:02.457', 5, NULL, NULL, '&lt;p&gt;This is relatively simple to do mathematically.  First, fit a regression line to the scatter plot of "total graduates" (y) vs. "total students" (x).  You will probably see a downward sloping line if your assertion is correct (smaller schools graduate a higher %).&lt;/p&gt;

&lt;p&gt;You can identify the slope and y-intercept for this line to convert it into an equation y = mx + b, and then do a little algebra to convert the equation  into normalized form: "y / x = m + b / x"&lt;/p&gt;

&lt;p&gt;Then, with all the ratios in your data , you should &lt;em&gt;subtract&lt;/em&gt; this RHS:  &lt;/p&gt;

&lt;p&gt;normalized ratio = (total grads / total students) - (m + b / total students)&lt;/p&gt;

&lt;p&gt;If the result is postive, then the ratio is above normal for that size (i.e. above the regression line) and if it is negative it is below the regression line.  If you want all positive numbers, you can add a positive constant to move all results above zero.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;This is how to do it mathematically, but I suggest that you consider whether it is wise, from a data analysis point of view, to normalize by school size.  This depends on the purpose of your analysis and specifically how this ratio is being analyzed in relation to other data.&lt;/p&gt;
', NULL, NULL, '''/em'':99C ''/p'':44C,85C,102C,114C,164C,217C ''add'':154C ''algebra'':72C ''analysi'':187C,203C ''analyz'':211C ''assert'':36C ''b'':66C,83C,111C ''consid'':179C ''constant'':157C ''convert'':59C,74C ''correct'':38C ''data'':94C,186C,216C ''depend'':197C ''downward'':31C ''em'':97C ''equat'':63C,76C ''first'':9C ''fit'':10C ''form'':79C ''grad'':107C ''graduat'':20C,41C ''higher'':43C ''hr'':165C ''i.e'':130C ''identifi'':48C ''intercept'':54C ''line'':13C,33C,57C,134C,145C ''littl'':71C ''m'':82C,110C ''mathemat'':8C,173C ''move'':159C ''mx'':65C ''negat'':139C ''normal'':78C,104C,126C,192C ''number'':151C ''p'':1C,45C,86C,103C,115C,166C ''plot'':17C ''point'':188C ''posit'':150C,156C ''postiv'':120C ''probabl'':28C ''purpos'':200C ''ratio'':91C,105C,123C,208C ''regress'':12C,133C,144C ''relat'':4C,213C ''result'':118C,161C ''rhs'':101C ''scatter'':16C ''school'':40C,194C ''see'':29C ''simpl'':5C ''size'':129C,195C ''slope'':32C,50C ''smaller'':39C ''specif'':205C ''student'':24C,109C,113C ''subtract'':98C ''suggest'':176C ''total'':19C,23C,106C,108C,112C ''view'':190C ''vs'':22C ''want'':148C ''whether'':180C ''wise'':183C ''x'':25C,81C,84C ''y'':21C,53C,64C,80C ''y-intercept'':52C ''zero'':163C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (573, 2, '2014-06-25 03:30:19.787', 2, NULL, NULL, '&lt;p&gt;OpenCV''s &lt;a href="http://docs.opencv.org/trunk/doc/tutorials/video/background_subtraction/background_subtraction.html" rel="nofollow" title="background subtraction"&gt;background subtraction&lt;/a&gt; will find objects moving about your harden. After that you could use a classifier or shape analysis to differentiate between cats, people, trees and etc.&lt;/p&gt;
', NULL, NULL, '''/a'':16C ''/p'':42C ''/trunk/doc/tutorials/video/background_subtraction/background_subtraction.html'':8C ''analysi'':33C ''background'':12C,14C ''cat'':37C ''classifi'':30C ''could'':27C ''differenti'':35C ''docs.opencv.org'':7C ''docs.opencv.org/trunk/doc/tutorials/video/background_subtraction/background_subtraction.html'':6C ''etc'':41C ''find'':18C ''harden'':23C ''href'':5C ''move'':20C ''nofollow'':10C ''object'':19C ''opencv'':2C ''p'':1C ''peopl'':38C ''rel'':9C ''shape'':32C ''subtract'':13C,15C ''titl'':11C ''tree'':39C ''use'':28C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (574, 2, '2014-06-25 07:22:33.17', 1, NULL, NULL, '&lt;p&gt;This is a very interesting problem.&lt;/p&gt;

&lt;p&gt;I faced a similar one by analyzing the pictures users upload to the social network. I did the following approach:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Rather than associating data to ages (15 y.o., 27 y.o., ...) what I did is to establish different groups of ages: Less than 18, from 18 to 30 and greater than 30 (this is due to the specific problem we were facing, but you can choose whatever intervals you want). This division helps a lot to solve the problem.&lt;/li&gt;
&lt;li&gt;Afterwards, I created a hierarchical clustering (divisive or aggregative). Then I choose those branches where I had users with known ages (or group ages) and then for that branch I extended the same age to that group.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;This approach is &lt;strong&gt;semi-supervised learning&lt;/strong&gt; and I recommended it in case you only have some data labeled.&lt;/p&gt;

&lt;p&gt;Please, notice that on a social network, people usually lie about the age (just for fun, or sometimes because they want to camuflate themselves on the social net).&lt;/p&gt;
', NULL, NULL, '''/li'':91C,130C ''/p'':8C,30C,154C,184C ''/strong'':141C ''/ul'':131C ''15'':39C ''18'':55C,57C ''27'':41C ''30'':59C,63C ''afterward'':93C ''age'':38C,52C,113C,116C,126C,168C ''aggreg'':101C ''analyz'':16C ''approach'':29C,134C ''associ'':35C ''branch'':106C,121C ''camufl'':178C ''case'':147C ''choos'':77C,104C ''cluster'':98C ''creat'':95C ''data'':36C,152C ''differ'':49C ''divis'':83C,99C ''due'':66C ''establish'':48C ''extend'':123C ''face'':11C,73C ''follow'':28C ''fun'':171C ''greater'':61C ''group'':50C,115C,129C ''help'':84C ''hierarch'':97C ''interest'':6C ''interv'':79C ''known'':112C ''label'':153C ''learn'':140C ''less'':53C ''li'':32C,92C ''lie'':165C ''lot'':86C ''net'':183C ''network'':24C,162C ''notic'':157C ''one'':14C ''p'':1C,9C,132C,155C ''peopl'':163C ''pictur'':18C ''pleas'':156C ''problem'':7C,70C,90C ''rather'':33C ''recommend'':144C ''semi'':138C ''semi-supervis'':137C ''similar'':13C ''social'':23C,161C,182C ''solv'':88C ''sometim'':173C ''specif'':69C ''strong'':136C ''supervis'':139C ''ul'':31C ''upload'':20C ''user'':19C,110C ''usual'':164C ''want'':81C,176C ''whatev'':78C ''y.o'':40C,42C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (575, 2, '2014-06-25 09:19:25.56', 3, NULL, NULL, '&lt;p&gt;This is a very good question and a common situation.&lt;/p&gt;

&lt;p&gt;In my opinion there are three different factors that must be controlled:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Data: There exist already different benchmarks in order to evaluate algorithms and architectures. These data must be publicly available so that everybody can contrast their approaches.&lt;/li&gt;
&lt;li&gt;Architecture: My suggestion is to test everything on the cloud, so that everyone can contrast their results and also there is no doubt the same machines and software is used.&lt;/li&gt;
&lt;li&gt;Algorithms: If you have developed a distributed algorithm, it would be great to compare your algorithm on a specific data. In this case, algorithms must not be public.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So, answering your question, if you want to compare different experiments and state to what extent your distributed algorithm outperforms others, you should try to replicate as accurate as possible the same environment (data and architecture) where the experiments were carried out.&lt;/p&gt;

&lt;p&gt;If this is not possible, my suggestion is that you test your algorithm with public data and cloud architecture so that &lt;strong&gt;you become a referent&lt;/strong&gt; as you are facilitating the comparison of future algorithms.&lt;/p&gt;
', NULL, NULL, '''/li'':55C,87C,117C ''/p'':12C,26C,162C,200C ''/strong'':190C ''/ul'':118C ''accur'':147C ''algorithm'':39C,89C,96C,104C,112C,138C,176C,199C ''alreadi'':32C ''also'':75C ''answer'':121C ''approach'':54C ''architectur'':41C,57C,155C,182C ''avail'':47C ''becom'':187C ''benchmark'':34C ''carri'':160C ''case'':111C ''cloud'':66C,181C ''common'':10C ''compar'':102C,128C ''comparison'':196C ''contrast'':52C,71C ''control'':25C ''data'':29C,43C,108C,153C,179C ''develop'':93C ''differ'':20C,33C,129C ''distribut'':95C,137C ''doubt'':79C ''environ'':152C ''evalu'':38C ''everybodi'':50C ''everyon'':69C ''everyth'':63C ''exist'':31C ''experi'':130C,158C ''extent'':135C ''facilit'':194C ''factor'':21C ''futur'':198C ''good'':6C ''great'':100C ''li'':28C,56C,88C ''machin'':82C ''must'':23C,44C,113C ''opinion'':16C ''order'':36C ''other'':140C ''outperform'':139C ''p'':1C,13C,119C,163C ''possibl'':149C,168C ''public'':46C,116C,178C ''question'':7C,123C ''refer'':189C ''replic'':145C ''result'':73C ''situat'':11C ''softwar'':84C ''specif'':107C ''state'':132C ''strong'':185C ''suggest'':59C,170C ''test'':62C,174C ''three'':19C ''tri'':143C ''ul'':27C ''use'':86C ''want'':126C ''would'':98C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (576, 2, '2014-06-25 10:15:31.477', 0, NULL, NULL, '&lt;p&gt;You should try &lt;em&gt;arules&lt;/em&gt; package in R. It allows you to create not only the association rules but also to specify the length of each rule, the importance of each rule and also you can filter them, which is what you are looking for (try the rhs() command of this package).&lt;/p&gt;
', NULL, NULL, '''/em'':7C ''/p'':55C ''allow'':12C ''also'':22C,36C ''arul'':6C ''associ'':19C ''command'':51C ''creat'':15C ''em'':5C ''filter'':39C ''import'':31C ''length'':26C ''look'':46C ''p'':1C ''packag'':8C,54C ''r'':10C ''rhs'':50C ''rule'':20C,29C,34C ''specifi'':24C ''tri'':4C,48C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (577, 2, '2014-06-25 10:59:56.737', 2, NULL, NULL, '&lt;p&gt;Though it''s easy to say, it''s better to treat the environment that changes as variables, describe/estimate your algorithm''s performance base on these variables. And hopefully others will do the same. Of interest, &lt;a href="http://infolab.stanford.edu/~ullman/pub/experiments.pdf" rel="nofollow"&gt;Experiments as Research Validation -- Have We Gone too Far?&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':53C ''/p'':54C ''/~ullman/pub/experiments.pdf'':41C ''algorithm'':21C ''base'':24C ''better'':10C ''chang'':16C ''describe/estimate'':19C ''easi'':5C ''environ'':14C ''experi'':44C ''far'':52C ''gone'':50C ''hope'':29C ''href'':38C ''infolab.stanford.edu'':40C ''infolab.stanford.edu/~ullman/pub/experiments.pdf'':39C ''interest'':36C ''nofollow'':43C ''other'':30C ''p'':1C ''perform'':23C ''rel'':42C ''research'':46C ''say'':7C ''though'':2C ''treat'':12C ''valid'':47C ''variabl'':18C,27C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (584, 2, '2014-06-25 15:46:21.267', 6, NULL, NULL, '&lt;p&gt;The original MacQueen k-means publication (the first to use the name "kmeans") is an online algorithm.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;MacQueen, J. B. (1967). "Some Methods for classification and Analysis of Multivariate Observations". Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability 1. University of California Press. pp. 281â297&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;After assigning each point, the mean is incrementally updated using a simple weighted-average formula (old mean is weighted with n, the new observation is weighted with 1, if the mean had n observations before).&lt;/p&gt;

&lt;p&gt;As far as I can tell, it was also meant to be a single pass over the data only, although it can be trivially repeated multiple times to reassign points until convergence.&lt;/p&gt;

&lt;p&gt;MacQueen usually takes fewer iterations than Lloyds to converge if your data is shuffled (because it updates the mean faster!). On ordered data, it can have problems. On the downside, it requires more computation for each object, so each iteration takes slightly longer (additional math operations, obviously).&lt;/p&gt;
', NULL, NULL, '''/blockquote'':55C ''/p'':20C,54C,93C,127C,176C ''1'':46C,85C ''1967'':26C ''281â'':52C ''297'':53C ''5th'':38C ''addit'':172C ''algorithm'':19C ''also'':103C ''although'':114C ''analysi'':32C ''assign'':58C ''averag'':71C ''b'':25C ''berkeley'':39C ''blockquot'':21C ''california'':49C ''classif'':30C ''comput'':162C ''converg'':126C,137C ''data'':112C,140C,151C ''downsid'':158C ''far'':96C ''faster'':148C ''fewer'':132C ''first'':10C ''formula'':72C ''increment'':64C ''iter'':133C,168C ''j'':24C ''k'':6C ''k-mean'':5C ''kmean'':15C ''lloyd'':135C ''longer'':171C ''macqueen'':4C,23C,129C ''math'':173C ''mathemat'':42C ''mean'':7C,62C,74C,88C,147C ''meant'':104C ''method'':28C ''multipl'':120C ''multivari'':34C ''n'':78C,90C ''name'':14C ''new'':80C ''object'':165C ''observ'':35C,81C,91C ''obvious'':175C ''old'':73C ''onlin'':18C ''oper'':174C ''order'':150C ''origin'':3C ''p'':1C,22C,56C,94C,128C ''pass'':109C ''point'':60C,124C ''pp'':51C ''press'':50C ''probabl'':45C ''problem'':155C ''proceed'':36C ''public'':8C ''reassign'':123C ''repeat'':119C ''requir'':160C ''shuffl'':142C ''simpl'':68C ''singl'':108C ''slight'':170C ''statist'':43C ''symposium'':40C ''take'':131C,169C ''tell'':100C ''time'':121C ''trivial'':118C ''univers'':47C ''updat'':65C,145C ''use'':12C,66C ''usual'':130C ''weight'':70C,76C,83C ''weighted-averag'':69C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (585, 2, '2014-06-25 15:53:30.723', 1, NULL, NULL, '&lt;p&gt;DBSCAN (see also: Generalized DBSCAN) does not require a distance.
All it needs is a &lt;em&gt;binary decision&lt;/em&gt;. Commonly, one would use "distance &amp;lt; epsilon" but nothing says you cannot use "similarity &gt; epsilon" instead. Triangle inequality etc. are not required.&lt;/p&gt;

&lt;p&gt;Affinity propagation, as the name says, uses similarities.&lt;/p&gt;

&lt;p&gt;Hierarchical clustering, except for maybe Ward linkage, does not make any assumption. In many implementations you can just use negative distances when you have similarities, and it will work just fine. Because all that is needed is min, max, and &amp;lt;.&lt;/p&gt;

&lt;p&gt;Kernel k-means could work IF your similarity is a good kernel function. Think of it as computing k-means in a different vector space, where Euclidean distance corresponds to your similarity function. But then you need to know k.&lt;/p&gt;

&lt;p&gt;PAM (K-medoids) should work. Assign each object to the most similary medoid, then choose the object with the highest average similarity as new medoid... no triangle inequality needed.&lt;/p&gt;

&lt;p&gt;... and probably many many more. There are literally hundreds of clustering algorithms. &lt;strong&gt;Most should work&lt;/strong&gt; IMHO. Very few seem to actually require metric properties. K-means has probably the strongest requirements: it minimizes &lt;em&gt;variance&lt;/em&gt; (not distance, or similarity), and you must be able to compute means.&lt;/p&gt;
', NULL, NULL, '''/em'':20C,212C ''/p'':43C,53C,96C,140C,172C,225C ''/strong'':190C ''abl'':221C ''actual'':196C ''affin'':45C ''algorithm'':185C ''also'':4C ''assign'':148C ''assumpt'':66C ''averag'':163C ''binari'':18C ''cannot'':32C ''choos'':157C ''cluster'':56C,184C ''common'':21C ''comput'':116C,223C ''correspond'':128C ''could'':102C ''dbscan'':2C,6C ''decis'':19C ''differ'':122C ''distanc'':11C,25C,75C,127C,214C ''em'':17C,210C ''epsilon'':27C,35C ''etc'':39C ''euclidean'':126C ''except'':57C ''fine'':85C ''function'':111C,132C ''general'':5C ''good'':109C ''hierarch'':55C ''highest'':162C ''hundr'':182C ''imho'':191C ''implement'':69C ''inequ'':38C,170C ''instead'':36C ''k'':100C,118C,139C,144C,201C ''k-mean'':99C,117C,200C ''k-medoid'':143C ''kernel'':98C,110C ''know'':138C ''linkag'':61C ''liter'':181C ''lt'':26C,95C ''make'':64C ''mani'':68C,176C,177C ''max'':93C ''mayb'':59C ''mean'':101C,119C,202C,224C ''medoid'':145C,155C,167C ''metric'':198C ''min'':92C ''minim'':209C ''must'':219C ''name'':49C ''need'':14C,90C,136C,171C ''negat'':74C ''new'':166C ''noth'':29C ''object'':150C,159C ''one'':22C ''p'':1C,44C,54C,97C,141C,173C ''pam'':142C ''probabl'':175C,204C ''propag'':46C ''properti'':199C ''requir'':9C,42C,197C,207C ''say'':30C,50C ''see'':3C ''seem'':194C ''similar'':34C,52C,79C,106C,131C,164C,216C ''similari'':154C ''space'':124C ''strong'':186C ''strongest'':206C ''think'':112C ''triangl'':37C,169C ''use'':24C,33C,51C,73C ''varianc'':211C ''vector'':123C ''ward'':60C ''work'':83C,103C,147C,189C ''would'':23C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (587, 1, '2014-06-25 18:06:14.293', 8, 4238, 'Where can I download historical market capitalization and daily turnover data for stocks?', '&lt;p&gt;There are plenty of sources which provide the historical stock data but they only provide the OHLC fields along with volume and adjusted close. Also a couple of sources I found provide market cap data sets but they''re restricted to US stocks. Yahoo Finance provides this data online but there''s no option to download it ( or none I am aware of ). &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Where can I download this data for stocks belonging to various top stock exchanges across countries by using their ticker name ?&lt;/li&gt;
&lt;li&gt;Is there some way to download it via Yahoo Finance or Google Finance ?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I need data for the last decade or so and hence need some script or API which would do this.&lt;/p&gt;
', 'dataset', NULL, '''/li'':103C,118C ''/p'':79C,141C ''/ul'':119C ''across'':96C ''adjust'':38C ''along'':34C ''also'':40C ''api'':136C ''awar'':77C ''belong'':90C ''cap'':49C ''capit'':7A ''close'':39C ''countri'':97C ''coupl'':42C ''daili'':9A ''data'':11A,26C,50C,63C,87C,123C ''dataset'':14B ''decad'':127C ''download'':4A,71C,85C,110C ''exchang'':95C ''field'':33C ''financ'':60C,114C,117C ''found'':46C ''googl'':116C ''henc'':131C ''histor'':5A,24C ''last'':126C ''li'':81C,104C ''market'':6A,48C ''name'':102C ''need'':122C,132C ''none'':74C ''ohlc'':32C ''onlin'':64C ''option'':69C ''p'':15C,120C ''plenti'':18C ''provid'':22C,30C,47C,61C ''re'':54C ''restrict'':55C ''script'':134C ''set'':51C ''sourc'':20C,44C ''stock'':13A,25C,58C,89C,94C ''ticker'':101C ''top'':93C ''turnov'':10A ''ul'':80C ''us'':57C ''use'':99C ''various'':92C ''via'':112C ''volum'':36C ''way'':108C ''would'':138C ''yahoo'':59C,113C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (588, 2, '2014-06-25 18:12:04.01', 4, NULL, NULL, '&lt;p&gt;Quant SE is better place for questions related to getting financial data:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://quant.stackexchange.com/questions/141/what-data-sources-are-available-online"&gt;What data sources are available online&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://quant.stackexchange.com/search?q=market+capitalization+data"&gt;http://quant.stackexchange.com/search?q=market+capitalization+data&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':28C ''/li'':29C ''/p'':14C ''/questions/141/what-data-sources-are-available-online'':21C ''/search?q=market+capitalization+data'':35C ''/search?q=market+capitalization+data&lt;/a&gt;&lt;/li&gt;'':38C ''/ul'':39C ''avail'':26C ''better'':5C ''data'':13C,23C ''financi'':12C ''get'':11C ''href'':18C,32C ''li'':16C,30C ''onlin'':27C ''p'':1C ''place'':6C ''quant'':2C ''quant.stackexchange.com'':20C,34C,37C ''quant.stackexchange.com/questions/141/what-data-sources-are-available-online'':19C ''quant.stackexchange.com/search?q=market+capitalization+data'':33C ''quant.stackexchange.com/search?q=market+capitalization+data&lt;/a&gt;&lt;/li&gt;'':36C ''question'':8C ''relat'':9C ''se'':3C ''sourc'':24C ''ul'':15C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (591, 2, '2014-06-26 06:51:12.243', 2, NULL, NULL, '&lt;p&gt;If you are totally unfamiliar with ordinal regression, I would try to read the Tabachnick / Fidell (&lt;a href="http://www.pearsonhighered.com/educator/product/Using-Multivariate-Statistics-6E/0205849571.page" rel="nofollow"&gt;http://www.pearsonhighered.com/educator/product/Using-Multivariate-Statistics-6E/0205849571.page&lt;/a&gt;)  chapter on the topic first - while not written for R, the book is very good at conveying the general logic and the "do''s" and "do nots". &lt;/p&gt;

&lt;p&gt;As a question: What are your response catgeories exactly? If they are some sort of scale, like "good - bad" it would be ok to use a linear regression (market research does it all the time...), but if the items are more disjunct, an ordinal regression might be better.
I dimly remember that some books about structural equatiotion modelling mentioned that linear regression was superior for good scales than probit - bit I cannot recall the book at the moment, sorry!&lt;/p&gt;

&lt;p&gt;The most serious problem might be the number of dummy variables - a couple of hundred dummy variables will make the analysis slow, hard to interpret and probably unstable - are there enough cases for each dummy / dummy-combination?&lt;/p&gt;
', NULL, NULL, '''/educator/product/using-multivariate-statistics-6e/0205849571.page'':22C ''/educator/product/using-multivariate-statistics-6e/0205849571.page&lt;/a&gt;)'':27C ''/p'':55C,136C,176C ''analysi'':158C ''bad'':75C ''better'':104C ''bit'':126C ''book'':39C,110C,131C ''cannot'':128C ''case'':169C ''catgeori'':64C ''chapter'':28C ''combin'':175C ''convey'':44C ''coupl'':150C ''dim'':106C ''disjunct'':98C ''dummi'':147C,153C,172C,174C ''dummy-combin'':173C ''enough'':168C ''equatiot'':113C ''exact'':65C ''fidel'':17C ''first'':32C ''general'':46C ''good'':42C,74C,122C ''hard'':160C ''href'':19C ''hundr'':152C ''interpret'':162C ''item'':95C ''like'':73C ''linear'':83C,117C ''logic'':47C ''make'':156C ''market'':85C ''mention'':115C ''might'':102C,142C ''model'':114C ''moment'':134C ''nofollow'':24C ''not'':54C ''number'':145C ''ok'':79C ''ordin'':8C,100C ''p'':1C,56C,137C ''probabl'':164C ''probit'':125C ''problem'':141C ''question'':59C ''r'':37C ''read'':14C ''recal'':129C ''regress'':9C,84C,101C,118C ''rel'':23C ''rememb'':107C ''research'':86C ''respons'':63C ''scale'':72C,123C ''serious'':140C ''slow'':159C ''sorri'':135C ''sort'':70C ''structur'':112C ''superior'':120C ''tabachnick'':16C ''time'':91C ''topic'':31C ''total'':5C ''tri'':12C ''unfamiliar'':6C ''unstabl'':165C ''use'':81C ''variabl'':148C,154C ''would'':11C,77C ''written'':35C ''www.pearsonhighered.com'':21C,26C ''www.pearsonhighered.com/educator/product/using-multivariate-statistics-6e/0205849571.page'':20C ''www.pearsonhighered.com/educator/product/using-multivariate-statistics-6e/0205849571.page&lt;/a&gt;)'':25C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (592, 2, '2014-06-26 07:41:52.403', 4, NULL, NULL, '&lt;p&gt;I usually take a two-step approach &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;compute univariate (variable by variable) summary statistics such as mean, range, variance, number of missing, cardinality, etc. for each variable and look for oddities (e.g. range not plausible given the meaning of the variable). Plot histograms for those odd variables.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;split the data into manageable subsets (choose a meaningful variable and split the data according to it e.g. all positive examples, and all negative) and explore them visually (e.g. with &lt;a href="http://www.ggobi.org/" rel="nofollow"&gt;ggobi&lt;/a&gt; ). Especially use tools like brushing and scatter plots to understand how variables are linked together.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;And when you start building models, make sure to plot the residuals, looking for extreme errors that might be due to an outlier, or look at the confusion matrix and make sure it is balanced. Use k-fold cross validation to optimize your models and look at the variance of the training error for each fold, if one fold performs much worse than the others, it may contain outliers.&lt;/p&gt;
', NULL, NULL, '''/a'':94C ''/li'':55C,111C ''/ol'':112C ''/p'':10C,54C,110C,184C ''accord'':72C ''approach'':9C ''balanc'':148C ''brush'':99C ''build'':118C ''cardin'':29C ''choos'':64C ''comput'':14C ''confus'':141C ''contain'':182C ''cross'':153C ''data'':60C,71C ''due'':133C ''e.g'':38C,75C,86C ''error'':129C,167C ''especi'':95C ''etc'':30C ''exampl'':78C ''explor'':83C ''extrem'':128C ''fold'':152C,170C,173C ''ggobi'':93C ''given'':42C ''histogram'':49C ''href'':89C ''k'':151C ''k-fold'':150C ''li'':12C,56C ''like'':98C ''link'':108C ''look'':35C,126C,138C,160C ''make'':120C,144C ''manag'':62C ''matrix'':142C ''may'':181C ''mean'':23C,44C ''meaning'':66C ''might'':131C ''miss'':28C ''model'':119C,158C ''much'':175C ''negat'':81C ''nofollow'':92C ''number'':26C ''odd'':52C ''odditi'':37C ''ol'':11C ''one'':172C ''optim'':156C ''other'':179C ''outlier'':136C,183C ''p'':1C,13C,57C,113C ''perform'':174C ''plausibl'':41C ''plot'':48C,102C,123C ''posit'':77C ''rang'':24C,39C ''rel'':91C ''residu'':125C ''scatter'':101C ''split'':58C,69C ''start'':117C ''statist'':20C ''step'':8C ''subset'':63C ''summari'':19C ''sure'':121C,145C ''take'':4C ''togeth'':109C ''tool'':97C ''train'':166C ''two'':7C ''two-step'':6C ''understand'':104C ''univari'':15C ''use'':96C,149C ''usual'':3C ''valid'':154C ''variabl'':16C,18C,33C,47C,53C,67C,106C ''varianc'':25C,163C ''visual'':85C ''wors'':176C ''www.ggobi.org'':90C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (868, 2, '2014-07-30 15:32:44.557', 4, NULL, NULL, '&lt;p&gt;There is a paper you should look into:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://cs.smith.edu/dftwiki/images/6/68/MapReduceDistributedComputingMachineLearning.pdf" rel="nofollow"&gt;MapReduce: Distributed Computing for Machine Learning&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;They distinguish 3 classes of machine-learning problems that are reasonable to address with MapReduce:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Single pass algorithms&lt;/li&gt;
&lt;li&gt;Iterative algorithms&lt;/li&gt;
&lt;li&gt;Query based algorithms&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;They also give examples for each class.&lt;/p&gt;
', NULL, NULL, '''/a'':25C ''/dftwiki/images/6/68/mapreducedistributedcomputingmachinelearning.pdf'':16C ''/li'':50C,54C,59C ''/ol'':60C ''/p'':10C,26C,44C,69C ''3'':30C ''address'':41C ''algorithm'':49C,53C,58C ''also'':63C ''base'':57C ''class'':31C,68C ''comput'':21C ''cs.smith.edu'':15C ''cs.smith.edu/dftwiki/images/6/68/mapreducedistributedcomputingmachinelearning.pdf'':14C ''distinguish'':29C ''distribut'':20C ''exampl'':65C ''give'':64C ''href'':13C ''iter'':52C ''learn'':24C,35C ''li'':46C,51C,55C ''look'':8C ''machin'':23C,34C ''machine-learn'':33C ''mapreduc'':19C,43C ''nofollow'':18C ''ol'':45C ''p'':1C,11C,27C,61C ''paper'':5C ''pass'':48C ''problem'':36C ''queri'':56C ''reason'':39C ''rel'':17C ''singl'':47C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (593, 2, '2014-06-26 08:01:40.763', 3, NULL, NULL, '&lt;p&gt;As many have answered, it is always best to avoid anything done manually as it is less reproducible/documentable. Your point about the overhead of writing a script vs. opening and fixing the file is valid, though.&lt;/p&gt;

&lt;p&gt;Best practice is often to&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;keep an untouched version of the data&lt;/li&gt;
&lt;li&gt;build a working copy of the data with errors fixed&lt;/li&gt;
&lt;li&gt;have a way to re-create a working copy from the original data&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The last point can be done with a script. Then make sure to be as specific as needed to modify only the data you want to modify, and to write the script in such a way that adding a fix by modifying the script is as easy as modifying the data directly.&lt;/p&gt;

&lt;p&gt;If your data lie in files, you can also use diffs/patches to store the original data along with the patches needed to produce the working data. To generate them, duplicate your working copy, perform the change, extract the diff/patch, save it, and delete the previous working copy.&lt;/p&gt;
', NULL, NULL, '''/li'':55C,67C,83C ''/p'':38C,45C,138C,187C ''/ul'':84C ''ad'':123C ''along'':156C ''also'':148C ''alway'':8C ''answer'':5C ''anyth'':12C ''avoid'':11C ''best'':9C,40C ''build'':57C ''chang'':175C ''copi'':60C,78C,172C,186C ''creat'':75C ''data'':54C,63C,82C,108C,136C,142C,155C,165C ''delet'':182C ''diff/patch'':178C ''diffs/patches'':150C ''direct'':137C ''done'':13C,91C ''duplic'':169C ''easi'':132C ''error'':65C ''extract'':176C ''file'':34C,145C ''fix'':32C,66C,125C ''generat'':167C ''keep'':48C ''last'':87C ''less'':18C ''li'':47C,56C,68C ''lie'':143C ''make'':96C ''mani'':3C ''manual'':14C ''modifi'':105C,112C,127C,134C ''need'':103C,160C ''often'':43C ''open'':30C ''origin'':81C,154C ''overhead'':24C ''p'':1C,39C,85C,139C ''patch'':159C ''perform'':173C ''point'':21C,88C ''practic'':41C ''previous'':184C ''produc'':162C ''re'':74C ''re-creat'':73C ''reproducible/documentable'':19C ''save'':179C ''script'':28C,94C,117C,129C ''specif'':101C ''store'':152C ''sure'':97C ''though'':37C ''ul'':46C ''untouch'':50C ''use'':149C ''valid'':36C ''version'':51C ''vs'':29C ''want'':110C ''way'':71C,121C ''work'':59C,77C,164C,171C,185C ''write'':26C,115C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8818, 1, '2015-11-12 15:41:30.003', 4, 54, 'how to increase the coverage of wordnet using wikipedia', '&lt;p&gt;I want to increase the coverage of wordnet by increasing the synset count.&lt;/p&gt;

&lt;p&gt;Is there a way in which I can proceed and increase sysnets. I am trying to work on Hindi wordnet with Princeton wordnet  as the  standard&lt;/p&gt;
', 'knowledge-base', NULL, '''/p'':27C,55C ''base'':12B ''count'':26C ''coverag'':5A,19C ''hindi'':47C ''increas'':3A,17C,23C,39C ''knowledg'':11B ''knowledge-bas'':10B ''p'':13C,28C ''princeton'':50C ''proceed'':37C ''standard'':54C ''synset'':25C ''sysnet'':40C ''tri'':43C ''use'':8A ''want'':15C ''way'':32C ''wikipedia'':9A ''wordnet'':7A,21C,48C,51C ''work'':45C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (594, 1, '2014-06-26 12:11:51.253', 5, 156, 'How does Google categorize results from its image search?', '&lt;p&gt;While doing a Google image search, the page displays some figured out categories for the images of the topic being searched for. I''m interested in learning how this works, and how it chooses and creates categories.&lt;/p&gt;

&lt;p&gt;Unfortunately, I couldn''t find much about it at all. Is anyone able to shed some light on algorithms they may be using to do this, and what basis these categories are created from?&lt;/p&gt;

&lt;p&gt;For example, if I search for "animals" I get the categories:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;"cute", "baby", "wild", "farm", "zoo", "clipart".&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;If I go into "wild", I then have subcategories:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;"forest", "baby", "africa", "clipart", "rainforest", "domestic".&lt;/p&gt;
&lt;/blockquote&gt;
', 'machine-learning  classification  google  search', NULL, '''/blockquote'':113C,134C ''/p'':54C,90C,103C,112C,124C,133C ''abl'':68C ''africa'':129C ''algorithm'':74C ''anim'':98C ''anyon'':67C ''babi'':107C,128C ''basi'':84C ''blockquot'':104C,125C ''categor'':4A ''categori'':29C,53C,86C,102C ''choos'':50C ''classif'':13B ''clipart'':111C,130C ''couldn'':58C ''creat'':52C,88C ''cute'':106C ''display'':25C ''domest'':132C ''exampl'':93C ''farm'':109C ''figur'':27C ''find'':60C ''forest'':127C ''get'':100C ''go'':117C ''googl'':3A,14B,20C ''imag'':8A,21C,32C ''interest'':41C ''learn'':12B,43C ''light'':72C ''m'':40C ''machin'':11B ''machine-learn'':10B ''may'':76C ''much'':61C ''p'':16C,55C,91C,105C,114C,126C ''page'':24C ''rainforest'':131C ''result'':5A ''search'':9A,15B,22C,37C,96C ''shed'':70C ''subcategori'':123C ''topic'':35C ''unfortun'':56C ''use'':78C ''wild'':108C,119C ''work'':46C ''zoo'':110C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (595, 1, '2014-06-26 12:25:55.663', 1, 178, 'How to use neural networks with large and variable number of inputs?', '&lt;p&gt;I''m new to machine learning, but I have an interesting problem. I have a large sample of people and visited sites. Some people have indicated gender, age, and other parameters. Now I want to restore these parameters to each user.&lt;/p&gt;

&lt;p&gt;Which way do I look for? Which algorithm is suitable to solve this problem? I''m familiar with Neural Networks (supervised learning), but it seems they don''t fit.&lt;/p&gt;
', 'machine-learning  data-mining  algorithms  neuralnetwork', NULL, '''/p'':63C,94C ''age'':49C ''algorithm'':19B,72C ''data'':17B ''data-min'':16B ''familiar'':81C ''fit'':93C ''gender'':48C ''indic'':47C ''input'':12A ''interest'':32C ''larg'':7A,37C ''learn'':15B,27C,86C ''look'':69C ''m'':23C,80C ''machin'':14B,26C ''machine-learn'':13B ''mine'':18B ''network'':5A,84C ''neural'':4A,83C ''neuralnetwork'':20B ''new'':24C ''number'':10A ''p'':21C,64C ''paramet'':52C,59C ''peopl'':40C,45C ''problem'':33C,78C ''restor'':57C ''sampl'':38C ''seem'':89C ''site'':43C ''solv'':76C ''suitabl'':74C ''supervis'':85C ''use'':3A ''user'':62C ''variabl'':9A ''visit'':42C ''want'':55C ''way'':66C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (964, 2, '2014-08-12 07:59:13.06', 4, NULL, NULL, '&lt;p&gt;Topic models are usually &lt;strong&gt;unsupervised&lt;/strong&gt;. There are "supervised topic models", too; but even then they try to model &lt;strong&gt;topics within a classes&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;E.g. you may have a class "football", but there may be topics inside this class that relate to particular matches or teams.&lt;/p&gt;

&lt;p&gt;The challenge with topics is that they change over time; consider the matches example above. Such topics may emerge, and disappear again.&lt;/p&gt;
', NULL, NULL, '''/p'':28C,52C,76C ''/strong'':8C,27C ''challeng'':55C ''chang'':61C ''class'':26C,35C,44C ''consid'':64C ''disappear'':74C ''e.g'':30C ''emerg'':72C ''even'':16C ''exampl'':67C ''footbal'':36C ''insid'':42C ''match'':49C,66C ''may'':32C,39C,71C ''model'':3C,13C,21C ''p'':1C,29C,53C ''particular'':48C ''relat'':46C ''strong'':6C,22C ''supervis'':11C ''team'':51C ''time'':63C ''topic'':2C,12C,23C,41C,57C,70C ''tri'':19C ''unsupervis'':7C ''usual'':5C ''within'':24C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (596, 1, '2014-06-26 13:32:18.05', 8, 170, 'Network analysis classic datasets', '&lt;p&gt;There are several classic datasets for machine learning classification/regression tasks. The most popular are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html"&gt;Iris Flower Data Set&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.kaggle.com/c/titanic-gettingStarted"&gt;Titanic Data Set&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stat.ethz.ch/R-manual/R-devel/library/datasets/html/mtcars.html"&gt;Motor Trend Cars&lt;/a&gt;;&lt;/li&gt;
&lt;li&gt;etc.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;But does anyone know similar datasets for networks analysis / graph theory? More concrete - I''m looking for &lt;strong&gt;Gold standard&lt;/strong&gt; datasets for comparing/evaluating/learning:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;centrality measures;&lt;/li&gt;
&lt;li&gt;network clustering algorithms.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I don''t need a huge list of publicly available networks/graphs, but a couple of actually must-know datasets.&lt;/p&gt;

&lt;p&gt;EDIT:&lt;/p&gt;

&lt;p&gt;It''s quite difficult to provide exact features for "gold standard dataset", but here are some thoughts. I think, real classic dataset should satisfy these criteria:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Multiple references in articles and textbooks;&lt;/li&gt;
&lt;li&gt;Inclusion in well-known network analysis software packages;&lt;/li&gt;
&lt;li&gt;Sufficient time of existence;&lt;/li&gt;
&lt;li&gt;Usage in a number of courses on graph analysis.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Concerning my field of interest, I also need labeled classes for vertices and/or precomputed (or predefined) "authority scores" (i.e. centrality estimates). After asking this question I continued searching, and here are some suitable examples:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://networkdata.ics.uci.edu/data.php?id=105"&gt;Zachary''s Karate Club&lt;/a&gt;: introduced in 1977, cited more than 1.5k times (according to Google Scholar), vertexes have attribute Faction (which can be used for clustering).&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.orgnet.com/Erdos.html"&gt;Erdos Collaboration Network&lt;/a&gt;: unfortunately, I haven''t find this network in form of data-file, but it''s rather famous, and if someone will enrich network with  mathematicians'' specialisations data, it also could be used for testing clustering algorithms.&lt;/li&gt;
&lt;/ul&gt;
', 'dataset  graphs', NULL, '''/a'':34C,45C,56C,237C,271C ''/c/titanic-gettingstarted'':41C ''/data.php?id=105'':232C ''/erdos.html'':267C ''/li'':35C,46C,57C,60C,92C,97C,160C,171C,177C,188C,261C,309C ''/ol'':98C ''/p'':22C,87C,120C,123C,151C,225C ''/r-manual/r-devel/library/datasets/html/mtcars.html'':52C ''/stable/auto_examples/datasets/plot_iris_dataset.html'':29C ''/strong'':83C ''/ul'':61C,189C,310C ''1.5'':244C ''1977'':240C ''accord'':247C ''actual'':115C ''algorithm'':96C,308C ''also'':197C,301C ''analysi'':2A,71C,168C,187C ''and/or'':203C ''anyon'':65C ''articl'':157C ''ask'':213C ''attribut'':253C ''author'':207C ''avail'':109C ''car'':55C ''central'':90C,210C ''cite'':241C ''class'':200C ''classic'':3A,11C,145C ''classification/regression'':16C ''club'':236C ''cluster'':95C,260C,307C ''collabor'':269C ''comparing/evaluating/learning'':86C ''concern'':191C ''concret'':75C ''continu'':217C ''could'':302C ''coupl'':113C ''cours'':184C ''criteria'':150C ''data'':32C,43C,283C,299C ''data-fil'':282C ''dataset'':4A,5B,12C,68C,84C,119C,136C,146C ''difficult'':128C ''edit'':122C ''enrich'':294C ''erdo'':268C ''estim'':211C ''etc'':59C ''exact'':131C ''exampl'':224C ''exist'':176C ''faction'':254C ''famous'':289C ''featur'':132C ''field'':193C ''file'':284C ''find'':276C ''flower'':31C ''form'':280C ''gold'':81C,134C ''googl'':249C ''graph'':6B,72C,186C ''haven'':274C ''href'':26C,38C,49C,229C,264C ''huge'':105C ''i.e'':209C ''inclus'':162C ''interest'':195C ''introduc'':238C ''iri'':30C ''k'':245C ''karat'':235C ''know'':66C,118C ''known'':166C ''label'':199C ''learn'':15C ''li'':24C,36C,47C,58C,89C,93C,153C,161C,172C,178C,227C,262C ''list'':106C ''look'':78C ''m'':77C ''machin'':14C ''mathematician'':297C ''measur'':91C ''motor'':53C ''multipl'':154C ''must'':117C ''must-know'':116C ''need'':103C,198C ''network'':1A,70C,94C,167C,270C,278C,295C ''networkdata.ics.uci.edu'':231C ''networkdata.ics.uci.edu/data.php?id=105'':230C ''networks/graphs'':110C ''number'':182C ''ol'':88C ''p'':7C,62C,99C,121C,124C,190C ''packag'':170C ''popular'':20C ''precomput'':204C ''predefin'':206C ''provid'':130C ''public'':108C ''question'':215C ''quit'':127C ''rather'':288C ''real'':144C ''refer'':155C ''satisfi'':148C ''scholar'':250C ''scikit-learn.org'':28C ''scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html'':27C ''score'':208C ''search'':218C ''set'':33C,44C ''sever'':10C ''similar'':67C ''softwar'':169C ''someon'':292C ''specialis'':298C ''standard'':82C,135C ''stat.ethz.ch'':51C ''stat.ethz.ch/r-manual/r-devel/library/datasets/html/mtcars.html'':50C ''strong'':80C ''suffici'':173C ''suitabl'':223C ''task'':17C ''test'':306C ''textbook'':159C ''theori'':73C ''think'':143C ''thought'':141C ''time'':174C,246C ''titan'':42C ''trend'':54C ''ul'':23C,152C,226C ''unfortun'':272C ''usag'':179C ''use'':258C,304C ''vertex'':251C ''vertic'':202C ''well'':165C ''well-known'':164C ''www.kaggle.com'':40C ''www.kaggle.com/c/titanic-gettingstarted'':39C ''www.orgnet.com'':266C ''www.orgnet.com/erdos.html'':265C ''zachari'':233C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (597, 2, '2014-06-26 13:45:16.573', 2, NULL, NULL, '&lt;p&gt;I am not working at Google, but I think it is some sort of recommendation system based on the words which millions of users searched before. So those people who search for "animals" often search for "wild animals" for example. Like in many online stores they recommend you to buy something in addition to the product you are looking for based on the previous purchases of other users.&lt;/p&gt;

&lt;p&gt;There are many approaches how to build such recommendation system using machine learning, no one knows for sure what google uses.&lt;/p&gt;
', NULL, NULL, '''/p'':70C,93C ''addit'':54C ''anim'':34C,39C ''approach'':75C ''base'':18C,62C ''build'':78C ''buy'':51C ''exampl'':41C ''googl'':7C,91C ''know'':87C ''learn'':84C ''like'':42C ''look'':60C ''machin'':83C ''mani'':44C,74C ''million'':23C ''often'':35C ''one'':86C ''onlin'':45C ''p'':1C,71C ''peopl'':30C ''previous'':65C ''product'':57C ''purchas'':66C ''recommend'':16C,48C,80C ''search'':26C,32C,36C ''someth'':52C ''sort'':14C ''store'':46C ''sure'':89C ''system'':17C,81C ''think'':10C ''use'':82C,92C ''user'':25C,69C ''wild'':38C ''word'':21C ''work'':5C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (615, 2, '2014-06-27 13:22:22.577', 2, NULL, NULL, '&lt;p&gt;One standard reference written from social science perspective is &lt;a href="http://rads.stackoverflow.com/amzn/click/0803973748" rel="nofollow"&gt;J Scott Long''s Limited Dependent Variables&lt;/a&gt; book. It goes much deeper than say Tabachnik suggested in &lt;a href="http://datascience.stackexchange.com/a/591/1237"&gt;another answer&lt;/a&gt;: Tabachnik is a cookbook at best, with little to no explanations of the "why", and it seems like you would benefit from figuring this out in more detail that can be found in Long''s book. Ordinal regression should be covered in most introductory econometrics courses (Wooldridge''s &lt;a href="http://rads.stackoverflow.com/amzn/click/0262232588" rel="nofollow"&gt;Cross-Section and Panel Data&lt;/a&gt; is a great graduate-level book), as well as quantitative social science courses (sociology, psychology), although I would imagine that the latter will loop back to Long''s book.&lt;/p&gt;

&lt;p&gt;Given that your number of variables is wa-a-ay lower than the sample size, the R package you should be looking is probably &lt;a href="http://cran.r-project.org/web/packages/ordinal/" rel="nofollow"&gt;&lt;code&gt;ordinal&lt;/code&gt;&lt;/a&gt; rather than &lt;code&gt;glmnetcr&lt;/code&gt;. &lt;a href="http://datascience.stackexchange.com/a/469/1237"&gt;Another answer&lt;/a&gt; mentioned that you can find this functionality in a more mainstream &lt;code&gt;MASS&lt;/code&gt; package.&lt;/p&gt;
', NULL, NULL, '''/a'':25C,43C,105C,173C,186C ''/a/469/1237'':183C ''/a/591/1237'':40C ''/amzn/click/0262232588'':96C ''/amzn/click/0803973748'':15C ''/code'':172C,178C,200C ''/p'':136C,202C ''/web/packages/ordinal/'':167C ''although'':122C ''anoth'':41C,184C ''answer'':42C,185C ''ay'':148C ''back'':131C ''benefit'':64C ''best'':49C ''book'':26C,79C,112C,135C ''code'':170C,176C,198C ''cookbook'':47C ''cours'':89C,119C ''cover'':84C ''cran.r-project.org'':166C ''cran.r-project.org/web/packages/ordinal/'':165C ''cross'':100C ''cross-sect'':99C ''data'':104C ''datascience.stackexchange.com'':39C,182C ''datascience.stackexchange.com/a/469/1237'':181C ''datascience.stackexchange.com/a/591/1237'':38C ''deeper'':30C ''depend'':23C ''detail'':71C ''econometr'':88C ''explan'':54C ''figur'':66C ''find'':191C ''found'':75C ''function'':193C ''given'':138C ''glmnetcr'':177C ''goe'':28C ''graduat'':110C ''graduate-level'':109C ''great'':108C ''href'':12C,37C,93C,164C,180C ''imagin'':125C ''introductori'':87C ''j'':18C ''latter'':128C ''level'':111C ''like'':61C ''limit'':22C ''littl'':51C ''long'':20C,77C,133C ''look'':160C ''loop'':130C ''lower'':149C ''mainstream'':197C ''mass'':199C ''mention'':187C ''much'':29C ''nofollow'':17C,98C,169C ''number'':141C ''one'':2C ''ordin'':80C,171C ''p'':1C,137C ''packag'':156C,201C ''panel'':103C ''perspect'':9C ''probabl'':162C ''psycholog'':121C ''quantit'':116C ''r'':155C ''rads.stackoverflow.com'':14C,95C ''rads.stackoverflow.com/amzn/click/0262232588'':94C ''rads.stackoverflow.com/amzn/click/0803973748'':13C ''rather'':174C ''refer'':4C ''regress'':81C ''rel'':16C,97C,168C ''sampl'':152C ''say'':32C ''scienc'':8C,118C ''scott'':19C ''section'':101C ''seem'':60C ''size'':153C ''social'':7C,117C ''sociolog'':120C ''standard'':3C ''suggest'':34C ''tabachnik'':33C,44C ''variabl'':24C,143C ''wa'':146C ''wa-a-ay'':145C ''well'':114C ''wooldridg'':90C ''would'':63C,124C ''written'':5C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (598, 2, '2014-06-26 14:17:44.077', 2, NULL, NULL, '&lt;p&gt;There exist many possibilities for populating empty gaps on data.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Most repeated value&lt;/strong&gt;: Fill the gaps with the most common value.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Create a distribution&lt;/strong&gt;: Make the histogram and drop values according to that distribution.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Create a new label&lt;/strong&gt;: Since you do not have information, do not assume any value and create another label/category to indicate that value is empty.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Create a classifier&lt;/strong&gt;: Make a relation among the variable with empty gaps and the rest of the data and create a simple classifier. With this, populate the rest of the data.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;There exist many others, but these are the most common strategies. My suggestion is not to populate and to keep unknown what is unknown.&lt;/p&gt;
', NULL, NULL, '''/li'':28C,45C,74C,109C ''/p'':12C,136C ''/strong'':19C,34C,52C,80C ''/ul'':110C ''accord'':41C ''among'':84C ''anoth'':66C ''assum'':61C ''classifi'':79C,100C ''common'':26C,121C ''creat'':31C,48C,65C,77C,97C ''data'':11C,95C,108C ''distribut'':33C,44C ''drop'':39C ''empti'':8C,73C,88C ''exist'':3C,113C ''fill'':20C ''gap'':9C,22C,89C ''histogram'':37C ''indic'':69C ''inform'':58C ''keep'':131C ''label'':51C ''label/category'':67C ''li'':14C,29C,46C,75C ''make'':35C,81C ''mani'':4C,114C ''new'':50C ''other'':115C ''p'':1C,111C ''popul'':7C,103C,128C ''possibl'':5C ''relat'':83C ''repeat'':17C ''rest'':92C,105C ''simpl'':99C ''sinc'':53C ''strategi'':122C ''strong'':15C,30C,47C,76C ''suggest'':124C ''ul'':13C ''unknown'':132C,135C ''valu'':18C,27C,40C,63C,71C ''variabl'':86C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (599, 2, '2014-06-26 14:21:10.513', 1, NULL, NULL, '&lt;p&gt;The only thing I know about is benchmark data for Graph Databases, such as Neo4j.&lt;/p&gt;

&lt;p&gt;You may find links similar to this one:
&lt;a href="http://istc-bigdata.org/index.php/benchmarking-graph-databases/" rel="nofollow"&gt;http://istc-bigdata.org/index.php/benchmarking-graph-databases/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;where you can find data to test network analysis and graph theory.&lt;/p&gt;

&lt;p&gt;Furthermore, you could play with the API of Twitter/Facebook to collect your own data. This is also a suggestion in case you do not find the data you are looking for.&lt;/p&gt;
', NULL, NULL, '''/index.php/benchmarking-graph-databases/'':31C ''/index.php/benchmarking-graph-databases/&lt;/a&gt;&lt;/p&gt;'':36C ''/p'':17C,50C,83C ''also'':68C ''analysi'':46C ''api'':58C ''benchmark'':9C ''case'':72C ''collect'':62C ''could'':54C ''data'':10C,42C,65C,78C ''databas'':13C ''find'':21C,41C,76C ''furthermor'':52C ''graph'':12C,48C ''href'':28C ''istc-bigdata.org'':30C,35C ''istc-bigdata.org/index.php/benchmarking-graph-databases/'':29C ''istc-bigdata.org/index.php/benchmarking-graph-databases/&lt;/a&gt;&lt;/p&gt;'':34C ''know'':6C ''link'':22C ''look'':81C ''may'':20C ''neo4j'':16C ''network'':45C ''nofollow'':33C ''one'':26C ''p'':1C,18C,37C,51C ''play'':55C ''rel'':32C ''similar'':23C ''suggest'':70C ''test'':44C ''theori'':49C ''thing'':4C ''twitter/facebook'':60C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (600, 1, '2014-06-26 14:39:06.41', 7, 276, 'How do you create an optimized walk list given longitude and latitude coordinates?', '&lt;p&gt;I am working on a political campaign where dozens of volunteers will be conducting door-knocking promotions over the next few weeks. Given a list with names, addresses and long/lat coordinates, what algorithms can be used to create an optimized walk list.&lt;/p&gt;
', 'algorithms', NULL, '''/p'':59C ''address'':44C ''algorithm'':14B,49C ''campaign'':22C ''conduct'':29C ''coordin'':13A,47C ''creat'':4A,54C ''door'':31C ''door-knock'':30C ''dozen'':24C ''given'':9A,39C ''knock'':32C ''latitud'':12A ''list'':8A,41C,58C ''long/lat'':46C ''longitud'':10A ''name'':43C ''next'':36C ''optim'':6A,56C ''p'':15C ''polit'':21C ''promot'':33C ''use'':52C ''volunt'':26C ''walk'':7A,57C ''week'':38C ''work'':18C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (606, 2, '2014-06-26 20:49:38.47', 3, NULL, NULL, '&lt;p&gt;Folks here stated great steps, but I think there are great information at the following link &lt;a href="http://simplystatistics.org/2014/06/13/what-i-do-when-i-get-a-new-data-set-as-told-through-tweets/" rel="nofollow"&gt;what I do when I get a new data set as told through tweets&lt;/a&gt;,
It sums up the steps the folks tweeted answering the great @hmason question "Data people: What is the very first thing you do when you get your hands on a new data set?"&lt;/p&gt;

&lt;p&gt;Hope it will be useful. &lt;/p&gt;
', NULL, NULL, '''/2014/06/13/what-i-do-when-i-get-a-new-data-set-as-told-through-tweets/'':22C ''/a'':39C ''/p'':73C,80C ''answer'':48C ''data'':33C,53C,71C ''first'':59C ''folk'':2C,46C ''follow'':16C ''get'':30C,65C ''great'':5C,12C,50C ''hand'':67C ''hmason'':51C ''hope'':75C ''href'':19C ''inform'':13C ''link'':17C ''new'':32C,70C ''nofollow'':24C ''p'':1C,74C ''peopl'':54C ''question'':52C ''rel'':23C ''set'':34C,72C ''simplystatistics.org'':21C ''simplystatistics.org/2014/06/13/what-i-do-when-i-get-a-new-data-set-as-told-through-tweets/'':20C ''state'':4C ''step'':6C,44C ''sum'':41C ''thing'':60C ''think'':9C ''told'':36C ''tweet'':38C,47C ''use'':79C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (621, 2, '2014-06-28 01:07:27.263', 3, NULL, NULL, '&lt;p&gt;There is a bunch of datasets made free by UC Irvine &lt;a href="https://archive.ics.uci.edu/ml/datasets.html" rel="nofollow"&gt;to play with here&lt;/a&gt;. Among those datasets, &lt;a href="https://archive.ics.uci.edu/ml/datasets.html?format=&amp;amp;task=&amp;amp;att=&amp;amp;area=&amp;amp;numAtt=&amp;amp;numIns=&amp;amp;type=text&amp;amp;sort=nameUp&amp;amp;view=table" rel="nofollow"&gt;there are a few dozen textual datasets&lt;/a&gt; that might help you guys with your task.&lt;/p&gt;

&lt;p&gt;Those are kind of generic datasets, so depending on your purpose they should not be used as the only data to train your models, or else your model -- while it might work -- will not produce quality results.&lt;/p&gt;
', NULL, NULL, '''/a'':24C,42C ''/ml/datasets.html'':17C ''/ml/datasets.html?format=&amp;amp;task=&amp;amp;att=&amp;amp;area=&amp;amp;numatt=&amp;amp;numins=&amp;amp;type=text&amp;amp;sort=nameup&amp;amp;view=table'':32C ''/p'':51C,90C ''among'':25C ''archive.ics.uci.edu'':16C,31C ''archive.ics.uci.edu/ml/datasets.html'':15C ''archive.ics.uci.edu/ml/datasets.html?format=&amp;amp;task=&amp;amp;att=&amp;amp;area=&amp;amp;numatt=&amp;amp;numins=&amp;amp;type=text&amp;amp;sort=nameup&amp;amp;view=table'':30C ''bunch'':5C ''data'':72C ''dataset'':7C,27C,41C,58C ''depend'':60C ''dozen'':39C ''els'':78C ''free'':9C ''generic'':57C ''guy'':47C ''help'':45C ''href'':14C,29C ''irvin'':12C ''kind'':55C ''made'':8C ''might'':44C,83C ''model'':76C,80C ''nofollow'':19C,34C ''p'':1C,52C ''play'':21C ''produc'':87C ''purpos'':63C ''qualiti'':88C ''rel'':18C,33C ''result'':89C ''task'':50C ''textual'':40C ''train'':74C ''uc'':11C ''use'':68C ''work'':84C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (622, 2, '2014-06-28 14:34:17.487', 1, NULL, NULL, '&lt;p&gt;I''m not sure if your question classifies as a clustering problem. In clustering you are trying to discover clusters of similar examples using unlabelled data. Here, it seems you wish to enumerate existing "clusters" of nearby nodes. &lt;/p&gt;

&lt;p&gt;To be honest, I have no idea of such a function in R. But, as far as the algorithm is concerned, I believe what you are looking for is &lt;a href="http://en.wikipedia.org/wiki/Connected-component_labeling" rel="nofollow"&gt;Connected-Component Labeling&lt;/a&gt;. Kind of a bucket fill, for matrices.&lt;/p&gt;

&lt;p&gt;The wikipedia article is linked above. One of the algorithms presented there, termed as single-pass algorithm, is as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;One-Pass(Image)
        [M, N]=size(Image);
        Connected = zeros(M,N);
        Mark = Value;
        Difference = Increment;
        Offsets = [-1; M; 1; -M];
        Index = [];
        No_of_Objects = 0; 

   for i: 1:M :
       for j: 1:N:
            if(Image(i,j)==1)            
                 No_of_Objects = No_of_Objects +1;            
                 Index = [((j-1)*M + i)];           
                 Connected(Index)=Mark;            
                 while ~isempty(Index)                
                      Image(Index)=0;                
                      Neighbors = bsxfun(@plus, Index, Offsets'');
                      Neighbors = unique(Neighbors(:));                
                      Index = Neighbors(find(Image(Neighbors)));                                
                      Connected(Index)=Mark;
                 end            
                 Mark = Mark + Difference;
            end
      end
  end
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I guess it''d be easy to roll your own using the above.&lt;/p&gt;
', NULL, NULL, '''+1'':161C ''-1'':133C,164C ''/a'':82C ''/code'':199C ''/p'':40C,90C,113C,215C ''/pre'':200C ''/wiki/connected-component_labeling'':75C ''0'':141C,175C ''1'':135C,144C,148C,154C ''algorithm'':60C,101C,109C ''articl'':94C ''believ'':64C ''bsxfun'':177C ''bucket'':86C ''classifi'':9C ''cluster'':12C,15C,21C,36C ''code'':115C ''compon'':80C ''concern'':62C ''connect'':79C,124C,167C,189C ''connected-compon'':78C ''d'':205C ''data'':27C ''differ'':130C,195C ''discov'':20C ''easi'':207C ''en.wikipedia.org'':74C ''en.wikipedia.org/wiki/connected-component_labeling'':73C ''end'':192C,196C,197C,198C ''enumer'':34C ''exampl'':24C ''exist'':35C ''far'':57C ''fill'':87C ''find'':186C ''follow'':112C ''function'':52C ''guess'':203C ''honest'':44C ''href'':72C ''idea'':48C ''imag'':119C,123C,151C,173C,187C ''increment'':131C ''index'':137C,162C,168C,172C,174C,179C,184C,190C ''isempti'':171C ''j'':147C,153C,163C ''kind'':83C ''label'':81C ''link'':96C ''look'':68C ''m'':3C,120C,126C,134C,136C,145C,165C ''mark'':128C,169C,191C,193C,194C ''matric'':89C ''n'':121C,127C,149C ''nearbi'':38C ''neighbor'':176C,181C,183C,185C,188C ''node'':39C ''nofollow'':77C ''object'':140C,157C,160C ''offset'':132C,180C ''one'':98C,117C ''one-pass'':116C ''p'':1C,41C,91C,201C ''pass'':108C,118C ''plus'':178C ''pre'':114C ''present'':102C ''problem'':13C ''question'':8C ''r'':54C ''rel'':76C ''roll'':209C ''seem'':30C ''similar'':23C ''singl'':107C ''single-pass'':106C ''size'':122C ''sure'':5C ''term'':104C ''tri'':18C ''uniqu'':182C ''unlabel'':26C ''use'':25C,212C ''valu'':129C ''wikipedia'':93C ''wish'':32C ''zero'':125C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8969, 4, '2015-11-22 05:40:44.037', 0, NULL, NULL, 'A measure of the degree of linear association among a pair of variables.', NULL, NULL, '''among'':9C ''associ'':8C ''degre'':5C ''linear'':7C ''measur'':2C ''pair'':11C ''variabl'':13C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (602, 2, '2014-06-26 16:19:46.48', 3, NULL, NULL, '&lt;p&gt;I thought of expanding a bit on the answer by Stanpol. While recommendation system is one approach of suggesting related queries, one more standard information retrieval based approach is the query expansion technique.&lt;/p&gt;

&lt;p&gt;Generally speaking, query expansion involves selecting additional terms from the top ranked documents retrieved in response to an initial query. Terms are typically selected by a combination of a term scoring function such as tf-idf and a co-occurrence based measure.&lt;/p&gt;

&lt;p&gt;For example, in response to a query term "animal", a term selection function may choose the term "zoo", because&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;"zoo" may be a dominating term (high tf-idf) in the top (say 10) documents retrieved in response to the query "animal"&lt;/li&gt;
&lt;li&gt;"zoo" may co-occur frequently (in close proximity) with the original query term "animal" in these documents&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':128C,148C ''/p'':35C,81C,102C ''/ul'':149C ''10'':119C ''addit'':43C ''anim'':91C,127C,144C ''answer'':10C ''approach'':18C,29C ''base'':28C,79C ''bit'':7C ''choos'':97C ''close'':137C ''co'':77C,133C ''co-occur'':132C ''co-occurr'':76C ''combin'':63C ''document'':49C,120C,147C ''domin'':109C ''exampl'':84C ''expand'':5C ''expans'':33C,40C ''frequent'':135C ''function'':68C,95C ''general'':37C ''high'':111C ''idf'':73C,114C ''inform'':26C ''initi'':55C ''involv'':41C ''li'':104C,129C ''may'':96C,106C,131C ''measur'':80C ''occur'':134C ''occurr'':78C ''one'':17C,23C ''origin'':141C ''p'':1C,36C,82C ''proxim'':138C ''queri'':22C,32C,39C,56C,89C,126C,142C ''rank'':48C ''recommend'':14C ''relat'':21C ''respons'':52C,86C,123C ''retriev'':27C,50C,121C ''say'':118C ''score'':67C ''select'':42C,60C,94C ''speak'':38C ''standard'':25C ''stanpol'':12C ''suggest'':20C ''system'':15C ''techniqu'':34C ''term'':44C,57C,66C,90C,93C,99C,110C,143C ''tf'':72C,113C ''tf-idf'':71C,112C ''thought'':3C ''top'':47C,117C ''typic'':59C ''ul'':103C ''zoo'':100C,105C,130C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (641, 1, '2014-06-30 21:02:05.053', 9, 1225, 'Dataset for Named Entity Recognition on Informal Text', '&lt;p&gt;I''m currently searching for labeled datasets to use to train a model to extract named entities from informal text (think something similar to tweets). Because capitalization and grammar are often lacking in the documents in my dataset, I''m looking for out of domain data that''s a bit more "informal" than the news articles and journal entries that many of today''s state of the art named entity recognition systems are trained on.  Any recommendations?  So far I''ve only been able to locate 50k tokens from twitter published here: &lt;a href="https://github.com/aritter/twitter_nlp/blob/master/data/annotated/ner.txt"&gt;https://github.com/aritter/twitter_nlp/blob/master/data/annotated/ner.txt&lt;/a&gt;&lt;/p&gt;
', 'dataset  nlp', NULL, '''/aritter/twitter_nlp/blob/master/data/annotated/ner.txt'':108C ''/aritter/twitter_nlp/blob/master/data/annotated/ner.txt&lt;/a&gt;&lt;/p&gt;'':111C ''50k'':98C ''abl'':95C ''art'':79C ''articl'':67C ''bit'':61C ''capit'':38C ''current'':14C ''data'':57C ''dataset'':1A,9B,18C,49C ''document'':46C ''domain'':56C ''entiti'':4A,28C,81C ''entri'':70C ''extract'':26C ''far'':90C ''github.com'':107C,110C ''github.com/aritter/twitter_nlp/blob/master/data/annotated/ner.txt'':106C ''github.com/aritter/twitter_nlp/blob/master/data/annotated/ner.txt&lt;/a&gt;&lt;/p&gt;'':109C ''grammar'':40C ''href'':105C ''inform'':7A,30C,63C ''journal'':69C ''label'':17C ''lack'':43C ''locat'':97C ''look'':52C ''m'':13C,51C ''mani'':72C ''model'':24C ''name'':3A,27C,80C ''news'':66C ''nlp'':10B ''often'':42C ''p'':11C ''publish'':102C ''recognit'':5A,82C ''recommend'':88C ''search'':15C ''similar'':34C ''someth'':33C ''state'':76C ''system'':83C ''text'':8A,31C ''think'':32C ''today'':74C ''token'':99C ''train'':22C,85C ''tweet'':36C ''twitter'':101C ''use'':20C ''ve'':92C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (603, 2, '2014-06-26 18:01:06.443', 4, NULL, NULL, '&lt;p&gt;People see something closely related to the &lt;a href="http://en.wikipedia.org/wiki/Travelling_salesman_problem" rel="nofollow"&gt;Travelling Salesman Problem&lt;/a&gt; and think that it can''t be solved. &lt;/p&gt;

&lt;p&gt;A good deal of work has been done on this topic and not all of it indicates that a solution is not available.  Depending on the parameters and the desired solution, you may be able to find something that will work.&lt;/p&gt;

&lt;p&gt;You may want to give a look at the &lt;a href="http://openopt.org/TSP" rel="nofollow"&gt;OpenOpt&lt;/a&gt; python library. &lt;/p&gt;

&lt;p&gt;Another resource to look at would  be the &lt;a href="http://tspsg.info/" rel="nofollow"&gt;TSP Solver and Generator&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;If you are using R, there is a &lt;a href="http://tsp.r-forge.r-project.org/" rel="nofollow"&gt;TSP package available&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Actually implementing a solution to your problem is a little too much to cover here, but this should provide a good starting point.  Within these packages and at the documentation within the links that I provided for you, you will find that there are a fairly wide variety of algorithmic strategies available.  You have a small geographic region and a small set of "salespeople", so the computational power needed to calculate a strategy within a reasonable time frame should be available on your desktop.  &lt;/p&gt;

&lt;p&gt;In practical terms, you don''t need to find the absolutely most optimal strategy.  You just need a very good one.  Pick a TSP package that looks the least overwhelming and give it a go.&lt;/p&gt;
', NULL, NULL, '''/a'':19C,90C,112C,131C ''/p'':28C,71C,93C,113C,132C,218C,255C ''/tsp'':86C ''/wiki/travelling_salesman_problem'':13C ''abl'':64C ''absolut'':230C ''actual'':134C ''algorithm'':183C ''anoth'':95C ''avail'':52C,130C,185C,214C ''calcul'':204C ''close'':5C ''comput'':200C ''cover'':147C ''deal'':32C ''depend'':53C ''desir'':59C ''desktop'':217C ''document'':163C ''done'':37C ''en.wikipedia.org'':12C ''en.wikipedia.org/wiki/travelling_salesman_problem'':11C ''fair'':179C ''find'':66C,174C,228C ''frame'':211C ''generat'':111C ''geograph'':190C ''give'':77C,251C ''go'':254C ''good'':31C,154C,239C ''href'':10C,83C,104C,124C ''implement'':135C ''indic'':46C ''least'':248C ''librari'':92C ''link'':166C ''littl'':143C ''look'':79C,98C,246C ''may'':62C,74C ''much'':145C ''need'':202C,226C,236C ''nofollow'':15C,88C,107C,127C ''one'':240C ''openopt'':89C ''openopt.org'':85C ''openopt.org/tsp'':84C ''optim'':232C ''overwhelm'':249C ''p'':1C,29C,72C,94C,114C,133C,219C ''packag'':129C,159C,244C ''paramet'':56C ''peopl'':2C ''pick'':241C ''point'':156C ''power'':201C ''practic'':221C ''problem'':18C,140C ''provid'':152C,169C ''python'':91C ''r'':119C ''reason'':209C ''region'':191C ''rel'':14C,87C,106C,126C ''relat'':6C ''resourc'':96C ''salesman'':17C ''salespeopl'':197C ''see'':3C ''set'':195C ''small'':189C,194C ''solut'':49C,60C,137C ''solv'':27C ''solver'':109C ''someth'':4C,67C ''start'':155C ''strategi'':184C,206C,233C ''term'':222C ''think'':21C ''time'':210C ''topic'':40C ''travel'':16C ''tsp'':108C,128C,243C ''tsp.r-forge.r-project.org'':125C ''tspsg.info'':105C ''use'':118C ''varieti'':181C ''want'':75C ''wide'':180C ''within'':157C,164C,207C ''work'':34C,70C ''would'':100C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (604, 2, '2014-06-26 19:16:31.47', 2, NULL, NULL, '&lt;p&gt;Google isn''t going to give away their proprietary work, but we can speculate.&lt;/p&gt;

&lt;p&gt;Here''s what I can gather from my limited usage:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;The recommendations do not seem to be user, geography, or history specific.&lt;/li&gt;
&lt;li&gt;There is never an empty recommendation (one that returns no results)&lt;/li&gt;
&lt;li&gt;There is not always a recommendation (some searches just return images)&lt;/li&gt;
&lt;li&gt;The recommendations are not always the same (consecutive searches sometimes return different recommendations)&lt;/li&gt;
&lt;li&gt;Result ordering shifts regularly (search for a specific image and it won''t always be in the same place)&lt;/li&gt;
&lt;li&gt;Very popular searches seem to be pre-calculated and more static than unpopular searches.&lt;/li&gt;
&lt;li&gt;Recommendations are not always one additional word, and recommendations do not always include the base query.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;It seems to me that they do this based on the history of the general end user population, they rotate recommendations when there are many popular ones, and they do some additional processing to determine that the result set is of a reasonable size.&lt;/p&gt;

&lt;p&gt;I would postulate that it works as follows:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Use consecutive search strings from users (short-to-long-tail searches) as training data for a machine-learning algorithm.&lt;/li&gt;
&lt;li&gt;Run searches that occur &gt; N amount of times a week against that recommendation algorithm.&lt;/li&gt;
&lt;li&gt;Validate and clean results.&lt;/li&gt;
&lt;li&gt;Push them out to the general population in rotation/A-B testing.&lt;/li&gt;
&lt;li&gt;Track click-throughs.&lt;/li&gt;
&lt;li&gt;Refine results over time.&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':43C,56C,69C,84C,105C,122C,140C,221C,237C,243C,255C,261C,267C ''/ol'':141C,268C ''/p'':16C,28C,187C,197C ''addit'':129C,174C ''algorithm'':220C,236C ''alway'':61C,75C,99C,127C,135C ''amount'':228C ''away'':8C ''base'':138C,151C ''calcul'':115C ''clean'':241C ''click'':259C ''click-through'':258C ''consecut'':78C,201C ''data'':214C ''determin'':177C ''differ'':82C ''empti'':49C ''end'':158C ''follow'':196C ''gather'':23C ''general'':157C,250C ''geographi'':39C ''give'':7C ''go'':5C ''googl'':2C ''histori'':41C,154C ''imag'':68C,94C ''includ'':136C ''isn'':3C ''learn'':219C ''li'':30C,44C,57C,70C,85C,106C,123C,199C,222C,238C,244C,256C,262C ''limit'':26C ''long'':209C ''machin'':218C ''machine-learn'':217C ''mani'':167C ''n'':227C ''never'':47C ''occur'':226C ''ol'':29C,198C ''one'':51C,128C,169C ''order'':87C ''p'':1C,17C,142C,188C ''place'':104C ''popul'':160C,251C ''popular'':108C,168C ''postul'':191C ''pre'':114C ''pre-calcul'':113C ''process'':175C ''proprietari'':10C ''push'':245C ''queri'':139C ''reason'':185C ''recommend'':32C,50C,63C,72C,83C,124C,132C,163C,235C ''refin'':263C ''regular'':89C ''result'':55C,86C,180C,242C,264C ''return'':53C,67C,81C ''rotat'':162C ''rotation/a-b'':253C ''run'':223C ''search'':65C,79C,90C,109C,121C,202C,211C,224C ''seem'':35C,110C,144C ''set'':181C ''shift'':88C ''short'':207C ''short-to-long-tail'':206C ''size'':186C ''sometim'':80C ''specif'':42C,93C ''specul'':15C ''static'':118C ''string'':203C ''tail'':210C ''test'':254C ''through'':260C ''time'':230C,266C ''track'':257C ''train'':213C ''unpopular'':120C ''usag'':27C ''use'':200C ''user'':38C,159C,205C ''valid'':239C ''week'':232C ''won'':97C ''word'':130C ''work'':11C,194C ''would'':190C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (654, 2, '2014-07-02 14:14:22.593', 7, NULL, NULL, '&lt;p&gt;What you are asking about is &lt;a href="http://en.wikipedia.org/wiki/Instance-based_learning"&gt;Instance-Based Learning&lt;/a&gt;. k-Nearest Neighbors (kNN) appears to be the most popular of these methods and is applicable to a wide variety of problem domains. Another general type of instance-based learning is &lt;a href="http://en.wikipedia.org/wiki/Analogical_modeling"&gt;Analogical Modeling&lt;/a&gt;, which uses instances as exemplars for comparison with new data.&lt;/p&gt;

&lt;p&gt;You referred to kNN as an application that uses training but that is not correct (the Wikipedia entry you linked is somewhat misleading in that regard). Yes, there are "training examples" (labeled instances) but the classifier doesn''t learn/train from these data. Rather, they are only used whenever you actually want to classify a new instance, which is why it is considered a "lazy" learner.&lt;/p&gt;

&lt;p&gt;Note that the Nearest Template Prediction method you mention effectively is a form of kNN with &lt;code&gt;k=1&lt;/code&gt; and cosine distance as the distance measure.&lt;/p&gt;
', NULL, NULL, '''/a'':17C,58C ''/code'':157C ''/p'':69C,136C,165C ''/wiki/analogical_modeling'':55C ''/wiki/instance-based_learning'':12C ''1'':156C ''actual'':120C ''analog'':56C ''anoth'':42C ''appear'':23C ''applic'':34C,77C ''ask'':5C ''base'':15C,48C ''classifi'':106C,123C ''code'':154C ''comparison'':65C ''consid'':132C ''correct'':85C ''cosin'':159C ''data'':68C,112C ''distanc'':160C,163C ''doesn'':107C ''domain'':41C ''effect'':147C ''en.wikipedia.org'':11C,54C ''en.wikipedia.org/wiki/analogical_modeling'':53C ''en.wikipedia.org/wiki/instance-based_learning'':10C ''entri'':88C ''exampl'':101C ''exemplar'':63C ''form'':150C ''general'':43C ''href'':9C,52C ''instanc'':14C,47C,61C,103C,126C ''instance-bas'':13C,46C ''k'':19C,155C ''k-nearest'':18C ''knn'':22C,74C,152C ''label'':102C ''lazi'':134C ''learn'':16C,49C ''learn/train'':109C ''learner'':135C ''link'':90C ''measur'':164C ''mention'':146C ''method'':31C,144C ''mislead'':93C ''model'':57C ''nearest'':20C,141C ''neighbor'':21C ''new'':67C,125C ''note'':138C ''p'':1C,70C,137C ''popular'':28C ''predict'':143C ''problem'':40C ''rather'':113C ''refer'':72C ''regard'':96C ''somewhat'':92C ''templat'':142C ''train'':80C,100C ''type'':44C ''use'':60C,79C,117C ''varieti'':38C ''want'':121C ''whenev'':118C ''wide'':37C ''wikipedia'':87C ''yes'':97C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (605, 1, '2014-06-26 19:23:46.043', 6, 283, 'How to normalize results of Singular Value Decomposition (SVD) between 0 and 1?', '&lt;p&gt;I''m building a recommender system and using SVD as one of the preprocessing techniques.
However, I want to normalize all my preprocessed data between 0 and 1 because all of my similarity measures (cosine, pearson, euclidean) depend on that assumption.&lt;/p&gt;

&lt;p&gt;After I take the SVD (A = USV^T), is there a standard way to normalize the matrix ''A'' between 0 and 1? Thanks!&lt;/p&gt;

&lt;p&gt;Edit: I want all of my similarity measurements to give results between 0 and 1 and my normalized euclidean distance in particular fails if the input matrix does not have values between 0 and 1.&lt;/p&gt;
', 'machine-learning  statistics  recommendation  feature-selection', NULL, '''/p'':64C,89C,126C ''0'':11A,48C,85C,103C,123C ''1'':13A,50C,87C,105C,125C ''assumpt'':63C ''build'':25C ''cosin'':57C ''data'':46C ''decomposit'':8A ''depend'':60C ''distanc'':110C ''edit'':91C ''euclidean'':59C,109C ''fail'':113C ''featur'':20B ''feature-select'':19B ''give'':100C ''howev'':38C ''input'':116C ''learn'':16B ''m'':24C ''machin'':15B ''machine-learn'':14B ''matrix'':82C,117C ''measur'':56C,98C ''normal'':3A,42C,80C,108C ''one'':33C ''p'':22C,65C,90C ''particular'':112C ''pearson'':58C ''preprocess'':36C,45C ''recommend'':18B,27C ''result'':4A,101C ''select'':21B ''similar'':55C,97C ''singular'':6A ''standard'':77C ''statist'':17B ''svd'':9A,31C,70C ''system'':28C ''take'':68C ''techniqu'':37C ''thank'':88C ''use'':30C ''usv'':72C ''valu'':7A,121C ''want'':40C,93C ''way'':78C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (645, 2, '2014-07-01 17:01:12.263', 3, NULL, NULL, '&lt;p&gt;You need to look at the confidence interval of the statistic.  This helps measure how much uncertainty in the statistic, which is largely a function of sample size.&lt;/p&gt;
', NULL, NULL, '''/p'':30C ''confid'':8C ''function'':26C ''help'':14C ''interv'':9C ''larg'':24C ''look'':5C ''measur'':15C ''much'':17C ''need'':3C ''p'':1C ''sampl'':28C ''size'':29C ''statist'':12C,21C ''uncertainti'':18C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (607, 1, '2014-06-26 22:17:52.57', 5, 277, 'Do you need a virtual machine as an instrument for your data science practice?', '&lt;p&gt;I am brand new to the field of data science, want to break into it, and there are so many tools out there.  These VMs have alot of software on them, but I haven''t been able to find any side-by-side comparison.  &lt;/p&gt;

&lt;p&gt;Here''s a start from my research, but if someone could tell me that one is objectively more rich-featured, with a larger community of support, and useful to get started then that would help greatly:&lt;/p&gt;

&lt;p&gt;datasciencetoolKIT.org -&gt; vm is on vagrant cloud (4 GB) and seems to be more "hip" with R, iPython notebook, and other useful command-line tools (html-&gt;txt, json-&gt;xml, etc). There is a book being released in August with detail.&lt;/p&gt;

&lt;p&gt;datasciencetoolBOX.org -&gt; vm is a vagrant box (24 GB) downloadable from their website. There seems to be more features here, and more literature.&lt;/p&gt;
', 'tools', NULL, '''/p'':62C,101C,143C,167C ''24'':151C ''4'':109C ''abl'':53C ''alot'':43C ''august'':140C ''book'':136C ''box'':150C ''brand'':19C ''break'':29C ''cloud'':108C ''command'':125C ''command-lin'':124C ''communiti'':88C ''comparison'':61C ''could'':74C ''data'':12A,25C ''datasciencetoolbox.org'':145C ''datasciencetoolkit.org'':103C ''detail'':142C ''download'':153C ''etc'':132C ''featur'':84C,162C ''field'':23C ''find'':55C ''gb'':110C,152C ''get'':94C ''great'':100C ''haven'':50C ''help'':99C ''hip'':116C ''html'':128C ''instrument'':9A ''ipython'':119C ''json'':130C ''larger'':87C ''line'':126C ''literatur'':166C ''machin'':6A ''mani'':36C ''need'':3A ''new'':20C ''notebook'':120C ''object'':80C ''one'':78C ''p'':16C,63C,102C,144C ''practic'':14A ''r'':118C ''releas'':138C ''research'':70C ''rich'':83C ''rich-featur'':82C ''scienc'':13A,26C ''seem'':112C,158C ''side'':58C,60C ''side-by-sid'':57C ''softwar'':45C ''someon'':73C ''start'':67C,95C ''support'':90C ''tell'':75C ''tool'':15B,37C,127C ''txt'':129C ''use'':92C,123C ''vagrant'':107C,149C ''virtual'':5A ''vm'':104C,146C ''vms'':41C ''want'':27C ''websit'':156C ''would'':98C ''xml'':131C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (651, 2, '2014-07-02 08:50:55.98', 2, NULL, NULL, '&lt;p&gt;The number of data in the class is sometimes referred to as the &lt;code&gt;support&lt;/code&gt; of the classifier. It tells how much you can trust your result, like a p-value would allow you to trust or distrust some test.&lt;/p&gt;

&lt;p&gt;One approach you can use is to compute several classifier performance measures, not only precision and recall, but also true positive rate, false positive rate, specificity, sensitivity, positive likelihood, negative likelihood, etc. and see whether they are consistent with one another. If one of the measure maxes out (100%) and the other do not, it is often, in my experience, indicative of something went wrong (e.g. poor support, trivial classifier, biased classifier, etc.). See &lt;a href="http://www.damienfrancois.be/blog/files/modelperfcheatsheet.pdf" rel="nofollow"&gt;this&lt;/a&gt; for a list of classifier performance measures.&lt;/p&gt;
', NULL, NULL, '''/a'':128C ''/blog/files/modelperfcheatsheet.pdf'':124C ''/code'':17C ''/p'':44C,136C ''100'':94C ''allow'':36C ''also'':64C ''anoth'':86C ''approach'':47C ''bias'':116C ''class'':8C ''classifi'':20C,55C,115C,117C,133C ''code'':15C ''comput'':53C ''consist'':83C ''data'':5C ''distrust'':41C ''e.g'':111C ''etc'':77C,118C ''experi'':105C ''fals'':68C ''href'':121C ''indic'':106C ''like'':30C ''likelihood'':74C,76C ''list'':131C ''max'':92C ''measur'':57C,91C,135C ''much'':24C ''negat'':75C ''nofollow'':126C ''number'':3C ''often'':102C ''one'':46C,85C,88C ''p'':1C,33C,45C ''p-valu'':32C ''perform'':56C,134C ''poor'':112C ''posit'':66C,69C,73C ''precis'':60C ''rate'':67C,70C ''recal'':62C ''refer'':11C ''rel'':125C ''result'':29C ''see'':79C,119C ''sensit'':72C ''sever'':54C ''someth'':108C ''sometim'':10C ''specif'':71C ''support'':16C,113C ''tell'':22C ''test'':43C ''trivial'':114C ''true'':65C ''trust'':27C,39C ''use'':50C ''valu'':34C ''went'':109C ''whether'':80C ''would'':35C ''wrong'':110C ''www.damienfrancois.be'':123C ''www.damienfrancois.be/blog/files/modelperfcheatsheet.pdf'':122C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (627, 2, '2014-06-29 22:44:02.56', 8, NULL, NULL, '&lt;p&gt;Roughly speaking, over-fitting typically occurs when the ratio&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/AVVpB.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;is too high.&lt;/p&gt;

&lt;p&gt;Think of over-fitting as a situation where your model learn the training data by heart instead of learning the big pictures which prevent it from being able to generalized to the test data: this happens when the model is too complex with respect to the size of the training data, that is to say when the size of the training data is to small in comparison with the model complexity.&lt;/p&gt;

&lt;p&gt;Examples: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;if your data is in two dimensions, you have 10000 points in the training set and the model is a line, you are likely to &lt;em&gt;under&lt;/em&gt;-fit.&lt;/li&gt;
&lt;li&gt;if your data is in two dimensions, you have 10 points in the training set and the model is 100-degree polynomial, you are likely to &lt;em&gt;over&lt;/em&gt;-fit.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/HH23h.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;From a theoretical standpoint, the amount of data you need to properly train your model is a crucial yet far-to-be-answered question in machine learning. One such approach to answer this question is the &lt;a href="http://math.stackexchange.com/a/656167/24265"&gt;VC dimension&lt;/a&gt;. Another is the &lt;a href="https://www.quora.com/Machine-Learning/What-is-an-intuitive-explanation-for-bias-variance-tradeoff/answer/Franck-Dernoncourt?share=1"&gt;bias-variance tradeoff&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;From an empirical standpoint, people typically plot the training error and the test error on the same plot and make sure that they don''t reduce the training error at the expense of the test error:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/I7LiT.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;I would advise to watch &lt;a href="https://www.coursera.org/course/ml"&gt;Coursera'' Machine Learning course&lt;/a&gt;, section "10: Advice for applying Machine Learning".&lt;/p&gt;

&lt;p&gt;(PS: please go &lt;a href="http://meta.datascience.stackexchange.com/q/6/843"&gt;here&lt;/a&gt; to ask for TeX support on this SE.) &lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (608, 1, '2014-06-26 22:58:32.38', 5, 79, 'Any differences in regularisation in MLP between batch and individual updates?', '&lt;p&gt;I have just learned about regularisation as an approach to control over-fitting, and I would like to incorporate the idea into a simple implementation of backpropagation and &lt;a href="http://en.wikipedia.org/wiki/Multilayer_perceptron" rel="nofollow"&gt;Multilayer perceptron&lt;/a&gt; (MLP) that I put together.&lt;/p&gt;

&lt;p&gt;Currently to avoid over-fitting, I cross-validate and keep the network with best score so far on the validation set. This works OK, but adding regularisation would benefit me in that correct choice of the regularisation algorithm and parameter would make my network converge on a non-overfit model more systematically.&lt;/p&gt;

&lt;p&gt;The formula I have for the update term (from Coursera ML course) is stated as a batch update e.g. for each weight, after summing all the applicable deltas for the whole training set from error propagation, an adjustment of &lt;code&gt;lambda * current_weight&lt;/code&gt; is added as well before the combined delta is subtracted at the end of the batch, where &lt;code&gt;lambda&lt;/code&gt; is the regularisation parameter.&lt;/p&gt;

&lt;p&gt;My implementation of backpropagation uses per-item weight updates. I am concerned that I cannot just copy the batch approach, although it looks OK intuitively to me. &lt;em&gt;Does a smaller regularisation term per item work just as well?&lt;/em&gt; &lt;/p&gt;

&lt;p&gt;For instance &lt;code&gt;lambda * current_weight / N&lt;/code&gt; where N is size of training set - at first glance this looks reasonable. I could not find anything on the subject though, and I wonder if that is because regularisation does not work as well with a per-item update, or even goes under a different name or altered formula.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (609, 2, '2014-06-26 23:46:22.487', 6, NULL, NULL, '&lt;p&gt;Below is a very good note (page 12) on learning rate in Neural Nets (Back Propagation) by Andrew Ng. You will find details relating to learning rate. &lt;/p&gt;

&lt;p&gt;&lt;a href="http://web.stanford.edu/class/cs294a/sparseAutoencoder_2011new.pdf" rel="nofollow"&gt;http://web.stanford.edu/class/cs294a/sparseAutoencoder_2011new.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;For your 4th point, you''re right that normally one has to choose a "balanced" learning rate, that should neither overshoot nor converge too slowly. One can plot the learning rate w.r.t. the descent of the cost function to diagnose/fine tune. In practice, Andrew normally uses the L-BFGS algorithm (mentioned in page 12) to get a "good enough" learning rate.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (611, 2, '2014-06-27 07:06:14.297', 0, NULL, NULL, '&lt;p&gt;I had almost the same problem: ''restoring'' age, gender, location for social network users. But I used users'' ego-networks, not visited sites statistics. And I faced with two almost independent tasks:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;''Restoring'' or ''predicting'' data. You can use a bunch of different technics to complete this task, but my vote is for simplest ones (KISS, yes). E.g., in my case, for age prediction, mean of ego-network users'' ages gave satisfactory results (for about 70% of users error was less than +/-3 years, in my case it was enough). It''s just an idea, but you can try to use for age prediction weighted average, defining weight as similarity measure between visited sites sets of current user and others.&lt;/li&gt;
&lt;li&gt;Evaluating prediction quality. Algorithm from task-1 will produce prediction almost in all cases. And second task is to determine, if prediction is reliable. E.g., in case of ego network and age prediction: can we trust in prediction, if a user has only one ''friend'' in his ego network? This task is more about machine-learning: it''s a binary classification problem. You need to compose features set, form training and test samples from your data with both right and wrong predictions. Creating appropriate classifier will help you to filter out unpredictable users. But you need to determine, what are your features set. I used a number of network metrics, and summary statistics on feature of interest distribution among ego-network.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;This approach wouldn''t populate all the gaps, but only predictable ones.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (612, 2, '2014-06-27 07:19:11.42', 2, NULL, NULL, '&lt;p&gt;Regularization is relevant in per-item learning as well. I would suggest to start with a basic validation approach for finding out lambda, whether you are doing batch or per-item learning. This is the easiest and safest approach. Try manually with a number of different values. e.g. 0.001. 0.003, 0.01, 0.03, 0.1 etc. and see how your validation set behaves. Later on you may automate this process by introducing a linear or local search method. &lt;/p&gt;

&lt;p&gt;As a side note, I believe the value of lambda should be considered in relation to the updates of the parameter vector, rather than the training set size. For batch training you have one parameter update &lt;em&gt;per dataset pass&lt;/em&gt;, while for online one update &lt;em&gt;per sample&lt;/em&gt; (regardless of the training set size).&lt;/p&gt;

&lt;p&gt;I recently stumbled upon this &lt;a href="http://stats.stackexchange.com/questions/64224/regularization-and-feature-scaling-in-online-learning"&gt;Crossvalidated Question&lt;/a&gt;, which seems quite similar to yours. There is a link to a paper about &lt;a href="http://leon.bottou.org/publications/pdf/jmlr-2009.pdf" rel="nofollow"&gt;a new SGD algorithm&lt;/a&gt;, with some relevant content. It might be useful to take a look (especially pages 1742-1743).&lt;/p&gt;
', NULL, NULL, '''-1743'':194C ''/a'':152C,178C ''/em'':122C,131C ''/p'':80C,138C,195C ''/publications/pdf/jmlr-2009.pdf'':171C ''/questions/64224/regularization-and-feature-scaling-in-online-learning'':149C ''0.001'':52C ''0.003'':53C ''0.01'':54C ''0.03'':55C ''0.1'':56C ''1742'':193C ''algorithm'':177C ''approach'':21C,42C ''autom'':69C ''basic'':19C ''batch'':30C,111C ''behav'':64C ''believ'':87C ''consid'':94C ''content'':182C ''crossvalid'':150C ''dataset'':120C ''differ'':49C ''e.g'':51C ''easiest'':39C ''em'':118C,128C ''especi'':191C ''etc'':57C ''find'':23C ''href'':146C,168C ''introduc'':73C ''item'':8C,34C ''lambda'':25C,91C ''later'':65C ''learn'':9C,35C ''leon.bottou.org'':170C ''leon.bottou.org/publications/pdf/jmlr-2009.pdf'':169C ''linear'':75C ''link'':162C ''local'':77C ''look'':190C ''manual'':44C ''may'':68C ''method'':79C ''might'':184C ''new'':175C ''nofollow'':173C ''note'':85C ''number'':47C ''one'':115C,126C ''onlin'':125C ''p'':1C,81C,139C ''page'':192C ''paper'':165C ''paramet'':102C,116C ''pass'':121C ''per'':7C,33C,119C,129C ''per-item'':6C,32C ''process'':71C ''question'':151C ''quit'':155C ''rather'':104C ''recent'':141C ''regardless'':132C ''regular'':2C ''rel'':172C ''relat'':96C ''relev'':4C,181C ''safest'':41C ''sampl'':130C ''search'':78C ''see'':59C ''seem'':154C ''set'':63C,108C,136C ''sgd'':176C ''side'':84C ''similar'':156C ''size'':109C,137C ''start'':16C ''stats.stackexchange.com'':148C ''stats.stackexchange.com/questions/64224/regularization-and-feature-scaling-in-online-learning'':147C ''stumbl'':142C ''suggest'':14C ''take'':188C ''train'':107C,112C,135C ''tri'':43C ''updat'':99C,117C,127C ''upon'':143C ''use'':186C ''valid'':20C,62C ''valu'':50C,89C ''vector'':103C ''well'':11C ''whether'':26C ''would'':13C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (613, 1, '2014-06-27 12:03:53.357', 2, 515, 'Difference Between Hadoop Mapreduce(Java) and RHadoop mapreduce', '&lt;p&gt;I understand Hadoop MapReduce and its features but I am confused about R MapReduce.&lt;/p&gt;

&lt;p&gt;One difference I have read is that R utilizes maximum RAM. So do perform parallel processing integrated R with Hadoop.&lt;/p&gt;

&lt;h2&gt;My doubt is:&lt;/h2&gt;

&lt;ol&gt;
&lt;li&gt;R can do all stats, math and data science related stuff, but why R MapReduce?&lt;/li&gt;
&lt;li&gt;Is there any new task I can achieve by using R MapReduce instead of Hadoop MapReduce? If yes, please specify.&lt;/li&gt;
&lt;li&gt;We can achieve the task by using R with Hadoop (directly) but what is the importance of MapReduce in R and how it is different from normal MapReduce?&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  r  hadoop  map-reduce', NULL, '''/h2'':59C ''/li'':77C,99C,129C ''/ol'':130C ''/p'':32C,54C ''achiev'':86C,103C ''confus'':28C ''data'':69C ''differ'':1A,35C,125C ''direct'':111C ''doubt'':57C ''featur'':24C ''h2'':55C ''hadoop'':3A,13B,20C,53C,93C,110C ''import'':116C ''instead'':91C ''integr'':50C ''java'':5A ''learn'':11B ''li'':61C,78C,100C ''machin'':10B ''machine-learn'':9B ''map'':15B ''map-reduc'':14B ''mapreduc'':4A,8A,21C,31C,76C,90C,94C,118C,128C ''math'':67C ''maximum'':43C ''new'':82C ''normal'':127C ''ol'':60C ''one'':34C ''p'':17C,33C ''parallel'':48C ''perform'':47C ''pleas'':97C ''process'':49C ''r'':12B,30C,41C,51C,62C,75C,89C,108C,120C ''ram'':44C ''read'':38C ''reduc'':16B ''relat'':71C ''rhadoop'':7A ''scienc'':70C ''specifi'':98C ''stat'':66C ''stuff'':72C ''task'':83C,105C ''understand'':19C ''use'':88C,107C ''util'':42C ''yes'':96C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (614, 2, '2014-06-27 12:28:25.467', 2, NULL, NULL, '&lt;p&gt;To complement what &lt;strong&gt;insys&lt;/strong&gt; said :&lt;/p&gt;

&lt;p&gt;Regularization is used when computing the backpropagation for all the weights in your MLP.
Therefore, instead of computing the gradient in regard to all the input of the training set (&lt;code&gt;batch&lt;/code&gt;) you only use some/one item(s) (&lt;code&gt;stochastic or semi-stochastic&lt;/code&gt;).
You will end up limiting a result of the update in regard to one item instead of all which is also correct.&lt;/p&gt;

&lt;p&gt;Also, if i remember correctly, Andrew NG used &lt;code&gt;L2-regularization&lt;/code&gt;.
The &lt;code&gt;/N&lt;/code&gt; in &lt;code&gt;lambda * current_weight / N&lt;/code&gt; is not mandatory, it just helps rescaling the input. However if you choose not to use it, you will have (in most of the case) to select another value for &lt;code&gt;lambda&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;You can also use the &lt;a href="http://en.wikipedia.org/wiki/Hyperparameter_optimization" rel="nofollow"&gt;Grid-search algorithm&lt;/a&gt; to choose the best value for &lt;code&gt;lambda&lt;/code&gt; (the &lt;em&gt;hyperparameter&lt;/em&gt; =&gt; the one you have to choose).&lt;/p&gt;
', NULL, NULL, '''/a'':156C ''/code'':43C,56C,93C,97C,104C,137C,165C ''/em'':169C ''/n'':96C ''/p'':9C,79C,138C,176C ''/strong'':7C ''/wiki/hyperparameter_optimization'':149C ''algorithm'':155C ''also'':77C,81C,142C ''andrew'':86C ''anoth'':132C ''backpropag'':17C ''batch'':42C ''best'':160C ''case'':129C ''choos'':117C,158C,175C ''code'':41C,50C,89C,95C,99C,135C,163C ''complement'':3C ''comput'':15C,28C ''correct'':78C,85C ''current'':101C ''em'':167C ''en.wikipedia.org'':148C ''en.wikipedia.org/wiki/hyperparameter_optimization'':147C ''end'':59C ''gradient'':30C ''grid'':153C ''grid-search'':152C ''help'':110C ''howev'':114C ''href'':146C ''hyperparamet'':168C ''input'':36C,113C ''insi'':6C ''instead'':26C,72C ''item'':48C,71C ''l2'':91C ''l2-regularization'':90C ''lambda'':100C,136C,164C ''limit'':61C ''mandatori'':107C ''mlp'':24C ''n'':103C ''ng'':87C ''nofollow'':151C ''one'':70C,171C ''p'':1C,10C,80C,139C ''regard'':32C,68C ''regular'':11C,92C ''rel'':150C ''rememb'':84C ''rescal'':111C ''result'':63C ''said'':8C ''search'':154C ''select'':131C ''semi'':54C ''semi-stochast'':53C ''set'':40C ''some/one'':47C ''stochast'':51C,55C ''strong'':5C ''therefor'':25C ''train'':39C ''updat'':66C ''use'':13C,46C,88C,120C,143C ''valu'':133C,161C ''weight'':21C,102C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (807, 2, '2014-07-22 04:10:18.353', 16, NULL, NULL, '&lt;p&gt;Really great question, and one that I find that most people don''t really understand on an intuitive level. &lt;code&gt;AUC&lt;/code&gt; is in fact often predicted over accuracy for binary classification for a number of different reasons. First though, let''s talk about exactly what &lt;code&gt;AUC&lt;/code&gt; is. Honestly, for being one of the most widely used efficacy metrics, it''s surprisingly obtuse to figure out exactly how &lt;code&gt;AUC&lt;/code&gt; works.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;AUC&lt;/code&gt; stands for &lt;code&gt;Area Under the Curve&lt;/code&gt;, which curve you ask? Well that would be the &lt;code&gt;ROC&lt;/code&gt; curve. &lt;code&gt;ROC&lt;/code&gt; stands for &lt;a href="http://en.wikipedia.org/wiki/Receiver_operating_characteristic"&gt;Receiver Operating Characteristic&lt;/a&gt;, which is actually slightly non-intuitive. The implicit goal of &lt;code&gt;AUC&lt;/code&gt; is to deal with situations where you have a very skewed sample distribution, and don''t want to overfit to a single class.&lt;/p&gt;

&lt;p&gt;A great example is in spam detection. Generally spam data sets are STRONGLY biased towards ham, or not-spam. If your data set is 90% ham, you can get a pretty damn good accuracy by just saying that every single email is ham, which is obviously something that indicates a non-ideal classifier. Let''s start with a couple of metrics that are a little more useful for us, specifically the true positive rate (&lt;code&gt;TPR&lt;/code&gt;) and the false positive rate (&lt;code&gt;FPR&lt;/code&gt;):&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/hNxTl.png" alt="ROC axes"&gt;&lt;/p&gt;

&lt;p&gt;Now in this graph, &lt;code&gt;TPR&lt;/code&gt; is specifically the ratio of true positive to all positives, and &lt;code&gt;FPR&lt;/code&gt; is the ratio of false positives to all negatives. (Keep in mind, this is only for binary classification.) On a graph like this, it should be pretty straightforward to figure out that a prediction of all 0''s or all 1''s will result in the points of &lt;code&gt;(0,0)&lt;/code&gt; and &lt;code&gt;(1,1)&lt;/code&gt; respectively. If you draw a line through these lines you get something like this:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/B1WT1.png" alt="Kind of like a triangle"&gt;&lt;/p&gt;

&lt;p&gt;Which looks basically like a diagonal line (it is), and by some easy geometry, you can see that the &lt;code&gt;AUC&lt;/code&gt; of such a model would be &lt;code&gt;0.5&lt;/code&gt; (height and base are both 1). Similarly, if you predict a random assortment of 0''s and 1''s, let''s say 90% 1''s, you could get the point &lt;code&gt;(0.9, 0.9)&lt;/code&gt;, which again falls along that diagonal line.&lt;/p&gt;

&lt;p&gt;Now comes the interesting part. What if we weren''t only predicting 0''s and 1''s? What if instead we wanted to say that, theoretically we were going to set a cutoff, above which every result was a 1, and below which every result were a 0. This would mean that at the extremes you get the original situation where you have all 0''s and all 1''s (at a cutoff of 0 and 1 respectively), but also a series of intermediate states that fall within the &lt;code&gt;1x1&lt;/code&gt; graph that contains your &lt;code&gt;ROC&lt;/code&gt;. In practice you get something like this:
&lt;img src="http://i.stack.imgur.com/13McM.png" alt="Courtesy of Wikipedia"&gt;&lt;/p&gt;

&lt;p&gt;So basically, what you''re actually getting when you do an &lt;code&gt;AUC&lt;/code&gt; over accuracy is something that will strongly discourage people going for models that are representative, but not discriminative, as this will only actually select for models that achieve false positive and true positive rates that are significantly above random chance, which is not guaranteed for accuracy.&lt;/p&gt;
', NULL, NULL, '''/13mcm.png'':545C ''/a'':115C ''/b1wt1.png'':352C ''/code'':23C,50C,74C,80C,88C,100C,104C,129C,233C,241C,260C,274C,326C,331C,382C,391C,425C,526C,533C,565C ''/hnxtl.png'':248C ''/p'':76C,153C,242C,252C,346C,359C,433C,550C,612C ''/wiki/receiver_operating_characteristic'':111C ''0'':311C,324C,325C,406C,447C,482C,499C,509C ''0.5'':390C ''0.9'':423C,424C ''1'':315C,329C,330C,397C,409C,415C,450C,474C,503C,511C ''1x1'':525C ''90'':180C,414C ''accuraci'':30C,189C,567C,611C ''achiev'':593C ''actual'':118C,557C,588C ''along'':429C ''also'':514C ''alt'':249C,353C,546C ''area'':84C ''ask'':92C ''assort'':404C ''auc'':22C,49C,73C,79C,128C,381C,564C ''axe'':251C ''base'':394C ''basic'':363C,553C ''bias'':168C ''binari'':32C,291C ''chanc'':605C ''characterist'':114C ''class'':152C ''classif'':33C,292C ''classifi'':209C ''code'':21C,48C,72C,78C,83C,98C,102C,127C,231C,239C,258C,272C,323C,328C,380C,389C,422C,524C,531C,563C ''come'':436C ''contain'':529C ''could'':418C ''coupl'':215C ''courtesi'':547C ''curv'':87C,90C,101C ''cutoff'':467C,507C ''damn'':187C ''data'':164C,177C ''deal'':132C ''detect'':161C ''diagon'':366C,431C ''differ'':38C ''discourag'':573C ''discrimin'':583C ''distribut'':142C ''draw'':335C ''easi'':373C ''efficaci'':61C ''email'':196C ''en.wikipedia.org'':110C ''en.wikipedia.org/wiki/receiver_operating_characteristic'':109C ''everi'':194C,470C,478C ''exact'':46C,70C ''exampl'':157C ''extrem'':489C ''fact'':26C ''fall'':428C,521C ''fals'':236C,279C,594C ''figur'':68C,304C ''find'':9C ''first'':40C ''fpr'':240C,273C ''general'':162C ''geometri'':374C ''get'':184C,342C,419C,491C,537C,558C ''go'':463C,575C ''goal'':125C ''good'':188C ''graph'':257C,295C,527C ''great'':3C,156C ''guarante'':609C ''ham'':170C,181C,198C ''height'':392C ''honest'':52C ''href'':108C ''i.stack.imgur.com'':247C,351C,544C ''i.stack.imgur.com/13mcm.png'':543C ''i.stack.imgur.com/b1wt1.png'':350C ''i.stack.imgur.com/hnxtl.png'':246C ''ideal'':208C ''img'':244C,348C,541C ''implicit'':124C ''indic'':204C ''instead'':454C ''interest'':438C ''intermedi'':518C ''intuit'':19C,122C ''keep'':284C ''kind'':354C ''let'':42C,210C,411C ''level'':20C ''like'':296C,344C,356C,364C,539C ''line'':337C,340C,367C,432C ''littl'':221C ''look'':362C ''mean'':485C ''metric'':62C,217C ''mind'':286C ''model'':386C,577C,591C ''negat'':283C ''non'':121C,207C ''non-id'':206C ''non-intuit'':120C ''not-spam'':172C ''number'':36C ''obtus'':66C ''obvious'':201C ''often'':27C ''one'':6C,55C ''oper'':113C ''origin'':493C ''overfit'':148C ''p'':1C,77C,154C,243C,253C,347C,360C,434C,551C ''part'':439C ''peopl'':12C,574C ''point'':321C,421C ''posit'':229C,237C,267C,270C,280C,595C,598C ''practic'':535C ''predict'':28C,308C,401C,446C ''pretti'':186C,301C ''question'':4C ''random'':403C,604C ''rate'':230C,238C,599C ''ratio'':264C,277C ''re'':556C ''realli'':2C,15C ''reason'':39C ''receiv'':112C ''repres'':580C ''respect'':332C,512C ''result'':318C,471C,479C ''roc'':99C,103C,250C,532C ''sampl'':141C ''say'':192C,413C,458C ''see'':377C ''select'':589C ''seri'':516C ''set'':165C,178C,465C ''signific'':602C ''similar'':398C ''singl'':151C,195C ''situat'':134C,494C ''skew'':140C ''slight'':119C ''someth'':202C,343C,538C,569C ''spam'':160C,163C,174C ''specif'':226C,262C ''src'':245C,349C,542C ''stand'':81C,105C ''start'':212C ''state'':519C ''straightforward'':302C ''strong'':167C,572C ''surpris'':65C ''talk'':44C ''theoret'':460C ''though'':41C ''toward'':169C ''tpr'':232C,259C ''triangl'':358C ''true'':228C,266C,597C ''understand'':16C ''us'':225C ''use'':60C,223C ''want'':146C,456C ''well'':93C ''weren'':443C ''wide'':59C ''wikipedia'':549C ''within'':522C ''work'':75C ''would'':95C,387C,484C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (616, 1, '2014-06-27 15:58:19.34', 5, 187, 'Identifying âclustersâ or âgroupsâ in a matrix', '&lt;p&gt;I have a matrix that is populated with discrete elements, and I need to cluster them (using R) into intact groups. So, for example, take this matrix:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[A B B C A]  
[A A B A A]  
[A B B C C]  
[A A A A A]  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;There would be two separate clusters for A, two separate clusters for C, and one cluster for B.&lt;/p&gt;

&lt;p&gt;The output I''m looking for would ideally assign a unique ID to each cluster, something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[1 2 2 3 4]  
[1 1 2 4 4]  
[1 2 2 5 5]  
[1 1 1 1 1]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Right now I wrote a code that does this recursively by just iteratively checking nearest neighbor, but it quickly overflows when the matrix gets large (i.e., 100x100).&lt;/p&gt;

&lt;p&gt;Is there a built in function in R that can do this? I looked into raster and image processing, but no luck. I''m convinced it must be out there.&lt;/p&gt;
', 'r  clustering', NULL, '''/code'':63C,127C ''/p'':40C,84C,104C,157C,189C ''/pre'':64C,128C ''1'':107C,112C,113C,117C,122C,123C,124C,125C,126C ''100x100'':156C ''2'':108C,109C,114C,118C,119C ''3'':110C ''4'':111C,115C,116C ''5'':120C,121C ''assign'':94C ''b'':44C,45C,50C,54C,55C,83C ''built'':162C ''c'':46C,56C,57C,78C ''check'':143C ''cluster'':11B,27C,71C,76C,81C,100C ''clustersâ'':3A ''code'':42C,106C,135C ''convinc'':183C ''discret'':21C ''element'':22C ''exampl'':36C ''function'':164C ''get'':153C ''group'':33C ''groupsâ'':6A ''i.e'':155C ''id'':97C ''ideal'':93C ''identifi'':1A ''imag'':176C ''intact'':32C ''iter'':142C ''larg'':154C ''like'':102C ''look'':90C,172C ''luck'':180C ''m'':89C,182C ''matrix'':9A,16C,39C,152C ''must'':185C ''nearest'':144C ''need'':25C ''neighbor'':145C ''one'':80C ''output'':87C ''overflow'':149C ''p'':12C,65C,85C,129C,158C ''popul'':19C ''pre'':41C,105C ''process'':177C ''quick'':148C ''r'':10B,30C,166C ''raster'':174C ''recurs'':139C ''right'':130C ''separ'':70C,75C ''someth'':101C ''take'':37C ''two'':69C,74C ''uniqu'':96C ''use'':29C ''would'':67C,92C ''wrote'':133C ''â'':2A,5A');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (617, 2, '2014-06-27 16:40:17.393', 2, NULL, NULL, '&lt;p&gt;What do you think is distance measure in your case?&lt;/p&gt;

&lt;p&gt;I assume there are three dimensions here:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;code&gt;RowN&lt;/code&gt; (row number)&lt;/li&gt;
&lt;li&gt;&lt;code&gt;ColN&lt;/code&gt; (column number)&lt;/li&gt;
&lt;li&gt;&lt;code&gt;Value&lt;/code&gt; (value: A, B or C)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;That means data you get from &lt;code&gt;4x5&lt;/code&gt; matrix looks like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Sample1 -&amp;gt; (1, 1, A)
Sample2 -&amp;gt; (1, 2, B)
...
Sample5 -&amp;gt; (1, 5, A)
Sample6 -&amp;gt; (2, 1, A)
...
Sample15 -&amp;gt; (3, 5, C)
...
Sample20 -&amp;gt; (4, 5, A)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is &lt;code&gt;value&lt;/code&gt; scaled? In other words, is &lt;code&gt;A &amp;lt; B &amp;lt; C&lt;/code&gt;? &lt;/p&gt;

&lt;p&gt;If yes, then&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;you can replace &lt;code&gt;{A, B, C}&lt;/code&gt; with &lt;code&gt;{0, 1, 2}&lt;/code&gt; (or may be &lt;code&gt;{10, 11, 12}&lt;/code&gt;, if you want this difference be less important than RowN and ColN attributes)&lt;/li&gt;
&lt;li&gt;normalize your data&lt;/li&gt;
&lt;li&gt;use, for example, K-Means clustering algorithm (&lt;a href="http://stat.ethz.ch/R-manual/R-patched/library/stats/html/kmeans.html" rel="nofollow"&gt;http://stat.ethz.ch/R-manual/R-patched/library/stats/html/kmeans.html&lt;/a&gt;) from &lt;code&gt;stats&lt;/code&gt; R package&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In that case the distance between two will be:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Sqrt( (RowN1-RowN2)^2 + (ColN1-ColN2)^2 + (Value1-Value2)^2 )
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;If &lt;code&gt;value&lt;/code&gt; is not scaled (regular categorical variable), use some &lt;a href="http://datascience.stackexchange.com/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data"&gt;modifications of K-Means that work with categorical data&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;So in case of 100x100 matrix you have 10000 observations and three variables, which is pretty trivial sample size.&lt;/p&gt;
', NULL, NULL, '''/a'':245C ''/code'':26C,33C,40C,57C,94C,100C,112C,128C,134C,142C,184C,215C,221C ''/li'':29C,36C,46C,156C,161C,187C ''/p'':12C,21C,61C,113C,118C,199C,246C,267C ''/pre'':95C,216C ''/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data'':234C ''/r-manual/r-patched/library/stats/html/kmeans.html'':175C ''/r-manual/r-patched/library/stats/html/kmeans.html&lt;/a&gt;)'':180C ''/ul'':47C,188C ''0'':131C ''1'':66C,67C,71C,76C,82C,132C ''10'':139C ''10000'':256C ''100x100'':252C ''11'':140C ''12'':141C ''2'':72C,81C,133C,206C,210C,214C ''3'':86C ''4'':91C ''4x5'':56C ''5'':77C,87C,92C ''algorithm'':170C ''assum'':15C ''attribut'':155C ''b'':43C,73C,109C,126C ''c'':45C,88C,111C,127C ''case'':11C,192C,250C ''categor'':226C,243C ''cluster'':169C ''code'':24C,31C,38C,55C,63C,98C,106C,124C,130C,138C,182C,201C,219C ''coln'':32C,154C ''coln1'':208C ''coln1-coln2'':207C ''coln2'':209C ''column'':34C ''data'':51C,160C,244C ''datascience.stackexchange.com'':233C ''datascience.stackexchange.com/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data'':232C ''differ'':147C ''dimens'':19C ''distanc'':7C,194C ''exampl'':165C ''get'':53C ''gt'':65C,70C,75C,80C,85C,90C ''href'':172C,231C ''import'':150C ''k'':167C,238C ''k-mean'':166C,237C ''less'':149C ''li'':23C,30C,37C,120C,157C,162C ''like'':60C ''look'':59C ''lt'':108C,110C ''matrix'':58C,253C ''may'':136C ''mean'':50C,168C,239C ''measur'':8C ''modif'':235C ''nofollow'':177C ''normal'':158C ''number'':28C,35C ''observ'':257C ''p'':1C,13C,48C,96C,114C,189C,217C,247C ''packag'':186C ''pre'':62C,200C ''pretti'':263C ''r'':185C ''regular'':225C ''rel'':176C ''replac'':123C ''row'':27C ''rown'':25C,152C ''rown1'':204C ''rown1-rown2'':203C ''rown2'':205C ''sampl'':265C ''sample1'':64C ''sample15'':84C ''sample2'':69C ''sample20'':89C ''sample5'':74C ''sample6'':79C ''scale'':101C,224C ''size'':266C ''sqrt'':202C ''stat'':183C ''stat.ethz.ch'':174C,179C ''stat.ethz.ch/r-manual/r-patched/library/stats/html/kmeans.html'':173C ''stat.ethz.ch/r-manual/r-patched/library/stats/html/kmeans.html&lt;/a&gt;)'':178C ''think'':5C ''three'':18C,259C ''trivial'':264C ''two'':196C ''ul'':22C,119C ''use'':163C,228C ''valu'':39C,41C,99C,220C ''value1'':212C ''value1-value2'':211C ''value2'':213C ''variabl'':227C,260C ''want'':145C ''word'':104C ''work'':241C ''yes'':116C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (618, 2, '2014-06-27 19:18:11.433', 1, NULL, NULL, '&lt;p&gt;Although adesantos has already given a good answer, I would like to add a little background information.&lt;/p&gt;

&lt;p&gt;The name for the problem you are looking at is "imputation". As adesantos already said, one of the possibilities is to fit a distribution. For example, you could fit a multivariate Gaussian to the data. You will get the mean only from the samples you know and you calculate the covariances only from the samples you know. You can then use standard MVG results to impute the missing data linearly.&lt;/p&gt;

&lt;p&gt;This is probably the simplest probabilistic method of imputation and it is already quite involved. If you are a neural networks, a recently proposed method that can do so are deep latent gaussian models by Rezende et al. However, understand the method will require a lot of neural net knowledge, quite some variational Bayes knowledge about Markov chains.&lt;/p&gt;

&lt;p&gt;Another method, which I have hear to work well is to train a generative stochastic network (Bengio et al). This is done by training a denoising auto encoder on the data you have (neglecting missing values in the reconstruction loss). Say you have a reconstruction function f and a input x. Then you will reconstruct it via x'' = f(x). You then reset the values of x'' with those you know from x. (I.e. you only keep the values that were missing before reconstruction.) If you do so many times, you are guaranteed to sample from the distribution given the values you know.&lt;/p&gt;

&lt;p&gt;But in either case, these methods require quite some knowledge about statistics and neural nets.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (619, 2, '2014-06-27 19:37:44.847', 1, NULL, NULL, '&lt;p&gt;&lt;a href="https://github.com/RevolutionAnalytics/RHadoop" rel="nofollow"&gt;rhadoop&lt;/a&gt; (the part you are interested in is now called &lt;a href="https://github.com/RevolutionAnalytics/rmr2" rel="nofollow"&gt;rmr2&lt;/a&gt;) is simply a client API for MapReduce written in R. You invoke MapReduce using R package APIs, and send an R function to the workers, where it is executed by an R interpreter locally. But it is otherwise exactly the same MapReduce.&lt;/p&gt;

&lt;p&gt;You can call anything you like in R this way, but no R functions are themselves parallelized to use MapReduce in this way. The point is simply that you can invoke M/R from R. I don''t think it somehow lets you do anything more magical than that.&lt;/p&gt;
', NULL, NULL, '''/a'':10C,28C ''/p'':71C,121C ''/revolutionanalytics/rhadoop'':6C ''/revolutionanalytics/rmr2'':24C ''anyth'':76C,116C ''api'':33C,45C ''call'':19C,75C ''client'':32C ''exact'':67C ''execut'':57C ''function'':50C,86C ''github.com'':5C,23C ''github.com/revolutionanalytics/rhadoop'':4C ''github.com/revolutionanalytics/rmr2'':22C ''href'':3C,21C ''interest'':15C ''interpret'':61C ''invok'':40C,103C ''let'':113C ''like'':78C ''local'':62C ''m/r'':104C ''magic'':118C ''mapreduc'':35C,41C,70C,92C ''nofollow'':8C,26C ''otherwis'':66C ''p'':1C,72C ''packag'':44C ''parallel'':89C ''part'':12C ''point'':97C ''r'':38C,43C,49C,60C,80C,85C,106C ''rel'':7C,25C ''rhadoop'':9C ''rmr2'':27C ''send'':47C ''simpli'':30C,99C ''somehow'':112C ''think'':110C ''use'':42C,91C ''way'':82C,95C ''worker'':53C ''written'':36C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (620, 2, '2014-06-27 21:45:23.237', 6, NULL, NULL, '&lt;p&gt;Yes, it''s problematic.  If you oversample the minority, you risk overfitting.  If you undersample the majority, you risk missing aspects of the majority class.  Stratified sampling, btw, is the equivalent to assigning non-uniform misclassification costs.  &lt;/p&gt;

&lt;p&gt;Alternatives:&lt;/p&gt;

&lt;p&gt;(1) Independently sampling several subsets from the majority class and making multiple classifiers by combining each subset with all the minority class data, as suggested in the answer from @Debasis and described in this &lt;a href="http://cse.seu.edu.cn/people/xyliu/publication/tsmcb09.pdf"&gt;EasyEnsemble paper&lt;/a&gt;, &lt;/p&gt;

&lt;p&gt;(2) &lt;a href="http://arxiv.org/pdf/1106.1813.pdf"&gt;SMOTE (Synthetic Minority Oversampling Technique)&lt;/a&gt; or &lt;a href="http://www3.nd.edu/~nchawla/papers/ECML03.pdf"&gt;SMOTEBoost, (combining SMOTE with boosting)&lt;/a&gt; to create synthetic instances of the minority class by making nearest neighbors in the feature space.  SMOTE is implemented in R in &lt;a href="http://cran.r-project.org/web/packages/DMwR/index.html"&gt;the DMwR package&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':86C,100C,112C,143C ''/p'':40C,43C,87C,144C ''/pdf/1106.1813.pdf'':94C ''/people/xyliu/publication/tsmcb09.pdf'':83C ''/web/packages/dmwr/index.html'':139C ''/~nchawla/papers/ecml03.pdf'':106C ''1'':45C ''2'':89C ''altern'':42C ''answer'':72C ''arxiv.org'':93C ''arxiv.org/pdf/1106.1813.pdf'':92C ''aspect'':22C ''assign'':34C ''boost'':111C ''btw'':29C ''class'':26C,53C,66C,120C ''classifi'':57C ''combin'':59C,108C ''cost'':39C ''cran.r-project.org'':138C ''cran.r-project.org/web/packages/dmwr/index.html'':137C ''creat'':114C ''cse.seu.edu.cn'':82C ''cse.seu.edu.cn/people/xyliu/publication/tsmcb09.pdf'':81C ''data'':67C ''debasi'':74C ''describ'':76C ''dmwr'':141C ''easyensembl'':84C ''equival'':32C ''featur'':127C ''href'':80C,91C,103C,136C ''implement'':131C ''independ'':46C ''instanc'':116C ''major'':18C,25C,52C ''make'':55C,122C ''minor'':10C,65C,97C,119C ''misclassif'':38C ''miss'':21C ''multipl'':56C ''nearest'':123C ''neighbor'':124C ''non'':36C ''non-uniform'':35C ''overfit'':13C ''oversampl'':8C,98C ''p'':1C,41C,44C,88C ''packag'':142C ''paper'':85C ''problemat'':5C ''r'':133C ''risk'':12C,20C ''sampl'':28C,47C ''sever'':48C ''smote'':95C,109C,129C ''smoteboost'':107C ''space'':128C ''stratifi'':27C ''subset'':49C,61C ''suggest'':69C ''synthet'':96C,115C ''techniqu'':99C ''undersampl'':16C ''uniform'':37C ''www3.nd.edu'':105C ''www3.nd.edu/~nchawla/papers/ecml03.pdf'':104C ''yes'':2C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1055, 2, '2014-08-28 11:24:38.743', 1, NULL, NULL, '&lt;p&gt;Scala is the only real language that has Big Data at it''s core. You have MLLib that sits on Spark, and as Scala is Functional it makes parallel computing really natural.  R, Python and Matlab are not suitable for industry productization, well some would say Python''s horrible dynamic typing can be handled a little using special build tools, but really its not type safe and there is no way to solve that problem.&lt;/p&gt;
', NULL, NULL, '''/p'':77C ''big'':10C ''build'':60C ''comput'':31C ''core'':15C ''data'':11C ''dynam'':51C ''function'':27C ''handl'':55C ''horribl'':50C ''industri'':42C ''languag'':7C ''littl'':57C ''make'':29C ''matlab'':37C ''mllib'':18C ''natur'':33C ''p'':1C ''parallel'':30C ''problem'':76C ''product'':43C ''python'':35C,48C ''r'':34C ''real'':6C ''realli'':32C,63C ''safe'':67C ''say'':47C ''scala'':2C,25C ''sit'':20C ''solv'':74C ''spark'':22C ''special'':59C ''suitabl'':40C ''tool'':61C ''type'':52C,66C ''use'':58C ''way'':72C ''well'':44C ''would'':46C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (623, 1, '2014-06-28 14:57:18.177', 9, 153, 'Measuring performance of different classifiers with different sample sizes', '&lt;p&gt;I''m currently using several different classifiers on various entities extracted from text, and using precision/recall as a summary of how well each separate classifier performs across a given dataset.&lt;/p&gt;

&lt;p&gt;I''m wondering if there''s a meaningful way of comparing the performance of these classifiers in a similar way, but which also takes into account the total numbers of each entity in the test data that''s being classified?&lt;/p&gt;

&lt;p&gt;Currently, I''m using precision/recall as a measure of performance, so might have something like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;                    Precision Recall
Person classifier   65%       40%
Company classifier  98%       90%
Cheese classifier   10%       50%
Egg classifier      100%      100%
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;However, the dataset I''m running these on might contain 100k people, 5k companies, 500 cheeses, and 1 egg.&lt;/p&gt;

&lt;p&gt;So is there a summary statistic I can add to the above table which also takes into account the total number of each item? Or is there some way of measuring the fact that e.g. 100% prec/rec on the Egg classifier might not be meaningful with only 1 data item?&lt;/p&gt;

&lt;p&gt;Let''s say we had hundreds of such classifiers, I guess I''m looking for a good way to answer questions like "Which classifiers are underperforming? Which classifiers lack sufficient test data to tell whether they''re underperforming?". &lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (624, 2, '2014-06-29 07:06:00.43', 2, NULL, NULL, '&lt;p&gt;In most cases a practicing data scientist creates his own working environment on personal computed installing preferred software packages. Normally it is sufficient and efficient use of computing resources, because to run a virtual machine (VM) on your main machine you have to allocate a significant portion of RAM for it. The software will run noticeably slower on both the main and the virtual machine unless a lot of RAM. &lt;/p&gt;

&lt;p&gt;Due to this impact on speed it is not common to use VMs as main working environment but they are a good solution in several cases when there is a need of additional working environment. &lt;/p&gt;

&lt;p&gt;The VMs be considered when:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;There is a need to easily replicate a number of identical computing
environments when teaching a course or doing a presentation on a
conference.&lt;/li&gt;
&lt;li&gt;There is a need to save and recreate an exact environment for an experiment or a calculation.&lt;/li&gt;
&lt;li&gt;There is a need to run a different OS or to test a solution on a tool that runs on a different OS. &lt;/li&gt;
&lt;li&gt;One wants to try out a bundle of software tools before installing
them on the main machine. E.g. there is an opportunity to instal an instance of Hadoop (CDH) on a VM during an &lt;a href="https://www.udacity.com/course/ud617" rel="nofollow"&gt;Intro to Hadoop&lt;/a&gt; course on Udacity. &lt;/li&gt;
&lt;li&gt;VMs are sometimes used for fast deployment in the cloud like AWS EC, Rackspace etc.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The VMs mentioned in the original question are made as easily installable data science software bundles. There are more than these two. This &lt;a href="http://jeroenjanssens.com/2013/12/07/lean-mean-data-science-machine.html" rel="nofollow"&gt;blog post&lt;/a&gt; by Jeroen Janssens gives a comparison of at least four:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Data Science Toolbox &lt;/li&gt;
&lt;li&gt;Mining the Social Web &lt;/li&gt;
&lt;li&gt;Data Science Toolkit &lt;/li&gt;
&lt;li&gt;Data Science Box  &lt;/li&gt;
&lt;/ol&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (625, 2, '2014-06-29 12:48:08.84', 3, NULL, NULL, '&lt;p&gt;Maybe you can check here - &lt;a href="http://snap.stanford.edu/data/" rel="nofollow"&gt;http://snap.stanford.edu/data/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;For each data set you will also see references of the works where they have been used&lt;/p&gt;
', NULL, NULL, '''/data/'':11C ''/data/&lt;/a&gt;&lt;/p&gt;'':16C ''/p'':35C ''also'':24C ''check'':5C ''data'':20C ''href'':8C ''mayb'':2C ''nofollow'':13C ''p'':1C,17C ''refer'':26C ''rel'':12C ''see'':25C ''set'':21C ''snap.stanford.edu'':10C,15C ''snap.stanford.edu/data/'':9C ''snap.stanford.edu/data/&lt;/a&gt;&lt;/p&gt;'':14C ''use'':34C ''work'':29C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (626, 2, '2014-06-29 21:11:47.727', 2, NULL, NULL, '&lt;p&gt;In my opinion, it is difficult to compare the performance when there is such a big difference of size. On this link, (please check it out here in Wikipedia &lt;a href="http://en.wikipedia.org/wiki/Effect_size" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Effect_size&lt;/a&gt;), you may see different strategies.&lt;/p&gt;

&lt;p&gt;The one I suggest is one related to the variance. For instance, consider the performance of the classifier (100%) and the person classifier (65%). The minimum error you commit with the former classifier is 100%. However, the minimum error you can commit with the latter classifier is 10e-5.&lt;/p&gt;

&lt;p&gt;So one way to compare classifier is to have on mind this &lt;strong&gt;Rule of Three&lt;/strong&gt; (&lt;a href="http://en.wikipedia.org/wiki/Rule_of_three_%28statistics%29" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Rule_of_three_(statistics)&lt;/a&gt; where you can compare the performance and its variability.&lt;/p&gt;

&lt;p&gt;Other possibility is &lt;strong&gt;F-measure&lt;/strong&gt; which is a combination of Precision and Recall and it is somehow independent to the effect size.&lt;/p&gt;
', NULL, NULL, '''/p'':46C,96C,134C,161C ''/strong'':114C,143C ''/wiki/effect_size'':35C ''/wiki/effect_size&lt;/a&gt;),'':40C ''/wiki/rule_of_three_%28statistics%29'':119C ''/wiki/rule_of_three_(statistics)&lt;/a&gt;'':124C ''100'':66C,82C ''10e-5'':95C ''65'':71C ''big'':17C ''check'':25C ''classifi'':65C,70C,80C,93C,103C ''combin'':147C ''commit'':76C,89C ''compar'':9C,102C,128C ''consid'':60C ''differ'':18C,44C ''difficult'':7C ''effect'':159C ''en.wikipedia.org'':34C,39C,118C,123C ''en.wikipedia.org/wiki/effect_size'':33C ''en.wikipedia.org/wiki/effect_size&lt;/a&gt;),'':38C ''en.wikipedia.org/wiki/rule_of_three_%28statistics%29'':117C ''en.wikipedia.org/wiki/rule_of_three_(statistics)&lt;/a&gt;'':122C ''error'':74C,86C ''f'':141C ''f-measur'':140C ''former'':79C ''howev'':83C ''href'':32C,116C ''independ'':156C ''instanc'':59C ''latter'':92C ''link'':23C ''may'':42C ''measur'':142C ''mind'':108C ''minimum'':73C,85C ''nofollow'':37C,121C ''one'':49C,53C,99C ''opinion'':4C ''p'':1C,47C,97C,135C ''perform'':11C,62C,130C ''person'':69C ''pleas'':24C ''possibl'':137C ''precis'':149C ''recal'':151C ''rel'':36C,120C ''relat'':54C ''rule'':111C ''see'':43C ''size'':20C,160C ''somehow'':155C ''strategi'':45C ''strong'':110C,139C ''suggest'':51C ''three'':113C ''variabl'':133C ''varianc'':57C ''way'':100C ''wikipedia'':30C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (965, 1, '2014-08-12 08:54:34.903', 1, 42, 'Tool for finding correlations between data after some event', '&lt;p&gt;I''m looking for a product that allows us to take in a collection of datastreams, and then after some event, will find any data that changes or correlates with that event. (For example, having a headache, and identifying that I drank too much beer last night and didn''t drink enough water)&lt;/p&gt;
', 'correlation', NULL, '''/p'':65C ''allow'':19C ''beer'':56C ''chang'':38C ''collect'':25C ''correl'':4A,10B,40C ''data'':6A,36C ''datastream'':27C ''didn'':60C ''drank'':53C ''drink'':62C ''enough'':63C ''event'':9A,32C,43C ''exampl'':45C ''find'':3A,34C ''headach'':48C ''identifi'':50C ''last'':57C ''look'':14C ''m'':13C ''much'':55C ''night'':58C ''p'':11C ''product'':17C ''take'':22C ''tool'':1A ''us'':20C ''water'':64C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (628, 2, '2014-06-29 23:14:39.513', 16, NULL, NULL, '&lt;p&gt;A model underfits when it is too simple with regards to the data it is trying to model.&lt;/p&gt;

&lt;p&gt;One way to detect such situation is to use the &lt;a href="http://en.wikipedia.org/wiki/Bias%E2%80%93variance_dilemma"&gt;biasâvariance approach&lt;/a&gt;, which can represented like this:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/t0zit.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Your model is underfitted when you have a high bias.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;To know whether you have a too high bias or a too high variance, you view the phenomenon in terms of training and test errors:&lt;/p&gt;

&lt;p&gt;High bias: This learning curve shows high error on both the training and test sets, so the algorithm is suffering from high bias:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/KFjM4.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;High variance: This learning curve shows a large gap between training and test set errors, so the algorithm is suffering from high variance.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Ypj9y.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;If an algorithm is suffering from high variance:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;more data will probably help&lt;/li&gt;
&lt;li&gt;otherwise reduce the model complexity&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If an algorithm is suffering from high bias:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;increase the model complexity&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I would advise to watch &lt;a href="https://www.coursera.org/course/ml"&gt;Coursera'' Machine Learning course&lt;/a&gt;, section "10: Advice for applying Machine Learning", from which I took the above graphs.&lt;/p&gt;
', NULL, NULL, '''/a'':40C,232C ''/course/ml'':227C ''/kfjm4.png'':129C ''/li'':190C,197C,215C ''/p'':20C,46C,58C,70C,98C,123C,135C,160C,172C,182C,208C,247C ''/t0zit.png'':52C ''/ul'':198C,216C ''/wiki/bias%e2%80%93variance_dilemma'':36C ''/ypj9y.png'':166C ''10'':234C ''advic'':235C ''advis'':220C ''algorithm'':117C,154C,176C,202C ''alt'':53C,130C,167C ''appli'':237C ''approach'':39C ''bias'':69C,81C,101C,122C,207C ''biasâ'':37C ''complex'':196C,214C ''cours'':231C ''coursera'':228C ''curv'':104C,141C ''data'':14C,186C ''descript'':56C,133C,170C ''detect'':25C ''en.wikipedia.org'':35C ''en.wikipedia.org/wiki/bias%e2%80%93variance_dilemma'':34C ''enter'':54C,131C,168C ''error'':97C,107C,151C ''gap'':145C ''graph'':246C ''help'':189C ''high'':68C,80C,85C,100C,106C,121C,137C,158C,180C,206C ''hr'':71C ''href'':33C,224C ''i.stack.imgur.com'':51C,128C,165C ''i.stack.imgur.com/kfjm4.png'':127C ''i.stack.imgur.com/t0zit.png'':50C ''i.stack.imgur.com/ypj9y.png'':164C ''imag'':55C,132C,169C ''img'':48C,125C,162C ''increas'':211C ''know'':74C ''larg'':144C ''learn'':103C,140C,230C,239C ''li'':184C,191C,210C ''like'':44C ''machin'':229C,238C ''model'':3C,19C,61C,195C,213C ''one'':22C ''otherwis'':192C ''p'':1C,21C,47C,59C,72C,99C,124C,136C,161C,173C,199C,217C ''phenomenon'':90C ''probabl'':188C ''reduc'':193C ''regard'':11C ''repres'':43C ''section'':233C ''set'':114C,150C ''show'':105C,142C ''simpl'':9C ''situat'':27C ''src'':49C,126C,163C ''suffer'':119C,156C,178C,204C ''term'':92C ''test'':96C,113C,149C ''took'':243C ''train'':94C,111C,147C ''tri'':17C ''ul'':183C,209C ''underfit'':4C,63C ''use'':30C ''varianc'':38C,86C,138C,159C,181C ''view'':88C ''watch'':222C ''way'':23C ''whether'':75C ''would'':219C ''www.coursera.org'':226C ''www.coursera.org/course/ml'':225C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (754, 2, '2014-07-16 14:22:38.677', 2, NULL, NULL, '&lt;p&gt;Most of the recent Frequent Pattern approaches that I''ve seen in the literature are based on optimizing FPGrowth. I have to admit, I haven''t seen many developments within the literature in FPM in many years.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Frequent_Pattern_Mining/The_FP-Growth_Algorithm" rel="nofollow"&gt;This wikibook&lt;/a&gt; highlights many of the variants on FPGrowth that are out there.&lt;/p&gt;
', NULL, NULL, '''/a'':50C ''/p'':39C,62C ''/wiki/data_mining_algorithms_in_r/frequent_pattern_mining/the_fp-growth_algorithm'':45C ''admit'':24C ''approach'':8C ''base'':17C ''develop'':30C ''en.wikibooks.org'':44C ''en.wikibooks.org/wiki/data_mining_algorithms_in_r/frequent_pattern_mining/the_fp-growth_algorithm'':43C ''fpgrowth'':20C,57C ''fpm'':35C ''frequent'':6C ''haven'':26C ''highlight'':51C ''href'':42C ''literatur'':15C,33C ''mani'':29C,37C,52C ''nofollow'':47C ''optim'':19C ''p'':1C,40C ''pattern'':7C ''recent'':5C ''rel'':46C ''seen'':12C,28C ''variant'':55C ''ve'':11C ''wikibook'':49C ''within'':31C ''year'':38C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (630, 2, '2014-06-30 04:00:35.077', 1, NULL, NULL, '&lt;p&gt;Here''s a crazy idea: talk to the volunteers who know the neighborhoods and who have done door-to-door work before.  Get their advice and ideas.  They will probably have insights that no algorithm will produce, and those modifications will be valuable to any computer-generated route list.  One example: Avoiding crossing heavily traveled streets with slow lights or no lights.  Another example: pairs of volunteers working on opposite sides of the same street will feel safer than a volunteer working that street alone.&lt;/p&gt;
', NULL, NULL, '''/p'':89C ''advic'':27C ''algorithm'':37C ''alon'':88C ''anoth'':66C ''avoid'':55C ''comput'':49C ''computer-gener'':48C ''crazi'':5C ''cross'':56C ''done'':18C ''door'':20C,22C ''door-to-door'':19C ''exampl'':54C,67C ''feel'':80C ''generat'':50C ''get'':25C ''heavili'':57C ''idea'':6C,29C ''insight'':34C ''know'':12C ''light'':62C,65C ''list'':52C ''modif'':42C ''neighborhood'':14C ''one'':53C ''opposit'':73C ''p'':1C ''pair'':68C ''probabl'':32C ''produc'':39C ''rout'':51C ''safer'':81C ''side'':74C ''slow'':61C ''street'':59C,78C,87C ''talk'':7C ''travel'':58C ''valuabl'':45C ''volunt'':10C,70C,84C ''work'':23C,71C,85C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (634, 1, '2014-06-30 09:43:01.94', 8, 86, 'Handling a regularly increasing feature set', '&lt;p&gt;I''m working on a fraud detection system. In this field, new frauds appear regularly, so that new features have to be added to the model on ongoing basis. &lt;/p&gt;

&lt;p&gt;I wonder what is the best way to handle it (from the development process perspective)? Just adding a new feature into the feature vector and re-training the classifier seems to be a naive approach, because too much time will be spent for re-learning of the old features.&lt;/p&gt;

&lt;p&gt;I''m thinking along the way of training a classifier for each feature (or a couple of related features), and then combining the results of those classifiers with an overall classifier. Are there any drawbacks of this approach? How can I choose an algorithm for the overall classifier?&lt;/p&gt;
', 'machine-learning  bigdata', NULL, '''/p'':41C,94C,144C ''ad'':34C,59C ''algorithm'':139C ''along'':99C ''appear'':25C ''approach'':78C,133C ''basi'':40C ''best'':48C ''bigdata'':10B ''choos'':137C ''classifi'':72C,105C,122C,126C,143C ''combin'':117C ''coupl'':111C ''detect'':18C ''develop'':55C ''drawback'':130C ''featur'':5A,30C,62C,65C,93C,108C,114C ''field'':22C ''fraud'':17C,24C ''handl'':1A,51C ''increas'':4A ''learn'':9B,89C ''m'':13C,97C ''machin'':8B ''machine-learn'':7B ''model'':37C ''much'':81C ''naiv'':77C ''new'':23C,29C,61C ''old'':92C ''ongo'':39C ''overal'':125C,142C ''p'':11C,42C,95C ''perspect'':57C ''process'':56C ''re'':69C,88C ''re-learn'':87C ''re-train'':68C ''regular'':3A,26C ''relat'':113C ''result'':119C ''seem'':73C ''set'':6A ''spent'':85C ''system'':19C ''think'':98C ''time'':82C ''train'':70C,103C ''vector'':66C ''way'':49C,101C ''wonder'':44C ''work'':14C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (635, 2, '2014-06-30 10:47:06.453', 4, NULL, NULL, '&lt;p&gt;In an ideal world, you retain all of your historical data, and do indeed run a new model with the new feature extracted retroactively from historical data. I''d argue that the computing resource spent on this is quite useful actually. Is it really a problem?&lt;/p&gt;

&lt;p&gt;Yes, it''s a widely accepted technique to build an ensemble of classifiers and combine their results. You can build a new model in parallel just on new features and average in its prediction. This should add value, but, you will never capture interaction between the new and old features this way, since they will never appear together in a classifier. &lt;/p&gt;
', NULL, NULL, '''/p'':48C,111C ''accept'':55C ''actual'':42C ''add'':86C ''appear'':106C ''argu'':31C ''averag'':80C ''build'':58C,69C ''captur'':92C ''classifi'':62C,110C ''combin'':64C ''comput'':34C ''d'':30C ''data'':12C,28C ''ensembl'':60C ''extract'':24C ''featur'':23C,78C,99C ''histor'':11C,27C ''ideal'':4C ''inde'':15C ''interact'':93C ''model'':19C,72C ''never'':91C,105C ''new'':18C,22C,71C,77C,96C ''old'':98C ''p'':1C,49C ''parallel'':74C ''predict'':83C ''problem'':47C ''quit'':40C ''realli'':45C ''resourc'':35C ''result'':66C ''retain'':7C ''retroact'':25C ''run'':16C ''sinc'':102C ''spent'':36C ''techniqu'':56C ''togeth'':107C ''use'':41C ''valu'':87C ''way'':101C ''wide'':54C ''world'':5C ''yes'':50C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (636, 1, '2014-06-30 12:05:38.597', 2, 390, 'Data preparation and machine learning algorithm for click prediction', '&lt;p&gt;I am new to machine learning. I have a task at hand of predicting click probability given user information like city, state, OS version, OS family, device, browser family, browser version, etc. I have been advised to try logit since logit seems to be what MS and Google are using. I have some questions regarding logistic regression:&lt;/p&gt;

&lt;p&gt;Click and non click is a very very unbalanced class and the simple GLM predictions do not look good. How can I make the data work better with the GLM?&lt;/p&gt;

&lt;p&gt;All the variables I have are categorical and things like device and city can be numerous. Also the frequency of occurrence of some devices or some cities can be very very low. How can I deal with this distribution of categorical variables?&lt;/p&gt;

&lt;p&gt;One of the variables that we get is device ID. This is a very unique feature that can be translated to a user''s identity. How can I make use of it in logit, or should it be used in a completely different model based on user identity?&lt;/p&gt;
', 'machine-learning  bigdata  data-mining  dataset  data-cleaning', NULL, '''/p'':79C,111C,155C,205C ''advis'':57C ''algorithm'':6A ''also'':129C ''base'':201C ''better'':107C ''bigdata'':13B ''browser'':49C,51C ''categor'':119C,153C ''citi'':42C,125C,139C ''class'':90C ''clean'':20B ''click'':8A,36C,81C,84C ''complet'':198C ''data'':1A,15B,19B,105C ''data-clean'':18B ''data-min'':14B ''dataset'':17B ''deal'':148C ''devic'':48C,123C,136C,165C ''differ'':199C ''distribut'':151C ''etc'':53C ''famili'':47C,50C ''featur'':172C ''frequenc'':131C ''get'':163C ''given'':38C ''glm'':94C,110C ''good'':99C ''googl'':69C ''hand'':33C ''id'':166C ''ident'':181C,204C ''inform'':40C ''learn'':5A,12B,27C ''like'':41C,122C ''logist'':77C ''logit'':60C,62C,190C ''look'':98C ''low'':144C ''machin'':4A,11B,26C ''machine-learn'':10B ''make'':103C,185C ''mine'':16B ''model'':200C ''ms'':67C ''new'':24C ''non'':83C ''numer'':128C ''occurr'':133C ''one'':157C ''os'':44C,46C ''p'':21C,80C,112C,156C ''predict'':9A,35C,95C ''prepar'':2A ''probabl'':37C ''question'':75C ''regard'':76C ''regress'':78C ''seem'':63C ''simpl'':93C ''sinc'':61C ''state'':43C ''task'':31C ''thing'':121C ''translat'':176C ''tri'':59C ''unbalanc'':89C ''uniqu'':171C ''use'':71C,186C,195C ''user'':39C,179C,203C ''variabl'':115C,154C,160C ''version'':45C,52C ''work'':106C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (637, 2, '2014-06-30 13:56:10.753', 2, NULL, NULL, '&lt;p&gt;Here''s an idea that just popped out of the blue â what if you make use of &lt;a href="http://en.wikipedia.org/wiki/Random_subspace_method" rel="nofollow"&gt;Random Subspace Sampling&lt;/a&gt; (as in fact Sean Owen already suggested) to train a bunch of new classifiers every time a new feature appears (using a random feature subset, including the new set of features). You could train those models on a subset of samples as well to save some training time.&lt;/p&gt;

&lt;p&gt;This way you can have new classifiers possibly taking on both new and old features, and at the same time keeping your old classifiers. You might even, perhaps using a cross validation technique to measure each classifier''s performance, be able to kill-off the worst performing ones after a while, to avoid a bloated model.&lt;/p&gt;
', NULL, NULL, '''/a'':30C ''/p'':79C,138C ''/wiki/random_subspace_method'':24C ''abl'':121C ''alreadi'':36C ''appear'':50C ''avoid'':134C ''bloat'':136C ''blue'':12C ''bunch'':41C ''classifi'':44C,87C,104C,117C ''could'':63C ''cross'':111C ''en.wikipedia.org'':23C ''en.wikipedia.org/wiki/random_subspace_method'':22C ''even'':107C ''everi'':45C ''fact'':33C ''featur'':49C,54C,61C,95C ''href'':21C ''idea'':5C ''includ'':56C ''keep'':101C ''kill'':124C ''kill-off'':123C ''make'':17C ''measur'':115C ''might'':106C ''model'':66C,137C ''new'':43C,48C,58C,86C,92C ''nofollow'':26C ''old'':94C,103C ''one'':129C ''owen'':35C ''p'':1C,80C ''perform'':119C,128C ''perhap'':108C ''pop'':8C ''possibl'':88C ''random'':27C,53C ''rel'':25C ''sampl'':29C,71C ''save'':75C ''sean'':34C ''set'':59C ''subset'':55C,69C ''subspac'':28C ''suggest'':37C ''take'':89C ''techniqu'':113C ''time'':46C,78C,100C ''train'':39C,64C,77C ''use'':18C,51C,109C ''valid'':112C ''way'':82C ''well'':73C ''worst'':127C ''â'':13C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5034, 2, '2015-02-04 00:16:15.323', 0, NULL, NULL, '&lt;p&gt;The hotel_id should not be a feature.&lt;/p&gt;

&lt;p&gt;Let''s see if I understand you correctly.&lt;/p&gt;

&lt;p&gt;At testing time you give your model a whole set of feature values for a particular hotel you are interested in. This hotel has an id, which is known to you.&lt;/p&gt;

&lt;p&gt;Your model should be able to take both an id and a set of feature values as input, so it can print both to an output file.&lt;/p&gt;

&lt;p&gt;Does that answer your question?&lt;/p&gt;
', NULL, NULL, '''/p'':10C,20C,53C,82C,89C ''abl'':59C ''answer'':86C ''correct'':19C ''featur'':9C,33C,69C ''file'':81C ''give'':26C ''hotel'':3C,38C,44C ''id'':4C,47C,64C ''input'':72C ''interest'':41C ''known'':50C ''let'':12C ''model'':28C,56C ''output'':80C ''p'':1C,11C,21C,54C,83C ''particular'':37C ''print'':76C ''question'':88C ''see'':14C ''set'':31C,67C ''take'':61C ''test'':23C ''time'':24C ''understand'':17C ''valu'':34C,70C ''whole'':30C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (640, 1, '2014-06-30 20:51:58.64', 4, 63, 'Large Scale Personalization - Per User vs Global Models', '&lt;p&gt;I''m currently working on a project that would benefit from personalized predictions.  Given an input document, a set of output documents, and a history of user behavior, I''d like to predict which of the output documents are clicked.  &lt;/p&gt;

&lt;p&gt;In short, I''m wondering what the typical approach to this kind of personalization problem is.  Are models trained per user, or does a single global model take in summary statistics of past user behavior to help inform that decision?  Per user models won''t be accurate until the user has been active for a while, while most global models have to take in a fixed length feature vector (meaning we more or less have to compress a stream of past events into a smaller number of summary statistics).  &lt;/p&gt;
', 'classification', NULL, '''/p'':51C,142C ''accur'':99C ''activ'':105C ''approach'':61C ''behavior'':38C,87C ''benefit'':20C ''classif'':9B ''click'':50C ''compress'':129C ''current'':13C ''d'':40C ''decis'':92C ''document'':27C,32C,48C ''event'':134C ''featur'':120C ''fix'':118C ''given'':24C ''global'':7A,78C,111C ''help'':89C ''histori'':35C ''inform'':90C ''input'':26C ''kind'':64C ''larg'':1A ''length'':119C ''less'':126C ''like'':41C ''m'':12C,56C ''mean'':122C ''model'':8A,70C,79C,95C,112C ''number'':138C ''output'':31C,47C ''p'':10C,52C ''past'':85C,133C ''per'':4A,72C,93C ''person'':3A,22C,66C ''predict'':23C,43C ''problem'':67C ''project'':17C ''scale'':2A ''set'':29C ''short'':54C ''singl'':77C ''smaller'':137C ''statist'':83C,141C ''stream'':131C ''summari'':82C,140C ''take'':80C,115C ''train'':71C ''typic'':60C ''user'':5A,37C,73C,86C,94C,102C ''vector'':121C ''vs'':6A ''won'':96C ''wonder'':57C ''work'':14C ''would'':19C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (642, 2, '2014-06-30 23:10:53.397', 3, NULL, NULL, '&lt;p&gt;The answer to this question is going to vary pretty wildly depending on the size and nature of your data. At a high level, you could think of it as a special case of multilevel models; you have the option of estimating a model with complete pooling (i.e., a universal model that doesn''t distinguish between users), models with no pooling (a separate model for each user), and partially pooled models (a mixture of the two). You should really read Andrew Gelman on this topic if you''re interested.&lt;/p&gt;

&lt;p&gt;You can also think of this as a learning-to-rank problem that either tries to produce point-wise estimates using a single function or instead tries to optimize on some list-wise loss function (e.g., NDCG). &lt;/p&gt;

&lt;p&gt;As with most machine learning problems, it all depends on what kind of data you have, the quality of it, the sparseness of it, and what kinds of features you are able to extract from it. If you have reason to believe that each and every user is going to be pretty unique in their behavior, you might want to build a per-user model, but that''s going to be unwieldy fast -- and what do you do when you are faced with a new user?&lt;/p&gt;
', NULL, NULL, '''/p'':91C,133C,222C ''abl'':166C ''also'':95C ''andrew'':82C ''answer'':3C ''behavior'':190C ''believ'':176C ''build'':195C ''case'':34C ''complet'':47C ''could'':27C ''data'':21C,148C ''depend'':13C,143C ''distinguish'':56C ''doesn'':54C ''e.g'':131C ''either'':107C ''estim'':43C,114C ''everi'':180C ''extract'':168C ''face'':217C ''fast'':208C ''featur'':163C ''function'':118C,130C ''gelman'':83C ''go'':8C,183C,204C ''high'':24C ''i.e'':49C ''instead'':120C ''interest'':90C ''kind'':146C,161C ''learn'':102C,139C ''learning-to-rank'':101C ''level'':25C ''list'':127C ''list-wis'':126C ''loss'':129C ''machin'':138C ''might'':192C ''mixtur'':74C ''model'':37C,45C,52C,59C,65C,72C,200C ''multilevel'':36C ''natur'':18C ''ndcg'':132C ''new'':220C ''optim'':123C ''option'':41C ''p'':1C,92C,134C ''partial'':70C ''per'':198C ''per-us'':197C ''point'':112C ''point-wis'':111C ''pool'':48C,62C,71C ''pretti'':11C,186C ''problem'':105C,140C ''produc'':110C ''qualiti'':152C ''question'':6C ''rank'':104C ''re'':89C ''read'':81C ''realli'':80C ''reason'':174C ''separ'':64C ''singl'':117C ''size'':16C ''spars'':156C ''special'':33C ''think'':28C,96C ''topic'':86C ''tri'':108C,121C ''two'':77C ''uniqu'':187C ''univers'':51C ''unwieldi'':207C ''use'':115C ''user'':58C,68C,181C,199C,221C ''vari'':10C ''want'':193C ''wild'':12C ''wise'':113C,128C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (643, 2, '2014-07-01 08:04:30.127', 2, NULL, NULL, '&lt;p&gt;I guess you say that you want to use 3-fold cross-validation because you know something about your data (that using k=10 would cause overfitting? I''m curious to your reasoning). I am not sure that you know this, if not then you can simply use a larger k.&lt;/p&gt;

&lt;p&gt;If you still think that you cannot use standard k-fold cross-validation, then you could modify the algorithm a bit: say that you split the data into 30 folds and each time use 20 for training and 10 for evaluation (and then shift up one fold and use the first and the last 9 as evaluation and the rest as training). This means that you''re able to use all your data.&lt;/p&gt;

&lt;p&gt;When I use k-fold cross-validation I usually run the process multiple times with a different randomisation to make sure that I have sufficient data, if you don''t you will see different performances depending on the randomisation. In such cases I would suggest sampling. The trick then is to do it often enough.&lt;/p&gt;
', NULL, NULL, '''/p'':54C,131C,190C ''10'':26C,96C ''20'':92C ''3'':11C ''30'':86C ''9'':112C ''abl'':125C ''algorithm'':76C ''bit'':78C ''cannot'':62C ''case'':176C ''caus'':28C ''could'':73C ''cross'':14C,69C,140C ''cross-valid'':13C,68C,139C ''curious'':32C ''data'':22C,84C,130C,160C ''depend'':170C ''differ'':151C,168C ''enough'':189C ''evalu'':98C,114C ''first'':108C ''fold'':12C,67C,87C,104C,138C ''guess'':3C ''k'':25C,53C,66C,137C ''k-fold'':65C,136C ''know'':18C,42C ''larger'':52C ''last'':111C ''m'':31C ''make'':154C ''mean'':121C ''modifi'':74C ''multipl'':147C ''often'':188C ''one'':103C ''overfit'':29C ''p'':1C,55C,132C ''perform'':169C ''process'':146C ''randomis'':152C,173C ''re'':124C ''reason'':35C ''rest'':117C ''run'':144C ''sampl'':180C ''say'':5C,79C ''see'':167C ''shift'':101C ''simpli'':49C ''someth'':19C ''split'':82C ''standard'':64C ''still'':58C ''suffici'':159C ''suggest'':179C ''sure'':39C,155C ''think'':59C ''time'':90C,148C ''train'':94C,119C ''trick'':182C ''use'':10C,24C,50C,63C,91C,106C,127C,135C ''usual'':143C ''valid'':15C,70C,141C ''want'':8C ''would'':27C,178C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (644, 1, '2014-07-01 13:44:23.29', 4, 310, 'Create most "average" cosine similarity observation', '&lt;p&gt;For a recommendation system I''m using cosine similarity to compute similarities between items. However, for items with small amounts of data I''d like to bin them under a general "average" category (in the general not mathematical sense). To accomplish this I''m currently trying to create a synthetic observation to represent that middle of the road point.&lt;/p&gt;

&lt;p&gt;So for example if these were my observations (rows are observations, cols are features):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[[0, 0, 0, 1, 1, 1, 0, 1, 0],
 [1, 0, 1, 0, 0, 0, 1, 0, 0],
 [1, 1, 1, 1, 0, 1, 0, 1, 1],
 [0, 0, 1, 0, 0, 1, 0, 1, 0]]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;A strategy where I''d simply take the actual average of all features across observations would generate a synthetic datapoint such as follows, which I''d then append to the matrix before doing the similarity calculation.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[ 0.5 ,  0.25,  0.75,  0.5 ,  0.25,  0.75,  0.25,  0.75,  0.25]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;While this might work well with certain similarity metrics (e.g. L1 distance) I''m sure there are much better ways for cosine similarity. Though, at the moment, I''m having trouble reasoning my way through angles between lines in high dimensional space.&lt;/p&gt;

&lt;p&gt;Any ideas?&lt;/p&gt;
', 'recommendation  similarity', NULL, '''/code'':124C,175C ''/p'':69C,85C,163C,220C,224C ''/pre'':125C,176C ''0'':88C,89C,90C,94C,96C,98C,100C,101C,102C,104C,105C,110C,112C,115C,116C,118C,119C,121C,123C ''0.25'':167C,170C,172C,174C ''0.5'':166C,169C ''0.75'':168C,171C,173C ''1'':91C,92C,93C,95C,97C,99C,103C,106C,107C,108C,109C,111C,113C,114C,117C,120C,122C ''accomplish'':50C ''across'':140C ''actual'':135C ''amount'':29C ''angl'':213C ''append'':154C ''averag'':3A,41C,136C ''better'':196C ''bin'':36C ''calcul'':162C ''categori'':42C ''certain'':184C ''code'':87C,165C ''col'':82C ''comput'':20C ''cosin'':4A,17C,199C ''creat'':1A,57C ''current'':54C ''d'':33C,131C,152C ''data'':31C ''datapoint'':146C ''dimension'':218C ''distanc'':189C ''e.g'':187C ''exampl'':73C ''featur'':84C,139C ''follow'':149C ''general'':40C,45C ''generat'':143C ''high'':217C ''howev'':24C ''idea'':223C ''item'':23C,26C ''l1'':188C ''like'':34C ''line'':215C ''m'':15C,53C,191C,206C ''mathemat'':47C ''matrix'':157C ''metric'':186C ''middl'':64C ''might'':180C ''moment'':204C ''much'':195C ''observ'':6A,60C,78C,81C,141C ''p'':9C,70C,126C,177C,221C ''point'':68C ''pre'':86C,164C ''reason'':209C ''recommend'':7B,12C ''repres'':62C ''road'':67C ''row'':79C ''sens'':48C ''similar'':5A,8B,18C,21C,161C,185C,200C ''simpli'':132C ''small'':28C ''space'':219C ''strategi'':128C ''sure'':192C ''synthet'':59C,145C ''system'':13C ''take'':133C ''though'':201C ''tri'':55C ''troubl'':208C ''use'':16C ''way'':197C,211C ''well'':182C ''work'':181C ''would'':142C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (646, 1, '2014-07-01 17:03:31.907', 2, 19, 'Spatial index for variable kernel nonparametric density', '&lt;p&gt;I''m trying to build a nonparametric density function for a fairly large dataset that can be evaluated efficently, and can be updated efficiently when new points are added. There will only ever be a maximum of 4 independent variables, but we can start off with 2. Lets use a gaussian kernel. Let the result be a probability density function, i.e. its volume will be 1.&lt;/p&gt;

&lt;p&gt;In each evaluation, we can omit all points for which the evaluation point is outside a certain ellipsoid corresponding to the minimum gaussian value we care about. We can change this threshold for accuracy or performance, and the maximum number of points inside the threshold will depend on the chosen covariance matrix of the kernel. Then, we can evaluate the distribution approximately using the subset of points.&lt;/p&gt;

&lt;p&gt;If we use a fixed kernel, then we can use the eigenvalues and eigenvectors we get from the covariance matrix to transform each point so that the threshold ellipsoid is a fixed circle. We can then shove all the transformed points into a spatial index, and efficiently find all points within the required radius of the evaluation point.&lt;/p&gt;

&lt;p&gt;However, we would the kernel to be variable for two reasons. (1) to fit the data better, and (2) because adding or modifying points would require the fixed kernel to be updated, which would mean that the entire data set would need to be reindexed. With a variable kernel, we could make new/updated points only affect the closest points.&lt;/p&gt;

&lt;p&gt;Specifically, is there a spatial index that can efficiently find ellipses surrounding a given point from a set of around 10 million ellipses of different shapes and sizes?&lt;/p&gt;

&lt;p&gt;More generally though, does my approach look sound? I am open to answers like "give up and precalculate a grid of results". Answers much appreciated!&lt;/p&gt;
', 'data-indexing-techniques', NULL, '''/p'':79C,148C,208C,269C,299C,326C ''1'':78C,221C ''10'':291C ''2'':59C,228C ''4'':50C ''accuraci'':114C ''ad'':41C,230C ''affect'':265C ''answer'':313C,323C ''appreci'':325C ''approach'':306C ''approxim'':142C ''around'':290C ''better'':226C ''build'':17C ''care'':106C ''certain'':97C ''chang'':110C ''chosen'':130C ''circl'':182C ''closest'':267C ''correspond'':99C ''could'':260C ''covari'':131C,168C ''data'':9B,225C,248C ''data-indexing-techniqu'':8B ''dataset'':26C ''densiti'':7A,20C,71C ''depend'':127C ''differ'':295C ''distribut'':141C ''effic'':31C ''effici'':36C,196C,279C ''eigenvalu'':161C ''eigenvector'':163C ''ellips'':281C,293C ''ellipsoid'':98C,178C ''entir'':247C ''evalu'':30C,83C,92C,139C,206C ''ever'':45C ''fair'':24C ''find'':197C,280C ''fit'':223C ''fix'':154C,181C,237C ''function'':21C,72C ''gaussian'':63C,103C ''general'':302C ''get'':165C ''give'':315C ''given'':284C ''grid'':320C ''howev'':210C ''i.e'':73C ''independ'':51C ''index'':2A,10B,194C,276C ''insid'':123C ''kernel'':5A,64C,135C,155C,214C,238C,258C ''larg'':25C ''let'':60C,65C ''like'':314C ''look'':307C ''m'':14C ''make'':261C ''matrix'':132C,169C ''maximum'':48C,119C ''mean'':244C ''million'':292C ''minimum'':102C ''modifi'':232C ''much'':324C ''need'':251C ''new'':38C ''new/updated'':262C ''nonparametr'':6A,19C ''number'':120C ''omit'':86C ''open'':311C ''outsid'':95C ''p'':12C,80C,149C,209C,270C,300C ''perform'':116C ''point'':39C,88C,93C,122C,147C,173C,190C,199C,207C,233C,263C,268C,285C ''precalcul'':318C ''probabl'':70C ''radius'':203C ''reason'':220C ''reindex'':254C ''requir'':202C,235C ''result'':67C,322C ''set'':249C,288C ''shape'':296C ''shove'':186C ''size'':298C ''sound'':308C ''spatial'':1A,193C,275C ''specif'':271C ''start'':56C ''subset'':145C ''surround'':282C ''techniqu'':11B ''though'':303C ''threshold'':112C,125C,177C ''transform'':171C,189C ''tri'':15C ''two'':219C ''updat'':35C,241C ''use'':61C,143C,152C,159C ''valu'':104C ''variabl'':4A,52C,217C,257C ''volum'':75C ''within'':200C ''would'':212C,234C,243C,250C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (648, 2, '2014-07-01 18:48:13.757', 3, NULL, NULL, '&lt;p&gt;Singular Value Decomposition is a linear algebraic technique as a result of which the notion of normalization is hard to define. In principle, you can do this normalization by dividing each element A(i,j) of the matrix by the sum (or max) of the elements in that particular (ith) row, i.e. A(i,j) = A(i, j) / \sum_{k=1}^{n} A(i,k)&lt;/p&gt;

&lt;p&gt;However, a more elegant way to achieve this would be to apply a probabilistic dimensionality reduction technique such as &lt;a href="http://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis" rel="nofollow"&gt;PLSA&lt;/a&gt; or &lt;a href="http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation" rel="nofollow"&gt;LDA&lt;/a&gt;. These dimensionality reduction techniques ensure that you always end up with probability values strictly between 0 and 1.  &lt;/p&gt;
', NULL, NULL, '''/a'':96C,106C ''/p'':67C,125C ''/wiki/latent_dirichlet_allocation'':102C ''/wiki/probabilistic_latent_semantic_analysis'':92C ''0'':122C ''1'':62C,124C ''achiev'':75C ''algebra'':8C ''alway'':114C ''appli'':80C ''decomposit'':4C ''defin'':22C ''dimension'':83C,108C ''divid'':31C ''eleg'':72C ''element'':33C,47C ''en.wikipedia.org'':91C,101C ''en.wikipedia.org/wiki/latent_dirichlet_allocation'':100C ''en.wikipedia.org/wiki/probabilistic_latent_semantic_analysis'':90C ''end'':115C ''ensur'':111C ''hard'':20C ''howev'':69C ''href'':89C,99C ''i.e'':53C ''ith'':51C ''j'':36C,56C,59C ''k'':61C,66C ''lda'':105C ''linear'':7C ''matrix'':39C ''max'':44C ''n'':63C ''nofollow'':94C,104C ''normal'':18C,29C ''notion'':16C ''p'':1C,68C ''particular'':50C ''plsa'':95C ''principl'':24C ''probabilist'':82C ''probabl'':118C ''reduct'':84C,109C ''rel'':93C,103C ''result'':12C ''row'':52C ''singular'':2C ''strict'':120C ''sum'':42C,60C ''techniqu'':9C,85C,110C ''valu'':3C,119C ''way'':73C ''would'':77C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (649, 2, '2014-07-01 23:11:35.627', 6, NULL, NULL, '&lt;p&gt;You are doing the correct thing. Technically, this averaging leads to computing the &lt;a href="http://en.wikipedia.org/wiki/Centroid"&gt;centroid&lt;/a&gt; in the Euclidean space of a set of N points. The centroid works pretty well with cosine similarities (cosine of the angles between normalized vectors), e.g. &lt;a href="http://en.wikipedia.org/wiki/Rocchio_algorithm"&gt;the Rocchio algorithm&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':21C,56C ''/p'':57C ''/wiki/centroid'':19C ''/wiki/rocchio_algorithm'':52C ''algorithm'':55C ''angl'':43C ''averag'':10C ''centroid'':20C,33C ''comput'':13C ''correct'':6C ''cosin'':38C,40C ''e.g'':47C ''en.wikipedia.org'':18C,51C ''en.wikipedia.org/wiki/centroid'':17C ''en.wikipedia.org/wiki/rocchio_algorithm'':50C ''euclidean'':24C ''href'':16C,49C ''lead'':11C ''n'':30C ''normal'':45C ''p'':1C ''point'':31C ''pretti'':35C ''rocchio'':54C ''set'':28C ''similar'':39C ''space'':25C ''technic'':8C ''thing'':7C ''vector'':46C ''well'':36C ''work'':34C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (650, 2, '2014-07-02 08:40:09.773', 1, NULL, NULL, '&lt;p&gt;As @Debasis writes, what you are doing is correct. Do not worry about the relative values as the cosine similarity focusses on the angle, not the length of the vector. &lt;/p&gt;

&lt;p&gt;Note that the averages that you compute actually are the proportion of observations that have that feature in your subset. If the subset is sufficiently large, you can interpret them as probabilities.&lt;/p&gt;
', NULL, NULL, '''/p'':32C,66C ''actual'':41C ''angl'':25C ''averag'':37C ''comput'':40C ''correct'':10C ''cosin'':20C ''debasi'':3C ''featur'':50C ''focuss'':22C ''interpret'':62C ''larg'':59C ''length'':28C ''note'':34C ''observ'':46C ''p'':1C,33C ''probabl'':65C ''proport'':44C ''relat'':16C ''similar'':21C ''subset'':53C,56C ''suffici'':58C ''valu'':17C ''vector'':31C ''worri'':13C ''write'':4C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (652, 2, '2014-07-02 08:58:25.413', 3, NULL, NULL, '&lt;p&gt;Simply, one common approach is to increase the complexity of the model, making it simple, and most probably underfitting at first, and increasing the complexity of the model  until early signs of overfitting are witnessed using a resampling technique such as cross validation, bootstrap, etc.&lt;/p&gt;

&lt;p&gt;You increase the complexity either by adding parameters (number of hidden neurons for artificial neural networks, number of trees in a random forest) or by relaxing the regularization (often named lambda, or C for support vector machines) term in your model.&lt;/p&gt;
', NULL, NULL, '''/p'':47C,90C ''ad'':55C ''approach'':5C ''artifici'':62C ''bootstrap'':45C ''c'':81C ''common'':4C ''complex'':10C,26C,52C ''cross'':43C ''earli'':31C ''either'':53C ''etc'':46C ''first'':22C ''forest'':71C ''hidden'':59C ''increas'':8C,24C,50C ''lambda'':79C ''machin'':85C ''make'':14C ''model'':13C,29C,89C ''name'':78C ''network'':64C ''neural'':63C ''neuron'':60C ''number'':57C,65C ''often'':77C ''one'':3C ''overfit'':34C ''p'':1C,48C ''paramet'':56C ''probabl'':19C ''random'':70C ''regular'':76C ''relax'':74C ''resampl'':39C ''sign'':32C ''simpl'':16C ''simpli'':2C ''support'':83C ''techniqu'':40C ''term'':86C ''tree'':67C ''underfit'':20C ''use'':37C ''valid'':44C ''vector'':84C ''wit'':36C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6486, 1, '2015-07-17 08:58:09.017', 2, 372, 'Data.frame vs Data.table in R?', '&lt;p&gt;Data.table package is claimed to be faster than Data.frame. what are the implementation changes that made this possible? How can one leverage the power of this package for data analysis?&lt;/p&gt;
', 'data-mining  r  data-cleaning', NULL, '''/p'':44C ''analysi'':43C ''chang'':27C ''claim'':17C ''clean'':12B ''data'':7B,11B,42C ''data-clean'':10B ''data-min'':6B ''data.frame'':1A,22C ''data.table'':3A,14C ''faster'':20C ''implement'':26C ''leverag'':35C ''made'':29C ''mine'':8B ''one'':34C ''p'':13C ''packag'':15C,40C ''possibl'':31C ''power'':37C ''r'':5A,9B ''vs'':2A');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (653, 1, '2014-07-02 13:40:27', 4, 132, 'Which non-training classification methods are available?', '&lt;p&gt;I am trying to find which classification methods, that do not use a training phase, are available. &lt;/p&gt;

&lt;p&gt;The scenario is gene expression based classification, in which you have a matrix of gene expression of m genes (features) and n samples (observations).
A signature for each class is also provided (that is a list of the features to consider to define to which class belongs a sample).&lt;/p&gt;

&lt;p&gt;An application (non-training) is the &lt;a href="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0015543" rel="nofollow"&gt;Nearest Template Prediction&lt;/a&gt; method. In this case it is computed the cosine distance between each sample and each signature (on the common set of features). Then each sample is assigned to the nearest class (the sample-class comparison resulting in a smaller distance). No already classified samples are needed in this case.&lt;/p&gt;

&lt;p&gt;A different application (training) is the &lt;a href="http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm" rel="nofollow"&gt;kNN&lt;/a&gt; method, in which we have a set of already labeled samples. Then, each new sample is labeled depending on how are labeled the k nearest samples.&lt;/p&gt;

&lt;p&gt;Are there any other non-training methods?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'classification', NULL, '''/a'':98C,165C ''/article/info%3adoi%2f10.1371%2fjournal.pone.0015543'':92C ''/p'':28C,79C,149C,192C,202C,205C ''/wiki/k-nearest_neighbors_algorithm'':161C ''alreadi'':141C,174C ''also'':60C ''applic'':82C,153C ''assign'':125C ''avail'':8A,27C ''base'':35C ''belong'':76C ''case'':102C,148C ''class'':58C,75C,129C,133C ''classif'':5A,9B,17C,36C ''classifi'':142C ''common'':117C ''comparison'':134C ''comput'':105C ''consid'':70C ''cosin'':107C ''defin'':72C ''depend'':183C ''differ'':152C ''distanc'':108C,139C ''en.wikipedia.org'':160C ''en.wikipedia.org/wiki/k-nearest_neighbors_algorithm'':159C ''express'':34C,45C ''featur'':49C,68C,120C ''find'':15C ''gene'':33C,44C,48C ''href'':89C,158C ''k'':189C ''knn'':164C ''label'':175C,182C,187C ''list'':65C ''m'':47C ''matrix'':42C ''method'':6A,18C,99C,166C,201C ''n'':51C ''nearest'':95C,128C,190C ''need'':145C ''new'':179C ''nofollow'':94C,163C ''non'':3A,84C,199C ''non-train'':2A,83C,198C ''observ'':53C ''p'':10C,29C,80C,150C,193C,203C ''phase'':25C ''predict'':97C ''provid'':61C ''rel'':93C,162C ''result'':135C ''sampl'':52C,78C,111C,123C,132C,143C,176C,180C,191C ''sample-class'':131C ''scenario'':31C ''set'':118C,172C ''signatur'':55C,114C ''smaller'':138C ''templat'':96C ''thank'':204C ''train'':4A,24C,85C,154C,200C ''tri'':13C ''use'':22C ''www.plosone.org'':91C ''www.plosone.org/article/info%3adoi%2f10.1371%2fjournal.pone.0015543'':90C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (732, 2, '2014-07-13 09:58:16.387', 21, NULL, NULL, '&lt;p&gt;The problem with deep networks is that they have lots of hyperparameters to tune and very small solution space. Thus, finding good ones is more like an art rather than engineering task. I would start with working example from tutorial and play around with its parameters to see how results change - this gives a good intuition (though not formal explanation) about dependencies between parameters and results (both - final and intermediate). &lt;/p&gt;

&lt;p&gt;Also I found following papers very useful: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://yosinski.com/media/papers/Yosinski2012VisuallyDebuggingRestrictedBoltzmannMachine.pdf"&gt;Visually Debugging Restricted Boltzmann Machine Training
with a 3D Example&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf"&gt;A Practical Guide to Training Restricted Boltzmann
Machines&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;They both describe RBMs, but contain some insights on deep networks in general. For example, one of key points is that networks need to be debugged layer-wise - if previous layer doesn''t provide good representation of features, further layers have almost no chance to fix it. &lt;/p&gt;
', NULL, NULL, '''/a'':99C,115C ''/li'':100C,116C ''/media/papers/yosinski2012visuallydebuggingrestrictedboltzmannmachine.pdf'':88C ''/p'':72C,81C,167C ''/ul'':117C ''/~hinton/absps/guidetr.pdf'':106C ''3d'':97C ''almost'':161C ''also'':74C ''around'':44C ''art'':29C ''boltzmann'':92C,113C ''chanc'':163C ''chang'':52C ''contain'':124C ''debug'':90C,144C ''deep'':5C,128C ''depend'':63C ''describ'':121C ''doesn'':151C ''engin'':32C ''exampl'':39C,98C,133C ''explan'':61C ''featur'':157C ''final'':69C ''find'':22C ''fix'':165C ''follow'':77C ''formal'':60C ''found'':76C ''general'':131C ''give'':54C ''good'':23C,56C,154C ''guid'':109C ''href'':85C,103C ''hyperparamet'':13C ''insight'':126C ''intermedi'':71C ''intuit'':57C ''key'':136C ''layer'':146C,150C,159C ''layer-wis'':145C ''li'':83C,101C ''like'':27C ''lot'':11C ''machin'':93C,114C ''need'':141C ''network'':6C,129C,140C ''one'':24C,134C ''p'':1C,73C,118C ''paper'':78C ''paramet'':47C,65C ''play'':43C ''point'':137C ''practic'':108C ''previous'':149C ''problem'':3C ''provid'':153C ''rather'':30C ''rbms'':122C ''represent'':155C ''restrict'':91C,112C ''result'':51C,67C ''see'':49C ''small'':18C ''solut'':19C ''space'':20C ''start'':36C ''task'':33C ''though'':58C ''thus'':21C ''train'':94C,111C ''tune'':15C ''tutori'':41C ''ul'':82C ''use'':80C ''visual'':89C ''wise'':147C ''work'':38C ''would'':35C ''www.cs.toronto.edu'':105C ''www.cs.toronto.edu/~hinton/absps/guidetr.pdf'':104C ''yosinski.com'':87C ''yosinski.com/media/papers/yosinski2012visuallydebuggingrestrictedboltzmannmachine.pdf'':86C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (655, 1, '2014-07-02 14:40:55.413', 8, 315, 'Foreign exchange market forecasting with neural networks', '&lt;p&gt;I would like to use ANN to automate trading currencies, preferably USD/EUR or USD/GBP. I know this is hard and may not be straightforward. I have already read some papers and done some experiments but without much luck. I would like to get advice from EXPERTS to make this work.&lt;/p&gt;

&lt;p&gt;Here is what I did so far:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;I got tick by tick data for the month of july 2013. It has bid/ask/bid volume/ask volume.&lt;/li&gt;
&lt;li&gt;Extracted all ticks for the time frame 12PM to 14PM for all days.&lt;/li&gt;
&lt;li&gt;From this data, created a data set where each entry consists of n bid values in sequence. &lt;/li&gt;
&lt;li&gt;Used that data to train an ANN with n-1 inputs and the output is the forecasted nth bid value.&lt;/li&gt;
&lt;li&gt;The ANN had n-1 inputs neurons, (n-1)*2 + 1 hidden and 1 output neuron. Input layer had linear TF, hidden had log TF and output had linear TF.&lt;/li&gt;
&lt;li&gt;Trained the network with back propagation with n-125 first and then 10.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;For both n, the MSE did not drop below 0.5 and stayed at that value during full training. Assuming that this could be due to the time series being totally random, I used the R package to find partial autocorrelation on the data set (pacf). This gave non zero values for 2 and 3 lags only. &lt;/p&gt;

&lt;p&gt;Question 1: What does this mean exactly? &lt;/p&gt;

&lt;p&gt;Then I used hurst exponent to evaluate the randomness. In R, hurst(values) showed values above 0.9.&lt;/p&gt;

&lt;p&gt;Question 2: It is supposed to be nearly random. Should it have a value closer to 0.5?&lt;/p&gt;

&lt;p&gt;I repeated the training of the ANN with n=3. The ANN was trained and was able to obtain a pretty low value for MSE. However, the calculated output from this ANN does not differ much from the (n-1)th bid value. It looks like ANN just takes the last bid as the next bid! I tried different network structures (all multilayer perceptions), different training parameters, etc, but results are same.&lt;/p&gt;

&lt;p&gt;Question 3: How can I improve the accuracy? Are there any other training methods than backpropagation?&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (656, 2, '2014-07-02 15:52:44.47', 3, NULL, NULL, '&lt;p&gt;Some suggestions&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Remove items appearing too infrequently in the data. That will reduce the dimensionality by several orders of magnitude. If a feature occurs less than say 10 times, it''s likely that it''s not adding any predictive value, and it may lead to overfitting due to low frequency&lt;/li&gt;
&lt;li&gt;Try a Linear SVM instead. They handle large dimensional data very well in terms of not overfitting. They also often have the option to assign relative weights to different classes, which may help address your unbalanced problem above. The sklearn svm (which simply wraps some other packages such as libsvm) has this option.&lt;/li&gt;
&lt;li&gt;Don''t use the ID column. Producing a model per user will most probably lead to overfitting. Instead, feed in attributes that describe the user that allows the model to generalize over similar users. You could try fitting a separate model per user, but you need a lot of data per user to do this well.&lt;/li&gt;
&lt;li&gt;It sounds like you really need to try some feature selection here, to reduce the dimensionality of the problem. But try 1 and 2 first, as they may give you good results sooner (although the end solution may still work better with some good feature selection). Sklearn again has a number of options for feature selection.&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':55C,110C,168C,226C ''/ol'':227C ''/p'':4C ''1'':191C ''10'':32C ''2'':193C ''ad'':41C ''address'':90C ''allow'':138C ''also'':75C ''although'':203C ''appear'':9C ''assign'':81C ''attribut'':132C ''better'':210C ''class'':86C ''column'':117C ''could'':147C ''data'':14C,66C,161C ''describ'':134C ''differ'':85C ''dimension'':19C,65C,185C ''due'':51C ''end'':205C ''featur'':27C,179C,214C,224C ''feed'':130C ''first'':194C ''fit'':149C ''frequenc'':54C ''general'':142C ''give'':198C ''good'':200C,213C ''handl'':63C ''help'':89C ''id'':116C ''infrequ'':11C ''instead'':61C,129C ''item'':8C ''larg'':64C ''lead'':48C,126C ''less'':29C ''li'':6C,56C,111C,169C ''libsvm'':106C ''like'':36C,172C ''linear'':59C ''lot'':159C ''low'':53C ''magnitud'':24C ''may'':47C,88C,197C,207C ''model'':120C,140C,152C ''need'':157C,175C ''number'':220C ''occur'':28C ''often'':76C ''ol'':5C ''option'':79C,109C,222C ''order'':22C ''overfit'':50C,73C,128C ''p'':1C ''packag'':103C ''per'':121C,153C,162C ''predict'':43C ''probabl'':125C ''problem'':93C,188C ''produc'':118C ''realli'':174C ''reduc'':17C,183C ''relat'':82C ''remov'':7C ''result'':201C ''say'':31C ''select'':180C,215C,225C ''separ'':151C ''sever'':21C ''similar'':144C ''simpli'':99C ''sklearn'':96C,216C ''solut'':206C ''sooner'':202C ''sound'':171C ''still'':208C ''suggest'':3C ''svm'':60C,97C ''term'':70C ''time'':33C ''tri'':57C,148C,177C,190C ''unbalanc'':92C ''use'':114C ''user'':122C,136C,145C,154C,163C ''valu'':44C ''weight'':83C ''well'':68C,167C ''work'':209C ''wrap'':100C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (657, 1, '2014-07-02 16:41:47.467', 3, 48, 'How are selected the features for a decision tree in CART?', '&lt;p&gt;Suppose I want to use CART as classification tree (I want a categorical response). I have the training set, and I split it using observation labels.&lt;/p&gt;

&lt;p&gt;Now, to build the decision tree (classification tree) how are selected the features to decide which label apply to testing observations?&lt;/p&gt;

&lt;p&gt;Supposing we are working on gene expression matrix, in which each element is a real number, is that done using features that are more distant between classes?&lt;/p&gt;
', 'classification', NULL, '''/p'':40C,63C,92C ''appli'':59C ''build'':44C ''cart'':11A,19C ''categor'':26C ''class'':91C ''classif'':12B,21C,48C ''decid'':56C ''decis'':8A,46C ''distant'':89C ''done'':83C ''element'':76C ''express'':71C ''featur'':5A,54C,85C ''gene'':70C ''label'':39C,58C ''matrix'':72C ''number'':80C ''observ'':38C,62C ''p'':13C,41C,64C ''real'':79C ''respons'':27C ''select'':3A,52C ''set'':32C ''split'':35C ''suppos'':14C,65C ''test'':61C ''train'':31C ''tree'':9A,22C,47C,49C ''use'':18C,37C,84C ''want'':16C,24C ''work'':68C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (658, 1, '2014-07-02 20:54:53.767', 8, 108, 'Prediction with non-atomic features', '&lt;p&gt;I would like to use non-atomic data, as a feature for a prediction. 
Suppose I have a Table with these features:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;- Column 1: Categorical - House
- Column 2: Numerical - 23.22
- Column 3: A Vector - [ 12, 22, 32 ]
- Column 4: A Tree - [ [ 2323, 2323 ],[2323, 2323] , [ Boolean, Categorical ] ]
- Column 5: A List [ 122, Boolean ]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I would like to predict/classify, for instance, Column 2.&lt;/p&gt;

&lt;p&gt;I am making something to automatically respond to questions, any type of question, like "Where was Foo Born?" ...&lt;/p&gt;

&lt;p&gt;I first make a query to a search engine, then I get some text data as a result, then I do all the parsing stuff (tagging, stemming, parsing, splitting ... )&lt;/p&gt;

&lt;p&gt;My first approach was to make a table, each row with a line of text and a lot of features, like "First Word", "Tag of First Word", "Chunks", etc...&lt;/p&gt;

&lt;p&gt;But with this approach I am missing the relationships between the sentences. &lt;/p&gt;

&lt;p&gt;I would like to know if there is an algorithm that looks inside the tree structures (or vectors) and makes the relations and extract whatever is relevant for predicting/classifying. I''d prefer to know about a library that does that than an algorithm that I have to implement.&lt;/p&gt;
', 'machine-learning', NULL, '''/code'':68C ''/p'':34C,80C,100C,131C,162C,176C,226C ''/pre'':69C ''1'':38C ''12'':49C ''122'':66C ''2'':42C,79C ''22'':50C ''23.22'':44C ''2323'':56C,57C,58C,59C ''3'':46C ''32'':51C ''4'':53C ''5'':63C ''algorithm'':187C,220C ''approach'':135C,167C ''atom'':5A,18C ''automat'':87C ''boolean'':60C,67C ''born'':99C ''categor'':39C,61C ''chunk'':160C ''code'':36C ''column'':37C,41C,45C,52C,62C,78C ''d'':208C ''data'':19C,116C ''engin'':110C ''etc'':161C ''extract'':201C ''featur'':6A,22C,33C,152C ''first'':103C,134C,154C,158C ''foo'':98C ''get'':113C ''hous'':40C ''implement'':225C ''insid'':190C ''instanc'':77C ''know'':182C,211C ''learn'':9B ''librari'':214C ''like'':13C,73C,95C,153C,180C ''line'':145C ''list'':65C ''look'':189C ''lot'':150C ''machin'':8B ''machine-learn'':7B ''make'':84C,104C,138C,197C ''miss'':170C ''non'':4A,17C ''non-atom'':3A,16C ''numer'':43C ''p'':10C,70C,81C,101C,132C,163C,177C ''pars'':125C,129C ''pre'':35C ''predict'':1A,25C ''predict/classify'':75C ''predicting/classifying'':206C ''prefer'':209C ''queri'':106C ''question'':90C,94C ''relat'':199C ''relationship'':172C ''relev'':204C ''respond'':88C ''result'':119C ''row'':142C ''search'':109C ''sentenc'':175C ''someth'':85C ''split'':130C ''stem'':128C ''structur'':193C ''stuff'':126C ''suppos'':26C ''tabl'':30C,140C ''tag'':127C,156C ''text'':115C,147C ''tree'':55C,192C ''type'':92C ''use'':15C ''vector'':48C,195C ''whatev'':202C ''word'':155C,159C ''would'':12C,72C,179C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (659, 2, '2014-07-03 08:00:10.823', 6, NULL, NULL, '&lt;p&gt;As Steve Kallestad has said, this is a TSP problem, and there are wonderful free solvers to find approximate solutions.&lt;/p&gt;

&lt;p&gt;It may be too much work for what you are looking for, but you may try use one of those solvers in combination with the Google Maps API, to find real walking distances beetwen your coordinates:
&lt;a href="https://developers.google.com/maps/documentation/directions/#DirectionsRequests"&gt;https://developers.google.com/maps/documentation/directions/#DirectionsRequests&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;(I have never used this API, so I don''t know how easy or effective it would be)&lt;/p&gt;
', NULL, NULL, '''/maps/documentation/directions/#directionsrequests'':64C ''/maps/documentation/directions/#directionsrequests&lt;/a&gt;&lt;/p&gt;'':67C ''/p'':22C,87C ''api'':51C,74C ''approxim'':20C ''beetwen'':57C ''combin'':46C ''coordin'':59C ''developers.google.com'':63C,66C ''developers.google.com/maps/documentation/directions/#directionsrequests'':62C ''developers.google.com/maps/documentation/directions/#directionsrequests&lt;/a&gt;&lt;/p&gt;'':65C ''distanc'':56C ''easi'':81C ''effect'':83C ''find'':19C,53C ''free'':16C ''googl'':49C ''href'':61C ''kallestad'':4C ''know'':79C ''look'':34C ''map'':50C ''may'':25C,38C ''much'':28C ''never'':71C ''one'':41C ''p'':1C,23C,68C ''problem'':11C ''real'':54C ''said'':6C ''solut'':21C ''solver'':17C,44C ''steve'':3C ''tri'':39C ''tsp'':10C ''use'':40C,72C ''walk'':55C ''wonder'':15C ''work'':29C ''would'':85C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (792, 2, '2014-07-21 11:59:49.203', 2, NULL, NULL, '&lt;p&gt;To handle such amount of data, programming language is not the main concern but the programming framework is. Frameworks such as MapReduce or Spark have bindings to many languages including Python. These frameworks certainly have many ready-to-use packages for data analysis tasks. But in the end it all comes to your requirement, i.e., what is your task? People have different definitions of data analysis tasks, some of them can be easily solved with relational databases. In that case, SQL is much better than all other alternatives.&lt;/p&gt;
', NULL, NULL, '''/p'':91C ''altern'':90C ''amount'':5C ''analysi'':45C,68C ''better'':86C ''bind'':27C ''case'':82C ''certain'':35C ''come'':53C ''concern'':14C ''data'':7C,44C,67C ''databas'':79C ''definit'':65C ''differ'':64C ''easili'':75C ''end'':50C ''framework'':18C,20C,34C ''handl'':3C ''i.e'':57C ''includ'':31C ''languag'':9C,30C ''main'':13C ''mani'':29C,37C ''mapreduc'':23C ''much'':85C ''p'':1C ''packag'':42C ''peopl'':62C ''program'':8C,17C ''python'':32C ''readi'':39C ''ready-to-us'':38C ''relat'':78C ''requir'':56C ''solv'':76C ''spark'':25C ''sql'':83C ''task'':46C,61C,69C ''use'':41C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (660, 1, '2014-07-03 10:49:50.993', 6, 764, 'Linear Regression in R Mapreduce(RHadoop)', '&lt;p&gt;I m new to RHadoop and also to RMR...
I had an requirement to write a Mapreduce Job in R Mapreduce. I have Tried writing but While executing this it gives an Error.
Tring to read the file from hdfs&lt;/p&gt;

&lt;h2&gt;Error:&lt;/h2&gt;

&lt;pre&gt;&lt;code&gt;Error in mr(map = map, reduce = reduce, combine = combine, vectorized.reduce,  : 
   hadoop streaming failed with error code 1
&lt;/code&gt;&lt;/pre&gt;

&lt;h2&gt;Code :&lt;/h2&gt;

&lt;pre&gt;&lt;code&gt;Sys.setenv(HADOOP_HOME="/opt/cloudera/parcels/CDH-4.7.0-1.cdh4.7.0.p0.40/lib/hadoop")
Sys.setenv(HADOOP_CMD="/opt/cloudera/parcels/CDH-4.7.0-1.cdh4.7.0.p0.40/bin/hadoop")

Sys.setenv(HADOOP_STREAMING="/opt/cloudera/parcels/CDH-4.7.0-1.cdh4.7.0.p0.40/lib/hadoop-0.20-mapreduce/contrib/streaming/hadoop-streaming-2.0.0-mr1-cdh4.7.0.jar")
library(rmr2)
library(rhdfs)
hdfs.init()
day_file = hdfs.file("/hdfs/bikes_LR/day.csv","r")
day_read = hdfs.read(day_file)
c = rawToChar(day_read)

XtX =
  values(from.dfs(
    mapreduce(
      input = "/hdfs/bikes_LR/day.csv",
      map=
        function(.,Xi){
         yi =c[Xi[,1],]
         Xi = Xi[,-1]
         keyval(1,list(t(Xi)%*%Xi))
       },
  reduce = function(k,v )
  {
    vals =as.numeric(v)
    keyval(k,sum(vals))
  } ,
  combine = TRUE)))[[1]]

XtY =
 values(from.dfs(
    mapreduce(
     input = "/hdfs/bikes_LR/day.csv",
     map=
       function(.,Xi){
         yi =c[Xi[,1],]
         Xi = Xi[,-1]
        keyval(1,list(t(Xi)%*%yi))
       },
     reduce = TRUE ,
     combine = TRUE)))[[1]]
solve(XtX,XtY)



Input:
------------

instant,dteday,season,yr,mnth,holiday,weekday,workingday,weathersit,temp,atemp,hum,windspeed,casual,registered,cnt
1,2011-01-01,1,0,1,0,6,0,2,0.344167,0.363625,0.805833,0.160446,331,654,985
2,2011-01-02,1,0,1,0,0,0,2,0.363478,0.353739,0.696087,0.248539,131,670,801
3,2011-01-03,1,0,1,0,1,1,1,0.196364,0.189405,0.437273,0.248309,120,1229,1349
4,2011-01-04,1,0,1,0,2,1,1,0.2,0.212122,0.590435,0.160296,108,1454,1562
5,2011-01-05,1,0,1,0,3,1,1,0.226957,0.22927,0.436957,0.1869,82,1518,1600
6,2011-01-06,1,0,1,0,4,1,1,0.204348,0.233209,0.518261,0.0895652,88,1518,1606
7,2011-01-07,1,0,1,0,5,1,2,0.196522,0.208839,0.498696,0.168726,148,1362,1510
8,2011-01-08,1,0,1,0,6,0,2,0.165,0.162254,0.535833,0.266804,68,891,959
9,2011-01-09,1,0,1,0,0,0,1,0.138333,0.116175,0.434167,0.36195,54,768,822
10,2011-01-10,1,0,1,0,1,1,1,0.150833,0.150888,0.482917,0.223267,41,1280,1321



 Please Suggest me any mistakes.
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (661, 1, '2014-07-03 14:18:14.387', 2, 906, 'Cannot make user directory on a new CDH5 installation (Hadoop)', '&lt;p&gt;I download and installed CDH 5 package succesfully on a single linux node in pseudo-distributed Mode on my CentOS 6.5&lt;/p&gt;

&lt;p&gt;Starting Hadoop and verifying it is Working Properly as in &lt;a href="http://www.cloudera.com/content/cloudera-content/cloudera-docs/CDH5/latest/CDH5-Quick-Start/cdh5qs_mrv1_pseudo.html" rel="nofollow"&gt;this link&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I succesfully finished the following steps&lt;/p&gt;

&lt;p&gt;Step 1: Format the NameNode.&lt;/p&gt;

&lt;p&gt;Step 2: Start HDFS&lt;/p&gt;

&lt;p&gt;Step 3: Create the /tmp Directory&lt;/p&gt;

&lt;p&gt;Step 4: Create the MapReduce system directories:&lt;/p&gt;

&lt;p&gt;Step 5: Verify the HDFS File Structure&lt;/p&gt;

&lt;p&gt;Step 6: Start MapReduce&lt;/p&gt;

&lt;p&gt;while following command in step 7 I get the following error.&lt;/p&gt;

&lt;p&gt;Step 7: Create User Directories&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;$ sudo -u hdfs hadoop fs -mkdir -p /user/hadoopuser&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;&lt;em&gt;mkdir: ''/user/hadoopuser'': No such file or directory&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;(where hadoopuser is my linux login username)&lt;/p&gt;

&lt;p&gt;If I create the directory manually as /user/hadoopuser in the filesystem, it is not accepting.&lt;/p&gt;

&lt;p&gt;How to success the step 7:?&lt;/p&gt;

&lt;p&gt;Please provide the sloution to procced the remaining installation.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (662, 1, '2014-07-03 16:11:22.637', 9, 3317, 'What algorithms should I use to perform job classification based on resume data?', '&lt;p&gt;Note that I am doing everything in R. &lt;/p&gt;

&lt;p&gt;The problem goes as follow: &lt;/p&gt;

&lt;p&gt;Basically, I have a list of resumes (CVs). Some candidates will have work experience before and some don''t. The goal here is to: based on the text on their CVs, I want to classify them into different job sectors. I am particular in those cases, in which the candidates do not have any experience / is a student, and I want to make a prediction to classify which job sectors this candidate will most likely belongs to after graduation . &lt;/p&gt;

&lt;p&gt;Question 1: I know machine learning algorithms. However, I have never done NLP before. I came across Latent Dirichlet allocation on the internet. However, I am not sure if this is the best approach to tackle my problem. &lt;/p&gt;

&lt;p&gt;My original idea:  &lt;em&gt;make this a supervised learning problem&lt;/em&gt;. 
Suppose we already have large amount of labelled data, meaning that we have correctly labelled the job sectors for a list of candidates. We train the model up using ML algorithms (i.e. nearest neighbor... )and feed in those &lt;em&gt;unlabelled data&lt;/em&gt;, which are candidates that have no work experience / are students, and try to predict which job sector they will belong to. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Update&lt;/strong&gt;
Question 2: Would it be a good idea to create an text file by extracting everything in a resume and print these data out in the text file, so that each resume is associated with a text file,which contains unstructured strings, and then we applied text mining techniques to the text files and make the data become structured or even to create a  frequency matrix of terms used out of the text files ? For example, the text file may look something like this:&lt;/p&gt;

&lt;p&gt;&lt;code&gt;I deployed ML algorithm in this project and... Skills: Java, Python, c++ ...&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;This is what I meant by ''unstructured'', i.e. collapsing everything into a single line string.&lt;/p&gt;

&lt;p&gt;Is this approach wrong ? Please correct me if you think my approach is wrong. &lt;/p&gt;

&lt;p&gt;Question 3: The tricky part is: how to &lt;strong&gt;identify and extract the keywords&lt;/strong&gt; ? Using the &lt;code&gt;tm&lt;/code&gt; package in R ? what algorithm is the &lt;code&gt;tm&lt;/code&gt;   package based on ?  Should I use NLP algorithms ? If yes, what algorithms should I look at ? Please point me to some good resources to look at as well. &lt;/p&gt;

&lt;p&gt;Any ideas would be great.&lt;/p&gt;
', 'machine-learning  classification  nlp  text-mining', NULL, '''/code'':339C,394C,404C ''/em'':171C,213C ''/p'':31C,38C,119C,159C,235C,324C,340C,357C,373C,433C,440C ''/strong'':239C,389C ''1'':122C ''2'':241C ''3'':376C ''across'':137C ''algorithm'':2A,127C,202C,330C,399C,412C,416C ''alloc'':140C ''alreadi'':174C ''amount'':177C ''appli'':285C ''approach'':154C,361C,370C ''associ'':273C ''base'':10A,64C,406C ''basic'':40C ''becom'':297C ''belong'':115C,233C ''best'':153C ''c'':338C ''came'':136C ''candid'':49C,89C,111C,194C,216C ''case'':85C ''classif'':9A,17B ''classifi'':74C,106C ''code'':326C,392C,402C ''collaps'':350C ''contain'':279C ''correct'':185C,364C ''creat'':249C,302C ''cvs'':47C,70C ''data'':13A,180C,212C,262C,296C ''deploy'':328C ''differ'':77C ''dirichlet'':139C ''done'':132C ''em'':164C,210C ''even'':300C ''everyth'':28C,255C,351C ''exampl'':315C ''experi'':53C,94C,221C ''extract'':254C,386C ''feed'':207C ''file'':252C,267C,277C,292C,313C,318C ''follow'':37C ''frequenc'':304C ''goal'':60C ''goe'':35C ''good'':246C,426C ''graduat'':118C ''great'':439C ''howev'':128C,144C ''i.e'':203C,349C ''idea'':163C,247C,436C ''identifi'':384C ''internet'':143C ''java'':336C ''job'':8A,78C,108C,188C,229C ''keyword'':388C ''know'':124C ''label'':179C,186C ''larg'':176C ''latent'':138C ''learn'':16B,126C,169C ''like'':114C,322C ''line'':355C ''list'':44C,192C ''look'':320C,419C,429C ''machin'':15B,125C ''machine-learn'':14B ''make'':102C,165C,294C ''matrix'':305C ''may'':319C ''mean'':181C ''meant'':346C ''mine'':21B,287C ''ml'':201C,329C ''model'':198C ''nearest'':204C ''neighbor'':205C ''never'':131C ''nlp'':18B,133C,411C ''note'':23C ''origin'':162C ''p'':22C,32C,39C,120C,160C,236C,325C,341C,358C,374C,434C ''packag'':395C,405C ''part'':379C ''particular'':82C ''perform'':7A ''pleas'':363C,421C ''point'':422C ''predict'':104C,227C ''print'':260C ''problem'':34C,158C,170C ''project'':333C ''python'':337C ''question'':121C,240C,375C ''r'':30C,397C ''resourc'':427C ''resum'':12A,46C,258C,271C ''sector'':79C,109C,189C,230C ''singl'':354C ''skill'':335C ''someth'':321C ''string'':281C,356C ''strong'':237C,383C ''structur'':298C ''student'':97C,223C ''supervis'':168C ''suppos'':172C ''sure'':148C ''tackl'':156C ''techniqu'':288C ''term'':307C ''text'':20B,67C,251C,266C,276C,286C,291C,312C,317C ''text-min'':19B ''think'':368C ''tm'':393C,403C ''train'':196C ''tri'':225C ''tricki'':378C ''unlabel'':211C ''unstructur'':280C,348C ''updat'':238C ''use'':5A,200C,308C,390C,410C ''want'':72C,100C ''well'':432C ''work'':52C,220C ''would'':242C,437C ''wrong'':362C,372C ''yes'':414C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (664, 1, '2014-07-03 17:11:01.77', 9, 92, 'Fisher Scoring v/s Coordinate Descent for MLE in R', '&lt;p&gt;R base function &lt;code&gt;glm()&lt;/code&gt; uses Fishers Scoring for MLE, while the &lt;code&gt;glmnet&lt;/code&gt; appears to use the coordinate descent method to solve the same equation. Coordinate descent is more time-efficient than Fisher Scoring, as Fisher Scoring calculates the second order derivative matrix, in addition to some other matrix operations. which makes expensive to perform, while coordinate descent can do the same task in O(np) time.&lt;/p&gt;

&lt;p&gt;Why would R base function use Fisher Scoring? Does this method have an advantage over other optimization methods? How does coordinate descent and Fisher Scoring compare? I am relatively new to do this field so any help or resource will be helpful.&lt;/p&gt;
', 'machine-learning  r  algorithms  optimization', NULL, '''/code'':22C,32C ''/p'':88C,132C ''addit'':65C ''advantag'':103C ''algorithm'':14B ''appear'':33C ''base'':18C,93C ''calcul'':58C ''code'':20C,30C ''compar'':115C ''coordin'':4A,37C,45C,77C,110C ''deriv'':62C ''descent'':5A,38C,46C,78C,111C ''effici'':51C ''equat'':44C ''expens'':73C ''field'':123C ''fisher'':1A,24C,53C,56C,96C,113C ''function'':19C,94C ''glm'':21C ''glmnet'':31C ''help'':126C,131C ''learn'':12B ''machin'':11B ''machine-learn'':10B ''make'':72C ''matrix'':63C,69C ''method'':39C,100C,107C ''mle'':7A,27C ''new'':119C ''np'':86C ''o'':85C ''oper'':70C ''optim'':15B,106C ''order'':61C ''p'':16C,89C ''perform'':75C ''r'':9A,13B,17C,92C ''relat'':118C ''resourc'':128C ''score'':2A,25C,54C,57C,97C,114C ''second'':60C ''solv'':41C ''task'':83C ''time'':50C,87C ''time-effici'':49C ''use'':23C,35C,95C ''v/s'':3A ''would'':91C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (665, 2, '2014-07-03 17:52:00.963', 3, NULL, NULL, '&lt;p&gt;At each split point, CART will choose the feature which "best" splits the observations. What qualifies as best varies, but generally the split is done so that the subsequent nodes are more homogenous/pure with respect to the target. There are different ways of measuring homogeneity, for example Gini, Entropy, Chi-square. If you are using software, it may allow you to choose the measure of homogenity that the tree algorithm will use.&lt;/p&gt;

&lt;p&gt;Distance is not a factor with trees - what matters is whether the value is greater than or less than the split point, not the distance from the split point.&lt;/p&gt;
', NULL, NULL, '''/p'':75C,106C ''algorithm'':72C ''allow'':61C ''best'':12C,19C ''cart'':6C ''chi'':52C ''chi-squar'':51C ''choos'':8C,64C ''differ'':42C ''distanc'':77C,101C ''done'':26C ''entropi'':50C ''exampl'':48C ''factor'':81C ''featur'':10C ''general'':22C ''gini'':49C ''greater'':91C ''homogen'':46C,68C ''homogenous/pure'':34C ''less'':94C ''matter'':85C ''may'':60C ''measur'':45C,66C ''node'':31C ''observ'':15C ''p'':1C,76C ''point'':5C,98C,105C ''qualifi'':17C ''respect'':36C ''softwar'':58C ''split'':4C,13C,24C,97C,104C ''squar'':53C ''subsequ'':30C ''target'':39C ''tree'':71C,83C ''use'':57C,74C ''valu'':89C ''vari'':20C ''way'':43C ''whether'':87C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (863, 2, '2014-07-29 13:43:39.06', 13, NULL, NULL, '&lt;p&gt;Let''s first split it into parts. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Data Science&lt;/strong&gt; is about making knowledge from raw data. It uses machine learning, statistics and other fields to simplify (or even automate) decision making. Data science techniques may work with any data size, but more data means better predictions and thus more precise decisions. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Hadoop&lt;/strong&gt; is a common name for a set of tools intended to work with large amounts of data. Two most important components in Hadoop are HDFS and MapReduce.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;HDFS&lt;/strong&gt;, or Hadoop Distributed File System, is a special distributed storage capable of holding really large data amounts. Large files on HDFS are split into blocks, and for each block HDFS API exposes its &lt;em&gt;location&lt;/em&gt;. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;MapReduce&lt;/strong&gt; is framework for running computations on nodes with data. MapReduce heavily uses &lt;em&gt;data locality&lt;/em&gt; exposed by HDFS: when possible, data is not transferred between nodes, but instead code is copied to the nodes with data. &lt;/p&gt;

&lt;p&gt;So basically any problem (including data science tasks) that doesn''t break data locality principle may be efficiently implemented using MapReduce (and a number of other problems may be solved not that efficiently, but still simply enough).&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Let''s take some examples. Very often analyst only needs some simple statistics over his tabular data. In this case &lt;a href="https://hive.apache.org/"&gt;&lt;strong&gt;Hive&lt;/strong&gt;&lt;/a&gt;, which is basically SQL engine over MapReduce, works pretty well (there are also Impala, Shark and others, but they don''t use Hadoop''s MapReduce, so more on them later). &lt;/p&gt;

&lt;p&gt;In other cases analyst (or developer) may want to work with previously unstructured data. Pure MapReduce is pretty good for &lt;strong&gt;transforming&lt;/strong&gt; and &lt;strong&gt;standardizing&lt;/strong&gt; data.&lt;/p&gt;

&lt;p&gt;Some people are used to exploratory statistics and visualization using tools like R. It''s possible to apply this approach to big data amounts using &lt;a href="https://github.com/RevolutionAnalytics/RHadoop/wiki"&gt;&lt;strong&gt;RHadoop&lt;/strong&gt;&lt;/a&gt; package. &lt;/p&gt;

&lt;p&gt;And when it comes to MapReduce-based machine learning &lt;a href="https://mahout.apache.org/"&gt;&lt;strong&gt;Apache Mahout&lt;/strong&gt;&lt;/a&gt; is the first to mention. &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;There''s, however, one type of algorithms that work pretty slowly on Hadoop even in presence of data locality, namely, iterative algorithms. Iterative algorithms tend to have multiple Map and Reduce stages. Hadoop''s MR framework &lt;strong&gt;reads&lt;/strong&gt; and &lt;strong&gt;writes&lt;/strong&gt; data to disk on each stage (and sometimes in between), which makes iterative (as well as any multi-stage) tasks terribly slow. &lt;/p&gt;

&lt;p&gt;Fortunately, there are alternative frameworks that can both - use data locality and keep data in memory between stages. Probably, the most notable of them is &lt;a href="https://spark.apache.org/"&gt;&lt;strong&gt;Apache Spark&lt;/strong&gt;&lt;/a&gt;. Spark is complete replacement for Hadoop''s MapReduce that uses its own runtime and exposes pretty rich API for manipulating your distributed dataset. Spark has several sub-projects, closely related to data science: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://shark.cs.berkeley.edu/"&gt;&lt;strong&gt;Shark&lt;/strong&gt;&lt;/a&gt; and &lt;a href="https://spark.apache.org/sql/"&gt;&lt;strong&gt;Spark SQL&lt;/strong&gt;&lt;/a&gt;  provide alternative SQL-like interfaces to data stored on HDFS&lt;/li&gt;
&lt;li&gt;&lt;a href="https://spark.apache.org/streaming/"&gt;&lt;strong&gt;Spark Streaming&lt;/strong&gt;&lt;/a&gt; makes it easy to work with continuous data streams (e.g. Twitter feed)&lt;/li&gt;
&lt;li&gt;&lt;a href="https://spark.apache.org/mllib/"&gt;&lt;strong&gt;MLlib&lt;/strong&gt;&lt;/a&gt; implements a number of machine learning algorithms with a pretty simple and flexible API&lt;/li&gt;
&lt;li&gt;&lt;a href="https://spark.apache.org/graphx/"&gt;&lt;strong&gt;GraphX&lt;/strong&gt;&lt;/a&gt; enables large-scale graph processing&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So there''s pretty large set of data science problems that you can solve with Hadoop and related projects. &lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (666, 2, '2014-07-03 18:36:36.05', 2, NULL, NULL, '&lt;p&gt;As far as gathering data goes, you can check out &lt;strong&gt;Quandl&lt;/strong&gt; (there''s a tutorial on using it with &lt;strong&gt;R&lt;/strong&gt; on &lt;a href="https://www.datacamp.com/courses/how-to-work-with-quandl-in-r" rel="nofollow"&gt;DataCamp&lt;/a&gt; if you''re interested).&lt;/p&gt;

&lt;p&gt;In addition, Aswath Damodaran''s &lt;a href="http://people.stern.nyu.edu/adamodar/New_Home_Page/data.html" rel="nofollow"&gt;site&lt;/a&gt; contains a lot of helpful datasets. Though they aren''t updated that frequently, they may still be useful, especially as a benchmark for comparing your own output (from the scripts you will inevitably need to write to calculate the necessary metrics).  &lt;/p&gt;

&lt;p&gt;And, again, &lt;strong&gt;Quant SE&lt;/strong&gt; is probably a better place to be looking...&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (667, 2, '2014-07-03 20:47:20.147', 5, NULL, NULL, '&lt;p&gt;This is a tricky problem. There are many ways to handle it. I guess, resumes can be treated as semi-structured documents. Sometimes, it''s beneficial to have some minimal structure in the documents. I believe, in resumes you would see some tabular data. You might want to treat these as attribute value pairs. For example, you would get a list of terms for the attribute "Skill set".&lt;/p&gt;

&lt;p&gt;The key idea is to manually configure a list of key phrases such as "skill", "education", "publication" etc. The next step is to extract terms which pertain to these key phrases either by exploiting the structure in some way (such as tables) or by utilizing the proximity of terms around these key phrases, e.g. the fact that the word "Java" is in close proximity to the term "skill" might indicate that the person is skilled in Java.&lt;/p&gt;

&lt;p&gt;After you extract these information, the next step could be to build up a feature vector for each of these key phrases. You can then represent a document as a vector with different fields (one each for a key phrase). For example, consider the following two resumes represented with two fields, namely &lt;em&gt;project&lt;/em&gt; and &lt;em&gt;education&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Doc1: {project: (java, 3) (c, 4)}, {education: (computer, 2), (physics, 1)}&lt;/p&gt;

&lt;p&gt;Doc2: {project: (java, 3) (python, 2)}, {education: (maths, 3), (computer, 2)}&lt;/p&gt;

&lt;p&gt;In the above example, I show a term with the frequency. Of course, while extracting the terms you need to stem and remove stop-words. It is clear from the examples that the person whose resume is Doc1 is more skilled in C than that of D2. Implementation wise, it''s very easy to represent documents as field vectors in Lucene.&lt;/p&gt;

&lt;p&gt;Now, the next step is to retrieve a ranked list of resumes given a job specification. In fact, that''s fairly straight forward if you represent queries (job specs) as field vectors as well. You just need to retrieve a ranked list of candidates (resumes) using Lucene from a collection of indexed resumes.&lt;/p&gt;
', NULL, NULL, '''/em'':206C,210C ''/p'':71C,150C,211C,224C,237C,301C,356C ''1'':223C ''2'':221C,231C,236C ''3'':216C,229C,234C ''4'':218C ''around'':122C ''attribut'':54C,68C ''believ'':38C ''benefici'':28C ''build'':163C ''c'':217C,282C ''candid'':346C ''clear'':267C ''close'':135C ''collect'':352C ''comput'':220C,235C ''configur'':79C ''consid'':194C ''could'':160C ''cours'':251C ''d2'':286C ''data'':46C ''differ'':184C ''doc1'':213C,277C ''doc2'':226C ''document'':24C,36C,179C,295C ''e.g'':126C ''easi'':292C ''educ'':88C,209C,219C,232C ''either'':104C ''em'':204C,208C ''etc'':90C ''exampl'':58C,193C,242C,270C ''exploit'':106C ''extract'':96C,154C,253C ''fact'':128C,320C ''fair'':323C ''featur'':166C ''field'':185C,202C,297C,333C ''follow'':196C ''forward'':325C ''frequenc'':249C ''get'':61C ''given'':315C ''guess'':15C ''handl'':12C ''idea'':75C ''implement'':287C ''index'':354C ''indic'':142C ''inform'':156C ''java'':132C,149C,215C,228C ''job'':317C,330C ''key'':74C,83C,102C,124C,172C,190C ''list'':63C,81C,312C,344C ''lucen'':300C,349C ''mani'':9C ''manual'':78C ''math'':233C ''might'':48C,141C ''minim'':32C ''name'':203C ''need'':257C,339C ''next'':92C,158C,305C ''one'':186C ''p'':1C,72C,151C,212C,225C,238C,302C ''pair'':56C ''person'':145C,273C ''pertain'':99C ''phrase'':84C,103C,125C,173C,191C ''physic'':222C ''problem'':6C ''project'':205C,214C,227C ''proxim'':119C,136C ''public'':89C ''python'':230C ''queri'':329C ''rank'':311C,343C ''remov'':261C ''repres'':177C,199C,294C,328C ''resum'':16C,40C,198C,275C,314C,347C,355C ''retriev'':309C,341C ''see'':43C ''semi'':22C ''semi-structur'':21C ''set'':70C ''show'':244C ''skill'':69C,87C,140C,147C,280C ''sometim'':25C ''spec'':331C ''specif'':318C ''stem'':259C ''step'':93C,159C,306C ''stop'':263C ''stop-word'':262C ''straight'':324C ''structur'':23C,33C,108C ''tabl'':114C ''tabular'':45C ''term'':65C,97C,121C,139C,246C,255C ''treat'':19C,51C ''tricki'':5C ''two'':197C,201C ''use'':348C ''util'':117C ''valu'':55C ''vector'':167C,182C,298C,334C ''want'':49C ''way'':10C,111C ''well'':336C ''whose'':274C ''wise'':288C ''word'':131C,264C ''would'':42C,60C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (668, 2, '2014-07-03 20:51:59.51', 1, NULL, NULL, '&lt;p&gt;I''ve done some research in this area. I''ve found first order Markov chains work well for predicting within game scoring dynamics across a variety of sports.&lt;/p&gt;

&lt;p&gt;You can read in more detail here:
&lt;a href="http://www.epjdatascience.com/content/3/1/4" rel="nofollow"&gt;http://www.epjdatascience.com/content/3/1/4&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/content/3/1/4'':43C ''/content/3/1/4&lt;/a&gt;&lt;/p&gt;'':48C ''/p'':30C ''across'':25C ''area'':9C ''chain'':16C ''detail'':37C ''done'':4C ''dynam'':24C ''first'':13C ''found'':12C ''game'':22C ''href'':40C ''markov'':15C ''nofollow'':45C ''order'':14C ''p'':1C,31C ''predict'':20C ''read'':34C ''rel'':44C ''research'':6C ''score'':23C ''sport'':29C ''varieti'':27C ''ve'':3C,11C ''well'':18C ''within'':21C ''work'':17C ''www.epjdatascience.com'':42C,47C ''www.epjdatascience.com/content/3/1/4'':41C ''www.epjdatascience.com/content/3/1/4&lt;/a&gt;&lt;/p&gt;'':46C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (669, 2, '2014-07-03 23:00:42.35', 6, NULL, NULL, '&lt;p&gt;The results you''re seeing aren''t a byproduct of your training product, but rather that &lt;code&gt;neural nets&lt;/code&gt; are not a great choice for this task. &lt;code&gt;Neural nets&lt;/code&gt; are effectively a means to create a high order non-linear function by composing a number of simpler functions. This is often a really good thing, because it allows neural nets to fit very complex patterns.&lt;/p&gt;

&lt;p&gt;However, in a stock exchange any complex pattern, when traded upon will quickly decay. Detecting a complicated pattern will generally not generate useful results, because it is typically complex patterns in the short term. Additionally, depending on the metric you choose, there are a number of ways of performing well that actually won''t pay off in investing (such as just predicting the last value in your example).&lt;/p&gt;

&lt;p&gt;In addition the stock market is startlingly chaotic which can result in a &lt;code&gt;neural net&lt;/code&gt; overfitting. This means that the patterns it learns will generalize poorly. Something along the lines of just seeing a stock decrease over a day and uniformly deciding that the stock will always decrease just because it was seen on a relatively short term. Instead techniques like &lt;code&gt;ridge&lt;/code&gt; and &lt;code&gt;robust regression&lt;/code&gt;, which will identify more general, less complex patterns, do better.&lt;/p&gt;

&lt;p&gt;The winner of a similar Kaggle competition used &lt;code&gt;robust regression&lt;/code&gt; for this very reason. You are likely to see better results if you switch to a shallow learning model that will find functions of a lower polynomial order, over the deep complex functions of a neural net.&lt;/p&gt;
', NULL, NULL, '''/code'':21C,33C,159C,208C,213C,237C ''/p'':71C,141C,224C,275C ''actual'':124C ''addit'':107C,144C ''allow'':63C ''along'':172C ''alway'':191C ''aren'':7C ''better'':223C,247C ''byproduct'':10C ''chaotic'':150C ''choic'':26C ''choos'':113C ''code'':18C,30C,156C,206C,210C,234C ''competit'':232C ''complex'':69C,79C,101C,220C,269C ''complic'':89C ''compos'':48C ''creat'':39C ''day'':183C ''decay'':86C ''decid'':186C ''decreas'':180C,192C ''deep'':268C ''depend'':108C ''detect'':87C ''effect'':35C ''exampl'':140C ''exchang'':77C ''find'':259C ''fit'':67C ''function'':46C,53C,260C,270C ''general'':92C,169C,218C ''generat'':94C ''good'':59C ''great'':25C ''high'':41C ''howev'':73C ''identifi'':216C ''instead'':203C ''invest'':130C ''kaggl'':231C ''last'':136C ''learn'':167C,255C ''less'':219C ''like'':205C,244C ''line'':174C ''linear'':45C ''lower'':263C ''market'':147C ''mean'':37C,162C ''metric'':111C ''model'':256C ''net'':20C,32C,65C,158C,274C ''neural'':19C,31C,64C,157C,273C ''non'':44C ''non-linear'':43C ''number'':50C,117C ''often'':56C ''order'':42C,265C ''overfit'':160C ''p'':1C,72C,142C,225C ''pattern'':70C,80C,90C,102C,165C,221C ''pay'':127C ''perform'':121C ''polynomi'':264C ''poor'':170C ''predict'':134C ''product'':14C ''quick'':85C ''rather'':16C ''re'':5C ''realli'':58C ''reason'':241C ''regress'':212C,236C ''relat'':200C ''result'':3C,96C,153C,248C ''ridg'':207C ''robust'':211C,235C ''see'':6C,177C,246C ''seen'':197C ''shallow'':254C ''short'':105C,201C ''similar'':230C ''simpler'':52C ''someth'':171C ''startl'':149C ''stock'':76C,146C,179C,189C ''switch'':251C ''task'':29C ''techniqu'':204C ''term'':106C,202C ''thing'':60C ''trade'':82C ''train'':13C ''typic'':100C ''uniform'':185C ''upon'':83C ''use'':95C,233C ''valu'':137C ''way'':119C ''well'':122C ''winner'':227C ''won'':125C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (670, 1, '2014-07-04 02:31:42.08', 3, 56, 'Dealing with events that have not yet happened when building a model', '&lt;p&gt;I was building a model that predicts user churn for a website, where I have data on all users, both past and present.&lt;/p&gt;

&lt;p&gt;I can build a model that only uses those users that have left, but then I''m leaving 2/3 of the total user population unused.&lt;/p&gt;

&lt;p&gt;Is there a good way to incorporate data from these users into a model from a conceptual standpoint?&lt;/p&gt;
', 'data-mining', NULL, '''/p'':40C,67C,87C ''2/3'':60C ''build'':10A,19C,44C ''churn'':25C ''conceptu'':85C ''data'':14B,32C,76C ''data-min'':13B ''deal'':1A ''event'':3A ''good'':72C ''happen'':8A ''incorpor'':75C ''leav'':59C ''left'':54C ''m'':58C ''mine'':15B ''model'':12A,21C,46C,82C ''p'':16C,41C,68C ''past'':37C ''popul'':65C ''predict'':23C ''present'':39C ''standpoint'':86C ''total'':63C ''unus'':66C ''use'':49C ''user'':24C,35C,51C,64C,79C ''way'':73C ''websit'':28C ''yet'':7A');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (671, 1, '2014-07-04 05:12:44.707', 6, 110, 'Linearly increasing data with manual reset', '&lt;p&gt;I have a linearly increasing time series dataset of a sensor, with value ranges between 50 and 150. I''ve implemented a &lt;a href="http://en.wikipedia.org/wiki/Simple_linear_regression" rel="nofollow"&gt;Simple Linear Regression&lt;/a&gt; algorithm to fit a regression line on such data, and I''m predicting the date when the series would reach 120.&lt;/p&gt;

&lt;p&gt;All works fine when the series move upwards. But, there are cases in which the sensor reaches around 110 or 115, and it is reset; in such cases the values would start over again at, say, 50 or 60.&lt;/p&gt;

&lt;p&gt;This is where I start facing issues with the regression line, as it starts moving downwards, and it starts predicting old date. I think I should be considering only the subset of data from where it was previously reset. However, I''m trying to understand if there are any algorithms available that consider this case.&lt;/p&gt;

&lt;p&gt;I''m new to data science, would appreciate any pointers to move further.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit: nfmcclure''s suggestions applied&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Before applying the suggestions&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ZsyyQ.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Below is the snapshot of what I''ve got after splitting the dataset where the reset occurs, and the slope of two set.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/OEQCw.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;finding the mean of the two slopes and drawing the line from the mean.&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/i2qv5.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Is this OK?&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (874, 2, '2014-07-30 21:53:13.79', 4, NULL, NULL, '&lt;p&gt;I have some thoughts about your question. I hope it may help you to solve your problem.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;I''m planning to run some experiments with very large data sets, and I''d like to distribute the computation.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;In &lt;a href="http://www.datasciencecentral.com/profiles/blogs/meeting-justice-of-data-science" rel="nofollow"&gt;one of my posts&lt;/a&gt;, I have done research on topic of &lt;em&gt;evaluation methods of Data Science&lt;/em&gt;. With &lt;strong&gt;Learning Curve&lt;/strong&gt;, you can evaluate your experiments &lt;em&gt;learning ability&lt;/em&gt;. To talk a bit more, you will &lt;strong&gt;fix&lt;/strong&gt; commodity configuration, and then will run same experiment on the same number of machines with different size of data set you have (from starting from small chunk in size, incrementally increase the size until you reach the whole data set). &lt;/p&gt;

&lt;p&gt;To point on, you should avoid having &lt;em&gt;power distribution&lt;/em&gt; for result of performance test being run with different size of data sets. To avoid, you should carefully choose &lt;em&gt;step size&lt;/em&gt; (step size = amount of increments).&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;I have about ten machines available, each with 200 GB of free space on hard disk. However, I would like to perform experiments on a greater number of nodes, to measure scalability more precisely.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;For this type question, I have intuitively searched and read materials; afterwards, published as a &lt;a href="http://www.datasciencecentral.com/profiles/blogs/scale-your-vision" rel="nofollow"&gt;blog post&lt;/a&gt;. At the end of the post, I have briefly talked about how to test your hypothesis on real complex system. If you let me, I want to briefly talk about;&lt;/p&gt;

&lt;p&gt;First of all, &lt;strong&gt;base requirement&lt;/strong&gt; should be formed in order to run data set as a whole. The minimum requirement will build your &lt;strong&gt;baseline evaluation score&lt;/strong&gt; which is calculated with one/combination of evaluation metrics you have chosen, or with one/combination of methods being used to calculate &lt;code&gt;Running Time = Computation complexity + Communication cost + Synchronization cost&lt;/code&gt;. &lt;/p&gt;

&lt;p&gt;After those steps, with an &lt;em&gt;evaluation strategy&lt;/em&gt;, add new elements, e.g. new node, to the system you have doing scalability test; meanwhile, for each addition, measure performance w.r.t new system configuration. &lt;/p&gt;

&lt;p&gt;Just to note, &lt;em&gt;evaluation strategy&lt;/em&gt; must be planned along with considerations of default behavior of parallel and distributed systems. For example, what I mean by behavior is that adding just more cores will, after some point, automatically drop performance of the system not due to your algorithm characteristics. It is because more cores need more RAMs, more hard driver, or etc. In other words, there is a &lt;strong&gt;N-Way&lt;/strong&gt; relationship between hardware components. As a second example, adding more nodes to the distributed system will punished you with more communication and synchronization costs.&lt;/p&gt;

&lt;p&gt;As a last step, you will sketch two different graphs with your evaluation results via data analysis program or language (As a recommendation, use GNU Plot or R programming language). Print out and put those results at your desktop, and start to examine them, carefully. According to your investigation, modify/erase + rebuild &lt;em&gt;evaluation strategy&lt;/em&gt; and re-do the performance test.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Are there commodity services which would grant me that only my application would be running at a given time? Has anyone used such services yet?&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I have no much experiment on commodity services, but I can easily say whether it grants or not depends on your configuration of services. If you configured say Hadoop to your node as an only service, Hadoop will grant your code will be only running at any time.&lt;/p&gt;
', NULL, NULL, '''/a'':57C,241C ''/blockquote'':43C,215C,562C ''/code'':333C ''/em'':71C,85C,148C,171C,344C,377C,525C ''/p'':19C,42C,136C,177C,214C,272C,334C,369C,469C,533C,561C,612C ''/profiles/blogs/meeting-justice-of-data-science'':50C ''/profiles/blogs/scale-your-vision'':236C ''/strong'':76C,95C,280C,303C,444C ''200'':188C ''abil'':84C ''accord'':516C ''ad'':401C,453C ''add'':345C ''addit'':362C ''afterward'':228C ''algorithm'':419C ''along'':381C ''amount'':174C ''analysi'':487C ''anyon'':556C ''applic'':547C ''automat'':409C ''avail'':185C ''avoid'':143C,163C ''base'':278C ''baselin'':300C ''behavior'':386C,398C ''bit'':89C ''blockquot'':20C,178C,534C ''blog'':239C ''briefli'':250C,269C ''build'':297C ''calcul'':306C,323C ''care'':166C,515C ''characterist'':420C ''choos'':167C ''chosen'':314C ''chunk'':122C ''code'':324C,604C ''commod'':96C,538C,570C ''communic'':329C,465C ''complex'':260C,328C ''compon'':448C ''comput'':41C,327C ''configur'':97C,368C,585C,590C ''consider'':383C ''core'':404C,425C ''cost'':330C,332C,468C ''curv'':75C ''d'':36C ''data'':32C,69C,114C,134C,160C,288C,486C ''default'':385C ''depend'':582C ''desktop'':509C ''differ'':111C,157C,479C ''disk'':195C ''distribut'':39C,147C,390C,458C ''done'':60C ''driver'':431C ''drop'':410C ''due'':416C ''e.g'':348C ''easili'':575C ''element'':347C ''em'':65C,82C,145C,168C,341C,374C,522C ''end'':244C ''etc'':433C ''evalu'':66C,79C,301C,310C,342C,375C,483C,523C ''examin'':513C ''exampl'':393C,452C ''experi'':28C,81C,103C,202C,568C ''first'':274C ''fix'':94C ''form'':283C ''free'':191C ''gb'':189C ''given'':553C ''gnu'':495C ''grant'':542C,579C,602C ''graph'':480C ''greater'':205C ''hadoop'':592C,600C ''hard'':194C,430C ''hardwar'':447C ''help'':13C ''hope'':10C ''howev'':196C ''href'':47C,233C ''hypothesi'':257C ''increas'':126C ''increment'':125C,176C ''intuit'':223C ''investig'':519C ''languag'':490C,500C ''larg'':31C ''last'':473C ''learn'':74C,83C ''let'':264C ''like'':37C,199C ''m'':23C ''machin'':109C,184C ''materi'':227C ''may'':12C ''mean'':396C ''meanwhil'':359C ''measur'':210C,363C ''method'':67C,319C ''metric'':311C ''minimum'':294C ''modify/erase'':520C ''much'':567C ''must'':378C ''n'':442C ''n-way'':441C ''need'':426C ''new'':346C,349C,366C ''node'':208C,350C,455C,595C ''nofollow'':52C,238C ''note'':373C ''number'':107C,206C ''one'':53C ''one/combination'':308C,317C ''order'':285C ''p'':1C,21C,44C,137C,179C,216C,273C,335C,370C,470C,535C,563C ''parallel'':388C ''perform'':152C,201C,364C,411C,531C ''plan'':24C,380C ''plot'':496C ''point'':139C,408C ''post'':56C,240C,247C ''power'':146C ''precis'':213C ''print'':501C ''problem'':18C ''program'':488C,499C ''publish'':229C ''punish'':461C ''put'':504C ''question'':8C,220C ''r'':498C ''ram'':428C ''re'':528C ''re-do'':527C ''reach'':131C ''read'':226C ''real'':259C ''rebuild'':521C ''recommend'':493C ''rel'':51C,237C ''relationship'':445C ''requir'':279C,295C ''research'':61C ''result'':150C,484C,506C ''run'':26C,101C,155C,287C,325C,550C,608C ''say'':576C,591C ''scalabl'':211C,357C ''scienc'':70C ''score'':302C ''search'':224C ''second'':451C ''servic'':539C,559C,571C,587C,599C ''set'':33C,115C,135C,161C,289C ''size'':112C,124C,128C,158C,170C,173C ''sketch'':477C ''small'':121C ''solv'':16C ''space'':192C ''start'':119C,511C ''step'':169C,172C,338C,474C ''strategi'':343C,376C,524C ''strong'':73C,93C,277C,299C,440C ''synchron'':331C,467C ''system'':261C,353C,367C,391C,414C,459C ''talk'':87C,251C,270C ''ten'':183C ''test'':153C,255C,358C,532C ''thought'':5C ''time'':326C,554C,611C ''topic'':63C ''two'':478C ''type'':219C ''use'':321C,494C,557C ''via'':485C ''w.r.t'':365C ''want'':267C ''way'':443C ''whether'':577C ''whole'':133C,292C ''word'':436C ''would'':198C,541C,548C ''www.datasciencecentral.com'':49C,235C ''www.datasciencecentral.com/profiles/blogs/meeting-justice-of-data-science'':48C ''www.datasciencecentral.com/profiles/blogs/scale-your-vision'':234C ''yet'':560C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (680, 2, '2014-07-06 00:01:41.327', 4, NULL, NULL, '&lt;p&gt;There''s a number of semantic distance measures, each with its pros and cons. Here are just a few of them: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Cosine_similarity" rel="nofollow"&gt;&lt;strong&gt;cosine distance&lt;/strong&gt;&lt;/a&gt;, inner product between document feature vectors;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Latent_semantic_analysis" rel="nofollow"&gt;&lt;strong&gt;LSA&lt;/strong&gt;&lt;/a&gt;, another vector-based model, but utilizing SVD for de-noising original term-document matrix;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/WordNet" rel="nofollow"&gt;&lt;strong&gt;WordNet&lt;/strong&gt;&lt;/a&gt;-based, human verified, though hardly extensible.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Start with a simplest approach and then move further based on issues for your specific case. &lt;/p&gt;
', NULL, NULL, '''/a'':37C,56C,86C ''/li'':44C,74C,93C ''/p'':23C,112C ''/strong'':36C,55C,85C ''/ul'':94C ''/wiki/cosine_similarity'':30C ''/wiki/latent_semantic_analysis'':50C ''/wiki/wordnet'':80C ''anoth'':57C ''approach'':100C ''base'':60C,87C,105C ''case'':111C ''con'':15C ''cosin'':34C ''de'':67C ''de-nois'':66C ''distanc'':8C,35C ''document'':41C,72C ''en.wikipedia.org'':29C,49C,79C ''en.wikipedia.org/wiki/cosine_similarity'':28C ''en.wikipedia.org/wiki/latent_semantic_analysis'':48C ''en.wikipedia.org/wiki/wordnet'':78C ''extens'':92C ''featur'':42C ''hard'':91C ''href'':27C,47C,77C ''human'':88C ''inner'':38C ''issu'':107C ''li'':25C,45C,75C ''lsa'':54C ''matrix'':73C ''measur'':9C ''model'':61C ''move'':103C ''nofollow'':32C,52C,82C ''nois'':68C ''number'':5C ''origin'':69C ''p'':1C,95C ''product'':39C ''pros'':13C ''rel'':31C,51C,81C ''semant'':7C ''simplest'':99C ''specif'':110C ''start'':96C ''strong'':33C,53C,83C ''svd'':64C ''term'':71C ''term-docu'':70C ''though'':90C ''ul'':24C ''util'':63C ''vector'':43C,59C ''vector-bas'':58C ''verifi'':89C ''wordnet'':84C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (672, 2, '2014-07-04 06:46:19.18', 5, NULL, NULL, '&lt;p&gt;This setting is common in reliability, health care, and mortality. The statistical analysis method is called &lt;a href="http://en.wikipedia.org/wiki/Survival_analysis"&gt;Survival Analysis&lt;/a&gt;. All users are coded according to their start date (or week or month).  You use the empirical data to estimate the survival function, which is the probability that the time of defection is later than some specified time &lt;strong&gt;&lt;em&gt;t&lt;/em&gt;&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Your baseline model will estimate survival function for all users.  Then you can do more sophisticated modeling to estimate what factors or behaviors might predict defection (churn), given your baseline survival function.  Basically, any model that is predictive will yield a survival probability that is significantly lower than the baseline.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;There''s another approach which involves attempting to identify precursor events patterns or user behavior pattern that foreshadow defection. Any given event/behavior pattern might occur for users that defect, or for users that stay. For this analysis, you may need to censor your data to only include users that have been members for some minimum period of time. The minimum time period can be estimated using your estimate of survival function, or even simple histogram analysis of the distribution of membership period for users who have defected. &lt;/p&gt;
', NULL, NULL, '''/a'':25C ''/em'':67C ''/p'':69C,121C,211C ''/strong'':68C ''/wiki/survival_analysis'':22C ''accord'':30C ''analysi'':14C,24C,160C,199C ''anoth'':126C ''approach'':127C ''attempt'':130C ''baselin'':72C,100C,120C ''basic'':103C ''behavior'':93C,138C ''call'':17C ''care'':9C ''censor'':165C ''churn'':97C ''code'':29C ''common'':5C ''data'':43C,167C ''date'':34C ''defect'':57C,96C,142C,152C,210C ''distribut'':202C ''em'':65C ''empir'':42C ''en.wikipedia.org'':21C ''en.wikipedia.org/wiki/survival_analysis'':20C ''estim'':45C,75C,89C,188C,191C ''even'':196C ''event'':134C ''event/behavior'':145C ''factor'':91C ''foreshadow'':141C ''function'':48C,77C,102C,194C ''given'':98C,144C ''health'':8C ''histogram'':198C ''hr'':122C ''href'':19C ''identifi'':132C ''includ'':170C ''involv'':129C ''later'':59C ''lower'':117C ''may'':162C ''member'':175C ''membership'':204C ''method'':15C ''might'':94C,147C ''minimum'':178C,183C ''model'':73C,87C,105C ''month'':38C ''mortal'':11C ''need'':163C ''occur'':148C ''p'':1C,70C,123C ''pattern'':135C,139C,146C ''period'':179C,185C,205C ''precursor'':133C ''predict'':95C,108C ''probabl'':52C,113C ''reliabl'':7C ''set'':3C ''signific'':116C ''simpl'':197C ''sophist'':86C ''specifi'':62C ''start'':33C ''statist'':13C ''stay'':157C ''strong'':64C ''surviv'':23C,47C,76C,101C,112C,193C ''time'':55C,63C,181C,184C ''use'':40C,189C ''user'':27C,80C,137C,150C,155C,171C,207C ''week'':36C ''yield'':110C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (675, 2, '2014-07-04 22:46:32.13', 5, NULL, NULL, '&lt;p&gt;Just extract &lt;strong&gt;keywords&lt;/strong&gt; and train a &lt;strong&gt;classifier&lt;/strong&gt; on them. That''s all, really. &lt;/p&gt;

&lt;p&gt;Most of the text in CVs is not actually related to skills. E.g. consider sentence "I''m experienced and highly efficient in Java". Here only 1 out of 7 words is a skill name, the rest is just a noise that''s going to put your classification accuracy down. &lt;/p&gt;

&lt;p&gt;Most of CVs are not really structured. Or structured too freely. Or use unusual names for sections. Or file formats that don''t preserve structure when translated to text. I have experience extracting dates, times, names, addresses and even people intents from unstructured text, but not a skill (or university or anything) list, not even closely. &lt;/p&gt;

&lt;p&gt;So just tokenize (and possibly &lt;a href="http://stackoverflow.com/questions/16069406/text-mining-with-the-tm-package-word-stemming"&gt;stem&lt;/a&gt;) your CVs, select only words from predefined list (you can use LinkedIn or something similar to grab this list), create a feature vector and try out a couple of classifiers (say, SVM and Naive Bayes). &lt;/p&gt;

&lt;p&gt;(Note: I used a similar approach to classify LinkedIn profiles into more than 50 classes with accuracy &gt; 90%, so I''m pretty sure even naive implementation will work well.)&lt;/p&gt;
', NULL, NULL, '''/a'':141C ''/p'':19C,70C,128C,177C,208C ''/questions/16069406/text-mining-with-the-tm-package-word-stemming'':139C ''/strong'':6C,12C ''1'':46C ''50'':192C ''7'':49C ''90'':196C ''accuraci'':68C,195C ''actual'':29C ''address'':108C ''anyth'':123C ''approach'':184C ''bay'':176C ''class'':193C ''classif'':67C ''classifi'':11C,171C,186C ''close'':127C ''consid'':34C ''coupl'':169C ''creat'':161C ''cvs'':26C,74C,143C ''date'':105C ''e.g'':33C ''effici'':41C ''even'':110C,126C,202C ''experi'':103C ''experienc'':38C ''extract'':3C,104C ''featur'':163C ''file'':90C ''format'':91C ''freeli'':82C ''go'':63C ''grab'':158C ''high'':40C ''href'':136C ''implement'':204C ''intent'':112C ''java'':43C ''keyword'':5C ''linkedin'':153C,187C ''list'':124C,149C,160C ''m'':37C,199C ''naiv'':175C,203C ''name'':54C,86C,107C ''nois'':60C ''note'':179C ''p'':1C,20C,71C,129C,178C ''peopl'':111C ''possibl'':134C ''predefin'':148C ''preserv'':95C ''pretti'':200C ''profil'':188C ''put'':65C ''realli'':18C,77C ''relat'':30C ''rest'':56C ''say'':172C ''section'':88C ''select'':144C ''sentenc'':35C ''similar'':156C,183C ''skill'':32C,53C,119C ''someth'':155C ''stackoverflow.com'':138C ''stackoverflow.com/questions/16069406/text-mining-with-the-tm-package-word-stemming'':137C ''stem'':140C ''strong'':4C,10C ''structur'':78C,80C,96C ''sure'':201C ''svm'':173C ''text'':24C,100C,115C ''time'':106C ''token'':132C ''train'':8C ''translat'':98C ''tri'':166C ''univers'':121C ''unstructur'':114C ''unusu'':85C ''use'':84C,152C,181C ''vector'':164C ''well'':207C ''word'':50C,146C ''work'':206C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (676, 2, '2014-07-05 14:48:10.57', 4, NULL, NULL, '&lt;p&gt;In general regression models (any) can behave in an arbitrary way beyond the domain spanned by training samples. In particular, they are free to assume linearity of the modeled function, so if you for instance train a regression model with points:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;X     Y
10    0
20    1
30    2
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;it is reasonable to build a model &lt;code&gt;f(x) = x/10-1&lt;/code&gt;, which for &lt;code&gt;x&amp;lt;10&lt;/code&gt; returns negative values.&lt;/p&gt;

&lt;p&gt;The same applies "in between" your data points, it is always possible that due to the assumed famility of functions (which can be modeled by particular method) you will get values "out of your training samples". &lt;/p&gt;

&lt;p&gt;You can think about this in another way - "what is so special about negative values?", why do you find existance of negative values weird (if not provided in training set) while you don''t get alarmed by existance of lets say... value 2131.23? Unless developed in such a way, no model will treat negative values "different" than positive ones. This is just a natural element of the real values which can be attained as any other value.&lt;/p&gt;
', NULL, NULL, '''/code'':54C,68C,75C ''/p'':43C,79C,117C,196C ''/pre'':55C ''0'':49C ''1'':51C ''10'':48C,74C ''2'':53C ''20'':50C ''2131.23'':161C ''30'':52C ''alarm'':154C ''alway'':91C ''anoth'':125C ''appli'':83C ''arbitrari'':11C ''assum'':26C,97C ''attain'':191C ''behav'':8C ''beyond'':13C ''build'':61C ''code'':45C,64C,71C ''data'':87C ''develop'':163C ''differ'':174C ''domain'':15C ''due'':94C ''element'':183C ''exist'':138C,156C ''f'':65C ''famil'':98C ''find'':137C ''free'':24C ''function'':31C,100C ''general'':3C ''get'':110C,153C ''instanc'':36C ''let'':158C ''linear'':27C ''lt'':73C ''method'':107C ''model'':5C,30C,40C,63C,104C,169C ''natur'':182C ''negat'':77C,132C,140C,172C ''one'':177C ''p'':1C,56C,80C,118C ''particular'':21C,106C ''point'':42C,88C ''posit'':176C ''possibl'':92C ''pre'':44C ''provid'':145C ''real'':186C ''reason'':59C ''regress'':4C,39C ''return'':76C ''sampl'':19C,116C ''say'':159C ''set'':148C ''span'':16C ''special'':130C ''think'':121C ''train'':18C,37C,115C,147C ''treat'':171C ''unless'':162C ''valu'':78C,111C,133C,141C,160C,173C,187C,195C ''way'':12C,126C,167C ''weird'':142C ''x'':46C,66C,72C ''x/10-1'':67C ''y'':47C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8961, 1, '2015-11-21 07:17:14.963', 1, 23, 'Can we predict correlation between independent variables based on dependent variables', '&lt;p&gt;I have a correlation matrix for dependent var Vs independent variable as below:&lt;/p&gt;

&lt;p&gt;Year   1       Sales
Sales -0.5453  1.0000
PriceIndex    -0.6089 
Income        -0.5033 
Interest      -0.3842 &lt;/p&gt;

&lt;p&gt;As we can see that all independent var have negative correlation with the dependent variable. However as one of the requirement that i have for my project is that based on these values can i predict correlation between my independent variables.&lt;/p&gt;

&lt;p&gt;Kindly suggest if we can predict the same.
Thanks, Shivi&lt;/p&gt;
', 'statistics  correlation  linear-regression', NULL, '''-0.3842'':44C ''-0.5033'':42C ''-0.5453'':37C ''-0.6089'':40C ''/p'':31C,45C,88C,100C ''1'':34C ''1.0000'':38C ''base'':8A,76C ''correl'':4A,13B,21C,57C,83C ''depend'':10A,24C,60C ''howev'':62C ''incom'':41C ''independ'':6A,27C,53C,86C ''interest'':43C ''kind'':90C ''linear'':15B ''linear-regress'':14B ''matrix'':22C ''negat'':56C ''one'':64C ''p'':17C,32C,46C,89C ''predict'':3A,82C,95C ''priceindex'':39C ''project'':73C ''regress'':16B ''requir'':67C ''sale'':35C,36C ''see'':50C ''shivi'':99C ''statist'':12B ''suggest'':91C ''thank'':98C ''valu'':79C ''var'':25C,54C ''variabl'':7A,11A,28C,61C,87C ''vs'':26C ''year'':33C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (758, 1, '2014-07-16 20:09:08.64', 18, 2450, 'Tools and protocol for reproducible data science using Python', '&lt;p&gt;I am working on a data science project using Python.
The project has several stages.
Each stage comprises of taking a data set, using Python scripts, auxiliary data, configuration and parameters, and creating another data set.
I store the code in git, so that part is covered.
I would like to hear about:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Tools for data version control.&lt;/li&gt;
&lt;li&gt;Tools enabling to reproduce stages and experiments.&lt;/li&gt;
&lt;li&gt;Protocol and suggested directory structure for such a project.&lt;/li&gt;
&lt;li&gt;Automated build/run tools.&lt;/li&gt;
&lt;/ol&gt;
', 'python  tools  version-control  reproducibility', NULL, '''/li'':78C,87C,98C,103C ''/ol'':104C ''/p'':70C ''anoth'':50C ''autom'':100C ''auxiliari'':43C ''build/run'':101C ''code'':56C ''compris'':34C ''configur'':45C ''control'':14B,77C ''cover'':63C ''creat'':49C ''data'':6A,22C,38C,44C,51C,75C ''directori'':92C ''enabl'':81C ''experi'':86C ''git'':58C ''hear'':68C ''li'':72C,79C,88C,99C ''like'':66C ''ol'':71C ''p'':16C ''paramet'':47C ''part'':61C ''project'':24C,28C,97C ''protocol'':3A,89C ''python'':9A,10B,26C,41C ''reproduc'':5A,15B,83C ''scienc'':7A,23C ''script'':42C ''set'':39C,52C ''sever'':30C ''stage'':31C,33C,84C ''store'':54C ''structur'':93C ''suggest'':91C ''take'':36C ''tool'':1A,11B,73C,80C,102C ''use'':8A,25C,40C ''version'':13B,76C ''version-control'':12B ''work'':19C ''would'':65C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (677, 1, '2014-07-05 15:01:43.94', 3, 1076, 'Struggling to integrate sklearn and pandas in simple Kaggle task', '&lt;p&gt;I''m trying to use the sklearn_pandas module to extend the work I do in pandas and dip a toe into machine learning but I''m struggling with an error I don''t really understand how to fix.&lt;/p&gt;

&lt;p&gt;I was working through the following dataset on &lt;a href="https://www.kaggle.com/c/data-science-london-scikit-learn/data" rel="nofollow"&gt;Kaggle&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;It''s essentially an unheadered table (1000 rows, 40 features) with floating point values.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import pandas as pdfrom sklearn import neighbors
from sklearn_pandas import DataFrameMapper, cross_val_score
path_train ="../kaggle/scikitlearn/train.csv"
path_labels ="../kaggle/scikitlearn/trainLabels.csv"
path_test = "../kaggle/scikitlearn/test.csv"

train = pd.read_csv(path_train, header=None)
labels = pd.read_csv(path_labels, header=None)
test = pd.read_csv(path_test, header=None)
mapper_train = DataFrameMapper([(list(train.columns),neighbors.KNeighborsClassifier(n_neighbors=3))])
mapper_train
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Output:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;DataFrameMapper(features=[([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], KNeighborsClassifier(algorithm=''auto'', leaf_size=30, metric=''minkowski'',
       n_neighbors=3, p=2, weights=''uniform''))])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So far so good. But then I try the fit&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;mapper_train.fit_transform(train, labels)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Output:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
&amp;lt;ipython-input-6-e3897d6db1b5&amp;gt; in &amp;lt;module&amp;gt;()
----&amp;gt; 1 mapper_train.fit_transform(train, labels)

//anaconda/lib/python2.7/site-packages/sklearn/base.pyc in fit_transform(self, X, y,     **fit_params)
    409         else:
    410             # fit method of arity 2 (supervised transformation)
--&amp;gt; 411             return self.fit(X, y, **fit_params).transform(X)
    412 
    413 

//anaconda/lib/python2.7/site-packages/sklearn_pandas/__init__.pyc in fit(self, X, y)
    116         for columns, transformer in self.features:
    117             if transformer is not None:
--&amp;gt; 118                 transformer.fit(self._get_col_subset(X, columns))
    119         return self
    120 

TypeError: fit() takes exactly 3 arguments (2 given)`
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What am I doing wrong? While the data in this case is all the same, I''m planning to work up a workflow for mixtures categorical, nominal and floating point features and sklearn_pandas seemed to be a logical fit.&lt;/p&gt;
', 'python  pandas  sklearn', NULL, '''/a'':72C ''/anaconda/lib/python2.7/site-packages/sklearn/base.pyc'':262C ''/anaconda/lib/python2.7/site-packages/sklearn_pandas/__init__.pyc'':293C ''/c/data-science-london-scikit-learn/data'':68C ''/code'':148C,212C,232C,329C ''/kaggle/scikitlearn/test.csv'':115C ''/kaggle/scikitlearn/train.csv'':109C ''/kaggle/scikitlearn/trainlabels.csv'':112C ''/p'':54C,73C,89C,152C,225C,236C,372C ''/pre'':149C,213C,233C,330C ''0'':157C ''1'':158C,257C ''10'':167C ''1000'':81C ''11'':168C ''116'':299C ''117'':305C ''118'':312C ''119'':317C ''12'':169C ''120'':320C ''13'':170C ''14'':171C ''15'':172C ''16'':173C ''17'':174C ''18'':175C ''19'':176C ''2'':159C,209C,278C,327C ''20'':177C ''21'':178C ''22'':179C ''23'':180C ''24'':181C ''25'':182C ''26'':183C ''27'':184C ''28'':185C ''29'':186C ''3'':145C,160C,207C,325C ''30'':187C,202C ''31'':188C ''32'':189C ''33'':190C ''34'':191C ''35'':192C ''36'':193C ''37'':194C ''38'':195C ''39'':196C ''4'':161C ''40'':83C ''409'':271C ''410'':273C ''411'':282C ''412'':291C ''413'':292C ''5'':162C ''6'':163C,249C ''7'':164C ''8'':165C ''9'':166C ''algorithm'':198C ''argument'':326C ''ariti'':277C ''auto'':199C ''call'':243C ''case'':342C ''categor'':357C ''code'':91C,154C,227C,238C ''column'':301C,316C ''cross'':104C ''csv'':118C,125C,132C ''data'':339C ''dataframemapp'':103C,139C,155C ''dataset'':62C ''dip'':33C ''e3897d6db1b5'':250C ''els'':272C ''error'':45C ''essenti'':77C ''exact'':324C ''extend'':25C ''far'':216C ''featur'':84C,156C,362C ''fit'':224C,264C,269C,274C,287C,295C,322C,371C ''fix'':53C ''float'':86C,360C ''follow'':61C ''given'':328C ''good'':218C ''gt'':251C,255C,256C,281C,311C ''header'':121C,128C,135C ''href'':65C ''import'':92C,97C,102C ''input'':248C ''integr'':3A ''ipython'':247C ''ipython-input'':246C ''kaggl'':9A,71C ''kneighborsclassifi'':197C ''label'':111C,123C,127C,231C,261C ''last'':244C ''leaf'':200C ''learn'':38C ''list'':140C ''logic'':370C ''lt'':245C,253C ''m'':16C,41C,348C ''machin'':37C ''mapper'':137C,146C ''mapper_train.fit'':228C,258C ''method'':275C ''metric'':203C ''minkowski'':204C ''mixtur'':356C ''modul'':23C,254C ''n'':143C,205C ''neighbor'':98C,144C,206C ''neighbors.kneighborsclassifier'':142C ''nofollow'':70C ''nomin'':358C ''none'':122C,129C,136C,310C ''output'':151C,235C ''p'':14C,55C,74C,150C,208C,214C,234C,331C ''panda'':6A,12B,22C,31C,93C,101C,365C ''param'':270C,288C ''path'':107C,110C,113C,119C,126C,133C ''pd.read'':117C,124C,131C ''pdfrom'':95C ''plan'':349C ''point'':87C,361C ''pre'':90C,153C,226C,237C ''python'':11B ''realli'':49C ''recent'':242C ''rel'':69C ''return'':283C,318C ''row'':82C ''score'':106C ''seem'':366C ''self'':266C,296C,319C ''self._get_col_subset'':314C ''self.features'':304C ''self.fit'':284C ''simpl'':8A ''size'':201C ''sklearn'':4A,13B,21C,96C,100C,364C ''struggl'':1A,42C ''supervis'':279C ''tabl'':80C ''take'':323C ''task'':10A ''test'':114C,130C,134C ''toe'':35C ''traceback'':240C ''train'':108C,116C,120C,138C,147C,230C,260C ''train.columns'':141C ''transform'':229C,259C,265C,280C,289C,302C,307C ''transformer.fit'':313C ''tri'':17C,222C ''typeerror'':239C,321C ''understand'':50C ''unhead'':79C ''uniform'':211C ''use'':19C ''val'':105C ''valu'':88C ''weight'':210C ''work'':27C,58C,351C ''workflow'':354C ''wrong'':336C ''www.kaggle.com'':67C ''www.kaggle.com/c/data-science-london-scikit-learn/data'':66C ''x'':267C,285C,290C,297C,315C ''y'':268C,286C,298C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (678, 1, '2014-07-05 16:10:21.58', 12, 3293, 'What are some standard ways of computing the distance between documents?', '&lt;p&gt;When I say "document", I have in mind web pages like Wikipedia articles and news stories.  I prefer answers giving either vanilla lexical distance metrics or state-of-the-art semantic distance metrics, with stronger preference for the latter.&lt;/p&gt;
', 'machine-learning  data-mining  nlp  text-mining  similarity', NULL, '''/p'':64C ''answer'':42C ''art'':54C ''articl'':36C ''comput'':7A ''data'':16B ''data-min'':15B ''distanc'':9A,47C,56C ''document'':11A,27C ''either'':44C ''give'':43C ''latter'':63C ''learn'':14B ''lexic'':46C ''like'':34C ''machin'':13B ''machine-learn'':12B ''metric'':48C,57C ''mind'':31C ''mine'':17B,21B ''news'':38C ''nlp'':18B ''p'':23C ''page'':33C ''prefer'':41C,60C ''say'':26C ''semant'':55C ''similar'':22B ''standard'':4A ''state'':51C ''state-of-the-art'':50C ''stori'':39C ''stronger'':59C ''text'':20B ''text-min'':19B ''vanilla'':45C ''way'':5A ''web'':32C ''wikipedia'':35C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (679, 1, '2014-07-05 16:20:17.963', 7, 110, 'What are some standard ways of computing the distance between individual search queries?', '&lt;p&gt;I made a similar question asking about distance between "documents" (Wikipedia articles, news stories, etc.).  I made this a separate question because search queries are considerably smaller than documents and are considerably noisier.  I hence don''t know (and doubt) if the same distance metrics would be used here.  &lt;/p&gt;

&lt;p&gt;Either vanilla lexical distance metrics or state-of-the-art semantic distance metrics are preferred, with stronger preference for the latter.&lt;/p&gt;
', 'machine-learning  nlp  search', NULL, '''/p'':69C,93C ''art'':81C ''articl'':31C ''ask'':25C ''comput'':7A ''consider'':45C,51C ''distanc'':9A,27C,63C,74C,83C ''document'':29C,48C ''doubt'':59C ''either'':71C ''etc'':34C ''henc'':54C ''individu'':11A ''know'':57C ''latter'':92C ''learn'':16B ''lexic'':73C ''machin'':15B ''machine-learn'':14B ''made'':21C,36C ''metric'':64C,75C,84C ''news'':32C ''nlp'':17B ''noisier'':52C ''p'':19C,70C ''prefer'':86C,89C ''queri'':13A,43C ''question'':24C,40C ''search'':12A,18B,42C ''semant'':82C ''separ'':39C ''similar'':23C ''smaller'':46C ''standard'':4A ''state'':78C ''state-of-the-art'':77C ''stori'':33C ''stronger'':88C ''use'':67C ''vanilla'':72C ''way'':5A ''wikipedia'':30C ''would'':65C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (682, 2, '2014-07-06 14:01:57.467', 1, NULL, NULL, '&lt;p&gt;Michael Maouboussin, in his book, "The Success Equation," looks at differentiating luck from skill in various endeavors, including sports.  He actually ranks sports by the amount of luck that contributes to performance in the different sports (p. 23) and about 2/3 of performance in football is attributable to skill.  By contrast, I used MM''s technique to analyze performance in Formula 1 racing, and found that 60% is attributable to skill (less than I was expecting.)&lt;/p&gt;

&lt;p&gt;That said, it seems this kind of analysis would imply that a sufficiently detailed and crafted feature set would allow ML algorithms to predict performance of NFL teams, perhaps even to the play level, with the caveat that significant variance will still exist because of the influence of luck in the game.&lt;/p&gt;
', NULL, NULL, '''/p'':78C,132C ''1'':63C ''2/3'':42C ''23'':39C ''60'':68C ''actual'':22C ''algorithm'':101C ''allow'':99C ''amount'':27C ''analysi'':87C ''analyz'':59C ''attribut'':48C,70C ''book'':6C ''caveat'':116C ''contrast'':52C ''contribut'':31C ''craft'':95C ''detail'':93C ''differ'':36C ''differenti'':12C ''endeavor'':18C ''equat'':9C ''even'':109C ''exist'':122C ''expect'':77C ''featur'':96C ''footbal'':46C ''formula'':62C ''found'':66C ''game'':131C ''impli'':89C ''includ'':19C ''influenc'':126C ''kind'':85C ''less'':73C ''level'':113C ''look'':10C ''luck'':13C,29C,128C ''maouboussin'':3C ''michael'':2C ''ml'':100C ''mm'':55C ''nfl'':106C ''p'':1C,38C,79C ''perform'':33C,44C,60C,104C ''perhap'':108C ''play'':112C ''predict'':103C ''race'':64C ''rank'':23C ''said'':81C ''seem'':83C ''set'':97C ''signific'':118C ''skill'':15C,50C,72C ''sport'':20C,24C,37C ''still'':121C ''success'':8C ''suffici'':92C ''team'':107C ''techniqu'':57C ''use'':54C ''varianc'':119C ''various'':17C ''would'':88C,98C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (683, 1, '2014-07-07 09:34:36.95', 9, 136, 'Build a binary classifier with only positive and unlabeled data', '&lt;p&gt;I have 2 datasets, one with positive instances of what I would like to detect, and one with unlabeled instances. What methods can I use ?&lt;/p&gt;

&lt;p&gt;As an example, suppose we want to understand detect spam email based on a few structured email characteristics. We have one dataset of 10000 spam emails, and one dataset of 100000 emails for which we don''t know whether they are spam or not.&lt;/p&gt;

&lt;p&gt;How can we tackle this problem (without labeling manually any of the unlabeled data) ?&lt;/p&gt;

&lt;p&gt;What can we do if we have additional information about the proportion of spam in the unlabeled data (i.e. what if we estimate that between 20-40% of the 100000 unlabeled emails are spam) ?&lt;/p&gt;
', 'classification  semi-supervised-learning', NULL, '''-40'':132C ''/p'':42C,88C,104C,140C ''10000'':67C ''100000'':74C,135C ''2'':19C ''20'':131C ''addit'':113C ''base'':55C ''binari'':3A ''build'':1A ''characterist'':61C ''classif'':11B ''classifi'':4A ''data'':10A,103C,123C ''dataset'':20C,65C,72C ''detect'':31C,52C ''email'':54C,60C,69C,75C,137C ''estim'':128C ''exampl'':46C ''i.e'':124C ''inform'':114C ''instanc'':24C,36C ''know'':81C ''label'':97C ''learn'':15B ''like'':29C ''manual'':98C ''method'':38C ''one'':21C,33C,64C,71C ''p'':16C,43C,89C,105C ''posit'':7A,23C ''problem'':95C ''proport'':117C ''semi'':13B ''semi-supervised-learn'':12B ''spam'':53C,68C,85C,119C,139C ''structur'':59C ''supervis'':14B ''suppos'':47C ''tackl'':93C ''understand'':51C ''unlabel'':9A,35C,102C,122C,136C ''use'':41C ''want'':49C ''whether'':82C ''without'':96C ''would'':28C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (684, 2, '2014-07-07 10:25:55.323', 6, NULL, NULL, '&lt;p&gt;I have never used &lt;code&gt;sklearn_pandas&lt;/code&gt;, but from reading their source code, it looks like this is a bug on their side. If you look for &lt;a href="https://github.com/paulgb/sklearn-pandas/blob/master/sklearn_pandas/__init__.py"&gt;the function that is throwing the exception&lt;/a&gt;, you can notice that they are discarding the &lt;code&gt;y&lt;/code&gt; argument (it does not even survive until the docstring), and the inner &lt;code&gt;fit&lt;/code&gt; function expects one argument more, which is probably &lt;code&gt;y&lt;/code&gt;:&lt;/p&gt;



&lt;pre&gt;&lt;code&gt;def fit(self, X, y=None):
    ''''''
    Fit a transformation from the pipeline

    X       the data to fit
    ''''''
    for columns, transformer in self.features:
        if transformer is not None:
            transformer.fit(self._get_col_subset(X, columns))
    return self
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I would recommend that you open an issue in &lt;a href="https://github.com/paulgb/sklearn-pandas/issues"&gt;their bug tracker&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;You can test this if you run your code from IPython. To summarize, if you use the &lt;code&gt;%pdb on&lt;/code&gt; magic right before you run the problematic call, the exception is captured by the Python debugger, so you can play around a bit and see that calling the &lt;code&gt;fit&lt;/code&gt; function with the label values &lt;code&gt;y[0]&lt;/code&gt; does work  -- see the last line with the &lt;code&gt;pdb&amp;gt;&lt;/code&gt; prompt. (The CSV files are downloaded from Kaggle, except for the largest one which is just a part of the real file).&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;In [1]: import pandas as pd

In [2]: from sklearn import neighbors

In [3]: from sklearn_pandas import DataFrameMapper, cross_val_score

In [4]: path_train ="train.csv"

In [5]: path_labels ="trainLabels.csv"

In [6]: path_test = "test.csv"

In [7]: train = pd.read_csv(path_train, header=None)

In [8]: labels = pd.read_csv(path_labels, header=None)

In [9]: test = pd.read_csv(path_test, header=None)

In [10]: mapper_train = DataFrameMapper([(list(train.columns),neighbors.KNeighborsClassifier(n_neighbors=3))])

In [13]: %pdb on

In [14]: mapper_train.fit_transform(train, labels)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
&amp;lt;ipython-input-14-e3897d6db1b5&amp;gt; in &amp;lt;module&amp;gt;()
----&amp;gt; 1 mapper_train.fit_transform(train, labels)

/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/base.pyc in fit_transform(self, X, y, **fit_params)
    409         else:
    410             # fit method of arity 2 (supervised transformation)
--&amp;gt; 411             return self.fit(X, y, **fit_params).transform(X)
    412
    413

/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn_pandas/__init__.pyc in fit(self, X, y)
    116         for columns, transformer in self.features:
    117             if transformer is not None:
--&amp;gt; 118                 transformer.fit(self._get_col_subset(X, columns))
    119         return self
    120

TypeError: fit() takes exactly 3 arguments (2 given)
&amp;gt; /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn_pandas/__init__.py(118)fit()
    117             if transformer is not None:
--&amp;gt; 118                 transformer.fit(self._get_col_subset(X, columns))
    119         return self

ipdb&amp;gt; l
    113
    114         X       the data to fit
    115         ''''''
    116         for columns, transformer in self.features:
    117             if transformer is not None:
--&amp;gt; 118                 transformer.fit(self._get_col_subset(X, columns))
    119         return self
    120
    121
    122     def transform(self, X):
    123         ''''''
ipdb&amp;gt; transformer.fit(self._get_col_subset(X, columns), y[0])
KNeighborsClassifier(algorithm=''auto'', leaf_size=30, metric=''minkowski'',
           n_neighbors=3, p=2, weights=''uniform'')
&lt;/code&gt;&lt;/pre&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (685, 1, '2014-07-07 11:43:48.43', 4, 642, 'Stochastic gradient descent in logistic regression', '&lt;p&gt;I am very new to machine learning and in my first project have stumbled across a lot of issues which I really want to get through.&lt;/p&gt;

&lt;p&gt;I''m using logistic regression with R''s &lt;code&gt;glmnet&lt;/code&gt; package and alpha = 0 for ridge regression.&lt;/p&gt;

&lt;p&gt;I''m using ridge regression actually since lasso deleted all my variables and gave very low area under curve (0.52) but with ridge there isn''t much of a difference (0.61).&lt;/p&gt;

&lt;p&gt;My dependent variable/output is probability of click, based on if there is a click or not in historical data.&lt;/p&gt;

&lt;p&gt;The independent variables are state, city, device, user age, user gender, IP carrier, keyword, mobile manufacturer, ad template, browser version, browser family, OS version and OS family.&lt;/p&gt;

&lt;p&gt;Of these, for prediction I''m using state, device, user age, user gender, IP carrier, browser version, browser family, OS version and OS family; I am not using keyword or template since we want to reject a user request before deep diving in our system and selecting a keyword or template. I am not using city because they are too many or mobile manufacturer because they are too few.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is that okay or should I be using the rejected variables?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;To start, I create a sparse matrix from my variables which are mapped against the column of clicks that have yes or no values.&lt;/p&gt;

&lt;p&gt;After training the model, I save the coefficients and intercept. These are used for new incoming requests using the formula for logistic regression:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;img src="http://i.stack.imgur.com/AZBRq.png" alt="1 / (1+e^-1*sum(a+k(ith)*x(ith)))"&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Where &lt;code&gt;a&lt;/code&gt; is intercept, &lt;code&gt;k&lt;/code&gt; is the &lt;code&gt;i&lt;/code&gt;th coefficient and &lt;code&gt;x&lt;/code&gt; is the &lt;code&gt;i&lt;/code&gt;th variable value.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Is my approach correct so far?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Simple GLM in R (that is where there is no regularized regression, right?) gave me 0.56 AUC. With regularization I get 0.61 but there is no distinct threshold where we could say that above 0.xx its mostly ones and below it most zeros are covered; actually, the max probability that a click didn''t happen is almost always greater than the max probability that a click happened.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;So basically what should I do?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I have read how stochastic gradient descent is an effective technique in logit so how do I implement stochastic gradient descent in R? If it''s not straightforward, is there a way to implement this system in Python? Is SGD implemented after generating a regularized logistic regression model or is it a different process altogether?&lt;/p&gt;

&lt;p&gt;Also there is an algorithm called follow the regularized leader (FTRL) that is used in click-through rate prediction. Is there a sample code and use of FTRL that I could go through?&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (686, 2, '2014-07-07 13:23:28.22', 3, NULL, NULL, '&lt;p&gt;State of the art appears to be "paragraph vectors" introduced in a recent paper: &lt;a href="http://cs.stanford.edu/~quocle/paragraph_vector.pdf" rel="nofollow"&gt;http://cs.stanford.edu/~quocle/paragraph_vector.pdf&lt;/a&gt;. Cosine/Euclidean distance between paragraph vectors would likely work better than any other approach. This probably isn''t feasible yet due to lack of open source implementations.&lt;/p&gt;

&lt;p&gt;Next best thing is cosine distance between LSA vectors or cosine distance between raw BOW vectors. Sometimes it works better to choose different weighting schemes, like TF-IDF. &lt;/p&gt;
', NULL, NULL, '''/p'':52C,83C ''/~quocle/paragraph_vector.pdf'':20C ''/~quocle/paragraph_vector.pdf&lt;/a&gt;.'':25C ''appear'':6C ''approach'':38C ''art'':5C ''best'':55C ''better'':34C,73C ''bow'':68C ''choos'':75C ''cosin'':58C,64C ''cosine/euclidean'':26C ''cs.stanford.edu'':19C,24C ''cs.stanford.edu/~quocle/paragraph_vector.pdf'':18C ''cs.stanford.edu/~quocle/paragraph_vector.pdf&lt;/a&gt;.'':23C ''differ'':76C ''distanc'':27C,59C,65C ''due'':45C ''feasibl'':43C ''href'':17C ''idf'':82C ''implement'':51C ''introduc'':11C ''isn'':41C ''lack'':47C ''like'':32C,79C ''lsa'':61C ''next'':54C ''nofollow'':22C ''open'':49C ''p'':1C,53C ''paper'':15C ''paragraph'':9C,29C ''probabl'':40C ''raw'':67C ''recent'':14C ''rel'':21C ''scheme'':78C ''sometim'':70C ''sourc'':50C ''state'':2C ''tf'':81C ''tf-idf'':80C ''thing'':56C ''vector'':10C,30C,62C,69C ''weight'':77C ''work'':33C,72C ''would'':31C ''yet'':44C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10394, 1, '2016-02-25 18:11:34.943', -1, 75, 'which technology should I invest into SAS In-memory analytics or R?', '&lt;p&gt;As I''m starting my career in analytics, I have to choose between SAS in-memory analytics and Openware and largely adopted R, which one should I choose now so that I will have good market value in short-term as well as long-term?&lt;/p&gt;
', 'r  sas', NULL, '''/p'':65C ''adopt'':39C ''analyt'':11A,24C,34C ''career'':22C ''choos'':28C,45C ''good'':52C ''in-memori'':8A,31C ''invest'':5A ''larg'':38C ''long'':63C ''long-term'':62C ''m'':19C ''market'':53C ''memori'':10A,33C ''one'':42C ''openwar'':36C ''p'':16C ''r'':13A,14B,40C ''sas'':7A,15B,30C ''short'':57C ''short-term'':56C ''start'':20C ''technolog'':2A ''term'':58C,64C ''valu'':54C ''well'':60C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (693, 2, '2014-07-07 18:36:23.43', 3, NULL, NULL, '&lt;p&gt;I work for an online jobs site and we build solutions to recommend jobs based on resumes. Our approach take''s a person''s job title (or desired job title if a student and known), along with skills we extract from their resume, and their location (which is very important to most people) and find matches with jobs based on that. &lt;/p&gt;

&lt;p&gt;in terms of document classification, I would take a similar approach. I would recommend computing a tf idf matrix for each resume as a standard bag of words model, extracting just the person''s job title and skills (for which you will need to define a list of skills to look for), and feed that into a ML algorithm. I would recommend trying knn, and an SVM, the latter works very well with high dimensional text data. Linear SVM''s tend to do better than non-linear (e.g. using RBf kernels). If you have that outputting reasonable results, I would then play with extracting features using a natural language parser \ chunker, and also some custom built phrases matched by regex''s.&lt;/p&gt;
', NULL, NULL, '''/p'':63C,187C ''algorithm'':123C ''along'':37C ''also'':178C ''approach'':20C,75C ''bag'':90C ''base'':16C,60C ''better'':148C ''build'':11C ''built'':181C ''chunker'':176C ''classif'':69C ''comput'':79C ''custom'':180C ''data'':141C ''defin'':109C ''desir'':29C ''dimension'':139C ''document'':68C ''e.g'':153C ''extract'':41C,94C,169C ''featur'':170C ''feed'':118C ''find'':56C ''high'':138C ''idf'':82C ''import'':51C ''job'':7C,15C,26C,30C,59C,99C ''kernel'':156C ''knn'':128C ''known'':36C ''languag'':174C ''latter'':133C ''linear'':142C,152C ''list'':111C ''locat'':47C ''look'':115C ''match'':57C,183C ''matrix'':83C ''ml'':122C ''model'':93C ''natur'':173C ''need'':107C ''non'':151C ''non-linear'':150C ''onlin'':6C ''output'':161C ''p'':1C,64C ''parser'':175C ''peopl'':54C ''person'':24C,97C ''phrase'':182C ''play'':167C ''rbf'':155C ''reason'':162C ''recommend'':14C,78C,126C ''regex'':185C ''result'':163C ''resum'':18C,44C,86C ''similar'':74C ''site'':8C ''skill'':39C,102C,113C ''solut'':12C ''standard'':89C ''student'':34C ''svm'':131C,143C ''take'':21C,72C ''tend'':145C ''term'':66C ''text'':140C ''tf'':81C ''titl'':27C,31C,100C ''tri'':127C ''use'':154C,171C ''well'':136C ''word'':92C ''work'':3C,134C ''would'':71C,77C,125C,165C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (694, 1, '2014-07-07 19:17:04.973', 60, 22834, 'Best python library for neural networks', '&lt;p&gt;I''m using Neural Networks to solve different Machine learning problems. I''m using Python and &lt;a href="http://pybrain.org/"&gt;pybrain&lt;/a&gt; but this library is almost discontinued. Are there other good alternatives in Python?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'machine-learning  python  neuralnetwork', NULL, '''/a'':33C ''/p'':47C,50C ''almost'':38C ''altern'':44C ''best'':1A ''differ'':20C ''discontinu'':39C ''good'':43C ''href'':30C ''learn'':9B,22C ''librari'':3A,36C ''m'':14C,25C ''machin'':8B,21C ''machine-learn'':7B ''network'':6A,17C ''neural'':5A,16C ''neuralnetwork'':11B ''p'':12C,48C ''problem'':23C ''pybrain'':32C ''pybrain.org'':31C ''python'':2A,10B,27C,46C ''solv'':19C ''thank'':49C ''use'':15C,26C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (687, 2, '2014-07-07 14:15:51.117', 7, NULL, NULL, '&lt;p&gt;My suggestion would be to attempt to build some kind of clustering on your unlabeled data that somewhat approximates a labelled dataset. The rationale is more or less as follows:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;You have some feature vector for representing your documents&lt;/li&gt;
&lt;li&gt;Based on that feature vector, you can come up with a number of different clusterings, with either fuzzy, rough, or class-based clustering methods&lt;/li&gt;
&lt;li&gt;Knowing what a positive example looks like, you can quickly evaluate the overall similarity of a cluster to your positive cluster&lt;/li&gt;
&lt;li&gt;Knowing that there should really only be two clusters, you can adjust the hyperparameters on your clustering method so that the above two metrics are closer and closer to satisfaction&lt;/li&gt;
&lt;li&gt;With the two clusters, you have what is likely a close approximation of a labelled dataset, which you can then use as a silver-standard corpus of sorts to actually train your model&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Hope that makes sense, if you''re specifically looking for clustering algorithms, a few that I personally enjoy that might be good in this scenario are &lt;a href="http://en.wikipedia.org/wiki/FLAME_clustering"&gt;FLAME&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding"&gt;tsne&lt;/a&gt;. Alternately, looking at the spectacular &lt;a href="http://radimrehurek.com/gensim/"&gt;gensim&lt;/a&gt; library in python will get you a long way toward the clustering you''re looking for.&lt;/p&gt;

&lt;p&gt;Hope that helps and makes sense, leave a comment if you''ve got any questions.&lt;/p&gt;
', NULL, NULL, '''/a'':197C,205C,217C ''/gensim/'':215C ''/li'':44C,71C,94C,126C,162C ''/p'':32C,234C,251C ''/ul'':163C ''/wiki/flame_clustering'':195C ''/wiki/t-distributed_stochastic_neighbor_embedding'':203C ''actual'':158C ''adjust'':107C ''algorithm'':176C ''altern'':206C ''approxim'':20C,139C ''attempt'':7C ''base'':46C,68C ''build'':9C ''class'':67C ''class-bas'':66C ''close'':138C ''closer'':121C,123C ''cluster'':13C,60C,69C,89C,93C,104C,112C,131C,175C,229C ''come'':53C ''comment'':244C ''corpus'':154C ''data'':17C ''dataset'':23C,143C ''differ'':59C ''document'':43C ''either'':62C ''en.wikipedia.org'':194C,202C ''en.wikipedia.org/wiki/flame_clustering'':193C ''en.wikipedia.org/wiki/t-distributed_stochastic_neighbor_embedding'':201C ''enjoy'':182C ''evalu'':83C ''exampl'':77C ''featur'':38C,49C ''flame'':196C ''follow'':31C ''fuzzi'':63C ''gensim'':216C ''get'':222C ''good'':186C ''got'':248C ''help'':238C ''hope'':165C,236C ''href'':192C,200C,212C ''hyperparamet'':109C ''kind'':11C ''know'':73C,96C ''label'':22C,142C ''leav'':242C ''less'':29C ''li'':34C,45C,72C,95C,127C ''librari'':218C ''like'':79C,136C ''long'':225C ''look'':78C,173C,207C,232C ''make'':167C,240C ''method'':70C,113C ''metric'':119C ''might'':184C ''model'':161C ''number'':57C ''overal'':85C ''p'':1C,164C,235C ''person'':181C ''posit'':76C,92C ''python'':220C ''question'':250C ''quick'':82C ''radimrehurek.com'':214C ''radimrehurek.com/gensim/'':213C ''rational'':25C ''re'':171C,231C ''realli'':100C ''repres'':41C ''rough'':64C ''satisfact'':125C ''scenario'':189C ''sens'':168C,241C ''silver'':152C ''silver-standard'':151C ''similar'':86C ''somewhat'':19C ''sort'':156C ''specif'':172C ''spectacular'':210C ''standard'':153C ''suggest'':3C ''toward'':227C ''train'':159C ''tsne'':204C ''two'':103C,118C,130C ''ul'':33C ''unlabel'':16C ''use'':148C ''ve'':247C ''vector'':39C,50C ''way'':226C ''would'':4C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (688, 2, '2014-07-07 14:41:59.523', 2, NULL, NULL, '&lt;p&gt;As @SpacedMan has noted &lt;a href="http://datascience.stackexchange.com/questions/600/how-do-you-create-an-optimized-walk-list-given-longitude-and-latitude-coordinate#comment1670_600"&gt;in a comment&lt;/a&gt;, the street layout will have a massive influence on the optimization of the walk list.  You have included only "latitude and longitude" in your question''s title; but solving that problem does not lead to a "walk list", but to a "as-the-crow-flies list".&lt;/p&gt;

&lt;p&gt;Looking at your street layout as a graph, with edge weights describing distances, and trying to find the shortest traversal between all required addresses, will lead you to think of your problem as a "&lt;a href="http://en.wikipedia.org/wiki/Shortest_path_problem" rel="nofollow"&gt;Shortest path problem&lt;/a&gt;". &lt;a href="http://en.wikipedia.org/wiki/Dijkstra%27s_algorithm" rel="nofollow"&gt;Dijkstra''s algorithm&lt;/a&gt; is the best known solution (there are others); in its naive implementation it converges in &lt;em&gt;O(n&lt;sup&gt;2&lt;/sup&gt;)&lt;/em&gt;, which may be acceptable if your lists of addresses are moderate in size.  Otherwise, look for optimized versions in the above links.&lt;/p&gt;

&lt;p&gt;As for libraries and resources to start tackling the problem, since you do not specify languages or platforms, let me point to the &lt;a href="http://wiki.openstreetmap.org/wiki/Routing#Developers" rel="nofollow"&gt;compilation of routing solvers in the Open Street Maps wiki&lt;/a&gt; and in general &lt;a href="http://wiki.openstreetmap.org/wiki/Frameworks" rel="nofollow"&gt;their frameworks and libraries page&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (689, 2, '2014-07-07 15:36:40.96', 26, NULL, NULL, '&lt;p&gt;There''s a number of different ways of going about this depending on exactly how much semantic information you want to retain and how easy your documents are to tokenize (html documents would probably be pretty difficult to tokenize, but you could conceivably do something with tags and context.)&lt;/p&gt;

&lt;p&gt;Some of them have been mentioned by ffriend, and the paragraph vectors by user1133029 is a really solid one, but I just figured I would go into some more depth about plusses and minuses of different approaches.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Cosine_similarity"&gt;Cosine Distance&lt;/a&gt; - Tried a true, cosine distance is probably the most common distance metric used generically across multiple domains. With that said, there''s very little information in cosine distance that can actually be mapped back to anything semantic, which seems to be non-ideal for this situation.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Levenshtein_distance"&gt;Levenshtein Distance&lt;/a&gt; - Also known as &lt;code&gt;edit distance&lt;/code&gt;, this is usually just used on the individual token level (words, bigrams, etc...). In general I wouldn''t recommend this metric as it not only discards any semantic information, but also tends to treat very different word alterations very similarly, but it is an extremely common metric for this kind of thing&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Latent_semantic_analysis"&gt;LSA&lt;/a&gt; - Is a part of a large arsenal of techniques when it comes to evaluating document similarity called &lt;code&gt;topic modeling&lt;/code&gt;. LSA has gone out of fashion pretty recently, and in my experience, it''s not quite the strongest topic modeling approach, but it is relatively straightforward to implement and has a few open source implementations&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation"&gt;LDA&lt;/a&gt; - Is also a technique used for &lt;code&gt;topic modeling&lt;/code&gt;, but it''s different from &lt;code&gt;LSA&lt;/code&gt; in that it actually learns internal representations that tend to be more smooth and intuitive. In general, the results you get from &lt;code&gt;LDA&lt;/code&gt; are better for modeling document similarity than &lt;code&gt;LSA&lt;/code&gt;, but not quite as good for learning how to discriminate strongly between topics.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://en.wikipedia.org/wiki/Pachinko_allocation"&gt;Pachinko Allocation&lt;/a&gt; - Is a really neat extension on top of LDA. In general, this is just a significantly improved version of &lt;code&gt;LDA&lt;/code&gt;, with the only downside being that it takes a bit longer to train and open-source implementations are a little harder to come by&lt;/li&gt;
&lt;li&gt;&lt;a href="https://code.google.com/p/word2vec/"&gt;word2vec&lt;/a&gt; - Google has been working on a series of techniques for intelligently reducing words and documents to more reasonable vectors than the sparse vectors yielded by techniques such as &lt;code&gt;Count Vectorizers&lt;/code&gt; and &lt;code&gt;TF-IDF&lt;/code&gt;. Word2vec is great because it has a number of open source implementations. Once you have the vector, any other similarity metric (like cosine distance) can be used on top of it with significantly more efficacy.&lt;/li&gt;
&lt;li&gt;&lt;a href="http://cs.stanford.edu/~quocle/paragraph_vector.pdf"&gt;doc2vec&lt;/a&gt; - Also known as &lt;code&gt;paragraph vectors&lt;/code&gt;, this is the latest and greatest in a series of papers by Google, looking into dense vector representations of documents. The &lt;code&gt;gensim&lt;/code&gt; library in python has an implementation of &lt;code&gt;word2vec&lt;/code&gt; that is straightforward enough that it can pretty reasonably be leveraged to build &lt;code&gt;doc2vec&lt;/code&gt;, but make sure to keep the license in mind if you want to go down this route&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Hope that helps, let me know if you''ve got any questions.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (690, 2, '2014-07-07 18:06:24.583', 2, NULL, NULL, '&lt;p&gt;Train 2 generative models, one for each dataset (spam only, spam plus ham), that will give you the probability that a datapoint is drawn from the same probability distribution of the training data. Assign emails as spam or ham based on which model gives you the highest probability of the document arising from the training data used to train it. Example generative models are RBM''s, autoencoders (in that case, which model has the lowest reconstruction error). There are likely some bayesian generative models also that will assign a probability to a data point based on some training data.&lt;/p&gt;

&lt;p&gt;The best option though would be to take time to curate a second dataset containing only ham. That will give you higher classification accuracy. Assuming a lower proportion of spam to ham emails, that should not be too hard. You can even use Mechanical Turk if you lack the time or resources (or interns \ graduates students or other cheap labor).&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (691, 2, '2014-07-07 18:20:36.09', 3, NULL, NULL, '&lt;p&gt;Empirically I have found LSA vastly superior to LDA every time and on every dataset I have tried it on. I have talked to other people who have said the same thing. It''s also been used to win a number of the SemEval competitions for measuring semantic similarity between documents, often in combinations with a wordnet based measure, so I wouldn''t say it''s going out of fashion, or is definitely inferior to LDA, which is better for topic modelling and not semantic similarity in my experience, contrary to what some responders have stated. &lt;/p&gt;

&lt;p&gt;If you use gensim (a python library), it has LSA, LDA and word2vec, so you can easily compare the 3. doc2vec is a cool idea, but does not scale very well and you will likely have to implement it yourself as I am unaware of any open source implementations. It does not scale well as for each document, a new and separate model has to be built using SGD, a slow machine learning algorithm. But it will probably give you the most accurate results. LSA and LDA also don''t scale well (word2vec does however), LDA scales worse in general. Gensim''s implementations are very fast however, as it uses iterative SVD.&lt;/p&gt;

&lt;p&gt;One other note, if you use word2vec, you will still have to determine a way to compose vectors from documents, as it gives you a different vector per word. The simplest way to do this is to normalize each vector and take the mean over all word vectors in the document, or take a weighted mean by idf weighting of each word. So it''s not as simple as ''use word2vec'', you will need to do something further to compute document similarity. &lt;/p&gt;

&lt;p&gt;I would personally go with LSA, as I have seen it work well empirically, and gensim''s library scales very well. However, there''s no free lunch, so preferably try each method and see which works better for your data.&lt;/p&gt;
', NULL, NULL, '''/p'':98C,212C,296C,338C ''3'':119C ''accur'':182C ''algorithm'':173C ''also'':36C,187C ''base'':59C ''better'':80C,334C ''built'':166C ''combin'':55C ''compar'':117C ''competit'':46C ''compos'':230C ''comput'':293C ''contrari'':91C ''cool'':123C ''data'':337C ''dataset'':16C ''definit'':74C ''determin'':226C ''differ'':239C ''doc2vec'':120C ''document'':52C,157C,233C,264C,294C ''easili'':116C ''empir'':2C,311C ''everi'':11C,15C ''experi'':90C ''fashion'':71C ''fast'':205C ''found'':5C ''free'':323C ''general'':199C ''gensim'':103C,200C,313C ''give'':178C,236C ''go'':68C,301C ''howev'':194C,206C,319C ''idea'':124C ''idf'':271C ''implement'':137C,148C,202C ''inferior'':75C ''iter'':210C ''lda'':10C,77C,110C,186C,195C ''learn'':172C ''librari'':106C,315C ''like'':134C ''lsa'':6C,109C,184C,303C ''lunch'':324C ''machin'':171C ''mean'':257C,269C ''measur'':48C,60C ''method'':329C ''model'':83C,162C ''need'':287C ''new'':159C ''normal'':251C ''note'':216C ''number'':42C ''often'':53C ''one'':214C ''open'':146C ''p'':1C,99C,213C,297C ''peopl'':27C ''per'':241C ''person'':300C ''prefer'':326C ''probabl'':177C ''python'':105C ''respond'':95C ''result'':183C ''said'':30C ''say'':65C ''scale'':128C,152C,190C,196C,316C ''see'':331C ''seen'':307C ''semant'':49C,86C ''semev'':45C ''separ'':161C ''sgd'':168C ''similar'':50C,87C,295C ''simpl'':281C ''simplest'':244C ''slow'':170C ''someth'':290C ''sourc'':147C ''state'':97C ''still'':223C ''superior'':8C ''svd'':211C ''take'':255C,266C ''talk'':24C ''thing'':33C ''time'':12C ''topic'':82C ''tri'':19C,327C ''unawar'':143C ''use'':38C,102C,167C,209C,219C,283C ''vast'':7C ''vector'':231C,240C,253C,261C ''way'':228C,245C ''weight'':268C,272C ''well'':130C,153C,191C,310C,318C ''win'':40C ''word'':242C,260C,275C ''word2vec'':112C,192C,220C,284C ''wordnet'':58C ''work'':309C,333C ''wors'':197C ''would'':299C ''wouldn'':63C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (692, 2, '2014-07-07 18:28:50.42', 4, NULL, NULL, '&lt;p&gt;The cosine similarity metric does a good (if not perfect) job of controlling for the document length, so comparing the similarity of 2 documents or 2 queries using the cosine metric and tf idf weights for the words should work well in either case. I would also recommend doing LSA first on tf idf weights, and then computing the cosine distance\similarities.&lt;/p&gt;

&lt;p&gt;If you are trying to build a search engine, I would recommend using a free open source search engine like solr or elastic search, or just the raw lucene libraries, as they do most of the work for you, and have good built in methods for handling the query to document similarity problem.&lt;/p&gt;
', NULL, NULL, '''/p'':64C,119C ''2'':24C,27C ''also'':48C ''build'':71C ''built'':108C ''case'':45C ''compar'':20C ''comput'':59C ''control'':14C ''cosin'':3C,31C,61C ''distanc'':62C ''document'':17C,25C,116C ''either'':44C ''elast'':88C ''engin'':74C,84C ''first'':52C ''free'':80C ''good'':8C,107C ''handl'':112C ''idf'':35C,55C ''job'':12C ''length'':18C ''librari'':95C ''like'':85C ''lsa'':51C ''lucen'':94C ''method'':110C ''metric'':5C,32C ''open'':81C ''p'':1C,65C ''perfect'':11C ''problem'':118C ''queri'':28C,114C ''raw'':93C ''recommend'':49C,77C ''search'':73C,83C,89C ''similar'':4C,22C,63C,117C ''solr'':86C ''sourc'':82C ''tf'':34C,54C ''tri'':69C ''use'':29C,78C ''weight'':36C,56C ''well'':42C ''word'':39C ''work'':41C,102C ''would'':47C,76C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9805, 1, '2016-01-15 17:53:32.933', 0, 29, 'What techniques do Google use to interpret text typed into the search bar?', '&lt;p&gt;I have a project that I''m working on where I''d love to be able to take in questions from a user and give our results without the rigidity of having explicit questions.&lt;/p&gt;
', 'machine-learning', NULL, '''/p'':52C ''abl'':33C ''bar'':13A ''d'':29C ''explicit'':50C ''give'':42C ''googl'':4A ''interpret'':7A ''learn'':16B ''love'':30C ''m'':24C ''machin'':15B ''machine-learn'':14B ''p'':17C ''project'':21C ''question'':37C,51C ''result'':44C ''rigid'':47C ''search'':12A ''take'':35C ''techniqu'':2A ''text'':8A ''type'':9A ''use'':5A ''user'':40C ''without'':45C ''work'':25C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (701, 1, '2014-07-08 07:37:57.123', 3, 179, 'Clustering pair-wise distance dataset', '&lt;p&gt;I have generated a dataset of pairwise distances as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;id_1 id_2 dist_12
id_2 id_3 dist_23
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I want to cluster this data so as to identify the pattern. I have been looking at Spectral clustering and DBSCAN, but I haven''t been able to come to a conclusion and have been ambiguous on how to make use of the existing implementations of these algorithms. I have been looking at Python and Java implementations so far.&lt;/p&gt;

&lt;p&gt;Could anyone point me to a tutorial or demo on how to make use of these clustering algorithms to handle the situation in hand?&lt;/p&gt;
', 'data-mining  clustering  dbscan', NULL, '''/code'':38C ''/p'':23C,100C,126C ''/pre'':39C ''1'':27C ''12'':31C ''2'':29C,33C ''23'':37C ''3'':35C ''abl'':67C ''algorithm'':88C,119C ''ambigu'':76C ''anyon'':103C ''cluster'':1A,10B,44C,59C,118C ''code'':25C ''come'':69C ''conclus'':72C ''could'':102C ''data'':8B,46C ''data-min'':7B ''dataset'':6A,17C ''dbscan'':11B,61C ''demo'':110C ''dist'':30C,36C ''distanc'':5A,20C ''exist'':84C ''far'':99C ''follow'':22C ''generat'':15C ''hand'':125C ''handl'':121C ''haven'':64C ''id'':26C,28C,32C,34C ''identifi'':50C ''implement'':85C,97C ''java'':96C ''look'':56C,92C ''make'':80C,114C ''mine'':9B ''p'':12C,40C,101C ''pair'':3A ''pair-wis'':2A ''pairwis'':19C ''pattern'':52C ''point'':104C ''pre'':24C ''python'':94C ''situat'':123C ''spectral'':58C ''tutori'':108C ''use'':81C,115C ''want'':42C ''wise'':4A');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (719, 2, '2014-07-10 22:42:13.72', 4, NULL, NULL, '&lt;p&gt;nsl-
I''m a beginner at machine learning, so forgive the lay-like description here, but it sounds like you might be able to use topic modelling, like latent dirichlet analysis (LDA). It''s an algorithm widely used to classify documents, according to what topics they are about, based on the words found and the relative frequencies of those words in the overall corpus.  I bring it up mainly because, in LDA it''s not necessary to define the topics in advance.&lt;/p&gt;

&lt;p&gt;Since the help pages on LDA are mostly written for text analysis, the analogy I would use, in order to apply it to your question, is:
- Treat each gene expression, or feature, as a ''word'' (sometimes called a token in typical LDA text-classification applications)
- Treat each sample as a document (ie it contains an assortment of words, or gene expressions)
- Treat the signatures as pre-existing topics&lt;/p&gt;

&lt;p&gt;If I''m not mistaken, LDA should give weighted probabilities for each topic, as to how strongly it is present in each document.&lt;/p&gt;
', NULL, NULL, '''/p'':85C,157C,182C ''abl'':25C ''accord'':44C ''advanc'':84C ''algorithm'':38C ''analog'':100C ''analysi'':33C,98C ''appli'':107C ''applic'':132C ''assort'':143C ''base'':51C ''beginn'':6C ''bring'':68C ''call'':123C ''classif'':131C ''classifi'':42C ''contain'':141C ''corpus'':66C ''defin'':80C ''descript'':16C ''dirichlet'':32C ''document'':43C,138C,181C ''exist'':155C ''express'':116C,148C ''featur'':118C ''forgiv'':11C ''found'':55C ''frequenc'':59C ''gene'':115C,147C ''give'':166C ''help'':89C ''ie'':139C ''latent'':31C ''lay'':14C ''lay-lik'':13C ''lda'':34C,74C,92C,128C,164C ''learn'':9C ''like'':15C,21C,30C ''m'':4C,161C ''machin'':8C ''main'':71C ''might'':23C ''mistaken'':163C ''model'':29C ''most'':94C ''necessari'':78C ''nsl'':2C ''order'':105C ''overal'':65C ''p'':1C,86C,158C ''page'':90C ''pre'':154C ''pre-exist'':153C ''present'':178C ''probabl'':168C ''question'':111C ''relat'':58C ''sampl'':135C ''signatur'':151C ''sinc'':87C ''sometim'':122C ''sound'':20C ''strong'':175C ''text'':97C,130C ''text-classif'':129C ''token'':125C ''topic'':28C,47C,82C,156C,171C ''treat'':113C,133C,149C ''typic'':127C ''use'':27C,40C,103C ''weight'':167C ''wide'':39C ''word'':54C,62C,121C,145C ''would'':102C ''written'':95C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (718, 2, '2014-07-10 15:43:53.177', 8, NULL, NULL, '&lt;p&gt;A very strong correlation between the new feature and an existing feature is a fairly good sign that the new feature provides little new information.  A low correlation between the new feature and existing features is likely preferable.&lt;/p&gt;

&lt;p&gt;A strong linear correlation between the new feature and the predicted variable is an good sign that a new feature will be valuable, but the absence of a high correlation is not necessary a sign of a poor feature, because neural networks are not restricted to linear combinations of variables.   &lt;/p&gt;

&lt;p&gt;If the new feature was manually constructed from a combination of existing features, consider leaving it out.  The beauty of neural networks is that little feature engineering and preprocessing is required -- features are instead learned by intermediate layers.  Whenever possible, prefer learning features to engineering them.&lt;/p&gt;
', NULL, NULL, '''/p'':40C,92C,140C ''absenc'':67C ''beauti'':112C ''combin'':89C,103C ''consid'':107C ''construct'':100C ''correl'':5C,29C,45C,71C ''engin'':120C,138C ''exist'':12C,35C,105C ''fair'':16C ''featur'':9C,13C,22C,33C,36C,49C,61C,80C,97C,106C,119C,125C,136C ''good'':17C,56C ''high'':70C ''inform'':26C ''instead'':127C ''intermedi'':130C ''layer'':131C ''learn'':128C,135C ''leav'':108C ''like'':38C ''linear'':44C,88C ''littl'':24C,118C ''low'':28C ''manual'':99C ''necessari'':74C ''network'':83C,115C ''neural'':82C,114C ''new'':8C,21C,25C,32C,48C,60C,96C ''p'':1C,41C,93C ''poor'':79C ''possibl'':133C ''predict'':52C ''prefer'':39C,134C ''preprocess'':122C ''provid'':23C ''requir'':124C ''restrict'':86C ''sign'':18C,57C,76C ''strong'':4C,43C ''valuabl'':64C ''variabl'':53C,91C ''whenev'':132C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (695, 2, '2014-07-07 19:55:51.057', 53, NULL, NULL, '&lt;p&gt;UPDATE: the landscape has changed quite a bit since I answered this question in July ''14, and some new players have entered the space.  In particular I would recommend checking out:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Lasagne: &lt;a href="https://github.com/Lasagne/Lasagne"&gt;https://github.com/Lasagne/Lasagne&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Keras: &lt;a href="https://github.com/fchollet/keras"&gt;https://github.com/fchollet/keras&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Deepy: &lt;a href="https://github.com/uaca/deepy"&gt;https://github.com/uaca/deepy&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Nolearn: &lt;a href="https://github.com/dnouri/nolearn"&gt;https://github.com/dnouri/nolearn&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Blocks: &lt;a href="https://github.com/mila-udem/blocks"&gt;https://github.com/mila-udem/blocks&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;TensorFlow: &lt;a href="https://github.com/tensorflow/tensorflow"&gt;https://github.com/tensorflow/tensorflow&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;They each have their strengths and weaknesses, so give them all a go and see which best suits your use case.  Although I would have recommended using pylearn2 a year ago, the community is no longer active so I would recommend looking elsewhere.  My original response to the answer is included below, but is largely irrelevant at this point.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;a href="http://deeplearning.net/software/pylearn2/"&gt;Pylearn2&lt;/a&gt; is generally considered the library of choice for neural networks and deep learning in python.  It''s designed for easy scientific experimentation rather than ease of use, so the learning curve is rather steep, but if you take your time and follow the tutorials I think you''ll be happy with the functionality it provides.  Everything from standard Multilayer Perceptrons to Restricted Boltzmann Machines to Convolutional Nets to Autoencoders is provided.  There''s great GPU support and everything is built on top of Theano, so performance is typically quite good.  The source for Pylearn2 is available &lt;a href="https://github.com/lisa-lab/pylearn2"&gt;on github&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Be aware that Pylearn2 has the opposite problem of pybrain at the moment -- rather than being abandoned, Pylearn2 is under active development and is subject to frequent changes.&lt;/p&gt;
', NULL, NULL, '''/a'':165C,269C ''/dnouri/nolearn'':71C ''/dnouri/nolearn&lt;/a&gt;&lt;/li&gt;'':74C ''/fchollet/keras'':51C ''/fchollet/keras&lt;/a&gt;&lt;/li&gt;'':54C ''/lasagne/lasagne'':41C ''/lasagne/lasagne&lt;/a&gt;&lt;/li&gt;'':44C ''/lisa-lab/pylearn2'':266C ''/mila-udem/blocks'':81C ''/mila-udem/blocks&lt;/a&gt;&lt;/li&gt;'':84C ''/p'':33C,156C,270C,300C ''/software/pylearn2/'':163C ''/tensorflow/tensorflow'':91C ''/tensorflow/tensorflow&lt;/a&gt;&lt;/li&gt;'':94C ''/uaca/deepy'':61C ''/uaca/deepy&lt;/a&gt;&lt;/li&gt;'':64C ''/ul'':95C ''14'':17C ''abandon'':288C ''activ'':133C,292C ''ago'':127C ''although'':118C ''answer'':12C,145C ''autoencod'':234C ''avail'':261C ''awar'':273C ''best'':113C ''bit'':9C ''block'':76C ''boltzmann'':228C ''built'':245C ''case'':117C ''chang'':6C,299C ''check'':31C ''choic'':172C ''communiti'':129C ''consid'':168C ''convolut'':231C ''curv'':196C ''deep'':177C ''deepi'':56C ''deeplearning.net'':162C ''deeplearning.net/software/pylearn2/'':161C ''design'':183C ''develop'':293C ''eas'':190C ''easi'':185C ''elsewher'':139C ''enter'':23C ''everyth'':221C,243C ''experiment'':187C ''follow'':207C ''frequent'':298C ''function'':218C ''general'':167C ''github'':268C ''github.com'':40C,43C,50C,53C,60C,63C,70C,73C,80C,83C,90C,93C,265C ''github.com/dnouri/nolearn'':69C ''github.com/dnouri/nolearn&lt;/a&gt;&lt;/li&gt;'':72C ''github.com/fchollet/keras'':49C ''github.com/fchollet/keras&lt;/a&gt;&lt;/li&gt;'':52C ''github.com/lasagne/lasagne'':39C ''github.com/lasagne/lasagne&lt;/a&gt;&lt;/li&gt;'':42C ''github.com/lisa-lab/pylearn2'':264C ''github.com/mila-udem/blocks'':79C ''github.com/mila-udem/blocks&lt;/a&gt;&lt;/li&gt;'':82C ''github.com/tensorflow/tensorflow'':89C ''github.com/tensorflow/tensorflow&lt;/a&gt;&lt;/li&gt;'':92C ''github.com/uaca/deepy'':59C ''github.com/uaca/deepy&lt;/a&gt;&lt;/li&gt;'':62C ''give'':105C ''go'':109C ''good'':255C ''gpu'':240C ''great'':239C ''happi'':215C ''hr'':157C ''href'':38C,48C,58C,68C,78C,88C,160C,263C ''includ'':147C ''irrelev'':152C ''juli'':16C ''kera'':46C ''landscap'':4C ''larg'':151C ''lasagn'':36C ''learn'':178C,195C ''li'':35C,45C,55C,65C,75C,85C ''librari'':170C ''ll'':213C ''longer'':132C ''look'':138C ''machin'':229C ''moment'':284C ''multilay'':224C ''net'':232C ''network'':175C ''neural'':174C ''new'':20C ''nolearn'':66C ''opposit'':278C ''origin'':141C ''p'':1C,96C,158C,271C ''particular'':27C ''perceptron'':225C ''perform'':251C ''player'':21C ''point'':155C ''problem'':279C ''provid'':220C,236C ''pybrain'':281C ''pylearn2'':124C,164C,259C,275C,289C ''python'':180C ''question'':14C ''quit'':7C,254C ''rather'':188C,198C,285C ''recommend'':30C,122C,137C ''respons'':142C ''restrict'':227C ''scientif'':186C ''see'':111C ''sinc'':10C ''sourc'':257C ''space'':25C ''standard'':223C ''steep'':199C ''strength'':101C ''subject'':296C ''suit'':114C ''support'':241C ''take'':203C ''tensorflow'':86C ''theano'':249C ''think'':211C ''time'':205C ''top'':247C ''tutori'':209C ''typic'':253C ''ul'':34C ''updat'':2C ''use'':116C,123C,192C ''weak'':103C ''would'':29C,120C,136C ''year'':126C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (697, 1, '2014-07-07 21:26:36.83', -1, 178, 'Running huge datasets with R', '&lt;p&gt;I''m trying to run some analysis with some big datasets (eg 400k rows vs. 400 columns) with R (e.g. using neural networks and recommendation systems).
But, it''s taking too long to process the data (with huge matrices, e.g. 400k rows vs. 400k columns).
What are some free/cheap ways to improve R performance?&lt;/p&gt;

&lt;p&gt;I''m accepting packages or web services suggestions (other options are welcome).&lt;/p&gt;
', 'bigdata  r  optimization  processing', NULL, '''/p'':65C,79C ''400'':26C ''400k'':23C,51C,54C ''accept'':69C ''analysi'':17C ''big'':20C ''bigdata'':6B ''column'':27C,55C ''data'':46C ''dataset'':3A,21C ''e.g'':30C,50C ''eg'':22C ''free/cheap'':59C ''huge'':2A,48C ''improv'':62C ''long'':42C ''m'':12C,68C ''matric'':49C ''network'':33C ''neural'':32C ''optim'':8B ''option'':76C ''p'':10C,66C ''packag'':70C ''perform'':64C ''process'':9B,44C ''r'':5A,7B,29C,63C ''recommend'':35C ''row'':24C,52C ''run'':1A,15C ''servic'':73C ''suggest'':74C ''system'':36C ''take'':40C ''tri'':13C ''use'':31C ''vs'':25C,53C ''way'':60C ''web'':72C ''welcom'':78C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (755, 2, '2014-07-16 17:34:24.88', 4, NULL, NULL, '&lt;p&gt;&lt;em&gt;I expected more from random trees&lt;/em&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;With random forests, typically for N features, sqrt(N) features are used for each decision tree construction. Since in your case &lt;em&gt;N&lt;/em&gt;=20, you could try setting &lt;em&gt;max_depth&lt;/em&gt; (the number of sub-features to construct each decision tree) to 5.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Instead of decision trees, linear models have been proposed and evaluated as base estimators in random forests, in particular multinomial logistic regression and naive Bayes. This might improve your accuracy.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;em&gt;On MNIST kNN gives better accuracy, any ideas how to get it higher?&lt;/em&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Try with a higher value of &lt;em&gt;K&lt;/em&gt; (say 5 or 7). A higher value of K would give you more supportive evidence about the class label of a point.&lt;/li&gt;
&lt;li&gt;You could run PCA or Fisher''s Linear Discriminant Analysis before running k-nearest neighbour. By this you could potentially get rid of correlated features while computing distances between the points, and hence your k neighbours would be more robust.&lt;/li&gt;
&lt;li&gt;Try different K values for different points based on the variance in the distances between the K neighbours.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/em'':9C,37C,46C,112C,124C ''/li'':61C,95C,147C,190C,210C ''/p'':10C,60C,94C,113C ''/ul'':96C,211C ''20'':38C ''5'':59C,126C ''7'':128C ''accuraci'':93C,104C ''analysi'':158C ''base'':76C,199C ''bay'':88C ''better'':103C ''case'':34C ''class'':142C ''comput'':176C ''construct'':30C,54C ''correl'':173C ''could'':40C,150C,168C ''decis'':28C,56C,66C ''depth'':45C ''differ'':193C,197C ''discrimin'':157C ''distanc'':177C,205C ''em'':2C,35C,43C,98C,122C ''estim'':77C ''evalu'':74C ''evid'':139C ''expect'':4C ''featur'':20C,23C,52C,174C ''fisher'':154C ''forest'':16C,80C ''get'':109C,170C ''give'':102C,135C ''henc'':182C ''higher'':111C,119C,130C ''idea'':106C ''improv'':91C ''instead'':64C ''k'':123C,133C,162C,184C,194C,208C ''k-nearest'':161C ''knn'':101C ''label'':143C ''li'':12C,62C,115C,148C,191C ''linear'':68C,156C ''logist'':84C ''max'':44C ''might'':90C ''mnist'':100C ''model'':69C ''multinomi'':83C ''n'':19C,22C,36C ''naiv'':87C ''nearest'':163C ''neighbour'':164C,185C,209C ''number'':48C ''p'':1C,13C,63C,97C ''particular'':82C ''pca'':152C ''point'':146C,180C,198C ''potenti'':169C ''propos'':72C ''random'':7C,15C,79C ''regress'':85C ''rid'':171C ''robust'':189C ''run'':151C,160C ''say'':125C ''set'':42C ''sinc'':31C ''sqrt'':21C ''sub'':51C ''sub-featur'':50C ''support'':138C ''tree'':8C,29C,57C,67C ''tri'':41C,116C,192C ''typic'':17C ''ul'':11C,114C ''use'':25C ''valu'':120C,131C,195C ''varianc'':202C ''would'':134C,186C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (698, 2, '2014-07-08 02:08:04.56', 1, NULL, NULL, '&lt;p&gt;They can''t predict, but they can tell you the most likely result. There''s an study about this kind of approach from &lt;strong&gt;Etienne&lt;/strong&gt; - &lt;a href="http://blog.wolfram.com/2014/06/20/predicting-who-will-win-the-world-cup-with-wolfram-language/" rel="nofollow"&gt;Predicting Who Will Win the World Cup with Wolfram Language&lt;/a&gt;. This is a very detailed study, so you can check all the methodology used to get the predictions. &lt;/p&gt;

&lt;p&gt;Interesting enough, 11 from 15 matches were correct!&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;As one might expect, Brazil is the favorite, with a probability to win of 42.5%. This striking result is due to the fact that Brazil has both the highest Elo ranking and plays at home.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;(Let''s go Brazil!)&lt;/p&gt;
', NULL, NULL, '''/2014/06/20/predicting-who-will-win-the-world-cup-with-wolfram-language/'':32C ''/a'':45C ''/blockquote'':113C ''/p'':64C,74C,112C,119C ''/strong'':27C ''11'':68C ''15'':70C ''42.5'':91C ''approach'':23C ''blockquot'':75C ''blog.wolfram.com'':31C ''blog.wolfram.com/2014/06/20/predicting-who-will-win-the-world-cup-with-wolfram-language/'':30C ''brazil'':81C,101C,118C ''check'':55C ''correct'':73C ''cup'':41C ''detail'':50C ''due'':96C ''elo'':106C ''enough'':67C ''etienn'':26C ''expect'':80C ''fact'':99C ''favorit'':84C ''get'':61C ''go'':117C ''highest'':105C ''home'':111C ''href'':29C ''interest'':66C ''kind'':21C ''languag'':44C ''let'':115C ''like'':13C ''match'':71C ''methodolog'':58C ''might'':79C ''nofollow'':34C ''one'':78C ''p'':1C,65C,76C,114C ''play'':109C ''predict'':5C,35C,63C ''probabl'':87C ''rank'':107C ''rel'':33C ''result'':14C,94C ''strike'':93C ''strong'':25C ''studi'':18C,51C ''tell'':9C ''use'':59C ''win'':38C,89C ''wolfram'':43C ''world'':40C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (699, 2, '2014-07-08 06:40:36.923', 4, NULL, NULL, '&lt;p&gt;From my experience only some classes of queries can be classified on lexical features (due to ambiguity of natural language). Instead you can try to use boolean search results (sites or segments of sites, not documents, without ranking) as features for classification (instead on words). This approach works well in classes where there is a big lexical ambiguity in a query but exists a lot of good sites relevant to the query (e.g. movies, music, commercial queries and so on).&lt;/p&gt;

&lt;p&gt;Also, for offline classification you can do LSI on query-site matrix. See "Introduction to Information Retrieval" book for details.&lt;/p&gt;
', NULL, NULL, '''/p'':82C,105C ''also'':84C ''ambigu'':18C,59C ''approach'':48C ''big'':57C ''book'':102C ''boolean'':28C ''class'':7C,52C ''classif'':43C,87C ''classifi'':12C ''commerci'':77C ''detail'':104C ''document'':37C ''due'':16C ''e.g'':74C ''exist'':64C ''experi'':4C ''featur'':15C,41C ''good'':68C ''inform'':100C ''instead'':22C,44C ''introduct'':98C ''languag'':21C ''lexic'':14C,58C ''lot'':66C ''lsi'':91C ''matrix'':96C ''movi'':75C ''music'':76C ''natur'':20C ''offlin'':86C ''p'':1C,83C ''queri'':9C,62C,73C,78C,94C ''query-sit'':93C ''rank'':39C ''relev'':70C ''result'':30C ''retriev'':101C ''search'':29C ''see'':97C ''segment'':33C ''site'':31C,35C,69C,95C ''tri'':25C ''use'':27C ''well'':50C ''without'':38C ''word'':46C ''work'':49C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (700, 1, '2014-07-08 07:29:34.167', 5, 125, 'Efficient dynamic clustering', '&lt;p&gt;I have a set of datapoints from the unit interval (i.e. 1-dimensional dataset with numerical values). I receive some additional datapoints online, and moreover the value of some datapoints might change dynamically. I''m looking for an ideal clustering algorithm which can handle these issues efficiently.&lt;/p&gt;

&lt;p&gt;I know &lt;a href="https://www.cs.princeton.edu/courses/archive/fall08/cos436/Duda/C/sk_means.htm"&gt;sequential k-means clustering&lt;/a&gt; copes with the addition of new instances, and I suppose with minor modification it can work with dynamic instance values (i.e. first taking the modified instance from the respective cluster, then updating the mean of the cluster and finally giving the modified instance as an input to the algorithm just as the addition of an unseen instance).&lt;/p&gt;

&lt;p&gt;My concern with using the k-means algorithm is the requirement of supplying the number of clusters as an input. I know that they beat other clustering algorithms (GAs, MSTs, Hierarchical Methods etc.) in time&amp;amp;space complexity. Honestly I''m not sure, but maybe I can get away with using one of the aforementioned algorithms. Even that my datasets are relatively large, the existence of a single dimension makes me wonder.&lt;/p&gt;

&lt;p&gt;More specifically a typical test case of mine would contain about 10K-200K 1-dimensional datapoints. I would like to complete the clustering preferably under a second. The dynamic changes in the value points are assumed to be smooth, i.e. relatively small. Thus being able to use existing solutions (i.e. being able to continue clustering on the existing one when a value is changed or new one is added) is highly preferred. &lt;/p&gt;

&lt;p&gt;So all in all:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Can you think of an algorithm which will provide a sweet spot between computational efficiency and the accuracy of clusters wrt. the problem defined above?&lt;/p&gt;
  
  &lt;p&gt;Are there some nice heuristics for the k-means algorithm to automatically compute the value of K beforehand?&lt;/p&gt;
&lt;/blockquote&gt;
', 'machine-learning  algorithms  clustering  k-means  hierarchical-data-format', NULL, '''/a'':78C ''/blockquote'':342C ''/courses/archive/fall08/cos436/duda/c/sk_means.htm'':72C ''/p'':64C,136C,211C,286C,292C,320C,341C ''1'':28C,227C ''10k'':225C ''10k-200k'':224C ''200k'':226C ''abl'':258C,265C ''accuraci'':312C ''ad'':282C ''addit'':37C,82C,131C ''aforement'':193C ''algorithm'':7B,57C,127C,146C,166C,194C,300C,332C ''amp'':174C ''assum'':249C ''automat'':334C ''away'':187C ''beat'':163C ''beforehand'':340C ''blockquot'':293C ''case'':218C ''chang'':48C,243C,277C ''cluster'':3A,8B,56C,77C,108C,115C,155C,165C,236C,268C,314C ''complet'':234C ''complex'':176C ''comput'':308C,335C ''concern'':139C ''contain'':222C ''continu'':267C ''cope'':79C ''data'':14B ''datapoint'':22C,38C,46C,229C ''dataset'':30C,198C ''defin'':318C ''dimens'':207C ''dimension'':29C,228C ''dynam'':2A,49C,96C,242C ''effici'':1A,63C,309C ''etc'':171C ''even'':195C ''exist'':203C,261C,271C ''final'':117C ''first'':100C ''format'':15B ''gas'':167C ''get'':186C ''give'':118C ''handl'':60C ''heurist'':326C ''hierarch'':13B,169C ''hierarchical-data-format'':12B ''high'':284C ''honest'':177C ''href'':69C ''i.e'':27C,99C,253C,263C ''ideal'':55C ''input'':124C,158C ''instanc'':85C,97C,104C,121C,135C ''interv'':26C ''issu'':62C ''k'':10B,75C,144C,330C,339C ''k-mean'':9B,74C,143C,329C ''know'':67C,160C ''larg'':201C ''learn'':6B ''like'':232C ''look'':52C ''m'':51C,179C ''machin'':5B ''machine-learn'':4B ''make'':208C ''mayb'':183C ''mean'':11B,76C,112C,145C,331C ''method'':170C ''might'':47C ''mine'':220C ''minor'':90C ''modif'':91C ''modifi'':103C,120C ''moreov'':41C ''msts'':168C ''new'':84C,279C ''nice'':325C ''number'':153C ''numer'':32C ''one'':190C,272C,280C ''onlin'':39C ''p'':16C,65C,137C,212C,287C,294C,321C ''point'':247C ''prefer'':237C,285C ''problem'':317C ''provid'':303C ''receiv'':35C ''relat'':200C,254C ''requir'':149C ''respect'':107C ''second'':240C ''sequenti'':73C ''set'':20C ''singl'':206C ''small'':255C ''smooth'':252C ''solut'':262C ''space'':175C ''specif'':214C ''spot'':306C ''suppli'':151C ''suppos'':88C ''sure'':181C ''sweet'':305C ''take'':101C ''test'':217C ''think'':297C ''thus'':256C ''time'':173C ''typic'':216C ''unit'':25C ''unseen'':134C ''updat'':110C ''use'':141C,189C,260C ''valu'':33C,43C,98C,246C,275C,337C ''wonder'':210C ''work'':94C ''would'':221C,231C ''wrt'':315C ''www.cs.princeton.edu'':71C ''www.cs.princeton.edu/courses/archive/fall08/cos436/duda/c/sk_means.htm'':70C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (709, 2, '2014-07-09 07:08:34.74', 2, NULL, NULL, '&lt;p&gt;Since you mention you are building a recommendation system, I believe you have a sparse matrix which you are working on. Check &lt;a href="http://stat.ethz.ch/R-manual/R-devel/library/Matrix/html/sparseMatrix.html" rel="nofollow"&gt;sparseMatrix&lt;/a&gt; from Matrix package. This should be able to help you with storing your large size matrix in memory and train your model. &lt;/p&gt;
', NULL, NULL, '''/a'':32C ''/p'':55C ''/r-manual/r-devel/library/matrix/html/sparsematrix.html'':28C ''abl'':39C ''believ'':12C ''build'':7C ''check'':23C ''help'':41C ''href'':25C ''larg'':46C ''matrix'':17C,34C,48C ''memori'':50C ''mention'':4C ''model'':54C ''nofollow'':30C ''p'':1C ''packag'':35C ''recommend'':9C ''rel'':29C ''sinc'':2C ''size'':47C ''spars'':16C ''sparsematrix'':31C ''stat.ethz.ch'':27C ''stat.ethz.ch/r-manual/r-devel/library/matrix/html/sparsematrix.html'':26C ''store'':44C ''system'':10C ''train'':52C ''work'':21C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (702, 2, '2014-07-08 09:18:17.99', 1, NULL, NULL, '&lt;p&gt;In the scikit-learn implementation of Spectral clustering and DBSCAN you do not need to precompute the distances, you should input the sample coordinates for all &lt;code&gt;id_1&lt;/code&gt; ... &lt;code&gt;id_n&lt;/code&gt;.  Here is a simplification of the &lt;a href="http://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html" rel="nofollow"&gt;documented example comparison of clustering algorithms&lt;/a&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
from sklearn import cluster
from sklearn.preprocessing import StandardScaler

## Prepare the data
X = np.random.rand(1500, 2)
# When reading from a file of the form: `id_n coord_x coord_y`
# you will need this call instead:
# X = np.loadtxt(''coords.csv'', usecols=(1, 2))
X = StandardScaler().fit_transform(X)

## Instantiate the algorithms
spectral = cluster.SpectralClustering(n_clusters=2,
                                      eigen_solver=''arpack'',
                                      affinity="nearest_neighbors")
dbscan = cluster.DBSCAN(eps=.2)

## Use the algorithms
spectral_labels = spectral.fit_predict(X)
dbscan_labels = dbscan.fit_predict(X)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':56C ''/code'':32C,36C,141C ''/p'':57C ''/pre'':142C ''/stable/auto_examples/cluster/plot_cluster_comparison.html'':47C ''1'':31C,103C ''1500'':77C ''2'':78C,104C,117C,127C ''affin'':121C ''algorithm'':55C,112C,130C ''arpack'':120C ''call'':97C ''cluster'':10C,54C,67C,116C ''cluster.dbscan'':125C ''cluster.spectralclustering'':114C ''code'':29C,33C,59C ''comparison'':52C ''coord'':89C,91C ''coordin'':26C ''coords.csv'':101C ''data'':74C ''dbscan'':12C,124C,136C ''dbscan.fit'':138C ''distanc'':20C ''document'':50C ''eigen'':118C ''ep'':126C ''exampl'':51C ''file'':83C ''fit'':107C ''form'':86C ''href'':44C ''id'':30C,34C,87C ''implement'':7C ''import'':60C,66C,70C ''input'':23C ''instanti'':110C ''instead'':98C ''label'':132C,137C ''learn'':6C ''n'':35C,88C,115C ''nearest'':122C ''need'':16C,95C ''neighbor'':123C ''nofollow'':49C ''np'':63C ''np.loadtxt'':100C ''np.random.rand'':76C ''numpi'':61C ''p'':1C ''pre'':58C ''precomput'':18C ''predict'':134C,139C ''prepar'':72C ''read'':80C ''rel'':48C ''sampl'':25C ''scikit'':5C ''scikit-learn'':4C ''scikit-learn.org'':46C ''scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html'':45C ''simplif'':40C ''sklearn'':65C ''sklearn.preprocessing'':69C ''solver'':119C ''spectral'':9C,113C,131C ''spectral.fit'':133C ''standardscal'':71C,106C ''transform'':108C ''use'':128C ''usecol'':102C ''x'':75C,90C,99C,105C,109C,135C,140C ''y'':92C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (708, 2, '2014-07-09 00:44:28.207', 4, NULL, NULL, '&lt;p&gt;There are many solutions to ease the burden of copying the file from a local machine to the computing nodes in the clusters. A simple approach is to use an interface that allows multi-access to the machines in the cluster, like &lt;a href="http://sourceforge.net/projects/clusterssh/" rel="nofollow"&gt;clusterssh&lt;/a&gt; (cssh). It allows you to type commands to multiple machines at once via a set of terminal screens (each one a ssh connection to a different machine in the cluster).&lt;/p&gt;

&lt;p&gt;Since your cluster seem to have &lt;code&gt;qsub&lt;/code&gt; set up, your problem may be rather related to &lt;em&gt;replicating&lt;/em&gt; the data along the machines (other than simply running a command in each node). So, to address this point, you may either write an &lt;code&gt;scp&lt;/code&gt; script, to copy things to and from each node in the cluster (which is surely better addressed with SVN), or you may set up a NFS. This would allow for a simple and transparent access to the data, and also reduce the need for replicating unnecessary data.&lt;/p&gt;

&lt;p&gt;For example, you could access a node, copy the data to such place, and simply use the data &lt;em&gt;remotely&lt;/em&gt;, via network communication. I''m not acquainted with how to set up a NFS, but you already have access to it (in case your home folder is the same across the machines you access). Then, the scripts and data could be sent to a single place, and later accessed from others. This is akin to the SVN approach, except it''s more transparent/straightforward.&lt;/p&gt;
', NULL, NULL, '''/a'':53C ''/code'':94C,133C ''/em'':106C,203C ''/p'':84C,181C,267C ''/projects/clusterssh/'':49C ''access'':37C,168C,187C,222C,237C,252C ''acquaint'':210C ''across'':233C ''address'':123C,150C ''akin'':257C ''allow'':34C,56C,162C ''along'':109C ''alreadi'':220C ''also'':173C ''approach'':27C,261C ''better'':149C ''burden'':9C ''case'':226C ''cluster'':24C,43C,83C,88C,145C ''clusterssh'':52C ''code'':92C,131C ''command'':60C,117C ''communic'':206C ''comput'':20C ''connect'':76C ''copi'':11C,136C,190C ''could'':186C,243C ''cssh'':54C ''data'':108C,171C,180C,192C,200C,242C ''differ'':79C ''eas'':7C ''either'':128C ''em'':104C,201C ''exampl'':184C ''except'':262C ''file'':13C ''folder'':229C ''home'':228C ''href'':46C ''interfac'':32C ''later'':251C ''like'':44C ''local'':16C ''m'':208C ''machin'':17C,40C,63C,80C,111C,235C ''mani'':4C ''may'':99C,127C,155C ''multi'':36C ''multi-access'':35C ''multipl'':62C ''need'':176C ''network'':205C ''nfs'':159C,217C ''node'':21C,120C,142C,189C ''nofollow'':51C ''one'':73C ''other'':254C ''p'':1C,85C,182C ''place'':195C,249C ''point'':125C ''problem'':98C ''qsub'':93C ''rather'':101C ''reduc'':174C ''rel'':50C ''relat'':102C ''remot'':202C ''replic'':105C,178C ''run'':115C ''scp'':132C ''screen'':71C ''script'':134C,240C ''seem'':89C ''sent'':245C ''set'':68C,95C,156C,214C ''simpl'':26C,165C ''simpli'':114C,197C ''sinc'':86C ''singl'':248C ''solut'':5C ''sourceforge.net'':48C ''sourceforge.net/projects/clusterssh/'':47C ''ssh'':75C ''sure'':148C ''svn'':152C,260C ''termin'':70C ''thing'':137C ''transpar'':167C ''transparent/straightforward'':266C ''type'':59C ''unnecessari'':179C ''use'':30C,198C ''via'':66C,204C ''would'':161C ''write'':129C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (897, 2, '2014-08-03 20:21:08.15', 3, NULL, NULL, '&lt;p&gt;Having worked with Facebook data a bit (harvested from Facebook users) we stored it just as a pair of values: USER_ID, FRIEND_USER_ID.&lt;/p&gt;

&lt;p&gt;But I guess your questions is a bit deeper? You can store it in different ways, depending on your research question. One interesting option is triads for example - &lt;a href="http://mypersonality.org/wiki/doku.php?id=list_of_variables_available#triads" rel="nofollow"&gt;http://mypersonality.org/wiki/doku.php?id=list_of_variables_available#triads&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':27C ''/wiki/doku.php?id=list_of_variables_available#triads'':61C ''/wiki/doku.php?id=list_of_variables_available#triads&lt;/a&gt;&lt;/p&gt;'':66C ''bit'':8C,36C ''data'':6C ''deeper'':37C ''depend'':45C ''differ'':43C ''exampl'':56C ''facebook'':5C,11C ''friend'':24C ''guess'':31C ''harvest'':9C ''href'':58C ''id'':23C,26C ''interest'':51C ''mypersonality.org'':60C,65C ''mypersonality.org/wiki/doku.php?id=list_of_variables_available#triads'':59C ''mypersonality.org/wiki/doku.php?id=list_of_variables_available#triads&lt;/a&gt;&lt;/p&gt;'':64C ''nofollow'':63C ''one'':50C ''option'':52C ''p'':1C,28C ''pair'':19C ''question'':33C,49C ''rel'':62C ''research'':48C ''store'':14C,40C ''triad'':54C ''user'':12C,22C,25C ''valu'':21C ''way'':44C ''work'':3C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (898, 2, '2014-08-04 09:42:03.59', 10, NULL, NULL, '&lt;h2&gt;Two Categorical Variables&lt;/h2&gt;

&lt;p&gt;Checking if two categorical variables are independent can be done with Chi-Squared test of independence. &lt;/p&gt;

&lt;p&gt;This is a typical &lt;a href="https://en.wikipedia.org/wiki/Chi-square_test"&gt;Chi-Square test&lt;/a&gt;: if we assume that two variables are independent, then the values of the contingency table for these variables should be distributed uniformly. And then we check how far away from uniform the actual values are.&lt;/p&gt;

&lt;p&gt;There also exists a &lt;a href="http://en.wikipedia.org/wiki/Cram%C3%A9r%27s_V"&gt;Crammer''s V&lt;/a&gt; that is a measure of correlation that follows from this test&lt;/p&gt;

&lt;h3&gt;Example&lt;/h3&gt;

&lt;p&gt;Suppose we have two variables&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;gender: male and female&lt;/li&gt;
&lt;li&gt;city: Blois and Tours&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;We observed the following data:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/zcCfV.png" alt="observed values"&gt; &lt;/p&gt;

&lt;p&gt;Are gender and city independent? Let''s perform a Chi-Squred test. Null hypothesis: they are independent, Alternative hypothesis is that they are correlated in some way. &lt;/p&gt;

&lt;p&gt;Under the Null hypothesis, we assume uniform distribution. So our expected values are the following&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/H8bKJ.png" alt="expected value"&gt;&lt;/p&gt;

&lt;p&gt;So we run the chi-squared test and the resulting p-value here can be seen as a measure of correlation between these two variables.&lt;/p&gt;

&lt;p&gt;To compute Crammer''s V we first find the normalizing factor chi-squared-max which is typically the size of the sample, divide the chi-square by it and take a square root &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/v7HY6.png" alt="crammers v"&gt;&lt;/p&gt;

&lt;h3&gt;R&lt;/h3&gt;

&lt;pre&gt;&lt;code&gt;tbl = matrix(data=c(55, 45, 20, 30), nrow=2, ncol=2, byrow=T)
dimnames(tbl) = list(City=c(''B'', ''T''), Gender=c(''M'', ''F''))

chi2 = chisq.test(tbl, correct=F)
c(chi2$statistic, chi2$p.value)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here the p value is 0.08 - quite small, but still not enough to reject the hypothesis of independence. So we can say that the "correlation" here is 0.08&lt;/p&gt;

&lt;p&gt;We also compute V: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;sqrt(chi2$statistic / sum(tbl))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And get 0.14 (the smaller v, the lower the correlation) &lt;/p&gt;

&lt;p&gt;Consider another dataset &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;    Gender
City  M  F
   B 51 49
   T 24 26
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For this, it would give the following&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;tbl = matrix(data=c(51, 49, 24, 26), nrow=2, ncol=2, byrow=T)
dimnames(tbl) = list(City=c(''B'', ''T''), Gender=c(''M'', ''F''))

chi2 = chisq.test(tbl, correct=F)
c(chi2$statistic, chi2$p.value)

sqrt(chi2$statistic / sum(tbl))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The p-value is 0.72 which is far closer to 1, and v is 0.03 - very close to 0&lt;/p&gt;

&lt;h2&gt;Categorical vs Numerical Variables&lt;/h2&gt;

&lt;p&gt;For this type we typically perform &lt;a href="http://en.wikipedia.org/wiki/F_test#One-way_ANOVA_example"&gt;One-way ANOVA test&lt;/a&gt;: we calculate in-group variance and intra-group variance and then compare them.&lt;/p&gt;

&lt;h3&gt;Example&lt;/h3&gt;

&lt;p&gt;We want to study the relationship between absorbed fat from donuts vs the type of fat used to produce donuts (example is taken from &lt;a href="http://courses.statistics.com/software/R/R1way.htm"&gt;here&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/LMOS3.png" alt="donuts"&gt;&lt;/p&gt;

&lt;p&gt;Is there any dependence between the variables?
For that we conduct ANOVA test and see that the p-value is just 0.007 - there''s no correlation between these variables. &lt;/p&gt;

&lt;h3&gt;R&lt;/h3&gt;

&lt;pre&gt;&lt;code&gt;t1 = c(164, 172, 168, 177, 156, 195)
t2 = c(178, 191, 197, 182, 185, 177)
t3 = c(175, 193, 178, 171, 163, 176)
t4 = c(155, 166, 149, 164, 170, 168)

val = c(t1, t2, t3, t4)
fac = gl(n=4, k=6, labels=c(''type1'', ''type2'', ''type3'', ''type4''))

aov1 = aov(val ~ fac)
summary(aov1)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Output is &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;            Df Sum Sq Mean Sq F value  Pr(&amp;gt;F)   
fac          3   1636   545.5   5.406 0.00688 **
Residuals   20   2018   100.9                   
---
Signif. codes:  0 â***â 0.001 â**â 0.01 â*â 0.05 â.â 0.1 â â 1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So we can take the p-value as the measure of correlation here as well.&lt;/p&gt;

&lt;h2&gt;References&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://en.wikipedia.org/wiki/Chi-square_test"&gt;https://en.wikipedia.org/wiki/Chi-square_test&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://0agr.ru/wiki/index.php/Chi-square_Test_of_Independence"&gt;http://0agr.ru/wiki/index.php/Chi-square_Test_of_Independence&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://courses.statistics.com/software/R/R1way.htm"&gt;http://courses.statistics.com/software/R/R1way.htm&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://0agr.ru/wiki/index.php/One-Way_ANOVA_F-Test"&gt;http://0agr.ru/wiki/index.php/One-Way_ANOVA_F-Test&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://0agr.ru/wiki/index.php/Crammer%27s_Coefficient"&gt;http://0agr.ru/wiki/index.php/Crammer%27s_Coefficient&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (703, 2, '2014-07-08 10:36:44.22', 14, NULL, NULL, '&lt;p&gt;Pylearn relies on Theano and as mentioned in the other answer to use the library is quite complicated, until you get the hold of it.&lt;/p&gt;

&lt;p&gt;In the meantime I would suggest using &lt;a href="https://github.com/lmjohns3/theano-nets/" rel="nofollow"&gt;Theanets&lt;/a&gt;. It also built on top of Theano, but is much more easier to work with. It might be true, that it doesn''t have all the features of Pylearn, but for the basic work it''s sufficient.&lt;/p&gt;

&lt;p&gt;Also it''s open source, so you can add custom networks on the fly, if you dare. :)&lt;/p&gt;

&lt;p&gt;EDIT: Dec 2015. Recently I have started using &lt;a href="http://keras.io/" rel="nofollow"&gt;Keras&lt;/a&gt;. It is a bit lower level than Theanets, but much more powerful. For basic tests the Theanets is appropriate. But if you want to do some research in field of ANN Keras is much more flexible. Plus the Keras can use &lt;strong&gt;Tensorflow&lt;/strong&gt; as a backend.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (770, 2, '2014-07-17 15:35:17.487', 2, NULL, NULL, '&lt;p&gt;I am not an export in using SVMs, but usually (if you are using a machine learning library like Python''s &lt;code&gt;scikit-learn&lt;/code&gt; or R''s &lt;code&gt;libsvm&lt;/code&gt;, there is the &lt;code&gt;class_weight&lt;/code&gt; parameter, or &lt;code&gt;class.weights&lt;/code&gt;, respectively. &lt;/p&gt;

&lt;p&gt;Or if you''d use a Bayes classifier, you would take this "skew" into account via the "prior (class) probabilities" P(&amp;omega;&lt;sub&gt;j&lt;/sub&gt;)&lt;/p&gt;
', NULL, NULL, '''/code'':27C,33C,40C,45C ''/p'':47C,74C ''/sub'':73C ''account'':63C ''bay'':55C ''class'':38C,67C ''class.weights'':44C ''classifi'':56C ''code'':23C,31C,37C,43C ''d'':52C ''export'':6C ''j'':72C ''learn'':18C,26C ''librari'':19C ''libsvm'':32C ''like'':20C ''machin'':17C ''omega'':70C ''p'':1C,48C,69C ''paramet'':41C ''prior'':66C ''probabl'':68C ''python'':21C ''r'':29C ''respect'':46C ''scikit'':25C ''scikit-learn'':24C ''skew'':61C ''sub'':71C ''svms'':9C ''take'':59C ''use'':8C,15C,53C ''usual'':11C ''via'':64C ''weight'':39C ''would'':58C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (704, 1, '2014-07-08 13:45:07.583', 8, 155, 'Working with HPC clusters', '&lt;p&gt;In my university, we have an HPC computing cluster. I use the cluster to train classifiers and so on. So, usually, to send a job to the cluster, (e.g. python scikit-learn script), I need to write a Bash script that contains (among others) a command like &lt;code&gt;qsub script.py&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;However, I find this process very very frustrating. Usually what happens is that I write the python script on my laptop and then I login to the server and update the SVN repository, so I get the same python script there. Then I write that Bash script or edit it, so I can run the bash script.&lt;/p&gt;

&lt;p&gt;As you see this is really frustrating since, for every little update for the python script, I need to do many steps to have it executed at the computing cluster. Of course the task gets even more complicated when I have to put the data on the server and use the datasets'' path on the server.&lt;/p&gt;

&lt;p&gt;I''m sure many people here are using computing clusters for their data science tasks. I just want to know how you guys manage sending the jobs to the clusters?&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (705, 2, '2014-07-08 14:32:55.827', 4, NULL, NULL, '&lt;p&gt;Stochastic gradient descent is a method of setting the parameters of the regressor; since the objective for logistic regression is convex (has only one maximum), this won''t be an issue and SGD is generally only needed to improve convergence speed with masses of training data.&lt;/p&gt;

&lt;p&gt;What your numbers suggest to me is that your features are not adequate to separate the classes. Consider adding extra features if you can think any any that are useful. You might also consider interactions and quadratic features in your original feature space.&lt;/p&gt;
', NULL, NULL, '''/p'':48C,93C ''ad'':68C ''adequ'':62C ''also'':82C ''class'':66C ''consid'':67C,83C ''converg'':41C ''convex'':22C ''data'':47C ''descent'':4C ''extra'':69C ''featur'':59C,70C,87C,91C ''general'':36C ''gradient'':3C ''improv'':40C ''interact'':84C ''issu'':32C ''logist'':19C ''mass'':44C ''maximum'':26C ''method'':7C ''might'':81C ''need'':38C ''number'':52C ''object'':17C ''one'':25C ''origin'':90C ''p'':1C,49C ''paramet'':11C ''quadrat'':86C ''regress'':20C ''regressor'':14C ''separ'':64C ''set'':9C ''sgd'':34C ''sinc'':15C ''space'':92C ''speed'':42C ''stochast'':2C ''suggest'':53C ''think'':74C ''train'':46C ''use'':79C ''won'':28C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (707, 2, '2014-07-09 00:37:51.167', 5, NULL, NULL, '&lt;p&gt;Try a recurrent neural network, a model well suited for time series data. They''re notoriously difficult to train, but seem to perform well when trained properly: &lt;a href="http://cs229.stanford.edu/proj2012/BernalFokPidaparthi-FinancialMarketTimeSeriesPredictionwithRecurrentNeural.pdf"&gt;http://cs229.stanford.edu/proj2012/BernalFokPidaparthi-FinancialMarketTimeSeriesPredictionwithRecurrentNeural.pdf&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/proj2012/bernalfokpidaparthi-financialmarkettimeseriespredictionwithrecurrentneural.pdf'':33C ''/proj2012/bernalfokpidaparthi-financialmarkettimeseriespredictionwithrecurrentneural.pdf&lt;/a&gt;&lt;/p&gt;'':36C ''cs229.stanford.edu'':32C,35C ''cs229.stanford.edu/proj2012/bernalfokpidaparthi-financialmarkettimeseriespredictionwithrecurrentneural.pdf'':31C ''cs229.stanford.edu/proj2012/bernalfokpidaparthi-financialmarkettimeseriespredictionwithrecurrentneural.pdf&lt;/a&gt;&lt;/p&gt;'':34C ''data'':14C ''difficult'':18C ''href'':30C ''model'':8C ''network'':6C ''neural'':5C ''notori'':17C ''p'':1C ''perform'':24C ''proper'':28C ''re'':16C ''recurr'':4C ''seem'':22C ''seri'':13C ''suit'':10C ''time'':12C ''train'':20C,27C ''tri'':2C ''well'':9C,25C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (711, 1, '2014-07-09 12:22:22.4', 31, 1672, 'Are Support Vector Machines still considered "state of the art" in their niche?', '&lt;p&gt;This question is in response to a comment I saw on another question.&lt;/p&gt;

&lt;p&gt;The comment was regarding the Machine Learning course syllabus on Coursera, and along the lines of "SVMs are not used so much nowadays".&lt;/p&gt;

&lt;p&gt;I have only just finished the relevant lectures myself, and my understanding of SVMs is that they are a robust and efficient learning algorithm for classification, and that when using a kernel, they have a "niche" covering number of features perhaps 10 to 1000 and number of training samples perhaps 100 to 10,000. The limit on training samples is because the core algorithm revolves around optimising results generated from a square matrix with dimensions based on number of training samples, not number of original features.&lt;/p&gt;

&lt;p&gt;So does the comment I saw refer some real change since the course was made, and if so, what is that change: A new algorithm that covers SVM''s "sweet spot" just as well, better CPUs meaning SVM''s computational advantages are not worth as much? Or is it perhaps opinion or personal experience of the commenter?&lt;/p&gt;

&lt;p&gt;I tried a search for e.g. "are support vector machines out of fashion" and found nothing to imply they were being dropped in favour of anything else.&lt;/p&gt;

&lt;p&gt;And Wikipedia has this: &lt;a href="http://en.wikipedia.org/wiki/Support_vector_machine#Issues"&gt;http://en.wikipedia.org/wiki/Support_vector_machine#Issues&lt;/a&gt; . . . the main sticking point appears to be difficulty of interpreting the model. Which makes SVM fine for a black-box predicting engine, but not so good for generating insights. I don''t see that as a major issue, just another minor thing to take into account when picking the right tool for the job (along with nature of the training data and learning task etc).&lt;/p&gt;
', 'data-mining  svm  state-of-the-art', NULL, '''/p'':37C,62C,150C,209C,238C,318C ''/wiki/support_vector_machine#issues'':248C ''/wiki/support_vector_machine#issues&lt;/a&gt;'':251C ''000'':117C ''10'':105C,116C ''100'':114C ''1000'':107C ''account'':298C ''advantag'':192C ''algorithm'':87C,127C,176C ''along'':51C,307C ''anoth'':35C,292C ''anyth'':236C ''appear'':256C ''around'':129C ''art'':10A,22B ''base'':139C ''better'':186C ''black'':271C ''black-box'':270C ''box'':272C ''chang'':161C,173C ''classif'':89C ''comment'':31C,40C,155C,208C ''comput'':191C ''consid'':6A ''core'':126C ''cours'':46C,164C ''coursera'':49C ''cover'':100C,178C ''cpus'':187C ''data'':15B,313C ''data-min'':14B ''difficulti'':259C ''dimens'':138C ''drop'':232C ''e.g'':216C ''effici'':85C ''els'':237C ''en.wikipedia.org'':247C,250C ''en.wikipedia.org/wiki/support_vector_machine#issues'':246C ''en.wikipedia.org/wiki/support_vector_machine#issues&lt;/a&gt;'':249C ''engin'':274C ''etc'':317C ''experi'':205C ''fashion'':223C ''favour'':234C ''featur'':103C,149C ''fine'':267C ''finish'':68C ''found'':225C ''generat'':132C,280C ''good'':278C ''href'':245C ''impli'':228C ''insight'':281C ''interpret'':261C ''issu'':290C ''job'':306C ''kernel'':95C ''learn'':45C,86C,315C ''lectur'':71C ''limit'':119C ''line'':53C ''machin'':4A,44C,220C ''made'':166C ''main'':253C ''major'':289C ''make'':265C ''matrix'':136C ''mean'':188C ''mine'':16B ''minor'':293C ''model'':263C ''much'':60C,197C ''natur'':309C ''new'':175C ''nich'':13A,99C ''noth'':226C ''nowaday'':61C ''number'':101C,109C,141C,146C ''opinion'':202C ''optimis'':130C ''origin'':148C ''p'':23C,38C,63C,151C,210C,239C ''perhap'':104C,113C,201C ''person'':204C ''pick'':300C ''point'':255C ''predict'':273C ''question'':25C,36C ''real'':160C ''refer'':158C ''regard'':42C ''relev'':70C ''respons'':28C ''result'':131C ''revolv'':128C ''right'':302C ''robust'':83C ''sampl'':112C,122C,144C ''saw'':33C,157C ''search'':214C ''see'':285C ''sinc'':162C ''spot'':182C ''squar'':135C ''state'':7A,19B ''state-of-the-art'':18B ''stick'':254C ''still'':5A ''support'':2A,218C ''svm'':17B,179C,189C,266C ''svms'':55C,77C ''sweet'':181C ''syllabus'':47C ''take'':296C ''task'':316C ''thing'':294C ''tool'':303C ''train'':111C,121C,143C,312C ''tri'':212C ''understand'':75C ''use'':58C,93C ''vector'':3A,219C ''well'':185C ''wikipedia'':241C ''worth'':195C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (712, 2, '2014-07-09 13:07:13.303', 25, NULL, NULL, '&lt;p&gt;SVM is a powerful classifier. It has some nice advantages (which I guess were responsible for its popularity)... These are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Efficiency: Only the support vectors play a role in determining the classification boundary. All other points from the training set needn''t be stored in memory.&lt;/li&gt;
&lt;li&gt;The so-called power of kernels: With appropriate kernels you can transform feature space into a higher dimension so that it becomes linearly separable. The notion of kernels work with arbitrary objects on which you can define some notion of similarity with the help of inner products... and hence SVMs can classify arbitrary objects such as trees, graphs etc.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;There are some significant disadvantages as well.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Parameter sensitivity: The performance is highly sensitive to the choice of the regularization parameter C, which allows some variance in the model.&lt;/li&gt;
&lt;li&gt;Extra parameter for the Gaussian kernel: The radius of the Gaussian kernel can have a significant impact on classifier accuracy. Typically a grid search has to be conducted to find optimal parameters. LibSVM has a support for grid search.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;SVMs generally belong to the class of "Sparse Kernel Machines". The sparse vectors in the case of SVM are the support vectors which are chosen from the maximum margin criterion. Other sparse vector machines such as the &lt;strong&gt;Relevance Vector Machine&lt;/strong&gt; (RVM) perform better than SVM. The following figure shows a comparative performance of the two. In the figure, the x-axis shows one dimensional data from two classes y={0,1}. The mixture model is defined as P(x|y=0)=Unif(0,1) and P(x|y=1)=Unif(.5,1.5) (Unif denotes uniform distribution). 1000 points were sampled from this mixture and an SVM and an RVM were used to estimate the posterior. The problem of SVM is that the predicted values are far off from the true log odds.  &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/zNYbt.png" alt="RVM vs. SVM"&gt; &lt;/p&gt;

&lt;p&gt;A very effective classifier, which is very popular nowadays, is the &lt;strong&gt;Random Forest&lt;/strong&gt;. The main advantages are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Only one parameter to tune (i.e. the number of trees in the forest)&lt;/li&gt;
&lt;li&gt;Not utterly parameter sensitive&lt;/li&gt;
&lt;li&gt;Can easily be extended to multiple classes&lt;/li&gt;
&lt;li&gt;Is based on probabilistic principles (maximizing mutual information gain with the help of decision trees)&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':51C,113C,148C,189C,375C,381C,390C,407C ''/p'':22C,123C,327C,338C,359C ''/strong'':233C,354C ''/ul'':114C,190C,408C ''/znybt.png'':333C ''0'':264C,275C,277C ''1'':265C,278C,283C ''1.5'':286C ''1000'':291C ''5'':285C ''accuraci'':169C ''advantag'':11C,357C ''allow'':142C ''alt'':334C ''appropri'':61C ''arbitrari'':84C,106C ''axi'':255C ''base'':393C ''becom'':75C ''belong'':194C ''better'':236C ''boundari'':37C ''c'':140C ''call'':56C ''case'':207C ''choic'':135C ''chosen'':216C ''class'':197C,262C,389C ''classif'':36C ''classifi'':6C,105C,168C,343C ''compar'':244C ''conduct'':177C ''criterion'':221C ''data'':259C ''decis'':405C ''defin'':90C,270C ''denot'':288C ''determin'':34C ''dimens'':71C ''dimension'':258C ''disadvantag'':120C ''distribut'':290C ''easili'':384C ''effect'':342C ''effici'':25C ''estim'':307C ''etc'':112C ''extend'':386C ''extra'':150C ''far'':320C ''featur'':66C ''figur'':241C,251C ''find'':179C ''follow'':240C ''forest'':353C,374C ''gain'':400C ''gaussian'':154C,160C ''general'':193C ''graph'':111C ''grid'':172C,187C ''guess'':14C ''help'':97C,403C ''henc'':102C ''high'':131C ''higher'':70C ''i.e'':367C ''i.stack.imgur.com'':332C ''i.stack.imgur.com/znybt.png'':331C ''img'':329C ''impact'':166C ''inform'':399C ''inner'':99C ''kernel'':59C,62C,81C,155C,161C,200C ''li'':24C,52C,125C,149C,361C,376C,382C,391C ''libsvm'':182C ''linear'':76C ''log'':325C ''machin'':201C,225C,232C ''main'':356C ''margin'':220C ''maxim'':397C ''maximum'':219C ''memori'':50C ''mixtur'':267C,297C ''model'':147C,268C ''multipl'':388C ''mutual'':398C ''needn'':45C ''nice'':10C ''notion'':79C,92C ''nowaday'':348C ''number'':369C ''object'':85C,107C ''odd'':326C ''one'':257C,363C ''optim'':180C ''p'':1C,115C,191C,272C,280C,328C,339C ''paramet'':126C,139C,151C,181C,364C,379C ''perform'':129C,235C,245C ''play'':30C ''point'':40C,292C ''popular'':19C,347C ''posterior'':309C ''power'':5C,57C ''predict'':317C ''principl'':396C ''probabilist'':395C ''problem'':311C ''product'':100C ''radius'':157C ''random'':352C ''regular'':138C ''relev'':230C ''respons'':16C ''role'':32C ''rvm'':234C,303C,335C ''sampl'':294C ''search'':173C,188C ''sensit'':127C,132C,380C ''separ'':77C ''set'':44C ''show'':242C,256C ''signific'':119C,165C ''similar'':94C ''so-cal'':54C ''space'':67C ''spars'':199C,203C,223C ''src'':330C ''store'':48C ''strong'':229C,351C ''support'':28C,185C,212C ''svm'':2C,209C,238C,300C,313C,337C ''svms'':103C,192C ''train'':43C ''transform'':65C ''tree'':110C,371C,406C ''true'':324C ''tune'':366C ''two'':248C,261C ''typic'':170C ''ul'':23C,124C,360C ''unif'':276C,284C,287C ''uniform'':289C ''use'':305C ''utter'':378C ''valu'':318C ''varianc'':144C ''vector'':29C,204C,213C,224C,231C ''vs'':336C ''well'':122C ''work'':82C ''x'':254C,273C,281C ''x-axi'':253C ''y'':263C,274C,282C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (713, 1, '2014-07-09 17:51:40.583', 3, 840, 'Cloudera QuickStart VM Error', '&lt;p&gt;I have installed cloudera CDH5 Quick start VM on VM player. When I login through HUE in the first page I am the following error&lt;/p&gt;

&lt;p&gt;âPotential misconfiguration detected. Fix and restart Hue.â&lt;img src="http://i.stack.imgur.com/vnq5P.png" alt="Potential misconfiguration detected. Fix and restart Hue"&gt;&lt;/p&gt;

&lt;p&gt;How to solve this issue.&lt;/p&gt;

&lt;p&gt;Thanks,
Green&lt;/p&gt;
', 'hadoop', NULL, '''/p'':32C,56C,63C,67C ''/vnq5p.png'':47C ''alt'':48C ''cdh5'':11C ''cloudera'':1A,10C ''detect'':37C,51C ''error'':4A,31C ''first'':25C ''fix'':38C,52C ''follow'':30C ''green'':66C ''hadoop'':5B ''hue'':22C,41C,55C ''i.stack.imgur.com'':46C ''i.stack.imgur.com/vnq5p.png'':45C ''img'':43C ''instal'':9C ''issu'':62C ''login'':20C ''misconfigur'':36C,50C ''p'':6C,33C,57C,64C ''page'':26C ''player'':17C ''potenti'':35C,49C ''quick'':12C ''quickstart'':2A ''restart'':40C,54C ''solv'':60C ''src'':44C ''start'':13C ''thank'':65C ''vm'':3A,14C,16C ''â'':34C,42C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (714, 2, '2014-07-10 08:38:43.353', 5, NULL, NULL, '&lt;p&gt;Although your question is not very specific so I''ll try to give you some generic solutions. There are couple of things you can do here:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Check sparseMatrix from Matrix package as mentioned by @Sidhha&lt;/li&gt;
&lt;li&gt;Try running your model in parallel using packages like snowfall, &lt;a href="https://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf"&gt;Parallel&lt;/a&gt;. Check this &lt;a href="http://cran.r-project.org/web/views/HighPerformanceComputing.html"&gt;list of packages on Cran&lt;/a&gt; which can help you runnning your model in multicore parallel mode.&lt;/li&gt;
&lt;li&gt;You can also try &lt;a href="http://datatable.r-forge.r-project.org/"&gt;data.table&lt;/a&gt; package. It is quite phenomenal in speed.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Good reads:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="http://theodi.org/blog/fig-data-11-tips-how-handle-big-data-r-and-1-bad-pun"&gt;11 Tips on How to Handle Big Data in R (and 1 Bad Pun)&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://adv-r.had.co.nz/Performance.html"&gt;Why R is slow &amp;amp; how to improve its Performance?&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/a'':58C,71C,93C,128C,146C ''/blog/fig-data-11-tips-how-handle-big-data-r-and-1-bad-pun'':113C ''/li'':40C,83C,101C,129C,147C ''/ol'':148C ''/p'':28C,106C ''/performance.html'':135C ''/r-manual/r-devel/library/parallel/doc/parallel.pdf'':56C ''/ul'':102C ''/web/views/highperformancecomputing.html'':65C ''1'':125C ''11'':114C ''adv-r.had.co.nz'':134C ''adv-r.had.co.nz/performance.html'':133C ''also'':87C ''although'':2C ''amp'':140C ''bad'':126C ''big'':120C ''check'':31C,59C ''coupl'':21C ''cran'':70C ''cran.r-project.org'':64C ''cran.r-project.org/web/views/highperformancecomputing.html'':63C ''data'':121C ''data.table'':92C ''datatable.r-forge.r-project.org'':91C ''generic'':17C ''give'':14C ''good'':104C ''handl'':119C ''help'':74C ''href'':53C,62C,90C,110C,132C ''improv'':143C ''li'':30C,41C,84C,108C,130C ''like'':50C ''list'':66C ''ll'':11C ''matrix'':34C ''mention'':37C ''mode'':82C ''model'':45C,78C ''multicor'':80C ''ol'':107C ''p'':1C,103C ''packag'':35C,49C,68C,94C ''parallel'':47C,57C,81C ''perform'':145C ''phenomen'':98C ''pun'':127C ''question'':4C ''quit'':97C ''r'':123C,137C ''read'':105C ''run'':43C ''runn'':76C ''sidhha'':39C ''slow'':139C ''snowfal'':51C ''solut'':18C ''sparsematrix'':32C ''specif'':8C ''speed'':100C ''stat.ethz.ch'':55C ''stat.ethz.ch/r-manual/r-devel/library/parallel/doc/parallel.pdf'':54C ''theodi.org'':112C ''theodi.org/blog/fig-data-11-tips-how-handle-big-data-r-and-1-bad-pun'':111C ''thing'':23C ''tip'':115C ''tri'':12C,42C,88C ''ul'':29C ''use'':48C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (715, 2, '2014-07-10 09:16:39.937', 2, NULL, NULL, '&lt;p&gt;Go into the other link from home - to the cloudera manager.&lt;/p&gt;

&lt;p&gt;From there, you''ll see Hue can be restarted, but there is probably an alert that needs to be resolved in there first.&lt;/p&gt;

&lt;p&gt;If I remember right there''s some initial configuration that needs to be done on the quickstart VM that''s spelled out as soon as you log into the manager application.&lt;/p&gt;
', NULL, NULL, '''/p'':13C,38C,71C ''alert'':29C ''applic'':70C ''cloudera'':11C ''configur'':48C ''done'':53C ''first'':37C ''go'':2C ''home'':8C ''hue'':20C ''initi'':47C ''link'':6C ''ll'':18C ''log'':66C ''manag'':12C,69C ''need'':31C,50C ''p'':1C,14C,39C ''probabl'':27C ''quickstart'':56C ''rememb'':42C ''resolv'':34C ''restart'':23C ''right'':43C ''see'':19C ''soon'':63C ''spell'':60C ''vm'':57C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (716, 1, '2014-07-10 10:07:13.523', 6, 465, 'How to choose the features for a neural network?', '&lt;p&gt;I know that there is no a clear answer for this question, but let''s suppose that I have a huge neural network, with a lot of data and I want to add a new feature in input. The "best" way would be to test the network with the new feature and see the results, but is there a method to test if the feature IS UNLIKELY helpful? Like correlation measures (&lt;a href="http://www3.nd.edu/~mclark19/learn/CorrelationComparison.pdf"&gt;http://www3.nd.edu/~mclark19/learn/CorrelationComparison.pdf&lt;/a&gt;) etc?&lt;/p&gt;
', 'machine-learning  neuralnetwork  feature-selection  feature-extraction', NULL, '''/p'':101C ''/~mclark19/learn/correlationcomparison.pdf'':96C ''/~mclark19/learn/correlationcomparison.pdf&lt;/a&gt;)'':99C ''add'':53C ''answer'':29C ''best'':60C ''choos'':3A ''clear'':28C ''correl'':90C ''data'':48C ''etc'':100C ''extract'':19B ''featur'':5A,15B,18B,56C,71C,85C ''feature-extract'':17B ''feature-select'':14B ''help'':88C ''href'':93C ''huge'':41C ''input'':58C ''know'':22C ''learn'':12B ''let'':34C ''like'':89C ''lot'':46C ''machin'':11B ''machine-learn'':10B ''measur'':91C ''method'':80C ''network'':9A,43C,67C ''neural'':8A,42C ''neuralnetwork'':13B ''new'':55C,70C ''p'':20C ''question'':32C ''result'':75C ''see'':73C ''select'':16B ''suppos'':36C ''test'':65C,82C ''unlik'':87C ''want'':51C ''way'':61C ''would'':62C ''www3.nd.edu'':95C,98C ''www3.nd.edu/~mclark19/learn/correlationcomparison.pdf'':94C ''www3.nd.edu/~mclark19/learn/correlationcomparison.pdf&lt;/a&gt;)'':97C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (717, 1, '2014-07-10 11:55:49.637', 5, 47, 'How to define a custom resampling methodology', '&lt;p&gt;I''m using an experimental design to test the robustness of different classification methods, and now I''m searching for the correct definition of such design.&lt;/p&gt;

&lt;p&gt;I''m creating different subsets of the full dataset by cutting away some samples. Each subset is created independently with respect to the others. Then, I run each classification method on every subset. Finally, I estimate the accuracy of each method as how many classifications on subsets are in agreement with the classification on the full dataset. For example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Classification-full     1    2    3    2    1    1    2

Classification-subset1  1    2         2    3    1   
Classification-subset2       2    3         1    1    2
...

Accuracy                1    1    1    1  0.5    1    1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is there a correct name to this methodology? I thought it can fall under &lt;a href="http://en.wikipedia.org/wiki/Bootstrapping_(statistics)" rel="nofollow"&gt;bootstrapping&lt;/a&gt; but I''m not sure about this.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (720, 2, '2014-07-10 23:38:58.153', 2, NULL, NULL, '&lt;p&gt;There are a couple of open source options I know of - &lt;/p&gt;

&lt;p&gt;LibOTS - &lt;a href="http://libots.sourceforge.net/" rel="nofollow"&gt;http://libots.sourceforge.net/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;DocSum - &lt;a href="http://docsum.sourceforge.net/docsum/web/about.php" rel="nofollow"&gt;http://docsum.sourceforge.net/docsum/web/about.php&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;A couple of commercial solutions - &lt;/p&gt;

&lt;p&gt;Intellix Summarizer Pro - &lt;a href="http://summarizer.intellexer.com/order_summarizer_pro.php" rel="nofollow"&gt;http://summarizer.intellexer.com/order_summarizer_pro.php&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Copernic Summarizer - &lt;a href="http://www.copernic.com/en/products/summarizer/" rel="nofollow"&gt;http://www.copernic.com/en/products/summarizer/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;And this one is a web service - &lt;/p&gt;

&lt;p&gt;TextTeaser - &lt;a href="http://www.textteaser.com/" rel="nofollow"&gt;http://www.textteaser.com/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I''m sure there are plenty of others out there.  I have used Copernic a good deal and it''s pretty good, but I was hoping it could be automated easily, which it can''t - at least it couldn''t when I used it.  &lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':23C,88C ''/docsum/web/about.php'':30C ''/docsum/web/about.php&lt;/a&gt;&lt;/p&gt;'':35C ''/en/products/summarizer/'':64C ''/en/products/summarizer/&lt;/a&gt;&lt;/p&gt;'':69C ''/order_summarizer_pro.php'':51C ''/order_summarizer_pro.php&lt;/a&gt;&lt;/p&gt;'':56C ''/p'':13C,42C,78C,134C ''autom'':119C ''commerci'':40C ''copern'':58C,103C ''could'':117C ''couldn'':128C ''coupl'':5C,38C ''deal'':106C ''docsum'':25C ''docsum.sourceforge.net'':29C,34C ''docsum.sourceforge.net/docsum/web/about.php'':28C ''docsum.sourceforge.net/docsum/web/about.php&lt;/a&gt;&lt;/p&gt;'':33C ''easili'':120C ''good'':105C,111C ''hope'':115C ''href'':17C,27C,48C,61C,82C ''intellix'':44C ''know'':11C ''least'':126C ''libot'':15C ''libots.sourceforge.net'':18C,22C ''libots.sourceforge.net/&lt;/a&gt;&lt;/p&gt;'':21C ''m'':91C ''nofollow'':20C,32C,53C,66C,85C ''one'':73C ''open'':7C ''option'':9C ''other'':97C ''p'':1C,14C,24C,36C,43C,57C,70C,79C,89C ''plenti'':95C ''pretti'':110C ''pro'':46C ''rel'':19C,31C,52C,65C,84C ''servic'':77C ''solut'':41C ''sourc'':8C ''summar'':45C,59C ''summarizer.intellexer.com'':50C,55C ''summarizer.intellexer.com/order_summarizer_pro.php'':49C ''summarizer.intellexer.com/order_summarizer_pro.php&lt;/a&gt;&lt;/p&gt;'':54C ''sure'':92C ''textteas'':80C ''use'':102C,132C ''web'':76C ''www.copernic.com'':63C,68C ''www.copernic.com/en/products/summarizer/'':62C ''www.copernic.com/en/products/summarizer/&lt;/a&gt;&lt;/p&gt;'':67C ''www.textteaser.com'':83C,87C ''www.textteaser.com/&lt;/a&gt;&lt;/p&gt;'':86C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (721, 2, '2014-07-11 01:28:24.957', 7, NULL, NULL, '&lt;p&gt;When it comes to dealing with many disparate kinds of data, especially when the relationships between them are unclear, I would strongly recommend a technique based on &lt;a href="http://en.wikipedia.org/wiki/Decision_tree_learning"&gt;decision trees&lt;/a&gt;, the most popular ones today to the best of my knowledge are &lt;a href="http://en.wikipedia.org/wiki/Random_forest"&gt;random forest&lt;/a&gt;, and &lt;a href="http://www.montefiore.ulg.ac.be/~ernst/uploads/news/id63/extremely-randomized-trees.pdf"&gt;extremely randomized trees&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Both have implementations in &lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier.fit_transform"&gt;sklearn&lt;/a&gt;, and they are pretty straightforward to use. At a very high level, the reason that a &lt;code&gt;decision tree&lt;/code&gt;-based approach is advantageous for multiple disparate kinds of data is because decision trees are largely independent from the specific data they are dealing with, just so long as they are capable of understanding your representation.&lt;/p&gt;

&lt;p&gt;You''ll still have to fit your data into a feature vector of some kind, but based on your example that seems like a pretty straightforward task, and if you''re willing to go a little deeper on your implementation you could certainly come up with a custom tree-splitting rule without actually having to change anything in the underlying algorithm. The &lt;a href="http://www.cs.nyu.edu/~roweis/csc2515-2006/readings/quinlan.pdf"&gt;original paper&lt;/a&gt; is a pretty decent place to start if you want to give that a shot.&lt;/p&gt;

&lt;p&gt;If you want pseudo-structural data from your text data though, I might suggest looking into &lt;code&gt;doc2vec&lt;/code&gt;, recently developed by Google. I don''t think there are any good open-source implementations now, but it''s a pretty straightforward improvement on the &lt;code&gt;word2vec&lt;/code&gt; algorithm, which has implementations in at least &lt;code&gt;C&lt;/code&gt; and &lt;code&gt;python&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Hope that helps! Let me know if you''ve got any more questions.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (946, 1, '2014-08-08 13:44:52.803', 2, 348, 'Analysis of Split (A/B) tests using Poisson and/or Binomial Distribution', '&lt;p&gt;Cross posting this from Cross Validated:&lt;/p&gt;

&lt;p&gt;I''ve seen this question asked before, but I have yet to come across a definitive source answering the specific questions:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;What''s the most appropriate statistical test to apply to a small A/B test?&lt;/li&gt;
&lt;li&gt;What''s the R code and interpretation to analyze a small A/B test?&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I''m running a small test to figure out which ads perform better. I have the following results:&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Position 1:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;code&gt;variation,impressions,clicks
row-1,753,26
row-3,767 7
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Position 2:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;code&gt;variation,impressions,clicks
row-1,753,16
row-3,767 13
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Position 3:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;code&gt;variation,impressions,clicks
row-1,753,2
row-3,767 7
&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;I think it''s safe to say these numbers are small and likely to be not normally distributed. Also, it''s click data so there''s a binary outcome of clicked or not and the trials are independent.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Appropriate test&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;In analyzing each position for significance, I think comparison with a binomial or Poisson distribution makes the most sense. &lt;/p&gt;

&lt;p&gt;According to &lt;a href="http://www.openintro.org/stat/textbook.php?stat_book=os" rel="nofollow"&gt;the OpenIntro Stats&lt;/a&gt; (and other sources) book, a variable follows a Poisson distribution "... if the event being considered is rare, the population is large, and the events occur independently of each other."&lt;/p&gt;

&lt;p&gt;The same source classifies a binomial variable approximately the same way adding that the probability of success is the same and the number of trials is fixed.&lt;/p&gt;

&lt;p&gt;I appreciate this is not an either/or decision and analysis can be done using both distributions.&lt;/p&gt;

&lt;p&gt;Given A/B (split) testing is a science that has been practiced for several years, I imagine that there is a canonical test. However, looking around the internet, I mostly come across analysis that uses the standard normal distribution. That just seems wrong :)&lt;/p&gt;

&lt;p&gt;Is there a canonical test to use for A/B tests with small #''s of clicks?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Interpretation and R code&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I''ve used the following R code to test significance for each position:&lt;/p&gt;

&lt;p&gt;Position 1:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;binom.test(7, 767, p=(26/753))

Exact binomial test

data:  7 and 767
number of successes = 7, number of trials = 767, p-value = 1.077e-05
alternative hypothesis: true probability of success is not equal to 0.03452855
95 percent confidence interval:
 0.003676962 0.018713125
sample estimates:
probability of success 
           0.009126467
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I interpret this result to mean: The probability of success in the test group is indeed different than the control group with a 95% confidence interval that the success probability is between .368% and 1.87%&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ppois(((26-1)/753), lambda=(7/767), lower.tail = F)
[1] 0.009084947
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I interpret this result to mean: Given a Poisson distribution with a click rate of 7 per 767 trials, there is a 0.9% chance of having a click rate of 26 or more per 753 trials in the same distribution. Contextualized in the ad example,
there is a .1% chance that the control ad actually performs the same as the test ad.&lt;/p&gt;

&lt;p&gt;Is the above interpretation correct? Does the test and interpretation change with the different positions (i.e. are the results of the Poisson test more appropriate for Position 3 given the small numbers)?&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (947, 2, '2014-08-08 13:53:36.847', 5, NULL, NULL, '&lt;p&gt;First of all, some basics of classification (and in general any supervised ML tasks), just to make sure we have same set of concepts in mind. &lt;/p&gt;

&lt;p&gt;Any supervised ML algorithm consists of at least 2 components: &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Dataset to train and test on.&lt;/li&gt;
&lt;li&gt;Algorithm(s) to handle these data.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Training dataset consists of a set of pairs &lt;code&gt;(x, y)&lt;/code&gt;, where &lt;code&gt;x&lt;/code&gt; is a &lt;strong&gt;vector of features&lt;/strong&gt; and &lt;code&gt;y&lt;/code&gt; is &lt;strong&gt;predicted variable&lt;/strong&gt;. Predicted variable is just what you want to know, i.e. in your case it is exception type. Features are more tricky. You cannot just throw raw text into an algorithm, you need to extract meaningful parts of it and organize them as feature vectors first. You''ve already mentioned a couple of useful features - exception class name (e.g. &lt;code&gt;com.acme.PrintException&lt;/code&gt;) and contained words ("Timeout"). All you need is to translate your row exceptions (and human-categorized exception types) into suitable dataset, e.g.: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ex_class                  contains_timeout  ...   | ex_type
-----------------------------------------------------------
[com.acme.PrintException, 1                , ...] | Availability
[java.lang.Exception    , 0                , ...] | Network
 ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This representation is already much better for ML algorithms. But which one to take? &lt;/p&gt;

&lt;p&gt;Taking into account nature of the task and your current approach natural choice is to use &lt;strong&gt;decision trees&lt;/strong&gt;. This class of algorithms will compute optimal decision criteria for all your exception types and print out resulting tree. This is especially useful, because you will have possibility to manually inspect how decision is made and see how much it corresponds to your manually-crafted rules. &lt;/p&gt;

&lt;p&gt;There''s, however, possibility that some exceptions with exactly the same features will belong to different exception types. In this case probabilistic approach may work well. Despite its name, &lt;strong&gt;Naive Bayes&lt;/strong&gt; classifier works pretty well in most cases. There''s one issue with NB and our dataset representation, though: dataset contains &lt;em&gt;categorical&lt;/em&gt; variables, and Naive Bayes can work with &lt;em&gt;numerical&lt;/em&gt; attributes only*. Standard way to overcome this problem is to use &lt;a href="http://en.wikipedia.org/wiki/Dummy_variable_%28statistics%29"&gt;dummy variables&lt;/a&gt;. In short, dummy variables are binary variables that simply indicate whether specific category presents or not. For example, single variable &lt;code&gt;ex_class&lt;/code&gt; with values &lt;code&gt;{com.acme.PrintException, java.lang.Exception, ...}&lt;/code&gt;, etc. may be split into several variables &lt;code&gt;ex_class_printexception&lt;/code&gt;, &lt;code&gt;ex_class_exception&lt;/code&gt;, etc. with values &lt;code&gt;{0, 1}&lt;/code&gt;:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;ex_class_printexception  ex_class_exception  contains_timeout | ex_type
-----------------------------------------------------------------------
[1,                    , 0                 , 1              ] | Availability
[0,                    , 1                 , 0              ] | Network
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;One last algorithm to try is &lt;strong&gt;Support Vector Machines (SVM)&lt;/strong&gt;. It neither provides helpful visualisation, nor is probabilistic, but often gives superior results. &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;&lt;em&gt;*&lt;/em&gt; - in fact, neither Bayes theorem, nor Naive Bayes itself state anything about variable type, but most software packages that come to mind rely on numerical features. &lt;/p&gt;
', NULL, NULL, '''/a'':363C ''/code'':71C,75C,86C,152C,191C,387C,393C,405C,410C,417C,439C ''/em'':334C,344C,471C ''/li'':49C,57C ''/ol'':58C ''/p'':28C,40C,176C,208C,277C,418C,467C,498C ''/pre'':192C,440C ''/strong'':82C,91C,229C,311C,453C ''/wiki/dummy_variable_%28statistics%29'':360C ''0'':189C,415C,432C,435C,437C ''1'':186C,416C,431C,433C,436C ''2'':38C ''account'':212C ''algorithm'':33C,51C,121C,202C,233C,444C ''alreadi'':139C,197C ''anyth'':482C ''approach'':220C,301C ''attribut'':345C ''avail'':187C,434C ''basic'':6C ''bay'':310C,338C,475C,479C ''belong'':292C ''better'':199C ''binari'':369C ''cannot'':114C ''case'':104C,299C,318C ''categor'':169C,333C ''categori'':376C ''choic'':222C ''class'':147C,180C,231C,386C,403C,408C,422C,425C ''classif'':8C ''classifi'':312C ''code'':68C,73C,84C,150C,178C,384C,390C,401C,406C,414C,420C ''com.acme.printexception'':151C,185C,391C ''come'':491C ''compon'':39C ''comput'':235C ''concept'':25C ''consist'':34C,62C ''contain'':154C,181C,331C,427C ''correspond'':270C ''coupl'':142C ''craft'':275C ''criteria'':238C ''current'':219C ''data'':56C ''dataset'':43C,61C,174C,327C,330C ''decis'':227C,237C,262C ''despit'':305C ''differ'':294C ''dummi'':361C,366C ''e.g'':149C,175C ''em'':332C,342C,470C ''en.wikipedia.org'':359C ''en.wikipedia.org/wiki/dummy_variable_%28statistics%29'':358C ''especi'':251C ''etc'':394C,411C ''ex'':179C,183C,385C,402C,407C,421C,424C,429C ''exact'':287C ''exampl'':381C ''except'':107C,146C,165C,170C,242C,285C,295C,409C,426C ''extract'':125C ''fact'':473C ''featur'':81C,109C,134C,145C,290C,497C ''first'':2C,136C ''general'':11C ''give'':464C ''handl'':54C ''help'':457C ''howev'':281C ''hr'':468C ''href'':357C ''human'':168C ''human-categor'':167C ''i.e'':101C ''indic'':373C ''inspect'':260C ''issu'':322C ''java.lang.exception'':188C,392C ''know'':100C ''last'':443C ''least'':37C ''li'':42C,50C ''machin'':451C ''made'':264C ''make'':18C ''manual'':259C,274C ''manually-craft'':273C ''may'':302C,395C ''meaning'':126C ''mention'':140C ''mind'':27C,493C ''ml'':14C,32C,201C ''much'':198C,268C ''naiv'':309C,337C,478C ''name'':148C,307C ''natur'':213C,221C ''nb'':324C ''need'':123C,159C ''neither'':455C,474C ''network'':190C,438C ''numer'':343C,496C ''often'':463C ''ol'':41C ''one'':205C,321C,442C ''optim'':236C ''organ'':131C ''overcom'':350C ''p'':1C,29C,59C,193C,209C,278C,441C,469C ''packag'':489C ''pair'':67C ''part'':127C ''possibl'':257C,282C ''pre'':177C,419C ''predict'':89C,92C ''present'':377C ''pretti'':314C ''print'':245C ''printexcept'':404C,423C ''probabilist'':300C,461C ''problem'':352C ''provid'':456C ''raw'':117C ''reli'':494C ''represent'':195C,328C ''result'':247C,466C ''row'':164C ''rule'':276C ''see'':266C ''set'':23C,65C ''sever'':399C ''short'':365C ''simpli'':372C ''singl'':382C ''softwar'':488C ''specif'':375C ''split'':397C ''standard'':347C ''state'':481C ''strong'':78C,88C,226C,308C,448C ''suitabl'':173C ''superior'':465C ''supervis'':13C,31C ''support'':449C ''sure'':19C ''svm'':452C ''take'':207C,210C ''task'':15C,216C ''test'':47C ''text'':118C ''theorem'':476C ''though'':329C ''throw'':116C ''timeout'':156C,182C,428C ''train'':45C,60C ''translat'':162C ''tree'':228C,248C ''tri'':446C ''tricki'':112C ''type'':108C,171C,184C,243C,296C,430C,485C ''use'':144C,225C,252C,355C ''valu'':389C,413C ''variabl'':90C,93C,335C,362C,367C,370C,383C,400C,484C ''ve'':138C ''vector'':79C,135C,450C ''visualis'':458C ''want'':98C ''way'':348C ''well'':304C,315C ''whether'':374C ''word'':155C ''work'':303C,313C,340C ''x'':69C,74C ''y'':70C,85C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (722, 2, '2014-07-11 09:49:32.933', 19, NULL, NULL, '&lt;p&gt;R contains some &lt;em&gt;standard&lt;/em&gt; functions for data manipulation, which can be used for data cleaning, in its &lt;strong&gt;base&lt;/strong&gt; package (&lt;code&gt;gsub&lt;/code&gt;, &lt;code&gt;transform&lt;/code&gt;, etc.), as well as in various third-party packages, such as &lt;strong&gt;stringr&lt;/strong&gt;, &lt;strong&gt;reshape&lt;/strong&gt;/&lt;strong&gt;reshape2&lt;/strong&gt;, and &lt;strong&gt;plyr&lt;/strong&gt;/&lt;strong&gt;dplyr&lt;/strong&gt;. Examples and best practices of usage for these packages and their functions are described in the following paper: &lt;a href="http://vita.had.co.nz/papers/tidy-data.pdf"&gt;http://vita.had.co.nz/papers/tidy-data.pdf&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Additionally, R offers some packages specifically &lt;em&gt;focused&lt;/em&gt; on data cleaning and transformation:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;editrules&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/editrules/index.html"&gt;http://cran.r-project.org/web/packages/editrules/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;deducorrect&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/deducorrect/index.html"&gt;http://cran.r-project.org/web/packages/deducorrect/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;StatMatch&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/StatMatch/index.html"&gt;http://cran.r-project.org/web/packages/StatMatch/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;MatchIt&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/MatchIt/index.html"&gt;http://cran.r-project.org/web/packages/MatchIt/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;DataCombine&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/DataCombine"&gt;http://cran.r-project.org/web/packages/DataCombine&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;data.table&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/data.table"&gt;http://cran.r-project.org/web/packages/data.table&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;A comprehensive and coherent approach to &lt;strong&gt;data cleaning&lt;/strong&gt; in R, including examples and use of &lt;strong&gt;editrules&lt;/strong&gt; and &lt;strong&gt;deducorrect&lt;/strong&gt; packages, as well as a description of &lt;em&gt;workflow&lt;/em&gt; (&lt;em&gt;framework&lt;/em&gt;) of data cleaning in R, is presented in the following paper, which I highly recommend: &lt;a href="http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf"&gt;http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/code'':27C,30C ''/doc/contrib/de_jonge+van_der_loo-introduction_to_data_cleaning_with_r.pdf'':232C ''/doc/contrib/de_jonge+van_der_loo-introduction_to_data_cleaning_with_r.pdf&lt;/a&gt;.&lt;/p&gt;'':235C ''/em'':7C,94C,209C,212C ''/p'':100C ''/papers/tidy-data.pdf'':81C ''/papers/tidy-data.pdf&lt;/a&gt;.&lt;/p&gt;'':84C ''/strong'':23C,45C,48C,51C,55C,58C,105C,117C,129C,141C,153C,165C,185C,195C,199C ''/ul'':174C ''/web/packages/data.table'':170C ''/web/packages/data.table&lt;/a&gt;)&lt;/li&gt;'':173C ''/web/packages/datacombine'':158C ''/web/packages/datacombine&lt;/a&gt;)&lt;/li&gt;'':161C ''/web/packages/deducorrect/index.html'':122C ''/web/packages/deducorrect/index.html&lt;/a&gt;)&lt;/li&gt;'':125C ''/web/packages/editrules/index.html'':110C ''/web/packages/editrules/index.html&lt;/a&gt;)&lt;/li&gt;'':113C ''/web/packages/matchit/index.html'':146C ''/web/packages/matchit/index.html&lt;/a&gt;)&lt;/li&gt;'':149C ''/web/packages/statmatch/index.html'':134C ''/web/packages/statmatch/index.html&lt;/a&gt;)&lt;/li&gt;'':137C ''addit'':86C ''approach'':180C ''base'':22C ''best'':61C ''clean'':18C,97C,184C,215C ''code'':25C,28C ''coher'':179C ''comprehens'':177C ''contain'':3C ''cran.r-project.org'':109C,112C,121C,124C,133C,136C,145C,148C,157C,160C,169C,172C,231C,234C ''cran.r-project.org/doc/contrib/de_jonge+van_der_loo-introduction_to_data_cleaning_with_r.pdf'':230C ''cran.r-project.org/doc/contrib/de_jonge+van_der_loo-introduction_to_data_cleaning_with_r.pdf&lt;/a&gt;.&lt;/p&gt;'':233C ''cran.r-project.org/web/packages/data.table'':168C ''cran.r-project.org/web/packages/data.table&lt;/a&gt;)&lt;/li&gt;'':171C ''cran.r-project.org/web/packages/datacombine'':156C ''cran.r-project.org/web/packages/datacombine&lt;/a&gt;)&lt;/li&gt;'':159C ''cran.r-project.org/web/packages/deducorrect/index.html'':120C ''cran.r-project.org/web/packages/deducorrect/index.html&lt;/a&gt;)&lt;/li&gt;'':123C ''cran.r-project.org/web/packages/editrules/index.html'':108C ''cran.r-project.org/web/packages/editrules/index.html&lt;/a&gt;)&lt;/li&gt;'':111C ''cran.r-project.org/web/packages/matchit/index.html'':144C ''cran.r-project.org/web/packages/matchit/index.html&lt;/a&gt;)&lt;/li&gt;'':147C ''cran.r-project.org/web/packages/statmatch/index.html'':132C ''cran.r-project.org/web/packages/statmatch/index.html&lt;/a&gt;)&lt;/li&gt;'':135C ''data'':10C,17C,96C,183C,214C ''data.table'':164C ''datacombin'':152C ''deducorrect'':116C,198C ''describ'':72C ''descript'':205C ''dplyr'':57C ''editrul'':104C,194C ''em'':5C,92C,207C,210C ''etc'':31C ''exampl'':59C,189C ''focus'':93C ''follow'':75C,222C ''framework'':211C ''function'':8C,70C ''gsub'':26C ''high'':226C ''href'':78C,107C,119C,131C,143C,155C,167C,229C ''includ'':188C ''li'':102C,114C,126C,138C,150C,162C ''manipul'':11C ''matchit'':140C ''offer'':88C ''p'':1C,85C,175C ''packag'':24C,40C,67C,90C,200C ''paper'':76C,223C ''parti'':39C ''plyr'':54C ''practic'':62C ''present'':219C ''r'':2C,87C,187C,217C ''recommend'':227C ''reshap'':47C ''reshape2'':50C ''specif'':91C ''standard'':6C ''statmatch'':128C ''stringr'':44C ''strong'':21C,43C,46C,49C,53C,56C,103C,115C,127C,139C,151C,163C,182C,193C,197C ''third'':38C ''third-parti'':37C ''transform'':29C,99C ''ul'':101C ''usag'':64C ''use'':15C,191C ''various'':36C ''vita.had.co.nz'':80C,83C ''vita.had.co.nz/papers/tidy-data.pdf'':79C ''vita.had.co.nz/papers/tidy-data.pdf&lt;/a&gt;.&lt;/p&gt;'':82C ''well'':33C,202C ''workflow'':208C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (723, 2, '2014-07-11 11:32:13.55', 2, NULL, NULL, '&lt;p&gt;Random subsampling seems appropriate, bootstrapping is a bit more generic, but also correct.&lt;/p&gt;

&lt;p&gt;Here are some references and synonyms: &lt;a href="http://www.frank-dieterle.com/phd/2_4_3.html" rel="nofollow"&gt;http://www.frank-dieterle.com/phd/2_4_3.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':15C ''/phd/2_4_3.html'':27C ''/phd/2_4_3.html&lt;/a&gt;&lt;/p&gt;'':32C ''also'':13C ''appropri'':5C ''bit'':9C ''bootstrap'':6C ''correct'':14C ''generic'':11C ''href'':24C ''nofollow'':29C ''p'':1C,16C ''random'':2C ''refer'':20C ''rel'':28C ''seem'':4C ''subsampl'':3C ''synonym'':22C ''www.frank-dieterle.com'':26C,31C ''www.frank-dieterle.com/phd/2_4_3.html'':25C ''www.frank-dieterle.com/phd/2_4_3.html&lt;/a&gt;&lt;/p&gt;'':30C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (724, 2, '2014-07-11 14:13:30.403', 4, NULL, NULL, '&lt;p&gt;Your approach of using a source version repository is a good one and it actually allows you also working on the cluster and then copying everything back.&lt;/p&gt;

&lt;p&gt;If you find yourself making minor edits to your Python script on your laptop, then updating your SVN directory on the cluster, why not work directly on the cluster frontend, make all needed minor edits, and then, at the end of the day, commit everything there and update on your laptop? &lt;/p&gt;

&lt;p&gt;All you need is to get familiar with the environment there (OS, editor, etc.) or install your own environment (I usually install in my home directory the latest version of &lt;a href="http://www.vim.org/" rel="nofollow"&gt;Vim&lt;/a&gt;, &lt;a href="http://tmux.sourceforge.net/" rel="nofollow"&gt;Tmux&lt;/a&gt;, etc. with the proper dotfiles so I feel at home there.)&lt;/p&gt;

&lt;p&gt;Also, you can version your data, and even your intermediate results if size permits. My repositories often comprise code, data (original and cleaned versions), documentation, and paper sources for publishing (latex)&lt;/p&gt;

&lt;p&gt;Finally, you can script your job submission to avoid modifying scripts manually. &lt;code&gt;qsub&lt;/code&gt; accepts a script from stdin and also accepts all &lt;code&gt;#$&lt;/code&gt; comments as command-line arguments. &lt;/p&gt;
', NULL, NULL, '''/a'':120C,127C ''/code'':188C,199C ''/p'':29C,82C,139C,172C,206C ''accept'':189C,196C ''actual'':16C ''allow'':17C ''also'':19C,141C,195C ''approach'':3C ''argument'':205C ''avoid'':182C ''back'':28C ''clean'':163C ''cluster'':23C,52C,59C ''code'':159C,186C,198C ''command'':203C ''command-lin'':202C ''comment'':200C ''commit'':74C ''compris'':158C ''copi'':26C ''data'':146C,160C ''day'':73C ''direct'':56C ''directori'':49C,109C ''document'':165C ''dotfil'':132C ''edit'':37C,65C ''editor'':96C ''end'':70C ''environ'':93C,102C ''etc'':97C,128C ''even'':148C ''everyth'':27C,75C ''familiar'':90C ''feel'':135C ''final'':174C ''find'':33C ''frontend'':60C ''get'':89C ''good'':12C ''home'':108C,137C ''href'':115C,122C ''instal'':99C,105C ''intermedi'':150C ''job'':179C ''laptop'':44C,81C ''latest'':111C ''latex'':171C ''line'':204C ''make'':35C,61C ''manual'':185C ''minor'':36C,64C ''modifi'':183C ''need'':63C,86C ''nofollow'':118C,125C ''often'':157C ''one'':13C ''origin'':161C ''os'':95C ''p'':1C,30C,83C,140C,173C ''paper'':167C ''permit'':154C ''proper'':131C ''publish'':170C ''python'':40C ''qsub'':187C ''rel'':117C,124C ''repositori'':9C,156C ''result'':151C ''script'':41C,177C,184C,191C ''size'':153C ''sourc'':7C,168C ''stdin'':193C ''submiss'':180C ''svn'':48C ''tmux'':126C ''tmux.sourceforge.net'':123C ''updat'':46C,78C ''use'':5C ''usual'':104C ''version'':8C,112C,144C,164C ''vim'':119C ''work'':20C,55C ''www.vim.org'':116C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (725, 2, '2014-07-11 14:27:01.603', 0, NULL, NULL, '&lt;p&gt;What you describe falls in the category of &lt;a href="http://en.wikipedia.org/wiki/Concept_drift" rel="nofollow"&gt;concept drift&lt;/a&gt; in machine learning.
You might find interesting and actionable ideas in this &lt;a href="http://arxiv.org/pdf/1010.4784.pdf" rel="nofollow"&gt;summary paper&lt;/a&gt; and you''ll find a taxonomy of the possible approaches in &lt;a href="http://www.cs.waikato.ac.nz/~abifet/PAKDD2011/PAKDD11Tutorial_Handling_Concept_Drift.pdf" rel="nofollow"&gt;these slides&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':19C,41C,62C ''/p'':63C ''/pdf/1010.4784.pdf'':36C ''/wiki/concept_drift'':14C ''/~abifet/pakdd2011/pakdd11tutorial_handling_concept_drift.pdf'':57C ''action'':28C ''approach'':51C ''arxiv.org'':35C ''arxiv.org/pdf/1010.4784.pdf'':34C ''categori'':8C ''concept'':17C ''describ'':4C ''drift'':18C ''en.wikipedia.org'':13C ''en.wikipedia.org/wiki/concept_drift'':12C ''fall'':5C ''find'':25C,45C ''href'':11C,33C,54C ''idea'':29C ''interest'':26C ''learn'':22C ''ll'':44C ''machin'':21C ''might'':24C ''nofollow'':16C,38C,59C ''p'':1C ''paper'':40C ''possibl'':50C ''rel'':15C,37C,58C ''slide'':61C ''summari'':39C ''taxonomi'':47C ''www.cs.waikato.ac.nz'':56C ''www.cs.waikato.ac.nz/~abifet/pakdd2011/pakdd11tutorial_handling_concept_drift.pdf'':55C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (726, 1, '2014-07-11 21:09:58.873', 5, 120, 'Trying to understand free-energy equations in a Karl Friston neuroscience article', '&lt;p&gt;I am trying to understand a neuroscience article:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Friston, Karl J., et al. "Action and behavior: a free-energy formulation." &lt;em&gt;Biological cybernetics&lt;/em&gt; 102.3 (2010): 227-260. (&lt;a href="http://link.springer.com/article/10.1007%2Fs00422-010-0364-z" rel="nofollow"&gt;DOI 10.1007/s00422-010-0364-z&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;In this article, Friston gives three equations that are, as I understand him, equivalent or inter-convertertable and refer to both physical and Shannon entropy. They appear on page 231 of the article as equation (5):&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;The resulting expression for free-energy can be expressed in three ways (with the use of the Bayes rules and simple rearrangements):&lt;/p&gt;
  
  &lt;p&gt;â¢ Energy minus entropy&lt;/p&gt;
  
  &lt;p&gt;â¢ Divergence plus surprise&lt;/p&gt;
  
  &lt;p&gt;â¢ Complexity minus accuracy&lt;/p&gt;
  
  &lt;p&gt;Mathematically, these correspond to:&lt;/p&gt;
  
  &lt;p&gt;&lt;img src="http://i.stack.imgur.com/0eQgg.png" alt="\begin {aligned} F &amp;amp;= - \langle \ln p(\tilde s, \Psi \vert m) \rangle _q + \langle \ln q(\Psi \vert \mu) \rangle _q \ &amp;amp;= D(q(\Psi \vert \mu) \parallel p(\Psi \vert \tilde s, m)) - \ln p(\tilde s \vert m) &amp;amp; \qquad \qquad &amp;amp; (5) \ &amp;amp;= D(q(\Psi \vert \mu) \parallel p(\Psi \vert m)) - \langle \ln p(\tilde s \vert \Psi, m) \rangle _q \end{aligned}"&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The things I am struggling with at this point are:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;the meaning of the || in the 2nd and 3rd versions of the equations;&lt;/li&gt;
&lt;li&gt;and the negative logs.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Any help in understanding how these equations are actually what Fristen claims them to be would be greatly appreciated. For example, in the 1st equation, in what sense is the first term energy, etc?&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5037, 2, '2015-02-04 02:54:52.773', 0, NULL, NULL, '&lt;p&gt;I''ve found an interesting project With tons of data available. It''s a real data benchmark executed over an industrial valve. This is the website.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://diag.mchtr.pw.edu.pl/damadics/" rel="nofollow"&gt;Industrial Actuator Real Data Benchmark Study.&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':43C ''/damadics/'':34C ''/p'':28C,44C ''actuat'':38C ''avail'':12C ''benchmark'':18C,41C ''data'':11C,17C,40C ''diag.mchtr.pw.edu.pl'':33C ''diag.mchtr.pw.edu.pl/damadics/'':32C ''execut'':19C ''found'':4C ''href'':31C ''industri'':22C,37C ''interest'':6C ''nofollow'':36C ''p'':1C,29C ''project'':7C ''real'':16C,39C ''rel'':35C ''studi'':42C ''ton'':9C ''valv'':23C ''ve'':3C ''websit'':27C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (728, 2, '2014-07-11 22:03:31.95', 4, NULL, NULL, '&lt;p&gt;In addition to excellent previous answers, I''d like to recommend two papers on &lt;strong&gt;data cleaning&lt;/strong&gt;. They are not specific to &lt;em&gt;manual&lt;/em&gt; data cleaning, but, considering the benefits and advice (which I completely agree with) of expressing even &lt;strong&gt;manual&lt;/strong&gt; data transformations &lt;strong&gt;in code&lt;/strong&gt;, these resources can be as valuable. Also, despite the fact that following papers are somewhat R-focused, I believe that general &lt;em&gt;ideas&lt;/em&gt; and &lt;em&gt;workflows&lt;/em&gt; for data cleaning can be easily extracted and are equally applicable to non-R environments, as well.&lt;/p&gt;

&lt;p&gt;The first paper presents the concept of &lt;em&gt;tidy data&lt;/em&gt;, as well as examples and best practices of use of standard and specific R packages in data cleaning: &lt;a href="http://vita.had.co.nz/papers/tidy-data.pdf" rel="nofollow"&gt;http://vita.had.co.nz/papers/tidy-data.pdf&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;A comprehensive and coherent approach to data cleaning in R, including examples, as well as a description of &lt;em&gt;workflow&lt;/em&gt; (&lt;em&gt;framework&lt;/em&gt;) of data cleaning in R, is presented in the following paper, which I highly recommend: &lt;a href="http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf" rel="nofollow"&gt;http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (730, 1, '2014-07-12 17:25:52.907', 8, 264, 'Is FPGrowth still considered "state of the art" in frequent pattern mining?', '&lt;p&gt;As far as I know the development of algorithms to solve the Frequent Pattern Mining (FPM) problem, the road of improvements have some main checkpoints. Firstly, the &lt;a href="http://en.wikipedia.org/wiki/Apriori_algorithm"&gt;Apriori&lt;/a&gt; algorithm was proposed in 1993, by &lt;a href="http://dl.acm.org/citation.cfm?id=170072"&gt;Agrawal et al.&lt;/a&gt;, along with the formalization of the problem. The algorithm was able to &lt;em&gt;strip-off&lt;/em&gt; some sets from the &lt;code&gt;2^n - 1&lt;/code&gt; sets (powerset) by using a lattice to maintain the data. A drawback of the approach was the need to re-read the database to compute the frequency of each set expanded.&lt;/p&gt;

&lt;p&gt;Later, on year 1997, &lt;a href="http://www.computer.org/csdl/trans/tk/2000/03/k0372-abs.html"&gt;Zaki et al.&lt;/a&gt; proposed the algorithm &lt;a href="http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Frequent_Pattern_Mining/The_Eclat_Algorithm"&gt;Eclat&lt;/a&gt;, which &lt;em&gt;inserted&lt;/em&gt; the resulting frequency of each set inside the lattice. This was done by adding, at each node of the lattice, the set of transaction-ids that had the items from root to the referred node. The main contribution is that one does not have to re-read the entire dataset to know the frequency of each set, but the memory required to keep such data structure built may exceed the size of the dataset itself.&lt;/p&gt;

&lt;p&gt;In 2000, &lt;a href="http://dl.acm.org/citation.cfm?doid=335191.335372"&gt;Han et al.&lt;/a&gt; proposed an algorithm named &lt;a href="http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Frequent_Pattern_Mining/The_FP-Growth_Algorithm"&gt;FPGrowth&lt;/a&gt;, along with a prefix-tree data structure named FPTree. The algorithm was able to provide significant data compression, while also granting that only frequent itemsets would be yielded (without candidate itemset generation). This was done mainly by sorting the items of each transaction in decreasing order, so that the most frequent items are the ones with the least repetitions in the tree data structure. Since the frequency only descends while traversing the tree in-depth, the algorithm is able to &lt;em&gt;strip-off&lt;/em&gt; non-frequent itemsets.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Edit&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;&lt;strike&gt;As far as I know, this may be considered a state-of-the-art algorithm, but I''d like to know about other proposed solutions. What other algorithms for FPM are considered "state-of-the-art"? What is the &lt;em&gt;intuition&lt;/em&gt;/&lt;em&gt;main-contribution&lt;/em&gt; of such algorithms?&lt;/strike&gt;&lt;/p&gt;

&lt;p&gt;Is the FPGrowth algorithm still considered "state of the art" in frequent pattern mining? If not, what algorithm(s) may extract frequent itemsets from large datasets more efficiently?&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (731, 1, '2014-07-13 09:04:39.703', 26, 966, 'How to fight underfitting in a deep neural net', '&lt;p&gt;When I started with artificial neural networks (NN) I thought I''d have to fight overfitting as the main problem. But in practice I can''t even get my NN to pass the 20% error rate barrier. I can''t even beat my score on random forest!&lt;/p&gt;

&lt;p&gt;I''m seeking some very general or not so general advice on what should one do to make a NN start capturing trends in data.&lt;/p&gt;

&lt;p&gt;For implementing NN I use Theano Stacked Auto Encoder with &lt;a href="https://github.com/lisa-lab/DeepLearningTutorials/blob/master/code/SdA.py"&gt;the code from tutorial&lt;/a&gt; that works great (less than 5% error rate) for classifying the MNIST dataset. It is a multilayer perceptron, with softmax layer on top with each hidden later being pre-trained as autoencoder (fully described at &lt;a href="http://deeplearning.net/tutorial/deeplearning.pdf"&gt;tutorial&lt;/a&gt;, chapter 8). There are ~50 input features and ~10 output classes. The NN has sigmoid neurons and all data are normalized to [0,1]. I tried lots of different configurations: number of hidden layers and neurons in them (100-&gt;100-&gt;100, 60-&gt;60-&gt;60, 60-&gt;30-&gt;15, etc.), different learning and pre-train rates, etc.&lt;/p&gt;

&lt;p&gt;And the best thing I can get is a 20% error rate on the validation set and a 40% error rate on the test set.&lt;/p&gt;

&lt;p&gt;On the other hand, when I try to use Random Forest (from scikit-learn) I easily get a 12% error rate on the validation set and 25%(!) on the test set.&lt;/p&gt;

&lt;p&gt;How can it be that my deep NN with pre-training behaves so badly? What should I try? &lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (733, 2, '2014-07-13 12:24:36.43', 5, NULL, NULL, '&lt;p&gt;Ask your grid administrator to add your local machine as a "submit host", and install SGE (which we assume you are using, you don''t actually say) so then you can &lt;code&gt;qsub&lt;/code&gt; from your machine.&lt;/p&gt;

&lt;p&gt;OR....&lt;/p&gt;

&lt;p&gt;Use emacs, then you can edit on your HPC via emacs''s "tramp" ssh-connection facilities, and keep a shell open in another emacs window. You don''t say what editor/operating system you like to use. You can even configure emacs to save a file in two places, so you could save to your local machine for running tests and to the HPC file system simultaneously for big jobs.&lt;/p&gt;
', NULL, NULL, '''/code'':35C ''/p'':39C,42C,114C ''actual'':27C ''add'':7C ''administr'':5C ''anoth'':67C ''ask'':2C ''assum'':20C ''big'':112C ''code'':33C ''configur'':84C ''connect'':59C ''could'':95C ''edit'':49C ''editor/operating'':75C ''emac'':45C,54C,68C,85C ''even'':83C ''facil'':60C ''file'':89C,108C ''grid'':4C ''host'':14C ''hpc'':52C,107C ''instal'':16C ''job'':113C ''keep'':62C ''like'':78C ''local'':9C,99C ''machin'':10C,38C,100C ''open'':65C ''p'':1C,40C,43C ''place'':92C ''qsub'':34C ''run'':102C ''save'':87C,96C ''say'':28C,73C ''sge'':17C ''shell'':64C ''simultan'':110C ''ssh'':58C ''ssh-connect'':57C ''submit'':13C ''system'':76C,109C ''test'':103C ''tramp'':56C ''two'':91C ''use'':23C,44C,80C ''via'':53C ''window'':69C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (793, 1, '2014-07-21 13:41:13.427', 10, 1960, 'Uses of NoSQL database in data science', '&lt;p&gt;How can &lt;a href="http://en.wikipedia.org/wiki/NoSQL"&gt;NoSQL&lt;/a&gt; databases like &lt;a href="http://en.wikipedia.org/wiki/MongoDB"&gt;MongoDB&lt;/a&gt; be used for data analysis? What are the features in them that can make data analysis faster and powerful?&lt;/p&gt;
', 'bigdata  nosql  mongodb', NULL, '''/a'':20C,29C ''/p'':49C ''/wiki/mongodb'':27C ''/wiki/nosql'':18C ''analysi'':34C,45C ''bigdata'':8B ''data'':6A,33C,44C ''databas'':4A,21C ''en.wikipedia.org'':17C,26C ''en.wikipedia.org/wiki/mongodb'':25C ''en.wikipedia.org/wiki/nosql'':16C ''faster'':46C ''featur'':38C ''href'':15C,24C ''like'':22C ''make'':43C ''mongodb'':10B,28C ''nosql'':3A,9B,19C ''p'':11C ''power'':48C ''scienc'':7A ''use'':1A,31C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5355, 2, '2015-03-19 16:35:56.013', 2, NULL, NULL, '&lt;p&gt;What about &lt;a href="http://ess.r-project.org" rel="nofollow"&gt;ESS&lt;/a&gt;, the R (and other stats languages) package for the Emacs editor?
It''s not formally an IDE, though it has many, if not more of the features of RStudio, just in a different UI (code completion, inline help, object-aware autocomplete, debugging etc.).&lt;/p&gt;
', NULL, NULL, '''/a'':10C ''/p'':55C ''autocomplet'':52C ''awar'':51C ''code'':45C ''complet'':46C ''debug'':53C ''differ'':43C ''editor'':21C ''emac'':20C ''ess'':9C ''ess.r-project.org'':6C ''etc'':54C ''featur'':37C ''formal'':25C ''help'':48C ''href'':5C ''ide'':27C ''inlin'':47C ''languag'':16C ''mani'':31C ''nofollow'':8C ''object'':50C ''object-awar'':49C ''p'':1C ''packag'':17C ''r'':12C ''rel'':7C ''rstudio'':39C ''stat'':15C ''though'':28C ''ui'':44C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (742, 2, '2014-07-15 06:19:31.82', 17, NULL, NULL, '&lt;p&gt;1) I think that there''s no need to question whether your background is adequate for a career in data science. CS degree IMHO is &lt;strong&gt;more than enough&lt;/strong&gt; for data scientist from software engineering point of view. Having said that, theoretical knowledge is not very helpful without matching &lt;strong&gt;practical experience&lt;/strong&gt;, so I would definitely try to &lt;strong&gt;enrich&lt;/strong&gt; my experience through participating in &lt;em&gt;additional school projects, internships or open source projects&lt;/em&gt; (maybe ones, focused on data science / machine learning / artificial intelligence).&lt;/p&gt;

&lt;p&gt;2) I believe your concern about &lt;strong&gt;focusing&lt;/strong&gt; on data science &lt;strong&gt;too early&lt;/strong&gt; is unfounded, as long as you will be practicing software engineering either as a part of your data science job, or additionally in your spare time.&lt;/p&gt;

&lt;p&gt;3) I find the following &lt;strong&gt;definition of a data scientist&lt;/strong&gt; rather accurate and hope it will be helpful in your future career success:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;A &lt;em&gt;data scientist&lt;/em&gt; is someone who is better at statistics than any
  software engineer and better at software engineering than any
  statistician.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;P.S. Today''s &lt;strong&gt;enormous&lt;/strong&gt; number of various resources on data science topics is mind-blowing, but this &lt;strong&gt;open source curriculum for learning data science&lt;/strong&gt; might fill some gaps between your BSc/MSc respective curricula and reality of the data science career (or, at least, provide some direction for further research and maybe answer some of your concerns): &lt;a href="http://datasciencemasters.org"&gt;http://datasciencemasters.org&lt;/a&gt;, or on GitHub: &lt;a href="https://github.com/datasciencemasters/go"&gt;https://github.com/datasciencemasters/go&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':256C ''/blockquote'':189C ''/datasciencemasters/go'':264C ''/datasciencemasters/go&lt;/a&gt;.&lt;/p&gt;'':267C ''/em'':79C,168C ''/p'':90C,134C,161C,188C ''/strong'':31C,55C,64C,100C,107C,147C,196C,219C ''1'':2C ''2'':92C ''3'':136C ''accur'':149C ''addit'':71C,129C ''adequ'':16C ''answer'':247C ''artifici'':88C ''background'':14C ''believ'':94C ''better'':173C,181C ''blockquot'':162C ''blow'':208C ''bsc/msc'':226C ''career'':19C,159C,235C ''concern'':96C,251C ''cs'':23C ''curricula'':228C ''curriculum'':214C ''data'':21C,33C,84C,102C,125C,145C,166C,202C,217C,233C ''datasciencemasters.org'':254C,255C ''definit'':59C,142C ''degre'':24C ''direct'':241C ''earli'':106C ''either'':119C ''em'':70C,165C ''engin'':37C,118C,179C,184C ''enorm'':195C ''enough'':30C ''enrich'':63C ''experi'':54C,66C ''fill'':221C ''find'':138C ''focus'':82C,99C ''follow'':140C ''futur'':158C ''gap'':223C ''github'':259C ''github.com'':263C,266C ''github.com/datasciencemasters/go'':262C ''github.com/datasciencemasters/go&lt;/a&gt;.&lt;/p&gt;'':265C ''help'':49C,155C ''hope'':151C ''href'':253C,261C ''imho'':25C ''intellig'':89C ''internship'':74C ''job'':127C ''knowledg'':45C ''learn'':87C,216C ''least'':238C ''long'':111C ''machin'':86C ''match'':51C ''mayb'':80C,246C ''might'':220C ''mind'':207C ''mind-blow'':206C ''need'':9C ''number'':197C ''one'':81C ''open'':76C,212C ''p'':1C,91C,135C,163C,190C ''p.s'':191C ''part'':122C ''particip'':68C ''point'':38C ''practic'':53C,116C ''project'':73C,78C ''provid'':239C ''question'':11C ''rather'':148C ''realiti'':230C ''research'':244C ''resourc'':200C ''respect'':227C ''said'':42C ''school'':72C ''scienc'':22C,85C,103C,126C,203C,218C,234C ''scientist'':34C,146C,167C ''softwar'':36C,117C,178C,183C ''someon'':170C ''sourc'':77C,213C ''spare'':132C ''statist'':175C ''statistician'':187C ''strong'':27C,52C,62C,98C,104C,141C,194C,211C ''success'':160C ''theoret'':44C ''think'':4C ''time'':133C ''today'':192C ''topic'':204C ''tri'':60C ''unfound'':109C ''various'':199C ''view'':40C ''whether'':12C ''without'':50C ''would'':58C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (734, 2, '2014-07-14 12:08:13.373', 3, NULL, NULL, '&lt;p&gt;I''m not qualified to understand almost all of that paper, but, I might be able to give some intuitions from information theory that help you parse the paper.&lt;/p&gt;

&lt;p&gt;&lt;code&gt;||&lt;/code&gt; denotes the &lt;a href="http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence" rel="nofollow"&gt;Kullback-Leibler divergence&lt;/a&gt;. It measures an information gain between two distributions. I suppose you could say it indicates the information in the real distribution of data that a model fails to capture.&lt;/p&gt;

&lt;p&gt;When you see "negative log" think &lt;a href="http://en.wikipedia.org/wiki/Information_entropy" rel="nofollow"&gt;"entropy"&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In the first equation, think of it as "-ln(...) - -ln(...)". This may help think of it as the difference of entropies. Likewise in the second, read it as "D(...) + -ln(...)". This may help think of it as "plus entropy".&lt;/p&gt;

&lt;p&gt;If you look at the divergence definition, you''ll see it is defined as the log of the ratio of the PDFs. This may help connect it to logs and negative logs. Look at the definition that writes it as cross-entropy minus entropy. Then this is all a question of differences of entropies of things which may be clearer.&lt;/p&gt;
', NULL, NULL, '''/a'':48C,94C ''/code'':34C ''/p'':31C,78C,95C,136C,199C ''/wiki/information_entropy'':90C ''/wiki/kullback%e2%80%93leibler_divergence'':41C ''abl'':17C ''almost'':8C ''captur'':77C ''clearer'':198C ''code'':33C ''connect'':163C ''could'':60C ''cross'':179C ''cross-entropi'':178C ''d'':125C ''data'':71C ''defin'':150C ''definit'':144C,173C ''denot'':35C ''differ'':115C,190C ''distribut'':56C,69C ''diverg'':47C,143C ''en.wikipedia.org'':40C,89C ''en.wikipedia.org/wiki/information_entropy'':88C ''en.wikipedia.org/wiki/kullback%e2%80%93leibler_divergence'':39C ''entropi'':93C,117C,135C,180C,182C,192C ''equat'':100C ''fail'':75C ''first'':99C ''gain'':53C ''give'':19C ''help'':26C,109C,129C,162C ''href'':38C,87C ''indic'':63C ''inform'':23C,52C,65C ''intuit'':21C ''kullback'':45C ''kullback-leibl'':44C ''leibler'':46C ''likewis'':118C ''ll'':146C ''ln'':105C,106C,126C ''log'':84C,153C,166C,169C ''look'':140C,170C ''m'':3C ''may'':108C,128C,161C,196C ''measur'':50C ''might'':15C ''minus'':181C ''model'':74C ''negat'':83C,168C ''nofollow'':43C,92C ''p'':1C,32C,79C,96C,137C ''paper'':12C,30C ''pars'':28C ''pdfs'':159C ''plus'':134C ''qualifi'':5C ''question'':188C ''ratio'':156C ''read'':122C ''real'':68C ''rel'':42C,91C ''say'':61C ''second'':121C ''see'':82C,147C ''suppos'':58C ''theori'':24C ''thing'':194C ''think'':85C,101C,110C,130C ''two'':55C ''understand'':7C ''write'':175C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (735, 2, '2014-07-14 13:06:05.523', 3, NULL, NULL, '&lt;p&gt;From your question''s wording I assume that you have a local machine and a remote machine where you update two files &amp;mdash; a Python script and a Bash script.  Both files are under SVN control, and both machines have access to the same SVN server.&lt;/p&gt;

&lt;p&gt;I am sorry I do not have any advice specific to your grid system, but let me list some general points I have found important for any deployment.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Keep production changes limited to configuration changes&lt;/strong&gt;. You write that you have to "use the datasets'' path on the server"; this sounds to me like you have the paths hardcoded into your Python script.  This is not a good idea, precisely because you will need to change those paths in every other machine where you move the script to.  If you commit those changes back to SVN, then on your local machine you will have the remote paths, and on and on ...  (What if there are not only paths, but also passwords?  You should not have production passwords in an SVN server.)&lt;/p&gt;

&lt;p&gt;So, keep paths and other setup informations in a &lt;code&gt;.ini&lt;/code&gt; file and use &lt;a href="https://docs.python.org/2/library/configparser.html" rel="nofollow"&gt;ConfigParser&lt;/a&gt; to read it, or use a &lt;code&gt;.json&lt;/code&gt; file and use the &lt;a href="https://docs.python.org/2/library/json.html" rel="nofollow"&gt;json&lt;/a&gt; module. Keep one copy of the file locally and one remotely, both under the same path, both without SVN control, and just keep the path to that configuration file in the Python script (or get it from the command line if you can''t keep both configurations under the same path).&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Keep configuration as small as possible&lt;/strong&gt;. Any configuration is a "moving part" of your application, and any system is more robust the less it has moving parts.  A good indicator of something that belongs into configuration is exactly that you have to edit it every time you move the code; things that have not needed editing can remain as constants in the code.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Automate your deployment&lt;/strong&gt;.  You can do it via a Bash script on your local machine; note that you can &lt;a href="http://malcontentcomics.com/systemsboy/2006/07/send-remote-commands-via-ssh.html" rel="nofollow"&gt;run any command on a remote machine&lt;/a&gt; through &lt;code&gt;ssh&lt;/code&gt;.  For instance:&lt;/p&gt;



&lt;pre&gt;&lt;code&gt;svn export yourprojectpath /tmp/exportedproject
tar czf /tmp/yourproject.tgz /tmp/exportedproject
scp /tmp/myproject.tgz youruser@remotemachine:~/dev

## Remote commands are in the right hand side, between ''''
ssh youruser@remotemachine ''tar xzf ~/dev/yourproject.tgz''
ssh youruser@remotemachine ''qsub ~/dev/yourproject/script.py''
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For this to work, you need of course to have a &lt;a href="http://www.linuxproblem.org/art_9.html" rel="nofollow"&gt;passwordless login&lt;/a&gt;, based on public/private keys, set up between your local and the remote machine.&lt;/p&gt;

&lt;p&gt;If you need more than this, you can think of using Python''s &lt;a href="http://www.fabfile.org" rel="nofollow"&gt;Fabric&lt;/a&gt; or the higher-level &lt;a href="https://github.com/sebastien/cuisine" rel="nofollow"&gt;cuisine&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/2/library/configparser.html'':205C ''/2/library/json.html'':227C ''/a'':209C,231C,387C,453C,488C,502C ''/art_9.html'':448C ''/code'':197C,218C,391C,430C ''/dev'':409C ''/dev/yourproject.tgz'':424C ''/dev/yourproject/script.py'':429C ''/p'':48C,78C,184C,283C,350C,394C,467C,503C ''/pre'':431C ''/sebastien/cuisine'':498C ''/strong'':88C,292C,356C ''/systemsboy/2006/07/send-remote-commands-via-ssh.html'':377C ''/tmp/exportedproject'':400C,404C ''/tmp/myproject.tgz'':406C ''/tmp/yourproject.tgz'':403C ''access'':42C ''advic'':58C ''also'':172C ''applic'':301C ''assum'':8C ''autom'':353C ''back'':146C ''base'':454C ''bash'':30C,363C ''belong'':320C ''chang'':83C,87C,128C,145C ''code'':195C,216C,336C,349C,389C,396C ''command'':270C,382C,411C ''commit'':143C ''configpars'':208C ''configur'':86C,259C,278C,287C,294C,322C ''constant'':346C ''control'':37C,251C ''copi'':235C ''cours'':440C ''cuisin'':501C ''czf'':402C ''dataset'':97C ''deploy'':77C,355C ''docs.python.org'':204C,226C ''docs.python.org/2/library/configparser.html'':203C ''docs.python.org/2/library/json.html'':225C ''edit'':329C,342C ''everi'':132C,331C ''exact'':324C ''export'':398C ''fabric'':487C ''file'':23C,33C,198C,219C,238C,260C ''found'':73C ''general'':69C ''get'':266C ''github.com'':497C ''github.com/sebastien/cuisine'':496C ''good'':120C,315C ''grid'':62C ''hand'':416C ''hardcod'':111C ''higher'':492C ''higher-level'':491C ''href'':202C,224C,374C,445C,483C,495C ''idea'':121C ''import'':74C ''indic'':316C ''inform'':192C ''ini'':196C ''instanc'':393C ''json'':217C,230C ''keep'':81C,187C,233C,254C,276C,286C ''key'':457C ''less'':309C ''let'':65C ''level'':493C ''like'':106C ''limit'':84C ''line'':271C ''list'':67C ''local'':13C,152C,239C,367C,462C ''login'':452C ''machin'':14C,18C,40C,134C,153C,368C,386C,466C ''malcontentcomics.com'':376C ''malcontentcomics.com/systemsboy/2006/07/send-remote-commands-via-ssh.html'':375C ''mdash'':24C ''modul'':232C ''move'':137C,297C,312C,334C ''need'':126C,341C,438C,471C ''nofollow'':207C,229C,379C,450C,486C,500C ''note'':369C ''one'':234C,241C ''p'':1C,49C,79C,185C,284C,351C,432C,468C ''part'':298C,313C ''password'':173C,179C ''passwordless'':451C ''path'':98C,110C,130C,159C,170C,188C,247C,256C,282C ''point'':70C ''possibl'':291C ''pre'':395C ''precis'':122C ''product'':82C,178C ''public/private'':456C ''python'':26C,114C,263C,480C ''qsub'':428C ''question'':4C ''read'':211C ''rel'':206C,228C,378C,449C,485C,499C ''remain'':344C ''remot'':17C,158C,242C,385C,410C,465C ''remotemachin'':408C,421C,427C ''right'':415C ''robust'':307C ''run'':380C ''scp'':405C ''script'':27C,31C,115C,139C,264C,364C ''server'':47C,101C,183C ''set'':458C ''setup'':191C ''side'':417C ''small'':289C ''someth'':318C ''sorri'':52C ''sound'':103C ''specif'':59C ''ssh'':390C,419C,425C ''strong'':80C,285C,352C ''svn'':36C,46C,148C,182C,250C,397C ''system'':63C,304C ''tar'':401C,422C ''thing'':337C ''think'':477C ''time'':332C ''two'':22C ''updat'':21C ''use'':95C,200C,214C,221C,479C ''via'':361C ''without'':249C ''word'':6C ''work'':436C ''write'':90C ''www.fabfile.org'':484C ''www.linuxproblem.org'':447C ''www.linuxproblem.org/art_9.html'':446C ''xzf'':423C ''yourprojectpath'':399C ''yourus'':407C,420C,426C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6702, 2, '2015-08-05 11:24:34.843', 1, NULL, NULL, '&lt;p&gt;You can answer this using tapply, which will give you the mean for all your subsets, e.g.&lt;/p&gt;

&lt;p&gt;dummy data&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;df = data.frame(mon=rep(month.abb, times=10)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;calculate mean for all months&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;tapply(df$temp, df$mon, mean, na.rm=T)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':33C,52C ''/p'':19C,23C,41C ''/pre'':34C,53C ''10'':32C ''answer'':4C ''calcul'':36C ''code'':25C,43C ''data'':22C ''data.frame'':27C ''df'':26C,45C,47C ''dummi'':21C ''e.g'':18C ''give'':10C ''mean'':13C,37C,49C ''mon'':28C,48C ''month'':40C ''month.abb'':30C ''na.rm'':50C ''p'':1C,20C,35C ''pre'':24C,42C ''rep'':29C ''subset'':17C ''tappli'':7C,44C ''temp'':46C ''time'':31C ''use'':6C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (736, 1, '2014-07-14 13:53:28.437', 7, 572, 'Skewed multi-class data', '&lt;p&gt;I have a dataset which contains ~100,000 samples of 50 classes. I have been using SVM with an RBF kernel to train and predict new data. The problem though is the dataset is skewed towards different classes. &lt;/p&gt;

&lt;p&gt;For example, Class 1 - 30 (~3% each), Class 31 - 45 (~0.6% each), Class 46 - 50 (~0.2% each)&lt;/p&gt;

&lt;p&gt;I see that the model tends to very rarely predict the classes which occur less frequent in the training set, even though the test set has the same class distribution as the training set. &lt;/p&gt;

&lt;p&gt;I am aware that there are technique such as ''undersampling'' where the majority class is scaled down to the minor class. However, is this applicable here where there are so many different classes? Are there other methods to help handle this case?&lt;/p&gt;
', 'classification  svm', NULL, '''/p'':47C,66C,102C,146C ''0.2'':64C ''0.6'':59C ''000'':16C ''1'':52C ''100'':15C ''3'':54C ''30'':53C ''31'':57C ''45'':58C ''46'':62C ''50'':19C,63C ''applic'':128C ''awar'':106C ''case'':145C ''class'':4A,20C,46C,51C,56C,61C,79C,96C,117C,124C,136C ''classif'':6B ''contain'':14C ''data'':5A,35C ''dataset'':12C,41C ''differ'':45C,135C ''distribut'':97C ''even'':88C ''exampl'':50C ''frequent'':83C ''handl'':143C ''help'':142C ''howev'':125C ''kernel'':29C ''less'':82C ''major'':116C ''mani'':134C ''method'':140C ''minor'':123C ''model'':72C ''multi'':3A ''multi-class'':2A ''new'':34C ''occur'':81C ''p'':8C,48C,67C,103C ''predict'':33C,77C ''problem'':37C ''rare'':76C ''rbf'':28C ''sampl'':17C ''scale'':119C ''see'':69C ''set'':87C,92C,101C ''skew'':1A,43C ''svm'':7B,25C ''techniqu'':110C ''tend'':73C ''test'':91C ''though'':38C,89C ''toward'':44C ''train'':31C,86C,100C ''undersampl'':113C ''use'':24C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (737, 2, '2014-07-14 15:58:42.517', 1, NULL, NULL, '&lt;p&gt;I have faced this problem many times while using SVM with Rbf kernel. Using Linear kernel instead of Rbf kernel solved my problem, but I dealt with lesser number of classes. The results were less skewed and more accurate with the linear kernel. Hope this solves your problem.&lt;/p&gt;

&lt;p&gt;Edit: While I wrote original answer I was naive enough to not consider weighting the classes as one of them correctly answered. Also, while using rbf kernel its important to make sure that the penalty parameter or the ''C'' value as per sklearn''s svm module is too generic. I find that the default value of C=1 is too generic most of the time and I typically end up with a value of C=10000. Hope this helps others who get skewed results with svm(rbf) despite of having good distribution of classes in data.&lt;/p&gt;
', NULL, NULL, '''/p'':50C,148C ''1'':109C ''10000'':127C ''accur'':40C ''also'':74C ''answer'':57C,73C ''c'':90C,108C,126C ''class'':32C,67C,145C ''consid'':64C ''correct'':72C ''data'':147C ''dealt'':27C ''default'':105C ''despit'':139C ''distribut'':143C ''edit'':52C ''end'':120C ''enough'':61C ''face'':4C ''find'':102C ''generic'':100C,112C ''get'':133C ''good'':142C ''help'':130C ''hope'':45C,128C ''import'':80C ''instead'':18C ''kernel'':14C,17C,21C,44C,78C ''less'':36C ''lesser'':29C ''linear'':16C,43C ''make'':82C ''mani'':7C ''modul'':97C ''naiv'':60C ''number'':30C ''one'':69C ''origin'':56C ''other'':131C ''p'':1C,51C ''paramet'':87C ''penalti'':86C ''per'':93C ''problem'':6C,24C,49C ''rbf'':13C,20C,77C,138C ''result'':34C,135C ''skew'':37C,134C ''sklearn'':94C ''solv'':22C,47C ''sure'':83C ''svm'':11C,96C,137C ''time'':8C,116C ''typic'':119C ''use'':10C,15C,76C ''valu'':91C,106C,124C ''weight'':65C ''wrote'':55C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9958, 1, '2016-01-25 23:45:28.583', 2, 57, 'What would be the best way to structure and mine this set of data?', '&lt;p&gt;&lt;a href="http://pastebin.com/K0eq8cyZ" rel="nofollow"&gt;http://pastebin.com/K0eq8cyZ&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I went through each season of "It''s Always Sunny in Philadelphia" and determined the character groupings (D=Dennis, F=Frank, C=Charlie, M=Mac, B=Sweet Dee) for each episode. I also starred "winners" for some episodes. How best could I organize this data, in what type of database, and what data science tools would extract the most information out of it?&lt;/p&gt;

&lt;p&gt;I was thinking of making an SQL table like so:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;             (1)     (2)      (3)     (4)     (5)
Episode# | Dennis | Frank | Charlie | Mac | Sweet Dee 
008      |    5   |  3,4  |  2,4    | 2,3 |    1
010      |    5   |  3,4,6|  2,4,6  |2,3,6|    1  
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;...where all the values are arrays of ints. 6 represents that the character won the episode and each number represents one of the 5 characters. Thoughts?&lt;/p&gt;
', 'machine-learning  clustering  dataset  visualization  data-cleaning', NULL, '''/code'':147C ''/k0eq8cyz'':29C ''/k0eq8cyz&lt;/a&gt;&lt;/p&gt;'':34C ''/p'':99C,111C,176C ''/pre'':148C ''008'':126C ''010'':135C ''1'':114C,134C,146C ''2'':115C,130C,132C,140C,143C ''3'':116C,128C,133C,137C,144C ''4'':117C,129C,131C,138C,141C ''5'':118C,127C,136C,173C ''6'':139C,142C,145C,158C ''also'':68C ''alway'':44C ''array'':155C ''b'':61C ''best'':5A,75C ''c'':57C ''charact'':51C,162C,174C ''charli'':58C,122C ''clean'':23B ''cluster'':18B ''code'':113C ''could'':76C ''d'':53C ''data'':14A,22B,80C,88C ''data-clean'':21B ''databas'':85C ''dataset'':19B ''dee'':63C,125C ''denni'':54C,120C ''determin'':49C ''episod'':66C,73C,119C,165C ''extract'':92C ''f'':55C ''frank'':56C,121C ''group'':52C ''href'':26C ''inform'':95C ''int'':157C ''learn'':17B ''like'':109C ''m'':59C ''mac'':60C,123C ''machin'':16B ''machine-learn'':15B ''make'':105C ''mine'':10A ''nofollow'':31C ''number'':168C ''one'':170C ''organ'':78C ''p'':24C,35C,100C,149C ''pastebin.com'':28C,33C ''pastebin.com/k0eq8cyz'':27C ''pastebin.com/k0eq8cyz&lt;/a&gt;&lt;/p&gt;'':32C ''philadelphia'':47C ''pre'':112C ''rel'':30C ''repres'':159C,169C ''scienc'':89C ''season'':40C ''set'':12A ''sql'':107C ''star'':69C ''structur'':8A ''sunni'':45C ''sweet'':62C,124C ''tabl'':108C ''think'':103C ''thought'':175C ''tool'':90C ''type'':83C ''valu'':153C ''visual'':20B ''way'':6A ''went'':37C ''winner'':70C ''won'':163C ''would'':2A,91C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (739, 1, '2014-07-14 19:02:01.67', 11, 1998, 'Starting my career as Data Scientist, is Software Engineering experience required?', '&lt;p&gt;I am an MSc student at the University of Edinburgh, specialized in machine learning and natural language processing. I had some practical courses focused on data mining, and others dealing with machine learning, bayesian statistics and graphical models. My background is a BSc in Computer Science.&lt;/p&gt;

&lt;p&gt;I did some software engineering and I learnt the basic concepts, such as design patterns, but I have never been involved in a large software development project. However, I had a data mining project in my MSc. My question is, if I want to go for a career as Data Scientist, should I apply for a graduate data scientist position first, or should I get a position as graduate software engineer first, maybe something related to data science, such as big data infrastructure or machine learning software development?&lt;/p&gt;

&lt;p&gt;My concern is that I might need good software engineering skills for data science, and I am not sure if these can be obtained by working as a graduate data scientist directly.&lt;/p&gt;

&lt;p&gt;Moreover, at the moment I like Data Mining, but what if I want to change my career to software engineering in the future? It might be difficult if I specialised so much in data science.&lt;/p&gt;

&lt;p&gt;I have not been employed yet, so my knowledge is still limited. Any clarification or advice are welcome, as I am about to finish my MSc and I want to start applying for graduate positions in early October.&lt;/p&gt;
', 'education  definitions  career', NULL, '''/p'':62C,152C,186C,223C,263C ''advic'':240C ''appli'':117C,256C ''background'':55C ''basic'':73C ''bayesian'':49C ''big'':144C ''bsc'':58C ''career'':3A,14B,111C,204C ''chang'':202C ''clarif'':238C ''comput'':60C ''concept'':74C ''concern'':155C ''cours'':38C ''data'':5A,41C,95C,113C,121C,140C,145C,166C,183C,194C,221C ''deal'':45C ''definit'':13B ''design'':77C ''develop'':89C,151C ''difficult'':214C ''direct'':185C ''earli'':261C ''edinburgh'':25C ''educ'':12B ''employ'':229C ''engin'':9A,68C,134C,163C,207C ''experi'':10A ''finish'':248C ''first'':124C,135C ''focus'':39C ''futur'':210C ''get'':128C ''go'':108C ''good'':161C ''graduat'':120C,132C,182C,258C ''graphic'':52C ''howev'':91C ''infrastructur'':146C ''involv'':84C ''knowledg'':233C ''languag'':32C ''larg'':87C ''learn'':29C,48C,149C ''learnt'':71C ''like'':193C ''limit'':236C ''machin'':28C,47C,148C ''mayb'':136C ''might'':159C,212C ''mine'':42C,96C,195C ''model'':53C ''moment'':191C ''moreov'':188C ''msc'':19C,100C,250C ''much'':219C ''natur'':31C ''need'':160C ''never'':82C ''obtain'':177C ''octob'':262C ''other'':44C ''p'':15C,63C,153C,187C,224C ''pattern'':78C ''posit'':123C,130C,259C ''practic'':37C ''process'':33C ''project'':90C,97C ''question'':102C ''relat'':138C ''requir'':11A ''scienc'':61C,141C,167C,222C ''scientist'':6A,114C,122C,184C ''skill'':164C ''softwar'':8A,67C,88C,133C,150C,162C,206C ''someth'':137C ''special'':26C ''specialis'':217C ''start'':1A,255C ''statist'':50C ''still'':235C ''student'':20C ''sure'':172C ''univers'':23C ''want'':106C,200C,253C ''welcom'':242C ''work'':179C ''yet'':230C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (740, 2, '2014-07-14 19:06:44.09', 5, NULL, NULL, '&lt;p&gt;I would suggest you to use libsvm, which already has adjustable class weights implemented in it. Rather than replicating the training samples, one modifies the C parameter for different classes in the SVM optimization. For example if your data has 2 classes, and the first class is only 10% of the data, you would choose class weights to be 10 and 1 for class 1 and 2 respectively. Therefore, margin violations of the first class would cost 10 times more than the margin violations for second class, and per-class accuracies would be more balanced.&lt;/p&gt;
', NULL, NULL, '''/p'':98C ''1'':63C,66C ''10'':50C,61C,79C ''2'':42C,68C ''accuraci'':93C ''adjust'':12C ''alreadi'':10C ''balanc'':97C ''c'':27C ''choos'':56C ''class'':13C,31C,43C,47C,57C,65C,76C,88C,92C ''cost'':78C ''data'':40C,53C ''differ'':30C ''exampl'':37C ''first'':46C,75C ''implement'':15C ''libsvm'':8C ''margin'':71C,84C ''modifi'':25C ''one'':24C ''optim'':35C ''p'':1C ''paramet'':28C ''per'':91C ''per-class'':90C ''rather'':18C ''replic'':20C ''respect'':69C ''sampl'':23C ''second'':87C ''suggest'':4C ''svm'':34C ''therefor'':70C ''time'':80C ''train'':22C ''use'':7C ''violat'':72C,85C ''weight'':14C,58C ''would'':3C,55C,77C,94C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (741, 2, '2014-07-15 04:39:11.67', 4, NULL, NULL, '&lt;p&gt;Absolutely. Keep your software skills sharp. You can do this in an academic program if you simply implement by yourself all the algorithms you learn about. &lt;/p&gt;

&lt;p&gt;Good selection of courses, btw. Consider getting an internship too.&lt;/p&gt;
', NULL, NULL, '''/p'':28C,40C ''absolut'':2C ''academ'':14C ''algorithm'':24C ''btw'':34C ''consid'':35C ''cours'':33C ''get'':36C ''good'':30C ''implement'':19C ''internship'':38C ''keep'':3C ''learn'':26C ''p'':1C,29C ''program'':15C ''select'':31C ''sharp'':7C ''simpli'':18C ''skill'':6C ''softwar'':5C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9489, 2, '2015-12-22 17:26:15.973', 3, NULL, NULL, '&lt;p&gt;You could try building multiple xgboost models, with some of them being limited to more recent data, then weighting those results together.  Another idea would be to make a customized evaluation metric that penalizes recent points more heavily which would give them more importance.&lt;/p&gt;
', NULL, NULL, '''/p'':46C ''anoth'':24C ''build'':5C ''could'':3C ''custom'':31C ''data'':18C ''evalu'':32C ''give'':42C ''heavili'':39C ''idea'':25C ''import'':45C ''limit'':14C ''make'':29C ''metric'':33C ''model'':8C ''multipl'':6C ''p'':1C ''penal'':35C ''point'':37C ''recent'':17C,36C ''result'':22C ''togeth'':23C ''tri'':4C ''weight'':20C ''would'':26C,41C ''xgboost'':7C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6703, 2, '2015-08-05 11:28:25.06', 0, NULL, NULL, '&lt;p&gt;To just get the mean for month 6:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;mean(df$temp[df$mon=="Jun"], na.rm=T)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You were nearly there but didn''t assign your subset to a value, if you had:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;x = data[data$X..Month.. == 6,]
mean(x$X..Temp.., na.rm=TRUE)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;that should work.&lt;/p&gt;
', NULL, NULL, '''..'':48C ''/code'':21C,56C ''/p'':10C,40C,62C ''/pre'':22C,57C ''6'':9C,49C ''assign'':31C ''code'':12C,42C ''data'':44C,45C ''df'':14C,16C ''didn'':29C ''get'':4C ''jun'':18C ''mean'':6C,13C,50C ''mon'':17C ''month'':8C,47C ''na.rm'':19C,54C ''near'':26C ''p'':1C,23C,58C ''pre'':11C,41C ''subset'':33C ''temp'':15C,53C ''true'':55C ''valu'':36C ''work'':61C ''x'':43C,46C,51C,52C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (743, 2, '2014-07-15 09:30:02.183', 6, NULL, NULL, '&lt;p&gt;From the job ads I have seen, the answer depends: There are jobs which are more technical in nature (designing big data projects, doing some analysis) or the exact opposite (doing analysis, storage etc. is someone elses job).&lt;/p&gt;

&lt;p&gt;So I would say that SOME software design skills are extremely useful , but you don''t need the abillity to build a huge program in C# / Java or whatever.
Why I like some SW skills is simply that your code probably looks way better than code from someone who never programmed for the sake of programming. Most of the time, the latter code is very hard do understand / debug for outsiders. Also, sometimes your analysis needs to be integrated in a bigger program,an understand of the needs of the programms certainly helps.&lt;/p&gt;
', NULL, NULL, '''/p'':40C,135C ''abil'':60C ''ad'':5C ''also'':113C ''analysi'':27C,33C,116C ''answer'':10C ''better'':85C ''big'':22C ''bigger'':123C ''build'':62C ''c'':67C ''certain'':133C ''code'':81C,87C,104C ''data'':23C ''debug'':110C ''depend'':11C ''design'':21C,49C ''els'':38C ''etc'':35C ''exact'':30C ''extrem'':52C ''hard'':107C ''help'':134C ''huge'':64C ''integr'':120C ''java'':68C ''job'':4C,14C,39C ''latter'':103C ''like'':73C ''look'':83C ''natur'':20C ''need'':58C,117C,129C ''never'':91C ''opposit'':31C ''outsid'':112C ''p'':1C,41C ''probabl'':82C ''program'':65C,92C,97C,124C ''programm'':132C ''project'':24C ''sake'':95C ''say'':45C ''seen'':8C ''simpli'':78C ''skill'':50C,76C ''softwar'':48C ''someon'':37C,89C ''sometim'':114C ''storag'':34C ''sw'':75C ''technic'':18C ''time'':101C ''understand'':109C,126C ''use'':53C ''way'':84C ''whatev'':70C ''would'':44C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8667, 1, '2015-10-31 05:21:03.62', 5, 158, 'Which algorithms or methods can be used to detect an outlier from this data set?', '&lt;p&gt;Suppose I have a data set : &lt;code&gt;Amount of money (100, 50, 150, 200, 35, 60 ,50, 20, 500)&lt;/code&gt;. I have &lt;a href="https://www.google.co.zw/webhp?sourceid=chrome-instant&amp;amp;ion=1&amp;amp;espv=2&amp;amp;ie=UTF-8#q=Outlier+detection+Algorithms"&gt;Googled&lt;/a&gt; the web looking for techniques that can be used to find a possible outlier in this data set but I ended up confused.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;My question is&lt;/strong&gt;: Which algorithms, techniques or methods can be used to detect possible outlier in this data set?&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;PS&lt;/strong&gt;:Consider that the data does not follow a normal distribution. Thanks.&lt;/p&gt;
', 'algorithms  outlier', NULL, '''/a'':47C ''/code'':38C ''/p'':71C,94C,110C ''/strong'':77C,98C ''/webhp?sourceid=chrome-instant&amp;amp;ion=1&amp;amp;espv=2&amp;amp;ie=utf-8#q=outlier+detection+algorithms'':45C ''100'':29C ''150'':31C ''20'':36C ''200'':32C ''35'':33C ''50'':30C,35C ''500'':37C ''60'':34C ''algorithm'':2A,16B,79C ''amount'':26C ''code'':25C ''confus'':70C ''consid'':99C ''data'':14A,23C,64C,92C,102C ''detect'':9A,87C ''distribut'':108C ''end'':68C ''find'':58C ''follow'':105C ''googl'':46C ''href'':42C ''look'':50C ''method'':4A,82C ''money'':28C ''normal'':107C ''outlier'':11A,17B,61C,89C ''p'':18C,72C,95C ''possibl'':60C,88C ''ps'':97C ''question'':75C ''set'':15A,24C,65C,93C ''strong'':73C,96C ''suppos'':19C ''techniqu'':52C,80C ''thank'':109C ''use'':7A,56C,85C ''web'':49C ''www.google.co.zw'':44C ''www.google.co.zw/webhp?sourceid=chrome-instant&amp;amp;ion=1&amp;amp;espv=2&amp;amp;ie=utf-8#q=outlier+detection+algorithms'':43C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (746, 2, '2014-07-16 03:32:16.27', 5, NULL, NULL, '&lt;p&gt;I''m not an expert on time series, but I have a &lt;strong&gt;general advice&lt;/strong&gt;: may I suggest you to try other packages (and various parameters) to see, if there are any differences in results.&lt;/p&gt;

&lt;p&gt;Also, unless you have to use &lt;code&gt;Python&lt;/code&gt;, I''d recommend to take a look at the &lt;code&gt;R&lt;/code&gt;''s extensive &lt;em&gt;ecosystem&lt;/em&gt; for &lt;strong&gt;time series analysis&lt;/strong&gt;: see &lt;a href="http://www.statmethods.net/advstats/timeseries.html"&gt;http://www.statmethods.net/advstats/timeseries.html&lt;/a&gt; and &lt;a href="http://cran.r-project.org/web/views/TimeSeries.html"&gt;http://cran.r-project.org/web/views/TimeSeries.html&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In particular, you may want to check the standard &lt;code&gt;stats&lt;/code&gt; package (including functions &lt;code&gt;arima()&lt;/code&gt; and &lt;code&gt;arima0&lt;/code&gt;), as well as some other packages: &lt;code&gt;FitARMA&lt;/code&gt; (&lt;a href="http://cran.r-project.org/web/packages/FitARMA"&gt;http://cran.r-project.org/web/packages/FitARMA&lt;/a&gt;), &lt;code&gt;forecast&lt;/code&gt; (&lt;a href="http://cran.r-project.org/web/packages/forecast"&gt;http://cran.r-project.org/web/packages/forecast&lt;/a&gt;) and education-focused &lt;code&gt;fArma&lt;/code&gt; (cran.r-project.org/web/packages/fArma), to mention just a few. I hope this is helpful.&lt;/p&gt;
', NULL, NULL, '''/advstats/timeseries.html'':77C ''/advstats/timeseries.html&lt;/a&gt;'':80C ''/code'':48C,60C,102C,108C,112C,121C,132C,147C ''/em'':65C ''/p'':38C,161C ''/strong'':17C,71C ''/web/packages/farma),'':150C ''/web/packages/fitarma'':126C ''/web/packages/fitarma&lt;/a&gt;),'':129C ''/web/packages/forecast'':137C ''/web/packages/forecast&lt;/a&gt;)'':140C ''/web/views/timeseries.html'':86C ''/web/views/timeseries.html&lt;/a&gt;.&lt;/p&gt;'':89C ''advic'':16C ''also'':40C ''analysi'':70C ''arima'':107C ''arima0'':111C ''check'':97C ''code'':46C,58C,100C,106C,110C,119C,130C,145C ''cran.r-project.org'':85C,88C,125C,128C,136C,139C,149C ''cran.r-project.org/web/packages/farma),'':148C ''cran.r-project.org/web/packages/fitarma'':124C ''cran.r-project.org/web/packages/fitarma&lt;/a&gt;),'':127C ''cran.r-project.org/web/packages/forecast'':135C ''cran.r-project.org/web/packages/forecast&lt;/a&gt;)'':138C ''cran.r-project.org/web/views/timeseries.html'':84C ''cran.r-project.org/web/views/timeseries.html&lt;/a&gt;.&lt;/p&gt;'':87C ''d'':50C ''differ'':35C ''ecosystem'':64C ''educ'':143C ''education-focus'':142C ''em'':63C ''expert'':6C ''extens'':62C ''farma'':146C ''fitarma'':120C ''focus'':144C ''forecast'':131C ''function'':105C ''general'':15C ''help'':160C ''hope'':157C ''href'':74C,83C,123C,134C ''includ'':104C ''look'':55C ''m'':3C ''may'':18C,94C ''mention'':152C ''p'':1C,39C,90C ''packag'':25C,103C,118C ''paramet'':28C ''particular'':92C ''python'':47C ''r'':59C ''recommend'':51C ''result'':37C ''see'':30C,72C ''seri'':9C,69C ''standard'':99C ''stat'':101C ''strong'':14C,67C ''suggest'':20C ''take'':53C ''time'':8C,68C ''tri'':23C ''unless'':41C ''use'':45C ''various'':27C ''want'':95C ''well'':114C ''www.statmethods.net'':76C,79C ''www.statmethods.net/advstats/timeseries.html'':75C ''www.statmethods.net/advstats/timeseries.html&lt;/a&gt;'':78C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (747, 2, '2014-07-16 06:45:36.74', 4, NULL, NULL, '&lt;p&gt;About automatic cleaning: You really cannot clean data automatically, because the number of errors and the definition of an error is often dependent on the data. E.g.: Your column "Income" might contain negative values, which are an error - you have to do something about the cases. On the other hand a column "monthly savings" could reasonably contain negative values. &lt;/p&gt;

&lt;p&gt;Such errors are highly domain dependent - so to find them, you must have domain knowledge, something at which humans excel, automated processes not so much.&lt;/p&gt;

&lt;p&gt;Where you can and should automate is repeated projects. E.g. a report which has to produced monthly. If you spot errors, you should place some automated process which can spot  these kinds of errors in subsequent months, freeing your time. &lt;/p&gt;
', NULL, NULL, '''/p'':61C,88C,130C ''autom'':83C,95C,115C ''automat'':3C,10C ''cannot'':7C ''case'':47C ''clean'':4C,8C ''column'':30C,53C ''contain'':33C,58C ''could'':56C ''data'':9C,27C ''definit'':18C ''depend'':24C,68C ''domain'':67C,76C ''e.g'':28C,99C ''error'':15C,21C,39C,64C,110C,123C ''excel'':82C ''find'':71C ''free'':127C ''hand'':51C ''high'':66C ''human'':81C ''incom'':31C ''kind'':121C ''knowledg'':77C ''might'':32C ''month'':54C,106C,126C ''much'':87C ''must'':74C ''negat'':34C,59C ''number'':13C ''often'':23C ''p'':1C,62C,89C ''place'':113C ''process'':84C,116C ''produc'':105C ''project'':98C ''realli'':6C ''reason'':57C ''repeat'':97C ''report'':101C ''save'':55C ''someth'':44C,78C ''spot'':109C,119C ''subsequ'':125C ''time'':129C ''valu'':35C,60C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (748, 1, '2014-07-16 07:47:48.603', 6, 65, 'Why use mean revenue in a split test?', '&lt;p&gt;I asked a data science question regarding how to decide on the best variation of a split test on the Statistics section of StackExchange. I hope I will have better luck here. The question is basically, "Why is mean revenue per user the best metric to make your decision on in a split test?"&lt;/p&gt;

&lt;p&gt;The original question is here: &lt;a href="http://stats.stackexchange.com/questions/107599/better-estimator-of-expected-sum-than-mean"&gt;http://stats.stackexchange.com/questions/107599/better-estimator-of-expected-sum-than-mean&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Since it was not well received/understood I simplified the problem to a discrete set of purchases and phrased it as a classical probability problem. That question is here: &lt;a href="http://stats.stackexchange.com/questions/107848/drawing-numbered-balls-from-an-urn"&gt;http://stats.stackexchange.com/questions/107848/drawing-numbered-balls-from-an-urn&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The mean may be the best metric for such a decision but I am not convinced. We often have a lot of prior information so a Bayesian method would likely improve our estimates. I realize that this is a difficult question but Data Scientists are doing such split tests everyday. &lt;/p&gt;
', 'research  cross-validation', NULL, '''/p'':68C,171C ''/questions/107599/better-estimator-of-expected-sum-than-mean'':79C ''/questions/107599/better-estimator-of-expected-sum-than-mean&lt;/a&gt;&lt;/p&gt;'':82C ''/questions/107848/drawing-numbered-balls-from-an-urn'':116C ''/questions/107848/drawing-numbered-balls-from-an-urn&lt;/a&gt;&lt;/p&gt;'':119C ''ask'':15C ''basic'':49C ''bayesian'':147C ''best'':26C,57C,126C ''better'':43C ''classic'':105C ''convinc'':136C ''cross'':11B ''cross-valid'':10B ''data'':17C,163C ''decid'':23C ''decis'':62C,131C ''difficult'':160C ''discret'':96C ''estim'':153C ''everyday'':170C ''hope'':39C ''href'':76C,113C ''improv'':151C ''inform'':144C ''like'':150C ''lot'':141C ''luck'':44C ''make'':60C ''may'':123C ''mean'':3A,52C,122C ''method'':148C ''metric'':58C,127C ''often'':138C ''origin'':71C ''p'':13C,69C,83C,120C ''per'':54C ''phrase'':101C ''prior'':143C ''probabl'':106C ''problem'':93C,107C ''purchas'':99C ''question'':19C,47C,72C,109C,161C ''realiz'':155C ''received/understood'':89C ''regard'':20C ''research'':9B ''revenu'':4A,53C ''scienc'':18C ''scientist'':164C ''section'':35C ''set'':97C ''simplifi'':91C ''sinc'':84C ''split'':7A,30C,66C,168C ''stackexchang'':37C ''statist'':34C ''stats.stackexchange.com'':78C,81C,115C,118C ''stats.stackexchange.com/questions/107599/better-estimator-of-expected-sum-than-mean'':77C ''stats.stackexchange.com/questions/107599/better-estimator-of-expected-sum-than-mean&lt;/a&gt;&lt;/p&gt;'':80C ''stats.stackexchange.com/questions/107848/drawing-numbered-balls-from-an-urn'':114C ''stats.stackexchange.com/questions/107848/drawing-numbered-balls-from-an-urn&lt;/a&gt;&lt;/p&gt;'':117C ''test'':8A,31C,67C,169C ''use'':2A ''user'':55C ''valid'':12B ''variat'':27C ''well'':88C ''would'':149C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5624, 1, '2015-04-27 09:09:55.17', 1, 44, 'Which classifier is efficient in dealing with test query which belongs to no trained class?', '&lt;p&gt;Suppose classifier trained with 5 class, and input query content does not belong to any of the trained class data.&lt;/p&gt;

&lt;p&gt;Naive bayes provides and random class as a result here. Which classifier deals best in such scenario?&lt;/p&gt;
', 'machine-learning  classification  svm', NULL, '''/p'':42C,61C ''5'':26C ''bay'':45C ''belong'':11A,34C ''best'':57C ''class'':15A,27C,40C,49C ''classif'':19B ''classifi'':2A,23C,55C ''content'':31C ''data'':41C ''deal'':6A,56C ''effici'':4A ''input'':29C ''learn'':18B ''machin'':17B ''machine-learn'':16B ''naiv'':44C ''p'':21C,43C ''provid'':46C ''queri'':9A,30C ''random'':48C ''result'':52C ''scenario'':60C ''suppos'':22C ''svm'':20B ''test'':8A ''train'':14A,24C,39C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (756, 2, '2014-07-16 18:15:42.343', 9, NULL, NULL, '&lt;p&gt;At the expense of over-simplication, latent features are ''hidden'' features to distinguish them from observed features. Latent features are computed from observed features using matrix factorization. An example would be text document analysis. ''words'' extracted from the documents are features. If you factorize the data of words you can find ''topics'', where ''topic'' is a group of words with semantic relevance. Low-rank matrix factorization maps several rows (observed features) to a smaller set of rows (latent features).
To elaborate, the document could have observed features (words) like [sail-boat, schooner, yatch, steamer, cruiser] which would ''factorize'' to latent feature (topic) like ''ship'' and ''boat''. &lt;/p&gt;

&lt;p&gt;[sail-boat, schooner, yatch, steamer, cruiser, ...] -&gt; [ship, boat]&lt;/p&gt;

&lt;p&gt;The underlying idea is that latent features are semantically relevant ''aggregates'' of observered features. When you have large-scale, high-dimensional, and noisy observered features, it makes sense to build your classifier on latent features.&lt;/p&gt;

&lt;p&gt;This is a of course a simplified description to elucidate the concept. You can read the details on Latent Dirichlet Allocation (LDA) or probabilistic Latent Semantic Analysis (pLSA) models for an accurate description.&lt;/p&gt;
', NULL, NULL, '''/p'':111C,122C,161C,196C ''accur'':194C ''aggreg'':134C ''alloc'':183C ''analysi'':36C,189C ''boat'':95C,110C,115C,121C ''build'':155C ''classifi'':157C ''comput'':23C ''concept'':174C ''could'':87C ''cours'':167C ''cruiser'':99C,119C ''data'':48C ''descript'':170C,195C ''detail'':179C ''dimension'':146C ''dirichlet'':182C ''distinguish'':15C ''document'':35C,41C,86C ''elabor'':84C ''elucid'':172C ''exampl'':31C ''expens'':4C ''extract'':38C ''factor'':29C,46C,69C,102C ''featur'':10C,13C,19C,21C,26C,43C,74C,82C,90C,105C,130C,137C,150C,160C ''find'':53C ''group'':59C ''hidden'':12C ''high'':145C ''high-dimension'':144C ''idea'':126C ''larg'':142C ''large-scal'':141C ''latent'':9C,20C,81C,104C,129C,159C,181C,187C ''lda'':184C ''like'':92C,107C ''low'':66C ''low-rank'':65C ''make'':152C ''map'':70C ''matrix'':28C,68C ''model'':191C ''noisi'':148C ''observ'':18C,25C,73C,89C,136C,149C ''over-simpl'':6C ''p'':1C,112C,123C,162C ''plsa'':190C ''probabilist'':186C ''rank'':67C ''read'':177C ''relev'':64C,133C ''row'':72C,80C ''sail'':94C,114C ''sail-boat'':93C,113C ''scale'':143C ''schooner'':96C,116C ''semant'':63C,132C,188C ''sens'':153C ''set'':78C ''sever'':71C ''ship'':108C,120C ''simplic'':8C ''simplifi'':169C ''smaller'':77C ''steamer'':98C,118C ''text'':34C ''topic'':54C,56C,106C ''under'':125C ''use'':27C ''word'':37C,50C,61C,91C ''would'':32C,101C ''yatch'':97C,117C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (750, 1, '2014-07-16 09:49:15.933', 11, 1413, 'How to increase accuracy of classifiers?', '&lt;p&gt;I am using OpenCV letter_recog.cpp example to experiment on random trees and other classifiers. This example has implementations of six classifiers - random trees, boosting, MLP, kNN, naive Bayes and SVM. UCI letter recognition dataset with 20000 instances and 16 features is used, which I split in half for training and testing. I have experience with SVM so I quickly set its recognition error to 3.3%. After some experimentation what I got was:&lt;/p&gt;

&lt;p&gt;UCI letter recognition:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;RTrees - 5.3% &lt;/li&gt;
&lt;li&gt;Boost -  13% &lt;/li&gt;
&lt;li&gt;MLP -    7.9% &lt;/li&gt;
&lt;li&gt;kNN(k=3) -   6.5%  &lt;/li&gt;
&lt;li&gt;Bayes -  11.5%  &lt;/li&gt;
&lt;li&gt;SVM -    3.3%&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Parameters used:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;RTrees - max_num_of_trees_in_the_forrest=200, max_depth=20,
min_sample_count=1&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Boost -  boost_type=REAL, weak_count=200, weight_trim_rate=0.95,
max_depth=7&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;MLP -    method=BACKPROP, param=0.001, max_iter=300 (default values - too
slow to experiment) &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;kNN(k=3) -   k=3&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Bayes -  none&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;SVM -    RBF kernel, C=10, gamma=0.01&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;After that I used same parameters and tested on Digits and MNIST datasets by extracting gradient features first (vector size 200 elements):&lt;/p&gt;

&lt;p&gt;Digits:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;RTrees - 5.1%&lt;/li&gt;
&lt;li&gt;Boost -  23.4%&lt;/li&gt;
&lt;li&gt;MLP -    4.3%&lt;/li&gt;
&lt;li&gt;kNN(k=3) -   7.3%&lt;/li&gt;
&lt;li&gt;Bayes -  17.7%&lt;/li&gt;
&lt;li&gt;SVM -    4.2%&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;MNIST:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;RTrees - 1.4%&lt;/li&gt;
&lt;li&gt;Boost -  out of memory&lt;/li&gt;
&lt;li&gt;MLP -    1.0%&lt;/li&gt;
&lt;li&gt;kNN(k=3) -   1.2%&lt;/li&gt;
&lt;li&gt;Bayes -  34.33%&lt;/li&gt;
&lt;li&gt;SVM -    0.6%&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I am new to all classifiers except SVM and kNN, for these two I can say the results seem fine. What about others? I expected more from random trees, on MNIST kNN gives better accuracy, any ideas how to get it higher? Boost and Bayes give very low accuracy. In the end I''d like to use these classifiers to make a multiple classifier system. Any advice?&lt;/p&gt;
', 'machine-learning  classification  svm  accuracy  random-forest', NULL, '''/li'':99C,103C,107C,113C,117C,121C,147C,165C,183C,192C,198C,209C,242C,246C,250C,256C,260C,264C,273C,279C,283C,289C,293C,297C ''/p'':89C,94C,126C,146C,164C,182C,191C,197C,208C,234C,237C,268C,367C ''/ul'':122C,210C,265C,298C ''0.001'':172C ''0.01'':207C ''0.6'':296C ''0.95'':160C ''1'':145C ''1.0'':282C ''1.2'':288C ''1.4'':272C ''10'':205C ''11.5'':116C ''13'':102C ''16'':55C ''17.7'':259C ''20'':141C ''200'':138C,156C,232C ''20000'':52C ''23.4'':245C ''3'':111C,188C,190C,254C,287C ''3.3'':81C,120C ''300'':175C ''34.33'':292C ''4.2'':263C ''4.3'':249C ''5.1'':241C ''5.3'':98C ''6.5'':112C ''7'':163C ''7.3'':255C ''7.9'':106C ''accuraci'':4A,12B,334C,348C ''advic'':366C ''backprop'':170C ''bay'':44C,115C,195C,258C,291C,344C ''better'':333C ''boost'':40C,101C,150C,151C,244C,275C,342C ''c'':204C ''classif'':10B ''classifi'':6A,30C,37C,305C,358C,363C ''count'':144C,155C ''d'':353C ''dataset'':50C,224C ''default'':176C ''depth'':140C,162C ''digit'':221C,236C ''element'':233C ''end'':351C ''error'':79C ''exampl'':22C,32C ''except'':306C ''expect'':324C ''experi'':24C,70C,181C ''experiment'':84C ''extract'':226C ''featur'':56C,228C ''fine'':319C ''first'':229C ''forest'':15B ''forrest'':137C ''gamma'':206C ''get'':339C ''give'':332C,345C ''got'':87C ''gradient'':227C ''half'':63C ''higher'':341C ''idea'':336C ''implement'':34C ''increas'':3A ''instanc'':53C ''iter'':174C ''k'':110C,187C,189C,253C,286C ''kernel'':203C ''knn'':42C,109C,186C,252C,285C,309C,331C ''learn'':9B ''letter'':48C,92C ''letter_recog.cpp'':21C ''li'':96C,100C,104C,108C,114C,118C,128C,148C,166C,184C,193C,199C,239C,243C,247C,251C,257C,261C,270C,274C,280C,284C,290C,294C ''like'':354C ''low'':347C ''machin'':8B ''machine-learn'':7B ''make'':360C ''max'':131C,139C,161C,173C ''memori'':278C ''method'':169C ''min'':142C ''mlp'':41C,105C,168C,248C,281C ''mnist'':223C,267C,330C ''multipl'':362C ''naiv'':43C ''new'':302C ''none'':196C ''num'':132C ''opencv'':20C ''other'':322C ''p'':16C,90C,123C,129C,149C,167C,185C,194C,200C,211C,235C,266C,299C ''param'':171C ''paramet'':124C,217C ''quick'':75C ''random'':14B,26C,38C,327C ''random-forest'':13B ''rate'':159C ''rbf'':202C ''real'':153C ''recognit'':49C,78C,93C ''result'':317C ''rtree'':97C,130C,240C,271C ''sampl'':143C ''say'':315C ''seem'':318C ''set'':76C ''six'':36C ''size'':231C ''slow'':179C ''split'':61C ''svm'':11B,46C,72C,119C,201C,262C,295C,307C ''system'':364C ''test'':67C,219C ''train'':65C ''tree'':27C,39C,134C,328C ''trim'':158C ''two'':312C ''type'':152C ''uci'':47C,91C ''ul'':95C,127C,238C,269C ''use'':19C,58C,125C,215C,356C ''valu'':177C ''vector'':230C ''weak'':154C ''weight'':157C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (751, 2, '2014-07-16 10:42:55.793', 3, NULL, NULL, '&lt;p&gt;You are right, cosine similarity has a lot of common with dot product of vectors. Indeed, it is a dot product, scaled by magnitude. And because of scaling it is normalized between 0 and 1. CS is preferable because it takes into account variability of data and features'' relative frequencies. On the other hand, plain dot product is a little bit "cheaper" (in terms of complexity and implementation).&lt;/p&gt;
', NULL, NULL, '''/p'':70C ''0'':34C ''1'':36C ''account'':44C ''bit'':62C ''cheaper'':63C ''common'':11C ''complex'':67C ''cosin'':5C ''cs'':37C ''data'':47C ''dot'':13C,21C,57C ''featur'':49C ''frequenc'':51C ''hand'':55C ''implement'':69C ''inde'':17C ''littl'':61C ''lot'':9C ''magnitud'':25C ''normal'':32C ''p'':1C ''plain'':56C ''prefer'':39C ''product'':14C,22C,58C ''relat'':50C ''right'':4C ''scale'':23C,29C ''similar'':6C ''take'':42C ''term'':65C ''variabl'':45C ''vector'':16C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (761, 1, '2014-07-17 09:50:41.437', 11, 3905, 'Clustering geo location coordinates (lat,long pairs)', '&lt;p&gt;What is the right approach and clustering algorithm for geo location clustering?&lt;/p&gt;

&lt;p&gt;I''m using the following code to cluster geolocation coordinates :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
import matplotlib.pyplot as plt
from scipy.cluster.vq import kmeans2, whiten

coordinates= np.array([
           [lat, long],
           [lat, long],
            ...
           [lat, long]
           ])
x, y = kmeans2(whiten(coordinates), 3, iter = 20)  
plt.scatter(coordinates[:,0], coordinates[:,1], c=y);
plt.show()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Is it right to use Kmeans for location clustering, as it uses Euclidean distance and not Haversine formula as a distance function?&lt;/p&gt;
', 'machine-learning  python  clustering  k-means  clusters', NULL, '''/code'':82C ''/p'':30C,42C,107C ''/pre'':83C ''0'':76C ''1'':78C ''20'':73C ''3'':71C ''algorithm'':25C ''approach'':22C ''c'':79C ''cluster'':1A,12B,16B,24C,29C,39C,93C ''code'':37C,44C ''coordin'':4A,41C,58C,70C,75C,77C ''distanc'':98C,105C ''euclidean'':97C ''follow'':36C ''formula'':102C ''function'':106C ''geo'':2A,27C ''geoloc'':40C ''haversin'':101C ''import'':45C,49C,55C ''iter'':72C ''k'':14B ''k-mean'':13B ''kmean'':90C ''kmeans2'':56C,68C ''lat'':5A,60C,62C,64C ''learn'':10B ''locat'':3A,28C,92C ''long'':6A,61C,63C,65C ''m'':33C ''machin'':9B ''machine-learn'':8B ''matplotlib.pyplot'':50C ''mean'':15B ''np'':48C ''np.array'':59C ''numpi'':46C ''p'':17C,31C,84C ''pair'':7A ''plt'':52C ''plt.scatter'':74C ''plt.show'':81C ''pre'':43C ''python'':11B ''right'':21C,87C ''scipy.cluster.vq'':54C ''use'':34C,89C,96C ''whiten'':57C,69C ''x'':66C ''y'':67C,80C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (762, 1, '2014-07-17 10:04:29.797', 4, 1174, 't-SNE Python implementation: Kullback-Leibler divergence', '&lt;p&gt;t-SNE, as in [1], works by progressively reducing the Kullback-Leibler (KL) divergence, until a certain condition is met.
The creators of t-SNE suggests to use KL divergence as a performance criterion for the visualizations:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;you can compare the Kullback-Leibler divergences that t-SNE reports. It is perfectly fine to run t-SNE ten times, and select the solution with the lowest KL divergence [2]&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I tried two implementations of t-SNE:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;python&lt;/strong&gt;: sklearn.manifold.TSNE().&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;R&lt;/strong&gt;: tsne, from library(tsne).&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Both these implementations, when verbosity is set, print the error (Kullback-Leibler divergence) for each iteration. However, they don''t allow the user to get this information, which looks a bit strange to me.&lt;/p&gt;

&lt;p&gt;For example, the code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import numpy as np
from sklearn.manifold import TSNE
X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
model = TSNE(n_components=2, verbose=2, n_iter=200)
t = model.fit_transform(X)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;produces:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[t-SNE] Computing pairwise distances...
[t-SNE] Computed conditional probabilities for sample 4 / 4
[t-SNE] Mean sigma: 1125899906842624.000000
[t-SNE] Iteration 10: error = 6.7213750, gradient norm = 0.0012028
[t-SNE] Iteration 20: error = 6.7192064, gradient norm = 0.0012062
[t-SNE] Iteration 30: error = 6.7178683, gradient norm = 0.0012114
...
[t-SNE] Error after 200 iterations: 0.270186
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Now, as far as I understand, &lt;strong&gt;0.270186&lt;/strong&gt; should be the KL divergence. However i cannot get this information, neither from &lt;strong&gt;model&lt;/strong&gt; nor from &lt;strong&gt;t&lt;/strong&gt; (which is a simple numpy.ndarray).&lt;/p&gt;

&lt;p&gt;To solve this problem I could: i) Calculate KL divergence by my self, ii) Do something nasty in python for capturing and parsing TSNE() function''s output [3]. However: i) would be quite stupid to re-calculate KL divergence, when TSNE() has already computed it, ii) would be a bit unusual in terms of code.&lt;/p&gt;

&lt;p&gt;Do you have any other suggestion? Is there a standard way to get this information using this library?&lt;/p&gt;

&lt;p&gt;I mentioned I tried &lt;em&gt;R&lt;/em&gt;''s tsne library, but I''d prefer the answers to focus on the &lt;em&gt;python&lt;/em&gt; sklearn implementation.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;References&lt;/p&gt;

&lt;p&gt;[1] &lt;a href="http://nbviewer.ipython.org/urls/gist.githubusercontent.com/AlexanderFabisch/1a0c648de22eff4a2a3e/raw/59d5bc5ed8f8bfd9ff1f7faa749d1b095aa97d5a/t-SNE.ipynb" rel="nofollow"&gt;http://nbviewer.ipython.org/urls/gist.githubusercontent.com/AlexanderFabisch/1a0c648de22eff4a2a3e/raw/59d5bc5ed8f8bfd9ff1f7faa749d1b095aa97d5a/t-SNE.ipynb&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;[2] &lt;a href="http://homepage.tudelft.nl/19j49/t-SNE.html" rel="nofollow"&gt;http://homepage.tudelft.nl/19j49/t-SNE.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;[3] &lt;a href="http://stackoverflow.com/questions/16571150/how-to-capture-stdout-output-from-a-python-function-call"&gt;http://stackoverflow.com/questions/16571150/how-to-capture-stdout-output-from-a-python-function-call&lt;/a&gt;&lt;/p&gt;
', 'machine-learning  python', NULL, '''/19j49/t-sne.html'':435C ''/19j49/t-sne.html&lt;/a&gt;&lt;/p&gt;'':440C ''/blockquote'':93C ''/code'':202C,269C ''/em'':393C,409C ''/li'':110C,119C ''/p'':55C,92C,103C,157C,163C,206C,307C,365C,385C,412C,416C ''/pre'':203C,270C ''/questions/16571150/how-to-capture-stdout-output-from-a-python-function-call'':447C ''/questions/16571150/how-to-capture-stdout-output-from-a-python-function-call&lt;/a&gt;&lt;/p&gt;'':450C ''/strong'':108C,114C,280C,296C,301C ''/ul'':120C ''/urls/gist.githubusercontent.com/alexanderfabisch/1a0c648de22eff4a2a3e/raw/59d5bc5ed8f8bfd9ff1f7faa749d1b095aa97d5a/t-sne.ipynb'':423C ''/urls/gist.githubusercontent.com/alexanderfabisch/1a0c648de22eff4a2a3e/raw/59d5bc5ed8f8bfd9ff1f7faa749d1b095aa97d5a/t-sne.ipynb&lt;/a&gt;&lt;/p&gt;'':428C ''0'':176C,177C,178C,179C,183C ''0.0012028'':240C ''0.0012062'':250C ''0.0012114'':260C ''0.270186'':268C,279C ''1'':20C,180C,181C,182C,184C,185C,186C,187C,418C ''10'':235C ''1125899906842624.000000'':230C ''2'':91C,192C,194C,430C ''20'':245C ''200'':197C,266C ''3'':336C,442C ''30'':255C ''4'':223C,224C ''6.7178683'':257C ''6.7192064'':247C ''6.7213750'':237C ''allow'':143C ''alreadi'':352C ''answer'':402C ''bit'':153C,359C ''blockquot'':56C ''calcul'':316C,346C ''cannot'':288C ''captur'':329C ''certain'':33C ''code'':162C,165C,208C,364C ''compar'':60C ''compon'':191C ''comput'':212C,218C,353C ''condit'':34C,219C ''could'':314C ''creator'':38C ''criterion'':51C ''d'':399C ''distanc'':214C ''diverg'':9A,30C,47C,65C,90C,135C,285C,318C,348C ''em'':391C,407C ''error'':131C,236C,246C,256C,264C ''exampl'':160C ''far'':274C ''fine'':74C ''focus'':404C ''function'':333C ''get'':147C,289C,379C ''gradient'':238C,248C,258C ''homepage.tudelft.nl'':434C,439C ''homepage.tudelft.nl/19j49/t-sne.html'':433C ''homepage.tudelft.nl/19j49/t-sne.html&lt;/a&gt;&lt;/p&gt;'':438C ''howev'':139C,286C,337C ''hr'':413C ''href'':420C,432C,444C ''ii'':322C,355C ''implement'':5A,98C,124C,411C ''import'':166C,172C ''inform'':149C,291C,381C ''iter'':138C,196C,234C,244C,254C,267C ''kl'':29C,46C,89C,284C,317C,347C ''kullback'':7A,27C,63C,133C ''kullback-leibl'':6A,26C,62C,132C ''learn'':12B ''leibler'':8A,28C,64C,134C ''li'':105C,111C ''librari'':117C,384C,396C ''look'':151C ''lowest'':88C ''machin'':11B ''machine-learn'':10B ''mean'':228C ''mention'':388C ''met'':36C ''model'':188C,295C ''model.fit'':199C ''n'':190C,195C ''nasti'':325C ''nbviewer.ipython.org'':422C,427C ''nbviewer.ipython.org/urls/gist.githubusercontent.com/alexanderfabisch/1a0c648de22eff4a2a3e/raw/59d5bc5ed8f8bfd9ff1f7faa749d1b095aa97d5a/t-sne.ipynb'':421C ''nbviewer.ipython.org/urls/gist.githubusercontent.com/alexanderfabisch/1a0c648de22eff4a2a3e/raw/59d5bc5ed8f8bfd9ff1f7faa749d1b095aa97d5a/t-sne.ipynb&lt;/a&gt;&lt;/p&gt;'':426C ''neither'':292C ''nofollow'':425C,437C ''norm'':239C,249C,259C ''np'':169C ''np.array'':175C ''numpi'':167C ''numpy.ndarray'':306C ''output'':335C ''p'':14C,57C,94C,121C,158C,204C,271C,308C,366C,386C,414C,417C,429C,441C ''pairwis'':213C ''pars'':331C ''perfect'':73C ''perform'':50C ''pre'':164C,207C ''prefer'':400C ''print'':129C ''probabl'':220C ''problem'':312C ''produc'':205C ''progress'':23C ''python'':4A,13B,107C,327C,408C ''quit'':341C ''r'':113C,392C ''re'':345C ''re-calcul'':344C ''reduc'':24C ''refer'':415C ''rel'':424C,436C ''report'':70C ''run'':76C ''sampl'':222C ''select'':83C ''self'':321C ''set'':128C ''sigma'':229C ''simpl'':305C ''sklearn'':410C ''sklearn.manifold'':171C ''sklearn.manifold.tsne'':109C ''sne'':3A,17C,42C,69C,79C,102C,211C,217C,227C,233C,243C,253C,263C ''solut'':85C ''solv'':310C ''someth'':324C ''stackoverflow.com'':446C,449C ''stackoverflow.com/questions/16571150/how-to-capture-stdout-output-from-a-python-function-call'':445C ''stackoverflow.com/questions/16571150/how-to-capture-stdout-output-from-a-python-function-call&lt;/a&gt;&lt;/p&gt;'':448C ''standard'':376C ''strang'':154C ''strong'':106C,112C,278C,294C,299C ''stupid'':342C ''suggest'':43C,372C ''t-sne'':1A,15C,40C,67C,77C,100C,209C,215C,225C,231C,241C,251C,261C ''ten'':80C ''term'':362C ''time'':81C ''transform'':200C ''tri'':96C,390C ''tsne'':115C,118C,173C,189C,332C,350C,395C ''two'':97C ''ul'':104C ''understand'':277C ''unusu'':360C ''use'':45C,382C ''user'':145C ''verbos'':126C,193C ''visual'':54C ''way'':377C ''work'':21C ''would'':339C,356C ''x'':174C,201C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (752, 2, '2014-07-16 11:07:56.467', 2, NULL, NULL, '&lt;p&gt;It seems to me that &lt;em&gt;latent features&lt;/em&gt; is a term used to describe criteria for &lt;strong&gt;classifying entities&lt;/strong&gt; by their structure, in other words, by &lt;em&gt;features&lt;/em&gt; (traits) they contain, instead of &lt;em&gt;classes&lt;/em&gt; they belong to. Meaning of the word "latent" here is most likely similar to its meaning in social sciences, where very popular term latent variable (&lt;a href="http://en.wikipedia.org/wiki/Latent_variable" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Latent_variable&lt;/a&gt;) means unobservable variable (concept).&lt;/p&gt;

&lt;p&gt;Section "Introduction" in the following paper provides a good explanation of latent features'' &lt;strong&gt;meaning&lt;/strong&gt; and use in &lt;strong&gt;modeling&lt;/strong&gt; of social sciences phenomena: &lt;a href="http://papers.nips.cc/paper/3846-nonparametric-latent-feature-models-for-link-prediction.pdf" rel="nofollow"&gt;http://papers.nips.cc/paper/3846-nonparametric-latent-feature-models-for-link-prediction.pdf&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/em'':10C,32C,40C ''/p'':80C ''/paper/3846-nonparametric-latent-feature-models-for-link-prediction.pdf'':112C ''/paper/3846-nonparametric-latent-feature-models-for-link-prediction.pdf&lt;/a&gt;.&lt;/p&gt;'':117C ''/strong'':22C,97C,103C ''/wiki/latent_variable'':70C ''/wiki/latent_variable&lt;/a&gt;)'':75C ''belong'':42C ''class'':39C ''classifi'':20C ''concept'':79C ''contain'':35C ''criteria'':17C ''describ'':16C ''em'':7C,30C,38C ''en.wikipedia.org'':69C,74C ''en.wikipedia.org/wiki/latent_variable'':68C ''en.wikipedia.org/wiki/latent_variable&lt;/a&gt;)'':73C ''entiti'':21C ''explan'':91C ''featur'':9C,31C,94C ''follow'':86C ''good'':90C ''href'':67C,109C ''instead'':36C ''introduct'':83C ''latent'':8C,48C,64C,93C ''like'':52C ''mean'':44C,56C,76C,96C ''model'':102C ''nofollow'':72C,114C ''p'':1C,81C ''paper'':87C ''papers.nips.cc'':111C,116C ''papers.nips.cc/paper/3846-nonparametric-latent-feature-models-for-link-prediction.pdf'':110C ''papers.nips.cc/paper/3846-nonparametric-latent-feature-models-for-link-prediction.pdf&lt;/a&gt;.&lt;/p&gt;'':115C ''phenomena'':107C ''popular'':62C ''provid'':88C ''rel'':71C,113C ''scienc'':59C,106C ''section'':82C ''seem'':3C ''similar'':53C ''social'':58C,105C ''strong'':19C,95C,101C ''structur'':25C ''term'':13C,63C ''trait'':33C ''unobserv'':77C ''use'':14C,99C ''variabl'':65C,78C ''word'':28C,47C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (753, 2, '2014-07-16 13:05:41.123', 0, NULL, NULL, '&lt;p&gt;I could try to explain you with words, but these slides explain it very well with pictures. Hope it helps.
&lt;a href="http://www.inf.ed.ac.uk/teaching/courses/mt/lectures/phrase-model.pdf" rel="nofollow"&gt;http://www.inf.ed.ac.uk/teaching/courses/mt/lectures/phrase-model.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Note this slides correspond to the chapter 5 of "Statistical Machine Translation" by Philipp Koehn, highly recommended if you are working on machine translation, and it is easy to read.&lt;/p&gt;
', NULL, NULL, '''/p'':63C ''/teaching/courses/mt/lectures/phrase-model.pdf'':26C ''/teaching/courses/mt/lectures/phrase-model.pdf&lt;/a&gt;&lt;/p&gt;'':31C ''5'':40C ''chapter'':39C ''correspond'':36C ''could'':3C ''easi'':60C ''explain'':6C,13C ''help'':21C ''high'':48C ''hope'':19C ''href'':23C ''koehn'':47C ''machin'':43C,55C ''nofollow'':28C ''note'':33C ''p'':1C,32C ''philipp'':46C ''pictur'':18C ''read'':62C ''recommend'':49C ''rel'':27C ''slide'':12C,35C ''statist'':42C ''translat'':44C,56C ''tri'':4C ''well'':16C ''word'':9C ''work'':53C ''www.inf.ed.ac.uk'':25C,30C ''www.inf.ed.ac.uk/teaching/courses/mt/lectures/phrase-model.pdf'':24C ''www.inf.ed.ac.uk/teaching/courses/mt/lectures/phrase-model.pdf&lt;/a&gt;&lt;/p&gt;'':29C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1228, 2, '2014-10-08 22:12:31.823', -1, NULL, NULL, '&lt;p&gt;Use one of Pandas'' built in functions:
&lt;a href="http://pandas.pydata.org/pandas-docs/stable/computation.html#correlation" rel="nofollow"&gt;http://pandas.pydata.org/pandas-docs/stable/computation.html#correlation&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/pandas-docs/stable/computation.html#correlation'':13C ''/pandas-docs/stable/computation.html#correlation&lt;/a&gt;&lt;/p&gt;'':18C ''built'':6C ''function'':8C ''href'':10C ''nofollow'':15C ''one'':3C ''p'':1C ''panda'':5C ''pandas.pydata.org'':12C,17C ''pandas.pydata.org/pandas-docs/stable/computation.html#correlation'':11C ''pandas.pydata.org/pandas-docs/stable/computation.html#correlation&lt;/a&gt;&lt;/p&gt;'':16C ''rel'':14C ''use'':2C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8671, 1, '2015-10-31 13:35:09.233', 0, 25, '$\chi^{2}$ kernel SVM performance issue', '&lt;p&gt;I am using $\chi^{2}$ kernel for non-linear SVM (using libSVM) for classifying MNIST digits. I am getting very bad performance (worse than random guessing). The $\chi^{2}$ kernel code (in MATLAB) is as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;function D = chi2_kernel(X,Y)
%Computes the chi2 kernel for two Matrices X and Y

 D = zeros(size(X,1),size(Y,1));

for i = 1:size(Y,1)

    d = bsxfun(@minus,X, Y(i,:));
    s = bsxfun(@plus,X,Y(i,:));
    D(:,i) = sum(d.^2 ./ (s/2),2);
end

D = exp(-0.0001.*(1-D));

end
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Using this I computed the kernel matrix as follows :&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;% Gram matrix for chi2 kernel
 dist = chi2_kernel(X_train2,X_train2);
dist1 = chi2_kernel(X_test,X_train2);

% Gram matrix for training set to training set


KTrT = [(1:numTrain/50)'', dist];

% Gram matrix for training to test set 

KTT = [(1:numTest)'', dist1];


model = svmtrain(Y_train2, KTrT,''-t 4'');

predClass = svmpredict(Y_test, KTT,model);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Does anyone have suggestions for how to better optimize this code, or an explanation for why I''m observing such poor performance?&lt;/p&gt;
', 'svm  matlab  libsvm', NULL, '''-0.0001'':103C ''/code'':107C,177C ''/p'':48C,119C,202C ''/pre'':108C,178C ''1'':71C,74C,77C,80C,104C,150C,161C ''2'':2A,15C,40C,97C,99C ''4'':170C ''anyon'':181C ''bad'':32C ''better'':187C ''bsxfun'':82C,88C ''chi'':1A,14C,39C ''chi2'':53C,59C,125C,128C,135C ''classifi'':25C ''code'':42C,50C,121C,190C ''comput'':57C,113C ''d'':52C,67C,81C,93C,96C,101C,105C ''digit'':27C ''dist'':127C,152C ''dist1'':134C,163C ''end'':100C,106C ''exp'':102C ''explan'':193C ''follow'':47C,118C ''function'':51C ''get'':30C ''gram'':122C,141C,153C ''guess'':37C ''issu'':6A ''kernel'':3A,16C,41C,54C,60C,115C,126C,129C,136C ''ktrt'':149C,168C ''ktt'':160C,175C ''libsvm'':9B,23C ''linear'':20C ''m'':197C ''matlab'':8B,44C ''matric'':63C ''matrix'':116C,123C,142C,154C ''minus'':83C ''mnist'':26C ''model'':164C,176C ''non'':19C ''non-linear'':18C ''numtest'':162C ''numtrain/50'':151C ''observ'':198C ''optim'':188C ''p'':10C,109C,179C ''perform'':5A,33C,201C ''plus'':89C ''poor'':200C ''pre'':49C,120C ''predclass'':171C ''random'':36C ''s/2'':98C ''set'':145C,148C,159C ''size'':69C,72C,78C ''suggest'':183C ''sum'':95C ''svm'':4A,7B,21C ''svmpredict'':172C ''svmtrain'':165C ''test'':138C,158C,174C ''train'':144C,147C,156C ''train2'':131C,133C,140C,167C ''two'':62C ''use'':13C,22C,110C ''wors'':34C ''x'':55C,64C,70C,84C,90C,130C,132C,137C,139C ''y'':56C,66C,73C,79C,85C,91C,166C,173C ''zero'':68C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (757, 2, '2014-07-16 18:26:54.233', 3, NULL, NULL, '&lt;p&gt;Gas usage has a daily cycle but there are also secondary weekly and annual cycles that the ARIMA may not be able to capture.&lt;/p&gt;

&lt;p&gt;There is a very noticeable difference between the weekday and Saturday data. Try creating a subset of the data for each day of the week or splitting the data into weekday and weekend and applying the model.&lt;/p&gt;

&lt;p&gt;If you can obtain temperature data for the same period check if there is a correlation between the temperature and gas usage.&lt;/p&gt;

&lt;p&gt;As @Aleksandr Blekh said R does have good packages for ARIMA models&lt;/p&gt;
', NULL, NULL, '''/p'':26C,65C,89C,103C ''abl'':23C ''aleksandr'':92C ''also'':11C ''annual'':15C ''appli'':62C ''arima'':19C,101C ''blekh'':93C ''captur'':25C ''check'':77C ''correl'':82C ''creat'':41C ''cycl'':7C,16C ''daili'':6C ''data'':39C,46C,56C,72C ''day'':49C ''differ'':33C ''gas'':2C,87C ''good'':98C ''may'':20C ''model'':64C,102C ''notic'':32C ''obtain'':70C ''p'':1C,27C,66C,90C ''packag'':99C ''period'':76C ''r'':95C ''said'':94C ''saturday'':38C ''secondari'':12C ''split'':54C ''subset'':43C ''temperatur'':71C,85C ''tri'':40C ''usag'':3C,88C ''week'':13C,52C ''weekday'':36C,58C ''weekend'':60C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8285, 2, '2015-10-03 00:44:15', 0, NULL, NULL, '&lt;p&gt;You don''t give many clues about what properties of the images you might be considering, but it seems that what you might want to measure is the difference in the distributions of the training and tests sets. A useful place to start would be with the KullbackâLeibler divergence, which is a measure of the difference of two distributions.&lt;/p&gt;
', NULL, NULL, '''/p'':62C ''clue'':7C ''consid'':17C ''differ'':30C,58C ''distribut'':33C,61C ''diverg'':51C ''give'':5C ''imag'':13C ''kullbackâ'':49C ''leibler'':50C ''mani'':6C ''measur'':27C,55C ''might'':15C,24C ''p'':1C ''place'':42C ''properti'':10C ''seem'':20C ''set'':39C ''start'':44C ''test'':38C ''train'':36C ''two'':60C ''use'':41C ''want'':25C ''would'':45C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (760, 1, '2014-07-17 09:26:11.833', 2, 131, 'Given time series data, how to model the frequency of someone changes his job?', '&lt;p&gt;I am given a time series data vector (ordered by months and years),which contains only &lt;code&gt;0&lt;/code&gt;s and &lt;code&gt;1&lt;/code&gt;s. &lt;code&gt;1&lt;/code&gt; s represent a person changes his job at a particular a month. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Questions:&lt;/strong&gt; What model can i use to determine model how frequently this person change his job ? In addition, this model should be able to predict the probability of this person changing his in the next 6 months.  &lt;/p&gt;

&lt;p&gt;A poisson process ? (I have studied poisson process before however I have no idea when and how to apply it). Any assumptions that data need to meet before applying the poisson process ? &lt;/p&gt;

&lt;p&gt;Would love to gather more information on how to model something like this. Thanks&lt;/p&gt;
', 'data-mining  time-series', NULL, '''/code'':40C,45C,49C ''/p'':62C,103C,137C,153C ''/strong'':66C ''0'':39C ''1'':44C,48C ''6'':101C ''abl'':88C ''addit'':83C ''appli'':123C,133C ''assumpt'':126C ''chang'':12A,54C,79C,96C ''code'':38C,43C,47C ''contain'':36C ''data'':4A,16B,28C,128C ''data-min'':15B ''determin'':73C ''frequenc'':9A ''frequent'':76C ''gather'':142C ''given'':1A,24C ''howev'':114C ''idea'':118C ''inform'':144C ''job'':14A,56C,81C ''like'':150C ''love'':140C ''meet'':131C ''mine'':17B ''model'':7A,68C,74C,85C,148C ''month'':32C,61C,102C ''need'':129C ''next'':100C ''order'':30C ''p'':21C,63C,104C,138C ''particular'':59C ''person'':53C,78C,95C ''poisson'':106C,111C,135C ''predict'':90C ''probabl'':92C ''process'':107C,112C,136C ''question'':65C ''repres'':51C ''seri'':3A,20B,27C ''someon'':11A ''someth'':149C ''strong'':64C ''studi'':110C ''thank'':152C ''time'':2A,19B,26C ''time-seri'':18B ''use'':71C ''vector'':29C ''would'':139C ''year'':34C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (786, 1, '2014-07-19 20:46:52.74', 6, 269, 'NASDAQ Trade Data', '&lt;p&gt;I am trying to find stock data to practice with, is there a good resource for this? I found this: &lt;a href="ftp://emi.nasdaq.com/ITCH/"&gt;ftp://emi.nasdaq.com/ITCH/&lt;/a&gt; but it only has the current year.&lt;/p&gt;

&lt;p&gt;I already have a way of parsing the protocol, but would like to have some more data to compare with. It doesn''t have to be in the same format, as long as it has price, trades, and date statistics.&lt;/p&gt;
', 'data-mining  dataset', NULL, '''/itch/'':33C ''/itch/&lt;/a&gt;'':36C ''/p'':44C,86C ''alreadi'':47C ''compar'':64C ''current'':42C ''data'':3A,5B,15C,62C ''data-min'':4B ''dataset'':7B ''date'':84C ''doesn'':67C ''emi.nasdaq.com'':32C,35C ''emi.nasdaq.com/itch/'':31C ''emi.nasdaq.com/itch/&lt;/a&gt;'':34C ''find'':13C ''format'':75C ''found'':27C ''good'':22C ''href'':30C ''like'':57C ''long'':77C ''mine'':6B ''nasdaq'':1A ''p'':8C,45C ''pars'':52C ''practic'':17C ''price'':81C ''protocol'':54C ''resourc'':23C ''statist'':85C ''stock'':14C ''trade'':2A,82C ''tri'':11C ''way'':50C ''would'':56C ''year'':43C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (763, 2, '2014-07-17 10:26:45.547', 2, NULL, NULL, '&lt;p&gt;A simple and perhaps somewhat naive approach would be to assume that a person changes jobs at a constant rate and that previous job changes have no influence on future ones. Under these assumptions you could model the job changes as a Poisson process and estimate the rate parameter using MLE (&lt;a href="http://en.wikipedia.org/wiki/Poisson_process" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Poisson_process&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/Poisson_distribution" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Poisson_distribution&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;Of course one should explore how well these assumptions hold in the data. To do this, you could study whether or not job changes are independent of one another by computing the correlation between events at various lags (&lt;a href="http://en.wikipedia.org/wiki/Correlation" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Correlation&lt;/a&gt;). You could also plot the distribution of time between job change events. If the process is Poisson-like then you should observe little to no correlation between events at any number of lags and the distribution of time between job change events should be exponentially distributed (&lt;a href="http://en.wikipedia.org/wiki/Exponential_distribution" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Exponential_distribution&lt;/a&gt;).&lt;/p&gt;
', NULL, NULL, '''/wiki/correlation'':117C ''/wiki/correlation&lt;/a&gt;).'':122C ''/wiki/exponential_distribution'':174C ''/wiki/exponential_distribution&lt;/a&gt;).&lt;/p&gt;'':179C ''/wiki/poisson_distribution'':68C ''/wiki/poisson_distribution&lt;/a&gt;).&lt;/p&gt;'':73C ''/wiki/poisson_process'':57C ''/wiki/poisson_process&lt;/a&gt;'':62C ''also'':125C ''anoth'':103C ''approach'':8C ''assum'':12C ''assumpt'':35C,83C ''chang'':16C,26C,41C,98C,133C,164C ''comput'':105C ''constant'':20C ''correl'':107C,149C ''could'':37C,92C,124C ''cours'':76C ''data'':87C ''distribut'':128C,159C,169C ''en.wikipedia.org'':56C,61C,67C,72C,116C,121C,173C,178C ''en.wikipedia.org/wiki/correlation'':115C ''en.wikipedia.org/wiki/correlation&lt;/a&gt;).'':120C ''en.wikipedia.org/wiki/exponential_distribution'':172C ''en.wikipedia.org/wiki/exponential_distribution&lt;/a&gt;).&lt;/p&gt;'':177C ''en.wikipedia.org/wiki/poisson_distribution'':66C ''en.wikipedia.org/wiki/poisson_distribution&lt;/a&gt;).&lt;/p&gt;'':71C ''en.wikipedia.org/wiki/poisson_process'':55C ''en.wikipedia.org/wiki/poisson_process&lt;/a&gt;'':60C ''estim'':47C ''event'':109C,134C,151C,165C ''explor'':79C ''exponenti'':168C ''futur'':31C ''hold'':84C ''href'':54C,65C,114C,171C ''independ'':100C ''influenc'':29C ''job'':17C,25C,40C,97C,132C,163C ''lag'':112C,156C ''like'':141C ''littl'':146C ''mle'':52C ''model'':38C ''naiv'':7C ''nofollow'':59C,70C,119C,176C ''number'':154C ''observ'':145C ''one'':32C,77C,102C ''p'':1C,74C ''paramet'':50C ''perhap'':5C ''person'':15C ''plot'':126C ''poisson'':44C,140C ''poisson-lik'':139C ''previous'':24C ''process'':45C,137C ''rate'':21C,49C ''rel'':58C,69C,118C,175C ''simpl'':3C ''somewhat'':6C ''studi'':93C ''time'':130C,161C ''use'':51C ''various'':111C ''well'':81C ''whether'':94C ''would'':9C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (773, 2, '2014-07-17 20:02:11.227', 11, NULL, NULL, '&lt;p&gt;Think geometrically. Cosine similarity only cares about angle difference, while dot product cares about angle and magnitude. If you normalize your data to have the same magnitude, the two are indistinguishable. Sometimes it is desirable to ignore the magnitude, hence cosine similarity is nice, but if magnitude plays a role, dot product would be better as a similarity measure. Note that neither of them is a "distance metric".&lt;/p&gt;
', NULL, NULL, '''/p'':70C ''angl'':9C,16C ''better'':56C ''care'':7C,14C ''cosin'':4C,42C ''data'':23C ''desir'':36C ''differ'':10C ''distanc'':68C ''dot'':12C,52C ''geometr'':3C ''henc'':41C ''ignor'':38C ''indistinguish'':32C ''magnitud'':18C,28C,40C,48C ''measur'':60C ''metric'':69C ''neither'':63C ''nice'':45C ''normal'':21C ''note'':61C ''p'':1C ''play'':49C ''product'':13C,53C ''role'':51C ''similar'':5C,43C,59C ''sometim'':33C ''think'':2C ''two'':30C ''would'':54C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (764, 2, '2014-07-17 12:34:11.397', 3, NULL, NULL, '&lt;p&gt;K-means should be right in this case. Since k-means tries to group based solely on euclidean distance between objects you will get back clusters of locations that are close to each other. &lt;/p&gt;

&lt;p&gt;To find the optimal number of clusters you can try making an ''elbow'' plot of the within group sum of square distance. This may be helpful (&lt;a href="http://nbviewer.ipython.org/github/nborwankar/LearnDataScience/blob/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb" rel="nofollow"&gt;http://nbviewer.ipython.org/github/nborwankar/LearnDataScience/blob/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb&lt;/a&gt;)&lt;/p&gt;
', NULL, NULL, '''/github/nborwankar/learndatascience/blob/master/notebooks/d3.%20k-means%20clustering%20analysis.ipynb'':70C ''/github/nborwankar/learndatascience/blob/master/notebooks/d3.%20k-means%20clustering%20analysis.ipynb&lt;/a&gt;)&lt;/p&gt;'':75C ''/p'':38C ''back'':28C ''base'':18C ''case'':10C ''close'':34C ''cluster'':29C,46C ''distanc'':22C,61C ''elbow'':52C ''euclidean'':21C ''find'':41C ''get'':27C ''group'':17C,57C ''help'':65C ''href'':67C ''k'':3C,13C ''k-mean'':2C,12C ''locat'':31C ''make'':50C ''may'':63C ''mean'':4C,14C ''nbviewer.ipython.org'':69C,74C ''nbviewer.ipython.org/github/nborwankar/learndatascience/blob/master/notebooks/d3.%20k-means%20clustering%20analysis.ipynb'':68C ''nbviewer.ipython.org/github/nborwankar/learndatascience/blob/master/notebooks/d3.%20k-means%20clustering%20analysis.ipynb&lt;/a&gt;)&lt;/p&gt;'':73C ''nofollow'':72C ''number'':44C ''object'':24C ''optim'':43C ''p'':1C,39C ''plot'':53C ''rel'':71C ''right'':7C ''sinc'':11C ''sole'':19C ''squar'':60C ''sum'':58C ''tri'':15C,49C ''within'':56C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (765, 2, '2014-07-17 13:19:46.817', 0, NULL, NULL, '&lt;p&gt;I''ve seen the same thing: My target variables are non-negative, but sklearn''s GBR sometimes makes negative predictions.  To grossly oversimplify, GBR is just using averages of subsets of the target variable to make predictions, so if the target variables are non-negative, then the predictions should be non-negative. &lt;/p&gt;

&lt;p&gt;Also, I can provide an example, if anyone''s interested.&lt;/p&gt;

&lt;p&gt;I''d make this a comment, but I don''t have enough reputation.&lt;/p&gt;
', NULL, NULL, '''/p'':57C,69C,84C ''also'':59C ''anyon'':66C ''averag'':30C ''comment'':76C ''d'':72C ''enough'':82C ''exampl'':64C ''gbr'':18C,26C ''grossli'':24C ''interest'':68C ''make'':20C,38C,73C ''negat'':14C,21C,48C,56C ''non'':13C,47C,55C ''non-neg'':12C,46C,54C ''oversimplifi'':25C ''p'':1C,58C,70C ''predict'':22C,39C,51C ''provid'':62C ''reput'':83C ''seen'':4C ''sklearn'':16C ''sometim'':19C ''subset'':32C ''target'':9C,35C,43C ''thing'':7C ''use'':29C ''variabl'':10C,36C,44C ''ve'':3C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (766, 2, '2014-07-17 14:07:12.643', 3, NULL, NULL, '&lt;p&gt;The TSNE source in scikit-learn is in pure Python. Fit &lt;code&gt;fit_transform()&lt;/code&gt; method is actually calling a private &lt;code&gt;_fit()&lt;/code&gt; function which then calls a private &lt;code&gt;_tsne()&lt;/code&gt; function. That &lt;code&gt;_tsne()&lt;/code&gt; function has a local variable &lt;code&gt;error&lt;/code&gt; which is printed out at the end of the fit. Seems like you could pretty easily change one or two lines of source code to have that value returned to &lt;code&gt;fit_transform()&lt;/code&gt;.&lt;/p&gt;
', NULL, NULL, '''/code'':17C,26C,35C,40C,48C,82C ''/p'':83C ''actual'':20C ''call'':21C,30C ''chang'':65C ''code'':14C,24C,33C,38C,46C,72C,79C ''could'':62C ''easili'':64C ''end'':55C ''error'':47C ''fit'':13C,15C,25C,58C,80C ''function'':27C,36C,41C ''learn'':8C ''like'':60C ''line'':69C ''local'':44C ''method'':18C ''one'':66C ''p'':1C ''pretti'':63C ''print'':51C ''privat'':23C,32C ''pure'':11C ''python'':12C ''return'':77C ''scikit'':7C ''scikit-learn'':6C ''seem'':59C ''sourc'':4C,71C ''transform'':16C,81C ''tsne'':3C,34C,39C ''two'':68C ''valu'':76C ''variabl'':45C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (767, 2, '2014-07-17 14:28:38.207', 12, NULL, NULL, '&lt;p&gt;The best reproducibility tool is to make a log of your actions, something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;experiment/input ; expected ; observation/output ; current hypothesis and if supported or rejected
exp1 ; expected1 ; obs1 ; some fancy hypothesis, supported
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This can be written down on a paper, but, if your experiments fit in a computational framework, you can use computational tools to partly or completely automate that logging process (particularly by helping you track the input datasets which can be huge, and the output figures).&lt;/p&gt;

&lt;p&gt;A great reproducibility tool for Python with a low learning curve is of course &lt;a href="http://ipython.org/" rel="nofollow"&gt;&lt;strong&gt;IPython/Jupyter Notebook&lt;/strong&gt;&lt;/a&gt; (don''t forget the &lt;a href="https://damontallen.github.io/IPython-quick-ref-sheets/" rel="nofollow"&gt;%logon and %logstart&lt;/a&gt; magics).&lt;/p&gt;

&lt;p&gt;Another great tool that is very recent (2015) is &lt;a href="https://github.com/recipy/recipy" rel="nofollow"&gt;recipy&lt;/a&gt;, which is very like sumatra (see below), but made specifically for Python. I don''t know if it works with Jupyter Notebooks, but I know the author frequently uses them so I guess that if it''s not currently supported, it will be in the future.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://en.wikipedia.org/wiki/Git_(software)" rel="nofollow"&gt;Git&lt;/a&gt; is also awesome, and it''s not tied to Python. It will help you not only to keep a history of all your experiments, code, datasets, figures, etc. but also provide you with tools to maintain (&lt;a href="http://www.philandstuff.com/2014/02/09/git-pickaxe.html" rel="nofollow"&gt;git pickaxe&lt;/a&gt;), collaborate (&lt;a href="http://jfire.io/blog/2012/03/07/code-archaeology-with-git/" rel="nofollow"&gt;blame&lt;/a&gt;) and debug (&lt;a href="http://blogs.perl.org/users/ovid/2014/07/making-git-bisect-more-useful.html" rel="nofollow"&gt;git&lt;/a&gt;-&lt;a href="https://lwn.net/Articles/317154/" rel="nofollow"&gt;bisect&lt;/a&gt;) using a scientific method of debugging (called &lt;a href="https://en.wikipedia.org/wiki/Delta_Debugging" rel="nofollow"&gt;delta debugging&lt;/a&gt;). &lt;a href="https://matthew-brett.github.io/pydagogue/curious_git.html" rel="nofollow"&gt;Here''s a story&lt;/a&gt; of a fictional researcher trying to make his own experiments logging system, until it ends up being a facsimile of Git.&lt;/p&gt;

&lt;p&gt;Another general tool working with any language (with a Python API on &lt;a href="https://pypi.python.org/pypi/Sumatra" rel="nofollow"&gt;pypi&lt;/a&gt;) is &lt;a href="http://neuralensemble.org/sumatra/" rel="nofollow"&gt;&lt;strong&gt;Sumatra&lt;/strong&gt;&lt;/a&gt;, which is specifically designed to help you do &lt;strong&gt;replicable&lt;/strong&gt; research (&lt;em&gt;replicable&lt;/em&gt; aims to produce the same results given the exact same code and softwares, whereas &lt;em&gt;reproducibility&lt;/em&gt; aims to produce the same results given any medium, which is a lot harder and time consuming and not automatable).&lt;/p&gt;

&lt;p&gt;Here is how Sumatra works: for each experiment that you conduct through Sumatra, this software will act like a "save game state" often found in videogames. More precisely, it will will save:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;all the parameters you provided;&lt;/li&gt;
&lt;li&gt;the exact sourcecode state of your whole experimental application and config files;&lt;/li&gt;
&lt;li&gt;the output/plots/results and also any file produced by your experimental application.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;It will then construct a database with the timestamp and other metadatas for each of your experiments, that you can later crawl using the webGUI. Since Sumatra saved the full state of your application for a specific experiment at one specific point in time, you can restore the code that produced a specific result at any moment you want, thus you have replicable research at a low cost (except for storage if you work on huge datasets, but you can configure exceptions if you don''t want to save everything everytime).&lt;/p&gt;

&lt;p&gt;In the end, you can use either Git or Sumatra, they will provide you with about the same replicability power, but Sumatra is specifically tailored for scientific research so it provides a few fancy tools like a web GUI to crawl your results, while Git is more tailored towards code maintenance (but it has debugging tools like git-bisect so if your experiments involve codes, it may actually be better). Or of course you can use both!&lt;/p&gt;

&lt;p&gt;/EDIT: &lt;a href="http://datascience.stackexchange.com/a/775"&gt;dsign&lt;/a&gt; touched a very important point here: the replicability of your setup is as important as the replicability of your application. In other words, you should at least provide a &lt;strong&gt;full list of the libraries and compilers&lt;/strong&gt; you used along with their exact &lt;strong&gt;versions&lt;/strong&gt; and the details of your &lt;strong&gt;platform&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Personally, in scientific computing with Python, I have found that packaging an application along with the libraries is just too painful, thus I now just use an all-in-one scientific python package such as &lt;a href="https://store.continuum.io/cshop/anaconda/" rel="nofollow"&gt;Anaconda&lt;/a&gt; (with the great package manager &lt;a href="http://www.continuum.io/blog/conda" rel="nofollow"&gt;conda&lt;/a&gt;), and just advise users to use the same package. Another solution could be to provide a script to automatically generate a &lt;a href="http://docs.python-guide.org/en/latest/dev/virtualenvs/" rel="nofollow"&gt;virtualenv&lt;/a&gt;, or to package everything using the commercial &lt;a href="http://datascience.stackexchange.com/a/775"&gt;Docker application as cited by dsign&lt;/a&gt; or the opensource &lt;a href="http://www.vagrantup.com/" rel="nofollow"&gt;Vagrant&lt;/a&gt; (with for example &lt;a href="http://deeplearning.net/software/pylearn2/#other-methods" rel="nofollow"&gt;pylearn2-in-a-box&lt;/a&gt; which use Vagrant to produce an easily redistributable virtual environment package).&lt;/p&gt;

&lt;p&gt;/EDIT2: Here''s a &lt;a href="https://www.udacity.com/course/viewer#!/c-cs259/l-48648797/m-48680672" rel="nofollow"&gt;great video&lt;/a&gt; summarizing (for debugging but this can also be applied to research) what is fundamental to do reproducible research: logging your experiments and each other steps of the scientific method, a sort of &lt;em&gt;"explicit experimenting"&lt;/em&gt;.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (768, 2, '2014-07-17 15:19:32.237', 13, NULL, NULL, '&lt;p&gt;Since I started doing research in academia I was constantly looking for a satisfactory workflow.
I think that I finally found something I am happy with:&lt;/p&gt;

&lt;p&gt;1) Put everything under version control, e.g., Git:&lt;/p&gt;

&lt;p&gt;For hobby research projects I use GitHub, for research at work I use the private GitLab server that is provided by our university. I also keep my datasets there.&lt;/p&gt;

&lt;p&gt;2) I do most of my analyses along with the documentation on IPython notebooks. It is very organized (for me) to have the code, the plots, and the discussion/conclusion all in one document
If I am running larger scripts, I would usually put them into separate script .py files, but I would still execute them from the IPython notebook via the %run magic to add information about the purpose, outcome, and other parameters.&lt;/p&gt;

&lt;p&gt;I have written a small cell-magic extension for IPython and IPython notebooks, called "watermark" that I use to conveniently create time stamps and keep track of the different package versions I used and also Git hashs&lt;/p&gt;

&lt;p&gt;For example&lt;/p&gt;

&lt;p&gt;&lt;br&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;%watermark

29/06/2014 01:19:10

CPython 3.4.1
IPython 2.1.0

compiler   : GCC 4.2.1 (Apple Inc. build 5577)
system     : Darwin
release    : 13.2.0
machine    : x86_64
processor  : i386
CPU cores  : 2
interpreter: 64bit
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;br&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;%watermark -d -t

29/06/2014 01:19:11 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;br&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;%watermark -v -m -p numpy,scipy

CPython 3.4.1
IPython 2.1.0

numpy 1.8.1
scipy 0.14.0

compiler   : GCC 4.2.1 (Apple Inc. build 5577)
system     : Darwin
release    : 13.2.0
machine    : x86_64
processor  : i386
CPU cores  : 2
interpreter: 64bit
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;For more info, see the &lt;a href="http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb"&gt;documentation here&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (769, 2, '2014-07-17 15:28:25.94', 7, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Dimensionality Reduction&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Another important procedure is to compare the error rates on training and test dataset to see if you are overfitting (due to the "curse of dimensionality"). E.g., if your error rate on the test dataset is much larger than the error on the training data set, this would be one indicator.&lt;br&gt;
In this case, you could try dimensionality reduction techniques, such as PCA or LDA.&lt;/p&gt;

&lt;p&gt;If you are interested, I have written about PCA, LDA and some other techniques here: &lt;a href="http://sebastianraschka.com/index.html#machine_learning"&gt;http://sebastianraschka.com/index.html#machine_learning&lt;/a&gt; and in my GitHub repo here: &lt;a href="https://github.com/rasbt/pattern_classification"&gt;https://github.com/rasbt/pattern_classification&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Cross validation&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Also you may want to take a look at cross-validation techniques in order to evaluate the performance of your classifiers in a more objective manner&lt;/p&gt;
', NULL, NULL, '''/index.html#machine_learning'':95C ''/index.html#machine_learning&lt;/a&gt;'':98C ''/p'':6C,74C,118C,147C ''/rasbt/pattern_classification'':109C ''/rasbt/pattern_classification&lt;/a&gt;&lt;/p&gt;'':112C ''/strong'':5C,117C ''also'':120C ''anoth'':8C ''br'':59C ''case'':62C ''classifi'':141C ''compar'':13C ''could'':64C ''cross'':115C,130C ''cross-valid'':129C ''curs'':31C ''data'':52C ''dataset'':21C,42C ''dimension'':3C,33C,66C ''due'':28C ''e.g'':34C ''error'':15C,37C,48C ''evalu'':136C ''github'':102C ''github.com'':108C,111C ''github.com/rasbt/pattern_classification'':107C ''github.com/rasbt/pattern_classification&lt;/a&gt;&lt;/p&gt;'':110C ''href'':92C,106C ''import'':9C ''indic'':58C ''interest'':79C ''larger'':45C ''lda'':73C,85C ''look'':127C ''manner'':146C ''may'':122C ''much'':44C ''object'':145C ''one'':57C ''order'':134C ''overfit'':27C ''p'':1C,7C,75C,113C,119C ''pca'':71C,84C ''perform'':138C ''procedur'':10C ''rate'':16C,38C ''reduct'':4C,67C ''repo'':103C ''sebastianraschka.com'':94C,97C ''sebastianraschka.com/index.html#machine_learning'':93C ''sebastianraschka.com/index.html#machine_learning&lt;/a&gt;'':96C ''see'':23C ''set'':53C ''strong'':2C,114C ''take'':125C ''techniqu'':68C,89C,132C ''test'':20C,41C ''train'':18C,51C ''tri'':65C ''valid'':116C,131C ''want'':123C ''would'':55C ''written'':82C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (771, 2, '2014-07-17 18:25:44.683', 4, NULL, NULL, '&lt;p&gt;From what I heard, Pylearn2 might be currently the library of choice for most people. This reminds me of a recent blog post a few month ago that lists all the different machine learning libraries with a short explanation&lt;/p&gt;

&lt;p&gt;&lt;a href="https://www.cbinsights.com/blog/python-tools-machine-learning" rel="nofollow"&gt;https://www.cbinsights.com/blog/python-tools-machine-learning&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The section you might be interested in here would be "Deep Learning". About Pylearn2, he writes&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;PyLearn2&lt;/p&gt;
  
  &lt;p&gt;There is another library built on top of Theano, called PyLearn2 which
  brings modularity and configurability to Theano where you could create
  your neural network through different configuration files so that it
  would be easier to experiment different parameters. Arguably, it
  provides more modularity by separating the parameters and properties
  of neural network to the configuration file.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/blockquote'':134C ''/blog/python-tools-machine-learning'':47C ''/blog/python-tools-machine-learning&lt;/a&gt;&lt;/p&gt;'':52C ''/p'':41C,70C,74C,133C ''ago'':28C ''anoth'':78C ''arguabl'':115C ''blockquot'':71C ''blog'':23C ''bring'':88C ''built'':80C ''call'':85C ''choic'':13C ''configur'':91C,103C,131C ''could'':96C ''creat'':97C ''current'':9C ''deep'':64C ''differ'':33C,102C,113C ''easier'':110C ''experi'':112C ''explan'':40C ''file'':104C,132C ''heard'':5C ''href'':44C ''interest'':59C ''learn'':35C,65C ''librari'':11C,36C,79C ''list'':30C ''machin'':34C ''might'':7C,57C ''modular'':89C,119C ''month'':27C ''network'':100C,128C ''neural'':99C,127C ''nofollow'':49C ''p'':1C,42C,53C,72C,75C ''paramet'':114C,123C ''peopl'':16C ''post'':24C ''properti'':125C ''provid'':117C ''pylearn2'':6C,67C,73C,86C ''recent'':22C ''rel'':48C ''remind'':18C ''section'':55C ''separ'':121C ''short'':39C ''theano'':84C,93C ''top'':82C ''would'':62C,108C ''write'':69C ''www.cbinsights.com'':46C,51C ''www.cbinsights.com/blog/python-tools-machine-learning'':45C ''www.cbinsights.com/blog/python-tools-machine-learning&lt;/a&gt;&lt;/p&gt;'':50C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (774, 2, '2014-07-18 04:50:58.287', 2, NULL, NULL, '&lt;p&gt;The following &lt;strong&gt;general&lt;/strong&gt; answer is my uneducated guess, so take it with grain of salt. Hopefully, it makes sense. I think that the best way to describe or analyze &lt;em&gt;experiments&lt;/em&gt; (as any other &lt;em&gt;systems&lt;/em&gt;, in general) is to build their &lt;strong&gt;statistical (multivariate) models&lt;/strong&gt; and evaluate them. Depending on whether &lt;em&gt;environments&lt;/em&gt; for your set of experiments are represented by the &lt;strong&gt;same model or different&lt;/strong&gt;, I see the following &lt;strong&gt;approaches&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;1) &lt;strong&gt;Single model approach.&lt;/strong&gt; &lt;em&gt;Define&lt;/em&gt; experiments'' statistical model for all environments (dependent and independent variables, data types, assumptions, constraints). &lt;em&gt;Analyze&lt;/em&gt; it (most likely, using &lt;em&gt;regression analysis&lt;/em&gt;). &lt;em&gt;Compare&lt;/em&gt; results &lt;strong&gt;across variables&lt;/strong&gt;, which determine (influence) different environments.&lt;/p&gt;

&lt;p&gt;2) &lt;strong&gt;Multiple models approach.&lt;/strong&gt; The same steps as previous case, but &lt;em&gt;compare&lt;/em&gt; results &lt;strong&gt;across models&lt;/strong&gt;, corresponding to different environments.&lt;/p&gt;
', NULL, NULL, '''/em'':35C,41C,61C,94C,111C,119C,122C,150C ''/p'':84C,133C,160C ''/strong'':6C,52C,76C,83C,91C,127C,140C,155C ''1'':86C ''2'':135C ''across'':125C,153C ''analysi'':118C ''analyz'':32C,110C ''answer'':7C ''approach'':82C,90C,139C ''assumpt'':107C ''best'':27C ''build'':46C ''case'':146C ''compar'':121C,149C ''constraint'':108C ''correspond'':156C ''data'':105C ''defin'':93C ''depend'':56C,101C ''describ'':30C ''determin'':129C ''differ'':75C,131C,158C ''em'':33C,39C,59C,92C,109C,116C,120C,148C ''environ'':60C,100C,132C,159C ''evalu'':54C ''experi'':34C,66C,95C ''follow'':3C,80C ''general'':5C,43C ''grain'':16C ''guess'':11C ''hope'':19C ''independ'':103C ''influenc'':130C ''like'':114C ''make'':21C ''model'':51C,73C,89C,97C,138C,154C ''multipl'':137C ''multivari'':50C ''p'':1C,85C,134C ''previous'':145C ''regress'':117C ''repres'':68C ''result'':123C,151C ''salt'':18C ''see'':78C ''sens'':22C ''set'':64C ''singl'':88C ''statist'':49C,96C ''step'':143C ''strong'':4C,48C,71C,81C,87C,124C,136C,152C ''system'':40C ''take'':13C ''think'':24C ''type'':106C ''uneduc'':10C ''use'':115C ''variabl'':104C,126C ''way'':28C ''whether'':58C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (775, 2, '2014-07-18 07:43:56.823', 9, NULL, NULL, '&lt;p&gt;Be sure to check out &lt;a href="https://www.docker.com/"&gt;docker&lt;/a&gt;! And in general, all the other good things that software engineering has created along decades for ensuring isolation and reproductibility. &lt;/p&gt;

&lt;p&gt;I would like to stress that it is not enough to have &lt;em&gt;just&lt;/em&gt; reproducible workflows, but also &lt;em&gt;easy&lt;/em&gt; to reproduce workflows. Let me show what I mean. Suppose that your project uses Python, a database X and Scipy. Most surely you will be using a specific library to connect to your database from Python, and Scipy will be in turn using some sparse algebraic routines. This is by all means a very simple setup, but not entirely simple to setup, pun intended. If somebody wants to execute your scripts, she will have to install all the dependencies. Or worse, she might have incompatible versions of it already installed. Fixing those things takes time. It will also take time to you if you at some moment need to move your computations to a cluster, to a different cluster, or to some cloud servers. &lt;/p&gt;

&lt;p&gt;Here is where I find docker useful. Docker is a way to formalize and compile recipes for binary environments. You can write the following in a dockerfile (I''m using here plain English instead of the Dockerfile syntax):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Start with a basic binary environment, like Ubuntu''s&lt;/li&gt;
&lt;li&gt;Install libsparse-dev&lt;/li&gt;
&lt;li&gt;(Pip) Install numpy and scipy&lt;/li&gt;
&lt;li&gt;Install X&lt;/li&gt;
&lt;li&gt;Install libX-dev&lt;/li&gt;
&lt;li&gt;(Pip) Install python-X&lt;/li&gt;
&lt;li&gt;Install IPython-Notebook&lt;/li&gt;
&lt;li&gt;Copy my python scripts/notebooks to my binary environment, these datafiles, and these configurations to do other miscellaneous things. To ensure reproductibility, copy them from a named url instead of a local file. &lt;/li&gt;
&lt;li&gt;Maybe run IPython-Notebook.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Some of the lines will be installing things in Python using pip, since pip can do a very clean work in selecting specific package versions. Check it out too!&lt;/p&gt;

&lt;p&gt;And that''s it. If after you create your Dockerfile it can be built, then it can be built anywhere, by anybody (provided they also have access to your project-specific files, e.g. because you put them in a public url referenced from the Dockerfile). What is best, you can upload the resulting environment (called an "image") to a public or private server (called a "register") for other people to use. So, when you publish your workflow, you have both a fully reproducible recipe in the form of a Dockerfile, and an easy way for you or other people to reproduce what you do:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;docker run dockerregistery.thewheezylab.org/nowyouwillbelieveme
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Or if they want to poke around in your scripts and so forth:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;docker run -i -t dockerregistery.thewheezylab.org/nowyouwillbelieveme /bin/bash
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':11C ''/bin/bash'':482C ''/code'':456C,483C ''/em'':48C,55C ''/li'':232C,238C,245C,249C,255C,262C,268C,302C,309C ''/nowyouwillbelieveme'':455C,481C ''/p'':32C,180C,220C,341C,448C,472C ''/pre'':457C,484C ''/ul'':310C ''access'':369C ''algebra'':101C ''along'':25C ''alreadi'':144C ''also'':52C,153C,367C ''anybodi'':364C ''anywher'':362C ''around'':465C ''basic'':226C ''best'':391C ''binari'':199C,227C,276C ''built'':356C,361C ''call'':398C,407C ''check'':5C,337C ''clean'':330C ''cloud'':178C ''cluster'':170C,174C ''code'':450C,474C ''compil'':196C ''comput'':167C ''configur'':282C ''connect'':86C ''copi'':270C,291C ''creat'':24C,350C ''databas'':72C,89C ''datafil'':279C ''decad'':26C ''depend'':134C ''dev'':237C,254C ''differ'':173C ''docker'':10C,187C,189C,451C,475C ''dockerfil'':208C,218C,352C,388C,433C ''dockerregistery.thewheezylab.org'':454C,480C ''dockerregistery.thewheezylab.org/nowyouwillbelieveme'':453C,479C ''e.g'':376C ''easi'':54C,436C ''em'':46C,53C ''engin'':22C ''english'':214C ''enough'':43C ''ensur'':28C,289C ''entir'':114C ''environ'':200C,228C,277C,397C ''execut'':124C ''file'':301C,375C ''find'':186C ''fix'':146C ''follow'':205C ''form'':430C ''formal'':194C ''forth'':471C ''fulli'':425C ''general'':14C ''good'':18C ''href'':8C ''imag'':400C ''incompat'':140C ''instal'':131C,145C,234C,241C,247C,251C,258C,264C,318C ''instead'':215C,297C ''intend'':119C ''ipython'':266C,307C ''ipython-notebook'':265C,306C ''isol'':29C ''let'':59C ''li'':222C,233C,239C,246C,250C,256C,263C,269C,303C ''librari'':84C ''libspars'':236C ''libsparse-dev'':235C ''libx'':253C ''libx-dev'':252C ''like'':36C,229C ''line'':315C ''local'':300C ''m'':210C ''mayb'':304C ''mean'':64C,107C ''might'':138C ''miscellan'':286C ''moment'':162C ''move'':165C ''name'':295C ''need'':163C ''notebook'':267C,308C ''numpi'':242C ''p'':1C,33C,181C,311C,342C,458C ''packag'':335C ''peopl'':412C,442C ''pip'':240C,257C,323C,325C ''plain'':213C ''poke'':464C ''pre'':449C,473C ''privat'':405C ''project'':68C,373C ''project-specif'':372C ''provid'':365C ''public'':383C,403C ''publish'':418C ''pun'':118C ''put'':379C ''python'':70C,91C,260C,272C,321C ''python-x'':259C ''recip'':197C,427C ''referenc'':385C ''regist'':409C ''reproduc'':49C,57C,426C,444C ''reproduct'':31C,290C ''result'':396C ''routin'':102C ''run'':305C,452C,476C ''scipi'':75C,93C,244C ''script'':126C,468C ''scripts/notebooks'':273C ''select'':333C ''server'':179C,406C ''setup'':111C,117C ''show'':61C ''simpl'':110C,115C ''sinc'':324C ''softwar'':21C ''somebodi'':121C ''spars'':100C ''specif'':83C,334C,374C ''start'':223C ''stress'':38C ''suppos'':65C ''sure'':3C,77C ''syntax'':219C ''take'':149C,154C ''thing'':19C,148C,287C,319C ''time'':150C,155C ''turn'':97C ''ubuntu'':230C ''ul'':221C ''upload'':394C ''url'':296C,384C ''use'':69C,81C,98C,188C,211C,322C,414C ''version'':141C,336C ''want'':122C,462C ''way'':192C,437C ''work'':331C ''workflow'':50C,58C,420C ''wors'':136C ''would'':35C ''write'':203C ''www.docker.com'':9C ''x'':73C,248C,261C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (776, 2, '2014-07-18 15:24:38.007', 0, NULL, NULL, '&lt;p&gt;I am far from an expert, but my understanding of the subject tells me that R (superb in statistics) and e.g. Python (superb in several of those things where R is lacking) complements each other quite well (as pointed out by previous posts). &lt;/p&gt;
', NULL, NULL, '''/p'':45C ''complement'':34C ''e.g'':22C ''expert'':7C ''far'':4C ''lack'':33C ''p'':1C ''point'':40C ''post'':44C ''previous'':43C ''python'':23C ''quit'':37C ''r'':17C,31C ''sever'':26C ''statist'':20C ''subject'':13C ''superb'':18C,24C ''tell'':14C ''thing'':29C ''understand'':10C ''well'':38C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (777, 1, '2014-07-18 22:29:05.017', 1, 81, 'Getting GitHub repository information by different criteria', '&lt;p&gt;New to the Data Science forum, and first poster here!&lt;/p&gt;

&lt;p&gt;This may be kind of a specific question (hopefully not too much so), but one I''d imagine others might be interested in.&lt;/p&gt;

&lt;p&gt;I''m looking for a way to basically query GitHub with something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Give me a collection of all of the public repositories that have more than 10 stars, at
least two forks, and more than three committers.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The result could take any viable form: a JSON data dump, a URL to the web page, etc. It more than likely will consist of information from 10,000 repos or something large.&lt;/p&gt;

&lt;p&gt;Is this sort of thing possible using the API or some other pre-built way, or am I going to have to build out my own custom solution where I try to scrape every page? If so, how feasible is this and how might I approach it?&lt;/p&gt;
', 'bigdata  data-mining  python  dataset', NULL, '''/code'':94C ''/p'':25C,50C,66C,130C,180C ''/pre'':95C ''000'':125C ''10'':83C,124C ''api'':140C ''approach'':178C ''basic'':59C ''bigdata'':8B ''build'':155C ''built'':146C ''code'':68C ''collect'':72C ''committ'':93C ''consist'':120C ''could'':99C ''criteria'':7A ''custom'':159C ''d'':43C ''data'':10B,18C,106C ''data-min'':9B ''dataset'':13B ''differ'':6A ''dump'':107C ''etc'':114C ''everi'':166C ''feasibl'':171C ''first'':22C ''fork'':88C ''form'':103C ''forum'':20C ''get'':1A ''github'':2A,61C ''give'':69C ''go'':151C ''hope'':35C ''imagin'':44C ''inform'':4A,122C ''interest'':48C ''json'':105C ''kind'':30C ''larg'':129C ''least'':86C ''like'':64C,118C ''look'':54C ''m'':53C ''may'':28C ''might'':46C,176C ''mine'':11B ''much'':38C ''new'':15C ''one'':41C ''other'':45C ''p'':14C,26C,51C,96C,131C ''page'':113C,167C ''possibl'':137C ''poster'':23C ''pre'':67C,145C ''pre-built'':144C ''public'':77C ''python'':12B ''queri'':60C ''question'':34C ''repo'':126C ''repositori'':3A,78C ''result'':98C ''scienc'':19C ''scrape'':165C ''solut'':160C ''someth'':63C,128C ''sort'':134C ''specif'':33C ''star'':84C ''take'':100C ''thing'':136C ''three'':92C ''tri'':163C ''two'':87C ''url'':109C ''use'':138C ''viabl'':102C ''way'':57C,147C ''web'':112C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (778, 1, '2014-07-18 22:34:48.08', 11, 1148, 'Is Python suitable for big data', '&lt;p&gt;I read in this post &lt;a href="https://datascience.stackexchange.com/questions/41/is-the-r-language-suitable-for-big-data"&gt;Is the R language suitable for Big Data&lt;/a&gt; that big data constitutes &lt;code&gt;5TB&lt;/code&gt;, and while it does a good job of providing information about the feasibility of working with this type of data in &lt;code&gt;R&lt;/code&gt; it provides very little information about &lt;code&gt;Python&lt;/code&gt;. I was wondering if &lt;code&gt;Python&lt;/code&gt; can work with this much data as well. &lt;/p&gt;
', 'bigdata  python', NULL, '''/a'':28C ''/code'':35C,59C,68C,75C ''/p'':84C ''/questions/41/is-the-r-language-suitable-for-big-data'':19C ''5tb'':34C ''big'':5A,26C,30C ''bigdata'':7B ''code'':33C,57C,66C,73C ''constitut'':32C ''data'':6A,27C,31C,55C,81C ''datascience.stackexchange.com'':18C ''datascience.stackexchange.com/questions/41/is-the-r-language-suitable-for-big-data'':17C ''feasibl'':48C ''good'':41C ''href'':16C ''inform'':45C,64C ''job'':42C ''languag'':23C ''littl'':63C ''much'':80C ''p'':9C ''post'':14C ''provid'':44C,61C ''python'':2A,8B,67C,74C ''r'':22C,58C ''read'':11C ''suitabl'':3A,24C ''type'':53C ''well'':83C ''wonder'':71C ''work'':50C,77C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (779, 2, '2014-07-18 22:59:39.1', 2, NULL, NULL, '&lt;p&gt;I believe the language itself has little to do with performance capabilities, when it comes to large data. What matters is:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;How large is the data actually&lt;/li&gt;
&lt;li&gt;What processing are you going to perform on it&lt;/li&gt;
&lt;li&gt;What hardware are you going to use&lt;/li&gt;
&lt;li&gt;Which are the specific libraries that you plan to use&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Anyway, Python is well adopted in data science communities.&lt;/p&gt;
', NULL, NULL, '''/li'':32C,43C,52C,64C ''/p'':23C,76C ''/ul'':65C ''actual'':31C ''adopt'':71C ''anyway'':67C ''believ'':3C ''capabl'':13C ''come'':16C ''communiti'':75C ''data'':19C,30C,73C ''go'':38C,49C ''hardwar'':46C ''languag'':5C ''larg'':18C,27C ''li'':25C,33C,44C,53C ''librari'':58C ''littl'':8C ''matter'':21C ''p'':1C,66C ''perform'':12C,40C ''plan'':61C ''process'':35C ''python'':68C ''scienc'':74C ''specif'':57C ''ul'':24C ''use'':51C,63C ''well'':70C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5626, 2, '2015-04-27 11:58:50.443', 0, NULL, NULL, '&lt;p&gt;Another good normalization is &lt;a href="http://en.wikipedia.org/wiki/Standard_score" rel="nofollow"&gt;zScore&lt;/a&gt; normalization. Was already implemented in python in the module &lt;a href="http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.mstats.zscore.html" rel="nofollow"&gt;scipy.stats&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':14C,32C ''/doc/scipy-0.14.0/reference/generated/scipy.stats.mstats.zscore.html'':28C ''/p'':33C ''/wiki/standard_score'':10C ''alreadi'':17C ''anoth'':2C ''docs.scipy.org'':27C ''docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.mstats.zscore.html'':26C ''en.wikipedia.org'':9C ''en.wikipedia.org/wiki/standard_score'':8C ''good'':3C ''href'':7C,25C ''implement'':18C ''modul'':23C ''nofollow'':12C,30C ''normal'':4C,15C ''p'':1C ''python'':20C ''rel'':11C,29C ''scipy.stats'':31C ''zscore'':13C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (780, 2, '2014-07-19 02:19:46.53', 10, NULL, NULL, '&lt;p&gt;Some good answers here. I would like to join the discussion by adding the following two &lt;strong&gt;notes&lt;/strong&gt;:&lt;/p&gt;

&lt;p&gt;1) The question''s emphasis on the &lt;strong&gt;volume of data&lt;/strong&gt; while referring to &lt;em&gt;Big Data&lt;/em&gt; is certainly understandable and valid, especially considering the &lt;strong&gt;problem&lt;/strong&gt; of data volume growth &lt;strong&gt;outpacing&lt;/strong&gt; technological capacities'' exponential growth per &lt;em&gt;Moore''s Law&lt;/em&gt; (&lt;a href="http://en.wikipedia.org/wiki/Moore%27s_law" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Moore%27s_law&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;2) Having said that, it is important to remember about other aspects of big data concept, based on &lt;em&gt;Gartner&lt;/em&gt;''s definition (emphasis mine - AB): "&lt;em&gt;Big data&lt;/em&gt; is high &lt;strong&gt;volume&lt;/strong&gt;, high &lt;strong&gt;velocity&lt;/strong&gt;, and/or high &lt;strong&gt;variety&lt;/strong&gt; information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization." (usually referred to as the "&lt;strong&gt;3Vs model&lt;/strong&gt;"). I mention this, because it forces data scientists and other analysts to look for and use R packages that focus on &lt;strong&gt;other than volume&lt;/strong&gt; aspects of big data (enabled by the &lt;strong&gt;richness&lt;/strong&gt; of enormous &lt;em&gt;R ecosystem&lt;/em&gt;).&lt;/p&gt;

&lt;p&gt;3) While existing answers mention some R packages, related to big data, for a more &lt;strong&gt;comprehensive coverage&lt;/strong&gt;, I''d recommend to refer to &lt;em&gt;CRAN Task View&lt;/em&gt; &lt;strong&gt;"High-Performance and Parallel Computing with R"&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/views/HighPerformanceComputing.html" rel="nofollow"&gt;http://cran.r-project.org/web/views/HighPerformanceComputing.html&lt;/a&gt;), in particular, sections &lt;strong&gt;"Parallel computing: Hadoop"&lt;/strong&gt; and &lt;strong&gt;"Large memory and out-of-memory data"&lt;/strong&gt;.&lt;/p&gt;
', NULL, NULL, '''/em'':41C,69C,101C,110C,193C,225C ''/p'':21C,194C,265C ''/strong'':20C,34C,52C,59C,115C,119C,124C,151C,177C,187C,214C,235C,253C,264C ''/web/views/highperformancecomputing.html'':240C ''/web/views/highperformancecomputing.html&lt;/a&gt;),'':245C ''/wiki/moore%27s_law'':74C ''/wiki/moore%27s_law&lt;/a&gt;).&lt;/p&gt;'':79C ''1'':23C ''2'':81C ''3'':196C ''3vs'':149C ''ab'':106C ''ad'':14C ''analyst'':162C ''and/or'':120C ''answer'':4C,199C ''aspect'':92C,178C ''asset'':126C ''base'':97C ''big'':39C,94C,108C,180C,206C ''capac'':61C ''certain'':43C ''comprehens'':212C ''comput'':232C,251C ''concept'':96C ''consid'':48C ''coverag'':213C ''cran'':222C ''cran.r-project.org'':239C,244C ''cran.r-project.org/web/views/highperformancecomputing.html'':238C ''cran.r-project.org/web/views/highperformancecomputing.html&lt;/a&gt;),'':243C ''d'':216C ''data'':33C,40C,54C,95C,109C,158C,181C,207C,263C ''decis'':136C ''definit'':103C ''discoveri'':139C ''discuss'':12C ''ecosystem'':192C ''em'':38C,65C,99C,107C,190C,221C ''emphasi'':27C,104C ''en.wikipedia.org'':73C,78C ''en.wikipedia.org/wiki/moore%27s_law'':72C ''en.wikipedia.org/wiki/moore%27s_law&lt;/a&gt;).&lt;/p&gt;'':77C ''enabl'':134C,182C ''enhanc'':135C ''enorm'':189C ''especi'':47C ''exist'':198C ''exponenti'':62C ''focus'':171C ''follow'':16C ''forc'':157C ''form'':130C ''gartner'':100C ''good'':3C ''growth'':56C,63C ''hadoop'':252C ''high'':112C,116C,121C,228C ''high-perform'':227C ''href'':71C,237C ''import'':87C ''inform'':125C ''insight'':138C ''join'':10C ''larg'':256C ''law'':68C ''like'':8C ''look'':164C ''make'':137C ''memori'':257C,262C ''mention'':153C,200C ''mine'':105C ''model'':150C ''moor'':66C ''new'':129C ''nofollow'':76C,242C ''note'':19C ''optim'':142C ''out-of-memori'':259C ''outpac'':58C ''p'':1C,22C,80C,195C ''packag'':169C,203C ''parallel'':231C,250C ''particular'':247C ''per'':64C ''perform'':229C ''problem'':51C ''process'':132C,141C ''question'':25C ''r'':168C,191C,202C,234C ''recommend'':217C ''refer'':36C,144C,219C ''rel'':75C,241C ''relat'':204C ''rememb'':89C ''requir'':128C ''rich'':186C ''said'':83C ''scientist'':159C ''section'':248C ''strong'':18C,30C,50C,57C,113C,117C,122C,148C,173C,185C,211C,226C,249C,255C ''task'':223C ''technolog'':60C ''two'':17C ''understand'':44C ''use'':167C ''usual'':143C ''valid'':46C ''varieti'':123C ''veloc'':118C ''view'':224C ''volum'':31C,55C,114C,176C ''would'':7C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8923, 2, '2015-11-19 13:17:24.097', 5, NULL, NULL, '&lt;p&gt;&lt;a href="https://github.com/pluskid/Mocha.jl"&gt;Mocha.jl&lt;/a&gt; - Mocha is a Deep Learning framework for Julia, inspired by the C++ framework Caffe.&lt;/p&gt;

&lt;p&gt;Project with good &lt;a href="http://mochajl.readthedocs.org/en/latest/"&gt;documentation&lt;/a&gt; and examples.
Can be run on CPU and GPU backend.&lt;/p&gt;
', NULL, NULL, '''/a'':8C,34C ''/en/latest/'':32C ''/p'':23C,45C ''/pluskid/mocha.jl'':6C ''backend'':44C ''c'':20C ''caff'':22C ''cpu'':41C ''deep'':12C ''document'':33C ''exampl'':36C ''framework'':14C,21C ''github.com'':5C ''github.com/pluskid/mocha.jl'':4C ''good'':27C ''gpu'':43C ''href'':3C,29C ''inspir'':17C ''julia'':16C ''learn'':13C ''mocha'':9C ''mocha.jl'':7C ''mochajl.readthedocs.org'':31C ''mochajl.readthedocs.org/en/latest/'':30C ''p'':1C,24C ''project'':25C ''run'':39C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (781, 2, '2014-07-19 03:29:02.647', 12, NULL, NULL, '&lt;p&gt;To clarify, I feel like the original question references by OP probably isn''t be best for a SO-type format, but I will certainly represent &lt;code&gt;python&lt;/code&gt; in this particular case.&lt;/p&gt;

&lt;p&gt;Let me just start by saying that regardless of your data size, &lt;code&gt;python&lt;/code&gt; shouldn''t be your limiting factor. In fact, there are just a couple main issues that you''re going to run into dealing with large datasets:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Reading data into memory&lt;/strong&gt; - This is by far the most common issue faced in the world of big data. Basically, you can''t read in more data than you have memory (RAM) for. The best way to fix this is by making atomic operations on your data instead of trying to read everything in at once.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Storing data&lt;/strong&gt; - This is actually just another form of the earlier issue, by the time to get up to about &lt;code&gt;1TB&lt;/code&gt;, you start having to look elsewhere for storage. AWS S3 is the most common resource, and &lt;code&gt;python&lt;/code&gt; has the fantastic &lt;code&gt;boto&lt;/code&gt; library to facilitate leading with large pieces of data.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Network latency&lt;/strong&gt; - Moving data around between different services is going to be your bottleneck. There''s not a huge amount you can do to fix this, other than trying to pick co-located resources and plugging into the wall.&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (782, 2, '2014-07-19 03:42:34.433', 0, NULL, NULL, '&lt;p&gt;My limited understanding, based on brief browsing &lt;em&gt;GitHub API&lt;/em&gt; documentation, is that currently there is NO &lt;strong&gt;single API request&lt;/strong&gt; that supports &lt;strong&gt;all&lt;/strong&gt; your listed criteria &lt;strong&gt;at once&lt;/strong&gt;. However, I think that you could use the following sequence in order to achieve the goal from your example (at least, I would use this approach):&lt;/p&gt;

&lt;p&gt;1) &lt;strong&gt;Request&lt;/strong&gt; information on all public repositories (API returns &lt;em&gt;summary representations&lt;/em&gt; only): &lt;a href="https://developer.github.com/v3/repos/#list-all-public-repositories" rel="nofollow"&gt;https://developer.github.com/v3/repos/#list-all-public-repositories&lt;/a&gt;;&lt;/p&gt;

&lt;p&gt;2) &lt;strong&gt;Loop&lt;/strong&gt; through the list of all public repositories retrieved in step 1, requesting individual resources, and save it as new (detailed) list (this returns &lt;em&gt;detailed representations&lt;/em&gt;, in other words, all attributes): &lt;a href="https://developer.github.com/v3/repos/#get" rel="nofollow"&gt;https://developer.github.com/v3/repos/#get&lt;/a&gt;;&lt;/p&gt;

&lt;p&gt;3) &lt;strong&gt;Loop&lt;/strong&gt; through the detailed list of all repositories, filtering corresponding fields by your criteria. For your example request, you''d be interested in the following attributes of the &lt;strong&gt;parent&lt;/strong&gt; object: &lt;em&gt;stargazers_count&lt;/em&gt;, &lt;em&gt;forks_count&lt;/em&gt;. In order to filter the repositories by number of committers, you could use a separate API: &lt;a href="https://developer.github.com/v3/repos/#list-contributors" rel="nofollow"&gt;https://developer.github.com/v3/repos/#list-contributors&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Updates or comments from people more familiar with GitHub API are welcome!&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (783, 1, '2014-07-19 05:27:22.773', 9, 314, 'Data visualization for pattern analysis (language-independent, but R preferred)', '&lt;p&gt;I want to plot the bytes from a disk image in order to understand a pattern in them. This is mainly an academic task, since I''m almost sure this pattern was created by a disk testing program, but I''d like to reverse-engineer it anyway.&lt;/p&gt;

&lt;p&gt;I already know that the pattern is aligned, with a periodicity of 256 characters.&lt;/p&gt;

&lt;p&gt;I can envision two ways of visualizing this information: either a 16x16 plane viewed through time (3 dimensions), where each pixel''s color is the ASCII code for the character, or a 256 pixel line for each period (2 dimensions).&lt;/p&gt;

&lt;p&gt;This is a snapshot of the pattern (you can see more than one), seen through &lt;code&gt;xxd&lt;/code&gt; (32x16):&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/zOFSK.gif" alt="Pattern to analyze"&gt;&lt;/p&gt;

&lt;p&gt;Either way, I am trying to find a way of visualizing this information. This probably isn''t hard for anyone into signal analysis, but I can''t seem to find a way using open-source software.&lt;/p&gt;

&lt;p&gt;I''d like to avoid Matlab or Mathematica and I''d prefer an answer in R, since I have been learning it recently, but nonetheless, any language is welcome.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;Update, 2014-07-25: given Emre''s answer below, this is what the pattern looks like, given the first 30MB of the pattern, aligned at 512 instead of 256 (this alignment looks better):&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/4tDIA.png" alt="Graphical pattern"&gt;&lt;/p&gt;

&lt;p&gt;Any further ideas are welcome!&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (796, 2, '2014-07-21 16:58:22.66', 7, NULL, NULL, '&lt;p&gt;There are couple off things you need to understand when dealing with Big data - &lt;/p&gt;

&lt;h3&gt;What is Big data?&lt;/h3&gt;

&lt;p&gt;You might be aware of famous V''s of Big data - Volume, Velocity, Variety... So, Python may not be suitable for all. And it goes with all data science tools available. You need to know which tool is good for what purpose.&lt;/p&gt;

&lt;p&gt;If dealing with large Volume of data:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Pig/Hive/Shark - Data cleaning and ETL work&lt;/li&gt;
&lt;li&gt;Hadoop/Spark - Distributed parallel computing&lt;/li&gt;
&lt;li&gt;Mahout/ML-Lib - Machine Learning&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Now, you can use R/Python in intermediate stages but you''ll realize that they become bottleneck in your entire process.&lt;/p&gt;

&lt;p&gt;If dealing with Velocity of data:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Kafka/Storm - High throughput system&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;People are trying to R/Python here but again it depends on kind of parallelism you want and your model complexity.&lt;/p&gt;

&lt;h3&gt;What sort of analysis you wish to do?&lt;/h3&gt;

&lt;p&gt;If your model demands the entire data to be first brought into memory then your model should not be complex because if the intermediate data is large then the code will break. And if you think of writing it into disk then you''ll face additional delay because disk read/write is slow as compared to RAM.&lt;/p&gt;

&lt;h2&gt;Conclusion&lt;/h2&gt;

&lt;p&gt;You can definitely use Python in Big data space (Definitely, since people are trying with R, why not Python) but know your data and business requirement first. There may be better tools available for same and always remember:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Your tools shouldnât determine how you answer questions. Your questions should determine what tools you use.&lt;/p&gt;
&lt;/blockquote&gt;
', NULL, NULL, '''/blockquote'':288C ''/h2'':227C ''/h3'':22C,166C ''/li'':84C,90C,95C,133C ''/p'':16C,66C,75C,118C,126C,156C,224C,267C,287C ''/ul'':96C,134C ''addit'':213C ''alway'':265C ''analysi'':161C ''answer'':277C ''avail'':54C,261C ''awar'':27C ''becom'':112C ''better'':259C ''big'':14C,20C,33C,235C ''blockquot'':268C ''bottleneck'':113C ''break'':199C ''brought'':178C ''busi'':253C ''clean'':80C ''code'':197C ''compar'':221C ''complex'':155C,187C ''comput'':89C ''conclus'':226C ''coupl'':4C ''data'':15C,21C,34C,51C,74C,79C,125C,174C,192C,236C,251C ''deal'':12C,69C,121C ''definit'':231C,238C ''delay'':214C ''demand'':171C ''depend'':145C ''determin'':274C,282C ''disk'':208C,216C ''distribut'':87C ''entir'':116C,173C ''etl'':82C ''face'':212C ''famous'':29C ''first'':177C,255C ''goe'':48C ''good'':62C ''h2'':225C ''h3'':17C,157C ''hadoop/spark'':86C ''high'':130C ''intermedi'':104C,191C ''kafka/storm'':129C ''kind'':147C ''know'':58C,249C ''larg'':71C,194C ''learn'':94C ''li'':77C,85C,91C,128C ''ll'':108C,211C ''machin'':93C ''mahout/ml-lib'':92C ''may'':40C,257C ''memori'':180C ''might'':25C ''model'':154C,170C,183C ''need'':8C,56C ''p'':1C,23C,67C,97C,119C,135C,167C,228C,269C ''parallel'':88C,149C ''peopl'':136C,240C ''pig/hive/shark'':78C ''process'':117C ''purpos'':65C ''python'':39C,233C,247C ''question'':278C,280C ''r'':244C ''r/python'':102C,140C ''ram'':223C ''read/write'':217C ''realiz'':109C ''rememb'':266C ''requir'':254C ''scienc'':52C ''shouldnâ'':272C ''sinc'':239C ''slow'':219C ''sort'':159C ''space'':237C ''stage'':105C ''suitabl'':43C ''system'':132C ''thing'':6C ''think'':203C ''throughput'':131C ''tool'':53C,60C,260C,271C,284C ''tri'':138C,242C ''ul'':76C,127C ''understand'':10C ''use'':101C,232C,286C ''v'':30C ''varieti'':37C ''veloc'':36C,123C ''volum'':35C,72C ''want'':151C ''wish'':163C ''work'':83C ''write'':205C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (784, 1, '2014-07-19 18:31:36.573', 5, 269, 'Why should I care about seasonal data when I forecast?', '&lt;p&gt;I have a timeseries with hourly gas consumption. I want to use &lt;a href="http://en.wikipedia.org/wiki/Autoregressive%E2%80%93moving-average_model"&gt;ARMA&lt;/a&gt;/&lt;a href="http://en.wikipedia.org/wiki/Autoregressive_integrated_moving_average"&gt;ARIMA&lt;/a&gt; to forecast the consumption on the next hour, basing on the previous. Why should I analyze/find the seasonality (with &lt;a href="https://www.otexts.org/fpp/6/5"&gt;Seasonal and Trend decomposition using Loess&lt;/a&gt; (STL)?)?&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/hYyH8.png" alt="enter image description here"&gt;&lt;/p&gt;
', 'machine-learning  time-series', NULL, '''/a'':36C,43C,74C ''/fpp/6/5'':67C ''/hyyh8.png'':82C ''/p'':76C,88C ''/wiki/autoregressive%e2%80%93moving-average_model'':34C ''/wiki/autoregressive_integrated_moving_average'':41C ''alt'':83C ''analyze/find'':59C ''arima'':42C ''arma'':35C ''base'':52C ''care'':4A ''consumpt'':25C,47C ''data'':7A ''decomposit'':71C ''descript'':86C ''en.wikipedia.org'':33C,40C ''en.wikipedia.org/wiki/autoregressive%e2%80%93moving-average_model'':32C ''en.wikipedia.org/wiki/autoregressive_integrated_moving_average'':39C ''enter'':84C ''forecast'':10A,45C ''gas'':24C ''hour'':23C,51C ''href'':31C,38C,64C ''i.stack.imgur.com'':81C ''i.stack.imgur.com/hyyh8.png'':80C ''imag'':85C ''img'':78C ''learn'':13B ''loess'':73C ''machin'':12B ''machine-learn'':11B ''next'':50C ''p'':17C,77C ''previous'':55C ''season'':6A,61C,68C ''seri'':16B ''src'':79C ''stl'':75C ''time'':15B ''time-seri'':14B ''timeseri'':21C ''trend'':70C ''use'':29C,72C ''want'':27C ''www.otexts.org'':66C ''www.otexts.org/fpp/6/5'':65C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (800, 2, '2014-07-21 21:29:26.27', 3, NULL, NULL, '&lt;p&gt;One benefit of the schema-free NoSQL approach is that you don''t commit prematurely and you can apply the right schema at query time using an appropriate tool like &lt;a href="http://incubator.apache.org/drill/" rel="nofollow"&gt;Apache Drill&lt;/a&gt;. See &lt;a href="http://wiki.apache.org/incubator/DrillProposal?action=AttachFile&amp;amp;do=get&amp;amp;target=Drill+slides.pdf" rel="nofollow"&gt;this presentation&lt;/a&gt; for details. MySQL wouldn''t be my first choice in a big data setting.&lt;/p&gt;
', NULL, NULL, '''/a'':42C,53C ''/drill/'':37C ''/incubator/drillproposal?action=attachfile&amp;amp;do=get&amp;amp;target=drill+slides.pdf'':48C ''/p'':68C ''apach'':40C ''appli'':21C ''approach'':10C ''appropri'':30C ''benefit'':3C ''big'':65C ''choic'':62C ''commit'':16C ''data'':66C ''detail'':55C ''drill'':41C ''first'':61C ''free'':8C ''href'':34C,45C ''incubator.apache.org'':36C ''incubator.apache.org/drill/'':35C ''like'':32C ''mysql'':56C ''nofollow'':39C,50C ''nosql'':9C ''one'':2C ''p'':1C ''prematur'':17C ''present'':52C ''queri'':26C ''rel'':38C,49C ''right'':23C ''schema'':7C,24C ''schema-fre'':6C ''see'':43C ''set'':67C ''time'':27C ''tool'':31C ''use'':28C ''wiki.apache.org'':47C ''wiki.apache.org/incubator/drillproposal?action=attachfile&amp;amp;do=get&amp;amp;target=drill+slides.pdf'':46C ''wouldn'':57C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (785, 2, '2014-07-19 18:47:38.787', 1, NULL, NULL, '&lt;p&gt;I know almost nothing about signal analysis, but 2-dimensional visualization could be easily done using R. Particularly you will need &lt;code&gt;reshape2&lt;/code&gt; and &lt;code&gt;ggplot2&lt;/code&gt; packages. Assuming your data is &lt;strong&gt;wide&lt;/strong&gt; (e.g. [n X 256] size), first you need to transform it to &lt;a href="http://www.cookbook-r.com/Manipulating_data/Converting_data_between_wide_and_long_format/" rel="nofollow"&gt;&lt;strong&gt;long&lt;/strong&gt;&lt;/a&gt; format using &lt;code&gt;melt()&lt;/code&gt; function from &lt;code&gt;reshape2&lt;/code&gt; package. Then use &lt;a href="http://docs.ggplot2.org/current/geom_tile.html" rel="nofollow"&gt;&lt;code&gt;geom_tile&lt;/code&gt;&lt;/a&gt; geometry from &lt;code&gt;ggplot2&lt;/code&gt;. Here is a nice &lt;a href="http://www.r-bloggers.com/simplest-possible-heatmap-with-ggplot2/" rel="nofollow"&gt;recipe&lt;/a&gt; with &lt;a href="https://gist.github.com/dsparks/3710171" rel="nofollow"&gt;gist&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':60C,85C,103C,113C ''/code'':25C,29C,65C,70C,84C,90C ''/current/geom_tile.html'':78C ''/dsparks/3710171'':109C ''/manipulating_data/converting_data_between_wide_and_long_format/'':54C ''/p'':114C ''/simplest-possible-heatmap-with-ggplot2/'':99C ''/strong'':37C,59C ''2'':10C ''256'':41C ''almost'':4C ''analysi'':8C ''assum'':31C ''code'':23C,27C,63C,68C,81C,88C ''could'':13C ''data'':33C ''dimension'':11C ''docs.ggplot2.org'':77C ''docs.ggplot2.org/current/geom_tile.html'':76C ''done'':16C ''e.g'':38C ''easili'':15C ''first'':43C ''format'':61C ''function'':66C ''geom'':82C ''geometri'':86C ''ggplot2'':28C,89C ''gist'':112C ''gist.github.com'':108C ''gist.github.com/dsparks/3710171'':107C ''href'':51C,75C,96C,106C ''know'':3C ''long'':58C ''melt'':64C ''n'':39C ''need'':22C,45C ''nice'':94C ''nofollow'':56C,80C,101C,111C ''noth'':5C ''p'':1C ''packag'':30C,71C ''particular'':19C ''r'':18C ''recip'':102C ''rel'':55C,79C,100C,110C ''reshape2'':24C,69C ''signal'':7C ''size'':42C ''strong'':35C,57C ''tile'':83C ''transform'':47C ''use'':17C,62C,73C ''visual'':12C ''wide'':36C ''www.cookbook-r.com'':53C ''www.cookbook-r.com/manipulating_data/converting_data_between_wide_and_long_format/'':52C ''www.r-bloggers.com'':98C ''www.r-bloggers.com/simplest-possible-heatmap-with-ggplot2/'':97C ''x'':40C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (787, 1, '2014-07-19 20:51:58.527', 2, 1276, 'Hadoop Resource Manager Won''t Start', '&lt;p&gt;I am a relatively new user to Hadoop (using version 2.4.1). I installed hadoop on my first node without a hitch, but I can''t seem to get the Resource Manager to start on my second node. &lt;/p&gt;

&lt;p&gt;I cleared up some "shared library" problems by adding this to yarn-env.sh and hadoop-env.sh:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;export HADOOP_HOME="/usr/local/hadoop"&lt;/p&gt;
  
  &lt;p&gt;export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib"&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I also added this to hadoop-env.sh:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;export HADOOP_COMMON_LIB_NATIVE_DIR=${HADOOP_PREFIX}/lib/native&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;based on the advice of this post at horton works &lt;a href="http://hortonworks.com/community/forums/topic/hdfs-tmp-dir-issue/" rel="nofollow"&gt;http://hortonworks.com/community/forums/topic/hdfs-tmp-dir-issue/&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;That cleared up all of my error messages; when I run /sbin/start-yarn.sh I get this:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;starting yarn daemons&lt;/p&gt;
  
  &lt;p&gt;starting resourcemanager, 
  logging to /usr/local/hadoop/logs/yarn-hduser-resourcemanager-HdNode.out&lt;/p&gt;
  
  &lt;p&gt;localhost: starting nodemanager, 
  logging to /usr/local/hadoop/logs/yarn-hduser-nodemanager-HdNode.out&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;The only problem is, JPS says that the Resource Manager isn''t running. &lt;/p&gt;

&lt;p&gt;What''s going on here?&lt;/p&gt;
', 'hadoop', NULL, '''/blockquote'':78C,99C,160C ''/community/forums/topic/hdfs-tmp-dir-issue/'':115C ''/community/forums/topic/hdfs-tmp-dir-issue/&lt;/a&gt;'':120C ''/lib/native'':97C ''/p'':46C,62C,69C,77C,86C,98C,121C,138C,144C,151C,159C,175C,182C ''/sbin/start-yarn.sh'':134C ''/usr/local/hadoop'':68C ''/usr/local/hadoop/logs/yarn-hduser-nodemanager-hdnode.out'':158C ''/usr/local/hadoop/logs/yarn-hduser-resourcemanager-hdnode.out'':150C ''2.4.1'':19C ''ad'':56C,82C ''advic'':104C ''also'':81C ''base'':101C ''blockquot'':63C,87C,139C ''clear'':49C,124C ''common'':91C ''daemon'':143C ''dir'':94C ''djava.library.path'':74C ''error'':129C ''export'':65C,71C,89C ''first'':25C ''get'':36C,136C ''go'':179C ''hadoop'':1A,7B,16C,22C,66C,72C,75C,90C,95C ''hadoop-env.sh'':61C,85C ''hitch'':29C ''home'':67C ''home/lib'':76C ''horton'':109C ''hortonworks.com'':114C,119C ''hortonworks.com/community/forums/topic/hdfs-tmp-dir-issue/'':113C ''hortonworks.com/community/forums/topic/hdfs-tmp-dir-issue/&lt;/a&gt;'':118C ''href'':112C ''instal'':21C ''isn'':172C ''jps'':166C ''lib'':92C ''librari'':53C ''localhost'':153C ''log'':148C,156C ''manag'':3A,39C,171C ''messag'':130C ''nativ'':93C ''new'':13C ''node'':26C,45C ''nodemanag'':155C ''nofollow'':117C ''opt'':73C ''p'':8C,47C,64C,70C,79C,88C,100C,122C,140C,145C,152C,161C,176C ''post'':107C ''prefix'':96C ''problem'':54C,164C ''rel'':116C ''relat'':12C ''resourc'':2A,38C,170C ''resourcemanag'':147C ''run'':133C,174C ''say'':167C ''second'':44C ''seem'':34C ''share'':52C ''start'':6A,41C,141C,146C,154C ''use'':17C ''user'':14C ''version'':18C ''without'':27C ''won'':4A ''work'':110C ''yarn'':142C ''yarn-env.sh'':59C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (788, 2, '2014-07-20 21:37:27.857', 1, NULL, NULL, '&lt;p&gt;Check your version of JPS and make sure it''s the same as the version of java that you are running.  Sometimes you start out with an out of the box jdk install, upgrade, set alternatives for the java bin, but still have the original jps binary being referenced.&lt;/p&gt;

&lt;p&gt;Run &lt;code&gt;ps -ef |grep java&lt;/code&gt; and look for the resource manager threads.  Maybe it''s actually running.  If it is, try &lt;code&gt;update-alternatives --config jps&lt;/code&gt; to see what binary jps is pointing at and compare it with the java binary that you are using.&lt;/p&gt;

&lt;p&gt;If the resource manager is not running, it''s time to do some basic linux troubleshooting.  Check log files and barring that check actual command output.&lt;/p&gt;

&lt;p&gt;On the system I''m looking at now, the log files for resource manager are placed in the &lt;code&gt;hadoop-install/logs&lt;/code&gt; directory in &lt;code&gt;yarn-username-resourcemanager-hostname.log&lt;/code&gt; and &lt;code&gt;yarn-user-resourcemanager-hostname.out&lt;/code&gt;.  Your configuration may place them in /var/logs or what have you.  Also, have a look at the syslog.&lt;/p&gt;

&lt;p&gt;If the logs don''t yield any good information, which can happen, my process is to generally try to figure out the command line from the startup script (usually by prefixing the command line with &lt;code&gt;echo&lt;/code&gt;), and then trying to run the command directly to see the output as it comes out.&lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;I have actually run into this problem before, but I can''t remember the specific issue.  I''m sure the same result can manifest itself from a variety of problems.  Considering that you are as far as you are in the process of getting set up, I believe it''s likely to be a minor configuration issue.&lt;/p&gt;
', NULL, NULL, '''/code'':59C,82C,155C,160C,164C,222C ''/logs'':154C ''/p'':51C,102C,130C,183C,239C,299C ''/var/logs'':171C ''actual'':70C,127C,244C ''also'':176C ''altern'':37C,79C ''bar'':124C ''basic'':117C ''believ'':289C ''bin'':41C ''binari'':48C,86C,97C ''box'':32C ''check'':2C,120C,126C ''code'':54C,76C,150C,158C,162C,220C ''come'':237C ''command'':128C,207C,217C,229C ''compar'':92C ''config'':80C ''configur'':166C,297C ''consid'':272C ''direct'':230C ''directori'':156C ''echo'':221C ''ef'':56C ''far'':277C ''figur'':204C ''file'':122C,142C ''general'':201C ''get'':285C ''good'':192C ''grep'':57C ''hadoop'':152C ''hadoop-instal'':151C ''happen'':196C ''hr'':240C ''inform'':193C ''instal'':34C,153C ''issu'':257C,298C ''java'':18C,40C,58C,96C ''jdk'':33C ''jps'':6C,47C,81C,87C ''like'':292C ''line'':208C,218C ''linux'':118C ''log'':121C,141C,187C ''look'':61C,137C,179C ''m'':136C,259C ''make'':8C ''manag'':65C,107C,145C ''manifest'':265C ''may'':167C ''mayb'':67C ''minor'':296C ''origin'':46C ''output'':129C,234C ''p'':1C,52C,103C,131C,184C,241C ''place'':147C,168C ''point'':89C ''prefix'':215C ''problem'':248C,271C ''process'':198C,283C ''ps'':55C ''referenc'':50C ''rememb'':254C ''resourc'':64C,106C,144C ''result'':263C ''run'':22C,53C,71C,110C,227C,245C ''script'':212C ''see'':84C,232C ''set'':36C,286C ''sometim'':23C ''specif'':256C ''start'':25C ''startup'':211C ''still'':43C ''sure'':9C,260C ''syslog'':182C ''system'':134C ''thread'':66C ''time'':113C ''tri'':75C,202C,225C ''troubleshoot'':119C ''updat'':78C ''update-altern'':77C ''upgrad'':35C ''use'':101C ''usual'':213C ''varieti'':269C ''version'':4C,16C ''yarn-user-resourcemanager-hostname.out'':163C ''yarn-username-resourcemanager-hostname.log'':159C ''yield'':190C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2272, 2, '2014-10-14 07:02:47.5', 21, NULL, NULL, '&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.r-fiddle.org"&gt;R-fiddle&lt;/a&gt; provides an easy-to-use interactive R console that you can run from your browser. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/cZJu6.png" alt="screenshot-rfiddle"&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://roncloud.com"&gt;R On Cloud&lt;/a&gt; provides also a browser-embedded R-console&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ELpbR.png" alt="screenshot-roncloud"&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.getdatajoy.com"&gt;DataJoy&lt;/a&gt; provides easy to use, online data processing with Python and R (sign-up is required; the free basic plan has the following restrictions: low CPU priority, 256MB memory, 1 minute script timeout). &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/c75NS.png" alt="screenshot-datajoy"&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://jupyter.org/"&gt;Jupyter.org&lt;/a&gt; evolved from the &lt;a href="http://ipython.org/"&gt;IPython Project&lt;/a&gt; (the language-agnostic parts of IPython); supports Python 3, Julia, R, Haskell, Ruby, etc.  &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/mhHkx.png" alt="jupyter screenshot"&gt;&lt;/p&gt;
', NULL, NULL, '''/a'':9C,47C,76C,129C,138C ''/c75ns.png'':117C ''/czju6.png'':33C ''/elpbr.png'':64C ''/li'':26C,57C,110C,154C ''/mhhkx.png'':161C ''/p'':38C,69C,122C,165C ''/ul'':27C,58C,111C,155C ''1'':106C ''256mb'':104C ''3'':148C ''agnost'':142C ''also'':49C ''alt'':34C,65C,118C,162C ''basic'':95C ''browser'':25C,52C ''browser-embed'':51C ''cloud'':46C ''consol'':18C,56C ''cpu'':102C ''data'':82C ''datajoy'':75C,121C ''easi'':13C,78C ''easy-to-us'':12C ''embed'':53C ''etc'':153C ''evolv'':130C ''fiddl'':8C ''follow'':99C ''free'':94C ''haskel'':151C ''href'':4C,42C,73C,126C,134C ''i.stack.imgur.com'':32C,63C,116C,160C ''i.stack.imgur.com/c75ns.png'':115C ''i.stack.imgur.com/czju6.png'':31C ''i.stack.imgur.com/elpbr.png'':62C ''i.stack.imgur.com/mhhkx.png'':159C ''img'':29C,60C,113C,157C ''interact'':16C ''ipython'':136C,145C ''ipython.org'':135C ''julia'':149C ''jupyt'':163C ''jupyter.org'':127C,128C ''languag'':141C ''language-agnost'':140C ''li'':2C,40C,71C,124C ''low'':101C ''memori'':105C ''minut'':107C ''onlin'':81C ''p'':28C,59C,112C,156C ''part'':143C ''plan'':96C ''prioriti'':103C ''process'':83C ''project'':137C ''provid'':10C,48C,77C ''python'':85C,147C ''r'':7C,17C,44C,55C,87C,150C ''r-consol'':54C ''r-fiddl'':6C ''requir'':92C ''restrict'':100C ''rfiddl'':37C ''roncloud'':68C ''roncloud.com'':43C ''rubi'':152C ''run'':22C ''screenshot'':36C,67C,120C,164C ''screenshot-datajoy'':119C ''screenshot-rfiddl'':35C ''screenshot-roncloud'':66C ''script'':108C ''sign'':89C ''sign-up'':88C ''src'':30C,61C,114C,158C ''support'':146C ''timeout'':109C ''ul'':1C,39C,70C,123C ''use'':15C,80C ''www.getdatajoy.com'':74C ''www.r-fiddle.org'':5C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (789, 2, '2014-07-20 23:54:09.07', 3, NULL, NULL, '&lt;p&gt;You can pull stock data very easyly in python and R (probably other languages as well) with the following packages:&lt;/p&gt;

&lt;p&gt;In python with: &lt;a href="https://pypi.python.org/pypi/ystockquote" rel="nofollow"&gt;https://pypi.python.org/pypi/ystockquote&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;This is also a really nice tutorial in iPython which shows you how to pull the stock data and play with it: &lt;a href="http://nbviewer.ipython.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20Pandas%20Basics.ipynb" rel="nofollow"&gt;http://nbviewer.ipython.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20Pandas%20Basics.ipynb&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;In R with: &lt;a href="http://www.quantmod.com/" rel="nofollow"&gt;http://www.quantmod.com/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;HTH. &lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;&lt;/p&gt;'':81C ''/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20pandas%20basics.ipynb'':64C ''/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20pandas%20basics.ipynb&lt;/a&gt;&lt;/p&gt;'':69C ''/p'':22C,84C ''/pypi/ystockquote'':31C ''/pypi/ystockquote&lt;/a&gt;&lt;/p&gt;'':36C ''also'':40C ''data'':6C,55C ''easyli'':8C ''follow'':20C ''href'':28C,61C,75C ''hth'':83C ''ipython'':46C ''languag'':15C ''nbviewer.ipython.org'':63C,68C ''nbviewer.ipython.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20pandas%20basics.ipynb'':62C ''nbviewer.ipython.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20pandas%20basics.ipynb&lt;/a&gt;&lt;/p&gt;'':67C ''nice'':43C ''nofollow'':33C,66C,78C ''p'':1C,23C,37C,70C,82C ''packag'':21C ''play'':57C ''probabl'':13C ''pull'':4C,52C ''pypi.python.org'':30C,35C ''pypi.python.org/pypi/ystockquote'':29C ''pypi.python.org/pypi/ystockquote&lt;/a&gt;&lt;/p&gt;'':34C ''python'':10C,25C ''r'':12C,72C ''realli'':42C ''rel'':32C,65C,77C ''show'':48C ''stock'':5C,54C ''tutori'':44C ''well'':17C ''www.quantmod.com'':76C,80C ''www.quantmod.com/&lt;/a&gt;&lt;/p&gt;'':79C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (790, 2, '2014-07-21 00:35:13.44', 4, NULL, NULL, '&lt;p&gt;Absolutely. When you''re working with data at that scale it''s common to use a big data framework, in which case python or whatever language you''re using is merely an interface. See for example &lt;a href="http://spark.apache.org/docs/0.9.1/python-programming-guide.html" rel="nofollow"&gt;Spark''s Python Programming Guide&lt;/a&gt;. What kind of data do you have and what do you want to do with it?&lt;/p&gt;
', NULL, NULL, '''/a'':50C ''/docs/0.9.1/python-programming-guide.html'':42C ''/p'':67C ''absolut'':2C ''big'':18C ''case'':23C ''common'':14C ''data'':8C,19C,54C ''exampl'':37C ''framework'':20C ''guid'':49C ''href'':39C ''interfac'':34C ''kind'':52C ''languag'':27C ''mere'':32C ''nofollow'':44C ''p'':1C ''program'':48C ''python'':24C,47C ''re'':5C,29C ''rel'':43C ''scale'':11C ''see'':35C ''spark'':45C ''spark.apache.org'':41C ''spark.apache.org/docs/0.9.1/python-programming-guide.html'':40C ''use'':16C,30C ''want'':62C ''whatev'':26C ''work'':6C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (791, 1, '2014-07-21 09:00:04.917', 5, 237, 'Job title similarity', '&lt;p&gt;I''m trying to define a metric between job titles in IT field. For this I need some metric between words of job titles that are not appearing together in the same job title, e.g. metric between the words &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;senior, primary, lead, head, vp, director, stuff, principal, chief, &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;or the words &lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;analyst, expert, modeler, researcher, scientist, developer, engineer, architect.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;How can I get all such possible words with their distance ?&lt;/p&gt;
', 'machine-learning  dataset', NULL, '''/blockquote'':61C,78C ''/p'':48C,60C,66C,77C,91C ''analyst'':69C ''appear'':36C ''architect'':76C ''blockquot'':49C,67C ''chief'':59C ''dataset'':7B ''defin'':13C ''develop'':74C ''director'':56C ''distanc'':90C ''e.g'':43C ''engin'':75C ''expert'':70C ''field'':21C ''get'':83C ''head'':54C ''job'':1A,17C,31C,41C ''lead'':53C ''learn'':6B ''m'':10C ''machin'':5B ''machine-learn'':4B ''metric'':15C,27C,44C ''model'':71C ''need'':25C ''p'':8C,50C,62C,68C,79C ''possibl'':86C ''primari'':52C ''princip'':58C ''research'':72C ''scientist'':73C ''senior'':51C ''similar'':3A ''stuff'':57C ''titl'':2A,18C,32C,42C ''togeth'':37C ''tri'':11C ''vp'':55C ''word'':29C,47C,65C,87C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5316, 1, '2015-03-13 16:41:29.28', 6, 710, 'General approach to extract key text from sentence (nlp)', '&lt;p&gt;Given a sentence like:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Complimentary gym access for two for the length of stay ($12 value per person per day)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;What general approach can I take to identify the word gym or gym access?&lt;/p&gt;
', 'machine-learning  nlp  text-mining  data-cleaning', NULL, '''/code'':44C ''/p'':25C,61C ''/pre'':45C ''12'':38C ''access'':30C,60C ''approach'':2A,49C ''clean'':19B ''code'':27C ''complimentari'':28C ''data'':18B ''data-clean'':17B ''day'':43C ''extract'':4A ''general'':1A,48C ''given'':21C ''gym'':29C,57C,59C ''identifi'':54C ''key'':5A ''learn'':12B ''length'':35C ''like'':24C ''machin'':11B ''machine-learn'':10B ''mine'':16B ''nlp'':9A,13B ''p'':20C,46C ''per'':40C,42C ''person'':41C ''pre'':26C ''sentenc'':8A,23C ''stay'':37C ''take'':52C ''text'':6A,15B ''text-min'':14B ''two'':32C ''valu'':39C ''word'':56C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (794, 2, '2014-07-21 15:35:59.54', 6, NULL, NULL, '&lt;p&gt;Pylearn2 seems to be the library of choice, however I find their YAML configuration files off-putting.&lt;/p&gt;

&lt;p&gt;Python itself was designed to be an easy language for prototyping, why would you &lt;strong&gt;not&lt;/strong&gt; use it to define the network properties themselves? We have great editors with autocompletion that would make your life much easier and Python is not like C++ where you have to wait for long builds to finish before you can run your code.&lt;/p&gt;

&lt;p&gt;YAML files on the other hand you have to edit using a standard text editor with no assistance whatsoever and this makes the learning curve even steeper. &lt;/p&gt;

&lt;p&gt;I may be missing the big picture but I still don''t understand what were they thinking, I don''t think prototyping in code would be much slower. For that reason I''m considering Theanets or using Theano directly.&lt;/p&gt;
', NULL, NULL, '''/p'':20C,82C,111C,152C ''/strong'':38C ''assist'':101C ''autocomplet'':52C ''big'':118C ''build'':73C ''c'':65C ''choic'':9C ''code'':81C,136C ''configur'':15C ''consid'':146C ''curv'':108C ''defin'':42C ''design'':25C ''direct'':151C ''easi'':29C ''easier'':59C ''edit'':93C ''editor'':50C,98C ''even'':109C ''file'':16C,85C ''find'':12C ''finish'':75C ''great'':49C ''hand'':89C ''howev'':10C ''languag'':30C ''learn'':107C ''librari'':7C ''life'':57C ''like'':64C ''long'':72C ''m'':145C ''make'':55C,105C ''may'':114C ''miss'':116C ''much'':58C,139C ''network'':44C ''off-put'':17C ''p'':1C,21C,83C,112C ''pictur'':119C ''properti'':45C ''prototyp'':32C,134C ''put'':19C ''pylearn2'':2C ''python'':22C,61C ''reason'':143C ''run'':79C ''seem'':3C ''slower'':140C ''standard'':96C ''steeper'':110C ''still'':122C ''strong'':36C ''text'':97C ''theanet'':147C ''theano'':150C ''think'':129C,133C ''understand'':125C ''use'':39C,94C,149C ''wait'':70C ''whatsoev'':102C ''would'':34C,54C,137C ''yaml'':14C,84C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (795, 2, '2014-07-21 16:15:07.01', 1, NULL, NULL, '&lt;p&gt;Not sure if this is exactly what you''re looking for, but r-base has a function called "adist" which creates a distance matrix of approximate string distances (according to the Levenshtein distance). Type ''?adist'' for more.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;words = c("senior", "primary", "lead", "head", "vp", "director", "stuff", "principal", "chief")
adist(words)

      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
 [1,]    0    6    5    5    6    5    5    7    5
 [2,]    6    0    6    6    7    7    7    6    6
 [3,]    5    6    0    1    4    7    5    8    5
 [4,]    5    6    1    0    4    7    5    8    4
 [5,]    6    7    4    4    0    8    5    8    5
 [6,]    5    7    7    7    8    0    8    8    7
 [7,]    5    7    5    5    5    8    0    9    4
 [8,]    7    6    8    8    8    8    9    0    8
 [9,]    5    6    5    4    5    7    4    8    0
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Also, if R isn''t an option, the Levenshtein distance algorithm is implemented in many languages here:
&lt;a href="http://en.wikibooks.org/wiki/Algorithm_Implementation/Strings/Levenshtein_distance" rel="nofollow"&gt;http://en.wikibooks.org/wiki/Algorithm_Implementation/Strings/Levenshtein_distance&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/code'':155C ''/p'':40C ''/pre'':156C ''/wiki/algorithm_implementation/strings/levenshtein_distance'':179C ''/wiki/algorithm_implementation/strings/levenshtein_distance&lt;/a&gt;&lt;/p&gt;'':184C ''0'':66C,77C,88C,99C,110C,121C,132C,143C,154C ''1'':56C,65C,89C,98C ''2'':57C,75C ''3'':58C,85C ''4'':59C,90C,95C,100C,104C,108C,109C,134C,149C,152C ''5'':60C,68C,69C,71C,72C,74C,86C,92C,94C,96C,102C,105C,112C,114C,116C,126C,128C,129C,130C,146C,148C,150C ''6'':61C,67C,70C,76C,78C,79C,83C,84C,87C,97C,106C,115C,137C,147C ''7'':62C,73C,80C,81C,82C,91C,101C,107C,117C,118C,119C,124C,125C,127C,136C,151C ''8'':63C,93C,103C,111C,113C,120C,122C,123C,131C,135C,138C,139C,140C,141C,144C,153C ''9'':64C,133C,142C,145C ''accord'':31C ''adist'':21C,37C,54C ''algorithm'':168C ''also'':158C ''approxim'':28C ''base'':16C ''c'':44C ''call'':20C ''chief'':53C ''code'':42C ''creat'':23C ''director'':50C ''distanc'':25C,30C,35C,167C ''en.wikibooks.org'':178C,183C ''en.wikibooks.org/wiki/algorithm_implementation/strings/levenshtein_distance'':177C ''en.wikibooks.org/wiki/algorithm_implementation/strings/levenshtein_distance&lt;/a&gt;&lt;/p&gt;'':182C ''exact'':7C ''function'':19C ''head'':48C ''href'':176C ''implement'':170C ''isn'':161C ''languag'':173C ''lead'':47C ''levenshtein'':34C,166C ''look'':11C ''mani'':172C ''matrix'':26C ''nofollow'':181C ''option'':164C ''p'':1C,157C ''pre'':41C ''primari'':46C ''princip'':52C ''r'':15C,160C ''r-base'':14C ''re'':10C ''rel'':180C ''senior'':45C ''string'':29C ''stuff'':51C ''sure'':3C ''type'':36C ''vp'':49C ''word'':43C,55C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (797, 2, '2014-07-21 19:06:43.223', 17, NULL, NULL, '&lt;p&gt;To be perfectly honest, most NoSQL databases are not very well suited to applications in big data. For the vast majority of all big data applications, the performance of &lt;a href="https://en.wikipedia.org/wiki/MongoDB" rel="nofollow"&gt;MongoDB&lt;/a&gt; compared to a relational database like &lt;a href="http://en.wikipedia.org/wiki/MySQL" rel="nofollow"&gt;MySQL&lt;/a&gt; is &lt;a href="http://www.moredevs.com/mysql-vs-mongodb-performance-benchmark/" rel="nofollow"&gt;significantly&lt;/a&gt; is poor enough to warrant staying away from something like MongoDB entirely.&lt;/p&gt;

&lt;p&gt;With that said, there are a couple of really useful properties of NoSQL databases that certainly work in your favor when you''re working with large data sets, though the chance of those benefits outweighing the generally poor performance of NoSQL compared to &lt;a href="http://en.wikipedia.org/wiki/SQL" rel="nofollow"&gt;SQL&lt;/a&gt; for read-intensive operations (most similar to typical big data use cases) is low.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;No Schema&lt;/strong&gt; - If you''re working with a lot of unstructured data, it might be hard to actually decide on and rigidly apply a schema. NoSQL databases in general are very supporting of this, and will allow you to insert schema-less documents on the fly, which is certainly not something an SQL database will support.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;&lt;a href="http://en.wikipedia.org/wiki/JSON" rel="nofollow"&gt;JSON&lt;/a&gt;&lt;/strong&gt; - If you happen to be working with JSON-style documents instead of with &lt;a href="http://en.wikipedia.org/wiki/Comma-separated_values" rel="nofollow"&gt;CSV&lt;/a&gt; files, then you''ll see a lot of advantage in using something like MongoDB for a database-layer. Generally the workflow savings don''t outweigh the increased query-times though.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Ease of Use&lt;/strong&gt; - I''m not saying that SQL databases are always hard to use, or that &lt;a href="http://en.wikipedia.org/wiki/Apache_Cassandra" rel="nofollow"&gt;Cassandra&lt;/a&gt; is the easiest thing in the world to set up, but in general NoSQL databases are easier to set up and use than SQL databases. MongoDB is a particularly strong example of this, known for being one of the easiest database layers to use (outside of &lt;a href="http://en.wikipedia.org/wiki/SQLite" rel="nofollow"&gt;SQLite&lt;/a&gt;). SQL also deals with a lot of normalization and there''s a large legacy of SQL best practices that just generally bogs down the development process.&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Personally I might suggest you also check out &lt;a href="http://en.wikipedia.org/wiki/Graph_database" rel="nofollow"&gt;graph databases&lt;/a&gt; such as &lt;a href="http://en.wikipedia.org/wiki/Neo4j" rel="nofollow"&gt;Neo4j&lt;/a&gt; that show really good performance for certain types of queries if you''re looking into picking out a backend for your data science applications.&lt;/p&gt;
', NULL, NULL, '''/a'':39C,54C,64C,130C,219C,243C,305C,360C,407C,418C ''/li'':208C,276C,387C ''/mysql-vs-mongodb-performance-benchmark/'':60C ''/p'':77C,146C,443C ''/strong'':152C,220C,282C ''/ul'':388C ''/wiki/apache_cassandra'':301C ''/wiki/comma-separated_values'':239C ''/wiki/graph_database'':402C ''/wiki/json'':215C ''/wiki/mongodb'':35C ''/wiki/mysql'':50C ''/wiki/neo4j'':414C ''/wiki/sql'':126C ''/wiki/sqlite'':356C ''actual'':168C ''advantag'':252C ''allow'':187C ''also'':362C,395C ''alway'':291C ''appli'':173C ''applic'':15C,27C,442C ''away'':71C ''backend'':437C ''benefit'':112C ''best'':377C ''big'':17C,25C,140C ''bog'':382C ''case'':143C ''cassandra'':304C ''certain'':94C,200C,425C ''chanc'':109C ''check'':396C ''compar'':40C,120C ''coupl'':85C ''csv'':242C ''data'':18C,26C,105C,141C,162C,440C ''databas'':8C,44C,92C,177C,205C,261C,289C,320C,330C,346C,406C ''database-lay'':260C ''deal'':363C ''decid'':169C ''develop'':385C ''document'':194C,231C ''eas'':279C ''easier'':322C ''easiest'':308C,345C ''en.wikipedia.org'':34C,49C,125C,214C,238C,300C,355C,401C,413C ''en.wikipedia.org/wiki/apache_cassandra'':299C ''en.wikipedia.org/wiki/comma-separated_values'':237C ''en.wikipedia.org/wiki/graph_database'':400C ''en.wikipedia.org/wiki/json'':213C ''en.wikipedia.org/wiki/mongodb'':33C ''en.wikipedia.org/wiki/mysql'':48C ''en.wikipedia.org/wiki/neo4j'':412C ''en.wikipedia.org/wiki/sql'':124C ''en.wikipedia.org/wiki/sqlite'':354C ''enough'':67C ''entir'':76C ''exampl'':336C ''favor'':98C ''file'':244C ''fli'':197C ''general'':115C,179C,263C,318C,381C ''good'':422C ''graph'':405C ''happen'':223C ''hard'':166C,292C ''honest'':5C ''href'':32C,47C,57C,123C,212C,236C,298C,353C,399C,411C ''increas'':271C ''insert'':190C ''instead'':232C ''intens'':134C ''json'':218C,229C ''json-styl'':228C ''known'':339C ''larg'':104C,373C ''layer'':262C,347C ''legaci'':374C ''less'':193C ''li'':148C,209C,277C ''like'':45C,74C,256C ''ll'':247C ''look'':432C ''lot'':159C,250C,366C ''low'':145C ''m'':284C ''major'':22C ''might'':164C,392C ''mongodb'':38C,75C,257C,331C ''mysql'':53C ''neo4j'':417C ''nofollow'':37C,52C,62C,128C,217C,241C,303C,358C,404C,416C ''normal'':368C ''nosql'':7C,91C,119C,176C,319C ''one'':342C ''oper'':135C ''outsid'':350C ''outweigh'':113C,269C ''p'':1C,78C,389C ''particular'':334C ''perfect'':4C ''perform'':29C,117C,423C ''person'':390C ''pick'':434C ''poor'':66C,116C ''practic'':378C ''process'':386C ''properti'':89C ''queri'':273C,428C ''query-tim'':272C ''re'':101C,155C,431C ''read'':133C ''read-intens'':132C ''realli'':87C,421C ''rel'':36C,51C,61C,127C,216C,240C,302C,357C,403C,415C ''relat'':43C ''rigid'':172C ''said'':81C ''save'':266C ''say'':286C ''schema'':151C,175C,192C ''schema-less'':191C ''scienc'':441C ''see'':248C ''set'':106C,314C,324C ''show'':420C ''signific'':63C ''similar'':137C ''someth'':73C,202C,255C ''sql'':129C,204C,288C,329C,361C,376C ''sqlite'':359C ''stay'':70C ''strong'':149C,210C,278C,335C ''style'':230C ''suggest'':393C ''suit'':13C ''support'':182C,207C ''thing'':309C ''though'':107C,275C ''time'':274C ''type'':426C ''typic'':139C ''ul'':147C ''unstructur'':161C ''use'':88C,142C,254C,281C,294C,327C,349C ''vast'':21C ''warrant'':69C ''well'':12C ''work'':95C,102C,156C,226C ''workflow'':265C ''world'':312C ''www.moredevs.com'':59C ''www.moredevs.com/mysql-vs-mongodb-performance-benchmark/'':58C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (798, 2, '2014-07-21 20:42:00.143', 2, NULL, NULL, '&lt;p&gt;If I understand your question, you can look at the co-occurrence matrix formed using the terms following the title; e.g., senior FOO, primary BAR, etc. Then you can compute the similarity between any pair of terms, such as "senior" and "primary", using a suitable metric; e.g., the cosine similarity.&lt;/p&gt;
', NULL, NULL, '''/p'':53C ''bar'':27C ''co'':13C ''co-occurr'':12C ''comput'':32C ''cosin'':51C ''e.g'':23C,49C ''etc'':28C ''follow'':20C ''foo'':25C ''form'':16C ''look'':9C ''matrix'':15C ''metric'':48C ''occurr'':14C ''p'':1C ''pair'':37C ''primari'':26C,44C ''question'':6C ''senior'':24C,42C ''similar'':34C,52C ''suitabl'':47C ''term'':19C,39C ''titl'':22C ''understand'':4C ''use'':17C,45C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (799, 2, '2014-07-21 21:17:07.303', 5, NULL, NULL, '&lt;p&gt;I would use a visual analysis. Since you know there is a repetition every 256 bytes, create an image 256 pixels wide by however many deep, and encode the data using brightness. In (i)python it would look like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;import os, numpy, matplotlib.pyplot as plt

%matplotlib inline

def read_in_chunks(infile, chunk_size=256):
    while True:
        chunk = infile.read(chunk_size)
        if chunk:
            yield chunk
        else:
            # The chunk was empty, which means we''re at the end
            # of the file
            return

fname = ''enter something here''
srcfile = open(fname, ''rb'')
height = 1 + os.path.getsize(fname)/256
data = numpy.zeros((height, 256), dtype=numpy.uint8)    

for i, line in enumerate(read_in_chunks(srcfile)):
    vals = list(map(int, line))
    data[i,:len(vals)] = vals

plt.imshow(data, aspect=1e-2);
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;This is what a PDF looks like:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/bicgF.png" alt="A PDF file visualized"&gt;&lt;/p&gt;

&lt;p&gt;A 256 byte periodic pattern would have manifested itself as vertical lines. Except for the header and tail it looks pretty noisy.&lt;/p&gt;
', NULL, NULL, '''/256'':99C ''/bicgf.png'':145C ''/code'':129C ''/p'':42C,139C,151C,175C ''/pre'':130C ''1'':96C ''1e-2'':128C ''256'':16C,21C,60C,103C,154C ''alt'':146C ''analysi'':7C ''aspect'':127C ''bright'':33C ''byte'':17C,155C ''chunk'':56C,58C,63C,65C,68C,70C,73C,113C ''code'':44C ''creat'':18C ''data'':31C,100C,120C,126C ''deep'':27C ''def'':53C ''dtype'':104C ''els'':71C ''empti'':75C ''encod'':29C ''end'':82C ''enter'':88C ''enumer'':110C ''everi'':15C ''except'':165C ''file'':85C,149C ''fname'':87C,93C,98C ''header'':168C ''height'':95C,102C ''howev'':25C ''i.stack.imgur.com'':144C ''i.stack.imgur.com/bicgf.png'':143C ''imag'':20C ''img'':141C ''import'':45C ''infil'':57C ''infile.read'':64C ''inlin'':52C ''int'':118C ''know'':10C ''len'':122C ''like'':40C,138C ''line'':108C,119C,164C ''list'':116C ''look'':39C,137C,172C ''mani'':26C ''manifest'':160C ''map'':117C ''matplotlib'':51C ''matplotlib.pyplot'':48C ''mean'':77C ''noisi'':174C ''numpi'':47C ''numpy.uint8'':105C ''numpy.zeros'':101C ''open'':92C ''os'':46C ''os.path.getsize'':97C ''p'':1C,131C,140C,152C ''pattern'':157C ''pdf'':136C,148C ''period'':156C ''pixel'':22C ''plt'':50C ''plt.imshow'':125C ''pre'':43C ''pretti'':173C ''python'':36C ''rb'':94C ''re'':79C ''read'':54C,111C ''repetit'':14C ''return'':86C ''sinc'':8C ''size'':59C,66C ''someth'':89C ''src'':142C ''srcfile'':91C,114C ''tail'':170C ''true'':62C ''use'':4C,32C ''val'':115C,123C,124C ''vertic'':163C ''visual'':6C,150C ''wide'':23C ''would'':3C,38C,158C ''yield'':69C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5927, 1, '2015-05-27 15:20:16.18', 1, 45, 'Wordnet Lemmatization', '&lt;p&gt;I tried finding about exception lists in wordnet lemmatizers. "Morphy() uses inflectional ending rules and exception lists to handle different possibilities" which I read from &lt;a href="http://www.nltk.org/howto/wordnet.html" rel="nofollow"&gt;http://www.nltk.org/howto/wordnet.html&lt;/a&gt; . Can you explain what is an exception list. Thank you.&lt;/p&gt;
', 'machine-learning  nlp  text-mining', NULL, '''/howto/wordnet.html'':40C ''/howto/wordnet.html&lt;/a&gt;'':45C ''/p'':56C ''differ'':30C ''end'':23C ''except'':15C,26C,52C ''explain'':48C ''find'':13C ''handl'':29C ''href'':37C ''inflect'':22C ''learn'':5B ''lemmat'':2A,19C ''list'':16C,27C,53C ''machin'':4B ''machine-learn'':3B ''mine'':9B ''morphi'':20C ''nlp'':6B ''nofollow'':42C ''p'':10C ''possibl'':31C ''read'':34C ''rel'':41C ''rule'':24C ''text'':8B ''text-min'':7B ''thank'':54C ''tri'':12C ''use'':21C ''wordnet'':1A,18C ''www.nltk.org'':39C,44C ''www.nltk.org/howto/wordnet.html'':38C ''www.nltk.org/howto/wordnet.html&lt;/a&gt;'':43C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (801, 2, '2014-07-21 21:32:12.963', 4, NULL, NULL, '&lt;p&gt;That''s an interesting problem, thanks for bring out here on stack.&lt;/p&gt;

&lt;p&gt;I think this problem is similar to when we apply &lt;a href="http://en.wikipedia.org/wiki/Latent_semantic_analysis" rel="nofollow"&gt;LSA(Latent Semantic Analysis)&lt;/a&gt; in sentiment analysis to find list of positive and negative words with polarity with respect to some predefined positive and negative words.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Good reads:&lt;/em&gt;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://cs.stanford.edu/people/ang/papers/acl11-WordVectorsSentimentAnalysis.pdf" rel="nofollow"&gt;Learning Word Vectors for Sentiment Analysis&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://arxiv.org/pdf/cs/0212012.pdf" rel="nofollow"&gt;Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word Corpus&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So, according to me LSA is your best approach to begin with in this situation as it learns the underlying relation between the words from the corpus and probably that''s what you are looking for.&lt;/p&gt;
', NULL, NULL, '''/a'':37C,82C,104C ''/em'':65C ''/li'':83C,105C ''/p'':14C,60C,66C,144C ''/pdf/cs/0212012.pdf'':89C ''/people/ang/papers/acl11-wordvectorssentimentanalysis.pdf'':73C ''/ul'':106C ''/wiki/latent_semantic_analysis'':30C ''accord'':109C ''analysi'':36C,40C,81C ''appli'':25C ''approach'':116C ''arxiv.org'':88C ''arxiv.org/pdf/cs/0212012.pdf'':87C ''begin'':118C ''best'':115C ''billion'':101C ''bring'':9C ''corpus'':103C,134C ''cs.stanford.edu'':72C ''cs.stanford.edu/people/ang/papers/acl11-wordvectorssentimentanalysis.pdf'':71C ''em'':62C ''en.wikipedia.org'':29C ''en.wikipedia.org/wiki/latent_semantic_analysis'':28C ''find'':42C ''good'':63C ''href'':27C,70C,86C ''hundr'':100C ''hundred-billion-word'':99C ''interest'':5C ''latent'':34C ''learn'':76C,93C,125C ''li'':68C,84C ''list'':43C ''look'':142C ''lsa'':33C,112C ''negat'':47C,58C ''nofollow'':32C,75C,91C ''orient'':96C ''p'':1C,15C,61C,107C ''polar'':50C ''posit'':45C,56C ''predefin'':55C ''probabl'':136C ''problem'':6C,19C ''read'':64C ''rel'':31C,74C,90C ''relat'':128C ''respect'':52C ''semant'':35C,95C ''sentiment'':39C,80C ''similar'':21C ''situat'':122C ''stack'':13C ''thank'':7C ''think'':17C ''ul'':67C ''under'':127C ''unsupervis'':92C ''vector'':78C ''word'':48C,59C,77C,102C,131C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (867, 1, '2014-07-30 13:55:36.187', 2, 418, 'Relative merits of different open source natural language generators', '&lt;p&gt;Does anyone know what (from your experience) is the best open source natural language generators (NLG) out there? What are the relative merits of each?&lt;/p&gt;

&lt;p&gt;I''m looking to do sophisticated text summarization and would like to use theme extraction/semantic modeling in conjunction with NLG tools to create accurate, context-aware, and natural-sounding text summaries.&lt;/p&gt;
', 'nlp  text-mining', NULL, '''/p'':40C,75C ''accur'':65C ''anyon'':16C ''awar'':68C ''best'':24C ''conjunct'':59C ''context'':67C ''context-awar'':66C ''creat'':64C ''differ'':4A ''experi'':21C ''extraction/semantic'':56C ''generat'':9A,29C ''know'':17C ''languag'':8A,28C ''like'':52C ''look'':44C ''m'':43C ''merit'':2A,37C ''mine'':13B ''model'':57C ''natur'':7A,27C,71C ''natural-sound'':70C ''nlg'':30C,61C ''nlp'':10B ''open'':5A,25C ''p'':14C,41C ''relat'':1A,36C ''sophist'':47C ''sound'':72C ''sourc'':6A,26C ''summar'':49C ''summari'':74C ''text'':12B,48C,73C ''text-min'':11B ''theme'':55C ''tool'':62C ''use'':54C ''would'':51C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (814, 2, '2014-07-22 16:40:17.733', 13, NULL, NULL, '&lt;p&gt;I also suggest &lt;code&gt;Gephi&lt;/code&gt; software (&lt;a href="https://gephi.github.io"&gt;https://gephi.github.io&lt;/a&gt;), which seems to be quite powerful. Some additional information on using &lt;code&gt;Gephi&lt;/code&gt; with &lt;strong&gt;large networks&lt;/strong&gt; can be found &lt;a href="https://forum.gephi.org/viewtopic.php?t=1554"&gt;here&lt;/a&gt; and, more generally, &lt;a href="https://forum.gephi.org/viewforum.php?f=25"&gt;here&lt;/a&gt;. &lt;code&gt;Cytoscape&lt;/code&gt; (&lt;a href="http://www.cytoscape.org"&gt;http://www.cytoscape.org&lt;/a&gt;) is an alternative to &lt;code&gt;Gephi&lt;/code&gt;, being an another popular platform for complex network analysis and visualization.&lt;/p&gt;

&lt;p&gt;If you''d like to work with networks &lt;strong&gt;programmatically&lt;/strong&gt; (including visualization) in R, Python or C/C++, you can check &lt;code&gt;igraph&lt;/code&gt; collection of libraries. Speaking of R, you may find interesting the following blog posts: on &lt;strong&gt;using R with Cytoscape&lt;/strong&gt; (&lt;a href="http://www.vesnam.com/Rblog/viznets1"&gt;http://www.vesnam.com/Rblog/viznets1&lt;/a&gt;) and on &lt;strong&gt;using R with Gephi&lt;/strong&gt; (&lt;a href="http://www.vesnam.com/Rblog/viznets2"&gt;http://www.vesnam.com/Rblog/viznets2&lt;/a&gt;).&lt;/p&gt;

&lt;p&gt;For &lt;strong&gt;extensive lists&lt;/strong&gt; of &lt;em&gt;network analysis and visualization software&lt;/em&gt;, including some comparison and reviews, you might want to check the following pages: 1) &lt;a href="http://wiki.cytoscape.org/Network_analysis_links"&gt;http://wiki.cytoscape.org/Network_analysis_links&lt;/a&gt;; 2) &lt;a href="http://www.kdnuggets.com/software/social-network-analysis.html"&gt;http://www.kdnuggets.com/software/social-network-analysis.html&lt;/a&gt;; 3) &lt;a href="http://www.activatenetworks.net/social-network-analysis-sna-software-review"&gt;http://www.activatenetworks.net/social-network-analysis-sna-software-review&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':13C,42C,52C,60C ''/code'':7C,27C,55C,67C,104C ''/em'':163C ''/network_analysis_links'':182C ''/network_analysis_links&lt;/a&gt;;'':185C ''/p'':79C ''/rblog/viznets1'':130C ''/rblog/viznets1&lt;/a&gt;)'':133C ''/rblog/viznets2'':146C ''/rblog/viznets2&lt;/a&gt;).&lt;/p&gt;'':149C ''/social-network-analysis-sna-software-review'':200C ''/social-network-analysis-sna-software-review&lt;/a&gt;.&lt;/p&gt;'':203C ''/software/social-network-analysis.html'':191C ''/software/social-network-analysis.html&lt;/a&gt;;'':194C ''/strong'':32C,91C,125C,141C,155C ''/viewforum.php?f=25'':50C ''/viewtopic.php?t=1554'':40C ''1'':177C ''2'':186C ''3'':195C ''addit'':21C ''also'':3C ''altern'':63C ''analysi'':76C,159C ''anoth'':70C ''blog'':117C ''c/c'':98C ''check'':101C,173C ''code'':5C,25C,53C,65C,102C ''collect'':105C ''comparison'':166C ''complex'':74C ''cytoscap'':54C,124C ''d'':83C ''em'':157C ''extens'':153C ''find'':113C ''follow'':116C,175C ''forum.gephi.org'':39C,49C ''forum.gephi.org/viewforum.php?f=25'':48C ''forum.gephi.org/viewtopic.php?t=1554'':38C ''found'':35C ''general'':45C ''gephi'':6C,26C,66C,140C ''gephi.github.io'':11C,12C ''href'':10C,37C,47C,57C,127C,143C,179C,188C,197C ''igraph'':103C ''includ'':92C,164C ''inform'':22C ''interest'':114C ''larg'':30C ''librari'':107C ''like'':84C ''list'':154C ''may'':112C ''might'':170C ''network'':31C,75C,88C,158C ''p'':1C,80C,150C ''page'':176C ''platform'':72C ''popular'':71C ''post'':118C ''power'':19C ''programmat'':90C ''python'':96C ''quit'':18C ''r'':95C,110C,122C,138C ''review'':168C ''seem'':15C ''softwar'':8C,162C ''speak'':108C ''strong'':29C,89C,120C,136C,152C ''suggest'':4C ''use'':24C,121C,137C ''visual'':78C,93C,161C ''want'':171C ''wiki.cytoscape.org'':181C,184C ''wiki.cytoscape.org/network_analysis_links'':180C ''wiki.cytoscape.org/network_analysis_links&lt;/a&gt;;'':183C ''work'':86C ''www.activatenetworks.net'':199C,202C ''www.activatenetworks.net/social-network-analysis-sna-software-review'':198C ''www.activatenetworks.net/social-network-analysis-sna-software-review&lt;/a&gt;.&lt;/p&gt;'':201C ''www.cytoscape.org'':58C,59C ''www.kdnuggets.com'':190C,193C ''www.kdnuggets.com/software/social-network-analysis.html'':189C ''www.kdnuggets.com/software/social-network-analysis.html&lt;/a&gt;;'':192C ''www.vesnam.com'':129C,132C,145C,148C ''www.vesnam.com/rblog/viznets1'':128C ''www.vesnam.com/rblog/viznets1&lt;/a&gt;)'':131C ''www.vesnam.com/rblog/viznets2'':144C ''www.vesnam.com/rblog/viznets2&lt;/a&gt;).&lt;/p&gt;'':147C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (802, 1, '2014-07-21 23:53:11.12', 10, 528, 'Efficient database model for storing data indexed by n-grams', '&lt;p&gt;I''m working on an application which requires creating a very large database of n-grams that exist in a large text corpus.&lt;/p&gt;

&lt;p&gt;I need three efficient operation types: Lookup and insertion indexed by the n-gram itself, and querying for all n-grams that contain a sub-n-gram.&lt;/p&gt;

&lt;p&gt;This sounds to me like the database should be a gigantic document tree, and document databases, e.g. Mongo, should be able to do the job well, but I''ve never used those at scale.&lt;/p&gt;

&lt;p&gt;Knowing the Stack Exchange question format, I''d like to clarify that I''m not asking for suggestions on specific technologies, but rather a type of database that I should be looking for to implement something like this at scale.&lt;/p&gt;
', 'nlp  databases', NULL, '''/p'':39C,71C,107C,149C ''abl'':93C ''applic'':20C ''ask'':124C ''clarifi'':119C ''contain'':65C ''corpus'':38C ''creat'':23C ''d'':116C ''data'':6A ''databas'':2A,13B,27C,79C,88C,135C ''document'':84C,87C ''e.g'':89C ''effici'':1A,44C ''exchang'':112C ''exist'':33C ''format'':114C ''gigant'':83C ''gram'':11A,31C,55C,63C,70C ''implement'':143C ''index'':7A,50C ''insert'':49C ''job'':97C ''know'':109C ''larg'':26C,36C ''like'':77C,117C,145C ''look'':140C ''lookup'':47C ''m'':16C,122C ''model'':3A ''mongo'':90C ''n'':10A,30C,54C,62C,69C ''n-gram'':9A,29C,53C,61C ''need'':42C ''never'':102C ''nlp'':12B ''oper'':45C ''p'':14C,40C,72C,108C ''queri'':58C ''question'':113C ''rather'':131C ''requir'':22C ''scale'':106C,148C ''someth'':144C ''sound'':74C ''specif'':128C ''stack'':111C ''store'':5A ''sub'':68C ''sub-n-gram'':67C ''suggest'':126C ''technolog'':129C ''text'':37C ''three'':43C ''tree'':85C ''type'':46C,133C ''use'':103C ''ve'':101C ''well'':98C ''work'':17C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (803, 2, '2014-07-22 00:06:10.5', 1, NULL, NULL, '&lt;p&gt;I haven''t done this before but it sounds like a job for a graph database given the functionality you want. &lt;a href="http://www.rene-pickhardt.de/download-google-n-gram-data-set-and-neo4j-source-code-for-storing-it/" rel="nofollow"&gt;Here''s a demo in neo4j&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':36C ''/download-google-n-gram-data-set-and-neo4j-source-code-for-storing-it/'':27C ''/p'':37C ''databas'':17C ''demo'':33C ''done'':5C ''function'':20C ''given'':18C ''graph'':16C ''haven'':3C ''href'':24C ''job'':13C ''like'':11C ''neo4j'':35C ''nofollow'':29C ''p'':1C ''rel'':28C ''sound'':10C ''want'':22C ''www.rene-pickhardt.de'':26C ''www.rene-pickhardt.de/download-google-n-gram-data-set-and-neo4j-source-code-for-storing-it/'':25C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (804, 1, '2014-07-22 01:12:03.86', 3, 38, 'What''s the difference between data products and intelligent systems?', '&lt;p&gt;Basically, both are software systems that are based on data and algorithms.&lt;/p&gt;
', 'tools  education  definitions', NULL, '''/p'':27C ''algorithm'':26C ''base'':22C ''basic'':15C ''data'':6A,24C ''definit'':13B ''differ'':4A ''educ'':12B ''intellig'':9A ''p'':14C ''product'':7A ''softwar'':18C ''system'':10A,19C ''tool'':11B');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (805, 2, '2014-07-22 02:27:43.71', 1, NULL, NULL, '&lt;p&gt;This is a very vague question. However, I will try to make sense of it. Considering rules of &lt;em&gt;logic&lt;/em&gt; as well as your statement that both &lt;em&gt;entities&lt;/em&gt; are "software systems that are based on data and algorithms", it appears that &lt;strong&gt;data products&lt;/strong&gt; are &lt;em&gt;intelligent systems&lt;/em&gt; and &lt;strong&gt;intelligent systems&lt;/strong&gt; are, to some degree, &lt;em&gt;data products&lt;/em&gt;. Therefore, it can be argued that the difference between the terms "data products" and "intelligent systems" is purely in the &lt;strong&gt;focus&lt;/strong&gt; (&lt;em&gt;source&lt;/em&gt; of information or &lt;em&gt;purpose&lt;/em&gt; of system dimensions) of each type of systems (&lt;em&gt;data&lt;/em&gt; &lt;strong&gt;vs.&lt;/strong&gt; &lt;em&gt;intelligence/algorithms&lt;/em&gt;).&lt;/p&gt;
', NULL, NULL, '''/em'':22C,32C,54C,67C,93C,99C,110C,116C ''/p'':117C ''/strong'':49C,59C,90C,113C ''algorithm'':42C ''appear'':44C ''argu'':72C ''base'':38C ''consid'':17C ''data'':40C,47C,65C,79C,109C ''degre'':63C ''differ'':75C ''dimens'':102C ''em'':20C,30C,51C,64C,91C,97C,108C,114C ''entiti'':31C ''focus'':89C ''howev'':8C ''inform'':95C ''intellig'':52C,57C,82C ''intelligence/algorithms'':115C ''logic'':21C ''make'':13C ''p'':1C ''product'':48C,66C,80C ''pure'':85C ''purpos'':98C ''question'':7C ''rule'':18C ''sens'':14C ''softwar'':34C ''sourc'':92C ''statement'':27C ''strong'':46C,56C,88C,111C ''system'':35C,53C,58C,83C,101C,107C ''term'':78C ''therefor'':68C ''tri'':11C ''type'':105C ''vagu'':6C ''vs'':112C ''well'':24C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (806, 1, '2014-07-22 03:43:20.327', 16, 1830, 'Advantages of AUC vs standard accuracy', '&lt;p&gt;I was starting to look into area under curve(AUC) and am a little confused about its usefulness. When first explained to me, AUC seemed to be a great measure of performance but in my research I''ve found that some claim its advantage is mostly marginal in that it is best for catching ''lucky'' models with high standard accuracy measurements and low AUC.&lt;/p&gt;

&lt;p&gt;So should I avoid relying on AUC for validating models or would a combination be best? Thanks for all your help.&lt;/p&gt;
', 'machine-learning  accuracy', NULL, '''/p'':76C,99C ''accuraci'':6A,10B,71C ''advantag'':1A,55C ''area'':18C ''auc'':3A,21C,35C,75C,84C ''avoid'':81C ''best'':63C,93C ''catch'':65C ''claim'':53C ''combin'':91C ''confus'':26C ''curv'':20C ''explain'':32C ''first'':31C ''found'':50C ''great'':40C ''help'':98C ''high'':69C ''learn'':9B ''littl'':25C ''look'':16C ''low'':74C ''lucki'':66C ''machin'':8B ''machine-learn'':7B ''margin'':58C ''measur'':41C,72C ''model'':67C,87C ''most'':57C ''p'':11C,77C ''perform'':43C ''reli'':82C ''research'':47C ''seem'':36C ''standard'':5A,70C ''start'':14C ''thank'':94C ''use'':29C ''valid'':86C ''ve'':49C ''vs'':4A ''would'':89C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (813, 2, '2014-07-22 15:44:04.16', 7, NULL, NULL, '&lt;p&gt;&lt;a href="https://gephi.github.io/"&gt;https://gephi.github.io/&lt;/a&gt; says it can handle a million edges. If your graph has 1000000 vertices and only 50000 edges then most of your vertices won''t have any edges anyway.&lt;/p&gt;

&lt;p&gt;In fact the Gephi spec is the dual of your example: "Networks up to 50,000 nodes and 1,000,000 edges"&lt;/p&gt;
', NULL, NULL, '''/&lt;/a&gt;'':7C ''/p'':36C,60C ''000'':53C,57C,58C ''1'':56C ''1000000'':19C ''50'':52C ''50000'':23C ''anyway'':35C ''dual'':45C ''edg'':14C,24C,34C,59C ''exampl'':48C ''fact'':39C ''gephi'':41C ''gephi.github.io'':4C,6C ''gephi.github.io/&lt;/a&gt;'':5C ''graph'':17C ''handl'':11C ''href'':3C ''million'':13C ''network'':49C ''node'':54C ''p'':1C,37C ''say'':8C ''spec'':42C ''vertic'':20C,29C ''won'':30C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (808, 1, '2014-07-22 08:39:33.81', 8, 589, 'Statistics + Computer Science = Data Science?', '&lt;p&gt;i want to become a &lt;strong&gt;data scientist&lt;/strong&gt;. I studied applied &lt;strong&gt;statistics&lt;/strong&gt; (actuarial science), so i have a great statistical background (regression, stochastic process, time series, just for mention a few). But now,  I am going to do a master degree in &lt;strong&gt;Computer Science&lt;/strong&gt; focus in Intelligent Systems.&lt;/p&gt;

&lt;p&gt;Here is my study plan:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Machine learning&lt;/li&gt;
&lt;li&gt;Advanced machine learning&lt;/li&gt;
&lt;li&gt;Data mining&lt;/li&gt;
&lt;li&gt;Fuzzy logic&lt;/li&gt;
&lt;li&gt;Recommendation Systems&lt;/li&gt;
&lt;li&gt;Distributed Data Systems&lt;/li&gt;
&lt;li&gt;Cloud Computing&lt;/li&gt;
&lt;li&gt;Knowledge discovery&lt;/li&gt;
&lt;li&gt;Business Intelligence&lt;/li&gt;
&lt;li&gt;Information retrieval&lt;/li&gt;
&lt;li&gt;Text mining&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;At the end, with all my statistical  and  computer science knowledge, can i call myself a data scientist? , or am i wrong?&lt;/p&gt;

&lt;p&gt;Thanks for the answers.&lt;/p&gt;
', 'machine-learning  statistics  career', NULL, '''/li'':77C,82C,86C,90C,94C,99C,103C,107C,111C,115C,119C ''/p'':65C,72C,144C,150C ''/strong'':20C,26C,60C ''/ul'':120C ''actuari'':27C ''advanc'':79C ''answer'':149C ''appli'':23C ''background'':35C ''becom'':15C ''busi'':109C ''call'':135C ''career'':10B ''cloud'':101C ''comput'':2A,58C,102C,130C ''data'':4A,18C,84C,97C,138C ''degre'':55C ''discoveri'':106C ''distribut'':96C ''end'':124C ''focus'':61C ''fuzzi'':88C ''go'':50C ''great'':33C ''inform'':113C ''intellig'':63C,110C ''knowledg'':105C,132C ''learn'':8B,76C,81C ''li'':74C,78C,83C,87C,91C,95C,100C,104C,108C,112C,116C ''logic'':89C ''machin'':7B,75C,80C ''machine-learn'':6B ''master'':54C ''mention'':43C ''mine'':85C,118C ''p'':11C,66C,121C,145C ''plan'':71C ''process'':38C ''recommend'':92C ''regress'':36C ''retriev'':114C ''scienc'':3A,5A,28C,59C,131C ''scientist'':19C,139C ''seri'':40C ''statist'':1A,9B,25C,34C,128C ''stochast'':37C ''strong'':17C,24C,57C ''studi'':22C,70C ''system'':64C,93C,98C ''text'':117C ''thank'':146C ''time'':39C ''ul'':73C ''want'':13C ''wrong'':143C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (809, 2, '2014-07-22 09:03:49.27', 1, NULL, NULL, '&lt;p&gt;I think that you''re on the right track toward becoming an &lt;strong&gt;expert&lt;/strong&gt; &lt;em&gt;data scientist&lt;/em&gt;. Recently I have answered related question here on Data Science StackExchange: &lt;a href="http://datascience.stackexchange.com/a/742/2452"&gt;http://datascience.stackexchange.com/a/742/2452&lt;/a&gt; (pay attention to the &lt;em&gt;definition&lt;/em&gt; I mention there, as it essentially answers your question by itself, as well as to aspects of &lt;strong&gt;practicing&lt;/strong&gt; &lt;em&gt;software engineering&lt;/em&gt; and &lt;strong&gt;applying&lt;/strong&gt; knowledge to solving &lt;em&gt;real-world&lt;/em&gt; problems). I hope that you will find all that useful. Good luck in your career!&lt;/p&gt;
', NULL, NULL, '''/a/742/2452'':36C ''/a/742/2452&lt;/a&gt;'':39C ''/em'':20C,46C,70C,82C ''/p'':98C ''/strong'':16C,66C,74C ''answer'':24C,53C ''appli'':73C ''aspect'':62C ''attent'':41C ''becom'':12C ''career'':97C ''data'':18C,29C ''datascience.stackexchange.com'':35C,38C ''datascience.stackexchange.com/a/742/2452'':34C ''datascience.stackexchange.com/a/742/2452&lt;/a&gt;'':37C ''definit'':45C ''em'':17C,44C,67C,78C ''engin'':69C ''essenti'':52C ''expert'':15C ''find'':89C ''good'':93C ''hope'':85C ''href'':33C ''knowledg'':75C ''luck'':94C ''mention'':48C ''p'':1C ''pay'':40C ''practic'':65C ''problem'':83C ''question'':26C,55C ''re'':6C ''real'':80C ''real-world'':79C ''recent'':21C ''relat'':25C ''right'':9C ''scienc'':30C ''scientist'':19C ''softwar'':68C ''solv'':77C ''stackexchang'':31C ''strong'':14C,64C,72C ''think'':3C ''toward'':11C ''track'':10C ''use'':92C ''well'':59C ''world'':81C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (810, 1, '2014-07-22 12:29:10.05', 20, 826, 'Should I go for a ''balanced'' dataset or a ''representative'' dataset?', '&lt;p&gt;My ''machine learning'' task is of separating benign Internet traffic from malicious traffic. In the real world scenario, most (say 90% or more) of Internet traffic is benign. Thus I felt that I should go with the similar kind of data for training my models as well. But then I did come across a research paper or two (in my area of work) which have used a balanced data to train models, implying equal number of instances of benign and malicious traffic.&lt;/p&gt;

&lt;p&gt;In general, if I am building ML models, should I go for a dataset which is representative of the real world problem, or is a balanced dataset better suited for building the models (since certain classifiers do not behave well with class imbalance, or due to other reasons not known to me)?&lt;/p&gt;

&lt;p&gt;Can someone shed more light on the &lt;em&gt;pros&lt;/em&gt; and &lt;em&gt;cons&lt;/em&gt; of both the choices, and how to decide which one to go for?&lt;/p&gt;
', 'machine-learning  dataset  unbalanced-classes', NULL, '''/em'':167C,171C ''/p'':102C,156C,185C ''90'':40C ''across'':72C ''area'':80C ''balanc'':6A,87C,129C ''behav'':142C ''benign'':27C,47C,98C ''better'':131C ''build'':109C,134C ''certain'':138C ''choic'':175C ''class'':18B,145C ''classifi'':139C ''come'':71C ''con'':170C ''data'':60C,88C ''dataset'':7A,11A,15B,117C,130C ''decid'':179C ''due'':148C ''em'':165C,169C ''equal'':93C ''felt'':50C ''general'':105C ''go'':3A,54C,114C,183C ''imbal'':146C ''impli'':92C ''instanc'':96C ''internet'':28C,44C ''kind'':58C ''known'':153C ''learn'':14B,22C ''light'':162C ''machin'':13B,21C ''machine-learn'':12B ''malici'':31C,100C ''ml'':110C ''model'':64C,91C,111C,136C ''number'':94C ''one'':181C ''p'':19C,103C,157C ''paper'':75C ''problem'':125C ''pros'':166C ''real'':35C,123C ''reason'':151C ''repres'':10A,120C ''research'':74C ''say'':39C ''scenario'':37C ''separ'':26C ''shed'':160C ''similar'':57C ''sinc'':137C ''someon'':159C ''suit'':132C ''task'':23C ''thus'':48C ''traffic'':29C,32C,45C,101C ''train'':62C,90C ''two'':77C ''unbalanc'':17B ''unbalanced-class'':16B ''use'':85C ''well'':66C,143C ''work'':82C ''world'':36C,124C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8322, 1, '2015-10-06 10:51:52.883', 2, 155, 'filling missing data with other than mean values', '&lt;p&gt;What all options are available for filling missing data ?&lt;/p&gt;

&lt;p&gt;One obvious choice is the mean, but if the percentage of missing data is large, it will decrease the accuracy.&lt;/p&gt;

&lt;p&gt;So how do we deal with missing values if they are in great number ?&lt;/p&gt;
', 'data-mining  missing-data', NULL, '''/p'':25C,47C,63C ''accuraci'':46C ''avail'':20C ''choic'':29C ''data'':3A,10B,14B,24C,39C ''data-min'':9B ''deal'':53C ''decreas'':44C ''fill'':1A,22C ''great'':61C ''larg'':41C ''mean'':7A,32C ''mine'':11B ''miss'':2A,13B,23C,38C,55C ''missing-data'':12B ''number'':62C ''obvious'':28C ''one'':27C ''option'':18C ''p'':15C,26C,48C ''percentag'':36C ''valu'':8A,56C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (811, 2, '2014-07-22 14:22:49.137', 17, NULL, NULL, '&lt;p&gt;I would say the answer depends on your use case. Based on my experience:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;If you''re trying to build a representative model -- one that describes the data rather than necessarily predicts -- then I would suggest using a representative sample of your data.&lt;/li&gt;
&lt;li&gt;If you want to build a predictive model, particularly one that performs well by measure of AUC or rank-order and plan to use a basic ML framework (i.e. Decision Tree, SVM, Naive Bayes, etc), then I would suggest you feed the framework a balanced dataset. Much of the literature on class imbalance finds that random undersampling (down sampling the majority class to the size of the minority class) can drive performance gains.&lt;/li&gt;
&lt;li&gt;If you''re building a predictive model, but are using a more advanced framework (i.e. something that determines sampling parameters via wrapper or a modification of a bagging framework that samples to class equivalence), then I would suggest again feeding the representative sample and letting the algorithm take care of balancing the data for training.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/li'':48C,124C,181C ''/p'':16C ''/ul'':182C ''advanc'':138C ''algorithm'':172C ''answer'':6C ''auc'':66C ''bag'':153C ''balanc'':95C,176C ''base'':12C ''basic'':76C ''bay'':84C ''build'':24C,54C,129C ''care'':174C ''case'':11C ''class'':102C,112C,119C,158C ''data'':32C,47C,178C ''dataset'':96C ''decis'':80C ''depend'':7C ''describ'':30C ''determin'':143C ''drive'':121C ''equival'':159C ''etc'':85C ''experi'':15C ''feed'':91C,165C ''find'':104C ''framework'':78C,93C,139C,154C ''gain'':123C ''i.e'':79C,140C ''imbal'':103C ''let'':170C ''li'':18C,49C,125C ''literatur'':100C ''major'':111C ''measur'':64C ''minor'':118C ''ml'':77C ''model'':27C,57C,132C ''modif'':150C ''much'':97C ''naiv'':83C ''necessarili'':35C ''one'':28C,59C ''order'':70C ''p'':1C ''paramet'':145C ''particular'':58C ''perform'':61C,122C ''plan'':72C ''predict'':36C,56C,131C ''random'':106C ''rank'':69C ''rank-ord'':68C ''rather'':33C ''re'':21C,128C ''repres'':26C,43C,167C ''sampl'':44C,109C,144C,156C,168C ''say'':4C ''size'':115C ''someth'':141C ''suggest'':40C,89C,163C ''svm'':82C ''take'':173C ''train'':180C ''tree'':81C ''tri'':22C ''ul'':17C ''undersampl'':107C ''use'':10C,41C,74C,135C ''via'':146C ''want'':52C ''well'':62C ''would'':3C,39C,88C,162C ''wrapper'':147C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (812, 1, '2014-07-22 15:17:04.81', 12, 2507, 'Visualizing a graph with a million vertices', '&lt;p&gt;What is the best tool to use to visualize (draw the vertices and edges) a graph with 1000000 vertices? There are about 50000 edges in the graph. And I can compute the location of individual vertices and edges.&lt;/p&gt;

&lt;p&gt;I am thinking about writing a program to generate a svg. Any other suggestions?  &lt;/p&gt;
', 'visualization  graphs', NULL, '''/p'':49C,65C ''1000000'':28C ''50000'':33C ''best'':14C ''comput'':41C ''draw'':20C ''edg'':24C,34C,48C ''generat'':59C ''graph'':3A,9B,26C,37C ''individu'':45C ''locat'':43C ''million'':6A ''p'':10C,50C ''program'':57C ''suggest'':64C ''svg'':61C ''think'':53C ''tool'':15C ''use'':17C ''vertic'':7A,22C,29C,46C ''visual'':1A,8B,19C ''write'':55C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2329, 2, '2014-10-21 10:22:21.39', 2, NULL, NULL, '&lt;p&gt;You could use the &lt;code&gt;wireframe&lt;/code&gt; function from the &lt;code&gt;lattice&lt;/code&gt; package:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;library("lattice")
wireframe(volcano[1:30, 1:30], shade=TRUE, zlab="")
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/BQHGX.png" alt="wireframe plot example"&gt;&lt;/p&gt;
', NULL, NULL, '''/bqhgx.png'':37C ''/code'':8C,14C,30C ''/p'':16C,42C ''/pre'':31C ''1'':23C,25C ''30'':24C,26C ''alt'':38C ''code'':6C,12C,18C ''could'':3C ''exampl'':41C ''function'':9C ''i.stack.imgur.com'':36C ''i.stack.imgur.com/bqhgx.png'':35C ''img'':33C ''lattic'':13C,20C ''librari'':19C ''p'':1C,32C ''packag'':15C ''plot'':40C ''pre'':17C ''shade'':27C ''src'':34C ''true'':28C ''use'':4C ''volcano'':22C ''wirefram'':7C,21C,39C ''zlab'':29C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (815, 1, '2014-07-22 19:20:47.58', 4, 95, 'is this a good case for NOSQL?', '&lt;p&gt;I''m currently facing a project that I could solve with a relational database in a relatively painful way. Having heard so much about NOSQL, I''m wondering if there is not a more appropriate way of tackling it:&lt;/p&gt;

&lt;p&gt;Suppose we are tracking a group of animals in a forest (n ~ 500) and would like to keep a record of a set of observations (this is a fictional scenario).&lt;/p&gt;

&lt;p&gt;We would like to store the following information in a database:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;a unique identifier for each animal&lt;/li&gt;
&lt;li&gt;a description of the animal with structured fields: Species, Genus, Family, ...&lt;/li&gt;
&lt;li&gt;a free text field with additional information&lt;/li&gt;
&lt;li&gt;each time-point at which it was detected close to a reference point&lt;/li&gt;
&lt;li&gt;a picture of the animal&lt;/li&gt;
&lt;li&gt;an indication whether two given animals are siblings&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;And:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;there might be additional features appearing later as more data comes in&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;We would like to be able to execute the following types of queries:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;return all the animals spotted between in a given time interval&lt;/li&gt;
&lt;li&gt;return all the animals of a given Species or Family&lt;/li&gt;
&lt;li&gt;perform a text search on the free text field&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Which particular database system would you recommend ? Is there any tutorial / examples that I could use as a starting point ?&lt;/p&gt;
', 'nosql  databases', NULL, '''/li'':104C,117C,126C,142C,149C,159C,178C,208C,220C,231C ''/p'':50C,82C,95C,163C,194C,254C ''/ul'':160C,179C,232C ''500'':64C ''abl'':186C ''addit'':124C,169C ''anim'':59C,103C,110C,148C,156C,200C,213C ''appear'':171C ''appropri'':45C ''case'':5A ''close'':137C ''come'':176C ''could'':19C,248C ''current'':13C ''data'':175C ''databas'':9B,24C,94C,236C ''descript'':107C ''detect'':136C ''exampl'':245C ''execut'':188C ''face'':14C ''famili'':116C,219C ''featur'':170C ''fiction'':80C ''field'':113C,122C,230C ''follow'':90C,190C ''forest'':62C ''free'':120C,228C ''genus'':115C ''given'':155C,205C,216C ''good'':4A ''group'':57C ''heard'':31C ''identifi'':100C ''indic'':152C ''inform'':91C,125C ''interv'':207C ''keep'':69C ''later'':172C ''li'':97C,105C,118C,127C,143C,150C,165C,196C,209C,221C ''like'':67C,86C,183C ''m'':12C,37C ''might'':167C ''much'':33C ''n'':63C ''nosql'':7A,8B,35C ''observ'':76C ''p'':10C,51C,83C,161C,180C,233C ''pain'':28C ''particular'':235C ''perform'':222C ''pictur'':145C ''point'':131C,141C,253C ''project'':16C ''queri'':193C ''recommend'':240C ''record'':71C ''refer'':140C ''relat'':23C,27C ''return'':197C,210C ''scenario'':81C ''search'':225C ''set'':74C ''sibl'':158C ''solv'':20C ''speci'':114C,217C ''spot'':201C ''start'':252C ''store'':88C ''structur'':112C ''suppos'':52C ''system'':237C ''tackl'':48C ''text'':121C,224C,229C ''time'':130C,206C ''time-point'':129C ''track'':55C ''tutori'':244C ''two'':154C ''type'':191C ''ul'':96C,164C,195C ''uniqu'':99C ''use'':249C ''way'':29C,46C ''whether'':153C ''wonder'':38C ''would'':66C,85C,182C,238C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (816, 2, '2014-07-22 21:12:08.333', 8, NULL, NULL, '&lt;p&gt;Well it depends on what kind of "Data Science" you wish to get in to.  For basic analytics and reporting statistics will certainly help, but for Machine Learning and Artificial Intelligence then you''ll want a few more skills&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Probability theory&lt;/strong&gt; - you must have a solid background in pure probability so that you can decompose any problem, whether seen before or not, into probabilistic principles.  Statistics helps a lot for already solved problems, but new and unsolved problems require a deep understanding of probability so that you can design appropriate techniques.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Information Theory&lt;/strong&gt; - this (relative to statistics) is quite a new field (though still decades old), the most important work was by Shannon, but even more important and often neglected note in literature is work by Hobson that proved that Kullback-Leibler Divergence is the only mathematical definition that truly captures the notion of a &lt;em&gt;"measure of information"&lt;/em&gt;. Now fundamental to artificial intellgence is being able to quantify information.  Suggest reading "Concepts in Statistical Mechanics" - Arthur Hobson (very expensive book, only available in academic libraries).&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Complexity Theory&lt;/strong&gt; - A big problem many Data Scientists face that do not have a solid complexity theory background is that their algorithms do not scale, or just take an extremely long time to run on large data.  Take PCA for example, many peoples favourite answer to the interview question "how do you reduce the number of features in our dataset", but even if you tell the candidate "the data set is really really really large" they still propose various forms of PCA that are O(n^3).  If you want to stand out, you want to be able to solve each problem on it''s own, NOT throw some text book solution at it designed a long time ago before Big Data was such a hip thing.  For that you need to understand how long things take to run, not only theoretically, but practically - so how to use a cluster of computers to distribute an algorithm, or which data structures take up less memory.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;&lt;strong&gt;Communication Skills&lt;/strong&gt; - A huge part of Data Science is understanding business.  Whether it''s inventing a product driven by data science, or giving business insight driven by data science, being able to communicate well with both the Project and Product Managers, the tech teams, and your fellow data scientists is very important.  You can have an amazing idea, say an awesome AI solution, but if you cannot effectively (a) communicate WHY that will make the business money, (b) convince your collegues it will work and (c) explain to tech people how you need their help to build it, then it wont get done.&lt;/p&gt;&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (831, 1, '2014-07-24 07:38:07.163', 3, 63, 'Data Science Association?', '&lt;p&gt;Have you heard of the "Data Science Association"? 
&lt;br&gt;URL: &lt;a href="http://www.datascienceassn.org/" rel="nofollow"&gt;http://www.datascienceassn.org/&lt;/a&gt;
&lt;br&gt;Do you expect it to become a professional body like the Actuaries Institute?
&lt;br&gt;If yes, then why?
&lt;br&gt;If no, then why not and do you see anyone else becoming the professional body?&lt;/p&gt;

&lt;p&gt;Lastly, is this question "on-topic" ?&lt;/p&gt;
', 'knowledge-base', NULL, '''/&lt;/a&gt;'':25C ''/p'':61C,70C ''actuari'':38C ''anyon'':55C ''associ'':3A,15C ''base'':6B ''becom'':32C,57C ''bodi'':35C,60C ''br'':16C,26C,40C,45C ''data'':1A,13C ''els'':56C ''expect'':29C ''heard'':10C ''href'':19C ''institut'':39C ''knowledg'':5B ''knowledge-bas'':4B ''last'':63C ''like'':36C ''nofollow'':22C ''on-top'':67C ''p'':7C,62C ''profession'':34C,59C ''question'':66C ''rel'':21C ''scienc'':2A,14C ''see'':54C ''topic'':69C ''url'':17C ''www.datascienceassn.org'':20C,24C ''www.datascienceassn.org/&lt;/a&gt;'':23C ''yes'':42C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (920, 1, '2014-08-06 09:02:01.033', 1, 99, 'SUMMARIST: Automated Text Summarization', '&lt;p&gt;There is a text summarization project called SUMMARIST. Apparently it is able to perform abstractive text summarization. I want to give it a try but unfortunately the demo links on the website do not work. Does anybody have any information regarding this? How can I test this tool?&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.isi.edu/natural-language/projects/SUMMARIST.html" rel="nofollow"&gt;http://www.isi.edu/natural-language/projects/SUMMARIST.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Regards,
PasMod&lt;/p&gt;
', 'text-mining', NULL, '''/natural-language/projects/summarist.html'':63C ''/natural-language/projects/summarist.html&lt;/a&gt;&lt;/p&gt;'':68C ''/p'':57C,72C ''abl'':20C ''abstract'':23C ''anybodi'':45C ''appar'':17C ''autom'':2A ''call'':15C ''demo'':36C ''give'':29C ''href'':60C ''inform'':48C ''link'':37C ''mine'':7B ''nofollow'':65C ''p'':8C,58C,69C ''pasmod'':71C ''perform'':22C ''project'':14C ''regard'':49C,70C ''rel'':64C ''summar'':4A,13C,25C ''summarist'':1A,16C ''test'':54C ''text'':3A,6B,12C,24C ''text-min'':5B ''tool'':56C ''tri'':32C ''unfortun'':34C ''want'':27C ''websit'':40C ''work'':43C ''www.isi.edu'':62C,67C ''www.isi.edu/natural-language/projects/summarist.html'':61C ''www.isi.edu/natural-language/projects/summarist.html&lt;/a&gt;&lt;/p&gt;'':66C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (864, 2, '2014-07-30 07:49:14.467', 2, NULL, NULL, '&lt;p&gt;map/reduce is most appropriate for parallelizable offline computations. To be more precise, it works best when the result can be found from the result of some function of a partition of the input. Averaging is a trivial example; you can do this with map/reduce by summing each partition, returning the sum and the number of elements in the partition, then computing the overall mean using these intermediate results. It is less appropriate when the intermediate steps depend on the state of the other partitions.&lt;/p&gt;
', NULL, NULL, '''/p'':86C ''appropri'':5C,73C ''averag'':35C ''best'':16C ''comput'':9C,62C ''depend'':78C ''element'':57C ''exampl'':39C ''found'':22C ''function'':28C ''input'':34C ''intermedi'':68C,76C ''less'':72C ''map/reduce'':2C,45C ''mean'':65C ''number'':55C ''offlin'':8C ''overal'':64C ''p'':1C ''paralleliz'':7C ''partit'':31C,49C,60C,85C ''precis'':13C ''result'':19C,25C,69C ''return'':50C ''state'':81C ''step'':77C ''sum'':47C,52C ''trivial'':38C ''use'':66C ''work'':15C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9938, 1, '2016-01-25 10:33:46.983', 3, 54, 'How to define user churn', '&lt;p&gt;I''d like to define our app user churn,  Generally, people will define their app user churn by some simply way, like: if the user do not logon continually 7 days or 1 month, we will define them churn user. So is there a better way?&lt;/p&gt;
', 'bigdata  churn', NULL, '''/p'':55C ''1'':41C ''7'':38C ''app'':15C,23C ''better'':53C ''bigdata'':6B ''churn'':5A,7B,17C,25C,47C ''continu'':37C ''d'':10C ''day'':39C ''defin'':3A,13C,21C,45C ''general'':18C ''like'':11C,30C ''logon'':36C ''month'':42C ''p'':8C ''peopl'':19C ''simpli'':28C ''user'':4A,16C,24C,33C,48C ''way'':29C,54C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (817, 2, '2014-07-22 21:24:33.82', 6, NULL, NULL, '&lt;p&gt;Data scientist (to me) a big umbrella term. I would see a data scientist as a person who can proficiently use techniques from the fields of data mining, machine learning, pattern classification, and statistics. &lt;/p&gt;

&lt;p&gt;However, those terms are intertwined to: machine learning is tied together with pattern classification, and also data mining overlaps when it comes finding patterns in data. And all techniques have their underlying statistical principles. I always picture this as a Venn diagram with a huge intersection. &lt;/p&gt;

&lt;p&gt;Computer sciences is related to all those fields too. I would say that you need "data science" techniques to do computer-scientific research, but computer science knowledge is not necessarily implied in "data science". However, programming skills - I see programming and computer science as different professions, where programming is more the tool in order solve problems - are also important to work with the data and to conduct data analysis.&lt;/p&gt;

&lt;p&gt;You have a really nice study plan, and it all makes sense. But I am not sure if you "want" to call yourself just "data scientist", I have the impression that "data scientist" is such a ambiguous term that can mean everything or nothing. What I want to convey is that you will end up being something more - more "specialized" - than "just" a data scientist.&lt;/p&gt;
', NULL, NULL, '''/p'':36C,84C,156C,223C ''also'':53C,144C ''alway'':73C ''ambigu'':194C ''analysi'':155C ''big'':7C ''call'':179C ''classif'':33C,51C ''come'':59C ''comput'':86C,107C,111C,128C ''computer-scientif'':106C ''conduct'':153C ''convey'':206C ''data'':2C,14C,28C,54C,63C,101C,119C,150C,154C,182C,189C,221C ''diagram'':79C ''differ'':131C ''end'':211C ''everyth'':199C ''field'':26C,93C ''find'':60C ''howev'':38C,121C ''huge'':82C ''impli'':117C ''import'':145C ''impress'':187C ''intersect'':83C ''intertwin'':42C ''knowledg'':113C ''learn'':31C,45C ''machin'':30C,44C ''make'':168C ''mean'':198C ''mine'':29C,55C ''necessarili'':116C ''need'':100C ''nice'':162C ''noth'':201C ''order'':140C ''overlap'':56C ''p'':1C,37C,85C,157C ''pattern'':32C,50C,61C ''person'':18C ''pictur'':74C ''plan'':164C ''principl'':71C ''problem'':142C ''profess'':132C ''profici'':21C ''program'':122C,126C,134C ''realli'':161C ''relat'':89C ''research'':109C ''say'':97C ''scienc'':87C,102C,112C,120C,129C ''scientif'':108C ''scientist'':3C,15C,183C,190C,222C ''see'':12C,125C ''sens'':169C ''skill'':123C ''solv'':141C ''someth'':214C ''special'':217C ''statist'':35C,70C ''studi'':163C ''sure'':174C ''techniqu'':23C,66C,103C ''term'':9C,40C,195C ''tie'':47C ''togeth'':48C ''tool'':138C ''umbrella'':8C ''under'':69C ''use'':22C ''venn'':78C ''want'':177C,204C ''work'':147C ''would'':11C,96C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (818, 2, '2014-07-23 05:29:06.903', 7, NULL, NULL, '&lt;p&gt;I think, that &lt;code&gt;Gephi&lt;/code&gt; could face with lack-of-memory issues, you will need at least 8Gb of RAM. Though number of edges is not extremely huge. &lt;/p&gt;

&lt;p&gt;Possibly, more appropriate tool in this case would be &lt;a href="http://www.graphviz.org/"&gt;&lt;code&gt;GraphViz&lt;/code&gt;&lt;/a&gt;. It''s a command line tool for network visualizations, and presumably would be more tolerant to graph size. Moreover, as I remember, in &lt;code&gt;GraphViz&lt;/code&gt; it is possible to use precomputed coordinates to facilitate computations.&lt;/p&gt;

&lt;p&gt;I''ve tried to find a real-world examples of using &lt;code&gt;GraphViz&lt;/code&gt; with huge graphs, but didn''t succeed. Though I found similar discussion on &lt;a href="http://scicomp.stackexchange.com/questions/3315/visualizing-very-large-link-graphs"&gt;&lt;strong&gt;Computational Science&lt;/strong&gt;&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (819, 2, '2014-07-23 07:01:01.14', 7, NULL, NULL, '&lt;p&gt;Three tables: &lt;strong&gt;animal&lt;/strong&gt;, &lt;strong&gt;observation&lt;/strong&gt;, and &lt;strong&gt;sibling&lt;/strong&gt;. The observation has an &lt;strong&gt;animal_id&lt;/strong&gt; column which links to the animal table, and the sibling table has &lt;strong&gt;animal_1_id&lt;/strong&gt; and &lt;strong&gt;animal_2_id&lt;/strong&gt; columns that indicates two animals are siblings for each row.&lt;/p&gt;

&lt;p&gt;Even with 5000 animals and 100000 observations I don''t think query time will be a problem for something like PostgreSQL for most reasonable queries (obviously you can construct unreasonable queries but you can do that in any system). &lt;/p&gt;

&lt;p&gt;So I don''t see how this is "relatively painful". Relative to what? The only complexity is the sibling table. In NOSQL you might store the full list of siblings in the record for each animal, but then when you add a sibling relationship you have to add it to both sibling''s animal records. With the relational table approach I''ve outlined, it only exists once, but at the expense of having to test against both columns to find an animal''s siblings. &lt;/p&gt;

&lt;p&gt;I''d use PostgreSQL, and that gives you the option of using PostGIS if you have location data - this is a geospatial extension to PostgreSQL that lets you do spatial queries (point in polygon, points near a point etc) which might be something for you. &lt;/p&gt;

&lt;p&gt;I really don''t think the properties of NOSQL databases are a problem here for you - you aren''t changing your schema every ten minutes, you probably &lt;strong&gt;do&lt;/strong&gt; care that your database is ACID-compliant, and you don''t need something web-scale.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.mongodb-is-web-scale.com/"&gt;http://www.mongodb-is-web-scale.com/&lt;/a&gt; [warning: strong language]&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (820, 1, '2014-07-23 08:06:21.417', 6, 76, 'How can we effectively measure the impact of our data decisions', '&lt;p&gt;Apologies if this is very broad question, what I would like to know is how effective is A/B testing (or other methods) of effectively measuring the effects of a design decision.&lt;/p&gt;

&lt;p&gt;For instance we can analyse user interactions or click results, purchase/ browse decisions and then modify/tailor the results presented to the user.&lt;/p&gt;

&lt;p&gt;We could then test the effectiveness of this design change by subjecting 10% of users to the alternative model randomly but then how objective is this?&lt;/p&gt;

&lt;p&gt;How do we avoid influencing the user by the model change, for instance we could decided that search queries for ''David Beckham'' are probably about football so search results become biased towards this but we could equally say that his lifestyle is just as relevant but this never makes it into the top 10 results that are returned.&lt;/p&gt;

&lt;p&gt;I am curious how this is dealt with and how to measure this effectively.&lt;/p&gt;

&lt;p&gt;My thoughts are that you could be in danger of pushing a model that you think is correct and the user obliges and this becomes a self-fulfilling prophecy.&lt;/p&gt;

&lt;p&gt;I''ve read an article on this: &lt;a href="http://techcrunch.com/2014/06/29/ethics-in-a-data-driven-world/"&gt;http://techcrunch.com/2014/06/29/ethics-in-a-data-driven-world/&lt;/a&gt; and also the book: &lt;a href="http://shop.oreilly.com/product/0636920028529.do"&gt;http://shop.oreilly.com/product/0636920028529.do&lt;/a&gt; which discussed this so it piqued my interest.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (821, 2, '2014-07-23 12:57:37.073', 7, NULL, NULL, '&lt;p&gt;"Because its there".&lt;/p&gt;

&lt;p&gt;The data has a seasonal pattern. So you model it. The data has a trend. So you model it. Maybe the data is correlated with the number of sunspots. So you model that. Eventually you hope to get nothing left to model than uncorrelated random noise.&lt;/p&gt;

&lt;p&gt;But I think you''ve screwed up your STL computation here. Your residuals are clearly not serially uncorrelated. I rather suspect you''ve not told the function that your "seasonality" is a 24-hour cycle rather than an annual one. But hey you haven''t given us any code or data so we don''t really have a clue what you''ve done, do we? What do you think "seasonality" even means here? Do you have any idea?&lt;/p&gt;

&lt;p&gt;Your data seems the have three peaks every 24 hours. Really? Is this ''gas''=''gasoline''=''petrol'' or gas in some heating/electric generating system? Either way if you know a priori there''s an 8 hour cycle, or an 8 hour cycle on top of a 24 hour cycle on top of what looks like a very high frequency one or two hour cycle you &lt;strong&gt;put that in your model&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;Actually you don''t even say what your x-axis is so maybe its days and then I''d fit a daily cycle, a weekly cycle, and then an annual cycle. But given how it all changes at time=85 or so I''d not expect a model to do well on both sides of that.&lt;/p&gt;

&lt;p&gt;With statistics (which is what this is, sorry to disappoint you but you''re not a data scientist yet) you don''t just robotically go "And.. Now.. I.. Fit.. An... S TL model....". You look at your data, try and get some understanding, then propose a model, fit it, test it, and use the parameters it make inferences about the data. Fitting cyclic seasonal patterns is part of that.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (822, 2, '2014-07-23 13:49:01.423', 3, NULL, NULL, '&lt;p&gt;In A/B testing, bias is handled very well by ensuring visitors are randomly assigned to either version A or version B of the site.  This creates independent samples drawn from the same population.  Because the groups are independent and, on average, only differ in the version of the site seen, the test measures the effect of the design decision.&lt;/p&gt;

&lt;p&gt;&lt;em&gt;Slight aside&lt;/em&gt;: Now you might argue that the A group or B group may differ in some demographic.  That commonly happens by random chance.  To a certain degree this can be taken care of by covariate adjusted randomization.  It can also be taken care of by adding covariates to the model that tests the effect of the design decision.  It should be noted that there is still some discussion about the proper way to do this within the statistics community.  Essentially A/B testing is an application of a &lt;a href="http://en.wikipedia.org/wiki/Randomized_controlled_trial" rel="nofollow"&gt;Randomized Control Trial&lt;/a&gt; to website design.  Some people disagree with adding covariates to the test.  Others, such as Frank Harrel (see &lt;a href="http://rads.stackoverflow.com/amzn/click/0387952322" rel="nofollow"&gt;Regression Modeling Strategies&lt;/a&gt;) argue for the use of covariates in such models.&lt;/p&gt;

&lt;p&gt;I would offer the following suggestions:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Design the study in advance so as to take care of as much sources of bias and variation as possible.   &lt;/li&gt;
&lt;li&gt;Let the data speak for itself.  As you get more data (like about searches for David Beckham), let it dominate your assumptions about how the data should be (as how the posterior dominates the prior in Bayesian analysis when the sample size becomes large).    &lt;/li&gt;
&lt;li&gt;Make sure your data matches the assumptions of the model.&lt;/li&gt;
&lt;/ul&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (823, 1, '2014-07-23 14:04:31.057', 14, 172, 'How should ethics be applied in data science', '&lt;p&gt;There was a recent furore with &lt;a href="http://online.wsj.com/articles/furor-erupts-over-facebook-experiment-on-users-1404085840"&gt;facebook experimenting on their users to see if they could alter user''s emotions&lt;/a&gt; and now &lt;a href="http://www.bbc.co.uk/news/technology-28542642"&gt;okcupid&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Whilst I am not a professional data scientist I read about &lt;a href="http://columbiadatascience.com/2013/11/25/data-science-ethics/"&gt;data science ethics&lt;/a&gt; from &lt;a href="http://shop.oreilly.com/product/0636920028529.do"&gt;Cathy O''Neill''s book ''Doing Data Science''&lt;/a&gt; and would like to know if this is something that professionals are taught at academic level (I would expect so) or something that is ignored or is lightly applied in the professional world. Particularly for those who ended up doing data science &lt;em&gt;accidentally&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;Whilst the linked article touched on data integrity, the book also discussed the moral ethics behind understanding the impact of the data models that are created and the impact of those models which can have adverse effects when used inappropriately (sometimes unwittingly) or when the models are inaccurate, again producing adverse results.&lt;/p&gt;

&lt;p&gt;The article discusses a code of practice and mentions the &lt;a href="http://www.datascienceassn.org/code-of-conduct.html"&gt;Data Science Association''s Code of conduct&lt;/a&gt;, is this something that is in use? Rule 7 is of particular interest (quoted from their website):&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;(a) A person who consults with a data scientist about the possibility
  of forming a client-data scientist relationship with respect to a
  matter is a prospective client.&lt;/p&gt;
  
  &lt;p&gt;(b) Even when no client-data scientist relationship ensues, a data
  scientist who has learned information from a prospective client shall
  not use or reveal that information.&lt;/p&gt;
  
  &lt;p&gt;(c) A data scientist subject to paragraph (b) shall not provide
  professional data science services for a client with interests
  materially adverse to those of a prospective client in the same or a
  substantially related industry if the data scientist received
  information from the prospective client that could be significantly
  harmful to that person in the matter&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Is this something that is practiced professionally? Many users blindly accept that we get some free service (mail, social network, image hosting, blog platform etc..) and agree to an EULA in order to have ads pushed at us. &lt;/p&gt;

&lt;p&gt;Finally how is this regulated, I often read about users being up in arms when the terms of a service change but it seems that it requires some liberty organisation, class action or a &lt;a href="http://www.cnet.com/news/senator-asks-ftc-to-investigate-facebooks-mood-study/"&gt;senator&lt;/a&gt; to react to such things before something happens.&lt;/p&gt;

&lt;p&gt;By the way I am not making any judgements here or saying that all data scientists behave like this, I''m interested in what is taught academically and practiced professionally.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5411, 2, '2015-03-27 15:45:50.857', 1, NULL, NULL, '&lt;p&gt;I have had success using &lt;a href="https://bitbucket.org/wellaware/mogwai" rel="nofollow"&gt;Mogwai Python Library&lt;/a&gt;  which is more actively maintained than Bulbs at this point.&lt;/p&gt;

&lt;p&gt;Though I agree that this belongs on stack overflow.  &lt;/p&gt;
', NULL, NULL, '''/a'':17C ''/p'':28C,39C ''/wellaware/mogwai'':11C ''activ'':21C ''agre'':32C ''belong'':35C ''bitbucket.org'':10C ''bitbucket.org/wellaware/mogwai'':9C ''bulb'':24C ''href'':8C ''librari'':16C ''maintain'':22C ''mogwai'':14C ''nofollow'':13C ''overflow'':38C ''p'':1C,29C ''point'':27C ''python'':15C ''rel'':12C ''stack'':37C ''success'':5C ''though'':30C ''use'':6C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (824, 2, '2014-07-23 15:46:48.977', 1, NULL, NULL, '&lt;p&gt;(too long for a comment)&lt;/p&gt;

&lt;p&gt;Basically, @Emre''s answer is correct: simple correlation matrix and cosine distance should work well*. There''s one subtlety, though - job titles are too short to carry important context. Let me explain this. &lt;/p&gt;

&lt;p&gt;Imagine LinkedIn profiles (which is pretty good source for data). Normally, they contain 4-10 sentences describing person''s skills and qualifications. It''s pretty likely that you find phrases like "lead data scientist" and "professional knowledge of Matlab and R" in a same profile, but it''s very unlikely to also see "junior Java developer" in it. So we may say that "lead" and "professional" (as well as "data scientist" and "Matlab" and "R") often occur in same contexts, but they are rarely found together with "junior" and "Java". &lt;/p&gt;

&lt;p&gt;Co-occurrence matrix shows exactly this. The more 2 words occur in same context, the more similar their vectors in the matrix will look like. And cosine distance is just a good way to measure this similarity. &lt;/p&gt;

&lt;p&gt;But what about job titles? Normally they are much shorter and don''t actually create enough context to catch similarities. Luckily, you don''t need source data to be titles themselves - you need to find similarities between skills in general, not specifically in titles. So you can simply build co-occurrence matrix from (long) profiles and then use it to measure similarity of titles. &lt;/p&gt;

&lt;p&gt;&lt;em&gt;*&lt;/em&gt; - in fact, it''s already worked for me on a similar project. &lt;/p&gt;
', NULL, NULL, '''-10'':58C ''/em'':244C ''/p'':7C,42C,134C,174C,241C,257C ''2'':145C ''4'':57C ''actual'':189C ''alreadi'':249C ''also'':95C ''answer'':12C ''basic'':9C ''build'':224C ''carri'':35C ''catch'':194C ''co'':137C,226C ''co-occurr'':136C,225C ''comment'':6C ''contain'':56C ''context'':37C,123C,150C,192C ''correct'':14C ''correl'':16C ''cosin'':19C,163C ''creat'':190C ''data'':53C,76C,113C,202C ''describ'':60C ''develop'':99C ''distanc'':20C,164C ''em'':243C ''emr'':10C ''enough'':191C ''exact'':141C ''explain'':40C ''fact'':246C ''find'':72C,210C ''found'':128C ''general'':215C ''good'':50C,168C ''imagin'':44C ''import'':36C ''java'':98C,133C ''job'':29C,179C ''junior'':97C,131C ''knowledg'':80C ''lead'':75C,107C ''let'':38C ''like'':69C,74C,161C ''linkedin'':45C ''long'':3C,230C ''look'':160C ''luckili'':196C ''matlab'':82C,116C ''matrix'':17C,139C,158C,228C ''may'':104C ''measur'':171C,237C ''much'':184C ''need'':200C,208C ''normal'':54C,181C ''occur'':120C,147C ''occurr'':138C,227C ''often'':119C ''one'':26C ''p'':1C,8C,43C,135C,175C,242C ''person'':61C ''phrase'':73C ''pretti'':49C,68C ''profession'':79C,109C ''profil'':46C,88C,231C ''project'':256C ''qualif'':65C ''r'':84C,118C ''rare'':127C ''say'':105C ''scientist'':77C,114C ''see'':96C ''sentenc'':59C ''short'':33C ''shorter'':185C ''show'':140C ''similar'':153C,173C,195C,211C,238C,255C ''simpl'':15C ''simpli'':223C ''skill'':63C,213C ''sourc'':51C,201C ''specif'':217C ''subtleti'':27C ''though'':28C ''titl'':30C,180C,205C,219C,240C ''togeth'':129C ''unlik'':93C ''use'':234C ''vector'':155C ''way'':169C ''well'':23C,111C ''word'':146C ''work'':22C,250C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (825, 1, '2014-07-24 00:01:40.76', 5, 69, 'Are there any annotators or Named Entity Recognition for license plate numbers?', '&lt;p&gt;Most vehicle license/number plate extractors I''ve found involve reading a plate from an image (OCR) but I''m interested in something that could tag instances of license plates in a body of text. Are there any such annotators out there?&lt;/p&gt;
', 'text-mining', NULL, '''/p'':58C ''annot'':4A,55C ''bodi'':48C ''could'':40C ''entiti'':7A ''extractor'':21C ''found'':24C ''imag'':31C ''instanc'':42C ''interest'':36C ''involv'':25C ''licens'':10A,44C ''license/number'':19C ''m'':35C ''mine'':15B ''name'':6A ''number'':12A ''ocr'':32C ''p'':16C ''plate'':11A,20C,28C,45C ''read'':26C ''recognit'':8A ''someth'':38C ''tag'':41C ''text'':14B,50C ''text-min'':13B ''ve'':23C ''vehicl'':18C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (826, 2, '2014-07-24 00:34:41.13', -1, NULL, NULL, '&lt;p&gt;This can be done using &lt;code&gt;regular expressions&lt;/code&gt;. &lt;/p&gt;

&lt;p&gt;2 letters followed by a number (\d denotes digits) would be&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[A-Z]{2} \d*
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;2 or 3 letters followed by a number is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;[A-Z]{2,3} \d*
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':10C,32C,53C ''/p'':11C,24C,44C ''/pre'':33C,54C ''2'':13C,30C,35C,50C ''3'':37C,51C ''a-z'':27C,47C ''code'':7C,26C,46C ''d'':19C,31C,52C ''denot'':20C ''digit'':21C ''done'':5C ''express'':9C ''follow'':15C,39C ''letter'':14C,38C ''number'':18C,42C ''p'':1C,12C,34C ''pre'':25C,45C ''regular'':8C ''use'':6C ''would'':22C ''z'':29C,49C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (827, 2, '2014-07-24 02:23:23.703', 4, NULL, NULL, '&lt;p&gt;There are a lot of pretty decent tools out there for text annotation in general, and given the broad nature of the task you''re approaching (license plates are about as general as words), the annotation tools you are looking at should probably come from the more classical tools for annotation.&lt;/p&gt;

&lt;p&gt;There was actually a pretty good discussion about annotation tools on &lt;a href="http://datascience.stackexchange.com/questions/223/how-to-annotate-text-documents-with-meta-data/404#404"&gt;this&lt;/a&gt; question, which should actually apply to this problem. The most relied-upon thing in annotation right now is probably &lt;code&gt;brat&lt;/code&gt;. You can learn more about &lt;code&gt;brat&lt;/code&gt; &lt;a href="http://brat.nlplab.org/" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Hope that helps! Let me know if you''ve got any more questions.&lt;/p&gt;
', NULL, NULL, '''/a'':72C,110C ''/code'':95C,103C ''/p'':53C,111C,126C ''/questions/223/how-to-annotate-text-documents-with-meta-data/404#404'':70C ''actual'':57C,76C ''annot'':14C,37C,52C,63C,88C ''appli'':77C ''approach'':27C ''brat'':94C,102C ''brat.nlplab.org'':106C ''broad'':20C ''classic'':49C ''code'':93C,101C ''come'':45C ''datascience.stackexchange.com'':69C ''datascience.stackexchange.com/questions/223/how-to-annotate-text-documents-with-meta-data/404#404'':68C ''decent'':8C ''discuss'':61C ''general'':16C,33C ''given'':18C ''good'':60C ''got'':122C ''help'':115C ''hope'':113C ''href'':67C,105C ''know'':118C ''learn'':98C ''let'':116C ''licens'':28C ''look'':41C ''lot'':5C ''natur'':21C ''nofollow'':108C ''p'':1C,54C,112C ''plate'':29C ''pretti'':7C,59C ''probabl'':44C,92C ''problem'':80C ''question'':73C,125C ''re'':26C ''rel'':107C ''reli'':84C ''relied-upon'':83C ''right'':89C ''task'':24C ''text'':13C ''thing'':86C ''tool'':9C,38C,50C,64C ''upon'':85C ''ve'':121C ''word'':35C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (828, 2, '2014-07-24 03:31:32.087', 3, NULL, NULL, '&lt;p&gt;Reporting back: I ended up coding graphml and using yEd for visualization (just because I am familiar with this combination. I bet gephi or graphviz would work fine and might even be better). Since I computed the location of all nodes, memory was not such big of an issue. Coding graphml is a little easier comparing to coding svg, since I don''t have to explicitly specify the placement of edges.&lt;/p&gt;
', NULL, NULL, '''/p'':73C ''back'':3C ''bet'':23C ''better'':34C ''big'':47C ''code'':7C,51C,59C ''combin'':21C ''compar'':57C ''comput'':37C ''easier'':56C ''edg'':72C ''end'':5C ''even'':32C ''explicit'':67C ''familiar'':18C ''fine'':29C ''gephi'':24C ''graphml'':8C,52C ''graphviz'':26C ''issu'':50C ''littl'':55C ''locat'':39C ''memori'':43C ''might'':31C ''node'':42C ''p'':1C ''placement'':70C ''report'':2C ''sinc'':35C,61C ''specifi'':68C ''svg'':60C ''use'':10C ''visual'':13C ''work'':28C ''would'':27C ''yed'':11C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (829, 1, '2014-07-24 06:26:07.29', 2, 53, 'Pig script code error?', '&lt;p&gt;While running the below pig script I am getting error in line4: 
If it is GROUP then I am getting error.
If I change from ''GROUP'' TO ''group'' in line4, then the script is running.
What is the difference between group and GROUP.&lt;/p&gt;

&lt;p&gt;LINES = LOAD ''/user/cloudera/datapeople.csv'' USING PigStorage('','') AS ( firstname:chararray, lastname:chararray, address:chararray, city:chararray, state:chararray, zip:chararray );&lt;/p&gt;

&lt;p&gt;WORDS = FOREACH LINES GENERATE FLATTEN(TOKENIZE(zip)) AS ZIPS;&lt;/p&gt;

&lt;p&gt;WORDSGROUPED = GROUP WORDS BY ZIPS;&lt;/p&gt;

&lt;p&gt;WORDBYCOUNT = FOREACH WORDSGROUPED GENERATE GROUP AS ZIPS, COUNT(WORDS);&lt;/p&gt;

&lt;p&gt;WORDSSORT = ORDER WORDBYCOUNT BY $1 DESC;&lt;/p&gt;

&lt;p&gt;DUMP WORDSSORT;&lt;/p&gt;
', 'bigdata  hadoop', NULL, '''/p'':51C,71C,82C,89C,100C,108C,112C ''/user/cloudera/datapeople.csv'':55C ''1'':106C ''address'':63C ''bigdata'':5B ''chang'':31C ''chararray'':60C,62C,64C,66C,68C,70C ''citi'':65C ''code'':3A ''count'':98C ''desc'':107C ''differ'':46C ''dump'':110C ''error'':4A,17C,28C ''firstnam'':59C ''flatten'':77C ''foreach'':74C,92C ''generat'':76C,94C ''get'':16C,27C ''group'':23C,33C,35C,48C,50C,85C,95C ''hadoop'':6B ''lastnam'':61C ''line'':53C,75C ''line4'':19C,37C ''load'':54C ''order'':103C ''p'':7C,52C,72C,83C,90C,101C,109C ''pig'':1A,12C ''pigstorag'':57C ''run'':9C,42C ''script'':2A,13C,40C ''state'':67C ''token'':78C ''use'':56C ''word'':73C,86C,99C ''wordbycount'':91C,104C ''wordsgroup'':84C,93C ''wordssort'':102C,111C ''zip'':69C,79C,81C,88C,97C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (875, 2, '2014-07-30 23:40:54.7', 2, NULL, NULL, '&lt;p&gt;I suggest you use &lt;a href="http://www.antlr.org/" rel="nofollow"&gt;ANTLR&lt;/a&gt;, which is a very powerful parser generator. It has a good GUI for entering your BNF. It has a &lt;a href="https://theantlrguy.atlassian.net/wiki/display/ANTLR4/Python+Target" rel="nofollow"&gt;Python target&lt;/a&gt; capability.&lt;/p&gt;
', NULL, NULL, '''/a'':12C,41C ''/p'':43C ''/wiki/display/antlr4/python+target'':36C ''antlr'':11C ''bnf'':28C ''capabl'':42C ''enter'':26C ''generat'':19C ''good'':23C ''gui'':24C ''href'':7C,33C ''nofollow'':10C,38C ''p'':1C ''parser'':18C ''power'':17C ''python'':39C ''rel'':9C,37C ''suggest'':3C ''target'':40C ''theantlrguy.atlassian.net'':35C ''theantlrguy.atlassian.net/wiki/display/antlr4/python+target'':34C ''use'':5C ''www.antlr.org'':8C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (830, 2, '2014-07-24 07:00:13.187', 2, NULL, NULL, '&lt;p&gt;''group'' in strictly lower case in the FOREACH is the thing you are looping/grouping over.&lt;/p&gt;

&lt;p&gt;&lt;a href="http://squarecog.wordpress.com/2010/05/11/group-operator-in-apache-pig/" rel="nofollow"&gt;http://squarecog.wordpress.com/2010/05/11/group-operator-in-apache-pig/&lt;/a&gt; says:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;When you group a relation, the result is a new relation with two
  columns: âgroupâ and the name of the original relation.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Column names are case sensitive, so you have to use lower-case ''group'' in your FOREACH.&lt;/p&gt;

&lt;p&gt;''GROUP'' in upper case is the grouping operator. You can''t mix them. So don''t do that.&lt;/p&gt;
', NULL, NULL, '''/2010/05/11/group-operator-in-apache-pig/'':23C ''/2010/05/11/group-operator-in-apache-pig/&lt;/a&gt;'':28C ''/blockquote'':57C ''/p'':17C,30C,56C,76C,96C ''blockquot'':31C ''case'':6C,62C,71C,81C ''column'':46C,59C ''foreach'':9C,75C ''group'':2C,35C,72C,78C,84C ''groupâ'':48C ''href'':20C ''looping/grouping'':15C ''lower'':5C,70C ''lower-cas'':69C ''mix'':89C ''name'':51C,60C ''new'':42C ''nofollow'':25C ''oper'':85C ''origin'':54C ''p'':1C,18C,32C,58C,77C ''rel'':24C ''relat'':37C,43C,55C ''result'':39C ''say'':29C ''sensit'':63C ''squarecog.wordpress.com'':22C,27C ''squarecog.wordpress.com/2010/05/11/group-operator-in-apache-pig/'':21C ''squarecog.wordpress.com/2010/05/11/group-operator-in-apache-pig/&lt;/a&gt;'':26C ''strict'':4C ''thing'':12C ''two'':45C ''upper'':80C ''use'':68C ''â'':47C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8334, 1, '2015-10-06 21:01:20.73', 1, 18, 'finding maximum depth of random forest given the number of features', '&lt;p&gt;How do we find maximum depth of Random Forest if we know the number of features ?&lt;/p&gt;

&lt;p&gt;This is needed for regularizing random forest classifier.&lt;/p&gt;
', 'machine-learning  random-forest', NULL, '''/p'':35C,45C ''classifi'':44C ''depth'':3A,24C ''featur'':11A,34C ''find'':1A,22C ''forest'':6A,17B,27C,43C ''given'':7A ''know'':30C ''learn'':14B ''machin'':13B ''machine-learn'':12B ''maximum'':2A,23C ''need'':39C ''number'':9A,32C ''p'':18C,36C ''random'':5A,16B,26C,42C ''random-forest'':15B ''regular'':41C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (844, 1, '2014-07-25 21:03:44.663', 5, 754, 'Using Apache Spark to do ML. Keep getting serializing errors', '&lt;p&gt;so I''m using Spark to do sentiment analysis, and I keep getting errors with the serializers it uses (I think) to pass python objects around.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;PySpark worker failed with exception:
Traceback (most recent call last):
  File "/Users/abdul/Desktop/RSI/spark-1.0.1-bin-    hadoop1/python/pyspark/worker.py", line 77, in main
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/Users/abdul/Desktop/RSI/spark-1.0.1-bin-    hadoop1/python/pyspark/serializers.py", line 191, in dump_stream
    self.serializer.dump_stream(self._batched(iterator), stream)
  File "/Users/abdul/Desktop/RSI/spark-1.0.1-bin-    hadoop1/python/pyspark/serializers.py", line 123, in dump_stream
    for obj in iterator:
  File "/Users/abdul/Desktop/RSI/spark-1.0.1-bin-    hadoop1/python/pyspark/serializers.py", line 180, in _batched
    for item in iterator:
TypeError: __init__() takes exactly 3 arguments (2 given)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and the code for serializers is available &lt;a href="https://spark.apache.org/docs/latest/api/python/pyspark.serializers-pysrc.html#PickleSerializer.dumps"&gt;here&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;and my code is &lt;a href="https://github.com/seashark97/Scalable-Sentiment-Analysis/blob/master/spark_test.py"&gt;here&lt;/a&gt;&lt;/p&gt;
', 'bigdata  hadoop  distributed  scalability  map-reduce', NULL, '''/a'':132C,145C ''/code'':116C ''/docs/latest/api/python/pyspark.serializers-pysrc.html#pickleserializer.dumps'':130C ''/p'':45C,133C,146C ''/pre'':117C ''/seashark97/scalable-sentiment-analysis/blob/master/spark_test.py'':143C ''/users/abdul/desktop/rsi/spark-1.0.1-bin-'':59C,73C,86C,98C ''123'':89C ''180'':101C ''191'':76C ''2'':114C ''3'':112C ''77'':62C ''analysi'':27C ''apach'':2A ''argument'':113C ''around'':44C ''avail'':125C ''batch'':103C ''bigdata'':11B ''call'':56C ''code'':47C,121C,137C ''distribut'':13B ''dump'':78C,91C ''error'':10A,32C ''exact'':111C ''except'':52C ''fail'':50C ''file'':58C,72C,85C,97C ''func'':67C ''get'':8A,31C ''github.com'':142C ''github.com/seashark97/scalable-sentiment-analysis/blob/master/spark_test.py'':141C ''given'':115C ''hadoop'':12B ''hadoop1/python/pyspark/serializers.py'':74C,87C,99C ''hadoop1/python/pyspark/worker.py'':60C ''href'':127C,140C ''index'':69C ''init'':109C ''item'':105C ''iter'':70C,83C,96C,107C ''keep'':7A,30C ''last'':57C ''line'':61C,75C,88C,100C ''m'':21C ''main'':64C ''map'':16B ''map-reduc'':15B ''ml'':6A ''obj'':94C ''object'':43C ''outfil'':71C ''p'':18C,118C,134C ''pass'':41C ''pre'':46C ''pyspark'':48C ''python'':42C ''recent'':55C ''reduc'':17B ''scalabl'':14B ''self._batched'':82C ''self.serializer.dump'':80C ''sentiment'':26C ''serial'':9A,35C,123C ''serializer.dump'':65C ''spark'':3A,23C ''spark.apache.org'':129C ''spark.apache.org/docs/latest/api/python/pyspark.serializers-pysrc.html#pickleserializer.dumps'':128C ''split'':68C ''stream'':66C,79C,81C,84C,92C ''take'':110C ''think'':39C ''traceback'':53C ''typeerror'':108C ''use'':1A,22C,37C ''worker'':49C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (832, 1, '2014-07-24 14:04:09.533', 5, 123, 'How do you plot overlapping durations?', '&lt;p&gt;My data contains a set of start times and duration for an action. I would like to plot this so that for a given time slice I can see how many actions are active. I''m currently thinking of this as a histogram with time on the x axis and number of active actions on the y axis.&lt;/p&gt;

&lt;p&gt;My question is, how should I adjust the data so that this is able to be plotted?&lt;/p&gt;

&lt;p&gt;The times for an action can be between 2 seconds and a minute. And, at any given time I would estimate there could be about 100 actions taking place. Ideally a single plot would be able to show hours of data. The accuracy of the data is in milliseconds.&lt;/p&gt;

&lt;p&gt;In the past the way that I have done this is to count for each second how many actions started , ended, or were active. This gave me a count of active actions for each second. The issue I found with this technique was that it made it difficult to adjust the time slice that I was looking at. Looking at a time slice of a minute was difficult to compute and looking at time slices of less than a second was impossible.&lt;/p&gt;

&lt;p&gt;I''m open to any advice on how to think about this issue.&lt;/p&gt;

&lt;p&gt;Thanks in advance!&lt;/p&gt;
', 'visualization', NULL, '''/p'':67C,86C,137C,221C,236C,241C ''100'':113C ''2'':96C ''abl'':82C,123C ''accuraci'':130C ''action'':21C,40C,62C,92C,114C,157C,170C ''activ'':42C,61C,162C,169C ''adjust'':75C,188C ''advanc'':240C ''advic'':228C ''axi'':57C,66C ''comput'':208C ''contain'':11C ''could'':110C ''count'':151C,167C ''current'':45C ''data'':10C,77C,128C,133C ''difficult'':186C,206C ''done'':147C ''durat'':6A,18C ''end'':159C ''estim'':108C ''found'':177C ''gave'':164C ''given'':32C,104C ''histogram'':51C ''hour'':126C ''ideal'':117C ''imposs'':220C ''issu'':175C,235C ''less'':215C ''like'':24C ''look'':195C,197C,210C ''m'':44C,224C ''made'':184C ''mani'':39C,156C ''millisecond'':136C ''minut'':100C,204C ''number'':59C ''open'':225C ''overlap'':5A ''p'':8C,68C,87C,138C,222C,237C ''past'':141C ''place'':116C ''plot'':4A,26C,85C,120C ''question'':70C ''second'':97C,154C,173C,218C ''see'':37C ''set'':13C ''show'':125C ''singl'':119C ''slice'':34C,191C,201C,213C ''start'':15C,158C ''take'':115C ''techniqu'':180C ''thank'':238C ''think'':46C,232C ''time'':16C,33C,53C,89C,105C,190C,200C,212C ''visual'':7B ''way'':143C ''would'':23C,107C,121C ''x'':56C ''y'':65C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (833, 2, '2014-07-24 21:14:45.793', 1, NULL, NULL, '&lt;p&gt;This can be done in &lt;code&gt;R&lt;/code&gt; using &lt;code&gt;ggplot&lt;/code&gt;. Based on &lt;a href="http://stackoverflow.com/questions/18102224/drawing-gantt-charts-with-r-to-sub-second-accuracy"&gt;this&lt;/a&gt; question, it could be done with this code where &lt;code&gt;limits&lt;/code&gt; is the date range of the plot.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;tasks &amp;lt;- c("Task1", "Task2")
dfr &amp;lt;- data.frame(
name        = factor(tasks, levels = tasks),
start.date  = c("2014-08-07 09:03:25.815", "2014-08-07 09:03:25.956"),
end.date    = c("2014-08-07 09:03:28.300", "2014-08-07 09:03:30.409")
)

mdfr &amp;lt;- melt(dfr, measure.vars = c("start.date", "end.date"))


mdfr$time&amp;lt;-as.POSIXct(mdfr$value)

ggplot(mdfr, aes(time,name)) + 
geom_line(size = 6) +
xlab("") + ylab("") +
theme_bw()+
scale_x_datetime(breaks=date_breaks("2 sec"),
limits = as.POSIXct(c(''2014-08-07 09:03:24'',''2014-08-07 09:03:29'')))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Bwwod.png" alt="enter image description here"&gt;&lt;/p&gt;
', NULL, NULL, '''-07'':62C,68C,76C,82C,126C,132C ''-08'':61C,67C,75C,81C,125C,131C ''/a'':22C ''/bwwod.png'':143C ''/code'':9C,13C,34C,136C ''/p'':42C,149C ''/pre'':137C ''/questions/18102224/drawing-gantt-charts-with-r-to-sub-second-accuracy'':20C ''03'':64C,70C,78C,84C,128C,134C ''09'':63C,69C,77C,83C,127C,133C ''2'':119C ''2014'':60C,66C,74C,80C,124C,130C ''24'':129C ''25.815'':65C ''25.956'':71C ''28.300'':79C ''29'':135C ''30.409'':85C ''6'':108C ''ae'':102C ''alt'':144C ''as.posixct'':97C,122C ''base'':14C ''break'':116C,118C ''bw'':112C ''c'':47C,59C,73C,91C,123C ''code'':7C,11C,30C,32C,44C ''could'':25C ''data.frame'':52C ''date'':37C,117C ''datetim'':115C ''descript'':147C ''dfr'':50C,89C ''done'':5C,27C ''end.date'':72C,93C ''enter'':145C ''factor'':54C ''geom'':105C ''ggplot'':12C,100C ''href'':17C ''i.stack.imgur.com'':142C ''i.stack.imgur.com/bwwod.png'':141C ''imag'':146C ''img'':139C ''level'':56C ''limit'':33C,121C ''line'':106C ''lt'':46C,51C,87C,96C ''mdfr'':86C,94C,98C,101C ''measure.vars'':90C ''melt'':88C ''name'':53C,104C ''p'':1C,138C ''plot'':41C ''pre'':43C ''question'':23C ''r'':8C ''rang'':38C ''scale'':113C ''sec'':120C ''size'':107C ''src'':140C ''stackoverflow.com'':19C ''stackoverflow.com/questions/18102224/drawing-gantt-charts-with-r-to-sub-second-accuracy'':18C ''start.date'':58C,92C ''task'':45C,55C,57C ''task1'':48C ''task2'':49C ''theme'':111C ''time'':95C,103C ''use'':10C ''valu'':99C ''x'':114C ''xlab'':109C ''ylab'':110C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (834, 1, '2014-07-25 00:58:12.253', 11, 3780, 'Recommending movies with additional features using collaborative filtering', '&lt;p&gt;I am trying to build a recommendation engine using collaborative filtering. I have the usual [user, movie, rating] information. I would like to incorporate an additional feature like ''language'' or ''duration of movie''. I am not sure what techniques I could use for such a problem. Please suggest references or packages in python/R. &lt;/p&gt;
', 'python  r  recommendation', NULL, '''/p'':66C ''addit'':4A,38C ''build'':17C ''collabor'':7A,22C ''could'':53C ''durat'':43C ''engin'':20C ''featur'':5A,39C ''filter'':8A,23C ''incorpor'':36C ''inform'':31C ''languag'':41C ''like'':34C,40C ''movi'':2A,29C,45C ''p'':12C ''packag'':63C ''pleas'':59C ''problem'':58C ''python'':9B ''python/r'':65C ''r'':10B ''rate'':30C ''recommend'':1A,11B,19C ''refer'':61C ''suggest'':60C ''sure'':49C ''techniqu'':51C ''tri'':15C ''use'':6A,21C,54C ''user'':28C ''usual'':27C ''would'':33C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (886, 2, '2014-08-01 23:21:34.36', 4, NULL, NULL, '&lt;p&gt;I am going to agree with @user1683454''s comment. After importing, your DATE column is of either &lt;code&gt;character&lt;/code&gt;, or &lt;code&gt;factor&lt;/code&gt; class (depending on your settings for &lt;code&gt;stringsAsFactors&lt;/code&gt;). Therefore, I think that you can solve this issue in at least several ways, as follows:&lt;/p&gt;

&lt;p&gt;1) &lt;strong&gt;Convert data&lt;/strong&gt; to correct type &lt;strong&gt;during import&lt;/strong&gt;. To do this, just use the following options of &lt;code&gt;read.csv()&lt;/code&gt;: &lt;code&gt;stringsAsFactors&lt;/code&gt; (or &lt;code&gt;as.is&lt;/code&gt;) and &lt;code&gt;colClasses&lt;/code&gt;. By default, you can specify conversion to &lt;code&gt;Date&lt;/code&gt; or &lt;code&gt;POSIXct&lt;/code&gt; classes. If you need a non-standard format, you have two options. First, if you have a single Date column, you can use &lt;code&gt;as.Date.character()&lt;/code&gt; to pass the desired format to &lt;code&gt;colClasses&lt;/code&gt;. Second, if you have multiple Date columns, you can write a function for that and pass it to &lt;code&gt;colClasses&lt;/code&gt; via &lt;code&gt;setAs()&lt;/code&gt;. Both options are discussed here: &lt;a href="http://stackoverflow.com/questions/13022299/specify-date-format-for-colclasses-argument-in-read-table-read-csv"&gt;http://stackoverflow.com/questions/13022299/specify-date-format-for-colclasses-argument-in-read-table-read-csv&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;2) &lt;strong&gt;Convert data&lt;/strong&gt; to correct format &lt;strong&gt;after import&lt;/strong&gt;. Thus, after calling &lt;code&gt;read.csv()&lt;/code&gt;, you would have to execute the following code: &lt;code&gt;dateColumn &amp;lt;- as.Date(dateColumn, "%m/%d/%Y")&lt;/code&gt; or &lt;code&gt;dateColumn &amp;lt;- strptime(dateColumn, "%m/%d/%Y")&lt;/code&gt; (adjust the format to whatever Date format you need).&lt;/p&gt;
', NULL, NULL, '''/code'':21C,25C,34C,76C,79C,83C,87C,97C,101C,128C,137C,158C,162C,194C,211C,221C ''/p'':51C,231C ''/questions/13022299/specify-date-format-for-colclasses-argument-in-read-table-read-csv'':172C ''/questions/13022299/specify-date-format-for-colclasses-argument-in-read-table-read-csv&lt;/a&gt;.&lt;/p&gt;'':175C ''/strong'':57C,64C,181C,188C ''1'':53C ''2'':177C ''adjust'':222C ''agre'':6C ''as.date'':206C ''as.date.character'':127C ''as.is'':82C ''call'':191C ''charact'':20C ''class'':26C,102C ''code'':19C,23C,32C,74C,77C,81C,85C,95C,99C,126C,135C,156C,160C,192C,202C,203C,213C ''colclass'':86C,136C,157C ''column'':15C,122C,144C ''comment'':10C ''convers'':93C ''convert'':55C,179C ''correct'':59C,183C ''d'':209C,219C ''data'':56C,180C ''date'':14C,96C,121C,143C,227C ''datecolumn'':204C,207C,214C,217C ''default'':89C ''depend'':27C ''desir'':132C ''discuss'':166C ''either'':18C ''execut'':199C ''factor'':24C ''first'':115C ''follow'':50C,71C,201C ''format'':110C,133C,184C,224C,228C ''function'':149C ''go'':4C ''href'':169C ''import'':12C,63C,187C ''issu'':43C ''least'':46C ''lt'':205C,215C ''m'':208C,218C ''multipl'':142C ''need'':105C,230C ''non'':108C ''non-standard'':107C ''option'':72C,114C,164C ''p'':1C,52C,176C ''pass'':130C,153C ''posixct'':100C ''read.csv'':75C,193C ''second'':138C ''set'':30C ''seta'':161C ''sever'':47C ''singl'':120C ''solv'':41C ''specifi'':92C ''stackoverflow.com'':171C,174C ''stackoverflow.com/questions/13022299/specify-date-format-for-colclasses-argument-in-read-table-read-csv'':170C ''stackoverflow.com/questions/13022299/specify-date-format-for-colclasses-argument-in-read-table-read-csv&lt;/a&gt;.&lt;/p&gt;'':173C ''standard'':109C ''stringsasfactor'':33C,78C ''strong'':54C,61C,178C,185C ''strptime'':216C ''therefor'':35C ''think'':37C ''thus'':189C ''two'':113C ''type'':60C ''use'':69C,125C ''user1683454'':8C ''via'':159C ''way'':48C ''whatev'':226C ''would'':196C ''write'':147C ''y'':210C,220C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (835, 2, '2014-07-25 03:05:38.107', 1, NULL, NULL, '&lt;p&gt;Consider, try, and perhaps even use multiple databases. It''s not just a "performance" issue at play here. It''s really going to come down to your requirements. How much data are you talking about? what kind of data? how fast do you need it? Are you more read heavy or write heavy?&lt;/p&gt;

&lt;p&gt;Here''s one thing you can''t do in a SQL database: Calculate sentiment. &lt;a href="http://www.slideshare.net/shift8/mongodb-machine-learning" rel="nofollow"&gt;http://www.slideshare.net/shift8/mongodb-machine-learning&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Of course the speed in that case may not be fast enough for your needs, but it is something that''s possible. With some caching of specific aggregate values, it was quite acceptable even. Why would you do this? Convenience.&lt;/p&gt;

&lt;p&gt;Convenience really is something that you''re going to be persuaded by. That''s exactly why (in my opinion) NoSQL databases were created. Performance too of course, but I''m trying to discount benchmarks and focus more on other concerns.&lt;/p&gt;

&lt;p&gt;MongoDB (and some other NoSQL) databases have some very powerful features such as built-in map/reduce. This could result in a savings both in cost and time over using something like Hadoop. Or it could provide a prototype or MVP to launch a larger business.&lt;/p&gt;

&lt;p&gt;What about graph databases? They''re "NoSQL" too. Look at databases like OrientDB. If you want to argue performance ...I don''t think you''re gonna show me a SQL database that''s faster there =) ...and graph databases have some really amazing application based on what you need to do.&lt;/p&gt;

&lt;p&gt;Rule of technology (and the internet) don''t get too comfortable with one thing. You''re gonna be limited and set yourself up for failure.&lt;/p&gt;
', NULL, NULL, '''/p'':55C,122C,164C,212C,264C,291C ''/shift8/mongodb-machine-learning'':75C ''/shift8/mongodb-machine-learning&lt;/a&gt;&lt;/p&gt;'':80C ''accept'':114C ''aggreg'':109C ''amaz'':255C ''applic'':256C ''argu'':231C ''base'':257C ''benchmark'':157C ''built'':180C ''built-in'':179C ''busi'':211C ''cach'':106C ''calcul'':69C ''case'':88C ''come'':25C ''comfort'':276C ''concern'':163C ''consid'':2C ''conveni'':121C,124C ''cost'':191C ''could'':184C,201C ''cours'':83C,150C ''creat'':146C ''data'':32C,40C ''databas'':9C,68C,144C,171C,217C,224C,244C,251C ''discount'':156C ''enough'':93C ''even'':6C,115C ''exact'':138C ''failur'':290C ''fast'':42C,92C ''faster'':247C ''featur'':176C ''focus'':159C ''get'':274C ''go'':23C,131C ''gonna'':239C,282C ''graph'':216C,250C ''hadoop'':198C ''heavi'':51C,54C ''href'':72C ''internet'':271C ''issu'':16C ''kind'':38C ''larger'':210C ''launch'':208C ''like'':197C,225C ''limit'':284C ''look'':222C ''m'':153C ''map/reduce'':182C ''may'':89C ''mongodb'':166C ''much'':31C ''multipl'':8C ''mvp'':206C ''need'':45C,96C,261C ''nofollow'':77C ''nosql'':143C,170C,220C ''one'':59C,278C ''opinion'':142C ''orientdb'':226C ''p'':1C,56C,81C,123C,165C,213C,265C ''perform'':15C,147C,232C ''perhap'':5C ''persuad'':134C ''play'':18C ''possibl'':103C ''power'':175C ''prototyp'':204C ''provid'':202C ''quit'':113C ''re'':130C,219C,238C,281C ''read'':50C ''realli'':22C,125C,254C ''rel'':76C ''requir'':29C ''result'':185C ''rule'':266C ''save'':188C ''sentiment'':70C ''set'':286C ''show'':240C ''someth'':100C,127C,196C ''specif'':108C ''speed'':85C ''sql'':67C,243C ''talk'':35C ''technolog'':268C ''thing'':60C,279C ''think'':236C ''time'':193C ''tri'':3C,154C ''use'':7C,195C ''valu'':110C ''want'':229C ''would'':117C ''write'':53C ''www.slideshare.net'':74C,79C ''www.slideshare.net/shift8/mongodb-machine-learning'':73C ''www.slideshare.net/shift8/mongodb-machine-learning&lt;/a&gt;&lt;/p&gt;'':78C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (837, 2, '2014-07-25 12:59:00.85', 1, NULL, NULL, '&lt;p&gt;I would look at the &lt;code&gt;raster&lt;/code&gt; package for this, which can read in raw binary data and present it as NxM grids. It can even extract subsets of large binary grids without having to read in the whole file (the R raster object itself is just a proxy to the data, not the data itself).&lt;/p&gt;
', NULL, NULL, '''/code'':9C ''/p'':59C ''binari'':18C,33C ''code'':7C ''data'':19C,54C,57C ''even'':28C ''extract'':29C ''file'':42C ''grid'':25C,34C ''larg'':32C ''look'':4C ''nxm'':24C ''object'':46C ''p'':1C ''packag'':10C ''present'':21C ''proxi'':51C ''r'':44C ''raster'':8C,45C ''raw'':17C ''read'':15C,38C ''subset'':30C ''whole'':41C ''without'':35C ''would'':3C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (838, 1, '2014-07-24 06:34:50.083', 2, 46, 'Pig latin code error', '&lt;p&gt;While running the below pig script I am getting error in line4: If it is &lt;code&gt;GROUP&lt;/code&gt; then I am getting error. If I change from &lt;code&gt;GROUP&lt;/code&gt; TO &lt;code&gt;group&lt;/code&gt; in line4, then the script is running. What is the difference between group and GROUP.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;LINES = LOAD ''/user/cloudera/datapeople.csv'' USING PigStorage('','') AS ( firstname:chararray, lastname:chararray, address:chararray, city:chararray, state:chararray, zip:chararray );

WORDS = FOREACH LINES GENERATE FLATTEN(TOKENIZE(zip)) AS ZIPS;

WORDSGROUPED = GROUP WORDS BY ZIPS;

WORDBYCOUNT = FOREACH WORDSGROUPED GENERATE GROUP AS ZIPS, COUNT(WORDS);

WORDSSORT = ORDER WORDBYCOUNT BY $1 DESC;

DUMP WORDSSORT;
&lt;/code&gt;&lt;/pre&gt;
', 'hadoop  bigdata', NULL, '''/code'':25C,37C,41C,109C ''/p'':57C ''/pre'':110C ''/user/cloudera/datapeople.csv'':62C ''1'':105C ''address'':70C ''bigdata'':6B ''chang'':33C ''chararray'':67C,69C,71C,73C,75C,77C ''citi'':72C ''code'':3A,23C,35C,39C,59C ''count'':99C ''desc'':106C ''differ'':52C ''dump'':107C ''error'':4A,17C,30C ''firstnam'':66C ''flatten'':82C ''foreach'':79C,93C ''generat'':81C,95C ''get'':16C,29C ''group'':24C,36C,40C,54C,56C,88C,96C ''hadoop'':5B ''lastnam'':68C ''latin'':2A ''line'':60C,80C ''line4'':19C,43C ''load'':61C ''order'':102C ''p'':7C ''pig'':1A,12C ''pigstorag'':64C ''pre'':58C ''run'':9C,48C ''script'':13C,46C ''state'':74C ''token'':83C ''use'':63C ''word'':78C,89C,100C ''wordbycount'':92C,103C ''wordsgroup'':87C,94C ''wordssort'':101C,108C ''zip'':76C,84C,86C,91C,98C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2332, 2, '2014-10-22 08:30:00.99', 3, NULL, NULL, '&lt;p&gt;Since it''s a master''s thesis, how about writing something regarding decision trees, and their "upgrades": boosting and Random Forests? And then integrate that with Map/Reduce, together with showing how to scale a Random Forest on Hadoop using M/R?&lt;/p&gt;
', NULL, NULL, '''/p'':42C ''boost'':19C ''decis'':14C ''forest'':22C,37C ''hadoop'':39C ''integr'':25C ''m/r'':41C ''map/reduce'':28C ''master'':6C ''p'':1C ''random'':21C,36C ''regard'':13C ''scale'':34C ''show'':31C ''sinc'':2C ''someth'':12C ''thesi'':8C ''togeth'':29C ''tree'':15C ''upgrad'':18C ''use'':40C ''write'':11C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (839, 2, '2014-07-25 09:05:09.493', 0, NULL, NULL, '&lt;p&gt;When we do grouping of the data, Pig creates a new key named "group" and puts all the tuple matching that key into a bag and associates the bag with the key. So after the group operation schema of the grouped data will be something like &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;raw = load ''$input'' using PigStorage(''\u0001'') as (id1:int, name:chararray);
groupdata1 = group raw by (id1,name);  
describe groupdata1;
{group: (id1: int,name: chararray),raw: {(id1: int,name: chararray)}}
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The ''GROUP'' in line 4 you are trying to access is one of the attribute of the schema from the last statement. These attribute name are case sensitive. It will produce the error saying it doesn''t exist in the schema. So you need to use ''group'' only to access it.&lt;/p&gt;
', NULL, NULL, '''/code'':80C ''/p'':48C,134C ''/pre'':81C ''4'':87C ''access'':92C,132C ''associ'':28C ''attribut'':97C,106C ''bag'':26C,30C ''case'':109C ''chararray'':61C,74C,79C ''code'':50C ''creat'':10C ''data'':8C,43C ''describ'':68C ''doesn'':118C ''error'':115C ''exist'':120C ''group'':5C,15C,37C,42C,63C,70C,84C,129C ''groupdata1'':62C,69C ''id1'':58C,66C,71C,76C ''input'':53C ''int'':59C,72C,77C ''key'':13C,23C,33C ''last'':103C ''like'':47C ''line'':86C ''load'':52C ''match'':21C ''name'':14C,60C,67C,73C,78C,107C ''need'':126C ''new'':12C ''one'':94C ''oper'':38C ''p'':1C,82C ''pig'':9C ''pigstorag'':55C ''pre'':49C ''produc'':113C ''put'':17C ''raw'':51C,64C,75C ''say'':116C ''schema'':39C,100C,123C ''sensit'':110C ''someth'':46C ''statement'':104C ''tri'':90C ''tupl'':20C ''u0001'':56C ''use'':54C,128C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (847, 2, '2014-07-26 08:37:52.823', 2, NULL, NULL, '&lt;p&gt;&lt;a href="https://www.coursera.org/course/datasci" rel="nofollow"&gt;Introduction to Data Science&lt;/a&gt; course that is being run on Coursera now includes real-world project assignment where companies post their problems and students are encouraged to solve them. This is done via &lt;a href="https://www.coursolve.org/" rel="nofollow"&gt;coursolve.com&lt;/a&gt; (already mentioned here).&lt;/p&gt;

&lt;p&gt;More information &lt;a href="https://class.coursera.org/datasci-002/wiki/OptionalRealWorldProject" rel="nofollow"&gt;here&lt;/a&gt; (you have to be enrolled in the course to see that link)&lt;/p&gt;
', NULL, NULL, '''/a'':13C,50C,66C ''/course/datasci'':6C ''/datasci-002/wiki/optionalrealworldproject'':62C ''/p'':54C,79C ''alreadi'':51C ''assign'':27C ''class.coursera.org'':61C ''class.coursera.org/datasci-002/wiki/optionalrealworldproject'':60C ''compani'':29C ''cours'':14C,74C ''coursera'':20C ''coursolve.com'':49C ''data'':11C ''done'':42C ''encourag'':36C ''enrol'':71C ''href'':3C,45C,59C ''includ'':22C ''inform'':57C ''introduct'':9C ''link'':78C ''mention'':52C ''nofollow'':8C,48C,64C ''p'':1C,55C ''post'':30C ''problem'':32C ''project'':26C ''real'':24C ''real-world'':23C ''rel'':7C,47C,63C ''run'':18C ''scienc'':12C ''see'':76C ''solv'':38C ''student'':34C ''via'':43C ''world'':25C ''www.coursera.org'':5C ''www.coursera.org/course/datasci'':4C ''www.coursolve.org'':46C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6759, 2, '2015-08-11 12:22:01.997', 1, NULL, NULL, '&lt;p&gt;You are looking for Recurrent Neural Networks for character-level language models. &lt;/p&gt;

&lt;p&gt;Have a look at this. &lt;a href="https://github.com/karpathy/char-rnn" rel="nofollow"&gt;https://github.com/karpathy/char-rnn&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/karpathy/char-rnn'':26C ''/karpathy/char-rnn&lt;/a&gt;&lt;/p&gt;'':31C ''/p'':15C ''charact'':11C ''character-level'':10C ''github.com'':25C,30C ''github.com/karpathy/char-rnn'':24C ''github.com/karpathy/char-rnn&lt;/a&gt;&lt;/p&gt;'':29C ''href'':23C ''languag'':13C ''level'':12C ''look'':4C,19C ''model'':14C ''network'':8C ''neural'':7C ''nofollow'':28C ''p'':1C,16C ''recurr'':6C ''rel'':27C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (840, 1, '2014-07-25 17:18:21.393', 1, 108, 'How to fix similarity matrix in Pandas returning all NaNs?', '&lt;p&gt;I''ve been trying to create a similarity matrix in Pandas from with a matrix multiplication operation on a document-term count matrix with 2264 rows and 20475 columns.&lt;/p&gt;

&lt;p&gt;The calculation completes in IPython but inspection shows the results all come back as NaN.&lt;/p&gt;

&lt;p&gt;I''ve also tried doing the same job in numpy, tried converting the original matrix to_sparse and even re-casting the values as integers, but still no joy.&lt;/p&gt;

&lt;p&gt;Can anyone suggest the best approach to tackle the problem?&lt;/p&gt;

&lt;p&gt;EDIT: Here''s my code thus far:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;path = "../../reuters.db"
%pylab inline
import pandas as pd
import numpy as np
import pandas.io.sql as psql
import sqlite3 as lite
con = lite.connect(path)
with con:
    sql = "SELECT * FROM Frequency"
    df = psql.frame_query(sql, con)
    print df.shape
df = df.rename(columns={"term":"term_id", "count":"count_id"})
pivoted = df.pivot(''docid'', ''term_id'', ''count_id'')
pivoted.to_sparse()
similarity_matrix = pivoted.dot(pivoted.T)
&lt;/code&gt;&lt;/pre&gt;
', 'pandas  similarity', NULL, '''/../reuters.db'':118C ''/code'':175C ''/p'':44C,61C,93C,105C,114C ''/pre'':176C ''20475'':42C ''2264'':39C ''also'':65C ''anyon'':96C ''approach'':100C ''back'':58C ''best'':99C ''calcul'':47C ''cast'':84C ''code'':111C,116C ''column'':43C,155C ''come'':57C ''complet'':48C ''con'':137C,141C,150C ''convert'':74C ''count'':36C,159C,160C,167C ''creat'':19C ''df'':146C,153C ''df.pivot'':163C ''df.rename'':154C ''df.shape'':152C ''docid'':164C ''document'':34C ''document-term'':33C ''edit'':107C ''even'':81C ''far'':113C ''fix'':3A ''frequenc'':145C ''id'':158C,161C,166C,168C ''import'':121C,125C,129C,133C ''inlin'':120C ''inspect'':52C ''integ'':88C ''ipython'':50C ''job'':70C ''joy'':92C ''lite'':136C ''lite.connect'':138C ''matrix'':5A,22C,28C,37C,77C,172C ''multipl'':29C ''nan'':10A,60C ''np'':128C ''numpi'':72C,126C ''oper'':30C ''origin'':76C ''p'':13C,45C,62C,94C,106C ''panda'':7A,11B,24C,122C ''pandas.io.sql'':130C ''path'':117C,139C ''pd'':124C ''pivot'':162C ''pivoted.dot'':173C ''pivoted.t'':174C ''pivoted.to'':169C ''pre'':115C ''print'':151C ''problem'':104C ''psql'':132C ''psql.frame'':147C ''pylab'':119C ''queri'':148C ''re'':83C ''re-cast'':82C ''result'':55C ''return'':8A ''row'':40C ''select'':143C ''show'':53C ''similar'':4A,12B,21C,171C ''spars'':79C,170C ''sql'':142C,149C ''sqlite3'':134C ''still'':90C ''suggest'':97C ''tackl'':102C ''term'':35C,156C,157C,165C ''thus'':112C ''tri'':17C,66C,73C ''valu'':86C ''ve'':15C,64C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (841, 2, '2014-07-25 18:12:49.847', 4, NULL, NULL, '&lt;p&gt;Instead of collaborative filtering I would use the matrix factorization approach, wherein users and movies alike a represented by vectors of latent features whose dot products yield the ratings. Normally one merely selects the rank (number of features) without regard to what the features represent, and the algorithm does the rest. Like PCA, the result is not immediately interpretable but it yields good results. What you want to do is extend the movie matrix to include the additional features you mentioned and make sure that they stay fixed as the algorithm estimates the two matrices using regularizastion. The corresponding entries in the user matrix will be initialized randomly, then estimated by the matrix factorization algorithm. It''s a versatile and performant approach but it takes some understanding of machine learning, or linear algebra at least.&lt;/p&gt;

&lt;p&gt;I saw a nice ipython notebook a while back but I can''t find it right now, so I''ll refer you to &lt;a href="http://nbviewer.ipython.org/github/diktat/CPSC540machinelearning/blob/master/1.4%20Collaborative%20Filtering%20for%20Movie%20Recommendation.ipynb" rel="nofollow"&gt;another one&lt;/a&gt; which, while not as nice, still clarifies some of the maths.&lt;/p&gt;
', NULL, NULL, '''/a'':171C ''/github/diktat/cpsc540machinelearning/blob/master/1.4%20collaborative%20filtering%20for%20movie%20recommendation.ipynb'':166C ''/p'':137C,183C ''addit'':79C ''algebra'':134C ''algorithm'':49C,92C,116C ''alik'':17C ''anoth'':169C ''approach'':12C,123C ''back'':147C ''clarifi'':178C ''collabor'':4C ''correspond'':100C ''dot'':26C ''entri'':101C ''estim'':93C,111C ''extend'':72C ''factor'':11C,115C ''featur'':24C,39C,45C,80C ''filter'':5C ''find'':152C ''fix'':89C ''good'':64C ''href'':163C ''immedi'':59C ''includ'':77C ''initi'':108C ''instead'':2C ''interpret'':60C ''ipython'':143C ''latent'':23C ''learn'':131C ''least'':136C ''like'':53C ''linear'':133C ''ll'':158C ''machin'':130C ''make'':84C ''math'':182C ''matric'':96C ''matrix'':10C,75C,105C,114C ''mention'':82C ''mere'':33C ''movi'':16C,74C ''nbviewer.ipython.org'':165C ''nbviewer.ipython.org/github/diktat/cpsc540machinelearning/blob/master/1.4%20collaborative%20filtering%20for%20movie%20recommendation.ipynb'':164C ''nice'':142C,176C ''nofollow'':168C ''normal'':31C ''notebook'':144C ''number'':37C ''one'':32C,170C ''p'':1C,138C ''pca'':54C ''perform'':122C ''product'':27C ''random'':109C ''rank'':36C ''rate'':30C ''refer'':159C ''regard'':41C ''regularizast'':98C ''rel'':167C ''repres'':19C,46C ''rest'':52C ''result'':56C,65C ''right'':154C ''saw'':140C ''select'':34C ''stay'':88C ''still'':177C ''sure'':85C ''take'':126C ''two'':95C ''understand'':128C ''use'':8C,97C ''user'':14C,104C ''vector'':21C ''versatil'':120C ''want'':68C ''wherein'':13C ''whose'':25C ''without'':40C ''would'':7C ''yield'':28C,63C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (842, 1, '2014-07-25 18:36:31.34', 12, 17577, 'Data Science Project Ideas', '&lt;p&gt;I don''t know if this is a right place to ask this question, but a community dedicated to Data Science should be the most appropriate place in my opinion.&lt;/p&gt;

&lt;p&gt;I have just started with Data Science and Machine learning. I am looking for long term project ideas which I can work on for like 8 months.&lt;/p&gt;

&lt;p&gt;A mix of Data Science and Machine learning would be great.&lt;/p&gt;

&lt;p&gt;A project big enough to help me understand the core concepts and also implement them at the same time would be very beneficial.&lt;/p&gt;
', 'machine-learning  bigdata  dataset', NULL, '''/p'':41C,70C,83C,108C ''8'':68C ''also'':97C ''appropri'':36C ''ask'':22C ''benefici'':107C ''big'':87C ''bigdata'':8B ''communiti'':27C ''concept'':95C ''core'':94C ''data'':1A,30C,48C,75C ''dataset'':9B ''dedic'':28C ''enough'':88C ''great'':82C ''help'':90C ''idea'':4A,60C ''implement'':98C ''know'':14C ''learn'':7B,52C,79C ''like'':67C ''long'':57C ''look'':55C ''machin'':6B,51C,78C ''machine-learn'':5B ''mix'':73C ''month'':69C ''opinion'':40C ''p'':10C,42C,71C,84C ''place'':20C,37C ''project'':3A,59C,86C ''question'':24C ''right'':19C ''scienc'':2A,31C,49C,76C ''start'':46C ''term'':58C ''time'':103C ''understand'':92C ''work'':64C ''would'':80C,104C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (843, 2, '2014-07-25 20:50:14.54', 18, NULL, NULL, '&lt;p&gt;I would try to analyze and solve one or more of the problems published on &lt;strong&gt;Kaggle Competitions&lt;/strong&gt; (&lt;a href="https://www.kaggle.com/competitions"&gt;https://www.kaggle.com/competitions&lt;/a&gt;). Note that the competitions are grouped by their expected &lt;em&gt;complexity&lt;/em&gt;, from &lt;code&gt;101&lt;/code&gt; (bottom of the list) to &lt;code&gt;Research&lt;/code&gt; and &lt;code&gt;Featured&lt;/code&gt; (top of the list). A color-coded vertical band is a &lt;em&gt;visual guideline&lt;/em&gt; for grouping. You can &lt;strong&gt;assess time&lt;/strong&gt; you could spend on a project by &lt;strong&gt;adjusting&lt;/strong&gt; the expected &lt;em&gt;length&lt;/em&gt; of corresponding competition, based on your &lt;em&gt;skills&lt;/em&gt; and &lt;em&gt;experience&lt;/em&gt;.&lt;/p&gt;

&lt;p&gt;A number of &lt;strong&gt;data science project ideas&lt;/strong&gt; can be found by browsing the following &lt;code&gt;Coursolve&lt;/code&gt; webpage: &lt;a href="https://www.coursolve.org/browse-needs?query=Data%20Science"&gt;https://www.coursolve.org/browse-needs?query=Data%20Science&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;If you have skills and desire to work on a &lt;strong&gt;real data science project&lt;/strong&gt;, focused on &lt;strong&gt;social impacts&lt;/strong&gt;, visit &lt;code&gt;DataKind&lt;/code&gt; projects page: &lt;a href="http://www.datakind.org/projects"&gt;http://www.datakind.org/projects&lt;/a&gt;. More projects with social impacts focus can be found at &lt;code&gt;Data Science for Social Good&lt;/code&gt; fellowship webpage: &lt;a href="http://dssg.io/projects"&gt;http://dssg.io/projects&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Science Project Ideas&lt;/strong&gt; page at &lt;code&gt;My NASA Data&lt;/code&gt; site looks like another place to visit for inspiration: &lt;a href="http://mynasadata.larc.nasa.gov/804-2"&gt;http://mynasadata.larc.nasa.gov/804-2&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;If you would like to use &lt;strong&gt;open data&lt;/strong&gt;, this long list of applications on &lt;code&gt;Data.gov&lt;/code&gt; can provide you with some interesting &lt;em&gt;data science&lt;/em&gt; project ideas: &lt;a href="http://www.data.gov/applications"&gt;http://www.data.gov/applications&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/804-2'':229C ''/804-2&lt;/a&gt;.&lt;/p&gt;'':232C ''/applications'':269C ''/applications&lt;/a&gt;.&lt;/p&gt;'':272C ''/browse-needs?query=data%20science'':135C ''/browse-needs?query=data%20science&lt;/a&gt;.&lt;/p&gt;'':138C ''/code'':44C,52C,56C,129C,165C,192C,215C,252C ''/competitions'':25C ''/competitions&lt;/a&gt;).'':28C ''/em'':40C,72C,95C,104C,108C,262C ''/p'':109C ''/projects'':172C,199C ''/projects&lt;/a&gt;.'':175C ''/projects&lt;/a&gt;.&lt;/p&gt;'':202C ''/strong'':20C,80C,90C,119C,155C,161C,208C,243C ''101'':43C ''adjust'':89C ''analyz'':6C ''anoth'':219C ''applic'':248C ''assess'':78C ''band'':66C ''base'':99C ''bottom'':45C ''brows'':124C ''code'':42C,50C,54C,64C,127C,163C,186C,211C,250C ''color'':63C ''color-cod'':62C ''competit'':19C,32C,98C ''complex'':39C ''correspond'':97C ''could'':82C ''coursolv'':128C ''data'':115C,152C,187C,214C,242C,260C ''data.gov'':251C ''datakind'':164C ''desir'':145C ''dssg.io'':198C,201C ''dssg.io/projects'':197C ''dssg.io/projects&lt;/a&gt;.&lt;/p&gt;'':200C ''em'':38C,69C,93C,102C,106C,259C ''expect'':37C,92C ''experi'':107C ''featur'':55C ''fellowship'':193C ''focus'':156C,181C ''follow'':126C ''found'':122C,184C ''good'':191C ''group'':34C,74C ''guidelin'':71C ''href'':22C,132C,169C,196C,226C,266C ''idea'':118C,207C,264C ''impact'':160C,180C ''inspir'':224C ''interest'':258C ''kaggl'':18C ''length'':94C ''like'':218C,237C ''list'':48C,60C,246C ''long'':245C ''look'':217C ''mynasadata.larc.nasa.gov'':228C,231C ''mynasadata.larc.nasa.gov/804-2'':227C ''mynasadata.larc.nasa.gov/804-2&lt;/a&gt;.&lt;/p&gt;'':230C ''nasa'':213C ''note'':29C ''number'':112C ''one'':9C ''open'':241C ''p'':1C,110C,139C,203C,233C ''page'':167C,209C ''place'':220C ''problem'':14C ''project'':86C,117C,154C,166C,177C,206C,263C ''provid'':254C ''publish'':15C ''real'':151C ''research'':51C ''scienc'':116C,153C,188C,205C,261C ''site'':216C ''skill'':103C,143C ''social'':159C,179C,190C ''solv'':8C ''spend'':83C ''strong'':17C,77C,88C,114C,150C,158C,204C,240C ''time'':79C ''top'':57C ''tri'':4C ''use'':239C ''vertic'':65C ''visit'':162C,222C ''visual'':70C ''webpag'':130C,194C ''work'':147C ''would'':3C,236C ''www.coursolve.org'':134C,137C ''www.coursolve.org/browse-needs?query=data%20science'':133C ''www.coursolve.org/browse-needs?query=data%20science&lt;/a&gt;.&lt;/p&gt;'':136C ''www.data.gov'':268C,271C ''www.data.gov/applications'':267C ''www.data.gov/applications&lt;/a&gt;.&lt;/p&gt;'':270C ''www.datakind.org'':171C,174C ''www.datakind.org/projects'':170C ''www.datakind.org/projects&lt;/a&gt;.'':173C ''www.kaggle.com'':24C,27C ''www.kaggle.com/competitions'':23C ''www.kaggle.com/competitions&lt;/a&gt;).'':26C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (845, 2, '2014-07-26 00:11:03.637', 6, NULL, NULL, '&lt;p&gt;Most often serialization error in (Py)Spark means that some part of your distributed code (e.g. functions passed to &lt;code&gt;map&lt;/code&gt;) has &lt;strong&gt;dependencies&lt;/strong&gt; on &lt;strong&gt;non-serializable data&lt;/strong&gt;. Consider following example: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;rdd = sc.parallelize(range(5))
rdd = rdd.map(lambda x: x + 1)
rdd.collect()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Here you have distributed collection and lambda function to send to all workers. Lambda is completely self-containing, so it''s easy to copy its binary representation to other nodes without any worries. &lt;/p&gt;

&lt;p&gt;Now let''s make things a bit more interesting: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;f = open("/etc/hosts")
rdd = sc.parallelize(range(100))
rdd = rdd.map(lambda x: f.read())
rdd.collect()
f.close()
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Boom! Strange error in serialization module! What just happened is that we had attempted to pass &lt;code&gt;f&lt;/code&gt;, which is a file object, to workers. Obviously, file object is a handle to &lt;em&gt;local&lt;/em&gt; data and thus cannot be sent to other machines. &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;So what''s happening in your specific code? Without actual data and knowing record format, I cannot debug it completely, but I guess that problem goes from this line: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def vectorizer(text, vocab=vocab_dict):
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In Python, keyword arguments are initialized when function is called for the first time. When you call &lt;code&gt;sc.parallelize(...).map(vectorizer)&lt;/code&gt; just after its definition, &lt;code&gt;vocab_dict&lt;/code&gt; is available &lt;em&gt;locally&lt;/em&gt;, but &lt;em&gt;remote&lt;/em&gt; workers know absolutely nothing about it. Thus function is called with fewer parameters than it expects which results in &lt;code&gt;__init__() takes exactly 3 arguments (2 given)&lt;/code&gt; error. &lt;/p&gt;

&lt;p&gt;Also note, that you follow very bad pattern of  &lt;code&gt;sc.parallelize(...)...collect()&lt;/code&gt; calls. First you spread your collection to entire cluster, do some computations, and then pull the result. But sending data back and forth is pretty pointless here. Instead, you can just do these computations locally, and run Spark''s parallel processes only when you work with really big datasets (like you main &lt;code&gt;amazon_dataset&lt;/code&gt;, I guess).&lt;/p&gt;
', NULL, NULL, '''/code'':23C,52C,117C,138C,206C,230C,238C,275C,291C,347C ''/em'':155C,243C,247C ''/etc/hosts'':105C ''/p'':38C,89C,100C,165C,197C,277C,350C ''/pre'':53C,118C,207C ''/strong'':27C,34C ''1'':50C ''100'':109C ''2'':273C ''3'':271C ''5'':44C ''absolut'':250C ''actual'':177C ''also'':279C ''amazon'':345C ''argument'':212C,272C ''attempt'':133C ''avail'':240C ''back'':312C ''bad'':285C ''big'':339C ''binari'':81C ''bit'':97C ''boom'':120C ''call'':218C,225C,257C,292C ''cannot'':159C,184C ''cluster'':300C ''code'':16C,21C,40C,102C,136C,175C,199C,226C,235C,267C,288C,344C ''collect'':59C,290C,297C ''complet'':70C,187C ''comput'':303C,325C ''consid'':35C ''contain'':73C ''copi'':79C ''data'':33C,156C,178C,311C ''dataset'':340C,346C ''debug'':185C ''def'':200C ''definit'':234C ''depend'':26C ''dict'':205C,237C ''distribut'':15C,58C ''e.g'':17C ''easi'':77C ''em'':153C,241C,245C ''entir'':299C ''error'':5C,122C,276C ''exact'':270C ''exampl'':37C ''expect'':263C ''f'':103C,137C ''f.close'':116C ''f.read'':114C ''fewer'':259C ''file'':142C,147C ''first'':221C,293C ''follow'':36C,283C ''format'':182C ''forth'':314C ''function'':18C,62C,216C,255C ''given'':274C ''goe'':193C ''guess'':190C,349C ''handl'':151C ''happen'':128C,171C ''hr'':166C ''init'':268C ''initi'':214C ''instead'':319C ''interest'':99C ''keyword'':211C ''know'':180C,249C ''lambda'':47C,61C,68C,112C ''let'':92C ''like'':341C ''line'':196C ''local'':154C,242C,326C ''machin'':164C ''main'':343C ''make'':94C ''map'':22C,228C ''mean'':9C ''modul'':125C ''node'':85C ''non'':31C ''non-serializ'':30C ''note'':280C ''noth'':251C ''object'':143C,148C ''obvious'':146C ''often'':3C ''open'':104C ''p'':1C,54C,90C,119C,167C,208C,278C ''parallel'':331C ''paramet'':260C ''part'':12C ''pass'':19C,135C ''pattern'':286C ''pointless'':317C ''pre'':39C,101C,198C ''pretti'':316C ''problem'':192C ''process'':332C ''pull'':306C ''py'':7C ''python'':210C ''rang'':43C,108C ''rdd'':41C,45C,106C,110C ''rdd.collect'':51C,115C ''rdd.map'':46C,111C ''realli'':338C ''record'':181C ''remot'':246C ''represent'':82C ''result'':265C,308C ''run'':328C ''sc.parallelize'':42C,107C,227C,289C ''self'':72C ''self-contain'':71C ''send'':64C,310C ''sent'':161C ''serial'':4C,124C ''serializ'':32C ''spark'':8C,329C ''specif'':174C ''spread'':295C ''strang'':121C ''strong'':25C,29C ''take'':269C ''text'':202C ''thing'':95C ''thus'':158C,254C ''time'':222C ''vector'':201C,229C ''vocab'':203C,204C,236C ''without'':86C,176C ''work'':336C ''worker'':67C,145C,248C ''worri'':88C ''x'':48C,49C,113C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (846, 2, '2014-07-26 01:12:08.167', 4, NULL, NULL, '&lt;p&gt;Take something from your everyday life. Create predictor of traffic jams in your region, craft personalised music recommender, analyse car market, etc. Choose &lt;strong&gt;real problem&lt;/strong&gt; that you &lt;strong&gt;want to solve&lt;/strong&gt; - this will not only keep you motivated, but also make you go through the whole development circle from data collection to hypothesis testing. &lt;/p&gt;
', NULL, NULL, '''/p'':59C ''/strong'':28C,35C ''also'':44C ''analys'':20C ''car'':21C ''choos'':24C ''circl'':52C ''collect'':55C ''craft'':16C ''creat'':8C ''data'':54C ''develop'':51C ''etc'':23C ''everyday'':6C ''go'':47C ''hypothesi'':57C ''jam'':12C ''keep'':40C ''life'':7C ''make'':45C ''market'':22C ''motiv'':42C ''music'':18C ''p'':1C ''personalis'':17C ''predictor'':9C ''problem'':27C ''real'':26C ''recommend'':19C ''region'':15C ''solv'':34C ''someth'':3C ''strong'':25C,31C ''take'':2C ''test'':58C ''traffic'':11C ''want'':32C ''whole'':50C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (848, 2, '2014-07-26 16:04:25.363', 11, NULL, NULL, '&lt;p&gt;K-means is not the most appropriate algorithm here.&lt;/p&gt;

&lt;p&gt;The reason is that k-means is designed to &lt;strong&gt;minimize variance&lt;/strong&gt;. This is, of course, appearling from a statistical and signal procssing point of view, but your data is not "linear".&lt;/p&gt;

&lt;p&gt;Since your data is in latitude, longitude format, you should use an algorithm that can handle &lt;em&gt;arbitrary&lt;/em&gt; distance functions, in particular geodetic distance functions. Hierarchical clustering, PAM, CLARA, and DBSCAN are popular examples of this.&lt;/p&gt;

&lt;p&gt;The problems of k-means are easy to see when you consider points close to the +-180 degrees wrap-around. Even if you hacked k-means to use Haversine distance, in the update step when it recomputes the &lt;em&gt;mean&lt;/em&gt; the result will be badly screwed. &lt;strong&gt;Worst case is, k-means will never converge!&lt;/strong&gt;&lt;/p&gt;
', NULL, NULL, '''-180'':106C ''/em'':68C,132C ''/p'':12C,48C,87C,150C ''/strong'':27C,149C ''algorithm'':10C,62C ''appearl'':32C ''appropri'':9C ''arbitrari'':67C ''around'':110C ''bad'':137C ''case'':141C ''clara'':79C ''close'':103C ''cluster'':77C ''consid'':101C ''converg'':148C ''cours'':31C ''data'':44C,52C ''dbscan'':81C ''degre'':107C ''design'':22C ''distanc'':69C,74C,121C ''easi'':96C ''em'':66C,130C ''even'':111C ''exampl'':84C ''format'':57C ''function'':70C,75C ''geodet'':73C ''hack'':114C ''handl'':65C ''haversin'':120C ''hierarch'':76C ''k'':3C,19C,93C,116C,144C ''k-mean'':2C,18C,92C,115C,143C ''latitud'':55C ''linear'':47C ''longitud'':56C ''mean'':4C,20C,94C,117C,131C,145C ''minim'':25C ''never'':147C ''p'':1C,13C,49C,88C ''pam'':78C ''particular'':72C ''point'':39C,102C ''popular'':83C ''problem'':90C ''procss'':38C ''reason'':15C ''recomput'':128C ''result'':134C ''screw'':138C ''see'':98C ''signal'':37C ''sinc'':50C ''statist'':35C ''step'':125C ''strong'':24C,139C ''updat'':124C ''use'':60C,119C ''varianc'':26C ''view'':41C ''worst'':140C ''wrap'':109C ''wrap-around'':108C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (849, 2, '2014-07-26 21:03:13.47', 2, NULL, NULL, '&lt;p&gt;Since you want to show so much data, I think that your best choice is going interactive. Check out this &lt;a href="http://demo.zunzun.se/intervals/index.html" rel="nofollow"&gt;demo&lt;/a&gt;, it is close to what you want but not quite. &lt;/p&gt;

&lt;p&gt;It is very difficult to show a lot of data in a single diagram, together with the finest details and the bird-eyes view. But you can let the user interact and look for the details. To show counts, one option is to use color-coding. Take a look at this image (code &lt;a href="https://gist.github.com/dsign/4c598cfbfc81f6d491d5" rel="nofollow"&gt;here&lt;/a&gt;): &lt;img src="http://i.stack.imgur.com/xmgHi.png" alt="image"&gt;.&lt;/p&gt;

&lt;p&gt;Here rgb channels have been used to encode (the logarithm of) the number of active events (red), events starting (green) and events ending (blue) for windows of different size. The X axis is time, and the Y axis represents window size, that is, duration. Thus, a point with coordinates (10, 4) represents the interval of time that goes from 10 to 14. &lt;/p&gt;

&lt;p&gt;To make a lot of data more detailed, it could be a good idea to make the diagram zoomable (like in the demo before), and to give the user the possibility of visualizing just one channel/magnitude.&lt;/p&gt;
', NULL, NULL, '''/a'':30C,106C ''/dsign/4c598cfbfc81f6d491d5'':102C ''/intervals/index.html'':26C ''/p'':41C,114C,178C,216C ''/xmghi.png'':111C ''10'':165C,175C ''14'':177C ''4'':166C ''activ'':130C ''alt'':112C ''axi'':147C,153C ''best'':14C ''bird'':65C ''bird-ey'':64C ''blue'':139C ''channel'':118C ''channel/magnitude'':215C ''check'':19C ''choic'':15C ''close'':33C ''code'':90C,97C ''color'':89C ''color-cod'':88C ''coordin'':164C ''could'':189C ''count'':82C ''data'':9C,52C,185C ''demo'':29C,202C ''demo.zunzun.se'':25C ''demo.zunzun.se/intervals/index.html'':24C ''detail'':61C,79C,187C ''diagram'':56C,197C ''differ'':143C ''difficult'':46C ''durat'':159C ''encod'':123C ''end'':138C ''event'':131C,133C,137C ''eye'':66C ''finest'':60C ''gist.github.com'':101C ''gist.github.com/dsign/4c598cfbfc81f6d491d5'':100C ''give'':206C ''go'':17C ''goe'':173C ''good'':192C ''green'':135C ''href'':23C,99C ''i.stack.imgur.com'':110C ''i.stack.imgur.com/xmghi.png'':109C ''idea'':193C ''imag'':96C,113C ''img'':107C ''interact'':18C,74C ''interv'':169C ''let'':71C ''like'':199C ''logarithm'':125C ''look'':76C,93C ''lot'':50C,183C ''make'':181C,195C ''much'':8C ''nofollow'':28C,104C ''number'':128C ''one'':83C,214C ''option'':84C ''p'':1C,42C,115C,179C ''point'':162C ''possibl'':210C ''quit'':40C ''red'':132C ''rel'':27C,103C ''repres'':154C,167C ''rgb'':117C ''show'':6C,48C,81C ''sinc'':2C ''singl'':55C ''size'':144C,156C ''src'':108C ''start'':134C ''take'':91C ''think'':11C ''thus'':160C ''time'':149C,171C ''togeth'':57C ''use'':87C,121C ''user'':73C,208C ''view'':67C ''visual'':212C ''want'':4C,37C ''window'':141C,155C ''x'':146C ''y'':152C ''zoomabl'':198C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (850, 2, '2014-07-27 03:58:22.907', 6, NULL, NULL, '&lt;p&gt;If you''ve got prior information then you should certainly not use simple mean in a split test. I assume you''re trying to just predict which group will produce the greatest amount of revenue overall, by trying to emulate the underlying distribution.&lt;/p&gt;

&lt;p&gt;Firstly, it''s worth noting that any metrics you choose will actually reduce to mean in a pretty trivial way. Eventually mean will necessarily work out, though using a standard bayesian method to estimate the mean is probably your best bet.&lt;/p&gt;

&lt;p&gt;If you''ve got a prior then using a standard bayesian approach to update the prior on your mean revenue is probably the best way to do it. Basically, just take the individual results you get and update a multinomial distribution representing your prior in each case.&lt;/p&gt;

&lt;p&gt;If you want some more full background on multinomial distributions as bayesian priors are pretty well, &lt;a href="http://research.microsoft.com/en-us/um/people/minka/papers/minka-multinomial.pdf"&gt;this&lt;/a&gt; Microsoft paper does a pretty good job of outlining it. In general, I wouldn''t care so much about the fact that your distribution is technically discrete, as a multinomial distribution will effectively interpolate across your solution space, giving you a continuous distribution that is a very good approximation of your discrete space.&lt;/p&gt;
', NULL, NULL, '''/a'':161C ''/en-us/um/people/minka/papers/minka-multinomial.pdf'':159C ''/p'':45C,88C,137C,215C ''across'':196C ''actual'':58C ''amount'':34C ''approach'':101C ''approxim'':210C ''assum'':21C ''background'':145C ''basic'':118C ''bayesian'':77C,100C,150C ''best'':86C,113C ''bet'':87C ''care'':177C ''case'':136C ''certain'':11C ''choos'':56C ''continu'':203C ''discret'':188C,213C ''distribut'':44C,130C,148C,185C,192C,204C ''effect'':194C ''emul'':41C ''estim'':80C ''eventu'':67C ''fact'':182C ''first'':47C ''full'':144C ''general'':173C ''get'':125C ''give'':200C ''good'':167C,209C ''got'':5C,93C ''greatest'':33C ''group'':29C ''href'':156C ''individu'':122C ''inform'':7C ''interpol'':195C ''job'':168C ''mean'':15C,61C,68C,82C,108C ''method'':78C ''metric'':54C ''microsoft'':162C ''much'':179C ''multinomi'':129C,147C,191C ''necessarili'':70C ''note'':51C ''outlin'':170C ''overal'':37C ''p'':1C,46C,89C,138C ''paper'':163C ''predict'':27C ''pretti'':64C,153C,166C ''prior'':6C,95C,105C,133C,151C ''probabl'':84C,111C ''produc'':31C ''re'':23C ''reduc'':59C ''repres'':131C ''research.microsoft.com'':158C ''research.microsoft.com/en-us/um/people/minka/papers/minka-multinomial.pdf'':157C ''result'':123C ''revenu'':36C,109C ''simpl'':14C ''solut'':198C ''space'':199C,214C ''split'':18C ''standard'':76C,99C ''take'':120C ''technic'':187C ''test'':19C ''though'':73C ''tri'':24C,39C ''trivial'':65C ''under'':43C ''updat'':103C,127C ''use'':13C,74C,97C ''ve'':4C,92C ''want'':141C ''way'':66C,114C ''well'':154C ''work'':71C ''worth'':50C ''wouldn'':175C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8947, 2, '2015-11-20 21:11:17.623', 1, NULL, NULL, '&lt;p&gt;Annual Wolfram Technology Conference.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="https://www.wolfram.com/events/technology-conference/2015/" rel="nofollow"&gt;Wolfram Technology Conference 2015&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Technical training, analysis topics on a wide range of industries, software development, curated data, on so on. &lt;/p&gt;
', NULL, NULL, '''/a'':20C ''/events/technology-conference/2015/'':13C ''/li'':21C ''/p'':6C,41C ''/ul'':22C ''2015'':19C ''analysi'':26C ''annual'':2C ''confer'':5C,18C ''curat'':36C ''data'':37C ''develop'':35C ''href'':10C ''industri'':33C ''li'':8C ''nofollow'':15C ''p'':1C,23C ''rang'':31C ''rel'':14C ''softwar'':34C ''technic'':24C ''technolog'':4C,17C ''topic'':27C ''train'':25C ''ul'':7C ''wide'':30C ''wolfram'':3C,16C ''www.wolfram.com'':12C ''www.wolfram.com/events/technology-conference/2015/'':11C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (851, 1, '2014-07-27 21:54:05.003', 6, 138, 'Computing Image Similarity based on Color Distribution', '&lt;h2&gt;Image Similarity based on Color Palette Distribution&lt;/h2&gt;

&lt;p&gt;I am trying to compute similarity between two images based on their color palette distribution, let''s say I have two sets of key value pairs as follows,&lt;/p&gt;

&lt;p&gt;Img1: &lt;code&gt;{''Brown'': 14, ''White'': 13, ''Black'': 40, ''Gray'': 31}&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Img2: &lt;code&gt;{''Pink'': 82, ''Brown'': 8, ''White'': 7}&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;Where the numbers denote the % of that color present in the image. What would be the best way to compute similarity on a scale of 0-100 between the two images?&lt;/p&gt;
', 'data-mining  clustering  similarity', NULL, '''-100'':103C ''/code'':63C,74C ''/h2'':21C ''/p'':51C,64C,75C,108C ''0'':102C ''13'':58C ''14'':56C ''31'':62C ''40'':60C ''7'':73C ''8'':71C ''82'':69C ''base'':4A,16C,32C ''best'':93C ''black'':59C ''brown'':55C,70C ''cluster'':11B ''code'':54C,67C ''color'':6A,18C,35C,84C ''comput'':1A,27C,96C ''data'':9B ''data-min'':8B ''denot'':80C ''distribut'':7A,20C,37C ''follow'':50C ''gray'':61C ''h2'':13C ''imag'':2A,14C,31C,88C,107C ''img1'':53C ''img2'':66C ''key'':46C ''let'':38C ''mine'':10B ''number'':79C ''p'':22C,52C,65C,76C ''pair'':48C ''palett'':19C,36C ''pink'':68C ''present'':85C ''say'':40C ''scale'':100C ''set'':44C ''similar'':3A,12B,15C,28C,97C ''tri'':25C ''two'':30C,43C,106C ''valu'':47C ''way'':94C ''white'':57C,72C ''would'':90C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (872, 1, '2014-07-30 18:45:13.79', 5, 1120, 'R error using package tm (text-mining)', '&lt;p&gt;I am attempting to use the tm package to convert a vector of text strings to a corpus element.&lt;/p&gt;

&lt;p&gt;My code looks something like this&lt;/p&gt;

&lt;p&gt;Corpus(d1$Yes)&lt;/p&gt;

&lt;p&gt;where d1$Yes is a factor with 124 levels, each containing a text string.&lt;/p&gt;

&lt;p&gt;For example, d1$Yes[246] = "So we can get the boat out!"&lt;/p&gt;

&lt;p&gt;I''m receiving the following error:
"Error: inherits(x, "Source") is not TRUE"&lt;/p&gt;

&lt;p&gt;I''m not sure how to remedy this.&lt;/p&gt;
', 'r  text-mining', NULL, '''/p'':33C,41C,46C,62C,76C,91C,101C ''124'':55C ''246'':68C ''attempt'':16C ''boat'':74C ''code'':36C ''contain'':58C ''convert'':23C ''corpus'':31C,43C ''d1'':44C,49C,66C ''element'':32C ''error'':2A,83C,84C ''exampl'':65C ''factor'':53C ''follow'':82C ''get'':72C ''inherit'':85C ''level'':56C ''like'':39C ''look'':37C ''m'':79C,94C ''mine'':8A,12B ''p'':13C,34C,42C,47C,63C,77C,92C ''packag'':4A,21C ''r'':1A,9B ''receiv'':80C ''remedi'':99C ''someth'':38C ''sourc'':87C ''string'':28C,61C ''sure'':96C ''text'':7A,11B,27C,60C ''text-min'':6A,10B ''tm'':5A,20C ''true'':90C ''use'':3A,18C ''vector'':25C ''x'':86C ''yes'':45C,50C,67C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (853, 1, '2014-07-28 07:19:49.877', 5, 484, 'Unsupervised Feature Learning for NER', '&lt;p&gt;I have implemented NER system with the use of CRF algorithm with my handcrafted features that gave quite good results. The thing is that I used lots of different features including POS tags and lemmas.&lt;/p&gt;

&lt;p&gt;Now I want to make the same NER for different language. The problem here is that I can''t use POS tags and lemmas. I started reading articles about deep learning and unsupervised feature learning.&lt;/p&gt;

&lt;p&gt;My question is, if it''s possible to use methods for unsupervised feature learning with CRF algorithm. Did anyone try this and got any good result? Is there any article or tutorial about this matter.&lt;/p&gt;

&lt;p&gt;I still don''t completely understand this way of feature creation so I don''t want to spend to much time for something that won''t work. So any information would be really helpful. To create whole NER system based on deep learning is a bit to much for now.&lt;/p&gt;

&lt;p&gt;Thank you in advance.&lt;/p&gt;
', 'nlp  text-mining  feature-extraction', NULL, '''/p'':49C,86C,123C,175C,181C ''advanc'':180C ''algorithm'':24C,104C ''anyon'':106C ''articl'':78C,117C ''base'':164C ''bit'':170C ''complet'':129C ''creat'':160C ''creation'':135C ''crf'':23C,103C ''deep'':80C,166C ''differ'':42C,60C ''extract'':12B ''featur'':2A,11B,28C,43C,84C,100C,134C ''feature-extract'':10B ''gave'':30C ''good'':32C,112C ''got'':110C ''handcraft'':27C ''help'':158C ''implement'':16C ''includ'':44C ''inform'':154C ''languag'':61C ''learn'':3A,81C,85C,101C,167C ''lemma'':48C,74C ''lot'':40C ''make'':55C ''matter'':122C ''method'':97C ''mine'':9B ''much'':144C,172C ''ner'':5A,17C,58C,162C ''nlp'':6B ''p'':13C,50C,87C,124C,176C ''pos'':45C,71C ''possibl'':94C ''problem'':63C ''question'':89C ''quit'':31C ''read'':77C ''realli'':157C ''result'':33C,113C ''someth'':147C ''spend'':142C ''start'':76C ''still'':126C ''system'':18C,163C ''tag'':46C,72C ''text'':8B ''text-min'':7B ''thank'':177C ''thing'':35C ''time'':145C ''tri'':107C ''tutori'':119C ''understand'':130C ''unsupervis'':1A,83C,99C ''use'':21C,39C,70C,96C ''want'':53C,140C ''way'':132C ''whole'':161C ''won'':149C ''work'':151C ''would'':155C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (854, 2, '2014-07-28 08:36:53.633', 5, NULL, NULL, '&lt;p&gt;I like @Kallestad answer very much, but I would like to add a meta-step: Make sure that you understand how the data where collected, and what types of constraints there are. 
I think it is very common to think that there where no non-obvious steps when the data where collected, but this is not the case: Most of the time, some process or indivudal did somethink with the data, and these steps can and will influence the shape of the data.&lt;/p&gt;

&lt;p&gt;Two examples:
I had a study recently where the data where collected by various con
tractors worldwide. I was not at the briefing, so that was opaque to me. Unfortunately, the measurements where off for some parts of france: People all liked ice cram, but we expected a random distribution. There was no obvious reason for this uniformity, so I began to hunt the errors. When I queried the contractors, one had misunderstood the briefing and selected only ice-cream lovers from his database.&lt;/p&gt;

&lt;p&gt;The second error was more challenging: When doing some geographic analysis, I found that a lot of people had extremely large movement patterns, which suggested that a lot of them traveled from Munich to Hamburg in minutes. When I spoke with ppeople upstream, they found a subtle bug in their data aggregation software, which was unnoticed before.&lt;/p&gt;

&lt;p&gt;Conclusions:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Do not assume that your data was collected by perfect processes /humans.&lt;/li&gt;
&lt;li&gt;Do try to understand the limits of your data providers.&lt;/li&gt;
&lt;li&gt;Look at individual patterns / values and try to determine if they are logical (easy for movement / geographic data)&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/humans'':250C ''/li'':251C,263C,283C ''/p'':87C,174C,233C,236C ''/ul'':284C ''add'':13C ''aggreg'':227C ''analysi'':186C ''answer'':5C ''assum'':241C ''began'':149C ''brief'':111C,163C ''bug'':223C ''case'':61C ''challeng'':181C ''collect'':27C,55C,100C,246C ''common'':40C ''con'':103C ''conclus'':235C ''constraint'':32C ''contractor'':158C ''cram'':132C ''cream'':169C ''data'':25C,53C,74C,86C,98C,226C,244C,261C,282C ''databas'':173C ''determin'':273C ''distribut'':138C ''easi'':278C ''error'':153C,178C ''exampl'':90C ''expect'':135C ''extrem'':195C ''found'':188C,220C ''franc'':127C ''geograph'':185C,281C ''hamburg'':210C ''hunt'':151C ''ice'':131C,168C ''ice-cream'':167C ''individu'':267C ''indivud'':69C ''influenc'':81C ''kallestad'':4C ''larg'':196C ''li'':238C,252C,264C ''like'':3C,11C,130C ''limit'':258C ''logic'':277C ''look'':265C ''lot'':191C,203C ''lover'':170C ''make'':18C ''measur'':120C ''meta'':16C ''meta-step'':15C ''minut'':212C ''misunderstood'':161C ''movement'':197C,280C ''much'':7C ''munich'':208C ''non'':48C ''non-obvi'':47C ''obvious'':49C,142C ''one'':159C ''opaqu'':115C ''p'':1C,88C,175C,234C ''part'':125C ''pattern'':198C,268C ''peopl'':128C,193C ''perfect'':248C ''ppeopl'':217C ''process'':67C,249C ''provid'':262C ''queri'':156C ''random'':137C ''reason'':143C ''recent'':95C ''second'':177C ''select'':165C ''shape'':83C ''softwar'':228C ''somethink'':71C ''spoke'':215C ''step'':17C,50C,77C ''studi'':94C ''subtl'':222C ''suggest'':200C ''sure'':19C ''think'':36C,42C ''time'':65C ''tractor'':104C ''travel'':206C ''tri'':254C,271C ''two'':89C ''type'':30C ''ul'':237C ''understand'':22C,256C ''unfortun'':118C ''uniform'':146C ''unnot'':231C ''upstream'':218C ''valu'':269C ''various'':102C ''worldwid'':105C ''would'':10C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (855, 2, '2014-07-28 12:07:25.573', 4, NULL, NULL, '&lt;p&gt;Below you can find a copy of my answer to a related (however, focused on data cleaning aspect) question here on &lt;em&gt;Data Science StackExchange&lt;/em&gt; (&lt;a href="http://datascience.stackexchange.com/a/722/2452"&gt;http://datascience.stackexchange.com/a/722/2452&lt;/a&gt;), provided in its entirety for readers'' convenience. I believe that it partially answers your question as well and hope it is helpful. While the answer is focused on &lt;code&gt;R&lt;/code&gt; ecosystem, similar packages and/or libraries can be found for other &lt;em&gt;data analysis environments&lt;/em&gt;. Moreover, while the two &lt;strong&gt;cited papers&lt;/strong&gt; on data preparation also contain examples in R, these papers present &lt;strong&gt;general&lt;/strong&gt; &lt;strong&gt;workflow (framework)&lt;/strong&gt; and &lt;strong&gt;best practices&lt;/strong&gt; that are applicable to &lt;strong&gt;any&lt;/strong&gt; data analysis environment.&lt;/p&gt;

&lt;p&gt;R contains some &lt;em&gt;standard&lt;/em&gt; functions for data manipulation, which can be used for data cleaning, in its &lt;strong&gt;base&lt;/strong&gt; package (&lt;code&gt;gsub&lt;/code&gt;, &lt;code&gt;transform&lt;/code&gt;, etc.), as well as in various third-party packages, such as &lt;strong&gt;stringr&lt;/strong&gt;, &lt;strong&gt;reshape&lt;/strong&gt;, &lt;strong&gt;reshape2&lt;/strong&gt;, and &lt;strong&gt;plyr&lt;/strong&gt;. Examples and best practices of usage for these packages and their functions are described in the following paper: &lt;a href="http://vita.had.co.nz/papers/tidy-data.pdf" rel="nofollow"&gt;http://vita.had.co.nz/papers/tidy-data.pdf&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Additionally, R offers some packages specifically &lt;em&gt;focused&lt;/em&gt; on data cleaning and transformation:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;editrules&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/editrules/index.html" rel="nofollow"&gt;http://cran.r-project.org/web/packages/editrules/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;deducorrect&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/deducorrect/index.html" rel="nofollow"&gt;http://cran.r-project.org/web/packages/deducorrect/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;StatMatch&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/StatMatch/index.html" rel="nofollow"&gt;http://cran.r-project.org/web/packages/StatMatch/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;MatchIt&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/MatchIt/index.html" rel="nofollow"&gt;http://cran.r-project.org/web/packages/MatchIt/index.html&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;DataCombine&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/DataCombine" rel="nofollow"&gt;http://cran.r-project.org/web/packages/DataCombine&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;A comprehensive and coherent approach to &lt;strong&gt;data cleaning&lt;/strong&gt; in R, including examples and use of &lt;strong&gt;editrules&lt;/strong&gt; and &lt;strong&gt;deducorrect&lt;/strong&gt; packages, as well as a description of &lt;em&gt;workflow&lt;/em&gt; (&lt;em&gt;framework&lt;/em&gt;) of data cleaning in R, is presented in the following paper, which I highly recommend: &lt;a href="http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf" rel="nofollow"&gt;http://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3703, 2, '2014-12-14 03:42:25.213', 1, NULL, NULL, '&lt;p&gt;Instead of &lt;em&gt;exporting&lt;/em&gt; your models, consider creating an R-based &lt;strong&gt;interoperable environment&lt;/strong&gt; for your &lt;em&gt;modeling&lt;/em&gt; needs. Such environment would consists of &lt;em&gt;R environment&lt;/em&gt; proper as well as &lt;em&gt;integration layers&lt;/em&gt; for your third-party libraries. In particular, for the &lt;em&gt;OpenCV&lt;/em&gt; project, consider either using &lt;code&gt;r-opencv&lt;/code&gt; open source project (&lt;a href="https://code.google.com/p/r-opencv" rel="nofollow"&gt;https://code.google.com/p/r-opencv&lt;/a&gt;), or integration via &lt;em&gt;OpenCV&lt;/em&gt; C++ APIs and &lt;em&gt;R&lt;/em&gt; &lt;code&gt;Rcpp&lt;/code&gt; package (&lt;a href="http://dirk.eddelbuettel.com/code/rcpp.html" rel="nofollow"&gt;http://dirk.eddelbuettel.com/code/rcpp.html&lt;/a&gt;). Finally, if you want to add &lt;em&gt;PMML&lt;/em&gt; support to the mix and create a &lt;strong&gt;deployable-to-cloud solution&lt;/strong&gt;, take a look at the following excellent blog post with relevant examples: &lt;a href="http://things-about-r.tumblr.com/post/37861967022/predictive-modeling-using-r-and-the" rel="nofollow"&gt;http://things-about-r.tumblr.com/post/37861967022/predictive-modeling-using-r-and-the&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (858, 2, '2014-07-28 14:48:37.743', 3, NULL, NULL, '&lt;p&gt;Yes, it is entirely possible to combine unsupervised learning with the CRF model.  In particular, I would recommend that you explore the possibility of using &lt;a href="https://code.google.com/p/word2vec/" rel="nofollow"&gt;word2vec&lt;/a&gt; features as inputs to your CRF.&lt;/p&gt;

&lt;p&gt;Word2vec trains a  to distinguish between words that are appropriate for a given context and words that are randomly selected.  Select weights of the model can then be interpreted as a dense vector representation of a given word.  &lt;/p&gt;

&lt;p&gt;These dense vectors have the appealing property that words that are semantically or syntactically similar have similar vector representations.  Basic vector arithmetic even reveals some interesting learned relationships between words.&lt;br&gt;
For example, vector("Paris") - vector("France") + vector("Italy") yields a vector that is quite similar to vector("Rome").  &lt;/p&gt;

&lt;p&gt;At a high level, you can think of word2vec representations as being similar to LDA or LSA representations, in the sense that you can convert a sparse input vector into a dense output vector that contains word similarity information. &lt;/p&gt;

&lt;p&gt;For that matter, LDA and LSA are also valid options for unsupervised feature learning -- both attempt to represent words as combinations of "topics" and output dense word representations.  &lt;/p&gt;

&lt;p&gt;For English text Google distributes word2vec models pretrained on a huge 100 billion word Google News dataset, but for other languages you''ll have to train your own model.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (859, 1, '2014-07-28 16:17:49.823', 7, 505, 'Data science and MapReduce programming model of Hadoop', '&lt;p&gt;What are the different classes of data science problems that can be solved using mapreduce programming model?&lt;/p&gt;
', 'hadoop  map-reduce', NULL, '''/p'':31C ''class'':18C ''data'':1A,20C ''differ'':17C ''hadoop'':8A,9B ''map'':11B ''map-reduc'':10B ''mapreduc'':4A,28C ''model'':6A,30C ''p'':13C ''problem'':22C ''program'':5A,29C ''reduc'':12B ''scienc'':2A,21C ''solv'':26C ''use'':27C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (860, 2, '2014-07-28 16:23:39.6', 1, NULL, NULL, '&lt;p&gt;I''ll add one thing- if possible, do a reasonableness check by comparing you data against some other source. It seems that whenever I fail to do this, I get burnt:(&lt;/p&gt;
', NULL, NULL, '''/p'':33C ''add'':4C ''burnt'':32C ''check'':12C ''compar'':14C ''data'':16C ''fail'':26C ''get'':31C ''ll'':3C ''one'':5C ''p'':1C ''possibl'':8C ''reason'':11C ''seem'':22C ''sourc'':20C ''thing'':6C ''whenev'':24C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (873, 2, '2014-07-30 19:15:09.383', 5, NULL, NULL, '&lt;p&gt;You have to tell Corpus what kind of source you are using.  Try:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Corpus(VectorSource(d1$Yes))
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':22C ''/p'':15C ''/pre'':23C ''code'':17C ''corpus'':6C,18C ''d1'':20C ''kind'':8C ''p'':1C ''pre'':16C ''sourc'':10C ''tell'':5C ''tri'':14C ''use'':13C ''vectorsourc'':19C ''yes'':21C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (861, 2, '2014-07-28 16:39:45.703', 9, NULL, NULL, '&lt;p&gt;Data Science has many different sub-areas as described in &lt;a href="http://www.datasciencecentral.com/profiles/blogs/ingredients-of-data-science-1"&gt;my post&lt;/a&gt;). Nearly for each area, scientists and developer has significant contributions. To learn more about what can be done, please look at following websites:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Data Mining Algorithms &amp;amp; Machine Learning -&gt; &lt;a href="https://mahout.apache.org/"&gt;Apache Mahout&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Statistics -&gt; &lt;a href="https://github.com/RevolutionAnalytics/RHadoop/wiki"&gt;RHadoop&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Data Warehousing &amp;amp; Database Querying -&gt; &lt;a href="http://www.teradata.com.tr/Teradata-Aster-SQL-MapReduce/?LangType=1055&amp;amp;LangSelect=true"&gt;SQL-MapReduce&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Social Network Analysis -&gt; &lt;a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&amp;amp;arnumber=5636636"&gt;Article&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Bio-informatics -&gt; &lt;a href="http://abhishek-tiwari.com/post/mapreduce-and-hadoop-algorithms-in-bioinformatics-papers?ModPagespeed=noscript"&gt;Article - 1 &lt;/a&gt;, &lt;a href="http://www.biomedcentral.com/1471-2105/11/S12/S1"&gt;Article - 2&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Also, there are some work on MapReduce + Excel + Cloud combination but I have not found the link.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;What are the different classes of Data Science problems ...&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Each "classes" is not purely homogeneous problem domain, i.e. some problem cannot be solved via map and reduce approach due to its communication cost, or algorithm behavior. What I mean by behavior is that some problem wants to have control on all data sets instead of chunks. Thus, I refuse to list type of problem "classes".&lt;/p&gt;

&lt;p&gt;Do not forget that knowing what MapReduce can do is not enough for Data Science. You should also aware of &lt;a href="http://www.analyticbridge.com/profiles/blogs/what-mapreduce-can-t-do"&gt;What MapReduce can''t do&lt;/a&gt;, too.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (862, 2, '2014-07-28 17:33:43.28', 4, NULL, NULL, '&lt;p&gt;I think that there is no universal technique for "cleaning" data before doing actual research. On the other hand, I''m aiming for doing as much &lt;a href="https://en.wikipedia.org/wiki/Reproducibility#Reproducible_research" rel="nofollow"&gt;reproducible research&lt;/a&gt; as possible. By doing reproducible research, if you used cleaning techniques with bugs or with poor parameters/assumptions it could be spot by others.&lt;/p&gt;

&lt;p&gt;There is nice R package &lt;a href="http://yihui.name/knitr/" rel="nofollow"&gt;knitr&lt;/a&gt; which helps a lot in reproducible research.&lt;/p&gt;

&lt;p&gt;Of course, not all research could be fully reproduced (for example live Twitter data) , but at least you can document cleaning, formating and preprocessing steps easily.&lt;/p&gt;

&lt;p&gt;You can check my &lt;a href="http://rpubs.com/QuatnumDamage/storm" rel="nofollow"&gt;assessment&lt;/a&gt; prepared for &lt;a href="https://www.coursera.org/course/repdata" rel="nofollow"&gt;Reproducible Research course at Coursera&lt;/a&gt;. &lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (869, 1, '2014-07-30 16:27:45.177', 8, 1260, 'Neural Network parse string data?', '&lt;p&gt;So, I''m just starting to learn how a neural network can operate to recognize patterns and categorize inputs, and I''ve seen how an artificial neural network can parse image data and categorize the images (&lt;a href="http://cs.stanford.edu/people/karpathy/convnetjs/demo/mnist.html"&gt;demo with convnetjs&lt;/a&gt;), and the key there is to downsample the image and each pixel stimulates one input neuron into the network.&lt;/p&gt;

&lt;p&gt;However, I''m trying to wrap my head around if this is possible to be done with string inputs? The use-case I''ve got is a "recommendation engine" for movies a user has watched. Movies have lots of string data (title, plot, tags), and I could imagine "downsampling" the text down to a few key words that describe that movie, but even if I parse out the top five words that describe this movie, I think I''d need input neurons for every english word in order to compare a set of movies? I could limit the input neurons just to the words used in the set, but then could it grow/learn by adding new movies (user watches a new movie, with new words)? Most of the libraries I''ve seen don''t allow adding new neurons after the system has been trained?&lt;/p&gt;

&lt;p&gt;Is there a standard way to map string/word/character data to inputs into a neural network? Or is a neural network really not the right tool for the job of parsing string data like this (what''s a better tool for pattern-matching in string data)?&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (870, 2, '2014-07-30 17:42:09.797', 2, NULL, NULL, '&lt;p&gt;This is not a problem about neural networks per se, but about representing textual data in machine learning. You can represent the movies, cast, and theme as categorical variables. The plot is more complicated; you''d probably want a &lt;a href="http://en.wikipedia.org/wiki/Topic_model" rel="nofollow"&gt;topic model&lt;/a&gt; for that, but I''d leave that out until you get the hang of things. It does precisely that textual "downsampling" you mentioned.&lt;/p&gt;

&lt;p&gt;Take a look at &lt;a href="http://visualstudiomagazine.com/articles/2013/07/01/neural-network-data-normalization-and-encoding.aspx" rel="nofollow"&gt;this&lt;/a&gt; tutorial to learn how to encode categorical variables for neural networks. And good luck!&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (871, 2, '2014-07-30 17:53:40.33', 8, NULL, NULL, '&lt;p&gt;Using a neural network for prediction on natural language data can be a tricky task, but there are tried and true methods for making it possible.  &lt;/p&gt;

&lt;p&gt;In the Natural Language Processing (NLP) field, text is often represented using the bag of words model.  In other words, you have a vector of length n, where n is the number of words in your vocabulary, and each word corresponds to an element in the vector.  In order to convert text to numeric data, you simply count the number of occurrences of each word and place that value at the index of the vector that corresponds to the word. &lt;a href="http://en.wikipedia.org/wiki/Bag-of-words_model."&gt;Wikipedia does an excellent job of describing this conversion process.&lt;/a&gt;  Because the length of the vector is fixed, its difficult to deal with new words that don''t map to an index, but there are ways to help mitigate this problem (lookup &lt;a href="http://en.wikipedia.org/wiki/Feature_hashing"&gt;feature hashing&lt;/a&gt;).  &lt;/p&gt;

&lt;p&gt;This method of representation has many disadvantages -- it does not preserve the relationship between adjacent words, and results in very sparse vectors.  Looking at &lt;a href="http://en.wikipedia.org/wiki/N-gram"&gt;n-grams&lt;/a&gt; helps to fix the problem of preserving word relationships, but for now let''s focus on the second problem: sparsity.  &lt;/p&gt;

&lt;p&gt;It''s difficult to deal directly with these sparse vectors (many linear algebra libraries do a poor job of handling sparse inputs), so often the next step is dimensionality reduction. For that we can refer to the field of &lt;a href="http://en.wikipedia.org/wiki/Topic_model"&gt;topic modeling&lt;/a&gt;:  Techniques like &lt;a href="http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation"&gt;Latent Dirichlet Allocation&lt;/a&gt; (LDA) and &lt;a href="http://en.wikipedia.org/wiki/Latent_semantic_analysis"&gt;Latent Semantic Analysis&lt;/a&gt; (LSA) allow the compression of these sparse vectors into dense vectors by representing a document as a combination of topics.  You can fix the number of topics used, and in doing so fix the size of the output vector producted by LDA or LSA. This dimensionality reduction process drastically reduces the size of the input vector while attempting to lose a minimal amount of information.  &lt;/p&gt;

&lt;p&gt;Finally, after all of these conversions, you can feed the outputs of the topic modeling process into the inputs of your neural network.     &lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (876, 2, '2014-07-31 16:39:37.127', 3, NULL, NULL, '&lt;p&gt;"Identify the most influential precursor conditions (comorbidities) for a medical condition like chronic kidney disease"&lt;/p&gt;

&lt;p&gt;I''m not sure that it''s possible to ID &lt;em&gt;the&lt;/em&gt; most influential conditions; I think it will depend on what model you''re using. Just yesterday I fit a random forest and a boosted regression tree to the same data, and the order and relative importance each model gave for the variables were quite different.&lt;/p&gt;
', NULL, NULL, '''/em'':31C ''/p'':17C,77C ''boost'':55C ''chronic'':14C ''comorbid'':8C ''condit'':7C,12C,34C ''data'':61C ''depend'':39C ''differ'':76C ''diseas'':16C ''em'':29C ''fit'':49C ''forest'':52C ''gave'':70C ''id'':28C ''identifi'':2C ''import'':67C ''influenti'':5C,33C ''kidney'':15C ''like'':13C ''m'':20C ''medic'':11C ''model'':42C,69C ''order'':64C ''p'':1C,18C ''possibl'':26C ''precursor'':6C ''quit'':75C ''random'':51C ''re'':44C ''regress'':56C ''relat'':66C ''sure'':22C ''think'':36C ''tree'':57C ''use'':45C ''variabl'':73C ''yesterday'':47C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (878, 2, '2014-07-31 23:03:33.767', 7, NULL, NULL, '&lt;p&gt;A couple of days ago developers from one product company asked me how they can understand why new users were leaving their website. My first question to them was what these users'' profiles looked like and how they were different from those who stayed. &lt;/p&gt;

&lt;p&gt;Advertising is only top of an iceberg. User profiles (either filled by users themselves or computed from users'' behaviour) hold information about: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;user categories&lt;/strong&gt;, i.e. what kind of people tend to use your website/product&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;paying client portraits&lt;/strong&gt;, i.e. who is more likely to use your paid services&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;UX component performance&lt;/strong&gt;, e.g. how long it takes people to find the button they need&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;action performance comparison&lt;/strong&gt;, e.g. what was more efficient - lower price for a weekend or propose gifts with each buy, etc. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;So it''s more about improving product and making better user experience rather than selling this data to advertisers. &lt;/p&gt;
', NULL, NULL, '''/li'':87C,104C,123C,147C ''/p'':46C,70C,169C ''/strong'':76C,93C,110C,129C ''/ul'':148C ''action'':126C ''advertis'':48C,168C ''ago'':6C ''ask'':12C ''behaviour'':66C ''better'':159C ''button'':120C ''buy'':145C ''categori'':75C ''client'':91C ''compani'':11C ''comparison'':128C ''compon'':108C ''comput'':63C ''coupl'':3C ''data'':166C ''day'':5C ''develop'':7C ''differ'':41C ''e.g'':111C,130C ''effici'':134C ''either'':57C ''etc'':146C ''experi'':161C ''fill'':58C ''find'':118C ''first'':26C ''gift'':142C ''hold'':67C ''i.e'':77C,94C ''iceberg'':54C ''improv'':155C ''inform'':68C ''kind'':79C ''leav'':22C ''li'':72C,88C,105C,124C ''like'':36C,98C ''long'':113C ''look'':35C ''lower'':135C ''make'':158C ''need'':122C ''new'':19C ''one'':9C ''p'':1C,47C,149C ''paid'':102C ''pay'':90C ''peopl'':81C,116C ''perform'':109C,127C ''portrait'':92C ''price'':136C ''product'':10C,156C ''profil'':34C,56C ''propos'':141C ''question'':27C ''rather'':162C ''sell'':164C ''servic'':103C ''stay'':45C ''strong'':73C,89C,106C,125C ''take'':115C ''tend'':82C ''top'':51C ''ul'':71C ''understand'':17C ''use'':84C,100C ''user'':20C,33C,55C,60C,65C,74C,160C ''ux'':107C ''websit'':24C ''website/product'':86C ''weekend'':139C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8948, 2, '2015-11-20 21:41:21.573', 1, NULL, NULL, '&lt;p&gt;Mean normalization is a form of feature scaling, so these are not really two different approaches. Feature scaling is just a more general term. What kind of feature scaling, e.g. mean normalization,  you need to use ultimately depends on the data.&lt;/p&gt;
', NULL, NULL, '''/p'':43C ''approach'':17C ''data'':42C ''depend'':39C ''differ'':16C ''e.g'':31C ''featur'':8C,18C,29C ''form'':6C ''general'':24C ''kind'':27C ''mean'':2C,32C ''need'':35C ''normal'':3C,33C ''p'':1C ''realli'':14C ''scale'':9C,19C,30C ''term'':25C ''two'':15C ''ultim'':38C ''use'':37C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (879, 2, '2014-08-01 06:03:47.7', 6, NULL, NULL, '&lt;p&gt;Most companies won''t sell the data, not on any small scale anyways.  Most will use it internally.&lt;/p&gt;

&lt;p&gt;User tracking data is important for understanding a lot of things.  There''s basic A/B testing where you provide different experiences to see which is more effective.  There is understanding how your UI is utilized.  Categorizing your end users in different ways for a variety of reasons.  Figuring out where your end user base is, and within that group where the end users that matter are.  Correlating user experiences with social network updates.  Figuring out what will draw people to your product and what drives them away.  The list of potential for data mining and analysis projects could go on for days.  &lt;/p&gt;

&lt;p&gt;Data storage is cheap.  If you track everything out of the gate, you can figure out what you want to do with that data later.  &lt;/p&gt;

&lt;p&gt;Scanning text messages is sketchy territory when there isn''t a good reason for it.  Even when there is a good reason it''s sketchy territory.  I''d love to say that nobody does it, but there have been instances where big companies have done it and there are a lot of cases where no-name apps at least require access to that kind of data for installation.  I generally frown on that kind of thing myself as a consumer, but the data analyst in me would love to see if I could build anything useful from a set of information like that.&lt;/p&gt;
', NULL, NULL, '''/p'':20C,124C,151C,257C ''a/b'':36C ''access'':214C ''analysi'':117C ''analyst'':237C ''anyth'':248C ''anyway'':14C ''app'':210C ''away'':108C ''base'':75C ''basic'':35C ''big'':194C ''build'':247C ''case'':205C ''categor'':57C ''cheap'':129C ''compani'':3C,195C ''consum'':233C ''correl'':88C ''could'':119C,246C ''d'':180C ''data'':8C,24C,114C,126C,149C,219C,236C ''day'':123C ''differ'':41C,62C ''done'':197C ''draw'':99C ''drive'':106C ''effect'':48C ''end'':59C,73C,83C ''even'':168C ''everyth'':133C ''experi'':42C,90C ''figur'':69C,95C,140C ''frown'':224C ''gate'':137C ''general'':223C ''go'':120C ''good'':164C,173C ''group'':80C ''import'':26C ''inform'':254C ''instal'':221C ''instanc'':192C ''intern'':19C ''isn'':161C ''kind'':217C,227C ''later'':150C ''least'':212C ''like'':255C ''list'':110C ''lot'':30C,203C ''love'':181C,241C ''matter'':86C ''messag'':155C ''mine'':115C ''name'':209C ''network'':93C ''no-nam'':207C ''nobodi'':185C ''p'':1C,21C,125C,152C ''peopl'':100C ''potenti'':112C ''product'':103C ''project'':118C ''provid'':40C ''reason'':68C,165C,174C ''requir'':213C ''say'':183C ''scale'':13C ''scan'':153C ''see'':44C,243C ''sell'':6C ''set'':252C ''sketchi'':157C,177C ''small'':12C ''social'':92C ''storag'':127C ''territori'':158C,178C ''test'':37C ''text'':154C ''thing'':32C,229C ''track'':23C,132C ''ui'':54C ''understand'':28C,51C ''updat'':94C ''use'':17C,249C ''user'':22C,60C,74C,84C,89C ''util'':56C ''varieti'':66C ''want'':144C ''way'':63C ''within'':78C ''won'':4C ''would'':240C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (880, 2, '2014-08-01 12:27:41.58', 3, NULL, NULL, '&lt;p&gt;There always is the solution to try both approaches and keep the one that maximizes the expected performances. &lt;/p&gt;

&lt;p&gt;In your case, I would assume you prefer minimizing false negatives at the cost of some false positive, so you want to bias your classifier against the strong negative prior, and address the imbalance by reducing the number of negative examples in your training set.&lt;/p&gt;

&lt;p&gt;Then compute the precision/recall, or sensitivity/specificity, or whatever criterion suits you on the full, imbalanced, dataset to make sure you haven''t ignored a significant pattern present in the real data while building the model on the reduced data.&lt;/p&gt;
', NULL, NULL, '''/p'':20C,67C,108C ''address'':53C ''alway'':3C ''approach'':10C ''assum'':27C ''bias'':44C ''build'':101C ''case'':24C ''classifi'':46C ''comput'':70C ''cost'':35C ''criterion'':77C ''data'':99C,107C ''dataset'':84C ''exampl'':62C ''expect'':18C ''fals'':31C,38C ''full'':82C ''haven'':89C ''ignor'':91C ''imbal'':55C ''imbalanc'':83C ''keep'':12C ''make'':86C ''maxim'':16C ''minim'':30C ''model'':103C ''negat'':32C,50C,61C ''number'':59C ''one'':14C ''p'':1C,21C,68C ''pattern'':94C ''perform'':19C ''posit'':39C ''precision/recall'':72C ''prefer'':29C ''present'':95C ''prior'':51C ''real'':98C ''reduc'':57C,106C ''sensitivity/specificity'':74C ''set'':66C ''signific'':93C ''solut'':6C ''strong'':49C ''suit'':78C ''sure'':87C ''train'':65C ''tri'':8C ''want'':42C ''whatev'':76C ''would'':26C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (881, 2, '2014-08-01 14:08:13.043', 6, NULL, NULL, '&lt;p&gt;I''ve never worked with medical data, but from general reasoning I''d say that relations between variables in healthcare are pretty complicated. Different models, such as random forests, regression, etc. could capture only part of relations and ignore others. In such circumstances it makes sense to use general &lt;strong&gt;statistical exploration&lt;/strong&gt; and &lt;strong&gt;modelling&lt;/strong&gt;. &lt;/p&gt;

&lt;p&gt;For example, the very first thing I would do is finding out &lt;strong&gt;correlations&lt;/strong&gt; between possible precursor conditions and diagnoses. E.g. in what percent of cases chronic kidney disease was preceded by long flu? If it is high, it &lt;a href="http://en.wikipedia.org/wiki/Correlation_does_not_imply_causation"&gt;doesn''t always mean causality&lt;/a&gt;, but gives pretty good food for thought and helps to better understand relations between different conditions. &lt;/p&gt;

&lt;p&gt;Another important step is data visualisation. Does CKD happens in males more often than in females? What about their place of residence? What is distribution of CKD cases by age? It''s hard to grasp large dataset as a set of numbers, plotting them out makes it much easier. &lt;/p&gt;

&lt;p&gt;When you have an idea of what''s going on, perform &lt;a href="http://en.wikipedia.org/wiki/Statistical_hypothesis_testing"&gt;&lt;strong&gt;hypothesis testing&lt;/strong&gt;&lt;/a&gt; to check your assumption. If you reject null hypothesis (basic assumption) in favour of alternative one, congratulations, you''ve made "something real". &lt;/p&gt;

&lt;p&gt;Finally, when you have a good understanding of your data, try to create complete &lt;strong&gt;model&lt;/strong&gt;. It may be something general like &lt;a href="https://www.coursera.org/course/pgm"&gt;PGM&lt;/a&gt; (e.g. manually-crafted Bayesian network), or something more specific like linear regression or &lt;a href="http://en.wikipedia.org/wiki/Support_vector_machine"&gt;SVM&lt;/a&gt;, or anything. But in any way you will already know how this model corresponds to your data and how you can measure its efficiency. &lt;/p&gt;

&lt;hr&gt;

&lt;p&gt;As a good starting resource for learning statistical approach I would recommend &lt;a href="https://www.udacity.com/course/st101"&gt;Intro to Statistics&lt;/a&gt; course by Sebastian Thrun. While it''s pretty basic and doesn''t include advanced topics, it describes most important concepts and gives systematic understanding of probability theory and statistics. &lt;/p&gt;
', NULL, NULL, '''/a'':111C,201C,255C,276C,324C ''/course/pgm'':253C ''/course/st101'':320C ''/p'':59C,128C,179C,224C,301C,354C ''/strong'':54C,58C,75C,200C,242C ''/wiki/correlation_does_not_imply_causation'':105C ''/wiki/statistical_hypothesis_testing'':196C ''/wiki/support_vector_machine'':274C ''advanc'':338C ''age'':159C ''alreadi'':285C ''altern'':216C ''alway'':108C ''anoth'':130C ''anyth'':278C ''approach'':312C ''assumpt'':205C,212C ''basic'':211C,333C ''bayesian'':260C ''better'':122C ''captur'':34C ''case'':87C,157C ''causal'':110C ''check'':203C ''chronic'':88C ''circumst'':44C ''ckd'':137C,156C ''complet'':239C ''complic'':24C ''concept'':344C ''condit'':79C,127C ''congratul'':218C ''correl'':74C ''correspond'':290C ''could'':33C ''cours'':325C ''craft'':259C ''creat'':238C ''d'':14C ''data'':8C,134C,235C,293C ''dataset'':166C ''describ'':341C ''diagnos'':81C ''differ'':25C,126C ''diseas'':90C ''distribut'':154C ''doesn'':106C,335C ''e.g'':82C,256C ''easier'':178C ''effici'':300C ''en.wikipedia.org'':104C,195C,273C ''en.wikipedia.org/wiki/correlation_does_not_imply_causation'':103C ''en.wikipedia.org/wiki/statistical_hypothesis_testing'':194C ''en.wikipedia.org/wiki/support_vector_machine'':272C ''etc'':32C ''exampl'':62C ''explor'':53C ''favour'':214C ''femal'':145C ''final'':226C ''find'':71C ''first'':65C ''flu'':95C ''food'':116C ''forest'':30C ''general'':11C,50C,247C ''give'':113C,346C ''go'':189C ''good'':115C,231C,306C ''grasp'':164C ''happen'':138C ''hard'':162C ''healthcar'':21C ''help'':120C ''high'':99C ''hr'':302C ''href'':102C,193C,250C,271C,317C ''hypothesi'':198C,210C ''idea'':185C ''ignor'':40C ''import'':131C,343C ''includ'':337C ''intro'':321C ''kidney'':89C ''know'':286C ''larg'':165C ''learn'':310C ''like'':248C,266C ''linear'':267C ''long'':94C ''made'':221C ''make'':46C,175C ''male'':140C ''manual'':258C ''manually-craft'':257C ''may'':244C ''mean'':109C ''measur'':298C ''medic'':7C ''model'':26C,57C,241C,289C ''much'':177C ''network'':261C ''never'':4C ''null'':209C ''number'':171C ''often'':142C ''one'':217C ''other'':41C ''p'':1C,60C,129C,180C,225C,303C ''part'':36C ''percent'':85C ''perform'':191C ''pgm'':254C ''place'':149C ''plot'':172C ''possibl'':77C ''preced'':92C ''precursor'':78C ''pretti'':23C,114C,332C ''probabl'':350C ''random'':29C ''real'':223C ''reason'':12C ''recommend'':315C ''regress'':31C,268C ''reject'':208C ''relat'':17C,38C,124C ''resid'':151C ''resourc'':308C ''say'':15C ''sebastian'':327C ''sens'':47C ''set'':169C ''someth'':222C,246C,263C ''specif'':265C ''start'':307C ''statist'':52C,311C,323C,353C ''step'':132C ''strong'':51C,56C,73C,197C,240C ''svm'':275C ''systemat'':347C ''test'':199C ''theori'':351C ''thing'':66C ''thought'':118C ''thrun'':328C ''topic'':339C ''tri'':236C ''understand'':123C,232C,348C ''use'':49C ''variabl'':19C ''ve'':3C,220C ''visualis'':135C ''way'':282C ''work'':5C ''would'':68C,314C ''www.coursera.org'':252C ''www.coursera.org/course/pgm'':251C ''www.udacity.com'':319C ''www.udacity.com/course/st101'':318C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (882, 2, '2014-08-01 14:10:38.267', 3, NULL, NULL, '&lt;p&gt;Here''s a practical example of using web data for something other than advertising. Distil Networks (disclaimer, I work there) uses network traffic to determine whether page accesses are from humans or bots - scrapers, click fraud, form spam, etc.&lt;/p&gt;

&lt;p&gt;Another example is some of the work that Webtrends is doing. They allow site users to build a model for each visitor to predict whether they''ll leave, buy, add to cart, etc. Then based on the probability of each action you can change the users experience (e.g. if they''re about to leave, give them a coupon). Here''s the slides from a talk by them: &lt;a href="http://www.oscon.com/oscon2014/public/schedule/detail/34809" rel="nofollow"&gt;http://www.oscon.com/oscon2014/public/schedule/detail/34809&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/oscon2014/public/schedule/detail/34809'':114C ''/oscon2014/public/schedule/detail/34809&lt;/a&gt;&lt;/p&gt;'':119C ''/p'':41C ''access'':29C ''action'':83C ''add'':72C ''advertis'':15C ''allow'':55C ''anoth'':43C ''base'':77C ''bot'':34C ''build'':59C ''buy'':71C ''cart'':74C ''chang'':86C ''click'':36C ''coupon'':100C ''data'':10C ''determin'':26C ''disclaim'':18C ''distil'':16C ''e.g'':90C ''etc'':40C,75C ''exampl'':6C,44C ''experi'':89C ''form'':38C ''fraud'':37C ''give'':97C ''href'':111C ''human'':32C ''leav'':70C,96C ''ll'':69C ''model'':61C ''network'':17C,23C ''nofollow'':116C ''p'':1C,42C ''page'':28C ''practic'':5C ''predict'':66C ''probabl'':80C ''re'':93C ''rel'':115C ''scraper'':35C ''site'':56C ''slide'':104C ''someth'':12C ''spam'':39C ''talk'':107C ''traffic'':24C ''use'':8C,22C ''user'':57C,88C ''visitor'':64C ''web'':9C ''webtrend'':51C ''whether'':27C,67C ''work'':20C,49C ''www.oscon.com'':113C,118C ''www.oscon.com/oscon2014/public/schedule/detail/34809'':112C ''www.oscon.com/oscon2014/public/schedule/detail/34809&lt;/a&gt;&lt;/p&gt;'':117C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (883, 1, '2014-08-01 18:13:06.063', 3, 452, 'R aggregate() with dates', '&lt;p&gt;I am working on a data set that has multiple traffic speed measurements per day. My data is from the city of chicago, and it is taken every minute for about six months. I wanted to consolidate this data into days only, so this is what I did:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;traffic &amp;lt;- read.csv("path.csv",header=TRUE)
traffic2 &amp;lt;- aggregate(SPEED~DATE, data=traffic, FUN=MEAN)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;this was perfect because it took all of my data and averaged it by date. For example, my original data looked something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;DATE        SPEED  
12/31/2012   22
12/31/2012   25
12/31/2012   23
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;and the final looked like this: &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;DATE        SPEED 
10/1/2012    22
10/2/2012    23
10/3/2012    22
...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The only problem, is my data is supposed to start at 9/1/2012. I plotted this data, and it turns out the data goes from 10/1/2012-12/31/2012 and then 9/1/2012-9/30/2012.&lt;/p&gt;

&lt;p&gt;What in the world is going on here?&lt;/p&gt;
', 'r  dataset  beginner', NULL, '''/code'':75C,113C,133C ''/p'':57C,102C,122C,164C,174C ''/pre'':76C,114C,134C ''10/1/2012'':127C ''10/1/2012-12/31/2012'':160C ''10/2/2012'':129C ''10/3/2012'':131C ''12/31/2012'':107C,109C,111C ''22'':108C,128C,132C ''23'':112C,130C ''25'':110C ''9/1/2012'':147C ''9/1/2012-9/30/2012'':163C ''aggreg'':2A,68C ''averag'':89C ''beginn'':7B ''chicago'':31C ''citi'':29C ''code'':59C,104C,124C ''consolid'':45C ''data'':14C,25C,47C,71C,87C,97C,141C,151C,157C ''dataset'':6B ''date'':4A,92C,105C,125C ''day'':23C,49C ''everi'':36C ''exampl'':94C ''final'':118C ''fun'':73C ''go'':171C ''goe'':158C ''header'':64C ''like'':100C,120C ''look'':98C,119C ''lt'':61C,67C ''mean'':74C ''measur'':21C ''minut'':37C ''month'':41C ''multipl'':18C ''origin'':96C ''p'':8C,77C,115C,135C,165C ''path.csv'':63C ''per'':22C ''perfect'':80C ''plot'':149C ''pre'':58C,103C,123C ''problem'':138C ''r'':1A,5B ''read.csv'':62C ''set'':15C ''six'':40C ''someth'':99C ''speed'':20C,69C,106C,126C ''start'':145C ''suppos'':143C ''taken'':35C ''took'':83C ''traffic'':19C,60C,72C ''traffic2'':66C ''true'':65C ''turn'':154C ''want'':43C ''work'':11C ''world'':169C ''~date'':70C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (884, 2, '2014-08-01 18:16:57.543', 1, NULL, NULL, '&lt;p&gt;So I was never able to find the error by looking through my logs. I ended up reinstalling it with CDH5 (which was MUCH easier than installing "poor" Hadoop)
Now everything runs fine! &lt;/p&gt;

&lt;p&gt;I''m still having trouble getting things to save to the hdfs, but thats a question for another day... &lt;/p&gt;
', NULL, NULL, '''/p'':35C,56C ''abl'':6C ''anoth'':54C ''cdh5'':22C ''day'':55C ''easier'':26C ''end'':17C ''error'':10C ''everyth'':32C ''find'':8C ''fine'':34C ''get'':42C ''hadoop'':30C ''hdfs'':48C ''instal'':28C ''log'':15C ''look'':12C ''m'':38C ''much'':25C ''never'':5C ''p'':1C,36C ''poor'':29C ''question'':52C ''reinstal'':19C ''run'':33C ''save'':45C ''still'':39C ''that'':50C ''thing'':43C ''troubl'':41C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (896, 1, '2014-08-03 16:38:38.853', 1, 393, 'How to implement Brown Clustering Algorithm in O(|V|k^2)', '&lt;p&gt;I am trying to implement the Brown Clustering Algorithm &lt;a href="http://delivery.acm.org/10.1145/180000/176316/p467-brown.pdf?ip=197.78.151.216&amp;amp;id=176316&amp;amp;acc=OPEN&amp;amp;key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&amp;amp;CFID=522814165&amp;amp;CFTOKEN=99738065&amp;amp;__acm__=1407081874_5a9649adaf4a9d43b8faa5c1a2da47f8" rel="nofollow"&gt;(link to paper)&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The algorithm is supposed to in O(|V|k^2) where |V| is the size of the vocabulary and k is the number of clusters. I am unable to implement it this efficiently. In fact, the best I can manage is O(|V|k^3) which is too slow. My current implementation for the main part of the algorithm is as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for w = number of clusters + 1 to |V|
{
   word = next most frequent word in the corpus

   assign word to a new cluster 

   initialize MaxQuality to 0

   initialize ArgMax vector to (0,0)

   for i = 0 to number of clusters - 1 
   {
      for j = i to number of clusters
      {
         Quality = Mutual Information if we merge cluster i and cluster j

         if Quality &amp;gt; MaxQuality
         {
            MaxQuality = Quality 

            ArgMax = (i,j) 
         }
      }
   }
} 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I compute quality as follows:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1. Before entering the second loop compute the pre-merge quality i.e. quality before doing any merges.
2. Every time a cluster-pair merge step is considered:
    i. assign quality := pre-merge quality
   ii. quality = quality - any terms in the mutual information equation that contain cluster i or cluster j (pre-merge)
  iii. quality = quality + any terms in the mutual information equation that contain (cluster i U cluster j)  (post-merge)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In my implementation, the first loop has approx |V| iterations, the second and third loop approx k iterations each. To compute quality at each step requires approx a further k iterations. In total it runs in (|V|k^3) time.&lt;/p&gt;

&lt;p&gt;How do you get it to run in (|V|k^2)?&lt;/p&gt;
', 'nlp  efficiency  clustering', NULL, '''/10.1145/180000/176316/p467-brown.pdf?ip=197.78.151.216&amp;amp;id=176316&amp;amp;acc=open&amp;amp;key=4d4702b0c3e38b35%2e4d4702b0c3e38b35%2e4d4702b0c3e38b35%2e6d218144511f3437&amp;amp;cfid=522814165&amp;amp;cftoken=99738065&amp;amp;__acm__=1407081874_5a9649adaf4a9d43b8faa5c1a2da47f8'':29C ''/a'':35C ''/code'':170C,257C ''/p'':36C,100C,178C,300C,313C ''/pre'':171C,258C ''0'':128C,133C,134C,137C ''1'':108C,142C,181C ''2'':11A,47C,199C,312C ''3'':82C,298C ''algorithm'':6A,24C,39C,96C ''approx'':267C,275C,286C ''argmax'':130C,167C ''assign'':119C,211C ''best'':74C ''brown'':4A,22C ''cluster'':5A,14B,23C,62C,107C,124C,141C,149C,156C,159C,204C,229C,232C,249C,252C ''cluster-pair'':203C ''code'':102C,180C ''comput'':174C,187C,280C ''consid'':209C ''contain'':228C,248C ''corpus'':118C ''current'':88C ''delivery.acm.org'':28C ''delivery.acm.org/10.1145/180000/176316/p467-brown.pdf?ip=197.78.151.216&amp;amp;id=176316&amp;amp;acc=open&amp;amp;key=4d4702b0c3e38b35%2e4d4702b0c3e38b35%2e4d4702b0c3e38b35%2e6d218144511f3437&amp;amp;cfid=522814165&amp;amp;cftoken=99738065&amp;amp;__acm__=1407081874_5a9649adaf4a9d43b8faa5c1a2da47f8'':27C ''effici'':13B,70C ''enter'':183C ''equat'':226C,246C ''everi'':200C ''fact'':72C ''first'':264C ''follow'':99C,177C ''frequent'':114C ''get'':305C ''gt'':163C ''href'':26C ''i.e'':193C ''ii'':217C ''iii'':237C ''implement'':3A,20C,67C,89C,262C ''inform'':152C,225C,245C ''initi'':125C,129C ''iter'':269C,277C,290C ''j'':144C,160C,169C,233C,253C ''k'':10A,46C,57C,81C,276C,289C,297C,311C ''link'':32C ''loop'':186C,265C,274C ''main'':92C ''manag'':77C ''maxqual'':126C,164C,165C ''merg'':155C,191C,198C,206C,215C,236C,256C ''mutual'':151C,224C,244C ''new'':123C ''next'':112C ''nlp'':12B ''nofollow'':31C ''number'':60C,105C,139C,147C ''o'':8A,44C,79C ''p'':15C,37C,172C,259C,301C ''pair'':205C ''paper'':34C ''part'':93C ''post'':255C ''post-merg'':254C ''pre'':101C,179C,190C,214C,235C ''pre-merg'':189C,213C,234C ''qualiti'':150C,162C,166C,175C,192C,194C,212C,216C,218C,219C,238C,239C,281C ''rel'':30C ''requir'':285C ''run'':294C,308C ''second'':185C,271C ''size'':52C ''slow'':86C ''step'':207C,284C ''suppos'':41C ''term'':221C,241C ''third'':273C ''time'':201C,299C ''total'':292C ''tri'':18C ''u'':251C ''unabl'':65C ''v'':9A,45C,49C,80C,110C,268C,296C,310C ''vector'':131C ''vocabulari'':55C ''w'':104C ''word'':111C,115C,120C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (887, 1, '2014-08-02 00:07:09.267', 1, 38, 'Modelling on one Population and Evaluating on another Population', '&lt;p&gt;I am currently on a project that will build a model (train and test) on Client-side Web data, but evaluate this model on Sever-side Web data.  Unfortunately building the model on Server-side data is not an option, nor is it an option to evaluate this model on Client-side data.&lt;/p&gt;

&lt;p&gt;This model will be based on metrics collected on specific visitors.  This is a real time system that will be calculating a likelihood based on metrics collected while visitors browse the website.&lt;/p&gt;

&lt;p&gt;I am looking for approaches to ensure the highest possible accuracy on the model evaluation.&lt;/p&gt;

&lt;p&gt;So far I have the following ideas,&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Clean the Server-side data by removing webpages that are never seen Client-side.&lt;/li&gt;
&lt;li&gt;Collect additional data Server-side data to make the Server-side data more closely resemble Client-side data.&lt;/li&gt;
&lt;li&gt;Collect data on the Client and send this data to the Server.  This is possible and may be the best solution, but is currently undesirable. &lt;/li&gt;
&lt;li&gt;Build one or more models that estimate Client-side Visitor metrics from Server-side Visitor metrics and use these estimates in the Likelihood model.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Any other thoughts on evaluating over one Population while training (and testing) on another Population?&lt;/p&gt;
', 'data-cleaning  evaluation', NULL, '''/li'':151C,174C,201C,229C ''/ol'':230C ''/p'':72C,106C,123C,132C,247C ''accuraci'':118C ''addit'':154C ''anoth'':8A,245C ''approach'':112C ''base'':78C,97C ''best'':195C ''brows'':103C ''build'':23C,46C,203C ''calcul'':94C ''clean'':12B,135C ''client'':31C,69C,149C,171C,180C,211C ''client-sid'':30C,68C,148C,170C,210C ''close'':168C ''collect'':81C,100C,153C,176C ''current'':17C,199C ''data'':11B,34C,44C,53C,71C,140C,155C,159C,166C,173C,177C,184C ''data-clean'':10B ''ensur'':114C ''estim'':209C,224C ''evalu'':6A,13B,36C,64C,122C,236C ''far'':126C ''follow'':130C ''highest'':116C ''idea'':131C ''li'':134C,152C,175C,202C ''likelihood'':96C,227C ''look'':110C ''make'':161C ''may'':192C ''metric'':80C,99C,214C,220C ''model'':1A,25C,38C,48C,66C,75C,121C,207C,228C ''never'':146C ''ol'':133C ''one'':3A,204C,238C ''option'':57C,62C ''p'':14C,73C,107C,124C,231C ''popul'':4A,9A,239C,246C ''possibl'':117C,190C ''project'':20C ''real'':88C ''remov'':142C ''resembl'':169C ''seen'':147C ''send'':182C ''server'':51C,138C,157C,164C,187C,217C ''server-sid'':50C,137C,156C,163C,216C ''sever'':41C ''sever-sid'':40C ''side'':32C,42C,52C,70C,139C,150C,158C,165C,172C,212C,218C ''solut'':196C ''specif'':83C ''system'':90C ''test'':28C,243C ''thought'':234C ''time'':89C ''train'':26C,241C ''undesir'':200C ''unfortun'':45C ''use'':222C ''visitor'':84C,102C,213C,219C ''web'':33C,43C ''webpag'':143C ''websit'':105C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (922, 1, '2014-08-06 09:08:08.113', 2, 40, 'Can I classify set of documents using classifying method using limited number of concepts ?', '&lt;p&gt;I have  set of documents and I want classify them to true and false &lt;/p&gt;

&lt;p&gt;My question is I have to take the whole words in the documents then I classify them depend on the similarity words in these documents or I can take only some words that I interested in then I compare it with the documents. Which one is more efficient in classify document and can work with SVM.       &lt;/p&gt;
', 'machine-learning  classification  text-mining', NULL, '''/p'':37C,95C ''classif'':18B ''classifi'':3A,8A,31C,54C,88C ''compar'':77C ''concept'':14A ''depend'':56C ''document'':6A,27C,51C,63C,81C,89C ''effici'':86C ''fals'':36C ''interest'':73C ''learn'':17B ''limit'':11A ''machin'':16B ''machine-learn'':15B ''method'':9A ''mine'':21B ''number'':12A ''one'':83C ''p'':22C,38C ''question'':40C ''set'':4A,25C ''similar'':59C ''svm'':94C ''take'':45C,67C ''text'':20B ''text-min'':19B ''true'':34C ''use'':7A,10A ''want'':30C ''whole'':47C ''word'':48C,60C,70C ''work'':92C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (888, 2, '2014-08-02 04:21:49.927', 0, NULL, NULL, '&lt;p&gt;I''m not an expert on this, so take my advice with a grain of salt. It''s not clear for me what is the relationship between server-side and client-side data. Are they both &lt;strong&gt;representative&lt;/strong&gt; of the same population? If Yes, I think it''s OK to use different data sets for testing/training and evaluating your models. If No, I think it might be a good idea to use some &lt;strong&gt;resampling&lt;/strong&gt; technique, such as &lt;em&gt;bootstrapping&lt;/em&gt;, &lt;em&gt;jackknifing&lt;/em&gt; or &lt;em&gt;cross-validation&lt;/em&gt;.&lt;/p&gt;
', NULL, NULL, '''/em'':86C,89C,95C ''/p'':96C ''/strong'':42C,80C ''advic'':12C ''bootstrap'':85C ''clear'':21C ''client'':34C ''client-sid'':33C ''cross'':93C ''cross-valid'':92C ''data'':36C,57C ''differ'':56C ''em'':84C,87C,91C ''evalu'':62C ''expert'':6C ''good'':73C ''grain'':15C ''idea'':74C ''jackknif'':88C ''m'':3C ''might'':70C ''model'':64C ''ok'':53C ''p'':1C ''popul'':46C ''relationship'':27C ''repres'':41C ''resampl'':79C ''salt'':17C ''server'':30C ''server-sid'':29C ''set'':58C ''side'':31C,35C ''strong'':40C,78C ''take'':10C ''techniqu'':81C ''testing/training'':60C ''think'':50C,68C ''use'':55C,76C ''valid'':94C ''yes'':48C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (889, 2, '2014-08-02 04:54:14.757', 2, NULL, NULL, '&lt;p&gt;If the users who you are getting client-side data from are from the same population of users who you would get server-side data from. If that is true, then you aren''t really training on one population and applying to another. The main difference is that the client side data happened in the past (by necessity unless you are constantly refitting your model) and the server side data will come in the future.&lt;/p&gt;

&lt;p&gt;Let''s reformulate the question in terms of models rather than web clients and servers.&lt;/p&gt;

&lt;p&gt;You are fitting a model on one dataset and applying it to another. That is the classic use of predictive modeling/machine learning. Models use features from the data to make estimates of some parameter or parameters. Once you have a fitted (and tested) model, all that you need is the same set of features to feed into the model to get your estimates.&lt;/p&gt;

&lt;p&gt;Just make sure to model on a set of features (aka variables) that are available on the client-side and server-side. If that isn''t possible, ask that question separately.&lt;/p&gt;
', NULL, NULL, '''/p'':79C,96C,161C,196C ''aka'':173C ''anoth'':46C,110C ''appli'':44C,107C ''aren'':36C ''ask'':192C ''avail'':177C ''classic'':114C ''client'':10C,53C,93C,181C ''client-sid'':9C,180C ''come'':75C ''constant'':65C ''data'':12C,28C,55C,73C,125C ''dataset'':105C ''differ'':49C ''estim'':128C,160C ''featur'':122C,151C,172C ''feed'':153C ''fit'':100C,138C ''futur'':78C ''get'':8C,24C,158C ''happen'':56C ''isn'':189C ''learn'':119C ''let'':81C ''main'':48C ''make'':127C,164C ''model'':68C,89C,102C,120C,141C,156C,167C ''modeling/machine'':118C ''necess'':61C ''need'':145C ''one'':41C,104C ''p'':1C,80C,97C,162C ''paramet'':131C,133C ''past'':59C ''popul'':18C,42C ''possibl'':191C ''predict'':117C ''question'':85C,194C ''rather'':90C ''realli'':38C ''refit'':66C ''reformul'':83C ''separ'':195C ''server'':26C,71C,95C,185C ''server-sid'':25C,184C ''set'':149C,170C ''side'':11C,27C,54C,72C,182C,186C ''sure'':165C ''term'':87C ''test'':140C ''train'':39C ''true'':33C ''unless'':62C ''use'':115C,121C ''user'':4C,20C ''variabl'':174C ''web'':92C ''would'':23C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (890, 2, '2014-08-02 14:55:37.347', 1, NULL, NULL, '&lt;p&gt;If your work is parallelizable enough for a distributed network of cpus to make a difference, why not try to run it on the gpu instead? That will require rather less investment than a large network of cpus with individual software licenses and still provide parallel processing which you can do runtime tracking on yourself.&lt;/p&gt;
', NULL, NULL, '''/p'':57C ''cpus'':13C,39C ''differ'':17C ''distribut'':10C ''enough'':7C ''gpu'':26C ''individu'':41C ''instead'':27C ''invest'':33C ''larg'':36C ''less'':32C ''licens'':43C ''make'':15C ''network'':11C,37C ''p'':1C ''parallel'':47C ''paralleliz'':6C ''process'':48C ''provid'':46C ''rather'':31C ''requir'':30C ''run'':22C ''runtim'':53C ''softwar'':42C ''still'':45C ''track'':54C ''tri'':20C ''work'':4C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (891, 2, '2014-08-02 15:02:42.437', 1, NULL, NULL, '&lt;p&gt;As others have pointed out, these are not distance "metrics", because they do not satisfy the metric criteria. Say instead "distance measure".&lt;/p&gt;

&lt;p&gt;Anyway, what are you measuring and why? That information will help us give a more useful answer for your situation.&lt;/p&gt;
', NULL, NULL, '''/p'':24C,46C ''answer'':42C ''anyway'':26C ''criteria'':19C ''distanc'':10C,22C ''give'':38C ''help'':36C ''inform'':34C ''instead'':21C ''measur'':23C,30C ''metric'':11C,18C ''other'':3C ''p'':1C,25C ''point'':5C ''satisfi'':16C ''say'':20C ''situat'':45C ''us'':37C ''use'':41C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (893, 1, '2014-08-03 13:07:24.143', 9, 13035, 'How to get correlation between two categorical variable and a categorical variable and continuous variable?', '&lt;p&gt;I am building a regression model and I need to calculate the below to check for correlations&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Correlation between 2 Multi level categorical variables&lt;/li&gt;
&lt;li&gt;Correlation between a Multi level categorical variable and
continuous variable &lt;/li&gt;
&lt;li&gt;VIF(variance inflation factor) for a Multi
level categorical variables&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I believe its wrong to use Pearson correlation coefficient for the above scenarios because Pearson only works for 2 continuous variables. &lt;/p&gt;

&lt;p&gt;Please answer the below questions&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Which correlation coefficient works best for the above cases ? &lt;/li&gt;
&lt;li&gt;VIF calculation only works for continuous data so what is the
alternative? &lt;/li&gt;
&lt;li&gt;What are the assumptions I need to check before I use the correlation coefficient you suggest? &lt;/li&gt;
&lt;li&gt;How to implement them in SAS &amp;amp; R?&lt;/li&gt;
&lt;/ol&gt;
', 'r  statistics  correlation', NULL, '''/li'':47C,59C,71C,114C,128C,146C,156C ''/ol'':72C,157C ''/p'':37C,95C,102C ''2'':42C,92C ''altern'':127C ''amp'':154C ''answer'':98C ''assumpt'':133C ''believ'':75C ''best'':109C ''build'':22C ''calcul'':30C,117C ''case'':113C ''categor'':7A,11A,45C,54C,69C ''check'':34C,137C ''coeffici'':82C,107C,143C ''continu'':14A,57C,93C,121C ''correl'':4A,18B,36C,40C,49C,81C,106C,142C ''data'':122C ''factor'':64C ''get'':3A ''implement'':150C ''inflat'':63C ''level'':44C,53C,68C ''li'':39C,48C,60C,104C,115C,129C,147C ''model'':25C ''multi'':43C,52C,67C ''need'':28C,135C ''ol'':38C,103C ''p'':19C,73C,96C ''pearson'':80C,88C ''pleas'':97C ''question'':101C ''r'':16B,155C ''regress'':24C ''sas'':153C ''scenario'':86C ''statist'':17B ''suggest'':145C ''two'':6A ''use'':79C,140C ''variabl'':8A,12A,15A,46C,55C,58C,70C,94C ''varianc'':62C ''vif'':61C,116C ''work'':90C,108C,119C ''wrong'':77C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (895, 1, '2014-08-03 16:09:35.75', 4, 134, 'Facebook''s Huge Database', '&lt;p&gt;I assume that each person on Facebook is represented as a node (of a Graph) in Facebook, and relationship/friendship between each person(node) is represented as an edge between the involved nodes.&lt;/p&gt;

&lt;p&gt;Given that there are millions of people on Facebook, how is the Graph stored?&lt;/p&gt;
', 'graphs', NULL, '''/p'':39C,55C ''assum'':8C ''databas'':4A ''edg'':34C ''facebook'':1A,13C,23C,49C ''given'':41C ''graph'':5B,21C,53C ''huge'':3A ''involv'':37C ''million'':45C ''node'':18C,29C,38C ''p'':6C,40C ''peopl'':47C ''person'':11C,28C ''relationship/friendship'':25C ''repres'':15C,31C ''store'':54C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (923, 2, '2014-08-06 09:08:12.117', 3, NULL, NULL, '&lt;p&gt;I think in your case a 10-fold CV will be O.K. &lt;/p&gt;

&lt;p&gt;I think it is more important to randomize the cross validation process than selecting the ideal value for k.&lt;/p&gt;

&lt;p&gt;So repeat the CV process several times randomly and compute the variance of your classification result to determine if the results are realiable or not.&lt;/p&gt;
', NULL, NULL, '''/p'':14C,35C,62C ''10'':8C ''case'':6C ''classif'':51C ''comput'':46C ''cross'':25C ''cv'':10C,40C ''determin'':54C ''fold'':9C ''ideal'':31C ''import'':21C ''k'':34C ''o.k'':13C ''p'':1C,15C,36C ''process'':27C,41C ''random'':23C,44C ''realiabl'':59C ''repeat'':38C ''result'':52C,57C ''select'':29C ''sever'':42C ''think'':3C,17C ''time'':43C ''valid'':26C ''valu'':32C ''varianc'':48C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (899, 2, '2014-08-04 09:48:09.66', 1, NULL, NULL, '&lt;p&gt;When I worked with social network data, we stoted the "friendship" relation in a database in the table &lt;code&gt;Friends(friend_a, friend_b, ...)&lt;/code&gt; with a B-Tree index on &lt;code&gt;(friend_a, friend_b)&lt;/code&gt; plus also some partitioning.&lt;/p&gt;

&lt;p&gt;In our case it was a little bit different since the graph was directed, so it wasn''t really "friendship", but rather "following/follower" relationship. But for friendship I would just store two edges: both &lt;code&gt;(friend_a, friend_b)&lt;/code&gt; and &lt;code&gt;(friend_b, friend_a)&lt;/code&gt;&lt;/p&gt;

&lt;p&gt;We used MySQL to store the data, if it matters, but I guess it shouldn''t.&lt;/p&gt;
', NULL, NULL, '''/code'':26C,39C,85C,92C ''/p'':44C,93C,111C ''also'':41C ''b'':25C,30C,38C,84C,89C ''b-tree'':29C ''bit'':53C ''case'':48C ''code'':20C,34C,80C,87C ''data'':8C,101C ''databas'':16C ''differ'':54C ''direct'':59C ''edg'':78C ''following/follower'':68C ''friend'':21C,22C,24C,35C,37C,81C,83C,88C,90C ''friendship'':12C,65C,72C ''graph'':57C ''guess'':107C ''index'':32C ''littl'':52C ''matter'':104C ''mysql'':97C ''network'':7C ''p'':1C,45C,94C ''partit'':43C ''plus'':40C ''rather'':67C ''realli'':64C ''relat'':13C ''relationship'':69C ''shouldn'':109C ''sinc'':55C ''social'':6C ''store'':76C,99C ''stote'':10C ''tabl'':19C ''tree'':31C ''two'':77C ''use'':96C ''wasn'':62C ''work'':4C ''would'':74C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (900, 2, '2014-08-04 15:48:29.757', 2, NULL, NULL, '&lt;p&gt;I think hierarchical clustering would be more time efficient in your case (with a single dimension).
Depending on your task, you may implement something like this:&lt;/p&gt;

&lt;p&gt;Having N datapoints d&lt;sub&gt;i&lt;/sub&gt; with their 1-dimension value x&lt;sub&gt;i&lt;/sub&gt;:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Sort datapoints based on their x&lt;sub&gt;i&lt;/sub&gt; value. &lt;/li&gt;
&lt;li&gt;Calculate distances between adjacent datapoints (N-1 distances). Each distance must be assigned a pair of original datapoints (d&lt;sub&gt;i&lt;/sub&gt;, d&lt;sub&gt;j&lt;/sub&gt;).&lt;/li&gt;
&lt;li&gt;Sort distances in descending order to generate list of datapoint pairs (d&lt;sub&gt;i&lt;/sub&gt;, d&lt;sub&gt;j&lt;/sub&gt;), starting from the closest one.&lt;/li&gt;
&lt;li&gt;Iteratively unite datapoints (d&lt;sub&gt;i&lt;/sub&gt;, d&lt;sub&gt;j&lt;/sub&gt;) into clusters, starting from beginning of the list (the closest pair). (Depending on current state of d&lt;sub&gt;i&lt;/sub&gt; and d&lt;sub&gt;j&lt;/sub&gt;, uniting them means: (a) creating new cluster for two unclustered datapoints, (b) adding a datapoint to existing cluster and (c) uniting two clusters.)&lt;/li&gt;
&lt;li&gt;Stop uniting, if the distance is over some threshold.&lt;/li&gt;
&lt;li&gt;Create singleton clusters for datapoints which did not get into clusters.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;This algorithm implements &lt;a href="http://en.wikipedia.org/wiki/Single-linkage_clustering" rel="nofollow"&gt;single linkage&lt;/a&gt; clustering. It can be tuned easily to implement average linkage. &lt;a href="http://en.wikipedia.org/wiki/Complete_linkage_clustering" rel="nofollow"&gt;Complete linkage&lt;/a&gt; will be less efficient, but maybe easier ones will give good results depending on your data and task.&lt;/p&gt;

&lt;p&gt;I believe for 200K datapoints it must take under second, if you use proper data structures for above operations.&lt;/p&gt;
', NULL, NULL, '''-1'':67C ''/a'':213C,233C ''/li'':59C,87C,113C,174C,185C,198C ''/ol'':199C ''/p'':28C,46C,252C,273C ''/sub'':36C,45C,57C,82C,86C,103C,107C,121C,125C,145C,150C ''/wiki/complete_linkage_clustering'':228C ''/wiki/single-linkage_clustering'':208C ''1'':39C ''200k'':257C ''ad'':163C ''adjac'':64C ''algorithm'':202C ''assign'':73C ''averag'':222C ''b'':162C ''base'':51C ''begin'':130C ''believ'':255C ''c'':170C ''calcul'':61C ''case'':13C ''closest'':111C,135C ''cluster'':5C,127C,157C,168C,173C,189C,197C,214C ''complet'':231C ''creat'':155C,187C ''current'':139C ''d'':33C,79C,83C,100C,104C,118C,122C,142C,147C ''data'':249C,268C ''datapoint'':32C,50C,65C,78C,98C,117C,161C,165C,191C,258C ''depend'':18C,137C,246C ''descend'':92C ''dimens'':17C,40C ''distanc'':62C,68C,70C,90C,180C ''easier'':240C ''easili'':219C ''effici'':10C,237C ''en.wikipedia.org'':207C,227C ''en.wikipedia.org/wiki/complete_linkage_clustering'':226C ''en.wikipedia.org/wiki/single-linkage_clustering'':206C ''exist'':167C ''generat'':95C ''get'':195C ''give'':243C ''good'':244C ''hierarch'':4C ''href'':205C,225C ''implement'':24C,203C,221C ''iter'':115C ''j'':85C,106C,124C,149C ''less'':236C ''li'':48C,60C,88C,114C,175C,186C ''like'':26C ''linkag'':212C,223C,232C ''list'':96C,133C ''may'':23C ''mayb'':239C ''mean'':153C ''must'':71C,260C ''n'':31C,66C ''new'':156C ''nofollow'':210C,230C ''ol'':47C ''one'':112C,241C ''oper'':272C ''order'':93C ''origin'':77C ''p'':1C,29C,200C,253C ''pair'':75C,99C,136C ''proper'':267C ''rel'':209C,229C ''result'':245C ''second'':263C ''singl'':16C,211C ''singleton'':188C ''someth'':25C ''sort'':49C,89C ''start'':108C,128C ''state'':140C ''stop'':176C ''structur'':269C ''sub'':34C,43C,55C,80C,84C,101C,105C,119C,123C,143C,148C ''take'':261C ''task'':21C,251C ''think'':3C ''threshold'':184C ''time'':9C ''tune'':218C ''two'':159C,172C ''unclust'':160C ''unit'':116C,151C,171C,177C ''use'':266C ''valu'':41C,58C ''would'':6C ''x'':42C,54C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (901, 2, '2014-08-04 15:59:12.573', 6, NULL, NULL, '&lt;p&gt;Strange as it sounds, graphs and graph databases are typically implemented as &lt;a href="http://docs.oracle.com/javase/7/docs/api/java/util/LinkedList.html"&gt;linked lists&lt;/a&gt;. As alluded to &lt;a href="http://docs.neo4j.org/chunked/stable/cypher-cookbook-newsfeed.html"&gt;here&lt;/a&gt;, even the most popular/performant graph database out there (neo4j), is secretly using something akin to a doubly-linked list.&lt;/p&gt;

&lt;p&gt;Representing a graph this way has a number of significant benefits, but also a few drawbacks. Firstly, representing a graph this way means that you can do edge-based insertions in near-constant time. Secondly, this means that traversing the graph can happen extremely quickly, if we''re only looking to either step up or down a linked list.&lt;/p&gt;

&lt;p&gt;The biggest drawback of this though comes from something sometimes called The Justin Bieber Effect, where nodes with a large number of connections tend to be extremely slow to evaluate. Imagine having to traverse a million semi-redundant links every time someone linked to Justin Bieber.&lt;/p&gt;

&lt;p&gt;I know that the awesome folks over at Neo4j are working on the second problem, but I''m not sure how they''re going about it, or how much success they''ve had.&lt;/p&gt;
', NULL, NULL, '''/a'':21C,31C ''/chunked/stable/cypher-cookbook-newsfeed.html'':29C ''/javase/7/docs/api/java/util/linkedlist.html'':18C ''/p'':52C,115C,164C,199C ''akin'':45C ''allud'':23C ''also'':66C ''awesom'':170C ''base'':83C ''benefit'':64C ''bieber'':130C,163C ''biggest'':118C ''call'':127C ''come'':123C ''connect'':139C ''constant'':88C ''databas'':9C,37C ''docs.neo4j.org'':28C ''docs.neo4j.org/chunked/stable/cypher-cookbook-newsfeed.html'':27C ''docs.oracle.com'':17C ''docs.oracle.com/javase/7/docs/api/java/util/linkedlist.html'':16C ''doubli'':49C ''doubly-link'':48C ''drawback'':69C,119C ''edg'':82C ''edge-bas'':81C ''effect'':131C ''either'':107C ''evalu'':146C ''even'':32C ''everi'':157C ''extrem'':99C,143C ''first'':70C ''folk'':171C ''go'':189C ''graph'':6C,8C,36C,56C,73C,96C ''happen'':98C ''href'':15C,26C ''imagin'':147C ''implement'':12C ''insert'':84C ''justin'':129C,162C ''know'':167C ''larg'':136C ''link'':19C,50C,113C,156C,160C ''list'':20C,51C,114C ''look'':105C ''m'':183C ''mean'':76C,92C ''million'':152C ''much'':194C ''near'':87C ''near-const'':86C ''neo4j'':40C,174C ''node'':133C ''number'':61C,137C ''p'':1C,53C,116C,165C ''popular/performant'':35C ''problem'':180C ''quick'':100C ''re'':103C,188C ''redund'':155C ''repres'':54C,71C ''second'':90C,179C ''secret'':42C ''semi'':154C ''semi-redund'':153C ''signific'':63C ''slow'':144C ''someon'':159C ''someth'':44C,125C ''sometim'':126C ''sound'':5C ''step'':108C ''strang'':2C ''success'':195C ''sure'':185C ''tend'':140C ''though'':122C ''time'':89C,158C ''travers'':94C,150C ''typic'':11C ''use'':43C ''ve'':197C ''way'':58C,75C ''work'':176C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (902, 1, '2014-08-04 19:10:57.187', 4, 132, 'When is there enough data for generalization?', '&lt;p&gt;Are there any general rules that one can use to infer what can be learned/generalized from a particular data set?  Suppose the dataset was taken from a sample of people.  Can these rules be stated as functions of the sample or total population?&lt;/p&gt;

&lt;p&gt;I understand the above may be vague, so a case scenario: Users participate in a search task, where the data are their queries, clicked results, and the HTML content (text only) of those results.  Each of these are tagged with their user and timestamp.  A user may generate a few pages - for a simple fact-finding task - or hundreds of pages - for a longer-term search task, like for class report.&lt;/p&gt;

&lt;p&gt;Edit:  In addition to generalizing about a population, given a sample, I''m interested in generalizing about an individual''s overall search behavior, given a time slice.  Theory and paper references are a plus!&lt;/p&gt;
', 'machine-learning  data-mining  statistics  search', NULL, '''/p'':60C,135C,171C ''addit'':139C ''behavior'':159C ''case'':71C ''class'':133C ''click'':85C ''content'':90C ''data'':5A,12B,35C,81C ''data-min'':11B ''dataset'':39C ''edit'':137C ''enough'':4A ''fact'':117C ''fact-find'':116C ''find'':118C ''function'':53C ''general'':7A,20C,141C,152C ''generat'':109C ''given'':145C,160C ''html'':89C ''hundr'':121C ''individu'':155C ''infer'':27C ''interest'':150C ''learn'':10B ''learned/generalized'':31C ''like'':131C ''longer'':127C ''longer-term'':126C ''m'':149C ''machin'':9B ''machine-learn'':8B ''may'':66C,108C ''mine'':13B ''one'':23C ''overal'':157C ''p'':16C,61C,136C ''page'':112C,123C ''paper'':166C ''particip'':74C ''particular'':34C ''peopl'':46C ''plus'':170C ''popul'':59C,144C ''queri'':84C ''refer'':167C ''report'':134C ''result'':86C,95C ''rule'':21C,49C ''sampl'':44C,56C,147C ''scenario'':72C ''search'':15B,77C,129C,158C ''set'':36C ''simpl'':115C ''slice'':163C ''state'':51C ''statist'':14B ''suppos'':37C ''tag'':100C ''taken'':41C ''task'':78C,119C,130C ''term'':128C ''text'':91C ''theori'':164C ''time'':162C ''timestamp'':105C ''total'':58C ''understand'':63C ''use'':25C ''user'':73C,103C,107C ''vagu'':68C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (903, 1, '2014-08-04 22:27:10.837', 8, 297, 'Analyzing A/B test results which are not normally distributed, using independent t-test', '&lt;p&gt;I have a set of results from an A/B test (one control group, one feature group) which do not fit a Normal Distribution. 
In fact the distribution resembles more closely the Landau Distribution.&lt;/p&gt;

&lt;p&gt;I believe the independent t-test requires that the samples be at least approximately normally distributed, which discourages me using the t-test as a valid method of significance testing.&lt;/p&gt;

&lt;p&gt;But my question is: 
&lt;strong&gt;At what point can one say that the t-test is not a good method of significance testing?&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Or put another way, how can one qualify how reliable the p-values of a t-test are, given only the data set?&lt;/p&gt;
', 'dataset  statistics  ab-test', NULL, '''/p'':54C,88C,115C,142C ''/strong'':114C ''a/b'':2A,29C ''ab'':18B ''ab-test'':17B ''analyz'':1A ''anoth'':119C ''approxim'':70C ''believ'':57C ''close'':50C ''control'':32C ''data'':140C ''dataset'':15B ''discourag'':74C ''distribut'':9A,43C,47C,53C,72C ''fact'':45C ''featur'':35C ''fit'':40C ''given'':137C ''good'':109C ''group'':33C,36C ''independ'':11A,59C ''landau'':52C ''least'':69C ''method'':84C,110C ''normal'':8A,42C,71C ''one'':31C,34C,99C,123C ''p'':20C,55C,89C,116C,129C ''p-valu'':128C ''point'':97C ''put'':118C ''qualifi'':124C ''question'':92C ''reliabl'':126C ''requir'':63C ''resembl'':48C ''result'':4A,26C ''sampl'':66C ''say'':100C ''set'':24C,141C ''signific'':86C,112C ''statist'':16B ''strong'':94C ''t-test'':12A,60C,78C,103C,133C ''test'':3A,14A,19B,30C,62C,80C,87C,105C,113C,135C ''use'':10A,76C ''valid'':83C ''valu'':130C ''way'':120C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (909, 2, '2014-08-05 08:12:15.647', 5, NULL, NULL, '&lt;p&gt;The distribution of your data doesn''t need to be normal, it''s the &lt;a href="https://en.wikipedia.org/wiki/Sampling_distribution"&gt;Sampling Distribution&lt;/a&gt; that has to be nearly normal. If your sample size is big enough, then the sampling distribution of means from Landau Distribution should to be nearly normal, due to the &lt;a href="https://en.wikipedia.org/wiki/Central_limit_theorem"&gt;Central Limit Theorem&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;So it means you should be able to safely use t-test with your data.&lt;/p&gt;

&lt;h3&gt;Example&lt;/h3&gt;

&lt;p&gt;Let''s consider this example: suppose we have a population with &lt;a href="https://en.wikipedia.org/wiki/Log-normal_distribution"&gt;Lognormal distribution&lt;/a&gt; with mu=0 and sd=0.5 (it looks a bit similar to Landau)&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/Hw5mM.png" alt="lognormal density"&gt;&lt;/p&gt;

&lt;p&gt;So we sample 30 observations 5000 times from this distribution each time calculating the mean of the sample &lt;/p&gt;

&lt;p&gt;And this is what we get &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/wjueS.png" alt="sampling distribution"&gt;&lt;/p&gt;

&lt;p&gt;Looks quite normal, doesn''t it? If we increase the sample size, it''s even more apparent&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/M0FQS.png" alt="sampling distribution"&gt;&lt;/p&gt;

&lt;h3&gt;R code&lt;/h3&gt;

&lt;pre&gt;&lt;code&gt;x = seq(0, 4, 0.05)
y = dlnorm(x, mean=0, sd=0.5)
plot(x, y, type=''l'', bty=''n'')


n = 30
m = 1000

set.seed(0)
samp = rep(NA, m)

for (i in 1:m) {
  samp[i] = mean(rlnorm(n, mean=0, sd=0.5))
}

hist(samp, col=''orange'', probability=T, breaks=25, main=''sample size = 30'')
x = seq(0.5, 1.5, 0.01)
lines(x, dnorm(x, mean=mean(samp), sd=sd(samp)))


n = 300
samp = rep(NA, m)

for (i in 1:m) {
  samp[i] = mean(rlnorm(n, mean=0, sd=0.5))
}

hist(samp, col=''orange'', probability=T, breaks=25, main=''sample size = 300'')
x = seq(1, 1.25, 0.005)
lines(x, dnorm(x, mean=mean(samp), sd=sd(samp)))
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/a'':23C,62C,104C ''/code'':319C ''/h3'':84C,199C ''/hw5mm.png'':124C ''/m0fqs.png'':191C ''/p'':63C,81C,118C,128C,148C,156C,166C,185C,195C ''/pre'':320C ''/wiki/central_limit_theorem'':58C ''/wiki/log-normal_distribution'':101C ''/wiki/sampling_distribution'':20C ''/wjues.png'':162C ''0'':107C,204C,211C,226C,242C,289C ''0.005'':308C ''0.01'':261C ''0.05'':206C ''0.5'':110C,213C,244C,259C,291C ''1'':234C,281C,306C ''1.25'':307C ''1.5'':260C ''1000'':224C ''25'':252C,299C ''30'':133C,222C,256C ''300'':273C,303C ''4'':205C ''5000'':135C ''abl'':71C ''alt'':125C,163C,192C ''appar'':184C ''big'':35C ''bit'':114C ''break'':251C,298C ''bti'':219C ''calcul'':142C ''central'':59C ''code'':198C,201C ''col'':247C,294C ''consid'':88C ''data'':6C,80C ''densiti'':127C ''distribut'':3C,22C,40C,45C,103C,139C,165C,194C ''dlnorm'':208C ''dnorm'':264C,311C ''doesn'':7C,171C ''due'':51C ''en.wikipedia.org'':19C,57C,100C ''en.wikipedia.org/wiki/central_limit_theorem'':56C ''en.wikipedia.org/wiki/log-normal_distribution'':99C ''en.wikipedia.org/wiki/sampling_distribution'':18C ''enough'':36C ''even'':182C ''exampl'':83C,90C ''get'':155C ''h3'':82C,196C ''hist'':245C,292C ''href'':17C,55C,98C ''i.stack.imgur.com'':123C,161C,190C ''i.stack.imgur.com/hw5mm.png'':122C ''i.stack.imgur.com/m0fqs.png'':189C ''i.stack.imgur.com/wjues.png'':160C ''img'':120C,158C,187C ''increas'':176C ''l'':218C ''landau'':44C,117C ''let'':86C ''limit'':60C ''line'':262C,309C ''lognorm'':102C,126C ''look'':112C,168C ''m'':223C,230C,235C,277C,282C ''main'':253C,300C ''mean'':42C,67C,144C,210C,238C,241C,266C,267C,285C,288C,313C,314C ''mu'':106C ''n'':220C,221C,240C,272C,287C ''na'':229C,276C ''near'':28C,49C ''need'':9C ''normal'':12C,29C,50C,170C ''observ'':134C ''orang'':248C,295C ''p'':1C,64C,85C,119C,129C,149C,157C,167C,186C ''plot'':214C ''popul'':95C ''pre'':200C ''probabl'':249C,296C ''quit'':169C ''r'':197C ''rep'':228C,275C ''rlnorm'':239C,286C ''safe'':73C ''samp'':227C,236C,246C,268C,271C,274C,283C,293C,315C,318C ''sampl'':21C,32C,39C,132C,147C,164C,178C,193C,254C,301C ''sd'':109C,212C,243C,269C,270C,290C,316C,317C ''seq'':203C,258C,305C ''set.seed'':225C ''similar'':115C ''size'':33C,179C,255C,302C ''src'':121C,159C,188C ''suppos'':91C ''t-test'':75C ''test'':77C ''theorem'':61C ''time'':136C,141C ''type'':217C ''use'':74C ''x'':202C,209C,215C,257C,263C,265C,304C,310C,312C ''y'':207C,216C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (904, 1, '2014-08-04 19:21:45.067', 12, 4572, 'What do you use to generate a dashboard in R?', '&lt;p&gt;I need to generate periodic (daily, monthly) web analytics dashboard reports. They will be static and don''t require interaction, so imagine a PDF file as the target output. The reports will mix tables and charts (mainly sparkline and bullet graphs created with ggplot2). Think Stephen Few/Perceptual Edge style dashboards, such as: &lt;img src="http://i.stack.imgur.com/Edh2e.png" alt="sample dashboard"&gt;&lt;/p&gt;

&lt;p&gt;but applied to web analytics. &lt;/p&gt;

&lt;p&gt;Any suggestions on what packages to use creating these dashboard reports? &lt;/p&gt;

&lt;p&gt;My first intuition is to use R markdown and knitr, but perhaps you''ve found a better solution. I can''t seem to find rich examples of dashboards generated from R. &lt;/p&gt;
', 'r  visualization', NULL, '''/edh2e.png'':70C ''/p'':74C,81C,94C,127C ''alt'':71C ''analyt'':22C,80C ''appli'':77C ''better'':112C ''bullet'':53C ''chart'':49C ''creat'':55C,90C ''daili'':19C ''dashboard'':8A,23C,63C,73C,92C,123C ''edg'':61C ''exampl'':121C ''few/perceptual'':60C ''file'':38C ''find'':119C ''first'':97C ''found'':110C ''generat'':6A,17C,124C ''ggplot2'':57C ''graph'':54C ''i.stack.imgur.com'':69C ''i.stack.imgur.com/edh2e.png'':68C ''imagin'':35C ''img'':66C ''interact'':33C ''intuit'':98C ''knitr'':105C ''main'':50C ''markdown'':103C ''mix'':46C ''month'':20C ''need'':15C ''output'':42C ''p'':13C,75C,82C,95C ''packag'':87C ''pdf'':37C ''perhap'':107C ''period'':18C ''r'':10A,11B,102C,126C ''report'':24C,44C,93C ''requir'':32C ''rich'':120C ''sampl'':72C ''seem'':117C ''solut'':113C ''sparklin'':51C ''src'':67C ''static'':28C ''stephen'':59C ''style'':62C ''suggest'':84C ''tabl'':47C ''target'':41C ''think'':58C ''use'':4A,89C,101C ''ve'':109C ''visual'':12B ''web'':21C,79C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1168, 2, '2014-09-26 08:51:46.727', 1, NULL, NULL, '&lt;p&gt;Please take a look at this paper: &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Survey on Various Natural Language Processing Toolkits&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;In this work several NLP tools are introduced. Examples are:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Carabao Language Toolkit&lt;/li&gt;
&lt;li&gt;GATE&lt;/li&gt;
&lt;li&gt;Stanford NLP toolkit&lt;/li&gt;
&lt;li&gt;NLTK&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Just google the title of the paper and you will find the .pdf file.&lt;/p&gt;
', NULL, NULL, '''/li'':38C,41C,46C,49C ''/p'':9C,20C,32C,66C ''/strong'':19C ''/ul'':50C ''carabao'':35C ''exampl'':30C ''file'':65C ''find'':62C ''gate'':40C ''googl'':53C ''introduc'':29C ''languag'':16C,36C ''li'':34C,39C,42C,47C ''look'':5C ''natur'':15C ''nlp'':26C,44C ''nltk'':48C ''p'':1C,10C,21C,51C ''paper'':8C,58C ''pdf'':64C ''pleas'':2C ''process'':17C ''sever'':25C ''stanford'':43C ''strong'':11C ''survey'':12C ''take'':3C ''titl'':55C ''tool'':27C ''toolkit'':18C,37C,45C ''ul'':33C ''various'':14C ''work'':24C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (921, 1, '2014-08-06 09:03:20.857', 3, 102, 'Avoid iterations while calculating average model accuracy', '&lt;p&gt;I am fitting a model in R.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;use &lt;code&gt;createFolds&lt;/code&gt; method to create several &lt;code&gt;k&lt;/code&gt; folds from the data set&lt;/li&gt;
&lt;li&gt;loop through the folds, repeating the following on each iteration:
&lt;ul&gt;
&lt;li&gt;&lt;code&gt;train&lt;/code&gt; the model on k-1 folds&lt;/li&gt;
&lt;li&gt;&lt;code&gt;predict&lt;/code&gt; the outcomes for the i-th fold&lt;/li&gt;
&lt;li&gt;calculate prediction accuracy&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;average the accuracy&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Does R have a function that makes folds itself, repeats model tuning/predictions and gives the average accuracy back?&lt;/p&gt;
', 'r  accuracy  cross-validation  sampling  beginner', NULL, '''-1'':63C ''/code'':29C,36C,58C,69C ''/li'':42C,65C,78C,83C,85C,90C ''/p'':23C,111C ''/ul'':84C,91C ''accuraci'':7A,9B,82C,89C,109C ''averag'':5A,87C,108C ''avoid'':1A ''back'':110C ''beginn'':14B ''calcul'':4A,80C ''code'':27C,34C,56C,67C ''creat'':32C ''createfold'':28C ''cross'':11B ''cross-valid'':10B ''data'':40C ''fit'':18C ''fold'':37C,47C,64C,77C,100C ''follow'':50C ''function'':97C ''give'':106C ''i-th'':74C ''iter'':2A,53C ''k'':35C,62C ''li'':25C,43C,55C,66C,79C,86C ''loop'':44C ''make'':99C ''method'':30C ''model'':6A,20C,60C,103C ''outcom'':71C ''p'':15C,92C ''predict'':68C,81C ''r'':8B,22C,94C ''repeat'':48C,102C ''sampl'':13B ''set'':41C ''sever'':33C ''th'':76C ''train'':57C ''tuning/predictions'':104C ''ul'':24C,54C ''use'':26C ''valid'':12B');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (905, 2, '2014-08-04 19:28:38.173', 8, NULL, NULL, '&lt;p&gt;&lt;a href="http://shiny.rstudio.com"&gt;Shiny&lt;/a&gt; is a framework for generating HTML-based apps that execute R code dynamically. Shiny apps can stand alone or be built into Markdown documents with &lt;code&gt;knitr&lt;/code&gt;, and Shiny development is fully integrated into RStudio. There''s even a free service called &lt;a href="https://www.shinyapps.io"&gt;shinyapps.io&lt;/a&gt; for hosting Shiny apps, the &lt;code&gt;shiny&lt;/code&gt; package has functions for deploying Shiny apps directly from R, and RStudio has a GUI interface for calling those functions. There''s plenty more info in the Tutorial section of the site.&lt;/p&gt;

&lt;p&gt;Since it essentially "compiles" the whole thing to JavaScript and HTML, you can use CSS to freely change the formatting and layout, although Shiny has decent wrapper functionality for this. But it just so happens that their default color scheme is similar to the one in the screenshot you posted.&lt;/p&gt;

&lt;p&gt;edit: I just realized you don''t need them to be dynamic. Shiny still makes very nice-looking webpages out of the box, with lots of options for rearranging elements. There''s also functionality for downloading plots, so you can generate your dashboard every month by just updating your data files in the app, and then saving the resulting image to PDF.&lt;/p&gt;
', NULL, NULL, '''/a'':6C,55C ''/code'':35C,63C ''/p'':96C,148C,213C ''alon'':25C ''also'':183C ''although'':120C ''app'':15C,22C,59C,70C,204C ''base'':14C ''box'':173C ''built'':28C ''call'':50C,81C ''chang'':115C ''code'':19C,33C,61C ''color'':136C ''compil'':101C ''css'':112C ''dashboard'':193C ''data'':200C ''decent'':123C ''default'':135C ''deploy'':68C ''develop'':38C ''direct'':71C ''document'':31C ''download'':186C ''dynam'':20C,161C ''edit'':150C ''element'':180C ''essenti'':100C ''even'':46C ''everi'':194C ''execut'':17C ''file'':201C ''format'':117C ''framework'':9C ''free'':48C ''freeli'':114C ''fulli'':40C ''function'':66C,83C,125C,184C ''generat'':11C,191C ''gui'':78C ''happen'':132C ''host'':57C ''href'':3C,52C ''html'':13C,108C ''html-base'':12C ''imag'':210C ''info'':88C ''integr'':41C ''interfac'':79C ''javascript'':106C ''knitr'':34C ''layout'':119C ''look'':168C ''lot'':175C ''make'':164C ''markdown'':30C ''month'':195C ''need'':157C ''nice'':167C ''nice-look'':166C ''one'':142C ''option'':177C ''p'':1C,97C,149C ''packag'':64C ''pdf'':212C ''plenti'':86C ''plot'':187C ''post'':147C ''r'':18C,73C ''realiz'':153C ''rearrang'':179C ''result'':209C ''rstudio'':43C,75C ''save'':207C ''scheme'':137C ''screenshot'':145C ''section'':92C ''servic'':49C ''shini'':5C,21C,37C,58C,62C,69C,121C,162C ''shiny.rstudio.com'':4C ''shinyapps.io'':54C ''similar'':139C ''sinc'':98C ''site'':95C ''stand'':24C ''still'':163C ''thing'':104C ''tutori'':91C ''updat'':198C ''use'':111C ''webpag'':169C ''whole'':103C ''wrapper'':124C ''www.shinyapps.io'':53C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (906, 2, '2014-08-04 23:37:27.877', 2, NULL, NULL, '&lt;p&gt;&lt;a href="http://mrvar.fdv.uni-lj.si/pajek/PajekXXL.htm" rel="nofollow"&gt;PajekXXL&lt;/a&gt; is designed to handle enormous networks. But Pajek is also kind of a bizarre program with an unintuitive interface.&lt;/p&gt;
', NULL, NULL, '''/a'':10C ''/p'':30C ''/pajek/pajekxxl.htm'':6C ''also'':20C ''bizarr'':24C ''design'':12C ''enorm'':15C ''handl'':14C ''href'':3C ''interfac'':29C ''kind'':21C ''mrvar.fdv.uni-lj.si'':5C ''mrvar.fdv.uni-lj.si/pajek/pajekxxl.htm'':4C ''network'':16C ''nofollow'':8C ''p'':1C ''pajek'':18C ''pajekxxl'':9C ''program'':25C ''rel'':7C ''unintuit'':28C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (907, 2, '2014-08-05 07:17:34.75', 13, NULL, NULL, '&lt;p&gt;I think that &lt;code&gt;Shiny&lt;/code&gt; is an overkill in this situation and doesn''t match your requirement of &lt;em&gt;dashboard reports&lt;/em&gt; to be &lt;strong&gt;static&lt;/strong&gt;. I guess, that your use of the term "dashboard" is a bit confusing, as some people might consider that it has more emphasis of &lt;strong&gt;interactivity&lt;/strong&gt; (&lt;em&gt;real-time dashboards&lt;/em&gt;), rather than &lt;strong&gt;information layout&lt;/strong&gt;, as is my understanding (confirmed by the "static" requirement).&lt;/p&gt;

&lt;p&gt;My recommendation to you is to use &lt;strong&gt;R Markdown&lt;/strong&gt; and &lt;strong&gt;knitr&lt;/strong&gt;, especially since these packages have much lower learning curve than &lt;strong&gt;Shiny&lt;/strong&gt;. Moreover, I have recently run across an R package, which, in my view, ideally suits your requirement of embedding small charts/plots in a report, as presented on your picture above. This package generates static or dynamic &lt;em&gt;graphical tables&lt;/em&gt; and is called &lt;strong&gt;sparkTable&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/sparkTable"&gt;http://cran.r-project.org/web/packages/sparkTable&lt;/a&gt;). Its vignette is available here (there is no link to it on the package''s home page): &lt;a href="http://publik.tuwien.ac.at/files/PubDat_228663.pdf"&gt;http://publik.tuwien.ac.at/files/PubDat_228663.pdf&lt;/a&gt;. Should you ever need some &lt;em&gt;interactivity&lt;/em&gt;, &lt;code&gt;sparkTable&lt;/code&gt; provides some via its simple interface to &lt;code&gt;Shiny&lt;/code&gt;.&lt;/p&gt;
', NULL, NULL, '''/code'':7C,197C,207C ''/em'':24C,62C,147C,194C ''/files/pubdat_228663.pdf'':183C ''/files/pubdat_228663.pdf&lt;/a&gt;.'':186C ''/p'':78C,208C ''/strong'':29C,56C,68C,90C,94C,107C,153C ''/web/packages/sparktable'':158C ''/web/packages/sparktable&lt;/a&gt;).'':161C ''across'':113C ''avail'':165C ''bit'':41C ''call'':150C ''charts/plots'':128C ''code'':5C,195C,205C ''confirm'':73C ''confus'':42C ''consid'':47C ''cran.r-project.org'':157C,160C ''cran.r-project.org/web/packages/sparktable'':156C ''cran.r-project.org/web/packages/sparktable&lt;/a&gt;).'':159C ''curv'':103C ''dashboard'':22C,38C,61C ''doesn'':15C ''dynam'':143C ''em'':21C,57C,144C,192C ''embed'':126C ''emphasi'':52C ''especi'':95C ''ever'':189C ''generat'':140C ''graphic'':145C ''guess'':31C ''home'':177C ''href'':155C,180C ''ideal'':121C ''inform'':66C ''interact'':55C,193C ''interfac'':203C ''knitr'':93C ''layout'':67C ''learn'':102C ''link'':170C ''lower'':101C ''markdown'':89C ''match'':17C ''might'':46C ''moreov'':108C ''much'':100C ''need'':190C ''overkil'':10C ''p'':1C,79C ''packag'':98C,116C,139C,175C ''page'':178C ''peopl'':45C ''pictur'':136C ''present'':133C ''provid'':198C ''publik.tuwien.ac.at'':182C,185C ''publik.tuwien.ac.at/files/pubdat_228663.pdf'':181C ''publik.tuwien.ac.at/files/pubdat_228663.pdf&lt;/a&gt;.'':184C ''r'':88C,115C ''rather'':63C ''real'':59C ''real-tim'':58C ''recent'':111C ''recommend'':81C ''report'':23C,131C ''requir'':19C,77C,124C ''run'':112C ''shini'':6C,106C,206C ''simpl'':202C ''sinc'':96C ''situat'':13C ''small'':127C ''sparktabl'':152C,196C ''static'':28C,76C,141C ''strong'':27C,54C,65C,87C,92C,105C,151C ''suit'':122C ''tabl'':146C ''term'':37C ''think'':3C ''time'':60C ''understand'':72C ''use'':34C,86C ''via'':200C ''view'':120C ''vignett'':163C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (929, 2, '2014-08-06 17:07:17.52', 1, NULL, NULL, '&lt;p&gt;Can you just cast your nparray from train_test_split back into a pandas dataFrame so you can carry out your same strategy. This is very common to what I do when dealing with pandas and scikit. For example,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt; a = train_test_split
 new_df = pd.DataFrame(a)
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''/code'':52C ''/p'':41C ''/pre'':53C ''back'':12C ''carri'':20C ''cast'':5C ''code'':43C ''common'':28C ''datafram'':16C ''deal'':34C ''df'':49C ''exampl'':40C ''new'':48C ''nparray'':7C ''p'':1C ''panda'':15C,36C ''pd.dataframe'':50C ''pre'':42C ''scikit'':38C ''split'':11C,47C ''strategi'':24C ''test'':10C,46C ''train'':9C,45C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (908, 2, '2014-08-05 08:09:17.24', 6, NULL, NULL, '&lt;p&gt;It is my understanding that &lt;em&gt;random sampling&lt;/em&gt; is a &lt;strong&gt;mandatory condition&lt;/strong&gt; for making any &lt;em&gt;generalization&lt;/em&gt; statements. IMHO, other parameters, such as sample size, just affect probability level (confidence) of generalization. Furthermore, clarifying the @ffriend''s comment, I believe that you have to &lt;strong&gt;calculate&lt;/strong&gt; needed &lt;em&gt;sample size&lt;/em&gt;, based on desired values of &lt;em&gt;confidence interval&lt;/em&gt;, &lt;em&gt;effect size&lt;/em&gt;, &lt;em&gt;statistical power&lt;/em&gt; and &lt;em&gt;number of predictors&lt;/em&gt; (this is based on Cohen''s work - see References section at the following link). For multiple regression, you can use the following calculator: &lt;a href="http://www.danielsoper.com/statcalc3/calc.aspx?id=1"&gt;http://www.danielsoper.com/statcalc3/calc.aspx?id=1&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;More information on &lt;strong&gt;how to select, calculate and interpret effect sizes&lt;/strong&gt; can be found in the following nice and comprehensive paper, which is freely available: &lt;a href="http://jpepsy.oxfordjournals.org/content/34/9/917.full"&gt;http://jpepsy.oxfordjournals.org/content/34/9/917.full&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;If you''re using &lt;code&gt;R&lt;/code&gt; (and even, if you don''t), you may find the following Web page on &lt;strong&gt;confidence intervals and R&lt;/strong&gt; interesting and useful: &lt;a href="http://osc.centerforopenscience.org/static/CIs_in_r.html"&gt;http://osc.centerforopenscience.org/static/CIs_in_r.html&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Finally, the following &lt;strong&gt;comprehensive guide&lt;/strong&gt; to survey &lt;strong&gt;sampling&lt;/strong&gt; can be helpful, even if you''re not using survey research designs. In my opinion, it contains a wealth of useful information on &lt;em&gt;sampling methods&lt;/em&gt;, &lt;em&gt;sampling size determination&lt;/em&gt; (including calculator) and much more: &lt;a href="http://home.ubalt.edu/ntsbarsh/stat-data/Surveys.htm"&gt;http://home.ubalt.edu/ntsbarsh/stat-data/Surveys.htm&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (911, 2, '2014-08-05 10:15:33.713', 1, NULL, NULL, '&lt;p&gt;Basically an independent t-test or a 2 sample t-test is used to check if the averages of the two samples are significantly different. Or, to put in another words, if there is a significant difference between the means of the two samples. &lt;/p&gt;

&lt;p&gt;Now, the means of those 2 samples are two statistics, which according with CLT, have a normal distribution, if provided enough samples. Note that CLT works no matter of the distribution from which the mean statistic is built. &lt;/p&gt;

&lt;p&gt;Normally one can use a z-test, but if the variances are estimated from the sample (because it is unknown), some additional uncertainty is introduced, which is incorporated in t distribution. That''s why 2-sample t-test applies here.  &lt;/p&gt;
', NULL, NULL, '''/p'':48C,88C,132C ''2'':10C,55C,125C ''accord'':61C ''addit'':112C ''anoth'':33C ''appli'':130C ''averag'':21C ''basic'':2C ''built'':87C ''check'':18C ''clt'':63C,74C ''differ'':28C,40C ''distribut'':67C,80C,121C ''enough'':70C ''estim'':103C ''incorpor'':118C ''independ'':4C ''introduc'':115C ''matter'':77C ''mean'':43C,52C,84C ''normal'':66C,90C ''note'':72C ''one'':91C ''p'':1C,49C,89C ''provid'':69C ''put'':31C ''sampl'':11C,25C,47C,56C,71C,106C,126C ''signific'':27C,39C ''statist'':59C,85C ''t-test'':5C,12C,127C ''test'':7C,14C,97C,129C ''two'':24C,46C,58C ''uncertainti'':113C ''unknown'':110C ''use'':16C,93C ''varianc'':101C ''word'':34C ''work'':75C ''z'':96C ''z-test'':95C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (912, 2, '2014-08-05 12:29:05.3', 2, NULL, NULL, '&lt;p&gt;There is an excellent comparison of the common inner-product-based similarity metrics &lt;a href="http://brenocon.com/blog/2012/03/cosine-similarity-pearson-correlation-and-ols-coefficients/" rel="nofollow"&gt;here&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;In particular, Cosine Similarity is normalized to lie within [0,1], unlike the dot product which can be any real number, but, as everyone else is saying, that will require ignoring the magnitude of the vectors. Personally, I think that''s a good thing. I think of magnitude as an internal (within-vector) structure, and angle between vectors as external (between vector) structure. They are different things and (in my opinion) are often best analyzed separately. I can''t imagine a situation where I would rather compute inner products than compute cosine similarities and just compare the magnitudes afterward.&lt;/p&gt;
', NULL, NULL, '''/a'':24C ''/blog/2012/03/cosine-similarity-pearson-correlation-and-ols-coefficients/'':20C ''/p'':25C,127C ''0'':36C ''1'':37C ''afterward'':126C ''analyz'':102C ''angl'':83C ''base'':13C ''best'':101C ''brenocon.com'':19C ''brenocon.com/blog/2012/03/cosine-similarity-pearson-correlation-and-ols-coefficients/'':18C ''common'':9C ''compar'':123C ''comparison'':6C ''comput'':114C,118C ''cosin'':29C,119C ''differ'':93C ''dot'':40C ''els'':51C ''everyon'':50C ''excel'':5C ''extern'':87C ''good'':69C ''href'':17C ''ignor'':57C ''imagin'':107C ''inner'':11C,115C ''inner-product-bas'':10C ''intern'':77C ''lie'':34C ''magnitud'':59C,74C,125C ''metric'':15C ''nofollow'':22C ''normal'':32C ''number'':47C ''often'':100C ''opinion'':98C ''p'':1C,26C ''particular'':28C ''person'':63C ''product'':12C,41C,116C ''rather'':113C ''real'':46C ''rel'':21C ''requir'':56C ''say'':53C ''separ'':103C ''similar'':14C,30C,120C ''situat'':109C ''structur'':81C,90C ''thing'':70C,94C ''think'':65C,72C ''unlik'':38C ''vector'':62C,80C,85C,89C ''within'':35C,79C ''within-vector'':78C ''would'':112C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (972, 1, '2014-08-13 20:30:18.143', 1, 21, 'Subgraph isomorphism and Anti-monotone property', '&lt;p&gt;While finding frequent subgraphs in single large graph, subgraph isomorphism (test) is not considered because its not anti-monotone. How and why subgraph isomorphism is not anti-monotone ?&lt;/p&gt;
', 'bigdata  data-mining  dataset  graphs  similarity', NULL, '''/p'':46C ''anti'':5A,34C,44C ''anti-monoton'':4A,33C,43C ''bigdata'':8B ''consid'':29C ''data'':10B ''data-min'':9B ''dataset'':12B ''find'':17C ''frequent'':18C ''graph'':13B,23C ''isomorph'':2A,25C,40C ''larg'':22C ''mine'':11B ''monoton'':6A,35C,45C ''p'':15C ''properti'':7A ''similar'':14B ''singl'':21C ''subgraph'':1A,19C,24C,39C ''test'':26C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9512, 1, '2015-12-25 09:04:20.853', 2, 24, 'Are there other large margin classifiers than SVMs?', '&lt;p&gt;When reading about SVMs (e.g. on the German Wikipedia) there is a sentence like "an svm is a large-margin classifier).&lt;/p&gt;

&lt;p&gt;Are there other large margin classifiers than SVMs?&lt;/p&gt;
', 'machine-learning  classification  svm', NULL, '''/p'':37C,47C ''classif'':12B ''classifi'':6A,36C,44C ''e.g'':19C ''german'':22C ''larg'':4A,34C,42C ''large-margin'':33C ''learn'':11B ''like'':28C ''machin'':10B ''machine-learn'':9B ''margin'':5A,35C,43C ''p'':14C,38C ''read'':16C ''sentenc'':27C ''svm'':13B,30C ''svms'':8A,18C,46C ''wikipedia'':23C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (913, 2, '2014-08-05 12:58:16', 4, NULL, NULL, '&lt;p&gt;There are two rules for generalizability:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;The sample must be &lt;strong&gt;representative&lt;/strong&gt;. In expectation, at least, the distribution of features in your sample must match the distribution of features in the population. When you are fitting a model with a response variable, &lt;em&gt;this includes features that you do not observe, but that affect any response variables in your model&lt;/em&gt;. Since it is, in many cases, impossible to know what you do not observe, &lt;strong&gt;random sampling&lt;/strong&gt; is used.&lt;/p&gt;

&lt;p&gt;The idea with randomization is that a random sample, up to sampling error, &lt;em&gt;must&lt;/em&gt; accurately reflect the distribution of all features in the population, observed and otherwise. This is why &lt;strong&gt;randomization is the "gold standard,"&lt;/strong&gt; but if sample control is available by some other technique, or it is defensible to argue that there are no omitted features, then it isn''t always necessary.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Your sample must be &lt;strong&gt;large enough&lt;/strong&gt; that the effect of &lt;strong&gt;sampling error&lt;/strong&gt; on the feature distribution is relatively small. This is, again, to ensure representativeness. But deciding who to sample is different from deciding how many people to sample.&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Since it sounds like you''re fitting a model, there''s the additional consideration that certain important combinations of features could be relatively rare in the population. This is not an issue for generalizability, but it bears heavily on your considerations for sample size. For instance, I''m working on a project now with (non-big) data that was originally collected to understand the experiences of minorities in college. As such, it was critically important to ensure that &lt;strong&gt;statistical power&lt;/strong&gt; was high &lt;em&gt;specifically in the minority subpopulation&lt;/em&gt;. For this reason, blacks and Latinos were deliberately &lt;strong&gt;oversampled&lt;/strong&gt;. However, the proportion by which they were oversampled was also recorded. These are used to compute survey weights. These can be used to  re-weight the sample so as to reflect the estimated population proportions, in the event that a representative sample is required.&lt;/p&gt;

&lt;p&gt;An additional consideration arises if your model is hierarchical. A canonical use for a hierarchical model is one of children''s behavior in schools. Children are "grouped" by school and share school-level traits. Therefore a representative sample of schools is required, and within each school a representative sample of children is required. This leads to &lt;strong&gt;stratified sampling&lt;/strong&gt;. This and some other sampling designs are reviewed in surprising depth on &lt;a href="http://en.wikipedia.org/wiki/Sampling_(statistics)#Sampling_methods" rel="nofollow"&gt;Wikipedia&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':439C ''/em'':67C,105C,299C ''/li'':158C,205C ''/ol'':206C ''/p'':8C,88C,157C,204C,356C,440C ''/strong'':18C,85C,128C,168C,176C,290C,310C,418C ''/wiki/sampling_(statistics)#sampling_methods'':435C ''accur'':106C ''addit'':220C,359C ''affect'':60C ''also'':320C ''alway'':155C ''argu'':144C ''aris'':361C ''avail'':134C ''bear'':244C ''behavior'':379C ''big'':264C ''black'':303C ''canon'':368C ''case'':73C ''certain'':223C ''children'':377C,382C,409C ''collect'':269C ''colleg'':277C ''combin'':225C ''comput'':326C ''consider'':221C,248C,360C ''control'':132C ''could'':228C ''critic'':282C ''data'':265C ''decid'':191C,198C ''defens'':142C ''deliber'':307C ''depth'':429C ''design'':424C ''differ'':196C ''distribut'':24C,33C,109C,180C ''effect'':171C ''em'':49C,103C,293C ''en.wikipedia.org'':434C ''en.wikipedia.org/wiki/sampling_(statistics)#sampling_methods'':433C ''enough'':167C ''ensur'':188C,285C ''error'':102C,175C ''estim'':344C ''event'':349C ''expect'':20C ''experi'':273C ''featur'':26C,35C,52C,112C,150C,179C,227C ''fit'':42C,214C ''generaliz'':7C,241C ''gold'':126C ''group'':384C ''heavili'':245C ''hierarch'':366C,372C ''high'':292C ''howev'':311C ''href'':432C ''idea'':91C ''import'':224C,283C ''imposs'':74C ''includ'':51C ''instanc'':253C ''isn'':153C ''issu'':239C ''know'':76C ''larg'':166C ''latino'':305C ''lead'':413C ''least'':22C ''level'':391C ''li'':10C,159C ''like'':211C ''m'':255C ''mani'':72C,200C ''match'':31C ''minor'':275C,297C ''model'':44C,66C,216C,364C,373C ''must'':14C,30C,104C,163C ''necessari'':156C ''nofollow'':437C ''non'':263C ''non-big'':262C ''observ'':57C,81C,116C ''ol'':9C ''omit'':149C ''one'':375C ''origin'':268C ''otherwis'':118C ''oversampl'':309C,318C ''p'':1C,11C,89C,160C,207C,357C ''peopl'':201C ''popul'':38C,115C,234C,345C ''power'':289C ''project'':259C ''proport'':313C,346C ''random'':83C,93C,97C,123C ''rare'':231C ''re'':213C,335C ''re-weight'':334C ''reason'':302C ''record'':321C ''reflect'':107C,342C ''rel'':436C ''relat'':182C,230C ''repres'':17C,189C,352C,395C,406C ''requir'':355C,400C,411C ''respons'':47C,62C ''review'':426C ''rule'':5C ''sampl'':13C,29C,84C,98C,101C,131C,162C,174C,194C,203C,250C,338C,353C,396C,407C,417C,423C ''school'':381C,386C,390C,398C,404C ''school-level'':389C ''share'':388C ''sinc'':68C,208C ''size'':251C ''small'':183C ''sound'':210C ''specif'':294C ''standard'':127C ''statist'':288C ''stratifi'':416C ''strong'':16C,82C,122C,165C,173C,287C,308C,415C ''subpopul'':298C ''surpris'':428C ''survey'':327C ''techniqu'':138C ''therefor'':393C ''trait'':392C ''two'':4C ''understand'':271C ''use'':87C,324C,332C,369C ''variabl'':48C,63C ''weight'':328C,336C ''wikipedia'':438C ''within'':402C ''work'':256C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (915, 1, '2014-08-05 18:36:12.753', 7, 165, 'How do various statistical techniques (regression, PCA, etc) scale with sample size and dimension?', '&lt;p&gt;Is there a known general table of statistical techniques that explain how they scale with sample size and dimension? For example, a friend of mine told me the other day that the computation time of simply quick-sorting one dimensional data of size n goes as n*log(n).&lt;/p&gt;

&lt;p&gt;So, for example, if we regress y against X where X is a d-dimensional variable, does it go as O(n^2*d)? How does it scale if I want to find the solution via exact Gauss-Markov solution vs numerical least squares with Newton method? Or simply getting the solution vs using significance tests?&lt;/p&gt;

&lt;p&gt;I guess I more want a good source of answers (like a paper that summarizes the scaling of various statistical techniques) than a good answer here. Like, say, a list that includes the scaling of multiple regression, logistic regression, PCA, cox proportional hazard regression, K-means clustering, etc.&lt;/p&gt;
', 'bigdata  statistics  efficiency  scalability', NULL, '''/p'':70C,130C,181C ''2'':95C ''answer'':141C,156C ''bigdata'':15B ''cluster'':179C ''comput'':52C ''cox'':172C ''d'':86C,96C ''d-dimension'':85C ''data'':61C ''day'':49C ''dimens'':14A,38C ''dimension'':60C,87C ''effici'':17B ''etc'':8A,180C ''exact'':109C ''exampl'':40C,74C ''explain'':30C ''find'':105C ''friend'':42C ''gauss'':111C ''gauss-markov'':110C ''general'':24C ''get'':123C ''go'':91C ''goe'':65C ''good'':138C,155C ''guess'':133C ''hazard'':174C ''includ'':163C ''k'':177C ''k-mean'':176C ''known'':23C ''least'':116C ''like'':142C,158C ''list'':161C ''log'':68C ''logist'':169C ''markov'':112C ''mean'':178C ''method'':120C ''mine'':44C ''multipl'':167C ''n'':64C,67C,69C,94C ''newton'':119C ''numer'':115C ''o'':93C ''one'':59C ''p'':19C,71C,131C ''paper'':144C ''pca'':7A,171C ''proport'':173C ''quick'':57C ''quick-sort'':56C ''regress'':6A,77C,168C,170C,175C ''sampl'':11A,35C ''say'':159C ''scalabl'':18B ''scale'':9A,33C,100C,148C,165C ''signific'':128C ''simpli'':55C,122C ''size'':12A,36C,63C ''solut'':107C,113C,125C ''sort'':58C ''sourc'':139C ''squar'':117C ''statist'':4A,16B,27C,151C ''summar'':146C ''tabl'':25C ''techniqu'':5A,28C,152C ''test'':129C ''time'':53C ''told'':45C ''use'':127C ''variabl'':88C ''various'':3A,150C ''via'':108C ''vs'':114C,126C ''want'':103C,136C ''x'':80C,82C ''y'':78C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (916, 2, '2014-08-05 20:24:09.2', 4, NULL, NULL, '&lt;p&gt;Most of the efficient (and non trivial) statistic algorithms are iterative in nature so that the worst case analysis &lt;code&gt;O()&lt;/code&gt; is irrelevant as the worst case is ''it fails to converge''.&lt;/p&gt;

&lt;p&gt;Nevertheless, when you have a lot of data, even the linear algorithms (&lt;code&gt;O(n)&lt;/code&gt;) can be slow and you then need to focus on the constant ''hidden'' behind the notation. For instance, computing the variance of a single variate is naively done scanning the data twice (once for computing an estimate of the mean, and then once to estimate the variance). But it also can be done in &lt;a href="http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Online_algorithm" rel="nofollow"&gt;one pass&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;For iterative algorithms, what is more important is convergence rate and number of parameters as a function of the data dimensionality, an element that greatly influences convergence. Many models/algorithm grow a number of parameters that is exponential with the number of variables (e.g. splines) while some other grow linearly (e.g. support vector machines, random forests, ...) &lt;/p&gt;
', NULL, NULL, '''/a'':116C ''/code'':23C,52C ''/p'':35C,117C,174C ''/wiki/algorithms_for_calculating_variance#online_algorithm'':111C ''algorithm'':10C,48C,121C ''also'':102C ''analysi'':20C ''behind'':66C ''case'':19C,29C ''code'':21C,49C ''comput'':71C,87C ''constant'':64C ''converg'':34C,127C,145C ''data'':44C,83C,138C ''dimension'':139C ''done'':80C,105C ''e.g'':161C,168C ''effici'':5C ''element'':141C ''en.wikipedia.org'':110C ''en.wikipedia.org/wiki/algorithms_for_calculating_variance#online_algorithm'':109C ''estim'':89C,97C ''even'':45C ''exponenti'':155C ''fail'':32C ''focus'':61C ''forest'':173C ''function'':135C ''great'':143C ''grow'':148C,166C ''hidden'':65C ''href'':108C ''import'':125C ''influenc'':144C ''instanc'':70C ''irrelev'':25C ''iter'':12C,120C ''linear'':47C,167C ''lot'':42C ''machin'':171C ''mani'':146C ''mean'':92C ''models/algorithm'':147C ''n'':51C ''naiv'':79C ''natur'':14C ''need'':59C ''nevertheless'':37C ''nofollow'':113C ''non'':7C ''notat'':68C ''number'':130C,150C,158C ''o'':22C,50C ''one'':114C ''p'':1C,36C,118C ''paramet'':132C,152C ''pass'':115C ''random'':172C ''rate'':128C ''rel'':112C ''scan'':81C ''singl'':76C ''slow'':55C ''spline'':162C ''statist'':9C ''support'':169C ''trivial'':8C ''twice'':84C ''variabl'':160C ''varianc'':73C,99C ''variat'':77C ''vector'':170C ''worst'':18C,28C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (917, 1, '2014-08-05 20:45:01.383', 6, 390, 'Solving a system of equations with sparse data', '&lt;p&gt;I am attempting to solve a set of equations which has 40 independent variables (x1, ..., x40) and one dependent variable (y). The total number of equations (number of rows) is ~300, and I want to solve for the set of 40 coefficients that minimizes the total sum-of-square error between y and the predicted value. &lt;/p&gt;

&lt;p&gt;My problem is that the matrix is very sparse and I do not know the best way to solve the system of equations with sparse data. An example of the dataset is shown below:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;   y    x1  x2 x3 x4 x5 x6 ... x40
87169   14  0  1  0  0  2  ... 0 
46449   0   0  4  0  1  4  ... 12
846449  0   0  0  0  0  3  ... 0
....
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;I am currently using a Genetic Algorithm to solve this and the results are coming out 
with roughly a factor of two difference between observed and expected. &lt;/p&gt;

&lt;p&gt;Can anyone suggest different methods or techniques which are capable of solving a set of equations with sparse data.&lt;/p&gt;
', 'machine-learning  algorithms  genetic', NULL, '''/code'':143C ''/p'':72C,108C,173C,194C ''/pre'':144C ''0'':121C,123C,124C,126C,128C,129C,131C,136C,137C,138C,139C,140C,142C ''1'':122C,132C ''12'':134C ''14'':120C ''2'':125C ''3'':141C ''300'':45C ''4'':130C,133C ''40'':26C,55C ''46449'':127C ''846449'':135C ''87169'':119C ''algorithm'':12B,152C ''anyon'':176C ''attempt'':17C ''best'':89C ''capabl'':184C ''code'':110C ''coeffici'':56C ''come'':160C ''current'':148C ''data'':8A,99C,193C ''dataset'':104C ''depend'':33C ''differ'':168C,178C ''equat'':5A,23C,40C,96C,190C ''error'':65C ''exampl'':101C ''expect'':172C ''factor'':165C ''genet'':13B,151C ''independ'':27C ''know'':87C ''learn'':11B ''machin'':10B ''machine-learn'':9B ''matrix'':79C ''method'':179C ''minim'':58C ''number'':38C,41C ''observ'':170C ''one'':32C ''p'':14C,73C,145C,174C ''pre'':109C ''predict'':70C ''problem'':75C ''result'':158C ''rough'':163C ''row'':43C ''set'':21C,53C,188C ''shown'':106C ''solv'':1A,19C,50C,92C,154C,186C ''spars'':7A,82C,98C,192C ''squar'':64C ''suggest'':177C ''sum'':62C ''sum-of-squar'':61C ''system'':3A,94C ''techniqu'':181C ''total'':37C,60C ''two'':167C ''use'':149C ''valu'':71C ''variabl'':28C,34C ''want'':48C ''way'':90C ''x1'':29C,112C ''x2'':113C ''x3'':114C ''x4'':115C ''x40'':30C,118C ''x5'':116C ''x6'':117C ''y'':35C,67C,111C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (918, 2, '2014-08-05 22:34:04.55', 8, NULL, NULL, '&lt;p&gt;If I understand you correctly, this is the case of &lt;strong&gt;multiple linear regression with sparse data&lt;/strong&gt; (&lt;em&gt;sparse regression&lt;/em&gt;). Assuming that, I hope you will find the following &lt;strong&gt;resources&lt;/strong&gt; useful.&lt;/p&gt;

&lt;p&gt;1) NCSU &lt;strong&gt;lecture slides on sparse regression&lt;/strong&gt; with overview of algorithms, notes, formulas, graphics and references to literature: &lt;a href="http://www.stat.ncsu.edu/people/zhou/courses/st810/notes/lect23sparse.pdf"&gt;http://www.stat.ncsu.edu/people/zhou/courses/st810/notes/lect23sparse.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;2) &lt;code&gt;R&lt;/code&gt; ecosystem offers many &lt;strong&gt;packages&lt;/strong&gt;, useful for sparse regression analysis, including:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Matrix&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/Matrix"&gt;http://cran.r-project.org/web/packages/Matrix&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;SparseM&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/SparseM"&gt;http://cran.r-project.org/web/packages/SparseM&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;MatrixModels&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/MatrixModels"&gt;http://cran.r-project.org/web/packages/MatrixModels&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;glmnet&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/glmnet"&gt;http://cran.r-project.org/web/packages/glmnet&lt;/a&gt;)&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;flare&lt;/strong&gt; (&lt;a href="http://cran.r-project.org/web/packages/flare"&gt;http://cran.r-project.org/web/packages/flare&lt;/a&gt;)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;3) A blog post with an &lt;strong&gt;example of sparse regression solution&lt;/strong&gt;, based on &lt;code&gt;SparseM&lt;/code&gt;: &lt;a href="http://aleph-nought.blogspot.com/2012/03/multiple-linear-regression-with-sparse.html"&gt;http://aleph-nought.blogspot.com/2012/03/multiple-linear-regression-with-sparse.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;4) A blog post on using &lt;strong&gt;sparse matrices in R&lt;/strong&gt;, which includes a &lt;strong&gt;primer&lt;/strong&gt; on using &lt;code&gt;glmnet&lt;/code&gt;: &lt;a href="http://www.johnmyleswhite.com/notebook/2011/10/31/using-sparse-matrices-in-r"&gt;http://www.johnmyleswhite.com/notebook/2011/10/31/using-sparse-matrices-in-r&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;5) &lt;strong&gt;More examples and some discussion&lt;/strong&gt; on the topic can be found on &lt;strong&gt;StackOverflow&lt;/strong&gt;: &lt;a href="http://stackoverflow.com/questions/3169371/large-scale-regression-in-r-with-a-sparse-feature-matrix"&gt;http://stackoverflow.com/questions/3169371/large-scale-regression-in-r-with-a-sparse-feature-matrix&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE&lt;/strong&gt; (based on your comment):&lt;/p&gt;

&lt;p&gt;If you''re trying to solve an LP problem with constraints, you may find this &lt;strong&gt;theoretical paper&lt;/strong&gt; useful: &lt;a href="http://web.stanford.edu/group/SOL/papers/gmsw84.pdf"&gt;http://web.stanford.edu/group/SOL/papers/gmsw84.pdf&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Also, check R package &lt;strong&gt;limSolve&lt;/strong&gt;: &lt;a href="http://cran.r-project.org/web/packages/limSolve"&gt;http://cran.r-project.org/web/packages/limSolve&lt;/a&gt;. And, in general, check packages in &lt;em&gt;CRAN Task View&lt;/em&gt; &lt;strong&gt;"Optimization and Mathematical Programming"&lt;/strong&gt;: &lt;a href="http://cran.r-project.org/web/views/Optimization.html"&gt;http://cran.r-project.org/web/views/Optimization.html&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Finally, check the book &lt;strong&gt;"Using R for Numerical Analysis in Science and Engineering"&lt;/strong&gt; (by Victor A. Bloomfield). It has a section on solving systems of equations, represented by &lt;strong&gt;sparse matrices&lt;/strong&gt; (section 5.7, pages 99-104), which includes examples, based on some of the above-mentioned packages: &lt;a href="http://books.google.com/books?id=9ph_AwAAQBAJ&amp;amp;pg=PA99&amp;amp;lpg=PA99&amp;amp;dq=r+limsolve+sparse+matrix&amp;amp;source=bl&amp;amp;ots=PHDE8nXljQ&amp;amp;sig=sPi4n5Wk0M02ywkubq7R7KD_b04&amp;amp;hl=en&amp;amp;sa=X&amp;amp;ei=FZjiU-ioIcjmsATGkYDAAg&amp;amp;ved=0CDUQ6AEwAw#v=onepage&amp;amp;q=r%20limsolve%20sparse%20matrix&amp;amp;f=false"&gt;http://books.google.com/books?id=9ph_AwAAQBAJ&amp;amp;pg=PA99&amp;amp;lpg=PA99&amp;amp;dq=r+limsolve+sparse+matrix&amp;amp;source=bl&amp;amp;ots=PHDE8nXljQ&amp;amp;sig=sPi4n5Wk0M02ywkubq7R7KD_b04&amp;amp;hl=en&amp;amp;sa=X&amp;amp;ei=FZjiU-ioIcjmsATGkYDAAg&amp;amp;ved=0CDUQ6AEwAw#v=onepage&amp;amp;q=r%20limsolve%20sparse%20matrix&amp;amp;f=false&lt;/a&gt;. &lt;/p&gt;
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''www.stat.ncsu.edu/people/zhou/courses/st810/notes/lect23sparse.pdf'':61C ''www.stat.ncsu.edu/people/zhou/courses/st810/notes/lect23sparse.pdf&lt;/a&gt;&lt;/p&gt;'':64C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (919, 1, '2014-08-06 08:41:44.967', 5, 188, 'Which cross-validation type best suits to binary classification problem', '&lt;p&gt;Data set looks like:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;25000 observations&lt;/li&gt;
&lt;li&gt;up to 15 predictors of different types: numeric, multi-class categorical, binary&lt;/li&gt;
&lt;li&gt;target variable is binary&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Which cross validation method is typical for this type of problems?&lt;/p&gt;

&lt;p&gt;By default I''m using K-Fold. How many folds is enough in this case? (One of the models I use is random forest, which is time consuming...)&lt;/p&gt;
', 'classification  cross-validation', NULL, '''/li'':26C,41C,47C ''/p'':21C,61C,92C ''/ul'':48C ''15'':30C ''25000'':24C ''best'':6A ''binari'':9A,40C,46C ''case'':78C ''categor'':39C ''class'':38C ''classif'':10A,12B ''consum'':91C ''cross'':3A,14B,51C ''cross-valid'':2A,13B ''data'':17C ''default'':64C ''differ'':33C ''enough'':75C ''fold'':70C,73C ''forest'':87C ''k'':69C ''k-fold'':68C ''li'':23C,27C,42C ''like'':20C ''look'':19C ''m'':66C ''mani'':72C ''method'':53C ''model'':82C ''multi'':37C ''multi-class'':36C ''numer'':35C ''observ'':25C ''one'':79C ''p'':16C,49C,62C ''predictor'':31C ''problem'':11A,60C ''random'':86C ''set'':18C ''suit'':7A ''target'':43C ''time'':90C ''type'':5A,34C,58C ''typic'':55C ''ul'':22C ''use'':67C,84C ''valid'':4A,15B,52C ''variabl'':44C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8974, 5, '2015-11-22 05:42:25.853', 0, NULL, NULL, '', NULL, NULL, '');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8665, 1, '2015-10-31 00:28:41.197', 1, 65, 'Master of Statistics or Master of Data Science', '&lt;p&gt;Many Universities now offer Masters Degrees in data science; I''m curious as to people''s opinions on whether a traditional master of statistics is a better to fit for working in the field of in data science.&lt;/p&gt;
', 'statistics  education  academia', NULL, '''/p'':51C ''academia'':11B ''better'':39C ''curious'':24C ''data'':7A,20C,49C ''degre'':18C ''educ'':10B ''field'':46C ''fit'':41C ''m'':23C ''mani'':13C ''master'':1A,5A,17C,34C ''offer'':16C ''opinion'':29C ''p'':12C ''peopl'':27C ''scienc'':8A,21C,50C ''statist'':3A,9B,36C ''tradit'':33C ''univers'':14C ''whether'':31C ''work'':43C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8031, 2, '2015-09-07 17:53:45.567', 1, NULL, NULL, '&lt;p&gt;You have the option of joining a PhD program in business school and information school as well. There are quantitative professors and data scientists in business schools and information schools as well (About US, I am sure there are a lot of schools). This way you are qualified or even over-qualified in terms of quantitative and technical skills and you can spend your time on reinforcing other skills.&lt;/p&gt;
', NULL, NULL, '''/p'':72C ''busi'':12C,27C ''data'':24C ''even'':51C ''inform'':15C,30C ''join'':7C ''lot'':42C ''option'':5C ''over-qualifi'':52C ''p'':1C ''phd'':9C ''professor'':22C ''program'':10C ''qualifi'':49C,54C ''quantit'':21C,58C ''reinforc'':69C ''school'':13C,16C,28C,31C,44C ''scientist'':25C ''skill'':61C,71C ''spend'':65C ''sure'':38C ''technic'':60C ''term'':56C ''time'':67C ''us'':35C ''way'':46C ''well'':18C,33C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (924, 2, '2014-08-06 12:16:22.85', 4, NULL, NULL, '&lt;p&gt;Yes, you can do all this using the Caret (&lt;a href="http://caret.r-forge.r-project.org/training.html" rel="nofollow"&gt;http://caret.r-forge.r-project.org/training.html&lt;/a&gt;) package in R. For example,&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;fitControl &amp;lt;- trainControl(## 10-fold CV
                           method = "repeatedcv",
                           number = 10,
                           ## repeated ten times
                           repeats = 10)

gbmFit1 &amp;lt;- train(Class ~ ., data = training,
                 method = "gbm",
                 trControl = fitControl,
                ## This last option is actually one
                ## for gbm() that passes through
                verbose = FALSE)
gbmFit1
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;which will give the output&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Stochastic Gradient Boosting 

157 samples
 60 predictors
  2 classes: ''M'', ''R'' 

No pre-processing
Resampling: Cross-Validated (10 fold, repeated 10 times) 

Summary of sample sizes: 142, 142, 140, 142, 142, 141, ... 

Resampling results across tuning parameters:

  interaction.depth  n.trees  Accuracy  Kappa  Accuracy SD  Kappa SD
  1                  50       0.8       0.5    0.1          0.2     
  1                  100      0.8       0.6    0.1          0.2     
  1                  200      0.8       0.6    0.09         0.2     
  2                  50       0.8       0.6    0.1          0.2     
  2                  100      0.8       0.6    0.09         0.2     
  2                  200      0.8       0.6    0.1          0.2     
  3                  50       0.8       0.6    0.09         0.2     
  3                  100      0.8       0.6    0.09         0.2     
  3                  200      0.8       0.6    0.08         0.2     

Tuning parameter ''shrinkage'' was held constant at a value of 0.1
Accuracy was used to select the optimal model using  the largest value.
The final values used for the model were n.trees = 150, interaction.depth = 3     
and shrinkage = 0.1.
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Caret offers many other options as well so should be able to suit your needs. &lt;/p&gt;
', NULL, NULL, '''/code'':68C,218C ''/p'':26C,76C,236C ''/pre'':69C,219C ''/training.html'':15C ''/training.html&lt;/a&gt;)'':20C ''0.08'':178C ''0.09'':142C,154C,166C,172C ''0.1'':130C,136C,148C,160C,190C,217C ''0.2'':131C,137C,143C,149C,155C,161C,167C,173C,179C ''0.5'':129C ''0.6'':135C,141C,147C,153C,159C,165C,171C,177C ''0.8'':128C,134C,140C,146C,152C,158C,164C,170C,176C ''1'':126C,132C,138C ''10'':32C,38C,43C,98C,101C ''100'':133C,151C,169C ''140'':109C ''141'':112C ''142'':107C,108C,110C,111C ''150'':212C ''157'':82C ''2'':86C,144C,150C,156C ''200'':139C,157C,175C ''3'':162C,168C,174C,214C ''50'':127C,145C,163C ''60'':84C ''abl'':231C ''accuraci'':120C,122C,191C ''across'':115C ''actual'':58C ''boost'':81C ''caret'':10C,221C ''caret.r-forge.r-project.org'':14C,19C ''caret.r-forge.r-project.org/training.html'':13C ''caret.r-forge.r-project.org/training.html&lt;/a&gt;)'':18C ''class'':47C,87C ''code'':28C,78C ''constant'':185C ''cross'':96C ''cross-valid'':95C ''cv'':34C ''data'':48C ''exampl'':25C ''fals'':66C ''final'':204C ''fitcontrol'':29C,53C ''fold'':33C,99C ''gbm'':51C,61C ''gbmfit1'':44C,67C ''give'':73C ''gradient'':80C ''held'':184C ''href'':12C ''interaction.depth'':118C,213C ''kappa'':121C,124C ''largest'':201C ''last'':55C ''lt'':30C,45C ''m'':88C ''mani'':223C ''method'':35C,50C ''model'':198C,209C ''n.trees'':119C,211C ''need'':235C ''nofollow'':17C ''number'':37C ''offer'':222C ''one'':59C ''optim'':197C ''option'':56C,225C ''output'':75C ''p'':1C,70C,220C ''packag'':21C ''paramet'':117C,181C ''pass'':63C ''pre'':27C,77C,92C ''pre-process'':91C ''predictor'':85C ''process'':93C ''r'':23C,89C ''rel'':16C ''repeat'':39C,42C,100C ''repeatedcv'':36C ''resampl'':94C,113C ''result'':114C ''sampl'':83C,105C ''sd'':123C,125C ''select'':195C ''shrinkag'':182C,216C ''size'':106C ''stochast'':79C ''suit'':233C ''summari'':103C ''ten'':40C ''time'':41C,102C ''train'':46C,49C ''traincontrol'':31C ''trcontrol'':52C ''tune'':116C,180C ''use'':8C,193C,199C,206C ''valid'':97C ''valu'':188C,202C,205C ''verbos'':65C ''well'':227C ''yes'':2C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (925, 2, '2014-08-06 12:27:31.277', 0, NULL, NULL, '&lt;p&gt;Both methods work. However, if you retain all words in documents you would essentially be working with high dimensional vectors (each term representing one dimension). Consequently, a classifier, e.g. SVM, would take more time to converge.&lt;/p&gt;

&lt;p&gt;It is thus a standard practice to reduce the term-space dimensionality by pre-processing steps such as stop-word removal, stemming, Principal Component Analysis (PCA) etc.&lt;/p&gt;

&lt;p&gt;One approach could be to analyze the document corpora by a topic modelling technique such as LDA and then retaining only those words which are representative of the topics, i.e. those which have high membership values in a single topic class.&lt;/p&gt;

&lt;p&gt;Another approach (inspired by information retrieval) could be to retain the top K tf-idf terms from each document.&lt;/p&gt;
', NULL, NULL, '''/p'':38C,70C,113C,135C ''analysi'':67C ''analyz'':77C ''anoth'':115C ''approach'':73C,116C ''class'':112C ''classifi'':29C ''compon'':66C ''consequ'':27C ''converg'':37C ''corpora'':80C ''could'':74C,121C ''dimens'':26C ''dimension'':20C,52C ''document'':12C,79C,134C ''e.g'':30C ''essenti'':15C ''etc'':69C ''high'':19C,105C ''howev'':5C ''i.e'':101C ''idf'':130C ''inform'':119C ''inspir'':117C ''k'':127C ''lda'':88C ''membership'':106C ''method'':3C ''model'':84C ''one'':25C,72C ''p'':1C,39C,71C,114C ''pca'':68C ''practic'':45C ''pre'':55C ''pre-process'':54C ''princip'':65C ''process'':56C ''reduc'':47C ''remov'':63C ''repres'':24C,97C ''retain'':8C,91C,124C ''retriev'':120C ''singl'':110C ''space'':51C ''standard'':44C ''stem'':64C ''step'':57C ''stop'':61C ''stop-word'':60C ''svm'':31C ''take'':33C ''techniqu'':85C ''term'':23C,50C,131C ''term-spac'':49C ''tf'':129C ''tf-idf'':128C ''thus'':42C ''time'':35C ''top'':126C ''topic'':83C,100C,111C ''valu'':107C ''vector'':21C ''word'':10C,62C,94C ''work'':4C,17C ''would'':14C,32C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (926, 2, '2014-08-06 13:42:54.083', 6, NULL, NULL, '&lt;p&gt;&lt;a href="http://datascience.stackexchange.com/a/918/2853"&gt;Aleksandr''s answer&lt;/a&gt; is completely correct.&lt;/p&gt;

&lt;p&gt;However, the way the question is posed implies that this is a straightforward ordinary least squares regression question: minimizing the sum of squared residuals between a dependent variable and a linear combination of predictors.&lt;/p&gt;

&lt;p&gt;Now, while there may be many zeros in your design matrix, your system as such is not overly large: 300 observations on 40 predictors is no more than medium-sized. You can run such a regression using R without any special efforts for sparse data. Just use the &lt;code&gt;lm()&lt;/code&gt; command (for "linear model"). Use &lt;code&gt;?lm&lt;/code&gt; to see the help page. And note that &lt;code&gt;lm&lt;/code&gt; will by default silently add a constant column of ones to your design matrix (the intercept) - include a &lt;code&gt;-1&lt;/code&gt; on the right hand side of your formula to suppress this. Overall, assuming all your data (and nothing else) is in a &lt;code&gt;data.frame&lt;/code&gt; called &lt;code&gt;foo&lt;/code&gt;, you can do this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;model &amp;lt;- lm(y~.-1,data=foo)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;And then you can look at parameter estimates etc. like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;summary(model)
residuals(model)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;em&gt;If&lt;/em&gt; your system is &lt;em&gt;much&lt;/em&gt; larger, say on the order of 10,000 observations and hundreds of predictors, looking at specialized sparse solvers as per &lt;a href="http://datascience.stackexchange.com/a/918/2853"&gt;Aleksandr''s answer&lt;/a&gt; may start to make sense.&lt;/p&gt;

&lt;p&gt;Finally, in your comment to &lt;a href="http://datascience.stackexchange.com/a/918/2853"&gt;Aleksandr''s answer&lt;/a&gt;, you mention constraints on your equation. If that is actually your key issue, there are ways to calculate constrained least squares in R. I personally like &lt;code&gt;pcls()&lt;/code&gt; in the &lt;code&gt;mgcv&lt;/code&gt; package. Perhaps you want to edit your question to include the type of constraints (box constraints, nonnegativity constraints, integrality constraints, linear constraints, ...) you face?&lt;/p&gt;
', NULL, NULL, '''-1'':142C,184C ''/a'':10C,248C,269C ''/a/918/2853'':6C,244C,265C ''/code'':103C,111C,122C,143C,168C,172C,187C,208C,298C,303C ''/em'':213C,219C ''/p'':14C,50C,177C,201C,254C,328C ''/pre'':188C,209C ''000'':227C ''10'':226C ''300'':71C ''40'':74C ''actual'':279C ''add'':127C ''aleksandr'':7C,245C,266C ''answer'':9C,247C,268C ''assum'':156C ''box'':318C ''calcul'':287C ''call'':169C ''code'':101C,109C,120C,141C,166C,170C,179C,203C,296C,301C ''column'':130C ''combin'':47C ''command'':104C ''comment'':259C ''complet'':12C ''constant'':129C ''constrain'':288C ''constraint'':272C,317C,319C,321C,323C,325C ''correct'':13C ''data'':97C,159C,185C ''data.frame'':167C ''datascience.stackexchange.com'':5C,243C,264C ''datascience.stackexchange.com/a/918/2853'':4C,242C,263C ''default'':125C ''depend'':42C ''design'':61C,135C ''edit'':309C ''effort'':94C ''els'':162C ''em'':211C,217C ''equat'':275C ''estim'':197C ''etc'':198C ''face'':327C ''final'':256C ''foo'':171C,186C ''formula'':151C ''hand'':147C ''help'':115C ''howev'':16C ''href'':3C,241C,262C ''hundr'':230C ''impli'':23C ''includ'':139C,313C ''integr'':322C ''intercept'':138C ''issu'':282C ''key'':281C ''larg'':70C ''larger'':220C ''least'':30C,289C ''like'':199C,295C ''linear'':46C,106C,324C ''lm'':102C,110C,121C,182C ''look'':194C,233C ''lt'':181C ''make'':252C ''mani'':57C ''matrix'':62C,136C ''may'':55C,249C ''medium'':81C ''medium-s'':80C ''mention'':271C ''mgcv'':302C ''minim'':34C ''model'':107C,180C,205C,207C ''much'':218C ''nonneg'':320C ''note'':118C ''noth'':161C ''observ'':72C,228C ''one'':132C ''order'':224C ''ordinari'':29C ''over'':69C ''overal'':155C ''p'':1C,15C,51C,189C,210C,255C ''packag'':304C ''page'':116C ''paramet'':196C ''pcls'':297C ''per'':239C ''perhap'':305C ''person'':294C ''pose'':22C ''pre'':178C,202C ''predictor'':49C,75C,232C ''question'':20C,33C,311C ''r'':90C,292C ''regress'':32C,88C ''residu'':39C,206C ''right'':146C ''run'':85C ''say'':221C ''see'':113C ''sens'':253C ''side'':148C ''silent'':126C ''size'':82C ''solver'':237C ''spars'':96C,236C ''special'':93C,235C ''squar'':31C,38C,290C ''start'':250C ''straightforward'':28C ''sum'':36C ''summari'':204C ''suppress'':153C ''system'':64C,215C ''type'':315C ''use'':89C,99C,108C ''variabl'':43C ''want'':307C ''way'':18C,285C ''without'':91C ''y'':183C ''zero'':58C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (927, 1, '2014-08-06 15:07:07.457', 2, 357, 'how to impute missing values on numpy array created by train_test_split from pandas.DataFrame?', '&lt;p&gt;I''m working on the dataset with lots of NA values with sklearn and pandas.DataFrame. I implemented different imputation strategies for different columns of the dataFrame based column names. For example NAs predictor ''var1'' I impute with 0''s and for ''var2'' with mean.&lt;/p&gt;

&lt;p&gt;When I try to cross validate my model using train_test_split it returns me a nparray which does not have column names. How can I impute missing values in this nparray?&lt;/p&gt;

&lt;p&gt;P.S. I do not impute missing values in the original data set before splitting on purpose so I keep test and validation sets separately.&lt;/p&gt;
', 'pandas  cross-validation  sklearn', NULL, '''/p'':66C,100C,126C ''0'':59C ''array'':8A ''base'':48C ''column'':44C,49C,89C ''creat'':9A ''cross'':18B,72C ''cross-valid'':17B ''data'':112C ''datafram'':47C ''dataset'':27C ''differ'':39C,43C ''exampl'':52C ''implement'':38C ''imput'':3A,40C,57C,94C,106C ''keep'':120C ''lot'':29C ''m'':23C ''mean'':65C ''miss'':4A,95C,107C ''model'':75C ''na'':31C ''name'':50C,90C ''nas'':53C ''nparray'':84C,99C ''numpi'':7A ''origin'':111C ''p'':21C,67C,101C ''p.s'':102C ''panda'':16B ''pandas.dataframe'':15A,36C ''predictor'':54C ''purpos'':117C ''return'':81C ''separ'':125C ''set'':113C,124C ''sklearn'':20B,34C ''split'':13A,79C,115C ''strategi'':41C ''test'':12A,78C,121C ''train'':11A,77C ''tri'':70C ''use'':76C ''valid'':19B,73C,123C ''valu'':5A,32C,96C,108C ''var1'':55C ''var2'':63C ''work'':24C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (928, 2, '2014-08-06 15:10:59.6', 0, NULL, NULL, '&lt;p&gt;K-Fold should do just fine for binary classification problem. Depending on the time it is taking to train your model and predict the outcome I would use 10-20 folds.&lt;/p&gt;

&lt;p&gt;However sometimes a single fold takes several minutes, in this case I use 3-5 folds but not less than 3. Hope it helps.&lt;/p&gt;
', NULL, NULL, '''-20'':32C ''-5'':50C ''/p'':34C,60C ''10'':31C ''3'':49C,56C ''binari'':10C ''case'':46C ''classif'':11C ''depend'':13C ''fine'':8C ''fold'':4C,33C,40C,51C ''help'':59C ''hope'':57C ''howev'':36C ''k'':3C ''k-fold'':2C ''less'':54C ''minut'':43C ''model'':23C ''outcom'':27C ''p'':1C,35C ''predict'':25C ''problem'':12C ''sever'':42C ''singl'':39C ''sometim'':37C ''take'':19C,41C ''time'':16C ''train'':21C ''use'':30C,48C ''would'':29C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (930, 2, '2014-08-06 18:17:29.7', 0, NULL, NULL, '&lt;p&gt;From the link you mentioned in the comment, the train and test sets should be in the form of a  dataframe if you followed the first explanation.&lt;/p&gt;

&lt;p&gt;In that case, you could do something like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;df[variable] = df[variable].fillna(df[variable].median())
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;You have options on what to fill the N/A values with, check out the link.
&lt;a href="http://pandas.pydata.org/pandas-docs/stable/missing_data.html" rel="nofollow"&gt;http://pandas.pydata.org/pandas-docs/stable/missing_data.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;If you followed the second explanation, using sklearn''s cross-validation, you could implement  mike1886''s suggestion of transforming the arrays into dataframes and then use the fillna option.&lt;/p&gt;
', NULL, NULL, '''/code'':51C ''/p'':29C,40C,110C ''/pandas-docs/stable/missing_data.html'':73C ''/pandas-docs/stable/missing_data.html&lt;/a&gt;&lt;/p&gt;'':78C ''/pre'':52C ''array'':101C ''case'':33C ''check'':65C ''code'':42C ''comment'':9C ''could'':35C,93C ''cross'':90C ''cross-valid'':89C ''datafram'':22C,103C ''df'':43C,45C,48C ''explan'':28C,85C ''fill'':60C ''fillna'':47C,108C ''first'':27C ''follow'':25C,82C ''form'':19C ''href'':70C ''implement'':94C ''like'':38C ''link'':4C,68C ''median'':50C ''mention'':6C ''mike1886'':95C ''n/a'':62C ''nofollow'':75C ''option'':56C,109C ''p'':1C,30C,53C,79C ''pandas.pydata.org'':72C,77C ''pandas.pydata.org/pandas-docs/stable/missing_data.html'':71C ''pandas.pydata.org/pandas-docs/stable/missing_data.html&lt;/a&gt;&lt;/p&gt;'':76C ''pre'':41C ''rel'':74C ''second'':84C ''set'':14C ''sklearn'':87C ''someth'':37C ''suggest'':97C ''test'':13C ''train'':11C ''transform'':99C ''use'':86C,106C ''valid'':91C ''valu'':63C ''variabl'':44C,46C,49C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5618, 1, '2015-04-26 11:09:49.133', 2, 68, 'Correlation between time to event data and continuous data', '&lt;p&gt;I want to measure the correlation between the survival time which is a time to event data and the patient''s activity count which is measured on continuous scale. What type of correlation coefficient is available to measure the strength of these two variables?&lt;/p&gt;
', 'statistics', NULL, '''/p'':56C ''activ'':33C ''avail'':47C ''coeffici'':45C ''continu'':8A,39C ''correl'':1A,17C,44C ''count'':34C ''data'':6A,9A,28C ''event'':5A,27C ''measur'':15C,37C,49C ''p'':11C ''patient'':31C ''scale'':40C ''statist'':10B ''strength'':51C ''surviv'':20C ''time'':3A,21C,25C ''two'':54C ''type'':42C ''variabl'':55C ''want'':13C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (973, 1, '2014-08-13 20:53:15.443', 1, 45, 'feature weights in structured support vector machine', '&lt;p&gt;I like to find the weight vector for input-space features in a &lt;a href="http://en.wikipedia.org/wiki/Structured_support_vector_machine" rel="nofollow"&gt;structured SVM&lt;/a&gt;. The idea is to identify the most important set of input-space features (based on the magnitude of their corresponding weights). I know that in a binary SVM the weight vector can be written as a &lt;a href="http://pyml.sourceforge.net/doc/howto.pdf" rel="nofollow"&gt;linear combination of examples&lt;/a&gt;, and the magnitude of those weights represents how much they were effective for the prediction problem at hand. But how do you compute the same for an SSVM?&lt;/p&gt;
', 'svm', NULL, '''/a'':33C,82C ''/doc/howto.pdf'':75C ''/p'':111C ''/wiki/structured_support_vector_machine'':28C ''base'':48C ''binari'':61C ''combin'':79C ''comput'':105C ''correspond'':54C ''effect'':94C ''en.wikipedia.org'':27C ''en.wikipedia.org/wiki/structured_support_vector_machine'':26C ''exampl'':81C ''featur'':1A,21C,47C ''find'':13C ''hand'':100C ''href'':25C,72C ''idea'':35C ''identifi'':38C ''import'':41C ''input'':19C,45C ''input-spac'':18C,44C ''know'':57C ''like'':11C ''linear'':78C ''machin'':7A ''magnitud'':51C,85C ''much'':91C ''nofollow'':30C,77C ''p'':9C ''predict'':97C ''problem'':98C ''pyml.sourceforge.net'':74C ''pyml.sourceforge.net/doc/howto.pdf'':73C ''rel'':29C,76C ''repres'':89C ''set'':42C ''space'':20C,46C ''ssvm'':110C ''structur'':4A,31C ''support'':5A ''svm'':8B,32C,62C ''vector'':6A,16C,65C ''weight'':2A,15C,55C,64C,88C ''written'':68C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (931, 2, '2014-08-06 23:37:15.293', 2, NULL, NULL, '&lt;p&gt;To answer a simpler, but related question, namely ''How well can my model generalize on the data that I have?'' the method of learning curves might be applicable. &lt;a href="https://www.youtube.com/watch?v=g4XluwGYPaA" rel="nofollow"&gt;This&lt;/a&gt; is a lecture given by Andrew Ng about them.&lt;/p&gt;

&lt;p&gt;The basic idea is to plot test set error and training set error vs. the complexity of the model you are using (this can be somewhat complicated). If the model is powerful enough to fully ''understand'' your data, at some point the complexity of the model will be high enough that performance on the training set will be close to perfect. However, the variance of a complex model will likely cause the test set performance to increase at some point.&lt;/p&gt;

&lt;p&gt;This analysis tells you two main things, I think. The first is an upper limit on performance. It''s pretty unlikely that you''ll do better on data that you haven''t seen than on your training data. The other thing it tells you is whether or not getting more data might help. If you can demonstrate that you fully understand your training data by driving training error to zero it might be possible, through the inclusion of more data, to drive your test error further down by getting a more complete sample and then training a powerful model on that. &lt;/p&gt;
', NULL, NULL, '''/a'':38C ''/p'':48C,130C,233C ''/watch?v=g4xluwgypaa'':34C ''analysi'':133C ''andrew'':44C ''answer'':3C ''applic'':29C ''basic'':51C ''better'':157C ''caus'':120C ''close'':108C ''complet'':223C ''complex'':65C,92C,116C ''complic'':76C ''curv'':26C ''data'':18C,87C,159C,169C,182C,195C,211C ''demonstr'':188C ''drive'':197C,213C ''enough'':82C,99C ''error'':58C,62C,199C,216C ''first'':142C ''fulli'':84C,191C ''general'':15C ''get'':180C,220C ''given'':42C ''haven'':162C ''help'':184C ''high'':98C ''howev'':111C ''href'':31C ''idea'':52C ''inclus'':208C ''increas'':126C ''learn'':25C ''lectur'':41C ''like'':119C ''limit'':146C ''ll'':155C ''main'':137C ''method'':23C ''might'':27C,183C,203C ''model'':14C,68C,79C,95C,117C,230C ''name'':9C ''ng'':45C ''nofollow'':36C ''p'':1C,49C,131C ''perfect'':110C ''perform'':101C,124C,148C ''plot'':55C ''point'':90C,129C ''possibl'':205C ''power'':81C,229C ''pretti'':151C ''question'':8C ''rel'':35C ''relat'':7C ''sampl'':224C ''seen'':164C ''set'':57C,61C,105C,123C ''simpler'':5C ''somewhat'':75C ''tell'':134C,174C ''test'':56C,122C,215C ''thing'':138C,172C ''think'':140C ''train'':60C,104C,168C,194C,198C,227C ''two'':136C ''understand'':85C,192C ''unlik'':152C ''upper'':145C ''use'':71C ''varianc'':113C ''vs'':63C ''well'':11C ''whether'':177C ''www.youtube.com'':33C ''www.youtube.com/watch?v=g4xluwgypaa'':32C ''zero'':201C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (932, 2, '2014-08-07 03:15:09.933', 1, NULL, NULL, '&lt;p&gt;I have managed to resolve this. There is an excellent and thorough explanation of the optimization steps in the following thesis: &lt;a href="http://cs.stanford.edu/~pliang/papers/meng-thesis.pdf" rel="nofollow"&gt;Semi-Supervised Learning for Natural Language by Percy Liang&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;My mistake was trying to update the quality for all potential clusters pairs. Instead, you should initialize a table with the quality changes of doing each merge. Use this table to find the best merge, and the update the relevant terms that make up the table entries.&lt;/p&gt;
', NULL, NULL, '''/a'':40C ''/p'':41C,90C ''/~pliang/papers/meng-thesis.pdf'':27C ''best'':76C ''chang'':65C ''cluster'':54C ''cs.stanford.edu'':26C ''cs.stanford.edu/~pliang/papers/meng-thesis.pdf'':25C ''entri'':89C ''excel'':11C ''explan'':14C ''find'':74C ''follow'':21C ''href'':24C ''initi'':59C ''instead'':56C ''languag'':36C ''learn'':33C ''liang'':39C ''make'':85C ''manag'':4C ''merg'':69C,77C ''mistak'':44C ''natur'':35C ''nofollow'':29C ''optim'':17C ''p'':1C,42C ''pair'':55C ''perci'':38C ''potenti'':53C ''qualiti'':50C,64C ''rel'':28C ''relev'':82C ''resolv'':6C ''semi'':31C ''semi-supervis'':30C ''step'':18C ''supervis'':32C ''tabl'':61C,72C,88C ''term'':83C ''thesi'':22C ''thorough'':13C ''tri'':46C ''updat'':48C,80C ''use'':70C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (933, 2, '2014-08-07 09:33:50.817', 1, NULL, NULL, '&lt;p&gt;It dates back to 1998, so most likely has been abandoned, or "acquired" by microsoft as the creator currently works there and has done since publishing that research.&lt;/p&gt;

&lt;p&gt;see &lt;a href="http://research.microsoft.com/en-us/people/cyl/ists97.pdf" rel="nofollow"&gt;http://research.microsoft.com/en-us/people/cyl/ists97.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;and &lt;a href="http://research.microsoft.com/en-us/people/cyl" rel="nofollow"&gt;http://research.microsoft.com/en-us/people/cyl&lt;/a&gt; for the author. Maybe you could try to contact him.&lt;/p&gt;
', NULL, NULL, '''/en-us/people/cyl'':49C ''/en-us/people/cyl&lt;/a&gt;'':54C ''/en-us/people/cyl/ists97.pdf'':37C ''/en-us/people/cyl/ists97.pdf&lt;/a&gt;&lt;/p&gt;'':42C ''/p'':30C,65C ''1998'':6C ''abandon'':12C ''acquir'':14C ''author'':57C ''back'':4C ''contact'':63C ''could'':60C ''creator'':19C ''current'':20C ''date'':3C ''done'':25C ''href'':34C,46C ''like'':9C ''mayb'':58C ''microsoft'':16C ''nofollow'':39C,51C ''p'':1C,31C,43C ''publish'':27C ''rel'':38C,50C ''research'':29C ''research.microsoft.com'':36C,41C,48C,53C ''research.microsoft.com/en-us/people/cyl'':47C ''research.microsoft.com/en-us/people/cyl&lt;/a&gt;'':52C ''research.microsoft.com/en-us/people/cyl/ists97.pdf'':35C ''research.microsoft.com/en-us/people/cyl/ists97.pdf&lt;/a&gt;&lt;/p&gt;'':40C ''see'':32C ''sinc'':26C ''tri'':61C ''work'':21C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (934, 1, '2014-08-07 10:45:38.557', 3, 134, 'Handling huge dataset imbalance (2 class values) and appropriate ML algorithm', '&lt;p&gt;I have train and test sets of chronological data consisting of 305000 instances and 70000,appropriately. There are 15 features in each instance and only 2 possible class values ( NEW,OLD). The problem is that there are only 725 OLD instances in the train set and 95 in the test. &lt;/p&gt;

&lt;p&gt;The only algorithm which succeeds for me to handle imbalance is NaiveBayes in Weka (0.02 precision for OLD class), others (trees) classify each instance as NEW.
What is the best approach to handle the imbalance and the appropriate algorithm in such a case?&lt;/p&gt;

&lt;p&gt;Thank you in advance.&lt;/p&gt;
', 'machine-learning  dataset', NULL, '''/p'':67C,112C,118C ''0.02'':83C ''15'':35C ''2'':5A,42C ''305000'':28C ''70000'':31C ''725'':55C ''95'':63C ''advanc'':117C ''algorithm'':11A,71C,107C ''approach'':99C ''appropri'':9A,32C,106C ''best'':98C ''case'':111C ''chronolog'':24C ''class'':6A,44C,87C ''classifi'':90C ''consist'':26C ''data'':25C ''dataset'':3A,15B ''featur'':36C ''handl'':1A,77C,101C ''huge'':2A ''imbal'':4A,78C,103C ''instanc'':29C,39C,57C,92C ''learn'':14B ''machin'':13B ''machine-learn'':12B ''ml'':10A ''naivebay'':80C ''new'':46C,94C ''old'':47C,56C,86C ''other'':88C ''p'':16C,68C,113C ''possibl'':43C ''precis'':84C ''problem'':49C ''set'':22C,61C ''succeed'':73C ''test'':21C,66C ''thank'':114C ''train'':19C,60C ''tree'':89C ''valu'':7A,45C ''weka'':82C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (935, 2, '2014-08-07 12:46:25.123', 5, NULL, NULL, '&lt;p&gt;I''m not allowed to comment, but I have more a suggestion: you could try to implement some "Over-sampling Techniques" like SMOTE:
&lt;a href="http://scholar.google.com/scholar?q=oversampling+minority+classes"&gt;http://scholar.google.com/scholar?q=oversampling+minority+classes&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/scholar?q=oversampling+minority+classes'':30C ''/scholar?q=oversampling+minority+classes&lt;/a&gt;&lt;/p&gt;'':33C ''allow'':5C ''comment'':7C ''could'':15C ''href'':27C ''implement'':18C ''like'':24C ''m'':3C ''over-sampl'':20C ''p'':1C ''sampl'':22C ''scholar.google.com'':29C,32C ''scholar.google.com/scholar?q=oversampling+minority+classes'':28C ''scholar.google.com/scholar?q=oversampling+minority+classes&lt;/a&gt;&lt;/p&gt;'':31C ''smote'':25C ''suggest'':13C ''techniqu'':23C ''tri'':16C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (936, 1, '2014-08-07 15:12:48.617', -1, 97, 'R error using Knitr', '&lt;p&gt;I am attempting to compile code using Knitr in R.&lt;/p&gt;

&lt;p&gt;My code below is returning the following error, and causes errors in the rest of the document.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;miss&amp;lt;-sample$sensor_glucose[!is.na(sample$sensor_glucose)]
# Error: "## Warning: is.na() applied to non-(list or vector) of type ''NULL''"

str(miss)
# int [1:103] 213 113 46 268 186 196 187 153 43 175 ...
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Does anyone know how to remedy this problem?&lt;/p&gt;

&lt;p&gt;Thanks in advance!&lt;/p&gt;
', 'r  error-handling', NULL, '''/code'':78C ''/p'':20C,39C,89C,94C ''/pre'':79C ''1'':66C ''103'':67C ''113'':69C ''153'':75C ''175'':77C ''186'':72C ''187'':74C ''196'':73C ''213'':68C ''268'':71C ''43'':76C ''46'':70C ''advanc'':93C ''anyon'':82C ''appli'':54C ''attempt'':12C ''caus'':31C ''code'':15C,23C,41C ''compil'':14C ''document'':38C ''error'':2A,7B,29C,32C,51C ''error-handl'':6B ''follow'':28C ''glucos'':46C,50C ''handl'':8B ''int'':65C ''is.na'':47C,53C ''knitr'':4A,17C ''know'':83C ''list'':57C ''lt'':43C ''miss'':42C,64C ''non'':56C ''null'':62C ''p'':9C,21C,80C,90C ''pre'':40C ''problem'':88C ''r'':1A,5B,19C ''remedi'':86C ''rest'':35C ''return'':26C ''sampl'':44C,48C ''sensor'':45C,49C ''str'':63C ''thank'':91C ''type'':61C ''use'':3A,16C ''vector'':59C ''warn'':52C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (937, 1, '2014-08-07 15:33:43.793', 6, 2397, 'Does scikit-learn have forward selection/stepwise regression algorithm?', '&lt;p&gt;I''m working on the problem with too many features and training my models takes way too long. I implemented forward selection algorithm to choose features.&lt;/p&gt;

&lt;p&gt;However, I was wondering does scikit-learn have forward selection/stepwise regression algorithm?&lt;/p&gt;
', 'feature-selection  sklearn  scikit', NULL, '''/p'':42C,57C ''algorithm'':9A,38C,56C ''choos'':40C ''featur'':11B,25C,41C ''feature-select'':10B ''forward'':6A,36C,53C ''howev'':44C ''implement'':35C ''learn'':4A,51C ''long'':33C ''m'':17C ''mani'':24C ''model'':29C ''p'':15C,43C ''problem'':21C ''regress'':8A,55C ''scikit'':3A,14B,50C ''scikit-learn'':2A,49C ''select'':12B,37C ''selection/stepwise'':7A,54C ''sklearn'':13B ''take'':30C ''train'':27C ''way'':31C ''wonder'':47C ''work'':18C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (938, 2, '2014-08-07 17:49:05.64', 1, NULL, NULL, '&lt;p&gt;You can apply a clustering algorithm to the instances in the majority class and train a classifier with the centroids/medoids offered by the cluster algorithm. This is subsampling the majority class, the converse of oversampling the minority class. &lt;/p&gt;
', NULL, NULL, '''/p'':40C ''algorithm'':7C,26C ''appli'':4C ''centroids/medoids'':21C ''class'':14C,32C,39C ''classifi'':18C ''cluster'':6C,25C ''convers'':34C ''instanc'':10C ''major'':13C,31C ''minor'':38C ''offer'':22C ''oversampl'':36C ''p'':1C ''subsampl'':29C ''train'':16C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (939, 2, '2014-08-07 17:53:06.97', 3, NULL, NULL, '&lt;p&gt;You will have best results if you care to build the folds so that each variable (and most importantly the target variable) is approximately identically distributed in each fold. This is called, when applied to the target variable, stratified k-fold. One approach is to cluster the inputs and make sure each fold contains the same number of instances from each cluster proportional to their size.&lt;/p&gt;
', NULL, NULL, '''/p'':69C ''appli'':35C ''approach'':45C ''approxim'':25C ''best'':5C ''build'':11C ''call'':33C ''care'':9C ''cluster'':48C,64C ''contain'':56C ''distribut'':27C ''fold'':13C,30C,43C,55C ''ident'':26C ''import'':20C ''input'':50C ''instanc'':61C ''k'':42C ''k-fold'':41C ''make'':52C ''number'':59C ''one'':44C ''p'':1C ''proport'':65C ''result'':6C ''size'':68C ''stratifi'':40C ''sure'':53C ''target'':22C,38C ''variabl'':17C,23C,39C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (966, 2, '2014-08-12 09:52:01.153', 15, NULL, NULL, '&lt;p&gt;&lt;strong&gt;Text Classification&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I give you a bunch of documents, each of which has a label attached. I ask you to learn why you think the contents of the documents have been given these labels based on their words. Then I give you new documents and ask what you think the label for each one should be. The labels have meaning to me, not to you necessarily.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Topic Modeling&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I give you a bunch of documents, without labels. I ask you to explain why the documents have the words they do by identifying some topics that each is "about". You tell me the topics, by telling me how much of each is in each document, and I decide what the topics "mean" if anything.&lt;/p&gt;

&lt;p&gt;You''d have to clarify what you me by "identify one topic" or "classify the text".&lt;/p&gt;
', NULL, NULL, '''/p'':6C,72C,78C,135C,153C ''/strong'':5C,77C ''anyth'':134C ''ask'':23C,51C,90C ''attach'':21C ''base'':40C ''bunch'':12C,84C ''clarifi'':141C ''classif'':4C ''classifi'':150C ''content'':31C ''d'':138C ''decid'':128C ''document'':14C,34C,49C,86C,96C,125C ''explain'':93C ''give'':9C,46C,81C ''given'':37C ''identifi'':103C,146C ''label'':20C,39C,56C,63C,88C ''learn'':26C ''mean'':65C,132C ''model'':76C ''much'':119C ''necessarili'':71C ''new'':48C ''one'':59C,147C ''p'':1C,7C,73C,79C,136C ''strong'':2C,74C ''tell'':111C,116C ''text'':3C,152C ''think'':29C,54C ''topic'':75C,105C,114C,131C,148C ''without'':87C ''word'':43C,99C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (940, 1, '2014-08-07 22:30:52.913', 2, 208, 'Database for a trie, or other appropriate structure for recommendation engine', '&lt;p&gt;We are storing the information about our users showing interest in our items. Based on this information, we would like to create a simple recommendation engine that will take the items I1, I2, I3 etc of the current user, search for all other users that had shown interest in those items, and then output the items I4, I5, I6 etc of the other users, sorted by their decreasing popularity. So, basically, the standard "other buyer were also interested in..." functionality.&lt;/p&gt;

&lt;p&gt;I''m asking myself what kind of a database is suitable for a realtime recommendation engine like this. My current idea is to build a trie of item IDs, then sort the item IDs of the current user (as the order of items is irrelevant) and to go down the trie; the children of the last trie node will build the needed output.&lt;/p&gt;

&lt;p&gt;The problem is that we have 2 million items so that according to our estimation the trie will have at least 1E12 nodes, so that we probably need a distributed sharded database to store it. Before we reinvent the wheel, are there any ready-to-use databases or generally, non-cloud solutions for recommendation engines out there?&lt;/p&gt;
', 'bigdata  recommendation  databases', NULL, '''/p'':96C,161C,222C ''1e12'':184C ''2'':169C ''accord'':174C ''also'':92C ''appropri'':7A ''ask'':100C ''base'':29C ''basic'':86C ''bigdata'':12B ''build'':121C,157C ''buyer'':90C ''children'':150C ''cloud'':215C ''creat'':37C ''current'':53C,117C,134C ''databas'':1A,14B,106C,194C,210C ''decreas'':83C ''distribut'':192C ''engin'':11A,41C,113C,219C ''estim'':177C ''etc'':50C,75C ''function'':95C ''general'':212C ''go'':145C ''i1'':47C ''i2'':48C ''i3'':49C ''i4'':72C ''i5'':73C ''i6'':74C ''id'':126C,131C ''idea'':118C ''inform'':20C,32C ''interest'':25C,63C,93C ''irrelev'':142C ''item'':28C,46C,66C,71C,125C,130C,140C,171C ''kind'':103C ''last'':153C ''least'':183C ''like'':35C,114C ''m'':99C ''million'':170C ''need'':159C,190C ''node'':155C,185C ''non'':214C ''non-cloud'':213C ''order'':138C ''output'':69C,160C ''p'':15C,97C,162C ''popular'':84C ''probabl'':189C ''problem'':164C ''readi'':207C ''ready-to-us'':206C ''realtim'':111C ''recommend'':10A,13B,40C,112C,218C ''reinvent'':200C ''search'':55C ''shard'':193C ''show'':24C ''shown'':62C ''simpl'':39C ''solut'':216C ''sort'':80C,128C ''standard'':88C ''store'':18C,196C ''structur'':8A ''suitabl'':108C ''take'':44C ''trie'':4A,123C,148C,154C,179C ''use'':209C ''user'':23C,54C,59C,79C,135C ''wheel'':202C ''would'':34C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (3777, 2, '2014-12-29 09:21:26.78', 0, NULL, NULL, '&lt;p&gt;Essentially machine learning uses non parametric methods...The assumption is that you have enough data and (computation)  time
 You Identify best model by cross validation (rather than eg  assessing significance of coefficients) ,  and estimate prediction error by using test set.  Confidence intervals can also be generated by bootstrapping. &lt;/p&gt;
', NULL, NULL, '''/p'':50C ''also'':45C ''assess'':30C ''assumpt'':10C ''best'':22C ''bootstrap'':49C ''coeffici'':33C ''comput'':18C ''confid'':42C ''cross'':25C ''data'':16C ''eg'':29C ''enough'':15C ''error'':37C ''essenti'':2C ''estim'':35C ''generat'':47C ''identifi'':21C ''interv'':43C ''learn'':4C ''machin'':3C ''method'':8C ''model'':23C ''non'':6C ''p'':1C ''parametr'':7C ''predict'':36C ''rather'':27C ''set'':41C ''signific'':31C ''test'':40C ''time'':19C ''use'':5C,39C ''valid'':26C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1005, 2, '2014-08-20 03:06:01.753', 3, NULL, NULL, '&lt;p&gt;Another idea is to combine OpenStreetMap project map data, for example, using corresponding nice R package (&lt;a href="http://www.r-bloggers.com/the-openstreetmap-package-opens-up" rel="nofollow"&gt;http://www.r-bloggers.com/the-openstreetmap-package-opens-up&lt;/a&gt;), with census data (population census data, such as the US data: &lt;a href="http://www.census.gov/data/data-tools.html" rel="nofollow"&gt;http://www.census.gov/data/data-tools.html&lt;/a&gt;, as well as census data in other categories: &lt;a href="http://national.census.okfn.org" rel="nofollow"&gt;http://national.census.okfn.org&lt;/a&gt;) to analyze temporal patterns of geosocial trends. HTH.&lt;/p&gt;
', NULL, NULL, '''/a'':63C ''/data/data-tools.html'':43C ''/data/data-tools.html&lt;/a&gt;,'':48C ''/p'':72C ''/the-openstreetmap-package-opens-up'':22C ''/the-openstreetmap-package-opens-up&lt;/a&gt;),'':27C ''analyz'':65C ''anoth'':2C ''categori'':56C ''census'':29C,32C,52C ''combin'':6C ''correspond'':14C ''data'':10C,30C,33C,38C,53C ''exampl'':12C ''geosoci'':69C ''href'':19C,40C,58C ''hth'':71C ''idea'':3C ''map'':9C ''national.census.okfn.org'':59C,62C ''nice'':15C ''nofollow'':24C,45C,61C ''openstreetmap'':7C ''p'':1C ''packag'':17C ''pattern'':67C ''popul'':31C ''project'':8C ''r'':16C ''rel'':23C,44C,60C ''tempor'':66C ''trend'':70C ''us'':37C ''use'':13C ''well'':50C ''www.census.gov'':42C,47C ''www.census.gov/data/data-tools.html'':41C ''www.census.gov/data/data-tools.html&lt;/a&gt;,'':46C ''www.r-bloggers.com'':21C,26C ''www.r-bloggers.com/the-openstreetmap-package-opens-up'':20C ''www.r-bloggers.com/the-openstreetmap-package-opens-up&lt;/a&gt;),'':25C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (941, 2, '2014-08-08 00:15:05.527', 2, NULL, NULL, '&lt;p&gt;I agree with @ssdecontrol that a minimal &lt;em&gt;reproducible example&lt;/em&gt; would be the most helpful. However, looking at your code (pay attention to the sequence &lt;code&gt;Error: ... Warning: ...&lt;/code&gt;), I believe that the issue you are experiencing is due to an inappropriate setting of R''s global &lt;code&gt;warn&lt;/code&gt; &lt;strong&gt;option&lt;/strong&gt;. It appears that your current setting is likely &lt;code&gt;2&lt;/code&gt;, which refers to &lt;strong&gt;converting warnings to errors&lt;/strong&gt;, whereas, you, most likely want the setting &lt;code&gt;1&lt;/code&gt;, which is to treat warnings as such, without converting them to errors. If that is the case, you just need to set the option appropriately:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;options(warn=1)  # print warnings as they occur
options(warn=2)  # treat warnings as errors
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Note for moderators/administrators&lt;/strong&gt;: This question seems not to be a &lt;em&gt;data science&lt;/em&gt; question, but purely an &lt;em&gt;R&lt;/em&gt; question. Therefore, I think it should be moved to &lt;em&gt;StackOverflow&lt;/em&gt;, where it belongs.&lt;/p&gt;
', NULL, NULL, '''/code'':31C,52C,66C,85C,129C ''/em'':12C,147C,154C,166C ''/p'':111C,170C ''/pre'':130C ''/strong'':55C,75C,136C ''1'':84C,116C ''2'':65C,124C ''agre'':3C ''appear'':57C ''appropri'':110C ''attent'':24C ''believ'':33C ''belong'':169C ''case'':102C ''code'':22C,28C,50C,64C,83C,113C ''convert'':71C,94C ''current'':60C ''data'':145C ''due'':41C ''em'':9C,144C,152C,164C ''error'':29C,74C,97C,128C ''exampl'':11C ''experienc'':39C ''global'':49C ''help'':17C ''howev'':18C ''inappropri'':44C ''issu'':36C ''like'':63C,79C ''look'':19C ''minim'':8C ''moderators/administrators'':135C ''move'':162C ''need'':105C ''note'':133C ''occur'':121C ''option'':54C,109C,114C,122C ''p'':1C,131C ''pay'':23C ''pre'':112C ''print'':117C ''pure'':150C ''question'':138C,148C,155C ''r'':47C,153C ''refer'':68C ''reproduc'':10C ''scienc'':146C ''seem'':139C ''sequenc'':27C ''set'':45C,61C,82C,107C ''ssdecontrol'':5C ''stackoverflow'':165C ''strong'':53C,70C,132C ''therefor'':156C ''think'':158C ''treat'':89C,125C ''want'':80C ''warn'':30C,51C,72C,90C,115C,118C,123C,126C ''wherea'':76C ''without'':93C ''would'':13C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (8675, 2, '2015-10-31 18:51:06.72', 3, NULL, NULL, '&lt;p&gt;A simple approach would be using the same thing as box plots does: away than 1.5 (median-q1) or 1.5 (q3-median) = outlier.&lt;/p&gt;

&lt;p&gt;I find it useful in lots of cases even it not perfect and maybe too simple.&lt;/p&gt;

&lt;p&gt;It has the advantage to not suppose normality.&lt;/p&gt;
', NULL, NULL, '''/p'':27C,45C,55C ''1.5'':17C,22C ''advantag'':50C ''approach'':4C ''away'':15C ''box'':12C ''case'':36C ''even'':37C ''find'':30C ''lot'':34C ''mayb'':42C ''median'':19C,25C ''median-q1'':18C ''normal'':54C ''outlier'':26C ''p'':1C,28C,46C ''perfect'':40C ''plot'':13C ''q1'':20C ''q3'':24C ''q3-median'':23C ''simpl'':3C,44C ''suppos'':53C ''thing'':10C ''use'':7C,32C ''would'':5C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (942, 2, '2014-08-08 00:25:04.21', 4, NULL, NULL, '&lt;p&gt;You mentioned regression and PCA in the title, and there is a definite answer for each of those.&lt;/p&gt;

&lt;p&gt;The asymptotic complexity of linear regression reduces to O(P^2 * N) if N &gt; P, where P is the number of features and N is the number of observations. More detail in &lt;a href="http://math.stackexchange.com/a/84503/117452"&gt;Computational complexity of least square regression operation&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Vanilla PCA is O(P^2 * N + P ^ 3), as in &lt;a href="http://scicomp.stackexchange.com/q/3220"&gt;Fastest PCA algorithm for high-dimensional data&lt;/a&gt;. However fast algorithms exist for very large matrices, explained in that answer and &lt;a href="http://stats.stackexchange.com/q/2806/36229"&gt;Best PCA Algorithm For Huge Number of Features?&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;However I don''t think anyone''s compiled a single lit review or reference or  book on the subject. Might not be a bad project for my free time...&lt;/p&gt;
', NULL, NULL, '''/a'':66C,93C,120C ''/a/84503/117452'':58C ''/p'':20C,67C,121C,152C ''/q/2806/36229'':111C ''/q/3220'':84C ''2'':32C,74C ''3'':77C ''algorithm'':87C,96C,114C ''answer'':15C,105C ''anyon'':128C ''asymptot'':23C ''bad'':146C ''best'':112C ''book'':138C ''compil'':130C ''complex'':24C,60C ''comput'':59C ''data'':92C ''definit'':14C ''detail'':52C ''dimension'':91C ''exist'':97C ''explain'':102C ''fast'':95C ''fastest'':85C ''featur'':43C,119C ''free'':150C ''high'':90C ''high-dimension'':89C ''howev'':94C,123C ''href'':55C,81C,108C ''huge'':116C ''larg'':100C ''least'':62C ''linear'':26C ''lit'':133C ''math.stackexchange.com'':57C ''math.stackexchange.com/a/84503/117452'':56C ''matric'':101C ''mention'':3C ''might'':142C ''n'':33C,35C,45C,75C ''number'':41C,48C,117C ''o'':30C,72C ''observ'':50C ''oper'':65C ''p'':1C,21C,31C,36C,38C,68C,73C,76C,122C ''pca'':6C,70C,86C,113C ''project'':147C ''reduc'':28C ''refer'':136C ''regress'':4C,27C,64C ''review'':134C ''scicomp.stackexchange.com'':83C ''scicomp.stackexchange.com/q/3220'':82C ''singl'':132C ''squar'':63C ''stats.stackexchange.com'':110C ''stats.stackexchange.com/q/2806/36229'':109C ''subject'':141C ''think'':127C ''time'':151C ''titl'':9C ''vanilla'':69C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (944, 2, '2014-08-08 07:12:52.68', 1, NULL, NULL, '&lt;p&gt;In addition to undersampling the majority class (i.e. taking only a few NEW), you may consider oversampling the minority class (in essence, duplicating your OLDs, but there are other smarter ways to do that)&lt;/p&gt;

&lt;p&gt;Note that oversampling may lead to overfitting, so pay special attention to testing your classifiers &lt;/p&gt;

&lt;p&gt;Check also this answer on CV: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://stats.stackexchange.com/a/108325/49130"&gt;http://stats.stackexchange.com/a/108325/49130&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a/108325/49130'':68C ''/a/108325/49130&lt;/a&gt;&lt;/li&gt;'':71C ''/p'':36C,53C,61C ''/ul'':72C ''addit'':3C ''also'':56C ''answer'':58C ''attent'':48C ''check'':55C ''class'':8C,21C ''classifi'':52C ''consid'':17C ''cv'':60C ''duplic'':24C ''essenc'':23C ''href'':65C ''i.e'':9C ''lead'':42C ''li'':63C ''major'':7C ''may'':16C,41C ''minor'':20C ''new'':14C ''note'':38C ''old'':26C ''overfit'':44C ''oversampl'':18C,40C ''p'':1C,37C,54C ''pay'':46C ''smarter'':31C ''special'':47C ''stats.stackexchange.com'':67C,70C ''stats.stackexchange.com/a/108325/49130'':66C ''stats.stackexchange.com/a/108325/49130&lt;/a&gt;&lt;/li&gt;'':69C ''take'':10C ''test'':50C ''ul'':62C ''undersampl'':5C ''way'':32C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (950, 2, '2014-08-08 19:23:33.637', 0, NULL, NULL, '&lt;p&gt;The approximation Binomial(k,n,p) ~= Poisson(k,s) (where s = n*p) can be shown under the assumptions: &lt;br/&gt;
1) n &gt;&gt; k (to say that n!/(n-k)! ~= n^k), &lt;br/&gt;
2) p &amp;lt;&amp;lt;1 (to say that (1-p)^(n-k) ~= (1-p)^n). &lt;br/&gt;
It''s up to you whether those are sufficiently satisfied. If the exact calculation can be done quickly, in my opinion, it''s nice to stay with that.&lt;/p&gt;

&lt;p&gt;Also since, if the probability of row 3 sample is different from the row 1 sample, it would almost certainly be on the lower side. It would probably best for you to use &lt;br/&gt;
binom.test(7, 767, p=(26/753), alternative=''less'')
the final option indicating that the alternative to your null hypothesis is that the probability is less than 26/753, not equal to. Of course, that''s simply just the sum of Binomial probabilities from 0 to 7 (you can check yourself), the interpretation being that &lt;em&gt;this&lt;/em&gt; is the probability of having gotten at most 7 rolls from random chance, if the probability truly was 26/753.&lt;/p&gt;

&lt;p&gt;Keep in mind the interpretation of that last sentence. These kinds of tests are generally used when we &lt;em&gt;know&lt;/em&gt; what the inherent probability is that we''re comparing to (e.g. to see if the set of coin flips has a probability significantly different from 1/2 which is what we expect from a fair coin). In this case, we don''t &lt;em&gt;know&lt;/em&gt; what the probability is that we''re comparing to, we''re just making the very crude guess that the 26/753 outcome of row 1 reflects the true probability. It''s better than a regular Normal t-test in this case, but don''t put &lt;em&gt;too&lt;/em&gt; much stock in it unless you have a much higher sample size for row 1.&lt;/p&gt;
', NULL, NULL, '''/em'':170C,212C,256C,304C ''/p'':80C,190C,320C ''0'':157C ''1'':22C,39C,43C,48C,96C,280C,319C ''1/2'':238C ''2'':35C ''26/753'':120C,141C,189C,276C ''3'':89C ''7'':117C,159C,179C ''767'':118C ''almost'':100C ''also'':82C ''altern'':121C,129C ''approxim'':3C ''assumpt'':20C ''best'':110C ''better'':287C ''binom.test'':116C ''binomi'':4C,154C ''br'':21C,34C,51C,115C ''calcul'':65C ''case'':250C,297C ''certain'':101C ''chanc'':183C ''check'':162C ''coin'':230C,247C ''compar'':221C,264C ''cours'':146C ''crude'':272C ''differ'':92C,236C ''done'':68C ''e.g'':223C ''em'':168C,210C,254C,302C ''equal'':143C ''exact'':64C ''expect'':243C ''fair'':246C ''final'':124C ''flip'':231C ''general'':206C ''gotten'':176C ''guess'':273C ''higher'':314C ''hypothesi'':133C ''indic'':126C ''inher'':215C ''interpret'':165C,196C ''k'':5C,9C,24C,31C,33C,47C ''keep'':192C ''kind'':202C ''know'':211C,255C ''last'':199C ''less'':122C,139C ''lower'':105C ''lt'':37C,38C ''make'':269C ''mind'':194C ''much'':305C,313C ''n'':6C,13C,23C,28C,30C,32C,46C,50C ''n-k'':29C,45C ''nice'':75C ''normal'':291C ''null'':132C ''opinion'':72C ''option'':125C ''outcom'':277C ''p'':1C,7C,14C,36C,44C,49C,81C,119C,191C ''poisson'':8C ''probabl'':86C,109C,137C,155C,173C,186C,216C,234C,259C,284C ''put'':301C ''quick'':69C ''random'':182C ''re'':220C,263C,267C ''reflect'':281C ''regular'':290C ''roll'':180C ''row'':88C,95C,279C,318C ''sampl'':90C,97C,315C ''satisfi'':61C ''say'':26C,41C ''see'':225C ''sentenc'':200C ''set'':228C ''shown'':17C ''side'':106C ''signific'':235C ''simpli'':149C ''sinc'':83C ''size'':316C ''stay'':77C ''stock'':306C ''suffici'':60C ''sum'':152C ''t-test'':292C ''test'':204C,294C ''true'':283C ''truli'':187C ''unless'':309C ''use'':114C,207C ''whether'':57C ''would'':99C,108C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (988, 2, '2014-08-18 08:49:11.933', 2, NULL, NULL, '&lt;p&gt;Here is a couple of tools that may be worth a look:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Bart, an open source tool that have been used for several languages, including German. &lt;a href="http://www.bart-coref.org/" rel="nofollow"&gt;Available from the website&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;Sucre is a tool developed at the University of Stuttgart. I don''t know if it''s available easily. &lt;a href="http://hnk.ffzg.hr/bibl/acl2011/CoNLL-ST/pdf/CoNLL-ST3.pdf" rel="nofollow"&gt;You can see this paper about it&lt;/a&gt;.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':40C,76C ''/bibl/acl2011/conll-st/pdf/conll-st3.pdf'':66C ''/li'':41C,77C ''/p'':14C ''/ul'':78C ''avail'':36C,60C ''bart'':17C ''coupl'':5C ''develop'':47C ''easili'':61C ''german'':30C ''hnk.ffzg.hr'':65C ''hnk.ffzg.hr/bibl/acl2011/conll-st/pdf/conll-st3.pdf'':64C ''href'':32C,63C ''includ'':29C ''know'':56C ''languag'':28C ''li'':16C,42C ''look'':13C ''may'':9C ''nofollow'':35C,68C ''open'':19C ''p'':1C ''paper'':73C ''rel'':34C,67C ''see'':71C ''sever'':27C ''sourc'':20C ''stuttgart'':52C ''sucr'':43C ''tool'':7C,21C,46C ''ul'':15C ''univers'':50C ''use'':25C ''websit'':39C ''worth'':11C ''www.bart-coref.org'':33C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (945, 1, '2014-08-08 10:01:25.4', 4, 126, 'Classifying Java exceptions', '&lt;p&gt;We have a classification algorithm to categorize Java exceptions in Production.
This algorithm is based on hierarchical human defined rules so when a bunch of text forming an exception comes up, it determines what kind of exception is (development, availability, configuration, etc.) and the responsible component (the most inner component responsible of the exception). In Java an exception can have several causing exceptions, and the whole must be analyzed.&lt;/p&gt;

&lt;p&gt;For example, given the following example exception:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;com.myapp.CustomException: Error printing ...
... (stack)
Caused by: com.foo.webservice.RemoteException: Unable to communicate ...
... (stack)
Caused by: com.acme.PrintException: PrintServer002: Timeout ....
... (stack)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;First of all, our algorithm splits the whole stack in three isolated exceptions. Afterwards it starts analyzing these exceptions starting from the most inner one. In this case, it determines that this exception (the second caused by) is of type &lt;code&gt;Availability&lt;/code&gt; and that the responsible component is a "print server". This is because there is a rule that matches containing the word &lt;code&gt;Timeout&lt;/code&gt; associated to the &lt;code&gt;Availability&lt;/code&gt; type. There is also a rule that matches &lt;code&gt;com.acme.PrintException&lt;/code&gt; and determines that the responsible component is a print server. As all the information needed is determined using only the most inner exception, the upper exceptions are ignored, but this is not always the case.&lt;/p&gt;

&lt;p&gt;As you can see this kind of approximation is very complex (and chaotic) as a human have to create new rules as new exceptions appear. Besides, the new rules have to be compatible with the current ones because a new rule for classifying a new exception must not change the classification of any of the already classified exceptions.&lt;/p&gt;

&lt;p&gt;We are thinking about using Machine Learning to automate this process. Obviously, I am not asking for a solution here as I know the complexity but I''d really appreciate some advice to achieve our goal.&lt;/p&gt;
', 'machine-learning  classification  algorithms', NULL, '''/code'':108C,153C,177C,183C,194C ''/p'':79C,88C,230C,290C,328C ''/pre'':109C ''achiev'':325C ''advic'':323C ''afterward'':124C ''algorithm'':8B,14C,22C,115C ''alreadi'':287C ''also'':187C ''alway'':227C ''analyz'':78C,127C ''appear'':256C ''appreci'':321C ''approxim'':239C ''ask'':307C ''associ'':178C ''autom'':300C ''avail'':49C,152C,182C ''base'':24C ''besid'':257C ''bunch'':33C ''case'':138C,229C ''categor'':16C ''caus'':71C,95C,102C,146C ''chang'':280C ''chaotic'':244C ''classif'':7B,13C,282C ''classifi'':1A,274C,288C ''code'':90C,151C,175C,181C,192C ''com.acme.printexception'':104C,193C ''com.foo.webservice.remoteexception'':97C ''com.myapp.customexception'':91C ''come'':39C ''communic'':100C ''compat'':264C ''complex'':242C,316C ''compon'':55C,59C,158C,200C ''configur'':50C ''contain'':172C ''creat'':250C ''current'':267C ''d'':319C ''defin'':28C ''determin'':42C,140C,196C,211C ''develop'':48C ''error'':92C ''etc'':51C ''exampl'':82C,86C ''except'':3A,18C,38C,46C,63C,67C,72C,87C,123C,129C,143C,217C,220C,255C,277C,289C ''first'':111C ''follow'':85C ''form'':36C ''given'':83C ''goal'':327C ''hierarch'':26C ''human'':27C,247C ''ignor'':222C ''inform'':208C ''inner'':58C,134C,216C ''isol'':122C ''java'':2A,17C,65C ''kind'':44C,237C ''know'':314C ''learn'':6B,298C ''machin'':5B,297C ''machine-learn'':4B ''match'':171C,191C ''must'':76C,278C ''need'':209C ''new'':251C,254C,259C,271C,276C ''obvious'':303C ''one'':135C,268C ''p'':9C,80C,110C,231C,291C ''pre'':89C ''print'':93C,161C,203C ''printserver002'':105C ''process'':302C ''product'':20C ''realli'':320C ''respons'':54C,60C,157C,199C ''rule'':29C,169C,189C,252C,260C,272C ''second'':145C ''see'':235C ''server'':162C,204C ''sever'':70C ''solut'':310C ''split'':116C ''stack'':94C,101C,107C,119C ''start'':126C,130C ''text'':35C ''think'':294C ''three'':121C ''timeout'':106C,176C ''type'':150C,184C ''unabl'':98C ''upper'':219C ''use'':212C,296C ''whole'':75C,118C ''word'':174C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (948, 1, '2014-08-08 16:49:13.85', 1, 226, 'Any clear winner for Data Science in Scala?', '&lt;p&gt;I have found a number of libraries and tools for data science in Scala, I would like to know about which one has more adoption and which one is gaining adoption at a faster pace and to what extent this is the case. Basically, which one should I bet for (if any at this point).&lt;/p&gt;

&lt;p&gt;Some of the tools I''ve found are (in no particular order):&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Scalding&lt;/li&gt;
&lt;li&gt;Breeze&lt;/li&gt;
&lt;li&gt;Spark&lt;/li&gt;
&lt;li&gt;Saddle&lt;/li&gt;
&lt;li&gt;H2O&lt;/li&gt;
&lt;li&gt;Spire&lt;/li&gt;
&lt;li&gt;Mahout&lt;/li&gt;
&lt;li&gt;Hadoop&lt;/li&gt;
&lt;li&gt;MongoDB&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;If I need to be more specific to make the question answerable: I''m not particularly interested in clusters and Big Data at this moment, but I''m interested in sizable data (up to 100 GB) for information integration and predictive analytics.&lt;/p&gt;
', 'tools', NULL, '''/li'':84C,87C,90C,93C,96C,99C,102C,105C,108C ''/p'':66C,80C,153C ''/ul'':109C ''100'':145C ''adopt'':35C,41C ''analyt'':152C ''answer'':122C ''basic'':54C ''bet'':59C ''big'':131C ''breez'':86C ''case'':53C ''clear'':2A ''cluster'':129C ''data'':5A,21C,132C,142C ''extent'':49C ''faster'':44C ''found'':13C,74C ''gain'':40C ''gb'':146C ''h2o'':95C ''hadoop'':104C ''inform'':148C ''integr'':149C ''interest'':127C,139C ''know'':29C ''li'':82C,85C,88C,91C,94C,97C,100C,103C,106C ''librari'':17C ''like'':27C ''m'':124C,138C ''mahout'':101C ''make'':119C ''moment'':135C ''mongodb'':107C ''need'':113C ''number'':15C ''one'':32C,38C,56C ''order'':79C ''p'':10C,67C,110C ''pace'':45C ''particular'':78C,126C ''point'':65C ''predict'':151C ''question'':121C ''saddl'':92C ''scala'':8A,24C ''scald'':83C ''scienc'':6A,22C ''sizabl'':141C ''spark'':89C ''specif'':117C ''spire'':98C ''tool'':9B,19C,71C ''ul'':81C ''ve'':73C ''winner'':3A ''would'':26C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (962, 1, '2014-08-12 03:50:52.303', 10, 2204, 'what is difference between text classification and topic models?', '&lt;p&gt;I know the difference between clustering and classification in machine learning, but I don''t understand the difference between text classification and topic modeling for documents. Can I use topic modeling over documents to identify a topic? Can I use classification methods to classify the text inside these documents?  &lt;/p&gt;
', 'classification  text-mining  topic-model', NULL, '''/p'':67C ''classif'':6A,10B,25C,38C,58C ''classifi'':61C ''cluster'':23C ''differ'':3A,21C,35C ''document'':43C,50C,66C ''identifi'':52C ''insid'':64C ''know'':19C ''learn'':28C ''machin'':27C ''method'':59C ''mine'':13B ''model'':9A,16B,41C,48C ''p'':17C ''text'':5A,12B,37C,63C ''text-min'':11B ''topic'':8A,15B,40C,47C,54C ''topic-model'':14B ''understand'':33C ''use'':46C,57C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (949, 1, '2014-08-08 17:32:48.377', 2, 303, 'Pig Rank function not generating rank in output', '&lt;p&gt;I am facing this bizarre issue while using &lt;code&gt;Apache Pig&lt;/code&gt; &lt;strong&gt;rank&lt;/strong&gt; utility. I am executing the following code:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;email_id_ranked = rank email_id;
store email_id_ranked into ''/tmp/'';
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So, basically I am trying to get the following result&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;1,email1
2,email2
3,email3
... 
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Issue is sometime pig dumps the above result but sometimes it dumps only the emails without the rank. Also when I dump the data on screen using &lt;code&gt;dump&lt;/code&gt; function pig returns both the columns. I don''t know where the issue is. Kindly advice.&lt;/p&gt;

&lt;p&gt;Please let me know if you need any more information. Thanks in advance.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Pig version: Apache Pig version 0.11.0-cdh4.6.0&lt;/strong&gt;&lt;/p&gt;
', 'bigdata  hadoop  apache-pig', NULL, '''/code'':26C,52C,74C,106C ''/p'':37C,65C,123C,138C,149C ''/pre'':53C,75C ''/strong'':29C,148C ''/tmp'':51C ''0.11.0'':146C ''1'':68C ''2'':70C ''3'':72C ''advanc'':137C ''advic'':122C ''also'':95C ''apach'':12B,24C,143C ''apache-pig'':11B ''basic'':56C ''bigdata'':9B ''bizarr'':19C ''cdh4.6.0'':147C ''code'':23C,36C,39C,67C,104C ''column'':112C ''data'':100C ''dump'':81C,88C,98C,105C ''email'':40C,44C,47C,91C ''email1'':69C ''email2'':71C ''email3'':73C ''execut'':33C ''face'':17C ''follow'':35C,63C ''function'':3A,107C ''generat'':5A ''get'':61C ''hadoop'':10B ''id'':41C,45C,48C ''inform'':134C ''issu'':20C,77C,119C ''kind'':121C ''know'':116C,128C ''let'':126C ''need'':131C ''output'':8A ''p'':14C,54C,76C,124C,139C ''pig'':1A,13B,25C,80C,108C,141C,144C ''pleas'':125C ''pre'':38C,66C ''rank'':2A,6A,28C,42C,43C,49C,94C ''result'':64C,84C ''return'':109C ''screen'':102C ''sometim'':79C,86C ''store'':46C ''strong'':27C,140C ''thank'':135C ''tri'':59C ''use'':22C,103C ''util'':30C ''version'':142C,145C ''without'':92C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (974, 1, '2014-08-14 00:50:51.103', 9, 910, 'Nearest neighbors search for very high dimensional data', '&lt;p&gt;I have a big sparse matrix of users and items they like (in the order of 1M users and 100K items, with a very low level of sparsity). I''m exploring ways in which I could perform kNN search on it. Given the size of my dataset and some initial tests I performed, my assumption is that the method I will use will need to be either parallel or distributed. So I''m considering two classes of possible solutions: one that is either available (or implementable in a reasonably easy way) on a single multicore machine, the other on a Spark cluster, i.e. as a MapReduce program. Here are three broad ideas that I considered:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Assuming a cosine similarity metric, perform the full multiplication of the normalized matrix by its transpose (implemented as a sum of outer products)&lt;/li&gt;
&lt;li&gt;Using locality-sensitive hashing (LSH)&lt;/li&gt;
&lt;li&gt;Reducing first the dimensionality of the problem with a PCA&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I''d appreciate any thoughts or advices about possible other ways in which I could tackle this problem.&lt;/p&gt;
', 'machine-learning  distributed  map-reduce  dimensionality-reduction', NULL, '''/li'':161C,169C,181C ''/p'':135C,202C ''/ul'':182C ''100k'':39C ''1m'':36C ''advic'':190C ''appreci'':186C ''assum'':138C ''assumpt'':74C ''avail'':103C ''big'':23C ''broad'':130C ''class'':95C ''cluster'':121C ''consid'':93C,134C ''cosin'':140C ''could'':55C,198C ''d'':185C ''data'':8A ''dataset'':66C ''dimension'':7A,17B,174C ''dimensionality-reduct'':16B ''distribut'':12B,89C ''easi'':109C ''either'':86C,102C ''explor'':50C ''first'':172C ''full'':145C ''given'':61C ''hash'':167C ''high'':6A ''i.e'':122C ''idea'':131C ''implement'':105C,154C ''initi'':69C ''item'':29C,40C ''knn'':57C ''learn'':11B ''level'':45C ''li'':137C,162C,170C ''like'':31C ''local'':165C ''locality-sensit'':164C ''low'':44C ''lsh'':168C ''m'':49C,92C ''machin'':10B,115C ''machine-learn'':9B ''map'':14B ''map-reduc'':13B ''mapreduc'':125C ''matrix'':25C,150C ''method'':78C ''metric'':142C ''multicor'':114C ''multipl'':146C ''nearest'':1A ''need'':83C ''neighbor'':2A ''normal'':149C ''one'':99C ''order'':34C ''outer'':159C ''p'':19C,183C ''parallel'':87C ''pca'':180C ''perform'':56C,72C,143C ''possibl'':97C,192C ''problem'':177C,201C ''product'':160C ''program'':126C ''reason'':108C ''reduc'':15B,171C ''reduct'':18B ''search'':3A,58C ''sensit'':166C ''similar'':141C ''singl'':113C ''size'':63C ''solut'':98C ''spark'':120C ''spars'':24C ''sparsiti'':47C ''sum'':157C ''tackl'':199C ''test'':70C ''thought'':188C ''three'':129C ''transpos'':153C ''two'':94C ''ul'':136C ''use'':81C,163C ''user'':27C,37C ''way'':51C,110C,194C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (954, 1, '2014-08-11 07:59:22.78', 3, 178, 'OpenNLP Coreference Resolution (German)', '&lt;p&gt;I need to do coreference resolution for German texts and I plan to use OpenNLP to perform this task.&lt;/p&gt;

&lt;p&gt;As far as I know OpenNLP coreference resolution does not support the German language.&lt;/p&gt;

&lt;p&gt;Which components/data do I need to adapt the code such that it is possible to perform coreference resolution for German texts?&lt;/p&gt;
', 'nlp  text-mining', NULL, '''/p'':29C,45C,68C ''adapt'':53C ''code'':55C ''components/data'':48C ''corefer'':2A,14C,37C,63C ''far'':32C ''german'':4A,17C,43C,66C ''know'':35C ''languag'':44C ''mine'':8B ''need'':11C,51C ''nlp'':5B ''opennlp'':1A,24C,36C ''p'':9C,30C,46C ''perform'':26C,62C ''plan'':21C ''possibl'':60C ''resolut'':3A,15C,38C,64C ''support'':41C ''task'':28C ''text'':7B,18C,67C ''text-min'':6B ''use'':23C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (955, 1, '2014-08-11 12:25:47.7', 4, 99, 'Coreference Resolution for German Texts', '&lt;p&gt;does anybody know a libarary for performing coreference resolution on German texts?&lt;/p&gt;

&lt;p&gt;As far as I know OpenNLP and Standord NLP are not able to perform coreference resolution for German Texts.&lt;/p&gt;

&lt;p&gt;The only tool that I know is &lt;a href="http://www.cl.uzh.ch/static/news.php?om=view&amp;amp;nid=163" rel="nofollow"&gt;CorZu&lt;/a&gt; which is a python library.&lt;/p&gt;
', 'nlp', NULL, '''/a'':58C ''/p'':20C,41C,64C ''/static/news.php?om=view&amp;amp;nid=163'':54C ''abl'':33C ''anybodi'':9C ''corefer'':1A,15C,36C ''corzu'':57C ''far'':23C ''german'':4A,18C,39C ''href'':51C ''know'':10C,26C,48C ''libarari'':12C ''librari'':63C ''nlp'':6B,30C ''nofollow'':56C ''opennlp'':27C ''p'':7C,21C,42C ''perform'':14C,35C ''python'':62C ''rel'':55C ''resolut'':2A,16C,37C ''standord'':29C ''text'':5A,19C,40C ''tool'':45C ''www.cl.uzh.ch'':53C ''www.cl.uzh.ch/static/news.php?om=view&amp;amp;nid=163'':52C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (956, 2, '2014-08-11 12:48:52.32', 1, NULL, NULL, '&lt;p&gt;I had to create a web based dashboard. My main charting tool was d3js. But I needed to use ggplot2 to generate few charts. Through d3js''s ggplot2 extension, I could create the same. If your charts can be generated through existing extension, then web has better alternatives. Later you can export them to PDF for distribution. &lt;/p&gt;
', NULL, NULL, '''/p'':59C ''altern'':49C ''base'':8C ''better'':48C ''chart'':12C,25C,38C ''could'':32C ''creat'':5C,33C ''d3js'':15C,27C ''dashboard'':9C ''distribut'':58C ''exist'':43C ''export'':53C ''extens'':30C,44C ''generat'':23C,41C ''ggplot2'':21C,29C ''later'':50C ''main'':11C ''need'':18C ''p'':1C ''pdf'':56C ''tool'':13C ''use'':20C ''web'':7C,46C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (963, 1, '2014-08-12 07:57:03.283', 8, 815, 'Sentiment data for Emoji', '&lt;p&gt;For experimenting we''d like to use the &lt;a href="https://en.wikipedia.org/wiki/Emoji"&gt;Emoji&lt;/a&gt; embedded in many Tweets as a ground truth/training data for simple quantitative senitment analysis. Tweets are usually too unstructured for NLP to work well.&lt;/p&gt;

&lt;p&gt;Anyway, there are 722 Emoji in Unicode 6.0, and probably another 250 will be added in Unicode 7.0.&lt;/p&gt;

&lt;p&gt;Is there a database (like e.g. SentiWordNet) that contains sentiment annotations for them?&lt;/p&gt;

&lt;p&gt;(Note that SentiWordNet does allow for &lt;em&gt;ambiguous&lt;/em&gt; meanings, too. Consider e.g. &lt;a href="http://sentiwordnet.isti.cnr.it/search.php?q=funny"&gt;funny&lt;/a&gt;, which is not just positive: "this tastes funny" is probably not positive... same will hold for &lt;code&gt;;-)&lt;/code&gt; for example. But I don''t think this is harder for Emoji than it is for regular words...)&lt;/p&gt;

&lt;p&gt;Also, if you have experience with using them for sentiment analysis, I''d be interested to hear.&lt;/p&gt;
', 'machine-learning  classification  parsing', NULL, '''/a'':25C,106C ''/code'':124C ''/em'':95C ''/p'':50C,70C,85C,143C,162C ''/search.php?q=funny'':104C ''/wiki/emoji'':23C ''250'':63C ''6.0'':59C ''7.0'':69C ''722'':55C ''ad'':66C ''allow'':91C ''also'':145C ''ambigu'':94C ''analysi'':39C,155C ''annot'':82C ''anoth'':62C ''anyway'':52C ''classif'':8B ''code'':123C ''consid'':98C ''contain'':80C ''d'':14C,157C ''data'':2A,34C ''databas'':75C ''e.g'':77C,99C ''em'':93C ''embed'':26C ''emoji'':4A,24C,56C,136C ''en.wikipedia.org'':22C ''en.wikipedia.org/wiki/emoji'':21C ''exampl'':126C ''experi'':12C,149C ''funni'':105C,114C ''ground'':32C ''harder'':134C ''hear'':161C ''hold'':121C ''href'':20C,101C ''interest'':159C ''learn'':7B ''like'':15C,76C ''machin'':6B ''machine-learn'':5B ''mani'':28C ''mean'':96C ''nlp'':46C ''note'':87C ''p'':10C,51C,71C,86C,144C ''pars'':9B ''posit'':111C,118C ''probabl'':61C,116C ''quantit'':37C ''regular'':141C ''senit'':38C ''sentiment'':1A,81C,154C ''sentiwordnet'':78C,89C ''sentiwordnet.isti.cnr.it'':103C ''sentiwordnet.isti.cnr.it/search.php?q=funny'':102C ''simpl'':36C ''tast'':113C ''think'':131C ''truth/training'':33C ''tweet'':29C,40C ''unicod'':58C,68C ''unstructur'':44C ''use'':17C,151C ''usual'':42C ''well'':49C ''word'':142C ''work'':48C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1025, 1, '2014-08-23 13:56:43.813', 9, 133, 'implementing temporal difference in chess', '&lt;p&gt;I have been developing a chess program which makes use of alpha-beta pruning algorithm and an evaluation function that evaluates positions using the following features namely material, kingsafety, mobility, pawn-structure and trapped pieces etc..... My evaluation function is derived from the &lt;/p&gt;

&lt;p&gt;$$f(p) = w_1 \cdot \text{material} + w_2 \cdot \text{kingsafety} + w_3 \cdot \text{mobility} + w_4 \cdot \text{pawn-structure} + w_5 \cdot \text{trapped pieces}$$&lt;/p&gt;

&lt;p&gt;where $w$ is the weight assigned to each feature. At this point i want to tune the weights of my evaluation function using temporal difference, where the agent plays against itself and in the process gather training data from its environment (which is a form of reinforcement learning). I have read some books and articles in order to have an insight on how to implement this in Java but they seem to be theoretical rather than practical. I need a detailed explanation and pseudo codes on how to automatically tune the weights of my evaluation function based on previous games.&lt;/p&gt;
', 'machine-learning  algorithms  reinforcement-learning', NULL, '''/p'':59C,91C,193C ''1'':64C ''2'':69C ''3'':74C ''4'':79C ''5'':86C ''agent'':120C ''algorithm'':9B,29C ''alpha'':26C ''alpha-beta'':25C ''articl'':147C ''assign'':98C ''automat'':181C ''base'':189C ''beta'':27C ''book'':145C ''cdot'':65C,70C,75C,80C,87C ''chess'':5A,19C ''code'':177C ''data'':130C ''deriv'':56C ''detail'':173C ''develop'':17C ''differ'':3A,117C ''environ'':133C ''etc'':51C ''evalu'':32C,35C,53C,113C,187C ''explan'':174C ''f'':61C ''featur'':40C,101C ''follow'':39C ''form'':137C ''function'':33C,54C,114C,188C ''game'':192C ''gather'':128C ''implement'':1A,157C ''insight'':153C ''java'':160C ''kingsafeti'':43C,72C ''learn'':8B,12B,140C ''machin'':7B ''machine-learn'':6B ''make'':22C ''materi'':42C,67C ''mobil'':44C,77C ''name'':41C ''need'':171C ''order'':149C ''p'':13C,60C,62C,92C ''pawn'':46C,83C ''pawn-structur'':45C,82C ''piec'':50C,90C ''play'':121C ''point'':104C ''posit'':36C ''practic'':169C ''previous'':191C ''process'':127C ''program'':20C ''prune'':28C ''pseudo'':176C ''rather'':167C ''read'':143C ''reinforc'':11B,139C ''reinforcement-learn'':10B ''seem'':163C ''structur'':47C,84C ''tempor'':2A,116C ''text'':66C,71C,76C,81C,88C ''theoret'':166C ''train'':129C ''trap'':49C,89C ''tune'':108C,182C ''use'':23C,37C,115C ''w'':63C,68C,73C,78C,85C,94C ''want'':106C ''weight'':97C,110C,184C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (957, 1, '2014-08-11 15:19:02.047', 2, 143, 'Multiple seasonality with ARIMA?', '&lt;p&gt;I know that ARIMA can''t detect multiple seasonality, but it is possible to &lt;a href="http://robjhyndman.com/hyndsight/dailydata/" rel="nofollow"&gt;use fourier functions to add a second seasonality&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;I need to forecast gas consumption composed by a daily, weekly (week days-weekend), yearly seasonality. Does it make sense to apply three times the &lt;a href="http://stat.ethz.ch/R-manual/R-devel/library/stats/html/stl.html" rel="nofollow"&gt;STL decomposition by LOESS&lt;/a&gt;? The reason is that I applied the fourier method and I have bad results but I don''t know if it is only because I applied it wrong.&lt;/p&gt;

&lt;p&gt;I''m interested in the theoretical explanation, but here you find also the code:&lt;/p&gt;

&lt;p&gt;ARIMA + 2 STL:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;b &amp;lt;- ts(drop(coredata(dat.ts)), deltat=1/12/30/24, start=1)
fit &amp;lt;- stl(b, s.window="periodic")
b &amp;lt;- seasadj(fit)
dat.ts &amp;lt;- xts(b, index(dat.ts))

# The weekdays are extracted
dat.weekdays &amp;lt;- dat.ts[.indexwday(dat.ts) %in% 1:5]
dat.weekdaysTS &amp;lt;- ts(drop(coredata(dat.weekdays)), frequency=24, start=1)
fit &amp;lt;- stl(dat.weekdaysTS, s.window="periodic")
dat.weekdaysTS &amp;lt;- seasadj(fit)

arima &amp;lt;- Arima(dat.weekdaysTS, order=c(3,0,5))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;With fourier:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dat.weekdays &amp;lt;- dat.ts[.indexwday(dat.ts) %in% 1:5]
dat.weekdaysTS &amp;lt;- ts(drop(coredata(dat.weekdays)), frequency=24, start=1)
z &amp;lt;- fourier(ts(dat.weekdaysTS, frequency=365.25), K=5)
arima &amp;lt;- Arima(dat.weekdaysTS, order=c(3,0,5),xreg=z)
&lt;/code&gt;&lt;/pre&gt;
', 'time-series', NULL, '''/a'':38C,78C ''/code'':198C,244C ''/hyndsight/dailydata/'':27C ''/p'':39C,107C,123C,128C,203C ''/pre'':199C,245C ''/r-manual/r-devel/library/stats/html/stl.html'':71C ''0'':196C,240C ''1'':140C,167C,178C,212C,223C ''1/12/30/24'':138C ''2'':126C ''24'':176C,221C ''3'':195C,239C ''365.25'':230C ''5'':168C,197C,213C,232C,241C ''add'':34C ''also'':120C ''appli'':63C,84C,104C ''arima'':4A,12C,125C,189C,191C,233C,235C ''b'':131C,144C,147C,154C ''bad'':91C ''c'':194C,238C ''code'':122C,130C,205C ''compos'':47C ''consumpt'':46C ''coredata'':135C,173C,218C ''daili'':50C ''dat.ts'':136C,151C,156C,163C,165C,208C,210C ''dat.weekdays'':161C,174C,206C,219C ''dat.weekdaysts'':169C,182C,185C,192C,214C,228C,236C ''day'':54C ''days-weekend'':53C ''decomposit'':75C ''deltat'':137C ''detect'':15C ''drop'':134C,172C,217C ''explan'':115C ''extract'':160C ''find'':119C ''fit'':141C,150C,179C,188C ''forecast'':44C ''fourier'':31C,86C,202C,226C ''frequenc'':175C,220C,229C ''function'':32C ''gas'':45C ''href'':24C,68C ''index'':155C ''indexwday'':164C,209C ''interest'':111C ''k'':231C ''know'':10C,97C ''loess'':77C ''lt'':132C,142C,148C,152C,162C,170C,180C,186C,190C,207C,215C,225C,234C ''m'':110C ''make'':60C ''method'':87C ''multipl'':1A,16C ''need'':42C ''nofollow'':29C,73C ''order'':193C,237C ''p'':8C,40C,108C,124C,200C ''period'':146C,184C ''possibl'':21C ''pre'':129C,204C ''reason'':80C ''rel'':28C,72C ''result'':92C ''robjhyndman.com'':26C ''robjhyndman.com/hyndsight/dailydata/'':25C ''s.window'':145C,183C ''seasadj'':149C,187C ''season'':2A,17C,37C,57C ''second'':36C ''sens'':61C ''seri'':7B ''start'':139C,177C,222C ''stat.ethz.ch'':70C ''stat.ethz.ch/r-manual/r-devel/library/stats/html/stl.html'':69C ''stl'':74C,127C,143C,181C ''theoret'':114C ''three'':64C ''time'':6B,65C ''time-seri'':5B ''ts'':133C,171C,216C,227C ''use'':30C ''week'':51C,52C ''weekday'':158C ''weekend'':55C ''wrong'':106C ''xreg'':242C ''xts'':153C ''year'':56C ''z'':224C,243C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (958, 2, '2014-08-11 16:05:02.197', 1, NULL, NULL, '&lt;p&gt;Have a look at &lt;a href="https://mahout.apache.org/" rel="nofollow"&gt;Apache Mahout&lt;/a&gt;. Last version features also user-item-based recommenders.&lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/p'':23C ''also'':17C ''apach'':11C ''base'':21C ''featur'':16C ''href'':7C ''item'':20C ''last'':14C ''look'':4C ''mahout'':12C ''mahout.apache.org'':8C ''nofollow'':10C ''p'':1C ''recommend'':22C ''rel'':9C ''user'':19C ''user-item-bas'':18C ''version'':15C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (959, 2, '2014-08-11 18:33:37.023', 5, NULL, NULL, '&lt;p&gt;While I am not a data scientist, I am an epidemiologist working in a clinical setting. Your research question did not specify a time period (ie odds of developing CKD in 1 year, 10 years, lifetime?).&lt;/p&gt;

&lt;p&gt;Generally, I would go through a number of steps before even thinking about modeling (univariate analysis, bivariate analysis, colinearity checks, etc). However, the most commonly used method for trying to predict a binary event (using continuous OR binary variables) is logistic regression. If you wanted to look at CKD as a lab value (urine albumin, eGFR) you would use linear regression (continuous outcome).&lt;/p&gt;

&lt;p&gt;While the methods used should be informed by your data and questions, clinicians are used to seeing odds ratios and risk ratios as these the most commonly reported measures of association in medical journals such as NEJM and JAMA. &lt;/p&gt;

&lt;p&gt;If you are working on this problem from a human health perspective (as opposed to Business Intelligence) this Steyerberg''s &lt;a href="http://www.springer.com/medicine/internal/book/978-0-387-77243-1"&gt;Clinical Prediction Models&lt;/a&gt; is an excellent resource. &lt;/p&gt;
', NULL, NULL, '''/a'':174C ''/medicine/internal/book/978-0-387-77243-1'':170C ''/p'':38C,103C,144C,179C ''1'':33C ''10'':35C ''albumin'':94C ''analysi'':55C,57C ''associ'':135C ''binari'':72C,77C ''bivari'':56C ''busi'':161C ''check'':59C ''ckd'':31C,88C ''clinic'':16C,171C ''clinician'':117C ''colinear'':58C ''common'':64C,131C ''continu'':75C,101C ''data'':7C,114C ''develop'':30C ''egfr'':95C ''epidemiologist'':12C ''etc'':60C ''even'':50C ''event'':73C ''excel'':177C ''general'':40C ''go'':43C ''health'':156C ''howev'':61C ''href'':167C ''human'':155C ''ie'':27C ''inform'':111C ''intellig'':162C ''jama'':143C ''journal'':138C ''lab'':91C ''lifetim'':37C ''linear'':99C ''logist'':80C ''look'':86C ''measur'':133C ''medic'':137C ''method'':66C,107C ''model'':53C,173C ''nejm'':141C ''number'':46C ''odd'':28C,122C ''oppos'':159C ''outcom'':102C ''p'':1C,39C,104C,145C ''period'':26C ''perspect'':157C ''predict'':70C,172C ''problem'':152C ''question'':20C,116C ''ratio'':123C,126C ''regress'':81C,100C ''report'':132C ''research'':19C ''resourc'':178C ''risk'':125C ''scientist'':8C ''see'':121C ''set'':17C ''specifi'':23C ''step'':48C ''steyerberg'':164C ''think'':51C ''time'':25C ''tri'':68C ''univari'':54C ''urin'':93C ''use'':65C,74C,98C,108C,119C ''valu'':92C ''variabl'':78C ''want'':84C ''work'':13C,149C ''would'':42C,97C ''www.springer.com'':169C ''www.springer.com/medicine/internal/book/978-0-387-77243-1'':168C ''year'':34C,36C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (960, 2, '2014-08-11 19:21:50.137', 1, NULL, NULL, '&lt;p&gt;I have to agree that k-fold should do "just" fine. However, there is a nice article about the "Bootstrap .632+" method (basically a smoothened cross validation) that is supposed to be superior (however, they did the comparisons on not-binary data as far as I can tell)&lt;/p&gt;

&lt;p&gt;Maybe you want to check out this article here: &lt;a href="http://www.jstor.org/stable/2965703" rel="nofollow"&gt;http://www.jstor.org/stable/2965703&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':52C ''/stable/2965703'':67C ''/stable/2965703&lt;/a&gt;&lt;/p&gt;'':72C ''632'':23C ''agre'':5C ''articl'':19C,61C ''basic'':25C ''binari'':44C ''bootstrap'':22C ''check'':58C ''comparison'':40C ''cross'':28C ''data'':45C ''far'':47C ''fine'':13C ''fold'':9C ''howev'':14C,36C ''href'':64C ''k'':8C ''k-fold'':7C ''mayb'':54C ''method'':24C ''nice'':18C ''nofollow'':69C ''not-binari'':42C ''p'':1C,53C ''rel'':68C ''smoothen'':27C ''superior'':35C ''suppos'':32C ''tell'':51C ''valid'':29C ''want'':56C ''www.jstor.org'':66C,71C ''www.jstor.org/stable/2965703'':65C ''www.jstor.org/stable/2965703&lt;/a&gt;&lt;/p&gt;'':70C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9536, 1, '2015-12-27 16:52:01.12', 4, 131, 'How is it possible to process an image with a few neurons?', '&lt;p&gt;An 1024*1024 pixel image has around one million pixels. If I would like to connect each pixel to an R,G,B input neuron, then more than 3 million neurons are needed.&lt;/p&gt;

&lt;p&gt;It would be really hard, to train a neural network, which has millions of inputs. How is it possible, to reduce the number of neurons?&lt;/p&gt;
', 'machine-learning  neuralnetwork  deep-learning  image-classification', NULL, '''/p'':57C,84C ''1024'':25C,26C ''3'':52C ''around'':30C ''b'':46C ''classif'':22B ''connect'':39C ''deep'':18B ''deep-learn'':17B ''g'':45C ''hard'':63C ''imag'':8A,21B,28C ''image-classif'':20B ''input'':47C,73C ''learn'':15B,19B ''like'':37C ''machin'':14B ''machine-learn'':13B ''million'':32C,53C,71C ''need'':56C ''network'':68C ''neural'':67C ''neuralnetwork'':16B ''neuron'':12A,48C,54C,83C ''number'':81C ''one'':31C ''p'':23C,58C ''pixel'':27C,33C,41C ''possibl'':4A,77C ''process'':6A ''r'':44C ''realli'':62C ''reduc'':79C ''train'':65C ''would'':36C,60C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (994, 2, '2014-08-18 17:05:19.957', 4, NULL, NULL, '&lt;p&gt;its worth also looking at the training errors. &lt;/p&gt;

&lt;p&gt;basically I disagree with your  analysis. if logistic regression etc are all giving the same results it would suggest that the ''best model'' is a very simple one (that all models can fit equally well - eg basically linear).&lt;/p&gt;

&lt;p&gt;So then the question might be why is the best model a simple model?:
It might suggest that your variables are not very predictive. Its of course hard to analyse without knowing the data. &lt;/p&gt;
', NULL, NULL, '''/p'':10C,50C,86C ''also'':4C ''analys'':81C ''analysi'':17C ''basic'':12C,48C ''best'':33C,61C ''cours'':78C ''data'':85C ''disagre'':14C ''eg'':47C ''equal'':45C ''error'':9C ''etc'':21C ''fit'':44C ''give'':24C ''hard'':79C ''know'':83C ''linear'':49C ''logist'':19C ''look'':5C ''might'':56C,67C ''model'':34C,42C,62C,65C ''one'':39C ''p'':1C,11C,51C ''predict'':75C ''question'':55C ''regress'':20C ''result'':27C ''simpl'':38C,64C ''suggest'':30C,68C ''train'':8C ''variabl'':71C ''well'':46C ''without'':82C ''worth'':3C ''would'':29C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (989, 1, '2014-08-18 10:46:57.36', 7, 3195, 'SVM using scikit learn runs endlessly and never completes execution', '&lt;p&gt;I am trying to run SVR using scikit learn ( python ) on a training dataset having 595605 rows and 5 columns(features) and test dataset having 397070 rows. The data has been pre-processed and regularized.&lt;/p&gt;

&lt;p&gt;I am able to successfully run the test examples but on executing using my dataset and letting it run for over an hour, I could still not see any output or termination of program. I have tried executing using a different IDE and even from terminal but that doesn''t seem to be the issue.
I have also tried changing the ''C'' parameter value from 1 to 1e3.&lt;/p&gt;

&lt;p&gt;I am facing similar issues with all svm implementations using scikit.&lt;/p&gt;

&lt;p&gt;Am I not waiting enough for it to complete ?
How much time should this execution take ?&lt;/p&gt;

&lt;p&gt;From my experience it shouldn''t require over a few minutes.&lt;/p&gt;

&lt;p&gt;Here is my system configuration:
Ubuntu 14.04, 8GB RAM, lots of free memory, 4th gen i7 processor&lt;/p&gt;
', 'python  svm  scikit', NULL, '''/p'':51C,121C,134C,152C,165C,184C ''1'':118C ''14.04'':173C ''1e3'':120C ''397070'':40C ''4th'':180C ''5'':33C ''595605'':30C ''8gb'':174C ''abl'':55C ''also'':110C ''c'':114C ''chang'':112C ''column'':34C ''complet'':9A,144C ''configur'':171C ''could'':77C ''data'':43C ''dataset'':28C,38C,67C ''differ'':93C ''doesn'':101C ''endless'':6A ''enough'':140C ''even'':96C ''exampl'':61C ''execut'':10A,64C,90C,150C ''experi'':156C ''face'':125C ''featur'':35C ''free'':178C ''gen'':181C ''hour'':75C ''i7'':182C ''ide'':94C ''implement'':131C ''issu'':107C,127C ''learn'':4A,23C ''let'':69C ''lot'':176C ''memori'':179C ''minut'':164C ''much'':146C ''never'':8A ''output'':82C ''p'':14C,52C,122C,135C,153C,166C ''paramet'':115C ''pre'':47C ''pre-process'':46C ''process'':48C ''processor'':183C ''program'':86C ''python'':11B,24C ''ram'':175C ''regular'':50C ''requir'':160C ''row'':31C,41C ''run'':5A,19C,58C,71C ''scikit'':3A,13B,22C,133C ''see'':80C ''seem'':103C ''shouldn'':158C ''similar'':126C ''still'':78C ''success'':57C ''svm'':1A,12B,130C ''svr'':20C ''system'':170C ''take'':151C ''termin'':84C,98C ''test'':37C,60C ''time'':147C ''train'':27C ''tri'':17C,89C,111C ''ubuntu'':172C ''use'':2A,21C,65C,91C,132C ''valu'':116C ''wait'':139C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (961, 1, '2014-08-11 21:13:36.23', 5, 160, 'Why RBM tends to learn very similar weights?', '&lt;p&gt;&lt;img src="http://i.stack.imgur.com/lBxL5.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;These are 4 different weight matrices that I got after training &lt;strong&gt;RBM&lt;/strong&gt; with ~4k visible units and only 96 hidden units/weight vectors. As you can see, weights are extremely similar - even black pixels on the face are reproduced. The other 92 vectors are very similar too, though &lt;em&gt;none&lt;/em&gt; of weights are &lt;em&gt;exactly&lt;/em&gt; the same. &lt;/p&gt;

&lt;p&gt;I can overcome this by increasing number of weight vectors to 512 or more. But I encountered this problem several times earlier with different RBM types (binary, Gaussian, even convolutional), different number of hidden units (including pretty large), different hyper-parameters, etc. &lt;/p&gt;

&lt;p&gt;My question is: what is the &lt;strong&gt;most likely reason&lt;/strong&gt; for weights to get &lt;strong&gt;very similar values&lt;/strong&gt;? Do they all just get to some local minimum? Or is it a sign of overfitting? &lt;/p&gt;

&lt;p&gt;I currently use a kind of Gaussian-Bernoulli RBM, code may be found &lt;a href="https://github.com/faithlessfriend/Milk.jl/blob/master/src/rbm.jl" rel="nofollow"&gt;here&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPD.&lt;/strong&gt; My dataset is based on &lt;a href="http://www.pitt.edu/~emotion/ck-spread.htm" rel="nofollow"&gt;CK+&lt;/a&gt;, which contains &gt; 10k images of 327 individuals. However I do pretty heavy preprocessing. First, I clip only pixels inside of outer contour of a face. Second, I transform each face (using piecewise affine wrapping) to the same grid (e.g. eyebrows, nose, lips etc. are in the same (x,y) position on all images). After preprocessing images look like this: &lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/FQMSp.png" alt="enter image description here"&gt; &lt;img src="http://i.stack.imgur.com/Hjcaw.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;When training RBM, I take only non-zero pixels, so outer black region is ignored. &lt;/p&gt;
', 'rbm', NULL, '''/a'':190C,209C ''/em'':74C,80C ''/faithlessfriend/milk.jl/blob/master/src/rbm.jl'':186C ''/fqmsp.png'':275C ''/hjcaw.png'':285C ''/lbxl5.png'':15C ''/p'':21C,83C,128C,166C,191C,269C,291C,309C ''/strong'':36C,140C,149C,195C ''/~emotion/ck-spread.htm'':205C ''10k'':212C ''327'':215C ''4'':25C ''4k'':38C ''512'':96C ''92'':65C ''96'':43C ''affin'':242C ''alt'':16C,276C,286C ''base'':199C ''bernoulli'':176C ''binari'':111C ''black'':56C,305C ''ck'':208C ''clip'':225C ''code'':178C ''contain'':211C ''contour'':231C ''convolut'':114C ''current'':169C ''dataset'':197C ''descript'':19C,279C,289C ''differ'':26C,108C,115C,123C ''e.g'':248C ''earlier'':106C ''em'':72C,78C ''encount'':101C ''enter'':17C,277C,287C ''etc'':127C,252C ''even'':55C,113C ''exact'':79C ''extrem'':53C ''eyebrow'':249C ''face'':60C,234C,239C ''first'':223C ''found'':181C ''gaussian'':112C,175C ''gaussian-bernoulli'':174C ''get'':144C,154C ''github.com'':185C ''github.com/faithlessfriend/milk.jl/blob/master/src/rbm.jl'':184C ''got'':31C ''grid'':247C ''heavi'':221C ''hidden'':44C,118C ''howev'':217C ''href'':183C,202C ''hyper'':125C ''hyper-paramet'':124C ''i.stack.imgur.com'':14C,274C,284C ''i.stack.imgur.com/fqmsp.png'':273C ''i.stack.imgur.com/hjcaw.png'':283C ''i.stack.imgur.com/lbxl5.png'':13C ''ignor'':308C ''imag'':18C,213C,262C,265C,278C,288C ''img'':11C,271C,281C ''includ'':120C ''increas'':90C ''individu'':216C ''insid'':228C ''kind'':172C ''larg'':122C ''learn'':5A ''like'':138C,267C ''lip'':251C ''local'':157C ''look'':266C ''matric'':28C ''may'':179C ''minimum'':158C ''nofollow'':188C,207C ''non'':300C ''non-zero'':299C ''none'':73C ''nose'':250C ''number'':91C,116C ''outer'':230C,304C ''overcom'':87C ''overfit'':165C ''p'':10C,22C,84C,129C,167C,192C,270C,292C ''paramet'':126C ''piecewis'':241C ''pixel'':57C,227C,302C ''posit'':259C ''preprocess'':222C,264C ''pretti'':121C,220C ''problem'':103C ''question'':131C ''rbm'':2A,9B,35C,109C,177C,295C ''reason'':139C ''region'':306C ''rel'':187C,206C ''reproduc'':62C ''second'':235C ''see'':50C ''sever'':104C ''sign'':163C ''similar'':7A,54C,69C,147C ''src'':12C,272C,282C ''strong'':34C,136C,145C,193C ''take'':297C ''tend'':3A ''though'':71C ''time'':105C ''train'':33C,294C ''transform'':237C ''type'':110C ''unit'':40C,119C ''units/weight'':45C ''upd'':194C ''use'':170C,240C ''valu'':148C ''vector'':46C,66C,94C ''visibl'':39C ''weight'':8A,27C,51C,76C,93C,142C ''wrap'':243C ''www.pitt.edu'':204C ''www.pitt.edu/~emotion/ck-spread.htm'':203C ''x'':257C ''y'':258C ''zero'':301C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (5432, 2, '2015-04-02 14:08:05.547', 1, NULL, NULL, '&lt;p&gt;I am using this one, so far so good.&lt;br&gt;
Online terminals: &lt;a href="http://www.tutorialspoint.com/codingground.htm" rel="nofollow"&gt;http://www.tutorialspoint.com/codingground.htm&lt;/a&gt; &lt;br&gt;&lt;/p&gt;

&lt;p&gt;Also, R-Fiddle is an option.&lt;br&gt;
&lt;a href="http://www.r-fiddle.org/#/" rel="nofollow"&gt;http://www.r-fiddle.org/#/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/#/'':39C ''/#/&lt;/a&gt;&lt;/p&gt;'':44C ''/codingground.htm'':18C ''/codingground.htm&lt;/a&gt;'':23C ''/p'':25C ''also'':27C ''br'':11C,24C,34C ''far'':8C ''fiddl'':30C ''good'':10C ''href'':15C,36C ''nofollow'':20C,41C ''one'':6C ''onlin'':12C ''option'':33C ''p'':1C,26C ''r'':29C ''r-fiddl'':28C ''rel'':19C,40C ''termin'':13C ''use'':4C ''www.r-fiddle.org'':38C,43C ''www.r-fiddle.org/#/'':37C ''www.r-fiddle.org/#/&lt;/a&gt;&lt;/p&gt;'':42C ''www.tutorialspoint.com'':17C,22C ''www.tutorialspoint.com/codingground.htm'':16C ''www.tutorialspoint.com/codingground.htm&lt;/a&gt;'':21C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (967, 2, '2014-08-12 12:40:44.907', 4, NULL, NULL, '&lt;p&gt;Total of 972 emoji is not really that big not to be able to label them manually, but I doubt that they will work as a good ground truth. Sources like Twitter are full of irony, sarcasm and other tricky settings where emotional symbols (such as emoji or emoticon) mean something different from normal interpretation. For example, someone may write "xxx cheated their clients, and now they are cheated themselves! ha ha ha! :D". This is definitely negative comment, but author is glad to see xxx company in trouble and thus adds positive emoticon. These cases are not that frequent, but definitely not suitable for ground truth. &lt;/p&gt;

&lt;p&gt;Much more common approach is to use emoticon as a &lt;strong&gt;seed for collecting actual data set&lt;/strong&gt;. For example, in &lt;a href="http://www.saifmohammad.com/WebDocs/NRC-Sentiment-JAIR-2014.pdf" rel="nofollow"&gt;this paper&lt;/a&gt; authors use emoticon and emotional hash tags to grab lexicon of words useful for further classification. &lt;/p&gt;
', NULL, NULL, '''/a'':141C ''/p'':109C,158C ''/strong'':128C ''/webdocs/nrc-sentiment-jair-2014.pdf'':136C ''972'':4C ''abl'':14C ''actual'':125C ''add'':93C ''approach'':114C ''author'':82C,142C ''big'':10C ''case'':97C ''cheat'':63C,70C ''classif'':157C ''client'':65C ''collect'':124C ''comment'':80C ''common'':113C ''compani'':88C ''d'':75C ''data'':126C ''definit'':78C,103C ''differ'':53C ''doubt'':21C ''emoji'':5C,48C ''emot'':44C,146C ''emoticon'':50C,95C,118C,144C ''exampl'':58C,130C ''frequent'':101C ''full'':35C ''glad'':84C ''good'':28C ''grab'':150C ''ground'':29C,107C ''ha'':72C,73C,74C ''hash'':147C ''href'':133C ''interpret'':56C ''ironi'':37C ''label'':16C ''lexicon'':151C ''like'':32C ''manual'':18C ''may'':60C ''mean'':51C ''much'':111C ''negat'':79C ''nofollow'':138C ''normal'':55C ''p'':1C,110C ''paper'':140C ''posit'':94C ''realli'':8C ''rel'':137C ''sarcasm'':38C ''see'':86C ''seed'':122C ''set'':42C,127C ''someon'':59C ''someth'':52C ''sourc'':31C ''strong'':121C ''suitabl'':105C ''symbol'':45C ''tag'':148C ''thus'':92C ''total'':2C ''tricki'':41C ''troubl'':90C ''truth'':30C,108C ''twitter'':33C ''use'':117C,143C,154C ''word'':153C ''work'':25C ''write'':61C ''www.saifmohammad.com'':135C ''www.saifmohammad.com/webdocs/nrc-sentiment-jair-2014.pdf'':134C ''xxx'':62C,87C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (968, 1, '2014-08-12 17:11:45.447', 4, 214, 'Understanding output stepAIC', '&lt;p&gt;I am using the &lt;code&gt;stepAIC&lt;/code&gt; function in R to do a bi-directional (forward and backward) stepwise regression. I do not understand what each return value from the function means. &lt;/p&gt;

&lt;p&gt;The output is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;          Df     Sum of Sq    RSS       AIC
&amp;lt;none&amp;gt;                        350.71   -5406.0
- aaa      1     0.283        350.99   -5405.9
- bbb      1     0.339        351.05   -5405.4
- ccc      1     0.982        351.69   -5400.5
- ddd      1     0.989        351.70   -5400.5
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Question Are the values listed under &lt;code&gt;Df&lt;/code&gt;, &lt;code&gt;Sum of Sq&lt;/code&gt;, &lt;code&gt;RSS&lt;/code&gt;, and &lt;code&gt;AIC&lt;/code&gt; the values for a model where only one variable would be considered as the independent variable (i.e. y ~
aaa, y ~ bbb, etc.)? &lt;/p&gt;
', 'data-mining  r  feature-selection', NULL, '''-5400.5'':78C,83C ''-5405.4'':73C ''-5405.9'':68C ''-5406.0'':63C ''/code'':18C,84C,95C,100C,103C,107C ''/p'':45C,50C,130C ''/pre'':85C ''0.283'':66C ''0.339'':71C ''0.982'':76C ''0.989'':81C ''1'':65C,70C,75C,80C ''350.71'':62C ''350.99'':67C ''351.05'':72C ''351.69'':77C ''351.70'':82C ''aaa'':64C,126C ''aic'':58C,106C ''backward'':30C ''bbb'':69C,128C ''bi'':26C ''bi-direct'':25C ''ccc'':74C ''code'':16C,52C,93C,96C,101C,105C ''consid'':119C ''data'':5B ''data-min'':4B ''ddd'':79C ''df'':53C,94C ''direct'':27C ''etc'':129C ''featur'':9B ''feature-select'':8B ''forward'':28C ''function'':19C,43C ''gt'':61C ''i.e'':124C ''independ'':122C ''list'':91C ''lt'':59C ''mean'':44C ''mine'':6B ''model'':112C ''none'':60C ''one'':115C ''output'':2A,48C ''p'':11C,46C,86C ''pre'':51C ''question'':87C ''r'':7B,21C ''regress'':32C ''return'':39C ''rss'':57C,102C ''select'':10B ''sq'':56C,99C ''stepaic'':3A,17C ''stepwis'':31C ''sum'':54C,97C ''understand'':1A,36C ''use'':14C ''valu'':40C,90C,109C ''variabl'':116C,123C ''would'':117C ''y'':125C,127C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (969, 2, '2014-08-13 14:18:58.917', 1, NULL, NULL, '&lt;p&gt;After consulting with someone I found out that the  corresponds to a model that would include all the variables, in other words none of the variables were removed. So consider the line in the output for the variable aaa. The listed RSS and AIC are the values for a model that would include all variables but aaa and we see an increase in the RSS and AIC. The other listed results can be considered in the same fashion. The best model is then the one where none are removed since this has the smallest AIC. &lt;/p&gt;
', NULL, NULL, '''/p'':97C ''aaa'':40C,58C ''aic'':45C,68C,96C ''best'':81C ''consid'':31C,75C ''consult'':3C ''correspond'':11C ''fashion'':79C ''found'':7C ''includ'':17C,54C ''increas'':63C ''line'':33C ''list'':42C,71C ''model'':14C,51C,82C ''none'':24C,88C ''one'':86C ''output'':36C ''p'':1C ''remov'':29C,90C ''result'':72C ''rss'':43C,66C ''see'':61C ''sinc'':91C ''smallest'':95C ''someon'':5C ''valu'':48C ''variabl'':20C,27C,39C,56C ''word'':23C ''would'':16C,53C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (975, 2, '2014-08-14 04:28:36.933', 10, NULL, NULL, '&lt;p&gt;I hope that the following &lt;strong&gt;resources&lt;/strong&gt; might get you additional &lt;strong&gt;ideas&lt;/strong&gt; toward solving the problem:&lt;/p&gt;

&lt;p&gt;1) Research paper &lt;strong&gt;"Efficient K-Nearest Neighbor Join Algorithms for High Dimensional Sparse Data"&lt;/strong&gt;: &lt;a href="http://arxiv.org/abs/1011.2807"&gt;http://arxiv.org/abs/1011.2807&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;2) Class project paper &lt;strong&gt;"Recommendation System Based on Collaborative Filtering"&lt;/strong&gt; (Stanford University): &lt;a href="http://cs229.stanford.edu/proj2008/Wen-RecommendationSystemBasedOnCollaborativeFiltering.pdf"&gt;http://cs229.stanford.edu/proj2008/Wen-RecommendationSystemBasedOnCollaborativeFiltering.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;3) Project for the &lt;strong&gt;Netflix Prize Competition (&lt;em&gt;k-NN&lt;/em&gt;-based)&lt;/strong&gt;: &lt;a href="http://cs.carleton.edu/cs_comps/0910/netflixprize/final_results/knn/index.html"&gt;http://cs.carleton.edu/cs_comps/0910/netflixprize/final_results/knn/index.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;4) Research paper &lt;strong&gt;"Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data"&lt;/strong&gt; on the &lt;em&gt;curse of dimensionality&lt;/em&gt; phenomenon and its relation to &lt;em&gt;machine learning&lt;/em&gt;, in general, and &lt;em&gt;k-NN algorithm&lt;/em&gt;, in particular: &lt;a href="http://jmlr.org/papers/volume11/radovanovic10a/radovanovic10a.pdf"&gt;http://jmlr.org/papers/volume11/radovanovic10a/radovanovic10a.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;5) &lt;strong&gt;Software for sparse k-NN classification&lt;/strong&gt; (free, but appears not to be open source - might clarify with authors): &lt;a href="http://www.autonlab.org/autonweb/10408.html"&gt;http://www.autonlab.org/autonweb/10408.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;6) Several &lt;strong&gt;discussion threads&lt;/strong&gt; on &lt;em&gt;StackOverflow&lt;/em&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://stackoverflow.com/questions/20333092/knn-with-big-sparse-matrices-in-python"&gt;http://stackoverflow.com/questions/20333092/knn-with-big-sparse-matrices-in-python&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stackoverflow.com/questions/18164348/efficient-nearest-neighbour-search-for-sparse-matrices"&gt;http://stackoverflow.com/questions/18164348/efficient-nearest-neighbour-search-for-sparse-matrices&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stackoverflow.com/questions/21085990/scipy-sparse-distance-matrix-scikit-or-scipy"&gt;http://stackoverflow.com/questions/21085990/scipy-sparse-distance-matrix-scikit-or-scipy&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stackoverflow.com/questions/10472681/handling-incomplete-data-data-sparsity-in-knn"&gt;http://stackoverflow.com/questions/10472681/handling-incomplete-data-data-sparsity-in-knn&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://stackoverflow.com/questions/5560218/computing-sparse-pairwise-distance-matrix-in-r"&gt;http://stackoverflow.com/questions/5560218/computing-sparse-pairwise-distance-matrix-in-r&lt;/a&gt; (unlike all previous discussions, which refer to &lt;code&gt;Python&lt;/code&gt;, this one refers to &lt;code&gt;R&lt;/code&gt; ecosystem)&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;7) Pay attention to &lt;em&gt;GraphLab&lt;/em&gt;, an open source &lt;strong&gt;parallel framework for machine learning&lt;/strong&gt; (&lt;a href="http://select.cs.cmu.edu/code/graphlab"&gt;http://select.cs.cmu.edu/code/graphlab&lt;/a&gt;), which supports &lt;em&gt;parallel clustering&lt;/em&gt; via &lt;code&gt;MapReduce&lt;/code&gt; model: &lt;a href="http://select.cs.cmu.edu/code/graphlab/clustering.html"&gt;http://select.cs.cmu.edu/code/graphlab/clustering.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You might also check my answer here on Data Science StackExchange on &lt;em&gt;sparse regression&lt;/em&gt; for links to relevant &lt;code&gt;R&lt;/code&gt; packages and &lt;code&gt;CRAN Task View&lt;/code&gt; pages: &lt;a href="http://datascience.stackexchange.com/a/918/2452"&gt;http://datascience.stackexchange.com/a/918/2452&lt;/a&gt;.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1026, 2, '2014-08-23 14:15:58.66', 2, NULL, NULL, '&lt;p&gt;I think there are two separate issues to consider: Training time, and prediction accuracy. &lt;/p&gt;

&lt;p&gt;Take a simple example : consider you have two classes, that have a multivariate normal distribution. Basically you need to estimate the respective class means and class covariances.  Now the first thing you care about is your estimate of the difference in the class means: but your performance is limited by the accuracy of the worst estimated mean: its no good estimating one mean to the 100th decimal place - if the other mean is only estimated to 1 decimal place.  So its a waste of computing resources to use all the data - you can instead undersample the  more common class AND reweight the classes appropriately. ( those computing resources can then be used exploring different input variables etc)&lt;/p&gt;

&lt;p&gt;Now the second issue is predictive accuracy: different algorithms use different error metrics, which may or may not agree with your own objectives. eg logistic regression will penalise overall probability error,  so if  most of your data is from one class, then it will tend to try to improve accurate probability estimates ( eg 90 vs 95% probability) of that one class rather than trying to identify the rare class. in that case you would definitely want to try to reweight to emphasize the rare class ( and subsequently adjust the estimate [by adjusting the bias term] to get the probability estimates realigned)&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (976, 2, '2014-08-14 17:28:40.453', 4, NULL, NULL, '&lt;p&gt;I gave a very limited partial answer for the confirmatory factor analysis package that I developed for Stata in this &lt;a href="http://www.stata-journal.com/article.html?article=st0169" rel="nofollow"&gt;Stata Journal article&lt;/a&gt; based on timing the actual simulations. Confirmatory factor analysis was implemented as a maximum likelihood estimation technique, and I could see very easily how the computation time grew with each dimension (sample size &lt;code&gt;n&lt;/code&gt;, number of variables &lt;code&gt;p&lt;/code&gt;, number of factors &lt;code&gt;k&lt;/code&gt;). As it is heavily dependent on how Stata thinks about the data (optimized to compute across columns/observations rather than rows), I found performance to be &lt;code&gt;O(n^{0.68} (k+p)^{2.4})&lt;/code&gt; where 2.4 is the fastest matrix inversion asymptotics (and there''s hell of a lot of that in confirmatory factor analysis iterative maximization). I did not give a reference for the latter, but I think I got this from &lt;a href="http://en.wikipedia.org/wiki/Computational_complexity_of_mathematical_operations#Matrix_algebra" rel="nofollow"&gt;Wikipedia&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Note that there is also a matrix inversion step in OLS. However, for reasons of numerical accuracy, no one would really brute-force inverse the &lt;code&gt;X''X&lt;/code&gt; matrix, and would rather use sweep operators and identify the dangerously collinear variables to deal with precision issues. If you add up $10^8$ numbers that originally were in &lt;a href="http://en.wikipedia.org/wiki/Double-precision_floating-point_format" rel="nofollow"&gt;double precision&lt;/a&gt;, you will likely end up with a number that only has a single precision. Numerical computing issues may become a forgotten corner of big data calculations as you start optimizing for speed.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (987, 1, '2014-08-17 17:29:44.123', 10, 755, 'Text categorization: combining different kind of features', '&lt;p&gt;The problem I am tackling is categorizing short texts into multiple classes. My current approach is to use tf-idf weighted term frequencies and learn a simple linear classifier (logistic regression). This works reasonably well (around 90% macro F-1 on test set, nearly 100% on training set). A big problem are unseen words/n-grams. &lt;/p&gt;

&lt;p&gt;I am trying to improve the classifier by adding other features, e.g. a fixed sized vector computed using distributional similarities (as computed by word2vec) or other categorical features of the examples. My idea was to just add the features to the sparse input features from the bag of words. However, this results in worse performance on the test and training set. The additional features by themselves give about 80% F-1 on the test set, so they aren''t garbage. Scaling the features didn''t help as well. My current thinking is that these kind of features don''t mix well with the (sparse) bag of words features.&lt;/p&gt;

&lt;p&gt;So the question is: assuming the additional features provide additional information, what is the best way to incorporate them? Could training separate classifiers and combining them in some kind of ensemble work (this would probably have the drawback that no interaction between the features of the different classifiers could be captured)? Are there other more complex models I should consider?&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10395, 2, '2016-02-25 18:49:53.537', 1, NULL, NULL, '&lt;p&gt;This all depends. SAS is great and is backed by the SAS Institute, meaning if you''re working for an organization that has invested in SAS, you can contact the support team for anything funky happening with the software.&lt;/p&gt;

&lt;p&gt;R is free and open source, and there are also organizations being created that are building on to and supporting R much like SAS Institute has done. The difference being that SAS has been around much longer and is much more structured than R.&lt;/p&gt;

&lt;p&gt;They both have their ups and downs, and it depends on what type of analytics you plan on doing in your career. So, to answer your questions, I would decide what you envision yourself doing. To my understanding R is good for Big Data applications and Machine Learning, SAS is great for statistical analysis (ARIMA, etc).&lt;/p&gt;

&lt;p&gt;SAS and R comparisons:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://support.sas.com/resources/papers/proceedings13/348-2013.pdf" rel="nofollow"&gt;http://support.sas.com/resources/papers/proceedings13/348-2013.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="http://www.learnanalytics.in/blog/?p=9" rel="nofollow"&gt;http://www.learnanalytics.in/blog/?p=9&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (977, 1, '2014-08-14 19:09:29.523', 10, 161, 'Solutions for Continuous Online Cluster Identification?', '&lt;p&gt;Let me show you an example of a hypothetical online clustering application:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/ZVOQN.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;At time n points 1,2,3,4 are allocated to the blue cluster A and points b,5,6,7 are allocated to the red cluster B.&lt;/p&gt;

&lt;p&gt;At time n+1 a new point a is introduced which is assigned to the blue cluster A but also causes the point b to be assigned to the blue cluster A as well.&lt;/p&gt;

&lt;p&gt;In the end points 1,2,3,4,a,b belong to A and points 5,6,7 to B. To me this seems reasonable.&lt;/p&gt;

&lt;p&gt;What seems simple at first glance is actually a bit tricky - to maintain identifiers across time steps. Let me try to make this point clear with a more borderline example:&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/sQj4h.jpg" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;The green point will cause two blue and two red points to be merged into one cluster which I arbitrarily decided to color blue - mind this is already my human heuristical thinking at work!&lt;/p&gt;

&lt;p&gt;A computer to make this decision will have to use rules. For example when points are merged into a cluster then the identity of the cluster is determined by the majority. In this case we would face a draw - both blue and red might be valid choices for the new (here blue colored) cluster. &lt;/p&gt;

&lt;p&gt;Imagine a fifth red point close to the green one. Then the majority would be red (3 red vs 2 blue) so red would be a good choice for the new cluster - but this would contradict the even clearer choice of red for the rightmost cluster as those have been red and probably should stay that way.&lt;/p&gt;

&lt;p&gt;I find it fishy to think about this. At the end of the day I guess there are no perfect rules for this - rather heuristics optimizing some stability criterea.&lt;/p&gt;

&lt;p&gt;This finally leads to my questions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Does this "problem" have a name that it can be referred to?&lt;/li&gt;
&lt;li&gt;Are there "standard" solutions to this and ...&lt;/li&gt;
&lt;li&gt;... is there maybe even an R package for that?&lt;/li&gt;
&lt;/ol&gt;

&lt;hr&gt;

&lt;p&gt;&lt;a href="http://www.joyofdata.de/blog/reasonable-inheritance-of-cluster-identities-in-repetitive-clustering/"&gt;Reasonable Inheritance of Cluster Identities in Repetitive Clustering&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (978, 2, '2014-08-14 21:38:57.63', 7, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;But I don''t know what is difference between text classification and topic models in documents&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Text classification is a form of supervised learning -- the set of possible classes are known/defined in advance and don''t change.&lt;/p&gt;

&lt;p&gt;Topic modeling is a form of unsupervised learning (akin to clustering) -- the set of possible topics are unknown apriori. They''re defined as part of generating the topic models. With a non-deterministic algorithm like LDA, you''ll get different topics each time you run the algorithm.&lt;/p&gt;

&lt;p&gt;Text classification often involves mutually-exclusive classes -- think of these as buckets. But it doesn''t have to -- given the right kind of labeled input data, you can set of a series of non-mutually-exclusive binary classifiers.&lt;/p&gt;

&lt;p&gt;Topic modeling is generally not mutually-exclusive -- the same document can have its probability distribution spread across many topics. In addition, there are also hierarchical topic modeling methods, etc.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Also can I use topic model for the documents to identify one topic later on can I use the classification to classify the text inside this documents ? &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;If you''re asking whether you can take all of the documents assigned to one topic by a topic modeling algorithm and then apply a classifier to that collection, then yes, you certainly can do that. I''m not sure it makes much sense, though -- at a minimum, you''d need to pick a threshold for the topic probability distribution above which you''ll include documents in your collection (typically 0.05-0.1). Can you elaborate on your use case?&lt;/p&gt;

&lt;p&gt;By the way, there''s a great tutorial on topic modeling using the MALLET library for Java available here: &lt;a href="http://programminghistorian.org/lessons/topic-modeling-and-mallet"&gt;Getting Started with Topic Modeling and MALLET&lt;/a&gt;&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (979, 1, '2014-08-15 13:10:20.937', 9, 2417, 'Algorithms for text clustering', '&lt;p&gt;I have a problem of clustering huge amount of sentences into groups by their meanings. This is similar to a problem when you have lots of sentences and want to group them by their meanings.&lt;/p&gt;

&lt;p&gt;What algorithms are suggested to do this? I don''t know number of clusters in advance (and as more data is coming clusters can change as well), what features are normally used to represent each sentence?&lt;/p&gt;

&lt;p&gt;I''m trying now the simplest features with just list of words and distance between sentences as next   &lt;img src="http://i.stack.imgur.com/dHB9X.jpg" alt="enter image description here"&gt; A and B are corresponding sets of words in sentence A and B. Does it make sense at all? &lt;/p&gt;

&lt;p&gt;I''m trying to apply &lt;a href="http://scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html#example-cluster-plot-mean-shift-py"&gt;Mean-Shift&lt;/a&gt; algorithm from scikit library to this distance as it does not require number of clusters in advance.&lt;/p&gt;

&lt;p&gt;If anyone will advise better methods/approaches for the problem - it will be very much appreciated as I''m still new to the topic.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2511, 2, '2014-11-20 21:11:12.187', 1, NULL, NULL, '&lt;p&gt;I would add a comment but I do not have enough reputation points. I might suggest Using "R revolution open". It is a Build of R that includes a lot of native support for multi-core processing. I have not used it much as my computer is very old, but it is defiantly worth looking at. Plus it is free. &lt;/p&gt;
', NULL, NULL, '''/p'':63C ''add'':4C ''build'':25C ''comment'':6C ''comput'':48C ''core'':38C ''defiant'':55C ''enough'':12C ''free'':62C ''includ'':29C ''look'':57C ''lot'':31C ''might'':16C ''much'':45C ''multi'':37C ''multi-cor'':36C ''nativ'':33C ''old'':51C ''open'':21C ''p'':1C ''plus'':59C ''point'':14C ''process'':39C ''r'':19C,27C ''reput'':13C ''revolut'':20C ''suggest'':17C ''support'':34C ''use'':18C,43C ''worth'':56C ''would'':3C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (980, 2, '2014-08-15 14:00:14.927', 7, NULL, NULL, '&lt;p&gt;Check the &lt;strong&gt;Stanford NLP Group&lt;/strong&gt;''s open source software (&lt;a href="http://www-nlp.stanford.edu/software"&gt;http://www-nlp.stanford.edu/software&lt;/a&gt;), in particular, &lt;strong&gt;Stanford Classifier&lt;/strong&gt; (&lt;a href="http://www-nlp.stanford.edu/software/classifier.shtml"&gt;http://www-nlp.stanford.edu/software/classifier.shtml&lt;/a&gt;). The software is written in &lt;code&gt;Java&lt;/code&gt;, which will likely delight you, but also has bindings for some other languages. Note, the &lt;em&gt;licensing&lt;/em&gt; - if you plan to use their code in commercial products, you have to acquire commercial license.&lt;/p&gt;

&lt;p&gt;Another interesting set of open source libraries, IMHO suitable for this task and much more, is &lt;strong&gt;parallel framework for machine learning GraphLab&lt;/strong&gt; (&lt;a href="http://select.cs.cmu.edu/code/graphlab"&gt;http://select.cs.cmu.edu/code/graphlab&lt;/a&gt;), which includes &lt;strong&gt;clustering library&lt;/strong&gt;, implementing various clustering algorithms (&lt;a href="http://select.cs.cmu.edu/code/graphlab/clustering.html"&gt;http://select.cs.cmu.edu/code/graphlab/clustering.html&lt;/a&gt;). It is especially suitable for &lt;strong&gt;very large volume of data&lt;/strong&gt; (like you have), as it implements &lt;code&gt;MapReduce&lt;/code&gt; model and, thus, supports &lt;em&gt;multicore&lt;/em&gt; and &lt;em&gt;multiprocessor&lt;/em&gt; &lt;strong&gt;parallel processing&lt;/strong&gt;.&lt;/p&gt;

&lt;p&gt;You most likely are aware of the following, but I will mention it just in case. &lt;strong&gt;Natural Language Toolkit (NLTK)&lt;/strong&gt; for &lt;code&gt;Python&lt;/code&gt; (&lt;a href="http://www.nltk.org"&gt;http://www.nltk.org&lt;/a&gt;) contains modules for clustering/classifying/categorizing text. Check the relevant chapter in the &lt;code&gt;NLTK Book&lt;/code&gt;: &lt;a href="http://www.nltk.org/book/ch06.html"&gt;http://www.nltk.org/book/ch06.html&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;UPDATE:&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Speaking of &lt;strong&gt;algorithms&lt;/strong&gt;, it seems that you''ve tried most of the ones from &lt;code&gt;scikit-learn&lt;/code&gt;, such as illustrated in this topic extraction example: &lt;a href="http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf.html"&gt;http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf.html&lt;/a&gt;. However, you may find useful other libraries, which implement a wide variety of &lt;em&gt;clustering algorithms&lt;/em&gt;, including &lt;em&gt;Non-Negative Matrix Factorization (NMF)&lt;/em&gt;. One of such libraries is &lt;strong&gt;Python Matrix Factorization (PyMF)&lt;/strong&gt; with home page at &lt;a href="https://code.google.com/p/pymf"&gt;https://code.google.com/p/pymf&lt;/a&gt; and source code at &lt;a href="https://github.com/nils-werner/pymf"&gt;https://github.com/nils-werner/pymf&lt;/a&gt;. Another, even more interesting, library, also Python-based, is &lt;strong&gt;NIMFA&lt;/strong&gt;, which implements various &lt;em&gt;NMF algorithms&lt;/em&gt;: &lt;a href="http://nimfa.biolab.si"&gt;http://nimfa.biolab.si&lt;/a&gt;. Here''s a research paper, describing &lt;code&gt;NIMFA&lt;/code&gt;: &lt;a href="http://jmlr.org/papers/volume13/zitnik12a/zitnik12a.pdf"&gt;http://jmlr.org/papers/volume13/zitnik12a/zitnik12a.pdf&lt;/a&gt;. Here''s an example from its documentation, which presents the solution for very similar text processing problem of &lt;em&gt;topic clustering&lt;/em&gt;: &lt;a href="http://nimfa.biolab.si/nimfa.examples.documents.html"&gt;http://nimfa.biolab.si/nimfa.examples.documents.html&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':197C,349C ''/book/ch06.html'':217C ''/book/ch06.html&lt;/a&gt;.&lt;/p&gt;'':220C ''/code'':42C,149C,192C,212C,247C,358C ''/code/graphlab'':107C ''/code/graphlab&lt;/a&gt;),'':110C ''/code/graphlab/clustering.html'':125C ''/code/graphlab/clustering.html&lt;/a&gt;).'':128C ''/em'':60C,156C,160C,280C,289C,344C,388C ''/nils-werner/pymf'':321C ''/nils-werner/pymf&lt;/a&gt;.'':324C ''/nimfa.examples.documents.html'':393C ''/nimfa.examples.documents.html&lt;/a&gt;.&lt;/p&gt;'':396C ''/p'':77C,165C,225C ''/p/pymf'':309C ''/p/pymf&lt;/a&gt;'':312C ''/papers/volume13/zitnik12a/zitnik12a.pdf'':363C ''/papers/volume13/zitnik12a/zitnik12a.pdf&lt;/a&gt;.'':366C ''/software'':17C ''/software&lt;/a&gt;),'':20C ''/software/classifier.shtml'':31C ''/software/classifier.shtml&lt;/a&gt;).'':34C ''/stable/auto_examples/applications/topics_extraction_with_nmf.html'':260C ''/stable/auto_examples/applications/topics_extraction_with_nmf.html&lt;/a&gt;.'':263C ''/strong'':8C,26C,102C,116C,140C,164C,188C,224C,231C,300C,337C ''acquir'':74C ''algorithm'':120C,230C,279C,343C ''also'':49C,330C ''anoth'':79C,325C ''awar'':171C ''base'':333C ''bind'':51C ''book'':211C ''case'':182C ''chapter'':206C ''check'':2C,203C ''classifi'':25C ''cluster'':114C,119C,278C,387C ''clustering/classifying/categorizing'':201C ''code'':40C,67C,147C,190C,209C,243C,315C,356C ''code.google.com'':308C,311C ''code.google.com/p/pymf'':307C ''code.google.com/p/pymf&lt;/a&gt;'':310C ''commerci'':69C,75C ''contain'':198C ''data'':139C ''delight'':46C ''describ'':355C ''document'':373C ''em'':58C,154C,158C,277C,282C,341C,385C ''especi'':131C ''even'':326C ''exampl'':255C,370C ''extract'':254C ''factor'':287C,298C ''find'':267C ''follow'':174C ''framework'':97C ''github.com'':320C,323C ''github.com/nils-werner/pymf'':319C ''github.com/nils-werner/pymf&lt;/a&gt;.'':322C ''graphlab'':101C ''group'':7C ''home'':302C ''howev'':264C ''href'':14C,28C,104C,122C,194C,214C,257C,306C,318C,346C,360C,390C ''illustr'':250C ''imho'':86C ''implement'':117C,146C,272C,339C ''includ'':112C,281C ''interest'':80C,328C ''java'':41C ''jmlr.org'':362C,365C ''jmlr.org/papers/volume13/zitnik12a/zitnik12a.pdf'':361C ''jmlr.org/papers/volume13/zitnik12a/zitnik12a.pdf&lt;/a&gt;.'':364C ''languag'':55C,185C ''larg'':136C ''learn'':100C,246C ''librari'':85C,115C,270C,293C,329C ''licens'':59C,76C ''like'':45C,141C,169C ''machin'':99C ''mapreduc'':148C ''matrix'':286C,297C ''may'':266C ''mention'':178C ''model'':150C ''modul'':199C ''much'':92C ''multicor'':155C ''multiprocessor'':159C ''natur'':184C ''negat'':285C ''nimfa'':336C,357C ''nimfa.biolab.si'':347C,348C,392C,395C ''nimfa.biolab.si/nimfa.examples.documents.html'':391C ''nimfa.biolab.si/nimfa.examples.documents.html&lt;/a&gt;.&lt;/p&gt;'':394C ''nlp'':6C ''nltk'':187C,210C ''nmf'':288C,342C ''non'':284C ''non-neg'':283C ''note'':56C ''one'':241C,290C ''open'':10C,83C ''p'':1C,78C,166C,221C,226C ''page'':303C ''paper'':354C ''parallel'':96C,162C ''particular'':22C ''plan'':63C ''present'':375C ''problem'':383C ''process'':163C,382C ''product'':70C ''pymf'':299C ''python'':191C,296C,332C ''python-bas'':331C ''relev'':205C ''research'':353C ''scikit'':245C ''scikit-learn'':244C ''scikit-learn.org'':259C,262C ''scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf.html'':258C ''scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf.html&lt;/a&gt;.'':261C ''seem'':233C ''select.cs.cmu.edu'':106C,109C,124C,127C ''select.cs.cmu.edu/code/graphlab'':105C ''select.cs.cmu.edu/code/graphlab&lt;/a&gt;),'':108C ''select.cs.cmu.edu/code/graphlab/clustering.html'':123C ''select.cs.cmu.edu/code/graphlab/clustering.html&lt;/a&gt;).'':126C ''set'':81C ''similar'':380C ''softwar'':12C,36C ''solut'':377C ''sourc'':11C,84C,314C ''speak'':227C ''stanford'':5C,24C ''strong'':4C,23C,95C,113C,134C,161C,183C,222C,229C,295C,335C ''suitabl'':87C,132C ''support'':153C ''task'':90C ''text'':202C,381C ''thus'':152C ''toolkit'':186C ''topic'':253C,386C ''tri'':237C ''updat'':223C ''use'':65C,268C ''varieti'':275C ''various'':118C,340C ''ve'':236C ''volum'':137C ''wide'':274C ''written'':38C ''www-nlp.stanford.edu'':16C,19C,30C,33C ''www-nlp.stanford.edu/software'':15C ''www-nlp.stanford.edu/software&lt;/a&gt;),'':18C ''www-nlp.stanford.edu/software/classifier.shtml'':29C ''www-nlp.stanford.edu/software/classifier.shtml&lt;/a&gt;).'':32C ''www.nltk.org'':195C,196C,216C,219C ''www.nltk.org/book/ch06.html'':215C ''www.nltk.org/book/ch06.html&lt;/a&gt;.&lt;/p&gt;'':218C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (981, 2, '2014-08-15 20:22:11.75', 2, NULL, NULL, '&lt;p&gt;Basically for this task you can efficiently use any SQL database with good support of B+tree based indexes (MySQL will suite you needs just perfect). &lt;/p&gt;

&lt;p&gt;Create 3 tables:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Documents table, columns: id/document&lt;/li&gt;
&lt;li&gt;N-grams table: n_gram_id/n_gram&lt;/li&gt;
&lt;li&gt;Mapping between n-grams and documents: document_id/n_gram_id&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Create indexes on N-gram table/n_gram string and Mapping table/n_gram_id, also primary keys will be indexed by default well.&lt;/p&gt;

&lt;p&gt;Your operations will be efficient:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Insertion of document: just extract all n-grams and insert into document table and N-grams table&lt;/li&gt;
&lt;li&gt;Lookup for in_gram will be quick with support of index&lt;/li&gt;
&lt;li&gt;Querying for all n-grams that contain a sub-n-gram: in 2 steps - just query based on index all n-grams which contain sub-n-gram from 2nd table. Then - retrieve all corresponding documents for each of these n-grams.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;You don''t even need to use joins to achieve all these operations so indexes will help a lot. Also if data will not suite in one machine - you can implement sharding scheme, like storing n_grams started from a-n on one server and o-z on another or other suitable scheme. &lt;/p&gt;

&lt;p&gt;Also you can use MongoDB, but I''m not sure how exactly you need to implement indexing scheme. For MongoDB you will get sharding scheme for free as it is already built-in.&lt;/p&gt;
', NULL, NULL, '''/li'':40C,49C,60C,112C,125C,173C ''/ol'':61C,174C ''/p'':28C,33C,83C,90C,231C,267C ''2'':141C ''2nd'':159C ''3'':31C ''a-n'':215C ''achiev'':185C ''alreadi'':263C ''also'':74C,195C,233C ''anoth'':226C ''b'':17C ''base'':19C,145C ''basic'':2C ''built'':265C ''built-in'':264C ''column'':38C ''contain'':134C,153C ''correspond'':164C ''creat'':30C,63C ''data'':197C ''databas'':12C ''default'':81C ''document'':36C,57C,58C,95C,105C,165C ''effici'':8C,89C ''even'':179C ''exact'':244C ''extract'':97C ''free'':259C ''get'':255C ''good'':14C ''gram'':44C,47C,55C,68C,101C,110C,117C,132C,139C,151C,157C,172C,212C ''help'':192C ''id/document'':39C ''id/n_gram'':48C ''id/n_gram_id'':59C ''implement'':206C,248C ''index'':20C,64C,79C,124C,147C,190C,249C ''insert'':93C,103C ''join'':183C ''key'':76C ''li'':35C,41C,50C,92C,113C,126C ''like'':209C ''lookup'':114C ''lot'':194C ''m'':240C ''machin'':203C ''map'':51C,72C ''mongodb'':237C,252C ''mysql'':21C ''n'':43C,46C,54C,67C,100C,109C,131C,138C,150C,156C,171C,211C,217C ''n-gram'':42C,53C,66C,99C,108C,130C,149C,170C ''need'':25C,180C,246C ''o'':223C ''o-z'':222C ''ol'':34C,91C ''one'':202C,219C ''oper'':86C,188C ''p'':1C,29C,62C,84C,175C,232C ''perfect'':27C ''primari'':75C ''queri'':127C,144C ''quick'':120C ''retriev'':162C ''scheme'':208C,230C,250C,257C ''server'':220C ''shard'':207C,256C ''sql'':11C ''start'':213C ''step'':142C ''store'':210C ''string'':70C ''sub'':137C,155C ''sub-n-gram'':136C,154C ''suit'':23C,200C ''suitabl'':229C ''support'':15C,122C ''sure'':242C ''tabl'':32C,37C,45C,106C,111C,160C ''table/n_gram'':69C ''table/n_gram_id'':73C ''task'':5C ''tree'':18C ''use'':9C,182C,236C ''well'':82C ''z'':224C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (982, 2, '2014-08-16 10:25:19.687', 8, NULL, NULL, '&lt;p&gt;See &lt;a href="https://lucene.apache.org/core/4_4_0/analyzers-common/org/apache/lucene/analysis/ngram/NGramTokenizer.html"&gt;Lucene NGramTokenizer&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Are you sure you can''t just use lucene or similar indexing techniques?&lt;/p&gt;

&lt;p&gt;Inverted indexes will store the n-gram only once, then just the document ids that contain the ngram; they don''t store this as highly redundant raw text.&lt;/p&gt;

&lt;p&gt;As for finding ngrams that contain your query sub-n-gram, I would build an index on the observed ngrams, e.g. using a second lucene index, or &lt;a href="https://en.wikipedia.org/wiki/Substring_index"&gt;any other substring index&lt;/a&gt; such as a trie or suffix tree. If your data is dynamic, probably lucene is a reasonable choice, using phrase queries to find your n-grams.&lt;/p&gt;
', NULL, NULL, '''/a'':10C,96C ''/core/4_4_0/analyzers-common/org/apache/lucene/analysis/ngram/ngramtokenizer.html'':7C ''/p'':11C,26C,57C,124C ''/wiki/substring_index'':91C ''build'':73C ''choic'':114C ''contain'':44C,64C ''data'':106C ''document'':41C ''dynam'':108C ''e.g'':80C ''en.wikipedia.org'':90C ''en.wikipedia.org/wiki/substring_index'':89C ''find'':61C,119C ''gram'':35C,70C,123C ''high'':53C ''href'':4C,88C ''id'':42C ''index'':24C,29C,75C,85C,95C ''invert'':28C ''lucen'':8C,21C,84C,110C ''lucene.apache.org'':6C ''lucene.apache.org/core/4_4_0/analyzers-common/org/apache/lucene/analysis/ngram/ngramtokenizer.html'':5C ''n'':34C,69C,122C ''n-gram'':33C,121C ''ngram'':46C,62C,79C ''ngramtoken'':9C ''observ'':78C ''p'':1C,12C,27C,58C ''phrase'':116C ''probabl'':109C ''queri'':66C,117C ''raw'':55C ''reason'':113C ''redund'':54C ''second'':83C ''see'':2C ''similar'':23C ''store'':31C,50C ''sub'':68C ''sub-n-gram'':67C ''substr'':94C ''suffix'':102C ''sure'':15C ''techniqu'':25C ''text'':56C ''tree'':103C ''trie'':100C ''use'':20C,81C,115C ''would'':72C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (984, 1, '2014-08-16 10:42:58.337', 1, 194, 'Can anyone provide the 24 hour challenge dataset from Climate Corporation?', '&lt;p&gt;I have no knowledge about the climate or soil. And I just want to find out more about these kind of dataset. I heard that Climate Corporation asked its candidates to perform statistical analysis on various climate dataset. &lt;/p&gt;

&lt;p&gt;This is why I am asking this question. Please do not get me wrong. I am not trying to get the dataset to prepare myself for an interview, as I know they give out different dataset to people from different background. &lt;/p&gt;

&lt;p&gt;I know that Climate Corporation only hires PHD, which I am not. I only want to play around with their dataset such that I can learn and implement &lt;strong&gt;time series analysis&lt;/strong&gt;. That''s it. &lt;/p&gt;

&lt;p&gt;So, if anyone does not mind sharing their dataset. Please post the link them below. Thank you very much. &lt;/p&gt;
', 'dataset', NULL, '''/p'':52C,95C,133C,154C ''/strong'':129C ''24'':5A ''analysi'':47C,128C ''anyon'':2A,137C ''around'':114C ''ask'':41C,59C ''background'':94C ''candid'':43C ''challeng'':7A ''climat'':10A,20C,39C,50C,100C ''corpor'':11A,40C,101C ''dataset'':8A,12B,35C,51C,75C,89C,117C,143C ''differ'':88C,93C ''find'':28C ''get'':65C,73C ''give'':86C ''heard'':37C ''hire'':103C ''hour'':6A ''implement'':124C ''interview'':81C ''kind'':33C ''know'':84C,98C ''knowledg'':17C ''learn'':122C ''link'':147C ''mind'':140C ''much'':153C ''p'':13C,53C,96C,134C ''peopl'':91C ''perform'':45C ''phd'':104C ''play'':113C ''pleas'':62C,144C ''post'':145C ''prepar'':77C ''provid'':3A ''question'':61C ''seri'':127C ''share'':141C ''soil'':22C ''statist'':46C ''strong'':125C ''thank'':150C ''time'':126C ''tri'':71C ''various'':49C ''want'':26C,111C ''wrong'':67C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1008, 2, '2014-08-20 15:34:00.83', 1, NULL, NULL, '&lt;p&gt;Try BeautifulSoup - &lt;a href="http://www.crummy.com/software/BeautifulSoup/" rel="nofollow"&gt;http://www.crummy.com/software/BeautifulSoup/&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;From the website "Beautiful Soup is a Python library designed for quick turnaround projects like screen-scraping."
I have no personally used it, but it often comes up in regards to a nice library for scraping. Here''s a blog post on using it to scrape Craigslist &lt;a href="http://www.gregreda.com/2014/07/27/scraping-craigslist-for-tickets/" rel="nofollow"&gt;http://www.gregreda.com/2014/07/27/scraping-craigslist-for-tickets/&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/2014/07/27/scraping-craigslist-for-tickets/'':67C ''/2014/07/27/scraping-craigslist-for-tickets/&lt;/a&gt;&lt;/p&gt;'':72C ''/software/beautifulsoup/'':8C ''/software/beautifulsoup/&lt;/a&gt;&lt;/p&gt;'':13C ''beauti'':18C ''beautifulsoup'':3C ''blog'':55C ''come'':42C ''craigslist'':62C ''design'':24C ''href'':5C,64C ''librari'':23C,49C ''like'':29C ''nice'':48C ''nofollow'':10C,69C ''often'':41C ''p'':1C,14C ''person'':36C ''post'':56C ''project'':28C ''python'':22C ''quick'':26C ''regard'':45C ''rel'':9C,68C ''scrape'':32C,51C,61C ''screen'':31C ''screen-scrap'':30C ''soup'':19C ''tri'':2C ''turnaround'':27C ''use'':37C,58C ''websit'':17C ''www.crummy.com'':7C,12C ''www.crummy.com/software/beautifulsoup/'':6C ''www.crummy.com/software/beautifulsoup/&lt;/a&gt;&lt;/p&gt;'':11C ''www.gregreda.com'':66C,71C ''www.gregreda.com/2014/07/27/scraping-craigslist-for-tickets/'':65C ''www.gregreda.com/2014/07/27/scraping-craigslist-for-tickets/&lt;/a&gt;&lt;/p&gt;'':70C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (985, 1, '2014-08-16 11:05:43.353', 2, 621, 'Can I use unsupervised learning followed by supervised learning?', '&lt;p&gt;I have a question about classifying documents using supervised learning and unsupervised learning.&lt;/p&gt;

&lt;p&gt;For example: - I have a bunch of documents talking about football.&lt;br&gt;
As we know football has different meaning in UK, USA and Australia. Therefore, it is difficult to classify these documents to three different categorizations which are soccer, American football and Australian football.&lt;br&gt;
My approach tries to use cosine similarity terms which is based on unsupervised. After we use the cluster learning, we are able to create a number of clusters based on cosine similarity which each cluster will contain similar documents terms. After we create the clusters, we can use a semantic feature to identify these clusters depend on supervised model like SVM to make accurate categorizations.  &lt;/p&gt;

&lt;p&gt;My goal is to create more accurate categorizations because if I want to test a new document I want know if this document can be related to these categorizations or not.  &lt;/p&gt;
', 'machine-learning  classification  clustering  dimensionality-reduction', NULL, '''/p'':32C,144C,177C ''abl'':100C ''accur'':142C,152C ''american'':73C ''approach'':80C ''australia'':57C ''australian'':76C ''base'':89C,107C ''br'':45C,78C ''bunch'':39C ''categor'':69C,143C,153C,174C ''classif'':13B ''classifi'':24C,63C ''cluster'':14B,96C,106C,113C,123C,133C ''contain'':115C ''cosin'':84C,109C ''creat'':102C,121C,150C ''depend'':134C ''differ'':51C,68C ''difficult'':61C ''dimension'':16B ''dimensionality-reduct'':15B ''document'':25C,41C,65C,117C,162C,168C ''exampl'':35C ''featur'':129C ''follow'':6A ''footbal'':44C,49C,74C,77C ''goal'':147C ''identifi'':131C ''know'':48C,165C ''learn'':5A,9A,12B,28C,31C,97C ''like'':138C ''machin'':11B ''machine-learn'':10B ''make'':141C ''mean'':52C ''model'':137C ''new'':161C ''number'':104C ''p'':18C,33C,145C ''question'':22C ''reduct'':17B ''relat'':171C ''semant'':128C ''similar'':85C,110C,116C ''soccer'':72C ''supervis'':8A,27C,136C ''svm'':139C ''talk'':42C ''term'':86C,118C ''test'':159C ''therefor'':58C ''three'':67C ''tri'':81C ''uk'':54C ''unsupervis'':4A,30C,91C ''usa'':55C ''use'':3A,26C,83C,94C,126C ''want'':157C,164C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (10120, 1, '2016-02-07 15:34:18.73', 2, 18, 'PCA on acceleration time series data', '&lt;p&gt;Has anyone attempted Principal component analysis on time series and acceleration data(ie. data from accelerometer and sensors) and tried compressing it as well as regenerating back the data with minimum error?&lt;/p&gt;

&lt;p&gt;I wanted to know if this is possible using PCA. Kindly suggest alternate methods available to compress and regenrate my data.&lt;/p&gt;
', 'data', NULL, '''/p'':41C,64C ''acceler'':3A,19C ''acceleromet'':24C ''altern'':55C ''analysi'':14C ''anyon'':10C ''attempt'':11C ''avail'':57C ''back'':35C ''compon'':13C ''compress'':29C,59C ''data'':6A,7B,20C,22C,37C,63C ''error'':40C ''ie'':21C ''kind'':53C ''know'':46C ''method'':56C ''minimum'':39C ''p'':8C,42C ''pca'':1A,52C ''possibl'':50C ''princip'':12C ''regener'':34C ''regenr'':61C ''sensor'':26C ''seri'':5A,17C ''suggest'':54C ''time'':4A,16C ''tri'':28C ''use'':51C ''want'':44C ''well'':32C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (986, 2, '2014-08-17 14:17:41.307', 1, NULL, NULL, '&lt;p&gt;OK, so here''s your data. &lt;/p&gt;

&lt;pre&gt;&lt;code&gt;dd &amp;lt;- data.frame(position=rep(1:3, each=2), 
                 variation=rep(c(1,3), 3), 
                 impressions=rep(c(753, 767), 3), 
                 clicks=c(26,7,16,13,2,7))
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;which is&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;  position variation impressions clicks
1        1         1         753     26
2        1         3         767      7
3        2         1         753     16
4        2         3         767     13
5        3         1         753      2
6        3         3         767      7
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The two model assumptions you''re thinking about are Binomial&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;mod.bin &amp;lt;- glm(cbind(clicks, impressions-clicks) ~ variation + position,
               family=binomial, data=dd)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where the dependent variable is constructed to have the count of the event of interest in the first column, and the Poisson&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;md.pois &amp;lt;- glm(clicks ~ variation + position + offset(log(impressions)), 
               family=poisson, data=dd)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;where the &lt;code&gt;log(impressions)&lt;/code&gt; offset is necessary whenever the number of trials differs across observations.  This means coefficients are interpretable in terms of change in rate not change in count, which is what you want.  &lt;/p&gt;

&lt;p&gt;The first model generalises the &lt;code&gt;binom.test&lt;/code&gt; to a setting with covariates, which is what you have.  That gets you a more direct answer to your question, and better (if not perfect) measurement of the relevant uncertainty.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Notes&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Both models assume no interaction between variation and position (''independent effects'').  This may or may not be reasonable.  You''d want more replications to investigate that properly.  Swap the &lt;code&gt;+&lt;/code&gt; for a &lt;code&gt;*&lt;/code&gt; to do so.&lt;/p&gt;

&lt;p&gt;In this data &lt;code&gt;summary&lt;/code&gt; confirms that the two models give rather similar results, so concerns about Poisson vs Binomial don''t seem to matter much.&lt;/p&gt;

&lt;p&gt;In the wild, count data is usually overdispersed, that is: more variable than you''d expect from a Poisson with a constant rate or a Binomial with constant click probability, often due to unmodeled determinants of click rate / probability.  If that''s the case then prediction intervals from these models will be too narrow.&lt;/p&gt;
', NULL, NULL, '''/code'':40C,82C,112C,153C,161C,202C,270C,274C,285C ''/p'':8C,45C,95C,137C,193C,233C,238C,278C,307C,363C ''/pre'':41C,83C,113C,154C ''/strong'':237C ''1'':16C,23C,52C,53C,54C,58C,64C,74C ''13'':37C,71C ''16'':36C,66C ''2'':19C,38C,57C,63C,68C,76C ''26'':34C,56C ''3'':17C,24C,25C,31C,59C,62C,69C,73C,78C,79C ''4'':67C ''5'':72C ''6'':77C ''7'':35C,39C,61C,81C ''753'':29C,55C,65C,75C ''767'':30C,60C,70C,80C ''across'':171C ''answer'':219C ''assum'':242C ''assumpt'':88C ''better'':224C ''binom.test'':201C ''binomi'':94C,109C,300C,334C ''c'':22C,28C,33C ''case'':352C ''cbind'':101C ''chang'':181C,185C ''click'':32C,51C,102C,105C,143C,337C,345C ''code'':10C,47C,97C,139C,158C,200C,269C,273C,283C ''coeffici'':175C ''column'':133C ''concern'':296C ''confirm'':286C ''constant'':330C,336C ''construct'':120C ''count'':124C,187C,312C ''covari'':207C ''d'':259C,323C ''data'':7C,110C,151C,282C,313C ''data.frame'':13C ''dd'':11C,111C,152C ''depend'':117C ''determin'':343C ''differ'':170C ''direct'':218C ''due'':340C ''effect'':250C ''event'':127C ''expect'':324C ''famili'':108C,149C ''first'':132C,196C ''generalis'':198C ''get'':214C ''give'':291C ''glm'':100C,142C ''impress'':26C,50C,104C,148C,160C ''impressions-click'':103C ''independ'':249C ''interact'':244C ''interest'':129C ''interpret'':177C ''interv'':355C ''investig'':264C ''log'':147C,159C ''lt'':12C,99C,141C ''matter'':305C ''may'':252C,254C ''md.pois'':140C ''mean'':174C ''measur'':228C ''mod.bin'':98C ''model'':87C,197C,241C,290C,358C ''much'':306C ''narrow'':362C ''necessari'':164C ''note'':236C ''number'':167C ''observ'':172C ''offset'':146C,162C ''often'':339C ''ok'':2C ''overdispers'':316C ''p'':1C,42C,84C,114C,155C,194C,234C,239C,279C,308C ''perfect'':227C ''poisson'':136C,150C,298C,327C ''posit'':14C,48C,107C,145C,248C ''pre'':9C,46C,96C,138C ''predict'':354C ''probabl'':338C,347C ''proper'':266C ''question'':222C ''rate'':183C,331C,346C ''rather'':292C ''re'':90C ''reason'':257C ''relev'':231C ''rep'':15C,21C,27C ''replic'':262C ''result'':294C ''seem'':303C ''set'':205C ''similar'':293C ''strong'':235C ''summari'':284C ''swap'':267C ''term'':179C ''think'':91C ''trial'':169C ''two'':86C,289C ''uncertainti'':232C ''unmodel'':342C ''usual'':315C ''variabl'':118C,320C ''variat'':20C,49C,106C,144C,246C ''vs'':299C ''want'':192C,260C ''whenev'':165C ''wild'':311C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (990, 2, '2014-08-18 11:33:32.617', 3, NULL, NULL, '&lt;p&gt;This makes sense. IIUC, the speed of execution of support vector operations is bound by number of samples, not dimensionality. In other words, it is capped by CPU time and not RAM. I''m not sure exactly how much time this should take, but I''m running some benchmarks to find out.&lt;/p&gt;
', NULL, NULL, '''/p'':54C ''benchmark'':50C ''bound'':15C ''cap'':27C ''cpu'':29C ''dimension'':21C ''exact'':38C ''execut'':9C ''find'':52C ''iiuc'':5C ''m'':35C,47C ''make'':3C ''much'':40C ''number'':17C ''oper'':13C ''p'':1C ''ram'':33C ''run'':48C ''sampl'':19C ''sens'':4C ''speed'':7C ''support'':11C ''sure'':37C ''take'':44C ''time'':30C,41C ''vector'':12C ''word'':24C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (991, 2, '2014-08-18 13:35:28', 7, NULL, NULL, '&lt;p&gt;Linear models simply add their features multiplied by corresponding weights. If, for example, you have 1000 sparse features only 3 or 4 of which are active in each instance (and the others are zeros) and 20 dense features that are all non-zeros, then it''s pretty likely that dense features will make most of the impact while sparse features will add only a little value. You can check this by looking at feature weights for a few instances and how they influence resulting sum.&lt;/p&gt;

&lt;p&gt;One way to fix it is to go away from additive model. Here''s a couple of candidate models. &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;SVM&lt;/strong&gt; is based on separating hyperplanes. Though hyperplane is linear model itself, SVM doesn''t sum up its parameters, but instead tries to split feature space in an optimal way. Given the number of features, I''d say that linear SVM should work fine while more complicated kernels may tend to overfit the data. &lt;/p&gt;

&lt;p&gt;Despite its name, &lt;strong&gt;Naive Bayes&lt;/strong&gt; is pretty powerful statistical model that showed good results for text classification. It''s also flexible enough to capture imbalance in frequency of sparse and dense features, so you should definitely give it a try. &lt;/p&gt;

&lt;p&gt;Finally, &lt;strong&gt;random forests&lt;/strong&gt; may work as a good ensemble method in this case. Randomization will ensure that different kinds of features (sparse/dense) will be used as primary decision nodes in different trees. RF/decision trees are also good for inspecting features themselves, so it''s worth to note their structure anyway.&lt;/p&gt;

&lt;p&gt;Note that all of these methods have their drawbacks that may turn them into a garbage in your case. Combing sparse and dense features isn''t really well-studied task, so let us know what of these approaches works best for your case. &lt;/p&gt;
', NULL, NULL, '''/p'':88C,109C,167C,211C,265C,311C ''/strong'':113C,175C,217C ''1000'':17C ''20'':37C ''3'':21C ''4'':23C ''activ'':27C ''add'':5C,64C ''addit'':100C ''also'':190C,250C ''anyway'':264C ''approach'':305C ''away'':98C ''base'':115C ''bay'':174C ''best'':307C ''candid'':107C ''captur'':194C ''case'':227C,285C,310C ''check'':71C ''classif'':187C ''comb'':286C ''complic'':159C ''correspond'':10C ''coupl'':105C ''d'':149C ''data'':166C ''decis'':242C ''definit'':206C ''dens'':38C,52C,201C,289C ''despit'':169C ''differ'':232C,245C ''doesn'':126C ''drawback'':275C ''enough'':192C ''ensembl'':223C ''ensur'':230C ''exampl'':14C ''featur'':7C,19C,39C,53C,62C,76C,137C,147C,202C,235C,254C,290C ''final'':213C ''fine'':156C ''fix'':93C ''flexibl'':191C ''forest'':216C ''frequenc'':197C ''garbag'':282C ''give'':207C ''given'':143C ''go'':97C ''good'':183C,222C,251C ''hyperplan'':118C,120C ''imbal'':195C ''impact'':59C ''influenc'':85C ''inspect'':253C ''instanc'':30C,81C ''instead'':133C ''isn'':291C ''kernel'':160C ''kind'':233C ''know'':301C ''let'':299C ''like'':50C ''linear'':2C,122C,152C ''littl'':67C ''look'':74C ''make'':55C ''may'':161C,218C,277C ''method'':224C,272C ''model'':3C,101C,108C,123C,180C ''multipli'':8C ''naiv'':173C ''name'':171C ''node'':243C ''non'':44C ''non-zero'':43C ''note'':261C,267C ''number'':145C ''one'':90C ''optim'':141C ''other'':33C ''overfit'':164C ''p'':1C,89C,110C,168C,212C,266C ''paramet'':131C ''power'':178C ''pretti'':49C,177C ''primari'':241C ''random'':215C,228C ''realli'':293C ''result'':86C,184C ''rf/decision'':247C ''say'':150C ''separ'':117C ''show'':182C ''simpli'':4C ''space'':138C ''spars'':18C,61C,199C,287C ''sparse/dense'':236C ''split'':136C ''statist'':179C ''strong'':111C,172C,214C ''structur'':263C ''studi'':296C ''sum'':87C,128C ''svm'':112C,125C,153C ''task'':297C ''tend'':162C ''text'':186C ''though'':119C ''tree'':246C,248C ''tri'':134C,210C ''turn'':278C ''us'':300C ''use'':239C ''valu'':68C ''way'':91C,142C ''weight'':11C,77C ''well'':295C ''well-studi'':294C ''work'':155C,219C,306C ''worth'':259C ''zero'':35C,45C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (992, 1, '2014-08-18 14:56:13.8', 8, 218, 'Why might several types of models give almost identical results?', '&lt;p&gt;I''ve been analyzing a data set of ~400k records and 9 variables The dependent variable is binary. I''ve fitted a logistic regression, a regression tree, a random forest, and a gradient boosted tree. All of them give virtual identical goodness of fit numbers when I validate them on another data set.&lt;/p&gt;

&lt;p&gt;Why is this so? I''m guessing that it''s because my observations to variable ratio is so high. If this is correct, at what observation to variable ratio will different models start to give different results? &lt;/p&gt;
', 'data-mining  classification  binary', NULL, '''/p'':70C,109C ''400k'':25C ''9'':28C ''almost'':8A ''analyz'':20C ''anoth'':67C ''binari'':15B,34C ''boost'':50C ''classif'':14B ''correct'':94C ''data'':12B,22C,68C ''data-min'':11B ''depend'':31C ''differ'':102C,107C ''fit'':37C,60C ''forest'':46C ''give'':7A,55C,106C ''good'':58C ''gradient'':49C ''guess'':78C ''high'':90C ''ident'':9A,57C ''logist'':39C ''m'':77C ''might'':2A ''mine'':13B ''model'':6A,103C ''number'':61C ''observ'':84C,97C ''p'':16C,71C ''random'':45C ''ratio'':87C,100C ''record'':26C ''regress'':40C,42C ''result'':10A,108C ''set'':23C,69C ''sever'':3A ''start'':104C ''tree'':43C,51C ''type'':4A ''valid'':64C ''variabl'':29C,32C,86C,99C ''ve'':18C,36C ''virtual'':56C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1059, 1, '2014-08-29 06:00:53.42', 6, 433, 'Forecasting Foreign Exchange with Neural Network - Lag in Prediction', '&lt;p&gt;I have a question regarding the use of neural network. I am currently working with R (&lt;a href="http://cran.r-project.org/web/packages/neuralnet/index.html" rel="nofollow"&gt;neuralnet package&lt;/a&gt;) and I am facing the following issue.
My testing and validation set are always late with respect to the historical data. Is there a way of correcting the result?
Maybe something is wrong in my analysis&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;I use the daily log return 
&lt;img src="http://i.stack.imgur.com/A3r16.gif" alt="r(t) = ln(s(t)/s(t-1))"&gt;&lt;/li&gt;
&lt;li&gt;I normalise my data with the sigmoid function (sigma and mu computed on my whole set)&lt;/li&gt;
&lt;li&gt;I train my neural networks with 10 dates and the output is the normalised value that follows these 10 dates.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;I tried to add the trend but there is no improvement, I observed 1-2 days late. My process seems ok, what do you think about it?&lt;/p&gt;
', 'r  neuralnetwork  time-series  forecast', NULL, '''-1'':101C ''-2'':159C ''/a'':42C ''/a3r16.gif'':92C ''/li'':102C,120C,142C ''/ol'':143C ''/p'':79C,172C ''/s'':99C ''/web/packages/neuralnet/index.html'':37C ''1'':158C ''10'':128C,140C ''add'':148C ''alt'':93C ''alway'':56C ''analysi'':78C ''comput'':115C ''correct'':69C ''cran.r-project.org'':36C ''cran.r-project.org/web/packages/neuralnet/index.html'':35C ''current'':29C ''daili'':85C ''data'':63C,107C ''date'':129C,141C ''day'':160C ''exchang'':3A ''face'':46C ''follow'':48C,138C ''forecast'':1A,15B ''foreign'':2A ''function'':111C ''histor'':62C ''href'':34C ''i.stack.imgur.com'':91C ''i.stack.imgur.com/a3r16.gif'':90C ''img'':88C ''improv'':155C ''issu'':49C ''lag'':7A ''late'':57C,161C ''li'':81C,103C,121C ''ln'':96C ''log'':86C ''mayb'':72C ''mu'':114C ''network'':6A,26C,126C ''neural'':5A,25C,125C ''neuralnet'':40C ''neuralnetwork'':11B ''nofollow'':39C ''normalis'':105C,135C ''observ'':157C ''ok'':165C ''ol'':80C ''output'':132C ''p'':16C,144C ''packag'':41C ''predict'':9A ''process'':163C ''question'':20C ''r'':10B,32C,94C ''regard'':21C ''rel'':38C ''respect'':59C ''result'':71C ''return'':87C ''seem'':164C ''seri'':14B ''set'':54C,119C ''sigma'':112C ''sigmoid'':110C ''someth'':73C ''src'':89C ''test'':51C ''think'':169C ''time'':13B ''time-seri'':12B ''train'':123C ''trend'':150C ''tri'':146C ''use'':23C,83C ''valid'':53C ''valu'':136C ''way'':67C ''whole'':118C ''work'':30C ''wrong'':75C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (993, 2, '2014-08-18 16:28:02.147', 0, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;I''m guessing that it''s because my observations to variable ratio is so high.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;I think this explanation makes perfect sense.&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;If this is correct, at what observation to variable ratio will different models start to give different results? &lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;This will probably depend very much on your specific data (for instance, even whether your nine variables are continuous, factors, ordinary or binary), as well as any tuning decisions you made while fitting your model.&lt;/p&gt;

&lt;p&gt;But you can play around with the observation-to-variable ratio - not by increasing the number of variables, but by decreasing the number of observations. Randomly draw 100 observations, fit models and see whether different models yield different results. (I guess they will.) Do this multiple times with different samples drawn from your total number of observations. Then look at subsamples of 1,000 observations... 10,000 observations... and so forth.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':19C,50C ''/p'':18C,28C,49C,87C,161C ''000'':153C,156C ''1'':152C ''10'':155C ''100'':117C ''around'':93C ''binari'':74C ''blockquot'':1C,29C ''continu'':70C ''correct'':34C ''data'':61C ''decis'':80C ''decreas'':110C ''depend'':55C ''differ'':42C,47C,124C,127C,138C ''draw'':116C ''drawn'':140C ''even'':64C ''explan'':24C ''factor'':71C ''fit'':84C,119C ''forth'':160C ''give'':46C ''guess'':5C,130C ''high'':17C ''increas'':103C ''instanc'':63C ''look'':148C ''m'':4C ''made'':82C ''make'':25C ''model'':43C,86C,120C,125C ''much'':57C ''multipl'':135C ''nine'':67C ''number'':105C,112C,144C ''observ'':11C,37C,97C,114C,118C,146C,154C,157C ''observation-to-vari'':96C ''ordinari'':72C ''p'':2C,20C,30C,51C,88C ''perfect'':26C ''play'':92C ''probabl'':54C ''random'':115C ''ratio'':14C,40C,100C ''result'':48C,128C ''sampl'':139C ''see'':122C ''sens'':27C ''specif'':60C ''start'':44C ''subsampl'':150C ''think'':22C ''time'':136C ''total'':143C ''tune'':79C ''variabl'':13C,39C,68C,99C,107C ''well'':76C ''whether'':65C,123C ''yield'':126C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2365, 2, '2014-10-28 07:14:06.873', 1, NULL, NULL, '&lt;p&gt;Ok all sorted - Bit embarrassing but forgot to normalise the data!&lt;/p&gt;
', NULL, NULL, '''/p'':13C ''bit'':5C ''data'':12C ''embarrass'':6C ''forgot'':8C ''normalis'':10C ''ok'':2C ''p'':1C ''sort'':4C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (995, 2, '2014-08-18 17:25:27.007', 1, NULL, NULL, '&lt;p&gt;As @seanv507 suggested, the similar performance may simply be due to the data being best separated by a linear model. But in general, the statement that it is because the "observations to variable ratio is so high" is incorrect. Even as your ratio of sample size to number of variables goes to infinity, you should not expect different models to perform nearly identically, unless they all provide the same predictive bias.&lt;/p&gt;
', NULL, NULL, '''/p'':73C ''best'':16C ''bias'':72C ''data'':14C ''differ'':59C ''due'':11C ''even'':41C ''expect'':58C ''general'':24C ''goe'':52C ''high'':38C ''ident'':64C ''incorrect'':40C ''infin'':54C ''linear'':20C ''may'':8C ''model'':21C,60C ''near'':63C ''number'':49C ''observ'':32C ''p'':1C ''perform'':7C,62C ''predict'':71C ''provid'':68C ''ratio'':35C,44C ''sampl'':46C ''seanv507'':3C ''separ'':17C ''similar'':6C ''simpli'':9C ''size'':47C ''statement'':26C ''suggest'':4C ''unless'':65C ''variabl'':34C,51C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (996, 2, '2014-08-19 00:56:40.89', 8, NULL, NULL, '&lt;p&gt;Kernelized SVMs require the computation of a distance function between each point in the dataset, which is the dominating cost of $\mathcal{O}(n_\text{features} \times n_\text{observations}^2)$. The storage of the distances is a burden on memory, so they''re recomputed on the fly. Thankfully, only the points nearest the decision boundary are needed most of the time. Frequently computed distances are stored in a cache. If the cache is getting thrashed then the running time blows up to $\mathcal{O}(n_\text{features} \times n_\text{observations}^3)$.&lt;/p&gt;

&lt;p&gt;You can increase this cache by invoking SVR as&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;model = SVR(cache_size=7000)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;In general, this is not going to work. But all is not lost. You can subsample the data and use the rest as a validation set, or you can pick a different model. Above the 200,000 observation range, it''s wise to choose linear learners.&lt;/p&gt;

&lt;p&gt;Kernel SVM can be approximated, by approximating the kernel matrix and feeding it to a linear SVM. This allows you to trade off between accuracy and performance in linear time.&lt;/p&gt;

&lt;p&gt;A popular means of achieving this is to use 100 or so cluster centers found by kmeans/kmeans++ as the basis of your kernel function. The new derived features are then fed into a linear model. This works very well in practice. Tools like &lt;a href="https://code.google.com/p/sofia-ml/" rel="nofollow"&gt;sophia-ml&lt;/a&gt; and &lt;a href="https://github.com/JohnLangford/vowpal_wabbit/wiki" rel="nofollow"&gt;vowpal wabbit&lt;/a&gt; are how Google, Yahoo and Microsoft do this. Input/output becomes the dominating cost for simple linear learners.&lt;/p&gt;

&lt;p&gt;In the abundance of data, nonparametric models perform roughly the same for most problems. The exceptions being structured inputs, like text, images, time series, audio.&lt;/p&gt;

&lt;h2&gt;Further reading&lt;/h2&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://fastml.com/the-secret-of-the-big-guys/" rel="nofollow"&gt;How to implement this.&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://fastml.com/go-non-linear-with-vowpal-wabbit/" rel="nofollow"&gt;How to train an ngram neural network with dropout that scales linearly&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://peekaboo-vision.blogspot.co.uk/2012/12/kernel-approximations-for-efficient.html" rel="nofollow"&gt;Kernel Approximations&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.144.9009&amp;amp;rep=rep1&amp;amp;type=pdf" rel="nofollow"&gt;A formal paper on using kmeans to approximate kernel machines&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/2012/12/kernel-approximations-for-efficient.html'':353C ''/a'':250C,261C,324C,346C,358C,378C ''/code'':114C ''/go-non-linear-with-vowpal-wabbit/'':331C ''/h2'':310C ''/johnlangford/vowpal_wabbit/wiki'':256C ''/li'':325C,347C,359C,379C ''/p'':95C,106C,163C,195C,279C,306C ''/p/sofia-ml/'':244C ''/pre'':115C ''/the-secret-of-the-big-guys/'':317C ''/ul'':380C ''/viewdoc/download?doi=10.1.1.144.9009&amp;amp;rep=rep1&amp;amp;type=pdf'':365C ''000'':153C ''100'':206C ''2'':32C ''200'':152C ''3'':94C ''7000'':113C ''abund'':283C ''accuraci'':189C ''achiev'':201C ''allow'':183C ''approxim'':169C,171C,357C,375C ''audio'':305C ''basi'':216C ''becom'':271C ''blow'':82C ''boundari'':57C ''burden'':40C ''cach'':71C,74C,101C,111C ''center'':210C ''choos'':160C ''citeseerx.ist.psu.edu'':364C ''citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.144.9009&amp;amp;rep=rep1&amp;amp;type=pdf'':363C ''cluster'':209C ''code'':108C ''code.google.com'':243C ''code.google.com/p/sofia-ml/'':242C ''comput'':6C,65C ''cost'':21C,274C ''data'':134C,285C ''dataset'':16C ''decis'':56C ''deriv'':223C ''differ'':148C ''distanc'':9C,37C,66C ''domin'':20C,273C ''dropout'':342C ''except'':296C ''fastml.com'':316C,330C ''fastml.com/go-non-linear-with-vowpal-wabbit/'':329C ''fastml.com/the-secret-of-the-big-guys/'':315C ''featur'':27C,89C,224C ''fed'':227C ''feed'':176C ''fli'':49C ''formal'':369C ''found'':211C ''frequent'':64C ''function'':10C,220C ''general'':118C ''get'':76C ''github.com'':255C ''github.com/johnlangford/vowpal_wabbit/wiki'':254C ''go'':122C ''googl'':264C ''h2'':307C ''href'':241C,253C,314C,328C,350C,362C ''imag'':302C ''implement'':322C ''increas'':99C ''input'':299C ''input/output'':270C ''invok'':103C ''kernel'':2C,165C,173C,219C,356C,376C ''kmean'':373C ''kmeans/kmeans'':213C ''learner'':162C,278C ''li'':312C,326C,348C,360C ''like'':239C,300C ''linear'':161C,180C,193C,230C,277C,345C ''lost'':129C ''machin'':377C ''mathcal'':23C,85C ''matrix'':174C ''mean'':199C ''memori'':42C ''microsoft'':267C ''ml'':249C ''model'':109C,149C,231C,287C ''n'':25C,29C,87C,91C ''nearest'':54C ''need'':59C ''network'':340C ''neural'':339C ''new'':222C ''ngram'':338C ''nofollow'':246C,258C,319C,333C,355C,367C ''nonparametr'':286C ''o'':24C,86C ''observ'':31C,93C,154C ''p'':1C,96C,116C,164C,196C,280C ''paper'':370C ''peekaboo-vision.blogspot.co.uk'':352C ''peekaboo-vision.blogspot.co.uk/2012/12/kernel-approximations-for-efficient.html'':351C ''perform'':191C,288C ''pick'':146C ''point'':13C,53C ''popular'':198C ''practic'':237C ''pre'':107C ''problem'':294C ''rang'':155C ''re'':45C ''read'':309C ''recomput'':46C ''rel'':245C,257C,318C,332C,354C,366C ''requir'':4C ''rest'':138C ''rough'':289C ''run'':80C ''scale'':344C ''seri'':304C ''set'':142C ''simpl'':276C ''size'':112C ''sophia'':248C ''sophia-ml'':247C ''storag'':34C ''store'':68C ''structur'':298C ''subsampl'':132C ''svm'':166C,181C ''svms'':3C ''svr'':104C,110C ''text'':26C,30C,88C,92C,301C ''thank'':50C ''thrash'':77C ''time'':28C,63C,81C,90C,194C,303C ''tool'':238C ''trade'':186C ''train'':336C ''ul'':311C ''use'':136C,205C,372C ''valid'':141C ''vowpal'':259C ''wabbit'':260C ''well'':235C ''wise'':158C ''work'':124C,233C ''yahoo'':265C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (997, 1, '2014-08-19 03:41:24.207', 4, 875, 'Where can I find free spatio-temporal dataset for download?', '&lt;p&gt;Where can I find free spatio-temporal dataset for download so that I can play with it in R ? &lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;
', 'dataset  open-source  freebase', NULL, '''/p'':38C,41C ''dataset'':9A,12B,26C ''download'':11A,28C ''find'':4A,21C ''free'':5A,22C ''freebas'':16B ''open'':14B ''open-sourc'':13B ''p'':17C,39C ''play'':33C ''r'':37C ''sourc'':15B ''spatio'':7A,24C ''spatio-tempor'':6A,23C ''tempor'':8A,25C ''thank'':40C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (998, 2, '2014-08-19 13:33:32.453', 4, NULL, NULL, '&lt;p&gt;No, sklearn doesn''t seem to have a forward selection algorithm. However, it does provide recursive feature elimination, which is a greedy feature elimination algorithm similar to sequential backward selection. See the documentation here:&lt;/p&gt;

&lt;p&gt;&lt;a href="http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html" rel="nofollow"&gt;http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html&lt;/a&gt;&lt;/p&gt;
', NULL, NULL, '''/p'':36C ''/stable/modules/generated/sklearn.feature_selection.rfe.html'':42C ''/stable/modules/generated/sklearn.feature_selection.rfe.html&lt;/a&gt;&lt;/p&gt;'':47C ''algorithm'':12C,26C ''backward'':30C ''document'':34C ''doesn'':4C ''elimin'':19C,25C ''featur'':18C,24C ''forward'':10C ''greedi'':23C ''howev'':13C ''href'':39C ''nofollow'':44C ''p'':1C,37C ''provid'':16C ''recurs'':17C ''rel'':43C ''scikit-learn.org'':41C,46C ''scikit-learn.org/stable/modules/generated/sklearn.feature_selection.rfe.html'':40C ''scikit-learn.org/stable/modules/generated/sklearn.feature_selection.rfe.html&lt;/a&gt;&lt;/p&gt;'':45C ''see'':32C ''seem'':6C ''select'':11C,31C ''sequenti'':29C ''similar'':27C ''sklearn'':3C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (999, 2, '2014-08-19 14:13:07.147', 7, NULL, NULL, '&lt;p&gt;This results means that whatever method you use, you are able to get reasonably close to the optimal decision rule (aka &lt;a href="http://en.wikipedia.org/wiki/Admissible_decision_rule#Bayes_rules_and_generalized_Bayes_rules"&gt;Bayes rule&lt;/a&gt;). The underlying reasons have been explained in Hastie, Tibshirani and Friedman''s &lt;a href="http://statweb.stanford.edu/~tibs/ElemStatLearn/"&gt;"Elements of Statistical Learning"&lt;/a&gt;. They demonstrated how the different methods perform by comparing Figs. 2.1, 2.2, 2.3, 5.11 (in my first edition -- in section on multidimensional splines), 12.2, 12.3 (support vector machines), and probably some others. If you have not read that book, you need to drop everything &lt;strong&gt;RIGHT NOW&lt;/strong&gt; and read it up. (I mean, it isn''t worth losing your job, but it is worth missing a homework or two if you are a student.)&lt;/p&gt;

&lt;p&gt;I don''t think that observations to variable ratio is the explanation. In light of my rationale offered above, it is the relatively simple form of the boundary separating your classes in the multidimensional space that all of the methods you tried have been able to identify.&lt;/p&gt;
', NULL, NULL, '''/a'':30C,52C ''/p'':128C,177C ''/strong'':100C ''/wiki/admissible_decision_rule#bayes_rules_and_generalized_bayes_rules'':27C ''/~tibs/elemstatlearn/'':47C ''12.2'':76C ''12.3'':77C ''2.1'':63C ''2.2'':64C ''2.3'':65C ''5.11'':66C ''abl'':12C,174C ''aka'':22C ''bay'':28C ''book'':91C ''boundari'':157C ''class'':160C ''close'':16C ''compar'':61C ''decis'':20C ''demonstr'':54C ''differ'':57C ''drop'':95C ''edit'':70C ''element'':48C ''en.wikipedia.org'':26C ''en.wikipedia.org/wiki/admissible_decision_rule#bayes_rules_and_generalized_bayes_rules'':25C ''everyth'':96C ''explain'':36C ''explan'':141C ''fig'':62C ''first'':69C ''form'':154C ''friedman'':41C ''get'':14C ''hasti'':38C ''homework'':120C ''href'':24C,44C ''identifi'':176C ''isn'':108C ''job'':113C ''learn'':51C ''light'':143C ''lose'':111C ''machin'':80C ''mean'':4C,106C ''method'':7C,58C,169C ''miss'':118C ''multidimension'':74C,163C ''need'':93C ''observ'':135C ''offer'':147C ''optim'':19C ''other'':84C ''p'':1C,129C ''perform'':59C ''probabl'':82C ''ratio'':138C ''rational'':146C ''read'':89C,102C ''reason'':15C,33C ''relat'':152C ''result'':3C ''right'':98C ''rule'':21C,29C ''section'':72C ''separ'':158C ''simpl'':153C ''space'':164C ''spline'':75C ''statist'':50C ''statweb.stanford.edu'':46C ''statweb.stanford.edu/~tibs/elemstatlearn/'':45C ''strong'':97C ''student'':127C ''support'':78C ''think'':133C ''tibshirani'':39C ''tri'':171C ''two'':122C ''under'':32C ''use'':9C ''variabl'':137C ''vector'':79C ''whatev'':6C ''worth'':110C,117C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9705, 1, '2016-01-08 17:39:10.643', 1, 13, 'Calculate the overall accuracy of mined rules using apiriori algorithm', '&lt;p&gt;I have doing association rule mining to discover some interesting patterns on supermarket data - using the apriori algorithm.&lt;/p&gt;

&lt;p&gt;I got 194 rules with minimum support 0.01 and minimum confidence 0.01 and also I have got some measures like support, confidence, lift and conviction for each rule. &lt;/p&gt;

&lt;p&gt;Now I need to measure the overall accuracy of my model, which contains 194 rules. &lt;/p&gt;

&lt;p&gt;How do I calculate the overall accuracy of my model? &lt;/p&gt;
', 'data-mining  predictive-modeling  accuracy', NULL, '''/p'':37C,67C,84C,96C ''0.01'':46C,50C ''194'':41C,82C ''accuraci'':4A,17B,76C,92C ''algorithm'':10A,36C ''also'':52C ''apiriori'':9A ''apriori'':35C ''associ'':22C ''calcul'':1A,89C ''confid'':49C,60C ''contain'':81C ''convict'':63C ''data'':12B,32C ''data-min'':11B ''discov'':26C ''got'':40C,55C ''interest'':28C ''lift'':61C ''like'':58C ''measur'':57C,73C ''mine'':6A,13B,24C ''minimum'':44C,48C ''model'':16B,79C,95C ''need'':71C ''overal'':3A,75C,91C ''p'':18C,38C,68C,85C ''pattern'':29C ''predict'':15B ''predictive-model'':14B ''rule'':7A,23C,42C,66C,83C ''supermarket'':31C ''support'':45C,59C ''use'':8A,33C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1000, 2, '2014-08-19 15:21:57.833', 2, NULL, NULL, '&lt;p&gt;You can get some documented, publicly available EEG data from the HeadIT database at UCSD.
&lt;a href="http://headit-beta.ucsd.edu/studies" rel="nofollow"&gt;http://headit-beta.ucsd.edu/studies&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The data itself appears to be in Biosemi Data Format (.bdf) files, described here: &lt;a href="http://www.biosemi.com/faq/file_format.htm" rel="nofollow"&gt;http://www.biosemi.com/faq/file_format.htm&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Biosemi provides links to several open-source methods to access and import .bdf files on their website, including several functions for importing into Matlab, as well as into Python (BioSig) and C/C++ libraries:
&lt;a href="http://www.biosemi.com/download.htm" rel="nofollow"&gt;http://www.biosemi.com/download.htm&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Just as a forewarning, EEG data can be a bit of a bear to work with, due to it''s inherently low signal/noise ratio.&lt;/p&gt;
', NULL, NULL, '''/download.htm'':91C ''/download.htm&lt;/a&gt;&lt;/p&gt;'':96C ''/faq/file_format.htm'':46C ''/faq/file_format.htm&lt;/a&gt;&lt;/p&gt;'':51C ''/p'':122C ''/studies'':21C ''/studies&lt;/a&gt;&lt;/p&gt;'':26C ''access'':63C ''appear'':31C ''avail'':8C ''bdf'':38C,66C ''bear'':110C ''biosemi'':35C,53C ''biosig'':83C ''bit'':107C ''c/c'':85C ''data'':10C,29C,36C,103C ''databas'':14C ''describ'':40C ''document'':6C ''due'':114C ''eeg'':9C,102C ''file'':39C,67C ''forewarn'':101C ''format'':37C ''function'':73C ''get'':4C ''headit'':13C ''headit-beta.ucsd.edu'':20C,25C ''headit-beta.ucsd.edu/studies'':19C ''headit-beta.ucsd.edu/studies&lt;/a&gt;&lt;/p&gt;'':24C ''href'':18C,43C,88C ''import'':65C,75C ''includ'':71C ''inher'':118C ''librari'':86C ''link'':55C ''low'':119C ''matlab'':77C ''method'':61C ''nofollow'':23C,48C,93C ''open'':59C ''open-sourc'':58C ''p'':1C,27C,52C,97C ''provid'':54C ''public'':7C ''python'':82C ''ratio'':121C ''rel'':22C,47C,92C ''sever'':57C,72C ''signal/noise'':120C ''sourc'':60C ''ucsd'':16C ''websit'':70C ''well'':79C ''work'':112C ''www.biosemi.com'':45C,50C,90C,95C ''www.biosemi.com/download.htm'':89C ''www.biosemi.com/download.htm&lt;/a&gt;&lt;/p&gt;'':94C ''www.biosemi.com/faq/file_format.htm'':44C ''www.biosemi.com/faq/file_format.htm&lt;/a&gt;&lt;/p&gt;'':49C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1001, 2, '2014-08-19 15:47:29.413', 4, NULL, NULL, '&lt;p&gt;First thing that came to mind would be one''s personal workout data from running or biking apps.&lt;/p&gt;

&lt;p&gt;Otherwise there is a dataset around NYC''s taxi trip data. Quick Googling brought me this: &lt;a href="http://www.andresmh.com/nyctaxitrips/" rel="nofollow"&gt;http://www.andresmh.com/nyctaxitrips/&lt;/a&gt;. Variables include time and location for both pickups and dropoffs.&lt;/p&gt;

&lt;p&gt;Another dataset comes from Chicago''s bikesharing service. It can be found here: &lt;a href="https://www.divvybikes.com/datachallenge" rel="nofollow"&gt;https://www.divvybikes.com/datachallenge&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/datachallenge'':77C ''/datachallenge&lt;/a&gt;.&lt;/p&gt;'':82C ''/nyctaxitrips/'':42C ''/nyctaxitrips/&lt;/a&gt;.'':47C ''/p'':20C,58C ''anoth'':60C ''app'':19C ''around'':27C ''bike'':18C ''bikeshar'':66C ''brought'':35C ''came'':5C ''chicago'':64C ''come'':62C ''data'':14C,32C ''dataset'':26C,61C ''dropoff'':57C ''first'':2C ''found'':71C ''googl'':34C ''href'':39C,74C ''includ'':49C ''locat'':52C ''mind'':7C ''nofollow'':44C,79C ''nyc'':28C ''one'':10C ''otherwis'':22C ''p'':1C,21C,59C ''person'':12C ''pickup'':55C ''quick'':33C ''rel'':43C,78C ''run'':16C ''servic'':67C ''taxi'':30C ''thing'':3C ''time'':50C ''trip'':31C ''variabl'':48C ''workout'':13C ''would'':8C ''www.andresmh.com'':41C,46C ''www.andresmh.com/nyctaxitrips/'':40C ''www.andresmh.com/nyctaxitrips/&lt;/a&gt;.'':45C ''www.divvybikes.com'':76C,81C ''www.divvybikes.com/datachallenge'':75C ''www.divvybikes.com/datachallenge&lt;/a&gt;.&lt;/p&gt;'':80C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1037, 2, '2014-08-25 03:01:09.837', 2, NULL, NULL, '&lt;p&gt;If I understand you question correctly, there are two reasons why genetic algorithm might not a good idea for optimizing website features:&lt;/p&gt;

&lt;p&gt;1) Feedback data is coming in too slow, say once a day, genetic algorithm might take a while to converge.&lt;/p&gt;

&lt;p&gt;2) In the process of testing genetic algorithm will probably come up with combinations that are ''strange'' and that might not be the risk the company wants to take.&lt;/p&gt;
', NULL, NULL, '''/p'':24C,46C,77C ''1'':26C ''2'':48C ''algorithm'':14C,39C,55C ''combin'':61C ''come'':30C,58C ''compani'':73C ''converg'':45C ''correct'':7C ''data'':28C ''day'':37C ''featur'':23C ''feedback'':27C ''genet'':13C,38C,54C ''good'':18C ''idea'':19C ''might'':15C,40C,67C ''optim'':21C ''p'':1C,25C,47C ''probabl'':57C ''process'':51C ''question'':6C ''reason'':11C ''risk'':71C ''say'':34C ''slow'':33C ''strang'':64C ''take'':41C,76C ''test'':53C ''two'':10C ''understand'':4C ''want'':74C ''websit'':22C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1002, 1, '2014-08-19 17:50:52.583', 3, 363, 'Making sense of large data sets', '&lt;p&gt;Caveat: I am a complete beginner when it comes to machine learning, but eager to learn.&lt;/p&gt;

&lt;p&gt;I have a large dataset and I''m trying to find pattern in it. There may / may not be correlation across the data, either with known variables, or variables that are contained in the data but which I haven''t yet realised are actually variables / relevant.&lt;/p&gt;

&lt;p&gt;I''m guessing this would be a familiar problem in the world of data analysis, so I have a few questions:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;The ''silver bullet'' would be to throw this all this data into a stats / data analysis program and for it to crunch the data looking for known / unknown patterns trying to find relations. Is SPSS suitable, or are there other applications which may be better suited.&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Should I learn a language like R, and figure out how to manually process the data. Wouldn''t this comprimise finding relations as I would have to manually specify what and how to analyse the data?&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;How would a professional data miner approach this problem and what steps would s/he take?&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', 'machine-learning  data-mining  tools  beginner', NULL, '''/li'':154C,194C,213C ''/ol'':214C ''/p'':32C,80C,103C,153C,193C,212C ''across'':54C ''actual'':77C ''analys'':190C ''analysi'':96C,122C ''applic'':147C ''approach'':203C ''beginn'':14B,21C ''better'':151C ''bullet'':109C ''caveat'':16C ''come'':24C ''complet'':20C ''comprimis'':176C ''contain'':65C ''correl'':53C ''crunch'':128C ''data'':5A,11B,56C,68C,95C,117C,121C,130C,172C,192C,201C ''data-min'':10B ''dataset'':38C ''eager'':29C ''either'':57C ''familiar'':89C ''figur'':165C ''find'':44C,138C,177C ''guess'':84C ''haven'':72C ''known'':59C,133C ''languag'':161C ''larg'':4A,37C ''learn'':9B,27C,31C,159C ''li'':105C,155C,195C ''like'':162C ''look'':131C ''m'':41C,83C ''machin'':8B,26C ''machine-learn'':7B ''make'':1A ''manual'':169C,184C ''may'':49C,50C,149C ''mine'':12B ''miner'':202C ''ol'':104C ''p'':15C,33C,81C,106C,156C,196C ''pattern'':45C,135C ''problem'':90C,205C ''process'':170C ''profession'':200C ''program'':123C ''question'':102C ''r'':163C ''realis'':75C ''relat'':139C,178C ''relev'':79C ''s/he'':210C ''sens'':2A ''set'':6A ''silver'':108C ''specifi'':185C ''spss'':141C ''stat'':120C ''step'':208C ''suit'':152C ''suitabl'':142C ''take'':211C ''throw'':113C ''tool'':13B ''tri'':42C,136C ''unknown'':134C ''variabl'':60C,62C,78C ''world'':93C ''would'':86C,110C,181C,198C,209C ''wouldn'':173C ''yet'':74C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1003, 1, '2014-08-19 18:59:03.013', 3, 179, 'Query similarity: how much data is used in practice?', '&lt;p&gt;I recently read &lt;a href="http://research.microsoft.com/en-us/um/people/sdumais/ecir07-metzlerdumaismeek-final.pdf" rel="nofollow"&gt;Similarity Measures for Short Segments of Text&lt;/a&gt; (Metzler et al.).  It describes basic methods for measuring query similarity, and in the paper, the data consists of queries and their top results. Results are lists of page urls, page titles, and short page snippets.  In the paper, the authors collect 200 results per query.&lt;/p&gt;

&lt;p&gt;When using the public Google APIs to retrieve results, I was only able to collect 4-10 results per query.  There''s a substantial difference between 10 and 200.  Hence, how much data is commonly used in practice to measure query similarity (e.g., how many results per query)?&lt;/p&gt;

&lt;p&gt;References are a plus!&lt;/p&gt;
', 'machine-learning  dataset  text-mining  search', NULL, '''-10'':101C ''/a'':36C ''/en-us/um/people/sdumais/ecir07-metzlerdumaismeek-final.pdf'':26C ''/p'':83C,133C,139C ''10'':111C ''200'':79C,113C ''4'':100C ''abl'':97C ''al'':39C ''api'':90C ''author'':77C ''basic'':42C ''collect'':78C,99C ''common'':119C ''consist'':54C ''data'':5A,53C,117C ''dataset'':13B ''describ'':41C ''differ'':109C ''e.g'':127C ''et'':38C ''googl'':89C ''henc'':114C ''href'':23C ''learn'':12B ''list'':63C ''machin'':11B ''machine-learn'':10B ''mani'':129C ''measur'':30C,45C,124C ''method'':43C ''metzler'':37C ''mine'':16B ''much'':4A,116C ''nofollow'':28C ''p'':18C,84C,134C ''page'':65C,67C,71C ''paper'':51C,75C ''per'':81C,103C,131C ''plus'':138C ''practic'':9A,122C ''public'':88C ''queri'':1A,46C,56C,82C,104C,125C,132C ''read'':21C ''recent'':20C ''refer'':135C ''rel'':27C ''research.microsoft.com'':25C ''research.microsoft.com/en-us/um/people/sdumais/ecir07-metzlerdumaismeek-final.pdf'':24C ''result'':60C,61C,80C,93C,102C,130C ''retriev'':92C ''search'':17B ''segment'':33C ''short'':32C,70C ''similar'':2A,29C,47C,126C ''snippet'':72C ''substanti'':108C ''text'':15B,35C ''text-min'':14B ''titl'':68C ''top'':59C ''url'':66C ''use'':7A,86C,120C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1019, 2, '2014-08-22 09:03:13.333', 4, NULL, NULL, '&lt;p&gt;I think it always depends on the scenario. Using a representative data set is not always the solution. Assume that your training set with 1000 negative examples and 20 positive examples. Without any modeification of the classifier, your algorithm will tend to classify all new examples as negative. In some scenarios this is O.K. But in many cases the costs of missing postive examples is high so you have to find a solution for it.&lt;/p&gt;

&lt;p&gt;In such cases you can use a cost sensitive machine learning algorithm. For example in the case of medical diagnosis data analysis.&lt;/p&gt;

&lt;p&gt;In summary: Classification erros do not have the same cost! &lt;/p&gt;
', NULL, NULL, '''/p'':77C,101C,113C ''1000'':26C ''20'':30C ''algorithm'':40C,90C ''alway'':5C,17C ''analysi'':100C ''assum'':20C ''case'':59C,81C,95C ''classif'':105C ''classifi'':38C,44C ''cost'':61C,86C,112C ''data'':13C,99C ''depend'':6C ''diagnosi'':98C ''erro'':106C ''exampl'':28C,32C,47C,65C,92C ''find'':72C ''high'':67C ''learn'':89C ''machin'':88C ''mani'':58C ''medic'':97C ''miss'':63C ''modeif'':35C ''negat'':27C,49C ''new'':46C ''o.k'':55C ''p'':1C,78C,102C ''posit'':31C ''postiv'':64C ''repres'':12C ''scenario'':9C,52C ''sensit'':87C ''set'':14C,24C ''solut'':19C,74C ''summari'':104C ''tend'':42C ''think'':3C ''train'':23C ''use'':10C,84C ''without'':33C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2380, 2, '2014-10-31 04:44:23.1', 2, NULL, NULL, '&lt;p&gt;The importance of a concept in mathematics depends on the circumstances of its application. Sometimes, its importance relies on the fact that it allows you to carry on with what you are doing. &lt;/p&gt;

&lt;p&gt;For example, you usually need column independence (independent variables between predictors) because multiple regression will behave badly with highly correlated variables. Even worst, when some of your columns (or rows) are dependent, your matrix is not invertible. Why? Because matrix inversion A^-1 involves the determinant 1/|A|, which is 0 when columns or rows are linearly dependent.&lt;/p&gt;

&lt;p&gt;Eigenvalues is a common occurrence in calculations related to maximization/minimization in machine learning. Let''s say you are interested in principal component analysis. A very important idea there is dimensional reduction (you have a dataset with many variables and want to reduce the number of variables without losing too much explanatory power.) One solution is to project your data onto a lower dimensional space (e.g. taking your data with 50 variables and reducing them to 5 variables.) Turns out a good projection to use is one that includes as much variation as possible and maximization of this variation results in the eigenvalue equation S u  = Î» u. &lt;/p&gt;

&lt;p&gt;In other cases, you explicitly include the eigenvalue equation of some quantity of interest because in doing so, you''re changing the coordinate system in which you represent the variables. Take the case of a (multivariate) Gaussian distribution in which the argument of the exponent is given by Î = (x-Î¼)^T Î£ (x-Î¼). If you consider the eigenvalue equation of Î£, the exponent can be written as Î = y_1^2 / Î»_1 + y_2^2 / Î»_2 (in two dimensions) This is the equation of an ellipse only if Î»_1 and Î»_2 are positive. Therefore, you obtain the following graphical interpretation (Bishop, PRML, p.81):&lt;/p&gt;

&lt;p&gt;&lt;img src="http://i.stack.imgur.com/pNrLb.png" alt="enter image description here"&gt;&lt;/p&gt;

&lt;p&gt;Positive semi-definite matrices are used as a matter of convenience. They are well-behaved and well-understood. For instance, their eigenvalues are non-negative, and if you remember the previous paragraph, the argument Î required positive eigenvalues. By now, you can see why some concepts are very popular: You need them for your calculations or they need each other.&lt;/p&gt;

&lt;p&gt;I can recommend a couple of books:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/0534998453" rel="nofollow"&gt;Linear Algebra: A Modern Introduction&lt;/a&gt; by David Poole &lt;/li&gt;
&lt;li&gt;&lt;a href="http://rads.stackoverflow.com/amzn/click/1584888326" rel="nofollow"&gt;Understanding Complex Datasets: Data Mining with Matrix Decompositions &lt;/a&gt; by David Skillicorn. &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;The second recommendation is more specialized and requires a decent understanding of the basics, but it is of great help to understand matrix decompositions.&lt;/p&gt;
', NULL, NULL, '''-1'':79C ''/a'':419C,440C ''/amzn/click/0534998453'':411C ''/amzn/click/1584888326'':429C ''/li'':423C,444C ''/ol'':445C ''/p'':35C,95C,204C,317C,329C,395C,404C,471C ''/pnrlb.png'':323C ''0'':87C ''1'':83C,279C,282C,301C ''2'':280C,284C,285C,287C,304C ''5'':172C ''50'':166C ''algebra'':415C ''allow'':25C ''alt'':324C ''analysi'':119C ''applic'':15C ''argument'':247C,368C ''bad'':53C ''basic'':460C ''behav'':52C,347C ''bishop'':314C ''book'':403C ''calcul'':103C,389C ''carri'':28C ''case'':208C,238C ''chang'':226C ''circumst'':12C ''column'':42C,64C,89C ''common'':100C ''complex'':433C ''compon'':118C ''concept'':6C,380C ''consid'':265C ''conveni'':342C ''coordin'':228C ''correl'':56C ''coupl'':401C ''data'':155C,164C,435C ''dataset'':131C,434C ''david'':421C,442C ''decent'':456C ''decomposit'':439C,470C ''definit'':334C ''depend'':9C,68C,94C ''descript'':327C ''determin'':82C ''dimens'':290C ''dimension'':126C,159C ''distribut'':243C ''e.g'':161C ''eigenvalu'':97C,198C,213C,267C,355C,372C ''ellips'':297C ''enter'':325C ''equat'':199C,214C,268C,294C ''even'':58C ''exampl'':38C ''explanatori'':147C ''explicit'':210C ''expon'':250C,272C ''fact'':22C ''follow'':311C ''gaussian'':242C ''given'':252C ''good'':177C ''graphic'':312C ''great'':465C ''help'':466C ''high'':55C ''href'':408C,426C ''i.stack.imgur.com'':322C ''i.stack.imgur.com/pnrlb.png'':321C ''idea'':123C ''imag'':326C ''img'':319C ''import'':3C,18C,122C ''includ'':184C,211C ''independ'':43C,44C ''instanc'':353C ''interest'':115C,219C ''interpret'':313C ''introduct'':418C ''invers'':77C ''invert'':73C ''involv'':80C ''learn'':109C ''let'':110C ''li'':406C,424C ''linear'':93C,414C ''lose'':144C ''lower'':158C ''machin'':108C ''mani'':133C ''mathemat'':8C ''matric'':335C ''matrix'':70C,76C,438C,469C ''matter'':340C ''maxim'':191C ''maximization/minimization'':106C ''mine'':436C ''modern'':417C ''much'':146C,186C ''multipl'':49C ''multivari'':241C ''need'':41C,385C,392C ''negat'':359C ''nofollow'':413C,431C ''non'':358C ''non-neg'':357C ''number'':140C ''obtain'':309C ''occurr'':101C ''ol'':405C ''one'':149C,182C ''onto'':156C ''p'':1C,36C,96C,205C,318C,330C,396C,446C ''p.81'':316C ''paragraph'':366C ''pool'':422C ''popular'':383C ''posit'':306C,331C,371C ''possibl'':189C ''power'':148C ''predictor'':47C ''previous'':365C ''princip'':117C ''prml'':315C ''project'':153C,178C ''quantiti'':217C ''rads.stackoverflow.com'':410C,428C ''rads.stackoverflow.com/amzn/click/0534998453'':409C ''rads.stackoverflow.com/amzn/click/1584888326'':427C ''re'':225C ''recommend'':399C,449C ''reduc'':138C,169C ''reduct'':127C ''regress'':50C ''rel'':412C,430C ''relat'':104C ''reli'':19C ''rememb'':363C ''repres'':233C ''requir'':370C,454C ''result'':195C ''row'':66C,91C ''say'':112C ''second'':448C ''see'':377C ''semi'':333C ''semi-definit'':332C ''skillicorn'':443C ''solut'':150C ''sometim'':16C ''space'':160C ''special'':452C ''src'':320C ''system'':229C ''take'':162C,236C ''therefor'':307C ''turn'':174C ''two'':289C ''u'':201C,203C ''understand'':432C,457C,468C ''understood'':351C ''use'':180C,337C ''usual'':40C ''variabl'':45C,57C,134C,142C,167C,173C,235C ''variat'':187C,194C ''want'':136C ''well'':346C,350C ''well-behav'':345C ''well-understood'':349C ''without'':143C ''worst'':59C ''written'':275C ''x'':256C,261C ''x-î'':255C,260C ''y'':278C,283C ''î'':202C,254C,257C,259C,262C,270C,277C,281C,286C,300C,303C,369C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1009, 2, '2014-08-20 15:53:29.403', 6, NULL, NULL, '&lt;p&gt;If you have R and the &lt;code&gt;spacetime&lt;/code&gt; package then you are only &lt;code&gt;data(package="spacetime")&lt;/code&gt; away from a list of space-time data sets bundled with the package:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Data sets in package âspacetimeâ:

DE_NUTS1 (air)          Air quality data, rural background PM10 in
                        Germany, daily averages 1998-2009
fires                   Northern Los Angeles County Fires
rural (air)             Air quality data, rural background PM10 in
                        Germany, daily averages 1998-2009
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;then for example:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt; data(fires)
&amp;gt; str(fires)
''data.frame'':   313 obs. of  3 variables:
 $ Time: int  5863 5870 6017 6018 6034 6060 6176 6364 6366 6372 ...
 $ X   : num  63.9 64.3 64.1 64 64.4 ...
 $ Y   : num  19.4 20.1 19.7 19.8 20.3 ...
&lt;/code&gt;&lt;/pre&gt;
', NULL, NULL, '''-2009'':58C,78C ''/code'':10C,20C,79C,126C ''/p'':35C,85C ''/pre'':80C,127C ''19.4'':121C ''19.7'':123C ''19.8'':124C ''1998'':57C,77C ''20.1'':122C ''20.3'':125C ''3'':98C ''313'':95C ''5863'':102C ''5870'':103C ''6017'':104C ''6018'':105C ''6034'':106C ''6060'':107C ''6176'':108C ''63.9'':114C ''6364'':109C ''6366'':110C ''6372'':111C ''64'':117C ''64.1'':116C ''64.3'':115C ''64.4'':118C ''air'':46C,47C,66C,67C ''angel'':62C ''averag'':56C,76C ''away'':21C ''background'':51C,71C ''bundl'':31C ''code'':8C,16C,37C,87C ''counti'':63C ''daili'':55C,75C ''data'':17C,29C,38C,49C,69C,89C ''data.frame'':94C ''de'':44C ''exampl'':84C ''fire'':59C,64C,90C,93C ''germani'':54C,74C ''gt'':88C,91C ''int'':101C ''list'':24C ''los'':61C ''northern'':60C ''num'':113C,120C ''nuts1'':45C ''ob'':96C ''p'':1C,81C ''packag'':11C,18C,34C,41C ''pm10'':52C,72C ''pre'':36C,86C ''qualiti'':48C,68C ''r'':5C ''rural'':50C,65C,70C ''set'':30C,39C ''space'':27C ''space-tim'':26C ''spacetim'':9C,19C ''spacetimeâ'':43C ''str'':92C ''time'':28C,100C ''variabl'':99C ''x'':112C ''y'':119C ''â'':42C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1010, 2, '2014-08-20 19:08:39.807', 1, NULL, NULL, '&lt;p&gt;You don''t mention what language you''re programming in (please consider adding it as a tag), so general help would be to seek out a HTML parser and use that to pull the data. Some web sites can have simply awful HTML code and can be very difficult to scrape, and just when you think you have it...&lt;/p&gt;

&lt;p&gt;A HTML parser will parse all the html and allow you to access it in a structured sort of way, whether that''s from an array, an object etc.&lt;/p&gt;
', NULL, NULL, '''/p'':61C,92C ''access'':75C ''ad'':14C ''allow'':72C ''array'':88C ''aw'':43C ''code'':45C ''consid'':13C ''data'':36C ''difficult'':50C ''etc'':91C ''general'':20C ''help'':21C ''html'':28C,44C,64C,70C ''languag'':7C ''mention'':5C ''object'':90C ''p'':1C,62C ''pars'':67C ''parser'':29C,65C ''pleas'':12C ''program'':10C ''pull'':34C ''re'':9C ''scrape'':52C ''seek'':25C ''simpli'':42C ''site'':39C ''sort'':80C ''structur'':79C ''tag'':18C ''think'':57C ''use'':31C ''way'':82C ''web'':38C ''whether'':83C ''would'':22C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1013, 1, '2014-08-20 21:12:49.927', 2, 66, 'Using Heuristic Methods for AB Testing', '&lt;p&gt;I''ve just started reading about AB testing, as it pertains to optimizing website design.  I find it interesting that most of the methods assume that changes to the layout and appearance are independent of each other.  I understand that the most common method of optimization is the &lt;a href="http://en.wikipedia.org/wiki/Multi-armed_bandit" rel="nofollow"&gt;''multi-armed bandit''&lt;/a&gt; procedure.  While I grasp the concept of it, it seems to ignore the fact that changes (changes to the website in this case) are not independent to each other.&lt;/p&gt;

&lt;p&gt;For example, if company is testing the placement and color of the logo on the website, they find the optimal color first then the optimal placement.  Not that I''m some expert on human psychology, but shouldn''t these be related? Can the multi-armed bandit method be efficiently used in this case or more complicated cases?&lt;/p&gt;

&lt;p&gt;My first instinct is to say no.  On that note, why haven''t people used heuristic algorithms to optimize over complicated AB testing sample spaces?  For an example, I thought someone might have used a genetic algorithm to optimize a website layout, but I can find no examples of something like this out there. This leads me to believe that I''m missing something important in my understanding of AB testing as it applies to website optimization.&lt;/p&gt;

&lt;p&gt;Why isn''t heuristic optimization used on more complicated websites?&lt;/p&gt;
', 'optimization  consumerweb', NULL, '''/a'':69C ''/p'':99C,159C,238C,250C ''/wiki/multi-armed_bandit'':62C ''ab'':5A,16C,182C,230C ''algorithm'':177C,197C ''appear'':41C ''appli'':234C ''arm'':67C,146C ''assum'':34C ''bandit'':68C,147C ''believ'':219C ''case'':92C,154C,158C ''chang'':36C,85C,86C ''color'':110C,121C ''common'':52C ''compani'':104C ''complic'':157C,181C,248C ''concept'':75C ''consumerweb'':8B ''design'':24C ''effici'':150C ''en.wikipedia.org'':61C ''en.wikipedia.org/wiki/multi-armed_bandit'':60C ''exampl'':102C,188C,208C ''expert'':132C ''fact'':83C ''find'':26C,118C,206C ''first'':122C,162C ''genet'':196C ''grasp'':73C ''haven'':172C ''heurist'':2A,176C,243C ''href'':59C ''human'':134C ''ignor'':81C ''import'':225C ''independ'':43C,95C ''instinct'':163C ''interest'':28C ''isn'':241C ''layout'':39C,202C ''lead'':216C ''like'':211C ''logo'':113C ''m'':130C,222C ''method'':3A,33C,53C,148C ''might'':192C ''miss'':223C ''multi'':66C,145C ''multi-arm'':65C,144C ''nofollow'':64C ''note'':170C ''optim'':7B,22C,55C,120C,125C,179C,199C,237C,244C ''p'':9C,100C,160C,239C ''peopl'':174C ''pertain'':20C ''placement'':108C,126C ''procedur'':70C ''psycholog'':135C ''read'':14C ''rel'':63C ''relat'':141C ''sampl'':184C ''say'':166C ''seem'':79C ''shouldn'':137C ''someon'':191C ''someth'':210C,224C ''space'':185C ''start'':13C ''test'':6A,17C,106C,183C,231C ''thought'':190C ''understand'':48C,228C ''use'':1A,151C,175C,194C,245C ''ve'':11C ''websit'':23C,89C,116C,201C,236C,249C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1015, 1, '2014-08-21 06:31:50.197', 1, 88, 'Making Factual drake work on Windows 7 64-bit', '&lt;p&gt;I have installed &lt;a href="https://github.com/Factual/drake" rel="nofollow"&gt;Drake&lt;/a&gt; on Windows 7 64-bit.&lt;/p&gt;

&lt;p&gt;I am using JDK 1.7.0_51.&lt;/p&gt;

&lt;p&gt;I tried both using the pre-compiled jar file and
compiling from the Clojure source using &lt;a href="https://github.com/technomancy/leiningen" rel="nofollow"&gt;leiningen&lt;/a&gt;.
The resulting Drake version is 0.1.6, the current development version.&lt;/p&gt;

&lt;p&gt;When running Drake, I get the current version number.&lt;/p&gt;

&lt;p&gt;Next, I tried to go through &lt;a href="https://github.com/Factual/drake/wiki/Tutorial" rel="nofollow"&gt;the tutorial&lt;/a&gt;. The command:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;java -jar drake.jar  -w .\workflow.d
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;results in the following Exception:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;java.lang.Exception: no input data found in locations: D:\tools\drake\in.c
sv
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Even though the file exists and has text inside it. 
The same scenario works in a similar installation on Ubuntu 12.04.
Am I doing something wrong, or is this a Windows-specific bug?&lt;/p&gt;
', 'tools', NULL, '''/a'':23C,64C,103C ''/code'':114C,137C ''/factual/drake'':19C ''/factual/drake/wiki/tutorial'':98C ''/p'':29C,37C,75C,86C,106C,122C,174C ''/pre'':115C,138C ''/technomancy/leiningen'':60C ''0.1.6'':70C ''1.7.0'':35C ''12.04'':160C ''51'':36C ''64'':8A,27C ''7'':7A,26C ''bit'':9A,28C ''bug'':173C ''clojur'':53C ''code'':108C,124C ''command'':105C ''compil'':46C,50C ''current'':72C,83C ''d'':132C ''data'':128C ''develop'':73C ''drake'':3A,22C,67C,79C,134C ''drake.jar'':111C ''even'':140C ''except'':121C ''exist'':144C ''factual'':2A ''file'':48C,143C ''follow'':120C ''found'':129C ''get'':81C ''github.com'':18C,59C,97C ''github.com/factual/drake'':17C ''github.com/factual/drake/wiki/tutorial'':96C ''github.com/technomancy/leiningen'':58C ''go'':92C ''href'':16C,57C,95C ''in.c'':135C ''input'':127C ''insid'':148C ''instal'':14C,157C ''jar'':47C,110C ''java'':109C ''java.lang.exception'':125C ''jdk'':34C ''leiningen'':63C ''locat'':131C ''make'':1A ''next'':88C ''nofollow'':21C,62C,100C ''number'':85C ''p'':11C,30C,38C,76C,87C,116C,139C ''pre'':45C,107C,123C ''pre-compil'':44C ''rel'':20C,61C,99C ''result'':66C,117C ''run'':78C ''scenario'':152C ''similar'':156C ''someth'':164C ''sourc'':54C ''specif'':172C ''sv'':136C ''text'':147C ''though'':141C ''tool'':10B,133C ''tri'':40C,90C ''tutori'':102C ''ubuntu'':159C ''use'':33C,42C,55C ''version'':68C,74C,84C ''w'':112C ''window'':6A,25C,171C ''windows-specif'':170C ''work'':4A,153C ''workflow.d'':113C ''wrong'':165C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1029, 1, '2014-08-23 19:34:09.417', -1, 549, 'Which one will be the dominating programming language for next 5 years for analytics , machine learning . R or python or SAS', '&lt;p&gt;Which one will be the dominating programming language for next 5 years for analytics , machine learning . R verses python verses SAS. Advantage and disadvantage.&lt;/p&gt;
', 'machine-learning  r  python', NULL, '''/p'':52C ''5'':11A,38C ''advantag'':49C ''analyt'':14A,41C ''disadvantag'':51C ''domin'':6A,33C ''languag'':8A,35C ''learn'':16A,24B,43C ''machin'':15A,23B,42C ''machine-learn'':22B ''next'':10A,37C ''one'':2A,29C ''p'':27C ''program'':7A,34C ''python'':19A,26B,46C ''r'':17A,25B,44C ''sas'':21A,48C ''vers'':45C,47C ''year'':12A,39C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1017, 1, '2014-08-21 10:13:54.13', 3, 53, 'How can I model open environment in reinforcement learning?', '&lt;p&gt;I''m studying reinforcement learning in order to implement a kind of time series pattern analyzer such as market.&lt;/p&gt;

&lt;p&gt;The most examples I have seen are based on the maze environment.&lt;/p&gt;

&lt;p&gt;But in real market environment, the signal changes endlessly as time passes and I can not guess how can I model environment and states.&lt;/p&gt;

&lt;p&gt;Another question is about buy-sell modeling.&lt;/p&gt;

&lt;p&gt;Let''s assume that the agent randomly buy at time $t$ and sell at time $t + \alpha$.&lt;/p&gt;

&lt;p&gt;It''s simple to calculate reward.
The problem is how can I model $Q$ matrix and how can I model signals between buy and sell actions.&lt;/p&gt;

&lt;p&gt;Can you share some source code or guidance for similar situation?&lt;/p&gt;
', 'machine-learning  reinforcement-learning', NULL, '''/p'':36C,50C,76C,86C,105C,133C,146C ''action'':132C ''agent'':93C ''alpha'':104C ''analyz'':32C ''anoth'':78C ''assum'':90C ''base'':45C ''buy'':83C,95C,129C ''buy-sel'':82C ''calcul'':111C ''chang'':59C ''code'':140C ''endless'':60C ''environ'':6A,49C,56C,73C ''exampl'':40C ''guess'':68C ''guidanc'':142C ''implement'':25C ''kind'':27C ''learn'':9A,12B,15B,21C ''let'':88C ''m'':18C ''machin'':11B ''machine-learn'':10B ''market'':35C,55C ''matrix'':121C ''maze'':48C ''model'':4A,72C,85C,119C,126C ''open'':5A ''order'':23C ''p'':16C,37C,51C,77C,87C,106C,134C ''pass'':63C ''pattern'':31C ''problem'':114C ''q'':120C ''question'':79C ''random'':94C ''real'':54C ''reinforc'':8A,14B,20C ''reinforcement-learn'':13B ''reward'':112C ''seen'':43C ''sell'':84C,100C,131C ''seri'':30C ''share'':137C ''signal'':58C,127C ''similar'':144C ''simpl'':109C ''situat'':145C ''sourc'':139C ''state'':75C ''studi'':19C ''time'':29C,62C,97C,102C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1020, 1, '2014-08-22 14:01:23.64', 4, 62, 'Available data about ''likers'' as a page on Facebook', '&lt;p&gt;I am hoping to model the characteristics of the users of a specific page on Facebook, which has roughly 2 million likes. I have been looking at the Facebook SDK/API, but I can''t really see if what I would like to do is possible. It seems that the users share quite different amounts of data so I probably discard a lot of users and only use the ones with a quite open public profile. I would like to have the following data:&lt;/p&gt;

&lt;p&gt;1) See the individuals that have ''liked'' the page.&lt;/p&gt;

&lt;p&gt;2) See the list of friends for each person that have ''liked'' the page.&lt;/p&gt;

&lt;p&gt;3) See gender for each person (optional)&lt;/p&gt;

&lt;p&gt;4) See other pages that each person has liked (optional)&lt;/p&gt;

&lt;p&gt;Could anyone tell me if it is possible to get this data? As mentioned earlier it is okay if I discard data for users that don''t like to share this data.&lt;/p&gt;
', 'social-network-analysis', NULL, '''/p'':98C,109C,125C,134C,146C,180C ''1'':100C ''2'':34C,111C ''3'':127C ''4'':136C ''amount'':68C ''analysi'':13B ''anyon'':149C ''avail'':1A ''characterist'':21C ''could'':148C ''data'':2A,70C,97C,159C,169C,179C ''differ'':67C ''discard'':74C,168C ''earlier'':162C ''facebook'':9A,30C,43C ''follow'':96C ''friend'':116C ''gender'':129C ''get'':157C ''hope'':17C ''individu'':103C ''like'':36C,55C,92C,106C,122C,144C,175C ''liker'':4A ''list'':114C ''look'':40C ''lot'':76C ''mention'':161C ''million'':35C ''model'':19C ''network'':12B ''okay'':165C ''one'':83C ''open'':87C ''option'':133C,145C ''p'':14C,99C,110C,126C,135C,147C ''page'':7A,28C,108C,124C,139C ''person'':119C,132C,142C ''possibl'':59C,155C ''probabl'':73C ''profil'':89C ''public'':88C ''quit'':66C,86C ''realli'':49C ''rough'':33C ''sdk/api'':44C ''see'':50C,101C,112C,128C,137C ''seem'':61C ''share'':65C,177C ''social'':11B ''social-network-analysi'':10B ''specif'':27C ''tell'':150C ''use'':81C ''user'':24C,64C,78C,171C ''would'':54C,91C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1021, 1, '2014-08-22 15:59:07.097', 8, 148, 'Extract canonical string from a list of noisy strings', '&lt;p&gt;I have thousands of lists of strings, and each list has about 10 strings. Most strings in a given list are very similar, though some strings are (rarely) completely unrelated to the others and some strings contain irrelevant words. They can be considered to be noisy variations of a canonical string. I am looking for an algorithm or a library that will convert each list into this canonical string.&lt;/p&gt;

&lt;p&gt;Here is one such list.&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Star Wars: Episode IV A New Hope | StarWars.com&lt;/li&gt;
&lt;li&gt;Star Wars Episode IV - A New Hope (1977)&lt;/li&gt;
&lt;li&gt;Star Wars: Episode IV - A New Hope - Rotten Tomatoes&lt;/li&gt;
&lt;li&gt;Watch Star Wars: Episode IV - A New Hope Online Free&lt;/li&gt;
&lt;li&gt;Star Wars (1977) - Greatest Films&lt;/li&gt;
&lt;li&gt;[REC] 4 poster promises death by outboard motor - SciFiNow&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;For this list, any string matching the regular expression &lt;code&gt;^Star Wars:? Episode IV (- )?A New Hope$&lt;/code&gt; would be acceptable.&lt;/p&gt;

&lt;p&gt;I have looked at Andrew Ng''s course on Machine Learning on Coursera, but I was not able to find a similar problem.&lt;/p&gt;
', 'nlp  similarity  information-retrieval', NULL, '''/code'':174C ''/li'':103C,113C,124C,136C,143C,154C ''/p'':85C,92C,178C,203C ''/ul'':155C ''10'':28C ''1977'':112C,140C ''4'':146C ''abl'':197C ''accept'':177C ''algorithm'':72C ''andrew'':184C ''canon'':2A,65C,83C ''code'':166C ''complet'':44C ''consid'':58C ''contain'':52C ''convert'':78C ''cours'':187C ''coursera'':192C ''death'':149C ''episod'':97C,107C,117C,129C,169C ''express'':165C ''extract'':1A ''film'':142C ''find'':199C ''free'':135C ''given'':34C ''greatest'':141C ''hope'':101C,111C,121C,133C,173C ''inform'':13B ''information-retriev'':12B ''irrelev'':53C ''iv'':98C,108C,118C,130C,170C ''learn'':190C ''li'':94C,104C,114C,125C,137C,144C ''librari'':75C ''list'':6A,20C,25C,35C,80C,91C,159C ''look'':69C,182C ''machin'':189C ''match'':162C ''motor'':152C ''new'':100C,110C,120C,132C,172C ''ng'':185C ''nlp'':10B ''noisi'':8A,61C ''one'':89C ''onlin'':134C ''other'':48C ''outboard'':151C ''p'':15C,86C,156C,179C ''poster'':147C ''problem'':202C ''promis'':148C ''rare'':43C ''rec'':145C ''regular'':164C ''retriev'':14B ''rotten'':122C ''scifinow'':153C ''similar'':11B,38C,201C ''star'':95C,105C,115C,127C,138C,167C ''starwars.com'':102C ''string'':3A,9A,22C,29C,31C,41C,51C,66C,84C,161C ''though'':39C ''thousand'':18C ''tomato'':123C ''ul'':93C ''unrel'':45C ''variat'':62C ''war'':96C,106C,116C,128C,139C,168C ''watch'':126C ''word'':54C ''would'':175C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1022, 2, '2014-08-22 18:27:04.377', 2, NULL, NULL, '&lt;p&gt;Want to wish you good luck. Some time ago faced with the same problem, but didn''t find any satisfying solution. 
First of all, there is no way to get list of users, who "liked" a particular page. Even, if you are an administrator of this page (I was). One only can get list of last 3 or 5 hundred users. &lt;/p&gt;

&lt;p&gt;Friendships data for most of the users is also inaccessible. Looks like gender is the only thing from your list, that you can get. &lt;/p&gt;

&lt;p&gt;Data about pages, that exact user "likes", should be available (as it''s written in docs), but in reality, through API you can collect something only for friends and FoF. Even though this data is available through web interface. So the only way is to try dirty trick with parsing and scraping (but remember, that I didn''t advise it ;) ). &lt;/p&gt;
', NULL, NULL, '''/p'':63C,89C,151C ''3'':58C ''5'':60C ''administr'':45C ''advis'':149C ''ago'':10C ''also'':73C ''api'':111C ''avail'':100C,126C ''collect'':114C ''data'':66C,91C,124C ''didn'':17C,147C ''dirti'':137C ''doc'':106C ''even'':40C,121C ''exact'':95C ''face'':11C ''find'':19C ''first'':23C ''fof'':120C ''friend'':118C ''friendship'':65C ''gender'':77C ''get'':31C,54C,88C ''good'':6C ''hundr'':61C ''inaccess'':74C ''interfac'':129C ''last'':57C ''like'':36C,76C,97C ''list'':32C,55C,84C ''look'':75C ''luck'':7C ''one'':51C ''p'':1C,64C,90C ''page'':39C,48C,93C ''pars'':140C ''particular'':38C ''problem'':15C ''realiti'':109C ''rememb'':144C ''satisfi'':21C ''scrape'':142C ''solut'':22C ''someth'':115C ''thing'':81C ''though'':122C ''time'':9C ''tri'':136C ''trick'':138C ''user'':34C,62C,71C,96C ''want'':2C ''way'':29C,133C ''web'':128C ''wish'':4C ''written'':104C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1023, 2, '2014-08-23 09:19:08.577', 2, NULL, NULL, '&lt;p&gt;As a naive solution I would suggest to first select the strings which contain the most frequent tokens inside the list. In this way you can get rid of irrelevant string.&lt;/p&gt;

&lt;p&gt;In the second phrase I would do a majority voting. Assuming the 3 sentences:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Star Wars: Episode IV A New Hope | StarWars.com&lt;/li&gt;
&lt;li&gt;Star Wars Episode IV - A New Hope (1977)&lt;/li&gt;
&lt;li&gt;Star Wars: Episode IV - A New Hope - Rotten Tomatoes&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;I would go through the tokens one by one. We start by "Star". It wins as all the string start with it. "Wars" will also win. The next one is ":". It will also win.&lt;/p&gt;

&lt;p&gt;All the tokens will ein in majority voting till "Hope". The next token after "Hope" will be either "|", or "(" or "-". None of the will win in majority voting so I will stop here!&lt;/p&gt;

&lt;p&gt;Another solution would be probably to use &lt;a href="http://en.wikipedia.org/wiki/Longest_common_subsequence_problem" rel="nofollow"&gt;Longest common subsequence&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;As I said I have not though about it much. So there might be much more better solutions to your problem :-)&lt;/p&gt;
', NULL, NULL, '''/a'':172C ''/li'':60C,70C,81C ''/p'':33C,49C,118C,153C,173C,196C ''/ul'':82C ''/wiki/longest_common_subsequence_problem'':166C ''1977'':69C ''3'':47C ''also'':108C,116C ''anoth'':155C ''assum'':45C ''better'':191C ''common'':170C ''contain'':15C ''ein'':124C ''either'':137C ''en.wikipedia.org'':165C ''en.wikipedia.org/wiki/longest_common_subsequence_problem'':164C ''episod'':54C,64C,74C ''first'':10C ''frequent'':18C ''get'':28C ''go'':86C ''hope'':58C,68C,78C,129C,134C ''href'':163C ''insid'':20C ''irrelev'':31C ''iv'':55C,65C,75C ''li'':51C,61C,71C ''list'':22C ''longest'':169C ''major'':43C,126C,146C ''might'':187C ''much'':184C,189C ''naiv'':4C ''new'':57C,67C,77C ''next'':111C,131C ''nofollow'':168C ''none'':140C ''one'':90C,92C,112C ''p'':1C,34C,83C,119C,154C,174C ''phrase'':38C ''probabl'':159C ''problem'':195C ''rel'':167C ''rid'':29C ''rotten'':79C ''said'':177C ''second'':37C ''select'':11C ''sentenc'':48C ''solut'':5C,156C,192C ''star'':52C,62C,72C,96C ''start'':94C,103C ''starwars.com'':59C ''stop'':151C ''string'':13C,32C,102C ''subsequ'':171C ''suggest'':8C ''though'':181C ''till'':128C ''token'':19C,89C,122C,132C ''tomato'':80C ''ul'':50C ''use'':161C ''vote'':44C,127C,147C ''war'':53C,63C,73C,106C ''way'':25C ''win'':98C,109C,117C,144C ''would'':7C,40C,85C,157C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1024, 1, '2014-08-23 13:47:01.907', 3, 83, 'Extrapolating GLM coefficients for year a product was sold into future years?', '&lt;p&gt;I''ve fit a GLM (Poisson) to a data set where one of the variables is categorical for the year a customer bought a product from my company, ranging from 1999 to 2012. There''s a linear trend of the coefficients for the values of the variable as the year of sale increases.&lt;/p&gt;

&lt;p&gt;Is there any problem with trying to improve predictions for 2013 and maybe 2014 by extrapolating to get the coefficients for those years?&lt;/p&gt;
', 'statistics  glm  regression', NULL, '''/p'':70C,95C ''1999'':47C ''2012'':49C ''2013'':82C ''2014'':85C ''bought'':39C ''categor'':33C ''coeffici'':3A,57C,91C ''compani'':44C ''custom'':38C ''data'':25C ''extrapol'':1A,87C ''fit'':19C ''futur'':11A ''get'':89C ''glm'':2A,14B,21C ''improv'':79C ''increas'':69C ''linear'':53C ''mayb'':84C ''one'':28C ''p'':16C,71C ''poisson'':22C ''predict'':80C ''problem'':75C ''product'':7A,41C ''rang'':45C ''regress'':15B ''sale'':68C ''set'':26C ''sold'':9A ''statist'':13B ''trend'':54C ''tri'':77C ''valu'':60C ''variabl'':31C,63C ''ve'':18C ''year'':5A,12A,36C,66C,94C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1027, 2, '2014-08-23 15:25:44.903', 2, NULL, NULL, '&lt;p&gt;A first remark, you should watch ''Wargames'' to know what you''re getting yourself into.&lt;/p&gt;

&lt;p&gt;What you want is f(p) such that f(p) is as close as possible to strength of position.&lt;/p&gt;

&lt;p&gt;A very simple solution using genetic algo would be to setup 10000 players with different weights and see which wins. Then keep the top 1000 winners'' weight, copy them 10 times, alter them slightly to explore weight space, and run the simulation again. That''s standard GA, given a functional form, what are the best coefficients for it.&lt;/p&gt;

&lt;p&gt;Another solution is to extract the positions, so you have a table ''(material, kingsafety, mobility, pawn-structure, trappedpieces) -&gt; goodness of position'' where goodness of position is some objective factor (outcome win/lose computed using simulations above or known matches, depth of available tree, number of moves under the tree where one of the 5 factors gets better. You can then try different functional forms for your f(p), regression, svm.&lt;/p&gt;
', NULL, NULL, '''/p'':17C,38C,98C,170C ''10'':69C ''1000'':64C ''10000'':51C ''5'':153C ''algo'':46C ''alter'':71C ''anoth'':100C ''avail'':141C ''best'':94C ''better'':156C ''close'':31C ''coeffici'':95C ''comput'':132C ''copi'':67C ''depth'':139C ''differ'':54C,161C ''explor'':75C ''extract'':104C ''f'':23C,27C,166C ''factor'':129C,154C ''first'':3C ''form'':90C,163C ''function'':89C,162C ''ga'':86C ''genet'':45C ''get'':14C,155C ''given'':87C ''good'':119C,123C ''keep'':61C ''kingsafeti'':113C ''know'':10C ''known'':137C ''match'':138C ''materi'':112C ''mobil'':114C ''move'':145C ''number'':143C ''object'':128C ''one'':150C ''outcom'':130C ''p'':1C,18C,24C,28C,39C,99C,167C ''pawn'':116C ''pawn-structur'':115C ''player'':52C ''posit'':37C,106C,121C,125C ''possibl'':33C ''re'':13C ''regress'':168C ''remark'':4C ''run'':79C ''see'':57C ''setup'':50C ''simpl'':42C ''simul'':81C,134C ''slight'':73C ''solut'':43C,101C ''space'':77C ''standard'':85C ''strength'':35C ''structur'':117C ''svm'':169C ''tabl'':111C ''time'':70C ''top'':63C ''trappedpiec'':118C ''tree'':142C,148C ''tri'':160C ''use'':44C,133C ''want'':21C ''wargam'':8C ''watch'':7C ''weight'':55C,66C,76C ''win'':59C ''win/lose'':131C ''winner'':65C ''would'':47C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1028, 1, '2014-08-23 16:54:06.38', 13, 310, 'Do Random Forest overfit?', '&lt;p&gt;I have been reading around about Random Forests but I cannot really find a definitive answer about the problem of overfitting. According to the original paper of Breiman, they should not overfit when increasing the number of trees in the forest, but it seems that there is not consensus about this. This is creating me quite some confusion about the issue.&lt;/p&gt;

&lt;p&gt;Maybe someone more expert than me can give me a more concrete answer or point me in the right direction to better understand the problem.&lt;/p&gt;
', 'machine-learning  random-forest', NULL, '''/p'':73C,100C ''accord'':33C ''answer'':27C,87C ''around'':16C ''better'':96C ''breiman'':39C ''cannot'':22C ''concret'':86C ''confus'':69C ''consensus'':60C ''creat'':65C ''definit'':26C ''direct'':94C ''expert'':78C ''find'':24C ''forest'':3A,10B,19C,52C ''give'':82C ''increas'':45C ''issu'':72C ''learn'':7B ''machin'':6B ''machine-learn'':5B ''mayb'':75C ''number'':47C ''origin'':36C ''overfit'':4A,32C,43C ''p'':11C,74C ''paper'':37C ''point'':89C ''problem'':30C,99C ''quit'':67C ''random'':2A,9B,18C ''random-forest'':8B ''read'':15C ''realli'':23C ''right'':93C ''seem'':55C ''someon'':76C ''tree'':49C ''understand'':97C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1030, 2, '2014-08-23 20:05:38.7', 4, NULL, NULL, '&lt;p&gt;I believe that this is a case for applying &lt;em&gt;time series analysis&lt;/em&gt;, in particular &lt;em&gt;time series forecasting&lt;/em&gt; (&lt;a href="http://en.wikipedia.org/wiki/Time_series" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Time_series&lt;/a&gt;). Consider the following resources on &lt;strong&gt;time series regression&lt;/strong&gt;:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471363553.html" rel="nofollow"&gt;http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471363553.html&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href="http://www.stats.uwo.ca/faculty/aim/tsar/tsar.pdf" rel="nofollow"&gt;http://www.stats.uwo.ca/faculty/aim/tsar/tsar.pdf&lt;/a&gt; (especially section
4.6)&lt;/li&gt;
&lt;li&gt;&lt;a href="http://arxiv.org/abs/0802.0219" rel="nofollow"&gt;http://arxiv.org/abs/0802.0219&lt;/a&gt; (Bayesian approach)&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/abs/0802.0219'':76C ''/abs/0802.0219&lt;/a&gt;'':81C ''/em'':15C,22C ''/faculty/aim/tsar/tsar.pdf'':61C ''/faculty/aim/tsar/tsar.pdf&lt;/a&gt;'':66C ''/li'':70C,84C ''/p'':43C ''/strong'':42C ''/ul'':85C ''/wiki/time_series'':27C ''/wiki/time_series&lt;/a&gt;).'':32C ''/wileycda/wileytitle/productcd-0471363553.html'':50C ''/wileycda/wileytitle/productcd-0471363553.html&lt;/a&gt;&lt;/li&gt;'':55C ''4.6'':69C ''analysi'':14C ''appli'':10C ''approach'':83C ''arxiv.org'':75C,80C ''arxiv.org/abs/0802.0219'':74C ''arxiv.org/abs/0802.0219&lt;/a&gt;'':79C ''bayesian'':82C ''believ'':3C ''case'':8C ''consid'':33C ''em'':11C,18C ''en.wikipedia.org'':26C,31C ''en.wikipedia.org/wiki/time_series'':25C ''en.wikipedia.org/wiki/time_series&lt;/a&gt;).'':30C ''especi'':67C ''follow'':35C ''forecast'':21C ''href'':24C,47C,58C,73C ''li'':45C,56C,71C ''nofollow'':29C,52C,63C,78C ''p'':1C ''particular'':17C ''regress'':41C ''rel'':28C,51C,62C,77C ''resourc'':36C ''section'':68C ''seri'':13C,20C,40C ''strong'':38C ''time'':12C,19C,39C ''ul'':44C ''www.stats.uwo.ca'':60C,65C ''www.stats.uwo.ca/faculty/aim/tsar/tsar.pdf'':59C ''www.stats.uwo.ca/faculty/aim/tsar/tsar.pdf&lt;/a&gt;'':64C ''www.wiley.com'':49C,54C ''www.wiley.com/wileycda/wileytitle/productcd-0471363553.html'':48C ''www.wiley.com/wileycda/wileytitle/productcd-0471363553.html&lt;/a&gt;&lt;/li&gt;'':53C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1031, 2, '2014-08-24 00:46:03.813', 2, NULL, NULL, '&lt;p&gt;There is a great &lt;a href="http://blog.revolutionanalytics.com/2014/01/in-data-scientist-survey-r-is-the-most-used-tool-other-than-databases.html" rel="nofollow"&gt;survey&lt;/a&gt; published by O''Reilly collected at Strata.&lt;/p&gt;

&lt;p&gt;You can see that SAS is not widely popular, and there is no reason why that should change at this point. One can rule that out.&lt;/p&gt;

&lt;p&gt;R is barely ahead of Python, 43% vs 41%. You can find many blogs expressing the rise of Python in data science. I would go with Python in the near future.&lt;/p&gt;

&lt;p&gt;But 5 years is a very long time. I think Golang will steal a lot of developers from Python in general. This might spill over to data science usage as well. Code can be written to execute in parallel very easily, which makes it a perfect vehicle for Big Data processing. &lt;a href="http://julialang.org/" rel="nofollow"&gt;Julia''s&lt;/a&gt; benchmarks for technical computing are even more impressive, and you can have iPython like stuff with iJulia. Hence Python is likely to lose some steam to both. But there are ways to call Julia functions from R and Python, so you can experiment using best sides of each.&lt;/p&gt;
', NULL, NULL, '''/2014/01/in-data-scientist-survey-r-is-the-most-used-tool-other-than-databases.html'':10C ''/a'':14C,143C ''/p'':22C,50C,83C,192C ''41'':60C ''43'':58C ''5'':86C ''ahead'':55C ''bare'':54C ''benchmark'':144C ''best'':188C ''big'':133C ''blog'':65C ''blog.revolutionanalytics.com'':9C ''blog.revolutionanalytics.com/2014/01/in-data-scientist-survey-r-is-the-most-used-tool-other-than-databases.html'':8C ''call'':176C ''chang'':41C ''code'':116C ''collect'':19C ''comput'':147C ''data'':72C,111C,134C ''develop'':101C ''easili'':125C ''even'':149C ''execut'':121C ''experi'':186C ''express'':66C ''find'':63C ''function'':178C ''futur'':82C ''general'':105C ''go'':76C ''golang'':95C ''great'':5C ''henc'':161C ''href'':7C,137C ''ijulia'':160C ''impress'':151C ''ipython'':156C ''julia'':141C,177C ''julialang.org'':138C ''like'':157C,164C ''long'':91C ''lose'':166C ''lot'':99C ''make'':127C ''mani'':64C ''might'':107C ''near'':81C ''nofollow'':12C,140C ''o'':17C ''one'':45C ''p'':1C,23C,51C,84C ''parallel'':123C ''perfect'':130C ''point'':44C ''popular'':32C ''process'':135C ''publish'':15C ''python'':57C,70C,78C,103C,162C,182C ''r'':52C,180C ''reason'':37C ''reilli'':18C ''rel'':11C,139C ''rise'':68C ''rule'':47C ''sas'':28C ''scienc'':73C,112C ''see'':26C ''side'':189C ''spill'':108C ''steal'':97C ''steam'':168C ''strata'':21C ''stuff'':158C ''survey'':13C ''technic'':146C ''think'':94C ''time'':92C ''usag'':113C ''use'':187C ''vehicl'':131C ''vs'':59C ''way'':174C ''well'':115C ''wide'':31C ''would'':75C ''written'':119C ''year'':87C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1032, 2, '2014-08-24 08:22:23.497', 6, NULL, NULL, '&lt;p&gt;You may want to check &lt;a href="http://stats.stackexchange.com/"&gt;cross-validated&lt;/a&gt; - a stachexchange website for many things, including machine learning. &lt;/p&gt;

&lt;p&gt;In particular, this question (with exactly same title) has already been answered multiple times. Check these links: &lt;a href="http://stats.stackexchange.com/search?q=random+forest+overfit"&gt;http://stats.stackexchange.com/search?q=random+forest+overfit&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;But I may give you the short answer to it: yes, it does overfit, and sometimes you need to control the complexity of the trees in your forest, or even prune when they grow too much - but this depends on the library you use for building the forest. E.g. in &lt;code&gt;randomForest&lt;/code&gt; in R you can only control the complexity&lt;/p&gt;
', NULL, NULL, '''/a'':13C ''/code'':103C ''/p'':23C,112C ''/search?q=random+forest+overfit'':46C ''/search?q=random+forest+overfit&lt;/a&gt;&lt;/p&gt;'':49C ''alreadi'':34C ''answer'':36C,58C ''build'':96C ''check'':6C,39C ''code'':101C ''complex'':72C,111C ''control'':70C,109C ''cross'':11C ''cross-valid'':10C ''depend'':89C ''e.g'':99C ''even'':80C ''exact'':30C ''forest'':78C,98C ''give'':54C ''grow'':84C ''href'':8C,43C ''includ'':20C ''learn'':22C ''librari'':92C ''link'':41C ''machin'':21C ''mani'':18C ''may'':3C,53C ''much'':86C ''multipl'':37C ''need'':68C ''overfit'':64C ''p'':1C,24C,50C ''particular'':26C ''prune'':81C ''question'':28C ''r'':105C ''randomforest'':102C ''short'':57C ''sometim'':66C ''stachexchang'':15C ''stats.stackexchange.com'':9C,45C,48C ''stats.stackexchange.com/search?q=random+forest+overfit'':44C ''stats.stackexchange.com/search?q=random+forest+overfit&lt;/a&gt;&lt;/p&gt;'':47C ''thing'':19C ''time'':38C ''titl'':32C ''tree'':75C ''use'':94C ''valid'':12C ''want'':4C ''websit'':16C ''yes'':61C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1033, 2, '2014-08-24 11:03:08.773', 3, NULL, NULL, '&lt;p&gt;Due to the very Big increase in &lt;strong&gt;Big Data&lt;/strong&gt; (pun intended) and the desire for robust stable scalable applications I actually believe it to be &lt;strong&gt;Scala&lt;/strong&gt;.  &lt;strong&gt;Spark&lt;/strong&gt; will inevitably become the main Big Data Machine Learning tool, and it''s main API is in Scala.  Furthermore you simply cannot build a product with scripting languages like Python and R, one can only experiment with these languages.  What Scala brings is a way to BOTH experiment and produce a product.  More reasons&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Think functionally - write faster code and more readable code&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Scala means the end of the two team development cycle. So better product ownership, more agile cross functional teams, and half as many employees required to make a product as we will no longer need both a "research" team and an engineering team, Data Scientists will be able to do both.  This is because Scala is;&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;A production quality language - static typing, but with the flexibility of dynamic typing due to implicits&lt;/li&gt;
&lt;li&gt;Interoperable with rest of Java world (so Apache Commons Math, Databases, Cassandra, HBase, HDFS, Akka, Storm, many many databases, more spark components (e.g. graphx, SparkStreaming)&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Step into Spark code easily and understand it, also helps with debugging&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Scala is awesome:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Amazing IDE support due to static typing&lt;/li&gt;
&lt;li&gt;Property based tests with ScalaCheck - insane unit testing&lt;/li&gt;
&lt;li&gt;Very concise language&lt;/li&gt;
&lt;li&gt;Suits mathematicians perfectly (especially Pure Mathematicians)&lt;/li&gt;
&lt;/ul&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;A little more efficient as compiled not interpreted&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Python Spark API sits on Scala API and therefore will always be behind Scala API&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Much easier to do Mathematics in Scala as it''s a Scalable Language where one can easily define DSLs and due to being so functional&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Akka - another way other than storm to do High Velocity&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Pimp my library pattern makes adding methods to Spark RDDs really easy&lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':101C,179C,206C,208C,224C,240C,250C,255C,263C,265C,277C,296C,325C,339C,355C ''/ol'':356C ''/p'':89C,160C,223C,230C,276C,295C,324C,338C,354C ''/strong'':12C,31C,34C ''/ul'':207C,264C ''abl'':151C ''actual'':24C ''ad'':347C ''agil'':119C ''akka'':195C,328C ''also'':219C ''alway'':290C ''amaz'':233C ''anoth'':329C ''apach'':188C ''api'':49C,282C,286C,294C ''applic'':22C ''awesom'':229C ''base'':243C ''becom'':37C ''behind'':292C ''believ'':25C ''better'':115C ''big'':6C,10C,40C ''bring'':76C ''build'':57C ''cannot'':56C ''cassandra'':192C ''code'':96C,100C,214C ''common'':189C ''compil'':273C ''compon'':202C ''concis'':253C ''cross'':120C ''cycl'':113C ''data'':11C,41C,147C ''databas'':191C,199C ''debug'':222C ''defin'':316C ''desir'':17C ''develop'':112C ''dsls'':317C ''due'':2C,176C,236C,319C ''dynam'':174C ''e.g'':203C ''easi'':353C ''easier'':300C ''easili'':215C,315C ''effici'':271C ''employe'':127C ''end'':107C ''engin'':145C ''especi'':260C ''experi'':70C,82C ''faster'':95C ''flexibl'':172C ''function'':93C,121C,323C ''furthermor'':53C ''graphx'':204C ''half'':124C ''hbase'':193C ''hdfs'':194C ''help'':220C ''high'':336C ''ide'':234C ''implicit'':178C ''increas'':7C ''inevit'':36C ''insan'':247C ''intend'':14C ''interoper'':181C ''interpret'':275C ''java'':185C ''languag'':62C,73C,166C,254C,311C ''learn'':43C ''li'':91C,102C,162C,180C,209C,225C,232C,241C,251C,256C,266C,278C,297C,326C,340C ''librari'':344C ''like'':63C ''littl'':269C ''longer'':137C ''machin'':42C ''main'':39C,48C ''make'':130C,346C ''mani'':126C,197C,198C ''math'':190C ''mathemat'':303C ''mathematician'':258C,262C ''mean'':105C ''method'':348C ''much'':299C ''need'':138C ''ol'':90C ''one'':67C,313C ''ownership'':117C ''p'':1C,103C,210C,226C,267C,279C,298C,327C,341C ''pattern'':345C ''perfect'':259C ''pimp'':342C ''produc'':84C ''product'':59C,86C,116C,132C,164C ''properti'':242C ''pun'':13C ''pure'':261C ''python'':64C,280C ''qualiti'':165C ''r'':66C ''rdds'':351C ''readabl'':99C ''realli'':352C ''reason'':88C ''requir'':128C ''research'':141C ''rest'':183C ''robust'':19C ''scala'':30C,52C,75C,104C,158C,227C,285C,293C,305C ''scalabl'':21C,310C ''scalacheck'':246C ''scientist'':148C ''script'':61C ''simpli'':55C ''sit'':283C ''spark'':33C,201C,213C,281C,350C ''sparkstream'':205C ''stabl'':20C ''static'':167C,238C ''step'':211C ''storm'':196C,333C ''strong'':9C,29C,32C ''suit'':257C ''support'':235C ''team'':111C,122C,142C,146C ''test'':244C,249C ''therefor'':288C ''think'':92C ''tool'':44C ''two'':110C ''type'':168C,175C,239C ''ul'':161C,231C ''understand'':217C ''unit'':248C ''veloc'':337C ''way'':79C,330C ''world'':186C ''write'':94C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1039, 2, '2014-08-25 11:29:35.047', 1, NULL, NULL, '&lt;p&gt;I think dependencies can be used to improve the accurary of your sentiment classifier. Consider the following examples:&lt;/p&gt;

&lt;p&gt;E1: Bill is not a scientist&lt;/p&gt;

&lt;p&gt;and assume that the token "scientist" has a positive sentiment in a specific domain.&lt;/p&gt;

&lt;p&gt;Knowing the dependency neg(scientist, not) we can see that the example above has a negative sentiment. Without knowing this dependency we would probably classify the sentence as positive.&lt;/p&gt;

&lt;p&gt;Another types of dependencies can be used probably in the same way to improve the accuracy of the classifiers.&lt;/p&gt;
', NULL, NULL, '''/p'':20C,28C,44C,75C,96C ''accuraci'':92C ''accurari'':11C ''anoth'':77C ''assum'':31C ''bill'':23C ''classifi'':15C,70C,95C ''consid'':16C ''depend'':4C,48C,66C,80C ''domain'':43C ''e1'':22C ''exampl'':19C,57C ''follow'':18C ''improv'':9C,90C ''know'':46C,64C ''neg'':49C ''negat'':61C ''p'':1C,21C,29C,45C,76C ''posit'':38C,74C ''probabl'':69C,84C ''scientist'':27C,35C,50C ''see'':54C ''sentenc'':72C ''sentiment'':14C,39C,62C ''specif'':42C ''think'':3C ''token'':34C ''type'':78C ''use'':7C,83C ''way'':88C ''without'':63C ''would'':68C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1034, 1, '2014-08-24 17:09:40.51', 4, 348, 'What features are generally used from Parse trees in classification process in NLP?', '&lt;p&gt;I am exploring different types of parse tree structures. The two widely known parse tree structures are 
a) Constituency based parse tree and 
b) Dependency based parse tree structures. &lt;/p&gt;

&lt;p&gt;I am able to use generate both types of parse tree structures using Stanford NLP package. However, I am not sure how to use these tree structures for my classification task. &lt;/p&gt;

&lt;p&gt;For e.g If I want to do sentiment analysis and want to categorize text into positive and negative classes, what features can I derive from parse tree structures for my classification task?&lt;/p&gt;
', 'machine-learning  nlp  feature-selection  feature-extraction', NULL, '''/p'':54C,87C,121C ''abl'':58C ''analysi'':97C ''b'':48C ''base'':44C,50C ''categor'':101C ''class'':107C ''classif'':10A,85C,119C ''constitu'':43C ''depend'':49C ''deriv'':112C ''differ'':28C ''e.g'':90C ''explor'':27C ''extract'':23B ''featur'':2A,19B,22B,109C ''feature-extract'':21B ''feature-select'':18B ''general'':4A ''generat'':61C ''howev'':72C ''known'':37C ''learn'':16B ''machin'':15B ''machine-learn'':14B ''negat'':106C ''nlp'':13A,17B,70C ''p'':24C,55C,88C ''packag'':71C ''pars'':7A,31C,38C,45C,51C,65C,114C ''posit'':104C ''process'':11A ''select'':20B ''sentiment'':96C ''stanford'':69C ''structur'':33C,40C,53C,67C,82C,116C ''sure'':76C ''task'':86C,120C ''text'':102C ''tree'':8A,32C,39C,46C,52C,66C,81C,115C ''two'':35C ''type'':29C,63C ''use'':5A,60C,68C,79C ''want'':93C,99C ''wide'':36C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1074, 1, '2014-09-02 19:29:07.49', 4, 343, 'Polynomial Kernel Parameters in SVMs', '&lt;p&gt;In SVMs the polynomial kernel is defined as:&lt;/p&gt;

&lt;p&gt;(scale * crossprod(x, y) + offset)^degree&lt;/p&gt;

&lt;p&gt;How do the scale and offset parameters affect the model and what range should they be in? (intuitively please)&lt;/p&gt;

&lt;p&gt;Are the scale and offset for numeric stability only (that''s what it looks like to me), or do they influence prediction accuracy as well?&lt;/p&gt;

&lt;p&gt;Can good values for scale and offset be calculated/estimated when the data is known or is a grid search required? The caret package always sets the offset to 1, but it does a grid search for scale. (Why) is an offset of 1 a good value?&lt;/p&gt;

&lt;p&gt;Thanks&lt;/p&gt;

&lt;p&gt;PS.: Wikipedia didn''t really help my understanding:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;For degree-d polynomials, the polynomial kernel is defined as&lt;/p&gt;
  
  &lt;p&gt;&lt;img src="http://i.stack.imgur.com/3stCR.png" alt=""&gt;&lt;/p&gt;
  
  &lt;p&gt;where x and y are vectors in the input space, i.e. vectors of features
  computed from training or test samples, &lt;img src="http://i.stack.imgur.com/34dwi.png" alt=""&gt; &lt;strong&gt;is a constant trading&lt;/strong&gt;
  &lt;strong&gt;off the influence of higher-order versus lower-order terms&lt;/strong&gt; in the
  polynomial. When &lt;img src="http://i.stack.imgur.com/79cDG.png" alt=""&gt;, the kernel is called homogeneous.(&lt;strong&gt;A further&lt;/strong&gt;
  &lt;strong&gt;generalized polykernel divides &lt;img src="http://i.stack.imgur.com/Ps0qt.png" alt=""&gt; by a user-specified scalar&lt;/strong&gt;
  &lt;strong&gt;parameter &lt;img src="http://i.stack.imgur.com/sjfS1.png" alt=""&gt;.&lt;/strong&gt;)&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Neither did ?polydot''s explanation in R''s help system:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;strong&gt;scale&lt;/strong&gt;: The scaling parameter of the polynomial and tangent kernel is a
  convenient way of normalizing patterns (&amp;lt;-!?) without the need to modify the
  data itself&lt;/p&gt;
  
  &lt;p&gt;&lt;strong&gt;offset&lt;/strong&gt;: The offset used in a polynomial or hyperbolic tangent kernel (&amp;lt;- lol thanks)&lt;/p&gt;
&lt;/blockquote&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1035, 2, '2014-08-24 22:02:01.05', 2, NULL, NULL, '&lt;p&gt;First compute the edit distance between all pairs of strings.  See &lt;a href="http://en.wikipedia.org/wiki/Edit_distance" rel="nofollow"&gt;http://en.wikipedia.org/wiki/Edit_distance&lt;/a&gt; and &lt;a href="http://web.stanford.edu/class/cs124/lec/med.pdf" rel="nofollow"&gt;http://web.stanford.edu/class/cs124/lec/med.pdf&lt;/a&gt;.  Then exclude any outliers strings based on some distance threshold.&lt;/p&gt;

&lt;p&gt;With remaining strings, you can use the distance matrix to identify the most central string.  Depending on the method you use, you might get ambiguous results for some data. No method is perfect for all possibilities.  For your purposes, all you need is some heuristic rules to resolve ambiguities -- i.e. pick two or more candidates.&lt;/p&gt;

&lt;p&gt;Maybe you don''t want to pick "most central" from your list of strings, but instead want to generate a regular expression that captures the pattern common to all the non-outlier strings.  One way to do this is to synthesize a string that is equidistant from all the non-outlier strings.  You can work out the required edit distance from the matrix, and then you''d randomly generate regular using those distances as constraints.  Then you''d test candidate regular expressions and accept the first one that fits the constraints and also accepts all the strings in your non-outlier list.  (Start building regular expressions from longest common substring lists, because those are non-wildcard characters.)&lt;/p&gt;
', NULL, NULL, '''/class/cs124/lec/med.pdf'':28C ''/class/cs124/lec/med.pdf&lt;/a&gt;.'':33C ''/p'':44C,101C,224C ''/wiki/edit_distance'':17C ''/wiki/edit_distance&lt;/a&gt;'':22C ''accept'':188C,198C ''also'':197C ''ambigu'':70C,94C ''base'':39C ''build'':209C ''candid'':100C,184C ''captur'':126C ''central'':59C,111C ''charact'':223C ''common'':129C,214C ''comput'':3C ''constraint'':179C,195C ''d'':171C,182C ''data'':74C ''depend'':61C ''distanc'':6C,42C,53C,164C,177C ''edit'':5C,163C ''en.wikipedia.org'':16C,21C ''en.wikipedia.org/wiki/edit_distance'':15C ''en.wikipedia.org/wiki/edit_distance&lt;/a&gt;'':20C ''equidist'':149C ''exclud'':35C ''express'':124C,186C,211C ''first'':2C,190C ''fit'':193C ''generat'':121C,173C ''get'':69C ''heurist'':90C ''href'':14C,25C ''i.e'':95C ''identifi'':56C ''instead'':118C ''list'':114C,207C,216C ''longest'':213C ''matrix'':54C,167C ''mayb'':103C ''method'':64C,76C ''might'':68C ''need'':87C ''nofollow'':19C,30C ''non'':134C,154C,205C,221C ''non-outli'':133C,153C,204C ''non-wildcard'':220C ''one'':137C,191C ''outlier'':37C,135C,155C,206C ''p'':1C,45C,102C ''pair'':9C ''pattern'':128C ''perfect'':78C ''pick'':96C,109C ''possibl'':81C ''purpos'':84C ''random'':172C ''regular'':123C,174C,185C,210C ''rel'':18C,29C ''remain'':47C ''requir'':162C ''resolv'':93C ''result'':71C ''rule'':91C ''see'':12C ''start'':208C ''string'':11C,38C,48C,60C,116C,136C,146C,156C,201C ''substr'':215C ''synthes'':144C ''test'':183C ''threshold'':43C ''two'':97C ''use'':51C,66C,175C ''want'':107C,119C ''way'':138C ''web.stanford.edu'':27C,32C ''web.stanford.edu/class/cs124/lec/med.pdf'':26C ''web.stanford.edu/class/cs124/lec/med.pdf&lt;/a&gt;.'':31C ''wildcard'':222C ''work'':159C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1047, 2, '2014-08-26 22:48:16.617', 1, NULL, NULL, '&lt;ol&gt;
&lt;li&gt;&lt;p&gt;SPSS is a great tool, but you can accomplish a great deal with resources that you already have on your computer, like Excel, or that are free, like the R-project. Although these tools are powerful, and can help you identify patterns, you''ll need to have a firm grasp of your data before running analyses (I''d recommend running descriptive statistics on your data, and exploring the data with graphs to make sure everything is looking normal). In other words, the tool that you use won''t offer a "silver bullet", because the output will only be as valuable as the input (you know the saying... "garbage in, garbage out"). Much of what I''m saying has already been stated in the reply by Aleksandr - spot on. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;R can be challenging for those of us who aren''t savvy with coding, but the free resources associated with R and its packages are abundant. If you practice learning the program, you''ll quickly gain traction. Again, you''ll need to be familiar with your data and the analyses you want to run anyway, and that fact remains regardless of the statistical tools you utilize. &lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;I''d begin by getting super familiar with my data (follow the steps outlined in the reply from Aleksandr, for starters). You might consider picking up John Foreman''s book called Data Smart. It''s a hands-on book, as John provides datasets and you follow along with his examples (using Excel) to learn various ways of navigating and exploring data. For beginners, it''s a great resource. &lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;
', NULL, NULL, '''/li'':134C,204C,277C ''/ol'':278C ''/p'':133C,203C,276C ''abund'':162C ''accomplish'':12C ''aleksandr'':130C,225C ''along'':254C ''alreadi'':20C,123C ''although'':36C ''analys'':60C,186C ''anyway'':191C ''aren'':146C ''associ'':155C ''begin'':209C ''beginn'':270C ''book'':236C,246C ''bullet'':96C ''call'':237C ''challeng'':140C ''code'':150C ''comput'':24C ''consid'':230C ''d'':62C,208C ''data'':57C,69C,73C,183C,216C,238C,268C ''dataset'':250C ''deal'':15C ''descript'':65C ''everyth'':79C ''exampl'':257C ''excel'':26C,259C ''explor'':71C,267C ''fact'':194C ''familiar'':180C,213C ''firm'':53C ''follow'':217C,253C ''foreman'':234C ''free'':30C,153C ''gain'':172C ''garbag'':112C,114C ''get'':211C ''graph'':75C ''grasp'':54C ''great'':7C,14C,274C ''hand'':244C ''hands-on'':243C ''help'':43C ''identifi'':45C ''input'':107C ''john'':233C,248C ''know'':109C ''learn'':166C,261C ''li'':2C,135C,205C ''like'':25C,31C ''ll'':48C,170C,176C ''look'':81C ''m'':120C ''make'':77C ''might'':229C ''much'':116C ''navig'':265C ''need'':49C,177C ''normal'':82C ''offer'':93C ''ol'':1C ''outlin'':220C ''output'':99C ''p'':3C,136C,206C ''packag'':160C ''pattern'':46C ''pick'':231C ''power'':40C ''practic'':165C ''program'':168C ''project'':35C ''provid'':249C ''quick'':171C ''r'':34C,137C,157C ''r-project'':33C ''recommend'':63C ''regardless'':196C ''remain'':195C ''repli'':128C,223C ''resourc'':17C,154C,275C ''run'':59C,64C,190C ''savvi'':148C ''say'':111C,121C ''silver'':95C ''smart'':239C ''spot'':131C ''spss'':4C ''starter'':227C ''state'':125C ''statist'':66C,199C ''step'':219C ''super'':212C ''sure'':78C ''tool'':8C,38C,87C,200C ''traction'':173C ''us'':144C ''use'':90C,258C ''util'':202C ''valuabl'':104C ''various'':262C ''want'':188C ''way'':263C ''won'':91C ''word'':85C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1036, 1, '2014-08-25 00:28:09.003', 5, 223, 'How to numerically estimate MLE estimators in python when gradients are very small far from the optimal solution?', '&lt;p&gt;I am exploring how to model a data set using normal distributions with both mean and variance defined as linear functions of independent variables.&lt;/p&gt;

&lt;p&gt;Something like N ~ (f(x), g(x)).&lt;/p&gt;

&lt;p&gt;I generate a random sample like this:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def draw(x):
    return norm(5 * x + 2, 3 *x + 4).rvs(1)[0]
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So I want to retrieve 5, 2 and 4 as the parameters for my distribution.&lt;/p&gt;

&lt;p&gt;I generate my sample:&lt;/p&gt;

&lt;p&gt;smp = np.zeros((100,2))&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;for i in range(0, len(smp)):
    smp[i][0] = i
    smp[i][1] = draw(i)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;The likelihood function is:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;def lh(p):
    p_loc_b0 = p[0]
    p_loc_b1 = p[1]
    p_scl_b0 = p[2]
    p_scl_b1 = p[3]

    l = 1
    for i in range(0, len(smp)):
        x = smp[i][0]
        y = smp[i][1]
        l = l * norm(p_loc_b0 + p_loc_b1 * x, p_scl_b0 + p_scl_b1 * x).pdf(y)

    return -l
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;So the parameters for the linear functions used in the model are given in the p 4-variable vector.&lt;/p&gt;

&lt;p&gt;Using scipy.optimize, I can solve for the MLE parameters using an extremely low xtol, and already giving the solution as the starting point:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;fmin(lh, x0=[2,5,3,4], xtol=1e-35)
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Which does not work to well:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;Warning: Maximum number of function evaluations has been exceeded.
array([ 3.27491346,  4.69237042,  5.70317719,  3.30395462])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Raising the xtol to higher values does no good.&lt;/p&gt;

&lt;p&gt;So i try using a starting solution far from the real solution:&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;&amp;gt;&amp;gt;&amp;gt; fmin(lh, x0=[1,1,1,1], xtol=1e-8)
Optimization terminated successfully.
         Current function value: -0.000000
         Iterations: 24
         Function evaluations: 143
array([ 1.,  1.,  1.,  1.])
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Which makes me think:&lt;/p&gt;

&lt;p&gt;PDF are largely clustered around the mean, and have very low gradients only a few standard deviations away from the mean, which must be not too good for numerical methods.&lt;/p&gt;

&lt;p&gt;So how does one go about doing these kind of numerical estimation in functions where gradient is very near to zero away from the solution?&lt;/p&gt;
', 'python  statistics', NULL, '''-0.000000'':333C ''/code'':81C,130C,201C,260C,286C,344C ''/p'':46C,55C,64C,99C,105C,111C,137C,223C,248C,269C,298C,312C,351C,383C,410C ''/pre'':82C,131C,202C,261C,287C,345C ''0'':80C,118C,123C,147C,169C,175C ''1'':79C,127C,152C,164C,179C,321C,322C,323C,324C,340C,341C,342C,343C ''100'':109C ''143'':338C ''1e-35'':259C ''1e-8'':326C ''2'':74C,90C,110C,157C,254C ''24'':335C ''3'':75C,162C,256C ''3.27491346'':282C ''3.30395462'':285C ''4'':77C,92C,220C,257C ''4.69237042'':283C ''5'':72C,89C,255C ''5.70317719'':284C ''alreadi'':240C ''around'':357C ''array'':281C,339C ''away'':370C,406C ''b0'':145C,155C,185C,192C ''b1'':150C,160C,188C,195C ''cluster'':356C ''code'':66C,113C,139C,250C,271C,314C ''current'':330C ''data'':29C ''def'':67C,140C ''defin'':39C ''deviat'':369C ''distribut'':33C,98C ''draw'':68C,128C ''estim'':4A,6A,396C ''evalu'':277C,337C ''exceed'':280C ''explor'':24C ''extrem'':236C ''f'':51C ''far'':14A,307C ''fmin'':251C,318C ''function'':42C,135C,210C,276C,331C,336C,398C ''g'':53C ''generat'':58C,102C ''give'':241C ''given'':216C ''go'':389C ''good'':297C,379C ''gradient'':10A,364C,400C ''gt'':315C,316C,317C ''higher'':293C ''independ'':44C ''iter'':334C ''kind'':393C ''l'':163C,180C,181C,200C ''larg'':355C ''len'':119C,170C ''lh'':141C,252C,319C ''like'':49C,62C ''likelihood'':134C ''linear'':41C,209C ''loc'':144C,149C,184C,187C ''low'':237C,363C ''make'':348C ''maximum'':273C ''mean'':36C,359C,373C ''method'':382C ''mle'':5A,232C ''model'':27C,214C ''must'':375C ''n'':50C ''near'':403C ''norm'':71C,182C ''normal'':32C ''np.zeros'':108C ''number'':274C ''numer'':3A,381C,395C ''one'':388C ''optim'':17A,327C ''p'':21C,47C,56C,83C,100C,106C,132C,142C,143C,146C,148C,151C,153C,156C,158C,161C,183C,186C,190C,193C,203C,219C,224C,262C,288C,299C,346C,352C,384C ''paramet'':95C,206C,233C ''pdf'':197C,353C ''point'':247C ''pre'':65C,112C,138C,249C,270C,313C ''python'':8A,19B ''rais'':289C ''random'':60C ''rang'':117C,168C ''real'':310C ''retriev'':88C ''return'':70C,199C ''rvs'':78C ''sampl'':61C,104C ''scipy.optimize'':226C ''scl'':154C,159C,191C,194C ''set'':30C ''small'':13A ''smp'':107C,120C,121C,125C,171C,173C,177C ''solut'':18A,243C,306C,311C,409C ''solv'':229C ''someth'':48C ''standard'':368C ''start'':246C,305C ''statist'':20B ''success'':329C ''termin'':328C ''think'':350C ''tri'':302C ''use'':31C,211C,225C,234C,303C ''valu'':294C,332C ''variabl'':45C,221C ''varianc'':38C ''vector'':222C ''want'':86C ''warn'':272C ''well'':268C ''work'':266C ''x'':52C,54C,69C,73C,76C,172C,189C,196C ''x0'':253C,320C ''xtol'':238C,258C,291C,325C ''y'':176C,198C ''zero'':405C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (6157, 2, '2015-06-19 15:28:51.003', 2, NULL, NULL, '&lt;p&gt;Yes it is a challenging task to extract named entities in tweets. Give a go at NLTK NER and also Alan Ritter''s Twitter specific NER and evaluate on their performance and compare to Stanford NER and which one fits in your use. Maybe you want to use more than one to get more named entities if you don''t mind so much of false NEs..&lt;/p&gt;
', NULL, NULL, '''/p'':68C ''alan'':22C ''also'':21C ''challeng'':6C ''compar'':34C ''entiti'':11C,57C ''evalu'':29C ''extract'':9C ''fals'':66C ''fit'':41C ''get'':54C ''give'':14C ''go'':16C ''mayb'':45C ''mind'':62C ''much'':64C ''name'':10C,56C ''ner'':19C,27C,37C ''nes'':67C ''nltk'':18C ''one'':40C,52C ''p'':1C ''perform'':32C ''ritter'':23C ''specif'':26C ''stanford'':36C ''task'':7C ''tweet'':13C ''twitter'':25C ''use'':44C,49C ''want'':47C ''yes'':2C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1038, 1, '2014-08-25 09:53:13.823', 1, 249, 'Hive External table does not showing in Namenode (Cloudera-QuickstartVm)', '&lt;p&gt;I have created external table in Hive in the hdfs path ''hdfs://localhost.localdomain:8020/user/hive/training''. If I apply describe command I can find the table path as shown below. But when I browse through the namenode web page, the table name does not showing up in the path.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;hive&amp;gt; describe extended testtable4;
OK
firstname   string  
lastname    string  
address string  
city    string  
state   string  
country string  

    ***Detailed Table Information  Table(tableName:testtable4, dbName:default, owner:cloudera, createTime:1408765301, lastAccessTime:0, retention:0, sd:StorageDescriptor(cols:[FieldSchema(name:firstname, type:string, comment:null), FieldSchema(name:lastname, type:string, comment:null), FieldSchema(name:address, type:string, comment:null), FieldSchema(name:city, type:string, comment:null), FieldSchema(name:state, type:string, comment:null), FieldSchema(name:country, type:string, comment:null)], location:hdfs://localhost.localdomain:8020/user/hive/training, inputFormat:org.apache.hadoop.mapred.TextInputFormat, outputFormat:org.apache.hadoop.hive.ql.io.HiveIgnoreKeyTextOutputFormat, compressed:false, numBuckets:-1, serdeInfo:SerDeInfo(name:null, serializationLib:org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe, parameters:{serialization.format=,, field.delim=,, line.delim=    
    }), bucketCols:[], sortCols:[], parameters:{}, skewedInfo:SkewedInfo(skewedColNames:[], skewedColValues:[], skewedColValueLocationMaps:{}), storedAsSubDirectories:false), partitionKeys:[], parameters:{EXTERNAL=TRUE, transient_lastDdlTime=1408765301}, viewOriginalText:null, viewExpandedText:null, tableType:EXTERNAL_TABLE)       
    Time taken: 0.7 seconds***
&lt;/code&gt;&lt;/pre&gt;
', 'bigdata', NULL, '''-1'':154C ''/code'':193C ''/p'':61C ''/pre'':194C ''/user/hive/training''''.'':27C ''/user/hive/training,'':146C ''0'':95C,97C ''0.7'':191C ''1408765301'':93C,181C ''address'':74C,117C ''appli'':30C ''bigdata'':12B ''brows'':45C ''bucketcol'':165C ''citi'':76C,124C ''cloudera'':10A,91C ''cloudera-quickstartvm'':9A ''code'':63C ''col'':100C ''command'':32C ''comment'':106C,113C,120C,127C,134C,141C ''compress'':151C ''countri'':80C,138C ''creat'':16C ''createtim'':92C ''dbname'':88C ''default'':89C ''describ'':31C,66C ''detail'':82C ''extend'':67C ''extern'':2A,17C,177C,187C ''fals'':152C,174C ''field.delim'':163C ''fieldschema'':101C,108C,115C,122C,129C,136C ''find'':35C ''firstnam'':70C,103C ''gt'':65C ''hdfs'':23C ''hive'':1A,20C,64C ''inform'':84C ''inputformat'':147C ''lastaccesstim'':94C ''lastddltim'':180C ''lastnam'':72C,110C ''line.delim'':164C ''localhost.localdomain:8020'':26C,145C ''localhost.localdomain:8020/user/hive/training''''.'':25C ''localhost.localdomain:8020/user/hive/training,'':144C ''locat'':143C ''name'':53C,102C,109C,116C,123C,130C,137C,157C ''namenod'':8A,48C ''null'':107C,114C,121C,128C,135C,142C,158C,183C,185C ''numbucket'':153C ''ok'':69C ''org.apache.hadoop.hive.ql.io.hiveignorekeytextoutputformat'':150C ''org.apache.hadoop.hive.serde2.lazy.lazysimpleserde'':160C ''org.apache.hadoop.mapred.textinputformat'':148C ''outputformat'':149C ''owner'':90C ''p'':13C ''page'':50C ''paramet'':161C,167C,176C ''partitionkey'':175C ''path'':24C,38C,60C ''pre'':62C ''quickstartvm'':11A ''retent'':96C ''sd'':98C ''second'':192C ''serdeinfo'':155C,156C ''serialization.format'':162C ''serializationlib'':159C ''show'':6A,56C ''shown'':40C ''skewedcolnam'':170C ''skewedcolvalu'':171C ''skewedcolvaluelocationmap'':172C ''skewedinfo'':168C,169C ''sortcol'':166C ''state'':78C,131C ''storagedescriptor'':99C ''storedassubdirectori'':173C ''string'':71C,73C,75C,77C,79C,81C,105C,112C,119C,126C,133C,140C ''tabl'':3A,18C,37C,52C,83C,85C,188C ''tablenam'':86C ''tabletyp'':186C ''taken'':190C ''testtable4'':68C,87C ''time'':189C ''transient'':179C ''true'':178C ''type'':104C,111C,118C,125C,132C,139C ''viewexpandedtext'':184C ''vieworiginaltext'':182C ''web'':49C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1041, 2, '2014-08-26 07:14:54.767', 6, NULL, NULL, '&lt;p&gt;If you suspect your response is linear with year, then put year in as a numeric term in your model rather than a categorical.&lt;/p&gt;

&lt;p&gt;Extrapolation is then perfectly valid based on the usual assumptions of the GLM family. Make sure you correctly get the errors on your extrapolated estimates.&lt;/p&gt;

&lt;p&gt;Just extrapolating the parameters from a categorical variable is wrong for a number of reasons. The first one I can think of is that there may be more observations in some years than others, so any linear extrapolation needs to weight those year''s estimates more. Just eyeballing a line - or even fitting a line to the coefficients - won''t do this. &lt;/p&gt;
', NULL, NULL, '''/p'':26C,53C,117C ''assumpt'':37C ''base'':33C ''categor'':25C,61C ''coeffici'':112C ''correct'':45C ''error'':48C ''estim'':52C,99C ''even'':106C ''extrapol'':28C,51C,56C,92C ''eyebal'':102C ''famili'':41C ''first'':71C ''fit'':107C ''get'':46C ''glm'':40C ''line'':104C,109C ''linear'':8C,91C ''make'':42C ''may'':80C ''model'':21C ''need'':93C ''number'':67C ''numer'':17C ''observ'':83C ''one'':72C ''other'':88C ''p'':1C,27C,54C ''paramet'':58C ''perfect'':31C ''put'':12C ''rather'':22C ''reason'':69C ''respons'':6C ''sure'':43C ''suspect'':4C ''term'':18C ''think'':75C ''usual'':36C ''valid'':32C ''variabl'':62C ''weight'':95C ''won'':113C ''wrong'':64C ''year'':10C,13C,86C,97C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1042, 1, '2014-08-26 07:33:40.08', 4, 83, 'Studying social media platforms - key data analysis methods?', '&lt;p&gt;I am seeking a basic list of key data analysis methods used for studying social media platforms online. Are there such key methods, or does this process generally vary according to topic? And is there a standard order in which these methods are applied?(The particular context I''m interested in is how the news is impacting on social media)&lt;/p&gt;
', 'social-network-analysis', NULL, '''/p'':74C ''accord'':43C ''analysi'':7A,12B,23C ''appli'':57C ''basic'':18C ''context'':60C ''data'':6A,22C ''general'':41C ''impact'':70C ''interest'':63C ''key'':5A,21C,35C ''list'':19C ''m'':62C ''media'':3A,29C,73C ''method'':8A,24C,36C,55C ''network'':11B ''news'':68C ''onlin'':31C ''order'':51C ''p'':13C ''particular'':59C ''platform'':4A,30C ''process'':40C ''seek'':16C ''social'':2A,10B,28C,72C ''social-network-analysi'':9B ''standard'':50C ''studi'':1A,27C ''topic'':45C ''use'':25C ''vari'':42C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1044, 1, '2014-08-26 14:53:40.647', -3, 199, 'which programming language has a large library that can do machine learning algorithm, R, matlab or python', '&lt;p&gt;As what I described in the title, we are especially interested in those for dealing with big data----ts efficiency and stability, and used in industry not in experiment or university. Thanks!&lt;/p&gt;
', 'machine-learning  bigdata  data-mining  python  r', NULL, '''/p'':60C ''algorithm'':13A ''big'':44C ''bigdata'':21B ''data'':23B,45C ''data-min'':22B ''deal'':42C ''describ'':31C ''effici'':47C ''especi'':37C ''experi'':56C ''industri'':53C ''interest'':38C ''languag'':3A ''larg'':6A ''learn'':12A,20B ''librari'':7A ''machin'':11A,19B ''machine-learn'':18B ''matlab'':15A ''mine'':24B ''p'':27C ''program'':2A ''python'':17A,25B ''r'':14A,26B ''stabil'':49C ''thank'':59C ''titl'':34C ''ts'':46C ''univers'':58C ''use'':51C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1048, 2, '2014-08-27 03:41:20.317', 2, NULL, NULL, '&lt;p&gt;I''ve making some researches last months and I could find more libraries, contente and active community with Python. Actually I''m using it to ETL processes, some minning jobs and to make map/reduce.&lt;/p&gt;
', NULL, NULL, '''/p'':36C ''activ'':17C ''actual'':21C ''communiti'':18C ''content'':15C ''could'':11C ''etl'':27C ''find'':12C ''job'':31C ''last'':7C ''librari'':14C ''m'':23C ''make'':4C,34C ''map/reduce'':35C ''min'':30C ''month'':8C ''p'':1C ''process'':28C ''python'':20C ''research'':6C ''use'':24C ''ve'':3C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1050, 1, '2014-08-27 13:01:14.643', 7, 274, 'Libraries for (label propagation algorithms/frequent subgraph mining) for graphs in R', '&lt;p&gt;&lt;strong&gt;General description of the problem&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I have a graph where some vertices are labeled with a type with 3 or 4 possible values. For the other vertices, the type is unknown.
My goal is to use the graph to predict the type for vertices that are unlabeled.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Possible framework&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;I suspect this fits into the general framework of label propagation problems, based on my reading of the literature (e.g., see &lt;a href="http://lvk.cs.msu.su/~bruzz/articles/classification/zhu02learning.pdf" rel="nofollow"&gt;this paper&lt;/a&gt; and &lt;a href="http://www.csc.ncsu.edu/faculty/samatova/practical-graph-mining-with-R/slides/pdf/Frequent_Subgraph_Mining.pdf" rel="nofollow"&gt;this paper&lt;/a&gt;)&lt;/p&gt;

&lt;p&gt;Another method that is mentioned often is &lt;code&gt;Frequent Subgraph Mining&lt;/code&gt;, which includes algorithms like &lt;code&gt;SUBDUE&lt;/code&gt;,&lt;code&gt;SLEUTH&lt;/code&gt;, and &lt;code&gt;gSpan&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Found in R&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The only label propagation implementation I managed to find in &lt;code&gt;R&lt;/code&gt; is &lt;code&gt;label.propagation.community()&lt;/code&gt; from the &lt;code&gt;igraph&lt;/code&gt; library. 
However, as the name suggests, it is mostly used to find communities, not for classifying unlabeled vertices.&lt;/p&gt;

&lt;p&gt;There also seems to be several references to a &lt;code&gt;subgraphMining&lt;/code&gt; library (here for example), but it looks like it is missing from CRAN.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Question&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Do you know of a library or framework for the task described?&lt;/p&gt;
', 'classification  r  graphs', NULL, '''/a'':105C,116C ''/code'':130C,137C,140C,144C,166C,170C,175C,207C ''/faculty/samatova/practical-graph-mining-with-r/slides/pdf/frequent_subgraph_mining.pdf'':111C ''/p'':23C,67C,73C,117C,145C,152C,194C,221C,226C,240C ''/strong'':22C,72C,151C,225C ''/~bruzz/articles/classification/zhu02learning.pdf'':100C ''3'':38C ''4'':40C ''algorithm'':133C ''algorithms/frequent'':5A ''also'':197C ''anoth'':119C ''base'':87C ''classif'':12B ''classifi'':191C ''code'':126C,135C,138C,142C,164C,168C,173C,205C ''communiti'':188C ''cran'':220C ''describ'':239C ''descript'':18C ''e.g'':94C ''exampl'':211C ''find'':162C,187C ''fit'':78C ''found'':148C ''framework'':71C,82C,235C ''frequent'':127C ''general'':17C,81C ''goal'':52C ''graph'':9A,14B,28C,57C ''gspan'':143C ''howev'':177C ''href'':97C,108C ''igraph'':174C ''implement'':158C ''includ'':132C ''know'':230C ''label'':3A,33C,84C,156C ''label.propagation.community'':169C ''librari'':1A,176C,208C,233C ''like'':134C,215C ''literatur'':93C ''look'':214C ''lvk.cs.msu.su'':99C ''lvk.cs.msu.su/~bruzz/articles/classification/zhu02learning.pdf'':98C ''manag'':160C ''mention'':123C ''method'':120C ''mine'':7A,129C ''miss'':218C ''most'':184C ''name'':180C ''nofollow'':102C,113C ''often'':124C ''p'':15C,24C,68C,74C,118C,146C,153C,195C,222C,227C ''paper'':104C,115C ''possibl'':41C,70C ''predict'':59C ''problem'':21C,86C ''propag'':4A,85C,157C ''question'':224C ''r'':11A,13B,150C,165C ''read'':90C ''refer'':202C ''rel'':101C,112C ''see'':95C ''seem'':198C ''sever'':201C ''sleuth'':139C ''strong'':16C,69C,147C,223C ''subdu'':136C ''subgraph'':6A,128C ''subgraphmin'':206C ''suggest'':181C ''suspect'':76C ''task'':238C ''type'':36C,48C,61C ''unknown'':50C ''unlabel'':66C,192C ''use'':55C,185C ''valu'':42C ''vertic'':31C,46C,63C,193C ''www.csc.ncsu.edu'':110C ''www.csc.ncsu.edu/faculty/samatova/practical-graph-mining-with-r/slides/pdf/frequent_subgraph_mining.pdf'':109C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1171, 2, '2014-09-26 16:15:05.58', 0, NULL, NULL, '&lt;p&gt;There''s a NLP toolbox for MATLAB called &lt;a href="https://github.com/faridani/MatlabNLP" rel="nofollow"&gt;&lt;strong&gt;MatlabNLP&lt;/strong&gt;&lt;/a&gt;. It includes modules for tokenization, preprocessing (stop word removal, text cleaning, stemming), and learning algorithms (linear regression, decision trees, support vector machines and a NaÃ¯ve Bayes). A module for POS tagging is coming soon.&lt;/p&gt;
', NULL, NULL, '''/a'':20C ''/faridani/matlabnlp'':14C ''/p'':56C ''/strong'':19C ''algorithm'':35C ''bay'':47C ''call'':9C ''clean'':31C ''come'':54C ''decis'':38C ''github.com'':13C ''github.com/faridani/matlabnlp'':12C ''href'':11C ''includ'':22C ''learn'':34C ''linear'':36C ''machin'':42C ''matlab'':8C ''matlabnlp'':18C ''modul'':23C,49C ''naã'':45C ''nlp'':5C ''nofollow'':16C ''p'':1C ''pos'':51C ''preprocess'':26C ''regress'':37C ''rel'':15C ''remov'':29C ''soon'':55C ''stem'':32C ''stop'':27C ''strong'':17C ''support'':40C ''tag'':52C ''text'':30C ''token'':25C ''toolbox'':6C ''tree'':39C ''ve'':46C ''vector'':41C ''word'':28C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1051, 2, '2014-08-27 18:33:23.11', 1, NULL, NULL, '&lt;blockquote&gt;
  &lt;p&gt;When using the public Google APIs to retrieve results, I was only able to collect 4-10 results per query.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;Here''s how to get more than 10 results per query: &lt;a href="https://support.google.com/customsearch/answer/1361951?hl=en" rel="nofollow"&gt;https://support.google.com/customsearch/answer/1361951?hl=en&lt;/a&gt;&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;Google Custom Search and Google Site Search return up to 10 results per query. If you want to display more than 10 results to the user, you can issue multiple requests (using the start=0, start=11 ... parameters) and display the results on a single page. In this case, Google will consider each request as a separate query, and if you are using Google Site Search, each query will count towards your limit.&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;There are other search engine APIs as well (e.g., &lt;a href="http://datamarket.azure.com/dataset/bing/search" rel="nofollow"&gt;Bing&lt;/a&gt;)&lt;/p&gt;
', NULL, NULL, '''-10'':19C ''/a'':142C ''/blockquote'':24C,123C ''/customsearch/answer/1361951?hl=en'':41C ''/customsearch/answer/1361951?hl=en&lt;/a&gt;&lt;/p&gt;'':46C ''/dataset/bing/search'':138C ''/p'':23C,122C,143C ''0'':83C ''10'':33C,59C,70C ''11'':85C ''4'':18C ''abl'':15C ''api'':8C,130C ''bing'':141C ''blockquot'':1C,47C ''case'':97C ''collect'':17C ''consid'':100C ''count'':118C ''custom'':50C ''datamarket.azure.com'':137C ''datamarket.azure.com/dataset/bing/search'':136C ''display'':67C,88C ''e.g'':133C ''engin'':129C ''get'':30C ''googl'':7C,49C,53C,98C,112C ''href'':38C,135C ''issu'':77C ''limit'':121C ''multipl'':78C ''nofollow'':43C,140C ''p'':2C,25C,48C,124C ''page'':94C ''paramet'':86C ''per'':21C,35C,61C ''public'':6C ''queri'':22C,36C,62C,106C,116C ''rel'':42C,139C ''request'':79C,102C ''result'':11C,20C,34C,60C,71C,90C ''retriev'':10C ''return'':56C ''search'':51C,55C,114C,128C ''separ'':105C ''singl'':93C ''site'':54C,113C ''start'':82C,84C ''support.google.com'':40C,45C ''support.google.com/customsearch/answer/1361951?hl=en'':39C ''support.google.com/customsearch/answer/1361951?hl=en&lt;/a&gt;&lt;/p&gt;'':44C ''toward'':119C ''use'':4C,80C,111C ''user'':74C ''want'':65C ''well'':132C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1053, 1, '2014-08-28 01:36:40.54', 6, 569, 'Summarize and visualize a CSV in Java/Scala?', '&lt;p&gt;I would like to &lt;a href="http://stat.ethz.ch/R-manual/R-devel/library/base/html/summary.html"&gt;summarize&lt;/a&gt; (as in R) the contents of a CSV (possibly after &lt;a href="http://www.endmemo.com/program/R/readcsv.php"&gt;loading&lt;/a&gt; it, or storing it somewhere, that''s not a problem). The summary should contain the quartiles, mean, median, min and max of the data in a CSV file for each numeric (integer or real numbers) dimension. The standard deviation would be cool as well.&lt;/p&gt;

&lt;p&gt;I would also like to generate some plots to visualize the data, for example 3 plots for the 3 pairs of variables that are more correlated (&lt;a href="http://www.r-tutor.com/elementary-statistics/numerical-measures/correlation-coefficient"&gt;correlation coefficient&lt;/a&gt;) and 3 plots for the 3 pairs of variables that are least correlated.&lt;/p&gt;

&lt;p&gt;R requires only a few lines to implement this. Are there any libraries (or tools) that would allow a similarly simple (and efficient if possible) implementation in Java or Scala?&lt;/p&gt;

&lt;p&gt;PD: This is a specific use case for a &lt;a href="http://datascience.stackexchange.com/questions/948/any-clear-winner-for-data-science-in-scala"&gt;previous (too broad) question&lt;/a&gt;.&lt;/p&gt;
', 'tools  visualization  scala  csv', NULL, '''/a'':23C,40C,120C,186C ''/elementary-statistics/numerical-measures/correlation-coefficient'':117C ''/p'':85C,134C,166C,187C ''/program/r/readcsv.php'':38C ''/questions/948/any-clear-winner-for-data-science-in-scala'':181C ''/r-manual/r-devel/library/base/html/summary.html'':21C ''3'':101C,105C,122C,126C ''allow'':153C ''also'':89C ''broad'':184C ''case'':174C ''coeffici'':119C ''contain'':54C ''content'':28C ''cool'':82C ''correl'':112C,118C,133C ''csv'':5A,11B,31C,67C ''data'':64C,98C ''datascience.stackexchange.com'':180C ''datascience.stackexchange.com/questions/948/any-clear-winner-for-data-science-in-scala'':179C ''deviat'':79C ''dimens'':76C ''effici'':158C ''exampl'':100C ''file'':68C ''generat'':92C ''href'':18C,35C,114C,178C ''implement'':143C,161C ''integ'':72C ''java'':163C ''java/scala'':7A ''least'':132C ''librari'':148C ''like'':15C,90C ''line'':141C ''load'':39C ''max'':61C ''mean'':57C ''median'':58C ''min'':59C ''number'':75C ''numer'':71C ''p'':12C,86C,135C,167C ''pair'':106C,127C ''pd'':168C ''plot'':94C,102C,123C ''possibl'':32C,160C ''previous'':182C ''problem'':50C ''quartil'':56C ''question'':185C ''r'':26C,136C ''real'':74C ''requir'':137C ''scala'':10B,165C ''similar'':155C ''simpl'':156C ''somewher'':45C ''specif'':172C ''standard'':78C ''stat.ethz.ch'':20C ''stat.ethz.ch/r-manual/r-devel/library/base/html/summary.html'':19C ''store'':43C ''summar'':1A,22C ''summari'':52C ''tool'':8B,150C ''use'':173C ''variabl'':108C,129C ''visual'':3A,9B,96C ''well'':84C ''would'':14C,80C,88C,152C ''www.endmemo.com'':37C ''www.endmemo.com/program/r/readcsv.php'':36C ''www.r-tutor.com'':116C ''www.r-tutor.com/elementary-statistics/numerical-measures/correlation-coefficient'':115C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1054, 2, '2014-08-28 06:49:05.21', 5, NULL, NULL, '&lt;p&gt;There are several reasons why you are getting erroneous results.
First, you should consider using log likelihood instead of likelihood. There are numerical issues with multiplying many small numbers(imagine if you had millions of samples you had to multiply millions of small numbers for the lhd). Also taking gradients for optimization methods that require gradients is often easier when you are dealing with the log likelihood. In general, it is good to have an objective which is a sum rather than a product of variables when dealing with optimization problems.&lt;/p&gt;

&lt;p&gt;Second, fmin is using Nelder-Mead simplex algorithm which has no convergence guarantees according to &lt;a href="http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin.html#scipy.optimize.fmin"&gt;scipy documentation&lt;/a&gt;. This means the convergence is totally random and you should not expect to find parameters close to the originals. To get around this, I would suggest you to use a gradient based method like stochastic gradient descent or BFGS. Since you know the generative model (rvs are Gaussian distributed) you can write the likelihood and log likelihood as:
&lt;img src="http://i.stack.imgur.com/bfsvr.png" alt="equations"&gt;&lt;/p&gt;

&lt;p&gt;Where a,b,c and d are your model parameters 5,2,3 and 4 respectively.
Then take the &lt;a href="http://en.wikipedia.org/wiki/Gradient"&gt;gradient&lt;/a&gt; with respect to [a,b,c,d] and feed that into prime input of fmin_bfgs. Note that due to varying variance what could be solved by just linear regression is now a nastier problem.&lt;/p&gt;

&lt;p&gt;Finally, you may also want to check Generalized least squares on &lt;a href="http://en.wikipedia.org/wiki/Linear_regression#Least-squares_estimation_and_related_techniques"&gt;http://en.wikipedia.org/wiki/Linear_regression#Least-squares_estimation_and_related_techniques&lt;/a&gt; and &lt;a href="http://en.wikipedia.org/wiki/Heteroscedasticity"&gt;http://en.wikipedia.org/wiki/Heteroscedasticity&lt;/a&gt;, which talk about your problem and offer several available solutions.&lt;/p&gt;

&lt;p&gt;Good luck!&lt;/p&gt;
', NULL, NULL, '''/a'':118C,211C ''/bfsvr.png'':181C ''/doc/scipy/reference/generated/scipy.optimize.fmin.html#scipy.optimize.fmin'':115C ''/p'':93C,184C,247C,287C,291C ''/wiki/gradient'':209C ''/wiki/heteroscedasticity'':273C ''/wiki/heteroscedasticity&lt;/a&gt;,'':276C ''/wiki/linear_regression#least-squares_estimation_and_related_techniques'':264C ''/wiki/linear_regression#least-squares_estimation_and_related_techniques&lt;/a&gt;'':267C ''2'':197C ''3'':198C ''4'':200C ''5'':196C ''accord'':109C ''algorithm'':103C ''also'':49C,252C ''alt'':182C ''around'':140C ''avail'':285C ''b'':188C,216C ''base'':150C ''bfgs'':157C,227C ''c'':189C,217C ''check'':255C ''close'':134C ''consid'':15C ''converg'':107C,122C ''could'':235C ''d'':191C,218C ''deal'':64C,89C ''descent'':155C ''distribut'':167C ''docs.scipy.org'':114C ''docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin.html#scipy.optimize.fmin'':113C ''document'':117C ''due'':230C ''easier'':60C ''en.wikipedia.org'':208C,263C,266C,272C,275C ''en.wikipedia.org/wiki/gradient'':207C ''en.wikipedia.org/wiki/heteroscedasticity'':271C ''en.wikipedia.org/wiki/heteroscedasticity&lt;/a&gt;,'':274C ''en.wikipedia.org/wiki/linear_regression#least-squares_estimation_and_related_techniques'':262C ''en.wikipedia.org/wiki/linear_regression#least-squares_estimation_and_related_techniques&lt;/a&gt;'':265C ''equat'':183C ''erron'':10C ''expect'':130C ''feed'':220C ''final'':249C ''find'':132C ''first'':12C ''fmin'':96C,226C ''gaussian'':166C ''general'':70C,256C ''generat'':162C ''get'':9C,139C ''good'':73C,289C ''gradient'':51C,57C,149C,154C,210C ''guarante'':108C ''href'':112C,206C,261C,270C ''i.stack.imgur.com'':180C ''i.stack.imgur.com/bfsvr.png'':179C ''imagin'':31C ''img'':177C ''input'':224C ''instead'':19C ''issu'':25C ''know'':160C ''least'':257C ''lhd'':48C ''like'':152C ''likelihood'':18C,21C,68C,172C,175C ''linear'':240C ''log'':17C,67C,174C ''luck'':290C ''mani'':28C ''may'':251C ''mead'':101C ''mean'':120C ''method'':54C,151C ''million'':35C,42C ''model'':163C,194C ''multipli'':27C,41C ''nastier'':245C ''nelder'':100C ''nelder-mead'':99C ''note'':228C ''number'':30C,45C ''numer'':24C ''object'':77C ''offer'':283C ''often'':59C ''optim'':53C,91C ''origin'':137C ''p'':1C,94C,185C,248C,288C ''paramet'':133C,195C ''prime'':223C ''problem'':92C,246C,281C ''product'':85C ''random'':125C ''rather'':82C ''reason'':5C ''regress'':241C ''requir'':56C ''respect'':201C,213C ''result'':11C ''rvs'':164C ''sampl'':37C ''scipi'':116C ''second'':95C ''sever'':4C,284C ''simplex'':102C ''sinc'':158C ''small'':29C,44C ''solut'':286C ''solv'':237C ''squar'':258C ''src'':178C ''stochast'':153C ''suggest'':144C ''sum'':81C ''take'':50C,203C ''talk'':278C ''total'':124C ''use'':16C,98C,147C ''vari'':232C ''variabl'':87C ''varianc'':233C ''want'':253C ''would'':143C ''write'':170C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1056, 2, '2014-08-28 16:56:45.577', 1, NULL, NULL, '&lt;p&gt;It sounds as if you want to use unsupervized learning to create a training set. Am I right? You use your cluster analysis to determine which docs come from UK, US or Oz -- or which docs are talking about Soccer, Football or Australian football respectively? Then feed those tagged docs into a supervized learning algorithm of some sort?&lt;/p&gt;

&lt;p&gt;How well this works will depend entirely on how well you can distinguish UK, US and OZ. I would have thought it would be fairly straightforward to find documents where national origin was known, so that you could build a supervized algorithm for detecting language variant. You wouldn''t even need a corpus that talked about football, since dialectical differences show up in other ways that are subject matter independent. (For example, I am clearly from North America, since I just wrote "in ways that are subject matter independent" rather than "Since dialectical differences do not depend on subject matter").&lt;/p&gt;

&lt;p&gt;However, the answer to your question, "can I use unsupervized learning and then supervized learning" is No, if you are looking for supervized learning. If the results of an unsupervized learning algorithm are fed to a supervized learning algorithm, the net result is unsupervized --- there are still no grown-ups in the room. And the classification errors of the resulting process will contain error terms from both stages. You won''t get the same performance as you would if you did a SVM with properly tagged training data. This doesn''t mean you shouldn''t use the method you propose ... it might still work well ... but it won''t be a supervized learning algorithm.&lt;/p&gt;
', NULL, NULL, '''/p'':60C,162C,279C ''algorithm'':56C,103C,195C,202C,278C ''america'':139C ''analysi'':24C ''answer'':166C ''australian'':44C ''build'':100C ''classif'':220C ''clear'':136C ''cluster'':23C ''come'':29C ''contain'':227C ''corpus'':114C ''could'':99C ''creat'':13C ''data'':252C ''depend'':67C,158C ''detect'':105C ''determin'':26C ''dialect'':120C,154C ''differ'':121C,155C ''distinguish'':74C ''doc'':28C,37C,51C ''document'':90C ''doesn'':254C ''entir'':68C ''error'':221C,228C ''even'':111C ''exampl'':133C ''fair'':86C ''fed'':197C ''feed'':48C ''find'':89C ''footbal'':42C,45C,118C ''get'':236C ''grown'':213C ''grown-up'':212C ''howev'':164C ''independ'':131C,150C ''known'':95C ''languag'':106C ''learn'':11C,55C,174C,178C,187C,194C,201C,277C ''look'':184C ''matter'':130C,149C,161C ''mean'':256C ''method'':262C ''might'':266C ''nation'':92C ''need'':112C ''net'':204C ''north'':138C ''origin'':93C ''oz'':34C,78C ''p'':1C,61C,163C ''perform'':239C ''process'':225C ''proper'':249C ''propos'':264C ''question'':169C ''rather'':151C ''respect'':46C ''result'':190C,205C,224C ''right'':19C ''room'':217C ''set'':16C ''shouldn'':258C ''show'':122C ''sinc'':119C,140C,153C ''soccer'':41C ''sort'':59C ''sound'':3C ''stage'':232C ''still'':210C,267C ''straightforward'':87C ''subject'':129C,148C,160C ''superv'':54C,102C,177C,186C,200C,276C ''svm'':247C ''tag'':50C,250C ''talk'':39C,116C ''term'':229C ''thought'':82C ''train'':15C,251C ''uk'':31C,75C ''unsuperv'':10C,173C,193C,207C ''up'':214C ''us'':32C,76C ''use'':9C,21C,172C,260C ''variant'':107C ''want'':7C ''way'':126C,145C ''well'':63C,71C,269C ''won'':234C,272C ''work'':65C,268C ''would'':80C,84C,242C ''wouldn'':109C ''wrote'':143C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1057, 2, '2014-08-28 18:53:57.297', 1, NULL, NULL, '&lt;p&gt;You might want to try this book &lt;a href="http://shop.oreilly.com/product/0636920030195.do" rel="nofollow"&gt;Mining the Social Web&lt;/a&gt; for an overview of different techniques. Obviously, the methods you need will depend on the use case. A lot of people do interesting things with graphs, displaying relationships between users, with respect to certain topics. Or you might simply to a timeline showing how a news topic builds in interest and wanes.&lt;/p&gt;
', NULL, NULL, '''/a'':20C ''/p'':73C ''/product/0636920030195.do'':13C ''book'':8C ''build'':68C ''case'':37C ''certain'':54C ''depend'':33C ''differ'':25C ''display'':47C ''graph'':46C ''href'':10C ''interest'':43C,70C ''lot'':39C ''method'':29C ''might'':3C,58C ''mine'':16C ''need'':31C ''news'':66C ''nofollow'':15C ''obvious'':27C ''overview'':23C ''p'':1C ''peopl'':41C ''rel'':14C ''relationship'':48C ''respect'':52C ''shop.oreilly.com'':12C ''shop.oreilly.com/product/0636920030195.do'':11C ''show'':63C ''simpli'':59C ''social'':18C ''techniqu'':26C ''thing'':44C ''timelin'':62C ''topic'':55C,67C ''tri'':6C ''use'':36C ''user'':50C ''wane'':72C ''want'':4C ''web'':19C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1058, 1, '2014-08-29 01:36:36.32', -2, 78, 'How much data space is used by all scientific articles?', '&lt;p&gt;I was wondering if there is any research or study made to calculate the volume of space is used by all scientific articles. It could be in pdf, txt, compressed, or any other format. Is there even a way to measure it?&lt;/p&gt;

&lt;p&gt;Can some one point me towards realizing this study?&lt;/p&gt;

&lt;p&gt;Regards and thanks.&lt;/p&gt;
', 'bigdata  research', NULL, '''/p'':56C,67C,72C ''articl'':10A,36C ''bigdata'':11B ''calcul'':26C ''compress'':43C ''could'':38C ''data'':3A ''even'':50C ''format'':47C ''made'':24C ''measur'':54C ''much'':2A ''one'':60C ''p'':13C,57C,68C ''pdf'':41C ''point'':61C ''realiz'':64C ''regard'':69C ''research'':12B,21C ''scientif'':9A,35C ''space'':4A,30C ''studi'':23C,66C ''thank'':71C ''toward'':63C ''txt'':42C ''use'':6A,32C ''volum'':28C ''way'':52C ''wonder'':16C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1061, 1, '2014-08-29 11:40:28.657', 1, 21, 'Apps to manage/host data sets', '&lt;p&gt;I''m looking for the best solution to manage and host datasets for journalistic pursuits. I am assessing &lt;a href="https://www.documentcloud.org" rel="nofollow"&gt;https://www.documentcloud.org&lt;/a&gt; and &lt;a href="http://datahub.io/" rel="nofollow"&gt;http://datahub.io/&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Can anyone explain the differences between them, or recommend a superior solution?&lt;/p&gt;
', 'dataset  optimization', NULL, '''/&lt;/a&gt;.&lt;/p&gt;'':42C ''/a'':33C ''/p'':56C ''anyon'':45C ''app'':1A ''assess'':26C ''best'':14C ''data'':4A ''datahub.io'':37C,41C ''datahub.io/&lt;/a&gt;.&lt;/p&gt;'':40C ''dataset'':6B,20C ''differ'':48C ''explain'':46C ''host'':19C ''href'':28C,36C ''journalist'':22C ''look'':11C ''m'':10C ''manag'':17C ''manage/host'':3A ''nofollow'':31C,39C ''optim'':7B ''p'':8C,43C ''pursuit'':23C ''recommend'':52C ''rel'':30C,38C ''set'':5A ''solut'':15C,55C ''superior'':54C ''www.documentcloud.org'':29C,32C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1099, 2, '2014-09-10 08:15:17.343', 10, NULL, NULL, '&lt;p&gt;No, despite their names they are not equivalent or even that similar. Gini impurity is a measure of misclassification, which applies in a multiclass classifier context. Gini coefficient applies to binary classification, and requires a classifier that can in some way rank examples according to likelihood of being in the positive class. Both could be applied in some cases, but they are different measures for different things. Impurity is what is commonly used in decision trees.&lt;/p&gt;
', NULL, NULL, '''/p'':78C ''accord'':45C ''appli'':22C,30C,57C ''binari'':32C ''case'':60C ''class'':53C ''classif'':33C ''classifi'':26C,37C ''coeffici'':29C ''common'':73C ''context'':27C ''could'':55C ''decis'':76C ''despit'':3C ''differ'':64C,67C ''equival'':9C ''even'':11C ''exampl'':44C ''gini'':14C,28C ''impur'':15C,69C ''likelihood'':47C ''measur'':18C,65C ''misclassif'':20C ''multiclass'':25C ''name'':5C ''p'':1C ''posit'':52C ''rank'':43C ''requir'':35C ''similar'':13C ''thing'':68C ''tree'':77C ''use'':74C ''way'':42C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1062, 2, '2014-08-29 16:19:06.903', 3, NULL, NULL, '&lt;p&gt;You can definitely try to first cluster your data, and then try to see if the cluster information helps your classification task.&lt;/p&gt;

&lt;p&gt;For example if your data looked like this (in 1D):&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;AA A AA A A      BBB B B B BB BB BB      AA AA A A AAA
&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;then it may be reasonable to run a clustering algorithm on each class, to obtain &lt;em&gt;two different kinds of A&lt;/em&gt;, and learn two separate classifiers for A1 and A2, and just drop the cluster distinction for the final output.&lt;/p&gt;

&lt;p&gt;Other common unsupervised techniques used include PCA.&lt;/p&gt;

&lt;p&gt;As for your football example, the problem is that the unsupervised algorithm does not know what it should be looking for. Instead of learning to separate american football and soccer, it may just as well decide to cluster on international vs. national games. Or Europe vs. U.S.; which may look like it learned about american football and soccer at first, but it put american soccer into the same cluster as american football, and american football teams in Europe into the Europe cluster... because it does not have &lt;em&gt;guidance&lt;/em&gt; on what structure you are interested in; and the continents &lt;em&gt;are&lt;/em&gt; a valid structure, too!&lt;/p&gt;

&lt;p&gt;So usually, I would not blindly assume that unsupervised techniques yield a distrinction that matches your desired result. They can yield &lt;em&gt;any&lt;/em&gt; kind of structure, and you will want to carefully inspect what they found before using it. If you use it blindly, make sure you spend enough time on evaluation (e.g. if the clustering improves your classifier performance, then it probably worked as intended ...)&lt;/p&gt;
', NULL, NULL, '''/code'':56C ''/em'':80C,200C,213C,243C ''/p'':24C,36C,100C,109C,218C,287C ''/pre'':57C ''1d'':35C ''a1'':87C ''a2'':89C ''aa'':39C,41C,51C,52C ''aaa'':55C ''algorithm'':68C,122C ''american'':137C,165C,174C,181C,184C ''assum'':226C ''b'':45C,46C,47C ''bb'':48C,49C,50C ''bbb'':44C ''blind'':225C,264C ''care'':252C ''class'':71C ''classif'':22C ''classifi'':85C,279C ''cluster'':8C,18C,67C,94C,148C,179C,192C,276C ''code'':38C ''common'':103C ''contin'':210C ''data'':10C,30C ''decid'':146C ''definit'':4C ''desir'':236C ''differ'':76C ''distinct'':95C ''distrinct'':232C ''drop'':92C ''e.g'':273C ''em'':74C,198C,211C,241C ''enough'':269C ''europ'':155C,188C,191C ''evalu'':272C ''exampl'':27C,115C ''final'':98C ''first'':7C,170C ''footbal'':114C,138C,166C,182C,185C ''found'':256C ''game'':153C ''guidanc'':199C ''help'':20C ''improv'':277C ''includ'':107C ''inform'':19C ''inspect'':253C ''instead'':132C ''intend'':286C ''interest'':206C ''intern'':150C ''kind'':77C,244C ''know'':125C ''learn'':82C,134C,163C ''like'':32C,161C ''look'':31C,130C,160C ''make'':265C ''match'':234C ''may'':61C,142C,159C ''nation'':152C ''obtain'':73C ''output'':99C ''p'':1C,25C,58C,101C,110C,219C ''pca'':108C ''perform'':280C ''pre'':37C ''probabl'':283C ''problem'':117C ''put'':173C ''reason'':63C ''result'':237C ''run'':65C ''see'':15C ''separ'':84C,136C ''soccer'':140C,168C,175C ''spend'':268C ''structur'':203C,216C,246C ''sure'':266C ''task'':23C ''team'':186C ''techniqu'':105C,229C ''time'':270C ''tri'':5C,13C ''two'':75C,83C ''u.s'':157C ''unsupervis'':104C,121C,228C ''use'':106C,258C,262C ''usual'':221C ''valid'':215C ''vs'':151C,156C ''want'':250C ''well'':145C ''work'':284C ''would'':223C ''yield'':230C,240C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1063, 2, '2014-08-30 17:05:23.197', 1, NULL, NULL, '&lt;p&gt;If your data is numeric, try loading it into ELKI (Java). With the &lt;code&gt;NullAlgorithm&lt;/code&gt; it will give you scatterplots, histograms and parallel coordinate plots. It''s fast in reading the data; only the current Apache Batik-based visualization is slooow because it''s using SVG. :-( I''m mostly using it "headless".&lt;/p&gt;

&lt;p&gt;It also has classes for various statistics (including higher order moments on data streams), but I havn''t seen them in the default UI yet.&lt;/p&gt;
', NULL, NULL, '''/code'':17C ''/p'':56C,83C ''also'':59C ''apach'':38C ''base'':41C ''batik'':40C ''batik-bas'':39C ''class'':61C ''code'':15C ''coordin'':26C ''current'':37C ''data'':4C,34C,70C ''default'':80C ''elki'':11C ''fast'':30C ''give'':20C ''havn'':74C ''headless'':55C ''higher'':66C ''histogram'':23C ''includ'':65C ''java'':12C ''load'':8C ''m'':51C ''moment'':68C ''most'':52C ''nullalgorithm'':16C ''numer'':6C ''order'':67C ''p'':1C,57C ''parallel'':25C ''plot'':27C ''read'':32C ''scatterplot'':22C ''seen'':76C ''slooow'':44C ''statist'':64C ''stream'':71C ''svg'':49C ''tri'':7C ''ui'':81C ''use'':48C,53C ''various'':63C ''visual'':42C ''yet'':82C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1075, 1, '2014-09-04 18:13:57.343', 4, 146, 'Hadoop for grid computing', '&lt;p&gt;Background:
I run a product that compares sets of data (data matching and data reconciliation).
To get the result we need to compare each row in a data set with every N rows on the opposing data set
Now however we get sets of up to 300 000 rows of data in each set to compare and are getting 90 Billion computations to handle.&lt;/p&gt;

&lt;p&gt;So my question is this:
Even though we dont have the data volumes to use Hadoop, we have the computational need for something distributed. Is Hadoop a good choice for us?&lt;/p&gt;
', 'hadoop', NULL, '''/p'':71C,104C ''000'':54C ''300'':53C ''90'':66C ''background'':7C ''billion'':67C ''choic'':101C ''compar'':13C,29C,62C ''comput'':4A,68C,92C ''data'':16C,17C,20C,34C,43C,57C,84C ''distribut'':96C ''dont'':81C ''even'':78C ''everi'':37C ''get'':23C,48C,65C ''good'':100C ''grid'':3A ''hadoop'':1A,5B,88C,98C ''handl'':70C ''howev'':46C ''match'':18C ''n'':38C ''need'':27C,93C ''oppos'':42C ''p'':6C,72C ''product'':11C ''question'':75C ''reconcili'':21C ''result'':25C ''row'':31C,39C,55C ''run'':9C ''set'':14C,35C,44C,49C,60C ''someth'':95C ''though'':79C ''us'':103C ''use'':87C ''volum'':85C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1064, 2, '2014-08-30 18:14:05.577', 2, NULL, NULL, '&lt;p&gt;MapReduce is not used in searching. It was used a long time ago to build the index; but it is a batch processing framework, and most of the web does not change all the time, so the newer architectures are all &lt;em&gt;incremental&lt;/em&gt; instead of batch oriented.&lt;/p&gt;

&lt;p&gt;Search in Google will largely work the same it works in Lucene and Elastic Search, except for a lot of fine tuned extra weighting and optimizations. But at the very heart, they will use some form of an &lt;strong&gt;inverted index&lt;/strong&gt;. In other words, they do &lt;em&gt;not&lt;/em&gt; search several terabytes when you enter a search query (even when it is not cached). They likely don''t look at the actual documents at all. But they use a lookup table that lists which documents match your query term (with stemming, misspellings, synonyms etc. all preprocessed). They probably retrieve the &lt;em&gt;list&lt;/em&gt; of the top 10000 documents for each word (10k integers - just a few kb!) and compute the best matches from that. Only if there aren''t good matches in these lists, they expand to the next such blocks etc.&lt;/p&gt;

&lt;p&gt;Queries for common words can be easily cached; and via preprocessing you can build a list of the top 10k results and then rerank them according to the user profile. There is nothing to be gained by computing an "exact" answer, too. Looking at the top 10k results is likely enough; there is no correct answer; and if a better result somewhere at position 10001 is missed, nobody will know or notice (or care). It likely was already ranked down in preprocessing and would not have made it into the top 10 that is presented to the user at the end (or the top 3, the user actually looks at)&lt;/p&gt;

&lt;p&gt;Rare terms on the other hand aren''t much of a challenge either - one of the lists only contains a few matching documents, and you can immediately discard all others.&lt;/p&gt;

&lt;p&gt;I recommend reading this article:&lt;/p&gt;

&lt;blockquote&gt;
  &lt;p&gt;&lt;strong&gt;The Anatomy of a Large-Scale Hypertextual Web Search Engine&lt;/strong&gt;&lt;br /&gt;
  Sergey Brin and Lawrence Page&lt;br /&gt;
  Computer Science Department, Stanford University, Stanford, CA 94305&lt;br /&gt;
  &lt;a href="http://infolab.stanford.edu/~backrub/google.html" rel="nofollow"&gt;http://infolab.stanford.edu/~backrub/google.html&lt;/a&gt;&lt;/p&gt;
&lt;/blockquote&gt;

&lt;p&gt;And yes, that''s the Google founders who wrote this. It''s not the latest state, but it will already work at a pretty large scale.&lt;/p&gt;
', NULL, NULL, '''/blockquote'':388C ''/em'':45C,101C,155C ''/p'':50C,195C,307C,339C,346C,416C ''/strong'':93C,361C ''/~backrub/google.html'':382C ''/~backrub/google.html&lt;/a&gt;&lt;/p&gt;'':387C ''10'':288C ''10000'':159C ''10001'':261C ''10k'':164C,216C,243C ''3'':301C ''94305'':376C ''accord'':222C ''actual'':124C,304C ''ago'':14C ''alreadi'':274C,409C ''anatomi'':351C ''answer'':237C,252C ''architectur'':40C ''aren'':180C,315C ''articl'':345C ''batch'':23C,48C ''best'':173C ''better'':256C ''block'':193C ''blockquot'':347C ''br'':362C,368C,377C ''brin'':364C ''build'':16C,210C ''ca'':375C ''cach'':116C,204C ''care'':270C ''challeng'':320C ''chang'':33C ''common'':199C ''comput'':171C,234C,369C ''contain'':327C ''correct'':251C ''depart'':371C ''discard'':336C ''document'':125C,137C,160C,331C ''easili'':203C ''either'':321C ''elast'':65C ''em'':43C,99C,153C ''end'':297C ''engin'':360C ''enough'':247C ''enter'':107C ''etc'':146C,194C ''even'':111C ''exact'':236C ''except'':67C ''expand'':188C ''extra'':74C ''fine'':72C ''form'':87C ''founder'':396C ''framework'':25C ''gain'':232C ''good'':182C ''googl'':54C,395C ''hand'':314C ''heart'':82C ''href'':379C ''hypertextu'':357C ''immedi'':335C ''increment'':44C ''index'':18C,92C ''infolab.stanford.edu'':381C,386C ''infolab.stanford.edu/~backrub/google.html'':380C ''infolab.stanford.edu/~backrub/google.html&lt;/a&gt;&lt;/p&gt;'':385C ''instead'':46C ''integ'':165C ''invert'':91C ''kb'':169C ''know'':266C ''larg'':56C,355C,414C ''large-scal'':354C ''latest'':404C ''lawrenc'':366C ''like'':118C,246C,272C ''list'':135C,154C,186C,212C,325C ''long'':12C ''look'':121C,239C,305C ''lookup'':132C ''lot'':70C ''lucen'':63C ''made'':283C ''mapreduc'':2C ''match'':138C,174C,183C,330C ''miss'':263C ''misspel'':144C ''much'':317C ''newer'':39C ''next'':191C ''nobodi'':264C ''nofollow'':384C ''noth'':229C ''notic'':268C ''one'':322C ''optim'':77C ''orient'':49C ''other'':338C ''p'':1C,51C,196C,308C,340C,348C,389C ''page'':367C ''posit'':260C ''preprocess'':148C,207C,278C ''present'':291C ''pretti'':413C ''probabl'':150C ''process'':24C ''profil'':226C ''queri'':110C,140C,197C ''rank'':275C ''rare'':309C ''read'':343C ''recommend'':342C ''rel'':383C ''rerank'':220C ''result'':217C,244C,257C ''retriev'':151C ''scale'':356C,415C ''scienc'':370C ''search'':7C,52C,66C,102C,109C,359C ''sergey'':363C ''sever'':103C ''somewher'':258C ''stanford'':372C,374C ''state'':405C ''stem'':143C ''strong'':90C,349C ''synonym'':145C ''tabl'':133C ''terabyt'':104C ''term'':141C,310C ''time'':13C,36C ''top'':158C,215C,242C,287C,300C ''tune'':73C ''univers'':373C ''use'':5C,10C,85C,130C ''user'':225C,294C,303C ''via'':206C ''web'':30C,358C ''weight'':75C ''word'':96C,163C,200C ''work'':57C,61C,410C ''would'':280C ''wrote'':398C ''yes'':391C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1073, 1, '2014-09-02 19:17:43.21', 8, 630, 'Libraries for Online Machine Learning', '&lt;p&gt;I am looking for packages (either in python, R, or a standalone package) to perform online learning to predict stock data.&lt;/p&gt;

&lt;p&gt;I have found and read about Vowpal Wabbit (&lt;a href="https://github.com/JohnLangford/vowpal_wabbit/wiki"&gt;https://github.com/JohnLangford/vowpal_wabbit/wiki&lt;/a&gt;),
which seems to be quite promising but I am wondering if there are any other packages out there.&lt;/p&gt;

&lt;p&gt;Thanks in advance.&lt;/p&gt;
', 'machine-learning  online-learning', NULL, '''/johnlangford/vowpal_wabbit/wiki'':48C ''/johnlangford/vowpal_wabbit/wiki&lt;/a&gt;),'':51C ''/p'':34C,70C,75C ''advanc'':74C ''data'':33C ''either'':18C ''found'':38C ''github.com'':47C,50C ''github.com/johnlangford/vowpal_wabbit/wiki'':46C ''github.com/johnlangford/vowpal_wabbit/wiki&lt;/a&gt;),'':49C ''href'':45C ''learn'':5A,8B,11B,29C ''librari'':1A ''look'':15C ''machin'':4A,7B ''machine-learn'':6B ''onlin'':3A,10B,28C ''online-learn'':9B ''p'':12C,35C,71C ''packag'':17C,25C,67C ''perform'':27C ''predict'':31C ''promis'':57C ''python'':20C ''quit'':56C ''r'':21C ''read'':40C ''seem'':53C ''standalon'':24C ''stock'':32C ''thank'':72C ''vowpal'':42C ''wabbit'':43C ''wonder'':61C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1066, 2, '2014-09-01 00:01:58.917', 1, NULL, NULL, '&lt;p&gt;Perhaps you are looking to quantify the amount of filespace used by a specific subset of data that we will label as "academic publications." &lt;/p&gt;

&lt;p&gt;Well, to estimate, you could find stats on how many publications are housed at all the leading libraries (JSTOR, EBSCO, AcademicHost, etc) and then get the mean average size of each. Multiply that by the number of articles and whamo, you''ve got yourself an estimate.&lt;/p&gt;

&lt;p&gt;Here''s the problem, though: PDF files store the text from string &lt;code&gt;s&lt;/code&gt; differently (in size) than, say, a text document stores that same string. Likewise, a compressed JPEG will store an amount of information &lt;code&gt;i&lt;/code&gt; differently than a non-compressed JPEG. So you see we could have two of the same articles containing the same information &lt;code&gt;i&lt;/code&gt; but taking up different amounts of memory &lt;code&gt;m&lt;/code&gt;.&lt;/p&gt;

&lt;p&gt;Are you looking to get a wordcount on the amount of scientific literature?&lt;/p&gt;

&lt;p&gt;Are you looking to get an approximation of file system space used to store all academically published content in the world? &lt;/p&gt;
', NULL, NULL, '''/code'':90C,115C,140C,150C ''/p'':26C,74C,151C,166C,189C ''academ'':24C,183C ''academichost'':48C ''amount'':9C,110C,145C,162C ''approxim'':174C ''articl'':65C,133C ''averag'':55C ''code'':88C,113C,138C,148C ''compress'':105C,121C ''contain'':134C ''content'':185C ''could'':32C,127C ''data'':18C ''differ'':91C,116C,144C ''document'':98C ''ebsco'':47C ''estim'':30C,73C ''etc'':49C ''file'':82C,176C ''filespac'':11C ''find'':33C ''get'':52C,157C,172C ''got'':70C ''hous'':40C ''inform'':112C,137C ''jpeg'':106C,122C ''jstor'':46C ''label'':22C ''lead'':44C ''librari'':45C ''likewis'':103C ''literatur'':165C ''look'':5C,155C,170C ''m'':149C ''mani'':37C ''mean'':54C ''memori'':147C ''multipli'':59C ''non'':120C ''non-compress'':119C ''number'':63C ''p'':1C,27C,75C,152C,167C ''pdf'':81C ''perhap'':2C ''problem'':79C ''public'':25C,38C ''publish'':184C ''quantifi'':7C ''say'':95C ''scientif'':164C ''see'':125C ''size'':56C,93C ''space'':178C ''specif'':15C ''stat'':34C ''store'':83C,99C,108C,181C ''string'':87C,102C ''subset'':16C ''system'':177C ''take'':142C ''text'':85C,97C ''though'':80C ''two'':129C ''use'':12C,179C ''ve'':69C ''well'':28C ''whamo'':67C ''wordcount'':159C ''world'':188C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1067, 2, '2014-09-01 00:17:56.07', 0, NULL, NULL, '&lt;p&gt;It is likely to be very hard to draw any conclusion if you are training with only 10 input samples.  With more data, your diagnosis that the model is predicting lagged values would have more plausibility.  As it stands, it seems pretty likely that your model is just saying that the last observed value is pretty close to correct.  This isn''t the same as a real lag model, but it is a very reasonable thing to guess if you haven''t seen enough data.&lt;/p&gt;
', NULL, NULL, '''/p'':87C ''10'':19C ''close'':58C ''conclus'':12C ''correct'':60C ''data'':24C,86C ''diagnosi'':26C ''draw'':10C ''enough'':85C ''guess'':79C ''hard'':8C ''haven'':82C ''input'':20C ''isn'':62C ''lag'':32C,69C ''last'':53C ''like'':4C,44C ''model'':29C,47C,70C ''observ'':54C ''p'':1C ''plausibl'':37C ''predict'':31C ''pretti'':43C,57C ''real'':68C ''reason'':76C ''sampl'':21C ''say'':50C ''seem'':42C ''seen'':84C ''stand'':40C ''thing'':77C ''train'':16C ''valu'':33C,55C ''would'':34C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1068, 2, '2014-09-01 08:10:56.967', 0, NULL, NULL, '&lt;p&gt;I''d have a closer look at one of Apache Spark''s modules: &lt;a href="https://spark.apache.org/mllib/" rel="nofollow" title="MLlib"&gt;MLlib&lt;/a&gt;.&lt;/p&gt;
', NULL, NULL, '''/a'':25C ''/mllib/'':19C ''/p'':26C ''apach'':11C ''closer'':6C ''d'':3C ''href'':16C ''look'':7C ''mllib'':23C,24C ''modul'':14C ''nofollow'':21C ''one'':9C ''p'':1C ''rel'':20C ''spark'':12C ''spark.apache.org'':18C ''spark.apache.org/mllib/'':17C ''titl'':22C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1120, 2, '2014-09-15 18:14:16.18', 2, NULL, NULL, '&lt;p&gt;This may provide some answer: &lt;a href="http://cran.r-project.org/web/packages/caret/vignettes/caret.pdf" rel="nofollow"&gt;http://cran.r-project.org/web/packages/caret/vignettes/caret.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;You may also check out Max Kuhn''s "Applied Predictive Modeling" book. He talks about the caret package at length in this book, including the kappa statistics and how to use it. This may be of some help to you.&lt;/p&gt;
', NULL, NULL, '''/p'':58C ''/web/packages/caret/vignettes/caret.pdf'':11C ''/web/packages/caret/vignettes/caret.pdf&lt;/a&gt;&lt;/p&gt;'':16C ''also'':20C ''answer'':6C ''appli'':26C ''book'':29C,40C ''caret'':34C ''check'':21C ''cran.r-project.org'':10C,15C ''cran.r-project.org/web/packages/caret/vignettes/caret.pdf'':9C ''cran.r-project.org/web/packages/caret/vignettes/caret.pdf&lt;/a&gt;&lt;/p&gt;'':14C ''help'':55C ''href'':8C ''includ'':41C ''kappa'':43C ''kuhn'':24C ''length'':37C ''max'':23C ''may'':3C,19C,51C ''model'':28C ''nofollow'':13C ''p'':1C,17C ''packag'':35C ''predict'':27C ''provid'':4C ''rel'':12C ''statist'':44C ''talk'':31C ''use'':48C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1069, 1, '2014-09-02 03:24:12.793', 5, 72, 'Kappa From Combined Confusion Matrices', '&lt;p&gt;I am trying to evaluate and compare several different machine learning models built with different parameters (i.e. downsampling, outlier removal) and different classifiers (i.e. Bayes Net, SVM, Decision Tree).&lt;/p&gt;

&lt;p&gt;I am performing a type of cross validation where I randomly select 67% of the data for use in the training set and 33% of the data for use in the testing set. I perform this for several iterations, say, 20.&lt;/p&gt;

&lt;p&gt;Now, from each iteration I am able to generate a confusion matrix and compute a kappa. My question is, what are some ways to aggregate these across the iterations? I am also interested in aggregating accuracy and expected accuracy, among other things.&lt;/p&gt;

&lt;p&gt;For the kappa, accuracy, and expected accuracy, I have just been taking the average up to this point. One of the problems is that when I recompute kappa with the aggregated average and expected average, it is not the same with the aggregated kappa.&lt;/p&gt;

&lt;p&gt;For the confusion matrix, I have been first normalizing the confusion matrix from each iteration and then averaging them, in an attempt to avoid an issue of confusion matrices with different numbers of total cases (which is possible with my cross validation scheme).&lt;/p&gt;

&lt;p&gt;When I recompute the kappa from this aggregated confusion matrix, it is also different from the previous two.&lt;/p&gt;

&lt;p&gt;Which one is most correct? Is there another way of computing an average kappa that is more correct?&lt;/p&gt;

&lt;p&gt;Thanks, and if more concrete examples are needed in order to illustrate my question please let me know.&lt;/p&gt;
', 'machine-learning  confusion-matrix', NULL, '''/p'':42C,85C,129C,175C,220C,240C,260C,280C ''20'':84C ''33'':67C ''67'':56C ''abl'':93C ''accuraci'':122C,125C,134C,137C ''across'':113C ''aggreg'':111C,121C,161C,173C,229C ''also'':118C,234C ''among'':126C ''anoth'':249C ''attempt'':198C ''averag'':144C,162C,165C,194C,254C ''avoid'':200C ''bay'':37C ''built'':25C ''case'':211C ''classifi'':35C ''combin'':3A ''compar'':19C ''comput'':100C,252C ''concret'':266C ''confus'':4A,10B,97C,179C,187C,204C,230C ''confusion-matrix'':9B ''correct'':246C,259C ''cross'':50C,217C ''data'':59C,70C ''decis'':40C ''differ'':21C,27C,34C,207C,235C ''downsampl'':30C ''evalu'':17C ''exampl'':267C ''expect'':124C,136C,164C ''first'':184C ''generat'':95C ''i.e'':29C,36C ''illustr'':273C ''interest'':119C ''issu'':202C ''iter'':82C,90C,115C,191C ''kappa'':1A,102C,133C,158C,174C,226C,255C ''know'':279C ''learn'':8B,23C ''let'':277C ''machin'':7B,22C ''machine-learn'':6B ''matric'':5A,205C ''matrix'':11B,98C,180C,188C,231C ''model'':24C ''need'':269C ''net'':38C ''normal'':185C ''number'':208C ''one'':149C,243C ''order'':271C ''outlier'':31C ''p'':12C,43C,86C,130C,176C,221C,241C,261C ''paramet'':28C ''perform'':46C,78C ''pleas'':276C ''point'':148C ''possibl'':214C ''previous'':238C ''problem'':152C ''question'':104C,275C ''random'':54C ''recomput'':157C,224C ''remov'':32C ''say'':83C ''scheme'':219C ''select'':55C ''set'':65C,76C ''sever'':20C,81C ''svm'':39C ''take'':142C ''test'':75C ''thank'':262C ''thing'':128C ''total'':210C ''train'':64C ''tree'':41C ''tri'':15C ''two'':239C ''type'':48C ''use'':61C,72C ''valid'':51C,218C ''way'':109C,250C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1070, 1, '2014-09-02 08:47:38.737', 2, 457, 'SAP HANA vs Exasol', '&lt;p&gt;I am interested in knowing the differences in &lt;strong&gt;functionality&lt;/strong&gt; between SAP HANA and Exasol. Since this is a bit of an open ended question let me be clear. I am not interested in people debating which is "better" or faster. I am only interested in what each was designed to do so please keep your opinions out of it. I suspect it is a bit like comparing HANA to Oracle Exalytics where there is some overlap but the functionality goals are different. &lt;/p&gt;
', 'bigdata', NULL, '''/p'':91C ''/strong'':17C ''better'':46C ''bigdata'':5B ''bit'':27C,73C ''clear'':36C ''compar'':75C ''debat'':43C ''design'':57C ''differ'':13C,90C ''end'':31C ''exalyt'':79C ''exasol'':4A,22C ''faster'':48C ''function'':16C,87C ''goal'':88C ''hana'':2A,20C,76C ''interest'':9C,40C,52C ''keep'':62C ''know'':11C ''let'':33C ''like'':74C ''open'':30C ''opinion'':64C ''oracl'':78C ''overlap'':84C ''p'':6C ''peopl'':42C ''pleas'':61C ''question'':32C ''sap'':1A,19C ''sinc'':23C ''strong'':15C ''suspect'':69C ''vs'':3A');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1071, 1, '2014-09-02 09:48:08.15', 1, 34, 'How is the concept of data different for different disciplines?', '&lt;p&gt;How is the concept of data different for different disciplines? Obviously, for physicists and sociologists, "data" is something different.&lt;/p&gt;
', 'definitions', NULL, '''/p'':32C ''concept'':4A,16C ''data'':6A,18C,28C ''definit'':11B ''differ'':7A,9A,19C,21C,31C ''disciplin'':10A,22C ''obvious'':23C ''p'':12C ''physicist'':25C ''sociologist'':27C ''someth'':30C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1072, 2, '2014-09-02 14:01:07.407', 1, NULL, NULL, '&lt;p&gt;There''s not an enormous difference between what you can do with the two databases, it''s more a question of the focus and the way the functionality is implemented and that''s where it becomes difficult to explain without using words like "better" and "faster" (and for sure words like "cheaper") &lt;/p&gt;

&lt;p&gt;EXASOL was designed for speed and ease of use with Analytical processing and is designed to run on clusters of commodity hardware. SAP is a more complex, aims to do more than "just" Analytical processing and runs only on a range of "approved" hardware.&lt;/p&gt;

&lt;p&gt;What type of differences did you have in mind ?&lt;/p&gt;
', NULL, NULL, '''/p'':54C,100C,111C ''aim'':83C ''analyt'':66C,89C ''approv'':98C ''becom'':37C ''better'':45C ''cheaper'':53C ''cluster'':74C ''commod'':76C ''complex'':82C ''databas'':16C ''design'':58C,70C ''differ'':7C,105C ''difficult'':38C ''eas'':62C ''enorm'':6C ''exasol'':56C ''explain'':40C ''faster'':47C ''focus'':24C ''function'':29C ''hardwar'':77C,99C ''implement'':31C ''like'':44C,52C ''mind'':110C ''p'':1C,55C,101C ''process'':67C,90C ''question'':21C ''rang'':96C ''run'':72C,92C ''sap'':78C ''speed'':60C ''sure'':50C ''two'':15C ''type'':103C ''use'':42C,64C ''way'':27C ''without'':41C ''word'':43C,51C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1077, 2, '2014-09-05 11:00:22.053', 4, NULL, NULL, '&lt;p&gt;Your job seems like a map-reduce job and hence might be good for Hadoop. Hadoop has a zoo of an ecosystem though. &lt;/p&gt;

&lt;p&gt;Hadoop is a distributed file system. It distributes data on a cluster and because this data is split up it can be analysed in parallel. Out of the box, Hadoop allows you to write map reduce jobs on the platform and this is why it might help with your problem.&lt;/p&gt;

&lt;p&gt;The following technologies work on Hadoop: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;If the data can be represented in a table format you might want to check out technologies like &lt;a href="https://hive.apache.org/" rel="nofollow"&gt;hive&lt;/a&gt; and &lt;a href="http://www.cloudera.com/content/cloudera/en/products-and-services/cdh/impala.html" rel="nofollow"&gt;impala&lt;/a&gt;. Impala uses all the distributed memory across a cluster and is very performant while it allows you to still work with a table structure. &lt;/li&gt;
&lt;li&gt;A more new, but promising alternative might also be &lt;a href="https://spark.apache.org/" rel="nofollow"&gt;spark&lt;/a&gt; which allows for more iterative procedures to be run on the cluster. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;Don''t underestimate the amount of time setting up and the amount of time needed to understand Hadoop.&lt;/p&gt;
', NULL, NULL, '''/a'':113C,123C,165C ''/content/cloudera/en/products-and-services/cdh/impala.html'':119C ''/li'':148C,178C ''/p'':26C,78C,86C,199C ''/ul'':179C ''across'':130C ''allow'':58C,139C,167C ''also'':157C ''altern'':155C ''amount'':185C,192C ''analys'':50C ''box'':56C ''check'':103C ''cluster'':39C,132C,177C ''data'':36C,43C,91C ''distribut'':31C,35C,128C ''ecosystem'':24C ''file'':32C ''follow'':81C ''format'':98C ''good'':15C ''hadoop'':17C,18C,28C,57C,85C,198C ''help'':74C ''henc'':12C ''hive'':112C ''hive.apache.org'':109C ''href'':108C,116C,160C ''impala'':122C,124C ''iter'':170C ''job'':3C,10C,64C ''li'':88C,149C ''like'':5C,106C ''map'':8C,62C ''map-reduc'':7C ''memori'':129C ''might'':13C,73C,100C,156C ''need'':195C ''new'':152C ''nofollow'':111C,121C,163C ''p'':1C,27C,79C,180C ''parallel'':52C ''perform'':136C ''platform'':67C ''problem'':77C ''procedur'':171C ''promis'':154C ''reduc'':9C,63C ''rel'':110C,120C,162C ''repres'':94C ''run'':174C ''seem'':4C ''set'':188C ''spark'':164C ''spark.apache.org'':161C ''split'':45C ''still'':142C ''structur'':147C ''system'':33C ''tabl'':97C,146C ''technolog'':82C,105C ''though'':25C ''time'':187C,194C ''ul'':87C ''underestim'':183C ''understand'':197C ''use'':125C ''want'':101C ''work'':83C,143C ''write'':61C ''www.cloudera.com'':118C ''www.cloudera.com/content/cloudera/en/products-and-services/cdh/impala.html'':117C ''zoo'':21C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1078, 1, '2014-09-05 12:08:11.347', 7, 2154, 'Unstructured text classification', '&lt;p&gt;I''m going to classify unstructured text documents, namely web sites of unknown structure. The number of classes to which I am classifying is limited (at this point, I believe there is no more than three). Does anyone have a suggested for how I might get started?&lt;/p&gt;

&lt;p&gt;Is the "bag of words" approach feasible here? Later, I could add another classification stage based on document structure (perhaps decision trees).&lt;/p&gt;

&lt;p&gt;I am somewhat familiar with Mahout and Hadoop, so I prefer Java-based solutions. If needed, I can switch to Scala and/or Spark engine (the ML library).&lt;/p&gt;
', 'machine-learning  classification  text-mining  beginner', NULL, '''/p'':60C,84C,114C ''add'':73C ''and/or'':108C ''anoth'':74C ''anyon'':50C ''approach'':67C ''bag'':64C ''base'':77C,99C ''beginn'':11B ''believ'':42C ''class'':30C ''classif'':3A,7B,75C ''classifi'':17C,35C ''could'':72C ''decis'':82C ''document'':20C,79C ''engin'':110C ''familiar'':89C ''feasibl'':68C ''get'':58C ''go'':15C ''hadoop'':93C ''java'':98C ''java-bas'':97C ''later'':70C ''learn'':6B ''librari'':113C ''limit'':37C ''m'':14C ''machin'':5B ''machine-learn'':4B ''mahout'':91C ''might'':57C ''mine'':10B ''ml'':112C ''name'':21C ''need'':102C ''number'':28C ''p'':12C,61C,85C ''perhap'':81C ''point'':40C ''prefer'':96C ''scala'':107C ''site'':23C ''solut'':100C ''somewhat'':88C ''spark'':109C ''stage'':76C ''start'':59C ''structur'':26C,80C ''suggest'':53C ''switch'':105C ''text'':2A,9B,19C ''text-min'':8B ''three'':48C ''tree'':83C ''unknown'':25C ''unstructur'':1A,18C ''web'':22C ''word'':66C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1138, 2, '2014-09-18 13:14:37.94', 5, NULL, NULL, '&lt;p&gt;Decision trees (and hence Random Forests) are insensitive to monotone transformations of input features. &lt;/p&gt;

&lt;p&gt;Since multiplying by the same factor is a monotone transformation, I''d assume that for Random Forests there indeed is no difference. &lt;/p&gt;

&lt;p&gt;However, you eventually may consider using other classifiers that do not have this property, so it may still make sense to use the entire TF * IDF. &lt;/p&gt;
', NULL, NULL, '''/p'':16C,40C,68C ''assum'':30C ''classifi'':49C ''consid'':46C ''d'':29C ''decis'':2C ''differ'':39C ''entir'':65C ''eventu'':44C ''factor'':23C ''featur'':15C ''forest'':7C,34C ''henc'':5C ''howev'':42C ''idf'':67C ''inde'':36C ''input'':14C ''insensit'':9C ''make'':60C ''may'':45C,58C ''monoton'':11C,26C ''multipli'':19C ''p'':1C,17C,41C ''properti'':55C ''random'':6C,33C ''sens'':61C ''sinc'':18C ''still'':59C ''tf'':66C ''transform'':12C,27C ''tree'':3C ''use'':47C,63C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (2478, 2, '2014-11-15 03:18:13.223', 1, NULL, NULL, '&lt;p&gt;Data volume is not the only criterion for using Hadoop. Big Data is often characterized by the 3 V''s: &lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;volume, &lt;/li&gt;
&lt;li&gt;velocity, and &lt;/li&gt;
&lt;li&gt;variety. &lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;More V''s than these 3 have been invented since. I suppose the V''s were a catchy way to characterize what is Big Data.&lt;/p&gt;

&lt;p&gt;But as hinted, computational intensity is a perfect reason for using Hadoop (if your algorithm is computationally expensive). And, as hinted, the problem you describe is perfect for Hadoop, especially since it is embarrassingly parallel in nature. &lt;/p&gt;

&lt;p&gt;Is Hadoop a good choice for you? I would argue, yes. Why? Because &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Hadoop is open source (compared with proprietary systems which may be expensive and black boxes), &lt;/li&gt;
&lt;li&gt;your problem lends itself well to the MapReduce paradigm (embarassingly parallel, shared-nothing), &lt;/li&gt;
&lt;li&gt;Hadoop is easily scalable with commodity hardware (as opposed to specialized hardware, and you should get linear speed-up in your performance with just throwing hardware at the problem, and you can just spin a cluster as needed on cloud service providers),&lt;/li&gt;
&lt;li&gt;Hadoop allows multiple client languages (Java is only one of many supported languages),&lt;/li&gt;
&lt;li&gt;there might be a library available already to do your cross-product operation, and&lt;/li&gt;
&lt;li&gt;you''re shipping compute code, not data, around the network (which you should benefit from, and as opposed to other distributed platforms where you are shipping data to compute nodes which is the bottleneck).&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Please note, Hadoop is not a distributed file system (as mentioned, and corrected already). Hadoop is distributed storage and processing platform. The distributed storage component of Hadoop is called the Hadoop Distributed File System (HDFS), and the distributed processing component is called MapReduce. &lt;/p&gt;

&lt;p&gt;Hadoop has now evolved slightly. They keep the HDFS part for distributed storage. But they have a new component called YARN (Yet Another Resource Negotiator), which serves to appropriate resources (CPU, RAM) for any compute task (including MapReduce). &lt;/p&gt;

&lt;p&gt;On the "overhead" part, there is noticeable overhead with starting/stopping a Java Virtual Machine (JVM) per tasks (map tasks, reduce tasks). You can specify for your MapReduce Jobs to reuse JVMs to mitigate this issue. &lt;/p&gt;

&lt;p&gt;If "overhead" is really an issue, look into Apache Spark, which is part of the Hadoop ecosystem, and they are orders of magnitude faster than MapReduce, especially for iterative algorithms. &lt;/p&gt;

&lt;p&gt;I have used Hadoop to compute pairwise comparisons (e.g. correlation matrix, similarity matrix) that are O(N^2) (n choose 2) in worst case running time complexity. Imagine computing the correlations between 16,000 variables (16,000 choose 2); Hadoop can easily process and store the results if you have the commodity resources to support the cluster. I did this using the preeminent cloud service provider (I won''t name it, but you can surely guess who it is), and it cost me &amp;lt; $100 and under 18 hours.  &lt;/p&gt;
', NULL, NULL, '''/li'':26C,30C,33C,133C,149C,194C,209C,226C,262C ''/ol'':263C ''/p'':22C,61C,100C,115C,308C,348C,385C,417C,508C ''/ul'':34C ''000'':452C,455C ''100'':503C ''16'':451C,454C ''18'':506C ''2'':436C,439C,457C ''3'':19C,41C ''algorithm'':77C,416C ''allow'':197C ''alreadi'':217C,278C ''anoth'':332C ''apach'':395C ''appropri'':338C ''argu'':111C ''around'':235C ''avail'':216C ''benefit'':241C ''big'':12C,59C ''black'':131C ''bottleneck'':261C ''box'':132C ''call'':293C,306C,329C ''case'':442C ''catchi'':53C ''character'':16C,56C ''choic'':106C ''choos'':438C,456C ''client'':199C ''cloud'':191C,482C ''cluster'':187C,475C ''code'':232C ''commod'':156C,470C ''compar'':122C ''comparison'':426C ''complex'':445C ''compon'':289C,304C,328C ''comput'':66C,79C,231C,256C,344C,424C,447C ''correct'':277C ''correl'':428C,449C ''cost'':500C ''cpu'':340C ''criterion'':8C ''cross'':222C ''cross-product'':221C ''data'':2C,13C,60C,234C,254C ''describ'':87C ''distribut'':248C,271C,281C,287C,296C,302C,321C ''e.g'':427C ''easili'':153C,460C ''ecosystem'':403C ''embarass'':144C ''embarrass'':96C ''especi'':92C,413C ''evolv'':313C ''expens'':80C,129C ''faster'':410C ''file'':272C,297C ''get'':166C ''good'':105C ''guess'':494C ''hadoop'':11C,74C,91C,103C,118C,151C,196C,267C,279C,291C,295C,310C,402C,422C,458C ''hardwar'':157C,162C,177C ''hdfs'':299C,318C ''hint'':65C,83C ''hour'':507C ''imagin'':446C ''includ'':346C ''intens'':67C ''invent'':44C ''issu'':384C,392C ''iter'':415C ''java'':201C,361C ''job'':377C ''jvm'':364C ''jvms'':380C ''keep'':316C ''languag'':200C,208C ''lend'':137C ''li'':24C,27C,31C,117C,134C,150C,195C,210C,227C ''librari'':215C ''linear'':167C ''look'':393C ''lt'':502C ''machin'':363C ''magnitud'':409C ''mani'':206C ''map'':367C ''mapreduc'':142C,307C,347C,376C,412C ''matrix'':429C,431C ''may'':127C ''mention'':275C ''might'':212C ''mitig'':382C ''multipl'':198C ''n'':435C,437C ''name'':488C ''natur'':99C ''need'':189C ''negoti'':334C ''network'':237C ''new'':327C ''node'':257C ''note'':266C ''noth'':148C ''notic'':356C ''o'':434C ''often'':15C ''ol'':116C ''one'':204C ''open'':120C ''oper'':224C ''oppos'':159C,245C ''order'':407C ''overhead'':352C,357C,388C ''p'':1C,35C,62C,101C,264C,309C,349C,386C,418C ''pairwis'':425C ''paradigm'':143C ''parallel'':97C,145C ''part'':319C,353C,399C ''per'':365C ''perfect'':70C,89C ''perform'':173C ''platform'':249C,285C ''pleas'':265C ''preemin'':481C ''problem'':85C,136C,180C ''process'':284C,303C,461C ''product'':223C ''proprietari'':124C ''provid'':193C,484C ''ram'':341C ''re'':229C ''realli'':390C ''reason'':71C ''reduc'':369C ''resourc'':333C,339C,471C ''result'':465C ''reus'':379C ''run'':443C ''scalabl'':154C ''serv'':336C ''servic'':192C,483C ''share'':147C ''shared-noth'':146C ''ship'':230C,253C ''similar'':430C ''sinc'':45C,93C ''slight'':314C ''sourc'':121C ''spark'':396C ''special'':161C ''specifi'':373C ''speed'':169C ''speed-up'':168C ''spin'':185C ''starting/stopping'':359C ''storag'':282C,288C,322C ''store'':463C ''support'':207C,473C ''suppos'':47C ''sure'':493C ''system'':125C,273C,298C ''task'':345C,366C,368C,370C ''throw'':176C ''time'':444C ''ul'':23C ''use'':10C,73C,421C,479C ''v'':20C,37C,49C ''variabl'':453C ''varieti'':32C ''veloc'':28C ''virtual'':362C ''volum'':3C,25C ''way'':54C ''well'':139C ''won'':486C ''worst'':441C ''would'':110C ''yarn'':330C ''yes'':112C ''yet'':331C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1079, 1, '2014-09-05 14:47:52.127', 2, 420, 'Data sets for evaluating text retrieval quality', '&lt;p&gt;I''m searching for data sets for evaluating text retrieval quality.&lt;/p&gt;

&lt;p&gt;TF-IDF is a popular similarity measure, but is it the best choice? And which &lt;em&gt;variant&lt;/em&gt; is the best choice? &lt;a href="https://lucene.apache.org/core/3_6_1/api/all/org/apache/lucene/search/Similarity.html" rel="nofollow"&gt;Lucenes Scoring&lt;/a&gt; for example uses IDF^2, and IDF defined as 1+log(numdocs/(docFreq+1)). TF in lucene is defined as sqrt(frequency)...&lt;/p&gt;

&lt;p&gt;Many more variants exist, including &lt;a href="https://en.wikipedia.org/wiki/Okapi_BM25" rel="nofollow"&gt;Okapi BM25&lt;/a&gt;, which is used by the &lt;a href="http://xapian.org/docs/bm25.html" rel="nofollow"&gt;Xapian search engine&lt;/a&gt;...&lt;/p&gt;

&lt;p&gt;I''d like to study the different variants, and I''m looking for &lt;strong&gt;evaluation data sets&lt;/strong&gt;. Thanks!&lt;/p&gt;
', 'dataset  text-mining  similarity  information-retrieval', NULL, '''+1'':76C ''/a'':62C,101C,117C ''/core/3_6_1/api/all/org/apache/lucene/search/similarity.html'':57C ''/docs/bm25.html'':111C ''/em'':48C ''/p'':28C,85C,118C,139C ''/strong'':137C ''/wiki/okapi_bm25'':96C ''1'':72C ''2'':67C ''best'':42C,51C ''bm25'':100C ''choic'':43C,52C ''d'':121C ''data'':1A,21C,135C ''dataset'':8B ''defin'':70C,81C ''differ'':126C ''docfreq'':75C ''em'':46C ''en.wikipedia.org'':95C ''en.wikipedia.org/wiki/okapi_bm25'':94C ''engin'':116C ''evalu'':4A,24C,134C ''exampl'':64C ''exist'':90C ''frequenc'':84C ''href'':54C,93C,108C ''idf'':32C,66C,69C ''includ'':91C ''inform'':14B ''information-retriev'':13B ''like'':122C ''log'':73C ''look'':131C ''lucen'':60C,79C ''lucene.apache.org'':56C ''lucene.apache.org/core/3_6_1/api/all/org/apache/lucene/search/similarity.html'':55C ''m'':18C,130C ''mani'':87C ''measur'':37C ''mine'':11B ''nofollow'':59C,98C,113C ''numdoc'':74C ''okapi'':99C ''p'':16C,29C,86C,119C ''popular'':35C ''qualiti'':7A,27C ''rel'':58C,97C,112C ''retriev'':6A,15B,26C ''score'':61C ''search'':19C,115C ''set'':2A,22C,136C ''similar'':12B,36C ''sqrt'':83C ''strong'':133C ''studi'':124C ''text'':5A,10B,25C ''text-min'':9B ''tf'':31C,77C ''tf-idf'':30C ''thank'':138C ''use'':65C,104C ''variant'':47C,89C,127C ''xapian'':114C ''xapian.org'':110C ''xapian.org/docs/bm25.html'':109C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1081, 2, '2014-09-05 16:36:03.227', 1, NULL, NULL, '&lt;p&gt;Data is, at it''s most basic reduction, a raw element of something. Data is a raw "thing" that exists in any form from which we can analyze it and construct intelligence. When I was an Intelligence Analyst, we used to define data as "anything and everything that could be used to construct a hypothesis." &lt;/p&gt;

&lt;p&gt;Thus, data for any discipline is interchangeable; as a sociologist, I have a vector of discrete variables indicating ethnicity, as an economist I have a vector with housing prices, and as an anthropologist I have a vector of tablet names used in some long-gone civilization. &lt;/p&gt;

&lt;p&gt;Data is data.&lt;/p&gt;
', NULL, NULL, '''/p'':57C,106C,111C ''analyst'':39C ''analyz'':29C ''anthropologist'':91C ''anyth'':46C ''basic'':8C ''civil'':105C ''construct'':32C,54C ''could'':50C ''data'':2C,15C,44C,60C,108C,110C ''defin'':43C ''disciplin'':63C ''discret'':74C ''economist'':80C ''element'':12C ''ethnic'':77C ''everyth'':48C ''exist'':21C ''form'':24C ''gone'':104C ''hous'':86C ''hypothesi'':56C ''indic'':76C ''intellig'':33C,38C ''interchang'':65C ''long'':103C ''long-gon'':102C ''name'':98C ''p'':1C,58C,107C ''price'':87C ''raw'':11C,18C ''reduct'':9C ''sociologist'':68C ''someth'':14C ''tablet'':97C ''thing'':19C ''thus'':59C ''use'':41C,52C,99C ''variabl'':75C ''vector'':72C,84C,95C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1137, 1, '2014-09-18 06:22:13.94', 5, 276, 'Choosing a window size for DTW', '&lt;p&gt;I have time series data from mobile sensors for different motions such as walking, pushups, dumbellifts, rowing and so on. All these motions have different length of time series. For classifying them using &lt;a href="http://en.wikipedia.org/wiki/Dynamic_time_warping"&gt;Dynamic Time Warping (DTW)&lt;/a&gt;, how do I choose an appropriate window size that will give good results?&lt;/p&gt;
', 'classification  time-series  parameter', NULL, '''/a'':55C ''/p'':69C ''/wiki/dynamic_time_warping'':50C ''appropri'':61C ''choos'':1A,59C ''classif'':7B ''classifi'':43C ''data'':17C ''differ'':22C,37C ''dtw'':6A,54C ''dumbellift'':28C ''dynam'':51C ''en.wikipedia.org'':49C ''en.wikipedia.org/wiki/dynamic_time_warping'':48C ''give'':66C ''good'':67C ''href'':47C ''length'':38C ''mobil'':19C ''motion'':23C,35C ''p'':12C ''paramet'':11B ''pushup'':27C ''result'':68C ''row'':29C ''sensor'':20C ''seri'':10B,16C,41C ''size'':4A,63C ''time'':9B,15C,40C,52C ''time-seri'':8B ''use'':45C ''walk'':26C ''warp'':53C ''window'':3A,62C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1082, 2, '2014-09-05 19:13:39.993', 2, NULL, NULL, '&lt;p&gt;Here are a couple of really great open source software packages for text classification that should help get you started:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;&lt;a href="http://mallet.cs.umass.edu/" rel="nofollow"&gt;MALLET&lt;/a&gt; is a CPL-licensed Java-based machine learning toolkit built by UMass for working with text data. It includes implementations of several &lt;a href="http://mallet.cs.umass.edu/classification.php" rel="nofollow"&gt;classification&lt;/a&gt; algorithms (e.g., naÃ¯ve Bayes, maximum entropy, decision trees).&lt;/li&gt;
&lt;li&gt;The &lt;a href="http://nlp.stanford.edu/software/classifier.shtml" rel="nofollow"&gt;Stanford Classifier&lt;/a&gt; from the Stanford NLP Group is a GPL-licensed Java implementation of a maximum entropy classifier designed to work with text data.&lt;/li&gt;
&lt;/ul&gt;
', NULL, NULL, '''/a'':31C,64C,86C ''/classification.php'':60C ''/li'':74C,110C ''/p'':22C ''/software/classifier.shtml'':81C ''/ul'':111C ''algorithm'':65C ''base'':39C ''bay'':69C ''built'':43C ''classif'':15C,63C ''classifi'':85C,103C ''coupl'':5C ''cpl'':35C ''cpl-licens'':34C ''data'':50C,109C ''decis'':72C ''design'':104C ''e.g'':66C ''entropi'':71C,102C ''get'':19C ''gpl'':95C ''gpl-licens'':94C ''great'':8C ''group'':91C ''help'':18C ''href'':26C,57C,78C ''implement'':53C,98C ''includ'':52C ''java'':38C,97C ''java-bas'':37C ''learn'':41C ''li'':24C,75C ''licens'':36C,96C ''machin'':40C ''mallet'':30C ''mallet.cs.umass.edu'':27C,59C ''mallet.cs.umass.edu/classification.php'':58C ''maximum'':70C,101C ''naã'':67C ''nlp'':90C ''nlp.stanford.edu'':80C ''nlp.stanford.edu/software/classifier.shtml'':79C ''nofollow'':29C,62C,83C ''open'':9C ''p'':1C ''packag'':12C ''realli'':7C ''rel'':28C,61C,82C ''sever'':55C ''softwar'':11C ''sourc'':10C ''stanford'':84C,89C ''start'':21C ''text'':14C,49C,108C ''toolkit'':42C ''tree'':73C ''ul'':23C ''umass'':45C ''ve'':68C ''work'':47C,106C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1086, 1, '2014-09-07 22:51:27.49', 2, 99, 'non query-based document ranking', '&lt;p&gt;We have ~500 biomedical documents each of some 1-2 MB. We want to use a non query-based method to rank the documents in order of their unique content score. I''m calling it "unique content" because our researchers want to know from which document to start reading. All the documents are of the same topic, in the biomedical world we know that there is always a lot of content overlap. So all we want to do is to arrange the documents in the order of their unique content.&lt;/p&gt;

&lt;p&gt;Most Information Retrieval literature suggest query-based ranking which does not fit our need.&lt;/p&gt;
', 'machine-learning  data-mining  text-mining  information-retrieval', NULL, '''-2'':29C ''/p'':111C,128C ''1'':28C ''500'':22C ''alway'':87C ''arrang'':101C ''base'':4A,39C,120C ''biomed'':23C,80C ''call'':54C ''content'':50C,57C,91C,110C ''data'':11B ''data-min'':10B ''document'':5A,24C,44C,66C,72C,103C ''fit'':125C ''inform'':17B,114C ''information-retriev'':16B ''know'':63C,83C ''learn'':9B ''literatur'':116C ''lot'':89C ''m'':53C ''machin'':8B ''machine-learn'':7B ''mb'':30C ''method'':40C ''mine'':12B,15B ''need'':127C ''non'':1A,36C ''order'':46C,106C ''overlap'':92C ''p'':19C,112C ''queri'':3A,38C,119C ''query-bas'':2A,37C,118C ''rank'':6A,42C,121C ''read'':69C ''research'':60C ''retriev'':18B,115C ''score'':51C ''start'':68C ''suggest'':117C ''text'':14B ''text-min'':13B ''topic'':77C ''uniqu'':49C,56C,109C ''use'':34C ''want'':32C,61C,96C ''world'':81C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (9674, 4, '2016-01-07 11:23:52.943', 0, NULL, NULL, 'Area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.', NULL, NULL, '''action'':13C ''agent'':9C ''area'':1C ''concern'':5C ''cumul'':24C ''environ'':16C ''learn'':4C ''machin'':3C ''maxim'':20C ''notion'':22C ''ought'':10C ''reward'':25C ''softwar'':8C ''take'':12C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1087, 2, '2014-09-08 00:47:21.59', 0, NULL, NULL, '&lt;p&gt;Here''s a simple initial approach to try:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Calculate the &lt;a href="http://en.wikipedia.org/wiki/Tf%E2%80%93idf" rel="nofollow"&gt;TF-IDF&lt;/a&gt; score of each word in each document.&lt;/li&gt;
&lt;li&gt;Sort the documents by the average TF-IDF score of their words.&lt;/li&gt;
&lt;li&gt;The higher the average TF-IDF score, the more unique a document is with respect to the rest of the collection.&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;You might also try a clustering-based approach where you look for outliers, or perhaps something with the &lt;a href="http://en.wikipedia.org/wiki/Jaccard_index" rel="nofollow"&gt;Jaccard index&lt;/a&gt; using a bag-of-words model.&lt;/p&gt;
', NULL, NULL, '''/a'':25C,103C ''/li'':33C,48C,72C ''/ol'':73C ''/p'':10C,111C ''/wiki/jaccard_index'':98C ''/wiki/tf%e2%80%93idf'':19C ''also'':77C ''approach'':7C,83C ''averag'':40C,53C ''bag'':107C ''bag-of-word'':106C ''base'':82C ''calcul'':13C ''cluster'':81C ''clustering-bas'':80C ''collect'':71C ''document'':32C,37C,62C ''en.wikipedia.org'':18C,97C ''en.wikipedia.org/wiki/jaccard_index'':96C ''en.wikipedia.org/wiki/tf%e2%80%93idf'':17C ''higher'':51C ''href'':16C,95C ''idf'':24C,43C,56C ''index'':102C ''initi'':6C ''jaccard'':101C ''li'':12C,34C,49C ''look'':86C ''might'':76C ''model'':110C ''nofollow'':21C,100C ''ol'':11C ''outlier'':88C ''p'':1C,74C ''perhap'':90C ''rel'':20C,99C ''respect'':65C ''rest'':68C ''score'':26C,44C,57C ''simpl'':5C ''someth'':91C ''sort'':35C ''tf'':23C,42C,55C ''tf-idf'':22C,41C,54C ''tri'':9C,78C ''uniqu'':60C ''use'':104C ''word'':29C,47C,109C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1113, 1, '2014-09-13 17:13:23.373', 2, 107, 'General approahces for grouping a continuous variable based on text data?', '&lt;p&gt;I have a general methodological question. I have two columns of data, with one a column a numeric variable for age and another column a short character variable for text responses to a question. &lt;/p&gt;

&lt;p&gt;My goal is to group the age variable (that is, create cut points for the age variable), based on the text responses. I''m unfamiliar with any general approaches for doing this sort of analysis. What general approaches would you recommend? Ideally I''d like to categorize the age variable based on linguistic similarity of the text responses.&lt;/p&gt;
', 'bigdata  clustering  text-mining', NULL, '''/p'':52C,112C ''age'':38C,60C,69C,102C ''analysi'':88C ''anoth'':40C ''approach'':82C,91C ''approahc'':2A ''base'':8A,71C,104C ''bigdata'':12B ''categor'':100C ''charact'':44C ''cluster'':13B ''column'':27C,33C,41C ''continu'':6A ''creat'':64C ''cut'':65C ''d'':97C ''data'':11A,29C ''general'':1A,21C,81C,90C ''goal'':55C ''group'':4A,58C ''ideal'':95C ''like'':98C ''linguist'':106C ''m'':77C ''methodolog'':22C ''mine'':16B ''numer'':35C ''one'':31C ''p'':17C,53C ''point'':66C ''question'':23C,51C ''recommend'':94C ''respons'':48C,75C,111C ''short'':43C ''similar'':107C ''sort'':86C ''text'':10A,15B,47C,74C,110C ''text-min'':14B ''two'':26C ''unfamiliar'':78C ''variabl'':7A,36C,45C,61C,70C,103C ''would'':92C');
INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1088, 2, '2014-09-08 02:45:42.653', 3, NULL, NULL, '&lt;p&gt;Topic Modeling would be a very appropriate method for your problem.  Topic Models are a form of unsupervised learning/discovery, where a specified (or discovered) number of topics are defined by a list of words that have a high probability of appearing together. In a separate step, you can label each topic using subject matter experts, but for your purposes this isn''t necessary since you are only interested in getting to three clusters.&lt;/p&gt;

&lt;p&gt;You treat each document as a bag of words, and pre-process to remove stop words, etc.  With the simplest methods, you pre-specify the number of topics.  In your case, you could either specify "3", which is your fixed limit on categories, or pick a larger number of topics (between 10 and 100), and then in a separate step, form three clusters for documents with common emphasis on topics. K-means or other clustering methods could be used.  (I''d recommend the latter approach)&lt;/p&gt;

&lt;p&gt;You don''t need to code topic modeling software from scratch. Here''s a web page with many resources, including software libraries/packages: &lt;a href="http://www.cs.princeton.edu/~blei/topicmodeling.html" rel="nofollow"&gt;http://www.cs.princeton.edu/~blei/topicmodeling.html&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;None are in Java, but there are ways to run C++ and Python under Java.&lt;/p&gt;
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INSERT INTO "public"."roonga_posts" ("id", "post_type_id", "creation_date", "score", "view_count", "title", "body", "tags", "close_date", "fts") VALUES (1089, 2, '2014-09-08 03:07:07.413', 1, NULL, NULL, '&lt;p&gt;You could use Topic Modeling as described in this paper:
&lt;a href="http://faculty.chicagobooth.edu/workshops/orgs-markets/pdf/KaplanSwordWin2014.pdf" rel="nofollow"&gt;http://faculty.chicagobooth.edu/workshops/orgs-markets/pdf/KaplanSwordWin2014.pdf&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;They performed Topic Modeling on abstracts of patents (limited to 150 words).  They identified papers as "novel" if they were the first to introduce a topic, and measured degree of novelty by how many papers in the following year used the same topic. (Read the paper for details).&lt;/p&gt;

&lt;p&gt;I suggest that you follow their lead and only process paper abstracts.  Processing the body of each paper might reveal some novelty that the abstract does not, but you also run the risk of having much more noise in your topic model (i.e. extraneous topics, extraneous words).&lt;/p&gt;

&lt;p&gt;While you say that all 500 papers are on the same "topic", it''s probably safer to say that they are all on the same "theme" or in the same "sub-category" of Bio-medicine. Topic modeling permits decomposition of the "theme" into "topics".&lt;/p&gt;

&lt;p&gt;The good news is that there are plenty of good packages/libraries for Topic Modeling.  You still have to do preprocessing, but you don''t have to code the algorithms yourself.  See this page for many resources:
&lt;a href="http://www.cs.princeton.edu/~blei/topicmodeling.html" rel="nofollow"&gt;http://www.cs.princeton.edu/~blei/topicmodeling.html&lt;/a&gt;&lt;/p&gt;
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